diff --git a/.gitattributes b/.gitattributes
index 8fa5e9834741a384d25c61a2f0b43a16e539b593..8a1c9eb87617f00b028967fda13879caa2797029 100644
--- a/.gitattributes
+++ b/.gitattributes
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/LICENSE.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0879448151a25d80a2cbd83e4d9ee8ef598a783
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/LICENSE.txt
@@ -0,0 +1,971 @@
+Copyright (c) 2005-2024, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+----
+
+The NumPy repository and source distributions bundle several libraries that are
+compatibly licensed.  We list these here.
+
+Name: lapack-lite
+Files: numpy/linalg/lapack_lite/*
+License: BSD-3-Clause
+  For details, see numpy/linalg/lapack_lite/LICENSE.txt
+
+Name: dragon4
+Files: numpy/_core/src/multiarray/dragon4.c
+License: MIT
+  For license text, see numpy/_core/src/multiarray/dragon4.c
+
+Name: libdivide
+Files: numpy/_core/include/numpy/libdivide/*
+License: Zlib
+  For license text, see numpy/_core/include/numpy/libdivide/LICENSE.txt
+
+
+Note that the following files are vendored in the repository and sdist but not
+installed in built numpy packages:
+
+Name: Meson
+Files: vendored-meson/meson/*
+License: Apache 2.0
+  For license text, see vendored-meson/meson/COPYING
+
+Name: spin
+Files: .spin/cmds.py
+License: BSD-3
+  For license text, see .spin/LICENSE
+
+Name: tempita
+Files: numpy/_build_utils/tempita/*
+License: MIT
+  For details, see numpy/_build_utils/tempita/LICENCE.txt
+
+----
+
+This binary distribution of NumPy also bundles the following software:
+
+
+Name: OpenBLAS
+Files: numpy.libs/libscipy_openblas*.so
+Description: bundled as a dynamically linked library
+Availability: https://github.com/OpenMathLib/OpenBLAS/
+License: BSD-3-Clause
+  Copyright (c) 2011-2014, The OpenBLAS Project
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are
+  met:
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+     1. Redistributions of source code must retain the above copyright
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+     2. Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in
+        the documentation and/or other materials provided with the
+        distribution.
+     3. Neither the name of the OpenBLAS project nor the names of
+        its contributors may be used to endorse or promote products
+        derived from this software without specific prior written
+        permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
+  USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Name: LAPACK
+Files: numpy.libs/libscipy_openblas*.so
+Description: bundled in OpenBLAS
+Availability: https://github.com/OpenMathLib/OpenBLAS/
+License: BSD-3-Clause-Attribution
+  Copyright (c) 1992-2013 The University of Tennessee and The University
+                          of Tennessee Research Foundation.  All rights
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+  Copyright (c) 2000-2013 The University of California Berkeley. All
+                          rights reserved.
+  Copyright (c) 2006-2013 The University of Colorado Denver.  All rights
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+  $COPYRIGHT$
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+  - Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer listed
+    in this license in the documentation and/or other materials
+    provided with the distribution.
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+  - Neither the name of the copyright holders nor the names of its
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+  The copyright holders provide no reassurances that the source code
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+  intellectual property rights of third parties.  The copyright holders
+  disclaim any liability to any recipient for claims brought against
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+  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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+Name: GCC runtime library
+Files: numpy.libs/libgfortran*.so
+Description: dynamically linked to files compiled with gcc
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+License: GPL-3.0-with-GCC-exception
+  Copyright (C) 2002-2017 Free Software Foundation, Inc.
+
+  Libgfortran is free software; you can redistribute it and/or modify
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+  Libgfortran is distributed in the hope that it will be useful,
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+  Under Section 7 of GPL version 3, you are granted additional
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+  a copy of the GCC Runtime Library Exception along with this program;
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+  <http://www.gnu.org/licenses/>.
+
+----
+
+Full text of license texts referred to above follows (that they are
+listed below does not necessarily imply the conditions apply to the
+present binary release):
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+----
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+GCC RUNTIME LIBRARY EXCEPTION
+
+Version 3.1, 31 March 2009
+
+Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
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+The availability of this Exception does not imply any general
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+
+                    GNU GENERAL PUBLIC LICENSE
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+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
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+
+  The GNU General Public License does not permit incorporating your program
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+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+
+Name: libquadmath
+Files: numpy.libs/libquadmath*.so
+Description: dynamically linked to files compiled with gcc
+Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
+License: LGPL-2.1-or-later
+
+    GCC Quad-Precision Math Library
+    Copyright (C) 2010-2019 Free Software Foundation, Inc.
+    Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>
+
+    This file is part of the libquadmath library.
+    Libquadmath is free software; you can redistribute it and/or
+    modify it under the terms of the GNU Library General Public
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+    version 2.1 of the License, or (at your option) any later version.
+
+    Libquadmath is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+    Lesser General Public License for more details.
+    https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..1c3041826574afca64c0ed3aca89bd92337def13
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/METADATA
@@ -0,0 +1,1092 @@
+Metadata-Version: 2.1
+Name: numpy
+Version: 2.2.6
+Summary: Fundamental package for array computing in Python
+Author: Travis E. Oliphant et al.
+Maintainer-Email: NumPy Developers <numpy-discussion@python.org>
+License: Copyright (c) 2005-2024, NumPy Developers.
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+         
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+         Files: numpy.libs/libquadmath*.so
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+         License: LGPL-2.1-or-later
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+             https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
+         
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
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+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
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+
+
+NumPy is the fundamental package for scientific computing with Python.
+
+- **Website:** https://www.numpy.org
+- **Documentation:** https://numpy.org/doc
+- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion
+- **Source code:** https://github.com/numpy/numpy
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+- **Bug reports:** https://github.com/numpy/numpy/issues
+- **Report a security vulnerability:** https://tidelift.com/docs/security
+
+It provides:
+
+- a powerful N-dimensional array object
+- sophisticated (broadcasting) functions
+- tools for integrating C/C++ and Fortran code
+- useful linear algebra, Fourier transform, and random number capabilities
+
+Testing:
+
+NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:
+
+    python -c "import numpy, sys; sys.exit(numpy.test() is False)"
+
+Code of Conduct
+----------------------
+
+NumPy is a community-driven open source project developed by a diverse group of
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+commitment to creating an open, inclusive, and positive community. Please read the
+[NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact
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+
+Call for Contributions
+----------------------
+
+The NumPy project welcomes your expertise and enthusiasm!
+
+Small improvements or fixes are always appreciated. If you are considering larger contributions
+to the source code, please contact us through the [mailing
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+Writing code isn’t the only way to contribute to NumPy. You can also:
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+If you’re unsure where to start or how your skills fit in, reach out! You can
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+
+We also have a biweekly community call, details of which are announced on the
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+
+If you are new to contributing to open source, [this
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+and how to successfully get involved.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..10d09b41ee1ff5c627d27bf803db7e049711510d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/RECORD
@@ -0,0 +1,1498 @@
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+../../../bin/numpy-config,sha256=TmybERQmELBY8Xsx7zpAkoDrKjkFg1aorBxtrM3WQ50,238
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..4e4c38ae320920b8f083b87f408214cdecd350d2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_17_x86_64
+Tag: cp310-cp310-manylinux2014_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/entry_points.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..963c00f7069bbcd2075093df390c8bfd73a109ce
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy-2.2.6.dist-info/entry_points.txt
@@ -0,0 +1,10 @@
+[array_api]
+numpy = numpy
+
+[pyinstaller40]
+hook-dirs = numpy:_pyinstaller_hooks_dir
+
+[console_scripts]
+f2py = numpy.f2py.f2py2e:main
+numpy-config = numpy._configtool:main
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libgfortran-040039e1-0352e75f.so.5.0.0 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libgfortran-040039e1-0352e75f.so.5.0.0
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libgfortran-040039e1-0352e75f.so.5.0.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6090048eccc763522c12ef016f81da6b627cb3a044f55cf0479a839c41c0980
+size 2833617
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libquadmath-96973f99-934c22de.so.0.0.0 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libquadmath-96973f99-934c22de.so.0.0.0
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@@ -0,0 +1,3 @@
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+size 250985
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libscipy_openblas64_-56d6093b.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy.libs/libscipy_openblas64_-56d6093b.so
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0bd815d04b6b54990e3cccc7528fbb696456d09569f533d0390c13f0cdc4dd4a
+size 25021457
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/__init__.pyi
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index 0000000000000000000000000000000000000000..e8ec8b2488664922c0594dc4ad8313d1612058fb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/__init__.pyi
@@ -0,0 +1,4 @@
+from numpy import matrix
+from .defmatrix import bmat, asmatrix
+
+__all__ = ["matrix", "bmat", "asmatrix"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/defmatrix.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/defmatrix.py
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index 0000000000000000000000000000000000000000..6512a0246db6aa6adc9a0619ca22e75f833b61e2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/defmatrix.py
@@ -0,0 +1,1118 @@
+__all__ = ['matrix', 'bmat', 'asmatrix']
+
+import sys
+import warnings
+import ast
+
+from .._utils import set_module
+import numpy._core.numeric as N
+from numpy._core.numeric import concatenate, isscalar
+# While not in __all__, matrix_power used to be defined here, so we import
+# it for backward compatibility.
+from numpy.linalg import matrix_power
+
+
+def _convert_from_string(data):
+    for char in '[]':
+        data = data.replace(char, '')
+
+    rows = data.split(';')
+    newdata = []
+    for count, row in enumerate(rows):
+        trow = row.split(',')
+        newrow = []
+        for col in trow:
+            temp = col.split()
+            newrow.extend(map(ast.literal_eval, temp))
+        if count == 0:
+            Ncols = len(newrow)
+        elif len(newrow) != Ncols:
+            raise ValueError("Rows not the same size.")
+        newdata.append(newrow)
+    return newdata
+
+
+@set_module('numpy')
+def asmatrix(data, dtype=None):
+    """
+    Interpret the input as a matrix.
+
+    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
+    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.
+
+    Parameters
+    ----------
+    data : array_like
+        Input data.
+    dtype : data-type
+       Data-type of the output matrix.
+
+    Returns
+    -------
+    mat : matrix
+        `data` interpreted as a matrix.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([[1, 2], [3, 4]])
+
+    >>> m = np.asmatrix(x)
+
+    >>> x[0,0] = 5
+
+    >>> m
+    matrix([[5, 2],
+            [3, 4]])
+
+    """
+    return matrix(data, dtype=dtype, copy=False)
+
+
+@set_module('numpy')
+class matrix(N.ndarray):
+    """
+    matrix(data, dtype=None, copy=True)
+
+    Returns a matrix from an array-like object, or from a string of data.
+
+    A matrix is a specialized 2-D array that retains its 2-D nature
+    through operations.  It has certain special operators, such as ``*``
+    (matrix multiplication) and ``**`` (matrix power).
+
+    .. note:: It is no longer recommended to use this class, even for linear
+              algebra. Instead use regular arrays. The class may be removed
+              in the future.
+
+    Parameters
+    ----------
+    data : array_like or string
+       If `data` is a string, it is interpreted as a matrix with commas
+       or spaces separating columns, and semicolons separating rows.
+    dtype : data-type
+       Data-type of the output matrix.
+    copy : bool
+       If `data` is already an `ndarray`, then this flag determines
+       whether the data is copied (the default), or whether a view is
+       constructed.
+
+    See Also
+    --------
+    array
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.matrix('1 2; 3 4')
+    >>> a
+    matrix([[1, 2],
+            [3, 4]])
+
+    >>> np.matrix([[1, 2], [3, 4]])
+    matrix([[1, 2],
+            [3, 4]])
+
+    """
+    __array_priority__ = 10.0
+    def __new__(subtype, data, dtype=None, copy=True):
+        warnings.warn('the matrix subclass is not the recommended way to '
+                      'represent matrices or deal with linear algebra (see '
+                      'https://docs.scipy.org/doc/numpy/user/'
+                      'numpy-for-matlab-users.html). '
+                      'Please adjust your code to use regular ndarray.',
+                      PendingDeprecationWarning, stacklevel=2)
+        if isinstance(data, matrix):
+            dtype2 = data.dtype
+            if (dtype is None):
+                dtype = dtype2
+            if (dtype2 == dtype) and (not copy):
+                return data
+            return data.astype(dtype)
+
+        if isinstance(data, N.ndarray):
+            if dtype is None:
+                intype = data.dtype
+            else:
+                intype = N.dtype(dtype)
+            new = data.view(subtype)
+            if intype != data.dtype:
+                return new.astype(intype)
+            if copy:
+                return new.copy()
+            else:
+                return new
+
+        if isinstance(data, str):
+            data = _convert_from_string(data)
+
+        # now convert data to an array
+        copy = None if not copy else True
+        arr = N.array(data, dtype=dtype, copy=copy)
+        ndim = arr.ndim
+        shape = arr.shape
+        if (ndim > 2):
+            raise ValueError("matrix must be 2-dimensional")
+        elif ndim == 0:
+            shape = (1, 1)
+        elif ndim == 1:
+            shape = (1, shape[0])
+
+        order = 'C'
+        if (ndim == 2) and arr.flags.fortran:
+            order = 'F'
+
+        if not (order or arr.flags.contiguous):
+            arr = arr.copy()
+
+        ret = N.ndarray.__new__(subtype, shape, arr.dtype,
+                                buffer=arr,
+                                order=order)
+        return ret
+
+    def __array_finalize__(self, obj):
+        self._getitem = False
+        if (isinstance(obj, matrix) and obj._getitem):
+            return
+        ndim = self.ndim
+        if (ndim == 2):
+            return
+        if (ndim > 2):
+            newshape = tuple([x for x in self.shape if x > 1])
+            ndim = len(newshape)
+            if ndim == 2:
+                self.shape = newshape
+                return
+            elif (ndim > 2):
+                raise ValueError("shape too large to be a matrix.")
+        else:
+            newshape = self.shape
+        if ndim == 0:
+            self.shape = (1, 1)
+        elif ndim == 1:
+            self.shape = (1, newshape[0])
+        return
+
+    def __getitem__(self, index):
+        self._getitem = True
+
+        try:
+            out = N.ndarray.__getitem__(self, index)
+        finally:
+            self._getitem = False
+
+        if not isinstance(out, N.ndarray):
+            return out
+
+        if out.ndim == 0:
+            return out[()]
+        if out.ndim == 1:
+            sh = out.shape[0]
+            # Determine when we should have a column array
+            try:
+                n = len(index)
+            except Exception:
+                n = 0
+            if n > 1 and isscalar(index[1]):
+                out.shape = (sh, 1)
+            else:
+                out.shape = (1, sh)
+        return out
+
+    def __mul__(self, other):
+        if isinstance(other, (N.ndarray, list, tuple)) :
+            # This promotes 1-D vectors to row vectors
+            return N.dot(self, asmatrix(other))
+        if isscalar(other) or not hasattr(other, '__rmul__') :
+            return N.dot(self, other)
+        return NotImplemented
+
+    def __rmul__(self, other):
+        return N.dot(other, self)
+
+    def __imul__(self, other):
+        self[:] = self * other
+        return self
+
+    def __pow__(self, other):
+        return matrix_power(self, other)
+
+    def __ipow__(self, other):
+        self[:] = self ** other
+        return self
+
+    def __rpow__(self, other):
+        return NotImplemented
+
+    def _align(self, axis):
+        """A convenience function for operations that need to preserve axis
+        orientation.
+        """
+        if axis is None:
+            return self[0, 0]
+        elif axis==0:
+            return self
+        elif axis==1:
+            return self.transpose()
+        else:
+            raise ValueError("unsupported axis")
+
+    def _collapse(self, axis):
+        """A convenience function for operations that want to collapse
+        to a scalar like _align, but are using keepdims=True
+        """
+        if axis is None:
+            return self[0, 0]
+        else:
+            return self
+
+    # Necessary because base-class tolist expects dimension
+    #  reduction by x[0]
+    def tolist(self):
+        """
+        Return the matrix as a (possibly nested) list.
+
+        See `ndarray.tolist` for full documentation.
+
+        See Also
+        --------
+        ndarray.tolist
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.tolist()
+        [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]
+
+        """
+        return self.__array__().tolist()
+
+    # To preserve orientation of result...
+    def sum(self, axis=None, dtype=None, out=None):
+        """
+        Returns the sum of the matrix elements, along the given axis.
+
+        Refer to `numpy.sum` for full documentation.
+
+        See Also
+        --------
+        numpy.sum
+
+        Notes
+        -----
+        This is the same as `ndarray.sum`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix([[1, 2], [4, 3]])
+        >>> x.sum()
+        10
+        >>> x.sum(axis=1)
+        matrix([[3],
+                [7]])
+        >>> x.sum(axis=1, dtype='float')
+        matrix([[3.],
+                [7.]])
+        >>> out = np.zeros((2, 1), dtype='float')
+        >>> x.sum(axis=1, dtype='float', out=np.asmatrix(out))
+        matrix([[3.],
+                [7.]])
+
+        """
+        return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
+
+
+    # To update docstring from array to matrix...
+    def squeeze(self, axis=None):
+        """
+        Return a possibly reshaped matrix.
+
+        Refer to `numpy.squeeze` for more documentation.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Selects a subset of the axes of length one in the shape.
+            If an axis is selected with shape entry greater than one,
+            an error is raised.
+
+        Returns
+        -------
+        squeezed : matrix
+            The matrix, but as a (1, N) matrix if it had shape (N, 1).
+
+        See Also
+        --------
+        numpy.squeeze : related function
+
+        Notes
+        -----
+        If `m` has a single column then that column is returned
+        as the single row of a matrix.  Otherwise `m` is returned.
+        The returned matrix is always either `m` itself or a view into `m`.
+        Supplying an axis keyword argument will not affect the returned matrix
+        but it may cause an error to be raised.
+
+        Examples
+        --------
+        >>> c = np.matrix([[1], [2]])
+        >>> c
+        matrix([[1],
+                [2]])
+        >>> c.squeeze()
+        matrix([[1, 2]])
+        >>> r = c.T
+        >>> r
+        matrix([[1, 2]])
+        >>> r.squeeze()
+        matrix([[1, 2]])
+        >>> m = np.matrix([[1, 2], [3, 4]])
+        >>> m.squeeze()
+        matrix([[1, 2],
+                [3, 4]])
+
+        """
+        return N.ndarray.squeeze(self, axis=axis)
+
+
+    # To update docstring from array to matrix...
+    def flatten(self, order='C'):
+        """
+        Return a flattened copy of the matrix.
+
+        All `N` elements of the matrix are placed into a single row.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            'C' means to flatten in row-major (C-style) order. 'F' means to
+            flatten in column-major (Fortran-style) order. 'A' means to
+            flatten in column-major order if `m` is Fortran *contiguous* in
+            memory, row-major order otherwise. 'K' means to flatten `m` in
+            the order the elements occur in memory. The default is 'C'.
+
+        Returns
+        -------
+        y : matrix
+            A copy of the matrix, flattened to a `(1, N)` matrix where `N`
+            is the number of elements in the original matrix.
+
+        See Also
+        --------
+        ravel : Return a flattened array.
+        flat : A 1-D flat iterator over the matrix.
+
+        Examples
+        --------
+        >>> m = np.matrix([[1,2], [3,4]])
+        >>> m.flatten()
+        matrix([[1, 2, 3, 4]])
+        >>> m.flatten('F')
+        matrix([[1, 3, 2, 4]])
+
+        """
+        return N.ndarray.flatten(self, order=order)
+
+    def mean(self, axis=None, dtype=None, out=None):
+        """
+        Returns the average of the matrix elements along the given axis.
+
+        Refer to `numpy.mean` for full documentation.
+
+        See Also
+        --------
+        numpy.mean
+
+        Notes
+        -----
+        Same as `ndarray.mean` except that, where that returns an `ndarray`,
+        this returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.mean()
+        5.5
+        >>> x.mean(0)
+        matrix([[4., 5., 6., 7.]])
+        >>> x.mean(1)
+        matrix([[ 1.5],
+                [ 5.5],
+                [ 9.5]])
+
+        """
+        return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)
+
+    def std(self, axis=None, dtype=None, out=None, ddof=0):
+        """
+        Return the standard deviation of the array elements along the given axis.
+
+        Refer to `numpy.std` for full documentation.
+
+        See Also
+        --------
+        numpy.std
+
+        Notes
+        -----
+        This is the same as `ndarray.std`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.std()
+        3.4520525295346629 # may vary
+        >>> x.std(0)
+        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]]) # may vary
+        >>> x.std(1)
+        matrix([[ 1.11803399],
+                [ 1.11803399],
+                [ 1.11803399]])
+
+        """
+        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
+
+    def var(self, axis=None, dtype=None, out=None, ddof=0):
+        """
+        Returns the variance of the matrix elements, along the given axis.
+
+        Refer to `numpy.var` for full documentation.
+
+        See Also
+        --------
+        numpy.var
+
+        Notes
+        -----
+        This is the same as `ndarray.var`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.var()
+        11.916666666666666
+        >>> x.var(0)
+        matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]]) # may vary
+        >>> x.var(1)
+        matrix([[1.25],
+                [1.25],
+                [1.25]])
+
+        """
+        return N.ndarray.var(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
+
+    def prod(self, axis=None, dtype=None, out=None):
+        """
+        Return the product of the array elements over the given axis.
+
+        Refer to `prod` for full documentation.
+
+        See Also
+        --------
+        prod, ndarray.prod
+
+        Notes
+        -----
+        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
+        returns a `matrix` object instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.prod()
+        0
+        >>> x.prod(0)
+        matrix([[  0,  45, 120, 231]])
+        >>> x.prod(1)
+        matrix([[   0],
+                [ 840],
+                [7920]])
+
+        """
+        return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)
+
+    def any(self, axis=None, out=None):
+        """
+        Test whether any array element along a given axis evaluates to True.
+
+        Refer to `numpy.any` for full documentation.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis along which logical OR is performed
+        out : ndarray, optional
+            Output to existing array instead of creating new one, must have
+            same shape as expected output
+
+        Returns
+        -------
+            any : bool, ndarray
+                Returns a single bool if `axis` is ``None``; otherwise,
+                returns `ndarray`
+
+        """
+        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis)
+
+    def all(self, axis=None, out=None):
+        """
+        Test whether all matrix elements along a given axis evaluate to True.
+
+        Parameters
+        ----------
+        See `numpy.all` for complete descriptions
+
+        See Also
+        --------
+        numpy.all
+
+        Notes
+        -----
+        This is the same as `ndarray.all`, but it returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> y = x[0]; y
+        matrix([[0, 1, 2, 3]])
+        >>> (x == y)
+        matrix([[ True,  True,  True,  True],
+                [False, False, False, False],
+                [False, False, False, False]])
+        >>> (x == y).all()
+        False
+        >>> (x == y).all(0)
+        matrix([[False, False, False, False]])
+        >>> (x == y).all(1)
+        matrix([[ True],
+                [False],
+                [False]])
+
+        """
+        return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
+
+    def max(self, axis=None, out=None):
+        """
+        Return the maximum value along an axis.
+
+        Parameters
+        ----------
+        See `amax` for complete descriptions
+
+        See Also
+        --------
+        amax, ndarray.max
+
+        Notes
+        -----
+        This is the same as `ndarray.max`, but returns a `matrix` object
+        where `ndarray.max` would return an ndarray.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.max()
+        11
+        >>> x.max(0)
+        matrix([[ 8,  9, 10, 11]])
+        >>> x.max(1)
+        matrix([[ 3],
+                [ 7],
+                [11]])
+
+        """
+        return N.ndarray.max(self, axis, out, keepdims=True)._collapse(axis)
+
+    def argmax(self, axis=None, out=None):
+        """
+        Indexes of the maximum values along an axis.
+
+        Return the indexes of the first occurrences of the maximum values
+        along the specified axis.  If axis is None, the index is for the
+        flattened matrix.
+
+        Parameters
+        ----------
+        See `numpy.argmax` for complete descriptions
+
+        See Also
+        --------
+        numpy.argmax
+
+        Notes
+        -----
+        This is the same as `ndarray.argmax`, but returns a `matrix` object
+        where `ndarray.argmax` would return an `ndarray`.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.argmax()
+        11
+        >>> x.argmax(0)
+        matrix([[2, 2, 2, 2]])
+        >>> x.argmax(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+        """
+        return N.ndarray.argmax(self, axis, out)._align(axis)
+
+    def min(self, axis=None, out=None):
+        """
+        Return the minimum value along an axis.
+
+        Parameters
+        ----------
+        See `amin` for complete descriptions.
+
+        See Also
+        --------
+        amin, ndarray.min
+
+        Notes
+        -----
+        This is the same as `ndarray.min`, but returns a `matrix` object
+        where `ndarray.min` would return an ndarray.
+
+        Examples
+        --------
+        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[  0,  -1,  -2,  -3],
+                [ -4,  -5,  -6,  -7],
+                [ -8,  -9, -10, -11]])
+        >>> x.min()
+        -11
+        >>> x.min(0)
+        matrix([[ -8,  -9, -10, -11]])
+        >>> x.min(1)
+        matrix([[ -3],
+                [ -7],
+                [-11]])
+
+        """
+        return N.ndarray.min(self, axis, out, keepdims=True)._collapse(axis)
+
+    def argmin(self, axis=None, out=None):
+        """
+        Indexes of the minimum values along an axis.
+
+        Return the indexes of the first occurrences of the minimum values
+        along the specified axis.  If axis is None, the index is for the
+        flattened matrix.
+
+        Parameters
+        ----------
+        See `numpy.argmin` for complete descriptions.
+
+        See Also
+        --------
+        numpy.argmin
+
+        Notes
+        -----
+        This is the same as `ndarray.argmin`, but returns a `matrix` object
+        where `ndarray.argmin` would return an `ndarray`.
+
+        Examples
+        --------
+        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[  0,  -1,  -2,  -3],
+                [ -4,  -5,  -6,  -7],
+                [ -8,  -9, -10, -11]])
+        >>> x.argmin()
+        11
+        >>> x.argmin(0)
+        matrix([[2, 2, 2, 2]])
+        >>> x.argmin(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+        """
+        return N.ndarray.argmin(self, axis, out)._align(axis)
+
+    def ptp(self, axis=None, out=None):
+        """
+        Peak-to-peak (maximum - minimum) value along the given axis.
+
+        Refer to `numpy.ptp` for full documentation.
+
+        See Also
+        --------
+        numpy.ptp
+
+        Notes
+        -----
+        Same as `ndarray.ptp`, except, where that would return an `ndarray` object,
+        this returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.ptp()
+        11
+        >>> x.ptp(0)
+        matrix([[8, 8, 8, 8]])
+        >>> x.ptp(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+        """
+        return N.ptp(self, axis, out)._align(axis)
+
+    @property
+    def I(self):
+        """
+        Returns the (multiplicative) inverse of invertible `self`.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            If `self` is non-singular, `ret` is such that ``ret * self`` ==
+            ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return
+            ``True``.
+
+        Raises
+        ------
+        numpy.linalg.LinAlgError: Singular matrix
+            If `self` is singular.
+
+        See Also
+        --------
+        linalg.inv
+
+        Examples
+        --------
+        >>> m = np.matrix('[1, 2; 3, 4]'); m
+        matrix([[1, 2],
+                [3, 4]])
+        >>> m.getI()
+        matrix([[-2. ,  1. ],
+                [ 1.5, -0.5]])
+        >>> m.getI() * m
+        matrix([[ 1.,  0.], # may vary
+                [ 0.,  1.]])
+
+        """
+        M, N = self.shape
+        if M == N:
+            from numpy.linalg import inv as func
+        else:
+            from numpy.linalg import pinv as func
+        return asmatrix(func(self))
+
+    @property
+    def A(self):
+        """
+        Return `self` as an `ndarray` object.
+
+        Equivalent to ``np.asarray(self)``.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : ndarray
+            `self` as an `ndarray`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.getA()
+        array([[ 0,  1,  2,  3],
+               [ 4,  5,  6,  7],
+               [ 8,  9, 10, 11]])
+
+        """
+        return self.__array__()
+
+    @property
+    def A1(self):
+        """
+        Return `self` as a flattened `ndarray`.
+
+        Equivalent to ``np.asarray(x).ravel()``
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : ndarray
+            `self`, 1-D, as an `ndarray`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.getA1()
+        array([ 0,  1,  2, ...,  9, 10, 11])
+
+
+        """
+        return self.__array__().ravel()
+
+
+    def ravel(self, order='C'):
+        """
+        Return a flattened matrix.
+
+        Refer to `numpy.ravel` for more documentation.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            The elements of `m` are read using this index order. 'C' means to
+            index the elements in C-like order, with the last axis index
+            changing fastest, back to the first axis index changing slowest.
+            'F' means to index the elements in Fortran-like index order, with
+            the first index changing fastest, and the last index changing
+            slowest. Note that the 'C' and 'F' options take no account of the
+            memory layout of the underlying array, and only refer to the order
+            of axis indexing.  'A' means to read the elements in Fortran-like
+            index order if `m` is Fortran *contiguous* in memory, C-like order
+            otherwise.  'K' means to read the elements in the order they occur
+            in memory, except for reversing the data when strides are negative.
+            By default, 'C' index order is used.
+
+        Returns
+        -------
+        ret : matrix
+            Return the matrix flattened to shape `(1, N)` where `N`
+            is the number of elements in the original matrix.
+            A copy is made only if necessary.
+
+        See Also
+        --------
+        matrix.flatten : returns a similar output matrix but always a copy
+        matrix.flat : a flat iterator on the array.
+        numpy.ravel : related function which returns an ndarray
+
+        """
+        return N.ndarray.ravel(self, order=order)
+
+    @property
+    def T(self):
+        """
+        Returns the transpose of the matrix.
+
+        Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            The (non-conjugated) transpose of the matrix.
+
+        See Also
+        --------
+        transpose, getH
+
+        Examples
+        --------
+        >>> m = np.matrix('[1, 2; 3, 4]')
+        >>> m
+        matrix([[1, 2],
+                [3, 4]])
+        >>> m.getT()
+        matrix([[1, 3],
+                [2, 4]])
+
+        """
+        return self.transpose()
+
+    @property
+    def H(self):
+        """
+        Returns the (complex) conjugate transpose of `self`.
+
+        Equivalent to ``np.transpose(self)`` if `self` is real-valued.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            complex conjugate transpose of `self`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4)))
+        >>> z = x - 1j*x; z
+        matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
+                [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
+                [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
+        >>> z.getH()
+        matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
+                [ 1. +1.j,  5. +5.j,  9. +9.j],
+                [ 2. +2.j,  6. +6.j, 10.+10.j],
+                [ 3. +3.j,  7. +7.j, 11.+11.j]])
+
+        """
+        if issubclass(self.dtype.type, N.complexfloating):
+            return self.transpose().conjugate()
+        else:
+            return self.transpose()
+
+    # kept for compatibility
+    getT = T.fget
+    getA = A.fget
+    getA1 = A1.fget
+    getH = H.fget
+    getI = I.fget
+
+def _from_string(str, gdict, ldict):
+    rows = str.split(';')
+    rowtup = []
+    for row in rows:
+        trow = row.split(',')
+        newrow = []
+        for x in trow:
+            newrow.extend(x.split())
+        trow = newrow
+        coltup = []
+        for col in trow:
+            col = col.strip()
+            try:
+                thismat = ldict[col]
+            except KeyError:
+                try:
+                    thismat = gdict[col]
+                except KeyError as e:
+                    raise NameError(f"name {col!r} is not defined") from None
+
+            coltup.append(thismat)
+        rowtup.append(concatenate(coltup, axis=-1))
+    return concatenate(rowtup, axis=0)
+
+
+@set_module('numpy')
+def bmat(obj, ldict=None, gdict=None):
+    """
+    Build a matrix object from a string, nested sequence, or array.
+
+    Parameters
+    ----------
+    obj : str or array_like
+        Input data. If a string, variables in the current scope may be
+        referenced by name.
+    ldict : dict, optional
+        A dictionary that replaces local operands in current frame.
+        Ignored if `obj` is not a string or `gdict` is None.
+    gdict : dict, optional
+        A dictionary that replaces global operands in current frame.
+        Ignored if `obj` is not a string.
+
+    Returns
+    -------
+    out : matrix
+        Returns a matrix object, which is a specialized 2-D array.
+
+    See Also
+    --------
+    block :
+        A generalization of this function for N-d arrays, that returns normal
+        ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> A = np.asmatrix('1 1; 1 1')
+    >>> B = np.asmatrix('2 2; 2 2')
+    >>> C = np.asmatrix('3 4; 5 6')
+    >>> D = np.asmatrix('7 8; 9 0')
+
+    All the following expressions construct the same block matrix:
+
+    >>> np.bmat([[A, B], [C, D]])
+    matrix([[1, 1, 2, 2],
+            [1, 1, 2, 2],
+            [3, 4, 7, 8],
+            [5, 6, 9, 0]])
+    >>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
+    matrix([[1, 1, 2, 2],
+            [1, 1, 2, 2],
+            [3, 4, 7, 8],
+            [5, 6, 9, 0]])
+    >>> np.bmat('A,B; C,D')
+    matrix([[1, 1, 2, 2],
+            [1, 1, 2, 2],
+            [3, 4, 7, 8],
+            [5, 6, 9, 0]])
+
+    """
+    if isinstance(obj, str):
+        if gdict is None:
+            # get previous frame
+            frame = sys._getframe().f_back
+            glob_dict = frame.f_globals
+            loc_dict = frame.f_locals
+        else:
+            glob_dict = gdict
+            loc_dict = ldict
+
+        return matrix(_from_string(obj, glob_dict, loc_dict))
+
+    if isinstance(obj, (tuple, list)):
+        # [[A,B],[C,D]]
+        arr_rows = []
+        for row in obj:
+            if isinstance(row, N.ndarray):  # not 2-d
+                return matrix(concatenate(obj, axis=-1))
+            else:
+                arr_rows.append(concatenate(row, axis=-1))
+        return matrix(concatenate(arr_rows, axis=0))
+    if isinstance(obj, N.ndarray):
+        return matrix(obj)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/defmatrix.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/defmatrix.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a6095cc1155ab6a97be8142c5704dd553aedd602
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/defmatrix.pyi
@@ -0,0 +1,17 @@
+from collections.abc import Mapping, Sequence
+from typing import Any
+
+from numpy import matrix
+from numpy._typing import ArrayLike, DTypeLike, NDArray
+
+__all__ = ["asmatrix", "bmat", "matrix"]
+
+def bmat(
+    obj: str | Sequence[ArrayLike] | NDArray[Any],
+    ldict: None | Mapping[str, Any] = ...,
+    gdict: None | Mapping[str, Any] = ...,
+) -> matrix[tuple[int, int], Any]: ...
+
+def asmatrix(
+    data: ArrayLike, dtype: DTypeLike = ...
+) -> matrix[tuple[int, int], Any]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_defmatrix.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_defmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..81d955e86fa863043b82fa126f09528b02a3cff3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_defmatrix.py
@@ -0,0 +1,453 @@
+import collections.abc
+
+import numpy as np
+from numpy import matrix, asmatrix, bmat
+from numpy.testing import (
+    assert_, assert_equal, assert_almost_equal, assert_array_equal,
+    assert_array_almost_equal, assert_raises
+    )
+from numpy.linalg import matrix_power
+
+class TestCtor:
+    def test_basic(self):
+        A = np.array([[1, 2], [3, 4]])
+        mA = matrix(A)
+        assert_(np.all(mA.A == A))
+
+        B = bmat("A,A;A,A")
+        C = bmat([[A, A], [A, A]])
+        D = np.array([[1, 2, 1, 2],
+                      [3, 4, 3, 4],
+                      [1, 2, 1, 2],
+                      [3, 4, 3, 4]])
+        assert_(np.all(B.A == D))
+        assert_(np.all(C.A == D))
+
+        E = np.array([[5, 6], [7, 8]])
+        AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
+        assert_(np.all(bmat([A, E]) == AEresult))
+
+        vec = np.arange(5)
+        mvec = matrix(vec)
+        assert_(mvec.shape == (1, 5))
+
+    def test_exceptions(self):
+        # Check for ValueError when called with invalid string data.
+        assert_raises(ValueError, matrix, "invalid")
+
+    def test_bmat_nondefault_str(self):
+        A = np.array([[1, 2], [3, 4]])
+        B = np.array([[5, 6], [7, 8]])
+        Aresult = np.array([[1, 2, 1, 2],
+                            [3, 4, 3, 4],
+                            [1, 2, 1, 2],
+                            [3, 4, 3, 4]])
+        mixresult = np.array([[1, 2, 5, 6],
+                              [3, 4, 7, 8],
+                              [5, 6, 1, 2],
+                              [7, 8, 3, 4]])
+        assert_(np.all(bmat("A,A;A,A") == Aresult))
+        assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
+        assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
+        assert_(
+            np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
+        b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
+        assert_(np.all(b2 == mixresult))
+
+
+class TestProperties:
+    def test_sum(self):
+        """Test whether matrix.sum(axis=1) preserves orientation.
+        Fails in NumPy <= 0.9.6.2127.
+        """
+        M = matrix([[1, 2, 0, 0],
+                   [3, 4, 0, 0],
+                   [1, 2, 1, 2],
+                   [3, 4, 3, 4]])
+        sum0 = matrix([8, 12, 4, 6])
+        sum1 = matrix([3, 7, 6, 14]).T
+        sumall = 30
+        assert_array_equal(sum0, M.sum(axis=0))
+        assert_array_equal(sum1, M.sum(axis=1))
+        assert_equal(sumall, M.sum())
+
+        assert_array_equal(sum0, np.sum(M, axis=0))
+        assert_array_equal(sum1, np.sum(M, axis=1))
+        assert_equal(sumall, np.sum(M))
+
+    def test_prod(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.prod(), 720)
+        assert_equal(x.prod(0), matrix([[4, 10, 18]]))
+        assert_equal(x.prod(1), matrix([[6], [120]]))
+
+        assert_equal(np.prod(x), 720)
+        assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]]))
+        assert_equal(np.prod(x, axis=1), matrix([[6], [120]]))
+
+        y = matrix([0, 1, 3])
+        assert_(y.prod() == 0)
+
+    def test_max(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.max(), 6)
+        assert_equal(x.max(0), matrix([[4, 5, 6]]))
+        assert_equal(x.max(1), matrix([[3], [6]]))
+
+        assert_equal(np.max(x), 6)
+        assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
+        assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
+
+    def test_min(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.min(), 1)
+        assert_equal(x.min(0), matrix([[1, 2, 3]]))
+        assert_equal(x.min(1), matrix([[1], [4]]))
+
+        assert_equal(np.min(x), 1)
+        assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
+        assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
+
+    def test_ptp(self):
+        x = np.arange(4).reshape((2, 2))
+        mx = x.view(np.matrix)
+        assert_(mx.ptp() == 3)
+        assert_(np.all(mx.ptp(0) == np.array([2, 2])))
+        assert_(np.all(mx.ptp(1) == np.array([1, 1])))
+
+    def test_var(self):
+        x = np.arange(9).reshape((3, 3))
+        mx = x.view(np.matrix)
+        assert_equal(x.var(ddof=0), mx.var(ddof=0))
+        assert_equal(x.var(ddof=1), mx.var(ddof=1))
+
+    def test_basic(self):
+        import numpy.linalg as linalg
+
+        A = np.array([[1., 2.],
+                      [3., 4.]])
+        mA = matrix(A)
+        assert_(np.allclose(linalg.inv(A), mA.I))
+        assert_(np.all(np.array(np.transpose(A) == mA.T)))
+        assert_(np.all(np.array(np.transpose(A) == mA.H)))
+        assert_(np.all(A == mA.A))
+
+        B = A + 2j*A
+        mB = matrix(B)
+        assert_(np.allclose(linalg.inv(B), mB.I))
+        assert_(np.all(np.array(np.transpose(B) == mB.T)))
+        assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
+
+    def test_pinv(self):
+        x = matrix(np.arange(6).reshape(2, 3))
+        xpinv = matrix([[-0.77777778,  0.27777778],
+                        [-0.11111111,  0.11111111],
+                        [ 0.55555556, -0.05555556]])
+        assert_almost_equal(x.I, xpinv)
+
+    def test_comparisons(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = matrix(A)
+        mB = matrix(A) + 0.1
+        assert_(np.all(mB == A+0.1))
+        assert_(np.all(mB == matrix(A+0.1)))
+        assert_(not np.any(mB == matrix(A-0.1)))
+        assert_(np.all(mA < mB))
+        assert_(np.all(mA <= mB))
+        assert_(np.all(mA <= mA))
+        assert_(not np.any(mA < mA))
+
+        assert_(not np.any(mB < mA))
+        assert_(np.all(mB >= mA))
+        assert_(np.all(mB >= mB))
+        assert_(not np.any(mB > mB))
+
+        assert_(np.all(mA == mA))
+        assert_(not np.any(mA == mB))
+        assert_(np.all(mB != mA))
+
+        assert_(not np.all(abs(mA) > 0))
+        assert_(np.all(abs(mB > 0)))
+
+    def test_asmatrix(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = asmatrix(A)
+        A[0, 0] = -10
+        assert_(A[0, 0] == mA[0, 0])
+
+    def test_noaxis(self):
+        A = matrix([[1, 0], [0, 1]])
+        assert_(A.sum() == matrix(2))
+        assert_(A.mean() == matrix(0.5))
+
+    def test_repr(self):
+        A = matrix([[1, 0], [0, 1]])
+        assert_(repr(A) == "matrix([[1, 0],\n        [0, 1]])")
+
+    def test_make_bool_matrix_from_str(self):
+        A = matrix('True; True; False')
+        B = matrix([[True], [True], [False]])
+        assert_array_equal(A, B)
+
+class TestCasting:
+    def test_basic(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = matrix(A)
+
+        mB = mA.copy()
+        O = np.ones((10, 10), np.float64) * 0.1
+        mB = mB + O
+        assert_(mB.dtype.type == np.float64)
+        assert_(np.all(mA != mB))
+        assert_(np.all(mB == mA+0.1))
+
+        mC = mA.copy()
+        O = np.ones((10, 10), np.complex128)
+        mC = mC * O
+        assert_(mC.dtype.type == np.complex128)
+        assert_(np.all(mA != mB))
+
+
+class TestAlgebra:
+    def test_basic(self):
+        import numpy.linalg as linalg
+
+        A = np.array([[1., 2.], [3., 4.]])
+        mA = matrix(A)
+
+        B = np.identity(2)
+        for i in range(6):
+            assert_(np.allclose((mA ** i).A, B))
+            B = np.dot(B, A)
+
+        Ainv = linalg.inv(A)
+        B = np.identity(2)
+        for i in range(6):
+            assert_(np.allclose((mA ** -i).A, B))
+            B = np.dot(B, Ainv)
+
+        assert_(np.allclose((mA * mA).A, np.dot(A, A)))
+        assert_(np.allclose((mA + mA).A, (A + A)))
+        assert_(np.allclose((3*mA).A, (3*A)))
+
+        mA2 = matrix(A)
+        mA2 *= 3
+        assert_(np.allclose(mA2.A, 3*A))
+
+    def test_pow(self):
+        """Test raising a matrix to an integer power works as expected."""
+        m = matrix("1. 2.; 3. 4.")
+        m2 = m.copy()
+        m2 **= 2
+        mi = m.copy()
+        mi **= -1
+        m4 = m2.copy()
+        m4 **= 2
+        assert_array_almost_equal(m2, m**2)
+        assert_array_almost_equal(m4, np.dot(m2, m2))
+        assert_array_almost_equal(np.dot(mi, m), np.eye(2))
+
+    def test_scalar_type_pow(self):
+        m = matrix([[1, 2], [3, 4]])
+        for scalar_t in [np.int8, np.uint8]:
+            two = scalar_t(2)
+            assert_array_almost_equal(m ** 2, m ** two)
+
+    def test_notimplemented(self):
+        '''Check that 'not implemented' operations produce a failure.'''
+        A = matrix([[1., 2.],
+                    [3., 4.]])
+
+        # __rpow__
+        with assert_raises(TypeError):
+            1.0**A
+
+        # __mul__ with something not a list, ndarray, tuple, or scalar
+        with assert_raises(TypeError):
+            A*object()
+
+
+class TestMatrixReturn:
+    def test_instance_methods(self):
+        a = matrix([1.0], dtype='f8')
+        methodargs = {
+            'astype': ('intc',),
+            'clip': (0.0, 1.0),
+            'compress': ([1],),
+            'repeat': (1,),
+            'reshape': (1,),
+            'swapaxes': (0, 0),
+            'dot': np.array([1.0]),
+            }
+        excluded_methods = [
+            'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield',
+            'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize',
+            'searchsorted', 'setflags', 'setfield', 'sort',
+            'partition', 'argpartition', 'newbyteorder', 'to_device',
+            'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any',
+            'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp',
+            'prod', 'std', 'ctypes', 'itemset', 'bitwise_count',
+            ]
+        for attrib in dir(a):
+            if attrib.startswith('_') or attrib in excluded_methods:
+                continue
+            f = getattr(a, attrib)
+            if isinstance(f, collections.abc.Callable):
+                # reset contents of a
+                a.astype('f8')
+                a.fill(1.0)
+                if attrib in methodargs:
+                    args = methodargs[attrib]
+                else:
+                    args = ()
+                b = f(*args)
+                assert_(type(b) is matrix, "%s" % attrib)
+        assert_(type(a.real) is matrix)
+        assert_(type(a.imag) is matrix)
+        c, d = matrix([0.0]).nonzero()
+        assert_(type(c) is np.ndarray)
+        assert_(type(d) is np.ndarray)
+
+
+class TestIndexing:
+    def test_basic(self):
+        x = asmatrix(np.zeros((3, 2), float))
+        y = np.zeros((3, 1), float)
+        y[:, 0] = [0.8, 0.2, 0.3]
+        x[:, 1] = y > 0.5
+        assert_equal(x, [[0, 1], [0, 0], [0, 0]])
+
+
+class TestNewScalarIndexing:
+    a = matrix([[1, 2], [3, 4]])
+
+    def test_dimesions(self):
+        a = self.a
+        x = a[0]
+        assert_equal(x.ndim, 2)
+
+    def test_array_from_matrix_list(self):
+        a = self.a
+        x = np.array([a, a])
+        assert_equal(x.shape, [2, 2, 2])
+
+    def test_array_to_list(self):
+        a = self.a
+        assert_equal(a.tolist(), [[1, 2], [3, 4]])
+
+    def test_fancy_indexing(self):
+        a = self.a
+        x = a[1, [0, 1, 0]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[3,  4,  3]]))
+        x = a[[1, 0]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[3,  4], [1, 2]]))
+        x = a[[[1], [0]], [[1, 0], [0, 1]]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[4,  3], [1,  2]]))
+
+    def test_matrix_element(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x[0][0], matrix([[1, 2, 3]]))
+        assert_equal(x[0][0].shape, (1, 3))
+        assert_equal(x[0].shape, (1, 3))
+        assert_equal(x[:, 0].shape, (2, 1))
+
+        x = matrix(0)
+        assert_equal(x[0, 0], 0)
+        assert_equal(x[0], 0)
+        assert_equal(x[:, 0].shape, x.shape)
+
+    def test_scalar_indexing(self):
+        x = asmatrix(np.zeros((3, 2), float))
+        assert_equal(x[0, 0], x[0][0])
+
+    def test_row_column_indexing(self):
+        x = asmatrix(np.eye(2))
+        assert_array_equal(x[0,:], [[1, 0]])
+        assert_array_equal(x[1,:], [[0, 1]])
+        assert_array_equal(x[:, 0], [[1], [0]])
+        assert_array_equal(x[:, 1], [[0], [1]])
+
+    def test_boolean_indexing(self):
+        A = np.arange(6)
+        A.shape = (3, 2)
+        x = asmatrix(A)
+        assert_array_equal(x[:, np.array([True, False])], x[:, 0])
+        assert_array_equal(x[np.array([True, False, False]),:], x[0,:])
+
+    def test_list_indexing(self):
+        A = np.arange(6)
+        A.shape = (3, 2)
+        x = asmatrix(A)
+        assert_array_equal(x[:, [1, 0]], x[:, ::-1])
+        assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
+
+
+class TestPower:
+    def test_returntype(self):
+        a = np.array([[0, 1], [0, 0]])
+        assert_(type(matrix_power(a, 2)) is np.ndarray)
+        a = asmatrix(a)
+        assert_(type(matrix_power(a, 2)) is matrix)
+
+    def test_list(self):
+        assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]])
+
+
+class TestShape:
+
+    a = np.array([[1], [2]])
+    m = matrix([[1], [2]])
+
+    def test_shape(self):
+        assert_equal(self.a.shape, (2, 1))
+        assert_equal(self.m.shape, (2, 1))
+
+    def test_numpy_ravel(self):
+        assert_equal(np.ravel(self.a).shape, (2,))
+        assert_equal(np.ravel(self.m).shape, (2,))
+
+    def test_member_ravel(self):
+        assert_equal(self.a.ravel().shape, (2,))
+        assert_equal(self.m.ravel().shape, (1, 2))
+
+    def test_member_flatten(self):
+        assert_equal(self.a.flatten().shape, (2,))
+        assert_equal(self.m.flatten().shape, (1, 2))
+
+    def test_numpy_ravel_order(self):
+        x = np.array([[1, 2, 3], [4, 5, 6]])
+        assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+        assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+        assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+
+    def test_matrix_ravel_order(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
+        assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
+        assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
+        assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
+
+    def test_array_memory_sharing(self):
+        assert_(np.may_share_memory(self.a, self.a.ravel()))
+        assert_(not np.may_share_memory(self.a, self.a.flatten()))
+
+    def test_matrix_memory_sharing(self):
+        assert_(np.may_share_memory(self.m, self.m.ravel()))
+        assert_(not np.may_share_memory(self.m, self.m.flatten()))
+
+    def test_expand_dims_matrix(self):
+        # matrices are always 2d - so expand_dims only makes sense when the
+        # type is changed away from matrix.
+        a = np.arange(10).reshape((2, 5)).view(np.matrix)
+        expanded = np.expand_dims(a, axis=1)
+        assert_equal(expanded.ndim, 3)
+        assert_(not isinstance(expanded, np.matrix))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_interaction.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_interaction.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c6bf210e46e4f6a8fd53f4762acf27f1c74e6a1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_interaction.py
@@ -0,0 +1,354 @@
+"""Tests of interaction of matrix with other parts of numpy.
+
+Note that tests with MaskedArray and linalg are done in separate files.
+"""
+import pytest
+
+import textwrap
+import warnings
+
+import numpy as np
+from numpy.testing import (assert_, assert_equal, assert_raises,
+                           assert_raises_regex, assert_array_equal,
+                           assert_almost_equal, assert_array_almost_equal)
+
+
+def test_fancy_indexing():
+    # The matrix class messes with the shape. While this is always
+    # weird (getitem is not used, it does not have setitem nor knows
+    # about fancy indexing), this tests gh-3110
+    # 2018-04-29: moved here from core.tests.test_index.
+    m = np.matrix([[1, 2], [3, 4]])
+
+    assert_(isinstance(m[[0, 1, 0], :], np.matrix))
+
+    # gh-3110. Note the transpose currently because matrices do *not*
+    # support dimension fixing for fancy indexing correctly.
+    x = np.asmatrix(np.arange(50).reshape(5, 10))
+    assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
+
+
+def test_polynomial_mapdomain():
+    # test that polynomial preserved matrix subtype.
+    # 2018-04-29: moved here from polynomial.tests.polyutils.
+    dom1 = [0, 4]
+    dom2 = [1, 3]
+    x = np.matrix([dom1, dom1])
+    res = np.polynomial.polyutils.mapdomain(x, dom1, dom2)
+    assert_(isinstance(res, np.matrix))
+
+
+def test_sort_matrix_none():
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    a = np.matrix([[2, 1, 0]])
+    actual = np.sort(a, axis=None)
+    expected = np.matrix([[0, 1, 2]])
+    assert_equal(actual, expected)
+    assert_(type(expected) is np.matrix)
+
+
+def test_partition_matrix_none():
+    # gh-4301
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    a = np.matrix([[2, 1, 0]])
+    actual = np.partition(a, 1, axis=None)
+    expected = np.matrix([[0, 1, 2]])
+    assert_equal(actual, expected)
+    assert_(type(expected) is np.matrix)
+
+
+def test_dot_scalar_and_matrix_of_objects():
+    # Ticket #2469
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.dot(arr, 3), desired)
+    assert_equal(np.dot(3, arr), desired)
+
+
+def test_inner_scalar_and_matrix():
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
+        sca = np.array(3, dtype=dt)[()]
+        arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
+        desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
+        assert_equal(np.inner(arr, sca), desired)
+        assert_equal(np.inner(sca, arr), desired)
+
+
+def test_inner_scalar_and_matrix_of_objects():
+    # Ticket #4482
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.inner(arr, 3), desired)
+    assert_equal(np.inner(3, arr), desired)
+
+
+def test_iter_allocate_output_subtype():
+    # Make sure that the subtype with priority wins
+    # 2018-04-29: moved here from core.tests.test_nditer, given the
+    # matrix specific shape test.
+
+    # matrix vs ndarray
+    a = np.matrix([[1, 2], [3, 4]])
+    b = np.arange(4).reshape(2, 2).T
+    i = np.nditer([a, b, None], [],
+                  [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+    assert_(type(i.operands[2]) is np.matrix)
+    assert_(type(i.operands[2]) is not np.ndarray)
+    assert_equal(i.operands[2].shape, (2, 2))
+
+    # matrix always wants things to be 2D
+    b = np.arange(4).reshape(1, 2, 2)
+    assert_raises(RuntimeError, np.nditer, [a, b, None], [],
+                  [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+    # but if subtypes are disabled, the result can still work
+    i = np.nditer([a, b, None], [],
+                  [['readonly'], ['readonly'],
+                   ['writeonly', 'allocate', 'no_subtype']])
+    assert_(type(i.operands[2]) is np.ndarray)
+    assert_(type(i.operands[2]) is not np.matrix)
+    assert_equal(i.operands[2].shape, (1, 2, 2))
+
+
+def like_function():
+    # 2018-04-29: moved here from core.tests.test_numeric
+    a = np.matrix([[1, 2], [3, 4]])
+    for like_function in np.zeros_like, np.ones_like, np.empty_like:
+        b = like_function(a)
+        assert_(type(b) is np.matrix)
+
+        c = like_function(a, subok=False)
+        assert_(type(c) is not np.matrix)
+
+
+def test_array_astype():
+    # 2018-04-29: copied here from core.tests.test_api
+    # subok=True passes through a matrix
+    a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
+    b = a.astype('f4', subok=True, copy=False)
+    assert_(a is b)
+
+    # subok=True is default, and creates a subtype on a cast
+    b = a.astype('i4', copy=False)
+    assert_equal(a, b)
+    assert_equal(type(b), np.matrix)
+
+    # subok=False never returns a matrix
+    b = a.astype('f4', subok=False, copy=False)
+    assert_equal(a, b)
+    assert_(not (a is b))
+    assert_(type(b) is not np.matrix)
+
+
+def test_stack():
+    # 2018-04-29: copied here from core.tests.test_shape_base
+    # check np.matrix cannot be stacked
+    m = np.matrix([[1, 2], [3, 4]])
+    assert_raises_regex(ValueError, 'shape too large to be a matrix',
+                        np.stack, [m, m])
+
+
+def test_object_scalar_multiply():
+    # Tickets #2469 and #4482
+    # 2018-04-29: moved here from core.tests.test_ufunc
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.multiply(arr, 3), desired)
+    assert_equal(np.multiply(3, arr), desired)
+
+
+def test_nanfunctions_matrices():
+    # Check that it works and that type and
+    # shape are preserved
+    # 2018-04-29: moved here from core.tests.test_nanfunctions
+    mat = np.matrix(np.eye(3))
+    for f in [np.nanmin, np.nanmax]:
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 1))
+        res = f(mat)
+        assert_(np.isscalar(res))
+    # check that rows of nan are dealt with for subclasses (#4628)
+    mat[1] = np.nan
+    for f in [np.nanmin, np.nanmax]:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat, axis=0)
+            assert_(isinstance(res, np.matrix))
+            assert_(not np.any(np.isnan(res)))
+            assert_(len(w) == 0)
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat, axis=1)
+            assert_(isinstance(res, np.matrix))
+            assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
+                    and not np.isnan(res[2, 0]))
+            assert_(len(w) == 1, 'no warning raised')
+            assert_(issubclass(w[0].category, RuntimeWarning))
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat)
+            assert_(np.isscalar(res))
+            assert_(res != np.nan)
+            assert_(len(w) == 0)
+
+
+def test_nanfunctions_matrices_general():
+    # Check that it works and that type and
+    # shape are preserved
+    # 2018-04-29: moved here from core.tests.test_nanfunctions
+    mat = np.matrix(np.eye(3))
+    for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
+              np.nanmean, np.nanvar, np.nanstd):
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 1))
+        res = f(mat)
+        assert_(np.isscalar(res))
+
+    for f in np.nancumsum, np.nancumprod:
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 3))
+        res = f(mat)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3*3))
+
+
+def test_average_matrix():
+    # 2018-04-29: moved here from core.tests.test_function_base.
+    y = np.matrix(np.random.rand(5, 5))
+    assert_array_equal(y.mean(0), np.average(y, 0))
+
+    a = np.matrix([[1, 2], [3, 4]])
+    w = np.matrix([[1, 2], [3, 4]])
+
+    r = np.average(a, axis=0, weights=w)
+    assert_equal(type(r), np.matrix)
+    assert_equal(r, [[2.5, 10.0/3]])
+
+
+def test_dot_matrix():
+    # Test to make sure matrices give the same answer as ndarrays
+    # 2018-04-29: moved here from core.tests.test_function_base.
+    x = np.linspace(0, 5)
+    y = np.linspace(-5, 0)
+    mx = np.matrix(x)
+    my = np.matrix(y)
+    r = np.dot(x, y)
+    mr = np.dot(mx, my.T)
+    assert_almost_equal(mr, r)
+
+
+def test_ediff1d_matrix():
+    # 2018-04-29: moved here from core.tests.test_arraysetops.
+    assert(isinstance(np.ediff1d(np.matrix(1)), np.matrix))
+    assert(isinstance(np.ediff1d(np.matrix(1), to_begin=1), np.matrix))
+
+
+def test_apply_along_axis_matrix():
+    # this test is particularly malicious because matrix
+    # refuses to become 1d
+    # 2018-04-29: moved here from core.tests.test_shape_base.
+    def double(row):
+        return row * 2
+
+    m = np.matrix([[0, 1], [2, 3]])
+    expected = np.matrix([[0, 2], [4, 6]])
+
+    result = np.apply_along_axis(double, 0, m)
+    assert_(isinstance(result, np.matrix))
+    assert_array_equal(result, expected)
+
+    result = np.apply_along_axis(double, 1, m)
+    assert_(isinstance(result, np.matrix))
+    assert_array_equal(result, expected)
+
+
+def test_kron_matrix():
+    # 2018-04-29: moved here from core.tests.test_shape_base.
+    a = np.ones([2, 2])
+    m = np.asmatrix(a)
+    assert_equal(type(np.kron(a, a)), np.ndarray)
+    assert_equal(type(np.kron(m, m)), np.matrix)
+    assert_equal(type(np.kron(a, m)), np.matrix)
+    assert_equal(type(np.kron(m, a)), np.matrix)
+
+
+class TestConcatenatorMatrix:
+    # 2018-04-29: moved here from core.tests.test_index_tricks.
+    def test_matrix(self):
+        a = [1, 2]
+        b = [3, 4]
+
+        ab_r = np.r_['r', a, b]
+        ab_c = np.r_['c', a, b]
+
+        assert_equal(type(ab_r), np.matrix)
+        assert_equal(type(ab_c), np.matrix)
+
+        assert_equal(np.array(ab_r), [[1, 2, 3, 4]])
+        assert_equal(np.array(ab_c), [[1], [2], [3], [4]])
+
+        assert_raises(ValueError, lambda: np.r_['rc', a, b])
+
+    def test_matrix_scalar(self):
+        r = np.r_['r', [1, 2], 3]
+        assert_equal(type(r), np.matrix)
+        assert_equal(np.array(r), [[1, 2, 3]])
+
+    def test_matrix_builder(self):
+        a = np.array([1])
+        b = np.array([2])
+        c = np.array([3])
+        d = np.array([4])
+        actual = np.r_['a, b; c, d']
+        expected = np.bmat([[a, b], [c, d]])
+
+        assert_equal(actual, expected)
+        assert_equal(type(actual), type(expected))
+
+
+def test_array_equal_error_message_matrix():
+    # 2018-04-29: moved here from testing.tests.test_utils.
+    with pytest.raises(AssertionError) as exc_info:
+        assert_equal(np.array([1, 2]), np.matrix([1, 2]))
+    msg = str(exc_info.value)
+    msg_reference = textwrap.dedent("""\
+
+    Arrays are not equal
+
+    (shapes (2,), (1, 2) mismatch)
+     ACTUAL: array([1, 2])
+     DESIRED: matrix([[1, 2]])""")
+    assert_equal(msg, msg_reference)
+
+
+def test_array_almost_equal_matrix():
+    # Matrix slicing keeps things 2-D, while array does not necessarily.
+    # See gh-8452.
+    # 2018-04-29: moved here from testing.tests.test_utils.
+    m1 = np.matrix([[1., 2.]])
+    m2 = np.matrix([[1., np.nan]])
+    m3 = np.matrix([[1., -np.inf]])
+    m4 = np.matrix([[np.nan, np.inf]])
+    m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
+    for assert_func in assert_array_almost_equal, assert_almost_equal:
+        for m in m1, m2, m3, m4, m5:
+            assert_func(m, m)
+            a = np.array(m)
+            assert_func(a, m)
+            assert_func(m, a)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_masked_matrix.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_masked_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..5303e6ce723f69b6fa8007857e6f0943e5010f5a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_masked_matrix.py
@@ -0,0 +1,232 @@
+import pickle
+
+import numpy as np
+from numpy.testing import assert_warns
+from numpy.ma.testutils import (assert_, assert_equal, assert_raises,
+                                assert_array_equal)
+from numpy.ma.core import (masked_array, masked_values, masked, allequal,
+                           MaskType, getmask, MaskedArray, nomask,
+                           log, add, hypot, divide)
+from numpy.ma.extras import mr_
+
+
+class MMatrix(MaskedArray, np.matrix,):
+
+    def __new__(cls, data, mask=nomask):
+        mat = np.matrix(data)
+        _data = MaskedArray.__new__(cls, data=mat, mask=mask)
+        return _data
+
+    def __array_finalize__(self, obj):
+        np.matrix.__array_finalize__(self, obj)
+        MaskedArray.__array_finalize__(self, obj)
+        return
+
+    @property
+    def _series(self):
+        _view = self.view(MaskedArray)
+        _view._sharedmask = False
+        return _view
+
+
+class TestMaskedMatrix:
+    def test_matrix_indexing(self):
+        # Tests conversions and indexing
+        x1 = np.matrix([[1, 2, 3], [4, 3, 2]])
+        x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]])
+        x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]])
+        x4 = masked_array(x1)
+        # test conversion to strings
+        str(x2)  # raises?
+        repr(x2)  # raises?
+        # tests of indexing
+        assert_(type(x2[1, 0]) is type(x1[1, 0]))
+        assert_(x1[1, 0] == x2[1, 0])
+        assert_(x2[1, 1] is masked)
+        assert_equal(x1[0, 2], x2[0, 2])
+        assert_equal(x1[0, 1:], x2[0, 1:])
+        assert_equal(x1[:, 2], x2[:, 2])
+        assert_equal(x1[:], x2[:])
+        assert_equal(x1[1:], x3[1:])
+        x1[0, 2] = 9
+        x2[0, 2] = 9
+        assert_equal(x1, x2)
+        x1[0, 1:] = 99
+        x2[0, 1:] = 99
+        assert_equal(x1, x2)
+        x2[0, 1] = masked
+        assert_equal(x1, x2)
+        x2[0, 1:] = masked
+        assert_equal(x1, x2)
+        x2[0, :] = x1[0, :]
+        x2[0, 1] = masked
+        assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]])))
+        x3[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+        assert_(allequal(getmask(x3)[1], masked_array([1, 1, 0])))
+        assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0])))
+        x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+        assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0])))
+        assert_(allequal(x4[1], masked_array([1, 2, 3])))
+        x1 = np.matrix(np.arange(5) * 1.0)
+        x2 = masked_values(x1, 3.0)
+        assert_equal(x1, x2)
+        assert_(allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType),
+                         x2.mask))
+        assert_equal(3.0, x2.fill_value)
+
+    def test_pickling_subbaseclass(self):
+        # Test pickling w/ a subclass of ndarray
+        a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+            assert_equal(a_pickled._mask, a._mask)
+            assert_equal(a_pickled, a)
+            assert_(isinstance(a_pickled._data, np.matrix))
+
+    def test_count_mean_with_matrix(self):
+        m = masked_array(np.matrix([[1, 2], [3, 4]]), mask=np.zeros((2, 2)))
+
+        assert_equal(m.count(axis=0).shape, (1, 2))
+        assert_equal(m.count(axis=1).shape, (2, 1))
+
+        # Make sure broadcasting inside mean and var work
+        assert_equal(m.mean(axis=0), [[2., 3.]])
+        assert_equal(m.mean(axis=1), [[1.5], [3.5]])
+
+    def test_flat(self):
+        # Test that flat can return items even for matrices [#4585, #4615]
+        # test simple access
+        test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+        assert_equal(test.flat[1], 2)
+        assert_equal(test.flat[2], masked)
+        assert_(np.all(test.flat[0:2] == test[0, 0:2]))
+        # Test flat on masked_matrices
+        test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+        test.flat = masked_array([3, 2, 1], mask=[1, 0, 0])
+        control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0])
+        assert_equal(test, control)
+        # Test setting
+        test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+        testflat = test.flat
+        testflat[:] = testflat[[2, 1, 0]]
+        assert_equal(test, control)
+        testflat[0] = 9
+        # test that matrices keep the correct shape (#4615)
+        a = masked_array(np.matrix(np.eye(2)), mask=0)
+        b = a.flat
+        b01 = b[:2]
+        assert_equal(b01.data, np.array([[1., 0.]]))
+        assert_equal(b01.mask, np.array([[False, False]]))
+
+    def test_allany_onmatrices(self):
+        x = np.array([[0.13, 0.26, 0.90],
+                      [0.28, 0.33, 0.63],
+                      [0.31, 0.87, 0.70]])
+        X = np.matrix(x)
+        m = np.array([[True, False, False],
+                      [False, False, False],
+                      [True, True, False]], dtype=np.bool)
+        mX = masked_array(X, mask=m)
+        mXbig = (mX > 0.5)
+        mXsmall = (mX < 0.5)
+
+        assert_(not mXbig.all())
+        assert_(mXbig.any())
+        assert_equal(mXbig.all(0), np.matrix([False, False, True]))
+        assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
+        assert_equal(mXbig.any(0), np.matrix([False, False, True]))
+        assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)
+
+        assert_(not mXsmall.all())
+        assert_(mXsmall.any())
+        assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
+        assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
+        assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
+        assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T)
+
+    def test_compressed(self):
+        a = masked_array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
+        b = a.compressed()
+        assert_equal(b, a)
+        assert_(isinstance(b, np.matrix))
+        a[0, 0] = masked
+        b = a.compressed()
+        assert_equal(b, [[2, 3, 4]])
+
+    def test_ravel(self):
+        a = masked_array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
+        aravel = a.ravel()
+        assert_equal(aravel.shape, (1, 5))
+        assert_equal(aravel._mask.shape, a.shape)
+
+    def test_view(self):
+        # Test view w/ flexible dtype
+        iterator = list(zip(np.arange(10), np.random.rand(10)))
+        data = np.array(iterator)
+        a = masked_array(iterator, dtype=[('a', float), ('b', float)])
+        a.mask[0] = (1, 0)
+        test = a.view((float, 2), np.matrix)
+        assert_equal(test, data)
+        assert_(isinstance(test, np.matrix))
+        assert_(not isinstance(test, MaskedArray))
+
+
+class TestSubclassing:
+    # Test suite for masked subclasses of ndarray.
+
+    def setup_method(self):
+        x = np.arange(5, dtype='float')
+        mx = MMatrix(x, mask=[0, 1, 0, 0, 0])
+        self.data = (x, mx)
+
+    def test_maskedarray_subclassing(self):
+        # Tests subclassing MaskedArray
+        (x, mx) = self.data
+        assert_(isinstance(mx._data, np.matrix))
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (x, mx) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(log(mx), MMatrix))
+            assert_equal(log(x), np.log(x))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (x, mx) = self.data
+        # Result should be a MMatrix
+        assert_(isinstance(add(mx, mx), MMatrix))
+        assert_(isinstance(add(mx, x), MMatrix))
+        # Result should work
+        assert_equal(add(mx, x), mx+x)
+        assert_(isinstance(add(mx, mx)._data, np.matrix))
+        with assert_warns(DeprecationWarning):
+            assert_(isinstance(add.outer(mx, mx), MMatrix))
+        assert_(isinstance(hypot(mx, mx), MMatrix))
+        assert_(isinstance(hypot(mx, x), MMatrix))
+
+    def test_masked_binary_operations2(self):
+        # Tests domained_masked_binary_operation
+        (x, mx) = self.data
+        xmx = masked_array(mx.data.__array__(), mask=mx.mask)
+        assert_(isinstance(divide(mx, mx), MMatrix))
+        assert_(isinstance(divide(mx, x), MMatrix))
+        assert_equal(divide(mx, mx), divide(xmx, xmx))
+
+class TestConcatenator:
+    # Tests for mr_, the equivalent of r_ for masked arrays.
+
+    def test_matrix_builder(self):
+        assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4'])
+
+    def test_matrix(self):
+        # Test consistency with unmasked version.  If we ever deprecate
+        # matrix, this test should either still pass, or both actual and
+        # expected should fail to be build.
+        actual = mr_['r', 1, 2, 3]
+        expected = np.ma.array(np.r_['r', 1, 2, 3])
+        assert_array_equal(actual, expected)
+
+        # outer type is masked array, inner type is matrix
+        assert_equal(type(actual), type(expected))
+        assert_equal(type(actual.data), type(expected.data))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..106c2e38217a633829329a94df077c097fbcbf7a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py
@@ -0,0 +1,93 @@
+""" Test functions for linalg module using the matrix class."""
+import numpy as np
+
+from numpy.linalg.tests.test_linalg import (
+    LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase,
+    _TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base,
+    SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases,
+    PinvCases, DetCases, LstsqCases)
+
+
+CASES = []
+
+# square test cases
+CASES += apply_tag('square', [
+    LinalgCase("0x0_matrix",
+               np.empty((0, 0), dtype=np.double).view(np.matrix),
+               np.empty((0, 1), dtype=np.double).view(np.matrix),
+               tags={'size-0'}),
+    LinalgCase("matrix_b_only",
+               np.array([[1., 2.], [3., 4.]]),
+               np.matrix([2., 1.]).T),
+    LinalgCase("matrix_a_and_b",
+               np.matrix([[1., 2.], [3., 4.]]),
+               np.matrix([2., 1.]).T),
+])
+
+# hermitian test-cases
+CASES += apply_tag('hermitian', [
+    LinalgCase("hmatrix_a_and_b",
+               np.matrix([[1., 2.], [2., 1.]]),
+               None),
+])
+# No need to make generalized or strided cases for matrices.
+
+
+class MatrixTestCase(LinalgTestCase):
+    TEST_CASES = CASES
+
+
+class TestSolveMatrix(SolveCases, MatrixTestCase):
+    pass
+
+
+class TestInvMatrix(InvCases, MatrixTestCase):
+    pass
+
+
+class TestEigvalsMatrix(EigvalsCases, MatrixTestCase):
+    pass
+
+
+class TestEigMatrix(EigCases, MatrixTestCase):
+    pass
+
+
+class TestSVDMatrix(SVDCases, MatrixTestCase):
+    pass
+
+
+class TestCondMatrix(CondCases, MatrixTestCase):
+    pass
+
+
+class TestPinvMatrix(PinvCases, MatrixTestCase):
+    pass
+
+
+class TestDetMatrix(DetCases, MatrixTestCase):
+    pass
+
+
+class TestLstsqMatrix(LstsqCases, MatrixTestCase):
+    pass
+
+
+class _TestNorm2DMatrix(_TestNorm2D):
+    array = np.matrix
+
+
+class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase):
+    pass
+
+
+class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase):
+    pass
+
+
+class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base):
+    pass
+
+
+class TestQRMatrix(_TestQR):
+    array = np.matrix
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_multiarray.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_multiarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..638d0d1534deba060140ffda3b61950a0b4f815d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_multiarray.py
@@ -0,0 +1,16 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_array_equal
+
+class TestView:
+    def test_type(self):
+        x = np.array([1, 2, 3])
+        assert_(isinstance(x.view(np.matrix), np.matrix))
+
+    def test_keywords(self):
+        x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
+        # We must be specific about the endianness here:
+        y = x.view(dtype='<i2', type=np.matrix)
+        assert_array_equal(y, [[513]])
+
+        assert_(isinstance(y, np.matrix))
+        assert_equal(y.dtype, np.dtype('<i2'))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_numeric.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..a772bb388847404a0497efa3a6b4fd241c5ec8a8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_numeric.py
@@ -0,0 +1,17 @@
+import numpy as np
+from numpy.testing import assert_equal
+
+class TestDot:
+    def test_matscalar(self):
+        b1 = np.matrix(np.ones((3, 3), dtype=complex))
+        assert_equal(b1*1.0, b1)
+
+
+def test_diagonal():
+    b1 = np.matrix([[1,2],[3,4]])
+    diag_b1 = np.matrix([[1, 4]])
+    array_b1 = np.array([1, 4])
+
+    assert_equal(b1.diagonal(), diag_b1)
+    assert_equal(np.diagonal(b1), array_b1)
+    assert_equal(np.diag(b1), array_b1)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_regression.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..27ab63058da7876bd86e868ba1ff3829dd14758f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/matrixlib/tests/test_regression.py
@@ -0,0 +1,31 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_raises
+
+
+class TestRegression:
+    def test_kron_matrix(self):
+        # Ticket #71
+        x = np.matrix('[1 0; 1 0]')
+        assert_equal(type(np.kron(x, x)), type(x))
+
+    def test_matrix_properties(self):
+        # Ticket #125
+        a = np.matrix([1.0], dtype=float)
+        assert_(type(a.real) is np.matrix)
+        assert_(type(a.imag) is np.matrix)
+        c, d = np.matrix([0.0]).nonzero()
+        assert_(type(c) is np.ndarray)
+        assert_(type(d) is np.ndarray)
+
+    def test_matrix_multiply_by_1d_vector(self):
+        # Ticket #473
+        def mul():
+            np.asmatrix(np.eye(2))*np.ones(2)
+
+        assert_raises(ValueError, mul)
+
+    def test_matrix_std_argmax(self):
+        # Ticket #83
+        x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
+        assert_equal(x.std().shape, ())
+        assert_equal(x.argmax().shape, ())
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b22ade5e28a8cbdfab954a1216af3bf97389d683
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/__init__.py
@@ -0,0 +1,187 @@
+"""
+A sub-package for efficiently dealing with polynomials.
+
+Within the documentation for this sub-package, a "finite power series,"
+i.e., a polynomial (also referred to simply as a "series") is represented
+by a 1-D numpy array of the polynomial's coefficients, ordered from lowest
+order term to highest.  For example, array([1,2,3]) represents
+``P_0 + 2*P_1 + 3*P_2``, where P_n is the n-th order basis polynomial
+applicable to the specific module in question, e.g., `polynomial` (which
+"wraps" the "standard" basis) or `chebyshev`.  For optimal performance,
+all operations on polynomials, including evaluation at an argument, are
+implemented as operations on the coefficients.  Additional (module-specific)
+information can be found in the docstring for the module of interest.
+
+This package provides *convenience classes* for each of six different kinds
+of polynomials:
+
+========================    ================
+**Name**                    **Provides**
+========================    ================
+`~polynomial.Polynomial`    Power series
+`~chebyshev.Chebyshev`      Chebyshev series
+`~legendre.Legendre`        Legendre series
+`~laguerre.Laguerre`        Laguerre series
+`~hermite.Hermite`          Hermite series
+`~hermite_e.HermiteE`       HermiteE series
+========================    ================
+
+These *convenience classes* provide a consistent interface for creating,
+manipulating, and fitting data with polynomials of different bases.
+The convenience classes are the preferred interface for the `~numpy.polynomial`
+package, and are available from the ``numpy.polynomial`` namespace.
+This eliminates the need to navigate to the corresponding submodules, e.g.
+``np.polynomial.Polynomial`` or ``np.polynomial.Chebyshev`` instead of
+``np.polynomial.polynomial.Polynomial`` or
+``np.polynomial.chebyshev.Chebyshev``, respectively.
+The classes provide a more consistent and concise interface than the
+type-specific functions defined in the submodules for each type of polynomial.
+For example, to fit a Chebyshev polynomial with degree ``1`` to data given
+by arrays ``xdata`` and ``ydata``, the
+`~chebyshev.Chebyshev.fit` class method::
+
+    >>> from numpy.polynomial import Chebyshev
+    >>> xdata = [1, 2, 3, 4]
+    >>> ydata = [1, 4, 9, 16]
+    >>> c = Chebyshev.fit(xdata, ydata, deg=1)
+
+is preferred over the `chebyshev.chebfit` function from the
+``np.polynomial.chebyshev`` module::
+
+    >>> from numpy.polynomial.chebyshev import chebfit
+    >>> c = chebfit(xdata, ydata, deg=1)
+
+See :doc:`routines.polynomials.classes` for more details.
+
+Convenience Classes
+===================
+
+The following lists the various constants and methods common to all of
+the classes representing the various kinds of polynomials. In the following,
+the term ``Poly`` represents any one of the convenience classes (e.g.
+`~polynomial.Polynomial`, `~chebyshev.Chebyshev`, `~hermite.Hermite`, etc.)
+while the lowercase ``p`` represents an **instance** of a polynomial class.
+
+Constants
+---------
+
+- ``Poly.domain``     -- Default domain
+- ``Poly.window``     -- Default window
+- ``Poly.basis_name`` -- String used to represent the basis
+- ``Poly.maxpower``   -- Maximum value ``n`` such that ``p**n`` is allowed
+- ``Poly.nickname``   -- String used in printing
+
+Creation
+--------
+
+Methods for creating polynomial instances.
+
+- ``Poly.basis(degree)``    -- Basis polynomial of given degree
+- ``Poly.identity()``       -- ``p`` where ``p(x) = x`` for all ``x``
+- ``Poly.fit(x, y, deg)``   -- ``p`` of degree ``deg`` with coefficients
+  determined by the least-squares fit to the data ``x``, ``y``
+- ``Poly.fromroots(roots)`` -- ``p`` with specified roots
+- ``p.copy()``              -- Create a copy of ``p``
+
+Conversion
+----------
+
+Methods for converting a polynomial instance of one kind to another.
+
+- ``p.cast(Poly)``    -- Convert ``p`` to instance of kind ``Poly``
+- ``p.convert(Poly)`` -- Convert ``p`` to instance of kind ``Poly`` or map
+  between ``domain`` and ``window``
+
+Calculus
+--------
+- ``p.deriv()`` -- Take the derivative of ``p``
+- ``p.integ()`` -- Integrate ``p``
+
+Validation
+----------
+- ``Poly.has_samecoef(p1, p2)``   -- Check if coefficients match
+- ``Poly.has_samedomain(p1, p2)`` -- Check if domains match
+- ``Poly.has_sametype(p1, p2)``   -- Check if types match
+- ``Poly.has_samewindow(p1, p2)`` -- Check if windows match
+
+Misc
+----
+- ``p.linspace()`` -- Return ``x, p(x)`` at equally-spaced points in ``domain``
+- ``p.mapparms()`` -- Return the parameters for the linear mapping between
+  ``domain`` and ``window``.
+- ``p.roots()``    -- Return the roots of ``p``.
+- ``p.trim()``     -- Remove trailing coefficients.
+- ``p.cutdeg(degree)`` -- Truncate ``p`` to given degree
+- ``p.truncate(size)`` -- Truncate ``p`` to given size
+
+"""
+from .polynomial import Polynomial
+from .chebyshev import Chebyshev
+from .legendre import Legendre
+from .hermite import Hermite
+from .hermite_e import HermiteE
+from .laguerre import Laguerre
+
+__all__ = [
+    "set_default_printstyle",
+    "polynomial", "Polynomial",
+    "chebyshev", "Chebyshev",
+    "legendre", "Legendre",
+    "hermite", "Hermite",
+    "hermite_e", "HermiteE",
+    "laguerre", "Laguerre",
+]
+
+
+def set_default_printstyle(style):
+    """
+    Set the default format for the string representation of polynomials.
+
+    Values for ``style`` must be valid inputs to ``__format__``, i.e. 'ascii'
+    or 'unicode'.
+
+    Parameters
+    ----------
+    style : str
+        Format string for default printing style. Must be either 'ascii' or
+        'unicode'.
+
+    Notes
+    -----
+    The default format depends on the platform: 'unicode' is used on
+    Unix-based systems and 'ascii' on Windows. This determination is based on
+    default font support for the unicode superscript and subscript ranges.
+
+    Examples
+    --------
+    >>> p = np.polynomial.Polynomial([1, 2, 3])
+    >>> c = np.polynomial.Chebyshev([1, 2, 3])
+    >>> np.polynomial.set_default_printstyle('unicode')
+    >>> print(p)
+    1.0 + 2.0·x + 3.0·x²
+    >>> print(c)
+    1.0 + 2.0·T₁(x) + 3.0·T₂(x)
+    >>> np.polynomial.set_default_printstyle('ascii')
+    >>> print(p)
+    1.0 + 2.0 x + 3.0 x**2
+    >>> print(c)
+    1.0 + 2.0 T_1(x) + 3.0 T_2(x)
+    >>> # Formatting supersedes all class/package-level defaults
+    >>> print(f"{p:unicode}")
+    1.0 + 2.0·x + 3.0·x²
+    """
+    if style not in ('unicode', 'ascii'):
+        raise ValueError(
+            f"Unsupported format string '{style}'. Valid options are 'ascii' "
+            f"and 'unicode'"
+        )
+    _use_unicode = True
+    if style == 'ascii':
+        _use_unicode = False
+    from ._polybase import ABCPolyBase
+    ABCPolyBase._use_unicode = _use_unicode
+
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c5dccfe16dee8889508150ecfe963297f24a5fd0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/__init__.pyi
@@ -0,0 +1,24 @@
+from typing import Final, Literal
+
+from .polynomial import Polynomial
+from .chebyshev import Chebyshev
+from .legendre import Legendre
+from .hermite import Hermite
+from .hermite_e import HermiteE
+from .laguerre import Laguerre
+from . import polynomial, chebyshev, legendre, hermite, hermite_e, laguerre
+
+__all__ = [
+    "set_default_printstyle",
+    "polynomial", "Polynomial",
+    "chebyshev", "Chebyshev",
+    "legendre", "Legendre",
+    "hermite", "Hermite",
+    "hermite_e", "HermiteE",
+    "laguerre", "Laguerre",
+]
+
+def set_default_printstyle(style: Literal["ascii", "unicode"]) -> None: ...
+
+from numpy._pytesttester import PytestTester as _PytestTester
+test: Final[_PytestTester]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polybase.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polybase.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c3d16c6efd7af25ef0cdfc32083802ecce2a92f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polybase.py
@@ -0,0 +1,1197 @@
+"""
+Abstract base class for the various polynomial Classes.
+
+The ABCPolyBase class provides the methods needed to implement the common API
+for the various polynomial classes. It operates as a mixin, but uses the
+abc module from the stdlib, hence it is only available for Python >= 2.6.
+
+"""
+import os
+import abc
+import numbers
+from typing import Callable
+
+import numpy as np
+from . import polyutils as pu
+
+__all__ = ['ABCPolyBase']
+
+class ABCPolyBase(abc.ABC):
+    """An abstract base class for immutable series classes.
+
+    ABCPolyBase provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' along with the
+    methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        Series coefficients in order of increasing degree, i.e.,
+        ``(1, 2, 3)`` gives ``1*P_0(x) + 2*P_1(x) + 3*P_2(x)``, where
+        ``P_i`` is the basis polynomials of degree ``i``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is the derived class domain.
+    window : (2,) array_like, optional
+        Window, see domain for its use. The default value is the
+        derived class window.
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    Attributes
+    ----------
+    coef : (N,) ndarray
+        Series coefficients in order of increasing degree.
+    domain : (2,) ndarray
+        Domain that is mapped to window.
+    window : (2,) ndarray
+        Window that domain is mapped to.
+    symbol : str
+        Symbol representing the independent variable.
+
+    Class Attributes
+    ----------------
+    maxpower : int
+        Maximum power allowed, i.e., the largest number ``n`` such that
+        ``p(x)**n`` is allowed. This is to limit runaway polynomial size.
+    domain : (2,) ndarray
+        Default domain of the class.
+    window : (2,) ndarray
+        Default window of the class.
+
+    """
+
+    # Not hashable
+    __hash__ = None
+
+    # Opt out of numpy ufuncs and Python ops with ndarray subclasses.
+    __array_ufunc__ = None
+
+    # Limit runaway size. T_n^m has degree n*m
+    maxpower = 100
+
+    # Unicode character mappings for improved __str__
+    _superscript_mapping = str.maketrans({
+        "0": "⁰",
+        "1": "¹",
+        "2": "²",
+        "3": "³",
+        "4": "⁴",
+        "5": "⁵",
+        "6": "⁶",
+        "7": "⁷",
+        "8": "⁸",
+        "9": "⁹"
+    })
+    _subscript_mapping = str.maketrans({
+        "0": "₀",
+        "1": "₁",
+        "2": "₂",
+        "3": "₃",
+        "4": "₄",
+        "5": "₅",
+        "6": "₆",
+        "7": "₇",
+        "8": "₈",
+        "9": "₉"
+    })
+    # Some fonts don't support full unicode character ranges necessary for
+    # the full set of superscripts and subscripts, including common/default
+    # fonts in Windows shells/terminals. Therefore, default to ascii-only
+    # printing on windows.
+    _use_unicode = not os.name == 'nt'
+
+    @property
+    def symbol(self):
+        return self._symbol
+
+    @property
+    @abc.abstractmethod
+    def domain(self):
+        pass
+
+    @property
+    @abc.abstractmethod
+    def window(self):
+        pass
+
+    @property
+    @abc.abstractmethod
+    def basis_name(self):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _add(c1, c2):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _sub(c1, c2):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _mul(c1, c2):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _div(c1, c2):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _pow(c, pow, maxpower=None):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _val(x, c):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _int(c, m, k, lbnd, scl):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _der(c, m, scl):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _fit(x, y, deg, rcond, full):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _line(off, scl):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _roots(c):
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def _fromroots(r):
+        pass
+
+    def has_samecoef(self, other):
+        """Check if coefficients match.
+
+        Parameters
+        ----------
+        other : class instance
+            The other class must have the ``coef`` attribute.
+
+        Returns
+        -------
+        bool : boolean
+            True if the coefficients are the same, False otherwise.
+
+        """
+        if len(self.coef) != len(other.coef):
+            return False
+        elif not np.all(self.coef == other.coef):
+            return False
+        else:
+            return True
+
+    def has_samedomain(self, other):
+        """Check if domains match.
+
+        Parameters
+        ----------
+        other : class instance
+            The other class must have the ``domain`` attribute.
+
+        Returns
+        -------
+        bool : boolean
+            True if the domains are the same, False otherwise.
+
+        """
+        return np.all(self.domain == other.domain)
+
+    def has_samewindow(self, other):
+        """Check if windows match.
+
+        Parameters
+        ----------
+        other : class instance
+            The other class must have the ``window`` attribute.
+
+        Returns
+        -------
+        bool : boolean
+            True if the windows are the same, False otherwise.
+
+        """
+        return np.all(self.window == other.window)
+
+    def has_sametype(self, other):
+        """Check if types match.
+
+        Parameters
+        ----------
+        other : object
+            Class instance.
+
+        Returns
+        -------
+        bool : boolean
+            True if other is same class as self
+
+        """
+        return isinstance(other, self.__class__)
+
+    def _get_coefficients(self, other):
+        """Interpret other as polynomial coefficients.
+
+        The `other` argument is checked to see if it is of the same
+        class as self with identical domain and window. If so,
+        return its coefficients, otherwise return `other`.
+
+        Parameters
+        ----------
+        other : anything
+            Object to be checked.
+
+        Returns
+        -------
+        coef
+            The coefficients of`other` if it is a compatible instance,
+            of ABCPolyBase, otherwise `other`.
+
+        Raises
+        ------
+        TypeError
+            When `other` is an incompatible instance of ABCPolyBase.
+
+        """
+        if isinstance(other, ABCPolyBase):
+            if not isinstance(other, self.__class__):
+                raise TypeError("Polynomial types differ")
+            elif not np.all(self.domain == other.domain):
+                raise TypeError("Domains differ")
+            elif not np.all(self.window == other.window):
+                raise TypeError("Windows differ")
+            elif self.symbol != other.symbol:
+                raise ValueError("Polynomial symbols differ")
+            return other.coef
+        return other
+
+    def __init__(self, coef, domain=None, window=None, symbol='x'):
+        [coef] = pu.as_series([coef], trim=False)
+        self.coef = coef
+
+        if domain is not None:
+            [domain] = pu.as_series([domain], trim=False)
+            if len(domain) != 2:
+                raise ValueError("Domain has wrong number of elements.")
+            self.domain = domain
+
+        if window is not None:
+            [window] = pu.as_series([window], trim=False)
+            if len(window) != 2:
+                raise ValueError("Window has wrong number of elements.")
+            self.window = window
+
+        # Validation for symbol
+        try:
+            if not symbol.isidentifier():
+                raise ValueError(
+                    "Symbol string must be a valid Python identifier"
+                )
+        # If a user passes in something other than a string, the above
+        # results in an AttributeError. Catch this and raise a more
+        # informative exception
+        except AttributeError:
+            raise TypeError("Symbol must be a non-empty string")
+
+        self._symbol = symbol
+
+    def __repr__(self):
+        coef = repr(self.coef)[6:-1]
+        domain = repr(self.domain)[6:-1]
+        window = repr(self.window)[6:-1]
+        name = self.__class__.__name__
+        return (f"{name}({coef}, domain={domain}, window={window}, "
+                f"symbol='{self.symbol}')")
+
+    def __format__(self, fmt_str):
+        if fmt_str == '':
+            return self.__str__()
+        if fmt_str not in ('ascii', 'unicode'):
+            raise ValueError(
+                f"Unsupported format string '{fmt_str}' passed to "
+                f"{self.__class__}.__format__. Valid options are "
+                f"'ascii' and 'unicode'"
+            )
+        if fmt_str == 'ascii':
+            return self._generate_string(self._str_term_ascii)
+        return self._generate_string(self._str_term_unicode)
+
+    def __str__(self):
+        if self._use_unicode:
+            return self._generate_string(self._str_term_unicode)
+        return self._generate_string(self._str_term_ascii)
+
+    def _generate_string(self, term_method):
+        """
+        Generate the full string representation of the polynomial, using
+        ``term_method`` to generate each polynomial term.
+        """
+        # Get configuration for line breaks
+        linewidth = np.get_printoptions().get('linewidth', 75)
+        if linewidth < 1:
+            linewidth = 1
+        out = pu.format_float(self.coef[0])
+
+        off, scale = self.mapparms()
+
+        scaled_symbol, needs_parens = self._format_term(pu.format_float,
+                                                        off, scale)
+        if needs_parens:
+            scaled_symbol = '(' + scaled_symbol + ')'
+
+        for i, coef in enumerate(self.coef[1:]):
+            out += " "
+            power = str(i + 1)
+            # Polynomial coefficient
+            # The coefficient array can be an object array with elements that
+            # will raise a TypeError with >= 0 (e.g. strings or Python
+            # complex). In this case, represent the coefficient as-is.
+            try:
+                if coef >= 0:
+                    next_term = "+ " + pu.format_float(coef, parens=True)
+                else:
+                    next_term = "- " + pu.format_float(-coef, parens=True)
+            except TypeError:
+                next_term = f"+ {coef}"
+            # Polynomial term
+            next_term += term_method(power, scaled_symbol)
+            # Length of the current line with next term added
+            line_len = len(out.split('\n')[-1]) + len(next_term)
+            # If not the last term in the polynomial, it will be two
+            # characters longer due to the +/- with the next term
+            if i < len(self.coef[1:]) - 1:
+                line_len += 2
+            # Handle linebreaking
+            if line_len >= linewidth:
+                next_term = next_term.replace(" ", "\n", 1)
+            out += next_term
+        return out
+
+    @classmethod
+    def _str_term_unicode(cls, i, arg_str):
+        """
+        String representation of single polynomial term using unicode
+        characters for superscripts and subscripts.
+        """
+        if cls.basis_name is None:
+            raise NotImplementedError(
+                "Subclasses must define either a basis_name, or override "
+                "_str_term_unicode(cls, i, arg_str)"
+            )
+        return (f"·{cls.basis_name}{i.translate(cls._subscript_mapping)}"
+                f"({arg_str})")
+
+    @classmethod
+    def _str_term_ascii(cls, i, arg_str):
+        """
+        String representation of a single polynomial term using ** and _ to
+        represent superscripts and subscripts, respectively.
+        """
+        if cls.basis_name is None:
+            raise NotImplementedError(
+                "Subclasses must define either a basis_name, or override "
+                "_str_term_ascii(cls, i, arg_str)"
+            )
+        return f" {cls.basis_name}_{i}({arg_str})"
+
+    @classmethod
+    def _repr_latex_term(cls, i, arg_str, needs_parens):
+        if cls.basis_name is None:
+            raise NotImplementedError(
+                "Subclasses must define either a basis name, or override "
+                "_repr_latex_term(i, arg_str, needs_parens)")
+        # since we always add parens, we don't care if the expression needs them
+        return f"{{{cls.basis_name}}}_{{{i}}}({arg_str})"
+
+    @staticmethod
+    def _repr_latex_scalar(x, parens=False):
+        # TODO: we're stuck with disabling math formatting until we handle
+        # exponents in this function
+        return r'\text{{{}}}'.format(pu.format_float(x, parens=parens))
+
+    def _format_term(self, scalar_format: Callable, off: float, scale: float):
+        """ Format a single term in the expansion """
+        if off == 0 and scale == 1:
+            term = self.symbol
+            needs_parens = False
+        elif scale == 1:
+            term = f"{scalar_format(off)} + {self.symbol}"
+            needs_parens = True
+        elif off == 0:
+            term = f"{scalar_format(scale)}{self.symbol}"
+            needs_parens = True
+        else:
+            term = (
+                f"{scalar_format(off)} + "
+                f"{scalar_format(scale)}{self.symbol}"
+            )
+            needs_parens = True
+        return term, needs_parens
+
+    def _repr_latex_(self):
+        # get the scaled argument string to the basis functions
+        off, scale = self.mapparms()
+        term, needs_parens = self._format_term(self._repr_latex_scalar,
+                                               off, scale)
+
+        mute = r"\color{{LightGray}}{{{}}}".format
+
+        parts = []
+        for i, c in enumerate(self.coef):
+            # prevent duplication of + and - signs
+            if i == 0:
+                coef_str = f"{self._repr_latex_scalar(c)}"
+            elif not isinstance(c, numbers.Real):
+                coef_str = f" + ({self._repr_latex_scalar(c)})"
+            elif c >= 0:
+                coef_str = f" + {self._repr_latex_scalar(c, parens=True)}"
+            else:
+                coef_str = f" - {self._repr_latex_scalar(-c, parens=True)}"
+
+            # produce the string for the term
+            term_str = self._repr_latex_term(i, term, needs_parens)
+            if term_str == '1':
+                part = coef_str
+            else:
+                part = rf"{coef_str}\,{term_str}"
+
+            if c == 0:
+                part = mute(part)
+
+            parts.append(part)
+
+        if parts:
+            body = ''.join(parts)
+        else:
+            # in case somehow there are no coefficients at all
+            body = '0'
+
+        return rf"${self.symbol} \mapsto {body}$"
+
+
+
+    # Pickle and copy
+
+    def __getstate__(self):
+        ret = self.__dict__.copy()
+        ret['coef'] = self.coef.copy()
+        ret['domain'] = self.domain.copy()
+        ret['window'] = self.window.copy()
+        ret['symbol'] = self.symbol
+        return ret
+
+    def __setstate__(self, dict):
+        self.__dict__ = dict
+
+    # Call
+
+    def __call__(self, arg):
+        arg = pu.mapdomain(arg, self.domain, self.window)
+        return self._val(arg, self.coef)
+
+    def __iter__(self):
+        return iter(self.coef)
+
+    def __len__(self):
+        return len(self.coef)
+
+    # Numeric properties.
+
+    def __neg__(self):
+        return self.__class__(
+            -self.coef, self.domain, self.window, self.symbol
+        )
+
+    def __pos__(self):
+        return self
+
+    def __add__(self, other):
+        othercoef = self._get_coefficients(other)
+        try:
+            coef = self._add(self.coef, othercoef)
+        except Exception:
+            return NotImplemented
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def __sub__(self, other):
+        othercoef = self._get_coefficients(other)
+        try:
+            coef = self._sub(self.coef, othercoef)
+        except Exception:
+            return NotImplemented
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def __mul__(self, other):
+        othercoef = self._get_coefficients(other)
+        try:
+            coef = self._mul(self.coef, othercoef)
+        except Exception:
+            return NotImplemented
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def __truediv__(self, other):
+        # there is no true divide if the rhs is not a Number, although it
+        # could return the first n elements of an infinite series.
+        # It is hard to see where n would come from, though.
+        if not isinstance(other, numbers.Number) or isinstance(other, bool):
+            raise TypeError(
+                f"unsupported types for true division: "
+                f"'{type(self)}', '{type(other)}'"
+            )
+        return self.__floordiv__(other)
+
+    def __floordiv__(self, other):
+        res = self.__divmod__(other)
+        if res is NotImplemented:
+            return res
+        return res[0]
+
+    def __mod__(self, other):
+        res = self.__divmod__(other)
+        if res is NotImplemented:
+            return res
+        return res[1]
+
+    def __divmod__(self, other):
+        othercoef = self._get_coefficients(other)
+        try:
+            quo, rem = self._div(self.coef, othercoef)
+        except ZeroDivisionError:
+            raise
+        except Exception:
+            return NotImplemented
+        quo = self.__class__(quo, self.domain, self.window, self.symbol)
+        rem = self.__class__(rem, self.domain, self.window, self.symbol)
+        return quo, rem
+
+    def __pow__(self, other):
+        coef = self._pow(self.coef, other, maxpower=self.maxpower)
+        res = self.__class__(coef, self.domain, self.window, self.symbol)
+        return res
+
+    def __radd__(self, other):
+        try:
+            coef = self._add(other, self.coef)
+        except Exception:
+            return NotImplemented
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def __rsub__(self, other):
+        try:
+            coef = self._sub(other, self.coef)
+        except Exception:
+            return NotImplemented
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def __rmul__(self, other):
+        try:
+            coef = self._mul(other, self.coef)
+        except Exception:
+            return NotImplemented
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def __rdiv__(self, other):
+        # set to __floordiv__ /.
+        return self.__rfloordiv__(other)
+
+    def __rtruediv__(self, other):
+        # An instance of ABCPolyBase is not considered a
+        # Number.
+        return NotImplemented
+
+    def __rfloordiv__(self, other):
+        res = self.__rdivmod__(other)
+        if res is NotImplemented:
+            return res
+        return res[0]
+
+    def __rmod__(self, other):
+        res = self.__rdivmod__(other)
+        if res is NotImplemented:
+            return res
+        return res[1]
+
+    def __rdivmod__(self, other):
+        try:
+            quo, rem = self._div(other, self.coef)
+        except ZeroDivisionError:
+            raise
+        except Exception:
+            return NotImplemented
+        quo = self.__class__(quo, self.domain, self.window, self.symbol)
+        rem = self.__class__(rem, self.domain, self.window, self.symbol)
+        return quo, rem
+
+    def __eq__(self, other):
+        res = (isinstance(other, self.__class__) and
+               np.all(self.domain == other.domain) and
+               np.all(self.window == other.window) and
+               (self.coef.shape == other.coef.shape) and
+               np.all(self.coef == other.coef) and
+               (self.symbol == other.symbol))
+        return res
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    #
+    # Extra methods.
+    #
+
+    def copy(self):
+        """Return a copy.
+
+        Returns
+        -------
+        new_series : series
+            Copy of self.
+
+        """
+        return self.__class__(self.coef, self.domain, self.window, self.symbol)
+
+    def degree(self):
+        """The degree of the series.
+
+        Returns
+        -------
+        degree : int
+            Degree of the series, one less than the number of coefficients.
+
+        Examples
+        --------
+
+        Create a polynomial object for ``1 + 7*x + 4*x**2``:
+
+        >>> poly = np.polynomial.Polynomial([1, 7, 4])
+        >>> print(poly)
+        1.0 + 7.0·x + 4.0·x²
+        >>> poly.degree()
+        2
+
+        Note that this method does not check for non-zero coefficients.
+        You must trim the polynomial to remove any trailing zeroes:
+
+        >>> poly = np.polynomial.Polynomial([1, 7, 0])
+        >>> print(poly)
+        1.0 + 7.0·x + 0.0·x²
+        >>> poly.degree()
+        2
+        >>> poly.trim().degree()
+        1
+
+        """
+        return len(self) - 1
+
+    def cutdeg(self, deg):
+        """Truncate series to the given degree.
+
+        Reduce the degree of the series to `deg` by discarding the
+        high order terms. If `deg` is greater than the current degree a
+        copy of the current series is returned. This can be useful in least
+        squares where the coefficients of the high degree terms may be very
+        small.
+
+        Parameters
+        ----------
+        deg : non-negative int
+            The series is reduced to degree `deg` by discarding the high
+            order terms. The value of `deg` must be a non-negative integer.
+
+        Returns
+        -------
+        new_series : series
+            New instance of series with reduced degree.
+
+        """
+        return self.truncate(deg + 1)
+
+    def trim(self, tol=0):
+        """Remove trailing coefficients
+
+        Remove trailing coefficients until a coefficient is reached whose
+        absolute value greater than `tol` or the beginning of the series is
+        reached. If all the coefficients would be removed the series is set
+        to ``[0]``. A new series instance is returned with the new
+        coefficients.  The current instance remains unchanged.
+
+        Parameters
+        ----------
+        tol : non-negative number.
+            All trailing coefficients less than `tol` will be removed.
+
+        Returns
+        -------
+        new_series : series
+            New instance of series with trimmed coefficients.
+
+        """
+        coef = pu.trimcoef(self.coef, tol)
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def truncate(self, size):
+        """Truncate series to length `size`.
+
+        Reduce the series to length `size` by discarding the high
+        degree terms. The value of `size` must be a positive integer. This
+        can be useful in least squares where the coefficients of the
+        high degree terms may be very small.
+
+        Parameters
+        ----------
+        size : positive int
+            The series is reduced to length `size` by discarding the high
+            degree terms. The value of `size` must be a positive integer.
+
+        Returns
+        -------
+        new_series : series
+            New instance of series with truncated coefficients.
+
+        """
+        isize = int(size)
+        if isize != size or isize < 1:
+            raise ValueError("size must be a positive integer")
+        if isize >= len(self.coef):
+            coef = self.coef
+        else:
+            coef = self.coef[:isize]
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def convert(self, domain=None, kind=None, window=None):
+        """Convert series to a different kind and/or domain and/or window.
+
+        Parameters
+        ----------
+        domain : array_like, optional
+            The domain of the converted series. If the value is None,
+            the default domain of `kind` is used.
+        kind : class, optional
+            The polynomial series type class to which the current instance
+            should be converted. If kind is None, then the class of the
+            current instance is used.
+        window : array_like, optional
+            The window of the converted series. If the value is None,
+            the default window of `kind` is used.
+
+        Returns
+        -------
+        new_series : series
+            The returned class can be of different type than the current
+            instance and/or have a different domain and/or different
+            window.
+
+        Notes
+        -----
+        Conversion between domains and class types can result in
+        numerically ill defined series.
+
+        """
+        if kind is None:
+            kind = self.__class__
+        if domain is None:
+            domain = kind.domain
+        if window is None:
+            window = kind.window
+        return self(kind.identity(domain, window=window, symbol=self.symbol))
+
+    def mapparms(self):
+        """Return the mapping parameters.
+
+        The returned values define a linear map ``off + scl*x`` that is
+        applied to the input arguments before the series is evaluated. The
+        map depends on the ``domain`` and ``window``; if the current
+        ``domain`` is equal to the ``window`` the resulting map is the
+        identity.  If the coefficients of the series instance are to be
+        used by themselves outside this class, then the linear function
+        must be substituted for the ``x`` in the standard representation of
+        the base polynomials.
+
+        Returns
+        -------
+        off, scl : float or complex
+            The mapping function is defined by ``off + scl*x``.
+
+        Notes
+        -----
+        If the current domain is the interval ``[l1, r1]`` and the window
+        is ``[l2, r2]``, then the linear mapping function ``L`` is
+        defined by the equations::
+
+            L(l1) = l2
+            L(r1) = r2
+
+        """
+        return pu.mapparms(self.domain, self.window)
+
+    def integ(self, m=1, k=[], lbnd=None):
+        """Integrate.
+
+        Return a series instance that is the definite integral of the
+        current series.
+
+        Parameters
+        ----------
+        m : non-negative int
+            The number of integrations to perform.
+        k : array_like
+            Integration constants. The first constant is applied to the
+            first integration, the second to the second, and so on. The
+            list of values must less than or equal to `m` in length and any
+            missing values are set to zero.
+        lbnd : Scalar
+            The lower bound of the definite integral.
+
+        Returns
+        -------
+        new_series : series
+            A new series representing the integral. The domain is the same
+            as the domain of the integrated series.
+
+        """
+        off, scl = self.mapparms()
+        if lbnd is None:
+            lbnd = 0
+        else:
+            lbnd = off + scl*lbnd
+        coef = self._int(self.coef, m, k, lbnd, 1./scl)
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def deriv(self, m=1):
+        """Differentiate.
+
+        Return a series instance of that is the derivative of the current
+        series.
+
+        Parameters
+        ----------
+        m : non-negative int
+            Find the derivative of order `m`.
+
+        Returns
+        -------
+        new_series : series
+            A new series representing the derivative. The domain is the same
+            as the domain of the differentiated series.
+
+        """
+        off, scl = self.mapparms()
+        coef = self._der(self.coef, m, scl)
+        return self.__class__(coef, self.domain, self.window, self.symbol)
+
+    def roots(self):
+        """Return the roots of the series polynomial.
+
+        Compute the roots for the series. Note that the accuracy of the
+        roots decreases the further outside the `domain` they lie.
+
+        Returns
+        -------
+        roots : ndarray
+            Array containing the roots of the series.
+
+        """
+        roots = self._roots(self.coef)
+        return pu.mapdomain(roots, self.window, self.domain)
+
+    def linspace(self, n=100, domain=None):
+        """Return x, y values at equally spaced points in domain.
+
+        Returns the x, y values at `n` linearly spaced points across the
+        domain.  Here y is the value of the polynomial at the points x. By
+        default the domain is the same as that of the series instance.
+        This method is intended mostly as a plotting aid.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of point pairs to return. The default value is 100.
+        domain : {None, array_like}, optional
+            If not None, the specified domain is used instead of that of
+            the calling instance. It should be of the form ``[beg,end]``.
+            The default is None which case the class domain is used.
+
+        Returns
+        -------
+        x, y : ndarray
+            x is equal to linspace(self.domain[0], self.domain[1], n) and
+            y is the series evaluated at element of x.
+
+        """
+        if domain is None:
+            domain = self.domain
+        x = np.linspace(domain[0], domain[1], n)
+        y = self(x)
+        return x, y
+
+    @classmethod
+    def fit(cls, x, y, deg, domain=None, rcond=None, full=False, w=None,
+        window=None, symbol='x'):
+        """Least squares fit to data.
+
+        Return a series instance that is the least squares fit to the data
+        `y` sampled at `x`. The domain of the returned instance can be
+        specified and this will often result in a superior fit with less
+        chance of ill conditioning.
+
+        Parameters
+        ----------
+        x : array_like, shape (M,)
+            x-coordinates of the M sample points ``(x[i], y[i])``.
+        y : array_like, shape (M,)
+            y-coordinates of the M sample points ``(x[i], y[i])``.
+        deg : int or 1-D array_like
+            Degree(s) of the fitting polynomials. If `deg` is a single integer
+            all terms up to and including the `deg`'th term are included in the
+            fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+            degrees of the terms to include may be used instead.
+        domain : {None, [beg, end], []}, optional
+            Domain to use for the returned series. If ``None``,
+            then a minimal domain that covers the points `x` is chosen.  If
+            ``[]`` the class domain is used. The default value was the
+            class domain in NumPy 1.4 and ``None`` in later versions.
+            The ``[]`` option was added in numpy 1.5.0.
+        rcond : float, optional
+            Relative condition number of the fit. Singular values smaller
+            than this relative to the largest singular value will be
+            ignored. The default value is ``len(x)*eps``, where eps is the
+            relative precision of the float type, about 2e-16 in most
+            cases.
+        full : bool, optional
+            Switch determining nature of return value. When it is False
+            (the default) just the coefficients are returned, when True
+            diagnostic information from the singular value decomposition is
+            also returned.
+        w : array_like, shape (M,), optional
+            Weights. If not None, the weight ``w[i]`` applies to the unsquared
+            residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+            chosen so that the errors of the products ``w[i]*y[i]`` all have
+            the same variance.  When using inverse-variance weighting, use
+            ``w[i] = 1/sigma(y[i])``.  The default value is None.
+        window : {[beg, end]}, optional
+            Window to use for the returned series. The default
+            value is the default class domain
+        symbol : str, optional
+            Symbol representing the independent variable. Default is 'x'.
+
+        Returns
+        -------
+        new_series : series
+            A series that represents the least squares fit to the data and
+            has the domain and window specified in the call. If the
+            coefficients for the unscaled and unshifted basis polynomials are
+            of interest, do ``new_series.convert().coef``.
+
+        [resid, rank, sv, rcond] : list
+            These values are only returned if ``full == True``
+
+            - resid -- sum of squared residuals of the least squares fit
+            - rank -- the numerical rank of the scaled Vandermonde matrix
+            - sv -- singular values of the scaled Vandermonde matrix
+            - rcond -- value of `rcond`.
+
+            For more details, see `linalg.lstsq`.
+
+        """
+        if domain is None:
+            domain = pu.getdomain(x)
+            if domain[0] == domain[1]:
+                domain[0] -= 1
+                domain[1] += 1
+        elif type(domain) is list and len(domain) == 0:
+            domain = cls.domain
+
+        if window is None:
+            window = cls.window
+
+        xnew = pu.mapdomain(x, domain, window)
+        res = cls._fit(xnew, y, deg, w=w, rcond=rcond, full=full)
+        if full:
+            [coef, status] = res
+            return (
+                cls(coef, domain=domain, window=window, symbol=symbol), status
+            )
+        else:
+            coef = res
+            return cls(coef, domain=domain, window=window, symbol=symbol)
+
+    @classmethod
+    def fromroots(cls, roots, domain=[], window=None, symbol='x'):
+        """Return series instance that has the specified roots.
+
+        Returns a series representing the product
+        ``(x - r[0])*(x - r[1])*...*(x - r[n-1])``, where ``r`` is a
+        list of roots.
+
+        Parameters
+        ----------
+        roots : array_like
+            List of roots.
+        domain : {[], None, array_like}, optional
+            Domain for the resulting series. If None the domain is the
+            interval from the smallest root to the largest. If [] the
+            domain is the class domain. The default is [].
+        window : {None, array_like}, optional
+            Window for the returned series. If None the class window is
+            used. The default is None.
+        symbol : str, optional
+            Symbol representing the independent variable. Default is 'x'.
+
+        Returns
+        -------
+        new_series : series
+            Series with the specified roots.
+
+        """
+        [roots] = pu.as_series([roots], trim=False)
+        if domain is None:
+            domain = pu.getdomain(roots)
+        elif type(domain) is list and len(domain) == 0:
+            domain = cls.domain
+
+        if window is None:
+            window = cls.window
+
+        deg = len(roots)
+        off, scl = pu.mapparms(domain, window)
+        rnew = off + scl*roots
+        coef = cls._fromroots(rnew) / scl**deg
+        return cls(coef, domain=domain, window=window, symbol=symbol)
+
+    @classmethod
+    def identity(cls, domain=None, window=None, symbol='x'):
+        """Identity function.
+
+        If ``p`` is the returned series, then ``p(x) == x`` for all
+        values of x.
+
+        Parameters
+        ----------
+        domain : {None, array_like}, optional
+            If given, the array must be of the form ``[beg, end]``, where
+            ``beg`` and ``end`` are the endpoints of the domain. If None is
+            given then the class domain is used. The default is None.
+        window : {None, array_like}, optional
+            If given, the resulting array must be if the form
+            ``[beg, end]``, where ``beg`` and ``end`` are the endpoints of
+            the window. If None is given then the class window is used. The
+            default is None.
+        symbol : str, optional
+            Symbol representing the independent variable. Default is 'x'.
+
+        Returns
+        -------
+        new_series : series
+             Series of representing the identity.
+
+        """
+        if domain is None:
+            domain = cls.domain
+        if window is None:
+            window = cls.window
+        off, scl = pu.mapparms(window, domain)
+        coef = cls._line(off, scl)
+        return cls(coef, domain, window, symbol)
+
+    @classmethod
+    def basis(cls, deg, domain=None, window=None, symbol='x'):
+        """Series basis polynomial of degree `deg`.
+
+        Returns the series representing the basis polynomial of degree `deg`.
+
+        Parameters
+        ----------
+        deg : int
+            Degree of the basis polynomial for the series. Must be >= 0.
+        domain : {None, array_like}, optional
+            If given, the array must be of the form ``[beg, end]``, where
+            ``beg`` and ``end`` are the endpoints of the domain. If None is
+            given then the class domain is used. The default is None.
+        window : {None, array_like}, optional
+            If given, the resulting array must be if the form
+            ``[beg, end]``, where ``beg`` and ``end`` are the endpoints of
+            the window. If None is given then the class window is used. The
+            default is None.
+        symbol : str, optional
+            Symbol representing the independent variable. Default is 'x'.
+
+        Returns
+        -------
+        new_series : series
+            A series with the coefficient of the `deg` term set to one and
+            all others zero.
+
+        """
+        if domain is None:
+            domain = cls.domain
+        if window is None:
+            window = cls.window
+        ideg = int(deg)
+
+        if ideg != deg or ideg < 0:
+            raise ValueError("deg must be non-negative integer")
+        return cls([0]*ideg + [1], domain, window, symbol)
+
+    @classmethod
+    def cast(cls, series, domain=None, window=None):
+        """Convert series to series of this class.
+
+        The `series` is expected to be an instance of some polynomial
+        series of one of the types supported by by the numpy.polynomial
+        module, but could be some other class that supports the convert
+        method.
+
+        Parameters
+        ----------
+        series : series
+            The series instance to be converted.
+        domain : {None, array_like}, optional
+            If given, the array must be of the form ``[beg, end]``, where
+            ``beg`` and ``end`` are the endpoints of the domain. If None is
+            given then the class domain is used. The default is None.
+        window : {None, array_like}, optional
+            If given, the resulting array must be if the form
+            ``[beg, end]``, where ``beg`` and ``end`` are the endpoints of
+            the window. If None is given then the class window is used. The
+            default is None.
+
+        Returns
+        -------
+        new_series : series
+            A series of the same kind as the calling class and equal to
+            `series` when evaluated.
+
+        See Also
+        --------
+        convert : similar instance method
+
+        """
+        if domain is None:
+            domain = cls.domain
+        if window is None:
+            window = cls.window
+        return series.convert(domain, cls, window)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polybase.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polybase.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ca7ca628d5140c7584ef42a92fb633625ca8a657
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polybase.pyi
@@ -0,0 +1,287 @@
+import abc
+import decimal
+import numbers
+from collections.abc import Iterator, Mapping, Sequence
+from typing import (
+    Any,
+    ClassVar,
+    Final,
+    Generic,
+    Literal,
+    SupportsIndex,
+    TypeAlias,
+    TypeGuard,
+    overload,
+)
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import (
+    _FloatLike_co,
+    _NumberLike_co,
+
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+)
+
+from ._polytypes import (
+    _AnyInt,
+    _CoefLike_co,
+
+    _Array2,
+    _Tuple2,
+
+    _Series,
+    _CoefSeries,
+
+    _SeriesLikeInt_co,
+    _SeriesLikeCoef_co,
+
+    _ArrayLikeCoefObject_co,
+    _ArrayLikeCoef_co,
+)
+
+from typing_extensions import LiteralString, TypeVar
+
+
+__all__: Final[Sequence[str]] = ("ABCPolyBase",)
+
+
+_NameCo = TypeVar("_NameCo", bound=LiteralString | None, covariant=True, default=LiteralString | None)
+_Self = TypeVar("_Self")
+_Other = TypeVar("_Other", bound=ABCPolyBase)
+
+_AnyOther: TypeAlias = ABCPolyBase | _CoefLike_co | _SeriesLikeCoef_co
+_Hundred: TypeAlias = Literal[100]
+
+
+class ABCPolyBase(Generic[_NameCo], metaclass=abc.ABCMeta):
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+    __array_ufunc__: ClassVar[None]
+
+    maxpower: ClassVar[_Hundred]
+    _superscript_mapping: ClassVar[Mapping[int, str]]
+    _subscript_mapping: ClassVar[Mapping[int, str]]
+    _use_unicode: ClassVar[bool]
+
+    basis_name: _NameCo
+    coef: _CoefSeries
+    domain: _Array2[np.inexact[Any] | np.object_]
+    window: _Array2[np.inexact[Any] | np.object_]
+
+    _symbol: LiteralString
+    @property
+    def symbol(self, /) -> LiteralString: ...
+
+    def __init__(
+        self,
+        /,
+        coef: _SeriesLikeCoef_co,
+        domain: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> None: ...
+
+    @overload
+    def __call__(self, /, arg: _Other) -> _Other: ...
+    # TODO: Once `_ShapeType@ndarray` is covariant and bounded (see #26081),
+    # additionally include 0-d arrays as input types with scalar return type.
+    @overload
+    def __call__(
+        self,
+        /,
+        arg: _FloatLike_co | decimal.Decimal | numbers.Real | np.object_,
+    ) -> np.float64 | np.complex128: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        arg: _NumberLike_co | numbers.Complex,
+    ) -> np.complex128: ...
+    @overload
+    def __call__(self, /, arg: _ArrayLikeFloat_co) -> (
+        npt.NDArray[np.float64]
+        | npt.NDArray[np.complex128]
+        | npt.NDArray[np.object_]
+    ): ...
+    @overload
+    def __call__(
+        self,
+        /,
+        arg: _ArrayLikeComplex_co,
+    ) -> npt.NDArray[np.complex128] | npt.NDArray[np.object_]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        arg: _ArrayLikeCoefObject_co,
+    ) -> npt.NDArray[np.object_]: ...
+
+    def __format__(self, fmt_str: str, /) -> str: ...
+    def __eq__(self, x: object, /) -> bool: ...
+    def __ne__(self, x: object, /) -> bool: ...
+    def __neg__(self: _Self, /) -> _Self: ...
+    def __pos__(self: _Self, /) -> _Self: ...
+    def __add__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __sub__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __mul__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __truediv__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __floordiv__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __mod__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __divmod__(self: _Self, x: _AnyOther, /) -> _Tuple2[_Self]: ...
+    def __pow__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __radd__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __rsub__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __rmul__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __rtruediv__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __rfloordiv__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __rmod__(self: _Self, x: _AnyOther, /) -> _Self: ...
+    def __rdivmod__(self: _Self, x: _AnyOther, /) -> _Tuple2[_Self]: ...
+    def __len__(self, /) -> int: ...
+    def __iter__(self, /) -> Iterator[np.inexact[Any] | object]: ...
+    def __getstate__(self, /) -> dict[str, Any]: ...
+    def __setstate__(self, dict: dict[str, Any], /) -> None: ...
+
+    def has_samecoef(self, /, other: ABCPolyBase) -> bool: ...
+    def has_samedomain(self, /, other: ABCPolyBase) -> bool: ...
+    def has_samewindow(self, /, other: ABCPolyBase) -> bool: ...
+    @overload
+    def has_sametype(self: _Self, /, other: ABCPolyBase) -> TypeGuard[_Self]: ...
+    @overload
+    def has_sametype(self, /, other: object) -> Literal[False]: ...
+
+    def copy(self: _Self, /) -> _Self: ...
+    def degree(self, /) -> int: ...
+    def cutdeg(self: _Self, /) -> _Self: ...
+    def trim(self: _Self, /, tol: _FloatLike_co = ...) -> _Self: ...
+    def truncate(self: _Self, /, size: _AnyInt) -> _Self: ...
+
+    @overload
+    def convert(
+        self,
+        domain: None | _SeriesLikeCoef_co,
+        kind: type[_Other],
+        /,
+        window: None | _SeriesLikeCoef_co = ...,
+    ) -> _Other: ...
+    @overload
+    def convert(
+        self,
+        /,
+        domain: None | _SeriesLikeCoef_co = ...,
+        *,
+        kind: type[_Other],
+        window: None | _SeriesLikeCoef_co = ...,
+    ) -> _Other: ...
+    @overload
+    def convert(
+        self: _Self,
+        /,
+        domain: None | _SeriesLikeCoef_co = ...,
+        kind: None | type[_Self] = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+    ) -> _Self: ...
+
+    def mapparms(self, /) -> _Tuple2[Any]: ...
+
+    def integ(
+        self: _Self, /,
+        m: SupportsIndex = ...,
+        k: _CoefLike_co | _SeriesLikeCoef_co = ...,
+        lbnd: None | _CoefLike_co = ...,
+    ) -> _Self: ...
+
+    def deriv(self: _Self, /, m: SupportsIndex = ...) -> _Self: ...
+
+    def roots(self, /) -> _CoefSeries: ...
+
+    def linspace(
+        self, /,
+        n: SupportsIndex = ...,
+        domain: None | _SeriesLikeCoef_co = ...,
+    ) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...
+
+    @overload
+    @classmethod
+    def fit(
+        cls: type[_Self], /,
+        x: _SeriesLikeCoef_co,
+        y: _SeriesLikeCoef_co,
+        deg: int | _SeriesLikeInt_co,
+        domain: None | _SeriesLikeCoef_co = ...,
+        rcond: _FloatLike_co = ...,
+        full: Literal[False] = ...,
+        w: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> _Self: ...
+    @overload
+    @classmethod
+    def fit(
+        cls: type[_Self], /,
+        x: _SeriesLikeCoef_co,
+        y: _SeriesLikeCoef_co,
+        deg: int | _SeriesLikeInt_co,
+        domain: None | _SeriesLikeCoef_co = ...,
+        rcond: _FloatLike_co = ...,
+        *,
+        full: Literal[True],
+        w: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> tuple[_Self, Sequence[np.inexact[Any] | np.int32]]: ...
+    @overload
+    @classmethod
+    def fit(
+        cls: type[_Self],
+        x: _SeriesLikeCoef_co,
+        y: _SeriesLikeCoef_co,
+        deg: int | _SeriesLikeInt_co,
+        domain: None | _SeriesLikeCoef_co,
+        rcond: _FloatLike_co,
+        full: Literal[True], /,
+        w: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> tuple[_Self, Sequence[np.inexact[Any] | np.int32]]: ...
+
+    @classmethod
+    def fromroots(
+        cls: type[_Self], /,
+        roots: _ArrayLikeCoef_co,
+        domain: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> _Self: ...
+
+    @classmethod
+    def identity(
+        cls: type[_Self], /,
+        domain: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> _Self: ...
+
+    @classmethod
+    def basis(
+        cls: type[_Self], /,
+        deg: _AnyInt,
+        domain: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+        symbol: str = ...,
+    ) -> _Self: ...
+
+    @classmethod
+    def cast(
+        cls: type[_Self], /,
+        series: ABCPolyBase,
+        domain: None | _SeriesLikeCoef_co = ...,
+        window: None | _SeriesLikeCoef_co = ...,
+    ) -> _Self: ...
+
+    @classmethod
+    def _str_term_unicode(cls, /, i: str, arg_str: str) -> str: ...
+    @staticmethod
+    def _str_term_ascii(i: str, arg_str: str) -> str: ...
+    @staticmethod
+    def _repr_latex_term(i: str, arg_str: str, needs_parens: bool) -> str: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polytypes.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polytypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b0794eb61831d396c339d54f34dc43c1554657b9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/_polytypes.pyi
@@ -0,0 +1,888 @@
+from collections.abc import Callable, Sequence
+from typing import (
+    Any,
+    Literal,
+    NoReturn,
+    Protocol,
+    SupportsIndex,
+    SupportsInt,
+    TypeAlias,
+    overload,
+    type_check_only,
+)
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import (
+    # array-likes
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _ArrayLikeNumber_co,
+    _ArrayLikeObject_co,
+    _NestedSequence,
+    _SupportsArray,
+
+    # scalar-likes
+    _IntLike_co,
+    _FloatLike_co,
+    _ComplexLike_co,
+    _NumberLike_co,
+)
+
+from typing_extensions import LiteralString, TypeVar
+
+
+_T = TypeVar("_T")
+_T_contra = TypeVar("_T_contra", contravariant=True)
+_Self = TypeVar("_Self")
+_SCT = TypeVar("_SCT", bound=np.number[Any] | np.bool | np.object_)
+
+# compatible with e.g. int, float, complex, Decimal, Fraction, and ABCPolyBase
+@type_check_only
+class _SupportsCoefOps(Protocol[_T_contra]):
+    def __eq__(self, x: object, /) -> bool: ...
+    def __ne__(self, x: object, /) -> bool: ...
+
+    def __neg__(self: _Self, /) -> _Self: ...
+    def __pos__(self: _Self, /) -> _Self: ...
+
+    def __add__(self: _Self, x: _T_contra, /) -> _Self: ...
+    def __sub__(self: _Self, x: _T_contra, /) -> _Self: ...
+    def __mul__(self: _Self, x: _T_contra, /) -> _Self: ...
+    def __pow__(self: _Self, x: _T_contra, /) -> _Self | float: ...
+
+    def __radd__(self: _Self, x: _T_contra, /) -> _Self: ...
+    def __rsub__(self: _Self, x: _T_contra, /) -> _Self: ...
+    def __rmul__(self: _Self, x: _T_contra, /) -> _Self: ...
+
+_Series: TypeAlias = np.ndarray[tuple[int], np.dtype[_SCT]]
+
+_FloatSeries: TypeAlias = _Series[np.floating[Any]]
+_ComplexSeries: TypeAlias = _Series[np.complexfloating[Any, Any]]
+_ObjectSeries: TypeAlias = _Series[np.object_]
+_CoefSeries: TypeAlias = _Series[np.inexact[Any] | np.object_]
+
+_FloatArray: TypeAlias = npt.NDArray[np.floating[Any]]
+_ComplexArray: TypeAlias = npt.NDArray[np.complexfloating[Any, Any]]
+_ObjectArray: TypeAlias = npt.NDArray[np.object_]
+_CoefArray: TypeAlias = npt.NDArray[np.inexact[Any] | np.object_]
+
+_Tuple2: TypeAlias = tuple[_T, _T]
+_Array1: TypeAlias = np.ndarray[tuple[Literal[1]], np.dtype[_SCT]]
+_Array2: TypeAlias = np.ndarray[tuple[Literal[2]], np.dtype[_SCT]]
+
+_AnyInt: TypeAlias = SupportsInt | SupportsIndex
+
+_CoefObjectLike_co: TypeAlias = np.object_ | _SupportsCoefOps[Any]
+_CoefLike_co: TypeAlias = _NumberLike_co | _CoefObjectLike_co
+
+# The term "series" is used here to refer to 1-d arrays of numeric scalars.
+_SeriesLikeBool_co: TypeAlias = (
+    _SupportsArray[np.dtype[np.bool]]
+    | Sequence[bool | np.bool]
+)
+_SeriesLikeInt_co: TypeAlias = (
+    _SupportsArray[np.dtype[np.integer[Any] | np.bool]]
+    | Sequence[_IntLike_co]
+)
+_SeriesLikeFloat_co: TypeAlias = (
+    _SupportsArray[np.dtype[np.floating[Any] | np.integer[Any] | np.bool]]
+    | Sequence[_FloatLike_co]
+)
+_SeriesLikeComplex_co: TypeAlias = (
+    _SupportsArray[np.dtype[np.inexact[Any] | np.integer[Any] | np.bool]]
+    | Sequence[_ComplexLike_co]
+)
+_SeriesLikeObject_co: TypeAlias = (
+    _SupportsArray[np.dtype[np.object_]]
+    | Sequence[_CoefObjectLike_co]
+)
+_SeriesLikeCoef_co: TypeAlias = (
+    _SupportsArray[np.dtype[np.number[Any] | np.bool | np.object_]]
+    | Sequence[_CoefLike_co]
+)
+
+_ArrayLikeCoefObject_co: TypeAlias = (
+    _CoefObjectLike_co
+    | _SeriesLikeObject_co
+    | _NestedSequence[_SeriesLikeObject_co]
+)
+_ArrayLikeCoef_co: TypeAlias = (
+    npt.NDArray[np.number[Any] | np.bool | np.object_]
+    | _ArrayLikeNumber_co
+    | _ArrayLikeCoefObject_co
+)
+
+_Name_co = TypeVar("_Name_co", bound=LiteralString, covariant=True, default=LiteralString)
+
+@type_check_only
+class _Named(Protocol[_Name_co]):
+    @property
+    def __name__(self, /) -> _Name_co: ...
+
+_Line: TypeAlias = np.ndarray[tuple[Literal[1, 2]], np.dtype[_SCT]]
+
+@type_check_only
+class _FuncLine(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(self, /, off: _SCT, scl: _SCT) -> _Line[_SCT]: ...
+    @overload
+    def __call__(self, /, off: int, scl: int) -> _Line[np.int_] : ...
+    @overload
+    def __call__(self, /, off: float, scl: float) -> _Line[np.float64]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        off: complex,
+        scl: complex,
+    ) -> _Line[np.complex128]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        off: _SupportsCoefOps[Any],
+        scl: _SupportsCoefOps[Any],
+    ) -> _Line[np.object_]: ...
+
+@type_check_only
+class _FuncFromRoots(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(self, /, roots: _SeriesLikeFloat_co) -> _FloatSeries: ...
+    @overload
+    def __call__(self, /, roots: _SeriesLikeComplex_co) -> _ComplexSeries: ...
+    @overload
+    def __call__(self, /, roots: _SeriesLikeCoef_co) -> _ObjectSeries: ...
+
+@type_check_only
+class _FuncBinOp(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c1: _SeriesLikeBool_co,
+        c2: _SeriesLikeBool_co,
+    ) -> NoReturn: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c1: _SeriesLikeFloat_co,
+        c2: _SeriesLikeFloat_co,
+    ) -> _FloatSeries: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c1: _SeriesLikeComplex_co,
+        c2: _SeriesLikeComplex_co,
+    ) -> _ComplexSeries: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c1: _SeriesLikeCoef_co,
+        c2: _SeriesLikeCoef_co,
+    ) -> _ObjectSeries: ...
+
+@type_check_only
+class _FuncUnOp(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(self, /, c: _SeriesLikeFloat_co) -> _FloatSeries: ...
+    @overload
+    def __call__(self, /, c: _SeriesLikeComplex_co) -> _ComplexSeries: ...
+    @overload
+    def __call__(self, /, c: _SeriesLikeCoef_co) -> _ObjectSeries: ...
+
+@type_check_only
+class _FuncPoly2Ortho(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(self, /, pol: _SeriesLikeFloat_co) -> _FloatSeries: ...
+    @overload
+    def __call__(self, /, pol: _SeriesLikeComplex_co) -> _ComplexSeries: ...
+    @overload
+    def __call__(self, /, pol: _SeriesLikeCoef_co) -> _ObjectSeries: ...
+
+@type_check_only
+class _FuncPow(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeFloat_co,
+        pow: _IntLike_co,
+        maxpower: None | _IntLike_co = ...,
+    ) -> _FloatSeries: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeComplex_co,
+        pow: _IntLike_co,
+        maxpower: None | _IntLike_co = ...,
+    ) -> _ComplexSeries: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeCoef_co,
+        pow: _IntLike_co,
+        maxpower: None | _IntLike_co = ...,
+    ) -> _ObjectSeries: ...
+
+@type_check_only
+class _FuncDer(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeFloat_co,
+        m: SupportsIndex = ...,
+        scl: _FloatLike_co = ...,
+        axis: SupportsIndex = ...,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeComplex_co,
+        m: SupportsIndex = ...,
+        scl: _ComplexLike_co = ...,
+        axis: SupportsIndex = ...,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeCoef_co,
+        m: SupportsIndex = ...,
+        scl: _CoefLike_co = ...,
+        axis: SupportsIndex = ...,
+    ) -> _ObjectArray: ...
+
+@type_check_only
+class _FuncInteg(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeFloat_co,
+        m: SupportsIndex = ...,
+        k: _FloatLike_co | _SeriesLikeFloat_co = ...,
+        lbnd: _FloatLike_co = ...,
+        scl: _FloatLike_co = ...,
+        axis: SupportsIndex = ...,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeComplex_co,
+        m: SupportsIndex = ...,
+        k: _ComplexLike_co | _SeriesLikeComplex_co = ...,
+        lbnd: _ComplexLike_co = ...,
+        scl: _ComplexLike_co = ...,
+        axis: SupportsIndex = ...,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeCoef_co,
+        m: SupportsIndex = ...,
+        k: _CoefLike_co | _SeriesLikeCoef_co = ...,
+        lbnd: _CoefLike_co = ...,
+        scl: _CoefLike_co = ...,
+        axis: SupportsIndex = ...,
+    ) -> _ObjectArray: ...
+
+@type_check_only
+class _FuncValFromRoots(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _FloatLike_co,
+        r: _FloatLike_co,
+        tensor: bool = ...,
+    ) -> np.floating[Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _NumberLike_co,
+        r: _NumberLike_co,
+        tensor: bool = ...,
+    ) -> np.complexfloating[Any, Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _FloatLike_co | _ArrayLikeFloat_co,
+        r: _ArrayLikeFloat_co,
+        tensor: bool = ...,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _NumberLike_co | _ArrayLikeComplex_co,
+        r: _ArrayLikeComplex_co,
+        tensor: bool = ...,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _CoefLike_co | _ArrayLikeCoef_co,
+        r: _ArrayLikeCoef_co,
+        tensor: bool = ...,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _CoefLike_co,
+        r: _CoefLike_co,
+        tensor: bool = ...,
+    ) -> _SupportsCoefOps[Any]: ...
+
+@type_check_only
+class _FuncVal(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _FloatLike_co,
+        c: _SeriesLikeFloat_co,
+        tensor: bool = ...,
+    ) -> np.floating[Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _NumberLike_co,
+        c: _SeriesLikeComplex_co,
+        tensor: bool = ...,
+    ) -> np.complexfloating[Any, Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeFloat_co,
+        c: _ArrayLikeFloat_co,
+        tensor: bool = ...,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeComplex_co,
+        c: _ArrayLikeComplex_co,
+        tensor: bool = ...,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeCoef_co,
+        c: _ArrayLikeCoef_co,
+        tensor: bool = ...,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _CoefLike_co,
+        c: _SeriesLikeObject_co,
+        tensor: bool = ...,
+    ) -> _SupportsCoefOps[Any]: ...
+
+@type_check_only
+class _FuncVal2D(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _FloatLike_co,
+        y: _FloatLike_co,
+        c: _SeriesLikeFloat_co,
+    ) -> np.floating[Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _NumberLike_co,
+        y: _NumberLike_co,
+        c: _SeriesLikeComplex_co,
+    ) -> np.complexfloating[Any, Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        c: _ArrayLikeFloat_co,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        c: _ArrayLikeComplex_co,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeCoef_co,
+        y: _ArrayLikeCoef_co,
+        c: _ArrayLikeCoef_co,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _CoefLike_co,
+        y: _CoefLike_co,
+        c: _SeriesLikeCoef_co,
+    ) -> _SupportsCoefOps[Any]: ...
+
+@type_check_only
+class _FuncVal3D(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _FloatLike_co,
+        y: _FloatLike_co,
+        z: _FloatLike_co,
+        c: _SeriesLikeFloat_co
+    ) -> np.floating[Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _NumberLike_co,
+        y: _NumberLike_co,
+        z: _NumberLike_co,
+        c: _SeriesLikeComplex_co,
+    ) -> np.complexfloating[Any, Any]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        z: _ArrayLikeFloat_co,
+        c: _ArrayLikeFloat_co,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        z: _ArrayLikeComplex_co,
+        c: _ArrayLikeComplex_co,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeCoef_co,
+        y: _ArrayLikeCoef_co,
+        z: _ArrayLikeCoef_co,
+        c: _ArrayLikeCoef_co,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _CoefLike_co,
+        y: _CoefLike_co,
+        z: _CoefLike_co,
+        c: _SeriesLikeCoef_co,
+    ) -> _SupportsCoefOps[Any]: ...
+
+_AnyValF: TypeAlias = Callable[
+    [npt.ArrayLike, npt.ArrayLike, bool],
+    _CoefArray,
+]
+
+@type_check_only
+class _FuncValND(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        val_f: _AnyValF,
+        c: _SeriesLikeFloat_co,
+        /,
+        *args: _FloatLike_co,
+    ) -> np.floating[Any]: ...
+    @overload
+    def __call__(
+        self,
+        val_f: _AnyValF,
+        c: _SeriesLikeComplex_co,
+        /,
+        *args: _NumberLike_co,
+    ) -> np.complexfloating[Any, Any]: ...
+    @overload
+    def __call__(
+        self,
+        val_f: _AnyValF,
+        c: _ArrayLikeFloat_co,
+        /,
+        *args: _ArrayLikeFloat_co,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        val_f: _AnyValF,
+        c: _ArrayLikeComplex_co,
+        /,
+        *args: _ArrayLikeComplex_co,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        val_f: _AnyValF,
+        c: _SeriesLikeObject_co,
+        /,
+        *args: _CoefObjectLike_co,
+    ) -> _SupportsCoefOps[Any]: ...
+    @overload
+    def __call__(
+        self,
+        val_f: _AnyValF,
+        c: _ArrayLikeCoef_co,
+        /,
+        *args: _ArrayLikeCoef_co,
+    ) -> _ObjectArray: ...
+
+@type_check_only
+class _FuncVander(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeFloat_co,
+        deg: SupportsIndex,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeComplex_co,
+        deg: SupportsIndex,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeCoef_co,
+        deg: SupportsIndex,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: npt.ArrayLike,
+        deg: SupportsIndex,
+    ) -> _CoefArray: ...
+
+_AnyDegrees: TypeAlias = Sequence[SupportsIndex]
+
+@type_check_only
+class _FuncVander2D(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        deg: _AnyDegrees,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        deg: _AnyDegrees,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeCoef_co,
+        y: _ArrayLikeCoef_co,
+        deg: _AnyDegrees,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: npt.ArrayLike,
+        y: npt.ArrayLike,
+        deg: _AnyDegrees,
+    ) -> _CoefArray: ...
+
+@type_check_only
+class _FuncVander3D(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        z: _ArrayLikeFloat_co,
+        deg: _AnyDegrees,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        z: _ArrayLikeComplex_co,
+        deg: _AnyDegrees,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _ArrayLikeCoef_co,
+        y: _ArrayLikeCoef_co,
+        z: _ArrayLikeCoef_co,
+        deg: _AnyDegrees,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: npt.ArrayLike,
+        y: npt.ArrayLike,
+        z: npt.ArrayLike,
+        deg: _AnyDegrees,
+    ) -> _CoefArray: ...
+
+# keep in sync with the broadest overload of `._FuncVander`
+_AnyFuncVander: TypeAlias = Callable[
+    [npt.ArrayLike, SupportsIndex],
+    _CoefArray,
+]
+
+@type_check_only
+class _FuncVanderND(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        vander_fs: Sequence[_AnyFuncVander],
+        points: Sequence[_ArrayLikeFloat_co],
+        degrees: Sequence[SupportsIndex],
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        vander_fs: Sequence[_AnyFuncVander],
+        points: Sequence[_ArrayLikeComplex_co],
+        degrees: Sequence[SupportsIndex],
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        vander_fs: Sequence[_AnyFuncVander],
+        points: Sequence[
+            _ArrayLikeObject_co | _ArrayLikeComplex_co,
+        ],
+        degrees: Sequence[SupportsIndex],
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        vander_fs: Sequence[_AnyFuncVander],
+        points: Sequence[npt.ArrayLike],
+        degrees: Sequence[SupportsIndex],
+    ) -> _CoefArray: ...
+
+_FullFitResult: TypeAlias = Sequence[np.inexact[Any] | np.int32]
+
+@type_check_only
+class _FuncFit(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _SeriesLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float = ...,
+        full: Literal[False] = ...,
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> _FloatArray: ...
+    @overload
+    def __call__(
+        self,
+        x: _SeriesLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float,
+        full: Literal[True],
+        /,
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> tuple[_FloatArray, _FullFitResult]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _SeriesLikeFloat_co,
+        y: _ArrayLikeFloat_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float = ...,
+        *,
+        full: Literal[True],
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> tuple[_FloatArray, _FullFitResult]: ...
+
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _SeriesLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float = ...,
+        full: Literal[False] = ...,
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> _ComplexArray: ...
+    @overload
+    def __call__(
+        self,
+        x: _SeriesLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float,
+        full: Literal[True],
+        /,
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> tuple[_ComplexArray, _FullFitResult]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _SeriesLikeComplex_co,
+        y: _ArrayLikeComplex_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float = ...,
+        *,
+        full: Literal[True],
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> tuple[_ComplexArray, _FullFitResult]: ...
+
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _SeriesLikeComplex_co,
+        y: _ArrayLikeCoef_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float = ...,
+        full: Literal[False] = ...,
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> _ObjectArray: ...
+    @overload
+    def __call__(
+        self,
+        x: _SeriesLikeComplex_co,
+        y: _ArrayLikeCoef_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float,
+        full: Literal[True],
+        /,
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> tuple[_ObjectArray, _FullFitResult]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        x: _SeriesLikeComplex_co,
+        y: _ArrayLikeCoef_co,
+        deg: int | _SeriesLikeInt_co,
+        rcond: None | float = ...,
+        *,
+        full: Literal[True],
+        w: None | _SeriesLikeFloat_co = ...,
+    ) -> tuple[_ObjectArray, _FullFitResult]: ...
+
+@type_check_only
+class _FuncRoots(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeFloat_co,
+    ) -> _Series[np.float64]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeComplex_co,
+    ) -> _Series[np.complex128]: ...
+    @overload
+    def __call__(self, /, c: _SeriesLikeCoef_co) -> _ObjectSeries: ...
+
+
+_Companion: TypeAlias = np.ndarray[tuple[int, int], np.dtype[_SCT]]
+
+@type_check_only
+class _FuncCompanion(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeFloat_co,
+    ) -> _Companion[np.float64]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _SeriesLikeComplex_co,
+    ) -> _Companion[np.complex128]: ...
+    @overload
+    def __call__(self, /, c: _SeriesLikeCoef_co) -> _Companion[np.object_]: ...
+
+@type_check_only
+class _FuncGauss(_Named[_Name_co], Protocol[_Name_co]):
+    def __call__(
+        self,
+        /,
+        deg: SupportsIndex,
+    ) -> _Tuple2[_Series[np.float64]]: ...
+
+@type_check_only
+class _FuncWeight(_Named[_Name_co], Protocol[_Name_co]):
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeFloat_co,
+    ) -> npt.NDArray[np.float64]: ...
+    @overload
+    def __call__(
+        self,
+        /,
+        c: _ArrayLikeComplex_co,
+    ) -> npt.NDArray[np.complex128]: ...
+    @overload
+    def __call__(self, /, c: _ArrayLikeCoef_co) -> _ObjectArray: ...
+
+@type_check_only
+class _FuncPts(_Named[_Name_co], Protocol[_Name_co]):
+    def __call__(self, /, npts: _AnyInt) -> _Series[np.float64]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/chebyshev.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/chebyshev.py
new file mode 100644
index 0000000000000000000000000000000000000000..837847e45110a9cf5cf202c496c68c5e437c4e67
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/chebyshev.py
@@ -0,0 +1,2003 @@
+"""
+====================================================
+Chebyshev Series (:mod:`numpy.polynomial.chebyshev`)
+====================================================
+
+This module provides a number of objects (mostly functions) useful for
+dealing with Chebyshev series, including a `Chebyshev` class that
+encapsulates the usual arithmetic operations.  (General information
+on how this module represents and works with such polynomials is in the
+docstring for its "parent" sub-package, `numpy.polynomial`).
+
+Classes
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   Chebyshev
+
+
+Constants
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   chebdomain
+   chebzero
+   chebone
+   chebx
+
+Arithmetic
+----------
+
+.. autosummary::
+   :toctree: generated/
+
+   chebadd
+   chebsub
+   chebmulx
+   chebmul
+   chebdiv
+   chebpow
+   chebval
+   chebval2d
+   chebval3d
+   chebgrid2d
+   chebgrid3d
+
+Calculus
+--------
+
+.. autosummary::
+   :toctree: generated/
+
+   chebder
+   chebint
+
+Misc Functions
+--------------
+
+.. autosummary::
+   :toctree: generated/
+
+   chebfromroots
+   chebroots
+   chebvander
+   chebvander2d
+   chebvander3d
+   chebgauss
+   chebweight
+   chebcompanion
+   chebfit
+   chebpts1
+   chebpts2
+   chebtrim
+   chebline
+   cheb2poly
+   poly2cheb
+   chebinterpolate
+
+See also
+--------
+`numpy.polynomial`
+
+Notes
+-----
+The implementations of multiplication, division, integration, and
+differentiation use the algebraic identities [1]_:
+
+.. math::
+    T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\
+    z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}.
+
+where
+
+.. math:: x = \\frac{z + z^{-1}}{2}.
+
+These identities allow a Chebyshev series to be expressed as a finite,
+symmetric Laurent series.  In this module, this sort of Laurent series
+is referred to as a "z-series."
+
+References
+----------
+.. [1] A. T. Benjamin, et al., "Combinatorial Trigonometry with Chebyshev
+  Polynomials," *Journal of Statistical Planning and Inference 14*, 2008
+  (https://web.archive.org/web/20080221202153/https://www.math.hmc.edu/~benjamin/papers/CombTrig.pdf, pg. 4)
+
+"""
+import numpy as np
+import numpy.linalg as la
+from numpy.lib.array_utils import normalize_axis_index
+
+from . import polyutils as pu
+from ._polybase import ABCPolyBase
+
+__all__ = [
+    'chebzero', 'chebone', 'chebx', 'chebdomain', 'chebline', 'chebadd',
+    'chebsub', 'chebmulx', 'chebmul', 'chebdiv', 'chebpow', 'chebval',
+    'chebder', 'chebint', 'cheb2poly', 'poly2cheb', 'chebfromroots',
+    'chebvander', 'chebfit', 'chebtrim', 'chebroots', 'chebpts1',
+    'chebpts2', 'Chebyshev', 'chebval2d', 'chebval3d', 'chebgrid2d',
+    'chebgrid3d', 'chebvander2d', 'chebvander3d', 'chebcompanion',
+    'chebgauss', 'chebweight', 'chebinterpolate']
+
+chebtrim = pu.trimcoef
+
+#
+# A collection of functions for manipulating z-series. These are private
+# functions and do minimal error checking.
+#
+
+def _cseries_to_zseries(c):
+    """Convert Chebyshev series to z-series.
+
+    Convert a Chebyshev series to the equivalent z-series. The result is
+    never an empty array. The dtype of the return is the same as that of
+    the input. No checks are run on the arguments as this routine is for
+    internal use.
+
+    Parameters
+    ----------
+    c : 1-D ndarray
+        Chebyshev coefficients, ordered from low to high
+
+    Returns
+    -------
+    zs : 1-D ndarray
+        Odd length symmetric z-series, ordered from  low to high.
+
+    """
+    n = c.size
+    zs = np.zeros(2*n-1, dtype=c.dtype)
+    zs[n-1:] = c/2
+    return zs + zs[::-1]
+
+
+def _zseries_to_cseries(zs):
+    """Convert z-series to a Chebyshev series.
+
+    Convert a z series to the equivalent Chebyshev series. The result is
+    never an empty array. The dtype of the return is the same as that of
+    the input. No checks are run on the arguments as this routine is for
+    internal use.
+
+    Parameters
+    ----------
+    zs : 1-D ndarray
+        Odd length symmetric z-series, ordered from  low to high.
+
+    Returns
+    -------
+    c : 1-D ndarray
+        Chebyshev coefficients, ordered from  low to high.
+
+    """
+    n = (zs.size + 1)//2
+    c = zs[n-1:].copy()
+    c[1:n] *= 2
+    return c
+
+
+def _zseries_mul(z1, z2):
+    """Multiply two z-series.
+
+    Multiply two z-series to produce a z-series.
+
+    Parameters
+    ----------
+    z1, z2 : 1-D ndarray
+        The arrays must be 1-D but this is not checked.
+
+    Returns
+    -------
+    product : 1-D ndarray
+        The product z-series.
+
+    Notes
+    -----
+    This is simply convolution. If symmetric/anti-symmetric z-series are
+    denoted by S/A then the following rules apply:
+
+    S*S, A*A -> S
+    S*A, A*S -> A
+
+    """
+    return np.convolve(z1, z2)
+
+
+def _zseries_div(z1, z2):
+    """Divide the first z-series by the second.
+
+    Divide `z1` by `z2` and return the quotient and remainder as z-series.
+    Warning: this implementation only applies when both z1 and z2 have the
+    same symmetry, which is sufficient for present purposes.
+
+    Parameters
+    ----------
+    z1, z2 : 1-D ndarray
+        The arrays must be 1-D and have the same symmetry, but this is not
+        checked.
+
+    Returns
+    -------
+
+    (quotient, remainder) : 1-D ndarrays
+        Quotient and remainder as z-series.
+
+    Notes
+    -----
+    This is not the same as polynomial division on account of the desired form
+    of the remainder. If symmetric/anti-symmetric z-series are denoted by S/A
+    then the following rules apply:
+
+    S/S -> S,S
+    A/A -> S,A
+
+    The restriction to types of the same symmetry could be fixed but seems like
+    unneeded generality. There is no natural form for the remainder in the case
+    where there is no symmetry.
+
+    """
+    z1 = z1.copy()
+    z2 = z2.copy()
+    lc1 = len(z1)
+    lc2 = len(z2)
+    if lc2 == 1:
+        z1 /= z2
+        return z1, z1[:1]*0
+    elif lc1 < lc2:
+        return z1[:1]*0, z1
+    else:
+        dlen = lc1 - lc2
+        scl = z2[0]
+        z2 /= scl
+        quo = np.empty(dlen + 1, dtype=z1.dtype)
+        i = 0
+        j = dlen
+        while i < j:
+            r = z1[i]
+            quo[i] = z1[i]
+            quo[dlen - i] = r
+            tmp = r*z2
+            z1[i:i+lc2] -= tmp
+            z1[j:j+lc2] -= tmp
+            i += 1
+            j -= 1
+        r = z1[i]
+        quo[i] = r
+        tmp = r*z2
+        z1[i:i+lc2] -= tmp
+        quo /= scl
+        rem = z1[i+1:i-1+lc2].copy()
+        return quo, rem
+
+
+def _zseries_der(zs):
+    """Differentiate a z-series.
+
+    The derivative is with respect to x, not z. This is achieved using the
+    chain rule and the value of dx/dz given in the module notes.
+
+    Parameters
+    ----------
+    zs : z-series
+        The z-series to differentiate.
+
+    Returns
+    -------
+    derivative : z-series
+        The derivative
+
+    Notes
+    -----
+    The zseries for x (ns) has been multiplied by two in order to avoid
+    using floats that are incompatible with Decimal and likely other
+    specialized scalar types. This scaling has been compensated by
+    multiplying the value of zs by two also so that the two cancels in the
+    division.
+
+    """
+    n = len(zs)//2
+    ns = np.array([-1, 0, 1], dtype=zs.dtype)
+    zs *= np.arange(-n, n+1)*2
+    d, r = _zseries_div(zs, ns)
+    return d
+
+
+def _zseries_int(zs):
+    """Integrate a z-series.
+
+    The integral is with respect to x, not z. This is achieved by a change
+    of variable using dx/dz given in the module notes.
+
+    Parameters
+    ----------
+    zs : z-series
+        The z-series to integrate
+
+    Returns
+    -------
+    integral : z-series
+        The indefinite integral
+
+    Notes
+    -----
+    The zseries for x (ns) has been multiplied by two in order to avoid
+    using floats that are incompatible with Decimal and likely other
+    specialized scalar types. This scaling has been compensated by
+    dividing the resulting zs by two.
+
+    """
+    n = 1 + len(zs)//2
+    ns = np.array([-1, 0, 1], dtype=zs.dtype)
+    zs = _zseries_mul(zs, ns)
+    div = np.arange(-n, n+1)*2
+    zs[:n] /= div[:n]
+    zs[n+1:] /= div[n+1:]
+    zs[n] = 0
+    return zs
+
+#
+# Chebyshev series functions
+#
+
+
+def poly2cheb(pol):
+    """
+    Convert a polynomial to a Chebyshev series.
+
+    Convert an array representing the coefficients of a polynomial (relative
+    to the "standard" basis) ordered from lowest degree to highest, to an
+    array of the coefficients of the equivalent Chebyshev series, ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    pol : array_like
+        1-D array containing the polynomial coefficients
+
+    Returns
+    -------
+    c : ndarray
+        1-D array containing the coefficients of the equivalent Chebyshev
+        series.
+
+    See Also
+    --------
+    cheb2poly
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy import polynomial as P
+    >>> p = P.Polynomial(range(4))
+    >>> p
+    Polynomial([0., 1., 2., 3.], domain=[-1.,  1.], window=[-1.,  1.], symbol='x')
+    >>> c = p.convert(kind=P.Chebyshev)
+    >>> c
+    Chebyshev([1.  , 3.25, 1.  , 0.75], domain=[-1.,  1.], window=[-1., ...
+    >>> P.chebyshev.poly2cheb(range(4))
+    array([1.  , 3.25, 1.  , 0.75])
+
+    """
+    [pol] = pu.as_series([pol])
+    deg = len(pol) - 1
+    res = 0
+    for i in range(deg, -1, -1):
+        res = chebadd(chebmulx(res), pol[i])
+    return res
+
+
+def cheb2poly(c):
+    """
+    Convert a Chebyshev series to a polynomial.
+
+    Convert an array representing the coefficients of a Chebyshev series,
+    ordered from lowest degree to highest, to an array of the coefficients
+    of the equivalent polynomial (relative to the "standard" basis) ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array containing the Chebyshev series coefficients, ordered
+        from lowest order term to highest.
+
+    Returns
+    -------
+    pol : ndarray
+        1-D array containing the coefficients of the equivalent polynomial
+        (relative to the "standard" basis) ordered from lowest order term
+        to highest.
+
+    See Also
+    --------
+    poly2cheb
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy import polynomial as P
+    >>> c = P.Chebyshev(range(4))
+    >>> c
+    Chebyshev([0., 1., 2., 3.], domain=[-1.,  1.], window=[-1.,  1.], symbol='x')
+    >>> p = c.convert(kind=P.Polynomial)
+    >>> p
+    Polynomial([-2., -8.,  4., 12.], domain=[-1.,  1.], window=[-1.,  1.], ...
+    >>> P.chebyshev.cheb2poly(range(4))
+    array([-2.,  -8.,   4.,  12.])
+
+    """
+    from .polynomial import polyadd, polysub, polymulx
+
+    [c] = pu.as_series([c])
+    n = len(c)
+    if n < 3:
+        return c
+    else:
+        c0 = c[-2]
+        c1 = c[-1]
+        # i is the current degree of c1
+        for i in range(n - 1, 1, -1):
+            tmp = c0
+            c0 = polysub(c[i - 2], c1)
+            c1 = polyadd(tmp, polymulx(c1)*2)
+        return polyadd(c0, polymulx(c1))
+
+
+#
+# These are constant arrays are of integer type so as to be compatible
+# with the widest range of other types, such as Decimal.
+#
+
+# Chebyshev default domain.
+chebdomain = np.array([-1., 1.])
+
+# Chebyshev coefficients representing zero.
+chebzero = np.array([0])
+
+# Chebyshev coefficients representing one.
+chebone = np.array([1])
+
+# Chebyshev coefficients representing the identity x.
+chebx = np.array([0, 1])
+
+
+def chebline(off, scl):
+    """
+    Chebyshev series whose graph is a straight line.
+
+    Parameters
+    ----------
+    off, scl : scalars
+        The specified line is given by ``off + scl*x``.
+
+    Returns
+    -------
+    y : ndarray
+        This module's representation of the Chebyshev series for
+        ``off + scl*x``.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyline
+    numpy.polynomial.legendre.legline
+    numpy.polynomial.laguerre.lagline
+    numpy.polynomial.hermite.hermline
+    numpy.polynomial.hermite_e.hermeline
+
+    Examples
+    --------
+    >>> import numpy.polynomial.chebyshev as C
+    >>> C.chebline(3,2)
+    array([3, 2])
+    >>> C.chebval(-3, C.chebline(3,2)) # should be -3
+    -3.0
+
+    """
+    if scl != 0:
+        return np.array([off, scl])
+    else:
+        return np.array([off])
+
+
+def chebfromroots(roots):
+    """
+    Generate a Chebyshev series with given roots.
+
+    The function returns the coefficients of the polynomial
+
+    .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
+
+    in Chebyshev form, where the :math:`r_n` are the roots specified in
+    `roots`.  If a zero has multiplicity n, then it must appear in `roots`
+    n times.  For instance, if 2 is a root of multiplicity three and 3 is a
+    root of multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3].
+    The roots can appear in any order.
+
+    If the returned coefficients are `c`, then
+
+    .. math:: p(x) = c_0 + c_1 * T_1(x) + ... +  c_n * T_n(x)
+
+    The coefficient of the last term is not generally 1 for monic
+    polynomials in Chebyshev form.
+
+    Parameters
+    ----------
+    roots : array_like
+        Sequence containing the roots.
+
+    Returns
+    -------
+    out : ndarray
+        1-D array of coefficients.  If all roots are real then `out` is a
+        real array, if some of the roots are complex, then `out` is complex
+        even if all the coefficients in the result are real (see Examples
+        below).
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfromroots
+    numpy.polynomial.legendre.legfromroots
+    numpy.polynomial.laguerre.lagfromroots
+    numpy.polynomial.hermite.hermfromroots
+    numpy.polynomial.hermite_e.hermefromroots
+
+    Examples
+    --------
+    >>> import numpy.polynomial.chebyshev as C
+    >>> C.chebfromroots((-1,0,1)) # x^3 - x relative to the standard basis
+    array([ 0.  , -0.25,  0.  ,  0.25])
+    >>> j = complex(0,1)
+    >>> C.chebfromroots((-j,j)) # x^2 + 1 relative to the standard basis
+    array([1.5+0.j, 0. +0.j, 0.5+0.j])
+
+    """
+    return pu._fromroots(chebline, chebmul, roots)
+
+
+def chebadd(c1, c2):
+    """
+    Add one Chebyshev series to another.
+
+    Returns the sum of two Chebyshev series `c1` + `c2`.  The arguments
+    are sequences of coefficients ordered from lowest order term to
+    highest, i.e., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Chebyshev series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the Chebyshev series of their sum.
+
+    See Also
+    --------
+    chebsub, chebmulx, chebmul, chebdiv, chebpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the sum of two Chebyshev series
+    is a Chebyshev series (without having to "reproject" the result onto
+    the basis set) so addition, just like that of "standard" polynomials,
+    is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> C.chebadd(c1,c2)
+    array([4., 4., 4.])
+
+    """
+    return pu._add(c1, c2)
+
+
+def chebsub(c1, c2):
+    """
+    Subtract one Chebyshev series from another.
+
+    Returns the difference of two Chebyshev series `c1` - `c2`.  The
+    sequences of coefficients are from lowest order term to highest, i.e.,
+    [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Chebyshev series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Chebyshev series coefficients representing their difference.
+
+    See Also
+    --------
+    chebadd, chebmulx, chebmul, chebdiv, chebpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the difference of two Chebyshev
+    series is a Chebyshev series (without having to "reproject" the result
+    onto the basis set) so subtraction, just like that of "standard"
+    polynomials, is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> C.chebsub(c1,c2)
+    array([-2.,  0.,  2.])
+    >>> C.chebsub(c2,c1) # -C.chebsub(c1,c2)
+    array([ 2.,  0., -2.])
+
+    """
+    return pu._sub(c1, c2)
+
+
+def chebmulx(c):
+    """Multiply a Chebyshev series by x.
+
+    Multiply the polynomial `c` by x, where x is the independent
+    variable.
+
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Chebyshev series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the result of the multiplication.
+
+    See Also
+    --------
+    chebadd, chebsub, chebmul, chebdiv, chebpow
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> C.chebmulx([1,2,3])
+    array([1. , 2.5, 1. , 1.5])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    # The zero series needs special treatment
+    if len(c) == 1 and c[0] == 0:
+        return c
+
+    prd = np.empty(len(c) + 1, dtype=c.dtype)
+    prd[0] = c[0]*0
+    prd[1] = c[0]
+    if len(c) > 1:
+        tmp = c[1:]/2
+        prd[2:] = tmp
+        prd[0:-2] += tmp
+    return prd
+
+
+def chebmul(c1, c2):
+    """
+    Multiply one Chebyshev series by another.
+
+    Returns the product of two Chebyshev series `c1` * `c2`.  The arguments
+    are sequences of coefficients, from lowest order "term" to highest,
+    e.g., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Chebyshev series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Chebyshev series coefficients representing their product.
+
+    See Also
+    --------
+    chebadd, chebsub, chebmulx, chebdiv, chebpow
+
+    Notes
+    -----
+    In general, the (polynomial) product of two C-series results in terms
+    that are not in the Chebyshev polynomial basis set.  Thus, to express
+    the product as a C-series, it is typically necessary to "reproject"
+    the product onto said basis set, which typically produces
+    "unintuitive live" (but correct) results; see Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> C.chebmul(c1,c2) # multiplication requires "reprojection"
+    array([  6.5,  12. ,  12. ,   4. ,   1.5])
+
+    """
+    # c1, c2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+    z1 = _cseries_to_zseries(c1)
+    z2 = _cseries_to_zseries(c2)
+    prd = _zseries_mul(z1, z2)
+    ret = _zseries_to_cseries(prd)
+    return pu.trimseq(ret)
+
+
+def chebdiv(c1, c2):
+    """
+    Divide one Chebyshev series by another.
+
+    Returns the quotient-with-remainder of two Chebyshev series
+    `c1` / `c2`.  The arguments are sequences of coefficients from lowest
+    order "term" to highest, e.g., [1,2,3] represents the series
+    ``T_0 + 2*T_1 + 3*T_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Chebyshev series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    [quo, rem] : ndarrays
+        Of Chebyshev series coefficients representing the quotient and
+        remainder.
+
+    See Also
+    --------
+    chebadd, chebsub, chebmulx, chebmul, chebpow
+
+    Notes
+    -----
+    In general, the (polynomial) division of one C-series by another
+    results in quotient and remainder terms that are not in the Chebyshev
+    polynomial basis set.  Thus, to express these results as C-series, it
+    is typically necessary to "reproject" the results onto said basis
+    set, which typically produces "unintuitive" (but correct) results;
+    see Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> C.chebdiv(c1,c2) # quotient "intuitive," remainder not
+    (array([3.]), array([-8., -4.]))
+    >>> c2 = (0,1,2,3)
+    >>> C.chebdiv(c2,c1) # neither "intuitive"
+    (array([0., 2.]), array([-2., -4.]))
+
+    """
+    # c1, c2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+    if c2[-1] == 0:
+        raise ZeroDivisionError  # FIXME: add message with details to exception
+
+    # note: this is more efficient than `pu._div(chebmul, c1, c2)`
+    lc1 = len(c1)
+    lc2 = len(c2)
+    if lc1 < lc2:
+        return c1[:1]*0, c1
+    elif lc2 == 1:
+        return c1/c2[-1], c1[:1]*0
+    else:
+        z1 = _cseries_to_zseries(c1)
+        z2 = _cseries_to_zseries(c2)
+        quo, rem = _zseries_div(z1, z2)
+        quo = pu.trimseq(_zseries_to_cseries(quo))
+        rem = pu.trimseq(_zseries_to_cseries(rem))
+        return quo, rem
+
+
+def chebpow(c, pow, maxpower=16):
+    """Raise a Chebyshev series to a power.
+
+    Returns the Chebyshev series `c` raised to the power `pow`. The
+    argument `c` is a sequence of coefficients ordered from low to high.
+    i.e., [1,2,3] is the series  ``T_0 + 2*T_1 + 3*T_2.``
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Chebyshev series coefficients ordered from low to
+        high.
+    pow : integer
+        Power to which the series will be raised
+    maxpower : integer, optional
+        Maximum power allowed. This is mainly to limit growth of the series
+        to unmanageable size. Default is 16
+
+    Returns
+    -------
+    coef : ndarray
+        Chebyshev series of power.
+
+    See Also
+    --------
+    chebadd, chebsub, chebmulx, chebmul, chebdiv
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> C.chebpow([1, 2, 3, 4], 2)
+    array([15.5, 22. , 16. , ..., 12.5, 12. ,  8. ])
+
+    """
+    # note: this is more efficient than `pu._pow(chebmul, c1, c2)`, as it
+    # avoids converting between z and c series repeatedly
+
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    power = int(pow)
+    if power != pow or power < 0:
+        raise ValueError("Power must be a non-negative integer.")
+    elif maxpower is not None and power > maxpower:
+        raise ValueError("Power is too large")
+    elif power == 0:
+        return np.array([1], dtype=c.dtype)
+    elif power == 1:
+        return c
+    else:
+        # This can be made more efficient by using powers of two
+        # in the usual way.
+        zs = _cseries_to_zseries(c)
+        prd = zs
+        for i in range(2, power + 1):
+            prd = np.convolve(prd, zs)
+        return _zseries_to_cseries(prd)
+
+
+def chebder(c, m=1, scl=1, axis=0):
+    """
+    Differentiate a Chebyshev series.
+
+    Returns the Chebyshev series coefficients `c` differentiated `m` times
+    along `axis`.  At each iteration the result is multiplied by `scl` (the
+    scaling factor is for use in a linear change of variable). The argument
+    `c` is an array of coefficients from low to high degree along each
+    axis, e.g., [1,2,3] represents the series ``1*T_0 + 2*T_1 + 3*T_2``
+    while [[1,2],[1,2]] represents ``1*T_0(x)*T_0(y) + 1*T_1(x)*T_0(y) +
+    2*T_0(x)*T_1(y) + 2*T_1(x)*T_1(y)`` if axis=0 is ``x`` and axis=1 is
+    ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Chebyshev series coefficients. If c is multidimensional
+        the different axis correspond to different variables with the
+        degree in each axis given by the corresponding index.
+    m : int, optional
+        Number of derivatives taken, must be non-negative. (Default: 1)
+    scl : scalar, optional
+        Each differentiation is multiplied by `scl`.  The end result is
+        multiplication by ``scl**m``.  This is for use in a linear change of
+        variable. (Default: 1)
+    axis : int, optional
+        Axis over which the derivative is taken. (Default: 0).
+
+    Returns
+    -------
+    der : ndarray
+        Chebyshev series of the derivative.
+
+    See Also
+    --------
+    chebint
+
+    Notes
+    -----
+    In general, the result of differentiating a C-series needs to be
+    "reprojected" onto the C-series basis set. Thus, typically, the
+    result of this function is "unintuitive," albeit correct; see Examples
+    section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> c = (1,2,3,4)
+    >>> C.chebder(c)
+    array([14., 12., 24.])
+    >>> C.chebder(c,3)
+    array([96.])
+    >>> C.chebder(c,scl=-1)
+    array([-14., -12., -24.])
+    >>> C.chebder(c,2,-1)
+    array([12.,  96.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    cnt = pu._as_int(m, "the order of derivation")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of derivation must be non-negative")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    n = len(c)
+    if cnt >= n:
+        c = c[:1]*0
+    else:
+        for i in range(cnt):
+            n = n - 1
+            c *= scl
+            der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
+            for j in range(n, 2, -1):
+                der[j - 1] = (2*j)*c[j]
+                c[j - 2] += (j*c[j])/(j - 2)
+            if n > 1:
+                der[1] = 4*c[2]
+            der[0] = c[1]
+            c = der
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
+    """
+    Integrate a Chebyshev series.
+
+    Returns the Chebyshev series coefficients `c` integrated `m` times from
+    `lbnd` along `axis`. At each iteration the resulting series is
+    **multiplied** by `scl` and an integration constant, `k`, is added.
+    The scaling factor is for use in a linear change of variable.  ("Buyer
+    beware": note that, depending on what one is doing, one may want `scl`
+    to be the reciprocal of what one might expect; for more information,
+    see the Notes section below.)  The argument `c` is an array of
+    coefficients from low to high degree along each axis, e.g., [1,2,3]
+    represents the series ``T_0 + 2*T_1 + 3*T_2`` while [[1,2],[1,2]]
+    represents ``1*T_0(x)*T_0(y) + 1*T_1(x)*T_0(y) + 2*T_0(x)*T_1(y) +
+    2*T_1(x)*T_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Chebyshev series coefficients. If c is multidimensional
+        the different axis correspond to different variables with the
+        degree in each axis given by the corresponding index.
+    m : int, optional
+        Order of integration, must be positive. (Default: 1)
+    k : {[], list, scalar}, optional
+        Integration constant(s).  The value of the first integral at zero
+        is the first value in the list, the value of the second integral
+        at zero is the second value, etc.  If ``k == []`` (the default),
+        all constants are set to zero.  If ``m == 1``, a single scalar can
+        be given instead of a list.
+    lbnd : scalar, optional
+        The lower bound of the integral. (Default: 0)
+    scl : scalar, optional
+        Following each integration the result is *multiplied* by `scl`
+        before the integration constant is added. (Default: 1)
+    axis : int, optional
+        Axis over which the integral is taken. (Default: 0).
+
+    Returns
+    -------
+    S : ndarray
+        C-series coefficients of the integral.
+
+    Raises
+    ------
+    ValueError
+        If ``m < 1``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
+        ``np.ndim(scl) != 0``.
+
+    See Also
+    --------
+    chebder
+
+    Notes
+    -----
+    Note that the result of each integration is *multiplied* by `scl`.
+    Why is this important to note?  Say one is making a linear change of
+    variable :math:`u = ax + b` in an integral relative to `x`.  Then
+    :math:`dx = du/a`, so one will need to set `scl` equal to
+    :math:`1/a`- perhaps not what one would have first thought.
+
+    Also note that, in general, the result of integrating a C-series needs
+    to be "reprojected" onto the C-series basis set.  Thus, typically,
+    the result of this function is "unintuitive," albeit correct; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import chebyshev as C
+    >>> c = (1,2,3)
+    >>> C.chebint(c)
+    array([ 0.5, -0.5,  0.5,  0.5])
+    >>> C.chebint(c,3)
+    array([ 0.03125   , -0.1875    ,  0.04166667, -0.05208333,  0.01041667, # may vary
+        0.00625   ])
+    >>> C.chebint(c, k=3)
+    array([ 3.5, -0.5,  0.5,  0.5])
+    >>> C.chebint(c,lbnd=-2)
+    array([ 8.5, -0.5,  0.5,  0.5])
+    >>> C.chebint(c,scl=-2)
+    array([-1.,  1., -1., -1.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if not np.iterable(k):
+        k = [k]
+    cnt = pu._as_int(m, "the order of integration")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of integration must be non-negative")
+    if len(k) > cnt:
+        raise ValueError("Too many integration constants")
+    if np.ndim(lbnd) != 0:
+        raise ValueError("lbnd must be a scalar.")
+    if np.ndim(scl) != 0:
+        raise ValueError("scl must be a scalar.")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    k = list(k) + [0]*(cnt - len(k))
+    for i in range(cnt):
+        n = len(c)
+        c *= scl
+        if n == 1 and np.all(c[0] == 0):
+            c[0] += k[i]
+        else:
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
+            tmp[0] = c[0]*0
+            tmp[1] = c[0]
+            if n > 1:
+                tmp[2] = c[1]/4
+            for j in range(2, n):
+                tmp[j + 1] = c[j]/(2*(j + 1))
+                tmp[j - 1] -= c[j]/(2*(j - 1))
+            tmp[0] += k[i] - chebval(lbnd, tmp)
+            c = tmp
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def chebval(x, c, tensor=True):
+    """
+    Evaluate a Chebyshev series at points x.
+
+    If `c` is of length `n + 1`, this function returns the value:
+
+    .. math:: p(x) = c_0 * T_0(x) + c_1 * T_1(x) + ... + c_n * T_n(x)
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `c`.
+
+    If `c` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If
+    `c` is multidimensional, then the shape of the result depends on the
+    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
+    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
+    scalars have shape (,).
+
+    Trailing zeros in the coefficients will be used in the evaluation, so
+    they should be avoided if efficiency is a concern.
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        themselves and with the elements of `c`.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree n are contained in c[n]. If `c` is multidimensional the
+        remaining indices enumerate multiple polynomials. In the two
+        dimensional case the coefficients may be thought of as stored in
+        the columns of `c`.
+    tensor : boolean, optional
+        If True, the shape of the coefficient array is extended with ones
+        on the right, one for each dimension of `x`. Scalars have dimension 0
+        for this action. The result is that every column of coefficients in
+        `c` is evaluated for every element of `x`. If False, `x` is broadcast
+        over the columns of `c` for the evaluation.  This keyword is useful
+        when `c` is multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, algebra_like
+        The shape of the return value is described above.
+
+    See Also
+    --------
+    chebval2d, chebgrid2d, chebval3d, chebgrid3d
+
+    Notes
+    -----
+    The evaluation uses Clenshaw recursion, aka synthetic division.
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray) and tensor:
+        c = c.reshape(c.shape + (1,)*x.ndim)
+
+    if len(c) == 1:
+        c0 = c[0]
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]
+        c1 = c[1]
+    else:
+        x2 = 2*x
+        c0 = c[-2]
+        c1 = c[-1]
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            c0 = c[-i] - c1
+            c1 = tmp + c1*x2
+    return c0 + c1*x
+
+
+def chebval2d(x, y, c):
+    """
+    Evaluate a 2-D Chebyshev series at points (x, y).
+
+    This function returns the values:
+
+    .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * T_i(x) * T_j(y)
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars and they
+    must have the same shape after conversion. In either case, either `x`
+    and `y` or their elements must support multiplication and addition both
+    with themselves and with the elements of `c`.
+
+    If `c` is a 1-D array a one is implicitly appended to its shape to make
+    it 2-D. The shape of the result will be c.shape[2:] + x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points ``(x, y)``,
+        where `x` and `y` must have the same shape. If `x` or `y` is a list
+        or tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and if it isn't an ndarray it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term
+        of multi-degree i,j is contained in ``c[i,j]``. If `c` has
+        dimension greater than 2 the remaining indices enumerate multiple
+        sets of coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional Chebyshev series at points formed
+        from pairs of corresponding values from `x` and `y`.
+
+    See Also
+    --------
+    chebval, chebgrid2d, chebval3d, chebgrid3d
+    """
+    return pu._valnd(chebval, c, x, y)
+
+
+def chebgrid2d(x, y, c):
+    """
+    Evaluate a 2-D Chebyshev series on the Cartesian product of x and y.
+
+    This function returns the values:
+
+    .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * T_i(a) * T_j(b),
+
+    where the points `(a, b)` consist of all pairs formed by taking
+    `a` from `x` and `b` from `y`. The resulting points form a grid with
+    `x` in the first dimension and `y` in the second.
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars. In either
+    case, either `x` and `y` or their elements must support multiplication
+    and addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape + y.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points in the
+        Cartesian product of `x` and `y`.  If `x` or `y` is a list or
+        tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j is contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional Chebyshev series at points in the
+        Cartesian product of `x` and `y`.
+
+    See Also
+    --------
+    chebval, chebval2d, chebval3d, chebgrid3d
+    """
+    return pu._gridnd(chebval, c, x, y)
+
+
+def chebval3d(x, y, z, c):
+    """
+    Evaluate a 3-D Chebyshev series at points (x, y, z).
+
+    This function returns the values:
+
+    .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * T_i(x) * T_j(y) * T_k(z)
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if
+    they are tuples or a lists, otherwise they are treated as a scalars and
+    they must have the same shape after conversion. In either case, either
+    `x`, `y`, and `z` or their elements must support multiplication and
+    addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than 3 dimensions, ones are implicitly appended to its
+    shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible object
+        The three dimensional series is evaluated at the points
+        ``(x, y, z)``, where `x`, `y`, and `z` must have the same shape.  If
+        any of `x`, `y`, or `z` is a list or tuple, it is first converted
+        to an ndarray, otherwise it is left unchanged and if it isn't an
+        ndarray it is  treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
+        greater than 3 the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the multidimensional polynomial on points formed with
+        triples of corresponding values from `x`, `y`, and `z`.
+
+    See Also
+    --------
+    chebval, chebval2d, chebgrid2d, chebgrid3d
+    """
+    return pu._valnd(chebval, c, x, y, z)
+
+
+def chebgrid3d(x, y, z, c):
+    """
+    Evaluate a 3-D Chebyshev series on the Cartesian product of x, y, and z.
+
+    This function returns the values:
+
+    .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * T_i(a) * T_j(b) * T_k(c)
+
+    where the points ``(a, b, c)`` consist of all triples formed by taking
+    `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
+    a grid with `x` in the first dimension, `y` in the second, and `z` in
+    the third.
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if they
+    are tuples or a lists, otherwise they are treated as a scalars. In
+    either case, either `x`, `y`, and `z` or their elements must support
+    multiplication and addition both with themselves and with the elements
+    of `c`.
+
+    If `c` has fewer than three dimensions, ones are implicitly appended to
+    its shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape + y.shape + z.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible objects
+        The three dimensional series is evaluated at the points in the
+        Cartesian product of `x`, `y`, and `z`.  If `x`, `y`, or `z` is a
+        list or tuple, it is first converted to an ndarray, otherwise it is
+        left unchanged and, if it isn't an ndarray, it is treated as a
+        scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    chebval, chebval2d, chebgrid2d, chebval3d
+    """
+    return pu._gridnd(chebval, c, x, y, z)
+
+
+def chebvander(x, deg):
+    """Pseudo-Vandermonde matrix of given degree.
+
+    Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
+    `x`. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., i] = T_i(x),
+
+    where ``0 <= i <= deg``. The leading indices of `V` index the elements of
+    `x` and the last index is the degree of the Chebyshev polynomial.
+
+    If `c` is a 1-D array of coefficients of length ``n + 1`` and `V` is the
+    matrix ``V = chebvander(x, n)``, then ``np.dot(V, c)`` and
+    ``chebval(x, c)`` are the same up to roundoff.  This equivalence is
+    useful both for least squares fitting and for the evaluation of a large
+    number of Chebyshev series of the same degree and sample points.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of points. The dtype is converted to float64 or complex128
+        depending on whether any of the elements are complex. If `x` is
+        scalar it is converted to a 1-D array.
+    deg : int
+        Degree of the resulting matrix.
+
+    Returns
+    -------
+    vander : ndarray
+        The pseudo Vandermonde matrix. The shape of the returned matrix is
+        ``x.shape + (deg + 1,)``, where The last index is the degree of the
+        corresponding Chebyshev polynomial.  The dtype will be the same as
+        the converted `x`.
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg < 0:
+        raise ValueError("deg must be non-negative")
+
+    x = np.array(x, copy=None, ndmin=1) + 0.0
+    dims = (ideg + 1,) + x.shape
+    dtyp = x.dtype
+    v = np.empty(dims, dtype=dtyp)
+    # Use forward recursion to generate the entries.
+    v[0] = x*0 + 1
+    if ideg > 0:
+        x2 = 2*x
+        v[1] = x
+        for i in range(2, ideg + 1):
+            v[i] = v[i-1]*x2 - v[i-2]
+    return np.moveaxis(v, 0, -1)
+
+
+def chebvander2d(x, y, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y)``. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (deg[1] + 1)*i + j] = T_i(x) * T_j(y),
+
+    where ``0 <= i <= deg[0]`` and ``0 <= j <= deg[1]``. The leading indices of
+    `V` index the points ``(x, y)`` and the last index encodes the degrees of
+    the Chebyshev polynomials.
+
+    If ``V = chebvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
+    correspond to the elements of a 2-D coefficient array `c` of shape
+    (xdeg + 1, ydeg + 1) in the order
+
+    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+
+    and ``np.dot(V, c.flat)`` and ``chebval2d(x, y, c)`` will be the same
+    up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 2-D Chebyshev
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to
+        1-D arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg].
+
+    Returns
+    -------
+    vander2d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)`.  The dtype will be the same
+        as the converted `x` and `y`.
+
+    See Also
+    --------
+    chebvander, chebvander3d, chebval2d, chebval3d
+    """
+    return pu._vander_nd_flat((chebvander, chebvander), (x, y), deg)
+
+
+def chebvander3d(x, y, z, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y, z)``. If `l`, `m`, `n` are the given degrees in `x`, `y`, `z`,
+    then The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = T_i(x)*T_j(y)*T_k(z),
+
+    where ``0 <= i <= l``, ``0 <= j <= m``, and ``0 <= j <= n``.  The leading
+    indices of `V` index the points ``(x, y, z)`` and the last index encodes
+    the degrees of the Chebyshev polynomials.
+
+    If ``V = chebvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
+    of `V` correspond to the elements of a 3-D coefficient array `c` of
+    shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
+
+    .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
+
+    and ``np.dot(V, c.flat)`` and ``chebval3d(x, y, z, c)`` will be the
+    same up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 3-D Chebyshev
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y, z : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes will
+        be converted to either float64 or complex128 depending on whether
+        any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg, z_deg].
+
+    Returns
+    -------
+    vander3d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`.  The dtype will
+        be the same as the converted `x`, `y`, and `z`.
+
+    See Also
+    --------
+    chebvander, chebvander3d, chebval2d, chebval3d
+    """
+    return pu._vander_nd_flat((chebvander, chebvander, chebvander), (x, y, z), deg)
+
+
+def chebfit(x, y, deg, rcond=None, full=False, w=None):
+    """
+    Least squares fit of Chebyshev series to data.
+
+    Return the coefficients of a Chebyshev series of degree `deg` that is the
+    least squares fit to the data values `y` given at points `x`. If `y` is
+    1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
+    fits are done, one for each column of `y`, and the resulting
+    coefficients are stored in the corresponding columns of a 2-D return.
+    The fitted polynomial(s) are in the form
+
+    .. math::  p(x) = c_0 + c_1 * T_1(x) + ... + c_n * T_n(x),
+
+    where `n` is `deg`.
+
+    Parameters
+    ----------
+    x : array_like, shape (M,)
+        x-coordinates of the M sample points ``(x[i], y[i])``.
+    y : array_like, shape (M,) or (M, K)
+        y-coordinates of the sample points. Several data sets of sample
+        points sharing the same x-coordinates can be fitted at once by
+        passing in a 2D-array that contains one dataset per column.
+    deg : int or 1-D array_like
+        Degree(s) of the fitting polynomials. If `deg` is a single integer,
+        all terms up to and including the `deg`'th term are included in the
+        fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+        degrees of the terms to include may be used instead.
+    rcond : float, optional
+        Relative condition number of the fit. Singular values smaller than
+        this relative to the largest singular value will be ignored. The
+        default value is ``len(x)*eps``, where eps is the relative precision of
+        the float type, about 2e-16 in most cases.
+    full : bool, optional
+        Switch determining nature of return value. When it is False (the
+        default) just the coefficients are returned, when True diagnostic
+        information from the singular value decomposition is also returned.
+    w : array_like, shape (`M`,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+
+    Returns
+    -------
+    coef : ndarray, shape (M,) or (M, K)
+        Chebyshev coefficients ordered from low to high. If `y` was 2-D,
+        the coefficients for the data in column k  of `y` are in column
+        `k`.
+
+    [residuals, rank, singular_values, rcond] : list
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    Warns
+    -----
+    RankWarning
+        The rank of the coefficient matrix in the least-squares fit is
+        deficient. The warning is only raised if ``full == False``.  The
+        warnings can be turned off by
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfit
+    numpy.polynomial.legendre.legfit
+    numpy.polynomial.laguerre.lagfit
+    numpy.polynomial.hermite.hermfit
+    numpy.polynomial.hermite_e.hermefit
+    chebval : Evaluates a Chebyshev series.
+    chebvander : Vandermonde matrix of Chebyshev series.
+    chebweight : Chebyshev weight function.
+    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution is the coefficients of the Chebyshev series `p` that
+    minimizes the sum of the weighted squared errors
+
+    .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
+
+    where :math:`w_j` are the weights. This problem is solved by setting up
+    as the (typically) overdetermined matrix equation
+
+    .. math:: V(x) * c = w * y,
+
+    where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
+    coefficients to be solved for, `w` are the weights, and `y` are the
+    observed values.  This equation is then solved using the singular value
+    decomposition of `V`.
+
+    If some of the singular values of `V` are so small that they are
+    neglected, then a `~exceptions.RankWarning` will be issued. This means that
+    the coefficient values may be poorly determined. Using a lower order fit
+    will usually get rid of the warning.  The `rcond` parameter can also be
+    set to a value smaller than its default, but the resulting fit may be
+    spurious and have large contributions from roundoff error.
+
+    Fits using Chebyshev series are usually better conditioned than fits
+    using power series, but much can depend on the distribution of the
+    sample points and the smoothness of the data. If the quality of the fit
+    is inadequate splines may be a good alternative.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Curve fitting",
+           https://en.wikipedia.org/wiki/Curve_fitting
+
+    Examples
+    --------
+
+    """
+    return pu._fit(chebvander, x, y, deg, rcond, full, w)
+
+
+def chebcompanion(c):
+    """Return the scaled companion matrix of c.
+
+    The basis polynomials are scaled so that the companion matrix is
+    symmetric when `c` is a Chebyshev basis polynomial. This provides
+    better eigenvalue estimates than the unscaled case and for basis
+    polynomials the eigenvalues are guaranteed to be real if
+    `numpy.linalg.eigvalsh` is used to obtain them.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Chebyshev series coefficients ordered from low to high
+        degree.
+
+    Returns
+    -------
+    mat : ndarray
+        Scaled companion matrix of dimensions (deg, deg).
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        raise ValueError('Series must have maximum degree of at least 1.')
+    if len(c) == 2:
+        return np.array([[-c[0]/c[1]]])
+
+    n = len(c) - 1
+    mat = np.zeros((n, n), dtype=c.dtype)
+    scl = np.array([1.] + [np.sqrt(.5)]*(n-1))
+    top = mat.reshape(-1)[1::n+1]
+    bot = mat.reshape(-1)[n::n+1]
+    top[0] = np.sqrt(.5)
+    top[1:] = 1/2
+    bot[...] = top
+    mat[:, -1] -= (c[:-1]/c[-1])*(scl/scl[-1])*.5
+    return mat
+
+
+def chebroots(c):
+    """
+    Compute the roots of a Chebyshev series.
+
+    Return the roots (a.k.a. "zeros") of the polynomial
+
+    .. math:: p(x) = \\sum_i c[i] * T_i(x).
+
+    Parameters
+    ----------
+    c : 1-D array_like
+        1-D array of coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        Array of the roots of the series. If all the roots are real,
+        then `out` is also real, otherwise it is complex.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyroots
+    numpy.polynomial.legendre.legroots
+    numpy.polynomial.laguerre.lagroots
+    numpy.polynomial.hermite.hermroots
+    numpy.polynomial.hermite_e.hermeroots
+
+    Notes
+    -----
+    The root estimates are obtained as the eigenvalues of the companion
+    matrix, Roots far from the origin of the complex plane may have large
+    errors due to the numerical instability of the series for such
+    values. Roots with multiplicity greater than 1 will also show larger
+    errors as the value of the series near such points is relatively
+    insensitive to errors in the roots. Isolated roots near the origin can
+    be improved by a few iterations of Newton's method.
+
+    The Chebyshev series basis polynomials aren't powers of `x` so the
+    results of this function may seem unintuitive.
+
+    Examples
+    --------
+    >>> import numpy.polynomial.chebyshev as cheb
+    >>> cheb.chebroots((-1, 1,-1, 1)) # T3 - T2 + T1 - T0 has real roots
+    array([ -5.00000000e-01,   2.60860684e-17,   1.00000000e+00]) # may vary
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        return np.array([], dtype=c.dtype)
+    if len(c) == 2:
+        return np.array([-c[0]/c[1]])
+
+    # rotated companion matrix reduces error
+    m = chebcompanion(c)[::-1,::-1]
+    r = la.eigvals(m)
+    r.sort()
+    return r
+
+
+def chebinterpolate(func, deg, args=()):
+    """Interpolate a function at the Chebyshev points of the first kind.
+
+    Returns the Chebyshev series that interpolates `func` at the Chebyshev
+    points of the first kind in the interval [-1, 1]. The interpolating
+    series tends to a minmax approximation to `func` with increasing `deg`
+    if the function is continuous in the interval.
+
+    Parameters
+    ----------
+    func : function
+        The function to be approximated. It must be a function of a single
+        variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are
+        extra arguments passed in the `args` parameter.
+    deg : int
+        Degree of the interpolating polynomial
+    args : tuple, optional
+        Extra arguments to be used in the function call. Default is no extra
+        arguments.
+
+    Returns
+    -------
+    coef : ndarray, shape (deg + 1,)
+        Chebyshev coefficients of the interpolating series ordered from low to
+        high.
+
+    Examples
+    --------
+    >>> import numpy.polynomial.chebyshev as C
+    >>> C.chebinterpolate(lambda x: np.tanh(x) + 0.5, 8)
+    array([  5.00000000e-01,   8.11675684e-01,  -9.86864911e-17,
+            -5.42457905e-02,  -2.71387850e-16,   4.51658839e-03,
+             2.46716228e-17,  -3.79694221e-04,  -3.26899002e-16])
+
+    Notes
+    -----
+    The Chebyshev polynomials used in the interpolation are orthogonal when
+    sampled at the Chebyshev points of the first kind. If it is desired to
+    constrain some of the coefficients they can simply be set to the desired
+    value after the interpolation, no new interpolation or fit is needed. This
+    is especially useful if it is known apriori that some of coefficients are
+    zero. For instance, if the function is even then the coefficients of the
+    terms of odd degree in the result can be set to zero.
+
+    """
+    deg = np.asarray(deg)
+
+    # check arguments.
+    if deg.ndim > 0 or deg.dtype.kind not in 'iu' or deg.size == 0:
+        raise TypeError("deg must be an int")
+    if deg < 0:
+        raise ValueError("expected deg >= 0")
+
+    order = deg + 1
+    xcheb = chebpts1(order)
+    yfunc = func(xcheb, *args)
+    m = chebvander(xcheb, deg)
+    c = np.dot(m.T, yfunc)
+    c[0] /= order
+    c[1:] /= 0.5*order
+
+    return c
+
+
+def chebgauss(deg):
+    """
+    Gauss-Chebyshev quadrature.
+
+    Computes the sample points and weights for Gauss-Chebyshev quadrature.
+    These sample points and weights will correctly integrate polynomials of
+    degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with
+    the weight function :math:`f(x) = 1/\\sqrt{1 - x^2}`.
+
+    Parameters
+    ----------
+    deg : int
+        Number of sample points and weights. It must be >= 1.
+
+    Returns
+    -------
+    x : ndarray
+        1-D ndarray containing the sample points.
+    y : ndarray
+        1-D ndarray containing the weights.
+
+    Notes
+    -----
+    The results have only been tested up to degree 100, higher degrees may
+    be problematic. For Gauss-Chebyshev there are closed form solutions for
+    the sample points and weights. If n = `deg`, then
+
+    .. math:: x_i = \\cos(\\pi (2 i - 1) / (2 n))
+
+    .. math:: w_i = \\pi / n
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg <= 0:
+        raise ValueError("deg must be a positive integer")
+
+    x = np.cos(np.pi * np.arange(1, 2*ideg, 2) / (2.0*ideg))
+    w = np.ones(ideg)*(np.pi/ideg)
+
+    return x, w
+
+
+def chebweight(x):
+    """
+    The weight function of the Chebyshev polynomials.
+
+    The weight function is :math:`1/\\sqrt{1 - x^2}` and the interval of
+    integration is :math:`[-1, 1]`. The Chebyshev polynomials are
+    orthogonal, but not normalized, with respect to this weight function.
+
+    Parameters
+    ----------
+    x : array_like
+       Values at which the weight function will be computed.
+
+    Returns
+    -------
+    w : ndarray
+       The weight function at `x`.
+    """
+    w = 1./(np.sqrt(1. + x) * np.sqrt(1. - x))
+    return w
+
+
+def chebpts1(npts):
+    """
+    Chebyshev points of the first kind.
+
+    The Chebyshev points of the first kind are the points ``cos(x)``,
+    where ``x = [pi*(k + .5)/npts for k in range(npts)]``.
+
+    Parameters
+    ----------
+    npts : int
+        Number of sample points desired.
+
+    Returns
+    -------
+    pts : ndarray
+        The Chebyshev points of the first kind.
+
+    See Also
+    --------
+    chebpts2
+    """
+    _npts = int(npts)
+    if _npts != npts:
+        raise ValueError("npts must be integer")
+    if _npts < 1:
+        raise ValueError("npts must be >= 1")
+
+    x = 0.5 * np.pi / _npts * np.arange(-_npts+1, _npts+1, 2)
+    return np.sin(x)
+
+
+def chebpts2(npts):
+    """
+    Chebyshev points of the second kind.
+
+    The Chebyshev points of the second kind are the points ``cos(x)``,
+    where ``x = [pi*k/(npts - 1) for k in range(npts)]`` sorted in ascending
+    order.
+
+    Parameters
+    ----------
+    npts : int
+        Number of sample points desired.
+
+    Returns
+    -------
+    pts : ndarray
+        The Chebyshev points of the second kind.
+    """
+    _npts = int(npts)
+    if _npts != npts:
+        raise ValueError("npts must be integer")
+    if _npts < 2:
+        raise ValueError("npts must be >= 2")
+
+    x = np.linspace(-np.pi, 0, _npts)
+    return np.cos(x)
+
+
+#
+# Chebyshev series class
+#
+
+class Chebyshev(ABCPolyBase):
+    """A Chebyshev series class.
+
+    The Chebyshev class provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
+    attributes and methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        Chebyshev coefficients in order of increasing degree, i.e.,
+        ``(1, 2, 3)`` gives ``1*T_0(x) + 2*T_1(x) + 3*T_2(x)``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is [-1., 1.].
+    window : (2,) array_like, optional
+        Window, see `domain` for its use. The default value is [-1., 1.].
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    """
+    # Virtual Functions
+    _add = staticmethod(chebadd)
+    _sub = staticmethod(chebsub)
+    _mul = staticmethod(chebmul)
+    _div = staticmethod(chebdiv)
+    _pow = staticmethod(chebpow)
+    _val = staticmethod(chebval)
+    _int = staticmethod(chebint)
+    _der = staticmethod(chebder)
+    _fit = staticmethod(chebfit)
+    _line = staticmethod(chebline)
+    _roots = staticmethod(chebroots)
+    _fromroots = staticmethod(chebfromroots)
+
+    @classmethod
+    def interpolate(cls, func, deg, domain=None, args=()):
+        """Interpolate a function at the Chebyshev points of the first kind.
+
+        Returns the series that interpolates `func` at the Chebyshev points of
+        the first kind scaled and shifted to the `domain`. The resulting series
+        tends to a minmax approximation of `func` when the function is
+        continuous in the domain.
+
+        Parameters
+        ----------
+        func : function
+            The function to be interpolated. It must be a function of a single
+            variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are
+            extra arguments passed in the `args` parameter.
+        deg : int
+            Degree of the interpolating polynomial.
+        domain : {None, [beg, end]}, optional
+            Domain over which `func` is interpolated. The default is None, in
+            which case the domain is [-1, 1].
+        args : tuple, optional
+            Extra arguments to be used in the function call. Default is no
+            extra arguments.
+
+        Returns
+        -------
+        polynomial : Chebyshev instance
+            Interpolating Chebyshev instance.
+
+        Notes
+        -----
+        See `numpy.polynomial.chebinterpolate` for more details.
+
+        """
+        if domain is None:
+            domain = cls.domain
+        xfunc = lambda x: func(pu.mapdomain(x, cls.window, domain), *args)
+        coef = chebinterpolate(xfunc, deg)
+        return cls(coef, domain=domain)
+
+    # Virtual properties
+    domain = np.array(chebdomain)
+    window = np.array(chebdomain)
+    basis_name = 'T'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/chebyshev.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/chebyshev.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..067af81d635d75511469f6cd130d774f00391be6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/chebyshev.pyi
@@ -0,0 +1,192 @@
+from collections.abc import Callable, Iterable
+from typing import (
+    Any,
+    Concatenate,
+    Final,
+    Literal as L,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _IntLike_co
+
+from ._polybase import ABCPolyBase
+from ._polytypes import (
+    _SeriesLikeCoef_co,
+    _Array1,
+    _Series,
+    _Array2,
+    _CoefSeries,
+    _FuncBinOp,
+    _FuncCompanion,
+    _FuncDer,
+    _FuncFit,
+    _FuncFromRoots,
+    _FuncGauss,
+    _FuncInteg,
+    _FuncLine,
+    _FuncPoly2Ortho,
+    _FuncPow,
+    _FuncPts,
+    _FuncRoots,
+    _FuncUnOp,
+    _FuncVal,
+    _FuncVal2D,
+    _FuncVal3D,
+    _FuncValFromRoots,
+    _FuncVander,
+    _FuncVander2D,
+    _FuncVander3D,
+    _FuncWeight,
+)
+from .polyutils import trimcoef as chebtrim
+
+__all__ = [
+    "chebzero",
+    "chebone",
+    "chebx",
+    "chebdomain",
+    "chebline",
+    "chebadd",
+    "chebsub",
+    "chebmulx",
+    "chebmul",
+    "chebdiv",
+    "chebpow",
+    "chebval",
+    "chebder",
+    "chebint",
+    "cheb2poly",
+    "poly2cheb",
+    "chebfromroots",
+    "chebvander",
+    "chebfit",
+    "chebtrim",
+    "chebroots",
+    "chebpts1",
+    "chebpts2",
+    "Chebyshev",
+    "chebval2d",
+    "chebval3d",
+    "chebgrid2d",
+    "chebgrid3d",
+    "chebvander2d",
+    "chebvander3d",
+    "chebcompanion",
+    "chebgauss",
+    "chebweight",
+    "chebinterpolate",
+]
+
+_SCT = TypeVar("_SCT", bound=np.number[Any] | np.object_)
+def _cseries_to_zseries(c: npt.NDArray[_SCT]) -> _Series[_SCT]: ...
+def _zseries_to_cseries(zs: npt.NDArray[_SCT]) -> _Series[_SCT]: ...
+def _zseries_mul(
+    z1: npt.NDArray[_SCT],
+    z2: npt.NDArray[_SCT],
+) -> _Series[_SCT]: ...
+def _zseries_div(
+    z1: npt.NDArray[_SCT],
+    z2: npt.NDArray[_SCT],
+) -> _Series[_SCT]: ...
+def _zseries_der(zs: npt.NDArray[_SCT]) -> _Series[_SCT]: ...
+def _zseries_int(zs: npt.NDArray[_SCT]) -> _Series[_SCT]: ...
+
+poly2cheb: _FuncPoly2Ortho[L["poly2cheb"]]
+cheb2poly: _FuncUnOp[L["cheb2poly"]]
+
+chebdomain: Final[_Array2[np.float64]]
+chebzero: Final[_Array1[np.int_]]
+chebone: Final[_Array1[np.int_]]
+chebx: Final[_Array2[np.int_]]
+
+chebline: _FuncLine[L["chebline"]]
+chebfromroots: _FuncFromRoots[L["chebfromroots"]]
+chebadd: _FuncBinOp[L["chebadd"]]
+chebsub: _FuncBinOp[L["chebsub"]]
+chebmulx: _FuncUnOp[L["chebmulx"]]
+chebmul: _FuncBinOp[L["chebmul"]]
+chebdiv: _FuncBinOp[L["chebdiv"]]
+chebpow: _FuncPow[L["chebpow"]]
+chebder: _FuncDer[L["chebder"]]
+chebint: _FuncInteg[L["chebint"]]
+chebval: _FuncVal[L["chebval"]]
+chebval2d: _FuncVal2D[L["chebval2d"]]
+chebval3d: _FuncVal3D[L["chebval3d"]]
+chebvalfromroots: _FuncValFromRoots[L["chebvalfromroots"]]
+chebgrid2d: _FuncVal2D[L["chebgrid2d"]]
+chebgrid3d: _FuncVal3D[L["chebgrid3d"]]
+chebvander: _FuncVander[L["chebvander"]]
+chebvander2d: _FuncVander2D[L["chebvander2d"]]
+chebvander3d: _FuncVander3D[L["chebvander3d"]]
+chebfit: _FuncFit[L["chebfit"]]
+chebcompanion: _FuncCompanion[L["chebcompanion"]]
+chebroots: _FuncRoots[L["chebroots"]]
+chebgauss: _FuncGauss[L["chebgauss"]]
+chebweight: _FuncWeight[L["chebweight"]]
+chebpts1: _FuncPts[L["chebpts1"]]
+chebpts2: _FuncPts[L["chebpts2"]]
+
+# keep in sync with `Chebyshev.interpolate`
+_RT = TypeVar("_RT", bound=np.number[Any] | np.bool | np.object_)
+@overload
+def chebinterpolate(
+    func: np.ufunc,
+    deg: _IntLike_co,
+    args: tuple[()] = ...,
+) -> npt.NDArray[np.float64 | np.complex128 | np.object_]: ...
+@overload
+def chebinterpolate(
+    func: Callable[[npt.NDArray[np.float64]], _RT],
+    deg: _IntLike_co,
+    args: tuple[()] = ...,
+) -> npt.NDArray[_RT]: ...
+@overload
+def chebinterpolate(
+    func: Callable[Concatenate[npt.NDArray[np.float64], ...], _RT],
+    deg: _IntLike_co,
+    args: Iterable[Any],
+) -> npt.NDArray[_RT]: ...
+
+_Self = TypeVar("_Self", bound=object)
+
+class Chebyshev(ABCPolyBase[L["T"]]):
+    @overload
+    @classmethod
+    def interpolate(
+        cls: type[_Self],
+        /,
+        func: Callable[[npt.NDArray[np.float64]], _CoefSeries],
+        deg: _IntLike_co,
+        domain: None | _SeriesLikeCoef_co = ...,
+        args: tuple[()] = ...,
+    ) -> _Self: ...
+    @overload
+    @classmethod
+    def interpolate(
+        cls: type[_Self],
+        /,
+        func: Callable[
+            Concatenate[npt.NDArray[np.float64], ...],
+            _CoefSeries,
+        ],
+        deg: _IntLike_co,
+        domain: None | _SeriesLikeCoef_co = ...,
+        *,
+        args: Iterable[Any],
+    ) -> _Self: ...
+    @overload
+    @classmethod
+    def interpolate(
+        cls: type[_Self],
+        func: Callable[
+            Concatenate[npt.NDArray[np.float64], ...],
+            _CoefSeries,
+        ],
+        deg: _IntLike_co,
+        domain: None | _SeriesLikeCoef_co,
+        args: Iterable[Any],
+        /,
+    ) -> _Self: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e51dca7fa55c83dfa467013440e160b260d9d9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite.py
@@ -0,0 +1,1740 @@
+"""
+==============================================================
+Hermite Series, "Physicists" (:mod:`numpy.polynomial.hermite`)
+==============================================================
+
+This module provides a number of objects (mostly functions) useful for
+dealing with Hermite series, including a `Hermite` class that
+encapsulates the usual arithmetic operations.  (General information
+on how this module represents and works with such polynomials is in the
+docstring for its "parent" sub-package, `numpy.polynomial`).
+
+Classes
+-------
+.. autosummary::
+   :toctree: generated/
+
+   Hermite
+
+Constants
+---------
+.. autosummary::
+   :toctree: generated/
+
+   hermdomain
+   hermzero
+   hermone
+   hermx
+
+Arithmetic
+----------
+.. autosummary::
+   :toctree: generated/
+
+   hermadd
+   hermsub
+   hermmulx
+   hermmul
+   hermdiv
+   hermpow
+   hermval
+   hermval2d
+   hermval3d
+   hermgrid2d
+   hermgrid3d
+
+Calculus
+--------
+.. autosummary::
+   :toctree: generated/
+
+   hermder
+   hermint
+
+Misc Functions
+--------------
+.. autosummary::
+   :toctree: generated/
+
+   hermfromroots
+   hermroots
+   hermvander
+   hermvander2d
+   hermvander3d
+   hermgauss
+   hermweight
+   hermcompanion
+   hermfit
+   hermtrim
+   hermline
+   herm2poly
+   poly2herm
+
+See also
+--------
+`numpy.polynomial`
+
+"""
+import numpy as np
+import numpy.linalg as la
+from numpy.lib.array_utils import normalize_axis_index
+
+from . import polyutils as pu
+from ._polybase import ABCPolyBase
+
+__all__ = [
+    'hermzero', 'hermone', 'hermx', 'hermdomain', 'hermline', 'hermadd',
+    'hermsub', 'hermmulx', 'hermmul', 'hermdiv', 'hermpow', 'hermval',
+    'hermder', 'hermint', 'herm2poly', 'poly2herm', 'hermfromroots',
+    'hermvander', 'hermfit', 'hermtrim', 'hermroots', 'Hermite',
+    'hermval2d', 'hermval3d', 'hermgrid2d', 'hermgrid3d', 'hermvander2d',
+    'hermvander3d', 'hermcompanion', 'hermgauss', 'hermweight']
+
+hermtrim = pu.trimcoef
+
+
+def poly2herm(pol):
+    """
+    poly2herm(pol)
+
+    Convert a polynomial to a Hermite series.
+
+    Convert an array representing the coefficients of a polynomial (relative
+    to the "standard" basis) ordered from lowest degree to highest, to an
+    array of the coefficients of the equivalent Hermite series, ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    pol : array_like
+        1-D array containing the polynomial coefficients
+
+    Returns
+    -------
+    c : ndarray
+        1-D array containing the coefficients of the equivalent Hermite
+        series.
+
+    See Also
+    --------
+    herm2poly
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import poly2herm
+    >>> poly2herm(np.arange(4))
+    array([1.   ,  2.75 ,  0.5  ,  0.375])
+
+    """
+    [pol] = pu.as_series([pol])
+    deg = len(pol) - 1
+    res = 0
+    for i in range(deg, -1, -1):
+        res = hermadd(hermmulx(res), pol[i])
+    return res
+
+
+def herm2poly(c):
+    """
+    Convert a Hermite series to a polynomial.
+
+    Convert an array representing the coefficients of a Hermite series,
+    ordered from lowest degree to highest, to an array of the coefficients
+    of the equivalent polynomial (relative to the "standard" basis) ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array containing the Hermite series coefficients, ordered
+        from lowest order term to highest.
+
+    Returns
+    -------
+    pol : ndarray
+        1-D array containing the coefficients of the equivalent polynomial
+        (relative to the "standard" basis) ordered from lowest order term
+        to highest.
+
+    See Also
+    --------
+    poly2herm
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import herm2poly
+    >>> herm2poly([ 1.   ,  2.75 ,  0.5  ,  0.375])
+    array([0., 1., 2., 3.])
+
+    """
+    from .polynomial import polyadd, polysub, polymulx
+
+    [c] = pu.as_series([c])
+    n = len(c)
+    if n == 1:
+        return c
+    if n == 2:
+        c[1] *= 2
+        return c
+    else:
+        c0 = c[-2]
+        c1 = c[-1]
+        # i is the current degree of c1
+        for i in range(n - 1, 1, -1):
+            tmp = c0
+            c0 = polysub(c[i - 2], c1*(2*(i - 1)))
+            c1 = polyadd(tmp, polymulx(c1)*2)
+        return polyadd(c0, polymulx(c1)*2)
+
+
+#
+# These are constant arrays are of integer type so as to be compatible
+# with the widest range of other types, such as Decimal.
+#
+
+# Hermite
+hermdomain = np.array([-1., 1.])
+
+# Hermite coefficients representing zero.
+hermzero = np.array([0])
+
+# Hermite coefficients representing one.
+hermone = np.array([1])
+
+# Hermite coefficients representing the identity x.
+hermx = np.array([0, 1/2])
+
+
+def hermline(off, scl):
+    """
+    Hermite series whose graph is a straight line.
+
+
+
+    Parameters
+    ----------
+    off, scl : scalars
+        The specified line is given by ``off + scl*x``.
+
+    Returns
+    -------
+    y : ndarray
+        This module's representation of the Hermite series for
+        ``off + scl*x``.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyline
+    numpy.polynomial.chebyshev.chebline
+    numpy.polynomial.legendre.legline
+    numpy.polynomial.laguerre.lagline
+    numpy.polynomial.hermite_e.hermeline
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermline, hermval
+    >>> hermval(0,hermline(3, 2))
+    3.0
+    >>> hermval(1,hermline(3, 2))
+    5.0
+
+    """
+    if scl != 0:
+        return np.array([off, scl/2])
+    else:
+        return np.array([off])
+
+
+def hermfromroots(roots):
+    """
+    Generate a Hermite series with given roots.
+
+    The function returns the coefficients of the polynomial
+
+    .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
+
+    in Hermite form, where the :math:`r_n` are the roots specified in `roots`.
+    If a zero has multiplicity n, then it must appear in `roots` n times.
+    For instance, if 2 is a root of multiplicity three and 3 is a root of
+    multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The
+    roots can appear in any order.
+
+    If the returned coefficients are `c`, then
+
+    .. math:: p(x) = c_0 + c_1 * H_1(x) + ... +  c_n * H_n(x)
+
+    The coefficient of the last term is not generally 1 for monic
+    polynomials in Hermite form.
+
+    Parameters
+    ----------
+    roots : array_like
+        Sequence containing the roots.
+
+    Returns
+    -------
+    out : ndarray
+        1-D array of coefficients.  If all roots are real then `out` is a
+        real array, if some of the roots are complex, then `out` is complex
+        even if all the coefficients in the result are real (see Examples
+        below).
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfromroots
+    numpy.polynomial.legendre.legfromroots
+    numpy.polynomial.laguerre.lagfromroots
+    numpy.polynomial.chebyshev.chebfromroots
+    numpy.polynomial.hermite_e.hermefromroots
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermfromroots, hermval
+    >>> coef = hermfromroots((-1, 0, 1))
+    >>> hermval((-1, 0, 1), coef)
+    array([0.,  0.,  0.])
+    >>> coef = hermfromroots((-1j, 1j))
+    >>> hermval((-1j, 1j), coef)
+    array([0.+0.j, 0.+0.j])
+
+    """
+    return pu._fromroots(hermline, hermmul, roots)
+
+
+def hermadd(c1, c2):
+    """
+    Add one Hermite series to another.
+
+    Returns the sum of two Hermite series `c1` + `c2`.  The arguments
+    are sequences of coefficients ordered from lowest order term to
+    highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the Hermite series of their sum.
+
+    See Also
+    --------
+    hermsub, hermmulx, hermmul, hermdiv, hermpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the sum of two Hermite series
+    is a Hermite series (without having to "reproject" the result onto
+    the basis set) so addition, just like that of "standard" polynomials,
+    is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermadd
+    >>> hermadd([1, 2, 3], [1, 2, 3, 4])
+    array([2., 4., 6., 4.])
+
+    """
+    return pu._add(c1, c2)
+
+
+def hermsub(c1, c2):
+    """
+    Subtract one Hermite series from another.
+
+    Returns the difference of two Hermite series `c1` - `c2`.  The
+    sequences of coefficients are from lowest order term to highest, i.e.,
+    [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Hermite series coefficients representing their difference.
+
+    See Also
+    --------
+    hermadd, hermmulx, hermmul, hermdiv, hermpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the difference of two Hermite
+    series is a Hermite series (without having to "reproject" the result
+    onto the basis set) so subtraction, just like that of "standard"
+    polynomials, is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermsub
+    >>> hermsub([1, 2, 3, 4], [1, 2, 3])
+    array([0.,  0.,  0.,  4.])
+
+    """
+    return pu._sub(c1, c2)
+
+
+def hermmulx(c):
+    """Multiply a Hermite series by x.
+
+    Multiply the Hermite series `c` by x, where x is the independent
+    variable.
+
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the result of the multiplication.
+
+    See Also
+    --------
+    hermadd, hermsub, hermmul, hermdiv, hermpow
+
+    Notes
+    -----
+    The multiplication uses the recursion relationship for Hermite
+    polynomials in the form
+
+    .. math::
+
+        xP_i(x) = (P_{i + 1}(x)/2 + i*P_{i - 1}(x))
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermmulx
+    >>> hermmulx([1, 2, 3])
+    array([2. , 6.5, 1. , 1.5])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    # The zero series needs special treatment
+    if len(c) == 1 and c[0] == 0:
+        return c
+
+    prd = np.empty(len(c) + 1, dtype=c.dtype)
+    prd[0] = c[0]*0
+    prd[1] = c[0]/2
+    for i in range(1, len(c)):
+        prd[i + 1] = c[i]/2
+        prd[i - 1] += c[i]*i
+    return prd
+
+
+def hermmul(c1, c2):
+    """
+    Multiply one Hermite series by another.
+
+    Returns the product of two Hermite series `c1` * `c2`.  The arguments
+    are sequences of coefficients, from lowest order "term" to highest,
+    e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Hermite series coefficients representing their product.
+
+    See Also
+    --------
+    hermadd, hermsub, hermmulx, hermdiv, hermpow
+
+    Notes
+    -----
+    In general, the (polynomial) product of two C-series results in terms
+    that are not in the Hermite polynomial basis set.  Thus, to express
+    the product as a Hermite series, it is necessary to "reproject" the
+    product onto said basis set, which may produce "unintuitive" (but
+    correct) results; see Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermmul
+    >>> hermmul([1, 2, 3], [0, 1, 2])
+    array([52.,  29.,  52.,   7.,   6.])
+
+    """
+    # s1, s2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+
+    if len(c1) > len(c2):
+        c = c2
+        xs = c1
+    else:
+        c = c1
+        xs = c2
+
+    if len(c) == 1:
+        c0 = c[0]*xs
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]*xs
+        c1 = c[1]*xs
+    else:
+        nd = len(c)
+        c0 = c[-2]*xs
+        c1 = c[-1]*xs
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = hermsub(c[-i]*xs, c1*(2*(nd - 1)))
+            c1 = hermadd(tmp, hermmulx(c1)*2)
+    return hermadd(c0, hermmulx(c1)*2)
+
+
+def hermdiv(c1, c2):
+    """
+    Divide one Hermite series by another.
+
+    Returns the quotient-with-remainder of two Hermite series
+    `c1` / `c2`.  The arguments are sequences of coefficients from lowest
+    order "term" to highest, e.g., [1,2,3] represents the series
+    ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    [quo, rem] : ndarrays
+        Of Hermite series coefficients representing the quotient and
+        remainder.
+
+    See Also
+    --------
+    hermadd, hermsub, hermmulx, hermmul, hermpow
+
+    Notes
+    -----
+    In general, the (polynomial) division of one Hermite series by another
+    results in quotient and remainder terms that are not in the Hermite
+    polynomial basis set.  Thus, to express these results as a Hermite
+    series, it is necessary to "reproject" the results onto the Hermite
+    basis set, which may produce "unintuitive" (but correct) results; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermdiv
+    >>> hermdiv([ 52.,  29.,  52.,   7.,   6.], [0, 1, 2])
+    (array([1., 2., 3.]), array([0.]))
+    >>> hermdiv([ 54.,  31.,  52.,   7.,   6.], [0, 1, 2])
+    (array([1., 2., 3.]), array([2., 2.]))
+    >>> hermdiv([ 53.,  30.,  52.,   7.,   6.], [0, 1, 2])
+    (array([1., 2., 3.]), array([1., 1.]))
+
+    """
+    return pu._div(hermmul, c1, c2)
+
+
+def hermpow(c, pow, maxpower=16):
+    """Raise a Hermite series to a power.
+
+    Returns the Hermite series `c` raised to the power `pow`. The
+    argument `c` is a sequence of coefficients ordered from low to high.
+    i.e., [1,2,3] is the series  ``P_0 + 2*P_1 + 3*P_2.``
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Hermite series coefficients ordered from low to
+        high.
+    pow : integer
+        Power to which the series will be raised
+    maxpower : integer, optional
+        Maximum power allowed. This is mainly to limit growth of the series
+        to unmanageable size. Default is 16
+
+    Returns
+    -------
+    coef : ndarray
+        Hermite series of power.
+
+    See Also
+    --------
+    hermadd, hermsub, hermmulx, hermmul, hermdiv
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermpow
+    >>> hermpow([1, 2, 3], 2)
+    array([81.,  52.,  82.,  12.,   9.])
+
+    """
+    return pu._pow(hermmul, c, pow, maxpower)
+
+
+def hermder(c, m=1, scl=1, axis=0):
+    """
+    Differentiate a Hermite series.
+
+    Returns the Hermite series coefficients `c` differentiated `m` times
+    along `axis`.  At each iteration the result is multiplied by `scl` (the
+    scaling factor is for use in a linear change of variable). The argument
+    `c` is an array of coefficients from low to high degree along each
+    axis, e.g., [1,2,3] represents the series ``1*H_0 + 2*H_1 + 3*H_2``
+    while [[1,2],[1,2]] represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) +
+    2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is
+    ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Hermite series coefficients. If `c` is multidimensional the
+        different axis correspond to different variables with the degree in
+        each axis given by the corresponding index.
+    m : int, optional
+        Number of derivatives taken, must be non-negative. (Default: 1)
+    scl : scalar, optional
+        Each differentiation is multiplied by `scl`.  The end result is
+        multiplication by ``scl**m``.  This is for use in a linear change of
+        variable. (Default: 1)
+    axis : int, optional
+        Axis over which the derivative is taken. (Default: 0).
+
+    Returns
+    -------
+    der : ndarray
+        Hermite series of the derivative.
+
+    See Also
+    --------
+    hermint
+
+    Notes
+    -----
+    In general, the result of differentiating a Hermite series does not
+    resemble the same operation on a power series. Thus the result of this
+    function may be "unintuitive," albeit correct; see Examples section
+    below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermder
+    >>> hermder([ 1. ,  0.5,  0.5,  0.5])
+    array([1., 2., 3.])
+    >>> hermder([-0.5,  1./2.,  1./8.,  1./12.,  1./16.], m=2)
+    array([1., 2., 3.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    cnt = pu._as_int(m, "the order of derivation")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of derivation must be non-negative")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    n = len(c)
+    if cnt >= n:
+        c = c[:1]*0
+    else:
+        for i in range(cnt):
+            n = n - 1
+            c *= scl
+            der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
+            for j in range(n, 0, -1):
+                der[j - 1] = (2*j)*c[j]
+            c = der
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
+    """
+    Integrate a Hermite series.
+
+    Returns the Hermite series coefficients `c` integrated `m` times from
+    `lbnd` along `axis`. At each iteration the resulting series is
+    **multiplied** by `scl` and an integration constant, `k`, is added.
+    The scaling factor is for use in a linear change of variable.  ("Buyer
+    beware": note that, depending on what one is doing, one may want `scl`
+    to be the reciprocal of what one might expect; for more information,
+    see the Notes section below.)  The argument `c` is an array of
+    coefficients from low to high degree along each axis, e.g., [1,2,3]
+    represents the series ``H_0 + 2*H_1 + 3*H_2`` while [[1,2],[1,2]]
+    represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) + 2*H_0(x)*H_1(y) +
+    2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Hermite series coefficients. If c is multidimensional the
+        different axis correspond to different variables with the degree in
+        each axis given by the corresponding index.
+    m : int, optional
+        Order of integration, must be positive. (Default: 1)
+    k : {[], list, scalar}, optional
+        Integration constant(s).  The value of the first integral at
+        ``lbnd`` is the first value in the list, the value of the second
+        integral at ``lbnd`` is the second value, etc.  If ``k == []`` (the
+        default), all constants are set to zero.  If ``m == 1``, a single
+        scalar can be given instead of a list.
+    lbnd : scalar, optional
+        The lower bound of the integral. (Default: 0)
+    scl : scalar, optional
+        Following each integration the result is *multiplied* by `scl`
+        before the integration constant is added. (Default: 1)
+    axis : int, optional
+        Axis over which the integral is taken. (Default: 0).
+
+    Returns
+    -------
+    S : ndarray
+        Hermite series coefficients of the integral.
+
+    Raises
+    ------
+    ValueError
+        If ``m < 0``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
+        ``np.ndim(scl) != 0``.
+
+    See Also
+    --------
+    hermder
+
+    Notes
+    -----
+    Note that the result of each integration is *multiplied* by `scl`.
+    Why is this important to note?  Say one is making a linear change of
+    variable :math:`u = ax + b` in an integral relative to `x`.  Then
+    :math:`dx = du/a`, so one will need to set `scl` equal to
+    :math:`1/a` - perhaps not what one would have first thought.
+
+    Also note that, in general, the result of integrating a C-series needs
+    to be "reprojected" onto the C-series basis set.  Thus, typically,
+    the result of this function is "unintuitive," albeit correct; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermint
+    >>> hermint([1,2,3]) # integrate once, value 0 at 0.
+    array([1. , 0.5, 0.5, 0.5])
+    >>> hermint([1,2,3], m=2) # integrate twice, value & deriv 0 at 0
+    array([-0.5       ,  0.5       ,  0.125     ,  0.08333333,  0.0625    ]) # may vary
+    >>> hermint([1,2,3], k=1) # integrate once, value 1 at 0.
+    array([2. , 0.5, 0.5, 0.5])
+    >>> hermint([1,2,3], lbnd=-1) # integrate once, value 0 at -1
+    array([-2. ,  0.5,  0.5,  0.5])
+    >>> hermint([1,2,3], m=2, k=[1,2], lbnd=-1)
+    array([ 1.66666667, -0.5       ,  0.125     ,  0.08333333,  0.0625    ]) # may vary
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if not np.iterable(k):
+        k = [k]
+    cnt = pu._as_int(m, "the order of integration")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of integration must be non-negative")
+    if len(k) > cnt:
+        raise ValueError("Too many integration constants")
+    if np.ndim(lbnd) != 0:
+        raise ValueError("lbnd must be a scalar.")
+    if np.ndim(scl) != 0:
+        raise ValueError("scl must be a scalar.")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    k = list(k) + [0]*(cnt - len(k))
+    for i in range(cnt):
+        n = len(c)
+        c *= scl
+        if n == 1 and np.all(c[0] == 0):
+            c[0] += k[i]
+        else:
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
+            tmp[0] = c[0]*0
+            tmp[1] = c[0]/2
+            for j in range(1, n):
+                tmp[j + 1] = c[j]/(2*(j + 1))
+            tmp[0] += k[i] - hermval(lbnd, tmp)
+            c = tmp
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def hermval(x, c, tensor=True):
+    """
+    Evaluate an Hermite series at points x.
+
+    If `c` is of length ``n + 1``, this function returns the value:
+
+    .. math:: p(x) = c_0 * H_0(x) + c_1 * H_1(x) + ... + c_n * H_n(x)
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `c`.
+
+    If `c` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If
+    `c` is multidimensional, then the shape of the result depends on the
+    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
+    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
+    scalars have shape (,).
+
+    Trailing zeros in the coefficients will be used in the evaluation, so
+    they should be avoided if efficiency is a concern.
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        themselves and with the elements of `c`.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree n are contained in c[n]. If `c` is multidimensional the
+        remaining indices enumerate multiple polynomials. In the two
+        dimensional case the coefficients may be thought of as stored in
+        the columns of `c`.
+    tensor : boolean, optional
+        If True, the shape of the coefficient array is extended with ones
+        on the right, one for each dimension of `x`. Scalars have dimension 0
+        for this action. The result is that every column of coefficients in
+        `c` is evaluated for every element of `x`. If False, `x` is broadcast
+        over the columns of `c` for the evaluation.  This keyword is useful
+        when `c` is multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, algebra_like
+        The shape of the return value is described above.
+
+    See Also
+    --------
+    hermval2d, hermgrid2d, hermval3d, hermgrid3d
+
+    Notes
+    -----
+    The evaluation uses Clenshaw recursion, aka synthetic division.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermval
+    >>> coef = [1,2,3]
+    >>> hermval(1, coef)
+    11.0
+    >>> hermval([[1,2],[3,4]], coef)
+    array([[ 11.,   51.],
+           [115.,  203.]])
+
+    """
+    c = np.array(c, ndmin=1, copy=None)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray) and tensor:
+        c = c.reshape(c.shape + (1,)*x.ndim)
+
+    x2 = x*2
+    if len(c) == 1:
+        c0 = c[0]
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]
+        c1 = c[1]
+    else:
+        nd = len(c)
+        c0 = c[-2]
+        c1 = c[-1]
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = c[-i] - c1*(2*(nd - 1))
+            c1 = tmp + c1*x2
+    return c0 + c1*x2
+
+
+def hermval2d(x, y, c):
+    """
+    Evaluate a 2-D Hermite series at points (x, y).
+
+    This function returns the values:
+
+    .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * H_i(x) * H_j(y)
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars and they
+    must have the same shape after conversion. In either case, either `x`
+    and `y` or their elements must support multiplication and addition both
+    with themselves and with the elements of `c`.
+
+    If `c` is a 1-D array a one is implicitly appended to its shape to make
+    it 2-D. The shape of the result will be c.shape[2:] + x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points ``(x, y)``,
+        where `x` and `y` must have the same shape. If `x` or `y` is a list
+        or tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and if it isn't an ndarray it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term
+        of multi-degree i,j is contained in ``c[i,j]``. If `c` has
+        dimension greater than two the remaining indices enumerate multiple
+        sets of coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points formed with
+        pairs of corresponding values from `x` and `y`.
+
+    See Also
+    --------
+    hermval, hermgrid2d, hermval3d, hermgrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermval2d
+    >>> x = [1, 2]
+    >>> y = [4, 5]
+    >>> c = [[1, 2, 3], [4, 5, 6]]
+    >>> hermval2d(x, y, c)
+    array([1035., 2883.])
+
+    """
+    return pu._valnd(hermval, c, x, y)
+
+
+def hermgrid2d(x, y, c):
+    """
+    Evaluate a 2-D Hermite series on the Cartesian product of x and y.
+
+    This function returns the values:
+
+    .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * H_i(a) * H_j(b)
+
+    where the points ``(a, b)`` consist of all pairs formed by taking
+    `a` from `x` and `b` from `y`. The resulting points form a grid with
+    `x` in the first dimension and `y` in the second.
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars. In either
+    case, either `x` and `y` or their elements must support multiplication
+    and addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points in the
+        Cartesian product of `x` and `y`.  If `x` or `y` is a list or
+        tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    hermval, hermval2d, hermval3d, hermgrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermgrid2d
+    >>> x = [1, 2, 3]
+    >>> y = [4, 5]
+    >>> c = [[1, 2, 3], [4, 5, 6]]
+    >>> hermgrid2d(x, y, c)
+    array([[1035., 1599.],
+           [1867., 2883.],
+           [2699., 4167.]])
+
+    """
+    return pu._gridnd(hermval, c, x, y)
+
+
+def hermval3d(x, y, z, c):
+    """
+    Evaluate a 3-D Hermite series at points (x, y, z).
+
+    This function returns the values:
+
+    .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * H_i(x) * H_j(y) * H_k(z)
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if
+    they are tuples or a lists, otherwise they are treated as a scalars and
+    they must have the same shape after conversion. In either case, either
+    `x`, `y`, and `z` or their elements must support multiplication and
+    addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than 3 dimensions, ones are implicitly appended to its
+    shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible object
+        The three dimensional series is evaluated at the points
+        ``(x, y, z)``, where `x`, `y`, and `z` must have the same shape.  If
+        any of `x`, `y`, or `z` is a list or tuple, it is first converted
+        to an ndarray, otherwise it is left unchanged and if it isn't an
+        ndarray it is  treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
+        greater than 3 the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the multidimensional polynomial on points formed with
+        triples of corresponding values from `x`, `y`, and `z`.
+
+    See Also
+    --------
+    hermval, hermval2d, hermgrid2d, hermgrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermval3d
+    >>> x = [1, 2]
+    >>> y = [4, 5]
+    >>> z = [6, 7]
+    >>> c = [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]
+    >>> hermval3d(x, y, z, c)
+    array([ 40077., 120131.])
+
+    """
+    return pu._valnd(hermval, c, x, y, z)
+
+
+def hermgrid3d(x, y, z, c):
+    """
+    Evaluate a 3-D Hermite series on the Cartesian product of x, y, and z.
+
+    This function returns the values:
+
+    .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * H_i(a) * H_j(b) * H_k(c)
+
+    where the points ``(a, b, c)`` consist of all triples formed by taking
+    `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
+    a grid with `x` in the first dimension, `y` in the second, and `z` in
+    the third.
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if they
+    are tuples or a lists, otherwise they are treated as a scalars. In
+    either case, either `x`, `y`, and `z` or their elements must support
+    multiplication and addition both with themselves and with the elements
+    of `c`.
+
+    If `c` has fewer than three dimensions, ones are implicitly appended to
+    its shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape + y.shape + z.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible objects
+        The three dimensional series is evaluated at the points in the
+        Cartesian product of `x`, `y`, and `z`.  If `x`, `y`, or `z` is a
+        list or tuple, it is first converted to an ndarray, otherwise it is
+        left unchanged and, if it isn't an ndarray, it is treated as a
+        scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    hermval, hermval2d, hermgrid2d, hermval3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermgrid3d
+    >>> x = [1, 2]
+    >>> y = [4, 5]
+    >>> z = [6, 7]
+    >>> c = [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]
+    >>> hermgrid3d(x, y, z, c)
+    array([[[ 40077.,  54117.],
+            [ 49293.,  66561.]],
+           [[ 72375.,  97719.],
+            [ 88975., 120131.]]])
+
+    """
+    return pu._gridnd(hermval, c, x, y, z)
+
+
+def hermvander(x, deg):
+    """Pseudo-Vandermonde matrix of given degree.
+
+    Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
+    `x`. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., i] = H_i(x),
+
+    where ``0 <= i <= deg``. The leading indices of `V` index the elements of
+    `x` and the last index is the degree of the Hermite polynomial.
+
+    If `c` is a 1-D array of coefficients of length ``n + 1`` and `V` is the
+    array ``V = hermvander(x, n)``, then ``np.dot(V, c)`` and
+    ``hermval(x, c)`` are the same up to roundoff. This equivalence is
+    useful both for least squares fitting and for the evaluation of a large
+    number of Hermite series of the same degree and sample points.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of points. The dtype is converted to float64 or complex128
+        depending on whether any of the elements are complex. If `x` is
+        scalar it is converted to a 1-D array.
+    deg : int
+        Degree of the resulting matrix.
+
+    Returns
+    -------
+    vander : ndarray
+        The pseudo-Vandermonde matrix. The shape of the returned matrix is
+        ``x.shape + (deg + 1,)``, where The last index is the degree of the
+        corresponding Hermite polynomial.  The dtype will be the same as
+        the converted `x`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite import hermvander
+    >>> x = np.array([-1, 0, 1])
+    >>> hermvander(x, 3)
+    array([[ 1., -2.,  2.,  4.],
+           [ 1.,  0., -2., -0.],
+           [ 1.,  2.,  2., -4.]])
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg < 0:
+        raise ValueError("deg must be non-negative")
+
+    x = np.array(x, copy=None, ndmin=1) + 0.0
+    dims = (ideg + 1,) + x.shape
+    dtyp = x.dtype
+    v = np.empty(dims, dtype=dtyp)
+    v[0] = x*0 + 1
+    if ideg > 0:
+        x2 = x*2
+        v[1] = x2
+        for i in range(2, ideg + 1):
+            v[i] = (v[i-1]*x2 - v[i-2]*(2*(i - 1)))
+    return np.moveaxis(v, 0, -1)
+
+
+def hermvander2d(x, y, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y)``. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (deg[1] + 1)*i + j] = H_i(x) * H_j(y),
+
+    where ``0 <= i <= deg[0]`` and ``0 <= j <= deg[1]``. The leading indices of
+    `V` index the points ``(x, y)`` and the last index encodes the degrees of
+    the Hermite polynomials.
+
+    If ``V = hermvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
+    correspond to the elements of a 2-D coefficient array `c` of shape
+    (xdeg + 1, ydeg + 1) in the order
+
+    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+
+    and ``np.dot(V, c.flat)`` and ``hermval2d(x, y, c)`` will be the same
+    up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 2-D Hermite
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg].
+
+    Returns
+    -------
+    vander2d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)`.  The dtype will be the same
+        as the converted `x` and `y`.
+
+    See Also
+    --------
+    hermvander, hermvander3d, hermval2d, hermval3d
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite import hermvander2d
+    >>> x = np.array([-1, 0, 1])
+    >>> y = np.array([-1, 0, 1])
+    >>> hermvander2d(x, y, [2, 2])
+    array([[ 1., -2.,  2., -2.,  4., -4.,  2., -4.,  4.],
+           [ 1.,  0., -2.,  0.,  0., -0., -2., -0.,  4.],
+           [ 1.,  2.,  2.,  2.,  4.,  4.,  2.,  4.,  4.]])
+
+    """
+    return pu._vander_nd_flat((hermvander, hermvander), (x, y), deg)
+
+
+def hermvander3d(x, y, z, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y, z)``. If `l`, `m`, `n` are the given degrees in `x`, `y`, `z`,
+    then The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = H_i(x)*H_j(y)*H_k(z),
+
+    where ``0 <= i <= l``, ``0 <= j <= m``, and ``0 <= j <= n``.  The leading
+    indices of `V` index the points ``(x, y, z)`` and the last index encodes
+    the degrees of the Hermite polynomials.
+
+    If ``V = hermvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
+    of `V` correspond to the elements of a 3-D coefficient array `c` of
+    shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
+
+    .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
+
+    and  ``np.dot(V, c.flat)`` and ``hermval3d(x, y, z, c)`` will be the
+    same up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 3-D Hermite
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y, z : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes will
+        be converted to either float64 or complex128 depending on whether
+        any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg, z_deg].
+
+    Returns
+    -------
+    vander3d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`.  The dtype will
+        be the same as the converted `x`, `y`, and `z`.
+
+    See Also
+    --------
+    hermvander, hermvander3d, hermval2d, hermval3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermvander3d
+    >>> x = np.array([-1, 0, 1])
+    >>> y = np.array([-1, 0, 1])
+    >>> z = np.array([-1, 0, 1])
+    >>> hermvander3d(x, y, z, [0, 1, 2])
+    array([[ 1., -2.,  2., -2.,  4., -4.],
+           [ 1.,  0., -2.,  0.,  0., -0.],
+           [ 1.,  2.,  2.,  2.,  4.,  4.]])
+
+    """
+    return pu._vander_nd_flat((hermvander, hermvander, hermvander), (x, y, z), deg)
+
+
+def hermfit(x, y, deg, rcond=None, full=False, w=None):
+    """
+    Least squares fit of Hermite series to data.
+
+    Return the coefficients of a Hermite series of degree `deg` that is the
+    least squares fit to the data values `y` given at points `x`. If `y` is
+    1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
+    fits are done, one for each column of `y`, and the resulting
+    coefficients are stored in the corresponding columns of a 2-D return.
+    The fitted polynomial(s) are in the form
+
+    .. math::  p(x) = c_0 + c_1 * H_1(x) + ... + c_n * H_n(x),
+
+    where `n` is `deg`.
+
+    Parameters
+    ----------
+    x : array_like, shape (M,)
+        x-coordinates of the M sample points ``(x[i], y[i])``.
+    y : array_like, shape (M,) or (M, K)
+        y-coordinates of the sample points. Several data sets of sample
+        points sharing the same x-coordinates can be fitted at once by
+        passing in a 2D-array that contains one dataset per column.
+    deg : int or 1-D array_like
+        Degree(s) of the fitting polynomials. If `deg` is a single integer
+        all terms up to and including the `deg`'th term are included in the
+        fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+        degrees of the terms to include may be used instead.
+    rcond : float, optional
+        Relative condition number of the fit. Singular values smaller than
+        this relative to the largest singular value will be ignored. The
+        default value is len(x)*eps, where eps is the relative precision of
+        the float type, about 2e-16 in most cases.
+    full : bool, optional
+        Switch determining nature of return value. When it is False (the
+        default) just the coefficients are returned, when True diagnostic
+        information from the singular value decomposition is also returned.
+    w : array_like, shape (`M`,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+
+    Returns
+    -------
+    coef : ndarray, shape (M,) or (M, K)
+        Hermite coefficients ordered from low to high. If `y` was 2-D,
+        the coefficients for the data in column k  of `y` are in column
+        `k`.
+
+    [residuals, rank, singular_values, rcond] : list
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    Warns
+    -----
+    RankWarning
+        The rank of the coefficient matrix in the least-squares fit is
+        deficient. The warning is only raised if ``full == False``.  The
+        warnings can be turned off by
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    numpy.polynomial.chebyshev.chebfit
+    numpy.polynomial.legendre.legfit
+    numpy.polynomial.laguerre.lagfit
+    numpy.polynomial.polynomial.polyfit
+    numpy.polynomial.hermite_e.hermefit
+    hermval : Evaluates a Hermite series.
+    hermvander : Vandermonde matrix of Hermite series.
+    hermweight : Hermite weight function
+    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution is the coefficients of the Hermite series `p` that
+    minimizes the sum of the weighted squared errors
+
+    .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
+
+    where the :math:`w_j` are the weights. This problem is solved by
+    setting up the (typically) overdetermined matrix equation
+
+    .. math:: V(x) * c = w * y,
+
+    where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
+    coefficients to be solved for, `w` are the weights, `y` are the
+    observed values.  This equation is then solved using the singular value
+    decomposition of `V`.
+
+    If some of the singular values of `V` are so small that they are
+    neglected, then a `~exceptions.RankWarning` will be issued. This means that
+    the coefficient values may be poorly determined. Using a lower order fit
+    will usually get rid of the warning.  The `rcond` parameter can also be
+    set to a value smaller than its default, but the resulting fit may be
+    spurious and have large contributions from roundoff error.
+
+    Fits using Hermite series are probably most useful when the data can be
+    approximated by ``sqrt(w(x)) * p(x)``, where ``w(x)`` is the Hermite
+    weight. In that case the weight ``sqrt(w(x[i]))`` should be used
+    together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
+    available as `hermweight`.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Curve fitting",
+           https://en.wikipedia.org/wiki/Curve_fitting
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite import hermfit, hermval
+    >>> x = np.linspace(-10, 10)
+    >>> rng = np.random.default_rng()
+    >>> err = rng.normal(scale=1./10, size=len(x))
+    >>> y = hermval(x, [1, 2, 3]) + err
+    >>> hermfit(x, y, 2)
+    array([1.02294967, 2.00016403, 2.99994614]) # may vary
+
+    """
+    return pu._fit(hermvander, x, y, deg, rcond, full, w)
+
+
+def hermcompanion(c):
+    """Return the scaled companion matrix of c.
+
+    The basis polynomials are scaled so that the companion matrix is
+    symmetric when `c` is an Hermite basis polynomial. This provides
+    better eigenvalue estimates than the unscaled case and for basis
+    polynomials the eigenvalues are guaranteed to be real if
+    `numpy.linalg.eigvalsh` is used to obtain them.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Hermite series coefficients ordered from low to high
+        degree.
+
+    Returns
+    -------
+    mat : ndarray
+        Scaled companion matrix of dimensions (deg, deg).
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermcompanion
+    >>> hermcompanion([1, 0, 1])
+    array([[0.        , 0.35355339],
+           [0.70710678, 0.        ]])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        raise ValueError('Series must have maximum degree of at least 1.')
+    if len(c) == 2:
+        return np.array([[-.5*c[0]/c[1]]])
+
+    n = len(c) - 1
+    mat = np.zeros((n, n), dtype=c.dtype)
+    scl = np.hstack((1., 1./np.sqrt(2.*np.arange(n - 1, 0, -1))))
+    scl = np.multiply.accumulate(scl)[::-1]
+    top = mat.reshape(-1)[1::n+1]
+    bot = mat.reshape(-1)[n::n+1]
+    top[...] = np.sqrt(.5*np.arange(1, n))
+    bot[...] = top
+    mat[:, -1] -= scl*c[:-1]/(2.0*c[-1])
+    return mat
+
+
+def hermroots(c):
+    """
+    Compute the roots of a Hermite series.
+
+    Return the roots (a.k.a. "zeros") of the polynomial
+
+    .. math:: p(x) = \\sum_i c[i] * H_i(x).
+
+    Parameters
+    ----------
+    c : 1-D array_like
+        1-D array of coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        Array of the roots of the series. If all the roots are real,
+        then `out` is also real, otherwise it is complex.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyroots
+    numpy.polynomial.legendre.legroots
+    numpy.polynomial.laguerre.lagroots
+    numpy.polynomial.chebyshev.chebroots
+    numpy.polynomial.hermite_e.hermeroots
+
+    Notes
+    -----
+    The root estimates are obtained as the eigenvalues of the companion
+    matrix, Roots far from the origin of the complex plane may have large
+    errors due to the numerical instability of the series for such
+    values. Roots with multiplicity greater than 1 will also show larger
+    errors as the value of the series near such points is relatively
+    insensitive to errors in the roots. Isolated roots near the origin can
+    be improved by a few iterations of Newton's method.
+
+    The Hermite series basis polynomials aren't powers of `x` so the
+    results of this function may seem unintuitive.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermroots, hermfromroots
+    >>> coef = hermfromroots([-1, 0, 1])
+    >>> coef
+    array([0.   ,  0.25 ,  0.   ,  0.125])
+    >>> hermroots(coef)
+    array([-1.00000000e+00, -1.38777878e-17,  1.00000000e+00])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) <= 1:
+        return np.array([], dtype=c.dtype)
+    if len(c) == 2:
+        return np.array([-.5*c[0]/c[1]])
+
+    # rotated companion matrix reduces error
+    m = hermcompanion(c)[::-1,::-1]
+    r = la.eigvals(m)
+    r.sort()
+    return r
+
+
+def _normed_hermite_n(x, n):
+    """
+    Evaluate a normalized Hermite polynomial.
+
+    Compute the value of the normalized Hermite polynomial of degree ``n``
+    at the points ``x``.
+
+
+    Parameters
+    ----------
+    x : ndarray of double.
+        Points at which to evaluate the function
+    n : int
+        Degree of the normalized Hermite function to be evaluated.
+
+    Returns
+    -------
+    values : ndarray
+        The shape of the return value is described above.
+
+    Notes
+    -----
+    This function is needed for finding the Gauss points and integration
+    weights for high degrees. The values of the standard Hermite functions
+    overflow when n >= 207.
+
+    """
+    if n == 0:
+        return np.full(x.shape, 1/np.sqrt(np.sqrt(np.pi)))
+
+    c0 = 0.
+    c1 = 1./np.sqrt(np.sqrt(np.pi))
+    nd = float(n)
+    for i in range(n - 1):
+        tmp = c0
+        c0 = -c1*np.sqrt((nd - 1.)/nd)
+        c1 = tmp + c1*x*np.sqrt(2./nd)
+        nd = nd - 1.0
+    return c0 + c1*x*np.sqrt(2)
+
+
+def hermgauss(deg):
+    """
+    Gauss-Hermite quadrature.
+
+    Computes the sample points and weights for Gauss-Hermite quadrature.
+    These sample points and weights will correctly integrate polynomials of
+    degree :math:`2*deg - 1` or less over the interval :math:`[-\\inf, \\inf]`
+    with the weight function :math:`f(x) = \\exp(-x^2)`.
+
+    Parameters
+    ----------
+    deg : int
+        Number of sample points and weights. It must be >= 1.
+
+    Returns
+    -------
+    x : ndarray
+        1-D ndarray containing the sample points.
+    y : ndarray
+        1-D ndarray containing the weights.
+
+    Notes
+    -----
+    The results have only been tested up to degree 100, higher degrees may
+    be problematic. The weights are determined by using the fact that
+
+    .. math:: w_k = c / (H'_n(x_k) * H_{n-1}(x_k))
+
+    where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
+    is the k'th root of :math:`H_n`, and then scaling the results to get
+    the right value when integrating 1.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite import hermgauss
+    >>> hermgauss(2)
+    (array([-0.70710678,  0.70710678]), array([0.88622693, 0.88622693]))
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg <= 0:
+        raise ValueError("deg must be a positive integer")
+
+    # first approximation of roots. We use the fact that the companion
+    # matrix is symmetric in this case in order to obtain better zeros.
+    c = np.array([0]*deg + [1], dtype=np.float64)
+    m = hermcompanion(c)
+    x = la.eigvalsh(m)
+
+    # improve roots by one application of Newton
+    dy = _normed_hermite_n(x, ideg)
+    df = _normed_hermite_n(x, ideg - 1) * np.sqrt(2*ideg)
+    x -= dy/df
+
+    # compute the weights. We scale the factor to avoid possible numerical
+    # overflow.
+    fm = _normed_hermite_n(x, ideg - 1)
+    fm /= np.abs(fm).max()
+    w = 1/(fm * fm)
+
+    # for Hermite we can also symmetrize
+    w = (w + w[::-1])/2
+    x = (x - x[::-1])/2
+
+    # scale w to get the right value
+    w *= np.sqrt(np.pi) / w.sum()
+
+    return x, w
+
+
+def hermweight(x):
+    """
+    Weight function of the Hermite polynomials.
+
+    The weight function is :math:`\\exp(-x^2)` and the interval of
+    integration is :math:`[-\\inf, \\inf]`. the Hermite polynomials are
+    orthogonal, but not normalized, with respect to this weight function.
+
+    Parameters
+    ----------
+    x : array_like
+       Values at which the weight function will be computed.
+
+    Returns
+    -------
+    w : ndarray
+       The weight function at `x`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite import hermweight
+    >>> x = np.arange(-2, 2)
+    >>> hermweight(x)
+    array([0.01831564, 0.36787944, 1.        , 0.36787944])
+
+    """
+    w = np.exp(-x**2)
+    return w
+
+
+#
+# Hermite series class
+#
+
+class Hermite(ABCPolyBase):
+    """An Hermite series class.
+
+    The Hermite class provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
+    attributes and methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        Hermite coefficients in order of increasing degree, i.e,
+        ``(1, 2, 3)`` gives ``1*H_0(x) + 2*H_1(x) + 3*H_2(x)``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is [-1., 1.].
+    window : (2,) array_like, optional
+        Window, see `domain` for its use. The default value is [-1., 1.].
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    """
+    # Virtual Functions
+    _add = staticmethod(hermadd)
+    _sub = staticmethod(hermsub)
+    _mul = staticmethod(hermmul)
+    _div = staticmethod(hermdiv)
+    _pow = staticmethod(hermpow)
+    _val = staticmethod(hermval)
+    _int = staticmethod(hermint)
+    _der = staticmethod(hermder)
+    _fit = staticmethod(hermfit)
+    _line = staticmethod(hermline)
+    _roots = staticmethod(hermroots)
+    _fromroots = staticmethod(hermfromroots)
+
+    # Virtual properties
+    domain = np.array(hermdomain)
+    window = np.array(hermdomain)
+    basis_name = 'H'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..07db43d0c0006601781cd24ee3269ae2f32a0445
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite.pyi
@@ -0,0 +1,106 @@
+from typing import Any, Final, Literal as L, TypeVar
+
+import numpy as np
+
+from ._polybase import ABCPolyBase
+from ._polytypes import (
+    _Array1,
+    _Array2,
+    _FuncBinOp,
+    _FuncCompanion,
+    _FuncDer,
+    _FuncFit,
+    _FuncFromRoots,
+    _FuncGauss,
+    _FuncInteg,
+    _FuncLine,
+    _FuncPoly2Ortho,
+    _FuncPow,
+    _FuncRoots,
+    _FuncUnOp,
+    _FuncVal,
+    _FuncVal2D,
+    _FuncVal3D,
+    _FuncValFromRoots,
+    _FuncVander,
+    _FuncVander2D,
+    _FuncVander3D,
+    _FuncWeight,
+)
+from .polyutils import trimcoef as hermtrim
+
+__all__ = [
+    "hermzero",
+    "hermone",
+    "hermx",
+    "hermdomain",
+    "hermline",
+    "hermadd",
+    "hermsub",
+    "hermmulx",
+    "hermmul",
+    "hermdiv",
+    "hermpow",
+    "hermval",
+    "hermder",
+    "hermint",
+    "herm2poly",
+    "poly2herm",
+    "hermfromroots",
+    "hermvander",
+    "hermfit",
+    "hermtrim",
+    "hermroots",
+    "Hermite",
+    "hermval2d",
+    "hermval3d",
+    "hermgrid2d",
+    "hermgrid3d",
+    "hermvander2d",
+    "hermvander3d",
+    "hermcompanion",
+    "hermgauss",
+    "hermweight",
+]
+
+poly2herm: _FuncPoly2Ortho[L["poly2herm"]]
+herm2poly: _FuncUnOp[L["herm2poly"]]
+
+hermdomain: Final[_Array2[np.float64]]
+hermzero: Final[_Array1[np.int_]]
+hermone: Final[_Array1[np.int_]]
+hermx: Final[_Array2[np.int_]]
+
+hermline: _FuncLine[L["hermline"]]
+hermfromroots: _FuncFromRoots[L["hermfromroots"]]
+hermadd: _FuncBinOp[L["hermadd"]]
+hermsub: _FuncBinOp[L["hermsub"]]
+hermmulx: _FuncUnOp[L["hermmulx"]]
+hermmul: _FuncBinOp[L["hermmul"]]
+hermdiv: _FuncBinOp[L["hermdiv"]]
+hermpow: _FuncPow[L["hermpow"]]
+hermder: _FuncDer[L["hermder"]]
+hermint: _FuncInteg[L["hermint"]]
+hermval: _FuncVal[L["hermval"]]
+hermval2d: _FuncVal2D[L["hermval2d"]]
+hermval3d: _FuncVal3D[L["hermval3d"]]
+hermvalfromroots: _FuncValFromRoots[L["hermvalfromroots"]]
+hermgrid2d: _FuncVal2D[L["hermgrid2d"]]
+hermgrid3d: _FuncVal3D[L["hermgrid3d"]]
+hermvander: _FuncVander[L["hermvander"]]
+hermvander2d: _FuncVander2D[L["hermvander2d"]]
+hermvander3d: _FuncVander3D[L["hermvander3d"]]
+hermfit: _FuncFit[L["hermfit"]]
+hermcompanion: _FuncCompanion[L["hermcompanion"]]
+hermroots: _FuncRoots[L["hermroots"]]
+
+_ND = TypeVar("_ND", bound=Any)
+def _normed_hermite_n(
+    x: np.ndarray[_ND, np.dtype[np.float64]],
+    n: int | np.intp,
+) -> np.ndarray[_ND, np.dtype[np.float64]]: ...
+
+hermgauss: _FuncGauss[L["hermgauss"]]
+hermweight: _FuncWeight[L["hermweight"]]
+
+class Hermite(ABCPolyBase[L["H"]]): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite_e.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite_e.py
new file mode 100644
index 0000000000000000000000000000000000000000..c820760ef75c1db162b0a6e0897c88ba18582464
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite_e.py
@@ -0,0 +1,1642 @@
+"""
+===================================================================
+HermiteE Series, "Probabilists" (:mod:`numpy.polynomial.hermite_e`)
+===================================================================
+
+This module provides a number of objects (mostly functions) useful for
+dealing with Hermite_e series, including a `HermiteE` class that
+encapsulates the usual arithmetic operations.  (General information
+on how this module represents and works with such polynomials is in the
+docstring for its "parent" sub-package, `numpy.polynomial`).
+
+Classes
+-------
+.. autosummary::
+   :toctree: generated/
+
+   HermiteE
+
+Constants
+---------
+.. autosummary::
+   :toctree: generated/
+
+   hermedomain
+   hermezero
+   hermeone
+   hermex
+
+Arithmetic
+----------
+.. autosummary::
+   :toctree: generated/
+
+   hermeadd
+   hermesub
+   hermemulx
+   hermemul
+   hermediv
+   hermepow
+   hermeval
+   hermeval2d
+   hermeval3d
+   hermegrid2d
+   hermegrid3d
+
+Calculus
+--------
+.. autosummary::
+   :toctree: generated/
+
+   hermeder
+   hermeint
+
+Misc Functions
+--------------
+.. autosummary::
+   :toctree: generated/
+
+   hermefromroots
+   hermeroots
+   hermevander
+   hermevander2d
+   hermevander3d
+   hermegauss
+   hermeweight
+   hermecompanion
+   hermefit
+   hermetrim
+   hermeline
+   herme2poly
+   poly2herme
+
+See also
+--------
+`numpy.polynomial`
+
+"""
+import numpy as np
+import numpy.linalg as la
+from numpy.lib.array_utils import normalize_axis_index
+
+from . import polyutils as pu
+from ._polybase import ABCPolyBase
+
+__all__ = [
+    'hermezero', 'hermeone', 'hermex', 'hermedomain', 'hermeline',
+    'hermeadd', 'hermesub', 'hermemulx', 'hermemul', 'hermediv',
+    'hermepow', 'hermeval', 'hermeder', 'hermeint', 'herme2poly',
+    'poly2herme', 'hermefromroots', 'hermevander', 'hermefit', 'hermetrim',
+    'hermeroots', 'HermiteE', 'hermeval2d', 'hermeval3d', 'hermegrid2d',
+    'hermegrid3d', 'hermevander2d', 'hermevander3d', 'hermecompanion',
+    'hermegauss', 'hermeweight']
+
+hermetrim = pu.trimcoef
+
+
+def poly2herme(pol):
+    """
+    poly2herme(pol)
+
+    Convert a polynomial to a Hermite series.
+
+    Convert an array representing the coefficients of a polynomial (relative
+    to the "standard" basis) ordered from lowest degree to highest, to an
+    array of the coefficients of the equivalent Hermite series, ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    pol : array_like
+        1-D array containing the polynomial coefficients
+
+    Returns
+    -------
+    c : ndarray
+        1-D array containing the coefficients of the equivalent Hermite
+        series.
+
+    See Also
+    --------
+    herme2poly
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite_e import poly2herme
+    >>> poly2herme(np.arange(4))
+    array([  2.,  10.,   2.,   3.])
+
+    """
+    [pol] = pu.as_series([pol])
+    deg = len(pol) - 1
+    res = 0
+    for i in range(deg, -1, -1):
+        res = hermeadd(hermemulx(res), pol[i])
+    return res
+
+
+def herme2poly(c):
+    """
+    Convert a Hermite series to a polynomial.
+
+    Convert an array representing the coefficients of a Hermite series,
+    ordered from lowest degree to highest, to an array of the coefficients
+    of the equivalent polynomial (relative to the "standard" basis) ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array containing the Hermite series coefficients, ordered
+        from lowest order term to highest.
+
+    Returns
+    -------
+    pol : ndarray
+        1-D array containing the coefficients of the equivalent polynomial
+        (relative to the "standard" basis) ordered from lowest order term
+        to highest.
+
+    See Also
+    --------
+    poly2herme
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import herme2poly
+    >>> herme2poly([  2.,  10.,   2.,   3.])
+    array([0.,  1.,  2.,  3.])
+
+    """
+    from .polynomial import polyadd, polysub, polymulx
+
+    [c] = pu.as_series([c])
+    n = len(c)
+    if n == 1:
+        return c
+    if n == 2:
+        return c
+    else:
+        c0 = c[-2]
+        c1 = c[-1]
+        # i is the current degree of c1
+        for i in range(n - 1, 1, -1):
+            tmp = c0
+            c0 = polysub(c[i - 2], c1*(i - 1))
+            c1 = polyadd(tmp, polymulx(c1))
+        return polyadd(c0, polymulx(c1))
+
+
+#
+# These are constant arrays are of integer type so as to be compatible
+# with the widest range of other types, such as Decimal.
+#
+
+# Hermite
+hermedomain = np.array([-1., 1.])
+
+# Hermite coefficients representing zero.
+hermezero = np.array([0])
+
+# Hermite coefficients representing one.
+hermeone = np.array([1])
+
+# Hermite coefficients representing the identity x.
+hermex = np.array([0, 1])
+
+
+def hermeline(off, scl):
+    """
+    Hermite series whose graph is a straight line.
+
+    Parameters
+    ----------
+    off, scl : scalars
+        The specified line is given by ``off + scl*x``.
+
+    Returns
+    -------
+    y : ndarray
+        This module's representation of the Hermite series for
+        ``off + scl*x``.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyline
+    numpy.polynomial.chebyshev.chebline
+    numpy.polynomial.legendre.legline
+    numpy.polynomial.laguerre.lagline
+    numpy.polynomial.hermite.hermline
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermeline
+    >>> from numpy.polynomial.hermite_e import hermeline, hermeval
+    >>> hermeval(0,hermeline(3, 2))
+    3.0
+    >>> hermeval(1,hermeline(3, 2))
+    5.0
+
+    """
+    if scl != 0:
+        return np.array([off, scl])
+    else:
+        return np.array([off])
+
+
+def hermefromroots(roots):
+    """
+    Generate a HermiteE series with given roots.
+
+    The function returns the coefficients of the polynomial
+
+    .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
+
+    in HermiteE form, where the :math:`r_n` are the roots specified in `roots`.
+    If a zero has multiplicity n, then it must appear in `roots` n times.
+    For instance, if 2 is a root of multiplicity three and 3 is a root of
+    multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The
+    roots can appear in any order.
+
+    If the returned coefficients are `c`, then
+
+    .. math:: p(x) = c_0 + c_1 * He_1(x) + ... +  c_n * He_n(x)
+
+    The coefficient of the last term is not generally 1 for monic
+    polynomials in HermiteE form.
+
+    Parameters
+    ----------
+    roots : array_like
+        Sequence containing the roots.
+
+    Returns
+    -------
+    out : ndarray
+        1-D array of coefficients.  If all roots are real then `out` is a
+        real array, if some of the roots are complex, then `out` is complex
+        even if all the coefficients in the result are real (see Examples
+        below).
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfromroots
+    numpy.polynomial.legendre.legfromroots
+    numpy.polynomial.laguerre.lagfromroots
+    numpy.polynomial.hermite.hermfromroots
+    numpy.polynomial.chebyshev.chebfromroots
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermefromroots, hermeval
+    >>> coef = hermefromroots((-1, 0, 1))
+    >>> hermeval((-1, 0, 1), coef)
+    array([0., 0., 0.])
+    >>> coef = hermefromroots((-1j, 1j))
+    >>> hermeval((-1j, 1j), coef)
+    array([0.+0.j, 0.+0.j])
+
+    """
+    return pu._fromroots(hermeline, hermemul, roots)
+
+
+def hermeadd(c1, c2):
+    """
+    Add one Hermite series to another.
+
+    Returns the sum of two Hermite series `c1` + `c2`.  The arguments
+    are sequences of coefficients ordered from lowest order term to
+    highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the Hermite series of their sum.
+
+    See Also
+    --------
+    hermesub, hermemulx, hermemul, hermediv, hermepow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the sum of two Hermite series
+    is a Hermite series (without having to "reproject" the result onto
+    the basis set) so addition, just like that of "standard" polynomials,
+    is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermeadd
+    >>> hermeadd([1, 2, 3], [1, 2, 3, 4])
+    array([2.,  4.,  6.,  4.])
+
+    """
+    return pu._add(c1, c2)
+
+
+def hermesub(c1, c2):
+    """
+    Subtract one Hermite series from another.
+
+    Returns the difference of two Hermite series `c1` - `c2`.  The
+    sequences of coefficients are from lowest order term to highest, i.e.,
+    [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Hermite series coefficients representing their difference.
+
+    See Also
+    --------
+    hermeadd, hermemulx, hermemul, hermediv, hermepow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the difference of two Hermite
+    series is a Hermite series (without having to "reproject" the result
+    onto the basis set) so subtraction, just like that of "standard"
+    polynomials, is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermesub
+    >>> hermesub([1, 2, 3, 4], [1, 2, 3])
+    array([0., 0., 0., 4.])
+
+    """
+    return pu._sub(c1, c2)
+
+
+def hermemulx(c):
+    """Multiply a Hermite series by x.
+
+    Multiply the Hermite series `c` by x, where x is the independent
+    variable.
+
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the result of the multiplication.
+
+    See Also
+    --------
+    hermeadd, hermesub, hermemul, hermediv, hermepow
+
+    Notes
+    -----
+    The multiplication uses the recursion relationship for Hermite
+    polynomials in the form
+
+    .. math::
+
+        xP_i(x) = (P_{i + 1}(x) + iP_{i - 1}(x)))
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermemulx
+    >>> hermemulx([1, 2, 3])
+    array([2.,  7.,  2.,  3.])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    # The zero series needs special treatment
+    if len(c) == 1 and c[0] == 0:
+        return c
+
+    prd = np.empty(len(c) + 1, dtype=c.dtype)
+    prd[0] = c[0]*0
+    prd[1] = c[0]
+    for i in range(1, len(c)):
+        prd[i + 1] = c[i]
+        prd[i - 1] += c[i]*i
+    return prd
+
+
+def hermemul(c1, c2):
+    """
+    Multiply one Hermite series by another.
+
+    Returns the product of two Hermite series `c1` * `c2`.  The arguments
+    are sequences of coefficients, from lowest order "term" to highest,
+    e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Hermite series coefficients representing their product.
+
+    See Also
+    --------
+    hermeadd, hermesub, hermemulx, hermediv, hermepow
+
+    Notes
+    -----
+    In general, the (polynomial) product of two C-series results in terms
+    that are not in the Hermite polynomial basis set.  Thus, to express
+    the product as a Hermite series, it is necessary to "reproject" the
+    product onto said basis set, which may produce "unintuitive" (but
+    correct) results; see Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermemul
+    >>> hermemul([1, 2, 3], [0, 1, 2])
+    array([14.,  15.,  28.,   7.,   6.])
+
+    """
+    # s1, s2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+
+    if len(c1) > len(c2):
+        c = c2
+        xs = c1
+    else:
+        c = c1
+        xs = c2
+
+    if len(c) == 1:
+        c0 = c[0]*xs
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]*xs
+        c1 = c[1]*xs
+    else:
+        nd = len(c)
+        c0 = c[-2]*xs
+        c1 = c[-1]*xs
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = hermesub(c[-i]*xs, c1*(nd - 1))
+            c1 = hermeadd(tmp, hermemulx(c1))
+    return hermeadd(c0, hermemulx(c1))
+
+
+def hermediv(c1, c2):
+    """
+    Divide one Hermite series by another.
+
+    Returns the quotient-with-remainder of two Hermite series
+    `c1` / `c2`.  The arguments are sequences of coefficients from lowest
+    order "term" to highest, e.g., [1,2,3] represents the series
+    ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Hermite series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    [quo, rem] : ndarrays
+        Of Hermite series coefficients representing the quotient and
+        remainder.
+
+    See Also
+    --------
+    hermeadd, hermesub, hermemulx, hermemul, hermepow
+
+    Notes
+    -----
+    In general, the (polynomial) division of one Hermite series by another
+    results in quotient and remainder terms that are not in the Hermite
+    polynomial basis set.  Thus, to express these results as a Hermite
+    series, it is necessary to "reproject" the results onto the Hermite
+    basis set, which may produce "unintuitive" (but correct) results; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermediv
+    >>> hermediv([ 14.,  15.,  28.,   7.,   6.], [0, 1, 2])
+    (array([1., 2., 3.]), array([0.]))
+    >>> hermediv([ 15.,  17.,  28.,   7.,   6.], [0, 1, 2])
+    (array([1., 2., 3.]), array([1., 2.]))
+
+    """
+    return pu._div(hermemul, c1, c2)
+
+
+def hermepow(c, pow, maxpower=16):
+    """Raise a Hermite series to a power.
+
+    Returns the Hermite series `c` raised to the power `pow`. The
+    argument `c` is a sequence of coefficients ordered from low to high.
+    i.e., [1,2,3] is the series  ``P_0 + 2*P_1 + 3*P_2.``
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Hermite series coefficients ordered from low to
+        high.
+    pow : integer
+        Power to which the series will be raised
+    maxpower : integer, optional
+        Maximum power allowed. This is mainly to limit growth of the series
+        to unmanageable size. Default is 16
+
+    Returns
+    -------
+    coef : ndarray
+        Hermite series of power.
+
+    See Also
+    --------
+    hermeadd, hermesub, hermemulx, hermemul, hermediv
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermepow
+    >>> hermepow([1, 2, 3], 2)
+    array([23.,  28.,  46.,  12.,   9.])
+
+    """
+    return pu._pow(hermemul, c, pow, maxpower)
+
+
+def hermeder(c, m=1, scl=1, axis=0):
+    """
+    Differentiate a Hermite_e series.
+
+    Returns the series coefficients `c` differentiated `m` times along
+    `axis`.  At each iteration the result is multiplied by `scl` (the
+    scaling factor is for use in a linear change of variable). The argument
+    `c` is an array of coefficients from low to high degree along each
+    axis, e.g., [1,2,3] represents the series ``1*He_0 + 2*He_1 + 3*He_2``
+    while [[1,2],[1,2]] represents ``1*He_0(x)*He_0(y) + 1*He_1(x)*He_0(y)
+    + 2*He_0(x)*He_1(y) + 2*He_1(x)*He_1(y)`` if axis=0 is ``x`` and axis=1
+    is ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Hermite_e series coefficients. If `c` is multidimensional
+        the different axis correspond to different variables with the
+        degree in each axis given by the corresponding index.
+    m : int, optional
+        Number of derivatives taken, must be non-negative. (Default: 1)
+    scl : scalar, optional
+        Each differentiation is multiplied by `scl`.  The end result is
+        multiplication by ``scl**m``.  This is for use in a linear change of
+        variable. (Default: 1)
+    axis : int, optional
+        Axis over which the derivative is taken. (Default: 0).
+
+    Returns
+    -------
+    der : ndarray
+        Hermite series of the derivative.
+
+    See Also
+    --------
+    hermeint
+
+    Notes
+    -----
+    In general, the result of differentiating a Hermite series does not
+    resemble the same operation on a power series. Thus the result of this
+    function may be "unintuitive," albeit correct; see Examples section
+    below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermeder
+    >>> hermeder([ 1.,  1.,  1.,  1.])
+    array([1.,  2.,  3.])
+    >>> hermeder([-0.25,  1.,  1./2.,  1./3.,  1./4 ], m=2)
+    array([1.,  2.,  3.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    cnt = pu._as_int(m, "the order of derivation")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of derivation must be non-negative")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    n = len(c)
+    if cnt >= n:
+        return c[:1]*0
+    else:
+        for i in range(cnt):
+            n = n - 1
+            c *= scl
+            der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
+            for j in range(n, 0, -1):
+                der[j - 1] = j*c[j]
+            c = der
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
+    """
+    Integrate a Hermite_e series.
+
+    Returns the Hermite_e series coefficients `c` integrated `m` times from
+    `lbnd` along `axis`. At each iteration the resulting series is
+    **multiplied** by `scl` and an integration constant, `k`, is added.
+    The scaling factor is for use in a linear change of variable.  ("Buyer
+    beware": note that, depending on what one is doing, one may want `scl`
+    to be the reciprocal of what one might expect; for more information,
+    see the Notes section below.)  The argument `c` is an array of
+    coefficients from low to high degree along each axis, e.g., [1,2,3]
+    represents the series ``H_0 + 2*H_1 + 3*H_2`` while [[1,2],[1,2]]
+    represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) + 2*H_0(x)*H_1(y) +
+    2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Hermite_e series coefficients. If c is multidimensional
+        the different axis correspond to different variables with the
+        degree in each axis given by the corresponding index.
+    m : int, optional
+        Order of integration, must be positive. (Default: 1)
+    k : {[], list, scalar}, optional
+        Integration constant(s).  The value of the first integral at
+        ``lbnd`` is the first value in the list, the value of the second
+        integral at ``lbnd`` is the second value, etc.  If ``k == []`` (the
+        default), all constants are set to zero.  If ``m == 1``, a single
+        scalar can be given instead of a list.
+    lbnd : scalar, optional
+        The lower bound of the integral. (Default: 0)
+    scl : scalar, optional
+        Following each integration the result is *multiplied* by `scl`
+        before the integration constant is added. (Default: 1)
+    axis : int, optional
+        Axis over which the integral is taken. (Default: 0).
+
+    Returns
+    -------
+    S : ndarray
+        Hermite_e series coefficients of the integral.
+
+    Raises
+    ------
+    ValueError
+        If ``m < 0``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
+        ``np.ndim(scl) != 0``.
+
+    See Also
+    --------
+    hermeder
+
+    Notes
+    -----
+    Note that the result of each integration is *multiplied* by `scl`.
+    Why is this important to note?  Say one is making a linear change of
+    variable :math:`u = ax + b` in an integral relative to `x`.  Then
+    :math:`dx = du/a`, so one will need to set `scl` equal to
+    :math:`1/a` - perhaps not what one would have first thought.
+
+    Also note that, in general, the result of integrating a C-series needs
+    to be "reprojected" onto the C-series basis set.  Thus, typically,
+    the result of this function is "unintuitive," albeit correct; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermeint
+    >>> hermeint([1, 2, 3]) # integrate once, value 0 at 0.
+    array([1., 1., 1., 1.])
+    >>> hermeint([1, 2, 3], m=2) # integrate twice, value & deriv 0 at 0
+    array([-0.25      ,  1.        ,  0.5       ,  0.33333333,  0.25      ]) # may vary
+    >>> hermeint([1, 2, 3], k=1) # integrate once, value 1 at 0.
+    array([2., 1., 1., 1.])
+    >>> hermeint([1, 2, 3], lbnd=-1) # integrate once, value 0 at -1
+    array([-1.,  1.,  1.,  1.])
+    >>> hermeint([1, 2, 3], m=2, k=[1, 2], lbnd=-1)
+    array([ 1.83333333,  0.        ,  0.5       ,  0.33333333,  0.25      ]) # may vary
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if not np.iterable(k):
+        k = [k]
+    cnt = pu._as_int(m, "the order of integration")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of integration must be non-negative")
+    if len(k) > cnt:
+        raise ValueError("Too many integration constants")
+    if np.ndim(lbnd) != 0:
+        raise ValueError("lbnd must be a scalar.")
+    if np.ndim(scl) != 0:
+        raise ValueError("scl must be a scalar.")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    k = list(k) + [0]*(cnt - len(k))
+    for i in range(cnt):
+        n = len(c)
+        c *= scl
+        if n == 1 and np.all(c[0] == 0):
+            c[0] += k[i]
+        else:
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
+            tmp[0] = c[0]*0
+            tmp[1] = c[0]
+            for j in range(1, n):
+                tmp[j + 1] = c[j]/(j + 1)
+            tmp[0] += k[i] - hermeval(lbnd, tmp)
+            c = tmp
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def hermeval(x, c, tensor=True):
+    """
+    Evaluate an HermiteE series at points x.
+
+    If `c` is of length ``n + 1``, this function returns the value:
+
+    .. math:: p(x) = c_0 * He_0(x) + c_1 * He_1(x) + ... + c_n * He_n(x)
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `c`.
+
+    If `c` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If
+    `c` is multidimensional, then the shape of the result depends on the
+    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
+    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
+    scalars have shape (,).
+
+    Trailing zeros in the coefficients will be used in the evaluation, so
+    they should be avoided if efficiency is a concern.
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        with themselves and with the elements of `c`.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree n are contained in c[n]. If `c` is multidimensional the
+        remaining indices enumerate multiple polynomials. In the two
+        dimensional case the coefficients may be thought of as stored in
+        the columns of `c`.
+    tensor : boolean, optional
+        If True, the shape of the coefficient array is extended with ones
+        on the right, one for each dimension of `x`. Scalars have dimension 0
+        for this action. The result is that every column of coefficients in
+        `c` is evaluated for every element of `x`. If False, `x` is broadcast
+        over the columns of `c` for the evaluation.  This keyword is useful
+        when `c` is multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, algebra_like
+        The shape of the return value is described above.
+
+    See Also
+    --------
+    hermeval2d, hermegrid2d, hermeval3d, hermegrid3d
+
+    Notes
+    -----
+    The evaluation uses Clenshaw recursion, aka synthetic division.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermeval
+    >>> coef = [1,2,3]
+    >>> hermeval(1, coef)
+    3.0
+    >>> hermeval([[1,2],[3,4]], coef)
+    array([[ 3., 14.],
+           [31., 54.]])
+
+    """
+    c = np.array(c, ndmin=1, copy=None)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray) and tensor:
+        c = c.reshape(c.shape + (1,)*x.ndim)
+
+    if len(c) == 1:
+        c0 = c[0]
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]
+        c1 = c[1]
+    else:
+        nd = len(c)
+        c0 = c[-2]
+        c1 = c[-1]
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = c[-i] - c1*(nd - 1)
+            c1 = tmp + c1*x
+    return c0 + c1*x
+
+
+def hermeval2d(x, y, c):
+    """
+    Evaluate a 2-D HermiteE series at points (x, y).
+
+    This function returns the values:
+
+    .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * He_i(x) * He_j(y)
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars and they
+    must have the same shape after conversion. In either case, either `x`
+    and `y` or their elements must support multiplication and addition both
+    with themselves and with the elements of `c`.
+
+    If `c` is a 1-D array a one is implicitly appended to its shape to make
+    it 2-D. The shape of the result will be c.shape[2:] + x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points ``(x, y)``,
+        where `x` and `y` must have the same shape. If `x` or `y` is a list
+        or tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and if it isn't an ndarray it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term
+        of multi-degree i,j is contained in ``c[i,j]``. If `c` has
+        dimension greater than two the remaining indices enumerate multiple
+        sets of coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points formed with
+        pairs of corresponding values from `x` and `y`.
+
+    See Also
+    --------
+    hermeval, hermegrid2d, hermeval3d, hermegrid3d
+    """
+    return pu._valnd(hermeval, c, x, y)
+
+
+def hermegrid2d(x, y, c):
+    """
+    Evaluate a 2-D HermiteE series on the Cartesian product of x and y.
+
+    This function returns the values:
+
+    .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * H_i(a) * H_j(b)
+
+    where the points ``(a, b)`` consist of all pairs formed by taking
+    `a` from `x` and `b` from `y`. The resulting points form a grid with
+    `x` in the first dimension and `y` in the second.
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars. In either
+    case, either `x` and `y` or their elements must support multiplication
+    and addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points in the
+        Cartesian product of `x` and `y`.  If `x` or `y` is a list or
+        tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    hermeval, hermeval2d, hermeval3d, hermegrid3d
+    """
+    return pu._gridnd(hermeval, c, x, y)
+
+
+def hermeval3d(x, y, z, c):
+    """
+    Evaluate a 3-D Hermite_e series at points (x, y, z).
+
+    This function returns the values:
+
+    .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * He_i(x) * He_j(y) * He_k(z)
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if
+    they are tuples or a lists, otherwise they are treated as a scalars and
+    they must have the same shape after conversion. In either case, either
+    `x`, `y`, and `z` or their elements must support multiplication and
+    addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than 3 dimensions, ones are implicitly appended to its
+    shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible object
+        The three dimensional series is evaluated at the points
+        `(x, y, z)`, where `x`, `y`, and `z` must have the same shape.  If
+        any of `x`, `y`, or `z` is a list or tuple, it is first converted
+        to an ndarray, otherwise it is left unchanged and if it isn't an
+        ndarray it is  treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
+        greater than 3 the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the multidimensional polynomial on points formed with
+        triples of corresponding values from `x`, `y`, and `z`.
+
+    See Also
+    --------
+    hermeval, hermeval2d, hermegrid2d, hermegrid3d
+    """
+    return pu._valnd(hermeval, c, x, y, z)
+
+
+def hermegrid3d(x, y, z, c):
+    """
+    Evaluate a 3-D HermiteE series on the Cartesian product of x, y, and z.
+
+    This function returns the values:
+
+    .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * He_i(a) * He_j(b) * He_k(c)
+
+    where the points ``(a, b, c)`` consist of all triples formed by taking
+    `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
+    a grid with `x` in the first dimension, `y` in the second, and `z` in
+    the third.
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if they
+    are tuples or a lists, otherwise they are treated as a scalars. In
+    either case, either `x`, `y`, and `z` or their elements must support
+    multiplication and addition both with themselves and with the elements
+    of `c`.
+
+    If `c` has fewer than three dimensions, ones are implicitly appended to
+    its shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape + y.shape + z.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible objects
+        The three dimensional series is evaluated at the points in the
+        Cartesian product of `x`, `y`, and `z`.  If `x`, `y`, or `z` is a
+        list or tuple, it is first converted to an ndarray, otherwise it is
+        left unchanged and, if it isn't an ndarray, it is treated as a
+        scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    hermeval, hermeval2d, hermegrid2d, hermeval3d
+    """
+    return pu._gridnd(hermeval, c, x, y, z)
+
+
+def hermevander(x, deg):
+    """Pseudo-Vandermonde matrix of given degree.
+
+    Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
+    `x`. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., i] = He_i(x),
+
+    where ``0 <= i <= deg``. The leading indices of `V` index the elements of
+    `x` and the last index is the degree of the HermiteE polynomial.
+
+    If `c` is a 1-D array of coefficients of length ``n + 1`` and `V` is the
+    array ``V = hermevander(x, n)``, then ``np.dot(V, c)`` and
+    ``hermeval(x, c)`` are the same up to roundoff. This equivalence is
+    useful both for least squares fitting and for the evaluation of a large
+    number of HermiteE series of the same degree and sample points.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of points. The dtype is converted to float64 or complex128
+        depending on whether any of the elements are complex. If `x` is
+        scalar it is converted to a 1-D array.
+    deg : int
+        Degree of the resulting matrix.
+
+    Returns
+    -------
+    vander : ndarray
+        The pseudo-Vandermonde matrix. The shape of the returned matrix is
+        ``x.shape + (deg + 1,)``, where The last index is the degree of the
+        corresponding HermiteE polynomial.  The dtype will be the same as
+        the converted `x`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite_e import hermevander
+    >>> x = np.array([-1, 0, 1])
+    >>> hermevander(x, 3)
+    array([[ 1., -1.,  0.,  2.],
+           [ 1.,  0., -1., -0.],
+           [ 1.,  1.,  0., -2.]])
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg < 0:
+        raise ValueError("deg must be non-negative")
+
+    x = np.array(x, copy=None, ndmin=1) + 0.0
+    dims = (ideg + 1,) + x.shape
+    dtyp = x.dtype
+    v = np.empty(dims, dtype=dtyp)
+    v[0] = x*0 + 1
+    if ideg > 0:
+        v[1] = x
+        for i in range(2, ideg + 1):
+            v[i] = (v[i-1]*x - v[i-2]*(i - 1))
+    return np.moveaxis(v, 0, -1)
+
+
+def hermevander2d(x, y, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y)``. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (deg[1] + 1)*i + j] = He_i(x) * He_j(y),
+
+    where ``0 <= i <= deg[0]`` and ``0 <= j <= deg[1]``. The leading indices of
+    `V` index the points ``(x, y)`` and the last index encodes the degrees of
+    the HermiteE polynomials.
+
+    If ``V = hermevander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
+    correspond to the elements of a 2-D coefficient array `c` of shape
+    (xdeg + 1, ydeg + 1) in the order
+
+    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+
+    and ``np.dot(V, c.flat)`` and ``hermeval2d(x, y, c)`` will be the same
+    up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 2-D HermiteE
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to
+        1-D arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg].
+
+    Returns
+    -------
+    vander2d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)`.  The dtype will be the same
+        as the converted `x` and `y`.
+
+    See Also
+    --------
+    hermevander, hermevander3d, hermeval2d, hermeval3d
+    """
+    return pu._vander_nd_flat((hermevander, hermevander), (x, y), deg)
+
+
+def hermevander3d(x, y, z, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y, z)``. If `l`, `m`, `n` are the given degrees in `x`, `y`, `z`,
+    then Hehe pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = He_i(x)*He_j(y)*He_k(z),
+
+    where ``0 <= i <= l``, ``0 <= j <= m``, and ``0 <= j <= n``.  The leading
+    indices of `V` index the points ``(x, y, z)`` and the last index encodes
+    the degrees of the HermiteE polynomials.
+
+    If ``V = hermevander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
+    of `V` correspond to the elements of a 3-D coefficient array `c` of
+    shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
+
+    .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
+
+    and  ``np.dot(V, c.flat)`` and ``hermeval3d(x, y, z, c)`` will be the
+    same up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 3-D HermiteE
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y, z : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes will
+        be converted to either float64 or complex128 depending on whether
+        any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg, z_deg].
+
+    Returns
+    -------
+    vander3d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`.  The dtype will
+        be the same as the converted `x`, `y`, and `z`.
+
+    See Also
+    --------
+    hermevander, hermevander3d, hermeval2d, hermeval3d
+    """
+    return pu._vander_nd_flat((hermevander, hermevander, hermevander), (x, y, z), deg)
+
+
+def hermefit(x, y, deg, rcond=None, full=False, w=None):
+    """
+    Least squares fit of Hermite series to data.
+
+    Return the coefficients of a HermiteE series of degree `deg` that is
+    the least squares fit to the data values `y` given at points `x`. If
+    `y` is 1-D the returned coefficients will also be 1-D. If `y` is 2-D
+    multiple fits are done, one for each column of `y`, and the resulting
+    coefficients are stored in the corresponding columns of a 2-D return.
+    The fitted polynomial(s) are in the form
+
+    .. math::  p(x) = c_0 + c_1 * He_1(x) + ... + c_n * He_n(x),
+
+    where `n` is `deg`.
+
+    Parameters
+    ----------
+    x : array_like, shape (M,)
+        x-coordinates of the M sample points ``(x[i], y[i])``.
+    y : array_like, shape (M,) or (M, K)
+        y-coordinates of the sample points. Several data sets of sample
+        points sharing the same x-coordinates can be fitted at once by
+        passing in a 2D-array that contains one dataset per column.
+    deg : int or 1-D array_like
+        Degree(s) of the fitting polynomials. If `deg` is a single integer
+        all terms up to and including the `deg`'th term are included in the
+        fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+        degrees of the terms to include may be used instead.
+    rcond : float, optional
+        Relative condition number of the fit. Singular values smaller than
+        this relative to the largest singular value will be ignored. The
+        default value is len(x)*eps, where eps is the relative precision of
+        the float type, about 2e-16 in most cases.
+    full : bool, optional
+        Switch determining nature of return value. When it is False (the
+        default) just the coefficients are returned, when True diagnostic
+        information from the singular value decomposition is also returned.
+    w : array_like, shape (`M`,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+
+    Returns
+    -------
+    coef : ndarray, shape (M,) or (M, K)
+        Hermite coefficients ordered from low to high. If `y` was 2-D,
+        the coefficients for the data in column k  of `y` are in column
+        `k`.
+
+    [residuals, rank, singular_values, rcond] : list
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    Warns
+    -----
+    RankWarning
+        The rank of the coefficient matrix in the least-squares fit is
+        deficient. The warning is only raised if ``full = False``.  The
+        warnings can be turned off by
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    numpy.polynomial.chebyshev.chebfit
+    numpy.polynomial.legendre.legfit
+    numpy.polynomial.polynomial.polyfit
+    numpy.polynomial.hermite.hermfit
+    numpy.polynomial.laguerre.lagfit
+    hermeval : Evaluates a Hermite series.
+    hermevander : pseudo Vandermonde matrix of Hermite series.
+    hermeweight : HermiteE weight function.
+    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution is the coefficients of the HermiteE series `p` that
+    minimizes the sum of the weighted squared errors
+
+    .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
+
+    where the :math:`w_j` are the weights. This problem is solved by
+    setting up the (typically) overdetermined matrix equation
+
+    .. math:: V(x) * c = w * y,
+
+    where `V` is the pseudo Vandermonde matrix of `x`, the elements of `c`
+    are the coefficients to be solved for, and the elements of `y` are the
+    observed values.  This equation is then solved using the singular value
+    decomposition of `V`.
+
+    If some of the singular values of `V` are so small that they are
+    neglected, then a `~exceptions.RankWarning` will be issued. This means that
+    the coefficient values may be poorly determined. Using a lower order fit
+    will usually get rid of the warning.  The `rcond` parameter can also be
+    set to a value smaller than its default, but the resulting fit may be
+    spurious and have large contributions from roundoff error.
+
+    Fits using HermiteE series are probably most useful when the data can
+    be approximated by ``sqrt(w(x)) * p(x)``, where ``w(x)`` is the HermiteE
+    weight. In that case the weight ``sqrt(w(x[i]))`` should be used
+    together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
+    available as `hermeweight`.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Curve fitting",
+           https://en.wikipedia.org/wiki/Curve_fitting
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.hermite_e import hermefit, hermeval
+    >>> x = np.linspace(-10, 10)
+    >>> rng = np.random.default_rng()
+    >>> err = rng.normal(scale=1./10, size=len(x))
+    >>> y = hermeval(x, [1, 2, 3]) + err
+    >>> hermefit(x, y, 2)
+    array([1.02284196, 2.00032805, 2.99978457]) # may vary
+
+    """
+    return pu._fit(hermevander, x, y, deg, rcond, full, w)
+
+
+def hermecompanion(c):
+    """
+    Return the scaled companion matrix of c.
+
+    The basis polynomials are scaled so that the companion matrix is
+    symmetric when `c` is an HermiteE basis polynomial. This provides
+    better eigenvalue estimates than the unscaled case and for basis
+    polynomials the eigenvalues are guaranteed to be real if
+    `numpy.linalg.eigvalsh` is used to obtain them.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of HermiteE series coefficients ordered from low to high
+        degree.
+
+    Returns
+    -------
+    mat : ndarray
+        Scaled companion matrix of dimensions (deg, deg).
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        raise ValueError('Series must have maximum degree of at least 1.')
+    if len(c) == 2:
+        return np.array([[-c[0]/c[1]]])
+
+    n = len(c) - 1
+    mat = np.zeros((n, n), dtype=c.dtype)
+    scl = np.hstack((1., 1./np.sqrt(np.arange(n - 1, 0, -1))))
+    scl = np.multiply.accumulate(scl)[::-1]
+    top = mat.reshape(-1)[1::n+1]
+    bot = mat.reshape(-1)[n::n+1]
+    top[...] = np.sqrt(np.arange(1, n))
+    bot[...] = top
+    mat[:, -1] -= scl*c[:-1]/c[-1]
+    return mat
+
+
+def hermeroots(c):
+    """
+    Compute the roots of a HermiteE series.
+
+    Return the roots (a.k.a. "zeros") of the polynomial
+
+    .. math:: p(x) = \\sum_i c[i] * He_i(x).
+
+    Parameters
+    ----------
+    c : 1-D array_like
+        1-D array of coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        Array of the roots of the series. If all the roots are real,
+        then `out` is also real, otherwise it is complex.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyroots
+    numpy.polynomial.legendre.legroots
+    numpy.polynomial.laguerre.lagroots
+    numpy.polynomial.hermite.hermroots
+    numpy.polynomial.chebyshev.chebroots
+
+    Notes
+    -----
+    The root estimates are obtained as the eigenvalues of the companion
+    matrix, Roots far from the origin of the complex plane may have large
+    errors due to the numerical instability of the series for such
+    values. Roots with multiplicity greater than 1 will also show larger
+    errors as the value of the series near such points is relatively
+    insensitive to errors in the roots. Isolated roots near the origin can
+    be improved by a few iterations of Newton's method.
+
+    The HermiteE series basis polynomials aren't powers of `x` so the
+    results of this function may seem unintuitive.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.hermite_e import hermeroots, hermefromroots
+    >>> coef = hermefromroots([-1, 0, 1])
+    >>> coef
+    array([0., 2., 0., 1.])
+    >>> hermeroots(coef)
+    array([-1.,  0.,  1.]) # may vary
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) <= 1:
+        return np.array([], dtype=c.dtype)
+    if len(c) == 2:
+        return np.array([-c[0]/c[1]])
+
+    # rotated companion matrix reduces error
+    m = hermecompanion(c)[::-1,::-1]
+    r = la.eigvals(m)
+    r.sort()
+    return r
+
+
+def _normed_hermite_e_n(x, n):
+    """
+    Evaluate a normalized HermiteE polynomial.
+
+    Compute the value of the normalized HermiteE polynomial of degree ``n``
+    at the points ``x``.
+
+
+    Parameters
+    ----------
+    x : ndarray of double.
+        Points at which to evaluate the function
+    n : int
+        Degree of the normalized HermiteE function to be evaluated.
+
+    Returns
+    -------
+    values : ndarray
+        The shape of the return value is described above.
+
+    Notes
+    -----
+    This function is needed for finding the Gauss points and integration
+    weights for high degrees. The values of the standard HermiteE functions
+    overflow when n >= 207.
+
+    """
+    if n == 0:
+        return np.full(x.shape, 1/np.sqrt(np.sqrt(2*np.pi)))
+
+    c0 = 0.
+    c1 = 1./np.sqrt(np.sqrt(2*np.pi))
+    nd = float(n)
+    for i in range(n - 1):
+        tmp = c0
+        c0 = -c1*np.sqrt((nd - 1.)/nd)
+        c1 = tmp + c1*x*np.sqrt(1./nd)
+        nd = nd - 1.0
+    return c0 + c1*x
+
+
+def hermegauss(deg):
+    """
+    Gauss-HermiteE quadrature.
+
+    Computes the sample points and weights for Gauss-HermiteE quadrature.
+    These sample points and weights will correctly integrate polynomials of
+    degree :math:`2*deg - 1` or less over the interval :math:`[-\\inf, \\inf]`
+    with the weight function :math:`f(x) = \\exp(-x^2/2)`.
+
+    Parameters
+    ----------
+    deg : int
+        Number of sample points and weights. It must be >= 1.
+
+    Returns
+    -------
+    x : ndarray
+        1-D ndarray containing the sample points.
+    y : ndarray
+        1-D ndarray containing the weights.
+
+    Notes
+    -----
+    The results have only been tested up to degree 100, higher degrees may
+    be problematic. The weights are determined by using the fact that
+
+    .. math:: w_k = c / (He'_n(x_k) * He_{n-1}(x_k))
+
+    where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
+    is the k'th root of :math:`He_n`, and then scaling the results to get
+    the right value when integrating 1.
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg <= 0:
+        raise ValueError("deg must be a positive integer")
+
+    # first approximation of roots. We use the fact that the companion
+    # matrix is symmetric in this case in order to obtain better zeros.
+    c = np.array([0]*deg + [1])
+    m = hermecompanion(c)
+    x = la.eigvalsh(m)
+
+    # improve roots by one application of Newton
+    dy = _normed_hermite_e_n(x, ideg)
+    df = _normed_hermite_e_n(x, ideg - 1) * np.sqrt(ideg)
+    x -= dy/df
+
+    # compute the weights. We scale the factor to avoid possible numerical
+    # overflow.
+    fm = _normed_hermite_e_n(x, ideg - 1)
+    fm /= np.abs(fm).max()
+    w = 1/(fm * fm)
+
+    # for Hermite_e we can also symmetrize
+    w = (w + w[::-1])/2
+    x = (x - x[::-1])/2
+
+    # scale w to get the right value
+    w *= np.sqrt(2*np.pi) / w.sum()
+
+    return x, w
+
+
+def hermeweight(x):
+    """Weight function of the Hermite_e polynomials.
+
+    The weight function is :math:`\\exp(-x^2/2)` and the interval of
+    integration is :math:`[-\\inf, \\inf]`. the HermiteE polynomials are
+    orthogonal, but not normalized, with respect to this weight function.
+
+    Parameters
+    ----------
+    x : array_like
+       Values at which the weight function will be computed.
+
+    Returns
+    -------
+    w : ndarray
+       The weight function at `x`.
+    """
+    w = np.exp(-.5*x**2)
+    return w
+
+
+#
+# HermiteE series class
+#
+
+class HermiteE(ABCPolyBase):
+    """An HermiteE series class.
+
+    The HermiteE class provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
+    attributes and methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        HermiteE coefficients in order of increasing degree, i.e,
+        ``(1, 2, 3)`` gives ``1*He_0(x) + 2*He_1(X) + 3*He_2(x)``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is [-1., 1.].
+    window : (2,) array_like, optional
+        Window, see `domain` for its use. The default value is [-1., 1.].
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    """
+    # Virtual Functions
+    _add = staticmethod(hermeadd)
+    _sub = staticmethod(hermesub)
+    _mul = staticmethod(hermemul)
+    _div = staticmethod(hermediv)
+    _pow = staticmethod(hermepow)
+    _val = staticmethod(hermeval)
+    _int = staticmethod(hermeint)
+    _der = staticmethod(hermeder)
+    _fit = staticmethod(hermefit)
+    _line = staticmethod(hermeline)
+    _roots = staticmethod(hermeroots)
+    _fromroots = staticmethod(hermefromroots)
+
+    # Virtual properties
+    domain = np.array(hermedomain)
+    window = np.array(hermedomain)
+    basis_name = 'He'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite_e.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite_e.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..94ad7248f268b9d4e4de1685063187c94db25fd7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/hermite_e.pyi
@@ -0,0 +1,106 @@
+from typing import Any, Final, Literal as L, TypeVar
+
+import numpy as np
+
+from ._polybase import ABCPolyBase
+from ._polytypes import (
+    _Array1,
+    _Array2,
+    _FuncBinOp,
+    _FuncCompanion,
+    _FuncDer,
+    _FuncFit,
+    _FuncFromRoots,
+    _FuncGauss,
+    _FuncInteg,
+    _FuncLine,
+    _FuncPoly2Ortho,
+    _FuncPow,
+    _FuncRoots,
+    _FuncUnOp,
+    _FuncVal,
+    _FuncVal2D,
+    _FuncVal3D,
+    _FuncValFromRoots,
+    _FuncVander,
+    _FuncVander2D,
+    _FuncVander3D,
+    _FuncWeight,
+)
+from .polyutils import trimcoef as hermetrim
+
+__all__ = [
+    "hermezero",
+    "hermeone",
+    "hermex",
+    "hermedomain",
+    "hermeline",
+    "hermeadd",
+    "hermesub",
+    "hermemulx",
+    "hermemul",
+    "hermediv",
+    "hermepow",
+    "hermeval",
+    "hermeder",
+    "hermeint",
+    "herme2poly",
+    "poly2herme",
+    "hermefromroots",
+    "hermevander",
+    "hermefit",
+    "hermetrim",
+    "hermeroots",
+    "HermiteE",
+    "hermeval2d",
+    "hermeval3d",
+    "hermegrid2d",
+    "hermegrid3d",
+    "hermevander2d",
+    "hermevander3d",
+    "hermecompanion",
+    "hermegauss",
+    "hermeweight",
+]
+
+poly2herme: _FuncPoly2Ortho[L["poly2herme"]]
+herme2poly: _FuncUnOp[L["herme2poly"]]
+
+hermedomain: Final[_Array2[np.float64]]
+hermezero: Final[_Array1[np.int_]]
+hermeone: Final[_Array1[np.int_]]
+hermex: Final[_Array2[np.int_]]
+
+hermeline: _FuncLine[L["hermeline"]]
+hermefromroots: _FuncFromRoots[L["hermefromroots"]]
+hermeadd: _FuncBinOp[L["hermeadd"]]
+hermesub: _FuncBinOp[L["hermesub"]]
+hermemulx: _FuncUnOp[L["hermemulx"]]
+hermemul: _FuncBinOp[L["hermemul"]]
+hermediv: _FuncBinOp[L["hermediv"]]
+hermepow: _FuncPow[L["hermepow"]]
+hermeder: _FuncDer[L["hermeder"]]
+hermeint: _FuncInteg[L["hermeint"]]
+hermeval: _FuncVal[L["hermeval"]]
+hermeval2d: _FuncVal2D[L["hermeval2d"]]
+hermeval3d: _FuncVal3D[L["hermeval3d"]]
+hermevalfromroots: _FuncValFromRoots[L["hermevalfromroots"]]
+hermegrid2d: _FuncVal2D[L["hermegrid2d"]]
+hermegrid3d: _FuncVal3D[L["hermegrid3d"]]
+hermevander: _FuncVander[L["hermevander"]]
+hermevander2d: _FuncVander2D[L["hermevander2d"]]
+hermevander3d: _FuncVander3D[L["hermevander3d"]]
+hermefit: _FuncFit[L["hermefit"]]
+hermecompanion: _FuncCompanion[L["hermecompanion"]]
+hermeroots: _FuncRoots[L["hermeroots"]]
+
+_ND = TypeVar("_ND", bound=Any)
+def _normed_hermite_e_n(
+    x: np.ndarray[_ND, np.dtype[np.float64]],
+    n: int | np.intp,
+) -> np.ndarray[_ND, np.dtype[np.float64]]: ...
+
+hermegauss: _FuncGauss[L["hermegauss"]]
+hermeweight: _FuncWeight[L["hermeweight"]]
+
+class HermiteE(ABCPolyBase[L["He"]]): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/laguerre.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/laguerre.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2cc5817c30cb892f58f1c366746b5967670d2ad
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/laguerre.py
@@ -0,0 +1,1675 @@
+"""
+==================================================
+Laguerre Series (:mod:`numpy.polynomial.laguerre`)
+==================================================
+
+This module provides a number of objects (mostly functions) useful for
+dealing with Laguerre series, including a `Laguerre` class that
+encapsulates the usual arithmetic operations.  (General information
+on how this module represents and works with such polynomials is in the
+docstring for its "parent" sub-package, `numpy.polynomial`).
+
+Classes
+-------
+.. autosummary::
+   :toctree: generated/
+
+   Laguerre
+
+Constants
+---------
+.. autosummary::
+   :toctree: generated/
+
+   lagdomain
+   lagzero
+   lagone
+   lagx
+
+Arithmetic
+----------
+.. autosummary::
+   :toctree: generated/
+
+   lagadd
+   lagsub
+   lagmulx
+   lagmul
+   lagdiv
+   lagpow
+   lagval
+   lagval2d
+   lagval3d
+   laggrid2d
+   laggrid3d
+
+Calculus
+--------
+.. autosummary::
+   :toctree: generated/
+
+   lagder
+   lagint
+
+Misc Functions
+--------------
+.. autosummary::
+   :toctree: generated/
+
+   lagfromroots
+   lagroots
+   lagvander
+   lagvander2d
+   lagvander3d
+   laggauss
+   lagweight
+   lagcompanion
+   lagfit
+   lagtrim
+   lagline
+   lag2poly
+   poly2lag
+
+See also
+--------
+`numpy.polynomial`
+
+"""
+import numpy as np
+import numpy.linalg as la
+from numpy.lib.array_utils import normalize_axis_index
+
+from . import polyutils as pu
+from ._polybase import ABCPolyBase
+
+__all__ = [
+    'lagzero', 'lagone', 'lagx', 'lagdomain', 'lagline', 'lagadd',
+    'lagsub', 'lagmulx', 'lagmul', 'lagdiv', 'lagpow', 'lagval', 'lagder',
+    'lagint', 'lag2poly', 'poly2lag', 'lagfromroots', 'lagvander',
+    'lagfit', 'lagtrim', 'lagroots', 'Laguerre', 'lagval2d', 'lagval3d',
+    'laggrid2d', 'laggrid3d', 'lagvander2d', 'lagvander3d', 'lagcompanion',
+    'laggauss', 'lagweight']
+
+lagtrim = pu.trimcoef
+
+
+def poly2lag(pol):
+    """
+    poly2lag(pol)
+
+    Convert a polynomial to a Laguerre series.
+
+    Convert an array representing the coefficients of a polynomial (relative
+    to the "standard" basis) ordered from lowest degree to highest, to an
+    array of the coefficients of the equivalent Laguerre series, ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    pol : array_like
+        1-D array containing the polynomial coefficients
+
+    Returns
+    -------
+    c : ndarray
+        1-D array containing the coefficients of the equivalent Laguerre
+        series.
+
+    See Also
+    --------
+    lag2poly
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.laguerre import poly2lag
+    >>> poly2lag(np.arange(4))
+    array([ 23., -63.,  58., -18.])
+
+    """
+    [pol] = pu.as_series([pol])
+    res = 0
+    for p in pol[::-1]:
+        res = lagadd(lagmulx(res), p)
+    return res
+
+
+def lag2poly(c):
+    """
+    Convert a Laguerre series to a polynomial.
+
+    Convert an array representing the coefficients of a Laguerre series,
+    ordered from lowest degree to highest, to an array of the coefficients
+    of the equivalent polynomial (relative to the "standard" basis) ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array containing the Laguerre series coefficients, ordered
+        from lowest order term to highest.
+
+    Returns
+    -------
+    pol : ndarray
+        1-D array containing the coefficients of the equivalent polynomial
+        (relative to the "standard" basis) ordered from lowest order term
+        to highest.
+
+    See Also
+    --------
+    poly2lag
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lag2poly
+    >>> lag2poly([ 23., -63.,  58., -18.])
+    array([0., 1., 2., 3.])
+
+    """
+    from .polynomial import polyadd, polysub, polymulx
+
+    [c] = pu.as_series([c])
+    n = len(c)
+    if n == 1:
+        return c
+    else:
+        c0 = c[-2]
+        c1 = c[-1]
+        # i is the current degree of c1
+        for i in range(n - 1, 1, -1):
+            tmp = c0
+            c0 = polysub(c[i - 2], (c1*(i - 1))/i)
+            c1 = polyadd(tmp, polysub((2*i - 1)*c1, polymulx(c1))/i)
+        return polyadd(c0, polysub(c1, polymulx(c1)))
+
+
+#
+# These are constant arrays are of integer type so as to be compatible
+# with the widest range of other types, such as Decimal.
+#
+
+# Laguerre
+lagdomain = np.array([0., 1.])
+
+# Laguerre coefficients representing zero.
+lagzero = np.array([0])
+
+# Laguerre coefficients representing one.
+lagone = np.array([1])
+
+# Laguerre coefficients representing the identity x.
+lagx = np.array([1, -1])
+
+
+def lagline(off, scl):
+    """
+    Laguerre series whose graph is a straight line.
+
+    Parameters
+    ----------
+    off, scl : scalars
+        The specified line is given by ``off + scl*x``.
+
+    Returns
+    -------
+    y : ndarray
+        This module's representation of the Laguerre series for
+        ``off + scl*x``.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyline
+    numpy.polynomial.chebyshev.chebline
+    numpy.polynomial.legendre.legline
+    numpy.polynomial.hermite.hermline
+    numpy.polynomial.hermite_e.hermeline
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagline, lagval
+    >>> lagval(0,lagline(3, 2))
+    3.0
+    >>> lagval(1,lagline(3, 2))
+    5.0
+
+    """
+    if scl != 0:
+        return np.array([off + scl, -scl])
+    else:
+        return np.array([off])
+
+
+def lagfromroots(roots):
+    """
+    Generate a Laguerre series with given roots.
+
+    The function returns the coefficients of the polynomial
+
+    .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
+
+    in Laguerre form, where the :math:`r_n` are the roots specified in `roots`.
+    If a zero has multiplicity n, then it must appear in `roots` n times.
+    For instance, if 2 is a root of multiplicity three and 3 is a root of
+    multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The
+    roots can appear in any order.
+
+    If the returned coefficients are `c`, then
+
+    .. math:: p(x) = c_0 + c_1 * L_1(x) + ... +  c_n * L_n(x)
+
+    The coefficient of the last term is not generally 1 for monic
+    polynomials in Laguerre form.
+
+    Parameters
+    ----------
+    roots : array_like
+        Sequence containing the roots.
+
+    Returns
+    -------
+    out : ndarray
+        1-D array of coefficients.  If all roots are real then `out` is a
+        real array, if some of the roots are complex, then `out` is complex
+        even if all the coefficients in the result are real (see Examples
+        below).
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfromroots
+    numpy.polynomial.legendre.legfromroots
+    numpy.polynomial.chebyshev.chebfromroots
+    numpy.polynomial.hermite.hermfromroots
+    numpy.polynomial.hermite_e.hermefromroots
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagfromroots, lagval
+    >>> coef = lagfromroots((-1, 0, 1))
+    >>> lagval((-1, 0, 1), coef)
+    array([0.,  0.,  0.])
+    >>> coef = lagfromroots((-1j, 1j))
+    >>> lagval((-1j, 1j), coef)
+    array([0.+0.j, 0.+0.j])
+
+    """
+    return pu._fromroots(lagline, lagmul, roots)
+
+
+def lagadd(c1, c2):
+    """
+    Add one Laguerre series to another.
+
+    Returns the sum of two Laguerre series `c1` + `c2`.  The arguments
+    are sequences of coefficients ordered from lowest order term to
+    highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Laguerre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the Laguerre series of their sum.
+
+    See Also
+    --------
+    lagsub, lagmulx, lagmul, lagdiv, lagpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the sum of two Laguerre series
+    is a Laguerre series (without having to "reproject" the result onto
+    the basis set) so addition, just like that of "standard" polynomials,
+    is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagadd
+    >>> lagadd([1, 2, 3], [1, 2, 3, 4])
+    array([2.,  4.,  6.,  4.])
+
+    """
+    return pu._add(c1, c2)
+
+
+def lagsub(c1, c2):
+    """
+    Subtract one Laguerre series from another.
+
+    Returns the difference of two Laguerre series `c1` - `c2`.  The
+    sequences of coefficients are from lowest order term to highest, i.e.,
+    [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Laguerre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Laguerre series coefficients representing their difference.
+
+    See Also
+    --------
+    lagadd, lagmulx, lagmul, lagdiv, lagpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the difference of two Laguerre
+    series is a Laguerre series (without having to "reproject" the result
+    onto the basis set) so subtraction, just like that of "standard"
+    polynomials, is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagsub
+    >>> lagsub([1, 2, 3, 4], [1, 2, 3])
+    array([0.,  0.,  0.,  4.])
+
+    """
+    return pu._sub(c1, c2)
+
+
+def lagmulx(c):
+    """Multiply a Laguerre series by x.
+
+    Multiply the Laguerre series `c` by x, where x is the independent
+    variable.
+
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Laguerre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the result of the multiplication.
+
+    See Also
+    --------
+    lagadd, lagsub, lagmul, lagdiv, lagpow
+
+    Notes
+    -----
+    The multiplication uses the recursion relationship for Laguerre
+    polynomials in the form
+
+    .. math::
+
+        xP_i(x) = (-(i + 1)*P_{i + 1}(x) + (2i + 1)P_{i}(x) - iP_{i - 1}(x))
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagmulx
+    >>> lagmulx([1, 2, 3])
+    array([-1.,  -1.,  11.,  -9.])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    # The zero series needs special treatment
+    if len(c) == 1 and c[0] == 0:
+        return c
+
+    prd = np.empty(len(c) + 1, dtype=c.dtype)
+    prd[0] = c[0]
+    prd[1] = -c[0]
+    for i in range(1, len(c)):
+        prd[i + 1] = -c[i]*(i + 1)
+        prd[i] += c[i]*(2*i + 1)
+        prd[i - 1] -= c[i]*i
+    return prd
+
+
+def lagmul(c1, c2):
+    """
+    Multiply one Laguerre series by another.
+
+    Returns the product of two Laguerre series `c1` * `c2`.  The arguments
+    are sequences of coefficients, from lowest order "term" to highest,
+    e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Laguerre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Laguerre series coefficients representing their product.
+
+    See Also
+    --------
+    lagadd, lagsub, lagmulx, lagdiv, lagpow
+
+    Notes
+    -----
+    In general, the (polynomial) product of two C-series results in terms
+    that are not in the Laguerre polynomial basis set.  Thus, to express
+    the product as a Laguerre series, it is necessary to "reproject" the
+    product onto said basis set, which may produce "unintuitive" (but
+    correct) results; see Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagmul
+    >>> lagmul([1, 2, 3], [0, 1, 2])
+    array([  8., -13.,  38., -51.,  36.])
+
+    """
+    # s1, s2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+
+    if len(c1) > len(c2):
+        c = c2
+        xs = c1
+    else:
+        c = c1
+        xs = c2
+
+    if len(c) == 1:
+        c0 = c[0]*xs
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]*xs
+        c1 = c[1]*xs
+    else:
+        nd = len(c)
+        c0 = c[-2]*xs
+        c1 = c[-1]*xs
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = lagsub(c[-i]*xs, (c1*(nd - 1))/nd)
+            c1 = lagadd(tmp, lagsub((2*nd - 1)*c1, lagmulx(c1))/nd)
+    return lagadd(c0, lagsub(c1, lagmulx(c1)))
+
+
+def lagdiv(c1, c2):
+    """
+    Divide one Laguerre series by another.
+
+    Returns the quotient-with-remainder of two Laguerre series
+    `c1` / `c2`.  The arguments are sequences of coefficients from lowest
+    order "term" to highest, e.g., [1,2,3] represents the series
+    ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Laguerre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    [quo, rem] : ndarrays
+        Of Laguerre series coefficients representing the quotient and
+        remainder.
+
+    See Also
+    --------
+    lagadd, lagsub, lagmulx, lagmul, lagpow
+
+    Notes
+    -----
+    In general, the (polynomial) division of one Laguerre series by another
+    results in quotient and remainder terms that are not in the Laguerre
+    polynomial basis set.  Thus, to express these results as a Laguerre
+    series, it is necessary to "reproject" the results onto the Laguerre
+    basis set, which may produce "unintuitive" (but correct) results; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagdiv
+    >>> lagdiv([  8., -13.,  38., -51.,  36.], [0, 1, 2])
+    (array([1., 2., 3.]), array([0.]))
+    >>> lagdiv([  9., -12.,  38., -51.,  36.], [0, 1, 2])
+    (array([1., 2., 3.]), array([1., 1.]))
+
+    """
+    return pu._div(lagmul, c1, c2)
+
+
+def lagpow(c, pow, maxpower=16):
+    """Raise a Laguerre series to a power.
+
+    Returns the Laguerre series `c` raised to the power `pow`. The
+    argument `c` is a sequence of coefficients ordered from low to high.
+    i.e., [1,2,3] is the series  ``P_0 + 2*P_1 + 3*P_2.``
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Laguerre series coefficients ordered from low to
+        high.
+    pow : integer
+        Power to which the series will be raised
+    maxpower : integer, optional
+        Maximum power allowed. This is mainly to limit growth of the series
+        to unmanageable size. Default is 16
+
+    Returns
+    -------
+    coef : ndarray
+        Laguerre series of power.
+
+    See Also
+    --------
+    lagadd, lagsub, lagmulx, lagmul, lagdiv
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagpow
+    >>> lagpow([1, 2, 3], 2)
+    array([ 14., -16.,  56., -72.,  54.])
+
+    """
+    return pu._pow(lagmul, c, pow, maxpower)
+
+
+def lagder(c, m=1, scl=1, axis=0):
+    """
+    Differentiate a Laguerre series.
+
+    Returns the Laguerre series coefficients `c` differentiated `m` times
+    along `axis`.  At each iteration the result is multiplied by `scl` (the
+    scaling factor is for use in a linear change of variable). The argument
+    `c` is an array of coefficients from low to high degree along each
+    axis, e.g., [1,2,3] represents the series ``1*L_0 + 2*L_1 + 3*L_2``
+    while [[1,2],[1,2]] represents ``1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) +
+    2*L_0(x)*L_1(y) + 2*L_1(x)*L_1(y)`` if axis=0 is ``x`` and axis=1 is
+    ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Laguerre series coefficients. If `c` is multidimensional
+        the different axis correspond to different variables with the
+        degree in each axis given by the corresponding index.
+    m : int, optional
+        Number of derivatives taken, must be non-negative. (Default: 1)
+    scl : scalar, optional
+        Each differentiation is multiplied by `scl`.  The end result is
+        multiplication by ``scl**m``.  This is for use in a linear change of
+        variable. (Default: 1)
+    axis : int, optional
+        Axis over which the derivative is taken. (Default: 0).
+
+    Returns
+    -------
+    der : ndarray
+        Laguerre series of the derivative.
+
+    See Also
+    --------
+    lagint
+
+    Notes
+    -----
+    In general, the result of differentiating a Laguerre series does not
+    resemble the same operation on a power series. Thus the result of this
+    function may be "unintuitive," albeit correct; see Examples section
+    below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagder
+    >>> lagder([ 1.,  1.,  1., -3.])
+    array([1.,  2.,  3.])
+    >>> lagder([ 1.,  0.,  0., -4.,  3.], m=2)
+    array([1.,  2.,  3.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+
+    cnt = pu._as_int(m, "the order of derivation")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of derivation must be non-negative")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    n = len(c)
+    if cnt >= n:
+        c = c[:1]*0
+    else:
+        for i in range(cnt):
+            n = n - 1
+            c *= scl
+            der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
+            for j in range(n, 1, -1):
+                der[j - 1] = -c[j]
+                c[j - 1] += c[j]
+            der[0] = -c[1]
+            c = der
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def lagint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
+    """
+    Integrate a Laguerre series.
+
+    Returns the Laguerre series coefficients `c` integrated `m` times from
+    `lbnd` along `axis`. At each iteration the resulting series is
+    **multiplied** by `scl` and an integration constant, `k`, is added.
+    The scaling factor is for use in a linear change of variable.  ("Buyer
+    beware": note that, depending on what one is doing, one may want `scl`
+    to be the reciprocal of what one might expect; for more information,
+    see the Notes section below.)  The argument `c` is an array of
+    coefficients from low to high degree along each axis, e.g., [1,2,3]
+    represents the series ``L_0 + 2*L_1 + 3*L_2`` while [[1,2],[1,2]]
+    represents ``1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) + 2*L_0(x)*L_1(y) +
+    2*L_1(x)*L_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
+
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Laguerre series coefficients. If `c` is multidimensional
+        the different axis correspond to different variables with the
+        degree in each axis given by the corresponding index.
+    m : int, optional
+        Order of integration, must be positive. (Default: 1)
+    k : {[], list, scalar}, optional
+        Integration constant(s).  The value of the first integral at
+        ``lbnd`` is the first value in the list, the value of the second
+        integral at ``lbnd`` is the second value, etc.  If ``k == []`` (the
+        default), all constants are set to zero.  If ``m == 1``, a single
+        scalar can be given instead of a list.
+    lbnd : scalar, optional
+        The lower bound of the integral. (Default: 0)
+    scl : scalar, optional
+        Following each integration the result is *multiplied* by `scl`
+        before the integration constant is added. (Default: 1)
+    axis : int, optional
+        Axis over which the integral is taken. (Default: 0).
+
+    Returns
+    -------
+    S : ndarray
+        Laguerre series coefficients of the integral.
+
+    Raises
+    ------
+    ValueError
+        If ``m < 0``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
+        ``np.ndim(scl) != 0``.
+
+    See Also
+    --------
+    lagder
+
+    Notes
+    -----
+    Note that the result of each integration is *multiplied* by `scl`.
+    Why is this important to note?  Say one is making a linear change of
+    variable :math:`u = ax + b` in an integral relative to `x`.  Then
+    :math:`dx = du/a`, so one will need to set `scl` equal to
+    :math:`1/a` - perhaps not what one would have first thought.
+
+    Also note that, in general, the result of integrating a C-series needs
+    to be "reprojected" onto the C-series basis set.  Thus, typically,
+    the result of this function is "unintuitive," albeit correct; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagint
+    >>> lagint([1,2,3])
+    array([ 1.,  1.,  1., -3.])
+    >>> lagint([1,2,3], m=2)
+    array([ 1.,  0.,  0., -4.,  3.])
+    >>> lagint([1,2,3], k=1)
+    array([ 2.,  1.,  1., -3.])
+    >>> lagint([1,2,3], lbnd=-1)
+    array([11.5,  1. ,  1. , -3. ])
+    >>> lagint([1,2], m=2, k=[1,2], lbnd=-1)
+    array([ 11.16666667,  -5.        ,  -3.        ,   2.        ]) # may vary
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if not np.iterable(k):
+        k = [k]
+    cnt = pu._as_int(m, "the order of integration")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of integration must be non-negative")
+    if len(k) > cnt:
+        raise ValueError("Too many integration constants")
+    if np.ndim(lbnd) != 0:
+        raise ValueError("lbnd must be a scalar.")
+    if np.ndim(scl) != 0:
+        raise ValueError("scl must be a scalar.")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    k = list(k) + [0]*(cnt - len(k))
+    for i in range(cnt):
+        n = len(c)
+        c *= scl
+        if n == 1 and np.all(c[0] == 0):
+            c[0] += k[i]
+        else:
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
+            tmp[0] = c[0]
+            tmp[1] = -c[0]
+            for j in range(1, n):
+                tmp[j] += c[j]
+                tmp[j + 1] = -c[j]
+            tmp[0] += k[i] - lagval(lbnd, tmp)
+            c = tmp
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def lagval(x, c, tensor=True):
+    """
+    Evaluate a Laguerre series at points x.
+
+    If `c` is of length ``n + 1``, this function returns the value:
+
+    .. math:: p(x) = c_0 * L_0(x) + c_1 * L_1(x) + ... + c_n * L_n(x)
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `c`.
+
+    If `c` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If
+    `c` is multidimensional, then the shape of the result depends on the
+    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
+    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
+    scalars have shape (,).
+
+    Trailing zeros in the coefficients will be used in the evaluation, so
+    they should be avoided if efficiency is a concern.
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        themselves and with the elements of `c`.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree n are contained in c[n]. If `c` is multidimensional the
+        remaining indices enumerate multiple polynomials. In the two
+        dimensional case the coefficients may be thought of as stored in
+        the columns of `c`.
+    tensor : boolean, optional
+        If True, the shape of the coefficient array is extended with ones
+        on the right, one for each dimension of `x`. Scalars have dimension 0
+        for this action. The result is that every column of coefficients in
+        `c` is evaluated for every element of `x`. If False, `x` is broadcast
+        over the columns of `c` for the evaluation.  This keyword is useful
+        when `c` is multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, algebra_like
+        The shape of the return value is described above.
+
+    See Also
+    --------
+    lagval2d, laggrid2d, lagval3d, laggrid3d
+
+    Notes
+    -----
+    The evaluation uses Clenshaw recursion, aka synthetic division.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagval
+    >>> coef = [1, 2, 3]
+    >>> lagval(1, coef)
+    -0.5
+    >>> lagval([[1, 2],[3, 4]], coef)
+    array([[-0.5, -4. ],
+           [-4.5, -2. ]])
+
+    """
+    c = np.array(c, ndmin=1, copy=None)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray) and tensor:
+        c = c.reshape(c.shape + (1,)*x.ndim)
+
+    if len(c) == 1:
+        c0 = c[0]
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]
+        c1 = c[1]
+    else:
+        nd = len(c)
+        c0 = c[-2]
+        c1 = c[-1]
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = c[-i] - (c1*(nd - 1))/nd
+            c1 = tmp + (c1*((2*nd - 1) - x))/nd
+    return c0 + c1*(1 - x)
+
+
+def lagval2d(x, y, c):
+    """
+    Evaluate a 2-D Laguerre series at points (x, y).
+
+    This function returns the values:
+
+    .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * L_i(x) * L_j(y)
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars and they
+    must have the same shape after conversion. In either case, either `x`
+    and `y` or their elements must support multiplication and addition both
+    with themselves and with the elements of `c`.
+
+    If `c` is a 1-D array a one is implicitly appended to its shape to make
+    it 2-D. The shape of the result will be c.shape[2:] + x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points ``(x, y)``,
+        where `x` and `y` must have the same shape. If `x` or `y` is a list
+        or tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and if it isn't an ndarray it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term
+        of multi-degree i,j is contained in ``c[i,j]``. If `c` has
+        dimension greater than two the remaining indices enumerate multiple
+        sets of coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points formed with
+        pairs of corresponding values from `x` and `y`.
+
+    See Also
+    --------
+    lagval, laggrid2d, lagval3d, laggrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagval2d
+    >>> c = [[1, 2],[3, 4]]
+    >>> lagval2d(1, 1, c)
+    1.0
+    """
+    return pu._valnd(lagval, c, x, y)
+
+
+def laggrid2d(x, y, c):
+    """
+    Evaluate a 2-D Laguerre series on the Cartesian product of x and y.
+
+    This function returns the values:
+
+    .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * L_i(a) * L_j(b)
+
+    where the points ``(a, b)`` consist of all pairs formed by taking
+    `a` from `x` and `b` from `y`. The resulting points form a grid with
+    `x` in the first dimension and `y` in the second.
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars. In either
+    case, either `x` and `y` or their elements must support multiplication
+    and addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape + y.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points in the
+        Cartesian product of `x` and `y`.  If `x` or `y` is a list or
+        tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j is contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional Chebyshev series at points in the
+        Cartesian product of `x` and `y`.
+
+    See Also
+    --------
+    lagval, lagval2d, lagval3d, laggrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import laggrid2d
+    >>> c = [[1, 2], [3, 4]]
+    >>> laggrid2d([0, 1], [0, 1], c)
+    array([[10.,  4.],
+           [ 3.,  1.]])
+
+    """
+    return pu._gridnd(lagval, c, x, y)
+
+
+def lagval3d(x, y, z, c):
+    """
+    Evaluate a 3-D Laguerre series at points (x, y, z).
+
+    This function returns the values:
+
+    .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * L_i(x) * L_j(y) * L_k(z)
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if
+    they are tuples or a lists, otherwise they are treated as a scalars and
+    they must have the same shape after conversion. In either case, either
+    `x`, `y`, and `z` or their elements must support multiplication and
+    addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than 3 dimensions, ones are implicitly appended to its
+    shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible object
+        The three dimensional series is evaluated at the points
+        ``(x, y, z)``, where `x`, `y`, and `z` must have the same shape.  If
+        any of `x`, `y`, or `z` is a list or tuple, it is first converted
+        to an ndarray, otherwise it is left unchanged and if it isn't an
+        ndarray it is  treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
+        greater than 3 the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the multidimensional polynomial on points formed with
+        triples of corresponding values from `x`, `y`, and `z`.
+
+    See Also
+    --------
+    lagval, lagval2d, laggrid2d, laggrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagval3d
+    >>> c = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
+    >>> lagval3d(1, 1, 2, c)
+    -1.0
+
+    """
+    return pu._valnd(lagval, c, x, y, z)
+
+
+def laggrid3d(x, y, z, c):
+    """
+    Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z.
+
+    This function returns the values:
+
+    .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
+
+    where the points ``(a, b, c)`` consist of all triples formed by taking
+    `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
+    a grid with `x` in the first dimension, `y` in the second, and `z` in
+    the third.
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if they
+    are tuples or a lists, otherwise they are treated as a scalars. In
+    either case, either `x`, `y`, and `z` or their elements must support
+    multiplication and addition both with themselves and with the elements
+    of `c`.
+
+    If `c` has fewer than three dimensions, ones are implicitly appended to
+    its shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape + y.shape + z.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible objects
+        The three dimensional series is evaluated at the points in the
+        Cartesian product of `x`, `y`, and `z`.  If `x`, `y`, or `z` is a
+        list or tuple, it is first converted to an ndarray, otherwise it is
+        left unchanged and, if it isn't an ndarray, it is treated as a
+        scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    lagval, lagval2d, laggrid2d, lagval3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import laggrid3d
+    >>> c = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
+    >>> laggrid3d([0, 1], [0, 1], [2, 4], c)
+    array([[[ -4., -44.],
+            [ -2., -18.]],
+           [[ -2., -14.],
+            [ -1.,  -5.]]])
+
+    """
+    return pu._gridnd(lagval, c, x, y, z)
+
+
+def lagvander(x, deg):
+    """Pseudo-Vandermonde matrix of given degree.
+
+    Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
+    `x`. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., i] = L_i(x)
+
+    where ``0 <= i <= deg``. The leading indices of `V` index the elements of
+    `x` and the last index is the degree of the Laguerre polynomial.
+
+    If `c` is a 1-D array of coefficients of length ``n + 1`` and `V` is the
+    array ``V = lagvander(x, n)``, then ``np.dot(V, c)`` and
+    ``lagval(x, c)`` are the same up to roundoff. This equivalence is
+    useful both for least squares fitting and for the evaluation of a large
+    number of Laguerre series of the same degree and sample points.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of points. The dtype is converted to float64 or complex128
+        depending on whether any of the elements are complex. If `x` is
+        scalar it is converted to a 1-D array.
+    deg : int
+        Degree of the resulting matrix.
+
+    Returns
+    -------
+    vander : ndarray
+        The pseudo-Vandermonde matrix. The shape of the returned matrix is
+        ``x.shape + (deg + 1,)``, where The last index is the degree of the
+        corresponding Laguerre polynomial.  The dtype will be the same as
+        the converted `x`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.laguerre import lagvander
+    >>> x = np.array([0, 1, 2])
+    >>> lagvander(x, 3)
+    array([[ 1.        ,  1.        ,  1.        ,  1.        ],
+           [ 1.        ,  0.        , -0.5       , -0.66666667],
+           [ 1.        , -1.        , -1.        , -0.33333333]])
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg < 0:
+        raise ValueError("deg must be non-negative")
+
+    x = np.array(x, copy=None, ndmin=1) + 0.0
+    dims = (ideg + 1,) + x.shape
+    dtyp = x.dtype
+    v = np.empty(dims, dtype=dtyp)
+    v[0] = x*0 + 1
+    if ideg > 0:
+        v[1] = 1 - x
+        for i in range(2, ideg + 1):
+            v[i] = (v[i-1]*(2*i - 1 - x) - v[i-2]*(i - 1))/i
+    return np.moveaxis(v, 0, -1)
+
+
+def lagvander2d(x, y, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y)``. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (deg[1] + 1)*i + j] = L_i(x) * L_j(y),
+
+    where ``0 <= i <= deg[0]`` and ``0 <= j <= deg[1]``. The leading indices of
+    `V` index the points ``(x, y)`` and the last index encodes the degrees of
+    the Laguerre polynomials.
+
+    If ``V = lagvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
+    correspond to the elements of a 2-D coefficient array `c` of shape
+    (xdeg + 1, ydeg + 1) in the order
+
+    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+
+    and ``np.dot(V, c.flat)`` and ``lagval2d(x, y, c)`` will be the same
+    up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 2-D Laguerre
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to
+        1-D arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg].
+
+    Returns
+    -------
+    vander2d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)`.  The dtype will be the same
+        as the converted `x` and `y`.
+
+    See Also
+    --------
+    lagvander, lagvander3d, lagval2d, lagval3d
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.laguerre import lagvander2d
+    >>> x = np.array([0])
+    >>> y = np.array([2])
+    >>> lagvander2d(x, y, [2, 1])
+    array([[ 1., -1.,  1., -1.,  1., -1.]])
+
+    """
+    return pu._vander_nd_flat((lagvander, lagvander), (x, y), deg)
+
+
+def lagvander3d(x, y, z, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y, z)``. If `l`, `m`, `n` are the given degrees in `x`, `y`, `z`,
+    then The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = L_i(x)*L_j(y)*L_k(z),
+
+    where ``0 <= i <= l``, ``0 <= j <= m``, and ``0 <= j <= n``.  The leading
+    indices of `V` index the points ``(x, y, z)`` and the last index encodes
+    the degrees of the Laguerre polynomials.
+
+    If ``V = lagvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
+    of `V` correspond to the elements of a 3-D coefficient array `c` of
+    shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
+
+    .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
+
+    and  ``np.dot(V, c.flat)`` and ``lagval3d(x, y, z, c)`` will be the
+    same up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 3-D Laguerre
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y, z : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes will
+        be converted to either float64 or complex128 depending on whether
+        any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg, z_deg].
+
+    Returns
+    -------
+    vander3d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`.  The dtype will
+        be the same as the converted `x`, `y`, and `z`.
+
+    See Also
+    --------
+    lagvander, lagvander3d, lagval2d, lagval3d
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.laguerre import lagvander3d
+    >>> x = np.array([0])
+    >>> y = np.array([2])
+    >>> z = np.array([0])
+    >>> lagvander3d(x, y, z, [2, 1, 3])
+    array([[ 1.,  1.,  1.,  1., -1., -1., -1., -1.,  1.,  1.,  1.,  1., -1.,
+            -1., -1., -1.,  1.,  1.,  1.,  1., -1., -1., -1., -1.]])
+
+    """
+    return pu._vander_nd_flat((lagvander, lagvander, lagvander), (x, y, z), deg)
+
+
+def lagfit(x, y, deg, rcond=None, full=False, w=None):
+    """
+    Least squares fit of Laguerre series to data.
+
+    Return the coefficients of a Laguerre series of degree `deg` that is the
+    least squares fit to the data values `y` given at points `x`. If `y` is
+    1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
+    fits are done, one for each column of `y`, and the resulting
+    coefficients are stored in the corresponding columns of a 2-D return.
+    The fitted polynomial(s) are in the form
+
+    .. math::  p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),
+
+    where ``n`` is `deg`.
+
+    Parameters
+    ----------
+    x : array_like, shape (M,)
+        x-coordinates of the M sample points ``(x[i], y[i])``.
+    y : array_like, shape (M,) or (M, K)
+        y-coordinates of the sample points. Several data sets of sample
+        points sharing the same x-coordinates can be fitted at once by
+        passing in a 2D-array that contains one dataset per column.
+    deg : int or 1-D array_like
+        Degree(s) of the fitting polynomials. If `deg` is a single integer
+        all terms up to and including the `deg`'th term are included in the
+        fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+        degrees of the terms to include may be used instead.
+    rcond : float, optional
+        Relative condition number of the fit. Singular values smaller than
+        this relative to the largest singular value will be ignored. The
+        default value is len(x)*eps, where eps is the relative precision of
+        the float type, about 2e-16 in most cases.
+    full : bool, optional
+        Switch determining nature of return value. When it is False (the
+        default) just the coefficients are returned, when True diagnostic
+        information from the singular value decomposition is also returned.
+    w : array_like, shape (`M`,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+
+    Returns
+    -------
+    coef : ndarray, shape (M,) or (M, K)
+        Laguerre coefficients ordered from low to high. If `y` was 2-D,
+        the coefficients for the data in column *k*  of `y` are in column
+        *k*.
+
+    [residuals, rank, singular_values, rcond] : list
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    Warns
+    -----
+    RankWarning
+        The rank of the coefficient matrix in the least-squares fit is
+        deficient. The warning is only raised if ``full == False``.  The
+        warnings can be turned off by
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfit
+    numpy.polynomial.legendre.legfit
+    numpy.polynomial.chebyshev.chebfit
+    numpy.polynomial.hermite.hermfit
+    numpy.polynomial.hermite_e.hermefit
+    lagval : Evaluates a Laguerre series.
+    lagvander : pseudo Vandermonde matrix of Laguerre series.
+    lagweight : Laguerre weight function.
+    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution is the coefficients of the Laguerre series ``p`` that
+    minimizes the sum of the weighted squared errors
+
+    .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
+
+    where the :math:`w_j` are the weights. This problem is solved by
+    setting up as the (typically) overdetermined matrix equation
+
+    .. math:: V(x) * c = w * y,
+
+    where ``V`` is the weighted pseudo Vandermonde matrix of `x`, ``c`` are the
+    coefficients to be solved for, `w` are the weights, and `y` are the
+    observed values.  This equation is then solved using the singular value
+    decomposition of ``V``.
+
+    If some of the singular values of `V` are so small that they are
+    neglected, then a `~exceptions.RankWarning` will be issued. This means that
+    the coefficient values may be poorly determined. Using a lower order fit
+    will usually get rid of the warning.  The `rcond` parameter can also be
+    set to a value smaller than its default, but the resulting fit may be
+    spurious and have large contributions from roundoff error.
+
+    Fits using Laguerre series are probably most useful when the data can
+    be approximated by ``sqrt(w(x)) * p(x)``, where ``w(x)`` is the Laguerre
+    weight. In that case the weight ``sqrt(w(x[i]))`` should be used
+    together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
+    available as `lagweight`.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Curve fitting",
+           https://en.wikipedia.org/wiki/Curve_fitting
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.laguerre import lagfit, lagval
+    >>> x = np.linspace(0, 10)
+    >>> rng = np.random.default_rng()
+    >>> err = rng.normal(scale=1./10, size=len(x))
+    >>> y = lagval(x, [1, 2, 3]) + err
+    >>> lagfit(x, y, 2)
+    array([1.00578369, 1.99417356, 2.99827656]) # may vary
+
+    """
+    return pu._fit(lagvander, x, y, deg, rcond, full, w)
+
+
+def lagcompanion(c):
+    """
+    Return the companion matrix of c.
+
+    The usual companion matrix of the Laguerre polynomials is already
+    symmetric when `c` is a basis Laguerre polynomial, so no scaling is
+    applied.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Laguerre series coefficients ordered from low to high
+        degree.
+
+    Returns
+    -------
+    mat : ndarray
+        Companion matrix of dimensions (deg, deg).
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagcompanion
+    >>> lagcompanion([1, 2, 3])
+    array([[ 1.        , -0.33333333],
+           [-1.        ,  4.33333333]])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        raise ValueError('Series must have maximum degree of at least 1.')
+    if len(c) == 2:
+        return np.array([[1 + c[0]/c[1]]])
+
+    n = len(c) - 1
+    mat = np.zeros((n, n), dtype=c.dtype)
+    top = mat.reshape(-1)[1::n+1]
+    mid = mat.reshape(-1)[0::n+1]
+    bot = mat.reshape(-1)[n::n+1]
+    top[...] = -np.arange(1, n)
+    mid[...] = 2.*np.arange(n) + 1.
+    bot[...] = top
+    mat[:, -1] += (c[:-1]/c[-1])*n
+    return mat
+
+
+def lagroots(c):
+    """
+    Compute the roots of a Laguerre series.
+
+    Return the roots (a.k.a. "zeros") of the polynomial
+
+    .. math:: p(x) = \\sum_i c[i] * L_i(x).
+
+    Parameters
+    ----------
+    c : 1-D array_like
+        1-D array of coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        Array of the roots of the series. If all the roots are real,
+        then `out` is also real, otherwise it is complex.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyroots
+    numpy.polynomial.legendre.legroots
+    numpy.polynomial.chebyshev.chebroots
+    numpy.polynomial.hermite.hermroots
+    numpy.polynomial.hermite_e.hermeroots
+
+    Notes
+    -----
+    The root estimates are obtained as the eigenvalues of the companion
+    matrix, Roots far from the origin of the complex plane may have large
+    errors due to the numerical instability of the series for such
+    values. Roots with multiplicity greater than 1 will also show larger
+    errors as the value of the series near such points is relatively
+    insensitive to errors in the roots. Isolated roots near the origin can
+    be improved by a few iterations of Newton's method.
+
+    The Laguerre series basis polynomials aren't powers of `x` so the
+    results of this function may seem unintuitive.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagroots, lagfromroots
+    >>> coef = lagfromroots([0, 1, 2])
+    >>> coef
+    array([  2.,  -8.,  12.,  -6.])
+    >>> lagroots(coef)
+    array([-4.4408921e-16,  1.0000000e+00,  2.0000000e+00])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) <= 1:
+        return np.array([], dtype=c.dtype)
+    if len(c) == 2:
+        return np.array([1 + c[0]/c[1]])
+
+    # rotated companion matrix reduces error
+    m = lagcompanion(c)[::-1,::-1]
+    r = la.eigvals(m)
+    r.sort()
+    return r
+
+
+def laggauss(deg):
+    """
+    Gauss-Laguerre quadrature.
+
+    Computes the sample points and weights for Gauss-Laguerre quadrature.
+    These sample points and weights will correctly integrate polynomials of
+    degree :math:`2*deg - 1` or less over the interval :math:`[0, \\inf]`
+    with the weight function :math:`f(x) = \\exp(-x)`.
+
+    Parameters
+    ----------
+    deg : int
+        Number of sample points and weights. It must be >= 1.
+
+    Returns
+    -------
+    x : ndarray
+        1-D ndarray containing the sample points.
+    y : ndarray
+        1-D ndarray containing the weights.
+
+    Notes
+    -----
+    The results have only been tested up to degree 100 higher degrees may
+    be problematic. The weights are determined by using the fact that
+
+    .. math:: w_k = c / (L'_n(x_k) * L_{n-1}(x_k))
+
+    where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
+    is the k'th root of :math:`L_n`, and then scaling the results to get
+    the right value when integrating 1.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import laggauss
+    >>> laggauss(2)
+    (array([0.58578644, 3.41421356]), array([0.85355339, 0.14644661]))
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg <= 0:
+        raise ValueError("deg must be a positive integer")
+
+    # first approximation of roots. We use the fact that the companion
+    # matrix is symmetric in this case in order to obtain better zeros.
+    c = np.array([0]*deg + [1])
+    m = lagcompanion(c)
+    x = la.eigvalsh(m)
+
+    # improve roots by one application of Newton
+    dy = lagval(x, c)
+    df = lagval(x, lagder(c))
+    x -= dy/df
+
+    # compute the weights. We scale the factor to avoid possible numerical
+    # overflow.
+    fm = lagval(x, c[1:])
+    fm /= np.abs(fm).max()
+    df /= np.abs(df).max()
+    w = 1/(fm * df)
+
+    # scale w to get the right value, 1 in this case
+    w /= w.sum()
+
+    return x, w
+
+
+def lagweight(x):
+    """Weight function of the Laguerre polynomials.
+
+    The weight function is :math:`exp(-x)` and the interval of integration
+    is :math:`[0, \\inf]`. The Laguerre polynomials are orthogonal, but not
+    normalized, with respect to this weight function.
+
+    Parameters
+    ----------
+    x : array_like
+       Values at which the weight function will be computed.
+
+    Returns
+    -------
+    w : ndarray
+       The weight function at `x`.
+
+    Examples
+    --------
+    >>> from numpy.polynomial.laguerre import lagweight
+    >>> x = np.array([0, 1, 2])
+    >>> lagweight(x)
+    array([1.        , 0.36787944, 0.13533528])
+
+    """
+    w = np.exp(-x)
+    return w
+
+#
+# Laguerre series class
+#
+
+class Laguerre(ABCPolyBase):
+    """A Laguerre series class.
+
+    The Laguerre class provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
+    attributes and methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        Laguerre coefficients in order of increasing degree, i.e,
+        ``(1, 2, 3)`` gives ``1*L_0(x) + 2*L_1(X) + 3*L_2(x)``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is [0., 1.].
+    window : (2,) array_like, optional
+        Window, see `domain` for its use. The default value is [0., 1.].
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    """
+    # Virtual Functions
+    _add = staticmethod(lagadd)
+    _sub = staticmethod(lagsub)
+    _mul = staticmethod(lagmul)
+    _div = staticmethod(lagdiv)
+    _pow = staticmethod(lagpow)
+    _val = staticmethod(lagval)
+    _int = staticmethod(lagint)
+    _der = staticmethod(lagder)
+    _fit = staticmethod(lagfit)
+    _line = staticmethod(lagline)
+    _roots = staticmethod(lagroots)
+    _fromroots = staticmethod(lagfromroots)
+
+    # Virtual properties
+    domain = np.array(lagdomain)
+    window = np.array(lagdomain)
+    basis_name = 'L'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/laguerre.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/laguerre.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ee81157957482006cde90445fa73cf4223723d5f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/laguerre.pyi
@@ -0,0 +1,100 @@
+from typing import Final, Literal as L
+
+import numpy as np
+
+from ._polybase import ABCPolyBase
+from ._polytypes import (
+    _Array1,
+    _Array2,
+    _FuncBinOp,
+    _FuncCompanion,
+    _FuncDer,
+    _FuncFit,
+    _FuncFromRoots,
+    _FuncGauss,
+    _FuncInteg,
+    _FuncLine,
+    _FuncPoly2Ortho,
+    _FuncPow,
+    _FuncRoots,
+    _FuncUnOp,
+    _FuncVal,
+    _FuncVal2D,
+    _FuncVal3D,
+    _FuncValFromRoots,
+    _FuncVander,
+    _FuncVander2D,
+    _FuncVander3D,
+    _FuncWeight,
+)
+from .polyutils import trimcoef as lagtrim
+
+__all__ = [
+    "lagzero",
+    "lagone",
+    "lagx",
+    "lagdomain",
+    "lagline",
+    "lagadd",
+    "lagsub",
+    "lagmulx",
+    "lagmul",
+    "lagdiv",
+    "lagpow",
+    "lagval",
+    "lagder",
+    "lagint",
+    "lag2poly",
+    "poly2lag",
+    "lagfromroots",
+    "lagvander",
+    "lagfit",
+    "lagtrim",
+    "lagroots",
+    "Laguerre",
+    "lagval2d",
+    "lagval3d",
+    "laggrid2d",
+    "laggrid3d",
+    "lagvander2d",
+    "lagvander3d",
+    "lagcompanion",
+    "laggauss",
+    "lagweight",
+]
+
+poly2lag: _FuncPoly2Ortho[L["poly2lag"]]
+lag2poly: _FuncUnOp[L["lag2poly"]]
+
+lagdomain: Final[_Array2[np.float64]]
+lagzero: Final[_Array1[np.int_]]
+lagone: Final[_Array1[np.int_]]
+lagx: Final[_Array2[np.int_]]
+
+lagline: _FuncLine[L["lagline"]]
+lagfromroots: _FuncFromRoots[L["lagfromroots"]]
+lagadd: _FuncBinOp[L["lagadd"]]
+lagsub: _FuncBinOp[L["lagsub"]]
+lagmulx: _FuncUnOp[L["lagmulx"]]
+lagmul: _FuncBinOp[L["lagmul"]]
+lagdiv: _FuncBinOp[L["lagdiv"]]
+lagpow: _FuncPow[L["lagpow"]]
+lagder: _FuncDer[L["lagder"]]
+lagint: _FuncInteg[L["lagint"]]
+lagval: _FuncVal[L["lagval"]]
+lagval2d: _FuncVal2D[L["lagval2d"]]
+lagval3d: _FuncVal3D[L["lagval3d"]]
+lagvalfromroots: _FuncValFromRoots[L["lagvalfromroots"]]
+laggrid2d: _FuncVal2D[L["laggrid2d"]]
+laggrid3d: _FuncVal3D[L["laggrid3d"]]
+lagvander: _FuncVander[L["lagvander"]]
+lagvander2d: _FuncVander2D[L["lagvander2d"]]
+lagvander3d: _FuncVander3D[L["lagvander3d"]]
+lagfit: _FuncFit[L["lagfit"]]
+lagcompanion: _FuncCompanion[L["lagcompanion"]]
+lagroots: _FuncRoots[L["lagroots"]]
+laggauss: _FuncGauss[L["laggauss"]]
+lagweight: _FuncWeight[L["lagweight"]]
+
+
+class Laguerre(ABCPolyBase[L["L"]]): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/legendre.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/legendre.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2cd3fbfe76021c908b0e5a004f68617c1da6d7f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/legendre.py
@@ -0,0 +1,1605 @@
+"""
+==================================================
+Legendre Series (:mod:`numpy.polynomial.legendre`)
+==================================================
+
+This module provides a number of objects (mostly functions) useful for
+dealing with Legendre series, including a `Legendre` class that
+encapsulates the usual arithmetic operations.  (General information
+on how this module represents and works with such polynomials is in the
+docstring for its "parent" sub-package, `numpy.polynomial`).
+
+Classes
+-------
+.. autosummary::
+   :toctree: generated/
+
+    Legendre
+
+Constants
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   legdomain
+   legzero
+   legone
+   legx
+
+Arithmetic
+----------
+
+.. autosummary::
+   :toctree: generated/
+
+   legadd
+   legsub
+   legmulx
+   legmul
+   legdiv
+   legpow
+   legval
+   legval2d
+   legval3d
+   leggrid2d
+   leggrid3d
+
+Calculus
+--------
+
+.. autosummary::
+   :toctree: generated/
+
+   legder
+   legint
+
+Misc Functions
+--------------
+
+.. autosummary::
+   :toctree: generated/
+
+   legfromroots
+   legroots
+   legvander
+   legvander2d
+   legvander3d
+   leggauss
+   legweight
+   legcompanion
+   legfit
+   legtrim
+   legline
+   leg2poly
+   poly2leg
+
+See also
+--------
+numpy.polynomial
+
+"""
+import numpy as np
+import numpy.linalg as la
+from numpy.lib.array_utils import normalize_axis_index
+
+from . import polyutils as pu
+from ._polybase import ABCPolyBase
+
+__all__ = [
+    'legzero', 'legone', 'legx', 'legdomain', 'legline', 'legadd',
+    'legsub', 'legmulx', 'legmul', 'legdiv', 'legpow', 'legval', 'legder',
+    'legint', 'leg2poly', 'poly2leg', 'legfromroots', 'legvander',
+    'legfit', 'legtrim', 'legroots', 'Legendre', 'legval2d', 'legval3d',
+    'leggrid2d', 'leggrid3d', 'legvander2d', 'legvander3d', 'legcompanion',
+    'leggauss', 'legweight']
+
+legtrim = pu.trimcoef
+
+
+def poly2leg(pol):
+    """
+    Convert a polynomial to a Legendre series.
+
+    Convert an array representing the coefficients of a polynomial (relative
+    to the "standard" basis) ordered from lowest degree to highest, to an
+    array of the coefficients of the equivalent Legendre series, ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    pol : array_like
+        1-D array containing the polynomial coefficients
+
+    Returns
+    -------
+    c : ndarray
+        1-D array containing the coefficients of the equivalent Legendre
+        series.
+
+    See Also
+    --------
+    leg2poly
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy import polynomial as P
+    >>> p = P.Polynomial(np.arange(4))
+    >>> p
+    Polynomial([0.,  1.,  2.,  3.], domain=[-1.,  1.], window=[-1.,  1.], ...
+    >>> c = P.Legendre(P.legendre.poly2leg(p.coef))
+    >>> c
+    Legendre([ 1.  ,  3.25,  1.  ,  0.75], domain=[-1,  1], window=[-1,  1]) # may vary
+
+    """
+    [pol] = pu.as_series([pol])
+    deg = len(pol) - 1
+    res = 0
+    for i in range(deg, -1, -1):
+        res = legadd(legmulx(res), pol[i])
+    return res
+
+
+def leg2poly(c):
+    """
+    Convert a Legendre series to a polynomial.
+
+    Convert an array representing the coefficients of a Legendre series,
+    ordered from lowest degree to highest, to an array of the coefficients
+    of the equivalent polynomial (relative to the "standard" basis) ordered
+    from lowest to highest degree.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array containing the Legendre series coefficients, ordered
+        from lowest order term to highest.
+
+    Returns
+    -------
+    pol : ndarray
+        1-D array containing the coefficients of the equivalent polynomial
+        (relative to the "standard" basis) ordered from lowest order term
+        to highest.
+
+    See Also
+    --------
+    poly2leg
+
+    Notes
+    -----
+    The easy way to do conversions between polynomial basis sets
+    is to use the convert method of a class instance.
+
+    Examples
+    --------
+    >>> from numpy import polynomial as P
+    >>> c = P.Legendre(range(4))
+    >>> c
+    Legendre([0., 1., 2., 3.], domain=[-1.,  1.], window=[-1.,  1.], symbol='x')
+    >>> p = c.convert(kind=P.Polynomial)
+    >>> p
+    Polynomial([-1. , -3.5,  3. ,  7.5], domain=[-1.,  1.], window=[-1., ...
+    >>> P.legendre.leg2poly(range(4))
+    array([-1. , -3.5,  3. ,  7.5])
+
+
+    """
+    from .polynomial import polyadd, polysub, polymulx
+
+    [c] = pu.as_series([c])
+    n = len(c)
+    if n < 3:
+        return c
+    else:
+        c0 = c[-2]
+        c1 = c[-1]
+        # i is the current degree of c1
+        for i in range(n - 1, 1, -1):
+            tmp = c0
+            c0 = polysub(c[i - 2], (c1*(i - 1))/i)
+            c1 = polyadd(tmp, (polymulx(c1)*(2*i - 1))/i)
+        return polyadd(c0, polymulx(c1))
+
+
+#
+# These are constant arrays are of integer type so as to be compatible
+# with the widest range of other types, such as Decimal.
+#
+
+# Legendre
+legdomain = np.array([-1., 1.])
+
+# Legendre coefficients representing zero.
+legzero = np.array([0])
+
+# Legendre coefficients representing one.
+legone = np.array([1])
+
+# Legendre coefficients representing the identity x.
+legx = np.array([0, 1])
+
+
+def legline(off, scl):
+    """
+    Legendre series whose graph is a straight line.
+
+
+
+    Parameters
+    ----------
+    off, scl : scalars
+        The specified line is given by ``off + scl*x``.
+
+    Returns
+    -------
+    y : ndarray
+        This module's representation of the Legendre series for
+        ``off + scl*x``.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyline
+    numpy.polynomial.chebyshev.chebline
+    numpy.polynomial.laguerre.lagline
+    numpy.polynomial.hermite.hermline
+    numpy.polynomial.hermite_e.hermeline
+
+    Examples
+    --------
+    >>> import numpy.polynomial.legendre as L
+    >>> L.legline(3,2)
+    array([3, 2])
+    >>> L.legval(-3, L.legline(3,2)) # should be -3
+    -3.0
+
+    """
+    if scl != 0:
+        return np.array([off, scl])
+    else:
+        return np.array([off])
+
+
+def legfromroots(roots):
+    """
+    Generate a Legendre series with given roots.
+
+    The function returns the coefficients of the polynomial
+
+    .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
+
+    in Legendre form, where the :math:`r_n` are the roots specified in `roots`.
+    If a zero has multiplicity n, then it must appear in `roots` n times.
+    For instance, if 2 is a root of multiplicity three and 3 is a root of
+    multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The
+    roots can appear in any order.
+
+    If the returned coefficients are `c`, then
+
+    .. math:: p(x) = c_0 + c_1 * L_1(x) + ... +  c_n * L_n(x)
+
+    The coefficient of the last term is not generally 1 for monic
+    polynomials in Legendre form.
+
+    Parameters
+    ----------
+    roots : array_like
+        Sequence containing the roots.
+
+    Returns
+    -------
+    out : ndarray
+        1-D array of coefficients.  If all roots are real then `out` is a
+        real array, if some of the roots are complex, then `out` is complex
+        even if all the coefficients in the result are real (see Examples
+        below).
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfromroots
+    numpy.polynomial.chebyshev.chebfromroots
+    numpy.polynomial.laguerre.lagfromroots
+    numpy.polynomial.hermite.hermfromroots
+    numpy.polynomial.hermite_e.hermefromroots
+
+    Examples
+    --------
+    >>> import numpy.polynomial.legendre as L
+    >>> L.legfromroots((-1,0,1)) # x^3 - x relative to the standard basis
+    array([ 0. , -0.4,  0. ,  0.4])
+    >>> j = complex(0,1)
+    >>> L.legfromroots((-j,j)) # x^2 + 1 relative to the standard basis
+    array([ 1.33333333+0.j,  0.00000000+0.j,  0.66666667+0.j]) # may vary
+
+    """
+    return pu._fromroots(legline, legmul, roots)
+
+
+def legadd(c1, c2):
+    """
+    Add one Legendre series to another.
+
+    Returns the sum of two Legendre series `c1` + `c2`.  The arguments
+    are sequences of coefficients ordered from lowest order term to
+    highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Legendre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the Legendre series of their sum.
+
+    See Also
+    --------
+    legsub, legmulx, legmul, legdiv, legpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the sum of two Legendre series
+    is a Legendre series (without having to "reproject" the result onto
+    the basis set) so addition, just like that of "standard" polynomials,
+    is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> L.legadd(c1,c2)
+    array([4.,  4.,  4.])
+
+    """
+    return pu._add(c1, c2)
+
+
+def legsub(c1, c2):
+    """
+    Subtract one Legendre series from another.
+
+    Returns the difference of two Legendre series `c1` - `c2`.  The
+    sequences of coefficients are from lowest order term to highest, i.e.,
+    [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Legendre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Legendre series coefficients representing their difference.
+
+    See Also
+    --------
+    legadd, legmulx, legmul, legdiv, legpow
+
+    Notes
+    -----
+    Unlike multiplication, division, etc., the difference of two Legendre
+    series is a Legendre series (without having to "reproject" the result
+    onto the basis set) so subtraction, just like that of "standard"
+    polynomials, is simply "component-wise."
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> L.legsub(c1,c2)
+    array([-2.,  0.,  2.])
+    >>> L.legsub(c2,c1) # -C.legsub(c1,c2)
+    array([ 2.,  0., -2.])
+
+    """
+    return pu._sub(c1, c2)
+
+
+def legmulx(c):
+    """Multiply a Legendre series by x.
+
+    Multiply the Legendre series `c` by x, where x is the independent
+    variable.
+
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Legendre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the result of the multiplication.
+
+    See Also
+    --------
+    legadd, legsub, legmul, legdiv, legpow
+
+    Notes
+    -----
+    The multiplication uses the recursion relationship for Legendre
+    polynomials in the form
+
+    .. math::
+
+      xP_i(x) = ((i + 1)*P_{i + 1}(x) + i*P_{i - 1}(x))/(2i + 1)
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> L.legmulx([1,2,3])
+    array([ 0.66666667, 2.2, 1.33333333, 1.8]) # may vary
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    # The zero series needs special treatment
+    if len(c) == 1 and c[0] == 0:
+        return c
+
+    prd = np.empty(len(c) + 1, dtype=c.dtype)
+    prd[0] = c[0]*0
+    prd[1] = c[0]
+    for i in range(1, len(c)):
+        j = i + 1
+        k = i - 1
+        s = i + j
+        prd[j] = (c[i]*j)/s
+        prd[k] += (c[i]*i)/s
+    return prd
+
+
+def legmul(c1, c2):
+    """
+    Multiply one Legendre series by another.
+
+    Returns the product of two Legendre series `c1` * `c2`.  The arguments
+    are sequences of coefficients, from lowest order "term" to highest,
+    e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Legendre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of Legendre series coefficients representing their product.
+
+    See Also
+    --------
+    legadd, legsub, legmulx, legdiv, legpow
+
+    Notes
+    -----
+    In general, the (polynomial) product of two C-series results in terms
+    that are not in the Legendre polynomial basis set.  Thus, to express
+    the product as a Legendre series, it is necessary to "reproject" the
+    product onto said basis set, which may produce "unintuitive" (but
+    correct) results; see Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2)
+    >>> L.legmul(c1,c2) # multiplication requires "reprojection"
+    array([  4.33333333,  10.4       ,  11.66666667,   3.6       ]) # may vary
+
+    """
+    # s1, s2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+
+    if len(c1) > len(c2):
+        c = c2
+        xs = c1
+    else:
+        c = c1
+        xs = c2
+
+    if len(c) == 1:
+        c0 = c[0]*xs
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]*xs
+        c1 = c[1]*xs
+    else:
+        nd = len(c)
+        c0 = c[-2]*xs
+        c1 = c[-1]*xs
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = legsub(c[-i]*xs, (c1*(nd - 1))/nd)
+            c1 = legadd(tmp, (legmulx(c1)*(2*nd - 1))/nd)
+    return legadd(c0, legmulx(c1))
+
+
+def legdiv(c1, c2):
+    """
+    Divide one Legendre series by another.
+
+    Returns the quotient-with-remainder of two Legendre series
+    `c1` / `c2`.  The arguments are sequences of coefficients from lowest
+    order "term" to highest, e.g., [1,2,3] represents the series
+    ``P_0 + 2*P_1 + 3*P_2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of Legendre series coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    quo, rem : ndarrays
+        Of Legendre series coefficients representing the quotient and
+        remainder.
+
+    See Also
+    --------
+    legadd, legsub, legmulx, legmul, legpow
+
+    Notes
+    -----
+    In general, the (polynomial) division of one Legendre series by another
+    results in quotient and remainder terms that are not in the Legendre
+    polynomial basis set.  Thus, to express these results as a Legendre
+    series, it is necessary to "reproject" the results onto the Legendre
+    basis set, which may produce "unintuitive" (but correct) results; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> c1 = (1,2,3)
+    >>> c2 = (3,2,1)
+    >>> L.legdiv(c1,c2) # quotient "intuitive," remainder not
+    (array([3.]), array([-8., -4.]))
+    >>> c2 = (0,1,2,3)
+    >>> L.legdiv(c2,c1) # neither "intuitive"
+    (array([-0.07407407,  1.66666667]), array([-1.03703704, -2.51851852])) # may vary
+
+    """
+    return pu._div(legmul, c1, c2)
+
+
+def legpow(c, pow, maxpower=16):
+    """Raise a Legendre series to a power.
+
+    Returns the Legendre series `c` raised to the power `pow`. The
+    argument `c` is a sequence of coefficients ordered from low to high.
+    i.e., [1,2,3] is the series  ``P_0 + 2*P_1 + 3*P_2.``
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Legendre series coefficients ordered from low to
+        high.
+    pow : integer
+        Power to which the series will be raised
+    maxpower : integer, optional
+        Maximum power allowed. This is mainly to limit growth of the series
+        to unmanageable size. Default is 16
+
+    Returns
+    -------
+    coef : ndarray
+        Legendre series of power.
+
+    See Also
+    --------
+    legadd, legsub, legmulx, legmul, legdiv
+
+    """
+    return pu._pow(legmul, c, pow, maxpower)
+
+
+def legder(c, m=1, scl=1, axis=0):
+    """
+    Differentiate a Legendre series.
+
+    Returns the Legendre series coefficients `c` differentiated `m` times
+    along `axis`.  At each iteration the result is multiplied by `scl` (the
+    scaling factor is for use in a linear change of variable). The argument
+    `c` is an array of coefficients from low to high degree along each
+    axis, e.g., [1,2,3] represents the series ``1*L_0 + 2*L_1 + 3*L_2``
+    while [[1,2],[1,2]] represents ``1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) +
+    2*L_0(x)*L_1(y) + 2*L_1(x)*L_1(y)`` if axis=0 is ``x`` and axis=1 is
+    ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Legendre series coefficients. If c is multidimensional the
+        different axis correspond to different variables with the degree in
+        each axis given by the corresponding index.
+    m : int, optional
+        Number of derivatives taken, must be non-negative. (Default: 1)
+    scl : scalar, optional
+        Each differentiation is multiplied by `scl`.  The end result is
+        multiplication by ``scl**m``.  This is for use in a linear change of
+        variable. (Default: 1)
+    axis : int, optional
+        Axis over which the derivative is taken. (Default: 0).
+
+    Returns
+    -------
+    der : ndarray
+        Legendre series of the derivative.
+
+    See Also
+    --------
+    legint
+
+    Notes
+    -----
+    In general, the result of differentiating a Legendre series does not
+    resemble the same operation on a power series. Thus the result of this
+    function may be "unintuitive," albeit correct; see Examples section
+    below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> c = (1,2,3,4)
+    >>> L.legder(c)
+    array([  6.,   9.,  20.])
+    >>> L.legder(c, 3)
+    array([60.])
+    >>> L.legder(c, scl=-1)
+    array([ -6.,  -9., -20.])
+    >>> L.legder(c, 2,-1)
+    array([  9.,  60.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    cnt = pu._as_int(m, "the order of derivation")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of derivation must be non-negative")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    n = len(c)
+    if cnt >= n:
+        c = c[:1]*0
+    else:
+        for i in range(cnt):
+            n = n - 1
+            c *= scl
+            der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
+            for j in range(n, 2, -1):
+                der[j - 1] = (2*j - 1)*c[j]
+                c[j - 2] += c[j]
+            if n > 1:
+                der[1] = 3*c[2]
+            der[0] = c[1]
+            c = der
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def legint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
+    """
+    Integrate a Legendre series.
+
+    Returns the Legendre series coefficients `c` integrated `m` times from
+    `lbnd` along `axis`. At each iteration the resulting series is
+    **multiplied** by `scl` and an integration constant, `k`, is added.
+    The scaling factor is for use in a linear change of variable.  ("Buyer
+    beware": note that, depending on what one is doing, one may want `scl`
+    to be the reciprocal of what one might expect; for more information,
+    see the Notes section below.)  The argument `c` is an array of
+    coefficients from low to high degree along each axis, e.g., [1,2,3]
+    represents the series ``L_0 + 2*L_1 + 3*L_2`` while [[1,2],[1,2]]
+    represents ``1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) + 2*L_0(x)*L_1(y) +
+    2*L_1(x)*L_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of Legendre series coefficients. If c is multidimensional the
+        different axis correspond to different variables with the degree in
+        each axis given by the corresponding index.
+    m : int, optional
+        Order of integration, must be positive. (Default: 1)
+    k : {[], list, scalar}, optional
+        Integration constant(s).  The value of the first integral at
+        ``lbnd`` is the first value in the list, the value of the second
+        integral at ``lbnd`` is the second value, etc.  If ``k == []`` (the
+        default), all constants are set to zero.  If ``m == 1``, a single
+        scalar can be given instead of a list.
+    lbnd : scalar, optional
+        The lower bound of the integral. (Default: 0)
+    scl : scalar, optional
+        Following each integration the result is *multiplied* by `scl`
+        before the integration constant is added. (Default: 1)
+    axis : int, optional
+        Axis over which the integral is taken. (Default: 0).
+
+    Returns
+    -------
+    S : ndarray
+        Legendre series coefficient array of the integral.
+
+    Raises
+    ------
+    ValueError
+        If ``m < 0``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
+        ``np.ndim(scl) != 0``.
+
+    See Also
+    --------
+    legder
+
+    Notes
+    -----
+    Note that the result of each integration is *multiplied* by `scl`.
+    Why is this important to note?  Say one is making a linear change of
+    variable :math:`u = ax + b` in an integral relative to `x`.  Then
+    :math:`dx = du/a`, so one will need to set `scl` equal to
+    :math:`1/a` - perhaps not what one would have first thought.
+
+    Also note that, in general, the result of integrating a C-series needs
+    to be "reprojected" onto the C-series basis set.  Thus, typically,
+    the result of this function is "unintuitive," albeit correct; see
+    Examples section below.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import legendre as L
+    >>> c = (1,2,3)
+    >>> L.legint(c)
+    array([ 0.33333333,  0.4       ,  0.66666667,  0.6       ]) # may vary
+    >>> L.legint(c, 3)
+    array([  1.66666667e-02,  -1.78571429e-02,   4.76190476e-02, # may vary
+             -1.73472348e-18,   1.90476190e-02,   9.52380952e-03])
+    >>> L.legint(c, k=3)
+     array([ 3.33333333,  0.4       ,  0.66666667,  0.6       ]) # may vary
+    >>> L.legint(c, lbnd=-2)
+    array([ 7.33333333,  0.4       ,  0.66666667,  0.6       ]) # may vary
+    >>> L.legint(c, scl=2)
+    array([ 0.66666667,  0.8       ,  1.33333333,  1.2       ]) # may vary
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if not np.iterable(k):
+        k = [k]
+    cnt = pu._as_int(m, "the order of integration")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of integration must be non-negative")
+    if len(k) > cnt:
+        raise ValueError("Too many integration constants")
+    if np.ndim(lbnd) != 0:
+        raise ValueError("lbnd must be a scalar.")
+    if np.ndim(scl) != 0:
+        raise ValueError("scl must be a scalar.")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    k = list(k) + [0]*(cnt - len(k))
+    for i in range(cnt):
+        n = len(c)
+        c *= scl
+        if n == 1 and np.all(c[0] == 0):
+            c[0] += k[i]
+        else:
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
+            tmp[0] = c[0]*0
+            tmp[1] = c[0]
+            if n > 1:
+                tmp[2] = c[1]/3
+            for j in range(2, n):
+                t = c[j]/(2*j + 1)
+                tmp[j + 1] = t
+                tmp[j - 1] -= t
+            tmp[0] += k[i] - legval(lbnd, tmp)
+            c = tmp
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def legval(x, c, tensor=True):
+    """
+    Evaluate a Legendre series at points x.
+
+    If `c` is of length ``n + 1``, this function returns the value:
+
+    .. math:: p(x) = c_0 * L_0(x) + c_1 * L_1(x) + ... + c_n * L_n(x)
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `c`.
+
+    If `c` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If
+    `c` is multidimensional, then the shape of the result depends on the
+    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
+    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
+    scalars have shape (,).
+
+    Trailing zeros in the coefficients will be used in the evaluation, so
+    they should be avoided if efficiency is a concern.
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        themselves and with the elements of `c`.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree n are contained in c[n]. If `c` is multidimensional the
+        remaining indices enumerate multiple polynomials. In the two
+        dimensional case the coefficients may be thought of as stored in
+        the columns of `c`.
+    tensor : boolean, optional
+        If True, the shape of the coefficient array is extended with ones
+        on the right, one for each dimension of `x`. Scalars have dimension 0
+        for this action. The result is that every column of coefficients in
+        `c` is evaluated for every element of `x`. If False, `x` is broadcast
+        over the columns of `c` for the evaluation.  This keyword is useful
+        when `c` is multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, algebra_like
+        The shape of the return value is described above.
+
+    See Also
+    --------
+    legval2d, leggrid2d, legval3d, leggrid3d
+
+    Notes
+    -----
+    The evaluation uses Clenshaw recursion, aka synthetic division.
+
+    """
+    c = np.array(c, ndmin=1, copy=None)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        c = c.astype(np.double)
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray) and tensor:
+        c = c.reshape(c.shape + (1,)*x.ndim)
+
+    if len(c) == 1:
+        c0 = c[0]
+        c1 = 0
+    elif len(c) == 2:
+        c0 = c[0]
+        c1 = c[1]
+    else:
+        nd = len(c)
+        c0 = c[-2]
+        c1 = c[-1]
+        for i in range(3, len(c) + 1):
+            tmp = c0
+            nd = nd - 1
+            c0 = c[-i] - (c1*(nd - 1))/nd
+            c1 = tmp + (c1*x*(2*nd - 1))/nd
+    return c0 + c1*x
+
+
+def legval2d(x, y, c):
+    """
+    Evaluate a 2-D Legendre series at points (x, y).
+
+    This function returns the values:
+
+    .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * L_i(x) * L_j(y)
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars and they
+    must have the same shape after conversion. In either case, either `x`
+    and `y` or their elements must support multiplication and addition both
+    with themselves and with the elements of `c`.
+
+    If `c` is a 1-D array a one is implicitly appended to its shape to make
+    it 2-D. The shape of the result will be c.shape[2:] + x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points ``(x, y)``,
+        where `x` and `y` must have the same shape. If `x` or `y` is a list
+        or tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and if it isn't an ndarray it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term
+        of multi-degree i,j is contained in ``c[i,j]``. If `c` has
+        dimension greater than two the remaining indices enumerate multiple
+        sets of coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional Legendre series at points formed
+        from pairs of corresponding values from `x` and `y`.
+
+    See Also
+    --------
+    legval, leggrid2d, legval3d, leggrid3d
+    """
+    return pu._valnd(legval, c, x, y)
+
+
+def leggrid2d(x, y, c):
+    """
+    Evaluate a 2-D Legendre series on the Cartesian product of x and y.
+
+    This function returns the values:
+
+    .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * L_i(a) * L_j(b)
+
+    where the points ``(a, b)`` consist of all pairs formed by taking
+    `a` from `x` and `b` from `y`. The resulting points form a grid with
+    `x` in the first dimension and `y` in the second.
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars. In either
+    case, either `x` and `y` or their elements must support multiplication
+    and addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape + y.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points in the
+        Cartesian product of `x` and `y`.  If `x` or `y` is a list or
+        tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j is contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional Chebyshev series at points in the
+        Cartesian product of `x` and `y`.
+
+    See Also
+    --------
+    legval, legval2d, legval3d, leggrid3d
+    """
+    return pu._gridnd(legval, c, x, y)
+
+
+def legval3d(x, y, z, c):
+    """
+    Evaluate a 3-D Legendre series at points (x, y, z).
+
+    This function returns the values:
+
+    .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * L_i(x) * L_j(y) * L_k(z)
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if
+    they are tuples or a lists, otherwise they are treated as a scalars and
+    they must have the same shape after conversion. In either case, either
+    `x`, `y`, and `z` or their elements must support multiplication and
+    addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than 3 dimensions, ones are implicitly appended to its
+    shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible object
+        The three dimensional series is evaluated at the points
+        ``(x, y, z)``, where `x`, `y`, and `z` must have the same shape.  If
+        any of `x`, `y`, or `z` is a list or tuple, it is first converted
+        to an ndarray, otherwise it is left unchanged and if it isn't an
+        ndarray it is  treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
+        greater than 3 the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the multidimensional polynomial on points formed with
+        triples of corresponding values from `x`, `y`, and `z`.
+
+    See Also
+    --------
+    legval, legval2d, leggrid2d, leggrid3d
+    """
+    return pu._valnd(legval, c, x, y, z)
+
+
+def leggrid3d(x, y, z, c):
+    """
+    Evaluate a 3-D Legendre series on the Cartesian product of x, y, and z.
+
+    This function returns the values:
+
+    .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
+
+    where the points ``(a, b, c)`` consist of all triples formed by taking
+    `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
+    a grid with `x` in the first dimension, `y` in the second, and `z` in
+    the third.
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if they
+    are tuples or a lists, otherwise they are treated as a scalars. In
+    either case, either `x`, `y`, and `z` or their elements must support
+    multiplication and addition both with themselves and with the elements
+    of `c`.
+
+    If `c` has fewer than three dimensions, ones are implicitly appended to
+    its shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape + y.shape + z.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible objects
+        The three dimensional series is evaluated at the points in the
+        Cartesian product of `x`, `y`, and `z`.  If `x`, `y`, or `z` is a
+        list or tuple, it is first converted to an ndarray, otherwise it is
+        left unchanged and, if it isn't an ndarray, it is treated as a
+        scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    legval, legval2d, leggrid2d, legval3d
+    """
+    return pu._gridnd(legval, c, x, y, z)
+
+
+def legvander(x, deg):
+    """Pseudo-Vandermonde matrix of given degree.
+
+    Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
+    `x`. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., i] = L_i(x)
+
+    where ``0 <= i <= deg``. The leading indices of `V` index the elements of
+    `x` and the last index is the degree of the Legendre polynomial.
+
+    If `c` is a 1-D array of coefficients of length ``n + 1`` and `V` is the
+    array ``V = legvander(x, n)``, then ``np.dot(V, c)`` and
+    ``legval(x, c)`` are the same up to roundoff. This equivalence is
+    useful both for least squares fitting and for the evaluation of a large
+    number of Legendre series of the same degree and sample points.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of points. The dtype is converted to float64 or complex128
+        depending on whether any of the elements are complex. If `x` is
+        scalar it is converted to a 1-D array.
+    deg : int
+        Degree of the resulting matrix.
+
+    Returns
+    -------
+    vander : ndarray
+        The pseudo-Vandermonde matrix. The shape of the returned matrix is
+        ``x.shape + (deg + 1,)``, where The last index is the degree of the
+        corresponding Legendre polynomial.  The dtype will be the same as
+        the converted `x`.
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg < 0:
+        raise ValueError("deg must be non-negative")
+
+    x = np.array(x, copy=None, ndmin=1) + 0.0
+    dims = (ideg + 1,) + x.shape
+    dtyp = x.dtype
+    v = np.empty(dims, dtype=dtyp)
+    # Use forward recursion to generate the entries. This is not as accurate
+    # as reverse recursion in this application but it is more efficient.
+    v[0] = x*0 + 1
+    if ideg > 0:
+        v[1] = x
+        for i in range(2, ideg + 1):
+            v[i] = (v[i-1]*x*(2*i - 1) - v[i-2]*(i - 1))/i
+    return np.moveaxis(v, 0, -1)
+
+
+def legvander2d(x, y, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y)``. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (deg[1] + 1)*i + j] = L_i(x) * L_j(y),
+
+    where ``0 <= i <= deg[0]`` and ``0 <= j <= deg[1]``. The leading indices of
+    `V` index the points ``(x, y)`` and the last index encodes the degrees of
+    the Legendre polynomials.
+
+    If ``V = legvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
+    correspond to the elements of a 2-D coefficient array `c` of shape
+    (xdeg + 1, ydeg + 1) in the order
+
+    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+
+    and ``np.dot(V, c.flat)`` and ``legval2d(x, y, c)`` will be the same
+    up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 2-D Legendre
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to
+        1-D arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg].
+
+    Returns
+    -------
+    vander2d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)`.  The dtype will be the same
+        as the converted `x` and `y`.
+
+    See Also
+    --------
+    legvander, legvander3d, legval2d, legval3d
+    """
+    return pu._vander_nd_flat((legvander, legvander), (x, y), deg)
+
+
+def legvander3d(x, y, z, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y, z)``. If `l`, `m`, `n` are the given degrees in `x`, `y`, `z`,
+    then The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = L_i(x)*L_j(y)*L_k(z),
+
+    where ``0 <= i <= l``, ``0 <= j <= m``, and ``0 <= j <= n``.  The leading
+    indices of `V` index the points ``(x, y, z)`` and the last index encodes
+    the degrees of the Legendre polynomials.
+
+    If ``V = legvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
+    of `V` correspond to the elements of a 3-D coefficient array `c` of
+    shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
+
+    .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
+
+    and ``np.dot(V, c.flat)`` and ``legval3d(x, y, z, c)`` will be the
+    same up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 3-D Legendre
+    series of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y, z : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes will
+        be converted to either float64 or complex128 depending on whether
+        any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg, z_deg].
+
+    Returns
+    -------
+    vander3d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg[1]+1)*(deg[2]+1)`.  The dtype will
+        be the same as the converted `x`, `y`, and `z`.
+
+    See Also
+    --------
+    legvander, legvander3d, legval2d, legval3d
+    """
+    return pu._vander_nd_flat((legvander, legvander, legvander), (x, y, z), deg)
+
+
+def legfit(x, y, deg, rcond=None, full=False, w=None):
+    """
+    Least squares fit of Legendre series to data.
+
+    Return the coefficients of a Legendre series of degree `deg` that is the
+    least squares fit to the data values `y` given at points `x`. If `y` is
+    1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
+    fits are done, one for each column of `y`, and the resulting
+    coefficients are stored in the corresponding columns of a 2-D return.
+    The fitted polynomial(s) are in the form
+
+    .. math::  p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),
+
+    where `n` is `deg`.
+
+    Parameters
+    ----------
+    x : array_like, shape (M,)
+        x-coordinates of the M sample points ``(x[i], y[i])``.
+    y : array_like, shape (M,) or (M, K)
+        y-coordinates of the sample points. Several data sets of sample
+        points sharing the same x-coordinates can be fitted at once by
+        passing in a 2D-array that contains one dataset per column.
+    deg : int or 1-D array_like
+        Degree(s) of the fitting polynomials. If `deg` is a single integer
+        all terms up to and including the `deg`'th term are included in the
+        fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+        degrees of the terms to include may be used instead.
+    rcond : float, optional
+        Relative condition number of the fit. Singular values smaller than
+        this relative to the largest singular value will be ignored. The
+        default value is len(x)*eps, where eps is the relative precision of
+        the float type, about 2e-16 in most cases.
+    full : bool, optional
+        Switch determining nature of return value. When it is False (the
+        default) just the coefficients are returned, when True diagnostic
+        information from the singular value decomposition is also returned.
+    w : array_like, shape (`M`,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+
+    Returns
+    -------
+    coef : ndarray, shape (M,) or (M, K)
+        Legendre coefficients ordered from low to high. If `y` was
+        2-D, the coefficients for the data in column k of `y` are in
+        column `k`. If `deg` is specified as a list, coefficients for
+        terms not included in the fit are set equal to zero in the
+        returned `coef`.
+
+    [residuals, rank, singular_values, rcond] : list
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    Warns
+    -----
+    RankWarning
+        The rank of the coefficient matrix in the least-squares fit is
+        deficient. The warning is only raised if ``full == False``.  The
+        warnings can be turned off by
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyfit
+    numpy.polynomial.chebyshev.chebfit
+    numpy.polynomial.laguerre.lagfit
+    numpy.polynomial.hermite.hermfit
+    numpy.polynomial.hermite_e.hermefit
+    legval : Evaluates a Legendre series.
+    legvander : Vandermonde matrix of Legendre series.
+    legweight : Legendre weight function (= 1).
+    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution is the coefficients of the Legendre series `p` that
+    minimizes the sum of the weighted squared errors
+
+    .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
+
+    where :math:`w_j` are the weights. This problem is solved by setting up
+    as the (typically) overdetermined matrix equation
+
+    .. math:: V(x) * c = w * y,
+
+    where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
+    coefficients to be solved for, `w` are the weights, and `y` are the
+    observed values.  This equation is then solved using the singular value
+    decomposition of `V`.
+
+    If some of the singular values of `V` are so small that they are
+    neglected, then a `~exceptions.RankWarning` will be issued. This means that
+    the coefficient values may be poorly determined. Using a lower order fit
+    will usually get rid of the warning.  The `rcond` parameter can also be
+    set to a value smaller than its default, but the resulting fit may be
+    spurious and have large contributions from roundoff error.
+
+    Fits using Legendre series are usually better conditioned than fits
+    using power series, but much can depend on the distribution of the
+    sample points and the smoothness of the data. If the quality of the fit
+    is inadequate splines may be a good alternative.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Curve fitting",
+           https://en.wikipedia.org/wiki/Curve_fitting
+
+    Examples
+    --------
+
+    """
+    return pu._fit(legvander, x, y, deg, rcond, full, w)
+
+
+def legcompanion(c):
+    """Return the scaled companion matrix of c.
+
+    The basis polynomials are scaled so that the companion matrix is
+    symmetric when `c` is an Legendre basis polynomial. This provides
+    better eigenvalue estimates than the unscaled case and for basis
+    polynomials the eigenvalues are guaranteed to be real if
+    `numpy.linalg.eigvalsh` is used to obtain them.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of Legendre series coefficients ordered from low to high
+        degree.
+
+    Returns
+    -------
+    mat : ndarray
+        Scaled companion matrix of dimensions (deg, deg).
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        raise ValueError('Series must have maximum degree of at least 1.')
+    if len(c) == 2:
+        return np.array([[-c[0]/c[1]]])
+
+    n = len(c) - 1
+    mat = np.zeros((n, n), dtype=c.dtype)
+    scl = 1./np.sqrt(2*np.arange(n) + 1)
+    top = mat.reshape(-1)[1::n+1]
+    bot = mat.reshape(-1)[n::n+1]
+    top[...] = np.arange(1, n)*scl[:n-1]*scl[1:n]
+    bot[...] = top
+    mat[:, -1] -= (c[:-1]/c[-1])*(scl/scl[-1])*(n/(2*n - 1))
+    return mat
+
+
+def legroots(c):
+    """
+    Compute the roots of a Legendre series.
+
+    Return the roots (a.k.a. "zeros") of the polynomial
+
+    .. math:: p(x) = \\sum_i c[i] * L_i(x).
+
+    Parameters
+    ----------
+    c : 1-D array_like
+        1-D array of coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        Array of the roots of the series. If all the roots are real,
+        then `out` is also real, otherwise it is complex.
+
+    See Also
+    --------
+    numpy.polynomial.polynomial.polyroots
+    numpy.polynomial.chebyshev.chebroots
+    numpy.polynomial.laguerre.lagroots
+    numpy.polynomial.hermite.hermroots
+    numpy.polynomial.hermite_e.hermeroots
+
+    Notes
+    -----
+    The root estimates are obtained as the eigenvalues of the companion
+    matrix, Roots far from the origin of the complex plane may have large
+    errors due to the numerical instability of the series for such values.
+    Roots with multiplicity greater than 1 will also show larger errors as
+    the value of the series near such points is relatively insensitive to
+    errors in the roots. Isolated roots near the origin can be improved by
+    a few iterations of Newton's method.
+
+    The Legendre series basis polynomials aren't powers of ``x`` so the
+    results of this function may seem unintuitive.
+
+    Examples
+    --------
+    >>> import numpy.polynomial.legendre as leg
+    >>> leg.legroots((1, 2, 3, 4)) # 4L_3 + 3L_2 + 2L_1 + 1L_0, all real roots
+    array([-0.85099543, -0.11407192,  0.51506735]) # may vary
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        return np.array([], dtype=c.dtype)
+    if len(c) == 2:
+        return np.array([-c[0]/c[1]])
+
+    # rotated companion matrix reduces error
+    m = legcompanion(c)[::-1,::-1]
+    r = la.eigvals(m)
+    r.sort()
+    return r
+
+
+def leggauss(deg):
+    """
+    Gauss-Legendre quadrature.
+
+    Computes the sample points and weights for Gauss-Legendre quadrature.
+    These sample points and weights will correctly integrate polynomials of
+    degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with
+    the weight function :math:`f(x) = 1`.
+
+    Parameters
+    ----------
+    deg : int
+        Number of sample points and weights. It must be >= 1.
+
+    Returns
+    -------
+    x : ndarray
+        1-D ndarray containing the sample points.
+    y : ndarray
+        1-D ndarray containing the weights.
+
+    Notes
+    -----
+    The results have only been tested up to degree 100, higher degrees may
+    be problematic. The weights are determined by using the fact that
+
+    .. math:: w_k = c / (L'_n(x_k) * L_{n-1}(x_k))
+
+    where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
+    is the k'th root of :math:`L_n`, and then scaling the results to get
+    the right value when integrating 1.
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg <= 0:
+        raise ValueError("deg must be a positive integer")
+
+    # first approximation of roots. We use the fact that the companion
+    # matrix is symmetric in this case in order to obtain better zeros.
+    c = np.array([0]*deg + [1])
+    m = legcompanion(c)
+    x = la.eigvalsh(m)
+
+    # improve roots by one application of Newton
+    dy = legval(x, c)
+    df = legval(x, legder(c))
+    x -= dy/df
+
+    # compute the weights. We scale the factor to avoid possible numerical
+    # overflow.
+    fm = legval(x, c[1:])
+    fm /= np.abs(fm).max()
+    df /= np.abs(df).max()
+    w = 1/(fm * df)
+
+    # for Legendre we can also symmetrize
+    w = (w + w[::-1])/2
+    x = (x - x[::-1])/2
+
+    # scale w to get the right value
+    w *= 2. / w.sum()
+
+    return x, w
+
+
+def legweight(x):
+    """
+    Weight function of the Legendre polynomials.
+
+    The weight function is :math:`1` and the interval of integration is
+    :math:`[-1, 1]`. The Legendre polynomials are orthogonal, but not
+    normalized, with respect to this weight function.
+
+    Parameters
+    ----------
+    x : array_like
+       Values at which the weight function will be computed.
+
+    Returns
+    -------
+    w : ndarray
+       The weight function at `x`.
+    """
+    w = x*0.0 + 1.0
+    return w
+
+#
+# Legendre series class
+#
+
+class Legendre(ABCPolyBase):
+    """A Legendre series class.
+
+    The Legendre class provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
+    attributes and methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        Legendre coefficients in order of increasing degree, i.e.,
+        ``(1, 2, 3)`` gives ``1*P_0(x) + 2*P_1(x) + 3*P_2(x)``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is [-1., 1.].
+    window : (2,) array_like, optional
+        Window, see `domain` for its use. The default value is [-1., 1.].
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    """
+    # Virtual Functions
+    _add = staticmethod(legadd)
+    _sub = staticmethod(legsub)
+    _mul = staticmethod(legmul)
+    _div = staticmethod(legdiv)
+    _pow = staticmethod(legpow)
+    _val = staticmethod(legval)
+    _int = staticmethod(legint)
+    _der = staticmethod(legder)
+    _fit = staticmethod(legfit)
+    _line = staticmethod(legline)
+    _roots = staticmethod(legroots)
+    _fromroots = staticmethod(legfromroots)
+
+    # Virtual properties
+    domain = np.array(legdomain)
+    window = np.array(legdomain)
+    basis_name = 'P'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/legendre.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/legendre.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d81f3e6f54a4f72fd2cbc341f0efaa973aa3195a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/legendre.pyi
@@ -0,0 +1,99 @@
+from typing import Final, Literal as L
+
+import numpy as np
+
+from ._polybase import ABCPolyBase
+from ._polytypes import (
+    _Array1,
+    _Array2,
+    _FuncBinOp,
+    _FuncCompanion,
+    _FuncDer,
+    _FuncFit,
+    _FuncFromRoots,
+    _FuncGauss,
+    _FuncInteg,
+    _FuncLine,
+    _FuncPoly2Ortho,
+    _FuncPow,
+    _FuncRoots,
+    _FuncUnOp,
+    _FuncVal,
+    _FuncVal2D,
+    _FuncVal3D,
+    _FuncValFromRoots,
+    _FuncVander,
+    _FuncVander2D,
+    _FuncVander3D,
+    _FuncWeight,
+)
+from .polyutils import trimcoef as legtrim
+
+__all__ = [
+    "legzero",
+    "legone",
+    "legx",
+    "legdomain",
+    "legline",
+    "legadd",
+    "legsub",
+    "legmulx",
+    "legmul",
+    "legdiv",
+    "legpow",
+    "legval",
+    "legder",
+    "legint",
+    "leg2poly",
+    "poly2leg",
+    "legfromroots",
+    "legvander",
+    "legfit",
+    "legtrim",
+    "legroots",
+    "Legendre",
+    "legval2d",
+    "legval3d",
+    "leggrid2d",
+    "leggrid3d",
+    "legvander2d",
+    "legvander3d",
+    "legcompanion",
+    "leggauss",
+    "legweight",
+]
+
+poly2leg: _FuncPoly2Ortho[L["poly2leg"]]
+leg2poly: _FuncUnOp[L["leg2poly"]]
+
+legdomain: Final[_Array2[np.float64]]
+legzero: Final[_Array1[np.int_]]
+legone: Final[_Array1[np.int_]]
+legx: Final[_Array2[np.int_]]
+
+legline: _FuncLine[L["legline"]]
+legfromroots: _FuncFromRoots[L["legfromroots"]]
+legadd: _FuncBinOp[L["legadd"]]
+legsub: _FuncBinOp[L["legsub"]]
+legmulx: _FuncUnOp[L["legmulx"]]
+legmul: _FuncBinOp[L["legmul"]]
+legdiv: _FuncBinOp[L["legdiv"]]
+legpow: _FuncPow[L["legpow"]]
+legder: _FuncDer[L["legder"]]
+legint: _FuncInteg[L["legint"]]
+legval: _FuncVal[L["legval"]]
+legval2d: _FuncVal2D[L["legval2d"]]
+legval3d: _FuncVal3D[L["legval3d"]]
+legvalfromroots: _FuncValFromRoots[L["legvalfromroots"]]
+leggrid2d: _FuncVal2D[L["leggrid2d"]]
+leggrid3d: _FuncVal3D[L["leggrid3d"]]
+legvander: _FuncVander[L["legvander"]]
+legvander2d: _FuncVander2D[L["legvander2d"]]
+legvander3d: _FuncVander3D[L["legvander3d"]]
+legfit: _FuncFit[L["legfit"]]
+legcompanion: _FuncCompanion[L["legcompanion"]]
+legroots: _FuncRoots[L["legroots"]]
+leggauss: _FuncGauss[L["leggauss"]]
+legweight: _FuncWeight[L["legweight"]]
+
+class Legendre(ABCPolyBase[L["P"]]): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polynomial.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polynomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..86ea3a5d1d6e030929bc9de2f4744983a2a0417e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polynomial.py
@@ -0,0 +1,1617 @@
+"""
+=================================================
+Power Series (:mod:`numpy.polynomial.polynomial`)
+=================================================
+
+This module provides a number of objects (mostly functions) useful for
+dealing with polynomials, including a `Polynomial` class that
+encapsulates the usual arithmetic operations.  (General information
+on how this module represents and works with polynomial objects is in
+the docstring for its "parent" sub-package, `numpy.polynomial`).
+
+Classes
+-------
+.. autosummary::
+   :toctree: generated/
+
+   Polynomial
+
+Constants
+---------
+.. autosummary::
+   :toctree: generated/
+
+   polydomain
+   polyzero
+   polyone
+   polyx
+
+Arithmetic
+----------
+.. autosummary::
+   :toctree: generated/
+
+   polyadd
+   polysub
+   polymulx
+   polymul
+   polydiv
+   polypow
+   polyval
+   polyval2d
+   polyval3d
+   polygrid2d
+   polygrid3d
+
+Calculus
+--------
+.. autosummary::
+   :toctree: generated/
+
+   polyder
+   polyint
+
+Misc Functions
+--------------
+.. autosummary::
+   :toctree: generated/
+
+   polyfromroots
+   polyroots
+   polyvalfromroots
+   polyvander
+   polyvander2d
+   polyvander3d
+   polycompanion
+   polyfit
+   polytrim
+   polyline
+
+See Also
+--------
+`numpy.polynomial`
+
+"""
+__all__ = [
+    'polyzero', 'polyone', 'polyx', 'polydomain', 'polyline', 'polyadd',
+    'polysub', 'polymulx', 'polymul', 'polydiv', 'polypow', 'polyval',
+    'polyvalfromroots', 'polyder', 'polyint', 'polyfromroots', 'polyvander',
+    'polyfit', 'polytrim', 'polyroots', 'Polynomial', 'polyval2d', 'polyval3d',
+    'polygrid2d', 'polygrid3d', 'polyvander2d', 'polyvander3d',
+    'polycompanion']
+
+import numpy as np
+import numpy.linalg as la
+from numpy.lib.array_utils import normalize_axis_index
+
+from . import polyutils as pu
+from ._polybase import ABCPolyBase
+
+polytrim = pu.trimcoef
+
+#
+# These are constant arrays are of integer type so as to be compatible
+# with the widest range of other types, such as Decimal.
+#
+
+# Polynomial default domain.
+polydomain = np.array([-1., 1.])
+
+# Polynomial coefficients representing zero.
+polyzero = np.array([0])
+
+# Polynomial coefficients representing one.
+polyone = np.array([1])
+
+# Polynomial coefficients representing the identity x.
+polyx = np.array([0, 1])
+
+#
+# Polynomial series functions
+#
+
+
+def polyline(off, scl):
+    """
+    Returns an array representing a linear polynomial.
+
+    Parameters
+    ----------
+    off, scl : scalars
+        The "y-intercept" and "slope" of the line, respectively.
+
+    Returns
+    -------
+    y : ndarray
+        This module's representation of the linear polynomial ``off +
+        scl*x``.
+
+    See Also
+    --------
+    numpy.polynomial.chebyshev.chebline
+    numpy.polynomial.legendre.legline
+    numpy.polynomial.laguerre.lagline
+    numpy.polynomial.hermite.hermline
+    numpy.polynomial.hermite_e.hermeline
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> P.polyline(1, -1)
+    array([ 1, -1])
+    >>> P.polyval(1, P.polyline(1, -1))  # should be 0
+    0.0
+
+    """
+    if scl != 0:
+        return np.array([off, scl])
+    else:
+        return np.array([off])
+
+
+def polyfromroots(roots):
+    """
+    Generate a monic polynomial with given roots.
+
+    Return the coefficients of the polynomial
+
+    .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
+
+    where the :math:`r_n` are the roots specified in `roots`.  If a zero has
+    multiplicity n, then it must appear in `roots` n times. For instance,
+    if 2 is a root of multiplicity three and 3 is a root of multiplicity 2,
+    then `roots` looks something like [2, 2, 2, 3, 3]. The roots can appear
+    in any order.
+
+    If the returned coefficients are `c`, then
+
+    .. math:: p(x) = c_0 + c_1 * x + ... +  x^n
+
+    The coefficient of the last term is 1 for monic polynomials in this
+    form.
+
+    Parameters
+    ----------
+    roots : array_like
+        Sequence containing the roots.
+
+    Returns
+    -------
+    out : ndarray
+        1-D array of the polynomial's coefficients If all the roots are
+        real, then `out` is also real, otherwise it is complex.  (see
+        Examples below).
+
+    See Also
+    --------
+    numpy.polynomial.chebyshev.chebfromroots
+    numpy.polynomial.legendre.legfromroots
+    numpy.polynomial.laguerre.lagfromroots
+    numpy.polynomial.hermite.hermfromroots
+    numpy.polynomial.hermite_e.hermefromroots
+
+    Notes
+    -----
+    The coefficients are determined by multiplying together linear factors
+    of the form ``(x - r_i)``, i.e.
+
+    .. math:: p(x) = (x - r_0) (x - r_1) ... (x - r_n)
+
+    where ``n == len(roots) - 1``; note that this implies that ``1`` is always
+    returned for :math:`a_n`.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> P.polyfromroots((-1,0,1))  # x(x - 1)(x + 1) = x^3 - x
+    array([ 0., -1.,  0.,  1.])
+    >>> j = complex(0,1)
+    >>> P.polyfromroots((-j,j))  # complex returned, though values are real
+    array([1.+0.j,  0.+0.j,  1.+0.j])
+
+    """
+    return pu._fromroots(polyline, polymul, roots)
+
+
+def polyadd(c1, c2):
+    """
+    Add one polynomial to another.
+
+    Returns the sum of two polynomials `c1` + `c2`.  The arguments are
+    sequences of coefficients from lowest order term to highest, i.e.,
+    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of polynomial coefficients ordered from low to high.
+
+    Returns
+    -------
+    out : ndarray
+        The coefficient array representing their sum.
+
+    See Also
+    --------
+    polysub, polymulx, polymul, polydiv, polypow
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c1 = (1, 2, 3)
+    >>> c2 = (3, 2, 1)
+    >>> sum = P.polyadd(c1,c2); sum
+    array([4.,  4.,  4.])
+    >>> P.polyval(2, sum)  # 4 + 4(2) + 4(2**2)
+    28.0
+
+    """
+    return pu._add(c1, c2)
+
+
+def polysub(c1, c2):
+    """
+    Subtract one polynomial from another.
+
+    Returns the difference of two polynomials `c1` - `c2`.  The arguments
+    are sequences of coefficients from lowest order term to highest, i.e.,
+    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of polynomial coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Of coefficients representing their difference.
+
+    See Also
+    --------
+    polyadd, polymulx, polymul, polydiv, polypow
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c1 = (1, 2, 3)
+    >>> c2 = (3, 2, 1)
+    >>> P.polysub(c1,c2)
+    array([-2.,  0.,  2.])
+    >>> P.polysub(c2, c1)  # -P.polysub(c1,c2)
+    array([ 2.,  0., -2.])
+
+    """
+    return pu._sub(c1, c2)
+
+
+def polymulx(c):
+    """Multiply a polynomial by x.
+
+    Multiply the polynomial `c` by x, where x is the independent
+    variable.
+
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of polynomial coefficients ordered from low to
+        high.
+
+    Returns
+    -------
+    out : ndarray
+        Array representing the result of the multiplication.
+
+    See Also
+    --------
+    polyadd, polysub, polymul, polydiv, polypow
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = (1, 2, 3)
+    >>> P.polymulx(c)
+    array([0., 1., 2., 3.])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    # The zero series needs special treatment
+    if len(c) == 1 and c[0] == 0:
+        return c
+
+    prd = np.empty(len(c) + 1, dtype=c.dtype)
+    prd[0] = c[0]*0
+    prd[1:] = c
+    return prd
+
+
+def polymul(c1, c2):
+    """
+    Multiply one polynomial by another.
+
+    Returns the product of two polynomials `c1` * `c2`.  The arguments are
+    sequences of coefficients, from lowest order term to highest, e.g.,
+    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2.``
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of coefficients representing a polynomial, relative to the
+        "standard" basis, and ordered from lowest order term to highest.
+
+    Returns
+    -------
+    out : ndarray
+        Of the coefficients of their product.
+
+    See Also
+    --------
+    polyadd, polysub, polymulx, polydiv, polypow
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c1 = (1, 2, 3)
+    >>> c2 = (3, 2, 1)
+    >>> P.polymul(c1, c2)
+    array([  3.,   8.,  14.,   8.,   3.])
+
+    """
+    # c1, c2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+    ret = np.convolve(c1, c2)
+    return pu.trimseq(ret)
+
+
+def polydiv(c1, c2):
+    """
+    Divide one polynomial by another.
+
+    Returns the quotient-with-remainder of two polynomials `c1` / `c2`.
+    The arguments are sequences of coefficients, from lowest order term
+    to highest, e.g., [1,2,3] represents ``1 + 2*x + 3*x**2``.
+
+    Parameters
+    ----------
+    c1, c2 : array_like
+        1-D arrays of polynomial coefficients ordered from low to high.
+
+    Returns
+    -------
+    [quo, rem] : ndarrays
+        Of coefficient series representing the quotient and remainder.
+
+    See Also
+    --------
+    polyadd, polysub, polymulx, polymul, polypow
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c1 = (1, 2, 3)
+    >>> c2 = (3, 2, 1)
+    >>> P.polydiv(c1, c2)
+    (array([3.]), array([-8., -4.]))
+    >>> P.polydiv(c2, c1)
+    (array([ 0.33333333]), array([ 2.66666667,  1.33333333]))  # may vary
+
+    """
+    # c1, c2 are trimmed copies
+    [c1, c2] = pu.as_series([c1, c2])
+    if c2[-1] == 0:
+        raise ZeroDivisionError  # FIXME: add message with details to exception
+
+    # note: this is more efficient than `pu._div(polymul, c1, c2)`
+    lc1 = len(c1)
+    lc2 = len(c2)
+    if lc1 < lc2:
+        return c1[:1]*0, c1
+    elif lc2 == 1:
+        return c1/c2[-1], c1[:1]*0
+    else:
+        dlen = lc1 - lc2
+        scl = c2[-1]
+        c2 = c2[:-1]/scl
+        i = dlen
+        j = lc1 - 1
+        while i >= 0:
+            c1[i:j] -= c2*c1[j]
+            i -= 1
+            j -= 1
+        return c1[j+1:]/scl, pu.trimseq(c1[:j+1])
+
+
+def polypow(c, pow, maxpower=None):
+    """Raise a polynomial to a power.
+
+    Returns the polynomial `c` raised to the power `pow`. The argument
+    `c` is a sequence of coefficients ordered from low to high. i.e.,
+    [1,2,3] is the series  ``1 + 2*x + 3*x**2.``
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of array of series coefficients ordered from low to
+        high degree.
+    pow : integer
+        Power to which the series will be raised
+    maxpower : integer, optional
+        Maximum power allowed. This is mainly to limit growth of the series
+        to unmanageable size. Default is 16
+
+    Returns
+    -------
+    coef : ndarray
+        Power series of power.
+
+    See Also
+    --------
+    polyadd, polysub, polymulx, polymul, polydiv
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> P.polypow([1, 2, 3], 2)
+    array([ 1., 4., 10., 12., 9.])
+
+    """
+    # note: this is more efficient than `pu._pow(polymul, c1, c2)`, as it
+    # avoids calling `as_series` repeatedly
+    return pu._pow(np.convolve, c, pow, maxpower)
+
+
+def polyder(c, m=1, scl=1, axis=0):
+    """
+    Differentiate a polynomial.
+
+    Returns the polynomial coefficients `c` differentiated `m` times along
+    `axis`.  At each iteration the result is multiplied by `scl` (the
+    scaling factor is for use in a linear change of variable).  The
+    argument `c` is an array of coefficients from low to high degree along
+    each axis, e.g., [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``
+    while [[1,2],[1,2]] represents ``1 + 1*x + 2*y + 2*x*y`` if axis=0 is
+    ``x`` and axis=1 is ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        Array of polynomial coefficients. If c is multidimensional the
+        different axis correspond to different variables with the degree
+        in each axis given by the corresponding index.
+    m : int, optional
+        Number of derivatives taken, must be non-negative. (Default: 1)
+    scl : scalar, optional
+        Each differentiation is multiplied by `scl`.  The end result is
+        multiplication by ``scl**m``.  This is for use in a linear change
+        of variable. (Default: 1)
+    axis : int, optional
+        Axis over which the derivative is taken. (Default: 0).
+
+    Returns
+    -------
+    der : ndarray
+        Polynomial coefficients of the derivative.
+
+    See Also
+    --------
+    polyint
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = (1, 2, 3, 4)
+    >>> P.polyder(c)  # (d/dx)(c)
+    array([  2.,   6.,  12.])
+    >>> P.polyder(c, 3)  # (d**3/dx**3)(c)
+    array([24.])
+    >>> P.polyder(c, scl=-1)  # (d/d(-x))(c)
+    array([ -2.,  -6., -12.])
+    >>> P.polyder(c, 2, -1)  # (d**2/d(-x)**2)(c)
+    array([  6.,  24.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        # astype fails with NA
+        c = c + 0.0
+    cdt = c.dtype
+    cnt = pu._as_int(m, "the order of derivation")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of derivation must be non-negative")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    c = np.moveaxis(c, iaxis, 0)
+    n = len(c)
+    if cnt >= n:
+        c = c[:1]*0
+    else:
+        for i in range(cnt):
+            n = n - 1
+            c *= scl
+            der = np.empty((n,) + c.shape[1:], dtype=cdt)
+            for j in range(n, 0, -1):
+                der[j - 1] = j*c[j]
+            c = der
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
+    """
+    Integrate a polynomial.
+
+    Returns the polynomial coefficients `c` integrated `m` times from
+    `lbnd` along `axis`.  At each iteration the resulting series is
+    **multiplied** by `scl` and an integration constant, `k`, is added.
+    The scaling factor is for use in a linear change of variable.  ("Buyer
+    beware": note that, depending on what one is doing, one may want `scl`
+    to be the reciprocal of what one might expect; for more information,
+    see the Notes section below.) The argument `c` is an array of
+    coefficients, from low to high degree along each axis, e.g., [1,2,3]
+    represents the polynomial ``1 + 2*x + 3*x**2`` while [[1,2],[1,2]]
+    represents ``1 + 1*x + 2*y + 2*x*y`` if axis=0 is ``x`` and axis=1 is
+    ``y``.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of polynomial coefficients, ordered from low to high.
+    m : int, optional
+        Order of integration, must be positive. (Default: 1)
+    k : {[], list, scalar}, optional
+        Integration constant(s).  The value of the first integral at zero
+        is the first value in the list, the value of the second integral
+        at zero is the second value, etc.  If ``k == []`` (the default),
+        all constants are set to zero.  If ``m == 1``, a single scalar can
+        be given instead of a list.
+    lbnd : scalar, optional
+        The lower bound of the integral. (Default: 0)
+    scl : scalar, optional
+        Following each integration the result is *multiplied* by `scl`
+        before the integration constant is added. (Default: 1)
+    axis : int, optional
+        Axis over which the integral is taken. (Default: 0).
+
+    Returns
+    -------
+    S : ndarray
+        Coefficient array of the integral.
+
+    Raises
+    ------
+    ValueError
+        If ``m < 1``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
+        ``np.ndim(scl) != 0``.
+
+    See Also
+    --------
+    polyder
+
+    Notes
+    -----
+    Note that the result of each integration is *multiplied* by `scl`.  Why
+    is this important to note?  Say one is making a linear change of
+    variable :math:`u = ax + b` in an integral relative to `x`. Then
+    :math:`dx = du/a`, so one will need to set `scl` equal to
+    :math:`1/a` - perhaps not what one would have first thought.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = (1, 2, 3)
+    >>> P.polyint(c)  # should return array([0, 1, 1, 1])
+    array([0.,  1.,  1.,  1.])
+    >>> P.polyint(c, 3)  # should return array([0, 0, 0, 1/6, 1/12, 1/20])
+     array([ 0.        ,  0.        ,  0.        ,  0.16666667,  0.08333333, # may vary
+             0.05      ])
+    >>> P.polyint(c, k=3)  # should return array([3, 1, 1, 1])
+    array([3.,  1.,  1.,  1.])
+    >>> P.polyint(c,lbnd=-2)  # should return array([6, 1, 1, 1])
+    array([6.,  1.,  1.,  1.])
+    >>> P.polyint(c,scl=-2)  # should return array([0, -2, -2, -2])
+    array([ 0., -2., -2., -2.])
+
+    """
+    c = np.array(c, ndmin=1, copy=True)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        # astype doesn't preserve mask attribute.
+        c = c + 0.0
+    cdt = c.dtype
+    if not np.iterable(k):
+        k = [k]
+    cnt = pu._as_int(m, "the order of integration")
+    iaxis = pu._as_int(axis, "the axis")
+    if cnt < 0:
+        raise ValueError("The order of integration must be non-negative")
+    if len(k) > cnt:
+        raise ValueError("Too many integration constants")
+    if np.ndim(lbnd) != 0:
+        raise ValueError("lbnd must be a scalar.")
+    if np.ndim(scl) != 0:
+        raise ValueError("scl must be a scalar.")
+    iaxis = normalize_axis_index(iaxis, c.ndim)
+
+    if cnt == 0:
+        return c
+
+    k = list(k) + [0]*(cnt - len(k))
+    c = np.moveaxis(c, iaxis, 0)
+    for i in range(cnt):
+        n = len(c)
+        c *= scl
+        if n == 1 and np.all(c[0] == 0):
+            c[0] += k[i]
+        else:
+            tmp = np.empty((n + 1,) + c.shape[1:], dtype=cdt)
+            tmp[0] = c[0]*0
+            tmp[1] = c[0]
+            for j in range(1, n):
+                tmp[j + 1] = c[j]/(j + 1)
+            tmp[0] += k[i] - polyval(lbnd, tmp)
+            c = tmp
+    c = np.moveaxis(c, 0, iaxis)
+    return c
+
+
+def polyval(x, c, tensor=True):
+    """
+    Evaluate a polynomial at points x.
+
+    If `c` is of length ``n + 1``, this function returns the value
+
+    .. math:: p(x) = c_0 + c_1 * x + ... + c_n * x^n
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `c`.
+
+    If `c` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If
+    `c` is multidimensional, then the shape of the result depends on the
+    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
+    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
+    scalars have shape (,).
+
+    Trailing zeros in the coefficients will be used in the evaluation, so
+    they should be avoided if efficiency is a concern.
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        with themselves and with the elements of `c`.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree n are contained in c[n]. If `c` is multidimensional the
+        remaining indices enumerate multiple polynomials. In the two
+        dimensional case the coefficients may be thought of as stored in
+        the columns of `c`.
+    tensor : boolean, optional
+        If True, the shape of the coefficient array is extended with ones
+        on the right, one for each dimension of `x`. Scalars have dimension 0
+        for this action. The result is that every column of coefficients in
+        `c` is evaluated for every element of `x`. If False, `x` is broadcast
+        over the columns of `c` for the evaluation.  This keyword is useful
+        when `c` is multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The shape of the returned array is described above.
+
+    See Also
+    --------
+    polyval2d, polygrid2d, polyval3d, polygrid3d
+
+    Notes
+    -----
+    The evaluation uses Horner's method.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial.polynomial import polyval
+    >>> polyval(1, [1,2,3])
+    6.0
+    >>> a = np.arange(4).reshape(2,2)
+    >>> a
+    array([[0, 1],
+           [2, 3]])
+    >>> polyval(a, [1, 2, 3])
+    array([[ 1.,   6.],
+           [17.,  34.]])
+    >>> coef = np.arange(4).reshape(2, 2)  # multidimensional coefficients
+    >>> coef
+    array([[0, 1],
+           [2, 3]])
+    >>> polyval([1, 2], coef, tensor=True)
+    array([[2.,  4.],
+           [4.,  7.]])
+    >>> polyval([1, 2], coef, tensor=False)
+    array([2.,  7.])
+
+    """
+    c = np.array(c, ndmin=1, copy=None)
+    if c.dtype.char in '?bBhHiIlLqQpP':
+        # astype fails with NA
+        c = c + 0.0
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray) and tensor:
+        c = c.reshape(c.shape + (1,)*x.ndim)
+
+    c0 = c[-1] + x*0
+    for i in range(2, len(c) + 1):
+        c0 = c[-i] + c0*x
+    return c0
+
+
+def polyvalfromroots(x, r, tensor=True):
+    """
+    Evaluate a polynomial specified by its roots at points x.
+
+    If `r` is of length ``N``, this function returns the value
+
+    .. math:: p(x) = \\prod_{n=1}^{N} (x - r_n)
+
+    The parameter `x` is converted to an array only if it is a tuple or a
+    list, otherwise it is treated as a scalar. In either case, either `x`
+    or its elements must support multiplication and addition both with
+    themselves and with the elements of `r`.
+
+    If `r` is a 1-D array, then ``p(x)`` will have the same shape as `x`.  If `r`
+    is multidimensional, then the shape of the result depends on the value of
+    `tensor`. If `tensor` is ``True`` the shape will be r.shape[1:] + x.shape;
+    that is, each polynomial is evaluated at every value of `x`. If `tensor` is
+    ``False``, the shape will be r.shape[1:]; that is, each polynomial is
+    evaluated only for the corresponding broadcast value of `x`. Note that
+    scalars have shape (,).
+
+    Parameters
+    ----------
+    x : array_like, compatible object
+        If `x` is a list or tuple, it is converted to an ndarray, otherwise
+        it is left unchanged and treated as a scalar. In either case, `x`
+        or its elements must support addition and multiplication with
+        with themselves and with the elements of `r`.
+    r : array_like
+        Array of roots. If `r` is multidimensional the first index is the
+        root index, while the remaining indices enumerate multiple
+        polynomials. For instance, in the two dimensional case the roots
+        of each polynomial may be thought of as stored in the columns of `r`.
+    tensor : boolean, optional
+        If True, the shape of the roots array is extended with ones on the
+        right, one for each dimension of `x`. Scalars have dimension 0 for this
+        action. The result is that every column of coefficients in `r` is
+        evaluated for every element of `x`. If False, `x` is broadcast over the
+        columns of `r` for the evaluation.  This keyword is useful when `r` is
+        multidimensional. The default value is True.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The shape of the returned array is described above.
+
+    See Also
+    --------
+    polyroots, polyfromroots, polyval
+
+    Examples
+    --------
+    >>> from numpy.polynomial.polynomial import polyvalfromroots
+    >>> polyvalfromroots(1, [1, 2, 3])
+    0.0
+    >>> a = np.arange(4).reshape(2, 2)
+    >>> a
+    array([[0, 1],
+           [2, 3]])
+    >>> polyvalfromroots(a, [-1, 0, 1])
+    array([[-0.,   0.],
+           [ 6.,  24.]])
+    >>> r = np.arange(-2, 2).reshape(2,2)  # multidimensional coefficients
+    >>> r # each column of r defines one polynomial
+    array([[-2, -1],
+           [ 0,  1]])
+    >>> b = [-2, 1]
+    >>> polyvalfromroots(b, r, tensor=True)
+    array([[-0.,  3.],
+           [ 3., 0.]])
+    >>> polyvalfromroots(b, r, tensor=False)
+    array([-0.,  0.])
+
+    """
+    r = np.array(r, ndmin=1, copy=None)
+    if r.dtype.char in '?bBhHiIlLqQpP':
+        r = r.astype(np.double)
+    if isinstance(x, (tuple, list)):
+        x = np.asarray(x)
+    if isinstance(x, np.ndarray):
+        if tensor:
+            r = r.reshape(r.shape + (1,)*x.ndim)
+        elif x.ndim >= r.ndim:
+            raise ValueError("x.ndim must be < r.ndim when tensor == False")
+    return np.prod(x - r, axis=0)
+
+
+def polyval2d(x, y, c):
+    """
+    Evaluate a 2-D polynomial at points (x, y).
+
+    This function returns the value
+
+    .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * x^i * y^j
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars and they
+    must have the same shape after conversion. In either case, either `x`
+    and `y` or their elements must support multiplication and addition both
+    with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points ``(x, y)``,
+        where `x` and `y` must have the same shape. If `x` or `y` is a list
+        or tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term
+        of multi-degree i,j is contained in ``c[i,j]``. If `c` has
+        dimension greater than two the remaining indices enumerate multiple
+        sets of coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points formed with
+        pairs of corresponding values from `x` and `y`.
+
+    See Also
+    --------
+    polyval, polygrid2d, polyval3d, polygrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = ((1, 2, 3), (4, 5, 6))
+    >>> P.polyval2d(1, 1, c)
+    21.0
+
+    """
+    return pu._valnd(polyval, c, x, y)
+
+
+def polygrid2d(x, y, c):
+    """
+    Evaluate a 2-D polynomial on the Cartesian product of x and y.
+
+    This function returns the values:
+
+    .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * a^i * b^j
+
+    where the points ``(a, b)`` consist of all pairs formed by taking
+    `a` from `x` and `b` from `y`. The resulting points form a grid with
+    `x` in the first dimension and `y` in the second.
+
+    The parameters `x` and `y` are converted to arrays only if they are
+    tuples or a lists, otherwise they are treated as a scalars. In either
+    case, either `x` and `y` or their elements must support multiplication
+    and addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than two dimensions, ones are implicitly appended to
+    its shape to make it 2-D. The shape of the result will be c.shape[2:] +
+    x.shape + y.shape.
+
+    Parameters
+    ----------
+    x, y : array_like, compatible objects
+        The two dimensional series is evaluated at the points in the
+        Cartesian product of `x` and `y`.  If `x` or `y` is a list or
+        tuple, it is first converted to an ndarray, otherwise it is left
+        unchanged and, if it isn't an ndarray, it is treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    polyval, polyval2d, polyval3d, polygrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = ((1, 2, 3), (4, 5, 6))
+    >>> P.polygrid2d([0, 1], [0, 1], c)
+    array([[ 1.,  6.],
+           [ 5., 21.]])
+
+    """
+    return pu._gridnd(polyval, c, x, y)
+
+
+def polyval3d(x, y, z, c):
+    """
+    Evaluate a 3-D polynomial at points (x, y, z).
+
+    This function returns the values:
+
+    .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * x^i * y^j * z^k
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if
+    they are tuples or a lists, otherwise they are treated as a scalars and
+    they must have the same shape after conversion. In either case, either
+    `x`, `y`, and `z` or their elements must support multiplication and
+    addition both with themselves and with the elements of `c`.
+
+    If `c` has fewer than 3 dimensions, ones are implicitly appended to its
+    shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible object
+        The three dimensional series is evaluated at the points
+        ``(x, y, z)``, where `x`, `y`, and `z` must have the same shape.  If
+        any of `x`, `y`, or `z` is a list or tuple, it is first converted
+        to an ndarray, otherwise it is left unchanged and if it isn't an
+        ndarray it is  treated as a scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficient of the term of
+        multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
+        greater than 3 the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the multidimensional polynomial on points formed with
+        triples of corresponding values from `x`, `y`, and `z`.
+
+    See Also
+    --------
+    polyval, polyval2d, polygrid2d, polygrid3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = ((1, 2, 3), (4, 5, 6), (7, 8, 9))
+    >>> P.polyval3d(1, 1, 1, c)
+    45.0
+
+    """
+    return pu._valnd(polyval, c, x, y, z)
+
+
+def polygrid3d(x, y, z, c):
+    """
+    Evaluate a 3-D polynomial on the Cartesian product of x, y and z.
+
+    This function returns the values:
+
+    .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * a^i * b^j * c^k
+
+    where the points ``(a, b, c)`` consist of all triples formed by taking
+    `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
+    a grid with `x` in the first dimension, `y` in the second, and `z` in
+    the third.
+
+    The parameters `x`, `y`, and `z` are converted to arrays only if they
+    are tuples or a lists, otherwise they are treated as a scalars. In
+    either case, either `x`, `y`, and `z` or their elements must support
+    multiplication and addition both with themselves and with the elements
+    of `c`.
+
+    If `c` has fewer than three dimensions, ones are implicitly appended to
+    its shape to make it 3-D. The shape of the result will be c.shape[3:] +
+    x.shape + y.shape + z.shape.
+
+    Parameters
+    ----------
+    x, y, z : array_like, compatible objects
+        The three dimensional series is evaluated at the points in the
+        Cartesian product of `x`, `y`, and `z`.  If `x`, `y`, or `z` is a
+        list or tuple, it is first converted to an ndarray, otherwise it is
+        left unchanged and, if it isn't an ndarray, it is treated as a
+        scalar.
+    c : array_like
+        Array of coefficients ordered so that the coefficients for terms of
+        degree i,j are contained in ``c[i,j]``. If `c` has dimension
+        greater than two the remaining indices enumerate multiple sets of
+        coefficients.
+
+    Returns
+    -------
+    values : ndarray, compatible object
+        The values of the two dimensional polynomial at points in the Cartesian
+        product of `x` and `y`.
+
+    See Also
+    --------
+    polyval, polyval2d, polygrid2d, polyval3d
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = ((1, 2, 3), (4, 5, 6), (7, 8, 9))
+    >>> P.polygrid3d([0, 1], [0, 1], [0, 1], c)
+    array([[ 1., 13.],
+           [ 6., 51.]])
+
+    """
+    return pu._gridnd(polyval, c, x, y, z)
+
+
+def polyvander(x, deg):
+    """Vandermonde matrix of given degree.
+
+    Returns the Vandermonde matrix of degree `deg` and sample points
+    `x`. The Vandermonde matrix is defined by
+
+    .. math:: V[..., i] = x^i,
+
+    where ``0 <= i <= deg``. The leading indices of `V` index the elements of
+    `x` and the last index is the power of `x`.
+
+    If `c` is a 1-D array of coefficients of length ``n + 1`` and `V` is the
+    matrix ``V = polyvander(x, n)``, then ``np.dot(V, c)`` and
+    ``polyval(x, c)`` are the same up to roundoff. This equivalence is
+    useful both for least squares fitting and for the evaluation of a large
+    number of polynomials of the same degree and sample points.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of points. The dtype is converted to float64 or complex128
+        depending on whether any of the elements are complex. If `x` is
+        scalar it is converted to a 1-D array.
+    deg : int
+        Degree of the resulting matrix.
+
+    Returns
+    -------
+    vander : ndarray.
+        The Vandermonde matrix. The shape of the returned matrix is
+        ``x.shape + (deg + 1,)``, where the last index is the power of `x`.
+        The dtype will be the same as the converted `x`.
+
+    See Also
+    --------
+    polyvander2d, polyvander3d
+
+    Examples
+    --------
+    The Vandermonde matrix of degree ``deg = 5`` and sample points
+    ``x = [-1, 2, 3]`` contains the element-wise powers of `x`
+    from 0 to 5 as its columns.
+
+    >>> from numpy.polynomial import polynomial as P
+    >>> x, deg = [-1, 2, 3], 5
+    >>> P.polyvander(x=x, deg=deg)
+    array([[  1.,  -1.,   1.,  -1.,   1.,  -1.],
+           [  1.,   2.,   4.,   8.,  16.,  32.],
+           [  1.,   3.,   9.,  27.,  81., 243.]])
+
+    """
+    ideg = pu._as_int(deg, "deg")
+    if ideg < 0:
+        raise ValueError("deg must be non-negative")
+
+    x = np.array(x, copy=None, ndmin=1) + 0.0
+    dims = (ideg + 1,) + x.shape
+    dtyp = x.dtype
+    v = np.empty(dims, dtype=dtyp)
+    v[0] = x*0 + 1
+    if ideg > 0:
+        v[1] = x
+        for i in range(2, ideg + 1):
+            v[i] = v[i-1]*x
+    return np.moveaxis(v, 0, -1)
+
+
+def polyvander2d(x, y, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y)``. The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (deg[1] + 1)*i + j] = x^i * y^j,
+
+    where ``0 <= i <= deg[0]`` and ``0 <= j <= deg[1]``. The leading indices of
+    `V` index the points ``(x, y)`` and the last index encodes the powers of
+    `x` and `y`.
+
+    If ``V = polyvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
+    correspond to the elements of a 2-D coefficient array `c` of shape
+    (xdeg + 1, ydeg + 1) in the order
+
+    .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
+
+    and ``np.dot(V, c.flat)`` and ``polyval2d(x, y, c)`` will be the same
+    up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 2-D polynomials
+    of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to
+        1-D arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg].
+
+    Returns
+    -------
+    vander2d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg([1]+1)`.  The dtype will be the same
+        as the converted `x` and `y`.
+
+    See Also
+    --------
+    polyvander, polyvander3d, polyval2d, polyval3d
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    The 2-D pseudo-Vandermonde matrix of degree ``[1, 2]`` and sample
+    points ``x = [-1, 2]`` and ``y = [1, 3]`` is as follows:
+
+    >>> from numpy.polynomial import polynomial as P
+    >>> x = np.array([-1, 2])
+    >>> y = np.array([1, 3])
+    >>> m, n = 1, 2
+    >>> deg = np.array([m, n])
+    >>> V = P.polyvander2d(x=x, y=y, deg=deg)
+    >>> V
+    array([[ 1.,  1.,  1., -1., -1., -1.],
+           [ 1.,  3.,  9.,  2.,  6., 18.]])
+
+    We can verify the columns for any ``0 <= i <= m`` and ``0 <= j <= n``:
+
+    >>> i, j = 0, 1
+    >>> V[:, (deg[1]+1)*i + j] == x**i * y**j
+    array([ True,  True])
+
+    The (1D) Vandermonde matrix of sample points ``x`` and degree ``m`` is a
+    special case of the (2D) pseudo-Vandermonde matrix with ``y`` points all
+    zero and degree ``[m, 0]``.
+
+    >>> P.polyvander2d(x=x, y=0*x, deg=(m, 0)) == P.polyvander(x=x, deg=m)
+    array([[ True,  True],
+           [ True,  True]])
+
+    """
+    return pu._vander_nd_flat((polyvander, polyvander), (x, y), deg)
+
+
+def polyvander3d(x, y, z, deg):
+    """Pseudo-Vandermonde matrix of given degrees.
+
+    Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
+    points ``(x, y, z)``. If `l`, `m`, `n` are the given degrees in `x`, `y`, `z`,
+    then The pseudo-Vandermonde matrix is defined by
+
+    .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = x^i * y^j * z^k,
+
+    where ``0 <= i <= l``, ``0 <= j <= m``, and ``0 <= j <= n``.  The leading
+    indices of `V` index the points ``(x, y, z)`` and the last index encodes
+    the powers of `x`, `y`, and `z`.
+
+    If ``V = polyvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
+    of `V` correspond to the elements of a 3-D coefficient array `c` of
+    shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
+
+    .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
+
+    and  ``np.dot(V, c.flat)`` and ``polyval3d(x, y, z, c)`` will be the
+    same up to roundoff. This equivalence is useful both for least squares
+    fitting and for the evaluation of a large number of 3-D polynomials
+    of the same degrees and sample points.
+
+    Parameters
+    ----------
+    x, y, z : array_like
+        Arrays of point coordinates, all of the same shape. The dtypes will
+        be converted to either float64 or complex128 depending on whether
+        any of the elements are complex. Scalars are converted to 1-D
+        arrays.
+    deg : list of ints
+        List of maximum degrees of the form [x_deg, y_deg, z_deg].
+
+    Returns
+    -------
+    vander3d : ndarray
+        The shape of the returned matrix is ``x.shape + (order,)``, where
+        :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`.  The dtype will
+        be the same as the converted `x`, `y`, and `z`.
+
+    See Also
+    --------
+    polyvander, polyvander3d, polyval2d, polyval3d
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial import polynomial as P
+    >>> x = np.asarray([-1, 2, 1])
+    >>> y = np.asarray([1, -2, -3])
+    >>> z = np.asarray([2, 2, 5])
+    >>> l, m, n = [2, 2, 1]
+    >>> deg = [l, m, n]
+    >>> V = P.polyvander3d(x=x, y=y, z=z, deg=deg)
+    >>> V
+    array([[  1.,   2.,   1.,   2.,   1.,   2.,  -1.,  -2.,  -1.,
+             -2.,  -1.,  -2.,   1.,   2.,   1.,   2.,   1.,   2.],
+           [  1.,   2.,  -2.,  -4.,   4.,   8.,   2.,   4.,  -4.,
+             -8.,   8.,  16.,   4.,   8.,  -8., -16.,  16.,  32.],
+           [  1.,   5.,  -3., -15.,   9.,  45.,   1.,   5.,  -3.,
+            -15.,   9.,  45.,   1.,   5.,  -3., -15.,   9.,  45.]])
+
+    We can verify the columns for any ``0 <= i <= l``, ``0 <= j <= m``,
+    and ``0 <= k <= n``
+
+    >>> i, j, k = 2, 1, 0
+    >>> V[:, (m+1)*(n+1)*i + (n+1)*j + k] == x**i * y**j * z**k
+    array([ True,  True,  True])
+
+    """
+    return pu._vander_nd_flat((polyvander, polyvander, polyvander), (x, y, z), deg)
+
+
+def polyfit(x, y, deg, rcond=None, full=False, w=None):
+    """
+    Least-squares fit of a polynomial to data.
+
+    Return the coefficients of a polynomial of degree `deg` that is the
+    least squares fit to the data values `y` given at points `x`. If `y` is
+    1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
+    fits are done, one for each column of `y`, and the resulting
+    coefficients are stored in the corresponding columns of a 2-D return.
+    The fitted polynomial(s) are in the form
+
+    .. math::  p(x) = c_0 + c_1 * x + ... + c_n * x^n,
+
+    where `n` is `deg`.
+
+    Parameters
+    ----------
+    x : array_like, shape (`M`,)
+        x-coordinates of the `M` sample (data) points ``(x[i], y[i])``.
+    y : array_like, shape (`M`,) or (`M`, `K`)
+        y-coordinates of the sample points.  Several sets of sample points
+        sharing the same x-coordinates can be (independently) fit with one
+        call to `polyfit` by passing in for `y` a 2-D array that contains
+        one data set per column.
+    deg : int or 1-D array_like
+        Degree(s) of the fitting polynomials. If `deg` is a single integer
+        all terms up to and including the `deg`'th term are included in the
+        fit. For NumPy versions >= 1.11.0 a list of integers specifying the
+        degrees of the terms to include may be used instead.
+    rcond : float, optional
+        Relative condition number of the fit.  Singular values smaller
+        than `rcond`, relative to the largest singular value, will be
+        ignored.  The default value is ``len(x)*eps``, where `eps` is the
+        relative precision of the platform's float type, about 2e-16 in
+        most cases.
+    full : bool, optional
+        Switch determining the nature of the return value.  When ``False``
+        (the default) just the coefficients are returned; when ``True``,
+        diagnostic information from the singular value decomposition (used
+        to solve the fit's matrix equation) is also returned.
+    w : array_like, shape (`M`,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+
+    Returns
+    -------
+    coef : ndarray, shape (`deg` + 1,) or (`deg` + 1, `K`)
+        Polynomial coefficients ordered from low to high.  If `y` was 2-D,
+        the coefficients in column `k` of `coef` represent the polynomial
+        fit to the data in `y`'s `k`-th column.
+
+    [residuals, rank, singular_values, rcond] : list
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    Raises
+    ------
+    RankWarning
+        Raised if the matrix in the least-squares fit is rank deficient.
+        The warning is only raised if ``full == False``.  The warnings can
+        be turned off by:
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    numpy.polynomial.chebyshev.chebfit
+    numpy.polynomial.legendre.legfit
+    numpy.polynomial.laguerre.lagfit
+    numpy.polynomial.hermite.hermfit
+    numpy.polynomial.hermite_e.hermefit
+    polyval : Evaluates a polynomial.
+    polyvander : Vandermonde matrix for powers.
+    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution is the coefficients of the polynomial `p` that minimizes
+    the sum of the weighted squared errors
+
+    .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
+
+    where the :math:`w_j` are the weights. This problem is solved by
+    setting up the (typically) over-determined matrix equation:
+
+    .. math:: V(x) * c = w * y,
+
+    where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
+    coefficients to be solved for, `w` are the weights, and `y` are the
+    observed values.  This equation is then solved using the singular value
+    decomposition of `V`.
+
+    If some of the singular values of `V` are so small that they are
+    neglected (and `full` == ``False``), a `~exceptions.RankWarning` will be
+    raised.  This means that the coefficient values may be poorly determined.
+    Fitting to a lower order polynomial will usually get rid of the warning
+    (but may not be what you want, of course; if you have independent
+    reason(s) for choosing the degree which isn't working, you may have to:
+    a) reconsider those reasons, and/or b) reconsider the quality of your
+    data).  The `rcond` parameter can also be set to a value smaller than
+    its default, but the resulting fit may be spurious and have large
+    contributions from roundoff error.
+
+    Polynomial fits using double precision tend to "fail" at about
+    (polynomial) degree 20. Fits using Chebyshev or Legendre series are
+    generally better conditioned, but much can still depend on the
+    distribution of the sample points and the smoothness of the data.  If
+    the quality of the fit is inadequate, splines may be a good
+    alternative.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial import polynomial as P
+    >>> x = np.linspace(-1,1,51)  # x "data": [-1, -0.96, ..., 0.96, 1]
+    >>> rng = np.random.default_rng()
+    >>> err = rng.normal(size=len(x))
+    >>> y = x**3 - x + err  # x^3 - x + Gaussian noise
+    >>> c, stats = P.polyfit(x,y,3,full=True)
+    >>> c # c[0], c[1] approx. -1, c[2] should be approx. 0, c[3] approx. 1
+    array([ 0.23111996, -1.02785049, -0.2241444 ,  1.08405657]) # may vary
+    >>> stats # note the large SSR, explaining the rather poor results
+    [array([48.312088]),                                        # may vary
+     4,
+     array([1.38446749, 1.32119158, 0.50443316, 0.28853036]),
+     1.1324274851176597e-14]
+
+    Same thing without the added noise
+
+    >>> y = x**3 - x
+    >>> c, stats = P.polyfit(x,y,3,full=True)
+    >>> c # c[0], c[1] ~= -1, c[2] should be "very close to 0", c[3] ~= 1
+    array([-6.73496154e-17, -1.00000000e+00,  0.00000000e+00,  1.00000000e+00])
+    >>> stats # note the minuscule SSR
+    [array([8.79579319e-31]),
+     np.int32(4),
+     array([1.38446749, 1.32119158, 0.50443316, 0.28853036]),
+     1.1324274851176597e-14]
+
+    """
+    return pu._fit(polyvander, x, y, deg, rcond, full, w)
+
+
+def polycompanion(c):
+    """
+    Return the companion matrix of c.
+
+    The companion matrix for power series cannot be made symmetric by
+    scaling the basis, so this function differs from those for the
+    orthogonal polynomials.
+
+    Parameters
+    ----------
+    c : array_like
+        1-D array of polynomial coefficients ordered from low to high
+        degree.
+
+    Returns
+    -------
+    mat : ndarray
+        Companion matrix of dimensions (deg, deg).
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polynomial as P
+    >>> c = (1, 2, 3)
+    >>> P.polycompanion(c)
+    array([[ 0.        , -0.33333333],
+           [ 1.        , -0.66666667]])
+
+    """
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        raise ValueError('Series must have maximum degree of at least 1.')
+    if len(c) == 2:
+        return np.array([[-c[0]/c[1]]])
+
+    n = len(c) - 1
+    mat = np.zeros((n, n), dtype=c.dtype)
+    bot = mat.reshape(-1)[n::n+1]
+    bot[...] = 1
+    mat[:, -1] -= c[:-1]/c[-1]
+    return mat
+
+
+def polyroots(c):
+    """
+    Compute the roots of a polynomial.
+
+    Return the roots (a.k.a. "zeros") of the polynomial
+
+    .. math:: p(x) = \\sum_i c[i] * x^i.
+
+    Parameters
+    ----------
+    c : 1-D array_like
+        1-D array of polynomial coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        Array of the roots of the polynomial. If all the roots are real,
+        then `out` is also real, otherwise it is complex.
+
+    See Also
+    --------
+    numpy.polynomial.chebyshev.chebroots
+    numpy.polynomial.legendre.legroots
+    numpy.polynomial.laguerre.lagroots
+    numpy.polynomial.hermite.hermroots
+    numpy.polynomial.hermite_e.hermeroots
+
+    Notes
+    -----
+    The root estimates are obtained as the eigenvalues of the companion
+    matrix, Roots far from the origin of the complex plane may have large
+    errors due to the numerical instability of the power series for such
+    values. Roots with multiplicity greater than 1 will also show larger
+    errors as the value of the series near such points is relatively
+    insensitive to errors in the roots. Isolated roots near the origin can
+    be improved by a few iterations of Newton's method.
+
+    Examples
+    --------
+    >>> import numpy.polynomial.polynomial as poly
+    >>> poly.polyroots(poly.polyfromroots((-1,0,1)))
+    array([-1.,  0.,  1.])
+    >>> poly.polyroots(poly.polyfromroots((-1,0,1))).dtype
+    dtype('float64')
+    >>> j = complex(0,1)
+    >>> poly.polyroots(poly.polyfromroots((-j,0,j)))
+    array([  0.00000000e+00+0.j,   0.00000000e+00+1.j,   2.77555756e-17-1.j])  # may vary
+
+    """  # noqa: E501
+    # c is a trimmed copy
+    [c] = pu.as_series([c])
+    if len(c) < 2:
+        return np.array([], dtype=c.dtype)
+    if len(c) == 2:
+        return np.array([-c[0]/c[1]])
+
+    # rotated companion matrix reduces error
+    m = polycompanion(c)[::-1,::-1]
+    r = la.eigvals(m)
+    r.sort()
+    return r
+
+
+#
+# polynomial class
+#
+
+class Polynomial(ABCPolyBase):
+    """A power series class.
+
+    The Polynomial class provides the standard Python numerical methods
+    '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
+    attributes and methods listed below.
+
+    Parameters
+    ----------
+    coef : array_like
+        Polynomial coefficients in order of increasing degree, i.e.,
+        ``(1, 2, 3)`` give ``1 + 2*x + 3*x**2``.
+    domain : (2,) array_like, optional
+        Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
+        to the interval ``[window[0], window[1]]`` by shifting and scaling.
+        The default value is [-1., 1.].
+    window : (2,) array_like, optional
+        Window, see `domain` for its use. The default value is [-1., 1.].
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
+
+    """
+    # Virtual Functions
+    _add = staticmethod(polyadd)
+    _sub = staticmethod(polysub)
+    _mul = staticmethod(polymul)
+    _div = staticmethod(polydiv)
+    _pow = staticmethod(polypow)
+    _val = staticmethod(polyval)
+    _int = staticmethod(polyint)
+    _der = staticmethod(polyder)
+    _fit = staticmethod(polyfit)
+    _line = staticmethod(polyline)
+    _roots = staticmethod(polyroots)
+    _fromroots = staticmethod(polyfromroots)
+
+    # Virtual properties
+    domain = np.array(polydomain)
+    window = np.array(polydomain)
+    basis_name = None
+
+    @classmethod
+    def _str_term_unicode(cls, i, arg_str):
+        if i == '1':
+            return f"·{arg_str}"
+        else:
+            return f"·{arg_str}{i.translate(cls._superscript_mapping)}"
+
+    @staticmethod
+    def _str_term_ascii(i, arg_str):
+        if i == '1':
+            return f" {arg_str}"
+        else:
+            return f" {arg_str}**{i}"
+
+    @staticmethod
+    def _repr_latex_term(i, arg_str, needs_parens):
+        if needs_parens:
+            arg_str = rf"\left({arg_str}\right)"
+        if i == 0:
+            return '1'
+        elif i == 1:
+            return arg_str
+        else:
+            return f"{arg_str}^{{{i}}}"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polynomial.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polynomial.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..89a8b57185f3e326f8891e71ab2b47f48cd908e9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polynomial.pyi
@@ -0,0 +1,87 @@
+from typing import Final, Literal as L
+
+import numpy as np
+from ._polybase import ABCPolyBase
+from ._polytypes import (
+    _Array1,
+    _Array2,
+    _FuncVal2D,
+    _FuncVal3D,
+    _FuncBinOp,
+    _FuncCompanion,
+    _FuncDer,
+    _FuncFit,
+    _FuncFromRoots,
+    _FuncInteg,
+    _FuncLine,
+    _FuncPow,
+    _FuncRoots,
+    _FuncUnOp,
+    _FuncVal,
+    _FuncVander,
+    _FuncVander2D,
+    _FuncVander3D,
+    _FuncValFromRoots,
+)
+from .polyutils import trimcoef as polytrim
+
+__all__ = [
+    "polyzero",
+    "polyone",
+    "polyx",
+    "polydomain",
+    "polyline",
+    "polyadd",
+    "polysub",
+    "polymulx",
+    "polymul",
+    "polydiv",
+    "polypow",
+    "polyval",
+    "polyvalfromroots",
+    "polyder",
+    "polyint",
+    "polyfromroots",
+    "polyvander",
+    "polyfit",
+    "polytrim",
+    "polyroots",
+    "Polynomial",
+    "polyval2d",
+    "polyval3d",
+    "polygrid2d",
+    "polygrid3d",
+    "polyvander2d",
+    "polyvander3d",
+    "polycompanion",
+]
+
+polydomain: Final[_Array2[np.float64]]
+polyzero: Final[_Array1[np.int_]]
+polyone: Final[_Array1[np.int_]]
+polyx: Final[_Array2[np.int_]]
+
+polyline: _FuncLine[L["Polyline"]]
+polyfromroots: _FuncFromRoots[L["polyfromroots"]]
+polyadd: _FuncBinOp[L["polyadd"]]
+polysub: _FuncBinOp[L["polysub"]]
+polymulx: _FuncUnOp[L["polymulx"]]
+polymul: _FuncBinOp[L["polymul"]]
+polydiv: _FuncBinOp[L["polydiv"]]
+polypow: _FuncPow[L["polypow"]]
+polyder: _FuncDer[L["polyder"]]
+polyint: _FuncInteg[L["polyint"]]
+polyval: _FuncVal[L["polyval"]]
+polyval2d: _FuncVal2D[L["polyval2d"]]
+polyval3d: _FuncVal3D[L["polyval3d"]]
+polyvalfromroots: _FuncValFromRoots[L["polyvalfromroots"]]
+polygrid2d: _FuncVal2D[L["polygrid2d"]]
+polygrid3d: _FuncVal3D[L["polygrid3d"]]
+polyvander: _FuncVander[L["polyvander"]]
+polyvander2d: _FuncVander2D[L["polyvander2d"]]
+polyvander3d: _FuncVander3D[L["polyvander3d"]]
+polyfit: _FuncFit[L["polyfit"]]
+polycompanion: _FuncCompanion[L["polycompanion"]]
+polyroots: _FuncRoots[L["polyroots"]]
+
+class Polynomial(ABCPolyBase[None]): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polyutils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polyutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a6813b786c9bdd7eaa7961b5c50a5b187f7837a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polyutils.py
@@ -0,0 +1,757 @@
+"""
+Utility classes and functions for the polynomial modules.
+
+This module provides: error and warning objects; a polynomial base class;
+and some routines used in both the `polynomial` and `chebyshev` modules.
+
+Functions
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   as_series    convert list of array_likes into 1-D arrays of common type.
+   trimseq      remove trailing zeros.
+   trimcoef     remove small trailing coefficients.
+   getdomain    return the domain appropriate for a given set of abscissae.
+   mapdomain    maps points between domains.
+   mapparms     parameters of the linear map between domains.
+
+"""
+import operator
+import functools
+import warnings
+
+import numpy as np
+
+from numpy._core.multiarray import dragon4_positional, dragon4_scientific
+from numpy.exceptions import RankWarning
+
+__all__ = [
+    'as_series', 'trimseq', 'trimcoef', 'getdomain', 'mapdomain', 'mapparms',
+    'format_float']
+
+#
+# Helper functions to convert inputs to 1-D arrays
+#
+def trimseq(seq):
+    """Remove small Poly series coefficients.
+
+    Parameters
+    ----------
+    seq : sequence
+        Sequence of Poly series coefficients.
+
+    Returns
+    -------
+    series : sequence
+        Subsequence with trailing zeros removed. If the resulting sequence
+        would be empty, return the first element. The returned sequence may
+        or may not be a view.
+
+    Notes
+    -----
+    Do not lose the type info if the sequence contains unknown objects.
+
+    """
+    if len(seq) == 0 or seq[-1] != 0:
+        return seq
+    else:
+        for i in range(len(seq) - 1, -1, -1):
+            if seq[i] != 0:
+                break
+        return seq[:i+1]
+
+
+def as_series(alist, trim=True):
+    """
+    Return argument as a list of 1-d arrays.
+
+    The returned list contains array(s) of dtype double, complex double, or
+    object.  A 1-d argument of shape ``(N,)`` is parsed into ``N`` arrays of
+    size one; a 2-d argument of shape ``(M,N)`` is parsed into ``M`` arrays
+    of size ``N`` (i.e., is "parsed by row"); and a higher dimensional array
+    raises a Value Error if it is not first reshaped into either a 1-d or 2-d
+    array.
+
+    Parameters
+    ----------
+    alist : array_like
+        A 1- or 2-d array_like
+    trim : boolean, optional
+        When True, trailing zeros are removed from the inputs.
+        When False, the inputs are passed through intact.
+
+    Returns
+    -------
+    [a1, a2,...] : list of 1-D arrays
+        A copy of the input data as a list of 1-d arrays.
+
+    Raises
+    ------
+    ValueError
+        Raised when `as_series` cannot convert its input to 1-d arrays, or at
+        least one of the resulting arrays is empty.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial import polyutils as pu
+    >>> a = np.arange(4)
+    >>> pu.as_series(a)
+    [array([0.]), array([1.]), array([2.]), array([3.])]
+    >>> b = np.arange(6).reshape((2,3))
+    >>> pu.as_series(b)
+    [array([0., 1., 2.]), array([3., 4., 5.])]
+
+    >>> pu.as_series((1, np.arange(3), np.arange(2, dtype=np.float16)))
+    [array([1.]), array([0., 1., 2.]), array([0., 1.])]
+
+    >>> pu.as_series([2, [1.1, 0.]])
+    [array([2.]), array([1.1])]
+
+    >>> pu.as_series([2, [1.1, 0.]], trim=False)
+    [array([2.]), array([1.1, 0. ])]
+
+    """
+    arrays = [np.array(a, ndmin=1, copy=None) for a in alist]
+    for a in arrays:
+        if a.size == 0:
+            raise ValueError("Coefficient array is empty")
+    if any(a.ndim != 1 for a in arrays):
+        raise ValueError("Coefficient array is not 1-d")
+    if trim:
+        arrays = [trimseq(a) for a in arrays]
+
+    if any(a.dtype == np.dtype(object) for a in arrays):
+        ret = []
+        for a in arrays:
+            if a.dtype != np.dtype(object):
+                tmp = np.empty(len(a), dtype=np.dtype(object))
+                tmp[:] = a[:]
+                ret.append(tmp)
+            else:
+                ret.append(a.copy())
+    else:
+        try:
+            dtype = np.common_type(*arrays)
+        except Exception as e:
+            raise ValueError("Coefficient arrays have no common type") from e
+        ret = [np.array(a, copy=True, dtype=dtype) for a in arrays]
+    return ret
+
+
+def trimcoef(c, tol=0):
+    """
+    Remove "small" "trailing" coefficients from a polynomial.
+
+    "Small" means "small in absolute value" and is controlled by the
+    parameter `tol`; "trailing" means highest order coefficient(s), e.g., in
+    ``[0, 1, 1, 0, 0]`` (which represents ``0 + x + x**2 + 0*x**3 + 0*x**4``)
+    both the 3-rd and 4-th order coefficients would be "trimmed."
+
+    Parameters
+    ----------
+    c : array_like
+        1-d array of coefficients, ordered from lowest order to highest.
+    tol : number, optional
+        Trailing (i.e., highest order) elements with absolute value less
+        than or equal to `tol` (default value is zero) are removed.
+
+    Returns
+    -------
+    trimmed : ndarray
+        1-d array with trailing zeros removed.  If the resulting series
+        would be empty, a series containing a single zero is returned.
+
+    Raises
+    ------
+    ValueError
+        If `tol` < 0
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polyutils as pu
+    >>> pu.trimcoef((0,0,3,0,5,0,0))
+    array([0.,  0.,  3.,  0.,  5.])
+    >>> pu.trimcoef((0,0,1e-3,0,1e-5,0,0),1e-3) # item == tol is trimmed
+    array([0.])
+    >>> i = complex(0,1) # works for complex
+    >>> pu.trimcoef((3e-4,1e-3*(1-i),5e-4,2e-5*(1+i)), 1e-3)
+    array([0.0003+0.j   , 0.001 -0.001j])
+
+    """
+    if tol < 0:
+        raise ValueError("tol must be non-negative")
+
+    [c] = as_series([c])
+    [ind] = np.nonzero(np.abs(c) > tol)
+    if len(ind) == 0:
+        return c[:1]*0
+    else:
+        return c[:ind[-1] + 1].copy()
+
+def getdomain(x):
+    """
+    Return a domain suitable for given abscissae.
+
+    Find a domain suitable for a polynomial or Chebyshev series
+    defined at the values supplied.
+
+    Parameters
+    ----------
+    x : array_like
+        1-d array of abscissae whose domain will be determined.
+
+    Returns
+    -------
+    domain : ndarray
+        1-d array containing two values.  If the inputs are complex, then
+        the two returned points are the lower left and upper right corners
+        of the smallest rectangle (aligned with the axes) in the complex
+        plane containing the points `x`. If the inputs are real, then the
+        two points are the ends of the smallest interval containing the
+        points `x`.
+
+    See Also
+    --------
+    mapparms, mapdomain
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial import polyutils as pu
+    >>> points = np.arange(4)**2 - 5; points
+    array([-5, -4, -1,  4])
+    >>> pu.getdomain(points)
+    array([-5.,  4.])
+    >>> c = np.exp(complex(0,1)*np.pi*np.arange(12)/6) # unit circle
+    >>> pu.getdomain(c)
+    array([-1.-1.j,  1.+1.j])
+
+    """
+    [x] = as_series([x], trim=False)
+    if x.dtype.char in np.typecodes['Complex']:
+        rmin, rmax = x.real.min(), x.real.max()
+        imin, imax = x.imag.min(), x.imag.max()
+        return np.array((complex(rmin, imin), complex(rmax, imax)))
+    else:
+        return np.array((x.min(), x.max()))
+
+def mapparms(old, new):
+    """
+    Linear map parameters between domains.
+
+    Return the parameters of the linear map ``offset + scale*x`` that maps
+    `old` to `new` such that ``old[i] -> new[i]``, ``i = 0, 1``.
+
+    Parameters
+    ----------
+    old, new : array_like
+        Domains. Each domain must (successfully) convert to a 1-d array
+        containing precisely two values.
+
+    Returns
+    -------
+    offset, scale : scalars
+        The map ``L(x) = offset + scale*x`` maps the first domain to the
+        second.
+
+    See Also
+    --------
+    getdomain, mapdomain
+
+    Notes
+    -----
+    Also works for complex numbers, and thus can be used to calculate the
+    parameters required to map any line in the complex plane to any other
+    line therein.
+
+    Examples
+    --------
+    >>> from numpy.polynomial import polyutils as pu
+    >>> pu.mapparms((-1,1),(-1,1))
+    (0.0, 1.0)
+    >>> pu.mapparms((1,-1),(-1,1))
+    (-0.0, -1.0)
+    >>> i = complex(0,1)
+    >>> pu.mapparms((-i,-1),(1,i))
+    ((1+1j), (1-0j))
+
+    """
+    oldlen = old[1] - old[0]
+    newlen = new[1] - new[0]
+    off = (old[1]*new[0] - old[0]*new[1])/oldlen
+    scl = newlen/oldlen
+    return off, scl
+
+def mapdomain(x, old, new):
+    """
+    Apply linear map to input points.
+
+    The linear map ``offset + scale*x`` that maps the domain `old` to
+    the domain `new` is applied to the points `x`.
+
+    Parameters
+    ----------
+    x : array_like
+        Points to be mapped. If `x` is a subtype of ndarray the subtype
+        will be preserved.
+    old, new : array_like
+        The two domains that determine the map.  Each must (successfully)
+        convert to 1-d arrays containing precisely two values.
+
+    Returns
+    -------
+    x_out : ndarray
+        Array of points of the same shape as `x`, after application of the
+        linear map between the two domains.
+
+    See Also
+    --------
+    getdomain, mapparms
+
+    Notes
+    -----
+    Effectively, this implements:
+
+    .. math::
+        x\\_out = new[0] + m(x - old[0])
+
+    where
+
+    .. math::
+        m = \\frac{new[1]-new[0]}{old[1]-old[0]}
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.polynomial import polyutils as pu
+    >>> old_domain = (-1,1)
+    >>> new_domain = (0,2*np.pi)
+    >>> x = np.linspace(-1,1,6); x
+    array([-1. , -0.6, -0.2,  0.2,  0.6,  1. ])
+    >>> x_out = pu.mapdomain(x, old_domain, new_domain); x_out
+    array([ 0.        ,  1.25663706,  2.51327412,  3.76991118,  5.02654825, # may vary
+            6.28318531])
+    >>> x - pu.mapdomain(x_out, new_domain, old_domain)
+    array([0., 0., 0., 0., 0., 0.])
+
+    Also works for complex numbers (and thus can be used to map any line in
+    the complex plane to any other line therein).
+
+    >>> i = complex(0,1)
+    >>> old = (-1 - i, 1 + i)
+    >>> new = (-1 + i, 1 - i)
+    >>> z = np.linspace(old[0], old[1], 6); z
+    array([-1. -1.j , -0.6-0.6j, -0.2-0.2j,  0.2+0.2j,  0.6+0.6j,  1. +1.j ])
+    >>> new_z = pu.mapdomain(z, old, new); new_z
+    array([-1.0+1.j , -0.6+0.6j, -0.2+0.2j,  0.2-0.2j,  0.6-0.6j,  1.0-1.j ]) # may vary
+
+    """
+    if type(x) not in (int, float, complex) and not isinstance(x, np.generic):
+        x = np.asanyarray(x)
+    off, scl = mapparms(old, new)
+    return off + scl*x
+
+
+def _nth_slice(i, ndim):
+    sl = [np.newaxis] * ndim
+    sl[i] = slice(None)
+    return tuple(sl)
+
+
+def _vander_nd(vander_fs, points, degrees):
+    r"""
+    A generalization of the Vandermonde matrix for N dimensions
+
+    The result is built by combining the results of 1d Vandermonde matrices,
+
+    .. math::
+        W[i_0, \ldots, i_M, j_0, \ldots, j_N] = \prod_{k=0}^N{V_k(x_k)[i_0, \ldots, i_M, j_k]}
+
+    where
+
+    .. math::
+        N &= \texttt{len(points)} = \texttt{len(degrees)} = \texttt{len(vander\_fs)} \\
+        M &= \texttt{points[k].ndim} \\
+        V_k &= \texttt{vander\_fs[k]} \\
+        x_k &= \texttt{points[k]} \\
+        0 \le j_k &\le \texttt{degrees[k]}
+
+    Expanding the one-dimensional :math:`V_k` functions gives:
+
+    .. math::
+        W[i_0, \ldots, i_M, j_0, \ldots, j_N] = \prod_{k=0}^N{B_{k, j_k}(x_k[i_0, \ldots, i_M])}
+
+    where :math:`B_{k,m}` is the m'th basis of the polynomial construction used along
+    dimension :math:`k`. For a regular polynomial, :math:`B_{k, m}(x) = P_m(x) = x^m`.
+
+    Parameters
+    ----------
+    vander_fs : Sequence[function(array_like, int) -> ndarray]
+        The 1d vander function to use for each axis, such as ``polyvander``
+    points : Sequence[array_like]
+        Arrays of point coordinates, all of the same shape. The dtypes
+        will be converted to either float64 or complex128 depending on
+        whether any of the elements are complex. Scalars are converted to
+        1-D arrays.
+        This must be the same length as `vander_fs`.
+    degrees : Sequence[int]
+        The maximum degree (inclusive) to use for each axis.
+        This must be the same length as `vander_fs`.
+
+    Returns
+    -------
+    vander_nd : ndarray
+        An array of shape ``points[0].shape + tuple(d + 1 for d in degrees)``.
+    """
+    n_dims = len(vander_fs)
+    if n_dims != len(points):
+        raise ValueError(
+            f"Expected {n_dims} dimensions of sample points, got {len(points)}")
+    if n_dims != len(degrees):
+        raise ValueError(
+            f"Expected {n_dims} dimensions of degrees, got {len(degrees)}")
+    if n_dims == 0:
+        raise ValueError("Unable to guess a dtype or shape when no points are given")
+
+    # convert to the same shape and type
+    points = tuple(np.asarray(tuple(points)) + 0.0)
+
+    # produce the vandermonde matrix for each dimension, placing the last
+    # axis of each in an independent trailing axis of the output
+    vander_arrays = (
+        vander_fs[i](points[i], degrees[i])[(...,) + _nth_slice(i, n_dims)]
+        for i in range(n_dims)
+    )
+
+    # we checked this wasn't empty already, so no `initial` needed
+    return functools.reduce(operator.mul, vander_arrays)
+
+
+def _vander_nd_flat(vander_fs, points, degrees):
+    """
+    Like `_vander_nd`, but flattens the last ``len(degrees)`` axes into a single axis
+
+    Used to implement the public ``<type>vander<n>d`` functions.
+    """
+    v = _vander_nd(vander_fs, points, degrees)
+    return v.reshape(v.shape[:-len(degrees)] + (-1,))
+
+
+def _fromroots(line_f, mul_f, roots):
+    """
+    Helper function used to implement the ``<type>fromroots`` functions.
+
+    Parameters
+    ----------
+    line_f : function(float, float) -> ndarray
+        The ``<type>line`` function, such as ``polyline``
+    mul_f : function(array_like, array_like) -> ndarray
+        The ``<type>mul`` function, such as ``polymul``
+    roots
+        See the ``<type>fromroots`` functions for more detail
+    """
+    if len(roots) == 0:
+        return np.ones(1)
+    else:
+        [roots] = as_series([roots], trim=False)
+        roots.sort()
+        p = [line_f(-r, 1) for r in roots]
+        n = len(p)
+        while n > 1:
+            m, r = divmod(n, 2)
+            tmp = [mul_f(p[i], p[i+m]) for i in range(m)]
+            if r:
+                tmp[0] = mul_f(tmp[0], p[-1])
+            p = tmp
+            n = m
+        return p[0]
+
+
+def _valnd(val_f, c, *args):
+    """
+    Helper function used to implement the ``<type>val<n>d`` functions.
+
+    Parameters
+    ----------
+    val_f : function(array_like, array_like, tensor: bool) -> array_like
+        The ``<type>val`` function, such as ``polyval``
+    c, args
+        See the ``<type>val<n>d`` functions for more detail
+    """
+    args = [np.asanyarray(a) for a in args]
+    shape0 = args[0].shape
+    if not all(a.shape == shape0 for a in args[1:]):
+        if len(args) == 3:
+            raise ValueError('x, y, z are incompatible')
+        elif len(args) == 2:
+            raise ValueError('x, y are incompatible')
+        else:
+            raise ValueError('ordinates are incompatible')
+    it = iter(args)
+    x0 = next(it)
+
+    # use tensor on only the first
+    c = val_f(x0, c)
+    for xi in it:
+        c = val_f(xi, c, tensor=False)
+    return c
+
+
+def _gridnd(val_f, c, *args):
+    """
+    Helper function used to implement the ``<type>grid<n>d`` functions.
+
+    Parameters
+    ----------
+    val_f : function(array_like, array_like, tensor: bool) -> array_like
+        The ``<type>val`` function, such as ``polyval``
+    c, args
+        See the ``<type>grid<n>d`` functions for more detail
+    """
+    for xi in args:
+        c = val_f(xi, c)
+    return c
+
+
+def _div(mul_f, c1, c2):
+    """
+    Helper function used to implement the ``<type>div`` functions.
+
+    Implementation uses repeated subtraction of c2 multiplied by the nth basis.
+    For some polynomial types, a more efficient approach may be possible.
+
+    Parameters
+    ----------
+    mul_f : function(array_like, array_like) -> array_like
+        The ``<type>mul`` function, such as ``polymul``
+    c1, c2
+        See the ``<type>div`` functions for more detail
+    """
+    # c1, c2 are trimmed copies
+    [c1, c2] = as_series([c1, c2])
+    if c2[-1] == 0:
+        raise ZeroDivisionError  # FIXME: add message with details to exception
+
+    lc1 = len(c1)
+    lc2 = len(c2)
+    if lc1 < lc2:
+        return c1[:1]*0, c1
+    elif lc2 == 1:
+        return c1/c2[-1], c1[:1]*0
+    else:
+        quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype)
+        rem = c1
+        for i in range(lc1 - lc2, - 1, -1):
+            p = mul_f([0]*i + [1], c2)
+            q = rem[-1]/p[-1]
+            rem = rem[:-1] - q*p[:-1]
+            quo[i] = q
+        return quo, trimseq(rem)
+
+
+def _add(c1, c2):
+    """ Helper function used to implement the ``<type>add`` functions. """
+    # c1, c2 are trimmed copies
+    [c1, c2] = as_series([c1, c2])
+    if len(c1) > len(c2):
+        c1[:c2.size] += c2
+        ret = c1
+    else:
+        c2[:c1.size] += c1
+        ret = c2
+    return trimseq(ret)
+
+
+def _sub(c1, c2):
+    """ Helper function used to implement the ``<type>sub`` functions. """
+    # c1, c2 are trimmed copies
+    [c1, c2] = as_series([c1, c2])
+    if len(c1) > len(c2):
+        c1[:c2.size] -= c2
+        ret = c1
+    else:
+        c2 = -c2
+        c2[:c1.size] += c1
+        ret = c2
+    return trimseq(ret)
+
+
+def _fit(vander_f, x, y, deg, rcond=None, full=False, w=None):
+    """
+    Helper function used to implement the ``<type>fit`` functions.
+
+    Parameters
+    ----------
+    vander_f : function(array_like, int) -> ndarray
+        The 1d vander function, such as ``polyvander``
+    c1, c2
+        See the ``<type>fit`` functions for more detail
+    """
+    x = np.asarray(x) + 0.0
+    y = np.asarray(y) + 0.0
+    deg = np.asarray(deg)
+
+    # check arguments.
+    if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0:
+        raise TypeError("deg must be an int or non-empty 1-D array of int")
+    if deg.min() < 0:
+        raise ValueError("expected deg >= 0")
+    if x.ndim != 1:
+        raise TypeError("expected 1D vector for x")
+    if x.size == 0:
+        raise TypeError("expected non-empty vector for x")
+    if y.ndim < 1 or y.ndim > 2:
+        raise TypeError("expected 1D or 2D array for y")
+    if len(x) != len(y):
+        raise TypeError("expected x and y to have same length")
+
+    if deg.ndim == 0:
+        lmax = deg
+        order = lmax + 1
+        van = vander_f(x, lmax)
+    else:
+        deg = np.sort(deg)
+        lmax = deg[-1]
+        order = len(deg)
+        van = vander_f(x, lmax)[:, deg]
+
+    # set up the least squares matrices in transposed form
+    lhs = van.T
+    rhs = y.T
+    if w is not None:
+        w = np.asarray(w) + 0.0
+        if w.ndim != 1:
+            raise TypeError("expected 1D vector for w")
+        if len(x) != len(w):
+            raise TypeError("expected x and w to have same length")
+        # apply weights. Don't use inplace operations as they
+        # can cause problems with NA.
+        lhs = lhs * w
+        rhs = rhs * w
+
+    # set rcond
+    if rcond is None:
+        rcond = len(x)*np.finfo(x.dtype).eps
+
+    # Determine the norms of the design matrix columns.
+    if issubclass(lhs.dtype.type, np.complexfloating):
+        scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1))
+    else:
+        scl = np.sqrt(np.square(lhs).sum(1))
+    scl[scl == 0] = 1
+
+    # Solve the least squares problem.
+    c, resids, rank, s = np.linalg.lstsq(lhs.T/scl, rhs.T, rcond)
+    c = (c.T/scl).T
+
+    # Expand c to include non-fitted coefficients which are set to zero
+    if deg.ndim > 0:
+        if c.ndim == 2:
+            cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype)
+        else:
+            cc = np.zeros(lmax+1, dtype=c.dtype)
+        cc[deg] = c
+        c = cc
+
+    # warn on rank reduction
+    if rank != order and not full:
+        msg = "The fit may be poorly conditioned"
+        warnings.warn(msg, RankWarning, stacklevel=2)
+
+    if full:
+        return c, [resids, rank, s, rcond]
+    else:
+        return c
+
+
+def _pow(mul_f, c, pow, maxpower):
+    """
+    Helper function used to implement the ``<type>pow`` functions.
+
+    Parameters
+    ----------
+    mul_f : function(array_like, array_like) -> ndarray
+        The ``<type>mul`` function, such as ``polymul``
+    c : array_like
+        1-D array of array of series coefficients
+    pow, maxpower
+        See the ``<type>pow`` functions for more detail
+    """
+    # c is a trimmed copy
+    [c] = as_series([c])
+    power = int(pow)
+    if power != pow or power < 0:
+        raise ValueError("Power must be a non-negative integer.")
+    elif maxpower is not None and power > maxpower:
+        raise ValueError("Power is too large")
+    elif power == 0:
+        return np.array([1], dtype=c.dtype)
+    elif power == 1:
+        return c
+    else:
+        # This can be made more efficient by using powers of two
+        # in the usual way.
+        prd = c
+        for i in range(2, power + 1):
+            prd = mul_f(prd, c)
+        return prd
+
+
+def _as_int(x, desc):
+    """
+    Like `operator.index`, but emits a custom exception when passed an
+    incorrect type
+
+    Parameters
+    ----------
+    x : int-like
+        Value to interpret as an integer
+    desc : str
+        description to include in any error message
+
+    Raises
+    ------
+    TypeError : if x is a float or non-numeric
+    """
+    try:
+        return operator.index(x)
+    except TypeError as e:
+        raise TypeError(f"{desc} must be an integer, received {x}") from e
+
+
+def format_float(x, parens=False):
+    if not np.issubdtype(type(x), np.floating):
+        return str(x)
+
+    opts = np.get_printoptions()
+
+    if np.isnan(x):
+        return opts['nanstr']
+    elif np.isinf(x):
+        return opts['infstr']
+
+    exp_format = False
+    if x != 0:
+        a = np.abs(x)
+        if a >= 1.e8 or a < 10**min(0, -(opts['precision']-1)//2):
+            exp_format = True
+
+    trim, unique = '0', True
+    if opts['floatmode'] == 'fixed':
+        trim, unique = 'k', False
+
+    if exp_format:
+        s = dragon4_scientific(x, precision=opts['precision'],
+                               unique=unique, trim=trim,
+                               sign=opts['sign'] == '+')
+        if parens:
+            s = '(' + s + ')'
+    else:
+        s = dragon4_positional(x, precision=opts['precision'],
+                               fractional=True,
+                               unique=unique, trim=trim,
+                               sign=opts['sign'] == '+')
+    return s
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polyutils.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polyutils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9299b23975b1ff9c59d36c9e6e804e06d415cf4b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/polyutils.pyi
@@ -0,0 +1,431 @@
+from collections.abc import Callable, Iterable, Sequence
+from typing import (
+    Any,
+    Final,
+    Literal,
+    SupportsIndex,
+    TypeAlias,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import (
+    _FloatLike_co,
+    _NumberLike_co,
+
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+)
+
+from ._polytypes import (
+    _AnyInt,
+    _CoefLike_co,
+
+    _Array2,
+    _Tuple2,
+
+    _FloatSeries,
+    _CoefSeries,
+    _ComplexSeries,
+    _ObjectSeries,
+
+    _ComplexArray,
+    _FloatArray,
+    _CoefArray,
+    _ObjectArray,
+
+    _SeriesLikeInt_co,
+    _SeriesLikeFloat_co,
+    _SeriesLikeComplex_co,
+    _SeriesLikeCoef_co,
+
+    _ArrayLikeCoef_co,
+
+    _FuncBinOp,
+    _FuncValND,
+    _FuncVanderND,
+)
+
+__all__: Final[Sequence[str]] = [
+    "as_series",
+    "format_float",
+    "getdomain",
+    "mapdomain",
+    "mapparms",
+    "trimcoef",
+    "trimseq",
+]
+
+_AnyLineF: TypeAlias = Callable[
+    [_CoefLike_co, _CoefLike_co],
+    _CoefArray,
+]
+_AnyMulF: TypeAlias = Callable[
+    [npt.ArrayLike, npt.ArrayLike],
+    _CoefArray,
+]
+_AnyVanderF: TypeAlias = Callable[
+    [npt.ArrayLike, SupportsIndex],
+    _CoefArray,
+]
+
+@overload
+def as_series(
+    alist: npt.NDArray[np.integer[Any]] | _FloatArray,
+    trim: bool = ...,
+) -> list[_FloatSeries]: ...
+@overload
+def as_series(
+    alist: _ComplexArray,
+    trim: bool = ...,
+) -> list[_ComplexSeries]: ...
+@overload
+def as_series(
+    alist: _ObjectArray,
+    trim: bool = ...,
+) -> list[_ObjectSeries]: ...
+@overload
+def as_series(  # type: ignore[overload-overlap]
+    alist: Iterable[_FloatArray | npt.NDArray[np.integer[Any]]],
+    trim: bool = ...,
+) -> list[_FloatSeries]: ...
+@overload
+def as_series(
+    alist: Iterable[_ComplexArray],
+    trim: bool = ...,
+) -> list[_ComplexSeries]: ...
+@overload
+def as_series(
+    alist: Iterable[_ObjectArray],
+    trim: bool = ...,
+) -> list[_ObjectSeries]: ...
+@overload
+def as_series(  # type: ignore[overload-overlap]
+    alist: Iterable[_SeriesLikeFloat_co | float],
+    trim: bool = ...,
+) -> list[_FloatSeries]: ...
+@overload
+def as_series(
+    alist: Iterable[_SeriesLikeComplex_co | complex],
+    trim: bool = ...,
+) -> list[_ComplexSeries]: ...
+@overload
+def as_series(
+    alist: Iterable[_SeriesLikeCoef_co | object],
+    trim: bool = ...,
+) -> list[_ObjectSeries]: ...
+
+_T_seq = TypeVar("_T_seq", bound=_CoefArray | Sequence[_CoefLike_co])
+def trimseq(seq: _T_seq) -> _T_seq: ...
+
+@overload
+def trimcoef(  # type: ignore[overload-overlap]
+    c: npt.NDArray[np.integer[Any]] | _FloatArray,
+    tol: _FloatLike_co = ...,
+) -> _FloatSeries: ...
+@overload
+def trimcoef(
+    c: _ComplexArray,
+    tol: _FloatLike_co = ...,
+) -> _ComplexSeries: ...
+@overload
+def trimcoef(
+    c: _ObjectArray,
+    tol: _FloatLike_co = ...,
+) -> _ObjectSeries: ...
+@overload
+def trimcoef(  # type: ignore[overload-overlap]
+    c: _SeriesLikeFloat_co | float,
+    tol: _FloatLike_co = ...,
+) -> _FloatSeries: ...
+@overload
+def trimcoef(
+    c: _SeriesLikeComplex_co | complex,
+    tol: _FloatLike_co = ...,
+) -> _ComplexSeries: ...
+@overload
+def trimcoef(
+    c: _SeriesLikeCoef_co | object,
+    tol: _FloatLike_co = ...,
+) -> _ObjectSeries: ...
+
+@overload
+def getdomain(  # type: ignore[overload-overlap]
+    x: _FloatArray | npt.NDArray[np.integer[Any]],
+) -> _Array2[np.float64]: ...
+@overload
+def getdomain(
+    x: _ComplexArray,
+) -> _Array2[np.complex128]: ...
+@overload
+def getdomain(
+    x: _ObjectArray,
+) -> _Array2[np.object_]: ...
+@overload
+def getdomain(  # type: ignore[overload-overlap]
+    x: _SeriesLikeFloat_co | float,
+) -> _Array2[np.float64]: ...
+@overload
+def getdomain(
+    x: _SeriesLikeComplex_co | complex,
+) -> _Array2[np.complex128]: ...
+@overload
+def getdomain(
+    x: _SeriesLikeCoef_co | object,
+) -> _Array2[np.object_]: ...
+
+@overload
+def mapparms(  # type: ignore[overload-overlap]
+    old: npt.NDArray[np.floating[Any] | np.integer[Any]],
+    new: npt.NDArray[np.floating[Any] | np.integer[Any]],
+) -> _Tuple2[np.floating[Any]]: ...
+@overload
+def mapparms(
+    old: npt.NDArray[np.number[Any]],
+    new: npt.NDArray[np.number[Any]],
+) -> _Tuple2[np.complexfloating[Any, Any]]: ...
+@overload
+def mapparms(
+    old: npt.NDArray[np.object_ | np.number[Any]],
+    new: npt.NDArray[np.object_ | np.number[Any]],
+) -> _Tuple2[object]: ...
+@overload
+def mapparms(  # type: ignore[overload-overlap]
+    old: Sequence[float],
+    new: Sequence[float],
+) -> _Tuple2[float]: ...
+@overload
+def mapparms(
+    old: Sequence[complex],
+    new: Sequence[complex],
+) -> _Tuple2[complex]: ...
+@overload
+def mapparms(
+    old: _SeriesLikeFloat_co,
+    new: _SeriesLikeFloat_co,
+) -> _Tuple2[np.floating[Any]]: ...
+@overload
+def mapparms(
+    old: _SeriesLikeComplex_co,
+    new: _SeriesLikeComplex_co,
+) -> _Tuple2[np.complexfloating[Any, Any]]: ...
+@overload
+def mapparms(
+    old: _SeriesLikeCoef_co,
+    new: _SeriesLikeCoef_co,
+) -> _Tuple2[object]: ...
+
+@overload
+def mapdomain(  # type: ignore[overload-overlap]
+    x: _FloatLike_co,
+    old: _SeriesLikeFloat_co,
+    new: _SeriesLikeFloat_co,
+) -> np.floating[Any]: ...
+@overload
+def mapdomain(
+    x: _NumberLike_co,
+    old: _SeriesLikeComplex_co,
+    new: _SeriesLikeComplex_co,
+) -> np.complexfloating[Any, Any]: ...
+@overload
+def mapdomain(  # type: ignore[overload-overlap]
+    x: npt.NDArray[np.floating[Any] | np.integer[Any]],
+    old: npt.NDArray[np.floating[Any] | np.integer[Any]],
+    new: npt.NDArray[np.floating[Any] | np.integer[Any]],
+) -> _FloatSeries: ...
+@overload
+def mapdomain(
+    x: npt.NDArray[np.number[Any]],
+    old: npt.NDArray[np.number[Any]],
+    new: npt.NDArray[np.number[Any]],
+) -> _ComplexSeries: ...
+@overload
+def mapdomain(
+    x: npt.NDArray[np.object_ | np.number[Any]],
+    old: npt.NDArray[np.object_ | np.number[Any]],
+    new: npt.NDArray[np.object_ | np.number[Any]],
+) -> _ObjectSeries: ...
+@overload
+def mapdomain(  # type: ignore[overload-overlap]
+    x: _SeriesLikeFloat_co,
+    old: _SeriesLikeFloat_co,
+    new: _SeriesLikeFloat_co,
+) -> _FloatSeries: ...
+@overload
+def mapdomain(
+    x: _SeriesLikeComplex_co,
+    old: _SeriesLikeComplex_co,
+    new: _SeriesLikeComplex_co,
+) -> _ComplexSeries: ...
+@overload
+def mapdomain(
+    x: _SeriesLikeCoef_co,
+    old:_SeriesLikeCoef_co,
+    new: _SeriesLikeCoef_co,
+) -> _ObjectSeries: ...
+@overload
+def mapdomain(
+    x: _CoefLike_co,
+    old: _SeriesLikeCoef_co,
+    new: _SeriesLikeCoef_co,
+) -> object: ...
+
+def _nth_slice(
+    i: SupportsIndex,
+    ndim: SupportsIndex,
+) -> tuple[None | slice, ...]: ...
+
+_vander_nd: _FuncVanderND[Literal["_vander_nd"]]
+_vander_nd_flat: _FuncVanderND[Literal["_vander_nd_flat"]]
+
+# keep in sync with `._polytypes._FuncFromRoots`
+@overload
+def _fromroots(  # type: ignore[overload-overlap]
+    line_f: _AnyLineF,
+    mul_f: _AnyMulF,
+    roots: _SeriesLikeFloat_co,
+) -> _FloatSeries: ...
+@overload
+def _fromroots(
+    line_f: _AnyLineF,
+    mul_f: _AnyMulF,
+    roots: _SeriesLikeComplex_co,
+) -> _ComplexSeries: ...
+@overload
+def _fromroots(
+    line_f: _AnyLineF,
+    mul_f: _AnyMulF,
+    roots: _SeriesLikeCoef_co,
+) -> _ObjectSeries: ...
+@overload
+def _fromroots(
+    line_f: _AnyLineF,
+    mul_f: _AnyMulF,
+    roots: _SeriesLikeCoef_co,
+) -> _CoefSeries: ...
+
+_valnd: _FuncValND[Literal["_valnd"]]
+_gridnd: _FuncValND[Literal["_gridnd"]]
+
+# keep in sync with `_polytypes._FuncBinOp`
+@overload
+def _div(  # type: ignore[overload-overlap]
+    mul_f: _AnyMulF,
+    c1: _SeriesLikeFloat_co,
+    c2: _SeriesLikeFloat_co,
+) -> _Tuple2[_FloatSeries]: ...
+@overload
+def _div(
+    mul_f: _AnyMulF,
+    c1: _SeriesLikeComplex_co,
+    c2: _SeriesLikeComplex_co,
+) -> _Tuple2[_ComplexSeries]: ...
+@overload
+def _div(
+    mul_f: _AnyMulF,
+    c1: _SeriesLikeCoef_co,
+    c2: _SeriesLikeCoef_co,
+) -> _Tuple2[_ObjectSeries]: ...
+@overload
+def _div(
+    mul_f: _AnyMulF,
+    c1: _SeriesLikeCoef_co,
+    c2: _SeriesLikeCoef_co,
+) -> _Tuple2[_CoefSeries]: ...
+
+_add: Final[_FuncBinOp]
+_sub: Final[_FuncBinOp]
+
+# keep in sync with `_polytypes._FuncPow`
+@overload
+def _pow(  # type: ignore[overload-overlap]
+    mul_f: _AnyMulF,
+    c: _SeriesLikeFloat_co,
+    pow: _AnyInt,
+    maxpower: None | _AnyInt = ...,
+) -> _FloatSeries: ...
+@overload
+def _pow(
+    mul_f: _AnyMulF,
+    c: _SeriesLikeComplex_co,
+    pow: _AnyInt,
+    maxpower: None | _AnyInt = ...,
+) -> _ComplexSeries: ...
+@overload
+def _pow(
+    mul_f: _AnyMulF,
+    c: _SeriesLikeCoef_co,
+    pow: _AnyInt,
+    maxpower: None | _AnyInt = ...,
+) -> _ObjectSeries: ...
+@overload
+def _pow(
+    mul_f: _AnyMulF,
+    c: _SeriesLikeCoef_co,
+    pow: _AnyInt,
+    maxpower: None | _AnyInt = ...,
+) -> _CoefSeries: ...
+
+# keep in sync with `_polytypes._FuncFit`
+@overload
+def _fit(  # type: ignore[overload-overlap]
+    vander_f: _AnyVanderF,
+    x: _SeriesLikeFloat_co,
+    y: _ArrayLikeFloat_co,
+    deg: _SeriesLikeInt_co,
+    domain: None | _SeriesLikeFloat_co = ...,
+    rcond: None | _FloatLike_co = ...,
+    full: Literal[False] = ...,
+    w: None | _SeriesLikeFloat_co = ...,
+) -> _FloatArray: ...
+@overload
+def _fit(
+    vander_f: _AnyVanderF,
+    x: _SeriesLikeComplex_co,
+    y: _ArrayLikeComplex_co,
+    deg: _SeriesLikeInt_co,
+    domain: None | _SeriesLikeComplex_co = ...,
+    rcond: None | _FloatLike_co = ...,
+    full: Literal[False] = ...,
+    w: None | _SeriesLikeComplex_co = ...,
+) -> _ComplexArray: ...
+@overload
+def _fit(
+    vander_f: _AnyVanderF,
+    x: _SeriesLikeCoef_co,
+    y: _ArrayLikeCoef_co,
+    deg: _SeriesLikeInt_co,
+    domain: None | _SeriesLikeCoef_co = ...,
+    rcond: None | _FloatLike_co = ...,
+    full: Literal[False] = ...,
+    w: None | _SeriesLikeCoef_co = ...,
+) -> _CoefArray: ...
+@overload
+def _fit(
+    vander_f: _AnyVanderF,
+    x: _SeriesLikeCoef_co,
+    y: _SeriesLikeCoef_co,
+    deg: _SeriesLikeInt_co,
+    domain: None | _SeriesLikeCoef_co,
+    rcond: None | _FloatLike_co ,
+    full: Literal[True],
+    /,
+    w: None | _SeriesLikeCoef_co = ...,
+) -> tuple[_CoefSeries, Sequence[np.inexact[Any] | np.int32]]: ...
+@overload
+def _fit(
+    vander_f: _AnyVanderF,
+    x: _SeriesLikeCoef_co,
+    y: _SeriesLikeCoef_co,
+    deg: _SeriesLikeInt_co,
+    domain: None | _SeriesLikeCoef_co = ...,
+    rcond: None | _FloatLike_co = ...,
+    *,
+    full: Literal[True],
+    w: None | _SeriesLikeCoef_co = ...,
+) -> tuple[_CoefSeries, Sequence[np.inexact[Any] | np.int32]]: ...
+
+def _as_int(x: SupportsIndex, desc: str) -> int: ...
+def format_float(x: _FloatLike_co, parens: bool = ...) -> str: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_chebyshev.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_chebyshev.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f54bebfdb27d54f436378e4ab6d6c8f2426dd90
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_chebyshev.py
@@ -0,0 +1,619 @@
+"""Tests for chebyshev module.
+
+"""
+from functools import reduce
+
+import numpy as np
+import numpy.polynomial.chebyshev as cheb
+from numpy.polynomial.polynomial import polyval
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+
+
+def trim(x):
+    return cheb.chebtrim(x, tol=1e-6)
+
+T0 = [1]
+T1 = [0, 1]
+T2 = [-1, 0, 2]
+T3 = [0, -3, 0, 4]
+T4 = [1, 0, -8, 0, 8]
+T5 = [0, 5, 0, -20, 0, 16]
+T6 = [-1, 0, 18, 0, -48, 0, 32]
+T7 = [0, -7, 0, 56, 0, -112, 0, 64]
+T8 = [1, 0, -32, 0, 160, 0, -256, 0, 128]
+T9 = [0, 9, 0, -120, 0, 432, 0, -576, 0, 256]
+
+Tlist = [T0, T1, T2, T3, T4, T5, T6, T7, T8, T9]
+
+
+class TestPrivate:
+
+    def test__cseries_to_zseries(self):
+        for i in range(5):
+            inp = np.array([2] + [1]*i, np.double)
+            tgt = np.array([.5]*i + [2] + [.5]*i, np.double)
+            res = cheb._cseries_to_zseries(inp)
+            assert_equal(res, tgt)
+
+    def test__zseries_to_cseries(self):
+        for i in range(5):
+            inp = np.array([.5]*i + [2] + [.5]*i, np.double)
+            tgt = np.array([2] + [1]*i, np.double)
+            res = cheb._zseries_to_cseries(inp)
+            assert_equal(res, tgt)
+
+
+class TestConstants:
+
+    def test_chebdomain(self):
+        assert_equal(cheb.chebdomain, [-1, 1])
+
+    def test_chebzero(self):
+        assert_equal(cheb.chebzero, [0])
+
+    def test_chebone(self):
+        assert_equal(cheb.chebone, [1])
+
+    def test_chebx(self):
+        assert_equal(cheb.chebx, [0, 1])
+
+
+class TestArithmetic:
+
+    def test_chebadd(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] += 1
+                res = cheb.chebadd([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_chebsub(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] -= 1
+                res = cheb.chebsub([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_chebmulx(self):
+        assert_equal(cheb.chebmulx([0]), [0])
+        assert_equal(cheb.chebmulx([1]), [0, 1])
+        for i in range(1, 5):
+            ser = [0]*i + [1]
+            tgt = [0]*(i - 1) + [.5, 0, .5]
+            assert_equal(cheb.chebmulx(ser), tgt)
+
+    def test_chebmul(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(i + j + 1)
+                tgt[i + j] += .5
+                tgt[abs(i - j)] += .5
+                res = cheb.chebmul([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_chebdiv(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                ci = [0]*i + [1]
+                cj = [0]*j + [1]
+                tgt = cheb.chebadd(ci, cj)
+                quo, rem = cheb.chebdiv(tgt, ci)
+                res = cheb.chebadd(cheb.chebmul(quo, ci), rem)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_chebpow(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                c = np.arange(i + 1)
+                tgt = reduce(cheb.chebmul, [c]*j, np.array([1]))
+                res = cheb.chebpow(c, j)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+
+class TestEvaluation:
+    # coefficients of 1 + 2*x + 3*x**2
+    c1d = np.array([2.5, 2., 1.5])
+    c2d = np.einsum('i,j->ij', c1d, c1d)
+    c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d)
+
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+    y = polyval(x, [1., 2., 3.])
+
+    def test_chebval(self):
+        #check empty input
+        assert_equal(cheb.chebval([], [1]).size, 0)
+
+        #check normal input)
+        x = np.linspace(-1, 1)
+        y = [polyval(x, c) for c in Tlist]
+        for i in range(10):
+            msg = f"At i={i}"
+            tgt = y[i]
+            res = cheb.chebval(x, [0]*i + [1])
+            assert_almost_equal(res, tgt, err_msg=msg)
+
+        #check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(cheb.chebval(x, [1]).shape, dims)
+            assert_equal(cheb.chebval(x, [1, 0]).shape, dims)
+            assert_equal(cheb.chebval(x, [1, 0, 0]).shape, dims)
+
+    def test_chebval2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, cheb.chebval2d, x1, x2[:2], self.c2d)
+
+        #test values
+        tgt = y1*y2
+        res = cheb.chebval2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = cheb.chebval2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3))
+
+    def test_chebval3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, cheb.chebval3d, x1, x2, x3[:2], self.c3d)
+
+        #test values
+        tgt = y1*y2*y3
+        res = cheb.chebval3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = cheb.chebval3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3))
+
+    def test_chebgrid2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j->ij', y1, y2)
+        res = cheb.chebgrid2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = cheb.chebgrid2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3)*2)
+
+    def test_chebgrid3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j,k->ijk', y1, y2, y3)
+        res = cheb.chebgrid3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = cheb.chebgrid3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3)*3)
+
+
+class TestIntegral:
+
+    def test_chebint(self):
+        # check exceptions
+        assert_raises(TypeError, cheb.chebint, [0], .5)
+        assert_raises(ValueError, cheb.chebint, [0], -1)
+        assert_raises(ValueError, cheb.chebint, [0], 1, [0, 0])
+        assert_raises(ValueError, cheb.chebint, [0], lbnd=[0])
+        assert_raises(ValueError, cheb.chebint, [0], scl=[0])
+        assert_raises(TypeError, cheb.chebint, [0], axis=.5)
+
+        # test integration of zero polynomial
+        for i in range(2, 5):
+            k = [0]*(i - 2) + [1]
+            res = cheb.chebint([0], m=i, k=k)
+            assert_almost_equal(res, [0, 1])
+
+        # check single integration with integration constant
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [1/scl]
+            chebpol = cheb.poly2cheb(pol)
+            chebint = cheb.chebint(chebpol, m=1, k=[i])
+            res = cheb.cheb2poly(chebint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check single integration with integration constant and lbnd
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            chebpol = cheb.poly2cheb(pol)
+            chebint = cheb.chebint(chebpol, m=1, k=[i], lbnd=-1)
+            assert_almost_equal(cheb.chebval(-1, chebint), i)
+
+        # check single integration with integration constant and scaling
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [2/scl]
+            chebpol = cheb.poly2cheb(pol)
+            chebint = cheb.chebint(chebpol, m=1, k=[i], scl=2)
+            res = cheb.cheb2poly(chebint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with default k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = cheb.chebint(tgt, m=1)
+                res = cheb.chebint(pol, m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with defined k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = cheb.chebint(tgt, m=1, k=[k])
+                res = cheb.chebint(pol, m=j, k=list(range(j)))
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with lbnd
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = cheb.chebint(tgt, m=1, k=[k], lbnd=-1)
+                res = cheb.chebint(pol, m=j, k=list(range(j)), lbnd=-1)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = cheb.chebint(tgt, m=1, k=[k], scl=2)
+                res = cheb.chebint(pol, m=j, k=list(range(j)), scl=2)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_chebint_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([cheb.chebint(c) for c in c2d.T]).T
+        res = cheb.chebint(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([cheb.chebint(c) for c in c2d])
+        res = cheb.chebint(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([cheb.chebint(c, k=3) for c in c2d])
+        res = cheb.chebint(c2d, k=3, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestDerivative:
+
+    def test_chebder(self):
+        # check exceptions
+        assert_raises(TypeError, cheb.chebder, [0], .5)
+        assert_raises(ValueError, cheb.chebder, [0], -1)
+
+        # check that zeroth derivative does nothing
+        for i in range(5):
+            tgt = [0]*i + [1]
+            res = cheb.chebder(tgt, m=0)
+            assert_equal(trim(res), trim(tgt))
+
+        # check that derivation is the inverse of integration
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = cheb.chebder(cheb.chebint(tgt, m=j), m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check derivation with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = cheb.chebder(cheb.chebint(tgt, m=j, scl=2), m=j, scl=.5)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_chebder_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([cheb.chebder(c) for c in c2d.T]).T
+        res = cheb.chebder(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([cheb.chebder(c) for c in c2d])
+        res = cheb.chebder(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestVander:
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+
+    def test_chebvander(self):
+        # check for 1d x
+        x = np.arange(3)
+        v = cheb.chebvander(x, 3)
+        assert_(v.shape == (3, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], cheb.chebval(x, coef))
+
+        # check for 2d x
+        x = np.array([[1, 2], [3, 4], [5, 6]])
+        v = cheb.chebvander(x, 3)
+        assert_(v.shape == (3, 2, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], cheb.chebval(x, coef))
+
+    def test_chebvander2d(self):
+        # also tests chebval2d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3))
+        van = cheb.chebvander2d(x1, x2, [1, 2])
+        tgt = cheb.chebval2d(x1, x2, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = cheb.chebvander2d([x1], [x2], [1, 2])
+        assert_(van.shape == (1, 5, 6))
+
+    def test_chebvander3d(self):
+        # also tests chebval3d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3, 4))
+        van = cheb.chebvander3d(x1, x2, x3, [1, 2, 3])
+        tgt = cheb.chebval3d(x1, x2, x3, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = cheb.chebvander3d([x1], [x2], [x3], [1, 2, 3])
+        assert_(van.shape == (1, 5, 24))
+
+
+class TestFitting:
+
+    def test_chebfit(self):
+        def f(x):
+            return x*(x - 1)*(x - 2)
+
+        def f2(x):
+            return x**4 + x**2 + 1
+
+        # Test exceptions
+        assert_raises(ValueError, cheb.chebfit, [1], [1], -1)
+        assert_raises(TypeError, cheb.chebfit, [[1]], [1], 0)
+        assert_raises(TypeError, cheb.chebfit, [], [1], 0)
+        assert_raises(TypeError, cheb.chebfit, [1], [[[1]]], 0)
+        assert_raises(TypeError, cheb.chebfit, [1, 2], [1], 0)
+        assert_raises(TypeError, cheb.chebfit, [1], [1, 2], 0)
+        assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[[1]])
+        assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[1, 1])
+        assert_raises(ValueError, cheb.chebfit, [1], [1], [-1,])
+        assert_raises(ValueError, cheb.chebfit, [1], [1], [2, -1, 6])
+        assert_raises(TypeError, cheb.chebfit, [1], [1], [])
+
+        # Test fit
+        x = np.linspace(0, 2)
+        y = f(x)
+        #
+        coef3 = cheb.chebfit(x, y, 3)
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(cheb.chebval(x, coef3), y)
+        coef3 = cheb.chebfit(x, y, [0, 1, 2, 3])
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(cheb.chebval(x, coef3), y)
+        #
+        coef4 = cheb.chebfit(x, y, 4)
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(cheb.chebval(x, coef4), y)
+        coef4 = cheb.chebfit(x, y, [0, 1, 2, 3, 4])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(cheb.chebval(x, coef4), y)
+        # check things still work if deg is not in strict increasing
+        coef4 = cheb.chebfit(x, y, [2, 3, 4, 1, 0])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(cheb.chebval(x, coef4), y)
+        #
+        coef2d = cheb.chebfit(x, np.array([y, y]).T, 3)
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        coef2d = cheb.chebfit(x, np.array([y, y]).T, [0, 1, 2, 3])
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        # test weighting
+        w = np.zeros_like(x)
+        yw = y.copy()
+        w[1::2] = 1
+        y[0::2] = 0
+        wcoef3 = cheb.chebfit(x, yw, 3, w=w)
+        assert_almost_equal(wcoef3, coef3)
+        wcoef3 = cheb.chebfit(x, yw, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef3, coef3)
+        #
+        wcoef2d = cheb.chebfit(x, np.array([yw, yw]).T, 3, w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        wcoef2d = cheb.chebfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        # test scaling with complex values x points whose square
+        # is zero when summed.
+        x = [1, 1j, -1, -1j]
+        assert_almost_equal(cheb.chebfit(x, x, 1), [0, 1])
+        assert_almost_equal(cheb.chebfit(x, x, [0, 1]), [0, 1])
+        # test fitting only even polynomials
+        x = np.linspace(-1, 1)
+        y = f2(x)
+        coef1 = cheb.chebfit(x, y, 4)
+        assert_almost_equal(cheb.chebval(x, coef1), y)
+        coef2 = cheb.chebfit(x, y, [0, 2, 4])
+        assert_almost_equal(cheb.chebval(x, coef2), y)
+        assert_almost_equal(coef1, coef2)
+
+
+class TestInterpolate:
+
+    def f(self, x):
+        return x * (x - 1) * (x - 2)
+
+    def test_raises(self):
+        assert_raises(ValueError, cheb.chebinterpolate, self.f, -1)
+        assert_raises(TypeError, cheb.chebinterpolate, self.f, 10.)
+
+    def test_dimensions(self):
+        for deg in range(1, 5):
+            assert_(cheb.chebinterpolate(self.f, deg).shape == (deg + 1,))
+
+    def test_approximation(self):
+
+        def powx(x, p):
+            return x**p
+
+        x = np.linspace(-1, 1, 10)
+        for deg in range(0, 10):
+            for p in range(0, deg + 1):
+                c = cheb.chebinterpolate(powx, deg, (p,))
+                assert_almost_equal(cheb.chebval(x, c), powx(x, p), decimal=12)
+
+
+class TestCompanion:
+
+    def test_raises(self):
+        assert_raises(ValueError, cheb.chebcompanion, [])
+        assert_raises(ValueError, cheb.chebcompanion, [1])
+
+    def test_dimensions(self):
+        for i in range(1, 5):
+            coef = [0]*i + [1]
+            assert_(cheb.chebcompanion(coef).shape == (i, i))
+
+    def test_linear_root(self):
+        assert_(cheb.chebcompanion([1, 2])[0, 0] == -.5)
+
+
+class TestGauss:
+
+    def test_100(self):
+        x, w = cheb.chebgauss(100)
+
+        # test orthogonality. Note that the results need to be normalized,
+        # otherwise the huge values that can arise from fast growing
+        # functions like Laguerre can be very confusing.
+        v = cheb.chebvander(x, 99)
+        vv = np.dot(v.T * w, v)
+        vd = 1/np.sqrt(vv.diagonal())
+        vv = vd[:, None] * vv * vd
+        assert_almost_equal(vv, np.eye(100))
+
+        # check that the integral of 1 is correct
+        tgt = np.pi
+        assert_almost_equal(w.sum(), tgt)
+
+
+class TestMisc:
+
+    def test_chebfromroots(self):
+        res = cheb.chebfromroots([])
+        assert_almost_equal(trim(res), [1])
+        for i in range(1, 5):
+            roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
+            tgt = [0]*i + [1]
+            res = cheb.chebfromroots(roots)*2**(i-1)
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_chebroots(self):
+        assert_almost_equal(cheb.chebroots([1]), [])
+        assert_almost_equal(cheb.chebroots([1, 2]), [-.5])
+        for i in range(2, 5):
+            tgt = np.linspace(-1, 1, i)
+            res = cheb.chebroots(cheb.chebfromroots(tgt))
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_chebtrim(self):
+        coef = [2, -1, 1, 0]
+
+        # Test exceptions
+        assert_raises(ValueError, cheb.chebtrim, coef, -1)
+
+        # Test results
+        assert_equal(cheb.chebtrim(coef), coef[:-1])
+        assert_equal(cheb.chebtrim(coef, 1), coef[:-3])
+        assert_equal(cheb.chebtrim(coef, 2), [0])
+
+    def test_chebline(self):
+        assert_equal(cheb.chebline(3, 4), [3, 4])
+
+    def test_cheb2poly(self):
+        for i in range(10):
+            assert_almost_equal(cheb.cheb2poly([0]*i + [1]), Tlist[i])
+
+    def test_poly2cheb(self):
+        for i in range(10):
+            assert_almost_equal(cheb.poly2cheb(Tlist[i]), [0]*i + [1])
+
+    def test_weight(self):
+        x = np.linspace(-1, 1, 11)[1:-1]
+        tgt = 1./(np.sqrt(1 + x) * np.sqrt(1 - x))
+        res = cheb.chebweight(x)
+        assert_almost_equal(res, tgt)
+
+    def test_chebpts1(self):
+        #test exceptions
+        assert_raises(ValueError, cheb.chebpts1, 1.5)
+        assert_raises(ValueError, cheb.chebpts1, 0)
+
+        #test points
+        tgt = [0]
+        assert_almost_equal(cheb.chebpts1(1), tgt)
+        tgt = [-0.70710678118654746, 0.70710678118654746]
+        assert_almost_equal(cheb.chebpts1(2), tgt)
+        tgt = [-0.86602540378443871, 0, 0.86602540378443871]
+        assert_almost_equal(cheb.chebpts1(3), tgt)
+        tgt = [-0.9238795325, -0.3826834323, 0.3826834323, 0.9238795325]
+        assert_almost_equal(cheb.chebpts1(4), tgt)
+
+    def test_chebpts2(self):
+        #test exceptions
+        assert_raises(ValueError, cheb.chebpts2, 1.5)
+        assert_raises(ValueError, cheb.chebpts2, 1)
+
+        #test points
+        tgt = [-1, 1]
+        assert_almost_equal(cheb.chebpts2(2), tgt)
+        tgt = [-1, 0, 1]
+        assert_almost_equal(cheb.chebpts2(3), tgt)
+        tgt = [-1, -0.5, .5, 1]
+        assert_almost_equal(cheb.chebpts2(4), tgt)
+        tgt = [-1.0, -0.707106781187, 0, 0.707106781187, 1.0]
+        assert_almost_equal(cheb.chebpts2(5), tgt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_classes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..75672a148524d8887663b986ec5d9e6c13d1193a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_classes.py
@@ -0,0 +1,607 @@
+"""Test inter-conversion of different polynomial classes.
+
+This tests the convert and cast methods of all the polynomial classes.
+
+"""
+import operator as op
+from numbers import Number
+
+import pytest
+import numpy as np
+from numpy.polynomial import (
+    Polynomial, Legendre, Chebyshev, Laguerre, Hermite, HermiteE)
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+from numpy.exceptions import RankWarning
+
+#
+# fixtures
+#
+
+classes = (
+    Polynomial, Legendre, Chebyshev, Laguerre,
+    Hermite, HermiteE
+    )
+classids = tuple(cls.__name__ for cls in classes)
+
+@pytest.fixture(params=classes, ids=classids)
+def Poly(request):
+    return request.param
+
+#
+# helper functions
+#
+random = np.random.random
+
+
+def assert_poly_almost_equal(p1, p2, msg=""):
+    try:
+        assert_(np.all(p1.domain == p2.domain))
+        assert_(np.all(p1.window == p2.window))
+        assert_almost_equal(p1.coef, p2.coef)
+    except AssertionError:
+        msg = f"Result: {p1}\nTarget: {p2}"
+        raise AssertionError(msg)
+
+
+#
+# Test conversion methods that depend on combinations of two classes.
+#
+
+Poly1 = Poly
+Poly2 = Poly
+
+
+def test_conversion(Poly1, Poly2):
+    x = np.linspace(0, 1, 10)
+    coef = random((3,))
+
+    d1 = Poly1.domain + random((2,))*.25
+    w1 = Poly1.window + random((2,))*.25
+    p1 = Poly1(coef, domain=d1, window=w1)
+
+    d2 = Poly2.domain + random((2,))*.25
+    w2 = Poly2.window + random((2,))*.25
+    p2 = p1.convert(kind=Poly2, domain=d2, window=w2)
+
+    assert_almost_equal(p2.domain, d2)
+    assert_almost_equal(p2.window, w2)
+    assert_almost_equal(p2(x), p1(x))
+
+
+def test_cast(Poly1, Poly2):
+    x = np.linspace(0, 1, 10)
+    coef = random((3,))
+
+    d1 = Poly1.domain + random((2,))*.25
+    w1 = Poly1.window + random((2,))*.25
+    p1 = Poly1(coef, domain=d1, window=w1)
+
+    d2 = Poly2.domain + random((2,))*.25
+    w2 = Poly2.window + random((2,))*.25
+    p2 = Poly2.cast(p1, domain=d2, window=w2)
+
+    assert_almost_equal(p2.domain, d2)
+    assert_almost_equal(p2.window, w2)
+    assert_almost_equal(p2(x), p1(x))
+
+
+#
+# test methods that depend on one class
+#
+
+
+def test_identity(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    x = np.linspace(d[0], d[1], 11)
+    p = Poly.identity(domain=d, window=w)
+    assert_equal(p.domain, d)
+    assert_equal(p.window, w)
+    assert_almost_equal(p(x), x)
+
+
+def test_basis(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly.basis(5, domain=d, window=w)
+    assert_equal(p.domain, d)
+    assert_equal(p.window, w)
+    assert_equal(p.coef, [0]*5 + [1])
+
+
+def test_fromroots(Poly):
+    # check that requested roots are zeros of a polynomial
+    # of correct degree, domain, and window.
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    r = random((5,))
+    p1 = Poly.fromroots(r, domain=d, window=w)
+    assert_equal(p1.degree(), len(r))
+    assert_equal(p1.domain, d)
+    assert_equal(p1.window, w)
+    assert_almost_equal(p1(r), 0)
+
+    # check that polynomial is monic
+    pdom = Polynomial.domain
+    pwin = Polynomial.window
+    p2 = Polynomial.cast(p1, domain=pdom, window=pwin)
+    assert_almost_equal(p2.coef[-1], 1)
+
+
+def test_bad_conditioned_fit(Poly):
+
+    x = [0., 0., 1.]
+    y = [1., 2., 3.]
+
+    # check RankWarning is raised
+    with pytest.warns(RankWarning) as record:
+        Poly.fit(x, y, 2)
+    assert record[0].message.args[0] == "The fit may be poorly conditioned"
+
+
+def test_fit(Poly):
+
+    def f(x):
+        return x*(x - 1)*(x - 2)
+    x = np.linspace(0, 3)
+    y = f(x)
+
+    # check default value of domain and window
+    p = Poly.fit(x, y, 3)
+    assert_almost_equal(p.domain, [0, 3])
+    assert_almost_equal(p(x), y)
+    assert_equal(p.degree(), 3)
+
+    # check with given domains and window
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly.fit(x, y, 3, domain=d, window=w)
+    assert_almost_equal(p(x), y)
+    assert_almost_equal(p.domain, d)
+    assert_almost_equal(p.window, w)
+    p = Poly.fit(x, y, [0, 1, 2, 3], domain=d, window=w)
+    assert_almost_equal(p(x), y)
+    assert_almost_equal(p.domain, d)
+    assert_almost_equal(p.window, w)
+
+    # check with class domain default
+    p = Poly.fit(x, y, 3, [])
+    assert_equal(p.domain, Poly.domain)
+    assert_equal(p.window, Poly.window)
+    p = Poly.fit(x, y, [0, 1, 2, 3], [])
+    assert_equal(p.domain, Poly.domain)
+    assert_equal(p.window, Poly.window)
+
+    # check that fit accepts weights.
+    w = np.zeros_like(x)
+    z = y + random(y.shape)*.25
+    w[::2] = 1
+    p1 = Poly.fit(x[::2], z[::2], 3)
+    p2 = Poly.fit(x, z, 3, w=w)
+    p3 = Poly.fit(x, z, [0, 1, 2, 3], w=w)
+    assert_almost_equal(p1(x), p2(x))
+    assert_almost_equal(p2(x), p3(x))
+
+
+def test_equal(Poly):
+    p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
+    p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
+    p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
+    p4 = Poly([1, 2, 3], domain=[0, 1], window=[1, 2])
+    assert_(p1 == p1)
+    assert_(not p1 == p2)
+    assert_(not p1 == p3)
+    assert_(not p1 == p4)
+
+
+def test_not_equal(Poly):
+    p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
+    p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
+    p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
+    p4 = Poly([1, 2, 3], domain=[0, 1], window=[1, 2])
+    assert_(not p1 != p1)
+    assert_(p1 != p2)
+    assert_(p1 != p3)
+    assert_(p1 != p4)
+
+
+def test_add(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 + p2
+    assert_poly_almost_equal(p2 + p1, p3)
+    assert_poly_almost_equal(p1 + c2, p3)
+    assert_poly_almost_equal(c2 + p1, p3)
+    assert_poly_almost_equal(p1 + tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) + p1, p3)
+    assert_poly_almost_equal(p1 + np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) + p1, p3)
+    assert_raises(TypeError, op.add, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.add, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.add, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.add, p1, Polynomial([0]))
+
+
+def test_sub(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 - p2
+    assert_poly_almost_equal(p2 - p1, -p3)
+    assert_poly_almost_equal(p1 - c2, p3)
+    assert_poly_almost_equal(c2 - p1, -p3)
+    assert_poly_almost_equal(p1 - tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) - p1, -p3)
+    assert_poly_almost_equal(p1 - np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) - p1, -p3)
+    assert_raises(TypeError, op.sub, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.sub, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.sub, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.sub, p1, Polynomial([0]))
+
+
+def test_mul(Poly):
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 * p2
+    assert_poly_almost_equal(p2 * p1, p3)
+    assert_poly_almost_equal(p1 * c2, p3)
+    assert_poly_almost_equal(c2 * p1, p3)
+    assert_poly_almost_equal(p1 * tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) * p1, p3)
+    assert_poly_almost_equal(p1 * np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) * p1, p3)
+    assert_poly_almost_equal(p1 * 2, p1 * Poly([2]))
+    assert_poly_almost_equal(2 * p1, p1 * Poly([2]))
+    assert_raises(TypeError, op.mul, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.mul, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.mul, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.mul, p1, Polynomial([0]))
+
+
+def test_floordiv(Poly):
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    assert_poly_almost_equal(p4 // p2, p1)
+    assert_poly_almost_equal(p4 // c2, p1)
+    assert_poly_almost_equal(c4 // p2, p1)
+    assert_poly_almost_equal(p4 // tuple(c2), p1)
+    assert_poly_almost_equal(tuple(c4) // p2, p1)
+    assert_poly_almost_equal(p4 // np.array(c2), p1)
+    assert_poly_almost_equal(np.array(c4) // p2, p1)
+    assert_poly_almost_equal(2 // p2, Poly([0]))
+    assert_poly_almost_equal(p2 // 2, 0.5*p2)
+    assert_raises(
+        TypeError, op.floordiv, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(
+        TypeError, op.floordiv, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.floordiv, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.floordiv, p1, Polynomial([0]))
+
+
+def test_truediv(Poly):
+    # true division is valid only if the denominator is a Number and
+    # not a python bool.
+    p1 = Poly([1,2,3])
+    p2 = p1 * 5
+
+    for stype in np.ScalarType:
+        if not issubclass(stype, Number) or issubclass(stype, bool):
+            continue
+        s = stype(5)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for stype in (int, float):
+        s = stype(5)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for stype in [complex]:
+        s = stype(5, 0)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for s in [tuple(), list(), dict(), bool(), np.array([1])]:
+        assert_raises(TypeError, op.truediv, p2, s)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for ptype in classes:
+        assert_raises(TypeError, op.truediv, p2, ptype(1))
+
+
+def test_mod(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    assert_poly_almost_equal(p4 % p2, p3)
+    assert_poly_almost_equal(p4 % c2, p3)
+    assert_poly_almost_equal(c4 % p2, p3)
+    assert_poly_almost_equal(p4 % tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c4) % p2, p3)
+    assert_poly_almost_equal(p4 % np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c4) % p2, p3)
+    assert_poly_almost_equal(2 % p2, Poly([2]))
+    assert_poly_almost_equal(p2 % 2, Poly([0]))
+    assert_raises(TypeError, op.mod, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.mod, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.mod, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.mod, p1, Polynomial([0]))
+
+
+def test_divmod(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    quo, rem = divmod(p4, p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, c2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(c4, p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, tuple(c2))
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(tuple(c4), p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, np.array(c2))
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(np.array(c4), p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p2, 2)
+    assert_poly_almost_equal(quo, 0.5*p2)
+    assert_poly_almost_equal(rem, Poly([0]))
+    quo, rem = divmod(2, p2)
+    assert_poly_almost_equal(quo, Poly([0]))
+    assert_poly_almost_equal(rem, Poly([2]))
+    assert_raises(TypeError, divmod, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, divmod, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, divmod, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, divmod, p1, Polynomial([0]))
+
+
+def test_roots(Poly):
+    d = Poly.domain * 1.25 + .25
+    w = Poly.window
+    tgt = np.linspace(d[0], d[1], 5)
+    res = np.sort(Poly.fromroots(tgt, domain=d, window=w).roots())
+    assert_almost_equal(res, tgt)
+    # default domain and window
+    res = np.sort(Poly.fromroots(tgt).roots())
+    assert_almost_equal(res, tgt)
+
+
+def test_degree(Poly):
+    p = Poly.basis(5)
+    assert_equal(p.degree(), 5)
+
+
+def test_copy(Poly):
+    p1 = Poly.basis(5)
+    p2 = p1.copy()
+    assert_(p1 == p2)
+    assert_(p1 is not p2)
+    assert_(p1.coef is not p2.coef)
+    assert_(p1.domain is not p2.domain)
+    assert_(p1.window is not p2.window)
+
+
+def test_integ(Poly):
+    P = Polynomial
+    # Check defaults
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ())
+    p2 = P.cast(p0.integ(2))
+    assert_poly_almost_equal(p1, P([0, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
+    # Check with k
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ(k=1))
+    p2 = P.cast(p0.integ(2, k=[1, 1]))
+    assert_poly_almost_equal(p1, P([1, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([1, 1, 1, 1, 1]))
+    # Check with lbnd
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ(lbnd=1))
+    p2 = P.cast(p0.integ(2, lbnd=1))
+    assert_poly_almost_equal(p1, P([-9, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([6, -9, 1, 1, 1]))
+    # Check scaling
+    d = 2*Poly.domain
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]), domain=d)
+    p1 = P.cast(p0.integ())
+    p2 = P.cast(p0.integ(2))
+    assert_poly_almost_equal(p1, P([0, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
+
+
+def test_deriv(Poly):
+    # Check that the derivative is the inverse of integration. It is
+    # assumes that the integration has been checked elsewhere.
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p1 = Poly([1, 2, 3], domain=d, window=w)
+    p2 = p1.integ(2, k=[1, 2])
+    p3 = p1.integ(1, k=[1])
+    assert_almost_equal(p2.deriv(1).coef, p3.coef)
+    assert_almost_equal(p2.deriv(2).coef, p1.coef)
+    # default domain and window
+    p1 = Poly([1, 2, 3])
+    p2 = p1.integ(2, k=[1, 2])
+    p3 = p1.integ(1, k=[1])
+    assert_almost_equal(p2.deriv(1).coef, p3.coef)
+    assert_almost_equal(p2.deriv(2).coef, p1.coef)
+
+
+def test_linspace(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly([1, 2, 3], domain=d, window=w)
+    # check default domain
+    xtgt = np.linspace(d[0], d[1], 20)
+    ytgt = p(xtgt)
+    xres, yres = p.linspace(20)
+    assert_almost_equal(xres, xtgt)
+    assert_almost_equal(yres, ytgt)
+    # check specified domain
+    xtgt = np.linspace(0, 2, 20)
+    ytgt = p(xtgt)
+    xres, yres = p.linspace(20, domain=[0, 2])
+    assert_almost_equal(xres, xtgt)
+    assert_almost_equal(yres, ytgt)
+
+
+def test_pow(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    tgt = Poly([1], domain=d, window=w)
+    tst = Poly([1, 2, 3], domain=d, window=w)
+    for i in range(5):
+        assert_poly_almost_equal(tst**i, tgt)
+        tgt = tgt * tst
+    # default domain and window
+    tgt = Poly([1])
+    tst = Poly([1, 2, 3])
+    for i in range(5):
+        assert_poly_almost_equal(tst**i, tgt)
+        tgt = tgt * tst
+    # check error for invalid powers
+    assert_raises(ValueError, op.pow, tgt, 1.5)
+    assert_raises(ValueError, op.pow, tgt, -1)
+
+
+def test_call(Poly):
+    P = Polynomial
+    d = Poly.domain
+    x = np.linspace(d[0], d[1], 11)
+
+    # Check defaults
+    p = Poly.cast(P([1, 2, 3]))
+    tgt = 1 + x*(2 + 3*x)
+    res = p(x)
+    assert_almost_equal(res, tgt)
+
+
+def test_call_with_list(Poly):
+    p = Poly([1, 2, 3])
+    x = [-1, 0, 2]
+    res = p(x)
+    assert_equal(res, p(np.array(x)))
+
+
+def test_cutdeg(Poly):
+    p = Poly([1, 2, 3])
+    assert_raises(ValueError, p.cutdeg, .5)
+    assert_raises(ValueError, p.cutdeg, -1)
+    assert_equal(len(p.cutdeg(3)), 3)
+    assert_equal(len(p.cutdeg(2)), 3)
+    assert_equal(len(p.cutdeg(1)), 2)
+    assert_equal(len(p.cutdeg(0)), 1)
+
+
+def test_truncate(Poly):
+    p = Poly([1, 2, 3])
+    assert_raises(ValueError, p.truncate, .5)
+    assert_raises(ValueError, p.truncate, 0)
+    assert_equal(len(p.truncate(4)), 3)
+    assert_equal(len(p.truncate(3)), 3)
+    assert_equal(len(p.truncate(2)), 2)
+    assert_equal(len(p.truncate(1)), 1)
+
+
+def test_trim(Poly):
+    c = [1, 1e-6, 1e-12, 0]
+    p = Poly(c)
+    assert_equal(p.trim().coef, c[:3])
+    assert_equal(p.trim(1e-10).coef, c[:2])
+    assert_equal(p.trim(1e-5).coef, c[:1])
+
+
+def test_mapparms(Poly):
+    # check with defaults. Should be identity.
+    d = Poly.domain
+    w = Poly.window
+    p = Poly([1], domain=d, window=w)
+    assert_almost_equal([0, 1], p.mapparms())
+    #
+    w = 2*d + 1
+    p = Poly([1], domain=d, window=w)
+    assert_almost_equal([1, 2], p.mapparms())
+
+
+def test_ufunc_override(Poly):
+    p = Poly([1, 2, 3])
+    x = np.ones(3)
+    assert_raises(TypeError, np.add, p, x)
+    assert_raises(TypeError, np.add, x, p)
+
+
+#
+# Test class method that only exists for some classes
+#
+
+
+class TestInterpolate:
+
+    def f(self, x):
+        return x * (x - 1) * (x - 2)
+
+    def test_raises(self):
+        assert_raises(ValueError, Chebyshev.interpolate, self.f, -1)
+        assert_raises(TypeError, Chebyshev.interpolate, self.f, 10.)
+
+    def test_dimensions(self):
+        for deg in range(1, 5):
+            assert_(Chebyshev.interpolate(self.f, deg).degree() == deg)
+
+    def test_approximation(self):
+
+        def powx(x, p):
+            return x**p
+
+        x = np.linspace(0, 2, 10)
+        for deg in range(0, 10):
+            for t in range(0, deg + 1):
+                p = Chebyshev.interpolate(powx, deg, domain=[0, 2], args=(t,))
+                assert_almost_equal(p(x), powx(x, t), decimal=11)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_hermite.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_hermite.py
new file mode 100644
index 0000000000000000000000000000000000000000..2188800853f2f5e9a98d2d7087893a7cf11440ef
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_hermite.py
@@ -0,0 +1,555 @@
+"""Tests for hermite module.
+
+"""
+from functools import reduce
+
+import numpy as np
+import numpy.polynomial.hermite as herm
+from numpy.polynomial.polynomial import polyval
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+
+H0 = np.array([1])
+H1 = np.array([0, 2])
+H2 = np.array([-2, 0, 4])
+H3 = np.array([0, -12, 0, 8])
+H4 = np.array([12, 0, -48, 0, 16])
+H5 = np.array([0, 120, 0, -160, 0, 32])
+H6 = np.array([-120, 0, 720, 0, -480, 0, 64])
+H7 = np.array([0, -1680, 0, 3360, 0, -1344, 0, 128])
+H8 = np.array([1680, 0, -13440, 0, 13440, 0, -3584, 0, 256])
+H9 = np.array([0, 30240, 0, -80640, 0, 48384, 0, -9216, 0, 512])
+
+Hlist = [H0, H1, H2, H3, H4, H5, H6, H7, H8, H9]
+
+
+def trim(x):
+    return herm.hermtrim(x, tol=1e-6)
+
+
+class TestConstants:
+
+    def test_hermdomain(self):
+        assert_equal(herm.hermdomain, [-1, 1])
+
+    def test_hermzero(self):
+        assert_equal(herm.hermzero, [0])
+
+    def test_hermone(self):
+        assert_equal(herm.hermone, [1])
+
+    def test_hermx(self):
+        assert_equal(herm.hermx, [0, .5])
+
+
+class TestArithmetic:
+    x = np.linspace(-3, 3, 100)
+
+    def test_hermadd(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] += 1
+                res = herm.hermadd([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_hermsub(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] -= 1
+                res = herm.hermsub([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_hermmulx(self):
+        assert_equal(herm.hermmulx([0]), [0])
+        assert_equal(herm.hermmulx([1]), [0, .5])
+        for i in range(1, 5):
+            ser = [0]*i + [1]
+            tgt = [0]*(i - 1) + [i, 0, .5]
+            assert_equal(herm.hermmulx(ser), tgt)
+
+    def test_hermmul(self):
+        # check values of result
+        for i in range(5):
+            pol1 = [0]*i + [1]
+            val1 = herm.hermval(self.x, pol1)
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                pol2 = [0]*j + [1]
+                val2 = herm.hermval(self.x, pol2)
+                pol3 = herm.hermmul(pol1, pol2)
+                val3 = herm.hermval(self.x, pol3)
+                assert_(len(pol3) == i + j + 1, msg)
+                assert_almost_equal(val3, val1*val2, err_msg=msg)
+
+    def test_hermdiv(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                ci = [0]*i + [1]
+                cj = [0]*j + [1]
+                tgt = herm.hermadd(ci, cj)
+                quo, rem = herm.hermdiv(tgt, ci)
+                res = herm.hermadd(herm.hermmul(quo, ci), rem)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_hermpow(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                c = np.arange(i + 1)
+                tgt = reduce(herm.hermmul, [c]*j, np.array([1]))
+                res = herm.hermpow(c, j)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+
+class TestEvaluation:
+    # coefficients of 1 + 2*x + 3*x**2
+    c1d = np.array([2.5, 1., .75])
+    c2d = np.einsum('i,j->ij', c1d, c1d)
+    c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d)
+
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+    y = polyval(x, [1., 2., 3.])
+
+    def test_hermval(self):
+        #check empty input
+        assert_equal(herm.hermval([], [1]).size, 0)
+
+        #check normal input)
+        x = np.linspace(-1, 1)
+        y = [polyval(x, c) for c in Hlist]
+        for i in range(10):
+            msg = f"At i={i}"
+            tgt = y[i]
+            res = herm.hermval(x, [0]*i + [1])
+            assert_almost_equal(res, tgt, err_msg=msg)
+
+        #check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(herm.hermval(x, [1]).shape, dims)
+            assert_equal(herm.hermval(x, [1, 0]).shape, dims)
+            assert_equal(herm.hermval(x, [1, 0, 0]).shape, dims)
+
+    def test_hermval2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, herm.hermval2d, x1, x2[:2], self.c2d)
+
+        #test values
+        tgt = y1*y2
+        res = herm.hermval2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herm.hermval2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3))
+
+    def test_hermval3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, herm.hermval3d, x1, x2, x3[:2], self.c3d)
+
+        #test values
+        tgt = y1*y2*y3
+        res = herm.hermval3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herm.hermval3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3))
+
+    def test_hermgrid2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j->ij', y1, y2)
+        res = herm.hermgrid2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herm.hermgrid2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3)*2)
+
+    def test_hermgrid3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j,k->ijk', y1, y2, y3)
+        res = herm.hermgrid3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herm.hermgrid3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3)*3)
+
+
+class TestIntegral:
+
+    def test_hermint(self):
+        # check exceptions
+        assert_raises(TypeError, herm.hermint, [0], .5)
+        assert_raises(ValueError, herm.hermint, [0], -1)
+        assert_raises(ValueError, herm.hermint, [0], 1, [0, 0])
+        assert_raises(ValueError, herm.hermint, [0], lbnd=[0])
+        assert_raises(ValueError, herm.hermint, [0], scl=[0])
+        assert_raises(TypeError, herm.hermint, [0], axis=.5)
+
+        # test integration of zero polynomial
+        for i in range(2, 5):
+            k = [0]*(i - 2) + [1]
+            res = herm.hermint([0], m=i, k=k)
+            assert_almost_equal(res, [0, .5])
+
+        # check single integration with integration constant
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [1/scl]
+            hermpol = herm.poly2herm(pol)
+            hermint = herm.hermint(hermpol, m=1, k=[i])
+            res = herm.herm2poly(hermint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check single integration with integration constant and lbnd
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            hermpol = herm.poly2herm(pol)
+            hermint = herm.hermint(hermpol, m=1, k=[i], lbnd=-1)
+            assert_almost_equal(herm.hermval(-1, hermint), i)
+
+        # check single integration with integration constant and scaling
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [2/scl]
+            hermpol = herm.poly2herm(pol)
+            hermint = herm.hermint(hermpol, m=1, k=[i], scl=2)
+            res = herm.herm2poly(hermint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with default k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herm.hermint(tgt, m=1)
+                res = herm.hermint(pol, m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with defined k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herm.hermint(tgt, m=1, k=[k])
+                res = herm.hermint(pol, m=j, k=list(range(j)))
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with lbnd
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herm.hermint(tgt, m=1, k=[k], lbnd=-1)
+                res = herm.hermint(pol, m=j, k=list(range(j)), lbnd=-1)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herm.hermint(tgt, m=1, k=[k], scl=2)
+                res = herm.hermint(pol, m=j, k=list(range(j)), scl=2)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_hermint_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([herm.hermint(c) for c in c2d.T]).T
+        res = herm.hermint(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([herm.hermint(c) for c in c2d])
+        res = herm.hermint(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([herm.hermint(c, k=3) for c in c2d])
+        res = herm.hermint(c2d, k=3, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestDerivative:
+
+    def test_hermder(self):
+        # check exceptions
+        assert_raises(TypeError, herm.hermder, [0], .5)
+        assert_raises(ValueError, herm.hermder, [0], -1)
+
+        # check that zeroth derivative does nothing
+        for i in range(5):
+            tgt = [0]*i + [1]
+            res = herm.hermder(tgt, m=0)
+            assert_equal(trim(res), trim(tgt))
+
+        # check that derivation is the inverse of integration
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = herm.hermder(herm.hermint(tgt, m=j), m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check derivation with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = herm.hermder(herm.hermint(tgt, m=j, scl=2), m=j, scl=.5)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_hermder_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([herm.hermder(c) for c in c2d.T]).T
+        res = herm.hermder(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([herm.hermder(c) for c in c2d])
+        res = herm.hermder(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestVander:
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+
+    def test_hermvander(self):
+        # check for 1d x
+        x = np.arange(3)
+        v = herm.hermvander(x, 3)
+        assert_(v.shape == (3, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], herm.hermval(x, coef))
+
+        # check for 2d x
+        x = np.array([[1, 2], [3, 4], [5, 6]])
+        v = herm.hermvander(x, 3)
+        assert_(v.shape == (3, 2, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], herm.hermval(x, coef))
+
+    def test_hermvander2d(self):
+        # also tests hermval2d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3))
+        van = herm.hermvander2d(x1, x2, [1, 2])
+        tgt = herm.hermval2d(x1, x2, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = herm.hermvander2d([x1], [x2], [1, 2])
+        assert_(van.shape == (1, 5, 6))
+
+    def test_hermvander3d(self):
+        # also tests hermval3d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3, 4))
+        van = herm.hermvander3d(x1, x2, x3, [1, 2, 3])
+        tgt = herm.hermval3d(x1, x2, x3, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = herm.hermvander3d([x1], [x2], [x3], [1, 2, 3])
+        assert_(van.shape == (1, 5, 24))
+
+
+class TestFitting:
+
+    def test_hermfit(self):
+        def f(x):
+            return x*(x - 1)*(x - 2)
+
+        def f2(x):
+            return x**4 + x**2 + 1
+
+        # Test exceptions
+        assert_raises(ValueError, herm.hermfit, [1], [1], -1)
+        assert_raises(TypeError, herm.hermfit, [[1]], [1], 0)
+        assert_raises(TypeError, herm.hermfit, [], [1], 0)
+        assert_raises(TypeError, herm.hermfit, [1], [[[1]]], 0)
+        assert_raises(TypeError, herm.hermfit, [1, 2], [1], 0)
+        assert_raises(TypeError, herm.hermfit, [1], [1, 2], 0)
+        assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[[1]])
+        assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[1, 1])
+        assert_raises(ValueError, herm.hermfit, [1], [1], [-1,])
+        assert_raises(ValueError, herm.hermfit, [1], [1], [2, -1, 6])
+        assert_raises(TypeError, herm.hermfit, [1], [1], [])
+
+        # Test fit
+        x = np.linspace(0, 2)
+        y = f(x)
+        #
+        coef3 = herm.hermfit(x, y, 3)
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(herm.hermval(x, coef3), y)
+        coef3 = herm.hermfit(x, y, [0, 1, 2, 3])
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(herm.hermval(x, coef3), y)
+        #
+        coef4 = herm.hermfit(x, y, 4)
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(herm.hermval(x, coef4), y)
+        coef4 = herm.hermfit(x, y, [0, 1, 2, 3, 4])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(herm.hermval(x, coef4), y)
+        # check things still work if deg is not in strict increasing
+        coef4 = herm.hermfit(x, y, [2, 3, 4, 1, 0])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(herm.hermval(x, coef4), y)
+        #
+        coef2d = herm.hermfit(x, np.array([y, y]).T, 3)
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        coef2d = herm.hermfit(x, np.array([y, y]).T, [0, 1, 2, 3])
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        # test weighting
+        w = np.zeros_like(x)
+        yw = y.copy()
+        w[1::2] = 1
+        y[0::2] = 0
+        wcoef3 = herm.hermfit(x, yw, 3, w=w)
+        assert_almost_equal(wcoef3, coef3)
+        wcoef3 = herm.hermfit(x, yw, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef3, coef3)
+        #
+        wcoef2d = herm.hermfit(x, np.array([yw, yw]).T, 3, w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        wcoef2d = herm.hermfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        # test scaling with complex values x points whose square
+        # is zero when summed.
+        x = [1, 1j, -1, -1j]
+        assert_almost_equal(herm.hermfit(x, x, 1), [0, .5])
+        assert_almost_equal(herm.hermfit(x, x, [0, 1]), [0, .5])
+        # test fitting only even Legendre polynomials
+        x = np.linspace(-1, 1)
+        y = f2(x)
+        coef1 = herm.hermfit(x, y, 4)
+        assert_almost_equal(herm.hermval(x, coef1), y)
+        coef2 = herm.hermfit(x, y, [0, 2, 4])
+        assert_almost_equal(herm.hermval(x, coef2), y)
+        assert_almost_equal(coef1, coef2)
+
+
+class TestCompanion:
+
+    def test_raises(self):
+        assert_raises(ValueError, herm.hermcompanion, [])
+        assert_raises(ValueError, herm.hermcompanion, [1])
+
+    def test_dimensions(self):
+        for i in range(1, 5):
+            coef = [0]*i + [1]
+            assert_(herm.hermcompanion(coef).shape == (i, i))
+
+    def test_linear_root(self):
+        assert_(herm.hermcompanion([1, 2])[0, 0] == -.25)
+
+
+class TestGauss:
+
+    def test_100(self):
+        x, w = herm.hermgauss(100)
+
+        # test orthogonality. Note that the results need to be normalized,
+        # otherwise the huge values that can arise from fast growing
+        # functions like Laguerre can be very confusing.
+        v = herm.hermvander(x, 99)
+        vv = np.dot(v.T * w, v)
+        vd = 1/np.sqrt(vv.diagonal())
+        vv = vd[:, None] * vv * vd
+        assert_almost_equal(vv, np.eye(100))
+
+        # check that the integral of 1 is correct
+        tgt = np.sqrt(np.pi)
+        assert_almost_equal(w.sum(), tgt)
+
+
+class TestMisc:
+
+    def test_hermfromroots(self):
+        res = herm.hermfromroots([])
+        assert_almost_equal(trim(res), [1])
+        for i in range(1, 5):
+            roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
+            pol = herm.hermfromroots(roots)
+            res = herm.hermval(roots, pol)
+            tgt = 0
+            assert_(len(pol) == i + 1)
+            assert_almost_equal(herm.herm2poly(pol)[-1], 1)
+            assert_almost_equal(res, tgt)
+
+    def test_hermroots(self):
+        assert_almost_equal(herm.hermroots([1]), [])
+        assert_almost_equal(herm.hermroots([1, 1]), [-.5])
+        for i in range(2, 5):
+            tgt = np.linspace(-1, 1, i)
+            res = herm.hermroots(herm.hermfromroots(tgt))
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_hermtrim(self):
+        coef = [2, -1, 1, 0]
+
+        # Test exceptions
+        assert_raises(ValueError, herm.hermtrim, coef, -1)
+
+        # Test results
+        assert_equal(herm.hermtrim(coef), coef[:-1])
+        assert_equal(herm.hermtrim(coef, 1), coef[:-3])
+        assert_equal(herm.hermtrim(coef, 2), [0])
+
+    def test_hermline(self):
+        assert_equal(herm.hermline(3, 4), [3, 2])
+
+    def test_herm2poly(self):
+        for i in range(10):
+            assert_almost_equal(herm.herm2poly([0]*i + [1]), Hlist[i])
+
+    def test_poly2herm(self):
+        for i in range(10):
+            assert_almost_equal(herm.poly2herm(Hlist[i]), [0]*i + [1])
+
+    def test_weight(self):
+        x = np.linspace(-5, 5, 11)
+        tgt = np.exp(-x**2)
+        res = herm.hermweight(x)
+        assert_almost_equal(res, tgt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_hermite_e.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_hermite_e.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d262a3306222bd79f682b09763b0bd2b90ba8fe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_hermite_e.py
@@ -0,0 +1,556 @@
+"""Tests for hermite_e module.
+
+"""
+from functools import reduce
+
+import numpy as np
+import numpy.polynomial.hermite_e as herme
+from numpy.polynomial.polynomial import polyval
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+
+He0 = np.array([1])
+He1 = np.array([0, 1])
+He2 = np.array([-1, 0, 1])
+He3 = np.array([0, -3, 0, 1])
+He4 = np.array([3, 0, -6, 0, 1])
+He5 = np.array([0, 15, 0, -10, 0, 1])
+He6 = np.array([-15, 0, 45, 0, -15, 0, 1])
+He7 = np.array([0, -105, 0, 105, 0, -21, 0, 1])
+He8 = np.array([105, 0, -420, 0, 210, 0, -28, 0, 1])
+He9 = np.array([0, 945, 0, -1260, 0, 378, 0, -36, 0, 1])
+
+Helist = [He0, He1, He2, He3, He4, He5, He6, He7, He8, He9]
+
+
+def trim(x):
+    return herme.hermetrim(x, tol=1e-6)
+
+
+class TestConstants:
+
+    def test_hermedomain(self):
+        assert_equal(herme.hermedomain, [-1, 1])
+
+    def test_hermezero(self):
+        assert_equal(herme.hermezero, [0])
+
+    def test_hermeone(self):
+        assert_equal(herme.hermeone, [1])
+
+    def test_hermex(self):
+        assert_equal(herme.hermex, [0, 1])
+
+
+class TestArithmetic:
+    x = np.linspace(-3, 3, 100)
+
+    def test_hermeadd(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] += 1
+                res = herme.hermeadd([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_hermesub(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] -= 1
+                res = herme.hermesub([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_hermemulx(self):
+        assert_equal(herme.hermemulx([0]), [0])
+        assert_equal(herme.hermemulx([1]), [0, 1])
+        for i in range(1, 5):
+            ser = [0]*i + [1]
+            tgt = [0]*(i - 1) + [i, 0, 1]
+            assert_equal(herme.hermemulx(ser), tgt)
+
+    def test_hermemul(self):
+        # check values of result
+        for i in range(5):
+            pol1 = [0]*i + [1]
+            val1 = herme.hermeval(self.x, pol1)
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                pol2 = [0]*j + [1]
+                val2 = herme.hermeval(self.x, pol2)
+                pol3 = herme.hermemul(pol1, pol2)
+                val3 = herme.hermeval(self.x, pol3)
+                assert_(len(pol3) == i + j + 1, msg)
+                assert_almost_equal(val3, val1*val2, err_msg=msg)
+
+    def test_hermediv(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                ci = [0]*i + [1]
+                cj = [0]*j + [1]
+                tgt = herme.hermeadd(ci, cj)
+                quo, rem = herme.hermediv(tgt, ci)
+                res = herme.hermeadd(herme.hermemul(quo, ci), rem)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_hermepow(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                c = np.arange(i + 1)
+                tgt = reduce(herme.hermemul, [c]*j, np.array([1]))
+                res = herme.hermepow(c, j)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+
+class TestEvaluation:
+    # coefficients of 1 + 2*x + 3*x**2
+    c1d = np.array([4., 2., 3.])
+    c2d = np.einsum('i,j->ij', c1d, c1d)
+    c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d)
+
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+    y = polyval(x, [1., 2., 3.])
+
+    def test_hermeval(self):
+        #check empty input
+        assert_equal(herme.hermeval([], [1]).size, 0)
+
+        #check normal input)
+        x = np.linspace(-1, 1)
+        y = [polyval(x, c) for c in Helist]
+        for i in range(10):
+            msg = f"At i={i}"
+            tgt = y[i]
+            res = herme.hermeval(x, [0]*i + [1])
+            assert_almost_equal(res, tgt, err_msg=msg)
+
+        #check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(herme.hermeval(x, [1]).shape, dims)
+            assert_equal(herme.hermeval(x, [1, 0]).shape, dims)
+            assert_equal(herme.hermeval(x, [1, 0, 0]).shape, dims)
+
+    def test_hermeval2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, herme.hermeval2d, x1, x2[:2], self.c2d)
+
+        #test values
+        tgt = y1*y2
+        res = herme.hermeval2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herme.hermeval2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3))
+
+    def test_hermeval3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, herme.hermeval3d, x1, x2, x3[:2], self.c3d)
+
+        #test values
+        tgt = y1*y2*y3
+        res = herme.hermeval3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herme.hermeval3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3))
+
+    def test_hermegrid2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j->ij', y1, y2)
+        res = herme.hermegrid2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herme.hermegrid2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3)*2)
+
+    def test_hermegrid3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j,k->ijk', y1, y2, y3)
+        res = herme.hermegrid3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = herme.hermegrid3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3)*3)
+
+
+class TestIntegral:
+
+    def test_hermeint(self):
+        # check exceptions
+        assert_raises(TypeError, herme.hermeint, [0], .5)
+        assert_raises(ValueError, herme.hermeint, [0], -1)
+        assert_raises(ValueError, herme.hermeint, [0], 1, [0, 0])
+        assert_raises(ValueError, herme.hermeint, [0], lbnd=[0])
+        assert_raises(ValueError, herme.hermeint, [0], scl=[0])
+        assert_raises(TypeError, herme.hermeint, [0], axis=.5)
+
+        # test integration of zero polynomial
+        for i in range(2, 5):
+            k = [0]*(i - 2) + [1]
+            res = herme.hermeint([0], m=i, k=k)
+            assert_almost_equal(res, [0, 1])
+
+        # check single integration with integration constant
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [1/scl]
+            hermepol = herme.poly2herme(pol)
+            hermeint = herme.hermeint(hermepol, m=1, k=[i])
+            res = herme.herme2poly(hermeint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check single integration with integration constant and lbnd
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            hermepol = herme.poly2herme(pol)
+            hermeint = herme.hermeint(hermepol, m=1, k=[i], lbnd=-1)
+            assert_almost_equal(herme.hermeval(-1, hermeint), i)
+
+        # check single integration with integration constant and scaling
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [2/scl]
+            hermepol = herme.poly2herme(pol)
+            hermeint = herme.hermeint(hermepol, m=1, k=[i], scl=2)
+            res = herme.herme2poly(hermeint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with default k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herme.hermeint(tgt, m=1)
+                res = herme.hermeint(pol, m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with defined k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herme.hermeint(tgt, m=1, k=[k])
+                res = herme.hermeint(pol, m=j, k=list(range(j)))
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with lbnd
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herme.hermeint(tgt, m=1, k=[k], lbnd=-1)
+                res = herme.hermeint(pol, m=j, k=list(range(j)), lbnd=-1)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = herme.hermeint(tgt, m=1, k=[k], scl=2)
+                res = herme.hermeint(pol, m=j, k=list(range(j)), scl=2)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_hermeint_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([herme.hermeint(c) for c in c2d.T]).T
+        res = herme.hermeint(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([herme.hermeint(c) for c in c2d])
+        res = herme.hermeint(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([herme.hermeint(c, k=3) for c in c2d])
+        res = herme.hermeint(c2d, k=3, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestDerivative:
+
+    def test_hermeder(self):
+        # check exceptions
+        assert_raises(TypeError, herme.hermeder, [0], .5)
+        assert_raises(ValueError, herme.hermeder, [0], -1)
+
+        # check that zeroth derivative does nothing
+        for i in range(5):
+            tgt = [0]*i + [1]
+            res = herme.hermeder(tgt, m=0)
+            assert_equal(trim(res), trim(tgt))
+
+        # check that derivation is the inverse of integration
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = herme.hermeder(herme.hermeint(tgt, m=j), m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check derivation with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = herme.hermeder(
+                    herme.hermeint(tgt, m=j, scl=2), m=j, scl=.5)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_hermeder_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([herme.hermeder(c) for c in c2d.T]).T
+        res = herme.hermeder(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([herme.hermeder(c) for c in c2d])
+        res = herme.hermeder(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestVander:
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+
+    def test_hermevander(self):
+        # check for 1d x
+        x = np.arange(3)
+        v = herme.hermevander(x, 3)
+        assert_(v.shape == (3, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], herme.hermeval(x, coef))
+
+        # check for 2d x
+        x = np.array([[1, 2], [3, 4], [5, 6]])
+        v = herme.hermevander(x, 3)
+        assert_(v.shape == (3, 2, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], herme.hermeval(x, coef))
+
+    def test_hermevander2d(self):
+        # also tests hermeval2d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3))
+        van = herme.hermevander2d(x1, x2, [1, 2])
+        tgt = herme.hermeval2d(x1, x2, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = herme.hermevander2d([x1], [x2], [1, 2])
+        assert_(van.shape == (1, 5, 6))
+
+    def test_hermevander3d(self):
+        # also tests hermeval3d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3, 4))
+        van = herme.hermevander3d(x1, x2, x3, [1, 2, 3])
+        tgt = herme.hermeval3d(x1, x2, x3, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = herme.hermevander3d([x1], [x2], [x3], [1, 2, 3])
+        assert_(van.shape == (1, 5, 24))
+
+
+class TestFitting:
+
+    def test_hermefit(self):
+        def f(x):
+            return x*(x - 1)*(x - 2)
+
+        def f2(x):
+            return x**4 + x**2 + 1
+
+        # Test exceptions
+        assert_raises(ValueError, herme.hermefit, [1], [1], -1)
+        assert_raises(TypeError, herme.hermefit, [[1]], [1], 0)
+        assert_raises(TypeError, herme.hermefit, [], [1], 0)
+        assert_raises(TypeError, herme.hermefit, [1], [[[1]]], 0)
+        assert_raises(TypeError, herme.hermefit, [1, 2], [1], 0)
+        assert_raises(TypeError, herme.hermefit, [1], [1, 2], 0)
+        assert_raises(TypeError, herme.hermefit, [1], [1], 0, w=[[1]])
+        assert_raises(TypeError, herme.hermefit, [1], [1], 0, w=[1, 1])
+        assert_raises(ValueError, herme.hermefit, [1], [1], [-1,])
+        assert_raises(ValueError, herme.hermefit, [1], [1], [2, -1, 6])
+        assert_raises(TypeError, herme.hermefit, [1], [1], [])
+
+        # Test fit
+        x = np.linspace(0, 2)
+        y = f(x)
+        #
+        coef3 = herme.hermefit(x, y, 3)
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(herme.hermeval(x, coef3), y)
+        coef3 = herme.hermefit(x, y, [0, 1, 2, 3])
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(herme.hermeval(x, coef3), y)
+        #
+        coef4 = herme.hermefit(x, y, 4)
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(herme.hermeval(x, coef4), y)
+        coef4 = herme.hermefit(x, y, [0, 1, 2, 3, 4])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(herme.hermeval(x, coef4), y)
+        # check things still work if deg is not in strict increasing
+        coef4 = herme.hermefit(x, y, [2, 3, 4, 1, 0])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(herme.hermeval(x, coef4), y)
+        #
+        coef2d = herme.hermefit(x, np.array([y, y]).T, 3)
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        coef2d = herme.hermefit(x, np.array([y, y]).T, [0, 1, 2, 3])
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        # test weighting
+        w = np.zeros_like(x)
+        yw = y.copy()
+        w[1::2] = 1
+        y[0::2] = 0
+        wcoef3 = herme.hermefit(x, yw, 3, w=w)
+        assert_almost_equal(wcoef3, coef3)
+        wcoef3 = herme.hermefit(x, yw, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef3, coef3)
+        #
+        wcoef2d = herme.hermefit(x, np.array([yw, yw]).T, 3, w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        wcoef2d = herme.hermefit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        # test scaling with complex values x points whose square
+        # is zero when summed.
+        x = [1, 1j, -1, -1j]
+        assert_almost_equal(herme.hermefit(x, x, 1), [0, 1])
+        assert_almost_equal(herme.hermefit(x, x, [0, 1]), [0, 1])
+        # test fitting only even Legendre polynomials
+        x = np.linspace(-1, 1)
+        y = f2(x)
+        coef1 = herme.hermefit(x, y, 4)
+        assert_almost_equal(herme.hermeval(x, coef1), y)
+        coef2 = herme.hermefit(x, y, [0, 2, 4])
+        assert_almost_equal(herme.hermeval(x, coef2), y)
+        assert_almost_equal(coef1, coef2)
+
+
+class TestCompanion:
+
+    def test_raises(self):
+        assert_raises(ValueError, herme.hermecompanion, [])
+        assert_raises(ValueError, herme.hermecompanion, [1])
+
+    def test_dimensions(self):
+        for i in range(1, 5):
+            coef = [0]*i + [1]
+            assert_(herme.hermecompanion(coef).shape == (i, i))
+
+    def test_linear_root(self):
+        assert_(herme.hermecompanion([1, 2])[0, 0] == -.5)
+
+
+class TestGauss:
+
+    def test_100(self):
+        x, w = herme.hermegauss(100)
+
+        # test orthogonality. Note that the results need to be normalized,
+        # otherwise the huge values that can arise from fast growing
+        # functions like Laguerre can be very confusing.
+        v = herme.hermevander(x, 99)
+        vv = np.dot(v.T * w, v)
+        vd = 1/np.sqrt(vv.diagonal())
+        vv = vd[:, None] * vv * vd
+        assert_almost_equal(vv, np.eye(100))
+
+        # check that the integral of 1 is correct
+        tgt = np.sqrt(2*np.pi)
+        assert_almost_equal(w.sum(), tgt)
+
+
+class TestMisc:
+
+    def test_hermefromroots(self):
+        res = herme.hermefromroots([])
+        assert_almost_equal(trim(res), [1])
+        for i in range(1, 5):
+            roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
+            pol = herme.hermefromroots(roots)
+            res = herme.hermeval(roots, pol)
+            tgt = 0
+            assert_(len(pol) == i + 1)
+            assert_almost_equal(herme.herme2poly(pol)[-1], 1)
+            assert_almost_equal(res, tgt)
+
+    def test_hermeroots(self):
+        assert_almost_equal(herme.hermeroots([1]), [])
+        assert_almost_equal(herme.hermeroots([1, 1]), [-1])
+        for i in range(2, 5):
+            tgt = np.linspace(-1, 1, i)
+            res = herme.hermeroots(herme.hermefromroots(tgt))
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_hermetrim(self):
+        coef = [2, -1, 1, 0]
+
+        # Test exceptions
+        assert_raises(ValueError, herme.hermetrim, coef, -1)
+
+        # Test results
+        assert_equal(herme.hermetrim(coef), coef[:-1])
+        assert_equal(herme.hermetrim(coef, 1), coef[:-3])
+        assert_equal(herme.hermetrim(coef, 2), [0])
+
+    def test_hermeline(self):
+        assert_equal(herme.hermeline(3, 4), [3, 4])
+
+    def test_herme2poly(self):
+        for i in range(10):
+            assert_almost_equal(herme.herme2poly([0]*i + [1]), Helist[i])
+
+    def test_poly2herme(self):
+        for i in range(10):
+            assert_almost_equal(herme.poly2herme(Helist[i]), [0]*i + [1])
+
+    def test_weight(self):
+        x = np.linspace(-5, 5, 11)
+        tgt = np.exp(-.5*x**2)
+        res = herme.hermeweight(x)
+        assert_almost_equal(res, tgt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_laguerre.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_laguerre.py
new file mode 100644
index 0000000000000000000000000000000000000000..49f7c7e115bec499a04f58c38d803d3e8be1247e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_laguerre.py
@@ -0,0 +1,537 @@
+"""Tests for laguerre module.
+
+"""
+from functools import reduce
+
+import numpy as np
+import numpy.polynomial.laguerre as lag
+from numpy.polynomial.polynomial import polyval
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+
+L0 = np.array([1])/1
+L1 = np.array([1, -1])/1
+L2 = np.array([2, -4, 1])/2
+L3 = np.array([6, -18, 9, -1])/6
+L4 = np.array([24, -96, 72, -16, 1])/24
+L5 = np.array([120, -600, 600, -200, 25, -1])/120
+L6 = np.array([720, -4320, 5400, -2400, 450, -36, 1])/720
+
+Llist = [L0, L1, L2, L3, L4, L5, L6]
+
+
+def trim(x):
+    return lag.lagtrim(x, tol=1e-6)
+
+
+class TestConstants:
+
+    def test_lagdomain(self):
+        assert_equal(lag.lagdomain, [0, 1])
+
+    def test_lagzero(self):
+        assert_equal(lag.lagzero, [0])
+
+    def test_lagone(self):
+        assert_equal(lag.lagone, [1])
+
+    def test_lagx(self):
+        assert_equal(lag.lagx, [1, -1])
+
+
+class TestArithmetic:
+    x = np.linspace(-3, 3, 100)
+
+    def test_lagadd(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] += 1
+                res = lag.lagadd([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_lagsub(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] -= 1
+                res = lag.lagsub([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_lagmulx(self):
+        assert_equal(lag.lagmulx([0]), [0])
+        assert_equal(lag.lagmulx([1]), [1, -1])
+        for i in range(1, 5):
+            ser = [0]*i + [1]
+            tgt = [0]*(i - 1) + [-i, 2*i + 1, -(i + 1)]
+            assert_almost_equal(lag.lagmulx(ser), tgt)
+
+    def test_lagmul(self):
+        # check values of result
+        for i in range(5):
+            pol1 = [0]*i + [1]
+            val1 = lag.lagval(self.x, pol1)
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                pol2 = [0]*j + [1]
+                val2 = lag.lagval(self.x, pol2)
+                pol3 = lag.lagmul(pol1, pol2)
+                val3 = lag.lagval(self.x, pol3)
+                assert_(len(pol3) == i + j + 1, msg)
+                assert_almost_equal(val3, val1*val2, err_msg=msg)
+
+    def test_lagdiv(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                ci = [0]*i + [1]
+                cj = [0]*j + [1]
+                tgt = lag.lagadd(ci, cj)
+                quo, rem = lag.lagdiv(tgt, ci)
+                res = lag.lagadd(lag.lagmul(quo, ci), rem)
+                assert_almost_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_lagpow(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                c = np.arange(i + 1)
+                tgt = reduce(lag.lagmul, [c]*j, np.array([1]))
+                res = lag.lagpow(c, j)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+
+class TestEvaluation:
+    # coefficients of 1 + 2*x + 3*x**2
+    c1d = np.array([9., -14., 6.])
+    c2d = np.einsum('i,j->ij', c1d, c1d)
+    c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d)
+
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+    y = polyval(x, [1., 2., 3.])
+
+    def test_lagval(self):
+        #check empty input
+        assert_equal(lag.lagval([], [1]).size, 0)
+
+        #check normal input)
+        x = np.linspace(-1, 1)
+        y = [polyval(x, c) for c in Llist]
+        for i in range(7):
+            msg = f"At i={i}"
+            tgt = y[i]
+            res = lag.lagval(x, [0]*i + [1])
+            assert_almost_equal(res, tgt, err_msg=msg)
+
+        #check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(lag.lagval(x, [1]).shape, dims)
+            assert_equal(lag.lagval(x, [1, 0]).shape, dims)
+            assert_equal(lag.lagval(x, [1, 0, 0]).shape, dims)
+
+    def test_lagval2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, lag.lagval2d, x1, x2[:2], self.c2d)
+
+        #test values
+        tgt = y1*y2
+        res = lag.lagval2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = lag.lagval2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3))
+
+    def test_lagval3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, lag.lagval3d, x1, x2, x3[:2], self.c3d)
+
+        #test values
+        tgt = y1*y2*y3
+        res = lag.lagval3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = lag.lagval3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3))
+
+    def test_laggrid2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j->ij', y1, y2)
+        res = lag.laggrid2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = lag.laggrid2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3)*2)
+
+    def test_laggrid3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j,k->ijk', y1, y2, y3)
+        res = lag.laggrid3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = lag.laggrid3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3)*3)
+
+
+class TestIntegral:
+
+    def test_lagint(self):
+        # check exceptions
+        assert_raises(TypeError, lag.lagint, [0], .5)
+        assert_raises(ValueError, lag.lagint, [0], -1)
+        assert_raises(ValueError, lag.lagint, [0], 1, [0, 0])
+        assert_raises(ValueError, lag.lagint, [0], lbnd=[0])
+        assert_raises(ValueError, lag.lagint, [0], scl=[0])
+        assert_raises(TypeError, lag.lagint, [0], axis=.5)
+
+        # test integration of zero polynomial
+        for i in range(2, 5):
+            k = [0]*(i - 2) + [1]
+            res = lag.lagint([0], m=i, k=k)
+            assert_almost_equal(res, [1, -1])
+
+        # check single integration with integration constant
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [1/scl]
+            lagpol = lag.poly2lag(pol)
+            lagint = lag.lagint(lagpol, m=1, k=[i])
+            res = lag.lag2poly(lagint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check single integration with integration constant and lbnd
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            lagpol = lag.poly2lag(pol)
+            lagint = lag.lagint(lagpol, m=1, k=[i], lbnd=-1)
+            assert_almost_equal(lag.lagval(-1, lagint), i)
+
+        # check single integration with integration constant and scaling
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [2/scl]
+            lagpol = lag.poly2lag(pol)
+            lagint = lag.lagint(lagpol, m=1, k=[i], scl=2)
+            res = lag.lag2poly(lagint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with default k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = lag.lagint(tgt, m=1)
+                res = lag.lagint(pol, m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with defined k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = lag.lagint(tgt, m=1, k=[k])
+                res = lag.lagint(pol, m=j, k=list(range(j)))
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with lbnd
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = lag.lagint(tgt, m=1, k=[k], lbnd=-1)
+                res = lag.lagint(pol, m=j, k=list(range(j)), lbnd=-1)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = lag.lagint(tgt, m=1, k=[k], scl=2)
+                res = lag.lagint(pol, m=j, k=list(range(j)), scl=2)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_lagint_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([lag.lagint(c) for c in c2d.T]).T
+        res = lag.lagint(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([lag.lagint(c) for c in c2d])
+        res = lag.lagint(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([lag.lagint(c, k=3) for c in c2d])
+        res = lag.lagint(c2d, k=3, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestDerivative:
+
+    def test_lagder(self):
+        # check exceptions
+        assert_raises(TypeError, lag.lagder, [0], .5)
+        assert_raises(ValueError, lag.lagder, [0], -1)
+
+        # check that zeroth derivative does nothing
+        for i in range(5):
+            tgt = [0]*i + [1]
+            res = lag.lagder(tgt, m=0)
+            assert_equal(trim(res), trim(tgt))
+
+        # check that derivation is the inverse of integration
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = lag.lagder(lag.lagint(tgt, m=j), m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check derivation with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = lag.lagder(lag.lagint(tgt, m=j, scl=2), m=j, scl=.5)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_lagder_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([lag.lagder(c) for c in c2d.T]).T
+        res = lag.lagder(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([lag.lagder(c) for c in c2d])
+        res = lag.lagder(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestVander:
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+
+    def test_lagvander(self):
+        # check for 1d x
+        x = np.arange(3)
+        v = lag.lagvander(x, 3)
+        assert_(v.shape == (3, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], lag.lagval(x, coef))
+
+        # check for 2d x
+        x = np.array([[1, 2], [3, 4], [5, 6]])
+        v = lag.lagvander(x, 3)
+        assert_(v.shape == (3, 2, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], lag.lagval(x, coef))
+
+    def test_lagvander2d(self):
+        # also tests lagval2d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3))
+        van = lag.lagvander2d(x1, x2, [1, 2])
+        tgt = lag.lagval2d(x1, x2, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = lag.lagvander2d([x1], [x2], [1, 2])
+        assert_(van.shape == (1, 5, 6))
+
+    def test_lagvander3d(self):
+        # also tests lagval3d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3, 4))
+        van = lag.lagvander3d(x1, x2, x3, [1, 2, 3])
+        tgt = lag.lagval3d(x1, x2, x3, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = lag.lagvander3d([x1], [x2], [x3], [1, 2, 3])
+        assert_(van.shape == (1, 5, 24))
+
+
+class TestFitting:
+
+    def test_lagfit(self):
+        def f(x):
+            return x*(x - 1)*(x - 2)
+
+        # Test exceptions
+        assert_raises(ValueError, lag.lagfit, [1], [1], -1)
+        assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
+        assert_raises(TypeError, lag.lagfit, [], [1], 0)
+        assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
+        assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
+        assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
+        assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
+        assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
+        assert_raises(ValueError, lag.lagfit, [1], [1], [-1,])
+        assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6])
+        assert_raises(TypeError, lag.lagfit, [1], [1], [])
+
+        # Test fit
+        x = np.linspace(0, 2)
+        y = f(x)
+        #
+        coef3 = lag.lagfit(x, y, 3)
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(lag.lagval(x, coef3), y)
+        coef3 = lag.lagfit(x, y, [0, 1, 2, 3])
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(lag.lagval(x, coef3), y)
+        #
+        coef4 = lag.lagfit(x, y, 4)
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(lag.lagval(x, coef4), y)
+        coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(lag.lagval(x, coef4), y)
+        #
+        coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3])
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        # test weighting
+        w = np.zeros_like(x)
+        yw = y.copy()
+        w[1::2] = 1
+        y[0::2] = 0
+        wcoef3 = lag.lagfit(x, yw, 3, w=w)
+        assert_almost_equal(wcoef3, coef3)
+        wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef3, coef3)
+        #
+        wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        # test scaling with complex values x points whose square
+        # is zero when summed.
+        x = [1, 1j, -1, -1j]
+        assert_almost_equal(lag.lagfit(x, x, 1), [1, -1])
+        assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1])
+
+
+class TestCompanion:
+
+    def test_raises(self):
+        assert_raises(ValueError, lag.lagcompanion, [])
+        assert_raises(ValueError, lag.lagcompanion, [1])
+
+    def test_dimensions(self):
+        for i in range(1, 5):
+            coef = [0]*i + [1]
+            assert_(lag.lagcompanion(coef).shape == (i, i))
+
+    def test_linear_root(self):
+        assert_(lag.lagcompanion([1, 2])[0, 0] == 1.5)
+
+
+class TestGauss:
+
+    def test_100(self):
+        x, w = lag.laggauss(100)
+
+        # test orthogonality. Note that the results need to be normalized,
+        # otherwise the huge values that can arise from fast growing
+        # functions like Laguerre can be very confusing.
+        v = lag.lagvander(x, 99)
+        vv = np.dot(v.T * w, v)
+        vd = 1/np.sqrt(vv.diagonal())
+        vv = vd[:, None] * vv * vd
+        assert_almost_equal(vv, np.eye(100))
+
+        # check that the integral of 1 is correct
+        tgt = 1.0
+        assert_almost_equal(w.sum(), tgt)
+
+
+class TestMisc:
+
+    def test_lagfromroots(self):
+        res = lag.lagfromroots([])
+        assert_almost_equal(trim(res), [1])
+        for i in range(1, 5):
+            roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
+            pol = lag.lagfromroots(roots)
+            res = lag.lagval(roots, pol)
+            tgt = 0
+            assert_(len(pol) == i + 1)
+            assert_almost_equal(lag.lag2poly(pol)[-1], 1)
+            assert_almost_equal(res, tgt)
+
+    def test_lagroots(self):
+        assert_almost_equal(lag.lagroots([1]), [])
+        assert_almost_equal(lag.lagroots([0, 1]), [1])
+        for i in range(2, 5):
+            tgt = np.linspace(0, 3, i)
+            res = lag.lagroots(lag.lagfromroots(tgt))
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_lagtrim(self):
+        coef = [2, -1, 1, 0]
+
+        # Test exceptions
+        assert_raises(ValueError, lag.lagtrim, coef, -1)
+
+        # Test results
+        assert_equal(lag.lagtrim(coef), coef[:-1])
+        assert_equal(lag.lagtrim(coef, 1), coef[:-3])
+        assert_equal(lag.lagtrim(coef, 2), [0])
+
+    def test_lagline(self):
+        assert_equal(lag.lagline(3, 4), [7, -4])
+
+    def test_lag2poly(self):
+        for i in range(7):
+            assert_almost_equal(lag.lag2poly([0]*i + [1]), Llist[i])
+
+    def test_poly2lag(self):
+        for i in range(7):
+            assert_almost_equal(lag.poly2lag(Llist[i]), [0]*i + [1])
+
+    def test_weight(self):
+        x = np.linspace(0, 10, 11)
+        tgt = np.exp(-x)
+        res = lag.lagweight(x)
+        assert_almost_equal(res, tgt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_legendre.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_legendre.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f1c9733a91121e208d7037f8e93b27f0cdbf9bb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_legendre.py
@@ -0,0 +1,568 @@
+"""Tests for legendre module.
+
+"""
+from functools import reduce
+
+import numpy as np
+import numpy.polynomial.legendre as leg
+from numpy.polynomial.polynomial import polyval
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+
+L0 = np.array([1])
+L1 = np.array([0, 1])
+L2 = np.array([-1, 0, 3])/2
+L3 = np.array([0, -3, 0, 5])/2
+L4 = np.array([3, 0, -30, 0, 35])/8
+L5 = np.array([0, 15, 0, -70, 0, 63])/8
+L6 = np.array([-5, 0, 105, 0, -315, 0, 231])/16
+L7 = np.array([0, -35, 0, 315, 0, -693, 0, 429])/16
+L8 = np.array([35, 0, -1260, 0, 6930, 0, -12012, 0, 6435])/128
+L9 = np.array([0, 315, 0, -4620, 0, 18018, 0, -25740, 0, 12155])/128
+
+Llist = [L0, L1, L2, L3, L4, L5, L6, L7, L8, L9]
+
+
+def trim(x):
+    return leg.legtrim(x, tol=1e-6)
+
+
+class TestConstants:
+
+    def test_legdomain(self):
+        assert_equal(leg.legdomain, [-1, 1])
+
+    def test_legzero(self):
+        assert_equal(leg.legzero, [0])
+
+    def test_legone(self):
+        assert_equal(leg.legone, [1])
+
+    def test_legx(self):
+        assert_equal(leg.legx, [0, 1])
+
+
+class TestArithmetic:
+    x = np.linspace(-1, 1, 100)
+
+    def test_legadd(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] += 1
+                res = leg.legadd([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_legsub(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] -= 1
+                res = leg.legsub([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_legmulx(self):
+        assert_equal(leg.legmulx([0]), [0])
+        assert_equal(leg.legmulx([1]), [0, 1])
+        for i in range(1, 5):
+            tmp = 2*i + 1
+            ser = [0]*i + [1]
+            tgt = [0]*(i - 1) + [i/tmp, 0, (i + 1)/tmp]
+            assert_equal(leg.legmulx(ser), tgt)
+
+    def test_legmul(self):
+        # check values of result
+        for i in range(5):
+            pol1 = [0]*i + [1]
+            val1 = leg.legval(self.x, pol1)
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                pol2 = [0]*j + [1]
+                val2 = leg.legval(self.x, pol2)
+                pol3 = leg.legmul(pol1, pol2)
+                val3 = leg.legval(self.x, pol3)
+                assert_(len(pol3) == i + j + 1, msg)
+                assert_almost_equal(val3, val1*val2, err_msg=msg)
+
+    def test_legdiv(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                ci = [0]*i + [1]
+                cj = [0]*j + [1]
+                tgt = leg.legadd(ci, cj)
+                quo, rem = leg.legdiv(tgt, ci)
+                res = leg.legadd(leg.legmul(quo, ci), rem)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_legpow(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                c = np.arange(i + 1)
+                tgt = reduce(leg.legmul, [c]*j, np.array([1]))
+                res = leg.legpow(c, j)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+
+class TestEvaluation:
+    # coefficients of 1 + 2*x + 3*x**2
+    c1d = np.array([2., 2., 2.])
+    c2d = np.einsum('i,j->ij', c1d, c1d)
+    c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d)
+
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+    y = polyval(x, [1., 2., 3.])
+
+    def test_legval(self):
+        #check empty input
+        assert_equal(leg.legval([], [1]).size, 0)
+
+        #check normal input)
+        x = np.linspace(-1, 1)
+        y = [polyval(x, c) for c in Llist]
+        for i in range(10):
+            msg = f"At i={i}"
+            tgt = y[i]
+            res = leg.legval(x, [0]*i + [1])
+            assert_almost_equal(res, tgt, err_msg=msg)
+
+        #check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(leg.legval(x, [1]).shape, dims)
+            assert_equal(leg.legval(x, [1, 0]).shape, dims)
+            assert_equal(leg.legval(x, [1, 0, 0]).shape, dims)
+
+    def test_legval2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, leg.legval2d, x1, x2[:2], self.c2d)
+
+        #test values
+        tgt = y1*y2
+        res = leg.legval2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = leg.legval2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3))
+
+    def test_legval3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises(ValueError, leg.legval3d, x1, x2, x3[:2], self.c3d)
+
+        #test values
+        tgt = y1*y2*y3
+        res = leg.legval3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = leg.legval3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3))
+
+    def test_leggrid2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j->ij', y1, y2)
+        res = leg.leggrid2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = leg.leggrid2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3)*2)
+
+    def test_leggrid3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j,k->ijk', y1, y2, y3)
+        res = leg.leggrid3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = leg.leggrid3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3)*3)
+
+
+class TestIntegral:
+
+    def test_legint(self):
+        # check exceptions
+        assert_raises(TypeError, leg.legint, [0], .5)
+        assert_raises(ValueError, leg.legint, [0], -1)
+        assert_raises(ValueError, leg.legint, [0], 1, [0, 0])
+        assert_raises(ValueError, leg.legint, [0], lbnd=[0])
+        assert_raises(ValueError, leg.legint, [0], scl=[0])
+        assert_raises(TypeError, leg.legint, [0], axis=.5)
+
+        # test integration of zero polynomial
+        for i in range(2, 5):
+            k = [0]*(i - 2) + [1]
+            res = leg.legint([0], m=i, k=k)
+            assert_almost_equal(res, [0, 1])
+
+        # check single integration with integration constant
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [1/scl]
+            legpol = leg.poly2leg(pol)
+            legint = leg.legint(legpol, m=1, k=[i])
+            res = leg.leg2poly(legint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check single integration with integration constant and lbnd
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            legpol = leg.poly2leg(pol)
+            legint = leg.legint(legpol, m=1, k=[i], lbnd=-1)
+            assert_almost_equal(leg.legval(-1, legint), i)
+
+        # check single integration with integration constant and scaling
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [2/scl]
+            legpol = leg.poly2leg(pol)
+            legint = leg.legint(legpol, m=1, k=[i], scl=2)
+            res = leg.leg2poly(legint)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with default k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = leg.legint(tgt, m=1)
+                res = leg.legint(pol, m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with defined k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = leg.legint(tgt, m=1, k=[k])
+                res = leg.legint(pol, m=j, k=list(range(j)))
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with lbnd
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = leg.legint(tgt, m=1, k=[k], lbnd=-1)
+                res = leg.legint(pol, m=j, k=list(range(j)), lbnd=-1)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = leg.legint(tgt, m=1, k=[k], scl=2)
+                res = leg.legint(pol, m=j, k=list(range(j)), scl=2)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_legint_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([leg.legint(c) for c in c2d.T]).T
+        res = leg.legint(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([leg.legint(c) for c in c2d])
+        res = leg.legint(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([leg.legint(c, k=3) for c in c2d])
+        res = leg.legint(c2d, k=3, axis=1)
+        assert_almost_equal(res, tgt)
+
+    def test_legint_zerointord(self):
+        assert_equal(leg.legint((1, 2, 3), 0), (1, 2, 3))
+
+
+class TestDerivative:
+
+    def test_legder(self):
+        # check exceptions
+        assert_raises(TypeError, leg.legder, [0], .5)
+        assert_raises(ValueError, leg.legder, [0], -1)
+
+        # check that zeroth derivative does nothing
+        for i in range(5):
+            tgt = [0]*i + [1]
+            res = leg.legder(tgt, m=0)
+            assert_equal(trim(res), trim(tgt))
+
+        # check that derivation is the inverse of integration
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = leg.legder(leg.legint(tgt, m=j), m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check derivation with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = leg.legder(leg.legint(tgt, m=j, scl=2), m=j, scl=.5)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_legder_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([leg.legder(c) for c in c2d.T]).T
+        res = leg.legder(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([leg.legder(c) for c in c2d])
+        res = leg.legder(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+    def test_legder_orderhigherthancoeff(self):
+        c = (1, 2, 3, 4)
+        assert_equal(leg.legder(c, 4), [0])
+
+class TestVander:
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+
+    def test_legvander(self):
+        # check for 1d x
+        x = np.arange(3)
+        v = leg.legvander(x, 3)
+        assert_(v.shape == (3, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], leg.legval(x, coef))
+
+        # check for 2d x
+        x = np.array([[1, 2], [3, 4], [5, 6]])
+        v = leg.legvander(x, 3)
+        assert_(v.shape == (3, 2, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], leg.legval(x, coef))
+
+    def test_legvander2d(self):
+        # also tests polyval2d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3))
+        van = leg.legvander2d(x1, x2, [1, 2])
+        tgt = leg.legval2d(x1, x2, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = leg.legvander2d([x1], [x2], [1, 2])
+        assert_(van.shape == (1, 5, 6))
+
+    def test_legvander3d(self):
+        # also tests polyval3d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3, 4))
+        van = leg.legvander3d(x1, x2, x3, [1, 2, 3])
+        tgt = leg.legval3d(x1, x2, x3, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = leg.legvander3d([x1], [x2], [x3], [1, 2, 3])
+        assert_(van.shape == (1, 5, 24))
+
+    def test_legvander_negdeg(self):
+        assert_raises(ValueError, leg.legvander, (1, 2, 3), -1)
+
+
+class TestFitting:
+
+    def test_legfit(self):
+        def f(x):
+            return x*(x - 1)*(x - 2)
+
+        def f2(x):
+            return x**4 + x**2 + 1
+
+        # Test exceptions
+        assert_raises(ValueError, leg.legfit, [1], [1], -1)
+        assert_raises(TypeError, leg.legfit, [[1]], [1], 0)
+        assert_raises(TypeError, leg.legfit, [], [1], 0)
+        assert_raises(TypeError, leg.legfit, [1], [[[1]]], 0)
+        assert_raises(TypeError, leg.legfit, [1, 2], [1], 0)
+        assert_raises(TypeError, leg.legfit, [1], [1, 2], 0)
+        assert_raises(TypeError, leg.legfit, [1], [1], 0, w=[[1]])
+        assert_raises(TypeError, leg.legfit, [1], [1], 0, w=[1, 1])
+        assert_raises(ValueError, leg.legfit, [1], [1], [-1,])
+        assert_raises(ValueError, leg.legfit, [1], [1], [2, -1, 6])
+        assert_raises(TypeError, leg.legfit, [1], [1], [])
+
+        # Test fit
+        x = np.linspace(0, 2)
+        y = f(x)
+        #
+        coef3 = leg.legfit(x, y, 3)
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(leg.legval(x, coef3), y)
+        coef3 = leg.legfit(x, y, [0, 1, 2, 3])
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(leg.legval(x, coef3), y)
+        #
+        coef4 = leg.legfit(x, y, 4)
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(leg.legval(x, coef4), y)
+        coef4 = leg.legfit(x, y, [0, 1, 2, 3, 4])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(leg.legval(x, coef4), y)
+        # check things still work if deg is not in strict increasing
+        coef4 = leg.legfit(x, y, [2, 3, 4, 1, 0])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(leg.legval(x, coef4), y)
+        #
+        coef2d = leg.legfit(x, np.array([y, y]).T, 3)
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        coef2d = leg.legfit(x, np.array([y, y]).T, [0, 1, 2, 3])
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        # test weighting
+        w = np.zeros_like(x)
+        yw = y.copy()
+        w[1::2] = 1
+        y[0::2] = 0
+        wcoef3 = leg.legfit(x, yw, 3, w=w)
+        assert_almost_equal(wcoef3, coef3)
+        wcoef3 = leg.legfit(x, yw, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef3, coef3)
+        #
+        wcoef2d = leg.legfit(x, np.array([yw, yw]).T, 3, w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        wcoef2d = leg.legfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        # test scaling with complex values x points whose square
+        # is zero when summed.
+        x = [1, 1j, -1, -1j]
+        assert_almost_equal(leg.legfit(x, x, 1), [0, 1])
+        assert_almost_equal(leg.legfit(x, x, [0, 1]), [0, 1])
+        # test fitting only even Legendre polynomials
+        x = np.linspace(-1, 1)
+        y = f2(x)
+        coef1 = leg.legfit(x, y, 4)
+        assert_almost_equal(leg.legval(x, coef1), y)
+        coef2 = leg.legfit(x, y, [0, 2, 4])
+        assert_almost_equal(leg.legval(x, coef2), y)
+        assert_almost_equal(coef1, coef2)
+
+
+class TestCompanion:
+
+    def test_raises(self):
+        assert_raises(ValueError, leg.legcompanion, [])
+        assert_raises(ValueError, leg.legcompanion, [1])
+
+    def test_dimensions(self):
+        for i in range(1, 5):
+            coef = [0]*i + [1]
+            assert_(leg.legcompanion(coef).shape == (i, i))
+
+    def test_linear_root(self):
+        assert_(leg.legcompanion([1, 2])[0, 0] == -.5)
+
+
+class TestGauss:
+
+    def test_100(self):
+        x, w = leg.leggauss(100)
+
+        # test orthogonality. Note that the results need to be normalized,
+        # otherwise the huge values that can arise from fast growing
+        # functions like Laguerre can be very confusing.
+        v = leg.legvander(x, 99)
+        vv = np.dot(v.T * w, v)
+        vd = 1/np.sqrt(vv.diagonal())
+        vv = vd[:, None] * vv * vd
+        assert_almost_equal(vv, np.eye(100))
+
+        # check that the integral of 1 is correct
+        tgt = 2.0
+        assert_almost_equal(w.sum(), tgt)
+
+
+class TestMisc:
+
+    def test_legfromroots(self):
+        res = leg.legfromroots([])
+        assert_almost_equal(trim(res), [1])
+        for i in range(1, 5):
+            roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
+            pol = leg.legfromroots(roots)
+            res = leg.legval(roots, pol)
+            tgt = 0
+            assert_(len(pol) == i + 1)
+            assert_almost_equal(leg.leg2poly(pol)[-1], 1)
+            assert_almost_equal(res, tgt)
+
+    def test_legroots(self):
+        assert_almost_equal(leg.legroots([1]), [])
+        assert_almost_equal(leg.legroots([1, 2]), [-.5])
+        for i in range(2, 5):
+            tgt = np.linspace(-1, 1, i)
+            res = leg.legroots(leg.legfromroots(tgt))
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_legtrim(self):
+        coef = [2, -1, 1, 0]
+
+        # Test exceptions
+        assert_raises(ValueError, leg.legtrim, coef, -1)
+
+        # Test results
+        assert_equal(leg.legtrim(coef), coef[:-1])
+        assert_equal(leg.legtrim(coef, 1), coef[:-3])
+        assert_equal(leg.legtrim(coef, 2), [0])
+
+    def test_legline(self):
+        assert_equal(leg.legline(3, 4), [3, 4])
+
+    def test_legline_zeroscl(self):
+        assert_equal(leg.legline(3, 0), [3])
+
+    def test_leg2poly(self):
+        for i in range(10):
+            assert_almost_equal(leg.leg2poly([0]*i + [1]), Llist[i])
+
+    def test_poly2leg(self):
+        for i in range(10):
+            assert_almost_equal(leg.poly2leg(Llist[i]), [0]*i + [1])
+
+    def test_weight(self):
+        x = np.linspace(-1, 1, 11)
+        tgt = 1.
+        res = leg.legweight(x)
+        assert_almost_equal(res, tgt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_polynomial.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_polynomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..d36b07dbd9536b4c1bd1f3129ae7ccaa2a320ed3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_polynomial.py
@@ -0,0 +1,647 @@
+"""Tests for polynomial module.
+
+"""
+from functools import reduce
+from fractions import Fraction
+import numpy as np
+import numpy.polynomial.polynomial as poly
+import numpy.polynomial.polyutils as pu
+import pickle
+from copy import deepcopy
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    assert_array_equal, assert_raises_regex, assert_warns)
+
+
+def trim(x):
+    return poly.polytrim(x, tol=1e-6)
+
+T0 = [1]
+T1 = [0, 1]
+T2 = [-1, 0, 2]
+T3 = [0, -3, 0, 4]
+T4 = [1, 0, -8, 0, 8]
+T5 = [0, 5, 0, -20, 0, 16]
+T6 = [-1, 0, 18, 0, -48, 0, 32]
+T7 = [0, -7, 0, 56, 0, -112, 0, 64]
+T8 = [1, 0, -32, 0, 160, 0, -256, 0, 128]
+T9 = [0, 9, 0, -120, 0, 432, 0, -576, 0, 256]
+
+Tlist = [T0, T1, T2, T3, T4, T5, T6, T7, T8, T9]
+
+
+class TestConstants:
+
+    def test_polydomain(self):
+        assert_equal(poly.polydomain, [-1, 1])
+
+    def test_polyzero(self):
+        assert_equal(poly.polyzero, [0])
+
+    def test_polyone(self):
+        assert_equal(poly.polyone, [1])
+
+    def test_polyx(self):
+        assert_equal(poly.polyx, [0, 1])
+
+    def test_copy(self):
+        x = poly.Polynomial([1, 2, 3])
+        y = deepcopy(x)
+        assert_equal(x, y)
+
+    def test_pickle(self):
+        x = poly.Polynomial([1, 2, 3])
+        y = pickle.loads(pickle.dumps(x))
+        assert_equal(x, y)
+
+class TestArithmetic:
+
+    def test_polyadd(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] += 1
+                res = poly.polyadd([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_polysub(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(max(i, j) + 1)
+                tgt[i] += 1
+                tgt[j] -= 1
+                res = poly.polysub([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_polymulx(self):
+        assert_equal(poly.polymulx([0]), [0])
+        assert_equal(poly.polymulx([1]), [0, 1])
+        for i in range(1, 5):
+            ser = [0]*i + [1]
+            tgt = [0]*(i + 1) + [1]
+            assert_equal(poly.polymulx(ser), tgt)
+
+    def test_polymul(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                tgt = np.zeros(i + j + 1)
+                tgt[i + j] += 1
+                res = poly.polymul([0]*i + [1], [0]*j + [1])
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+    def test_polydiv(self):
+        # check zero division
+        assert_raises(ZeroDivisionError, poly.polydiv, [1], [0])
+
+        # check scalar division
+        quo, rem = poly.polydiv([2], [2])
+        assert_equal((quo, rem), (1, 0))
+        quo, rem = poly.polydiv([2, 2], [2])
+        assert_equal((quo, rem), ((1, 1), 0))
+
+        # check rest.
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                ci = [0]*i + [1, 2]
+                cj = [0]*j + [1, 2]
+                tgt = poly.polyadd(ci, cj)
+                quo, rem = poly.polydiv(tgt, ci)
+                res = poly.polyadd(poly.polymul(quo, ci), rem)
+                assert_equal(res, tgt, err_msg=msg)
+
+    def test_polypow(self):
+        for i in range(5):
+            for j in range(5):
+                msg = f"At i={i}, j={j}"
+                c = np.arange(i + 1)
+                tgt = reduce(poly.polymul, [c]*j, np.array([1]))
+                res = poly.polypow(c, j)
+                assert_equal(trim(res), trim(tgt), err_msg=msg)
+
+class TestFraction:
+
+    def test_Fraction(self):
+        # assert we can use Polynomials with coefficients of object dtype
+        f = Fraction(2, 3)
+        one = Fraction(1, 1)
+        zero = Fraction(0, 1)
+        p = poly.Polynomial([f, f], domain=[zero, one], window=[zero, one])
+
+        x = 2 * p + p ** 2
+        assert_equal(x.coef, np.array([Fraction(16, 9), Fraction(20, 9),
+                                       Fraction(4, 9)], dtype=object))
+        assert_equal(p.domain, [zero, one])
+        assert_equal(p.coef.dtype, np.dtypes.ObjectDType())
+        assert_(isinstance(p(f), Fraction))
+        assert_equal(p(f), Fraction(10, 9))
+        p_deriv = poly.Polynomial([Fraction(2, 3)], domain=[zero, one],
+                                  window=[zero, one])
+        assert_equal(p.deriv(), p_deriv)
+
+class TestEvaluation:
+    # coefficients of 1 + 2*x + 3*x**2
+    c1d = np.array([1., 2., 3.])
+    c2d = np.einsum('i,j->ij', c1d, c1d)
+    c3d = np.einsum('i,j,k->ijk', c1d, c1d, c1d)
+
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+    y = poly.polyval(x, [1., 2., 3.])
+
+    def test_polyval(self):
+        #check empty input
+        assert_equal(poly.polyval([], [1]).size, 0)
+
+        #check normal input)
+        x = np.linspace(-1, 1)
+        y = [x**i for i in range(5)]
+        for i in range(5):
+            tgt = y[i]
+            res = poly.polyval(x, [0]*i + [1])
+            assert_almost_equal(res, tgt)
+        tgt = x*(x**2 - 1)
+        res = poly.polyval(x, [0, -1, 0, 1])
+        assert_almost_equal(res, tgt)
+
+        #check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(poly.polyval(x, [1]).shape, dims)
+            assert_equal(poly.polyval(x, [1, 0]).shape, dims)
+            assert_equal(poly.polyval(x, [1, 0, 0]).shape, dims)
+
+        #check masked arrays are processed correctly
+        mask = [False, True, False]
+        mx = np.ma.array([1, 2, 3], mask=mask)
+        res = np.polyval([7, 5, 3], mx)
+        assert_array_equal(res.mask, mask)
+
+        #check subtypes of ndarray are preserved
+        class C(np.ndarray):
+            pass
+
+        cx = np.array([1, 2, 3]).view(C)
+        assert_equal(type(np.polyval([2, 3, 4], cx)), C)
+
+    def test_polyvalfromroots(self):
+        # check exception for broadcasting x values over root array with
+        # too few dimensions
+        assert_raises(ValueError, poly.polyvalfromroots,
+                      [1], [1], tensor=False)
+
+        # check empty input
+        assert_equal(poly.polyvalfromroots([], [1]).size, 0)
+        assert_(poly.polyvalfromroots([], [1]).shape == (0,))
+
+        # check empty input + multidimensional roots
+        assert_equal(poly.polyvalfromroots([], [[1] * 5]).size, 0)
+        assert_(poly.polyvalfromroots([], [[1] * 5]).shape == (5, 0))
+
+        # check scalar input
+        assert_equal(poly.polyvalfromroots(1, 1), 0)
+        assert_(poly.polyvalfromroots(1, np.ones((3, 3))).shape == (3,))
+
+        # check normal input)
+        x = np.linspace(-1, 1)
+        y = [x**i for i in range(5)]
+        for i in range(1, 5):
+            tgt = y[i]
+            res = poly.polyvalfromroots(x, [0]*i)
+            assert_almost_equal(res, tgt)
+        tgt = x*(x - 1)*(x + 1)
+        res = poly.polyvalfromroots(x, [-1, 0, 1])
+        assert_almost_equal(res, tgt)
+
+        # check that shape is preserved
+        for i in range(3):
+            dims = [2]*i
+            x = np.zeros(dims)
+            assert_equal(poly.polyvalfromroots(x, [1]).shape, dims)
+            assert_equal(poly.polyvalfromroots(x, [1, 0]).shape, dims)
+            assert_equal(poly.polyvalfromroots(x, [1, 0, 0]).shape, dims)
+
+        # check compatibility with factorization
+        ptest = [15, 2, -16, -2, 1]
+        r = poly.polyroots(ptest)
+        x = np.linspace(-1, 1)
+        assert_almost_equal(poly.polyval(x, ptest),
+                            poly.polyvalfromroots(x, r))
+
+        # check multidimensional arrays of roots and values
+        # check tensor=False
+        rshape = (3, 5)
+        x = np.arange(-3, 2)
+        r = np.random.randint(-5, 5, size=rshape)
+        res = poly.polyvalfromroots(x, r, tensor=False)
+        tgt = np.empty(r.shape[1:])
+        for ii in range(tgt.size):
+            tgt[ii] = poly.polyvalfromroots(x[ii], r[:, ii])
+        assert_equal(res, tgt)
+
+        # check tensor=True
+        x = np.vstack([x, 2*x])
+        res = poly.polyvalfromroots(x, r, tensor=True)
+        tgt = np.empty(r.shape[1:] + x.shape)
+        for ii in range(r.shape[1]):
+            for jj in range(x.shape[0]):
+                tgt[ii, jj, :] = poly.polyvalfromroots(x[jj], r[:, ii])
+        assert_equal(res, tgt)
+
+    def test_polyval2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises_regex(ValueError, 'incompatible',
+                            poly.polyval2d, x1, x2[:2], self.c2d)
+
+        #test values
+        tgt = y1*y2
+        res = poly.polyval2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = poly.polyval2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3))
+
+    def test_polyval3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test exceptions
+        assert_raises_regex(ValueError, 'incompatible',
+                      poly.polyval3d, x1, x2, x3[:2], self.c3d)
+
+        #test values
+        tgt = y1*y2*y3
+        res = poly.polyval3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = poly.polyval3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3))
+
+    def test_polygrid2d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j->ij', y1, y2)
+        res = poly.polygrid2d(x1, x2, self.c2d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = poly.polygrid2d(z, z, self.c2d)
+        assert_(res.shape == (2, 3)*2)
+
+    def test_polygrid3d(self):
+        x1, x2, x3 = self.x
+        y1, y2, y3 = self.y
+
+        #test values
+        tgt = np.einsum('i,j,k->ijk', y1, y2, y3)
+        res = poly.polygrid3d(x1, x2, x3, self.c3d)
+        assert_almost_equal(res, tgt)
+
+        #test shape
+        z = np.ones((2, 3))
+        res = poly.polygrid3d(z, z, z, self.c3d)
+        assert_(res.shape == (2, 3)*3)
+
+
+class TestIntegral:
+
+    def test_polyint(self):
+        # check exceptions
+        assert_raises(TypeError, poly.polyint, [0], .5)
+        assert_raises(ValueError, poly.polyint, [0], -1)
+        assert_raises(ValueError, poly.polyint, [0], 1, [0, 0])
+        assert_raises(ValueError, poly.polyint, [0], lbnd=[0])
+        assert_raises(ValueError, poly.polyint, [0], scl=[0])
+        assert_raises(TypeError, poly.polyint, [0], axis=.5)
+        assert_raises(TypeError, poly.polyint, [1, 1], 1.)
+
+        # test integration of zero polynomial
+        for i in range(2, 5):
+            k = [0]*(i - 2) + [1]
+            res = poly.polyint([0], m=i, k=k)
+            assert_almost_equal(res, [0, 1])
+
+        # check single integration with integration constant
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [1/scl]
+            res = poly.polyint(pol, m=1, k=[i])
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check single integration with integration constant and lbnd
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            res = poly.polyint(pol, m=1, k=[i], lbnd=-1)
+            assert_almost_equal(poly.polyval(-1, res), i)
+
+        # check single integration with integration constant and scaling
+        for i in range(5):
+            scl = i + 1
+            pol = [0]*i + [1]
+            tgt = [i] + [0]*i + [2/scl]
+            res = poly.polyint(pol, m=1, k=[i], scl=2)
+            assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with default k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = poly.polyint(tgt, m=1)
+                res = poly.polyint(pol, m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with defined k
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = poly.polyint(tgt, m=1, k=[k])
+                res = poly.polyint(pol, m=j, k=list(range(j)))
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with lbnd
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = poly.polyint(tgt, m=1, k=[k], lbnd=-1)
+                res = poly.polyint(pol, m=j, k=list(range(j)), lbnd=-1)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check multiple integrations with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                pol = [0]*i + [1]
+                tgt = pol[:]
+                for k in range(j):
+                    tgt = poly.polyint(tgt, m=1, k=[k], scl=2)
+                res = poly.polyint(pol, m=j, k=list(range(j)), scl=2)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_polyint_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([poly.polyint(c) for c in c2d.T]).T
+        res = poly.polyint(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([poly.polyint(c) for c in c2d])
+        res = poly.polyint(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([poly.polyint(c, k=3) for c in c2d])
+        res = poly.polyint(c2d, k=3, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestDerivative:
+
+    def test_polyder(self):
+        # check exceptions
+        assert_raises(TypeError, poly.polyder, [0], .5)
+        assert_raises(ValueError, poly.polyder, [0], -1)
+
+        # check that zeroth derivative does nothing
+        for i in range(5):
+            tgt = [0]*i + [1]
+            res = poly.polyder(tgt, m=0)
+            assert_equal(trim(res), trim(tgt))
+
+        # check that derivation is the inverse of integration
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = poly.polyder(poly.polyint(tgt, m=j), m=j)
+                assert_almost_equal(trim(res), trim(tgt))
+
+        # check derivation with scaling
+        for i in range(5):
+            for j in range(2, 5):
+                tgt = [0]*i + [1]
+                res = poly.polyder(poly.polyint(tgt, m=j, scl=2), m=j, scl=.5)
+                assert_almost_equal(trim(res), trim(tgt))
+
+    def test_polyder_axis(self):
+        # check that axis keyword works
+        c2d = np.random.random((3, 4))
+
+        tgt = np.vstack([poly.polyder(c) for c in c2d.T]).T
+        res = poly.polyder(c2d, axis=0)
+        assert_almost_equal(res, tgt)
+
+        tgt = np.vstack([poly.polyder(c) for c in c2d])
+        res = poly.polyder(c2d, axis=1)
+        assert_almost_equal(res, tgt)
+
+
+class TestVander:
+    # some random values in [-1, 1)
+    x = np.random.random((3, 5))*2 - 1
+
+    def test_polyvander(self):
+        # check for 1d x
+        x = np.arange(3)
+        v = poly.polyvander(x, 3)
+        assert_(v.shape == (3, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], poly.polyval(x, coef))
+
+        # check for 2d x
+        x = np.array([[1, 2], [3, 4], [5, 6]])
+        v = poly.polyvander(x, 3)
+        assert_(v.shape == (3, 2, 4))
+        for i in range(4):
+            coef = [0]*i + [1]
+            assert_almost_equal(v[..., i], poly.polyval(x, coef))
+
+    def test_polyvander2d(self):
+        # also tests polyval2d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3))
+        van = poly.polyvander2d(x1, x2, [1, 2])
+        tgt = poly.polyval2d(x1, x2, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = poly.polyvander2d([x1], [x2], [1, 2])
+        assert_(van.shape == (1, 5, 6))
+
+    def test_polyvander3d(self):
+        # also tests polyval3d for non-square coefficient array
+        x1, x2, x3 = self.x
+        c = np.random.random((2, 3, 4))
+        van = poly.polyvander3d(x1, x2, x3, [1, 2, 3])
+        tgt = poly.polyval3d(x1, x2, x3, c)
+        res = np.dot(van, c.flat)
+        assert_almost_equal(res, tgt)
+
+        # check shape
+        van = poly.polyvander3d([x1], [x2], [x3], [1, 2, 3])
+        assert_(van.shape == (1, 5, 24))
+
+    def test_polyvandernegdeg(self):
+        x = np.arange(3)
+        assert_raises(ValueError, poly.polyvander, x, -1)
+
+
+class TestCompanion:
+
+    def test_raises(self):
+        assert_raises(ValueError, poly.polycompanion, [])
+        assert_raises(ValueError, poly.polycompanion, [1])
+
+    def test_dimensions(self):
+        for i in range(1, 5):
+            coef = [0]*i + [1]
+            assert_(poly.polycompanion(coef).shape == (i, i))
+
+    def test_linear_root(self):
+        assert_(poly.polycompanion([1, 2])[0, 0] == -.5)
+
+
+class TestMisc:
+
+    def test_polyfromroots(self):
+        res = poly.polyfromroots([])
+        assert_almost_equal(trim(res), [1])
+        for i in range(1, 5):
+            roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
+            tgt = Tlist[i]
+            res = poly.polyfromroots(roots)*2**(i-1)
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_polyroots(self):
+        assert_almost_equal(poly.polyroots([1]), [])
+        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
+        for i in range(2, 5):
+            tgt = np.linspace(-1, 1, i)
+            res = poly.polyroots(poly.polyfromroots(tgt))
+            assert_almost_equal(trim(res), trim(tgt))
+
+    def test_polyfit(self):
+        def f(x):
+            return x*(x - 1)*(x - 2)
+
+        def f2(x):
+            return x**4 + x**2 + 1
+
+        # Test exceptions
+        assert_raises(ValueError, poly.polyfit, [1], [1], -1)
+        assert_raises(TypeError, poly.polyfit, [[1]], [1], 0)
+        assert_raises(TypeError, poly.polyfit, [], [1], 0)
+        assert_raises(TypeError, poly.polyfit, [1], [[[1]]], 0)
+        assert_raises(TypeError, poly.polyfit, [1, 2], [1], 0)
+        assert_raises(TypeError, poly.polyfit, [1], [1, 2], 0)
+        assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[[1]])
+        assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[1, 1])
+        assert_raises(ValueError, poly.polyfit, [1], [1], [-1,])
+        assert_raises(ValueError, poly.polyfit, [1], [1], [2, -1, 6])
+        assert_raises(TypeError, poly.polyfit, [1], [1], [])
+
+        # Test fit
+        x = np.linspace(0, 2)
+        y = f(x)
+        #
+        coef3 = poly.polyfit(x, y, 3)
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(poly.polyval(x, coef3), y)
+        coef3 = poly.polyfit(x, y, [0, 1, 2, 3])
+        assert_equal(len(coef3), 4)
+        assert_almost_equal(poly.polyval(x, coef3), y)
+        #
+        coef4 = poly.polyfit(x, y, 4)
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(poly.polyval(x, coef4), y)
+        coef4 = poly.polyfit(x, y, [0, 1, 2, 3, 4])
+        assert_equal(len(coef4), 5)
+        assert_almost_equal(poly.polyval(x, coef4), y)
+        #
+        coef2d = poly.polyfit(x, np.array([y, y]).T, 3)
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        coef2d = poly.polyfit(x, np.array([y, y]).T, [0, 1, 2, 3])
+        assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
+        # test weighting
+        w = np.zeros_like(x)
+        yw = y.copy()
+        w[1::2] = 1
+        yw[0::2] = 0
+        wcoef3 = poly.polyfit(x, yw, 3, w=w)
+        assert_almost_equal(wcoef3, coef3)
+        wcoef3 = poly.polyfit(x, yw, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef3, coef3)
+        #
+        wcoef2d = poly.polyfit(x, np.array([yw, yw]).T, 3, w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        wcoef2d = poly.polyfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
+        assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
+        # test scaling with complex values x points whose square
+        # is zero when summed.
+        x = [1, 1j, -1, -1j]
+        assert_almost_equal(poly.polyfit(x, x, 1), [0, 1])
+        assert_almost_equal(poly.polyfit(x, x, [0, 1]), [0, 1])
+        # test fitting only even Polyendre polynomials
+        x = np.linspace(-1, 1)
+        y = f2(x)
+        coef1 = poly.polyfit(x, y, 4)
+        assert_almost_equal(poly.polyval(x, coef1), y)
+        coef2 = poly.polyfit(x, y, [0, 2, 4])
+        assert_almost_equal(poly.polyval(x, coef2), y)
+        assert_almost_equal(coef1, coef2)
+
+    def test_polytrim(self):
+        coef = [2, -1, 1, 0]
+
+        # Test exceptions
+        assert_raises(ValueError, poly.polytrim, coef, -1)
+
+        # Test results
+        assert_equal(poly.polytrim(coef), coef[:-1])
+        assert_equal(poly.polytrim(coef, 1), coef[:-3])
+        assert_equal(poly.polytrim(coef, 2), [0])
+
+    def test_polyline(self):
+        assert_equal(poly.polyline(3, 4), [3, 4])
+
+    def test_polyline_zero(self):
+        assert_equal(poly.polyline(3, 0), [3])
+
+    def test_fit_degenerate_domain(self):
+        p = poly.Polynomial.fit([1], [2], deg=0)
+        assert_equal(p.coef, [2.])
+        p = poly.Polynomial.fit([1, 1], [2, 2.1], deg=0)
+        assert_almost_equal(p.coef, [2.05])
+        with assert_warns(pu.RankWarning):
+            p = poly.Polynomial.fit([1, 1], [2, 2.1], deg=1)
+
+    def test_result_type(self):
+        w = np.array([-1, 1], dtype=np.float32)
+        p = np.polynomial.Polynomial(w, domain=w, window=w)
+        v = p(2)
+        assert_equal(v.dtype, np.float32)
+
+        arr = np.polydiv(1, np.float32(1))
+        assert_equal(arr[0].dtype, np.float64)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_polyutils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_polyutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5143ed5c3e4a1651c67b5260cef47112c5ea071
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_polyutils.py
@@ -0,0 +1,125 @@
+"""Tests for polyutils module.
+
+"""
+import numpy as np
+import numpy.polynomial.polyutils as pu
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+
+
+class TestMisc:
+
+    def test_trimseq(self):
+        tgt = [1]
+        for num_trailing_zeros in range(5):
+            res = pu.trimseq([1] + [0] * num_trailing_zeros)
+            assert_equal(res, tgt)
+
+    def test_trimseq_empty_input(self):
+        for empty_seq in [[], np.array([], dtype=np.int32)]:
+            assert_equal(pu.trimseq(empty_seq), empty_seq)
+
+    def test_as_series(self):
+        # check exceptions
+        assert_raises(ValueError, pu.as_series, [[]])
+        assert_raises(ValueError, pu.as_series, [[[1, 2]]])
+        assert_raises(ValueError, pu.as_series, [[1], ['a']])
+        # check common types
+        types = ['i', 'd', 'O']
+        for i in range(len(types)):
+            for j in range(i):
+                ci = np.ones(1, types[i])
+                cj = np.ones(1, types[j])
+                [resi, resj] = pu.as_series([ci, cj])
+                assert_(resi.dtype.char == resj.dtype.char)
+                assert_(resj.dtype.char == types[i])
+
+    def test_trimcoef(self):
+        coef = [2, -1, 1, 0]
+        # Test exceptions
+        assert_raises(ValueError, pu.trimcoef, coef, -1)
+        # Test results
+        assert_equal(pu.trimcoef(coef), coef[:-1])
+        assert_equal(pu.trimcoef(coef, 1), coef[:-3])
+        assert_equal(pu.trimcoef(coef, 2), [0])
+
+    def test_vander_nd_exception(self):
+        # n_dims != len(points)
+        assert_raises(ValueError, pu._vander_nd, (), (1, 2, 3), [90])
+        # n_dims != len(degrees)
+        assert_raises(ValueError, pu._vander_nd, (), (), [90.65])
+        # n_dims == 0
+        assert_raises(ValueError, pu._vander_nd, (), (), [])
+
+    def test_div_zerodiv(self):
+        # c2[-1] == 0
+        assert_raises(ZeroDivisionError, pu._div, pu._div, (1, 2, 3), [0])
+
+    def test_pow_too_large(self):
+        # power > maxpower
+        assert_raises(ValueError, pu._pow, (), [1, 2, 3], 5, 4)
+
+class TestDomain:
+
+    def test_getdomain(self):
+        # test for real values
+        x = [1, 10, 3, -1]
+        tgt = [-1, 10]
+        res = pu.getdomain(x)
+        assert_almost_equal(res, tgt)
+
+        # test for complex values
+        x = [1 + 1j, 1 - 1j, 0, 2]
+        tgt = [-1j, 2 + 1j]
+        res = pu.getdomain(x)
+        assert_almost_equal(res, tgt)
+
+    def test_mapdomain(self):
+        # test for real values
+        dom1 = [0, 4]
+        dom2 = [1, 3]
+        tgt = dom2
+        res = pu.mapdomain(dom1, dom1, dom2)
+        assert_almost_equal(res, tgt)
+
+        # test for complex values
+        dom1 = [0 - 1j, 2 + 1j]
+        dom2 = [-2, 2]
+        tgt = dom2
+        x = dom1
+        res = pu.mapdomain(x, dom1, dom2)
+        assert_almost_equal(res, tgt)
+
+        # test for multidimensional arrays
+        dom1 = [0, 4]
+        dom2 = [1, 3]
+        tgt = np.array([dom2, dom2])
+        x = np.array([dom1, dom1])
+        res = pu.mapdomain(x, dom1, dom2)
+        assert_almost_equal(res, tgt)
+
+        # test that subtypes are preserved.
+        class MyNDArray(np.ndarray):
+            pass
+
+        dom1 = [0, 4]
+        dom2 = [1, 3]
+        x = np.array([dom1, dom1]).view(MyNDArray)
+        res = pu.mapdomain(x, dom1, dom2)
+        assert_(isinstance(res, MyNDArray))
+
+    def test_mapparms(self):
+        # test for real values
+        dom1 = [0, 4]
+        dom2 = [1, 3]
+        tgt = [1, .5]
+        res = pu. mapparms(dom1, dom2)
+        assert_almost_equal(res, tgt)
+
+        # test for complex values
+        dom1 = [0 - 1j, 2 + 1j]
+        dom2 = [-2, 2]
+        tgt = [-1 + 1j, 1 - 1j]
+        res = pu.mapparms(dom1, dom2)
+        assert_almost_equal(res, tgt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_printing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_printing.py
new file mode 100644
index 0000000000000000000000000000000000000000..6651f6cd92056f94d19f62cd818eeed642df2b2e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_printing.py
@@ -0,0 +1,552 @@
+from math import nan, inf
+import pytest
+from numpy._core import array, arange, printoptions
+import numpy.polynomial as poly
+from numpy.testing import assert_equal, assert_
+
+# For testing polynomial printing with object arrays
+from fractions import Fraction
+from decimal import Decimal
+
+
+class TestStrUnicodeSuperSubscripts:
+
+    @pytest.fixture(scope='class', autouse=True)
+    def use_unicode(self):
+        poly.set_default_printstyle('unicode')
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0·x + 3.0·x²"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0·x + 3.0·x² - 1.0·x³"),
+        (arange(12), ("0.0 + 1.0·x + 2.0·x² + 3.0·x³ + 4.0·x⁴ + 5.0·x⁵ + "
+                      "6.0·x⁶ + 7.0·x⁷ +\n8.0·x⁸ + 9.0·x⁹ + 10.0·x¹⁰ + "
+                      "11.0·x¹¹")),
+    ))
+    def test_polynomial_str(self, inp, tgt):
+        p = poly.Polynomial(inp)
+        res = str(p)
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0·T₁(x) + 3.0·T₂(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0·T₁(x) + 3.0·T₂(x) - 1.0·T₃(x)"),
+        (arange(12), ("0.0 + 1.0·T₁(x) + 2.0·T₂(x) + 3.0·T₃(x) + 4.0·T₄(x) + "
+                      "5.0·T₅(x) +\n6.0·T₆(x) + 7.0·T₇(x) + 8.0·T₈(x) + "
+                      "9.0·T₉(x) + 10.0·T₁₀(x) + 11.0·T₁₁(x)")),
+    ))
+    def test_chebyshev_str(self, inp, tgt):
+        res = str(poly.Chebyshev(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0·P₁(x) + 3.0·P₂(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0·P₁(x) + 3.0·P₂(x) - 1.0·P₃(x)"),
+        (arange(12), ("0.0 + 1.0·P₁(x) + 2.0·P₂(x) + 3.0·P₃(x) + 4.0·P₄(x) + "
+                      "5.0·P₅(x) +\n6.0·P₆(x) + 7.0·P₇(x) + 8.0·P₈(x) + "
+                      "9.0·P₉(x) + 10.0·P₁₀(x) + 11.0·P₁₁(x)")),
+    ))
+    def test_legendre_str(self, inp, tgt):
+        res = str(poly.Legendre(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0·H₁(x) + 3.0·H₂(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0·H₁(x) + 3.0·H₂(x) - 1.0·H₃(x)"),
+        (arange(12), ("0.0 + 1.0·H₁(x) + 2.0·H₂(x) + 3.0·H₃(x) + 4.0·H₄(x) + "
+                      "5.0·H₅(x) +\n6.0·H₆(x) + 7.0·H₇(x) + 8.0·H₈(x) + "
+                      "9.0·H₉(x) + 10.0·H₁₀(x) + 11.0·H₁₁(x)")),
+    ))
+    def test_hermite_str(self, inp, tgt):
+        res = str(poly.Hermite(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0·He₁(x) + 3.0·He₂(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0·He₁(x) + 3.0·He₂(x) - 1.0·He₃(x)"),
+        (arange(12), ("0.0 + 1.0·He₁(x) + 2.0·He₂(x) + 3.0·He₃(x) + "
+                      "4.0·He₄(x) + 5.0·He₅(x) +\n6.0·He₆(x) + 7.0·He₇(x) + "
+                      "8.0·He₈(x) + 9.0·He₉(x) + 10.0·He₁₀(x) +\n"
+                      "11.0·He₁₁(x)")),
+    ))
+    def test_hermiteE_str(self, inp, tgt):
+        res = str(poly.HermiteE(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0·L₁(x) + 3.0·L₂(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0·L₁(x) + 3.0·L₂(x) - 1.0·L₃(x)"),
+        (arange(12), ("0.0 + 1.0·L₁(x) + 2.0·L₂(x) + 3.0·L₃(x) + 4.0·L₄(x) + "
+                      "5.0·L₅(x) +\n6.0·L₆(x) + 7.0·L₇(x) + 8.0·L₈(x) + "
+                      "9.0·L₉(x) + 10.0·L₁₀(x) + 11.0·L₁₁(x)")),
+    ))
+    def test_laguerre_str(self, inp, tgt):
+        res = str(poly.Laguerre(inp))
+        assert_equal(res, tgt)
+
+    def test_polynomial_str_domains(self):
+        res = str(poly.Polynomial([0, 1]))
+        tgt = '0.0 + 1.0·x'
+        assert_equal(res, tgt)
+
+        res = str(poly.Polynomial([0, 1], domain=[1, 2]))
+        tgt = '0.0 + 1.0·(-3.0 + 2.0x)'
+        assert_equal(res, tgt)
+
+class TestStrAscii:
+
+    @pytest.fixture(scope='class', autouse=True)
+    def use_ascii(self):
+        poly.set_default_printstyle('ascii')
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0 x + 3.0 x**2"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0 x + 3.0 x**2 - 1.0 x**3"),
+        (arange(12), ("0.0 + 1.0 x + 2.0 x**2 + 3.0 x**3 + 4.0 x**4 + "
+                      "5.0 x**5 + 6.0 x**6 +\n7.0 x**7 + 8.0 x**8 + "
+                      "9.0 x**9 + 10.0 x**10 + 11.0 x**11")),
+    ))
+    def test_polynomial_str(self, inp, tgt):
+        res = str(poly.Polynomial(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0 T_1(x) + 3.0 T_2(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0 T_1(x) + 3.0 T_2(x) - 1.0 T_3(x)"),
+        (arange(12), ("0.0 + 1.0 T_1(x) + 2.0 T_2(x) + 3.0 T_3(x) + "
+                      "4.0 T_4(x) + 5.0 T_5(x) +\n6.0 T_6(x) + 7.0 T_7(x) + "
+                      "8.0 T_8(x) + 9.0 T_9(x) + 10.0 T_10(x) +\n"
+                      "11.0 T_11(x)")),
+    ))
+    def test_chebyshev_str(self, inp, tgt):
+        res = str(poly.Chebyshev(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0 P_1(x) + 3.0 P_2(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0 P_1(x) + 3.0 P_2(x) - 1.0 P_3(x)"),
+        (arange(12), ("0.0 + 1.0 P_1(x) + 2.0 P_2(x) + 3.0 P_3(x) + "
+                      "4.0 P_4(x) + 5.0 P_5(x) +\n6.0 P_6(x) + 7.0 P_7(x) + "
+                      "8.0 P_8(x) + 9.0 P_9(x) + 10.0 P_10(x) +\n"
+                      "11.0 P_11(x)")),
+    ))
+    def test_legendre_str(self, inp, tgt):
+        res = str(poly.Legendre(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0 H_1(x) + 3.0 H_2(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0 H_1(x) + 3.0 H_2(x) - 1.0 H_3(x)"),
+        (arange(12), ("0.0 + 1.0 H_1(x) + 2.0 H_2(x) + 3.0 H_3(x) + "
+                      "4.0 H_4(x) + 5.0 H_5(x) +\n6.0 H_6(x) + 7.0 H_7(x) + "
+                      "8.0 H_8(x) + 9.0 H_9(x) + 10.0 H_10(x) +\n"
+                      "11.0 H_11(x)")),
+    ))
+    def test_hermite_str(self, inp, tgt):
+        res = str(poly.Hermite(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0 He_1(x) + 3.0 He_2(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0 He_1(x) + 3.0 He_2(x) - 1.0 He_3(x)"),
+        (arange(12), ("0.0 + 1.0 He_1(x) + 2.0 He_2(x) + 3.0 He_3(x) + "
+                      "4.0 He_4(x) +\n5.0 He_5(x) + 6.0 He_6(x) + "
+                      "7.0 He_7(x) + 8.0 He_8(x) + 9.0 He_9(x) +\n"
+                      "10.0 He_10(x) + 11.0 He_11(x)")),
+    ))
+    def test_hermiteE_str(self, inp, tgt):
+        res = str(poly.HermiteE(inp))
+        assert_equal(res, tgt)
+
+    @pytest.mark.parametrize(('inp', 'tgt'), (
+        ([1, 2, 3], "1.0 + 2.0 L_1(x) + 3.0 L_2(x)"),
+        ([-1, 0, 3, -1], "-1.0 + 0.0 L_1(x) + 3.0 L_2(x) - 1.0 L_3(x)"),
+        (arange(12), ("0.0 + 1.0 L_1(x) + 2.0 L_2(x) + 3.0 L_3(x) + "
+                      "4.0 L_4(x) + 5.0 L_5(x) +\n6.0 L_6(x) + 7.0 L_7(x) + "
+                      "8.0 L_8(x) + 9.0 L_9(x) + 10.0 L_10(x) +\n"
+                      "11.0 L_11(x)")),
+    ))
+    def test_laguerre_str(self, inp, tgt):
+        res = str(poly.Laguerre(inp))
+        assert_equal(res, tgt)
+
+    def test_polynomial_str_domains(self):
+        res = str(poly.Polynomial([0, 1]))
+        tgt = '0.0 + 1.0 x'
+        assert_equal(res, tgt)
+
+        res = str(poly.Polynomial([0, 1], domain=[1, 2]))
+        tgt = '0.0 + 1.0 (-3.0 + 2.0x)'
+        assert_equal(res, tgt)
+
+class TestLinebreaking:
+
+    @pytest.fixture(scope='class', autouse=True)
+    def use_ascii(self):
+        poly.set_default_printstyle('ascii')
+
+    def test_single_line_one_less(self):
+        # With 'ascii' style, len(str(p)) is default linewidth - 1 (i.e. 74)
+        p = poly.Polynomial([12345678, 12345678, 12345678, 12345678, 123])
+        assert_equal(len(str(p)), 74)
+        assert_equal(str(p), (
+            '12345678.0 + 12345678.0 x + 12345678.0 x**2 + '
+            '12345678.0 x**3 + 123.0 x**4'
+        ))
+
+    def test_num_chars_is_linewidth(self):
+        # len(str(p)) == default linewidth == 75
+        p = poly.Polynomial([12345678, 12345678, 12345678, 12345678, 1234])
+        assert_equal(len(str(p)), 75)
+        assert_equal(str(p), (
+            '12345678.0 + 12345678.0 x + 12345678.0 x**2 + '
+            '12345678.0 x**3 +\n1234.0 x**4'
+        ))
+
+    def test_first_linebreak_multiline_one_less_than_linewidth(self):
+        # Multiline str where len(first_line) + len(next_term) == lw - 1 == 74
+        p = poly.Polynomial(
+                [12345678, 12345678, 12345678, 12345678, 1, 12345678]
+            )
+        assert_equal(len(str(p).split('\n')[0]), 74)
+        assert_equal(str(p), (
+            '12345678.0 + 12345678.0 x + 12345678.0 x**2 + '
+            '12345678.0 x**3 + 1.0 x**4 +\n12345678.0 x**5'
+        ))
+
+    def test_first_linebreak_multiline_on_linewidth(self):
+        # First line is one character longer than previous test
+        p = poly.Polynomial(
+                [12345678, 12345678, 12345678, 12345678.12, 1, 12345678]
+            )
+        assert_equal(str(p), (
+            '12345678.0 + 12345678.0 x + 12345678.0 x**2 + '
+            '12345678.12 x**3 +\n1.0 x**4 + 12345678.0 x**5'
+        ))
+
+    @pytest.mark.parametrize(('lw', 'tgt'), (
+        (75, ('0.0 + 10.0 x + 200.0 x**2 + 3000.0 x**3 + 40000.0 x**4 + '
+              '500000.0 x**5 +\n600000.0 x**6 + 70000.0 x**7 + 8000.0 x**8 + '
+              '900.0 x**9')),
+        (45, ('0.0 + 10.0 x + 200.0 x**2 + 3000.0 x**3 +\n40000.0 x**4 + '
+              '500000.0 x**5 +\n600000.0 x**6 + 70000.0 x**7 + 8000.0 x**8 +\n'
+              '900.0 x**9')),
+        (132, ('0.0 + 10.0 x + 200.0 x**2 + 3000.0 x**3 + 40000.0 x**4 + '
+               '500000.0 x**5 + 600000.0 x**6 + 70000.0 x**7 + 8000.0 x**8 + '
+               '900.0 x**9')),
+    ))
+    def test_linewidth_printoption(self, lw, tgt):
+        p = poly.Polynomial(
+            [0, 10, 200, 3000, 40000, 500000, 600000, 70000, 8000, 900]
+        )
+        with printoptions(linewidth=lw):
+            assert_equal(str(p), tgt)
+            for line in str(p).split('\n'):
+                assert_(len(line) < lw)
+
+
+def test_set_default_printoptions():
+    p = poly.Polynomial([1, 2, 3])
+    c = poly.Chebyshev([1, 2, 3])
+    poly.set_default_printstyle('ascii')
+    assert_equal(str(p), "1.0 + 2.0 x + 3.0 x**2")
+    assert_equal(str(c), "1.0 + 2.0 T_1(x) + 3.0 T_2(x)")
+    poly.set_default_printstyle('unicode')
+    assert_equal(str(p), "1.0 + 2.0·x + 3.0·x²")
+    assert_equal(str(c), "1.0 + 2.0·T₁(x) + 3.0·T₂(x)")
+    with pytest.raises(ValueError):
+        poly.set_default_printstyle('invalid_input')
+
+
+def test_complex_coefficients():
+    """Test both numpy and built-in complex."""
+    coefs = [0+1j, 1+1j, -2+2j, 3+0j]
+    # numpy complex
+    p1 = poly.Polynomial(coefs)
+    # Python complex
+    p2 = poly.Polynomial(array(coefs, dtype=object))
+    poly.set_default_printstyle('unicode')
+    assert_equal(str(p1), "1j + (1+1j)·x - (2-2j)·x² + (3+0j)·x³")
+    assert_equal(str(p2), "1j + (1+1j)·x + (-2+2j)·x² + (3+0j)·x³")
+    poly.set_default_printstyle('ascii')
+    assert_equal(str(p1), "1j + (1+1j) x - (2-2j) x**2 + (3+0j) x**3")
+    assert_equal(str(p2), "1j + (1+1j) x + (-2+2j) x**2 + (3+0j) x**3")
+
+
+@pytest.mark.parametrize(('coefs', 'tgt'), (
+    (array([Fraction(1, 2), Fraction(3, 4)], dtype=object), (
+        "1/2 + 3/4·x"
+    )),
+    (array([1, 2, Fraction(5, 7)], dtype=object), (
+        "1 + 2·x + 5/7·x²"
+    )),
+    (array([Decimal('1.00'), Decimal('2.2'), 3], dtype=object), (
+        "1.00 + 2.2·x + 3·x²"
+    )),
+))
+def test_numeric_object_coefficients(coefs, tgt):
+    p = poly.Polynomial(coefs)
+    poly.set_default_printstyle('unicode')
+    assert_equal(str(p), tgt)
+
+
+@pytest.mark.parametrize(('coefs', 'tgt'), (
+    (array([1, 2, 'f'], dtype=object), '1 + 2·x + f·x²'),
+    (array([1, 2, [3, 4]], dtype=object), '1 + 2·x + [3, 4]·x²'),
+))
+def test_nonnumeric_object_coefficients(coefs, tgt):
+    """
+    Test coef fallback for object arrays of non-numeric coefficients.
+    """
+    p = poly.Polynomial(coefs)
+    poly.set_default_printstyle('unicode')
+    assert_equal(str(p), tgt)
+
+
+class TestFormat:
+    def test_format_unicode(self):
+        poly.set_default_printstyle('ascii')
+        p = poly.Polynomial([1, 2, 0, -1])
+        assert_equal(format(p, 'unicode'), "1.0 + 2.0·x + 0.0·x² - 1.0·x³")
+
+    def test_format_ascii(self):
+        poly.set_default_printstyle('unicode')
+        p = poly.Polynomial([1, 2, 0, -1])
+        assert_equal(
+            format(p, 'ascii'), "1.0 + 2.0 x + 0.0 x**2 - 1.0 x**3"
+        )
+
+    def test_empty_formatstr(self):
+        poly.set_default_printstyle('ascii')
+        p = poly.Polynomial([1, 2, 3])
+        assert_equal(format(p), "1.0 + 2.0 x + 3.0 x**2")
+        assert_equal(f"{p}", "1.0 + 2.0 x + 3.0 x**2")
+
+    def test_bad_formatstr(self):
+        p = poly.Polynomial([1, 2, 0, -1])
+        with pytest.raises(ValueError):
+            format(p, '.2f')
+
+
+@pytest.mark.parametrize(('poly', 'tgt'), (
+    (poly.Polynomial, '1.0 + 2.0·z + 3.0·z²'),
+    (poly.Chebyshev, '1.0 + 2.0·T₁(z) + 3.0·T₂(z)'),
+    (poly.Hermite, '1.0 + 2.0·H₁(z) + 3.0·H₂(z)'),
+    (poly.HermiteE, '1.0 + 2.0·He₁(z) + 3.0·He₂(z)'),
+    (poly.Laguerre, '1.0 + 2.0·L₁(z) + 3.0·L₂(z)'),
+    (poly.Legendre, '1.0 + 2.0·P₁(z) + 3.0·P₂(z)'),
+))
+def test_symbol(poly, tgt):
+    p = poly([1, 2, 3], symbol='z')
+    assert_equal(f"{p:unicode}", tgt)
+
+
+class TestRepr:
+    def test_polynomial_repr(self):
+        res = repr(poly.Polynomial([0, 1]))
+        tgt = (
+            "Polynomial([0., 1.], domain=[-1.,  1.], window=[-1.,  1.], "
+            "symbol='x')"
+        )
+        assert_equal(res, tgt)
+
+    def test_chebyshev_repr(self):
+        res = repr(poly.Chebyshev([0, 1]))
+        tgt = (
+            "Chebyshev([0., 1.], domain=[-1.,  1.], window=[-1.,  1.], "
+            "symbol='x')"
+        )
+        assert_equal(res, tgt)
+
+    def test_legendre_repr(self):
+        res = repr(poly.Legendre([0, 1]))
+        tgt = (
+            "Legendre([0., 1.], domain=[-1.,  1.], window=[-1.,  1.], "
+            "symbol='x')"
+        )
+        assert_equal(res, tgt)
+
+    def test_hermite_repr(self):
+        res = repr(poly.Hermite([0, 1]))
+        tgt = (
+            "Hermite([0., 1.], domain=[-1.,  1.], window=[-1.,  1.], "
+            "symbol='x')"
+        )
+        assert_equal(res, tgt)
+
+    def test_hermiteE_repr(self):
+        res = repr(poly.HermiteE([0, 1]))
+        tgt = (
+            "HermiteE([0., 1.], domain=[-1.,  1.], window=[-1.,  1.], "
+            "symbol='x')"
+        )
+        assert_equal(res, tgt)
+
+    def test_laguerre_repr(self):
+        res = repr(poly.Laguerre([0, 1]))
+        tgt = (
+            "Laguerre([0., 1.], domain=[0., 1.], window=[0., 1.], "
+            "symbol='x')"
+        )
+        assert_equal(res, tgt)
+
+
+class TestLatexRepr:
+    """Test the latex repr used by Jupyter"""
+
+    @staticmethod
+    def as_latex(obj):
+        # right now we ignore the formatting of scalars in our tests, since
+        # it makes them too verbose. Ideally, the formatting of scalars will
+        # be fixed such that tests below continue to pass
+        obj._repr_latex_scalar = lambda x, parens=False: str(x)
+        try:
+            return obj._repr_latex_()
+        finally:
+            del obj._repr_latex_scalar
+
+    def test_simple_polynomial(self):
+        # default input
+        p = poly.Polynomial([1, 2, 3])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0 + 2.0\,x + 3.0\,x^{2}$')
+
+        # translated input
+        p = poly.Polynomial([1, 2, 3], domain=[-2, 0])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0 + 2.0\,\left(1.0 + x\right) + 3.0\,\left(1.0 + x\right)^{2}$')
+
+        # scaled input
+        p = poly.Polynomial([1, 2, 3], domain=[-0.5, 0.5])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0 + 2.0\,\left(2.0x\right) + 3.0\,\left(2.0x\right)^{2}$')
+
+        # affine input
+        p = poly.Polynomial([1, 2, 3], domain=[-1, 0])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0 + 2.0\,\left(1.0 + 2.0x\right) + 3.0\,\left(1.0 + 2.0x\right)^{2}$')
+
+    def test_basis_func(self):
+        p = poly.Chebyshev([1, 2, 3])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0\,{T}_{0}(x) + 2.0\,{T}_{1}(x) + 3.0\,{T}_{2}(x)$')
+        # affine input - check no surplus parens are added
+        p = poly.Chebyshev([1, 2, 3], domain=[-1, 0])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0\,{T}_{0}(1.0 + 2.0x) + 2.0\,{T}_{1}(1.0 + 2.0x) + 3.0\,{T}_{2}(1.0 + 2.0x)$')
+
+    def test_multichar_basis_func(self):
+        p = poly.HermiteE([1, 2, 3])
+        assert_equal(self.as_latex(p),
+            r'$x \mapsto 1.0\,{He}_{0}(x) + 2.0\,{He}_{1}(x) + 3.0\,{He}_{2}(x)$')
+
+    def test_symbol_basic(self):
+        # default input
+        p = poly.Polynomial([1, 2, 3], symbol='z')
+        assert_equal(self.as_latex(p),
+            r'$z \mapsto 1.0 + 2.0\,z + 3.0\,z^{2}$')
+
+        # translated input
+        p = poly.Polynomial([1, 2, 3], domain=[-2, 0], symbol='z')
+        assert_equal(
+            self.as_latex(p),
+            (
+                r'$z \mapsto 1.0 + 2.0\,\left(1.0 + z\right) + 3.0\,'
+                r'\left(1.0 + z\right)^{2}$'
+            ),
+        )
+
+        # scaled input
+        p = poly.Polynomial([1, 2, 3], domain=[-0.5, 0.5], symbol='z')
+        assert_equal(
+            self.as_latex(p),
+            (
+                r'$z \mapsto 1.0 + 2.0\,\left(2.0z\right) + 3.0\,'
+                r'\left(2.0z\right)^{2}$'
+            ),
+        )
+
+        # affine input
+        p = poly.Polynomial([1, 2, 3], domain=[-1, 0], symbol='z')
+        assert_equal(
+            self.as_latex(p),
+            (
+                r'$z \mapsto 1.0 + 2.0\,\left(1.0 + 2.0z\right) + 3.0\,'
+                r'\left(1.0 + 2.0z\right)^{2}$'
+            ),
+        )
+
+    def test_numeric_object_coefficients(self):
+        coefs = array([Fraction(1, 2), Fraction(1)])
+        p = poly.Polynomial(coefs)
+        assert_equal(self.as_latex(p), '$x \\mapsto 1/2 + 1\\,x$')
+
+SWITCH_TO_EXP = (
+    '1.0 + (1.0e-01) x + (1.0e-02) x**2',
+    '1.2 + (1.2e-01) x + (1.2e-02) x**2',
+    '1.23 + 0.12 x + (1.23e-02) x**2 + (1.23e-03) x**3',
+    '1.235 + 0.123 x + (1.235e-02) x**2 + (1.235e-03) x**3',
+    '1.2346 + 0.1235 x + 0.0123 x**2 + (1.2346e-03) x**3 + (1.2346e-04) x**4',
+    '1.23457 + 0.12346 x + 0.01235 x**2 + (1.23457e-03) x**3 + '
+    '(1.23457e-04) x**4',
+    '1.234568 + 0.123457 x + 0.012346 x**2 + 0.001235 x**3 + '
+    '(1.234568e-04) x**4 + (1.234568e-05) x**5',
+    '1.2345679 + 0.1234568 x + 0.0123457 x**2 + 0.0012346 x**3 + '
+    '(1.2345679e-04) x**4 + (1.2345679e-05) x**5')
+
+class TestPrintOptions:
+    """
+    Test the output is properly configured via printoptions.
+    The exponential notation is enabled automatically when the values
+    are too small or too large.
+    """
+
+    @pytest.fixture(scope='class', autouse=True)
+    def use_ascii(self):
+        poly.set_default_printstyle('ascii')
+
+    def test_str(self):
+        p = poly.Polynomial([1/2, 1/7, 1/7*10**8, 1/7*10**9])
+        assert_equal(str(p), '0.5 + 0.14285714 x + 14285714.28571429 x**2 '
+                             '+ (1.42857143e+08) x**3')
+
+        with printoptions(precision=3):
+            assert_equal(str(p), '0.5 + 0.143 x + 14285714.286 x**2 '
+                                 '+ (1.429e+08) x**3')
+
+    def test_latex(self):
+        p = poly.Polynomial([1/2, 1/7, 1/7*10**8, 1/7*10**9])
+        assert_equal(p._repr_latex_(),
+            r'$x \mapsto \text{0.5} + \text{0.14285714}\,x + '
+            r'\text{14285714.28571429}\,x^{2} + '
+            r'\text{(1.42857143e+08)}\,x^{3}$')
+
+        with printoptions(precision=3):
+            assert_equal(p._repr_latex_(),
+                r'$x \mapsto \text{0.5} + \text{0.143}\,x + '
+                r'\text{14285714.286}\,x^{2} + \text{(1.429e+08)}\,x^{3}$')
+
+    def test_fixed(self):
+        p = poly.Polynomial([1/2])
+        assert_equal(str(p), '0.5')
+
+        with printoptions(floatmode='fixed'):
+            assert_equal(str(p), '0.50000000')
+
+        with printoptions(floatmode='fixed', precision=4):
+            assert_equal(str(p), '0.5000')
+
+    def test_switch_to_exp(self):
+        for i, s in enumerate(SWITCH_TO_EXP):
+            with printoptions(precision=i):
+                p = poly.Polynomial([1.23456789*10**-i
+                                     for i in range(i//2+3)])
+                assert str(p).replace('\n', ' ') == s
+
+    def test_non_finite(self):
+        p = poly.Polynomial([nan, inf])
+        assert str(p) == 'nan + inf x'
+        assert p._repr_latex_() == r'$x \mapsto \text{nan} + \text{inf}\,x$'
+        with printoptions(nanstr='NAN', infstr='INF'):
+            assert str(p) == 'NAN + INF x'
+            assert p._repr_latex_() == \
+                r'$x \mapsto \text{NAN} + \text{INF}\,x$'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_symbol.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_symbol.py
new file mode 100644
index 0000000000000000000000000000000000000000..f985533f9fe8c639f224daead98e31dc6f798cc4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/polynomial/tests/test_symbol.py
@@ -0,0 +1,216 @@
+"""
+Tests related to the ``symbol`` attribute of the ABCPolyBase class.
+"""
+
+import pytest
+import numpy.polynomial as poly
+from numpy._core import array
+from numpy.testing import assert_equal, assert_raises, assert_
+
+
+class TestInit:
+    """
+    Test polynomial creation with symbol kwarg.
+    """
+    c = [1, 2, 3]
+
+    def test_default_symbol(self):
+        p = poly.Polynomial(self.c)
+        assert_equal(p.symbol, 'x')
+
+    @pytest.mark.parametrize(('bad_input', 'exception'), (
+        ('', ValueError),
+        ('3', ValueError),
+        (None, TypeError),
+        (1, TypeError),
+    ))
+    def test_symbol_bad_input(self, bad_input, exception):
+        with pytest.raises(exception):
+            p = poly.Polynomial(self.c, symbol=bad_input)
+
+    @pytest.mark.parametrize('symbol', (
+        'x',
+        'x_1',
+        'A',
+        'xyz',
+        'β',
+    ))
+    def test_valid_symbols(self, symbol):
+        """
+        Values for symbol that should pass input validation.
+        """
+        p = poly.Polynomial(self.c, symbol=symbol)
+        assert_equal(p.symbol, symbol)
+
+    def test_property(self):
+        """
+        'symbol' attribute is read only.
+        """
+        p = poly.Polynomial(self.c, symbol='x')
+        with pytest.raises(AttributeError):
+            p.symbol = 'z'
+
+    def test_change_symbol(self):
+        p = poly.Polynomial(self.c, symbol='y')
+        # Create new polynomial from p with different symbol
+        pt = poly.Polynomial(p.coef, symbol='t')
+        assert_equal(pt.symbol, 't')
+
+
+class TestUnaryOperators:
+    p = poly.Polynomial([1, 2, 3], symbol='z')
+
+    def test_neg(self):
+        n = -self.p
+        assert_equal(n.symbol, 'z')
+
+    def test_scalarmul(self):
+        out = self.p * 10
+        assert_equal(out.symbol, 'z')
+
+    def test_rscalarmul(self):
+        out = 10 * self.p
+        assert_equal(out.symbol, 'z')
+
+    def test_pow(self):
+        out = self.p ** 3
+        assert_equal(out.symbol, 'z')
+
+
+@pytest.mark.parametrize(
+    'rhs',
+    (
+        poly.Polynomial([4, 5, 6], symbol='z'),
+        array([4, 5, 6]),
+    ),
+)
+class TestBinaryOperatorsSameSymbol:
+    """
+    Ensure symbol is preserved for numeric operations on polynomials with
+    the same symbol
+    """
+    p = poly.Polynomial([1, 2, 3], symbol='z')
+
+    def test_add(self, rhs):
+        out = self.p + rhs
+        assert_equal(out.symbol, 'z')
+
+    def test_sub(self, rhs):
+        out = self.p - rhs
+        assert_equal(out.symbol, 'z')
+
+    def test_polymul(self, rhs):
+        out = self.p * rhs
+        assert_equal(out.symbol, 'z')
+
+    def test_divmod(self, rhs):
+        for out in divmod(self.p, rhs):
+            assert_equal(out.symbol, 'z')
+
+    def test_radd(self, rhs):
+        out = rhs + self.p
+        assert_equal(out.symbol, 'z')
+
+    def test_rsub(self, rhs):
+        out = rhs - self.p
+        assert_equal(out.symbol, 'z')
+
+    def test_rmul(self, rhs):
+        out = rhs * self.p
+        assert_equal(out.symbol, 'z')
+
+    def test_rdivmod(self, rhs):
+        for out in divmod(rhs, self.p):
+            assert_equal(out.symbol, 'z')
+
+
+class TestBinaryOperatorsDifferentSymbol:
+    p = poly.Polynomial([1, 2, 3], symbol='x')
+    other = poly.Polynomial([4, 5, 6], symbol='y')
+    ops = (p.__add__, p.__sub__, p.__mul__, p.__floordiv__, p.__mod__)
+
+    @pytest.mark.parametrize('f', ops)
+    def test_binops_fails(self, f):
+        assert_raises(ValueError, f, self.other)
+
+
+class TestEquality:
+    p = poly.Polynomial([1, 2, 3], symbol='x')
+
+    def test_eq(self):
+        other = poly.Polynomial([1, 2, 3], symbol='x')
+        assert_(self.p == other)
+
+    def test_neq(self):
+        other = poly.Polynomial([1, 2, 3], symbol='y')
+        assert_(not self.p == other)
+
+
+class TestExtraMethods:
+    """
+    Test other methods for manipulating/creating polynomial objects.
+    """
+    p = poly.Polynomial([1, 2, 3, 0], symbol='z')
+
+    def test_copy(self):
+        other = self.p.copy()
+        assert_equal(other.symbol, 'z')
+
+    def test_trim(self):
+        other = self.p.trim()
+        assert_equal(other.symbol, 'z')
+
+    def test_truncate(self):
+        other = self.p.truncate(2)
+        assert_equal(other.symbol, 'z')
+
+    @pytest.mark.parametrize('kwarg', (
+        {'domain': [-10, 10]},
+        {'window': [-10, 10]},
+        {'kind': poly.Chebyshev},
+    ))
+    def test_convert(self, kwarg):
+        other = self.p.convert(**kwarg)
+        assert_equal(other.symbol, 'z')
+
+    def test_integ(self):
+        other = self.p.integ()
+        assert_equal(other.symbol, 'z')
+
+    def test_deriv(self):
+        other = self.p.deriv()
+        assert_equal(other.symbol, 'z')
+
+
+def test_composition():
+    p = poly.Polynomial([3, 2, 1], symbol="t")
+    q = poly.Polynomial([5, 1, 0, -1], symbol="λ_1")
+    r = p(q)
+    assert r.symbol == "λ_1"
+
+
+#
+# Class methods that result in new polynomial class instances
+#
+
+
+def test_fit():
+    x, y = (range(10),)*2
+    p = poly.Polynomial.fit(x, y, deg=1, symbol='z')
+    assert_equal(p.symbol, 'z')
+
+
+def test_froomroots():
+    roots = [-2, 2]
+    p = poly.Polynomial.fromroots(roots, symbol='z')
+    assert_equal(p.symbol, 'z')
+
+
+def test_identity():
+    p = poly.Polynomial.identity(domain=[-1, 1], window=[5, 20], symbol='z')
+    assert_equal(p.symbol, 'z')
+
+
+def test_basis():
+    p = poly.Polynomial.basis(3, symbol='z')
+    assert_equal(p.symbol, 'z')
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/LICENSE.md b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6cf1b17e99725556ac56ce3661498df1ee2276a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/LICENSE.md
@@ -0,0 +1,71 @@
+**This software is dual-licensed under the The University of Illinois/NCSA
+Open Source License (NCSA) and The 3-Clause BSD License**
+
+# NCSA Open Source License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,
+<kevin.k.sheppard@gmail.com>)
+[http://www.kevinsheppard.com](http://www.kevinsheppard.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal with
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimers.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimers in the documentation and/or
+other materials provided with the distribution.
+
+Neither the names of Kevin Sheppard, nor the names of any contributors may be
+used to endorse or promote products derived from this Software without specific
+prior written permission.
+
+**THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH
+THE SOFTWARE.**
+
+
+# 3-Clause BSD License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.**
+
+# Components
+
+Many parts of this module have been derived from original sources, 
+often the algorithm's designer. Component licenses are located with 
+the component code.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.pxd b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1f9057296ba9475574a191cf231dc04ace3f910c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.pxd
@@ -0,0 +1,14 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+from numpy.random.bit_generator cimport BitGenerator, SeedSequence
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e8f99fe3045b9c2b691a8ece67d0f06d9d73b08
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.py
@@ -0,0 +1,215 @@
+"""
+========================
+Random Number Generation
+========================
+
+Use ``default_rng()`` to create a `Generator` and call its methods.
+
+=============== =========================================================
+Generator
+--------------- ---------------------------------------------------------
+Generator       Class implementing all of the random number distributions
+default_rng     Default constructor for ``Generator``
+=============== =========================================================
+
+============================================= ===
+BitGenerator Streams that work with Generator
+--------------------------------------------- ---
+MT19937
+PCG64
+PCG64DXSM
+Philox
+SFC64
+============================================= ===
+
+============================================= ===
+Getting entropy to initialize a BitGenerator
+--------------------------------------------- ---
+SeedSequence
+============================================= ===
+
+
+Legacy
+------
+
+For backwards compatibility with previous versions of numpy before 1.17, the
+various aliases to the global `RandomState` methods are left alone and do not
+use the new `Generator` API.
+
+==================== =========================================================
+Utility functions
+-------------------- ---------------------------------------------------------
+random               Uniformly distributed floats over ``[0, 1)``
+bytes                Uniformly distributed random bytes.
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+choice               Random sample from 1-D array.
+==================== =========================================================
+
+==================== =========================================================
+Compatibility
+functions - removed
+in the new API
+-------------------- ---------------------------------------------------------
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+random_integers      Uniformly distributed integers in a given range.
+                     (deprecated, use ``integers(..., closed=True)`` instead)
+random_sample        Alias for `random_sample`
+randint              Uniformly distributed integers in a given range
+seed                 Seed the legacy random number generator.
+==================== =========================================================
+
+==================== =========================================================
+Univariate
+distributions
+-------------------- ---------------------------------------------------------
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== ==========================================================
+Multivariate
+distributions
+-------------------- ----------------------------------------------------------
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== ==========================================================
+
+==================== =========================================================
+Standard
+distributions
+-------------------- ---------------------------------------------------------
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+-------------------- ---------------------------------------------------------
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+
+"""
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'choice',
+    'dirichlet',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random',
+    'random_integers',
+    'random_sample',
+    'ranf',
+    'rayleigh',
+    'sample',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf',
+]
+
+# add these for module-freeze analysis (like PyInstaller)
+from . import _pickle
+from . import _common
+from . import _bounded_integers
+
+from ._generator import Generator, default_rng
+from .bit_generator import SeedSequence, BitGenerator
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .mtrand import *
+
+__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
+            'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
+            'BitGenerator']
+
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+
+    This function exists solely to assist (un)pickling.
+
+    Note that the state of the RandomState returned here is irrelevant, as this
+    function's entire purpose is to return a newly allocated RandomState whose
+    state pickle can set.  Consequently the RandomState returned by this function
+    is a freshly allocated copy with a seed=0.
+
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+
+    """
+    return RandomState(seed=0)
+
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8cfa9c0e1369812c5afe6e353d29e39793358715
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/__init__.pyi
@@ -0,0 +1,126 @@
+from ._generator import Generator
+from ._generator import default_rng
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .bit_generator import BitGenerator
+from .bit_generator import SeedSequence
+from .mtrand import (
+    RandomState,
+    beta,
+    binomial,
+    bytes,
+    chisquare,
+    choice,
+    dirichlet,
+    exponential,
+    f,
+    gamma,
+    geometric,
+    get_bit_generator,  # noqa: F401
+    get_state,
+    gumbel,
+    hypergeometric,
+    laplace,
+    logistic,
+    lognormal,
+    logseries,
+    multinomial,
+    multivariate_normal,
+    negative_binomial,
+    noncentral_chisquare,
+    noncentral_f,
+    normal,
+    pareto,
+    permutation,
+    poisson,
+    power,
+    rand,
+    randint,
+    randn,
+    random,
+    random_integers,
+    random_sample,
+    ranf,
+    rayleigh,
+    sample,
+    seed,
+    set_bit_generator,  # noqa: F401
+    set_state,
+    shuffle,
+    standard_cauchy,
+    standard_exponential,
+    standard_gamma,
+    standard_normal,
+    standard_t,
+    triangular,
+    uniform,
+    vonmises,
+    wald,
+    weibull,
+    zipf,
+)
+
+__all__ = [
+    "beta",
+    "binomial",
+    "bytes",
+    "chisquare",
+    "choice",
+    "dirichlet",
+    "exponential",
+    "f",
+    "gamma",
+    "geometric",
+    "get_state",
+    "gumbel",
+    "hypergeometric",
+    "laplace",
+    "logistic",
+    "lognormal",
+    "logseries",
+    "multinomial",
+    "multivariate_normal",
+    "negative_binomial",
+    "noncentral_chisquare",
+    "noncentral_f",
+    "normal",
+    "pareto",
+    "permutation",
+    "poisson",
+    "power",
+    "rand",
+    "randint",
+    "randn",
+    "random",
+    "random_integers",
+    "random_sample",
+    "ranf",
+    "rayleigh",
+    "sample",
+    "seed",
+    "set_state",
+    "shuffle",
+    "standard_cauchy",
+    "standard_exponential",
+    "standard_gamma",
+    "standard_normal",
+    "standard_t",
+    "triangular",
+    "uniform",
+    "vonmises",
+    "wald",
+    "weibull",
+    "zipf",
+    "Generator",
+    "RandomState",
+    "SeedSequence",
+    "MT19937",
+    "Philox",
+    "PCG64",
+    "PCG64DXSM",
+    "SFC64",
+    "default_rng",
+    "BitGenerator",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_bounded_integers.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_bounded_integers.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..98ac82392b1ec1ac48cb760c4de2c92e89fc483e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_bounded_integers.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b4ff98f2bb0f7db24091b7996dd4d5c6ba5ae584f3683fcfa72ad8bce926ef1
+size 362560
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_bounded_integers.pxd b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_bounded_integers.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..607014cbf5b42737669f699471082ab5642910d1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_bounded_integers.pxd
@@ -0,0 +1,29 @@
+from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
+                          int8_t, int16_t, int32_t, int64_t, intptr_t)
+import numpy as np
+cimport numpy as np
+ctypedef np.npy_bool bool_t
+
+from numpy.random cimport bitgen_t
+
+cdef inline uint64_t _gen_mask(uint64_t max_val) noexcept nogil:
+    """Mask generator for use in bounded random numbers"""
+    # Smallest bit mask >= max
+    cdef uint64_t mask = max_val
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+    mask |= mask >> 32
+    return mask
+
+cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_common.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_common.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..79aa4b6c20c7584869791de7ac092ea3d2fa9d0e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_common.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:209653a11ba1914f706e972fd071be37c3207e182918c81fc7fab43cd66c5c28
+size 251664
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_common.pxd b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_common.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0de4456d778f409f63d237d53eb083bf2c9949ae
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_common.pxd
@@ -0,0 +1,107 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t
+
+import numpy as np
+cimport numpy as np
+
+from numpy.random cimport bitgen_t
+
+cdef double POISSON_LAM_MAX
+cdef double LEGACY_POISSON_LAM_MAX
+cdef uint64_t MAXSIZE
+
+cdef enum ConstraintType:
+    CONS_NONE
+    CONS_NON_NEGATIVE
+    CONS_POSITIVE
+    CONS_POSITIVE_NOT_NAN
+    CONS_BOUNDED_0_1
+    CONS_BOUNDED_GT_0_1
+    CONS_BOUNDED_LT_0_1
+    CONS_GT_1
+    CONS_GTE_1
+    CONS_POISSON
+    LEGACY_CONS_POISSON
+    LEGACY_CONS_NON_NEGATIVE_INBOUNDS_LONG
+
+ctypedef ConstraintType constraint_type
+
+cdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)
+cdef object random_raw(bitgen_t *bitgen, object lock, object size, object output)
+cdef object prepare_cffi(bitgen_t *bitgen)
+cdef object prepare_ctypes(bitgen_t *bitgen)
+cdef int check_constraint(double val, object name, constraint_type cons) except -1
+cdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1
+
+cdef extern from "include/aligned_malloc.h":
+    cdef void *PyArray_realloc_aligned(void *p, size_t n)
+    cdef void *PyArray_malloc_aligned(size_t n)
+    cdef void *PyArray_calloc_aligned(size_t n, size_t s)
+    cdef void PyArray_free_aligned(void *p)
+
+ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out)  noexcept nogil
+ctypedef double (*random_double_0)(void *state)  noexcept nogil
+ctypedef double (*random_double_1)(void *state, double a)  noexcept nogil
+ctypedef double (*random_double_2)(void *state, double a, double b)  noexcept nogil
+ctypedef double (*random_double_3)(void *state, double a, double b, double c)  noexcept nogil
+
+ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out)  noexcept nogil
+ctypedef float (*random_float_0)(bitgen_t *state)  noexcept nogil
+ctypedef float (*random_float_1)(bitgen_t *state, float a)  noexcept nogil
+
+ctypedef int64_t (*random_uint_0)(void *state)  noexcept nogil
+ctypedef int64_t (*random_uint_d)(void *state, double a)  noexcept nogil
+ctypedef int64_t (*random_uint_dd)(void *state, double a, double b)  noexcept nogil
+ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b)  noexcept nogil
+ctypedef int64_t (*random_uint_i)(void *state, int64_t a)  noexcept nogil
+ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c)  noexcept nogil
+
+ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state)  noexcept nogil
+ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a)  noexcept nogil
+
+ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b)  noexcept nogil
+ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b)  noexcept nogil
+
+cdef double kahan_sum(double *darr, np.npy_intp n) noexcept
+
+cdef inline double uint64_to_double(uint64_t rnd) noexcept nogil:
+    return (rnd >> 11) * (1.0 / 9007199254740992.0)
+
+cdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill_from_double(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object wrap_int(object val, object bits)
+
+cdef np.ndarray int_to_array(object value, object name, object bits, object uint_size)
+
+cdef validate_output_shape(iter_shape, np.ndarray output)
+
+cdef object cont(void *func, void *state, object size, object lock, int narg,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint,
+                 object out)
+
+cdef object disc(void *func, void *state, object size, object lock,
+                 int narg_double, int narg_int64,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint)
+
+cdef object cont_f(void *func, bitgen_t *state, object size, object lock,
+                   object a, object a_name, constraint_type a_constraint,
+                   object out)
+
+cdef object cont_broadcast_3(void *func, void *state, object size, object lock,
+                             np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                             np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                             np.ndarray c_arr, object c_name, constraint_type c_constraint)
+
+cdef object discrete_broadcast_iii(void *func, void *state, object size, object lock,
+                                   np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                                   np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                                   np.ndarray c_arr, object c_name, constraint_type c_constraint)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cffi/extending.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cffi/extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..8440d400ea9178bb17efc68fde1f8cca1f66c189
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cffi/extending.py
@@ -0,0 +1,40 @@
+"""
+Use cffi to access any of the underlying C functions from distributions.h
+"""
+import os
+import numpy as np
+import cffi
+from .parse import parse_distributions_h
+ffi = cffi.FFI()
+
+inc_dir = os.path.join(np.get_include(), 'numpy')
+
+# Basic numpy types
+ffi.cdef('''
+    typedef intptr_t npy_intp;
+    typedef unsigned char npy_bool;
+
+''')
+
+parse_distributions_h(ffi, inc_dir)
+
+lib = ffi.dlopen(np.random._generator.__file__)
+
+# Compare the distributions.h random_standard_normal_fill to
+# Generator.standard_random
+bit_gen = np.random.PCG64()
+rng = np.random.Generator(bit_gen)
+state = bit_gen.state
+
+interface = rng.bit_generator.cffi
+n = 100
+vals_cffi = ffi.new('double[%d]' % n)
+lib.random_standard_normal_fill(interface.bit_generator, n, vals_cffi)
+
+# reset the state
+bit_gen.state = state
+
+vals = rng.standard_normal(n)
+
+for i in range(n):
+    assert vals[i] == vals_cffi[i]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cffi/parse.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cffi/parse.py
new file mode 100644
index 0000000000000000000000000000000000000000..993cedee05eb0219e3748c41efb575b87a0c56a7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cffi/parse.py
@@ -0,0 +1,54 @@
+import os
+
+
+def parse_distributions_h(ffi, inc_dir):
+    """
+    Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef
+
+    Read the function declarations without the "#define ..." macros that will
+    be filled in when loading the library.
+    """
+
+    with open(os.path.join(inc_dir, 'random', 'bitgen.h')) as fid:
+        s = []
+        for line in fid:
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+
+    with open(os.path.join(inc_dir, 'random', 'distributions.h')) as fid:
+        s = []
+        in_skip = 0
+        ignoring = False
+        for line in fid:
+            # check for and remove extern "C" guards
+            if ignoring:
+                if line.strip().startswith('#endif'):
+                    ignoring = False
+                continue
+            if line.strip().startswith('#ifdef __cplusplus'):
+                ignoring = True
+
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+
+            # skip any inlined function definition
+            # which starts with 'static inline xxx(...) {'
+            # and ends with a closing '}'
+            if line.strip().startswith('static inline'):
+                in_skip += line.count('{')
+                continue
+            elif in_skip > 0:
+                in_skip += line.count('{')
+                in_skip -= line.count('}')
+                continue
+
+            # replace defines with their value or remove them
+            line = line.replace('DECLDIR', '')
+            line = line.replace('RAND_INT_TYPE', 'int64_t')
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/extending.pyx b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/extending.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..6a0f45e1be9e6f32ac9ac39952cd01597b93a2e9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/extending.pyx
@@ -0,0 +1,77 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+
+import numpy as np
+cimport numpy as np
+cimport cython
+
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+
+np.import_array()
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniform_mean(Py_ssize_t n):
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+    cdef np.ndarray randoms
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n)
+    # Best practice is to acquire the lock whenever generating random values.
+    # This prevents other threads from modifying the state. Acquiring the lock
+    # is only necessary if the GIL is also released, as in this example.
+    with x.lock, nogil:
+        for i in range(n):
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+    return randoms.mean()
+
+
+# This function is declared nogil so it can be used without the GIL below
+cdef uint32_t bounded_uint(uint32_t lb, uint32_t ub, bitgen_t *rng) nogil:
+    cdef uint32_t mask, delta, val
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = rng.next_uint32(rng.state) & mask
+    while val > delta:
+        val = rng.next_uint32(rng.state) & mask
+
+    return lb + val
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def bounded_uints(uint32_t lb, uint32_t ub, Py_ssize_t n):
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef uint32_t[::1] out
+    cdef const char *capsule_name = "BitGenerator"
+
+    x = PCG64()
+    out = np.empty(n, dtype=np.uint32)
+    capsule = x.capsule
+
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *>PyCapsule_GetPointer(capsule, capsule_name)
+
+    with x.lock, nogil:
+        for i in range(n):
+            out[i] = bounded_uint(lb, ub, rng)
+    return np.asarray(out)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..59ecc4b36366f76d21289286d5c8780b3852e660
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx
@@ -0,0 +1,116 @@
+#cython: language_level=3
+"""
+This file shows how the to use a BitGenerator to create a distribution.
+"""
+import numpy as np
+cimport numpy as np
+cimport cython
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+from libc.stdint cimport uint16_t, uint64_t
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+from numpy.random.c_distributions cimport (
+      random_standard_uniform_fill, random_standard_uniform_fill_f)
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniforms(Py_ssize_t n):
+    """
+    Create an array of `n` uniformly distributed doubles.
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+
+    x = PCG64()
+    capsule = x.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='float64')
+    with x.lock, nogil:
+        for i in range(n):
+            # Call the function
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+
+    return randoms
+
+# cython example 2
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uint10_uniforms(Py_ssize_t n):
+    """Uniform 10 bit integers stored as 16-bit unsigned integers"""
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef uint16_t[::1] random_values
+    cdef int bits_remaining
+    cdef int width = 10
+    cdef uint64_t buff, mask = 0x3FF
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='uint16')
+    # Best practice is to release GIL and acquire the lock
+    bits_remaining = 0
+    with x.lock, nogil:
+        for i in range(n):
+            if bits_remaining < width:
+                buff = rng.next_uint64(rng.state)
+            random_values[i] = buff & mask
+            buff >>= width
+
+    randoms = np.asarray(random_values)
+    return randoms
+
+# cython example 3
+def uniforms_ex(bit_generator, Py_ssize_t n, dtype=np.float64):
+    """
+    Create an array of `n` uniformly distributed doubles via a "fill" function.
+
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+
+    Parameters
+    ----------
+    bit_generator: BitGenerator instance
+    n: int
+        Output vector length
+    dtype: {str, dtype}, optional
+        Desired dtype, either 'd' (or 'float64') or 'f' (or 'float32'). The
+        default dtype value is 'd'
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef np.ndarray randoms
+
+    capsule = bit_generator.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+
+    _dtype = np.dtype(dtype)
+    randoms = np.empty(n, dtype=_dtype)
+    if _dtype == np.float32:
+        with bit_generator.lock:
+            random_standard_uniform_fill_f(rng, n, <float*>np.PyArray_DATA(randoms))
+    elif _dtype == np.float64:
+        with bit_generator.lock:
+            random_standard_uniform_fill(rng, n, <double*>np.PyArray_DATA(randoms))
+    else:
+        raise TypeError('Unsupported dtype %r for random' % _dtype)
+    return randoms
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/meson.build b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..7aa367d13787c4f7ad5c2910bb044670b07eb012
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/cython/meson.build
@@ -0,0 +1,53 @@
+project('random-build-examples', 'c', 'cpp', 'cython')
+
+py_mod = import('python')
+py3 = py_mod.find_installation(pure: false)
+
+cc = meson.get_compiler('c')
+cy = meson.get_compiler('cython')
+
+# Keep synced with pyproject.toml
+if not cy.version().version_compare('>=3.0.6')
+  error('tests requires Cython >= 3.0.6')
+endif
+
+base_cython_args = []
+if cy.version().version_compare('>=3.1.0')
+  base_cython_args += ['-Xfreethreading_compatible=True']
+endif
+
+_numpy_abs = run_command(py3, ['-c',
+               'import os; os.chdir(".."); import numpy; print(os.path.abspath(numpy.get_include() + "../../.."))'],
+                         check: true).stdout().strip()
+
+npymath_path = _numpy_abs / '_core' / 'lib'
+npy_include_path = _numpy_abs / '_core' / 'include'
+npyrandom_path = _numpy_abs / 'random' / 'lib'
+npymath_lib = cc.find_library('npymath', dirs: npymath_path)
+npyrandom_lib = cc.find_library('npyrandom', dirs: npyrandom_path)
+
+py3.extension_module(
+    'extending_distributions',
+    'extending_distributions.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+    cython_args: base_cython_args,
+)
+py3.extension_module(
+    'extending',
+    'extending.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+    cython_args: base_cython_args,
+)
+py3.extension_module(
+    'extending_cpp',
+    'extending_distributions.pyx',
+    install: false,
+    override_options : ['cython_language=cpp'],
+    cython_args: base_cython_args + ['--module-name', 'extending_cpp'],
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/numba/extending.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/numba/extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..f387db69502a4bfe8731d540a7a741b062fea861
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/numba/extending.py
@@ -0,0 +1,84 @@
+import numpy as np
+import numba as nb
+
+from numpy.random import PCG64
+from timeit import timeit
+
+bit_gen = PCG64()
+next_d = bit_gen.cffi.next_double
+state_addr = bit_gen.cffi.state_address
+
+def normals(n, state):
+    out = np.empty(n)
+    for i in range((n + 1) // 2):
+        x1 = 2.0 * next_d(state) - 1.0
+        x2 = 2.0 * next_d(state) - 1.0
+        r2 = x1 * x1 + x2 * x2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * next_d(state) - 1.0
+            x2 = 2.0 * next_d(state) - 1.0
+            r2 = x1 * x1 + x2 * x2
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        out[2 * i] = f * x1
+        if 2 * i + 1 < n:
+            out[2 * i + 1] = f * x2
+    return out
+
+# Compile using Numba
+normalsj = nb.jit(normals, nopython=True)
+# Must use state address not state with numba
+n = 10000
+
+def numbacall():
+    return normalsj(n, state_addr)
+
+rg = np.random.Generator(PCG64())
+
+def numpycall():
+    return rg.normal(size=n)
+
+# Check that the functions work
+r1 = numbacall()
+r2 = numpycall()
+assert r1.shape == (n,)
+assert r1.shape == r2.shape
+
+t1 = timeit(numbacall, number=1000)
+print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')
+t2 = timeit(numpycall, number=1000)
+print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')
+
+# example 2
+
+next_u32 = bit_gen.ctypes.next_uint32
+ctypes_state = bit_gen.ctypes.state
+
+@nb.jit(nopython=True)
+def bounded_uint(lb, ub, state):
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = next_u32(state) & mask
+    while val > delta:
+        val = next_u32(state) & mask
+
+    return lb + val
+
+
+print(bounded_uint(323, 2394691, ctypes_state.value))
+
+
+@nb.jit(nopython=True)
+def bounded_uints(lb, ub, n, state):
+    out = np.empty(n, dtype=np.uint32)
+    for i in range(n):
+        out[i] = bounded_uint(lb, ub, state)
+
+
+bounded_uints(323, 2394691, 10000000, ctypes_state.value)
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/numba/extending_distributions.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/numba/extending_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ef0753d71d1a1033c0225f332bf1b75d832a598
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_examples/numba/extending_distributions.py
@@ -0,0 +1,67 @@
+r"""
+Building the required library in this example requires a source distribution
+of NumPy or clone of the NumPy git repository since distributions.c is not
+included in binary distributions.
+
+On *nix, execute in numpy/random/src/distributions
+
+export ${PYTHON_VERSION}=3.8 # Python version
+export PYTHON_INCLUDE=#path to Python's include folder, usually \
+    ${PYTHON_HOME}/include/python${PYTHON_VERSION}m
+export NUMPY_INCLUDE=#path to numpy's include folder, usually \
+    ${PYTHON_HOME}/lib/python${PYTHON_VERSION}/site-packages/numpy/_core/include
+gcc -shared -o libdistributions.so -fPIC distributions.c \
+    -I${NUMPY_INCLUDE} -I${PYTHON_INCLUDE}
+mv libdistributions.so ../../_examples/numba/
+
+On Windows
+
+rem PYTHON_HOME and PYTHON_VERSION are setup dependent, this is an example
+set PYTHON_HOME=c:\Anaconda
+set PYTHON_VERSION=38
+cl.exe /LD .\distributions.c -DDLL_EXPORT \
+    -I%PYTHON_HOME%\lib\site-packages\numpy\_core\include \
+    -I%PYTHON_HOME%\include %PYTHON_HOME%\libs\python%PYTHON_VERSION%.lib
+move distributions.dll ../../_examples/numba/
+"""
+import os
+
+import numba as nb
+import numpy as np
+from cffi import FFI
+
+from numpy.random import PCG64
+
+ffi = FFI()
+if os.path.exists('./distributions.dll'):
+    lib = ffi.dlopen('./distributions.dll')
+elif os.path.exists('./libdistributions.so'):
+    lib = ffi.dlopen('./libdistributions.so')
+else:
+    raise RuntimeError('Required DLL/so file was not found.')
+
+ffi.cdef("""
+double random_standard_normal(void *bitgen_state);
+""")
+x = PCG64()
+xffi = x.cffi
+bit_generator = xffi.bit_generator
+
+random_standard_normal = lib.random_standard_normal
+
+
+def normals(n, bit_generator):
+    out = np.empty(n)
+    for i in range(n):
+        out[i] = random_standard_normal(bit_generator)
+    return out
+
+
+normalsj = nb.jit(normals, nopython=True)
+
+# Numba requires a memory address for void *
+# Can also get address from x.ctypes.bit_generator.value
+bit_generator_address = int(ffi.cast('uintptr_t', bit_generator))
+
+norm = normalsj(1000, bit_generator_address)
+print(norm[:12])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_generator.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_generator.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..20c6df41d36c781659fba215561c28c358db1165
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_generator.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b71b3b7481e842a207aa2b06ac7547721ed56366c5bccb7943616591e6075fb9
+size 996144
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_generator.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_generator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7ed4a959625f92b19ec6e56bab54403706b6604f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_generator.pyi
@@ -0,0 +1,856 @@
+from collections.abc import Callable
+from typing import Any, Literal, TypeAlias, TypeVar, overload
+
+import numpy as np
+from numpy import dtype, float32, float64, int64
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NDArray,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _BoolCodes,
+    _DoubleCodes,
+    _DTypeLike,
+    _DTypeLikeBool,
+    _Float32Codes,
+    _Float64Codes,
+    _FloatLike_co,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntPCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntPCodes,
+)
+from numpy.random import BitGenerator, RandomState, SeedSequence
+
+_IntegerT = TypeVar("_IntegerT", bound=np.integer)
+
+_DTypeLikeFloat32: TypeAlias = (
+    dtype[float32]
+    | _SupportsDType[dtype[float32]]
+    | type[float32]
+    | _Float32Codes
+    | _SingleCodes
+)
+
+_DTypeLikeFloat64: TypeAlias = (
+    dtype[float64]
+    | _SupportsDType[dtype[float64]]
+    | type[float]
+    | type[float64]
+    | _Float64Codes
+    | _DoubleCodes
+)
+
+class Generator:
+    def __init__(self, bit_generator: BitGenerator) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> None: ...
+    def __setstate__(self, state: dict[str, Any] | None) -> None: ...
+    def __reduce__(self) -> tuple[
+        Callable[[BitGenerator], Generator],
+        tuple[BitGenerator],
+        None]: ...
+    @property
+    def bit_generator(self) -> BitGenerator: ...
+    def spawn(self, n_children: int) -> list[Generator]: ...
+    def bytes(self, length: int) -> bytes: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def permutation(self, x: int, axis: int = ...) -> NDArray[int64]: ...
+    @overload
+    def permutation(self, x: ArrayLike, axis: int = ...) -> NDArray[Any]: ...
+    @overload
+    def standard_exponential(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(
+        self,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        method: Literal["zig", "inv"] = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def random(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def random(
+        self,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def beta(
+        self,
+        a: _FloatLike_co,
+        b: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self,
+        a: _ArrayLikeFloat_co,
+        b: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> NDArray[float64]: ...
+
+    #
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        dtype: _DTypeLike[np.int64] | _Int64Codes = ...,
+        endpoint: bool = False,
+    ) -> np.int64: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: type[bool],
+        endpoint: bool = False,
+    ) -> bool: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: type[int],
+        endpoint: bool = False,
+    ) -> int: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _DTypeLike[np.bool] | _BoolCodes,
+        endpoint: bool = False,
+    ) -> np.bool: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _DTypeLike[_IntegerT],
+        endpoint: bool = False,
+    ) -> _IntegerT: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        dtype: _DTypeLike[np.int64] | _Int64Codes = ...,
+        endpoint: bool = False,
+    ) -> NDArray[np.int64]: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _DTypeLikeBool,
+        endpoint: bool = False,
+    ) -> NDArray[np.bool]: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _DTypeLike[_IntegerT],
+        endpoint: bool = False,
+    ) -> NDArray[_IntegerT]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _Int8Codes,
+        endpoint: bool = False,
+    ) -> np.int8: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _Int8Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.int8]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt8Codes,
+        endpoint: bool = False,
+    ) -> np.uint8: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt8Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint8]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _Int16Codes,
+        endpoint: bool = False,
+    ) -> np.int16: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _Int16Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.int16]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt16Codes,
+        endpoint: bool = False,
+    ) -> np.uint16: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt16Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint16]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _Int32Codes,
+        endpoint: bool = False,
+    ) -> np.int32: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _Int32Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.int32]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt32Codes,
+        endpoint: bool = False,
+    ) -> np.uint32: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt32Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint32]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt64Codes,
+        endpoint: bool = False,
+    ) -> np.uint64: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt64Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint64]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _IntPCodes,
+        endpoint: bool = False,
+    ) -> np.intp: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _IntPCodes,
+        endpoint: bool = False,
+    ) -> NDArray[np.intp]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UIntPCodes,
+        endpoint: bool = False,
+    ) -> np.uintp: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UIntPCodes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uintp]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        dtype: DTypeLike = ...,
+        endpoint: bool = False,
+    ) -> Any: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        dtype: DTypeLike = ...,
+        endpoint: bool = False,
+    ) -> NDArray[Any]: ...
+
+    # TODO: Use a TypeVar _T here to get away from Any output?
+    #       Should be int->NDArray[int64], ArrayLike[_T] -> _T | NDArray[Any]
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> NDArray[int64]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> NDArray[Any]: ...
+    @overload
+    def uniform(
+        self,
+        low: _FloatLike_co = ...,
+        high: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def normal(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: _FloatLike_co,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gamma(
+        self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def f(
+        self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _FloatLike_co,
+        dfden: _FloatLike_co,
+        nonc: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_chisquare(
+        self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self,
+        df: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def vonmises(
+        self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self,
+        mu: _ArrayLikeFloat_co,
+        kappa: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def power(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...
+    @overload
+    def laplace(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gumbel(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def logistic(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def lognormal(
+        self,
+        mean: _FloatLike_co = ...,
+        sigma: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def wald(
+        self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self,
+        mean: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def triangular(
+        self,
+        left: _FloatLike_co,
+        mode: _FloatLike_co,
+        right: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def negative_binomial(
+        self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...
+    ) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self,
+        n: _ArrayLikeFloat_co,
+        p: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def hypergeometric(
+        self, ngood: int, nbad: int, nsample: int, size: None = ...
+    ) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[int64]: ...
+    @overload
+    def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+        *,
+        method: Literal["svd", "eigh", "cholesky"] = ...,
+    ) -> NDArray[float64]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co,
+            pvals: _ArrayLikeFloat_co,
+            size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    def multivariate_hypergeometric(
+        self,
+        colors: _ArrayLikeInt_co,
+        nsample: int,
+        size: None | _ShapeLike = ...,
+        method: Literal["marginals", "count"] = ...,
+    ) -> NDArray[int64]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    def permuted(
+        self, x: ArrayLike, *, axis: None | int = ..., out: None | NDArray[Any] = ...
+    ) -> NDArray[Any]: ...
+    def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...
+
+def default_rng(
+    seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator | RandomState = ...
+) -> Generator: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_mt19937.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_mt19937.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..a883f37a51b6356f5cd065a6799e984e1bfd4f1c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_mt19937.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6245585dae2024446b9cc6f86f8f5d7ffb90054f2c50be0ede4b05663392f527
+size 129912
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_mt19937.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_mt19937.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..430dd8041f50221c92b297a3ee5e9fe767a8d176
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_mt19937.pyi
@@ -0,0 +1,25 @@
+from typing import TypedDict, type_check_only
+
+from numpy import uint32
+from numpy.typing import NDArray
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+@type_check_only
+class _MT19937Internal(TypedDict):
+    key: NDArray[uint32]
+    pos: int
+
+@type_check_only
+class _MT19937State(TypedDict):
+    bit_generator: str
+    state: _MT19937Internal
+
+class MT19937(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ...
+    def jumped(self, jumps: int = ...) -> MT19937: ...
+    @property
+    def state(self) -> _MT19937State: ...
+    @state.setter
+    def state(self, value: _MT19937State) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pcg64.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pcg64.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..24800bfb6e03ebd0582eda5c650499f1fee163ad
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pcg64.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:839bd03aa79e83d3aa67733423ca65fd47e4af535145c0f7384d7c88642d3ef8
+size 136480
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pcg64.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pcg64.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..15bb0525c9a532af49715242b0b2da6a5e7dbdbc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pcg64.pyi
@@ -0,0 +1,44 @@
+from typing import TypedDict, type_check_only
+
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+@type_check_only
+class _PCG64Internal(TypedDict):
+    state: int
+    inc: int
+
+@type_check_only
+class _PCG64State(TypedDict):
+    bit_generator: str
+    state: _PCG64Internal
+    has_uint32: int
+    uinteger: int
+
+class PCG64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64: ...
+
+class PCG64DXSM(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64DXSM: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64DXSM: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_philox.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_philox.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..9d312ddefd4d6b331b95b75353a48f32402ea619
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_philox.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b166c0672edb46c1c9cbf720867d95aea2e9a323bc069157c9f0c15884ea5882
+size 116832
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_philox.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_philox.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7206ae9702c002a7f893bbee3d485eef4c6ca240
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_philox.pyi
@@ -0,0 +1,39 @@
+from typing import TypedDict, type_check_only
+
+from numpy import uint64
+from numpy.typing import NDArray
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+@type_check_only
+class _PhiloxInternal(TypedDict):
+    counter: NDArray[uint64]
+    key: NDArray[uint64]
+
+@type_check_only
+class _PhiloxState(TypedDict):
+    bit_generator: str
+    state: _PhiloxInternal
+    buffer: NDArray[uint64]
+    buffer_pos: int
+    has_uint32: int
+    uinteger: int
+
+class Philox(BitGenerator):
+    def __init__(
+        self,
+        seed: None | _ArrayLikeInt_co | SeedSequence = ...,
+        counter: None | _ArrayLikeInt_co = ...,
+        key: None | _ArrayLikeInt_co = ...,
+    ) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _PhiloxState: ...
+    @state.setter
+    def state(
+        self,
+        value: _PhiloxState,
+    ) -> None: ...
+    def jumped(self, jumps: int = ...) -> Philox: ...
+    def advance(self, delta: int) -> Philox: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pickle.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..842bd441a50237765a543a13c878ce1ece828892
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pickle.py
@@ -0,0 +1,89 @@
+from .bit_generator import BitGenerator
+from .mtrand import RandomState
+from ._philox import Philox
+from ._pcg64 import PCG64, PCG64DXSM
+from ._sfc64 import SFC64
+
+from ._generator import Generator
+from ._mt19937 import MT19937
+
+BitGenerators = {'MT19937': MT19937,
+                 'PCG64': PCG64,
+                 'PCG64DXSM': PCG64DXSM,
+                 'Philox': Philox,
+                 'SFC64': SFC64,
+                 }
+
+
+def __bit_generator_ctor(bit_generator: str | type[BitGenerator] = 'MT19937'):
+    """
+    Pickling helper function that returns a bit generator object
+
+    Parameters
+    ----------
+    bit_generator : type[BitGenerator] or str
+        BitGenerator class or string containing the name of the BitGenerator
+
+    Returns
+    -------
+    BitGenerator
+        BitGenerator instance
+    """
+    if isinstance(bit_generator, type):
+        bit_gen_class = bit_generator
+    elif bit_generator in BitGenerators:
+        bit_gen_class = BitGenerators[bit_generator]
+    else:
+        raise ValueError(
+            str(bit_generator) + ' is not a known BitGenerator module.'
+        )
+
+    return bit_gen_class()
+
+
+def __generator_ctor(bit_generator_name="MT19937",
+                     bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a Generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str or BitGenerator
+        String containing the core BitGenerator's name or a
+        BitGenerator instance
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rg : Generator
+        Generator using the named core BitGenerator
+    """
+    if isinstance(bit_generator_name, BitGenerator):
+        return Generator(bit_generator_name)
+    # Legacy path that uses a bit generator name and ctor
+    return Generator(bit_generator_ctor(bit_generator_name))
+
+
+def __randomstate_ctor(bit_generator_name="MT19937",
+                       bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a legacy RandomState-like object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator's name
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rs : RandomState
+        Legacy RandomState using the named core BitGenerator
+    """
+    if isinstance(bit_generator_name, BitGenerator):
+        return RandomState(bit_generator_name)
+    return RandomState(bit_generator_ctor(bit_generator_name))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pickle.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pickle.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d4c6e8155ae9a800d3b0e3b320e6e552ce85f177
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_pickle.pyi
@@ -0,0 +1,43 @@
+from collections.abc import Callable
+from typing import Final, Literal, TypeVar, TypedDict, overload, type_check_only
+
+from numpy.random._generator import Generator
+from numpy.random._mt19937 import MT19937
+from numpy.random._pcg64 import PCG64, PCG64DXSM
+from numpy.random._philox import Philox
+from numpy.random._sfc64 import SFC64
+from numpy.random.bit_generator import BitGenerator
+from numpy.random.mtrand import RandomState
+
+_T = TypeVar("_T", bound=BitGenerator)
+
+@type_check_only
+class _BitGenerators(TypedDict):
+    MT19937: type[MT19937]
+    PCG64: type[PCG64]
+    PCG64DXSM: type[PCG64DXSM]
+    Philox: type[Philox]
+    SFC64: type[SFC64]
+
+BitGenerators: Final[_BitGenerators] = ...
+
+@overload
+def __bit_generator_ctor(bit_generator: Literal["MT19937"] = "MT19937") -> MT19937: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["PCG64"]) -> PCG64: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["PCG64DXSM"]) -> PCG64DXSM: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["Philox"]) -> Philox: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["SFC64"]) -> SFC64: ...
+@overload
+def __bit_generator_ctor(bit_generator: type[_T]) -> _T: ...
+def __generator_ctor(
+    bit_generator_name: str | type[BitGenerator] | BitGenerator = "MT19937",
+    bit_generator_ctor: Callable[[str | type[BitGenerator]], BitGenerator] = ...,
+) -> Generator: ...
+def __randomstate_ctor(
+    bit_generator_name: str | type[BitGenerator] | BitGenerator = "MT19937",
+    bit_generator_ctor: Callable[[str | type[BitGenerator]], BitGenerator] = ...,
+) -> RandomState: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..56bd3a7a29e025a6aaed9bd36b6df48b2483e583
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_sfc64.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_sfc64.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..baaae7c668fb61f950489f4486c1880ae7cd44e1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/_sfc64.pyi
@@ -0,0 +1,28 @@
+from typing import TypedDict, type_check_only
+
+from numpy import uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import NDArray, _ArrayLikeInt_co
+
+@type_check_only
+class _SFC64Internal(TypedDict):
+    state: NDArray[uint64]
+
+@type_check_only
+class _SFC64State(TypedDict):
+    bit_generator: str
+    state: _SFC64Internal
+    has_uint32: int
+    uinteger: int
+
+class SFC64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _SFC64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _SFC64State,
+    ) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..2856b1ac335d919dfe83131635f6f94ea616163d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa02602384f668d5551761a14c616893a01ac02965092cdaf52b86fd2584d9c3
+size 242528
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.pxd b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..dfa7d0a71c085dfa3dfb2819f47493cb8501d198
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.pxd
@@ -0,0 +1,35 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+cdef class BitGenerator():
+    cdef readonly object _seed_seq
+    cdef readonly object lock
+    cdef bitgen_t _bitgen
+    cdef readonly object _ctypes
+    cdef readonly object _cffi
+    cdef readonly object capsule
+
+
+cdef class SeedSequence():
+    cdef readonly object entropy
+    cdef readonly tuple spawn_key
+    cdef readonly Py_ssize_t pool_size
+    cdef readonly object pool
+    cdef readonly uint32_t n_children_spawned
+
+    cdef mix_entropy(self, np.ndarray[np.npy_uint32, ndim=1] mixer,
+                     np.ndarray[np.npy_uint32, ndim=1] entropy_array)
+    cdef get_assembled_entropy(self)
+
+cdef class SeedlessSequence():
+    pass
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..78fb769683d32f0ae2c1c663ff76d79429b2e6e7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/bit_generator.pyi
@@ -0,0 +1,107 @@
+import abc
+from collections.abc import Callable, Mapping, Sequence
+from threading import Lock
+from typing import Any, ClassVar, Literal, NamedTuple, TypeAlias, TypedDict, overload, type_check_only
+
+from _typeshed import Incomplete
+from typing_extensions import CapsuleType, Self
+
+import numpy as np
+from numpy._typing import NDArray, _ArrayLikeInt_co, _DTypeLike, _ShapeLike, _UInt32Codes, _UInt64Codes
+
+__all__ = ["BitGenerator", "SeedSequence"]
+
+###
+
+_DTypeLikeUint_: TypeAlias = _DTypeLike[np.uint32 | np.uint64] | _UInt32Codes | _UInt64Codes
+
+@type_check_only
+class _SeedSeqState(TypedDict):
+    entropy: int | Sequence[int] | None
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+
+@type_check_only
+class _Interface(NamedTuple):
+    state_address: Incomplete
+    state: Incomplete
+    next_uint64: Incomplete
+    next_uint32: Incomplete
+    next_double: Incomplete
+    bit_generator: Incomplete
+
+@type_check_only
+class _CythonMixin:
+    def __setstate_cython__(self, pyx_state: object, /) -> None: ...
+    def __reduce_cython__(self) -> Any: ...  # noqa: ANN401
+
+@type_check_only
+class _GenerateStateMixin(_CythonMixin):
+    def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...
+
+###
+
+class ISeedSequence(abc.ABC):
+    @abc.abstractmethod
+    def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...
+
+class ISpawnableSeedSequence(ISeedSequence, abc.ABC):
+    @abc.abstractmethod
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+
+class SeedlessSeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+
+class SeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):
+    __pyx_vtable__: ClassVar[CapsuleType] = ...
+
+    entropy: int | Sequence[int] | None
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+    pool: NDArray[np.uint32]
+
+    def __init__(
+        self,
+        /,
+        entropy: _ArrayLikeInt_co | None = None,
+        *,
+        spawn_key: Sequence[int] = (),
+        pool_size: int = 4,
+        n_children_spawned: int = ...,
+    ) -> None: ...
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+    @property
+    def state(self) -> _SeedSeqState: ...
+
+class BitGenerator(_CythonMixin, abc.ABC):
+    lock: Lock
+    @property
+    def state(self) -> Mapping[str, Any]: ...
+    @state.setter
+    def state(self, value: Mapping[str, Any], /) -> None: ...
+    @property
+    def seed_seq(self) -> ISeedSequence: ...
+    @property
+    def ctypes(self) -> _Interface: ...
+    @property
+    def cffi(self) -> _Interface: ...
+    @property
+    def capsule(self) -> CapsuleType: ...
+
+    #
+    def __init__(self, /, seed: _ArrayLikeInt_co | SeedSequence | None = None) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], Self], tuple[str], tuple[Mapping[str, Any], ISeedSequence]]: ...
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+    def _benchmark(self, /, cnt: int, method: str = "uint64") -> None: ...
+
+    #
+    @overload
+    def random_raw(self, /, size: None = None, output: Literal[True] = True) -> int: ...
+    @overload
+    def random_raw(self, /, size: _ShapeLike, output: Literal[True] = True) -> NDArray[np.uint64]: ...
+    @overload
+    def random_raw(self, /, size: _ShapeLike | None, output: Literal[False]) -> None: ...
+    @overload
+    def random_raw(self, /, size: _ShapeLike | None = None, *, output: Literal[False]) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/c_distributions.pxd b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/c_distributions.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..da790ca499df2aadb503d6a98182575fb0de67ed
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/c_distributions.pxd
@@ -0,0 +1,119 @@
+#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
+from numpy cimport npy_intp
+
+from libc.stdint cimport (uint64_t, int32_t, int64_t)
+from numpy.random cimport bitgen_t
+
+cdef extern from "numpy/random/distributions.h":
+
+    struct s_binomial_t:
+        int has_binomial
+        double psave
+        int64_t nsave
+        double r
+        double q
+        double fm
+        int64_t m
+        double p1
+        double xm
+        double xl
+        double xr
+        double c
+        double laml
+        double lamr
+        double p2
+        double p3
+        double p4
+
+    ctypedef s_binomial_t binomial_t
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    double random_standard_uniform(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_exponential(bitgen_t *bitgen_state) nogil
+    float random_standard_exponential_f(bitgen_t *bitgen_state) nogil
+    void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_normal(bitgen_t* bitgen_state) nogil
+    float random_standard_normal_f(bitgen_t *bitgen_state) nogil
+    void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil
+    void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil
+    double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil
+    float random_standard_normal_f(bitgen_t* bitgen_state) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    int64_t random_positive_int64(bitgen_t *bitgen_state) nogil
+    int32_t random_positive_int32(bitgen_t *bitgen_state) nogil
+    int64_t random_positive_int(bitgen_t *bitgen_state) nogil
+    uint64_t random_uint(bitgen_t *bitgen_state) nogil
+
+    double random_normal(bitgen_t *bitgen_state, double loc, double scale) nogil
+
+    double random_gamma(bitgen_t *bitgen_state, double shape, double scale) nogil
+    float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) nogil
+
+    double random_exponential(bitgen_t *bitgen_state, double scale) nogil
+    double random_uniform(bitgen_t *bitgen_state, double lower, double range) nogil
+    double random_beta(bitgen_t *bitgen_state, double a, double b) nogil
+    double random_chisquare(bitgen_t *bitgen_state, double df) nogil
+    double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) nogil
+    double random_standard_cauchy(bitgen_t *bitgen_state) nogil
+    double random_pareto(bitgen_t *bitgen_state, double a) nogil
+    double random_weibull(bitgen_t *bitgen_state, double a) nogil
+    double random_power(bitgen_t *bitgen_state, double a) nogil
+    double random_laplace(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_gumbel(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_logistic(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) nogil
+    double random_rayleigh(bitgen_t *bitgen_state, double mode) nogil
+    double random_standard_t(bitgen_t *bitgen_state, double df) nogil
+    double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
+                                       double nonc) nogil
+    double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
+                               double dfden, double nonc) nogil
+    double random_wald(bitgen_t *bitgen_state, double mean, double scale) nogil
+    double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa) nogil
+    double random_triangular(bitgen_t *bitgen_state, double left, double mode,
+                             double right) nogil
+
+    int64_t random_poisson(bitgen_t *bitgen_state, double lam) nogil
+    int64_t random_negative_binomial(bitgen_t *bitgen_state, double n, double p) nogil
+    int64_t random_binomial(bitgen_t *bitgen_state, double p, int64_t n, binomial_t *binomial) nogil
+    int64_t random_logseries(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_search(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_zipf(bitgen_t *bitgen_state, double a) nogil
+    int64_t random_hypergeometric(bitgen_t *bitgen_state, int64_t good, int64_t bad,
+                                    int64_t sample) nogil
+
+    uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) nogil
+
+    # Generate random uint64 numbers in closed interval [off, off + rng].
+    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
+                                   uint64_t off, uint64_t rng,
+                                   uint64_t mask, bint use_masked) nogil
+
+    void random_multinomial(bitgen_t *bitgen_state, int64_t n, int64_t *mnix,
+                            double *pix, npy_intp d, binomial_t *binomial) nogil
+
+    int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
+                          int64_t total,
+                          size_t num_colors, int64_t *colors,
+                          int64_t nsample,
+                          size_t num_variates, int64_t *variates) nogil
+    void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
+                               int64_t total,
+                               size_t num_colors, int64_t *colors,
+                               int64_t nsample,
+                               size_t num_variates, int64_t *variates) nogil
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/lib/libnpyrandom.a b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/lib/libnpyrandom.a
new file mode 100644
index 0000000000000000000000000000000000000000..946b059f1c5740b06d124641d66276cfc32ebccc
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/lib/libnpyrandom.a differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/mtrand.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/mtrand.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..bb5b6d958527d0d58bab1cd215f5643631d9566f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/mtrand.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ef34372d188edcd794777a72a2eb86010f17736d3d7195b4bd6bf848ea7906c
+size 797808
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/mtrand.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/mtrand.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..16a722c0038e4180cc68d4e528ef806a629fc3f5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/mtrand.pyi
@@ -0,0 +1,658 @@
+import builtins
+from collections.abc import Callable
+from typing import Any, overload, Literal
+
+import numpy as np
+from numpy import (
+    dtype,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    long,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    uint,
+    ulong,
+)
+from numpy.random.bit_generator import BitGenerator
+from numpy._typing import (
+    ArrayLike,
+    NDArray,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DTypeLikeBool,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _LongCodes,
+    _ShapeLike,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+    _ULongCodes,
+)
+
+
+class RandomState:
+    _bit_generator: BitGenerator
+    def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ...
+    def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ...
+    @overload
+    def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...
+    @overload
+    def get_state(
+        self, legacy: Literal[True] = ...
+    ) -> dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]: ...
+    def set_state(
+        self, state: dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]
+    ) -> None: ...
+    @overload
+    def random_sample(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ...
+    @overload
+    def random(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random(self, size: _ShapeLike) -> NDArray[float64]: ...
+    @overload
+    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ...
+    @overload
+    def tomaxint(self, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    # Generates long values, but stores it in a 64bit int:
+    def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: type[bool] = ...,
+    ) -> bool: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: type[np.bool] = ...,
+    ) -> np.bool: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: type[int] = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+    ) -> uint8: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+    ) -> uint16: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+    ) -> uint32: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+    ) -> uint: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ...,
+    ) -> ulong: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+    ) -> uint64: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+    ) -> int8: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+    ) -> int16: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+    ) -> int32: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int_] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+    ) -> int_: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[long] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ...,
+    ) -> long: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+    ) -> int64: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+    ) -> NDArray[np.bool]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+    ) -> NDArray[int8]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+    ) -> NDArray[int16]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+    ) -> NDArray[int32]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+    ) -> NDArray[int64]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+    ) -> NDArray[uint8]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+    ) -> NDArray[uint16]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+    ) -> NDArray[uint32]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+    ) -> NDArray[uint64]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[long] | type[int] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ...,
+    ) -> NDArray[ulong]: ...
+    def bytes(self, length: int) -> builtins.bytes: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> NDArray[Any]: ...
+    @overload
+    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def rand(self) -> float: ...
+    @overload
+    def rand(self, *args: int) -> NDArray[float64]: ...
+    @overload
+    def randn(self) -> float: ...
+    @overload
+    def randn(self, *args: int) -> NDArray[float64]: ...
+    @overload
+    def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def random_integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def standard_normal(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self, size: _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: float,
+        size: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...
+    @overload
+    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+    ) -> NDArray[float64]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    def shuffle(self, x: ArrayLike) -> None: ...
+    @overload
+    def permutation(self, x: int) -> NDArray[long]: ...
+    @overload
+    def permutation(self, x: ArrayLike) -> NDArray[Any]: ...
+
+_rand: RandomState
+
+beta = _rand.beta
+binomial = _rand.binomial
+bytes = _rand.bytes
+chisquare = _rand.chisquare
+choice = _rand.choice
+dirichlet = _rand.dirichlet
+exponential = _rand.exponential
+f = _rand.f
+gamma = _rand.gamma
+get_state = _rand.get_state
+geometric = _rand.geometric
+gumbel = _rand.gumbel
+hypergeometric = _rand.hypergeometric
+laplace = _rand.laplace
+logistic = _rand.logistic
+lognormal = _rand.lognormal
+logseries = _rand.logseries
+multinomial = _rand.multinomial
+multivariate_normal = _rand.multivariate_normal
+negative_binomial = _rand.negative_binomial
+noncentral_chisquare = _rand.noncentral_chisquare
+noncentral_f = _rand.noncentral_f
+normal = _rand.normal
+pareto = _rand.pareto
+permutation = _rand.permutation
+poisson = _rand.poisson
+power = _rand.power
+rand = _rand.rand
+randint = _rand.randint
+randn = _rand.randn
+random = _rand.random
+random_integers = _rand.random_integers
+random_sample = _rand.random_sample
+rayleigh = _rand.rayleigh
+seed = _rand.seed
+set_state = _rand.set_state
+shuffle = _rand.shuffle
+standard_cauchy = _rand.standard_cauchy
+standard_exponential = _rand.standard_exponential
+standard_gamma = _rand.standard_gamma
+standard_normal = _rand.standard_normal
+standard_t = _rand.standard_t
+triangular = _rand.triangular
+uniform = _rand.uniform
+vonmises = _rand.vonmises
+wald = _rand.wald
+weibull = _rand.weibull
+zipf = _rand.zipf
+# Two legacy that are trivial wrappers around random_sample
+sample = _rand.random_sample
+ranf = _rand.random_sample
+
+def set_bit_generator(bitgen: BitGenerator) -> None:
+    ...
+
+def get_bit_generator() -> BitGenerator:
+    ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/__init__.py
new file mode 100644
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new file mode 100644
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/generator_pcg64_np121.pkl.gz b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/generator_pcg64_np121.pkl.gz
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+size 203
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/generator_pcg64_np126.pkl.gz b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/generator_pcg64_np126.pkl.gz
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cdb8e4794ccdb30fa4fea3045b30a188f6ff5357
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-1.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0c8271fab6dff757e0b94443be8bc0e8b5f1e047
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-2.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7c13e3172d0e7798200f58cde9362ad0bd4a22e1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-2.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..39cef057f4490a70d7041bcfde4d3fec844732bb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..878c5ea7c3a5173467a4f945947174149f3913e6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/philox-testset-1.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/philox-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e448cbf73cc0774eca74df0d3d8866d8bd35bab3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/philox-testset-1.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/philox-testset-2.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/philox-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..69d24c38c28959361283624e02f8891fa69b6c5f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/philox-testset-2.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-1.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4fffe69591fea3f399f35034057096089d4017fb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-1.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-2.csv b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..70aebd5d539256e0abd453bb45828b691c6abdf0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-2.csv
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64_np126.pkl.gz b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64_np126.pkl.gz
new file mode 100644
index 0000000000000000000000000000000000000000..b9f88985ca2c492d4c96adab79151d824dfc6cad
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/data/sfc64_np126.pkl.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3156b5ca5172ec350f81404afa821e292755978518122377019ec6dec773cdac
+size 290
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_direct.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_direct.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ef94b63ac590c9dfb4224df785f977e59b05b51
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_direct.py
@@ -0,0 +1,580 @@
+import os
+from os.path import join
+import sys
+
+import numpy as np
+from numpy.testing import (assert_equal, assert_allclose, assert_array_equal,
+                           assert_raises)
+import pytest
+
+from numpy.random import (
+    Generator, MT19937, PCG64, PCG64DXSM, Philox, RandomState, SeedSequence,
+    SFC64, default_rng
+)
+from numpy.random._common import interface
+
+try:
+    import cffi  # noqa: F401
+
+    MISSING_CFFI = False
+except ImportError:
+    MISSING_CFFI = True
+
+try:
+    import ctypes  # noqa: F401
+
+    MISSING_CTYPES = False
+except ImportError:
+    MISSING_CTYPES = False
+
+if sys.flags.optimize > 1:
+    # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1
+    # cffi cannot succeed
+    MISSING_CFFI = True
+
+
+pwd = os.path.dirname(os.path.abspath(__file__))
+
+
+def assert_state_equal(actual, target):
+    for key in actual:
+        if isinstance(actual[key], dict):
+            assert_state_equal(actual[key], target[key])
+        elif isinstance(actual[key], np.ndarray):
+            assert_array_equal(actual[key], target[key])
+        else:
+            assert actual[key] == target[key]
+
+
+def uint32_to_float32(u):
+    return ((u >> np.uint32(8)) * (1.0 / 2**24)).astype(np.float32)
+
+
+def uniform32_from_uint64(x):
+    x = np.uint64(x)
+    upper = np.array(x >> np.uint64(32), dtype=np.uint32)
+    lower = np.uint64(0xffffffff)
+    lower = np.array(x & lower, dtype=np.uint32)
+    joined = np.column_stack([lower, upper]).ravel()
+    return uint32_to_float32(joined)
+
+
+def uniform32_from_uint53(x):
+    x = np.uint64(x) >> np.uint64(16)
+    x = np.uint32(x & np.uint64(0xffffffff))
+    return uint32_to_float32(x)
+
+
+def uniform32_from_uint32(x):
+    return uint32_to_float32(x)
+
+
+def uniform32_from_uint(x, bits):
+    if bits == 64:
+        return uniform32_from_uint64(x)
+    elif bits == 53:
+        return uniform32_from_uint53(x)
+    elif bits == 32:
+        return uniform32_from_uint32(x)
+    else:
+        raise NotImplementedError
+
+
+def uniform_from_uint(x, bits):
+    if bits in (64, 63, 53):
+        return uniform_from_uint64(x)
+    elif bits == 32:
+        return uniform_from_uint32(x)
+
+
+def uniform_from_uint64(x):
+    return (x >> np.uint64(11)) * (1.0 / 9007199254740992.0)
+
+
+def uniform_from_uint32(x):
+    out = np.empty(len(x) // 2)
+    for i in range(0, len(x), 2):
+        a = x[i] >> 5
+        b = x[i + 1] >> 6
+        out[i // 2] = (a * 67108864.0 + b) / 9007199254740992.0
+    return out
+
+
+def uniform_from_dsfmt(x):
+    return x.view(np.double) - 1.0
+
+
+def gauss_from_uint(x, n, bits):
+    if bits in (64, 63):
+        doubles = uniform_from_uint64(x)
+    elif bits == 32:
+        doubles = uniform_from_uint32(x)
+    else:  # bits == 'dsfmt'
+        doubles = uniform_from_dsfmt(x)
+    gauss = []
+    loc = 0
+    x1 = x2 = 0.0
+    while len(gauss) < n:
+        r2 = 2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * doubles[loc] - 1.0
+            x2 = 2.0 * doubles[loc + 1] - 1.0
+            r2 = x1 * x1 + x2 * x2
+            loc += 2
+
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        gauss.append(f * x2)
+        gauss.append(f * x1)
+
+    return gauss[:n]
+
+
+def test_seedsequence():
+    from numpy.random.bit_generator import (ISeedSequence,
+                                            ISpawnableSeedSequence,
+                                            SeedlessSeedSequence)
+
+    s1 = SeedSequence(range(10), spawn_key=(1, 2), pool_size=6)
+    s1.spawn(10)
+    s2 = SeedSequence(**s1.state)
+    assert_equal(s1.state, s2.state)
+    assert_equal(s1.n_children_spawned, s2.n_children_spawned)
+
+    # The interfaces cannot be instantiated themselves.
+    assert_raises(TypeError, ISeedSequence)
+    assert_raises(TypeError, ISpawnableSeedSequence)
+    dummy = SeedlessSeedSequence()
+    assert_raises(NotImplementedError, dummy.generate_state, 10)
+    assert len(dummy.spawn(10)) == 10
+
+
+def test_generator_spawning():
+    """ Test spawning new generators and bit_generators directly.
+    """
+    rng = np.random.default_rng()
+    seq = rng.bit_generator.seed_seq
+    new_ss = seq.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5)]
+    assert [c.spawn_key for c in new_ss] == expected_keys
+
+    new_bgs = rng.bit_generator.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5, 10)]
+    assert [bg.seed_seq.spawn_key for bg in new_bgs] == expected_keys
+
+    new_rngs = rng.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(10, 15)]
+    found_keys = [rng.bit_generator.seed_seq.spawn_key for rng in new_rngs]
+    assert found_keys == expected_keys
+
+    # Sanity check that streams are actually different:
+    assert new_rngs[0].uniform() != new_rngs[1].uniform()
+
+
+def test_non_spawnable():
+    from numpy.random.bit_generator import ISeedSequence
+
+    class FakeSeedSequence:
+        def generate_state(self, n_words, dtype=np.uint32):
+            return np.zeros(n_words, dtype=dtype)
+
+    ISeedSequence.register(FakeSeedSequence)
+
+    rng = np.random.default_rng(FakeSeedSequence())
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.spawn(5)
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.bit_generator.spawn(5)
+
+
+class Base:
+    dtype = np.uint64
+    data2 = data1 = {}
+
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.seed_error_type = TypeError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = []
+
+    @classmethod
+    def _read_csv(cls, filename):
+        with open(filename) as csv:
+            seed = csv.readline()
+            seed = seed.split(',')
+            seed = [int(s.strip(), 0) for s in seed[1:]]
+            data = []
+            for line in csv:
+                data.append(int(line.split(',')[-1].strip(), 0))
+            return {'seed': seed, 'data': np.array(data, dtype=cls.dtype)}
+
+    def test_raw(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw(1000)
+        assert_equal(uints, self.data1['data'])
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw()
+        assert_equal(uints, self.data1['data'][0])
+
+        bit_generator = self.bit_generator(*self.data2['seed'])
+        uints = bit_generator.random_raw(1000)
+        assert_equal(uints, self.data2['data'])
+
+    def test_random_raw(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw(output=False)
+        assert uints is None
+        uints = bit_generator.random_raw(1000, output=False)
+        assert uints is None
+
+    def test_gauss_inv(self):
+        n = 25
+        rs = RandomState(self.bit_generator(*self.data1['seed']))
+        gauss = rs.standard_normal(n)
+        assert_allclose(gauss,
+                        gauss_from_uint(self.data1['data'], n, self.bits))
+
+        rs = RandomState(self.bit_generator(*self.data2['seed']))
+        gauss = rs.standard_normal(25)
+        assert_allclose(gauss,
+                        gauss_from_uint(self.data2['data'], n, self.bits))
+
+    def test_uniform_double(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        vals = uniform_from_uint(self.data1['data'], self.bits)
+        uniforms = rs.random(len(vals))
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float64)
+
+        rs = Generator(self.bit_generator(*self.data2['seed']))
+        vals = uniform_from_uint(self.data2['data'], self.bits)
+        uniforms = rs.random(len(vals))
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float64)
+
+    def test_uniform_float(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        vals = uniform32_from_uint(self.data1['data'], self.bits)
+        uniforms = rs.random(len(vals), dtype=np.float32)
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float32)
+
+        rs = Generator(self.bit_generator(*self.data2['seed']))
+        vals = uniform32_from_uint(self.data2['data'], self.bits)
+        uniforms = rs.random(len(vals), dtype=np.float32)
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float32)
+
+    def test_repr(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        assert 'Generator' in repr(rs)
+        assert f'{id(rs):#x}'.upper().replace('X', 'x') in repr(rs)
+
+    def test_str(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        assert 'Generator' in str(rs)
+        assert str(self.bit_generator.__name__) in str(rs)
+        assert f'{id(rs):#x}'.upper().replace('X', 'x') not in str(rs)
+
+    def test_pickle(self):
+        import pickle
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        bitgen_pkl = pickle.dumps(bit_generator)
+        reloaded = pickle.loads(bitgen_pkl)
+        reloaded_state = reloaded.state
+        assert_array_equal(Generator(bit_generator).standard_normal(1000),
+                           Generator(reloaded).standard_normal(1000))
+        assert bit_generator is not reloaded
+        assert_state_equal(reloaded_state, state)
+
+        ss = SeedSequence(100)
+        aa = pickle.loads(pickle.dumps(ss))
+        assert_equal(ss.state, aa.state)
+
+    def test_pickle_preserves_seed_sequence(self):
+        # GH 26234
+        # Add explicit test that bit generators preserve seed sequences
+        import pickle
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        ss = bit_generator.seed_seq
+        bg_plk = pickle.loads(pickle.dumps(bit_generator))
+        ss_plk = bg_plk.seed_seq
+        assert_equal(ss.state, ss_plk.state)
+        assert_equal(ss.pool, ss_plk.pool)
+
+        bit_generator.seed_seq.spawn(10)
+        bg_plk = pickle.loads(pickle.dumps(bit_generator))
+        ss_plk = bg_plk.seed_seq
+        assert_equal(ss.state, ss_plk.state)
+        assert_equal(ss.n_children_spawned, ss_plk.n_children_spawned)
+
+    def test_invalid_state_type(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        with pytest.raises(TypeError):
+            bit_generator.state = {'1'}
+
+    def test_invalid_state_value(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        state['bit_generator'] = 'otherBitGenerator'
+        with pytest.raises(ValueError):
+            bit_generator.state = state
+
+    def test_invalid_init_type(self):
+        bit_generator = self.bit_generator
+        for st in self.invalid_init_types:
+            with pytest.raises(TypeError):
+                bit_generator(*st)
+
+    def test_invalid_init_values(self):
+        bit_generator = self.bit_generator
+        for st in self.invalid_init_values:
+            with pytest.raises((ValueError, OverflowError)):
+                bit_generator(*st)
+
+    def test_benchmark(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        bit_generator._benchmark(1)
+        bit_generator._benchmark(1, 'double')
+        with pytest.raises(ValueError):
+            bit_generator._benchmark(1, 'int32')
+
+    @pytest.mark.skipif(MISSING_CFFI, reason='cffi not available')
+    def test_cffi(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        cffi_interface = bit_generator.cffi
+        assert isinstance(cffi_interface, interface)
+        other_cffi_interface = bit_generator.cffi
+        assert other_cffi_interface is cffi_interface
+
+    @pytest.mark.skipif(MISSING_CTYPES, reason='ctypes not available')
+    def test_ctypes(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        ctypes_interface = bit_generator.ctypes
+        assert isinstance(ctypes_interface, interface)
+        other_ctypes_interface = bit_generator.ctypes
+        assert other_ctypes_interface is ctypes_interface
+
+    def test_getstate(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        alt_state = bit_generator.__getstate__()
+        assert isinstance(alt_state, tuple)
+        assert_state_equal(state, alt_state[0])
+        assert isinstance(alt_state[1], SeedSequence)
+
+class TestPhilox(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = Philox
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(
+            join(pwd, './data/philox-testset-1.csv'))
+        cls.data2 = cls._read_csv(
+            join(pwd, './data/philox-testset-2.csv'))
+        cls.seed_error_type = TypeError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = [(1, None, 1), (-1,), (None, None, 2 ** 257 + 1)]
+
+    def test_set_key(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        keyed = self.bit_generator(counter=state['state']['counter'],
+                                   key=state['state']['key'])
+        assert_state_equal(bit_generator.state, keyed.state)
+
+
+class TestPCG64(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(join(pwd, './data/pcg64-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/pcg64-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_advance_symmetry(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        state = rs.bit_generator.state
+        step = -0x9e3779b97f4a7c150000000000000000
+        rs.bit_generator.advance(step)
+        val_neg = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(2**128 + step)
+        val_pos = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(10 * 2**128 + step)
+        val_big = rs.integers(10)
+        assert val_neg == val_pos
+        assert val_big == val_pos
+
+    def test_advange_large(self):
+        rs = Generator(self.bit_generator(38219308213743))
+        pcg = rs.bit_generator
+        state = pcg.state["state"]
+        initial_state = 287608843259529770491897792873167516365
+        assert state["state"] == initial_state
+        pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))
+        state = pcg.state["state"]
+        advanced_state = 135275564607035429730177404003164635391
+        assert state["state"] == advanced_state
+
+
+
+class TestPCG64DXSM(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64DXSM
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_advance_symmetry(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        state = rs.bit_generator.state
+        step = -0x9e3779b97f4a7c150000000000000000
+        rs.bit_generator.advance(step)
+        val_neg = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(2**128 + step)
+        val_pos = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(10 * 2**128 + step)
+        val_big = rs.integers(10)
+        assert val_neg == val_pos
+        assert val_big == val_pos
+
+    def test_advange_large(self):
+        rs = Generator(self.bit_generator(38219308213743))
+        pcg = rs.bit_generator
+        state = pcg.state
+        initial_state = 287608843259529770491897792873167516365
+        assert state["state"]["state"] == initial_state
+        pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))
+        state = pcg.state["state"]
+        advanced_state = 277778083536782149546677086420637664879
+        assert state["state"] == advanced_state
+
+
+class TestMT19937(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = MT19937
+        cls.bits = 32
+        cls.dtype = np.uint32
+        cls.data1 = cls._read_csv(join(pwd, './data/mt19937-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/mt19937-testset-2.csv'))
+        cls.seed_error_type = ValueError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = [(-1,)]
+
+    def test_seed_float_array(self):
+        assert_raises(TypeError, self.bit_generator, np.array([np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([-np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([np.pi, -np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([0, np.pi]))
+        assert_raises(TypeError, self.bit_generator, [np.pi])
+        assert_raises(TypeError, self.bit_generator, [0, np.pi])
+
+    def test_state_tuple(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        bit_generator = rs.bit_generator
+        state = bit_generator.state
+        desired = rs.integers(2 ** 16)
+        tup = (state['bit_generator'], state['state']['key'],
+               state['state']['pos'])
+        bit_generator.state = tup
+        actual = rs.integers(2 ** 16)
+        assert_equal(actual, desired)
+        tup = tup + (0, 0.0)
+        bit_generator.state = tup
+        actual = rs.integers(2 ** 16)
+        assert_equal(actual, desired)
+
+
+class TestSFC64(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = SFC64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(
+            join(pwd, './data/sfc64-testset-1.csv'))
+        cls.data2 = cls._read_csv(
+            join(pwd, './data/sfc64-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_legacy_pickle(self):
+        # Pickling format was changed in 2.0.x
+        import gzip
+        import pickle
+
+        expected_state = np.array(
+            [
+                9957867060933711493,
+                532597980065565856,
+                14769588338631205282,
+                13
+            ],
+            dtype=np.uint64
+        )
+
+        base_path = os.path.split(os.path.abspath(__file__))[0]
+        pkl_file = os.path.join(base_path, "data", "sfc64_np126.pkl.gz")
+        with gzip.open(pkl_file) as gz:
+            sfc = pickle.load(gz)
+
+        assert isinstance(sfc, SFC64)
+        assert_equal(sfc.state["state"]["state"], expected_state)
+
+
+class TestDefaultRNG:
+    def test_seed(self):
+        for args in [(), (None,), (1234,), ([1234, 5678],)]:
+            rg = default_rng(*args)
+            assert isinstance(rg.bit_generator, PCG64)
+
+    def test_passthrough(self):
+        bg = Philox()
+        rg = default_rng(bg)
+        assert rg.bit_generator is bg
+        rg2 = default_rng(rg)
+        assert rg2 is rg
+        assert rg2.bit_generator is bg
+
+    def test_coercion_RandomState_Generator(self):
+        # use default_rng to coerce RandomState to Generator
+        rs = RandomState(1234)
+        rg = default_rng(rs)
+        assert isinstance(rg.bit_generator, MT19937)
+        assert rg.bit_generator is rs._bit_generator
+
+        # RandomState with a non MT19937 bit generator
+        _original = np.random.get_bit_generator()
+        bg = PCG64(12342298)
+        np.random.set_bit_generator(bg)
+        rs = np.random.mtrand._rand
+        rg = default_rng(rs)
+        assert rg.bit_generator is bg
+
+        # vital to get global state back to original, otherwise
+        # other tests start to fail.
+        np.random.set_bit_generator(_original)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_extending.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6ffea0b2dbf060180a747f2e549f5fc98d1cfed
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_extending.py
@@ -0,0 +1,126 @@
+from importlib.util import spec_from_file_location, module_from_spec
+import os
+import pytest
+import shutil
+import subprocess
+import sys
+import sysconfig
+import warnings
+
+import numpy as np
+from numpy.testing import IS_WASM, IS_EDITABLE
+
+
+try:
+    import cffi
+except ImportError:
+    cffi = None
+
+if sys.flags.optimize > 1:
+    # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1
+    # cffi cannot succeed
+    cffi = None
+
+try:
+    with warnings.catch_warnings(record=True) as w:
+        # numba issue gh-4733
+        warnings.filterwarnings('always', '', DeprecationWarning)
+        import numba
+except (ImportError, SystemError):
+    # Certain numpy/numba versions trigger a SystemError due to a numba bug
+    numba = None
+
+try:
+    import cython
+    from Cython.Compiler.Version import version as cython_version
+except ImportError:
+    cython = None
+else:
+    from numpy._utils import _pep440
+    # Note: keep in sync with the one in pyproject.toml
+    required_version = '3.0.6'
+    if _pep440.parse(cython_version) < _pep440.Version(required_version):
+        # too old or wrong cython, skip the test
+        cython = None
+
+
+@pytest.mark.skipif(
+    IS_EDITABLE,
+    reason='Editable install cannot find .pxd headers'
+)
+@pytest.mark.skipif(
+        sys.platform == "win32" and sys.maxsize < 2**32,
+        reason="Failing in 32-bit Windows wheel build job, skip for now"
+)
+@pytest.mark.skipif(IS_WASM, reason="Can't start subprocess")
+@pytest.mark.skipif(cython is None, reason="requires cython")
+@pytest.mark.slow
+def test_cython(tmp_path):
+    import glob
+    # build the examples in a temporary directory
+    srcdir = os.path.join(os.path.dirname(__file__), '..')
+    shutil.copytree(srcdir, tmp_path / 'random')
+    build_dir = tmp_path / 'random' / '_examples' / 'cython'
+    target_dir = build_dir / "build"
+    os.makedirs(target_dir, exist_ok=True)
+    # Ensure we use the correct Python interpreter even when `meson` is
+    # installed in a different Python environment (see gh-24956)
+    native_file = str(build_dir / 'interpreter-native-file.ini')
+    with open(native_file, 'w') as f:
+        f.write("[binaries]\n")
+        f.write(f"python = '{sys.executable}'\n")
+        f.write(f"python3 = '{sys.executable}'")
+    if sys.platform == "win32":
+        subprocess.check_call(["meson", "setup",
+                               "--buildtype=release",
+                               "--vsenv", "--native-file", native_file,
+                               str(build_dir)],
+                              cwd=target_dir,
+                              )
+    else:
+        subprocess.check_call(["meson", "setup",
+                               "--native-file", native_file, str(build_dir)],
+                              cwd=target_dir
+                              )
+    subprocess.check_call(["meson", "compile", "-vv"], cwd=target_dir)
+
+    # gh-16162: make sure numpy's __init__.pxd was used for cython
+    # not really part of this test, but it is a convenient place to check
+
+    g = glob.glob(str(target_dir / "*" / "extending.pyx.c"))
+    with open(g[0]) as fid:
+        txt_to_find = 'NumPy API declarations from "numpy/__init__'
+        for line in fid:
+            if txt_to_find in line:
+                break
+        else:
+            assert False, ("Could not find '{}' in C file, "
+                           "wrong pxd used".format(txt_to_find))
+    # import without adding the directory to sys.path
+    suffix = sysconfig.get_config_var('EXT_SUFFIX')
+
+    def load(modname):
+        so = (target_dir / modname).with_suffix(suffix)
+        spec = spec_from_file_location(modname, so)
+        mod = module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        return mod
+
+    # test that the module can be imported
+    load("extending")
+    load("extending_cpp")
+    # actually test the cython c-extension
+    extending_distributions = load("extending_distributions")
+    from numpy.random import PCG64
+    values = extending_distributions.uniforms_ex(PCG64(0), 10, 'd')
+    assert values.shape == (10,)
+    assert values.dtype == np.float64
+
+@pytest.mark.skipif(numba is None or cffi is None,
+                    reason="requires numba and cffi")
+def test_numba():
+    from numpy.random._examples.numba import extending  # noqa: F401
+
+@pytest.mark.skipif(cffi is None, reason="requires cffi")
+def test_cffi():
+    from numpy.random._examples.cffi import extending  # noqa: F401
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_generator_mt19937.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_generator_mt19937.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9dc81e96a37fbd13549272d70161847554923b0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_generator_mt19937.py
@@ -0,0 +1,2797 @@
+import os.path
+import sys
+import hashlib
+
+import pytest
+
+import numpy as np
+from numpy.exceptions import AxisError
+from numpy.linalg import LinAlgError
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_allclose,
+    assert_warns, assert_no_warnings, assert_array_equal,
+    assert_array_almost_equal, suppress_warnings, IS_WASM)
+
+from numpy.random import Generator, MT19937, SeedSequence, RandomState
+
+random = Generator(MT19937())
+
+JUMP_TEST_DATA = [
+    {
+        "seed": 0,
+        "steps": 10,
+        "initial": {"key_sha256": "bb1636883c2707b51c5b7fc26c6927af4430f2e0785a8c7bc886337f919f9edf", "pos": 9},
+        "jumped": {"key_sha256": "ff682ac12bb140f2d72fba8d3506cf4e46817a0db27aae1683867629031d8d55", "pos": 598},
+    },
+    {
+        "seed":384908324,
+        "steps":312,
+        "initial": {"key_sha256": "16b791a1e04886ccbbb4d448d6ff791267dc458ae599475d08d5cced29d11614", "pos": 311},
+        "jumped": {"key_sha256": "a0110a2cf23b56be0feaed8f787a7fc84bef0cb5623003d75b26bdfa1c18002c", "pos": 276},
+    },
+    {
+        "seed": [839438204, 980239840, 859048019, 821],
+        "steps": 511,
+        "initial": {"key_sha256": "d306cf01314d51bd37892d874308200951a35265ede54d200f1e065004c3e9ea", "pos": 510},
+        "jumped": {"key_sha256": "0e00ab449f01a5195a83b4aee0dfbc2ce8d46466a640b92e33977d2e42f777f8", "pos": 475},
+    },
+]
+
+
+@pytest.fixture(scope='module', params=[True, False])
+def endpoint(request):
+    return request.param
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = Generator(MT19937(0))
+        assert_equal(s.integers(1000), 479)
+        s = Generator(MT19937(4294967295))
+        assert_equal(s.integers(1000), 324)
+
+    def test_array(self):
+        s = Generator(MT19937(range(10)))
+        assert_equal(s.integers(1000), 465)
+        s = Generator(MT19937(np.arange(10)))
+        assert_equal(s.integers(1000), 465)
+        s = Generator(MT19937([0]))
+        assert_equal(s.integers(1000), 479)
+        s = Generator(MT19937([4294967295]))
+        assert_equal(s.integers(1000), 324)
+
+    def test_seedsequence(self):
+        s = MT19937(SeedSequence(0))
+        assert_equal(s.random_raw(1), 2058676884)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, MT19937, -0.5)
+        assert_raises(ValueError, MT19937, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned integer
+        assert_raises(TypeError, MT19937, [-0.5])
+        assert_raises(ValueError, MT19937, [-1])
+        assert_raises(ValueError, MT19937, [1, -2, 4294967296])
+
+    def test_noninstantized_bitgen(self):
+        assert_raises(ValueError, Generator, MT19937)
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.integers(-5, -1) < -1)
+        x = random.integers(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, random.multinomial, 1, p,
+                      float(1))
+
+    def test_invalid_prob(self):
+        assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
+        assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])
+
+    def test_invalid_n(self):
+        assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])
+        assert_raises(ValueError, random.multinomial, [-1] * 10, [0.8, 0.2])
+
+    def test_p_non_contiguous(self):
+        p = np.arange(15.)
+        p /= np.sum(p[1::3])
+        pvals = p[1::3]
+        random = Generator(MT19937(1432985819))
+        non_contig = random.multinomial(100, pvals=pvals)
+        random = Generator(MT19937(1432985819))
+        contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
+        assert_array_equal(non_contig, contig)
+
+    def test_multinomial_pvals_float32(self):
+        x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
+                      1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
+        pvals = x / x.sum()
+        random = Generator(MT19937(1432985819))
+        match = r"[\w\s]*pvals array is cast to 64-bit floating"
+        with pytest.raises(ValueError, match=match):
+            random.multinomial(1, pvals)
+
+
+class TestMultivariateHypergeometric:
+
+    def setup_method(self):
+        self.seed = 8675309
+
+    def test_argument_validation(self):
+        # Error cases...
+
+        # `colors` must be a 1-d sequence
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      10, 4)
+
+        # Negative nsample
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [2, 3, 4], -1)
+
+        # Negative color
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [-1, 2, 3], 2)
+
+        # nsample exceeds sum(colors)
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [2, 3, 4], 10)
+
+        # nsample exceeds sum(colors) (edge case of empty colors)
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [], 1)
+
+        # Validation errors associated with very large values in colors.
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [999999999, 101], 5, 1, 'marginals')
+
+        int64_info = np.iinfo(np.int64)
+        max_int64 = int64_info.max
+        max_int64_index = max_int64 // int64_info.dtype.itemsize
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [max_int64_index - 100, 101], 5, 1, 'count')
+
+    @pytest.mark.parametrize('method', ['count', 'marginals'])
+    def test_edge_cases(self, method):
+        # Set the seed, but in fact, all the results in this test are
+        # deterministic, so we don't really need this.
+        random = Generator(MT19937(self.seed))
+
+        x = random.multivariate_hypergeometric([0, 0, 0], 0, method=method)
+        assert_array_equal(x, [0, 0, 0])
+
+        x = random.multivariate_hypergeometric([], 0, method=method)
+        assert_array_equal(x, [])
+
+        x = random.multivariate_hypergeometric([], 0, size=1, method=method)
+        assert_array_equal(x, np.empty((1, 0), dtype=np.int64))
+
+        x = random.multivariate_hypergeometric([1, 2, 3], 0, method=method)
+        assert_array_equal(x, [0, 0, 0])
+
+        x = random.multivariate_hypergeometric([9, 0, 0], 3, method=method)
+        assert_array_equal(x, [3, 0, 0])
+
+        colors = [1, 1, 0, 1, 1]
+        x = random.multivariate_hypergeometric(colors, sum(colors),
+                                               method=method)
+        assert_array_equal(x, colors)
+
+        x = random.multivariate_hypergeometric([3, 4, 5], 12, size=3,
+                                               method=method)
+        assert_array_equal(x, [[3, 4, 5]]*3)
+
+    # Cases for nsample:
+    #     nsample < 10
+    #     10 <= nsample < colors.sum()/2
+    #     colors.sum()/2 < nsample < colors.sum() - 10
+    #     colors.sum() - 10 < nsample < colors.sum()
+    @pytest.mark.parametrize('nsample', [8, 25, 45, 55])
+    @pytest.mark.parametrize('method', ['count', 'marginals'])
+    @pytest.mark.parametrize('size', [5, (2, 3), 150000])
+    def test_typical_cases(self, nsample, method, size):
+        random = Generator(MT19937(self.seed))
+
+        colors = np.array([10, 5, 20, 25])
+        sample = random.multivariate_hypergeometric(colors, nsample, size,
+                                                    method=method)
+        if isinstance(size, int):
+            expected_shape = (size,) + colors.shape
+        else:
+            expected_shape = size + colors.shape
+        assert_equal(sample.shape, expected_shape)
+        assert_((sample >= 0).all())
+        assert_((sample <= colors).all())
+        assert_array_equal(sample.sum(axis=-1),
+                           np.full(size, fill_value=nsample, dtype=int))
+        if isinstance(size, int) and size >= 100000:
+            # This sample is large enough to compare its mean to
+            # the expected values.
+            assert_allclose(sample.mean(axis=0),
+                            nsample * colors / colors.sum(),
+                            rtol=1e-3, atol=0.005)
+
+    def test_repeatability1(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([3, 4, 5], 5, size=5,
+                                                    method='count')
+        expected = np.array([[2, 1, 2],
+                             [2, 1, 2],
+                             [1, 1, 3],
+                             [2, 0, 3],
+                             [2, 1, 2]])
+        assert_array_equal(sample, expected)
+
+    def test_repeatability2(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([20, 30, 50], 50,
+                                                    size=5,
+                                                    method='marginals')
+        expected = np.array([[ 9, 17, 24],
+                             [ 7, 13, 30],
+                             [ 9, 15, 26],
+                             [ 9, 17, 24],
+                             [12, 14, 24]])
+        assert_array_equal(sample, expected)
+
+    def test_repeatability3(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([20, 30, 50], 12,
+                                                    size=5,
+                                                    method='marginals')
+        expected = np.array([[2, 3, 7],
+                             [5, 3, 4],
+                             [2, 5, 5],
+                             [5, 3, 4],
+                             [1, 5, 6]])
+        assert_array_equal(sample, expected)
+
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.rg = Generator(MT19937(self.seed))
+        self.bit_generator = self.rg.bit_generator
+        self.state = self.bit_generator.state
+        self.legacy_state = (self.state['bit_generator'],
+                             self.state['state']['key'],
+                             self.state['state']['pos'])
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.rg.standard_normal(size=3)
+        self.bit_generator.state = self.state
+        new = self.rg.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.rg.standard_normal()
+        state = self.bit_generator.state
+        old = self.rg.standard_normal(size=3)
+        self.bit_generator.state = state
+        new = self.rg.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.rg.negative_binomial(0.5, 0.5)
+
+
+class TestIntegers:
+    rfunc = random.integers
+
+    # valid integer/boolean types
+    itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self, endpoint):
+        assert_raises(TypeError, self.rfunc, 1, endpoint=endpoint, dtype=float)
+
+    def test_bounds_checking(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, endpoint=endpoint,
+                          dtype=dt)
+
+            assert_raises(ValueError, self.rfunc, [lbnd - 1], ubnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [lbnd], [ubnd + 1],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd], [lbnd],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, [0],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd],
+                          endpoint=endpoint, dtype=dt)
+
+    def test_bounds_checking_array(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + (not endpoint)
+
+            assert_raises(ValueError, self.rfunc, [lbnd - 1] * 2, [ubnd] * 2,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [lbnd] * 2,
+                          [ubnd + 1] * 2, endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, [lbnd] * 2,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [1] * 2, 0,
+                          endpoint=endpoint, dtype=dt)
+
+    def test_rng_zero_and_extremes(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            is_open = not endpoint
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc([tgt], tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc(tgt, [tgt + is_open], size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd) // 2
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc([tgt], [tgt + is_open],
+                                    size=1000, endpoint=endpoint, dtype=dt),
+                         tgt)
+
+    def test_rng_zero_and_extremes_array(self, endpoint):
+        size = 1000
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd) // 2
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+    def test_full_range(self, endpoint):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            try:
+                self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_full_range_array(self, endpoint):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            try:
+                self.rfunc([lbnd] * 2, [ubnd], endpoint=endpoint, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self, endpoint):
+        # Don't use fixed seed
+        random = Generator(MT19937())
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd - endpoint, size=2 ** 16,
+                                  endpoint=endpoint, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2 - endpoint, size=2 ** 16, endpoint=endpoint,
+                          dtype=bool)
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_scalar_array_equiv(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            size = 1000
+            random = Generator(MT19937(1234))
+            scalar = random.integers(lbnd, ubnd, size=size, endpoint=endpoint,
+                                dtype=dt)
+
+            random = Generator(MT19937(1234))
+            scalar_array = random.integers([lbnd], [ubnd], size=size,
+                                      endpoint=endpoint, dtype=dt)
+
+            random = Generator(MT19937(1234))
+            array = random.integers([lbnd] * size, [ubnd] *
+                               size, size=size, endpoint=endpoint, dtype=dt)
+            assert_array_equal(scalar, scalar_array)
+            assert_array_equal(scalar, array)
+
+    def test_repeatability(self, endpoint):
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool':   '053594a9b82d656f967c54869bc6970aa0358cf94ad469c81478459c6a90eee3',
+               'int16':  '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
+               'int32':  'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
+               'int64':  '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
+               'int8':   '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1',
+               'uint16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
+               'uint32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
+               'uint64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
+               'uint8':  '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1'}
+
+        for dt in self.itype[1:]:
+            random = Generator(MT19937(1234))
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
+                                 dtype=dt)
+            else:
+                val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
+                                 dtype=dt).byteswap()
+
+            res = hashlib.sha256(val).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        random = Generator(MT19937(1234))
+        val = random.integers(0, 2 - endpoint, size=1000, endpoint=endpoint,
+                         dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    def test_repeatability_broadcasting(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt in (bool, np.bool) else np.iinfo(dt).min
+            ubnd = 2 if dt in (bool, np.bool) else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            # view as little endian for hash
+            random = Generator(MT19937(1234))
+            val = random.integers(lbnd, ubnd, size=1000, endpoint=endpoint,
+                             dtype=dt)
+
+            random = Generator(MT19937(1234))
+            val_bc = random.integers([lbnd] * 1000, ubnd, endpoint=endpoint,
+                                dtype=dt)
+
+            assert_array_equal(val, val_bc)
+
+            random = Generator(MT19937(1234))
+            val_bc = random.integers([lbnd] * 1000, [ubnd] * 1000,
+                                endpoint=endpoint, dtype=dt)
+
+            assert_array_equal(val, val_bc)
+
+    @pytest.mark.parametrize(
+        'bound, expected',
+        [(2**32 - 1, np.array([517043486, 1364798665, 1733884389, 1353720612,
+                               3769704066, 1170797179, 4108474671])),
+         (2**32, np.array([517043487, 1364798666, 1733884390, 1353720613,
+                           3769704067, 1170797180, 4108474672])),
+         (2**32 + 1, np.array([517043487, 1733884390, 3769704068, 4108474673,
+                               1831631863, 1215661561, 3869512430]))]
+    )
+    def test_repeatability_32bit_boundary(self, bound, expected):
+        for size in [None, len(expected)]:
+            random = Generator(MT19937(1234))
+            x = random.integers(bound, size=size)
+            assert_equal(x, expected if size is not None else expected[0])
+
+    def test_repeatability_32bit_boundary_broadcasting(self):
+        desired = np.array([[[1622936284, 3620788691, 1659384060],
+                             [1417365545,  760222891, 1909653332],
+                             [3788118662,  660249498, 4092002593]],
+                            [[3625610153, 2979601262, 3844162757],
+                             [ 685800658,  120261497, 2694012896],
+                             [1207779440, 1586594375, 3854335050]],
+                            [[3004074748, 2310761796, 3012642217],
+                             [2067714190, 2786677879, 1363865881],
+                             [ 791663441, 1867303284, 2169727960]],
+                            [[1939603804, 1250951100,  298950036],
+                             [1040128489, 3791912209, 3317053765],
+                             [3155528714,   61360675, 2305155588]],
+                            [[ 817688762, 1335621943, 3288952434],
+                             [1770890872, 1102951817, 1957607470],
+                             [3099996017,  798043451,   48334215]]])
+        for size in [None, (5, 3, 3)]:
+            random = Generator(MT19937(12345))
+            x = random.integers([[-1], [0], [1]],
+                                [2**32 - 1, 2**32, 2**32 + 1],
+                                size=size)
+            assert_array_equal(x, desired if size is not None else desired[0])
+
+    def test_int64_uint64_broadcast_exceptions(self, endpoint):
+        configs = {np.uint64: ((0, 2**65), (-1, 2**62), (10, 9), (0, 0)),
+                   np.int64: ((0, 2**64), (-(2**64), 2**62), (10, 9), (0, 0),
+                              (-2**63-1, -2**63-1))}
+        for dtype in configs:
+            for config in configs[dtype]:
+                low, high = config
+                high = high - endpoint
+                low_a = np.array([[low]*10])
+                high_a = np.array([high] * 10)
+                assert_raises(ValueError, random.integers, low, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_a, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low, high_a,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_a, high_a,
+                              endpoint=endpoint, dtype=dtype)
+
+                low_o = np.array([[low]*10], dtype=object)
+                high_o = np.array([high] * 10, dtype=object)
+                assert_raises(ValueError, random.integers, low_o, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low, high_o,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_o, high_o,
+                              endpoint=endpoint, dtype=dtype)
+
+    def test_int64_uint64_corner_case(self, endpoint):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1 - endpoint)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = random.integers(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            dt = np.bool if dt is bool else dt
+
+            sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            assert_equal(sample.dtype, dt)
+
+        for dt in (bool, int):
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            assert not hasattr(sample, 'dtype')
+            assert_equal(type(sample), dt)
+
+    def test_respect_dtype_array(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            dt = np.bool if dt is bool else dt
+
+            sample = self.rfunc([lbnd], [ubnd], endpoint=endpoint, dtype=dt)
+            assert_equal(sample.dtype, dt)
+            sample = self.rfunc([lbnd] * 2, [ubnd] * 2, endpoint=endpoint,
+                                dtype=dt)
+            assert_equal(sample.dtype, dt)
+
+    def test_zero_size(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            sample = self.rfunc(0, 0, (3, 0, 4), endpoint=endpoint, dtype=dt)
+            assert sample.shape == (3, 0, 4)
+            assert sample.dtype == dt
+            assert self.rfunc(0, -10, 0, endpoint=endpoint,
+                              dtype=dt).shape == (0,)
+            assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape,
+                         (3, 0, 4))
+            assert_equal(random.integers(0, -10, size=0).shape, (0,))
+            assert_equal(random.integers(10, 10, size=0).shape, (0,))
+
+    def test_error_byteorder(self):
+        other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
+        with pytest.raises(ValueError):
+            random.integers(0, 200, size=10, dtype=other_byteord_dt)
+
+    # chi2max is the maximum acceptable chi-squared value.
+    @pytest.mark.slow
+    @pytest.mark.parametrize('sample_size,high,dtype,chi2max',
+        [(5000000, 5, np.int8, 125.0),          # p-value ~4.6e-25
+         (5000000, 7, np.uint8, 150.0),         # p-value ~7.7e-30
+         (10000000, 2500, np.int16, 3300.0),    # p-value ~3.0e-25
+         (50000000, 5000, np.uint16, 6500.0),   # p-value ~3.5e-25
+        ])
+    def test_integers_small_dtype_chisquared(self, sample_size, high,
+                                             dtype, chi2max):
+        # Regression test for gh-14774.
+        samples = random.integers(high, size=sample_size, dtype=dtype)
+
+        values, counts = np.unique(samples, return_counts=True)
+        expected = sample_size / high
+        chi2 = ((counts - expected)**2 / expected).sum()
+        assert chi2 < chi2max
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_integers(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(-99, 99, size=(3, 2))
+        desired = np.array([[-80, -56], [41, 37], [-83, -16]])
+        assert_array_equal(actual, desired)
+
+    def test_integers_masked(self):
+        # Test masked rejection sampling algorithm to generate array of
+        # uint32 in an interval.
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(0, 99, size=(3, 2), dtype=np.uint32)
+        desired = np.array([[9, 21], [70, 68], [8, 41]], dtype=np.uint32)
+        assert_array_equal(actual, desired)
+
+    def test_integers_closed(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(-99, 99, size=(3, 2), endpoint=True)
+        desired = np.array([[-80, -56], [ 41, 38], [-83, -15]])
+        assert_array_equal(actual, desired)
+
+    def test_integers_max_int(self):
+        # Tests whether integers with closed=True can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        actual = random.integers(np.iinfo('l').max, np.iinfo('l').max,
+                                 endpoint=True)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+
+    def test_random(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random((3, 2))
+        desired = np.array([[0.096999199829214, 0.707517457682192],
+                            [0.084364834598269, 0.767731206553125],
+                            [0.665069021359413, 0.715487190596693]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.random()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_random_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random((3, 2))
+        desired = np.array([[0.0969992 , 0.70751746],
+                            [0.08436483, 0.76773121],
+                            [0.66506902, 0.71548719]])
+        assert_array_almost_equal(actual, desired, decimal=7)
+
+    def test_random_float_scalar(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random(dtype=np.float32)
+        desired = 0.0969992
+        assert_array_almost_equal(actual, desired, decimal=7)
+
+    @pytest.mark.parametrize('dtype, uint_view_type',
+                             [(np.float32, np.uint32),
+                              (np.float64, np.uint64)])
+    def test_random_distribution_of_lsb(self, dtype, uint_view_type):
+        random = Generator(MT19937(self.seed))
+        sample = random.random(100000, dtype=dtype)
+        num_ones_in_lsb = np.count_nonzero(sample.view(uint_view_type) & 1)
+        # The probability of a 1 in the least significant bit is 0.25.
+        # With a sample size of 100000, the probability that num_ones_in_lsb
+        # is outside the following range is less than 5e-11.
+        assert 24100 < num_ones_in_lsb < 25900
+
+    def test_random_unsupported_type(self):
+        assert_raises(TypeError, random.random, dtype='int32')
+
+    def test_choice_uniform_replace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 4)
+        desired = np.array([0, 0, 2, 2], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([0, 1, 0, 1], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 3, replace=False)
+        desired = np.array([2, 0, 3], dtype=np.int64)
+        assert_array_equal(actual, desired)
+        actual = random.choice(4, 4, replace=False, shuffle=False)
+        desired = np.arange(4, dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([0, 2, 3], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['a', 'a', 'c', 'c'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_multidimensional_default_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 3)
+        desired = np.array([[0, 1], [0, 1], [4, 5]])
+        assert_array_equal(actual, desired)
+
+    def test_choice_multidimensional_custom_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 1, axis=1)
+        desired = np.array([[0], [2], [4], [6]])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(random.choice(2, replace=True)))
+        assert_(np.isscalar(random.choice(2, replace=False)))
+        assert_(np.isscalar(random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(random.choice([1, 2], replace=True)))
+        assert_(random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
+        assert_(random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(random.choice(6, s, replace=True).shape, s)
+        assert_equal(random.choice(6, s, replace=False).shape, s)
+        assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(random.integers(0, -10, size=0).shape, (0,))
+        assert_equal(random.integers(10, 10, size=0).shape, (0,))
+        assert_equal(random.choice(0, size=0).shape, (0,))
+        assert_equal(random.choice([], size=(0,)).shape, (0,))
+        assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, random.choice, a, p=p)
+
+    def test_choice_p_non_contiguous(self):
+        p = np.ones(10) / 5
+        p[1::2] = 3.0
+        random = Generator(MT19937(self.seed))
+        non_contig = random.choice(5, 3, p=p[::2])
+        random = Generator(MT19937(self.seed))
+        contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
+        assert_array_equal(non_contig, contig)
+
+    def test_choice_return_type(self):
+        # gh 9867
+        p = np.ones(4) / 4.
+        actual = random.choice(4, 2)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, replace=False)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, p=p)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, p=p, replace=False)
+        assert actual.dtype == np.int64
+
+    def test_choice_large_sample(self):
+        choice_hash = '4266599d12bfcfb815213303432341c06b4349f5455890446578877bb322e222'
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(10000, 5000, replace=False)
+        if sys.byteorder != 'little':
+            actual = actual.byteswap()
+        res = hashlib.sha256(actual.view(np.int8)).hexdigest()
+        assert_(choice_hash == res)
+
+    def test_choice_array_size_empty_tuple(self):
+        random = Generator(MT19937(self.seed))
+        assert_array_equal(random.choice([1, 2, 3], size=()), np.array(1),
+                           strict=True)
+        assert_array_equal(random.choice([[1, 2, 3]], size=()), [1, 2, 3])
+        assert_array_equal(random.choice([[1]], size=()), [1], strict=True)
+        assert_array_equal(random.choice([[1]], size=(), axis=1), [1],
+                           strict=True)
+
+    def test_bytes(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.bytes(10)
+        desired = b'\x86\xf0\xd4\x18\xe1\x81\t8%\xdd'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object, (1,)),
+                                           ("b", np.int32, (1,))])]:
+            random = Generator(MT19937(self.seed))
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            random.shuffle(alist)
+            actual = alist
+            desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_custom_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = np.arange(16).reshape((4, 4))
+        random.shuffle(actual, axis=1)
+        desired = np.array([[ 0,  3,  1,  2],
+                            [ 4,  7,  5,  6],
+                            [ 8, 11,  9, 10],
+                            [12, 15, 13, 14]])
+        assert_array_equal(actual, desired)
+        random = Generator(MT19937(self.seed))
+        actual = np.arange(16).reshape((4, 4))
+        random.shuffle(actual, axis=-1)
+        assert_array_equal(actual, desired)
+
+    def test_shuffle_custom_axis_empty(self):
+        random = Generator(MT19937(self.seed))
+        desired = np.array([]).reshape((0, 6))
+        for axis in (0, 1):
+            actual = np.array([]).reshape((0, 6))
+            random.shuffle(actual, axis=axis)
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_axis_nonsquare(self):
+        y1 = np.arange(20).reshape(2, 10)
+        y2 = y1.copy()
+        random = Generator(MT19937(self.seed))
+        random.shuffle(y1, axis=1)
+        random = Generator(MT19937(self.seed))
+        random.shuffle(y2.T)
+        assert_array_equal(y1, y2)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+    def test_shuffle_exceptions(self):
+        random = Generator(MT19937(self.seed))
+        arr = np.arange(10)
+        assert_raises(AxisError, random.shuffle, arr, 1)
+        arr = np.arange(9).reshape((3, 3))
+        assert_raises(AxisError, random.shuffle, arr, 3)
+        assert_raises(TypeError, random.shuffle, arr, slice(1, 2, None))
+        arr = [[1, 2, 3], [4, 5, 6]]
+        assert_raises(NotImplementedError, random.shuffle, arr, 1)
+
+        arr = np.array(3)
+        assert_raises(TypeError, random.shuffle, arr)
+        arr = np.ones((3, 2))
+        assert_raises(AxisError, random.shuffle, arr, 2)
+
+    def test_shuffle_not_writeable(self):
+        random = Generator(MT19937(self.seed))
+        a = np.zeros(5)
+        a.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            random.shuffle(a)
+
+    def test_permutation(self):
+        random = Generator(MT19937(self.seed))
+        alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
+        actual = random.permutation(alist)
+        desired = [4, 1, 9, 8, 0, 5, 3, 6, 2, 7]
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
+        actual = random.permutation(arr_2d)
+        assert_array_equal(actual, np.atleast_2d(desired).T)
+
+        bad_x_str = "abcd"
+        assert_raises(AxisError, random.permutation, bad_x_str)
+
+        bad_x_float = 1.2
+        assert_raises(AxisError, random.permutation, bad_x_float)
+
+        random = Generator(MT19937(self.seed))
+        integer_val = 10
+        desired = [3, 0, 8, 7, 9, 4, 2, 5, 1, 6]
+
+        actual = random.permutation(integer_val)
+        assert_array_equal(actual, desired)
+
+    def test_permutation_custom_axis(self):
+        a = np.arange(16).reshape((4, 4))
+        desired = np.array([[ 0,  3,  1,  2],
+                            [ 4,  7,  5,  6],
+                            [ 8, 11,  9, 10],
+                            [12, 15, 13, 14]])
+        random = Generator(MT19937(self.seed))
+        actual = random.permutation(a, axis=1)
+        assert_array_equal(actual, desired)
+        random = Generator(MT19937(self.seed))
+        actual = random.permutation(a, axis=-1)
+        assert_array_equal(actual, desired)
+
+    def test_permutation_exceptions(self):
+        random = Generator(MT19937(self.seed))
+        arr = np.arange(10)
+        assert_raises(AxisError, random.permutation, arr, 1)
+        arr = np.arange(9).reshape((3, 3))
+        assert_raises(AxisError, random.permutation, arr, 3)
+        assert_raises(TypeError, random.permutation, arr, slice(1, 2, None))
+
+    @pytest.mark.parametrize("dtype", [int, object])
+    @pytest.mark.parametrize("axis, expected",
+                             [(None, np.array([[3, 7, 0, 9, 10, 11],
+                                               [8, 4, 2, 5,  1,  6]])),
+                              (0, np.array([[6, 1, 2, 9, 10, 11],
+                                            [0, 7, 8, 3,  4,  5]])),
+                              (1, np.array([[ 5, 3,  4, 0, 2, 1],
+                                            [11, 9, 10, 6, 8, 7]]))])
+    def test_permuted(self, dtype, axis, expected):
+        random = Generator(MT19937(self.seed))
+        x = np.arange(12).reshape(2, 6).astype(dtype)
+        random.permuted(x, axis=axis, out=x)
+        assert_array_equal(x, expected)
+
+        random = Generator(MT19937(self.seed))
+        x = np.arange(12).reshape(2, 6).astype(dtype)
+        y = random.permuted(x, axis=axis)
+        assert y.dtype == dtype
+        assert_array_equal(y, expected)
+
+    def test_permuted_with_strides(self):
+        random = Generator(MT19937(self.seed))
+        x0 = np.arange(22).reshape(2, 11)
+        x1 = x0.copy()
+        x = x0[:, ::3]
+        y = random.permuted(x, axis=1, out=x)
+        expected = np.array([[0, 9, 3, 6],
+                             [14, 20, 11, 17]])
+        assert_array_equal(y, expected)
+        x1[:, ::3] = expected
+        # Verify that the original x0 was modified in-place as expected.
+        assert_array_equal(x1, x0)
+
+    def test_permuted_empty(self):
+        y = random.permuted([])
+        assert_array_equal(y, [])
+
+    @pytest.mark.parametrize('outshape', [(2, 3), 5])
+    def test_permuted_out_with_wrong_shape(self, outshape):
+        a = np.array([1, 2, 3])
+        out = np.zeros(outshape, dtype=a.dtype)
+        with pytest.raises(ValueError, match='same shape'):
+            random.permuted(a, out=out)
+
+    def test_permuted_out_with_wrong_type(self):
+        out = np.zeros((3, 5), dtype=np.int32)
+        x = np.ones((3, 5))
+        with pytest.raises(TypeError, match='Cannot cast'):
+            random.permuted(x, axis=1, out=out)
+
+    def test_permuted_not_writeable(self):
+        x = np.zeros((2, 5))
+        x.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            random.permuted(x, axis=1, out=x)
+
+    def test_beta(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+            [[1.083029353267698e-10, 2.449965303168024e-11],
+             [2.397085162969853e-02, 3.590779671820755e-08],
+             [2.830254190078299e-04, 1.744709918330393e-01]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(100.123, .456, size=(3, 2))
+        desired = np.array([[42, 41],
+                            [42, 48],
+                            [44, 50]])
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(100.123, .456)
+        desired = 42
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.chisquare(50, size=(3, 2))
+        desired = np.array([[32.9850547060149, 39.0219480493301],
+                            [56.2006134779419, 57.3474165711485],
+                            [55.4243733880198, 55.4209797925213]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        random = Generator(MT19937(self.seed))
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.5439892869558927,  0.45601071304410745],
+                             [0.5588917345860708,  0.4411082654139292 ]],
+                            [[0.5632074165063435,  0.43679258349365657],
+                             [0.54862581112627,    0.45137418887373015]],
+                            [[0.49961831357047226, 0.5003816864295278 ],
+                             [0.52374806183482,    0.47625193816517997]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        bad_alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, bad_alpha)
+
+        random = Generator(MT19937(self.seed))
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha)
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, alpha)
+
+        # gh-15876
+        assert_raises(ValueError, random.dirichlet, [[5, 1]])
+        assert_raises(ValueError, random.dirichlet, [[5], [1]])
+        assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+        assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
+    def test_dirichlet_alpha_non_contiguous(self):
+        a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
+        alpha = a[::2]
+        random = Generator(MT19937(self.seed))
+        non_contig = random.dirichlet(alpha, size=(3, 2))
+        random = Generator(MT19937(self.seed))
+        contig = random.dirichlet(np.ascontiguousarray(alpha),
+                                  size=(3, 2))
+        assert_array_almost_equal(non_contig, contig)
+
+    def test_dirichlet_small_alpha(self):
+        eps = 1.0e-9  # 1.0e-10 -> runtime x 10; 1e-11 -> runtime x 200, etc.
+        alpha = eps * np.array([1., 1.0e-3])
+        random = Generator(MT19937(self.seed))
+        actual = random.dirichlet(alpha, size=(3, 2))
+        expected = np.array([
+            [[1., 0.],
+             [1., 0.]],
+            [[1., 0.],
+             [1., 0.]],
+            [[1., 0.],
+             [1., 0.]]
+        ])
+        assert_array_almost_equal(actual, expected, decimal=15)
+
+    @pytest.mark.slow
+    def test_dirichlet_moderately_small_alpha(self):
+        # Use alpha.max() < 0.1 to trigger stick breaking code path
+        alpha = np.array([0.02, 0.04])
+        exact_mean = alpha / alpha.sum()
+        random = Generator(MT19937(self.seed))
+        sample = random.dirichlet(alpha, size=20000000)
+        sample_mean = sample.mean(axis=0)
+        assert_allclose(sample_mean, exact_mean, rtol=1e-3)
+
+    # This set of parameters includes inputs with alpha.max() >= 0.1 and
+    # alpha.max() < 0.1 to exercise both generation methods within the
+    # dirichlet code.
+    @pytest.mark.parametrize(
+        'alpha',
+        [[5, 9, 0, 8],
+         [0.5, 0, 0, 0],
+         [1, 5, 0, 0, 1.5, 0, 0, 0],
+         [0.01, 0.03, 0, 0.005],
+         [1e-5, 0, 0, 0],
+         [0.002, 0.015, 0, 0, 0.04, 0, 0, 0],
+         [0.0],
+         [0, 0, 0]],
+    )
+    def test_dirichlet_multiple_zeros_in_alpha(self, alpha):
+        alpha = np.array(alpha)
+        y = random.dirichlet(alpha)
+        assert_equal(y[alpha == 0], 0.0)
+
+    def test_exponential(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[0.098845481066258, 1.560752510746964],
+                            [0.075730916041636, 1.769098974710777],
+                            [1.488602544592235, 2.49684815275751 ]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(random.exponential(scale=0), 0)
+        assert_raises(ValueError, random.exponential, scale=-0.)
+
+    def test_f(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.f(12, 77, size=(3, 2))
+        desired = np.array([[0.461720027077085, 1.100441958872451],
+                            [1.100337455217484, 0.91421736740018 ],
+                            [0.500811891303113, 0.826802454552058]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[ 5.03850858902096,  7.9228656732049 ],
+                            [18.73983605132985, 19.57961681699238],
+                            [18.17897755150825, 18.17653912505234]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[1, 11],
+                            [1, 12],
+                            [11, 17]])
+        assert_array_equal(actual, desired)
+
+    def test_geometric_exceptions(self):
+        assert_raises(ValueError, random.geometric, 1.1)
+        assert_raises(ValueError, random.geometric, [1.1] * 10)
+        assert_raises(ValueError, random.geometric, -0.1)
+        assert_raises(ValueError, random.geometric, [-0.1] * 10)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.geometric, np.nan)
+            assert_raises(ValueError, random.geometric, [np.nan] * 10)
+
+    def test_gumbel(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[ 4.688397515056245, -0.289514845417841],
+                            [ 4.981176042584683, -0.633224272589149],
+                            [-0.055915275687488, -0.333962478257953]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(random.gumbel(scale=0), 0)
+        assert_raises(ValueError, random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
+        desired = np.array([[ 9, 9],
+                            [ 9, 9],
+                            [10, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-3.156353949272393,  1.195863024830054],
+                            [-3.435458081645966,  1.656882398925444],
+                            [ 0.924824032467446,  1.251116432209336]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(random.laplace(scale=0), 0)
+        assert_raises(ValueError, random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-4.338584631510999,  1.890171436749954],
+                            [-4.64547787337966 ,  2.514545562919217],
+                            [ 1.495389489198666,  1.967827627577474]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[ 0.0268252166335, 13.9534486483053],
+                            [ 0.1204014788936,  2.2422077497792],
+                            [ 4.2484199496128, 12.0093343977523]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[14, 17],
+                            [3, 18],
+                            [5, 1]])
+        assert_array_equal(actual, desired)
+
+    def test_logseries_zero(self):
+        random = Generator(MT19937(self.seed))
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        random = Generator(MT19937(self.seed))
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
+
+    def test_multinomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[1, 5, 1, 6, 4, 3],
+                             [4, 2, 6, 2, 4, 2]],
+                            [[5, 3, 2, 6, 3, 1],
+                             [4, 4, 0, 2, 3, 7]],
+                            [[6, 3, 1, 5, 3, 2],
+                             [5, 5, 3, 1, 2, 4]]])
+        assert_array_equal(actual, desired)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+    def test_multivariate_normal(self, method):
+        random = Generator(MT19937(self.seed))
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = random.multivariate_normal(mean, cov, size, method=method)
+        desired = np.array([[[-1.747478062846581,  11.25613495182354  ],
+                             [-0.9967333370066214, 10.342002097029821 ]],
+                            [[ 0.7850019631242964, 11.181113712443013 ],
+                             [ 0.8901349653255224,  8.873825399642492 ]],
+                            [[ 0.7130260107430003,  9.551628690083056 ],
+                             [ 0.7127098726541128, 11.991709234143173 ]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = random.multivariate_normal(mean, cov, method=method)
+        desired = np.array([0.233278563284287, 9.424140804347195])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        # Check that non symmetric covariance input raises exception when
+        # check_valid='raises' if using default svd method.
+        mean = [0, 0]
+        cov = [[1, 2], [1, 2]]
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov,
+                     method='eigh')
+        assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+                      method='cholesky')
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise', method='eigh')
+
+        # check degenerate samples from singular covariance matrix
+        cov = [[1, 1], [1, 1]]
+        if method in ('svd', 'eigh'):
+            samples = random.multivariate_normal(mean, cov, size=(3, 2),
+                                                 method=method)
+            assert_array_almost_equal(samples[..., 0], samples[..., 1],
+                                      decimal=6)
+        else:
+            assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+                          method='cholesky')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            random.multivariate_normal(mean, cov, method=method)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+        mu = np.zeros(2)
+        cov = np.eye(2)
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='other')
+        assert_raises(ValueError, random.multivariate_normal,
+                      np.zeros((2, 1, 1)), cov)
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.empty((3, 2)))
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.eye(3))
+
+    @pytest.mark.parametrize('mean, cov', [([0], [[1+1j]]), ([0j], [[1]])])
+    def test_multivariate_normal_disallow_complex(self, mean, cov):
+        random = Generator(MT19937(self.seed))
+        with pytest.raises(TypeError, match="must not be complex"):
+            random.multivariate_normal(mean, cov)
+
+    @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+    def test_multivariate_normal_basic_stats(self, method):
+        random = Generator(MT19937(self.seed))
+        n_s = 1000
+        mean = np.array([1, 2])
+        cov = np.array([[2, 1], [1, 2]])
+        s = random.multivariate_normal(mean, cov, size=(n_s,), method=method)
+        s_center = s - mean
+        cov_emp = (s_center.T @ s_center) / (n_s - 1)
+        # these are pretty loose and are only designed to detect major errors
+        assert np.all(np.abs(s_center.mean(-2)) < 0.1)
+        assert np.all(np.abs(cov_emp - cov) < 0.2)
+
+    def test_negative_binomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[543, 727],
+                            [775, 760],
+                            [600, 674]])
+        assert_array_equal(actual, desired)
+
+    def test_negative_binomial_exceptions(self):
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.negative_binomial, 100, np.nan)
+            assert_raises(ValueError, random.negative_binomial, 100,
+                          [np.nan] * 10)
+
+    def test_negative_binomial_p0_exception(self):
+        # Verify that p=0 raises an exception.
+        with assert_raises(ValueError):
+            x = random.negative_binomial(1, 0)
+
+    def test_negative_binomial_invalid_p_n_combination(self):
+        # Verify that values of p and n that would result in an overflow
+        # or infinite loop raise an exception.
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.negative_binomial, 2**62, 0.1)
+            assert_raises(ValueError, random.negative_binomial, [2**62], [0.1])
+
+    def test_noncentral_chisquare(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[ 1.70561552362133, 15.97378184942111],
+                            [13.71483425173724, 20.17859633310629],
+                            [11.3615477156643 ,  3.67891108738029]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[9.41427665607629e-04, 1.70473157518850e-04],
+                            [1.14554372041263e+00, 1.38187755933435e-03],
+                            [1.90659181905387e+00, 1.21772577941822e+00]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[0.82947954590419, 1.80139670767078],
+                            [6.58720057417794, 7.00491463609814],
+                            [6.31101879073157, 6.30982307753005]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                     size=(3, 2))
+        desired = np.array([[0.060310671139  , 0.23866058175939],
+                            [0.86860246709073, 0.2668510459738 ],
+                            [0.23375780078364, 1.88922102885943]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f_nan(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
+        assert np.isnan(actual)
+
+    def test_normal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-3.618412914693162,  2.635726692647081],
+                            [-2.116923463013243,  0.807460983059643],
+                            [ 1.446547137248593,  2.485684213886024]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(random.normal(scale=0), 0)
+        assert_raises(ValueError, random.normal, scale=-0.)
+
+    def test_pareto(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array([[1.0394926776069018e+00, 7.7142534343505773e+04],
+                            [7.2640150889064703e-01, 3.4650454783825594e+05],
+                            [4.5852344481994740e+04, 6.5851383009539105e+07]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [0, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('int64').max
+        lamneg = -1
+        assert_raises(ValueError, random.poisson, lamneg)
+        assert_raises(ValueError, random.poisson, [lamneg] * 10)
+        assert_raises(ValueError, random.poisson, lambig)
+        assert_raises(ValueError, random.poisson, [lambig] * 10)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.poisson, np.nan)
+            assert_raises(ValueError, random.poisson, [np.nan] * 10)
+
+    def test_power(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[1.977857368842754e-09, 9.806792196620341e-02],
+                            [2.482442984543471e-10, 1.527108843266079e-01],
+                            [8.188283434244285e-02, 3.950547209346948e-01]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[4.19494429102666, 16.66920198906598],
+                            [3.67184544902662, 17.74695521962917],
+                            [16.27935397855501, 21.08355560691792]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_cauchy(size=(3, 2))
+        desired = np.array([[-1.489437778266206, -3.275389641569784],
+                            [ 0.560102864910406, -0.680780916282552],
+                            [-1.314912905226277,  0.295852965660225]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_exponential(size=(3, 2), method='inv')
+        desired = np.array([[0.102031839440643, 1.229350298474972],
+                            [0.088137284693098, 1.459859985522667],
+                            [1.093830802293668, 1.256977002164613]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_expoential_type_error(self):
+        assert_raises(TypeError, random.standard_exponential, dtype=np.int32)
+
+    def test_standard_gamma(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[0.62970724056362, 1.22379851271008],
+                            [3.899412530884  , 4.12479964250139],
+                            [3.74994102464584, 3.74929307690815]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gammma_scalar_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(3, dtype=np.float32)
+        desired = 2.9242148399353027
+        assert_array_almost_equal(actual, desired, decimal=6)
+
+    def test_standard_gamma_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[0.62971, 1.2238 ],
+                            [3.89941, 4.1248 ],
+                            [3.74994, 3.74929]])
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+    def test_standard_gammma_float_out(self):
+        actual = np.zeros((3, 2), dtype=np.float32)
+        random = Generator(MT19937(self.seed))
+        random.standard_gamma(10.0, out=actual, dtype=np.float32)
+        desired = np.array([[10.14987,  7.87012],
+                             [ 9.46284, 12.56832],
+                             [13.82495,  7.81533]], dtype=np.float32)
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+        random = Generator(MT19937(self.seed))
+        random.standard_gamma(10.0, out=actual, size=(3, 2), dtype=np.float32)
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+    def test_standard_gamma_unknown_type(self):
+        assert_raises(TypeError, random.standard_gamma, 1.,
+                      dtype='int32')
+
+    def test_out_size_mismatch(self):
+        out = np.zeros(10)
+        assert_raises(ValueError, random.standard_gamma, 10.0, size=20,
+                      out=out)
+        assert_raises(ValueError, random.standard_gamma, 10.0, size=(10, 1),
+                      out=out)
+
+    def test_standard_gamma_0(self):
+        assert_equal(random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_normal(size=(3, 2))
+        desired = np.array([[-1.870934851846581,  1.25613495182354 ],
+                            [-1.120190126006621,  0.342002097029821],
+                            [ 0.661545174124296,  1.181113712443012]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_normal_unsupported_type(self):
+        assert_raises(TypeError, random.standard_normal, dtype=np.int32)
+
+    def test_standard_t(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[-1.484666193042647,  0.30597891831161 ],
+                            [ 1.056684299648085, -0.407312602088507],
+                            [ 0.130704414281157, -2.038053410490321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.triangular(left=5.12, mode=10.23, right=20.34,
+                                   size=(3, 2))
+        desired = np.array([[ 7.86664070590917, 13.6313848513185 ],
+                            [ 7.68152445215983, 14.36169131136546],
+                            [13.16105603911429, 13.72341621856971]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[2.13306255040998 , 7.816987531021207],
+                            [2.015436610109887, 8.377577533009589],
+                            [7.421792588856135, 7.891185744455209]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func, 0, np.inf)
+        assert_raises(OverflowError, func, fmin, fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_uniform_zero_range(self):
+        func = random.uniform
+        result = func(1.5, 1.5)
+        assert_allclose(result, 1.5)
+        result = func([0.0, np.pi], [0.0, np.pi])
+        assert_allclose(result, [0.0, np.pi])
+        result = func([[2145.12], [2145.12]], [2145.12, 2145.12])
+        assert_allclose(result, 2145.12 + np.zeros((2, 2)))
+
+    def test_uniform_neg_range(self):
+        func = random.uniform
+        assert_raises(ValueError, func, 2, 1)
+        assert_raises(ValueError, func,  [1, 2], [1, 1])
+        assert_raises(ValueError, func,  [[0, 1],[2, 3]], 2)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[ 1.107972248690106,  2.841536476232361],
+                            [ 1.832602376042457,  1.945511926976032],
+                            [-0.260147475776542,  2.058047492231698]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        assert_(np.isfinite(r).all())
+
+    def test_vonmises_nan(self):
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu=0., kappa=np.nan)
+        assert_(np.isnan(r))
+
+    @pytest.mark.parametrize("kappa", [1e4, 1e15])
+    def test_vonmises_large_kappa(self, kappa):
+        random = Generator(MT19937(self.seed))
+        rs = RandomState(random.bit_generator)
+        state = random.bit_generator.state
+
+        random_state_vals = rs.vonmises(0, kappa, size=10)
+        random.bit_generator.state = state
+        gen_vals = random.vonmises(0, kappa, size=10)
+        if kappa < 1e6:
+            assert_allclose(random_state_vals, gen_vals)
+        else:
+            assert np.all(random_state_vals != gen_vals)
+
+    @pytest.mark.parametrize("mu", [-7., -np.pi, -3.1, np.pi, 3.2])
+    @pytest.mark.parametrize("kappa", [1e-9, 1e-6, 1, 1e3, 1e15])
+    def test_vonmises_large_kappa_range(self, mu, kappa):
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu, kappa, 50)
+        assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_wald(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[0.26871721804551, 3.2233942732115 ],
+                            [2.20328374987066, 2.40958405189353],
+                            [2.07093587449261, 0.73073890064369]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.138613914769468, 1.306463419753191],
+                            [0.111623365934763, 1.446570494646721],
+                            [1.257145775276011, 1.914247725027957]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        random = Generator(MT19937(self.seed))
+        assert_equal(random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, random.weibull, a=-0.)
+
+    def test_zipf(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[  1,   1],
+                            [ 10, 867],
+                            [354,   2]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def test_uniform(self):
+        random = Generator(MT19937(self.seed))
+        low = [0]
+        high = [1]
+        uniform = random.uniform
+        desired = np.array([0.16693771389729, 0.19635129550675, 0.75563050964095])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        random = Generator(MT19937(self.seed))
+        desired = np.array([-0.38736406738527,  0.79594375042255,  0.0197076236097])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.normal, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        normal = random.normal
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        desired = np.array([0.18719338682602, 0.73234824491364, 0.17928615186455])
+
+        random = Generator(MT19937(self.seed))
+        beta = random.beta
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        std_gamma = random.standard_gamma
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        desired = np.array([1.34491986425611, 0.42760990636187, 1.4355697857258])
+
+        random = Generator(MT19937(self.seed))
+        gamma = random.gamma
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        gamma = random.gamma
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        desired = np.array([0.07765056244107, 7.72951397913186, 0.05786093891763])
+
+        random = Generator(MT19937(self.seed))
+        f = random.f
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        random = Generator(MT19937(self.seed))
+        f = random.f
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        desired = np.array([2.02434240411421, 12.91838601070124, 1.24395160354629])
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))
+
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        random = Generator(MT19937(self.seed))
+        desired = np.array([0.04714867120827, 0.1239390327694])
+        actual = random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        desired = np.array([0.05573640064251, 1.47220224353539, 2.9469379318589])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        desired = np.array([0.07710766249436, 5.27829115110304, 0.630732147399])
+
+        random = Generator(MT19937(self.seed))
+        nonc_chi = random.noncentral_chisquare
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_chi = random.noncentral_chisquare
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        desired = np.array([-1.39498829447098, -1.23058658835223, 0.17207021065983])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.standard_t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        desired = np.array([2.25935584988528, 2.23326261461399, -2.84152146503326])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.vonmises, mu * 3, bad_kappa)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.95905052946317, 0.2383810889437 , 1.04988745750013])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.48954864361052, 0.19249412888486, 0.51216834058807])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([-1.09698732625119, -0.93470271947368, 0.71592671378202])
+
+        random = Generator(MT19937(self.seed))
+        laplace = random.laplace
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        laplace = random.laplace
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([1.70020068231762, 1.52054354273631, -0.34293267607081])
+
+        random = Generator(MT19937(self.seed))
+        gumbel = random.gumbel
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        gumbel = random.gumbel
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([-1.607487640433, -1.40925686003678, 1.12887112820397])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.logistic, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.logistic, loc, bad_scale * 3)
+        assert_equal(random.logistic(1.0, 0.0), 1.0)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        desired = np.array([0.67884390500697, 2.21653186290321, 1.01990310084276])
+
+        random = Generator(MT19937(self.seed))
+        lognormal = random.lognormal
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.lognormal(mean, sigma * 3)
+        assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array(
+            [1.1597068009872629,
+             0.6539188836253857,
+             1.1981526554349398]
+        )
+
+        random = Generator(MT19937(self.seed))
+        actual = random.rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        desired = np.array([0.38052407392905, 0.50701641508592, 0.484935249864])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.wald, bad_mean * 3, scale)
+        assert_raises(ValueError, random.wald, mean * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.wald, bad_mean, scale * 3)
+        assert_raises(ValueError, random.wald, mean, bad_scale * 3)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        desired = np.array([1.57781954604754, 1.62665986867957, 2.30090130831326])
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+        assert_raises(ValueError, triangular, 10., 0., 20.)
+        assert_raises(ValueError, triangular, 10., 25., 20.)
+        assert_raises(ValueError, triangular, 10., 10., 10.)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([0, 0, 1])
+
+        random = Generator(MT19937(self.seed))
+        binom = random.binomial
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([0, 2, 1], dtype=np.int64)
+
+        random = Generator(MT19937(self.seed))
+        neg_binom = random.negative_binomial
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        random = Generator(MT19937(self.seed))
+        neg_binom = random.negative_binomial
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+
+        lam = [1]
+        bad_lam_one = [-1]
+        desired = np.array([0, 0, 3])
+
+        random = Generator(MT19937(self.seed))
+        max_lam = random._poisson_lam_max
+        bad_lam_two = [max_lam * 2]
+        poisson = random.poisson
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        desired = np.array([1, 8, 1])
+
+        random = Generator(MT19937(self.seed))
+        zipf = random.zipf
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([1, 1, 3])
+
+        random = Generator(MT19937(self.seed))
+        geometric = random.geometric
+        actual = geometric(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geometric, bad_p_one * 3)
+        assert_raises(ValueError, geometric, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [-1]
+        bad_nsample_two = [4]
+        desired = np.array([0, 0, 1])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, random.hypergeometric, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_two)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, random.hypergeometric, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_two)
+
+        random = Generator(MT19937(self.seed))
+        hypergeom = random.hypergeometric
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+        assert_raises(ValueError, hypergeom, -1, 10, 20)
+        assert_raises(ValueError, hypergeom, 10, -1, 20)
+        assert_raises(ValueError, hypergeom, 10, 10, -1)
+        assert_raises(ValueError, hypergeom, 10, 10, 25)
+
+        # ValueError for arguments that are too big.
+        assert_raises(ValueError, hypergeom, 2**30, 10, 20)
+        assert_raises(ValueError, hypergeom, 999, 2**31, 50)
+        assert_raises(ValueError, hypergeom, 999, [2**29, 2**30], 1000)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        desired = np.array([1, 1, 1])
+
+        random = Generator(MT19937(self.seed))
+        logseries = random.logseries
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+    def test_multinomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[0, 0, 2, 1, 2, 0],
+                             [2, 3, 6, 4, 2, 3]],
+                            [[1, 0, 1, 0, 2, 1],
+                             [7, 2, 2, 1, 4, 4]],
+                            [[0, 2, 0, 1, 2, 0],
+                             [3, 2, 3, 3, 4, 5]]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [1 / 6.] * 6)
+        desired = np.array([[0, 0, 2, 1, 2, 0],
+                            [2, 3, 6, 4, 2, 3]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2)
+        desired = np.array([[0, 0, 2, 1, 2, 0],
+                            [2, 3, 6, 4, 2, 3]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2)
+        desired = np.array([[[0, 0, 2, 1, 2, 0],
+                             [0, 0, 2, 1, 1, 1]],
+                            [[4, 2, 3, 3, 5, 3],
+                             [7, 2, 2, 1, 4, 4]]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    @pytest.mark.parametrize("n", [10,
+                                   np.array([10, 10]),
+                                   np.array([[[10]], [[10]]])
+                                   ]
+                             )
+    def test_multinomial_pval_broadcast(self, n):
+        random = Generator(MT19937(self.seed))
+        pvals = np.array([1 / 4] * 4)
+        actual = random.multinomial(n, pvals)
+        n_shape = tuple() if isinstance(n, int) else n.shape
+        expected_shape = n_shape + (4,)
+        assert actual.shape == expected_shape
+        pvals = np.vstack([pvals, pvals])
+        actual = random.multinomial(n, pvals)
+        expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,)
+        assert actual.shape == expected_shape
+
+        pvals = np.vstack([[pvals], [pvals]])
+        actual = random.multinomial(n, pvals)
+        expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1])
+        assert actual.shape == expected_shape + (4,)
+        actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape)
+        assert actual.shape == (3, 2) + expected_shape + (4,)
+
+        with pytest.raises(ValueError):
+            # Ensure that size is not broadcast
+            actual = random.multinomial(n, pvals, size=(1,) * 6)
+
+    def test_invalid_pvals_broadcast(self):
+        random = Generator(MT19937(self.seed))
+        pvals = [[1 / 6] * 6, [1 / 4] * 6]
+        assert_raises(ValueError, random.multinomial, 1, pvals)
+        assert_raises(ValueError, random.multinomial, 6, 0.5)
+
+    def test_empty_outputs(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6)
+        assert actual.shape == (10, 0, 6, 6)
+        actual = random.multinomial(12, np.empty((10, 0, 10)))
+        assert actual.shape == (10, 0, 10)
+        actual = random.multinomial(np.empty((3, 0, 7), "i8"),
+                                    np.empty((3, 0, 7, 4)))
+        assert actual.shape == (3, 0, 7, 4)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(Generator(MT19937(s)), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(Generator(MT19937(s)), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)
+
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (random.exponential, random.standard_gamma,
+                 random.chisquare, random.standard_t,
+                 random.pareto, random.weibull,
+                 random.power, random.rayleigh,
+                 random.poisson, random.zipf,
+                 random.geometric, random.logseries)
+
+        probfuncs = (random.geometric, random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (random.uniform, random.normal,
+                 random.beta, random.gamma,
+                 random.f, random.noncentral_chisquare,
+                 random.vonmises, random.laplace,
+                 random.gumbel, random.logistic,
+                 random.lognormal, random.wald,
+                 random.binomial, random.negative_binomial)
+
+        probfuncs = (random.binomial, random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_integers(self, endpoint):
+        itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+                 np.int32, np.uint32, np.int64, np.uint64]
+        func = random.integers
+        high = np.array([1])
+        low = np.array([0])
+
+        for dt in itype:
+            out = func(low, high, endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low[0], high, endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low, high[0], endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [random.noncentral_f, random.triangular,
+                 random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+
+@pytest.mark.parametrize("config", JUMP_TEST_DATA)
+def test_jumped(config):
+    # Each config contains the initial seed, a number of raw steps
+    # the sha256 hashes of the initial and the final states' keys and
+    # the position of the initial and the final state.
+    # These were produced using the original C implementation.
+    seed = config["seed"]
+    steps = config["steps"]
+
+    mt19937 = MT19937(seed)
+    # Burn step
+    mt19937.random_raw(steps)
+    key = mt19937.state["state"]["key"]
+    if sys.byteorder == 'big':
+        key = key.byteswap()
+    sha256 = hashlib.sha256(key)
+    assert mt19937.state["state"]["pos"] == config["initial"]["pos"]
+    assert sha256.hexdigest() == config["initial"]["key_sha256"]
+
+    jumped = mt19937.jumped()
+    key = jumped.state["state"]["key"]
+    if sys.byteorder == 'big':
+        key = key.byteswap()
+    sha256 = hashlib.sha256(key)
+    assert jumped.state["state"]["pos"] == config["jumped"]["pos"]
+    assert sha256.hexdigest() == config["jumped"]["key_sha256"]
+
+
+def test_broadcast_size_error():
+    mu = np.ones(3)
+    sigma = np.ones((4, 3))
+    size = (10, 4, 2)
+    assert random.normal(mu, sigma, size=(5, 4, 3)).shape == (5, 4, 3)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=size)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=(1, 3))
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=(4, 1, 1))
+    # 1 arg
+    shape = np.ones((4, 3))
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=size)
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=(3,))
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=3)
+    # Check out
+    out = np.empty(size)
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, out=out)
+
+    # 2 arg
+    with pytest.raises(ValueError):
+        random.binomial(1, [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], 0.3, size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.multinomial([2, 2], [.3, .7], size=(2, 1))
+
+    # 3 arg
+    a = random.chisquare(5, size=3)
+    b = random.chisquare(5, size=(4, 3))
+    c = random.chisquare(5, size=(5, 4, 3))
+    assert random.noncentral_f(a, b, c).shape == (5, 4, 3)
+    with pytest.raises(ValueError, match=r"Output size \(6, 5, 1, 1\) is"):
+        random.noncentral_f(a, b, c, size=(6, 5, 1, 1))
+
+
+def test_broadcast_size_scalar():
+    mu = np.ones(3)
+    sigma = np.ones(3)
+    random.normal(mu, sigma, size=3)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=2)
+
+
+def test_ragged_shuffle():
+    # GH 18142
+    seq = [[], [], 1]
+    gen = Generator(MT19937(0))
+    assert_no_warnings(gen.shuffle, seq)
+    assert seq == [1, [], []]
+
+
+@pytest.mark.parametrize("high", [-2, [-2]])
+@pytest.mark.parametrize("endpoint", [True, False])
+def test_single_arg_integer_exception(high, endpoint):
+    # GH 14333
+    gen = Generator(MT19937(0))
+    msg = 'high < 0' if endpoint else 'high <= 0'
+    with pytest.raises(ValueError, match=msg):
+        gen.integers(high, endpoint=endpoint)
+    msg = 'low > high' if endpoint else 'low >= high'
+    with pytest.raises(ValueError, match=msg):
+        gen.integers(-1, high, endpoint=endpoint)
+    with pytest.raises(ValueError, match=msg):
+        gen.integers([-1], high, endpoint=endpoint)
+
+
+@pytest.mark.parametrize("dtype", ["f4", "f8"])
+def test_c_contig_req_out(dtype):
+    # GH 18704
+    out = np.empty((2, 3), order="F", dtype=dtype)
+    shape = [1, 2, 3]
+    with pytest.raises(ValueError, match="Supplied output array"):
+        random.standard_gamma(shape, out=out, dtype=dtype)
+    with pytest.raises(ValueError, match="Supplied output array"):
+        random.standard_gamma(shape, out=out, size=out.shape, dtype=dtype)
+
+
+@pytest.mark.parametrize("dtype", ["f4", "f8"])
+@pytest.mark.parametrize("order", ["F", "C"])
+@pytest.mark.parametrize("dist", [random.standard_normal, random.random])
+def test_contig_req_out(dist, order, dtype):
+    # GH 18704
+    out = np.empty((2, 3), dtype=dtype, order=order)
+    variates = dist(out=out, dtype=dtype)
+    assert variates is out
+    variates = dist(out=out, dtype=dtype, size=out.shape)
+    assert variates is out
+
+
+def test_generator_ctor_old_style_pickle():
+    rg = np.random.Generator(np.random.PCG64DXSM(0))
+    rg.standard_normal(1)
+    # Directly call reduce which is used in pickling
+    ctor, (bit_gen, ), _ = rg.__reduce__()
+    # Simulate unpickling an old pickle that only has the name
+    assert bit_gen.__class__.__name__ == "PCG64DXSM"
+    print(ctor)
+    b = ctor(*("PCG64DXSM",))
+    print(b)
+    b.bit_generator.state = bit_gen.state
+    state_b = b.bit_generator.state
+    assert bit_gen.state == state_b
+
+
+def test_pickle_preserves_seed_sequence():
+    # GH 26234
+    # Add explicit test that bit generators preserve seed sequences
+    import pickle
+
+    rg = np.random.Generator(np.random.PCG64DXSM(20240411))
+    ss = rg.bit_generator.seed_seq
+    rg_plk = pickle.loads(pickle.dumps(rg))
+    ss_plk = rg_plk.bit_generator.seed_seq
+    assert_equal(ss.state, ss_plk.state)
+    assert_equal(ss.pool, ss_plk.pool)
+
+    rg.bit_generator.seed_seq.spawn(10)
+    rg_plk = pickle.loads(pickle.dumps(rg))
+    ss_plk = rg_plk.bit_generator.seed_seq
+    assert_equal(ss.state, ss_plk.state)
+
+
+@pytest.mark.parametrize("version", [121, 126])
+def test_legacy_pickle(version):
+    # Pickling format was changes in 1.22.x and in 2.0.x
+    import pickle
+    import gzip
+
+    base_path = os.path.split(os.path.abspath(__file__))[0]
+    pkl_file = os.path.join(
+        base_path, "data", f"generator_pcg64_np{version}.pkl.gz"
+    )
+    with gzip.open(pkl_file) as gz:
+        rg = pickle.load(gz)
+    state = rg.bit_generator.state['state']
+
+    assert isinstance(rg, Generator)
+    assert isinstance(rg.bit_generator, np.random.PCG64)
+    assert state['state'] == 35399562948360463058890781895381311971
+    assert state['inc'] == 87136372517582989555478159403783844777
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py
new file mode 100644
index 0000000000000000000000000000000000000000..c34e6bb3ba74f2f9084a7400fc608776d8e278c0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py
@@ -0,0 +1,206 @@
+from numpy.testing import (assert_, assert_array_equal)
+import numpy as np
+import pytest
+from numpy.random import Generator, MT19937
+
+
+class TestRegression:
+
+    def setup_method(self):
+        self.mt19937 = Generator(MT19937(121263137472525314065))
+
+    def test_vonmises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = self.mt19937.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(self.mt19937.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(self.mt19937.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = (2**20 - 2, 2**20 - 2, 2**20 - 2)  # Check for 32-bit systems
+        assert_(self.mt19937.hypergeometric(*args) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        rvsn = self.mt19937.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            mt19937 = Generator(MT19937(12345))
+            shuffled = np.array(t, dtype=object)
+            mt19937.shuffle(shuffled)
+            expected = np.array([t[2], t[0], t[3], t[1]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom BitGenerator does not call into global state
+        res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4])
+        for i in range(3):
+            mt19937 = Generator(MT19937(i))
+            m = Generator(MT19937(4321))
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        self.mt19937.multivariate_normal([0], [[0]], size=1)
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        x = self.mt19937.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
+
+    def test_beta_very_small_parameters(self):
+        # gh-24203: beta would hang with very small parameters.
+        self.mt19937.beta(1e-49, 1e-40)
+
+    def test_beta_ridiculously_small_parameters(self):
+        # gh-24266: beta would generate nan when the parameters
+        # were subnormal or a small multiple of the smallest normal.
+        tiny = np.finfo(1.0).tiny
+        x = self.mt19937.beta(tiny/32, tiny/40, size=50)
+        assert not np.any(np.isnan(x))
+
+    def test_beta_expected_zero_frequency(self):
+        # gh-24475: For small a and b (e.g. a=0.0025, b=0.0025), beta
+        # would generate too many zeros.
+        a = 0.0025
+        b = 0.0025
+        n = 1000000
+        x = self.mt19937.beta(a, b, size=n)
+        nzeros = np.count_nonzero(x == 0)
+        # beta CDF at x = np.finfo(np.double).smallest_subnormal/2
+        # is p = 0.0776169083131899, e.g,
+        #
+        #    import numpy as np
+        #    from mpmath import mp
+        #    mp.dps = 160
+        #    x = mp.mpf(np.finfo(np.float64).smallest_subnormal)/2
+        #    # CDF of the beta distribution at x:
+        #    p = mp.betainc(a, b, x1=0, x2=x, regularized=True)
+        #    n = 1000000
+        #    exprected_freq = float(n*p)
+        #
+        expected_freq = 77616.90831318991
+        assert 0.95*expected_freq < nzeros < 1.05*expected_freq
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = self.mt19937.choice(a, p=probs)
+            assert_(c in a)
+            with pytest.raises(ValueError):
+                self.mt19937.choice(a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            self.mt19937.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            self.mt19937.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+
+        class N(np.ndarray):
+            pass
+
+        mt19937 = Generator(MT19937(1))
+        orig = np.arange(3).view(N)
+        perm = mt19937.permutation(orig)
+        assert_array_equal(perm, np.array([2, 0, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        mt19937 = Generator(MT19937(1))
+        m = M()
+        perm = mt19937.permutation(m)
+        assert_array_equal(perm, np.array([4, 1, 3, 0, 2]))
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_gamma_0(self):
+        assert self.mt19937.standard_gamma(0.0) == 0.0
+        assert_array_equal(self.mt19937.standard_gamma([0.0]), 0.0)
+
+        actual = self.mt19937.standard_gamma([0.0], dtype='float')
+        expected = np.array([0.], dtype=np.float32)
+        assert_array_equal(actual, expected)
+
+    def test_geometric_tiny_prob(self):
+        # Regression test for gh-17007.
+        # When p = 1e-30, the probability that a sample will exceed 2**63-1
+        # is 0.9999999999907766, so we expect the result to be all 2**63-1.
+        assert_array_equal(self.mt19937.geometric(p=1e-30, size=3),
+                           np.iinfo(np.int64).max)
+
+    def test_zipf_large_parameter(self):
+        # Regression test for part of gh-9829: a call such as rng.zipf(10000)
+        # would hang.
+        n = 8
+        sample = self.mt19937.zipf(10000, size=n)
+        assert_array_equal(sample, np.ones(n, dtype=np.int64))
+
+    def test_zipf_a_near_1(self):
+        # Regression test for gh-9829: a call such as rng.zipf(1.0000000000001)
+        # would hang.
+        n = 100000
+        sample = self.mt19937.zipf(1.0000000000001, size=n)
+        # Not much of a test, but let's do something more than verify that
+        # it doesn't hang.  Certainly for a monotonically decreasing
+        # discrete distribution truncated to signed 64 bit integers, more
+        # than half should be less than 2**62.
+        assert np.count_nonzero(sample < 2**62) > n/2
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_random.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_random.py
new file mode 100644
index 0000000000000000000000000000000000000000..c98584aeda9df3ea64209038521784c4b2c24ba9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_random.py
@@ -0,0 +1,1751 @@
+import warnings
+
+import pytest
+
+import numpy as np
+from numpy.testing import (
+        assert_, assert_raises, assert_equal, assert_warns,
+        assert_no_warnings, assert_array_equal, assert_array_almost_equal,
+        suppress_warnings, IS_WASM
+        )
+from numpy import random
+import sys
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = np.random.RandomState(0)
+        assert_equal(s.randint(1000), 684)
+        s = np.random.RandomState(4294967295)
+        assert_equal(s.randint(1000), 419)
+
+    def test_array(self):
+        s = np.random.RandomState(range(10))
+        assert_equal(s.randint(1000), 468)
+        s = np.random.RandomState(np.arange(10))
+        assert_equal(s.randint(1000), 468)
+        s = np.random.RandomState([0])
+        assert_equal(s.randint(1000), 973)
+        s = np.random.RandomState([4294967295])
+        assert_equal(s.randint(1000), 265)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, np.random.RandomState, -0.5)
+        assert_raises(ValueError, np.random.RandomState, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, np.random.RandomState, [-0.5])
+        assert_raises(ValueError, np.random.RandomState, [-1])
+        assert_raises(ValueError, np.random.RandomState, [4294967296])
+        assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296])
+        assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])
+
+    def test_invalid_array_shape(self):
+        # gh-9832
+        assert_raises(ValueError, np.random.RandomState,
+                      np.array([], dtype=np.int64))
+        assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]])
+        assert_raises(ValueError, np.random.RandomState, [[1, 2, 3],
+                                                          [4, 5, 6]])
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.randint(-5, -1) < -1)
+        x = random.randint(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, np.random.multinomial, 1, p,
+                      float(1))
+
+    def test_multidimensional_pvals(self):
+        assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]])
+        assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]])
+        assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]])
+        assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]]))
+
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.prng = random.RandomState(self.seed)
+        self.state = self.prng.get_state()
+
+    def test_basic(self):
+        old = self.prng.tomaxint(16)
+        self.prng.set_state(self.state)
+        new = self.prng.tomaxint(16)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.prng.standard_normal(size=3)
+        self.prng.set_state(self.state)
+        new = self.prng.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.prng.standard_normal()
+        state = self.prng.get_state()
+        old = self.prng.standard_normal(size=3)
+        self.prng.set_state(state)
+        new = self.prng.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_backwards_compatibility(self):
+        # Make sure we can accept old state tuples that do not have the
+        # cached Gaussian value.
+        old_state = self.state[:-2]
+        x1 = self.prng.standard_normal(size=16)
+        self.prng.set_state(old_state)
+        x2 = self.prng.standard_normal(size=16)
+        self.prng.set_state(self.state)
+        x3 = self.prng.standard_normal(size=16)
+        assert_(np.all(x1 == x2))
+        assert_(np.all(x1 == x3))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.prng.negative_binomial(0.5, 0.5)
+
+    def test_set_invalid_state(self):
+        # gh-25402
+        with pytest.raises(IndexError):
+            self.prng.set_state(())
+
+
+class TestRandint:
+
+    rfunc = np.random.randint
+
+    # valid integer/boolean types
+    itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self):
+        assert_raises(TypeError, self.rfunc, 1, dtype=float)
+
+    def test_bounds_checking(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
+
+    def test_rng_zero_and_extremes(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd)//2
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+    def test_full_range(self):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            try:
+                self.rfunc(lbnd, ubnd, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self):
+        # Don't use fixed seed
+        np.random.seed()
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)
+
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_repeatability(self):
+        import hashlib
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
+               'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
+               'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
+
+        for dt in self.itype[1:]:
+            np.random.seed(1234)
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = self.rfunc(0, 6, size=1000, dtype=dt)
+            else:
+                val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
+
+            res = hashlib.sha256(val.view(np.int8)).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        np.random.seed(1234)
+        val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    def test_int64_uint64_corner_case(self):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = np.random.randint(lbnd, ubnd, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_equal(sample.dtype, np.dtype(dt))
+
+        for dt in (bool, int):
+            # The legacy rng uses "long" as the default integer:
+            lbnd = 0 if dt is bool else np.iinfo("long").min
+            ubnd = 2 if dt is bool else np.iinfo("long").max + 1
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_(not hasattr(sample, 'dtype'))
+            assert_equal(type(sample), dt)
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_rand(self):
+        np.random.seed(self.seed)
+        actual = np.random.rand(3, 2)
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn(self):
+        np.random.seed(self.seed)
+        actual = np.random.randn(3, 2)
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                           [1.498988344300628, -0.2286433324536169],
+                           [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randint(self):
+        np.random.seed(self.seed)
+        actual = np.random.randint(-99, 99, size=(3, 2))
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers(self):
+        np.random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = np.random.random_integers(-99, 99, size=(3, 2))
+            assert_(len(w) == 1)
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers_max_int(self):
+        # Tests whether random_integers can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = np.random.random_integers(np.iinfo('l').max,
+                                               np.iinfo('l').max)
+            assert_(len(w) == 1)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+
+    def test_random_integers_deprecated(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+
+            # DeprecationWarning raised with high == None
+            assert_raises(DeprecationWarning,
+                          np.random.random_integers,
+                          np.iinfo('l').max)
+
+            # DeprecationWarning raised with high != None
+            assert_raises(DeprecationWarning,
+                          np.random.random_integers,
+                          np.iinfo('l').max, np.iinfo('l').max)
+
+    def test_random(self):
+        np.random.seed(self.seed)
+        actual = np.random.random((3, 2))
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_choice_uniform_replace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 4)
+        desired = np.array([2, 3, 2, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([1, 1, 2, 2])
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 3, replace=False)
+        desired = np.array([0, 1, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 3, replace=False,
+                                  p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([2, 3, 1])
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['c', 'd', 'c', 'd'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = np.random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(np.random.choice(2, replace=True)))
+        assert_(np.isscalar(np.random.choice(2, replace=False)))
+        assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(np.random.choice([1, 2], replace=True)))
+        assert_(np.random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(np.random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(np.random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))
+        assert_(np.random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(np.random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(np.random.choice(6, s, replace=True).shape, s)
+        assert_equal(np.random.choice(6, s, replace=False).shape, s)
+        assert_equal(np.random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(np.random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(np.random.randint(0, -10, size=0).shape, (0,))
+        assert_equal(np.random.randint(10, 10, size=0).shape, (0,))
+        assert_equal(np.random.choice(0, size=0).shape, (0,))
+        assert_equal(np.random.choice([], size=(0,)).shape, (0,))
+        assert_equal(np.random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, np.random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, np.random.choice, a, p=p)
+
+    def test_bytes(self):
+        np.random.seed(self.seed)
+        actual = np.random.bytes(10)
+        desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object), ("b", np.int32)])]:
+            np.random.seed(self.seed)
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            np.random.shuffle(alist)
+            actual = alist
+            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            np.random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            np.random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+    @pytest.mark.parametrize("random",
+            [np.random, np.random.RandomState(), np.random.default_rng()])
+    def test_shuffle_untyped_warning(self, random):
+        # Create a dict works like a sequence but isn't one
+        values = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}
+        with pytest.warns(UserWarning,
+                match="you are shuffling a 'dict' object") as rec:
+            random.shuffle(values)
+        assert "test_random" in rec[0].filename
+
+    @pytest.mark.parametrize("random",
+        [np.random, np.random.RandomState(), np.random.default_rng()])
+    @pytest.mark.parametrize("use_array_like", [True, False])
+    def test_shuffle_no_object_unpacking(self, random, use_array_like):
+        class MyArr(np.ndarray):
+            pass
+
+        items = [
+            None, np.array([3]), np.float64(3), np.array(10), np.float64(7)
+        ]
+        arr = np.array(items, dtype=object)
+        item_ids = {id(i) for i in items}
+        if use_array_like:
+            arr = arr.view(MyArr)
+
+        # The array was created fine, and did not modify any objects:
+        assert all(id(i) in item_ids for i in arr)
+
+        if use_array_like and not isinstance(random, np.random.Generator):
+            # The old API gives incorrect results, but warns about it.
+            with pytest.warns(UserWarning,
+                    match="Shuffling a one dimensional array.*"):
+                random.shuffle(arr)
+        else:
+            random.shuffle(arr)
+            assert all(id(i) in item_ids for i in arr)
+
+    def test_shuffle_memoryview(self):
+        # gh-18273
+        # allow graceful handling of memoryviews
+        # (treat the same as arrays)
+        np.random.seed(self.seed)
+        a = np.arange(5).data
+        np.random.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 4, 3, 2])
+        rng = np.random.RandomState(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 2, 3, 4])
+        rng = np.random.default_rng(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [4, 1, 0, 3, 2])
+
+    def test_shuffle_not_writeable(self):
+        a = np.zeros(3)
+        a.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            np.random.shuffle(a)
+
+    def test_beta(self):
+        np.random.seed(self.seed)
+        actual = np.random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+                [[1.45341850513746058e-02, 5.31297615662868145e-04],
+                 [1.85366619058432324e-06, 4.19214516800110563e-03],
+                 [1.58405155108498093e-04, 1.26252891949397652e-04]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.binomial(100, .456, size=(3, 2))
+        desired = np.array([[37, 43],
+                            [42, 48],
+                            [46, 45]])
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        np.random.seed(self.seed)
+        actual = np.random.chisquare(50, size=(3, 2))
+        desired = np.array([[63.87858175501090585, 68.68407748911370447],
+                            [65.77116116901505904, 47.09686762438974483],
+                            [72.3828403199695174, 74.18408615260374006]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        np.random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = np.random.mtrand.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.54539444573611562, 0.45460555426388438],
+                             [0.62345816822039413, 0.37654183177960598]],
+                            [[0.55206000085785778, 0.44793999914214233],
+                             [0.58964023305154301, 0.41035976694845688]],
+                            [[0.59266909280647828, 0.40733090719352177],
+                             [0.56974431743975207, 0.43025568256024799]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, np.random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, np.random.mtrand.dirichlet, alpha)
+
+        # gh-15876
+        assert_raises(ValueError, random.dirichlet, [[5, 1]])
+        assert_raises(ValueError, random.dirichlet, [[5], [1]])
+        assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+        assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
+    def test_exponential(self):
+        np.random.seed(self.seed)
+        actual = np.random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[1.08342649775011624, 1.00607889924557314],
+                            [2.46628830085216721, 2.49668106809923884],
+                            [0.68717433461363442, 1.69175666993575979]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(np.random.exponential(scale=0), 0)
+        assert_raises(ValueError, np.random.exponential, scale=-0.)
+
+    def test_f(self):
+        np.random.seed(self.seed)
+        actual = np.random.f(12, 77, size=(3, 2))
+        desired = np.array([[1.21975394418575878, 1.75135759791559775],
+                            [1.44803115017146489, 1.22108959480396262],
+                            [1.02176975757740629, 1.34431827623300415]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        np.random.seed(self.seed)
+        actual = np.random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[24.60509188649287182, 28.54993563207210627],
+                            [26.13476110204064184, 12.56988482927716078],
+                            [31.71863275789960568, 33.30143302795922011]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(np.random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, np.random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        np.random.seed(self.seed)
+        actual = np.random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[8, 7],
+                            [17, 17],
+                            [5, 12]])
+        assert_array_equal(actual, desired)
+
+    def test_gumbel(self):
+        np.random.seed(self.seed)
+        actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.19591898743416816, 0.34405539668096674],
+                            [-1.4492522252274278, -1.47374816298446865],
+                            [1.10651090478803416, -0.69535848626236174]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(np.random.gumbel(scale=0), 0)
+        assert_raises(ValueError, np.random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        np.random.seed(self.seed)
+        actual = np.random.hypergeometric(10, 5, 14, size=(3, 2))
+        desired = np.array([[10, 10],
+                            [10, 10],
+                            [9, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = np.random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = np.random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = np.random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = np.random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.66599721112760157, 0.52829452552221945],
+                            [3.12791959514407125, 3.18202813572992005],
+                            [-0.05391065675859356, 1.74901336242837324]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(np.random.laplace(scale=0), 0)
+        assert_raises(ValueError, np.random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        np.random.seed(self.seed)
+        actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[1.09232835305011444, 0.8648196662399954],
+                            [4.27818590694950185, 4.33897006346929714],
+                            [-0.21682183359214885, 2.63373365386060332]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        np.random.seed(self.seed)
+        actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[16.50698631688883822, 36.54846706092654784],
+                            [22.67886599981281748, 0.71617561058995771],
+                            [65.72798501792723869, 86.84341601437161273]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(np.random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, np.random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        np.random.seed(self.seed)
+        actual = np.random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[2, 2],
+                            [6, 17],
+                            [3, 6]])
+        assert_array_equal(actual, desired)
+
+    def test_multinomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2))
+        desired = np.array([[[4, 3, 5, 4, 2, 2],
+                             [5, 2, 8, 2, 2, 1]],
+                            [[3, 4, 3, 6, 0, 4],
+                             [2, 1, 4, 3, 6, 4]],
+                            [[4, 4, 2, 5, 2, 3],
+                             [4, 3, 4, 2, 3, 4]]])
+        assert_array_equal(actual, desired)
+
+    def test_multivariate_normal(self):
+        np.random.seed(self.seed)
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = np.random.multivariate_normal(mean, cov, size)
+        desired = np.array([[[1.463620246718631, 11.73759122771936],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = np.random.multivariate_normal(mean, cov)
+        desired = np.array([0.895289569463708, 9.17180864067987])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        mean = [0, 0]
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(np.random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, np.random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            np.random.multivariate_normal(mean, cov)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+    def test_negative_binomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[848, 841],
+                            [892, 611],
+                            [779, 647]])
+        assert_array_equal(actual, desired)
+
+    def test_noncentral_chisquare(self):
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[23.91905354498517511, 13.35324692733826346],
+                            [31.22452661329736401, 16.60047399466177254],
+                            [5.03461598262724586, 17.94973089023519464]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[1.47145377828516666,  0.15052899268012659],
+                            [0.00943803056963588,  1.02647251615666169],
+                            [0.332334982684171,  0.15451287602753125]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[9.597154162763948, 11.725484450296079],
+                            [10.413711048138335, 3.694475922923986],
+                            [13.484222138963087, 14.377255424602957]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                        size=(3, 2))
+        desired = np.array([[1.40598099674926669, 0.34207973179285761],
+                            [3.57715069265772545, 7.92632662577829805],
+                            [0.43741599463544162, 1.1774208752428319]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        np.random.seed(self.seed)
+        actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[2.80378370443726244, 3.59863924443872163],
+                            [3.121433477601256, -0.33382987590723379],
+                            [4.18552478636557357, 4.46410668111310471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(np.random.normal(scale=0), 0)
+        assert_raises(ValueError, np.random.normal, scale=-0.)
+
+    def test_pareto(self):
+        np.random.seed(self.seed)
+        actual = np.random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array(
+                [[2.46852460439034849e+03, 1.41286880810518346e+03],
+                 [5.28287797029485181e+07, 6.57720981047328785e+07],
+                 [1.40840323350391515e+02, 1.98390255135251704e+05]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        np.random.seed(self.seed)
+        actual = np.random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [1, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('l').max
+        lamneg = -1
+        assert_raises(ValueError, np.random.poisson, lamneg)
+        assert_raises(ValueError, np.random.poisson, [lamneg]*10)
+        assert_raises(ValueError, np.random.poisson, lambig)
+        assert_raises(ValueError, np.random.poisson, [lambig]*10)
+
+    def test_power(self):
+        np.random.seed(self.seed)
+        actual = np.random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[0.02048932883240791, 0.01424192241128213],
+                            [0.38446073748535298, 0.39499689943484395],
+                            [0.00177699707563439, 0.13115505880863756]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        np.random.seed(self.seed)
+        actual = np.random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[13.8882496494248393, 13.383318339044731],
+                            [20.95413364294492098, 21.08285015800712614],
+                            [11.06066537006854311, 17.35468505778271009]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(np.random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, np.random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_cauchy(size=(3, 2))
+        desired = np.array([[0.77127660196445336, -6.55601161955910605],
+                            [0.93582023391158309, -2.07479293013759447],
+                            [-4.74601644297011926, 0.18338989290760804]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_exponential(size=(3, 2))
+        desired = np.array([[0.96441739162374596, 0.89556604882105506],
+                            [2.1953785836319808, 2.22243285392490542],
+                            [0.6116915921431676, 1.50592546727413201]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_gamma(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[5.50841531318455058, 6.62953470301903103],
+                            [5.93988484943779227, 2.31044849402133989],
+                            [7.54838614231317084, 8.012756093271868]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gamma_0(self):
+        assert_equal(np.random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, np.random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_normal(size=(3, 2))
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                            [1.498988344300628, -0.2286433324536169],
+                            [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_t(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[0.97140611862659965, -0.08830486548450577],
+                            [1.36311143689505321, -0.55317463909867071],
+                            [-0.18473749069684214, 0.61181537341755321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        np.random.seed(self.seed)
+        actual = np.random.triangular(left=5.12, mode=10.23, right=20.34,
+                                      size=(3, 2))
+        desired = np.array([[12.68117178949215784, 12.4129206149193152],
+                            [16.20131377335158263, 16.25692138747600524],
+                            [11.20400690911820263, 14.4978144835829923]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        np.random.seed(self.seed)
+        actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[6.99097932346268003, 6.73801597444323974],
+                            [9.50364421400426274, 9.53130618907631089],
+                            [5.48995325769805476, 8.47493103280052118]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = np.random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func,  0,      np.inf)
+        assert_raises(OverflowError, func,  fmin,   fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        np.random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, np.random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+            __index__ = __int__
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, np.random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        np.random.seed(self.seed)
+        actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[2.28567572673902042, 2.89163838442285037],
+                            [0.38198375564286025, 2.57638023113890746],
+                            [1.19153771588353052, 1.83509849681825354]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        np.random.seed(self.seed)
+        r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        np.testing.assert_(np.isfinite(r).all())
+
+    def test_wald(self):
+        np.random.seed(self.seed)
+        actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[3.82935265715889983, 5.13125249184285526],
+                            [0.35045403618358717, 1.50832396872003538],
+                            [0.24124319895843183, 0.22031101461955038]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        np.random.seed(self.seed)
+        actual = np.random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.97097342648766727, 0.91422896443565516],
+                            [1.89517770034962929, 1.91414357960479564],
+                            [0.67057783752390987, 1.39494046635066793]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        np.random.seed(self.seed)
+        assert_equal(np.random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, np.random.weibull, a=-0.)
+
+    def test_zipf(self):
+        np.random.seed(self.seed)
+        actual = np.random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[66, 29],
+                            [1, 1],
+                            [3, 13]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def setSeed(self):
+        np.random.seed(self.seed)
+
+    # TODO: Include test for randint once it can broadcast
+    # Can steal the test written in PR #6938
+
+    def test_uniform(self):
+        low = [0]
+        high = [1]
+        uniform = np.random.uniform
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.setSeed()
+        actual = uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        self.setSeed()
+        actual = uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        normal = np.random.normal
+        desired = np.array([2.2129019979039612,
+                            2.1283977976520019,
+                            1.8417114045748335])
+
+        self.setSeed()
+        actual = normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        beta = np.random.beta
+        desired = np.array([0.19843558305989056,
+                            0.075230336409423643,
+                            0.24976865978980844])
+
+        self.setSeed()
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        self.setSeed()
+        actual = beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a, b * 3)
+        assert_raises(ValueError, beta, a, bad_b * 3)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        exponential = np.random.exponential
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        std_gamma = np.random.standard_gamma
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        gamma = np.random.gamma
+        desired = np.array([1.5221370731769048,
+                            1.5277256455738331,
+                            1.4248762625178359])
+
+        self.setSeed()
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        self.setSeed()
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        f = np.random.f
+        desired = np.array([0.80038951638264799,
+                            0.86768719635363512,
+                            2.7251095168386801])
+
+        self.setSeed()
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        self.setSeed()
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        nonc_f = np.random.noncentral_f
+        desired = np.array([9.1393943263705211,
+                            13.025456344595602,
+                            8.8018098359100545])
+
+        self.setSeed()
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        self.setSeed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = np.random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        chisquare = np.random.chisquare
+        desired = np.array([0.57022801133088286,
+                            0.51947702108840776,
+                            0.1320969254923558])
+
+        self.setSeed()
+        actual = chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        nonc_chi = np.random.noncentral_chisquare
+        desired = np.array([9.0015599467913763,
+                            4.5804135049718742,
+                            6.0872302432834564])
+
+        self.setSeed()
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        t = np.random.standard_t
+        desired = np.array([3.0702872575217643,
+                            5.8560725167361607,
+                            1.0274791436474273])
+
+        self.setSeed()
+        actual = t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        vonmises = np.random.vonmises
+        desired = np.array([2.9883443664201312,
+                            -2.7064099483995943,
+                            -1.8672476700665914])
+
+        self.setSeed()
+        actual = vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
+
+        self.setSeed()
+        actual = vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        pareto = np.random.pareto
+        desired = np.array([1.1405622680198362,
+                            1.1465519762044529,
+                            1.0389564467453547])
+
+        self.setSeed()
+        actual = pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        weibull = np.random.weibull
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        power = np.random.power
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.setSeed()
+        actual = power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        laplace = np.random.laplace
+        desired = np.array([0.067921356028507157,
+                            0.070715642226971326,
+                            0.019290950698972624])
+
+        self.setSeed()
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        gumbel = np.random.gumbel
+        desired = np.array([0.2730318639556768,
+                            0.26936705726291116,
+                            0.33906220393037939])
+
+        self.setSeed()
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        logistic = np.random.logistic
+        desired = np.array([0.13152135837586171,
+                            0.13675915696285773,
+                            0.038216792802833396])
+
+        self.setSeed()
+        actual = logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc, bad_scale * 3)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        lognormal = np.random.lognormal
+        desired = np.array([9.1422086044848427,
+                            8.4013952870126261,
+                            6.3073234116578671])
+
+        self.setSeed()
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+
+        self.setSeed()
+        actual = lognormal(mean, sigma * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        rayleigh = np.random.rayleigh
+        desired = np.array([1.2337491937897689,
+                            1.2360119924878694,
+                            1.1936818095781789])
+
+        self.setSeed()
+        actual = rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        wald = np.random.wald
+        desired = np.array([0.11873681120271318,
+                            0.12450084820795027,
+                            0.9096122728408238])
+
+        self.setSeed()
+        actual = wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean * 3, scale)
+        assert_raises(ValueError, wald, mean * 3, bad_scale)
+
+        self.setSeed()
+        actual = wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean, scale * 3)
+        assert_raises(ValueError, wald, mean, bad_scale * 3)
+        assert_raises(ValueError, wald, 0.0, 1)
+        assert_raises(ValueError, wald, 0.5, 0.0)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        triangular = np.random.triangular
+        desired = np.array([2.03339048710429,
+                            2.0347400359389356,
+                            2.0095991069536208])
+
+        self.setSeed()
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        self.setSeed()
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        self.setSeed()
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        binom = np.random.binomial
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        self.setSeed()
+        actual = binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        neg_binom = np.random.negative_binomial
+        desired = np.array([1, 0, 1])
+
+        self.setSeed()
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        self.setSeed()
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+        max_lam = np.random.RandomState()._poisson_lam_max
+
+        lam = [1]
+        bad_lam_one = [-1]
+        bad_lam_two = [max_lam * 2]
+        poisson = np.random.poisson
+        desired = np.array([1, 1, 0])
+
+        self.setSeed()
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        zipf = np.random.zipf
+        desired = np.array([2, 2, 1])
+
+        self.setSeed()
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        geom = np.random.geometric
+        desired = np.array([2, 2, 2])
+
+        self.setSeed()
+        actual = geom(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geom, bad_p_one * 3)
+        assert_raises(ValueError, geom, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [0]
+        bad_nsample_two = [4]
+        hypergeom = np.random.hypergeometric
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = hypergeom(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
+
+        self.setSeed()
+        actual = hypergeom(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
+
+        self.setSeed()
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        logseries = np.random.logseries
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(np.random.RandomState(s), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(np.random.RandomState(s), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1/6.]*6, size=10000)
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (np.random.exponential, np.random.standard_gamma,
+                 np.random.chisquare, np.random.standard_t,
+                 np.random.pareto, np.random.weibull,
+                 np.random.power, np.random.rayleigh,
+                 np.random.poisson, np.random.zipf,
+                 np.random.geometric, np.random.logseries)
+
+        probfuncs = (np.random.geometric, np.random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (np.random.uniform, np.random.normal,
+                 np.random.beta, np.random.gamma,
+                 np.random.f, np.random.noncentral_chisquare,
+                 np.random.vonmises, np.random.laplace,
+                 np.random.gumbel, np.random.logistic,
+                 np.random.lognormal, np.random.wald,
+                 np.random.binomial, np.random.negative_binomial)
+
+        probfuncs = (np.random.binomial, np.random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_randint(self):
+        itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
+                 np.int32, np.uint32, np.int64, np.uint64]
+        func = np.random.randint
+        high = np.array([1])
+        low = np.array([0])
+
+        for dt in itype:
+            out = func(low, high, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low[0], high, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low, high[0], dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [np.random.noncentral_f, np.random.triangular,
+                 np.random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_randomstate.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_randomstate.py
new file mode 100644
index 0000000000000000000000000000000000000000..5121a684f693a14febd473272509d491a4438631
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_randomstate.py
@@ -0,0 +1,2124 @@
+import hashlib
+import pickle
+import sys
+import warnings
+
+import numpy as np
+import pytest
+from numpy.testing import (
+        assert_, assert_raises, assert_equal, assert_warns,
+        assert_no_warnings, assert_array_equal, assert_array_almost_equal,
+        suppress_warnings, IS_WASM
+        )
+
+from numpy.random import MT19937, PCG64
+from numpy import random
+
+INT_FUNCS = {'binomial': (100.0, 0.6),
+             'geometric': (.5,),
+             'hypergeometric': (20, 20, 10),
+             'logseries': (.5,),
+             'multinomial': (20, np.ones(6) / 6.0),
+             'negative_binomial': (100, .5),
+             'poisson': (10.0,),
+             'zipf': (2,),
+             }
+
+if np.iinfo(np.long).max < 2**32:
+    # Windows and some 32-bit platforms, e.g., ARM
+    INT_FUNC_HASHES = {'binomial': '2fbead005fc63942decb5326d36a1f32fe2c9d32c904ee61e46866b88447c263',
+                       'logseries': '23ead5dcde35d4cfd4ef2c105e4c3d43304b45dc1b1444b7823b9ee4fa144ebb',
+                       'geometric': '0d764db64f5c3bad48c8c33551c13b4d07a1e7b470f77629bef6c985cac76fcf',
+                       'hypergeometric': '7b59bf2f1691626c5815cdcd9a49e1dd68697251d4521575219e4d2a1b8b2c67',
+                       'multinomial': 'd754fa5b92943a38ec07630de92362dd2e02c43577fc147417dc5b9db94ccdd3',
+                       'negative_binomial': '8eb216f7cb2a63cf55605422845caaff002fddc64a7dc8b2d45acd477a49e824',
+                       'poisson': '70c891d76104013ebd6f6bcf30d403a9074b886ff62e4e6b8eb605bf1a4673b7',
+                       'zipf': '01f074f97517cd5d21747148ac6ca4074dde7fcb7acbaec0a936606fecacd93f',
+                       }
+else:
+    INT_FUNC_HASHES = {'binomial': '8626dd9d052cb608e93d8868de0a7b347258b199493871a1dc56e2a26cacb112',
+                       'geometric': '8edd53d272e49c4fc8fbbe6c7d08d563d62e482921f3131d0a0e068af30f0db9',
+                       'hypergeometric': '83496cc4281c77b786c9b7ad88b74d42e01603a55c60577ebab81c3ba8d45657',
+                       'logseries': '65878a38747c176bc00e930ebafebb69d4e1e16cd3a704e264ea8f5e24f548db',
+                       'multinomial': '7a984ae6dca26fd25374479e118b22f55db0aedccd5a0f2584ceada33db98605',
+                       'negative_binomial': 'd636d968e6a24ae92ab52fe11c46ac45b0897e98714426764e820a7d77602a61',
+                       'poisson': '956552176f77e7c9cb20d0118fc9cf690be488d790ed4b4c4747b965e61b0bb4',
+                       'zipf': 'f84ba7feffda41e606e20b28dfc0f1ea9964a74574513d4a4cbc98433a8bfa45',
+                       }
+
+
+@pytest.fixture(scope='module', params=INT_FUNCS)
+def int_func(request):
+    return (request.param, INT_FUNCS[request.param],
+            INT_FUNC_HASHES[request.param])
+
+
+@pytest.fixture
+def restore_singleton_bitgen():
+    """Ensures that the singleton bitgen is restored after a test"""
+    orig_bitgen = np.random.get_bit_generator()
+    yield
+    np.random.set_bit_generator(orig_bitgen)
+
+
+def assert_mt19937_state_equal(a, b):
+    assert_equal(a['bit_generator'], b['bit_generator'])
+    assert_array_equal(a['state']['key'], b['state']['key'])
+    assert_array_equal(a['state']['pos'], b['state']['pos'])
+    assert_equal(a['has_gauss'], b['has_gauss'])
+    assert_equal(a['gauss'], b['gauss'])
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = random.RandomState(0)
+        assert_equal(s.randint(1000), 684)
+        s = random.RandomState(4294967295)
+        assert_equal(s.randint(1000), 419)
+
+    def test_array(self):
+        s = random.RandomState(range(10))
+        assert_equal(s.randint(1000), 468)
+        s = random.RandomState(np.arange(10))
+        assert_equal(s.randint(1000), 468)
+        s = random.RandomState([0])
+        assert_equal(s.randint(1000), 973)
+        s = random.RandomState([4294967295])
+        assert_equal(s.randint(1000), 265)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, random.RandomState, -0.5)
+        assert_raises(ValueError, random.RandomState, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, random.RandomState, [-0.5])
+        assert_raises(ValueError, random.RandomState, [-1])
+        assert_raises(ValueError, random.RandomState, [4294967296])
+        assert_raises(ValueError, random.RandomState, [1, 2, 4294967296])
+        assert_raises(ValueError, random.RandomState, [1, -2, 4294967296])
+
+    def test_invalid_array_shape(self):
+        # gh-9832
+        assert_raises(ValueError, random.RandomState, np.array([],
+                                                               dtype=np.int64))
+        assert_raises(ValueError, random.RandomState, [[1, 2, 3]])
+        assert_raises(ValueError, random.RandomState, [[1, 2, 3],
+                                                       [4, 5, 6]])
+
+    def test_cannot_seed(self):
+        rs = random.RandomState(PCG64(0))
+        with assert_raises(TypeError):
+            rs.seed(1234)
+
+    def test_invalid_initialization(self):
+        assert_raises(ValueError, random.RandomState, MT19937)
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.randint(-5, -1) < -1)
+        x = random.randint(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, random.multinomial, 1, p,
+                      float(1))
+
+    def test_invalid_prob(self):
+        assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
+        assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])
+
+    def test_invalid_n(self):
+        assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])
+
+    def test_p_non_contiguous(self):
+        p = np.arange(15.)
+        p /= np.sum(p[1::3])
+        pvals = p[1::3]
+        random.seed(1432985819)
+        non_contig = random.multinomial(100, pvals=pvals)
+        random.seed(1432985819)
+        contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
+        assert_array_equal(non_contig, contig)
+
+    def test_multinomial_pvals_float32(self):
+        x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
+                      1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
+        pvals = x / x.sum()
+        match = r"[\w\s]*pvals array is cast to 64-bit floating"
+        with pytest.raises(ValueError, match=match):
+            random.multinomial(1, pvals)
+
+    def test_multinomial_n_float(self):
+        # Non-index integer types should gracefully truncate floats
+        random.multinomial(100.5, [0.2, 0.8])
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.random_state = random.RandomState(self.seed)
+        self.state = self.random_state.get_state()
+
+    def test_basic(self):
+        old = self.random_state.tomaxint(16)
+        self.random_state.set_state(self.state)
+        new = self.random_state.tomaxint(16)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.random_state.standard_normal(size=3)
+        self.random_state.set_state(self.state)
+        new = self.random_state.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.random_state.standard_normal()
+        state = self.random_state.get_state()
+        old = self.random_state.standard_normal(size=3)
+        self.random_state.set_state(state)
+        new = self.random_state.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_backwards_compatibility(self):
+        # Make sure we can accept old state tuples that do not have the
+        # cached Gaussian value.
+        old_state = self.state[:-2]
+        x1 = self.random_state.standard_normal(size=16)
+        self.random_state.set_state(old_state)
+        x2 = self.random_state.standard_normal(size=16)
+        self.random_state.set_state(self.state)
+        x3 = self.random_state.standard_normal(size=16)
+        assert_(np.all(x1 == x2))
+        assert_(np.all(x1 == x3))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.random_state.negative_binomial(0.5, 0.5)
+
+    def test_get_state_warning(self):
+        rs = random.RandomState(PCG64())
+        with suppress_warnings() as sup:
+            w = sup.record(RuntimeWarning)
+            state = rs.get_state()
+            assert_(len(w) == 1)
+            assert isinstance(state, dict)
+            assert state['bit_generator'] == 'PCG64'
+
+    def test_invalid_legacy_state_setting(self):
+        state = self.random_state.get_state()
+        new_state = ('Unknown', ) + state[1:]
+        assert_raises(ValueError, self.random_state.set_state, new_state)
+        assert_raises(TypeError, self.random_state.set_state,
+                      np.array(new_state, dtype=object))
+        state = self.random_state.get_state(legacy=False)
+        del state['bit_generator']
+        assert_raises(ValueError, self.random_state.set_state, state)
+
+    def test_pickle(self):
+        self.random_state.seed(0)
+        self.random_state.random_sample(100)
+        self.random_state.standard_normal()
+        pickled = self.random_state.get_state(legacy=False)
+        assert_equal(pickled['has_gauss'], 1)
+        rs_unpick = pickle.loads(pickle.dumps(self.random_state))
+        unpickled = rs_unpick.get_state(legacy=False)
+        assert_mt19937_state_equal(pickled, unpickled)
+
+    def test_state_setting(self):
+        attr_state = self.random_state.__getstate__()
+        self.random_state.standard_normal()
+        self.random_state.__setstate__(attr_state)
+        state = self.random_state.get_state(legacy=False)
+        assert_mt19937_state_equal(attr_state, state)
+
+    def test_repr(self):
+        assert repr(self.random_state).startswith('RandomState(MT19937)')
+
+
+class TestRandint:
+
+    rfunc = random.randint
+
+    # valid integer/boolean types
+    itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self):
+        assert_raises(TypeError, self.rfunc, 1, dtype=float)
+
+    def test_bounds_checking(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
+
+    def test_rng_zero_and_extremes(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd)//2
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+    def test_full_range(self):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            try:
+                self.rfunc(lbnd, ubnd, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self):
+        # Don't use fixed seed
+        random.seed()
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)
+
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_repeatability(self):
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
+               'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
+               'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
+
+        for dt in self.itype[1:]:
+            random.seed(1234)
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = self.rfunc(0, 6, size=1000, dtype=dt)
+            else:
+                val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
+
+            res = hashlib.sha256(val.view(np.int8)).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        random.seed(1234)
+        val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_repeatability_32bit_boundary_broadcasting(self):
+        desired = np.array([[[3992670689, 2438360420, 2557845020],
+                             [4107320065, 4142558326, 3216529513],
+                             [1605979228, 2807061240,  665605495]],
+                            [[3211410639, 4128781000,  457175120],
+                             [1712592594, 1282922662, 3081439808],
+                             [3997822960, 2008322436, 1563495165]],
+                            [[1398375547, 4269260146,  115316740],
+                             [3414372578, 3437564012, 2112038651],
+                             [3572980305, 2260248732, 3908238631]],
+                            [[2561372503,  223155946, 3127879445],
+                             [ 441282060, 3514786552, 2148440361],
+                             [1629275283, 3479737011, 3003195987]],
+                            [[ 412181688,  940383289, 3047321305],
+                             [2978368172,  764731833, 2282559898],
+                             [ 105711276,  720447391, 3596512484]]])
+        for size in [None, (5, 3, 3)]:
+            random.seed(12345)
+            x = self.rfunc([[-1], [0], [1]], [2**32 - 1, 2**32, 2**32 + 1],
+                           size=size)
+            assert_array_equal(x, desired if size is not None else desired[0])
+
+    def test_int64_uint64_corner_case(self):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = random.randint(lbnd, ubnd, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_equal(sample.dtype, np.dtype(dt))
+
+        for dt in (bool, int):
+            # The legacy random generation forces the use of "long" on this
+            # branch even when the input is `int` and the default dtype
+            # for int changed (dtype=int is also the functions default)
+            op_dtype = "long" if dt is int else "bool"
+            lbnd = 0 if dt is bool else np.iinfo(op_dtype).min
+            ubnd = 2 if dt is bool else np.iinfo(op_dtype).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_(not hasattr(sample, 'dtype'))
+            assert_equal(type(sample), dt)
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_rand(self):
+        random.seed(self.seed)
+        actual = random.rand(3, 2)
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rand_singleton(self):
+        random.seed(self.seed)
+        actual = random.rand()
+        desired = 0.61879477158567997
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn(self):
+        random.seed(self.seed)
+        actual = random.randn(3, 2)
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                           [1.498988344300628, -0.2286433324536169],
+                           [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random.seed(self.seed)
+        actual = random.randn()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_randint(self):
+        random.seed(self.seed)
+        actual = random.randint(-99, 99, size=(3, 2))
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers(self):
+        random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(-99, 99, size=(3, 2))
+            assert_(len(w) == 1)
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(198, size=(3, 2))
+            assert_(len(w) == 1)
+        assert_array_equal(actual, desired + 100)
+
+    def test_tomaxint(self):
+        random.seed(self.seed)
+        rs = random.RandomState(self.seed)
+        actual = rs.tomaxint(size=(3, 2))
+        if np.iinfo(np.long).max == 2147483647:
+            desired = np.array([[1328851649,  731237375],
+                                [1270502067,  320041495],
+                                [1908433478,  499156889]], dtype=np.int64)
+        else:
+            desired = np.array([[5707374374421908479, 5456764827585442327],
+                                [8196659375100692377, 8224063923314595285],
+                                [4220315081820346526, 7177518203184491332]],
+                               dtype=np.int64)
+
+        assert_equal(actual, desired)
+
+        rs.seed(self.seed)
+        actual = rs.tomaxint()
+        assert_equal(actual, desired[0, 0])
+
+    def test_random_integers_max_int(self):
+        # Tests whether random_integers can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(np.iinfo('l').max,
+                                            np.iinfo('l').max)
+            assert_(len(w) == 1)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            typer = np.dtype('l').type
+            actual = random.random_integers(typer(np.iinfo('l').max),
+                                            typer(np.iinfo('l').max))
+            assert_(len(w) == 1)
+        assert_equal(actual, desired)
+
+    def test_random_integers_deprecated(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+
+            # DeprecationWarning raised with high == None
+            assert_raises(DeprecationWarning,
+                          random.random_integers,
+                          np.iinfo('l').max)
+
+            # DeprecationWarning raised with high != None
+            assert_raises(DeprecationWarning,
+                          random.random_integers,
+                          np.iinfo('l').max, np.iinfo('l').max)
+
+    def test_random_sample(self):
+        random.seed(self.seed)
+        actual = random.random_sample((3, 2))
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random.seed(self.seed)
+        actual = random.random_sample()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_choice_uniform_replace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 4)
+        desired = np.array([2, 3, 2, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([1, 1, 2, 2])
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 3, replace=False)
+        desired = np.array([0, 1, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([2, 3, 1])
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        random.seed(self.seed)
+        actual = random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['c', 'd', 'c', 'd'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(random.choice(2, replace=True)))
+        assert_(np.isscalar(random.choice(2, replace=False)))
+        assert_(np.isscalar(random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(random.choice([1, 2], replace=True)))
+        assert_(random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
+        assert_(random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(random.choice(6, s, replace=True).shape, s)
+        assert_equal(random.choice(6, s, replace=False).shape, s)
+        assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(random.randint(0, -10, size=0).shape, (0,))
+        assert_equal(random.randint(10, 10, size=0).shape, (0,))
+        assert_equal(random.choice(0, size=0).shape, (0,))
+        assert_equal(random.choice([], size=(0,)).shape, (0,))
+        assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, random.choice, a, p=p)
+
+    def test_choice_p_non_contiguous(self):
+        p = np.ones(10) / 5
+        p[1::2] = 3.0
+        random.seed(self.seed)
+        non_contig = random.choice(5, 3, p=p[::2])
+        random.seed(self.seed)
+        contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
+        assert_array_equal(non_contig, contig)
+
+    def test_bytes(self):
+        random.seed(self.seed)
+        actual = random.bytes(10)
+        desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object, (1,)),
+                                           ("b", np.int32, (1,))])]:
+            random.seed(self.seed)
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            random.shuffle(alist)
+            actual = alist
+            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+        def test_shuffle_invalid_objects(self):
+            x = np.array(3)
+            assert_raises(TypeError, random.shuffle, x)
+
+    def test_permutation(self):
+        random.seed(self.seed)
+        alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
+        actual = random.permutation(alist)
+        desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3]
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
+        actual = random.permutation(arr_2d)
+        assert_array_equal(actual, np.atleast_2d(desired).T)
+
+        random.seed(self.seed)
+        bad_x_str = "abcd"
+        assert_raises(IndexError, random.permutation, bad_x_str)
+
+        random.seed(self.seed)
+        bad_x_float = 1.2
+        assert_raises(IndexError, random.permutation, bad_x_float)
+
+        integer_val = 10
+        desired = [9, 0, 8, 5, 1, 3, 4, 7, 6, 2]
+
+        random.seed(self.seed)
+        actual = random.permutation(integer_val)
+        assert_array_equal(actual, desired)
+
+    def test_beta(self):
+        random.seed(self.seed)
+        actual = random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+                [[1.45341850513746058e-02, 5.31297615662868145e-04],
+                 [1.85366619058432324e-06, 4.19214516800110563e-03],
+                 [1.58405155108498093e-04, 1.26252891949397652e-04]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        random.seed(self.seed)
+        actual = random.binomial(100.123, .456, size=(3, 2))
+        desired = np.array([[37, 43],
+                            [42, 48],
+                            [46, 45]])
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        actual = random.binomial(100.123, .456)
+        desired = 37
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        random.seed(self.seed)
+        actual = random.chisquare(50, size=(3, 2))
+        desired = np.array([[63.87858175501090585, 68.68407748911370447],
+                            [65.77116116901505904, 47.09686762438974483],
+                            [72.3828403199695174, 74.18408615260374006]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.54539444573611562, 0.45460555426388438],
+                             [0.62345816822039413, 0.37654183177960598]],
+                            [[0.55206000085785778, 0.44793999914214233],
+                             [0.58964023305154301, 0.41035976694845688]],
+                            [[0.59266909280647828, 0.40733090719352177],
+                             [0.56974431743975207, 0.43025568256024799]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        bad_alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, bad_alpha)
+
+        random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha)
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, alpha)
+
+    def test_dirichlet_alpha_non_contiguous(self):
+        a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
+        alpha = a[::2]
+        random.seed(self.seed)
+        non_contig = random.dirichlet(alpha, size=(3, 2))
+        random.seed(self.seed)
+        contig = random.dirichlet(np.ascontiguousarray(alpha),
+                                  size=(3, 2))
+        assert_array_almost_equal(non_contig, contig)
+
+    def test_exponential(self):
+        random.seed(self.seed)
+        actual = random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[1.08342649775011624, 1.00607889924557314],
+                            [2.46628830085216721, 2.49668106809923884],
+                            [0.68717433461363442, 1.69175666993575979]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(random.exponential(scale=0), 0)
+        assert_raises(ValueError, random.exponential, scale=-0.)
+
+    def test_f(self):
+        random.seed(self.seed)
+        actual = random.f(12, 77, size=(3, 2))
+        desired = np.array([[1.21975394418575878, 1.75135759791559775],
+                            [1.44803115017146489, 1.22108959480396262],
+                            [1.02176975757740629, 1.34431827623300415]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        random.seed(self.seed)
+        actual = random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[24.60509188649287182, 28.54993563207210627],
+                            [26.13476110204064184, 12.56988482927716078],
+                            [31.71863275789960568, 33.30143302795922011]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        random.seed(self.seed)
+        actual = random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[8, 7],
+                            [17, 17],
+                            [5, 12]])
+        assert_array_equal(actual, desired)
+
+    def test_geometric_exceptions(self):
+        assert_raises(ValueError, random.geometric, 1.1)
+        assert_raises(ValueError, random.geometric, [1.1] * 10)
+        assert_raises(ValueError, random.geometric, -0.1)
+        assert_raises(ValueError, random.geometric, [-0.1] * 10)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.geometric, np.nan)
+            assert_raises(ValueError, random.geometric, [np.nan] * 10)
+
+    def test_gumbel(self):
+        random.seed(self.seed)
+        actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.19591898743416816, 0.34405539668096674],
+                            [-1.4492522252274278, -1.47374816298446865],
+                            [1.10651090478803416, -0.69535848626236174]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(random.gumbel(scale=0), 0)
+        assert_raises(ValueError, random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        random.seed(self.seed)
+        actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
+        desired = np.array([[10, 10],
+                            [10, 10],
+                            [9, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        random.seed(self.seed)
+        actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.66599721112760157, 0.52829452552221945],
+                            [3.12791959514407125, 3.18202813572992005],
+                            [-0.05391065675859356, 1.74901336242837324]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(random.laplace(scale=0), 0)
+        assert_raises(ValueError, random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        random.seed(self.seed)
+        actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[1.09232835305011444, 0.8648196662399954],
+                            [4.27818590694950185, 4.33897006346929714],
+                            [-0.21682183359214885, 2.63373365386060332]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        random.seed(self.seed)
+        actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[16.50698631688883822, 36.54846706092654784],
+                            [22.67886599981281748, 0.71617561058995771],
+                            [65.72798501792723869, 86.84341601437161273]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        random.seed(self.seed)
+        actual = random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[2, 2],
+                            [6, 17],
+                            [3, 6]])
+        assert_array_equal(actual, desired)
+
+    def test_logseries_zero(self):
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
+
+    def test_multinomial(self):
+        random.seed(self.seed)
+        actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[4, 3, 5, 4, 2, 2],
+                             [5, 2, 8, 2, 2, 1]],
+                            [[3, 4, 3, 6, 0, 4],
+                             [2, 1, 4, 3, 6, 4]],
+                            [[4, 4, 2, 5, 2, 3],
+                             [4, 3, 4, 2, 3, 4]]])
+        assert_array_equal(actual, desired)
+
+    def test_multivariate_normal(self):
+        random.seed(self.seed)
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = random.multivariate_normal(mean, cov, size)
+        desired = np.array([[[1.463620246718631, 11.73759122771936],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = random.multivariate_normal(mean, cov)
+        desired = np.array([0.895289569463708, 9.17180864067987])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        mean = [0, 0]
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            random.multivariate_normal(mean, cov)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+        mu = np.zeros(2)
+        cov = np.eye(2)
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='other')
+        assert_raises(ValueError, random.multivariate_normal,
+                      np.zeros((2, 1, 1)), cov)
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.empty((3, 2)))
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.eye(3))
+
+    def test_negative_binomial(self):
+        random.seed(self.seed)
+        actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[848, 841],
+                            [892, 611],
+                            [779, 647]])
+        assert_array_equal(actual, desired)
+
+    def test_negative_binomial_exceptions(self):
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.negative_binomial, 100, np.nan)
+            assert_raises(ValueError, random.negative_binomial, 100,
+                          [np.nan] * 10)
+
+    def test_noncentral_chisquare(self):
+        random.seed(self.seed)
+        actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[23.91905354498517511, 13.35324692733826346],
+                            [31.22452661329736401, 16.60047399466177254],
+                            [5.03461598262724586, 17.94973089023519464]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[1.47145377828516666,  0.15052899268012659],
+                            [0.00943803056963588,  1.02647251615666169],
+                            [0.332334982684171,  0.15451287602753125]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random.seed(self.seed)
+        actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[9.597154162763948, 11.725484450296079],
+                            [10.413711048138335, 3.694475922923986],
+                            [13.484222138963087, 14.377255424602957]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        random.seed(self.seed)
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                     size=(3, 2))
+        desired = np.array([[1.40598099674926669, 0.34207973179285761],
+                            [3.57715069265772545, 7.92632662577829805],
+                            [0.43741599463544162, 1.1774208752428319]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f_nan(self):
+        random.seed(self.seed)
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
+        assert np.isnan(actual)
+
+    def test_normal(self):
+        random.seed(self.seed)
+        actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[2.80378370443726244, 3.59863924443872163],
+                            [3.121433477601256, -0.33382987590723379],
+                            [4.18552478636557357, 4.46410668111310471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(random.normal(scale=0), 0)
+        assert_raises(ValueError, random.normal, scale=-0.)
+
+    def test_pareto(self):
+        random.seed(self.seed)
+        actual = random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array(
+                [[2.46852460439034849e+03, 1.41286880810518346e+03],
+                 [5.28287797029485181e+07, 6.57720981047328785e+07],
+                 [1.40840323350391515e+02, 1.98390255135251704e+05]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        random.seed(self.seed)
+        actual = random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [1, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('l').max
+        lamneg = -1
+        assert_raises(ValueError, random.poisson, lamneg)
+        assert_raises(ValueError, random.poisson, [lamneg] * 10)
+        assert_raises(ValueError, random.poisson, lambig)
+        assert_raises(ValueError, random.poisson, [lambig] * 10)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.poisson, np.nan)
+            assert_raises(ValueError, random.poisson, [np.nan] * 10)
+
+    def test_power(self):
+        random.seed(self.seed)
+        actual = random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[0.02048932883240791, 0.01424192241128213],
+                            [0.38446073748535298, 0.39499689943484395],
+                            [0.00177699707563439, 0.13115505880863756]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        random.seed(self.seed)
+        actual = random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[13.8882496494248393, 13.383318339044731],
+                            [20.95413364294492098, 21.08285015800712614],
+                            [11.06066537006854311, 17.35468505778271009]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        random.seed(self.seed)
+        actual = random.standard_cauchy(size=(3, 2))
+        desired = np.array([[0.77127660196445336, -6.55601161955910605],
+                            [0.93582023391158309, -2.07479293013759447],
+                            [-4.74601644297011926, 0.18338989290760804]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        random.seed(self.seed)
+        actual = random.standard_exponential(size=(3, 2))
+        desired = np.array([[0.96441739162374596, 0.89556604882105506],
+                            [2.1953785836319808, 2.22243285392490542],
+                            [0.6116915921431676, 1.50592546727413201]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_gamma(self):
+        random.seed(self.seed)
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[5.50841531318455058, 6.62953470301903103],
+                            [5.93988484943779227, 2.31044849402133989],
+                            [7.54838614231317084, 8.012756093271868]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gamma_0(self):
+        assert_equal(random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        random.seed(self.seed)
+        actual = random.standard_normal(size=(3, 2))
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                            [1.498988344300628, -0.2286433324536169],
+                            [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn_singleton(self):
+        random.seed(self.seed)
+        actual = random.randn()
+        desired = np.array(1.34016345771863121)
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_t(self):
+        random.seed(self.seed)
+        actual = random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[0.97140611862659965, -0.08830486548450577],
+                            [1.36311143689505321, -0.55317463909867071],
+                            [-0.18473749069684214, 0.61181537341755321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        random.seed(self.seed)
+        actual = random.triangular(left=5.12, mode=10.23, right=20.34,
+                                   size=(3, 2))
+        desired = np.array([[12.68117178949215784, 12.4129206149193152],
+                            [16.20131377335158263, 16.25692138747600524],
+                            [11.20400690911820263, 14.4978144835829923]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        random.seed(self.seed)
+        actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[6.99097932346268003, 6.73801597444323974],
+                            [9.50364421400426274, 9.53130618907631089],
+                            [5.48995325769805476, 8.47493103280052118]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func, 0, np.inf)
+        assert_raises(OverflowError, func, fmin, fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        random.seed(self.seed)
+        actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[2.28567572673902042, 2.89163838442285037],
+                            [0.38198375564286025, 2.57638023113890746],
+                            [1.19153771588353052, 1.83509849681825354]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        random.seed(self.seed)
+        r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        assert_(np.isfinite(r).all())
+
+    def test_vonmises_large(self):
+        # guard against changes in RandomState when Generator is fixed
+        random.seed(self.seed)
+        actual = random.vonmises(mu=0., kappa=1e7, size=3)
+        desired = np.array([4.634253748521111e-04,
+                            3.558873596114509e-04,
+                            -2.337119622577433e-04])
+        assert_array_almost_equal(actual, desired, decimal=8)
+
+    def test_vonmises_nan(self):
+        random.seed(self.seed)
+        r = random.vonmises(mu=0., kappa=np.nan)
+        assert_(np.isnan(r))
+
+    def test_wald(self):
+        random.seed(self.seed)
+        actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[3.82935265715889983, 5.13125249184285526],
+                            [0.35045403618358717, 1.50832396872003538],
+                            [0.24124319895843183, 0.22031101461955038]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        random.seed(self.seed)
+        actual = random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.97097342648766727, 0.91422896443565516],
+                            [1.89517770034962929, 1.91414357960479564],
+                            [0.67057783752390987, 1.39494046635066793]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        random.seed(self.seed)
+        assert_equal(random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, random.weibull, a=-0.)
+
+    def test_zipf(self):
+        random.seed(self.seed)
+        actual = random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[66, 29],
+                            [1, 1],
+                            [3, 13]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def set_seed(self):
+        random.seed(self.seed)
+
+    def test_uniform(self):
+        low = [0]
+        high = [1]
+        uniform = random.uniform
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.set_seed()
+        actual = uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        self.set_seed()
+        actual = uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        normal = random.normal
+        desired = np.array([2.2129019979039612,
+                            2.1283977976520019,
+                            1.8417114045748335])
+
+        self.set_seed()
+        actual = normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        beta = random.beta
+        desired = np.array([0.19843558305989056,
+                            0.075230336409423643,
+                            0.24976865978980844])
+
+        self.set_seed()
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        self.set_seed()
+        actual = beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a, b * 3)
+        assert_raises(ValueError, beta, a, bad_b * 3)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        exponential = random.exponential
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        std_gamma = random.standard_gamma
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        gamma = random.gamma
+        desired = np.array([1.5221370731769048,
+                            1.5277256455738331,
+                            1.4248762625178359])
+
+        self.set_seed()
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        self.set_seed()
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        f = random.f
+        desired = np.array([0.80038951638264799,
+                            0.86768719635363512,
+                            2.7251095168386801])
+
+        self.set_seed()
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        self.set_seed()
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        nonc_f = random.noncentral_f
+        desired = np.array([9.1393943263705211,
+                            13.025456344595602,
+                            8.8018098359100545])
+
+        self.set_seed()
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))
+
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        self.set_seed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        chisquare = random.chisquare
+        desired = np.array([0.57022801133088286,
+                            0.51947702108840776,
+                            0.1320969254923558])
+
+        self.set_seed()
+        actual = chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        nonc_chi = random.noncentral_chisquare
+        desired = np.array([9.0015599467913763,
+                            4.5804135049718742,
+                            6.0872302432834564])
+
+        self.set_seed()
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        t = random.standard_t
+        desired = np.array([3.0702872575217643,
+                            5.8560725167361607,
+                            1.0274791436474273])
+
+        self.set_seed()
+        actual = t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, t, bad_df * 3)
+        assert_raises(ValueError, random.standard_t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        vonmises = random.vonmises
+        desired = np.array([2.9883443664201312,
+                            -2.7064099483995943,
+                            -1.8672476700665914])
+
+        self.set_seed()
+        actual = vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
+
+        self.set_seed()
+        actual = vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        pareto = random.pareto
+        desired = np.array([1.1405622680198362,
+                            1.1465519762044529,
+                            1.0389564467453547])
+
+        self.set_seed()
+        actual = pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, pareto, bad_a * 3)
+        assert_raises(ValueError, random.pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        weibull = random.weibull
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, weibull, bad_a * 3)
+        assert_raises(ValueError, random.weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        power = random.power
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.set_seed()
+        actual = power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, power, bad_a * 3)
+        assert_raises(ValueError, random.power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        laplace = random.laplace
+        desired = np.array([0.067921356028507157,
+                            0.070715642226971326,
+                            0.019290950698972624])
+
+        self.set_seed()
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        gumbel = random.gumbel
+        desired = np.array([0.2730318639556768,
+                            0.26936705726291116,
+                            0.33906220393037939])
+
+        self.set_seed()
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        logistic = random.logistic
+        desired = np.array([0.13152135837586171,
+                            0.13675915696285773,
+                            0.038216792802833396])
+
+        self.set_seed()
+        actual = logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc, bad_scale * 3)
+        assert_equal(random.logistic(1.0, 0.0), 1.0)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        lognormal = random.lognormal
+        desired = np.array([9.1422086044848427,
+                            8.4013952870126261,
+                            6.3073234116578671])
+
+        self.set_seed()
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+        assert_raises(ValueError, random.lognormal, mean * 3, bad_sigma)
+
+        self.set_seed()
+        actual = lognormal(mean, sigma * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
+        assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        rayleigh = random.rayleigh
+        desired = np.array([1.2337491937897689,
+                            1.2360119924878694,
+                            1.1936818095781789])
+
+        self.set_seed()
+        actual = rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        wald = random.wald
+        desired = np.array([0.11873681120271318,
+                            0.12450084820795027,
+                            0.9096122728408238])
+
+        self.set_seed()
+        actual = wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean * 3, scale)
+        assert_raises(ValueError, wald, mean * 3, bad_scale)
+        assert_raises(ValueError, random.wald, bad_mean * 3, scale)
+        assert_raises(ValueError, random.wald, mean * 3, bad_scale)
+
+        self.set_seed()
+        actual = wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean, scale * 3)
+        assert_raises(ValueError, wald, mean, bad_scale * 3)
+        assert_raises(ValueError, wald, 0.0, 1)
+        assert_raises(ValueError, wald, 0.5, 0.0)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        triangular = random.triangular
+        desired = np.array([2.03339048710429,
+                            2.0347400359389356,
+                            2.0095991069536208])
+
+        self.set_seed()
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        self.set_seed()
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        self.set_seed()
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+        assert_raises(ValueError, triangular, 10., 0., 20.)
+        assert_raises(ValueError, triangular, 10., 25., 20.)
+        assert_raises(ValueError, triangular, 10., 10., 10.)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        binom = random.binomial
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        self.set_seed()
+        actual = binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        neg_binom = random.negative_binomial
+        desired = np.array([1, 0, 1])
+
+        self.set_seed()
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        self.set_seed()
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+        max_lam = random.RandomState()._poisson_lam_max
+
+        lam = [1]
+        bad_lam_one = [-1]
+        bad_lam_two = [max_lam * 2]
+        poisson = random.poisson
+        desired = np.array([1, 1, 0])
+
+        self.set_seed()
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        zipf = random.zipf
+        desired = np.array([2, 2, 1])
+
+        self.set_seed()
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        geom = random.geometric
+        desired = np.array([2, 2, 2])
+
+        self.set_seed()
+        actual = geom(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geom, bad_p_one * 3)
+        assert_raises(ValueError, geom, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [0]
+        bad_nsample_two = [4]
+        hypergeom = random.hypergeometric
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = hypergeom(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
+
+        self.set_seed()
+        actual = hypergeom(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
+
+        self.set_seed()
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+        assert_raises(ValueError, hypergeom, -1, 10, 20)
+        assert_raises(ValueError, hypergeom, 10, -1, 20)
+        assert_raises(ValueError, hypergeom, 10, 10, 0)
+        assert_raises(ValueError, hypergeom, 10, 10, 25)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        logseries = random.logseries
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(random.RandomState(s), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(random.RandomState(s), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)
+
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (random.exponential, random.standard_gamma,
+                 random.chisquare, random.standard_t,
+                 random.pareto, random.weibull,
+                 random.power, random.rayleigh,
+                 random.poisson, random.zipf,
+                 random.geometric, random.logseries)
+
+        probfuncs = (random.geometric, random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (random.uniform, random.normal,
+                 random.beta, random.gamma,
+                 random.f, random.noncentral_chisquare,
+                 random.vonmises, random.laplace,
+                 random.gumbel, random.logistic,
+                 random.lognormal, random.wald,
+                 random.binomial, random.negative_binomial)
+
+        probfuncs = (random.binomial, random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [random.noncentral_f, random.triangular,
+                 random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+
+# Ensure returned array dtype is correct for platform
+def test_integer_dtype(int_func):
+    random.seed(123456789)
+    fname, args, sha256 = int_func
+    f = getattr(random, fname)
+    actual = f(*args, size=2)
+    assert_(actual.dtype == np.dtype('l'))
+
+
+def test_integer_repeat(int_func):
+    random.seed(123456789)
+    fname, args, sha256 = int_func
+    f = getattr(random, fname)
+    val = f(*args, size=1000000)
+    if sys.byteorder != 'little':
+        val = val.byteswap()
+    res = hashlib.sha256(val.view(np.int8)).hexdigest()
+    assert_(res == sha256)
+
+
+def test_broadcast_size_error():
+    # GH-16833
+    with pytest.raises(ValueError):
+        random.binomial(1, [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], 0.3, size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], [0.3, 0.7], size=(2, 1))
+
+
+def test_randomstate_ctor_old_style_pickle():
+    rs = np.random.RandomState(MT19937(0))
+    rs.standard_normal(1)
+    # Directly call reduce which is used in pickling
+    ctor, args, state_a = rs.__reduce__()
+    # Simulate unpickling an old pickle that only has the name
+    assert args[0].__class__.__name__ == "MT19937"
+    b = ctor(*("MT19937",))
+    b.set_state(state_a)
+    state_b = b.get_state(legacy=False)
+
+    assert_equal(state_a['bit_generator'], state_b['bit_generator'])
+    assert_array_equal(state_a['state']['key'], state_b['state']['key'])
+    assert_array_equal(state_a['state']['pos'], state_b['state']['pos'])
+    assert_equal(state_a['has_gauss'], state_b['has_gauss'])
+    assert_equal(state_a['gauss'], state_b['gauss'])
+
+
+def test_hot_swap(restore_singleton_bitgen):
+    # GH 21808
+    def_bg = np.random.default_rng(0)
+    bg = def_bg.bit_generator
+    np.random.set_bit_generator(bg)
+    assert isinstance(np.random.mtrand._rand._bit_generator, type(bg))
+
+    second_bg = np.random.get_bit_generator()
+    assert bg is second_bg
+
+
+def test_seed_alt_bit_gen(restore_singleton_bitgen):
+    # GH 21808
+    bg = PCG64(0)
+    np.random.set_bit_generator(bg)
+    state = np.random.get_state(legacy=False)
+    np.random.seed(1)
+    new_state = np.random.get_state(legacy=False)
+    print(state)
+    print(new_state)
+    assert state["bit_generator"] == "PCG64"
+    assert state["state"]["state"] != new_state["state"]["state"]
+    assert state["state"]["inc"] != new_state["state"]["inc"]
+
+
+def test_state_error_alt_bit_gen(restore_singleton_bitgen):
+    # GH 21808
+    state = np.random.get_state()
+    bg = PCG64(0)
+    np.random.set_bit_generator(bg)
+    with pytest.raises(ValueError, match="state must be for a PCG64"):
+        np.random.set_state(state)
+
+
+def test_swap_worked(restore_singleton_bitgen):
+    # GH 21808
+    np.random.seed(98765)
+    vals = np.random.randint(0, 2 ** 30, 10)
+    bg = PCG64(0)
+    state = bg.state
+    np.random.set_bit_generator(bg)
+    state_direct = np.random.get_state(legacy=False)
+    for field in state:
+        assert state[field] == state_direct[field]
+    np.random.seed(98765)
+    pcg_vals = np.random.randint(0, 2 ** 30, 10)
+    assert not np.all(vals == pcg_vals)
+    new_state = bg.state
+    assert new_state["state"]["state"] != state["state"]["state"]
+    assert new_state["state"]["inc"] == new_state["state"]["inc"]
+
+
+def test_swapped_singleton_against_direct(restore_singleton_bitgen):
+    np.random.set_bit_generator(PCG64(98765))
+    singleton_vals = np.random.randint(0, 2 ** 30, 10)
+    rg = np.random.RandomState(PCG64(98765))
+    non_singleton_vals = rg.randint(0, 2 ** 30, 10)
+    assert_equal(non_singleton_vals, singleton_vals)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_randomstate_regression.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_randomstate_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fd8776c7f969c71c7d0046142598219bd3374b3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_randomstate_regression.py
@@ -0,0 +1,216 @@
+import sys
+
+import pytest
+
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises,
+    )
+import numpy as np
+
+from numpy import random
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = random.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        is_64bits = sys.maxsize > 2**32
+        if is_64bits and sys.platform != 'win32':
+            # Check for 64-bit systems
+            args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
+        for arg in args:
+            assert_(random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        random.seed(0)
+        rvsn = random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            random.seed(12345)
+            shuffled = list(t)
+            random.shuffle(shuffled)
+            expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = random.RandomState()
+        res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
+        for i in range(3):
+            random.seed(i)
+            m.seed(4321)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        random.multivariate_normal([0], [[0]], size=1)
+        random.multivariate_normal([0], [[0]], size=np.int_(1))
+        random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        random.seed(1234567890)
+        x = random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        random.seed(1)
+        orig = np.arange(3).view(N)
+        perm = random.permutation(orig)
+        assert_array_equal(perm, np.array([0, 2, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        random.seed(1)
+        m = M()
+        perm = random.permutation(m)
+        assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_warns_byteorder(self):
+        # GH 13159
+        other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
+        with pytest.deprecated_call(match='non-native byteorder is not'):
+            random.randint(0, 200, size=10, dtype=other_byteord_dt)
+
+    def test_named_argument_initialization(self):
+        # GH 13669
+        rs1 = np.random.RandomState(123456789)
+        rs2 = np.random.RandomState(seed=123456789)
+        assert rs1.randint(0, 100) == rs2.randint(0, 100)
+
+    def test_choice_retun_dtype(self):
+        # GH 9867, now long since the NumPy default changed.
+        c = np.random.choice(10, p=[.1]*10, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = np.random.choice(10, p=[.1]*10, replace=False, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = np.random.choice(10, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = np.random.choice(10, replace=False, size=2)
+        assert c.dtype == np.dtype(np.long)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_randint_117(self):
+        # GH 14189
+        random.seed(0)
+        expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
+                             2588848963, 3684848379, 2340255427, 3638918503,
+                             1819583497, 2678185683], dtype='int64')
+        actual = random.randint(2**32, size=10)
+        assert_array_equal(actual, expected)
+
+    def test_p_zero_stream(self):
+        # Regression test for gh-14522.  Ensure that future versions
+        # generate the same variates as version 1.16.
+        np.random.seed(12345)
+        assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),
+                           [0, 0, 0, 1, 1])
+
+    def test_n_zero_stream(self):
+        # Regression test for gh-14522.  Ensure that future versions
+        # generate the same variates as version 1.16.
+        np.random.seed(8675309)
+        expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                             [3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])
+        assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),
+                           expected)
+
+
+def test_multinomial_empty():
+    # gh-20483
+    # Ensure that empty p-vals are correctly handled
+    assert random.multinomial(10, []).shape == (0,)
+    assert random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0)
+
+
+def test_multinomial_1d_pval():
+    # gh-20483
+    with pytest.raises(TypeError, match="pvals must be a 1-d"):
+        random.multinomial(10, 0.3)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_regression.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7b02dc4f7d7f161631e193caf43e3a3e109909c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_regression.py
@@ -0,0 +1,149 @@
+import sys
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises,
+    )
+from numpy import random
+import numpy as np
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = random.mtrand.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        is_64bits = sys.maxsize > 2**32
+        if is_64bits and sys.platform != 'win32':
+            # Check for 64-bit systems
+            args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
+        for arg in args:
+            assert_(np.random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        np.random.seed(0)
+        rvsn = np.random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            np.random.seed(12345)
+            shuffled = list(t)
+            random.shuffle(shuffled)
+            expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = np.random.RandomState()
+        res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
+        for i in range(3):
+            np.random.seed(i)
+            m.seed(4321)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        np.random.multivariate_normal([0], [[0]], size=1)
+        np.random.multivariate_normal([0], [[0]], size=np.int_(1))
+        np.random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        np.random.seed(1234567890)
+        x = np.random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        np.random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = np.random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        np.random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        np.random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        np.random.seed(1)
+        orig = np.arange(3).view(N)
+        perm = np.random.permutation(orig)
+        assert_array_equal(perm, np.array([0, 2, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        np.random.seed(1)
+        m = M()
+        perm = np.random.permutation(m)
+        assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
+        assert_array_equal(m.__array__(), np.arange(5))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_seed_sequence.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_seed_sequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..f08cf80faafa2fc1a369eaf7dd4d6fcccd5e9158
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_seed_sequence.py
@@ -0,0 +1,80 @@
+import numpy as np
+from numpy.testing import assert_array_equal, assert_array_compare
+
+from numpy.random import SeedSequence
+
+
+def test_reference_data():
+    """ Check that SeedSequence generates data the same as the C++ reference.
+
+    https://gist.github.com/imneme/540829265469e673d045
+    """
+    inputs = [
+        [3735928559, 195939070, 229505742, 305419896],
+        [3668361503, 4165561550, 1661411377, 3634257570],
+        [164546577, 4166754639, 1765190214, 1303880213],
+        [446610472, 3941463886, 522937693, 1882353782],
+        [1864922766, 1719732118, 3882010307, 1776744564],
+        [4141682960, 3310988675, 553637289, 902896340],
+        [1134851934, 2352871630, 3699409824, 2648159817],
+        [1240956131, 3107113773, 1283198141, 1924506131],
+        [2669565031, 579818610, 3042504477, 2774880435],
+        [2766103236, 2883057919, 4029656435, 862374500],
+    ]
+    outputs = [
+        [3914649087, 576849849, 3593928901, 2229911004],
+        [2240804226, 3691353228, 1365957195, 2654016646],
+        [3562296087, 3191708229, 1147942216, 3726991905],
+        [1403443605, 3591372999, 1291086759, 441919183],
+        [1086200464, 2191331643, 560336446, 3658716651],
+        [3249937430, 2346751812, 847844327, 2996632307],
+        [2584285912, 4034195531, 3523502488, 169742686],
+        [959045797, 3875435559, 1886309314, 359682705],
+        [3978441347, 432478529, 3223635119, 138903045],
+        [296367413, 4262059219, 13109864, 3283683422],
+    ]
+    outputs64 = [
+        [2477551240072187391, 9577394838764454085],
+        [15854241394484835714, 11398914698975566411],
+        [13708282465491374871, 16007308345579681096],
+        [15424829579845884309, 1898028439751125927],
+        [9411697742461147792, 15714068361935982142],
+        [10079222287618677782, 12870437757549876199],
+        [17326737873898640088, 729039288628699544],
+        [16644868984619524261, 1544825456798124994],
+        [1857481142255628931, 596584038813451439],
+        [18305404959516669237, 14103312907920476776],
+    ]
+    for seed, expected, expected64 in zip(inputs, outputs, outputs64):
+        expected = np.array(expected, dtype=np.uint32)
+        ss = SeedSequence(seed)
+        state = ss.generate_state(len(expected))
+        assert_array_equal(state, expected)
+        state64 = ss.generate_state(len(expected64), dtype=np.uint64)
+        assert_array_equal(state64, expected64)
+
+
+def test_zero_padding():
+    """ Ensure that the implicit zero-padding does not cause problems.
+    """
+    # Ensure that large integers are inserted in little-endian fashion to avoid
+    # trailing 0s.
+    ss0 = SeedSequence(42)
+    ss1 = SeedSequence(42 << 32)
+    assert_array_compare(
+        np.not_equal,
+        ss0.generate_state(4),
+        ss1.generate_state(4))
+
+    # Ensure backwards compatibility with the original 0.17 release for small
+    # integers and no spawn key.
+    expected42 = np.array([3444837047, 2669555309, 2046530742, 3581440988],
+                          dtype=np.uint32)
+    assert_array_equal(SeedSequence(42).generate_state(4), expected42)
+
+    # Regression test for gh-16539 to ensure that the implicit 0s don't
+    # conflict with spawn keys.
+    assert_array_compare(
+        np.not_equal,
+        SeedSequence(42, spawn_key=(0,)).generate_state(4),
+        expected42)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_smoke.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_smoke.py
new file mode 100644
index 0000000000000000000000000000000000000000..b402e87384d6fcee08b6351bbbf8ef7587b890e8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/random/tests/test_smoke.py
@@ -0,0 +1,818 @@
+import pickle
+from functools import partial
+
+import numpy as np
+import pytest
+from numpy.testing import assert_equal, assert_, assert_array_equal
+from numpy.random import (Generator, MT19937, PCG64, PCG64DXSM, Philox, SFC64)
+
+@pytest.fixture(scope='module',
+                params=(np.bool, np.int8, np.int16, np.int32, np.int64,
+                        np.uint8, np.uint16, np.uint32, np.uint64))
+def dtype(request):
+    return request.param
+
+
+def params_0(f):
+    val = f()
+    assert_(np.isscalar(val))
+    val = f(10)
+    assert_(val.shape == (10,))
+    val = f((10, 10))
+    assert_(val.shape == (10, 10))
+    val = f((10, 10, 10))
+    assert_(val.shape == (10, 10, 10))
+    val = f(size=(5, 5))
+    assert_(val.shape == (5, 5))
+
+
+def params_1(f, bounded=False):
+    a = 5.0
+    b = np.arange(2.0, 12.0)
+    c = np.arange(2.0, 102.0).reshape((10, 10))
+    d = np.arange(2.0, 1002.0).reshape((10, 10, 10))
+    e = np.array([2.0, 3.0])
+    g = np.arange(2.0, 12.0).reshape((1, 10, 1))
+    if bounded:
+        a = 0.5
+        b = b / (1.5 * b.max())
+        c = c / (1.5 * c.max())
+        d = d / (1.5 * d.max())
+        e = e / (1.5 * e.max())
+        g = g / (1.5 * g.max())
+
+    # Scalar
+    f(a)
+    # Scalar - size
+    f(a, size=(10, 10))
+    # 1d
+    f(b)
+    # 2d
+    f(c)
+    # 3d
+    f(d)
+    # 1d size
+    f(b, size=10)
+    # 2d - size - broadcast
+    f(e, size=(10, 2))
+    # 3d - size
+    f(g, size=(10, 10, 10))
+
+
+def comp_state(state1, state2):
+    identical = True
+    if isinstance(state1, dict):
+        for key in state1:
+            identical &= comp_state(state1[key], state2[key])
+    elif type(state1) != type(state2):
+        identical &= type(state1) == type(state2)
+    else:
+        if (isinstance(state1, (list, tuple, np.ndarray)) and isinstance(
+                state2, (list, tuple, np.ndarray))):
+            for s1, s2 in zip(state1, state2):
+                identical &= comp_state(s1, s2)
+        else:
+            identical &= state1 == state2
+    return identical
+
+
+def warmup(rg, n=None):
+    if n is None:
+        n = 11 + np.random.randint(0, 20)
+    rg.standard_normal(n)
+    rg.standard_normal(n)
+    rg.standard_normal(n, dtype=np.float32)
+    rg.standard_normal(n, dtype=np.float32)
+    rg.integers(0, 2 ** 24, n, dtype=np.uint64)
+    rg.integers(0, 2 ** 48, n, dtype=np.uint64)
+    rg.standard_gamma(11.0, n)
+    rg.standard_gamma(11.0, n, dtype=np.float32)
+    rg.random(n, dtype=np.float64)
+    rg.random(n, dtype=np.float32)
+
+
+class RNG:
+    @classmethod
+    def setup_class(cls):
+        # Overridden in test classes. Place holder to silence IDE noise
+        cls.bit_generator = PCG64
+        cls.advance = None
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+    @classmethod
+    def _extra_setup(cls):
+        cls.vec_1d = np.arange(2.0, 102.0)
+        cls.vec_2d = np.arange(2.0, 102.0)[None, :]
+        cls.mat = np.arange(2.0, 102.0, 0.01).reshape((100, 100))
+        cls.seed_error = TypeError
+
+    def _reset_state(self):
+        self.rg.bit_generator.state = self.initial_state
+
+    def test_init(self):
+        rg = Generator(self.bit_generator())
+        state = rg.bit_generator.state
+        rg.standard_normal(1)
+        rg.standard_normal(1)
+        rg.bit_generator.state = state
+        new_state = rg.bit_generator.state
+        assert_(comp_state(state, new_state))
+
+    def test_advance(self):
+        state = self.rg.bit_generator.state
+        if hasattr(self.rg.bit_generator, 'advance'):
+            self.rg.bit_generator.advance(self.advance)
+            assert_(not comp_state(state, self.rg.bit_generator.state))
+        else:
+            bitgen_name = self.rg.bit_generator.__class__.__name__
+            pytest.skip(f'Advance is not supported by {bitgen_name}')
+
+    def test_jump(self):
+        state = self.rg.bit_generator.state
+        if hasattr(self.rg.bit_generator, 'jumped'):
+            bit_gen2 = self.rg.bit_generator.jumped()
+            jumped_state = bit_gen2.state
+            assert_(not comp_state(state, jumped_state))
+            self.rg.random(2 * 3 * 5 * 7 * 11 * 13 * 17)
+            self.rg.bit_generator.state = state
+            bit_gen3 = self.rg.bit_generator.jumped()
+            rejumped_state = bit_gen3.state
+            assert_(comp_state(jumped_state, rejumped_state))
+        else:
+            bitgen_name = self.rg.bit_generator.__class__.__name__
+            if bitgen_name not in ('SFC64',):
+                raise AttributeError(f'no "jumped" in {bitgen_name}')
+            pytest.skip(f'Jump is not supported by {bitgen_name}')
+
+    def test_uniform(self):
+        r = self.rg.uniform(-1.0, 0.0, size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+
+    def test_uniform_array(self):
+        r = self.rg.uniform(np.array([-1.0] * 10), 0.0, size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+        r = self.rg.uniform(np.array([-1.0] * 10),
+                            np.array([0.0] * 10), size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+        r = self.rg.uniform(-1.0, np.array([0.0] * 10), size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+
+    def test_random(self):
+        assert_(len(self.rg.random(10)) == 10)
+        params_0(self.rg.random)
+
+    def test_standard_normal_zig(self):
+        assert_(len(self.rg.standard_normal(10)) == 10)
+
+    def test_standard_normal(self):
+        assert_(len(self.rg.standard_normal(10)) == 10)
+        params_0(self.rg.standard_normal)
+
+    def test_standard_gamma(self):
+        assert_(len(self.rg.standard_gamma(10, 10)) == 10)
+        assert_(len(self.rg.standard_gamma(np.array([10] * 10), 10)) == 10)
+        params_1(self.rg.standard_gamma)
+
+    def test_standard_exponential(self):
+        assert_(len(self.rg.standard_exponential(10)) == 10)
+        params_0(self.rg.standard_exponential)
+
+    def test_standard_exponential_float(self):
+        randoms = self.rg.standard_exponential(10, dtype='float32')
+        assert_(len(randoms) == 10)
+        assert randoms.dtype == np.float32
+        params_0(partial(self.rg.standard_exponential, dtype='float32'))
+
+    def test_standard_exponential_float_log(self):
+        randoms = self.rg.standard_exponential(10, dtype='float32',
+                                               method='inv')
+        assert_(len(randoms) == 10)
+        assert randoms.dtype == np.float32
+        params_0(partial(self.rg.standard_exponential, dtype='float32',
+                         method='inv'))
+
+    def test_standard_cauchy(self):
+        assert_(len(self.rg.standard_cauchy(10)) == 10)
+        params_0(self.rg.standard_cauchy)
+
+    def test_standard_t(self):
+        assert_(len(self.rg.standard_t(10, 10)) == 10)
+        params_1(self.rg.standard_t)
+
+    def test_binomial(self):
+        assert_(self.rg.binomial(10, .5) >= 0)
+        assert_(self.rg.binomial(1000, .5) >= 0)
+
+    def test_reset_state(self):
+        state = self.rg.bit_generator.state
+        int_1 = self.rg.integers(2**31)
+        self.rg.bit_generator.state = state
+        int_2 = self.rg.integers(2**31)
+        assert_(int_1 == int_2)
+
+    def test_entropy_init(self):
+        rg = Generator(self.bit_generator())
+        rg2 = Generator(self.bit_generator())
+        assert_(not comp_state(rg.bit_generator.state,
+                               rg2.bit_generator.state))
+
+    def test_seed(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg2 = Generator(self.bit_generator(*self.seed))
+        rg.random()
+        rg2.random()
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_reset_state_gauss(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.standard_normal()
+        state = rg.bit_generator.state
+        n1 = rg.standard_normal(size=10)
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.standard_normal(size=10)
+        assert_array_equal(n1, n2)
+
+    def test_reset_state_uint32(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.integers(0, 2 ** 24, 120, dtype=np.uint32)
+        state = rg.bit_generator.state
+        n1 = rg.integers(0, 2 ** 24, 10, dtype=np.uint32)
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.integers(0, 2 ** 24, 10, dtype=np.uint32)
+        assert_array_equal(n1, n2)
+
+    def test_reset_state_float(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.random(dtype='float32')
+        state = rg.bit_generator.state
+        n1 = rg.random(size=10, dtype='float32')
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.random(size=10, dtype='float32')
+        assert_((n1 == n2).all())
+
+    def test_shuffle(self):
+        original = np.arange(200, 0, -1)
+        permuted = self.rg.permutation(original)
+        assert_((original != permuted).any())
+
+    def test_permutation(self):
+        original = np.arange(200, 0, -1)
+        permuted = self.rg.permutation(original)
+        assert_((original != permuted).any())
+
+    def test_beta(self):
+        vals = self.rg.beta(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), 2.0)
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(2.0, np.array([2.0] * 10))
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), np.array([2.0] * 10))
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), np.array([[2.0]] * 10))
+        assert_(vals.shape == (10, 10))
+
+    def test_bytes(self):
+        vals = self.rg.bytes(10)
+        assert_(len(vals) == 10)
+
+    def test_chisquare(self):
+        vals = self.rg.chisquare(2.0, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.chisquare)
+
+    def test_exponential(self):
+        vals = self.rg.exponential(2.0, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.exponential)
+
+    def test_f(self):
+        vals = self.rg.f(3, 1000, 10)
+        assert_(len(vals) == 10)
+
+    def test_gamma(self):
+        vals = self.rg.gamma(3, 2, 10)
+        assert_(len(vals) == 10)
+
+    def test_geometric(self):
+        vals = self.rg.geometric(0.5, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.exponential, bounded=True)
+
+    def test_gumbel(self):
+        vals = self.rg.gumbel(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_laplace(self):
+        vals = self.rg.laplace(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_logitic(self):
+        vals = self.rg.logistic(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_logseries(self):
+        vals = self.rg.logseries(0.5, 10)
+        assert_(len(vals) == 10)
+
+    def test_negative_binomial(self):
+        vals = self.rg.negative_binomial(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_noncentral_chisquare(self):
+        vals = self.rg.noncentral_chisquare(10, 2, 10)
+        assert_(len(vals) == 10)
+
+    def test_noncentral_f(self):
+        vals = self.rg.noncentral_f(3, 1000, 2, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(np.array([3] * 10), 1000, 2)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(3, np.array([1000] * 10), 2)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(3, 1000, np.array([2] * 10))
+        assert_(len(vals) == 10)
+
+    def test_normal(self):
+        vals = self.rg.normal(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_pareto(self):
+        vals = self.rg.pareto(3.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_poisson(self):
+        vals = self.rg.poisson(10, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.poisson(np.array([10] * 10))
+        assert_(len(vals) == 10)
+        params_1(self.rg.poisson)
+
+    def test_power(self):
+        vals = self.rg.power(0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_integers(self):
+        vals = self.rg.integers(10, 20, 10)
+        assert_(len(vals) == 10)
+
+    def test_rayleigh(self):
+        vals = self.rg.rayleigh(0.2, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.rayleigh, bounded=True)
+
+    def test_vonmises(self):
+        vals = self.rg.vonmises(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_wald(self):
+        vals = self.rg.wald(1.0, 1.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_weibull(self):
+        vals = self.rg.weibull(1.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_zipf(self):
+        vals = self.rg.zipf(10, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.zipf(self.vec_1d)
+        assert_(len(vals) == 100)
+        vals = self.rg.zipf(self.vec_2d)
+        assert_(vals.shape == (1, 100))
+        vals = self.rg.zipf(self.mat)
+        assert_(vals.shape == (100, 100))
+
+    def test_hypergeometric(self):
+        vals = self.rg.hypergeometric(25, 25, 20)
+        assert_(np.isscalar(vals))
+        vals = self.rg.hypergeometric(np.array([25] * 10), 25, 20)
+        assert_(vals.shape == (10,))
+
+    def test_triangular(self):
+        vals = self.rg.triangular(-5, 0, 5)
+        assert_(np.isscalar(vals))
+        vals = self.rg.triangular(-5, np.array([0] * 10), 5)
+        assert_(vals.shape == (10,))
+
+    def test_multivariate_normal(self):
+        mean = [0, 0]
+        cov = [[1, 0], [0, 100]]  # diagonal covariance
+        x = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        x_zig = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        x_inv = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        assert_((x_zig != x_inv).any())
+
+    def test_multinomial(self):
+        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3])
+        assert_(vals.shape == (2,))
+        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3], size=10)
+        assert_(vals.shape == (10, 2))
+
+    def test_dirichlet(self):
+        s = self.rg.dirichlet((10, 5, 3), 20)
+        assert_(s.shape == (20, 3))
+
+    def test_pickle(self):
+        pick = pickle.dumps(self.rg)
+        unpick = pickle.loads(pick)
+        assert_(type(self.rg) == type(unpick))
+        assert_(comp_state(self.rg.bit_generator.state,
+                           unpick.bit_generator.state))
+
+        pick = pickle.dumps(self.rg)
+        unpick = pickle.loads(pick)
+        assert_(type(self.rg) == type(unpick))
+        assert_(comp_state(self.rg.bit_generator.state,
+                           unpick.bit_generator.state))
+
+    def test_seed_array(self):
+        if self.seed_vector_bits is None:
+            bitgen_name = self.bit_generator.__name__
+            pytest.skip(f'Vector seeding is not supported by {bitgen_name}')
+
+        if self.seed_vector_bits == 32:
+            dtype = np.uint32
+        else:
+            dtype = np.uint64
+        seed = np.array([1], dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(1)
+        state2 = bg.state
+        assert_(comp_state(state1, state2))
+
+        seed = np.arange(4, dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+        seed = np.arange(1500, dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+        seed = 2 ** np.mod(np.arange(1500, dtype=dtype),
+                           self.seed_vector_bits - 1) + 1
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg  = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+    def test_uniform_float(self):
+        rg = Generator(self.bit_generator(12345))
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.random(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.random(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_gamma_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_gamma(4.0, 11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_gamma(4.0, 11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_normal_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_normal(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_normal(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_normal_zig_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_normal(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_normal(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_output_fill(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_normal(size=size)
+        assert_equal(direct, existing)
+
+        sized = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=sized, size=sized.shape)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_normal(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_uniform(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.random(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.random(size=size)
+        assert_equal(direct, existing)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.random(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.random(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_exponential(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_exponential(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_exponential(size=size)
+        assert_equal(direct, existing)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_exponential(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_exponential(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_gamma(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.zeros(size)
+        rg.bit_generator.state = state
+        rg.standard_gamma(1.0, out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(1.0, size=size)
+        assert_equal(direct, existing)
+
+        existing = np.zeros(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_gamma(1.0, out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(1.0, size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_gamma_broadcast(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        mu = np.arange(97.0) + 1.0
+        existing = np.zeros(size)
+        rg.bit_generator.state = state
+        rg.standard_gamma(mu, out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(mu, size=size)
+        assert_equal(direct, existing)
+
+        existing = np.zeros(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_gamma(mu, out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(mu, size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_fill_error(self):
+        rg = self.rg
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        with pytest.raises(TypeError):
+            rg.standard_normal(out=existing, dtype=np.float32)
+        with pytest.raises(ValueError):
+            rg.standard_normal(out=existing[::3])
+        existing = np.empty(size, dtype=np.float32)
+        with pytest.raises(TypeError):
+            rg.standard_normal(out=existing, dtype=np.float64)
+
+        existing = np.zeros(size, dtype=np.float32)
+        with pytest.raises(TypeError):
+            rg.standard_gamma(1.0, out=existing, dtype=np.float64)
+        with pytest.raises(ValueError):
+            rg.standard_gamma(1.0, out=existing[::3], dtype=np.float32)
+        existing = np.zeros(size, dtype=np.float64)
+        with pytest.raises(TypeError):
+            rg.standard_gamma(1.0, out=existing, dtype=np.float32)
+        with pytest.raises(ValueError):
+            rg.standard_gamma(1.0, out=existing[::3])
+
+    def test_integers_broadcast(self, dtype):
+        if dtype == np.bool:
+            upper = 2
+            lower = 0
+        else:
+            info = np.iinfo(dtype)
+            upper = int(info.max) + 1
+            lower = info.min
+        self._reset_state()
+        a = self.rg.integers(lower, [upper] * 10, dtype=dtype)
+        self._reset_state()
+        b = self.rg.integers([lower] * 10, upper, dtype=dtype)
+        assert_equal(a, b)
+        self._reset_state()
+        c = self.rg.integers(lower, upper, size=10, dtype=dtype)
+        assert_equal(a, c)
+        self._reset_state()
+        d = self.rg.integers(np.array(
+            [lower] * 10), np.array([upper], dtype=object), size=10,
+            dtype=dtype)
+        assert_equal(a, d)
+        self._reset_state()
+        e = self.rg.integers(
+            np.array([lower] * 10), np.array([upper] * 10), size=10,
+            dtype=dtype)
+        assert_equal(a, e)
+
+        self._reset_state()
+        a = self.rg.integers(0, upper, size=10, dtype=dtype)
+        self._reset_state()
+        b = self.rg.integers([upper] * 10, dtype=dtype)
+        assert_equal(a, b)
+
+    def test_integers_numpy(self, dtype):
+        high = np.array([1])
+        low = np.array([0])
+
+        out = self.rg.integers(low, high, dtype=dtype)
+        assert out.shape == (1,)
+
+        out = self.rg.integers(low[0], high, dtype=dtype)
+        assert out.shape == (1,)
+
+        out = self.rg.integers(low, high[0], dtype=dtype)
+        assert out.shape == (1,)
+
+    def test_integers_broadcast_errors(self, dtype):
+        if dtype == np.bool:
+            upper = 2
+            lower = 0
+        else:
+            info = np.iinfo(dtype)
+            upper = int(info.max) + 1
+            lower = info.min
+        with pytest.raises(ValueError):
+            self.rg.integers(lower, [upper + 1] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers(lower - 1, [upper] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers([lower - 1], [upper] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers([0], [0], dtype=dtype)
+
+
+class TestMT19937(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = MT19937
+        cls.advance = None
+        cls.seed = [2 ** 21 + 2 ** 16 + 2 ** 5 + 1]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 32
+        cls._extra_setup()
+        cls.seed_error = ValueError
+
+    def test_numpy_state(self):
+        nprg = np.random.RandomState()
+        nprg.standard_normal(99)
+        state = nprg.get_state()
+        self.rg.bit_generator.state = state
+        state2 = self.rg.bit_generator.state
+        assert_((state[1] == state2['state']['key']).all())
+        assert_(state[2] == state2['state']['pos'])
+
+
+class TestPhilox(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = Philox
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestSFC64(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = SFC64
+        cls.advance = None
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 192
+        cls._extra_setup()
+
+
+class TestPCG64(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestPCG64DXSM(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64DXSM
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestDefaultRNG(RNG):
+    @classmethod
+    def setup_class(cls):
+        # This will duplicate some tests that directly instantiate a fresh
+        # Generator(), but that's okay.
+        cls.bit_generator = PCG64
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = np.random.default_rng(*cls.seed)
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+    def test_default_is_pcg64(self):
+        # In order to change the default BitGenerator, we'll go through
+        # a deprecation cycle to move to a different function.
+        assert_(isinstance(self.rg.bit_generator, PCG64))
+
+    def test_seed(self):
+        np.random.default_rng()
+        np.random.default_rng(None)
+        np.random.default_rng(12345)
+        np.random.default_rng(0)
+        np.random.default_rng(43660444402423911716352051725018508569)
+        np.random.default_rng([43660444402423911716352051725018508569,
+                               279705150948142787361475340226491943209])
+        with pytest.raises(ValueError):
+            np.random.default_rng(-1)
+        with pytest.raises(ValueError):
+            np.random.default_rng([12345, -1])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/rec/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/rec/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a439ada8c35a6971b5fa8507381bde63ead8a6e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/rec/__init__.py
@@ -0,0 +1,2 @@
+from numpy._core.records import __all__, __doc__
+from numpy._core.records import *
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/rec/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/rec/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..605770f7c9c0695bcbe71a3832690d9045a6038c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/rec/__init__.pyi
@@ -0,0 +1,22 @@
+from numpy._core.records import (
+    record,
+    recarray,
+    find_duplicate,
+    format_parser,
+    fromarrays,
+    fromrecords,
+    fromstring,
+    fromfile,
+    array,
+)
+__all__ = [
+    "record",
+    "recarray",
+    "format_parser",
+    "fromarrays",
+    "fromrecords",
+    "fromstring",
+    "fromfile",
+    "array",
+    "find_duplicate",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/strings/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/strings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f370ba71f296be0129c3e7aebc9af769dd83e94e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/strings/__init__.py
@@ -0,0 +1,2 @@
+from numpy._core.strings import __all__, __doc__
+from numpy._core.strings import *
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/strings/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/strings/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fb03e9c8b5e62912182623c5787ea554ed2a0bb9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/strings/__init__.pyi
@@ -0,0 +1,95 @@
+from numpy._core.strings import (
+    equal,
+    not_equal,
+    greater_equal,
+    less_equal,
+    greater,
+    less,
+    add,
+    multiply,
+    mod,
+    isalpha,
+    isalnum,
+    isdigit,
+    isspace,
+    isnumeric,
+    isdecimal,
+    islower,
+    isupper,
+    istitle,
+    str_len,
+    find,
+    rfind,
+    index,
+    rindex,
+    count,
+    startswith,
+    endswith,
+    decode,
+    encode,
+    expandtabs,
+    center,
+    ljust,
+    rjust,
+    lstrip,
+    rstrip,
+    strip,
+    zfill,
+    upper,
+    lower,
+    swapcase,
+    capitalize,
+    title,
+    replace,
+    partition,
+    rpartition,
+    translate,
+)
+
+__all__ = [
+    "equal",
+    "not_equal",
+    "less",
+    "less_equal",
+    "greater",
+    "greater_equal",
+    "add",
+    "multiply",
+    "isalpha",
+    "isdigit",
+    "isspace",
+    "isalnum",
+    "islower",
+    "isupper",
+    "istitle",
+    "isdecimal",
+    "isnumeric",
+    "str_len",
+    "find",
+    "rfind",
+    "index",
+    "rindex",
+    "count",
+    "startswith",
+    "endswith",
+    "lstrip",
+    "rstrip",
+    "strip",
+    "replace",
+    "expandtabs",
+    "center",
+    "ljust",
+    "rjust",
+    "zfill",
+    "partition",
+    "rpartition",
+    "upper",
+    "lower",
+    "swapcase",
+    "capitalize",
+    "title",
+    "mod",
+    "decode",
+    "encode",
+    "translate",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a34221e4dde5f8a1eeab7446193344915467769
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/__init__.py
@@ -0,0 +1,22 @@
+"""Common test support for all numpy test scripts.
+
+This single module should provide all the common functionality for numpy tests
+in a single location, so that test scripts can just import it and work right
+away.
+
+"""
+from unittest import TestCase
+
+from . import _private
+from ._private.utils import *
+from ._private.utils import (_assert_valid_refcount, _gen_alignment_data)
+from ._private import extbuild
+from . import overrides
+
+__all__ = (
+    _private.utils.__all__ + ['TestCase', 'overrides']
+)
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ba3c9a2b7a44bb8f4639fb8e4ab2e528b0a4e572
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/__init__.pyi
@@ -0,0 +1,102 @@
+from unittest import TestCase
+
+from . import overrides
+from ._private.utils import (
+    HAS_LAPACK64,
+    HAS_REFCOUNT,
+    IS_EDITABLE,
+    IS_INSTALLED,
+    IS_MUSL,
+    IS_PYPY,
+    IS_PYSTON,
+    IS_WASM,
+    NOGIL_BUILD,
+    NUMPY_ROOT,
+    IgnoreException,
+    KnownFailureException,
+    SkipTest,
+    assert_,
+    assert_allclose,
+    assert_almost_equal,
+    assert_approx_equal,
+    assert_array_almost_equal,
+    assert_array_almost_equal_nulp,
+    assert_array_compare,
+    assert_array_equal,
+    assert_array_less,
+    assert_array_max_ulp,
+    assert_equal,
+    assert_no_gc_cycles,
+    assert_no_warnings,
+    assert_raises,
+    assert_raises_regex,
+    assert_string_equal,
+    assert_warns,
+    break_cycles,
+    build_err_msg,
+    check_support_sve,
+    clear_and_catch_warnings,
+    decorate_methods,
+    jiffies,
+    measure,
+    memusage,
+    print_assert_equal,
+    run_threaded,
+    rundocs,
+    runstring,
+    suppress_warnings,
+    tempdir,
+    temppath,
+    verbose,
+)
+
+__all__ = [
+    "HAS_LAPACK64",
+    "HAS_REFCOUNT",
+    "IS_EDITABLE",
+    "IS_INSTALLED",
+    "IS_MUSL",
+    "IS_PYPY",
+    "IS_PYSTON",
+    "IS_WASM",
+    "NOGIL_BUILD",
+    "NUMPY_ROOT",
+    "IgnoreException",
+    "KnownFailureException",
+    "SkipTest",
+    "TestCase",
+    "assert_",
+    "assert_allclose",
+    "assert_almost_equal",
+    "assert_approx_equal",
+    "assert_array_almost_equal",
+    "assert_array_almost_equal_nulp",
+    "assert_array_compare",
+    "assert_array_equal",
+    "assert_array_less",
+    "assert_array_max_ulp",
+    "assert_equal",
+    "assert_no_gc_cycles",
+    "assert_no_warnings",
+    "assert_raises",
+    "assert_raises_regex",
+    "assert_string_equal",
+    "assert_warns",
+    "break_cycles",
+    "build_err_msg",
+    "check_support_sve",
+    "clear_and_catch_warnings",
+    "decorate_methods",
+    "jiffies",
+    "measure",
+    "memusage",
+    "overrides",
+    "print_assert_equal",
+    "run_threaded",
+    "rundocs",
+    "runstring",
+    "suppress_warnings",
+    "tempdir",
+    "temppath",
+    "verbose",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/__init__.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/extbuild.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/extbuild.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fd0d839f24962d17b974d59b08ded8c03277fac
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/extbuild.py
@@ -0,0 +1,252 @@
+"""
+Build a c-extension module on-the-fly in tests.
+See build_and_import_extensions for usage hints
+
+"""
+
+import os
+import pathlib
+import subprocess
+import sys
+import sysconfig
+import textwrap
+
+__all__ = ['build_and_import_extension', 'compile_extension_module']
+
+
+def build_and_import_extension(
+        modname, functions, *, prologue="", build_dir=None,
+        include_dirs=[], more_init=""):
+    """
+    Build and imports a c-extension module `modname` from a list of function
+    fragments `functions`.
+
+
+    Parameters
+    ----------
+    functions : list of fragments
+        Each fragment is a sequence of func_name, calling convention, snippet.
+    prologue : string
+        Code to precede the rest, usually extra ``#include`` or ``#define``
+        macros.
+    build_dir : pathlib.Path
+        Where to build the module, usually a temporary directory
+    include_dirs : list
+        Extra directories to find include files when compiling
+    more_init : string
+        Code to appear in the module PyMODINIT_FUNC
+
+    Returns
+    -------
+    out: module
+        The module will have been loaded and is ready for use
+
+    Examples
+    --------
+    >>> functions = [("test_bytes", "METH_O", \"\"\"
+        if ( !PyBytesCheck(args)) {
+            Py_RETURN_FALSE;
+        }
+        Py_RETURN_TRUE;
+    \"\"\")]
+    >>> mod = build_and_import_extension("testme", functions)
+    >>> assert not mod.test_bytes('abc')
+    >>> assert mod.test_bytes(b'abc')
+    """
+    body = prologue + _make_methods(functions, modname)
+    init = """
+    PyObject *mod = PyModule_Create(&moduledef);
+    #ifdef Py_GIL_DISABLED
+    PyUnstable_Module_SetGIL(mod, Py_MOD_GIL_NOT_USED);
+    #endif
+           """
+    if not build_dir:
+        build_dir = pathlib.Path('.')
+    if more_init:
+        init += """#define INITERROR return NULL
+                """
+        init += more_init
+    init += "\nreturn mod;"
+    source_string = _make_source(modname, init, body)
+    try:
+        mod_so = compile_extension_module(
+            modname, build_dir, include_dirs, source_string)
+    except Exception as e:
+        # shorten the exception chain
+        raise RuntimeError(f"could not compile in {build_dir}:") from e
+    import importlib.util
+    spec = importlib.util.spec_from_file_location(modname, mod_so)
+    foo = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(foo)
+    return foo
+
+
+def compile_extension_module(
+        name, builddir, include_dirs,
+        source_string, libraries=[], library_dirs=[]):
+    """
+    Build an extension module and return the filename of the resulting
+    native code file.
+
+    Parameters
+    ----------
+    name : string
+        name of the module, possibly including dots if it is a module inside a
+        package.
+    builddir : pathlib.Path
+        Where to build the module, usually a temporary directory
+    include_dirs : list
+        Extra directories to find include files when compiling
+    libraries : list
+        Libraries to link into the extension module
+    library_dirs: list
+        Where to find the libraries, ``-L`` passed to the linker
+    """
+    modname = name.split('.')[-1]
+    dirname = builddir / name
+    dirname.mkdir(exist_ok=True)
+    cfile = _convert_str_to_file(source_string, dirname)
+    include_dirs = include_dirs + [sysconfig.get_config_var('INCLUDEPY')]
+
+    return _c_compile(
+        cfile, outputfilename=dirname / modname,
+        include_dirs=include_dirs, libraries=[], library_dirs=[],
+        )
+
+
+def _convert_str_to_file(source, dirname):
+    """Helper function to create a file ``source.c`` in `dirname` that contains
+    the string in `source`. Returns the file name
+    """
+    filename = dirname / 'source.c'
+    with filename.open('w') as f:
+        f.write(str(source))
+    return filename
+
+
+def _make_methods(functions, modname):
+    """ Turns the name, signature, code in functions into complete functions
+    and lists them in a methods_table. Then turns the methods_table into a
+    ``PyMethodDef`` structure and returns the resulting code fragment ready
+    for compilation
+    """
+    methods_table = []
+    codes = []
+    for funcname, flags, code in functions:
+        cfuncname = "%s_%s" % (modname, funcname)
+        if 'METH_KEYWORDS' in flags:
+            signature = '(PyObject *self, PyObject *args, PyObject *kwargs)'
+        else:
+            signature = '(PyObject *self, PyObject *args)'
+        methods_table.append(
+            "{\"%s\", (PyCFunction)%s, %s}," % (funcname, cfuncname, flags))
+        func_code = """
+        static PyObject* {cfuncname}{signature}
+        {{
+        {code}
+        }}
+        """.format(cfuncname=cfuncname, signature=signature, code=code)
+        codes.append(func_code)
+
+    body = "\n".join(codes) + """
+    static PyMethodDef methods[] = {
+    %(methods)s
+    { NULL }
+    };
+    static struct PyModuleDef moduledef = {
+        PyModuleDef_HEAD_INIT,
+        "%(modname)s",  /* m_name */
+        NULL,           /* m_doc */
+        -1,             /* m_size */
+        methods,        /* m_methods */
+    };
+    """ % dict(methods='\n'.join(methods_table), modname=modname)
+    return body
+
+
+def _make_source(name, init, body):
+    """ Combines the code fragments into source code ready to be compiled
+    """
+    code = """
+    #include <Python.h>
+
+    %(body)s
+
+    PyMODINIT_FUNC
+    PyInit_%(name)s(void) {
+    %(init)s
+    }
+    """ % dict(
+        name=name, init=init, body=body,
+    )
+    return code
+
+
+def _c_compile(cfile, outputfilename, include_dirs=[], libraries=[],
+               library_dirs=[]):
+    if sys.platform == 'win32':
+        compile_extra = ["/we4013"]
+        link_extra = ["/LIBPATH:" + os.path.join(sys.base_prefix, 'libs')]
+    elif sys.platform.startswith('linux'):
+        compile_extra = [
+            "-O0", "-g", "-Werror=implicit-function-declaration", "-fPIC"]
+        link_extra = []
+    else:
+        compile_extra = link_extra = []
+        pass
+    if sys.platform == 'win32':
+        link_extra = link_extra + ['/DEBUG']  # generate .pdb file
+    if sys.platform == 'darwin':
+        # support Fink & Darwinports
+        for s in ('/sw/', '/opt/local/'):
+            if (s + 'include' not in include_dirs
+                    and os.path.exists(s + 'include')):
+                include_dirs.append(s + 'include')
+            if s + 'lib' not in library_dirs and os.path.exists(s + 'lib'):
+                library_dirs.append(s + 'lib')
+
+    outputfilename = outputfilename.with_suffix(get_so_suffix())
+    build(
+        cfile, outputfilename,
+        compile_extra, link_extra,
+        include_dirs, libraries, library_dirs)
+    return outputfilename
+
+
+def build(cfile, outputfilename, compile_extra, link_extra,
+          include_dirs, libraries, library_dirs):
+    "use meson to build"
+
+    build_dir = cfile.parent / "build"
+    os.makedirs(build_dir, exist_ok=True)
+    so_name = outputfilename.parts[-1]
+    with open(cfile.parent / "meson.build", "wt") as fid:
+        includes = ['-I' + d for d in include_dirs]
+        link_dirs = ['-L' + d for d in library_dirs]
+        fid.write(textwrap.dedent(f"""\
+            project('foo', 'c')
+            shared_module('{so_name}', '{cfile.parts[-1]}',
+                c_args: {includes} + {compile_extra},
+                link_args: {link_dirs} + {link_extra},
+                link_with: {libraries},
+                name_prefix: '',
+                name_suffix: 'dummy',
+            )
+        """))
+    if sys.platform == "win32":
+        subprocess.check_call(["meson", "setup",
+                               "--buildtype=release",
+                               "--vsenv", ".."],
+                              cwd=build_dir,
+                              )
+    else:
+        subprocess.check_call(["meson", "setup", "--vsenv", ".."],
+                              cwd=build_dir
+                              )
+    subprocess.check_call(["meson", "compile"], cwd=build_dir)
+    os.rename(str(build_dir / so_name) + ".dummy", cfile.parent / so_name)
+
+def get_so_suffix():
+    ret = sysconfig.get_config_var('EXT_SUFFIX')
+    assert ret
+    return ret
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/extbuild.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/extbuild.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..609a45e79d1614bb920b312ecd4449ef3b05a3f2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/extbuild.pyi
@@ -0,0 +1,25 @@
+import pathlib
+import types
+from collections.abc import Sequence
+
+__all__ = ["build_and_import_extension", "compile_extension_module"]
+
+def build_and_import_extension(
+    modname: str,
+    functions: Sequence[tuple[str, str, str]],
+    *,
+    prologue: str = "",
+    build_dir: pathlib.Path | None = None,
+    include_dirs: Sequence[str] = [],
+    more_init: str = "",
+) -> types.ModuleType: ...
+
+#
+def compile_extension_module(
+    name: str,
+    builddir: pathlib.Path,
+    include_dirs: Sequence[str],
+    source_string: str,
+    libraries: Sequence[str] = [],
+    library_dirs: Sequence[str] = [],
+) -> pathlib.Path: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..42e43e21f37bb68695c6383232ea2082c39898a4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/utils.py
@@ -0,0 +1,2760 @@
+"""
+Utility function to facilitate testing.
+
+"""
+import os
+import sys
+import pathlib
+import platform
+import re
+import gc
+import operator
+import warnings
+from functools import partial, wraps
+import shutil
+import contextlib
+from tempfile import mkdtemp, mkstemp
+from unittest.case import SkipTest
+from warnings import WarningMessage
+import pprint
+import sysconfig
+import concurrent.futures
+import threading
+import importlib.metadata
+
+import numpy as np
+from numpy._core import (
+     intp, float32, empty, arange, array_repr, ndarray, isnat, array)
+from numpy import isfinite, isnan, isinf
+import numpy.linalg._umath_linalg
+from numpy._utils import _rename_parameter
+from numpy._core.tests._natype import pd_NA
+
+from io import StringIO
+
+
+__all__ = [
+        'assert_equal', 'assert_almost_equal', 'assert_approx_equal',
+        'assert_array_equal', 'assert_array_less', 'assert_string_equal',
+        'assert_array_almost_equal', 'assert_raises', 'build_err_msg',
+        'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal',
+        'rundocs', 'runstring', 'verbose', 'measure',
+        'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex',
+        'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings',
+        'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings',
+        'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY',
+        'HAS_REFCOUNT', "IS_WASM", 'suppress_warnings', 'assert_array_compare',
+        'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON',
+        'IS_MUSL', 'check_support_sve', 'NOGIL_BUILD',
+        'IS_EDITABLE', 'IS_INSTALLED', 'NUMPY_ROOT', 'run_threaded', 'IS_64BIT',
+        ]
+
+
+class KnownFailureException(Exception):
+    '''Raise this exception to mark a test as a known failing test.'''
+    pass
+
+
+KnownFailureTest = KnownFailureException  # backwards compat
+verbose = 0
+
+NUMPY_ROOT = pathlib.Path(np.__file__).parent
+
+try:
+    np_dist = importlib.metadata.distribution('numpy')
+except importlib.metadata.PackageNotFoundError:
+    IS_INSTALLED = IS_EDITABLE = False
+else:
+    IS_INSTALLED = True
+    try:
+        if sys.version_info >= (3, 13):
+            IS_EDITABLE = np_dist.origin.dir_info.editable
+        else:
+            # Backport importlib.metadata.Distribution.origin
+            import json, types  # noqa: E401
+            origin = json.loads(
+                np_dist.read_text('direct_url.json') or '{}',
+                object_hook=lambda data: types.SimpleNamespace(**data),
+            )
+            IS_EDITABLE = origin.dir_info.editable
+    except AttributeError:
+        IS_EDITABLE = False
+
+    # spin installs numpy directly via meson, instead of using meson-python, and
+    # runs the module by setting PYTHONPATH. This is problematic because the
+    # resulting installation lacks the Python metadata (.dist-info), and numpy
+    # might already be installed on the environment, causing us to find its
+    # metadata, even though we are not actually loading that package.
+    # Work around this issue by checking if the numpy root matches.
+    if not IS_EDITABLE and np_dist.locate_file('numpy') != NUMPY_ROOT:
+        IS_INSTALLED = False
+
+IS_WASM = platform.machine() in ["wasm32", "wasm64"]
+IS_PYPY = sys.implementation.name == 'pypy'
+IS_PYSTON = hasattr(sys, "pyston_version_info")
+HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None and not IS_PYSTON
+HAS_LAPACK64 = numpy.linalg._umath_linalg._ilp64
+
+IS_MUSL = False
+# alternate way is
+# from packaging.tags import sys_tags
+#     _tags = list(sys_tags())
+#     if 'musllinux' in _tags[0].platform:
+_v = sysconfig.get_config_var('HOST_GNU_TYPE') or ''
+if 'musl' in _v:
+    IS_MUSL = True
+
+NOGIL_BUILD = bool(sysconfig.get_config_var("Py_GIL_DISABLED"))
+IS_64BIT = np.dtype(np.intp).itemsize == 8
+
+def assert_(val, msg=''):
+    """
+    Assert that works in release mode.
+    Accepts callable msg to allow deferring evaluation until failure.
+
+    The Python built-in ``assert`` does not work when executing code in
+    optimized mode (the ``-O`` flag) - no byte-code is generated for it.
+
+    For documentation on usage, refer to the Python documentation.
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if not val:
+        try:
+            smsg = msg()
+        except TypeError:
+            smsg = msg
+        raise AssertionError(smsg)
+
+
+if os.name == 'nt':
+    # Code "stolen" from enthought/debug/memusage.py
+    def GetPerformanceAttributes(object, counter, instance=None,
+                                 inum=-1, format=None, machine=None):
+        # NOTE: Many counters require 2 samples to give accurate results,
+        # including "% Processor Time" (as by definition, at any instant, a
+        # thread's CPU usage is either 0 or 100).  To read counters like this,
+        # you should copy this function, but keep the counter open, and call
+        # CollectQueryData() each time you need to know.
+        # See http://msdn.microsoft.com/library/en-us/dnperfmo/html/perfmonpt2.asp
+        #(dead link)
+        # My older explanation for this was that the "AddCounter" process
+        # forced the CPU to 100%, but the above makes more sense :)
+        import win32pdh
+        if format is None:
+            format = win32pdh.PDH_FMT_LONG
+        path = win32pdh.MakeCounterPath((machine, object, instance, None,
+                                         inum, counter))
+        hq = win32pdh.OpenQuery()
+        try:
+            hc = win32pdh.AddCounter(hq, path)
+            try:
+                win32pdh.CollectQueryData(hq)
+                type, val = win32pdh.GetFormattedCounterValue(hc, format)
+                return val
+            finally:
+                win32pdh.RemoveCounter(hc)
+        finally:
+            win32pdh.CloseQuery(hq)
+
+    def memusage(processName="python", instance=0):
+        # from win32pdhutil, part of the win32all package
+        import win32pdh
+        return GetPerformanceAttributes("Process", "Virtual Bytes",
+                                        processName, instance,
+                                        win32pdh.PDH_FMT_LONG, None)
+elif sys.platform[:5] == 'linux':
+
+    def memusage(_proc_pid_stat=f'/proc/{os.getpid()}/stat'):
+        """
+        Return virtual memory size in bytes of the running python.
+
+        """
+        try:
+            with open(_proc_pid_stat) as f:
+                l = f.readline().split(' ')
+            return int(l[22])
+        except Exception:
+            return
+else:
+    def memusage():
+        """
+        Return memory usage of running python. [Not implemented]
+
+        """
+        raise NotImplementedError
+
+
+if sys.platform[:5] == 'linux':
+    def jiffies(_proc_pid_stat=f'/proc/{os.getpid()}/stat', _load_time=[]):
+        """
+        Return number of jiffies elapsed.
+
+        Return number of jiffies (1/100ths of a second) that this
+        process has been scheduled in user mode. See man 5 proc.
+
+        """
+        import time
+        if not _load_time:
+            _load_time.append(time.time())
+        try:
+            with open(_proc_pid_stat) as f:
+                l = f.readline().split(' ')
+            return int(l[13])
+        except Exception:
+            return int(100 * (time.time() - _load_time[0]))
+else:
+    # os.getpid is not in all platforms available.
+    # Using time is safe but inaccurate, especially when process
+    # was suspended or sleeping.
+    def jiffies(_load_time=[]):
+        """
+        Return number of jiffies elapsed.
+
+        Return number of jiffies (1/100ths of a second) that this
+        process has been scheduled in user mode. See man 5 proc.
+
+        """
+        import time
+        if not _load_time:
+            _load_time.append(time.time())
+        return int(100 * (time.time() - _load_time[0]))
+
+
+def build_err_msg(arrays, err_msg, header='Items are not equal:',
+                  verbose=True, names=('ACTUAL', 'DESIRED'), precision=8):
+    msg = ['\n' + header]
+    err_msg = str(err_msg)
+    if err_msg:
+        if err_msg.find('\n') == -1 and len(err_msg) < 79 - len(header):
+            msg = [msg[0] + ' ' + err_msg]
+        else:
+            msg.append(err_msg)
+    if verbose:
+        for i, a in enumerate(arrays):
+
+            if isinstance(a, ndarray):
+                # precision argument is only needed if the objects are ndarrays
+                r_func = partial(array_repr, precision=precision)
+            else:
+                r_func = repr
+
+            try:
+                r = r_func(a)
+            except Exception as exc:
+                r = f'[repr failed for <{type(a).__name__}>: {exc}]'
+            if r.count('\n') > 3:
+                r = '\n'.join(r.splitlines()[:3])
+                r += '...'
+            msg.append(f' {names[i]}: {r}')
+    return '\n'.join(msg)
+
+
+def assert_equal(actual, desired, err_msg='', verbose=True, *, strict=False):
+    """
+    Raises an AssertionError if two objects are not equal.
+
+    Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
+    check that all elements of these objects are equal. An exception is raised
+    at the first conflicting values.
+
+    This function handles NaN comparisons as if NaN was a "normal" number.
+    That is, AssertionError is not raised if both objects have NaNs in the same
+    positions.  This is in contrast to the IEEE standard on NaNs, which says
+    that NaN compared to anything must return False.
+
+    Parameters
+    ----------
+    actual : array_like
+        The object to check.
+    desired : array_like
+        The expected object.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True and either of the `actual` and `desired` arguments is an array,
+        raise an ``AssertionError`` when either the shape or the data type of
+        the arguments does not match. If neither argument is an array, this
+        parameter has no effect.
+
+        .. versionadded:: 2.0.0
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired are not equal.
+
+    See Also
+    --------
+    assert_allclose
+    assert_array_almost_equal_nulp,
+    assert_array_max_ulp,
+
+    Notes
+    -----
+    By default, when one of `actual` and `desired` is a scalar and the other is
+    an array, the function checks that each element of the array is equal to
+    the scalar. This behaviour can be disabled by setting ``strict==True``.
+
+    Examples
+    --------
+    >>> np.testing.assert_equal([4, 5], [4, 6])
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Items are not equal:
+    item=1
+     ACTUAL: 5
+     DESIRED: 6
+
+    The following comparison does not raise an exception.  There are NaNs
+    in the inputs, but they are in the same positions.
+
+    >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])
+
+    As mentioned in the Notes section, `assert_equal` has special
+    handling for scalars when one of the arguments is an array.
+    Here, the test checks that each value in `x` is 3:
+
+    >>> x = np.full((2, 5), fill_value=3)
+    >>> np.testing.assert_equal(x, 3)
+
+    Use `strict` to raise an AssertionError when comparing a scalar with an
+    array of a different shape:
+
+    >>> np.testing.assert_equal(x, 3, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    <BLANKLINE>
+    (shapes (2, 5), () mismatch)
+     ACTUAL: array([[3, 3, 3, 3, 3],
+           [3, 3, 3, 3, 3]])
+     DESIRED: array(3)
+
+    The `strict` parameter also ensures that the array data types match:
+
+    >>> x = np.array([2, 2, 2])
+    >>> y = np.array([2., 2., 2.], dtype=np.float32)
+    >>> np.testing.assert_equal(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    <BLANKLINE>
+    (dtypes int64, float32 mismatch)
+     ACTUAL: array([2, 2, 2])
+     DESIRED: array([2., 2., 2.], dtype=float32)
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if isinstance(desired, dict):
+        if not isinstance(actual, dict):
+            raise AssertionError(repr(type(actual)))
+        assert_equal(len(actual), len(desired), err_msg, verbose)
+        for k, i in desired.items():
+            if k not in actual:
+                raise AssertionError(repr(k))
+            assert_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}',
+                         verbose)
+        return
+    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
+        assert_equal(len(actual), len(desired), err_msg, verbose)
+        for k in range(len(desired)):
+            assert_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}',
+                         verbose)
+        return
+    from numpy._core import ndarray, isscalar, signbit
+    from numpy import iscomplexobj, real, imag
+    if isinstance(actual, ndarray) or isinstance(desired, ndarray):
+        return assert_array_equal(actual, desired, err_msg, verbose,
+                                  strict=strict)
+    msg = build_err_msg([actual, desired], err_msg, verbose=verbose)
+
+    # Handle complex numbers: separate into real/imag to handle
+    # nan/inf/negative zero correctly
+    # XXX: catch ValueError for subclasses of ndarray where iscomplex fail
+    try:
+        usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
+    except (ValueError, TypeError):
+        usecomplex = False
+
+    if usecomplex:
+        if iscomplexobj(actual):
+            actualr = real(actual)
+            actuali = imag(actual)
+        else:
+            actualr = actual
+            actuali = 0
+        if iscomplexobj(desired):
+            desiredr = real(desired)
+            desiredi = imag(desired)
+        else:
+            desiredr = desired
+            desiredi = 0
+        try:
+            assert_equal(actualr, desiredr)
+            assert_equal(actuali, desiredi)
+        except AssertionError:
+            raise AssertionError(msg)
+
+    # isscalar test to check cases such as [np.nan] != np.nan
+    if isscalar(desired) != isscalar(actual):
+        raise AssertionError(msg)
+
+    try:
+        isdesnat = isnat(desired)
+        isactnat = isnat(actual)
+        dtypes_match = (np.asarray(desired).dtype.type ==
+                        np.asarray(actual).dtype.type)
+        if isdesnat and isactnat:
+            # If both are NaT (and have the same dtype -- datetime or
+            # timedelta) they are considered equal.
+            if dtypes_match:
+                return
+            else:
+                raise AssertionError(msg)
+
+    except (TypeError, ValueError, NotImplementedError):
+        pass
+
+    # Inf/nan/negative zero handling
+    try:
+        isdesnan = isnan(desired)
+        isactnan = isnan(actual)
+        if isdesnan and isactnan:
+            return  # both nan, so equal
+
+        # handle signed zero specially for floats
+        array_actual = np.asarray(actual)
+        array_desired = np.asarray(desired)
+        if (array_actual.dtype.char in 'Mm' or
+                array_desired.dtype.char in 'Mm'):
+            # version 1.18
+            # until this version, isnan failed for datetime64 and timedelta64.
+            # Now it succeeds but comparison to scalar with a different type
+            # emits a DeprecationWarning.
+            # Avoid that by skipping the next check
+            raise NotImplementedError('cannot compare to a scalar '
+                                      'with a different type')
+
+        if desired == 0 and actual == 0:
+            if not signbit(desired) == signbit(actual):
+                raise AssertionError(msg)
+
+    except (TypeError, ValueError, NotImplementedError):
+        pass
+
+    try:
+        # Explicitly use __eq__ for comparison, gh-2552
+        if not (desired == actual):
+            raise AssertionError(msg)
+
+    except (DeprecationWarning, FutureWarning) as e:
+        # this handles the case when the two types are not even comparable
+        if 'elementwise == comparison' in e.args[0]:
+            raise AssertionError(msg)
+        else:
+            raise
+
+
+def print_assert_equal(test_string, actual, desired):
+    """
+    Test if two objects are equal, and print an error message if test fails.
+
+    The test is performed with ``actual == desired``.
+
+    Parameters
+    ----------
+    test_string : str
+        The message supplied to AssertionError.
+    actual : object
+        The object to test for equality against `desired`.
+    desired : object
+        The expected result.
+
+    Examples
+    --------
+    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
+    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
+    Traceback (most recent call last):
+    ...
+    AssertionError: Test XYZ of func xyz failed
+    ACTUAL:
+    [0, 1]
+    DESIRED:
+    [0, 2]
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import pprint
+
+    if not (actual == desired):
+        msg = StringIO()
+        msg.write(test_string)
+        msg.write(' failed\nACTUAL: \n')
+        pprint.pprint(actual, msg)
+        msg.write('DESIRED: \n')
+        pprint.pprint(desired, msg)
+        raise AssertionError(msg.getvalue())
+
+
+def assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True):
+    """
+    Raises an AssertionError if two items are not equal up to desired
+    precision.
+
+    .. note:: It is recommended to use one of `assert_allclose`,
+              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
+              instead of this function for more consistent floating point
+              comparisons.
+
+    The test verifies that the elements of `actual` and `desired` satisfy::
+
+        abs(desired-actual) < float64(1.5 * 10**(-decimal))
+
+    That is a looser test than originally documented, but agrees with what the
+    actual implementation in `assert_array_almost_equal` did up to rounding
+    vagaries. An exception is raised at conflicting values. For ndarrays this
+    delegates to assert_array_almost_equal
+
+    Parameters
+    ----------
+    actual : array_like
+        The object to check.
+    desired : array_like
+        The expected object.
+    decimal : int, optional
+        Desired precision, default is 7.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+
+    Raises
+    ------
+    AssertionError
+      If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Examples
+    --------
+    >>> from numpy.testing import assert_almost_equal
+    >>> assert_almost_equal(2.3333333333333, 2.33333334)
+    >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 10 decimals
+     ACTUAL: 2.3333333333333
+     DESIRED: 2.33333334
+
+    >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
+    ...                     np.array([1.0,2.33333334]), decimal=9)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 9 decimals
+    <BLANKLINE>
+    Mismatched elements: 1 / 2 (50%)
+    Max absolute difference among violations: 6.66669964e-09
+    Max relative difference among violations: 2.85715698e-09
+     ACTUAL: array([1.         , 2.333333333])
+     DESIRED: array([1.        , 2.33333334])
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    from numpy._core import ndarray
+    from numpy import iscomplexobj, real, imag
+
+    # Handle complex numbers: separate into real/imag to handle
+    # nan/inf/negative zero correctly
+    # XXX: catch ValueError for subclasses of ndarray where iscomplex fail
+    try:
+        usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
+    except ValueError:
+        usecomplex = False
+
+    def _build_err_msg():
+        header = ('Arrays are not almost equal to %d decimals' % decimal)
+        return build_err_msg([actual, desired], err_msg, verbose=verbose,
+                             header=header)
+
+    if usecomplex:
+        if iscomplexobj(actual):
+            actualr = real(actual)
+            actuali = imag(actual)
+        else:
+            actualr = actual
+            actuali = 0
+        if iscomplexobj(desired):
+            desiredr = real(desired)
+            desiredi = imag(desired)
+        else:
+            desiredr = desired
+            desiredi = 0
+        try:
+            assert_almost_equal(actualr, desiredr, decimal=decimal)
+            assert_almost_equal(actuali, desiredi, decimal=decimal)
+        except AssertionError:
+            raise AssertionError(_build_err_msg())
+
+    if isinstance(actual, (ndarray, tuple, list)) \
+            or isinstance(desired, (ndarray, tuple, list)):
+        return assert_array_almost_equal(actual, desired, decimal, err_msg)
+    try:
+        # If one of desired/actual is not finite, handle it specially here:
+        # check that both are nan if any is a nan, and test for equality
+        # otherwise
+        if not (isfinite(desired) and isfinite(actual)):
+            if isnan(desired) or isnan(actual):
+                if not (isnan(desired) and isnan(actual)):
+                    raise AssertionError(_build_err_msg())
+            else:
+                if not desired == actual:
+                    raise AssertionError(_build_err_msg())
+            return
+    except (NotImplementedError, TypeError):
+        pass
+    if abs(desired - actual) >= np.float64(1.5 * 10.0**(-decimal)):
+        raise AssertionError(_build_err_msg())
+
+
+def assert_approx_equal(actual, desired, significant=7, err_msg='',
+                        verbose=True):
+    """
+    Raises an AssertionError if two items are not equal up to significant
+    digits.
+
+    .. note:: It is recommended to use one of `assert_allclose`,
+              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
+              instead of this function for more consistent floating point
+              comparisons.
+
+    Given two numbers, check that they are approximately equal.
+    Approximately equal is defined as the number of significant digits
+    that agree.
+
+    Parameters
+    ----------
+    actual : scalar
+        The object to check.
+    desired : scalar
+        The expected object.
+    significant : int, optional
+        Desired precision, default is 7.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+
+    Raises
+    ------
+    AssertionError
+      If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Examples
+    --------
+    >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)
+    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,
+    ...                                significant=8)
+    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,
+    ...                                significant=8)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Items are not equal to 8 significant digits:
+     ACTUAL: 1.234567e-21
+     DESIRED: 1.2345672e-21
+
+    the evaluated condition that raises the exception is
+
+    >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
+    True
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+
+    (actual, desired) = map(float, (actual, desired))
+    if desired == actual:
+        return
+    # Normalized the numbers to be in range (-10.0,10.0)
+    # scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual))))))
+    with np.errstate(invalid='ignore'):
+        scale = 0.5 * (np.abs(desired) + np.abs(actual))
+        scale = np.power(10, np.floor(np.log10(scale)))
+    try:
+        sc_desired = desired / scale
+    except ZeroDivisionError:
+        sc_desired = 0.0
+    try:
+        sc_actual = actual / scale
+    except ZeroDivisionError:
+        sc_actual = 0.0
+    msg = build_err_msg(
+        [actual, desired], err_msg,
+        header='Items are not equal to %d significant digits:' % significant,
+        verbose=verbose)
+    try:
+        # If one of desired/actual is not finite, handle it specially here:
+        # check that both are nan if any is a nan, and test for equality
+        # otherwise
+        if not (isfinite(desired) and isfinite(actual)):
+            if isnan(desired) or isnan(actual):
+                if not (isnan(desired) and isnan(actual)):
+                    raise AssertionError(msg)
+            else:
+                if not desired == actual:
+                    raise AssertionError(msg)
+            return
+    except (TypeError, NotImplementedError):
+        pass
+    if np.abs(sc_desired - sc_actual) >= np.power(10., -(significant - 1)):
+        raise AssertionError(msg)
+
+
+def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
+                         precision=6, equal_nan=True, equal_inf=True,
+                         *, strict=False, names=('ACTUAL', 'DESIRED')):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    from numpy._core import (array2string, isnan, inf, errstate,
+                            all, max, object_)
+
+    x = np.asanyarray(x)
+    y = np.asanyarray(y)
+
+    # original array for output formatting
+    ox, oy = x, y
+
+    def isnumber(x):
+        return x.dtype.char in '?bhilqpBHILQPefdgFDG'
+
+    def istime(x):
+        return x.dtype.char in "Mm"
+
+    def isvstring(x):
+        return x.dtype.char == "T"
+
+    def func_assert_same_pos(x, y, func=isnan, hasval='nan'):
+        """Handling nan/inf.
+
+        Combine results of running func on x and y, checking that they are True
+        at the same locations.
+
+        """
+        __tracebackhide__ = True  # Hide traceback for py.test
+
+        x_id = func(x)
+        y_id = func(y)
+        # We include work-arounds here to handle three types of slightly
+        # pathological ndarray subclasses:
+        # (1) all() on `masked` array scalars can return masked arrays, so we
+        #     use != True
+        # (2) __eq__ on some ndarray subclasses returns Python booleans
+        #     instead of element-wise comparisons, so we cast to np.bool() and
+        #     use isinstance(..., bool) checks
+        # (3) subclasses with bare-bones __array_function__ implementations may
+        #     not implement np.all(), so favor using the .all() method
+        # We are not committed to supporting such subclasses, but it's nice to
+        # support them if possible.
+        if np.bool(x_id == y_id).all() != True:
+            msg = build_err_msg(
+                [x, y],
+                err_msg + '\n%s location mismatch:'
+                % (hasval), verbose=verbose, header=header,
+                names=names,
+                precision=precision)
+            raise AssertionError(msg)
+        # If there is a scalar, then here we know the array has the same
+        # flag as it everywhere, so we should return the scalar flag.
+        if isinstance(x_id, bool) or x_id.ndim == 0:
+            return np.bool(x_id)
+        elif isinstance(y_id, bool) or y_id.ndim == 0:
+            return np.bool(y_id)
+        else:
+            return y_id
+
+    try:
+        if strict:
+            cond = x.shape == y.shape and x.dtype == y.dtype
+        else:
+            cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
+        if not cond:
+            if x.shape != y.shape:
+                reason = f'\n(shapes {x.shape}, {y.shape} mismatch)'
+            else:
+                reason = f'\n(dtypes {x.dtype}, {y.dtype} mismatch)'
+            msg = build_err_msg([x, y],
+                                err_msg
+                                + reason,
+                                verbose=verbose, header=header,
+                                names=names,
+                                precision=precision)
+            raise AssertionError(msg)
+
+        flagged = np.bool(False)
+        if isnumber(x) and isnumber(y):
+            if equal_nan:
+                flagged = func_assert_same_pos(x, y, func=isnan, hasval='nan')
+
+            if equal_inf:
+                flagged |= func_assert_same_pos(x, y,
+                                                func=lambda xy: xy == +inf,
+                                                hasval='+inf')
+                flagged |= func_assert_same_pos(x, y,
+                                                func=lambda xy: xy == -inf,
+                                                hasval='-inf')
+
+        elif istime(x) and istime(y):
+            # If one is datetime64 and the other timedelta64 there is no point
+            if equal_nan and x.dtype.type == y.dtype.type:
+                flagged = func_assert_same_pos(x, y, func=isnat, hasval="NaT")
+
+        elif isvstring(x) and isvstring(y):
+            dt = x.dtype
+            if equal_nan and dt == y.dtype and hasattr(dt, 'na_object'):
+                is_nan = (isinstance(dt.na_object, float) and
+                          np.isnan(dt.na_object))
+                bool_errors = 0
+                try:
+                    bool(dt.na_object)
+                except TypeError:
+                    bool_errors = 1
+                if is_nan or bool_errors:
+                    # nan-like NA object
+                    flagged = func_assert_same_pos(
+                        x, y, func=isnan, hasval=x.dtype.na_object)
+
+        if flagged.ndim > 0:
+            x, y = x[~flagged], y[~flagged]
+            # Only do the comparison if actual values are left
+            if x.size == 0:
+                return
+        elif flagged:
+            # no sense doing comparison if everything is flagged.
+            return
+
+        val = comparison(x, y)
+        invalids = np.logical_not(val)
+
+        if isinstance(val, bool):
+            cond = val
+            reduced = array([val])
+        else:
+            reduced = val.ravel()
+            cond = reduced.all()
+
+        # The below comparison is a hack to ensure that fully masked
+        # results, for which val.ravel().all() returns np.ma.masked,
+        # do not trigger a failure (np.ma.masked != True evaluates as
+        # np.ma.masked, which is falsy).
+        if cond != True:
+            n_mismatch = reduced.size - reduced.sum(dtype=intp)
+            n_elements = flagged.size if flagged.ndim != 0 else reduced.size
+            percent_mismatch = 100 * n_mismatch / n_elements
+            remarks = [
+                'Mismatched elements: {} / {} ({:.3g}%)'.format(
+                    n_mismatch, n_elements, percent_mismatch)]
+
+            with errstate(all='ignore'):
+                # ignore errors for non-numeric types
+                with contextlib.suppress(TypeError):
+                    error = abs(x - y)
+                    if np.issubdtype(x.dtype, np.unsignedinteger):
+                        error2 = abs(y - x)
+                        np.minimum(error, error2, out=error)
+
+                    reduced_error = error[invalids]
+                    max_abs_error = max(reduced_error)
+                    if getattr(error, 'dtype', object_) == object_:
+                        remarks.append(
+                            'Max absolute difference among violations: '
+                            + str(max_abs_error))
+                    else:
+                        remarks.append(
+                            'Max absolute difference among violations: '
+                            + array2string(max_abs_error))
+
+                    # note: this definition of relative error matches that one
+                    # used by assert_allclose (found in np.isclose)
+                    # Filter values where the divisor would be zero
+                    nonzero = np.bool(y != 0)
+                    nonzero_and_invalid = np.logical_and(invalids, nonzero)
+
+                    if all(~nonzero_and_invalid):
+                        max_rel_error = array(inf)
+                    else:
+                        nonzero_invalid_error = error[nonzero_and_invalid]
+                        broadcasted_y = np.broadcast_to(y, error.shape)
+                        nonzero_invalid_y = broadcasted_y[nonzero_and_invalid]
+                        max_rel_error = max(nonzero_invalid_error
+                                            / abs(nonzero_invalid_y))
+
+                    if getattr(error, 'dtype', object_) == object_:
+                        remarks.append(
+                            'Max relative difference among violations: '
+                            + str(max_rel_error))
+                    else:
+                        remarks.append(
+                            'Max relative difference among violations: '
+                            + array2string(max_rel_error))
+            err_msg = str(err_msg)
+            err_msg += '\n' + '\n'.join(remarks)
+            msg = build_err_msg([ox, oy], err_msg,
+                                verbose=verbose, header=header,
+                                names=names,
+                                precision=precision)
+            raise AssertionError(msg)
+    except ValueError:
+        import traceback
+        efmt = traceback.format_exc()
+        header = f'error during assertion:\n\n{efmt}\n\n{header}'
+
+        msg = build_err_msg([x, y], err_msg, verbose=verbose, header=header,
+                            names=names, precision=precision)
+        raise ValueError(msg)
+
+
+@_rename_parameter(['x', 'y'], ['actual', 'desired'], dep_version='2.0.0')
+def assert_array_equal(actual, desired, err_msg='', verbose=True, *,
+                       strict=False):
+    """
+    Raises an AssertionError if two array_like objects are not equal.
+
+    Given two array_like objects, check that the shape is equal and all
+    elements of these objects are equal (but see the Notes for the special
+    handling of a scalar). An exception is raised at shape mismatch or
+    conflicting values. In contrast to the standard usage in numpy, NaNs
+    are compared like numbers, no assertion is raised if both objects have
+    NaNs in the same positions.
+
+    The usual caution for verifying equality with floating point numbers is
+    advised.
+
+    .. note:: When either `actual` or `desired` is already an instance of
+        `numpy.ndarray` and `desired` is not a ``dict``, the behavior of
+        ``assert_equal(actual, desired)`` is identical to the behavior of this
+        function. Otherwise, this function performs `np.asanyarray` on the
+        inputs before comparison, whereas `assert_equal` defines special
+        comparison rules for common Python types. For example, only
+        `assert_equal` can be used to compare nested Python lists. In new code,
+        consider using only `assert_equal`, explicitly converting either
+        `actual` or `desired` to arrays if the behavior of `assert_array_equal`
+        is desired.
+
+    Parameters
+    ----------
+    actual : array_like
+        The actual object to check.
+    desired : array_like
+        The desired, expected object.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True, raise an AssertionError when either the shape or the data
+        type of the array_like objects does not match. The special
+        handling for scalars mentioned in the Notes section is disabled.
+
+        .. versionadded:: 1.24.0
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired objects are not equal.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Notes
+    -----
+    When one of `actual` and `desired` is a scalar and the other is array_like,
+    the function checks that each element of the array_like object is equal to
+    the scalar. This behaviour can be disabled with the `strict` parameter.
+
+    Examples
+    --------
+    The first assert does not raise an exception:
+
+    >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
+    ...                               [np.exp(0),2.33333, np.nan])
+
+    Assert fails with numerical imprecision with floats:
+
+    >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
+    ...                               [1, np.sqrt(np.pi)**2, np.nan])
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    <BLANKLINE>
+    Mismatched elements: 1 / 3 (33.3%)
+    Max absolute difference among violations: 4.4408921e-16
+    Max relative difference among violations: 1.41357986e-16
+     ACTUAL: array([1.      , 3.141593,      nan])
+     DESIRED: array([1.      , 3.141593,      nan])
+
+    Use `assert_allclose` or one of the nulp (number of floating point values)
+    functions for these cases instead:
+
+    >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
+    ...                            [1, np.sqrt(np.pi)**2, np.nan],
+    ...                            rtol=1e-10, atol=0)
+
+    As mentioned in the Notes section, `assert_array_equal` has special
+    handling for scalars. Here the test checks that each value in `x` is 3:
+
+    >>> x = np.full((2, 5), fill_value=3)
+    >>> np.testing.assert_array_equal(x, 3)
+
+    Use `strict` to raise an AssertionError when comparing a scalar with an
+    array:
+
+    >>> np.testing.assert_array_equal(x, 3, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    <BLANKLINE>
+    (shapes (2, 5), () mismatch)
+     ACTUAL: array([[3, 3, 3, 3, 3],
+           [3, 3, 3, 3, 3]])
+     DESIRED: array(3)
+
+    The `strict` parameter also ensures that the array data types match:
+
+    >>> x = np.array([2, 2, 2])
+    >>> y = np.array([2., 2., 2.], dtype=np.float32)
+    >>> np.testing.assert_array_equal(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    <BLANKLINE>
+    (dtypes int64, float32 mismatch)
+     ACTUAL: array([2, 2, 2])
+     DESIRED: array([2., 2., 2.], dtype=float32)
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    assert_array_compare(operator.__eq__, actual, desired, err_msg=err_msg,
+                         verbose=verbose, header='Arrays are not equal',
+                         strict=strict)
+
+
+@_rename_parameter(['x', 'y'], ['actual', 'desired'], dep_version='2.0.0')
+def assert_array_almost_equal(actual, desired, decimal=6, err_msg='',
+                              verbose=True):
+    """
+    Raises an AssertionError if two objects are not equal up to desired
+    precision.
+
+    .. note:: It is recommended to use one of `assert_allclose`,
+              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
+              instead of this function for more consistent floating point
+              comparisons.
+
+    The test verifies identical shapes and that the elements of ``actual`` and
+    ``desired`` satisfy::
+
+        abs(desired-actual) < 1.5 * 10**(-decimal)
+
+    That is a looser test than originally documented, but agrees with what the
+    actual implementation did up to rounding vagaries. An exception is raised
+    at shape mismatch or conflicting values. In contrast to the standard usage
+    in numpy, NaNs are compared like numbers, no assertion is raised if both
+    objects have NaNs in the same positions.
+
+    Parameters
+    ----------
+    actual : array_like
+        The actual object to check.
+    desired : array_like
+        The desired, expected object.
+    decimal : int, optional
+        Desired precision, default is 6.
+    err_msg : str, optional
+      The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Examples
+    --------
+    the first assert does not raise an exception
+
+    >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
+    ...                                      [1.0,2.333,np.nan])
+
+    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
+    ...                                      [1.0,2.33339,np.nan], decimal=5)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 5 decimals
+    <BLANKLINE>
+    Mismatched elements: 1 / 3 (33.3%)
+    Max absolute difference among violations: 6.e-05
+    Max relative difference among violations: 2.57136612e-05
+     ACTUAL: array([1.     , 2.33333,     nan])
+     DESIRED: array([1.     , 2.33339,     nan])
+
+    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
+    ...                                      [1.0,2.33333, 5], decimal=5)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 5 decimals
+    <BLANKLINE>
+    nan location mismatch:
+     ACTUAL: array([1.     , 2.33333,     nan])
+     DESIRED: array([1.     , 2.33333, 5.     ])
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    from numpy._core import number, result_type
+    from numpy._core.numerictypes import issubdtype
+    from numpy._core.fromnumeric import any as npany
+
+    def compare(x, y):
+        try:
+            if npany(isinf(x)) or npany(isinf(y)):
+                xinfid = isinf(x)
+                yinfid = isinf(y)
+                if not (xinfid == yinfid).all():
+                    return False
+                # if one item, x and y is +- inf
+                if x.size == y.size == 1:
+                    return x == y
+                x = x[~xinfid]
+                y = y[~yinfid]
+        except (TypeError, NotImplementedError):
+            pass
+
+        # make sure y is an inexact type to avoid abs(MIN_INT); will cause
+        # casting of x later.
+        dtype = result_type(y, 1.)
+        y = np.asanyarray(y, dtype)
+        z = abs(x - y)
+
+        if not issubdtype(z.dtype, number):
+            z = z.astype(np.float64)  # handle object arrays
+
+        return z < 1.5 * 10.0**(-decimal)
+
+    assert_array_compare(compare, actual, desired, err_msg=err_msg,
+                         verbose=verbose,
+             header=('Arrays are not almost equal to %d decimals' % decimal),
+             precision=decimal)
+
+
+def assert_array_less(x, y, err_msg='', verbose=True, *, strict=False):
+    """
+    Raises an AssertionError if two array_like objects are not ordered by less
+    than.
+
+    Given two array_like objects `x` and `y`, check that the shape is equal and
+    all elements of `x` are strictly less than the corresponding elements of
+    `y` (but see the Notes for the special handling of a scalar). An exception
+    is raised at shape mismatch or values that are not correctly ordered. In
+    contrast to the  standard usage in NumPy, no assertion is raised if both
+    objects have NaNs in the same positions.
+
+    Parameters
+    ----------
+    x : array_like
+      The smaller object to check.
+    y : array_like
+      The larger object to compare.
+    err_msg : string
+      The error message to be printed in case of failure.
+    verbose : bool
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True, raise an AssertionError when either the shape or the data
+        type of the array_like objects does not match. The special
+        handling for scalars mentioned in the Notes section is disabled.
+
+        .. versionadded:: 2.0.0
+
+    Raises
+    ------
+    AssertionError
+      If x is not strictly smaller than y, element-wise.
+
+    See Also
+    --------
+    assert_array_equal: tests objects for equality
+    assert_array_almost_equal: test objects for equality up to precision
+
+    Notes
+    -----
+    When one of `x` and `y` is a scalar and the other is array_like, the
+    function performs the comparison as though the scalar were broadcasted
+    to the shape of the array. This behaviour can be disabled with the `strict`
+    parameter.
+
+    Examples
+    --------
+    The following assertion passes because each finite element of `x` is
+    strictly less than the corresponding element of `y`, and the NaNs are in
+    corresponding locations.
+
+    >>> x = [1.0, 1.0, np.nan]
+    >>> y = [1.1, 2.0, np.nan]
+    >>> np.testing.assert_array_less(x, y)
+
+    The following assertion fails because the zeroth element of `x` is no
+    longer strictly less than the zeroth element of `y`.
+
+    >>> y[0] = 1
+    >>> np.testing.assert_array_less(x, y)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not strictly ordered `x < y`
+    <BLANKLINE>
+    Mismatched elements: 1 / 3 (33.3%)
+    Max absolute difference among violations: 0.
+    Max relative difference among violations: 0.
+     x: array([ 1.,  1., nan])
+     y: array([ 1.,  2., nan])
+
+    Here, `y` is a scalar, so each element of `x` is compared to `y`, and
+    the assertion passes.
+
+    >>> x = [1.0, 4.0]
+    >>> y = 5.0
+    >>> np.testing.assert_array_less(x, y)
+
+    However, with ``strict=True``, the assertion will fail because the shapes
+    do not match.
+
+    >>> np.testing.assert_array_less(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not strictly ordered `x < y`
+    <BLANKLINE>
+    (shapes (2,), () mismatch)
+     x: array([1., 4.])
+     y: array(5.)
+
+    With ``strict=True``, the assertion also fails if the dtypes of the two
+    arrays do not match.
+
+    >>> y = [5, 5]
+    >>> np.testing.assert_array_less(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not strictly ordered `x < y`
+    <BLANKLINE>
+    (dtypes float64, int64 mismatch)
+     x: array([1., 4.])
+     y: array([5, 5])
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    assert_array_compare(operator.__lt__, x, y, err_msg=err_msg,
+                         verbose=verbose,
+                         header='Arrays are not strictly ordered `x < y`',
+                         equal_inf=False,
+                         strict=strict,
+                         names=('x', 'y'))
+
+
+def runstring(astr, dict):
+    exec(astr, dict)
+
+
+def assert_string_equal(actual, desired):
+    """
+    Test if two strings are equal.
+
+    If the given strings are equal, `assert_string_equal` does nothing.
+    If they are not equal, an AssertionError is raised, and the diff
+    between the strings is shown.
+
+    Parameters
+    ----------
+    actual : str
+        The string to test for equality against the expected string.
+    desired : str
+        The expected string.
+
+    Examples
+    --------
+    >>> np.testing.assert_string_equal('abc', 'abc')
+    >>> np.testing.assert_string_equal('abc', 'abcd')
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    ...
+    AssertionError: Differences in strings:
+    - abc+ abcd?    +
+
+    """
+    # delay import of difflib to reduce startup time
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import difflib
+
+    if not isinstance(actual, str):
+        raise AssertionError(repr(type(actual)))
+    if not isinstance(desired, str):
+        raise AssertionError(repr(type(desired)))
+    if desired == actual:
+        return
+
+    diff = list(difflib.Differ().compare(actual.splitlines(True),
+                desired.splitlines(True)))
+    diff_list = []
+    while diff:
+        d1 = diff.pop(0)
+        if d1.startswith('  '):
+            continue
+        if d1.startswith('- '):
+            l = [d1]
+            d2 = diff.pop(0)
+            if d2.startswith('? '):
+                l.append(d2)
+                d2 = diff.pop(0)
+            if not d2.startswith('+ '):
+                raise AssertionError(repr(d2))
+            l.append(d2)
+            if diff:
+                d3 = diff.pop(0)
+                if d3.startswith('? '):
+                    l.append(d3)
+                else:
+                    diff.insert(0, d3)
+            if d2[2:] == d1[2:]:
+                continue
+            diff_list.extend(l)
+            continue
+        raise AssertionError(repr(d1))
+    if not diff_list:
+        return
+    msg = f"Differences in strings:\n{''.join(diff_list).rstrip()}"
+    if actual != desired:
+        raise AssertionError(msg)
+
+
+def rundocs(filename=None, raise_on_error=True):
+    """
+    Run doctests found in the given file.
+
+    By default `rundocs` raises an AssertionError on failure.
+
+    Parameters
+    ----------
+    filename : str
+        The path to the file for which the doctests are run.
+    raise_on_error : bool
+        Whether to raise an AssertionError when a doctest fails. Default is
+        True.
+
+    Notes
+    -----
+    The doctests can be run by the user/developer by adding the ``doctests``
+    argument to the ``test()`` call. For example, to run all tests (including
+    doctests) for ``numpy.lib``:
+
+    >>> np.lib.test(doctests=True)  # doctest: +SKIP
+    """
+    from numpy.distutils.misc_util import exec_mod_from_location
+    import doctest
+    if filename is None:
+        f = sys._getframe(1)
+        filename = f.f_globals['__file__']
+    name = os.path.splitext(os.path.basename(filename))[0]
+    m = exec_mod_from_location(name, filename)
+
+    tests = doctest.DocTestFinder().find(m)
+    runner = doctest.DocTestRunner(verbose=False)
+
+    msg = []
+    if raise_on_error:
+        out = lambda s: msg.append(s)
+    else:
+        out = None
+
+    for test in tests:
+        runner.run(test, out=out)
+
+    if runner.failures > 0 and raise_on_error:
+        raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg))
+
+
+def check_support_sve(__cache=[]):
+    """
+    gh-22982
+    """
+
+    if __cache:
+        return __cache[0]
+
+    import subprocess
+    cmd = 'lscpu'
+    try:
+        output = subprocess.run(cmd, capture_output=True, text=True)
+        result = 'sve' in output.stdout
+    except (OSError, subprocess.SubprocessError):
+        result = False
+    __cache.append(result)
+    return __cache[0]
+
+
+#
+# assert_raises and assert_raises_regex are taken from unittest.
+#
+import unittest
+
+
+class _Dummy(unittest.TestCase):
+    def nop(self):
+        pass
+
+
+_d = _Dummy('nop')
+
+
+def assert_raises(*args, **kwargs):
+    """
+    assert_raises(exception_class, callable, *args, **kwargs)
+    assert_raises(exception_class)
+
+    Fail unless an exception of class exception_class is thrown
+    by callable when invoked with arguments args and keyword
+    arguments kwargs. If a different type of exception is
+    thrown, it will not be caught, and the test case will be
+    deemed to have suffered an error, exactly as for an
+    unexpected exception.
+
+    Alternatively, `assert_raises` can be used as a context manager:
+
+    >>> from numpy.testing import assert_raises
+    >>> with assert_raises(ZeroDivisionError):
+    ...     1 / 0
+
+    is equivalent to
+
+    >>> def div(x, y):
+    ...     return x / y
+    >>> assert_raises(ZeroDivisionError, div, 1, 0)
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    return _d.assertRaises(*args, **kwargs)
+
+
+def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):
+    """
+    assert_raises_regex(exception_class, expected_regexp, callable, *args,
+                        **kwargs)
+    assert_raises_regex(exception_class, expected_regexp)
+
+    Fail unless an exception of class exception_class and with message that
+    matches expected_regexp is thrown by callable when invoked with arguments
+    args and keyword arguments kwargs.
+
+    Alternatively, can be used as a context manager like `assert_raises`.
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    return _d.assertRaisesRegex(exception_class, expected_regexp, *args, **kwargs)
+
+
+def decorate_methods(cls, decorator, testmatch=None):
+    """
+    Apply a decorator to all methods in a class matching a regular expression.
+
+    The given decorator is applied to all public methods of `cls` that are
+    matched by the regular expression `testmatch`
+    (``testmatch.search(methodname)``). Methods that are private, i.e. start
+    with an underscore, are ignored.
+
+    Parameters
+    ----------
+    cls : class
+        Class whose methods to decorate.
+    decorator : function
+        Decorator to apply to methods
+    testmatch : compiled regexp or str, optional
+        The regular expression. Default value is None, in which case the
+        nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
+        is used.
+        If `testmatch` is a string, it is compiled to a regular expression
+        first.
+
+    """
+    if testmatch is None:
+        testmatch = re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)
+    else:
+        testmatch = re.compile(testmatch)
+    cls_attr = cls.__dict__
+
+    # delayed import to reduce startup time
+    from inspect import isfunction
+
+    methods = [_m for _m in cls_attr.values() if isfunction(_m)]
+    for function in methods:
+        try:
+            if hasattr(function, 'compat_func_name'):
+                funcname = function.compat_func_name
+            else:
+                funcname = function.__name__
+        except AttributeError:
+            # not a function
+            continue
+        if testmatch.search(funcname) and not funcname.startswith('_'):
+            setattr(cls, funcname, decorator(function))
+    return
+
+
+def measure(code_str, times=1, label=None):
+    """
+    Return elapsed time for executing code in the namespace of the caller.
+
+    The supplied code string is compiled with the Python builtin ``compile``.
+    The precision of the timing is 10 milli-seconds. If the code will execute
+    fast on this timescale, it can be executed many times to get reasonable
+    timing accuracy.
+
+    Parameters
+    ----------
+    code_str : str
+        The code to be timed.
+    times : int, optional
+        The number of times the code is executed. Default is 1. The code is
+        only compiled once.
+    label : str, optional
+        A label to identify `code_str` with. This is passed into ``compile``
+        as the second argument (for run-time error messages).
+
+    Returns
+    -------
+    elapsed : float
+        Total elapsed time in seconds for executing `code_str` `times` times.
+
+    Examples
+    --------
+    >>> times = 10
+    >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)', times=times)
+    >>> print("Time for a single execution : ", etime / times, "s")  # doctest: +SKIP
+    Time for a single execution :  0.005 s
+
+    """
+    frame = sys._getframe(1)
+    locs, globs = frame.f_locals, frame.f_globals
+
+    code = compile(code_str, f'Test name: {label} ', 'exec')
+    i = 0
+    elapsed = jiffies()
+    while i < times:
+        i += 1
+        exec(code, globs, locs)
+    elapsed = jiffies() - elapsed
+    return 0.01 * elapsed
+
+
+def _assert_valid_refcount(op):
+    """
+    Check that ufuncs don't mishandle refcount of object `1`.
+    Used in a few regression tests.
+    """
+    if not HAS_REFCOUNT:
+        return True
+
+    import gc
+    import numpy as np
+
+    b = np.arange(100 * 100).reshape(100, 100)
+    c = b
+    i = 1
+
+    gc.disable()
+    try:
+        rc = sys.getrefcount(i)
+        for j in range(15):
+            d = op(b, c)
+        assert_(sys.getrefcount(i) >= rc)
+    finally:
+        gc.enable()
+    del d  # for pyflakes
+
+
+def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
+                    err_msg='', verbose=True, *, strict=False):
+    """
+    Raises an AssertionError if two objects are not equal up to desired
+    tolerance.
+
+    Given two array_like objects, check that their shapes and all elements
+    are equal (but see the Notes for the special handling of a scalar). An
+    exception is raised if the shapes mismatch or any values conflict. In
+    contrast to the standard usage in numpy, NaNs are compared like numbers,
+    no assertion is raised if both objects have NaNs in the same positions.
+
+    The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note
+    that ``allclose`` has different default values). It compares the difference
+    between `actual` and `desired` to ``atol + rtol * abs(desired)``.
+
+    Parameters
+    ----------
+    actual : array_like
+        Array obtained.
+    desired : array_like
+        Array desired.
+    rtol : float, optional
+        Relative tolerance.
+    atol : float, optional
+        Absolute tolerance.
+    equal_nan : bool, optional.
+        If True, NaNs will compare equal.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True, raise an ``AssertionError`` when either the shape or the data
+        type of the arguments does not match. The special handling of scalars
+        mentioned in the Notes section is disabled.
+
+        .. versionadded:: 2.0.0
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_array_almost_equal_nulp, assert_array_max_ulp
+
+    Notes
+    -----
+    When one of `actual` and `desired` is a scalar and the other is
+    array_like, the function performs the comparison as if the scalar were
+    broadcasted to the shape of the array.
+    This behaviour can be disabled with the `strict` parameter.
+
+    Examples
+    --------
+    >>> x = [1e-5, 1e-3, 1e-1]
+    >>> y = np.arccos(np.cos(x))
+    >>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)
+
+    As mentioned in the Notes section, `assert_allclose` has special
+    handling for scalars. Here, the test checks that the value of `numpy.sin`
+    is nearly zero at integer multiples of π.
+
+    >>> x = np.arange(3) * np.pi
+    >>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15)
+
+    Use `strict` to raise an ``AssertionError`` when comparing an array
+    with one or more dimensions against a scalar.
+
+    >>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Not equal to tolerance rtol=1e-07, atol=1e-15
+    <BLANKLINE>
+    (shapes (3,), () mismatch)
+     ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
+     DESIRED: array(0)
+
+    The `strict` parameter also ensures that the array data types match:
+
+    >>> y = np.zeros(3, dtype=np.float32)
+    >>> np.testing.assert_allclose(np.sin(x), y, atol=1e-15, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Not equal to tolerance rtol=1e-07, atol=1e-15
+    <BLANKLINE>
+    (dtypes float64, float32 mismatch)
+     ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
+     DESIRED: array([0., 0., 0.], dtype=float32)
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+
+    def compare(x, y):
+        return np._core.numeric.isclose(x, y, rtol=rtol, atol=atol,
+                                       equal_nan=equal_nan)
+
+    actual, desired = np.asanyarray(actual), np.asanyarray(desired)
+    header = f'Not equal to tolerance rtol={rtol:g}, atol={atol:g}'
+    assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
+                         verbose=verbose, header=header, equal_nan=equal_nan,
+                         strict=strict)
+
+
+def assert_array_almost_equal_nulp(x, y, nulp=1):
+    """
+    Compare two arrays relatively to their spacing.
+
+    This is a relatively robust method to compare two arrays whose amplitude
+    is variable.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Input arrays.
+    nulp : int, optional
+        The maximum number of unit in the last place for tolerance (see Notes).
+        Default is 1.
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    AssertionError
+        If the spacing between `x` and `y` for one or more elements is larger
+        than `nulp`.
+
+    See Also
+    --------
+    assert_array_max_ulp : Check that all items of arrays differ in at most
+        N Units in the Last Place.
+    spacing : Return the distance between x and the nearest adjacent number.
+
+    Notes
+    -----
+    An assertion is raised if the following condition is not met::
+
+        abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y)))
+
+    Examples
+    --------
+    >>> x = np.array([1., 1e-10, 1e-20])
+    >>> eps = np.finfo(x.dtype).eps
+    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)
+
+    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
+    Traceback (most recent call last):
+      ...
+    AssertionError: Arrays are not equal to 1 ULP (max is 2)
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+    ax = np.abs(x)
+    ay = np.abs(y)
+    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
+    if not np.all(np.abs(x - y) <= ref):
+        if np.iscomplexobj(x) or np.iscomplexobj(y):
+            msg = f"Arrays are not equal to {nulp} ULP"
+        else:
+            max_nulp = np.max(nulp_diff(x, y))
+            msg = f"Arrays are not equal to {nulp} ULP (max is {max_nulp:g})"
+        raise AssertionError(msg)
+
+
+def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
+    """
+    Check that all items of arrays differ in at most N Units in the Last Place.
+
+    Parameters
+    ----------
+    a, b : array_like
+        Input arrays to be compared.
+    maxulp : int, optional
+        The maximum number of units in the last place that elements of `a` and
+        `b` can differ. Default is 1.
+    dtype : dtype, optional
+        Data-type to convert `a` and `b` to if given. Default is None.
+
+    Returns
+    -------
+    ret : ndarray
+        Array containing number of representable floating point numbers between
+        items in `a` and `b`.
+
+    Raises
+    ------
+    AssertionError
+        If one or more elements differ by more than `maxulp`.
+
+    Notes
+    -----
+    For computing the ULP difference, this API does not differentiate between
+    various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
+    is zero).
+
+    See Also
+    --------
+    assert_array_almost_equal_nulp : Compare two arrays relatively to their
+        spacing.
+
+    Examples
+    --------
+    >>> a = np.linspace(0., 1., 100)
+    >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+    ret = nulp_diff(a, b, dtype)
+    if not np.all(ret <= maxulp):
+        raise AssertionError("Arrays are not almost equal up to %g "
+                             "ULP (max difference is %g ULP)" %
+                             (maxulp, np.max(ret)))
+    return ret
+
+
+def nulp_diff(x, y, dtype=None):
+    """For each item in x and y, return the number of representable floating
+    points between them.
+
+    Parameters
+    ----------
+    x : array_like
+        first input array
+    y : array_like
+        second input array
+    dtype : dtype, optional
+        Data-type to convert `x` and `y` to if given. Default is None.
+
+    Returns
+    -------
+    nulp : array_like
+        number of representable floating point numbers between each item in x
+        and y.
+
+    Notes
+    -----
+    For computing the ULP difference, this API does not differentiate between
+    various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
+    is zero).
+
+    Examples
+    --------
+    # By definition, epsilon is the smallest number such as 1 + eps != 1, so
+    # there should be exactly one ULP between 1 and 1 + eps
+    >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps)
+    1.0
+    """
+    import numpy as np
+    if dtype:
+        x = np.asarray(x, dtype=dtype)
+        y = np.asarray(y, dtype=dtype)
+    else:
+        x = np.asarray(x)
+        y = np.asarray(y)
+
+    t = np.common_type(x, y)
+    if np.iscomplexobj(x) or np.iscomplexobj(y):
+        raise NotImplementedError("_nulp not implemented for complex array")
+
+    x = np.array([x], dtype=t)
+    y = np.array([y], dtype=t)
+
+    x[np.isnan(x)] = np.nan
+    y[np.isnan(y)] = np.nan
+
+    if not x.shape == y.shape:
+        raise ValueError("Arrays do not have the same shape: %s - %s" %
+                         (x.shape, y.shape))
+
+    def _diff(rx, ry, vdt):
+        diff = np.asarray(rx - ry, dtype=vdt)
+        return np.abs(diff)
+
+    rx = integer_repr(x)
+    ry = integer_repr(y)
+    return _diff(rx, ry, t)
+
+
+def _integer_repr(x, vdt, comp):
+    # Reinterpret binary representation of the float as sign-magnitude:
+    # take into account two-complement representation
+    # See also
+    # https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
+    rx = x.view(vdt)
+    if not (rx.size == 1):
+        rx[rx < 0] = comp - rx[rx < 0]
+    else:
+        if rx < 0:
+            rx = comp - rx
+
+    return rx
+
+
+def integer_repr(x):
+    """Return the signed-magnitude interpretation of the binary representation
+    of x."""
+    import numpy as np
+    if x.dtype == np.float16:
+        return _integer_repr(x, np.int16, np.int16(-2**15))
+    elif x.dtype == np.float32:
+        return _integer_repr(x, np.int32, np.int32(-2**31))
+    elif x.dtype == np.float64:
+        return _integer_repr(x, np.int64, np.int64(-2**63))
+    else:
+        raise ValueError(f'Unsupported dtype {x.dtype}')
+
+
+@contextlib.contextmanager
+def _assert_warns_context(warning_class, name=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    with suppress_warnings() as sup:
+        l = sup.record(warning_class)
+        yield
+        if not len(l) > 0:
+            name_str = f' when calling {name}' if name is not None else ''
+            raise AssertionError("No warning raised" + name_str)
+
+
+def assert_warns(warning_class, *args, **kwargs):
+    """
+    Fail unless the given callable throws the specified warning.
+
+    A warning of class warning_class should be thrown by the callable when
+    invoked with arguments args and keyword arguments kwargs.
+    If a different type of warning is thrown, it will not be caught.
+
+    If called with all arguments other than the warning class omitted, may be
+    used as a context manager::
+
+        with assert_warns(SomeWarning):
+            do_something()
+
+    The ability to be used as a context manager is new in NumPy v1.11.0.
+
+    Parameters
+    ----------
+    warning_class : class
+        The class defining the warning that `func` is expected to throw.
+    func : callable, optional
+        Callable to test
+    *args : Arguments
+        Arguments for `func`.
+    **kwargs : Kwargs
+        Keyword arguments for `func`.
+
+    Returns
+    -------
+    The value returned by `func`.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> def deprecated_func(num):
+    ...     warnings.warn("Please upgrade", DeprecationWarning)
+    ...     return num*num
+    >>> with np.testing.assert_warns(DeprecationWarning):
+    ...     assert deprecated_func(4) == 16
+    >>> # or passing a func
+    >>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4)
+    >>> assert ret == 16
+    """
+    if not args and not kwargs:
+        return _assert_warns_context(warning_class)
+    elif len(args) < 1:
+        if "match" in kwargs:
+            raise RuntimeError(
+                "assert_warns does not use 'match' kwarg, "
+                "use pytest.warns instead"
+                )
+        raise RuntimeError("assert_warns(...) needs at least one arg")
+
+    func = args[0]
+    args = args[1:]
+    with _assert_warns_context(warning_class, name=func.__name__):
+        return func(*args, **kwargs)
+
+
+@contextlib.contextmanager
+def _assert_no_warnings_context(name=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    with warnings.catch_warnings(record=True) as l:
+        warnings.simplefilter('always')
+        yield
+        if len(l) > 0:
+            name_str = f' when calling {name}' if name is not None else ''
+            raise AssertionError(f'Got warnings{name_str}: {l}')
+
+
+def assert_no_warnings(*args, **kwargs):
+    """
+    Fail if the given callable produces any warnings.
+
+    If called with all arguments omitted, may be used as a context manager::
+
+        with assert_no_warnings():
+            do_something()
+
+    The ability to be used as a context manager is new in NumPy v1.11.0.
+
+    Parameters
+    ----------
+    func : callable
+        The callable to test.
+    \\*args : Arguments
+        Arguments passed to `func`.
+    \\*\\*kwargs : Kwargs
+        Keyword arguments passed to `func`.
+
+    Returns
+    -------
+    The value returned by `func`.
+
+    """
+    if not args:
+        return _assert_no_warnings_context()
+
+    func = args[0]
+    args = args[1:]
+    with _assert_no_warnings_context(name=func.__name__):
+        return func(*args, **kwargs)
+
+
+def _gen_alignment_data(dtype=float32, type='binary', max_size=24):
+    """
+    generator producing data with different alignment and offsets
+    to test simd vectorization
+
+    Parameters
+    ----------
+    dtype : dtype
+        data type to produce
+    type : string
+        'unary': create data for unary operations, creates one input
+                 and output array
+        'binary': create data for unary operations, creates two input
+                 and output array
+    max_size : integer
+        maximum size of data to produce
+
+    Returns
+    -------
+    if type is 'unary' yields one output, one input array and a message
+    containing information on the data
+    if type is 'binary' yields one output array, two input array and a message
+    containing information on the data
+
+    """
+    ufmt = 'unary offset=(%d, %d), size=%d, dtype=%r, %s'
+    bfmt = 'binary offset=(%d, %d, %d), size=%d, dtype=%r, %s'
+    for o in range(3):
+        for s in range(o + 2, max(o + 3, max_size)):
+            if type == 'unary':
+                inp = lambda: arange(s, dtype=dtype)[o:]
+                out = empty((s,), dtype=dtype)[o:]
+                yield out, inp(), ufmt % (o, o, s, dtype, 'out of place')
+                d = inp()
+                yield d, d, ufmt % (o, o, s, dtype, 'in place')
+                yield out[1:], inp()[:-1], ufmt % \
+                    (o + 1, o, s - 1, dtype, 'out of place')
+                yield out[:-1], inp()[1:], ufmt % \
+                    (o, o + 1, s - 1, dtype, 'out of place')
+                yield inp()[:-1], inp()[1:], ufmt % \
+                    (o, o + 1, s - 1, dtype, 'aliased')
+                yield inp()[1:], inp()[:-1], ufmt % \
+                    (o + 1, o, s - 1, dtype, 'aliased')
+            if type == 'binary':
+                inp1 = lambda: arange(s, dtype=dtype)[o:]
+                inp2 = lambda: arange(s, dtype=dtype)[o:]
+                out = empty((s,), dtype=dtype)[o:]
+                yield out, inp1(), inp2(),  bfmt % \
+                    (o, o, o, s, dtype, 'out of place')
+                d = inp1()
+                yield d, d, inp2(), bfmt % \
+                    (o, o, o, s, dtype, 'in place1')
+                d = inp2()
+                yield d, inp1(), d, bfmt % \
+                    (o, o, o, s, dtype, 'in place2')
+                yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % \
+                    (o + 1, o, o, s - 1, dtype, 'out of place')
+                yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % \
+                    (o, o + 1, o, s - 1, dtype, 'out of place')
+                yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % \
+                    (o, o, o + 1, s - 1, dtype, 'out of place')
+                yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % \
+                    (o + 1, o, o, s - 1, dtype, 'aliased')
+                yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % \
+                    (o, o + 1, o, s - 1, dtype, 'aliased')
+                yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % \
+                    (o, o, o + 1, s - 1, dtype, 'aliased')
+
+
+class IgnoreException(Exception):
+    "Ignoring this exception due to disabled feature"
+    pass
+
+
+@contextlib.contextmanager
+def tempdir(*args, **kwargs):
+    """Context manager to provide a temporary test folder.
+
+    All arguments are passed as this to the underlying tempfile.mkdtemp
+    function.
+
+    """
+    tmpdir = mkdtemp(*args, **kwargs)
+    try:
+        yield tmpdir
+    finally:
+        shutil.rmtree(tmpdir)
+
+
+@contextlib.contextmanager
+def temppath(*args, **kwargs):
+    """Context manager for temporary files.
+
+    Context manager that returns the path to a closed temporary file. Its
+    parameters are the same as for tempfile.mkstemp and are passed directly
+    to that function. The underlying file is removed when the context is
+    exited, so it should be closed at that time.
+
+    Windows does not allow a temporary file to be opened if it is already
+    open, so the underlying file must be closed after opening before it
+    can be opened again.
+
+    """
+    fd, path = mkstemp(*args, **kwargs)
+    os.close(fd)
+    try:
+        yield path
+    finally:
+        os.remove(path)
+
+
+class clear_and_catch_warnings(warnings.catch_warnings):
+    """ Context manager that resets warning registry for catching warnings
+
+    Warnings can be slippery, because, whenever a warning is triggered, Python
+    adds a ``__warningregistry__`` member to the *calling* module.  This makes
+    it impossible to retrigger the warning in this module, whatever you put in
+    the warnings filters.  This context manager accepts a sequence of `modules`
+    as a keyword argument to its constructor and:
+
+    * stores and removes any ``__warningregistry__`` entries in given `modules`
+      on entry;
+    * resets ``__warningregistry__`` to its previous state on exit.
+
+    This makes it possible to trigger any warning afresh inside the context
+    manager without disturbing the state of warnings outside.
+
+    For compatibility with Python 3.0, please consider all arguments to be
+    keyword-only.
+
+    Parameters
+    ----------
+    record : bool, optional
+        Specifies whether warnings should be captured by a custom
+        implementation of ``warnings.showwarning()`` and be appended to a list
+        returned by the context manager. Otherwise None is returned by the
+        context manager. The objects appended to the list are arguments whose
+        attributes mirror the arguments to ``showwarning()``.
+    modules : sequence, optional
+        Sequence of modules for which to reset warnings registry on entry and
+        restore on exit. To work correctly, all 'ignore' filters should
+        filter by one of these modules.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> with np.testing.clear_and_catch_warnings(
+    ...         modules=[np._core.fromnumeric]):
+    ...     warnings.simplefilter('always')
+    ...     warnings.filterwarnings('ignore', module='np._core.fromnumeric')
+    ...     # do something that raises a warning but ignore those in
+    ...     # np._core.fromnumeric
+    """
+    class_modules = ()
+
+    def __init__(self, record=False, modules=()):
+        self.modules = set(modules).union(self.class_modules)
+        self._warnreg_copies = {}
+        super().__init__(record=record)
+
+    def __enter__(self):
+        for mod in self.modules:
+            if hasattr(mod, '__warningregistry__'):
+                mod_reg = mod.__warningregistry__
+                self._warnreg_copies[mod] = mod_reg.copy()
+                mod_reg.clear()
+        return super().__enter__()
+
+    def __exit__(self, *exc_info):
+        super().__exit__(*exc_info)
+        for mod in self.modules:
+            if hasattr(mod, '__warningregistry__'):
+                mod.__warningregistry__.clear()
+            if mod in self._warnreg_copies:
+                mod.__warningregistry__.update(self._warnreg_copies[mod])
+
+
+class suppress_warnings:
+    """
+    Context manager and decorator doing much the same as
+    ``warnings.catch_warnings``.
+
+    However, it also provides a filter mechanism to work around
+    https://bugs.python.org/issue4180.
+
+    This bug causes Python before 3.4 to not reliably show warnings again
+    after they have been ignored once (even within catch_warnings). It
+    means that no "ignore" filter can be used easily, since following
+    tests might need to see the warning. Additionally it allows easier
+    specificity for testing warnings and can be nested.
+
+    Parameters
+    ----------
+    forwarding_rule : str, optional
+        One of "always", "once", "module", or "location". Analogous to
+        the usual warnings module filter mode, it is useful to reduce
+        noise mostly on the outmost level. Unsuppressed and unrecorded
+        warnings will be forwarded based on this rule. Defaults to "always".
+        "location" is equivalent to the warnings "default", match by exact
+        location the warning warning originated from.
+
+    Notes
+    -----
+    Filters added inside the context manager will be discarded again
+    when leaving it. Upon entering all filters defined outside a
+    context will be applied automatically.
+
+    When a recording filter is added, matching warnings are stored in the
+    ``log`` attribute as well as in the list returned by ``record``.
+
+    If filters are added and the ``module`` keyword is given, the
+    warning registry of this module will additionally be cleared when
+    applying it, entering the context, or exiting it. This could cause
+    warnings to appear a second time after leaving the context if they
+    were configured to be printed once (default) and were already
+    printed before the context was entered.
+
+    Nesting this context manager will work as expected when the
+    forwarding rule is "always" (default). Unfiltered and unrecorded
+    warnings will be passed out and be matched by the outer level.
+    On the outmost level they will be printed (or caught by another
+    warnings context). The forwarding rule argument can modify this
+    behaviour.
+
+    Like ``catch_warnings`` this context manager is not threadsafe.
+
+    Examples
+    --------
+
+    With a context manager::
+
+        with np.testing.suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "Some text")
+            sup.filter(module=np.ma.core)
+            log = sup.record(FutureWarning, "Does this occur?")
+            command_giving_warnings()
+            # The FutureWarning was given once, the filtered warnings were
+            # ignored. All other warnings abide outside settings (may be
+            # printed/error)
+            assert_(len(log) == 1)
+            assert_(len(sup.log) == 1)  # also stored in log attribute
+
+    Or as a decorator::
+
+        sup = np.testing.suppress_warnings()
+        sup.filter(module=np.ma.core)  # module must match exactly
+        @sup
+        def some_function():
+            # do something which causes a warning in np.ma.core
+            pass
+    """
+    def __init__(self, forwarding_rule="always"):
+        self._entered = False
+
+        # Suppressions are either instance or defined inside one with block:
+        self._suppressions = []
+
+        if forwarding_rule not in {"always", "module", "once", "location"}:
+            raise ValueError("unsupported forwarding rule.")
+        self._forwarding_rule = forwarding_rule
+
+    def _clear_registries(self):
+        if hasattr(warnings, "_filters_mutated"):
+            # clearing the registry should not be necessary on new pythons,
+            # instead the filters should be mutated.
+            warnings._filters_mutated()
+            return
+        # Simply clear the registry, this should normally be harmless,
+        # note that on new pythons it would be invalidated anyway.
+        for module in self._tmp_modules:
+            if hasattr(module, "__warningregistry__"):
+                module.__warningregistry__.clear()
+
+    def _filter(self, category=Warning, message="", module=None, record=False):
+        if record:
+            record = []  # The log where to store warnings
+        else:
+            record = None
+        if self._entered:
+            if module is None:
+                warnings.filterwarnings(
+                    "always", category=category, message=message)
+            else:
+                module_regex = module.__name__.replace('.', r'\.') + '$'
+                warnings.filterwarnings(
+                    "always", category=category, message=message,
+                    module=module_regex)
+                self._tmp_modules.add(module)
+                self._clear_registries()
+
+            self._tmp_suppressions.append(
+                (category, message, re.compile(message, re.I), module, record))
+        else:
+            self._suppressions.append(
+                (category, message, re.compile(message, re.I), module, record))
+
+        return record
+
+    def filter(self, category=Warning, message="", module=None):
+        """
+        Add a new suppressing filter or apply it if the state is entered.
+
+        Parameters
+        ----------
+        category : class, optional
+            Warning class to filter
+        message : string, optional
+            Regular expression matching the warning message.
+        module : module, optional
+            Module to filter for. Note that the module (and its file)
+            must match exactly and cannot be a submodule. This may make
+            it unreliable for external modules.
+
+        Notes
+        -----
+        When added within a context, filters are only added inside
+        the context and will be forgotten when the context is exited.
+        """
+        self._filter(category=category, message=message, module=module,
+                     record=False)
+
+    def record(self, category=Warning, message="", module=None):
+        """
+        Append a new recording filter or apply it if the state is entered.
+
+        All warnings matching will be appended to the ``log`` attribute.
+
+        Parameters
+        ----------
+        category : class, optional
+            Warning class to filter
+        message : string, optional
+            Regular expression matching the warning message.
+        module : module, optional
+            Module to filter for. Note that the module (and its file)
+            must match exactly and cannot be a submodule. This may make
+            it unreliable for external modules.
+
+        Returns
+        -------
+        log : list
+            A list which will be filled with all matched warnings.
+
+        Notes
+        -----
+        When added within a context, filters are only added inside
+        the context and will be forgotten when the context is exited.
+        """
+        return self._filter(category=category, message=message, module=module,
+                            record=True)
+
+    def __enter__(self):
+        if self._entered:
+            raise RuntimeError("cannot enter suppress_warnings twice.")
+
+        self._orig_show = warnings.showwarning
+        self._filters = warnings.filters
+        warnings.filters = self._filters[:]
+
+        self._entered = True
+        self._tmp_suppressions = []
+        self._tmp_modules = set()
+        self._forwarded = set()
+
+        self.log = []  # reset global log (no need to keep same list)
+
+        for cat, mess, _, mod, log in self._suppressions:
+            if log is not None:
+                del log[:]  # clear the log
+            if mod is None:
+                warnings.filterwarnings(
+                    "always", category=cat, message=mess)
+            else:
+                module_regex = mod.__name__.replace('.', r'\.') + '$'
+                warnings.filterwarnings(
+                    "always", category=cat, message=mess,
+                    module=module_regex)
+                self._tmp_modules.add(mod)
+        warnings.showwarning = self._showwarning
+        self._clear_registries()
+
+        return self
+
+    def __exit__(self, *exc_info):
+        warnings.showwarning = self._orig_show
+        warnings.filters = self._filters
+        self._clear_registries()
+        self._entered = False
+        del self._orig_show
+        del self._filters
+
+    def _showwarning(self, message, category, filename, lineno,
+                     *args, use_warnmsg=None, **kwargs):
+        for cat, _, pattern, mod, rec in (
+                self._suppressions + self._tmp_suppressions)[::-1]:
+            if (issubclass(category, cat) and
+                    pattern.match(message.args[0]) is not None):
+                if mod is None:
+                    # Message and category match, either recorded or ignored
+                    if rec is not None:
+                        msg = WarningMessage(message, category, filename,
+                                             lineno, **kwargs)
+                        self.log.append(msg)
+                        rec.append(msg)
+                    return
+                # Use startswith, because warnings strips the c or o from
+                # .pyc/.pyo files.
+                elif mod.__file__.startswith(filename):
+                    # The message and module (filename) match
+                    if rec is not None:
+                        msg = WarningMessage(message, category, filename,
+                                             lineno, **kwargs)
+                        self.log.append(msg)
+                        rec.append(msg)
+                    return
+
+        # There is no filter in place, so pass to the outside handler
+        # unless we should only pass it once
+        if self._forwarding_rule == "always":
+            if use_warnmsg is None:
+                self._orig_show(message, category, filename, lineno,
+                                *args, **kwargs)
+            else:
+                self._orig_showmsg(use_warnmsg)
+            return
+
+        if self._forwarding_rule == "once":
+            signature = (message.args, category)
+        elif self._forwarding_rule == "module":
+            signature = (message.args, category, filename)
+        elif self._forwarding_rule == "location":
+            signature = (message.args, category, filename, lineno)
+
+        if signature in self._forwarded:
+            return
+        self._forwarded.add(signature)
+        if use_warnmsg is None:
+            self._orig_show(message, category, filename, lineno, *args,
+                            **kwargs)
+        else:
+            self._orig_showmsg(use_warnmsg)
+
+    def __call__(self, func):
+        """
+        Function decorator to apply certain suppressions to a whole
+        function.
+        """
+        @wraps(func)
+        def new_func(*args, **kwargs):
+            with self:
+                return func(*args, **kwargs)
+
+        return new_func
+
+
+@contextlib.contextmanager
+def _assert_no_gc_cycles_context(name=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+
+    # not meaningful to test if there is no refcounting
+    if not HAS_REFCOUNT:
+        yield
+        return
+
+    assert_(gc.isenabled())
+    gc.disable()
+    gc_debug = gc.get_debug()
+    try:
+        for i in range(100):
+            if gc.collect() == 0:
+                break
+        else:
+            raise RuntimeError(
+                "Unable to fully collect garbage - perhaps a __del__ method "
+                "is creating more reference cycles?")
+
+        gc.set_debug(gc.DEBUG_SAVEALL)
+        yield
+        # gc.collect returns the number of unreachable objects in cycles that
+        # were found -- we are checking that no cycles were created in the context
+        n_objects_in_cycles = gc.collect()
+        objects_in_cycles = gc.garbage[:]
+    finally:
+        del gc.garbage[:]
+        gc.set_debug(gc_debug)
+        gc.enable()
+
+    if n_objects_in_cycles:
+        name_str = f' when calling {name}' if name is not None else ''
+        raise AssertionError(
+            "Reference cycles were found{}: {} objects were collected, "
+            "of which {} are shown below:{}"
+            .format(
+                name_str,
+                n_objects_in_cycles,
+                len(objects_in_cycles),
+                ''.join(
+                    "\n  {} object with id={}:\n    {}".format(
+                        type(o).__name__,
+                        id(o),
+                        pprint.pformat(o).replace('\n', '\n    ')
+                    ) for o in objects_in_cycles
+                )
+            )
+        )
+
+
+def assert_no_gc_cycles(*args, **kwargs):
+    """
+    Fail if the given callable produces any reference cycles.
+
+    If called with all arguments omitted, may be used as a context manager::
+
+        with assert_no_gc_cycles():
+            do_something()
+
+    Parameters
+    ----------
+    func : callable
+        The callable to test.
+    \\*args : Arguments
+        Arguments passed to `func`.
+    \\*\\*kwargs : Kwargs
+        Keyword arguments passed to `func`.
+
+    Returns
+    -------
+    Nothing. The result is deliberately discarded to ensure that all cycles
+    are found.
+
+    """
+    if not args:
+        return _assert_no_gc_cycles_context()
+
+    func = args[0]
+    args = args[1:]
+    with _assert_no_gc_cycles_context(name=func.__name__):
+        func(*args, **kwargs)
+
+
+def break_cycles():
+    """
+    Break reference cycles by calling gc.collect
+    Objects can call other objects' methods (for instance, another object's
+     __del__) inside their own __del__. On PyPy, the interpreter only runs
+    between calls to gc.collect, so multiple calls are needed to completely
+    release all cycles.
+    """
+
+    gc.collect()
+    if IS_PYPY:
+        # a few more, just to make sure all the finalizers are called
+        gc.collect()
+        gc.collect()
+        gc.collect()
+        gc.collect()
+
+
+def requires_memory(free_bytes):
+    """Decorator to skip a test if not enough memory is available"""
+    import pytest
+
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*a, **kw):
+            msg = check_free_memory(free_bytes)
+            if msg is not None:
+                pytest.skip(msg)
+
+            try:
+                return func(*a, **kw)
+            except MemoryError:
+                # Probably ran out of memory regardless: don't regard as failure
+                pytest.xfail("MemoryError raised")
+
+        return wrapper
+
+    return decorator
+
+
+def check_free_memory(free_bytes):
+    """
+    Check whether `free_bytes` amount of memory is currently free.
+    Returns: None if enough memory available, otherwise error message
+    """
+    env_var = 'NPY_AVAILABLE_MEM'
+    env_value = os.environ.get(env_var)
+    if env_value is not None:
+        try:
+            mem_free = _parse_size(env_value)
+        except ValueError as exc:
+            raise ValueError(f'Invalid environment variable {env_var}: {exc}')
+
+        msg = (f'{free_bytes / 1e9} GB memory required, but environment variable '
+               f'NPY_AVAILABLE_MEM={env_value} set')
+    else:
+        mem_free = _get_mem_available()
+
+        if mem_free is None:
+            msg = ("Could not determine available memory; set NPY_AVAILABLE_MEM "
+                   "environment variable (e.g. NPY_AVAILABLE_MEM=16GB) to run "
+                   "the test.")
+            mem_free = -1
+        else:
+            free_bytes_gb = free_bytes / 1e9
+            mem_free_gb = mem_free / 1e9
+            msg = f'{free_bytes_gb} GB memory required, but {mem_free_gb} GB available'
+
+    return msg if mem_free < free_bytes else None
+
+
+def _parse_size(size_str):
+    """Convert memory size strings ('12 GB' etc.) to float"""
+    suffixes = {'': 1, 'b': 1,
+                'k': 1000, 'm': 1000**2, 'g': 1000**3, 't': 1000**4,
+                'kb': 1000, 'mb': 1000**2, 'gb': 1000**3, 'tb': 1000**4,
+                'kib': 1024, 'mib': 1024**2, 'gib': 1024**3, 'tib': 1024**4}
+
+    size_re = re.compile(r'^\s*(\d+|\d+\.\d+)\s*({0})\s*$'.format(
+        '|'.join(suffixes.keys())), re.I)
+
+    m = size_re.match(size_str.lower())
+    if not m or m.group(2) not in suffixes:
+        raise ValueError(f'value {size_str!r} not a valid size')
+    return int(float(m.group(1)) * suffixes[m.group(2)])
+
+
+def _get_mem_available():
+    """Return available memory in bytes, or None if unknown."""
+    try:
+        import psutil
+        return psutil.virtual_memory().available
+    except (ImportError, AttributeError):
+        pass
+
+    if sys.platform.startswith('linux'):
+        info = {}
+        with open('/proc/meminfo') as f:
+            for line in f:
+                p = line.split()
+                info[p[0].strip(':').lower()] = int(p[1]) * 1024
+
+        if 'memavailable' in info:
+            # Linux >= 3.14
+            return info['memavailable']
+        else:
+            return info['memfree'] + info['cached']
+
+    return None
+
+
+def _no_tracing(func):
+    """
+    Decorator to temporarily turn off tracing for the duration of a test.
+    Needed in tests that check refcounting, otherwise the tracing itself
+    influences the refcounts
+    """
+    if not hasattr(sys, 'gettrace'):
+        return func
+    else:
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            original_trace = sys.gettrace()
+            try:
+                sys.settrace(None)
+                return func(*args, **kwargs)
+            finally:
+                sys.settrace(original_trace)
+        return wrapper
+
+
+def _get_glibc_version():
+    try:
+        ver = os.confstr('CS_GNU_LIBC_VERSION').rsplit(' ')[1]
+    except Exception:
+        ver = '0.0'
+
+    return ver
+
+
+_glibcver = _get_glibc_version()
+_glibc_older_than = lambda x: (_glibcver != '0.0' and _glibcver < x)
+
+
+def run_threaded(func, max_workers=8, pass_count=False,
+                 pass_barrier=False, outer_iterations=1,
+                 prepare_args=None):
+    """Runs a function many times in parallel"""
+    for _ in range(outer_iterations):
+        with (concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
+              as tpe):
+            if prepare_args is None:
+                args = []
+            else:
+                args = prepare_args()
+            if pass_barrier:
+                barrier = threading.Barrier(max_workers)
+                args.append(barrier)
+            if pass_count:
+                all_args = [(func, i, *args) for i in range(max_workers)]
+            else:
+                all_args = [(func, *args) for i in range(max_workers)]
+            try:
+                futures = []
+                for arg in all_args:
+                    futures.append(tpe.submit(*arg))
+            finally:
+                if len(futures) < max_workers and pass_barrier:
+                    barrier.abort()
+            for f in futures:
+                f.result()
+
+
+def get_stringdtype_dtype(na_object, coerce=True):
+    # explicit is check for pd_NA because != with pd_NA returns pd_NA
+    if na_object is pd_NA or na_object != "unset":
+        return np.dtypes.StringDType(na_object=na_object, coerce=coerce)
+    else:
+        return np.dtypes.StringDType(coerce=coerce)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/utils.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..75ea45d3a72118fa6d17298fe85ccf7078caaed3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/_private/utils.pyi
@@ -0,0 +1,496 @@
+import ast
+import sys
+import types
+import unittest
+import warnings
+from collections.abc import Callable, Iterable, Sequence
+from contextlib import _GeneratorContextManager
+from pathlib import Path
+from re import Pattern
+from typing import (
+    Any,
+    AnyStr,
+    ClassVar,
+    Final,
+    Generic,
+    NoReturn,
+    SupportsIndex,
+    TypeAlias,
+    overload,
+    type_check_only,
+)
+from typing import Literal as L
+from unittest.case import SkipTest
+
+from _typeshed import ConvertibleToFloat, GenericPath, StrOrBytesPath, StrPath
+from typing_extensions import ParamSpec, Self, TypeVar, TypeVarTuple, Unpack
+
+import numpy as np
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NDArray,
+    _ArrayLikeDT64_co,
+    _ArrayLikeNumber_co,
+    _ArrayLikeObject_co,
+    _ArrayLikeTD64_co,
+)
+
+__all__ = [  # noqa: RUF022
+    "IS_EDITABLE",
+    "IS_MUSL",
+    "IS_PYPY",
+    "IS_PYSTON",
+    "IS_WASM",
+    "HAS_LAPACK64",
+    "HAS_REFCOUNT",
+    "NOGIL_BUILD",
+    "assert_",
+    "assert_array_almost_equal_nulp",
+    "assert_raises_regex",
+    "assert_array_max_ulp",
+    "assert_warns",
+    "assert_no_warnings",
+    "assert_allclose",
+    "assert_equal",
+    "assert_almost_equal",
+    "assert_approx_equal",
+    "assert_array_equal",
+    "assert_array_less",
+    "assert_string_equal",
+    "assert_array_almost_equal",
+    "assert_raises",
+    "build_err_msg",
+    "decorate_methods",
+    "jiffies",
+    "memusage",
+    "print_assert_equal",
+    "rundocs",
+    "runstring",
+    "verbose",
+    "measure",
+    "IgnoreException",
+    "clear_and_catch_warnings",
+    "SkipTest",
+    "KnownFailureException",
+    "temppath",
+    "tempdir",
+    "suppress_warnings",
+    "assert_array_compare",
+    "assert_no_gc_cycles",
+    "break_cycles",
+    "check_support_sve",
+    "run_threaded",
+]
+
+###
+
+_T = TypeVar("_T")
+_Ts = TypeVarTuple("_Ts")
+_Tss = ParamSpec("_Tss")
+_ET = TypeVar("_ET", bound=BaseException, default=BaseException)
+_FT = TypeVar("_FT", bound=Callable[..., Any])
+_W_co = TypeVar("_W_co", bound=_WarnLog | None, default=_WarnLog | None, covariant=True)
+_T_or_bool = TypeVar("_T_or_bool", default=bool)
+
+_StrLike: TypeAlias = str | bytes
+_RegexLike: TypeAlias = _StrLike | Pattern[Any]
+_NumericArrayLike: TypeAlias = _ArrayLikeNumber_co | _ArrayLikeObject_co
+
+_ExceptionSpec: TypeAlias = type[_ET] | tuple[type[_ET], ...]
+_WarningSpec: TypeAlias = type[Warning]
+_WarnLog: TypeAlias = list[warnings.WarningMessage]
+_ToModules: TypeAlias = Iterable[types.ModuleType]
+
+# Must return a bool or an ndarray/generic type that is supported by `np.logical_and.reduce`
+_ComparisonFunc: TypeAlias = Callable[
+    [NDArray[Any], NDArray[Any]],
+    bool | np.bool | np.number | NDArray[np.bool | np.number | np.object_],
+]
+
+# Type-check only `clear_and_catch_warnings` subclasses for both values of the
+# `record` parameter. Copied from the stdlib `warnings` stubs.
+@type_check_only
+class _clear_and_catch_warnings_with_records(clear_and_catch_warnings):
+    def __enter__(self) -> list[warnings.WarningMessage]: ...
+
+@type_check_only
+class _clear_and_catch_warnings_without_records(clear_and_catch_warnings):
+    def __enter__(self) -> None: ...
+
+###
+
+verbose: int = 0
+NUMPY_ROOT: Final[Path] = ...
+IS_INSTALLED: Final[bool] = ...
+IS_EDITABLE: Final[bool] = ...
+IS_MUSL: Final[bool] = ...
+IS_PYPY: Final[bool] = ...
+IS_PYSTON: Final[bool] = ...
+IS_WASM: Final[bool] = ...
+HAS_REFCOUNT: Final[bool] = ...
+HAS_LAPACK64: Final[bool] = ...
+NOGIL_BUILD: Final[bool] = ...
+
+class KnownFailureException(Exception): ...
+class IgnoreException(Exception): ...
+
+# NOTE: `warnings.catch_warnings` is incorrectly defined as invariant in typeshed
+class clear_and_catch_warnings(warnings.catch_warnings[_W_co], Generic[_W_co]):  # type: ignore[type-var]  # pyright: ignore[reportInvalidTypeArguments]
+    class_modules: ClassVar[tuple[types.ModuleType, ...]] = ()
+    modules: Final[set[types.ModuleType]]
+    @overload  # record: True
+    def __init__(self: clear_and_catch_warnings[_WarnLog], /, record: L[True], modules: _ToModules = ()) -> None: ...
+    @overload  # record: False (default)
+    def __init__(self: clear_and_catch_warnings[None], /, record: L[False] = False, modules: _ToModules = ()) -> None: ...
+    @overload  # record; bool
+    def __init__(self, /, record: bool, modules: _ToModules = ()) -> None: ...
+
+class suppress_warnings:
+    log: Final[_WarnLog]
+    def __init__(self, /, forwarding_rule: L["always", "module", "once", "location"] = "always") -> None: ...
+    def __enter__(self) -> Self: ...
+    def __exit__(self, cls: type[BaseException] | None, exc: BaseException | None, tb: types.TracebackType | None, /) -> None: ...
+    def __call__(self, /, func: _FT) -> _FT: ...
+
+    #
+    def filter(self, /, category: type[Warning] = ..., message: str = "", module: types.ModuleType | None = None) -> None: ...
+    def record(self, /, category: type[Warning] = ..., message: str = "", module: types.ModuleType | None = None) -> _WarnLog: ...
+
+# Contrary to runtime we can't do `os.name` checks while type checking,
+# only `sys.platform` checks
+if sys.platform == "win32" or sys.platform == "cygwin":
+    def memusage(processName: str = ..., instance: int = ...) -> int: ...
+elif sys.platform == "linux":
+    def memusage(_proc_pid_stat: StrOrBytesPath = ...) -> int | None: ...
+else:
+    def memusage() -> NoReturn: ...
+
+if sys.platform == "linux":
+    def jiffies(_proc_pid_stat: StrOrBytesPath = ..., _load_time: list[float] = []) -> int: ...
+else:
+    def jiffies(_load_time: list[float] = []) -> int: ...
+
+#
+def build_err_msg(
+    arrays: Iterable[object],
+    err_msg: object,
+    header: str = ...,
+    verbose: bool = ...,
+    names: Sequence[str] = ...,
+    precision: SupportsIndex | None = ...,
+) -> str: ...
+
+#
+def print_assert_equal(test_string: str, actual: object, desired: object) -> None: ...
+
+#
+def assert_(val: object, msg: str | Callable[[], str] = "") -> None: ...
+
+#
+def assert_equal(
+    actual: object,
+    desired: object,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+def assert_almost_equal(
+    actual: _NumericArrayLike,
+    desired: _NumericArrayLike,
+    decimal: int = 7,
+    err_msg: object = "",
+    verbose: bool = True,
+) -> None: ...
+
+#
+def assert_approx_equal(
+    actual: ConvertibleToFloat,
+    desired: ConvertibleToFloat,
+    significant: int = 7,
+    err_msg: object = "",
+    verbose: bool = True,
+) -> None: ...
+
+#
+def assert_array_compare(
+    comparison: _ComparisonFunc,
+    x: ArrayLike,
+    y: ArrayLike,
+    err_msg: object = "",
+    verbose: bool = True,
+    header: str = "",
+    precision: SupportsIndex = 6,
+    equal_nan: bool = True,
+    equal_inf: bool = True,
+    *,
+    strict: bool = False,
+    names: tuple[str, str] = ("ACTUAL", "DESIRED"),
+) -> None: ...
+
+#
+def assert_array_equal(
+    actual: object,
+    desired: object,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+#
+def assert_array_almost_equal(
+    actual: _NumericArrayLike,
+    desired: _NumericArrayLike,
+    decimal: float = 6,
+    err_msg: object = "",
+    verbose: bool = True,
+) -> None: ...
+
+@overload
+def assert_array_less(
+    x: _ArrayLikeDT64_co,
+    y: _ArrayLikeDT64_co,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+@overload
+def assert_array_less(
+    x: _ArrayLikeTD64_co,
+    y: _ArrayLikeTD64_co,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+@overload
+def assert_array_less(
+    x: _NumericArrayLike,
+    y: _NumericArrayLike,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+#
+def assert_string_equal(actual: str, desired: str) -> None: ...
+
+#
+@overload
+def assert_raises(
+    exception_class: _ExceptionSpec[_ET],
+    /,
+    *,
+    msg: str | None = None,
+) -> unittest.case._AssertRaisesContext[_ET]: ...
+@overload
+def assert_raises(
+    exception_class: _ExceptionSpec,
+    callable: Callable[_Tss, Any],
+    /,
+    *args: _Tss.args,
+    **kwargs: _Tss.kwargs,
+) -> None: ...
+
+#
+@overload
+def assert_raises_regex(
+    exception_class: _ExceptionSpec[_ET],
+    expected_regexp: _RegexLike,
+    *,
+    msg: str | None = None,
+) -> unittest.case._AssertRaisesContext[_ET]: ...
+@overload
+def assert_raises_regex(
+    exception_class: _ExceptionSpec,
+    expected_regexp: _RegexLike,
+    callable: Callable[_Tss, Any],
+    *args: _Tss.args,
+    **kwargs: _Tss.kwargs,
+) -> None: ...
+
+#
+@overload
+def assert_allclose(
+    actual: _ArrayLikeTD64_co,
+    desired: _ArrayLikeTD64_co,
+    rtol: float = 1e-7,
+    atol: float = 0,
+    equal_nan: bool = True,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+@overload
+def assert_allclose(
+    actual: _NumericArrayLike,
+    desired: _NumericArrayLike,
+    rtol: float = 1e-7,
+    atol: float = 0,
+    equal_nan: bool = True,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+#
+def assert_array_almost_equal_nulp(
+    x: _ArrayLikeNumber_co,
+    y: _ArrayLikeNumber_co,
+    nulp: float = 1,
+) -> None: ...
+
+#
+def assert_array_max_ulp(
+    a: _ArrayLikeNumber_co,
+    b: _ArrayLikeNumber_co,
+    maxulp: float = 1,
+    dtype: DTypeLike | None = None,
+) -> NDArray[Any]: ...
+
+#
+@overload
+def assert_warns(warning_class: _WarningSpec) -> _GeneratorContextManager[None]: ...
+@overload
+def assert_warns(warning_class: _WarningSpec, func: Callable[_Tss, _T], *args: _Tss.args, **kwargs: _Tss.kwargs) -> _T: ...
+
+#
+@overload
+def assert_no_warnings() -> _GeneratorContextManager[None]: ...
+@overload
+def assert_no_warnings(func: Callable[_Tss, _T], /, *args: _Tss.args, **kwargs: _Tss.kwargs) -> _T: ...
+
+#
+@overload
+def assert_no_gc_cycles() -> _GeneratorContextManager[None]: ...
+@overload
+def assert_no_gc_cycles(func: Callable[_Tss, Any], /, *args: _Tss.args, **kwargs: _Tss.kwargs) -> None: ...
+
+###
+
+#
+@overload
+def tempdir(
+    suffix: None = None,
+    prefix: None = None,
+    dir: None = None,
+) -> _GeneratorContextManager[str]: ...
+@overload
+def tempdir(
+    suffix: AnyStr | None = None,
+    prefix: AnyStr | None = None,
+    *,
+    dir: GenericPath[AnyStr],
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def tempdir(
+    suffix: AnyStr | None = None,
+    *,
+    prefix: AnyStr,
+    dir: GenericPath[AnyStr] | None = None,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def tempdir(
+    suffix: AnyStr,
+    prefix: AnyStr | None = None,
+    dir: GenericPath[AnyStr] | None = None,
+) -> _GeneratorContextManager[AnyStr]: ...
+
+#
+@overload
+def temppath(
+    suffix: None = None,
+    prefix: None = None,
+    dir: None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[str]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None,
+    prefix: AnyStr | None,
+    dir: GenericPath[AnyStr],
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None = None,
+    prefix: AnyStr | None = None,
+    *,
+    dir: GenericPath[AnyStr],
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None,
+    prefix: AnyStr,
+    dir: GenericPath[AnyStr] | None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None = None,
+    *,
+    prefix: AnyStr,
+    dir: GenericPath[AnyStr] | None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr,
+    prefix: AnyStr | None = None,
+    dir: GenericPath[AnyStr] | None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+
+#
+def check_support_sve(__cache: list[_T_or_bool] = []) -> _T_or_bool: ...  # noqa: PYI063
+
+#
+def decorate_methods(
+    cls: type,
+    decorator: Callable[[Callable[..., Any]], Any],
+    testmatch: _RegexLike | None = None,
+) -> None: ...
+
+#
+@overload
+def run_threaded(
+    func: Callable[[], None],
+    max_workers: int = 8,
+    pass_count: bool = False,
+    pass_barrier: bool = False,
+    outer_iterations: int = 1,
+    prepare_args: None = None,
+) -> None: ...
+@overload
+def run_threaded(
+    func: Callable[[Unpack[_Ts]], None],
+    max_workers: int,
+    pass_count: bool,
+    pass_barrier: bool,
+    outer_iterations: int,
+    prepare_args: tuple[Unpack[_Ts]],
+) -> None: ...
+@overload
+def run_threaded(
+    func: Callable[[Unpack[_Ts]], None],
+    max_workers: int = 8,
+    pass_count: bool = False,
+    pass_barrier: bool = False,
+    outer_iterations: int = 1,
+    *,
+    prepare_args: tuple[Unpack[_Ts]],
+) -> None: ...
+
+#
+def runstring(astr: _StrLike | types.CodeType, dict: dict[str, Any] | None) -> Any: ...  # noqa: ANN401
+def rundocs(filename: StrPath | None = None, raise_on_error: bool = True) -> None: ...
+def measure(code_str: _StrLike | ast.AST, times: int = 1, label: str | None = None) -> float: ...
+def break_cycles() -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/overrides.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/overrides.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e61534c323648f3def69c24e61d7d6e6c79d970
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/overrides.py
@@ -0,0 +1,83 @@
+"""Tools for testing implementations of __array_function__ and ufunc overrides
+
+
+"""
+
+from numpy._core.overrides import ARRAY_FUNCTIONS as _array_functions
+from numpy import ufunc as _ufunc
+import numpy._core.umath as _umath
+
+def get_overridable_numpy_ufuncs():
+    """List all numpy ufuncs overridable via `__array_ufunc__`
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    set
+        A set containing all overridable ufuncs in the public numpy API.
+    """
+    ufuncs = {obj for obj in _umath.__dict__.values()
+              if isinstance(obj, _ufunc)}
+    return ufuncs
+
+
+def allows_array_ufunc_override(func):
+    """Determine if a function can be overridden via `__array_ufunc__`
+
+    Parameters
+    ----------
+    func : callable
+        Function that may be overridable via `__array_ufunc__`
+
+    Returns
+    -------
+    bool
+        `True` if `func` is overridable via `__array_ufunc__` and
+        `False` otherwise.
+
+    Notes
+    -----
+    This function is equivalent to ``isinstance(func, np.ufunc)`` and
+    will work correctly for ufuncs defined outside of Numpy.
+
+    """
+    return isinstance(func, _ufunc)
+
+
+def get_overridable_numpy_array_functions():
+    """List all numpy functions overridable via `__array_function__`
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    set
+        A set containing all functions in the public numpy API that are
+        overridable via `__array_function__`.
+
+    """
+    # 'import numpy' doesn't import recfunctions, so make sure it's imported
+    # so ufuncs defined there show up in the ufunc listing
+    from numpy.lib import recfunctions  # noqa: F401
+    return _array_functions.copy()
+
+def allows_array_function_override(func):
+    """Determine if a Numpy function can be overridden via `__array_function__`
+
+    Parameters
+    ----------
+    func : callable
+        Function that may be overridable via `__array_function__`
+
+    Returns
+    -------
+    bool
+        `True` if `func` is a function in the Numpy API that is
+        overridable via `__array_function__` and `False` otherwise.
+    """
+    return func in _array_functions
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/overrides.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/overrides.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3fefc3f350dacbd223c1fcc94db1c634d1b6c6b1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/overrides.pyi
@@ -0,0 +1,11 @@
+from collections.abc import Callable, Hashable
+from typing import Any
+
+from typing_extensions import TypeIs
+
+import numpy as np
+
+def get_overridable_numpy_ufuncs() -> set[np.ufunc]: ...
+def get_overridable_numpy_array_functions() -> set[Callable[..., Any]]: ...
+def allows_array_ufunc_override(func: object) -> TypeIs[np.ufunc]: ...
+def allows_array_function_override(func: Hashable) -> bool: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/print_coercion_tables.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/print_coercion_tables.py
new file mode 100644
index 0000000000000000000000000000000000000000..649c1cd6bc21720ace7d4a6597061242a9d2ccde
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/print_coercion_tables.py
@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""Prints type-coercion tables for the built-in NumPy types
+
+"""
+import numpy as np
+from numpy._core.numerictypes import obj2sctype
+from collections import namedtuple
+
+# Generic object that can be added, but doesn't do anything else
+class GenericObject:
+    def __init__(self, v):
+        self.v = v
+
+    def __add__(self, other):
+        return self
+
+    def __radd__(self, other):
+        return self
+
+    dtype = np.dtype('O')
+
+def print_cancast_table(ntypes):
+    print('X', end=' ')
+    for char in ntypes:
+        print(char, end=' ')
+    print()
+    for row in ntypes:
+        print(row, end=' ')
+        for col in ntypes:
+            if np.can_cast(row, col, "equiv"):
+                cast = "#"
+            elif np.can_cast(row, col, "safe"):
+                cast = "="
+            elif np.can_cast(row, col, "same_kind"):
+                cast = "~"
+            elif np.can_cast(row, col, "unsafe"):
+                cast = "."
+            else:
+                cast = " "
+            print(cast, end=' ')
+        print()
+
+def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
+    print('+', end=' ')
+    for char in ntypes:
+        print(char, end=' ')
+    print()
+    for row in ntypes:
+        if row == 'O':
+            rowtype = GenericObject
+        else:
+            rowtype = obj2sctype(row)
+
+        print(row, end=' ')
+        for col in ntypes:
+            if col == 'O':
+                coltype = GenericObject
+            else:
+                coltype = obj2sctype(col)
+            try:
+                if firstarray:
+                    rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
+                else:
+                    rowvalue = rowtype(inputfirstvalue)
+                colvalue = coltype(inputsecondvalue)
+                if use_promote_types:
+                    char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
+                else:
+                    value = np.add(rowvalue, colvalue)
+                    if isinstance(value, np.ndarray):
+                        char = value.dtype.char
+                    else:
+                        char = np.dtype(type(value)).char
+            except ValueError:
+                char = '!'
+            except OverflowError:
+                char = '@'
+            except TypeError:
+                char = '#'
+            print(char, end=' ')
+        print()
+
+
+def print_new_cast_table(*, can_cast=True, legacy=False, flags=False):
+    """Prints new casts, the values given are default "can-cast" values, not
+    actual ones.
+    """
+    from numpy._core._multiarray_tests import get_all_cast_information
+
+    cast_table = {
+        -1: " ",
+        0: "#",  # No cast (classify as equivalent here)
+        1: "#",  # equivalent casting
+        2: "=",  # safe casting
+        3: "~",  # same-kind casting
+        4: ".",  # unsafe casting
+    }
+    flags_table = {
+        0 : "▗", 7: "█",
+        1: "▚", 2: "▐", 4: "▄",
+                3: "▜", 5: "▙",
+                        6: "▟",
+    }
+
+    cast_info = namedtuple("cast_info", ["can_cast", "legacy", "flags"])
+    no_cast_info = cast_info(" ", " ", " ")
+
+    casts = get_all_cast_information()
+    table = {}
+    dtypes = set()
+    for cast in casts:
+        dtypes.add(cast["from"])
+        dtypes.add(cast["to"])
+
+        if cast["from"] not in table:
+            table[cast["from"]] = {}
+        to_dict = table[cast["from"]]
+
+        can_cast = cast_table[cast["casting"]]
+        legacy = "L" if cast["legacy"] else "."
+        flags = 0
+        if cast["requires_pyapi"]:
+            flags |= 1
+        if cast["supports_unaligned"]:
+            flags |= 2
+        if cast["no_floatingpoint_errors"]:
+            flags |= 4
+
+        flags = flags_table[flags]
+        to_dict[cast["to"]] = cast_info(can_cast=can_cast, legacy=legacy, flags=flags)
+
+    # The np.dtype(x.type) is a bit strange, because dtype classes do
+    # not expose much yet.
+    types = np.typecodes["All"]
+    def sorter(x):
+        # This is a bit weird hack, to get a table as close as possible to
+        # the one printing all typecodes (but expecting user-dtypes).
+        dtype = np.dtype(x.type)
+        try:
+            indx = types.index(dtype.char)
+        except ValueError:
+            indx = np.inf
+        return (indx, dtype.char)
+
+    dtypes = sorted(dtypes, key=sorter)
+
+    def print_table(field="can_cast"):
+        print('X', end=' ')
+        for dt in dtypes:
+            print(np.dtype(dt.type).char, end=' ')
+        print()
+        for from_dt in dtypes:
+            print(np.dtype(from_dt.type).char, end=' ')
+            row = table.get(from_dt, {})
+            for to_dt in dtypes:
+                print(getattr(row.get(to_dt, no_cast_info), field), end=' ')
+            print()
+
+    if can_cast:
+        # Print the actual table:
+        print()
+        print("Casting: # is equivalent, = is safe, ~ is same-kind, and . is unsafe")
+        print()
+        print_table("can_cast")
+
+    if legacy:
+        print()
+        print("L denotes a legacy cast . a non-legacy one.")
+        print()
+        print_table("legacy")
+
+    if flags:
+        print()
+        print(f"{flags_table[0]}: no flags, {flags_table[1]}: PyAPI, "
+              f"{flags_table[2]}: supports unaligned, {flags_table[4]}: no-float-errors")
+        print()
+        print_table("flags")
+
+
+if __name__ == '__main__':
+    print("can cast")
+    print_cancast_table(np.typecodes['All'])
+    print()
+    print("In these tables, ValueError is '!', OverflowError is '@', TypeError is '#'")
+    print()
+    print("scalar + scalar")
+    print_coercion_table(np.typecodes['All'], 0, 0, False)
+    print()
+    print("scalar + neg scalar")
+    print_coercion_table(np.typecodes['All'], 0, -1, False)
+    print()
+    print("array + scalar")
+    print_coercion_table(np.typecodes['All'], 0, 0, True)
+    print()
+    print("array + neg scalar")
+    print_coercion_table(np.typecodes['All'], 0, -1, True)
+    print()
+    print("promote_types")
+    print_coercion_table(np.typecodes['All'], 0, 0, False, True)
+    print("New casting type promotion:")
+    print_new_cast_table(can_cast=True, legacy=True, flags=True)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/print_coercion_tables.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/print_coercion_tables.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e6430304675e430753a8caa72ffcb2570736a618
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/print_coercion_tables.pyi
@@ -0,0 +1,27 @@
+from collections.abc import Iterable
+from typing import ClassVar, Generic
+
+from typing_extensions import Self, TypeVar
+
+import numpy as np
+
+_VT_co = TypeVar("_VT_co", default=object, covariant=True)
+
+# undocumented
+class GenericObject(Generic[_VT_co]):
+    dtype: ClassVar[np.dtype[np.object_]] = ...
+    v: _VT_co
+
+    def __init__(self, /, v: _VT_co) -> None: ...
+    def __add__(self, other: object, /) -> Self: ...
+    def __radd__(self, other: object, /) -> Self: ...
+
+def print_cancast_table(ntypes: Iterable[str]) -> None: ...
+def print_coercion_table(
+    ntypes: Iterable[str],
+    inputfirstvalue: int,
+    inputsecondvalue: int,
+    firstarray: bool,
+    use_promote_types: bool = False,
+) -> None: ...
+def print_new_cast_table(*, can_cast: bool = True, legacy: bool = False, flags: bool = False) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/tests/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/tests/test_utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/tests/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..df9fce8fd79afbcec85d94bb37ee034a9d1f4668
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/testing/tests/test_utils.py
@@ -0,0 +1,1929 @@
+import warnings
+import sys
+import os
+import itertools
+import pytest
+import weakref
+import re
+
+import numpy as np
+import numpy._core._multiarray_umath as ncu
+from numpy.testing import (
+    assert_equal, assert_array_equal, assert_almost_equal,
+    assert_array_almost_equal, assert_array_less, build_err_msg,
+    assert_raises, assert_warns, assert_no_warnings, assert_allclose,
+    assert_approx_equal, assert_array_almost_equal_nulp, assert_array_max_ulp,
+    clear_and_catch_warnings, suppress_warnings, assert_string_equal, assert_,
+    tempdir, temppath, assert_no_gc_cycles, HAS_REFCOUNT
+)
+
+
+class _GenericTest:
+
+    def _test_equal(self, a, b):
+        self._assert_func(a, b)
+
+    def _test_not_equal(self, a, b):
+        with assert_raises(AssertionError):
+            self._assert_func(a, b)
+
+    def test_array_rank1_eq(self):
+        """Test two equal array of rank 1 are found equal."""
+        a = np.array([1, 2])
+        b = np.array([1, 2])
+
+        self._test_equal(a, b)
+
+    def test_array_rank1_noteq(self):
+        """Test two different array of rank 1 are found not equal."""
+        a = np.array([1, 2])
+        b = np.array([2, 2])
+
+        self._test_not_equal(a, b)
+
+    def test_array_rank2_eq(self):
+        """Test two equal array of rank 2 are found equal."""
+        a = np.array([[1, 2], [3, 4]])
+        b = np.array([[1, 2], [3, 4]])
+
+        self._test_equal(a, b)
+
+    def test_array_diffshape(self):
+        """Test two arrays with different shapes are found not equal."""
+        a = np.array([1, 2])
+        b = np.array([[1, 2], [1, 2]])
+
+        self._test_not_equal(a, b)
+
+    def test_objarray(self):
+        """Test object arrays."""
+        a = np.array([1, 1], dtype=object)
+        self._test_equal(a, 1)
+
+    def test_array_likes(self):
+        self._test_equal([1, 2, 3], (1, 2, 3))
+
+
+class TestArrayEqual(_GenericTest):
+
+    def setup_method(self):
+        self._assert_func = assert_array_equal
+
+    def test_generic_rank1(self):
+        """Test rank 1 array for all dtypes."""
+        def foo(t):
+            a = np.empty(2, t)
+            a.fill(1)
+            b = a.copy()
+            c = a.copy()
+            c.fill(0)
+            self._test_equal(a, b)
+            self._test_not_equal(c, b)
+
+        # Test numeric types and object
+        for t in '?bhilqpBHILQPfdgFDG':
+            foo(t)
+
+        # Test strings
+        for t in ['S1', 'U1']:
+            foo(t)
+
+    def test_0_ndim_array(self):
+        x = np.array(473963742225900817127911193656584771)
+        y = np.array(18535119325151578301457182298393896)
+
+        with pytest.raises(AssertionError) as exc_info:
+            self._assert_func(x, y)
+        msg = str(exc_info.value)
+        assert_('Mismatched elements: 1 / 1 (100%)\n'
+                in msg)
+
+        y = x
+        self._assert_func(x, y)
+
+        x = np.array(4395065348745.5643764887869876)
+        y = np.array(0)
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: '
+                        '4.39506535e+12\n'
+                        'Max relative difference among violations: inf\n')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        x = y
+        self._assert_func(x, y)
+
+    def test_generic_rank3(self):
+        """Test rank 3 array for all dtypes."""
+        def foo(t):
+            a = np.empty((4, 2, 3), t)
+            a.fill(1)
+            b = a.copy()
+            c = a.copy()
+            c.fill(0)
+            self._test_equal(a, b)
+            self._test_not_equal(c, b)
+
+        # Test numeric types and object
+        for t in '?bhilqpBHILQPfdgFDG':
+            foo(t)
+
+        # Test strings
+        for t in ['S1', 'U1']:
+            foo(t)
+
+    def test_nan_array(self):
+        """Test arrays with nan values in them."""
+        a = np.array([1, 2, np.nan])
+        b = np.array([1, 2, np.nan])
+
+        self._test_equal(a, b)
+
+        c = np.array([1, 2, 3])
+        self._test_not_equal(c, b)
+
+    def test_string_arrays(self):
+        """Test two arrays with different shapes are found not equal."""
+        a = np.array(['floupi', 'floupa'])
+        b = np.array(['floupi', 'floupa'])
+
+        self._test_equal(a, b)
+
+        c = np.array(['floupipi', 'floupa'])
+
+        self._test_not_equal(c, b)
+
+    def test_recarrays(self):
+        """Test record arrays."""
+        a = np.empty(2, [('floupi', float), ('floupa', float)])
+        a['floupi'] = [1, 2]
+        a['floupa'] = [1, 2]
+        b = a.copy()
+
+        self._test_equal(a, b)
+
+        c = np.empty(2, [('floupipi', float),
+                         ('floupi', float), ('floupa', float)])
+        c['floupipi'] = a['floupi'].copy()
+        c['floupa'] = a['floupa'].copy()
+
+        with pytest.raises(TypeError):
+            self._test_not_equal(c, b)
+
+    def test_masked_nan_inf(self):
+        # Regression test for gh-11121
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[False, True, False])
+        b = np.array([3., np.nan, 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, False, False])
+        b = np.array([np.inf, 4., 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+
+    def test_subclass_that_overrides_eq(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return bool(np.equal(self, other).all())
+
+            def __ne__(self, other):
+                return not self == other
+
+        a = np.array([1., 2.]).view(MyArray)
+        b = np.array([2., 3.]).view(MyArray)
+        assert_(type(a == a), bool)
+        assert_(a == a)
+        assert_(a != b)
+        self._test_equal(a, a)
+        self._test_not_equal(a, b)
+        self._test_not_equal(b, a)
+
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 0.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._test_equal(a, b)
+
+        c = np.array([0., 2.9]).view(MyArray)
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 2.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._test_equal(b, c)
+
+    def test_subclass_that_does_not_implement_npall(self):
+        class MyArray(np.ndarray):
+            def __array_function__(self, *args, **kwargs):
+                return NotImplemented
+
+        a = np.array([1., 2.]).view(MyArray)
+        b = np.array([2., 3.]).view(MyArray)
+        with assert_raises(TypeError):
+            np.all(a)
+        self._test_equal(a, a)
+        self._test_not_equal(a, b)
+        self._test_not_equal(b, a)
+
+    def test_suppress_overflow_warnings(self):
+        # Based on issue #18992
+        with pytest.raises(AssertionError):
+            with np.errstate(all="raise"):
+                np.testing.assert_array_equal(
+                    np.array([1, 2, 3], np.float32),
+                    np.array([1, 1e-40, 3], np.float32))
+
+    def test_array_vs_scalar_is_equal(self):
+        """Test comparing an array with a scalar when all values are equal."""
+        a = np.array([1., 1., 1.])
+        b = 1.
+
+        self._test_equal(a, b)
+
+    def test_array_vs_array_not_equal(self):
+        """Test comparing an array with a scalar when not all values equal."""
+        a = np.array([34986, 545676, 439655, 563766])
+        b = np.array([34986, 545676, 439655, 0])
+
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 563766\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+        a = np.array([34986, 545676, 439655.2, 563766])
+        expected_msg = ('Mismatched elements: 2 / 4 (50%)\n'
+                        'Max absolute difference among violations: '
+                        '563766.\n'
+                        'Max relative difference among violations: '
+                        '4.54902139e-07')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+    def test_array_vs_scalar_strict(self):
+        """Test comparing an array with a scalar with strict option."""
+        a = np.array([1., 1., 1.])
+        b = 1.
+
+        with pytest.raises(AssertionError):
+            self._assert_func(a, b, strict=True)
+
+    def test_array_vs_array_strict(self):
+        """Test comparing two arrays with strict option."""
+        a = np.array([1., 1., 1.])
+        b = np.array([1., 1., 1.])
+
+        self._assert_func(a, b, strict=True)
+
+    def test_array_vs_float_array_strict(self):
+        """Test comparing two arrays with strict option."""
+        a = np.array([1, 1, 1])
+        b = np.array([1., 1., 1.])
+
+        with pytest.raises(AssertionError):
+            self._assert_func(a, b, strict=True)
+
+
+class TestBuildErrorMessage:
+
+    def test_build_err_msg_defaults(self):
+        x = np.array([1.00001, 2.00002, 3.00003])
+        y = np.array([1.00002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg)
+        b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
+             '1.00001, 2.00002, 3.00003])\n DESIRED: array([1.00002, '
+             '2.00003, 3.00004])')
+        assert_equal(a, b)
+
+    def test_build_err_msg_no_verbose(self):
+        x = np.array([1.00001, 2.00002, 3.00003])
+        y = np.array([1.00002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg, verbose=False)
+        b = '\nItems are not equal: There is a mismatch'
+        assert_equal(a, b)
+
+    def test_build_err_msg_custom_names(self):
+        x = np.array([1.00001, 2.00002, 3.00003])
+        y = np.array([1.00002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg, names=('FOO', 'BAR'))
+        b = ('\nItems are not equal: There is a mismatch\n FOO: array(['
+             '1.00001, 2.00002, 3.00003])\n BAR: array([1.00002, 2.00003, '
+             '3.00004])')
+        assert_equal(a, b)
+
+    def test_build_err_msg_custom_precision(self):
+        x = np.array([1.000000001, 2.00002, 3.00003])
+        y = np.array([1.000000002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg, precision=10)
+        b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
+             '1.000000001, 2.00002    , 3.00003    ])\n DESIRED: array(['
+             '1.000000002, 2.00003    , 3.00004    ])')
+        assert_equal(a, b)
+
+
+class TestEqual(TestArrayEqual):
+
+    def setup_method(self):
+        self._assert_func = assert_equal
+
+    def test_nan_items(self):
+        self._assert_func(np.nan, np.nan)
+        self._assert_func([np.nan], [np.nan])
+        self._test_not_equal(np.nan, [np.nan])
+        self._test_not_equal(np.nan, 1)
+
+    def test_inf_items(self):
+        self._assert_func(np.inf, np.inf)
+        self._assert_func([np.inf], [np.inf])
+        self._test_not_equal(np.inf, [np.inf])
+
+    def test_datetime(self):
+        self._test_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-01", "s")
+        )
+        self._test_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-01", "m")
+        )
+
+        # gh-10081
+        self._test_not_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-02", "s")
+        )
+        self._test_not_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-02", "m")
+        )
+
+    def test_nat_items(self):
+        # not a datetime
+        nadt_no_unit = np.datetime64("NaT")
+        nadt_s = np.datetime64("NaT", "s")
+        nadt_d = np.datetime64("NaT", "ns")
+        # not a timedelta
+        natd_no_unit = np.timedelta64("NaT")
+        natd_s = np.timedelta64("NaT", "s")
+        natd_d = np.timedelta64("NaT", "ns")
+
+        dts = [nadt_no_unit, nadt_s, nadt_d]
+        tds = [natd_no_unit, natd_s, natd_d]
+        for a, b in itertools.product(dts, dts):
+            self._assert_func(a, b)
+            self._assert_func([a], [b])
+            self._test_not_equal([a], b)
+
+        for a, b in itertools.product(tds, tds):
+            self._assert_func(a, b)
+            self._assert_func([a], [b])
+            self._test_not_equal([a], b)
+
+        for a, b in itertools.product(tds, dts):
+            self._test_not_equal(a, b)
+            self._test_not_equal(a, [b])
+            self._test_not_equal([a], [b])
+            self._test_not_equal([a], np.datetime64("2017-01-01", "s"))
+            self._test_not_equal([b], np.datetime64("2017-01-01", "s"))
+            self._test_not_equal([a], np.timedelta64(123, "s"))
+            self._test_not_equal([b], np.timedelta64(123, "s"))
+
+    def test_non_numeric(self):
+        self._assert_func('ab', 'ab')
+        self._test_not_equal('ab', 'abb')
+
+    def test_complex_item(self):
+        self._assert_func(complex(1, 2), complex(1, 2))
+        self._assert_func(complex(1, np.nan), complex(1, np.nan))
+        self._test_not_equal(complex(1, np.nan), complex(1, 2))
+        self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
+        self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
+
+    def test_negative_zero(self):
+        self._test_not_equal(ncu.PZERO, ncu.NZERO)
+
+    def test_complex(self):
+        x = np.array([complex(1, 2), complex(1, np.nan)])
+        y = np.array([complex(1, 2), complex(1, 2)])
+        self._assert_func(x, x)
+        self._test_not_equal(x, y)
+
+    def test_object(self):
+        # gh-12942
+        import datetime
+        a = np.array([datetime.datetime(2000, 1, 1),
+                      datetime.datetime(2000, 1, 2)])
+        self._test_not_equal(a, a[::-1])
+
+
+class TestArrayAlmostEqual(_GenericTest):
+
+    def setup_method(self):
+        self._assert_func = assert_array_almost_equal
+
+    def test_closeness(self):
+        # Note that in the course of time we ended up with
+        #     `abs(x - y) < 1.5 * 10**(-decimal)`
+        # instead of the previously documented
+        #     `abs(x - y) < 0.5 * 10**(-decimal)`
+        # so this check serves to preserve the wrongness.
+
+        # test scalars
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.5\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(1.5, 0.0, decimal=0)
+
+        # test arrays
+        self._assert_func([1.499999], [0.0], decimal=0)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.5\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func([1.5], [0.0], decimal=0)
+
+        a = [1.4999999, 0.00003]
+        b = [1.49999991, 0]
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 3.e-05\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b, decimal=7)
+
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 3.e-05\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(b, a, decimal=7)
+
+    def test_simple(self):
+        x = np.array([1234.2222])
+        y = np.array([1234.2223])
+
+        self._assert_func(x, y, decimal=3)
+        self._assert_func(x, y, decimal=4)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: '
+                        '1.e-04\n'
+                        'Max relative difference among violations: '
+                        '8.10226812e-08')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y, decimal=5)
+
+    def test_array_vs_scalar(self):
+        a = [5498.42354, 849.54345, 0.00]
+        b = 5498.42354
+        expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b, decimal=9)
+
+        expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: 5.4722099')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(b, a, decimal=9)
+
+        a = [5498.42354, 0.00]
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(b, a, decimal=7)
+
+        b = 0
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b, decimal=7)
+
+    def test_nan(self):
+        anan = np.array([np.nan])
+        aone = np.array([1])
+        ainf = np.array([np.inf])
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(ainf, anan))
+
+    def test_inf(self):
+        a = np.array([[1., 2.], [3., 4.]])
+        b = a.copy()
+        a[0, 0] = np.inf
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(a, b))
+        b[0, 0] = -np.inf
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(a, b))
+
+    def test_subclass(self):
+        a = np.array([[1., 2.], [3., 4.]])
+        b = np.ma.masked_array([[1., 2.], [0., 4.]],
+                               [[False, False], [True, False]])
+        self._assert_func(a, b)
+        self._assert_func(b, a)
+        self._assert_func(b, b)
+
+        # Test fully masked as well (see gh-11123).
+        a = np.ma.MaskedArray(3.5, mask=True)
+        b = np.array([3., 4., 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.masked
+        b = np.array([3., 4., 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])
+        b = np.array([1., 2., 3.])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])
+        b = np.array(1.)
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+
+    def test_subclass_2(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return super().__eq__(other).view(np.ndarray)
+
+            def __lt__(self, other):
+                return super().__lt__(other).view(np.ndarray)
+
+            def all(self, *args, **kwargs):
+                return all(self)
+
+        a = np.array([1., 2.]).view(MyArray)
+        self._assert_func(a, a)
+
+        z = np.array([True, True]).view(MyArray)
+        all(z)
+        b = np.array([1., 202]).view(MyArray)
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 200.\n'
+                        'Max relative difference among violations: 0.99009')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+    def test_subclass_that_cannot_be_bool(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return super().__eq__(other).view(np.ndarray)
+
+            def __lt__(self, other):
+                return super().__lt__(other).view(np.ndarray)
+
+            def all(self, *args, **kwargs):
+                raise NotImplementedError
+
+        a = np.array([1., 2.]).view(MyArray)
+        self._assert_func(a, a)
+
+
+class TestAlmostEqual(_GenericTest):
+
+    def setup_method(self):
+        self._assert_func = assert_almost_equal
+
+    def test_closeness(self):
+        # Note that in the course of time we ended up with
+        #     `abs(x - y) < 1.5 * 10**(-decimal)`
+        # instead of the previously documented
+        #     `abs(x - y) < 0.5 * 10**(-decimal)`
+        # so this check serves to preserve the wrongness.
+
+        # test scalars
+        self._assert_func(1.499999, 0.0, decimal=0)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(1.5, 0.0, decimal=0))
+
+        # test arrays
+        self._assert_func([1.499999], [0.0], decimal=0)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func([1.5], [0.0], decimal=0))
+
+    def test_nan_item(self):
+        self._assert_func(np.nan, np.nan)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.nan, 1))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.nan, np.inf))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.inf, np.nan))
+
+    def test_inf_item(self):
+        self._assert_func(np.inf, np.inf)
+        self._assert_func(-np.inf, -np.inf)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.inf, 1))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(-np.inf, np.inf))
+
+    def test_simple_item(self):
+        self._test_not_equal(1, 2)
+
+    def test_complex_item(self):
+        self._assert_func(complex(1, 2), complex(1, 2))
+        self._assert_func(complex(1, np.nan), complex(1, np.nan))
+        self._assert_func(complex(np.inf, np.nan), complex(np.inf, np.nan))
+        self._test_not_equal(complex(1, np.nan), complex(1, 2))
+        self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
+        self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
+
+    def test_complex(self):
+        x = np.array([complex(1, 2), complex(1, np.nan)])
+        z = np.array([complex(1, 2), complex(np.nan, 1)])
+        y = np.array([complex(1, 2), complex(1, 2)])
+        self._assert_func(x, x)
+        self._test_not_equal(x, y)
+        self._test_not_equal(x, z)
+
+    def test_error_message(self):
+        """Check the message is formatted correctly for the decimal value.
+           Also check the message when input includes inf or nan (gh12200)"""
+        x = np.array([1.00000000001, 2.00000000002, 3.00003])
+        y = np.array([1.00000000002, 2.00000000003, 3.00004])
+
+        # Test with a different amount of decimal digits
+        expected_msg = ('Mismatched elements: 3 / 3 (100%)\n'
+                        'Max absolute difference among violations: 1.e-05\n'
+                        'Max relative difference among violations: '
+                        '3.33328889e-06\n'
+                        ' ACTUAL: array([1.00000000001, '
+                        '2.00000000002, '
+                        '3.00003      ])\n'
+                        ' DESIRED: array([1.00000000002, 2.00000000003, '
+                        '3.00004      ])')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y, decimal=12)
+
+        # With the default value of decimal digits, only the 3rd element
+        # differs. Note that we only check for the formatting of the arrays
+        # themselves.
+        expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'
+                        'Max absolute difference among violations: 1.e-05\n'
+                        'Max relative difference among violations: '
+                        '3.33328889e-06\n'
+                        ' ACTUAL: array([1.     , 2.     , 3.00003])\n'
+                        ' DESIRED: array([1.     , 2.     , 3.00004])')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        # Check the error message when input includes inf
+        x = np.array([np.inf, 0])
+        y = np.array([np.inf, 1])
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 1.\n'
+                        ' ACTUAL: array([inf,  0.])\n'
+                        ' DESIRED: array([inf,  1.])')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        # Check the error message when dividing by zero
+        x = np.array([1, 2])
+        y = np.array([0, 0])
+        expected_msg = ('Mismatched elements: 2 / 2 (100%)\n'
+                        'Max absolute difference among violations: 2\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+    def test_error_message_2(self):
+        """Check the message is formatted correctly """
+        """when either x or y is a scalar."""
+        x = 2
+        y = np.ones(20)
+        expected_msg = ('Mismatched elements: 20 / 20 (100%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        y = 2
+        x = np.ones(20)
+        expected_msg = ('Mismatched elements: 20 / 20 (100%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 0.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+    def test_subclass_that_cannot_be_bool(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return super().__eq__(other).view(np.ndarray)
+
+            def __lt__(self, other):
+                return super().__lt__(other).view(np.ndarray)
+
+            def all(self, *args, **kwargs):
+                raise NotImplementedError
+
+        a = np.array([1., 2.]).view(MyArray)
+        self._assert_func(a, a)
+
+
+class TestApproxEqual:
+
+    def setup_method(self):
+        self._assert_func = assert_approx_equal
+
+    def test_simple_0d_arrays(self):
+        x = np.array(1234.22)
+        y = np.array(1234.23)
+
+        self._assert_func(x, y, significant=5)
+        self._assert_func(x, y, significant=6)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(x, y, significant=7))
+
+    def test_simple_items(self):
+        x = 1234.22
+        y = 1234.23
+
+        self._assert_func(x, y, significant=4)
+        self._assert_func(x, y, significant=5)
+        self._assert_func(x, y, significant=6)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(x, y, significant=7))
+
+    def test_nan_array(self):
+        anan = np.array(np.nan)
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
+
+    def test_nan_items(self):
+        anan = np.array(np.nan)
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
+
+
+class TestArrayAssertLess:
+
+    def setup_method(self):
+        self._assert_func = assert_array_less
+
+    def test_simple_arrays(self):
+        x = np.array([1.1, 2.2])
+        y = np.array([1.2, 2.3])
+
+        self._assert_func(x, y)
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        y = np.array([1.0, 2.3])
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        a = np.array([1, 3, 6, 20])
+        b = np.array([2, 4, 6, 8])
+
+        expected_msg = ('Mismatched elements: 2 / 4 (50%)\n'
+                        'Max absolute difference among violations: 12\n'
+                        'Max relative difference among violations: 1.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+    def test_rank2(self):
+        x = np.array([[1.1, 2.2], [3.3, 4.4]])
+        y = np.array([[1.2, 2.3], [3.4, 4.5]])
+
+        self._assert_func(x, y)
+        expected_msg = ('Mismatched elements: 4 / 4 (100%)\n'
+                        'Max absolute difference among violations: 0.1\n'
+                        'Max relative difference among violations: 0.09090909')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+        y = np.array([[1.0, 2.3], [3.4, 4.5]])
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+    def test_rank3(self):
+        x = np.ones(shape=(2, 2, 2))
+        y = np.ones(shape=(2, 2, 2))+1
+
+        self._assert_func(x, y)
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        y[0, 0, 0] = 0
+        expected_msg = ('Mismatched elements: 1 / 8 (12.5%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+    def test_simple_items(self):
+        x = 1.1
+        y = 2.2
+
+        self._assert_func(x, y)
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.1\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+        y = np.array([2.2, 3.3])
+
+        self._assert_func(x, y)
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        y = np.array([1.0, 3.3])
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+
+    def test_simple_items_and_array(self):
+        x = np.array([[621.345454, 390.5436, 43.54657, 626.4535],
+                      [54.54, 627.3399, 13., 405.5435],
+                      [543.545, 8.34, 91.543, 333.3]])
+        y = 627.34
+        self._assert_func(x, y)
+
+        y = 8.339999
+        self._assert_func(y, x)
+
+        x = np.array([[3.4536, 2390.5436, 435.54657, 324525.4535],
+                      [5449.54, 999090.54, 130303.54, 405.5435],
+                      [543.545, 8.34, 91.543, 999090.53999]])
+        y = 999090.54
+
+        expected_msg = ('Mismatched elements: 1 / 12 (8.33%)\n'
+                        'Max absolute difference among violations: 0.\n'
+                        'Max relative difference among violations: 0.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        expected_msg = ('Mismatched elements: 12 / 12 (100%)\n'
+                        'Max absolute difference among violations: '
+                        '999087.0864\n'
+                        'Max relative difference among violations: '
+                        '289288.5934676')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+    def test_zeroes(self):
+        x = np.array([546456., 0, 15.455])
+        y = np.array(87654.)
+
+        expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'
+                        'Max absolute difference among violations: 458802.\n'
+                        'Max relative difference among violations: 5.23423917')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'
+                        'Max absolute difference among violations: 87654.\n'
+                        'Max relative difference among violations: '
+                        '5670.5626011')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+        y = 0
+
+        expected_msg = ('Mismatched elements: 3 / 3 (100%)\n'
+                        'Max absolute difference among violations: 546456.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'
+                        'Max absolute difference among violations: 0.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+    def test_nan_noncompare(self):
+        anan = np.array(np.nan)
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError, lambda: self._assert_func(aone, anan))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
+
+    def test_nan_noncompare_array(self):
+        x = np.array([1.1, 2.2, 3.3])
+        anan = np.array(np.nan)
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, anan))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, x))
+
+        x = np.array([1.1, 2.2, np.nan])
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, anan))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, x))
+
+        y = np.array([1.0, 2.0, np.nan])
+
+        self._assert_func(y, x)
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+
+    def test_inf_compare(self):
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+
+        self._assert_func(aone, ainf)
+        self._assert_func(-ainf, aone)
+        self._assert_func(-ainf, ainf)
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(aone, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(-ainf, -ainf))
+
+    def test_inf_compare_array(self):
+        x = np.array([1.1, 2.2, np.inf])
+        ainf = np.array(np.inf)
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, x))
+        assert_raises(AssertionError, lambda: self._assert_func(x, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(-x, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(-ainf, -x))
+        self._assert_func(-ainf, x)
+
+    def test_strict(self):
+        """Test the behavior of the `strict` option."""
+        x = np.zeros(3)
+        y = np.ones(())
+        self._assert_func(x, y)
+        with pytest.raises(AssertionError):
+            self._assert_func(x, y, strict=True)
+        y = np.broadcast_to(y, x.shape)
+        self._assert_func(x, y)
+        with pytest.raises(AssertionError):
+            self._assert_func(x, y.astype(np.float32), strict=True)
+
+
+class TestWarns:
+
+    def test_warn(self):
+        def f():
+            warnings.warn("yo")
+            return 3
+
+        before_filters = sys.modules['warnings'].filters[:]
+        assert_equal(assert_warns(UserWarning, f), 3)
+        after_filters = sys.modules['warnings'].filters
+
+        assert_raises(AssertionError, assert_no_warnings, f)
+        assert_equal(assert_no_warnings(lambda x: x, 1), 1)
+
+        # Check that the warnings state is unchanged
+        assert_equal(before_filters, after_filters,
+                     "assert_warns does not preserver warnings state")
+
+    def test_context_manager(self):
+
+        before_filters = sys.modules['warnings'].filters[:]
+        with assert_warns(UserWarning):
+            warnings.warn("yo")
+        after_filters = sys.modules['warnings'].filters
+
+        def no_warnings():
+            with assert_no_warnings():
+                warnings.warn("yo")
+
+        assert_raises(AssertionError, no_warnings)
+        assert_equal(before_filters, after_filters,
+                     "assert_warns does not preserver warnings state")
+
+    def test_args(self):
+        def f(a=0, b=1):
+            warnings.warn("yo")
+            return a + b
+
+        assert assert_warns(UserWarning, f, b=20) == 20
+
+        with pytest.raises(RuntimeError) as exc:
+            # assert_warns cannot do regexp matching, use pytest.warns
+            with assert_warns(UserWarning, match="A"):
+                warnings.warn("B", UserWarning)
+        assert "assert_warns" in str(exc)
+        assert "pytest.warns" in str(exc)
+
+        with pytest.raises(RuntimeError) as exc:
+            # assert_warns cannot do regexp matching, use pytest.warns
+            with assert_warns(UserWarning, wrong="A"):
+                warnings.warn("B", UserWarning)
+        assert "assert_warns" in str(exc)
+        assert "pytest.warns" not in str(exc)
+
+    def test_warn_wrong_warning(self):
+        def f():
+            warnings.warn("yo", DeprecationWarning)
+
+        failed = False
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+            try:
+                # Should raise a DeprecationWarning
+                assert_warns(UserWarning, f)
+                failed = True
+            except DeprecationWarning:
+                pass
+
+        if failed:
+            raise AssertionError("wrong warning caught by assert_warn")
+
+
+class TestAssertAllclose:
+
+    def test_simple(self):
+        x = 1e-3
+        y = 1e-9
+
+        assert_allclose(x, y, atol=1)
+        assert_raises(AssertionError, assert_allclose, x, y)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 0.001\n'
+                        'Max relative difference among violations: 999999.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(x, y)
+
+        z = 0
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.e-09\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(y, z)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.e-09\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(z, y)
+
+        a = np.array([x, y, x, y])
+        b = np.array([x, y, x, x])
+
+        assert_allclose(a, b, atol=1)
+        assert_raises(AssertionError, assert_allclose, a, b)
+
+        b[-1] = y * (1 + 1e-8)
+        assert_allclose(a, b)
+        assert_raises(AssertionError, assert_allclose, a, b, rtol=1e-9)
+
+        assert_allclose(6, 10, rtol=0.5)
+        assert_raises(AssertionError, assert_allclose, 10, 6, rtol=0.5)
+
+        b = np.array([x, y, x, x])
+        c = np.array([x, y, x, z])
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 0.001\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(b, c)
+
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 0.001\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(c, b)
+
+    def test_min_int(self):
+        a = np.array([np.iinfo(np.int_).min], dtype=np.int_)
+        # Should not raise:
+        assert_allclose(a, a)
+
+    def test_report_fail_percentage(self):
+        a = np.array([1, 1, 1, 1])
+        b = np.array([1, 1, 1, 2])
+
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 1\n'
+                        'Max relative difference among violations: 0.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(a, b)
+
+    def test_equal_nan(self):
+        a = np.array([np.nan])
+        b = np.array([np.nan])
+        # Should not raise:
+        assert_allclose(a, b, equal_nan=True)
+
+    def test_not_equal_nan(self):
+        a = np.array([np.nan])
+        b = np.array([np.nan])
+        assert_raises(AssertionError, assert_allclose, a, b, equal_nan=False)
+
+    def test_equal_nan_default(self):
+        # Make sure equal_nan default behavior remains unchanged. (All
+        # of these functions use assert_array_compare under the hood.)
+        # None of these should raise.
+        a = np.array([np.nan])
+        b = np.array([np.nan])
+        assert_array_equal(a, b)
+        assert_array_almost_equal(a, b)
+        assert_array_less(a, b)
+        assert_allclose(a, b)
+
+    def test_report_max_relative_error(self):
+        a = np.array([0, 1])
+        b = np.array([0, 2])
+
+        expected_msg = 'Max relative difference among violations: 0.5'
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(a, b)
+
+    def test_timedelta(self):
+        # see gh-18286
+        a = np.array([[1, 2, 3, "NaT"]], dtype="m8[ns]")
+        assert_allclose(a, a)
+
+    def test_error_message_unsigned(self):
+        """Check the message is formatted correctly when overflow can occur
+           (gh21768)"""
+        # Ensure to test for potential overflow in the case of:
+        #        x - y
+        # and
+        #        y - x
+        x = np.asarray([0, 1, 8], dtype='uint8')
+        y = np.asarray([4, 4, 4], dtype='uint8')
+        expected_msg = 'Max absolute difference among violations: 4'
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(x, y, atol=3)
+
+    def test_strict(self):
+        """Test the behavior of the `strict` option."""
+        x = np.ones(3)
+        y = np.ones(())
+        assert_allclose(x, y)
+        with pytest.raises(AssertionError):
+            assert_allclose(x, y, strict=True)
+        assert_allclose(x, x)
+        with pytest.raises(AssertionError):
+            assert_allclose(x, x.astype(np.float32), strict=True)
+
+
+class TestArrayAlmostEqualNulp:
+
+    def test_float64_pass(self):
+        # The number of units of least precision
+        # In this case, use a few places above the lowest level (ie nulp=1)
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        # Addition
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+        # Subtraction
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+    def test_float64_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+    def test_float64_ignore_nan(self):
+        # Ignore ULP differences between various NAN's
+        # Note that MIPS may reverse quiet and signaling nans
+        # so we use the builtin version as a base.
+        offset = np.uint64(0xffffffff)
+        nan1_i64 = np.array(np.nan, dtype=np.float64).view(np.uint64)
+        nan2_i64 = nan1_i64 ^ offset  # nan payload on MIPS is all ones.
+        nan1_f64 = nan1_i64.view(np.float64)
+        nan2_f64 = nan2_i64.view(np.float64)
+        assert_array_max_ulp(nan1_f64, nan2_f64, 0)
+
+    def test_float32_pass(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+    def test_float32_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+    def test_float32_ignore_nan(self):
+        # Ignore ULP differences between various NAN's
+        # Note that MIPS may reverse quiet and signaling nans
+        # so we use the builtin version as a base.
+        offset = np.uint32(0xffff)
+        nan1_i32 = np.array(np.nan, dtype=np.float32).view(np.uint32)
+        nan2_i32 = nan1_i32 ^ offset  # nan payload on MIPS is all ones.
+        nan1_f32 = nan1_i32.view(np.float32)
+        nan2_f32 = nan2_i32.view(np.float32)
+        assert_array_max_ulp(nan1_f32, nan2_f32, 0)
+
+    def test_float16_pass(self):
+        nulp = 5
+        x = np.linspace(-4, 4, 10, dtype=np.float16)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+    def test_float16_fail(self):
+        nulp = 5
+        x = np.linspace(-4, 4, 10, dtype=np.float16)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+    def test_float16_ignore_nan(self):
+        # Ignore ULP differences between various NAN's
+        # Note that MIPS may reverse quiet and signaling nans
+        # so we use the builtin version as a base.
+        offset = np.uint16(0xff)
+        nan1_i16 = np.array(np.nan, dtype=np.float16).view(np.uint16)
+        nan2_i16 = nan1_i16 ^ offset  # nan payload on MIPS is all ones.
+        nan1_f16 = nan1_i16.view(np.float16)
+        nan2_f16 = nan2_i16.view(np.float16)
+        assert_array_max_ulp(nan1_f16, nan2_f16, 0)
+
+    def test_complex128_pass(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        # The test condition needs to be at least a factor of sqrt(2) smaller
+        # because the real and imaginary parts both change
+        y = x + x*eps*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+    def test_complex128_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        # The test condition needs to be at least a factor of sqrt(2) smaller
+        # because the real and imaginary parts both change
+        y = x + x*eps*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+    def test_complex64_pass(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        y = x + x*eps*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+    def test_complex64_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        y = x + x*eps*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+
+class TestULP:
+
+    def test_equal(self):
+        x = np.random.randn(10)
+        assert_array_max_ulp(x, x, maxulp=0)
+
+    def test_single(self):
+        # Generate 1 + small deviation, check that adding eps gives a few UNL
+        x = np.ones(10).astype(np.float32)
+        x += 0.01 * np.random.randn(10).astype(np.float32)
+        eps = np.finfo(np.float32).eps
+        assert_array_max_ulp(x, x+eps, maxulp=20)
+
+    def test_double(self):
+        # Generate 1 + small deviation, check that adding eps gives a few UNL
+        x = np.ones(10).astype(np.float64)
+        x += 0.01 * np.random.randn(10).astype(np.float64)
+        eps = np.finfo(np.float64).eps
+        assert_array_max_ulp(x, x+eps, maxulp=200)
+
+    def test_inf(self):
+        for dt in [np.float32, np.float64]:
+            inf = np.array([np.inf]).astype(dt)
+            big = np.array([np.finfo(dt).max])
+            assert_array_max_ulp(inf, big, maxulp=200)
+
+    def test_nan(self):
+        # Test that nan is 'far' from small, tiny, inf, max and min
+        for dt in [np.float32, np.float64]:
+            if dt == np.float32:
+                maxulp = 1e6
+            else:
+                maxulp = 1e12
+            inf = np.array([np.inf]).astype(dt)
+            nan = np.array([np.nan]).astype(dt)
+            big = np.array([np.finfo(dt).max])
+            tiny = np.array([np.finfo(dt).tiny])
+            zero = np.array([0.0]).astype(dt)
+            nzero = np.array([-0.0]).astype(dt)
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, inf,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, big,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, tiny,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, zero,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, nzero,
+                                                       maxulp=maxulp))
+
+
+class TestStringEqual:
+    def test_simple(self):
+        assert_string_equal("hello", "hello")
+        assert_string_equal("hello\nmultiline", "hello\nmultiline")
+
+        with pytest.raises(AssertionError) as exc_info:
+            assert_string_equal("foo\nbar", "hello\nbar")
+        msg = str(exc_info.value)
+        assert_equal(msg, "Differences in strings:\n- foo\n+ hello")
+
+        assert_raises(AssertionError,
+                      lambda: assert_string_equal("foo", "hello"))
+
+    def test_regex(self):
+        assert_string_equal("a+*b", "a+*b")
+
+        assert_raises(AssertionError,
+                      lambda: assert_string_equal("aaa", "a+b"))
+
+
+def assert_warn_len_equal(mod, n_in_context):
+    try:
+        mod_warns = mod.__warningregistry__
+    except AttributeError:
+        # the lack of a __warningregistry__
+        # attribute means that no warning has
+        # occurred; this can be triggered in
+        # a parallel test scenario, while in
+        # a serial test scenario an initial
+        # warning (and therefore the attribute)
+        # are always created first
+        mod_warns = {}
+
+    num_warns = len(mod_warns)
+
+    if 'version' in mod_warns:
+        # Python 3 adds a 'version' entry to the registry,
+        # do not count it.
+        num_warns -= 1
+
+    assert_equal(num_warns, n_in_context)
+
+
+def test_warn_len_equal_call_scenarios():
+    # assert_warn_len_equal is called under
+    # varying circumstances depending on serial
+    # vs. parallel test scenarios; this test
+    # simply aims to probe both code paths and
+    # check that no assertion is uncaught
+
+    # parallel scenario -- no warning issued yet
+    class mod:
+        pass
+
+    mod_inst = mod()
+
+    assert_warn_len_equal(mod=mod_inst,
+                          n_in_context=0)
+
+    # serial test scenario -- the __warningregistry__
+    # attribute should be present
+    class mod:
+        def __init__(self):
+            self.__warningregistry__ = {'warning1': 1,
+                                        'warning2': 2}
+
+    mod_inst = mod()
+    assert_warn_len_equal(mod=mod_inst,
+                          n_in_context=2)
+
+
+def _get_fresh_mod():
+    # Get this module, with warning registry empty
+    my_mod = sys.modules[__name__]
+    try:
+        my_mod.__warningregistry__.clear()
+    except AttributeError:
+        # will not have a __warningregistry__ unless warning has been
+        # raised in the module at some point
+        pass
+    return my_mod
+
+
+def test_clear_and_catch_warnings():
+    # Initial state of module, no warnings
+    my_mod = _get_fresh_mod()
+    assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
+    with clear_and_catch_warnings(modules=[my_mod]):
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_equal(my_mod.__warningregistry__, {})
+    # Without specified modules, don't clear warnings during context.
+    # catch_warnings doesn't make an entry for 'ignore'.
+    with clear_and_catch_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+    # Manually adding two warnings to the registry:
+    my_mod.__warningregistry__ = {'warning1': 1,
+                                  'warning2': 2}
+
+    # Confirm that specifying module keeps old warning, does not add new
+    with clear_and_catch_warnings(modules=[my_mod]):
+        warnings.simplefilter('ignore')
+        warnings.warn('Another warning')
+    assert_warn_len_equal(my_mod, 2)
+
+    # Another warning, no module spec it clears up registry
+    with clear_and_catch_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Another warning')
+    assert_warn_len_equal(my_mod, 0)
+
+
+def test_suppress_warnings_module():
+    # Initial state of module, no warnings
+    my_mod = _get_fresh_mod()
+    assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
+
+    def warn_other_module():
+        # Apply along axis is implemented in python; stacklevel=2 means
+        # we end up inside its module, not ours.
+        def warn(arr):
+            warnings.warn("Some warning 2", stacklevel=2)
+            return arr
+        np.apply_along_axis(warn, 0, [0])
+
+    # Test module based warning suppression:
+    assert_warn_len_equal(my_mod, 0)
+    with suppress_warnings() as sup:
+        sup.record(UserWarning)
+        # suppress warning from other module (may have .pyc ending),
+        # if apply_along_axis is moved, had to be changed.
+        sup.filter(module=np.lib._shape_base_impl)
+        warnings.warn("Some warning")
+        warn_other_module()
+    # Check that the suppression did test the file correctly (this module
+    # got filtered)
+    assert_equal(len(sup.log), 1)
+    assert_equal(sup.log[0].message.args[0], "Some warning")
+    assert_warn_len_equal(my_mod, 0)
+    sup = suppress_warnings()
+    # Will have to be changed if apply_along_axis is moved:
+    sup.filter(module=my_mod)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+    # And test repeat works:
+    sup.filter(module=my_mod)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+    # Without specified modules
+    with suppress_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+
+def test_suppress_warnings_type():
+    # Initial state of module, no warnings
+    my_mod = _get_fresh_mod()
+    assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
+
+    # Test module based warning suppression:
+    with suppress_warnings() as sup:
+        sup.filter(UserWarning)
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+    sup = suppress_warnings()
+    sup.filter(UserWarning)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+    # And test repeat works:
+    sup.filter(module=my_mod)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+    # Without specified modules
+    with suppress_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+
+def test_suppress_warnings_decorate_no_record():
+    sup = suppress_warnings()
+    sup.filter(UserWarning)
+
+    @sup
+    def warn(category):
+        warnings.warn('Some warning', category)
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        warn(UserWarning)  # should be suppressed
+        warn(RuntimeWarning)
+        assert_equal(len(w), 1)
+
+
+def test_suppress_warnings_record():
+    sup = suppress_warnings()
+    log1 = sup.record()
+
+    with sup:
+        log2 = sup.record(message='Some other warning 2')
+        sup.filter(message='Some warning')
+        warnings.warn('Some warning')
+        warnings.warn('Some other warning')
+        warnings.warn('Some other warning 2')
+
+        assert_equal(len(sup.log), 2)
+        assert_equal(len(log1), 1)
+        assert_equal(len(log2), 1)
+        assert_equal(log2[0].message.args[0], 'Some other warning 2')
+
+    # Do it again, with the same context to see if some warnings survived:
+    with sup:
+        log2 = sup.record(message='Some other warning 2')
+        sup.filter(message='Some warning')
+        warnings.warn('Some warning')
+        warnings.warn('Some other warning')
+        warnings.warn('Some other warning 2')
+
+        assert_equal(len(sup.log), 2)
+        assert_equal(len(log1), 1)
+        assert_equal(len(log2), 1)
+        assert_equal(log2[0].message.args[0], 'Some other warning 2')
+
+    # Test nested:
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings() as sup2:
+            sup2.record(message='Some warning')
+            warnings.warn('Some warning')
+            warnings.warn('Some other warning')
+            assert_equal(len(sup2.log), 1)
+        assert_equal(len(sup.log), 1)
+
+
+def test_suppress_warnings_forwarding():
+    def warn_other_module():
+        # Apply along axis is implemented in python; stacklevel=2 means
+        # we end up inside its module, not ours.
+        def warn(arr):
+            warnings.warn("Some warning", stacklevel=2)
+            return arr
+        np.apply_along_axis(warn, 0, [0])
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("always"):
+            for i in range(2):
+                warnings.warn("Some warning")
+
+        assert_equal(len(sup.log), 2)
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("location"):
+            for i in range(2):
+                warnings.warn("Some warning")
+                warnings.warn("Some warning")
+
+        assert_equal(len(sup.log), 2)
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("module"):
+            for i in range(2):
+                warnings.warn("Some warning")
+                warnings.warn("Some warning")
+                warn_other_module()
+
+        assert_equal(len(sup.log), 2)
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("once"):
+            for i in range(2):
+                warnings.warn("Some warning")
+                warnings.warn("Some other warning")
+                warn_other_module()
+
+        assert_equal(len(sup.log), 2)
+
+
+def test_tempdir():
+    with tempdir() as tdir:
+        fpath = os.path.join(tdir, 'tmp')
+        with open(fpath, 'w'):
+            pass
+    assert_(not os.path.isdir(tdir))
+
+    raised = False
+    try:
+        with tempdir() as tdir:
+            raise ValueError
+    except ValueError:
+        raised = True
+    assert_(raised)
+    assert_(not os.path.isdir(tdir))
+
+
+def test_temppath():
+    with temppath() as fpath:
+        with open(fpath, 'w'):
+            pass
+    assert_(not os.path.isfile(fpath))
+
+    raised = False
+    try:
+        with temppath() as fpath:
+            raise ValueError
+    except ValueError:
+        raised = True
+    assert_(raised)
+    assert_(not os.path.isfile(fpath))
+
+
+class my_cacw(clear_and_catch_warnings):
+
+    class_modules = (sys.modules[__name__],)
+
+
+def test_clear_and_catch_warnings_inherit():
+    # Test can subclass and add default modules
+    my_mod = _get_fresh_mod()
+    with my_cacw():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_equal(my_mod.__warningregistry__, {})
+
+
+@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+class TestAssertNoGcCycles:
+    """ Test assert_no_gc_cycles """
+
+    def test_passes(self):
+        def no_cycle():
+            b = []
+            b.append([])
+            return b
+
+        with assert_no_gc_cycles():
+            no_cycle()
+
+        assert_no_gc_cycles(no_cycle)
+
+    def test_asserts(self):
+        def make_cycle():
+            a = []
+            a.append(a)
+            a.append(a)
+            return a
+
+        with assert_raises(AssertionError):
+            with assert_no_gc_cycles():
+                make_cycle()
+
+        with assert_raises(AssertionError):
+            assert_no_gc_cycles(make_cycle)
+
+    @pytest.mark.slow
+    def test_fails(self):
+        """
+        Test that in cases where the garbage cannot be collected, we raise an
+        error, instead of hanging forever trying to clear it.
+        """
+
+        class ReferenceCycleInDel:
+            """
+            An object that not only contains a reference cycle, but creates new
+            cycles whenever it's garbage-collected and its __del__ runs
+            """
+            make_cycle = True
+
+            def __init__(self):
+                self.cycle = self
+
+            def __del__(self):
+                # break the current cycle so that `self` can be freed
+                self.cycle = None
+
+                if ReferenceCycleInDel.make_cycle:
+                    # but create a new one so that the garbage collector has more
+                    # work to do.
+                    ReferenceCycleInDel()
+
+        try:
+            w = weakref.ref(ReferenceCycleInDel())
+            try:
+                with assert_raises(RuntimeError):
+                    # this will be unable to get a baseline empty garbage
+                    assert_no_gc_cycles(lambda: None)
+            except AssertionError:
+                # the above test is only necessary if the GC actually tried to free
+                # our object anyway, which python 2.7 does not.
+                if w() is not None:
+                    pytest.skip("GC does not call __del__ on cyclic objects")
+                    raise
+
+        finally:
+            # make sure that we stop creating reference cycles
+            ReferenceCycleInDel.make_cycle = False
+
+
+@pytest.mark.parametrize('assert_func', [assert_array_equal,
+                                         assert_array_almost_equal])
+def test_xy_rename(assert_func):
+    # Test that keywords `x` and `y` have been renamed to `actual` and
+    # `desired`, respectively. These tests and use of `_rename_parameter`
+    # decorator can be removed before the release of NumPy 2.2.0.
+    assert_func(1, 1)
+    assert_func(actual=1, desired=1)
+
+    assert_message = "Arrays are not..."
+    with pytest.raises(AssertionError, match=assert_message):
+        assert_func(1, 2)
+    with pytest.raises(AssertionError, match=assert_message):
+        assert_func(actual=1, desired=2)
+
+    dep_message = 'Use of keyword argument...'
+    with pytest.warns(DeprecationWarning, match=dep_message):
+        assert_func(x=1, desired=1)
+    with pytest.warns(DeprecationWarning, match=dep_message):
+        assert_func(1, y=1)
+
+    type_message = '...got multiple values for argument'
+    with (pytest.warns(DeprecationWarning, match=dep_message),
+          pytest.raises(TypeError, match=type_message)):
+        assert_func(1, x=1)
+        assert_func(1, 2, y=2)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test__all__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test__all__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e44bda3d58ab92e614905f6f20f102242d6d6b0c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test__all__.py
@@ -0,0 +1,9 @@
+
+import collections
+import numpy as np
+
+
+def test_no_duplicates_in_np__all__():
+    # Regression test for gh-10198.
+    dups = {k: v for k, v in collections.Counter(np.__all__).items() if v > 1}
+    assert len(dups) == 0
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_configtool.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_configtool.py
new file mode 100644
index 0000000000000000000000000000000000000000..5215057f644a5573a0ef8938f19f9876b04de2c1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_configtool.py
@@ -0,0 +1,43 @@
+import os
+import subprocess
+import sysconfig
+
+import pytest
+import numpy as np
+
+from numpy.testing import IS_WASM
+
+
+is_editable = not bool(np.__path__)
+numpy_in_sitepackages = sysconfig.get_path('platlib') in np.__file__
+# We only expect to have a `numpy-config` available if NumPy was installed via
+# a build frontend (and not `spin` for example)
+if not (numpy_in_sitepackages or is_editable):
+    pytest.skip("`numpy-config` not expected to be installed",
+                allow_module_level=True)
+
+
+def check_numpyconfig(arg):
+    p = subprocess.run(['numpy-config', arg], capture_output=True, text=True)
+    p.check_returncode()
+    return p.stdout.strip()
+
+@pytest.mark.skipif(IS_WASM, reason="wasm interpreter cannot start subprocess")
+def test_configtool_version():
+    stdout = check_numpyconfig('--version')
+    assert stdout == np.__version__
+
+@pytest.mark.skipif(IS_WASM, reason="wasm interpreter cannot start subprocess")
+def test_configtool_cflags():
+    stdout = check_numpyconfig('--cflags')
+    assert stdout.endswith(os.path.join('numpy', '_core', 'include'))
+
+@pytest.mark.skipif(IS_WASM, reason="wasm interpreter cannot start subprocess")
+def test_configtool_pkgconfigdir():
+    stdout = check_numpyconfig('--pkgconfigdir')
+    assert stdout.endswith(os.path.join('numpy', '_core', 'lib', 'pkgconfig'))
+
+    if not is_editable:
+        # Also check that the .pc file actually exists (unless we're using an
+        # editable install, then it'll be hiding in the build dir)
+        assert os.path.exists(os.path.join(stdout, 'numpy.pc'))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_ctypeslib.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_ctypeslib.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fd0c042f2caa7af8922c47d4d7d75ec9df549c0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_ctypeslib.py
@@ -0,0 +1,377 @@
+import sys
+import sysconfig
+import weakref
+from pathlib import Path
+
+import pytest
+
+import numpy as np
+from numpy.ctypeslib import ndpointer, load_library, as_array
+from numpy.testing import assert_, assert_array_equal, assert_raises, assert_equal
+
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+else:
+    cdll = None
+    test_cdll = None
+    if hasattr(sys, 'gettotalrefcount'):
+        try:
+            cdll = load_library(
+                '_multiarray_umath_d', np._core._multiarray_umath.__file__
+            )
+        except OSError:
+            pass
+        try:
+            test_cdll = load_library(
+                '_multiarray_tests', np._core._multiarray_tests.__file__
+            )
+        except OSError:
+            pass
+    if cdll is None:
+        cdll = load_library(
+            '_multiarray_umath', np._core._multiarray_umath.__file__)
+    if test_cdll is None:
+        test_cdll = load_library(
+            '_multiarray_tests', np._core._multiarray_tests.__file__
+        )
+
+    c_forward_pointer = test_cdll.forward_pointer
+
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available in this python")
+@pytest.mark.skipif(sys.platform == 'cygwin',
+                    reason="Known to fail on cygwin")
+class TestLoadLibrary:
+    def test_basic(self):
+        loader_path = np._core._multiarray_umath.__file__
+
+        out1 = load_library('_multiarray_umath', loader_path)
+        out2 = load_library(Path('_multiarray_umath'), loader_path)
+        out3 = load_library('_multiarray_umath', Path(loader_path))
+        out4 = load_library(b'_multiarray_umath', loader_path)
+
+        assert isinstance(out1, ctypes.CDLL)
+        assert out1 is out2 is out3 is out4
+
+    def test_basic2(self):
+        # Regression for #801: load_library with a full library name
+        # (including extension) does not work.
+        try:
+            so_ext = sysconfig.get_config_var('EXT_SUFFIX')
+            load_library('_multiarray_umath%s' % so_ext,
+                         np._core._multiarray_umath.__file__)
+        except ImportError as e:
+            msg = ("ctypes is not available on this python: skipping the test"
+                   " (import error was: %s)" % str(e))
+            print(msg)
+
+
+class TestNdpointer:
+    def test_dtype(self):
+        dt = np.intc
+        p = ndpointer(dtype=dt)
+        assert_(p.from_param(np.array([1], dt)))
+        dt = '<i4'
+        p = ndpointer(dtype=dt)
+        assert_(p.from_param(np.array([1], dt)))
+        dt = np.dtype('>i4')
+        p = ndpointer(dtype=dt)
+        p.from_param(np.array([1], dt))
+        assert_raises(TypeError, p.from_param,
+                          np.array([1], dt.newbyteorder('swap')))
+        dtnames = ['x', 'y']
+        dtformats = [np.intc, np.float64]
+        dtdescr = {'names': dtnames, 'formats': dtformats}
+        dt = np.dtype(dtdescr)
+        p = ndpointer(dtype=dt)
+        assert_(p.from_param(np.zeros((10,), dt)))
+        samedt = np.dtype(dtdescr)
+        p = ndpointer(dtype=samedt)
+        assert_(p.from_param(np.zeros((10,), dt)))
+        dt2 = np.dtype(dtdescr, align=True)
+        if dt.itemsize != dt2.itemsize:
+            assert_raises(TypeError, p.from_param, np.zeros((10,), dt2))
+        else:
+            assert_(p.from_param(np.zeros((10,), dt2)))
+
+    def test_ndim(self):
+        p = ndpointer(ndim=0)
+        assert_(p.from_param(np.array(1)))
+        assert_raises(TypeError, p.from_param, np.array([1]))
+        p = ndpointer(ndim=1)
+        assert_raises(TypeError, p.from_param, np.array(1))
+        assert_(p.from_param(np.array([1])))
+        p = ndpointer(ndim=2)
+        assert_(p.from_param(np.array([[1]])))
+
+    def test_shape(self):
+        p = ndpointer(shape=(1, 2))
+        assert_(p.from_param(np.array([[1, 2]])))
+        assert_raises(TypeError, p.from_param, np.array([[1], [2]]))
+        p = ndpointer(shape=())
+        assert_(p.from_param(np.array(1)))
+
+    def test_flags(self):
+        x = np.array([[1, 2], [3, 4]], order='F')
+        p = ndpointer(flags='FORTRAN')
+        assert_(p.from_param(x))
+        p = ndpointer(flags='CONTIGUOUS')
+        assert_raises(TypeError, p.from_param, x)
+        p = ndpointer(flags=x.flags.num)
+        assert_(p.from_param(x))
+        assert_raises(TypeError, p.from_param, np.array([[1, 2], [3, 4]]))
+
+    def test_cache(self):
+        assert_(ndpointer(dtype=np.float64) is ndpointer(dtype=np.float64))
+
+        # shapes are normalized
+        assert_(ndpointer(shape=2) is ndpointer(shape=(2,)))
+
+        # 1.12 <= v < 1.16 had a bug that made these fail
+        assert_(ndpointer(shape=2) is not ndpointer(ndim=2))
+        assert_(ndpointer(ndim=2) is not ndpointer(shape=2))
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available on this python installation")
+class TestNdpointerCFunc:
+    def test_arguments(self):
+        """ Test that arguments are coerced from arrays """
+        c_forward_pointer.restype = ctypes.c_void_p
+        c_forward_pointer.argtypes = (ndpointer(ndim=2),)
+
+        c_forward_pointer(np.zeros((2, 3)))
+        # too many dimensions
+        assert_raises(
+            ctypes.ArgumentError, c_forward_pointer, np.zeros((2, 3, 4)))
+
+    @pytest.mark.parametrize(
+        'dt', [
+            float,
+            np.dtype(dict(
+                formats=['<i4', '<i4'],
+                names=['a', 'b'],
+                offsets=[0, 2],
+                itemsize=6
+            ))
+        ], ids=[
+            'float',
+            'overlapping-fields'
+        ]
+    )
+    def test_return(self, dt):
+        """ Test that return values are coerced to arrays """
+        arr = np.zeros((2, 3), dt)
+        ptr_type = ndpointer(shape=arr.shape, dtype=arr.dtype)
+
+        c_forward_pointer.restype = ptr_type
+        c_forward_pointer.argtypes = (ptr_type,)
+
+        # check that the arrays are equivalent views on the same data
+        arr2 = c_forward_pointer(arr)
+        assert_equal(arr2.dtype, arr.dtype)
+        assert_equal(arr2.shape, arr.shape)
+        assert_equal(
+            arr2.__array_interface__['data'],
+            arr.__array_interface__['data']
+        )
+
+    def test_vague_return_value(self):
+        """ Test that vague ndpointer return values do not promote to arrays """
+        arr = np.zeros((2, 3))
+        ptr_type = ndpointer(dtype=arr.dtype)
+
+        c_forward_pointer.restype = ptr_type
+        c_forward_pointer.argtypes = (ptr_type,)
+
+        ret = c_forward_pointer(arr)
+        assert_(isinstance(ret, ptr_type))
+
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available on this python installation")
+class TestAsArray:
+    def test_array(self):
+        from ctypes import c_int
+
+        pair_t = c_int * 2
+        a = as_array(pair_t(1, 2))
+        assert_equal(a.shape, (2,))
+        assert_array_equal(a, np.array([1, 2]))
+        a = as_array((pair_t * 3)(pair_t(1, 2), pair_t(3, 4), pair_t(5, 6)))
+        assert_equal(a.shape, (3, 2))
+        assert_array_equal(a, np.array([[1, 2], [3, 4], [5, 6]]))
+
+    def test_pointer(self):
+        from ctypes import c_int, cast, POINTER
+
+        p = cast((c_int * 10)(*range(10)), POINTER(c_int))
+
+        a = as_array(p, shape=(10,))
+        assert_equal(a.shape, (10,))
+        assert_array_equal(a, np.arange(10))
+
+        a = as_array(p, shape=(2, 5))
+        assert_equal(a.shape, (2, 5))
+        assert_array_equal(a, np.arange(10).reshape((2, 5)))
+
+        # shape argument is required
+        assert_raises(TypeError, as_array, p)
+
+    @pytest.mark.skipif(
+            sys.version_info[:2] == (3, 12),
+            reason="Broken in 3.12.0rc1, see gh-24399",
+    )
+    def test_struct_array_pointer(self):
+        from ctypes import c_int16, Structure, pointer
+
+        class Struct(Structure):
+            _fields_ = [('a', c_int16)]
+
+        Struct3 = 3 * Struct
+
+        c_array = (2 * Struct3)(
+            Struct3(Struct(a=1), Struct(a=2), Struct(a=3)),
+            Struct3(Struct(a=4), Struct(a=5), Struct(a=6))
+        )
+
+        expected = np.array([
+            [(1,), (2,), (3,)],
+            [(4,), (5,), (6,)],
+        ], dtype=[('a', np.int16)])
+
+        def check(x):
+            assert_equal(x.dtype, expected.dtype)
+            assert_equal(x, expected)
+
+        # all of these should be equivalent
+        check(as_array(c_array))
+        check(as_array(pointer(c_array), shape=()))
+        check(as_array(pointer(c_array[0]), shape=(2,)))
+        check(as_array(pointer(c_array[0][0]), shape=(2, 3)))
+
+    def test_reference_cycles(self):
+        # related to gh-6511
+        import ctypes
+
+        # create array to work with
+        # don't use int/long to avoid running into bpo-10746
+        N = 100
+        a = np.arange(N, dtype=np.short)
+
+        # get pointer to array
+        pnt = np.ctypeslib.as_ctypes(a)
+
+        with np.testing.assert_no_gc_cycles():
+            # decay the array above to a pointer to its first element
+            newpnt = ctypes.cast(pnt, ctypes.POINTER(ctypes.c_short))
+            # and construct an array using this data
+            b = np.ctypeslib.as_array(newpnt, (N,))
+            # now delete both, which should cleanup both objects
+            del newpnt, b
+
+    def test_segmentation_fault(self):
+        arr = np.zeros((224, 224, 3))
+        c_arr = np.ctypeslib.as_ctypes(arr)
+        arr_ref = weakref.ref(arr)
+        del arr
+
+        # check the reference wasn't cleaned up
+        assert_(arr_ref() is not None)
+
+        # check we avoid the segfault
+        c_arr[0][0][0]
+
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available on this python installation")
+class TestAsCtypesType:
+    """ Test conversion from dtypes to ctypes types """
+    def test_scalar(self):
+        dt = np.dtype('<u2')
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, ctypes.c_uint16.__ctype_le__)
+
+        dt = np.dtype('>u2')
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, ctypes.c_uint16.__ctype_be__)
+
+        dt = np.dtype('u2')
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, ctypes.c_uint16)
+
+    def test_subarray(self):
+        dt = np.dtype((np.int32, (2, 3)))
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, 2 * (3 * ctypes.c_int32))
+
+    def test_structure(self):
+        dt = np.dtype([
+            ('a', np.uint16),
+            ('b', np.uint32),
+        ])
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Structure))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('b', ctypes.c_uint32),
+        ])
+
+    def test_structure_aligned(self):
+        dt = np.dtype([
+            ('a', np.uint16),
+            ('b', np.uint32),
+        ], align=True)
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Structure))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('', ctypes.c_char * 2),  # padding
+            ('b', ctypes.c_uint32),
+        ])
+
+    def test_union(self):
+        dt = np.dtype(dict(
+            names=['a', 'b'],
+            offsets=[0, 0],
+            formats=[np.uint16, np.uint32]
+        ))
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Union))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('b', ctypes.c_uint32),
+        ])
+
+    def test_padded_union(self):
+        dt = np.dtype(dict(
+            names=['a', 'b'],
+            offsets=[0, 0],
+            formats=[np.uint16, np.uint32],
+            itemsize=5,
+        ))
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Union))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('b', ctypes.c_uint32),
+            ('', ctypes.c_char * 5),  # padding
+        ])
+
+    def test_overlapping(self):
+        dt = np.dtype(dict(
+            names=['a', 'b'],
+            offsets=[0, 2],
+            formats=[np.uint32, np.uint32]
+        ))
+        assert_raises(NotImplementedError, np.ctypeslib.as_ctypes_type, dt)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_lazyloading.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_lazyloading.py
new file mode 100644
index 0000000000000000000000000000000000000000..1298fadc5618069776c02f192afcaf742679f860
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_lazyloading.py
@@ -0,0 +1,37 @@
+import sys
+from importlib.util import LazyLoader, find_spec, module_from_spec
+import pytest
+
+
+# Warning raised by _reload_guard() in numpy/__init__.py
+@pytest.mark.filterwarnings("ignore:The NumPy module was reloaded")
+def test_lazy_load():
+    # gh-22045. lazyload doesn't import submodule names into the namespace
+    # muck with sys.modules to test the importing system
+    old_numpy = sys.modules.pop("numpy")
+
+    numpy_modules = {}
+    for mod_name, mod in list(sys.modules.items()):
+        if mod_name[:6] == "numpy.":
+            numpy_modules[mod_name] = mod
+            sys.modules.pop(mod_name)
+
+    try:
+        # create lazy load of numpy as np
+        spec = find_spec("numpy")
+        module = module_from_spec(spec)
+        sys.modules["numpy"] = module
+        loader = LazyLoader(spec.loader)
+        loader.exec_module(module)
+        np = module
+
+        # test a subpackage import
+        from numpy.lib import recfunctions  # noqa: F401
+
+        # test triggering the import of the package
+        np.ndarray
+
+    finally:
+        if old_numpy:
+            sys.modules["numpy"] = old_numpy
+            sys.modules.update(numpy_modules)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_matlib.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_matlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e93c4848d75432c97189273f4f2e0cbc6c04e20
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_matlib.py
@@ -0,0 +1,58 @@
+import numpy as np
+import numpy.matlib
+from numpy.testing import assert_array_equal, assert_
+
+def test_empty():
+    x = numpy.matlib.empty((2,))
+    assert_(isinstance(x, np.matrix))
+    assert_(x.shape, (1, 2))
+
+def test_ones():
+    assert_array_equal(numpy.matlib.ones((2, 3)),
+                       np.matrix([[ 1.,  1.,  1.],
+                                 [ 1.,  1.,  1.]]))
+
+    assert_array_equal(numpy.matlib.ones(2), np.matrix([[ 1.,  1.]]))
+
+def test_zeros():
+    assert_array_equal(numpy.matlib.zeros((2, 3)),
+                       np.matrix([[ 0.,  0.,  0.],
+                                 [ 0.,  0.,  0.]]))
+
+    assert_array_equal(numpy.matlib.zeros(2), np.matrix([[ 0.,  0.]]))
+
+def test_identity():
+    x = numpy.matlib.identity(2, dtype=int)
+    assert_array_equal(x, np.matrix([[1, 0], [0, 1]]))
+
+def test_eye():
+    xc = numpy.matlib.eye(3, k=1, dtype=int)
+    assert_array_equal(xc, np.matrix([[ 0,  1,  0],
+                                      [ 0,  0,  1],
+                                      [ 0,  0,  0]]))
+    assert xc.flags.c_contiguous
+    assert not xc.flags.f_contiguous
+
+    xf = numpy.matlib.eye(3, 4, dtype=int, order='F')
+    assert_array_equal(xf, np.matrix([[ 1,  0,  0,  0],
+                                      [ 0,  1,  0,  0],
+                                      [ 0,  0,  1,  0]]))
+    assert not xf.flags.c_contiguous
+    assert xf.flags.f_contiguous
+
+def test_rand():
+    x = numpy.matlib.rand(3)
+    # check matrix type, array would have shape (3,)
+    assert_(x.ndim == 2)
+
+def test_randn():
+    x = np.matlib.randn(3)
+    # check matrix type, array would have shape (3,)
+    assert_(x.ndim == 2)
+
+def test_repmat():
+    a1 = np.arange(4)
+    x = numpy.matlib.repmat(a1, 2, 2)
+    y = np.array([[0, 1, 2, 3, 0, 1, 2, 3],
+                  [0, 1, 2, 3, 0, 1, 2, 3]])
+    assert_array_equal(x, y)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_numpy_config.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_numpy_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e225b2bd7b4c0136e813ce8baf1063d4692ba84
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_numpy_config.py
@@ -0,0 +1,44 @@
+"""
+Check the numpy config is valid.
+"""
+import numpy as np
+import pytest
+from unittest.mock import patch
+
+pytestmark = pytest.mark.skipif(
+    not hasattr(np.__config__, "_built_with_meson"),
+    reason="Requires Meson builds",
+)
+
+
+class TestNumPyConfigs:
+    REQUIRED_CONFIG_KEYS = [
+        "Compilers",
+        "Machine Information",
+        "Python Information",
+    ]
+
+    @patch("numpy.__config__._check_pyyaml")
+    def test_pyyaml_not_found(self, mock_yaml_importer):
+        mock_yaml_importer.side_effect = ModuleNotFoundError()
+        with pytest.warns(UserWarning):
+            np.show_config()
+
+    def test_dict_mode(self):
+        config = np.show_config(mode="dicts")
+
+        assert isinstance(config, dict)
+        assert all(key in config for key in self.REQUIRED_CONFIG_KEYS), (
+            "Required key missing,"
+            " see index of `False` with `REQUIRED_CONFIG_KEYS`"
+        )
+
+    def test_invalid_mode(self):
+        with pytest.raises(AttributeError):
+            np.show_config(mode="foo")
+
+    def test_warn_to_add_tests(self):
+        assert len(np.__config__.DisplayModes) == 2, (
+            "New mode detected,"
+            " please add UT if applicable and increment this count"
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_numpy_version.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_numpy_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3abcb92c1c3d8b16651b0d05d47021912915855
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_numpy_version.py
@@ -0,0 +1,54 @@
+"""
+Check the numpy version is valid.
+
+Note that a development version is marked by the presence of 'dev0' or '+'
+in the version string, all else is treated as a release. The version string
+itself is set from the output of ``git describe`` which relies on tags.
+
+Examples
+--------
+
+Valid Development: 1.22.0.dev0 1.22.0.dev0+5-g7999db4df2 1.22.0+5-g7999db4df2
+Valid Release: 1.21.0.rc1, 1.21.0.b1, 1.21.0
+Invalid: 1.22.0.dev, 1.22.0.dev0-5-g7999db4dfB, 1.21.0.d1, 1.21.a
+
+Note that a release is determined by the version string, which in turn
+is controlled by the result of the ``git describe`` command.
+"""
+import re
+
+import numpy as np
+from numpy.testing import assert_
+
+
+def test_valid_numpy_version():
+    # Verify that the numpy version is a valid one (no .post suffix or other
+    # nonsense).  See gh-6431 for an issue caused by an invalid version.
+    version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(a[0-9]|b[0-9]|rc[0-9])?"
+    dev_suffix = r"(\.dev[0-9]+(\+git[0-9]+\.[0-9a-f]+)?)?"
+    res = re.match(version_pattern + dev_suffix + '$', np.__version__)
+
+    assert_(res is not None, np.__version__)
+
+
+def test_short_version():
+    # Check numpy.short_version actually exists
+    if np.version.release:
+        assert_(np.__version__ == np.version.short_version,
+                "short_version mismatch in release version")
+    else:
+        assert_(np.__version__.split("+")[0] == np.version.short_version,
+                "short_version mismatch in development version")
+
+
+def test_version_module():
+    contents = set([s for s in dir(np.version) if not s.startswith('_')])
+    expected = set([
+        'full_version',
+        'git_revision',
+        'release',
+        'short_version',
+        'version',
+    ])
+
+    assert contents == expected
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_public_api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_public_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b25818c62d3176026d2a91072bf39e932083b47f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_public_api.py
@@ -0,0 +1,810 @@
+import functools
+import sys
+import sysconfig
+import subprocess
+import pkgutil
+import types
+import importlib
+import inspect
+import warnings
+
+import numpy as np
+import numpy
+from numpy.testing import IS_WASM
+
+import pytest
+
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+
+
+def check_dir(module, module_name=None):
+    """Returns a mapping of all objects with the wrong __module__ attribute."""
+    if module_name is None:
+        module_name = module.__name__
+    results = {}
+    for name in dir(module):
+        if name == "core":
+            continue
+        item = getattr(module, name)
+        if (hasattr(item, '__module__') and hasattr(item, '__name__')
+                and item.__module__ != module_name):
+            results[name] = item.__module__ + '.' + item.__name__
+    return results
+
+
+def test_numpy_namespace():
+    # We override dir to not show these members
+    allowlist = {
+        'recarray': 'numpy.rec.recarray',
+    }
+    bad_results = check_dir(np)
+    # pytest gives better error messages with the builtin assert than with
+    # assert_equal
+    assert bad_results == allowlist
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
+@pytest.mark.parametrize('name', ['testing'])
+def test_import_lazy_import(name):
+    """Make sure we can actually use the modules we lazy load.
+
+    While not exported as part of the public API, it was accessible.  With the
+    use of __getattr__ and __dir__, this isn't always true It can happen that
+    an infinite recursion may happen.
+
+    This is the only way I found that would force the failure to appear on the
+    badly implemented code.
+
+    We also test for the presence of the lazily imported modules in dir
+
+    """
+    exe = (sys.executable, '-c', "import numpy; numpy." + name)
+    result = subprocess.check_output(exe)
+    assert not result
+
+    # Make sure they are still in the __dir__
+    assert name in dir(np)
+
+
+def test_dir_testing():
+    """Assert that output of dir has only one "testing/tester"
+    attribute without duplicate"""
+    assert len(dir(np)) == len(set(dir(np)))
+
+
+def test_numpy_linalg():
+    bad_results = check_dir(np.linalg)
+    assert bad_results == {}
+
+
+def test_numpy_fft():
+    bad_results = check_dir(np.fft)
+    assert bad_results == {}
+
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available in this python")
+def test_NPY_NO_EXPORT():
+    cdll = ctypes.CDLL(np._core._multiarray_tests.__file__)
+    # Make sure an arbitrary NPY_NO_EXPORT function is actually hidden
+    f = getattr(cdll, 'test_not_exported', None)
+    assert f is None, ("'test_not_exported' is mistakenly exported, "
+                      "NPY_NO_EXPORT does not work")
+
+
+# Historically NumPy has not used leading underscores for private submodules
+# much.  This has resulted in lots of things that look like public modules
+# (i.e. things that can be imported as `import numpy.somesubmodule.somefile`),
+# but were never intended to be public.  The PUBLIC_MODULES list contains
+# modules that are either public because they were meant to be, or because they
+# contain public functions/objects that aren't present in any other namespace
+# for whatever reason and therefore should be treated as public.
+#
+# The PRIVATE_BUT_PRESENT_MODULES list contains modules that look public (lack
+# of underscores) but should not be used.  For many of those modules the
+# current status is fine.  For others it may make sense to work on making them
+# private, to clean up our public API and avoid confusion.
+PUBLIC_MODULES = ['numpy.' + s for s in [
+    "ctypeslib",
+    "dtypes",
+    "exceptions",
+    "f2py",
+    "fft",
+    "lib",
+    "lib.array_utils",
+    "lib.format",
+    "lib.introspect",
+    "lib.mixins",
+    "lib.npyio",
+    "lib.recfunctions",  # note: still needs cleaning, was forgotten for 2.0
+    "lib.scimath",
+    "lib.stride_tricks",
+    "linalg",
+    "ma",
+    "ma.extras",
+    "ma.mrecords",
+    "polynomial",
+    "polynomial.chebyshev",
+    "polynomial.hermite",
+    "polynomial.hermite_e",
+    "polynomial.laguerre",
+    "polynomial.legendre",
+    "polynomial.polynomial",
+    "random",
+    "strings",
+    "testing",
+    "testing.overrides",
+    "typing",
+    "typing.mypy_plugin",
+    "version",
+]]
+if sys.version_info < (3, 12):
+    PUBLIC_MODULES += [
+        'numpy.' + s for s in [
+            "distutils",
+            "distutils.cpuinfo",
+            "distutils.exec_command",
+            "distutils.misc_util",
+            "distutils.log",
+            "distutils.system_info",
+        ]
+    ]
+
+
+
+PUBLIC_ALIASED_MODULES = [
+    "numpy.char",
+    "numpy.emath",
+    "numpy.rec",
+]
+
+
+PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
+    "compat",
+    "compat.py3k",
+    "conftest",
+    "core",
+    "core.multiarray",
+    "core.numeric",
+    "core.umath",
+    "core.arrayprint",
+    "core.defchararray",
+    "core.einsumfunc",
+    "core.fromnumeric",
+    "core.function_base",
+    "core.getlimits",
+    "core.numerictypes",
+    "core.overrides",
+    "core.records",
+    "core.shape_base",
+    "f2py.auxfuncs",
+    "f2py.capi_maps",
+    "f2py.cb_rules",
+    "f2py.cfuncs",
+    "f2py.common_rules",
+    "f2py.crackfortran",
+    "f2py.diagnose",
+    "f2py.f2py2e",
+    "f2py.f90mod_rules",
+    "f2py.func2subr",
+    "f2py.rules",
+    "f2py.symbolic",
+    "f2py.use_rules",
+    "fft.helper",
+    "lib.user_array",  # note: not in np.lib, but probably should just be deleted
+    "linalg.lapack_lite",
+    "linalg.linalg",
+    "ma.core",
+    "ma.testutils",
+    "ma.timer_comparison",
+    "matlib",
+    "matrixlib",
+    "matrixlib.defmatrix",
+    "polynomial.polyutils",
+    "random.mtrand",
+    "random.bit_generator",
+    "testing.print_coercion_tables",
+]]
+if sys.version_info < (3, 12):
+    PRIVATE_BUT_PRESENT_MODULES += [
+        'numpy.' + s for s in [
+            "distutils.armccompiler",
+            "distutils.fujitsuccompiler",
+            "distutils.ccompiler",
+            'distutils.ccompiler_opt',
+            "distutils.command",
+            "distutils.command.autodist",
+            "distutils.command.bdist_rpm",
+            "distutils.command.build",
+            "distutils.command.build_clib",
+            "distutils.command.build_ext",
+            "distutils.command.build_py",
+            "distutils.command.build_scripts",
+            "distutils.command.build_src",
+            "distutils.command.config",
+            "distutils.command.config_compiler",
+            "distutils.command.develop",
+            "distutils.command.egg_info",
+            "distutils.command.install",
+            "distutils.command.install_clib",
+            "distutils.command.install_data",
+            "distutils.command.install_headers",
+            "distutils.command.sdist",
+            "distutils.conv_template",
+            "distutils.core",
+            "distutils.extension",
+            "distutils.fcompiler",
+            "distutils.fcompiler.absoft",
+            "distutils.fcompiler.arm",
+            "distutils.fcompiler.compaq",
+            "distutils.fcompiler.environment",
+            "distutils.fcompiler.g95",
+            "distutils.fcompiler.gnu",
+            "distutils.fcompiler.hpux",
+            "distutils.fcompiler.ibm",
+            "distutils.fcompiler.intel",
+            "distutils.fcompiler.lahey",
+            "distutils.fcompiler.mips",
+            "distutils.fcompiler.nag",
+            "distutils.fcompiler.none",
+            "distutils.fcompiler.pathf95",
+            "distutils.fcompiler.pg",
+            "distutils.fcompiler.nv",
+            "distutils.fcompiler.sun",
+            "distutils.fcompiler.vast",
+            "distutils.fcompiler.fujitsu",
+            "distutils.from_template",
+            "distutils.intelccompiler",
+            "distutils.lib2def",
+            "distutils.line_endings",
+            "distutils.mingw32ccompiler",
+            "distutils.msvccompiler",
+            "distutils.npy_pkg_config",
+            "distutils.numpy_distribution",
+            "distutils.pathccompiler",
+            "distutils.unixccompiler",
+        ]
+    ]
+
+
+def is_unexpected(name):
+    """Check if this needs to be considered."""
+    if '._' in name or '.tests' in name or '.setup' in name:
+        return False
+
+    if name in PUBLIC_MODULES:
+        return False
+
+    if name in PUBLIC_ALIASED_MODULES:
+        return False
+
+    if name in PRIVATE_BUT_PRESENT_MODULES:
+        return False
+
+    return True
+
+
+if sys.version_info < (3, 12):
+    SKIP_LIST = ["numpy.distutils.msvc9compiler"]
+else:
+    SKIP_LIST = []
+
+
+# suppressing warnings from deprecated modules
+@pytest.mark.filterwarnings("ignore:.*np.compat.*:DeprecationWarning")
+def test_all_modules_are_expected():
+    """
+    Test that we don't add anything that looks like a new public module by
+    accident.  Check is based on filenames.
+    """
+
+    modnames = []
+    for _, modname, ispkg in pkgutil.walk_packages(path=np.__path__,
+                                                   prefix=np.__name__ + '.',
+                                                   onerror=None):
+        if is_unexpected(modname) and modname not in SKIP_LIST:
+            # We have a name that is new.  If that's on purpose, add it to
+            # PUBLIC_MODULES.  We don't expect to have to add anything to
+            # PRIVATE_BUT_PRESENT_MODULES.  Use an underscore in the name!
+            modnames.append(modname)
+
+    if modnames:
+        raise AssertionError(f'Found unexpected modules: {modnames}')
+
+
+# Stuff that clearly shouldn't be in the API and is detected by the next test
+# below
+SKIP_LIST_2 = [
+    'numpy.lib.math',
+    'numpy.matlib.char',
+    'numpy.matlib.rec',
+    'numpy.matlib.emath',
+    'numpy.matlib.exceptions',
+    'numpy.matlib.math',
+    'numpy.matlib.linalg',
+    'numpy.matlib.fft',
+    'numpy.matlib.random',
+    'numpy.matlib.ctypeslib',
+    'numpy.matlib.ma',
+]
+if sys.version_info < (3, 12):
+    SKIP_LIST_2 += [
+        'numpy.distutils.log.sys',
+        'numpy.distutils.log.logging',
+        'numpy.distutils.log.warnings',
+    ]
+
+
+def test_all_modules_are_expected_2():
+    """
+    Method checking all objects. The pkgutil-based method in
+    `test_all_modules_are_expected` does not catch imports into a namespace,
+    only filenames.  So this test is more thorough, and checks this like:
+
+        import .lib.scimath as emath
+
+    To check if something in a module is (effectively) public, one can check if
+    there's anything in that namespace that's a public function/object but is
+    not exposed in a higher-level namespace.  For example for a `numpy.lib`
+    submodule::
+
+        mod = np.lib.mixins
+        for obj in mod.__all__:
+            if obj in np.__all__:
+                continue
+            elif obj in np.lib.__all__:
+                continue
+
+            else:
+                print(obj)
+
+    """
+
+    def find_unexpected_members(mod_name):
+        members = []
+        module = importlib.import_module(mod_name)
+        if hasattr(module, '__all__'):
+            objnames = module.__all__
+        else:
+            objnames = dir(module)
+
+        for objname in objnames:
+            if not objname.startswith('_'):
+                fullobjname = mod_name + '.' + objname
+                if isinstance(getattr(module, objname), types.ModuleType):
+                    if is_unexpected(fullobjname):
+                        if fullobjname not in SKIP_LIST_2:
+                            members.append(fullobjname)
+
+        return members
+
+    unexpected_members = find_unexpected_members("numpy")
+    for modname in PUBLIC_MODULES:
+        unexpected_members.extend(find_unexpected_members(modname))
+
+    if unexpected_members:
+        raise AssertionError("Found unexpected object(s) that look like "
+                             "modules: {}".format(unexpected_members))
+
+
+def test_api_importable():
+    """
+    Check that all submodules listed higher up in this file can be imported
+
+    Note that if a PRIVATE_BUT_PRESENT_MODULES entry goes missing, it may
+    simply need to be removed from the list (deprecation may or may not be
+    needed - apply common sense).
+    """
+    def check_importable(module_name):
+        try:
+            importlib.import_module(module_name)
+        except (ImportError, AttributeError):
+            return False
+
+        return True
+
+    module_names = []
+    for module_name in PUBLIC_MODULES:
+        if not check_importable(module_name):
+            module_names.append(module_name)
+
+    if module_names:
+        raise AssertionError("Modules in the public API that cannot be "
+                             "imported: {}".format(module_names))
+
+    for module_name in PUBLIC_ALIASED_MODULES:
+        try:
+            eval(module_name)
+        except AttributeError:
+            module_names.append(module_name)
+
+    if module_names:
+        raise AssertionError("Modules in the public API that were not "
+                             "found: {}".format(module_names))
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.filterwarnings('always', category=DeprecationWarning)
+        warnings.filterwarnings('always', category=ImportWarning)
+        for module_name in PRIVATE_BUT_PRESENT_MODULES:
+            if not check_importable(module_name):
+                module_names.append(module_name)
+
+    if module_names:
+        raise AssertionError("Modules that are not really public but looked "
+                             "public and can not be imported: "
+                             "{}".format(module_names))
+
+
+@pytest.mark.xfail(
+    sysconfig.get_config_var("Py_DEBUG") not in (None, 0, "0"),
+    reason=(
+        "NumPy possibly built with `USE_DEBUG=True ./tools/travis-test.sh`, "
+        "which does not expose the `array_api` entry point. "
+        "See https://github.com/numpy/numpy/pull/19800"
+    ),
+)
+def test_array_api_entry_point():
+    """
+    Entry point for Array API implementation can be found with importlib and
+    returns the main numpy namespace.
+    """
+    # For a development install that did not go through meson-python,
+    # the entrypoint will not have been installed. So ensure this test fails
+    # only if numpy is inside site-packages.
+    numpy_in_sitepackages = sysconfig.get_path('platlib') in np.__file__
+
+    eps = importlib.metadata.entry_points()
+    try:
+        xp_eps = eps.select(group="array_api")
+    except AttributeError:
+        # The select interface for entry_points was introduced in py3.10,
+        # deprecating its dict interface. We fallback to dict keys for finding
+        # Array API entry points so that running this test in <=3.9 will
+        # still work - see https://github.com/numpy/numpy/pull/19800.
+        xp_eps = eps.get("array_api", [])
+    if len(xp_eps) == 0:
+        if numpy_in_sitepackages:
+            msg = "No entry points for 'array_api' found"
+            raise AssertionError(msg) from None
+        return
+
+    try:
+        ep = next(ep for ep in xp_eps if ep.name == "numpy")
+    except StopIteration:
+        if numpy_in_sitepackages:
+            msg = "'numpy' not in array_api entry points"
+            raise AssertionError(msg) from None
+        return
+
+    if ep.value == 'numpy.array_api':
+        # Looks like the entrypoint for the current numpy build isn't
+        # installed, but an older numpy is also installed and hence the
+        # entrypoint is pointing to the old (no longer existing) location.
+        # This isn't a problem except for when running tests with `spin` or an
+        # in-place build.
+        return
+
+    xp = ep.load()
+    msg = (
+        f"numpy entry point value '{ep.value}' "
+        "does not point to our Array API implementation"
+    )
+    assert xp is numpy, msg
+
+
+def test_main_namespace_all_dir_coherence():
+    """
+    Checks if `dir(np)` and `np.__all__` are consistent and return
+    the same content, excluding exceptions and private members.
+    """
+    def _remove_private_members(member_set):
+        return {m for m in member_set if not m.startswith('_')}
+
+    def _remove_exceptions(member_set):
+        return member_set.difference({
+            "bool"  # included only in __dir__
+        })
+
+    all_members = _remove_private_members(np.__all__)
+    all_members = _remove_exceptions(all_members)
+
+    dir_members = _remove_private_members(np.__dir__())
+    dir_members = _remove_exceptions(dir_members)
+
+    assert all_members == dir_members, (
+        "Members that break symmetry: "
+        f"{all_members.symmetric_difference(dir_members)}"
+    )
+
+
+@pytest.mark.filterwarnings(
+    r"ignore:numpy.core(\.\w+)? is deprecated:DeprecationWarning"
+)
+def test_core_shims_coherence():
+    """
+    Check that all "semi-public" members of `numpy._core` are also accessible
+    from `numpy.core` shims.
+    """
+    import numpy.core as core
+
+    for member_name in dir(np._core):
+        # Skip private and test members. Also if a module is aliased,
+        # no need to add it to np.core
+        if (
+            member_name.startswith("_")
+            or member_name in ["tests", "strings"]
+            or f"numpy.{member_name}" in PUBLIC_ALIASED_MODULES
+        ):
+            continue
+
+        member = getattr(np._core, member_name)
+
+        # np.core is a shim and all submodules of np.core are shims
+        # but we should be able to import everything in those shims
+        # that are available in the "real" modules in np._core
+        if inspect.ismodule(member):
+            submodule = member
+            submodule_name = member_name
+            for submodule_member_name in dir(submodule):
+                # ignore dunder names
+                if submodule_member_name.startswith("__"):
+                    continue
+                submodule_member = getattr(submodule, submodule_member_name)
+
+                core_submodule = __import__(
+                    f"numpy.core.{submodule_name}",
+                    fromlist=[submodule_member_name]
+                )
+
+                assert submodule_member is getattr(
+                    core_submodule, submodule_member_name
+                )
+
+        else:
+            assert member is getattr(core, member_name)
+
+
+def test_functions_single_location():
+    """
+    Check that each public function is available from one location only.
+
+    Test performs BFS search traversing NumPy's public API. It flags
+    any function-like object that is accessible from more that one place.
+    """
+    from typing import Any, Callable, Dict, List, Set, Tuple
+    from numpy._core._multiarray_umath import (
+        _ArrayFunctionDispatcher as dispatched_function
+    )
+
+    visited_modules: Set[types.ModuleType] = {np}
+    visited_functions: Set[Callable[..., Any]] = set()
+    # Functions often have `__name__` overridden, therefore we need
+    # to keep track of locations where functions have been found.
+    functions_original_paths: Dict[Callable[..., Any], str] = dict()
+
+    # Here we aggregate functions with more than one location.
+    # It must be empty for the test to pass.
+    duplicated_functions: List[Tuple] = []
+
+    modules_queue = [np]
+
+    while len(modules_queue) > 0:
+
+        module = modules_queue.pop()
+
+        for member_name in dir(module):
+            member = getattr(module, member_name)
+
+            # first check if we got a module
+            if (
+                inspect.ismodule(member) and  # it's a module
+                "numpy" in member.__name__ and  # inside NumPy
+                not member_name.startswith("_") and  # not private
+                "numpy._core" not in member.__name__ and  # outside _core
+                # not a legacy or testing module
+                member_name not in ["f2py", "ma", "testing", "tests"] and
+                member not in visited_modules  # not visited yet
+            ):
+                modules_queue.append(member)
+                visited_modules.add(member)
+
+            # else check if we got a function-like object
+            elif (
+                inspect.isfunction(member) or
+                isinstance(member, (dispatched_function, np.ufunc))
+            ):
+                if member in visited_functions:
+
+                    # skip main namespace functions with aliases
+                    if (
+                        member.__name__ in [
+                            "absolute",  # np.abs
+                            "arccos",  # np.acos
+                            "arccosh",  # np.acosh
+                            "arcsin",  # np.asin
+                            "arcsinh",  # np.asinh
+                            "arctan",  # np.atan
+                            "arctan2",  # np.atan2
+                            "arctanh",  # np.atanh
+                            "left_shift",  # np.bitwise_left_shift
+                            "right_shift",  # np.bitwise_right_shift
+                            "conjugate",  # np.conj
+                            "invert",  # np.bitwise_not & np.bitwise_invert
+                            "remainder",  # np.mod
+                            "divide",  # np.true_divide
+                            "concatenate",  # np.concat
+                            "power",  # np.pow
+                            "transpose",  # np.permute_dims
+                        ] and
+                        module.__name__ == "numpy"
+                    ):
+                        continue
+                    # skip trimcoef from numpy.polynomial as it is
+                    # duplicated by design.
+                    if (
+                        member.__name__ == "trimcoef" and
+                        module.__name__.startswith("numpy.polynomial")
+                    ):
+                        continue
+
+                    # skip ufuncs that are exported in np.strings as well
+                    if member.__name__ in (
+                        "add",
+                        "equal",
+                        "not_equal",
+                        "greater",
+                        "greater_equal",
+                        "less",
+                        "less_equal",
+                    ) and module.__name__ == "numpy.strings":
+                        continue
+
+                    # numpy.char reexports all numpy.strings functions for
+                    # backwards-compatibility
+                    if module.__name__ == "numpy.char":
+                        continue
+
+                    # function is present in more than one location!
+                    duplicated_functions.append(
+                        (member.__name__,
+                         module.__name__,
+                         functions_original_paths[member])
+                    )
+                else:
+                    visited_functions.add(member)
+                    functions_original_paths[member] = module.__name__
+
+    del visited_functions, visited_modules, functions_original_paths
+
+    assert len(duplicated_functions) == 0, duplicated_functions
+
+
+def test___module___attribute():
+    modules_queue = [np]
+    visited_modules = {np}
+    visited_functions = set()
+    incorrect_entries = []
+
+    while len(modules_queue) > 0:
+        module = modules_queue.pop()
+        for member_name in dir(module):
+            member = getattr(module, member_name)
+            # first check if we got a module
+            if (
+                inspect.ismodule(member) and  # it's a module
+                "numpy" in member.__name__ and  # inside NumPy
+                not member_name.startswith("_") and  # not private
+                "numpy._core" not in member.__name__ and  # outside _core
+                # not in a skip module list
+                member_name not in [
+                    "char", "core", "ctypeslib", "f2py", "ma", "lapack_lite",
+                    "mrecords", "testing", "tests", "polynomial", "typing",
+                    "mtrand", "bit_generator",
+                ] and
+                member not in visited_modules  # not visited yet
+            ):
+                modules_queue.append(member)
+                visited_modules.add(member)
+            elif (
+                not inspect.ismodule(member) and
+                hasattr(member, "__name__") and
+                not member.__name__.startswith("_") and
+                member.__module__ != module.__name__ and
+                member not in visited_functions
+            ):
+                # skip ufuncs that are exported in np.strings as well
+                if member.__name__ in (
+                    "add", "equal", "not_equal", "greater", "greater_equal",
+                    "less", "less_equal",
+                ) and module.__name__ == "numpy.strings":
+                    continue
+
+                # recarray and record are exported in np and np.rec
+                if (
+                    (member.__name__ == "recarray" and module.__name__ == "numpy") or
+                    (member.__name__ == "record" and module.__name__ == "numpy.rec")
+                ):
+                    continue
+
+                # skip cdef classes
+                if member.__name__ in (
+                    "BitGenerator", "Generator", "MT19937", "PCG64", "PCG64DXSM",
+                    "Philox", "RandomState", "SFC64", "SeedSequence",
+                ):
+                    continue
+
+                incorrect_entries.append(
+                    dict(
+                        Func=member.__name__,
+                        actual=member.__module__,
+                        expected=module.__name__,
+                    )
+                )
+                visited_functions.add(member)
+
+    if incorrect_entries:
+        assert len(incorrect_entries) == 0, incorrect_entries
+
+
+def _check___qualname__(obj) -> bool:
+    qualname = obj.__qualname__
+    name = obj.__name__
+    module_name = obj.__module__
+    assert name == qualname.split(".")[-1]
+
+    module = sys.modules[module_name]
+    actual_obj = functools.reduce(getattr, qualname.split("."), module)
+    return (
+        actual_obj is obj or
+        (
+            # for bound methods check qualname match
+            module_name.startswith("numpy.random") and
+            actual_obj.__qualname__ == qualname
+        )
+    )
+
+
+def test___qualname___attribute():
+    modules_queue = [np]
+    visited_modules = {np}
+    visited_functions = set()
+    incorrect_entries = []
+
+    while len(modules_queue) > 0:
+        module = modules_queue.pop()
+        for member_name in dir(module):
+            member = getattr(module, member_name)
+            # first check if we got a module
+            if (
+                inspect.ismodule(member) and  # it's a module
+                "numpy" in member.__name__ and  # inside NumPy
+                not member_name.startswith("_") and  # not private
+                member_name not in [
+                    "f2py", "ma", "tests", "testing", "typing",
+                    "bit_generator", "ctypeslib", "lapack_lite",
+                ] and  # skip modules
+                "numpy._core" not in member.__name__ and  # outside _core
+                member not in visited_modules  # not visited yet
+            ):
+                modules_queue.append(member)
+                visited_modules.add(member)
+            elif (
+                not inspect.ismodule(member) and
+                hasattr(member, "__name__") and
+                not member.__name__.startswith("_") and
+                not member_name.startswith("_") and
+                not _check___qualname__(member) and
+                member not in visited_functions
+            ):
+                incorrect_entries.append(
+                    dict(
+                        actual=member.__qualname__, expected=member.__name__,
+                    )
+                )
+                visited_functions.add(member)
+
+    if incorrect_entries:
+        assert len(incorrect_entries) == 0, incorrect_entries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_reloading.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_reloading.py
new file mode 100644
index 0000000000000000000000000000000000000000..22bff7212e59288ffe5179655a23985cd29d3b5c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_reloading.py
@@ -0,0 +1,74 @@
+import sys
+import subprocess
+import textwrap
+from importlib import reload
+import pickle
+
+import pytest
+
+import numpy.exceptions as ex
+from numpy.testing import (
+    assert_raises,
+    assert_warns,
+    assert_,
+    assert_equal,
+    IS_WASM,
+)
+
+
+def test_numpy_reloading():
+    # gh-7844. Also check that relevant globals retain their identity.
+    import numpy as np
+    import numpy._globals
+
+    _NoValue = np._NoValue
+    VisibleDeprecationWarning = ex.VisibleDeprecationWarning
+    ModuleDeprecationWarning = ex.ModuleDeprecationWarning
+
+    with assert_warns(UserWarning):
+        reload(np)
+    assert_(_NoValue is np._NoValue)
+    assert_(ModuleDeprecationWarning is ex.ModuleDeprecationWarning)
+    assert_(VisibleDeprecationWarning is ex.VisibleDeprecationWarning)
+
+    assert_raises(RuntimeError, reload, numpy._globals)
+    with assert_warns(UserWarning):
+        reload(np)
+    assert_(_NoValue is np._NoValue)
+    assert_(ModuleDeprecationWarning is ex.ModuleDeprecationWarning)
+    assert_(VisibleDeprecationWarning is ex.VisibleDeprecationWarning)
+
+def test_novalue():
+    import numpy as np
+    for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+        assert_equal(repr(np._NoValue), '<no value>')
+        assert_(pickle.loads(pickle.dumps(np._NoValue,
+                                          protocol=proto)) is np._NoValue)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
+def test_full_reimport():
+    """At the time of writing this, it is *not* truly supported, but
+    apparently enough users rely on it, for it to be an annoying change
+    when it started failing previously.
+    """
+    # Test within a new process, to ensure that we do not mess with the
+    # global state during the test run (could lead to cryptic test failures).
+    # This is generally unsafe, especially, since we also reload the C-modules.
+    code = textwrap.dedent(r"""
+        import sys
+        from pytest import warns
+        import numpy as np
+
+        for k in list(sys.modules.keys()):
+            if "numpy" in k:
+                del sys.modules[k]
+
+        with warns(UserWarning):
+            import numpy as np
+        """)
+    p = subprocess.run([sys.executable, '-c', code], capture_output=True)
+    if p.returncode:
+        raise AssertionError(
+            f"Non-zero return code: {p.returncode!r}\n\n{p.stderr.decode()}"
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_scripts.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..892c04eef0bed4b9d92408419c547f8258a005e3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_scripts.py
@@ -0,0 +1,47 @@
+""" Test scripts
+
+Test that we can run executable scripts that have been installed with numpy.
+"""
+import sys
+import os
+import pytest
+from os.path import join as pathjoin, isfile, dirname
+import subprocess
+
+import numpy as np
+from numpy.testing import assert_equal, IS_WASM
+
+is_inplace = isfile(pathjoin(dirname(np.__file__),  '..', 'setup.py'))
+
+
+def find_f2py_commands():
+    if sys.platform == 'win32':
+        exe_dir = dirname(sys.executable)
+        if exe_dir.endswith('Scripts'): # virtualenv
+            return [os.path.join(exe_dir, 'f2py')]
+        else:
+            return [os.path.join(exe_dir, "Scripts", 'f2py')]
+    else:
+        # Three scripts are installed in Unix-like systems:
+        # 'f2py', 'f2py{major}', and 'f2py{major.minor}'. For example,
+        # if installed with python3.9 the scripts would be named
+        # 'f2py', 'f2py3', and 'f2py3.9'.
+        version = sys.version_info
+        major = str(version.major)
+        minor = str(version.minor)
+        return ['f2py', 'f2py' + major, 'f2py' + major + '.' + minor]
+
+
+@pytest.mark.skipif(is_inplace, reason="Cannot test f2py command inplace")
+@pytest.mark.xfail(reason="Test is unreliable")
+@pytest.mark.parametrize('f2py_cmd', find_f2py_commands())
+def test_f2py(f2py_cmd):
+    # test that we can run f2py script
+    stdout = subprocess.check_output([f2py_cmd, '-v'])
+    assert_equal(stdout.strip(), np.__version__.encode('ascii'))
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+def test_pep338():
+    stdout = subprocess.check_output([sys.executable, '-mnumpy.f2py', '-v'])
+    assert_equal(stdout.strip(), np.__version__.encode('ascii'))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_warnings.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_warnings.py
new file mode 100644
index 0000000000000000000000000000000000000000..9304c1346cbff578eb57da65f034499cd665ba41
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/tests/test_warnings.py
@@ -0,0 +1,76 @@
+"""
+Tests which scan for certain occurrences in the code, they may not find
+all of these occurrences but should catch almost all.
+"""
+import pytest
+
+from pathlib import Path
+import ast
+import tokenize
+import numpy
+
+class ParseCall(ast.NodeVisitor):
+    def __init__(self):
+        self.ls = []
+
+    def visit_Attribute(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+        self.ls.append(node.attr)
+
+    def visit_Name(self, node):
+        self.ls.append(node.id)
+
+
+class FindFuncs(ast.NodeVisitor):
+    def __init__(self, filename):
+        super().__init__()
+        self.__filename = filename
+
+    def visit_Call(self, node):
+        p = ParseCall()
+        p.visit(node.func)
+        ast.NodeVisitor.generic_visit(self, node)
+
+        if p.ls[-1] == 'simplefilter' or p.ls[-1] == 'filterwarnings':
+            if node.args[0].value == "ignore":
+                raise AssertionError(
+                    "warnings should have an appropriate stacklevel; found in "
+                    "{} on line {}".format(self.__filename, node.lineno))
+
+        if p.ls[-1] == 'warn' and (
+                len(p.ls) == 1 or p.ls[-2] == 'warnings'):
+
+            if "testing/tests/test_warnings.py" == self.__filename:
+                # This file
+                return
+
+            # See if stacklevel exists:
+            if len(node.args) == 3:
+                return
+            args = {kw.arg for kw in node.keywords}
+            if "stacklevel" in args:
+                return
+            raise AssertionError(
+                "warnings should have an appropriate stacklevel; found in "
+                "{} on line {}".format(self.__filename, node.lineno))
+
+
+@pytest.mark.slow
+def test_warning_calls():
+    # combined "ignore" and stacklevel error
+    base = Path(numpy.__file__).parent
+
+    for path in base.rglob("*.py"):
+        if base / "testing" in path.parents:
+            continue
+        if path == base / "__init__.py":
+            continue
+        if path == base / "random" / "__init__.py":
+            continue
+        if path == base / "conftest.py":
+            continue
+        # use tokenize to auto-detect encoding on systems where no
+        # default encoding is defined (e.g. LANG='C')
+        with tokenize.open(str(path)) as file:
+            tree = ast.parse(file.read())
+            FindFuncs(path).visit(tree)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b247921818e27d603dc653098b51f53d8e3187e1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/__init__.py
@@ -0,0 +1,175 @@
+"""
+============================
+Typing (:mod:`numpy.typing`)
+============================
+
+.. versionadded:: 1.20
+
+Large parts of the NumPy API have :pep:`484`-style type annotations. In
+addition a number of type aliases are available to users, most prominently
+the two below:
+
+- `ArrayLike`: objects that can be converted to arrays
+- `DTypeLike`: objects that can be converted to dtypes
+
+.. _typing-extensions: https://pypi.org/project/typing-extensions/
+
+Mypy plugin
+-----------
+
+.. versionadded:: 1.21
+
+.. automodule:: numpy.typing.mypy_plugin
+
+.. currentmodule:: numpy.typing
+
+Differences from the runtime NumPy API
+--------------------------------------
+
+NumPy is very flexible. Trying to describe the full range of
+possibilities statically would result in types that are not very
+helpful. For that reason, the typed NumPy API is often stricter than
+the runtime NumPy API. This section describes some notable
+differences.
+
+ArrayLike
+~~~~~~~~~
+
+The `ArrayLike` type tries to avoid creating object arrays. For
+example,
+
+.. code-block:: python
+
+    >>> np.array(x**2 for x in range(10))
+    array(<generator object <genexpr> at ...>, dtype=object)
+
+is valid NumPy code which will create a 0-dimensional object
+array. Type checkers will complain about the above example when using
+the NumPy types however. If you really intended to do the above, then
+you can either use a ``# type: ignore`` comment:
+
+.. code-block:: python
+
+    >>> np.array(x**2 for x in range(10))  # type: ignore
+
+or explicitly type the array like object as `~typing.Any`:
+
+.. code-block:: python
+
+    >>> from typing import Any
+    >>> array_like: Any = (x**2 for x in range(10))
+    >>> np.array(array_like)
+    array(<generator object <genexpr> at ...>, dtype=object)
+
+ndarray
+~~~~~~~
+
+It's possible to mutate the dtype of an array at runtime. For example,
+the following code is valid:
+
+.. code-block:: python
+
+    >>> x = np.array([1, 2])
+    >>> x.dtype = np.bool
+
+This sort of mutation is not allowed by the types. Users who want to
+write statically typed code should instead use the `numpy.ndarray.view`
+method to create a view of the array with a different dtype.
+
+DTypeLike
+~~~~~~~~~
+
+The `DTypeLike` type tries to avoid creation of dtype objects using
+dictionary of fields like below:
+
+.. code-block:: python
+
+    >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)})
+
+Although this is valid NumPy code, the type checker will complain about it,
+since its usage is discouraged.
+Please see : :ref:`Data type objects <arrays.dtypes>`
+
+Number precision
+~~~~~~~~~~~~~~~~
+
+The precision of `numpy.number` subclasses is treated as a invariant generic
+parameter (see :class:`~NBitBase`), simplifying the annotating of processes
+involving precision-based casting.
+
+.. code-block:: python
+
+    >>> from typing import TypeVar
+    >>> import numpy as np
+    >>> import numpy.typing as npt
+
+    >>> T = TypeVar("T", bound=npt.NBitBase)
+    >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]":
+    ...     ...
+
+Consequently, the likes of `~numpy.float16`, `~numpy.float32` and
+`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to
+runtime, they're not necessarily considered as sub-classes.
+
+Timedelta64
+~~~~~~~~~~~
+
+The `~numpy.timedelta64` class is not considered a subclass of
+`~numpy.signedinteger`, the former only inheriting from `~numpy.generic`
+while static type checking.
+
+0D arrays
+~~~~~~~~~
+
+During runtime numpy aggressively casts any passed 0D arrays into their
+corresponding `~numpy.generic` instance. Until the introduction of shape
+typing (see :pep:`646`) it is unfortunately not possible to make the
+necessary distinction between 0D and >0D arrays. While thus not strictly
+correct, all operations are that can potentially perform a 0D-array -> scalar
+cast are currently annotated as exclusively returning an `~numpy.ndarray`.
+
+If it is known in advance that an operation *will* perform a
+0D-array -> scalar cast, then one can consider manually remedying the
+situation with either `typing.cast` or a ``# type: ignore`` comment.
+
+Record array dtypes
+~~~~~~~~~~~~~~~~~~~
+
+The dtype of `numpy.recarray`, and the :ref:`routines.array-creation.rec`
+functions in general, can be specified in one of two ways:
+
+* Directly via the ``dtype`` argument.
+* With up to five helper arguments that operate via `numpy.rec.format_parser`:
+  ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``.
+
+These two approaches are currently typed as being mutually exclusive,
+*i.e.* if ``dtype`` is specified than one may not specify ``formats``.
+While this mutual exclusivity is not (strictly) enforced during runtime,
+combining both dtype specifiers can lead to unexpected or even downright
+buggy behavior.
+
+API
+---
+
+"""
+# NOTE: The API section will be appended with additional entries
+# further down in this file
+
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NBitBase,
+    NDArray,
+)
+
+__all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"]
+
+if __doc__ is not None:
+    from numpy._typing._add_docstring import _docstrings
+    __doc__ += _docstrings
+    __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n'
+    del _docstrings
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/mypy_plugin.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/mypy_plugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce9b0d9582ad164d3c6d2747dc941e841907eba6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/mypy_plugin.py
@@ -0,0 +1,199 @@
+"""A mypy_ plugin for managing a number of platform-specific annotations.
+Its functionality can be split into three distinct parts:
+
+* Assigning the (platform-dependent) precisions of certain `~numpy.number`
+  subclasses, including the likes of `~numpy.int_`, `~numpy.intp` and
+  `~numpy.longlong`. See the documentation on
+  :ref:`scalar types <arrays.scalars.built-in>` for a comprehensive overview
+  of the affected classes. Without the plugin the precision of all relevant
+  classes will be inferred as `~typing.Any`.
+* Removing all extended-precision `~numpy.number` subclasses that are
+  unavailable for the platform in question. Most notably this includes the
+  likes of `~numpy.float128` and `~numpy.complex256`. Without the plugin *all*
+  extended-precision types will, as far as mypy is concerned, be available
+  to all platforms.
+* Assigning the (platform-dependent) precision of `~numpy.ctypeslib.c_intp`.
+  Without the plugin the type will default to `ctypes.c_int64`.
+
+  .. versionadded:: 1.22
+
+Examples
+--------
+To enable the plugin, one must add it to their mypy `configuration file`_:
+
+.. code-block:: ini
+
+    [mypy]
+    plugins = numpy.typing.mypy_plugin
+
+.. _mypy: https://mypy-lang.org/
+.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html
+
+"""
+
+from __future__ import annotations
+
+from typing import Final, TYPE_CHECKING, Callable
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+try:
+    import mypy.types
+    from mypy.types import Type
+    from mypy.plugin import Plugin, AnalyzeTypeContext
+    from mypy.nodes import MypyFile, ImportFrom, Statement
+    from mypy.build import PRI_MED
+
+    _HookFunc = Callable[[AnalyzeTypeContext], Type]
+    MYPY_EX: None | ModuleNotFoundError = None
+except ModuleNotFoundError as ex:
+    MYPY_EX = ex
+
+__all__: list[str] = []
+
+
+def _get_precision_dict() -> dict[str, str]:
+    names = [
+        ("_NBitByte", np.byte),
+        ("_NBitShort", np.short),
+        ("_NBitIntC", np.intc),
+        ("_NBitIntP", np.intp),
+        ("_NBitInt", np.int_),
+        ("_NBitLong", np.long),
+        ("_NBitLongLong", np.longlong),
+
+        ("_NBitHalf", np.half),
+        ("_NBitSingle", np.single),
+        ("_NBitDouble", np.double),
+        ("_NBitLongDouble", np.longdouble),
+    ]
+    ret = {}
+    module = "numpy._typing"
+    for name, typ in names:
+        n: int = 8 * typ().dtype.itemsize
+        ret[f'{module}._nbit.{name}'] = f"{module}._nbit_base._{n}Bit"
+    return ret
+
+
+def _get_extended_precision_list() -> list[str]:
+    extended_names = [
+        "uint128",
+        "uint256",
+        "int128",
+        "int256",
+        "float80",
+        "float96",
+        "float128",
+        "float256",
+        "complex160",
+        "complex192",
+        "complex256",
+        "complex512",
+    ]
+    return [i for i in extended_names if hasattr(np, i)]
+
+def _get_c_intp_name() -> str:
+    # Adapted from `np.core._internal._getintp_ctype`
+    char = np.dtype('n').char
+    if char == 'i':
+        return "c_int"
+    elif char == 'l':
+        return "c_long"
+    elif char == 'q':
+        return "c_longlong"
+    else:
+        return "c_long"
+
+
+#: A dictionary mapping type-aliases in `numpy._typing._nbit` to
+#: concrete `numpy.typing.NBitBase` subclasses.
+_PRECISION_DICT: Final = _get_precision_dict()
+
+#: A list with the names of all extended precision `np.number` subclasses.
+_EXTENDED_PRECISION_LIST: Final = _get_extended_precision_list()
+
+#: The name of the ctypes equivalent of `np.intp`
+_C_INTP: Final = _get_c_intp_name()
+
+
+def _hook(ctx: AnalyzeTypeContext) -> Type:
+    """Replace a type-alias with a concrete ``NBitBase`` subclass."""
+    typ, _, api = ctx
+    name = typ.name.split(".")[-1]
+    name_new = _PRECISION_DICT[f"numpy._typing._nbit.{name}"]
+    return api.named_type(name_new)
+
+
+if TYPE_CHECKING or MYPY_EX is None:
+    def _index(iterable: Iterable[Statement], id: str) -> int:
+        """Identify the first ``ImportFrom`` instance the specified `id`."""
+        for i, value in enumerate(iterable):
+            if getattr(value, "id", None) == id:
+                return i
+        raise ValueError("Failed to identify a `ImportFrom` instance "
+                         f"with the following id: {id!r}")
+
+    def _override_imports(
+        file: MypyFile,
+        module: str,
+        imports: list[tuple[str, None | str]],
+    ) -> None:
+        """Override the first `module`-based import with new `imports`."""
+        # Construct a new `from module import y` statement
+        import_obj = ImportFrom(module, 0, names=imports)
+        import_obj.is_top_level = True
+
+        # Replace the first `module`-based import statement with `import_obj`
+        for lst in [file.defs, file.imports]:  # type: list[Statement]
+            i = _index(lst, module)
+            lst[i] = import_obj
+
+    class _NumpyPlugin(Plugin):
+        """A mypy plugin for handling versus numpy-specific typing tasks."""
+
+        def get_type_analyze_hook(self, fullname: str) -> None | _HookFunc:
+            """Set the precision of platform-specific `numpy.number`
+            subclasses.
+
+            For example: `numpy.int_`, `numpy.longlong` and `numpy.longdouble`.
+            """
+            if fullname in _PRECISION_DICT:
+                return _hook
+            return None
+
+        def get_additional_deps(
+            self, file: MypyFile
+        ) -> list[tuple[int, str, int]]:
+            """Handle all import-based overrides.
+
+            * Import platform-specific extended-precision `numpy.number`
+              subclasses (*e.g.* `numpy.float96`, `numpy.float128` and
+              `numpy.complex256`).
+            * Import the appropriate `ctypes` equivalent to `numpy.intp`.
+
+            """
+            ret = [(PRI_MED, file.fullname, -1)]
+
+            if file.fullname == "numpy":
+                _override_imports(
+                    file, "numpy._typing._extended_precision",
+                    imports=[(v, v) for v in _EXTENDED_PRECISION_LIST],
+                )
+            elif file.fullname == "numpy.ctypeslib":
+                _override_imports(
+                    file, "ctypes",
+                    imports=[(_C_INTP, "_c_intp")],
+                )
+            return ret
+
+    def plugin(version: str) -> type[_NumpyPlugin]:
+        """An entry-point for mypy."""
+        return _NumpyPlugin
+
+else:
+    def plugin(version: str) -> type[_NumpyPlugin]:
+        """An entry-point for mypy."""
+        raise MYPY_EX
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..29f3ab4e28d36b02b537880d4d5656d6cb4e5481
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi
@@ -0,0 +1,128 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+b_ = np.bool()
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint32]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.longdouble]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+ANY: Any
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_m: list[np.timedelta64]
+AR_LIKE_M: list[np.datetime64]
+
+# Array subtraction
+
+# NOTE: mypys `NoReturn` errors are, unfortunately, not that great
+_1 = AR_b - AR_LIKE_b  # E: Need type annotation
+_2 = AR_LIKE_b - AR_b  # E: Need type annotation
+AR_i - bytes()  # E: No overload variant
+
+AR_f - AR_LIKE_m  # E: Unsupported operand types
+AR_f - AR_LIKE_M  # E: Unsupported operand types
+AR_c - AR_LIKE_m  # E: Unsupported operand types
+AR_c - AR_LIKE_M  # E: Unsupported operand types
+
+AR_m - AR_LIKE_f  # E: Unsupported operand types
+AR_M - AR_LIKE_f  # E: Unsupported operand types
+AR_m - AR_LIKE_c  # E: Unsupported operand types
+AR_M - AR_LIKE_c  # E: Unsupported operand types
+
+AR_m - AR_LIKE_M  # E: Unsupported operand types
+AR_LIKE_m - AR_M  # E: Unsupported operand types
+
+# array floor division
+
+AR_M // AR_LIKE_b  # E: Unsupported operand types
+AR_M // AR_LIKE_u  # E: Unsupported operand types
+AR_M // AR_LIKE_i  # E: Unsupported operand types
+AR_M // AR_LIKE_f  # E: Unsupported operand types
+AR_M // AR_LIKE_c  # E: Unsupported operand types
+AR_M // AR_LIKE_m  # E: Unsupported operand types
+AR_M // AR_LIKE_M  # E: Unsupported operand types
+
+AR_b // AR_LIKE_M  # E: Unsupported operand types
+AR_u // AR_LIKE_M  # E: Unsupported operand types
+AR_i // AR_LIKE_M  # E: Unsupported operand types
+AR_f // AR_LIKE_M  # E: Unsupported operand types
+AR_c // AR_LIKE_M  # E: Unsupported operand types
+AR_m // AR_LIKE_M  # E: Unsupported operand types
+AR_M // AR_LIKE_M  # E: Unsupported operand types
+
+_3 = AR_m // AR_LIKE_b  # E: Need type annotation
+AR_m // AR_LIKE_c  # E: Unsupported operand types
+
+AR_b // AR_LIKE_m  # E: Unsupported operand types
+AR_u // AR_LIKE_m  # E: Unsupported operand types
+AR_i // AR_LIKE_m  # E: Unsupported operand types
+AR_f // AR_LIKE_m  # E: Unsupported operand types
+AR_c // AR_LIKE_m  # E: Unsupported operand types
+
+# regression tests for https://github.com/numpy/numpy/issues/28957
+AR_c // 2  # type: ignore[operator]
+AR_c // AR_i  # type: ignore[operator]
+AR_c // AR_c  # type: ignore[operator]
+
+# Array multiplication
+
+AR_b *= AR_LIKE_u  # E: incompatible type
+AR_b *= AR_LIKE_i  # E: incompatible type
+AR_b *= AR_LIKE_f  # E: incompatible type
+AR_b *= AR_LIKE_c  # E: incompatible type
+AR_b *= AR_LIKE_m  # E: incompatible type
+
+AR_u *= AR_LIKE_i  # E: incompatible type
+AR_u *= AR_LIKE_f  # E: incompatible type
+AR_u *= AR_LIKE_c  # E: incompatible type
+AR_u *= AR_LIKE_m  # E: incompatible type
+
+AR_i *= AR_LIKE_f  # E: incompatible type
+AR_i *= AR_LIKE_c  # E: incompatible type
+AR_i *= AR_LIKE_m  # E: incompatible type
+
+AR_f *= AR_LIKE_c  # E: incompatible type
+AR_f *= AR_LIKE_m  # E: incompatible type
+
+# Array power
+
+AR_b **= AR_LIKE_b  # E: Invalid self argument
+AR_b **= AR_LIKE_u  # E: Invalid self argument
+AR_b **= AR_LIKE_i  # E: Invalid self argument
+AR_b **= AR_LIKE_f  # E: Invalid self argument
+AR_b **= AR_LIKE_c  # E: Invalid self argument
+
+AR_u **= AR_LIKE_i  # E: incompatible type
+AR_u **= AR_LIKE_f  # E: incompatible type
+AR_u **= AR_LIKE_c  # E: incompatible type
+
+AR_i **= AR_LIKE_f  # E: incompatible type
+AR_i **= AR_LIKE_c  # E: incompatible type
+
+AR_f **= AR_LIKE_c  # E: incompatible type
+
+# Scalars
+
+b_ - b_  # E: No overload variant
+
+dt + dt  # E: Unsupported operand types
+td - dt  # E: Unsupported operand types
+td % 1  # E: Unsupported operand types
+td / dt  # E: No overload
+td % dt  # E: Unsupported operand types
+
+-b_  # E: Unsupported operand type
++b_  # E: Unsupported operand type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..27eefe3c918d87c871d018f5d29246d681ed4033
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi
@@ -0,0 +1,34 @@
+import numpy as np
+import numpy.typing as npt
+
+a: npt.NDArray[np.float64]
+generator = (i for i in range(10))
+
+np.require(a, requirements=1)  # E: No overload variant
+np.require(a, requirements="TEST")  # E: incompatible type
+
+np.zeros("test")  # E: incompatible type
+np.zeros()  # E: require at least one argument
+
+np.ones("test")  # E: incompatible type
+np.ones()  # E: require at least one argument
+
+np.array(0, float, True)  # E: No overload variant
+
+np.linspace(None, 'bob')  # E: No overload variant
+np.linspace(0, 2, num=10.0)  # E: No overload variant
+np.linspace(0, 2, endpoint='True')  # E: No overload variant
+np.linspace(0, 2, retstep=b'False')  # E: No overload variant
+np.linspace(0, 2, dtype=0)  # E: No overload variant
+np.linspace(0, 2, axis=None)  # E: No overload variant
+
+np.logspace(None, 'bob')  # E: No overload variant
+np.logspace(0, 2, base=None)  # E: No overload variant
+
+np.geomspace(None, 'bob')  # E: No overload variant
+
+np.stack(generator)  # E: No overload variant
+np.hstack({1, 2})  # E: No overload variant
+np.vstack(1)  # E: No overload variant
+
+np.array([1], like=1)  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_like.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_like.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a21101a993c768210f0befbba319ca21ca50723b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_like.pyi
@@ -0,0 +1,18 @@
+import numpy as np
+from numpy._typing import ArrayLike
+
+
+class A:
+    pass
+
+
+x1: ArrayLike = (i for i in range(10))  # E: Incompatible types in assignment
+x2: ArrayLike = A()  # E: Incompatible types in assignment
+x3: ArrayLike = {1: "foo", 2: "bar"}  # E: Incompatible types in assignment
+
+scalar = np.int64(1)
+scalar.__array__(dtype=np.float64)  # E: No overload variant
+array = np.array([1])
+array.__array__(dtype=np.float64)  # E: No overload variant
+
+array.setfield(np.eye(1), np.int32, (0, 1))  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_pad.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_pad.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2be51a87181dcc14068d7036fe44d1d3cc9d9d6f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/array_pad.pyi
@@ -0,0 +1,6 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+
+np.pad(AR_i8, 2, mode="bob")  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..486c11e79868f8d88ac093d789633ec5fb0953d4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi
@@ -0,0 +1,16 @@
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+AR: npt.NDArray[np.float64]
+func1: Callable[[Any], str]
+func2: Callable[[np.integer], str]
+
+np.array2string(AR, style=None)  # E: No overload variant
+np.array2string(AR, legacy="1.14")  # E: No overload variant
+np.array2string(AR, sign="*")  # E: No overload variant
+np.array2string(AR, floatmode="default")  # E: No overload variant
+np.array2string(AR, formatter={"A": func1})  # E: No overload variant
+np.array2string(AR, formatter={"float": func2})  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..00280b3a6a2c523ff0f92ed5f2110a103a2a8740
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi
@@ -0,0 +1,14 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+np.lib.Arrayterator(np.int64())  # E: incompatible type
+ar_iter.shape = (10, 5)  # E: is read-only
+ar_iter[None]  # E: Invalid index type
+ar_iter[None, 1]  # E: Invalid index type
+ar_iter[np.intp()]  # E: Invalid index type
+ar_iter[np.intp(), ...]  # E: Invalid index type
+ar_iter[AR_i8]  # E: Invalid index type
+ar_iter[AR_i8, :]  # E: Invalid index type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..13b47c485b417fdf2008f09706b98ed3311bb8f7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi
@@ -0,0 +1,21 @@
+import numpy as np
+
+i8 = np.int64()
+i4 = np.int32()
+u8 = np.uint64()
+b_ = np.bool()
+i = int()
+
+f8 = np.float64()
+
+b_ >> f8  # E: No overload variant
+i8 << f8  # E: No overload variant
+i | f8  # E: Unsupported operand types
+i8 ^ f8  # E: No overload variant
+u8 & f8  # E: No overload variant
+~f8  # E: Unsupported operand type
+# TODO: Certain mixes like i4 << u8 go to float and thus should fail
+
+# mypys' error message for `NoReturn` is unfortunately pretty bad
+# TODO: Re-enable this once we add support for numerical precision for `number`s
+# a = u8 | 0  # E: Need type annotation
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/char.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/char.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..542a273baef54c802d0033019926b7a50861c21f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/char.pyi
@@ -0,0 +1,69 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+
+np.char.equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.not_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.greater_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.less_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.greater(AR_U, AR_S)  # E: incompatible type
+
+np.char.less(AR_U, AR_S)  # E: incompatible type
+
+np.char.encode(AR_S)  # E: incompatible type
+np.char.decode(AR_U)  # E: incompatible type
+
+np.char.join(AR_U, b"_")  # E: incompatible type
+np.char.join(AR_S, "_")  # E: incompatible type
+
+np.char.ljust(AR_U, 5, fillchar=b"a")  # E: incompatible type
+np.char.ljust(AR_S, 5, fillchar="a")  # E: incompatible type
+np.char.rjust(AR_U, 5, fillchar=b"a")  # E: incompatible type
+np.char.rjust(AR_S, 5, fillchar="a")  # E: incompatible type
+
+np.char.lstrip(AR_U, chars=b"a")  # E: incompatible type
+np.char.lstrip(AR_S, chars="a")  # E: incompatible type
+np.char.strip(AR_U, chars=b"a")  # E: incompatible type
+np.char.strip(AR_S, chars="a")  # E: incompatible type
+np.char.rstrip(AR_U, chars=b"a")  # E: incompatible type
+np.char.rstrip(AR_S, chars="a")  # E: incompatible type
+
+np.char.partition(AR_U, b"a")  # E: incompatible type
+np.char.partition(AR_S, "a")  # E: incompatible type
+np.char.rpartition(AR_U, b"a")  # E: incompatible type
+np.char.rpartition(AR_S, "a")  # E: incompatible type
+
+np.char.replace(AR_U, b"_", b"-")  # E: incompatible type
+np.char.replace(AR_S, "_", "-")  # E: incompatible type
+
+np.char.split(AR_U, b"_")  # E: incompatible type
+np.char.split(AR_S, "_")  # E: incompatible type
+np.char.rsplit(AR_U, b"_")  # E: incompatible type
+np.char.rsplit(AR_S, "_")  # E: incompatible type
+
+np.char.count(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.count(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.endswith(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.endswith(AR_S, "a", end=9)  # E: incompatible type
+np.char.startswith(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.startswith(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.find(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.find(AR_S, "a", end=9)  # E: incompatible type
+np.char.rfind(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.rfind(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.index(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.index(AR_S, "a", end=9)  # E: incompatible type
+np.char.rindex(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.rindex(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.isdecimal(AR_S)  # E: incompatible type
+np.char.isnumeric(AR_S)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/chararray.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/chararray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e484b644e4b8ec75cdc8ea404a457cf9d1a78b58
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/chararray.pyi
@@ -0,0 +1,61 @@
+import numpy as np
+
+AR_U: np.char.chararray[tuple[int, ...], np.dtype[np.str_]]
+AR_S: np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]]
+
+AR_S.encode()  # E: Invalid self argument
+AR_U.decode()  # E: Invalid self argument
+
+AR_U.join(b"_")  # E: incompatible type
+AR_S.join("_")  # E: incompatible type
+
+AR_U.ljust(5, fillchar=b"a")  # E: incompatible type
+AR_S.ljust(5, fillchar="a")  # E: incompatible type
+AR_U.rjust(5, fillchar=b"a")  # E: incompatible type
+AR_S.rjust(5, fillchar="a")  # E: incompatible type
+
+AR_U.lstrip(chars=b"a")  # E: incompatible type
+AR_S.lstrip(chars="a")  # E: incompatible type
+AR_U.strip(chars=b"a")  # E: incompatible type
+AR_S.strip(chars="a")  # E: incompatible type
+AR_U.rstrip(chars=b"a")  # E: incompatible type
+AR_S.rstrip(chars="a")  # E: incompatible type
+
+AR_U.partition(b"a")  # E: incompatible type
+AR_S.partition("a")  # E: incompatible type
+AR_U.rpartition(b"a")  # E: incompatible type
+AR_S.rpartition("a")  # E: incompatible type
+
+AR_U.replace(b"_", b"-")  # E: incompatible type
+AR_S.replace("_", "-")  # E: incompatible type
+
+AR_U.split(b"_")  # E: incompatible type
+AR_S.split("_")  # E: incompatible type
+AR_S.split(1)  # E: incompatible type
+AR_U.rsplit(b"_")  # E: incompatible type
+AR_S.rsplit("_")  # E: incompatible type
+
+AR_U.count(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.count("a", end=9)  # E: incompatible type
+
+AR_U.endswith(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.endswith("a", end=9)  # E: incompatible type
+AR_U.startswith(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.startswith("a", end=9)  # E: incompatible type
+
+AR_U.find(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.find("a", end=9)  # E: incompatible type
+AR_U.rfind(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.rfind("a", end=9)  # E: incompatible type
+
+AR_U.index(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.index("a", end=9)  # E: incompatible type
+AR_U.rindex(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.rindex("a", end=9)  # E: incompatible type
+
+AR_U == AR_S  # E: Unsupported operand types
+AR_U != AR_S  # E: Unsupported operand types
+AR_U >= AR_S  # E: Unsupported operand types
+AR_U <= AR_S  # E: Unsupported operand types
+AR_U > AR_S  # E: Unsupported operand types
+AR_U < AR_S  # E: Unsupported operand types
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/comparisons.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/comparisons.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1ae8149082b6b726989a1e2dd01933388f49eece
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/comparisons.pyi
@@ -0,0 +1,27 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+AR_f > AR_m  # E: Unsupported operand types
+AR_c > AR_m  # E: Unsupported operand types
+
+AR_m > AR_f  # E: Unsupported operand types
+AR_m > AR_c  # E: Unsupported operand types
+
+AR_i > AR_M  # E: Unsupported operand types
+AR_f > AR_M  # E: Unsupported operand types
+AR_m > AR_M  # E: Unsupported operand types
+
+AR_M > AR_i  # E: Unsupported operand types
+AR_M > AR_f  # E: Unsupported operand types
+AR_M > AR_m  # E: Unsupported operand types
+
+AR_i > str()  # E: No overload variant
+AR_i > bytes()  # E: No overload variant
+str() > AR_M  # E: Unsupported operand types
+bytes() > AR_M  # E: Unsupported operand types
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/constants.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/constants.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b5d6d27eae46086e8241b276895516f8bad226c5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/constants.pyi
@@ -0,0 +1,3 @@
+import numpy as np
+
+np.little_endian = np.little_endian  # E: Cannot assign to final
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/datasource.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/datasource.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..44f4fa27307addb3ee54ebfe840ccbfa1e4cc72d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/datasource.pyi
@@ -0,0 +1,15 @@
+from pathlib import Path
+import numpy as np
+
+path: Path
+d1: np.lib.npyio.DataSource
+
+d1.abspath(path)  # E: incompatible type
+d1.abspath(b"...")  # E: incompatible type
+
+d1.exists(path)  # E: incompatible type
+d1.exists(b"...")  # E: incompatible type
+
+d1.open(path, "r")  # E: incompatible type
+d1.open(b"...", encoding="utf8")  # E: incompatible type
+d1.open(None, newline="/n")  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/dtype.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/dtype.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0f3810f3c014aafac0e149cfc6da0ec38c61f165
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/dtype.pyi
@@ -0,0 +1,20 @@
+import numpy as np
+
+
+class Test1:
+    not_dtype = np.dtype(float)
+
+
+class Test2:
+    dtype = float
+
+
+np.dtype(Test1())  # E: No overload variant of "dtype" matches
+np.dtype(Test2())  # E: incompatible type
+
+np.dtype(  # E: No overload variant of "dtype" matches
+    {
+        "field1": (float, 1),
+        "field2": (int, 3),
+    }
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e51f72e47b25b798c9655b749fc01911c824c2dd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi
@@ -0,0 +1,12 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_m: npt.NDArray[np.timedelta64]
+AR_U: npt.NDArray[np.str_]
+
+np.einsum("i,i->i", AR_i, AR_m)  # E: incompatible type
+np.einsum("i,i->i", AR_f, AR_f, dtype=np.int32)  # E: incompatible type
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U)  # E: Value of type variable "_ArrayType" of "einsum" cannot be
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U, casting="unsafe")  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/flatiter.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/flatiter.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b0c3b023f16b3b36a28421938fe4eda4d411a68b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/flatiter.pyi
@@ -0,0 +1,25 @@
+from typing import Any
+
+import numpy as np
+import numpy._typing as npt
+
+
+class Index:
+    def __index__(self) -> int:
+        ...
+
+
+a: np.flatiter[npt.NDArray[np.float64]]
+supports_array: npt._SupportsArray[np.dtype[np.float64]]
+
+a.base = Any  # E: Property "base" defined in "flatiter" is read-only
+a.coords = Any  # E: Property "coords" defined in "flatiter" is read-only
+a.index = Any  # E: Property "index" defined in "flatiter" is read-only
+a.copy(order='C')  # E: Unexpected keyword argument
+
+# NOTE: Contrary to `ndarray.__getitem__` its counterpart in `flatiter`
+# does not accept objects with the `__array__` or `__index__` protocols;
+# boolean indexing is just plain broken (gh-17175)
+a[np.bool()]  # E: No overload variant of "__getitem__"
+a[Index()]  # E: No overload variant of "__getitem__"
+a[supports_array]  # E: No overload variant of "__getitem__"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fb666986a7e03fc6eb7aceeba235299ec9bfa14f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi
@@ -0,0 +1,165 @@
+"""Tests for :mod:`numpy._core.fromnumeric`."""
+
+import numpy as np
+import numpy.typing as npt
+
+A = np.array(True, ndmin=2, dtype=bool)
+A.setflags(write=False)
+AR_U: npt.NDArray[np.str_]
+AR_M: npt.NDArray[np.datetime64]
+
+a = np.bool(True)
+
+np.take(a, None)  # E: No overload variant
+np.take(a, axis=1.0)  # E: No overload variant
+np.take(A, out=1)  # E: No overload variant
+np.take(A, mode="bob")  # E: No overload variant
+
+np.reshape(a, None)  # E: No overload variant
+np.reshape(A, 1, order="bob")  # E: No overload variant
+
+np.choose(a, None)  # E: No overload variant
+np.choose(a, out=1.0)  # E: No overload variant
+np.choose(A, mode="bob")  # E: No overload variant
+
+np.repeat(a, None)  # E: No overload variant
+np.repeat(A, 1, axis=1.0)  # E: No overload variant
+
+np.swapaxes(A, None, 1)  # E: No overload variant
+np.swapaxes(A, 1, [0])  # E: No overload variant
+
+np.transpose(A, axes=1.0)  # E: No overload variant
+
+np.partition(a, None)  # E: No overload variant
+np.partition(  # E: No overload variant
+    a, 0, axis="bob"
+)
+np.partition(  # E: No overload variant
+    A, 0, kind="bob"
+)
+np.partition(
+    A, 0, order=range(5)  # E: Argument "order" to "partition" has incompatible type
+)
+
+np.argpartition(
+    a, None  # E: incompatible type
+)
+np.argpartition(
+    a, 0, axis="bob"  # E: incompatible type
+)
+np.argpartition(
+    A, 0, kind="bob"  # E: incompatible type
+)
+np.argpartition(
+    A, 0, order=range(5)  # E: Argument "order" to "argpartition" has incompatible type
+)
+
+np.sort(A, axis="bob")  # E: No overload variant
+np.sort(A, kind="bob")  # E: No overload variant
+np.sort(A, order=range(5))  # E: Argument "order" to "sort" has incompatible type
+
+np.argsort(A, axis="bob")  # E: Argument "axis" to "argsort" has incompatible type
+np.argsort(A, kind="bob")  # E: Argument "kind" to "argsort" has incompatible type
+np.argsort(A, order=range(5))  # E: Argument "order" to "argsort" has incompatible type
+
+np.argmax(A, axis="bob")  # E: No overload variant of "argmax" matches argument type
+np.argmax(A, kind="bob")  # E: No overload variant of "argmax" matches argument type
+
+np.argmin(A, axis="bob")  # E: No overload variant of "argmin" matches argument type
+np.argmin(A, kind="bob")  # E: No overload variant of "argmin" matches argument type
+
+np.searchsorted(  # E: No overload variant of "searchsorted" matches argument type
+    A[0], 0, side="bob"
+)
+np.searchsorted(  # E: No overload variant of "searchsorted" matches argument type
+    A[0], 0, sorter=1.0
+)
+
+np.resize(A, 1.0)  # E: No overload variant
+
+np.squeeze(A, 1.0)  # E: No overload variant of "squeeze" matches argument type
+
+np.diagonal(A, offset=None)  # E: No overload variant
+np.diagonal(A, axis1="bob")  # E: No overload variant
+np.diagonal(A, axis2=[])  # E: No overload variant
+
+np.trace(A, offset=None)  # E: No overload variant
+np.trace(A, axis1="bob")  # E: No overload variant
+np.trace(A, axis2=[])  # E: No overload variant
+
+np.ravel(a, order="bob")  # E: No overload variant
+
+np.nonzero(0)  # E: No overload variant
+
+np.compress(  # E: No overload variant
+    [True], A, axis=1.0
+)
+
+np.clip(a, 1, 2, out=1)  # E: No overload variant of "clip" matches argument type
+
+np.sum(a, axis=1.0)  # E: No overload variant
+np.sum(a, keepdims=1.0)  # E: No overload variant
+np.sum(a, initial=[1])  # E: No overload variant
+
+np.all(a, axis=1.0)  # E: No overload variant
+np.all(a, keepdims=1.0)  # E: No overload variant
+np.all(a, out=1.0)  # E: No overload variant
+
+np.any(a, axis=1.0)  # E: No overload variant
+np.any(a, keepdims=1.0)  # E: No overload variant
+np.any(a, out=1.0)  # E: No overload variant
+
+np.cumsum(a, axis=1.0)  # E: No overload variant
+np.cumsum(a, dtype=1.0)  # E: No overload variant
+np.cumsum(a, out=1.0)  # E: No overload variant
+
+np.ptp(a, axis=1.0)  # E: No overload variant
+np.ptp(a, keepdims=1.0)  # E: No overload variant
+np.ptp(a, out=1.0)  # E: No overload variant
+
+np.amax(a, axis=1.0)  # E: No overload variant
+np.amax(a, keepdims=1.0)  # E: No overload variant
+np.amax(a, out=1.0)  # E: No overload variant
+np.amax(a, initial=[1.0])  # E: No overload variant
+np.amax(a, where=[1.0])  # E: incompatible type
+
+np.amin(a, axis=1.0)  # E: No overload variant
+np.amin(a, keepdims=1.0)  # E: No overload variant
+np.amin(a, out=1.0)  # E: No overload variant
+np.amin(a, initial=[1.0])  # E: No overload variant
+np.amin(a, where=[1.0])  # E: incompatible type
+
+np.prod(a, axis=1.0)  # E: No overload variant
+np.prod(a, out=False)  # E: No overload variant
+np.prod(a, keepdims=1.0)  # E: No overload variant
+np.prod(a, initial=int)  # E: No overload variant
+np.prod(a, where=1.0)  # E: No overload variant
+np.prod(AR_U)  # E: incompatible type
+
+np.cumprod(a, axis=1.0)  # E: No overload variant
+np.cumprod(a, out=False)  # E: No overload variant
+np.cumprod(AR_U)  # E: incompatible type
+
+np.size(a, axis=1.0)  # E: Argument "axis" to "size" has incompatible type
+
+np.around(a, decimals=1.0)  # E: No overload variant
+np.around(a, out=type)  # E: No overload variant
+np.around(AR_U)  # E: incompatible type
+
+np.mean(a, axis=1.0)  # E: No overload variant
+np.mean(a, out=False)  # E: No overload variant
+np.mean(a, keepdims=1.0)  # E: No overload variant
+np.mean(AR_U)  # E: incompatible type
+np.mean(AR_M)  # E: incompatible type
+
+np.std(a, axis=1.0)  # E: No overload variant
+np.std(a, out=False)  # E: No overload variant
+np.std(a, ddof='test')  # E: No overload variant
+np.std(a, keepdims=1.0)  # E: No overload variant
+np.std(AR_U)  # E: incompatible type
+
+np.var(a, axis=1.0)  # E: No overload variant
+np.var(a, out=False)  # E: No overload variant
+np.var(a, ddof='test')  # E: No overload variant
+np.var(a, keepdims=1.0)  # E: No overload variant
+np.var(AR_U)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/histograms.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/histograms.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22499d39175ac4252d6ebc7a8c9c63421d64faee
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/histograms.pyi
@@ -0,0 +1,12 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+np.histogram_bin_edges(AR_i8, range=(0, 1, 2))  # E: incompatible type
+
+np.histogram(AR_i8, range=(0, 1, 2))  # E: incompatible type
+
+np.histogramdd(AR_i8, range=(0, 1))  # E: incompatible type
+np.histogramdd(AR_i8, range=[(0, 1, 2)])  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22f6f4a61e8e11079e40d3755b0c01200ffdf762
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi
@@ -0,0 +1,14 @@
+import numpy as np
+
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+
+np.ndindex([1, 2, 3])  # E: No overload variant
+np.unravel_index(AR_LIKE_f, (1, 2, 3))  # E: incompatible type
+np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob")  # E: No overload variant
+np.mgrid[1]  # E: Invalid index type
+np.mgrid[...]  # E: Invalid index type
+np.ogrid[1]  # E: Invalid index type
+np.ogrid[...]  # E: Invalid index type
+np.fill_diagonal(AR_LIKE_f, 2)  # E: incompatible type
+np.diag_indices(1.0)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..de4e56b07ba1ed189300ae9cbdf585144bb0d880
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi
@@ -0,0 +1,62 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_b_list: list[npt.NDArray[np.bool]]
+
+def fn_none_i(a: None, /) -> npt.NDArray[Any]: ...
+def fn_ar_i(a: npt.NDArray[np.float64], posarg: int, /) -> npt.NDArray[Any]: ...
+
+np.average(AR_m)  # E: incompatible type
+np.select(1, [AR_f8])  # E: incompatible type
+np.angle(AR_m)  # E: incompatible type
+np.unwrap(AR_m)  # E: incompatible type
+np.unwrap(AR_c16)  # E: incompatible type
+np.trim_zeros(1)  # E: incompatible type
+np.place(1, [True], 1.5)  # E: incompatible type
+np.vectorize(1)  # E: incompatible type
+np.place(AR_f8, slice(None), 5)  # E: incompatible type
+
+np.piecewise(AR_f8, True, [fn_ar_i], 42)  # E: No overload variants
+# TODO: enable these once mypy actually supports ParamSpec (released in 2021)
+# NOTE: pyright correctly reports errors for these (`reportCallIssue`)
+# np.piecewise(AR_f8, AR_b_list, [fn_none_i])  # E: No overload variants
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i])  # E: No overload variant
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 3.14)  # E: No overload variant
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 42, None)  # E: No overload variant
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 42, _=None)  # E: No overload variant
+
+np.interp(AR_f8, AR_c16, AR_f8)  # E: incompatible type
+np.interp(AR_c16, AR_f8, AR_f8)  # E: incompatible type
+np.interp(AR_f8, AR_f8, AR_f8, period=AR_c16)  # E: No overload variant
+np.interp(AR_f8, AR_f8, AR_O)  # E: incompatible type
+
+np.cov(AR_m)  # E: incompatible type
+np.cov(AR_O)  # E: incompatible type
+np.corrcoef(AR_m)  # E: incompatible type
+np.corrcoef(AR_O)  # E: incompatible type
+np.corrcoef(AR_f8, bias=True)  # E: No overload variant
+np.corrcoef(AR_f8, ddof=2)  # E: No overload variant
+np.blackman(1j)  # E: incompatible type
+np.bartlett(1j)  # E: incompatible type
+np.hanning(1j)  # E: incompatible type
+np.hamming(1j)  # E: incompatible type
+np.hamming(AR_c16)  # E: incompatible type
+np.kaiser(1j, 1)  # E: incompatible type
+np.sinc(AR_O)  # E: incompatible type
+np.median(AR_M)  # E: incompatible type
+
+np.percentile(AR_f8, 50j)  # E: No overload variant
+np.percentile(AR_f8, 50, interpolation="bob")  # E: No overload variant
+np.quantile(AR_f8, 0.5j)  # E: No overload variant
+np.quantile(AR_f8, 0.5, interpolation="bob")  # E: No overload variant
+np.meshgrid(AR_f8, AR_f8, indexing="bob")  # E: incompatible type
+np.delete(AR_f8, AR_f8)  # E: incompatible type
+np.insert(AR_f8, AR_f8, 1.5)  # E: incompatible type
+np.digitize(AR_f8, 1j)  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e51b6b58e30751bef821a44127e96be71483005b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi
@@ -0,0 +1,29 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+AR_U: npt.NDArray[np.str_]
+
+poly_obj: np.poly1d
+
+np.polymul(AR_f8, AR_U)  # E: incompatible type
+np.polydiv(AR_f8, AR_U)  # E: incompatible type
+
+5**poly_obj  # E: No overload variant
+
+np.polyint(AR_U)  # E: incompatible type
+np.polyint(AR_f8, m=1j)  # E: No overload variant
+
+np.polyder(AR_U)  # E: incompatible type
+np.polyder(AR_f8, m=1j)  # E: No overload variant
+
+np.polyfit(AR_O, AR_f8, 1)  # E: incompatible type
+np.polyfit(AR_f8, AR_f8, 1, rcond=1j)  # E: No overload variant
+np.polyfit(AR_f8, AR_f8, 1, w=AR_c16)  # E: incompatible type
+np.polyfit(AR_f8, AR_f8, 1, cov="bob")  # E: No overload variant
+
+np.polyval(AR_f8, AR_U)  # E: incompatible type
+np.polyadd(AR_f8, AR_U)  # E: incompatible type
+np.polysub(AR_f8, AR_U)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8b8482eeff6d93510dcabeece5bf7d6f85f627aa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi
@@ -0,0 +1,3 @@
+import numpy.lib.array_utils as array_utils
+
+array_utils.byte_bounds(1)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_version.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2758cfe4043883eaaa3651efe726bd31b853e603
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/lib_version.pyi
@@ -0,0 +1,6 @@
+from numpy.lib import NumpyVersion
+
+version: NumpyVersion
+
+NumpyVersion(b"1.8.0")  # E: incompatible type
+version >= b"1.8.0"  # E: Unsupported operand types
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/linalg.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..da9390328bd7ca1ebcab5a1ce0736f7f4df57d96
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/linalg.pyi
@@ -0,0 +1,48 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_O: npt.NDArray[np.object_]
+AR_M: npt.NDArray[np.datetime64]
+
+np.linalg.tensorsolve(AR_O, AR_O)  # E: incompatible type
+
+np.linalg.solve(AR_O, AR_O)  # E: incompatible type
+
+np.linalg.tensorinv(AR_O)  # E: incompatible type
+
+np.linalg.inv(AR_O)  # E: incompatible type
+
+np.linalg.matrix_power(AR_M, 5)  # E: incompatible type
+
+np.linalg.cholesky(AR_O)  # E: incompatible type
+
+np.linalg.qr(AR_O)  # E: incompatible type
+np.linalg.qr(AR_f8, mode="bob")  # E: No overload variant
+
+np.linalg.eigvals(AR_O)  # E: incompatible type
+
+np.linalg.eigvalsh(AR_O)  # E: incompatible type
+np.linalg.eigvalsh(AR_O, UPLO="bob")  # E: No overload variant
+
+np.linalg.eig(AR_O)  # E: incompatible type
+
+np.linalg.eigh(AR_O)  # E: incompatible type
+np.linalg.eigh(AR_O, UPLO="bob")  # E: No overload variant
+
+np.linalg.svd(AR_O)  # E: incompatible type
+
+np.linalg.cond(AR_O)  # E: incompatible type
+np.linalg.cond(AR_f8, p="bob")  # E: incompatible type
+
+np.linalg.matrix_rank(AR_O)  # E: incompatible type
+
+np.linalg.pinv(AR_O)  # E: incompatible type
+
+np.linalg.slogdet(AR_O)  # E: incompatible type
+
+np.linalg.det(AR_O)  # E: incompatible type
+
+np.linalg.norm(AR_f8, ord="bob")  # E: No overload variant
+
+np.linalg.multi_dot([AR_M])  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/memmap.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/memmap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..434870b60e41948afc6fb3f593742deb2cc11e3e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/memmap.pyi
@@ -0,0 +1,5 @@
+import numpy as np
+
+with open("file.txt", "r") as f:
+    np.memmap(f)  # E: No overload variant
+np.memmap("test.txt", shape=[10, 5])  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/modules.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/modules.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..541be15b24ae3a20c7ce24d474e9af6adc804c69
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/modules.pyi
@@ -0,0 +1,17 @@
+import numpy as np
+
+np.testing.bob  # E: Module has no attribute
+np.bob  # E: Module has no attribute
+
+# Stdlib modules in the namespace by accident
+np.warnings  # E: Module has no attribute
+np.sys  # E: Module has no attribute
+np.os  # E: Module "numpy" does not explicitly export
+np.math  # E: Module has no attribute
+
+# Public sub-modules that are not imported to their parent module by default;
+# e.g. one must first execute `import numpy.lib.recfunctions`
+np.lib.recfunctions  # E: Module has no attribute
+
+np.__deprecated_attrs__  # E: Module has no attribute
+np.__expired_functions__  # E: Module has no attribute
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/multiarray.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/multiarray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0ee6c11c6dfff268efd1af3083210d430699f04c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/multiarray.pyi
@@ -0,0 +1,53 @@
+import numpy as np
+import numpy.typing as npt
+
+i8: np.int64
+
+AR_b: npt.NDArray[np.bool]
+AR_u1: npt.NDArray[np.uint8]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+
+M: np.datetime64
+
+AR_LIKE_f: list[float]
+
+def func(a: int) -> None: ...
+
+np.where(AR_b, 1)  # E: No overload variant
+
+np.can_cast(AR_f8, 1)  # E: incompatible type
+
+np.vdot(AR_M, AR_M)  # E: incompatible type
+
+np.copyto(AR_LIKE_f, AR_f8)  # E: incompatible type
+
+np.putmask(AR_LIKE_f, [True, True, False], 1.5)  # E: incompatible type
+
+np.packbits(AR_f8)  # E: incompatible type
+np.packbits(AR_u1, bitorder=">")  # E: incompatible type
+
+np.unpackbits(AR_i8)  # E: incompatible type
+np.unpackbits(AR_u1, bitorder=">")  # E: incompatible type
+
+np.shares_memory(1, 1, max_work=i8)  # E: incompatible type
+np.may_share_memory(1, 1, max_work=i8)  # E: incompatible type
+
+np.arange(M)  # E: No overload variant
+np.arange(stop=10)  # E: No overload variant
+
+np.datetime_data(int)  # E: incompatible type
+
+np.busday_offset("2012", 10)  # E: No overload variant
+
+np.datetime_as_string("2012")  # E: No overload variant
+
+np.char.compare_chararrays("a", b"a", "==", False)  # E: No overload variant
+
+np.nested_iters([AR_i8, AR_i8])  # E: Missing positional argument
+np.nested_iters([AR_i8, AR_i8], 0)  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [0])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["test"])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["test"]])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], buffersize=1.0)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5ecae02e6178c3ced44031358a6f24047552651e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray.pyi
@@ -0,0 +1,11 @@
+import numpy as np
+
+# Ban setting dtype since mutating the type of the array in place
+# makes having ndarray be generic over dtype impossible. Generally
+# users should use `ndarray.view` in this situation anyway. See
+#
+# https://github.com/numpy/numpy-stubs/issues/7
+#
+# for more context.
+float_array = np.array([1.0])
+float_array.dtype = np.bool  # E: Property "dtype" defined in "ndarray" is read-only
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..489aefca7ffcc3d2673f23b80ea5da16742d189e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi
@@ -0,0 +1,36 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+import numpy as np
+import numpy.typing as npt
+
+f8: np.float64
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+AR_b: npt.NDArray[np.bool]
+
+ctypes_obj = AR_f8.ctypes
+
+f8.argpartition(0)  # E: has no attribute
+f8.diagonal()  # E: has no attribute
+f8.dot(1)  # E: has no attribute
+f8.nonzero()  # E: has no attribute
+f8.partition(0)  # E: has no attribute
+f8.put(0, 2)  # E: has no attribute
+f8.setfield(2, np.float64)  # E: has no attribute
+f8.sort()  # E: has no attribute
+f8.trace()  # E: has no attribute
+
+AR_M.__complex__()  # E: Invalid self argument
+AR_b.__index__()  # E: Invalid self argument
+
+AR_f8[1.5]  # E: No overload variant
+AR_f8["field_a"]  # E: No overload variant
+AR_f8[["field_a", "field_b"]]  # E: Invalid index type
+
+AR_f8.__array_finalize__(object())  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/nditer.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/nditer.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1e8e37ee5fe09373a6be5e8a2b2ddb9f84725eb0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/nditer.pyi
@@ -0,0 +1,8 @@
+import numpy as np
+
+class Test(np.nditer): ...  # E: Cannot inherit from final class
+
+np.nditer([0, 1], flags=["test"])  # E: incompatible type
+np.nditer([0, 1], op_flags=[["test"]])  # E: incompatible type
+np.nditer([0, 1], itershape=(1.0,))  # E: incompatible type
+np.nditer([0, 1], buffersize=1.0)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6301e51769fee30db50bfaf1e2777bf894166de8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi
@@ -0,0 +1,17 @@
+from collections.abc import Sequence
+from numpy._typing import _NestedSequence
+
+a: Sequence[float]
+b: list[complex]
+c: tuple[str, ...]
+d: int
+e: str
+
+def func(a: _NestedSequence[int]) -> None:
+    ...
+
+reveal_type(func(a))  # E: incompatible type
+reveal_type(func(b))  # E: incompatible type
+reveal_type(func(c))  # E: incompatible type
+reveal_type(func(d))  # E: incompatible type
+reveal_type(func(e))  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/npyio.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/npyio.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6ba6a6be17978fbb68a300f2d67c56a22b80a4a8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/npyio.pyi
@@ -0,0 +1,25 @@
+import pathlib
+from typing import IO
+
+import numpy.typing as npt
+import numpy as np
+
+str_path: str
+bytes_path: bytes
+pathlib_path: pathlib.Path
+str_file: IO[str]
+AR_i8: npt.NDArray[np.int64]
+
+np.load(str_file)  # E: incompatible type
+
+np.save(bytes_path, AR_i8)  # E: No overload variant
+# https://github.com/python/mypy/issues/16111
+# np.save(str_path, AR_i8, fix_imports=True)  # W: deprecated
+
+np.savez(bytes_path, AR_i8)  # E: incompatible type
+
+np.savez_compressed(bytes_path, AR_i8)  # E: incompatible type
+
+np.loadtxt(bytes_path)  # E: incompatible type
+
+np.fromregex(bytes_path, ".", np.int64)  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..29a3cf30dd959d7e05ce688413741b9ccc0060a3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi
@@ -0,0 +1,5 @@
+import numpy as np
+
+np.isdtype(1, np.int64)  # E: incompatible type
+
+np.issubdtype(1, np.int64)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/random.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/random.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..aa1eae4424e2ba07b6f662ccc5e2f523fcb6dcf8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/random.pyi
@@ -0,0 +1,62 @@
+import numpy as np
+import numpy.typing as npt
+
+SEED_FLOAT: float = 457.3
+SEED_ARR_FLOAT: npt.NDArray[np.float64] = np.array([1.0, 2, 3, 4])
+SEED_ARRLIKE_FLOAT: list[float] = [1.0, 2.0, 3.0, 4.0]
+SEED_SEED_SEQ: np.random.SeedSequence = np.random.SeedSequence(0)
+SEED_STR: str = "String seeding not allowed"
+
+# default rng
+np.random.default_rng(SEED_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_STR)  # E: incompatible type
+
+# Seed Sequence
+np.random.SeedSequence(SEED_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_SEED_SEQ)  # E: incompatible type
+np.random.SeedSequence(SEED_STR)  # E: incompatible type
+
+seed_seq: np.random.bit_generator.SeedSequence = np.random.SeedSequence()
+seed_seq.spawn(11.5)  # E: incompatible type
+seed_seq.generate_state(3.14)  # E: incompatible type
+seed_seq.generate_state(3, np.uint8)  # E: incompatible type
+seed_seq.generate_state(3, "uint8")  # E: incompatible type
+seed_seq.generate_state(3, "u1")  # E: incompatible type
+seed_seq.generate_state(3, np.uint16)  # E: incompatible type
+seed_seq.generate_state(3, "uint16")  # E: incompatible type
+seed_seq.generate_state(3, "u2")  # E: incompatible type
+seed_seq.generate_state(3, np.int32)  # E: incompatible type
+seed_seq.generate_state(3, "int32")  # E: incompatible type
+seed_seq.generate_state(3, "i4")  # E: incompatible type
+
+# Bit Generators
+np.random.MT19937(SEED_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_STR)  # E: incompatible type
+
+np.random.PCG64(SEED_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_STR)  # E: incompatible type
+
+np.random.Philox(SEED_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_STR)  # E: incompatible type
+
+np.random.SFC64(SEED_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_STR)  # E: incompatible type
+
+# Generator
+np.random.Generator(None)  # E: incompatible type
+np.random.Generator(12333283902830213)  # E: incompatible type
+np.random.Generator("OxFEEDF00D")  # E: incompatible type
+np.random.Generator([123, 234])  # E: incompatible type
+np.random.Generator(np.array([123, 234], dtype="u4"))  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/rec.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/rec.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a57f1ba27d74504ff59232a4a5929ccaf55dd445
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/rec.pyi
@@ -0,0 +1,17 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+
+np.rec.fromarrays(1)  # E: No overload variant
+np.rec.fromarrays([1, 2, 3], dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+np.rec.fromrecords(AR_i8)  # E: incompatible type
+np.rec.fromrecords([(1.5,)], dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+np.rec.fromstring("string", dtype=[("f8", "f8")])  # E: No overload variant
+np.rec.fromstring(b"bytes")  # E: No overload variant
+np.rec.fromstring(b"(1.5,)", dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+with open("test", "r") as f:
+    np.rec.fromfile(f, dtype=[("f8", "f8")])  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/scalars.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/scalars.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e847d8d6c45a0158818aa5f90da756ec36080b5d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/scalars.pyi
@@ -0,0 +1,89 @@
+import sys
+import numpy as np
+
+f2: np.float16
+f8: np.float64
+c8: np.complex64
+
+# Construction
+
+np.float32(3j)  # E: incompatible type
+
+# Technically the following examples are valid NumPy code. But they
+# are not considered a best practice, and people who wish to use the
+# stubs should instead do
+#
+# np.array([1.0, 0.0, 0.0], dtype=np.float32)
+# np.array([], dtype=np.complex64)
+#
+# See e.g. the discussion on the mailing list
+#
+# https://mail.python.org/pipermail/numpy-discussion/2020-April/080566.html
+#
+# and the issue
+#
+# https://github.com/numpy/numpy-stubs/issues/41
+#
+# for more context.
+np.float32([1.0, 0.0, 0.0])  # E: incompatible type
+np.complex64([])  # E: incompatible type
+
+# TODO: protocols (can't check for non-existent protocols w/ __getattr__)
+
+np.datetime64(0)  # E: No overload variant
+
+class A:
+    def __float__(self):
+        return 1.0
+
+
+np.int8(A())  # E: incompatible type
+np.int16(A())  # E: incompatible type
+np.int32(A())  # E: incompatible type
+np.int64(A())  # E: incompatible type
+np.uint8(A())  # E: incompatible type
+np.uint16(A())  # E: incompatible type
+np.uint32(A())  # E: incompatible type
+np.uint64(A())  # E: incompatible type
+
+np.void("test")  # E: No overload variant
+np.void("test", dtype=None)  # E: No overload variant
+
+np.generic(1)  # E: Cannot instantiate abstract class
+np.number(1)  # E: Cannot instantiate abstract class
+np.integer(1)  # E: Cannot instantiate abstract class
+np.inexact(1)  # E: Cannot instantiate abstract class
+np.character("test")  # E: Cannot instantiate abstract class
+np.flexible(b"test")  # E: Cannot instantiate abstract class
+
+np.float64(value=0.0)  # E: Unexpected keyword argument
+np.int64(value=0)  # E: Unexpected keyword argument
+np.uint64(value=0)  # E: Unexpected keyword argument
+np.complex128(value=0.0j)  # E: No overload variant
+np.str_(value='bob')  # E: No overload variant
+np.bytes_(value=b'test')  # E: No overload variant
+np.void(value=b'test')  # E: No overload variant
+np.bool(value=True)  # E: Unexpected keyword argument
+np.datetime64(value="2019")  # E: No overload variant
+np.timedelta64(value=0)  # E: Unexpected keyword argument
+
+np.bytes_(b"hello", encoding='utf-8')  # E: No overload variant
+np.str_("hello", encoding='utf-8')  # E: No overload variant
+
+f8.item(1)  # E: incompatible type
+f8.item((0, 1))  # E: incompatible type
+f8.squeeze(axis=1)  # E: incompatible type
+f8.squeeze(axis=(0, 1))  # E: incompatible type
+f8.transpose(1)  # E: incompatible type
+
+def func(a: np.float32) -> None: ...
+
+func(f2)  # E: incompatible type
+func(f8)  # E: incompatible type
+
+c8.__getnewargs__()  # E: Invalid self argument
+f2.__getnewargs__()  # E: Invalid self argument
+f2.hex()  # E: Invalid self argument
+np.float16.fromhex("0x0.0p+0")  # E: Invalid self argument
+f2.__trunc__()  # E: Invalid self argument
+f2.__getformat__("float")  # E: Invalid self argument
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/shape.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/shape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3dd6d14f4222de8872f85bde7d69cc3a65aef4a7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/shape.pyi
@@ -0,0 +1,6 @@
+from typing import Any
+import numpy as np
+
+# test bounds of _ShapeType_co
+
+np.ndarray[tuple[str, str], Any]  # E: Value of type variable
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/shape_base.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/shape_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e709741b7935ec7269affd836f5256a0842ddd0a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/shape_base.pyi
@@ -0,0 +1,8 @@
+import numpy as np
+
+class DTypeLike:
+    dtype: np.dtype[np.int_]
+
+dtype_like: DTypeLike
+
+np.expand_dims(dtype_like, (5, 10))  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f2bfba7432a89b41e095377e1d7e0e5f87d07109
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi
@@ -0,0 +1,9 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.lib.stride_tricks.as_strided(AR_f8, shape=8)  # E: No overload variant
+np.lib.stride_tricks.as_strided(AR_f8, strides=8)  # E: No overload variant
+
+np.lib.stride_tricks.sliding_window_view(AR_f8, axis=(1,))  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/strings.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/strings.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..25c3c2ecc0d76c6bbd2ec897ef9f8b09b4078715
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/strings.pyi
@@ -0,0 +1,59 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+
+np.strings.equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.not_equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.greater_equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.less_equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.greater(AR_U, AR_S)  # E: incompatible type
+
+np.strings.less(AR_U, AR_S)  # E: incompatible type
+
+np.strings.encode(AR_S)  # E: incompatible type
+np.strings.decode(AR_U)  # E: incompatible type
+
+np.strings.join(AR_U, b"_")  # E: incompatible type
+np.strings.join(AR_S, "_")  # E: incompatible type
+
+np.strings.lstrip(AR_U, b"a")  # E: incompatible type
+np.strings.lstrip(AR_S, "a")  # E: incompatible type
+np.strings.strip(AR_U, b"a")  # E: incompatible type
+np.strings.strip(AR_S, "a")  # E: incompatible type
+np.strings.rstrip(AR_U, b"a")  # E: incompatible type
+np.strings.rstrip(AR_S, "a")  # E: incompatible type
+
+np.strings.partition(AR_U, b"a")  # E: incompatible type
+np.strings.partition(AR_S, "a")  # E: incompatible type
+np.strings.rpartition(AR_U, b"a")  # E: incompatible type
+np.strings.rpartition(AR_S, "a")  # E: incompatible type
+
+np.strings.count(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.count(AR_S, "a", 0, 9)  # E: incompatible type
+
+np.strings.endswith(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.endswith(AR_S, "a", 0, 9)  # E: incompatible type
+np.strings.startswith(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.startswith(AR_S, "a", 0, 9)  # E: incompatible type
+
+np.strings.find(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.find(AR_S, "a", 0, 9)  # E: incompatible type
+np.strings.rfind(AR_U, b"a", [1, 2, 3], [1, 2 , 3])  # E: incompatible type
+np.strings.rfind(AR_S, "a", 0, 9)  # E: incompatible type
+
+np.strings.index(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.strings.index(AR_S, "a", end=9)  # E: incompatible type
+np.strings.rindex(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.strings.rindex(AR_S, "a", end=9)  # E: incompatible type
+
+np.strings.isdecimal(AR_S)  # E: incompatible type
+np.strings.isnumeric(AR_S)  # E: incompatible type
+
+np.strings.replace(AR_U, b"_", b"-", 10)  # E: incompatible type
+np.strings.replace(AR_S, "_", "-", 1)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/testing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f7eaa7d20836946144d7e1f7aa2d40a6d0f00948
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/testing.pyi
@@ -0,0 +1,28 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+
+def func(x: object) -> bool: ...
+
+np.testing.assert_(True, msg=1)  # E: incompatible type
+np.testing.build_err_msg(1, "test")  # E: incompatible type
+np.testing.assert_almost_equal(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_approx_equal([1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.testing.assert_array_almost_equal(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_less(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_string_equal(b"a", b"a")  # E: incompatible type
+
+np.testing.assert_raises(expected_exception=TypeError, callable=func)  # E: No overload variant
+np.testing.assert_raises_regex(expected_exception=TypeError, expected_regex="T", callable=func)  # E: No overload variant
+
+np.testing.assert_allclose(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_almost_equal_nulp(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_max_ulp(AR_U, AR_U)  # E: incompatible type
+
+np.testing.assert_warns(RuntimeWarning, func)  # E: No overload variant
+np.testing.assert_no_warnings(func=func)  # E: No overload variant
+np.testing.assert_no_warnings(func)  # E: Too many arguments
+np.testing.assert_no_warnings(func, y=None)  # E: No overload variant
+
+np.testing.assert_no_gc_cycles(func=func)  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..76186285669b34e42f0e387e94435f593954781f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi
@@ -0,0 +1,37 @@
+from typing import Any, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+
+def func1(ar: npt.NDArray[Any], a: int) -> npt.NDArray[np.str_]:
+    pass
+
+
+def func2(ar: npt.NDArray[Any], a: float) -> float:
+    pass
+
+
+AR_b: npt.NDArray[np.bool]
+AR_m: npt.NDArray[np.timedelta64]
+
+AR_LIKE_b: list[bool]
+
+np.eye(10, M=20.0)  # E: No overload variant
+np.eye(10, k=2.5, dtype=int)  # E: No overload variant
+
+np.diag(AR_b, k=0.5)  # E: No overload variant
+np.diagflat(AR_b, k=0.5)  # E: No overload variant
+
+np.tri(10, M=20.0)  # E: No overload variant
+np.tri(10, k=2.5, dtype=int)  # E: No overload variant
+
+np.tril(AR_b, k=0.5)  # E: No overload variant
+np.triu(AR_b, k=0.5)  # E: No overload variant
+
+np.vander(AR_m)  # E: incompatible type
+
+np.histogram2d(AR_m)  # E: No overload variant
+
+np.mask_indices(10, func1)  # E: incompatible type
+np.mask_indices(10, func2, 10.5)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/type_check.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/type_check.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..95f52bfbd260914c429cbf0ca57f1ff4b03cbb1d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/type_check.pyi
@@ -0,0 +1,13 @@
+import numpy as np
+import numpy.typing as npt
+
+DTYPE_i8: np.dtype[np.int64]
+
+np.mintypecode(DTYPE_i8)  # E: incompatible type
+np.iscomplexobj(DTYPE_i8)  # E: incompatible type
+np.isrealobj(DTYPE_i8)  # E: incompatible type
+
+np.typename(DTYPE_i8)  # E: No overload variant
+np.typename("invalid")  # E: No overload variant
+
+np.common_type(np.timedelta64())  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b080804b0fcf21a09cedb2abeba9f2baa9592dde
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi
@@ -0,0 +1,21 @@
+"""Typing tests for `numpy._core._ufunc_config`."""
+
+import numpy as np
+
+def func1(a: str, b: int, c: float) -> None: ...
+def func2(a: str, *, b: int) -> None: ...
+
+class Write1:
+    def write1(self, a: str) -> None: ...
+
+class Write2:
+    def write(self, a: str, b: str) -> None: ...
+
+class Write3:
+    def write(self, *, a: str) -> None: ...
+
+np.seterrcall(func1)  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(func2)  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write1())  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write2())  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write3())  # E: Argument 1 to "seterrcall" has incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..be5e6a1530c2e5415042840ee83ed4a9c8182daf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi
@@ -0,0 +1,21 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+
+np.fix(AR_c)  # E: incompatible type
+np.fix(AR_m)  # E: incompatible type
+np.fix(AR_M)  # E: incompatible type
+
+np.isposinf(AR_c)  # E: incompatible type
+np.isposinf(AR_m)  # E: incompatible type
+np.isposinf(AR_M)  # E: incompatible type
+np.isposinf(AR_O)  # E: incompatible type
+
+np.isneginf(AR_c)  # E: incompatible type
+np.isneginf(AR_m)  # E: incompatible type
+np.isneginf(AR_M)  # E: incompatible type
+np.isneginf(AR_O)  # E: incompatible type
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bbab0dfe3fc2fe363dc50a7a1f3c0b11f3d22f97
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi
@@ -0,0 +1,17 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.sin.nin + "foo"  # E: Unsupported operand types
+np.sin(1, foo="bar")  # E: No overload variant
+
+np.abs(None)  # E: No overload variant
+
+np.add(1, 1, 1)  # E: No overload variant
+np.add(1, 1, axis=0)  # E: No overload variant
+
+np.matmul(AR_f8, AR_f8, where=True)  # E: No overload variant
+
+np.frexp(AR_f8, out=None)  # E: No overload variant
+np.frexp(AR_f8, out=AR_f8)  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fae96d6bf01641d0cf7dc8f7883f09c9492cb383
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi
@@ -0,0 +1,5 @@
+import numpy.exceptions as ex
+
+ex.AxisError(1.0)  # E: No overload variant
+ex.AxisError(1, ndim=2.0)  # E: No overload variant
+ex.AxisError(2, msg_prefix=404)  # E: No overload variant
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..78d8d93c6560616c3495dcdf801befce51997c00
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi
@@ -0,0 +1,25 @@
+import sys
+
+import numpy as np
+from numpy._typing import _80Bit, _96Bit, _128Bit, _256Bit
+
+if sys.version_info >= (3, 11):
+    from typing import assert_type
+else:
+    from typing_extensions import assert_type
+
+assert_type(np.uint128(), np.unsignedinteger[_128Bit])
+assert_type(np.uint256(), np.unsignedinteger[_256Bit])
+
+assert_type(np.int128(), np.signedinteger[_128Bit])
+assert_type(np.int256(), np.signedinteger[_256Bit])
+
+assert_type(np.float80(), np.floating[_80Bit])
+assert_type(np.float96(), np.floating[_96Bit])
+assert_type(np.float128(), np.floating[_128Bit])
+assert_type(np.float256(), np.floating[_256Bit])
+
+assert_type(np.complex160(), np.complexfloating[_80Bit, _80Bit])
+assert_type(np.complex192(), np.complexfloating[_96Bit, _96Bit])
+assert_type(np.complex256(), np.complexfloating[_128Bit, _128Bit])
+assert_type(np.complex512(), np.complexfloating[_256Bit, _256Bit])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/mypy.ini b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/mypy.ini
new file mode 100644
index 0000000000000000000000000000000000000000..3bd7887c12091fc77cf4b872a61ec364d77d3eb5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/mypy.ini
@@ -0,0 +1,10 @@
+[mypy]
+plugins = numpy.typing.mypy_plugin
+show_absolute_path = True
+implicit_reexport = False
+pretty = True
+disallow_any_unimported = True
+disallow_any_generics = True
+; https://github.com/python/mypy/issues/15313
+disable_bytearray_promotion = true
+disable_memoryview_promotion = true
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arithmetic.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..93fda1d291c043b109ac2d8d151b83e45bd0f43a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arithmetic.py
@@ -0,0 +1,596 @@
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+import numpy.typing as npt
+import pytest
+
+c16 = np.complex128(1)
+f8 = np.float64(1)
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+c8 = np.complex64(1)
+f4 = np.float32(1)
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+dt = np.datetime64(1, "D")
+td = np.timedelta64(1, "D")
+
+b_ = np.bool(1)
+
+b = bool(1)
+c = complex(1)
+f = float(1)
+i = int(1)
+
+
+class Object:
+    def __array__(self, dtype: np.typing.DTypeLike = None,
+                  copy: bool | None = None) -> np.ndarray[Any, np.dtype[np.object_]]:
+        ret = np.empty((), dtype=object)
+        ret[()] = self
+        return ret
+
+    def __sub__(self, value: Any) -> Object:
+        return self
+
+    def __rsub__(self, value: Any) -> Object:
+        return self
+
+    def __floordiv__(self, value: Any) -> Object:
+        return self
+
+    def __rfloordiv__(self, value: Any) -> Object:
+        return self
+
+    def __mul__(self, value: Any) -> Object:
+        return self
+
+    def __rmul__(self, value: Any) -> Object:
+        return self
+
+    def __pow__(self, value: Any) -> Object:
+        return self
+
+    def __rpow__(self, value: Any) -> Object:
+        return self
+
+
+AR_b: npt.NDArray[np.bool] = np.array([True])
+AR_u: npt.NDArray[np.uint32] = np.array([1], dtype=np.uint32)
+AR_i: npt.NDArray[np.int64] = np.array([1])
+AR_f: npt.NDArray[np.float64] = np.array([1.0])
+AR_c: npt.NDArray[np.complex128] = np.array([1j])
+AR_m: npt.NDArray[np.timedelta64] = np.array([np.timedelta64(1, "D")])
+AR_M: npt.NDArray[np.datetime64] = np.array([np.datetime64(1, "D")])
+AR_O: npt.NDArray[np.object_] = np.array([Object()])
+
+AR_LIKE_b = [True]
+AR_LIKE_u = [np.uint32(1)]
+AR_LIKE_i = [1]
+AR_LIKE_f = [1.0]
+AR_LIKE_c = [1j]
+AR_LIKE_m = [np.timedelta64(1, "D")]
+AR_LIKE_M = [np.datetime64(1, "D")]
+AR_LIKE_O = [Object()]
+
+# Array subtractions
+
+AR_b - AR_LIKE_u
+AR_b - AR_LIKE_i
+AR_b - AR_LIKE_f
+AR_b - AR_LIKE_c
+AR_b - AR_LIKE_m
+AR_b - AR_LIKE_O
+
+AR_LIKE_u - AR_b
+AR_LIKE_i - AR_b
+AR_LIKE_f - AR_b
+AR_LIKE_c - AR_b
+AR_LIKE_m - AR_b
+AR_LIKE_M - AR_b
+AR_LIKE_O - AR_b
+
+AR_u - AR_LIKE_b
+AR_u - AR_LIKE_u
+AR_u - AR_LIKE_i
+AR_u - AR_LIKE_f
+AR_u - AR_LIKE_c
+AR_u - AR_LIKE_m
+AR_u - AR_LIKE_O
+
+AR_LIKE_b - AR_u
+AR_LIKE_u - AR_u
+AR_LIKE_i - AR_u
+AR_LIKE_f - AR_u
+AR_LIKE_c - AR_u
+AR_LIKE_m - AR_u
+AR_LIKE_M - AR_u
+AR_LIKE_O - AR_u
+
+AR_i - AR_LIKE_b
+AR_i - AR_LIKE_u
+AR_i - AR_LIKE_i
+AR_i - AR_LIKE_f
+AR_i - AR_LIKE_c
+AR_i - AR_LIKE_m
+AR_i - AR_LIKE_O
+
+AR_LIKE_b - AR_i
+AR_LIKE_u - AR_i
+AR_LIKE_i - AR_i
+AR_LIKE_f - AR_i
+AR_LIKE_c - AR_i
+AR_LIKE_m - AR_i
+AR_LIKE_M - AR_i
+AR_LIKE_O - AR_i
+
+AR_f - AR_LIKE_b
+AR_f - AR_LIKE_u
+AR_f - AR_LIKE_i
+AR_f - AR_LIKE_f
+AR_f - AR_LIKE_c
+AR_f - AR_LIKE_O
+
+AR_LIKE_b - AR_f
+AR_LIKE_u - AR_f
+AR_LIKE_i - AR_f
+AR_LIKE_f - AR_f
+AR_LIKE_c - AR_f
+AR_LIKE_O - AR_f
+
+AR_c - AR_LIKE_b
+AR_c - AR_LIKE_u
+AR_c - AR_LIKE_i
+AR_c - AR_LIKE_f
+AR_c - AR_LIKE_c
+AR_c - AR_LIKE_O
+
+AR_LIKE_b - AR_c
+AR_LIKE_u - AR_c
+AR_LIKE_i - AR_c
+AR_LIKE_f - AR_c
+AR_LIKE_c - AR_c
+AR_LIKE_O - AR_c
+
+AR_m - AR_LIKE_b
+AR_m - AR_LIKE_u
+AR_m - AR_LIKE_i
+AR_m - AR_LIKE_m
+
+AR_LIKE_b - AR_m
+AR_LIKE_u - AR_m
+AR_LIKE_i - AR_m
+AR_LIKE_m - AR_m
+AR_LIKE_M - AR_m
+
+AR_M - AR_LIKE_b
+AR_M - AR_LIKE_u
+AR_M - AR_LIKE_i
+AR_M - AR_LIKE_m
+AR_M - AR_LIKE_M
+
+AR_LIKE_M - AR_M
+
+AR_O - AR_LIKE_b
+AR_O - AR_LIKE_u
+AR_O - AR_LIKE_i
+AR_O - AR_LIKE_f
+AR_O - AR_LIKE_c
+AR_O - AR_LIKE_O
+
+AR_LIKE_b - AR_O
+AR_LIKE_u - AR_O
+AR_LIKE_i - AR_O
+AR_LIKE_f - AR_O
+AR_LIKE_c - AR_O
+AR_LIKE_O - AR_O
+
+AR_u += AR_b
+AR_u += AR_u
+AR_u += 1  # Allowed during runtime as long as the object is 0D and >=0
+
+# Array floor division
+
+AR_b // AR_LIKE_b
+AR_b // AR_LIKE_u
+AR_b // AR_LIKE_i
+AR_b // AR_LIKE_f
+AR_b // AR_LIKE_O
+
+AR_LIKE_b // AR_b
+AR_LIKE_u // AR_b
+AR_LIKE_i // AR_b
+AR_LIKE_f // AR_b
+AR_LIKE_O // AR_b
+
+AR_u // AR_LIKE_b
+AR_u // AR_LIKE_u
+AR_u // AR_LIKE_i
+AR_u // AR_LIKE_f
+AR_u // AR_LIKE_O
+
+AR_LIKE_b // AR_u
+AR_LIKE_u // AR_u
+AR_LIKE_i // AR_u
+AR_LIKE_f // AR_u
+AR_LIKE_m // AR_u
+AR_LIKE_O // AR_u
+
+AR_i // AR_LIKE_b
+AR_i // AR_LIKE_u
+AR_i // AR_LIKE_i
+AR_i // AR_LIKE_f
+AR_i // AR_LIKE_O
+
+AR_LIKE_b // AR_i
+AR_LIKE_u // AR_i
+AR_LIKE_i // AR_i
+AR_LIKE_f // AR_i
+AR_LIKE_m // AR_i
+AR_LIKE_O // AR_i
+
+AR_f // AR_LIKE_b
+AR_f // AR_LIKE_u
+AR_f // AR_LIKE_i
+AR_f // AR_LIKE_f
+AR_f // AR_LIKE_O
+
+AR_LIKE_b // AR_f
+AR_LIKE_u // AR_f
+AR_LIKE_i // AR_f
+AR_LIKE_f // AR_f
+AR_LIKE_m // AR_f
+AR_LIKE_O // AR_f
+
+AR_m // AR_LIKE_u
+AR_m // AR_LIKE_i
+AR_m // AR_LIKE_f
+AR_m // AR_LIKE_m
+
+AR_LIKE_m // AR_m
+
+AR_O // AR_LIKE_b
+AR_O // AR_LIKE_u
+AR_O // AR_LIKE_i
+AR_O // AR_LIKE_f
+AR_O // AR_LIKE_O
+
+AR_LIKE_b // AR_O
+AR_LIKE_u // AR_O
+AR_LIKE_i // AR_O
+AR_LIKE_f // AR_O
+AR_LIKE_O // AR_O
+
+# Inplace multiplication
+
+AR_b *= AR_LIKE_b
+
+AR_u *= AR_LIKE_b
+AR_u *= AR_LIKE_u
+
+AR_i *= AR_LIKE_b
+AR_i *= AR_LIKE_u
+AR_i *= AR_LIKE_i
+
+AR_f *= AR_LIKE_b
+AR_f *= AR_LIKE_u
+AR_f *= AR_LIKE_i
+AR_f *= AR_LIKE_f
+
+AR_c *= AR_LIKE_b
+AR_c *= AR_LIKE_u
+AR_c *= AR_LIKE_i
+AR_c *= AR_LIKE_f
+AR_c *= AR_LIKE_c
+
+AR_m *= AR_LIKE_b
+AR_m *= AR_LIKE_u
+AR_m *= AR_LIKE_i
+AR_m *= AR_LIKE_f
+
+AR_O *= AR_LIKE_b
+AR_O *= AR_LIKE_u
+AR_O *= AR_LIKE_i
+AR_O *= AR_LIKE_f
+AR_O *= AR_LIKE_c
+AR_O *= AR_LIKE_O
+
+# Inplace power
+
+AR_u **= AR_LIKE_b
+AR_u **= AR_LIKE_u
+
+AR_i **= AR_LIKE_b
+AR_i **= AR_LIKE_u
+AR_i **= AR_LIKE_i
+
+AR_f **= AR_LIKE_b
+AR_f **= AR_LIKE_u
+AR_f **= AR_LIKE_i
+AR_f **= AR_LIKE_f
+
+AR_c **= AR_LIKE_b
+AR_c **= AR_LIKE_u
+AR_c **= AR_LIKE_i
+AR_c **= AR_LIKE_f
+AR_c **= AR_LIKE_c
+
+AR_O **= AR_LIKE_b
+AR_O **= AR_LIKE_u
+AR_O **= AR_LIKE_i
+AR_O **= AR_LIKE_f
+AR_O **= AR_LIKE_c
+AR_O **= AR_LIKE_O
+
+# unary ops
+
+-c16
+-c8
+-f8
+-f4
+-i8
+-i4
+with pytest.warns(RuntimeWarning):
+    -u8
+    -u4
+-td
+-AR_f
+
++c16
++c8
++f8
++f4
++i8
++i4
++u8
++u4
++td
++AR_f
+
+abs(c16)
+abs(c8)
+abs(f8)
+abs(f4)
+abs(i8)
+abs(i4)
+abs(u8)
+abs(u4)
+abs(td)
+abs(b_)
+abs(AR_f)
+
+# Time structures
+
+dt + td
+dt + i
+dt + i4
+dt + i8
+dt - dt
+dt - i
+dt - i4
+dt - i8
+
+td + td
+td + i
+td + i4
+td + i8
+td - td
+td - i
+td - i4
+td - i8
+td / f
+td / f4
+td / f8
+td / td
+td // td
+td % td
+
+
+# boolean
+
+b_ / b
+b_ / b_
+b_ / i
+b_ / i8
+b_ / i4
+b_ / u8
+b_ / u4
+b_ / f
+b_ / f8
+b_ / f4
+b_ / c
+b_ / c16
+b_ / c8
+
+b / b_
+b_ / b_
+i / b_
+i8 / b_
+i4 / b_
+u8 / b_
+u4 / b_
+f / b_
+f8 / b_
+f4 / b_
+c / b_
+c16 / b_
+c8 / b_
+
+# Complex
+
+c16 + c16
+c16 + f8
+c16 + i8
+c16 + c8
+c16 + f4
+c16 + i4
+c16 + b_
+c16 + b
+c16 + c
+c16 + f
+c16 + i
+c16 + AR_f
+
+c16 + c16
+f8 + c16
+i8 + c16
+c8 + c16
+f4 + c16
+i4 + c16
+b_ + c16
+b + c16
+c + c16
+f + c16
+i + c16
+AR_f + c16
+
+c8 + c16
+c8 + f8
+c8 + i8
+c8 + c8
+c8 + f4
+c8 + i4
+c8 + b_
+c8 + b
+c8 + c
+c8 + f
+c8 + i
+c8 + AR_f
+
+c16 + c8
+f8 + c8
+i8 + c8
+c8 + c8
+f4 + c8
+i4 + c8
+b_ + c8
+b + c8
+c + c8
+f + c8
+i + c8
+AR_f + c8
+
+# Float
+
+f8 + f8
+f8 + i8
+f8 + f4
+f8 + i4
+f8 + b_
+f8 + b
+f8 + c
+f8 + f
+f8 + i
+f8 + AR_f
+
+f8 + f8
+i8 + f8
+f4 + f8
+i4 + f8
+b_ + f8
+b + f8
+c + f8
+f + f8
+i + f8
+AR_f + f8
+
+f4 + f8
+f4 + i8
+f4 + f4
+f4 + i4
+f4 + b_
+f4 + b
+f4 + c
+f4 + f
+f4 + i
+f4 + AR_f
+
+f8 + f4
+i8 + f4
+f4 + f4
+i4 + f4
+b_ + f4
+b + f4
+c + f4
+f + f4
+i + f4
+AR_f + f4
+
+# Int
+
+i8 + i8
+i8 + u8
+i8 + i4
+i8 + u4
+i8 + b_
+i8 + b
+i8 + c
+i8 + f
+i8 + i
+i8 + AR_f
+
+u8 + u8
+u8 + i4
+u8 + u4
+u8 + b_
+u8 + b
+u8 + c
+u8 + f
+u8 + i
+u8 + AR_f
+
+i8 + i8
+u8 + i8
+i4 + i8
+u4 + i8
+b_ + i8
+b + i8
+c + i8
+f + i8
+i + i8
+AR_f + i8
+
+u8 + u8
+i4 + u8
+u4 + u8
+b_ + u8
+b + u8
+c + u8
+f + u8
+i + u8
+AR_f + u8
+
+i4 + i8
+i4 + i4
+i4 + i
+i4 + b_
+i4 + b
+i4 + AR_f
+
+u4 + i8
+u4 + i4
+u4 + u8
+u4 + u4
+u4 + i
+u4 + b_
+u4 + b
+u4 + AR_f
+
+i8 + i4
+i4 + i4
+i + i4
+b_ + i4
+b + i4
+AR_f + i4
+
+i8 + u4
+i4 + u4
+u8 + u4
+u4 + u4
+b_ + u4
+b + u4
+i + u4
+AR_f + u4
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/array_constructors.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/array_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..17b6fab93ad877b5eff31e65afadabcda8bd1c9e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/array_constructors.py
@@ -0,0 +1,137 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+class Index:
+    def __index__(self) -> int:
+        return 0
+
+
+class SubClass(npt.NDArray[np.float64]):
+    pass
+
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass:
+    return B
+
+
+i8 = np.int64(1)
+
+A = np.array([1])
+B = A.view(SubClass).copy()
+B_stack = np.array([[1], [1]]).view(SubClass)
+C = [1]
+
+np.ndarray(Index())
+np.ndarray([Index()])
+
+np.array(1, dtype=float)
+np.array(1, copy=None)
+np.array(1, order='F')
+np.array(1, order=None)
+np.array(1, subok=True)
+np.array(1, ndmin=3)
+np.array(1, str, copy=True, order='C', subok=False, ndmin=2)
+
+np.asarray(A)
+np.asarray(B)
+np.asarray(C)
+
+np.asanyarray(A)
+np.asanyarray(B)
+np.asanyarray(B, dtype=int)
+np.asanyarray(C)
+
+np.ascontiguousarray(A)
+np.ascontiguousarray(B)
+np.ascontiguousarray(C)
+
+np.asfortranarray(A)
+np.asfortranarray(B)
+np.asfortranarray(C)
+
+np.require(A)
+np.require(B)
+np.require(B, dtype=int)
+np.require(B, requirements=None)
+np.require(B, requirements="E")
+np.require(B, requirements=["ENSUREARRAY"])
+np.require(B, requirements={"F", "E"})
+np.require(B, requirements=["C", "OWNDATA"])
+np.require(B, requirements="W")
+np.require(B, requirements="A")
+np.require(C)
+
+np.linspace(0, 2)
+np.linspace(0.5, [0, 1, 2])
+np.linspace([0, 1, 2], 3)
+np.linspace(0j, 2)
+np.linspace(0, 2, num=10)
+np.linspace(0, 2, endpoint=True)
+np.linspace(0, 2, retstep=True)
+np.linspace(0j, 2j, retstep=True)
+np.linspace(0, 2, dtype=bool)
+np.linspace([0, 1], [2, 3], axis=Index())
+
+np.logspace(0, 2, base=2)
+np.logspace(0, 2, base=2)
+np.logspace(0, 2, base=[1j, 2j], num=2)
+
+np.geomspace(1, 2)
+
+np.zeros_like(A)
+np.zeros_like(C)
+np.zeros_like(B)
+np.zeros_like(B, dtype=np.int64)
+
+np.ones_like(A)
+np.ones_like(C)
+np.ones_like(B)
+np.ones_like(B, dtype=np.int64)
+
+np.empty_like(A)
+np.empty_like(C)
+np.empty_like(B)
+np.empty_like(B, dtype=np.int64)
+
+np.full_like(A, i8)
+np.full_like(C, i8)
+np.full_like(B, i8)
+np.full_like(B, i8, dtype=np.int64)
+
+np.ones(1)
+np.ones([1, 1, 1])
+
+np.full(1, i8)
+np.full([1, 1, 1], i8)
+
+np.indices([1, 2, 3])
+np.indices([1, 2, 3], sparse=True)
+
+np.fromfunction(func, (3, 5))
+
+np.identity(10)
+
+np.atleast_1d(C)
+np.atleast_1d(A)
+np.atleast_1d(C, C)
+np.atleast_1d(C, A)
+np.atleast_1d(A, A)
+
+np.atleast_2d(C)
+
+np.atleast_3d(C)
+
+np.vstack([C, C])
+np.vstack([C, A])
+np.vstack([A, A])
+
+np.hstack([C, C])
+
+np.stack([C, C])
+np.stack([C, C], axis=0)
+np.stack([C, C], out=B_stack)
+
+np.block([[C, C], [C, C]])
+np.block(A)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/array_like.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/array_like.py
new file mode 100644
index 0000000000000000000000000000000000000000..730eb46d1c925474508e863a133e256007bd734b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/array_like.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from numpy._typing import NDArray, ArrayLike, _SupportsArray
+
+x1: ArrayLike = True
+x2: ArrayLike = 5
+x3: ArrayLike = 1.0
+x4: ArrayLike = 1 + 1j
+x5: ArrayLike = np.int8(1)
+x6: ArrayLike = np.float64(1)
+x7: ArrayLike = np.complex128(1)
+x8: ArrayLike = np.array([1, 2, 3])
+x9: ArrayLike = [1, 2, 3]
+x10: ArrayLike = (1, 2, 3)
+x11: ArrayLike = "foo"
+x12: ArrayLike = memoryview(b'foo')
+
+
+class A:
+    def __array__(
+        self, dtype: None | np.dtype[Any] = None
+    ) -> NDArray[np.float64]:
+        return np.array([1.0, 2.0, 3.0])
+
+
+x13: ArrayLike = A()
+
+scalar: _SupportsArray[np.dtype[np.int64]] = np.int64(1)
+scalar.__array__()
+array: _SupportsArray[np.dtype[np.int_]] = np.array(1)
+array.__array__()
+
+a: _SupportsArray[np.dtype[np.float64]] = A()
+a.__array__()
+a.__array__()
+
+# Escape hatch for when you mean to make something like an object
+# array.
+object_array_scalar: object = (i for i in range(10))
+np.array(object_array_scalar)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arrayprint.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arrayprint.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c704c755570d1508424af92a0eb5aa1353666a0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arrayprint.py
@@ -0,0 +1,37 @@
+import numpy as np
+
+AR = np.arange(10)
+AR.setflags(write=False)
+
+with np.printoptions():
+    np.set_printoptions(
+        precision=1,
+        threshold=2,
+        edgeitems=3,
+        linewidth=4,
+        suppress=False,
+        nanstr="Bob",
+        infstr="Bill",
+        formatter={},
+        sign="+",
+        floatmode="unique",
+    )
+    np.get_printoptions()
+    str(AR)
+
+    np.array2string(
+        AR,
+        max_line_width=5,
+        precision=2,
+        suppress_small=True,
+        separator=";",
+        prefix="test",
+        threshold=5,
+        floatmode="fixed",
+        suffix="?",
+        legacy="1.13",
+    )
+    np.format_float_scientific(1, precision=5)
+    np.format_float_positional(1, trim="k")
+    np.array_repr(AR)
+    np.array_str(AR)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arrayterator.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arrayterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..572be5e2fe29ba978b78c8b65b116b5b54a4d01a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/arrayterator.py
@@ -0,0 +1,27 @@
+
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+AR_i8: np.ndarray[Any, np.dtype[np.int_]] = np.arange(10)
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+ar_iter.var
+ar_iter.buf_size
+ar_iter.start
+ar_iter.stop
+ar_iter.step
+ar_iter.shape
+ar_iter.flat
+
+ar_iter.__array__()
+
+for i in ar_iter:
+    pass
+
+ar_iter[0]
+ar_iter[...]
+ar_iter[:]
+ar_iter[0, 0, 0]
+ar_iter[..., 0, :]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..22a245d2180979e6e0fa0cb5780713aa3ac86439
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py
@@ -0,0 +1,131 @@
+import numpy as np
+
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+b_ = np.bool(1)
+
+b = bool(1)
+i = int(1)
+
+AR = np.array([0, 1, 2], dtype=np.int32)
+AR.setflags(write=False)
+
+
+i8 << i8
+i8 >> i8
+i8 | i8
+i8 ^ i8
+i8 & i8
+
+i << AR
+i >> AR
+i | AR
+i ^ AR
+i & AR
+
+i8 << AR
+i8 >> AR
+i8 | AR
+i8 ^ AR
+i8 & AR
+
+i4 << i4
+i4 >> i4
+i4 | i4
+i4 ^ i4
+i4 & i4
+
+i8 << i4
+i8 >> i4
+i8 | i4
+i8 ^ i4
+i8 & i4
+
+i8 << i
+i8 >> i
+i8 | i
+i8 ^ i
+i8 & i
+
+i8 << b_
+i8 >> b_
+i8 | b_
+i8 ^ b_
+i8 & b_
+
+i8 << b
+i8 >> b
+i8 | b
+i8 ^ b
+i8 & b
+
+u8 << u8
+u8 >> u8
+u8 | u8
+u8 ^ u8
+u8 & u8
+
+u4 << u4
+u4 >> u4
+u4 | u4
+u4 ^ u4
+u4 & u4
+
+u4 << i4
+u4 >> i4
+u4 | i4
+u4 ^ i4
+u4 & i4
+
+u4 << i
+u4 >> i
+u4 | i
+u4 ^ i
+u4 & i
+
+u8 << b_
+u8 >> b_
+u8 | b_
+u8 ^ b_
+u8 & b_
+
+u8 << b
+u8 >> b
+u8 | b
+u8 ^ b
+u8 & b
+
+b_ << b_
+b_ >> b_
+b_ | b_
+b_ ^ b_
+b_ & b_
+
+b_ << AR
+b_ >> AR
+b_ | AR
+b_ ^ AR
+b_ & AR
+
+b_ << b
+b_ >> b
+b_ | b
+b_ ^ b
+b_ & b
+
+b_ << i
+b_ >> i
+b_ | i
+b_ ^ i
+b_ & i
+
+~i8
+~i4
+~u8
+~u4
+~b_
+~AR
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/comparisons.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/comparisons.py
new file mode 100644
index 0000000000000000000000000000000000000000..a461d8b660da6f38ca6ddd416d4857d7b865aa9e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/comparisons.py
@@ -0,0 +1,315 @@
+from __future__ import annotations
+
+from typing import cast, Any
+import numpy as np
+
+c16 = np.complex128()
+f8 = np.float64()
+i8 = np.int64()
+u8 = np.uint64()
+
+c8 = np.complex64()
+f4 = np.float32()
+i4 = np.int32()
+u4 = np.uint32()
+
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+b_ = np.bool()
+
+b = bool()
+c = complex()
+f = float()
+i = int()
+
+SEQ = (0, 1, 2, 3, 4)
+
+AR_b: np.ndarray[Any, np.dtype[np.bool]] = np.array([True])
+AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
+AR_i: np.ndarray[Any, np.dtype[np.int_]] = np.array([1])
+AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
+AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1.0j])
+AR_S: np.ndarray[Any, np.dtype[np.bytes_]] = np.array([b"a"], "S")
+AR_T = cast(np.ndarray[Any, np.dtypes.StringDType], np.array(["a"], "T"))
+AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.array(["a"], "U")
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64("1")])
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64("1")])
+AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([1], dtype=object)
+
+# Arrays
+
+AR_b > AR_b
+AR_b > AR_u
+AR_b > AR_i
+AR_b > AR_f
+AR_b > AR_c
+
+AR_u > AR_b
+AR_u > AR_u
+AR_u > AR_i
+AR_u > AR_f
+AR_u > AR_c
+
+AR_i > AR_b
+AR_i > AR_u
+AR_i > AR_i
+AR_i > AR_f
+AR_i > AR_c
+
+AR_f > AR_b
+AR_f > AR_u
+AR_f > AR_i
+AR_f > AR_f
+AR_f > AR_c
+
+AR_c > AR_b
+AR_c > AR_u
+AR_c > AR_i
+AR_c > AR_f
+AR_c > AR_c
+
+AR_S > AR_S
+AR_S > b""
+
+AR_T > AR_T
+AR_T > AR_U
+AR_T > ""
+
+AR_U > AR_U
+AR_U > AR_T
+AR_U > ""
+
+AR_m > AR_b
+AR_m > AR_u
+AR_m > AR_i
+AR_b > AR_m
+AR_u > AR_m
+AR_i > AR_m
+
+AR_M > AR_M
+
+AR_O > AR_O
+1 > AR_O
+AR_O > 1
+
+# Time structures
+
+dt > dt
+
+td > td
+td > i
+td > i4
+td > i8
+td > AR_i
+td > SEQ
+
+# boolean
+
+b_ > b
+b_ > b_
+b_ > i
+b_ > i8
+b_ > i4
+b_ > u8
+b_ > u4
+b_ > f
+b_ > f8
+b_ > f4
+b_ > c
+b_ > c16
+b_ > c8
+b_ > AR_i
+b_ > SEQ
+
+# Complex
+
+c16 > c16
+c16 > f8
+c16 > i8
+c16 > c8
+c16 > f4
+c16 > i4
+c16 > b_
+c16 > b
+c16 > c
+c16 > f
+c16 > i
+c16 > AR_i
+c16 > SEQ
+
+c16 > c16
+f8 > c16
+i8 > c16
+c8 > c16
+f4 > c16
+i4 > c16
+b_ > c16
+b > c16
+c > c16
+f > c16
+i > c16
+AR_i > c16
+SEQ > c16
+
+c8 > c16
+c8 > f8
+c8 > i8
+c8 > c8
+c8 > f4
+c8 > i4
+c8 > b_
+c8 > b
+c8 > c
+c8 > f
+c8 > i
+c8 > AR_i
+c8 > SEQ
+
+c16 > c8
+f8 > c8
+i8 > c8
+c8 > c8
+f4 > c8
+i4 > c8
+b_ > c8
+b > c8
+c > c8
+f > c8
+i > c8
+AR_i > c8
+SEQ > c8
+
+# Float
+
+f8 > f8
+f8 > i8
+f8 > f4
+f8 > i4
+f8 > b_
+f8 > b
+f8 > c
+f8 > f
+f8 > i
+f8 > AR_i
+f8 > SEQ
+
+f8 > f8
+i8 > f8
+f4 > f8
+i4 > f8
+b_ > f8
+b > f8
+c > f8
+f > f8
+i > f8
+AR_i > f8
+SEQ > f8
+
+f4 > f8
+f4 > i8
+f4 > f4
+f4 > i4
+f4 > b_
+f4 > b
+f4 > c
+f4 > f
+f4 > i
+f4 > AR_i
+f4 > SEQ
+
+f8 > f4
+i8 > f4
+f4 > f4
+i4 > f4
+b_ > f4
+b > f4
+c > f4
+f > f4
+i > f4
+AR_i > f4
+SEQ > f4
+
+# Int
+
+i8 > i8
+i8 > u8
+i8 > i4
+i8 > u4
+i8 > b_
+i8 > b
+i8 > c
+i8 > f
+i8 > i
+i8 > AR_i
+i8 > SEQ
+
+u8 > u8
+u8 > i4
+u8 > u4
+u8 > b_
+u8 > b
+u8 > c
+u8 > f
+u8 > i
+u8 > AR_i
+u8 > SEQ
+
+i8 > i8
+u8 > i8
+i4 > i8
+u4 > i8
+b_ > i8
+b > i8
+c > i8
+f > i8
+i > i8
+AR_i > i8
+SEQ > i8
+
+u8 > u8
+i4 > u8
+u4 > u8
+b_ > u8
+b > u8
+c > u8
+f > u8
+i > u8
+AR_i > u8
+SEQ > u8
+
+i4 > i8
+i4 > i4
+i4 > i
+i4 > b_
+i4 > b
+i4 > AR_i
+i4 > SEQ
+
+u4 > i8
+u4 > i4
+u4 > u8
+u4 > u4
+u4 > i
+u4 > b_
+u4 > b
+u4 > AR_i
+u4 > SEQ
+
+i8 > i4
+i4 > i4
+i > i4
+b_ > i4
+b > i4
+AR_i > i4
+SEQ > i4
+
+i8 > u4
+i4 > u4
+u8 > u4
+u4 > u4
+b_ > u4
+b > u4
+i > u4
+AR_i > u4
+SEQ > u4
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/dtype.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f11518276c842f2601e3f1420020f00e3b775fb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/dtype.py
@@ -0,0 +1,57 @@
+import numpy as np
+
+dtype_obj = np.dtype(np.str_)
+void_dtype_obj = np.dtype([("f0", np.float64), ("f1", np.float32)])
+
+np.dtype(dtype=np.int64)
+np.dtype(int)
+np.dtype("int")
+np.dtype(None)
+
+np.dtype((int, 2))
+np.dtype((int, (1,)))
+
+np.dtype({"names": ["a", "b"], "formats": [int, float]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object()]})
+
+np.dtype([("name", np.str_, 16), ("grades", np.float64, (2,)), ("age", "int32")])
+
+np.dtype(
+    {
+        "names": ["a", "b"],
+        "formats": [int, float],
+        "itemsize": 9,
+        "aligned": False,
+        "titles": ["x", "y"],
+        "offsets": [0, 1],
+    }
+)
+
+np.dtype((np.float64, float))
+
+
+class Test:
+    dtype = np.dtype(float)
+
+
+np.dtype(Test())
+
+# Methods and attributes
+dtype_obj.base
+dtype_obj.subdtype
+dtype_obj.newbyteorder()
+dtype_obj.type
+dtype_obj.name
+dtype_obj.names
+
+dtype_obj * 0
+dtype_obj * 2
+
+0 * dtype_obj
+2 * dtype_obj
+
+void_dtype_obj["f0"]
+void_dtype_obj[0]
+void_dtype_obj[["f0", "f1"]]
+void_dtype_obj[["f0"]]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/einsumfunc.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/einsumfunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..429764e67eccc7855d363da20d432fdb45e66971
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/einsumfunc.py
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+AR_LIKE_b = [True, True, True]
+AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
+AR_LIKE_i = [1, 2, 3]
+AR_LIKE_f = [1.0, 2.0, 3.0]
+AR_LIKE_c = [1j, 2j, 3j]
+AR_LIKE_U = ["1", "2", "3"]
+
+OUT_f: np.ndarray[Any, np.dtype[np.float64]] = np.empty(3, dtype=np.float64)
+OUT_c: np.ndarray[Any, np.dtype[np.complex128]] = np.empty(3, dtype=np.complex128)
+
+np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)
+np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)
+np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)
+np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)
+np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)
+np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)
+
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")
+np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe")
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, out=OUT_c)
+np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=int, casting="unsafe", out=OUT_f)
+
+np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b)
+np.einsum_path("i,i->i", AR_LIKE_u, AR_LIKE_u)
+np.einsum_path("i,i->i", AR_LIKE_i, AR_LIKE_i)
+np.einsum_path("i,i->i", AR_LIKE_f, AR_LIKE_f)
+np.einsum_path("i,i->i", AR_LIKE_c, AR_LIKE_c)
+np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_i)
+np.einsum_path("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/flatiter.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/flatiter.py
new file mode 100644
index 0000000000000000000000000000000000000000..63c839af4b23f0ba3bea8c56f2bbb7c03e7bc44a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/flatiter.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+a = np.empty((2, 2)).flat
+
+a.base
+a.copy()
+a.coords
+a.index
+iter(a)
+next(a)
+a[0]
+a[[0, 1, 2]]
+a[...]
+a[:]
+a.__array__()
+a.__array__(np.dtype(np.float64))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/fromnumeric.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/fromnumeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cc2bcfd8b50f235081e737c3420e1db34191637
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/fromnumeric.py
@@ -0,0 +1,272 @@
+"""Tests for :mod:`numpy._core.fromnumeric`."""
+
+import numpy as np
+
+A = np.array(True, ndmin=2, dtype=bool)
+B = np.array(1.0, ndmin=2, dtype=np.float32)
+A.setflags(write=False)
+B.setflags(write=False)
+
+a = np.bool(True)
+b = np.float32(1.0)
+c = 1.0
+d = np.array(1.0, dtype=np.float32)  # writeable
+
+np.take(a, 0)
+np.take(b, 0)
+np.take(c, 0)
+np.take(A, 0)
+np.take(B, 0)
+np.take(A, [0])
+np.take(B, [0])
+
+np.reshape(a, 1)
+np.reshape(b, 1)
+np.reshape(c, 1)
+np.reshape(A, 1)
+np.reshape(B, 1)
+
+np.choose(a, [True, True])
+np.choose(A, [1.0, 1.0])
+
+np.repeat(a, 1)
+np.repeat(b, 1)
+np.repeat(c, 1)
+np.repeat(A, 1)
+np.repeat(B, 1)
+
+np.swapaxes(A, 0, 0)
+np.swapaxes(B, 0, 0)
+
+np.transpose(a)
+np.transpose(b)
+np.transpose(c)
+np.transpose(A)
+np.transpose(B)
+
+np.partition(a, 0, axis=None)
+np.partition(b, 0, axis=None)
+np.partition(c, 0, axis=None)
+np.partition(A, 0)
+np.partition(B, 0)
+
+np.argpartition(a, 0)
+np.argpartition(b, 0)
+np.argpartition(c, 0)
+np.argpartition(A, 0)
+np.argpartition(B, 0)
+
+np.sort(A, 0)
+np.sort(B, 0)
+
+np.argsort(A, 0)
+np.argsort(B, 0)
+
+np.argmax(A)
+np.argmax(B)
+np.argmax(A, axis=0)
+np.argmax(B, axis=0)
+
+np.argmin(A)
+np.argmin(B)
+np.argmin(A, axis=0)
+np.argmin(B, axis=0)
+
+np.searchsorted(A[0], 0)
+np.searchsorted(B[0], 0)
+np.searchsorted(A[0], [0])
+np.searchsorted(B[0], [0])
+
+np.resize(a, (5, 5))
+np.resize(b, (5, 5))
+np.resize(c, (5, 5))
+np.resize(A, (5, 5))
+np.resize(B, (5, 5))
+
+np.squeeze(a)
+np.squeeze(b)
+np.squeeze(c)
+np.squeeze(A)
+np.squeeze(B)
+
+np.diagonal(A)
+np.diagonal(B)
+
+np.trace(A)
+np.trace(B)
+
+np.ravel(a)
+np.ravel(b)
+np.ravel(c)
+np.ravel(A)
+np.ravel(B)
+
+np.nonzero(A)
+np.nonzero(B)
+
+np.shape(a)
+np.shape(b)
+np.shape(c)
+np.shape(A)
+np.shape(B)
+
+np.compress([True], a)
+np.compress([True], b)
+np.compress([True], c)
+np.compress([True], A)
+np.compress([True], B)
+
+np.clip(a, 0, 1.0)
+np.clip(b, -1, 1)
+np.clip(a, 0, None)
+np.clip(b, None, 1)
+np.clip(c, 0, 1)
+np.clip(A, 0, 1)
+np.clip(B, 0, 1)
+np.clip(B, [0, 1], [1, 2])
+
+np.sum(a)
+np.sum(b)
+np.sum(c)
+np.sum(A)
+np.sum(B)
+np.sum(A, axis=0)
+np.sum(B, axis=0)
+
+np.all(a)
+np.all(b)
+np.all(c)
+np.all(A)
+np.all(B)
+np.all(A, axis=0)
+np.all(B, axis=0)
+np.all(A, keepdims=True)
+np.all(B, keepdims=True)
+
+np.any(a)
+np.any(b)
+np.any(c)
+np.any(A)
+np.any(B)
+np.any(A, axis=0)
+np.any(B, axis=0)
+np.any(A, keepdims=True)
+np.any(B, keepdims=True)
+
+np.cumsum(a)
+np.cumsum(b)
+np.cumsum(c)
+np.cumsum(A)
+np.cumsum(B)
+
+np.cumulative_sum(a)
+np.cumulative_sum(b)
+np.cumulative_sum(c)
+np.cumulative_sum(A, axis=0)
+np.cumulative_sum(B, axis=0)
+
+np.ptp(b)
+np.ptp(c)
+np.ptp(B)
+np.ptp(B, axis=0)
+np.ptp(B, keepdims=True)
+
+np.amax(a)
+np.amax(b)
+np.amax(c)
+np.amax(A)
+np.amax(B)
+np.amax(A, axis=0)
+np.amax(B, axis=0)
+np.amax(A, keepdims=True)
+np.amax(B, keepdims=True)
+
+np.amin(a)
+np.amin(b)
+np.amin(c)
+np.amin(A)
+np.amin(B)
+np.amin(A, axis=0)
+np.amin(B, axis=0)
+np.amin(A, keepdims=True)
+np.amin(B, keepdims=True)
+
+np.prod(a)
+np.prod(b)
+np.prod(c)
+np.prod(A)
+np.prod(B)
+np.prod(a, dtype=None)
+np.prod(A, dtype=None)
+np.prod(A, axis=0)
+np.prod(B, axis=0)
+np.prod(A, keepdims=True)
+np.prod(B, keepdims=True)
+np.prod(b, out=d)
+np.prod(B, out=d)
+
+np.cumprod(a)
+np.cumprod(b)
+np.cumprod(c)
+np.cumprod(A)
+np.cumprod(B)
+
+np.cumulative_prod(a)
+np.cumulative_prod(b)
+np.cumulative_prod(c)
+np.cumulative_prod(A, axis=0)
+np.cumulative_prod(B, axis=0)
+
+np.ndim(a)
+np.ndim(b)
+np.ndim(c)
+np.ndim(A)
+np.ndim(B)
+
+np.size(a)
+np.size(b)
+np.size(c)
+np.size(A)
+np.size(B)
+
+np.around(a)
+np.around(b)
+np.around(c)
+np.around(A)
+np.around(B)
+
+np.mean(a)
+np.mean(b)
+np.mean(c)
+np.mean(A)
+np.mean(B)
+np.mean(A, axis=0)
+np.mean(B, axis=0)
+np.mean(A, keepdims=True)
+np.mean(B, keepdims=True)
+np.mean(b, out=d)
+np.mean(B, out=d)
+
+np.std(a)
+np.std(b)
+np.std(c)
+np.std(A)
+np.std(B)
+np.std(A, axis=0)
+np.std(B, axis=0)
+np.std(A, keepdims=True)
+np.std(B, keepdims=True)
+np.std(b, out=d)
+np.std(B, out=d)
+
+np.var(a)
+np.var(b)
+np.var(c)
+np.var(A)
+np.var(B)
+np.var(A, axis=0)
+np.var(B, axis=0)
+np.var(A, keepdims=True)
+np.var(B, keepdims=True)
+np.var(b, out=d)
+np.var(B, out=d)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/index_tricks.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/index_tricks.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfc4ff2f314aa2417cd5205a86386d4bd6211273
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/index_tricks.py
@@ -0,0 +1,60 @@
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+AR_LIKE_b = [[True, True], [True, True]]
+AR_LIKE_i = [[1, 2], [3, 4]]
+AR_LIKE_f = [[1.0, 2.0], [3.0, 4.0]]
+AR_LIKE_U = [["1", "2"], ["3", "4"]]
+
+AR_i8: np.ndarray[Any, np.dtype[np.int64]] = np.array(AR_LIKE_i, dtype=np.int64)
+
+np.ndenumerate(AR_i8)
+np.ndenumerate(AR_LIKE_f)
+np.ndenumerate(AR_LIKE_U)
+
+next(np.ndenumerate(AR_i8))
+next(np.ndenumerate(AR_LIKE_f))
+next(np.ndenumerate(AR_LIKE_U))
+
+iter(np.ndenumerate(AR_i8))
+iter(np.ndenumerate(AR_LIKE_f))
+iter(np.ndenumerate(AR_LIKE_U))
+
+iter(np.ndindex(1, 2, 3))
+next(np.ndindex(1, 2, 3))
+
+np.unravel_index([22, 41, 37], (7, 6))
+np.unravel_index([31, 41, 13], (7, 6), order='F')
+np.unravel_index(1621, (6, 7, 8, 9))
+
+np.ravel_multi_index(AR_LIKE_i, (7, 6))
+np.ravel_multi_index(AR_LIKE_i, (7, 6), order='F')
+np.ravel_multi_index(AR_LIKE_i, (4, 6), mode='clip')
+np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=('clip', 'wrap'))
+np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))
+
+np.mgrid[1:1:2]
+np.mgrid[1:1:2, None:10]
+
+np.ogrid[1:1:2]
+np.ogrid[1:1:2, None:10]
+
+np.index_exp[0:1]
+np.index_exp[0:1, None:3]
+np.index_exp[0, 0:1, ..., [0, 1, 3]]
+
+np.s_[0:1]
+np.s_[0:1, None:3]
+np.s_[0, 0:1, ..., [0, 1, 3]]
+
+np.ix_(AR_LIKE_b[0])
+np.ix_(AR_LIKE_i[0], AR_LIKE_f[0])
+np.ix_(AR_i8[0])
+
+np.fill_diagonal(AR_i8, 5)
+
+np.diag_indices(4)
+np.diag_indices(2, 3)
+
+np.diag_indices_from(AR_i8)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_user_array.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_user_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..62b7e85d7ff1e49fecbbe88a921c931a5b8ae745
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_user_array.py
@@ -0,0 +1,22 @@
+"""Based on the `if __name__ == "__main__"` test code in `lib/_user_array_impl.py`."""
+
+from __future__ import annotations
+
+import numpy as np
+from numpy.lib.user_array import container
+
+N = 10_000
+W = H = int(N**0.5)
+
+a: np.ndarray[tuple[int, int], np.dtype[np.int32]]
+ua: container[tuple[int, int], np.dtype[np.int32]]
+
+a = np.arange(N, dtype=np.int32).reshape(W, H)
+ua = container(a)
+
+ua_small: container[tuple[int, int], np.dtype[np.int32]] = ua[:3, :5]
+ua_small[0, 0] = 10
+
+ua_bool: container[tuple[int, int], np.dtype[np.bool]] = ua_small > 1
+
+# shape: tuple[int, int] = np.shape(ua)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9b3381e13d26adf44130fc62aa8bfaacfe6a658
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_utils.py
@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from io import StringIO
+
+import numpy as np
+import numpy.lib.array_utils as array_utils
+
+FILE = StringIO()
+AR = np.arange(10, dtype=np.float64)
+
+
+def func(a: int) -> bool:
+    return True
+
+
+array_utils.byte_bounds(AR)
+array_utils.byte_bounds(np.float64())
+
+np.info(1, output=FILE)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_version.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3825eca524795f4d9742873a773cd7749636e2a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/lib_version.py
@@ -0,0 +1,18 @@
+from numpy.lib import NumpyVersion
+
+version = NumpyVersion("1.8.0")
+
+version.vstring
+version.version
+version.major
+version.minor
+version.bugfix
+version.pre_release
+version.is_devversion
+
+version == version
+version != version
+version < "1.8.0"
+version <= version
+version > version
+version >= "1.8.0"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/literal.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/literal.py
new file mode 100644
index 0000000000000000000000000000000000000000..2238618eb67c904335d731332d5afa5eb988d7f1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/literal.py
@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING
+from functools import partial
+
+import pytest
+import numpy as np
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+AR = np.array(0)
+AR.setflags(write=False)
+
+KACF = frozenset({None, "K", "A", "C", "F"})
+ACF = frozenset({None, "A", "C", "F"})
+CF = frozenset({None, "C", "F"})
+
+order_list: list[tuple[frozenset[str | None], Callable[..., Any]]] = [
+    (KACF, partial(np.ndarray, 1)),
+    (KACF, AR.tobytes),
+    (KACF, partial(AR.astype, int)),
+    (KACF, AR.copy),
+    (ACF, partial(AR.reshape, 1)),
+    (KACF, AR.flatten),
+    (KACF, AR.ravel),
+    (KACF, partial(np.array, 1)),
+    # NOTE: __call__ is needed due to mypy 1.11 bugs (#17620, #17631)
+    (CF, partial(np.zeros.__call__, 1)),
+    (CF, partial(np.ones.__call__, 1)),
+    (CF, partial(np.empty.__call__, 1)),
+    (CF, partial(np.full, 1, 1)),
+    (KACF, partial(np.zeros_like, AR)),
+    (KACF, partial(np.ones_like, AR)),
+    (KACF, partial(np.empty_like, AR)),
+    (KACF, partial(np.full_like, AR, 1)),
+    (KACF, partial(np.add.__call__, 1, 1)),  # i.e. np.ufunc.__call__
+    (ACF, partial(np.reshape, AR, 1)),
+    (KACF, partial(np.ravel, AR)),
+    (KACF, partial(np.asarray, 1)),
+    (KACF, partial(np.asanyarray, 1)),
+]
+
+for order_set, func in order_list:
+    for order in order_set:
+        func(order=order)
+
+    invalid_orders = KACF - order_set
+    for order in invalid_orders:
+        with pytest.raises(ValueError):
+            func(order=order)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ma.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ma.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b3b138119bbd01a8241a68f4f172ce1d26b5833
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ma.py
@@ -0,0 +1,8 @@
+from typing import Any
+
+import numpy as np
+import numpy.ma
+
+
+m : np.ma.MaskedArray[Any, np.dtype[np.float64]] = np.ma.masked_array([1.5, 2, 3], mask=[True, False, True])
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/mod.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/mod.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b7e6cd85c73cfce452c3bd8bf91f174f134966c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/mod.py
@@ -0,0 +1,149 @@
+import numpy as np
+
+f8 = np.float64(1)
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+f4 = np.float32(1)
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+td = np.timedelta64(1, "D")
+b_ = np.bool(1)
+
+b = bool(1)
+f = float(1)
+i = int(1)
+
+AR = np.array([1], dtype=np.bool)
+AR.setflags(write=False)
+
+AR2 = np.array([1], dtype=np.timedelta64)
+AR2.setflags(write=False)
+
+# Time structures
+
+td % td
+td % AR2
+AR2 % td
+
+divmod(td, td)
+divmod(td, AR2)
+divmod(AR2, td)
+
+# Bool
+
+b_ % b
+b_ % i
+b_ % f
+b_ % b_
+b_ % i8
+b_ % u8
+b_ % f8
+b_ % AR
+
+divmod(b_, b)
+divmod(b_, i)
+divmod(b_, f)
+divmod(b_, b_)
+divmod(b_, i8)
+divmod(b_, u8)
+divmod(b_, f8)
+divmod(b_, AR)
+
+b % b_
+i % b_
+f % b_
+b_ % b_
+i8 % b_
+u8 % b_
+f8 % b_
+AR % b_
+
+divmod(b, b_)
+divmod(i, b_)
+divmod(f, b_)
+divmod(b_, b_)
+divmod(i8, b_)
+divmod(u8, b_)
+divmod(f8, b_)
+divmod(AR, b_)
+
+# int
+
+i8 % b
+i8 % i
+i8 % f
+i8 % i8
+i8 % f8
+i4 % i8
+i4 % f8
+i4 % i4
+i4 % f4
+i8 % AR
+
+divmod(i8, b)
+divmod(i8, i)
+divmod(i8, f)
+divmod(i8, i8)
+divmod(i8, f8)
+divmod(i8, i4)
+divmod(i8, f4)
+divmod(i4, i4)
+divmod(i4, f4)
+divmod(i8, AR)
+
+b % i8
+i % i8
+f % i8
+i8 % i8
+f8 % i8
+i8 % i4
+f8 % i4
+i4 % i4
+f4 % i4
+AR % i8
+
+divmod(b, i8)
+divmod(i, i8)
+divmod(f, i8)
+divmod(i8, i8)
+divmod(f8, i8)
+divmod(i4, i8)
+divmod(f4, i8)
+divmod(i4, i4)
+divmod(f4, i4)
+divmod(AR, i8)
+
+# float
+
+f8 % b
+f8 % i
+f8 % f
+i8 % f4
+f4 % f4
+f8 % AR
+
+divmod(f8, b)
+divmod(f8, i)
+divmod(f8, f)
+divmod(f8, f8)
+divmod(f8, f4)
+divmod(f4, f4)
+divmod(f8, AR)
+
+b % f8
+i % f8
+f % f8
+f8 % f8
+f8 % f8
+f4 % f4
+AR % f8
+
+divmod(b, f8)
+divmod(i, f8)
+divmod(f, f8)
+divmod(f8, f8)
+divmod(f4, f8)
+divmod(f4, f4)
+divmod(AR, f8)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/modules.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c2fd4b7e7c3a2d0ed1be39928edf5b2d591e16c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/modules.py
@@ -0,0 +1,45 @@
+import numpy as np
+from numpy import f2py
+
+np.char
+np.ctypeslib
+np.emath
+np.fft
+np.lib
+np.linalg
+np.ma
+np.matrixlib
+np.polynomial
+np.random
+np.rec
+np.strings
+np.testing
+np.version
+
+np.lib.format
+np.lib.mixins
+np.lib.scimath
+np.lib.stride_tricks
+np.lib.array_utils
+np.ma.extras
+np.polynomial.chebyshev
+np.polynomial.hermite
+np.polynomial.hermite_e
+np.polynomial.laguerre
+np.polynomial.legendre
+np.polynomial.polynomial
+
+np.__path__
+np.__version__
+
+np.__all__
+np.char.__all__
+np.ctypeslib.__all__
+np.emath.__all__
+np.lib.__all__
+np.ma.__all__
+np.random.__all__
+np.rec.__all__
+np.strings.__all__
+np.testing.__all__
+f2py.__all__
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/multiarray.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/multiarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..26cedfd77566e7b7865345e0775af88153e74ffc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/multiarray.py
@@ -0,0 +1,76 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64] = np.array([1.0])
+AR_i4 = np.array([1], dtype=np.int32)
+AR_u1 = np.array([1], dtype=np.uint8)
+
+AR_LIKE_f = [1.5]
+AR_LIKE_i = [1]
+
+b_f8 = np.broadcast(AR_f8)
+b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8)
+
+next(b_f8)
+b_f8.reset()
+b_f8.index
+b_f8.iters
+b_f8.nd
+b_f8.ndim
+b_f8.numiter
+b_f8.shape
+b_f8.size
+
+next(b_i4_f8_f8)
+b_i4_f8_f8.reset()
+b_i4_f8_f8.ndim
+b_i4_f8_f8.index
+b_i4_f8_f8.iters
+b_i4_f8_f8.nd
+b_i4_f8_f8.numiter
+b_i4_f8_f8.shape
+b_i4_f8_f8.size
+
+np.inner(AR_f8, AR_i4)
+
+np.where([True, True, False])
+np.where([True, True, False], 1, 0)
+
+np.lexsort([0, 1, 2])
+
+np.can_cast(np.dtype("i8"), int)
+np.can_cast(AR_f8, "f8")
+np.can_cast(AR_f8, np.complex128, casting="unsafe")
+
+np.min_scalar_type([1])
+np.min_scalar_type(AR_f8)
+
+np.result_type(int, AR_i4)
+np.result_type(AR_f8, AR_u1)
+np.result_type(AR_f8, np.complex128)
+
+np.dot(AR_LIKE_f, AR_i4)
+np.dot(AR_u1, 1)
+np.dot(1.5j, 1)
+np.dot(AR_u1, 1, out=AR_f8)
+
+np.vdot(AR_LIKE_f, AR_i4)
+np.vdot(AR_u1, 1)
+np.vdot(1.5j, 1)
+
+np.bincount(AR_i4)
+
+np.copyto(AR_f8, [1.6])
+
+np.putmask(AR_f8, [True], 1.5)
+
+np.packbits(AR_i4)
+np.packbits(AR_u1)
+
+np.unpackbits(AR_u1)
+
+np.shares_memory(1, 2)
+np.shares_memory(AR_f8, AR_f8, max_work=1)
+
+np.may_share_memory(1, 2)
+np.may_share_memory(AR_f8, AR_f8, max_work=1)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..76da1dadd327861fdb576f1f492fbc44a4881168
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py
@@ -0,0 +1,87 @@
+import os
+import tempfile
+
+import numpy as np
+
+nd = np.array([[1, 2], [3, 4]])
+scalar_array = np.array(1)
+
+# item
+scalar_array.item()
+nd.item(1)
+nd.item(0, 1)
+nd.item((0, 1))
+
+# tobytes
+nd.tobytes()
+nd.tobytes("C")
+nd.tobytes(None)
+
+# tofile
+if os.name != "nt":
+    with tempfile.NamedTemporaryFile(suffix=".txt") as tmp:
+        nd.tofile(tmp.name)
+        nd.tofile(tmp.name, "")
+        nd.tofile(tmp.name, sep="")
+
+        nd.tofile(tmp.name, "", "%s")
+        nd.tofile(tmp.name, format="%s")
+
+        nd.tofile(tmp)
+
+# dump is pretty simple
+# dumps is pretty simple
+
+# astype
+nd.astype("float")
+nd.astype(float)
+
+nd.astype(float, "K")
+nd.astype(float, order="K")
+
+nd.astype(float, "K", "unsafe")
+nd.astype(float, casting="unsafe")
+
+nd.astype(float, "K", "unsafe", True)
+nd.astype(float, subok=True)
+
+nd.astype(float, "K", "unsafe", True, True)
+nd.astype(float, copy=True)
+
+# byteswap
+nd.byteswap()
+nd.byteswap(True)
+
+# copy
+nd.copy()
+nd.copy("C")
+
+# view
+nd.view()
+nd.view(np.int64)
+nd.view(dtype=np.int64)
+nd.view(np.int64, np.matrix)
+nd.view(type=np.matrix)
+
+# getfield
+complex_array = np.array([[1 + 1j, 0], [0, 1 - 1j]], dtype=np.complex128)
+
+complex_array.getfield("float")
+complex_array.getfield(float)
+
+complex_array.getfield("float", 8)
+complex_array.getfield(float, offset=8)
+
+# setflags
+nd.setflags()
+
+nd.setflags(True)
+nd.setflags(write=True)
+
+nd.setflags(True, True)
+nd.setflags(write=True, align=True)
+
+nd.setflags(True, True, False)
+nd.setflags(write=True, align=True, uic=False)
+
+# fill is pretty simple
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..758626e18dd69077380ea177cc698e7a535e7937
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py
@@ -0,0 +1,196 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import cast, Any
+
+import numpy as np
+import numpy.typing as npt
+
+class SubClass(npt.NDArray[np.float64]): ...
+
+i4 = np.int32(1)
+A: np.ndarray[Any, np.dtype[np.int32]] = np.array([[1]], dtype=np.int32)
+B0 = np.empty((), dtype=np.int32).view(SubClass)
+B1 = np.empty((1,), dtype=np.int32).view(SubClass)
+B2 = np.empty((1, 1), dtype=np.int32).view(SubClass)
+C: np.ndarray[Any, np.dtype[np.int32]] = np.array([0, 1, 2], dtype=np.int32)
+D = np.ones(3).view(SubClass)
+
+ctypes_obj = A.ctypes
+
+i4.all()
+A.all()
+A.all(axis=0)
+A.all(keepdims=True)
+A.all(out=B0)
+
+i4.any()
+A.any()
+A.any(axis=0)
+A.any(keepdims=True)
+A.any(out=B0)
+
+i4.argmax()
+A.argmax()
+A.argmax(axis=0)
+A.argmax(out=B0)
+
+i4.argmin()
+A.argmin()
+A.argmin(axis=0)
+A.argmin(out=B0)
+
+i4.argsort()
+A.argsort()
+
+i4.choose([()])
+_choices = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=np.int32)
+C.choose(_choices)
+C.choose(_choices, out=D)
+
+i4.clip(1)
+A.clip(1)
+A.clip(None, 1)
+A.clip(1, out=B2)
+A.clip(None, 1, out=B2)
+
+i4.compress([1])
+A.compress([1])
+A.compress([1], out=B1)
+
+i4.conj()
+A.conj()
+B0.conj()
+
+i4.conjugate()
+A.conjugate()
+B0.conjugate()
+
+i4.cumprod()
+A.cumprod()
+A.cumprod(out=B1)
+
+i4.cumsum()
+A.cumsum()
+A.cumsum(out=B1)
+
+i4.max()
+A.max()
+A.max(axis=0)
+A.max(keepdims=True)
+A.max(out=B0)
+
+i4.mean()
+A.mean()
+A.mean(axis=0)
+A.mean(keepdims=True)
+A.mean(out=B0)
+
+i4.min()
+A.min()
+A.min(axis=0)
+A.min(keepdims=True)
+A.min(out=B0)
+
+i4.prod()
+A.prod()
+A.prod(axis=0)
+A.prod(keepdims=True)
+A.prod(out=B0)
+
+i4.round()
+A.round()
+A.round(out=B2)
+
+i4.repeat(1)
+A.repeat(1)
+B0.repeat(1)
+
+i4.std()
+A.std()
+A.std(axis=0)
+A.std(keepdims=True)
+A.std(out=B0.astype(np.float64))
+
+i4.sum()
+A.sum()
+A.sum(axis=0)
+A.sum(keepdims=True)
+A.sum(out=B0)
+
+i4.take(0)
+A.take(0)
+A.take([0])
+A.take(0, out=B0)
+A.take([0], out=B1)
+
+i4.var()
+A.var()
+A.var(axis=0)
+A.var(keepdims=True)
+A.var(out=B0)
+
+A.argpartition([0])
+
+A.diagonal()
+
+A.dot(1)
+A.dot(1, out=B2)
+
+A.nonzero()
+
+C.searchsorted(1)
+
+A.trace()
+A.trace(out=B0)
+
+void = cast(np.void, np.array(1, dtype=[("f", np.float64)]).take(0))
+void.setfield(10, np.float64)
+
+A.item(0)
+C.item(0)
+
+A.ravel()
+C.ravel()
+
+A.flatten()
+C.flatten()
+
+A.reshape(1)
+C.reshape(3)
+
+int(np.array(1.0, dtype=np.float64))
+int(np.array("1", dtype=np.str_))
+
+float(np.array(1.0, dtype=np.float64))
+float(np.array("1", dtype=np.str_))
+
+complex(np.array(1.0, dtype=np.float64))
+
+operator.index(np.array(1, dtype=np.int64))
+
+# this fails on numpy 2.2.1
+# https://github.com/scipy/scipy/blob/a755ee77ec47a64849abe42c349936475a6c2f24/scipy/io/arff/tests/test_arffread.py#L41-L44
+A_float = np.array([[1, 5], [2, 4], [np.nan, np.nan]])
+A_void: npt.NDArray[np.void] = np.empty(3, [("yop", float), ("yap", float)])
+A_void["yop"] = A_float[:, 0]
+A_void["yap"] = A_float[:, 1]
+
+# deprecated
+
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_data()  # pyright: ignore[reportDeprecated]
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_shape()  # pyright: ignore[reportDeprecated]
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_strides()  # pyright: ignore[reportDeprecated]
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_as_parameter()  # pyright: ignore[reportDeprecated]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ca3dff392e12a4122740ade2ceb71b3d6e7bb08
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py
@@ -0,0 +1,47 @@
+import numpy as np
+
+nd1 = np.array([[1, 2], [3, 4]])
+
+# reshape
+nd1.reshape(4)
+nd1.reshape(2, 2)
+nd1.reshape((2, 2))
+
+nd1.reshape((2, 2), order="C")
+nd1.reshape(4, order="C")
+
+# resize
+nd1.resize()
+nd1.resize(4)
+nd1.resize(2, 2)
+nd1.resize((2, 2))
+
+nd1.resize((2, 2), refcheck=True)
+nd1.resize(4, refcheck=True)
+
+nd2 = np.array([[1, 2], [3, 4]])
+
+# transpose
+nd2.transpose()
+nd2.transpose(1, 0)
+nd2.transpose((1, 0))
+
+# swapaxes
+nd2.swapaxes(0, 1)
+
+# flatten
+nd2.flatten()
+nd2.flatten("C")
+
+# ravel
+nd2.ravel()
+nd2.ravel("C")
+
+# squeeze
+nd2.squeeze()
+
+nd3 = np.array([[1, 2]])
+nd3.squeeze(0)
+
+nd4 = np.array([[[1, 2]]])
+nd4.squeeze((0, 1))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/nditer.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/nditer.py
new file mode 100644
index 0000000000000000000000000000000000000000..25a5b44d7aecf6486be76ae0dca0e3e8ef60699b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/nditer.py
@@ -0,0 +1,4 @@
+import numpy as np
+
+arr = np.array([1])
+np.nditer([arr, None])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/numeric.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e12fb5d70e60eb1d9a29f7d79e8e1ca4fd47efa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/numeric.py
@@ -0,0 +1,95 @@
+"""
+Tests for :mod:`numpy._core.numeric`.
+
+Does not include tests which fall under ``array_constructors``.
+
+"""
+
+from __future__ import annotations
+from typing import cast
+
+import numpy as np
+import numpy.typing as npt
+
+class SubClass(npt.NDArray[np.float64]):
+    ...
+
+i8 = np.int64(1)
+
+A = cast(
+    np.ndarray[tuple[int, int, int], np.dtype[np.intp]],
+    np.arange(27).reshape(3, 3, 3),
+)
+B: list[list[list[int]]] = A.tolist()
+C = np.empty((27, 27)).view(SubClass)
+
+np.count_nonzero(i8)
+np.count_nonzero(A)
+np.count_nonzero(B)
+np.count_nonzero(A, keepdims=True)
+np.count_nonzero(A, axis=0)
+
+np.isfortran(i8)
+np.isfortran(A)
+
+np.argwhere(i8)
+np.argwhere(A)
+
+np.flatnonzero(i8)
+np.flatnonzero(A)
+
+np.correlate(B[0][0], A.ravel(), mode="valid")
+np.correlate(A.ravel(), A.ravel(), mode="same")
+
+np.convolve(B[0][0], A.ravel(), mode="valid")
+np.convolve(A.ravel(), A.ravel(), mode="same")
+
+np.outer(i8, A)
+np.outer(B, A)
+np.outer(A, A)
+np.outer(A, A, out=C)
+
+np.tensordot(B, A)
+np.tensordot(A, A)
+np.tensordot(A, A, axes=0)
+np.tensordot(A, A, axes=(0, 1))
+
+np.isscalar(i8)
+np.isscalar(A)
+np.isscalar(B)
+
+np.roll(A, 1)
+np.roll(A, (1, 2))
+np.roll(B, 1)
+
+np.rollaxis(A, 0, 1)
+
+np.moveaxis(A, 0, 1)
+np.moveaxis(A, (0, 1), (1, 2))
+
+np.cross(B, A)
+np.cross(A, A)
+
+np.indices([0, 1, 2])
+np.indices([0, 1, 2], sparse=False)
+np.indices([0, 1, 2], sparse=True)
+
+np.binary_repr(1)
+
+np.base_repr(1)
+
+np.allclose(i8, A)
+np.allclose(B, A)
+np.allclose(A, A)
+
+np.isclose(i8, A)
+np.isclose(B, A)
+np.isclose(A, A)
+
+np.array_equal(i8, A)
+np.array_equal(B, A)
+np.array_equal(A, A)
+
+np.array_equiv(i8, A)
+np.array_equiv(B, A)
+np.array_equiv(A, A)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/numerictypes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/numerictypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e1a9986d88ab320bdfab54a050ed27aec4d8b5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/numerictypes.py
@@ -0,0 +1,17 @@
+import numpy as np
+
+np.isdtype(np.float64, (np.int64, np.float64))
+np.isdtype(np.int64, "signed integer")
+
+np.issubdtype("S1", np.bytes_)
+np.issubdtype(np.float64, np.float32)
+
+np.ScalarType
+np.ScalarType[0]
+np.ScalarType[3]
+np.ScalarType[8]
+np.ScalarType[10]
+
+np.typecodes["Character"]
+np.typecodes["Complex"]
+np.typecodes["All"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/random.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/random.py
new file mode 100644
index 0000000000000000000000000000000000000000..bce204a7378e8a8c8761d02a08686206f59ca6ce
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/random.py
@@ -0,0 +1,1497 @@
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+SEED_NONE = None
+SEED_INT = 4579435749574957634658964293569
+SEED_ARR: np.ndarray[Any, np.dtype[np.int64]] = np.array([1, 2, 3, 4], dtype=np.int64)
+SEED_ARRLIKE: list[int] = [1, 2, 3, 4]
+SEED_SEED_SEQ: np.random.SeedSequence = np.random.SeedSequence(0)
+SEED_MT19937: np.random.MT19937 = np.random.MT19937(0)
+SEED_PCG64: np.random.PCG64 = np.random.PCG64(0)
+SEED_PHILOX: np.random.Philox = np.random.Philox(0)
+SEED_SFC64: np.random.SFC64 = np.random.SFC64(0)
+
+# default rng
+np.random.default_rng()
+np.random.default_rng(SEED_NONE)
+np.random.default_rng(SEED_INT)
+np.random.default_rng(SEED_ARR)
+np.random.default_rng(SEED_ARRLIKE)
+np.random.default_rng(SEED_SEED_SEQ)
+np.random.default_rng(SEED_MT19937)
+np.random.default_rng(SEED_PCG64)
+np.random.default_rng(SEED_PHILOX)
+np.random.default_rng(SEED_SFC64)
+
+# Seed Sequence
+np.random.SeedSequence(SEED_NONE)
+np.random.SeedSequence(SEED_INT)
+np.random.SeedSequence(SEED_ARR)
+np.random.SeedSequence(SEED_ARRLIKE)
+
+# Bit Generators
+np.random.MT19937(SEED_NONE)
+np.random.MT19937(SEED_INT)
+np.random.MT19937(SEED_ARR)
+np.random.MT19937(SEED_ARRLIKE)
+np.random.MT19937(SEED_SEED_SEQ)
+
+np.random.PCG64(SEED_NONE)
+np.random.PCG64(SEED_INT)
+np.random.PCG64(SEED_ARR)
+np.random.PCG64(SEED_ARRLIKE)
+np.random.PCG64(SEED_SEED_SEQ)
+
+np.random.Philox(SEED_NONE)
+np.random.Philox(SEED_INT)
+np.random.Philox(SEED_ARR)
+np.random.Philox(SEED_ARRLIKE)
+np.random.Philox(SEED_SEED_SEQ)
+
+np.random.SFC64(SEED_NONE)
+np.random.SFC64(SEED_INT)
+np.random.SFC64(SEED_ARR)
+np.random.SFC64(SEED_ARRLIKE)
+np.random.SFC64(SEED_SEED_SEQ)
+
+seed_seq: np.random.bit_generator.SeedSequence = np.random.SeedSequence(SEED_NONE)
+seed_seq.spawn(10)
+seed_seq.generate_state(3)
+seed_seq.generate_state(3, "u4")
+seed_seq.generate_state(3, "uint32")
+seed_seq.generate_state(3, "u8")
+seed_seq.generate_state(3, "uint64")
+seed_seq.generate_state(3, np.uint32)
+seed_seq.generate_state(3, np.uint64)
+
+
+def_gen: np.random.Generator = np.random.default_rng()
+
+D_arr_0p1: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.1])
+D_arr_0p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.5])
+D_arr_0p9: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.9])
+D_arr_1p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.5])
+I_arr_10: np.ndarray[Any, np.dtype[np.int_]] = np.array([10], dtype=np.int_)
+I_arr_20: np.ndarray[Any, np.dtype[np.int_]] = np.array([20], dtype=np.int_)
+D_arr_like_0p1: list[float] = [0.1]
+D_arr_like_0p5: list[float] = [0.5]
+D_arr_like_0p9: list[float] = [0.9]
+D_arr_like_1p5: list[float] = [1.5]
+I_arr_like_10: list[int] = [10]
+I_arr_like_20: list[int] = [20]
+D_2D_like: list[list[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]]
+D_2D: np.ndarray[Any, np.dtype[np.float64]] = np.array(D_2D_like)
+
+S_out: np.ndarray[Any, np.dtype[np.float32]] = np.empty(1, dtype=np.float32)
+D_out: np.ndarray[Any, np.dtype[np.float64]] = np.empty(1)
+
+def_gen.standard_normal()
+def_gen.standard_normal(dtype=np.float32)
+def_gen.standard_normal(dtype="float32")
+def_gen.standard_normal(dtype="double")
+def_gen.standard_normal(dtype=np.float64)
+def_gen.standard_normal(size=None)
+def_gen.standard_normal(size=1)
+def_gen.standard_normal(size=1, dtype=np.float32)
+def_gen.standard_normal(size=1, dtype="f4")
+def_gen.standard_normal(size=1, dtype="float32", out=S_out)
+def_gen.standard_normal(dtype=np.float32, out=S_out)
+def_gen.standard_normal(size=1, dtype=np.float64)
+def_gen.standard_normal(size=1, dtype="float64")
+def_gen.standard_normal(size=1, dtype="f8")
+def_gen.standard_normal(out=D_out)
+def_gen.standard_normal(size=1, dtype="float64")
+def_gen.standard_normal(size=1, dtype="float64", out=D_out)
+
+def_gen.random()
+def_gen.random(dtype=np.float32)
+def_gen.random(dtype="float32")
+def_gen.random(dtype="double")
+def_gen.random(dtype=np.float64)
+def_gen.random(size=None)
+def_gen.random(size=1)
+def_gen.random(size=1, dtype=np.float32)
+def_gen.random(size=1, dtype="f4")
+def_gen.random(size=1, dtype="float32", out=S_out)
+def_gen.random(dtype=np.float32, out=S_out)
+def_gen.random(size=1, dtype=np.float64)
+def_gen.random(size=1, dtype="float64")
+def_gen.random(size=1, dtype="f8")
+def_gen.random(out=D_out)
+def_gen.random(size=1, dtype="float64")
+def_gen.random(size=1, dtype="float64", out=D_out)
+
+def_gen.standard_cauchy()
+def_gen.standard_cauchy(size=None)
+def_gen.standard_cauchy(size=1)
+
+def_gen.standard_exponential()
+def_gen.standard_exponential(method="inv")
+def_gen.standard_exponential(dtype=np.float32)
+def_gen.standard_exponential(dtype="float32")
+def_gen.standard_exponential(dtype="double")
+def_gen.standard_exponential(dtype=np.float64)
+def_gen.standard_exponential(size=None)
+def_gen.standard_exponential(size=None, method="inv")
+def_gen.standard_exponential(size=1, method="inv")
+def_gen.standard_exponential(size=1, dtype=np.float32)
+def_gen.standard_exponential(size=1, dtype="f4", method="inv")
+def_gen.standard_exponential(size=1, dtype="float32", out=S_out)
+def_gen.standard_exponential(dtype=np.float32, out=S_out)
+def_gen.standard_exponential(size=1, dtype=np.float64, method="inv")
+def_gen.standard_exponential(size=1, dtype="float64")
+def_gen.standard_exponential(size=1, dtype="f8")
+def_gen.standard_exponential(out=D_out)
+def_gen.standard_exponential(size=1, dtype="float64")
+def_gen.standard_exponential(size=1, dtype="float64", out=D_out)
+
+def_gen.zipf(1.5)
+def_gen.zipf(1.5, size=None)
+def_gen.zipf(1.5, size=1)
+def_gen.zipf(D_arr_1p5)
+def_gen.zipf(D_arr_1p5, size=1)
+def_gen.zipf(D_arr_like_1p5)
+def_gen.zipf(D_arr_like_1p5, size=1)
+
+def_gen.weibull(0.5)
+def_gen.weibull(0.5, size=None)
+def_gen.weibull(0.5, size=1)
+def_gen.weibull(D_arr_0p5)
+def_gen.weibull(D_arr_0p5, size=1)
+def_gen.weibull(D_arr_like_0p5)
+def_gen.weibull(D_arr_like_0p5, size=1)
+
+def_gen.standard_t(0.5)
+def_gen.standard_t(0.5, size=None)
+def_gen.standard_t(0.5, size=1)
+def_gen.standard_t(D_arr_0p5)
+def_gen.standard_t(D_arr_0p5, size=1)
+def_gen.standard_t(D_arr_like_0p5)
+def_gen.standard_t(D_arr_like_0p5, size=1)
+
+def_gen.poisson(0.5)
+def_gen.poisson(0.5, size=None)
+def_gen.poisson(0.5, size=1)
+def_gen.poisson(D_arr_0p5)
+def_gen.poisson(D_arr_0p5, size=1)
+def_gen.poisson(D_arr_like_0p5)
+def_gen.poisson(D_arr_like_0p5, size=1)
+
+def_gen.power(0.5)
+def_gen.power(0.5, size=None)
+def_gen.power(0.5, size=1)
+def_gen.power(D_arr_0p5)
+def_gen.power(D_arr_0p5, size=1)
+def_gen.power(D_arr_like_0p5)
+def_gen.power(D_arr_like_0p5, size=1)
+
+def_gen.pareto(0.5)
+def_gen.pareto(0.5, size=None)
+def_gen.pareto(0.5, size=1)
+def_gen.pareto(D_arr_0p5)
+def_gen.pareto(D_arr_0p5, size=1)
+def_gen.pareto(D_arr_like_0p5)
+def_gen.pareto(D_arr_like_0p5, size=1)
+
+def_gen.chisquare(0.5)
+def_gen.chisquare(0.5, size=None)
+def_gen.chisquare(0.5, size=1)
+def_gen.chisquare(D_arr_0p5)
+def_gen.chisquare(D_arr_0p5, size=1)
+def_gen.chisquare(D_arr_like_0p5)
+def_gen.chisquare(D_arr_like_0p5, size=1)
+
+def_gen.exponential(0.5)
+def_gen.exponential(0.5, size=None)
+def_gen.exponential(0.5, size=1)
+def_gen.exponential(D_arr_0p5)
+def_gen.exponential(D_arr_0p5, size=1)
+def_gen.exponential(D_arr_like_0p5)
+def_gen.exponential(D_arr_like_0p5, size=1)
+
+def_gen.geometric(0.5)
+def_gen.geometric(0.5, size=None)
+def_gen.geometric(0.5, size=1)
+def_gen.geometric(D_arr_0p5)
+def_gen.geometric(D_arr_0p5, size=1)
+def_gen.geometric(D_arr_like_0p5)
+def_gen.geometric(D_arr_like_0p5, size=1)
+
+def_gen.logseries(0.5)
+def_gen.logseries(0.5, size=None)
+def_gen.logseries(0.5, size=1)
+def_gen.logseries(D_arr_0p5)
+def_gen.logseries(D_arr_0p5, size=1)
+def_gen.logseries(D_arr_like_0p5)
+def_gen.logseries(D_arr_like_0p5, size=1)
+
+def_gen.rayleigh(0.5)
+def_gen.rayleigh(0.5, size=None)
+def_gen.rayleigh(0.5, size=1)
+def_gen.rayleigh(D_arr_0p5)
+def_gen.rayleigh(D_arr_0p5, size=1)
+def_gen.rayleigh(D_arr_like_0p5)
+def_gen.rayleigh(D_arr_like_0p5, size=1)
+
+def_gen.standard_gamma(0.5)
+def_gen.standard_gamma(0.5, size=None)
+def_gen.standard_gamma(0.5, dtype="float32")
+def_gen.standard_gamma(0.5, size=None, dtype="float32")
+def_gen.standard_gamma(0.5, size=1)
+def_gen.standard_gamma(D_arr_0p5)
+def_gen.standard_gamma(D_arr_0p5, dtype="f4")
+def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out)
+def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out)
+def_gen.standard_gamma(D_arr_0p5, size=1)
+def_gen.standard_gamma(D_arr_like_0p5)
+def_gen.standard_gamma(D_arr_like_0p5, size=1)
+def_gen.standard_gamma(0.5, out=D_out)
+def_gen.standard_gamma(D_arr_like_0p5, out=D_out)
+def_gen.standard_gamma(D_arr_like_0p5, size=1)
+def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64)
+
+def_gen.vonmises(0.5, 0.5)
+def_gen.vonmises(0.5, 0.5, size=None)
+def_gen.vonmises(0.5, 0.5, size=1)
+def_gen.vonmises(D_arr_0p5, 0.5)
+def_gen.vonmises(0.5, D_arr_0p5)
+def_gen.vonmises(D_arr_0p5, 0.5, size=1)
+def_gen.vonmises(0.5, D_arr_0p5, size=1)
+def_gen.vonmises(D_arr_like_0p5, 0.5)
+def_gen.vonmises(0.5, D_arr_like_0p5)
+def_gen.vonmises(D_arr_0p5, D_arr_0p5)
+def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.wald(0.5, 0.5)
+def_gen.wald(0.5, 0.5, size=None)
+def_gen.wald(0.5, 0.5, size=1)
+def_gen.wald(D_arr_0p5, 0.5)
+def_gen.wald(0.5, D_arr_0p5)
+def_gen.wald(D_arr_0p5, 0.5, size=1)
+def_gen.wald(0.5, D_arr_0p5, size=1)
+def_gen.wald(D_arr_like_0p5, 0.5)
+def_gen.wald(0.5, D_arr_like_0p5)
+def_gen.wald(D_arr_0p5, D_arr_0p5)
+def_gen.wald(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.wald(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.uniform(0.5, 0.5)
+def_gen.uniform(0.5, 0.5, size=None)
+def_gen.uniform(0.5, 0.5, size=1)
+def_gen.uniform(D_arr_0p5, 0.5)
+def_gen.uniform(0.5, D_arr_0p5)
+def_gen.uniform(D_arr_0p5, 0.5, size=1)
+def_gen.uniform(0.5, D_arr_0p5, size=1)
+def_gen.uniform(D_arr_like_0p5, 0.5)
+def_gen.uniform(0.5, D_arr_like_0p5)
+def_gen.uniform(D_arr_0p5, D_arr_0p5)
+def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.beta(0.5, 0.5)
+def_gen.beta(0.5, 0.5, size=None)
+def_gen.beta(0.5, 0.5, size=1)
+def_gen.beta(D_arr_0p5, 0.5)
+def_gen.beta(0.5, D_arr_0p5)
+def_gen.beta(D_arr_0p5, 0.5, size=1)
+def_gen.beta(0.5, D_arr_0p5, size=1)
+def_gen.beta(D_arr_like_0p5, 0.5)
+def_gen.beta(0.5, D_arr_like_0p5)
+def_gen.beta(D_arr_0p5, D_arr_0p5)
+def_gen.beta(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.beta(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.f(0.5, 0.5)
+def_gen.f(0.5, 0.5, size=None)
+def_gen.f(0.5, 0.5, size=1)
+def_gen.f(D_arr_0p5, 0.5)
+def_gen.f(0.5, D_arr_0p5)
+def_gen.f(D_arr_0p5, 0.5, size=1)
+def_gen.f(0.5, D_arr_0p5, size=1)
+def_gen.f(D_arr_like_0p5, 0.5)
+def_gen.f(0.5, D_arr_like_0p5)
+def_gen.f(D_arr_0p5, D_arr_0p5)
+def_gen.f(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.f(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.gamma(0.5, 0.5)
+def_gen.gamma(0.5, 0.5, size=None)
+def_gen.gamma(0.5, 0.5, size=1)
+def_gen.gamma(D_arr_0p5, 0.5)
+def_gen.gamma(0.5, D_arr_0p5)
+def_gen.gamma(D_arr_0p5, 0.5, size=1)
+def_gen.gamma(0.5, D_arr_0p5, size=1)
+def_gen.gamma(D_arr_like_0p5, 0.5)
+def_gen.gamma(0.5, D_arr_like_0p5)
+def_gen.gamma(D_arr_0p5, D_arr_0p5)
+def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.gumbel(0.5, 0.5)
+def_gen.gumbel(0.5, 0.5, size=None)
+def_gen.gumbel(0.5, 0.5, size=1)
+def_gen.gumbel(D_arr_0p5, 0.5)
+def_gen.gumbel(0.5, D_arr_0p5)
+def_gen.gumbel(D_arr_0p5, 0.5, size=1)
+def_gen.gumbel(0.5, D_arr_0p5, size=1)
+def_gen.gumbel(D_arr_like_0p5, 0.5)
+def_gen.gumbel(0.5, D_arr_like_0p5)
+def_gen.gumbel(D_arr_0p5, D_arr_0p5)
+def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.laplace(0.5, 0.5)
+def_gen.laplace(0.5, 0.5, size=None)
+def_gen.laplace(0.5, 0.5, size=1)
+def_gen.laplace(D_arr_0p5, 0.5)
+def_gen.laplace(0.5, D_arr_0p5)
+def_gen.laplace(D_arr_0p5, 0.5, size=1)
+def_gen.laplace(0.5, D_arr_0p5, size=1)
+def_gen.laplace(D_arr_like_0p5, 0.5)
+def_gen.laplace(0.5, D_arr_like_0p5)
+def_gen.laplace(D_arr_0p5, D_arr_0p5)
+def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.logistic(0.5, 0.5)
+def_gen.logistic(0.5, 0.5, size=None)
+def_gen.logistic(0.5, 0.5, size=1)
+def_gen.logistic(D_arr_0p5, 0.5)
+def_gen.logistic(0.5, D_arr_0p5)
+def_gen.logistic(D_arr_0p5, 0.5, size=1)
+def_gen.logistic(0.5, D_arr_0p5, size=1)
+def_gen.logistic(D_arr_like_0p5, 0.5)
+def_gen.logistic(0.5, D_arr_like_0p5)
+def_gen.logistic(D_arr_0p5, D_arr_0p5)
+def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.lognormal(0.5, 0.5)
+def_gen.lognormal(0.5, 0.5, size=None)
+def_gen.lognormal(0.5, 0.5, size=1)
+def_gen.lognormal(D_arr_0p5, 0.5)
+def_gen.lognormal(0.5, D_arr_0p5)
+def_gen.lognormal(D_arr_0p5, 0.5, size=1)
+def_gen.lognormal(0.5, D_arr_0p5, size=1)
+def_gen.lognormal(D_arr_like_0p5, 0.5)
+def_gen.lognormal(0.5, D_arr_like_0p5)
+def_gen.lognormal(D_arr_0p5, D_arr_0p5)
+def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.noncentral_chisquare(0.5, 0.5)
+def_gen.noncentral_chisquare(0.5, 0.5, size=None)
+def_gen.noncentral_chisquare(0.5, 0.5, size=1)
+def_gen.noncentral_chisquare(D_arr_0p5, 0.5)
+def_gen.noncentral_chisquare(0.5, D_arr_0p5)
+def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1)
+def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1)
+def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5)
+def_gen.noncentral_chisquare(0.5, D_arr_like_0p5)
+def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5)
+def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.normal(0.5, 0.5)
+def_gen.normal(0.5, 0.5, size=None)
+def_gen.normal(0.5, 0.5, size=1)
+def_gen.normal(D_arr_0p5, 0.5)
+def_gen.normal(0.5, D_arr_0p5)
+def_gen.normal(D_arr_0p5, 0.5, size=1)
+def_gen.normal(0.5, D_arr_0p5, size=1)
+def_gen.normal(D_arr_like_0p5, 0.5)
+def_gen.normal(0.5, D_arr_like_0p5)
+def_gen.normal(D_arr_0p5, D_arr_0p5)
+def_gen.normal(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.normal(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.triangular(0.1, 0.5, 0.9)
+def_gen.triangular(0.1, 0.5, 0.9, size=None)
+def_gen.triangular(0.1, 0.5, 0.9, size=1)
+def_gen.triangular(D_arr_0p1, 0.5, 0.9)
+def_gen.triangular(0.1, D_arr_0p5, 0.9)
+def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1)
+def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)
+def_gen.triangular(0.5, D_arr_like_0p5, 0.9)
+def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9)
+def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+def_gen.noncentral_f(0.1, 0.5, 0.9)
+def_gen.noncentral_f(0.1, 0.5, 0.9, size=None)
+def_gen.noncentral_f(0.1, 0.5, 0.9, size=1)
+def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9)
+def_gen.noncentral_f(0.1, D_arr_0p5, 0.9)
+def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)
+def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)
+def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9)
+def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)
+def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+def_gen.binomial(10, 0.5)
+def_gen.binomial(10, 0.5, size=None)
+def_gen.binomial(10, 0.5, size=1)
+def_gen.binomial(I_arr_10, 0.5)
+def_gen.binomial(10, D_arr_0p5)
+def_gen.binomial(I_arr_10, 0.5, size=1)
+def_gen.binomial(10, D_arr_0p5, size=1)
+def_gen.binomial(I_arr_like_10, 0.5)
+def_gen.binomial(10, D_arr_like_0p5)
+def_gen.binomial(I_arr_10, D_arr_0p5)
+def_gen.binomial(I_arr_like_10, D_arr_like_0p5)
+def_gen.binomial(I_arr_10, D_arr_0p5, size=1)
+def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+def_gen.negative_binomial(10, 0.5)
+def_gen.negative_binomial(10, 0.5, size=None)
+def_gen.negative_binomial(10, 0.5, size=1)
+def_gen.negative_binomial(I_arr_10, 0.5)
+def_gen.negative_binomial(10, D_arr_0p5)
+def_gen.negative_binomial(I_arr_10, 0.5, size=1)
+def_gen.negative_binomial(10, D_arr_0p5, size=1)
+def_gen.negative_binomial(I_arr_like_10, 0.5)
+def_gen.negative_binomial(10, D_arr_like_0p5)
+def_gen.negative_binomial(I_arr_10, D_arr_0p5)
+def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5)
+def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1)
+def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+def_gen.hypergeometric(20, 20, 10)
+def_gen.hypergeometric(20, 20, 10, size=None)
+def_gen.hypergeometric(20, 20, 10, size=1)
+def_gen.hypergeometric(I_arr_20, 20, 10)
+def_gen.hypergeometric(20, I_arr_20, 10)
+def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)
+def_gen.hypergeometric(20, I_arr_20, 10, size=1)
+def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10)
+def_gen.hypergeometric(20, I_arr_like_20, 10)
+def_gen.hypergeometric(I_arr_20, I_arr_20, 10)
+def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10)
+def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)
+def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)
+
+I_int64_100: np.ndarray[Any, np.dtype[np.int64]] = np.array([100], dtype=np.int64)
+
+def_gen.integers(0, 100)
+def_gen.integers(100)
+def_gen.integers([100])
+def_gen.integers(0, [100])
+
+I_bool_low: np.ndarray[Any, np.dtype[np.bool]] = np.array([0], dtype=np.bool)
+I_bool_low_like: list[int] = [0]
+I_bool_high_open: np.ndarray[Any, np.dtype[np.bool]] = np.array([1], dtype=np.bool)
+I_bool_high_closed: np.ndarray[Any, np.dtype[np.bool]] = np.array([1], dtype=np.bool)
+
+def_gen.integers(2, dtype=bool)
+def_gen.integers(0, 2, dtype=bool)
+def_gen.integers(1, dtype=bool, endpoint=True)
+def_gen.integers(0, 1, dtype=bool, endpoint=True)
+def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True)
+def_gen.integers(I_bool_high_open, dtype=bool)
+def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool)
+def_gen.integers(0, I_bool_high_open, dtype=bool)
+def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True)
+def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True)
+def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True)
+
+def_gen.integers(2, dtype=np.bool)
+def_gen.integers(0, 2, dtype=np.bool)
+def_gen.integers(1, dtype=np.bool, endpoint=True)
+def_gen.integers(0, 1, dtype=np.bool, endpoint=True)
+def_gen.integers(I_bool_low_like, 1, dtype=np.bool, endpoint=True)
+def_gen.integers(I_bool_high_open, dtype=np.bool)
+def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool)
+def_gen.integers(0, I_bool_high_open, dtype=np.bool)
+def_gen.integers(I_bool_high_closed, dtype=np.bool, endpoint=True)
+def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool, endpoint=True)
+def_gen.integers(0, I_bool_high_closed, dtype=np.bool, endpoint=True)
+
+I_u1_low: np.ndarray[Any, np.dtype[np.uint8]] = np.array([0], dtype=np.uint8)
+I_u1_low_like: list[int] = [0]
+I_u1_high_open: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8)
+I_u1_high_closed: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8)
+
+def_gen.integers(256, dtype="u1")
+def_gen.integers(0, 256, dtype="u1")
+def_gen.integers(255, dtype="u1", endpoint=True)
+def_gen.integers(0, 255, dtype="u1", endpoint=True)
+def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True)
+def_gen.integers(I_u1_high_open, dtype="u1")
+def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1")
+def_gen.integers(0, I_u1_high_open, dtype="u1")
+def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True)
+def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True)
+def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True)
+
+def_gen.integers(256, dtype="uint8")
+def_gen.integers(0, 256, dtype="uint8")
+def_gen.integers(255, dtype="uint8", endpoint=True)
+def_gen.integers(0, 255, dtype="uint8", endpoint=True)
+def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True)
+def_gen.integers(I_u1_high_open, dtype="uint8")
+def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8")
+def_gen.integers(0, I_u1_high_open, dtype="uint8")
+def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True)
+def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True)
+def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True)
+
+def_gen.integers(256, dtype=np.uint8)
+def_gen.integers(0, 256, dtype=np.uint8)
+def_gen.integers(255, dtype=np.uint8, endpoint=True)
+def_gen.integers(0, 255, dtype=np.uint8, endpoint=True)
+def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True)
+def_gen.integers(I_u1_high_open, dtype=np.uint8)
+def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8)
+def_gen.integers(0, I_u1_high_open, dtype=np.uint8)
+def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True)
+def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True)
+def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True)
+
+I_u2_low: np.ndarray[Any, np.dtype[np.uint16]] = np.array([0], dtype=np.uint16)
+I_u2_low_like: list[int] = [0]
+I_u2_high_open: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16)
+I_u2_high_closed: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16)
+
+def_gen.integers(65536, dtype="u2")
+def_gen.integers(0, 65536, dtype="u2")
+def_gen.integers(65535, dtype="u2", endpoint=True)
+def_gen.integers(0, 65535, dtype="u2", endpoint=True)
+def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True)
+def_gen.integers(I_u2_high_open, dtype="u2")
+def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2")
+def_gen.integers(0, I_u2_high_open, dtype="u2")
+def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True)
+def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True)
+def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True)
+
+def_gen.integers(65536, dtype="uint16")
+def_gen.integers(0, 65536, dtype="uint16")
+def_gen.integers(65535, dtype="uint16", endpoint=True)
+def_gen.integers(0, 65535, dtype="uint16", endpoint=True)
+def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True)
+def_gen.integers(I_u2_high_open, dtype="uint16")
+def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16")
+def_gen.integers(0, I_u2_high_open, dtype="uint16")
+def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True)
+def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True)
+def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True)
+
+def_gen.integers(65536, dtype=np.uint16)
+def_gen.integers(0, 65536, dtype=np.uint16)
+def_gen.integers(65535, dtype=np.uint16, endpoint=True)
+def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True)
+def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True)
+def_gen.integers(I_u2_high_open, dtype=np.uint16)
+def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16)
+def_gen.integers(0, I_u2_high_open, dtype=np.uint16)
+def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True)
+def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True)
+def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True)
+
+I_u4_low: np.ndarray[Any, np.dtype[np.uint32]] = np.array([0], dtype=np.uint32)
+I_u4_low_like: list[int] = [0]
+I_u4_high_open: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32)
+I_u4_high_closed: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32)
+
+def_gen.integers(4294967296, dtype="u4")
+def_gen.integers(0, 4294967296, dtype="u4")
+def_gen.integers(4294967295, dtype="u4", endpoint=True)
+def_gen.integers(0, 4294967295, dtype="u4", endpoint=True)
+def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True)
+def_gen.integers(I_u4_high_open, dtype="u4")
+def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4")
+def_gen.integers(0, I_u4_high_open, dtype="u4")
+def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True)
+def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True)
+def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True)
+
+def_gen.integers(4294967296, dtype="uint32")
+def_gen.integers(0, 4294967296, dtype="uint32")
+def_gen.integers(4294967295, dtype="uint32", endpoint=True)
+def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True)
+def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True)
+def_gen.integers(I_u4_high_open, dtype="uint32")
+def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32")
+def_gen.integers(0, I_u4_high_open, dtype="uint32")
+def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True)
+def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True)
+def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True)
+
+def_gen.integers(4294967296, dtype=np.uint32)
+def_gen.integers(0, 4294967296, dtype=np.uint32)
+def_gen.integers(4294967295, dtype=np.uint32, endpoint=True)
+def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True)
+def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True)
+def_gen.integers(I_u4_high_open, dtype=np.uint32)
+def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32)
+def_gen.integers(0, I_u4_high_open, dtype=np.uint32)
+def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True)
+def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True)
+def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True)
+
+I_u8_low: np.ndarray[Any, np.dtype[np.uint64]] = np.array([0], dtype=np.uint64)
+I_u8_low_like: list[int] = [0]
+I_u8_high_open: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64)
+I_u8_high_closed: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64)
+
+def_gen.integers(18446744073709551616, dtype="u8")
+def_gen.integers(0, 18446744073709551616, dtype="u8")
+def_gen.integers(18446744073709551615, dtype="u8", endpoint=True)
+def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True)
+def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True)
+def_gen.integers(I_u8_high_open, dtype="u8")
+def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8")
+def_gen.integers(0, I_u8_high_open, dtype="u8")
+def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True)
+def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True)
+def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True)
+
+def_gen.integers(18446744073709551616, dtype="uint64")
+def_gen.integers(0, 18446744073709551616, dtype="uint64")
+def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True)
+def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True)
+def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True)
+def_gen.integers(I_u8_high_open, dtype="uint64")
+def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64")
+def_gen.integers(0, I_u8_high_open, dtype="uint64")
+def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True)
+def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True)
+def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True)
+
+def_gen.integers(18446744073709551616, dtype=np.uint64)
+def_gen.integers(0, 18446744073709551616, dtype=np.uint64)
+def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True)
+def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True)
+def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True)
+def_gen.integers(I_u8_high_open, dtype=np.uint64)
+def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64)
+def_gen.integers(0, I_u8_high_open, dtype=np.uint64)
+def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True)
+def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True)
+def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True)
+
+I_i1_low: np.ndarray[Any, np.dtype[np.int8]] = np.array([-128], dtype=np.int8)
+I_i1_low_like: list[int] = [-128]
+I_i1_high_open: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8)
+I_i1_high_closed: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8)
+
+def_gen.integers(128, dtype="i1")
+def_gen.integers(-128, 128, dtype="i1")
+def_gen.integers(127, dtype="i1", endpoint=True)
+def_gen.integers(-128, 127, dtype="i1", endpoint=True)
+def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True)
+def_gen.integers(I_i1_high_open, dtype="i1")
+def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1")
+def_gen.integers(-128, I_i1_high_open, dtype="i1")
+def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True)
+def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True)
+def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True)
+
+def_gen.integers(128, dtype="int8")
+def_gen.integers(-128, 128, dtype="int8")
+def_gen.integers(127, dtype="int8", endpoint=True)
+def_gen.integers(-128, 127, dtype="int8", endpoint=True)
+def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True)
+def_gen.integers(I_i1_high_open, dtype="int8")
+def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8")
+def_gen.integers(-128, I_i1_high_open, dtype="int8")
+def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True)
+def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True)
+def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True)
+
+def_gen.integers(128, dtype=np.int8)
+def_gen.integers(-128, 128, dtype=np.int8)
+def_gen.integers(127, dtype=np.int8, endpoint=True)
+def_gen.integers(-128, 127, dtype=np.int8, endpoint=True)
+def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True)
+def_gen.integers(I_i1_high_open, dtype=np.int8)
+def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8)
+def_gen.integers(-128, I_i1_high_open, dtype=np.int8)
+def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True)
+def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True)
+def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True)
+
+I_i2_low: np.ndarray[Any, np.dtype[np.int16]] = np.array([-32768], dtype=np.int16)
+I_i2_low_like: list[int] = [-32768]
+I_i2_high_open: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16)
+I_i2_high_closed: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16)
+
+def_gen.integers(32768, dtype="i2")
+def_gen.integers(-32768, 32768, dtype="i2")
+def_gen.integers(32767, dtype="i2", endpoint=True)
+def_gen.integers(-32768, 32767, dtype="i2", endpoint=True)
+def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True)
+def_gen.integers(I_i2_high_open, dtype="i2")
+def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2")
+def_gen.integers(-32768, I_i2_high_open, dtype="i2")
+def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True)
+def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True)
+def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True)
+
+def_gen.integers(32768, dtype="int16")
+def_gen.integers(-32768, 32768, dtype="int16")
+def_gen.integers(32767, dtype="int16", endpoint=True)
+def_gen.integers(-32768, 32767, dtype="int16", endpoint=True)
+def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True)
+def_gen.integers(I_i2_high_open, dtype="int16")
+def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16")
+def_gen.integers(-32768, I_i2_high_open, dtype="int16")
+def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True)
+def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True)
+def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True)
+
+def_gen.integers(32768, dtype=np.int16)
+def_gen.integers(-32768, 32768, dtype=np.int16)
+def_gen.integers(32767, dtype=np.int16, endpoint=True)
+def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True)
+def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True)
+def_gen.integers(I_i2_high_open, dtype=np.int16)
+def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16)
+def_gen.integers(-32768, I_i2_high_open, dtype=np.int16)
+def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True)
+def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True)
+def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True)
+
+I_i4_low: np.ndarray[Any, np.dtype[np.int32]] = np.array([-2147483648], dtype=np.int32)
+I_i4_low_like: list[int] = [-2147483648]
+I_i4_high_open: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32)
+I_i4_high_closed: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32)
+
+def_gen.integers(2147483648, dtype="i4")
+def_gen.integers(-2147483648, 2147483648, dtype="i4")
+def_gen.integers(2147483647, dtype="i4", endpoint=True)
+def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True)
+def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True)
+def_gen.integers(I_i4_high_open, dtype="i4")
+def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4")
+def_gen.integers(-2147483648, I_i4_high_open, dtype="i4")
+def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True)
+def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True)
+def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True)
+
+def_gen.integers(2147483648, dtype="int32")
+def_gen.integers(-2147483648, 2147483648, dtype="int32")
+def_gen.integers(2147483647, dtype="int32", endpoint=True)
+def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True)
+def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True)
+def_gen.integers(I_i4_high_open, dtype="int32")
+def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32")
+def_gen.integers(-2147483648, I_i4_high_open, dtype="int32")
+def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True)
+def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True)
+def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True)
+
+def_gen.integers(2147483648, dtype=np.int32)
+def_gen.integers(-2147483648, 2147483648, dtype=np.int32)
+def_gen.integers(2147483647, dtype=np.int32, endpoint=True)
+def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True)
+def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True)
+def_gen.integers(I_i4_high_open, dtype=np.int32)
+def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32)
+def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32)
+def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True)
+def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True)
+def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True)
+
+I_i8_low: np.ndarray[Any, np.dtype[np.int64]] = np.array([-9223372036854775808], dtype=np.int64)
+I_i8_low_like: list[int] = [-9223372036854775808]
+I_i8_high_open: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64)
+I_i8_high_closed: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64)
+
+def_gen.integers(9223372036854775808, dtype="i8")
+def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8")
+def_gen.integers(9223372036854775807, dtype="i8", endpoint=True)
+def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True)
+def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True)
+def_gen.integers(I_i8_high_open, dtype="i8")
+def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8")
+def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8")
+def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True)
+def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True)
+def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True)
+
+def_gen.integers(9223372036854775808, dtype="int64")
+def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64")
+def_gen.integers(9223372036854775807, dtype="int64", endpoint=True)
+def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True)
+def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True)
+def_gen.integers(I_i8_high_open, dtype="int64")
+def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64")
+def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64")
+def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True)
+def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True)
+def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True)
+
+def_gen.integers(9223372036854775808, dtype=np.int64)
+def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64)
+def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True)
+def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True)
+def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True)
+def_gen.integers(I_i8_high_open, dtype=np.int64)
+def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64)
+def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64)
+def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True)
+def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True)
+def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True)
+
+
+def_gen.bit_generator
+
+def_gen.bytes(2)
+
+def_gen.choice(5)
+def_gen.choice(5, 3)
+def_gen.choice(5, 3, replace=True)
+def_gen.choice(5, 3, p=[1 / 5] * 5)
+def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False)
+
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"])
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))
+
+def_gen.dirichlet([0.5, 0.5])
+def_gen.dirichlet(np.array([0.5, 0.5]))
+def_gen.dirichlet(np.array([0.5, 0.5]), size=3)
+
+def_gen.multinomial(20, [1 / 6.0] * 6)
+def_gen.multinomial(20, np.array([0.5, 0.5]))
+def_gen.multinomial(20, [1 / 6.0] * 6, size=2)
+def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2))
+def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2))
+
+def_gen.multivariate_hypergeometric([3, 5, 7], 2)
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2)
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4)
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7))
+def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count")
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals")
+
+def_gen.multivariate_normal([0.0], [[1.0]])
+def_gen.multivariate_normal([0.0], np.array([[1.0]]))
+def_gen.multivariate_normal(np.array([0.0]), [[1.0]])
+def_gen.multivariate_normal([0.0], np.array([[1.0]]))
+
+def_gen.permutation(10)
+def_gen.permutation([1, 2, 3, 4])
+def_gen.permutation(np.array([1, 2, 3, 4]))
+def_gen.permutation(D_2D, axis=1)
+def_gen.permuted(D_2D)
+def_gen.permuted(D_2D_like)
+def_gen.permuted(D_2D, axis=1)
+def_gen.permuted(D_2D, out=D_2D)
+def_gen.permuted(D_2D_like, out=D_2D)
+def_gen.permuted(D_2D_like, out=D_2D)
+def_gen.permuted(D_2D, axis=1, out=D_2D)
+
+def_gen.shuffle(np.arange(10))
+def_gen.shuffle([1, 2, 3, 4, 5])
+def_gen.shuffle(D_2D, axis=1)
+
+def_gen.__str__()
+def_gen.__repr__()
+def_gen.__setstate__(dict(def_gen.bit_generator.state))
+
+# RandomState
+random_st: np.random.RandomState = np.random.RandomState()
+
+random_st.standard_normal()
+random_st.standard_normal(size=None)
+random_st.standard_normal(size=1)
+
+random_st.random()
+random_st.random(size=None)
+random_st.random(size=1)
+
+random_st.standard_cauchy()
+random_st.standard_cauchy(size=None)
+random_st.standard_cauchy(size=1)
+
+random_st.standard_exponential()
+random_st.standard_exponential(size=None)
+random_st.standard_exponential(size=1)
+
+random_st.zipf(1.5)
+random_st.zipf(1.5, size=None)
+random_st.zipf(1.5, size=1)
+random_st.zipf(D_arr_1p5)
+random_st.zipf(D_arr_1p5, size=1)
+random_st.zipf(D_arr_like_1p5)
+random_st.zipf(D_arr_like_1p5, size=1)
+
+random_st.weibull(0.5)
+random_st.weibull(0.5, size=None)
+random_st.weibull(0.5, size=1)
+random_st.weibull(D_arr_0p5)
+random_st.weibull(D_arr_0p5, size=1)
+random_st.weibull(D_arr_like_0p5)
+random_st.weibull(D_arr_like_0p5, size=1)
+
+random_st.standard_t(0.5)
+random_st.standard_t(0.5, size=None)
+random_st.standard_t(0.5, size=1)
+random_st.standard_t(D_arr_0p5)
+random_st.standard_t(D_arr_0p5, size=1)
+random_st.standard_t(D_arr_like_0p5)
+random_st.standard_t(D_arr_like_0p5, size=1)
+
+random_st.poisson(0.5)
+random_st.poisson(0.5, size=None)
+random_st.poisson(0.5, size=1)
+random_st.poisson(D_arr_0p5)
+random_st.poisson(D_arr_0p5, size=1)
+random_st.poisson(D_arr_like_0p5)
+random_st.poisson(D_arr_like_0p5, size=1)
+
+random_st.power(0.5)
+random_st.power(0.5, size=None)
+random_st.power(0.5, size=1)
+random_st.power(D_arr_0p5)
+random_st.power(D_arr_0p5, size=1)
+random_st.power(D_arr_like_0p5)
+random_st.power(D_arr_like_0p5, size=1)
+
+random_st.pareto(0.5)
+random_st.pareto(0.5, size=None)
+random_st.pareto(0.5, size=1)
+random_st.pareto(D_arr_0p5)
+random_st.pareto(D_arr_0p5, size=1)
+random_st.pareto(D_arr_like_0p5)
+random_st.pareto(D_arr_like_0p5, size=1)
+
+random_st.chisquare(0.5)
+random_st.chisquare(0.5, size=None)
+random_st.chisquare(0.5, size=1)
+random_st.chisquare(D_arr_0p5)
+random_st.chisquare(D_arr_0p5, size=1)
+random_st.chisquare(D_arr_like_0p5)
+random_st.chisquare(D_arr_like_0p5, size=1)
+
+random_st.exponential(0.5)
+random_st.exponential(0.5, size=None)
+random_st.exponential(0.5, size=1)
+random_st.exponential(D_arr_0p5)
+random_st.exponential(D_arr_0p5, size=1)
+random_st.exponential(D_arr_like_0p5)
+random_st.exponential(D_arr_like_0p5, size=1)
+
+random_st.geometric(0.5)
+random_st.geometric(0.5, size=None)
+random_st.geometric(0.5, size=1)
+random_st.geometric(D_arr_0p5)
+random_st.geometric(D_arr_0p5, size=1)
+random_st.geometric(D_arr_like_0p5)
+random_st.geometric(D_arr_like_0p5, size=1)
+
+random_st.logseries(0.5)
+random_st.logseries(0.5, size=None)
+random_st.logseries(0.5, size=1)
+random_st.logseries(D_arr_0p5)
+random_st.logseries(D_arr_0p5, size=1)
+random_st.logseries(D_arr_like_0p5)
+random_st.logseries(D_arr_like_0p5, size=1)
+
+random_st.rayleigh(0.5)
+random_st.rayleigh(0.5, size=None)
+random_st.rayleigh(0.5, size=1)
+random_st.rayleigh(D_arr_0p5)
+random_st.rayleigh(D_arr_0p5, size=1)
+random_st.rayleigh(D_arr_like_0p5)
+random_st.rayleigh(D_arr_like_0p5, size=1)
+
+random_st.standard_gamma(0.5)
+random_st.standard_gamma(0.5, size=None)
+random_st.standard_gamma(0.5, size=1)
+random_st.standard_gamma(D_arr_0p5)
+random_st.standard_gamma(D_arr_0p5, size=1)
+random_st.standard_gamma(D_arr_like_0p5)
+random_st.standard_gamma(D_arr_like_0p5, size=1)
+random_st.standard_gamma(D_arr_like_0p5, size=1)
+
+random_st.vonmises(0.5, 0.5)
+random_st.vonmises(0.5, 0.5, size=None)
+random_st.vonmises(0.5, 0.5, size=1)
+random_st.vonmises(D_arr_0p5, 0.5)
+random_st.vonmises(0.5, D_arr_0p5)
+random_st.vonmises(D_arr_0p5, 0.5, size=1)
+random_st.vonmises(0.5, D_arr_0p5, size=1)
+random_st.vonmises(D_arr_like_0p5, 0.5)
+random_st.vonmises(0.5, D_arr_like_0p5)
+random_st.vonmises(D_arr_0p5, D_arr_0p5)
+random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5)
+random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1)
+random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.wald(0.5, 0.5)
+random_st.wald(0.5, 0.5, size=None)
+random_st.wald(0.5, 0.5, size=1)
+random_st.wald(D_arr_0p5, 0.5)
+random_st.wald(0.5, D_arr_0p5)
+random_st.wald(D_arr_0p5, 0.5, size=1)
+random_st.wald(0.5, D_arr_0p5, size=1)
+random_st.wald(D_arr_like_0p5, 0.5)
+random_st.wald(0.5, D_arr_like_0p5)
+random_st.wald(D_arr_0p5, D_arr_0p5)
+random_st.wald(D_arr_like_0p5, D_arr_like_0p5)
+random_st.wald(D_arr_0p5, D_arr_0p5, size=1)
+random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.uniform(0.5, 0.5)
+random_st.uniform(0.5, 0.5, size=None)
+random_st.uniform(0.5, 0.5, size=1)
+random_st.uniform(D_arr_0p5, 0.5)
+random_st.uniform(0.5, D_arr_0p5)
+random_st.uniform(D_arr_0p5, 0.5, size=1)
+random_st.uniform(0.5, D_arr_0p5, size=1)
+random_st.uniform(D_arr_like_0p5, 0.5)
+random_st.uniform(0.5, D_arr_like_0p5)
+random_st.uniform(D_arr_0p5, D_arr_0p5)
+random_st.uniform(D_arr_like_0p5, D_arr_like_0p5)
+random_st.uniform(D_arr_0p5, D_arr_0p5, size=1)
+random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.beta(0.5, 0.5)
+random_st.beta(0.5, 0.5, size=None)
+random_st.beta(0.5, 0.5, size=1)
+random_st.beta(D_arr_0p5, 0.5)
+random_st.beta(0.5, D_arr_0p5)
+random_st.beta(D_arr_0p5, 0.5, size=1)
+random_st.beta(0.5, D_arr_0p5, size=1)
+random_st.beta(D_arr_like_0p5, 0.5)
+random_st.beta(0.5, D_arr_like_0p5)
+random_st.beta(D_arr_0p5, D_arr_0p5)
+random_st.beta(D_arr_like_0p5, D_arr_like_0p5)
+random_st.beta(D_arr_0p5, D_arr_0p5, size=1)
+random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.f(0.5, 0.5)
+random_st.f(0.5, 0.5, size=None)
+random_st.f(0.5, 0.5, size=1)
+random_st.f(D_arr_0p5, 0.5)
+random_st.f(0.5, D_arr_0p5)
+random_st.f(D_arr_0p5, 0.5, size=1)
+random_st.f(0.5, D_arr_0p5, size=1)
+random_st.f(D_arr_like_0p5, 0.5)
+random_st.f(0.5, D_arr_like_0p5)
+random_st.f(D_arr_0p5, D_arr_0p5)
+random_st.f(D_arr_like_0p5, D_arr_like_0p5)
+random_st.f(D_arr_0p5, D_arr_0p5, size=1)
+random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.gamma(0.5, 0.5)
+random_st.gamma(0.5, 0.5, size=None)
+random_st.gamma(0.5, 0.5, size=1)
+random_st.gamma(D_arr_0p5, 0.5)
+random_st.gamma(0.5, D_arr_0p5)
+random_st.gamma(D_arr_0p5, 0.5, size=1)
+random_st.gamma(0.5, D_arr_0p5, size=1)
+random_st.gamma(D_arr_like_0p5, 0.5)
+random_st.gamma(0.5, D_arr_like_0p5)
+random_st.gamma(D_arr_0p5, D_arr_0p5)
+random_st.gamma(D_arr_like_0p5, D_arr_like_0p5)
+random_st.gamma(D_arr_0p5, D_arr_0p5, size=1)
+random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.gumbel(0.5, 0.5)
+random_st.gumbel(0.5, 0.5, size=None)
+random_st.gumbel(0.5, 0.5, size=1)
+random_st.gumbel(D_arr_0p5, 0.5)
+random_st.gumbel(0.5, D_arr_0p5)
+random_st.gumbel(D_arr_0p5, 0.5, size=1)
+random_st.gumbel(0.5, D_arr_0p5, size=1)
+random_st.gumbel(D_arr_like_0p5, 0.5)
+random_st.gumbel(0.5, D_arr_like_0p5)
+random_st.gumbel(D_arr_0p5, D_arr_0p5)
+random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5)
+random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1)
+random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.laplace(0.5, 0.5)
+random_st.laplace(0.5, 0.5, size=None)
+random_st.laplace(0.5, 0.5, size=1)
+random_st.laplace(D_arr_0p5, 0.5)
+random_st.laplace(0.5, D_arr_0p5)
+random_st.laplace(D_arr_0p5, 0.5, size=1)
+random_st.laplace(0.5, D_arr_0p5, size=1)
+random_st.laplace(D_arr_like_0p5, 0.5)
+random_st.laplace(0.5, D_arr_like_0p5)
+random_st.laplace(D_arr_0p5, D_arr_0p5)
+random_st.laplace(D_arr_like_0p5, D_arr_like_0p5)
+random_st.laplace(D_arr_0p5, D_arr_0p5, size=1)
+random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.logistic(0.5, 0.5)
+random_st.logistic(0.5, 0.5, size=None)
+random_st.logistic(0.5, 0.5, size=1)
+random_st.logistic(D_arr_0p5, 0.5)
+random_st.logistic(0.5, D_arr_0p5)
+random_st.logistic(D_arr_0p5, 0.5, size=1)
+random_st.logistic(0.5, D_arr_0p5, size=1)
+random_st.logistic(D_arr_like_0p5, 0.5)
+random_st.logistic(0.5, D_arr_like_0p5)
+random_st.logistic(D_arr_0p5, D_arr_0p5)
+random_st.logistic(D_arr_like_0p5, D_arr_like_0p5)
+random_st.logistic(D_arr_0p5, D_arr_0p5, size=1)
+random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.lognormal(0.5, 0.5)
+random_st.lognormal(0.5, 0.5, size=None)
+random_st.lognormal(0.5, 0.5, size=1)
+random_st.lognormal(D_arr_0p5, 0.5)
+random_st.lognormal(0.5, D_arr_0p5)
+random_st.lognormal(D_arr_0p5, 0.5, size=1)
+random_st.lognormal(0.5, D_arr_0p5, size=1)
+random_st.lognormal(D_arr_like_0p5, 0.5)
+random_st.lognormal(0.5, D_arr_like_0p5)
+random_st.lognormal(D_arr_0p5, D_arr_0p5)
+random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5)
+random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1)
+random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.noncentral_chisquare(0.5, 0.5)
+random_st.noncentral_chisquare(0.5, 0.5, size=None)
+random_st.noncentral_chisquare(0.5, 0.5, size=1)
+random_st.noncentral_chisquare(D_arr_0p5, 0.5)
+random_st.noncentral_chisquare(0.5, D_arr_0p5)
+random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1)
+random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1)
+random_st.noncentral_chisquare(D_arr_like_0p5, 0.5)
+random_st.noncentral_chisquare(0.5, D_arr_like_0p5)
+random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5)
+random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)
+random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)
+random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.normal(0.5, 0.5)
+random_st.normal(0.5, 0.5, size=None)
+random_st.normal(0.5, 0.5, size=1)
+random_st.normal(D_arr_0p5, 0.5)
+random_st.normal(0.5, D_arr_0p5)
+random_st.normal(D_arr_0p5, 0.5, size=1)
+random_st.normal(0.5, D_arr_0p5, size=1)
+random_st.normal(D_arr_like_0p5, 0.5)
+random_st.normal(0.5, D_arr_like_0p5)
+random_st.normal(D_arr_0p5, D_arr_0p5)
+random_st.normal(D_arr_like_0p5, D_arr_like_0p5)
+random_st.normal(D_arr_0p5, D_arr_0p5, size=1)
+random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.triangular(0.1, 0.5, 0.9)
+random_st.triangular(0.1, 0.5, 0.9, size=None)
+random_st.triangular(0.1, 0.5, 0.9, size=1)
+random_st.triangular(D_arr_0p1, 0.5, 0.9)
+random_st.triangular(0.1, D_arr_0p5, 0.9)
+random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+random_st.triangular(0.1, D_arr_0p5, 0.9, size=1)
+random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)
+random_st.triangular(0.5, D_arr_like_0p5, 0.9)
+random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9)
+random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+random_st.noncentral_f(0.1, 0.5, 0.9)
+random_st.noncentral_f(0.1, 0.5, 0.9, size=None)
+random_st.noncentral_f(0.1, 0.5, 0.9, size=1)
+random_st.noncentral_f(D_arr_0p1, 0.5, 0.9)
+random_st.noncentral_f(0.1, D_arr_0p5, 0.9)
+random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)
+random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)
+random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9)
+random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)
+random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+random_st.binomial(10, 0.5)
+random_st.binomial(10, 0.5, size=None)
+random_st.binomial(10, 0.5, size=1)
+random_st.binomial(I_arr_10, 0.5)
+random_st.binomial(10, D_arr_0p5)
+random_st.binomial(I_arr_10, 0.5, size=1)
+random_st.binomial(10, D_arr_0p5, size=1)
+random_st.binomial(I_arr_like_10, 0.5)
+random_st.binomial(10, D_arr_like_0p5)
+random_st.binomial(I_arr_10, D_arr_0p5)
+random_st.binomial(I_arr_like_10, D_arr_like_0p5)
+random_st.binomial(I_arr_10, D_arr_0p5, size=1)
+random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+random_st.negative_binomial(10, 0.5)
+random_st.negative_binomial(10, 0.5, size=None)
+random_st.negative_binomial(10, 0.5, size=1)
+random_st.negative_binomial(I_arr_10, 0.5)
+random_st.negative_binomial(10, D_arr_0p5)
+random_st.negative_binomial(I_arr_10, 0.5, size=1)
+random_st.negative_binomial(10, D_arr_0p5, size=1)
+random_st.negative_binomial(I_arr_like_10, 0.5)
+random_st.negative_binomial(10, D_arr_like_0p5)
+random_st.negative_binomial(I_arr_10, D_arr_0p5)
+random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5)
+random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1)
+random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+random_st.hypergeometric(20, 20, 10)
+random_st.hypergeometric(20, 20, 10, size=None)
+random_st.hypergeometric(20, 20, 10, size=1)
+random_st.hypergeometric(I_arr_20, 20, 10)
+random_st.hypergeometric(20, I_arr_20, 10)
+random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)
+random_st.hypergeometric(20, I_arr_20, 10, size=1)
+random_st.hypergeometric(I_arr_like_20, 20, I_arr_10)
+random_st.hypergeometric(20, I_arr_like_20, 10)
+random_st.hypergeometric(I_arr_20, I_arr_20, 10)
+random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10)
+random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)
+random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)
+
+random_st.randint(0, 100)
+random_st.randint(100)
+random_st.randint([100])
+random_st.randint(0, [100])
+
+random_st.randint(2, dtype=bool)
+random_st.randint(0, 2, dtype=bool)
+random_st.randint(I_bool_high_open, dtype=bool)
+random_st.randint(I_bool_low, I_bool_high_open, dtype=bool)
+random_st.randint(0, I_bool_high_open, dtype=bool)
+
+random_st.randint(2, dtype=np.bool)
+random_st.randint(0, 2, dtype=np.bool)
+random_st.randint(I_bool_high_open, dtype=np.bool)
+random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool)
+random_st.randint(0, I_bool_high_open, dtype=np.bool)
+
+random_st.randint(256, dtype="u1")
+random_st.randint(0, 256, dtype="u1")
+random_st.randint(I_u1_high_open, dtype="u1")
+random_st.randint(I_u1_low, I_u1_high_open, dtype="u1")
+random_st.randint(0, I_u1_high_open, dtype="u1")
+
+random_st.randint(256, dtype="uint8")
+random_st.randint(0, 256, dtype="uint8")
+random_st.randint(I_u1_high_open, dtype="uint8")
+random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8")
+random_st.randint(0, I_u1_high_open, dtype="uint8")
+
+random_st.randint(256, dtype=np.uint8)
+random_st.randint(0, 256, dtype=np.uint8)
+random_st.randint(I_u1_high_open, dtype=np.uint8)
+random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8)
+random_st.randint(0, I_u1_high_open, dtype=np.uint8)
+
+random_st.randint(65536, dtype="u2")
+random_st.randint(0, 65536, dtype="u2")
+random_st.randint(I_u2_high_open, dtype="u2")
+random_st.randint(I_u2_low, I_u2_high_open, dtype="u2")
+random_st.randint(0, I_u2_high_open, dtype="u2")
+
+random_st.randint(65536, dtype="uint16")
+random_st.randint(0, 65536, dtype="uint16")
+random_st.randint(I_u2_high_open, dtype="uint16")
+random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16")
+random_st.randint(0, I_u2_high_open, dtype="uint16")
+
+random_st.randint(65536, dtype=np.uint16)
+random_st.randint(0, 65536, dtype=np.uint16)
+random_st.randint(I_u2_high_open, dtype=np.uint16)
+random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16)
+random_st.randint(0, I_u2_high_open, dtype=np.uint16)
+
+random_st.randint(4294967296, dtype="u4")
+random_st.randint(0, 4294967296, dtype="u4")
+random_st.randint(I_u4_high_open, dtype="u4")
+random_st.randint(I_u4_low, I_u4_high_open, dtype="u4")
+random_st.randint(0, I_u4_high_open, dtype="u4")
+
+random_st.randint(4294967296, dtype="uint32")
+random_st.randint(0, 4294967296, dtype="uint32")
+random_st.randint(I_u4_high_open, dtype="uint32")
+random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32")
+random_st.randint(0, I_u4_high_open, dtype="uint32")
+
+random_st.randint(4294967296, dtype=np.uint32)
+random_st.randint(0, 4294967296, dtype=np.uint32)
+random_st.randint(I_u4_high_open, dtype=np.uint32)
+random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32)
+random_st.randint(0, I_u4_high_open, dtype=np.uint32)
+
+
+random_st.randint(18446744073709551616, dtype="u8")
+random_st.randint(0, 18446744073709551616, dtype="u8")
+random_st.randint(I_u8_high_open, dtype="u8")
+random_st.randint(I_u8_low, I_u8_high_open, dtype="u8")
+random_st.randint(0, I_u8_high_open, dtype="u8")
+
+random_st.randint(18446744073709551616, dtype="uint64")
+random_st.randint(0, 18446744073709551616, dtype="uint64")
+random_st.randint(I_u8_high_open, dtype="uint64")
+random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64")
+random_st.randint(0, I_u8_high_open, dtype="uint64")
+
+random_st.randint(18446744073709551616, dtype=np.uint64)
+random_st.randint(0, 18446744073709551616, dtype=np.uint64)
+random_st.randint(I_u8_high_open, dtype=np.uint64)
+random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64)
+random_st.randint(0, I_u8_high_open, dtype=np.uint64)
+
+random_st.randint(128, dtype="i1")
+random_st.randint(-128, 128, dtype="i1")
+random_st.randint(I_i1_high_open, dtype="i1")
+random_st.randint(I_i1_low, I_i1_high_open, dtype="i1")
+random_st.randint(-128, I_i1_high_open, dtype="i1")
+
+random_st.randint(128, dtype="int8")
+random_st.randint(-128, 128, dtype="int8")
+random_st.randint(I_i1_high_open, dtype="int8")
+random_st.randint(I_i1_low, I_i1_high_open, dtype="int8")
+random_st.randint(-128, I_i1_high_open, dtype="int8")
+
+random_st.randint(128, dtype=np.int8)
+random_st.randint(-128, 128, dtype=np.int8)
+random_st.randint(I_i1_high_open, dtype=np.int8)
+random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8)
+random_st.randint(-128, I_i1_high_open, dtype=np.int8)
+
+random_st.randint(32768, dtype="i2")
+random_st.randint(-32768, 32768, dtype="i2")
+random_st.randint(I_i2_high_open, dtype="i2")
+random_st.randint(I_i2_low, I_i2_high_open, dtype="i2")
+random_st.randint(-32768, I_i2_high_open, dtype="i2")
+random_st.randint(32768, dtype="int16")
+random_st.randint(-32768, 32768, dtype="int16")
+random_st.randint(I_i2_high_open, dtype="int16")
+random_st.randint(I_i2_low, I_i2_high_open, dtype="int16")
+random_st.randint(-32768, I_i2_high_open, dtype="int16")
+random_st.randint(32768, dtype=np.int16)
+random_st.randint(-32768, 32768, dtype=np.int16)
+random_st.randint(I_i2_high_open, dtype=np.int16)
+random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16)
+random_st.randint(-32768, I_i2_high_open, dtype=np.int16)
+
+random_st.randint(2147483648, dtype="i4")
+random_st.randint(-2147483648, 2147483648, dtype="i4")
+random_st.randint(I_i4_high_open, dtype="i4")
+random_st.randint(I_i4_low, I_i4_high_open, dtype="i4")
+random_st.randint(-2147483648, I_i4_high_open, dtype="i4")
+
+random_st.randint(2147483648, dtype="int32")
+random_st.randint(-2147483648, 2147483648, dtype="int32")
+random_st.randint(I_i4_high_open, dtype="int32")
+random_st.randint(I_i4_low, I_i4_high_open, dtype="int32")
+random_st.randint(-2147483648, I_i4_high_open, dtype="int32")
+
+random_st.randint(2147483648, dtype=np.int32)
+random_st.randint(-2147483648, 2147483648, dtype=np.int32)
+random_st.randint(I_i4_high_open, dtype=np.int32)
+random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32)
+random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32)
+
+random_st.randint(9223372036854775808, dtype="i8")
+random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8")
+random_st.randint(I_i8_high_open, dtype="i8")
+random_st.randint(I_i8_low, I_i8_high_open, dtype="i8")
+random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8")
+
+random_st.randint(9223372036854775808, dtype="int64")
+random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64")
+random_st.randint(I_i8_high_open, dtype="int64")
+random_st.randint(I_i8_low, I_i8_high_open, dtype="int64")
+random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64")
+
+random_st.randint(9223372036854775808, dtype=np.int64)
+random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64)
+random_st.randint(I_i8_high_open, dtype=np.int64)
+random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64)
+random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64)
+
+bg: np.random.BitGenerator = random_st._bit_generator
+
+random_st.bytes(2)
+
+random_st.choice(5)
+random_st.choice(5, 3)
+random_st.choice(5, 3, replace=True)
+random_st.choice(5, 3, p=[1 / 5] * 5)
+random_st.choice(5, 3, p=[1 / 5] * 5, replace=False)
+
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"])
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))
+
+random_st.dirichlet([0.5, 0.5])
+random_st.dirichlet(np.array([0.5, 0.5]))
+random_st.dirichlet(np.array([0.5, 0.5]), size=3)
+
+random_st.multinomial(20, [1 / 6.0] * 6)
+random_st.multinomial(20, np.array([0.5, 0.5]))
+random_st.multinomial(20, [1 / 6.0] * 6, size=2)
+
+random_st.multivariate_normal([0.0], [[1.0]])
+random_st.multivariate_normal([0.0], np.array([[1.0]]))
+random_st.multivariate_normal(np.array([0.0]), [[1.0]])
+random_st.multivariate_normal([0.0], np.array([[1.0]]))
+
+random_st.permutation(10)
+random_st.permutation([1, 2, 3, 4])
+random_st.permutation(np.array([1, 2, 3, 4]))
+random_st.permutation(D_2D)
+
+random_st.shuffle(np.arange(10))
+random_st.shuffle([1, 2, 3, 4, 5])
+random_st.shuffle(D_2D)
+
+np.random.RandomState(SEED_PCG64)
+np.random.RandomState(0)
+np.random.RandomState([0, 1, 2])
+random_st.__str__()
+random_st.__repr__()
+random_st_state = random_st.__getstate__()
+random_st.__setstate__(random_st_state)
+random_st.seed()
+random_st.seed(1)
+random_st.seed([0, 1])
+random_st_get_state = random_st.get_state()
+random_st_get_state_legacy = random_st.get_state(legacy=True)
+random_st.set_state(random_st_get_state)
+
+random_st.rand()
+random_st.rand(1)
+random_st.rand(1, 2)
+random_st.randn()
+random_st.randn(1)
+random_st.randn(1, 2)
+random_st.random_sample()
+random_st.random_sample(1)
+random_st.random_sample(size=(1, 2))
+
+random_st.tomaxint()
+random_st.tomaxint(1)
+random_st.tomaxint((1,))
+
+np.random.mtrand.set_bit_generator(SEED_PCG64)
+np.random.mtrand.get_bit_generator()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/recfunctions.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/recfunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..03322e064be4407c8748d415cc26f7e5861e1b7a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/recfunctions.py
@@ -0,0 +1,162 @@
+"""These tests are based on the doctests from `numpy/lib/recfunctions.py`."""
+
+from typing import Any
+from typing_extensions import assert_type
+
+import numpy as np
+import numpy.typing as npt
+from numpy.lib import recfunctions as rfn
+
+
+def test_recursive_fill_fields() -> None:
+    a: npt.NDArray[np.void] = np.array(
+        [(1, 10.0), (2, 20.0)],
+        dtype=[("A", np.int64), ("B", np.float64)],
+    )
+    b = np.zeros((int(3),), dtype=a.dtype)
+    out = rfn.recursive_fill_fields(a, b)
+    assert_type(out, np.ndarray[tuple[int], np.dtype[np.void]])
+
+
+def test_get_names() -> None:
+    names: tuple[str | Any, ...]
+    names = rfn.get_names(np.empty((1,), dtype=[("A", int)]).dtype)
+    names = rfn.get_names(np.empty((1,), dtype=[("A", int), ("B", float)]).dtype)
+
+    adtype = np.dtype([("a", int), ("b", [("b_a", int), ("b_b", int)])])
+    names = rfn.get_names(adtype)
+
+
+def test_get_names_flat() -> None:
+    names: tuple[str, ...]
+    names = rfn.get_names_flat(np.empty((1,), dtype=[("A", int)]).dtype)
+    names = rfn.get_names_flat(np.empty((1,), dtype=[("A", int), ("B", float)]).dtype)
+
+    adtype = np.dtype([("a", int), ("b", [("b_a", int), ("b_b", int)])])
+    names = rfn.get_names_flat(adtype)
+
+
+def test_flatten_descr() -> None:
+    ndtype = np.dtype([("a", "<i4"), ("b", [("b_a", "<f8"), ("b_b", "<i4")])])
+    assert_type(rfn.flatten_descr(ndtype), tuple[tuple[str, np.dtype[Any]]])
+
+
+def test_get_fieldstructure() -> None:
+    ndtype = np.dtype([
+        ("A", int),
+        ("B", [("B_A", int), ("B_B", [("B_B_A", int), ("B_B_B", int)])]),
+    ])
+    assert_type(rfn.get_fieldstructure(ndtype), dict[str, list[str]])
+
+
+def test_merge_arrays() -> None:
+    assert_type(
+        rfn.merge_arrays((
+            np.ones((int(2),), np.int_),
+            np.ones((int(3),), np.float64),
+        )),
+        np.recarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_drop_fields() -> None:
+    ndtype = [("a", np.int64), ("b", [("b_a", np.double), ("b_b", np.int64)])]
+    a = np.ones((int(3),), dtype=ndtype)
+
+    assert_type(
+        rfn.drop_fields(a, "a"),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+    assert_type(
+        rfn.drop_fields(a, "a", asrecarray=True),
+        np.rec.recarray[tuple[int], np.dtype[np.void]],
+    )
+    assert_type(
+        rfn.rec_drop_fields(a, "a"),
+        np.rec.recarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_rename_fields() -> None:
+    ndtype = [("a", np.int64), ("b", [("b_a", np.double), ("b_b", np.int64)])]
+    a = np.ones((int(3),), dtype=ndtype)
+
+    assert_type(
+        rfn.rename_fields(a, {"a": "A", "b_b": "B_B"}),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_repack_fields() -> None:
+    dt: np.dtype[np.void] = np.dtype("u1, <i8, <f8", align=True)
+
+    assert_type(rfn.repack_fields(dt), np.dtype[np.void])
+    assert_type(rfn.repack_fields(dt.type(0)), np.void)
+    assert_type(
+        rfn.repack_fields(np.ones((int(3),), dtype=dt)),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_structured_to_unstructured() -> None:
+    a = np.zeros(4, dtype=[("a", "i4"), ("b", "f4,u2"), ("c", "f4", 2)])
+    assert_type(rfn.structured_to_unstructured(a), npt.NDArray[Any])
+
+
+def unstructured_to_structured() -> None:
+    dt: np.dtype[np.void] = np.dtype([("a", "i4"), ("b", "f4,u2"), ("c", "f4", 2)])
+    a = np.arange(20, dtype=np.int32).reshape((4, 5))
+    assert_type(rfn.unstructured_to_structured(a, dt), npt.NDArray[np.void])
+
+
+def test_apply_along_fields() -> None:
+    b = np.ones(4, dtype=[("x", "i4"), ("y", "f4"), ("z", "f8")])
+    assert_type(
+        rfn.apply_along_fields(np.mean, b),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_assign_fields_by_name() -> None:
+    b = np.ones(4, dtype=[("x", "i4"), ("y", "f4"), ("z", "f8")])
+    assert_type(
+        rfn.apply_along_fields(np.mean, b),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_require_fields() -> None:
+    a = np.ones(4, dtype=[("a", "i4"), ("b", "f8"), ("c", "u1")])
+    assert_type(
+        rfn.require_fields(a, [("b", "f4"), ("c", "u1")]),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_stack_arrays() -> None:
+    x = np.zeros((int(2),), np.int32)
+    assert_type(
+        rfn.stack_arrays(x),
+        np.ndarray[tuple[int], np.dtype[np.int32]],
+    )
+
+    z = np.ones((int(2),), [("A", "|S3"), ("B", float)])
+    zz = np.ones((int(2),), [("A", "|S3"), ("B", np.float64), ("C", np.float64)])
+    assert_type(
+        rfn.stack_arrays((z, zz)),
+        np.ma.MaskedArray[tuple[int, ...], np.dtype[np.void]],
+    )
+
+
+def test_find_duplicates() -> None:
+    ndtype = np.dtype([("a", int)])
+
+    a = np.ma.ones(7, mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
+    assert_type(rfn.find_duplicates(a), np.ma.MaskedArray[Any, np.dtype[np.void]])
+    assert_type(
+        rfn.find_duplicates(a, ignoremask=True, return_index=True),
+        tuple[
+            np.ma.MaskedArray[Any, np.dtype[np.void]],
+            np.ndarray[Any, np.dtype[np.int_]],
+        ],
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/scalars.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/scalars.py
new file mode 100644
index 0000000000000000000000000000000000000000..89f24cb92991ba6586b21e1d707fb0d023731b89
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/scalars.py
@@ -0,0 +1,248 @@
+import datetime as dt
+
+import pytest
+import numpy as np
+
+b =  np.bool()
+b_ = np.bool_()
+u8 = np.uint64()
+i8 = np.int64()
+f8 = np.float64()
+c16 = np.complex128()
+U = np.str_()
+S = np.bytes_()
+
+
+# Construction
+class D:
+    def __index__(self) -> int:
+        return 0
+
+
+class C:
+    def __complex__(self) -> complex:
+        return 3j
+
+
+class B:
+    def __int__(self) -> int:
+        return 4
+
+
+class A:
+    def __float__(self) -> float:
+        return 4.0
+
+
+np.complex64(3j)
+np.complex64(A())
+np.complex64(C())
+np.complex128(3j)
+np.complex128(C())
+np.complex128(None)
+np.complex64("1.2")
+np.complex128(b"2j")
+
+np.int8(4)
+np.int16(3.4)
+np.int32(4)
+np.int64(-1)
+np.uint8(B())
+np.uint32()
+np.int32("1")
+np.int64(b"2")
+
+np.float16(A())
+np.float32(16)
+np.float64(3.0)
+np.float64(None)
+np.float32("1")
+np.float16(b"2.5")
+
+np.uint64(D())
+np.float32(D())
+np.complex64(D())
+
+np.bytes_(b"hello")
+np.bytes_("hello", 'utf-8')
+np.bytes_("hello", encoding='utf-8')
+np.str_("hello")
+np.str_(b"hello", 'utf-8')
+np.str_(b"hello", encoding='utf-8')
+
+# Array-ish semantics
+np.int8().real
+np.int16().imag
+np.int32().data
+np.int64().flags
+
+np.uint8().itemsize * 2
+np.uint16().ndim + 1
+np.uint32().strides
+np.uint64().shape
+
+# Time structures
+np.datetime64()
+np.datetime64(0, "D")
+np.datetime64(0, b"D")
+np.datetime64(0, ('ms', 3))
+np.datetime64("2019")
+np.datetime64(b"2019")
+np.datetime64("2019", "D")
+np.datetime64("2019", "us")
+np.datetime64("2019", "as")
+np.datetime64(np.datetime64())
+np.datetime64(np.datetime64())
+np.datetime64(dt.datetime(2000, 5, 3))
+np.datetime64(dt.datetime(2000, 5, 3), "D")
+np.datetime64(dt.datetime(2000, 5, 3), "us")
+np.datetime64(dt.datetime(2000, 5, 3), "as")
+np.datetime64(dt.date(2000, 5, 3))
+np.datetime64(dt.date(2000, 5, 3), "D")
+np.datetime64(dt.date(2000, 5, 3), "us")
+np.datetime64(dt.date(2000, 5, 3), "as")
+np.datetime64(None)
+np.datetime64(None, "D")
+
+np.timedelta64()
+np.timedelta64(0)
+np.timedelta64(0, "D")
+np.timedelta64(0, ('ms', 3))
+np.timedelta64(0, b"D")
+np.timedelta64("3")
+np.timedelta64(b"5")
+np.timedelta64(np.timedelta64(2))
+np.timedelta64(dt.timedelta(2))
+np.timedelta64(None)
+np.timedelta64(None, "D")
+
+np.void(1)
+np.void(np.int64(1))
+np.void(True)
+np.void(np.bool(True))
+np.void(b"test")
+np.void(np.bytes_("test"))
+np.void(object(), [("a", "O"), ("b", "O")])
+np.void(object(), dtype=[("a", "O"), ("b", "O")])
+
+# Protocols
+i8 = np.int64()
+u8 = np.uint64()
+f8 = np.float64()
+c16 = np.complex128()
+b = np.bool()
+td = np.timedelta64()
+U = np.str_("1")
+S = np.bytes_("1")
+AR = np.array(1, dtype=np.float64)
+
+int(i8)
+int(u8)
+int(f8)
+int(b)
+int(td)
+int(U)
+int(S)
+int(AR)
+with pytest.warns(np.exceptions.ComplexWarning):
+    int(c16)
+
+float(i8)
+float(u8)
+float(f8)
+float(b_)
+float(td)
+float(U)
+float(S)
+float(AR)
+with pytest.warns(np.exceptions.ComplexWarning):
+    float(c16)
+
+complex(i8)
+complex(u8)
+complex(f8)
+complex(c16)
+complex(b_)
+complex(td)
+complex(U)
+complex(AR)
+
+
+# Misc
+c16.dtype
+c16.real
+c16.imag
+c16.real.real
+c16.real.imag
+c16.ndim
+c16.size
+c16.itemsize
+c16.shape
+c16.strides
+c16.squeeze()
+c16.byteswap()
+c16.transpose()
+
+# Aliases
+np.byte()
+np.short()
+np.intc()
+np.intp()
+np.int_()
+np.longlong()
+
+np.ubyte()
+np.ushort()
+np.uintc()
+np.uintp()
+np.uint()
+np.ulonglong()
+
+np.half()
+np.single()
+np.double()
+np.longdouble()
+
+np.csingle()
+np.cdouble()
+np.clongdouble()
+
+b.item()
+i8.item()
+u8.item()
+f8.item()
+c16.item()
+U.item()
+S.item()
+
+b.tolist()
+i8.tolist()
+u8.tolist()
+f8.tolist()
+c16.tolist()
+U.tolist()
+S.tolist()
+
+b.ravel()
+i8.ravel()
+u8.ravel()
+f8.ravel()
+c16.ravel()
+U.ravel()
+S.ravel()
+
+b.flatten()
+i8.flatten()
+u8.flatten()
+f8.flatten()
+c16.flatten()
+U.flatten()
+S.flatten()
+
+b.reshape(1)
+i8.reshape(1)
+u8.reshape(1)
+f8.reshape(1)
+c16.reshape(1)
+U.reshape(1)
+S.reshape(1)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/shape.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/shape.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab1ae3d9bc79e19d6000bee350b18fe34d9abdc0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/shape.py
@@ -0,0 +1,21 @@
+from typing import Any, NamedTuple, cast
+
+import numpy as np
+
+
+# Subtype of tuple[int, int]
+class XYGrid(NamedTuple):
+    x_axis: int
+    y_axis: int
+
+# TODO: remove this cast after: https://github.com/numpy/numpy/pull/27171
+arr: np.ndarray[XYGrid, Any] = cast(
+    np.ndarray[XYGrid, Any],
+    np.empty(XYGrid(2, 2)),
+)
+
+# Test variance of _ShapeType_co
+def accepts_2d(a: np.ndarray[tuple[int, int], Any]) -> None:
+    return None
+
+accepts_2d(arr)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/simple.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/simple.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f44e6e76f8353d55a29f6cc0608d39e7db433dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/simple.py
@@ -0,0 +1,168 @@
+"""Simple expression that should pass with mypy."""
+import operator
+
+import numpy as np
+import numpy.typing as npt
+from collections.abc import Iterable
+
+# Basic checks
+array = np.array([1, 2])
+
+
+def ndarray_func(x: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
+    return x
+
+
+ndarray_func(np.array([1, 2], dtype=np.float64))
+array == 1
+array.dtype == float
+
+# Dtype construction
+np.dtype(float)
+np.dtype(np.float64)
+np.dtype(None)
+np.dtype("float64")
+np.dtype(np.dtype(float))
+np.dtype(("U", 10))
+np.dtype((np.int32, (2, 2)))
+# Define the arguments on the previous line to prevent bidirectional
+# type inference in mypy from broadening the types.
+two_tuples_dtype = [("R", "u1"), ("G", "u1"), ("B", "u1")]
+np.dtype(two_tuples_dtype)
+
+three_tuples_dtype = [("R", "u1", 2)]
+np.dtype(three_tuples_dtype)
+
+mixed_tuples_dtype = [("R", "u1"), ("G", np.str_, 1)]
+np.dtype(mixed_tuples_dtype)
+
+shape_tuple_dtype = [("R", "u1", (2, 2))]
+np.dtype(shape_tuple_dtype)
+
+shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.str_, 1)]
+np.dtype(shape_like_dtype)
+
+object_dtype = [("field1", object)]
+np.dtype(object_dtype)
+
+np.dtype((np.int32, (np.int8, 4)))
+
+# Dtype comparison
+np.dtype(float) == float
+np.dtype(float) != np.float64
+np.dtype(float) < None
+np.dtype(float) <= "float64"
+np.dtype(float) > np.dtype(float)
+np.dtype(float) >= np.dtype(("U", 10))
+
+# Iteration and indexing
+def iterable_func(x: Iterable[object]) -> Iterable[object]:
+    return x
+
+
+iterable_func(array)
+list(array)
+iter(array)
+zip(array, array)
+array[1]
+array[:]
+array[...]
+array[:] = 0
+
+array_2d = np.ones((3, 3))
+array_2d[:2, :2]
+array_2d[:2, :2] = 0
+array_2d[..., 0]
+array_2d[..., 0] = 2
+array_2d[-1, -1] = None
+
+array_obj = np.zeros(1, dtype=np.object_)
+array_obj[0] = slice(None)
+
+# Other special methods
+len(array)
+str(array)
+array_scalar = np.array(1)
+int(array_scalar)
+float(array_scalar)
+complex(array_scalar)
+bytes(array_scalar)
+operator.index(array_scalar)
+bool(array_scalar)
+
+# comparisons
+array < 1
+array <= 1
+array == 1
+array != 1
+array > 1
+array >= 1
+1 < array
+1 <= array
+1 == array
+1 != array
+1 > array
+1 >= array
+
+# binary arithmetic
+array + 1
+1 + array
+array += 1
+
+array - 1
+1 - array
+array -= 1
+
+array * 1
+1 * array
+array *= 1
+
+nonzero_array = np.array([1, 2])
+array / 1
+1 / nonzero_array
+float_array = np.array([1.0, 2.0])
+float_array /= 1
+
+array // 1
+1 // nonzero_array
+array //= 1
+
+array % 1
+1 % nonzero_array
+array %= 1
+
+divmod(array, 1)
+divmod(1, nonzero_array)
+
+array ** 1
+1 ** array
+array **= 1
+
+array << 1
+1 << array
+array <<= 1
+
+array >> 1
+1 >> array
+array >>= 1
+
+array & 1
+1 & array
+array &= 1
+
+array ^ 1
+1 ^ array
+array ^= 1
+
+array | 1
+1 | array
+array |= 1
+
+# unary arithmetic
+-array
++array
+abs(array)
+~array
+
+# Other methods
+np.array([1, 2]).transpose()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/simple_py3.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/simple_py3.py
new file mode 100644
index 0000000000000000000000000000000000000000..c05a1ce612ac5e40d0914732c4c72ad1d3f2d552
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/simple_py3.py
@@ -0,0 +1,6 @@
+import numpy as np
+
+array = np.array([1, 2])
+
+# The @ operator is not in python 2
+array @ array
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufunc_config.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufunc_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..778e1b57f7e3831464f0bc9ba37e4baddcdf49d8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufunc_config.py
@@ -0,0 +1,64 @@
+"""Typing tests for `numpy._core._ufunc_config`."""
+
+import numpy as np
+
+
+def func1(a: str, b: int) -> None:
+    return None
+
+
+def func2(a: str, b: int, c: float = 1.0) -> None:
+    return None
+
+
+def func3(a: str, b: int) -> int:
+    return 0
+
+
+class Write1:
+    def write(self, a: str) -> None:
+        return None
+
+
+class Write2:
+    def write(self, a: str, b: int = 1) -> None:
+        return None
+
+
+class Write3:
+    def write(self, a: str) -> int:
+        return 0
+
+
+_err_default = np.geterr()
+_bufsize_default = np.getbufsize()
+_errcall_default = np.geterrcall()
+
+try:
+    np.seterr(all=None)
+    np.seterr(divide="ignore")
+    np.seterr(over="warn")
+    np.seterr(under="call")
+    np.seterr(invalid="raise")
+    np.geterr()
+
+    np.setbufsize(4096)
+    np.getbufsize()
+
+    np.seterrcall(func1)
+    np.seterrcall(func2)
+    np.seterrcall(func3)
+    np.seterrcall(Write1())
+    np.seterrcall(Write2())
+    np.seterrcall(Write3())
+    np.geterrcall()
+
+    with np.errstate(call=func1, all="call"):
+        pass
+    with np.errstate(call=Write1(), divide="log", over="log"):
+        pass
+
+finally:
+    np.seterr(**_err_default)
+    np.setbufsize(_bufsize_default)
+    np.seterrcall(_errcall_default)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufunclike.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufunclike.py
new file mode 100644
index 0000000000000000000000000000000000000000..f993939ddba12851c5c93d3eed0f6d0923d0b98d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufunclike.py
@@ -0,0 +1,47 @@
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+
+class Object:
+    def __ceil__(self) -> Object:
+        return self
+
+    def __floor__(self) -> Object:
+        return self
+
+    def __ge__(self, value: object) -> bool:
+        return True
+
+    def __array__(self, dtype: np.typing.DTypeLike | None = None,
+                  copy: bool | None = None) -> np.ndarray[Any, np.dtype[np.object_]]:
+        ret = np.empty((), dtype=object)
+        ret[()] = self
+        return ret
+
+
+AR_LIKE_b = [True, True, False]
+AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
+AR_LIKE_i = [1, 2, 3]
+AR_LIKE_f = [1.0, 2.0, 3.0]
+AR_LIKE_O = [Object(), Object(), Object()]
+AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.zeros(3, dtype="U5")
+
+np.fix(AR_LIKE_b)
+np.fix(AR_LIKE_u)
+np.fix(AR_LIKE_i)
+np.fix(AR_LIKE_f)
+np.fix(AR_LIKE_O)
+np.fix(AR_LIKE_f, out=AR_U)
+
+np.isposinf(AR_LIKE_b)
+np.isposinf(AR_LIKE_u)
+np.isposinf(AR_LIKE_i)
+np.isposinf(AR_LIKE_f)
+np.isposinf(AR_LIKE_f, out=AR_U)
+
+np.isneginf(AR_LIKE_b)
+np.isneginf(AR_LIKE_u)
+np.isneginf(AR_LIKE_i)
+np.isneginf(AR_LIKE_f)
+np.isneginf(AR_LIKE_f, out=AR_U)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufuncs.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbc61bb0b17b594865bc909787798238c5b32bf5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/ufuncs.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+np.sin(1)
+np.sin([1, 2, 3])
+np.sin(1, out=np.empty(1))
+np.matmul(np.ones((2, 2, 2)), np.ones((2, 2, 2)), axes=[(0, 1), (0, 1), (0, 1)])
+np.sin(1, signature="D->D")
+# NOTE: `np.generic` subclasses are not guaranteed to support addition;
+# re-enable this we can infer the exact return type of `np.sin(...)`.
+#
+# np.sin(1) + np.sin(1)
+np.sin.types[0]
+np.sin.__name__
+np.sin.__doc__
+
+np.abs(np.array([1]))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..c351afb084c57b2d1264a3ff020ed1e63703a623
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py
@@ -0,0 +1,6 @@
+import numpy.exceptions as ex
+
+ex.AxisError("test")
+ex.AxisError(1, ndim=2)
+ex.AxisError(1, ndim=2, msg_prefix="error")
+ex.AxisError(1, ndim=2, msg_prefix=None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c0b94bae08a10aca0a72cba8ee54ef87e19c7ab7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi
@@ -0,0 +1,677 @@
+import datetime as dt
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _32Bit,_64Bit, _128Bit
+
+from typing_extensions import assert_type
+
+b: bool
+c: complex
+f: float
+i: int
+
+c16: np.complex128
+c8: np.complex64
+
+# Can't directly import `np.float128` as it is not available on all platforms
+f16: np.floating[_128Bit]
+f8: np.float64
+f4: np.float32
+
+i8: np.int64
+i4: np.int32
+
+u8: np.uint64
+u4: np.uint32
+
+b_: np.bool
+
+M8: np.datetime64
+M8_none: np.datetime64[None]
+M8_date: np.datetime64[dt.date]
+M8_time: np.datetime64[dt.datetime]
+M8_int: np.datetime64[int]
+date: dt.date
+time: dt.datetime
+
+m8: np.timedelta64
+m8_none: np.timedelta64[None]
+m8_int: np.timedelta64[int]
+m8_delta: np.timedelta64[dt.timedelta]
+delta: dt.timedelta
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint32]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_floating: npt.NDArray[np.floating]
+AR_number: npt.NDArray[np.number]
+AR_Any: npt.NDArray[Any]
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_m: list[np.timedelta64]
+AR_LIKE_M: list[np.datetime64]
+AR_LIKE_O: list[np.object_]
+
+
+# Array subtraction
+
+assert_type(AR_number - AR_number, npt.NDArray[np.number[Any]])
+
+assert_type(AR_b - AR_LIKE_u, npt.NDArray[np.uint32])
+assert_type(AR_b - AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_b - AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_b - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_b - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_b - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_u - AR_b, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_i - AR_b, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f - AR_b, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_c - AR_b, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_m - AR_b, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_b, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_b, Any)
+
+assert_type(AR_u - AR_LIKE_b, npt.NDArray[np.uint32])
+assert_type(AR_u - AR_LIKE_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_u - AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_u - AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_u - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_u - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_u - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_u, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_u - AR_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_LIKE_i - AR_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f - AR_u, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_c - AR_u, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_m - AR_u, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_u, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_u, Any)
+
+assert_type(AR_i - AR_LIKE_b, npt.NDArray[np.int64])
+assert_type(AR_i - AR_LIKE_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i - AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i - AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_i - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_i - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_i - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_i, npt.NDArray[np.int64])
+assert_type(AR_LIKE_u - AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_i - AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f - AR_i, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_c - AR_i, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_m - AR_i, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_i, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_i, Any)
+
+assert_type(AR_f - AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_f - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_c - AR_f, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_O - AR_f, Any)
+
+assert_type(AR_c - AR_LIKE_b, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_u, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_i, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_f, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_c, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_u - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_i - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_f - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_c - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_O - AR_c, Any)
+
+assert_type(AR_m - AR_LIKE_b, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_u, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_i, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_u - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_i - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_m - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_m, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_m, Any)
+
+assert_type(AR_M - AR_LIKE_b, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_u, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_i, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_m, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_M, npt.NDArray[np.timedelta64])
+assert_type(AR_M - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_M - AR_M, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O - AR_M, Any)
+
+assert_type(AR_O - AR_LIKE_b, Any)
+assert_type(AR_O - AR_LIKE_u, Any)
+assert_type(AR_O - AR_LIKE_i, Any)
+assert_type(AR_O - AR_LIKE_f, Any)
+assert_type(AR_O - AR_LIKE_c, Any)
+assert_type(AR_O - AR_LIKE_m, Any)
+assert_type(AR_O - AR_LIKE_M, Any)
+assert_type(AR_O - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_O, Any)
+assert_type(AR_LIKE_u - AR_O, Any)
+assert_type(AR_LIKE_i - AR_O, Any)
+assert_type(AR_LIKE_f - AR_O, Any)
+assert_type(AR_LIKE_c - AR_O, Any)
+assert_type(AR_LIKE_m - AR_O, Any)
+assert_type(AR_LIKE_M - AR_O, Any)
+assert_type(AR_LIKE_O - AR_O, Any)
+
+# Array "true" division
+
+assert_type(AR_f / b, npt.NDArray[np.float64])
+assert_type(AR_f / i, npt.NDArray[np.float64])
+assert_type(AR_f / f, npt.NDArray[np.float64])
+
+assert_type(b / AR_f, npt.NDArray[np.float64])
+assert_type(i / AR_f, npt.NDArray[np.float64])
+assert_type(f / AR_f, npt.NDArray[np.float64])
+
+assert_type(AR_b / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_O / AR_b, Any)
+
+assert_type(AR_u / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m / AR_u, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O / AR_u, Any)
+
+assert_type(AR_i / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m / AR_i, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O / AR_i, Any)
+
+assert_type(AR_f / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m / AR_f, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O / AR_f, Any)
+
+assert_type(AR_m / AR_LIKE_u, npt.NDArray[np.timedelta64])
+assert_type(AR_m / AR_LIKE_i, npt.NDArray[np.timedelta64])
+assert_type(AR_m / AR_LIKE_f, npt.NDArray[np.timedelta64])
+assert_type(AR_m / AR_LIKE_m, npt.NDArray[np.float64])
+assert_type(AR_m / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_m / AR_m, npt.NDArray[np.float64])
+assert_type(AR_LIKE_O / AR_m, Any)
+
+assert_type(AR_O / AR_LIKE_b, Any)
+assert_type(AR_O / AR_LIKE_u, Any)
+assert_type(AR_O / AR_LIKE_i, Any)
+assert_type(AR_O / AR_LIKE_f, Any)
+assert_type(AR_O / AR_LIKE_m, Any)
+assert_type(AR_O / AR_LIKE_M, Any)
+assert_type(AR_O / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_O, Any)
+assert_type(AR_LIKE_u / AR_O, Any)
+assert_type(AR_LIKE_i / AR_O, Any)
+assert_type(AR_LIKE_f / AR_O, Any)
+assert_type(AR_LIKE_m / AR_O, Any)
+assert_type(AR_LIKE_M / AR_O, Any)
+assert_type(AR_LIKE_O / AR_O, Any)
+
+# Array floor division
+
+assert_type(AR_b // AR_LIKE_b, npt.NDArray[np.int8])
+assert_type(AR_b // AR_LIKE_u, npt.NDArray[np.uint32])
+assert_type(AR_b // AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_b // AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_b // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_b, npt.NDArray[np.int8])
+assert_type(AR_LIKE_u // AR_b, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_i // AR_b, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f // AR_b, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_O // AR_b, Any)
+
+assert_type(AR_u // AR_LIKE_b, npt.NDArray[np.uint32])
+assert_type(AR_u // AR_LIKE_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_u // AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_u // AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_u // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_u, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_u // AR_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_LIKE_i // AR_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f // AR_u, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_m // AR_u, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O // AR_u, Any)
+
+assert_type(AR_i // AR_LIKE_b, npt.NDArray[np.int64])
+assert_type(AR_i // AR_LIKE_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i // AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i // AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_i // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_i, npt.NDArray[np.int64])
+assert_type(AR_LIKE_u // AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_i // AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f // AR_i, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_m // AR_i, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O // AR_i, Any)
+
+assert_type(AR_f // AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m // AR_f, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O // AR_f, Any)
+
+assert_type(AR_m // AR_LIKE_u, npt.NDArray[np.timedelta64])
+assert_type(AR_m // AR_LIKE_i, npt.NDArray[np.timedelta64])
+assert_type(AR_m // AR_LIKE_f, npt.NDArray[np.timedelta64])
+assert_type(AR_m // AR_LIKE_m, npt.NDArray[np.int64])
+assert_type(AR_m // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_m // AR_m, npt.NDArray[np.int64])
+assert_type(AR_LIKE_O // AR_m, Any)
+
+assert_type(AR_O // AR_LIKE_b, Any)
+assert_type(AR_O // AR_LIKE_u, Any)
+assert_type(AR_O // AR_LIKE_i, Any)
+assert_type(AR_O // AR_LIKE_f, Any)
+assert_type(AR_O // AR_LIKE_m, Any)
+assert_type(AR_O // AR_LIKE_M, Any)
+assert_type(AR_O // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_O, Any)
+assert_type(AR_LIKE_u // AR_O, Any)
+assert_type(AR_LIKE_i // AR_O, Any)
+assert_type(AR_LIKE_f // AR_O, Any)
+assert_type(AR_LIKE_m // AR_O, Any)
+assert_type(AR_LIKE_M // AR_O, Any)
+assert_type(AR_LIKE_O // AR_O, Any)
+
+# unary ops
+
+assert_type(-f16, np.floating[_128Bit])
+assert_type(-c16, np.complex128)
+assert_type(-c8, np.complex64)
+assert_type(-f8, np.float64)
+assert_type(-f4, np.float32)
+assert_type(-i8, np.int64)
+assert_type(-i4, np.int32)
+assert_type(-u8, np.uint64)
+assert_type(-u4, np.uint32)
+assert_type(-m8, np.timedelta64)
+assert_type(-m8_none, np.timedelta64[None])
+assert_type(-m8_int, np.timedelta64[int])
+assert_type(-m8_delta, np.timedelta64[dt.timedelta])
+assert_type(-AR_f, npt.NDArray[np.float64])
+
+assert_type(+f16, np.floating[_128Bit])
+assert_type(+c16, np.complex128)
+assert_type(+c8, np.complex64)
+assert_type(+f8, np.float64)
+assert_type(+f4, np.float32)
+assert_type(+i8, np.int64)
+assert_type(+i4, np.int32)
+assert_type(+u8, np.uint64)
+assert_type(+u4, np.uint32)
+assert_type(+m8_none, np.timedelta64[None])
+assert_type(+m8_int, np.timedelta64[int])
+assert_type(+m8_delta, np.timedelta64[dt.timedelta])
+assert_type(+AR_f, npt.NDArray[np.float64])
+
+assert_type(abs(f16), np.floating[_128Bit])
+assert_type(abs(c16), np.float64)
+assert_type(abs(c8), np.float32)
+assert_type(abs(f8), np.float64)
+assert_type(abs(f4), np.float32)
+assert_type(abs(i8), np.int64)
+assert_type(abs(i4), np.int32)
+assert_type(abs(u8), np.uint64)
+assert_type(abs(u4), np.uint32)
+assert_type(abs(m8), np.timedelta64)
+assert_type(abs(m8_none), np.timedelta64[None])
+assert_type(abs(m8_int), np.timedelta64[int])
+assert_type(abs(m8_delta), np.timedelta64[dt.timedelta])
+assert_type(abs(b_), np.bool)
+assert_type(abs(AR_O), npt.NDArray[np.object_])
+
+# Time structures
+
+assert_type(M8 + m8, np.datetime64)
+assert_type(M8 + i, np.datetime64)
+assert_type(M8 + i8, np.datetime64)
+assert_type(M8 - M8, np.timedelta64)
+assert_type(M8 - i, np.datetime64)
+assert_type(M8 - i8, np.datetime64)
+
+assert_type(M8_none + m8, np.datetime64[None])
+assert_type(M8_none + i, np.datetime64[None])
+assert_type(M8_none + i8, np.datetime64[None])
+assert_type(M8_none - M8, np.timedelta64[None])
+assert_type(M8_none - m8, np.datetime64[None])
+assert_type(M8_none - i, np.datetime64[None])
+assert_type(M8_none - i8, np.datetime64[None])
+
+assert_type(m8 + m8, np.timedelta64)
+assert_type(m8 + i, np.timedelta64)
+assert_type(m8 + i8, np.timedelta64)
+assert_type(m8 - m8, np.timedelta64)
+assert_type(m8 - i, np.timedelta64)
+assert_type(m8 - i8, np.timedelta64)
+assert_type(m8 * f, np.timedelta64)
+assert_type(m8 * f4, np.timedelta64)
+assert_type(m8 * np.True_, np.timedelta64)
+assert_type(m8 / f, np.timedelta64)
+assert_type(m8 / f4, np.timedelta64)
+assert_type(m8 / m8, np.float64)
+assert_type(m8 // m8, np.int64)
+assert_type(m8 % m8, np.timedelta64)
+assert_type(divmod(m8, m8), tuple[np.int64, np.timedelta64])
+
+assert_type(m8_none + m8, np.timedelta64[None])
+assert_type(m8_none + i, np.timedelta64[None])
+assert_type(m8_none + i8, np.timedelta64[None])
+assert_type(m8_none - i, np.timedelta64[None])
+assert_type(m8_none - i8, np.timedelta64[None])
+
+assert_type(m8_int + i, np.timedelta64[int])
+assert_type(m8_int + m8_delta, np.timedelta64[int])
+assert_type(m8_int + m8, np.timedelta64[int | None])
+assert_type(m8_int - i, np.timedelta64[int])
+assert_type(m8_int - m8_delta, np.timedelta64[int])
+assert_type(m8_int - m8, np.timedelta64[int | None])
+
+assert_type(m8_delta + date, dt.date)
+assert_type(m8_delta + time, dt.datetime)
+assert_type(m8_delta + delta, dt.timedelta)
+assert_type(m8_delta - delta, dt.timedelta)
+assert_type(m8_delta / delta, float)
+assert_type(m8_delta // delta, int)
+assert_type(m8_delta % delta, dt.timedelta)
+assert_type(divmod(m8_delta, delta), tuple[int, dt.timedelta])
+
+# boolean
+
+assert_type(b_ / b, np.float64)
+assert_type(b_ / b_, np.float64)
+assert_type(b_ / i, np.float64)
+assert_type(b_ / i8, np.float64)
+assert_type(b_ / i4, np.float64)
+assert_type(b_ / u8, np.float64)
+assert_type(b_ / u4, np.float64)
+assert_type(b_ / f, np.float64)
+assert_type(b_ / f16, np.floating[_128Bit])
+assert_type(b_ / f8, np.float64)
+assert_type(b_ / f4, np.float32)
+assert_type(b_ / c, np.complex128)
+assert_type(b_ / c16, np.complex128)
+assert_type(b_ / c8, np.complex64)
+
+assert_type(b / b_, np.float64)
+assert_type(b_ / b_, np.float64)
+assert_type(i / b_, np.float64)
+assert_type(i8 / b_, np.float64)
+assert_type(i4 / b_, np.float64)
+assert_type(u8 / b_, np.float64)
+assert_type(u4 / b_, np.float64)
+assert_type(f / b_, np.float64)
+assert_type(f16 / b_, np.floating[_128Bit])
+assert_type(f8 / b_, np.float64)
+assert_type(f4 / b_, np.float32)
+assert_type(c / b_, np.complex128)
+assert_type(c16 / b_, np.complex128)
+assert_type(c8 / b_, np.complex64)
+
+# Complex
+
+assert_type(c16 + f16, np.complex128 | np.complexfloating[_128Bit, _128Bit])
+assert_type(c16 + c16, np.complex128)
+assert_type(c16 + f8, np.complex128)
+assert_type(c16 + i8, np.complex128)
+assert_type(c16 + c8, np.complex128)
+assert_type(c16 + f4, np.complex128)
+assert_type(c16 + i4, np.complex128)
+assert_type(c16 + b_, np.complex128)
+assert_type(c16 + b, np.complex128)
+assert_type(c16 + c, np.complex128)
+assert_type(c16 + f, np.complex128)
+assert_type(c16 + AR_f, npt.NDArray[np.complex128])
+
+assert_type(f16 + c16, np.complex128 | np.complexfloating[_128Bit, _128Bit])
+assert_type(c16 + c16, np.complex128)
+assert_type(f8 + c16, np.complex128)
+assert_type(i8 + c16, np.complex128)
+assert_type(c8 + c16, np.complex128 | np.complex64)
+assert_type(f4 + c16, np.complex128 | np.complex64)
+assert_type(i4 + c16, np.complex128)
+assert_type(b_ + c16, np.complex128)
+assert_type(b + c16, np.complex128)
+assert_type(c + c16, np.complex128)
+assert_type(f + c16, np.complex128)
+assert_type(AR_f + c16, npt.NDArray[np.complex128])
+
+assert_type(c8 + f16, np.complexfloating[_32Bit, _32Bit] | np.complexfloating[_128Bit, _128Bit])
+assert_type(c8 + c16, np.complex64 | np.complex128)
+assert_type(c8 + f8, np.complex64 | np.complex128)
+assert_type(c8 + i8, np.complexfloating[_32Bit, _32Bit] | np.complexfloating[_64Bit, _64Bit])
+assert_type(c8 + c8, np.complex64)
+assert_type(c8 + f4, np.complex64)
+assert_type(c8 + i4, np.complex64)
+assert_type(c8 + b_, np.complex64)
+assert_type(c8 + b, np.complex64)
+assert_type(c8 + c, np.complex64 | np.complex128)
+assert_type(c8 + f, np.complex64 | np.complex128)
+assert_type(c8 + AR_f, npt.NDArray[np.complexfloating])
+
+assert_type(f16 + c8, np.complexfloating[_128Bit, _128Bit] | np.complex64)
+assert_type(c16 + c8, np.complex128)
+assert_type(f8 + c8, np.complexfloating[_64Bit, _64Bit])
+assert_type(i8 + c8, np.complexfloating[_64Bit, _64Bit] | np.complex64)
+assert_type(c8 + c8, np.complex64)
+assert_type(f4 + c8, np.complex64)
+assert_type(i4 + c8, np.complex64)
+assert_type(b_ + c8, np.complex64)
+assert_type(b + c8, np.complex64)
+assert_type(c + c8, np.complex64 | np.complex128)
+assert_type(f + c8, np.complex64 | np.complex128)
+assert_type(AR_f + c8, npt.NDArray[np.complexfloating])
+
+# Float
+
+assert_type(f8 + f16, np.float64| np.floating[_128Bit])
+assert_type(f8 + f8, np.float64)
+assert_type(f8 + i8, np.float64)
+assert_type(f8 + f4, np.float64)
+assert_type(f8 + i4, np.float64)
+assert_type(f8 + b_, np.float64)
+assert_type(f8 + b, np.float64)
+assert_type(f8 + c, np.float64 | np.complex128)
+assert_type(f8 + f, np.float64)
+assert_type(f8 + AR_f, npt.NDArray[np.float64])
+
+assert_type(f16 + f8, np.floating[_128Bit] | np.float64)
+assert_type(f8 + f8, np.float64)
+assert_type(i8 + f8, np.float64)
+assert_type(f4 + f8, np.float32 | np.float64)
+assert_type(i4 + f8,np.float64)
+assert_type(b_ + f8, np.float64)
+assert_type(b + f8, np.float64)
+assert_type(c + f8, np.complex128 | np.float64)
+assert_type(f + f8, np.float64)
+assert_type(AR_f + f8, npt.NDArray[np.float64])
+
+assert_type(f4 + f16, np.float32 | np.floating[_128Bit])
+assert_type(f4 + f8, np.float32 | np.float64)
+assert_type(f4 + i8, np.float32 | np.floating[_64Bit])
+assert_type(f4 + f4, np.float32)
+assert_type(f4 + i4, np.float32)
+assert_type(f4 + b_, np.float32)
+assert_type(f4 + b, np.float32)
+assert_type(f4 + c, np.complex64 | np.complex128)
+assert_type(f4 + f, np.float32 | np.float64)
+assert_type(f4 + AR_f, npt.NDArray[np.float64])
+
+assert_type(f16 + f4, np.floating[_128Bit] | np.float32)
+assert_type(f8 + f4, np.float64)
+assert_type(i8 + f4, np.floating[_32Bit] | np.floating[_64Bit])
+assert_type(f4 + f4, np.float32)
+assert_type(i4 + f4, np.float32)
+assert_type(b_ + f4, np.float32)
+assert_type(b + f4, np.float32)
+assert_type(c + f4, np.complex64 | np.complex128)
+assert_type(f + f4, np.float64 | np.float32)
+assert_type(AR_f + f4, npt.NDArray[np.float64])
+
+# Int
+
+assert_type(i8 + i8, np.int64)
+assert_type(i8 + u8, Any)
+assert_type(i8 + i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 + u4, Any)
+assert_type(i8 + b_, np.int64)
+assert_type(i8 + b, np.int64)
+assert_type(i8 + c, np.complex128)
+assert_type(i8 + f, np.float64)
+assert_type(i8 + AR_f, npt.NDArray[np.float64])
+
+assert_type(u8 + u8, np.uint64)
+assert_type(u8 + i4, Any)
+assert_type(u8 + u4, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(u8 + b_, np.uint64)
+assert_type(u8 + b, np.uint64)
+assert_type(u8 + c, np.complex128)
+assert_type(u8 + f, np.float64)
+assert_type(u8 + AR_f, npt.NDArray[np.float64])
+
+assert_type(i8 + i8, np.int64)
+assert_type(u8 + i8, Any)
+assert_type(i4 + i8, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(u4 + i8, Any)
+assert_type(b_ + i8, np.int64)
+assert_type(b + i8, np.int64)
+assert_type(c + i8, np.complex128)
+assert_type(f + i8, np.float64)
+assert_type(AR_f + i8, npt.NDArray[np.float64])
+
+assert_type(u8 + u8, np.uint64)
+assert_type(i4 + u8, Any)
+assert_type(u4 + u8, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(b_ + u8, np.uint64)
+assert_type(b + u8, np.uint64)
+assert_type(c + u8, np.complex128)
+assert_type(f + u8, np.float64)
+assert_type(AR_f + u8, npt.NDArray[np.float64])
+
+assert_type(i4 + i8, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i4 + i4, np.int32)
+assert_type(i4 + b_, np.int32)
+assert_type(i4 + b, np.int32)
+assert_type(i4 + AR_f, npt.NDArray[np.float64])
+
+assert_type(u4 + i8, Any)
+assert_type(u4 + i4, Any)
+assert_type(u4 + u8, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(u4 + u4, np.uint32)
+assert_type(u4 + b_, np.uint32)
+assert_type(u4 + b, np.uint32)
+assert_type(u4 + AR_f, npt.NDArray[np.float64])
+
+assert_type(i8 + i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i4 + i4, np.int32)
+assert_type(b_ + i4, np.int32)
+assert_type(b + i4, np.int32)
+assert_type(AR_f + i4, npt.NDArray[np.float64])
+
+assert_type(i8 + u4, Any)
+assert_type(i4 + u4, Any)
+assert_type(u8 + u4, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(u4 + u4, np.uint32)
+assert_type(b_ + u4, np.uint32)
+assert_type(b + u4, np.uint32)
+assert_type(AR_f + u4, npt.NDArray[np.float64])
+
+# Any
+
+assert_type(AR_Any + 2, npt.NDArray[Any])
+
+# regression tests for https://github.com/numpy/numpy/issues/28805
+
+assert_type(AR_floating + f, npt.NDArray[np.floating])
+assert_type(AR_floating - f, npt.NDArray[np.floating])
+assert_type(AR_floating * f, npt.NDArray[np.floating])
+assert_type(AR_floating ** f, npt.NDArray[np.floating])
+assert_type(AR_floating / f, npt.NDArray[np.floating])
+assert_type(AR_floating // f, npt.NDArray[np.floating])
+assert_type(AR_floating % f, npt.NDArray[np.floating])
+assert_type(divmod(AR_floating, f), tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]])
+
+assert_type(f + AR_floating, npt.NDArray[np.floating])
+assert_type(f - AR_floating, npt.NDArray[np.floating])
+assert_type(f * AR_floating, npt.NDArray[np.floating])
+assert_type(f ** AR_floating, npt.NDArray[np.floating])
+assert_type(f / AR_floating, npt.NDArray[np.floating])
+assert_type(f // AR_floating, npt.NDArray[np.floating])
+assert_type(f % AR_floating, npt.NDArray[np.floating])
+assert_type(divmod(f, AR_floating), tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/array_api_info.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/array_api_info.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e4110b7344e2324b59c2c421cffeeb596dd4ca9a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/array_api_info.pyi
@@ -0,0 +1,72 @@
+from typing import Literal
+
+import numpy as np
+
+from typing_extensions import Never, assert_type
+
+info = np.__array_namespace_info__()
+
+assert_type(info.__module__, Literal["numpy"])
+
+assert_type(info.default_device(), Literal["cpu"])
+assert_type(info.devices()[0], Literal["cpu"])
+assert_type(info.devices()[-1], Literal["cpu"])
+
+assert_type(info.capabilities()["boolean indexing"], Literal[True])
+assert_type(info.capabilities()["data-dependent shapes"], Literal[True])
+
+assert_type(info.default_dtypes()["real floating"], np.dtype[np.float64])
+assert_type(info.default_dtypes()["complex floating"], np.dtype[np.complex128])
+assert_type(info.default_dtypes()["integral"], np.dtype[np.int_])
+assert_type(info.default_dtypes()["indexing"], np.dtype[np.intp])
+
+assert_type(info.dtypes()["bool"], np.dtype[np.bool])
+assert_type(info.dtypes()["int8"], np.dtype[np.int8])
+assert_type(info.dtypes()["uint8"], np.dtype[np.uint8])
+assert_type(info.dtypes()["float32"], np.dtype[np.float32])
+assert_type(info.dtypes()["complex64"], np.dtype[np.complex64])
+
+assert_type(info.dtypes(kind="bool")["bool"], np.dtype[np.bool])
+assert_type(info.dtypes(kind="signed integer")["int64"], np.dtype[np.int64])
+assert_type(info.dtypes(kind="unsigned integer")["uint64"], np.dtype[np.uint64])
+assert_type(info.dtypes(kind="integral")["int32"], np.dtype[np.int32])
+assert_type(info.dtypes(kind="integral")["uint32"], np.dtype[np.uint32])
+assert_type(info.dtypes(kind="real floating")["float64"], np.dtype[np.float64])
+assert_type(info.dtypes(kind="complex floating")["complex128"], np.dtype[np.complex128])
+assert_type(info.dtypes(kind="numeric")["int16"], np.dtype[np.int16])
+assert_type(info.dtypes(kind="numeric")["uint16"], np.dtype[np.uint16])
+assert_type(info.dtypes(kind="numeric")["float64"], np.dtype[np.float64])
+assert_type(info.dtypes(kind="numeric")["complex128"], np.dtype[np.complex128])
+
+assert_type(info.dtypes(kind=()), dict[Never, Never])
+
+assert_type(info.dtypes(kind=("bool",))["bool"], np.dtype[np.bool])
+assert_type(info.dtypes(kind=("signed integer",))["int64"], np.dtype[np.int64])
+assert_type(info.dtypes(kind=("integral",))["uint32"], np.dtype[np.uint32])
+assert_type(info.dtypes(kind=("complex floating",))["complex128"], np.dtype[np.complex128])
+assert_type(info.dtypes(kind=("numeric",))["float64"], np.dtype[np.float64])
+
+assert_type(
+    info.dtypes(kind=("signed integer", "unsigned integer"))["int8"],
+    np.dtype[np.int8],
+)
+assert_type(
+    info.dtypes(kind=("signed integer", "unsigned integer"))["uint8"],
+    np.dtype[np.uint8],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["int16"],
+    np.dtype[np.int16],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["uint16"],
+    np.dtype[np.uint16],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["float32"],
+    np.dtype[np.float32],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["complex64"],
+    np.dtype[np.complex64],
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..35861cc0e94205cb13739187647975f1e0cf3a1e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi
@@ -0,0 +1,244 @@
+import sys
+from typing import Any, Literal as L, TypeVar
+from pathlib import Path
+from collections import deque
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_SCT = TypeVar("_SCT", bound=np.generic, covariant=True)
+
+class SubClass(npt.NDArray[_SCT]): ...
+
+i8: np.int64
+
+A: npt.NDArray[np.float64]
+B: SubClass[np.float64]
+C: list[int]
+D: SubClass[np.float64 | np.int64]
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass[np.float64]: ...
+
+assert_type(np.empty_like(A), npt.NDArray[np.float64])
+assert_type(np.empty_like(B), SubClass[np.float64])
+assert_type(np.empty_like([1, 1.0]), npt.NDArray[Any])
+assert_type(np.empty_like(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.empty_like(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.array(A), npt.NDArray[np.float64])
+assert_type(np.array(B), npt.NDArray[np.float64])
+assert_type(np.array([1, 1.0]), npt.NDArray[Any])
+assert_type(np.array(deque([1, 2, 3])), npt.NDArray[Any])
+assert_type(np.array(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.array(A, dtype='c16'), npt.NDArray[Any])
+assert_type(np.array(A, like=A), npt.NDArray[np.float64])
+assert_type(np.array(A, subok=True), npt.NDArray[np.float64])
+assert_type(np.array(B, subok=True), SubClass[np.float64])
+assert_type(np.array(B, subok=True, ndmin=0), SubClass[np.float64])
+assert_type(np.array(B, subok=True, ndmin=1), SubClass[np.float64])
+assert_type(np.array(D), npt.NDArray[np.float64 | np.int64])
+
+assert_type(np.zeros([1, 5, 6]), npt.NDArray[np.float64])
+assert_type(np.zeros([1, 5, 6], dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.zeros([1, 5, 6], dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.empty([1, 5, 6]), npt.NDArray[np.float64])
+assert_type(np.empty([1, 5, 6], dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.empty([1, 5, 6], dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.concatenate(A), npt.NDArray[np.float64])
+assert_type(np.concatenate([A, A]), npt.NDArray[Any])
+assert_type(np.concatenate([[1], A]), npt.NDArray[Any])
+assert_type(np.concatenate([[1], [1]]), npt.NDArray[Any])
+assert_type(np.concatenate((A, A)), npt.NDArray[np.float64])
+assert_type(np.concatenate(([1], [1])), npt.NDArray[Any])
+assert_type(np.concatenate([1, 1.0]), npt.NDArray[Any])
+assert_type(np.concatenate(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.concatenate(A, dtype='c16'), npt.NDArray[Any])
+assert_type(np.concatenate([1, 1.0], out=A), npt.NDArray[np.float64])
+
+assert_type(np.asarray(A), npt.NDArray[np.float64])
+assert_type(np.asarray(B), npt.NDArray[np.float64])
+assert_type(np.asarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.asarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.asarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.asanyarray(A), npt.NDArray[np.float64])
+assert_type(np.asanyarray(B), SubClass[np.float64])
+assert_type(np.asanyarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.asanyarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.asanyarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.ascontiguousarray(A), npt.NDArray[np.float64])
+assert_type(np.ascontiguousarray(B), npt.NDArray[np.float64])
+assert_type(np.ascontiguousarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.ascontiguousarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.ascontiguousarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.asfortranarray(A), npt.NDArray[np.float64])
+assert_type(np.asfortranarray(B), npt.NDArray[np.float64])
+assert_type(np.asfortranarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.asfortranarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.asfortranarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.fromstring("1 1 1", sep=" "), npt.NDArray[np.float64])
+assert_type(np.fromstring(b"1 1 1", sep=" "), npt.NDArray[np.float64])
+assert_type(np.fromstring("1 1 1", dtype=np.int64, sep=" "), npt.NDArray[np.int64])
+assert_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" "), npt.NDArray[np.int64])
+assert_type(np.fromstring("1 1 1", dtype="c16", sep=" "), npt.NDArray[Any])
+assert_type(np.fromstring(b"1 1 1", dtype="c16", sep=" "), npt.NDArray[Any])
+
+assert_type(np.fromfile("test.txt", sep=" "), npt.NDArray[np.float64])
+assert_type(np.fromfile("test.txt", dtype=np.int64, sep=" "), npt.NDArray[np.int64])
+assert_type(np.fromfile("test.txt", dtype="c16", sep=" "), npt.NDArray[Any])
+with open("test.txt") as f:
+    assert_type(np.fromfile(f, sep=" "), npt.NDArray[np.float64])
+    assert_type(np.fromfile(b"test.txt", sep=" "), npt.NDArray[np.float64])
+    assert_type(np.fromfile(Path("test.txt"), sep=" "), npt.NDArray[np.float64])
+
+assert_type(np.fromiter("12345", np.float64), npt.NDArray[np.float64])
+assert_type(np.fromiter("12345", float), npt.NDArray[Any])
+
+assert_type(np.frombuffer(A), npt.NDArray[np.float64])
+assert_type(np.frombuffer(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.frombuffer(A, dtype="c16"), npt.NDArray[Any])
+
+assert_type(np.arange(False, True), np.ndarray[tuple[int], np.dtype[np.signedinteger[Any]]])
+assert_type(np.arange(10), np.ndarray[tuple[int], np.dtype[np.signedinteger[Any]]])
+assert_type(np.arange(0, 10, step=2), np.ndarray[tuple[int], np.dtype[np.signedinteger[Any]]])
+assert_type(np.arange(10.0), np.ndarray[tuple[int], np.dtype[np.floating[Any]]])
+assert_type(np.arange(start=0, stop=10.0), np.ndarray[tuple[int], np.dtype[np.floating[Any]]])
+assert_type(np.arange(np.timedelta64(0)), np.ndarray[tuple[int], np.dtype[np.timedelta64]])
+assert_type(np.arange(0, np.timedelta64(10)), np.ndarray[tuple[int], np.dtype[np.timedelta64]])
+assert_type(np.arange(np.datetime64("0"), np.datetime64("10")), np.ndarray[tuple[int], np.dtype[np.datetime64]])
+assert_type(np.arange(10, dtype=np.float64), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(np.arange(0, 10, step=2, dtype=np.int16), np.ndarray[tuple[int], np.dtype[np.int16]])
+assert_type(np.arange(10, dtype=int), np.ndarray[tuple[int], np.dtype[Any]])
+assert_type(np.arange(0, 10, dtype="f8"), np.ndarray[tuple[int], np.dtype[Any]])
+
+assert_type(np.require(A), npt.NDArray[np.float64])
+assert_type(np.require(B), SubClass[np.float64])
+assert_type(np.require(B, requirements=None), SubClass[np.float64])
+assert_type(np.require(B, dtype=int), npt.NDArray[Any])
+assert_type(np.require(B, requirements="E"), npt.NDArray[Any])
+assert_type(np.require(B, requirements=["ENSUREARRAY"]), npt.NDArray[Any])
+assert_type(np.require(B, requirements={"F", "E"}), npt.NDArray[Any])
+assert_type(np.require(B, requirements=["C", "OWNDATA"]), SubClass[np.float64])
+assert_type(np.require(B, requirements="W"), SubClass[np.float64])
+assert_type(np.require(B, requirements="A"), SubClass[np.float64])
+assert_type(np.require(C), npt.NDArray[Any])
+
+assert_type(np.linspace(0, 10), npt.NDArray[np.floating[Any]])
+assert_type(np.linspace(0, 10j), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.linspace(0, 10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.linspace(0, 10, dtype=int), npt.NDArray[Any])
+assert_type(np.linspace(0, 10, retstep=True), tuple[npt.NDArray[np.floating[Any]], np.floating[Any]])
+assert_type(np.linspace(0j, 10, retstep=True), tuple[npt.NDArray[np.complexfloating[Any, Any]], np.complexfloating[Any, Any]])
+assert_type(np.linspace(0, 10, retstep=True, dtype=np.int64), tuple[npt.NDArray[np.int64], np.int64])
+assert_type(np.linspace(0j, 10, retstep=True, dtype=int), tuple[npt.NDArray[Any], Any])
+
+assert_type(np.logspace(0, 10), npt.NDArray[np.floating[Any]])
+assert_type(np.logspace(0, 10j), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.logspace(0, 10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.logspace(0, 10, dtype=int), npt.NDArray[Any])
+
+assert_type(np.geomspace(0, 10), npt.NDArray[np.floating[Any]])
+assert_type(np.geomspace(0, 10j), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.geomspace(0, 10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.geomspace(0, 10, dtype=int), npt.NDArray[Any])
+
+assert_type(np.zeros_like(A), npt.NDArray[np.float64])
+assert_type(np.zeros_like(C), npt.NDArray[Any])
+assert_type(np.zeros_like(A, dtype=float), npt.NDArray[Any])
+assert_type(np.zeros_like(B), SubClass[np.float64])
+assert_type(np.zeros_like(B, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(np.ones_like(A), npt.NDArray[np.float64])
+assert_type(np.ones_like(C), npt.NDArray[Any])
+assert_type(np.ones_like(A, dtype=float), npt.NDArray[Any])
+assert_type(np.ones_like(B), SubClass[np.float64])
+assert_type(np.ones_like(B, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(np.full_like(A, i8), npt.NDArray[np.float64])
+assert_type(np.full_like(C, i8), npt.NDArray[Any])
+assert_type(np.full_like(A, i8, dtype=int), npt.NDArray[Any])
+assert_type(np.full_like(B, i8), SubClass[np.float64])
+assert_type(np.full_like(B, i8, dtype=np.int64), npt.NDArray[np.int64])
+
+_size: int
+_shape_0d: tuple[()]
+_shape_1d: tuple[int]
+_shape_2d: tuple[int, int]
+_shape_nd: tuple[int, ...]
+_shape_like: list[int]
+
+assert_type(np.ones(_shape_0d), np.ndarray[tuple[()], np.dtype[np.float64]])
+assert_type(np.ones(_size), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(np.ones(_shape_2d), np.ndarray[tuple[int, int], np.dtype[np.float64]])
+assert_type(np.ones(_shape_nd), np.ndarray[tuple[int, ...], np.dtype[np.float64]])
+assert_type(np.ones(_shape_1d, dtype=np.int64), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(np.ones(_shape_like), npt.NDArray[np.float64])
+assert_type(np.ones(_shape_like, dtype=np.dtypes.Int64DType()), np.ndarray[Any, np.dtypes.Int64DType])
+assert_type(np.ones(_shape_like, dtype=int), npt.NDArray[Any])
+
+assert_type(np.full(_size, i8), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(np.full(_shape_2d, i8), np.ndarray[tuple[int, int], np.dtype[np.int64]])
+assert_type(np.full(_shape_like, i8), npt.NDArray[np.int64])
+assert_type(np.full(_shape_like, 42), npt.NDArray[Any])
+assert_type(np.full(_size, i8, dtype=np.float64), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(np.full(_size, i8, dtype=float), np.ndarray[tuple[int], np.dtype[Any]])
+assert_type(np.full(_shape_like, 42, dtype=float), npt.NDArray[Any])
+assert_type(np.full(_shape_0d, i8, dtype=object), np.ndarray[tuple[()], np.dtype[Any]])
+
+assert_type(np.indices([1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.indices([1, 2, 3], sparse=True), tuple[npt.NDArray[np.int_], ...])
+
+assert_type(np.fromfunction(func, (3, 5)), SubClass[np.float64])
+
+assert_type(np.identity(10), npt.NDArray[np.float64])
+assert_type(np.identity(10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.identity(10, dtype=int), npt.NDArray[Any])
+
+assert_type(np.atleast_1d(A), npt.NDArray[np.float64])
+assert_type(np.atleast_1d(C), npt.NDArray[Any])
+assert_type(np.atleast_1d(A, A), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+assert_type(np.atleast_1d(A, C), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.atleast_1d(C, C), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.atleast_1d(A, A, A), tuple[npt.NDArray[np.float64], ...])
+assert_type(np.atleast_1d(C, C, C), tuple[npt.NDArray[Any], ...])
+
+assert_type(np.atleast_2d(A), npt.NDArray[np.float64])
+assert_type(np.atleast_2d(A, A), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+assert_type(np.atleast_2d(A, A, A), tuple[npt.NDArray[np.float64], ...])
+
+assert_type(np.atleast_3d(A), npt.NDArray[np.float64])
+assert_type(np.atleast_3d(A, A), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+assert_type(np.atleast_3d(A, A, A), tuple[npt.NDArray[np.float64], ...])
+
+assert_type(np.vstack([A, A]), npt.NDArray[np.float64])
+assert_type(np.vstack([A, A], dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.vstack([A, C]), npt.NDArray[Any])
+assert_type(np.vstack([C, C]), npt.NDArray[Any])
+
+assert_type(np.hstack([A, A]), npt.NDArray[np.float64])
+assert_type(np.hstack([A, A], dtype=np.float32), npt.NDArray[np.float32])
+
+assert_type(np.stack([A, A]), npt.NDArray[np.float64])
+assert_type(np.stack([A, A], dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.stack([A, C]), npt.NDArray[Any])
+assert_type(np.stack([C, C]), npt.NDArray[Any])
+assert_type(np.stack([A, A], axis=0), npt.NDArray[np.float64])
+assert_type(np.stack([A, A], out=B), SubClass[np.float64])
+
+assert_type(np.block([[A, A], [A, A]]), npt.NDArray[Any])
+assert_type(np.block(C), npt.NDArray[Any])
+
+if sys.version_info >= (3, 12):
+    from collections.abc import Buffer
+
+    def create_array(obj: npt.ArrayLike) -> npt.NDArray[Any]: ...
+
+    buffer: Buffer
+    assert_type(create_array(buffer), npt.NDArray[Any])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d053dab1c76f9383f0d50041e20301bd2cde52fc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi
@@ -0,0 +1,24 @@
+from collections.abc import Mapping
+from typing import Any, SupportsIndex
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+def mode_func(
+    ar: npt.NDArray[np.number[Any]],
+    width: tuple[int, int],
+    iaxis: SupportsIndex,
+    kwargs: Mapping[str, Any],
+) -> None: ...
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_LIKE: list[int]
+
+assert_type(np.pad(AR_i8, (2, 3), "constant"), npt.NDArray[np.int64])
+assert_type(np.pad(AR_LIKE, (2, 3), "constant"), npt.NDArray[Any])
+
+assert_type(np.pad(AR_f8, (2, 3), mode_func), npt.NDArray[np.float64])
+assert_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2), npt.NDArray[np.float64])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f19f1536d416edec0d49b6a2ad043b68d92c7ad9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi
@@ -0,0 +1,27 @@
+import contextlib
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy._core.arrayprint import _FormatOptions
+
+from typing_extensions import assert_type
+
+AR: npt.NDArray[np.int64]
+func_float: Callable[[np.floating[Any]], str]
+func_int: Callable[[np.integer[Any]], str]
+
+assert_type(np.get_printoptions(), _FormatOptions)
+assert_type(
+    np.array2string(AR, formatter={'float_kind': func_float, 'int_kind': func_int}),
+    str,
+)
+assert_type(np.format_float_scientific(1.0), str)
+assert_type(np.format_float_positional(1), str)
+assert_type(np.array_repr(AR), str)
+assert_type(np.array_str(AR), str)
+
+assert_type(np.printoptions(), contextlib._GeneratorContextManager[_FormatOptions])
+with np.printoptions() as dct:
+    assert_type(dct, _FormatOptions)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eabc7677cde9655914e0366cfadc13c9758830dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi
@@ -0,0 +1,72 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy.lib._arraysetops_impl import UniqueAllResult, UniqueCountsResult, UniqueInverseResult
+
+from typing_extensions import assert_type
+
+AR_b: npt.NDArray[np.bool]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+
+AR_LIKE_f8: list[float]
+
+assert_type(np.ediff1d(AR_b), npt.NDArray[np.int8])
+assert_type(np.ediff1d(AR_i8, to_end=[1, 2, 3]), npt.NDArray[np.int64])
+assert_type(np.ediff1d(AR_M), npt.NDArray[np.timedelta64])
+assert_type(np.ediff1d(AR_O), npt.NDArray[np.object_])
+assert_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5]), npt.NDArray[Any])
+
+assert_type(np.intersect1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.intersect1d(AR_M, AR_M, assume_unique=True), npt.NDArray[np.datetime64])
+assert_type(np.intersect1d(AR_f8, AR_i8), npt.NDArray[Any])
+assert_type(
+    np.intersect1d(AR_f8, AR_f8, return_indices=True),
+    tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]],
+)
+
+assert_type(np.setxor1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.setxor1d(AR_M, AR_M, assume_unique=True), npt.NDArray[np.datetime64])
+assert_type(np.setxor1d(AR_f8, AR_i8), npt.NDArray[Any])
+
+assert_type(np.isin(AR_i8, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isin(AR_M, AR_M, assume_unique=True), npt.NDArray[np.bool])
+assert_type(np.isin(AR_f8, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isin(AR_f8, AR_LIKE_f8, invert=True), npt.NDArray[np.bool])
+
+assert_type(np.union1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.union1d(AR_M, AR_M), npt.NDArray[np.datetime64])
+assert_type(np.union1d(AR_f8, AR_i8), npt.NDArray[Any])
+
+assert_type(np.setdiff1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.setdiff1d(AR_M, AR_M, assume_unique=True), npt.NDArray[np.datetime64])
+assert_type(np.setdiff1d(AR_f8, AR_i8), npt.NDArray[Any])
+
+assert_type(np.unique(AR_f8), npt.NDArray[np.float64])
+assert_type(np.unique(AR_LIKE_f8, axis=0), npt.NDArray[Any])
+assert_type(np.unique(AR_f8, return_index=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_inverse=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_inverse=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_index=True, return_inverse=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_index=True, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_inverse=True, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+
+assert_type(np.unique_all(AR_f8), UniqueAllResult[np.float64])
+assert_type(np.unique_all(AR_LIKE_f8), UniqueAllResult[Any])
+assert_type(np.unique_counts(AR_f8), UniqueCountsResult[np.float64])
+assert_type(np.unique_counts(AR_LIKE_f8), UniqueCountsResult[Any])
+assert_type(np.unique_inverse(AR_f8), UniqueInverseResult[np.float64])
+assert_type(np.unique_inverse(AR_LIKE_f8), UniqueInverseResult[Any])
+assert_type(np.unique_values(AR_f8), npt.NDArray[np.float64])
+assert_type(np.unique_values(AR_LIKE_f8), npt.NDArray[Any])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..332e5da9bc961ef1a49883449ed4641d55aaf216
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi
@@ -0,0 +1,29 @@
+from typing import Any
+from collections.abc import Generator
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+assert_type(ar_iter.var, npt.NDArray[np.int64])
+assert_type(ar_iter.buf_size, None | int)
+assert_type(ar_iter.start, list[int])
+assert_type(ar_iter.stop, list[int])
+assert_type(ar_iter.step, list[int])
+assert_type(ar_iter.shape, tuple[int, ...])
+assert_type(ar_iter.flat, Generator[np.int64, None, None])
+
+assert_type(ar_iter.__array__(), npt.NDArray[np.int64])
+
+for i in ar_iter:
+    assert_type(i, npt.NDArray[np.int64])
+
+assert_type(ar_iter[0], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[...], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[:], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[0, 0, 0], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[..., 0, :], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/bitwise_ops.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/bitwise_ops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..384932a2c823528dad842aaac26a00b0c666133d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/bitwise_ops.pyi
@@ -0,0 +1,170 @@
+from typing import Any, Literal as L, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _64Bit, _32Bit
+
+from typing_extensions import assert_type
+
+FalseType: TypeAlias = L[False]
+TrueType: TypeAlias = L[True]
+
+i4: np.int32
+i8: np.int64
+
+u4: np.uint32
+u8: np.uint64
+
+b_: np.bool[bool]
+b0_: np.bool[FalseType]
+b1_: np.bool[TrueType]
+
+b: bool
+b0: FalseType
+b1: TrueType
+
+i: int
+
+AR: npt.NDArray[np.int32]
+
+
+assert_type(i8 << i8, np.int64)
+assert_type(i8 >> i8, np.int64)
+assert_type(i8 | i8, np.int64)
+assert_type(i8 ^ i8, np.int64)
+assert_type(i8 & i8, np.int64)
+
+assert_type(i8 << AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 >> AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 | AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 ^ AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 & AR, npt.NDArray[np.signedinteger[Any]])
+
+assert_type(i4 << i4, np.int32)
+assert_type(i4 >> i4, np.int32)
+assert_type(i4 | i4, np.int32)
+assert_type(i4 ^ i4, np.int32)
+assert_type(i4 & i4, np.int32)
+
+assert_type(i8 << i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 >> i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 | i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 ^ i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 & i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+
+assert_type(i8 << b_, np.int64)
+assert_type(i8 >> b_, np.int64)
+assert_type(i8 | b_, np.int64)
+assert_type(i8 ^ b_, np.int64)
+assert_type(i8 & b_, np.int64)
+
+assert_type(i8 << b, np.int64)
+assert_type(i8 >> b, np.int64)
+assert_type(i8 | b, np.int64)
+assert_type(i8 ^ b, np.int64)
+assert_type(i8 & b, np.int64)
+
+assert_type(u8 << u8, np.uint64)
+assert_type(u8 >> u8, np.uint64)
+assert_type(u8 | u8, np.uint64)
+assert_type(u8 ^ u8, np.uint64)
+assert_type(u8 & u8, np.uint64)
+
+assert_type(u8 << AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 >> AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 | AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 ^ AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 & AR, npt.NDArray[np.signedinteger[Any]])
+
+assert_type(u4 << u4, np.uint32)
+assert_type(u4 >> u4, np.uint32)
+assert_type(u4 | u4, np.uint32)
+assert_type(u4 ^ u4, np.uint32)
+assert_type(u4 & u4, np.uint32)
+
+assert_type(u4 << i4, np.signedinteger[Any])
+assert_type(u4 >> i4, np.signedinteger[Any])
+assert_type(u4 | i4, np.signedinteger[Any])
+assert_type(u4 ^ i4, np.signedinteger[Any])
+assert_type(u4 & i4, np.signedinteger[Any])
+
+assert_type(u4 << i, np.signedinteger[Any])
+assert_type(u4 >> i, np.signedinteger[Any])
+assert_type(u4 | i, np.signedinteger[Any])
+assert_type(u4 ^ i, np.signedinteger[Any])
+assert_type(u4 & i, np.signedinteger[Any])
+
+assert_type(u8 << b_, np.uint64)
+assert_type(u8 >> b_, np.uint64)
+assert_type(u8 | b_, np.uint64)
+assert_type(u8 ^ b_, np.uint64)
+assert_type(u8 & b_, np.uint64)
+
+assert_type(u8 << b, np.uint64)
+assert_type(u8 >> b, np.uint64)
+assert_type(u8 | b, np.uint64)
+assert_type(u8 ^ b, np.uint64)
+assert_type(u8 & b, np.uint64)
+
+assert_type(b_ << b_, np.int8)
+assert_type(b_ >> b_, np.int8)
+assert_type(b_ | b_, np.bool)
+assert_type(b_ ^ b_, np.bool)
+assert_type(b_ & b_, np.bool)
+
+assert_type(b_ << AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ >> AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ | AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ ^ AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ & AR, npt.NDArray[np.signedinteger[Any]])
+
+assert_type(b_ << b, np.int8)
+assert_type(b_ >> b, np.int8)
+assert_type(b_ | b, np.bool)
+assert_type(b_ ^ b, np.bool)
+assert_type(b_ & b, np.bool)
+
+assert_type(b_ << i, np.int_)
+assert_type(b_ >> i, np.int_)
+assert_type(b_ | i, np.bool | np.int_)
+assert_type(b_ ^ i, np.bool | np.int_)
+assert_type(b_ & i, np.bool | np.int_)
+
+assert_type(~i8, np.int64)
+assert_type(~i4, np.int32)
+assert_type(~u8, np.uint64)
+assert_type(~u4, np.uint32)
+assert_type(~b_, np.bool)
+assert_type(~b0_, np.bool[TrueType])
+assert_type(~b1_, np.bool[FalseType])
+assert_type(~AR, npt.NDArray[np.int32])
+
+assert_type(b_ | b0_, np.bool)
+assert_type(b0_ | b_, np.bool)
+assert_type(b_ | b1_, np.bool[TrueType])
+assert_type(b1_ | b_, np.bool[TrueType])
+
+assert_type(b_ ^ b0_, np.bool)
+assert_type(b0_ ^ b_, np.bool)
+assert_type(b_ ^ b1_, np.bool)
+assert_type(b1_ ^ b_, np.bool)
+
+assert_type(b_ & b0_, np.bool[FalseType])
+assert_type(b0_ & b_, np.bool[FalseType])
+assert_type(b_ & b1_, np.bool)
+assert_type(b1_ & b_, np.bool)
+
+assert_type(b0_ | b0_, np.bool[FalseType])
+assert_type(b0_ | b1_, np.bool[TrueType])
+assert_type(b1_ | b0_, np.bool[TrueType])
+assert_type(b1_ | b1_, np.bool[TrueType])
+
+assert_type(b0_ ^ b0_, np.bool[FalseType])
+assert_type(b0_ ^ b1_, np.bool[TrueType])
+assert_type(b1_ ^ b0_, np.bool[TrueType])
+assert_type(b1_ ^ b1_, np.bool[FalseType])
+
+assert_type(b0_ & b0_, np.bool[FalseType])
+assert_type(b0_ & b1_, np.bool[FalseType])
+assert_type(b1_ & b0_, np.bool[FalseType])
+assert_type(b1_ & b1_, np.bool[TrueType])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/char.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/char.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..19ca211bec1a906cdf75fbd619239b9fda68d435
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/char.pyi
@@ -0,0 +1,219 @@
+import numpy as np
+import numpy.typing as npt
+import numpy._typing as np_t
+
+from typing_extensions import assert_type
+from typing import TypeAlias
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+AR_T: np.ndarray[np_t._Shape, np.dtypes.StringDType]
+
+AR_T_alias: TypeAlias = np.ndarray[np_t._Shape, np.dtypes.StringDType]
+AR_TU_alias: TypeAlias = AR_T_alias | npt.NDArray[np.str_]
+
+assert_type(np.char.equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.not_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.not_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.not_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.greater_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.greater_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.greater_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.less_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.less_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.less_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.greater(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.greater(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.greater(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.less(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.less(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.less(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.multiply(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.multiply(AR_S, [5, 4, 3]), npt.NDArray[np.bytes_])
+assert_type(np.char.multiply(AR_T, 5), AR_T_alias)
+
+assert_type(np.char.mod(AR_U, "test"), npt.NDArray[np.str_])
+assert_type(np.char.mod(AR_S, "test"), npt.NDArray[np.bytes_])
+assert_type(np.char.mod(AR_T, "test"), AR_T_alias)
+
+assert_type(np.char.capitalize(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.capitalize(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.capitalize(AR_T), AR_T_alias)
+
+assert_type(np.char.center(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.center(AR_S, [2, 3, 4], b"a"), npt.NDArray[np.bytes_])
+assert_type(np.char.center(AR_T, 5), AR_T_alias)
+
+assert_type(np.char.encode(AR_U), npt.NDArray[np.bytes_])
+assert_type(np.char.encode(AR_T), npt.NDArray[np.bytes_])
+assert_type(np.char.decode(AR_S), npt.NDArray[np.str_])
+
+assert_type(np.char.expandtabs(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.expandtabs(AR_S, tabsize=4), npt.NDArray[np.bytes_])
+assert_type(np.char.expandtabs(AR_T), AR_T_alias)
+
+assert_type(np.char.join(AR_U, "_"), npt.NDArray[np.str_])
+assert_type(np.char.join(AR_S, [b"_", b""]), npt.NDArray[np.bytes_])
+assert_type(np.char.join(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.ljust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.ljust(AR_T, 5), AR_T_alias)
+assert_type(np.char.ljust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_TU_alias)
+
+assert_type(np.char.rjust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.rjust(AR_T, 5), AR_T_alias)
+assert_type(np.char.rjust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_TU_alias)
+
+assert_type(np.char.lstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.lstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.char.lstrip(AR_T), AR_T_alias)
+assert_type(np.char.lstrip(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.rstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.rstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.char.rstrip(AR_T), AR_T_alias)
+assert_type(np.char.rstrip(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.strip(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.strip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.char.strip(AR_T), AR_T_alias)
+assert_type(np.char.strip(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.count(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.count(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.count(AR_T, AR_T, start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.count(AR_T, ["a", "b", "c"], end=9), npt.NDArray[np.int_])
+
+assert_type(np.char.partition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.char.partition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.partition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.char.rpartition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.char.rpartition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.rpartition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.char.replace(AR_U, "_", "-"), npt.NDArray[np.str_])
+assert_type(np.char.replace(AR_S, [b"_", b""], [b"a", b"b"]), npt.NDArray[np.bytes_])
+assert_type(np.char.replace(AR_T, "_", "_"), AR_TU_alias)
+
+assert_type(np.char.split(AR_U, "_"), npt.NDArray[np.object_])
+assert_type(np.char.split(AR_S, maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+assert_type(np.char.split(AR_T, "_"), npt.NDArray[np.object_])
+
+assert_type(np.char.rsplit(AR_U, "_"), npt.NDArray[np.object_])
+assert_type(np.char.rsplit(AR_S, maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+assert_type(np.char.rsplit(AR_T, "_"), npt.NDArray[np.object_])
+
+assert_type(np.char.splitlines(AR_U), npt.NDArray[np.object_])
+assert_type(np.char.splitlines(AR_S, keepends=[True, True, False]), npt.NDArray[np.object_])
+assert_type(np.char.splitlines(AR_T), npt.NDArray[np.object_])
+
+assert_type(np.char.lower(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.lower(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.lower(AR_T), AR_T_alias)
+
+assert_type(np.char.upper(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.upper(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.upper(AR_T), AR_T_alias)
+
+assert_type(np.char.swapcase(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.swapcase(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.swapcase(AR_T), AR_T_alias)
+
+assert_type(np.char.title(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.title(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.title(AR_T), AR_T_alias)
+
+assert_type(np.char.zfill(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.zfill(AR_S, [2, 3, 4]), npt.NDArray[np.bytes_])
+assert_type(np.char.zfill(AR_T, 5), AR_T_alias)
+
+assert_type(np.char.endswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.char.endswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.char.endswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.char.startswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.char.startswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.char.startswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.char.find(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.find(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.find(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.rfind(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.rfind(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.rfind(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.index(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.index(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.index(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.rindex(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.rindex(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.rindex(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.isalpha(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isalpha(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isalpha(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isalnum(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isalnum(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isalnum(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isdecimal(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isdecimal(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isdigit(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isdigit(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isdigit(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.islower(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.islower(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.islower(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isnumeric(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isnumeric(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isspace(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isspace(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isspace(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.istitle(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.istitle(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.istitle(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isupper(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isupper(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isupper(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.str_len(AR_U), npt.NDArray[np.int_])
+assert_type(np.char.str_len(AR_S), npt.NDArray[np.int_])
+assert_type(np.char.str_len(AR_T), npt.NDArray[np.int_])
+
+assert_type(np.char.translate(AR_U, ""), npt.NDArray[np.str_])
+assert_type(np.char.translate(AR_S, ""), npt.NDArray[np.bytes_])
+assert_type(np.char.translate(AR_T, ""), AR_T_alias)
+
+assert_type(np.char.array(AR_U), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.array(AR_S, order="K"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.array("bob", copy=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.array(b"bob", itemsize=5), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.array(1, unicode=False), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.array(1, unicode=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+
+assert_type(np.char.asarray(AR_U), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.asarray(AR_S, order="K"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.asarray("bob"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.asarray(b"bob", itemsize=5), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.asarray(1, unicode=False), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.asarray(1, unicode=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/chararray.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/chararray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..116880f44356564e4e42cebd2a4854f42ca8fe70
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/chararray.pyi
@@ -0,0 +1,136 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_U: np.char.chararray[tuple[int, ...], np.dtype[np.str_]]
+AR_S: np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]]
+
+assert_type(AR_U == AR_U, npt.NDArray[np.bool])
+assert_type(AR_S == AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U != AR_U, npt.NDArray[np.bool])
+assert_type(AR_S != AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U >= AR_U, npt.NDArray[np.bool])
+assert_type(AR_S >= AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U <= AR_U, npt.NDArray[np.bool])
+assert_type(AR_S <= AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U > AR_U, npt.NDArray[np.bool])
+assert_type(AR_S > AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U < AR_U, npt.NDArray[np.bool])
+assert_type(AR_S < AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U * 5, np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S * [5], np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U % "test", np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S % b"test", np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.capitalize(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.capitalize(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.center(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.center([2, 3, 4], b"a"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.encode(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_S.decode(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+
+assert_type(AR_U.expandtabs(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.expandtabs(tabsize=4), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.join("_"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.join([b"_", b""]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.ljust(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.ljust([4, 3, 1], fillchar=[b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.rjust(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.rjust([4, 3, 1], fillchar=[b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.lstrip(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.lstrip(chars=b"_"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.rstrip(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.rstrip(chars=b"_"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.strip(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.strip(chars=b"_"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.partition("\n"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.partition([b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.rpartition("\n"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.rpartition([b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.replace("_", "-"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.replace([b"_", b""], [b"a", b"b"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.split("_"), npt.NDArray[np.object_])
+assert_type(AR_S.split(maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+assert_type(AR_U.rsplit("_"), npt.NDArray[np.object_])
+assert_type(AR_S.rsplit(maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+
+assert_type(AR_U.splitlines(), npt.NDArray[np.object_])
+assert_type(AR_S.splitlines(keepends=[True, True, False]), npt.NDArray[np.object_])
+
+assert_type(AR_U.swapcase(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.swapcase(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.title(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.title(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.upper(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.upper(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.zfill(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.zfill([2, 3, 4]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.count("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.count([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+
+assert_type(AR_U.endswith("a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(AR_S.endswith([b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(AR_U.startswith("a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(AR_S.startswith([b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+
+assert_type(AR_U.find("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.find([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(AR_U.rfind("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.rfind([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+
+assert_type(AR_U.index("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.index([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(AR_U.rindex("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.rindex([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+
+assert_type(AR_U.isalpha(), npt.NDArray[np.bool])
+assert_type(AR_S.isalpha(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isalnum(), npt.NDArray[np.bool])
+assert_type(AR_S.isalnum(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isdecimal(), npt.NDArray[np.bool])
+assert_type(AR_S.isdecimal(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isdigit(), npt.NDArray[np.bool])
+assert_type(AR_S.isdigit(), npt.NDArray[np.bool])
+
+assert_type(AR_U.islower(), npt.NDArray[np.bool])
+assert_type(AR_S.islower(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isnumeric(), npt.NDArray[np.bool])
+assert_type(AR_S.isnumeric(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isspace(), npt.NDArray[np.bool])
+assert_type(AR_S.isspace(), npt.NDArray[np.bool])
+
+assert_type(AR_U.istitle(), npt.NDArray[np.bool])
+assert_type(AR_S.istitle(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isupper(), npt.NDArray[np.bool])
+assert_type(AR_S.isupper(), npt.NDArray[np.bool])
+
+assert_type(AR_U.__array_finalize__(object()), None)
+assert_type(AR_S.__array_finalize__(object()), None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/comparisons.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/comparisons.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b71ef1d1b79f952cc03e9924f30c52bd821cb72b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/comparisons.pyi
@@ -0,0 +1,266 @@
+import fractions
+import decimal
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+c16 = np.complex128()
+f8 = np.float64()
+i8 = np.int64()
+u8 = np.uint64()
+
+c8 = np.complex64()
+f4 = np.float32()
+i4 = np.int32()
+u4 = np.uint32()
+
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+b_ = np.bool()
+
+b = bool()
+c = complex()
+f = float()
+i = int()
+
+AR = np.array([0], dtype=np.int64)
+AR.setflags(write=False)
+
+SEQ = (0, 1, 2, 3, 4)
+
+# object-like comparisons
+
+assert_type(i8 > fractions.Fraction(1, 5), np.bool)
+assert_type(i8 > [fractions.Fraction(1, 5)], npt.NDArray[np.bool])
+assert_type(i8 > decimal.Decimal("1.5"), np.bool)
+assert_type(i8 > [decimal.Decimal("1.5")], npt.NDArray[np.bool])
+
+# Time structures
+
+assert_type(dt > dt, np.bool)
+
+assert_type(td > td, np.bool)
+assert_type(td > i, np.bool)
+assert_type(td > i4, np.bool)
+assert_type(td > i8, np.bool)
+
+assert_type(td > AR, npt.NDArray[np.bool])
+assert_type(td > SEQ, npt.NDArray[np.bool])
+assert_type(AR > SEQ, npt.NDArray[np.bool])
+assert_type(AR > td, npt.NDArray[np.bool])
+assert_type(SEQ > td, npt.NDArray[np.bool])
+assert_type(SEQ > AR, npt.NDArray[np.bool])
+
+# boolean
+
+assert_type(b_ > b, np.bool)
+assert_type(b_ > b_, np.bool)
+assert_type(b_ > i, np.bool)
+assert_type(b_ > i8, np.bool)
+assert_type(b_ > i4, np.bool)
+assert_type(b_ > u8, np.bool)
+assert_type(b_ > u4, np.bool)
+assert_type(b_ > f, np.bool)
+assert_type(b_ > f8, np.bool)
+assert_type(b_ > f4, np.bool)
+assert_type(b_ > c, np.bool)
+assert_type(b_ > c16, np.bool)
+assert_type(b_ > c8, np.bool)
+assert_type(b_ > AR, npt.NDArray[np.bool])
+assert_type(b_ > SEQ, npt.NDArray[np.bool])
+
+# Complex
+
+assert_type(c16 > c16, np.bool)
+assert_type(c16 > f8, np.bool)
+assert_type(c16 > i8, np.bool)
+assert_type(c16 > c8, np.bool)
+assert_type(c16 > f4, np.bool)
+assert_type(c16 > i4, np.bool)
+assert_type(c16 > b_, np.bool)
+assert_type(c16 > b, np.bool)
+assert_type(c16 > c, np.bool)
+assert_type(c16 > f, np.bool)
+assert_type(c16 > i, np.bool)
+assert_type(c16 > AR, npt.NDArray[np.bool])
+assert_type(c16 > SEQ, npt.NDArray[np.bool])
+
+assert_type(c16 > c16, np.bool)
+assert_type(f8 > c16, np.bool)
+assert_type(i8 > c16, np.bool)
+assert_type(c8 > c16, np.bool)
+assert_type(f4 > c16, np.bool)
+assert_type(i4 > c16, np.bool)
+assert_type(b_ > c16, np.bool)
+assert_type(b > c16, np.bool)
+assert_type(c > c16, np.bool)
+assert_type(f > c16, np.bool)
+assert_type(i > c16, np.bool)
+assert_type(AR > c16, npt.NDArray[np.bool])
+assert_type(SEQ > c16, npt.NDArray[np.bool])
+
+assert_type(c8 > c16, np.bool)
+assert_type(c8 > f8, np.bool)
+assert_type(c8 > i8, np.bool)
+assert_type(c8 > c8, np.bool)
+assert_type(c8 > f4, np.bool)
+assert_type(c8 > i4, np.bool)
+assert_type(c8 > b_, np.bool)
+assert_type(c8 > b, np.bool)
+assert_type(c8 > c, np.bool)
+assert_type(c8 > f, np.bool)
+assert_type(c8 > i, np.bool)
+assert_type(c8 > AR, npt.NDArray[np.bool])
+assert_type(c8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(c16 > c8, np.bool)
+assert_type(f8 > c8, np.bool)
+assert_type(i8 > c8, np.bool)
+assert_type(c8 > c8, np.bool)
+assert_type(f4 > c8, np.bool)
+assert_type(i4 > c8, np.bool)
+assert_type(b_ > c8, np.bool)
+assert_type(b > c8, np.bool)
+assert_type(c > c8, np.bool)
+assert_type(f > c8, np.bool)
+assert_type(i > c8, np.bool)
+assert_type(AR > c8, npt.NDArray[np.bool])
+assert_type(SEQ > c8, npt.NDArray[np.bool])
+
+# Float
+
+assert_type(f8 > f8, np.bool)
+assert_type(f8 > i8, np.bool)
+assert_type(f8 > f4, np.bool)
+assert_type(f8 > i4, np.bool)
+assert_type(f8 > b_, np.bool)
+assert_type(f8 > b, np.bool)
+assert_type(f8 > c, np.bool)
+assert_type(f8 > f, np.bool)
+assert_type(f8 > i, np.bool)
+assert_type(f8 > AR, npt.NDArray[np.bool])
+assert_type(f8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(f8 > f8, np.bool)
+assert_type(i8 > f8, np.bool)
+assert_type(f4 > f8, np.bool)
+assert_type(i4 > f8, np.bool)
+assert_type(b_ > f8, np.bool)
+assert_type(b > f8, np.bool)
+assert_type(c > f8, np.bool)
+assert_type(f > f8, np.bool)
+assert_type(i > f8, np.bool)
+assert_type(AR > f8, npt.NDArray[np.bool])
+assert_type(SEQ > f8, npt.NDArray[np.bool])
+
+assert_type(f4 > f8, np.bool)
+assert_type(f4 > i8, np.bool)
+assert_type(f4 > f4, np.bool)
+assert_type(f4 > i4, np.bool)
+assert_type(f4 > b_, np.bool)
+assert_type(f4 > b, np.bool)
+assert_type(f4 > c, np.bool)
+assert_type(f4 > f, np.bool)
+assert_type(f4 > i, np.bool)
+assert_type(f4 > AR, npt.NDArray[np.bool])
+assert_type(f4 > SEQ, npt.NDArray[np.bool])
+
+assert_type(f8 > f4, np.bool)
+assert_type(i8 > f4, np.bool)
+assert_type(f4 > f4, np.bool)
+assert_type(i4 > f4, np.bool)
+assert_type(b_ > f4, np.bool)
+assert_type(b > f4, np.bool)
+assert_type(c > f4, np.bool)
+assert_type(f > f4, np.bool)
+assert_type(i > f4, np.bool)
+assert_type(AR > f4, npt.NDArray[np.bool])
+assert_type(SEQ > f4, npt.NDArray[np.bool])
+
+# Int
+
+assert_type(i8 > i8, np.bool)
+assert_type(i8 > u8, np.bool)
+assert_type(i8 > i4, np.bool)
+assert_type(i8 > u4, np.bool)
+assert_type(i8 > b_, np.bool)
+assert_type(i8 > b, np.bool)
+assert_type(i8 > c, np.bool)
+assert_type(i8 > f, np.bool)
+assert_type(i8 > i, np.bool)
+assert_type(i8 > AR, npt.NDArray[np.bool])
+assert_type(i8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(u8 > u8, np.bool)
+assert_type(u8 > i4, np.bool)
+assert_type(u8 > u4, np.bool)
+assert_type(u8 > b_, np.bool)
+assert_type(u8 > b, np.bool)
+assert_type(u8 > c, np.bool)
+assert_type(u8 > f, np.bool)
+assert_type(u8 > i, np.bool)
+assert_type(u8 > AR, npt.NDArray[np.bool])
+assert_type(u8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(i8 > i8, np.bool)
+assert_type(u8 > i8, np.bool)
+assert_type(i4 > i8, np.bool)
+assert_type(u4 > i8, np.bool)
+assert_type(b_ > i8, np.bool)
+assert_type(b > i8, np.bool)
+assert_type(c > i8, np.bool)
+assert_type(f > i8, np.bool)
+assert_type(i > i8, np.bool)
+assert_type(AR > i8, npt.NDArray[np.bool])
+assert_type(SEQ > i8, npt.NDArray[np.bool])
+
+assert_type(u8 > u8, np.bool)
+assert_type(i4 > u8, np.bool)
+assert_type(u4 > u8, np.bool)
+assert_type(b_ > u8, np.bool)
+assert_type(b > u8, np.bool)
+assert_type(c > u8, np.bool)
+assert_type(f > u8, np.bool)
+assert_type(i > u8, np.bool)
+assert_type(AR > u8, npt.NDArray[np.bool])
+assert_type(SEQ > u8, npt.NDArray[np.bool])
+
+assert_type(i4 > i8, np.bool)
+assert_type(i4 > i4, np.bool)
+assert_type(i4 > i, np.bool)
+assert_type(i4 > b_, np.bool)
+assert_type(i4 > b, np.bool)
+assert_type(i4 > AR, npt.NDArray[np.bool])
+assert_type(i4 > SEQ, npt.NDArray[np.bool])
+
+assert_type(u4 > i8, np.bool)
+assert_type(u4 > i4, np.bool)
+assert_type(u4 > u8, np.bool)
+assert_type(u4 > u4, np.bool)
+assert_type(u4 > i, np.bool)
+assert_type(u4 > b_, np.bool)
+assert_type(u4 > b, np.bool)
+assert_type(u4 > AR, npt.NDArray[np.bool])
+assert_type(u4 > SEQ, npt.NDArray[np.bool])
+
+assert_type(i8 > i4, np.bool)
+assert_type(i4 > i4, np.bool)
+assert_type(i > i4, np.bool)
+assert_type(b_ > i4, np.bool)
+assert_type(b > i4, np.bool)
+assert_type(AR > i4, npt.NDArray[np.bool])
+assert_type(SEQ > i4, npt.NDArray[np.bool])
+
+assert_type(i8 > u4, np.bool)
+assert_type(i4 > u4, np.bool)
+assert_type(u8 > u4, np.bool)
+assert_type(u4 > u4, np.bool)
+assert_type(b_ > u4, np.bool)
+assert_type(b > u4, np.bool)
+assert_type(i > u4, np.bool)
+assert_type(AR > u4, npt.NDArray[np.bool])
+assert_type(SEQ > u4, npt.NDArray[np.bool])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/constants.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/constants.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..146a40cf467ffd22355a261eef5ad1687a62ad2e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/constants.pyi
@@ -0,0 +1,15 @@
+from typing import Literal
+from typing_extensions import assert_type
+
+import numpy as np
+
+assert_type(np.e, float)
+assert_type(np.euler_gamma, float)
+assert_type(np.inf, float)
+assert_type(np.nan, float)
+assert_type(np.pi, float)
+
+assert_type(np.little_endian, bool)
+
+assert_type(np.True_, np.bool[Literal[True]])
+assert_type(np.False_, np.bool[Literal[False]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ctypeslib.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ctypeslib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..80928a93444cf0a1af8b92fcdaccf8b45be0d98b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ctypeslib.pyi
@@ -0,0 +1,93 @@
+import sys
+import ctypes as ct
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy import ctypeslib
+
+from typing_extensions import assert_type
+
+AR_bool: npt.NDArray[np.bool]
+AR_ubyte: npt.NDArray[np.ubyte]
+AR_ushort: npt.NDArray[np.ushort]
+AR_uintc: npt.NDArray[np.uintc]
+AR_ulong: npt.NDArray[np.ulong]
+AR_ulonglong: npt.NDArray[np.ulonglong]
+AR_byte: npt.NDArray[np.byte]
+AR_short: npt.NDArray[np.short]
+AR_intc: npt.NDArray[np.intc]
+AR_long: npt.NDArray[np.long]
+AR_longlong: npt.NDArray[np.longlong]
+AR_single: npt.NDArray[np.single]
+AR_double: npt.NDArray[np.double]
+AR_longdouble: npt.NDArray[np.longdouble]
+AR_void: npt.NDArray[np.void]
+
+pointer: ct._Pointer[Any]
+
+assert_type(np.ctypeslib.c_intp(), ctypeslib.c_intp)
+
+assert_type(np.ctypeslib.ndpointer(), type[ctypeslib._ndptr[None]])
+assert_type(np.ctypeslib.ndpointer(dtype=np.float64), type[ctypeslib._ndptr[np.dtype[np.float64]]])
+assert_type(np.ctypeslib.ndpointer(dtype=float), type[ctypeslib._ndptr[np.dtype[Any]]])
+assert_type(np.ctypeslib.ndpointer(shape=(10, 3)), type[ctypeslib._ndptr[None]])
+assert_type(np.ctypeslib.ndpointer(np.int64, shape=(10, 3)), type[ctypeslib._concrete_ndptr[np.dtype[np.int64]]])
+assert_type(np.ctypeslib.ndpointer(int, shape=(1,)), type[np.ctypeslib._concrete_ndptr[np.dtype[Any]]])
+
+assert_type(np.ctypeslib.as_ctypes_type(np.bool), type[ct.c_bool])
+assert_type(np.ctypeslib.as_ctypes_type(np.ubyte), type[ct.c_ubyte])
+assert_type(np.ctypeslib.as_ctypes_type(np.ushort), type[ct.c_ushort])
+assert_type(np.ctypeslib.as_ctypes_type(np.uintc), type[ct.c_uint])
+assert_type(np.ctypeslib.as_ctypes_type(np.byte), type[ct.c_byte])
+assert_type(np.ctypeslib.as_ctypes_type(np.short), type[ct.c_short])
+assert_type(np.ctypeslib.as_ctypes_type(np.intc), type[ct.c_int])
+assert_type(np.ctypeslib.as_ctypes_type(np.single), type[ct.c_float])
+assert_type(np.ctypeslib.as_ctypes_type(np.double), type[ct.c_double])
+assert_type(np.ctypeslib.as_ctypes_type(ct.c_double), type[ct.c_double])
+assert_type(np.ctypeslib.as_ctypes_type("q"), type[ct.c_longlong])
+assert_type(np.ctypeslib.as_ctypes_type([("i8", np.int64), ("f8", np.float64)]), type[Any])
+assert_type(np.ctypeslib.as_ctypes_type("i8"), type[Any])
+assert_type(np.ctypeslib.as_ctypes_type("f8"), type[Any])
+
+assert_type(np.ctypeslib.as_ctypes(AR_bool.take(0)), ct.c_bool)
+assert_type(np.ctypeslib.as_ctypes(AR_ubyte.take(0)), ct.c_ubyte)
+assert_type(np.ctypeslib.as_ctypes(AR_ushort.take(0)), ct.c_ushort)
+assert_type(np.ctypeslib.as_ctypes(AR_uintc.take(0)), ct.c_uint)
+
+assert_type(np.ctypeslib.as_ctypes(AR_byte.take(0)), ct.c_byte)
+assert_type(np.ctypeslib.as_ctypes(AR_short.take(0)), ct.c_short)
+assert_type(np.ctypeslib.as_ctypes(AR_intc.take(0)), ct.c_int)
+assert_type(np.ctypeslib.as_ctypes(AR_single.take(0)), ct.c_float)
+assert_type(np.ctypeslib.as_ctypes(AR_double.take(0)), ct.c_double)
+assert_type(np.ctypeslib.as_ctypes(AR_void.take(0)), Any)
+assert_type(np.ctypeslib.as_ctypes(AR_bool), ct.Array[ct.c_bool])
+assert_type(np.ctypeslib.as_ctypes(AR_ubyte), ct.Array[ct.c_ubyte])
+assert_type(np.ctypeslib.as_ctypes(AR_ushort), ct.Array[ct.c_ushort])
+assert_type(np.ctypeslib.as_ctypes(AR_uintc), ct.Array[ct.c_uint])
+assert_type(np.ctypeslib.as_ctypes(AR_byte), ct.Array[ct.c_byte])
+assert_type(np.ctypeslib.as_ctypes(AR_short), ct.Array[ct.c_short])
+assert_type(np.ctypeslib.as_ctypes(AR_intc), ct.Array[ct.c_int])
+assert_type(np.ctypeslib.as_ctypes(AR_single), ct.Array[ct.c_float])
+assert_type(np.ctypeslib.as_ctypes(AR_double), ct.Array[ct.c_double])
+assert_type(np.ctypeslib.as_ctypes(AR_void), ct.Array[Any])
+
+assert_type(np.ctypeslib.as_array(AR_ubyte), npt.NDArray[np.ubyte])
+assert_type(np.ctypeslib.as_array(1), npt.NDArray[Any])
+assert_type(np.ctypeslib.as_array(pointer), npt.NDArray[Any])
+
+if sys.platform == "win32":
+    # Mainly on windows int is the same size as long but gets picked first:
+    assert_type(np.ctypeslib.as_ctypes_type(np.long), type[ct.c_int])
+    assert_type(np.ctypeslib.as_ctypes_type(np.ulong), type[ct.c_uint])
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong), ct.Array[ct.c_uint])
+    assert_type(np.ctypeslib.as_ctypes(AR_long), ct.Array[ct.c_int])
+    assert_type(np.ctypeslib.as_ctypes(AR_long.take(0)), ct.c_int)
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong.take(0)), ct.c_uint)
+else:
+    assert_type(np.ctypeslib.as_ctypes_type(np.long), type[ct.c_long])
+    assert_type(np.ctypeslib.as_ctypes_type(np.ulong), type[ct.c_ulong])
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong), ct.Array[ct.c_ulong])
+    assert_type(np.ctypeslib.as_ctypes(AR_long), ct.Array[ct.c_long])
+    assert_type(np.ctypeslib.as_ctypes(AR_long.take(0)), ct.c_long)
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong.take(0)), ct.c_ulong)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/datasource.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/datasource.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..88f2b076be8481ffb58618afa4b25d51bf18bf60
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/datasource.pyi
@@ -0,0 +1,25 @@
+from pathlib import Path
+from typing import IO, Any
+
+import numpy as np
+
+from typing_extensions import assert_type
+
+path1: Path
+path2: str
+
+d1 = np.lib.npyio.DataSource(path1)
+d2 = np.lib.npyio.DataSource(path2)
+d3 = np.lib.npyio.DataSource(None)
+
+assert_type(d1.abspath("..."), str)
+assert_type(d2.abspath("..."), str)
+assert_type(d3.abspath("..."), str)
+
+assert_type(d1.exists("..."), bool)
+assert_type(d2.exists("..."), bool)
+assert_type(d3.exists("..."), bool)
+
+assert_type(d1.open("...", "r"), IO[Any])
+assert_type(d2.open("...", encoding="utf8"), IO[Any])
+assert_type(d3.open("...", newline="/n"), IO[Any])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/dtype.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/dtype.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..da37778b177bd08c7919f3bcc0a4ee83a07f94a2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/dtype.pyi
@@ -0,0 +1,140 @@
+import ctypes as ct
+import datetime as dt
+from decimal import Decimal
+from fractions import Fraction
+from typing import Any, Literal, TypeAlias
+
+import numpy as np
+from numpy.dtypes import StringDType
+
+from typing_extensions import LiteralString, assert_type
+
+# a combination of likely `object` dtype-like candidates (no `_co`)
+_PyObjectLike: TypeAlias = Decimal | Fraction | dt.datetime | dt.timedelta
+
+dtype_U: np.dtype[np.str_]
+dtype_V: np.dtype[np.void]
+dtype_i8: np.dtype[np.int64]
+
+py_int_co: type[int | bool]
+py_float_co: type[float | int | bool]
+py_complex_co: type[complex | float | int | bool]
+py_object: type[_PyObjectLike]
+py_character: type[str | bytes]
+py_flexible: type[str | bytes | memoryview]
+
+ct_floating: type[ct.c_float | ct.c_double | ct.c_longdouble]
+ct_number: type[ct.c_uint8 | ct.c_float]
+ct_generic: type[ct.c_bool | ct.c_char]
+
+cs_integer: Literal["u1", "<i2", "L"]
+cs_number: Literal["=L" ,"i", "c16"]
+cs_flex: Literal[">V", "S"]
+cs_generic: Literal["H", "U", "h", "|M8[Y]", "?"]
+
+dt_inexact: np.dtype[np.inexact[Any]]
+dt_string: StringDType
+
+
+assert_type(np.dtype(np.float64), np.dtype[np.float64])
+assert_type(np.dtype(np.float64, metadata={"test": "test"}), np.dtype[np.float64])
+assert_type(np.dtype(np.int64), np.dtype[np.int64])
+
+# String aliases
+assert_type(np.dtype("float64"), np.dtype[np.float64])
+assert_type(np.dtype("float32"), np.dtype[np.float32])
+assert_type(np.dtype("int64"), np.dtype[np.int64])
+assert_type(np.dtype("int32"), np.dtype[np.int32])
+assert_type(np.dtype("bool"), np.dtype[np.bool])
+assert_type(np.dtype("bytes"), np.dtype[np.bytes_])
+assert_type(np.dtype("str"), np.dtype[np.str_])
+
+# Python types
+assert_type(np.dtype(bool), np.dtype[np.bool])
+assert_type(np.dtype(py_int_co), np.dtype[np.int_ | np.bool])
+assert_type(np.dtype(int), np.dtype[np.int_ | np.bool])
+assert_type(np.dtype(py_float_co), np.dtype[np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(float), np.dtype[np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(py_complex_co), np.dtype[np.complex128 | np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(complex), np.dtype[np.complex128 | np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(py_object), np.dtype[np.object_])
+assert_type(np.dtype(str), np.dtype[np.str_])
+assert_type(np.dtype(bytes), np.dtype[np.bytes_])
+assert_type(np.dtype(py_character), np.dtype[np.character])
+assert_type(np.dtype(memoryview), np.dtype[np.void])
+assert_type(np.dtype(py_flexible), np.dtype[np.flexible])
+
+assert_type(np.dtype(list), np.dtype[np.object_])
+assert_type(np.dtype(dt.datetime), np.dtype[np.object_])
+assert_type(np.dtype(dt.timedelta), np.dtype[np.object_])
+assert_type(np.dtype(Decimal), np.dtype[np.object_])
+assert_type(np.dtype(Fraction), np.dtype[np.object_])
+
+# char-codes
+assert_type(np.dtype("?"), np.dtype[np.bool])
+assert_type(np.dtype("|b1"), np.dtype[np.bool])
+assert_type(np.dtype("u1"), np.dtype[np.uint8])
+assert_type(np.dtype("l"), np.dtype[np.long])
+assert_type(np.dtype("longlong"), np.dtype[np.longlong])
+assert_type(np.dtype(">g"), np.dtype[np.longdouble])
+assert_type(np.dtype(cs_integer), np.dtype[np.integer[Any]])
+assert_type(np.dtype(cs_number), np.dtype[np.number[Any]])
+assert_type(np.dtype(cs_flex), np.dtype[np.flexible])
+assert_type(np.dtype(cs_generic), np.dtype[np.generic])
+
+# ctypes
+assert_type(np.dtype(ct.c_double), np.dtype[np.double])
+assert_type(np.dtype(ct.c_longlong), np.dtype[np.longlong])
+assert_type(np.dtype(ct.c_uint32), np.dtype[np.uint32])
+assert_type(np.dtype(ct.c_bool), np.dtype[np.bool])
+assert_type(np.dtype(ct.c_char), np.dtype[np.bytes_])
+assert_type(np.dtype(ct.py_object), np.dtype[np.object_])
+
+# Special case for None
+assert_type(np.dtype(None), np.dtype[np.float64])
+
+# Dypes of dtypes
+assert_type(np.dtype(np.dtype(np.float64)), np.dtype[np.float64])
+assert_type(np.dtype(dt_inexact), np.dtype[np.inexact[Any]])
+
+# Parameterized dtypes
+assert_type(np.dtype("S8"), np.dtype[Any])
+
+# Void
+assert_type(np.dtype(("U", 10)), np.dtype[np.void])
+
+# StringDType
+assert_type(np.dtype(dt_string), StringDType)
+assert_type(np.dtype("T"), StringDType)
+assert_type(np.dtype("=T"), StringDType)
+assert_type(np.dtype("|T"), StringDType)
+
+
+# Methods and attributes
+assert_type(dtype_U.base, np.dtype[Any])
+assert_type(dtype_U.subdtype, None | tuple[np.dtype[Any], tuple[int, ...]])
+assert_type(dtype_U.newbyteorder(), np.dtype[np.str_])
+assert_type(dtype_U.type, type[np.str_])
+assert_type(dtype_U.name, LiteralString)
+assert_type(dtype_U.names, None | tuple[str, ...])
+
+assert_type(dtype_U * 0, np.dtype[np.str_])
+assert_type(dtype_U * 1, np.dtype[np.str_])
+assert_type(dtype_U * 2, np.dtype[np.str_])
+
+assert_type(dtype_i8 * 0, np.dtype[np.void])
+assert_type(dtype_i8 * 1, np.dtype[np.int64])
+assert_type(dtype_i8 * 2, np.dtype[np.void])
+
+assert_type(0 * dtype_U, np.dtype[np.str_])
+assert_type(1 * dtype_U, np.dtype[np.str_])
+assert_type(2 * dtype_U, np.dtype[np.str_])
+
+assert_type(0 * dtype_i8, np.dtype[Any])
+assert_type(1 * dtype_i8, np.dtype[Any])
+assert_type(2 * dtype_i8, np.dtype[Any])
+
+assert_type(dtype_V["f0"], np.dtype[Any])
+assert_type(dtype_V[0], np.dtype[Any])
+assert_type(dtype_V[["f0", "f1"]], np.dtype[np.void])
+assert_type(dtype_V[["f0"]], np.dtype[np.void])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/einsumfunc.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/einsumfunc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6dc44e23bda0523c528f502ed224aa2d64a1970a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/einsumfunc.pyi
@@ -0,0 +1,41 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_U: list[str]
+AR_o: npt.NDArray[np.object_]
+
+OUT_f: npt.NDArray[np.float64]
+
+assert_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b), Any)
+assert_type(np.einsum("i,i->i", AR_o, AR_o), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i), Any)
+assert_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c), Any)
+
+assert_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f), npt.NDArray[np.float64])
+assert_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f), npt.NDArray[np.float64])
+assert_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16"), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe"), Any)
+
+assert_type(np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_u, AR_LIKE_u), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_i, AR_LIKE_i), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_f, AR_LIKE_f), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_c, AR_LIKE_c), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_i), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c), tuple[list[Any], str])
+
+assert_type(np.einsum([[1, 1], [1, 1]], AR_LIKE_i, AR_LIKE_i), Any)
+assert_type(np.einsum_path([[1, 1], [1, 1]], AR_LIKE_i, AR_LIKE_i), tuple[list[Any], str])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/emath.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/emath.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cc6579cf3b33a9cc831720ae9d6382ab2b710c3b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/emath.pyi
@@ -0,0 +1,56 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+f8: np.float64
+c16: np.complex128
+
+assert_type(np.emath.sqrt(f8), Any)
+assert_type(np.emath.sqrt(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.sqrt(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.sqrt(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.log(f8), Any)
+assert_type(np.emath.log(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.log(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.log(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.log10(f8), Any)
+assert_type(np.emath.log10(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.log10(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.log10(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.log2(f8), Any)
+assert_type(np.emath.log2(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.log2(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.log2(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.logn(f8, 2), Any)
+assert_type(np.emath.logn(AR_f8, 4), npt.NDArray[Any])
+assert_type(np.emath.logn(f8, 1j), np.complexfloating[Any, Any])
+assert_type(np.emath.logn(AR_c16, 1.5), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.power(f8, 2), Any)
+assert_type(np.emath.power(AR_f8, 4), npt.NDArray[Any])
+assert_type(np.emath.power(f8, 2j), np.complexfloating[Any, Any])
+assert_type(np.emath.power(AR_c16, 1.5), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.arccos(f8), Any)
+assert_type(np.emath.arccos(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.arccos(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.arccos(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.arcsin(f8), Any)
+assert_type(np.emath.arcsin(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.arcsin(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.arcsin(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.arctanh(f8), Any)
+assert_type(np.emath.arctanh(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.arctanh(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.arctanh(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/fft.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/fft.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f3a29c75615ce86d9d977445b82b41ab33f470e7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/fft.pyi
@@ -0,0 +1,39 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_LIKE_f8: list[float]
+
+assert_type(np.fft.fftshift(AR_f8), npt.NDArray[np.float64])
+assert_type(np.fft.fftshift(AR_LIKE_f8, axes=0), npt.NDArray[Any])
+
+assert_type(np.fft.ifftshift(AR_f8), npt.NDArray[np.float64])
+assert_type(np.fft.ifftshift(AR_LIKE_f8, axes=0), npt.NDArray[Any])
+
+assert_type(np.fft.fftfreq(5, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.fft.fftfreq(np.int64(), AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.fft.fftfreq(5, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.fft.fftfreq(np.int64(), AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.fft.fft(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.ifft(AR_f8, axis=1), npt.NDArray[np.complex128])
+assert_type(np.fft.rfft(AR_f8, n=None), npt.NDArray[np.complex128])
+assert_type(np.fft.irfft(AR_f8, norm="ortho"), npt.NDArray[np.float64])
+assert_type(np.fft.hfft(AR_f8, n=2), npt.NDArray[np.float64])
+assert_type(np.fft.ihfft(AR_f8), npt.NDArray[np.complex128])
+
+assert_type(np.fft.fftn(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.ifftn(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.rfftn(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.irfftn(AR_f8), npt.NDArray[np.float64])
+
+assert_type(np.fft.rfft2(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.ifft2(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.fft2(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.irfft2(AR_f8), npt.NDArray[np.float64])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/flatiter.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/flatiter.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6891ce7382fe93b32e5a7cb87def3fde36dfb2a5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/flatiter.pyi
@@ -0,0 +1,49 @@
+from typing import Literal, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+a: np.flatiter[npt.NDArray[np.str_]]
+a_1d: np.flatiter[np.ndarray[tuple[int], np.dtype[np.bytes_]]]
+
+Size: TypeAlias = Literal[42]
+a_1d_fixed: np.flatiter[np.ndarray[tuple[Size], np.dtype[np.object_]]]
+
+assert_type(a.base, npt.NDArray[np.str_])
+assert_type(a.copy(), npt.NDArray[np.str_])
+assert_type(a.coords, tuple[int, ...])
+assert_type(a.index, int)
+assert_type(iter(a), np.flatiter[npt.NDArray[np.str_]])
+assert_type(next(a), np.str_)
+assert_type(a[0], np.str_)
+assert_type(a[[0, 1, 2]], npt.NDArray[np.str_])
+assert_type(a[...], npt.NDArray[np.str_])
+assert_type(a[:], npt.NDArray[np.str_])
+assert_type(a[(...,)], npt.NDArray[np.str_])
+assert_type(a[(0,)], np.str_)
+
+assert_type(a.__array__(), npt.NDArray[np.str_])
+assert_type(a.__array__(np.dtype(np.float64)), npt.NDArray[np.float64])
+assert_type(
+    a_1d.__array__(),
+    np.ndarray[tuple[int], np.dtype[np.bytes_]],
+)
+assert_type(
+    a_1d.__array__(np.dtype(np.float64)),
+    np.ndarray[tuple[int], np.dtype[np.float64]],
+)
+assert_type(
+    a_1d_fixed.__array__(),
+    np.ndarray[tuple[Size], np.dtype[np.object_]],
+)
+assert_type(
+    a_1d_fixed.__array__(np.dtype(np.float64)),
+    np.ndarray[tuple[Size], np.dtype[np.float64]],
+)
+
+a[0] = "a"
+a[:5] = "a"
+a[...] = "a"
+a[(...,)] = "a"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/fromnumeric.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/fromnumeric.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7e778dc58410e3cdf2a552497bde5c92df9a9b34
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/fromnumeric.pyi
@@ -0,0 +1,343 @@
+"""Tests for :mod:`_core.fromnumeric`."""
+
+from typing import Any, Literal as L
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+class NDArraySubclass(npt.NDArray[np.complex128]):
+    ...
+
+AR_b: npt.NDArray[np.bool]
+AR_f4: npt.NDArray[np.float32]
+AR_c16: npt.NDArray[np.complex128]
+AR_u8: npt.NDArray[np.uint64]
+AR_i8: npt.NDArray[np.int64]
+AR_O: npt.NDArray[np.object_]
+AR_subclass: NDArraySubclass
+AR_m: npt.NDArray[np.timedelta64]
+AR_0d: np.ndarray[tuple[()], np.dtype[Any]]
+AR_1d: np.ndarray[tuple[int], np.dtype[Any]]
+AR_nd: np.ndarray[tuple[int, ...], np.dtype[Any]]
+
+b: np.bool
+f4: np.float32
+i8: np.int64
+f: float
+
+assert_type(np.take(b, 0), np.bool)
+assert_type(np.take(f4, 0), np.float32)
+assert_type(np.take(f, 0), Any)
+assert_type(np.take(AR_b, 0), np.bool)
+assert_type(np.take(AR_f4, 0), np.float32)
+assert_type(np.take(AR_b, [0]), npt.NDArray[np.bool])
+assert_type(np.take(AR_f4, [0]), npt.NDArray[np.float32])
+assert_type(np.take([1], [0]), npt.NDArray[Any])
+assert_type(np.take(AR_f4, [0], out=AR_subclass), NDArraySubclass)
+
+assert_type(np.reshape(b, 1), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.reshape(f4, 1), np.ndarray[tuple[int], np.dtype[np.float32]])
+assert_type(np.reshape(f, 1), np.ndarray[tuple[int], np.dtype[Any]])
+assert_type(np.reshape(AR_b, 1), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.reshape(AR_f4, 1), np.ndarray[tuple[int], np.dtype[np.float32]])
+
+assert_type(np.choose(1, [True, True]), Any)
+assert_type(np.choose([1], [True, True]), npt.NDArray[Any])
+assert_type(np.choose([1], AR_b), npt.NDArray[np.bool])
+assert_type(np.choose([1], AR_b, out=AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.repeat(b, 1), npt.NDArray[np.bool])
+assert_type(np.repeat(f4, 1), npt.NDArray[np.float32])
+assert_type(np.repeat(f, 1), npt.NDArray[Any])
+assert_type(np.repeat(AR_b, 1), npt.NDArray[np.bool])
+assert_type(np.repeat(AR_f4, 1), npt.NDArray[np.float32])
+
+# TODO: array_bdd tests for np.put()
+
+assert_type(np.swapaxes([[0, 1]], 0, 0), npt.NDArray[Any])
+assert_type(np.swapaxes(AR_b, 0, 0), npt.NDArray[np.bool])
+assert_type(np.swapaxes(AR_f4, 0, 0), npt.NDArray[np.float32])
+
+assert_type(np.transpose(b), npt.NDArray[np.bool])
+assert_type(np.transpose(f4), npt.NDArray[np.float32])
+assert_type(np.transpose(f), npt.NDArray[Any])
+assert_type(np.transpose(AR_b), npt.NDArray[np.bool])
+assert_type(np.transpose(AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.partition(b, 0, axis=None), npt.NDArray[np.bool])
+assert_type(np.partition(f4, 0, axis=None), npt.NDArray[np.float32])
+assert_type(np.partition(f, 0, axis=None), npt.NDArray[Any])
+assert_type(np.partition(AR_b, 0), npt.NDArray[np.bool])
+assert_type(np.partition(AR_f4, 0), npt.NDArray[np.float32])
+
+assert_type(np.argpartition(b, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(f4, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(f, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(AR_b, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(AR_f4, 0), npt.NDArray[np.intp])
+
+assert_type(np.sort([2, 1], 0), npt.NDArray[Any])
+assert_type(np.sort(AR_b, 0), npt.NDArray[np.bool])
+assert_type(np.sort(AR_f4, 0), npt.NDArray[np.float32])
+
+assert_type(np.argsort(AR_b, 0), npt.NDArray[np.intp])
+assert_type(np.argsort(AR_f4, 0), npt.NDArray[np.intp])
+
+assert_type(np.argmax(AR_b), np.intp)
+assert_type(np.argmax(AR_f4), np.intp)
+assert_type(np.argmax(AR_b, axis=0), Any)
+assert_type(np.argmax(AR_f4, axis=0), Any)
+assert_type(np.argmax(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.argmin(AR_b), np.intp)
+assert_type(np.argmin(AR_f4), np.intp)
+assert_type(np.argmin(AR_b, axis=0), Any)
+assert_type(np.argmin(AR_f4, axis=0), Any)
+assert_type(np.argmin(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.searchsorted(AR_b[0], 0), np.intp)
+assert_type(np.searchsorted(AR_f4[0], 0), np.intp)
+assert_type(np.searchsorted(AR_b[0], [0]), npt.NDArray[np.intp])
+assert_type(np.searchsorted(AR_f4[0], [0]), npt.NDArray[np.intp])
+
+assert_type(np.resize(b, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.bool]])
+assert_type(np.resize(f4, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.float32]])
+assert_type(np.resize(f, (5, 5)), np.ndarray[tuple[int, int], np.dtype[Any]])
+assert_type(np.resize(AR_b, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.bool]])
+assert_type(np.resize(AR_f4, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.float32]])
+
+assert_type(np.squeeze(b), np.bool)
+assert_type(np.squeeze(f4), np.float32)
+assert_type(np.squeeze(f), npt.NDArray[Any])
+assert_type(np.squeeze(AR_b), npt.NDArray[np.bool])
+assert_type(np.squeeze(AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.diagonal(AR_b), npt.NDArray[np.bool])
+assert_type(np.diagonal(AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.trace(AR_b), Any)
+assert_type(np.trace(AR_f4), Any)
+assert_type(np.trace(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.ravel(b), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.ravel(f4), np.ndarray[tuple[int], np.dtype[np.float32]])
+assert_type(np.ravel(f), np.ndarray[tuple[int], np.dtype[np.float64 | np.int_ | np.bool]])
+assert_type(np.ravel(AR_b), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.ravel(AR_f4), np.ndarray[tuple[int], np.dtype[np.float32]])
+
+assert_type(np.nonzero(AR_b), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.nonzero(AR_f4), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.nonzero(AR_1d), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.nonzero(AR_nd), tuple[npt.NDArray[np.intp], ...])
+
+assert_type(np.shape(b), tuple[()])
+assert_type(np.shape(f), tuple[()])
+assert_type(np.shape([1]), tuple[int])
+assert_type(np.shape([[2]]), tuple[int, int])
+assert_type(np.shape([[[3]]]), tuple[int, ...])
+assert_type(np.shape(AR_b), tuple[int, ...])
+assert_type(np.shape(AR_nd), tuple[int, ...])
+# these fail on mypy, but it works as expected with pyright/pylance
+# assert_type(np.shape(AR_0d), tuple[()])
+# assert_type(np.shape(AR_1d), tuple[int])
+# assert_type(np.shape(AR_2d), tuple[int, int])
+
+assert_type(np.compress([True], b), npt.NDArray[np.bool])
+assert_type(np.compress([True], f4), npt.NDArray[np.float32])
+assert_type(np.compress([True], f), npt.NDArray[Any])
+assert_type(np.compress([True], AR_b), npt.NDArray[np.bool])
+assert_type(np.compress([True], AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.clip(b, 0, 1.0), np.bool)
+assert_type(np.clip(f4, -1, 1), np.float32)
+assert_type(np.clip(f, 0, 1), Any)
+assert_type(np.clip(AR_b, 0, 1), npt.NDArray[np.bool])
+assert_type(np.clip(AR_f4, 0, 1), npt.NDArray[np.float32])
+assert_type(np.clip([0], 0, 1), npt.NDArray[Any])
+assert_type(np.clip(AR_b, 0, 1, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.sum(b), np.bool)
+assert_type(np.sum(f4), np.float32)
+assert_type(np.sum(f), Any)
+assert_type(np.sum(AR_b), np.bool)
+assert_type(np.sum(AR_f4), np.float32)
+assert_type(np.sum(AR_b, axis=0), Any)
+assert_type(np.sum(AR_f4, axis=0), Any)
+assert_type(np.sum(AR_f4, out=AR_subclass), NDArraySubclass)
+assert_type(np.sum(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.sum(AR_f4, None, np.float64), np.float64)
+assert_type(np.sum(AR_f4, dtype=np.float64, keepdims=False), np.float64)
+assert_type(np.sum(AR_f4, None, np.float64, keepdims=False), np.float64)
+assert_type(np.sum(AR_f4, dtype=np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+assert_type(np.sum(AR_f4, None, np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+
+assert_type(np.all(b), np.bool)
+assert_type(np.all(f4), np.bool)
+assert_type(np.all(f), np.bool)
+assert_type(np.all(AR_b), np.bool)
+assert_type(np.all(AR_f4), np.bool)
+assert_type(np.all(AR_b, axis=0), Any)
+assert_type(np.all(AR_f4, axis=0), Any)
+assert_type(np.all(AR_b, keepdims=True), Any)
+assert_type(np.all(AR_f4, keepdims=True), Any)
+assert_type(np.all(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.any(b), np.bool)
+assert_type(np.any(f4), np.bool)
+assert_type(np.any(f), np.bool)
+assert_type(np.any(AR_b), np.bool)
+assert_type(np.any(AR_f4), np.bool)
+assert_type(np.any(AR_b, axis=0), Any)
+assert_type(np.any(AR_f4, axis=0), Any)
+assert_type(np.any(AR_b, keepdims=True), Any)
+assert_type(np.any(AR_f4, keepdims=True), Any)
+assert_type(np.any(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumsum(b), npt.NDArray[np.bool])
+assert_type(np.cumsum(f4), npt.NDArray[np.float32])
+assert_type(np.cumsum(f), npt.NDArray[Any])
+assert_type(np.cumsum(AR_b), npt.NDArray[np.bool])
+assert_type(np.cumsum(AR_f4), npt.NDArray[np.float32])
+assert_type(np.cumsum(f, dtype=float), npt.NDArray[Any])
+assert_type(np.cumsum(f, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumsum(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumulative_sum(b), npt.NDArray[np.bool])
+assert_type(np.cumulative_sum(f4), npt.NDArray[np.float32])
+assert_type(np.cumulative_sum(f), npt.NDArray[Any])
+assert_type(np.cumulative_sum(AR_b), npt.NDArray[np.bool])
+assert_type(np.cumulative_sum(AR_f4), npt.NDArray[np.float32])
+assert_type(np.cumulative_sum(f, dtype=float), npt.NDArray[Any])
+assert_type(np.cumulative_sum(f, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumulative_sum(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.ptp(b), np.bool)
+assert_type(np.ptp(f4), np.float32)
+assert_type(np.ptp(f), Any)
+assert_type(np.ptp(AR_b), np.bool)
+assert_type(np.ptp(AR_f4), np.float32)
+assert_type(np.ptp(AR_b, axis=0), Any)
+assert_type(np.ptp(AR_f4, axis=0), Any)
+assert_type(np.ptp(AR_b, keepdims=True), Any)
+assert_type(np.ptp(AR_f4, keepdims=True), Any)
+assert_type(np.ptp(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.amax(b), np.bool)
+assert_type(np.amax(f4), np.float32)
+assert_type(np.amax(f), Any)
+assert_type(np.amax(AR_b), np.bool)
+assert_type(np.amax(AR_f4), np.float32)
+assert_type(np.amax(AR_b, axis=0), Any)
+assert_type(np.amax(AR_f4, axis=0), Any)
+assert_type(np.amax(AR_b, keepdims=True), Any)
+assert_type(np.amax(AR_f4, keepdims=True), Any)
+assert_type(np.amax(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.amin(b), np.bool)
+assert_type(np.amin(f4), np.float32)
+assert_type(np.amin(f), Any)
+assert_type(np.amin(AR_b), np.bool)
+assert_type(np.amin(AR_f4), np.float32)
+assert_type(np.amin(AR_b, axis=0), Any)
+assert_type(np.amin(AR_f4, axis=0), Any)
+assert_type(np.amin(AR_b, keepdims=True), Any)
+assert_type(np.amin(AR_f4, keepdims=True), Any)
+assert_type(np.amin(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.prod(AR_b), np.int_)
+assert_type(np.prod(AR_u8), np.uint64)
+assert_type(np.prod(AR_i8), np.int64)
+assert_type(np.prod(AR_f4), np.floating[Any])
+assert_type(np.prod(AR_c16), np.complexfloating[Any, Any])
+assert_type(np.prod(AR_O), Any)
+assert_type(np.prod(AR_f4, axis=0), Any)
+assert_type(np.prod(AR_f4, keepdims=True), Any)
+assert_type(np.prod(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.prod(AR_f4, dtype=float), Any)
+assert_type(np.prod(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumprod(AR_b), npt.NDArray[np.int_])
+assert_type(np.cumprod(AR_u8), npt.NDArray[np.uint64])
+assert_type(np.cumprod(AR_i8), npt.NDArray[np.int64])
+assert_type(np.cumprod(AR_f4), npt.NDArray[np.floating[Any]])
+assert_type(np.cumprod(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cumprod(AR_O), npt.NDArray[np.object_])
+assert_type(np.cumprod(AR_f4, axis=0), npt.NDArray[np.floating[Any]])
+assert_type(np.cumprod(AR_f4, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumprod(AR_f4, dtype=float), npt.NDArray[Any])
+assert_type(np.cumprod(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumulative_prod(AR_b), npt.NDArray[np.int_])
+assert_type(np.cumulative_prod(AR_u8), npt.NDArray[np.uint64])
+assert_type(np.cumulative_prod(AR_i8), npt.NDArray[np.int64])
+assert_type(np.cumulative_prod(AR_f4), npt.NDArray[np.floating[Any]])
+assert_type(np.cumulative_prod(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cumulative_prod(AR_O), npt.NDArray[np.object_])
+assert_type(np.cumulative_prod(AR_f4, axis=0), npt.NDArray[np.floating[Any]])
+assert_type(np.cumulative_prod(AR_f4, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumulative_prod(AR_f4, dtype=float), npt.NDArray[Any])
+assert_type(np.cumulative_prod(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.ndim(b), int)
+assert_type(np.ndim(f4), int)
+assert_type(np.ndim(f), int)
+assert_type(np.ndim(AR_b), int)
+assert_type(np.ndim(AR_f4), int)
+
+assert_type(np.size(b), int)
+assert_type(np.size(f4), int)
+assert_type(np.size(f), int)
+assert_type(np.size(AR_b), int)
+assert_type(np.size(AR_f4), int)
+
+assert_type(np.around(b), np.float16)
+assert_type(np.around(f), Any)
+assert_type(np.around(i8), np.int64)
+assert_type(np.around(f4), np.float32)
+assert_type(np.around(AR_b), npt.NDArray[np.float16])
+assert_type(np.around(AR_i8), npt.NDArray[np.int64])
+assert_type(np.around(AR_f4), npt.NDArray[np.float32])
+assert_type(np.around([1.5]), npt.NDArray[Any])
+assert_type(np.around(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.mean(AR_b), np.floating[Any])
+assert_type(np.mean(AR_i8), np.floating[Any])
+assert_type(np.mean(AR_f4), np.floating[Any])
+assert_type(np.mean(AR_m), np.timedelta64)
+assert_type(np.mean(AR_c16), np.complexfloating[Any, Any])
+assert_type(np.mean(AR_O), Any)
+assert_type(np.mean(AR_f4, axis=0), Any)
+assert_type(np.mean(AR_f4, keepdims=True), Any)
+assert_type(np.mean(AR_f4, dtype=float), Any)
+assert_type(np.mean(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.mean(AR_f4, out=AR_subclass), NDArraySubclass)
+assert_type(np.mean(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.mean(AR_f4, None, np.float64), np.float64)
+assert_type(np.mean(AR_f4, dtype=np.float64, keepdims=False), np.float64)
+assert_type(np.mean(AR_f4, None, np.float64, keepdims=False), np.float64)
+assert_type(np.mean(AR_f4, dtype=np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+assert_type(np.mean(AR_f4, None, np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+
+assert_type(np.std(AR_b), np.floating[Any])
+assert_type(np.std(AR_i8), np.floating[Any])
+assert_type(np.std(AR_f4), np.floating[Any])
+assert_type(np.std(AR_c16), np.floating[Any])
+assert_type(np.std(AR_O), Any)
+assert_type(np.std(AR_f4, axis=0), Any)
+assert_type(np.std(AR_f4, keepdims=True), Any)
+assert_type(np.std(AR_f4, dtype=float), Any)
+assert_type(np.std(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.std(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.var(AR_b), np.floating[Any])
+assert_type(np.var(AR_i8), np.floating[Any])
+assert_type(np.var(AR_f4), np.floating[Any])
+assert_type(np.var(AR_c16), np.floating[Any])
+assert_type(np.var(AR_O), Any)
+assert_type(np.var(AR_f4, axis=0), Any)
+assert_type(np.var(AR_f4, keepdims=True), Any)
+assert_type(np.var(AR_f4, dtype=float), Any)
+assert_type(np.var(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.var(AR_f4, out=AR_subclass), NDArraySubclass)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/getlimits.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/getlimits.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f058382f2042e38267fccf0e772907420c4dc51c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/getlimits.pyi
@@ -0,0 +1,53 @@
+from typing import Any
+
+import numpy as np
+from numpy._typing import _64Bit
+
+from typing_extensions import assert_type, LiteralString
+
+f: float
+f8: np.float64
+c8: np.complex64
+
+i: int
+i8: np.int64
+u4: np.uint32
+
+finfo_f8: np.finfo[np.float64]
+iinfo_i8: np.iinfo[np.int64]
+
+assert_type(np.finfo(f), np.finfo[np.float64])
+assert_type(np.finfo(f8), np.finfo[np.floating[_64Bit]])
+assert_type(np.finfo(c8), np.finfo[np.float32])
+assert_type(np.finfo('f2'), np.finfo[np.floating[Any]])
+
+assert_type(finfo_f8.dtype, np.dtype[np.float64])
+assert_type(finfo_f8.bits, int)
+assert_type(finfo_f8.eps, np.float64)
+assert_type(finfo_f8.epsneg, np.float64)
+assert_type(finfo_f8.iexp, int)
+assert_type(finfo_f8.machep, int)
+assert_type(finfo_f8.max, np.float64)
+assert_type(finfo_f8.maxexp, int)
+assert_type(finfo_f8.min, np.float64)
+assert_type(finfo_f8.minexp, int)
+assert_type(finfo_f8.negep, int)
+assert_type(finfo_f8.nexp, int)
+assert_type(finfo_f8.nmant, int)
+assert_type(finfo_f8.precision, int)
+assert_type(finfo_f8.resolution, np.float64)
+assert_type(finfo_f8.tiny, np.float64)
+assert_type(finfo_f8.smallest_normal, np.float64)
+assert_type(finfo_f8.smallest_subnormal, np.float64)
+
+assert_type(np.iinfo(i), np.iinfo[np.int_])
+assert_type(np.iinfo(i8), np.iinfo[np.int64])
+assert_type(np.iinfo(u4), np.iinfo[np.uint32])
+assert_type(np.iinfo('i2'), np.iinfo[Any])
+
+assert_type(iinfo_i8.dtype, np.dtype[np.int64])
+assert_type(iinfo_i8.kind, LiteralString)
+assert_type(iinfo_i8.bits, int)
+assert_type(iinfo_i8.key, LiteralString)
+assert_type(iinfo_i8.min, int)
+assert_type(iinfo_i8.max, int)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/histograms.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/histograms.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..91a7d0394d20c575af6a7763ef35a9469d369a2b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/histograms.pyi
@@ -0,0 +1,27 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+assert_type(np.histogram_bin_edges(AR_i8, bins="auto"), npt.NDArray[Any])
+assert_type(np.histogram_bin_edges(AR_i8, bins="rice", range=(0, 3)), npt.NDArray[Any])
+assert_type(np.histogram_bin_edges(AR_i8, bins="scott", weights=AR_f8), npt.NDArray[Any])
+
+assert_type(np.histogram(AR_i8, bins="auto"), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.histogram(AR_i8, bins="rice", range=(0, 3)), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.histogram(AR_i8, bins="scott", weights=AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.histogram(AR_f8, bins=1, density=True), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.histogramdd(AR_i8, bins=[1]),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
+assert_type(np.histogramdd(AR_i8, range=[(0, 3)]),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
+assert_type(np.histogramdd(AR_i8, weights=AR_f8),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
+assert_type(np.histogramdd(AR_f8, density=True),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/index_tricks.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/index_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..06071feddd79f9bd3c0d61ec10c489d62e06e0fa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/index_tricks.pyi
@@ -0,0 +1,72 @@
+from types import EllipsisType
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_LIKE_b: list[bool]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_U: list[str]
+AR_LIKE_O: list[object]
+
+AR_i8: npt.NDArray[np.int64]
+AR_O: npt.NDArray[np.object_]
+
+assert_type(np.ndenumerate(AR_i8), np.ndenumerate[np.int64])
+assert_type(np.ndenumerate(AR_LIKE_f), np.ndenumerate[np.float64])
+assert_type(np.ndenumerate(AR_LIKE_U), np.ndenumerate[np.str_])
+assert_type(np.ndenumerate(AR_LIKE_O), np.ndenumerate[Any])
+
+assert_type(next(np.ndenumerate(AR_i8)), tuple[tuple[int, ...], np.int64])
+assert_type(next(np.ndenumerate(AR_LIKE_f)), tuple[tuple[int, ...], np.float64])
+assert_type(next(np.ndenumerate(AR_LIKE_U)), tuple[tuple[int, ...], np.str_])
+assert_type(next(np.ndenumerate(AR_LIKE_O)), tuple[tuple[int, ...], Any])
+
+assert_type(iter(np.ndenumerate(AR_i8)), np.ndenumerate[np.int64])
+assert_type(iter(np.ndenumerate(AR_LIKE_f)), np.ndenumerate[np.float64])
+assert_type(iter(np.ndenumerate(AR_LIKE_U)), np.ndenumerate[np.str_])
+assert_type(iter(np.ndenumerate(AR_LIKE_O)), np.ndenumerate[Any])
+
+assert_type(np.ndindex(1, 2, 3), np.ndindex)
+assert_type(np.ndindex((1, 2, 3)), np.ndindex)
+assert_type(iter(np.ndindex(1, 2, 3)), np.ndindex)
+assert_type(next(np.ndindex(1, 2, 3)), tuple[int, ...])
+
+assert_type(np.unravel_index([22, 41, 37], (7, 6)), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.unravel_index([31, 41, 13], (7, 6), order="F"), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.unravel_index(1621, (6, 7, 8, 9)), tuple[np.intp, ...])
+
+assert_type(np.ravel_multi_index([[1]], (7, 6)), npt.NDArray[np.intp])
+assert_type(np.ravel_multi_index(AR_LIKE_i, (7, 6)), np.intp)
+assert_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F"), np.intp)
+assert_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip"), np.intp)
+assert_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap")), np.intp)
+assert_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), np.intp)
+
+assert_type(np.mgrid[1:1:2], npt.NDArray[Any])
+assert_type(np.mgrid[1:1:2, None:10], npt.NDArray[Any])
+
+assert_type(np.ogrid[1:1:2], tuple[npt.NDArray[Any], ...])
+assert_type(np.ogrid[1:1:2, None:10], tuple[npt.NDArray[Any], ...])
+
+assert_type(np.index_exp[0:1], tuple[slice[int, int, None]])
+assert_type(np.index_exp[0:1, None:3], tuple[slice[int, int, None], slice[None, int, None]])
+assert_type(np.index_exp[0, 0:1, ..., [0, 1, 3]], tuple[Literal[0], slice[int, int, None], EllipsisType, list[int]])
+
+assert_type(np.s_[0:1], slice[int, int, None])
+assert_type(np.s_[0:1, None:3], tuple[slice[int, int, None], slice[None, int, None]])
+assert_type(np.s_[0, 0:1, ..., [0, 1, 3]], tuple[Literal[0], slice[int, int, None], EllipsisType, list[int]])
+
+assert_type(np.ix_(AR_LIKE_b), tuple[npt.NDArray[np.bool], ...])
+assert_type(np.ix_(AR_LIKE_i, AR_LIKE_f), tuple[npt.NDArray[np.float64], ...])
+assert_type(np.ix_(AR_i8), tuple[npt.NDArray[np.int64], ...])
+
+assert_type(np.fill_diagonal(AR_i8, 5), None)
+
+assert_type(np.diag_indices(4), tuple[npt.NDArray[np.int_], ...])
+assert_type(np.diag_indices(2, 3), tuple[npt.NDArray[np.int_], ...])
+
+assert_type(np.diag_indices_from(AR_i8), tuple[npt.NDArray[np.int_], ...])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_function_base.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_function_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9cd06a36f3e08e21b0b8e538bc45d1cfd37de083
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_function_base.pyi
@@ -0,0 +1,210 @@
+from fractions import Fraction
+from typing import Any
+from collections.abc import Callable
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+vectorized_func: np.vectorize
+
+f8: np.float64
+AR_LIKE_f8: list[float]
+AR_LIKE_c16: list[complex]
+AR_LIKE_O: list[Fraction]
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_b: npt.NDArray[np.bool]
+AR_U: npt.NDArray[np.str_]
+CHAR_AR_U: np.char.chararray[tuple[int, ...], np.dtype[np.str_]]
+
+AR_b_list: list[npt.NDArray[np.bool]]
+
+def func(
+    a: npt.NDArray[Any],
+    posarg: bool = ...,
+    /,
+    arg: int = ...,
+    *,
+    kwarg: str = ...,
+) -> npt.NDArray[Any]: ...
+
+assert_type(vectorized_func.pyfunc, Callable[..., Any])
+assert_type(vectorized_func.cache, bool)
+assert_type(vectorized_func.signature, None | str)
+assert_type(vectorized_func.otypes, None | str)
+assert_type(vectorized_func.excluded, set[int | str])
+assert_type(vectorized_func.__doc__, None | str)
+assert_type(vectorized_func([1]), Any)
+assert_type(np.vectorize(int), np.vectorize)
+assert_type(
+    np.vectorize(int, otypes="i", doc="doc", excluded=(), cache=True, signature=None),
+    np.vectorize,
+)
+
+assert_type(np.rot90(AR_f8, k=2), npt.NDArray[np.float64])
+assert_type(np.rot90(AR_LIKE_f8, axes=(0, 1)), npt.NDArray[Any])
+
+assert_type(np.flip(f8), np.float64)
+assert_type(np.flip(1.0), Any)
+assert_type(np.flip(AR_f8, axis=(0, 1)), npt.NDArray[np.float64])
+assert_type(np.flip(AR_LIKE_f8, axis=0), npt.NDArray[Any])
+
+assert_type(np.iterable(1), bool)
+assert_type(np.iterable([1]), bool)
+
+assert_type(np.average(AR_f8), np.floating[Any])
+assert_type(np.average(AR_f8, weights=AR_c16), np.complexfloating[Any, Any])
+assert_type(np.average(AR_O), Any)
+assert_type(np.average(AR_f8, returned=True), tuple[np.floating[Any], np.floating[Any]])
+assert_type(np.average(AR_f8, weights=AR_c16, returned=True), tuple[np.complexfloating[Any, Any], np.complexfloating[Any, Any]])
+assert_type(np.average(AR_O, returned=True), tuple[Any, Any])
+assert_type(np.average(AR_f8, axis=0), Any)
+assert_type(np.average(AR_f8, axis=0, returned=True), tuple[Any, Any])
+
+assert_type(np.asarray_chkfinite(AR_f8), npt.NDArray[np.float64])
+assert_type(np.asarray_chkfinite(AR_LIKE_f8), npt.NDArray[Any])
+assert_type(np.asarray_chkfinite(AR_f8, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.asarray_chkfinite(AR_f8, dtype=float), npt.NDArray[Any])
+
+assert_type(np.piecewise(AR_f8, AR_b, [func]), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_f8, AR_b_list, [func]), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_f8, AR_b_list, [func], True, -1, kwarg=''), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_f8, AR_b_list, [func], True, arg=-1, kwarg=''), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_LIKE_f8, AR_b_list, [func]), npt.NDArray[Any])
+
+assert_type(np.select([AR_f8], [AR_f8]), npt.NDArray[Any])
+
+assert_type(np.copy(AR_LIKE_f8), npt.NDArray[Any])
+assert_type(np.copy(AR_U), npt.NDArray[np.str_])
+assert_type(np.copy(CHAR_AR_U), npt.NDArray[np.str_])
+assert_type(np.copy(CHAR_AR_U, "K", subok=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.copy(CHAR_AR_U, subok=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+
+assert_type(np.gradient(AR_f8, axis=None), Any)
+assert_type(np.gradient(AR_LIKE_f8, edge_order=2), Any)
+
+assert_type(np.diff("bob", n=0), str)
+assert_type(np.diff(AR_f8, axis=0), npt.NDArray[Any])
+assert_type(np.diff(AR_LIKE_f8, prepend=1.5), npt.NDArray[Any])
+
+assert_type(np.interp(1, [1], AR_f8), np.float64)
+assert_type(np.interp(1, [1], [1]), np.float64)
+assert_type(np.interp(1, [1], AR_c16), np.complex128)
+assert_type(np.interp(1, [1], [1j]), np.complex128)  # pyright correctly infers `complex128 | float64`
+assert_type(np.interp([1], [1], AR_f8), npt.NDArray[np.float64])
+assert_type(np.interp([1], [1], [1]), npt.NDArray[np.float64])
+assert_type(np.interp([1], [1], AR_c16), npt.NDArray[np.complex128])
+assert_type(np.interp([1], [1], [1j]), npt.NDArray[np.complex128])  # pyright correctly infers `NDArray[complex128 | float64]`
+
+assert_type(np.angle(f8), np.floating[Any])
+assert_type(np.angle(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.angle(AR_c16, deg=True), npt.NDArray[np.floating[Any]])
+assert_type(np.angle(AR_O), npt.NDArray[np.object_])
+
+assert_type(np.unwrap(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.unwrap(AR_O), npt.NDArray[np.object_])
+
+assert_type(np.sort_complex(AR_f8), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.trim_zeros(AR_f8), npt.NDArray[np.float64])
+assert_type(np.trim_zeros(AR_LIKE_f8), list[float])
+
+assert_type(np.extract(AR_i8, AR_f8), npt.NDArray[np.float64])
+assert_type(np.extract(AR_i8, AR_LIKE_f8), npt.NDArray[Any])
+
+assert_type(np.place(AR_f8, mask=AR_i8, vals=5.0), None)
+
+assert_type(np.cov(AR_f8, bias=True), npt.NDArray[np.floating[Any]])
+assert_type(np.cov(AR_f8, AR_c16, ddof=1), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cov(AR_f8, aweights=AR_f8, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.cov(AR_f8, fweights=AR_f8, dtype=float), npt.NDArray[Any])
+
+assert_type(np.corrcoef(AR_f8, rowvar=True), npt.NDArray[np.floating[Any]])
+assert_type(np.corrcoef(AR_f8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.corrcoef(AR_f8, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.corrcoef(AR_f8, dtype=float), npt.NDArray[Any])
+
+assert_type(np.blackman(5), npt.NDArray[np.floating[Any]])
+assert_type(np.bartlett(6), npt.NDArray[np.floating[Any]])
+assert_type(np.hanning(4.5), npt.NDArray[np.floating[Any]])
+assert_type(np.hamming(0), npt.NDArray[np.floating[Any]])
+assert_type(np.i0(AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.kaiser(4, 5.9), npt.NDArray[np.floating[Any]])
+
+assert_type(np.sinc(1.0), np.floating[Any])
+assert_type(np.sinc(1j), np.complexfloating[Any, Any])
+assert_type(np.sinc(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.sinc(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.median(AR_f8, keepdims=False), np.floating[Any])
+assert_type(np.median(AR_c16, overwrite_input=True), np.complexfloating[Any, Any])
+assert_type(np.median(AR_m), np.timedelta64)
+assert_type(np.median(AR_O), Any)
+assert_type(np.median(AR_f8, keepdims=True), Any)
+assert_type(np.median(AR_c16, axis=0), Any)
+assert_type(np.median(AR_LIKE_f8, out=AR_c16), npt.NDArray[np.complex128])
+
+assert_type(np.percentile(AR_f8, 50), np.floating[Any])
+assert_type(np.percentile(AR_c16, 50), np.complexfloating[Any, Any])
+assert_type(np.percentile(AR_m, 50), np.timedelta64)
+assert_type(np.percentile(AR_M, 50, overwrite_input=True), np.datetime64)
+assert_type(np.percentile(AR_O, 50), Any)
+assert_type(np.percentile(AR_f8, [50]), npt.NDArray[np.floating[Any]])
+assert_type(np.percentile(AR_c16, [50]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.percentile(AR_m, [50]), npt.NDArray[np.timedelta64])
+assert_type(np.percentile(AR_M, [50], method="nearest"), npt.NDArray[np.datetime64])
+assert_type(np.percentile(AR_O, [50]), npt.NDArray[np.object_])
+assert_type(np.percentile(AR_f8, [50], keepdims=True), Any)
+assert_type(np.percentile(AR_f8, [50], axis=[1]), Any)
+assert_type(np.percentile(AR_f8, [50], out=AR_c16), npt.NDArray[np.complex128])
+
+assert_type(np.quantile(AR_f8, 0.5), np.floating[Any])
+assert_type(np.quantile(AR_c16, 0.5), np.complexfloating[Any, Any])
+assert_type(np.quantile(AR_m, 0.5), np.timedelta64)
+assert_type(np.quantile(AR_M, 0.5, overwrite_input=True), np.datetime64)
+assert_type(np.quantile(AR_O, 0.5), Any)
+assert_type(np.quantile(AR_f8, [0.5]), npt.NDArray[np.floating[Any]])
+assert_type(np.quantile(AR_c16, [0.5]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.quantile(AR_m, [0.5]), npt.NDArray[np.timedelta64])
+assert_type(np.quantile(AR_M, [0.5], method="nearest"), npt.NDArray[np.datetime64])
+assert_type(np.quantile(AR_O, [0.5]), npt.NDArray[np.object_])
+assert_type(np.quantile(AR_f8, [0.5], keepdims=True), Any)
+assert_type(np.quantile(AR_f8, [0.5], axis=[1]), Any)
+assert_type(np.quantile(AR_f8, [0.5], out=AR_c16), npt.NDArray[np.complex128])
+
+assert_type(np.trapezoid(AR_LIKE_f8), np.float64)
+assert_type(np.trapezoid(AR_LIKE_f8, AR_LIKE_f8), np.float64)
+assert_type(np.trapezoid(AR_LIKE_c16), np.complex128)
+assert_type(np.trapezoid(AR_LIKE_c16, AR_LIKE_f8), np.complex128)
+assert_type(np.trapezoid(AR_LIKE_f8, AR_LIKE_c16), np.complex128)
+assert_type(np.trapezoid(AR_LIKE_O), float)
+assert_type(np.trapezoid(AR_LIKE_O, AR_LIKE_f8), float)
+assert_type(np.trapezoid(AR_f8), np.float64 | npt.NDArray[np.float64])
+assert_type(np.trapezoid(AR_f8, AR_f8), np.float64 | npt.NDArray[np.float64])
+assert_type(np.trapezoid(AR_c16), np.complex128 | npt.NDArray[np.complex128])
+assert_type(np.trapezoid(AR_c16, AR_c16), np.complex128 | npt.NDArray[np.complex128])
+assert_type(np.trapezoid(AR_m), np.timedelta64 | npt.NDArray[np.timedelta64])
+assert_type(np.trapezoid(AR_O), float | npt.NDArray[np.object_])
+assert_type(np.trapezoid(AR_O, AR_LIKE_f8), float | npt.NDArray[np.object_])
+
+assert_type(np.meshgrid(AR_f8, AR_i8, copy=False), tuple[npt.NDArray[Any], ...])
+assert_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij"), tuple[npt.NDArray[Any], ...])
+
+assert_type(np.delete(AR_f8, np.s_[:5]), npt.NDArray[np.float64])
+assert_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0), npt.NDArray[Any])
+
+assert_type(np.insert(AR_f8, np.s_[:5], 5), npt.NDArray[np.float64])
+assert_type(np.insert(AR_LIKE_f8, [0, 4, 9], [0.5, 9.2, 7], axis=0), npt.NDArray[Any])
+
+assert_type(np.append(AR_f8, 5), npt.NDArray[Any])
+assert_type(np.append(AR_LIKE_f8, 1j, axis=0), npt.NDArray[Any])
+
+assert_type(np.digitize(4.5, [1]), np.intp)
+assert_type(np.digitize(AR_f8, [1, 2, 3]), npt.NDArray[np.intp])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_polynomial.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_polynomial.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d41b1d56b75a7d3f2e119bc0801b052cbc8ec858
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_polynomial.pyi
@@ -0,0 +1,146 @@
+from typing import Any, NoReturn
+from collections.abc import Iterator
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_b: npt.NDArray[np.bool]
+AR_u4: npt.NDArray[np.uint32]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+
+poly_obj: np.poly1d
+
+assert_type(poly_obj.variable, str)
+assert_type(poly_obj.order, int)
+assert_type(poly_obj.o, int)
+assert_type(poly_obj.roots, npt.NDArray[Any])
+assert_type(poly_obj.r, npt.NDArray[Any])
+assert_type(poly_obj.coeffs, npt.NDArray[Any])
+assert_type(poly_obj.c, npt.NDArray[Any])
+assert_type(poly_obj.coef, npt.NDArray[Any])
+assert_type(poly_obj.coefficients, npt.NDArray[Any])
+assert_type(poly_obj.__hash__, None)
+
+assert_type(poly_obj(1), Any)
+assert_type(poly_obj([1]), npt.NDArray[Any])
+assert_type(poly_obj(poly_obj), np.poly1d)
+
+assert_type(len(poly_obj), int)
+assert_type(-poly_obj, np.poly1d)
+assert_type(+poly_obj, np.poly1d)
+
+assert_type(poly_obj * 5, np.poly1d)
+assert_type(5 * poly_obj, np.poly1d)
+assert_type(poly_obj + 5, np.poly1d)
+assert_type(5 + poly_obj, np.poly1d)
+assert_type(poly_obj - 5, np.poly1d)
+assert_type(5 - poly_obj, np.poly1d)
+assert_type(poly_obj**1, np.poly1d)
+assert_type(poly_obj**1.0, np.poly1d)
+assert_type(poly_obj / 5, np.poly1d)
+assert_type(5 / poly_obj, np.poly1d)
+
+assert_type(poly_obj[0], Any)
+poly_obj[0] = 5
+assert_type(iter(poly_obj), Iterator[Any])
+assert_type(poly_obj.deriv(), np.poly1d)
+assert_type(poly_obj.integ(), np.poly1d)
+
+assert_type(np.poly(poly_obj), npt.NDArray[np.floating[Any]])
+assert_type(np.poly(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.poly(AR_c16), npt.NDArray[np.floating[Any]])
+
+assert_type(np.polyint(poly_obj), np.poly1d)
+assert_type(np.polyint(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyint(AR_f8, k=AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyint(AR_O, m=2), npt.NDArray[np.object_])
+
+assert_type(np.polyder(poly_obj), np.poly1d)
+assert_type(np.polyder(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyder(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyder(AR_O, m=2), npt.NDArray[np.object_])
+
+assert_type(np.polyfit(AR_f8, AR_f8, 2), npt.NDArray[np.float64])
+assert_type(
+    np.polyfit(AR_f8, AR_i8, 1, full=True),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.int32],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.polyfit(AR_u4, AR_f8, 1.0, cov="unscaled"),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(np.polyfit(AR_c16, AR_f8, 2), npt.NDArray[np.complex128])
+assert_type(
+    np.polyfit(AR_f8, AR_c16, 1, full=True),
+    tuple[
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.int32],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.polyfit(AR_u4, AR_c16, 1.0, cov=True),
+    tuple[
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+
+assert_type(np.polyval(AR_b, AR_b), npt.NDArray[np.int64])
+assert_type(np.polyval(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polyval(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polyval(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyval(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyval(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polyadd(poly_obj, AR_i8), np.poly1d)
+assert_type(np.polyadd(AR_f8, poly_obj), np.poly1d)
+assert_type(np.polyadd(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.polyadd(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polyadd(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polyadd(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyadd(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyadd(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polysub(poly_obj, AR_i8), np.poly1d)
+assert_type(np.polysub(AR_f8, poly_obj), np.poly1d)
+assert_type(np.polysub(AR_b, AR_b), NoReturn)
+assert_type(np.polysub(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polysub(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polysub(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polysub(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polysub(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polymul(poly_obj, AR_i8), np.poly1d)
+assert_type(np.polymul(AR_f8, poly_obj), np.poly1d)
+assert_type(np.polymul(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.polymul(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polymul(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polymul(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polymul(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polymul(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polydiv(poly_obj, AR_i8), tuple[np.poly1d, np.poly1d])
+assert_type(np.polydiv(AR_f8, poly_obj), tuple[np.poly1d, np.poly1d])
+assert_type(np.polydiv(AR_b, AR_b), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_u4, AR_b), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_i8, AR_i8), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_f8, AR_i8), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_i8, AR_c16), tuple[npt.NDArray[np.complexfloating[Any, Any]], npt.NDArray[np.complexfloating[Any, Any]]])
+assert_type(np.polydiv(AR_O, AR_O), tuple[npt.NDArray[Any], npt.NDArray[Any]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_utils.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..44ae59234c42405cdb717b74bb213f057449cfad
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_utils.pyi
@@ -0,0 +1,18 @@
+from io import StringIO
+
+import numpy as np
+import numpy.typing as npt
+import numpy.lib.array_utils as array_utils
+
+from typing_extensions import assert_type
+
+AR: npt.NDArray[np.float64]
+AR_DICT: dict[str, npt.NDArray[np.float64]]
+FILE: StringIO
+
+def func(a: int) -> bool: ...
+
+assert_type(array_utils.byte_bounds(AR), tuple[int, int])
+assert_type(array_utils.byte_bounds(np.float64()), tuple[int, int])
+
+assert_type(np.info(1, output=FILE), None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_version.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..52c1218e9dfde3b0241ebb739aec6e27f91e0eb0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_version.pyi
@@ -0,0 +1,20 @@
+from numpy.lib import NumpyVersion
+
+from typing_extensions import assert_type
+
+version = NumpyVersion("1.8.0")
+
+assert_type(version.vstring, str)
+assert_type(version.version, str)
+assert_type(version.major, int)
+assert_type(version.minor, int)
+assert_type(version.bugfix, int)
+assert_type(version.pre_release, str)
+assert_type(version.is_devversion, bool)
+
+assert_type(version == version, bool)
+assert_type(version != version, bool)
+assert_type(version < "1.8.0", bool)
+assert_type(version <= version, bool)
+assert_type(version > version, bool)
+assert_type(version >= "1.8.0", bool)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/linalg.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f9aaa71ef4bc44a4fdc22f9224912b16e086a439
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/linalg.pyi
@@ -0,0 +1,130 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy.linalg._linalg import (
+    QRResult, EigResult, EighResult, SVDResult, SlogdetResult
+)
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+AR_m: npt.NDArray[np.timedelta64]
+AR_S: npt.NDArray[np.str_]
+AR_b: npt.NDArray[np.bool]
+
+assert_type(np.linalg.tensorsolve(AR_i8, AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.tensorsolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.tensorsolve(AR_c16, AR_f8), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.solve(AR_i8, AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.solve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.solve(AR_c16, AR_f8), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.tensorinv(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.tensorinv(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.tensorinv(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.inv(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.inv(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.inv(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.matrix_power(AR_i8, -1), npt.NDArray[Any])
+assert_type(np.linalg.matrix_power(AR_f8, 0), npt.NDArray[Any])
+assert_type(np.linalg.matrix_power(AR_c16, 1), npt.NDArray[Any])
+assert_type(np.linalg.matrix_power(AR_O, 2), npt.NDArray[Any])
+
+assert_type(np.linalg.cholesky(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.cholesky(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.cholesky(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.outer(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.linalg.outer(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.outer(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.linalg.outer(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.linalg.outer(AR_O, AR_O), npt.NDArray[np.object_])
+assert_type(np.linalg.outer(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+
+assert_type(np.linalg.qr(AR_i8), QRResult)
+assert_type(np.linalg.qr(AR_f8), QRResult)
+assert_type(np.linalg.qr(AR_c16), QRResult)
+
+assert_type(np.linalg.eigvals(AR_i8), npt.NDArray[np.float64] | npt.NDArray[np.complex128])
+assert_type(np.linalg.eigvals(AR_f8), npt.NDArray[np.floating[Any]] | npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.linalg.eigvals(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.eigvalsh(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.eigvalsh(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.eigvalsh(AR_c16), npt.NDArray[np.floating[Any]])
+
+assert_type(np.linalg.eig(AR_i8), EigResult)
+assert_type(np.linalg.eig(AR_f8), EigResult)
+assert_type(np.linalg.eig(AR_c16), EigResult)
+
+assert_type(np.linalg.eigh(AR_i8), EighResult)
+assert_type(np.linalg.eigh(AR_f8), EighResult)
+assert_type(np.linalg.eigh(AR_c16), EighResult)
+
+assert_type(np.linalg.svd(AR_i8), SVDResult)
+assert_type(np.linalg.svd(AR_f8), SVDResult)
+assert_type(np.linalg.svd(AR_c16), SVDResult)
+assert_type(np.linalg.svd(AR_i8, compute_uv=False), npt.NDArray[np.float64])
+assert_type(np.linalg.svd(AR_f8, compute_uv=False), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.svd(AR_c16, compute_uv=False), npt.NDArray[np.floating[Any]])
+
+assert_type(np.linalg.cond(AR_i8), Any)
+assert_type(np.linalg.cond(AR_f8), Any)
+assert_type(np.linalg.cond(AR_c16), Any)
+
+assert_type(np.linalg.matrix_rank(AR_i8), Any)
+assert_type(np.linalg.matrix_rank(AR_f8), Any)
+assert_type(np.linalg.matrix_rank(AR_c16), Any)
+
+assert_type(np.linalg.pinv(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.pinv(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.pinv(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.slogdet(AR_i8), SlogdetResult)
+assert_type(np.linalg.slogdet(AR_f8), SlogdetResult)
+assert_type(np.linalg.slogdet(AR_c16), SlogdetResult)
+
+assert_type(np.linalg.det(AR_i8), Any)
+assert_type(np.linalg.det(AR_f8), Any)
+assert_type(np.linalg.det(AR_c16), Any)
+
+assert_type(np.linalg.lstsq(AR_i8, AR_i8), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], np.int32, npt.NDArray[np.float64]])
+assert_type(np.linalg.lstsq(AR_i8, AR_f8), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]], np.int32, npt.NDArray[np.floating[Any]]])
+assert_type(np.linalg.lstsq(AR_f8, AR_c16), tuple[npt.NDArray[np.complexfloating[Any, Any]], npt.NDArray[np.floating[Any]], np.int32, npt.NDArray[np.floating[Any]]])
+
+assert_type(np.linalg.norm(AR_i8), np.floating[Any])
+assert_type(np.linalg.norm(AR_f8), np.floating[Any])
+assert_type(np.linalg.norm(AR_c16), np.floating[Any])
+assert_type(np.linalg.norm(AR_S), np.floating[Any])
+assert_type(np.linalg.norm(AR_f8, axis=0), Any)
+
+assert_type(np.linalg.matrix_norm(AR_i8), np.floating[Any])
+assert_type(np.linalg.matrix_norm(AR_f8), np.floating[Any])
+assert_type(np.linalg.matrix_norm(AR_c16), np.floating[Any])
+assert_type(np.linalg.matrix_norm(AR_S), np.floating[Any])
+
+assert_type(np.linalg.vector_norm(AR_i8), np.floating[Any])
+assert_type(np.linalg.vector_norm(AR_f8), np.floating[Any])
+assert_type(np.linalg.vector_norm(AR_c16), np.floating[Any])
+assert_type(np.linalg.vector_norm(AR_S), np.floating[Any])
+
+assert_type(np.linalg.multi_dot([AR_i8, AR_i8]), Any)
+assert_type(np.linalg.multi_dot([AR_i8, AR_f8]), Any)
+assert_type(np.linalg.multi_dot([AR_f8, AR_c16]), Any)
+assert_type(np.linalg.multi_dot([AR_O, AR_O]), Any)
+assert_type(np.linalg.multi_dot([AR_m, AR_m]), Any)
+
+assert_type(np.linalg.cross(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.linalg.cross(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.cross(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.matmul(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.linalg.matmul(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.matmul(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/matrix.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/matrix.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..28a2531b4db26b0c074236906bdedb1022217d4a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/matrix.pyi
@@ -0,0 +1,74 @@
+from typing import Any, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_Shape2D: TypeAlias = tuple[int, int]
+
+mat: np.matrix[_Shape2D, np.dtype[np.int64]]
+ar_f8: npt.NDArray[np.float64]
+
+assert_type(mat * 5, np.matrix[_Shape2D, Any])
+assert_type(5 * mat, np.matrix[_Shape2D, Any])
+mat *= 5
+
+assert_type(mat**5, np.matrix[_Shape2D, Any])
+mat **= 5
+
+assert_type(mat.sum(), Any)
+assert_type(mat.mean(), Any)
+assert_type(mat.std(), Any)
+assert_type(mat.var(), Any)
+assert_type(mat.prod(), Any)
+assert_type(mat.any(), np.bool)
+assert_type(mat.all(), np.bool)
+assert_type(mat.max(), np.int64)
+assert_type(mat.min(), np.int64)
+assert_type(mat.argmax(), np.intp)
+assert_type(mat.argmin(), np.intp)
+assert_type(mat.ptp(), np.int64)
+
+assert_type(mat.sum(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.mean(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.std(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.var(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.prod(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.any(axis=0), np.matrix[_Shape2D, np.dtype[np.bool]])
+assert_type(mat.all(axis=0), np.matrix[_Shape2D, np.dtype[np.bool]])
+assert_type(mat.max(axis=0), np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.min(axis=0), np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.argmax(axis=0), np.matrix[_Shape2D, np.dtype[np.intp]])
+assert_type(mat.argmin(axis=0), np.matrix[_Shape2D, np.dtype[np.intp]])
+assert_type(mat.ptp(axis=0), np.matrix[_Shape2D, np.dtype[np.int64]])
+
+assert_type(mat.sum(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.mean(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.std(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.var(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.prod(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.any(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.all(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.max(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.min(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.argmax(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.argmin(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.ptp(out=ar_f8), npt.NDArray[np.float64])
+
+assert_type(mat.T, np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.I, np.matrix[_Shape2D, Any])
+assert_type(mat.A, np.ndarray[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.A1, npt.NDArray[np.int64])
+assert_type(mat.H, np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.getT(), np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.getI(), np.matrix[_Shape2D, Any])
+assert_type(mat.getA(), np.ndarray[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.getA1(), npt.NDArray[np.int64])
+assert_type(mat.getH(), np.matrix[_Shape2D, np.dtype[np.int64]])
+
+assert_type(np.bmat(ar_f8), np.matrix[_Shape2D, Any])
+assert_type(np.bmat([[0, 1, 2]]), np.matrix[_Shape2D, Any])
+assert_type(np.bmat("mat"), np.matrix[_Shape2D, Any])
+
+assert_type(np.asmatrix(ar_f8, dtype=np.int64), np.matrix[_Shape2D, Any])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/memmap.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/memmap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b1f985382c6b63b2dd9f6619ab5d60c90efc783b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/memmap.pyi
@@ -0,0 +1,21 @@
+from typing import Any
+
+import numpy as np
+
+from typing_extensions import assert_type
+
+memmap_obj: np.memmap[Any, np.dtype[np.str_]]
+
+assert_type(np.memmap.__array_priority__, float)
+assert_type(memmap_obj.__array_priority__, float)
+assert_type(memmap_obj.filename, str | None)
+assert_type(memmap_obj.offset, int)
+assert_type(memmap_obj.mode, str)
+assert_type(memmap_obj.flush(), None)
+
+assert_type(np.memmap("file.txt", offset=5), np.memmap[Any, np.dtype[np.uint8]])
+assert_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3)), np.memmap[Any, np.dtype[np.float64]])
+with open("file.txt", "rb") as f:
+    assert_type(np.memmap(f, dtype=float, order="K"), np.memmap[Any, np.dtype[Any]])
+
+assert_type(memmap_obj.__array_finalize__(object()), None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/mod.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/mod.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..db79504fdd1f472bb004f5db1ef3864f1a0e5ab1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/mod.pyi
@@ -0,0 +1,181 @@
+import datetime as dt
+from typing import Literal as L
+
+from typing_extensions import assert_type
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _64Bit
+
+f8: np.float64
+i8: np.int64
+u8: np.uint64
+
+f4: np.float32
+i4: np.int32
+u4: np.uint32
+
+m: np.timedelta64
+m_nat: np.timedelta64[None]
+m_int0: np.timedelta64[L[0]]
+m_int: np.timedelta64[int]
+m_td: np.timedelta64[dt.timedelta]
+
+b_: np.bool
+
+b: bool
+i: int
+f: float
+
+AR_b: npt.NDArray[np.bool]
+AR_m: npt.NDArray[np.timedelta64]
+
+# Time structures
+
+assert_type(m % m, np.timedelta64)
+assert_type(m % m_nat, np.timedelta64[None])
+assert_type(m % m_int0, np.timedelta64[None])
+assert_type(m % m_int, np.timedelta64[int | None])
+assert_type(m_nat % m, np.timedelta64[None])
+assert_type(m_int % m_nat, np.timedelta64[None])
+assert_type(m_int % m_int0, np.timedelta64[None])
+assert_type(m_int % m_int, np.timedelta64[int | None])
+assert_type(m_int % m_td, np.timedelta64[int | None])
+assert_type(m_td % m_nat, np.timedelta64[None])
+assert_type(m_td % m_int0, np.timedelta64[None])
+assert_type(m_td % m_int, np.timedelta64[int | None])
+assert_type(m_td % m_td, np.timedelta64[dt.timedelta | None])
+
+assert_type(AR_m % m, npt.NDArray[np.timedelta64])
+assert_type(m % AR_m, npt.NDArray[np.timedelta64])
+
+assert_type(divmod(m, m), tuple[np.int64, np.timedelta64])
+assert_type(divmod(m, m_nat), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m, m_int0), tuple[np.int64, np.timedelta64[None]])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(m.__divmod__(m_int), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_nat, m), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_int, m_nat), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_int, m_int0), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_int, m_int), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_int, m_td), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_td, m_nat), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_td, m_int0), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_td, m_int), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_td, m_td), tuple[np.int64, np.timedelta64[dt.timedelta | None]])
+
+assert_type(divmod(AR_m, m), tuple[npt.NDArray[np.int64], npt.NDArray[np.timedelta64]])
+assert_type(divmod(m, AR_m), tuple[npt.NDArray[np.int64], npt.NDArray[np.timedelta64]])
+
+# Bool
+
+assert_type(b_ % b, np.int8)
+assert_type(b_ % i, np.int_)
+assert_type(b_ % f, np.float64)
+assert_type(b_ % b_, np.int8)
+assert_type(b_ % i8, np.int64)
+assert_type(b_ % u8, np.uint64)
+assert_type(b_ % f8, np.float64)
+assert_type(b_ % AR_b, npt.NDArray[np.int8])
+
+assert_type(divmod(b_, b), tuple[np.int8, np.int8])
+assert_type(divmod(b_, b_), tuple[np.int8, np.int8])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(b_.__divmod__(i), tuple[np.int_, np.int_])
+assert_type(b_.__divmod__(f), tuple[np.float64, np.float64])
+assert_type(b_.__divmod__(i8), tuple[np.int64, np.int64])
+assert_type(b_.__divmod__(u8), tuple[np.uint64, np.uint64])
+assert_type(divmod(b_, f8), tuple[np.float64, np.float64])
+assert_type(divmod(b_, AR_b), tuple[npt.NDArray[np.int8], npt.NDArray[np.int8]])
+
+assert_type(b % b_, np.int8)
+assert_type(i % b_, np.int_)
+assert_type(f % b_, np.float64)
+assert_type(b_ % b_, np.int8)
+assert_type(i8 % b_, np.int64)
+assert_type(u8 % b_, np.uint64)
+assert_type(f8 % b_, np.float64)
+assert_type(AR_b % b_, npt.NDArray[np.int8])
+
+assert_type(divmod(b, b_), tuple[np.int8, np.int8])
+assert_type(divmod(i, b_), tuple[np.int_, np.int_])
+assert_type(divmod(f, b_), tuple[np.float64, np.float64])
+assert_type(divmod(b_, b_), tuple[np.int8, np.int8])
+assert_type(divmod(i8, b_), tuple[np.int64, np.int64])
+assert_type(divmod(u8, b_), tuple[np.uint64, np.uint64])
+assert_type(divmod(f8, b_), tuple[np.float64, np.float64])
+assert_type(divmod(AR_b, b_), tuple[npt.NDArray[np.int8], npt.NDArray[np.int8]])
+
+# int
+
+assert_type(i8 % b, np.int64)
+assert_type(i8 % i8, np.int64)
+assert_type(i8 % f, np.float64 | np.floating[_64Bit])
+assert_type(i8 % f8, np.float64 | np.floating[_64Bit])
+assert_type(i4 % i8, np.int64 | np.int32)
+assert_type(i4 % f8, np.float64 | np.float32)
+assert_type(i4 % i4, np.int32)
+assert_type(i4 % f4, np.float32)
+assert_type(i8 % AR_b, npt.NDArray[np.int64])
+
+assert_type(divmod(i8, b), tuple[np.int64, np.int64])
+assert_type(divmod(i8, i4), tuple[np.int64, np.int64] | tuple[np.int32, np.int32])
+assert_type(divmod(i8, i8), tuple[np.int64, np.int64])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(i8.__divmod__(f), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float64, np.float64])
+assert_type(i8.__divmod__(f8), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float64, np.float64])
+assert_type(divmod(i8, f4), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float32, np.float32])
+assert_type(divmod(i4, i4), tuple[np.int32, np.int32])
+assert_type(divmod(i4, f4), tuple[np.float32, np.float32])
+assert_type(divmod(i8, AR_b), tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]])
+
+assert_type(b % i8, np.int64)
+assert_type(f % i8, np.float64 | np.floating[_64Bit])
+assert_type(i8 % i8, np.int64)
+assert_type(f8 % i8, np.float64)
+assert_type(i8 % i4, np.int64 | np.int32)
+assert_type(f8 % i4, np.float64)
+assert_type(i4 % i4, np.int32)
+assert_type(f4 % i4, np.float32)
+assert_type(AR_b % i8, npt.NDArray[np.int64])
+
+assert_type(divmod(b, i8), tuple[np.int64, np.int64])
+assert_type(divmod(f, i8), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float64, np.float64])
+assert_type(divmod(i8, i8), tuple[np.int64, np.int64])
+assert_type(divmod(f8, i8), tuple[np.float64, np.float64])
+assert_type(divmod(i4, i8), tuple[np.int64, np.int64] | tuple[np.int32, np.int32])
+assert_type(divmod(i4, i4), tuple[np.int32, np.int32])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(f4.__divmod__(i8), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float32, np.float32])
+assert_type(f4.__divmod__(i4), tuple[np.float32, np.float32])
+assert_type(AR_b.__divmod__(i8), tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]])
+
+# float
+
+assert_type(f8 % b, np.float64)
+assert_type(f8 % f, np.float64)
+assert_type(i8 % f4, np.floating[_64Bit] | np.float32)
+assert_type(f4 % f4, np.float32)
+assert_type(f8 % AR_b, npt.NDArray[np.float64])
+
+assert_type(divmod(f8, b), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f8), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f4), tuple[np.float64, np.float64])
+assert_type(divmod(f4, f4), tuple[np.float32, np.float32])
+assert_type(divmod(f8, AR_b), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+
+assert_type(b % f8, np.float64)
+assert_type(f % f8, np.float64)  # pyright: ignore[reportAssertTypeFailure]  # pyright incorrectly infers `builtins.float`
+assert_type(f8 % f8, np.float64)
+assert_type(f8 % f8, np.float64)
+assert_type(f4 % f4, np.float32)
+assert_type(AR_b % f8, npt.NDArray[np.float64])
+
+assert_type(divmod(b, f8), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f8), tuple[np.float64, np.float64])
+assert_type(divmod(f4, f4), tuple[np.float32, np.float32])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(f8.__rdivmod__(f), tuple[np.float64, np.float64])
+assert_type(f8.__rdivmod__(f4), tuple[np.float64, np.float64])
+assert_type(AR_b.__divmod__(f8), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/modules.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/modules.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1e4e895bf5f864b902c68c27f0725f7fa3da553e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/modules.pyi
@@ -0,0 +1,52 @@
+import types
+
+import numpy as np
+from numpy import f2py
+
+from typing_extensions import assert_type
+
+assert_type(np, types.ModuleType)
+
+assert_type(np.char, types.ModuleType)
+assert_type(np.ctypeslib, types.ModuleType)
+assert_type(np.emath, types.ModuleType)
+assert_type(np.fft, types.ModuleType)
+assert_type(np.lib, types.ModuleType)
+assert_type(np.linalg, types.ModuleType)
+assert_type(np.ma, types.ModuleType)
+assert_type(np.matrixlib, types.ModuleType)
+assert_type(np.polynomial, types.ModuleType)
+assert_type(np.random, types.ModuleType)
+assert_type(np.rec, types.ModuleType)
+assert_type(np.testing, types.ModuleType)
+assert_type(np.version, types.ModuleType)
+assert_type(np.exceptions, types.ModuleType)
+assert_type(np.dtypes, types.ModuleType)
+
+assert_type(np.lib.format, types.ModuleType)
+assert_type(np.lib.mixins, types.ModuleType)
+assert_type(np.lib.scimath, types.ModuleType)
+assert_type(np.lib.stride_tricks, types.ModuleType)
+assert_type(np.ma.extras, types.ModuleType)
+assert_type(np.polynomial.chebyshev, types.ModuleType)
+assert_type(np.polynomial.hermite, types.ModuleType)
+assert_type(np.polynomial.hermite_e, types.ModuleType)
+assert_type(np.polynomial.laguerre, types.ModuleType)
+assert_type(np.polynomial.legendre, types.ModuleType)
+assert_type(np.polynomial.polynomial, types.ModuleType)
+
+assert_type(np.__path__, list[str])
+assert_type(np.__version__, str)
+assert_type(np.test, np._pytesttester.PytestTester)
+assert_type(np.test.module_name, str)
+
+assert_type(np.__all__, list[str])
+assert_type(np.char.__all__, list[str])
+assert_type(np.ctypeslib.__all__, list[str])
+assert_type(np.emath.__all__, list[str])
+assert_type(np.lib.__all__, list[str])
+assert_type(np.ma.__all__, list[str])
+assert_type(np.random.__all__, list[str])
+assert_type(np.rec.__all__, list[str])
+assert_type(np.testing.__all__, list[str])
+assert_type(f2py.__all__, list[str])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/multiarray.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/multiarray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cae14ee57e22f492629a23d8bed0e2038741e0b3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/multiarray.pyi
@@ -0,0 +1,196 @@
+import datetime as dt
+from typing import Any, Literal, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import Unpack, assert_type
+
+_SCT = TypeVar("_SCT", bound=np.generic, covariant=True)
+
+class SubClass(npt.NDArray[_SCT]): ...
+
+subclass: SubClass[np.float64]
+
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+AR_u1: npt.NDArray[np.uint8]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+AR_LIKE_f: list[float]
+AR_LIKE_i: list[int]
+
+m: np.timedelta64
+M: np.datetime64
+
+b_f8 = np.broadcast(AR_f8)
+b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8)
+
+nditer_obj: np.nditer
+
+date_scalar: dt.date
+date_seq: list[dt.date]
+timedelta_seq: list[dt.timedelta]
+
+n1: Literal[1]
+n2: Literal[2]
+n3: Literal[3]
+
+f8: np.float64
+
+def func11(a: int) -> bool: ...
+def func21(a: int, b: int) -> int: ...
+def func12(a: int) -> tuple[complex, bool]: ...
+
+assert_type(next(b_f8), tuple[Any, ...])
+assert_type(b_f8.reset(), None)
+assert_type(b_f8.index, int)
+assert_type(b_f8.iters, tuple[np.flatiter[Any], ...])
+assert_type(b_f8.nd, int)
+assert_type(b_f8.ndim, int)
+assert_type(b_f8.numiter, int)
+assert_type(b_f8.shape, tuple[int, ...])
+assert_type(b_f8.size, int)
+
+assert_type(next(b_i8_f8_f8), tuple[Any, ...])
+assert_type(b_i8_f8_f8.reset(), None)
+assert_type(b_i8_f8_f8.index, int)
+assert_type(b_i8_f8_f8.iters, tuple[np.flatiter[Any], ...])
+assert_type(b_i8_f8_f8.nd, int)
+assert_type(b_i8_f8_f8.ndim, int)
+assert_type(b_i8_f8_f8.numiter, int)
+assert_type(b_i8_f8_f8.shape, tuple[int, ...])
+assert_type(b_i8_f8_f8.size, int)
+
+assert_type(np.inner(AR_f8, AR_i8), Any)
+
+assert_type(np.where([True, True, False]), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.where([True, True, False], 1, 0), npt.NDArray[Any])
+
+assert_type(np.lexsort([0, 1, 2]), Any)
+
+assert_type(np.can_cast(np.dtype("i8"), int), bool)
+assert_type(np.can_cast(AR_f8, "f8"), bool)
+assert_type(np.can_cast(AR_f8, np.complex128, casting="unsafe"), bool)
+
+assert_type(np.min_scalar_type([1]), np.dtype[Any])
+assert_type(np.min_scalar_type(AR_f8), np.dtype[Any])
+
+assert_type(np.result_type(int, [1]), np.dtype[Any])
+assert_type(np.result_type(AR_f8, AR_u1), np.dtype[Any])
+assert_type(np.result_type(AR_f8, np.complex128), np.dtype[Any])
+
+assert_type(np.dot(AR_LIKE_f, AR_i8), Any)
+assert_type(np.dot(AR_u1, 1), Any)
+assert_type(np.dot(1.5j, 1), Any)
+assert_type(np.dot(AR_u1, 1, out=AR_f8), npt.NDArray[np.float64])
+
+assert_type(np.vdot(AR_LIKE_f, AR_i8), np.floating[Any])
+assert_type(np.vdot(AR_u1, 1), np.signedinteger[Any])
+assert_type(np.vdot(1.5j, 1), np.complexfloating[Any, Any])
+
+assert_type(np.bincount(AR_i8), npt.NDArray[np.intp])
+
+assert_type(np.copyto(AR_f8, [1., 1.5, 1.6]), None)
+
+assert_type(np.putmask(AR_f8, [True, True, False], 1.5), None)
+
+assert_type(np.packbits(AR_i8), npt.NDArray[np.uint8])
+assert_type(np.packbits(AR_u1), npt.NDArray[np.uint8])
+
+assert_type(np.unpackbits(AR_u1), npt.NDArray[np.uint8])
+
+assert_type(np.shares_memory(1, 2), bool)
+assert_type(np.shares_memory(AR_f8, AR_f8, max_work=1), bool)
+
+assert_type(np.may_share_memory(1, 2), bool)
+assert_type(np.may_share_memory(AR_f8, AR_f8, max_work=1), bool)
+
+assert_type(np.promote_types(np.int32, np.int64), np.dtype[Any])
+assert_type(np.promote_types("f4", float), np.dtype[Any])
+
+assert_type(np.frompyfunc(func11, n1, n1).nin, Literal[1])
+assert_type(np.frompyfunc(func11, n1, n1).nout, Literal[1])
+assert_type(np.frompyfunc(func11, n1, n1).nargs, Literal[2])
+assert_type(np.frompyfunc(func11, n1, n1).ntypes, Literal[1])
+assert_type(np.frompyfunc(func11, n1, n1).identity, None)
+assert_type(np.frompyfunc(func11, n1, n1).signature, None)
+assert_type(np.frompyfunc(func11, n1, n1)(f8), bool)
+assert_type(np.frompyfunc(func11, n1, n1)(AR_f8), bool | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func11, n1, n1).at(AR_f8, AR_i8), None)
+
+assert_type(np.frompyfunc(func21, n2, n1).nin, Literal[2])
+assert_type(np.frompyfunc(func21, n2, n1).nout, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1).nargs, Literal[3])
+assert_type(np.frompyfunc(func21, n2, n1).ntypes, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1).identity, None)
+assert_type(np.frompyfunc(func21, n2, n1).signature, None)
+assert_type(np.frompyfunc(func21, n2, n1)(f8, f8), int)
+assert_type(np.frompyfunc(func21, n2, n1)(AR_f8, f8), int | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1)(f8, AR_f8), int | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).reduce(AR_f8, axis=0), int | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).accumulate(AR_f8), npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).reduceat(AR_f8, AR_i8), npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).outer(f8, f8), int)
+assert_type(np.frompyfunc(func21, n2, n1).outer(AR_f8, f8), int | npt.NDArray[np.object_])
+
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).nin, Literal[2])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).nout, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).nargs, Literal[3])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).ntypes, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).identity, int)
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).signature, None)
+
+assert_type(np.frompyfunc(func12, n1, n2).nin, Literal[1])
+assert_type(np.frompyfunc(func12, n1, n2).nout, Literal[2])
+assert_type(np.frompyfunc(func12, n1, n2).nargs, int)
+assert_type(np.frompyfunc(func12, n1, n2).ntypes, Literal[1])
+assert_type(np.frompyfunc(func12, n1, n2).identity, None)
+assert_type(np.frompyfunc(func12, n1, n2).signature, None)
+assert_type(
+    np.frompyfunc(func12, n2, n2)(f8, f8),
+    tuple[complex, complex, Unpack[tuple[complex, ...]]],
+)
+assert_type(
+    np.frompyfunc(func12, n2, n2)(AR_f8, f8),
+    tuple[
+        complex | npt.NDArray[np.object_],
+        complex | npt.NDArray[np.object_],
+        Unpack[tuple[complex | npt.NDArray[np.object_], ...]],
+    ],
+)
+
+assert_type(np.datetime_data("m8[D]"), tuple[str, int])
+assert_type(np.datetime_data(np.datetime64), tuple[str, int])
+assert_type(np.datetime_data(np.dtype(np.timedelta64)), tuple[str, int])
+
+assert_type(np.busday_count("2011-01", "2011-02"), np.int_)
+assert_type(np.busday_count(["2011-01"], "2011-02"), npt.NDArray[np.int_])
+assert_type(np.busday_count(["2011-01"], date_scalar), npt.NDArray[np.int_])
+
+assert_type(np.busday_offset(M, m), np.datetime64)
+assert_type(np.busday_offset(date_scalar, m), np.datetime64)
+assert_type(np.busday_offset(M, 5), np.datetime64)
+assert_type(np.busday_offset(AR_M, m), npt.NDArray[np.datetime64])
+assert_type(np.busday_offset(M, timedelta_seq), npt.NDArray[np.datetime64])
+assert_type(np.busday_offset("2011-01", "2011-02", roll="forward"), np.datetime64)
+assert_type(np.busday_offset(["2011-01"], "2011-02", roll="forward"), npt.NDArray[np.datetime64])
+
+assert_type(np.is_busday("2012"), np.bool)
+assert_type(np.is_busday(date_scalar), np.bool)
+assert_type(np.is_busday(["2012"]), npt.NDArray[np.bool])
+
+assert_type(np.datetime_as_string(M), np.str_)
+assert_type(np.datetime_as_string(AR_M), npt.NDArray[np.str_])
+
+assert_type(np.busdaycalendar(holidays=date_seq), np.busdaycalendar)
+assert_type(np.busdaycalendar(holidays=[M]), np.busdaycalendar)
+
+assert_type(np.char.compare_chararrays("a", "b", "!=", rstrip=False), npt.NDArray[np.bool])
+assert_type(np.char.compare_chararrays(b"a", b"a", "==", True), npt.NDArray[np.bool])
+
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["c_index"]), tuple[np.nditer, ...])
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]]), tuple[np.nditer, ...])
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_), tuple[np.nditer, ...])
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no"), tuple[np.nditer, ...])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nbit_base_example.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nbit_base_example.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..add031ac884affb8ea7f5e3c8d48b1dc366ac206
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nbit_base_example.pyi
@@ -0,0 +1,23 @@
+from typing import TypeVar
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _64Bit, _32Bit
+
+from typing_extensions import assert_type
+
+T1 = TypeVar("T1", bound=npt.NBitBase)
+T2 = TypeVar("T2", bound=npt.NBitBase)
+
+def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
+    return a + b
+
+i8: np.int64
+i4: np.int32
+f8: np.float64
+f4: np.float32
+
+assert_type(add(f8, i8), np.floating[_64Bit])
+assert_type(add(f4, i8), np.floating[_32Bit | _64Bit])
+assert_type(add(f8, i4), np.floating[_32Bit | _64Bit])
+assert_type(add(f4, i4), np.floating[_32Bit])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_assignability.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_assignability.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22f0d005a7d2eec43b30cd7be51847233acda211
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_assignability.pyi
@@ -0,0 +1,79 @@
+from typing import Protocol, TypeAlias, TypeVar
+from typing_extensions import assert_type
+import numpy as np
+
+from numpy._typing import _64Bit
+
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+
+class CanAbs(Protocol[_T_co]):
+    def __abs__(self, /) -> _T_co: ...
+
+class CanInvert(Protocol[_T_co]):
+    def __invert__(self, /) -> _T_co: ...
+
+class CanNeg(Protocol[_T_co]):
+    def __neg__(self, /) -> _T_co: ...
+
+class CanPos(Protocol[_T_co]):
+    def __pos__(self, /) -> _T_co: ...
+
+def do_abs(x: CanAbs[_T]) -> _T: ...
+def do_invert(x: CanInvert[_T]) -> _T: ...
+def do_neg(x: CanNeg[_T]) -> _T: ...
+def do_pos(x: CanPos[_T]) -> _T: ...
+
+_Bool_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.bool]]
+_UInt8_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.uint8]]
+_Int16_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.int16]]
+_LongLong_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.longlong]]
+_Float32_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.float32]]
+_Float64_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.float64]]
+_LongDouble_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.longdouble]]
+_Complex64_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complex64]]
+_Complex128_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complex128]]
+_CLongDouble_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.clongdouble]]
+
+b1_1d: _Bool_1d
+u1_1d: _UInt8_1d
+i2_1d: _Int16_1d
+q_1d: _LongLong_1d
+f4_1d: _Float32_1d
+f8_1d: _Float64_1d
+g_1d: _LongDouble_1d
+c8_1d: _Complex64_1d
+c16_1d: _Complex128_1d
+G_1d: _CLongDouble_1d
+
+assert_type(do_abs(b1_1d), _Bool_1d)
+assert_type(do_abs(u1_1d), _UInt8_1d)
+assert_type(do_abs(i2_1d), _Int16_1d)
+assert_type(do_abs(q_1d), _LongLong_1d)
+assert_type(do_abs(f4_1d), _Float32_1d)
+assert_type(do_abs(f8_1d), _Float64_1d)
+assert_type(do_abs(g_1d), _LongDouble_1d)
+
+assert_type(do_abs(c8_1d), _Float32_1d)
+# NOTE: Unfortunately it's not possible to have this return a `float64` sctype, see
+# https://github.com/python/mypy/issues/14070
+assert_type(do_abs(c16_1d), np.ndarray[tuple[int], np.dtype[np.floating[_64Bit]]])
+assert_type(do_abs(G_1d), _LongDouble_1d)
+
+assert_type(do_invert(b1_1d), _Bool_1d)
+assert_type(do_invert(u1_1d), _UInt8_1d)
+assert_type(do_invert(i2_1d), _Int16_1d)
+assert_type(do_invert(q_1d), _LongLong_1d)
+
+assert_type(do_neg(u1_1d), _UInt8_1d)
+assert_type(do_neg(i2_1d), _Int16_1d)
+assert_type(do_neg(q_1d), _LongLong_1d)
+assert_type(do_neg(f4_1d), _Float32_1d)
+assert_type(do_neg(c16_1d), _Complex128_1d)
+
+assert_type(do_pos(u1_1d), _UInt8_1d)
+assert_type(do_pos(i2_1d), _Int16_1d)
+assert_type(do_pos(q_1d), _LongLong_1d)
+assert_type(do_pos(f4_1d), _Float32_1d)
+assert_type(do_pos(c16_1d), _Complex128_1d)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_conversion.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_conversion.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..49181d2c98a61730e86748dd8823fe7549a781ff
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_conversion.pyi
@@ -0,0 +1,88 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+b1_0d: np.ndarray[tuple[()], np.dtype[np.bool]]
+u2_1d: np.ndarray[tuple[int], np.dtype[np.uint16]]
+i4_2d: np.ndarray[tuple[int, int], np.dtype[np.int32]]
+f8_3d: np.ndarray[tuple[int, int, int], np.dtype[np.float64]]
+cG_4d: np.ndarray[tuple[int, int, int, int], np.dtype[np.clongdouble]]
+i0_nd: npt.NDArray[np.int_]
+uncertain_dtype: np.int32 | np.float64 | np.str_
+
+# item
+assert_type(i0_nd.item(), int)
+assert_type(i0_nd.item(1), int)
+assert_type(i0_nd.item(0, 1), int)
+assert_type(i0_nd.item((0, 1)), int)
+
+assert_type(b1_0d.item(()), bool)
+assert_type(u2_1d.item((0,)), int)
+assert_type(i4_2d.item(-1, 2), int)
+assert_type(f8_3d.item(2, 1, -1), float)
+assert_type(cG_4d.item(-0xEd_fed_Deb_a_dead_bee), complex)  # c'mon Ed, we talked about this...
+
+# tolist
+assert_type(b1_0d.tolist(), bool)
+assert_type(u2_1d.tolist(), list[int])
+assert_type(i4_2d.tolist(), list[list[int]])
+assert_type(f8_3d.tolist(), list[list[list[float]]])
+assert_type(cG_4d.tolist(), Any)
+assert_type(i0_nd.tolist(), Any)
+
+# regression tests for numpy/numpy#27944
+any_dtype: np.ndarray[Any, Any]
+any_sctype: np.ndarray[Any, Any]
+assert_type(any_dtype.tolist(), Any)
+assert_type(any_sctype.tolist(), Any)
+
+
+# itemset does not return a value
+# tostring is pretty simple
+# tobytes is pretty simple
+# tofile does not return a value
+# dump does not return a value
+# dumps is pretty simple
+
+# astype
+assert_type(i0_nd.astype("float"), npt.NDArray[Any])
+assert_type(i0_nd.astype(float), npt.NDArray[Any])
+assert_type(i0_nd.astype(np.float64), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K"), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K", "unsafe"), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K", "unsafe", True), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K", "unsafe", True, True), npt.NDArray[np.float64])
+
+assert_type(np.astype(i0_nd, np.float64), npt.NDArray[np.float64])
+
+assert_type(i4_2d.astype(np.uint16), np.ndarray[tuple[int, int], np.dtype[np.uint16]])
+assert_type(np.astype(i4_2d, np.uint16), np.ndarray[tuple[int, int], np.dtype[np.uint16]])
+assert_type(f8_3d.astype(np.int16), np.ndarray[tuple[int, int, int], np.dtype[np.int16]])
+assert_type(np.astype(f8_3d, np.int16), np.ndarray[tuple[int, int, int], np.dtype[np.int16]])
+assert_type(i4_2d.astype(uncertain_dtype), np.ndarray[tuple[int, int], np.dtype[np.generic[Any]]])
+assert_type(np.astype(i4_2d, uncertain_dtype), np.ndarray[tuple[int, int], np.dtype[Any]])
+
+# byteswap
+assert_type(i0_nd.byteswap(), npt.NDArray[np.int_])
+assert_type(i0_nd.byteswap(True), npt.NDArray[np.int_])
+
+# copy
+assert_type(i0_nd.copy(), npt.NDArray[np.int_])
+assert_type(i0_nd.copy("C"), npt.NDArray[np.int_])
+
+assert_type(i0_nd.view(), npt.NDArray[np.int_])
+assert_type(i0_nd.view(np.float64), npt.NDArray[np.float64])
+assert_type(i0_nd.view(float), npt.NDArray[Any])
+assert_type(i0_nd.view(np.float64, np.matrix), np.matrix[tuple[int, int], Any])
+
+# getfield
+assert_type(i0_nd.getfield("float"), npt.NDArray[Any])
+assert_type(i0_nd.getfield(float), npt.NDArray[Any])
+assert_type(i0_nd.getfield(np.float64), npt.NDArray[np.float64])
+assert_type(i0_nd.getfield(np.float64, 8), npt.NDArray[np.float64])
+
+# setflags does not return a value
+# fill does not return a value
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_misc.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_misc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7c619c1e156e313f1eb3d8f4af6c535065ec0b55
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_misc.pyi
@@ -0,0 +1,234 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+import operator
+import ctypes as ct
+from types import ModuleType
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import CapsuleType, assert_type
+
+class SubClass(npt.NDArray[np.object_]): ...
+
+f8: np.float64
+i8: np.int64
+B: SubClass
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+AR_u1: npt.NDArray[np.uint8]
+AR_c8: npt.NDArray[np.complex64]
+AR_m: npt.NDArray[np.timedelta64]
+AR_U: npt.NDArray[np.str_]
+AR_V: npt.NDArray[np.void]
+
+ctypes_obj = AR_f8.ctypes
+
+assert_type(AR_f8.__dlpack__(), CapsuleType)
+assert_type(AR_f8.__dlpack_device__(), tuple[Literal[1], Literal[0]])
+
+assert_type(ctypes_obj.data, int)
+assert_type(ctypes_obj.shape, ct.Array[np.ctypeslib.c_intp])
+assert_type(ctypes_obj.strides, ct.Array[np.ctypeslib.c_intp])
+assert_type(ctypes_obj._as_parameter_, ct.c_void_p)
+
+assert_type(ctypes_obj.data_as(ct.c_void_p), ct.c_void_p)
+assert_type(ctypes_obj.shape_as(ct.c_longlong), ct.Array[ct.c_longlong])
+assert_type(ctypes_obj.strides_as(ct.c_ubyte), ct.Array[ct.c_ubyte])
+
+assert_type(f8.all(), np.bool)
+assert_type(AR_f8.all(), np.bool)
+assert_type(AR_f8.all(axis=0), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.all(keepdims=True), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.all(out=B), SubClass)
+
+assert_type(f8.any(), np.bool)
+assert_type(AR_f8.any(), np.bool)
+assert_type(AR_f8.any(axis=0), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.any(keepdims=True), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.any(out=B), SubClass)
+
+assert_type(f8.argmax(), np.intp)
+assert_type(AR_f8.argmax(), np.intp)
+assert_type(AR_f8.argmax(axis=0), Any)
+assert_type(AR_f8.argmax(out=B), SubClass)
+
+assert_type(f8.argmin(), np.intp)
+assert_type(AR_f8.argmin(), np.intp)
+assert_type(AR_f8.argmin(axis=0), Any)
+assert_type(AR_f8.argmin(out=B), SubClass)
+
+assert_type(f8.argsort(), npt.NDArray[Any])
+assert_type(AR_f8.argsort(), npt.NDArray[Any])
+
+assert_type(f8.astype(np.int64).choose([()]), npt.NDArray[Any])
+assert_type(AR_f8.choose([0]), npt.NDArray[Any])
+assert_type(AR_f8.choose([0], out=B), SubClass)
+
+assert_type(f8.clip(1), npt.NDArray[Any])
+assert_type(AR_f8.clip(1), npt.NDArray[Any])
+assert_type(AR_f8.clip(None, 1), npt.NDArray[Any])
+assert_type(AR_f8.clip(1, out=B), SubClass)
+assert_type(AR_f8.clip(None, 1, out=B), SubClass)
+
+assert_type(f8.compress([0]), npt.NDArray[Any])
+assert_type(AR_f8.compress([0]), npt.NDArray[Any])
+assert_type(AR_f8.compress([0], out=B), SubClass)
+
+assert_type(f8.conj(), np.float64)
+assert_type(AR_f8.conj(), npt.NDArray[np.float64])
+assert_type(B.conj(), SubClass)
+
+assert_type(f8.conjugate(), np.float64)
+assert_type(AR_f8.conjugate(), npt.NDArray[np.float64])
+assert_type(B.conjugate(), SubClass)
+
+assert_type(f8.cumprod(), npt.NDArray[Any])
+assert_type(AR_f8.cumprod(), npt.NDArray[Any])
+assert_type(AR_f8.cumprod(out=B), SubClass)
+
+assert_type(f8.cumsum(), npt.NDArray[Any])
+assert_type(AR_f8.cumsum(), npt.NDArray[Any])
+assert_type(AR_f8.cumsum(out=B), SubClass)
+
+assert_type(f8.max(), Any)
+assert_type(AR_f8.max(), Any)
+assert_type(AR_f8.max(axis=0), Any)
+assert_type(AR_f8.max(keepdims=True), Any)
+assert_type(AR_f8.max(out=B), SubClass)
+
+assert_type(f8.mean(), Any)
+assert_type(AR_f8.mean(), Any)
+assert_type(AR_f8.mean(axis=0), Any)
+assert_type(AR_f8.mean(keepdims=True), Any)
+assert_type(AR_f8.mean(out=B), SubClass)
+
+assert_type(f8.min(), Any)
+assert_type(AR_f8.min(), Any)
+assert_type(AR_f8.min(axis=0), Any)
+assert_type(AR_f8.min(keepdims=True), Any)
+assert_type(AR_f8.min(out=B), SubClass)
+
+assert_type(f8.prod(), Any)
+assert_type(AR_f8.prod(), Any)
+assert_type(AR_f8.prod(axis=0), Any)
+assert_type(AR_f8.prod(keepdims=True), Any)
+assert_type(AR_f8.prod(out=B), SubClass)
+
+assert_type(f8.round(), np.float64)
+assert_type(AR_f8.round(), npt.NDArray[np.float64])
+assert_type(AR_f8.round(out=B), SubClass)
+
+assert_type(f8.repeat(1), npt.NDArray[np.float64])
+assert_type(AR_f8.repeat(1), npt.NDArray[np.float64])
+assert_type(B.repeat(1), npt.NDArray[np.object_])
+
+assert_type(f8.std(), Any)
+assert_type(AR_f8.std(), Any)
+assert_type(AR_f8.std(axis=0), Any)
+assert_type(AR_f8.std(keepdims=True), Any)
+assert_type(AR_f8.std(out=B), SubClass)
+
+assert_type(f8.sum(), Any)
+assert_type(AR_f8.sum(), Any)
+assert_type(AR_f8.sum(axis=0), Any)
+assert_type(AR_f8.sum(keepdims=True), Any)
+assert_type(AR_f8.sum(out=B), SubClass)
+
+assert_type(f8.take(0), np.float64)
+assert_type(AR_f8.take(0), np.float64)
+assert_type(AR_f8.take([0]), npt.NDArray[np.float64])
+assert_type(AR_f8.take(0, out=B), SubClass)
+assert_type(AR_f8.take([0], out=B), SubClass)
+
+assert_type(f8.var(), Any)
+assert_type(AR_f8.var(), Any)
+assert_type(AR_f8.var(axis=0), Any)
+assert_type(AR_f8.var(keepdims=True), Any)
+assert_type(AR_f8.var(out=B), SubClass)
+
+assert_type(AR_f8.argpartition([0]), npt.NDArray[np.intp])
+
+assert_type(AR_f8.diagonal(), npt.NDArray[np.float64])
+
+assert_type(AR_f8.dot(1), npt.NDArray[Any])
+assert_type(AR_f8.dot([1]), Any)
+assert_type(AR_f8.dot(1, out=B), SubClass)
+
+assert_type(AR_f8.nonzero(), tuple[npt.NDArray[np.intp], ...])
+
+assert_type(AR_f8.searchsorted(1), np.intp)
+assert_type(AR_f8.searchsorted([1]), npt.NDArray[np.intp])
+
+assert_type(AR_f8.trace(), Any)
+assert_type(AR_f8.trace(out=B), SubClass)
+
+assert_type(AR_f8.item(), float)
+assert_type(AR_U.item(), str)
+
+assert_type(AR_f8.ravel(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(AR_U.ravel(), np.ndarray[tuple[int], np.dtype[np.str_]])
+
+assert_type(AR_f8.flatten(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(AR_U.flatten(), np.ndarray[tuple[int], np.dtype[np.str_]])
+
+assert_type(AR_i8.reshape(None), npt.NDArray[np.int64])
+assert_type(AR_f8.reshape(-1), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(AR_c8.reshape(2, 3, 4, 5), np.ndarray[tuple[int, int, int, int], np.dtype[np.complex64]])
+assert_type(AR_m.reshape(()), np.ndarray[tuple[()], np.dtype[np.timedelta64]])
+assert_type(AR_U.reshape([]), np.ndarray[tuple[()], np.dtype[np.str_]])
+assert_type(AR_V.reshape((480, 720, 4)), np.ndarray[tuple[int, int, int], np.dtype[np.void]])
+
+assert_type(int(AR_f8), int)
+assert_type(int(AR_U), int)
+
+assert_type(float(AR_f8), float)
+assert_type(float(AR_U), float)
+
+assert_type(complex(AR_f8), complex)
+
+assert_type(operator.index(AR_i8), int)
+
+assert_type(AR_f8.__array_wrap__(B), npt.NDArray[np.object_])
+
+assert_type(AR_V[0], Any)
+assert_type(AR_V[0, 0], Any)
+assert_type(AR_V[AR_i8], npt.NDArray[np.void])
+assert_type(AR_V[AR_i8, AR_i8], npt.NDArray[np.void])
+assert_type(AR_V[AR_i8, None], npt.NDArray[np.void])
+assert_type(AR_V[0, ...], npt.NDArray[np.void])
+assert_type(AR_V[[0]], npt.NDArray[np.void])
+assert_type(AR_V[[0], [0]], npt.NDArray[np.void])
+assert_type(AR_V[:], npt.NDArray[np.void])
+assert_type(AR_V["a"], npt.NDArray[Any])
+assert_type(AR_V[["a", "b"]], npt.NDArray[np.void])
+
+assert_type(AR_f8.dump("test_file"), None)
+assert_type(AR_f8.dump(b"test_file"), None)
+with open("test_file", "wb") as f:
+    assert_type(AR_f8.dump(f), None)
+
+assert_type(AR_f8.__array_finalize__(None), None)
+assert_type(AR_f8.__array_finalize__(B), None)
+assert_type(AR_f8.__array_finalize__(AR_f8), None)
+
+assert_type(f8.device, Literal["cpu"])
+assert_type(AR_f8.device, Literal["cpu"])
+
+assert_type(f8.to_device("cpu"), np.float64)
+assert_type(i8.to_device("cpu"), np.int64)
+assert_type(AR_f8.to_device("cpu"), npt.NDArray[np.float64])
+assert_type(AR_i8.to_device("cpu"), npt.NDArray[np.int64])
+assert_type(AR_u1.to_device("cpu"), npt.NDArray[np.uint8])
+assert_type(AR_c8.to_device("cpu"), npt.NDArray[np.complex64])
+assert_type(AR_m.to_device("cpu"), npt.NDArray[np.timedelta64])
+
+assert_type(f8.__array_namespace__(), ModuleType)
+assert_type(AR_f8.__array_namespace__(), ModuleType)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..25637134088c7c9990c4fc257ed4e779d9ac34aa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi
@@ -0,0 +1,39 @@
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+nd: npt.NDArray[np.int64]
+
+# reshape
+assert_type(nd.reshape(None), npt.NDArray[np.int64])
+assert_type(nd.reshape(4), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.reshape((4,)), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.reshape(2, 2), np.ndarray[tuple[int, int], np.dtype[np.int64]])
+assert_type(nd.reshape((2, 2)), np.ndarray[tuple[int, int], np.dtype[np.int64]])
+
+assert_type(nd.reshape((2, 2), order="C"),  np.ndarray[tuple[int, int], np.dtype[np.int64]])
+assert_type(nd.reshape(4, order="C"),  np.ndarray[tuple[int], np.dtype[np.int64]])
+
+# resize does not return a value
+
+# transpose
+assert_type(nd.transpose(), npt.NDArray[np.int64])
+assert_type(nd.transpose(1, 0), npt.NDArray[np.int64])
+assert_type(nd.transpose((1, 0)), npt.NDArray[np.int64])
+
+# swapaxes
+assert_type(nd.swapaxes(0, 1), npt.NDArray[np.int64])
+
+# flatten
+assert_type(nd.flatten(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.flatten("C"), np.ndarray[tuple[int], np.dtype[np.int64]])
+
+# ravel
+assert_type(nd.ravel(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.ravel("C"), np.ndarray[tuple[int], np.dtype[np.int64]])
+
+# squeeze
+assert_type(nd.squeeze(), npt.NDArray[np.int64])
+assert_type(nd.squeeze(0), npt.NDArray[np.int64])
+assert_type(nd.squeeze((0, 2)), npt.NDArray[np.int64])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nditer.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nditer.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b5723c41310e84318b6d436bb305b994a61b61b3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nditer.pyi
@@ -0,0 +1,51 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+nditer_obj: np.nditer
+
+assert_type(np.nditer([0, 1], flags=["c_index"]), np.nditer)
+assert_type(np.nditer([0, 1], op_flags=[["readonly", "readonly"]]), np.nditer)
+assert_type(np.nditer([0, 1], op_dtypes=np.int_), np.nditer)
+assert_type(np.nditer([0, 1], order="C", casting="no"), np.nditer)
+
+assert_type(nditer_obj.dtypes, tuple[np.dtype[Any], ...])
+assert_type(nditer_obj.finished, bool)
+assert_type(nditer_obj.has_delayed_bufalloc, bool)
+assert_type(nditer_obj.has_index, bool)
+assert_type(nditer_obj.has_multi_index, bool)
+assert_type(nditer_obj.index, int)
+assert_type(nditer_obj.iterationneedsapi, bool)
+assert_type(nditer_obj.iterindex, int)
+assert_type(nditer_obj.iterrange, tuple[int, ...])
+assert_type(nditer_obj.itersize, int)
+assert_type(nditer_obj.itviews, tuple[npt.NDArray[Any], ...])
+assert_type(nditer_obj.multi_index, tuple[int, ...])
+assert_type(nditer_obj.ndim, int)
+assert_type(nditer_obj.nop, int)
+assert_type(nditer_obj.operands, tuple[npt.NDArray[Any], ...])
+assert_type(nditer_obj.shape, tuple[int, ...])
+assert_type(nditer_obj.value, tuple[npt.NDArray[Any], ...])
+
+assert_type(nditer_obj.close(), None)
+assert_type(nditer_obj.copy(), np.nditer)
+assert_type(nditer_obj.debug_print(), None)
+assert_type(nditer_obj.enable_external_loop(), None)
+assert_type(nditer_obj.iternext(), bool)
+assert_type(nditer_obj.remove_axis(0), None)
+assert_type(nditer_obj.remove_multi_index(), None)
+assert_type(nditer_obj.reset(), None)
+
+assert_type(len(nditer_obj), int)
+assert_type(iter(nditer_obj), np.nditer)
+assert_type(next(nditer_obj), tuple[npt.NDArray[Any], ...])
+assert_type(nditer_obj.__copy__(), np.nditer)
+with nditer_obj as f:
+    assert_type(f, np.nditer)
+assert_type(nditer_obj[0], npt.NDArray[Any])
+assert_type(nditer_obj[:], tuple[npt.NDArray[Any], ...])
+nditer_obj[0] = 0
+nditer_obj[:] = [0, 1]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nested_sequence.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nested_sequence.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..06acbbd9ce84e41481053de8e3b0b7c402e6a296
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/nested_sequence.pyi
@@ -0,0 +1,28 @@
+from collections.abc import Sequence
+from typing import Any
+
+from numpy._typing import _NestedSequence
+
+from typing_extensions import assert_type
+
+a: Sequence[int]
+b: Sequence[Sequence[int]]
+c: Sequence[Sequence[Sequence[int]]]
+d: Sequence[Sequence[Sequence[Sequence[int]]]]
+e: Sequence[bool]
+f: tuple[int, ...]
+g: list[int]
+h: Sequence[Any]
+
+def func(a: _NestedSequence[int]) -> None:
+    ...
+
+assert_type(func(a), None)
+assert_type(func(b), None)
+assert_type(func(c), None)
+assert_type(func(d), None)
+assert_type(func(e), None)
+assert_type(func(f), None)
+assert_type(func(g), None)
+assert_type(func(h), None)
+assert_type(func(range(15)), None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/npyio.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/npyio.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d4c47b665ca50d5d5ecd503bcaeaea717bdc889b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/npyio.pyi
@@ -0,0 +1,85 @@
+import re
+import zipfile
+import pathlib
+from typing import IO, Any
+from collections.abc import Mapping
+
+import numpy.typing as npt
+import numpy as np
+from numpy.lib._npyio_impl import BagObj
+
+from typing_extensions import assert_type
+
+str_path: str
+pathlib_path: pathlib.Path
+str_file: IO[str]
+bytes_file: IO[bytes]
+
+npz_file: np.lib.npyio.NpzFile
+
+AR_i8: npt.NDArray[np.int64]
+AR_LIKE_f8: list[float]
+
+class BytesWriter:
+    def write(self, data: bytes) -> None: ...
+
+class BytesReader:
+    def read(self, n: int = ...) -> bytes: ...
+    def seek(self, offset: int, whence: int = ...) -> int: ...
+
+bytes_writer: BytesWriter
+bytes_reader: BytesReader
+
+assert_type(npz_file.zip, zipfile.ZipFile)
+assert_type(npz_file.fid, None | IO[str])
+assert_type(npz_file.files, list[str])
+assert_type(npz_file.allow_pickle, bool)
+assert_type(npz_file.pickle_kwargs, None | Mapping[str, Any])
+assert_type(npz_file.f, BagObj[np.lib.npyio.NpzFile])
+assert_type(npz_file["test"], npt.NDArray[Any])
+assert_type(len(npz_file), int)
+with npz_file as f:
+    assert_type(f, np.lib.npyio.NpzFile)
+
+assert_type(np.load(bytes_file), Any)
+assert_type(np.load(pathlib_path, allow_pickle=True), Any)
+assert_type(np.load(str_path, encoding="bytes"), Any)
+assert_type(np.load(bytes_reader), Any)
+
+assert_type(np.save(bytes_file, AR_LIKE_f8), None)
+assert_type(np.save(pathlib_path, AR_i8, allow_pickle=True), None)
+assert_type(np.save(str_path, AR_LIKE_f8), None)
+assert_type(np.save(bytes_writer, AR_LIKE_f8), None)
+
+assert_type(np.savez(bytes_file, AR_LIKE_f8), None)
+assert_type(np.savez(pathlib_path, ar1=AR_i8, ar2=AR_i8), None)
+assert_type(np.savez(str_path, AR_LIKE_f8, ar1=AR_i8), None)
+assert_type(np.savez(bytes_writer, AR_LIKE_f8, ar1=AR_i8), None)
+
+assert_type(np.savez_compressed(bytes_file, AR_LIKE_f8), None)
+assert_type(np.savez_compressed(pathlib_path, ar1=AR_i8, ar2=AR_i8), None)
+assert_type(np.savez_compressed(str_path, AR_LIKE_f8, ar1=AR_i8), None)
+assert_type(np.savez_compressed(bytes_writer, AR_LIKE_f8, ar1=AR_i8), None)
+
+assert_type(np.loadtxt(bytes_file), npt.NDArray[np.float64])
+assert_type(np.loadtxt(pathlib_path, dtype=np.str_), npt.NDArray[np.str_])
+assert_type(np.loadtxt(str_path, dtype=str, skiprows=2), npt.NDArray[Any])
+assert_type(np.loadtxt(str_file, comments="test"), npt.NDArray[np.float64])
+assert_type(np.loadtxt(str_file, comments=None), npt.NDArray[np.float64])
+assert_type(np.loadtxt(str_path, delimiter="\n"), npt.NDArray[np.float64])
+assert_type(np.loadtxt(str_path, ndmin=2), npt.NDArray[np.float64])
+assert_type(np.loadtxt(["1", "2", "3"]), npt.NDArray[np.float64])
+
+assert_type(np.fromregex(bytes_file, "test", np.float64), npt.NDArray[np.float64])
+assert_type(np.fromregex(str_file, b"test", dtype=float), npt.NDArray[Any])
+assert_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="utf8"), npt.NDArray[np.str_])
+assert_type(np.fromregex(pathlib_path, "test", np.float64), npt.NDArray[np.float64])
+assert_type(np.fromregex(bytes_reader, "test", np.float64), npt.NDArray[np.float64])
+
+assert_type(np.genfromtxt(bytes_file), npt.NDArray[Any])
+assert_type(np.genfromtxt(pathlib_path, dtype=np.str_), npt.NDArray[np.str_])
+assert_type(np.genfromtxt(str_path, dtype=str, skip_header=2), npt.NDArray[Any])
+assert_type(np.genfromtxt(str_file, comments="test"), npt.NDArray[Any])
+assert_type(np.genfromtxt(str_path, delimiter="\n"), npt.NDArray[Any])
+assert_type(np.genfromtxt(str_path, ndmin=2), npt.NDArray[Any])
+assert_type(np.genfromtxt(["1", "2", "3"], ndmin=2), npt.NDArray[Any])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/numeric.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/numeric.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..90e6674a85e39a806020d5768883356c9e85bed4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/numeric.pyi
@@ -0,0 +1,137 @@
+"""
+Tests for :mod:`_core.numeric`.
+
+Does not include tests which fall under ``array_constructors``.
+
+"""
+
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+class SubClass(npt.NDArray[np.int64]):
+    ...
+
+i8: np.int64
+
+AR_b: npt.NDArray[np.bool]
+AR_u8: npt.NDArray[np.uint64]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_O: npt.NDArray[np.object_]
+
+B: list[int]
+C: SubClass
+
+assert_type(np.count_nonzero(i8), int)
+assert_type(np.count_nonzero(AR_i8), int)
+assert_type(np.count_nonzero(B), int)
+assert_type(np.count_nonzero(AR_i8, keepdims=True), npt.NDArray[np.intp])
+assert_type(np.count_nonzero(AR_i8, axis=0), Any)
+
+assert_type(np.isfortran(i8), bool)
+assert_type(np.isfortran(AR_i8), bool)
+
+assert_type(np.argwhere(i8), npt.NDArray[np.intp])
+assert_type(np.argwhere(AR_i8), npt.NDArray[np.intp])
+
+assert_type(np.flatnonzero(i8), npt.NDArray[np.intp])
+assert_type(np.flatnonzero(AR_i8), npt.NDArray[np.intp])
+
+assert_type(np.correlate(B, AR_i8, mode="valid"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.correlate(AR_i8, AR_i8, mode="same"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.correlate(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.correlate(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.correlate(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.correlate(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.correlate(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.correlate(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.correlate(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.convolve(B, AR_i8, mode="valid"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_i8, mode="same"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.convolve(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.convolve(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.convolve(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.convolve(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.outer(i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.outer(B, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.outer(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.outer(AR_i8, AR_i8, out=C), SubClass)
+assert_type(np.outer(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.outer(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.outer(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.outer(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.outer(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.outer(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.tensordot(B, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_i8, axes=0), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_i8, axes=(0, 1)), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.tensordot(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.tensordot(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.tensordot(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.tensordot(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.isscalar(i8), bool)
+assert_type(np.isscalar(AR_i8), bool)
+assert_type(np.isscalar(B), bool)
+
+assert_type(np.roll(AR_i8, 1), npt.NDArray[np.int64])
+assert_type(np.roll(AR_i8, (1, 2)), npt.NDArray[np.int64])
+assert_type(np.roll(B, 1), npt.NDArray[Any])
+
+assert_type(np.rollaxis(AR_i8, 0, 1), npt.NDArray[np.int64])
+
+assert_type(np.moveaxis(AR_i8, 0, 1), npt.NDArray[np.int64])
+assert_type(np.moveaxis(AR_i8, (0, 1), (1, 2)), npt.NDArray[np.int64])
+
+assert_type(np.cross(B, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.cross(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.cross(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.cross(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.cross(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.cross(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cross(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.indices([0, 1, 2]), npt.NDArray[np.int_])
+assert_type(np.indices([0, 1, 2], sparse=True), tuple[npt.NDArray[np.int_], ...])
+assert_type(np.indices([0, 1, 2], dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.indices([0, 1, 2], sparse=True, dtype=np.float64), tuple[npt.NDArray[np.float64], ...])
+assert_type(np.indices([0, 1, 2], dtype=float), npt.NDArray[Any])
+assert_type(np.indices([0, 1, 2], sparse=True, dtype=float), tuple[npt.NDArray[Any], ...])
+
+assert_type(np.binary_repr(1), str)
+
+assert_type(np.base_repr(1), str)
+
+assert_type(np.allclose(i8, AR_i8), bool)
+assert_type(np.allclose(B, AR_i8), bool)
+assert_type(np.allclose(AR_i8, AR_i8), bool)
+
+assert_type(np.isclose(i8, i8), np.bool)
+assert_type(np.isclose(i8, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isclose(B, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isclose(AR_i8, AR_i8), npt.NDArray[np.bool])
+
+assert_type(np.array_equal(i8, AR_i8), bool)
+assert_type(np.array_equal(B, AR_i8), bool)
+assert_type(np.array_equal(AR_i8, AR_i8), bool)
+
+assert_type(np.array_equiv(i8, AR_i8), bool)
+assert_type(np.array_equiv(B, AR_i8), bool)
+assert_type(np.array_equiv(AR_i8, AR_i8), bool)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/numerictypes.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/numerictypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a8ad4e0e1f4b287245ae3d457ccf3c67b5483b10
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/numerictypes.pyi
@@ -0,0 +1,53 @@
+from typing import Literal
+from typing_extensions import assert_type
+
+import numpy as np
+
+
+assert_type(
+    np.ScalarType,
+    tuple[
+        type[int],
+        type[float],
+        type[complex],
+        type[bool],
+        type[bytes],
+        type[str],
+        type[memoryview],
+        type[np.bool],
+        type[np.csingle],
+        type[np.cdouble],
+        type[np.clongdouble],
+        type[np.half],
+        type[np.single],
+        type[np.double],
+        type[np.longdouble],
+        type[np.byte],
+        type[np.short],
+        type[np.intc],
+        type[np.long],
+        type[np.longlong],
+        type[np.timedelta64],
+        type[np.datetime64],
+        type[np.object_],
+        type[np.bytes_],
+        type[np.str_],
+        type[np.ubyte],
+        type[np.ushort],
+        type[np.uintc],
+        type[np.ulong],
+        type[np.ulonglong],
+        type[np.void],
+    ],
+)
+assert_type(np.ScalarType[0], type[int])
+assert_type(np.ScalarType[3], type[bool])
+assert_type(np.ScalarType[8], type[np.csingle])
+assert_type(np.ScalarType[10], type[np.clongdouble])
+assert_type(np.bool_(object()), np.bool)
+
+assert_type(np.typecodes["Character"], Literal["c"])
+assert_type(np.typecodes["Complex"], Literal["FDG"])
+assert_type(np.typecodes["All"], Literal["?bhilqnpBHILQNPefdgFDGSUVOMm"])
+
+assert_type(np.sctypeDict['uint8'], type[np.generic])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polybase.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polybase.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..40c13e646f4a31a4e1c1196ebeb953098c745b91
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polybase.pyi
@@ -0,0 +1,221 @@
+from fractions import Fraction
+from collections.abc import Sequence
+from decimal import Decimal
+from typing import Any, Literal as L, TypeAlias, TypeVar
+
+import numpy as np
+import numpy.polynomial as npp
+import numpy.typing as npt
+
+from typing_extensions import assert_type, LiteralString
+
+_Ar_x: TypeAlias = npt.NDArray[np.inexact[Any] | np.object_]
+_Ar_f: TypeAlias = npt.NDArray[np.floating[Any]]
+_Ar_c: TypeAlias = npt.NDArray[np.complexfloating[Any, Any]]
+_Ar_O: TypeAlias = npt.NDArray[np.object_]
+
+_Ar_x_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.inexact[Any] | np.object_]]
+_Ar_f_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.floating[Any]]]
+_Ar_c_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complexfloating[Any, Any]]]
+_Ar_O_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.object_]]
+
+_Ar_x_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.inexact[Any] | np.object_]]
+_Ar_f_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.floating[Any]]]
+_Ar_c_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.complexfloating[Any, Any]]]
+_Ar_O_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.object_]]
+
+_SCT = TypeVar("_SCT", bound=np.generic)
+_Ar_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[_SCT]]
+
+_BasisName: TypeAlias = L["X"]
+
+SC_i: np.int_
+SC_i_co: int | np.int_
+SC_f: np.float64
+SC_f_co: float | np.float64 | np.int_
+SC_c: np.complex128
+SC_c_co: complex | np.complex128
+SC_O: Decimal
+
+AR_i: npt.NDArray[np.int_]
+AR_f: npt.NDArray[np.float64]
+AR_f_co: npt.NDArray[np.float64] | npt.NDArray[np.int_]
+AR_c: npt.NDArray[np.complex128]
+AR_c_co: npt.NDArray[np.complex128] |npt.NDArray[np.float64] | npt.NDArray[np.int_]
+AR_O: npt.NDArray[np.object_]
+AR_O_co: npt.NDArray[np.object_ | np.number[Any]]
+
+SQ_i: Sequence[int]
+SQ_f: Sequence[float]
+SQ_c: Sequence[complex]
+SQ_O: Sequence[Decimal]
+
+PS_poly: npp.Polynomial
+PS_cheb: npp.Chebyshev
+PS_herm: npp.Hermite
+PS_herme: npp.HermiteE
+PS_lag: npp.Laguerre
+PS_leg: npp.Legendre
+PS_all: (
+    npp.Polynomial
+    | npp.Chebyshev
+    | npp.Hermite
+    | npp.HermiteE
+    | npp.Laguerre
+    | npp.Legendre
+)
+
+# static- and classmethods
+
+assert_type(type(PS_poly).basis_name, None)
+assert_type(type(PS_cheb).basis_name, L['T'])
+assert_type(type(PS_herm).basis_name, L['H'])
+assert_type(type(PS_herme).basis_name, L['He'])
+assert_type(type(PS_lag).basis_name, L['L'])
+assert_type(type(PS_leg).basis_name, L['P'])
+
+assert_type(type(PS_all).__hash__, None)
+assert_type(type(PS_all).__array_ufunc__, None)
+assert_type(type(PS_all).maxpower, L[100])
+
+assert_type(type(PS_poly).fromroots(SC_i), npp.Polynomial)
+assert_type(type(PS_poly).fromroots(SQ_i), npp.Polynomial)
+assert_type(type(PS_poly).fromroots(AR_i), npp.Polynomial)
+assert_type(type(PS_cheb).fromroots(SC_f), npp.Chebyshev)
+assert_type(type(PS_cheb).fromroots(SQ_f), npp.Chebyshev)
+assert_type(type(PS_cheb).fromroots(AR_f_co), npp.Chebyshev)
+assert_type(type(PS_herm).fromroots(SC_c), npp.Hermite)
+assert_type(type(PS_herm).fromroots(SQ_c), npp.Hermite)
+assert_type(type(PS_herm).fromroots(AR_c_co), npp.Hermite)
+assert_type(type(PS_leg).fromroots(SC_O), npp.Legendre)
+assert_type(type(PS_leg).fromroots(SQ_O), npp.Legendre)
+assert_type(type(PS_leg).fromroots(AR_O_co), npp.Legendre)
+
+assert_type(type(PS_poly).identity(), npp.Polynomial)
+assert_type(type(PS_cheb).identity(symbol='z'), npp.Chebyshev)
+
+assert_type(type(PS_lag).basis(SC_i), npp.Laguerre)
+assert_type(type(PS_leg).basis(32, symbol='u'), npp.Legendre)
+
+assert_type(type(PS_herm).cast(PS_poly), npp.Hermite)
+assert_type(type(PS_herme).cast(PS_leg), npp.HermiteE)
+
+# attributes / properties
+
+assert_type(PS_all.coef, _Ar_x_n)
+assert_type(PS_all.domain, _Ar_x_2)
+assert_type(PS_all.window, _Ar_x_2)
+assert_type(PS_all.symbol, LiteralString)
+
+# instance methods
+
+assert_type(PS_all.has_samecoef(PS_all), bool)
+assert_type(PS_all.has_samedomain(PS_all), bool)
+assert_type(PS_all.has_samewindow(PS_all), bool)
+assert_type(PS_all.has_sametype(PS_all), bool)
+assert_type(PS_poly.has_sametype(PS_poly), bool)
+assert_type(PS_poly.has_sametype(PS_leg), bool)
+assert_type(PS_poly.has_sametype(NotADirectoryError), L[False])
+
+assert_type(PS_poly.copy(), npp.Polynomial)
+assert_type(PS_cheb.copy(), npp.Chebyshev)
+assert_type(PS_herm.copy(), npp.Hermite)
+assert_type(PS_herme.copy(), npp.HermiteE)
+assert_type(PS_lag.copy(), npp.Laguerre)
+assert_type(PS_leg.copy(), npp.Legendre)
+
+assert_type(PS_leg.cutdeg(), npp.Legendre)
+assert_type(PS_leg.trim(), npp.Legendre)
+assert_type(PS_leg.trim(tol=SC_f_co), npp.Legendre)
+assert_type(PS_leg.truncate(SC_i_co), npp.Legendre)
+
+assert_type(PS_all.convert(None, npp.Chebyshev), npp.Chebyshev)
+assert_type(PS_all.convert((0, 1), npp.Laguerre), npp.Laguerre)
+assert_type(PS_all.convert([0, 1], npp.Hermite, [-1, 1]), npp.Hermite)
+
+assert_type(PS_all.degree(), int)
+assert_type(PS_all.mapparms(), tuple[Any, Any])
+
+assert_type(PS_poly.integ(), npp.Polynomial)
+assert_type(PS_herme.integ(SC_i_co), npp.HermiteE)
+assert_type(PS_lag.integ(SC_i_co, SC_f_co), npp.Laguerre)
+assert_type(PS_poly.deriv(), npp.Polynomial)
+assert_type(PS_herm.deriv(SC_i_co), npp.Hermite)
+
+assert_type(PS_poly.roots(), _Ar_x_n)
+
+assert_type(
+    PS_poly.linspace(),
+    tuple[_Ar_1d[np.float64 | np.complex128], _Ar_1d[np.float64 | np.complex128]],
+)
+
+assert_type(
+    PS_poly.linspace(9),
+    tuple[_Ar_1d[np.float64 | np.complex128], _Ar_1d[np.float64 | np.complex128]],
+)
+
+assert_type(PS_cheb.fit(AR_c_co, AR_c_co, SC_i_co), npp.Chebyshev)
+assert_type(PS_leg.fit(AR_c_co, AR_c_co, AR_i), npp.Legendre)
+assert_type(PS_herm.fit(AR_c_co, AR_c_co, SQ_i), npp.Hermite)
+assert_type(PS_poly.fit(AR_c_co, SQ_c, SQ_i), npp.Polynomial)
+assert_type(PS_lag.fit(SQ_c, SQ_c, SQ_i, full=False), npp.Laguerre)
+assert_type(
+    PS_herme.fit(SQ_c, AR_c_co, SC_i_co, full=True),
+    tuple[npp.HermiteE, Sequence[np.inexact[Any] | np.int32]],
+)
+
+# custom operations
+
+assert_type(PS_all.__hash__, None)
+assert_type(PS_all.__array_ufunc__, None)
+
+assert_type(str(PS_all), str)
+assert_type(repr(PS_all), str)
+assert_type(format(PS_all), str)
+
+assert_type(len(PS_all), int)
+assert_type(next(iter(PS_all)), np.inexact[Any] | object)
+
+assert_type(PS_all(SC_f_co), np.float64 | np.complex128)
+assert_type(PS_all(SC_c_co), np.complex128)
+assert_type(PS_all(Decimal()), np.float64 | np.complex128)
+assert_type(PS_all(Fraction()), np.float64 | np.complex128)
+assert_type(PS_poly(SQ_f), npt.NDArray[np.float64] | npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(SQ_c), npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(SQ_O), npt.NDArray[np.object_])
+assert_type(PS_poly(AR_f), npt.NDArray[np.float64] | npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(AR_c), npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(AR_O), npt.NDArray[np.object_])
+assert_type(PS_all(PS_poly), npp.Polynomial)
+
+assert_type(PS_poly == PS_poly, bool)
+assert_type(PS_poly != PS_poly, bool)
+
+assert_type(-PS_poly, npp.Polynomial)
+assert_type(+PS_poly, npp.Polynomial)
+
+assert_type(PS_poly + 5, npp.Polynomial)
+assert_type(PS_poly - 5, npp.Polynomial)
+assert_type(PS_poly * 5, npp.Polynomial)
+assert_type(PS_poly / 5, npp.Polynomial)
+assert_type(PS_poly // 5, npp.Polynomial)
+assert_type(PS_poly % 5, npp.Polynomial)
+
+assert_type(PS_poly + PS_leg, npp.Polynomial)
+assert_type(PS_poly - PS_leg, npp.Polynomial)
+assert_type(PS_poly * PS_leg, npp.Polynomial)
+assert_type(PS_poly / PS_leg, npp.Polynomial)
+assert_type(PS_poly // PS_leg, npp.Polynomial)
+assert_type(PS_poly % PS_leg, npp.Polynomial)
+
+assert_type(5 + PS_poly, npp.Polynomial)
+assert_type(5 - PS_poly, npp.Polynomial)
+assert_type(5 * PS_poly, npp.Polynomial)
+assert_type(5 / PS_poly, npp.Polynomial)
+assert_type(5 // PS_poly, npp.Polynomial)
+assert_type(5 % PS_poly, npp.Polynomial)
+assert_type(divmod(PS_poly, 5), tuple[npp.Polynomial, npp.Polynomial])
+assert_type(divmod(5, PS_poly), tuple[npp.Polynomial, npp.Polynomial])
+
+assert_type(PS_poly**1, npp.Polynomial)
+assert_type(PS_poly**1.0, npp.Polynomial)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polyutils.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polyutils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ca5852808ce7102460016a9e1fd5cad5faf781e7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polyutils.pyi
@@ -0,0 +1,220 @@
+from collections.abc import Sequence
+from decimal import Decimal
+from fractions import Fraction
+from typing import Any, Literal as L, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+import numpy.polynomial.polyutils as pu
+from numpy.polynomial._polytypes import _Tuple2
+
+from typing_extensions import assert_type
+
+_ArrFloat1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.floating[Any]]]
+_ArrComplex1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complexfloating[Any, Any]]]
+_ArrObject1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.object_]]
+
+_ArrFloat1D_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.float64]]
+_ArrComplex1D_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.complex128]]
+_ArrObject1D_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.object_]]
+
+num_int: int
+num_float: float
+num_complex: complex
+# will result in an `object_` dtype
+num_object: Decimal | Fraction
+
+sct_int: np.int_
+sct_float: np.float64
+sct_complex: np.complex128
+sct_object: np.object_  # doesn't exist at runtime
+
+arr_int: npt.NDArray[np.int_]
+arr_float: npt.NDArray[np.float64]
+arr_complex: npt.NDArray[np.complex128]
+arr_object: npt.NDArray[np.object_]
+
+seq_num_int: Sequence[int]
+seq_num_float: Sequence[float]
+seq_num_complex: Sequence[complex]
+seq_num_object: Sequence[Decimal | Fraction]
+
+seq_sct_int: Sequence[np.int_]
+seq_sct_float: Sequence[np.float64]
+seq_sct_complex: Sequence[np.complex128]
+seq_sct_object: Sequence[np.object_]
+
+seq_arr_int: Sequence[npt.NDArray[np.int_]]
+seq_arr_float: Sequence[npt.NDArray[np.float64]]
+seq_arr_complex: Sequence[npt.NDArray[np.complex128]]
+seq_arr_object: Sequence[npt.NDArray[np.object_]]
+
+seq_seq_num_int: Sequence[Sequence[int]]
+seq_seq_num_float: Sequence[Sequence[float]]
+seq_seq_num_complex: Sequence[Sequence[complex]]
+seq_seq_num_object: Sequence[Sequence[Decimal | Fraction]]
+
+seq_seq_sct_int: Sequence[Sequence[np.int_]]
+seq_seq_sct_float: Sequence[Sequence[np.float64]]
+seq_seq_sct_complex: Sequence[Sequence[np.complex128]]
+seq_seq_sct_object: Sequence[Sequence[np.object_]]  # doesn't exist at runtime
+
+# as_series
+
+assert_type(pu.as_series(arr_int), list[_ArrFloat1D])
+assert_type(pu.as_series(arr_float), list[_ArrFloat1D])
+assert_type(pu.as_series(arr_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(arr_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_num_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_num_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_num_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_num_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_sct_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_sct_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_sct_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_sct_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_arr_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_arr_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_arr_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_arr_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_seq_num_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_num_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_num_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_seq_num_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_seq_sct_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_sct_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_sct_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_seq_sct_object), list[_ArrObject1D])
+
+# trimcoef
+
+assert_type(pu.trimcoef(num_int), _ArrFloat1D)
+assert_type(pu.trimcoef(num_float), _ArrFloat1D)
+assert_type(pu.trimcoef(num_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(num_object), _ArrObject1D)
+assert_type(pu.trimcoef(num_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(sct_int), _ArrFloat1D)
+assert_type(pu.trimcoef(sct_float), _ArrFloat1D)
+assert_type(pu.trimcoef(sct_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(sct_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(arr_int), _ArrFloat1D)
+assert_type(pu.trimcoef(arr_float), _ArrFloat1D)
+assert_type(pu.trimcoef(arr_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(arr_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(seq_num_int), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_num_float), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_num_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(seq_num_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(seq_sct_int), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_sct_float), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_sct_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(seq_sct_object), _ArrObject1D)
+
+# getdomain
+
+assert_type(pu.getdomain(num_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(num_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(num_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(num_object), _ArrObject1D_2)
+assert_type(pu.getdomain(num_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(sct_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(sct_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(sct_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(sct_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(arr_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(arr_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(arr_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(arr_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(seq_num_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_num_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_num_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(seq_num_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(seq_sct_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_sct_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_sct_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(seq_sct_object), _ArrObject1D_2)
+
+# mapparms
+
+assert_type(pu.mapparms(seq_num_int, seq_num_int), _Tuple2[float])
+assert_type(pu.mapparms(seq_num_int, seq_num_float), _Tuple2[float])
+assert_type(pu.mapparms(seq_num_float, seq_num_float), _Tuple2[float])
+assert_type(pu.mapparms(seq_num_float, seq_num_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_num_complex, seq_num_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_num_complex, seq_num_object), _Tuple2[object])
+assert_type(pu.mapparms(seq_num_object, seq_num_object), _Tuple2[object])
+
+assert_type(pu.mapparms(seq_sct_int, seq_sct_int), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(seq_sct_int, seq_sct_float), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(seq_sct_float, seq_sct_float), _Tuple2[float])
+assert_type(pu.mapparms(seq_sct_float, seq_sct_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_sct_complex, seq_sct_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_sct_complex, seq_sct_object), _Tuple2[object])
+assert_type(pu.mapparms(seq_sct_object, seq_sct_object), _Tuple2[object])
+
+assert_type(pu.mapparms(arr_int, arr_int), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(arr_int, arr_float), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(arr_float, arr_float), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(arr_float, arr_complex), _Tuple2[np.complexfloating[Any, Any]])
+assert_type(pu.mapparms(arr_complex, arr_complex), _Tuple2[np.complexfloating[Any, Any]])
+assert_type(pu.mapparms(arr_complex, arr_object), _Tuple2[object])
+assert_type(pu.mapparms(arr_object, arr_object), _Tuple2[object])
+
+# mapdomain
+
+assert_type(pu.mapdomain(num_int, seq_num_int, seq_num_int), np.floating[Any])
+assert_type(pu.mapdomain(num_int, seq_num_int, seq_num_float), np.floating[Any])
+assert_type(pu.mapdomain(num_int, seq_num_float, seq_num_float), np.floating[Any])
+assert_type(pu.mapdomain(num_float, seq_num_float, seq_num_float), np.floating[Any])
+assert_type(pu.mapdomain(num_float, seq_num_float, seq_num_complex), np.complexfloating[Any, Any])
+assert_type(pu.mapdomain(num_float, seq_num_complex, seq_num_complex), np.complexfloating[Any, Any])
+assert_type(pu.mapdomain(num_complex, seq_num_complex, seq_num_complex), np.complexfloating[Any, Any])
+assert_type(pu.mapdomain(num_complex, seq_num_complex, seq_num_object), object)
+assert_type(pu.mapdomain(num_complex, seq_num_object, seq_num_object), object)
+assert_type(pu.mapdomain(num_object, seq_num_object, seq_num_object), object)
+
+assert_type(pu.mapdomain(seq_num_int, seq_num_int, seq_num_int), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_int, seq_num_int, seq_num_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_int, seq_num_float, seq_num_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_float, seq_num_float, seq_num_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_float, seq_num_float, seq_num_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_num_float, seq_num_complex, seq_num_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_num_complex, seq_num_complex, seq_num_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_num_complex, seq_num_complex, seq_num_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_num_complex, seq_num_object, seq_num_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_num_object, seq_num_object, seq_num_object), _ArrObject1D)
+
+assert_type(pu.mapdomain(seq_sct_int, seq_sct_int, seq_sct_int), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_int, seq_sct_int, seq_sct_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_int, seq_sct_float, seq_sct_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_float, seq_sct_float, seq_sct_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_float, seq_sct_float, seq_sct_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_sct_float, seq_sct_complex, seq_sct_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_sct_complex, seq_sct_complex, seq_sct_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_sct_complex, seq_sct_complex, seq_sct_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_sct_complex, seq_sct_object, seq_sct_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_sct_object, seq_sct_object, seq_sct_object), _ArrObject1D)
+
+assert_type(pu.mapdomain(arr_int, arr_int, arr_int), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_int, arr_int, arr_float), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_int, arr_float, arr_float), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_float, arr_float, arr_float), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_float, arr_float, arr_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(arr_float, arr_complex, arr_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(arr_complex, arr_complex, arr_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(arr_complex, arr_complex, arr_object), _ArrObject1D)
+assert_type(pu.mapdomain(arr_complex, arr_object, arr_object), _ArrObject1D)
+assert_type(pu.mapdomain(arr_object, arr_object, arr_object), _ArrObject1D)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_series.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_series.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..80ec9c0ff56ac1905b8a97483d26f85eba02e58f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_series.pyi
@@ -0,0 +1,140 @@
+from collections.abc import Sequence
+from typing import Any, TypeAlias
+
+import numpy as np
+import numpy.polynomial as npp
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_ArrFloat1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.floating[Any]]]
+_ArrFloat1D64: TypeAlias = np.ndarray[tuple[int], np.dtype[np.float64]]
+_ArrComplex1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complexfloating[Any, Any]]]
+_ArrComplex1D128: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complex128]]
+_ArrObject1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.object_]]
+
+AR_b: npt.NDArray[np.bool]
+AR_u4: npt.NDArray[np.uint32]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+
+PS_poly: npp.Polynomial
+PS_cheb: npp.Chebyshev
+
+assert_type(npp.polynomial.polyroots(AR_f8), _ArrFloat1D64)
+assert_type(npp.polynomial.polyroots(AR_c16), _ArrComplex1D128)
+assert_type(npp.polynomial.polyroots(AR_O), _ArrObject1D)
+
+assert_type(npp.polynomial.polyfromroots(AR_f8), _ArrFloat1D)
+assert_type(npp.polynomial.polyfromroots(AR_c16), _ArrComplex1D)
+assert_type(npp.polynomial.polyfromroots(AR_O), _ArrObject1D)
+
+# assert_type(npp.polynomial.polyadd(AR_b, AR_b), NoReturn)
+assert_type(npp.polynomial.polyadd(AR_u4, AR_b), _ArrFloat1D)
+assert_type(npp.polynomial.polyadd(AR_i8, AR_i8), _ArrFloat1D)
+assert_type(npp.polynomial.polyadd(AR_f8, AR_i8), _ArrFloat1D)
+assert_type(npp.polynomial.polyadd(AR_i8, AR_c16), _ArrComplex1D)
+assert_type(npp.polynomial.polyadd(AR_O, AR_O), _ArrObject1D)
+
+assert_type(npp.polynomial.polymulx(AR_u4), _ArrFloat1D)
+assert_type(npp.polynomial.polymulx(AR_i8), _ArrFloat1D)
+assert_type(npp.polynomial.polymulx(AR_f8), _ArrFloat1D)
+assert_type(npp.polynomial.polymulx(AR_c16), _ArrComplex1D)
+assert_type(npp.polynomial.polymulx(AR_O), _ArrObject1D)
+
+assert_type(npp.polynomial.polypow(AR_u4, 2), _ArrFloat1D)
+assert_type(npp.polynomial.polypow(AR_i8, 2), _ArrFloat1D)
+assert_type(npp.polynomial.polypow(AR_f8, 2), _ArrFloat1D)
+assert_type(npp.polynomial.polypow(AR_c16, 2), _ArrComplex1D)
+assert_type(npp.polynomial.polypow(AR_O, 2), _ArrObject1D)
+
+# assert_type(npp.polynomial.polyder(PS_poly), npt.NDArray[np.object_])
+assert_type(npp.polynomial.polyder(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyder(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyder(AR_O, m=2), npt.NDArray[np.object_])
+
+# assert_type(npp.polynomial.polyint(PS_poly), npt.NDArray[np.object_])
+assert_type(npp.polynomial.polyint(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyint(AR_f8, k=AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyint(AR_O, m=2), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyval(AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyval(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyval2d(AR_b, AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_u4, AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_i8, AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_f8, AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_i8, AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyval2d(AR_O, AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyval3d(AR_b, AR_b, AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_u4, AR_u4, AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_i8, AR_i8, AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_f8, AR_f8, AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_i8, AR_i8, AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyval3d(AR_O, AR_O, AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvalfromroots(AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvander(AR_f8, 3), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvander(AR_c16, 3), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvander(AR_O, 3), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvander2d(AR_f8, AR_f8, [4, 2]), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvander2d(AR_c16, AR_c16, [4, 2]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvander2d(AR_O, AR_O, [4, 2]), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvander3d(AR_f8, AR_f8, AR_f8, [4, 3, 2]), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvander3d(AR_c16, AR_c16, AR_c16, [4, 3, 2]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvander3d(AR_O, AR_O, AR_O, [4, 3, 2]), npt.NDArray[np.object_])
+
+assert_type(
+    npp.polynomial.polyfit(AR_f8, AR_f8, 2),
+    npt.NDArray[np.floating[Any]],
+)
+assert_type(
+    npp.polynomial.polyfit(AR_f8, AR_i8, 1, full=True),
+    tuple[npt.NDArray[np.floating[Any]], Sequence[np.inexact[Any] | np.int32]],
+)
+assert_type(
+    npp.polynomial.polyfit(AR_c16, AR_f8, 2),
+    npt.NDArray[np.complexfloating[Any, Any]],
+)
+assert_type(
+    npp.polynomial.polyfit(AR_f8, AR_c16, 1, full=True)[0],
+    npt.NDArray[np.complexfloating[Any, Any]],
+)
+
+assert_type(npp.chebyshev.chebgauss(2), tuple[_ArrFloat1D64, _ArrFloat1D64])
+
+assert_type(npp.chebyshev.chebweight(AR_f8), npt.NDArray[np.float64])
+assert_type(npp.chebyshev.chebweight(AR_c16), npt.NDArray[np.complex128])
+assert_type(npp.chebyshev.chebweight(AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.chebyshev.poly2cheb(AR_f8), _ArrFloat1D)
+assert_type(npp.chebyshev.poly2cheb(AR_c16), _ArrComplex1D)
+assert_type(npp.chebyshev.poly2cheb(AR_O), _ArrObject1D)
+
+assert_type(npp.chebyshev.cheb2poly(AR_f8), _ArrFloat1D)
+assert_type(npp.chebyshev.cheb2poly(AR_c16), _ArrComplex1D)
+assert_type(npp.chebyshev.cheb2poly(AR_O), _ArrObject1D)
+
+assert_type(npp.chebyshev.chebpts1(6), _ArrFloat1D64)
+assert_type(npp.chebyshev.chebpts2(6), _ArrFloat1D64)
+
+assert_type(
+    npp.chebyshev.chebinterpolate(np.tanh, 3),
+    npt.NDArray[np.float64 | np.complex128 | np.object_],
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/random.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/random.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4c1c8abd927c5950445e725f0ccefe7005cf96c2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/random.pyi
@@ -0,0 +1,1551 @@
+import threading
+from typing import Any
+from collections.abc import Sequence
+
+import numpy as np
+import numpy.typing as npt
+from numpy.random._generator import Generator
+from numpy.random._mt19937 import MT19937
+from numpy.random._pcg64 import PCG64
+from numpy.random._sfc64 import SFC64
+from numpy.random._philox import Philox
+from numpy.random.bit_generator import SeedSequence, SeedlessSeedSequence
+
+from typing_extensions import assert_type
+
+def_rng = np.random.default_rng()
+seed_seq = np.random.SeedSequence()
+mt19937 = np.random.MT19937()
+pcg64 = np.random.PCG64()
+sfc64 = np.random.SFC64()
+philox = np.random.Philox()
+seedless_seq = SeedlessSeedSequence()
+
+assert_type(def_rng, Generator)
+assert_type(mt19937, MT19937)
+assert_type(pcg64, PCG64)
+assert_type(sfc64, SFC64)
+assert_type(philox, Philox)
+assert_type(seed_seq, SeedSequence)
+assert_type(seedless_seq, SeedlessSeedSequence)
+
+mt19937_jumped = mt19937.jumped()
+mt19937_jumped3 = mt19937.jumped(3)
+mt19937_raw = mt19937.random_raw()
+mt19937_raw_arr = mt19937.random_raw(5)
+
+assert_type(mt19937_jumped, MT19937)
+assert_type(mt19937_jumped3, MT19937)
+assert_type(mt19937_raw, int)
+assert_type(mt19937_raw_arr, npt.NDArray[np.uint64])
+assert_type(mt19937.lock, threading.Lock)
+
+pcg64_jumped = pcg64.jumped()
+pcg64_jumped3 = pcg64.jumped(3)
+pcg64_adv = pcg64.advance(3)
+pcg64_raw = pcg64.random_raw()
+pcg64_raw_arr = pcg64.random_raw(5)
+
+assert_type(pcg64_jumped, PCG64)
+assert_type(pcg64_jumped3, PCG64)
+assert_type(pcg64_adv, PCG64)
+assert_type(pcg64_raw, int)
+assert_type(pcg64_raw_arr, npt.NDArray[np.uint64])
+assert_type(pcg64.lock, threading.Lock)
+
+philox_jumped = philox.jumped()
+philox_jumped3 = philox.jumped(3)
+philox_adv = philox.advance(3)
+philox_raw = philox.random_raw()
+philox_raw_arr = philox.random_raw(5)
+
+assert_type(philox_jumped, Philox)
+assert_type(philox_jumped3, Philox)
+assert_type(philox_adv, Philox)
+assert_type(philox_raw, int)
+assert_type(philox_raw_arr, npt.NDArray[np.uint64])
+assert_type(philox.lock, threading.Lock)
+
+sfc64_raw = sfc64.random_raw()
+sfc64_raw_arr = sfc64.random_raw(5)
+
+assert_type(sfc64_raw, int)
+assert_type(sfc64_raw_arr, npt.NDArray[np.uint64])
+assert_type(sfc64.lock, threading.Lock)
+
+assert_type(seed_seq.pool, npt.NDArray[np.uint32])
+assert_type(seed_seq.entropy, None | int | Sequence[int])
+assert_type(seed_seq.spawn(1), list[np.random.SeedSequence])
+assert_type(seed_seq.generate_state(8, "uint32"), npt.NDArray[np.uint32 | np.uint64])
+assert_type(seed_seq.generate_state(8, "uint64"), npt.NDArray[np.uint32 | np.uint64])
+
+
+def_gen: np.random.Generator = np.random.default_rng()
+
+D_arr_0p1: npt.NDArray[np.float64] = np.array([0.1])
+D_arr_0p5: npt.NDArray[np.float64] = np.array([0.5])
+D_arr_0p9: npt.NDArray[np.float64] = np.array([0.9])
+D_arr_1p5: npt.NDArray[np.float64] = np.array([1.5])
+I_arr_10: npt.NDArray[np.int_] = np.array([10], dtype=np.int_)
+I_arr_20: npt.NDArray[np.int_] = np.array([20], dtype=np.int_)
+D_arr_like_0p1: list[float] = [0.1]
+D_arr_like_0p5: list[float] = [0.5]
+D_arr_like_0p9: list[float] = [0.9]
+D_arr_like_1p5: list[float] = [1.5]
+I_arr_like_10: list[int] = [10]
+I_arr_like_20: list[int] = [20]
+D_2D_like: list[list[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]]
+D_2D: npt.NDArray[np.float64] = np.array(D_2D_like)
+S_out: npt.NDArray[np.float32] = np.empty(1, dtype=np.float32)
+D_out: npt.NDArray[np.float64] = np.empty(1)
+
+assert_type(def_gen.standard_normal(), float)
+assert_type(def_gen.standard_normal(dtype=np.float32), float)
+assert_type(def_gen.standard_normal(dtype="float32"), float)
+assert_type(def_gen.standard_normal(dtype="double"), float)
+assert_type(def_gen.standard_normal(dtype=np.float64), float)
+assert_type(def_gen.standard_normal(size=None), float)
+assert_type(def_gen.standard_normal(size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.random(), float)
+assert_type(def_gen.random(dtype=np.float32), float)
+assert_type(def_gen.random(dtype="float32"), float)
+assert_type(def_gen.random(dtype="double"), float)
+assert_type(def_gen.random(dtype=np.float64), float)
+assert_type(def_gen.random(size=None), float)
+assert_type(def_gen.random(size=1), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.random(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.random(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_cauchy(), float)
+assert_type(def_gen.standard_cauchy(size=None), float)
+assert_type(def_gen.standard_cauchy(size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_exponential(), float)
+assert_type(def_gen.standard_exponential(method="inv"), float)
+assert_type(def_gen.standard_exponential(dtype=np.float32), float)
+assert_type(def_gen.standard_exponential(dtype="float32"), float)
+assert_type(def_gen.standard_exponential(dtype="double"), float)
+assert_type(def_gen.standard_exponential(dtype=np.float64), float)
+assert_type(def_gen.standard_exponential(size=None), float)
+assert_type(def_gen.standard_exponential(size=None, method="inv"), float)
+assert_type(def_gen.standard_exponential(size=1, method="inv"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.zipf(1.5), int)
+assert_type(def_gen.zipf(1.5, size=None), int)
+assert_type(def_gen.zipf(1.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_1p5), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_1p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_like_1p5), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_like_1p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.weibull(0.5), float)
+assert_type(def_gen.weibull(0.5, size=None), float)
+assert_type(def_gen.weibull(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_t(0.5), float)
+assert_type(def_gen.standard_t(0.5, size=None), float)
+assert_type(def_gen.standard_t(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.poisson(0.5), int)
+assert_type(def_gen.poisson(0.5, size=None), int)
+assert_type(def_gen.poisson(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.power(0.5), float)
+assert_type(def_gen.power(0.5, size=None), float)
+assert_type(def_gen.power(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.pareto(0.5), float)
+assert_type(def_gen.pareto(0.5, size=None), float)
+assert_type(def_gen.pareto(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.chisquare(0.5), float)
+assert_type(def_gen.chisquare(0.5, size=None), float)
+assert_type(def_gen.chisquare(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.exponential(0.5), float)
+assert_type(def_gen.exponential(0.5, size=None), float)
+assert_type(def_gen.exponential(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.geometric(0.5), int)
+assert_type(def_gen.geometric(0.5, size=None), int)
+assert_type(def_gen.geometric(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.logseries(0.5), int)
+assert_type(def_gen.logseries(0.5, size=None), int)
+assert_type(def_gen.logseries(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.rayleigh(0.5), float)
+assert_type(def_gen.rayleigh(0.5, size=None), float)
+assert_type(def_gen.rayleigh(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_gamma(0.5), float)
+assert_type(def_gen.standard_gamma(0.5, size=None), float)
+assert_type(def_gen.standard_gamma(0.5, dtype="float32"), float)
+assert_type(def_gen.standard_gamma(0.5, size=None, dtype="float32"), float)
+assert_type(def_gen.standard_gamma(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(0.5, out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64), npt.NDArray[np.float64])
+
+assert_type(def_gen.vonmises(0.5, 0.5), float)
+assert_type(def_gen.vonmises(0.5, 0.5, size=None), float)
+assert_type(def_gen.vonmises(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.wald(0.5, 0.5), float)
+assert_type(def_gen.wald(0.5, 0.5, size=None), float)
+assert_type(def_gen.wald(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.uniform(0.5, 0.5), float)
+assert_type(def_gen.uniform(0.5, 0.5, size=None), float)
+assert_type(def_gen.uniform(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.beta(0.5, 0.5), float)
+assert_type(def_gen.beta(0.5, 0.5, size=None), float)
+assert_type(def_gen.beta(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.f(0.5, 0.5), float)
+assert_type(def_gen.f(0.5, 0.5, size=None), float)
+assert_type(def_gen.f(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.gamma(0.5, 0.5), float)
+assert_type(def_gen.gamma(0.5, 0.5, size=None), float)
+assert_type(def_gen.gamma(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.gumbel(0.5, 0.5), float)
+assert_type(def_gen.gumbel(0.5, 0.5, size=None), float)
+assert_type(def_gen.gumbel(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.laplace(0.5, 0.5), float)
+assert_type(def_gen.laplace(0.5, 0.5, size=None), float)
+assert_type(def_gen.laplace(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.logistic(0.5, 0.5), float)
+assert_type(def_gen.logistic(0.5, 0.5, size=None), float)
+assert_type(def_gen.logistic(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.lognormal(0.5, 0.5), float)
+assert_type(def_gen.lognormal(0.5, 0.5, size=None), float)
+assert_type(def_gen.lognormal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5), float)
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5, size=None), float)
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.normal(0.5, 0.5), float)
+assert_type(def_gen.normal(0.5, 0.5, size=None), float)
+assert_type(def_gen.normal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.triangular(0.1, 0.5, 0.9), float)
+assert_type(def_gen.triangular(0.1, 0.5, 0.9, size=None), float)
+assert_type(def_gen.triangular(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9), float)
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=None), float)
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.binomial(10, 0.5), int)
+assert_type(def_gen.binomial(10, 0.5, size=None), int)
+assert_type(def_gen.binomial(10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.negative_binomial(10, 0.5), int)
+assert_type(def_gen.negative_binomial(10, 0.5, size=None), int)
+assert_type(def_gen.negative_binomial(10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.hypergeometric(20, 20, 10), int)
+assert_type(def_gen.hypergeometric(20, 20, 10, size=None), int)
+assert_type(def_gen.hypergeometric(20, 20, 10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, 20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_like_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1), npt.NDArray[np.int64])
+
+I_int64_100: npt.NDArray[np.int64] = np.array([100], dtype=np.int64)
+
+assert_type(def_gen.integers(0, 100), np.int64)
+assert_type(def_gen.integers(100), np.int64)
+assert_type(def_gen.integers([100]), npt.NDArray[np.int64])
+assert_type(def_gen.integers(0, [100]), npt.NDArray[np.int64])
+
+I_bool_low: npt.NDArray[np.bool] = np.array([0], dtype=np.bool)
+I_bool_low_like: list[int] = [0]
+I_bool_high_open: npt.NDArray[np.bool] = np.array([1], dtype=np.bool)
+I_bool_high_closed: npt.NDArray[np.bool] = np.array([1], dtype=np.bool)
+
+assert_type(def_gen.integers(2, dtype=bool), bool)
+assert_type(def_gen.integers(0, 2, dtype=bool), bool)
+assert_type(def_gen.integers(1, dtype=bool, endpoint=True), bool)
+assert_type(def_gen.integers(0, 1, dtype=bool, endpoint=True), bool)
+assert_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+
+assert_type(def_gen.integers(2, dtype=np.bool), np.bool)
+assert_type(def_gen.integers(0, 2, dtype=np.bool), np.bool)
+assert_type(def_gen.integers(1, dtype=np.bool, endpoint=True), np.bool)
+assert_type(def_gen.integers(0, 1, dtype=np.bool, endpoint=True), np.bool)
+assert_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+
+I_u1_low: npt.NDArray[np.uint8] = np.array([0], dtype=np.uint8)
+I_u1_low_like: list[int] = [0]
+I_u1_high_open: npt.NDArray[np.uint8] = np.array([255], dtype=np.uint8)
+I_u1_high_closed: npt.NDArray[np.uint8] = np.array([255], dtype=np.uint8)
+
+assert_type(def_gen.integers(256, dtype="u1"), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype="u1"), np.uint8)
+assert_type(def_gen.integers(255, dtype="u1", endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype="u1", endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+
+assert_type(def_gen.integers(256, dtype="uint8"), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype="uint8"), np.uint8)
+assert_type(def_gen.integers(255, dtype="uint8", endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype="uint8", endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+
+assert_type(def_gen.integers(256, dtype=np.uint8), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype=np.uint8), np.uint8)
+assert_type(def_gen.integers(255, dtype=np.uint8, endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype=np.uint8, endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+
+I_u2_low: npt.NDArray[np.uint16] = np.array([0], dtype=np.uint16)
+I_u2_low_like: list[int] = [0]
+I_u2_high_open: npt.NDArray[np.uint16] = np.array([65535], dtype=np.uint16)
+I_u2_high_closed: npt.NDArray[np.uint16] = np.array([65535], dtype=np.uint16)
+
+assert_type(def_gen.integers(65536, dtype="u2"), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype="u2"), np.uint16)
+assert_type(def_gen.integers(65535, dtype="u2", endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype="u2", endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+
+assert_type(def_gen.integers(65536, dtype="uint16"), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype="uint16"), np.uint16)
+assert_type(def_gen.integers(65535, dtype="uint16", endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype="uint16", endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+
+assert_type(def_gen.integers(65536, dtype=np.uint16), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype=np.uint16), np.uint16)
+assert_type(def_gen.integers(65535, dtype=np.uint16, endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+
+I_u4_low: npt.NDArray[np.uint32] = np.array([0], dtype=np.uint32)
+I_u4_low_like: list[int] = [0]
+I_u4_high_open: npt.NDArray[np.uint32] = np.array([4294967295], dtype=np.uint32)
+I_u4_high_closed: npt.NDArray[np.uint32] = np.array([4294967295], dtype=np.uint32)
+
+assert_type(def_gen.integers(4294967296, dtype=np.int_), np.int_)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.int_), np.int_)
+assert_type(def_gen.integers(4294967295, dtype=np.int_, endpoint=True), np.int_)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.int_, endpoint=True), np.int_)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+
+
+assert_type(def_gen.integers(4294967296, dtype="u4"), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype="u4"), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype="u4", endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype="u4", endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype="uint32"), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype="uint32"), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype="uint32", endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype=np.uint32), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.uint32), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype=np.uint32, endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype=np.uint), np.uint)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.uint), np.uint)
+assert_type(def_gen.integers(4294967295, dtype=np.uint, endpoint=True), np.uint)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.uint, endpoint=True), np.uint)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+
+I_u8_low: npt.NDArray[np.uint64] = np.array([0], dtype=np.uint64)
+I_u8_low_like: list[int] = [0]
+I_u8_high_open: npt.NDArray[np.uint64] = np.array([18446744073709551615], dtype=np.uint64)
+I_u8_high_closed: npt.NDArray[np.uint64] = np.array([18446744073709551615], dtype=np.uint64)
+
+assert_type(def_gen.integers(18446744073709551616, dtype="u8"), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype="u8"), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype="u8", endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+
+assert_type(def_gen.integers(18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+
+assert_type(def_gen.integers(18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+
+I_i1_low: npt.NDArray[np.int8] = np.array([-128], dtype=np.int8)
+I_i1_low_like: list[int] = [-128]
+I_i1_high_open: npt.NDArray[np.int8] = np.array([127], dtype=np.int8)
+I_i1_high_closed: npt.NDArray[np.int8] = np.array([127], dtype=np.int8)
+
+assert_type(def_gen.integers(128, dtype="i1"), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype="i1"), np.int8)
+assert_type(def_gen.integers(127, dtype="i1", endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype="i1", endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+
+assert_type(def_gen.integers(128, dtype="int8"), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype="int8"), np.int8)
+assert_type(def_gen.integers(127, dtype="int8", endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype="int8", endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+
+assert_type(def_gen.integers(128, dtype=np.int8), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype=np.int8), np.int8)
+assert_type(def_gen.integers(127, dtype=np.int8, endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype=np.int8, endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+
+I_i2_low: npt.NDArray[np.int16] = np.array([-32768], dtype=np.int16)
+I_i2_low_like: list[int] = [-32768]
+I_i2_high_open: npt.NDArray[np.int16] = np.array([32767], dtype=np.int16)
+I_i2_high_closed: npt.NDArray[np.int16] = np.array([32767], dtype=np.int16)
+
+assert_type(def_gen.integers(32768, dtype="i2"), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype="i2"), np.int16)
+assert_type(def_gen.integers(32767, dtype="i2", endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype="i2", endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+
+assert_type(def_gen.integers(32768, dtype="int16"), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype="int16"), np.int16)
+assert_type(def_gen.integers(32767, dtype="int16", endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype="int16", endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+
+assert_type(def_gen.integers(32768, dtype=np.int16), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype=np.int16), np.int16)
+assert_type(def_gen.integers(32767, dtype=np.int16, endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+
+I_i4_low: npt.NDArray[np.int32] = np.array([-2147483648], dtype=np.int32)
+I_i4_low_like: list[int] = [-2147483648]
+I_i4_high_open: npt.NDArray[np.int32] = np.array([2147483647], dtype=np.int32)
+I_i4_high_closed: npt.NDArray[np.int32] = np.array([2147483647], dtype=np.int32)
+
+assert_type(def_gen.integers(2147483648, dtype="i4"), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype="i4"), np.int32)
+assert_type(def_gen.integers(2147483647, dtype="i4", endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+
+assert_type(def_gen.integers(2147483648, dtype="int32"), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype="int32"), np.int32)
+assert_type(def_gen.integers(2147483647, dtype="int32", endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+
+assert_type(def_gen.integers(2147483648, dtype=np.int32), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype=np.int32), np.int32)
+assert_type(def_gen.integers(2147483647, dtype=np.int32, endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+
+I_i8_low: npt.NDArray[np.int64] = np.array([-9223372036854775808], dtype=np.int64)
+I_i8_low_like: list[int] = [-9223372036854775808]
+I_i8_high_open: npt.NDArray[np.int64] = np.array([9223372036854775807], dtype=np.int64)
+I_i8_high_closed: npt.NDArray[np.int64] = np.array([9223372036854775807], dtype=np.int64)
+
+assert_type(def_gen.integers(9223372036854775808, dtype="i8"), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8"), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype="i8", endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.integers(9223372036854775808, dtype="int64"), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64"), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype="int64", endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.integers(9223372036854775808, dtype=np.int64), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+
+
+assert_type(def_gen.bit_generator, np.random.BitGenerator)
+
+assert_type(def_gen.bytes(2), bytes)
+
+assert_type(def_gen.choice(5), int)
+assert_type(def_gen.choice(5, 3), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, replace=True), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, p=[1 / 5] * 5), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False), npt.NDArray[np.int64])
+
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"]), Any)
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4])), npt.NDArray[Any])
+
+assert_type(def_gen.dirichlet([0.5, 0.5]), npt.NDArray[np.float64])
+assert_type(def_gen.dirichlet(np.array([0.5, 0.5])), npt.NDArray[np.float64])
+assert_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3), npt.NDArray[np.float64])
+
+assert_type(def_gen.multinomial(20, [1 / 6.0] * 6), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(20, np.array([0.5, 0.5])), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2)), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2)), npt.NDArray[np.int64])
+
+assert_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7)), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count"), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals"), npt.NDArray[np.int64])
+
+assert_type(def_gen.multivariate_normal([0.0], [[1.0]]), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]]), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+
+assert_type(def_gen.permutation(10), npt.NDArray[np.int64])
+assert_type(def_gen.permutation([1, 2, 3, 4]), npt.NDArray[Any])
+assert_type(def_gen.permutation(np.array([1, 2, 3, 4])), npt.NDArray[Any])
+assert_type(def_gen.permutation(D_2D, axis=1), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, axis=1), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, axis=1, out=D_2D), npt.NDArray[Any])
+
+assert_type(def_gen.shuffle(np.arange(10)), None)
+assert_type(def_gen.shuffle([1, 2, 3, 4, 5]), None)
+assert_type(def_gen.shuffle(D_2D, axis=1), None)
+
+assert_type(np.random.Generator(pcg64), np.random.Generator)
+assert_type(def_gen.__str__(), str)
+assert_type(def_gen.__repr__(), str)
+assert_type(def_gen.__setstate__(dict(def_gen.bit_generator.state)), None)
+
+# RandomState
+random_st: np.random.RandomState = np.random.RandomState()
+
+assert_type(random_st.standard_normal(), float)
+assert_type(random_st.standard_normal(size=None), float)
+assert_type(random_st.standard_normal(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.random(), float)
+assert_type(random_st.random(size=None), float)
+assert_type(random_st.random(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_cauchy(), float)
+assert_type(random_st.standard_cauchy(size=None), float)
+assert_type(random_st.standard_cauchy(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_exponential(), float)
+assert_type(random_st.standard_exponential(size=None), float)
+assert_type(random_st.standard_exponential(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.zipf(1.5), int)
+assert_type(random_st.zipf(1.5, size=None), int)
+assert_type(random_st.zipf(1.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_1p5), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_1p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_like_1p5), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_like_1p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.weibull(0.5), float)
+assert_type(random_st.weibull(0.5, size=None), float)
+assert_type(random_st.weibull(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_t(0.5), float)
+assert_type(random_st.standard_t(0.5, size=None), float)
+assert_type(random_st.standard_t(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.poisson(0.5), int)
+assert_type(random_st.poisson(0.5, size=None), int)
+assert_type(random_st.poisson(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.power(0.5), float)
+assert_type(random_st.power(0.5, size=None), float)
+assert_type(random_st.power(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.pareto(0.5), float)
+assert_type(random_st.pareto(0.5, size=None), float)
+assert_type(random_st.pareto(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.chisquare(0.5), float)
+assert_type(random_st.chisquare(0.5, size=None), float)
+assert_type(random_st.chisquare(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.exponential(0.5), float)
+assert_type(random_st.exponential(0.5, size=None), float)
+assert_type(random_st.exponential(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.geometric(0.5), int)
+assert_type(random_st.geometric(0.5, size=None), int)
+assert_type(random_st.geometric(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.logseries(0.5), int)
+assert_type(random_st.logseries(0.5, size=None), int)
+assert_type(random_st.logseries(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.rayleigh(0.5), float)
+assert_type(random_st.rayleigh(0.5, size=None), float)
+assert_type(random_st.rayleigh(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_gamma(0.5), float)
+assert_type(random_st.standard_gamma(0.5, size=None), float)
+assert_type(random_st.standard_gamma(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.vonmises(0.5, 0.5), float)
+assert_type(random_st.vonmises(0.5, 0.5, size=None), float)
+assert_type(random_st.vonmises(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.wald(0.5, 0.5), float)
+assert_type(random_st.wald(0.5, 0.5, size=None), float)
+assert_type(random_st.wald(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.uniform(0.5, 0.5), float)
+assert_type(random_st.uniform(0.5, 0.5, size=None), float)
+assert_type(random_st.uniform(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.beta(0.5, 0.5), float)
+assert_type(random_st.beta(0.5, 0.5, size=None), float)
+assert_type(random_st.beta(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.f(0.5, 0.5), float)
+assert_type(random_st.f(0.5, 0.5, size=None), float)
+assert_type(random_st.f(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.gamma(0.5, 0.5), float)
+assert_type(random_st.gamma(0.5, 0.5, size=None), float)
+assert_type(random_st.gamma(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.gumbel(0.5, 0.5), float)
+assert_type(random_st.gumbel(0.5, 0.5, size=None), float)
+assert_type(random_st.gumbel(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.laplace(0.5, 0.5), float)
+assert_type(random_st.laplace(0.5, 0.5, size=None), float)
+assert_type(random_st.laplace(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.logistic(0.5, 0.5), float)
+assert_type(random_st.logistic(0.5, 0.5, size=None), float)
+assert_type(random_st.logistic(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.lognormal(0.5, 0.5), float)
+assert_type(random_st.lognormal(0.5, 0.5, size=None), float)
+assert_type(random_st.lognormal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.noncentral_chisquare(0.5, 0.5), float)
+assert_type(random_st.noncentral_chisquare(0.5, 0.5, size=None), float)
+assert_type(random_st.noncentral_chisquare(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.normal(0.5, 0.5), float)
+assert_type(random_st.normal(0.5, 0.5, size=None), float)
+assert_type(random_st.normal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.triangular(0.1, 0.5, 0.9), float)
+assert_type(random_st.triangular(0.1, 0.5, 0.9, size=None), float)
+assert_type(random_st.triangular(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9), float)
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=None), float)
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.binomial(10, 0.5), int)
+assert_type(random_st.binomial(10, 0.5, size=None), int)
+assert_type(random_st.binomial(10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.negative_binomial(10, 0.5), int)
+assert_type(random_st.negative_binomial(10, 0.5, size=None), int)
+assert_type(random_st.negative_binomial(10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.hypergeometric(20, 20, 10), int)
+assert_type(random_st.hypergeometric(20, 20, 10, size=None), int)
+assert_type(random_st.hypergeometric(20, 20, 10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, 20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_20, 10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_like_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.randint(0, 100), int)
+assert_type(random_st.randint(100), int)
+assert_type(random_st.randint([100]), npt.NDArray[np.long])
+assert_type(random_st.randint(0, [100]), npt.NDArray[np.long])
+
+assert_type(random_st.randint(2, dtype=bool), bool)
+assert_type(random_st.randint(0, 2, dtype=bool), bool)
+assert_type(random_st.randint(I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(0, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+
+assert_type(random_st.randint(2, dtype=np.bool), np.bool)
+assert_type(random_st.randint(0, 2, dtype=np.bool), np.bool)
+assert_type(random_st.randint(I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(0, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+
+assert_type(random_st.randint(256, dtype="u1"), np.uint8)
+assert_type(random_st.randint(0, 256, dtype="u1"), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(256, dtype="uint8"), np.uint8)
+assert_type(random_st.randint(0, 256, dtype="uint8"), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(256, dtype=np.uint8), np.uint8)
+assert_type(random_st.randint(0, 256, dtype=np.uint8), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(65536, dtype="u2"), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype="u2"), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(65536, dtype="uint16"), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype="uint16"), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(65536, dtype=np.uint16), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype=np.uint16), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(4294967296, dtype="u4"), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype="u4"), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype="uint32"), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype="uint32"), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype=np.uint32), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype=np.uint32), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype=np.uint), np.uint)
+assert_type(random_st.randint(0, 4294967296, dtype=np.uint), np.uint)
+assert_type(random_st.randint(I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(random_st.randint(0, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+
+assert_type(random_st.randint(18446744073709551616, dtype="u8"), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype="u8"), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(128, dtype="i1"), np.int8)
+assert_type(random_st.randint(-128, 128, dtype="i1"), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(128, dtype="int8"), np.int8)
+assert_type(random_st.randint(-128, 128, dtype="int8"), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(128, dtype=np.int8), np.int8)
+assert_type(random_st.randint(-128, 128, dtype=np.int8), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(32768, dtype="i2"), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype="i2"), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(32768, dtype="int16"), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype="int16"), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(32768, dtype=np.int16), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype=np.int16), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(2147483648, dtype="i4"), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype="i4"), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype="int32"), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype="int32"), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype=np.int32), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype=np.int32), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype=np.int_), np.int_)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype=np.int_), np.int_)
+assert_type(random_st.randint(I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+
+assert_type(random_st.randint(9223372036854775808, dtype="i8"), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8"), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+
+assert_type(random_st.randint(9223372036854775808, dtype="int64"), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64"), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+
+assert_type(random_st.randint(9223372036854775808, dtype=np.int64), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(random_st._bit_generator, np.random.BitGenerator)
+
+assert_type(random_st.bytes(2), bytes)
+
+assert_type(random_st.choice(5), int)
+assert_type(random_st.choice(5, 3), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, replace=True), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, p=[1 / 5] * 5), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False), npt.NDArray[np.long])
+
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"]), Any)
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4])), npt.NDArray[Any])
+
+assert_type(random_st.dirichlet([0.5, 0.5]), npt.NDArray[np.float64])
+assert_type(random_st.dirichlet(np.array([0.5, 0.5])), npt.NDArray[np.float64])
+assert_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3), npt.NDArray[np.float64])
+
+assert_type(random_st.multinomial(20, [1 / 6.0] * 6), npt.NDArray[np.long])
+assert_type(random_st.multinomial(20, np.array([0.5, 0.5])), npt.NDArray[np.long])
+assert_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2), npt.NDArray[np.long])
+
+assert_type(random_st.multivariate_normal([0.0], [[1.0]]), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]]), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+
+assert_type(random_st.permutation(10), npt.NDArray[np.long])
+assert_type(random_st.permutation([1, 2, 3, 4]), npt.NDArray[Any])
+assert_type(random_st.permutation(np.array([1, 2, 3, 4])), npt.NDArray[Any])
+assert_type(random_st.permutation(D_2D), npt.NDArray[Any])
+
+assert_type(random_st.shuffle(np.arange(10)), None)
+assert_type(random_st.shuffle([1, 2, 3, 4, 5]), None)
+assert_type(random_st.shuffle(D_2D), None)
+
+assert_type(np.random.RandomState(pcg64), np.random.RandomState)
+assert_type(np.random.RandomState(0), np.random.RandomState)
+assert_type(np.random.RandomState([0, 1, 2]), np.random.RandomState)
+assert_type(random_st.__str__(), str)
+assert_type(random_st.__repr__(), str)
+random_st_state = random_st.__getstate__()
+assert_type(random_st_state, dict[str, Any])
+assert_type(random_st.__setstate__(random_st_state), None)
+assert_type(random_st.seed(), None)
+assert_type(random_st.seed(1), None)
+assert_type(random_st.seed([0, 1]), None)
+random_st_get_state = random_st.get_state()
+assert_type(random_st_state, dict[str, Any])
+random_st_get_state_legacy = random_st.get_state(legacy=True)
+assert_type(random_st_get_state_legacy, dict[str, Any] | tuple[str, npt.NDArray[np.uint32], int, int, float])
+assert_type(random_st.set_state(random_st_get_state), None)
+
+assert_type(random_st.rand(), float)
+assert_type(random_st.rand(1), npt.NDArray[np.float64])
+assert_type(random_st.rand(1, 2), npt.NDArray[np.float64])
+assert_type(random_st.randn(), float)
+assert_type(random_st.randn(1), npt.NDArray[np.float64])
+assert_type(random_st.randn(1, 2), npt.NDArray[np.float64])
+assert_type(random_st.random_sample(), float)
+assert_type(random_st.random_sample(1), npt.NDArray[np.float64])
+assert_type(random_st.random_sample(size=(1, 2)), npt.NDArray[np.float64])
+
+assert_type(random_st.tomaxint(), int)
+assert_type(random_st.tomaxint(1), npt.NDArray[np.int64])
+assert_type(random_st.tomaxint((1,)), npt.NDArray[np.int64])
+
+assert_type(np.random.mtrand.set_bit_generator(pcg64), None)
+assert_type(np.random.mtrand.get_bit_generator(), np.random.BitGenerator)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/rec.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/rec.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1b88f6b46316c11a742143c3f48982733ac19744
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/rec.pyi
@@ -0,0 +1,168 @@
+import io
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+REC_AR_V: np.recarray[tuple[int, ...], np.dtype[np.record]]
+AR_LIST: list[npt.NDArray[np.int64]]
+
+record: np.record
+file_obj: io.BufferedIOBase
+
+assert_type(np.rec.format_parser(
+    formats=[np.float64, np.int64, np.bool],
+    names=["f8", "i8", "?"],
+    titles=None,
+    aligned=True,
+), np.rec.format_parser)
+assert_type(np.rec.format_parser.dtype, np.dtype[np.void])
+
+assert_type(record.field_a, Any)
+assert_type(record.field_b, Any)
+assert_type(record["field_a"], Any)
+assert_type(record["field_b"], Any)
+assert_type(record.pprint(), str)
+record.field_c = 5
+
+assert_type(REC_AR_V.field(0), Any)
+assert_type(REC_AR_V.field("field_a"), Any)
+assert_type(REC_AR_V.field(0, AR_i8), None)
+assert_type(REC_AR_V.field("field_a", AR_i8), None)
+assert_type(REC_AR_V["field_a"], npt.NDArray[Any])
+assert_type(REC_AR_V.field_a, Any)
+assert_type(REC_AR_V.__array_finalize__(object()), None)
+
+assert_type(
+    np.recarray(
+        shape=(10, 5),
+        formats=[np.float64, np.int64, np.bool],
+        order="K",
+        byteorder="|",
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.recarray(
+        shape=(10, 5),
+        dtype=[("f8", np.float64), ("i8", np.int64)],
+        strides=(5, 5),
+    ),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(np.rec.fromarrays(AR_LIST), np.recarray[Any, np.dtype[Any]])
+assert_type(
+    np.rec.fromarrays(AR_LIST, dtype=np.int64),
+    np.recarray[Any, np.dtype[Any]],
+)
+assert_type(
+    np.rec.fromarrays(
+        AR_LIST,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromrecords((1, 1.5)),
+    np.recarray[Any, np.dtype[np.record]]
+)
+
+assert_type(
+    np.rec.fromrecords(
+        [(1, 1.5)],
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromrecords(
+        REC_AR_V,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromstring(
+        b"(1, 1.5)",
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromstring(
+        REC_AR_V,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(np.rec.fromfile(
+    "test_file.txt",
+    dtype=[("i8", np.int64), ("f8", np.float64)],
+), np.recarray[Any, np.dtype[Any]])
+
+assert_type(
+    np.rec.fromfile(
+        file_obj,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(np.rec.array(AR_i8), np.recarray[Any, np.dtype[np.int64]])
+
+assert_type(
+    np.rec.array([(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)]),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(
+    np.rec.array(
+        [(1, 1.5)],
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.array(
+        None,
+        dtype=np.float64,
+        shape=(10, 3),
+    ),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(
+    np.rec.array(
+        None,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"],
+        shape=(10, 3),
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.array(file_obj, dtype=np.float64),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(
+    np.rec.array(file_obj, formats=[np.int64, np.float64], names=["i8", "f8"]),
+    np.recarray[Any, np.dtype[np.record]],
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/scalars.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/scalars.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d3070437b740a3c122ae4d73dc5b971f4421c9d0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/scalars.pyi
@@ -0,0 +1,193 @@
+from typing import Any, Literal, TypeAlias
+from typing_extensions import Unpack, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+_1: TypeAlias = Literal[1]
+
+b: np.bool
+u8: np.uint64
+i8: np.int64
+f8: np.float64
+c8: np.complex64
+c16: np.complex128
+m: np.timedelta64
+U: np.str_
+S: np.bytes_
+V: np.void
+O: np.object_  # cannot exists at runtime
+
+array_nd: np.ndarray[Any, Any]
+array_0d: np.ndarray[tuple[()], Any]
+array_2d_2x2: np.ndarray[tuple[Literal[2], Literal[2]], Any]
+
+assert_type(c8.real, np.float32)
+assert_type(c8.imag, np.float32)
+
+assert_type(c8.real.real, np.float32)
+assert_type(c8.real.imag, np.float32)
+
+assert_type(c8.itemsize, int)
+assert_type(c8.shape, tuple[()])
+assert_type(c8.strides, tuple[()])
+
+assert_type(c8.ndim, Literal[0])
+assert_type(c8.size, Literal[1])
+
+assert_type(c8.squeeze(), np.complex64)
+assert_type(c8.byteswap(), np.complex64)
+assert_type(c8.transpose(), np.complex64)
+
+assert_type(c8.dtype, np.dtype[np.complex64])
+
+assert_type(c8.real, np.float32)
+assert_type(c16.imag, np.float64)
+
+assert_type(np.str_('foo'), np.str_)
+
+assert_type(V[0], Any)
+assert_type(V["field1"], Any)
+assert_type(V[["field1", "field2"]], np.void)
+V[0] = 5
+
+# Aliases
+assert_type(np.bool_(), np.bool[Literal[False]])
+assert_type(np.byte(), np.byte)
+assert_type(np.short(), np.short)
+assert_type(np.intc(), np.intc)
+assert_type(np.intp(), np.intp)
+assert_type(np.int_(), np.int_)
+assert_type(np.long(), np.long)
+assert_type(np.longlong(), np.longlong)
+
+assert_type(np.ubyte(), np.ubyte)
+assert_type(np.ushort(), np.ushort)
+assert_type(np.uintc(), np.uintc)
+assert_type(np.uintp(), np.uintp)
+assert_type(np.uint(), np.uint)
+assert_type(np.ulong(), np.ulong)
+assert_type(np.ulonglong(), np.ulonglong)
+
+assert_type(np.half(), np.half)
+assert_type(np.single(), np.single)
+assert_type(np.double(), np.double)
+assert_type(np.longdouble(), np.longdouble)
+
+assert_type(np.csingle(), np.csingle)
+assert_type(np.cdouble(), np.cdouble)
+assert_type(np.clongdouble(), np.clongdouble)
+
+assert_type(b.item(), bool)
+assert_type(i8.item(), int)
+assert_type(u8.item(), int)
+assert_type(f8.item(), float)
+assert_type(c16.item(), complex)
+assert_type(U.item(), str)
+assert_type(S.item(), bytes)
+
+assert_type(b.tolist(), bool)
+assert_type(i8.tolist(), int)
+assert_type(u8.tolist(), int)
+assert_type(f8.tolist(), float)
+assert_type(c16.tolist(), complex)
+assert_type(U.tolist(), str)
+assert_type(S.tolist(), bytes)
+
+assert_type(b.ravel(), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(i8.ravel(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(u8.ravel(), np.ndarray[tuple[int], np.dtype[np.uint64]])
+assert_type(f8.ravel(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(c16.ravel(), np.ndarray[tuple[int], np.dtype[np.complex128]])
+assert_type(U.ravel(), np.ndarray[tuple[int], np.dtype[np.str_]])
+assert_type(S.ravel(), np.ndarray[tuple[int], np.dtype[np.bytes_]])
+
+assert_type(b.flatten(), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(i8.flatten(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(u8.flatten(), np.ndarray[tuple[int], np.dtype[np.uint64]])
+assert_type(f8.flatten(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(c16.flatten(), np.ndarray[tuple[int], np.dtype[np.complex128]])
+assert_type(U.flatten(), np.ndarray[tuple[int], np.dtype[np.str_]])
+assert_type(S.flatten(), np.ndarray[tuple[int], np.dtype[np.bytes_]])
+
+assert_type(b.reshape(()), np.bool)
+assert_type(i8.reshape([]), np.int64)
+assert_type(b.reshape(1), np.ndarray[tuple[_1], np.dtype[np.bool]])
+assert_type(i8.reshape(-1), np.ndarray[tuple[_1], np.dtype[np.int64]])
+assert_type(u8.reshape(1, 1), np.ndarray[tuple[_1, _1], np.dtype[np.uint64]])
+assert_type(f8.reshape(1, -1), np.ndarray[tuple[_1, _1], np.dtype[np.float64]])
+assert_type(c16.reshape(1, 1, 1), np.ndarray[tuple[_1, _1, _1], np.dtype[np.complex128]])
+assert_type(U.reshape(1, 1, 1, 1), np.ndarray[tuple[_1, _1, _1, _1], np.dtype[np.str_]])
+assert_type(
+    S.reshape(1, 1, 1, 1, 1),
+    np.ndarray[
+        # len(shape) >= 5
+        tuple[_1, _1, _1, _1, _1, Unpack[tuple[_1, ...]]],
+        np.dtype[np.bytes_],
+    ],
+)
+
+assert_type(i8.astype(float), Any)
+assert_type(i8.astype(np.float64), np.float64)
+
+assert_type(i8.view(), np.int64)
+assert_type(i8.view(np.float64), np.float64)
+assert_type(i8.view(float), Any)
+assert_type(i8.view(np.float64, np.ndarray), np.float64)
+
+assert_type(i8.getfield(float), Any)
+assert_type(i8.getfield(np.float64), np.float64)
+assert_type(i8.getfield(np.float64, 8), np.float64)
+
+assert_type(f8.as_integer_ratio(), tuple[int, int])
+assert_type(f8.is_integer(), bool)
+assert_type(f8.__trunc__(), int)
+assert_type(f8.__getformat__("float"), str)
+assert_type(f8.hex(), str)
+assert_type(np.float64.fromhex("0x0.0p+0"), np.float64)
+
+assert_type(f8.__getnewargs__(), tuple[float])
+assert_type(c16.__getnewargs__(), tuple[float, float])
+
+assert_type(i8.numerator, np.int64)
+assert_type(i8.denominator, Literal[1])
+assert_type(u8.numerator, np.uint64)
+assert_type(u8.denominator, Literal[1])
+assert_type(m.numerator, np.timedelta64)
+assert_type(m.denominator, Literal[1])
+
+assert_type(round(i8), int)
+assert_type(round(i8, 3), np.int64)
+assert_type(round(u8), int)
+assert_type(round(u8, 3), np.uint64)
+assert_type(round(f8), int)
+assert_type(round(f8, 3), np.float64)
+
+assert_type(f8.__ceil__(), int)
+assert_type(f8.__floor__(), int)
+
+assert_type(i8.is_integer(), Literal[True])
+
+assert_type(O.real, np.object_)
+assert_type(O.imag, np.object_)
+assert_type(int(O), int)
+assert_type(float(O), float)
+assert_type(complex(O), complex)
+
+# These fail fail because of a mypy __new__ bug:
+# https://github.com/python/mypy/issues/15182
+# According to the typing spec, the following statements are valid, see
+# https://typing.readthedocs.io/en/latest/spec/constructors.html#new-method
+
+# assert_type(np.object_(), None)
+# assert_type(np.object_(None), None)
+# assert_type(np.object_(array_nd), np.ndarray[Any, np.dtype[np.object_]])
+# assert_type(np.object_([]), npt.NDArray[np.object_])
+# assert_type(np.object_(()), npt.NDArray[np.object_])
+# assert_type(np.object_(range(4)), npt.NDArray[np.object_])
+# assert_type(np.object_(+42), int)
+# assert_type(np.object_(1 / 137), float)
+# assert_type(np.object_('Developers! ' * (1 << 6)), str)
+# assert_type(np.object_(object()), object)
+# assert_type(np.object_({False, True, NotADirectoryError}), set[Any])
+# assert_type(np.object_({'spam': 'food', 'ham': 'food'}), dict[str, str])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/shape.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/shape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8f8d819cbcea964d9c66cc80b82c1622db2ce2a9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/shape.pyi
@@ -0,0 +1,15 @@
+from typing import Any, NamedTuple
+
+import numpy as np
+from typing_extensions import assert_type
+
+
+# Subtype of tuple[int, int]
+class XYGrid(NamedTuple):
+    x_axis: int
+    y_axis: int
+
+arr: np.ndarray[XYGrid, Any]
+
+# Test shape property matches shape typevar
+assert_type(arr.shape, XYGrid)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a4b4bba3f9fc68e233a4f06d80b837dea449cc0e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi
@@ -0,0 +1,54 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+i8: np.int64
+f8: np.float64
+
+AR_b: npt.NDArray[np.bool]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+AR_LIKE_f8: list[float]
+
+assert_type(np.take_along_axis(AR_f8, AR_i8, axis=1), npt.NDArray[np.float64])
+assert_type(np.take_along_axis(f8, AR_i8, axis=None), npt.NDArray[np.float64])
+
+assert_type(np.put_along_axis(AR_f8, AR_i8, "1.0", axis=1), None)
+
+assert_type(np.expand_dims(AR_i8, 2), npt.NDArray[np.int64])
+assert_type(np.expand_dims(AR_LIKE_f8, 2), npt.NDArray[Any])
+
+assert_type(np.column_stack([AR_i8]), npt.NDArray[np.int64])
+assert_type(np.column_stack([AR_LIKE_f8]), npt.NDArray[Any])
+
+assert_type(np.dstack([AR_i8]), npt.NDArray[np.int64])
+assert_type(np.dstack([AR_LIKE_f8]), npt.NDArray[Any])
+
+assert_type(np.array_split(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.split(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.split(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.hsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.vsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.dsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.kron(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.kron(AR_b, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.kron(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+
+assert_type(np.tile(AR_i8, 5), npt.NDArray[np.int64])
+assert_type(np.tile(AR_LIKE_f8, [2, 2]), npt.NDArray[Any])
+
+assert_type(np.unstack(AR_i8, axis=0), tuple[npt.NDArray[np.int64], ...])
+assert_type(np.unstack(AR_LIKE_f8, axis=0), tuple[npt.NDArray[Any], ...])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2ce666280f64a58a4d8f96efd1bca6d4ceeaffbe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi
@@ -0,0 +1,29 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_LIKE_f: list[float]
+interface_dict: dict[str, Any]
+
+assert_type(np.lib.stride_tricks.as_strided(AR_f8), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.as_strided(AR_LIKE_f), npt.NDArray[Any])
+assert_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5)), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20]), npt.NDArray[np.float64])
+
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_f8, 5), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5)), npt.NDArray[Any])
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1), npt.NDArray[np.float64])
+
+assert_type(np.broadcast_to(AR_f8, 5), npt.NDArray[np.float64])
+assert_type(np.broadcast_to(AR_LIKE_f, (1, 5)), npt.NDArray[Any])
+assert_type(np.broadcast_to(AR_f8, [4, 6], subok=True), npt.NDArray[np.float64])
+
+assert_type(np.broadcast_shapes((1, 2), [3, 1], (3, 2)), tuple[int, ...])
+assert_type(np.broadcast_shapes((6, 7), (5, 6, 1), 7, (5, 1, 7)), tuple[int, ...])
+
+assert_type(np.broadcast_arrays(AR_f8, AR_f8), tuple[npt.NDArray[Any], ...])
+assert_type(np.broadcast_arrays(AR_f8, AR_LIKE_f), tuple[npt.NDArray[Any], ...])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/strings.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/strings.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9339456b61aea342c9f00ac0c17d794ca12e35e8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/strings.pyi
@@ -0,0 +1,193 @@
+import numpy as np
+import numpy.typing as npt
+import numpy._typing as np_t
+
+from typing_extensions import assert_type
+from typing import TypeAlias
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+AR_T: np.ndarray[np_t._Shape, np.dtypes.StringDType]
+
+AR_T_alias: TypeAlias = np.ndarray[np_t._Shape, np.dtypes.StringDType]
+AR_TU_alias: TypeAlias = AR_T_alias | npt.NDArray[np.str_]
+
+assert_type(np.strings.equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.not_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.not_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.not_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.greater_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.greater_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.greater_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.less_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.less_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.less_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.greater(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.greater(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.greater(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.less(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.less(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.less(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.add(AR_U, AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.add(AR_S, AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.add(AR_T, AR_T), AR_T_alias)
+
+assert_type(np.strings.multiply(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.multiply(AR_S, [5, 4, 3]), npt.NDArray[np.bytes_])
+assert_type(np.strings.multiply(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.mod(AR_U, "test"), npt.NDArray[np.str_])
+assert_type(np.strings.mod(AR_S, "test"), npt.NDArray[np.bytes_])
+assert_type(np.strings.mod(AR_T, "test"), AR_T_alias)
+
+assert_type(np.strings.capitalize(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.capitalize(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.capitalize(AR_T), AR_T_alias)
+
+assert_type(np.strings.center(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.center(AR_S, [2, 3, 4], b"a"), npt.NDArray[np.bytes_])
+assert_type(np.strings.center(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.encode(AR_U), npt.NDArray[np.bytes_])
+assert_type(np.strings.encode(AR_T), npt.NDArray[np.bytes_])
+assert_type(np.strings.decode(AR_S), npt.NDArray[np.str_])
+
+assert_type(np.strings.expandtabs(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.expandtabs(AR_S, tabsize=4), npt.NDArray[np.bytes_])
+assert_type(np.strings.expandtabs(AR_T), AR_T_alias)
+
+assert_type(np.strings.ljust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.ljust(AR_T, 5), AR_T_alias)
+assert_type(np.strings.ljust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_T_alias)
+
+assert_type(np.strings.rjust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.rjust(AR_T, 5), AR_T_alias)
+assert_type(np.strings.rjust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_T_alias)
+
+assert_type(np.strings.lstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.lstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.lstrip(AR_T), AR_T_alias)
+assert_type(np.strings.lstrip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.rstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.rstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.rstrip(AR_T), AR_T_alias)
+assert_type(np.strings.rstrip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.strip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.strip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.strip(AR_T), AR_T_alias)
+assert_type(np.strings.strip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.count(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_T, ["a", "b", "c"], end=9), npt.NDArray[np.int_])
+
+assert_type(np.strings.partition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.strings.partition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.partition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.strings.rpartition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.strings.rpartition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.rpartition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.strings.replace(AR_U, "_", "-"), npt.NDArray[np.str_])
+assert_type(np.strings.replace(AR_S, [b"_", b""], [b"a", b"b"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.replace(AR_T, "_", "_"), AR_TU_alias)
+
+assert_type(np.strings.lower(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.lower(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.lower(AR_T), AR_T_alias)
+
+assert_type(np.strings.upper(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.upper(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.upper(AR_T), AR_T_alias)
+
+assert_type(np.strings.swapcase(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.swapcase(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.swapcase(AR_T), AR_T_alias)
+
+assert_type(np.strings.title(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.title(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.title(AR_T), AR_T_alias)
+
+assert_type(np.strings.zfill(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.zfill(AR_S, [2, 3, 4]), npt.NDArray[np.bytes_])
+assert_type(np.strings.zfill(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.endswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.strings.endswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.strings.endswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.strings.startswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.strings.startswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.strings.startswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.strings.find(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.find(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.find(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.rfind(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.rfind(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.rfind(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.index(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.index(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.index(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.rindex(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.rindex(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.rindex(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.isalpha(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isalpha(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isalpha(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isalnum(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isalnum(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isalnum(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isdecimal(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isdecimal(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isdigit(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isdigit(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isdigit(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.islower(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.islower(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.islower(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isnumeric(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isnumeric(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isspace(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isspace(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isspace(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.istitle(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.istitle(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.istitle(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isupper(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isupper(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isupper(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.str_len(AR_U), npt.NDArray[np.int_])
+assert_type(np.strings.str_len(AR_S), npt.NDArray[np.int_])
+assert_type(np.strings.str_len(AR_T), npt.NDArray[np.int_])
+
+assert_type(np.strings.translate(AR_U, ""), npt.NDArray[np.str_])
+assert_type(np.strings.translate(AR_S, ""), npt.NDArray[np.bytes_])
+assert_type(np.strings.translate(AR_T, ""), AR_T_alias)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/testing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..741c71f62a5bfbca0e3c8f2ed03917a46899d0ed
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/testing.pyi
@@ -0,0 +1,200 @@
+import re
+import sys
+import warnings
+import types
+import unittest
+import contextlib
+from collections.abc import Callable
+from typing import Any, TypeVar
+from pathlib import Path
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+
+bool_obj: bool
+suppress_obj: np.testing.suppress_warnings
+FT = TypeVar("FT", bound=Callable[..., Any])
+
+def func() -> int: ...
+
+def func2(
+    x: npt.NDArray[np.number[Any]],
+    y: npt.NDArray[np.number[Any]],
+) -> npt.NDArray[np.bool]: ...
+
+assert_type(np.testing.KnownFailureException(), np.testing.KnownFailureException)
+assert_type(np.testing.IgnoreException(), np.testing.IgnoreException)
+
+assert_type(
+    np.testing.clear_and_catch_warnings(modules=[np.testing]),
+    np.testing.clear_and_catch_warnings[None],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(True),
+    np.testing.clear_and_catch_warnings[list[warnings.WarningMessage]],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(False),
+    np.testing.clear_and_catch_warnings[None],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(bool_obj),
+    np.testing.clear_and_catch_warnings,
+)
+assert_type(
+    np.testing.clear_and_catch_warnings.class_modules,
+    tuple[types.ModuleType, ...],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings.modules,
+    set[types.ModuleType],
+)
+
+with np.testing.clear_and_catch_warnings(True) as c1:
+    assert_type(c1, list[warnings.WarningMessage])
+with np.testing.clear_and_catch_warnings() as c2:
+    assert_type(c2, None)
+
+assert_type(np.testing.suppress_warnings("once"), np.testing.suppress_warnings)
+assert_type(np.testing.suppress_warnings()(func), Callable[[], int])
+assert_type(suppress_obj.filter(RuntimeWarning), None)
+assert_type(suppress_obj.record(RuntimeWarning), list[warnings.WarningMessage])
+with suppress_obj as c3:
+    assert_type(c3, np.testing.suppress_warnings)
+
+assert_type(np.testing.verbose, int)
+assert_type(np.testing.IS_PYPY, bool)
+assert_type(np.testing.HAS_REFCOUNT, bool)
+assert_type(np.testing.HAS_LAPACK64, bool)
+
+assert_type(np.testing.assert_(1, msg="test"), None)
+assert_type(np.testing.assert_(2, msg=lambda: "test"), None)
+
+if sys.platform == "win32" or sys.platform == "cygwin":
+    assert_type(np.testing.memusage(), int)
+elif sys.platform == "linux":
+    assert_type(np.testing.memusage(), None | int)
+
+assert_type(np.testing.jiffies(), int)
+
+assert_type(np.testing.build_err_msg([0, 1, 2], "test"), str)
+assert_type(np.testing.build_err_msg(range(2), "test", header="header"), str)
+assert_type(np.testing.build_err_msg(np.arange(9).reshape(3, 3), "test", verbose=False), str)
+assert_type(np.testing.build_err_msg("abc", "test", names=["x", "y"]), str)
+assert_type(np.testing.build_err_msg([1.0, 2.0], "test", precision=5), str)
+
+assert_type(np.testing.assert_equal({1}, {1}), None)
+assert_type(np.testing.assert_equal([1, 2, 3], [1, 2, 3], err_msg="fail"), None)
+assert_type(np.testing.assert_equal(1, 1.0, verbose=True), None)
+
+assert_type(np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1]), None)
+
+assert_type(np.testing.assert_almost_equal(1.0, 1.1), None)
+assert_type(np.testing.assert_almost_equal([1, 2, 3], [1, 2, 3], err_msg="fail"), None)
+assert_type(np.testing.assert_almost_equal(1, 1.0, verbose=True), None)
+assert_type(np.testing.assert_almost_equal(1, 1.0001, decimal=2), None)
+
+assert_type(np.testing.assert_approx_equal(1.0, 1.1), None)
+assert_type(np.testing.assert_approx_equal("1", "2", err_msg="fail"), None)
+assert_type(np.testing.assert_approx_equal(1, 1.0, verbose=True), None)
+assert_type(np.testing.assert_approx_equal(1, 1.0001, significant=2), None)
+
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, verbose=True), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, header="header"), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, precision=np.int64()), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_nan=False), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_inf=True), None)
+
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8, verbose=True), None)
+
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, verbose=True), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, decimal=1), None)
+
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8, verbose=True), None)
+
+assert_type(np.testing.runstring("1 + 1", {}), Any)
+assert_type(np.testing.runstring("int64() + 1", {"int64": np.int64}), Any)
+
+assert_type(np.testing.assert_string_equal("1", "1"), None)
+
+assert_type(np.testing.rundocs(), None)
+assert_type(np.testing.rundocs("test.py"), None)
+assert_type(np.testing.rundocs(Path("test.py"), raise_on_error=True), None)
+
+def func3(a: int) -> bool: ...
+
+assert_type(
+    np.testing.assert_raises(RuntimeWarning),
+    unittest.case._AssertRaisesContext[RuntimeWarning],
+)
+assert_type(np.testing.assert_raises(RuntimeWarning, func3, 5), None)
+
+assert_type(
+    np.testing.assert_raises_regex(RuntimeWarning, r"test"),
+    unittest.case._AssertRaisesContext[RuntimeWarning],
+)
+assert_type(np.testing.assert_raises_regex(RuntimeWarning, b"test", func3, 5), None)
+assert_type(np.testing.assert_raises_regex(RuntimeWarning, re.compile(b"test"), func3, 5), None)
+
+class Test: ...
+
+def decorate(a: FT) -> FT:
+    return a
+
+assert_type(np.testing.decorate_methods(Test, decorate), None)
+assert_type(np.testing.decorate_methods(Test, decorate, None), None)
+assert_type(np.testing.decorate_methods(Test, decorate, "test"), None)
+assert_type(np.testing.decorate_methods(Test, decorate, b"test"), None)
+assert_type(np.testing.decorate_methods(Test, decorate, re.compile("test")), None)
+
+assert_type(np.testing.measure("for i in range(1000): np.sqrt(i**2)"), float)
+assert_type(np.testing.measure(b"for i in range(1000): np.sqrt(i**2)", times=5), float)
+
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, rtol=0.005), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, atol=1), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, equal_nan=True), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, err_msg="err"), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, verbose=False), None)
+
+assert_type(np.testing.assert_array_almost_equal_nulp(AR_i8, AR_f8, nulp=2), None)
+
+assert_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, maxulp=2), npt.NDArray[Any])
+assert_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32), npt.NDArray[Any])
+
+assert_type(np.testing.assert_warns(RuntimeWarning), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_warns(RuntimeWarning, func3, 5), bool)
+
+def func4(a: int, b: str) -> bool: ...
+
+assert_type(np.testing.assert_no_warnings(), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_no_warnings(func3, 5), bool)
+assert_type(np.testing.assert_no_warnings(func4, a=1, b="test"), bool)
+assert_type(np.testing.assert_no_warnings(func4, 1, "test"), bool)
+
+assert_type(np.testing.tempdir("test_dir"), contextlib._GeneratorContextManager[str])
+assert_type(np.testing.tempdir(prefix=b"test"), contextlib._GeneratorContextManager[bytes])
+assert_type(np.testing.tempdir("test_dir", dir=Path("here")), contextlib._GeneratorContextManager[str])
+
+assert_type(np.testing.temppath("test_dir", text=True), contextlib._GeneratorContextManager[str])
+assert_type(np.testing.temppath(prefix=b"test"), contextlib._GeneratorContextManager[bytes])
+assert_type(np.testing.temppath("test_dir", dir=Path("here")), contextlib._GeneratorContextManager[str])
+
+assert_type(np.testing.assert_no_gc_cycles(), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_no_gc_cycles(func3, 5), None)
+
+assert_type(np.testing.break_cycles(), None)
+
+assert_type(np.testing.TestCase(), unittest.case.TestCase)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2f1cd56d1e7b7b6941431a40fea1001bf5a7d7e3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi
@@ -0,0 +1,152 @@
+from typing import Any, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_SCT = TypeVar("_SCT", bound=np.generic)
+
+
+def func1(ar: npt.NDArray[_SCT], a: int) -> npt.NDArray[_SCT]:
+    pass
+
+
+def func2(ar: npt.NDArray[np.number[Any]], a: str) -> npt.NDArray[np.float64]:
+    pass
+
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint64]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+
+AR_LIKE_b: list[bool]
+AR_LIKE_c: list[complex]
+
+assert_type(np.fliplr(AR_b), npt.NDArray[np.bool])
+assert_type(np.fliplr(AR_LIKE_b), npt.NDArray[Any])
+
+assert_type(np.flipud(AR_b), npt.NDArray[np.bool])
+assert_type(np.flipud(AR_LIKE_b), npt.NDArray[Any])
+
+assert_type(np.eye(10), npt.NDArray[np.float64])
+assert_type(np.eye(10, M=20, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.eye(10, k=2, dtype=int), npt.NDArray[Any])
+
+assert_type(np.diag(AR_b), npt.NDArray[np.bool])
+assert_type(np.diag(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.diagflat(AR_b), npt.NDArray[np.bool])
+assert_type(np.diagflat(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.tri(10), npt.NDArray[np.float64])
+assert_type(np.tri(10, M=20, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.tri(10, k=2, dtype=int), npt.NDArray[Any])
+
+assert_type(np.tril(AR_b), npt.NDArray[np.bool])
+assert_type(np.tril(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.triu(AR_b), npt.NDArray[np.bool])
+assert_type(np.triu(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.vander(AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.vander(AR_u), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.vander(AR_i, N=2), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.vander(AR_f, increasing=True), npt.NDArray[np.floating[Any]])
+assert_type(np.vander(AR_c), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.vander(AR_O), npt.NDArray[np.object_])
+
+assert_type(
+    np.histogram2d(AR_LIKE_c, AR_LIKE_c),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128 | np.float64],
+        npt.NDArray[np.complex128 | np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_i, AR_b),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_f, AR_i),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_i, AR_f),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_f, AR_c, weights=AR_LIKE_b),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_f, AR_c, bins=8),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_f, bins=(8, 5)),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_i, bins=AR_u),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.uint64],
+        npt.NDArray[np.uint64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_c, bins=(AR_u, AR_u)),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.uint64],
+        npt.NDArray[np.uint64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_c, bins=(AR_b, 8)),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.bool | np.complex128],
+        npt.NDArray[np.bool | np.complex128],
+    ],
+)
+
+assert_type(np.mask_indices(10, func1), tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.mask_indices(8, func2, "0"), tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]])
+
+assert_type(np.tril_indices(10), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
+
+assert_type(np.tril_indices_from(AR_b), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
+
+assert_type(np.triu_indices(10), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
+
+assert_type(np.triu_indices_from(AR_b), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/type_check.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/type_check.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4a7ef36e9e2658c924e78604362f550a45679d0b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/type_check.pyi
@@ -0,0 +1,79 @@
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _16Bit, _32Bit, _64Bit, _128Bit
+
+from typing_extensions import assert_type
+
+f8: np.float64
+f: float
+
+# NOTE: Avoid importing the platform specific `np.float128` type
+AR_i8: npt.NDArray[np.int64]
+AR_i4: npt.NDArray[np.int32]
+AR_f2: npt.NDArray[np.float16]
+AR_f8: npt.NDArray[np.float64]
+AR_f16: npt.NDArray[np.floating[_128Bit]]
+AR_c8: npt.NDArray[np.complex64]
+AR_c16: npt.NDArray[np.complex128]
+
+AR_LIKE_f: list[float]
+
+class ComplexObj:
+    real: slice
+    imag: slice
+
+assert_type(np.mintypecode(["f8"], typeset="qfQF"), str)
+
+assert_type(np.real(ComplexObj()), slice)
+assert_type(np.real(AR_f8), npt.NDArray[np.float64])
+assert_type(np.real(AR_c16), npt.NDArray[np.float64])
+assert_type(np.real(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.imag(ComplexObj()), slice)
+assert_type(np.imag(AR_f8), npt.NDArray[np.float64])
+assert_type(np.imag(AR_c16), npt.NDArray[np.float64])
+assert_type(np.imag(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.iscomplex(f8), np.bool)
+assert_type(np.iscomplex(AR_f8), npt.NDArray[np.bool])
+assert_type(np.iscomplex(AR_LIKE_f), npt.NDArray[np.bool])
+
+assert_type(np.isreal(f8), np.bool)
+assert_type(np.isreal(AR_f8), npt.NDArray[np.bool])
+assert_type(np.isreal(AR_LIKE_f), npt.NDArray[np.bool])
+
+assert_type(np.iscomplexobj(f8), bool)
+assert_type(np.isrealobj(f8), bool)
+
+assert_type(np.nan_to_num(f8), np.float64)
+assert_type(np.nan_to_num(f, copy=True), Any)
+assert_type(np.nan_to_num(AR_f8, nan=1.5), npt.NDArray[np.float64])
+assert_type(np.nan_to_num(AR_LIKE_f, posinf=9999), npt.NDArray[Any])
+
+assert_type(np.real_if_close(AR_f8), npt.NDArray[np.float64])
+assert_type(
+    np.real_if_close(AR_c16),
+    npt.NDArray[np.floating[_64Bit]] | npt.NDArray[np.complexfloating[_64Bit, _64Bit]],
+)
+assert_type(np.real_if_close(AR_c8), npt.NDArray[np.float32] | npt.NDArray[np.complex64])
+assert_type(np.real_if_close(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.typename("h"), Literal["short"])
+assert_type(np.typename("B"), Literal["unsigned char"])
+assert_type(np.typename("V"), Literal["void"])
+assert_type(np.typename("S1"), Literal["character"])
+
+assert_type(np.common_type(AR_i4), type[np.floating[_64Bit]])
+assert_type(np.common_type(AR_f2), type[np.float16])
+assert_type(np.common_type(AR_f2, AR_i4), type[np.floating[_16Bit | _64Bit]])
+assert_type(np.common_type(AR_f16, AR_i4), type[np.floating[_64Bit | _128Bit]])
+assert_type(
+    np.common_type(AR_c8, AR_f2),
+    type[np.complexfloating[_16Bit | _32Bit, _16Bit | _32Bit]],
+)
+assert_type(
+    np.common_type(AR_f2, AR_c8, AR_i4),
+    type[np.complexfloating[_16Bit | _32Bit | _64Bit, _16Bit | _32Bit | _64Bit]],
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b98157d1d4515e411ade4e8957ac46844f3051e8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi
@@ -0,0 +1,31 @@
+"""Typing tests for `_core._ufunc_config`."""
+
+from _typeshed import SupportsWrite
+from typing import Any
+from collections.abc import Callable
+
+import numpy as np
+
+from typing_extensions import assert_type
+
+def func(a: str, b: int) -> None: ...
+
+class Write:
+    def write(self, value: str) -> None: ...
+
+assert_type(np.seterr(all=None), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(divide="ignore"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(over="warn"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(under="call"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(invalid="raise"), np._core._ufunc_config._ErrDict)
+assert_type(np.geterr(), np._core._ufunc_config._ErrDict)
+
+assert_type(np.setbufsize(4096), int)
+assert_type(np.getbufsize(), int)
+
+assert_type(np.seterrcall(func), Callable[[str, int], Any] | None | SupportsWrite[str])
+assert_type(np.seterrcall(Write()), Callable[[str, int], Any] | None | SupportsWrite[str])
+assert_type(np.geterrcall(), Callable[[str, int], Any] | None | SupportsWrite[str])
+
+assert_type(np.errstate(call=func, all="call"), np.errstate)
+assert_type(np.errstate(call=Write(), divide="log", over="log"), np.errstate)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2a0c6c65ea5da1ea26ce296dc2330e3544c7c3bd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi
@@ -0,0 +1,33 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_O: list[np.object_]
+
+AR_U: npt.NDArray[np.str_]
+
+assert_type(np.fix(AR_LIKE_b), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_u), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_i), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_f), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_O), npt.NDArray[np.object_])
+assert_type(np.fix(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
+
+assert_type(np.isposinf(AR_LIKE_b), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_u), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_i), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_f), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
+
+assert_type(np.isneginf(AR_LIKE_b), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_u), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_i), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_f), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8d3527ac84157d931bd3063d5afe6467fa39badb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi
@@ -0,0 +1,125 @@
+from typing import Literal, Any, NoReturn
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+i8: np.int64
+f8: np.float64
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+
+assert_type(np.absolute.__doc__, str)
+assert_type(np.absolute.types, list[str])
+
+assert_type(np.absolute.__name__, Literal["absolute"])
+assert_type(np.absolute.__qualname__, Literal["absolute"])
+assert_type(np.absolute.ntypes, Literal[20])
+assert_type(np.absolute.identity, None)
+assert_type(np.absolute.nin, Literal[1])
+assert_type(np.absolute.nin, Literal[1])
+assert_type(np.absolute.nout, Literal[1])
+assert_type(np.absolute.nargs, Literal[2])
+assert_type(np.absolute.signature, None)
+assert_type(np.absolute(f8), Any)
+assert_type(np.absolute(AR_f8), npt.NDArray[Any])
+assert_type(np.absolute.at(AR_f8, AR_i8), None)
+
+assert_type(np.add.__name__, Literal["add"])
+assert_type(np.add.__qualname__, Literal["add"])
+assert_type(np.add.ntypes, Literal[22])
+assert_type(np.add.identity, Literal[0])
+assert_type(np.add.nin, Literal[2])
+assert_type(np.add.nout, Literal[1])
+assert_type(np.add.nargs, Literal[3])
+assert_type(np.add.signature, None)
+assert_type(np.add(f8, f8), Any)
+assert_type(np.add(AR_f8, f8), npt.NDArray[Any])
+assert_type(np.add.at(AR_f8, AR_i8, f8), None)
+assert_type(np.add.reduce(AR_f8, axis=0), Any)
+assert_type(np.add.accumulate(AR_f8), npt.NDArray[Any])
+assert_type(np.add.reduceat(AR_f8, AR_i8), npt.NDArray[Any])
+assert_type(np.add.outer(f8, f8), Any)
+assert_type(np.add.outer(AR_f8, f8), npt.NDArray[Any])
+
+assert_type(np.frexp.__name__, Literal["frexp"])
+assert_type(np.frexp.__qualname__, Literal["frexp"])
+assert_type(np.frexp.ntypes, Literal[4])
+assert_type(np.frexp.identity, None)
+assert_type(np.frexp.nin, Literal[1])
+assert_type(np.frexp.nout, Literal[2])
+assert_type(np.frexp.nargs, Literal[3])
+assert_type(np.frexp.signature, None)
+assert_type(np.frexp(f8), tuple[Any, Any])
+assert_type(np.frexp(AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.divmod.__name__, Literal["divmod"])
+assert_type(np.divmod.__qualname__, Literal["divmod"])
+assert_type(np.divmod.ntypes, Literal[15])
+assert_type(np.divmod.identity, None)
+assert_type(np.divmod.nin, Literal[2])
+assert_type(np.divmod.nout, Literal[2])
+assert_type(np.divmod.nargs, Literal[4])
+assert_type(np.divmod.signature, None)
+assert_type(np.divmod(f8, f8), tuple[Any, Any])
+assert_type(np.divmod(AR_f8, f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.matmul.__name__, Literal["matmul"])
+assert_type(np.matmul.__qualname__, Literal["matmul"])
+assert_type(np.matmul.ntypes, Literal[19])
+assert_type(np.matmul.identity, None)
+assert_type(np.matmul.nin, Literal[2])
+assert_type(np.matmul.nout, Literal[1])
+assert_type(np.matmul.nargs, Literal[3])
+assert_type(np.matmul.signature, Literal["(n?,k),(k,m?)->(n?,m?)"])
+assert_type(np.matmul.identity, None)
+assert_type(np.matmul(AR_f8, AR_f8), Any)
+assert_type(np.matmul(AR_f8, AR_f8, axes=[(0, 1), (0, 1), (0, 1)]), Any)
+
+assert_type(np.vecdot.__name__, Literal["vecdot"])
+assert_type(np.vecdot.__qualname__, Literal["vecdot"])
+assert_type(np.vecdot.ntypes, Literal[19])
+assert_type(np.vecdot.identity, None)
+assert_type(np.vecdot.nin, Literal[2])
+assert_type(np.vecdot.nout, Literal[1])
+assert_type(np.vecdot.nargs, Literal[3])
+assert_type(np.vecdot.signature, Literal["(n),(n)->()"])
+assert_type(np.vecdot.identity, None)
+assert_type(np.vecdot(AR_f8, AR_f8), Any)
+
+assert_type(np.bitwise_count.__name__, Literal["bitwise_count"])
+assert_type(np.bitwise_count.__qualname__, Literal["bitwise_count"])
+assert_type(np.bitwise_count.ntypes, Literal[11])
+assert_type(np.bitwise_count.identity, None)
+assert_type(np.bitwise_count.nin, Literal[1])
+assert_type(np.bitwise_count.nout, Literal[1])
+assert_type(np.bitwise_count.nargs, Literal[2])
+assert_type(np.bitwise_count.signature, None)
+assert_type(np.bitwise_count.identity, None)
+assert_type(np.bitwise_count(i8), Any)
+assert_type(np.bitwise_count(AR_i8), npt.NDArray[Any])
+
+assert_type(np.absolute.outer(), NoReturn)
+assert_type(np.frexp.outer(), NoReturn)
+assert_type(np.divmod.outer(), NoReturn)
+assert_type(np.matmul.outer(), NoReturn)
+
+assert_type(np.absolute.reduceat(), NoReturn)
+assert_type(np.frexp.reduceat(), NoReturn)
+assert_type(np.divmod.reduceat(), NoReturn)
+assert_type(np.matmul.reduceat(), NoReturn)
+
+assert_type(np.absolute.reduce(), NoReturn)
+assert_type(np.frexp.reduce(), NoReturn)
+assert_type(np.divmod.reduce(), NoReturn)
+assert_type(np.matmul.reduce(), NoReturn)
+
+assert_type(np.absolute.accumulate(), NoReturn)
+assert_type(np.frexp.accumulate(), NoReturn)
+assert_type(np.divmod.accumulate(), NoReturn)
+assert_type(np.matmul.accumulate(), NoReturn)
+
+assert_type(np.frexp.at(), NoReturn)
+assert_type(np.divmod.at(), NoReturn)
+assert_type(np.matmul.at(), NoReturn)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9b1e23dfb081fc38213959d6a1937fc46f81daaf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi
@@ -0,0 +1,11 @@
+import numpy.exceptions as ex
+
+from typing_extensions import assert_type
+
+assert_type(ex.ModuleDeprecationWarning(), ex.ModuleDeprecationWarning)
+assert_type(ex.VisibleDeprecationWarning(), ex.VisibleDeprecationWarning)
+assert_type(ex.ComplexWarning(), ex.ComplexWarning)
+assert_type(ex.RankWarning(), ex.RankWarning)
+assert_type(ex.TooHardError(), ex.TooHardError)
+assert_type(ex.AxisError("test"), ex.AxisError)
+assert_type(ex.AxisError(5, 1), ex.AxisError)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_isfile.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_isfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..e77b560f8c762b5df16bdccd6fd4193583de5c21
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_isfile.py
@@ -0,0 +1,32 @@
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+from numpy.testing import assert_
+
+ROOT = Path(np.__file__).parents[0]
+FILES = [
+    ROOT / "py.typed",
+    ROOT / "__init__.pyi",
+    ROOT / "ctypeslib.pyi",
+    ROOT / "_core" / "__init__.pyi",
+    ROOT / "f2py" / "__init__.pyi",
+    ROOT / "fft" / "__init__.pyi",
+    ROOT / "lib" / "__init__.pyi",
+    ROOT / "linalg" / "__init__.pyi",
+    ROOT / "ma" / "__init__.pyi",
+    ROOT / "matrixlib" / "__init__.pyi",
+    ROOT / "polynomial" / "__init__.pyi",
+    ROOT / "random" / "__init__.pyi",
+    ROOT / "testing" / "__init__.pyi",
+]
+if sys.version_info < (3, 12):
+    FILES += [ROOT / "distutils" / "__init__.pyi"]
+
+
+class TestIsFile:
+    def test_isfile(self):
+        """Test if all ``.pyi`` files are properly installed."""
+        for file in FILES:
+            assert_(os.path.isfile(file))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_runtime.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..c32c5db3266aff7643cc70b1e139aa17e24a26f6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_runtime.py
@@ -0,0 +1,109 @@
+"""Test the runtime usage of `numpy.typing`."""
+
+from __future__ import annotations
+
+from typing import (
+    get_type_hints,
+    Union,
+    NamedTuple,
+    get_args,
+    get_origin,
+    Any,
+)
+
+import pytest
+import numpy as np
+import numpy.typing as npt
+import numpy._typing as _npt
+
+
+class TypeTup(NamedTuple):
+    typ: type
+    args: tuple[type, ...]
+    origin: None | type
+
+
+NDArrayTup = TypeTup(npt.NDArray, npt.NDArray.__args__, np.ndarray)
+
+TYPES = {
+    "ArrayLike": TypeTup(npt.ArrayLike, npt.ArrayLike.__args__, Union),
+    "DTypeLike": TypeTup(npt.DTypeLike, npt.DTypeLike.__args__, Union),
+    "NBitBase": TypeTup(npt.NBitBase, (), None),
+    "NDArray": NDArrayTup,
+}
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_args(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_args`."""
+    typ, ref = tup.typ, tup.args
+    out = get_args(typ)
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_origin(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_origin`."""
+    typ, ref = tup.typ, tup.origin
+    out = get_origin(typ)
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_type_hints(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_type_hints`."""
+    typ = tup.typ
+
+    # Explicitly set `__annotations__` in order to circumvent the
+    # stringification performed by `from __future__ import annotations`
+    def func(a): pass
+    func.__annotations__ = {"a": typ, "return": None}
+
+    out = get_type_hints(func)
+    ref = {"a": typ, "return": type(None)}
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_type_hints_str(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_type_hints` with string-representation of types."""
+    typ_str, typ = f"npt.{name}", tup.typ
+
+    # Explicitly set `__annotations__` in order to circumvent the
+    # stringification performed by `from __future__ import annotations`
+    def func(a): pass
+    func.__annotations__ = {"a": typ_str, "return": None}
+
+    out = get_type_hints(func)
+    ref = {"a": typ, "return": type(None)}
+    assert out == ref
+
+
+def test_keys() -> None:
+    """Test that ``TYPES.keys()`` and ``numpy.typing.__all__`` are synced."""
+    keys = TYPES.keys()
+    ref = set(npt.__all__)
+    assert keys == ref
+
+
+PROTOCOLS: dict[str, tuple[type[Any], object]] = {
+    "_SupportsDType": (_npt._SupportsDType, np.int64(1)),
+    "_SupportsArray": (_npt._SupportsArray, np.arange(10)),
+    "_SupportsArrayFunc": (_npt._SupportsArrayFunc, np.arange(10)),
+    "_NestedSequence": (_npt._NestedSequence, [1]),
+}
+
+
+@pytest.mark.parametrize("cls,obj", PROTOCOLS.values(), ids=PROTOCOLS.keys())
+class TestRuntimeProtocol:
+    def test_isinstance(self, cls: type[Any], obj: object) -> None:
+        assert isinstance(obj, cls)
+        assert not isinstance(None, cls)
+
+    def test_issubclass(self, cls: type[Any], obj: object) -> None:
+        if cls is _npt._SupportsDType:
+            pytest.xfail(
+                "Protocols with non-method members don't support issubclass()"
+            )
+        assert issubclass(type(obj), cls)
+        assert not issubclass(type(None), cls)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_typing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d6f0d4df26b435675b549ac322b4688651ddf2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/numpy/typing/tests/test_typing.py
@@ -0,0 +1,286 @@
+from __future__ import annotations
+
+import importlib.util
+import os
+import re
+import shutil
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+import pytest
+from numpy.typing.mypy_plugin import _EXTENDED_PRECISION_LIST
+
+
+# Only trigger a full `mypy` run if this environment variable is set
+# Note that these tests tend to take over a minute even on a macOS M1 CPU,
+# and more than that in CI.
+RUN_MYPY = "NPY_RUN_MYPY_IN_TESTSUITE" in os.environ
+if RUN_MYPY and RUN_MYPY not in ('0', '', 'false'):
+    RUN_MYPY = True
+
+# Skips all functions in this file
+pytestmark = pytest.mark.skipif(
+    not RUN_MYPY,
+    reason="`NPY_RUN_MYPY_IN_TESTSUITE` not set"
+)
+
+
+try:
+    from mypy import api
+except ImportError:
+    NO_MYPY = True
+else:
+    NO_MYPY = False
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+    # We need this as annotation, but it's located in a private namespace.
+    # As a compromise, do *not* import it during runtime
+    from _pytest.mark.structures import ParameterSet
+
+DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
+PASS_DIR = os.path.join(DATA_DIR, "pass")
+FAIL_DIR = os.path.join(DATA_DIR, "fail")
+REVEAL_DIR = os.path.join(DATA_DIR, "reveal")
+MISC_DIR = os.path.join(DATA_DIR, "misc")
+MYPY_INI = os.path.join(DATA_DIR, "mypy.ini")
+CACHE_DIR = os.path.join(DATA_DIR, ".mypy_cache")
+
+#: A dictionary with file names as keys and lists of the mypy stdout as values.
+#: To-be populated by `run_mypy`.
+OUTPUT_MYPY: defaultdict[str, list[str]] = defaultdict(list)
+
+
+def _key_func(key: str) -> str:
+    """Split at the first occurrence of the ``:`` character.
+
+    Windows drive-letters (*e.g.* ``C:``) are ignored herein.
+    """
+    drive, tail = os.path.splitdrive(key)
+    return os.path.join(drive, tail.split(":", 1)[0])
+
+
+def _strip_filename(msg: str) -> tuple[int, str]:
+    """Strip the filename and line number from a mypy message."""
+    _, tail = os.path.splitdrive(msg)
+    _, lineno, msg = tail.split(":", 2)
+    return int(lineno), msg.strip()
+
+
+def strip_func(match: re.Match[str]) -> str:
+    """`re.sub` helper function for stripping module names."""
+    return match.groups()[1]
+
+
+@pytest.fixture(scope="module", autouse=True)
+def run_mypy() -> None:
+    """Clears the cache and run mypy before running any of the typing tests.
+
+    The mypy results are cached in `OUTPUT_MYPY` for further use.
+
+    The cache refresh can be skipped using
+
+    NUMPY_TYPING_TEST_CLEAR_CACHE=0 pytest numpy/typing/tests
+    """
+    if (
+        os.path.isdir(CACHE_DIR)
+        and bool(os.environ.get("NUMPY_TYPING_TEST_CLEAR_CACHE", True))
+    ):
+        shutil.rmtree(CACHE_DIR)
+
+    split_pattern = re.compile(r"(\s+)?\^(\~+)?")
+    for directory in (PASS_DIR, REVEAL_DIR, FAIL_DIR, MISC_DIR):
+        # Run mypy
+        stdout, stderr, exit_code = api.run([
+            "--config-file",
+            MYPY_INI,
+            "--cache-dir",
+            CACHE_DIR,
+            directory,
+        ])
+        if stderr:
+            pytest.fail(f"Unexpected mypy standard error\n\n{stderr}")
+        elif exit_code not in {0, 1}:
+            pytest.fail(f"Unexpected mypy exit code: {exit_code}\n\n{stdout}")
+
+        str_concat = ""
+        filename: str | None = None
+        for i in stdout.split("\n"):
+            if "note:" in i:
+                continue
+            if filename is None:
+                filename = _key_func(i)
+
+            str_concat += f"{i}\n"
+            if split_pattern.match(i) is not None:
+                OUTPUT_MYPY[filename].append(str_concat)
+                str_concat = ""
+                filename = None
+
+
+def get_test_cases(directory: str) -> Iterator[ParameterSet]:
+    for root, _, files in os.walk(directory):
+        for fname in files:
+            short_fname, ext = os.path.splitext(fname)
+            if ext in (".pyi", ".py"):
+                fullpath = os.path.join(root, fname)
+                yield pytest.param(fullpath, id=short_fname)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
+def test_success(path) -> None:
+    # Alias `OUTPUT_MYPY` so that it appears in the local namespace
+    output_mypy = OUTPUT_MYPY
+    if path in output_mypy:
+        msg = "Unexpected mypy output\n\n"
+        msg += "\n".join(_strip_filename(v)[1] for v in output_mypy[path])
+        raise AssertionError(msg)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(FAIL_DIR))
+def test_fail(path: str) -> None:
+    __tracebackhide__ = True
+
+    with open(path) as fin:
+        lines = fin.readlines()
+
+    errors = defaultdict(lambda: "")
+
+    output_mypy = OUTPUT_MYPY
+    assert path in output_mypy
+
+    for error_line in output_mypy[path]:
+        lineno, error_line = _strip_filename(error_line)
+        errors[lineno] += f'{error_line}\n'
+
+    for i, line in enumerate(lines):
+        lineno = i + 1
+        if (
+            line.startswith('#')
+            or (" E:" not in line and lineno not in errors)
+        ):
+            continue
+
+        target_line = lines[lineno - 1]
+        if "# E:" in target_line:
+            expression, _, marker = target_line.partition("  # E: ")
+            error = errors[lineno].strip()
+            expected_error = marker.strip()
+            _test_fail(path, expression, error, expected_error, lineno)
+        else:
+            pytest.fail(
+                f"Unexpected mypy output at line {lineno}\n\n{errors[lineno]}"
+            )
+
+
+_FAIL_MSG1 = """Extra error at line {}
+
+Expression: {}
+Extra error: {!r}
+"""
+
+_FAIL_MSG2 = """Error mismatch at line {}
+
+Expression: {}
+Expected error: {}
+Observed error: {!r}
+"""
+
+
+def _test_fail(
+    path: str,
+    expression: str,
+    error: str,
+    expected_error: None | str,
+    lineno: int,
+) -> None:
+    if expected_error is None:
+        raise AssertionError(_FAIL_MSG1.format(lineno, expression, error))
+    elif expected_error not in error:
+        raise AssertionError(_FAIL_MSG2.format(
+            lineno, expression, expected_error, error
+        ))
+
+
+_REVEAL_MSG = """Reveal mismatch at line {}
+
+{}
+"""
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(REVEAL_DIR))
+def test_reveal(path: str) -> None:
+    """Validate that mypy correctly infers the return-types of
+    the expressions in `path`.
+    """
+    __tracebackhide__ = True
+
+    output_mypy = OUTPUT_MYPY
+    if path not in output_mypy:
+        return
+
+    for error_line in output_mypy[path]:
+        lineno, error_line = _strip_filename(error_line)
+        raise AssertionError(_REVEAL_MSG.format(lineno, error_line))
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
+def test_code_runs(path: str) -> None:
+    """Validate that the code in `path` properly during runtime."""
+    path_without_extension, _ = os.path.splitext(path)
+    dirname, filename = path.split(os.sep)[-2:]
+
+    spec = importlib.util.spec_from_file_location(
+        f"{dirname}.{filename}", path
+    )
+    assert spec is not None
+    assert spec.loader is not None
+
+    test_module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(test_module)
+
+
+LINENO_MAPPING = {
+    11: "uint128",
+    12: "uint256",
+    14: "int128",
+    15: "int256",
+    17: "float80",
+    18: "float96",
+    19: "float128",
+    20: "float256",
+    22: "complex160",
+    23: "complex192",
+    24: "complex256",
+    25: "complex512",
+}
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+def test_extended_precision() -> None:
+    path = os.path.join(MISC_DIR, "extended_precision.pyi")
+    output_mypy = OUTPUT_MYPY
+    assert path in output_mypy
+
+    with open(path) as f:
+        expression_list = f.readlines()
+
+    for _msg in output_mypy[path]:
+        lineno, msg = _strip_filename(_msg)
+        expression = expression_list[lineno - 1].rstrip("\n")
+
+        if LINENO_MAPPING[lineno] in _EXTENDED_PRECISION_LIST:
+            raise AssertionError(_REVEAL_MSG.format(lineno, msg))
+        elif "error" not in msg:
+            _test_fail(
+                path, expression, msg, 'Expression is of type "Any"', lineno
+            )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas.h
new file mode 100644
index 0000000000000000000000000000000000000000..96eadad8a8e8c3979b99910ceea41ceaf2c8b58e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas.h
@@ -0,0 +1,891 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * This is the public header file for the CUBLAS library, defining the API
+ *
+ * CUBLAS is an implementation of BLAS (Basic Linear Algebra Subroutines)
+ * on top of the CUDA runtime.
+ */
+
+#if !defined(CUBLAS_H_)
+#define CUBLAS_H_
+
+#if defined(CUBLAS_V2_H_)
+#error "It is an error to include both cublas.h and cublas_v2.h"
+#endif
+
+#include <cuda_runtime.h>
+
+#ifndef CUBLASWINAPI
+#ifdef _WIN32
+#define CUBLASWINAPI __stdcall
+#else
+#define CUBLASWINAPI
+#endif
+#endif
+
+#undef CUBLASAPI
+#ifdef __CUDACC__
+#define CUBLASAPI __host__
+#else
+#define CUBLASAPI
+#endif
+
+#include "cublas_api.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* CUBLAS data types */
+#define cublasStatus cublasStatus_t
+
+cublasStatus CUBLASWINAPI cublasInit(void);
+cublasStatus CUBLASWINAPI cublasShutdown(void);
+cublasStatus CUBLASWINAPI cublasGetError(void);
+
+cublasStatus CUBLASWINAPI cublasGetVersion(int* version);
+cublasStatus CUBLASWINAPI cublasAlloc(int n, int elemSize, void** devicePtr);
+
+cublasStatus CUBLASWINAPI cublasFree(void* devicePtr);
+
+cublasStatus CUBLASWINAPI cublasSetKernelStream(cudaStream_t stream);
+
+/* ---------------- CUBLAS BLAS1 functions ---------------- */
+/* NRM2 */
+float CUBLASWINAPI cublasSnrm2(int n, const float* x, int incx);
+double CUBLASWINAPI cublasDnrm2(int n, const double* x, int incx);
+float CUBLASWINAPI cublasScnrm2(int n, const cuComplex* x, int incx);
+double CUBLASWINAPI cublasDznrm2(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* DOT */
+float CUBLASWINAPI cublasSdot(int n, const float* x, int incx, const float* y, int incy);
+double CUBLASWINAPI cublasDdot(int n, const double* x, int incx, const double* y, int incy);
+cuComplex CUBLASWINAPI cublasCdotu(int n, const cuComplex* x, int incx, const cuComplex* y, int incy);
+cuComplex CUBLASWINAPI cublasCdotc(int n, const cuComplex* x, int incx, const cuComplex* y, int incy);
+cuDoubleComplex CUBLASWINAPI cublasZdotu(int n, const cuDoubleComplex* x, int incx, const cuDoubleComplex* y, int incy);
+cuDoubleComplex CUBLASWINAPI cublasZdotc(int n, const cuDoubleComplex* x, int incx, const cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* SCAL */
+void CUBLASWINAPI cublasSscal(int n, float alpha, float* x, int incx);
+void CUBLASWINAPI cublasDscal(int n, double alpha, double* x, int incx);
+void CUBLASWINAPI cublasCscal(int n, cuComplex alpha, cuComplex* x, int incx);
+void CUBLASWINAPI cublasZscal(int n, cuDoubleComplex alpha, cuDoubleComplex* x, int incx);
+
+void CUBLASWINAPI cublasCsscal(int n, float alpha, cuComplex* x, int incx);
+void CUBLASWINAPI cublasZdscal(int n, double alpha, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* AXPY */
+void CUBLASWINAPI cublasSaxpy(int n, float alpha, const float* x, int incx, float* y, int incy);
+void CUBLASWINAPI cublasDaxpy(int n, double alpha, const double* x, int incx, double* y, int incy);
+void CUBLASWINAPI cublasCaxpy(int n, cuComplex alpha, const cuComplex* x, int incx, cuComplex* y, int incy);
+void CUBLASWINAPI
+cublasZaxpy(int n, cuDoubleComplex alpha, const cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* COPY */
+void CUBLASWINAPI cublasScopy(int n, const float* x, int incx, float* y, int incy);
+void CUBLASWINAPI cublasDcopy(int n, const double* x, int incx, double* y, int incy);
+void CUBLASWINAPI cublasCcopy(int n, const cuComplex* x, int incx, cuComplex* y, int incy);
+void CUBLASWINAPI cublasZcopy(int n, const cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* SWAP */
+void CUBLASWINAPI cublasSswap(int n, float* x, int incx, float* y, int incy);
+void CUBLASWINAPI cublasDswap(int n, double* x, int incx, double* y, int incy);
+void CUBLASWINAPI cublasCswap(int n, cuComplex* x, int incx, cuComplex* y, int incy);
+void CUBLASWINAPI cublasZswap(int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* AMAX */
+int CUBLASWINAPI cublasIsamax(int n, const float* x, int incx);
+int CUBLASWINAPI cublasIdamax(int n, const double* x, int incx);
+int CUBLASWINAPI cublasIcamax(int n, const cuComplex* x, int incx);
+int CUBLASWINAPI cublasIzamax(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* AMIN */
+int CUBLASWINAPI cublasIsamin(int n, const float* x, int incx);
+int CUBLASWINAPI cublasIdamin(int n, const double* x, int incx);
+
+int CUBLASWINAPI cublasIcamin(int n, const cuComplex* x, int incx);
+int CUBLASWINAPI cublasIzamin(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* ASUM */
+float CUBLASWINAPI cublasSasum(int n, const float* x, int incx);
+double CUBLASWINAPI cublasDasum(int n, const double* x, int incx);
+float CUBLASWINAPI cublasScasum(int n, const cuComplex* x, int incx);
+double CUBLASWINAPI cublasDzasum(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* ROT */
+void CUBLASWINAPI cublasSrot(int n, float* x, int incx, float* y, int incy, float sc, float ss);
+void CUBLASWINAPI cublasDrot(int n, double* x, int incx, double* y, int incy, double sc, double ss);
+void CUBLASWINAPI cublasCrot(int n, cuComplex* x, int incx, cuComplex* y, int incy, float c, cuComplex s);
+void CUBLASWINAPI
+cublasZrot(int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy, double sc, cuDoubleComplex cs);
+void CUBLASWINAPI cublasCsrot(int n, cuComplex* x, int incx, cuComplex* y, int incy, float c, float s);
+void CUBLASWINAPI cublasZdrot(int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy, double c, double s);
+/*------------------------------------------------------------------------*/
+/* ROTG */
+void CUBLASWINAPI cublasSrotg(float* sa, float* sb, float* sc, float* ss);
+void CUBLASWINAPI cublasDrotg(double* sa, double* sb, double* sc, double* ss);
+void CUBLASWINAPI cublasCrotg(cuComplex* ca, cuComplex cb, float* sc, cuComplex* cs);
+void CUBLASWINAPI cublasZrotg(cuDoubleComplex* ca, cuDoubleComplex cb, double* sc, cuDoubleComplex* cs);
+/*------------------------------------------------------------------------*/
+/* ROTM */
+void CUBLASWINAPI cublasSrotm(int n, float* x, int incx, float* y, int incy, const float* sparam);
+void CUBLASWINAPI cublasDrotm(int n, double* x, int incx, double* y, int incy, const double* sparam);
+/*------------------------------------------------------------------------*/
+/* ROTMG */
+void CUBLASWINAPI cublasSrotmg(float* sd1, float* sd2, float* sx1, const float* sy1, float* sparam);
+void CUBLASWINAPI cublasDrotmg(double* sd1, double* sd2, double* sx1, const double* sy1, double* sparam);
+
+/* --------------- CUBLAS BLAS2 functions  ---------------- */
+/* GEMV */
+void CUBLASWINAPI cublasSgemv(char trans,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* x,
+                              int incx,
+                              float beta,
+                              float* y,
+                              int incy);
+void CUBLASWINAPI cublasDgemv(char trans,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasCgemv(char trans,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZgemv(char trans,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* GBMV */
+void CUBLASWINAPI cublasSgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* x,
+                              int incx,
+                              float beta,
+                              float* y,
+                              int incy);
+void CUBLASWINAPI cublasDgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasCgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* TRMV */
+void CUBLASWINAPI cublasStrmv(char uplo, char trans, char diag, int n, const float* A, int lda, float* x, int incx);
+void CUBLASWINAPI cublasDtrmv(char uplo, char trans, char diag, int n, const double* A, int lda, double* x, int incx);
+void CUBLASWINAPI
+cublasCtrmv(char uplo, char trans, char diag, int n, const cuComplex* A, int lda, cuComplex* x, int incx);
+void CUBLASWINAPI
+cublasZtrmv(char uplo, char trans, char diag, int n, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TBMV */
+void CUBLASWINAPI
+cublasStbmv(char uplo, char trans, char diag, int n, int k, const float* A, int lda, float* x, int incx);
+void CUBLASWINAPI
+cublasDtbmv(char uplo, char trans, char diag, int n, int k, const double* A, int lda, double* x, int incx);
+void CUBLASWINAPI
+cublasCtbmv(char uplo, char trans, char diag, int n, int k, const cuComplex* A, int lda, cuComplex* x, int incx);
+void CUBLASWINAPI cublasZtbmv(
+    char uplo, char trans, char diag, int n, int k, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TPMV */
+void CUBLASWINAPI cublasStpmv(char uplo, char trans, char diag, int n, const float* AP, float* x, int incx);
+
+void CUBLASWINAPI cublasDtpmv(char uplo, char trans, char diag, int n, const double* AP, double* x, int incx);
+
+void CUBLASWINAPI cublasCtpmv(char uplo, char trans, char diag, int n, const cuComplex* AP, cuComplex* x, int incx);
+
+void CUBLASWINAPI
+cublasZtpmv(char uplo, char trans, char diag, int n, const cuDoubleComplex* AP, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TRSV */
+void CUBLASWINAPI cublasStrsv(char uplo, char trans, char diag, int n, const float* A, int lda, float* x, int incx);
+
+void CUBLASWINAPI cublasDtrsv(char uplo, char trans, char diag, int n, const double* A, int lda, double* x, int incx);
+
+void CUBLASWINAPI
+cublasCtrsv(char uplo, char trans, char diag, int n, const cuComplex* A, int lda, cuComplex* x, int incx);
+
+void CUBLASWINAPI
+cublasZtrsv(char uplo, char trans, char diag, int n, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TPSV */
+void CUBLASWINAPI cublasStpsv(char uplo, char trans, char diag, int n, const float* AP, float* x, int incx);
+
+void CUBLASWINAPI cublasDtpsv(char uplo, char trans, char diag, int n, const double* AP, double* x, int incx);
+
+void CUBLASWINAPI cublasCtpsv(char uplo, char trans, char diag, int n, const cuComplex* AP, cuComplex* x, int incx);
+
+void CUBLASWINAPI
+cublasZtpsv(char uplo, char trans, char diag, int n, const cuDoubleComplex* AP, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TBSV */
+void CUBLASWINAPI
+cublasStbsv(char uplo, char trans, char diag, int n, int k, const float* A, int lda, float* x, int incx);
+
+void CUBLASWINAPI
+cublasDtbsv(char uplo, char trans, char diag, int n, int k, const double* A, int lda, double* x, int incx);
+void CUBLASWINAPI
+cublasCtbsv(char uplo, char trans, char diag, int n, int k, const cuComplex* A, int lda, cuComplex* x, int incx);
+
+void CUBLASWINAPI cublasZtbsv(
+    char uplo, char trans, char diag, int n, int k, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* SYMV/HEMV */
+void CUBLASWINAPI cublasSsymv(
+    char uplo, int n, float alpha, const float* A, int lda, const float* x, int incx, float beta, float* y, int incy);
+void CUBLASWINAPI cublasDsymv(char uplo,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasChemv(char uplo,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZhemv(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* SBMV/HBMV */
+void CUBLASWINAPI cublasSsbmv(char uplo,
+                              int n,
+                              int k,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* x,
+                              int incx,
+                              float beta,
+                              float* y,
+                              int incy);
+void CUBLASWINAPI cublasDsbmv(char uplo,
+                              int n,
+                              int k,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasChbmv(char uplo,
+                              int n,
+                              int k,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZhbmv(char uplo,
+                              int n,
+                              int k,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* SPMV/HPMV */
+void CUBLASWINAPI
+cublasSspmv(char uplo, int n, float alpha, const float* AP, const float* x, int incx, float beta, float* y, int incy);
+void CUBLASWINAPI cublasDspmv(
+    char uplo, int n, double alpha, const double* AP, const double* x, int incx, double beta, double* y, int incy);
+void CUBLASWINAPI cublasChpmv(char uplo,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* AP,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZhpmv(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* AP,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+
+/*------------------------------------------------------------------------*/
+/* GER */
+void CUBLASWINAPI
+cublasSger(int m, int n, float alpha, const float* x, int incx, const float* y, int incy, float* A, int lda);
+void CUBLASWINAPI
+cublasDger(int m, int n, double alpha, const double* x, int incx, const double* y, int incy, double* A, int lda);
+
+void CUBLASWINAPI cublasCgeru(
+    int m, int n, cuComplex alpha, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* A, int lda);
+void CUBLASWINAPI cublasCgerc(
+    int m, int n, cuComplex alpha, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* A, int lda);
+void CUBLASWINAPI cublasZgeru(int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* A,
+                              int lda);
+void CUBLASWINAPI cublasZgerc(int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* A,
+                              int lda);
+/*------------------------------------------------------------------------*/
+/* SYR/HER */
+void CUBLASWINAPI cublasSsyr(char uplo, int n, float alpha, const float* x, int incx, float* A, int lda);
+void CUBLASWINAPI cublasDsyr(char uplo, int n, double alpha, const double* x, int incx, double* A, int lda);
+
+void CUBLASWINAPI cublasCher(char uplo, int n, float alpha, const cuComplex* x, int incx, cuComplex* A, int lda);
+void CUBLASWINAPI
+cublasZher(char uplo, int n, double alpha, const cuDoubleComplex* x, int incx, cuDoubleComplex* A, int lda);
+
+/*------------------------------------------------------------------------*/
+/* SPR/HPR */
+void CUBLASWINAPI cublasSspr(char uplo, int n, float alpha, const float* x, int incx, float* AP);
+void CUBLASWINAPI cublasDspr(char uplo, int n, double alpha, const double* x, int incx, double* AP);
+void CUBLASWINAPI cublasChpr(char uplo, int n, float alpha, const cuComplex* x, int incx, cuComplex* AP);
+void CUBLASWINAPI cublasZhpr(char uplo, int n, double alpha, const cuDoubleComplex* x, int incx, cuDoubleComplex* AP);
+/*------------------------------------------------------------------------*/
+/* SYR2/HER2 */
+void CUBLASWINAPI
+cublasSsyr2(char uplo, int n, float alpha, const float* x, int incx, const float* y, int incy, float* A, int lda);
+void CUBLASWINAPI
+cublasDsyr2(char uplo, int n, double alpha, const double* x, int incx, const double* y, int incy, double* A, int lda);
+void CUBLASWINAPI cublasCher2(char uplo,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* x,
+                              int incx,
+                              const cuComplex* y,
+                              int incy,
+                              cuComplex* A,
+                              int lda);
+void CUBLASWINAPI cublasZher2(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* A,
+                              int lda);
+
+/*------------------------------------------------------------------------*/
+/* SPR2/HPR2 */
+void CUBLASWINAPI
+cublasSspr2(char uplo, int n, float alpha, const float* x, int incx, const float* y, int incy, float* AP);
+void CUBLASWINAPI
+cublasDspr2(char uplo, int n, double alpha, const double* x, int incx, const double* y, int incy, double* AP);
+void CUBLASWINAPI cublasChpr2(
+    char uplo, int n, cuComplex alpha, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* AP);
+void CUBLASWINAPI cublasZhpr2(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* AP);
+/* ------------------------BLAS3 Functions ------------------------------- */
+/* GEMM */
+void CUBLASWINAPI cublasSgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* B,
+                              int ldb,
+                              float beta,
+                              float* C,
+                              int ldc);
+void CUBLASWINAPI cublasDgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* B,
+                              int ldb,
+                              double beta,
+                              double* C,
+                              int ldc);
+void CUBLASWINAPI cublasCgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* B,
+                              int ldb,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+void CUBLASWINAPI cublasZgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* B,
+                              int ldb,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/* -------------------------------------------------------*/
+/* SYRK */
+void CUBLASWINAPI
+cublasSsyrk(char uplo, char trans, int n, int k, float alpha, const float* A, int lda, float beta, float* C, int ldc);
+void CUBLASWINAPI cublasDsyrk(
+    char uplo, char trans, int n, int k, double alpha, const double* A, int lda, double beta, double* C, int ldc);
+
+void CUBLASWINAPI cublasCsyrk(char uplo,
+                              char trans,
+                              int n,
+                              int k,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+void CUBLASWINAPI cublasZsyrk(char uplo,
+                              char trans,
+                              int n,
+                              int k,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/* ------------------------------------------------------- */
+/* HERK */
+void CUBLASWINAPI cublasCherk(
+    char uplo, char trans, int n, int k, float alpha, const cuComplex* A, int lda, float beta, cuComplex* C, int ldc);
+void CUBLASWINAPI cublasZherk(char uplo,
+                              char trans,
+                              int n,
+                              int k,
+                              double alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              double beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/* ------------------------------------------------------- */
+/* SYR2K */
+void CUBLASWINAPI cublasSsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               float alpha,
+                               const float* A,
+                               int lda,
+                               const float* B,
+                               int ldb,
+                               float beta,
+                               float* C,
+                               int ldc);
+
+void CUBLASWINAPI cublasDsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               double alpha,
+                               const double* A,
+                               int lda,
+                               const double* B,
+                               int ldb,
+                               double beta,
+                               double* C,
+                               int ldc);
+void CUBLASWINAPI cublasCsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuComplex alpha,
+                               const cuComplex* A,
+                               int lda,
+                               const cuComplex* B,
+                               int ldb,
+                               cuComplex beta,
+                               cuComplex* C,
+                               int ldc);
+
+void CUBLASWINAPI cublasZsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuDoubleComplex alpha,
+                               const cuDoubleComplex* A,
+                               int lda,
+                               const cuDoubleComplex* B,
+                               int ldb,
+                               cuDoubleComplex beta,
+                               cuDoubleComplex* C,
+                               int ldc);
+/* ------------------------------------------------------- */
+/* HER2K */
+void CUBLASWINAPI cublasCher2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuComplex alpha,
+                               const cuComplex* A,
+                               int lda,
+                               const cuComplex* B,
+                               int ldb,
+                               float beta,
+                               cuComplex* C,
+                               int ldc);
+
+void CUBLASWINAPI cublasZher2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuDoubleComplex alpha,
+                               const cuDoubleComplex* A,
+                               int lda,
+                               const cuDoubleComplex* B,
+                               int ldb,
+                               double beta,
+                               cuDoubleComplex* C,
+                               int ldc);
+
+/*------------------------------------------------------------------------*/
+/* SYMM*/
+void CUBLASWINAPI cublasSsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* B,
+                              int ldb,
+                              float beta,
+                              float* C,
+                              int ldc);
+void CUBLASWINAPI cublasDsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* B,
+                              int ldb,
+                              double beta,
+                              double* C,
+                              int ldc);
+
+void CUBLASWINAPI cublasCsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* B,
+                              int ldb,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+
+void CUBLASWINAPI cublasZsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* B,
+                              int ldb,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/*------------------------------------------------------------------------*/
+/* HEMM*/
+void CUBLASWINAPI cublasChemm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* B,
+                              int ldb,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+void CUBLASWINAPI cublasZhemm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* B,
+                              int ldb,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+
+/*------------------------------------------------------------------------*/
+/* TRSM*/
+void CUBLASWINAPI cublasStrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              float* B,
+                              int ldb);
+
+void CUBLASWINAPI cublasDtrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              double* B,
+                              int ldb);
+
+void CUBLASWINAPI cublasCtrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              cuComplex* B,
+                              int ldb);
+
+void CUBLASWINAPI cublasZtrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              cuDoubleComplex* B,
+                              int ldb);
+/*------------------------------------------------------------------------*/
+/* TRMM*/
+void CUBLASWINAPI cublasStrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              float* B,
+                              int ldb);
+void CUBLASWINAPI cublasDtrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              double* B,
+                              int ldb);
+void CUBLASWINAPI cublasCtrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              cuComplex* B,
+                              int ldb);
+void CUBLASWINAPI cublasZtrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              cuDoubleComplex* B,
+                              int ldb);
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* !defined(CUBLAS_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublasLt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublasLt.h
new file mode 100644
index 0000000000000000000000000000000000000000..217462f2ab4a16001f4ce6dc8c26a129d9f07b26
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublasLt.h
@@ -0,0 +1,2511 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+#pragma once
+
+#ifndef CUBLASAPI
+#ifdef __CUDACC__
+#define CUBLASAPI __host__ __device__
+#else
+#define CUBLASAPI
+#endif
+#endif
+
+#include <cublas_api.h>
+
+#include <stdint.h>
+#include <stddef.h>
+#include <stdio.h>
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+/** Opaque structure holding CUBLASLT context
+ */
+typedef struct cublasLtContext* cublasLtHandle_t;
+
+cublasStatus_t CUBLASWINAPI cublasLtCreate(cublasLtHandle_t* lightHandle);
+
+cublasStatus_t CUBLASWINAPI cublasLtDestroy(cublasLtHandle_t lightHandle);
+
+const char* CUBLASWINAPI cublasLtGetStatusName(cublasStatus_t status);
+
+const char* CUBLASWINAPI cublasLtGetStatusString(cublasStatus_t status);
+
+size_t CUBLASWINAPI cublasLtGetVersion(void);
+
+size_t CUBLASWINAPI cublasLtGetCudartVersion(void);
+
+cublasStatus_t CUBLASWINAPI cublasLtGetProperty(libraryPropertyType type, int* value);
+
+cublasStatus_t CUBLASWINAPI cublasLtHeuristicsCacheGetCapacity(size_t* capacity);
+cublasStatus_t CUBLASWINAPI cublasLtHeuristicsCacheSetCapacity(size_t capacity);
+
+/** Restricts usage of CPU instructions (ISA) specified by the flags in the mask.
+ *
+ * Flags can be combined with bitwise OR(|) operator. Supported flags:
+ * - 0x1 -- x86-64 AVX512 ISA
+ *
+ * Default mask: 0 (any applicable ISA is allowed).
+ *
+ * The function returns the previous value of the mask.
+ * The function takes precedence over the environment variable CUBLASLT_DISABLE_CPU_INSTRUCTIONS_MASK.
+ */
+unsigned CUBLASWINAPI cublasLtDisableCpuInstructionsSetMask(unsigned mask);
+
+/** Semi-opaque descriptor for matrix memory layout
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatrixLayoutOpaque_t;
+
+/** Opaque descriptor for matrix memory layout
+ */
+typedef cublasLtMatrixLayoutOpaque_t* cublasLtMatrixLayout_t;
+
+/** Semi-opaque algorithm descriptor (to avoid complicated alloc/free schemes)
+ *
+ * This structure can be trivially serialized and later restored for use with the same version of cuBLAS library to save
+ * on selecting the right configuration again.
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatmulAlgo_t;
+
+/** Semi-opaque descriptor for cublasLtMatmul() operation details
+ */
+typedef struct {
+  uint64_t data[32];
+} cublasLtMatmulDescOpaque_t;
+
+/** Opaque descriptor for cublasLtMatmul() operation details
+ */
+typedef cublasLtMatmulDescOpaque_t* cublasLtMatmulDesc_t;
+
+/** Semi-opaque descriptor for cublasLtMatrixTransform() operation details
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatrixTransformDescOpaque_t;
+
+/** Opaque descriptor for cublasLtMatrixTransform() operation details
+ */
+typedef cublasLtMatrixTransformDescOpaque_t* cublasLtMatrixTransformDesc_t;
+
+/** Semi-opaque descriptor for cublasLtMatmulPreference() operation details
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatmulPreferenceOpaque_t;
+
+/** Opaque descriptor for cublasLtMatmulAlgoGetHeuristic() configuration
+ */
+typedef cublasLtMatmulPreferenceOpaque_t* cublasLtMatmulPreference_t;
+
+/** Tile size (in C/D matrix Rows x Cols)
+ *
+ * General order of tile IDs is sorted by size first and by first dimension second.
+ */
+typedef enum {
+  CUBLASLT_MATMUL_TILE_UNDEFINED = 0,
+  CUBLASLT_MATMUL_TILE_8x8 = 1,
+  CUBLASLT_MATMUL_TILE_8x16 = 2,
+  CUBLASLT_MATMUL_TILE_16x8 = 3,
+  CUBLASLT_MATMUL_TILE_8x32 = 4,
+  CUBLASLT_MATMUL_TILE_16x16 = 5,
+  CUBLASLT_MATMUL_TILE_32x8 = 6,
+  CUBLASLT_MATMUL_TILE_8x64 = 7,
+  CUBLASLT_MATMUL_TILE_16x32 = 8,
+  CUBLASLT_MATMUL_TILE_32x16 = 9,
+  CUBLASLT_MATMUL_TILE_64x8 = 10,
+  CUBLASLT_MATMUL_TILE_32x32 = 11,
+  CUBLASLT_MATMUL_TILE_32x64 = 12,
+  CUBLASLT_MATMUL_TILE_64x32 = 13,
+  CUBLASLT_MATMUL_TILE_32x128 = 14,
+  CUBLASLT_MATMUL_TILE_64x64 = 15,
+  CUBLASLT_MATMUL_TILE_128x32 = 16,
+  CUBLASLT_MATMUL_TILE_64x128 = 17,
+  CUBLASLT_MATMUL_TILE_128x64 = 18,
+  CUBLASLT_MATMUL_TILE_64x256 = 19,
+  CUBLASLT_MATMUL_TILE_128x128 = 20,
+  CUBLASLT_MATMUL_TILE_256x64 = 21,
+  CUBLASLT_MATMUL_TILE_64x512 = 22,
+  CUBLASLT_MATMUL_TILE_128x256 = 23,
+  CUBLASLT_MATMUL_TILE_256x128 = 24,
+  CUBLASLT_MATMUL_TILE_512x64 = 25,
+  CUBLASLT_MATMUL_TILE_64x96 = 26,
+  CUBLASLT_MATMUL_TILE_96x64 = 27,
+  CUBLASLT_MATMUL_TILE_96x128 = 28,
+  CUBLASLT_MATMUL_TILE_128x160 = 29,
+  CUBLASLT_MATMUL_TILE_160x128 = 30,
+  CUBLASLT_MATMUL_TILE_192x128 = 31,
+  CUBLASLT_MATMUL_TILE_128x192 = 32,
+  CUBLASLT_MATMUL_TILE_128x96 = 33,
+  CUBLASLT_MATMUL_TILE_32x256 = 34,
+  CUBLASLT_MATMUL_TILE_256x32 = 35,
+  CUBLASLT_MATMUL_TILE_8x128 = 36,
+  CUBLASLT_MATMUL_TILE_8x192 = 37,
+  CUBLASLT_MATMUL_TILE_8x256 = 38,
+  CUBLASLT_MATMUL_TILE_8x320 = 39,
+  CUBLASLT_MATMUL_TILE_8x384 = 40,
+  CUBLASLT_MATMUL_TILE_8x448 = 41,
+  CUBLASLT_MATMUL_TILE_8x512 = 42,
+  CUBLASLT_MATMUL_TILE_8x576 = 43,
+  CUBLASLT_MATMUL_TILE_8x640 = 44,
+  CUBLASLT_MATMUL_TILE_8x704 = 45,
+  CUBLASLT_MATMUL_TILE_8x768 = 46,
+  CUBLASLT_MATMUL_TILE_16x64 = 47,
+  CUBLASLT_MATMUL_TILE_16x128 = 48,
+  CUBLASLT_MATMUL_TILE_16x192 = 49,
+  CUBLASLT_MATMUL_TILE_16x256 = 50,
+  CUBLASLT_MATMUL_TILE_16x320 = 51,
+  CUBLASLT_MATMUL_TILE_16x384 = 52,
+  CUBLASLT_MATMUL_TILE_16x448 = 53,
+  CUBLASLT_MATMUL_TILE_16x512 = 54,
+  CUBLASLT_MATMUL_TILE_16x576 = 55,
+  CUBLASLT_MATMUL_TILE_16x640 = 56,
+  CUBLASLT_MATMUL_TILE_16x704 = 57,
+  CUBLASLT_MATMUL_TILE_16x768 = 58,
+  CUBLASLT_MATMUL_TILE_24x64 = 59,
+  CUBLASLT_MATMUL_TILE_24x128 = 60,
+  CUBLASLT_MATMUL_TILE_24x192 = 61,
+  CUBLASLT_MATMUL_TILE_24x256 = 62,
+  CUBLASLT_MATMUL_TILE_24x320 = 63,
+  CUBLASLT_MATMUL_TILE_24x384 = 64,
+  CUBLASLT_MATMUL_TILE_24x448 = 65,
+  CUBLASLT_MATMUL_TILE_24x512 = 66,
+  CUBLASLT_MATMUL_TILE_24x576 = 67,
+  CUBLASLT_MATMUL_TILE_24x640 = 68,
+  CUBLASLT_MATMUL_TILE_24x704 = 69,
+  CUBLASLT_MATMUL_TILE_24x768 = 70,
+  CUBLASLT_MATMUL_TILE_32x192 = 71,
+  CUBLASLT_MATMUL_TILE_32x320 = 72,
+  CUBLASLT_MATMUL_TILE_32x384 = 73,
+  CUBLASLT_MATMUL_TILE_32x448 = 74,
+  CUBLASLT_MATMUL_TILE_32x512 = 75,
+  CUBLASLT_MATMUL_TILE_32x576 = 76,
+  CUBLASLT_MATMUL_TILE_32x640 = 77,
+  CUBLASLT_MATMUL_TILE_32x704 = 78,
+  CUBLASLT_MATMUL_TILE_32x768 = 79,
+  CUBLASLT_MATMUL_TILE_40x64 = 80,
+  CUBLASLT_MATMUL_TILE_40x128 = 81,
+  CUBLASLT_MATMUL_TILE_40x192 = 82,
+  CUBLASLT_MATMUL_TILE_40x256 = 83,
+  CUBLASLT_MATMUL_TILE_40x320 = 84,
+  CUBLASLT_MATMUL_TILE_40x384 = 85,
+  CUBLASLT_MATMUL_TILE_40x448 = 86,
+  CUBLASLT_MATMUL_TILE_40x512 = 87,
+  CUBLASLT_MATMUL_TILE_40x576 = 88,
+  CUBLASLT_MATMUL_TILE_40x640 = 89,
+  CUBLASLT_MATMUL_TILE_40x704 = 90,
+  CUBLASLT_MATMUL_TILE_40x768 = 91,
+  CUBLASLT_MATMUL_TILE_48x64 = 92,
+  CUBLASLT_MATMUL_TILE_48x128 = 93,
+  CUBLASLT_MATMUL_TILE_48x192 = 94,
+  CUBLASLT_MATMUL_TILE_48x256 = 95,
+  CUBLASLT_MATMUL_TILE_48x320 = 96,
+  CUBLASLT_MATMUL_TILE_48x384 = 97,
+  CUBLASLT_MATMUL_TILE_48x448 = 98,
+  CUBLASLT_MATMUL_TILE_48x512 = 99,
+  CUBLASLT_MATMUL_TILE_48x576 = 100,
+  CUBLASLT_MATMUL_TILE_48x640 = 101,
+  CUBLASLT_MATMUL_TILE_48x704 = 102,
+  CUBLASLT_MATMUL_TILE_48x768 = 103,
+  CUBLASLT_MATMUL_TILE_56x64 = 104,
+  CUBLASLT_MATMUL_TILE_56x128 = 105,
+  CUBLASLT_MATMUL_TILE_56x192 = 106,
+  CUBLASLT_MATMUL_TILE_56x256 = 107,
+  CUBLASLT_MATMUL_TILE_56x320 = 108,
+  CUBLASLT_MATMUL_TILE_56x384 = 109,
+  CUBLASLT_MATMUL_TILE_56x448 = 110,
+  CUBLASLT_MATMUL_TILE_56x512 = 111,
+  CUBLASLT_MATMUL_TILE_56x576 = 112,
+  CUBLASLT_MATMUL_TILE_56x640 = 113,
+  CUBLASLT_MATMUL_TILE_56x704 = 114,
+  CUBLASLT_MATMUL_TILE_56x768 = 115,
+  CUBLASLT_MATMUL_TILE_64x192 = 116,
+  CUBLASLT_MATMUL_TILE_64x320 = 117,
+  CUBLASLT_MATMUL_TILE_64x384 = 118,
+  CUBLASLT_MATMUL_TILE_64x448 = 119,
+  CUBLASLT_MATMUL_TILE_64x576 = 120,
+  CUBLASLT_MATMUL_TILE_64x640 = 121,
+  CUBLASLT_MATMUL_TILE_64x704 = 122,
+  CUBLASLT_MATMUL_TILE_64x768 = 123,
+  CUBLASLT_MATMUL_TILE_72x64 = 124,
+  CUBLASLT_MATMUL_TILE_72x128 = 125,
+  CUBLASLT_MATMUL_TILE_72x192 = 126,
+  CUBLASLT_MATMUL_TILE_72x256 = 127,
+  CUBLASLT_MATMUL_TILE_72x320 = 128,
+  CUBLASLT_MATMUL_TILE_72x384 = 129,
+  CUBLASLT_MATMUL_TILE_72x448 = 130,
+  CUBLASLT_MATMUL_TILE_72x512 = 131,
+  CUBLASLT_MATMUL_TILE_72x576 = 132,
+  CUBLASLT_MATMUL_TILE_72x640 = 133,
+  CUBLASLT_MATMUL_TILE_80x64 = 134,
+  CUBLASLT_MATMUL_TILE_80x128 = 135,
+  CUBLASLT_MATMUL_TILE_80x192 = 136,
+  CUBLASLT_MATMUL_TILE_80x256 = 137,
+  CUBLASLT_MATMUL_TILE_80x320 = 138,
+  CUBLASLT_MATMUL_TILE_80x384 = 139,
+  CUBLASLT_MATMUL_TILE_80x448 = 140,
+  CUBLASLT_MATMUL_TILE_80x512 = 141,
+  CUBLASLT_MATMUL_TILE_80x576 = 142,
+  CUBLASLT_MATMUL_TILE_88x64 = 143,
+  CUBLASLT_MATMUL_TILE_88x128 = 144,
+  CUBLASLT_MATMUL_TILE_88x192 = 145,
+  CUBLASLT_MATMUL_TILE_88x256 = 146,
+  CUBLASLT_MATMUL_TILE_88x320 = 147,
+  CUBLASLT_MATMUL_TILE_88x384 = 148,
+  CUBLASLT_MATMUL_TILE_88x448 = 149,
+  CUBLASLT_MATMUL_TILE_88x512 = 150,
+  CUBLASLT_MATMUL_TILE_96x192 = 151,
+  CUBLASLT_MATMUL_TILE_96x256 = 152,
+  CUBLASLT_MATMUL_TILE_96x320 = 153,
+  CUBLASLT_MATMUL_TILE_96x384 = 154,
+  CUBLASLT_MATMUL_TILE_96x448 = 155,
+  CUBLASLT_MATMUL_TILE_96x512 = 156,
+  CUBLASLT_MATMUL_TILE_104x64 = 157,
+  CUBLASLT_MATMUL_TILE_104x128 = 158,
+  CUBLASLT_MATMUL_TILE_104x192 = 159,
+  CUBLASLT_MATMUL_TILE_104x256 = 160,
+  CUBLASLT_MATMUL_TILE_104x320 = 161,
+  CUBLASLT_MATMUL_TILE_104x384 = 162,
+  CUBLASLT_MATMUL_TILE_104x448 = 163,
+  CUBLASLT_MATMUL_TILE_112x64 = 164,
+  CUBLASLT_MATMUL_TILE_112x128 = 165,
+  CUBLASLT_MATMUL_TILE_112x192 = 166,
+  CUBLASLT_MATMUL_TILE_112x256 = 167,
+  CUBLASLT_MATMUL_TILE_112x320 = 168,
+  CUBLASLT_MATMUL_TILE_112x384 = 169,
+  CUBLASLT_MATMUL_TILE_120x64 = 170,
+  CUBLASLT_MATMUL_TILE_120x128 = 171,
+  CUBLASLT_MATMUL_TILE_120x192 = 172,
+  CUBLASLT_MATMUL_TILE_120x256 = 173,
+  CUBLASLT_MATMUL_TILE_120x320 = 174,
+  CUBLASLT_MATMUL_TILE_120x384 = 175,
+  CUBLASLT_MATMUL_TILE_128x320 = 176,
+  CUBLASLT_MATMUL_TILE_128x384 = 177,
+  CUBLASLT_MATMUL_TILE_136x64 = 178,
+  CUBLASLT_MATMUL_TILE_136x128 = 179,
+  CUBLASLT_MATMUL_TILE_136x192 = 180,
+  CUBLASLT_MATMUL_TILE_136x256 = 181,
+  CUBLASLT_MATMUL_TILE_136x320 = 182,
+  CUBLASLT_MATMUL_TILE_144x64 = 183,
+  CUBLASLT_MATMUL_TILE_144x128 = 184,
+  CUBLASLT_MATMUL_TILE_144x192 = 185,
+  CUBLASLT_MATMUL_TILE_144x256 = 186,
+  CUBLASLT_MATMUL_TILE_144x320 = 187,
+  CUBLASLT_MATMUL_TILE_152x64 = 188,
+  CUBLASLT_MATMUL_TILE_152x128 = 189,
+  CUBLASLT_MATMUL_TILE_152x192 = 190,
+  CUBLASLT_MATMUL_TILE_152x256 = 191,
+  CUBLASLT_MATMUL_TILE_152x320 = 192,
+  CUBLASLT_MATMUL_TILE_160x64 = 193,
+  CUBLASLT_MATMUL_TILE_160x192 = 194,
+  CUBLASLT_MATMUL_TILE_160x256 = 195,
+  CUBLASLT_MATMUL_TILE_168x64 = 196,
+  CUBLASLT_MATMUL_TILE_168x128 = 197,
+  CUBLASLT_MATMUL_TILE_168x192 = 198,
+  CUBLASLT_MATMUL_TILE_168x256 = 199,
+  CUBLASLT_MATMUL_TILE_176x64 = 200,
+  CUBLASLT_MATMUL_TILE_176x128 = 201,
+  CUBLASLT_MATMUL_TILE_176x192 = 202,
+  CUBLASLT_MATMUL_TILE_176x256 = 203,
+  CUBLASLT_MATMUL_TILE_184x64 = 204,
+  CUBLASLT_MATMUL_TILE_184x128 = 205,
+  CUBLASLT_MATMUL_TILE_184x192 = 206,
+  CUBLASLT_MATMUL_TILE_184x256 = 207,
+  CUBLASLT_MATMUL_TILE_192x64 = 208,
+  CUBLASLT_MATMUL_TILE_192x192 = 209,
+  CUBLASLT_MATMUL_TILE_192x256 = 210,
+  CUBLASLT_MATMUL_TILE_200x64 = 211,
+  CUBLASLT_MATMUL_TILE_200x128 = 212,
+  CUBLASLT_MATMUL_TILE_200x192 = 213,
+  CUBLASLT_MATMUL_TILE_208x64 = 214,
+  CUBLASLT_MATMUL_TILE_208x128 = 215,
+  CUBLASLT_MATMUL_TILE_208x192 = 216,
+  CUBLASLT_MATMUL_TILE_216x64 = 217,
+  CUBLASLT_MATMUL_TILE_216x128 = 218,
+  CUBLASLT_MATMUL_TILE_216x192 = 219,
+  CUBLASLT_MATMUL_TILE_224x64 = 220,
+  CUBLASLT_MATMUL_TILE_224x128 = 221,
+  CUBLASLT_MATMUL_TILE_224x192 = 222,
+  CUBLASLT_MATMUL_TILE_232x64 = 223,
+  CUBLASLT_MATMUL_TILE_232x128 = 224,
+  CUBLASLT_MATMUL_TILE_232x192 = 225,
+  CUBLASLT_MATMUL_TILE_240x64 = 226,
+  CUBLASLT_MATMUL_TILE_240x128 = 227,
+  CUBLASLT_MATMUL_TILE_240x192 = 228,
+  CUBLASLT_MATMUL_TILE_248x64 = 229,
+  CUBLASLT_MATMUL_TILE_248x128 = 230,
+  CUBLASLT_MATMUL_TILE_248x192 = 231,
+  CUBLASLT_MATMUL_TILE_256x192 = 232,
+  CUBLASLT_MATMUL_TILE_264x64 = 233,
+  CUBLASLT_MATMUL_TILE_264x128 = 234,
+  CUBLASLT_MATMUL_TILE_272x64 = 235,
+  CUBLASLT_MATMUL_TILE_272x128 = 236,
+  CUBLASLT_MATMUL_TILE_280x64 = 237,
+  CUBLASLT_MATMUL_TILE_280x128 = 238,
+  CUBLASLT_MATMUL_TILE_288x64 = 239,
+  CUBLASLT_MATMUL_TILE_288x128 = 240,
+  CUBLASLT_MATMUL_TILE_296x64 = 241,
+  CUBLASLT_MATMUL_TILE_296x128 = 242,
+  CUBLASLT_MATMUL_TILE_304x64 = 243,
+  CUBLASLT_MATMUL_TILE_304x128 = 244,
+  CUBLASLT_MATMUL_TILE_312x64 = 245,
+  CUBLASLT_MATMUL_TILE_312x128 = 246,
+  CUBLASLT_MATMUL_TILE_320x64 = 247,
+  CUBLASLT_MATMUL_TILE_320x128 = 248,
+  CUBLASLT_MATMUL_TILE_328x64 = 249,
+  CUBLASLT_MATMUL_TILE_328x128 = 250,
+  CUBLASLT_MATMUL_TILE_336x64 = 251,
+  CUBLASLT_MATMUL_TILE_336x128 = 252,
+  CUBLASLT_MATMUL_TILE_344x64 = 253,
+  CUBLASLT_MATMUL_TILE_344x128 = 254,
+  CUBLASLT_MATMUL_TILE_352x64 = 255,
+  CUBLASLT_MATMUL_TILE_352x128 = 256,
+  CUBLASLT_MATMUL_TILE_360x64 = 257,
+  CUBLASLT_MATMUL_TILE_360x128 = 258,
+  CUBLASLT_MATMUL_TILE_368x64 = 259,
+  CUBLASLT_MATMUL_TILE_368x128 = 260,
+  CUBLASLT_MATMUL_TILE_376x64 = 261,
+  CUBLASLT_MATMUL_TILE_376x128 = 262,
+  CUBLASLT_MATMUL_TILE_384x64 = 263,
+  CUBLASLT_MATMUL_TILE_384x128 = 264,
+  CUBLASLT_MATMUL_TILE_392x64 = 265,
+  CUBLASLT_MATMUL_TILE_400x64 = 266,
+  CUBLASLT_MATMUL_TILE_408x64 = 267,
+  CUBLASLT_MATMUL_TILE_416x64 = 268,
+  CUBLASLT_MATMUL_TILE_424x64 = 269,
+  CUBLASLT_MATMUL_TILE_432x64 = 270,
+  CUBLASLT_MATMUL_TILE_440x64 = 271,
+  CUBLASLT_MATMUL_TILE_448x64 = 272,
+  CUBLASLT_MATMUL_TILE_456x64 = 273,
+  CUBLASLT_MATMUL_TILE_464x64 = 274,
+  CUBLASLT_MATMUL_TILE_472x64 = 275,
+  CUBLASLT_MATMUL_TILE_480x64 = 276,
+  CUBLASLT_MATMUL_TILE_488x64 = 277,
+  CUBLASLT_MATMUL_TILE_496x64 = 278,
+  CUBLASLT_MATMUL_TILE_504x64 = 279,
+  CUBLASLT_MATMUL_TILE_520x64 = 280,
+  CUBLASLT_MATMUL_TILE_528x64 = 281,
+  CUBLASLT_MATMUL_TILE_536x64 = 282,
+  CUBLASLT_MATMUL_TILE_544x64 = 283,
+  CUBLASLT_MATMUL_TILE_552x64 = 284,
+  CUBLASLT_MATMUL_TILE_560x64 = 285,
+  CUBLASLT_MATMUL_TILE_568x64 = 286,
+  CUBLASLT_MATMUL_TILE_576x64 = 287,
+  CUBLASLT_MATMUL_TILE_584x64 = 288,
+  CUBLASLT_MATMUL_TILE_592x64 = 289,
+  CUBLASLT_MATMUL_TILE_600x64 = 290,
+  CUBLASLT_MATMUL_TILE_608x64 = 291,
+  CUBLASLT_MATMUL_TILE_616x64 = 292,
+  CUBLASLT_MATMUL_TILE_624x64 = 293,
+  CUBLASLT_MATMUL_TILE_632x64 = 294,
+  CUBLASLT_MATMUL_TILE_640x64 = 295,
+  CUBLASLT_MATMUL_TILE_648x64 = 296,
+  CUBLASLT_MATMUL_TILE_656x64 = 297,
+  CUBLASLT_MATMUL_TILE_664x64 = 298,
+  CUBLASLT_MATMUL_TILE_672x64 = 299,
+  CUBLASLT_MATMUL_TILE_680x64 = 300,
+  CUBLASLT_MATMUL_TILE_688x64 = 301,
+  CUBLASLT_MATMUL_TILE_696x64 = 302,
+  CUBLASLT_MATMUL_TILE_704x64 = 303,
+  CUBLASLT_MATMUL_TILE_712x64 = 304,
+  CUBLASLT_MATMUL_TILE_720x64 = 305,
+  CUBLASLT_MATMUL_TILE_728x64 = 306,
+  CUBLASLT_MATMUL_TILE_736x64 = 307,
+  CUBLASLT_MATMUL_TILE_744x64 = 308,
+  CUBLASLT_MATMUL_TILE_752x64 = 309,
+  CUBLASLT_MATMUL_TILE_760x64 = 310,
+  CUBLASLT_MATMUL_TILE_768x64 = 311,
+  CUBLASLT_MATMUL_TILE_64x16 = 312,
+  CUBLASLT_MATMUL_TILE_64x24 = 313,
+  CUBLASLT_MATMUL_TILE_64x40 = 314,
+  CUBLASLT_MATMUL_TILE_64x48 = 315,
+  CUBLASLT_MATMUL_TILE_64x56 = 316,
+  CUBLASLT_MATMUL_TILE_64x72 = 317,
+  CUBLASLT_MATMUL_TILE_64x80 = 318,
+  CUBLASLT_MATMUL_TILE_64x88 = 319,
+  CUBLASLT_MATMUL_TILE_64x104 = 320,
+  CUBLASLT_MATMUL_TILE_64x112 = 321,
+  CUBLASLT_MATMUL_TILE_64x120 = 322,
+  CUBLASLT_MATMUL_TILE_64x136 = 323,
+  CUBLASLT_MATMUL_TILE_64x144 = 324,
+  CUBLASLT_MATMUL_TILE_64x152 = 325,
+  CUBLASLT_MATMUL_TILE_64x160 = 326,
+  CUBLASLT_MATMUL_TILE_64x168 = 327,
+  CUBLASLT_MATMUL_TILE_64x176 = 328,
+  CUBLASLT_MATMUL_TILE_64x184 = 329,
+  CUBLASLT_MATMUL_TILE_64x200 = 330,
+  CUBLASLT_MATMUL_TILE_64x208 = 331,
+  CUBLASLT_MATMUL_TILE_64x216 = 332,
+  CUBLASLT_MATMUL_TILE_64x224 = 333,
+  CUBLASLT_MATMUL_TILE_64x232 = 334,
+  CUBLASLT_MATMUL_TILE_64x240 = 335,
+  CUBLASLT_MATMUL_TILE_64x248 = 336,
+  CUBLASLT_MATMUL_TILE_64x264 = 337,
+  CUBLASLT_MATMUL_TILE_64x272 = 338,
+  CUBLASLT_MATMUL_TILE_64x280 = 339,
+  CUBLASLT_MATMUL_TILE_64x288 = 340,
+  CUBLASLT_MATMUL_TILE_64x296 = 341,
+  CUBLASLT_MATMUL_TILE_64x304 = 342,
+  CUBLASLT_MATMUL_TILE_64x312 = 343,
+  CUBLASLT_MATMUL_TILE_64x328 = 344,
+  CUBLASLT_MATMUL_TILE_64x336 = 345,
+  CUBLASLT_MATMUL_TILE_64x344 = 346,
+  CUBLASLT_MATMUL_TILE_64x352 = 347,
+  CUBLASLT_MATMUL_TILE_64x360 = 348,
+  CUBLASLT_MATMUL_TILE_64x368 = 349,
+  CUBLASLT_MATMUL_TILE_64x376 = 350,
+  CUBLASLT_MATMUL_TILE_64x392 = 351,
+  CUBLASLT_MATMUL_TILE_64x400 = 352,
+  CUBLASLT_MATMUL_TILE_64x408 = 353,
+  CUBLASLT_MATMUL_TILE_64x416 = 354,
+  CUBLASLT_MATMUL_TILE_64x424 = 355,
+  CUBLASLT_MATMUL_TILE_64x432 = 356,
+  CUBLASLT_MATMUL_TILE_64x440 = 357,
+  CUBLASLT_MATMUL_TILE_64x456 = 358,
+  CUBLASLT_MATMUL_TILE_64x464 = 359,
+  CUBLASLT_MATMUL_TILE_64x472 = 360,
+  CUBLASLT_MATMUL_TILE_64x480 = 361,
+  CUBLASLT_MATMUL_TILE_64x488 = 362,
+  CUBLASLT_MATMUL_TILE_64x496 = 363,
+  CUBLASLT_MATMUL_TILE_64x504 = 364,
+  CUBLASLT_MATMUL_TILE_64x520 = 365,
+  CUBLASLT_MATMUL_TILE_64x528 = 366,
+  CUBLASLT_MATMUL_TILE_64x536 = 367,
+  CUBLASLT_MATMUL_TILE_64x544 = 368,
+  CUBLASLT_MATMUL_TILE_64x552 = 369,
+  CUBLASLT_MATMUL_TILE_64x560 = 370,
+  CUBLASLT_MATMUL_TILE_64x568 = 371,
+  CUBLASLT_MATMUL_TILE_64x584 = 372,
+  CUBLASLT_MATMUL_TILE_64x592 = 373,
+  CUBLASLT_MATMUL_TILE_64x600 = 374,
+  CUBLASLT_MATMUL_TILE_64x608 = 375,
+  CUBLASLT_MATMUL_TILE_64x616 = 376,
+  CUBLASLT_MATMUL_TILE_64x624 = 377,
+  CUBLASLT_MATMUL_TILE_64x632 = 378,
+  CUBLASLT_MATMUL_TILE_64x648 = 379,
+  CUBLASLT_MATMUL_TILE_64x656 = 380,
+  CUBLASLT_MATMUL_TILE_64x664 = 381,
+  CUBLASLT_MATMUL_TILE_64x672 = 382,
+  CUBLASLT_MATMUL_TILE_64x680 = 383,
+  CUBLASLT_MATMUL_TILE_64x688 = 384,
+  CUBLASLT_MATMUL_TILE_64x696 = 385,
+  CUBLASLT_MATMUL_TILE_64x712 = 386,
+  CUBLASLT_MATMUL_TILE_64x720 = 387,
+  CUBLASLT_MATMUL_TILE_64x728 = 388,
+  CUBLASLT_MATMUL_TILE_64x736 = 389,
+  CUBLASLT_MATMUL_TILE_64x744 = 390,
+  CUBLASLT_MATMUL_TILE_64x752 = 391,
+  CUBLASLT_MATMUL_TILE_64x760 = 392,
+  CUBLASLT_MATMUL_TILE_128x8 = 393,
+  CUBLASLT_MATMUL_TILE_128x16 = 394,
+  CUBLASLT_MATMUL_TILE_128x24 = 395,
+  CUBLASLT_MATMUL_TILE_128x40 = 396,
+  CUBLASLT_MATMUL_TILE_128x48 = 397,
+  CUBLASLT_MATMUL_TILE_128x56 = 398,
+  CUBLASLT_MATMUL_TILE_128x72 = 399,
+  CUBLASLT_MATMUL_TILE_128x80 = 400,
+  CUBLASLT_MATMUL_TILE_128x88 = 401,
+  CUBLASLT_MATMUL_TILE_128x104 = 402,
+  CUBLASLT_MATMUL_TILE_128x112 = 403,
+  CUBLASLT_MATMUL_TILE_128x120 = 404,
+  CUBLASLT_MATMUL_TILE_128x136 = 405,
+  CUBLASLT_MATMUL_TILE_128x144 = 406,
+  CUBLASLT_MATMUL_TILE_128x152 = 407,
+  CUBLASLT_MATMUL_TILE_128x168 = 408,
+  CUBLASLT_MATMUL_TILE_128x176 = 409,
+  CUBLASLT_MATMUL_TILE_128x184 = 410,
+  CUBLASLT_MATMUL_TILE_128x200 = 411,
+  CUBLASLT_MATMUL_TILE_128x208 = 412,
+  CUBLASLT_MATMUL_TILE_128x216 = 413,
+  CUBLASLT_MATMUL_TILE_128x224 = 414,
+  CUBLASLT_MATMUL_TILE_128x232 = 415,
+  CUBLASLT_MATMUL_TILE_128x240 = 416,
+  CUBLASLT_MATMUL_TILE_128x248 = 417,
+  CUBLASLT_MATMUL_TILE_128x264 = 418,
+  CUBLASLT_MATMUL_TILE_128x272 = 419,
+  CUBLASLT_MATMUL_TILE_128x280 = 420,
+  CUBLASLT_MATMUL_TILE_128x288 = 421,
+  CUBLASLT_MATMUL_TILE_128x296 = 422,
+  CUBLASLT_MATMUL_TILE_128x304 = 423,
+  CUBLASLT_MATMUL_TILE_128x312 = 424,
+  CUBLASLT_MATMUL_TILE_128x328 = 425,
+  CUBLASLT_MATMUL_TILE_128x336 = 426,
+  CUBLASLT_MATMUL_TILE_128x344 = 427,
+  CUBLASLT_MATMUL_TILE_128x352 = 428,
+  CUBLASLT_MATMUL_TILE_128x360 = 429,
+  CUBLASLT_MATMUL_TILE_128x368 = 430,
+  CUBLASLT_MATMUL_TILE_128x376 = 431,
+  CUBLASLT_MATMUL_TILE_128x392 = 432,
+  CUBLASLT_MATMUL_TILE_128x400 = 433,
+  CUBLASLT_MATMUL_TILE_128x408 = 434,
+  CUBLASLT_MATMUL_TILE_128x416 = 435,
+  CUBLASLT_MATMUL_TILE_128x424 = 436,
+  CUBLASLT_MATMUL_TILE_128x432 = 437,
+  CUBLASLT_MATMUL_TILE_128x440 = 438,
+  CUBLASLT_MATMUL_TILE_128x448 = 439,
+  CUBLASLT_MATMUL_TILE_128x456 = 440,
+  CUBLASLT_MATMUL_TILE_128x464 = 441,
+  CUBLASLT_MATMUL_TILE_128x472 = 442,
+  CUBLASLT_MATMUL_TILE_128x480 = 443,
+  CUBLASLT_MATMUL_TILE_128x488 = 444,
+  CUBLASLT_MATMUL_TILE_128x496 = 445,
+  CUBLASLT_MATMUL_TILE_128x504 = 446,
+  CUBLASLT_MATMUL_TILE_128x512 = 447,
+  CUBLASLT_MATMUL_TILE_192x8 = 448,
+  CUBLASLT_MATMUL_TILE_192x16 = 449,
+  CUBLASLT_MATMUL_TILE_192x24 = 450,
+  CUBLASLT_MATMUL_TILE_192x32 = 451,
+  CUBLASLT_MATMUL_TILE_192x40 = 452,
+  CUBLASLT_MATMUL_TILE_192x48 = 453,
+  CUBLASLT_MATMUL_TILE_192x56 = 454,
+  CUBLASLT_MATMUL_TILE_192x72 = 455,
+  CUBLASLT_MATMUL_TILE_192x80 = 456,
+  CUBLASLT_MATMUL_TILE_192x88 = 457,
+  CUBLASLT_MATMUL_TILE_192x96 = 458,
+  CUBLASLT_MATMUL_TILE_192x104 = 459,
+  CUBLASLT_MATMUL_TILE_192x112 = 460,
+  CUBLASLT_MATMUL_TILE_192x120 = 461,
+  CUBLASLT_MATMUL_TILE_192x136 = 462,
+  CUBLASLT_MATMUL_TILE_192x144 = 463,
+  CUBLASLT_MATMUL_TILE_192x152 = 464,
+  CUBLASLT_MATMUL_TILE_192x160 = 465,
+  CUBLASLT_MATMUL_TILE_192x168 = 466,
+  CUBLASLT_MATMUL_TILE_192x176 = 467,
+  CUBLASLT_MATMUL_TILE_192x184 = 468,
+  CUBLASLT_MATMUL_TILE_192x200 = 469,
+  CUBLASLT_MATMUL_TILE_192x208 = 470,
+  CUBLASLT_MATMUL_TILE_192x216 = 471,
+  CUBLASLT_MATMUL_TILE_192x224 = 472,
+  CUBLASLT_MATMUL_TILE_192x232 = 473,
+  CUBLASLT_MATMUL_TILE_192x240 = 474,
+  CUBLASLT_MATMUL_TILE_192x248 = 475,
+  CUBLASLT_MATMUL_TILE_192x264 = 476,
+  CUBLASLT_MATMUL_TILE_192x272 = 477,
+  CUBLASLT_MATMUL_TILE_192x280 = 478,
+  CUBLASLT_MATMUL_TILE_192x288 = 479,
+  CUBLASLT_MATMUL_TILE_192x296 = 480,
+  CUBLASLT_MATMUL_TILE_192x304 = 481,
+  CUBLASLT_MATMUL_TILE_192x312 = 482,
+  CUBLASLT_MATMUL_TILE_192x320 = 483,
+  CUBLASLT_MATMUL_TILE_192x328 = 484,
+  CUBLASLT_MATMUL_TILE_192x336 = 485,
+  CUBLASLT_MATMUL_TILE_256x8 = 486,
+  CUBLASLT_MATMUL_TILE_256x16 = 487,
+  CUBLASLT_MATMUL_TILE_256x24 = 488,
+  CUBLASLT_MATMUL_TILE_256x40 = 489,
+  CUBLASLT_MATMUL_TILE_256x48 = 490,
+  CUBLASLT_MATMUL_TILE_256x56 = 491,
+  CUBLASLT_MATMUL_TILE_256x72 = 492,
+  CUBLASLT_MATMUL_TILE_256x80 = 493,
+  CUBLASLT_MATMUL_TILE_256x88 = 494,
+  CUBLASLT_MATMUL_TILE_256x96 = 495,
+  CUBLASLT_MATMUL_TILE_256x104 = 496,
+  CUBLASLT_MATMUL_TILE_256x112 = 497,
+  CUBLASLT_MATMUL_TILE_256x120 = 498,
+  CUBLASLT_MATMUL_TILE_256x136 = 499,
+  CUBLASLT_MATMUL_TILE_256x144 = 500,
+  CUBLASLT_MATMUL_TILE_256x152 = 501,
+  CUBLASLT_MATMUL_TILE_256x160 = 502,
+  CUBLASLT_MATMUL_TILE_256x168 = 503,
+  CUBLASLT_MATMUL_TILE_256x176 = 504,
+  CUBLASLT_MATMUL_TILE_256x184 = 505,
+  CUBLASLT_MATMUL_TILE_256x200 = 506,
+  CUBLASLT_MATMUL_TILE_256x208 = 507,
+  CUBLASLT_MATMUL_TILE_256x216 = 508,
+  CUBLASLT_MATMUL_TILE_256x224 = 509,
+  CUBLASLT_MATMUL_TILE_256x232 = 510,
+  CUBLASLT_MATMUL_TILE_256x240 = 511,
+  CUBLASLT_MATMUL_TILE_256x248 = 512,
+  CUBLASLT_MATMUL_TILE_256x256 = 513,
+  CUBLASLT_MATMUL_TILE_320x8 = 514,
+  CUBLASLT_MATMUL_TILE_320x16 = 515,
+  CUBLASLT_MATMUL_TILE_320x24 = 516,
+  CUBLASLT_MATMUL_TILE_320x32 = 517,
+  CUBLASLT_MATMUL_TILE_320x40 = 518,
+  CUBLASLT_MATMUL_TILE_320x48 = 519,
+  CUBLASLT_MATMUL_TILE_320x56 = 520,
+  CUBLASLT_MATMUL_TILE_320x72 = 521,
+  CUBLASLT_MATMUL_TILE_320x80 = 522,
+  CUBLASLT_MATMUL_TILE_320x88 = 523,
+  CUBLASLT_MATMUL_TILE_320x96 = 524,
+  CUBLASLT_MATMUL_TILE_320x104 = 525,
+  CUBLASLT_MATMUL_TILE_320x112 = 526,
+  CUBLASLT_MATMUL_TILE_320x120 = 527,
+  CUBLASLT_MATMUL_TILE_320x136 = 528,
+  CUBLASLT_MATMUL_TILE_320x144 = 529,
+  CUBLASLT_MATMUL_TILE_320x152 = 530,
+  CUBLASLT_MATMUL_TILE_320x160 = 531,
+  CUBLASLT_MATMUL_TILE_320x168 = 532,
+  CUBLASLT_MATMUL_TILE_320x176 = 533,
+  CUBLASLT_MATMUL_TILE_320x184 = 534,
+  CUBLASLT_MATMUL_TILE_320x192 = 535,
+  CUBLASLT_MATMUL_TILE_320x200 = 536,
+  CUBLASLT_MATMUL_TILE_384x8 = 537,
+  CUBLASLT_MATMUL_TILE_384x16 = 538,
+  CUBLASLT_MATMUL_TILE_384x24 = 539,
+  CUBLASLT_MATMUL_TILE_384x32 = 540,
+  CUBLASLT_MATMUL_TILE_384x40 = 541,
+  CUBLASLT_MATMUL_TILE_384x48 = 542,
+  CUBLASLT_MATMUL_TILE_384x56 = 543,
+  CUBLASLT_MATMUL_TILE_384x72 = 544,
+  CUBLASLT_MATMUL_TILE_384x80 = 545,
+  CUBLASLT_MATMUL_TILE_384x88 = 546,
+  CUBLASLT_MATMUL_TILE_384x96 = 547,
+  CUBLASLT_MATMUL_TILE_384x104 = 548,
+  CUBLASLT_MATMUL_TILE_384x112 = 549,
+  CUBLASLT_MATMUL_TILE_384x120 = 550,
+  CUBLASLT_MATMUL_TILE_384x136 = 551,
+  CUBLASLT_MATMUL_TILE_384x144 = 552,
+  CUBLASLT_MATMUL_TILE_384x152 = 553,
+  CUBLASLT_MATMUL_TILE_384x160 = 554,
+  CUBLASLT_MATMUL_TILE_384x168 = 555,
+  CUBLASLT_MATMUL_TILE_448x8 = 556,
+  CUBLASLT_MATMUL_TILE_448x16 = 557,
+  CUBLASLT_MATMUL_TILE_448x24 = 558,
+  CUBLASLT_MATMUL_TILE_448x32 = 559,
+  CUBLASLT_MATMUL_TILE_448x40 = 560,
+  CUBLASLT_MATMUL_TILE_448x48 = 561,
+  CUBLASLT_MATMUL_TILE_448x56 = 562,
+  CUBLASLT_MATMUL_TILE_448x72 = 563,
+  CUBLASLT_MATMUL_TILE_448x80 = 564,
+  CUBLASLT_MATMUL_TILE_448x88 = 565,
+  CUBLASLT_MATMUL_TILE_448x96 = 566,
+  CUBLASLT_MATMUL_TILE_448x104 = 567,
+  CUBLASLT_MATMUL_TILE_448x112 = 568,
+  CUBLASLT_MATMUL_TILE_448x120 = 569,
+  CUBLASLT_MATMUL_TILE_448x128 = 570,
+  CUBLASLT_MATMUL_TILE_448x136 = 571,
+  CUBLASLT_MATMUL_TILE_448x144 = 572,
+  CUBLASLT_MATMUL_TILE_512x8 = 573,
+  CUBLASLT_MATMUL_TILE_512x16 = 574,
+  CUBLASLT_MATMUL_TILE_512x24 = 575,
+  CUBLASLT_MATMUL_TILE_512x32 = 576,
+  CUBLASLT_MATMUL_TILE_512x40 = 577,
+  CUBLASLT_MATMUL_TILE_512x48 = 578,
+  CUBLASLT_MATMUL_TILE_512x56 = 579,
+  CUBLASLT_MATMUL_TILE_512x72 = 580,
+  CUBLASLT_MATMUL_TILE_512x80 = 581,
+  CUBLASLT_MATMUL_TILE_512x88 = 582,
+  CUBLASLT_MATMUL_TILE_512x96 = 583,
+  CUBLASLT_MATMUL_TILE_512x104 = 584,
+  CUBLASLT_MATMUL_TILE_512x112 = 585,
+  CUBLASLT_MATMUL_TILE_512x120 = 586,
+  CUBLASLT_MATMUL_TILE_512x128 = 587,
+  CUBLASLT_MATMUL_TILE_576x8 = 588,
+  CUBLASLT_MATMUL_TILE_576x16 = 589,
+  CUBLASLT_MATMUL_TILE_576x24 = 590,
+  CUBLASLT_MATMUL_TILE_576x32 = 591,
+  CUBLASLT_MATMUL_TILE_576x40 = 592,
+  CUBLASLT_MATMUL_TILE_576x48 = 593,
+  CUBLASLT_MATMUL_TILE_576x56 = 594,
+  CUBLASLT_MATMUL_TILE_576x72 = 595,
+  CUBLASLT_MATMUL_TILE_576x80 = 596,
+  CUBLASLT_MATMUL_TILE_576x88 = 597,
+  CUBLASLT_MATMUL_TILE_576x96 = 598,
+  CUBLASLT_MATMUL_TILE_576x104 = 599,
+  CUBLASLT_MATMUL_TILE_576x112 = 600,
+  CUBLASLT_MATMUL_TILE_640x8 = 601,
+  CUBLASLT_MATMUL_TILE_640x16 = 602,
+  CUBLASLT_MATMUL_TILE_640x24 = 603,
+  CUBLASLT_MATMUL_TILE_640x32 = 604,
+  CUBLASLT_MATMUL_TILE_640x40 = 605,
+  CUBLASLT_MATMUL_TILE_640x48 = 606,
+  CUBLASLT_MATMUL_TILE_640x56 = 607,
+  CUBLASLT_MATMUL_TILE_640x72 = 608,
+  CUBLASLT_MATMUL_TILE_640x80 = 609,
+  CUBLASLT_MATMUL_TILE_640x88 = 610,
+  CUBLASLT_MATMUL_TILE_640x96 = 611,
+  CUBLASLT_MATMUL_TILE_704x8 = 612,
+  CUBLASLT_MATMUL_TILE_704x16 = 613,
+  CUBLASLT_MATMUL_TILE_704x24 = 614,
+  CUBLASLT_MATMUL_TILE_704x32 = 615,
+  CUBLASLT_MATMUL_TILE_704x40 = 616,
+  CUBLASLT_MATMUL_TILE_704x48 = 617,
+  CUBLASLT_MATMUL_TILE_704x56 = 618,
+  CUBLASLT_MATMUL_TILE_704x72 = 619,
+  CUBLASLT_MATMUL_TILE_704x80 = 620,
+  CUBLASLT_MATMUL_TILE_704x88 = 621,
+  CUBLASLT_MATMUL_TILE_768x8 = 622,
+  CUBLASLT_MATMUL_TILE_768x16 = 623,
+  CUBLASLT_MATMUL_TILE_768x24 = 624,
+  CUBLASLT_MATMUL_TILE_768x32 = 625,
+  CUBLASLT_MATMUL_TILE_768x40 = 626,
+  CUBLASLT_MATMUL_TILE_768x48 = 627,
+  CUBLASLT_MATMUL_TILE_768x56 = 628,
+  CUBLASLT_MATMUL_TILE_768x72 = 629,
+  CUBLASLT_MATMUL_TILE_768x80 = 630,
+  CUBLASLT_MATMUL_TILE_256x512 = 631,
+  CUBLASLT_MATMUL_TILE_256x1024 = 632,
+  CUBLASLT_MATMUL_TILE_512x512 = 633,
+  CUBLASLT_MATMUL_TILE_512x1024 = 634,
+  CUBLASLT_MATMUL_TILE_END
+} cublasLtMatmulTile_t;
+
+/** Size and number of stages in which elements are read into shared memory
+ *
+ * General order of stages IDs is sorted by stage size first and by number of stages second.
+ */
+typedef enum {
+  CUBLASLT_MATMUL_STAGES_UNDEFINED = 0,
+  CUBLASLT_MATMUL_STAGES_16x1 = 1,
+  CUBLASLT_MATMUL_STAGES_16x2 = 2,
+  CUBLASLT_MATMUL_STAGES_16x3 = 3,
+  CUBLASLT_MATMUL_STAGES_16x4 = 4,
+  CUBLASLT_MATMUL_STAGES_16x5 = 5,
+  CUBLASLT_MATMUL_STAGES_16x6 = 6,
+  CUBLASLT_MATMUL_STAGES_32x1 = 7,
+  CUBLASLT_MATMUL_STAGES_32x2 = 8,
+  CUBLASLT_MATMUL_STAGES_32x3 = 9,
+  CUBLASLT_MATMUL_STAGES_32x4 = 10,
+  CUBLASLT_MATMUL_STAGES_32x5 = 11,
+  CUBLASLT_MATMUL_STAGES_32x6 = 12,
+  CUBLASLT_MATMUL_STAGES_64x1 = 13,
+  CUBLASLT_MATMUL_STAGES_64x2 = 14,
+  CUBLASLT_MATMUL_STAGES_64x3 = 15,
+  CUBLASLT_MATMUL_STAGES_64x4 = 16,
+  CUBLASLT_MATMUL_STAGES_64x5 = 17,
+  CUBLASLT_MATMUL_STAGES_64x6 = 18,
+  CUBLASLT_MATMUL_STAGES_128x1 = 19,
+  CUBLASLT_MATMUL_STAGES_128x2 = 20,
+  CUBLASLT_MATMUL_STAGES_128x3 = 21,
+  CUBLASLT_MATMUL_STAGES_128x4 = 22,
+  CUBLASLT_MATMUL_STAGES_128x5 = 23,
+  CUBLASLT_MATMUL_STAGES_128x6 = 24,
+  CUBLASLT_MATMUL_STAGES_32x10 = 25,
+  CUBLASLT_MATMUL_STAGES_8x4 = 26,
+  CUBLASLT_MATMUL_STAGES_16x10 = 27,
+  CUBLASLT_MATMUL_STAGES_8x5 = 28,
+  CUBLASLT_MATMUL_STAGES_8x3 = 31,
+  CUBLASLT_MATMUL_STAGES_8xAUTO = 32,
+  CUBLASLT_MATMUL_STAGES_16xAUTO = 33,
+  CUBLASLT_MATMUL_STAGES_32xAUTO = 34,
+  CUBLASLT_MATMUL_STAGES_64xAUTO = 35,
+  CUBLASLT_MATMUL_STAGES_128xAUTO = 36,
+  CUBLASLT_MATMUL_STAGES_256xAUTO = 37,
+  CUBLASLT_MATMUL_STAGES_END
+} cublasLtMatmulStages_t;
+
+/** Thread Block Cluster size
+ *
+ * Typically dimensioned similar to cublasLtMatmulTile_t, with the third coordinate unused at this time.
+ */
+typedef enum {
+  /** Let library pick cluster shape automatically */
+  CUBLASLT_CLUSTER_SHAPE_AUTO = 0,
+  CUBLASLT_CLUSTER_SHAPE_1x1x1 = 2,
+  CUBLASLT_CLUSTER_SHAPE_2x1x1 = 3,
+  CUBLASLT_CLUSTER_SHAPE_4x1x1 = 4,
+  CUBLASLT_CLUSTER_SHAPE_1x2x1 = 5,
+  CUBLASLT_CLUSTER_SHAPE_2x2x1 = 6,
+  CUBLASLT_CLUSTER_SHAPE_4x2x1 = 7,
+  CUBLASLT_CLUSTER_SHAPE_1x4x1 = 8,
+  CUBLASLT_CLUSTER_SHAPE_2x4x1 = 9,
+  CUBLASLT_CLUSTER_SHAPE_4x4x1 = 10,
+  CUBLASLT_CLUSTER_SHAPE_8x1x1 = 11,
+  CUBLASLT_CLUSTER_SHAPE_1x8x1 = 12,
+  CUBLASLT_CLUSTER_SHAPE_8x2x1 = 13,
+  CUBLASLT_CLUSTER_SHAPE_2x8x1 = 14,
+  CUBLASLT_CLUSTER_SHAPE_16x1x1 = 15,
+  CUBLASLT_CLUSTER_SHAPE_1x16x1 = 16,
+  CUBLASLT_CLUSTER_SHAPE_3x1x1 = 17,
+  CUBLASLT_CLUSTER_SHAPE_5x1x1 = 18,
+  CUBLASLT_CLUSTER_SHAPE_6x1x1 = 19,
+  CUBLASLT_CLUSTER_SHAPE_7x1x1 = 20,
+  CUBLASLT_CLUSTER_SHAPE_9x1x1 = 21,
+  CUBLASLT_CLUSTER_SHAPE_10x1x1 = 22,
+  CUBLASLT_CLUSTER_SHAPE_11x1x1 = 23,
+  CUBLASLT_CLUSTER_SHAPE_12x1x1 = 24,
+  CUBLASLT_CLUSTER_SHAPE_13x1x1 = 25,
+  CUBLASLT_CLUSTER_SHAPE_14x1x1 = 26,
+  CUBLASLT_CLUSTER_SHAPE_15x1x1 = 27,
+  CUBLASLT_CLUSTER_SHAPE_3x2x1 = 28,
+  CUBLASLT_CLUSTER_SHAPE_5x2x1 = 29,
+  CUBLASLT_CLUSTER_SHAPE_6x2x1 = 30,
+  CUBLASLT_CLUSTER_SHAPE_7x2x1 = 31,
+  CUBLASLT_CLUSTER_SHAPE_1x3x1 = 32,
+  CUBLASLT_CLUSTER_SHAPE_2x3x1 = 33,
+  CUBLASLT_CLUSTER_SHAPE_3x3x1 = 34,
+  CUBLASLT_CLUSTER_SHAPE_4x3x1 = 35,
+  CUBLASLT_CLUSTER_SHAPE_5x3x1 = 36,
+  CUBLASLT_CLUSTER_SHAPE_3x4x1 = 37,
+  CUBLASLT_CLUSTER_SHAPE_1x5x1 = 38,
+  CUBLASLT_CLUSTER_SHAPE_2x5x1 = 39,
+  CUBLASLT_CLUSTER_SHAPE_3x5x1 = 40,
+  CUBLASLT_CLUSTER_SHAPE_1x6x1 = 41,
+  CUBLASLT_CLUSTER_SHAPE_2x6x1 = 42,
+  CUBLASLT_CLUSTER_SHAPE_1x7x1 = 43,
+  CUBLASLT_CLUSTER_SHAPE_2x7x1 = 44,
+  CUBLASLT_CLUSTER_SHAPE_1x9x1 = 45,
+  CUBLASLT_CLUSTER_SHAPE_1x10x1 = 46,
+  CUBLASLT_CLUSTER_SHAPE_1x11x1 = 47,
+  CUBLASLT_CLUSTER_SHAPE_1x12x1 = 48,
+  CUBLASLT_CLUSTER_SHAPE_1x13x1 = 49,
+  CUBLASLT_CLUSTER_SHAPE_1x14x1 = 50,
+  CUBLASLT_CLUSTER_SHAPE_1x15x1 = 51,
+  CUBLASLT_CLUSTER_SHAPE_END
+} cublasLtClusterShape_t;
+
+/** Inner size of the kernel
+ *
+ * Represents various aspects of internal kernel design, that don't impact CUDA grid size but may have other more subtle
+ * effects.
+ *
+ */
+typedef enum {
+  CUBLASLT_MATMUL_INNER_SHAPE_UNDEFINED = 0,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA884 = 1,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA1684 = 2,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA1688 = 3,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA16816 = 4,
+  CUBLASLT_MATMUL_INNER_SHAPE_END
+} cublasLtMatmulInnerShape_t;
+
+/** Scaling mode for per-matrix scaling */
+typedef enum {
+  /** Scaling factors are single precision scalars applied to the whole tensor */
+  CUBLASLT_MATMUL_MATRIX_SCALE_SCALAR_32F = 0,
+  /** Scaling factors are tensors that contain a dedicated scaling factor stored as an 8-bit CUDA_R_8F_UE4M3 value for
+     each 16-element block in the innermost dimension of the corresponding data tensor */
+  CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3 = 1,
+  /** Same as above, except that scaling factor tensor elements have type CUDA_R_8F_UE8M0 and the block size is 32
+     elements*/
+  CUBLASLT_MATMUL_MATRIX_SCALE_VEC32_UE8M0 = 2,
+  CUBLASLT_MATMUL_MATRIX_SCALE_END
+} cublasLtMatmulMatrixScale_t;
+
+/** Pointer mode to use for alpha/beta */
+typedef enum {
+  /** matches CUBLAS_POINTER_MODE_HOST, pointer targets a single value host memory */
+  CUBLASLT_POINTER_MODE_HOST = CUBLAS_POINTER_MODE_HOST,
+  /** matches CUBLAS_POINTER_MODE_DEVICE, pointer targets a single value device memory */
+  CUBLASLT_POINTER_MODE_DEVICE = CUBLAS_POINTER_MODE_DEVICE,
+  /** pointer targets an array in device memory */
+  CUBLASLT_POINTER_MODE_DEVICE_VECTOR = 2,
+  /** alpha pointer targets an array in device memory, beta is zero. Note:
+     CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE is not supported, must be 0. */
+  CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO = 3,
+  /** alpha pointer targets an array in device memory, beta is a single value in host memory. */
+  CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST = 4,
+} cublasLtPointerMode_t;
+
+/** Mask to define pointer mode capability */
+typedef enum {
+  /** see CUBLASLT_POINTER_MODE_HOST */
+  CUBLASLT_POINTER_MODE_MASK_HOST = 1,
+  /** see CUBLASLT_POINTER_MODE_DEVICE */
+  CUBLASLT_POINTER_MODE_MASK_DEVICE = 2,
+  /** see CUBLASLT_POINTER_MODE_DEVICE_VECTOR */
+  CUBLASLT_POINTER_MODE_MASK_DEVICE_VECTOR = 4,
+  /** see CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO */
+  CUBLASLT_POINTER_MODE_MASK_ALPHA_DEVICE_VECTOR_BETA_ZERO = 8,
+  /** see CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST */
+  CUBLASLT_POINTER_MODE_MASK_ALPHA_DEVICE_VECTOR_BETA_HOST = 16,
+} cublasLtPointerModeMask_t;
+
+/** Implementation details that may affect numerical behavior of algorithms. */
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_FMA (0x01ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_HMMA (0x02ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_IMMA (0x04ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_DMMA (0x08ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_TENSOR_OP_MASK (0xfeull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_OP_TYPE_MASK (0xffull << 0)
+
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_16F (0x01ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_32F (0x02ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_64F (0x04ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_32I (0x08ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_TYPE_MASK (0xffull << 8)
+
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_16F (0x01ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_16BF (0x02ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_TF32 (0x04ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_32F (0x08ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_64F (0x10ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_8I (0x20ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_8F_E4M3 (0x40ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_8F_E5M2 (0x80ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_OP_INPUT_TYPE_MASK (0xffull << 16)
+
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_GAUSSIAN (0x01ull << 32)
+typedef uint64_t cublasLtNumericalImplFlags_t;
+
+/** Execute matrix multiplication (D = alpha * op(A) * op(B) + beta * C).
+ *
+ * \retval     CUBLAS_STATUS_NOT_INITIALIZED   if cuBLASLt handle has not been initialized
+ * \retval     CUBLAS_STATUS_INVALID_VALUE     if parameters are in conflict or in an impossible configuration; e.g.
+ *                                             when workspaceSizeInBytes is less than workspace required by configured
+ *                                             algo
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED     if current implementation on selected device doesn't support configured
+ *                                             operation
+ * \retval     CUBLAS_STATUS_ARCH_MISMATCH     if configured operation cannot be run using selected device
+ * \retval     CUBLAS_STATUS_EXECUTION_FAILED  if cuda reported execution error from the device
+ * \retval     CUBLAS_STATUS_SUCCESS           if the operation completed successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmul(cublasLtHandle_t lightHandle,
+                                           cublasLtMatmulDesc_t computeDesc,
+                                           const void* alpha, /* host or device pointer */
+                                           const void* A,
+                                           cublasLtMatrixLayout_t Adesc,
+                                           const void* B,
+                                           cublasLtMatrixLayout_t Bdesc,
+                                           const void* beta, /* host or device pointer */
+                                           const void* C,
+                                           cublasLtMatrixLayout_t Cdesc,
+                                           void* D,
+                                           cublasLtMatrixLayout_t Ddesc,
+                                           const cublasLtMatmulAlgo_t* algo,
+                                           void* workspace,
+                                           size_t workspaceSizeInBytes,
+                                           cudaStream_t stream);
+
+/** Matrix layout conversion helper (C = alpha * op(A) + beta * op(B))
+ *
+ * Can be used to change memory order of data or to scale and shift the values.
+ *
+ * \retval     CUBLAS_STATUS_NOT_INITIALIZED   if cuBLASLt handle has not been initialized
+ * \retval     CUBLAS_STATUS_INVALID_VALUE     if parameters are in conflict or in an impossible configuration; e.g.
+ *                                             when A is not NULL, but Adesc is NULL
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED     if current implementation on selected device doesn't support configured
+ *                                             operation
+ * \retval     CUBLAS_STATUS_ARCH_MISMATCH     if configured operation cannot be run using selected device
+ * \retval     CUBLAS_STATUS_EXECUTION_FAILED  if cuda reported execution error from the device
+ * \retval     CUBLAS_STATUS_SUCCESS           if the operation completed successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransform(cublasLtHandle_t lightHandle,
+                                                    cublasLtMatrixTransformDesc_t transformDesc,
+                                                    const void* alpha, /* host or device pointer */
+                                                    const void* A,
+                                                    cublasLtMatrixLayout_t Adesc,
+                                                    const void* beta, /* host or device pointer */
+                                                    const void* B,
+                                                    cublasLtMatrixLayout_t Bdesc,
+                                                    void* C,
+                                                    cublasLtMatrixLayout_t Cdesc,
+                                                    cudaStream_t stream);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Helper functions for cublasLtMatrixLayout_t */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Enum for data ordering */
+typedef enum {
+  /** Column-major
+   *
+   * Leading dimension is the stride (in elements) to the beginning of next column in memory.
+   */
+  CUBLASLT_ORDER_COL = 0,
+  /** Row major
+   *
+   * Leading dimension is the stride (in elements) to the beginning of next row in memory.
+   */
+  CUBLASLT_ORDER_ROW = 1,
+  /** Column-major ordered tiles of 32 columns.
+   *
+   * Leading dimension is the stride (in elements) to the beginning of next group of 32-columns. E.g. if matrix has 33
+   * columns and 2 rows, ld must be at least (32) * 2 = 64.
+   */
+  CUBLASLT_ORDER_COL32 = 2,
+  /** Column-major ordered tiles of composite tiles with total 32 columns and 8 rows, tile composed of interleaved
+   * inner tiles of 4 columns within 4 even or odd rows in an alternating pattern.
+   *
+   * Leading dimension is the stride (in elements) to the beginning of the first 32 column x 8 row tile for the next
+   * 32-wide group of columns. E.g. if matrix has 33 columns and 1 row, ld must be at least (32 * 8) * 1 = 256.
+   */
+  CUBLASLT_ORDER_COL4_4R2_8C = 3,
+  /** Column-major ordered tiles of composite tiles with total 32 columns ands 32 rows.
+   * Element offset within the tile is calculated as (((row%8)/2*4+row/8)*2+row%2)*32+col.
+   *
+   * Leading dimension is the stride (in elements) to the beginning of the first 32 column x 32 row tile for the next
+   * 32-wide group of columns. E.g. if matrix has 33 columns and 1 row, ld must be at least (32*32)*1 = 1024.
+   */
+  CUBLASLT_ORDER_COL32_2R_4R4 = 4,
+
+} cublasLtOrder_t;
+
+/** Attributes of memory layout */
+typedef enum {
+  /** Data type, see cudaDataType.
+   *
+   * uint32_t
+   */
+  CUBLASLT_MATRIX_LAYOUT_TYPE = 0,
+
+  /** Memory order of the data, see cublasLtOrder_t.
+   *
+   * int32_t, default: CUBLASLT_ORDER_COL
+   */
+  CUBLASLT_MATRIX_LAYOUT_ORDER = 1,
+
+  /** Number of rows.
+   *
+   * Usually only values that can be expressed as int32_t are supported.
+   *
+   * uint64_t
+   */
+  CUBLASLT_MATRIX_LAYOUT_ROWS = 2,
+
+  /** Number of columns.
+   *
+   * Usually only values that can be expressed as int32_t are supported.
+   *
+   * uint64_t
+   */
+  CUBLASLT_MATRIX_LAYOUT_COLS = 3,
+
+  /** Matrix leading dimension.
+   *
+   * For CUBLASLT_ORDER_COL this is stride (in elements) of matrix column, for more details and documentation for
+   * other memory orders see documentation for cublasLtOrder_t values.
+   *
+   * Currently only non-negative values are supported, must be large enough so that matrix memory locations are not
+   * overlapping (e.g. greater or equal to CUBLASLT_MATRIX_LAYOUT_ROWS in case of CUBLASLT_ORDER_COL).
+   *
+   * int64_t;
+   */
+  CUBLASLT_MATRIX_LAYOUT_LD = 4,
+
+  /** Number of matmul operations to perform in the batch.
+   *
+   * See also CUBLASLT_ALGO_CAP_STRIDED_BATCH_SUPPORT
+   *
+   * int32_t, default: 1
+   */
+  CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT = 5,
+
+  /** Stride (in elements) to the next matrix for strided batch operation.
+   *
+   * When matrix type is planar-complex (CUBLASLT_MATRIX_LAYOUT_PLANE_OFFSET != 0), batch stride
+   * is interpreted by cublasLtMatmul() in number of real valued sub-elements. E.g. for data of type CUDA_C_16F,
+   * offset of 1024B is encoded as a stride of value 512 (since each element of the real and imaginary matrices
+   * is a 2B (16bit) floating point type).
+   *
+   * NOTE: A bug in cublasLtMatrixTransform() causes it to interpret the batch stride for a planar-complex matrix
+   * as if it was specified in number of complex elements. Therefore an offset of 1024B must be encoded as stride
+   * value 256 when calling cublasLtMatrixTransform() (each complex element is 4B with real and imaginary values 2B
+   * each). This behavior is expected to be corrected in the next major cuBLAS version.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET = 6,
+
+  /** Stride (in bytes) to the imaginary plane for planar complex layout.
+   *
+   * int64_t, default: 0 - 0 means that layout is regular (real and imaginary parts of complex numbers are interleaved
+   * in memory in each element)
+   */
+  CUBLASLT_MATRIX_LAYOUT_PLANE_OFFSET = 7,
+} cublasLtMatrixLayoutAttribute_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutInit_internal(  //
+    cublasLtMatrixLayout_t matLayout,
+    size_t size,
+    cudaDataType type,
+    uint64_t rows,
+    uint64_t cols,
+    int64_t ld);
+
+/** Initialize matrix layout descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+static inline cublasStatus_t cublasLtMatrixLayoutInit(
+    cublasLtMatrixLayout_t matLayout, cudaDataType type, uint64_t rows, uint64_t cols, int64_t ld) {
+  return cublasLtMatrixLayoutInit_internal(matLayout, sizeof(*matLayout), type, rows, cols, ld);
+}
+
+/** Create new matrix layout descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutCreate(  //
+    cublasLtMatrixLayout_t* matLayout,
+    cudaDataType type,
+    uint64_t rows,
+    uint64_t cols,
+    int64_t ld);
+
+/** Destroy matrix layout descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutDestroy(cublasLtMatrixLayout_t matLayout);
+
+/** Set matrix layout descriptor attribute.
+ *
+ * \param[in]  matLayout    The descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutSetAttribute(  //
+    cublasLtMatrixLayout_t matLayout,
+    cublasLtMatrixLayoutAttribute_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matrix layout descriptor attribute.
+ *
+ * \param[in]  matLayout    The descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutGetAttribute(  //
+    cublasLtMatrixLayout_t matLayout,
+    cublasLtMatrixLayoutAttribute_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Helper functions for cublasLtMatmulDesc_t */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Matmul descriptor attributes to define details of the operation. */
+typedef enum {
+  /** Compute type, see cudaDataType. Defines data type used for multiply and accumulate operations and the
+   * accumulator during matrix multiplication.
+   *
+   * int32_t
+   */
+  CUBLASLT_MATMUL_DESC_COMPUTE_TYPE = 0,
+
+  /** Scale type, see cudaDataType. Defines data type of alpha and beta. Accumulator and value from matrix C are
+   * typically converted to scale type before final scaling. Value is then converted from scale type to type of matrix
+   * D before being stored in memory.
+   *
+   * int32_t, default: same as CUBLASLT_MATMUL_DESC_COMPUTE_TYPE
+   */
+  CUBLASLT_MATMUL_DESC_SCALE_TYPE = 1,
+
+  /** Pointer mode of alpha and beta, see cublasLtPointerMode_t. When CUBLASLT_POINTER_MODE_DEVICE_VECTOR is in use,
+   * alpha/beta vector lenghts must match number of output matrix rows.
+   *
+   * int32_t, default: CUBLASLT_POINTER_MODE_HOST
+   */
+  CUBLASLT_MATMUL_DESC_POINTER_MODE = 2,
+
+  /** Transform of matrix A, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATMUL_DESC_TRANSA = 3,
+
+  /** Transform of matrix B, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATMUL_DESC_TRANSB = 4,
+
+  /** Transform of matrix C, see cublasOperation_t.
+   *
+   * Currently only CUBLAS_OP_N is supported.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATMUL_DESC_TRANSC = 5,
+
+  /** Matrix fill mode, see cublasFillMode_t.
+   *
+   * int32_t, default: CUBLAS_FILL_MODE_FULL
+   */
+  CUBLASLT_MATMUL_DESC_FILL_MODE = 6,
+
+  /** Epilogue function, see cublasLtEpilogue_t.
+   *
+   * uint32_t, default: CUBLASLT_EPILOGUE_DEFAULT
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE = 7,
+
+  /** Bias or bias gradient vector pointer in the device memory.
+   *
+   * Bias case. See CUBLASLT_EPILOGUE_BIAS.
+   * For bias data type see CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE.
+   *
+   * Bias vector length must match matrix D rows count.
+   *
+   * Bias gradient case. See CUBLASLT_EPILOGUE_DRELU_BGRAD and CUBLASLT_EPILOGUE_DGELU_BGRAD.
+   * Bias gradient vector elements are the same type as the output elements
+   * (Ctype) with the exception of IMMA kernels (see above).
+   *
+   * Routines that don't dereference this pointer, like cublasLtMatmulAlgoGetHeuristic()
+   * depend on its value to determine expected pointer alignment.
+   *
+   * Bias case: const void *, default: NULL
+   * Bias gradient case: void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_BIAS_POINTER = 8,
+
+  /** Batch stride for bias or bias gradient vector.
+   *
+   * Used together with CUBLASLT_MATMUL_DESC_BIAS_POINTER when matrix D's CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT > 1.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_BIAS_BATCH_STRIDE = 10,
+
+  /** Pointer for epilogue auxiliary buffer.
+   *
+   * - Output vector for ReLu bit-mask in forward pass when CUBLASLT_EPILOGUE_RELU_AUX
+   *   or CUBLASLT_EPILOGUE_RELU_AUX_BIAS epilogue is used.
+   * - Input vector for ReLu bit-mask in backward pass when
+   *   CUBLASLT_EPILOGUE_DRELU_BGRAD epilogue is used.
+   *
+   * - Output of GELU input matrix in forward pass when
+   *   CUBLASLT_EPILOGUE_GELU_AUX_BIAS epilogue is used.
+   * - Input of GELU input matrix for backward pass when
+   *   CUBLASLT_EPILOGUE_DGELU_BGRAD epilogue is used.
+   *
+   * For aux data type see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE.
+   *
+   * Routines that don't dereference this pointer, like cublasLtMatmulAlgoGetHeuristic()
+   * depend on its value to determine expected pointer alignment.
+   *
+   * Requires setting CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD attribute.
+   *
+   * Forward pass: void *, default: NULL
+   * Backward pass: const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER = 11,
+
+  /** Leading dimension for epilogue auxiliary buffer.
+   *
+   * - ReLu bit-mask matrix leading dimension in elements (i.e. bits)
+   *   when CUBLASLT_EPILOGUE_RELU_AUX, CUBLASLT_EPILOGUE_RELU_AUX_BIAS or CUBLASLT_EPILOGUE_DRELU_BGRAD epilogue is
+   * used. Must be divisible by 128 and be no less than the number of rows in the output matrix.
+   *
+   * - GELU input matrix leading dimension in elements
+   *   when CUBLASLT_EPILOGUE_GELU_AUX_BIAS or CUBLASLT_EPILOGUE_DGELU_BGRAD epilogue used.
+   *   Must be divisible by 8 and be no less than the number of rows in the output matrix.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD = 12,
+
+  /** Batch stride for epilogue auxiliary buffer.
+   *
+   * - ReLu bit-mask matrix batch stride in elements (i.e. bits)
+   *   when CUBLASLT_EPILOGUE_RELU_AUX, CUBLASLT_EPILOGUE_RELU_AUX_BIAS or CUBLASLT_EPILOGUE_DRELU_BGRAD epilogue is
+   * used. Must be divisible by 128.
+   *
+   * - GELU input matrix batch stride in elements
+   *   when CUBLASLT_EPILOGUE_GELU_AUX_BIAS or CUBLASLT_EPILOGUE_DGELU_BGRAD epilogue used.
+   *   Must be divisible by 8.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_BATCH_STRIDE = 13,
+
+  /** Batch stride for alpha vector.
+   *
+   * Used together with CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST when matrix D's
+   * CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT > 1. If CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO is set then
+   * CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE must be set to 0 as this mode doesnt supported batched alpha vector.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE = 14,
+
+  /** Number of SMs to target for parallel execution. Optimizes heuristics for execution on a different number of SMs
+   *  when user expects a concurrent stream to be using some of the device resources.
+   *
+   *  int32_t, default: 0 - use the number reported by the device.
+   */
+  CUBLASLT_MATMUL_DESC_SM_COUNT_TARGET = 15,
+
+  /** Device pointer to the scale factor value that converts data in matrix A to the compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_A_SCALE_POINTER = 17,
+
+  /** Device pointer to the scale factor value to convert data in matrix B to compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_B_SCALE_POINTER = 18,
+
+  /** Device pointer to the scale factor value to convert data in matrix C to compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_C_SCALE_POINTER = 19,
+
+  /** Device pointer to the scale factor value to convert data in matrix D to compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_D_SCALE_POINTER = 20,
+
+  /** Device pointer to the memory location that on completion will be set to the maximum of absolute values in the
+   *  output matrix.
+   *
+   *  The computed value has the same type as the compute type.
+   *
+   *  If not specified or set to NULL, the maximum absolute value is not computed. If set for an unsupported matrix
+   *  data, scale, and compute type combination, calling cublasLtMatmul() will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_AMAX_D_POINTER = 21,
+
+  /** Type of the data to be stored to the memory pointed to by CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   *
+   *  If unset, the data type defaults to the type of elements of the output matrix with some exceptions, see details
+   * below.
+   *
+   *  ReLu uses a bit-mask.
+   *
+   *  GELU input matrix elements type is the same as the type of elements of
+   *  the output matrix with some exceptions, see details below.
+   *
+   *  For fp8 kernels with output type CUDA_R_8F_E4M3 the aux data type can be CUDA_R_8F_E4M3 or CUDA_R_16F with some
+   *  restrictions.  See https://docs.nvidia.com/cuda/cublas/index.html#cublasLtMatmulDescAttributes_t for more details.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  int32_t based on cudaDataType, default: -1
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE = 22,
+
+  /** Device pointer to the scaling factor value to convert results from compute type data range to storage
+   *  data range in the auxiliary matrix that is set via CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1. If set for an unsupported matrix data,
+   *  scale, and compute type combination, calling cublasLtMatmul() will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_SCALE_POINTER = 23,
+
+  /** Device pointer to the memory location that on completion will be set to the maximum of absolute values in the
+   *  buffer that is set via CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   *
+   *  The computed value has the same type as the compute type.
+   *
+   *  If not specified or set to NULL, the maximum absolute value is not computed. If set for an unsupported matrix
+   *  data, scale, and compute type combination, calling cublasLtMatmul() will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_AMAX_POINTER = 24,
+
+  /** Flag for managing fp8 fast accumulation mode.
+   *  When enabled, problem execution might be faster but at the cost of lower accuracy because intermediate results
+   *  will not periodically be promoted to a higher precision.
+   *
+   *  int8_t, default: 0 - fast accumulation mode is disabled.
+   */
+  CUBLASLT_MATMUL_DESC_FAST_ACCUM = 25,
+
+  /** Type of bias or bias gradient vector in the device memory.
+   *
+   * Bias case: see CUBLASLT_EPILOGUE_BIAS.
+   *
+   * Bias vector elements are the same type as the elements of output matrix (Dtype) with the following exceptions:
+   * - IMMA kernels with computeType=CUDA_R_32I and Ctype=CUDA_R_8I where the bias vector elements
+   *   are the same type as alpha, beta (CUBLASLT_MATMUL_DESC_SCALE_TYPE=CUDA_R_32F)
+   * - fp8 kernels with an output type of CUDA_R_32F, CUDA_R_8F_E4M3 or CUDA_R_8F_E5M2, See
+   *   https://docs.nvidia.com/cuda/cublas/index.html#cublasLtMatmul for details.
+   *
+   * int32_t based on cudaDataType, default: -1
+   */
+  CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE = 26,
+
+  /** EXPERIMENTAL, DEPRECATED: Number of atomic synchronization chunks in the row dimension of the output matrix D.
+   *
+   * int32_t, default 0 (atomic synchronization disabled)
+   */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_ROWS = 27,
+
+  /** EXPERIMENTAL, DEPRECATED: Number of atomic synchronization chunks in the column dimension of the output matrix D.
+   *
+   * int32_t, default 0 (atomic synchronization disabled)
+   */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_COLS = 28,
+
+  /** EXPERIMENTAL: Pointer to a device array of input atomic counters consumed by a matmul.
+   *
+   * int32_t *, default: NULL
+   * */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_IN_COUNTERS_POINTER = 29,
+
+  /** EXPERIMENTAL: Pointer to a device array of output atomic counters produced by a matmul.
+   *
+   * int32_t *, default: NULL
+   * */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_OUT_COUNTERS_POINTER = 30,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix A is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_A_SCALE_MODE = 31,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix B is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_B_SCALE_MODE = 32,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix C is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_C_SCALE_MODE = 33,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix D is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_D_SCALE_MODE = 34,
+
+  /** Scaling mode that defines how the matrix scaling factor for the auxiliary matrix is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_SCALE_MODE = 35,
+
+  /** Device pointer to the scale factors that are used to convert data in matrix D to the compute data type range.
+   *
+   *  The scaling factor value type is defined by the scaling mode (see CUBLASLT_MATMUL_DESC_D_OUT_SCALE_MODE)
+   *
+   *  If set for an unsupported matrix data, scale, scale mode, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_D_OUT_SCALE_POINTER = 36,
+
+  /** Scaling mode that defines how the output matrix scaling factor for matrix D is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_D_OUT_SCALE_MODE = 37,
+} cublasLtMatmulDescAttributes_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescInit_internal(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    size_t size,
+    cublasComputeType_t computeType,
+    cudaDataType_t scaleType);
+
+/** Initialize matmul operation descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was initialized successfully
+ */
+static inline cublasStatus_t cublasLtMatmulDescInit(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    cublasComputeType_t computeType,
+    cudaDataType_t scaleType) {
+  return cublasLtMatmulDescInit_internal(matmulDesc, sizeof(*matmulDesc), computeType, scaleType);
+}
+
+/** Create new matmul operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescCreate(cublasLtMatmulDesc_t* matmulDesc,
+                                                     cublasComputeType_t computeType,
+                                                     cudaDataType_t scaleType);
+
+/** Destroy matmul operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescDestroy(cublasLtMatmulDesc_t matmulDesc);
+
+/** Set matmul operation descriptor attribute.
+ *
+ * \param[in]  matmulDesc   The descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescSetAttribute(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    cublasLtMatmulDescAttributes_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matmul operation descriptor attribute.
+ *
+ * \param[in]  matmulDesc   The descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescGetAttribute(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    cublasLtMatmulDescAttributes_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Helper functions for cublasLtMatrixTransformDesc_t */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Matrix transform descriptor attributes to define details of the operation.
+ */
+typedef enum {
+  /** Scale type, see cudaDataType. Inputs are converted to scale type for scaling and summation and results are then
+   * converted to output type to store in memory.
+   *
+   * int32_t
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_SCALE_TYPE,
+
+  /** Pointer mode of alpha and beta, see cublasLtPointerMode_t.
+   *
+   * int32_t, default: CUBLASLT_POINTER_MODE_HOST
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_POINTER_MODE,
+
+  /** Transform of matrix A, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_TRANSA,
+
+  /** Transform of matrix B, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_TRANSB,
+} cublasLtMatrixTransformDescAttributes_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescInit_internal(cublasLtMatrixTransformDesc_t transformDesc,
+                                                                     size_t size,
+                                                                     cudaDataType scaleType);
+
+/** Initialize matrix transform operation descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+static inline cublasStatus_t cublasLtMatrixTransformDescInit(cublasLtMatrixTransformDesc_t transformDesc,
+                                                             cudaDataType scaleType) {
+  return cublasLtMatrixTransformDescInit_internal(transformDesc, sizeof(*transformDesc), scaleType);
+}
+
+/** Create new matrix transform operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescCreate(cublasLtMatrixTransformDesc_t* transformDesc,
+                                                              cudaDataType scaleType);
+
+/** Destroy matrix transform operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescDestroy(cublasLtMatrixTransformDesc_t transformDesc);
+
+/** Set matrix transform operation descriptor attribute.
+ *
+ * \param[in]  transformDesc  The descriptor
+ * \param[in]  attr           The attribute
+ * \param[in]  buf            memory address containing the new value
+ * \param[in]  sizeInBytes    size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescSetAttribute(  //
+    cublasLtMatrixTransformDesc_t transformDesc,
+    cublasLtMatrixTransformDescAttributes_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matrix transform operation descriptor attribute.
+ *
+ * \param[in]  transformDesc  The descriptor
+ * \param[in]  attr           The attribute
+ * \param[out] buf            memory address containing the new value
+ * \param[in]  sizeInBytes    size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten    only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number
+ * of bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescGetAttribute(  //
+    cublasLtMatrixTransformDesc_t transformDesc,
+    cublasLtMatrixTransformDescAttributes_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/** Reduction scheme for portions of the dot-product calculated in parallel (a. k. a. "split - K").
+ */
+typedef enum {
+  /** No reduction scheme, dot-product shall be performed in one sequence.
+   */
+  CUBLASLT_REDUCTION_SCHEME_NONE = 0,
+
+  /** Reduction is performed "in place" - using the output buffer (and output data type) and counters (in workspace) to
+   * guarantee the sequentiality.
+   */
+  CUBLASLT_REDUCTION_SCHEME_INPLACE = 1,
+
+  /** Intermediate results are stored in compute type in the workspace and reduced in a separate step.
+   */
+  CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE = 2,
+
+  /** Intermediate results are stored in output type in the workspace and reduced in a separate step.
+   */
+  CUBLASLT_REDUCTION_SCHEME_OUTPUT_TYPE = 4,
+
+  CUBLASLT_REDUCTION_SCHEME_MASK = 0x7,
+} cublasLtReductionScheme_t;
+
+/** Postprocessing options for the epilogue
+ */
+typedef enum {
+  /** No special postprocessing, just scale and quantize results if necessary.
+   */
+  CUBLASLT_EPILOGUE_DEFAULT = 1,
+
+  /** ReLu, apply ReLu point-wise transform to the results (x:=max(x, 0)).
+   */
+  CUBLASLT_EPILOGUE_RELU = 2,
+
+  /** ReLu, apply ReLu point-wise transform to the results (x:=max(x, 0)).
+   *
+   * This epilogue mode produces an extra output, a ReLu bit-mask matrix,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_RELU_AUX = (CUBLASLT_EPILOGUE_RELU | 128),
+
+  /** Bias, apply (broadcasted) Bias from bias vector. Bias vector length must match matrix D rows, it must be packed
+   * (stride between vector elements is 1). Bias vector is broadcasted to all columns and added before applying final
+   * postprocessing.
+   */
+  CUBLASLT_EPILOGUE_BIAS = 4,
+
+  /** ReLu and Bias, apply Bias and then ReLu transform
+   */
+  CUBLASLT_EPILOGUE_RELU_BIAS = (CUBLASLT_EPILOGUE_RELU | CUBLASLT_EPILOGUE_BIAS),
+
+  /** ReLu and Bias, apply Bias and then ReLu transform
+   *
+   * This epilogue mode produces an extra output, a ReLu bit-mask matrix,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_RELU_AUX_BIAS = (CUBLASLT_EPILOGUE_RELU_AUX | CUBLASLT_EPILOGUE_BIAS),
+
+  /* ReLu gradient. Apply ReLu gradient to matmul output. Store ReLu gradient in the output matrix.
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DRELU = 8 | 128,
+
+  /* ReLu and Bias gradients. Apply independently ReLu and Bias gradient to
+   * matmul output. Store ReLu gradient in the output matrix, and Bias gradient
+   * in the auxiliary output (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DRELU_BGRAD = CUBLASLT_EPILOGUE_DRELU | 16,
+
+  /** GELU, apply GELU point-wise transform to the results (x:=GELU(x)).
+   */
+  CUBLASLT_EPILOGUE_GELU = 32,
+
+  /** GELU, apply GELU point-wise transform to the results (x:=GELU(x)).
+   *
+   * This epilogue mode outputs GELU input as a separate matrix (useful for training).
+   * See CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_GELU_AUX = (CUBLASLT_EPILOGUE_GELU | 128),
+
+  /** GELU and Bias, apply Bias and then GELU transform
+   */
+  CUBLASLT_EPILOGUE_GELU_BIAS = (CUBLASLT_EPILOGUE_GELU | CUBLASLT_EPILOGUE_BIAS),
+
+  /** GELU and Bias, apply Bias and then GELU transform
+   *
+   * This epilogue mode outputs GELU input as a separate matrix (useful for training).
+   * See CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_GELU_AUX_BIAS = (CUBLASLT_EPILOGUE_GELU_AUX | CUBLASLT_EPILOGUE_BIAS),
+
+  /* GELU gradient. Apply GELU gradient to matmul output. Store GELU gradient in the output matrix.
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DGELU = 64 | 128,
+
+  /* GELU and Bias gradients. Apply independently GELU and Bias gradient to
+   * matmul output. Store GELU gradient in the output matrix, and Bias gradient
+   * in the auxiliary output (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DGELU_BGRAD = CUBLASLT_EPILOGUE_DGELU | 16,
+
+  /** Bias gradient based on the input matrix A.
+   *
+   * The bias size corresponds to the number of rows of the matrix D.
+   * The reduction happens over the GEMM's "k" dimension.
+   *
+   * Stores Bias gradient in the auxiliary output
+   * (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   */
+  CUBLASLT_EPILOGUE_BGRADA = 256,
+
+  /** Bias gradient based on the input matrix B.
+   *
+   * The bias size corresponds to the number of columns of the matrix D.
+   * The reduction happens over the GEMM's "k" dimension.
+   *
+   * Stores Bias gradient in the auxiliary output
+   * (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   */
+  CUBLASLT_EPILOGUE_BGRADB = 512,
+} cublasLtEpilogue_t;
+
+/** Matmul heuristic search mode
+ */
+typedef enum {
+  /** ask heuristics for best algo for given usecase
+   */
+  CUBLASLT_SEARCH_BEST_FIT = 0,
+  /** only try to find best config for preconfigured algo id
+   */
+  CUBLASLT_SEARCH_LIMITED_BY_ALGO_ID = 1,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_02 = 2,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_03 = 3,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_04 = 4,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_05 = 5,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_06 = 6,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_07 = 7,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_08 = 8,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_09 = 9,
+} cublasLtMatmulSearch_t;
+
+/** Algo search preference to fine tune the heuristic function. */
+typedef enum {
+  /** Search mode, see cublasLtMatmulSearch_t.
+   *
+   * uint32_t, default: CUBLASLT_SEARCH_BEST_FIT
+   */
+  CUBLASLT_MATMUL_PREF_SEARCH_MODE = 0,
+
+  /** Maximum allowed workspace size in bytes.
+   *
+   * uint64_t, default: 0 - no workspace allowed
+   */
+  CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES = 1,
+
+  /** Reduction scheme mask, see cublasLtReductionScheme_t. Filters heuristic result to only include algo configs that
+   * use one of the required modes.
+   *
+   * E.g. mask value of 0x03 will allow only INPLACE and COMPUTE_TYPE reduction schemes.
+   *
+   * uint32_t, default: CUBLASLT_REDUCTION_SCHEME_MASK (allows all reduction schemes)
+   */
+  CUBLASLT_MATMUL_PREF_REDUCTION_SCHEME_MASK = 3,
+
+  /** Minimum buffer alignment for matrix A (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix A that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_A_BYTES = 5,
+
+  /** Minimum buffer alignment for matrix B (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix B that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_B_BYTES = 6,
+
+  /** Minimum buffer alignment for matrix C (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix C that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_C_BYTES = 7,
+
+  /** Minimum buffer alignment for matrix D (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix D that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_D_BYTES = 8,
+
+  /** Maximum wave count.
+   *
+   * See cublasLtMatmulHeuristicResult_t::wavesCount.
+   *
+   * Selecting a non-zero value will exclude algorithms that report device utilization higher than specified.
+   *
+   * float, default: 0.0f
+   */
+  CUBLASLT_MATMUL_PREF_MAX_WAVES_COUNT = 9,
+
+  /** Numerical implementation details mask, see cublasLtNumericalImplFlags_t. Filters heuristic result to only include
+   * algorithms that use the allowed implementations.
+   *
+   * uint64_t, default: uint64_t(-1) (allow everything)
+   */
+  CUBLASLT_MATMUL_PREF_IMPL_MASK = 12,
+} cublasLtMatmulPreferenceAttributes_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceInit_internal(cublasLtMatmulPreference_t pref, size_t size);
+
+/** Initialize matmul heuristic search preference descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+static inline cublasStatus_t cublasLtMatmulPreferenceInit(cublasLtMatmulPreference_t pref) {
+  return cublasLtMatmulPreferenceInit_internal(pref, sizeof(*pref));
+}
+
+/** Create new matmul heuristic search preference descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceCreate(cublasLtMatmulPreference_t* pref);
+
+/** Destroy matmul heuristic search preference descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceDestroy(cublasLtMatmulPreference_t pref);
+
+/** Set matmul heuristic search preference descriptor attribute.
+ *
+ * \param[in]  pref         The descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceSetAttribute(  //
+    cublasLtMatmulPreference_t pref,
+    cublasLtMatmulPreferenceAttributes_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matmul heuristic search preference descriptor attribute.
+ *
+ * \param[in]  pref         The descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceGetAttribute(  //
+    cublasLtMatmulPreference_t pref,
+    cublasLtMatmulPreferenceAttributes_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/** Results structure used by cublasLtMatmulAlgoGetHeuristic
+ *
+ * Holds returned configured algo descriptor and its runtime properties.
+ */
+typedef struct {
+  /** Matmul algorithm descriptor.
+   *
+   * Must be initialized with cublasLtMatmulAlgoInit() if preferences' CUBLASLT_MATMUL_PERF_SEARCH_MODE is set to
+   * CUBLASLT_SEARCH_LIMITED_BY_ALGO_ID
+   */
+  cublasLtMatmulAlgo_t algo;
+
+  /** Actual size of workspace memory required.
+   */
+  size_t workspaceSize;
+
+  /** Result status, other fields are only valid if after call to cublasLtMatmulAlgoGetHeuristic() this member is set to
+   * CUBLAS_STATUS_SUCCESS.
+   */
+  cublasStatus_t state;
+
+  /** Waves count - a device utilization metric.
+   *
+   * wavesCount value of 1.0f suggests that when kernel is launched it will fully occupy the GPU.
+   */
+  float wavesCount;
+
+  int reserved[4];
+} cublasLtMatmulHeuristicResult_t;
+
+/** Query cublasLt heuristic for algorithm appropriate for given use case.
+ *
+ * \param[in]      lightHandle            Pointer to the allocated cuBLASLt handle for the cuBLASLt
+ *                                        context. See cublasLtHandle_t.
+ * \param[in]      operationDesc          Handle to the matrix multiplication descriptor.
+ * \param[in]      Adesc                  Handle to the layout descriptors for matrix A.
+ * \param[in]      Bdesc                  Handle to the layout descriptors for matrix B.
+ * \param[in]      Cdesc                  Handle to the layout descriptors for matrix C.
+ * \param[in]      Ddesc                  Handle to the layout descriptors for matrix D.
+ * \param[in]      preference             Pointer to the structure holding the heuristic search
+ *                                        preferences descriptor. See cublasLtMatrixLayout_t.
+ * \param[in]      requestedAlgoCount     Size of heuristicResultsArray (in elements) and requested
+ *                                        maximum number of algorithms to return.
+ * \param[in, out] heuristicResultsArray  Output algorithms and associated runtime characteristics,
+ *                                        ordered in increasing estimated compute time.
+ * \param[out]     returnAlgoCount        The number of heuristicResultsArray elements written.
+ *
+ * \retval  CUBLAS_STATUS_INVALID_VALUE   if requestedAlgoCount is less or equal to zero
+ * \retval  CUBLAS_STATUS_NOT_SUPPORTED   if no heuristic function available for current configuration
+ * \retval  CUBLAS_STATUS_SUCCESS         if query was successful, inspect
+ *                                        heuristicResultsArray[0 to (returnAlgoCount - 1)].state
+ *                                        for detail status of results
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoGetHeuristic(cublasLtHandle_t lightHandle,
+                                                           cublasLtMatmulDesc_t operationDesc,
+                                                           cublasLtMatrixLayout_t Adesc,
+                                                           cublasLtMatrixLayout_t Bdesc,
+                                                           cublasLtMatrixLayout_t Cdesc,
+                                                           cublasLtMatrixLayout_t Ddesc,
+                                                           cublasLtMatmulPreference_t preference,
+                                                           int requestedAlgoCount,
+                                                           cublasLtMatmulHeuristicResult_t heuristicResultsArray[],
+                                                           int* returnAlgoCount);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Lower level API to be able to implement own Heuristic and Find routines                */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Routine to get all algo IDs that can potentially run
+ *
+ * \param[in]  int              requestedAlgoCount requested number of algos (must be less or equal to size of algoIdsA
+ * (in elements)) \param[out] algoIdsA         array to write algoIds to \param[out] returnAlgoCount  number of algoIds
+ * actually written
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if requestedAlgoCount is less or equal to zero
+ * \retval     CUBLAS_STATUS_SUCCESS        if query was successful, inspect returnAlgoCount to get actual number of IDs
+ *                                          available
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoGetIds(cublasLtHandle_t lightHandle,
+                                                     cublasComputeType_t computeType,
+                                                     cudaDataType_t scaleType,
+                                                     cudaDataType_t Atype,
+                                                     cudaDataType_t Btype,
+                                                     cudaDataType_t Ctype,
+                                                     cudaDataType_t Dtype,
+                                                     int requestedAlgoCount,
+                                                     int algoIdsArray[],
+                                                     int* returnAlgoCount);
+
+/** Initialize algo structure
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if algo is NULL or algoId is outside of recognized range
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED  if algoId is not supported for given combination of data types
+ * \retval     CUBLAS_STATUS_SUCCESS        if the structure was successfully initialized
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoInit(cublasLtHandle_t lightHandle,
+                                                   cublasComputeType_t computeType,
+                                                   cudaDataType_t scaleType,
+                                                   cudaDataType_t Atype,
+                                                   cudaDataType_t Btype,
+                                                   cudaDataType_t Ctype,
+                                                   cudaDataType_t Dtype,
+                                                   int algoId,
+                                                   cublasLtMatmulAlgo_t* algo);
+
+/** Check configured algo descriptor for correctness and support on current device.
+ *
+ * Result includes required workspace size and calculated wave count.
+ *
+ * CUBLAS_STATUS_SUCCESS doesn't fully guarantee algo will run (will fail if e.g. buffers are not correctly aligned);
+ * but if cublasLtMatmulAlgoCheck fails, the algo will not run.
+ *
+ * \param[in]  algo    algo configuration to check
+ * \param[out] result  result structure to report algo runtime characteristics; algo field is never updated
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if matrix layout descriptors or operation descriptor don't match algo
+ *                                          descriptor
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED  if algo configuration or data type combination is not currently supported on
+ *                                          given device
+ * \retval     CUBLAS_STATUS_ARCH_MISMATCH  if algo configuration cannot be run using the selected device
+ * \retval     CUBLAS_STATUS_SUCCESS        if check was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoCheck(  //
+    cublasLtHandle_t lightHandle,
+    cublasLtMatmulDesc_t operationDesc,
+    cublasLtMatrixLayout_t Adesc,
+    cublasLtMatrixLayout_t Bdesc,
+    cublasLtMatrixLayout_t Cdesc,
+    cublasLtMatrixLayout_t Ddesc,
+    const cublasLtMatmulAlgo_t* algo,  ///< may point to result->algo
+    cublasLtMatmulHeuristicResult_t* result);
+
+/** Capabilities Attributes that can be retrieved from an initialized Algo structure
+ */
+typedef enum {
+  /** support for split K, see CUBLASLT_ALGO_CONFIG_SPLITK_NUM
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_SPLITK_SUPPORT = 0,
+
+  /** reduction scheme mask, see cublasLtReductionScheme_t; shows supported reduction schemes, if reduction scheme is
+   * not masked out it is supported.
+   *
+   * e.g. int isReductionSchemeComputeTypeSupported ? (reductionSchemeMask & CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE) ==
+   * CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE ? 1 : 0;
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK = 1,
+
+  /** support for cta swizzling, see CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING
+   *
+   * uint32_t, 0 means no support, 1 means supported value of 1, other values are reserved
+   */
+  CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT = 2,
+
+  /** support strided batch
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_STRIDED_BATCH_SUPPORT = 3,
+
+  /** support results out of place (D != C in D = alpha.A.B + beta.C)
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_OUT_OF_PLACE_RESULT_SUPPORT = 4,
+
+  /** syrk/herk support (on top of regular gemm)
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_UPLO_SUPPORT = 5,
+
+  /** tile ids possible to use, see cublasLtMatmulTile_t; if no tile ids are supported use
+   * CUBLASLT_MATMUL_TILE_UNDEFINED
+   *
+   * use cublasLtMatmulAlgoCapGetAttribute() with sizeInBytes=0 to query actual count
+   *
+   * array of uint32_t
+   */
+  CUBLASLT_ALGO_CAP_TILE_IDS = 6,
+
+  /** custom option range is from 0 to CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX (inclusive), see
+   * CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION
+   *
+   * int32_t
+   */
+  CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX = 7,
+
+  /** whether algorithm supports custom (not COL or ROW memory order), see cublasLtOrder_t
+   *
+   * int32_t 0 means only COL and ROW memory order is allowed, non-zero means that algo might have different
+   * requirements;
+   */
+  CUBLASLT_ALGO_CAP_CUSTOM_MEMORY_ORDER = 10,
+
+  /** bitmask enumerating pointer modes algorithm supports
+   *
+   * uint32_t, see cublasLtPointerModeMask_t
+   */
+  CUBLASLT_ALGO_CAP_POINTER_MODE_MASK = 11,
+
+  /** bitmask enumerating kinds of postprocessing algorithm supports in the epilogue
+   *
+   * uint32_t, see cublasLtEpilogue_t
+   */
+  CUBLASLT_ALGO_CAP_EPILOGUE_MASK = 12,
+
+  /** stages ids possible to use, see cublasLtMatmulStages_t; if no stages ids are supported use
+   * CUBLASLT_MATMUL_STAGES_UNDEFINED
+   *
+   * use cublasLtMatmulAlgoCapGetAttribute() with sizeInBytes=0 to query actual count
+   *
+   * array of uint32_t
+   */
+  CUBLASLT_ALGO_CAP_STAGES_IDS = 13,
+
+  /** support for nagative ld for all of the matrices
+   *
+   * int32_t 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_LD_NEGATIVE = 14,
+
+  /** details about algorithm's implementation that affect it's numerical behavior
+   *
+   * uint64_t, see cublasLtNumericalImplFlags_t
+   */
+  CUBLASLT_ALGO_CAP_NUMERICAL_IMPL_FLAGS = 15,
+
+  /** minimum alignment required for A matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_A_BYTES = 16,
+
+  /** minimum alignment required for B matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_B_BYTES = 17,
+
+  /** minimum alignment required for C matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_C_BYTES = 18,
+
+  /** minimum alignment required for D matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_D_BYTES = 19,
+
+  /** EXPERIMENTAL: support for synchronization via atomic counters
+   *
+   * int32_t
+   */
+  CUBLASLT_ALGO_CAP_ATOMIC_SYNC = 20,
+} cublasLtMatmulAlgoCapAttributes_t;
+
+/** Get algo capability attribute.
+ *
+ * E.g. to get list of supported Tile IDs:
+ *      cublasLtMatmulTile_t tiles[CUBLASLT_MATMUL_TILE_END];
+ *      size_t num_tiles, size_written;
+ *      if (cublasLtMatmulAlgoCapGetAttribute(algo, CUBLASLT_ALGO_CAP_TILE_IDS, tiles, sizeof(tiles), size_written) ==
+ * CUBLAS_STATUS_SUCCESS) { num_tiles = size_written / sizeof(tiles[0]);
+ *      }
+ *
+ * \param[in]  algo         The algo descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoCapGetAttribute(const cublasLtMatmulAlgo_t* algo,
+                                                              cublasLtMatmulAlgoCapAttributes_t attr,
+                                                              void* buf,
+                                                              size_t sizeInBytes,
+                                                              size_t* sizeWritten);
+
+/** Algo Configuration Attributes that can be set according to the Algo capabilities
+ */
+typedef enum {
+  /** algorithm index, see cublasLtMatmulAlgoGetIds()
+   *
+   * readonly, set by cublasLtMatmulAlgoInit()
+   * int32_t
+   */
+  CUBLASLT_ALGO_CONFIG_ID = 0,
+  /** tile id, see cublasLtMatmulTile_t
+   *
+   * uint32_t, default: CUBLASLT_MATMUL_TILE_UNDEFINED
+   */
+  CUBLASLT_ALGO_CONFIG_TILE_ID = 1,
+  /** Number of K splits. If the number of K splits is greater than one, SPLITK_NUM parts
+   * of matrix multiplication will be computed in parallel. The results will be accumulated
+   * according to CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME
+   *
+   * int32_t, default: 1
+   */
+  CUBLASLT_ALGO_CONFIG_SPLITK_NUM = 2,
+  /** reduction scheme, see cublasLtReductionScheme_t
+   *
+   * uint32_t, default: CUBLASLT_REDUCTION_SCHEME_NONE
+   */
+  CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME = 3,
+  /** cta swizzling, change mapping from CUDA grid coordinates to parts of the matrices
+   *
+   * possible values: 0, 1, other values reserved
+   *
+   * uint32_t, default: 0
+   */
+  CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING = 4,
+  /** custom option, each algorithm can support some custom options that don't fit description of the other config
+   * attributes, see CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX to get accepted range for any specific case
+   *
+   * uint32_t, default: 0
+   */
+  CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION = 5,
+  /** stages id, see cublasLtMatmulStages_t
+   *
+   * uint32_t, default: CUBLASLT_MATMUL_STAGES_UNDEFINED
+   */
+  CUBLASLT_ALGO_CONFIG_STAGES_ID = 6,
+  /** inner shape id, see cublasLtMatmulInnerShape_t
+   *
+   * uint16_t, default: 0 (CUBLASLT_MATMUL_INNER_SHAPE_UNDEFINED)
+   */
+  CUBLASLT_ALGO_CONFIG_INNER_SHAPE_ID = 7,
+  /** Thread Block Cluster shape id, see cublasLtClusterShape_t. Defines cluster size to use.
+   *
+   * uint16_t, default: 0 (CUBLASLT_CLUSTER_SHAPE_AUTO)
+   */
+  CUBLASLT_ALGO_CONFIG_CLUSTER_SHAPE_ID = 8,
+} cublasLtMatmulAlgoConfigAttributes_t;
+
+/** Set algo configuration attribute.
+ *
+ * \param[in]  algo         The algo descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoConfigSetAttribute(cublasLtMatmulAlgo_t* algo,
+                                                                 cublasLtMatmulAlgoConfigAttributes_t attr,
+                                                                 const void* buf,
+                                                                 size_t sizeInBytes);
+
+/** Get algo configuration attribute.
+ *
+ * \param[in]  algo         The algo descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoConfigGetAttribute(const cublasLtMatmulAlgo_t* algo,
+                                                                 cublasLtMatmulAlgoConfigAttributes_t attr,
+                                                                 void* buf,
+                                                                 size_t sizeInBytes,
+                                                                 size_t* sizeWritten);
+
+/** Experimental: Logger callback type.
+ */
+typedef void (*cublasLtLoggerCallback_t)(int logLevel, const char* functionName, const char* message);
+
+/** Experimental: Logger callback setter.
+ *
+ * \param[in]  callback                     a user defined callback function to be called by the logger
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if callback was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetCallback(cublasLtLoggerCallback_t callback);
+
+/** Experimental: Log file setter.
+ *
+ * \param[in]  file                         an open file with write permissions
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log file was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetFile(FILE* file);
+
+/** Experimental: Open log file.
+ *
+ * \param[in]  logFile                      log file path. if the log file does not exist, it will be created
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log file was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerOpenFile(const char* logFile);
+
+/** Experimental: Log level setter.
+ *
+ * \param[in]  level                        log level, should be one of the following:
+ *                                          0. Off
+ *                                          1. Errors
+ *                                          2. Performance Trace
+ *                                          3. Performance Hints
+ *                                          4. Heuristics Trace
+ *                                          5. API Trace
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if log level is not one of the above levels
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log level was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetLevel(int level);
+
+/** Experimental: Log mask setter.
+ *
+ * \param[in]  mask                         log mask, should be a combination of the following masks:
+ *                                          0.  Off
+ *                                          1.  Errors
+ *                                          2.  Performance Trace
+ *                                          4.  Performance Hints
+ *                                          8.  Heuristics Trace
+ *                                          16. API Trace
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log mask was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetMask(int mask);
+
+/** Experimental: Disable logging for the entire session.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if disabled logging
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerForceDisable();
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublasXt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublasXt.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe0e6f99b952514874c45208e751f5330e71570c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublasXt.h
@@ -0,0 +1,693 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cublasXt : Host API, Out of Core and Multi-GPU BLAS Library
+
+*/
+
+#if !defined(CUBLAS_XT_H_)
+#define CUBLAS_XT_H_
+
+#include "driver_types.h"
+#include "cuComplex.h" /* import complex data type */
+
+#include "cublas_v2.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+struct cublasXtContext;
+typedef struct cublasXtContext* cublasXtHandle_t;
+
+cublasStatus_t CUBLASWINAPI cublasXtCreate(cublasXtHandle_t* handle);
+cublasStatus_t CUBLASWINAPI cublasXtDestroy(cublasXtHandle_t handle);
+cublasStatus_t CUBLASWINAPI cublasXtGetNumBoards(int nbDevices, int deviceId[], int* nbBoards);
+cublasStatus_t CUBLASWINAPI cublasXtMaxBoards(int* nbGpuBoards);
+/* This routine selects the Gpus that the user want to use for CUBLAS-XT */
+cublasStatus_t CUBLASWINAPI cublasXtDeviceSelect(cublasXtHandle_t handle, int nbDevices, int deviceId[]);
+
+/* This routine allows to change the dimension of the tiles ( blockDim x blockDim ) */
+cublasStatus_t CUBLASWINAPI cublasXtSetBlockDim(cublasXtHandle_t handle, int blockDim);
+cublasStatus_t CUBLASWINAPI cublasXtGetBlockDim(cublasXtHandle_t handle, int* blockDim);
+
+typedef enum { CUBLASXT_PINNING_DISABLED = 0, CUBLASXT_PINNING_ENABLED = 1 } cublasXtPinnedMemMode_t;
+/* This routine allows to CUBLAS-XT to pin the Host memory if it find out that some of the matrix passed
+   are not pinned : Pinning/Unpinning the Host memory is still a costly operation
+   It is better if the user controls the memory on its own (by pinning/unpinning oly when necessary)
+*/
+cublasStatus_t CUBLASWINAPI cublasXtGetPinningMemMode(cublasXtHandle_t handle, cublasXtPinnedMemMode_t* mode);
+cublasStatus_t CUBLASWINAPI cublasXtSetPinningMemMode(cublasXtHandle_t handle, cublasXtPinnedMemMode_t mode);
+
+/* This routines is to provide a CPU Blas routines, used for too small sizes or hybrid computation */
+typedef enum {
+  CUBLASXT_FLOAT = 0,
+  CUBLASXT_DOUBLE = 1,
+  CUBLASXT_COMPLEX = 2,
+  CUBLASXT_DOUBLECOMPLEX = 3,
+} cublasXtOpType_t;
+
+typedef enum {
+  CUBLASXT_GEMM = 0,
+  CUBLASXT_SYRK = 1,
+  CUBLASXT_HERK = 2,
+  CUBLASXT_SYMM = 3,
+  CUBLASXT_HEMM = 4,
+  CUBLASXT_TRSM = 5,
+  CUBLASXT_SYR2K = 6,
+  CUBLASXT_HER2K = 7,
+
+  CUBLASXT_SPMM = 8,
+  CUBLASXT_SYRKX = 9,
+  CUBLASXT_HERKX = 10,
+  CUBLASXT_TRMM = 11,
+  CUBLASXT_ROUTINE_MAX = 12,
+} cublasXtBlasOp_t;
+
+/* Currently only 32-bit integer BLAS routines are supported */
+cublasStatus_t CUBLASWINAPI cublasXtSetCpuRoutine(cublasXtHandle_t handle,
+                                                  cublasXtBlasOp_t blasOp,
+                                                  cublasXtOpType_t type,
+                                                  void* blasFunctor);
+
+/* Specified the percentage of work that should done by the CPU, default is 0 (no work) */
+cublasStatus_t CUBLASWINAPI cublasXtSetCpuRatio(cublasXtHandle_t handle,
+                                                cublasXtBlasOp_t blasOp,
+                                                cublasXtOpType_t type,
+                                                float ratio);
+
+/* GEMM */
+cublasStatus_t CUBLASWINAPI cublasXtSgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* B,
+                                          size_t ldb,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* B,
+                                          size_t ldb,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* ------------------------------------------------------- */
+/* SYRK */
+cublasStatus_t CUBLASWINAPI cublasXtSsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HERK */
+cublasStatus_t CUBLASWINAPI cublasXtCherk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const float* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const float* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZherk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const double* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const double* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* -------------------------------------------------------------------- */
+/* SYR2K */
+cublasStatus_t CUBLASWINAPI cublasXtSsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const float* alpha,
+                                           const float* A,
+                                           size_t lda,
+                                           const float* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           float* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const double* alpha,
+                                           const double* A,
+                                           size_t lda,
+                                           const double* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           double* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const cuComplex* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const cuDoubleComplex* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HERKX : variant extension of HERK */
+cublasStatus_t CUBLASWINAPI cublasXtCherkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZherkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* TRSM */
+cublasStatus_t CUBLASWINAPI cublasXtStrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          float* B,
+                                          size_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasXtDtrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          double* B,
+                                          size_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasXtCtrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          cuComplex* B,
+                                          size_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasXtZtrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          cuDoubleComplex* B,
+                                          size_t ldb);
+/* -------------------------------------------------------------------- */
+/* SYMM : Symmetric Multiply Matrix*/
+cublasStatus_t CUBLASWINAPI cublasXtSsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* B,
+                                          size_t ldb,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* B,
+                                          size_t ldb,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HEMM : Hermitian Matrix Multiply */
+cublasStatus_t CUBLASWINAPI cublasXtChemm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZhemm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* SYRKX : variant extension of SYRK  */
+cublasStatus_t CUBLASWINAPI cublasXtSsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const float* alpha,
+                                           const float* A,
+                                           size_t lda,
+                                           const float* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           float* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const double* alpha,
+                                           const double* A,
+                                           size_t lda,
+                                           const double* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           double* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const cuComplex* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const cuDoubleComplex* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HER2K : variant extension of HERK  */
+cublasStatus_t CUBLASWINAPI cublasXtCher2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZher2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* SPMM : Symmetric Packed Multiply Matrix*/
+cublasStatus_t CUBLASWINAPI cublasXtSspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* AP,
+                                          const float* B,
+                                          size_t ldb,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* AP,
+                                          const double* B,
+                                          size_t ldb,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* AP,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* AP,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* TRMM */
+cublasStatus_t CUBLASWINAPI cublasXtStrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* B,
+                                          size_t ldb,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDtrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* B,
+                                          size_t ldb,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCtrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZtrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* !defined(CUBLAS_XT_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas_api.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas_api.h
new file mode 100644
index 0000000000000000000000000000000000000000..95f4bdebe37089a8a68c5ad3ec39fd9d7bf96571
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas_api.h
@@ -0,0 +1,5835 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * This is the public header file for the CUBLAS library, defining the API
+ *
+ * CUBLAS is an implementation of BLAS (Basic Linear Algebra Subroutines)
+ * on top of the CUDA runtime.
+ */
+
+#if !defined(CUBLAS_API_H_)
+#define CUBLAS_API_H_
+
+#ifndef CUBLASWINAPI
+#ifdef _WIN32
+#define CUBLASWINAPI __stdcall
+#else
+#define CUBLASWINAPI
+#endif
+#endif
+
+#ifndef CUBLASAPI
+#error "This file should not be included without defining CUBLASAPI"
+#endif
+
+#include <stdint.h>
+
+#include "driver_types.h"
+#include "cuComplex.h" /* import complex data type */
+
+#include <cuda_fp16.h>
+#include <cuda_bf16.h>
+
+#include <library_types.h>
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+#define CUBLAS_VER_MAJOR 12
+#define CUBLAS_VER_MINOR 8
+#define CUBLAS_VER_PATCH 4
+#define CUBLAS_VER_BUILD 1
+#define CUBLAS_VERSION (CUBLAS_VER_MAJOR * 10000 + CUBLAS_VER_MINOR * 100 + CUBLAS_VER_PATCH)
+
+/* CUBLAS status type returns */
+typedef enum {
+  CUBLAS_STATUS_SUCCESS = 0,
+  CUBLAS_STATUS_NOT_INITIALIZED = 1,
+  CUBLAS_STATUS_ALLOC_FAILED = 3,
+  CUBLAS_STATUS_INVALID_VALUE = 7,
+  CUBLAS_STATUS_ARCH_MISMATCH = 8,
+  CUBLAS_STATUS_MAPPING_ERROR = 11,
+  CUBLAS_STATUS_EXECUTION_FAILED = 13,
+  CUBLAS_STATUS_INTERNAL_ERROR = 14,
+  CUBLAS_STATUS_NOT_SUPPORTED = 15,
+  CUBLAS_STATUS_LICENSE_ERROR = 16
+} cublasStatus_t;
+
+typedef enum { CUBLAS_FILL_MODE_LOWER = 0, CUBLAS_FILL_MODE_UPPER = 1, CUBLAS_FILL_MODE_FULL = 2 } cublasFillMode_t;
+
+typedef enum { CUBLAS_DIAG_NON_UNIT = 0, CUBLAS_DIAG_UNIT = 1 } cublasDiagType_t;
+
+typedef enum { CUBLAS_SIDE_LEFT = 0, CUBLAS_SIDE_RIGHT = 1 } cublasSideMode_t;
+
+typedef enum {
+  CUBLAS_OP_N = 0,
+  CUBLAS_OP_T = 1,
+  CUBLAS_OP_C = 2,
+  CUBLAS_OP_HERMITAN = 2, /* synonym if CUBLAS_OP_C */
+  CUBLAS_OP_CONJG = 3     /* conjugate, placeholder - not supported in the current release */
+} cublasOperation_t;
+
+typedef enum { CUBLAS_POINTER_MODE_HOST = 0, CUBLAS_POINTER_MODE_DEVICE = 1 } cublasPointerMode_t;
+
+typedef enum { CUBLAS_ATOMICS_NOT_ALLOWED = 0, CUBLAS_ATOMICS_ALLOWED = 1 } cublasAtomicsMode_t;
+
+/*For different GEMM algorithm */
+typedef enum {
+  CUBLAS_GEMM_DFALT = -1,
+  CUBLAS_GEMM_DEFAULT = -1,
+  CUBLAS_GEMM_ALGO0 = 0,
+  CUBLAS_GEMM_ALGO1 = 1,
+  CUBLAS_GEMM_ALGO2 = 2,
+  CUBLAS_GEMM_ALGO3 = 3,
+  CUBLAS_GEMM_ALGO4 = 4,
+  CUBLAS_GEMM_ALGO5 = 5,
+  CUBLAS_GEMM_ALGO6 = 6,
+  CUBLAS_GEMM_ALGO7 = 7,
+  CUBLAS_GEMM_ALGO8 = 8,
+  CUBLAS_GEMM_ALGO9 = 9,
+  CUBLAS_GEMM_ALGO10 = 10,
+  CUBLAS_GEMM_ALGO11 = 11,
+  CUBLAS_GEMM_ALGO12 = 12,
+  CUBLAS_GEMM_ALGO13 = 13,
+  CUBLAS_GEMM_ALGO14 = 14,
+  CUBLAS_GEMM_ALGO15 = 15,
+  CUBLAS_GEMM_ALGO16 = 16,
+  CUBLAS_GEMM_ALGO17 = 17,
+  CUBLAS_GEMM_ALGO18 = 18,  // sliced 32x32
+  CUBLAS_GEMM_ALGO19 = 19,  // sliced 64x32
+  CUBLAS_GEMM_ALGO20 = 20,  // sliced 128x32
+  CUBLAS_GEMM_ALGO21 = 21,  // sliced 32x32  -splitK
+  CUBLAS_GEMM_ALGO22 = 22,  // sliced 64x32  -splitK
+  CUBLAS_GEMM_ALGO23 = 23,  // sliced 128x32 -splitK
+  CUBLAS_GEMM_DEFAULT_TENSOR_OP = 99,
+  CUBLAS_GEMM_DFALT_TENSOR_OP = 99,
+  CUBLAS_GEMM_ALGO0_TENSOR_OP = 100,
+  CUBLAS_GEMM_ALGO1_TENSOR_OP = 101,
+  CUBLAS_GEMM_ALGO2_TENSOR_OP = 102,
+  CUBLAS_GEMM_ALGO3_TENSOR_OP = 103,
+  CUBLAS_GEMM_ALGO4_TENSOR_OP = 104,
+  CUBLAS_GEMM_ALGO5_TENSOR_OP = 105,
+  CUBLAS_GEMM_ALGO6_TENSOR_OP = 106,
+  CUBLAS_GEMM_ALGO7_TENSOR_OP = 107,
+  CUBLAS_GEMM_ALGO8_TENSOR_OP = 108,
+  CUBLAS_GEMM_ALGO9_TENSOR_OP = 109,
+  CUBLAS_GEMM_ALGO10_TENSOR_OP = 110,
+  CUBLAS_GEMM_ALGO11_TENSOR_OP = 111,
+  CUBLAS_GEMM_ALGO12_TENSOR_OP = 112,
+  CUBLAS_GEMM_ALGO13_TENSOR_OP = 113,
+  CUBLAS_GEMM_ALGO14_TENSOR_OP = 114,
+  CUBLAS_GEMM_ALGO15_TENSOR_OP = 115
+} cublasGemmAlgo_t;
+
+/*Enum for default math mode/tensor operation*/
+typedef enum {
+  CUBLAS_DEFAULT_MATH = 0,
+
+  /* deprecated, same effect as using CUBLAS_COMPUTE_32F_FAST_16F, will be removed in a future release */
+  CUBLAS_TENSOR_OP_MATH = 1,
+
+  /* same as using matching _PEDANTIC compute type when using cublas<T>routine calls or cublasEx() calls with
+     cudaDataType as compute type */
+  CUBLAS_PEDANTIC_MATH = 2,
+
+  /* allow accelerating single precision routines using TF32 tensor cores */
+  CUBLAS_TF32_TENSOR_OP_MATH = 3,
+
+  /* flag to force any reductons to use the accumulator type and not output type in case of mixed precision routines
+     with lower size output type */
+  CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION = 16,
+} cublasMath_t;
+
+/* For backward compatibility purposes */
+typedef cudaDataType cublasDataType_t;
+
+/* Enum for compute type
+ *
+ * - default types provide best available performance using all available hardware features
+ *   and guarantee internal storage precision with at least the same precision and range;
+ * - _PEDANTIC types ensure standard arithmetic and exact specified internal storage format;
+ * - _FAST types allow for some loss of precision to enable higher throughput arithmetic.
+ */
+typedef enum {
+  CUBLAS_COMPUTE_16F = 64,           /* half - default */
+  CUBLAS_COMPUTE_16F_PEDANTIC = 65,  /* half - pedantic */
+  CUBLAS_COMPUTE_32F = 68,           /* float - default */
+  CUBLAS_COMPUTE_32F_PEDANTIC = 69,  /* float - pedantic */
+  CUBLAS_COMPUTE_32F_FAST_16F = 74,  /* float - fast, allows down-converting inputs to half or TF32 */
+  CUBLAS_COMPUTE_32F_FAST_16BF = 75, /* float - fast, allows down-converting inputs to bfloat16 or TF32 */
+  CUBLAS_COMPUTE_32F_FAST_TF32 = 77, /* float - fast, allows down-converting inputs to TF32 */
+  CUBLAS_COMPUTE_64F = 70,           /* double - default */
+  CUBLAS_COMPUTE_64F_PEDANTIC = 71,  /* double - pedantic */
+  CUBLAS_COMPUTE_32I = 72,           /* signed 32-bit int - default */
+  CUBLAS_COMPUTE_32I_PEDANTIC = 73,  /* signed 32-bit int - pedantic */
+} cublasComputeType_t;
+
+/* Opaque structure holding CUBLAS library context */
+struct cublasContext;
+typedef struct cublasContext* cublasHandle_t;
+
+/* Cublas logging */
+typedef void (*cublasLogCallback)(const char* msg);
+
+/* cuBLAS Exported API {{{ */
+
+/* --------------- CUBLAS Helper Functions  ---------------- */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCreate_v2(cublasHandle_t* handle);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDestroy_v2(cublasHandle_t handle);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetVersion_v2(cublasHandle_t handle, int* version);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetProperty(libraryPropertyType type, int* value);
+
+CUBLASAPI size_t CUBLASWINAPI cublasGetCudartVersion(void);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetWorkspace_v2(cublasHandle_t handle,
+                                                            void* workspace,
+                                                            size_t workspaceSizeInBytes);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetStream_v2(cublasHandle_t handle, cudaStream_t streamId);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetStream_v2(cublasHandle_t handle, cudaStream_t* streamId);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetPointerMode_v2(cublasHandle_t handle, cublasPointerMode_t* mode);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetPointerMode_v2(cublasHandle_t handle, cublasPointerMode_t mode);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetAtomicsMode(cublasHandle_t handle, cublasAtomicsMode_t* mode);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetAtomicsMode(cublasHandle_t handle, cublasAtomicsMode_t mode);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetMathMode(cublasHandle_t handle, cublasMath_t* mode);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetMathMode(cublasHandle_t handle, cublasMath_t mode);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetSmCountTarget(cublasHandle_t handle, int* smCountTarget);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetSmCountTarget(cublasHandle_t handle, int smCountTarget);
+
+CUBLASAPI const char* CUBLASWINAPI cublasGetStatusName(cublasStatus_t status);
+
+CUBLASAPI const char* CUBLASWINAPI cublasGetStatusString(cublasStatus_t status);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasLoggerConfigure(int logIsOn,
+                                                            int logToStdOut,
+                                                            int logToStdErr,
+                                                            const char* logFileName);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSetLoggerCallback(cublasLogCallback userCallback);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGetLoggerCallback(cublasLogCallback* userCallback);
+
+cublasStatus_t CUBLASWINAPI cublasSetVector(int n, int elemSize, const void* x, int incx, void* devicePtr, int incy);
+
+cublasStatus_t CUBLASWINAPI
+cublasSetVector_64(int64_t n, int64_t elemSize, const void* x, int64_t incx, void* devicePtr, int64_t incy);
+
+cublasStatus_t CUBLASWINAPI cublasGetVector(int n, int elemSize, const void* x, int incx, void* y, int incy);
+
+cublasStatus_t CUBLASWINAPI
+cublasGetVector_64(int64_t n, int64_t elemSize, const void* x, int64_t incx, void* y, int64_t incy);
+
+cublasStatus_t CUBLASWINAPI cublasSetMatrix(int rows, int cols, int elemSize, const void* A, int lda, void* B, int ldb);
+
+cublasStatus_t CUBLASWINAPI
+cublasSetMatrix_64(int64_t rows, int64_t cols, int64_t elemSize, const void* A, int64_t lda, void* B, int64_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasGetMatrix(int rows, int cols, int elemSize, const void* A, int lda, void* B, int ldb);
+
+cublasStatus_t CUBLASWINAPI
+cublasGetMatrix_64(int64_t rows, int64_t cols, int64_t elemSize, const void* A, int64_t lda, void* B, int64_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasSetVectorAsync(
+    int n, int elemSize, const void* hostPtr, int incx, void* devicePtr, int incy, cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI cublasSetVectorAsync_64(
+    int64_t n, int64_t elemSize, const void* hostPtr, int64_t incx, void* devicePtr, int64_t incy, cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI cublasGetVectorAsync(
+    int n, int elemSize, const void* devicePtr, int incx, void* hostPtr, int incy, cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI cublasGetVectorAsync_64(
+    int64_t n, int64_t elemSize, const void* devicePtr, int64_t incx, void* hostPtr, int64_t incy, cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI
+cublasSetMatrixAsync(int rows, int cols, int elemSize, const void* A, int lda, void* B, int ldb, cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI cublasSetMatrixAsync_64(int64_t rows,
+                                                    int64_t cols,
+                                                    int64_t elemSize,
+                                                    const void* A,
+                                                    int64_t lda,
+                                                    void* B,
+                                                    int64_t ldb,
+                                                    cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI
+cublasGetMatrixAsync(int rows, int cols, int elemSize, const void* A, int lda, void* B, int ldb, cudaStream_t stream);
+
+cublasStatus_t CUBLASWINAPI cublasGetMatrixAsync_64(int64_t rows,
+                                                    int64_t cols,
+                                                    int64_t elemSize,
+                                                    const void* A,
+                                                    int64_t lda,
+                                                    void* B,
+                                                    int64_t ldb,
+                                                    cudaStream_t stream);
+
+CUBLASAPI void CUBLASWINAPI cublasXerbla(const char* srName, int info);
+
+/* --------------- CUBLAS BLAS1 Functions  ---------------- */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasNrm2Ex(cublasHandle_t handle,
+                                                   int n,
+                                                   const void* x,
+                                                   cudaDataType xType,
+                                                   int incx,
+                                                   void* result,
+                                                   cudaDataType resultType,
+                                                   cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasNrm2Ex_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      const void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      void* result,
+                                                      cudaDataType resultType,
+                                                      cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSnrm2_v2(cublasHandle_t handle, int n, const float* x, int incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSnrm2_v2_64(cublasHandle_t handle, int64_t n, const float* x, int64_t incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDnrm2_v2(cublasHandle_t handle, int n, const double* x, int incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDnrm2_v2_64(cublasHandle_t handle, int64_t n, const double* x, int64_t incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasScnrm2_v2(cublasHandle_t handle, int n, const cuComplex* x, int incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasScnrm2_v2_64(cublasHandle_t handle, int64_t n, const cuComplex* x, int64_t incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDznrm2_v2(cublasHandle_t handle, int n, const cuDoubleComplex* x, int incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDznrm2_v2_64(cublasHandle_t handle, int64_t n, const cuDoubleComplex* x, int64_t incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDotEx(cublasHandle_t handle,
+                                                  int n,
+                                                  const void* x,
+                                                  cudaDataType xType,
+                                                  int incx,
+                                                  const void* y,
+                                                  cudaDataType yType,
+                                                  int incy,
+                                                  void* result,
+                                                  cudaDataType resultType,
+                                                  cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDotEx_64(cublasHandle_t handle,
+                                                     int64_t n,
+                                                     const void* x,
+                                                     cudaDataType xType,
+                                                     int64_t incx,
+                                                     const void* y,
+                                                     cudaDataType yType,
+                                                     int64_t incy,
+                                                     void* result,
+                                                     cudaDataType resultType,
+                                                     cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDotcEx(cublasHandle_t handle,
+                                                   int n,
+                                                   const void* x,
+                                                   cudaDataType xType,
+                                                   int incx,
+                                                   const void* y,
+                                                   cudaDataType yType,
+                                                   int incy,
+                                                   void* result,
+                                                   cudaDataType resultType,
+                                                   cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDotcEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      const void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      const void* y,
+                                                      cudaDataType yType,
+                                                      int64_t incy,
+                                                      void* result,
+                                                      cudaDataType resultType,
+                                                      cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSdot_v2(cublasHandle_t handle, int n, const float* x, int incx, const float* y, int incy, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSdot_v2_64(
+    cublasHandle_t handle, int64_t n, const float* x, int64_t incx, const float* y, int64_t incy, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDdot_v2(cublasHandle_t handle, int n, const double* x, int incx, const double* y, int incy, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDdot_v2_64(
+    cublasHandle_t handle, int64_t n, const double* x, int64_t incx, const double* y, int64_t incy, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCdotu_v2(
+    cublasHandle_t handle, int n, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCdotu_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCdotc_v2(
+    cublasHandle_t handle, int n, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCdotc_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdotu_v2(cublasHandle_t handle,
+                                                     int n,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdotu_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdotc_v2(cublasHandle_t handle,
+                                                     int n,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdotc_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasScalEx(cublasHandle_t handle,
+                                                   int n,
+                                                   const void* alpha,
+                                                   cudaDataType alphaType,
+                                                   void* x,
+                                                   cudaDataType xType,
+                                                   int incx,
+                                                   cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasScalEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      const void* alpha,
+                                                      cudaDataType alphaType,
+                                                      void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      cudaDataType executionType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSscal_v2(cublasHandle_t handle, int n, const float* alpha, float* x, int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSscal_v2_64(cublasHandle_t handle, int64_t n, const float* alpha, float* x, int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDscal_v2(cublasHandle_t handle, int n, const double* alpha, double* x, int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDscal_v2_64(cublasHandle_t handle, int64_t n, const double* alpha, double* x, int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCscal_v2(cublasHandle_t handle, int n, const cuComplex* alpha, cuComplex* x, int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCscal_v2_64(cublasHandle_t handle, int64_t n, const cuComplex* alpha, cuComplex* x, int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCsscal_v2(cublasHandle_t handle, int n, const float* alpha, cuComplex* x, int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCsscal_v2_64(cublasHandle_t handle, int64_t n, const float* alpha, cuComplex* x, int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZscal_v2(cublasHandle_t handle, int n, const cuDoubleComplex* alpha, cuDoubleComplex* x, int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZscal_v2_64(cublasHandle_t handle, int64_t n, const cuDoubleComplex* alpha, cuDoubleComplex* x, int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZdscal_v2(cublasHandle_t handle, int n, const double* alpha, cuDoubleComplex* x, int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZdscal_v2_64(cublasHandle_t handle, int64_t n, const double* alpha, cuDoubleComplex* x, int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasAxpyEx(cublasHandle_t handle,
+                                                   int n,
+                                                   const void* alpha,
+                                                   cudaDataType alphaType,
+                                                   const void* x,
+                                                   cudaDataType xType,
+                                                   int incx,
+                                                   void* y,
+                                                   cudaDataType yType,
+                                                   int incy,
+                                                   cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasAxpyEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      const void* alpha,
+                                                      cudaDataType alphaType,
+                                                      const void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      void* y,
+                                                      cudaDataType yType,
+                                                      int64_t incy,
+                                                      cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSaxpy_v2(cublasHandle_t handle, int n, const float* alpha, const float* x, int incx, float* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSaxpy_v2_64(
+    cublasHandle_t handle, int64_t n, const float* alpha, const float* x, int64_t incx, float* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDaxpy_v2(cublasHandle_t handle, int n, const double* alpha, const double* x, int incx, double* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDaxpy_v2_64(
+    cublasHandle_t handle, int64_t n, const double* alpha, const double* x, int64_t incx, double* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCaxpy_v2(
+    cublasHandle_t handle, int n, const cuComplex* alpha, const cuComplex* x, int incx, cuComplex* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCaxpy_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZaxpy_v2(cublasHandle_t handle,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZaxpy_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCopyEx(
+    cublasHandle_t handle, int n, const void* x, cudaDataType xType, int incx, void* y, cudaDataType yType, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCopyEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      const void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      void* y,
+                                                      cudaDataType yType,
+                                                      int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasScopy_v2(cublasHandle_t handle, int n, const float* x, int incx, float* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasScopy_v2_64(cublasHandle_t handle, int64_t n, const float* x, int64_t incx, float* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDcopy_v2(cublasHandle_t handle, int n, const double* x, int incx, double* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDcopy_v2_64(cublasHandle_t handle, int64_t n, const double* x, int64_t incx, double* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCcopy_v2(cublasHandle_t handle, int n, const cuComplex* x, int incx, cuComplex* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCcopy_v2_64(cublasHandle_t handle, int64_t n, const cuComplex* x, int64_t incx, cuComplex* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZcopy_v2(cublasHandle_t handle, int n, const cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZcopy_v2_64(
+    cublasHandle_t handle, int64_t n, const cuDoubleComplex* x, int64_t incx, cuDoubleComplex* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSswap_v2(cublasHandle_t handle, int n, float* x, int incx, float* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSswap_v2_64(cublasHandle_t handle, int64_t n, float* x, int64_t incx, float* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDswap_v2(cublasHandle_t handle, int n, double* x, int incx, double* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDswap_v2_64(cublasHandle_t handle, int64_t n, double* x, int64_t incx, double* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCswap_v2(cublasHandle_t handle, int n, cuComplex* x, int incx, cuComplex* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCswap_v2_64(cublasHandle_t handle, int64_t n, cuComplex* x, int64_t incx, cuComplex* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZswap_v2(cublasHandle_t handle, int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZswap_v2_64(cublasHandle_t handle, int64_t n, cuDoubleComplex* x, int64_t incx, cuDoubleComplex* y, int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSwapEx(
+    cublasHandle_t handle, int n, void* x, cudaDataType xType, int incx, void* y, cudaDataType yType, int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSwapEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      void* y,
+                                                      cudaDataType yType,
+                                                      int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIsamax_v2(cublasHandle_t handle, int n, const float* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIsamax_v2_64(cublasHandle_t handle, int64_t n, const float* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIdamax_v2(cublasHandle_t handle, int n, const double* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIdamax_v2_64(cublasHandle_t handle, int64_t n, const double* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIcamax_v2(cublasHandle_t handle, int n, const cuComplex* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIcamax_v2_64(cublasHandle_t handle, int64_t n, const cuComplex* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIzamax_v2(cublasHandle_t handle, int n, const cuDoubleComplex* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIzamax_v2_64(cublasHandle_t handle, int64_t n, const cuDoubleComplex* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIamaxEx(cublasHandle_t handle, int n, const void* x, cudaDataType xType, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIamaxEx_64(cublasHandle_t handle, int64_t n, const void* x, cudaDataType xType, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIsamin_v2(cublasHandle_t handle, int n, const float* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIsamin_v2_64(cublasHandle_t handle, int64_t n, const float* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIdamin_v2(cublasHandle_t handle, int n, const double* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIdamin_v2_64(cublasHandle_t handle, int64_t n, const double* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIcamin_v2(cublasHandle_t handle, int n, const cuComplex* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIcamin_v2_64(cublasHandle_t handle, int64_t n, const cuComplex* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIzamin_v2(cublasHandle_t handle, int n, const cuDoubleComplex* x, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIzamin_v2_64(cublasHandle_t handle, int64_t n, const cuDoubleComplex* x, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIaminEx(cublasHandle_t handle, int n, const void* x, cudaDataType xType, int incx, int* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasIaminEx_64(cublasHandle_t handle, int64_t n, const void* x, cudaDataType xType, int64_t incx, int64_t* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasAsumEx(cublasHandle_t handle,
+                                                   int n,
+                                                   const void* x,
+                                                   cudaDataType xType,
+                                                   int incx,
+                                                   void* result,
+                                                   cudaDataType resultType,
+                                                   cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasAsumEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      const void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      void* result,
+                                                      cudaDataType resultType,
+                                                      cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSasum_v2(cublasHandle_t handle, int n, const float* x, int incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSasum_v2_64(cublasHandle_t handle, int64_t n, const float* x, int64_t incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDasum_v2(cublasHandle_t handle, int n, const double* x, int incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDasum_v2_64(cublasHandle_t handle, int64_t n, const double* x, int64_t incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasScasum_v2(cublasHandle_t handle, int n, const cuComplex* x, int incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasScasum_v2_64(cublasHandle_t handle, int64_t n, const cuComplex* x, int64_t incx, float* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDzasum_v2(cublasHandle_t handle, int n, const cuDoubleComplex* x, int incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDzasum_v2_64(cublasHandle_t handle, int64_t n, const cuDoubleComplex* x, int64_t incx, double* result);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSrot_v2(cublasHandle_t handle, int n, float* x, int incx, float* y, int incy, const float* c, const float* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSrot_v2_64(
+    cublasHandle_t handle, int64_t n, float* x, int64_t incx, float* y, int64_t incy, const float* c, const float* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDrot_v2(cublasHandle_t handle, int n, double* x, int incx, double* y, int incy, const double* c, const double* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDrot_v2_64(cublasHandle_t handle,
+                                                       int64_t n,
+                                                       double* x,
+                                                       int64_t incx,
+                                                       double* y,
+                                                       int64_t incy,
+                                                       const double* c,
+                                                       const double* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCrot_v2(
+    cublasHandle_t handle, int n, cuComplex* x, int incx, cuComplex* y, int incy, const float* c, const cuComplex* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCrot_v2_64(cublasHandle_t handle,
+                                                       int64_t n,
+                                                       cuComplex* x,
+                                                       int64_t incx,
+                                                       cuComplex* y,
+                                                       int64_t incy,
+                                                       const float* c,
+                                                       const cuComplex* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsrot_v2(
+    cublasHandle_t handle, int n, cuComplex* x, int incx, cuComplex* y, int incy, const float* c, const float* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsrot_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        cuComplex* x,
+                                                        int64_t incx,
+                                                        cuComplex* y,
+                                                        int64_t incy,
+                                                        const float* c,
+                                                        const float* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZrot_v2(cublasHandle_t handle,
+                                                    int n,
+                                                    cuDoubleComplex* x,
+                                                    int incx,
+                                                    cuDoubleComplex* y,
+                                                    int incy,
+                                                    const double* c,
+                                                    const cuDoubleComplex* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZrot_v2_64(cublasHandle_t handle,
+                                                       int64_t n,
+                                                       cuDoubleComplex* x,
+                                                       int64_t incx,
+                                                       cuDoubleComplex* y,
+                                                       int64_t incy,
+                                                       const double* c,
+                                                       const cuDoubleComplex* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdrot_v2(cublasHandle_t handle,
+                                                     int n,
+                                                     cuDoubleComplex* x,
+                                                     int incx,
+                                                     cuDoubleComplex* y,
+                                                     int incy,
+                                                     const double* c,
+                                                     const double* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdrot_v2_64(cublasHandle_t handle,
+                                                        int64_t n,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        const double* c,
+                                                        const double* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasRotEx(cublasHandle_t handle,
+                                                  int n,
+                                                  void* x,
+                                                  cudaDataType xType,
+                                                  int incx,
+                                                  void* y,
+                                                  cudaDataType yType,
+                                                  int incy,
+                                                  const void* c,
+                                                  const void* s,
+                                                  cudaDataType csType,
+                                                  cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasRotEx_64(cublasHandle_t handle,
+                                                     int64_t n,
+                                                     void* x,
+                                                     cudaDataType xType,
+                                                     int64_t incx,
+                                                     void* y,
+                                                     cudaDataType yType,
+                                                     int64_t incy,
+                                                     const void* c,
+                                                     const void* s,
+                                                     cudaDataType csType,
+                                                     cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSrotg_v2(cublasHandle_t handle, float* a, float* b, float* c, float* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDrotg_v2(cublasHandle_t handle, double* a, double* b, double* c, double* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCrotg_v2(cublasHandle_t handle, cuComplex* a, cuComplex* b, float* c, cuComplex* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasZrotg_v2(cublasHandle_t handle, cuDoubleComplex* a, cuDoubleComplex* b, double* c, cuDoubleComplex* s);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasRotgEx(cublasHandle_t handle,
+                                                   void* a,
+                                                   void* b,
+                                                   cudaDataType abType,
+                                                   void* c,
+                                                   void* s,
+                                                   cudaDataType csType,
+                                                   cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSrotm_v2(cublasHandle_t handle, int n, float* x, int incx, float* y, int incy, const float* param);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSrotm_v2_64(cublasHandle_t handle, int64_t n, float* x, int64_t incx, float* y, int64_t incy, const float* param);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDrotm_v2(cublasHandle_t handle, int n, double* x, int incx, double* y, int incy, const double* param);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDrotm_v2_64(
+    cublasHandle_t handle, int64_t n, double* x, int64_t incx, double* y, int64_t incy, const double* param);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasRotmEx(cublasHandle_t handle,
+                                                   int n,
+                                                   void* x,
+                                                   cudaDataType xType,
+                                                   int incx,
+                                                   void* y,
+                                                   cudaDataType yType,
+                                                   int incy,
+                                                   const void* param,
+                                                   cudaDataType paramType,
+                                                   cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasRotmEx_64(cublasHandle_t handle,
+                                                      int64_t n,
+                                                      void* x,
+                                                      cudaDataType xType,
+                                                      int64_t incx,
+                                                      void* y,
+                                                      cudaDataType yType,
+                                                      int64_t incy,
+                                                      const void* param,
+                                                      cudaDataType paramType,
+                                                      cudaDataType executiontype);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSrotmg_v2(cublasHandle_t handle, float* d1, float* d2, float* x1, const float* y1, float* param);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDrotmg_v2(cublasHandle_t handle, double* d1, double* d2, double* x1, const double* y1, double* param);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasRotmgEx(cublasHandle_t handle,
+                                                    void* d1,
+                                                    cudaDataType d1Type,
+                                                    void* d2,
+                                                    cudaDataType d2Type,
+                                                    void* x1,
+                                                    cudaDataType x1Type,
+                                                    const void* y1,
+                                                    cudaDataType y1Type,
+                                                    void* param,
+                                                    cudaDataType paramType,
+                                                    cudaDataType executiontype);
+
+/* --------------- CUBLAS BLAS2 Functions  ---------------- */
+
+/* GEMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* beta,
+                                                     float* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* beta,
+                                                        float* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* beta,
+                                                     double* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* beta,
+                                                        double* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* beta,
+                                                     cuComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* beta,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+/* GBMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgbmv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     int kl,
+                                                     int ku,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* beta,
+                                                     float* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgbmv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t kl,
+                                                        int64_t ku,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* beta,
+                                                        float* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgbmv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     int kl,
+                                                     int ku,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* beta,
+                                                     double* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgbmv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t kl,
+                                                        int64_t ku,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* beta,
+                                                        double* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgbmv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     int kl,
+                                                     int ku,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* beta,
+                                                     cuComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgbmv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t kl,
+                                                        int64_t ku,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* beta,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgbmv_v2(cublasHandle_t handle,
+                                                     cublasOperation_t trans,
+                                                     int m,
+                                                     int n,
+                                                     int kl,
+                                                     int ku,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgbmv_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t trans,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t kl,
+                                                        int64_t ku,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+/* TRMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const float* A,
+                                                     int lda,
+                                                     float* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        float* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const double* A,
+                                                     int lda,
+                                                     double* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        double* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     cuComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        cuComplex* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     cuDoubleComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx);
+
+/* TBMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const float* A,
+                                                     int lda,
+                                                     float* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        float* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const double* A,
+                                                     int lda,
+                                                     double* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        double* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     cuComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        cuComplex* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     cuDoubleComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx);
+
+/* TPMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStpmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const float* AP,
+                                                     float* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStpmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const float* AP,
+                                                        float* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtpmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const double* AP,
+                                                     double* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtpmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const double* AP,
+                                                        double* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtpmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuComplex* AP,
+                                                     cuComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtpmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuComplex* AP,
+                                                        cuComplex* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtpmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuDoubleComplex* AP,
+                                                     cuDoubleComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtpmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* AP,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx);
+
+/* TRSV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const float* A,
+                                                     int lda,
+                                                     float* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        float* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const double* A,
+                                                     int lda,
+                                                     double* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        double* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     cuComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        cuComplex* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     cuDoubleComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx);
+
+/* TPSV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStpsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const float* AP,
+                                                     float* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStpsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const float* AP,
+                                                        float* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtpsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const double* AP,
+                                                     double* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtpsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const double* AP,
+                                                        double* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtpsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuComplex* AP,
+                                                     cuComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtpsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuComplex* AP,
+                                                        cuComplex* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtpsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     const cuDoubleComplex* AP,
+                                                     cuDoubleComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtpsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* AP,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx);
+
+/* TBSV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStbsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const float* A,
+                                                     int lda,
+                                                     float* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStbsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        float* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtbsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const double* A,
+                                                     int lda,
+                                                     double* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtbsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        double* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtbsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     cuComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtbsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        cuComplex* x,
+                                                        int64_t incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtbsv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int n,
+                                                     int k,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     cuDoubleComplex* x,
+                                                     int incx);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtbsv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        cuDoubleComplex* x,
+                                                        int64_t incx);
+
+/* SYMV/HEMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsymv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* beta,
+                                                     float* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsymv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* beta,
+                                                        float* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsymv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* beta,
+                                                     double* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsymv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* beta,
+                                                        double* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsymv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* beta,
+                                                     cuComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsymv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* beta,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsymv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsymv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChemv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* beta,
+                                                     cuComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChemv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* beta,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhemv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhemv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+/* SBMV/HBMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     int k,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* beta,
+                                                     float* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* beta,
+                                                        float* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     int k,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* beta,
+                                                     double* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* beta,
+                                                        double* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     int k,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* beta,
+                                                     cuComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* beta,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhbmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     int k,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhbmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+/* SPMV/HPMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSspmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* AP,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* beta,
+                                                     float* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSspmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* AP,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* beta,
+                                                        float* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDspmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* AP,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* beta,
+                                                     double* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDspmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* AP,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* beta,
+                                                        double* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChpmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* AP,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* beta,
+                                                     cuComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChpmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* AP,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* beta,
+                                                        cuComplex* y,
+                                                        int64_t incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhpmv_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* AP,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* y,
+                                                     int incy);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhpmv_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* AP,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* y,
+                                                        int64_t incy);
+
+/* GER */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSger_v2(cublasHandle_t handle,
+                                                    int m,
+                                                    int n,
+                                                    const float* alpha,
+                                                    const float* x,
+                                                    int incx,
+                                                    const float* y,
+                                                    int incy,
+                                                    float* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSger_v2_64(cublasHandle_t handle,
+                                                       int64_t m,
+                                                       int64_t n,
+                                                       const float* alpha,
+                                                       const float* x,
+                                                       int64_t incx,
+                                                       const float* y,
+                                                       int64_t incy,
+                                                       float* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDger_v2(cublasHandle_t handle,
+                                                    int m,
+                                                    int n,
+                                                    const double* alpha,
+                                                    const double* x,
+                                                    int incx,
+                                                    const double* y,
+                                                    int incy,
+                                                    double* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDger_v2_64(cublasHandle_t handle,
+                                                       int64_t m,
+                                                       int64_t n,
+                                                       const double* alpha,
+                                                       const double* x,
+                                                       int64_t incx,
+                                                       const double* y,
+                                                       int64_t incy,
+                                                       double* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgeru_v2(cublasHandle_t handle,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* y,
+                                                     int incy,
+                                                     cuComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgeru_v2_64(cublasHandle_t handle,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgerc_v2(cublasHandle_t handle,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* y,
+                                                     int incy,
+                                                     cuComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgerc_v2_64(cublasHandle_t handle,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgeru_v2(cublasHandle_t handle,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgeru_v2_64(cublasHandle_t handle,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgerc_v2(cublasHandle_t handle,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgerc_v2_64(cublasHandle_t handle,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* A,
+                                                        int64_t lda);
+
+/* SYR/HER */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyr_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const float* alpha,
+                                                    const float* x,
+                                                    int incx,
+                                                    float* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const float* alpha,
+                                                       const float* x,
+                                                       int64_t incx,
+                                                       float* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyr_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const double* alpha,
+                                                    const double* x,
+                                                    int incx,
+                                                    double* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const double* alpha,
+                                                       const double* x,
+                                                       int64_t incx,
+                                                       double* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyr_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const cuComplex* alpha,
+                                                    const cuComplex* x,
+                                                    int incx,
+                                                    cuComplex* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const cuComplex* alpha,
+                                                       const cuComplex* x,
+                                                       int64_t incx,
+                                                       cuComplex* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyr_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const cuDoubleComplex* alpha,
+                                                    const cuDoubleComplex* x,
+                                                    int incx,
+                                                    cuDoubleComplex* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const cuDoubleComplex* alpha,
+                                                       const cuDoubleComplex* x,
+                                                       int64_t incx,
+                                                       cuDoubleComplex* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCher_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const float* alpha,
+                                                    const cuComplex* x,
+                                                    int incx,
+                                                    cuComplex* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCher_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const float* alpha,
+                                                       const cuComplex* x,
+                                                       int64_t incx,
+                                                       cuComplex* A,
+                                                       int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZher_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const double* alpha,
+                                                    const cuDoubleComplex* x,
+                                                    int incx,
+                                                    cuDoubleComplex* A,
+                                                    int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZher_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const double* alpha,
+                                                       const cuDoubleComplex* x,
+                                                       int64_t incx,
+                                                       cuDoubleComplex* A,
+                                                       int64_t lda);
+
+/* SPR/HPR */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSspr_v2(
+    cublasHandle_t handle, cublasFillMode_t uplo, int n, const float* alpha, const float* x, int incx, float* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSspr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const float* alpha,
+                                                       const float* x,
+                                                       int64_t incx,
+                                                       float* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDspr_v2(
+    cublasHandle_t handle, cublasFillMode_t uplo, int n, const double* alpha, const double* x, int incx, double* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDspr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const double* alpha,
+                                                       const double* x,
+                                                       int64_t incx,
+                                                       double* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChpr_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const float* alpha,
+                                                    const cuComplex* x,
+                                                    int incx,
+                                                    cuComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChpr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const float* alpha,
+                                                       const cuComplex* x,
+                                                       int64_t incx,
+                                                       cuComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhpr_v2(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    int n,
+                                                    const double* alpha,
+                                                    const cuDoubleComplex* x,
+                                                    int incx,
+                                                    cuDoubleComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhpr_v2_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       int64_t n,
+                                                       const double* alpha,
+                                                       const cuDoubleComplex* x,
+                                                       int64_t incx,
+                                                       cuDoubleComplex* AP);
+
+/* SYR2/HER2 */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* y,
+                                                     int incy,
+                                                     float* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* y,
+                                                        int64_t incy,
+                                                        float* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* y,
+                                                     int incy,
+                                                     double* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* y,
+                                                        int64_t incy,
+                                                        double* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* y,
+                                                     int incy,
+                                                     cuComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCher2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* y,
+                                                     int incy,
+                                                     cuComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCher2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* A,
+                                                        int64_t lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZher2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* A,
+                                                     int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZher2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* A,
+                                                        int64_t lda);
+
+/* SPR2/HPR2 */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSspr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* x,
+                                                     int incx,
+                                                     const float* y,
+                                                     int incy,
+                                                     float* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSspr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* x,
+                                                        int64_t incx,
+                                                        const float* y,
+                                                        int64_t incy,
+                                                        float* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDspr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* x,
+                                                     int incx,
+                                                     const double* y,
+                                                     int incy,
+                                                     double* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDspr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* x,
+                                                        int64_t incx,
+                                                        const double* y,
+                                                        int64_t incy,
+                                                        double* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChpr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* x,
+                                                     int incx,
+                                                     const cuComplex* y,
+                                                     int incy,
+                                                     cuComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChpr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* x,
+                                                        int64_t incx,
+                                                        const cuComplex* y,
+                                                        int64_t incy,
+                                                        cuComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhpr2_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* x,
+                                                     int incx,
+                                                     const cuDoubleComplex* y,
+                                                     int incy,
+                                                     cuDoubleComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhpr2_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* x,
+                                                        int64_t incx,
+                                                        const cuDoubleComplex* y,
+                                                        int64_t incy,
+                                                        cuDoubleComplex* AP);
+
+/* BATCH GEMV */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemvBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         const float* alpha,
+                                                         const float* const Aarray[],
+                                                         int lda,
+                                                         const float* const xarray[],
+                                                         int incx,
+                                                         const float* beta,
+                                                         float* const yarray[],
+                                                         int incy,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemvBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t trans,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const float* alpha,
+                                                            const float* const Aarray[],
+                                                            int64_t lda,
+                                                            const float* const xarray[],
+                                                            int64_t incx,
+                                                            const float* beta,
+                                                            float* const yarray[],
+                                                            int64_t incy,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemvBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         const double* alpha,
+                                                         const double* const Aarray[],
+                                                         int lda,
+                                                         const double* const xarray[],
+                                                         int incx,
+                                                         const double* beta,
+                                                         double* const yarray[],
+                                                         int incy,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemvBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t trans,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const double* alpha,
+                                                            const double* const Aarray[],
+                                                            int64_t lda,
+                                                            const double* const xarray[],
+                                                            int64_t incx,
+                                                            const double* beta,
+                                                            double* const yarray[],
+                                                            int64_t incy,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemvBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         const cuComplex* alpha,
+                                                         const cuComplex* const Aarray[],
+                                                         int lda,
+                                                         const cuComplex* const xarray[],
+                                                         int incx,
+                                                         const cuComplex* beta,
+                                                         cuComplex* const yarray[],
+                                                         int incy,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemvBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t trans,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const cuComplex* alpha,
+                                                            const cuComplex* const Aarray[],
+                                                            int64_t lda,
+                                                            const cuComplex* const xarray[],
+                                                            int64_t incx,
+                                                            const cuComplex* beta,
+                                                            cuComplex* const yarray[],
+                                                            int64_t incy,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemvBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         const cuDoubleComplex* alpha,
+                                                         const cuDoubleComplex* const Aarray[],
+                                                         int lda,
+                                                         const cuDoubleComplex* const xarray[],
+                                                         int incx,
+                                                         const cuDoubleComplex* beta,
+                                                         cuDoubleComplex* const yarray[],
+                                                         int incy,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemvBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t trans,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const cuDoubleComplex* alpha,
+                                                            const cuDoubleComplex* const Aarray[],
+                                                            int64_t lda,
+                                                            const cuDoubleComplex* const xarray[],
+                                                            int64_t incx,
+                                                            const cuDoubleComplex* beta,
+                                                            cuDoubleComplex* const yarray[],
+                                                            int64_t incy,
+                                                            int64_t batchCount);
+
+#if defined(__cplusplus)
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSHgemvBatched(cublasHandle_t handle,
+                                                           cublasOperation_t trans,
+                                                           int m,
+                                                           int n,
+                                                           const float* alpha,
+                                                           const __half* const Aarray[],
+                                                           int lda,
+                                                           const __half* const xarray[],
+                                                           int incx,
+                                                           const float* beta,
+                                                           __half* const yarray[],
+                                                           int incy,
+                                                           int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSHgemvBatched_64(cublasHandle_t handle,
+                                                              cublasOperation_t trans,
+                                                              int64_t m,
+                                                              int64_t n,
+                                                              const float* alpha,
+                                                              const __half* const Aarray[],
+                                                              int64_t lda,
+                                                              const __half* const xarray[],
+                                                              int64_t incx,
+                                                              const float* beta,
+                                                              __half* const yarray[],
+                                                              int64_t incy,
+                                                              int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSSgemvBatched(cublasHandle_t handle,
+                                                           cublasOperation_t trans,
+                                                           int m,
+                                                           int n,
+                                                           const float* alpha,
+                                                           const __half* const Aarray[],
+                                                           int lda,
+                                                           const __half* const xarray[],
+                                                           int incx,
+                                                           const float* beta,
+                                                           float* const yarray[],
+                                                           int incy,
+                                                           int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSSgemvBatched_64(cublasHandle_t handle,
+                                                              cublasOperation_t trans,
+                                                              int64_t m,
+                                                              int64_t n,
+                                                              const float* alpha,
+                                                              const __half* const Aarray[],
+                                                              int64_t lda,
+                                                              const __half* const xarray[],
+                                                              int64_t incx,
+                                                              const float* beta,
+                                                              float* const yarray[],
+                                                              int64_t incy,
+                                                              int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSTgemvBatched(cublasHandle_t handle,
+                                                           cublasOperation_t trans,
+                                                           int m,
+                                                           int n,
+                                                           const float* alpha,
+                                                           const __nv_bfloat16* const Aarray[],
+                                                           int lda,
+                                                           const __nv_bfloat16* const xarray[],
+                                                           int incx,
+                                                           const float* beta,
+                                                           __nv_bfloat16* const yarray[],
+                                                           int incy,
+                                                           int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSTgemvBatched_64(cublasHandle_t handle,
+                                                              cublasOperation_t trans,
+                                                              int64_t m,
+                                                              int64_t n,
+                                                              const float* alpha,
+                                                              const __nv_bfloat16* const Aarray[],
+                                                              int64_t lda,
+                                                              const __nv_bfloat16* const xarray[],
+                                                              int64_t incx,
+                                                              const float* beta,
+                                                              __nv_bfloat16* const yarray[],
+                                                              int64_t incy,
+                                                              int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSSgemvBatched(cublasHandle_t handle,
+                                                           cublasOperation_t trans,
+                                                           int m,
+                                                           int n,
+                                                           const float* alpha,
+                                                           const __nv_bfloat16* const Aarray[],
+                                                           int lda,
+                                                           const __nv_bfloat16* const xarray[],
+                                                           int incx,
+                                                           const float* beta,
+                                                           float* const yarray[],
+                                                           int incy,
+                                                           int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSSgemvBatched_64(cublasHandle_t handle,
+                                                              cublasOperation_t trans,
+                                                              int64_t m,
+                                                              int64_t n,
+                                                              const float* alpha,
+                                                              const __nv_bfloat16* const Aarray[],
+                                                              int64_t lda,
+                                                              const __nv_bfloat16* const xarray[],
+                                                              int64_t incx,
+                                                              const float* beta,
+                                                              float* const yarray[],
+                                                              int64_t incy,
+                                                              int64_t batchCount);
+
+#endif
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemvStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t trans,
+                                                                int m,
+                                                                int n,
+                                                                const float* alpha,
+                                                                const float* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const float* x,
+                                                                int incx,
+                                                                long long int stridex,
+                                                                const float* beta,
+                                                                float* y,
+                                                                int incy,
+                                                                long long int stridey,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemvStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t trans,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   const float* alpha,
+                                                                   const float* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const float* x,
+                                                                   int64_t incx,
+                                                                   long long int stridex,
+                                                                   const float* beta,
+                                                                   float* y,
+                                                                   int64_t incy,
+                                                                   long long int stridey,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemvStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t trans,
+                                                                int m,
+                                                                int n,
+                                                                const double* alpha,
+                                                                const double* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const double* x,
+                                                                int incx,
+                                                                long long int stridex,
+                                                                const double* beta,
+                                                                double* y,
+                                                                int incy,
+                                                                long long int stridey,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemvStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t trans,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   const double* alpha,
+                                                                   const double* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const double* x,
+                                                                   int64_t incx,
+                                                                   long long int stridex,
+                                                                   const double* beta,
+                                                                   double* y,
+                                                                   int64_t incy,
+                                                                   long long int stridey,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemvStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t trans,
+                                                                int m,
+                                                                int n,
+                                                                const cuComplex* alpha,
+                                                                const cuComplex* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const cuComplex* x,
+                                                                int incx,
+                                                                long long int stridex,
+                                                                const cuComplex* beta,
+                                                                cuComplex* y,
+                                                                int incy,
+                                                                long long int stridey,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemvStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t trans,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   const cuComplex* alpha,
+                                                                   const cuComplex* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const cuComplex* x,
+                                                                   int64_t incx,
+                                                                   long long int stridex,
+                                                                   const cuComplex* beta,
+                                                                   cuComplex* y,
+                                                                   int64_t incy,
+                                                                   long long int stridey,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemvStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t trans,
+                                                                int m,
+                                                                int n,
+                                                                const cuDoubleComplex* alpha,
+                                                                const cuDoubleComplex* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const cuDoubleComplex* x,
+                                                                int incx,
+                                                                long long int stridex,
+                                                                const cuDoubleComplex* beta,
+                                                                cuDoubleComplex* y,
+                                                                int incy,
+                                                                long long int stridey,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemvStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t trans,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   const cuDoubleComplex* alpha,
+                                                                   const cuDoubleComplex* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const cuDoubleComplex* x,
+                                                                   int64_t incx,
+                                                                   long long int stridex,
+                                                                   const cuDoubleComplex* beta,
+                                                                   cuDoubleComplex* y,
+                                                                   int64_t incy,
+                                                                   long long int stridey,
+                                                                   int64_t batchCount);
+
+#if defined(__cplusplus)
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSHgemvStridedBatched(cublasHandle_t handle,
+                                                                  cublasOperation_t trans,
+                                                                  int m,
+                                                                  int n,
+                                                                  const float* alpha,
+                                                                  const __half* A,
+                                                                  int lda,
+                                                                  long long int strideA,
+                                                                  const __half* x,
+                                                                  int incx,
+                                                                  long long int stridex,
+                                                                  const float* beta,
+                                                                  __half* y,
+                                                                  int incy,
+                                                                  long long int stridey,
+                                                                  int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSHgemvStridedBatched_64(cublasHandle_t handle,
+                                                                     cublasOperation_t trans,
+                                                                     int64_t m,
+                                                                     int64_t n,
+                                                                     const float* alpha,
+                                                                     const __half* A,
+                                                                     int64_t lda,
+                                                                     long long int strideA,
+                                                                     const __half* x,
+                                                                     int64_t incx,
+                                                                     long long int stridex,
+                                                                     const float* beta,
+                                                                     __half* y,
+                                                                     int64_t incy,
+                                                                     long long int stridey,
+                                                                     int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSSgemvStridedBatched(cublasHandle_t handle,
+                                                                  cublasOperation_t trans,
+                                                                  int m,
+                                                                  int n,
+                                                                  const float* alpha,
+                                                                  const __half* A,
+                                                                  int lda,
+                                                                  long long int strideA,
+                                                                  const __half* x,
+                                                                  int incx,
+                                                                  long long int stridex,
+                                                                  const float* beta,
+                                                                  float* y,
+                                                                  int incy,
+                                                                  long long int stridey,
+                                                                  int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHSSgemvStridedBatched_64(cublasHandle_t handle,
+                                                                     cublasOperation_t trans,
+                                                                     int64_t m,
+                                                                     int64_t n,
+                                                                     const float* alpha,
+                                                                     const __half* A,
+                                                                     int64_t lda,
+                                                                     long long int strideA,
+                                                                     const __half* x,
+                                                                     int64_t incx,
+                                                                     long long int stridex,
+                                                                     const float* beta,
+                                                                     float* y,
+                                                                     int64_t incy,
+                                                                     long long int stridey,
+                                                                     int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSTgemvStridedBatched(cublasHandle_t handle,
+                                                                  cublasOperation_t trans,
+                                                                  int m,
+                                                                  int n,
+                                                                  const float* alpha,
+                                                                  const __nv_bfloat16* A,
+                                                                  int lda,
+                                                                  long long int strideA,
+                                                                  const __nv_bfloat16* x,
+                                                                  int incx,
+                                                                  long long int stridex,
+                                                                  const float* beta,
+                                                                  __nv_bfloat16* y,
+                                                                  int incy,
+                                                                  long long int stridey,
+                                                                  int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSTgemvStridedBatched_64(cublasHandle_t handle,
+                                                                     cublasOperation_t trans,
+                                                                     int64_t m,
+                                                                     int64_t n,
+                                                                     const float* alpha,
+                                                                     const __nv_bfloat16* A,
+                                                                     int64_t lda,
+                                                                     long long int strideA,
+                                                                     const __nv_bfloat16* x,
+                                                                     int64_t incx,
+                                                                     long long int stridex,
+                                                                     const float* beta,
+                                                                     __nv_bfloat16* y,
+                                                                     int64_t incy,
+                                                                     long long int stridey,
+                                                                     int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSSgemvStridedBatched(cublasHandle_t handle,
+                                                                  cublasOperation_t trans,
+                                                                  int m,
+                                                                  int n,
+                                                                  const float* alpha,
+                                                                  const __nv_bfloat16* A,
+                                                                  int lda,
+                                                                  long long int strideA,
+                                                                  const __nv_bfloat16* x,
+                                                                  int incx,
+                                                                  long long int stridex,
+                                                                  const float* beta,
+                                                                  float* y,
+                                                                  int incy,
+                                                                  long long int stridey,
+                                                                  int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasTSSgemvStridedBatched_64(cublasHandle_t handle,
+                                                                     cublasOperation_t trans,
+                                                                     int64_t m,
+                                                                     int64_t n,
+                                                                     const float* alpha,
+                                                                     const __nv_bfloat16* A,
+                                                                     int64_t lda,
+                                                                     long long int strideA,
+                                                                     const __nv_bfloat16* x,
+                                                                     int64_t incx,
+                                                                     long long int stridex,
+                                                                     const float* beta,
+                                                                     float* y,
+                                                                     int64_t incy,
+                                                                     long long int stridey,
+                                                                     int64_t batchCount);
+
+#endif
+
+/* ---------------- CUBLAS BLAS3 Functions ---------------- */
+
+/* GEMM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemm_v2(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int m,
+                                                     int n,
+                                                     int k,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* B,
+                                                     int ldb,
+                                                     const float* beta,
+                                                     float* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemm_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t transa,
+                                                        cublasOperation_t transb,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* B,
+                                                        int64_t ldb,
+                                                        const float* beta,
+                                                        float* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemm_v2(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int m,
+                                                     int n,
+                                                     int k,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* B,
+                                                     int ldb,
+                                                     const double* beta,
+                                                     double* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemm_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t transa,
+                                                        cublasOperation_t transb,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* B,
+                                                        int64_t ldb,
+                                                        const double* beta,
+                                                        double* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm_v2(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int m,
+                                                     int n,
+                                                     int k,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* B,
+                                                     int ldb,
+                                                     const cuComplex* beta,
+                                                     cuComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t transa,
+                                                        cublasOperation_t transb,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* B,
+                                                        int64_t ldb,
+                                                        const cuComplex* beta,
+                                                        cuComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3m(cublasHandle_t handle,
+                                                    cublasOperation_t transa,
+                                                    cublasOperation_t transb,
+                                                    int m,
+                                                    int n,
+                                                    int k,
+                                                    const cuComplex* alpha,
+                                                    const cuComplex* A,
+                                                    int lda,
+                                                    const cuComplex* B,
+                                                    int ldb,
+                                                    const cuComplex* beta,
+                                                    cuComplex* C,
+                                                    int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3m_64(cublasHandle_t handle,
+                                                       cublasOperation_t transa,
+                                                       cublasOperation_t transb,
+                                                       int64_t m,
+                                                       int64_t n,
+                                                       int64_t k,
+                                                       const cuComplex* alpha,
+                                                       const cuComplex* A,
+                                                       int64_t lda,
+                                                       const cuComplex* B,
+                                                       int64_t ldb,
+                                                       const cuComplex* beta,
+                                                       cuComplex* C,
+                                                       int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3mEx(cublasHandle_t handle,
+                                                      cublasOperation_t transa,
+                                                      cublasOperation_t transb,
+                                                      int m,
+                                                      int n,
+                                                      int k,
+                                                      const cuComplex* alpha,
+                                                      const void* A,
+                                                      cudaDataType Atype,
+                                                      int lda,
+                                                      const void* B,
+                                                      cudaDataType Btype,
+                                                      int ldb,
+                                                      const cuComplex* beta,
+                                                      void* C,
+                                                      cudaDataType Ctype,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3mEx_64(cublasHandle_t handle,
+                                                         cublasOperation_t transa,
+                                                         cublasOperation_t transb,
+                                                         int64_t m,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const cuComplex* alpha,
+                                                         const void* A,
+                                                         cudaDataType Atype,
+                                                         int64_t lda,
+                                                         const void* B,
+                                                         cudaDataType Btype,
+                                                         int64_t ldb,
+                                                         const cuComplex* beta,
+                                                         void* C,
+                                                         cudaDataType Ctype,
+                                                         int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemm_v2(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int m,
+                                                     int n,
+                                                     int k,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* B,
+                                                     int ldb,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemm_v2_64(cublasHandle_t handle,
+                                                        cublasOperation_t transa,
+                                                        cublasOperation_t transb,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* B,
+                                                        int64_t ldb,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemm3m(cublasHandle_t handle,
+                                                    cublasOperation_t transa,
+                                                    cublasOperation_t transb,
+                                                    int m,
+                                                    int n,
+                                                    int k,
+                                                    const cuDoubleComplex* alpha,
+                                                    const cuDoubleComplex* A,
+                                                    int lda,
+                                                    const cuDoubleComplex* B,
+                                                    int ldb,
+                                                    const cuDoubleComplex* beta,
+                                                    cuDoubleComplex* C,
+                                                    int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemm3m_64(cublasHandle_t handle,
+                                                       cublasOperation_t transa,
+                                                       cublasOperation_t transb,
+                                                       int64_t m,
+                                                       int64_t n,
+                                                       int64_t k,
+                                                       const cuDoubleComplex* alpha,
+                                                       const cuDoubleComplex* A,
+                                                       int64_t lda,
+                                                       const cuDoubleComplex* B,
+                                                       int64_t ldb,
+                                                       const cuDoubleComplex* beta,
+                                                       cuDoubleComplex* C,
+                                                       int64_t ldc);
+
+#if defined(__cplusplus)
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHgemm(cublasHandle_t handle,
+                                                  cublasOperation_t transa,
+                                                  cublasOperation_t transb,
+                                                  int m,
+                                                  int n,
+                                                  int k,
+                                                  const __half* alpha,
+                                                  const __half* A,
+                                                  int lda,
+                                                  const __half* B,
+                                                  int ldb,
+                                                  const __half* beta,
+                                                  __half* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHgemm_64(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     int64_t k,
+                                                     const __half* alpha,
+                                                     const __half* A,
+                                                     int64_t lda,
+                                                     const __half* B,
+                                                     int64_t ldb,
+                                                     const __half* beta,
+                                                     __half* C,
+                                                     int64_t ldc);
+
+#endif
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmEx(cublasHandle_t handle,
+                                                    cublasOperation_t transa,
+                                                    cublasOperation_t transb,
+                                                    int m,
+                                                    int n,
+                                                    int k,
+                                                    const float* alpha,
+                                                    const void* A,
+                                                    cudaDataType Atype,
+                                                    int lda,
+                                                    const void* B,
+                                                    cudaDataType Btype,
+                                                    int ldb,
+                                                    const float* beta,
+                                                    void* C,
+                                                    cudaDataType Ctype,
+                                                    int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmEx_64(cublasHandle_t handle,
+                                                       cublasOperation_t transa,
+                                                       cublasOperation_t transb,
+                                                       int64_t m,
+                                                       int64_t n,
+                                                       int64_t k,
+                                                       const float* alpha,
+                                                       const void* A,
+                                                       cudaDataType Atype,
+                                                       int64_t lda,
+                                                       const void* B,
+                                                       cudaDataType Btype,
+                                                       int64_t ldb,
+                                                       const float* beta,
+                                                       void* C,
+                                                       cudaDataType Ctype,
+                                                       int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmEx(cublasHandle_t handle,
+                                                   cublasOperation_t transa,
+                                                   cublasOperation_t transb,
+                                                   int m,
+                                                   int n,
+                                                   int k,
+                                                   const void* alpha,
+                                                   const void* A,
+                                                   cudaDataType Atype,
+                                                   int lda,
+                                                   const void* B,
+                                                   cudaDataType Btype,
+                                                   int ldb,
+                                                   const void* beta,
+                                                   void* C,
+                                                   cudaDataType Ctype,
+                                                   int ldc,
+                                                   cublasComputeType_t computeType,
+                                                   cublasGemmAlgo_t algo);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmEx_64(cublasHandle_t handle,
+                                                      cublasOperation_t transa,
+                                                      cublasOperation_t transb,
+                                                      int64_t m,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const void* alpha,
+                                                      const void* A,
+                                                      cudaDataType Atype,
+                                                      int64_t lda,
+                                                      const void* B,
+                                                      cudaDataType Btype,
+                                                      int64_t ldb,
+                                                      const void* beta,
+                                                      void* C,
+                                                      cudaDataType Ctype,
+                                                      int64_t ldc,
+                                                      cublasComputeType_t computeType,
+                                                      cublasGemmAlgo_t algo);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemmEx(cublasHandle_t handle,
+                                                    cublasOperation_t transa,
+                                                    cublasOperation_t transb,
+                                                    int m,
+                                                    int n,
+                                                    int k,
+                                                    const cuComplex* alpha,
+                                                    const void* A,
+                                                    cudaDataType Atype,
+                                                    int lda,
+                                                    const void* B,
+                                                    cudaDataType Btype,
+                                                    int ldb,
+                                                    const cuComplex* beta,
+                                                    void* C,
+                                                    cudaDataType Ctype,
+                                                    int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemmEx_64(cublasHandle_t handle,
+                                                       cublasOperation_t transa,
+                                                       cublasOperation_t transb,
+                                                       int64_t m,
+                                                       int64_t n,
+                                                       int64_t k,
+                                                       const cuComplex* alpha,
+                                                       const void* A,
+                                                       cudaDataType Atype,
+                                                       int64_t lda,
+                                                       const void* B,
+                                                       cudaDataType Btype,
+                                                       int64_t ldb,
+                                                       const cuComplex* beta,
+                                                       void* C,
+                                                       cudaDataType Ctype,
+                                                       int64_t ldc);
+
+/* SYRK */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyrk_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     int n,
+                                                     int k,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* beta,
+                                                     float* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyrk_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* beta,
+                                                        float* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyrk_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     int n,
+                                                     int k,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* beta,
+                                                     double* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyrk_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* beta,
+                                                        double* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrk_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     int n,
+                                                     int k,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* beta,
+                                                     cuComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrk_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* beta,
+                                                        cuComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyrk_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     int n,
+                                                     int k,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyrk_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrkEx(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    cublasOperation_t trans,
+                                                    int n,
+                                                    int k,
+                                                    const cuComplex* alpha,
+                                                    const void* A,
+                                                    cudaDataType Atype,
+                                                    int lda,
+                                                    const cuComplex* beta,
+                                                    void* C,
+                                                    cudaDataType Ctype,
+                                                    int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrkEx_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       cublasOperation_t trans,
+                                                       int64_t n,
+                                                       int64_t k,
+                                                       const cuComplex* alpha,
+                                                       const void* A,
+                                                       cudaDataType Atype,
+                                                       int64_t lda,
+                                                       const cuComplex* beta,
+                                                       void* C,
+                                                       cudaDataType Ctype,
+                                                       int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrk3mEx(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const cuComplex* alpha,
+                                                      const void* A,
+                                                      cudaDataType Atype,
+                                                      int lda,
+                                                      const cuComplex* beta,
+                                                      void* C,
+                                                      cudaDataType Ctype,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrk3mEx_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const cuComplex* alpha,
+                                                         const void* A,
+                                                         cudaDataType Atype,
+                                                         int64_t lda,
+                                                         const cuComplex* beta,
+                                                         void* C,
+                                                         cudaDataType Ctype,
+                                                         int64_t ldc);
+
+/* HERK */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherk_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     int n,
+                                                     int k,
+                                                     const float* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const float* beta,
+                                                     cuComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherk_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const float* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const float* beta,
+                                                        cuComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZherk_v2(cublasHandle_t handle,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     int n,
+                                                     int k,
+                                                     const double* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const double* beta,
+                                                     cuDoubleComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZherk_v2_64(cublasHandle_t handle,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        int64_t n,
+                                                        int64_t k,
+                                                        const double* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const double* beta,
+                                                        cuDoubleComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherkEx(cublasHandle_t handle,
+                                                    cublasFillMode_t uplo,
+                                                    cublasOperation_t trans,
+                                                    int n,
+                                                    int k,
+                                                    const float* alpha,
+                                                    const void* A,
+                                                    cudaDataType Atype,
+                                                    int lda,
+                                                    const float* beta,
+                                                    void* C,
+                                                    cudaDataType Ctype,
+                                                    int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherkEx_64(cublasHandle_t handle,
+                                                       cublasFillMode_t uplo,
+                                                       cublasOperation_t trans,
+                                                       int64_t n,
+                                                       int64_t k,
+                                                       const float* alpha,
+                                                       const void* A,
+                                                       cudaDataType Atype,
+                                                       int64_t lda,
+                                                       const float* beta,
+                                                       void* C,
+                                                       cudaDataType Ctype,
+                                                       int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherk3mEx(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const float* alpha,
+                                                      const void* A,
+                                                      cudaDataType Atype,
+                                                      int lda,
+                                                      const float* beta,
+                                                      void* C,
+                                                      cudaDataType Ctype,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherk3mEx_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const float* alpha,
+                                                         const void* A,
+                                                         cudaDataType Atype,
+                                                         int64_t lda,
+                                                         const float* beta,
+                                                         void* C,
+                                                         cudaDataType Ctype,
+                                                         int64_t ldc);
+
+/* SYR2K / HER2K */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyr2k_v2(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const float* alpha,
+                                                      const float* A,
+                                                      int lda,
+                                                      const float* B,
+                                                      int ldb,
+                                                      const float* beta,
+                                                      float* C,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyr2k_v2_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const float* alpha,
+                                                         const float* A,
+                                                         int64_t lda,
+                                                         const float* B,
+                                                         int64_t ldb,
+                                                         const float* beta,
+                                                         float* C,
+                                                         int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyr2k_v2(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const double* alpha,
+                                                      const double* A,
+                                                      int lda,
+                                                      const double* B,
+                                                      int ldb,
+                                                      const double* beta,
+                                                      double* C,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyr2k_v2_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const double* alpha,
+                                                         const double* A,
+                                                         int64_t lda,
+                                                         const double* B,
+                                                         int64_t ldb,
+                                                         const double* beta,
+                                                         double* C,
+                                                         int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyr2k_v2(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const cuComplex* alpha,
+                                                      const cuComplex* A,
+                                                      int lda,
+                                                      const cuComplex* B,
+                                                      int ldb,
+                                                      const cuComplex* beta,
+                                                      cuComplex* C,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyr2k_v2_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const cuComplex* alpha,
+                                                         const cuComplex* A,
+                                                         int64_t lda,
+                                                         const cuComplex* B,
+                                                         int64_t ldb,
+                                                         const cuComplex* beta,
+                                                         cuComplex* C,
+                                                         int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyr2k_v2(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const cuDoubleComplex* alpha,
+                                                      const cuDoubleComplex* A,
+                                                      int lda,
+                                                      const cuDoubleComplex* B,
+                                                      int ldb,
+                                                      const cuDoubleComplex* beta,
+                                                      cuDoubleComplex* C,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyr2k_v2_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const cuDoubleComplex* alpha,
+                                                         const cuDoubleComplex* A,
+                                                         int64_t lda,
+                                                         const cuDoubleComplex* B,
+                                                         int64_t ldb,
+                                                         const cuDoubleComplex* beta,
+                                                         cuDoubleComplex* C,
+                                                         int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCher2k_v2(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const cuComplex* alpha,
+                                                      const cuComplex* A,
+                                                      int lda,
+                                                      const cuComplex* B,
+                                                      int ldb,
+                                                      const float* beta,
+                                                      cuComplex* C,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCher2k_v2_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const cuComplex* alpha,
+                                                         const cuComplex* A,
+                                                         int64_t lda,
+                                                         const cuComplex* B,
+                                                         int64_t ldb,
+                                                         const float* beta,
+                                                         cuComplex* C,
+                                                         int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZher2k_v2(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int n,
+                                                      int k,
+                                                      const cuDoubleComplex* alpha,
+                                                      const cuDoubleComplex* A,
+                                                      int lda,
+                                                      const cuDoubleComplex* B,
+                                                      int ldb,
+                                                      const double* beta,
+                                                      cuDoubleComplex* C,
+                                                      int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZher2k_v2_64(cublasHandle_t handle,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         int64_t n,
+                                                         int64_t k,
+                                                         const cuDoubleComplex* alpha,
+                                                         const cuDoubleComplex* A,
+                                                         int64_t lda,
+                                                         const cuDoubleComplex* B,
+                                                         int64_t ldb,
+                                                         const double* beta,
+                                                         cuDoubleComplex* C,
+                                                         int64_t ldc);
+
+/* SYRKX / HERKX */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyrkx(cublasHandle_t handle,
+                                                   cublasFillMode_t uplo,
+                                                   cublasOperation_t trans,
+                                                   int n,
+                                                   int k,
+                                                   const float* alpha,
+                                                   const float* A,
+                                                   int lda,
+                                                   const float* B,
+                                                   int ldb,
+                                                   const float* beta,
+                                                   float* C,
+                                                   int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsyrkx_64(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const float* alpha,
+                                                      const float* A,
+                                                      int64_t lda,
+                                                      const float* B,
+                                                      int64_t ldb,
+                                                      const float* beta,
+                                                      float* C,
+                                                      int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyrkx(cublasHandle_t handle,
+                                                   cublasFillMode_t uplo,
+                                                   cublasOperation_t trans,
+                                                   int n,
+                                                   int k,
+                                                   const double* alpha,
+                                                   const double* A,
+                                                   int lda,
+                                                   const double* B,
+                                                   int ldb,
+                                                   const double* beta,
+                                                   double* C,
+                                                   int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsyrkx_64(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const double* alpha,
+                                                      const double* A,
+                                                      int64_t lda,
+                                                      const double* B,
+                                                      int64_t ldb,
+                                                      const double* beta,
+                                                      double* C,
+                                                      int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrkx(cublasHandle_t handle,
+                                                   cublasFillMode_t uplo,
+                                                   cublasOperation_t trans,
+                                                   int n,
+                                                   int k,
+                                                   const cuComplex* alpha,
+                                                   const cuComplex* A,
+                                                   int lda,
+                                                   const cuComplex* B,
+                                                   int ldb,
+                                                   const cuComplex* beta,
+                                                   cuComplex* C,
+                                                   int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsyrkx_64(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const cuComplex* alpha,
+                                                      const cuComplex* A,
+                                                      int64_t lda,
+                                                      const cuComplex* B,
+                                                      int64_t ldb,
+                                                      const cuComplex* beta,
+                                                      cuComplex* C,
+                                                      int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyrkx(cublasHandle_t handle,
+                                                   cublasFillMode_t uplo,
+                                                   cublasOperation_t trans,
+                                                   int n,
+                                                   int k,
+                                                   const cuDoubleComplex* alpha,
+                                                   const cuDoubleComplex* A,
+                                                   int lda,
+                                                   const cuDoubleComplex* B,
+                                                   int ldb,
+                                                   const cuDoubleComplex* beta,
+                                                   cuDoubleComplex* C,
+                                                   int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsyrkx_64(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const cuDoubleComplex* alpha,
+                                                      const cuDoubleComplex* A,
+                                                      int64_t lda,
+                                                      const cuDoubleComplex* B,
+                                                      int64_t ldb,
+                                                      const cuDoubleComplex* beta,
+                                                      cuDoubleComplex* C,
+                                                      int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherkx(cublasHandle_t handle,
+                                                   cublasFillMode_t uplo,
+                                                   cublasOperation_t trans,
+                                                   int n,
+                                                   int k,
+                                                   const cuComplex* alpha,
+                                                   const cuComplex* A,
+                                                   int lda,
+                                                   const cuComplex* B,
+                                                   int ldb,
+                                                   const float* beta,
+                                                   cuComplex* C,
+                                                   int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCherkx_64(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const cuComplex* alpha,
+                                                      const cuComplex* A,
+                                                      int64_t lda,
+                                                      const cuComplex* B,
+                                                      int64_t ldb,
+                                                      const float* beta,
+                                                      cuComplex* C,
+                                                      int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZherkx(cublasHandle_t handle,
+                                                   cublasFillMode_t uplo,
+                                                   cublasOperation_t trans,
+                                                   int n,
+                                                   int k,
+                                                   const cuDoubleComplex* alpha,
+                                                   const cuDoubleComplex* A,
+                                                   int lda,
+                                                   const cuDoubleComplex* B,
+                                                   int ldb,
+                                                   const double* beta,
+                                                   cuDoubleComplex* C,
+                                                   int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZherkx_64(cublasHandle_t handle,
+                                                      cublasFillMode_t uplo,
+                                                      cublasOperation_t trans,
+                                                      int64_t n,
+                                                      int64_t k,
+                                                      const cuDoubleComplex* alpha,
+                                                      const cuDoubleComplex* A,
+                                                      int64_t lda,
+                                                      const cuDoubleComplex* B,
+                                                      int64_t ldb,
+                                                      const double* beta,
+                                                      cuDoubleComplex* C,
+                                                      int64_t ldc);
+
+/* SYMM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsymm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     int m,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* B,
+                                                     int ldb,
+                                                     const float* beta,
+                                                     float* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSsymm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* B,
+                                                        int64_t ldb,
+                                                        const float* beta,
+                                                        float* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsymm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     int m,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* B,
+                                                     int ldb,
+                                                     const double* beta,
+                                                     double* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDsymm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* B,
+                                                        int64_t ldb,
+                                                        const double* beta,
+                                                        double* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsymm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* B,
+                                                     int ldb,
+                                                     const cuComplex* beta,
+                                                     cuComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCsymm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* B,
+                                                        int64_t ldb,
+                                                        const cuComplex* beta,
+                                                        cuComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsymm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* B,
+                                                     int ldb,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZsymm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* B,
+                                                        int64_t ldb,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* C,
+                                                        int64_t ldc);
+
+/* HEMM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChemm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* B,
+                                                     int ldb,
+                                                     const cuComplex* beta,
+                                                     cuComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasChemm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* B,
+                                                        int64_t ldb,
+                                                        const cuComplex* beta,
+                                                        cuComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhemm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* B,
+                                                     int ldb,
+                                                     const cuDoubleComplex* beta,
+                                                     cuDoubleComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZhemm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* B,
+                                                        int64_t ldb,
+                                                        const cuDoubleComplex* beta,
+                                                        cuDoubleComplex* C,
+                                                        int64_t ldc);
+
+/* TRSM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrsm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     float* B,
+                                                     int ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrsm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        float* B,
+                                                        int64_t ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrsm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     double* B,
+                                                     int ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrsm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        double* B,
+                                                        int64_t ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrsm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     cuComplex* B,
+                                                     int ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrsm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        cuComplex* B,
+                                                        int64_t ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrsm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     cuDoubleComplex* B,
+                                                     int ldb);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrsm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        cuDoubleComplex* B,
+                                                        int64_t ldb);
+
+/* TRMM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrmm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int lda,
+                                                     const float* B,
+                                                     int ldb,
+                                                     float* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrmm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const float* alpha,
+                                                        const float* A,
+                                                        int64_t lda,
+                                                        const float* B,
+                                                        int64_t ldb,
+                                                        float* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrmm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int lda,
+                                                     const double* B,
+                                                     int ldb,
+                                                     double* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrmm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const double* alpha,
+                                                        const double* A,
+                                                        int64_t lda,
+                                                        const double* B,
+                                                        int64_t ldb,
+                                                        double* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrmm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int lda,
+                                                     const cuComplex* B,
+                                                     int ldb,
+                                                     cuComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrmm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuComplex* alpha,
+                                                        const cuComplex* A,
+                                                        int64_t lda,
+                                                        const cuComplex* B,
+                                                        int64_t ldb,
+                                                        cuComplex* C,
+                                                        int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrmm_v2(cublasHandle_t handle,
+                                                     cublasSideMode_t side,
+                                                     cublasFillMode_t uplo,
+                                                     cublasOperation_t trans,
+                                                     cublasDiagType_t diag,
+                                                     int m,
+                                                     int n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int lda,
+                                                     const cuDoubleComplex* B,
+                                                     int ldb,
+                                                     cuDoubleComplex* C,
+                                                     int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrmm_v2_64(cublasHandle_t handle,
+                                                        cublasSideMode_t side,
+                                                        cublasFillMode_t uplo,
+                                                        cublasOperation_t trans,
+                                                        cublasDiagType_t diag,
+                                                        int64_t m,
+                                                        int64_t n,
+                                                        const cuDoubleComplex* alpha,
+                                                        const cuDoubleComplex* A,
+                                                        int64_t lda,
+                                                        const cuDoubleComplex* B,
+                                                        int64_t ldb,
+                                                        cuDoubleComplex* C,
+                                                        int64_t ldc);
+
+/* BATCH GEMM */
+
+#if defined(__cplusplus)
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHgemmBatched(cublasHandle_t handle,
+                                                         cublasOperation_t transa,
+                                                         cublasOperation_t transb,
+                                                         int m,
+                                                         int n,
+                                                         int k,
+                                                         const __half* alpha,
+                                                         const __half* const Aarray[],
+                                                         int lda,
+                                                         const __half* const Barray[],
+                                                         int ldb,
+                                                         const __half* beta,
+                                                         __half* const Carray[],
+                                                         int ldc,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHgemmBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t transa,
+                                                            cublasOperation_t transb,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            int64_t k,
+                                                            const __half* alpha,
+                                                            const __half* const Aarray[],
+                                                            int64_t lda,
+                                                            const __half* const Barray[],
+                                                            int64_t ldb,
+                                                            const __half* beta,
+                                                            __half* const Carray[],
+                                                            int64_t ldc,
+                                                            int64_t batchCount);
+
+#endif
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmBatched(cublasHandle_t handle,
+                                                         cublasOperation_t transa,
+                                                         cublasOperation_t transb,
+                                                         int m,
+                                                         int n,
+                                                         int k,
+                                                         const float* alpha,
+                                                         const float* const Aarray[],
+                                                         int lda,
+                                                         const float* const Barray[],
+                                                         int ldb,
+                                                         const float* beta,
+                                                         float* const Carray[],
+                                                         int ldc,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t transa,
+                                                            cublasOperation_t transb,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            int64_t k,
+                                                            const float* alpha,
+                                                            const float* const Aarray[],
+                                                            int64_t lda,
+                                                            const float* const Barray[],
+                                                            int64_t ldb,
+                                                            const float* beta,
+                                                            float* const Carray[],
+                                                            int64_t ldc,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemmBatched(cublasHandle_t handle,
+                                                         cublasOperation_t transa,
+                                                         cublasOperation_t transb,
+                                                         int m,
+                                                         int n,
+                                                         int k,
+                                                         const double* alpha,
+                                                         const double* const Aarray[],
+                                                         int lda,
+                                                         const double* const Barray[],
+                                                         int ldb,
+                                                         const double* beta,
+                                                         double* const Carray[],
+                                                         int ldc,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemmBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t transa,
+                                                            cublasOperation_t transb,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            int64_t k,
+                                                            const double* alpha,
+                                                            const double* const Aarray[],
+                                                            int64_t lda,
+                                                            const double* const Barray[],
+                                                            int64_t ldb,
+                                                            const double* beta,
+                                                            double* const Carray[],
+                                                            int64_t ldc,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemmBatched(cublasHandle_t handle,
+                                                         cublasOperation_t transa,
+                                                         cublasOperation_t transb,
+                                                         int m,
+                                                         int n,
+                                                         int k,
+                                                         const cuComplex* alpha,
+                                                         const cuComplex* const Aarray[],
+                                                         int lda,
+                                                         const cuComplex* const Barray[],
+                                                         int ldb,
+                                                         const cuComplex* beta,
+                                                         cuComplex* const Carray[],
+                                                         int ldc,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemmBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t transa,
+                                                            cublasOperation_t transb,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            int64_t k,
+                                                            const cuComplex* alpha,
+                                                            const cuComplex* const Aarray[],
+                                                            int64_t lda,
+                                                            const cuComplex* const Barray[],
+                                                            int64_t ldb,
+                                                            const cuComplex* beta,
+                                                            cuComplex* const Carray[],
+                                                            int64_t ldc,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3mBatched(cublasHandle_t handle,
+                                                           cublasOperation_t transa,
+                                                           cublasOperation_t transb,
+                                                           int m,
+                                                           int n,
+                                                           int k,
+                                                           const cuComplex* alpha,
+                                                           const cuComplex* const Aarray[],
+                                                           int lda,
+                                                           const cuComplex* const Barray[],
+                                                           int ldb,
+                                                           const cuComplex* beta,
+                                                           cuComplex* const Carray[],
+                                                           int ldc,
+                                                           int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3mBatched_64(cublasHandle_t handle,
+                                                              cublasOperation_t transa,
+                                                              cublasOperation_t transb,
+                                                              int64_t m,
+                                                              int64_t n,
+                                                              int64_t k,
+                                                              const cuComplex* alpha,
+                                                              const cuComplex* const Aarray[],
+                                                              int64_t lda,
+                                                              const cuComplex* const Barray[],
+                                                              int64_t ldb,
+                                                              const cuComplex* beta,
+                                                              cuComplex* const Carray[],
+                                                              int64_t ldc,
+                                                              int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemmBatched(cublasHandle_t handle,
+                                                         cublasOperation_t transa,
+                                                         cublasOperation_t transb,
+                                                         int m,
+                                                         int n,
+                                                         int k,
+                                                         const cuDoubleComplex* alpha,
+                                                         const cuDoubleComplex* const Aarray[],
+                                                         int lda,
+                                                         const cuDoubleComplex* const Barray[],
+                                                         int ldb,
+                                                         const cuDoubleComplex* beta,
+                                                         cuDoubleComplex* const Carray[],
+                                                         int ldc,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemmBatched_64(cublasHandle_t handle,
+                                                            cublasOperation_t transa,
+                                                            cublasOperation_t transb,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            int64_t k,
+                                                            const cuDoubleComplex* alpha,
+                                                            const cuDoubleComplex* const Aarray[],
+                                                            int64_t lda,
+                                                            const cuDoubleComplex* const Barray[],
+                                                            int64_t ldb,
+                                                            const cuDoubleComplex* beta,
+                                                            cuDoubleComplex* const Carray[],
+                                                            int64_t ldc,
+                                                            int64_t batchCount);
+
+#if defined(__cplusplus)
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHgemmStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t transa,
+                                                                cublasOperation_t transb,
+                                                                int m,
+                                                                int n,
+                                                                int k,
+                                                                const __half* alpha,
+                                                                const __half* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const __half* B,
+                                                                int ldb,
+                                                                long long int strideB,
+                                                                const __half* beta,
+                                                                __half* C,
+                                                                int ldc,
+                                                                long long int strideC,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasHgemmStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t transa,
+                                                                   cublasOperation_t transb,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   int64_t k,
+                                                                   const __half* alpha,
+                                                                   const __half* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const __half* B,
+                                                                   int64_t ldb,
+                                                                   long long int strideB,
+                                                                   const __half* beta,
+                                                                   __half* C,
+                                                                   int64_t ldc,
+                                                                   long long int strideC,
+                                                                   int64_t batchCount);
+
+#endif
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t transa,
+                                                                cublasOperation_t transb,
+                                                                int m,
+                                                                int n,
+                                                                int k,
+                                                                const float* alpha,
+                                                                const float* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const float* B,
+                                                                int ldb,
+                                                                long long int strideB,
+                                                                const float* beta,
+                                                                float* C,
+                                                                int ldc,
+                                                                long long int strideC,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t transa,
+                                                                   cublasOperation_t transb,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   int64_t k,
+                                                                   const float* alpha,
+                                                                   const float* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const float* B,
+                                                                   int64_t ldb,
+                                                                   long long int strideB,
+                                                                   const float* beta,
+                                                                   float* C,
+                                                                   int64_t ldc,
+                                                                   long long int strideC,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemmStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t transa,
+                                                                cublasOperation_t transb,
+                                                                int m,
+                                                                int n,
+                                                                int k,
+                                                                const double* alpha,
+                                                                const double* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const double* B,
+                                                                int ldb,
+                                                                long long int strideB,
+                                                                const double* beta,
+                                                                double* C,
+                                                                int ldc,
+                                                                long long int strideC,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemmStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t transa,
+                                                                   cublasOperation_t transb,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   int64_t k,
+                                                                   const double* alpha,
+                                                                   const double* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const double* B,
+                                                                   int64_t ldb,
+                                                                   long long int strideB,
+                                                                   const double* beta,
+                                                                   double* C,
+                                                                   int64_t ldc,
+                                                                   long long int strideC,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemmStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t transa,
+                                                                cublasOperation_t transb,
+                                                                int m,
+                                                                int n,
+                                                                int k,
+                                                                const cuComplex* alpha,
+                                                                const cuComplex* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const cuComplex* B,
+                                                                int ldb,
+                                                                long long int strideB,
+                                                                const cuComplex* beta,
+                                                                cuComplex* C,
+                                                                int ldc,
+                                                                long long int strideC,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemmStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t transa,
+                                                                   cublasOperation_t transb,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   int64_t k,
+                                                                   const cuComplex* alpha,
+                                                                   const cuComplex* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const cuComplex* B,
+                                                                   int64_t ldb,
+                                                                   long long int strideB,
+                                                                   const cuComplex* beta,
+                                                                   cuComplex* C,
+                                                                   int64_t ldc,
+                                                                   long long int strideC,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3mStridedBatched(cublasHandle_t handle,
+                                                                  cublasOperation_t transa,
+                                                                  cublasOperation_t transb,
+                                                                  int m,
+                                                                  int n,
+                                                                  int k,
+                                                                  const cuComplex* alpha,
+                                                                  const cuComplex* A,
+                                                                  int lda,
+                                                                  long long int strideA,
+                                                                  const cuComplex* B,
+                                                                  int ldb,
+                                                                  long long int strideB,
+                                                                  const cuComplex* beta,
+                                                                  cuComplex* C,
+                                                                  int ldc,
+                                                                  long long int strideC,
+                                                                  int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgemm3mStridedBatched_64(cublasHandle_t handle,
+                                                                     cublasOperation_t transa,
+                                                                     cublasOperation_t transb,
+                                                                     int64_t m,
+                                                                     int64_t n,
+                                                                     int64_t k,
+                                                                     const cuComplex* alpha,
+                                                                     const cuComplex* A,
+                                                                     int64_t lda,
+                                                                     long long int strideA,
+                                                                     const cuComplex* B,
+                                                                     int64_t ldb,
+                                                                     long long int strideB,
+                                                                     const cuComplex* beta,
+                                                                     cuComplex* C,
+                                                                     int64_t ldc,
+                                                                     long long int strideC,
+                                                                     int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemmStridedBatched(cublasHandle_t handle,
+                                                                cublasOperation_t transa,
+                                                                cublasOperation_t transb,
+                                                                int m,
+                                                                int n,
+                                                                int k,
+                                                                const cuDoubleComplex* alpha,
+                                                                const cuDoubleComplex* A,
+                                                                int lda,
+                                                                long long int strideA,
+                                                                const cuDoubleComplex* B,
+                                                                int ldb,
+                                                                long long int strideB,
+                                                                const cuDoubleComplex* beta,
+                                                                cuDoubleComplex* C,
+                                                                int ldc,
+                                                                long long int strideC,
+                                                                int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgemmStridedBatched_64(cublasHandle_t handle,
+                                                                   cublasOperation_t transa,
+                                                                   cublasOperation_t transb,
+                                                                   int64_t m,
+                                                                   int64_t n,
+                                                                   int64_t k,
+                                                                   const cuDoubleComplex* alpha,
+                                                                   const cuDoubleComplex* A,
+                                                                   int64_t lda,
+                                                                   long long int strideA,
+                                                                   const cuDoubleComplex* B,
+                                                                   int64_t ldb,
+                                                                   long long int strideB,
+                                                                   const cuDoubleComplex* beta,
+                                                                   cuDoubleComplex* C,
+                                                                   int64_t ldc,
+                                                                   long long int strideC,
+                                                                   int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmBatchedEx(cublasHandle_t handle,
+                                                          cublasOperation_t transa,
+                                                          cublasOperation_t transb,
+                                                          int m,
+                                                          int n,
+                                                          int k,
+                                                          const void* alpha,
+                                                          const void* const Aarray[],
+                                                          cudaDataType Atype,
+                                                          int lda,
+                                                          const void* const Barray[],
+                                                          cudaDataType Btype,
+                                                          int ldb,
+                                                          const void* beta,
+                                                          void* const Carray[],
+                                                          cudaDataType Ctype,
+                                                          int ldc,
+                                                          int batchCount,
+                                                          cublasComputeType_t computeType,
+                                                          cublasGemmAlgo_t algo);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmBatchedEx_64(cublasHandle_t handle,
+                                                             cublasOperation_t transa,
+                                                             cublasOperation_t transb,
+                                                             int64_t m,
+                                                             int64_t n,
+                                                             int64_t k,
+                                                             const void* alpha,
+                                                             const void* const Aarray[],
+                                                             cudaDataType Atype,
+                                                             int64_t lda,
+                                                             const void* const Barray[],
+                                                             cudaDataType Btype,
+                                                             int64_t ldb,
+                                                             const void* beta,
+                                                             void* const Carray[],
+                                                             cudaDataType Ctype,
+                                                             int64_t ldc,
+                                                             int64_t batchCount,
+                                                             cublasComputeType_t computeType,
+                                                             cublasGemmAlgo_t algo);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmStridedBatchedEx(cublasHandle_t handle,
+                                                                 cublasOperation_t transa,
+                                                                 cublasOperation_t transb,
+                                                                 int m,
+                                                                 int n,
+                                                                 int k,
+                                                                 const void* alpha,
+                                                                 const void* A,
+                                                                 cudaDataType Atype,
+                                                                 int lda,
+                                                                 long long int strideA,
+                                                                 const void* B,
+                                                                 cudaDataType Btype,
+                                                                 int ldb,
+                                                                 long long int strideB,
+                                                                 const void* beta,
+                                                                 void* C,
+                                                                 cudaDataType Ctype,
+                                                                 int ldc,
+                                                                 long long int strideC,
+                                                                 int batchCount,
+                                                                 cublasComputeType_t computeType,
+                                                                 cublasGemmAlgo_t algo);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmStridedBatchedEx_64(cublasHandle_t handle,
+                                                                    cublasOperation_t transa,
+                                                                    cublasOperation_t transb,
+                                                                    int64_t m,
+                                                                    int64_t n,
+                                                                    int64_t k,
+                                                                    const void* alpha,
+                                                                    const void* A,
+                                                                    cudaDataType Atype,
+                                                                    int64_t lda,
+                                                                    long long int strideA,
+                                                                    const void* B,
+                                                                    cudaDataType Btype,
+                                                                    int64_t ldb,
+                                                                    long long int strideB,
+                                                                    const void* beta,
+                                                                    void* C,
+                                                                    cudaDataType Ctype,
+                                                                    int64_t ldc,
+                                                                    long long int strideC,
+                                                                    int64_t batchCount,
+                                                                    cublasComputeType_t computeType,
+                                                                    cublasGemmAlgo_t algo);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmGroupedBatched(cublasHandle_t handle,
+                                                                const cublasOperation_t transa_array[],
+                                                                const cublasOperation_t transb_array[],
+                                                                const int m_array[],
+                                                                const int n_array[],
+                                                                const int k_array[],
+                                                                const float alpha_array[],
+                                                                const float* const Aarray[],
+                                                                const int lda_array[],
+                                                                const float* const Barray[],
+                                                                const int ldb_array[],
+                                                                const float beta_array[],
+                                                                float* const Carray[],
+                                                                const int ldc_array[],
+                                                                int group_count,
+                                                                const int group_size[]);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgemmGroupedBatched_64(cublasHandle_t handle,
+                                                                   const cublasOperation_t transa_array[],
+                                                                   const cublasOperation_t transb_array[],
+                                                                   const int64_t m_array[],
+                                                                   const int64_t n_array[],
+                                                                   const int64_t k_array[],
+                                                                   const float alpha_array[],
+                                                                   const float* const Aarray[],
+                                                                   const int64_t lda_array[],
+                                                                   const float* const Barray[],
+                                                                   const int64_t ldb_array[],
+                                                                   const float beta_array[],
+                                                                   float* const Carray[],
+                                                                   const int64_t ldc_array[],
+                                                                   int64_t group_count,
+                                                                   const int64_t group_size[]);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemmGroupedBatched(cublasHandle_t handle,
+                                                                const cublasOperation_t transa_array[],
+                                                                const cublasOperation_t transb_array[],
+                                                                const int m_array[],
+                                                                const int n_array[],
+                                                                const int k_array[],
+                                                                const double alpha_array[],
+                                                                const double* const Aarray[],
+                                                                const int lda_array[],
+                                                                const double* const Barray[],
+                                                                const int ldb_array[],
+                                                                const double beta_array[],
+                                                                double* const Carray[],
+                                                                const int ldc_array[],
+                                                                int group_count,
+                                                                const int group_size[]);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgemmGroupedBatched_64(cublasHandle_t handle,
+                                                                   const cublasOperation_t transa_array[],
+                                                                   const cublasOperation_t transb_array[],
+                                                                   const int64_t m_array[],
+                                                                   const int64_t n_array[],
+                                                                   const int64_t k_array[],
+                                                                   const double alpha_array[],
+                                                                   const double* const Aarray[],
+                                                                   const int64_t lda_array[],
+                                                                   const double* const Barray[],
+                                                                   const int64_t ldb_array[],
+                                                                   const double beta_array[],
+                                                                   double* const Carray[],
+                                                                   const int64_t ldc_array[],
+                                                                   int64_t group_count,
+                                                                   const int64_t group_size[]);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmGroupedBatchedEx(cublasHandle_t handle,
+                                                                 const cublasOperation_t transa_array[],
+                                                                 const cublasOperation_t transb_array[],
+                                                                 const int m_array[],
+                                                                 const int n_array[],
+                                                                 const int k_array[],
+                                                                 const void* alpha_array,
+                                                                 const void* const Aarray[],
+                                                                 cudaDataType_t Atype,
+                                                                 const int lda_array[],
+                                                                 const void* const Barray[],
+                                                                 cudaDataType_t Btype,
+                                                                 const int ldb_array[],
+                                                                 const void* beta_array,
+                                                                 void* const Carray[],
+                                                                 cudaDataType_t Ctype,
+                                                                 const int ldc_array[],
+                                                                 int group_count,
+                                                                 const int group_size[],
+                                                                 cublasComputeType_t computeType);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasGemmGroupedBatchedEx_64(cublasHandle_t handle,
+                                                                    const cublasOperation_t transa_array[],
+                                                                    const cublasOperation_t transb_array[],
+                                                                    const int64_t m_array[],
+                                                                    const int64_t n_array[],
+                                                                    const int64_t k_array[],
+                                                                    const void* alpha_array,
+                                                                    const void* const Aarray[],
+                                                                    cudaDataType_t Atype,
+                                                                    const int64_t lda_array[],
+                                                                    const void* const Barray[],
+                                                                    cudaDataType_t Btype,
+                                                                    const int64_t ldb_array[],
+                                                                    const void* beta_array,
+                                                                    void* const Carray[],
+                                                                    cudaDataType_t Ctype,
+                                                                    const int64_t ldc_array[],
+                                                                    int64_t group_count,
+                                                                    const int64_t group_size[],
+                                                                    cublasComputeType_t computeType);
+
+/* ---------------- CUBLAS BLAS-like Extension ---------------- */
+
+/* GEAM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgeam(cublasHandle_t handle,
+                                                  cublasOperation_t transa,
+                                                  cublasOperation_t transb,
+                                                  int m,
+                                                  int n,
+                                                  const float* alpha,
+                                                  const float* A,
+                                                  int lda,
+                                                  const float* beta,
+                                                  const float* B,
+                                                  int ldb,
+                                                  float* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgeam_64(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const float* alpha,
+                                                     const float* A,
+                                                     int64_t lda,
+                                                     const float* beta,
+                                                     const float* B,
+                                                     int64_t ldb,
+                                                     float* C,
+                                                     int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgeam(cublasHandle_t handle,
+                                                  cublasOperation_t transa,
+                                                  cublasOperation_t transb,
+                                                  int m,
+                                                  int n,
+                                                  const double* alpha,
+                                                  const double* A,
+                                                  int lda,
+                                                  const double* beta,
+                                                  const double* B,
+                                                  int ldb,
+                                                  double* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgeam_64(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const double* alpha,
+                                                     const double* A,
+                                                     int64_t lda,
+                                                     const double* beta,
+                                                     const double* B,
+                                                     int64_t ldb,
+                                                     double* C,
+                                                     int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgeam(cublasHandle_t handle,
+                                                  cublasOperation_t transa,
+                                                  cublasOperation_t transb,
+                                                  int m,
+                                                  int n,
+                                                  const cuComplex* alpha,
+                                                  const cuComplex* A,
+                                                  int lda,
+                                                  const cuComplex* beta,
+                                                  const cuComplex* B,
+                                                  int ldb,
+                                                  cuComplex* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgeam_64(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const cuComplex* alpha,
+                                                     const cuComplex* A,
+                                                     int64_t lda,
+                                                     const cuComplex* beta,
+                                                     const cuComplex* B,
+                                                     int64_t ldb,
+                                                     cuComplex* C,
+                                                     int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgeam(cublasHandle_t handle,
+                                                  cublasOperation_t transa,
+                                                  cublasOperation_t transb,
+                                                  int m,
+                                                  int n,
+                                                  const cuDoubleComplex* alpha,
+                                                  const cuDoubleComplex* A,
+                                                  int lda,
+                                                  const cuDoubleComplex* beta,
+                                                  const cuDoubleComplex* B,
+                                                  int ldb,
+                                                  cuDoubleComplex* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgeam_64(cublasHandle_t handle,
+                                                     cublasOperation_t transa,
+                                                     cublasOperation_t transb,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const cuDoubleComplex* alpha,
+                                                     const cuDoubleComplex* A,
+                                                     int64_t lda,
+                                                     const cuDoubleComplex* beta,
+                                                     const cuDoubleComplex* B,
+                                                     int64_t ldb,
+                                                     cuDoubleComplex* C,
+                                                     int64_t ldc);
+
+/* TRSM - Batched Triangular Solver */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrsmBatched(cublasHandle_t handle,
+                                                         cublasSideMode_t side,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         cublasDiagType_t diag,
+                                                         int m,
+                                                         int n,
+                                                         const float* alpha,
+                                                         const float* const A[],
+                                                         int lda,
+                                                         float* const B[],
+                                                         int ldb,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasStrsmBatched_64(cublasHandle_t handle,
+                                                            cublasSideMode_t side,
+                                                            cublasFillMode_t uplo,
+                                                            cublasOperation_t trans,
+                                                            cublasDiagType_t diag,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const float* alpha,
+                                                            const float* const A[],
+                                                            int64_t lda,
+                                                            float* const B[],
+                                                            int64_t ldb,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrsmBatched(cublasHandle_t handle,
+                                                         cublasSideMode_t side,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         cublasDiagType_t diag,
+                                                         int m,
+                                                         int n,
+                                                         const double* alpha,
+                                                         const double* const A[],
+                                                         int lda,
+                                                         double* const B[],
+                                                         int ldb,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDtrsmBatched_64(cublasHandle_t handle,
+                                                            cublasSideMode_t side,
+                                                            cublasFillMode_t uplo,
+                                                            cublasOperation_t trans,
+                                                            cublasDiagType_t diag,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const double* alpha,
+                                                            const double* const A[],
+                                                            int64_t lda,
+                                                            double* const B[],
+                                                            int64_t ldb,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrsmBatched(cublasHandle_t handle,
+                                                         cublasSideMode_t side,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         cublasDiagType_t diag,
+                                                         int m,
+                                                         int n,
+                                                         const cuComplex* alpha,
+                                                         const cuComplex* const A[],
+                                                         int lda,
+                                                         cuComplex* const B[],
+                                                         int ldb,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCtrsmBatched_64(cublasHandle_t handle,
+                                                            cublasSideMode_t side,
+                                                            cublasFillMode_t uplo,
+                                                            cublasOperation_t trans,
+                                                            cublasDiagType_t diag,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const cuComplex* alpha,
+                                                            const cuComplex* const A[],
+                                                            int64_t lda,
+                                                            cuComplex* const B[],
+                                                            int64_t ldb,
+                                                            int64_t batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrsmBatched(cublasHandle_t handle,
+                                                         cublasSideMode_t side,
+                                                         cublasFillMode_t uplo,
+                                                         cublasOperation_t trans,
+                                                         cublasDiagType_t diag,
+                                                         int m,
+                                                         int n,
+                                                         const cuDoubleComplex* alpha,
+                                                         const cuDoubleComplex* const A[],
+                                                         int lda,
+                                                         cuDoubleComplex* const B[],
+                                                         int ldb,
+                                                         int batchCount);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrsmBatched_64(cublasHandle_t handle,
+                                                            cublasSideMode_t side,
+                                                            cublasFillMode_t uplo,
+                                                            cublasOperation_t trans,
+                                                            cublasDiagType_t diag,
+                                                            int64_t m,
+                                                            int64_t n,
+                                                            const cuDoubleComplex* alpha,
+                                                            const cuDoubleComplex* const A[],
+                                                            int64_t lda,
+                                                            cuDoubleComplex* const B[],
+                                                            int64_t ldb,
+                                                            int64_t batchCount);
+
+/* DGMM */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSdgmm(cublasHandle_t handle,
+                                                  cublasSideMode_t mode,
+                                                  int m,
+                                                  int n,
+                                                  const float* A,
+                                                  int lda,
+                                                  const float* x,
+                                                  int incx,
+                                                  float* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSdgmm_64(cublasHandle_t handle,
+                                                     cublasSideMode_t mode,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const float* A,
+                                                     int64_t lda,
+                                                     const float* x,
+                                                     int64_t incx,
+                                                     float* C,
+                                                     int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDdgmm(cublasHandle_t handle,
+                                                  cublasSideMode_t mode,
+                                                  int m,
+                                                  int n,
+                                                  const double* A,
+                                                  int lda,
+                                                  const double* x,
+                                                  int incx,
+                                                  double* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDdgmm_64(cublasHandle_t handle,
+                                                     cublasSideMode_t mode,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const double* A,
+                                                     int64_t lda,
+                                                     const double* x,
+                                                     int64_t incx,
+                                                     double* C,
+                                                     int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCdgmm(cublasHandle_t handle,
+                                                  cublasSideMode_t mode,
+                                                  int m,
+                                                  int n,
+                                                  const cuComplex* A,
+                                                  int lda,
+                                                  const cuComplex* x,
+                                                  int incx,
+                                                  cuComplex* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCdgmm_64(cublasHandle_t handle,
+                                                     cublasSideMode_t mode,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const cuComplex* A,
+                                                     int64_t lda,
+                                                     const cuComplex* x,
+                                                     int64_t incx,
+                                                     cuComplex* C,
+                                                     int64_t ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdgmm(cublasHandle_t handle,
+                                                  cublasSideMode_t mode,
+                                                  int m,
+                                                  int n,
+                                                  const cuDoubleComplex* A,
+                                                  int lda,
+                                                  const cuDoubleComplex* x,
+                                                  int incx,
+                                                  cuDoubleComplex* C,
+                                                  int ldc);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZdgmm_64(cublasHandle_t handle,
+                                                     cublasSideMode_t mode,
+                                                     int64_t m,
+                                                     int64_t n,
+                                                     const cuDoubleComplex* A,
+                                                     int64_t lda,
+                                                     const cuDoubleComplex* x,
+                                                     int64_t incx,
+                                                     cuDoubleComplex* C,
+                                                     int64_t ldc);
+
+/* Batched - MATINV*/
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSmatinvBatched(cublasHandle_t handle,
+                                                           int n,
+                                                           const float* const A[],
+                                                           int lda,
+                                                           float* const Ainv[],
+                                                           int lda_inv,
+                                                           int* info,
+                                                           int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDmatinvBatched(cublasHandle_t handle,
+                                                           int n,
+                                                           const double* const A[],
+                                                           int lda,
+                                                           double* const Ainv[],
+                                                           int lda_inv,
+                                                           int* info,
+                                                           int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCmatinvBatched(cublasHandle_t handle,
+                                                           int n,
+                                                           const cuComplex* const A[],
+                                                           int lda,
+                                                           cuComplex* const Ainv[],
+                                                           int lda_inv,
+                                                           int* info,
+                                                           int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZmatinvBatched(cublasHandle_t handle,
+                                                           int n,
+                                                           const cuDoubleComplex* const A[],
+                                                           int lda,
+                                                           cuDoubleComplex* const Ainv[],
+                                                           int lda_inv,
+                                                           int* info,
+                                                           int batchSize);
+
+/* Batch QR Factorization */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgeqrfBatched(cublasHandle_t handle,
+                                                          int m,
+                                                          int n,
+                                                          float* const Aarray[],
+                                                          int lda,
+                                                          float* const TauArray[],
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgeqrfBatched(cublasHandle_t handle,
+                                                          int m,
+                                                          int n,
+                                                          double* const Aarray[],
+                                                          int lda,
+                                                          double* const TauArray[],
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgeqrfBatched(cublasHandle_t handle,
+                                                          int m,
+                                                          int n,
+                                                          cuComplex* const Aarray[],
+                                                          int lda,
+                                                          cuComplex* const TauArray[],
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgeqrfBatched(cublasHandle_t handle,
+                                                          int m,
+                                                          int n,
+                                                          cuDoubleComplex* const Aarray[],
+                                                          int lda,
+                                                          cuDoubleComplex* const TauArray[],
+                                                          int* info,
+                                                          int batchSize);
+
+/* Least Square Min only m >= n and Non-transpose supported */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgelsBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         int nrhs,
+                                                         float* const Aarray[],
+                                                         int lda,
+                                                         float* const Carray[],
+                                                         int ldc,
+                                                         int* info,
+                                                         int* devInfoArray,
+                                                         int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgelsBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         int nrhs,
+                                                         double* const Aarray[],
+                                                         int lda,
+                                                         double* const Carray[],
+                                                         int ldc,
+                                                         int* info,
+                                                         int* devInfoArray,
+                                                         int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgelsBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         int nrhs,
+                                                         cuComplex* const Aarray[],
+                                                         int lda,
+                                                         cuComplex* const Carray[],
+                                                         int ldc,
+                                                         int* info,
+                                                         int* devInfoArray,
+                                                         int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgelsBatched(cublasHandle_t handle,
+                                                         cublasOperation_t trans,
+                                                         int m,
+                                                         int n,
+                                                         int nrhs,
+                                                         cuDoubleComplex* const Aarray[],
+                                                         int lda,
+                                                         cuDoubleComplex* const Carray[],
+                                                         int ldc,
+                                                         int* info,
+                                                         int* devInfoArray,
+                                                         int batchSize);
+
+/* TPTTR : Triangular Pack format to Triangular format */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasStpttr(cublasHandle_t handle, cublasFillMode_t uplo, int n, const float* AP, float* A, int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDtpttr(cublasHandle_t handle, cublasFillMode_t uplo, int n, const double* AP, double* A, int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCtpttr(cublasHandle_t handle, cublasFillMode_t uplo, int n, const cuComplex* AP, cuComplex* A, int lda);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtpttr(
+    cublasHandle_t handle, cublasFillMode_t uplo, int n, const cuDoubleComplex* AP, cuDoubleComplex* A, int lda);
+
+/* TRTTP : Triangular format to Triangular Pack format */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasStrttp(cublasHandle_t handle, cublasFillMode_t uplo, int n, const float* A, int lda, float* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDtrttp(cublasHandle_t handle, cublasFillMode_t uplo, int n, const double* A, int lda, double* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCtrttp(cublasHandle_t handle, cublasFillMode_t uplo, int n, const cuComplex* A, int lda, cuComplex* AP);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZtrttp(
+    cublasHandle_t handle, cublasFillMode_t uplo, int n, const cuDoubleComplex* A, int lda, cuDoubleComplex* AP);
+
+/* Batched LU - GETRF*/
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasSgetrfBatched(cublasHandle_t handle, int n, float* const A[], int lda, int* P, int* info, int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasDgetrfBatched(cublasHandle_t handle, int n, double* const A[], int lda, int* P, int* info, int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI
+cublasCgetrfBatched(cublasHandle_t handle, int n, cuComplex* const A[], int lda, int* P, int* info, int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgetrfBatched(
+    cublasHandle_t handle, int n, cuDoubleComplex* const A[], int lda, int* P, int* info, int batchSize);
+
+/* Batched inversion based on LU factorization from getrf */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgetriBatched(cublasHandle_t handle,
+                                                          int n,
+                                                          const float* const A[],
+                                                          int lda,
+                                                          const int* P,
+                                                          float* const C[],
+                                                          int ldc,
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgetriBatched(cublasHandle_t handle,
+                                                          int n,
+                                                          const double* const A[],
+                                                          int lda,
+                                                          const int* P,
+                                                          double* const C[],
+                                                          int ldc,
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgetriBatched(cublasHandle_t handle,
+                                                          int n,
+                                                          const cuComplex* const A[],
+                                                          int lda,
+                                                          const int* P,
+                                                          cuComplex* const C[],
+                                                          int ldc,
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgetriBatched(cublasHandle_t handle,
+                                                          int n,
+                                                          const cuDoubleComplex* const A[],
+                                                          int lda,
+                                                          const int* P,
+                                                          cuDoubleComplex* const C[],
+                                                          int ldc,
+                                                          int* info,
+                                                          int batchSize);
+
+/* Batched solver based on LU factorization from getrf */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasSgetrsBatched(cublasHandle_t handle,
+                                                          cublasOperation_t trans,
+                                                          int n,
+                                                          int nrhs,
+                                                          const float* const Aarray[],
+                                                          int lda,
+                                                          const int* devIpiv,
+                                                          float* const Barray[],
+                                                          int ldb,
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasDgetrsBatched(cublasHandle_t handle,
+                                                          cublasOperation_t trans,
+                                                          int n,
+                                                          int nrhs,
+                                                          const double* const Aarray[],
+                                                          int lda,
+                                                          const int* devIpiv,
+                                                          double* const Barray[],
+                                                          int ldb,
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasCgetrsBatched(cublasHandle_t handle,
+                                                          cublasOperation_t trans,
+                                                          int n,
+                                                          int nrhs,
+                                                          const cuComplex* const Aarray[],
+                                                          int lda,
+                                                          const int* devIpiv,
+                                                          cuComplex* const Barray[],
+                                                          int ldb,
+                                                          int* info,
+                                                          int batchSize);
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasZgetrsBatched(cublasHandle_t handle,
+                                                          cublasOperation_t trans,
+                                                          int n,
+                                                          int nrhs,
+                                                          const cuDoubleComplex* const Aarray[],
+                                                          int lda,
+                                                          const int* devIpiv,
+                                                          cuDoubleComplex* const Barray[],
+                                                          int ldb,
+                                                          int* info,
+                                                          int batchSize);
+
+/* Deprecated */
+
+CUBLASAPI cublasStatus_t CUBLASWINAPI cublasUint8gemmBias(cublasHandle_t handle,
+                                                          cublasOperation_t transa,
+                                                          cublasOperation_t transb,
+                                                          cublasOperation_t transc,
+                                                          int m,
+                                                          int n,
+                                                          int k,
+                                                          const unsigned char* A,
+                                                          int A_bias,
+                                                          int lda,
+                                                          const unsigned char* B,
+                                                          int B_bias,
+                                                          int ldb,
+                                                          unsigned char* C,
+                                                          int C_bias,
+                                                          int ldc,
+                                                          int C_mult,
+                                                          int C_shift);
+
+/* }}} cuBLAS Exported API */
+
+#if defined(__cplusplus)
+}
+
+static inline cublasStatus_t cublasMigrateComputeType(cublasHandle_t handle,
+                                                      cudaDataType_t dataType,
+                                                      cublasComputeType_t* computeType) {
+  cublasMath_t mathMode = CUBLAS_DEFAULT_MATH;
+  cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
+
+  status = cublasGetMathMode(handle, &mathMode);
+  if (status != CUBLAS_STATUS_SUCCESS) {
+    return status;
+  }
+
+  bool isPedantic = ((mathMode & 0xf) == CUBLAS_PEDANTIC_MATH);
+
+  switch (dataType) {
+    case CUDA_R_32F:
+    case CUDA_C_32F:
+      *computeType = isPedantic ? CUBLAS_COMPUTE_32F_PEDANTIC : CUBLAS_COMPUTE_32F;
+      return CUBLAS_STATUS_SUCCESS;
+    case CUDA_R_64F:
+    case CUDA_C_64F:
+      *computeType = isPedantic ? CUBLAS_COMPUTE_64F_PEDANTIC : CUBLAS_COMPUTE_64F;
+      return CUBLAS_STATUS_SUCCESS;
+    case CUDA_R_16F:
+      *computeType = isPedantic ? CUBLAS_COMPUTE_16F_PEDANTIC : CUBLAS_COMPUTE_16F;
+      return CUBLAS_STATUS_SUCCESS;
+    case CUDA_R_32I:
+      *computeType = isPedantic ? CUBLAS_COMPUTE_32I_PEDANTIC : CUBLAS_COMPUTE_32I;
+      return CUBLAS_STATUS_SUCCESS;
+    default:
+      return CUBLAS_STATUS_NOT_SUPPORTED;
+  }
+}
+/* wrappers to accept old code with cudaDataType computeType when referenced from c++ code */
+static inline cublasStatus_t cublasGemmEx(cublasHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          int m,
+                                          int n,
+                                          int k,
+                                          const void* alpha, /* host or device pointer */
+                                          const void* A,
+                                          cudaDataType Atype,
+                                          int lda,
+                                          const void* B,
+                                          cudaDataType Btype,
+                                          int ldb,
+                                          const void* beta, /* host or device pointer */
+                                          void* C,
+                                          cudaDataType Ctype,
+                                          int ldc,
+                                          cudaDataType computeType,
+                                          cublasGemmAlgo_t algo) {
+  cublasComputeType_t migratedComputeType = CUBLAS_COMPUTE_32F;
+  cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
+  status = cublasMigrateComputeType(handle, computeType, &migratedComputeType);
+  if (status != CUBLAS_STATUS_SUCCESS) {
+    return status;
+  }
+
+  return cublasGemmEx(handle,
+                      transa,
+                      transb,
+                      m,
+                      n,
+                      k,
+                      alpha,
+                      A,
+                      Atype,
+                      lda,
+                      B,
+                      Btype,
+                      ldb,
+                      beta,
+                      C,
+                      Ctype,
+                      ldc,
+                      migratedComputeType,
+                      algo);
+}
+
+static inline cublasStatus_t cublasGemmBatchedEx(cublasHandle_t handle,
+                                                 cublasOperation_t transa,
+                                                 cublasOperation_t transb,
+                                                 int m,
+                                                 int n,
+                                                 int k,
+                                                 const void* alpha, /* host or device pointer */
+                                                 const void* const Aarray[],
+                                                 cudaDataType Atype,
+                                                 int lda,
+                                                 const void* const Barray[],
+                                                 cudaDataType Btype,
+                                                 int ldb,
+                                                 const void* beta, /* host or device pointer */
+                                                 void* const Carray[],
+                                                 cudaDataType Ctype,
+                                                 int ldc,
+                                                 int batchCount,
+                                                 cudaDataType computeType,
+                                                 cublasGemmAlgo_t algo) {
+  cublasComputeType_t migratedComputeType;
+  cublasStatus_t status;
+  status = cublasMigrateComputeType(handle, computeType, &migratedComputeType);
+  if (status != CUBLAS_STATUS_SUCCESS) {
+    return status;
+  }
+
+  return cublasGemmBatchedEx(handle,
+                             transa,
+                             transb,
+                             m,
+                             n,
+                             k,
+                             alpha,
+                             Aarray,
+                             Atype,
+                             lda,
+                             Barray,
+                             Btype,
+                             ldb,
+                             beta,
+                             Carray,
+                             Ctype,
+                             ldc,
+                             batchCount,
+                             migratedComputeType,
+                             algo);
+}
+
+static inline cublasStatus_t cublasGemmStridedBatchedEx(cublasHandle_t handle,
+                                                        cublasOperation_t transa,
+                                                        cublasOperation_t transb,
+                                                        int m,
+                                                        int n,
+                                                        int k,
+                                                        const void* alpha, /* host or device pointer */
+                                                        const void* A,
+                                                        cudaDataType Atype,
+                                                        int lda,
+                                                        long long int strideA, /* purposely signed */
+                                                        const void* B,
+                                                        cudaDataType Btype,
+                                                        int ldb,
+                                                        long long int strideB,
+                                                        const void* beta, /* host or device pointer */
+                                                        void* C,
+                                                        cudaDataType Ctype,
+                                                        int ldc,
+                                                        long long int strideC,
+                                                        int batchCount,
+                                                        cudaDataType computeType,
+                                                        cublasGemmAlgo_t algo) {
+  cublasComputeType_t migratedComputeType;
+  cublasStatus_t status;
+  status = cublasMigrateComputeType(handle, computeType, &migratedComputeType);
+  if (status != CUBLAS_STATUS_SUCCESS) {
+    return status;
+  }
+
+  return cublasGemmStridedBatchedEx(handle,
+                                    transa,
+                                    transb,
+                                    m,
+                                    n,
+                                    k,
+                                    alpha,
+                                    A,
+                                    Atype,
+                                    lda,
+                                    strideA,
+                                    B,
+                                    Btype,
+                                    ldb,
+                                    strideB,
+                                    beta,
+                                    C,
+                                    Ctype,
+                                    ldc,
+                                    strideC,
+                                    batchCount,
+                                    migratedComputeType,
+                                    algo);
+}
+#endif /* __cplusplus */
+
+#endif /* !defined(CUBLAS_API_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas_v2.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas_v2.h
new file mode 100644
index 0000000000000000000000000000000000000000..bd81a3b1d8e7e3d04d6c54f4c0640af7d8893eab
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/cublas_v2.h
@@ -0,0 +1,478 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * This is the public header file for the new CUBLAS library API, it mapped the generic
+ * Cublas name functions to the actual _v2 implementations.
+ */
+
+#if !defined(CUBLAS_V2_H_)
+#define CUBLAS_V2_H_
+
+#if defined(CUBLAS_H_)
+#error "It is an error to include both cublas.h and cublas_v2.h"
+#endif
+
+#undef CUBLASAPI
+#ifdef __CUDACC__
+#define CUBLASAPI __host__ __device__
+#else
+#define CUBLASAPI
+#endif
+
+#include "cublas_api.h"
+
+#define cublasCreate cublasCreate_v2
+#define cublasDestroy cublasDestroy_v2
+#define cublasGetVersion cublasGetVersion_v2
+#define cublasSetWorkspace cublasSetWorkspace_v2
+#define cublasSetStream cublasSetStream_v2
+#define cublasGetStream cublasGetStream_v2
+#define cublasGetPointerMode cublasGetPointerMode_v2
+#define cublasSetPointerMode cublasSetPointerMode_v2
+
+/* 32-bit integer */
+
+/* Blas1 Routines   */
+
+#define cublasSnrm2 cublasSnrm2_v2
+#define cublasDnrm2 cublasDnrm2_v2
+#define cublasScnrm2 cublasScnrm2_v2
+#define cublasDznrm2 cublasDznrm2_v2
+
+#define cublasSdot cublasSdot_v2
+#define cublasDdot cublasDdot_v2
+#define cublasCdotu cublasCdotu_v2
+#define cublasCdotc cublasCdotc_v2
+#define cublasZdotu cublasZdotu_v2
+#define cublasZdotc cublasZdotc_v2
+
+#define cublasSscal cublasSscal_v2
+#define cublasDscal cublasDscal_v2
+#define cublasCscal cublasCscal_v2
+#define cublasCsscal cublasCsscal_v2
+#define cublasZscal cublasZscal_v2
+#define cublasZdscal cublasZdscal_v2
+
+#define cublasSaxpy cublasSaxpy_v2
+#define cublasDaxpy cublasDaxpy_v2
+#define cublasCaxpy cublasCaxpy_v2
+#define cublasZaxpy cublasZaxpy_v2
+
+#define cublasScopy cublasScopy_v2
+#define cublasDcopy cublasDcopy_v2
+#define cublasCcopy cublasCcopy_v2
+#define cublasZcopy cublasZcopy_v2
+
+#define cublasSswap cublasSswap_v2
+#define cublasDswap cublasDswap_v2
+#define cublasCswap cublasCswap_v2
+#define cublasZswap cublasZswap_v2
+
+#define cublasIsamax cublasIsamax_v2
+#define cublasIdamax cublasIdamax_v2
+#define cublasIcamax cublasIcamax_v2
+#define cublasIzamax cublasIzamax_v2
+
+#define cublasIsamin cublasIsamin_v2
+#define cublasIdamin cublasIdamin_v2
+#define cublasIcamin cublasIcamin_v2
+#define cublasIzamin cublasIzamin_v2
+
+#define cublasSasum cublasSasum_v2
+#define cublasDasum cublasDasum_v2
+#define cublasScasum cublasScasum_v2
+#define cublasDzasum cublasDzasum_v2
+
+#define cublasSrot cublasSrot_v2
+#define cublasDrot cublasDrot_v2
+#define cublasCrot cublasCrot_v2
+#define cublasCsrot cublasCsrot_v2
+#define cublasZrot cublasZrot_v2
+#define cublasZdrot cublasZdrot_v2
+
+#define cublasSrotg cublasSrotg_v2
+#define cublasDrotg cublasDrotg_v2
+#define cublasCrotg cublasCrotg_v2
+#define cublasZrotg cublasZrotg_v2
+
+#define cublasSrotm cublasSrotm_v2
+#define cublasDrotm cublasDrotm_v2
+
+#define cublasSrotmg cublasSrotmg_v2
+#define cublasDrotmg cublasDrotmg_v2
+
+/* Blas2 Routines */
+
+#define cublasSgemv cublasSgemv_v2
+#define cublasDgemv cublasDgemv_v2
+#define cublasCgemv cublasCgemv_v2
+#define cublasZgemv cublasZgemv_v2
+
+#define cublasSgbmv cublasSgbmv_v2
+#define cublasDgbmv cublasDgbmv_v2
+#define cublasCgbmv cublasCgbmv_v2
+#define cublasZgbmv cublasZgbmv_v2
+
+#define cublasStrmv cublasStrmv_v2
+#define cublasDtrmv cublasDtrmv_v2
+#define cublasCtrmv cublasCtrmv_v2
+#define cublasZtrmv cublasZtrmv_v2
+
+#define cublasStbmv cublasStbmv_v2
+#define cublasDtbmv cublasDtbmv_v2
+#define cublasCtbmv cublasCtbmv_v2
+#define cublasZtbmv cublasZtbmv_v2
+
+#define cublasStpmv cublasStpmv_v2
+#define cublasDtpmv cublasDtpmv_v2
+#define cublasCtpmv cublasCtpmv_v2
+#define cublasZtpmv cublasZtpmv_v2
+
+#define cublasStrsv cublasStrsv_v2
+#define cublasDtrsv cublasDtrsv_v2
+#define cublasCtrsv cublasCtrsv_v2
+#define cublasZtrsv cublasZtrsv_v2
+
+#define cublasStpsv cublasStpsv_v2
+#define cublasDtpsv cublasDtpsv_v2
+#define cublasCtpsv cublasCtpsv_v2
+#define cublasZtpsv cublasZtpsv_v2
+
+#define cublasStbsv cublasStbsv_v2
+#define cublasDtbsv cublasDtbsv_v2
+#define cublasCtbsv cublasCtbsv_v2
+#define cublasZtbsv cublasZtbsv_v2
+
+#define cublasSsymv cublasSsymv_v2
+#define cublasDsymv cublasDsymv_v2
+#define cublasCsymv cublasCsymv_v2
+#define cublasZsymv cublasZsymv_v2
+#define cublasChemv cublasChemv_v2
+#define cublasZhemv cublasZhemv_v2
+
+#define cublasSsbmv cublasSsbmv_v2
+#define cublasDsbmv cublasDsbmv_v2
+#define cublasChbmv cublasChbmv_v2
+#define cublasZhbmv cublasZhbmv_v2
+
+#define cublasSspmv cublasSspmv_v2
+#define cublasDspmv cublasDspmv_v2
+#define cublasChpmv cublasChpmv_v2
+#define cublasZhpmv cublasZhpmv_v2
+
+#define cublasSger cublasSger_v2
+#define cublasDger cublasDger_v2
+#define cublasCgeru cublasCgeru_v2
+#define cublasCgerc cublasCgerc_v2
+#define cublasZgeru cublasZgeru_v2
+#define cublasZgerc cublasZgerc_v2
+
+#define cublasSsyr cublasSsyr_v2
+#define cublasDsyr cublasDsyr_v2
+#define cublasCsyr cublasCsyr_v2
+#define cublasZsyr cublasZsyr_v2
+#define cublasCher cublasCher_v2
+#define cublasZher cublasZher_v2
+
+#define cublasSspr cublasSspr_v2
+#define cublasDspr cublasDspr_v2
+#define cublasChpr cublasChpr_v2
+#define cublasZhpr cublasZhpr_v2
+
+#define cublasSsyr2 cublasSsyr2_v2
+#define cublasDsyr2 cublasDsyr2_v2
+#define cublasCsyr2 cublasCsyr2_v2
+#define cublasZsyr2 cublasZsyr2_v2
+#define cublasCher2 cublasCher2_v2
+#define cublasZher2 cublasZher2_v2
+
+#define cublasSspr2 cublasSspr2_v2
+#define cublasDspr2 cublasDspr2_v2
+#define cublasChpr2 cublasChpr2_v2
+#define cublasZhpr2 cublasZhpr2_v2
+
+/* Blas3 Routines   */
+
+#define cublasSgemm cublasSgemm_v2
+#define cublasDgemm cublasDgemm_v2
+#define cublasCgemm cublasCgemm_v2
+#define cublasZgemm cublasZgemm_v2
+
+#define cublasSsyrk cublasSsyrk_v2
+#define cublasDsyrk cublasDsyrk_v2
+#define cublasCsyrk cublasCsyrk_v2
+#define cublasZsyrk cublasZsyrk_v2
+#define cublasCherk cublasCherk_v2
+#define cublasZherk cublasZherk_v2
+
+#define cublasSsyr2k cublasSsyr2k_v2
+#define cublasDsyr2k cublasDsyr2k_v2
+#define cublasCsyr2k cublasCsyr2k_v2
+#define cublasZsyr2k cublasZsyr2k_v2
+#define cublasCher2k cublasCher2k_v2
+#define cublasZher2k cublasZher2k_v2
+
+#define cublasSsymm cublasSsymm_v2
+#define cublasDsymm cublasDsymm_v2
+#define cublasCsymm cublasCsymm_v2
+#define cublasZsymm cublasZsymm_v2
+#define cublasChemm cublasChemm_v2
+#define cublasZhemm cublasZhemm_v2
+
+#define cublasStrsm cublasStrsm_v2
+#define cublasDtrsm cublasDtrsm_v2
+#define cublasCtrsm cublasCtrsm_v2
+#define cublasZtrsm cublasZtrsm_v2
+
+#define cublasStrmm cublasStrmm_v2
+#define cublasDtrmm cublasDtrmm_v2
+#define cublasCtrmm cublasCtrmm_v2
+#define cublasZtrmm cublasZtrmm_v2
+
+/* 64-bit integer */
+
+/* Blas1 Routines   */
+
+#define cublasSnrm2_64 cublasSnrm2_v2_64
+#define cublasDnrm2_64 cublasDnrm2_v2_64
+#define cublasScnrm2_64 cublasScnrm2_v2_64
+#define cublasDznrm2_64 cublasDznrm2_v2_64
+
+#define cublasSdot_64 cublasSdot_v2_64
+#define cublasDdot_64 cublasDdot_v2_64
+#define cublasCdotu_64 cublasCdotu_v2_64
+#define cublasCdotc_64 cublasCdotc_v2_64
+#define cublasZdotu_64 cublasZdotu_v2_64
+#define cublasZdotc_64 cublasZdotc_v2_64
+
+#define cublasSscal_64 cublasSscal_v2_64
+#define cublasDscal_64 cublasDscal_v2_64
+#define cublasCscal_64 cublasCscal_v2_64
+#define cublasCsscal_64 cublasCsscal_v2_64
+#define cublasZscal_64 cublasZscal_v2_64
+#define cublasZdscal_64 cublasZdscal_v2_64
+
+#define cublasSaxpy_64 cublasSaxpy_v2_64
+#define cublasDaxpy_64 cublasDaxpy_v2_64
+#define cublasCaxpy_64 cublasCaxpy_v2_64
+#define cublasZaxpy_64 cublasZaxpy_v2_64
+
+#define cublasScopy_64 cublasScopy_v2_64
+#define cublasDcopy_64 cublasDcopy_v2_64
+#define cublasCcopy_64 cublasCcopy_v2_64
+#define cublasZcopy_64 cublasZcopy_v2_64
+
+#define cublasSswap_64 cublasSswap_v2_64
+#define cublasDswap_64 cublasDswap_v2_64
+#define cublasCswap_64 cublasCswap_v2_64
+#define cublasZswap_64 cublasZswap_v2_64
+
+#define cublasIsamax_64 cublasIsamax_v2_64
+#define cublasIdamax_64 cublasIdamax_v2_64
+#define cublasIcamax_64 cublasIcamax_v2_64
+#define cublasIzamax_64 cublasIzamax_v2_64
+
+#define cublasIsamin_64 cublasIsamin_v2_64
+#define cublasIdamin_64 cublasIdamin_v2_64
+#define cublasIcamin_64 cublasIcamin_v2_64
+#define cublasIzamin_64 cublasIzamin_v2_64
+
+#define cublasSasum_64 cublasSasum_v2_64
+#define cublasDasum_64 cublasDasum_v2_64
+#define cublasScasum_64 cublasScasum_v2_64
+#define cublasDzasum_64 cublasDzasum_v2_64
+
+#define cublasSrot_64 cublasSrot_v2_64
+#define cublasDrot_64 cublasDrot_v2_64
+#define cublasCrot_64 cublasCrot_v2_64
+#define cublasCsrot_64 cublasCsrot_v2_64
+#define cublasZrot_64 cublasZrot_v2_64
+#define cublasZdrot_64 cublasZdrot_v2_64
+
+#define cublasSrotg_64 cublasSrotg_v2_64
+#define cublasDrotg_64 cublasDrotg_v2_64
+#define cublasCrotg_64 cublasCrotg_v2_64
+#define cublasZrotg_64 cublasZrotg_v2_64
+
+#define cublasSrotm_64 cublasSrotm_v2_64
+#define cublasDrotm_64 cublasDrotm_v2_64
+
+#define cublasSrotmg_64 cublasSrotmg_v2_64
+#define cublasDrotmg_64 cublasDrotmg_v2_64
+
+/* Blas2 Routines */
+
+#define cublasSgemv_64 cublasSgemv_v2_64
+#define cublasDgemv_64 cublasDgemv_v2_64
+#define cublasCgemv_64 cublasCgemv_v2_64
+#define cublasZgemv_64 cublasZgemv_v2_64
+
+#define cublasSgbmv_64 cublasSgbmv_v2_64
+#define cublasDgbmv_64 cublasDgbmv_v2_64
+#define cublasCgbmv_64 cublasCgbmv_v2_64
+#define cublasZgbmv_64 cublasZgbmv_v2_64
+
+#define cublasStrmv_64 cublasStrmv_v2_64
+#define cublasDtrmv_64 cublasDtrmv_v2_64
+#define cublasCtrmv_64 cublasCtrmv_v2_64
+#define cublasZtrmv_64 cublasZtrmv_v2_64
+
+#define cublasStbmv_64 cublasStbmv_v2_64
+#define cublasDtbmv_64 cublasDtbmv_v2_64
+#define cublasCtbmv_64 cublasCtbmv_v2_64
+#define cublasZtbmv_64 cublasZtbmv_v2_64
+
+#define cublasStpmv_64 cublasStpmv_v2_64
+#define cublasDtpmv_64 cublasDtpmv_v2_64
+#define cublasCtpmv_64 cublasCtpmv_v2_64
+#define cublasZtpmv_64 cublasZtpmv_v2_64
+
+#define cublasStrsv_64 cublasStrsv_v2_64
+#define cublasDtrsv_64 cublasDtrsv_v2_64
+#define cublasCtrsv_64 cublasCtrsv_v2_64
+#define cublasZtrsv_64 cublasZtrsv_v2_64
+
+#define cublasStpsv_64 cublasStpsv_v2_64
+#define cublasDtpsv_64 cublasDtpsv_v2_64
+#define cublasCtpsv_64 cublasCtpsv_v2_64
+#define cublasZtpsv_64 cublasZtpsv_v2_64
+
+#define cublasStbsv_64 cublasStbsv_v2_64
+#define cublasDtbsv_64 cublasDtbsv_v2_64
+#define cublasCtbsv_64 cublasCtbsv_v2_64
+#define cublasZtbsv_64 cublasZtbsv_v2_64
+
+#define cublasSsymv_64 cublasSsymv_v2_64
+#define cublasDsymv_64 cublasDsymv_v2_64
+#define cublasCsymv_64 cublasCsymv_v2_64
+#define cublasZsymv_64 cublasZsymv_v2_64
+#define cublasChemv_64 cublasChemv_v2_64
+#define cublasZhemv_64 cublasZhemv_v2_64
+
+#define cublasSsbmv_64 cublasSsbmv_v2_64
+#define cublasDsbmv_64 cublasDsbmv_v2_64
+#define cublasChbmv_64 cublasChbmv_v2_64
+#define cublasZhbmv_64 cublasZhbmv_v2_64
+
+#define cublasSspmv_64 cublasSspmv_v2_64
+#define cublasDspmv_64 cublasDspmv_v2_64
+#define cublasChpmv_64 cublasChpmv_v2_64
+#define cublasZhpmv_64 cublasZhpmv_v2_64
+
+#define cublasSger_64 cublasSger_v2_64
+#define cublasDger_64 cublasDger_v2_64
+#define cublasCgeru_64 cublasCgeru_v2_64
+#define cublasCgerc_64 cublasCgerc_v2_64
+#define cublasZgeru_64 cublasZgeru_v2_64
+#define cublasZgerc_64 cublasZgerc_v2_64
+
+#define cublasSsyr_64 cublasSsyr_v2_64
+#define cublasDsyr_64 cublasDsyr_v2_64
+#define cublasCsyr_64 cublasCsyr_v2_64
+#define cublasZsyr_64 cublasZsyr_v2_64
+#define cublasCher_64 cublasCher_v2_64
+#define cublasZher_64 cublasZher_v2_64
+
+#define cublasSspr_64 cublasSspr_v2_64
+#define cublasDspr_64 cublasDspr_v2_64
+#define cublasChpr_64 cublasChpr_v2_64
+#define cublasZhpr_64 cublasZhpr_v2_64
+
+#define cublasSsyr2_64 cublasSsyr2_v2_64
+#define cublasDsyr2_64 cublasDsyr2_v2_64
+#define cublasCsyr2_64 cublasCsyr2_v2_64
+#define cublasZsyr2_64 cublasZsyr2_v2_64
+#define cublasCher2_64 cublasCher2_v2_64
+#define cublasZher2_64 cublasZher2_v2_64
+
+#define cublasSspr2_64 cublasSspr2_v2_64
+#define cublasDspr2_64 cublasDspr2_v2_64
+#define cublasChpr2_64 cublasChpr2_v2_64
+#define cublasZhpr2_64 cublasZhpr2_v2_64
+
+/* Blas3 Routines   */
+
+#define cublasSgemm_64 cublasSgemm_v2_64
+#define cublasDgemm_64 cublasDgemm_v2_64
+#define cublasCgemm_64 cublasCgemm_v2_64
+#define cublasZgemm_64 cublasZgemm_v2_64
+
+#define cublasSsyrk_64 cublasSsyrk_v2_64
+#define cublasDsyrk_64 cublasDsyrk_v2_64
+#define cublasCsyrk_64 cublasCsyrk_v2_64
+#define cublasZsyrk_64 cublasZsyrk_v2_64
+#define cublasCherk_64 cublasCherk_v2_64
+#define cublasZherk_64 cublasZherk_v2_64
+
+#define cublasSsyr2k_64 cublasSsyr2k_v2_64
+#define cublasDsyr2k_64 cublasDsyr2k_v2_64
+#define cublasCsyr2k_64 cublasCsyr2k_v2_64
+#define cublasZsyr2k_64 cublasZsyr2k_v2_64
+#define cublasCher2k_64 cublasCher2k_v2_64
+#define cublasZher2k_64 cublasZher2k_v2_64
+
+#define cublasSsymm_64 cublasSsymm_v2_64
+#define cublasDsymm_64 cublasDsymm_v2_64
+#define cublasCsymm_64 cublasCsymm_v2_64
+#define cublasZsymm_64 cublasZsymm_v2_64
+#define cublasChemm_64 cublasChemm_v2_64
+#define cublasZhemm_64 cublasZhemm_v2_64
+
+#define cublasStrsm_64 cublasStrsm_v2_64
+#define cublasDtrsm_64 cublasDtrsm_v2_64
+#define cublasCtrsm_64 cublasCtrsm_v2_64
+#define cublasZtrsm_64 cublasZtrsm_v2_64
+
+#define cublasStrmm_64 cublasStrmm_v2_64
+#define cublasDtrmm_64 cublasDtrmm_v2_64
+#define cublasCtrmm_64 cublasCtrmm_v2_64
+#define cublasZtrmm_64 cublasZtrmm_v2_64
+
+#endif /* !defined(CUBLAS_V2_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/nvblas.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/nvblas.h
new file mode 100644
index 0000000000000000000000000000000000000000..29ea9153faf7b3e62a6d53c0be1980ae79c49f51
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/include/nvblas.h
@@ -0,0 +1,824 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(NVBLAS_H_)
+#define NVBLAS_H_
+
+#include "driver_types.h"
+#include "cuComplex.h" /* import complex data type */
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* GEMM */
+void sgemm_(const char* transa,
+            const char* transb,
+            const int* m,
+            const int* n,
+            const int* k,
+            const float* alpha,
+            const float* a,
+            const int* lda,
+            const float* b,
+            const int* ldb,
+            const float* beta,
+            float* c,
+            const int* ldc);
+
+void dgemm_(const char* transa,
+            const char* transb,
+            const int* m,
+            const int* n,
+            const int* k,
+            const double* alpha,
+            const double* a,
+            const int* lda,
+            const double* b,
+            const int* ldb,
+            const double* beta,
+            double* c,
+            const int* ldc);
+
+void cgemm_(const char* transa,
+            const char* transb,
+            const int* m,
+            const int* n,
+            const int* k,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const cuComplex* b,
+            const int* ldb,
+            const cuComplex* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zgemm_(const char* transa,
+            const char* transb,
+            const int* m,
+            const int* n,
+            const int* k,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const cuDoubleComplex* b,
+            const int* ldb,
+            const cuDoubleComplex* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+void sgemm(const char* transa,
+           const char* transb,
+           const int* m,
+           const int* n,
+           const int* k,
+           const float* alpha,
+           const float* a,
+           const int* lda,
+           const float* b,
+           const int* ldb,
+           const float* beta,
+           float* c,
+           const int* ldc);
+
+void dgemm(const char* transa,
+           const char* transb,
+           const int* m,
+           const int* n,
+           const int* k,
+           const double* alpha,
+           const double* a,
+           const int* lda,
+           const double* b,
+           const int* ldb,
+           const double* beta,
+           double* c,
+           const int* ldc);
+
+void cgemm(const char* transa,
+           const char* transb,
+           const int* m,
+           const int* n,
+           const int* k,
+           const cuComplex* alpha,
+           const cuComplex* a,
+           const int* lda,
+           const cuComplex* b,
+           const int* ldb,
+           const cuComplex* beta,
+           cuComplex* c,
+           const int* ldc);
+
+void zgemm(const char* transa,
+           const char* transb,
+           const int* m,
+           const int* n,
+           const int* k,
+           const cuDoubleComplex* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           const cuDoubleComplex* b,
+           const int* ldb,
+           const cuDoubleComplex* beta,
+           cuDoubleComplex* c,
+           const int* ldc);
+
+/* SYRK */
+void ssyrk_(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const float* alpha,
+            const float* a,
+            const int* lda,
+            const float* beta,
+            float* c,
+            const int* ldc);
+
+void dsyrk_(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const double* alpha,
+            const double* a,
+            const int* lda,
+            const double* beta,
+            double* c,
+            const int* ldc);
+
+void csyrk_(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const cuComplex* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zsyrk_(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const cuDoubleComplex* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+void ssyrk(const char* uplo,
+           const char* trans,
+           const int* n,
+           const int* k,
+           const float* alpha,
+           const float* a,
+           const int* lda,
+           const float* beta,
+           float* c,
+           const int* ldc);
+
+void dsyrk(const char* uplo,
+           const char* trans,
+           const int* n,
+           const int* k,
+           const double* alpha,
+           const double* a,
+           const int* lda,
+           const double* beta,
+           double* c,
+           const int* ldc);
+
+void csyrk(const char* uplo,
+           const char* trans,
+           const int* n,
+           const int* k,
+           const cuComplex* alpha,
+           const cuComplex* a,
+           const int* lda,
+           const cuComplex* beta,
+           cuComplex* c,
+           const int* ldc);
+
+void zsyrk(const char* uplo,
+           const char* trans,
+           const int* n,
+           const int* k,
+           const cuDoubleComplex* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           const cuDoubleComplex* beta,
+           cuDoubleComplex* c,
+           const int* ldc);
+
+/* HERK */
+void cherk_(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const float* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const float* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zherk_(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const double* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const double* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+void cherk(const char* uplo,
+           const char* trans,
+           const int* n,
+           const int* k,
+           const float* alpha,
+           const cuComplex* a,
+           const int* lda,
+           const float* beta,
+           cuComplex* c,
+           const int* ldc);
+
+void zherk(const char* uplo,
+           const char* trans,
+           const int* n,
+           const int* k,
+           const double* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           const double* beta,
+           cuDoubleComplex* c,
+           const int* ldc);
+
+/* TRSM */
+void strsm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const float* alpha,
+            const float* a,
+            const int* lda,
+            float* b,
+            const int* ldb);
+
+void dtrsm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const double* alpha,
+            const double* a,
+            const int* lda,
+            double* b,
+            const int* ldb);
+
+void ctrsm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            cuComplex* b,
+            const int* ldb);
+
+void ztrsm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            cuDoubleComplex* b,
+            const int* ldb);
+
+void strsm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const float* alpha,
+           const float* a,
+           const int* lda,
+           float* b,
+           const int* ldb);
+
+void dtrsm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const double* alpha,
+           const double* a,
+           const int* lda,
+           double* b,
+           const int* ldb);
+
+void ctrsm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const cuComplex* alpha,
+           const cuComplex* a,
+           const int* lda,
+           cuComplex* b,
+           const int* ldb);
+
+void ztrsm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const cuDoubleComplex* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           cuDoubleComplex* b,
+           const int* ldb);
+
+/* SYMM */
+void ssymm_(const char* side,
+            const char* uplo,
+            const int* m,
+            const int* n,
+            const float* alpha,
+            const float* a,
+            const int* lda,
+            const float* b,
+            const int* ldb,
+            const float* beta,
+            float* c,
+            const int* ldc);
+
+void dsymm_(const char* side,
+            const char* uplo,
+            const int* m,
+            const int* n,
+            const double* alpha,
+            const double* a,
+            const int* lda,
+            const double* b,
+            const int* ldb,
+            const double* beta,
+            double* c,
+            const int* ldc);
+
+void csymm_(const char* side,
+            const char* uplo,
+            const int* m,
+            const int* n,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const cuComplex* b,
+            const int* ldb,
+            const cuComplex* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zsymm_(const char* side,
+            const char* uplo,
+            const int* m,
+            const int* n,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const cuDoubleComplex* b,
+            const int* ldb,
+            const cuDoubleComplex* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+void ssymm(const char* side,
+           const char* uplo,
+           const int* m,
+           const int* n,
+           const float* alpha,
+           const float* a,
+           const int* lda,
+           const float* b,
+           const int* ldb,
+           const float* beta,
+           float* c,
+           const int* ldc);
+
+void dsymm(const char* side,
+           const char* uplo,
+           const int* m,
+           const int* n,
+           const double* alpha,
+           const double* a,
+           const int* lda,
+           const double* b,
+           const int* ldb,
+           const double* beta,
+           double* c,
+           const int* ldc);
+
+void csymm(const char* side,
+           const char* uplo,
+           const int* m,
+           const int* n,
+           const cuComplex* alpha,
+           const cuComplex* a,
+           const int* lda,
+           const cuComplex* b,
+           const int* ldb,
+           const cuComplex* beta,
+           cuComplex* c,
+           const int* ldc);
+
+void zsymm(const char* side,
+           const char* uplo,
+           const int* m,
+           const int* n,
+           const cuDoubleComplex* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           const cuDoubleComplex* b,
+           const int* ldb,
+           const cuDoubleComplex* beta,
+           cuDoubleComplex* c,
+           const int* ldc);
+
+/* HEMM */
+void chemm_(const char* side,
+            const char* uplo,
+            const int* m,
+            const int* n,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const cuComplex* b,
+            const int* ldb,
+            const cuComplex* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zhemm_(const char* side,
+            const char* uplo,
+            const int* m,
+            const int* n,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const cuDoubleComplex* b,
+            const int* ldb,
+            const cuDoubleComplex* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+/* HEMM with no underscore*/
+void chemm(const char* side,
+           const char* uplo,
+           const int* m,
+           const int* n,
+           const cuComplex* alpha,
+           const cuComplex* a,
+           const int* lda,
+           const cuComplex* b,
+           const int* ldb,
+           const cuComplex* beta,
+           cuComplex* c,
+           const int* ldc);
+
+void zhemm(const char* side,
+           const char* uplo,
+           const int* m,
+           const int* n,
+           const cuDoubleComplex* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           const cuDoubleComplex* b,
+           const int* ldb,
+           const cuDoubleComplex* beta,
+           cuDoubleComplex* c,
+           const int* ldc);
+
+/* SYR2K */
+void ssyr2k_(const char* uplo,
+             const char* trans,
+             const int* n,
+             const int* k,
+             const float* alpha,
+             const float* a,
+             const int* lda,
+             const float* b,
+             const int* ldb,
+             const float* beta,
+             float* c,
+             const int* ldc);
+
+void dsyr2k_(const char* uplo,
+             const char* trans,
+             const int* n,
+             const int* k,
+             const double* alpha,
+             const double* a,
+             const int* lda,
+             const double* b,
+             const int* ldb,
+             const double* beta,
+             double* c,
+             const int* ldc);
+
+void csyr2k_(const char* uplo,
+             const char* trans,
+             const int* n,
+             const int* k,
+             const cuComplex* alpha,
+             const cuComplex* a,
+             const int* lda,
+             const cuComplex* b,
+             const int* ldb,
+             const cuComplex* beta,
+             cuComplex* c,
+             const int* ldc);
+
+void zsyr2k_(const char* uplo,
+             const char* trans,
+             const int* n,
+             const int* k,
+             const cuDoubleComplex* alpha,
+             const cuDoubleComplex* a,
+             const int* lda,
+             const cuDoubleComplex* b,
+             const int* ldb,
+             const cuDoubleComplex* beta,
+             cuDoubleComplex* c,
+             const int* ldc);
+
+/* SYR2K no_underscore*/
+void ssyr2k(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const float* alpha,
+            const float* a,
+            const int* lda,
+            const float* b,
+            const int* ldb,
+            const float* beta,
+            float* c,
+            const int* ldc);
+
+void dsyr2k(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const double* alpha,
+            const double* a,
+            const int* lda,
+            const double* b,
+            const int* ldb,
+            const double* beta,
+            double* c,
+            const int* ldc);
+
+void csyr2k(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const cuComplex* b,
+            const int* ldb,
+            const cuComplex* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zsyr2k(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const cuDoubleComplex* b,
+            const int* ldb,
+            const cuDoubleComplex* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+/* HERK */
+void cher2k_(const char* uplo,
+             const char* trans,
+             const int* n,
+             const int* k,
+             const cuComplex* alpha,
+             const cuComplex* a,
+             const int* lda,
+             const cuComplex* b,
+             const int* ldb,
+             const float* beta,
+             cuComplex* c,
+             const int* ldc);
+
+void zher2k_(const char* uplo,
+             const char* trans,
+             const int* n,
+             const int* k,
+             const cuDoubleComplex* alpha,
+             const cuDoubleComplex* a,
+             const int* lda,
+             const cuDoubleComplex* b,
+             const int* ldb,
+             const double* beta,
+             cuDoubleComplex* c,
+             const int* ldc);
+
+/* HER2K with no underscore */
+void cher2k(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            const cuComplex* b,
+            const int* ldb,
+            const float* beta,
+            cuComplex* c,
+            const int* ldc);
+
+void zher2k(const char* uplo,
+            const char* trans,
+            const int* n,
+            const int* k,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            const cuDoubleComplex* b,
+            const int* ldb,
+            const double* beta,
+            cuDoubleComplex* c,
+            const int* ldc);
+
+/* TRMM */
+void strmm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const float* alpha,
+            const float* a,
+            const int* lda,
+            float* b,
+            const int* ldb);
+
+void dtrmm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const double* alpha,
+            const double* a,
+            const int* lda,
+            double* b,
+            const int* ldb);
+
+void ctrmm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const cuComplex* alpha,
+            const cuComplex* a,
+            const int* lda,
+            cuComplex* b,
+            const int* ldb);
+
+void ztrmm_(const char* side,
+            const char* uplo,
+            const char* transa,
+            const char* diag,
+            const int* m,
+            const int* n,
+            const cuDoubleComplex* alpha,
+            const cuDoubleComplex* a,
+            const int* lda,
+            cuDoubleComplex* b,
+            const int* ldb);
+
+void strmm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const float* alpha,
+           const float* a,
+           const int* lda,
+           float* b,
+           const int* ldb);
+
+void dtrmm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const double* alpha,
+           const double* a,
+           const int* lda,
+           double* b,
+           const int* ldb);
+
+void ctrmm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const cuComplex* alpha,
+           const cuComplex* a,
+           const int* lda,
+           cuComplex* b,
+           const int* ldb);
+
+void ztrmm(const char* side,
+           const char* uplo,
+           const char* transa,
+           const char* diag,
+           const int* m,
+           const int* n,
+           const cuDoubleComplex* alpha,
+           const cuDoubleComplex* a,
+           const int* lda,
+           cuDoubleComplex* b,
+           const int* ldb);
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* !defined(NVBLAS_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/__init__.py
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libcublas.so.12 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libcublas.so.12
new file mode 100644
index 0000000000000000000000000000000000000000..bb58b05fe4679649023c94850f174af45c2880f7
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libcublas.so.12
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:031ce6c2cbfbb9468f040527cab5c599069ce5609e73e28f87503881063eac21
+size 116388640
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libcublasLt.so.12 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libcublasLt.so.12
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libcublasLt.so.12
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10b5e6631cf8115c661eb895ed1533826308b58f7956466f53d236a40c9b622c
+size 751771728
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libnvblas.so.12 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cublas/lib/libnvblas.so.12
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d244820d32966f0af47afe6c9ee6f34b230fafe87ea0141dc71241a5d2b340c
+size 753824
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openacc/cupti_openacc.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openacc/cupti_openacc.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7ea50da7beb2187e77f7606dd70faed0e4b4add
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openacc/cupti_openacc.h
@@ -0,0 +1,98 @@
+/*
+ * Copyright 2017 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#include <cuda_stdint.h>
+
+#if !defined(_CUPTI_OPENACC_H_)
+#define _CUPTI_OPENACC_H_
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__LP64__)
+#define CUPTILP64 1
+#elif defined(_WIN64)
+#define CUPTILP64 1
+#else
+#undef CUPTILP64
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \brief Initialize OpenACC support
+ *
+ * \param profRegister function of type acc_prof_reg as obtained from acc_register_library
+ * \param profUnregister function of type acc_prof_reg as obtained from acc_register_library
+ * \param profLookup function of type acc_prof_lookup as obtained from acc_register_library
+ */
+CUptiResult CUPTIAPI
+cuptiOpenACCInitialize(void *profRegister, void *profUnregister, void *profLookup);
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_OPENACC_H_*/
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openmp/cupti_openmp.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openmp/cupti_openmp.h
new file mode 100644
index 0000000000000000000000000000000000000000..303dd42878fb02774d872c197ccc27b17f2af69e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openmp/cupti_openmp.h
@@ -0,0 +1,100 @@
+/*
+ * Copyright 2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#include <cuda_stdint.h>
+#include "Openmp/omp-tools.h"
+
+#if !defined(_CUPTI_OPENMP_H_)
+#define _CUPTI_OPENMP_H_
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__LP64__)
+#define CUPTILP64 1
+#elif defined(_WIN64)
+#define CUPTILP64 1
+#else
+#undef CUPTILP64
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \brief Initialize OPENMP support (deprecated, used before OpenMP 5.0)
+ *
+ */
+int CUPTIAPI cuptiOpenMpInitialize(ompt_function_lookup_t ompt_fn_lookup, const char *runtime_version, unsigned int ompt_version);
+
+/**
+ * \brief Initialize OPENMP support
+ *
+ */
+int CUPTIAPI cuptiOpenMpInitialize_v2(ompt_function_lookup_t lookup, int initial_device_num, ompt_data_t *tool_data);
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_OPENMP_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openmp/omp-tools.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openmp/omp-tools.h
new file mode 100644
index 0000000000000000000000000000000000000000..276967d07e8f8c0f7686e5b3b15151edf2415ae7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/Openmp/omp-tools.h
@@ -0,0 +1,1083 @@
+/*
+ * include/50/omp-tools.h.var
+ */
+
+//===----------------------------------------------------------------------===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is dual licensed under the MIT and the University of Illinois Open
+// Source Licenses. See LICENSE.txt for details.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef __OMPT__
+#define __OMPT__
+
+/*****************************************************************************
+ * system include files
+ *****************************************************************************/
+
+#include <stdint.h>
+#include <stddef.h>
+
+/*****************************************************************************
+ * iteration macros
+ *****************************************************************************/
+
+#define FOREACH_OMPT_INQUIRY_FN(macro)      \
+    macro (ompt_enumerate_states)           \
+    macro (ompt_enumerate_mutex_impls)      \
+                                            \
+    macro (ompt_set_callback)               \
+    macro (ompt_get_callback)               \
+                                            \
+    macro (ompt_get_state)                  \
+                                            \
+    macro (ompt_get_parallel_info)          \
+    macro (ompt_get_task_info)              \
+    macro (ompt_get_task_memory)            \
+    macro (ompt_get_thread_data)            \
+    macro (ompt_get_unique_id)              \
+    macro (ompt_finalize_tool)              \
+                                            \
+    macro(ompt_get_num_procs)               \
+    macro(ompt_get_num_places)              \
+    macro(ompt_get_place_proc_ids)          \
+    macro(ompt_get_place_num)               \
+    macro(ompt_get_partition_place_nums)    \
+    macro(ompt_get_proc_id)                 \
+                                            \
+    macro(ompt_get_target_info)             \
+    macro(ompt_get_num_devices)
+
+#define FOREACH_OMPT_STATE(macro)                                                                \
+                                                                                                \
+    /* first available state */                                                                 \
+    macro (ompt_state_undefined, 0x102)      /* undefined thread state */                        \
+                                                                                                \
+    /* work states (0..15) */                                                                   \
+    macro (ompt_state_work_serial, 0x000)    /* working outside parallel */                      \
+    macro (ompt_state_work_parallel, 0x001)  /* working within parallel */                       \
+    macro (ompt_state_work_reduction, 0x002) /* performing a reduction */                        \
+                                                                                                \
+    /* barrier wait states (16..31) */                                                          \
+    macro (ompt_state_wait_barrier, 0x010)   /* waiting at a barrier */                          \
+    macro (ompt_state_wait_barrier_implicit_parallel, 0x011)                                     \
+                                            /* implicit barrier at the end of parallel region */\
+    macro (ompt_state_wait_barrier_implicit_workshare, 0x012)                                    \
+                                            /* implicit barrier at the end of worksharing */    \
+    macro (ompt_state_wait_barrier_implicit, 0x013)  /* implicit barrier */                      \
+    macro (ompt_state_wait_barrier_explicit, 0x014)  /* explicit barrier */                      \
+                                                                                                \
+    /* task wait states (32..63) */                                                             \
+    macro (ompt_state_wait_taskwait, 0x020)  /* waiting at a taskwait */                         \
+    macro (ompt_state_wait_taskgroup, 0x021) /* waiting at a taskgroup */                        \
+                                                                                                \
+    /* mutex wait states (64..127) */                                                           \
+    macro (ompt_state_wait_mutex, 0x040)                                                         \
+    macro (ompt_state_wait_lock, 0x041)      /* waiting for lock */                              \
+    macro (ompt_state_wait_critical, 0x042)  /* waiting for critical */                          \
+    macro (ompt_state_wait_atomic, 0x043)    /* waiting for atomic */                            \
+    macro (ompt_state_wait_ordered, 0x044)   /* waiting for ordered */                           \
+                                                                                                \
+    /* target wait states (128..255) */                                                         \
+    macro (ompt_state_wait_target, 0x080)        /* waiting for target region */                 \
+    macro (ompt_state_wait_target_map, 0x081)    /* waiting for target data mapping operation */ \
+    macro (ompt_state_wait_target_update, 0x082) /* waiting for target update operation */       \
+                                                                                                \
+    /* misc (256..511) */                                                                       \
+    macro (ompt_state_idle, 0x100)           /* waiting for work */                              \
+    macro (ompt_state_overhead, 0x101)       /* overhead excluding wait states */                \
+                                                                                                \
+    /* implementation-specific states (512..) */
+
+
+#define FOREACH_KMP_MUTEX_IMPL(macro)                                                \
+    macro (kmp_mutex_impl_none, 0)         /* unknown implementation */              \
+    macro (kmp_mutex_impl_spin, 1)         /* based on spin */                       \
+    macro (kmp_mutex_impl_queuing, 2)      /* based on some fair policy */           \
+    macro (kmp_mutex_impl_speculative, 3)  /* based on HW-supported speculation */
+
+#define FOREACH_OMPT_EVENT(macro)                                                                                        \
+                                                                                                                         \
+    /*--- Mandatory Events ---*/                                                                                         \
+    macro (ompt_callback_thread_begin,      ompt_callback_thread_begin_t,       1) /* thread begin                    */ \
+    macro (ompt_callback_thread_end,        ompt_callback_thread_end_t,         2) /* thread end                      */ \
+                                                                                                                         \
+    macro (ompt_callback_parallel_begin,    ompt_callback_parallel_begin_t,     3) /* parallel begin                  */ \
+    macro (ompt_callback_parallel_end,      ompt_callback_parallel_end_t,       4) /* parallel end                    */ \
+                                                                                                                         \
+    macro (ompt_callback_task_create,       ompt_callback_task_create_t,        5) /* task begin                      */ \
+    macro (ompt_callback_task_schedule,     ompt_callback_task_schedule_t,      6) /* task schedule                   */ \
+    macro (ompt_callback_implicit_task,     ompt_callback_implicit_task_t,      7) /* implicit task                   */ \
+                                                                                                                         \
+    macro (ompt_callback_target,            ompt_callback_target_t,             8) /* target                          */ \
+    macro (ompt_callback_target_data_op,    ompt_callback_target_data_op_t,     9) /* target data op                  */ \
+    macro (ompt_callback_target_submit,     ompt_callback_target_submit_t,     10) /* target  submit                  */ \
+                                                                                                                         \
+    macro (ompt_callback_control_tool,      ompt_callback_control_tool_t,      11) /* control tool                    */ \
+                                                                                                                         \
+    macro (ompt_callback_device_initialize, ompt_callback_device_initialize_t, 12) /* device initialize               */ \
+    macro (ompt_callback_device_finalize,   ompt_callback_device_finalize_t,   13) /* device finalize                 */ \
+                                                                                                                         \
+    macro (ompt_callback_device_load,       ompt_callback_device_load_t,       14) /* device load                     */ \
+    macro (ompt_callback_device_unload,     ompt_callback_device_unload_t,     15) /* device unload                   */ \
+                                                                                                                         \
+    /* Optional Events */                                                                                                \
+    macro (ompt_callback_sync_region_wait,  ompt_callback_sync_region_t,       16) /* sync region wait begin or end   */ \
+                                                                                                                         \
+    macro (ompt_callback_mutex_released,    ompt_callback_mutex_t,             17) /* mutex released                  */ \
+                                                                                                                         \
+    macro (ompt_callback_dependences,       ompt_callback_dependences_t,       18) /* report task dependences         */ \
+    macro (ompt_callback_task_dependence,   ompt_callback_task_dependence_t,   19) /* report task dependence          */ \
+                                                                                                                         \
+    macro (ompt_callback_work,              ompt_callback_work_t,              20) /* task at work begin or end       */ \
+                                                                                                                         \
+    macro (ompt_callback_master,            ompt_callback_master_t,            21) /* task at master begin or end     */ \
+                                                                                                                         \
+    macro (ompt_callback_target_map,        ompt_callback_target_map_t,        22) /* target map                      */ \
+                                                                                                                         \
+    macro (ompt_callback_sync_region,       ompt_callback_sync_region_t,       23) /* sync region begin or end        */ \
+                                                                                                                         \
+    macro (ompt_callback_lock_init,         ompt_callback_mutex_acquire_t,     24) /* lock init                       */ \
+    macro (ompt_callback_lock_destroy,      ompt_callback_mutex_t,             25) /* lock destroy                    */ \
+                                                                                                                         \
+    macro (ompt_callback_mutex_acquire,     ompt_callback_mutex_acquire_t,     26) /* mutex acquire                   */ \
+    macro (ompt_callback_mutex_acquired,    ompt_callback_mutex_t,             27) /* mutex acquired                  */ \
+                                                                                                                         \
+    macro (ompt_callback_nest_lock,         ompt_callback_nest_lock_t,         28) /* nest lock                       */ \
+                                                                                                                         \
+    macro (ompt_callback_flush,             ompt_callback_flush_t,             29) /* after executing flush           */ \
+                                                                                                                         \
+    macro (ompt_callback_cancel,            ompt_callback_cancel_t,            30) /* cancel innermost binding region */ \
+                                                                                                                         \
+    macro (ompt_callback_reduction,         ompt_callback_sync_region_t,       31) /* reduction                       */ \
+                                                                                                                         \
+    macro (ompt_callback_dispatch,          ompt_callback_dispatch_t,          32) /* dispatch of work                */
+
+/*****************************************************************************
+ * implementation specific types
+ *****************************************************************************/
+
+typedef enum kmp_mutex_impl_t {
+#define kmp_mutex_impl_macro(impl, code) impl = code,
+    FOREACH_KMP_MUTEX_IMPL(kmp_mutex_impl_macro)
+#undef kmp_mutex_impl_macro
+} kmp_mutex_impl_t;
+
+/*****************************************************************************
+ * definitions generated from spec
+ *****************************************************************************/
+
+typedef enum ompt_callbacks_t {
+  ompt_callback_thread_begin             = 1,
+  ompt_callback_thread_end               = 2,
+  ompt_callback_parallel_begin           = 3,
+  ompt_callback_parallel_end             = 4,
+  ompt_callback_task_create              = 5,
+  ompt_callback_task_schedule            = 6,
+  ompt_callback_implicit_task            = 7,
+  ompt_callback_target                   = 8,
+  ompt_callback_target_data_op           = 9,
+  ompt_callback_target_submit            = 10,
+  ompt_callback_control_tool             = 11,
+  ompt_callback_device_initialize        = 12,
+  ompt_callback_device_finalize          = 13,
+  ompt_callback_device_load              = 14,
+  ompt_callback_device_unload            = 15,
+  ompt_callback_sync_region_wait         = 16,
+  ompt_callback_mutex_released           = 17,
+  ompt_callback_dependences              = 18,
+  ompt_callback_task_dependence          = 19,
+  ompt_callback_work                     = 20,
+  ompt_callback_master                   = 21,
+  ompt_callback_target_map               = 22,
+  ompt_callback_sync_region              = 23,
+  ompt_callback_lock_init                = 24,
+  ompt_callback_lock_destroy             = 25,
+  ompt_callback_mutex_acquire            = 26,
+  ompt_callback_mutex_acquired           = 27,
+  ompt_callback_nest_lock                = 28,
+  ompt_callback_flush                    = 29,
+  ompt_callback_cancel                   = 30,
+  ompt_callback_reduction                = 31,
+  ompt_callback_dispatch                 = 32
+} ompt_callbacks_t;
+
+typedef enum ompt_record_t {
+  ompt_record_ompt               = 1,
+  ompt_record_native             = 2,
+  ompt_record_invalid            = 3
+} ompt_record_t;
+
+typedef enum ompt_record_native_t {
+  ompt_record_native_info  = 1,
+  ompt_record_native_event = 2
+} ompt_record_native_t;
+
+typedef enum ompt_set_result_t {
+  ompt_set_error            = 0,
+  ompt_set_never            = 1,
+  ompt_set_impossible       = 2,
+  ompt_set_sometimes        = 3,
+  ompt_set_sometimes_paired = 4,
+  ompt_set_always           = 5
+} ompt_set_result_t;
+
+typedef uint64_t ompt_id_t;
+
+typedef uint64_t ompt_device_time_t;
+
+typedef uint64_t ompt_buffer_cursor_t;
+
+typedef enum ompt_thread_t {
+  ompt_thread_initial                 = 1,
+  ompt_thread_worker                  = 2,
+  ompt_thread_other                   = 3,
+  ompt_thread_unknown                 = 4
+} ompt_thread_t;
+
+typedef enum ompt_scope_endpoint_t {
+  ompt_scope_begin                    = 1,
+  ompt_scope_end                      = 2
+} ompt_scope_endpoint_t;
+
+typedef enum ompt_dispatch_t {
+  ompt_dispatch_iteration             = 1,
+  ompt_dispatch_section               = 2
+} ompt_dispatch_t;
+
+typedef enum ompt_sync_region_t {
+  ompt_sync_region_barrier                = 1,
+  ompt_sync_region_barrier_implicit       = 2,
+  ompt_sync_region_barrier_explicit       = 3,
+  ompt_sync_region_barrier_implementation = 4,
+  ompt_sync_region_taskwait               = 5,
+  ompt_sync_region_taskgroup              = 6,
+  ompt_sync_region_reduction              = 7
+} ompt_sync_region_t;
+
+typedef enum ompt_target_data_op_t {
+  ompt_target_data_alloc                = 1,
+  ompt_target_data_transfer_to_device   = 2,
+  ompt_target_data_transfer_from_device = 3,
+  ompt_target_data_delete               = 4,
+  ompt_target_data_associate            = 5,
+  ompt_target_data_disassociate         = 6
+} ompt_target_data_op_t;
+
+typedef enum ompt_work_t {
+  ompt_work_loop               = 1,
+  ompt_work_sections           = 2,
+  ompt_work_single_executor    = 3,
+  ompt_work_single_other       = 4,
+  ompt_work_workshare          = 5,
+  ompt_work_distribute         = 6,
+  ompt_work_taskloop           = 7
+} ompt_work_t;
+
+typedef enum ompt_mutex_t {
+  ompt_mutex_lock                     = 1,
+  ompt_mutex_test_lock                = 2,
+  ompt_mutex_nest_lock                = 3,
+  ompt_mutex_test_nest_lock           = 4,
+  ompt_mutex_critical                 = 5,
+  ompt_mutex_atomic                   = 6,
+  ompt_mutex_ordered                  = 7
+} ompt_mutex_t;
+
+typedef enum ompt_native_mon_flag_t {
+  ompt_native_data_motion_explicit    = 0x01,
+  ompt_native_data_motion_implicit    = 0x02,
+  ompt_native_kernel_invocation       = 0x04,
+  ompt_native_kernel_execution        = 0x08,
+  ompt_native_driver                  = 0x10,
+  ompt_native_runtime                 = 0x20,
+  ompt_native_overhead                = 0x40,
+  ompt_native_idleness                = 0x80
+} ompt_native_mon_flag_t;
+
+typedef enum ompt_task_flag_t {
+  ompt_task_initial                   = 0x00000001,
+  ompt_task_implicit                  = 0x00000002,
+  ompt_task_explicit                  = 0x00000004,
+  ompt_task_target                    = 0x00000008,
+  ompt_task_undeferred                = 0x08000000,
+  ompt_task_untied                    = 0x10000000,
+  ompt_task_final                     = 0x20000000,
+  ompt_task_mergeable                 = 0x40000000,
+  ompt_task_merged                    = 0x80000000
+} ompt_task_flag_t;
+
+typedef enum ompt_task_status_t {
+  ompt_task_complete      = 1,
+  ompt_task_yield         = 2,
+  ompt_task_cancel        = 3,
+  ompt_task_detach        = 4,
+  ompt_task_early_fulfill = 5,
+  ompt_task_late_fulfill  = 6,
+  ompt_task_switch        = 7
+} ompt_task_status_t;
+
+typedef enum ompt_target_t {
+  ompt_target                         = 1,
+  ompt_target_enter_data              = 2,
+  ompt_target_exit_data               = 3,
+  ompt_target_update                  = 4
+} ompt_target_t;
+
+typedef enum ompt_parallel_flag_t {
+  ompt_parallel_invoker_program = 0x00000001,
+  ompt_parallel_invoker_runtime = 0x00000002,
+  ompt_parallel_league          = 0x40000000,
+  ompt_parallel_team            = 0x80000000
+} ompt_parallel_flag_t;
+
+typedef enum ompt_target_map_flag_t {
+  ompt_target_map_flag_to             = 0x01,
+  ompt_target_map_flag_from           = 0x02,
+  ompt_target_map_flag_alloc          = 0x04,
+  ompt_target_map_flag_release        = 0x08,
+  ompt_target_map_flag_delete         = 0x10,
+  ompt_target_map_flag_implicit       = 0x20
+} ompt_target_map_flag_t;
+
+typedef enum ompt_dependence_type_t {
+  ompt_dependence_type_in              = 1,
+  ompt_dependence_type_out             = 2,
+  ompt_dependence_type_inout           = 3,
+  ompt_dependence_type_mutexinoutset   = 4,
+  ompt_dependence_type_source          = 5,
+  ompt_dependence_type_sink            = 6
+} ompt_dependence_type_t;
+
+typedef enum ompt_cancel_flag_t {
+  ompt_cancel_parallel       = 0x01,
+  ompt_cancel_sections       = 0x02,
+  ompt_cancel_loop           = 0x04,
+  ompt_cancel_taskgroup      = 0x08,
+  ompt_cancel_activated      = 0x10,
+  ompt_cancel_detected       = 0x20,
+  ompt_cancel_discarded_task = 0x40
+} ompt_cancel_flag_t;
+
+typedef uint64_t ompt_hwid_t;
+
+typedef uint64_t ompt_wait_id_t;
+
+typedef enum ompt_frame_flag_t {
+  ompt_frame_runtime        = 0x00,
+  ompt_frame_application    = 0x01,
+  ompt_frame_cfa            = 0x10,
+  ompt_frame_framepointer   = 0x20,
+  ompt_frame_stackaddress   = 0x30
+} ompt_frame_flag_t; 
+
+typedef enum ompt_state_t {
+  ompt_state_work_serial                      = 0x000,
+  ompt_state_work_parallel                    = 0x001,
+  ompt_state_work_reduction                   = 0x002,
+
+  ompt_state_wait_barrier                     = 0x010,
+  ompt_state_wait_barrier_implicit_parallel   = 0x011,
+  ompt_state_wait_barrier_implicit_workshare  = 0x012,
+  ompt_state_wait_barrier_implicit            = 0x013,
+  ompt_state_wait_barrier_explicit            = 0x014,
+
+  ompt_state_wait_taskwait                    = 0x020,
+  ompt_state_wait_taskgroup                   = 0x021,
+
+  ompt_state_wait_mutex                       = 0x040,
+  ompt_state_wait_lock                        = 0x041,
+  ompt_state_wait_critical                    = 0x042,
+  ompt_state_wait_atomic                      = 0x043,
+  ompt_state_wait_ordered                     = 0x044,
+
+  ompt_state_wait_target                      = 0x080,
+  ompt_state_wait_target_map                  = 0x081,
+  ompt_state_wait_target_update               = 0x082,
+
+  ompt_state_idle                             = 0x100,
+  ompt_state_overhead                         = 0x101,
+  ompt_state_undefined                        = 0x102
+} ompt_state_t;
+
+typedef uint64_t (*ompt_get_unique_id_t) (void);
+
+typedef uint64_t ompd_size_t;
+
+typedef uint64_t ompd_wait_id_t;
+
+typedef uint64_t ompd_addr_t;
+typedef int64_t  ompd_word_t;
+typedef uint64_t ompd_seg_t;
+
+typedef uint64_t ompd_device_t;
+
+typedef uint64_t ompd_thread_id_t;
+
+typedef enum ompd_scope_t {
+  ompd_scope_global = 1,
+  ompd_scope_address_space = 2,
+  ompd_scope_thread = 3,
+  ompd_scope_parallel = 4,
+  ompd_scope_implicit_task = 5,
+  ompd_scope_task = 6
+} ompd_scope_t;
+
+typedef uint64_t ompd_icv_id_t;
+
+typedef enum ompd_rc_t {
+  ompd_rc_ok = 0,
+  ompd_rc_unavailable = 1,
+  ompd_rc_stale_handle = 2,
+  ompd_rc_bad_input = 3,
+  ompd_rc_error = 4,
+  ompd_rc_unsupported = 5,
+  ompd_rc_needs_state_tracking = 6,
+  ompd_rc_incompatible = 7,
+  ompd_rc_device_read_error = 8,
+  ompd_rc_device_write_error = 9,
+  ompd_rc_nomem = 10,
+} ompd_rc_t;
+
+typedef void (*ompt_interface_fn_t) (void);
+
+typedef ompt_interface_fn_t (*ompt_function_lookup_t) (
+  const char *interface_function_name
+);
+
+typedef union ompt_data_t {
+  uint64_t value;
+  void *ptr;
+} ompt_data_t;
+
+typedef struct ompt_frame_t {
+  ompt_data_t exit_frame;
+  ompt_data_t enter_frame;
+  int exit_frame_flags;
+  int enter_frame_flags;
+} ompt_frame_t;
+
+typedef void (*ompt_callback_t) (void);
+
+typedef void ompt_device_t;
+
+typedef void ompt_buffer_t;
+
+typedef void (*ompt_callback_buffer_request_t) (
+  int device_num,
+  ompt_buffer_t **buffer,
+  size_t *bytes
+);
+
+typedef void (*ompt_callback_buffer_complete_t) (
+  int device_num,
+  ompt_buffer_t *buffer,
+  size_t bytes,
+  ompt_buffer_cursor_t begin,
+  int buffer_owned
+);
+
+typedef void (*ompt_finalize_t) (
+  ompt_data_t *tool_data
+);
+
+typedef int (*ompt_initialize_t) (
+  ompt_function_lookup_t lookup,
+  int initial_device_num,
+  ompt_data_t *tool_data
+);
+
+typedef struct ompt_start_tool_result_t {
+  ompt_initialize_t initialize;
+  ompt_finalize_t finalize;
+  ompt_data_t tool_data;
+} ompt_start_tool_result_t;
+
+typedef struct ompt_record_abstract_t {
+  ompt_record_native_t rclass;
+  const char *type;
+  ompt_device_time_t start_time;
+  ompt_device_time_t end_time;
+  ompt_hwid_t hwid;
+} ompt_record_abstract_t;
+
+typedef struct ompt_dependence_t {
+  ompt_data_t variable;
+  ompt_dependence_type_t dependence_type;
+} ompt_dependence_t;
+
+typedef int (*ompt_enumerate_states_t) (
+  int current_state,
+  int *next_state,
+  const char **next_state_name
+);
+
+typedef int (*ompt_enumerate_mutex_impls_t) (
+  int current_impl,
+  int *next_impl,
+  const char **next_impl_name
+);
+
+typedef ompt_set_result_t (*ompt_set_callback_t) (
+  ompt_callbacks_t event,
+  ompt_callback_t callback
+);
+
+typedef int (*ompt_get_callback_t) (
+  ompt_callbacks_t event,
+  ompt_callback_t *callback
+);
+
+typedef ompt_data_t *(*ompt_get_thread_data_t) (void);
+
+typedef int (*ompt_get_num_procs_t) (void);
+
+typedef int (*ompt_get_num_places_t) (void);
+
+typedef int (*ompt_get_place_proc_ids_t) (
+  int place_num,
+  int ids_size,
+  int *ids
+);
+
+typedef int (*ompt_get_place_num_t) (void);
+
+typedef int (*ompt_get_partition_place_nums_t) (
+  int place_nums_size,
+  int *place_nums
+);
+
+typedef int (*ompt_get_proc_id_t) (void);
+
+typedef int (*ompt_get_state_t) (
+  ompt_wait_id_t *wait_id
+);
+
+typedef int (*ompt_get_parallel_info_t) (
+  int ancestor_level,
+  ompt_data_t **parallel_data,
+  int *team_size
+);
+
+typedef int (*ompt_get_task_info_t) (
+  int ancestor_level,
+  int *flags,
+  ompt_data_t **task_data,
+  ompt_frame_t **task_frame,
+  ompt_data_t **parallel_data,
+  int *thread_num
+);
+
+typedef int (*ompt_get_task_memory_t)(
+  void **addr,
+  size_t *size,
+  int block
+);
+
+typedef int (*ompt_get_target_info_t) (
+  uint64_t *device_num,
+  ompt_id_t *target_id,
+  ompt_id_t *host_op_id
+);
+
+typedef int (*ompt_get_num_devices_t) (void);
+
+typedef void (*ompt_finalize_tool_t) (void);
+
+typedef int (*ompt_get_device_num_procs_t) (
+  ompt_device_t *device
+);
+
+typedef ompt_device_time_t (*ompt_get_device_time_t) (
+  ompt_device_t *device
+);
+
+typedef double (*ompt_translate_time_t) (
+  ompt_device_t *device,
+  ompt_device_time_t time
+);
+
+typedef ompt_set_result_t (*ompt_set_trace_ompt_t) (
+  ompt_device_t *device,
+  unsigned int enable,
+  unsigned int etype
+);
+
+typedef ompt_set_result_t (*ompt_set_trace_native_t) (
+  ompt_device_t *device,
+  int enable,
+  int flags
+);
+
+typedef int (*ompt_start_trace_t) (
+  ompt_device_t *device,
+  ompt_callback_buffer_request_t request,
+  ompt_callback_buffer_complete_t complete
+);
+
+typedef int (*ompt_pause_trace_t) (
+  ompt_device_t *device,
+  int begin_pause
+);
+
+typedef int (*ompt_flush_trace_t) (
+  ompt_device_t *device
+);
+
+typedef int (*ompt_stop_trace_t) (
+  ompt_device_t *device
+);
+
+typedef int (*ompt_advance_buffer_cursor_t) (
+  ompt_device_t *device,
+  ompt_buffer_t *buffer,
+  size_t size,
+  ompt_buffer_cursor_t current,
+  ompt_buffer_cursor_t *next
+);
+
+typedef ompt_record_t (*ompt_get_record_type_t) (
+  ompt_buffer_t *buffer,
+  ompt_buffer_cursor_t current
+);
+
+typedef void *(*ompt_get_record_native_t) (
+  ompt_buffer_t *buffer,
+  ompt_buffer_cursor_t current,
+  ompt_id_t *host_op_id
+);
+
+typedef ompt_record_abstract_t *
+(*ompt_get_record_abstract_t) (
+  void *native_record
+);
+
+typedef void (*ompt_callback_thread_begin_t) (
+  ompt_thread_t thread_type,
+  ompt_data_t *thread_data
+);
+
+typedef struct ompt_record_thread_begin_t {
+  ompt_thread_t thread_type;
+} ompt_record_thread_begin_t;
+
+typedef void (*ompt_callback_thread_end_t) (
+  ompt_data_t *thread_data
+);
+
+typedef void (*ompt_callback_parallel_begin_t) (
+  ompt_data_t *encountering_task_data,
+  const ompt_frame_t *encountering_task_frame,
+  ompt_data_t *parallel_data,
+  unsigned int requested_parallelism,
+  int flags,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_parallel_begin_t {
+  ompt_id_t encountering_task_id;
+  ompt_id_t parallel_id;
+  unsigned int requested_parallelism;
+  int flags;
+  const void *codeptr_ra;
+} ompt_record_parallel_begin_t;
+
+typedef void (*ompt_callback_parallel_end_t) (
+  ompt_data_t *parallel_data,
+  ompt_data_t *encountering_task_data,
+  int flags,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_parallel_end_t {
+  ompt_id_t parallel_id;
+  ompt_id_t encountering_task_id;
+  int flags;
+  const void *codeptr_ra;
+} ompt_record_parallel_end_t;
+
+typedef void (*ompt_callback_work_t) (
+  ompt_work_t wstype,
+  ompt_scope_endpoint_t endpoint,
+  ompt_data_t *parallel_data,
+  ompt_data_t *task_data,
+  uint64_t count,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_work_t {
+  ompt_work_t wstype;
+  ompt_scope_endpoint_t endpoint;
+  ompt_id_t parallel_id;
+  ompt_id_t task_id;
+  uint64_t count;
+  const void *codeptr_ra;
+} ompt_record_work_t;
+
+typedef void (*ompt_callback_dispatch_t) (
+  ompt_data_t *parallel_data,
+  ompt_data_t *task_data,
+  ompt_dispatch_t kind,
+  ompt_data_t instance 
+);
+
+typedef struct ompt_record_dispatch_t {
+  ompt_id_t parallel_id;
+  ompt_id_t task_id;
+  ompt_dispatch_t kind;
+  ompt_data_t instance; 
+} ompt_record_dispatch_t;
+
+typedef void (*ompt_callback_task_create_t) (
+  ompt_data_t *encountering_task_data,
+  const ompt_frame_t *encountering_task_frame,
+  ompt_data_t *new_task_data,
+  int flags,
+  int has_dependences,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_task_create_t {
+  ompt_id_t encountering_task_id;
+  ompt_id_t new_task_id;
+  int flags;
+  int has_dependences;
+  const void *codeptr_ra;
+} ompt_record_task_create_t;
+
+typedef void (*ompt_callback_dependences_t) (
+  ompt_data_t *task_data,
+  const ompt_dependence_t *deps,
+  int ndeps
+);
+
+typedef struct ompt_record_dependences_t {
+  ompt_id_t task_id;
+  ompt_dependence_t dep;
+  int ndeps;
+} ompt_record_dependences_t;
+
+typedef void (*ompt_callback_task_dependence_t) (
+  ompt_data_t *src_task_data,
+  ompt_data_t *sink_task_data
+);
+
+typedef struct ompt_record_task_dependence_t {
+  ompt_id_t src_task_id;
+  ompt_id_t sink_task_id;
+} ompt_record_task_dependence_t;
+
+typedef void (*ompt_callback_task_schedule_t) (
+  ompt_data_t *prior_task_data,
+  ompt_task_status_t prior_task_status,
+  ompt_data_t *next_task_data
+);
+
+typedef struct ompt_record_task_schedule_t {
+  ompt_id_t prior_task_id;
+  ompt_task_status_t prior_task_status;
+  ompt_id_t next_task_id;
+} ompt_record_task_schedule_t;
+
+typedef void (*ompt_callback_implicit_task_t) (
+  ompt_scope_endpoint_t endpoint,
+  ompt_data_t *parallel_data,
+  ompt_data_t *task_data,
+  unsigned int actual_parallelism,
+  unsigned int index,
+  int flags
+);
+
+typedef struct ompt_record_implicit_task_t {
+  ompt_scope_endpoint_t endpoint;
+  ompt_id_t parallel_id;
+  ompt_id_t task_id;
+  unsigned int actual_parallelism;
+  unsigned int index;
+  int flags;
+} ompt_record_implicit_task_t;
+
+typedef void (*ompt_callback_master_t) (
+  ompt_scope_endpoint_t endpoint,
+  ompt_data_t *parallel_data,
+  ompt_data_t *task_data,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_master_t {
+  ompt_scope_endpoint_t endpoint;
+  ompt_id_t parallel_id;
+  ompt_id_t task_id;
+  const void *codeptr_ra;
+} ompt_record_master_t;
+
+typedef void (*ompt_callback_sync_region_t) (
+  ompt_sync_region_t kind,
+  ompt_scope_endpoint_t endpoint,
+  ompt_data_t *parallel_data,
+  ompt_data_t *task_data,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_sync_region_t {
+  ompt_sync_region_t kind;
+  ompt_scope_endpoint_t endpoint;
+  ompt_id_t parallel_id;
+  ompt_id_t task_id;
+  const void *codeptr_ra;
+} ompt_record_sync_region_t;
+
+typedef void (*ompt_callback_mutex_acquire_t) (
+  ompt_mutex_t kind,
+  unsigned int hint,
+  unsigned int impl,
+  ompt_wait_id_t wait_id,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_mutex_acquire_t {
+  ompt_mutex_t kind;
+  unsigned int hint;
+  unsigned int impl;
+  ompt_wait_id_t wait_id;
+  const void *codeptr_ra;
+} ompt_record_mutex_acquire_t;
+
+typedef void (*ompt_callback_mutex_t) (
+  ompt_mutex_t kind,
+  ompt_wait_id_t wait_id,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_mutex_t {
+  ompt_mutex_t kind;
+  ompt_wait_id_t wait_id;
+  const void *codeptr_ra;
+} ompt_record_mutex_t;
+
+typedef void (*ompt_callback_nest_lock_t) (
+  ompt_scope_endpoint_t endpoint,
+  ompt_wait_id_t wait_id,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_nest_lock_t {
+  ompt_scope_endpoint_t endpoint;
+  ompt_wait_id_t wait_id;
+  const void *codeptr_ra;
+} ompt_record_nest_lock_t;
+
+typedef void (*ompt_callback_flush_t) (
+  ompt_data_t *thread_data,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_flush_t {
+  const void *codeptr_ra;
+} ompt_record_flush_t;
+
+typedef void (*ompt_callback_cancel_t) (
+  ompt_data_t *task_data,
+  int flags,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_cancel_t {
+  ompt_id_t task_id;
+  int flags;
+  const void *codeptr_ra;
+} ompt_record_cancel_t;
+
+typedef void (*ompt_callback_device_initialize_t) (
+  int device_num,
+  const char *type,
+  ompt_device_t *device,
+  ompt_function_lookup_t lookup,
+  const char *documentation
+);
+
+typedef void (*ompt_callback_device_finalize_t) (
+  int device_num
+);
+
+typedef void (*ompt_callback_device_load_t) (
+  int device_num,
+  const char *filename,
+  int64_t offset_in_file,
+  void *vma_in_file,
+  size_t bytes,
+  void *host_addr,
+  void *device_addr,
+  uint64_t module_id
+);
+
+typedef void (*ompt_callback_device_unload_t) (
+  int device_num,
+  uint64_t module_id
+);
+
+typedef void (*ompt_callback_target_data_op_t) (
+  ompt_id_t target_id,
+  ompt_id_t host_op_id,
+  ompt_target_data_op_t optype,
+  void *src_addr,
+  int src_device_num,
+  void *dest_addr,
+  int dest_device_num,
+  size_t bytes,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_target_data_op_t {
+  ompt_id_t host_op_id;
+  ompt_target_data_op_t optype;
+  void *src_addr;
+  int src_device_num;
+  void *dest_addr;
+  int dest_device_num;
+  size_t bytes;
+  ompt_device_time_t end_time;
+  const void *codeptr_ra;
+} ompt_record_target_data_op_t;
+
+typedef void (*ompt_callback_target_t) (
+  ompt_target_t kind,
+  ompt_scope_endpoint_t endpoint,
+  int device_num,
+  ompt_data_t *task_data,
+  ompt_id_t target_id,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_target_t {
+  ompt_target_t kind;
+  ompt_scope_endpoint_t endpoint;
+  int device_num;
+  ompt_id_t task_id;
+  ompt_id_t target_id;
+  const void *codeptr_ra;
+} ompt_record_target_t;
+
+typedef void (*ompt_callback_target_map_t) (
+  ompt_id_t target_id,
+  unsigned int nitems,
+  void **host_addr,
+  void **device_addr,
+  size_t *bytes,
+  unsigned int *mapping_flags,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_target_map_t {
+  ompt_id_t target_id;
+  unsigned int nitems;
+  void **host_addr;
+  void **device_addr;
+  size_t *bytes;
+  unsigned int *mapping_flags;
+  const void *codeptr_ra;
+} ompt_record_target_map_t;
+
+typedef void (*ompt_callback_target_submit_t) (
+  ompt_id_t target_id,
+  ompt_id_t host_op_id,
+  unsigned int requested_num_teams
+);
+
+typedef struct ompt_record_target_kernel_t {
+  ompt_id_t host_op_id;
+  unsigned int requested_num_teams;
+  unsigned int granted_num_teams;
+  ompt_device_time_t end_time;
+} ompt_record_target_kernel_t;
+
+typedef int (*ompt_callback_control_tool_t) (
+  uint64_t command,
+  uint64_t modifier,
+  void *arg,
+  const void *codeptr_ra
+);
+
+typedef struct ompt_record_control_tool_t {
+  uint64_t command;
+  uint64_t modifier;
+  const void *codeptr_ra;
+} ompt_record_control_tool_t;
+
+typedef struct ompd_address_t {
+  ompd_seg_t segment;
+  ompd_addr_t address;
+} ompd_address_t;
+
+typedef struct ompd_frame_info_t {
+  ompd_address_t frame_address;
+  ompd_word_t frame_flag;
+} ompd_frame_info_t;
+
+typedef struct _ompd_aspace_handle ompd_address_space_handle_t;
+typedef struct _ompd_thread_handle ompd_thread_handle_t;
+typedef struct _ompd_parallel_handle ompd_parallel_handle_t;
+typedef struct _ompd_task_handle ompd_task_handle_t;
+
+typedef struct _ompd_aspace_cont ompd_address_space_context_t;
+typedef struct _ompd_thread_cont ompd_thread_context_t;
+
+typedef struct ompd_device_type_sizes_t {
+  uint8_t sizeof_char;
+  uint8_t sizeof_short;
+  uint8_t sizeof_int;
+  uint8_t sizeof_long;
+  uint8_t sizeof_long_long;
+  uint8_t sizeof_pointer;
+} ompd_device_type_sizes_t;
+
+typedef struct ompt_record_ompt_t {
+  ompt_callbacks_t type;
+  ompt_device_time_t time;
+  ompt_id_t thread_id;
+  ompt_id_t target_id;
+  union {
+    ompt_record_thread_begin_t thread_begin;
+    ompt_record_parallel_begin_t parallel_begin;
+    ompt_record_parallel_end_t parallel_end;
+    ompt_record_work_t work;
+    ompt_record_dispatch_t dispatch;
+    ompt_record_task_create_t task_create;
+    ompt_record_dependences_t dependences;
+    ompt_record_task_dependence_t task_dependence;
+    ompt_record_task_schedule_t task_schedule;
+    ompt_record_implicit_task_t implicit_task;
+    ompt_record_master_t master;
+    ompt_record_sync_region_t sync_region;
+    ompt_record_mutex_acquire_t mutex_acquire;
+    ompt_record_mutex_t mutex;
+    ompt_record_nest_lock_t nest_lock;
+    ompt_record_flush_t flush;
+    ompt_record_cancel_t cancel;
+    ompt_record_target_t target;
+    ompt_record_target_data_op_t target_data_op;
+    ompt_record_target_map_t target_map;
+    ompt_record_target_kernel_t target_kernel;
+    ompt_record_control_tool_t control_tool;
+  } record;
+} ompt_record_ompt_t;
+
+typedef ompt_record_ompt_t *(*ompt_get_record_ompt_t) (
+  ompt_buffer_t *buffer,
+  ompt_buffer_cursor_t current
+);
+
+#define ompt_id_none 0
+#define ompt_data_none {0}
+#define ompt_time_none 0
+#define ompt_hwid_none 0
+#define ompt_addr_none ~0
+#define ompt_mutex_impl_none 0
+#define ompt_wait_id_none 0
+
+#define ompd_segment_none 0
+
+#endif /* __OMPT__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cuda_stdint.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cuda_stdint.h
new file mode 100644
index 0000000000000000000000000000000000000000..8a9814410e4b6fb4f07ad9edc8394e956b77dbcd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cuda_stdint.h
@@ -0,0 +1,112 @@
+/*
+ * Copyright 2009-2017 NVIDIA Corporation.  All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *  * Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ *  * Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *  * Neither the name of NVIDIA CORPORATION nor the names of its
+ *    contributors may be used to endorse or promote products derived
+ *    from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#ifndef __cuda_stdint_h__
+#define __cuda_stdint_h__
+
+// Compiler-specific treatment for C99's stdint.h
+//
+// By default, this header will use the standard headers (so it
+// is your responsibility to make sure they are available), except
+// on MSVC before Visual Studio 2010, when they were not provided.
+// To support old MSVC, a few of the commonly-used definitions are
+// provided here.  If more definitions are needed, add them here,
+// or replace these definitions with a complete implementation,
+// such as the ones available from Google, Boost, or MSVC10.  You
+// can prevent the definition of any of these types (in order to
+// use your own) by #defining CU_STDINT_TYPES_ALREADY_DEFINED.
+
+#if !defined(CU_STDINT_TYPES_ALREADY_DEFINED)
+
+// In VS including stdint.h forces the C++ runtime dep - provide an opt-out
+// (CU_STDINT_VS_FORCE_NO_STDINT_H) for users that care (notably static
+// cudart).
+#if defined(_MSC_VER) && ((_MSC_VER < 1600) || defined(CU_STDINT_VS_FORCE_NO_STDINT_H))
+
+// These definitions can be used with MSVC 8 and 9,
+// which don't ship with stdint.h:
+
+typedef unsigned   char   uint8_t;
+
+typedef            short  int16_t;
+typedef unsigned   short uint16_t;
+
+// To keep it consistent with all MSVC build. define those types
+// in the exact same way they are defined with the MSVC headers
+#if defined(_MSC_VER)
+typedef signed     char    int8_t;
+
+typedef            int     int32_t;
+typedef unsigned   int     uint32_t;
+
+typedef long long          int64_t;
+typedef unsigned long long uint64_t;
+#else
+typedef            char    int8_t;
+
+typedef            long   int32_t;
+typedef unsigned   long  uint32_t;
+
+typedef          __int64  int64_t;
+typedef unsigned __int64 uint64_t;
+#endif
+
+#elif defined(__DJGPP__)
+
+// These definitions can be used when compiling
+// C code with DJGPP, which only provides stdint.h
+// when compiling C++ code with TR1 enabled.
+
+typedef               char    int8_t;
+typedef unsigned      char   uint8_t;
+
+typedef               short  int16_t;
+typedef unsigned      short uint16_t;
+
+typedef               long   int32_t;
+typedef unsigned      long  uint32_t;
+
+typedef          long long   int64_t;
+typedef unsigned long long  uint64_t;
+
+#else
+
+// Use standard headers, as specified by C99 and C++ TR1.
+// Known to be provided by:
+// - gcc/glibc, supported by all versions of glibc
+// - djgpp, supported since 2001
+// - MSVC, supported by Visual Studio 2010 and later
+
+#include <stdint.h>
+
+#endif
+
+#endif // !defined(CU_STDINT_TYPES_ALREADY_DEFINED)
+
+
+#endif // file guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti.h
new file mode 100644
index 0000000000000000000000000000000000000000..be316531dcfd846bcea8feadf3604437ce2447a1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti.h
@@ -0,0 +1,123 @@
+/*
+ * Copyright 2010-2017 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_H_)
+#define _CUPTI_H_
+
+#ifdef _WIN32
+#ifndef WIN32_LEAN_AND_MEAN
+#define WIN32_LEAN_AND_MEAN
+#endif
+#ifdef NOMINMAX
+#include <windows.h>
+#else
+#define NOMINMAX
+#include <windows.h>
+#undef NOMINMAX
+#endif
+#endif
+
+#include <cuda.h>
+#include <cupti_result.h>
+#include <cupti_version.h>
+
+/* Activity, callback, event and metric APIs */
+#include <cupti_activity.h>
+#include <cupti_callbacks.h>
+#include <cupti_events.h>
+#include <cupti_metrics.h>
+
+/* Runtime, driver, and nvtx function identifiers */
+#include <cupti_driver_cbid.h>
+#include <cupti_runtime_cbid.h>
+#include <cupti_nvtx_cbid.h>
+
+/* To support function parameter structures for obsoleted API. See
+   cuda.h for the actual definition of these structures. */
+typedef unsigned int CUdeviceptr_v1;
+typedef struct CUDA_MEMCPY2D_v1_st { int dummy; } CUDA_MEMCPY2D_v1;
+typedef struct CUDA_MEMCPY3D_v1_st { int dummy; } CUDA_MEMCPY3D_v1;
+typedef struct CUDA_ARRAY_DESCRIPTOR_v1_st { int dummy; } CUDA_ARRAY_DESCRIPTOR_v1;
+typedef struct CUDA_ARRAY3D_DESCRIPTOR_v1_st { int dummy; } CUDA_ARRAY3D_DESCRIPTOR_v1;
+
+/* Function parameter structures */
+#include <generated_cuda_runtime_api_meta.h>
+#include <generated_cuda_meta.h>
+
+/* The following parameter structures cannot be included unless a
+   header that defines GL_VERSION is included before including them.
+   If these are needed then make sure such a header is included
+   already. */
+#ifdef GL_VERSION
+#include <generated_cuda_gl_interop_meta.h>
+#include <generated_cudaGL_meta.h>
+#endif
+
+//#include <generated_nvtx_meta.h>
+
+/* The following parameter structures cannot be included by default as
+   they are not guaranteed to be available on all systems. Uncomment
+   the includes that are available, or use the include explicitly. */
+#if defined(__linux__)
+//#include <generated_cuda_vdpau_interop_meta.h>
+//#include <generated_cudaVDPAU_meta.h>
+#endif
+
+#ifdef _WIN32
+//#include <generated_cuda_d3d9_interop_meta.h>
+//#include <generated_cuda_d3d10_interop_meta.h>
+//#include <generated_cuda_d3d11_interop_meta.h>
+//#include <generated_cudaD3D9_meta.h>
+//#include <generated_cudaD3D10_meta.h>
+//#include <generated_cudaD3D11_meta.h>
+#endif
+
+#endif /*_CUPTI_H_*/
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_activity.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_activity.h
new file mode 100644
index 0000000000000000000000000000000000000000..cdb6b76f8d66e986b20bd481fbeb0a12a791e5a5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_activity.h
@@ -0,0 +1,8065 @@
+/*
+ * Copyright 2011-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_ACTIVITY_H_)
+#define _CUPTI_ACTIVITY_H_
+
+/**
+ * Deprecated APIs and structures have been moved to the
+ * header :doc: `cupti_activity_deprecated.h`, which is included at
+ * the bottom of this file. Header cupti_activity.h contains
+ * only the latest version of APIs and structures.
+ */
+
+#include <cuda.h>
+#include <cupti_callbacks.h>
+#include <cupti_events.h>
+#include <cupti_metrics.h>
+#include <cupti_result.h>
+
+#if defined(CUPTI_DIRECTIVE_SUPPORT)
+#include <Openacc/cupti_openacc.h>
+#include <Openmp/cupti_openmp.h>
+#endif
+
+#include <cupti_common.h>
+
+#define CUPTI_UNIFIED_MEMORY_CPU_DEVICE_ID ((uint32_t) 0xFFFFFFFFU)
+#define CUPTI_INVALID_CONTEXT_ID ((uint32_t) 0xFFFFFFFFU)
+#define CUPTI_INVALID_STREAM_ID ((uint32_t) 0xFFFFFFFFU)
+#define CUPTI_INVALID_CHANNEL_ID ((uint32_t) 0xFFFFFFFFU)
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+#define invalidNumaId ((uint32_t) 0xFFFFFFFF)
+
+/**
+ * \defgroup CUPTI_ACTIVITY_API CUPTI Activity API
+ * Functions, types, and enums that implement the CUPTI Activity API.
+ * @{
+ */
+
+/**
+ * \brief The kinds of activity records.
+ *
+ * Each activity record kind represents information about a GPU or an
+ * activity occurring on a CPU or GPU. Each kind is associated with a
+ * activity record structure that holds the information associated
+ * with the kind.
+ * \see CUpti_Activity
+ * \see CUpti_ActivityAPI
+ * \see CUpti_ActivityContext
+ * \see CUpti_ActivityContext2
+ * \see CUpti_ActivityContext3
+ * \see CUpti_ActivityDevice
+ * \see CUpti_ActivityDevice2
+ * \see CUpti_ActivityDevice3
+ * \see CUpti_ActivityDevice4
+ * \see CUpti_ActivityDeviceAttribute
+ * \see CUpti_ActivityEvent
+ * \see CUpti_ActivityEventInstance
+ * \see CUpti_ActivityKernel
+ * \see CUpti_ActivityKernel2
+ * \see CUpti_ActivityKernel3
+ * \see CUpti_ActivityKernel4
+ * \see CUpti_ActivityKernel5
+ * \see CUpti_ActivityKernel6
+ * \see CUpti_ActivityKernel7
+ * \see CUpti_ActivityKernel8
+ * \see CUpti_ActivityKernel9
+ * \see CUpti_ActivityCdpKernel
+ * \see CUpti_ActivityPreemption
+ * \see CUpti_ActivityMemcpy
+ * \see CUpti_ActivityMemcpy3
+ * \see CUpti_ActivityMemcpy4
+ * \see CUpti_ActivityMemcpy5
+ * \see CUpti_ActivityMemcpy6
+ * \see CUpti_ActivityMemcpyPtoP
+ * \see CUpti_ActivityMemcpyPtoP2
+ * \see CUpti_ActivityMemcpyPtoP3
+ * \see CUpti_ActivityMemcpyPtoP4
+ * \see CUpti_ActivityMemset
+ * \see CUpti_ActivityMemset2
+ * \see CUpti_ActivityMemset3
+ * \see CUpti_ActivityMemset4
+ * \see CUpti_ActivityMemory
+ * \see CUpti_ActivityMemory2
+ * \see CUpti_ActivityMemory3
+ * \see CUpti_ActivityMemory4
+ * \see CUpti_ActivityMemoryPool
+ * \see CUpti_ActivityMemoryPool2
+ * \see CUpti_ActivityMetric
+ * \see CUpti_ActivityMetricInstance
+ * \see CUpti_ActivityName
+ * \see CUpti_ActivityMarker
+ * \see CUpti_ActivityMarker2
+ * \see CUpti_ActivityMarkerData
+ * \see CUpti_ActivitySourceLocator
+ * \see CUpti_ActivityGlobalAccess
+ * \see CUpti_ActivityGlobalAccess2
+ * \see CUpti_ActivityGlobalAccess3
+ * \see CUpti_ActivityBranch
+ * \see CUpti_ActivityBranch2
+ * \see CUpti_ActivityOverhead3
+ * \see CUpti_ActivityEnvironment
+ * \see CUpti_ActivityInstructionExecution
+ * \see CUpti_ActivityUnifiedMemoryCounter
+ * \see CUpti_ActivityFunction
+ * \see CUpti_ActivityModule
+ * \see CUpti_ActivitySharedAccess
+ * \see CUpti_ActivityPCSampling
+ * \see CUpti_ActivityPCSampling2
+ * \see CUpti_ActivityPCSampling3
+ * \see CUpti_ActivityPCSamplingRecordInfo
+ * \see CUpti_ActivityCudaEvent2
+ * \see CUpti_ActivityStream
+ * \see CUpti_ActivitySynchronization2
+ * \see CUpti_ActivityInstructionCorrelation
+ * \see CUpti_ActivityExternalCorrelation
+ * \see CUpti_ActivityUnifiedMemoryCounter3
+ * \see CUpti_ActivityOpenAccData
+ * \see CUpti_ActivityOpenAccLaunch
+ * \see CUpti_ActivityOpenAccOther
+ * \see CUpti_ActivityOpenMp
+ * \see CUpti_ActivityNvLink
+ * \see CUpti_ActivityNvLink2
+ * \see CUpti_ActivityNvLink3
+ * \see CUpti_ActivityNvLink4
+ * \see CUpti_ActivityPcie
+ * \see CUpti_ActivityConfidentialComputeRotation
+ */
+
+typedef enum {
+  /**
+   * The activity record is invalid.
+   */
+  CUPTI_ACTIVITY_KIND_INVALID  = 0,
+
+  /**
+   * A host<->host, host<->device, or device<->device memory copy.
+   * For peer to peer memory copy, use the kind CUPTI_ACTIVITY_KIND_MEMCPY2.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityMemcpy6.
+   */
+  CUPTI_ACTIVITY_KIND_MEMCPY   = 1,
+
+  /**
+   * A memory set executing on the GPU. The corresponding activity
+   * record structure is \ref CUpti_ActivityMemset4.
+   */
+  CUPTI_ACTIVITY_KIND_MEMSET   = 2,
+
+  /**
+   * A kernel executing on the GPU. This activity kind may significantly change
+   * the overall performance characteristics of the application because all
+   * kernel executions are serialized on the GPU. Other activity kind for kernel
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL doesn't break kernel concurrency.
+   * The corresponding activity record structure is \ref CUpti_ActivityKernel9.
+   */
+  CUPTI_ACTIVITY_KIND_KERNEL   = 3,
+
+  /**
+   * A CUDA driver API function execution. The corresponding activity
+   * record structure is \ref CUpti_ActivityAPI.
+   */
+  CUPTI_ACTIVITY_KIND_DRIVER   = 4,
+
+  /**
+   * A CUDA runtime API function execution. The corresponding activity
+   * record structure is \ref CUpti_ActivityAPI.
+   */
+  CUPTI_ACTIVITY_KIND_RUNTIME  = 5,
+
+  /**
+   * A performance counter (aka event) value. The corresponding activity record 
+   * structure is \ref CUpti_ActivityEvent. This activity cannot be directly
+   * enabled or disabled. Information collected using the Event API.
+   * can be stored in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_EVENT    = 6,
+
+  /**
+   * A performance metric value. The corresponding activity record structure is
+   * \ref CUpti_ActivityMetric. This activity cannot be directly
+   * enabled or disabled. Information collected using the Metric API.
+   * can be stored in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_METRIC   = 7,
+
+  /**
+   * Information about a CUDA device. The corresponding activity record
+   * structure is \ref CUpti_ActivityDevice5.
+   */
+  CUPTI_ACTIVITY_KIND_DEVICE   = 8,
+
+  /**
+   * Information about a CUDA context. The corresponding activity record
+   * structure is \ref CUpti_ActivityContext3.
+   */
+  CUPTI_ACTIVITY_KIND_CONTEXT  = 9,
+
+  /**
+   * A kernel executing on the GPU. This activity kind doesn't break
+   * kernel concurrency. The corresponding activity record structure
+   * is \ref CUpti_ActivityKernel9.
+   */
+  CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL = 10,
+
+  /**
+   * Resource naming done via NVTX APIs for thread, device, context, etc.
+   * The corresponding activity record structure is \ref CUpti_ActivityName.
+   */
+  CUPTI_ACTIVITY_KIND_NAME     = 11,
+
+  /**
+   * Instantaneous, start, or end NVTX marker. The corresponding activity
+   * record structure is \ref CUpti_ActivityMarker2.
+   */
+  CUPTI_ACTIVITY_KIND_MARKER = 12,
+
+  /**
+   * Extended, optional, data about a NVTX marker. User must enable
+   * CUPTI_ACTIVITY_KIND_MARKER as well to get records for marker data.
+   * The corresponding activity record structure is \ref CUpti_ActivityMarkerData.
+   */
+  CUPTI_ACTIVITY_KIND_MARKER_DATA = 13,
+
+  /**
+   * Source information about source level result. The corresponding
+   * activity record structure is \ref CUpti_ActivitySourceLocator.
+   * In CUDA 12.6, this kind is deprecated for Volta and later GPU architectures
+   * in favor of SASS Metric APIs from the header cupti_sass_metrics.h.
+   */
+  CUPTI_ACTIVITY_KIND_SOURCE_LOCATOR = 14,
+
+  /**
+   * Results for source-level global access. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivityGlobalAccess3.
+   * In CUDA 12.6, this kind is deprecated for Volta and later GPU architectures
+   * in favor of SASS Metric APIs from the header cupti_sass_metrics.h.
+   */
+  CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS = 15,
+
+  /**
+   * Results for source-level branch. The corresponding
+   * activity record structure is \ref CUpti_ActivityBranch2.
+   * In CUDA 12.6, this kind is deprecated for Volta and later GPU architectures
+   * in favor of SASS Metric APIs from the header cupti_sass_metrics.h.
+   */
+  CUPTI_ACTIVITY_KIND_BRANCH = 16,
+
+  /**
+   * Overhead added by CUPTI, Compiler, CUDA driver etc. The
+   * corresponding activity record structure is
+   * \ref CUpti_ActivityOverhead3.
+   */
+  CUPTI_ACTIVITY_KIND_OVERHEAD = 17,
+
+  /**
+   * A CDP (CUDA Dynamic Parallel) kernel executing on the GPU. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivityCdpKernel. This activity cannot be directly
+   * enabled or disabled. It is enabled and disabled through
+   * concurrent kernel activity i.e. _CONCURRENT_KERNEL.
+   */
+  CUPTI_ACTIVITY_KIND_CDP_KERNEL = 18,
+  /**
+   * Preemption activity record indicating a preemption of a CDP (CUDA
+   * Dynamic Parallel) kernel executing on the GPU. The corresponding
+   * activity record structure is \ref CUpti_ActivityPreemption.
+   */
+  CUPTI_ACTIVITY_KIND_PREEMPTION = 19,
+
+  /**
+   * Environment activity records indicating power, clock, thermal,
+   * etc. levels of the GPU. The corresponding activity record
+   * structure is \ref CUpti_ActivityEnvironment.
+   */
+  CUPTI_ACTIVITY_KIND_ENVIRONMENT = 20,
+
+  /**
+   * An performance counter value associated with a specific event domain
+   * instance. The corresponding activity record structure is \ref
+   * CUpti_ActivityEventInstance. This activity cannot be directly
+   * enabled or disabled. Information collected using the Event API.
+   * can be stored in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_EVENT_INSTANCE = 21,
+
+  /**
+   * A peer to peer memory copy. The corresponding activity record
+   * structure is \ref CUpti_ActivityMemcpyPtoP4.
+   */
+  CUPTI_ACTIVITY_KIND_MEMCPY2 = 22,
+
+  /**
+   * A performance metric value associated with a specific metric domain
+   * instance. The corresponding activity record structure is \ref
+   * CUpti_ActivityMetricInstance. This activity cannot be directly
+   * enabled or disabled. Information collected using the Metric API.
+   * can be stored in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_METRIC_INSTANCE = 23,
+
+  /**
+   * Results for source-level instruction execution.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityInstructionExecution.
+   * In CUDA 12.6, this kind is deprecated for Volta and later GPU architectures
+   * in favor of SASS Metric APIs from the header cupti_sass_metrics.h.
+   */
+  CUPTI_ACTIVITY_KIND_INSTRUCTION_EXECUTION = 24,
+
+  /**
+   * Unified Memory counter record. The corresponding activity
+   * record structure is \ref CUpti_ActivityUnifiedMemoryCounter3.
+   */
+  CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER = 25,
+
+  /**
+   * Device global/function record. The corresponding activity
+   * record structure is \ref CUpti_ActivityFunction.
+   */
+  CUPTI_ACTIVITY_KIND_FUNCTION = 26,
+
+  /**
+   * CUDA Module record. The corresponding activity
+   * record structure is \ref CUpti_ActivityModule.
+   * This activity cannot be directly enabled or disabled.
+   * Information collected using the module callback can be
+   * be stored in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_MODULE = 27,
+
+  /**
+   * A device attribute value. The corresponding activity record
+   * structure is \ref CUpti_ActivityDeviceAttribute.
+   * This activity cannot be directly enabled or disabled.
+   * Information collected using attributes CUpti_DeviceAttribute
+   * or CUdevice_attribute can be stored in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_DEVICE_ATTRIBUTE   = 28,
+
+  /**
+   * Results for source-level shared access. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivitySharedAccess.
+   * In CUDA 12.6, this kind is deprecated for Volta and later GPU architectures
+   * in favor of SASS Metric APIs from the header cupti_sass_metrics.h.
+   */
+  CUPTI_ACTIVITY_KIND_SHARED_ACCESS = 29,
+
+  /**
+   * PC sampling information for kernels. This will serialize
+   * kernels. The corresponding activity record structure
+   * is \ref CUpti_ActivityPCSampling3. In CUDA 12.5, this kind
+   * is deprecated for Volta and later GPU architectures in favor
+   * of PC Sampling APIs from the header cupti_pcsampling.h which
+   * allows concurrent kernel execution.
+   */
+  CUPTI_ACTIVITY_KIND_PC_SAMPLING = 30,
+
+  /**
+   * Summary information about PC sampling records. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivityPCSamplingRecordInfo. In CUDA 12.5, this kind
+   * is deprecated for Volta and later GPU architectures in favor
+   * of PC Sampling APIs from the header cupti_pcsampling.h.
+   */
+  CUPTI_ACTIVITY_KIND_PC_SAMPLING_RECORD_INFO = 31,
+
+  /**
+   * SASS/Source line-by-line correlation record.
+   * This will generate sass/source correlation for functions that have source
+   * level analysis or pc sampling results. The records will be generated only
+   * when either of source level analysis or pc sampling activity is enabled.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityInstructionCorrelation.
+   * In CUDA 12.6, this kind is deprecated for Volta and later GPU architectures
+   * in favor of SASS Metric APIs from the header cupti_sass_metrics.h.
+   */
+  CUPTI_ACTIVITY_KIND_INSTRUCTION_CORRELATION = 32,
+
+  /**
+   * OpenACC data events.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityOpenAccData.
+   */
+  CUPTI_ACTIVITY_KIND_OPENACC_DATA = 33,
+
+  /**
+   * OpenACC launch events.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityOpenAccLaunch.
+   */
+  CUPTI_ACTIVITY_KIND_OPENACC_LAUNCH = 34,
+
+  /**
+   * OpenACC other events.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityOpenAccOther.
+   */
+  CUPTI_ACTIVITY_KIND_OPENACC_OTHER = 35,
+
+  /**
+   * Information about a CUDA event (cudaEvent). This activity cannot be
+   * directly enabled or disabled. It is enabled and disabled through
+   * the activity CUPTI_ACTIVITY_KIND_SYNCHRONIZATION.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityCudaEvent2.
+   */
+  CUPTI_ACTIVITY_KIND_CUDA_EVENT = 36,
+
+  /**
+   * Information about a CUDA stream. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivityStream.
+   */
+  CUPTI_ACTIVITY_KIND_STREAM = 37,
+
+  /**
+   * Records for CUDA synchronization primitives. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivitySynchronization2.
+   */
+  CUPTI_ACTIVITY_KIND_SYNCHRONIZATION = 38,
+
+  /**
+   * Records for correlation of different programming APIs. The
+   * corresponding activity record structure is \ref
+   * CUpti_ActivityExternalCorrelation.
+   */
+  CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION = 39,
+
+  /**
+   * NVLink topology information.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityNvLink4.
+   */
+  CUPTI_ACTIVITY_KIND_NVLINK = 40,
+
+  /**
+   * Instantaneous Event information.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityInstantaneousEvent.
+   * This activity can not be directly enabled or disabled.
+   * Information collected using the Event API can be stored
+   * in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_INSTANTANEOUS_EVENT = 41,
+
+  /**
+   * Instantaneous Event information for a specific event
+   * domain instance.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityInstantaneousEventInstance.
+   * This activity can not be directly enabled or disabled.
+   * Information collected using the Event API can be stored
+   * in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_INSTANTANEOUS_EVENT_INSTANCE = 42,
+
+  /**
+   * Instantaneous Metric information
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityInstantaneousMetric.
+   * This activity cannot be directly enabled or disabled.
+   * Information collected using the Metric API can be stored
+   * in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_INSTANTANEOUS_METRIC = 43,
+
+  /**
+   * Instantaneous Metric information for a specific metric
+   * domain instance.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityInstantaneousMetricInstance.
+   * This activity cannot be directly enabled or disabled.
+   * Information collected using the Metric API can be stored
+   * in the corresponding activity record.
+   */
+  CUPTI_ACTIVITY_KIND_INSTANTANEOUS_METRIC_INSTANCE = 44,
+
+  /**
+   * Memory activity tracking allocation and freeing of the memory
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityMemory.
+   */
+  CUPTI_ACTIVITY_KIND_MEMORY = 45,
+
+  /**
+   * PCI devices information used for PCI topology.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityPcie.
+   */
+  CUPTI_ACTIVITY_KIND_PCIE = 46,
+
+  /**
+   * OpenMP parallel events.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityOpenMp.
+   */
+  CUPTI_ACTIVITY_KIND_OPENMP = 47,
+
+  /**
+   * A CUDA driver kernel launch occurring outside of any
+   * public API function execution. Tools can handle these
+   * like records for driver API launch functions, although
+   * the cbid field is not used here.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityAPI.
+   */
+  CUPTI_ACTIVITY_KIND_INTERNAL_LAUNCH_API = 48,
+
+  /**
+   * Memory activity tracking allocation and freeing of the memory
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityMemory4.
+   */
+  CUPTI_ACTIVITY_KIND_MEMORY2 = 49,
+
+  /**
+   * Memory pool activity tracking creation, destruction and
+   * trimming of the memory pool.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityMemoryPool2.
+   */
+  CUPTI_ACTIVITY_KIND_MEMORY_POOL = 50,
+
+  /**
+   * Activity record for graph-level information.
+   * The corresponding activity record structure is
+   * \ref CUpti_ActivityGraphTrace2.
+   */
+  CUPTI_ACTIVITY_KIND_GRAPH_TRACE = 51,
+
+  /**
+   * JIT (Just-in-time) operation tracking.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityJit.
+   */
+  CUPTI_ACTIVITY_KIND_JIT = 52,
+
+  /**
+   * This activity can not be directly enabled or disabled.
+   * It is enabled when CUPTI_ACTIVITY_KIND_GRAPH_TRACE is enabled
+   * and device graph trace is enabled through API cuptiActivityEnableDeviceGraph().
+   * The corresponding activity record structure is
+   * \ref CUpti_ActivityDeviceGraphTrace.
+   */
+  CUPTI_ACTIVITY_KIND_DEVICE_GRAPH_TRACE = 53,
+
+  /**
+   * Tracing batches of copies that are to be decompressed.
+   * The corresponding activity record structure is \ref
+   * CUpti_ActivityMemDecompress.
+   */
+  CUPTI_ACTIVITY_KIND_MEM_DECOMPRESS = 54,
+
+
+
+  /**
+   * Count of supported activity kinds.
+   */
+  CUPTI_ACTIVITY_KIND_COUNT,
+
+  CUPTI_ACTIVITY_KIND_FORCE_INT     = 0x7fffffff
+} CUpti_ActivityKind;
+
+/**
+ * \brief The kinds of activity objects.
+ * \see CUpti_ActivityObjectKindId
+ */
+typedef enum {
+  /**
+   * The object kind is not known.
+   */
+  CUPTI_ACTIVITY_OBJECT_UNKNOWN  = 0,
+
+  /**
+   * A process.
+   */
+  CUPTI_ACTIVITY_OBJECT_PROCESS  = 1,
+
+  /**
+   * A thread.
+   */
+  CUPTI_ACTIVITY_OBJECT_THREAD   = 2,
+
+  /**
+   * A device.
+   */
+  CUPTI_ACTIVITY_OBJECT_DEVICE   = 3,
+
+  /**
+   * A context.
+   */
+  CUPTI_ACTIVITY_OBJECT_CONTEXT  = 4,
+
+  /**
+   * A stream.
+   */
+  CUPTI_ACTIVITY_OBJECT_STREAM   = 5,
+
+  CUPTI_ACTIVITY_OBJECT_FORCE_INT = 0x7fffffff
+} CUpti_ActivityObjectKind;
+
+/**
+ * \brief Identifiers for object kinds as specified by
+ * CUpti_ActivityObjectKind.
+ * \see CUpti_ActivityObjectKind
+ */
+typedef union {
+  /**
+   * A process object requires that we identify the process ID. A
+   * thread object requires that we identify both the process and
+   * thread ID.
+   */
+  struct {
+    uint32_t processId;
+    uint32_t threadId;
+  } pt;
+
+  /**
+   * A device object requires that we identify the device ID. A
+   * context object requires that we identify both the device and
+   * context ID. A stream object requires that we identify device,
+   * context, and stream ID.
+   */
+  struct {
+    uint32_t deviceId;
+    uint32_t contextId;
+    uint32_t streamId;
+  } dcs;
+} CUpti_ActivityObjectKindId;
+
+/**
+ * \brief The structure to provide additional data for CUPTI_ACTIVITY_OVERHEAD_COMMAND_BUFFER_FULL.
+ */
+typedef struct {
+  /**
+   * The remaining space in the command buffer. This field will always be zero
+   * when the command buffer is full, making it not useful in such cases.
+   *
+   */
+  uint32_t commandBufferLength;
+  /**
+   * The channel ID of the command buffer.
+   *
+   */
+  uint32_t channelID;
+  /**
+   * The channel type of the command buffer.
+   *
+   */
+  uint32_t channelType;
+} CUpti_ActivityOverheadCommandBufferFullData;
+
+/**
+ * \brief The kinds of activity overhead.
+ */
+typedef enum {
+  /**
+   * The overhead kind is not known.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_UNKNOWN               = 0,
+
+  /**
+   * Compiler overhead.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_DRIVER_COMPILER       = 1,
+
+  /**
+   * Activity buffer flush overhead.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_CUPTI_BUFFER_FLUSH               = 1<<16,
+
+  /**
+   * CUPTI instrumentation overhead.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_CUPTI_INSTRUMENTATION            = 2<<16,
+
+  /**
+   * CUPTI resource creation and destruction overhead.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_CUPTI_RESOURCE                   = 3<<16,
+
+  /**
+   * CUDA Runtime triggered module loading overhead.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_RUNTIME_TRIGGERED_MODULE_LOADING = 4<<16,
+
+  /**
+   * Lazy function loading overhead.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_LAZY_FUNCTION_LOADING            = 5<<16,
+
+  /**
+   * Overhead due to lack of command buffer space.
+   * Refer CUpti_ActivityOverheadCommandBufferFullData for more details.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_COMMAND_BUFFER_FULL              = 6<<16,
+
+  /**
+   * Overhead due to activity buffer request.
+   */
+  CUPTI_ACTIVITY_OVERHEAD_ACTIVITY_BUFFER_REQUEST          = 7<<16,
+
+  /**
+    * Overhead due to UVM activity initialization.
+    */
+   CUPTI_ACTIVITY_OVERHEAD_UVM_ACTIVITY_INIT                = 8<<16,
+
+  CUPTI_ACTIVITY_OVERHEAD_FORCE_INT             = 0x7fffffff
+} CUpti_ActivityOverheadKind;
+
+/**
+ * \brief The kind of a compute API.
+ */
+typedef enum {
+  /**
+   * The compute API is not known.
+   */
+  CUPTI_ACTIVITY_COMPUTE_API_UNKNOWN    = 0,
+
+  /**
+   * The compute APIs are for CUDA.
+   */
+  CUPTI_ACTIVITY_COMPUTE_API_CUDA       = 1,
+
+  /**
+   * The compute APIs are for CUDA running
+   * in MPS (Multi-Process Service) environment.
+   */
+  CUPTI_ACTIVITY_COMPUTE_API_CUDA_MPS   = 2,
+
+  CUPTI_ACTIVITY_COMPUTE_API_FORCE_INT  = 0x7fffffff
+} CUpti_ActivityComputeApiKind;
+
+/**
+ * \brief Flags associated with activity records.
+ *
+ * Activity record flags. Flags can be combined by bitwise OR to
+ * associated multiple flags with an activity record. Each flag is
+ * specific to a certain activity kind, as noted below.
+ */
+typedef enum {
+  /**
+   * Indicates the activity record has no flags.
+   */
+  CUPTI_ACTIVITY_FLAG_NONE          = 0,
+
+  /**
+   * Indicates the activity represents a device that supports
+   * concurrent kernel execution. Valid for
+   * CUPTI_ACTIVITY_KIND_DEVICE.
+   */
+  CUPTI_ACTIVITY_FLAG_DEVICE_CONCURRENT_KERNELS  = 1 << 0,
+
+  /**
+   * Indicates if the activity represents a CUdevice_attribute value
+   * or a CUpti_DeviceAttribute value. Valid for
+   * CUPTI_ACTIVITY_KIND_DEVICE_ATTRIBUTE.
+   */
+  CUPTI_ACTIVITY_FLAG_DEVICE_ATTRIBUTE_CUDEVICE  = 1 << 0,
+
+  /**
+   * Indicates the activity represents an asynchronous memcpy
+   * operation. Valid for CUPTI_ACTIVITY_KIND_MEMCPY.
+   */
+  CUPTI_ACTIVITY_FLAG_MEMCPY_ASYNC  = 1 << 0,
+
+  /**
+   * Indicates the activity represents an instantaneous marker. Valid
+   * for CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_INSTANTANEOUS  = 1 << 0,
+
+  /**
+   * Indicates the activity represents a region start marker. Valid
+   * for CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_START  = 1 << 1,
+
+  /**
+   * Indicates the activity represents a region end marker. Valid for
+   * CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_END  = 1 << 2,
+
+  /**
+   * Indicates the activity represents an attempt to acquire a user
+   * defined synchronization object.
+   * Valid for CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_SYNC_ACQUIRE = 1 << 3,
+
+  /**
+   * Indicates the activity represents success in acquiring the
+   * user defined synchronization object.
+   * Valid for CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_SYNC_ACQUIRE_SUCCESS = 1 << 4,
+
+  /**
+   * Indicates the activity represents failure in acquiring the
+   * user defined synchronization object.
+   * Valid for CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_SYNC_ACQUIRE_FAILED = 1 << 5,
+
+  /**
+   * Indicates the activity represents releasing a reservation on
+   * user defined synchronization object.
+   * Valid for CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_SYNC_RELEASE = 1 << 6,
+
+  /**
+   * Indicates the activity represents a marker that does not specify
+   * a color. Valid for CUPTI_ACTIVITY_KIND_MARKER_DATA.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_COLOR_NONE  = 1 << 0,
+
+  /**
+   * Indicates the activity represents a marker that specifies a color
+   * in alpha-red-green-blue format. Valid for
+   * CUPTI_ACTIVITY_KIND_MARKER_DATA.
+   */
+  CUPTI_ACTIVITY_FLAG_MARKER_COLOR_ARGB  = 1 << 1,
+
+  /**
+   * The number of bytes requested by each thread
+   * Valid for CUpti_ActivityGlobalAccess3.
+   */
+  CUPTI_ACTIVITY_FLAG_GLOBAL_ACCESS_KIND_SIZE_MASK  = 0xFF << 0,
+
+  /**
+   * If bit in this flag is set, the access was load, else it is a
+   * store access. Valid for CUpti_ActivityGlobalAccess3.
+   */
+  CUPTI_ACTIVITY_FLAG_GLOBAL_ACCESS_KIND_LOAD       = 1 << 8,
+
+  /**
+   * If this bit in flag is set, the load access was cached else it is
+   * uncached. Valid for CUpti_ActivityGlobalAccess3.
+   */
+  CUPTI_ACTIVITY_FLAG_GLOBAL_ACCESS_KIND_CACHED     = 1 << 9,
+
+  /**
+   * If this bit in flag is set, the metric value overflowed. Valid
+   * for CUpti_ActivityMetric and CUpti_ActivityMetricInstance.
+   */
+  CUPTI_ACTIVITY_FLAG_METRIC_OVERFLOWED     = 1 << 0,
+
+  /**
+   * If this bit in flag is set, the metric value couldn't be
+   * calculated. This occurs when a value(s) required to calculate the
+   * metric is missing.  Valid for CUpti_ActivityMetric and
+   * CUpti_ActivityMetricInstance.
+   */
+  CUPTI_ACTIVITY_FLAG_METRIC_VALUE_INVALID  = 1 << 1,
+
+  /**
+   * If this bit in flag is set, the source level metric value couldn't be
+   * calculated. This occurs when a value(s) required to calculate the
+   * source level metric cannot be evaluated.
+   * Valid for CUpti_ActivityInstructionExecution.
+   */
+  CUPTI_ACTIVITY_FLAG_INSTRUCTION_VALUE_INVALID  = 1 << 0,
+
+  /**
+   * The mask for the instruction class, \ref CUpti_ActivityInstructionClass
+   * Valid for CUpti_ActivityInstructionExecution and
+   * CUpti_ActivityInstructionCorrelation
+   */
+  CUPTI_ACTIVITY_FLAG_INSTRUCTION_CLASS_MASK    = 0xFF << 1,
+
+  /**
+   * When calling cuptiActivityFlushAll, this flag
+   * can be set to force CUPTI to flush all records in the buffer, whether
+   * finished or not
+   */
+  CUPTI_ACTIVITY_FLAG_FLUSH_FORCED = 1 << 0,
+
+  /**
+   * The number of bytes requested by each thread
+   * Valid for CUpti_ActivitySharedAccess.
+   */
+  CUPTI_ACTIVITY_FLAG_SHARED_ACCESS_KIND_SIZE_MASK  = 0xFF << 0,
+
+  /**
+   * If bit in this flag is set, the access was load, else it is a
+   * store access.  Valid for CUpti_ActivitySharedAccess.
+   */
+  CUPTI_ACTIVITY_FLAG_SHARED_ACCESS_KIND_LOAD       = 1 << 8,
+
+  /**
+   * Indicates the activity represents an asynchronous memset
+   * operation. Valid for CUPTI_ACTIVITY_KIND_MEMSET.
+   */
+  CUPTI_ACTIVITY_FLAG_MEMSET_ASYNC  = 1 << 0,
+
+  /**
+   * Indicates the activity represents thrashing in CPU.
+   * Valid for counter of kind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING in
+   * CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER
+   */
+  CUPTI_ACTIVITY_FLAG_THRASHING_IN_CPU = 1 << 0,
+
+  /**
+   * Indicates the activity represents page throttling in CPU.
+   * Valid for counter of kind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING in
+   * CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER
+   */
+  CUPTI_ACTIVITY_FLAG_THROTTLING_IN_CPU = 1 << 0,
+
+  CUPTI_ACTIVITY_FLAG_FORCE_INT = 0x7fffffff
+} CUpti_ActivityFlag;
+
+/**
+ * \brief The stall reason for PC sampling activity.
+ */
+typedef enum {
+  /**
+   * Invalid reason
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_INVALID      = 0,
+
+  /**
+   * No stall, instruction is selected for issue
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_NONE         = 1,
+
+  /**
+   * Warp is blocked because next instruction is not yet available,
+   * because of instruction cache miss, or because of branching effects
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_INST_FETCH   = 2,
+
+  /**
+   * Instruction is waiting on an arithmetic dependency
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_EXEC_DEPENDENCY   = 3,
+
+  /**
+   * Warp is blocked because it is waiting for a memory access to complete.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_MEMORY_DEPENDENCY   = 4,
+
+  /**
+   * Texture sub-system is fully utilized or has too many outstanding requests.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_TEXTURE   = 5,
+
+  /**
+   * Warp is blocked as it is waiting at __syncthreads() or at memory barrier.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_SYNC   = 6,
+
+  /**
+   * Warp is blocked waiting for __constant__ memory and immediate memory access to complete.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_CONSTANT_MEMORY_DEPENDENCY   = 7,
+
+  /**
+   * Compute operation cannot be performed due to the required resources not
+   * being available.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_PIPE_BUSY   = 8,
+
+  /**
+   * Warp is blocked because there are too many pending memory operations.
+   * In Kepler architecture it often indicates high number of memory replays.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_MEMORY_THROTTLE   = 9,
+
+  /**
+   * Warp was ready to issue, but some other warp issued instead.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_NOT_SELECTED   = 10,
+
+  /**
+   * Miscellaneous reasons
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_OTHER   = 11,
+
+  /**
+   * Sleeping.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_SLEEPING   = 12,
+
+  CUPTI_ACTIVITY_PC_SAMPLING_STALL_FORCE_INT  = 0x7fffffff
+} CUpti_ActivityPCSamplingStallReason;
+
+/**
+ * \brief Sampling period for PC sampling method
+ *
+ * Sampling period can be set using \ref cuptiActivityConfigurePCSampling
+ */
+typedef enum {
+  /**
+   * The PC sampling period is not set.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_INVALID = 0,
+
+  /**
+   * Minimum sampling period available on the device.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_MIN = 1,
+
+  /**
+   * Sampling period in lower range.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_LOW = 2,
+
+  /**
+   * Medium sampling period.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_MID = 3,
+
+  /**
+   * Sampling period in higher range.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_HIGH = 4,
+
+  /**
+   * Maximum sampling period available on the device.
+   */
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_MAX = 5,
+
+  CUPTI_ACTIVITY_PC_SAMPLING_PERIOD_FORCE_INT = 0x7fffffff
+} CUpti_ActivityPCSamplingPeriod;
+
+/**
+ * \brief The kind of a memory copy, indicating the source and
+ * destination targets of the copy.
+ *
+ * Each kind represents the source and destination targets of a memory
+ * copy. Targets are host, device, and array.
+ */
+typedef enum {
+  /**
+   * The memory copy kind is not known.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_UNKNOWN = 0,
+
+  /**
+   * A host to device memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_HTOD    = 1,
+
+  /**
+   * A device to host memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_DTOH    = 2,
+
+  /**
+   * A host to device array memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_HTOA    = 3,
+
+  /**
+   * A device array to host memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_ATOH    = 4,
+
+  /**
+   * A device array to device array memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_ATOA    = 5,
+
+  /**
+   * A device array to device memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_ATOD    = 6,
+
+  /**
+   * A device to device array memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_DTOA    = 7,
+
+  /**
+   * A device to device memory copy on the same device.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_DTOD    = 8,
+
+  /**
+   * A host to host memory copy.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_HTOH    = 9,
+
+  /**
+   * A peer to peer memory copy across different devices.
+   */
+  CUPTI_ACTIVITY_MEMCPY_KIND_PTOP    = 10,
+
+  CUPTI_ACTIVITY_MEMCPY_KIND_FORCE_INT = 0x7fffffff
+} CUpti_ActivityMemcpyKind;
+
+/**
+ * \brief The kinds of memory accessed by a memory operation/copy.
+ *
+ * Each kind represents the type of the memory
+ * accessed by a memory operation/copy.
+ */
+typedef enum {
+  /**
+   * The memory kind is unknown.
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_UNKNOWN            = 0,
+
+  /**
+   * The memory is pageable.
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_PAGEABLE           = 1,
+
+  /**
+   * The memory is pinned.
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_PINNED             = 2,
+
+  /**
+   * The memory is on the device.
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_DEVICE             = 3,
+
+  /**
+   * The memory is an array.
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_ARRAY              = 4,
+
+  /**
+   * The memory is managed
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_MANAGED            = 5,
+
+  /**
+   * The memory is device static
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_DEVICE_STATIC      = 6,
+
+  /**
+   * The memory is managed static
+   */
+  CUPTI_ACTIVITY_MEMORY_KIND_MANAGED_STATIC     = 7,
+
+  CUPTI_ACTIVITY_MEMORY_KIND_FORCE_INT          = 0x7fffffff
+} CUpti_ActivityMemoryKind;
+
+/**
+ * \brief The kind of a preemption activity.
+ */
+typedef enum {
+  /**
+   * The preemption kind is not known.
+   */
+  CUPTI_ACTIVITY_PREEMPTION_KIND_UNKNOWN    = 0,
+
+  /**
+   * Preemption to save CDP block.
+   */
+  CUPTI_ACTIVITY_PREEMPTION_KIND_SAVE       = 1,
+
+  /**
+   * Preemption to restore CDP block.
+   */
+  CUPTI_ACTIVITY_PREEMPTION_KIND_RESTORE    = 2,
+
+  CUPTI_ACTIVITY_PREEMPTION_KIND_FORCE_INT  = 0x7fffffff
+} CUpti_ActivityPreemptionKind;
+
+/**
+ * \brief The kind of environment data. Used to indicate what type of
+ * data is being reported by an environment activity record.
+ */
+typedef enum {
+  /**
+   * Unknown data.
+   */
+  CUPTI_ACTIVITY_ENVIRONMENT_UNKNOWN = 0,
+
+  /**
+   * The environment data is related to speed.
+   */
+  CUPTI_ACTIVITY_ENVIRONMENT_SPEED = 1,
+
+  /**
+   * The environment data is related to temperature.
+   */
+  CUPTI_ACTIVITY_ENVIRONMENT_TEMPERATURE = 2,
+
+  /**
+   * The environment data is related to power.
+   */
+  CUPTI_ACTIVITY_ENVIRONMENT_POWER = 3,
+
+  /**
+   * The environment data is related to cooling.
+   */
+  CUPTI_ACTIVITY_ENVIRONMENT_COOLING = 4,
+
+  CUPTI_ACTIVITY_ENVIRONMENT_COUNT,
+
+  CUPTI_ACTIVITY_ENVIRONMENT_KIND_FORCE_INT    = 0x7fffffff
+} CUpti_ActivityEnvironmentKind;
+
+/**
+ * \brief Reasons for clock throttling.
+ *
+ * The possible reasons that a clock can be throttled. There can be
+ * more than one reason that a clock is being throttled so these types
+ * can be combined by bitwise OR.  These are used in the
+ * clocksThrottleReason field in the Environment Activity Record.
+ */
+typedef enum {
+  /**
+   * Nothing is running on the GPU and the clocks are dropping to idle
+   * state.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_GPU_IDLE              = 0x00000001,
+
+  /**
+   * The GPU clocks are limited by a user specified limit.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_USER_DEFINED_CLOCKS   = 0x00000002,
+
+  /**
+   * A software power scaling algorithm is reducing the clocks below
+   * requested clocks.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_SW_POWER_CAP          = 0x00000004,
+
+  /**
+   * Hardware slowdown to reduce the clock by a factor of two or more
+   * is engaged.  This is an indicator of one of the following: 1)
+   * Temperature is too high, 2) External power brake assertion is
+   * being triggered (e.g. by the system power supply), 3) Change in
+   * power state.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_HW_SLOWDOWN           = 0x00000008,
+
+  /**
+   * Some unspecified factor is reducing the clocks.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_UNKNOWN               = 0x80000000,
+
+  /**
+   * Throttle reason is not supported for this GPU.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_UNSUPPORTED           = 0x40000000,
+
+  /**
+   * No clock throttling.
+   */
+  CUPTI_CLOCKS_THROTTLE_REASON_NONE                  = 0x00000000,
+
+  CUPTI_CLOCKS_THROTTLE_REASON_FORCE_INT             = 0x7fffffff
+} CUpti_EnvironmentClocksThrottleReason;
+
+/**
+ * \brief Scope of the unified memory counter (deprecated in CUDA 7.0)
+ */
+typedef enum {
+  /**
+   * The unified memory counter scope is not known.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_SCOPE_UNKNOWN = 0,
+
+  /**
+   * Collect unified memory counter for single process on one device
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_SCOPE_PROCESS_SINGLE_DEVICE = 1,
+
+  /**
+   * Collect unified memory counter for single process across all devices
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_SCOPE_PROCESS_ALL_DEVICES = 2,
+
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_SCOPE_COUNT,
+
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_SCOPE_FORCE_INT = 0x7fffffff
+} CUpti_ActivityUnifiedMemoryCounterScope;
+
+/**
+ * \brief Kind of the Unified Memory counter
+ *
+ * Many activities are associated with Unified Memory mechanism; among them
+ * are transfers from host to device, device to host, page fault at
+ * host side.
+ */
+typedef enum {
+  /**
+   * The unified memory counter kind is not known.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_UNKNOWN = 0,
+
+  /**
+   * Number of bytes transferred from host to device
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD = 1,
+
+  /**
+   * Number of bytes transferred from device to host
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH = 2,
+
+  /**
+   * Number of CPU page faults, this is only supported on 64 bit
+   * Linux and Mac platforms
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT = 3,
+
+  /**
+   * Number of GPU page faults, this is only supported on devices with
+   * compute capability 6.0 and higher and 64 bit Linux platforms
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT = 4,
+
+  /**
+   * Thrashing occurs when data is frequently accessed by
+   * multiple processors and has to be constantly migrated around
+   * to achieve data locality. In this case the overhead of migration
+   * may exceed the benefits of locality.
+   * This is only supported on 64 bit Linux platforms.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING = 5,
+
+  /**
+   * Throttling is a prevention technique used by the driver to avoid
+   * further thrashing. Here, the driver doesn't service the fault for
+   * one of the contending processors for a specific period of time,
+   * so that the other processor can run at full-speed.
+   * This is only supported on 64 bit Linux platforms.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING = 6,
+
+  /**
+   * In case throttling does not help, the driver tries to pin the memory
+   * to a processor for a specific period of time. One of the contending
+   * processors will have slow  access to the memory, while the other will
+   * have fast access.
+   * This is only supported on 64 bit Linux platforms.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP = 7,
+
+  /**
+   * Number of bytes transferred from one device to another device.
+   * This is only supported on 64 bit Linux platforms.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOD = 8,
+
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_COUNT,
+
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_FORCE_INT = 0x7fffffff
+} CUpti_ActivityUnifiedMemoryCounterKind;
+
+/**
+ * \brief Memory access type for unified memory page faults
+ *
+ * This is valid for \ref CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT
+ * and \ref CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT
+ */
+typedef enum {
+  /**
+   * The unified memory access type is not known
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_ACCESS_TYPE_UNKNOWN = 0,
+
+  /**
+   * The page fault was triggered by read memory instruction
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_ACCESS_TYPE_READ = 1,
+
+  /**
+   * The page fault was triggered by write memory instruction
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_ACCESS_TYPE_WRITE = 2,
+
+  /**
+   * The page fault was triggered by atomic memory instruction
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_ACCESS_TYPE_ATOMIC = 3,
+
+  /**
+   * The page fault was triggered by memory prefetch operation
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_ACCESS_TYPE_PREFETCH = 4
+} CUpti_ActivityUnifiedMemoryAccessType;
+
+/**
+ * \brief Migration cause of the Unified Memory counter
+ *
+ * This is valid for \ref CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD and
+ * \ref CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH
+ */
+typedef enum {
+  /**
+   * The unified memory migration cause is not known
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_MIGRATION_CAUSE_UNKNOWN = 0,
+
+  /**
+   * The unified memory migrated due to an explicit call from
+   * the user e.g. cudaMemPrefetchAsync
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_MIGRATION_CAUSE_USER = 1,
+
+  /**
+   * The unified memory migrated to guarantee data coherence
+   * e.g. CPU/GPU faults on Pascal+ and kernel launch on pre-Pascal GPUs
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_MIGRATION_CAUSE_COHERENCE = 2,
+
+  /**
+   * The unified memory was speculatively migrated by the UVM driver
+   * before being accessed by the destination processor to improve
+   * performance
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_MIGRATION_CAUSE_PREFETCH = 3,
+
+  /**
+   * The unified memory migrated to the CPU because it was evicted to make
+   * room for another block of memory on the GPU
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_MIGRATION_CAUSE_EVICTION = 4,
+
+  /**
+    * The unified memory migrated to another processor because of access counter
+    * notifications. Only frequently accessed pages are migrated between CPU and GPU, or
+    * between peer GPUs.
+    */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_MIGRATION_CAUSE_ACCESS_COUNTERS = 5,
+} CUpti_ActivityUnifiedMemoryMigrationCause;
+
+/**
+ * \brief Remote memory map cause of the Unified Memory counter
+ *
+ * This is valid for \ref CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP
+ */
+typedef enum {
+  /**
+   * The cause of mapping to remote memory was unknown
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_REMOTE_MAP_CAUSE_UNKNOWN = 0,
+
+  /**
+   * Mapping to remote memory was added to maintain data coherence.
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_REMOTE_MAP_CAUSE_COHERENCE = 1,
+
+  /**
+   * Mapping to remote memory was added to prevent further thrashing
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_REMOTE_MAP_CAUSE_THRASHING = 2,
+
+  /**
+   * Mapping to remote memory was added to enforce the hints
+   * specified by the programmer or by performance heuristics of the
+   * UVM driver
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_REMOTE_MAP_CAUSE_POLICY = 3,
+
+  /**
+   * Mapping to remote memory was added because there is no more
+   * memory available on the processor and eviction was not
+   * possible
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_REMOTE_MAP_CAUSE_OUT_OF_MEMORY = 4,
+
+  /**
+   * Mapping to remote memory was added after the memory was
+   * evicted to make room for another block of memory on the GPU
+   */
+  CUPTI_ACTIVITY_UNIFIED_MEMORY_REMOTE_MAP_CAUSE_EVICTION = 5,
+} CUpti_ActivityUnifiedMemoryRemoteMapCause;
+
+/**
+ * \brief SASS instruction classification.
+ *
+ * The sass instruction are broadly divided into different class. Each enum represents a classification.
+ */
+typedef enum {
+  /**
+   * The instruction class is not known.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_UNKNOWN = 0,
+
+  /**
+   * Represents a 32 bit floating point operation.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_FP_32 = 1,
+
+  /**
+   * Represents a 64 bit floating point operation.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_FP_64 = 2,
+
+  /**
+   * Represents an integer operation.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_INTEGER = 3,
+
+  /**
+   * Represents a bit conversion operation.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_BIT_CONVERSION = 4,
+
+  /**
+   * Represents a control flow instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_CONTROL_FLOW = 5,
+
+  /**
+   * Represents a global load-store instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_GLOBAL = 6,
+
+  /**
+   * Represents a shared load-store instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_SHARED = 7,
+
+  /**
+   * Represents a local load-store instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_LOCAL = 8,
+
+  /**
+   * Represents a generic load-store instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_GENERIC = 9,
+
+  /**
+   * Represents a surface load-store instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_SURFACE = 10,
+
+  /**
+   * Represents a constant load instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_CONSTANT = 11,
+
+  /**
+   * Represents a texture load-store instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_TEXTURE = 12,
+
+  /**
+   * Represents a global atomic instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_GLOBAL_ATOMIC = 13,
+
+  /**
+   * Represents a shared atomic instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_SHARED_ATOMIC = 14,
+
+  /**
+   * Represents a surface atomic instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_SURFACE_ATOMIC = 15,
+
+  /**
+   * Represents a inter-thread communication instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_INTER_THREAD_COMMUNICATION = 16,
+
+  /**
+   * Represents a barrier instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_BARRIER = 17,
+
+  /**
+   * Represents some miscellaneous instructions which do not fit in the above classification.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_MISCELLANEOUS = 18,
+
+  /**
+   * Represents a 16 bit floating point operation.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_FP_16 = 19,
+
+  /**
+   * Represents uniform instruction.
+   */
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_UNIFORM = 20,
+
+  CUPTI_ACTIVITY_INSTRUCTION_CLASS_KIND_FORCE_INT     = 0x7fffffff
+} CUpti_ActivityInstructionClass;
+
+/**
+ * \brief Partitioned global caching option
+ */
+typedef enum {
+  /**
+   * Partitioned global cache config unknown.
+   */
+  CUPTI_ACTIVITY_PARTITIONED_GLOBAL_CACHE_CONFIG_UNKNOWN       = 0,
+
+  /**
+   * Partitioned global cache not supported.
+   */
+  CUPTI_ACTIVITY_PARTITIONED_GLOBAL_CACHE_CONFIG_NOT_SUPPORTED = 1,
+
+  /**
+   * Partitioned global cache config off.
+   */
+  CUPTI_ACTIVITY_PARTITIONED_GLOBAL_CACHE_CONFIG_OFF           = 2,
+
+  /**
+   * Partitioned global cache config on.
+   */
+  CUPTI_ACTIVITY_PARTITIONED_GLOBAL_CACHE_CONFIG_ON            = 3,
+
+  CUPTI_ACTIVITY_PARTITIONED_GLOBAL_CACHE_CONFIG_FORCE_INT     = 0x7fffffff
+} CUpti_ActivityPartitionedGlobalCacheConfig;
+
+/**
+ * \brief Synchronization type.
+ *
+ * The types of synchronization to be used with
+ * CUpti_ActivitySynchronization2.
+ */
+
+typedef enum {
+  /**
+   * Unknown data.
+   */
+  CUPTI_ACTIVITY_SYNCHRONIZATION_TYPE_UNKNOWN             = 0,
+
+  /**
+   * Event synchronize API.
+   */
+  CUPTI_ACTIVITY_SYNCHRONIZATION_TYPE_EVENT_SYNCHRONIZE   = 1,
+
+  /**
+   * Stream wait event API.
+   */
+  CUPTI_ACTIVITY_SYNCHRONIZATION_TYPE_STREAM_WAIT_EVENT   = 2,
+
+  /**
+   * Stream synchronize API.
+   */
+  CUPTI_ACTIVITY_SYNCHRONIZATION_TYPE_STREAM_SYNCHRONIZE  = 3,
+
+  /**
+   * Context synchronize API.
+   */
+  CUPTI_ACTIVITY_SYNCHRONIZATION_TYPE_CONTEXT_SYNCHRONIZE = 4,
+
+  CUPTI_ACTIVITY_SYNCHRONIZATION_TYPE_FORCE_INT           = 0x7fffffff
+} CUpti_ActivitySynchronizationType;
+
+/**
+ * \brief stream type.
+ *
+ * The types of stream to be used with CUpti_ActivityStream.
+ */
+
+typedef enum {
+  /**
+   * Unknown data.
+   */
+  CUPTI_ACTIVITY_STREAM_CREATE_FLAG_UNKNOWN      = 0,
+
+  /**
+   * Default stream.
+   */
+  CUPTI_ACTIVITY_STREAM_CREATE_FLAG_DEFAULT      = 1,
+
+  /**
+   * Non-blocking stream.
+   */
+  CUPTI_ACTIVITY_STREAM_CREATE_FLAG_NON_BLOCKING = 2,
+
+  /**
+   * Null stream.
+   */
+  CUPTI_ACTIVITY_STREAM_CREATE_FLAG_NULL         = 3,
+
+  /**
+   * Stream create Mask
+   */
+  CUPTI_ACTIVITY_STREAM_CREATE_MASK              = 0xFFFF,
+
+  CUPTI_ACTIVITY_STREAM_CREATE_FLAG_FORCE_INT    = 0x7fffffff
+} CUpti_ActivityStreamFlag;
+
+/**
+* \brief Link flags.
+*
+* Describes link properties, to be used with CUpti_ActivityNvLink.
+*/
+
+typedef enum {
+  /**
+   * The flag is invalid.
+   */
+  CUPTI_LINK_FLAG_INVALID        = 0,
+
+  /**
+  * Is peer to peer access supported by this link.
+  */
+  CUPTI_LINK_FLAG_PEER_ACCESS    = (1 << 1),
+
+  /**
+  * Is system memory access supported by this link.
+  */
+  CUPTI_LINK_FLAG_SYSMEM_ACCESS  = (1 << 2),
+
+  /**
+  * Is peer atomic access supported by this link.
+  */
+  CUPTI_LINK_FLAG_PEER_ATOMICS   = (1 << 3),
+
+  /**
+  * Is system memory atomic access supported by this link.
+  */
+  CUPTI_LINK_FLAG_SYSMEM_ATOMICS = (1 << 4),
+
+  CUPTI_LINK_FLAG_FORCE_INT = 0x7fffffff
+} CUpti_LinkFlag;
+
+/**
+* \brief Memory operation types.
+*
+* Describes the type of memory operation, to be used with CUpti_ActivityMemory4.
+*/
+
+typedef enum {
+  /**
+   * The operation is invalid.
+   */
+  CUPTI_ACTIVITY_MEMORY_OPERATION_TYPE_INVALID   = 0,
+
+  /**
+  * Memory is allocated.
+  */
+  CUPTI_ACTIVITY_MEMORY_OPERATION_TYPE_ALLOCATION = 1,
+
+  /**
+  * Memory is released.
+  */
+  CUPTI_ACTIVITY_MEMORY_OPERATION_TYPE_RELEASE    = 2,
+
+  CUPTI_ACTIVITY_MEMORY_OPERATION_TYPE_FORCE_INT  = 0x7fffffff
+} CUpti_ActivityMemoryOperationType;
+
+/**
+* \brief Memory pool types.
+*
+* Describes the type of memory pool, to be used with CUpti_ActivityMemory4.
+*/
+
+typedef enum {
+  /**
+   * The operation is invalid.
+   */
+  CUPTI_ACTIVITY_MEMORY_POOL_TYPE_INVALID   = 0,
+
+  /**
+  * Memory pool is local to the process.
+  */
+  CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL     = 1,
+
+  /**
+  * Memory pool is imported by the process.
+  */
+  CUPTI_ACTIVITY_MEMORY_POOL_TYPE_IMPORTED  = 2,
+
+  CUPTI_ACTIVITY_MEMORY_POOL_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_ActivityMemoryPoolType;
+
+/**
+* \brief Memory pool operation types.
+*
+* Describes the type of memory pool operation, to be used with CUpti_ActivityMemoryPool2.
+*/
+
+typedef enum {
+  /**
+   * The operation is invalid.
+   */
+  CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_INVALID   = 0,
+
+  /**
+  * Memory pool is created.
+  */
+  CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_CREATED   = 1,
+
+  /**
+  * Memory pool is destroyed.
+  */
+  CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_DESTROYED = 2,
+
+  /**
+  * Memory pool is trimmed.
+  */
+  CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_TRIMMED   = 3,
+
+  CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_ActivityMemoryPoolOperationType;
+
+typedef enum {
+  CUPTI_CHANNEL_TYPE_INVALID      = 0,
+
+  /**
+   * Channel is used for standard work launch and tracking
+   */
+  CUPTI_CHANNEL_TYPE_COMPUTE      = 1,
+
+  /**
+   * Channel is used by an asynchronous copy engine
+   * For confidential compute configurations, work launch and
+   * completion are done using the copy engines.
+   */
+  CUPTI_CHANNEL_TYPE_ASYNC_MEMCPY = 2,
+
+
+  /**
+   * Channel is used for memory decompression operations
+   */
+    CUPTI_CHANNEL_TYPE_DECOMP ,
+
+  CUPTI_CHANNEL_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_ChannelType;
+
+/**
+* \brief CIG (CUDA in Graphics) Modes.
+*
+* Describes the CIG modes associated with the CUDA context.
+*/
+
+typedef enum
+{
+  /**
+   * Regular (non-CIG) mode
+   */
+  CUPTI_CONTEXT_CIG_MODE_NONE         = 0,
+  /**
+   * CIG mode
+   */
+  CUPTI_CONTEXT_CIG_MODE_CIG          = 1,
+  /**
+   * CIG fallback mode
+   */
+  CUPTI_CONTEXT_CIG_MODE_CIG_FALLBACK = 2,
+
+  CUPTI_CONTEXT_CIG_MODE_FORCE_INT    = 0x7fffffff
+} CUpti_ContextCigMode;
+
+/**
+ * The source-locator ID that indicates an unknown source
+ * location. There is not an actual CUpti_ActivitySourceLocator object
+ * corresponding to this value.
+ */
+#define CUPTI_SOURCE_LOCATOR_ID_UNKNOWN 0
+
+/**
+ * An invalid function index ID.
+ */
+#define CUPTI_FUNCTION_INDEX_ID_INVALID 0
+
+/**
+ * An invalid/unknown correlation ID. A correlation ID of this value
+ * indicates that there is no correlation for the activity record.
+ */
+#define CUPTI_CORRELATION_ID_UNKNOWN 0
+
+/**
+ * An invalid/unknown grid ID.
+ */
+#define CUPTI_GRID_ID_UNKNOWN 0LL
+
+/**
+ * An invalid/unknown timestamp for a start, end, queued, submitted,
+ * or completed time.
+ */
+#define CUPTI_TIMESTAMP_UNKNOWN 0LL
+
+/**
+ * An invalid/unknown value.
+ */
+#define CUPTI_SYNCHRONIZATION_INVALID_VALUE ((uint32_t) 0xFFFFFFFFU)
+
+/**
+ * An invalid/unknown process id.
+ */
+#define CUPTI_AUTO_BOOST_INVALID_CLIENT_PID 0
+
+/**
+ * Invalid/unknown NVLink port number.
+*/
+#define CUPTI_NVLINK_INVALID_PORT -1
+
+/**
+ * Maximum NVLink port numbers.
+*/
+#define CUPTI_MAX_NVLINK_PORTS 32
+
+/**
+ * An invalid/unknown value for decompressed bytes.
+*/
+#define CUPTI_DECOMPRESSED_BYTES_UNKNOWN 0LL
+
+START_PACKED_ALIGNMENT
+/**
+ * \brief Unified Memory counters configuration structure
+ *
+ * This structure controls the enable/disable of the various
+ * Unified Memory counters consisting of scope, kind and other parameters.
+ * See function \ref cuptiActivityConfigureUnifiedMemoryCounter
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * Unified Memory counter Counter scope. (deprecated in CUDA 7.0)
+   */
+  CUpti_ActivityUnifiedMemoryCounterScope scope;
+
+  /**
+   * Unified Memory counter Counter kind
+   */
+  CUpti_ActivityUnifiedMemoryCounterKind kind;
+
+  /**
+   * Device id of the target device. This is relevant only
+   * for single device scopes. (deprecated in CUDA 7.0)
+   */
+  uint32_t deviceId;
+
+  /**
+   * Control to enable/disable the counter. To enable the counter
+   * set it to non-zero value while disable is indicated by zero.
+   */
+  uint32_t enable;
+} CUpti_ActivityUnifiedMemoryCounterConfig;
+
+/**
+ * \brief Device auto boost state structure
+ *
+ * This structure defines auto boost state for a device.
+ * See function \ref cuptiGetAutoBoostState
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * Returned auto boost state. 1 is returned in case auto boost is enabled, 0
+   * otherwise
+   */
+  uint32_t enabled;
+
+  /**
+   * Id of process that has set the current boost state. The value will be
+   * CUPTI_AUTO_BOOST_INVALID_CLIENT_PID if the user does not have the
+   * permission to query process ids or there is an error in querying the
+   * process id.
+   */
+  uint32_t pid;
+
+} CUpti_ActivityAutoBoostState;
+
+/**
+ * \brief PC sampling configuration structure
+ *
+ * This structure defines the pc sampling configuration.
+ *
+ * See function \ref cuptiActivityConfigurePCSampling
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * Size of configuration structure.
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  uint32_t size;
+
+  /**
+   * There are 5 level provided for sampling period. The level
+   * internally maps to a period in terms of cycles. Same level can
+   * map to different number of cycles on different gpus. No of
+   * cycles will be chosen to minimize information loss. The period
+   * chosen will be given by samplingPeriodInCycles in
+   * \ref CUpti_ActivityPCSamplingRecordInfo for each kernel instance.
+   */
+  CUpti_ActivityPCSamplingPeriod samplingPeriod;
+
+  /**
+   * This will override the period set by samplingPeriod. Value 0 in samplingPeriod2 will be
+   * considered as samplingPeriod2 should not be used and samplingPeriod should be used.
+   * Valid values for samplingPeriod2 are between 5 to 31 both inclusive.
+   * This will set the sampling period to (2^samplingPeriod2) cycles.
+   */
+  uint32_t samplingPeriod2;
+} CUpti_ActivityPCSamplingConfig;
+
+/**
+ * \brief The base activity record.
+ *
+ * The activity API uses a CUpti_Activity as a generic representation
+ * for any activity. The 'kind' field is used to determine the
+ * specific activity kind, and from that the CUpti_Activity object can
+ * be cast to the specific activity record type appropriate for that kind.
+ *
+ * Note that all activity record types are padded and aligned to
+ * ensure that each member of the record is naturally aligned.
+ *
+ * \see CUpti_ActivityKind
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The kind of this activity.
+   */
+  CUpti_ActivityKind kind;
+} CUpti_Activity;
+
+/**
+ * \brief The activity record for memory copies.
+ *
+ * This activity record represents a memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size. \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory copy is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the memory copy.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The runtime correlation ID of the memory copy. Each memory copy
+   * is assigned a unique runtime correlation ID that is identical to
+   * the correlation ID in the runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t runtimeCorrelationId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * The ID of the HW channel on which the memory copy is occurring.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+
+  /**
+   *  Reserved for internal use.
+   */
+  uint32_t pad2;
+
+  /**
+   * The total number of memcopy operations traced in this record.
+   * This field is valid for memcpy operations happening using
+   * MemcpyBatchAsync APIs in CUDA.
+   * In MemcpyBatchAsync APIs, multiple memcpy operations are batched
+   * together for optimization purposes based on certain heuristics.
+   * For other memcpy operations, this field will be 1.
+   */
+   uint64_t copyCount;
+} CUpti_ActivityMemcpy6;
+
+/**
+ * \brief The activity record for peer-to-peer memory copies.
+ *
+ * This activity record represents a peer-to-peer memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY2).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY2.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size.  \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see
+   * CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+  * The ID of the device where the memory copy is occurring.
+  */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the device where memory is being copied from.
+   */
+  uint32_t srcDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied from.
+   */
+  uint32_t srcContextId;
+
+  /**
+   * The ID of the device where memory is being copied to.
+   */
+  uint32_t dstDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied to.
+   */
+  uint32_t dstContextId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t correlationId;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed the memcpy through graph launch.
+   * This field will be 0 if memcpy is not done using graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * The ID of the HW channel on which the memory copy is occurring.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+} CUpti_ActivityMemcpyPtoP4;
+
+/**
+ * \brief The activity record for memset.
+ *
+ * This activity record represents a memory set operation
+ * (CUPTI_ACTIVITY_KIND_MEMSET).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMSET.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The value being assigned to memory by the memory set.
+   */
+  uint32_t value;
+
+  /**
+   * The number of bytes being set by the memory set.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory set is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory set is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory set is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory set. Each memory set is assigned
+   * a unique correlation ID that is identical to the correlation ID
+   * in the driver API activity record that launched the memory set.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The flags associated with the memset. \see CUpti_ActivityFlag
+   */
+  uint16_t flags;
+
+  /**
+   * The memory kind of the memory set \see CUpti_ActivityMemoryKind
+   */
+  uint16_t memoryKind;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memset through graph launch.
+   * This field will be 0 if the memset is not executed through graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memset through graph launch.
+   * This field will be 0 if the memset is not executed through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * The ID of the HW channel on which the memory set is occurring.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+
+  /**
+   *  Undefined. Reserved for internal use
+   */
+  uint32_t pad2;
+} CUpti_ActivityMemset4;
+
+/**
+ * \brief The activity record for memory.
+ *
+ * This activity record represents a memory allocation and free operation
+ * (CUPTI_ACTIVITY_KIND_MEMORY).
+ * This activity record provides a single record for the memory
+ * allocation and memory release operations.
+ *
+ * Note: It is recommended to move to the new activity record \ref CUpti_ActivityMemory4
+ * enabled using the kind \ref CUPTI_ACTIVITY_KIND_MEMORY2.
+ * \ref CUpti_ActivityMemory4 provides separate records for memory
+ * allocation and memory release operations. This allows to correlate the
+ * corresponding driver and runtime API activity record with the memory operation.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMORY
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The memory kind requested by the user
+   */
+  CUpti_ActivityMemoryKind memoryKind;
+
+  /**
+   * The virtual address of the allocation
+   */
+  uint64_t address;
+
+  /**
+   * The number of bytes of memory allocated.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory operation, i.e.
+   * the time when memory was allocated, in ns.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory operation, i.e.
+   * the time when memory was freed, in ns.
+   * This will be 0 if memory is not freed in the application
+   */
+  uint64_t end;
+
+  /**
+   * The program counter of the allocation of memory
+   */
+  uint64_t allocPC;
+
+  /**
+   * The program counter of the freeing of memory. This will
+   * be 0 if memory is not freed in the application
+   */
+  uint64_t freePC;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the device where the memory allocation is taking place.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context. If context is NULL, \p contextId is set to CUPTI_INVALID_CONTEXT_ID.
+   */
+  uint32_t contextId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Variable name. This name is shared across all activity
+   * records representing the same symbol, and so should not be
+   * modified.
+   */
+  const char* name;
+} CUpti_ActivityMemory;
+
+/**
+ * \brief The activity record for memory.
+ *
+ * This activity record represents a memory allocation and free operation
+ * (CUPTI_ACTIVITY_KIND_MEMORY2).
+ * This activity record provides separate records for memory allocation and
+ * memory release operations.
+ * This allows to correlate the corresponding driver and runtime API
+ * activity record with the memory operation.
+ *
+ * Note: This activity record is an upgrade over \ref CUpti_ActivityMemory
+ * enabled using the kind \ref CUPTI_ACTIVITY_KIND_MEMORY.
+ * \ref CUpti_ActivityMemory provides a single record for the memory
+ * allocation and memory release operations.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMORY2
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The memory operation requested by the user, \ref CUpti_ActivityMemoryOperationType.
+   */
+  CUpti_ActivityMemoryOperationType memoryOperationType;
+
+  /**
+   * The memory kind requested by the user, \ref CUpti_ActivityMemoryKind.
+   */
+  CUpti_ActivityMemoryKind memoryKind;
+
+  /**
+   * The correlation ID of the memory operation. Each memory operation is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The virtual address of the allocation.
+   */
+  uint64_t address;
+
+  /**
+   * The number of bytes of memory allocated.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory operation, in ns.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The program counter of the memory operation.
+   */
+  uint64_t PC;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the device where the memory operation is taking place.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context. If context is NULL, \p contextId is set to CUPTI_INVALID_CONTEXT_ID.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream. If memory operation is not async, \p streamId is set to CUPTI_INVALID_STREAM_ID.
+   */
+  uint32_t streamId;
+
+  /**
+   * Variable name. This name is shared across all activity
+   * records representing the same symbol, and so should not be
+   * modified.
+   */
+  const char* name;
+
+  /**
+   * \p isAsync is set if memory operation happens through async memory APIs.
+   */
+  uint32_t isAsync;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad1;
+#endif
+
+  /**
+   * The memory pool configuration used for the memory operations.
+   */
+  struct PACKED_ALIGNMENT {
+    /**
+     * The type of the memory pool, \ref CUpti_ActivityMemoryPoolType
+     */
+    CUpti_ActivityMemoryPoolType memoryPoolType;
+
+#ifdef CUPTILP64
+    /**
+     * Undefined. Reserved for internal use.
+     */
+    uint32_t pad2;
+#endif
+
+    /**
+     * The base address of the memory pool.
+     */
+    uint64_t address;
+
+    /**
+     * The release threshold of the memory pool in bytes. \p releaseThreshold is
+     * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     */
+    uint64_t releaseThreshold;
+
+    /**
+     * The size of memory pool in bytes and the processId of the memory pools
+     * \p size is valid if \p memoryPoolType is
+     * CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     * \p processId is valid if \p memoryPoolType is
+     * CUPTI_ACTIVITY_MEMORY_POOL_TYPE_IMPORTED, \ref CUpti_ActivityMemoryPoolType
+     */
+    union {
+      uint64_t size;
+      uint64_t processId;
+    } pool;
+
+    /**
+     * The utilized size of the memory pool. \p utilizedSize is
+     * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     */
+    uint64_t utilizedSize;
+  } memoryPoolConfig;
+
+    /**
+     * The shared object or binary that the memory allocation request comes from.
+     */
+    const char* source;
+} CUpti_ActivityMemory4;
+
+/**
+ * \brief The activity record for memory pool.
+ *
+ * This activity record represents a memory pool creation, destruction and
+ * trimming (CUPTI_ACTIVITY_KIND_MEMORY_POOL).
+ * This activity record provides separate records for memory pool creation,
+ * destruction and trimming operations.
+ * This allows to correlate the corresponding driver and runtime API
+ * activity record with the memory pool operation.
+ *
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMORY_POOL
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The memory operation requested by the user, \ref CUpti_ActivityMemoryPoolOperationType.
+   */
+  CUpti_ActivityMemoryPoolOperationType memoryPoolOperationType;
+
+  /**
+   * The type of the memory pool, \ref CUpti_ActivityMemoryPoolType
+   */
+  CUpti_ActivityMemoryPoolType memoryPoolType;
+
+  /**
+   * The correlation ID of the memory pool operation. Each memory pool
+   * operation is assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the device where the memory pool is created.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The minimum bytes to keep of the memory pool. \p minBytesToKeep is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_TRIMMED,
+   * \ref CUpti_ActivityMemoryPoolOperationType
+   */
+  size_t minBytesToKeep;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The virtual address of the allocation.
+   */
+  uint64_t address;
+
+  /**
+   * The size of the memory pool operation in bytes. \p size is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+   */
+  uint64_t size;
+
+  /**
+   * The release threshold of the memory pool. \p releaseThreshold is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+   */
+  uint64_t releaseThreshold;
+
+  /**
+   * The start timestamp for the memory operation, in ns.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The utilized size of the memory pool. \p utilizedSize is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+   */
+  uint64_t utilizedSize;
+} CUpti_ActivityMemoryPool2;
+
+/**
+ * \brief The type of the CUDA kernel launch.
+ */
+typedef enum {
+  /**
+  * The kernel was launched via a regular kernel call
+  */
+  CUPTI_ACTIVITY_LAUNCH_TYPE_REGULAR = 0,
+
+  /**
+  * The kernel was launched via API \ref cudaLaunchCooperativeKernel() or
+  * \ref cuLaunchCooperativeKernel()
+  */
+  CUPTI_ACTIVITY_LAUNCH_TYPE_COOPERATIVE_SINGLE_DEVICE = 1,
+
+  /**
+  * The kernel was launched via API \ref cudaLaunchCooperativeKernelMultiDevice() or
+  * \ref cuLaunchCooperativeKernelMultiDevice()
+  */
+  CUPTI_ACTIVITY_LAUNCH_TYPE_COOPERATIVE_MULTI_DEVICE = 2,
+
+  /**
+  * The kernel was launched as a CBL commandlist
+  */
+  CUPTI_ACTIVITY_LAUNCH_TYPE_CBL_COMMANDLIST = 3,
+} CUpti_ActivityLaunchType;
+
+/**
+ * \brief The shared memory limit per block config for a kernel
+ * This should be used to set 'cudaOccFuncShmemConfig' field in occupancy calculator API
+ */
+typedef enum  {
+    /** The shared memory limit config is default
+     */
+    CUPTI_FUNC_SHMEM_LIMIT_DEFAULT              = 0x00,
+
+    /** User has opted for a higher dynamic shared memory limit using function attribute
+     * 'cudaFuncAttributeMaxDynamicSharedMemorySize' for runtime API or
+     * CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES for driver API
+     */
+    CUPTI_FUNC_SHMEM_LIMIT_OPTIN                = 0x01,
+
+    CUPTI_FUNC_SHMEM_LIMIT_FORCE_INT            = 0x7fffffff
+} CUpti_FuncShmemLimitConfig;
+
+/**
+ * \brief The activity record for kernel.
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL)
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * For devices with compute capability 7.0+ cacheConfig values are not updated
+   * in case field isSharedMemoryCarveoutRequested is set
+   */
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes (deprecated in CUDA 11.8).
+   * Refer field localMemoryTotal_v2
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The timestamp when the kernel is queued up in the command buffer, in ns.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time
+   * could not be collected for the kernel. This timestamp is not collected
+   * by default. Use API \ref cuptiActivityEnableLatencyTimestamps() to
+   * enable collection.
+   *
+   * Command buffer is a buffer written by CUDA driver to send commands
+   * like kernel launch, memory copy etc to the GPU. All launches of CUDA
+   * kernels are asynchronous with respect to the host, the host requests
+   * the launch by writing commands into the command buffer, then returns
+   * without checking the GPU's progress.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when the command buffer containing the kernel launch
+   * is submitted to the GPU, in ns. A value of CUPTI_TIMESTAMP_UNKNOWN
+   * indicates that the submitted time could not be collected for the kernel.
+   * This timestamp is not collected by default. Use API \ref
+   * cuptiActivityEnableLatencyTimestamps() to enable collection.
+   */
+  uint64_t submitted;
+
+  /**
+   * The indicates if the kernel was executed via a regular launch or via a
+   * single/multi device cooperative launch. \see CUpti_ActivityLaunchType
+   */
+  uint8_t launchType;
+
+  /**
+   * This indicates if CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT was
+   * updated for the kernel launch
+   */
+  uint8_t isSharedMemoryCarveoutRequested;
+
+  /**
+   * Shared memory carveout value requested for the function in percentage of
+   * the total resource. The value will be updated only if field
+   * isSharedMemoryCarveoutRequested is set.
+   */
+  uint8_t sharedMemoryCarveoutRequested;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t padding;
+
+ /**
+  * Shared memory size set by the driver.
+  */
+  uint32_t sharedMemoryExecuted;
+
+  /**
+   * The unique ID of the graph node that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The shared memory limit config for the kernel. This field shows whether user has opted for a
+   * higher per block limit of dynamic shared memory.
+   */
+  CUpti_FuncShmemLimitConfig shmemLimitConfig;
+
+  /**
+   * The unique ID of the graph that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint32_t graphId;
+
+  /**
+   * The pointer to the access policy window. The structure CUaccessPolicyWindow is
+   * defined in cuda.h.
+   */
+  CUaccessPolicyWindow *pAccessPolicyWindow;
+
+  /**
+   * The ID of the HW channel on which the kernel is launched.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+
+  /**
+   * The X-dimension cluster size for the kernel.
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterX;
+
+  /**
+   * The Y-dimension cluster size for the kernel.
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterY;
+
+  /**
+   * The Z-dimension cluster size for the kernel.
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterZ;
+
+  /**
+   * The cluster scheduling policy for the kernel. Refer CUclusterSchedulingPolicy
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterSchedulingPolicy;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint64_t localMemoryTotal_v2;
+
+  /**
+   * The maximum cluster size for the kernel
+   */
+  uint32_t maxPotentialClusterSize;
+
+  /**
+   * The maximum clusters that could co-exist on the target device for the kernel
+   */
+  uint32_t maxActiveClusters;
+} CUpti_ActivityKernel9;
+
+/**
+ * \brief The activity record for CDP (CUDA Dynamic Parallelism)
+ * kernel.
+ *
+ * This activity record represents a CDP kernel execution.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_CDP_KERNEL
+   */
+  CUpti_ActivityKind kind;
+
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel execution
+   * is assigned a unique grid ID.
+   */
+  int64_t gridId;
+
+  /**
+   * The grid ID of the parent kernel.
+   */
+  int64_t parentGridId;
+
+  /**
+   * The timestamp when kernel is queued up, in ns. A value of
+   * CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time is
+   * unknown.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when kernel is submitted to the gpu, in ns. A value
+   * of CUPTI_TIMESTAMP_UNKNOWN indicates that the submission time is
+   * unknown.
+   */
+  uint64_t submitted;
+
+  /**
+   * The timestamp when kernel is marked as completed, in ns. A value
+   * of CUPTI_TIMESTAMP_UNKNOWN indicates that the completion time is
+   * unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The X-dimension of the parent block.
+   */
+  uint32_t parentBlockX;
+
+  /**
+   * The Y-dimension of the parent block.
+   */
+  uint32_t parentBlockY;
+
+  /**
+   * The Z-dimension of the parent block.
+   */
+  uint32_t parentBlockZ;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+} CUpti_ActivityCdpKernel;
+
+/**
+ * \brief The activity record for a preemption of a CDP kernel.
+ *
+ * This activity record represents a preemption of a CDP kernel.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_PREEMPTION
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+  * kind of the preemption
+  */
+  CUpti_ActivityPreemptionKind preemptionKind;
+
+  /**
+   * The timestamp of the preemption, in ns. A value of 0 indicates
+   * that timestamp information could not be collected for the
+   * preemption.
+   */
+  uint64_t timestamp;
+
+  /**
+  * The grid-id of the block that is preempted
+  */
+  int64_t gridId;
+
+  /**
+   * The X-dimension of the block that is preempted
+   */
+  uint32_t blockX;
+
+  /**
+   * The Y-dimension of the block that is preempted
+   */
+  uint32_t blockY;
+
+  /**
+   * The Z-dimension of the block that is preempted
+   */
+  uint32_t blockZ;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityPreemption;
+
+/**
+ * \brief The activity record for a driver or runtime API invocation.
+ *
+ * This activity record represents an invocation of a driver or
+ * runtime API (CUPTI_ACTIVITY_KIND_DRIVER and
+ * CUPTI_ACTIVITY_KIND_RUNTIME).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DRIVER,
+   * CUPTI_ACTIVITY_KIND_RUNTIME, or CUPTI_ACTIVITY_KIND_INTERNAL_LAUNCH_API.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID of the driver or runtime function.
+   */
+  CUpti_CallbackId cbid;
+
+  /**
+   * The start timestamp for the function, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the function.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the function, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the function.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the process where the driver or runtime CUDA function
+   * is executing.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the thread where the driver or runtime CUDA function is
+   * executing.
+   */
+  uint32_t threadId;
+
+  /**
+   * The correlation ID of the driver or runtime CUDA function. Each
+   * function invocation is assigned a unique correlation ID that is
+   * identical to the correlation ID in the memcpy, memset, or kernel
+   * activity record that is associated with this function.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The return value for the function. For a CUDA driver function
+   * with will be a CUresult value, and for a CUDA runtime function
+   * this will be a cudaError_t value.
+   */
+  uint32_t returnValue;
+} CUpti_ActivityAPI;
+
+/**
+ * \brief The activity record for a CUPTI event.
+ *
+ * This activity record represents a CUPTI event value
+ * (CUPTI_ACTIVITY_KIND_EVENT). This activity record kind is not
+ * produced by the activity API but is included for completeness and
+ * ease-of-use. Profile frameworks built on top of CUPTI that collect
+ * event data may choose to use this type to store the collected event
+ * data.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_EVENT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The event ID.
+   */
+  CUpti_EventID id;
+
+  /**
+   * The event value.
+   */
+  uint64_t value;
+
+  /**
+   * The event domain ID.
+   */
+  CUpti_EventDomainID domain;
+
+  /**
+   * The correlation ID of the event. Use of this ID is user-defined,
+   * but typically this ID value will equal the correlation ID of the
+   * kernel for which the event was gathered.
+   */
+  uint32_t correlationId;
+} CUpti_ActivityEvent;
+
+/**
+ * \brief The activity record for a CUPTI event with instance
+ * information.
+ *
+ * This activity record represents the a CUPTI event value for a
+ * specific event domain instance
+ * (CUPTI_ACTIVITY_KIND_EVENT_INSTANCE). This activity record kind is
+ * not produced by the activity API but is included for completeness
+ * and ease-of-use. Profile frameworks built on top of CUPTI that
+ * collect event data may choose to use this type to store the
+ * collected event data. This activity record should be used when
+ * event domain instance information needs to be associated with the
+ * event.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be
+   * CUPTI_ACTIVITY_KIND_EVENT_INSTANCE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The event ID.
+   */
+  CUpti_EventID id;
+
+  /**
+   * The event domain ID.
+   */
+  CUpti_EventDomainID domain;
+
+  /**
+   * The event domain instance.
+   */
+  uint32_t instance;
+
+  /**
+   * The event value.
+   */
+  uint64_t value;
+
+  /**
+   * The correlation ID of the event. Use of this ID is user-defined,
+   * but typically this ID value will equal the correlation ID of the
+   * kernel for which the event was gathered.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityEventInstance;
+
+/**
+ * \brief The activity record for a CUPTI metric.
+ *
+ * This activity record represents the collection of a CUPTI metric
+ * value (CUPTI_ACTIVITY_KIND_METRIC). This activity record kind is not
+ * produced by the activity API but is included for completeness and
+ * ease-of-use. Profile frameworks built on top of CUPTI that collect
+ * metric data may choose to use this type to store the collected metric
+ * data.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_METRIC.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The metric ID.
+   */
+  CUpti_MetricID id;
+
+  /**
+   * The metric value.
+   */
+  CUpti_MetricValue value;
+
+  /**
+   * The correlation ID of the metric. Use of this ID is user-defined,
+   * but typically this ID value will equal the correlation ID of the
+   * kernel for which the metric was gathered.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The properties of this metric. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t pad[3];
+} CUpti_ActivityMetric;
+
+/**
+ * \brief The activity record for a CUPTI metric with instance
+ * information.
+ *
+ * This activity record represents a CUPTI metric value
+ * for a specific metric domain instance
+ * (CUPTI_ACTIVITY_KIND_METRIC_INSTANCE).  This activity record kind
+ * is not produced by the activity API but is included for
+ * completeness and ease-of-use. Profile frameworks built on top of
+ * CUPTI that collect metric data may choose to use this type to store
+ * the collected metric data. This activity record should be used when
+ * metric domain instance information needs to be associated with the
+ * metric.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be
+   * CUPTI_ACTIVITY_KIND_METRIC_INSTANCE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The metric ID.
+   */
+  CUpti_MetricID id;
+
+  /**
+   * The metric value.
+   */
+  CUpti_MetricValue value;
+
+  /**
+   * The metric domain instance.
+   */
+  uint32_t instance;
+
+  /**
+   * The correlation ID of the metric. Use of this ID is user-defined,
+   * but typically this ID value will equal the correlation ID of the
+   * kernel for which the metric was gathered.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The properties of this metric. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t pad[7];
+} CUpti_ActivityMetricInstance;
+
+/**
+ * \brief The activity record for source locator.
+ *
+ * This activity record represents a source locator
+ * (CUPTI_ACTIVITY_KIND_SOURCE_LOCATOR).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_SOURCE_LOCATOR.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID for the source path, will be used in all the source level
+   * results.
+   */
+  uint32_t id;
+
+  /**
+   * The line number in the source .
+   */
+  uint32_t lineNumber;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The path for the file.
+   */
+  const char *fileName;
+} CUpti_ActivitySourceLocator;
+
+/**
+ * \brief The activity record for source-level global
+ * access.
+ *
+ * This activity records the locations of the global
+ * accesses in the source (CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this global access.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * when at least one of thread among warp is active with predicate and condition code
+   * evaluating to true.
+   */
+  uint32_t executed;
+
+  /**
+   * The pc offset for the access.
+   */
+  uint64_t pcOffset;
+
+  /**
+   * This increments each time when this instruction is executed by number of
+   * threads that executed this instruction with predicate and condition code
+   * evaluating to true.
+   */
+  uint64_t threadsExecuted;
+
+  /**
+   * The total number of 32 bytes transactions to L2 cache generated by this
+     access
+   */
+  uint64_t l2_transactions;
+
+  /**
+   * The minimum number of L2 transactions possible based on the access pattern.
+   */
+  uint64_t theoreticalL2Transactions;
+} CUpti_ActivityGlobalAccess3;
+
+/**
+ * \brief The activity record for source level result
+ * branch.
+ *
+ * This activity record the locations of the branches in the
+ * source (CUPTI_ACTIVITY_KIND_BRANCH).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_BRANCH.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the branch.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * Number of times this branch diverged
+   */
+  uint32_t diverged;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction
+   */
+  uint64_t threadsExecuted;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * regardless of predicate or condition code.
+   */
+  uint32_t executed;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityBranch2;
+
+/**
+ * \brief The activity record for a device. (CUDA 11.6 onwards)
+ *
+ * This activity record represents information about a GPU device
+ * (CUPTI_ACTIVITY_KIND_DEVICE).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DEVICE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the device. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The global memory bandwidth available on the device, in
+   * kBytes/sec.
+   */
+  uint64_t globalMemoryBandwidth;
+
+  /**
+   * The amount of global memory on the device, in bytes.
+   */
+  uint64_t globalMemorySize;
+
+  /**
+   * The amount of constant memory on the device, in bytes.
+   */
+  uint32_t constantMemorySize;
+
+  /**
+   * The size of the L2 cache on the device, in bytes.
+   */
+  uint32_t l2CacheSize;
+
+  /**
+   * The number of threads per warp on the device.
+   */
+  uint32_t numThreadsPerWarp;
+
+  /**
+   * The core clock rate of the device, in kHz.
+   */
+  uint32_t coreClockRate;
+
+  /**
+   * Number of memory copy engines on the device.
+   */
+  uint32_t numMemcpyEngines;
+
+  /**
+   * Number of multiprocessors on the device.
+   */
+  uint32_t numMultiprocessors;
+
+  /**
+   * The maximum "instructions per cycle" possible on each device
+   * multiprocessor.
+   */
+  uint32_t maxIPC;
+
+  /**
+   * Maximum number of warps that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxWarpsPerMultiprocessor;
+
+  /**
+   * Maximum number of blocks that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxBlocksPerMultiprocessor;
+
+  /**
+   * Maximum amount of shared memory available per multiprocessor, in bytes.
+   */
+  uint32_t maxSharedMemoryPerMultiprocessor;
+
+  /**
+   * Maximum number of 32-bit registers available per multiprocessor.
+   */
+  uint32_t maxRegistersPerMultiprocessor;
+
+  /**
+   * Maximum number of registers that can be allocated to a block.
+   */
+  uint32_t maxRegistersPerBlock;
+
+  /**
+   * Maximum amount of shared memory that can be assigned to a block,
+   * in bytes.
+   */
+  uint32_t maxSharedMemoryPerBlock;
+
+  /**
+   * Maximum number of threads allowed in a block.
+   */
+  uint32_t maxThreadsPerBlock;
+
+  /**
+   * Maximum allowed X dimension for a block.
+   */
+  uint32_t maxBlockDimX;
+
+  /**
+   * Maximum allowed Y dimension for a block.
+   */
+  uint32_t maxBlockDimY;
+
+  /**
+   * Maximum allowed Z dimension for a block.
+   */
+  uint32_t maxBlockDimZ;
+
+  /**
+   * Maximum allowed X dimension for a grid.
+   */
+  uint32_t maxGridDimX;
+
+  /**
+   * Maximum allowed Y dimension for a grid.
+   */
+  uint32_t maxGridDimY;
+
+  /**
+   * Maximum allowed Z dimension for a grid.
+   */
+  uint32_t maxGridDimZ;
+
+  /**
+   * Compute capability for the device, major number.
+   */
+  uint32_t computeCapabilityMajor;
+
+  /**
+   * Compute capability for the device, minor number.
+   */
+  uint32_t computeCapabilityMinor;
+
+  /**
+   * The device ID.
+   */
+  uint32_t id;
+
+  /**
+   * ECC enabled flag for device
+   */
+  uint32_t eccEnabled;
+
+  /**
+   * The device UUID. This value is the globally unique immutable
+   * alphanumeric identifier of the device.
+   */
+  CUuuid uuid;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The device name. This name is shared across all activity records
+   * representing instances of the device, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Flag to indicate whether the device is visible to CUDA. Users can
+   * set the device visibility using CUDA_VISIBLE_DEVICES environment
+   */
+  uint8_t isCudaVisible;
+
+  /**
+   * MIG enabled flag for device
+   */
+  uint8_t isMigEnabled;
+
+  uint8_t reserved[6];
+
+  /**
+   * GPU Instance id for MIG enabled devices.
+   * If mig mode is disabled value is set to UINT32_MAX
+   */
+  uint32_t gpuInstanceId;
+
+  /**
+   * Compute Instance id for MIG enabled devices.
+   * If mig mode is disabled value is set to UINT32_MAX
+   */
+  uint32_t computeInstanceId;
+
+  /**
+   * The MIG UUID. This value is the globally unique immutable
+   * alphanumeric identifier of the device.
+   */
+  CUuuid migUuid;
+
+  /**
+   * Numa (Non-uniform memory access) information for device
+   * GPU is a NUMA node or not
+  */
+  uint32_t isNumaNode;
+
+  /**
+   * Numa (Non-uniform memory access) information for device
+   * NUMA node ID of the GPU memory
+   * if GPU is not a NUMA node, it returns invalidNumaId
+  */
+  uint32_t numaId;
+} CUpti_ActivityDevice5;
+
+/**
+ * \brief The activity record for a device attribute.
+ *
+ * This activity record represents information about a GPU device:
+ * either a CUpti_DeviceAttribute or CUdevice_attribute value
+ * (CUPTI_ACTIVITY_KIND_DEVICE_ATTRIBUTE).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be
+   * CUPTI_ACTIVITY_KIND_DEVICE_ATTRIBUTE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the device. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID of the device that this attribute applies to.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The attribute, either a CUpti_DeviceAttribute or
+   * CUdevice_attribute. Flag
+   * CUPTI_ACTIVITY_FLAG_DEVICE_ATTRIBUTE_CUDEVICE is used to indicate
+   * what kind of attribute this is. If
+   * CUPTI_ACTIVITY_FLAG_DEVICE_ATTRIBUTE_CUDEVICE is 1 then
+   * CUdevice_attribute field is value, otherwise
+   * CUpti_DeviceAttribute field is valid.
+   */
+  union {
+    CUdevice_attribute cu;
+    CUpti_DeviceAttribute cupti;
+  } attribute;
+
+  /**
+   * The value for the attribute. See CUpti_DeviceAttribute and
+   * CUdevice_attribute for the type of the value for a given
+   * attribute.
+   */
+  union {
+    double vDouble;
+    uint32_t vUint32;
+    uint64_t vUint64;
+    int32_t vInt32;
+    int64_t vInt64;
+  } value;
+} CUpti_ActivityDeviceAttribute;
+
+/**
+ * \brief The activity record for a context.
+ *
+ * This activity record represents information about a context
+ * (CUPTI_ACTIVITY_KIND_CONTEXT).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_CONTEXT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The context ID.
+   */
+  uint32_t contextId;
+
+  /**
+   * The device ID.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The compute API kind. \see CUpti_ActivityComputeApiKind
+   */
+  uint16_t computeApiKind;
+
+  /**
+   * The ID for the NULL stream in this context
+   */
+  uint16_t nullStreamId;
+
+  /**
+   * The ID of the parent context. It would be 0 if
+   * context does not have parent
+   */
+  uint32_t parentContextId;
+
+  /**
+   * This field indicates whether the context is a green context
+   */
+  uint8_t isGreenContext;
+
+  uint8_t padding;
+
+  /**
+   * Number of multiprocessors assigned to the green context
+   * Invalid if the field 'isGreenContext' is 0
+   */
+  uint16_t numMultiprocessors;
+
+  /**
+   * This field indicates the CIG mode
+   */
+  CUpti_ContextCigMode cigMode;
+
+  uint32_t padding2;
+
+} CUpti_ActivityContext3;
+
+/**
+ * \brief The activity record providing a name.
+ *
+ * This activity record provides a name for a device, context, thread,
+ * etc. and other resource naming done via NVTX APIs
+ * (CUPTI_ACTIVITY_KIND_NAME).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_NAME.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of activity object being named.
+   */
+  CUpti_ActivityObjectKind objectKind;
+
+  /**
+   * The identifier for the activity object. 'objectKind' indicates
+   * which ID is valid for this record.
+   */
+  CUpti_ActivityObjectKindId objectId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The name.
+   */
+  const char *name;
+
+} CUpti_ActivityName;
+
+/**
+ * \brief The activity record providing a marker which is an
+ * instantaneous point in time.
+ *
+ * The marker is specified with a descriptive name and unique id
+ * (CUPTI_ACTIVITY_KIND_MARKER).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the marker. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The timestamp for the marker, in ns. A value of 0 indicates that
+   * timestamp information could not be collected for the marker.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The marker ID.
+   */
+  uint32_t id;
+
+  /**
+   * The kind of activity object associated with this marker.
+   */
+  CUpti_ActivityObjectKind objectKind;
+
+  /**
+   * The identifier for the activity object associated with this
+   * marker. 'objectKind' indicates which ID is valid for this record.
+   */
+  CUpti_ActivityObjectKindId objectId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+
+
+  /**
+   * The marker name for an instantaneous or start marker. This will
+   * be NULL for an end marker.
+   */
+  const char *name;
+
+  /**
+   * The name of the domain to which this marker belongs to.
+   * This will be NULL for default domain.
+   */
+  const char *domain;
+
+} CUpti_ActivityMarker2;
+
+/**
+ * \brief The activity record providing detailed information for a marker.
+ *
+ * User must enable CUPTI_ACTIVITY_KIND_MARKER as well
+ * to get records for marker data.
+ * The marker data contains color, payload, and category.
+ * (CUPTI_ACTIVITY_KIND_MARKER_DATA).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be
+   * CUPTI_ACTIVITY_KIND_MARKER_DATA.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the marker. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The marker ID.
+   */
+  uint32_t id;
+
+  /**
+   * Defines the payload format for the value associated with the marker.
+   */
+  CUpti_MetricValueKind payloadKind;
+
+  /**
+   * The payload value.
+   */
+  CUpti_MetricValue payload;
+
+  /**
+   * The color for the marker.
+   */
+  uint32_t color;
+
+  /**
+   * The category for the marker.
+   */
+  uint32_t category;
+
+} CUpti_ActivityMarkerData;
+
+/**
+ * \brief The activity record for CUPTI and driver overheads.
+ *
+ * This activity record provides CUPTI and driver overhead information
+ * (CUPTI_ACTIVITY_KIND_OVERHEAD).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_OVERHEAD.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of overhead, CUPTI, DRIVER, COMPILER etc.
+   */
+  CUpti_ActivityOverheadKind overheadKind;
+
+  /**
+   * The kind of activity object that the overhead is associated with.
+   */
+  CUpti_ActivityObjectKind objectKind;
+
+  /**
+   * The identifier for the activity object. 'objectKind' indicates
+   * which ID is valid for this record.
+   */
+  CUpti_ActivityObjectKindId objectId;
+
+  /**
+   * The start timestamp for the overhead, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the overhead.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the overhead, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the overhead.
+   */
+  uint64_t end;
+
+  /**
+   * The correlation ID of the overhead operation to which
+   * records belong to. This ID is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the overhead operation.
+   * In some cases, it can be zero, such as for CUPTI_ACTIVITY_OVERHEAD_CUPTI_BUFFER_FLUSH records.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Reserved for internal use.
+   */
+  uint32_t reserved0;
+
+  /**
+   * Pointer to the struct with additional details about the overhead.
+   * Refer CUpti_ActivityOverheadKind enum and the corresponding structure to typecast and access additional overhead data.
+   * Client is responsible for freeing this memory using the free function when done.
+   */
+  void *overheadData;
+
+} CUpti_ActivityOverhead3;
+
+/**
+ * \brief The activity record for CUPTI environmental data.
+ *
+ * This activity record provides CUPTI environmental data, include
+ * power, clocks, and thermals.  This information is sampled at
+ * various rates and returned in this activity record.  The consumer
+ * of the record needs to check the environmentKind field to figure
+ * out what kind of environmental record this is.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_ENVIRONMENT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID of the device
+   */
+  uint32_t deviceId;
+
+  /**
+   * The timestamp when this sample was retrieved, in ns. A value of 0
+   * indicates that timestamp information could not be collected for
+   * the marker.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The kind of data reported in this record.
+   */
+  CUpti_ActivityEnvironmentKind environmentKind;
+
+  union {
+    /**
+     * Data returned for CUPTI_ACTIVITY_ENVIRONMENT_SPEED environment
+     * kind.
+     */
+    struct {
+      /**
+       * The SM frequency in MHz
+       */
+      uint32_t smClock;
+
+      /**
+       * The memory frequency in MHz
+       */
+      uint32_t memoryClock;
+
+      /**
+       * The PCIe link generation.
+       */
+      uint32_t pcieLinkGen;
+
+      /**
+       * The PCIe link width.
+       */
+      uint32_t pcieLinkWidth;
+
+      /**
+       * The clocks throttle reasons.
+       */
+      CUpti_EnvironmentClocksThrottleReason clocksThrottleReasons;
+    } speed;
+
+    /**
+     * Data returned for CUPTI_ACTIVITY_ENVIRONMENT_TEMPERATURE
+     * environment kind.
+     */
+    struct {
+      /**
+       * The GPU temperature in degrees C.
+       */
+      uint32_t gpuTemperature;
+    } temperature;
+
+    /**
+     * Data returned for CUPTI_ACTIVITY_ENVIRONMENT_POWER environment kind.
+     * The power in milliwatts consumed by GPU and associated circuitry.
+     * The power in milliwatts that will trigger power management algorithm.
+     */
+    struct {
+
+      uint32_t power;
+      uint32_t powerLimit;
+    } power;
+
+    /**
+     * Data returned for CUPTI_ACTIVITY_ENVIRONMENT_COOLING
+     * environment kind.
+     */
+    struct {
+      /**
+       * The fan speed as percentage of maximum.
+       */
+      uint32_t fanSpeed;
+    } cooling;
+  } data;
+} CUpti_ActivityEnvironment;
+
+/**
+ * \brief The activity record for source-level instruction execution.
+ *
+ * This activity records result for source level instruction execution.
+ * (CUPTI_ACTIVITY_KIND_INSTRUCTION_EXECUTION).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_INSTRUCTION_EXECUTION.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this instruction execution.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the instruction.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction, regardless of predicate or condition code.
+   */
+  uint64_t threadsExecuted;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction with predicate and condition code evaluating to true.
+   */
+  uint64_t notPredOffThreadsExecuted;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * regardless of predicate or condition code.
+   */
+  uint32_t executed;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityInstructionExecution;
+
+/**
+ * \brief The activity record for PC sampling.
+ *
+ * This activity records information obtained by sampling PC
+ * (CUPTI_ACTIVITY_KIND_PC_SAMPLING).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_PC_SAMPLING.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this instruction.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * Number of times the PC was sampled with the stallReason in the record.
+   * These samples indicate that no instruction was issued in that cycle from
+   * the warp scheduler from where the warp was sampled.
+   * Field is valid for devices with compute capability 6.0 and higher
+   */
+  uint32_t latencySamples;
+
+  /**
+   * Number of times the PC was sampled with the stallReason in the record.
+   * The same PC can be sampled with different stall reasons. The count includes
+   * latencySamples.
+   */
+  uint32_t samples;
+
+  /**
+   * Current stall reason. Includes one of the reasons from
+   * \ref CUpti_ActivityPCSamplingStallReason
+   */
+  CUpti_ActivityPCSamplingStallReason stallReason;
+
+  /**
+   * The pc offset for the instruction.
+   */
+  uint64_t pcOffset;
+} CUpti_ActivityPCSampling3;
+
+/**
+ * \brief The activity record for record status for PC sampling.
+ *
+ * This activity records information obtained by sampling PC
+ * (CUPTI_ACTIVITY_KIND_PC_SAMPLING_RECORD_INFO).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_PC_SAMPLING_RECORD_INFO.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Number of times the PC was sampled for this kernel instance including all
+   * dropped samples.
+   */
+  uint64_t totalSamples;
+
+  /**
+   * Number of samples that were dropped by hardware due to backpressure/overflow.
+   */
+  uint64_t droppedSamples;
+  /**
+   * Sampling period in terms of number of cycles .
+   */
+  uint64_t samplingPeriodInCycles;
+} CUpti_ActivityPCSamplingRecordInfo;
+
+/**
+ * \brief The activity record for Unified Memory counters (CUDA 7.0 and beyond)
+ *
+ * This activity record represents a Unified Memory counter
+ * (CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The Unified Memory counter kind
+   */
+  CUpti_ActivityUnifiedMemoryCounterKind counterKind;
+
+  /**
+   * Value of the counter
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD,
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH,
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THREASHING and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP, it is the size of the
+   * memory region in bytes.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT, it
+   * is the number of page fault groups for the same page.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT,
+   * it is the program counter for the instruction that caused fault.
+   */
+  uint64_t value;
+
+  /**
+   * The start timestamp of the counter, in ns.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH, timestamp is
+   * captured when activity starts on GPU.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT, timestamp is
+   * captured when CUDA driver started processing the fault.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING, timestamp
+   * is captured when CUDA driver detected thrashing of memory region.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING,
+   * timestamp is captured when throttling operation was started by CUDA driver.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP,
+   * timestamp is captured when CUDA driver has pushed all required operations
+   * to the processor specified by dstId.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp of the counter, in ns.
+   * Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH, timestamp is
+   * captured when activity finishes on GPU.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT, timestamp is
+   * captured when CUDA driver queues the replay of faulting memory accesses on the GPU
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING, timestamp
+   * is captured when throttling operation was finished by CUDA driver
+   */
+  uint64_t end;
+
+  /**
+   * This is the virtual base address of the page/s being transferred. For cpu and
+   * gpu faults, the virtual address for the page that faulted.
+   */
+  uint64_t address;
+
+  /**
+   * The ID of the source CPU/device involved in the memory transfer, page fault, thrashing,
+   * throttling or remote map operation. For counterKind
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING, it is a bitwise ORing of the
+   * device IDs fighting for the memory region, ONLY if there are less than 32 devices. Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT
+   */
+  uint32_t srcId;
+
+  /**
+   * The ID of the destination CPU/device involved in the memory transfer or remote map
+   * operation. Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING
+   */
+  uint32_t dstId;
+
+  /**
+   * The ID of the stream causing the transfer.
+   * This value of this field is invalid.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The flags associated with this record. See enums \ref CUpti_ActivityUnifiedMemoryAccessType
+   * if counterKind is CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT
+   * and \ref CUpti_ActivityUnifiedMemoryMigrationCause if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD
+   * and \ref CUpti_ActivityUnifiedMemoryRemoteMapCause if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP and \ref CUpti_ActivityFlag
+   * if counterKind is CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING
+   */
+  uint32_t flags;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+
+  /**
+   * \brief The bitmask of devices involved in the operation.
+   *
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING, it is a bitwise ORing of the
+   * device IDs fighting for the memory region. processors[0] represents the device ID of the device 0 to device 63,
+   * processors[1] represents device ID of device 64 to device 127 and so on.
+   * Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_DTOD or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_FAULT_REPLAY
+   */
+  uint64_t processors[5];
+} CUpti_ActivityUnifiedMemoryCounter3;
+
+/**
+ * \brief The activity record for global/device functions.
+ *
+ * This activity records function name and corresponding module
+ * information.
+ * (CUPTI_ACTIVITY_KIND_FUNCTION).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_FUNCTION.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+  * ID to uniquely identify the record
+  */
+  uint32_t id;
+
+  /**
+   * The ID of the context where the function is launched.
+   */
+  uint32_t contextId;
+
+  /**
+   * The module ID in which this global/device function is present.
+   */
+  uint32_t moduleId;
+
+  /**
+   * The function's unique symbol index in the module.
+   */
+  uint32_t functionIndex;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The name of the function. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+} CUpti_ActivityFunction;
+
+/**
+ * \brief The activity record for a CUDA module.
+ *
+ * This activity record represents a CUDA module
+ * (CUPTI_ACTIVITY_KIND_MODULE). This activity record kind is not
+ * produced by the activity API but is included for completeness and
+ * ease-of-use. Profile frameworks built on top of CUPTI that collect
+ * module data from the module callback may choose to use this type to
+ * store the collected module data.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MODULE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID of the context where the module is loaded.
+   */
+  uint32_t contextId;
+
+  /**
+   * The module ID.
+   */
+  uint32_t id;
+
+  /**
+   * The cubin size.
+   */
+  uint32_t cubinSize;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The pointer to cubin.
+   */
+  const void *cubin;
+} CUpti_ActivityModule;
+
+/**
+ * \brief The activity record for source-level shared
+ * access.
+ *
+ * This activity records the locations of the shared
+ * accesses in the source
+ * (CUPTI_ACTIVITY_KIND_SHARED_ACCESS).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_SHARED_ACCESS.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this shared access.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+ /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the access.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction with predicate and condition code evaluating to true.
+   */
+  uint64_t threadsExecuted;
+
+  /**
+   * The total number of shared memory transactions generated by this access
+   */
+  uint64_t sharedTransactions;
+
+  /**
+   * The minimum number of shared memory transactions possible based on the access pattern.
+   */
+  uint64_t theoreticalSharedTransactions;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * when at least one of thread among warp is active with predicate and condition code
+   * evaluating to true.
+   */
+  uint32_t executed;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivitySharedAccess;
+
+/**
+ * \brief The activity record for CUDA event.
+ *
+ * This activity is used to track recorded events.
+ * (CUPTI_ACTIVITY_KIND_CUDA_EVENT).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_CUDA_EVENT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The correlation ID of the API to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The ID of the context where the event was recorded.
+   */
+  uint32_t contextId;
+
+  /**
+   * The compute stream where the event was recorded.
+   */
+  uint32_t streamId;
+
+  /**
+   * A unique event ID to identify the event record.
+   */
+  uint32_t eventId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+
+  /**
+   * The ID of the device where the event was recorded.
+   */
+  uint32_t deviceId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad2;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The device-side timestamp on CUDA event record.
+   * Timestamp is in nanoseconds.
+   */
+  uint64_t deviceTimestamp;
+  /**
+   * A unique ID to associate event synchronization records
+   * with the latest CUDA Event record. Similar field is added
+   * in CUpti_ActivitySynchronization2 to associate CUDA Event
+   * record to the synchronization record.
+   *
+   * The same CUDA event can be used multiple times, so the
+   * event id will not be unique to correlate the synchronization
+   * record with the latest CUDA Event record.
+   * This field will be unique and can be used to do the required
+   * correlation.
+   */
+  uint64_t cudaEventSyncId;
+} CUpti_ActivityCudaEvent2;
+
+/**
+ * \brief The activity record for CUDA stream.
+ *
+ * This activity is used to track created streams.
+ * (CUPTI_ACTIVITY_KIND_STREAM).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_STREAM.
+   */
+  CUpti_ActivityKind kind;
+  /**
+   * The ID of the context where the stream was created.
+   */
+  uint32_t contextId;
+
+  /**
+   * A unique stream ID to identify the stream.
+   */
+  uint32_t streamId;
+
+  /**
+   * The clamped priority for the stream.
+   */
+  uint32_t priority;
+
+  /**
+   * Flags associated with the stream.
+   */
+  CUpti_ActivityStreamFlag flag;
+
+  /**
+   * The correlation ID of the API to which this result is associated.
+   */
+  uint32_t correlationId;
+} CUpti_ActivityStream;
+
+/**
+ * \brief The activity record for synchronization management.
+ *
+ * This activity is used to track various CUDA synchronization APIs.
+ * (CUPTI_ACTIVITY_KIND_SYNCHRONIZATION).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_SYNCHRONIZATION.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The type of record.
+   */
+  CUpti_ActivitySynchronizationType type;
+
+  /**
+   * The start timestamp for the function, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the function.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the function, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the function.
+   */
+  uint64_t end;
+
+  /**
+   * The correlation ID of the API to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The ID of the context for which the synchronization API is called.
+   * In case of context synchronization API it is the context id for which the API is called.
+   * In case of stream/event synchronization it is the ID of the context where the stream/event was created.
+   */
+  uint32_t contextId;
+
+  /**
+   * The compute stream for which the synchronization API is called.
+   * A CUPTI_SYNCHRONIZATION_INVALID_VALUE value indicate the field is not applicable for this record.
+   * Not valid for cuCtxSynchronize, cuEventSynchronize.
+   */
+  uint32_t streamId;
+
+  /**
+   * The event ID for which the synchronization API is called.
+   * A CUPTI_SYNCHRONIZATION_INVALID_VALUE value indicate the field is not applicable for this record.
+   * Not valid for cuCtxSynchronize, cuStreamSynchronize.
+   */
+  uint32_t cudaEventId;
+
+  /**
+   * A unique ID to associate event synchronization records
+   * with the latest CUDA Event record. Similar field is added
+   * in CUpti_ActivityCudaEvent2 to associate synchronization
+   * record to the CUDA Event record.
+   *
+   * The same CUDA event can be used multiple times, so the
+   * event id will not be unique to correlate the synchronization
+   * record with the latest CUDA Event record.
+   * This field will be unique and can be used to do the required
+   * correlation.
+   *
+   * A CUPTI_SYNCHRONIZATION_INVALID_VALUE value indicates that
+   * the field is not applicable for this record.
+   * Valid only for synchronization records related to CUDA Events.
+   */
+  uint64_t cudaEventSyncId;
+
+  /**
+   * The return value for the synchronization record.
+   * Use cuptiActivityEnableAllSyncRecords API to enable/disable
+   * collection of synchronization records with return value being
+   * non-zero. This will be a CUresult value.
+   */
+  uint32_t returnValue;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivitySynchronization2;
+
+/**
+ * \brief The activity record for source-level sass/source
+ * line-by-line correlation.
+ *
+ * This activity records source level sass/source correlation
+ * information.
+ * (CUPTI_ACTIVITY_KIND_INSTRUCTION_CORRELATION).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_INSTRUCTION_CORRELATION.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this instruction.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+ /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the instruction.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityInstructionCorrelation;
+
+/**
+ * \brief The OpenAcc event kind for OpenAcc activity records.
+ *
+ * \see CUpti_ActivityKindOpenAcc
+ */
+typedef enum {
+  CUPTI_OPENACC_EVENT_KIND_INVALID              = 0,
+  CUPTI_OPENACC_EVENT_KIND_DEVICE_INIT          = 1,
+  CUPTI_OPENACC_EVENT_KIND_DEVICE_SHUTDOWN      = 2,
+  CUPTI_OPENACC_EVENT_KIND_RUNTIME_SHUTDOWN     = 3,
+  CUPTI_OPENACC_EVENT_KIND_ENQUEUE_LAUNCH       = 4,
+  CUPTI_OPENACC_EVENT_KIND_ENQUEUE_UPLOAD       = 5,
+  CUPTI_OPENACC_EVENT_KIND_ENQUEUE_DOWNLOAD     = 6,
+  CUPTI_OPENACC_EVENT_KIND_WAIT                 = 7,
+  CUPTI_OPENACC_EVENT_KIND_IMPLICIT_WAIT        = 8,
+  CUPTI_OPENACC_EVENT_KIND_COMPUTE_CONSTRUCT    = 9,
+  CUPTI_OPENACC_EVENT_KIND_UPDATE               = 10,
+  CUPTI_OPENACC_EVENT_KIND_ENTER_DATA           = 11,
+  CUPTI_OPENACC_EVENT_KIND_EXIT_DATA            = 12,
+  CUPTI_OPENACC_EVENT_KIND_CREATE               = 13,
+  CUPTI_OPENACC_EVENT_KIND_DELETE               = 14,
+  CUPTI_OPENACC_EVENT_KIND_ALLOC                = 15,
+  CUPTI_OPENACC_EVENT_KIND_FREE                 = 16,
+  CUPTI_OPENACC_EVENT_KIND_FORCE_INT            = 0x7fffffff
+} CUpti_OpenAccEventKind;
+
+/**
+ * \brief The OpenAcc parent construct kind for OpenAcc activity records.
+ */
+typedef enum {
+  CUPTI_OPENACC_CONSTRUCT_KIND_UNKNOWN          = 0,
+  CUPTI_OPENACC_CONSTRUCT_KIND_PARALLEL         = 1,
+  CUPTI_OPENACC_CONSTRUCT_KIND_KERNELS          = 2,
+  CUPTI_OPENACC_CONSTRUCT_KIND_LOOP             = 3,
+  CUPTI_OPENACC_CONSTRUCT_KIND_DATA             = 4,
+  CUPTI_OPENACC_CONSTRUCT_KIND_ENTER_DATA       = 5,
+  CUPTI_OPENACC_CONSTRUCT_KIND_EXIT_DATA        = 6,
+  CUPTI_OPENACC_CONSTRUCT_KIND_HOST_DATA        = 7,
+  CUPTI_OPENACC_CONSTRUCT_KIND_ATOMIC           = 8,
+  CUPTI_OPENACC_CONSTRUCT_KIND_DECLARE          = 9,
+  CUPTI_OPENACC_CONSTRUCT_KIND_INIT             = 10,
+  CUPTI_OPENACC_CONSTRUCT_KIND_SHUTDOWN         = 11,
+  CUPTI_OPENACC_CONSTRUCT_KIND_SET              = 12,
+  CUPTI_OPENACC_CONSTRUCT_KIND_UPDATE           = 13,
+  CUPTI_OPENACC_CONSTRUCT_KIND_ROUTINE          = 14,
+  CUPTI_OPENACC_CONSTRUCT_KIND_WAIT             = 15,
+  CUPTI_OPENACC_CONSTRUCT_KIND_RUNTIME_API      = 16,
+  CUPTI_OPENACC_CONSTRUCT_KIND_FORCE_INT        = 0x7fffffff
+
+} CUpti_OpenAccConstructKind;
+
+typedef enum {
+  CUPTI_OPENMP_EVENT_KIND_INVALID               = 0,
+  CUPTI_OPENMP_EVENT_KIND_PARALLEL              = 1,
+  CUPTI_OPENMP_EVENT_KIND_TASK                  = 2,
+  CUPTI_OPENMP_EVENT_KIND_THREAD                = 3,
+  CUPTI_OPENMP_EVENT_KIND_IDLE                  = 4,
+  CUPTI_OPENMP_EVENT_KIND_WAIT_BARRIER          = 5,
+  CUPTI_OPENMP_EVENT_KIND_WAIT_TASKWAIT         = 6,
+  CUPTI_OPENMP_EVENT_KIND_FORCE_INT             = 0x7fffffff
+} CUpti_OpenMpEventKind;
+
+/**
+ * \brief The base activity record for OpenAcc records.
+ *
+ * The OpenACC activity API part uses a CUpti_ActivityOpenAcc as a generic
+ * representation for any OpenACC activity. The 'kind' field is used to determine the
+ * specific activity kind, and from that the CUpti_ActivityOpenAcc object can
+ * be cast to the specific OpenACC activity record type appropriate for that kind.
+ *
+ * Note that all OpenACC activity record types are padded and aligned to
+ * ensure that each member of the record is naturally aligned.
+ *
+ * \see CUpti_ActivityKind
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The kind of this activity.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * CUPTI OpenACC event kind (\see CUpti_OpenAccEventKind)
+   */
+  CUpti_OpenAccEventKind eventKind;
+
+  /**
+   * CUPTI OpenACC parent construct kind (\see CUpti_OpenAccConstructKind)
+   *
+   * Note that for applications using PGI OpenACC runtime < 16.1, this
+   * will always be CUPTI_OPENACC_CONSTRUCT_KIND_UNKNOWN.
+   */
+  CUpti_OpenAccConstructKind parentConstruct;
+
+  /**
+   * Version number
+   */
+  uint32_t version;
+
+  /**
+   * 1 for any implicit event, such as an implicit wait at a synchronous data construct
+   * 0 otherwise
+   */
+  uint32_t implicit;
+
+  /**
+   * Device type
+   */
+  uint32_t deviceType;
+
+  /**
+   * Device number
+   */
+  uint32_t deviceNumber;
+
+  /**
+   * ThreadId
+   */
+  uint32_t threadId;
+
+  /**
+   * Value of async() clause of the corresponding directive
+   */
+  uint64_t async;
+
+  /**
+   * Internal asynchronous queue number used
+   */
+  uint64_t asyncMap;
+
+  /**
+   * The line number of the directive or program construct or the starting line
+   * number of the OpenACC construct corresponding to the event.
+   * A zero value means the line number is not known.
+   */
+  uint32_t lineNo;
+
+  /**
+   * For an OpenACC construct, this contains the line number of the end
+   * of the construct. A zero value means the line number is not known.
+   */
+  uint32_t endLineNo;
+
+  /**
+   * The line number of the first line of the function named in funcName.
+   * A zero value means the line number is not known.
+   */
+  uint32_t funcLineNo;
+
+  /**
+   * The last line number of the function named in funcName.
+   * A zero value means the line number is not known.
+   */
+  uint32_t funcEndLineNo;
+
+  /**
+   * CUPTI start timestamp
+   */
+  uint64_t start;
+
+  /**
+   * CUPTI end timestamp
+   */
+  uint64_t end;
+
+  /**
+   * CUDA device id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuDeviceId;
+
+  /**
+   * CUDA context id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuContextId;
+
+  /**
+   * CUDA stream id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuStreamId;
+
+  /**
+   * The ID of the process where the OpenACC activity is executing.
+   */
+  uint32_t cuProcessId;
+
+  /**
+   * The ID of the thread where the OpenACC activity is executing.
+   */
+  uint32_t cuThreadId;
+
+  /**
+   * The OpenACC correlation ID.
+   * Valid only if deviceType is acc_device_nvidia.
+   * If not 0, it uniquely identifies this record. It is identical to the
+   * externalId in the preceding external correlation record of type
+   * CUPTI_EXTERNAL_CORRELATION_KIND_OPENACC.
+   */
+  uint32_t externalId;
+
+  /*
+   * A pointer to null-terminated string containing the name of or path to
+   * the source file, if known, or a null pointer if not.
+   */
+  const char *srcFile;
+
+  /*
+   * A pointer to a null-terminated string containing the name of the
+   * function in which the event occurred.
+   */
+  const char *funcName;
+} CUpti_ActivityOpenAcc;
+
+/**
+ * \brief The activity record for OpenACC data.
+ *
+ * (CUPTI_ACTIVITY_KIND_OPENACC_DATA).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_OPENACC_DATA.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * CUPTI OpenACC event kind (\see CUpti_OpenAccEventKind)
+   */
+  CUpti_OpenAccEventKind eventKind;
+
+  /*
+   * CUPTI OpenACC parent construct kind (\see CUpti_OpenAccConstructKind)
+   *
+   * Note that for applications using PGI OpenACC runtime < 16.1, this
+   * will always be CUPTI_OPENACC_CONSTRUCT_KIND_UNKNOWN.
+   */
+  CUpti_OpenAccConstructKind parentConstruct;
+
+  /*
+   * Version number
+   */
+  uint32_t version;
+
+  /*
+   * 1 for any implicit event, such as an implicit wait at a synchronous data construct
+   * 0 otherwise
+   */
+  uint32_t implicit;
+
+  /*
+   * Device type
+   */
+  uint32_t deviceType;
+
+  /*
+   * Device number
+   */
+  uint32_t deviceNumber;
+
+  /**
+   * ThreadId
+   */
+  uint32_t threadId;
+
+  /*
+   * Value of async() clause of the corresponding directive
+   */
+  uint64_t async;
+
+  /*
+   * Internal asynchronous queue number used
+   */
+  uint64_t asyncMap;
+
+  /*
+   * The line number of the directive or program construct or the starting line
+   * number of the OpenACC construct corresponding to the event.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t lineNo;
+
+  /*
+   * For an OpenACC construct, this contains the line number of the end
+   * of the construct. A negative or zero value means the line number is not known.
+   */
+  uint32_t endLineNo;
+
+  /*
+   * The line number of the first line of the function named in func_name.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t funcLineNo;
+
+  /*
+   * The last line number of the function named in func_name.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t funcEndLineNo;
+
+  /**
+   * CUPTI start timestamp
+   */
+  uint64_t start;
+
+  /**
+   * CUPTI end timestamp
+   */
+  uint64_t end;
+
+  /**
+   * CUDA device id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuDeviceId;
+
+  /**
+   * CUDA context id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuContextId;
+
+  /**
+   * CUDA stream id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuStreamId;
+
+  /**
+   * The ID of the process where the OpenACC activity is executing.
+   */
+  uint32_t cuProcessId;
+
+  /**
+   * The ID of the thread where the OpenACC activity is executing.
+   */
+  uint32_t cuThreadId;
+
+  /**
+   * The OpenACC correlation ID.
+   * Valid only if deviceType is acc_device_nvidia.
+   * If not 0, it uniquely identifies this record. It is identical to the
+   * externalId in the preceding external correlation record of type
+   * CUPTI_EXTERNAL_CORRELATION_KIND_OPENACC.
+   */
+  uint32_t externalId;
+
+  /*
+   * A pointer to null-terminated string containing the name of or path to
+   * the source file, if known, or a null pointer if not.
+   */
+  const char *srcFile;
+
+  /*
+   * A pointer to a null-terminated string containing the name of the
+   * function in which the event occurred.
+   */
+  const char *funcName;
+
+  /* --- end of common CUpti_ActivityOpenAcc part --- */
+
+  /**
+   * Number of bytes
+   */
+  uint64_t bytes;
+
+  /**
+   * Host pointer if available
+   */
+  uint64_t hostPtr;
+
+  /**
+   * Device pointer if available
+   */
+  uint64_t devicePtr;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad1;
+#endif
+
+  /*
+   * A pointer to null-terminated string containing the name of the variable
+   * for which this event is triggered, if known, or a null pointer if not.
+   */
+  const char *varName;
+
+} CUpti_ActivityOpenAccData;
+
+/**
+ * \brief The activity record for OpenACC launch.
+ *
+ * (CUPTI_ACTIVITY_KIND_OPENACC_LAUNCH).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_OPENACC_LAUNCH.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * CUPTI OpenACC event kind (\see CUpti_OpenAccEventKind)
+   */
+  CUpti_OpenAccEventKind eventKind;
+
+  /**
+   * CUPTI OpenACC parent construct kind (\see CUpti_OpenAccConstructKind)
+   *
+   * Note that for applications using PGI OpenACC runtime < 16.1, this
+   * will always be CUPTI_OPENACC_CONSTRUCT_KIND_UNKNOWN.
+   */
+  CUpti_OpenAccConstructKind parentConstruct;
+
+  /**
+   * Version number
+   */
+  uint32_t version;
+
+  /**
+   * 1 for any implicit event, such as an implicit wait at a synchronous data construct
+   * 0 otherwise
+   */
+  uint32_t implicit;
+
+  /**
+   * Device type
+   */
+  uint32_t deviceType;
+
+  /**
+   * Device number
+   */
+  uint32_t deviceNumber;
+
+  /**
+   * ThreadId
+   */
+  uint32_t threadId;
+
+  /**
+   * Value of async() clause of the corresponding directive
+   */
+  uint64_t async;
+
+  /**
+   * Internal asynchronous queue number used
+   */
+  uint64_t asyncMap;
+
+  /**
+   * The line number of the directive or program construct or the starting line
+   * number of the OpenACC construct corresponding to the event.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t lineNo;
+
+  /**
+   * For an OpenACC construct, this contains the line number of the end
+   * of the construct. A negative or zero value means the line number is not known.
+   */
+  uint32_t endLineNo;
+
+  /**
+   * The line number of the first line of the function named in func_name.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t funcLineNo;
+
+  /**
+   * The last line number of the function named in func_name.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t funcEndLineNo;
+
+  /**
+   * CUPTI start timestamp
+   */
+  uint64_t start;
+
+  /**
+   * CUPTI end timestamp
+   */
+  uint64_t end;
+
+  /**
+   * CUDA device id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuDeviceId;
+
+  /**
+   * CUDA context id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuContextId;
+
+  /**
+   * CUDA stream id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuStreamId;
+
+  /**
+   * The ID of the process where the OpenACC activity is executing.
+   */
+  uint32_t cuProcessId;
+
+  /**
+   * The ID of the thread where the OpenACC activity is executing.
+   */
+  uint32_t cuThreadId;
+
+  /**
+   * The OpenACC correlation ID.
+   * Valid only if deviceType is acc_device_nvidia.
+   * If not 0, it uniquely identifies this record. It is identical to the
+   * externalId in the preceding external correlation record of type
+   * CUPTI_EXTERNAL_CORRELATION_KIND_OPENACC.
+   */
+  uint32_t externalId;
+
+  /**
+   * A pointer to null-terminated string containing the name of or path to
+   * the source file, if known, or a null pointer if not.
+   */
+  const char *srcFile;
+
+  /**
+   * A pointer to a null-terminated string containing the name of the
+   * function in which the event occurred.
+   */
+  const char *funcName;
+
+  /* --- end of common CUpti_ActivityOpenAcc part --- */
+
+  /**
+   * The number of gangs created for this kernel launch
+   */
+  uint64_t numGangs;
+
+  /**
+   * The number of workers created for this kernel launch
+   */
+  uint64_t numWorkers;
+
+  /**
+   * The number of vector lanes created for this kernel launch
+   */
+  uint64_t vectorLength;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad1;
+#endif
+
+  /**
+   * A pointer to null-terminated string containing the name of the
+   * kernel being launched, if known, or a null pointer if not.
+   */
+  const char *kernelName;
+
+} CUpti_ActivityOpenAccLaunch;
+
+/**
+ * \brief The activity record for OpenACC other.
+ *
+ * (CUPTI_ACTIVITY_KIND_OPENACC_OTHER).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_OPENACC_OTHER.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * CUPTI OpenACC event kind (\see CUpti_OpenAccEventKind)
+   */
+  CUpti_OpenAccEventKind eventKind;
+
+  /**
+   * CUPTI OpenACC parent construct kind (\see CUpti_OpenAccConstructKind)
+   *
+   * Note that for applications using PGI OpenACC runtime < 16.1, this
+   * will always be CUPTI_OPENACC_CONSTRUCT_KIND_UNKNOWN.
+   */
+  CUpti_OpenAccConstructKind parentConstruct;
+
+  /**
+   * Version number
+   */
+  uint32_t version;
+
+  /**
+   * 1 for any implicit event, such as an implicit wait at a synchronous data construct
+   * 0 otherwise
+   */
+  uint32_t implicit;
+
+  /**
+   * Device type
+   */
+  uint32_t deviceType;
+
+  /**
+   * Device number
+   */
+  uint32_t deviceNumber;
+
+  /**
+   * ThreadId
+   */
+  uint32_t threadId;
+
+  /**
+   * Value of async() clause of the corresponding directive
+   */
+  uint64_t async;
+
+  /**
+   * Internal asynchronous queue number used
+   */
+  uint64_t asyncMap;
+
+  /**
+   * The line number of the directive or program construct or the starting line
+   * number of the OpenACC construct corresponding to the event.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t lineNo;
+
+  /**
+   * For an OpenACC construct, this contains the line number of the end
+   * of the construct. A negative or zero value means the line number is not known.
+   */
+  uint32_t endLineNo;
+
+  /**
+   * The line number of the first line of the function named in func_name.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t funcLineNo;
+
+  /**
+   * The last line number of the function named in func_name.
+   * A negative or zero value means the line number is not known.
+   */
+  uint32_t funcEndLineNo;
+
+  /**
+   * CUPTI start timestamp
+   */
+  uint64_t start;
+
+  /**
+   * CUPTI end timestamp
+   */
+  uint64_t end;
+
+  /**
+   * CUDA device id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuDeviceId;
+
+  /**
+   * CUDA context id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuContextId;
+
+  /**
+   * CUDA stream id
+   * Valid only if deviceType is acc_device_nvidia.
+   */
+  uint32_t cuStreamId;
+
+  /**
+   * The ID of the process where the OpenACC activity is executing.
+   */
+  uint32_t cuProcessId;
+
+  /**
+   * The ID of the thread where the OpenACC activity is executing.
+   */
+  uint32_t cuThreadId;
+
+  /**
+   * The OpenACC correlation ID.
+   * Valid only if deviceType is acc_device_nvidia.
+   * If not 0, it uniquely identifies this record. It is identical to the
+   * externalId in the preceding external correlation record of type
+   * CUPTI_EXTERNAL_CORRELATION_KIND_OPENACC.
+   */
+  uint32_t externalId;
+
+  /**
+   * A pointer to null-terminated string containing the name of or path to
+   * the source file, if known, or a null pointer if not.
+   */
+  const char *srcFile;
+
+  /**
+   * A pointer to a null-terminated string containing the name of the
+   * function in which the event occurred.
+   */
+  const char *funcName;
+
+  /* --- end of common CUpti_ActivityOpenAcc part --- */
+} CUpti_ActivityOpenAccOther;
+
+/**
+ * \brief The base activity record for OpenMp records.
+ *
+ * \see CUpti_ActivityKind
+ */
+typedef struct PACKED_ALIGNMENT {
+
+  /**
+   * The kind of this activity.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * CUPTI OpenMP event kind (\see CUpti_OpenMpEventKind)
+   */
+  CUpti_OpenMpEventKind eventKind;
+
+  /**
+   * Version number
+   */
+  uint32_t version;
+
+  /**
+   * ThreadId
+   */
+  uint32_t threadId;
+
+  /**
+   * CUPTI start timestamp
+   */
+  uint64_t start;
+
+  /**
+   * CUPTI end timestamp
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the process where the OpenMP activity is executing.
+   */
+  uint32_t cuProcessId;
+
+  /**
+   * The ID of the thread where the OpenMP activity is executing.
+   */
+  uint32_t cuThreadId;
+} CUpti_ActivityOpenMp;
+
+/**
+ * \brief The kind of external APIs supported for correlation.
+ *
+ * Custom correlation kinds are reserved for usage in external tools.
+ *
+ * \see CUpti_ActivityExternalCorrelation
+ */
+typedef enum {
+    CUPTI_EXTERNAL_CORRELATION_KIND_INVALID              = 0,
+
+    /**
+     * The external API is unknown to CUPTI
+     */
+    CUPTI_EXTERNAL_CORRELATION_KIND_UNKNOWN              = 1,
+
+    /**
+     * The external API is OpenACC
+     */
+    CUPTI_EXTERNAL_CORRELATION_KIND_OPENACC              = 2,
+
+    /**
+     * The external API is custom0
+     */
+    CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM0              = 3,
+
+    /**
+     * The external API is custom1
+     */
+    CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1              = 4,
+
+    /**
+     * The external API is custom2
+     */
+    CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM2              = 5,
+
+    /**
+     * Add new kinds before this line
+     */
+    CUPTI_EXTERNAL_CORRELATION_KIND_SIZE,
+
+    CUPTI_EXTERNAL_CORRELATION_KIND_FORCE_INT            = 0x7fffffff
+} CUpti_ExternalCorrelationKind;
+
+/**
+ * \brief The activity record for correlation with external records
+ *
+ * This activity record correlates native CUDA records (e.g. CUDA Driver API,
+ * kernels, memcpys, ...) with records from external APIs such as OpenACC.
+ * (CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION).
+ *
+ * \see CUpti_ActivityKind
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The kind of this activity.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of external API this record correlated to.
+   */
+  CUpti_ExternalCorrelationKind externalKind;
+
+  /**
+   * The correlation ID of the associated non-CUDA API record.
+   * The exact field in the associated external record depends
+   * on that record's activity kind (\see externalKind).
+   */
+  uint64_t externalId;
+
+  /**
+   * The correlation ID of the associated CUDA driver or runtime API record.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t reserved;
+} CUpti_ActivityExternalCorrelation;
+
+/**
+* \brief The device type for device connected to NVLink.
+*/
+typedef enum {
+    CUPTI_DEV_TYPE_INVALID = 0,
+
+    /**
+    * The device type is GPU.
+    */
+    CUPTI_DEV_TYPE_GPU = 1,
+
+    /**
+    * The device type is NVLink processing unit in CPU.
+    */
+    CUPTI_DEV_TYPE_NPU = 2,
+
+    CUPTI_DEV_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_DevType;
+
+/**
+* \brief NVLink information.
+*
+* This structure gives capabilities of each logical NVLink connection between two devices,
+* gpu<->gpu or gpu<->CPU which can be used to understand the topology.
+*/
+
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_NVLINK.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * NvLink version.
+   */
+  uint32_t nvlinkVersion;
+
+  /**
+   * Type of device 0 \ref CUpti_DevType
+   */
+  CUpti_DevType typeDev0;
+
+  /**
+   * Type of device 1 \ref CUpti_DevType
+   */
+  CUpti_DevType typeDev1;
+
+  /**
+  * If typeDev0 is CUPTI_DEV_TYPE_GPU, UUID for device 0. \ref CUpti_ActivityDevice5.
+  * If typeDev0 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+       * Index of the NPU. First index will always be zero.
+       */
+      uint32_t index;
+
+      /**
+       * Domain ID of NPU. On Linux, this can be queried using lspci.
+       */
+      uint32_t domainId;
+    } npu;
+  } idDev0;
+
+  /**
+  * If typeDev1 is CUPTI_DEV_TYPE_GPU, UUID for device 1. \ref CUpti_ActivityDevice5.
+  * If typeDev1 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+
+      /**
+       * Index of the NPU. First index will always be zero.
+       */
+      uint32_t index;
+
+      /**
+       * Domain ID of NPU. On Linux, this can be queried using lspci.
+       */
+      uint32_t domainId;
+    } npu;
+  } idDev1;
+
+  /**
+   * Flag gives capabilities of the link \see CUpti_LinkFlag
+   */
+  uint32_t flag;
+
+  /**
+   * Number of physical NVLinks present between two devices.
+   */
+  uint32_t  physicalNvLinkCount;
+
+  /**
+   * Port numbers for maximum 32 NVLinks connected to device 0.
+   * If typeDev0 is CUPTI_DEV_TYPE_NPU, ignore this field.
+   * In case of invalid/unknown port number, this field will be set
+   * to value CUPTI_NVLINK_INVALID_PORT.
+   * This will be used to correlate the metric values to individual
+   * physical link and attribute traffic to the logical NVLink in
+   * the topology.
+   */
+  int8_t  portDev0[CUPTI_MAX_NVLINK_PORTS];
+
+  /**
+   * Port numbers for maximum 32 NVLinks connected to device 1.
+   * If typeDev1 is CUPTI_DEV_TYPE_NPU, ignore this field.
+   * In case of invalid/unknown port number, this field will be set
+   * to value CUPTI_NVLINK_INVALID_PORT.
+   * This will be used to correlate the metric values to individual
+   * physical link and attribute traffic to the logical NVLink in
+   * the topology.
+   */
+  int8_t  portDev1[CUPTI_MAX_NVLINK_PORTS];
+
+  /**
+   * Bandwidth of NVLink in kbytes/sec
+   */
+  uint64_t  bandwidth;
+
+  /**
+   * NVSwitch is connected as an intermediate node.
+   */
+  uint8_t nvswitchConnected;
+
+  /**
+   * Undefined. reserved for internal use
+   */
+  uint8_t pad[7];
+} CUpti_ActivityNvLink4;
+
+#define CUPTI_MAX_GPUS 32
+/**
+ * Field to differentiate whether PCIE Activity record
+ * is of a GPU or a PCI Bridge
+ */
+typedef enum {
+    /**
+     * PCIE GPU record
+     */
+    CUPTI_PCIE_DEVICE_TYPE_GPU       = 0,
+
+    /**
+     * PCIE Bridge record
+     */
+    CUPTI_PCIE_DEVICE_TYPE_BRIDGE    = 1,
+
+    CUPTI_PCIE_DEVICE_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_PcieDeviceType;
+
+/**
+ * \brief PCI devices information required to construct topology
+ *
+ * This structure gives capabilities of GPU and PCI bridge connected to the PCIE bus
+ * which can be used to understand the topology.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_PCIE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * Type of device in topology, \ref CUpti_PcieDeviceType. If type is
+   * CUPTI_PCIE_DEVICE_TYPE_GPU use devId for id and gpuAttr and if type is
+   * CUPTI_PCIE_DEVICE_TYPE_BRIDGE use bridgeId for id and bridgeAttr.
+   */
+  CUpti_PcieDeviceType type;
+
+  /**
+   * A unique identifier for GPU or Bridge in Topology
+   */
+  union {
+    /**
+     * GPU device ID
+     */
+    CUdevice devId;
+
+    /**
+     * A unique identifier for Bridge in the Topology
+     */
+    uint32_t bridgeId;
+  } id;
+
+  /**
+   * Domain for the GPU or Bridge, required to identify which PCIE bus it belongs to in
+   * multiple NUMA systems.
+   */
+  uint32_t domain;
+
+  /**
+   * PCIE Generation of GPU or Bridge.
+   */
+  uint16_t pcieGeneration;
+
+  /**
+   * Link rate of the GPU or bridge in gigatransfers per second (GT/s)
+   */
+  uint16_t linkRate;
+
+  /**
+   * Link width of the GPU or bridge
+   */
+  uint16_t linkWidth;
+
+  /**
+   * Upstream bus ID for the GPU or PCI bridge. Required to identify which bus it is
+   * connected to in the topology.
+   */
+  uint16_t upstreamBus;
+
+  /**
+   * Attributes for more information about GPU (gpuAttr) or PCI Bridge (bridgeAttr)
+   */
+  union {
+    struct {
+      /**
+       * UUID for the device. \ref CUpti_ActivityDevice5.
+       */
+      CUuuid uuidDev;
+
+      /**
+       * CUdevice with which this device has P2P capability.
+       * This can also be obtained by querying cuDeviceCanAccessPeer or
+       * cudaDeviceCanAccessPeer APIs
+       */
+      CUdevice peerDev[CUPTI_MAX_GPUS];
+    } gpuAttr;
+
+    struct {
+      /**
+       * The downstream bus number, used to search downstream devices/bridges connected
+       * to this bridge.
+       */
+      uint16_t secondaryBus;
+
+      /**
+       * Device ID of the bridge
+       */
+      uint16_t deviceId;
+
+      /**
+       * Vendor ID of the bridge
+       */
+      uint16_t vendorId;
+
+      /**
+       * Padding for alignment
+       */
+      uint16_t pad0;
+    } bridgeAttr;
+  } attr;
+} CUpti_ActivityPcie;
+
+/**
+ * \brief PCIE Generation.
+ *
+ * Enumeration of PCIE Generation for
+ * pcie activity attribute pcieGeneration
+ */
+typedef enum {
+  /**
+  * PCIE Generation 1
+  */
+  CUPTI_PCIE_GEN_GEN1       = 1,
+
+  /**
+  * PCIE Generation 2
+  */
+  CUPTI_PCIE_GEN_GEN2       = 2,
+
+  /**
+  * PCIE Generation 3
+  */
+  CUPTI_PCIE_GEN_GEN3       = 3,
+
+  /**
+  * PCIE Generation 4
+  */
+  CUPTI_PCIE_GEN_GEN4       = 4,
+
+  /**
+  * PCIE Generation 5
+  */
+  CUPTI_PCIE_GEN_GEN5       = 5,
+
+  /**
+  * PCIE Generation 6
+  */
+  CUPTI_PCIE_GEN_GEN6       = 6,
+
+  CUPTI_PCIE_GEN_FORCE_INT  = 0x7fffffff
+} CUpti_PcieGen;
+
+
+/**
+ * \brief The activity record for an instantaneous CUPTI event.
+ *
+ * This activity record represents a CUPTI event value
+ * (CUPTI_ACTIVITY_KIND_EVENT) sampled at a particular instant.
+ * This activity record kind is not produced by the activity API but is
+ * included for completeness and ease-of-use. Profiler frameworks built on
+ * top of CUPTI that collect event data at a particular time may choose to
+ * use this type to store the collected event data.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_INSTANTANEOUS_EVENT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The event ID.
+   */
+  CUpti_EventID id;
+
+  /**
+   * The event value.
+   */
+  uint64_t value;
+
+  /**
+   * The timestamp at which event is sampled
+   */
+  uint64_t timestamp;
+
+  /**
+   * The device id
+   */
+  uint32_t deviceId;
+
+  /**
+   * Undefined. reserved for internal use
+   */
+  uint32_t reserved;
+} CUpti_ActivityInstantaneousEvent;
+
+/**
+ * \brief The activity record for an instantaneous CUPTI event
+ * with event domain instance information.
+ *
+ * This activity record represents the a CUPTI event value for a
+ * specific event domain instance
+ * (CUPTI_ACTIVITY_KIND_EVENT_INSTANCE) sampled at a particular instant.
+ * This activity record kind is not produced by the activity API but is
+ * included for completeness and ease-of-use. Profiler frameworks built on
+ * top of CUPTI that collect event data may choose to use this type to store the
+ * collected event data. This activity record should be used when
+ * event domain instance information needs to be associated with the
+ * event.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_INSTANTANEOUS_EVENT_INSTANCE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The event ID.
+   */
+  CUpti_EventID id;
+
+  /**
+   * The event value.
+   */
+  uint64_t value;
+
+  /**
+   * The timestamp at which event is sampled
+   */
+  uint64_t timestamp;
+
+  /**
+   * The device id
+   */
+  uint32_t deviceId;
+
+  /**
+   * The event domain instance
+   */
+  uint8_t instance;
+
+  /**
+   * Undefined. reserved for internal use
+   */
+  uint8_t pad[3];
+} CUpti_ActivityInstantaneousEventInstance;
+
+/**
+ * \brief The activity record for an instantaneous CUPTI metric.
+ *
+ * This activity record represents the collection of a CUPTI metric
+ * value (CUPTI_ACTIVITY_KIND_METRIC) at a particular instance.
+ * This activity record kind is not produced by the activity API but
+ * is included for completeness and ease-of-use. Profiler frameworks built
+ * on top of CUPTI that collect metric data may choose to use this type to
+ * store the collected metric data.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_INSTANTANEOUS_METRIC.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The metric ID.
+   */
+  CUpti_MetricID id;
+
+  /**
+   * The metric value.
+   */
+  CUpti_MetricValue value;
+
+  /**
+   * The timestamp at which metric is sampled
+   */
+  uint64_t timestamp;
+
+  /**
+   * The device id
+   */
+  uint32_t deviceId;
+
+  /**
+   * The properties of this metric. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * Undefined. reserved for internal use
+   */
+  uint8_t pad[3];
+} CUpti_ActivityInstantaneousMetric;
+
+/**
+ * \brief The instantaneous activity record for a CUPTI metric with instance
+ * information.
+
+ * This activity record represents a CUPTI metric value
+ * for a specific metric domain instance
+ * (CUPTI_ACTIVITY_KIND_METRIC_INSTANCE) sampled at a particular time. This
+ * activity record kind is not produced by the activity API but is included for
+ * completeness and ease-of-use. Profiler frameworks built on top of
+ * CUPTI that collect metric data may choose to use this type to store
+ * the collected metric data. This activity record should be used when
+ * metric domain instance information needs to be associated with the
+ * metric.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_INSTANTANEOUS_METRIC_INSTANCE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The metric ID.
+   */
+  CUpti_MetricID id;
+
+  /**
+   * The metric value.
+   */
+  CUpti_MetricValue value;
+
+  /**
+   * The timestamp at which metric is sampled
+   */
+  uint64_t timestamp;
+
+  /**
+   * The device id
+   */
+  uint32_t deviceId;
+
+  /**
+   * The properties of this metric. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The metric domain instance
+   */
+  uint8_t instance;
+
+  /**
+   * Undefined. reserved for internal use
+   */
+  uint8_t pad[2];
+} CUpti_ActivityInstantaneousMetricInstance;
+
+/**
+ * \brief The types of JIT entry.
+ *
+ * To be used in CUpti_ActivityJit.
+ */
+typedef enum {
+  CUPTI_ACTIVITY_JIT_ENTRY_INVALID= 0,
+
+  /**
+  * PTX to CUBIN.
+  */
+  CUPTI_ACTIVITY_JIT_ENTRY_PTX_TO_CUBIN = 1,
+
+  /**
+  * NVVM-IR to PTX
+  */
+  CUPTI_ACTIVITY_JIT_ENTRY_NVVM_IR_TO_PTX = 2,
+
+  CUPTI_ACTIVITY_JIT_ENTRY_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_ActivityJitEntryType;
+
+/**
+ * \brief The types of JIT compilation operations.
+ *
+ * To be used in CUpti_ActivityJit.
+ */
+
+typedef enum {
+  CUPTI_ACTIVITY_JIT_OPERATION_INVALID = 0,
+  /**
+  * Loaded from the compute cache.
+  */
+  CUPTI_ACTIVITY_JIT_OPERATION_CACHE_LOAD = 1,
+
+  /**
+  * Stored in the compute cache.
+  */
+  CUPTI_ACTIVITY_JIT_OPERATION_CACHE_STORE = 2,
+
+  /**
+  * JIT compilation.
+  */
+  CUPTI_ACTIVITY_JIT_OPERATION_COMPILE = 3,
+
+  CUPTI_ACTIVITY_JIT_OPERATION_TYPE_FORCE_INT = 0x7fffffff
+} CUpti_ActivityJitOperationType;
+
+/**
+ * \brief The activity record for JIT operations.
+ * This activity represents the JIT operations (compile, load, store) of a CUmodule
+ * from the Compute Cache.
+ * Gives the exact hashed path of where the cached module is loaded from,
+ * or where the module will be stored after Just-In-Time (JIT) compilation.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind must be CUPTI_ACTIVITY_KIND_JIT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+    * The JIT entry type.
+    */
+  CUpti_ActivityJitEntryType jitEntryType;
+
+  /**
+   * The JIT operation type.
+   */
+  CUpti_ActivityJitOperationType jitOperationType;
+
+  /**
+   * The device ID.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The start timestamp for the JIT operation, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the JIT operation.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the JIT operation, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the JIT operation.
+   */
+  uint64_t end;
+
+  /**
+   * The correlation ID of the JIT operation to which
+   * records belong to. Each JIT operation is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the JIT operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Internal use.
+   */
+  uint32_t padding;
+
+  /**
+   * The correlation ID to correlate JIT compilation, load and store operations.
+   * Each JIT compilation unit is assigned a unique correlation ID
+   * at the time of the JIT compilation. This correlation id can be used
+   * to find the matching JIT cache load/store records.
+   */
+  uint64_t jitOperationCorrelationId;
+
+  /**
+   * The size of compute cache.
+   */
+  uint64_t cacheSize;
+
+  /**
+   * The path where the fat binary is cached.
+   */
+  const char* cachePath;
+
+  /**
+   * The ID of the process where the JIT operation is executing.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the thread where the JIT operation is executing.
+   */
+  uint32_t threadId;
+} CUpti_ActivityJit2;
+
+
+/**
+ * \brief The activity record for trace of graph execution.
+ *
+ * This activity record represents execution for a graph without giving visibility
+ * about the execution of its nodes. This is intended to reduce overheads in tracing
+ * each node. The activity kind is CUPTI_ACTIVITY_KIND_GRAPH_TRACE
+ */
+typedef struct {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_GRAPH_TRACE
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The correlation ID of the graph launch. Each graph launch is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the graph.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The start timestamp for the graph execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the graph.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the graph execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the graph.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the first node of the graph is executed.
+   * If this is INT_MAX, then the start is on the host.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The unique ID of the graph that is launched.
+   */
+  uint32_t graphId;
+
+  /**
+   * The ID of the context where the first node of the graph is executed.
+   * If this is INT_MAX, then the start is on the host.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the graph is being launched.
+   */
+  uint32_t streamId;
+
+  /**
+   * This field is reserved for internal use
+   */
+  void *reserved;
+
+  /**
+   * The ID of the device where last node of the graph is executed
+   */
+  uint32_t endDeviceId;
+
+  /**
+   * The ID of the context where the last node of the graph is executed.
+   */
+  uint32_t endContextId;
+} CUpti_ActivityGraphTrace2;
+
+/**
+ * \brief The launch mode for device graph execution.
+ */
+typedef enum {
+    CUPTI_DEVICE_GRAPH_LAUNCH_MODE_INVALID = 0,
+    CUPTI_DEVICE_GRAPH_LAUNCH_MODE_FIRE_AND_FORGET = 1,
+    CUPTI_DEVICE_GRAPH_LAUNCH_MODE_TAIL = 2,
+    CUPTI_DEVICE_GRAPH_LAUNCH_MODE_FIRE_AND_FORGET_AS_SIBLING = 3,
+} CUpti_DeviceGraphLaunchMode;
+
+/**
+ * \brief The activity record for trace of device graph execution.
+ *
+ * This activity record represents execution for a device launched graph without giving visibility
+ * about the execution of its nodes. This is intended to reduce overheads in tracing
+ * each node. The activity kind is CUPTI_ACTIVITY_KIND_DEVICE_GRAPH_TRACE
+ */
+typedef struct {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DEVICE_GRAPH_TRACE
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID of the device where the first node of the graph is executed.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The start timestamp for the graph execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the graph.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the graph execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the graph.
+   */
+  uint64_t end;
+
+  /**
+   * The unique ID of the graph that is launched.
+   */
+  uint32_t graphId;
+
+  /**
+   * The unique ID of the graph that has launched this graph.
+   */
+  uint32_t launcherGraphId;
+
+  /**
+   * The type of launch. See \ref CUpti_DeviceGraphLaunchMode
+   */
+  uint32_t deviceLaunchMode;
+
+  /**
+   * The ID of the context where the first node of the graph is executed.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the graph is being launched.
+   */
+  uint64_t streamId;
+
+  /**
+   * This field is reserved for internal use
+   */
+  void *reserved;
+
+} CUpti_ActivityDeviceGraphTrace;
+
+/**
+ * \brief The activity record for trace of decompression operations.
+ *
+ * This activity record represents execution for a batch of decompression operatios.
+ * The activity kind is CUPTI_ACTIVITY_KIND_MEM_DECOMPRESS
+ */
+typedef struct {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEM_DECOMPRESS
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID of the device.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the HW channel on which the memory copy is occurring.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+
+  /**
+   * The correlation ID of the decompression operations. Each operation is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The number of operations in the batch.
+   */
+  uint32_t numberOfOperations;
+
+  /**
+   * The number of bytes to be read and decompressed in the
+   * batch operation.
+   */
+  uint64_t sourceBytes;
+
+  /**
+   * This field is reserved for internal use
+   */
+  void *reserved0;
+
+  /**
+   * The start timestamp.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the start time is unknown.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the start time is unknown.
+   */
+  uint64_t end;
+} CUpti_ActivityMemDecompress;
+
+END_PACKED_ALIGNMENT
+
+/**
+ * \brief Activity attributes.
+ *
+ * These attributes are used to control the behavior of the activity
+ * API.
+ */
+typedef enum {
+    /**
+     * The device memory size (in bytes) reserved for storing profiling data for concurrent
+     * kernels (activity kind \ref CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL), memcopies and memsets
+     * for each buffer on a context. The value is a size_t.
+     *
+     * There is a limit on how many device buffers can be allocated per context. User
+     * can query and set this limit using the attribute
+     * \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_POOL_LIMIT.
+     * CUPTI doesn't pre-allocate all the buffers, it pre-allocates only those many
+     * buffers as set by the attribute \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_PRE_ALLOCATE_VALUE.
+     * When all of the data in a buffer is consumed, it is added in the reuse pool, and
+     * CUPTI picks a buffer from this pool when a new buffer is needed. Thus memory
+     * footprint does not scale with the kernel count. Applications with the high density
+     * of kernels, memcopies and memsets might result in having CUPTI to allocate more device buffers.
+     * CUPTI allocates another buffer only when it runs out of the buffers in the
+     * reuse pool.
+     *
+     * Since buffer allocation happens in the main application thread, this might result
+     * in stalls in the critical path. CUPTI pre-allocates 3 buffers of the same size to
+     * mitigate this issue. User can query and set the pre-allocation limit using the
+     * attribute \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_PRE_ALLOCATE_VALUE.
+     *
+     * Having larger buffer size leaves less device memory for the application.
+     * Having smaller buffer size increases the risk of dropping timestamps for
+     * records if too many kernels or memcopies or memsets are launched at one time.
+     *
+     * This value only applies to new buffer allocations. Set this value before initializing
+     * CUDA or before creating a context to ensure it is considered for the following allocations.
+     *
+     * The default value is 3200000 (~3MB) which can accommodate profiling data
+     * up to 100,000 kernels, memcopies and memsets combined.
+     *
+     * Note: Starting with the CUDA 12.0 Update 1 release, CUPTI allocates profiling buffer in the
+     * device memory by default as this might help in improving the performance of the
+     * tracing run. Refer to the description of the attribute
+     * \ref CUPTI_ACTIVITY_ATTR_MEM_ALLOCATION_TYPE_HOST_PINNED for more details.
+     * Size of the memory and maximum number of pools are still controlled by the attributes
+     * \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_SIZE and \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_POOL_LIMIT.
+     *
+     * Note: The actual amount of device memory per buffer reserved by CUPTI might be larger.
+     */
+    CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_SIZE                      = 0,
+
+    /**
+     * The device memory size (in bytes) reserved for storing profiling
+     * data for CDP operations for each buffer on a context. The
+     * value is a size_t.
+     *
+     * Having larger buffer size means less flush operations but
+     * consumes more device memory. This value only applies to new
+     * allocations.
+     *
+     * Set this value before initializing CUDA or before creating a
+     * context to ensure it is considered for the following allocations.
+     *
+     * The default value is 8388608 (8MB).
+     *
+     * Note: The actual amount of device memory per context reserved by
+     * CUPTI might be larger.
+     */
+    CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_SIZE_CDP          = 1,
+
+    /**
+     * The maximum number of device memory buffers per context. The value is a size_t.
+     *
+     * For an application with high rate of kernel launches, memcopies and memsets having a bigger pool
+     * limit helps in timestamp collection for all these activities at the expense of a larger memory footprint.
+     * Refer to the description of the attribute \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_SIZE
+     * for more details.
+     *
+     * Setting this value will not modify the number of memory buffers
+     * currently stored.
+     *
+     * Set this value before initializing CUDA to ensure the limit is
+     * not exceeded.
+     *
+     * The default value is 250.
+     */
+    CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_POOL_LIMIT                = 2,
+
+    /**
+     * This attribute is not supported starting with CUDA 12.3
+     * CUPTI no longer uses profiling semaphore pool to store profiling data.
+     *
+     * There is a limit on how many semaphore pools can be allocated per context. User
+     * can query and set this limit using the attribute
+     * \ref CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_POOL_LIMIT.
+     * CUPTI doesn't pre-allocate all the semaphore pools, it pre-allocates only those many
+     * semaphore pools as set by the attribute \ref CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_PRE_ALLOCATE_VALUE.
+     * When all of the data in a semaphore pool is consumed, it is added in the reuse pool, and
+     * CUPTI picks a semaphore pool from the reuse pool when a new semaphore pool is needed. Thus memory
+     * footprint does not scale with the kernel count. Applications with the high density
+     * of kernels might result in having CUPTI to allocate more semaphore pools.
+     * CUPTI allocates another semaphore pool only when it runs out of the semaphore pools in the
+     * reuse pool.
+     *
+     * Since semaphore pool allocation happens in the main application thread, this might result
+     * in stalls in the critical path. CUPTI pre-allocates 3 semaphore pools of the same size to
+     * mitigate this issue. User can query and set the pre-allocation limit using the
+     * attribute \ref CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_PRE_ALLOCATE_VALUE.
+     *
+     * Having larger semaphore pool size leaves less device memory for the application.
+     * Having smaller semaphore pool size increases the risk of dropping timestamps for
+     * kernel records if too many kernels are issued/launched at one time.
+     *
+     * This value only applies to new semaphore pool allocations. Set this value before initializing
+     * CUDA or before creating a context to ensure it is considered for the following allocations.
+     *
+     * The default value is 25000 which can accommodate profiling data for upto 25,000 kernels.
+     *
+     */
+    CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_POOL_SIZE           = 3,
+
+    /**
+     * This attribute is not supported starting with CUDA 12.3
+     * CUPTI no longer uses profiling semaphore pool to store profiling data.
+     *
+     * The maximum number of profiling semaphore pools per context. The value is a size_t.
+     *
+     * Refer to the description of the attribute \ref CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_POOL_SIZE
+     * for more details.
+     *
+     * Set this value before initializing CUDA to ensure the limit is not exceeded.
+     *
+     * The default value is 250.
+     */
+    CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_POOL_LIMIT          = 4,
+
+    /**
+     * The flag to indicate whether user should provide activity buffer of zero value.
+     * The value is a uint8_t.
+     *
+     * If the value of this attribute is non-zero, user should provide
+     * a zero value buffer in the \ref CUpti_BuffersCallbackRequestFunc.
+     * If the user does not provide a zero value buffer after setting this to non-zero,
+     * the activity buffer may contain some uninitialized values when CUPTI returns it in
+     * \ref CUpti_BuffersCallbackCompleteFunc
+     *
+     * If the value of this attribute is zero, CUPTI will initialize the user buffer
+     * received in the \ref CUpti_BuffersCallbackRequestFunc to zero before filling it.
+     * If the user sets this to zero, a few stalls may appear in critical path because CUPTI
+     * will zero out the buffer in the main thread.
+     * Set this value before returning from \ref CUpti_BuffersCallbackRequestFunc to
+     * ensure it is considered for all the subsequent user buffers.
+     *
+     * The default value is 0.
+     */
+    CUPTI_ACTIVITY_ATTR_ZEROED_OUT_ACTIVITY_BUFFER              = 5,
+
+    /**
+     * Number of device buffers to pre-allocate for a context during the initialization phase.
+     * The value is a size_t.
+     *
+     * Refer to the description of the attribute \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_SIZE
+     * for details.
+     *
+     * This value must be less than the maximum number of device buffers set using
+     * the attribute \ref CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_POOL_LIMIT
+     *
+     * Set this value before initializing CUDA or before creating a context to ensure it
+     * is considered by the CUPTI.
+     *
+     * The default value is set to 3 to ping pong between these buffers (if possible).
+     */
+    CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_PRE_ALLOCATE_VALUE        = 6,
+
+    /**
+     * This attribute is not supported starting with CUDA 12.3
+     * CUPTI no longer uses profiling semaphore pool to store profiling data.
+     *
+     * Number of profiling semaphore pools to pre-allocate for a context during the
+     * initialization phase. The value is a size_t.
+     *
+     * Refer to the description of the attribute \ref CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_POOL_SIZE
+     * for details.
+     *
+     * This value must be less than the maximum number of profiling semaphore pools set
+     * using the attribute \ref CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_POOL_LIMIT
+     *
+     * Set this value before initializing CUDA or before creating a context to ensure it
+     * is considered by the CUPTI.
+     *
+     * The default value is set to 3 to ping pong between these pools (if possible).
+     */
+    CUPTI_ACTIVITY_ATTR_PROFILING_SEMAPHORE_PRE_ALLOCATE_VALUE  = 7,
+
+    /**
+     * Allocate page-locked (pinned) host memory for storing profiling data for concurrent
+     * kernels, memcopies and memsets for each buffer on a context. The value is a uint8_t.
+     *
+     * Starting with the CUDA 11.2 release, CUPTI allocates profiling buffer in the pinned host
+     * memory by default as this might help in improving the performance of the tracing run.
+     * Allocating excessive amounts of pinned memory may degrade system performance, since it
+     * reduces the amount of memory available to the system for paging. For this reason user
+     * might want to change the location from pinned host memory to device memory by setting
+     * value of this attribute to 0.
+     *
+     * Using page-locked (pinned) host memory buffers is not supported on confidential computing
+     * devices. On setting this attribute to 1, CUPTI will return CUPTI_ERROR_NOT_SUPPORTED.
+     *
+     * The default value is 1.
+     */
+    CUPTI_ACTIVITY_ATTR_MEM_ALLOCATION_TYPE_HOST_PINNED         = 8,
+
+    /**
+     * Request activity buffers per-thread to store CUPTI activity records
+     * in the activity buffer on per-thread basis. The value is a uint8_t.
+     *
+     * The attribute should be set before registering the buffer callbacks using
+     * cuptiActivityRegisterCallbacks API and before any of the CUPTI activity kinds are enabled.
+     * This makes sure that all the records are stored in activity buffers allocated per-thread.
+     * Changing this attribute in the middle of the profiling session will result in undefined behavior.
+     *
+     * The default value is 0.
+     */
+    CUPTI_ACTIVITY_ATTR_PER_THREAD_ACTIVITY_BUFFER,
+
+
+
+    CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_FORCE_INT                 = 0x7fffffff
+} CUpti_ActivityAttribute;
+
+/**
+ * \brief Thread-Id types.
+ *
+ * CUPTI uses different methods to obtain the thread-id depending on the
+ * support and the underlying platform. This enum documents these methods
+ * for each type. APIs \ref cuptiSetThreadIdType and \ref cuptiGetThreadIdType
+ * can be used to set and get the thread-id type.
+ */
+typedef enum {
+    /**
+     * Default type
+     * Windows uses API GetCurrentThreadId()
+     * Linux/Mac/Android/QNX use POSIX pthread API pthread_self()
+     */
+    CUPTI_ACTIVITY_THREAD_ID_TYPE_DEFAULT       = 0,
+
+    /**
+     * This type is based on the system API available on the underlying platform
+     * and thread-id obtained is supposed to be unique for the process lifetime.
+     * Windows uses API GetCurrentThreadId()
+     * Linux uses syscall SYS_gettid
+     * Mac uses syscall SYS_thread_selfid
+     * Android/QNX use gettid()
+     */
+    CUPTI_ACTIVITY_THREAD_ID_TYPE_SYSTEM        = 1,
+
+    /**
+     * Add new enums before this field.
+     */
+    CUPTI_ACTIVITY_THREAD_ID_TYPE_SIZE          = 2,
+
+    CUPTI_ACTIVITY_THREAD_ID_TYPE_FORCE_INT     = 0x7fffffff
+} CUpti_ActivityThreadIdType;
+
+/**
+ * \brief Get the CUPTI timestamp.
+ *
+ * Returns a timestamp normalized to correspond with the start and end
+ * timestamps reported in the CUPTI activity records. The timestamp is
+ * reported in nanoseconds.
+ *
+ * \param timestamp Returns the CUPTI timestamp
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p timestamp is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetTimestamp(uint64_t *timestamp);
+
+/**
+ * \brief Get the ID of a context.
+ *
+ * Get the ID of a context.
+ *
+ * \param context The context
+ * \param contextId Returns a process-unique ID for the context
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT The context is NULL or not valid.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p contextId is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetContextId(CUcontext context, uint32_t *contextId);
+
+/**
+ * \brief Get the ID of a stream.
+ *
+ * Get the ID of a stream. The stream ID is unique within a context
+ * (i.e. all streams within a context will have unique stream
+ * IDs).
+ *
+ * \param context If non-NULL then the stream is checked to ensure
+ * that it belongs to this context. Typically this parameter should be
+ * null.
+ * \param stream The stream
+ * \param streamId Returns a context-unique ID for the stream
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_STREAM if unable to get stream ID, or
+ * if \p context is non-NULL and \p stream does not belong to the
+ * context
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p streamId is NULL
+ *
+ * **DEPRECATED** This method is deprecated as of CUDA 8.0.
+ * Use method cuptiGetStreamIdEx instead.
+ */
+CUptiResult CUPTIAPI cuptiGetStreamId(CUcontext context, CUstream stream, uint32_t *streamId);
+
+/**
+* \brief Get the ID of a stream.
+*
+* Get the ID of a stream. The stream ID is unique within a context
+* (i.e. all streams within a context will have unique stream
+* IDs).
+*
+* \param context If non-NULL then the stream is checked to ensure
+* that it belongs to this context. Typically this parameter should be
+* null.
+* \param stream The stream
+* \param perThreadStream Flag to indicate if program is compiled for per-thread streams
+* \param streamId Returns a context-unique ID for the stream
+*
+* \retval CUPTI_SUCCESS
+* \retval CUPTI_ERROR_NOT_INITIALIZED
+* \retval CUPTI_ERROR_INVALID_STREAM if unable to get stream ID, or
+* if \p context is non-NULL and \p stream does not belong to the
+* context
+* \retval CUPTI_ERROR_INVALID_PARAMETER if \p streamId is NULL
+*/
+CUptiResult CUPTIAPI cuptiGetStreamIdEx(CUcontext context, CUstream stream, uint8_t perThreadStream, uint32_t *streamId);
+
+/**
+ * \brief Get the ID of a device
+ *
+ * If \p context is NULL, returns the ID of the device that contains
+ * the currently active context. If \p context is non-NULL, returns
+ * the ID of the device which contains that context. Operates in a
+ * similar manner to cudaGetDevice() or cuCtxGetDevice() but may be
+ * called from within callback functions.
+ *
+ * \param context The context, or NULL to indicate the current context.
+ * \param deviceId Returns the ID of the device that is current for
+ * the calling thread.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE if unable to get device ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p deviceId is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetDeviceId(CUcontext context, uint32_t *deviceId);
+
+/**
+ * \brief Get the unique ID of a graph node
+ *
+ * Returns the unique ID of the CUDA graph node.
+ *
+ * \param node The graph node.
+ * \param nodeId Returns the unique ID of the node
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p node is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetGraphNodeId(CUgraphNode node, uint64_t *nodeId);
+
+/**
+ * \brief Get the unique ID of graph
+ *
+ * Returns the unique ID of CUDA graph.
+ *
+ * \param graph The graph.
+ * \param pId Returns the unique ID of the graph
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p graph is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetGraphId(CUgraph graph, uint32_t *pId);
+
+/**
+ * \brief Get the unique ID of executable graph
+ *
+ * Returns the unique ID of executable CUDA graph.
+ *
+ * \param graphExec The executable graph.
+ * \param pId Returns the unique ID of the executable graph
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p graph is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetGraphExecId(CUgraphExec graphExec, uint32_t *pId);
+
+/**
+ * \brief Enable collection of a specific kind of activity record.
+ *
+ * Enable collection of a specific kind of activity record. Multiple
+ * kinds can be enabled by calling this function multiple times. By
+ * default all activity kinds are disabled for collection.
+ *
+ * \param kind The kind of activity record to collect
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if the activity kind cannot be enabled
+ * \retval CUPTI_ERROR_INVALID_KIND if the activity kind is not supported
+ */
+CUptiResult CUPTIAPI cuptiActivityEnable(CUpti_ActivityKind kind);
+
+/**
+ * \brief Enable collection of a specific kind of activity record. For certain activity kinds
+ * it dumps existing records.
+ *
+ * In general, the behavior of this API is similar to the API \ref cuptiActivityEnable i.e. it
+ * enables the collection of a specific kind of activity record.
+ * Additionally, this API can help in dumping the records for activities which happened in
+ * the past before enabling the corresponding activity kind.
+ * The API allows to get records for the current resource allocations done in CUDA
+ * For CUPTI_ACTIVITY_KIND_DEVICE, existing device records are dumped
+ * For CUPTI_ACTIVITY_KIND_CONTEXT, existing context records are dumped
+ * For CUPTI_ACTIVITY_KIND_STREAM, existing stream records are dumped
+ * For CUPTI_ACTIVITY_KIND_ NVLINK, existing NVLINK records are dumped
+ * For CUPTI_ACTIVITY_KIND_PCIE, existing PCIE records are dumped
+ * For other activities, the behavior is similar to the API \ref cuptiActivityEnable
+ *
+ * Device records are emitted in CUPTI on CUDA driver initialization. Those records
+ * can only be retrieved by the user if CUPTI is attached before CUDA initialization.
+ * Context and stream records are emitted on context and stream creation.
+ * The use case of the API is to provide the records for CUDA resources
+ * (contexts/streams/devices) that are currently active if user late attaches CUPTI.
+ *
+ * Before calling this function, the user must register buffer callbacks
+ * to get the activity records by calling \ref cuptiActivityRegisterCallbacks.
+ * If the user does not register the buffers and calls API \ref cuptiActivityEnableAndDump,
+ * then CUPTI will enable the activity kind but not provide any records for that
+ * activity kind.
+ *
+ * \param kind The kind of activity record to collect
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_UNKNOWN if buffer is not initialized.
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if the activity kind cannot be enabled
+ * \retval CUPTI_ERROR_INVALID_KIND if the activity kind is not supported
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableAndDump(CUpti_ActivityKind kind);
+
+/**
+ * \brief Disable collection of a specific kind of activity record.
+ *
+ * Disable collection of a specific kind of activity record. Multiple
+ * kinds can be disabled by calling this function multiple times. By
+ * default all activity kinds are disabled for collection.
+ *
+ * \param kind The kind of activity record to stop collecting
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_KIND if the activity kind is not supported
+ */
+CUptiResult CUPTIAPI cuptiActivityDisable(CUpti_ActivityKind kind);
+
+/**
+ * \brief Enable collection of a specific kind of activity record for
+ * a context.
+ *
+ * Enable collection of a specific kind of activity record for a
+ * context.  This setting done by this API will supersede the global
+ * settings for activity records enabled by \ref cuptiActivityEnable.
+ * Multiple kinds can be enabled by calling this function multiple
+ * times.
+ *
+ * \param context The context for which activity is to be enabled
+ * \param kind The kind of activity record to collect
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if the activity kind cannot be enabled
+ * \retval CUPTI_ERROR_INVALID_KIND if the activity kind is not supported
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableContext(CUcontext context, CUpti_ActivityKind kind);
+
+/**
+ * \brief Disable collection of a specific kind of activity record for
+ * a context.
+ *
+ * Disable collection of a specific kind of activity record for a context.
+ * This setting done by this API will supersede the global settings
+ * for activity records.
+ * Multiple kinds can be enabled by calling this function multiple times.
+ *
+ * \param context The context for which activity is to be disabled
+ * \param kind The kind of activity record to stop collecting
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_KIND if the activity kind is not supported
+ */
+CUptiResult CUPTIAPI cuptiActivityDisableContext(CUcontext context, CUpti_ActivityKind kind);
+
+/**
+ * \brief Get the number of activity records that were dropped of
+ * insufficient buffer space.
+ *
+ * Get the number of records that were dropped because of insufficient
+ * buffer space.  The dropped count includes records that could not be
+ * recorded because CUPTI did not have activity buffer space available
+ * for the record (because the CUpti_BuffersCallbackRequestFunc
+ * callback did not return an empty buffer of sufficient size) and
+ * also CDP records that could not be record because the device-size
+ * buffer was full (size is controlled by the
+ * CUPTI_ACTIVITY_ATTR_DEVICE_BUFFER_SIZE_CDP attribute). The dropped
+ * count maintained for the queue is reset to zero when this function
+ * is called.
+ *
+ * \param context The context, or NULL to get dropped count from global queue
+ * \param streamId The stream ID
+ * \param dropped The number of records that were dropped since the last call
+ * to this function.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p dropped is NULL
+ */
+CUptiResult CUPTIAPI cuptiActivityGetNumDroppedRecords(CUcontext context, uint32_t streamId,
+                                                       size_t *dropped);
+
+/**
+ * \brief Iterate over the activity records in a buffer.
+ *
+ * This is a helper function to iterate over the activity records in a
+ * buffer. A buffer of activity records is typically obtained by
+ * receiving a CUpti_BuffersCallbackCompleteFunc callback. Stop iterating
+ * the buffer when an error occurs.
+ *
+ * An example of typical usage:
+ * \code
+ * CUpti_Activity *record = NULL;
+ * CUptiResult status = CUPTI_SUCCESS;
+ *   do {
+ *      status = cuptiActivityGetNextRecord(buffer, validSize, &record);
+ *      if(status == CUPTI_SUCCESS) {
+ *           // Use record here...
+ *      }
+ *      else if (status == CUPTI_ERROR_MAX_LIMIT_REACHED)
+ *          break;
+ *      else if (status == CUPTI_ERROR_INVALID_KIND)
+ *          break;
+ *      else {
+ *          goto Error;
+ *      }
+ *    } while (1);
+ * \endcode
+ *
+ * \param buffer The buffer containing activity records
+ * \param record Inputs the previous record returned by
+ * cuptiActivityGetNextRecord and returns the next activity record
+ * from the buffer. If input value is NULL, returns the first activity
+ * record in the buffer. Records of certain kinds like CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL
+ * may contain invalid (0) timestamps, indicating that no timing information could
+ * be collected for lack of device memory.
+ * \param validBufferSizeBytes The number of valid bytes in the buffer.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_MAX_LIMIT_REACHED if no more records in the buffer
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p buffer is NULL.
+ * \retval CUPTI_ERROR_INVALID_KIND if activity record is either incomplete or invalid
+ */
+CUptiResult CUPTIAPI cuptiActivityGetNextRecord(uint8_t* buffer, size_t validBufferSizeBytes,
+                                                CUpti_Activity **record);
+
+/**
+ * \brief Function type for callback used by CUPTI to request an empty
+ * buffer for storing activity records.
+ *
+ * This callback function signals the CUPTI client that an activity
+ * buffer is needed by CUPTI. The activity buffer is used by CUPTI to
+ * store activity records. The callback function can decline the
+ * request by setting \p *buffer to NULL. In this case CUPTI may drop
+ * activity records.
+ *
+ * \param buffer Returns the new buffer. If set to NULL then no buffer
+ * is returned.
+ * \param size Returns the size of the returned buffer.
+ * \param maxNumRecords Returns the maximum number of records that
+ * should be placed in the buffer. If 0 then the buffer is filled with
+ * as many records as possible. If > 0 the buffer is filled with at
+ * most that many records before it is returned.
+ */
+typedef void (CUPTIAPI *CUpti_BuffersCallbackRequestFunc)(
+    uint8_t **buffer,
+    size_t *size,
+    size_t *maxNumRecords);
+
+/**
+ * \brief Function type for callback used by CUPTI to return a buffer
+ * of activity records.
+ *
+ * This callback function returns to the CUPTI client a buffer
+ * containing activity records.  The buffer contains \p validSize
+ * bytes of activity records which should be read using
+ * cuptiActivityGetNextRecord. The number of dropped records can be
+ * read using cuptiActivityGetNumDroppedRecords. After this call CUPTI
+ * relinquished ownership of the buffer and will not use it
+ * anymore. The client may return the buffer to CUPTI using the
+ * CUpti_BuffersCallbackRequestFunc callback.
+ * Note: CUDA 6.0 onwards, all buffers returned by this callback are
+ * global buffers i.e. there is no context/stream specific buffer.
+ * User needs to parse the global buffer to extract the context/stream
+ * specific activity records.
+ *
+ * \param context The context this buffer is associated with. If NULL, the
+ * buffer is associated with the global activities. This field is deprecated
+ * as of CUDA 6.0 and will always be NULL.
+ * \param streamId The stream id this buffer is associated with.
+ * This field is deprecated as of CUDA 6.0 and will always be NULL.
+ * \param buffer The activity record buffer.
+ * \param size The total size of the buffer in bytes as set in
+ * CUpti_BuffersCallbackRequestFunc.
+ * \param validSize The number of valid bytes in the buffer.
+ */
+typedef void (CUPTIAPI *CUpti_BuffersCallbackCompleteFunc)(
+    CUcontext context,
+    uint32_t streamId,
+    uint8_t *buffer,
+    size_t size,
+    size_t validSize);
+
+/**
+ * \brief Registers callback functions with CUPTI for activity buffer
+ * handling.
+ *
+ * This function registers two callback functions to be used in asynchronous
+ * buffer handling. If registered, activity record buffers are handled using
+ * asynchronous requested/completed callbacks from CUPTI.
+ *
+ * Registering these callbacks prevents the client from using CUPTI's
+ * blocking enqueue/dequeue functions.
+ *
+ * \param funcBufferRequested callback which is invoked when an empty
+ * buffer is requested by CUPTI
+ * \param funcBufferCompleted callback which is invoked when a buffer
+ * containing activity records is available from CUPTI
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if either \p
+ * funcBufferRequested or \p funcBufferCompleted is NULL
+ */
+CUptiResult CUPTIAPI cuptiActivityRegisterCallbacks(CUpti_BuffersCallbackRequestFunc funcBufferRequested,
+        CUpti_BuffersCallbackCompleteFunc funcBufferCompleted);
+
+/**
+ * \brief Wait for all activity records to be delivered via the
+ * completion callback.
+ *
+ * This function does not return until all activity records associated
+ * with the specified context/stream are returned to the CUPTI client
+ * using the callback registered in cuptiActivityRegisterCallbacks. To
+ * ensure that all activity records are complete, the requested
+ * stream(s), if any, are synchronized.
+ *
+ * If \p context is NULL, the global activity records (i.e. those not
+ * associated with a particular stream) are flushed (in this case no
+ * streams are synchronized).  If \p context is a valid CUcontext and
+ * \p streamId is 0, the buffers of all streams of this context are
+ * flushed.  Otherwise, the buffers of the specified stream in this
+ * context is flushed.
+ *
+ * Before calling this function, the buffer handling callback api
+ * must be activated by calling cuptiActivityRegisterCallbacks.
+ *
+ * \param context A valid CUcontext or NULL.
+ * \param streamId The stream ID.
+ * \param flag The flag can be set to indicate a forced flush. See CUpti_ActivityFlag
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_CUPTI_ERROR_INVALID_OPERATION if not preceded
+ * by a successful call to cuptiActivityRegisterCallbacks
+ * \retval CUPTI_ERROR_UNKNOWN an internal error occurred
+ *
+ * **DEPRECATED** This method is deprecated
+ * CONTEXT and STREAMID will be ignored. Use cuptiActivityFlushAll
+ * to flush all data.
+ */
+CUptiResult CUPTIAPI cuptiActivityFlush(CUcontext context, uint32_t streamId, uint32_t flag);
+
+/**
+ * \brief Request to deliver activity records via the buffer completion callback.
+ *
+ * This function returns the activity records associated with all contexts/streams
+ * (and the global buffers not associated with any stream) to the CUPTI client
+ * using the callback registered in cuptiActivityRegisterCallbacks.
+ *
+ * This is a blocking call but it doesn't issue any CUDA synchronization calls
+ * implicitly thus it's not guaranteed that all activities are completed on the
+ * underlying devices. Activity record is considered as completed if it has all
+ * the information filled up including the timestamps if any. It is the client's
+ * responsibility to issue necessary CUDA synchronization calls before calling
+ * this function if all activity records with complete information are expected
+ * to be delivered.
+ *
+ * Behavior of the function based on the input flag:
+ * (-) ::For default flush i.e. when flag is set as 0, it returns all the
+ * activity buffers which have all the activity records completed, buffers need not
+ * to be full though. It doesn't return buffers which have one or more incomplete
+ * records. Default flush can be done at a regular interval in a separate thread.
+ * (-) ::For forced flush i.e. when flag CUPTI_ACTIVITY_FLAG_FLUSH_FORCED is passed
+ * to the function, it returns all the activity buffers including the ones which have
+ * one or more incomplete activity records. It's suggested for clients to do the
+ * force flush before the termination of the profiling session to allow remaining
+ * buffers to be delivered. In general, it can be done in the at-exit handler.
+ *
+ * Before calling this function, the buffer handling callback api must be activated
+ * by calling cuptiActivityRegisterCallbacks.
+ *
+ * \param flag The flag can be set to indicate a forced flush. See CUpti_ActivityFlag
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if not preceded by a
+ * successful call to cuptiActivityRegisterCallbacks
+ * \retval CUPTI_ERROR_UNKNOWN an internal error occurred
+ *
+ * \see cuptiActivityFlushPeriod
+ */
+CUptiResult CUPTIAPI cuptiActivityFlushAll(uint32_t flag);
+
+/**
+ * \brief Read an activity API attribute.
+ *
+ * Read an activity API attribute and return it in \p *value.
+ *
+ * \param attr The attribute to read
+ * \param valueSize Size of buffer pointed by the value, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the value of the attribute
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value is NULL, or
+ * if \p attr is not an activity attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT Indicates that
+ * the \p value buffer is too small to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiActivityGetAttribute(CUpti_ActivityAttribute attr,
+        size_t *valueSize, void* value);
+
+/**
+ * \brief Write an activity API attribute.
+ *
+ * Write an activity API attribute.
+ *
+ * \param attr The attribute to write
+ * \param valueSize The size, in bytes, of the value
+ * \param value The attribute value to write
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value is NULL, or
+ * if \p attr is not an activity attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT Indicates that
+ * the \p value buffer is too small to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiActivitySetAttribute(CUpti_ActivityAttribute attr,
+        size_t *valueSize, void* value);
+
+
+/**
+ * \brief Set Unified Memory Counter configuration.
+ *
+ * Set the configuration before enabling the corresponding activity kind
+ * CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER.
+ * The API should be called after CUDA driver initialization.
+ *
+ * \param config A pointer to \ref CUpti_ActivityUnifiedMemoryCounterConfig structures
+ * containing Unified Memory counter configuration.
+ * \param count Number of Unified Memory counter configuration structures
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p config is NULL or
+ * any parameter in the \p config structures is not a valid value
+ * \retval CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED One potential reason is that
+ * platform (OS/arch) does not support the unified memory counters
+ * \retval CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED_ON_DEVICE Indicates that the device
+ * does not support the unified memory counters
+ * \retval CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED_ON_NON_P2P_DEVICES Indicates that
+ * multi-GPU configuration without P2P support between any pair of devices
+ * does not support the unified memory counters
+ */
+CUptiResult CUPTIAPI cuptiActivityConfigureUnifiedMemoryCounter(CUpti_ActivityUnifiedMemoryCounterConfig *config, uint32_t count);
+
+/**
+ * \brief Get auto boost state
+ *
+ * The profiling results can be inconsistent in case auto boost is enabled.
+ * CUPTI tries to disable auto boost while profiling. It can fail to disable in
+ * cases where user does not have the permissions or CUDA_AUTO_BOOST env
+ * variable is set. The function can be used to query whether auto boost is
+ * enabled.
+ *
+ * \param context A valid CUcontext.
+ * \param state A pointer to \ref CUpti_ActivityAutoBoostState structure which
+ * contains the current state and the id of the process that has requested the
+ * current state
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p CUcontext or \p state is NULL
+ * \retval CUPTI_ERROR_NOT_SUPPORTED Indicates that the device does not support auto boost
+ * \retval CUPTI_ERROR_UNKNOWN an internal error occurred
+ */
+CUptiResult CUPTIAPI cuptiGetAutoBoostState(CUcontext context, CUpti_ActivityAutoBoostState *state);
+
+/**
+ * \brief Set PC sampling configuration.
+ *
+ * For Pascal and older GPU architectures this API must be called before enabling
+ * activity kind CUPTI_ACTIVITY_KIND_PC_SAMPLING. There is no such requirement
+ * for Volta and newer GPU architectures.
+ *
+ * For Volta and newer GPU architectures if this API is called in the middle of
+ * execution, PC sampling configuration will be updated for subsequent kernel launches.
+ *
+ * \param ctx The context
+ * \param config A pointer to \ref CUpti_ActivityPCSamplingConfig structure
+ * containing PC sampling configuration.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this api is called while
+ * some valid event collection method is set.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p config is NULL or
+ * any parameter in the \p config structures is not a valid value
+ * \retval CUPTI_ERROR_NOT_SUPPORTED Indicates that the system/device
+ * does not support the unified memory counters
+ */
+CUptiResult CUPTIAPI cuptiActivityConfigurePCSampling(CUcontext ctx, CUpti_ActivityPCSamplingConfig *config);
+
+/**
+ * \brief Returns the last error from a cupti call or callback
+ *
+ * Returns the last error that has been produced by any of the cupti api calls
+ * or the callback in the same host thread and resets it to CUPTI_SUCCESS.
+ */
+CUptiResult CUPTIAPI cuptiGetLastError(void);
+
+/**
+ * \brief Set the thread-id type
+ *
+ * CUPTI uses the method corresponding to set type to generate the thread-id.
+ * See enum \ref CUpti_ActivityThreadIdType for the list of methods.
+ * Activity records having thread-id field contain the same value.
+ * Thread id type must not be changed during the profiling session to
+ * avoid thread-id value mismatch across activity records.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_SUPPORTED if \p type is not supported on the platform
+ */
+CUptiResult CUPTIAPI cuptiSetThreadIdType(CUpti_ActivityThreadIdType type);
+
+/**
+ * \brief Get the thread-id type
+ *
+ * Returns the thread-id type used in CUPTI
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p type is NULL
+  */
+CUptiResult CUPTIAPI cuptiGetThreadIdType(CUpti_ActivityThreadIdType *type);
+
+/**
+* \brief Check support for a compute capability
+*
+* This function is used to check the support for a device based on
+* it's compute capability. It sets the \p support when the compute
+* capability is supported by the current version of CUPTI, and clears
+* it otherwise. This version of CUPTI might not support all GPUs sharing
+* the same compute capability. It is suggested to use API \ref
+* cuptiDeviceSupported which provides correct information.
+*
+* \param major The major revision number of the compute capability
+* \param minor The minor revision number of the compute capability
+* \param support Pointer to an integer to return the support status
+*
+* \retval CUPTI_SUCCESS
+* \retval CUPTI_ERROR_INVALID_PARAMETER if \p support is NULL
+*
+* \sa ::cuptiDeviceSupported
+*/
+CUptiResult CUPTIAPI cuptiComputeCapabilitySupported(int major, int minor, int *support);
+
+/**
+* \brief Check support for a compute device
+*
+* This function is used to check the support for a compute device.
+* It sets the \p support when the device is supported by the current
+* version of CUPTI, and clears it otherwise.
+*
+* \param dev The device handle returned by CUDA Driver API cuDeviceGet
+* \param support Pointer to an integer to return the support status
+*
+* \retval CUPTI_SUCCESS
+* \retval CUPTI_ERROR_INVALID_PARAMETER if \p support is NULL
+* \retval CUPTI_ERROR_INVALID_DEVICE if \p dev is not a valid device
+*
+* \sa ::cuptiComputeCapabilitySupported
+*/
+CUptiResult CUPTIAPI cuptiDeviceSupported(CUdevice dev, int *support);
+
+/**
+ * This indicates the virtualization mode in which CUDA device is running
+ */
+typedef enum {
+  /**
+   * No virtualization mode is associated with the device
+   * i.e. it's a baremetal GPU
+   */
+  CUPTI_DEVICE_VIRTUALIZATION_MODE_NONE = 0,
+  /**
+   * The device is associated with the pass-through GPU.
+   * In this mode, an entire physical GPU is directly assigned
+   * to one virtual machine (VM).
+   */
+  CUPTI_DEVICE_VIRTUALIZATION_MODE_PASS_THROUGH = 1,
+  /**
+   * The device is associated with the virtual GPU (vGPU).
+   * In this mode multiple virtual machines (VMs) have simultaneous,
+   * direct access to a single physical GPU.
+   */
+  CUPTI_DEVICE_VIRTUALIZATION_MODE_VIRTUAL_GPU = 2,
+
+  CUPTI_DEVICE_VIRTUALIZATION_MODE_FORCE_INT = 0x7fffffff
+} CUpti_DeviceVirtualizationMode;
+
+/**
+ * \brief Query the virtualization mode of the device
+ *
+ * This function is used to query the virtualization mode of the CUDA device.
+ *
+ * \param dev The device handle returned by CUDA Driver API cuDeviceGet
+ * \param mode Pointer to an CUpti_DeviceVirtualizationMode to return the virtualization mode
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_DEVICE if \p dev is not a valid device
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p mode is NULL
+ *
+ */
+CUptiResult CUPTIAPI cuptiDeviceVirtualizationMode(CUdevice dev, CUpti_DeviceVirtualizationMode *mode);
+
+/**
+ * \brief Detach CUPTI from the running process
+ *
+ * This API detaches the CUPTI from the running process. It destroys and cleans up all the
+ * resources associated with CUPTI in the current process. After CUPTI detaches from the process,
+ * the process will keep on running with no CUPTI attached to it.
+ * For safe operation of the API, it is recommended this API is invoked from the exit callsite
+ * of any of the CUDA Driver or Runtime API. Otherwise CUPTI client needs to make sure that
+ * required CUDA synchronization and CUPTI activity buffer flush is done before calling the API.
+ * Sample code showing the usage of the API in the cupti callback handler code:
+ * \code
+  void CUPTIAPI
+  cuptiCallbackHandler(void *userdata, CUpti_CallbackDomain domain,
+      CUpti_CallbackId cbid, void *cbdata)
+  {
+    const CUpti_CallbackData *cbInfo = (CUpti_CallbackData *)cbdata;
+
+    // Take this code path when CUPTI detach is requested
+    if (detachCupti) {
+      switch(domain)
+      {
+        case CUPTI_CB_DOMAIN_RUNTIME_API:
+        case CUPTI_CB_DOMAIN_DRIVER_API:
+          if (cbInfo->callbackSite == CUPTI_API_EXIT) {
+              // call the CUPTI detach API
+              cuptiFinalize();
+          }
+          break;
+        default:
+          break;
+      }
+    }
+  }
+ \endcode
+ */
+CUptiResult CUPTIAPI cuptiFinalize(void);
+
+/**
+ * \brief Push an external correlation id for the calling thread
+ *
+ * This function notifies CUPTI that the calling thread is entering an external API region.
+ * When a CUPTI activity API record is created while within an external API region and
+ * CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION is enabled, the activity API record will
+ * be preceded by a CUpti_ActivityExternalCorrelation record for each \ref CUpti_ExternalCorrelationKind.
+ *
+ * \param kind The kind of external API activities should be correlated with.
+ * \param id External correlation id.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER The external API kind is invalid
+ */
+CUptiResult CUPTIAPI cuptiActivityPushExternalCorrelationId(CUpti_ExternalCorrelationKind kind, uint64_t id);
+
+/**
+ * \brief Pop an external correlation id for the calling thread
+ *
+ * This function notifies CUPTI that the calling thread is leaving an external API region.
+ *
+ * \param kind The kind of external API activities should be correlated with.
+ * \param lastId If the function returns successful, contains the last external correlation id for this \p kind, can be NULL.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER The external API kind is invalid.
+ * \retval CUPTI_ERROR_QUEUE_EMPTY No external id is currently associated with \p kind.
+ */
+CUptiResult CUPTIAPI cuptiActivityPopExternalCorrelationId(CUpti_ExternalCorrelationKind kind, uint64_t *lastId);
+
+/**
+ * \brief Controls the collection of queued and submitted timestamps for kernels.
+ *
+ * This API is used to control the collection of queued and submitted timestamps
+ * for kernels whose records are provided through the struct \ref CUpti_ActivityKernel9.
+ * Default value is 0, i.e. these timestamps are not collected. This API needs
+ * to be called before initialization of CUDA and this setting should not be
+ * changed during the profiling session.
+ * 
+ * This API is not supported if the HW trace is enabled through the API \ref cuptiActivityEnableHWTrace. 
+ * \param enable is a boolean, denoting whether these timestamps should be
+ * collected
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableLatencyTimestamps(uint8_t enable);
+
+/**
+ * \brief Sets the flush period for the worker thread
+ *
+ * CUPTI creates a worker thread to minimize the perturbance for the application created
+ * threads. CUPTI offloads certain operations from the application threads to the worker
+ * thread, this includes synchronization of profiling resources between host and device,
+ * delivery of the activity buffers to the client using the callback registered in
+ * cuptiActivityRegisterCallbacks. For performance reasons, CUPTI wakes up the worker
+ * thread based on certain heuristics.
+ *
+ * This API is used to control the flush period of the worker thread. This setting will
+ * override the CUPTI heuristics. Setting time to zero disables the periodic flush and
+ * restores the default behavior.
+ *
+ * Periodic flush can return only those activity buffers which are full and have all the
+ * activity records completed.
+ *
+ * It's allowed to use the API \ref cuptiActivityFlushAll to flush the data on-demand, even
+ * when client sets the periodic flush.
+ *
+ * \param time flush period in milliseconds (ms)
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ *
+ * \see cuptiActivityFlushAll
+ */
+CUptiResult CUPTIAPI cuptiActivityFlushPeriod(uint32_t time);
+
+/**
+ * \brief Controls the collection of launch attributes for kernels.
+ *
+ * This API is used to control the collection of launch attributes for kernels whose
+ * records are provided through the struct \ref CUpti_ActivityKernel9.
+ * Default value is 0, i.e. these attributes are not collected.
+ *
+ * \param enable is a boolean denoting whether these launch attributes should be collected
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableLaunchAttributes(uint8_t enable);
+
+/**
+ * \brief Function type for callback used by CUPTI to request a timestamp
+ * to be used in activity records.
+ *
+ * This callback function signals the CUPTI client that a timestamp needs
+ * to be returned. This timestamp would be treated as normalized timestamp
+ * to be used for various purposes in CUPTI. For example to store start and
+ * end timestamps reported in the CUPTI activity records.
+ * The returned timestamp must be in nanoseconds.
+ *
+ * \sa ::cuptiActivityRegisterTimestampCallback
+ */
+typedef uint64_t (CUPTIAPI *CUpti_TimestampCallbackFunc)(void);
+
+/**
+ * \brief Registers callback function with CUPTI for providing timestamp.
+ *
+ * This function registers a callback function to obtain timestamp of user's
+ * choice instead of using CUPTI provided timestamp.
+ * By default CUPTI uses different methods, based on the underlying platform,
+ * to retrieve the timestamp
+ * Linux and Android use clock_gettime(CLOCK_REALTIME, ..)
+ * Windows uses QueryPerformanceCounter()
+ * QNX uses ClockCycles()
+ * Timestamps retrieved using these methods are converted to nanosecond if needed
+ * before usage.
+ *
+ * Timestamps for GPU activities such as kernels, memory copies and memset operations are
+ * recorded directly on the GPU. To provide a unified and normalized view of these timestamps
+ * in relation to CPU time, CUPTI performs a linear interpolation to convert GPU timestamps
+ * into CPU timestamps during post-processing.
+ * For activities where timestamps are captured on the GPU, the timestamp callback is invoked
+ * during the post-processing phase, while converting GPU timestamps into CPU timestamps.
+ * For activities for which timestamps are captured directly on the CPU, the timestamp callback
+ * is invoked immediately at the time of the activity.
+ *
+ * The registration of timestamp callback should be done before any of the CUPTI
+ * activity kinds are enabled to make sure that all the records report the timestamp using
+ * the callback function registered through cuptiActivityRegisterTimestampCallback API.
+ *
+ * Changing the timestamp callback function in CUPTI through
+ * cuptiActivityRegisterTimestampCallback API in the middle of the profiling
+ * session can cause records generated prior to the change to report
+ * timestamps through previous timestamp method.
+ *
+ * \param funcTimestamp callback which is invoked when a timestamp is
+ * needed by CUPTI
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p funcTimestamp is NULL
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ */
+CUptiResult CUPTIAPI cuptiActivityRegisterTimestampCallback(CUpti_TimestampCallbackFunc funcTimestamp);
+
+/**
+ * \brief Controls the collection of records for device launched graphs.
+ *
+ * This API is used to control the collection of records for device launched graphs.
+ * Default value is 0, i.e. these records are not collected. This API needs
+ * to be called before initialization of CUDA and this setting should not be
+ * changed during the profiling session.
+ *
+ * \param enable is a boolean, denoting whether these records should be
+ * collected
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableDeviceGraph(uint8_t enable);
+
+/**
+ * \brief Controls the collection of activity records for specific CUDA Driver APIs.
+ *
+ * Activity kind CUPTI_ACTIVITY_KIND_DRIVER controls the collection of either all
+ * CUDA Driver APIs or none. API cuptiActivityEnableDriverApi can be used for fine-grained
+ * control, it allows enabling/disabling tracing of a specific set of CUDA Driver APIs.
+ * To disable collection of a small set of CUDA Driver APIs, user can
+ * first enable the collection of all Driver APIs using the activity kind
+ * CUPTI_ACTIVITY_KIND_DRIVER and call this API to disable specific Driver APIs.
+ * And to enable the collection of a small set of CUDA Driver APIs, user can
+ * call this API without using the activity kind CUPTI_ACTIVITY_KIND_DRIVER.
+ *
+ * Note: Activity kind CUPTI_ACTIVITY_KIND_DRIVER overrides the settings done by this API
+ * if it is called after the API.
+ *
+ * \param cbid callback id of the CUDA Driver API. This can be found in the header cupti_driver_cbid.h.
+ * \param enable is a boolean, denoting whether to enable or disable the collection
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableDriverApi(CUpti_CallbackId cbid, uint8_t enable);
+
+/**
+ * \brief Controls the collection of activity records for specific CUDA Runtime APIs.
+ *
+ * Activity kind CUPTI_ACTIVITY_KIND_RUNTIME controls the collection of either all
+ * CUDA Runtime APIs or none. API cuptiActivityEnableRuntimeApi can be used for fine-grained
+ * control, it allows enabling/disabling tracing of a specific set of CUDA Runtime APIs.
+ * To disable collection of a small set of CUDA Runtime APIs, user can
+ * first enable the collection of all Runtime APIs using the activity kind
+ * CUPTI_ACTIVITY_KIND_RUNTIME and call this API to disable specific Runtime APIs.
+ * And to enable the collection of a small set of CUDA Runtime APIs, user can
+ * call this API without using the activity kind CUPTI_ACTIVITY_KIND_RUNTIME.
+ *
+ * Note: Activity kind CUPTI_ACTIVITY_KIND_RUNTIME overrides the settings done by this API
+ * if it is called after the API.
+ *
+ * \param cbid callback id of the CUDA Runtime API. This can be found in the header cupti_runtime_cbid.h.
+ * \param enable is a boolean, denoting whether to enable or disable the collection
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableRuntimeApi(CUpti_CallbackId cbid, uint8_t enable);
+
+/**
+ * \brief Enables the collection of CUDA kernel timestamps through HW events.
+ *
+ * This API enables the collection of CUDA kernel timestamps through HW events instead
+ * of the traditional SW instrumentation and semaphore based approach.
+ * This option is only available on Blackwell architecture.
+ * This API should be called after driver is initialized.
+ *
+ * \param enable is a boolean, denoting whether to enable or disable the collection through HW events
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if CUPTI is not initialized or the CUDA driver is not initialized
+ * \retval CUPTI_ERROR_NOT_SUPPORTED if HW trace cannot be enabled on the current platform
+ * \retval CUPTI_ERROR_VIRTUALIZED_DEVICE_NOT_SUPPORTED
+ * \retval CUPTI_ERROR_CONFIDENTIAL_COMPUTING_NOT_SUPPORTED
+ * \retval CUPTI_ERROR_CMP_DEVICE_NOT_SUPPORTED
+ * \retval CUPTI_ERROR_MIG_DEVICE_NOT_SUPPORTED
+ * \retval CUPTI_ERROR_SLI_DEVICE_NOT_SUPPORTED
+ * \retval CUPTI_ERROR_WSL_DEVICE_NOT_SUPPORTED
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableHWTrace(uint8_t enable);
+
+
+/**
+ *  \brief Enables tracking the source library for memory allocation requests.
+ *
+ * This API is used to control whether or not we track the source library of
+ * memory allocation requests. Default value is 0, i.e. it is not tracked. The
+ * activity kind CUPTI_ACTIVITY_KIND_MEMORY2 needs to be enabled, and if this flag is
+ * set, we get the full path of the shared object responsible for the GPU memory allocation
+ * request in the member source in the CUpti_ActivityMemory4 records. Also note that this feature
+ * adds runtime overhead.
+ *
+ * \param enable is a boolean, denoting whether the source library of the memory allocation
+ * request needs to be tracked
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+*/
+CUptiResult CUPTIAPI cuptiActivityEnableAllocationSource (uint8_t enable);
+
+/**
+ * \brief Enables collecting records for all synchronization operations.
+ *
+ * CUPTI provides CUDA event query and stream query records via CUPTI_ACTIVTIY_KIND_SYNCHRONIZATION.
+ * Using this API, CUPTI client can enable to record all CUDA event query and stream query records
+ * even if the event has not yet been completed and all operations on stream have not yet been completed
+ * respectively.
+ *
+ * By default, the record is only generated if all captured work has been completed for the CUDA event.
+ * By default, the record is only generated if all operations have been completed on the stream.
+ *
+ * \param enable is a boolean, denoting whether to enable or disable the collection of all CUDA event query
+ * and stream query records
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ */
+CUptiResult CUPTIAPI cuptiActivityEnableAllSyncRecords(uint8_t enable);
+
+/** @} */ /* END CUPTI_ACTIVITY_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+// Including deprecated structures of CUPTI_ACTIVITY_API
+#include "cupti_activity_deprecated.h"
+
+#endif /*_CUPTI_ACTIVITY_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_activity_deprecated.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_activity_deprecated.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9d725499ffa13ac7de864719abee2baa88d6c13
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_activity_deprecated.h
@@ -0,0 +1,5335 @@
+/*
+ * Copyright 2011-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_ACTIVITY_DEPRECATED_H_)
+#define _CUPTI_ACTIVITY_DEPRECATED_H_
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \brief The kinds of activity records.
+ *
+ * Each activity record kind represents information about a GPU or an
+ * activity occurring on a CPU or GPU. Each kind is associated with a
+ * activity record structure that holds the information associated
+ * with the kind.
+ * \see CUpti_ActivityOverhead
+ * \see CUpti_ActivityOverhead2
+ * \see CUpti_ActivityDevice
+ * \see CUpti_ActivityDevice2
+ * \see CUpti_ActivityDevice3
+ * \see CUpti_ActivityDevice4
+ * \see CUpti_ActivityKernel
+ * \see CUpti_ActivityKernel2
+ * \see CUpti_ActivityKernel3
+ * \see CUpti_ActivityKernel4
+ * \see CUpti_ActivityKernel5
+ * \see CUpti_ActivityKernel6
+ * \see CUpti_ActivityKernel7
+ * \see CUpti_ActivityKernel8
+ * \see CUpti_ActivityMemcpy
+ * \see CUpti_ActivityMemcpy3
+ * \see CUpti_ActivityMemcpy4
+ * \see CUpti_ActivityMemcpyPtoP
+ * \see CUpti_ActivityMemcpyPtoP2
+ * \see CUpti_ActivityMemcpyPtoP3
+ * \see CUpti_ActivityMemset
+ * \see CUpti_ActivityMemset2
+ * \see CUpti_ActivityMemset3
+ * \see CUpti_ActivityMemory2
+ * \see CUpti_ActivityMemory3
+ * \see CUpti_ActivityMemoryPool
+ * \see CUpti_ActivityMarker
+ * \see CUpti_ActivityGlobalAccess
+ * \see CUpti_ActivityGlobalAccess2
+ * \see CUpti_ActivityBranch
+ * \see CUpti_ActivityPCSampling
+ * \see CUpti_ActivityPCSampling2
+ * \see CUpti_ActivityUnifiedMemoryCounter
+ * \see CUpti_ActivityUnifiedMemoryCounter2
+ * \see CUpti_ActivityNvLink
+ * \see CUpti_ActivityNvLink2
+ * \see CUpti_ActivityNvLink3
+ */
+
+/**
+ * \brief The activity record for CUPTI and driver overheads.
+ * (Deprecated in CUDA 12.2)
+ *
+ * This activity record provides CUPTI and driver overhead information
+ * (CUPTI_ACTIVITY_OVERHEAD). These records are now reported using
+ * CUpti_ActivityOverhead3
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_OVERHEAD.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of overhead, CUPTI, DRIVER, COMPILER etc.
+   */
+  CUpti_ActivityOverheadKind overheadKind;
+
+  /**
+   * The kind of activity object that the overhead is associated with.
+   */
+  CUpti_ActivityObjectKind objectKind;
+
+  /**
+   * The identifier for the activity object. 'objectKind' indicates
+   * which ID is valid for this record.
+   */
+  CUpti_ActivityObjectKindId objectId;
+
+  /**
+   * The start timestamp for the overhead, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the overhead.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the overhead, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the overhead.
+   */
+  uint64_t end;
+} CUpti_ActivityOverhead;
+
+/**
+ * \brief The activity record for CUPTI and driver overheads.
+ *
+ * This activity record provides CUPTI and driver overhead information
+ * (CUPTI_ACTIVITY_OVERHEAD).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_OVERHEAD.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of overhead, CUPTI, DRIVER, COMPILER etc.
+   */
+  CUpti_ActivityOverheadKind overheadKind;
+
+  /**
+   * The kind of activity object that the overhead is associated with.
+   */
+  CUpti_ActivityObjectKind objectKind;
+
+  /**
+   * The identifier for the activity object. 'objectKind' indicates
+   * which ID is valid for this record.
+   */
+  CUpti_ActivityObjectKindId objectId;
+
+  /**
+   * The start timestamp for the overhead, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the overhead.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the overhead, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the overhead.
+   */
+  uint64_t end;
+
+  /**
+   * The correlation ID of the overhead operation to which
+   * records belong to. This ID is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the overhead operation.
+   * In some cases, it can be zero, such as for CUPTI_ACTIVITY_OVERHEAD_CUPTI_BUFFER_FLUSH records.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Reserved for internal use.
+   */
+  uint32_t reserved0;
+} CUpti_ActivityOverhead2;
+
+/**
+ * \brief The activity record for a device. (deprecated)
+ *
+ * This activity record represents information about a GPU device
+ * (CUPTI_ACTIVITY_KIND_DEVICE).
+ * Device activity is now reported using the
+ * CUpti_ActivityDevice5 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DEVICE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the device. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The global memory bandwidth available on the device, in
+   * kBytes/sec.
+   */
+  uint64_t globalMemoryBandwidth;
+
+  /**
+   * The amount of global memory on the device, in bytes.
+   */
+  uint64_t globalMemorySize;
+
+  /**
+   * The amount of constant memory on the device, in bytes.
+   */
+  uint32_t constantMemorySize;
+
+  /**
+   * The size of the L2 cache on the device, in bytes.
+   */
+  uint32_t l2CacheSize;
+
+  /**
+   * The number of threads per warp on the device.
+   */
+  uint32_t numThreadsPerWarp;
+
+  /**
+   * The core clock rate of the device, in kHz.
+   */
+  uint32_t coreClockRate;
+
+  /**
+   * Number of memory copy engines on the device.
+   */
+  uint32_t numMemcpyEngines;
+
+  /**
+   * Number of multiprocessors on the device.
+   */
+  uint32_t numMultiprocessors;
+
+  /**
+   * The maximum "instructions per cycle" possible on each device
+   * multiprocessor.
+   */
+  uint32_t maxIPC;
+
+  /**
+   * Maximum number of warps that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxWarpsPerMultiprocessor;
+
+  /**
+   * Maximum number of blocks that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxBlocksPerMultiprocessor;
+
+  /**
+   * Maximum number of registers that can be allocated to a block.
+   */
+  uint32_t maxRegistersPerBlock;
+
+  /**
+   * Maximum amount of shared memory that can be assigned to a block,
+   * in bytes.
+   */
+  uint32_t maxSharedMemoryPerBlock;
+
+  /**
+   * Maximum number of threads allowed in a block.
+   */
+  uint32_t maxThreadsPerBlock;
+
+  /**
+   * Maximum allowed X dimension for a block.
+   */
+  uint32_t maxBlockDimX;
+
+  /**
+   * Maximum allowed Y dimension for a block.
+   */
+  uint32_t maxBlockDimY;
+
+  /**
+   * Maximum allowed Z dimension for a block.
+   */
+  uint32_t maxBlockDimZ;
+
+  /**
+   * Maximum allowed X dimension for a grid.
+   */
+  uint32_t maxGridDimX;
+
+  /**
+   * Maximum allowed Y dimension for a grid.
+   */
+  uint32_t maxGridDimY;
+
+  /**
+   * Maximum allowed Z dimension for a grid.
+   */
+  uint32_t maxGridDimZ;
+
+  /**
+   * Compute capability for the device, major number.
+   */
+  uint32_t computeCapabilityMajor;
+
+  /**
+   * Compute capability for the device, minor number.
+   */
+  uint32_t computeCapabilityMinor;
+
+  /**
+   * The device ID.
+   */
+  uint32_t id;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The device name. This name is shared across all activity records
+   * representing instances of the device, and so should not be
+   * modified.
+   */
+  const char *name;
+} CUpti_ActivityDevice;
+
+/**
+ * \brief The activity record for a device. (deprecated)
+ *
+ * This activity record represents information about a GPU device
+ * (CUPTI_ACTIVITY_KIND_DEVICE).
+ * Device activity is now reported using the
+ * CUpti_ActivityDevice5 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DEVICE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the device. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The global memory bandwidth available on the device, in
+   * kBytes/sec.
+   */
+  uint64_t globalMemoryBandwidth;
+
+  /**
+   * The amount of global memory on the device, in bytes.
+   */
+  uint64_t globalMemorySize;
+
+  /**
+   * The amount of constant memory on the device, in bytes.
+   */
+  uint32_t constantMemorySize;
+
+  /**
+   * The size of the L2 cache on the device, in bytes.
+   */
+  uint32_t l2CacheSize;
+
+  /**
+   * The number of threads per warp on the device.
+   */
+  uint32_t numThreadsPerWarp;
+
+  /**
+   * The core clock rate of the device, in kHz.
+   */
+  uint32_t coreClockRate;
+
+  /**
+   * Number of memory copy engines on the device.
+   */
+  uint32_t numMemcpyEngines;
+
+  /**
+   * Number of multiprocessors on the device.
+   */
+  uint32_t numMultiprocessors;
+
+  /**
+   * The maximum "instructions per cycle" possible on each device
+   * multiprocessor.
+   */
+  uint32_t maxIPC;
+
+  /**
+   * Maximum number of warps that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxWarpsPerMultiprocessor;
+
+  /**
+   * Maximum number of blocks that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxBlocksPerMultiprocessor;
+
+  /**
+   * Maximum amount of shared memory available per multiprocessor, in bytes.
+   */
+  uint32_t maxSharedMemoryPerMultiprocessor;
+
+  /**
+   * Maximum number of 32-bit registers available per multiprocessor.
+   */
+  uint32_t maxRegistersPerMultiprocessor;
+
+  /**
+   * Maximum number of registers that can be allocated to a block.
+   */
+  uint32_t maxRegistersPerBlock;
+
+  /**
+   * Maximum amount of shared memory that can be assigned to a block,
+   * in bytes.
+   */
+  uint32_t maxSharedMemoryPerBlock;
+
+  /**
+   * Maximum number of threads allowed in a block.
+   */
+  uint32_t maxThreadsPerBlock;
+
+  /**
+   * Maximum allowed X dimension for a block.
+   */
+  uint32_t maxBlockDimX;
+
+  /**
+   * Maximum allowed Y dimension for a block.
+   */
+  uint32_t maxBlockDimY;
+
+  /**
+   * Maximum allowed Z dimension for a block.
+   */
+  uint32_t maxBlockDimZ;
+
+  /**
+   * Maximum allowed X dimension for a grid.
+   */
+  uint32_t maxGridDimX;
+
+  /**
+   * Maximum allowed Y dimension for a grid.
+   */
+  uint32_t maxGridDimY;
+
+  /**
+   * Maximum allowed Z dimension for a grid.
+   */
+  uint32_t maxGridDimZ;
+
+  /**
+   * Compute capability for the device, major number.
+   */
+  uint32_t computeCapabilityMajor;
+
+  /**
+   * Compute capability for the device, minor number.
+   */
+  uint32_t computeCapabilityMinor;
+
+  /**
+   * The device ID.
+   */
+  uint32_t id;
+
+  /**
+   * ECC enabled flag for device
+   */
+  uint32_t eccEnabled;
+
+  /**
+   * The device UUID. This value is the globally unique immutable
+   * alphanumeric identifier of the device.
+   */
+  CUuuid uuid;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The device name. This name is shared across all activity records
+   * representing instances of the device, and so should not be
+   * modified.
+   */
+  const char *name;
+} CUpti_ActivityDevice2;
+
+/**
+ * \brief The activity record for a device. (CUDA 7.0 onwards)
+ *
+ * This activity record represents information about a GPU device
+ * (CUPTI_ACTIVITY_KIND_DEVICE).
+ * Device activity is now reported using the
+ * CUpti_ActivityDevice5 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DEVICE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the device. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The global memory bandwidth available on the device, in
+   * kBytes/sec.
+   */
+  uint64_t globalMemoryBandwidth;
+
+  /**
+   * The amount of global memory on the device, in bytes.
+   */
+  uint64_t globalMemorySize;
+
+  /**
+   * The amount of constant memory on the device, in bytes.
+   */
+  uint32_t constantMemorySize;
+
+  /**
+   * The size of the L2 cache on the device, in bytes.
+   */
+  uint32_t l2CacheSize;
+
+  /**
+   * The number of threads per warp on the device.
+   */
+  uint32_t numThreadsPerWarp;
+
+  /**
+   * The core clock rate of the device, in kHz.
+   */
+  uint32_t coreClockRate;
+
+  /**
+   * Number of memory copy engines on the device.
+   */
+  uint32_t numMemcpyEngines;
+
+  /**
+   * Number of multiprocessors on the device.
+   */
+  uint32_t numMultiprocessors;
+
+  /**
+   * The maximum "instructions per cycle" possible on each device
+   * multiprocessor.
+   */
+  uint32_t maxIPC;
+
+  /**
+   * Maximum number of warps that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxWarpsPerMultiprocessor;
+
+  /**
+   * Maximum number of blocks that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxBlocksPerMultiprocessor;
+
+  /**
+   * Maximum amount of shared memory available per multiprocessor, in bytes.
+   */
+  uint32_t maxSharedMemoryPerMultiprocessor;
+
+  /**
+   * Maximum number of 32-bit registers available per multiprocessor.
+   */
+  uint32_t maxRegistersPerMultiprocessor;
+
+  /**
+   * Maximum number of registers that can be allocated to a block.
+   */
+  uint32_t maxRegistersPerBlock;
+
+  /**
+   * Maximum amount of shared memory that can be assigned to a block,
+   * in bytes.
+   */
+  uint32_t maxSharedMemoryPerBlock;
+
+  /**
+   * Maximum number of threads allowed in a block.
+   */
+  uint32_t maxThreadsPerBlock;
+
+  /**
+   * Maximum allowed X dimension for a block.
+   */
+  uint32_t maxBlockDimX;
+
+  /**
+   * Maximum allowed Y dimension for a block.
+   */
+  uint32_t maxBlockDimY;
+
+  /**
+   * Maximum allowed Z dimension for a block.
+   */
+  uint32_t maxBlockDimZ;
+
+  /**
+   * Maximum allowed X dimension for a grid.
+   */
+  uint32_t maxGridDimX;
+
+  /**
+   * Maximum allowed Y dimension for a grid.
+   */
+  uint32_t maxGridDimY;
+
+  /**
+   * Maximum allowed Z dimension for a grid.
+   */
+  uint32_t maxGridDimZ;
+
+  /**
+   * Compute capability for the device, major number.
+   */
+  uint32_t computeCapabilityMajor;
+
+  /**
+   * Compute capability for the device, minor number.
+   */
+  uint32_t computeCapabilityMinor;
+
+  /**
+   * The device ID.
+   */
+  uint32_t id;
+
+  /**
+   * ECC enabled flag for device
+   */
+  uint32_t eccEnabled;
+
+  /**
+   * The device UUID. This value is the globally unique immutable
+   * alphanumeric identifier of the device.
+   */
+  CUuuid uuid;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The device name. This name is shared across all activity records
+   * representing instances of the device, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Flag to indicate whether the device is visible to CUDA. Users can
+   * set the device visibility using CUDA_VISIBLE_DEVICES environment
+   */
+  uint8_t isCudaVisible;
+
+  uint8_t reserved[7];
+} CUpti_ActivityDevice3;
+
+/**
+ * \brief The activity record for a device. (CUDA 11.6 onwards)
+ *
+ * This activity record represents information about a GPU device
+ * (CUPTI_ACTIVITY_KIND_DEVICE).
+ * Device activity is now reported using the
+ * CUpti_ActivityDevice5 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_DEVICE.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the device. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The global memory bandwidth available on the device, in
+   * kBytes/sec.
+   */
+  uint64_t globalMemoryBandwidth;
+
+  /**
+   * The amount of global memory on the device, in bytes.
+   */
+  uint64_t globalMemorySize;
+
+  /**
+   * The amount of constant memory on the device, in bytes.
+   */
+  uint32_t constantMemorySize;
+
+  /**
+   * The size of the L2 cache on the device, in bytes.
+   */
+  uint32_t l2CacheSize;
+
+  /**
+   * The number of threads per warp on the device.
+   */
+  uint32_t numThreadsPerWarp;
+
+  /**
+   * The core clock rate of the device, in kHz.
+   */
+  uint32_t coreClockRate;
+
+  /**
+   * Number of memory copy engines on the device.
+   */
+  uint32_t numMemcpyEngines;
+
+  /**
+   * Number of multiprocessors on the device.
+   */
+  uint32_t numMultiprocessors;
+
+  /**
+   * The maximum "instructions per cycle" possible on each device
+   * multiprocessor.
+   */
+  uint32_t maxIPC;
+
+  /**
+   * Maximum number of warps that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxWarpsPerMultiprocessor;
+
+  /**
+   * Maximum number of blocks that can be present on a multiprocessor
+   * at any given time.
+   */
+  uint32_t maxBlocksPerMultiprocessor;
+
+  /**
+   * Maximum amount of shared memory available per multiprocessor, in bytes.
+   */
+  uint32_t maxSharedMemoryPerMultiprocessor;
+
+  /**
+   * Maximum number of 32-bit registers available per multiprocessor.
+   */
+  uint32_t maxRegistersPerMultiprocessor;
+
+  /**
+   * Maximum number of registers that can be allocated to a block.
+   */
+  uint32_t maxRegistersPerBlock;
+
+  /**
+   * Maximum amount of shared memory that can be assigned to a block,
+   * in bytes.
+   */
+  uint32_t maxSharedMemoryPerBlock;
+
+  /**
+   * Maximum number of threads allowed in a block.
+   */
+  uint32_t maxThreadsPerBlock;
+
+  /**
+   * Maximum allowed X dimension for a block.
+   */
+  uint32_t maxBlockDimX;
+
+  /**
+   * Maximum allowed Y dimension for a block.
+   */
+  uint32_t maxBlockDimY;
+
+  /**
+   * Maximum allowed Z dimension for a block.
+   */
+  uint32_t maxBlockDimZ;
+
+  /**
+   * Maximum allowed X dimension for a grid.
+   */
+  uint32_t maxGridDimX;
+
+  /**
+   * Maximum allowed Y dimension for a grid.
+   */
+  uint32_t maxGridDimY;
+
+  /**
+   * Maximum allowed Z dimension for a grid.
+   */
+  uint32_t maxGridDimZ;
+
+  /**
+   * Compute capability for the device, major number.
+   */
+  uint32_t computeCapabilityMajor;
+
+  /**
+   * Compute capability for the device, minor number.
+   */
+  uint32_t computeCapabilityMinor;
+
+  /**
+   * The device ID.
+   */
+  uint32_t id;
+
+  /**
+   * ECC enabled flag for device
+   */
+  uint32_t eccEnabled;
+
+  /**
+   * The device UUID. This value is the globally unique immutable
+   * alphanumeric identifier of the device.
+   */
+  CUuuid uuid;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The device name. This name is shared across all activity records
+   * representing instances of the device, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Flag to indicate whether the device is visible to CUDA. Users can
+   * set the device visibility using CUDA_VISIBLE_DEVICES environment
+   */
+  uint8_t isCudaVisible;
+
+  /**
+   * MIG enabled flag for device
+   */
+  uint8_t isMigEnabled;
+
+  uint8_t reserved[6];
+
+  /**
+   * GPU Instance id for MIG enabled devices.
+   * If mig mode is disabled value is set to UINT32_MAX
+   */
+  uint32_t gpuInstanceId;
+
+  /**
+   * Compute Instance id for MIG enabled devices.
+   * If mig mode is disabled value is set to UINT32_MAX
+   */
+  uint32_t computeInstanceId;
+
+  /**
+   * The MIG UUID. This value is the globally unique immutable
+   * alphanumeric identifier of the device.
+   */
+  CUuuid migUuid;
+
+} CUpti_ActivityDevice4;
+
+/**
+ * \brief The activity record for kernel. (deprecated)
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL) but is no longer generated
+ * by CUPTI. Kernel activities are now reported using the
+ * CUpti_ActivityKernel9 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL
+   * or CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The cache configuration requested by the kernel. The value is one
+   * of the CUfunc_cache enumeration values from cuda.h.
+   */
+  uint8_t cacheConfigRequested;
+
+  /**
+   * The cache configuration used for the kernel. The value is one of
+   * the CUfunc_cache enumeration values from cuda.h.
+   */
+  uint8_t cacheConfigExecuted;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The runtime correlation ID of the kernel. Each kernel execution
+   * is assigned a unique runtime correlation ID that is identical to
+   * the correlation ID in the runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t runtimeCorrelationId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+} CUpti_ActivityKernel;
+
+/**
+ * \brief The activity record for kernel. (deprecated)
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL) but is no longer generated
+ * by CUPTI. Kernel activities are now reported using the
+ * CUpti_ActivityKernel9 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+} CUpti_ActivityKernel2;
+
+/**
+ * \brief The activity record for a kernel (CUDA 6.5(with sm_52 support) onwards).
+ * (deprecated in CUDA 9.0)
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL).
+ * Kernel activities are now reported using the CUpti_ActivityKernel9 activity
+ * record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+} CUpti_ActivityKernel3;
+
+/**
+ * \brief The activity record for a kernel (CUDA 9.0(with sm_70 support) onwards).
+ * (deprecated in CUDA 11.0)
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL).
+ * Kernel activities are now reported using the CUpti_ActivityKernel9 activity
+ * record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * For devices with compute capability 7.0+ cacheConfig values are not updated
+   * in case field isSharedMemoryCarveoutRequested is set
+   */
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The timestamp when the kernel is queued up in the command buffer, in ns.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time
+   * could not be collected for the kernel. This timestamp is not collected
+   * by default. Use API \ref cuptiActivityEnableLatencyTimestamps() to
+   * enable collection.
+   *
+   * Command buffer is a buffer written by CUDA driver to send commands
+   * like kernel launch, memory copy etc to the GPU. All launches of CUDA
+   * kernels are asynchronous with respect to the host, the host requests
+   * the launch by writing commands into the command buffer, then returns
+   * without checking the GPU's progress.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when the command buffer containing the kernel launch
+   * is submitted to the GPU, in ns. A value of CUPTI_TIMESTAMP_UNKNOWN
+   * indicates that the submitted time could not be collected for the kernel.
+   * This timestamp is not collected by default. Use API \ref
+   * cuptiActivityEnableLatencyTimestamps() to enable collection.
+   */
+  uint64_t submitted;
+
+  /**
+   * The indicates if the kernel was executed via a regular launch or via a
+   * single/multi device cooperative launch. \see CUpti_ActivityLaunchType
+   */
+  uint8_t launchType;
+
+  /**
+   * This indicates if CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT was
+   * updated for the kernel launch
+   */
+  uint8_t isSharedMemoryCarveoutRequested;
+
+  /**
+   * Shared memory carveout value requested for the function in percentage of
+   * the total resource. The value will be updated only if field
+   * isSharedMemoryCarveoutRequested is set.
+   */
+  uint8_t sharedMemoryCarveoutRequested;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t padding;
+
+ /**
+  * Shared memory size set by the driver.
+  */
+  uint32_t sharedMemoryExecuted;
+} CUpti_ActivityKernel4;
+
+/**
+ * \brief The activity record for a kernel (CUDA 11.0(with sm_80 support) onwards).
+ * (deprecated in CUDA 11.2)
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL) but is no longer generated
+ * by CUPTI. Kernel activities are now reported using the
+ * CUpti_ActivityKernel9 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * For devices with compute capability 7.0+ cacheConfig values are not updated
+   * in case field isSharedMemoryCarveoutRequested is set
+   */
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The timestamp when the kernel is queued up in the command buffer, in ns.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time
+   * could not be collected for the kernel. This timestamp is not collected
+   * by default. Use API \ref cuptiActivityEnableLatencyTimestamps() to
+   * enable collection.
+   *
+   * Command buffer is a buffer written by CUDA driver to send commands
+   * like kernel launch, memory copy etc to the GPU. All launches of CUDA
+   * kernels are asynchronous with respect to the host, the host requests
+   * the launch by writing commands into the command buffer, then returns
+   * without checking the GPU's progress.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when the command buffer containing the kernel launch
+   * is submitted to the GPU, in ns. A value of CUPTI_TIMESTAMP_UNKNOWN
+   * indicates that the submitted time could not be collected for the kernel.
+   * This timestamp is not collected by default. Use API \ref
+   * cuptiActivityEnableLatencyTimestamps() to enable collection.
+   */
+  uint64_t submitted;
+
+  /**
+   * The indicates if the kernel was executed via a regular launch or via a
+   * single/multi device cooperative launch. \see CUpti_ActivityLaunchType
+   */
+  uint8_t launchType;
+
+  /**
+   * This indicates if CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT was
+   * updated for the kernel launch
+   */
+  uint8_t isSharedMemoryCarveoutRequested;
+
+  /**
+   * Shared memory carveout value requested for the function in percentage of
+   * the total resource. The value will be updated only if field
+   * isSharedMemoryCarveoutRequested is set.
+   */
+  uint8_t sharedMemoryCarveoutRequested;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t padding;
+
+ /**
+  * Shared memory size set by the driver.
+  */
+  uint32_t sharedMemoryExecuted;
+
+  /**
+   * The unique ID of the graph node that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The shared memory limit config for the kernel. This field shows whether user has opted for a
+   * higher per block limit of dynamic shared memory.
+   */
+  CUpti_FuncShmemLimitConfig shmemLimitConfig;
+
+  /**
+   * The unique ID of the graph that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint32_t graphId;
+} CUpti_ActivityKernel5;
+
+/**
+ * \brief The activity record for kernel. (deprecated in CUDA 11.6)
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL) but is no longer generated
+ * by CUPTI. Kernel activities are now reported using the
+ * CUpti_ActivityKernel9 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * For devices with compute capability 7.0+ cacheConfig values are not updated
+   * in case field isSharedMemoryCarveoutRequested is set
+   */
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The timestamp when the kernel is queued up in the command buffer, in ns.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time
+   * could not be collected for the kernel. This timestamp is not collected
+   * by default. Use API \ref cuptiActivityEnableLatencyTimestamps() to
+   * enable collection.
+   *
+   * Command buffer is a buffer written by CUDA driver to send commands
+   * like kernel launch, memory copy etc to the GPU. All launches of CUDA
+   * kernels are asynchronous with respect to the host, the host requests
+   * the launch by writing commands into the command buffer, then returns
+   * without checking the GPU's progress.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when the command buffer containing the kernel launch
+   * is submitted to the GPU, in ns. A value of CUPTI_TIMESTAMP_UNKNOWN
+   * indicates that the submitted time could not be collected for the kernel.
+   * This timestamp is not collected by default. Use API \ref
+   * cuptiActivityEnableLatencyTimestamps() to enable collection.
+   */
+  uint64_t submitted;
+
+  /**
+   * The indicates if the kernel was executed via a regular launch or via a
+   * single/multi device cooperative launch. \see CUpti_ActivityLaunchType
+   */
+  uint8_t launchType;
+
+  /**
+   * This indicates if CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT was
+   * updated for the kernel launch
+   */
+  uint8_t isSharedMemoryCarveoutRequested;
+
+  /**
+   * Shared memory carveout value requested for the function in percentage of
+   * the total resource. The value will be updated only if field
+   * isSharedMemoryCarveoutRequested is set.
+   */
+  uint8_t sharedMemoryCarveoutRequested;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t padding;
+
+ /**
+  * Shared memory size set by the driver.
+  */
+  uint32_t sharedMemoryExecuted;
+
+  /**
+   * The unique ID of the graph node that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The shared memory limit config for the kernel. This field shows whether user has opted for a
+   * higher per block limit of dynamic shared memory.
+   */
+  CUpti_FuncShmemLimitConfig shmemLimitConfig;
+
+  /**
+   * The unique ID of the graph that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint32_t graphId;
+
+  /**
+   * The pointer to the access policy window. The structure CUaccessPolicyWindow is
+   * defined in cuda.h.
+   */
+  CUaccessPolicyWindow *pAccessPolicyWindow;
+} CUpti_ActivityKernel6;
+
+/**
+ * \brief The activity record for kernel. (deprecated in CUDA 11.8)
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL) but is no longer generated
+ * by CUPTI. Kernel activities are now reported using the
+ * CUpti_ActivityKernel9 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * For devices with compute capability 7.0+ cacheConfig values are not updated
+   * in case field isSharedMemoryCarveoutRequested is set
+   */
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The timestamp when the kernel is queued up in the command buffer, in ns.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time
+   * could not be collected for the kernel. This timestamp is not collected
+   * by default. Use API \ref cuptiActivityEnableLatencyTimestamps() to
+   * enable collection.
+   *
+   * Command buffer is a buffer written by CUDA driver to send commands
+   * like kernel launch, memory copy etc to the GPU. All launches of CUDA
+   * kernels are asynchronous with respect to the host, the host requests
+   * the launch by writing commands into the command buffer, then returns
+   * without checking the GPU's progress.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when the command buffer containing the kernel launch
+   * is submitted to the GPU, in ns. A value of CUPTI_TIMESTAMP_UNKNOWN
+   * indicates that the submitted time could not be collected for the kernel.
+   * This timestamp is not collected by default. Use API \ref
+   * cuptiActivityEnableLatencyTimestamps() to enable collection.
+   */
+  uint64_t submitted;
+
+  /**
+   * The indicates if the kernel was executed via a regular launch or via a
+   * single/multi device cooperative launch. \see CUpti_ActivityLaunchType
+   */
+  uint8_t launchType;
+
+  /**
+   * This indicates if CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT was
+   * updated for the kernel launch
+   */
+  uint8_t isSharedMemoryCarveoutRequested;
+
+  /**
+   * Shared memory carveout value requested for the function in percentage of
+   * the total resource. The value will be updated only if field
+   * isSharedMemoryCarveoutRequested is set.
+   */
+  uint8_t sharedMemoryCarveoutRequested;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t padding;
+
+ /**
+  * Shared memory size set by the driver.
+  */
+  uint32_t sharedMemoryExecuted;
+
+  /**
+   * The unique ID of the graph node that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The shared memory limit config for the kernel. This field shows whether user has opted for a
+   * higher per block limit of dynamic shared memory.
+   */
+  CUpti_FuncShmemLimitConfig shmemLimitConfig;
+
+  /**
+   * The unique ID of the graph that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint32_t graphId;
+
+  /**
+   * The pointer to the access policy window. The structure CUaccessPolicyWindow is
+   * defined in cuda.h.
+   */
+  CUaccessPolicyWindow *pAccessPolicyWindow;
+
+  /**
+   * The ID of the HW channel on which the kernel is launched.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+} CUpti_ActivityKernel7;
+
+/**
+ * \brief The activity record for kernel.
+ *
+ * This activity record represents a kernel execution
+ * (CUPTI_ACTIVITY_KIND_KERNEL and
+ * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL)
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_KERNEL or
+   * CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * For devices with compute capability 7.0+ cacheConfig values are not updated
+   * in case field isSharedMemoryCarveoutRequested is set
+   */
+  union {
+    uint8_t both;
+    struct {
+      /**
+       * The cache configuration requested by the kernel. The value is one
+       * of the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t requested:4;
+
+      /**
+       * The cache configuration used for the kernel. The value is one of
+       * the CUfunc_cache enumeration values from cuda.h.
+       */
+      uint8_t executed:4;
+    } config;
+  } cacheConfig;
+
+  /**
+   * The shared memory configuration used for the kernel. The value is one of
+   * the CUsharedconfig enumeration values from cuda.h.
+   */
+  uint8_t sharedMemoryConfig;
+
+  /**
+   * The number of registers required for each thread executing the
+   * kernel.
+   */
+  uint16_t registersPerThread;
+
+  /**
+   * The partitioned global caching requested for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheRequested;
+
+  /**
+   * The partitioned global caching executed for the kernel. Partitioned
+   * global caching is required to enable caching on certain chips, such as
+   * devices with compute capability 5.2. Partitioned global caching can be
+   * automatically disabled if the occupancy requirement of the launch cannot
+   * support caching.
+   */
+  CUpti_ActivityPartitionedGlobalCacheConfig partitionedGlobalCacheExecuted;
+
+  /**
+   * The start timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the kernel execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the kernel.
+   */
+  uint64_t end;
+
+  /**
+   * The completed timestamp for the kernel execution, in ns.  It
+   * represents the completion of all it's child kernels and the
+   * kernel itself. A value of CUPTI_TIMESTAMP_UNKNOWN indicates that
+   * the completion time is unknown.
+   */
+  uint64_t completed;
+
+  /**
+   * The ID of the device where the kernel is executing.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the kernel is executing.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the kernel is executing.
+   */
+  uint32_t streamId;
+
+  /**
+   * The X-dimension grid size for the kernel.
+   */
+  int32_t gridX;
+
+  /**
+   * The Y-dimension grid size for the kernel.
+   */
+  int32_t gridY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t gridZ;
+
+  /**
+   * The X-dimension block size for the kernel.
+   */
+  int32_t blockX;
+
+  /**
+   * The Y-dimension block size for the kernel.
+   */
+  int32_t blockY;
+
+  /**
+   * The Z-dimension grid size for the kernel.
+   */
+  int32_t blockZ;
+
+  /**
+   * The static shared memory allocated for the kernel, in bytes.
+   */
+  int32_t staticSharedMemory;
+
+  /**
+   * The dynamic shared memory reserved for the kernel, in bytes.
+   */
+  int32_t dynamicSharedMemory;
+
+  /**
+   * The amount of local memory reserved for each thread, in bytes.
+   */
+  uint32_t localMemoryPerThread;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes (deprecated in CUDA 11.8).
+   * Refer field localMemoryTotal_v2
+   */
+  uint32_t localMemoryTotal;
+
+  /**
+   * The correlation ID of the kernel. Each kernel execution is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the kernel.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The grid ID of the kernel. Each kernel is assigned a unique
+   * grid ID at runtime.
+   */
+  int64_t gridId;
+
+  /**
+   * The name of the kernel. This name is shared across all activity
+   * records representing the same kernel, and so should not be
+   * modified.
+   */
+  const char *name;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The timestamp when the kernel is queued up in the command buffer, in ns.
+   * A value of CUPTI_TIMESTAMP_UNKNOWN indicates that the queued time
+   * could not be collected for the kernel. This timestamp is not collected
+   * by default. Use API \ref cuptiActivityEnableLatencyTimestamps() to
+   * enable collection.
+   *
+   * Command buffer is a buffer written by CUDA driver to send commands
+   * like kernel launch, memory copy etc to the GPU. All launches of CUDA
+   * kernels are asynchronous with respect to the host, the host requests
+   * the launch by writing commands into the command buffer, then returns
+   * without checking the GPU's progress.
+   */
+  uint64_t queued;
+
+  /**
+   * The timestamp when the command buffer containing the kernel launch
+   * is submitted to the GPU, in ns. A value of CUPTI_TIMESTAMP_UNKNOWN
+   * indicates that the submitted time could not be collected for the kernel.
+   * This timestamp is not collected by default. Use API \ref
+   * cuptiActivityEnableLatencyTimestamps() to enable collection.
+   */
+  uint64_t submitted;
+
+  /**
+   * The indicates if the kernel was executed via a regular launch or via a
+   * single/multi device cooperative launch. \see CUpti_ActivityLaunchType
+   */
+  uint8_t launchType;
+
+  /**
+   * This indicates if CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT was
+   * updated for the kernel launch
+   */
+  uint8_t isSharedMemoryCarveoutRequested;
+
+  /**
+   * Shared memory carveout value requested for the function in percentage of
+   * the total resource. The value will be updated only if field
+   * isSharedMemoryCarveoutRequested is set.
+   */
+  uint8_t sharedMemoryCarveoutRequested;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint8_t padding;
+
+ /**
+  * Shared memory size set by the driver.
+  */
+  uint32_t sharedMemoryExecuted;
+
+  /**
+   * The unique ID of the graph node that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The shared memory limit config for the kernel. This field shows whether user has opted for a
+   * higher per block limit of dynamic shared memory.
+   */
+  CUpti_FuncShmemLimitConfig shmemLimitConfig;
+
+  /**
+   * The unique ID of the graph that launched this kernel through graph launch APIs.
+   * This field will be 0 if the kernel is not launched through graph launch APIs.
+   */
+  uint32_t graphId;
+
+  /**
+   * The pointer to the access policy window. The structure CUaccessPolicyWindow is
+   * defined in cuda.h.
+   */
+  CUaccessPolicyWindow *pAccessPolicyWindow;
+
+  /**
+   * The ID of the HW channel on which the kernel is launched.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+
+  /**
+   * The X-dimension cluster size for the kernel.
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterX;
+
+  /**
+   * The Y-dimension cluster size for the kernel.
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterY;
+
+  /**
+   * The Z-dimension cluster size for the kernel.
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterZ;
+
+  /**
+   * The cluster scheduling policy for the kernel. Refer CUclusterSchedulingPolicy
+   * Field is valid for devices with compute capability 9.0 and higher
+   */
+  uint32_t clusterSchedulingPolicy;
+
+  /**
+   * The total amount of local memory reserved for the kernel, in
+   * bytes.
+   */
+  uint64_t localMemoryTotal_v2;
+} CUpti_ActivityKernel8;
+
+/**
+ * \brief The activity record for memory copies. (deprecated)
+ *
+ * This activity record represents a memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size. \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory copy is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the memory copy.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The runtime correlation ID of the memory copy. Each memory copy
+   * is assigned a unique runtime correlation ID that is identical to
+   * the correlation ID in the runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t runtimeCorrelationId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+} CUpti_ActivityMemcpy;
+
+/**
+ * \brief The activity record for memory copies. (deprecated in CUDA 11.1)
+ *
+ * This activity record represents a memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size. \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory copy is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the memory copy.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The runtime correlation ID of the memory copy. Each memory copy
+   * is assigned a unique runtime correlation ID that is identical to
+   * the correlation ID in the runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t runtimeCorrelationId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint64_t graphNodeId;
+} CUpti_ActivityMemcpy3;
+
+/**
+ * \brief The activity record for memory copies. (deprecated in CUDA 11.6)
+ *
+ * This activity record represents a memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size. \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory copy is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the memory copy.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The runtime correlation ID of the memory copy. Each memory copy
+   * is assigned a unique runtime correlation ID that is identical to
+   * the correlation ID in the runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t runtimeCorrelationId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t padding;
+} CUpti_ActivityMemcpy4;
+
+/**
+ * \brief The activity record for peer-to-peer memory copies.
+ *
+ * This activity record represents a peer-to-peer memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY2) but is no longer generated
+ * by CUPTI. Peer-to-peer memory copy activities are now reported using the
+ * CUpti_ActivityMemcpyPtoP2 activity record..
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY2.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size.  \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see
+   * CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+  * The ID of the device where the memory copy is occurring.
+  */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the device where memory is being copied from.
+   */
+  uint32_t srcDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied from.
+   */
+  uint32_t srcContextId;
+
+  /**
+   * The ID of the device where memory is being copied to.
+   */
+  uint32_t dstDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied to.
+   */
+  uint32_t dstContextId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t correlationId;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+} CUpti_ActivityMemcpyPtoP;
+
+typedef CUpti_ActivityMemcpyPtoP CUpti_ActivityMemcpy2;
+
+/**
+ * \brief The activity record for peer-to-peer memory copies.
+ * (deprecated in CUDA 11.1)
+ *
+ * This activity record represents a peer-to-peer memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY2).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY2.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size.  \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see
+   * CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+  * The ID of the device where the memory copy is occurring.
+  */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the device where memory is being copied from.
+   */
+  uint32_t srcDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied from.
+   */
+  uint32_t srcContextId;
+
+  /**
+   * The ID of the device where memory is being copied to.
+   */
+  uint32_t dstDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied to.
+   */
+  uint32_t dstContextId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t correlationId;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed the memcpy through graph launch.
+   * This field will be 0 if memcpy is not done using graph launch.
+   */
+  uint64_t graphNodeId;
+} CUpti_ActivityMemcpyPtoP2;
+
+/**
+ * \brief The activity record for peer-to-peer memory copies.
+ * (deprecated in CUDA 11.6)
+ *
+ * This activity record represents a peer-to-peer memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY2).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY2.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size.  \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size.  \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see
+   * CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+  * The ID of the device where the memory copy is occurring.
+  */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the device where memory is being copied from.
+   */
+  uint32_t srcDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied from.
+   */
+  uint32_t srcContextId;
+
+  /**
+   * The ID of the device where memory is being copied to.
+   */
+  uint32_t dstDeviceId;
+
+  /**
+   * The ID of the context owning the memory being copied to.
+   */
+  uint32_t dstContextId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t correlationId;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed the memcpy through graph launch.
+   * This field will be 0 if memcpy is not done using graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t padding;
+} CUpti_ActivityMemcpyPtoP3;
+
+/**
+ * \brief The activity record for memset. (deprecated)
+ *
+ * This activity record represents a memory set operation
+ * (CUPTI_ACTIVITY_KIND_MEMSET).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMSET.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The value being assigned to memory by the memory set.
+   */
+  uint32_t value;
+
+  /**
+   * The number of bytes being set by the memory set.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory set is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory set is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory set is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory set. Each memory set is assigned
+   * a unique correlation ID that is identical to the correlation ID
+   * in the driver API activity record that launched the memory set.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The flags associated with the memset. \see CUpti_ActivityFlag
+   */
+  uint16_t flags;
+
+  /**
+   * The memory kind of the memory set \see CUpti_ActivityMemoryKind
+   */
+  uint16_t memoryKind;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+} CUpti_ActivityMemset;
+
+/**
+ * \brief The activity record for memset. (deprecated in CUDA 11.1)
+ *
+ * This activity record represents a memory set operation
+ * (CUPTI_ACTIVITY_KIND_MEMSET).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMSET.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The value being assigned to memory by the memory set.
+   */
+  uint32_t value;
+
+  /**
+   * The number of bytes being set by the memory set.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory set is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory set is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory set is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory set. Each memory set is assigned
+   * a unique correlation ID that is identical to the correlation ID
+   * in the driver API activity record that launched the memory set.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The flags associated with the memset. \see CUpti_ActivityFlag
+   */
+  uint16_t flags;
+
+  /**
+   * The memory kind of the memory set \see CUpti_ActivityMemoryKind
+   */
+  uint16_t memoryKind;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memset through graph launch.
+   * This field will be 0 if the memset is not executed through graph launch.
+   */
+  uint64_t graphNodeId;
+} CUpti_ActivityMemset2;
+
+/**
+ * \brief The activity record for memset. (deprecated in CUDA 11.6)
+ *
+ * This activity record represents a memory set operation
+ * (CUPTI_ACTIVITY_KIND_MEMSET).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMSET.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The value being assigned to memory by the memory set.
+   */
+  uint32_t value;
+
+  /**
+   * The number of bytes being set by the memory set.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory set, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory set.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory set is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory set is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory set is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory set. Each memory set is assigned
+   * a unique correlation ID that is identical to the correlation ID
+   * in the driver API activity record that launched the memory set.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The flags associated with the memset. \see CUpti_ActivityFlag
+   */
+  uint16_t flags;
+
+  /**
+   * The memory kind of the memory set \see CUpti_ActivityMemoryKind
+   */
+  uint16_t memoryKind;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memset through graph launch.
+   * This field will be 0 if the memset is not executed through graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memset through graph launch.
+   * This field will be 0 if the memset is not executed through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t padding;
+} CUpti_ActivityMemset3;
+
+/**
+ * \brief The activity record for memory.
+ *
+ * This activity record represents a memory allocation and free operation
+ * (CUPTI_ACTIVITY_KIND_MEMORY2).
+ * This activity record provides separate records for memory allocation and
+ * memory release operations.
+ * This allows to correlate the corresponding driver and runtime API
+ * activity record with the memory operation.
+ *
+ * Note: This activity record is an upgrade over \ref CUpti_ActivityMemory
+ * enabled using the kind \ref CUPTI_ACTIVITY_KIND_MEMORY.
+ * \ref CUpti_ActivityMemory provides a single record for the memory
+ * allocation and memory release operations.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMORY2
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The memory operation requested by the user, \ref CUpti_ActivityMemoryOperationType.
+   */
+  CUpti_ActivityMemoryOperationType memoryOperationType;
+
+  /**
+   * The memory kind requested by the user, \ref CUpti_ActivityMemoryKind.
+   */
+  CUpti_ActivityMemoryKind memoryKind;
+
+  /**
+   * The correlation ID of the memory operation. Each memory operation is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The virtual address of the allocation.
+   */
+  uint64_t address;
+
+  /**
+   * The number of bytes of memory allocated.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory operation, in ns.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The program counter of the memory operation.
+   */
+  uint64_t PC;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the device where the memory operation is taking place.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context. If context is NULL, \p contextId is set to CUPTI_INVALID_CONTEXT_ID.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream. If memory operation is not async, \p streamId is set to CUPTI_INVALID_STREAM_ID.
+   */
+  uint32_t streamId;
+
+  /**
+   * Variable name. This name is shared across all activity
+   * records representing the same symbol, and so should not be
+   * modified.
+   */
+  const char* name;
+
+  /**
+   * \p isAsync is set if memory operation happens through async memory APIs.
+   */
+  uint32_t isAsync;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad1;
+#endif
+
+  /**
+   * The memory pool configuration used for the memory operations.
+   */
+  struct {
+    /**
+     * The type of the memory pool, \ref CUpti_ActivityMemoryPoolType
+     */
+    CUpti_ActivityMemoryPoolType memoryPoolType;
+
+#ifdef CUPTILP64
+    /**
+     * Undefined. Reserved for internal use.
+     */
+    uint32_t pad2;
+#endif
+
+    /**
+     * The base address of the memory pool.
+     */
+    uint64_t address;
+
+    /**
+     * The release threshold of the memory pool in bytes. \p releaseThreshold is
+     * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     */
+    uint64_t releaseThreshold;
+
+   /**
+   * The size of the memory pool in bytes and the processID of the memory pool.
+   * \p size is valid if \p memoryPoolType is
+   * CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+   * \p processId is valid if \p memoryPoolType is
+   * CUPTI_ACTIVITY_MEMORY_POOL_TYPE_IMPORTED, \ref CUpti_ActivityMemoryPoolType.
+   */
+   union {
+      uint64_t size;
+      uint64_t processId;
+    } pool;
+  } memoryPoolConfig;
+
+} CUpti_ActivityMemory2;
+
+/**
+ * \brief The activity record for memory.
+ *
+ * This activity record represents a memory allocation and free operation
+ * (CUPTI_ACTIVITY_KIND_MEMORY2).
+ * This activity record provides separate records for memory allocation and
+ * memory release operations.
+ * This allows to correlate the corresponding driver and runtime API
+ * activity record with the memory operation.
+ *
+ * Note: This activity record is an upgrade over \ref CUpti_ActivityMemory2
+ * enabled using the kind \ref CUPTI_ACTIVITY_KIND_MEMORY.
+ * \ref CUpti_ActivityMemory provides a single record for the memory
+ * allocation and memory release operations.
+ */
+
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMORY2
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The memory operation requested by the user, \ref CUpti_ActivityMemoryOperationType.
+   */
+  CUpti_ActivityMemoryOperationType memoryOperationType;
+
+  /**
+   * The memory kind requested by the user, \ref CUpti_ActivityMemoryKind.
+   */
+  CUpti_ActivityMemoryKind memoryKind;
+
+  /**
+   * The correlation ID of the memory operation. Each memory operation is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The virtual address of the allocation.
+   */
+  uint64_t address;
+
+  /**
+   * The number of bytes of memory allocated.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory operation, in ns.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The program counter of the memory operation.
+   */
+  uint64_t PC;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the device where the memory operation is taking place.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context. If context is NULL, \p contextId is set to CUPTI_INVALID_CONTEXT_ID.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream. If memory operation is not async, \p streamId is set to CUPTI_INVALID_STREAM_ID.
+   */
+  uint32_t streamId;
+
+  /**
+   * Variable name. This name is shared across all activity
+   * records representing the same symbol, and so should not be
+   * modified.
+   */
+  const char* name;
+
+  /**
+   * \p isAsync is set if memory operation happens through async memory APIs.
+   */
+  uint32_t isAsync;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad1;
+#endif
+
+  /**
+   * The memory pool configuration used for the memory operations.
+   */
+  struct PACKED_ALIGNMENT {
+    /**
+     * The type of the memory pool, \ref CUpti_ActivityMemoryPoolType
+     */
+    CUpti_ActivityMemoryPoolType memoryPoolType;
+
+#ifdef CUPTILP64
+    /**
+     * Undefined. Reserved for internal use.
+     */
+    uint32_t pad2;
+#endif
+
+    /**
+     * The base address of the memory pool.
+     */
+    uint64_t address;
+
+    /**
+     * The release threshold of the memory pool in bytes. \p releaseThreshold is
+     * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     */
+    uint64_t releaseThreshold;
+
+    /**
+     * The size of memory pool in bytes and the processId of the memory pools
+     * \p size is valid if \p memoryPoolType is
+     * CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     * \p processId is valid if \p memoryPoolType is
+     * CUPTI_ACTIVITY_MEMORY_POOL_TYPE_IMPORTED, \ref CUpti_ActivityMemoryPoolType
+     */
+    union {
+      uint64_t size;
+      uint64_t processId;
+    } pool;
+
+    /**
+     * The utilized size of the memory pool. \p utilizedSize is
+     * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+     */
+    uint64_t utilizedSize;
+  } memoryPoolConfig;
+
+} CUpti_ActivityMemory3;
+
+/**
+ * \brief The activity record for memory pool.
+ *
+ * This activity record represents a memory pool creation, destruction and
+ * trimming (CUPTI_ACTIVITY_KIND_MEMORY_POOL).
+ * This activity record provides separate records for memory pool creation,
+ * destruction and trimming operations.
+ * This allows to correlate the corresponding driver and runtime API
+ * activity record with the memory pool operation.
+ *
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMORY_POOL
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The memory operation requested by the user, \ref CUpti_ActivityMemoryPoolOperationType.
+   */
+  CUpti_ActivityMemoryPoolOperationType memoryPoolOperationType;
+
+  /**
+   * The type of the memory pool, \ref CUpti_ActivityMemoryPoolType
+   */
+  CUpti_ActivityMemoryPoolType memoryPoolType;
+
+  /**
+   * The correlation ID of the memory pool operation. Each memory pool
+   * operation is assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver and runtime API activity record that
+   * launched the memory operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The ID of the device where the memory pool is created.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The minimum bytes to keep of the memory pool. \p minBytesToKeep is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_OPERATION_TYPE_TRIMMED,
+   * \ref CUpti_ActivityMemoryPoolOperationType
+   */
+  size_t minBytesToKeep;
+
+#ifndef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The virtual address of the allocation.
+   */
+  uint64_t address;
+
+  /**
+   * The size of the memory pool operation in bytes. \p size is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+   */
+  uint64_t size;
+
+  /**
+   * The release threshold of the memory pool. \p releaseThreshold is
+   * valid for CUPTI_ACTIVITY_MEMORY_POOL_TYPE_LOCAL, \ref CUpti_ActivityMemoryPoolType.
+   */
+  uint64_t releaseThreshold;
+
+  /**
+   * The start timestamp for the memory operation, in ns.
+   */
+  uint64_t timestamp;
+} CUpti_ActivityMemoryPool;
+
+/**
+ * \brief The activity record providing a marker which is an
+ * instantaneous point in time. (deprecated in CUDA 8.0)
+ *
+ * The marker is specified with a descriptive name and unique id
+ * (CUPTI_ACTIVITY_KIND_MARKER).
+ * Marker activity is now reported using the
+ * CUpti_ActivityMarker2 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MARKER.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The flags associated with the marker. \see CUpti_ActivityFlag
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The timestamp for the marker, in ns. A value of 0 indicates that
+   * timestamp information could not be collected for the marker.
+   */
+  uint64_t timestamp;
+
+  /**
+   * The marker ID.
+   */
+  uint32_t id;
+
+  /**
+   * The kind of activity object associated with this marker.
+   */
+  CUpti_ActivityObjectKind objectKind;
+
+  /**
+   * The identifier for the activity object associated with this
+   * marker. 'objectKind' indicates which ID is valid for this record.
+   */
+  CUpti_ActivityObjectKindId objectId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * The marker name for an instantaneous or start marker. This will
+   * be NULL for an end marker.
+   */
+  const char *name;
+
+} CUpti_ActivityMarker;
+
+/**
+ * \brief The activity record for source-level global
+ * access. (deprecated)
+ *
+ * This activity records the locations of the global
+ * accesses in the source (CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS).
+ * Global access activities are now reported using the
+ * CUpti_ActivityGlobalAccess3 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this global access.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The pc offset for the access.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * when at least one of thread among warp is active with predicate and condition code
+   * evaluating to true.
+   */
+  uint32_t executed;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction with predicate and condition code evaluating to true.
+   */
+  uint64_t threadsExecuted;
+
+  /**
+   * The total number of 32 bytes transactions to L2 cache generated by this access
+   */
+  uint64_t l2_transactions;
+} CUpti_ActivityGlobalAccess;
+
+/**
+ * \brief The activity record for source-level global
+ * access. (deprecated in CUDA 9.0)
+ *
+ * This activity records the locations of the global
+ * accesses in the source (CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS).
+ * Global access activities are now reported using the
+ * CUpti_ActivityGlobalAccess3 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_GLOBAL_ACCESS.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this global access.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the access.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction with predicate and condition code evaluating to true.
+   */
+  uint64_t threadsExecuted;
+
+  /**
+   * The total number of 32 bytes transactions to L2 cache generated by this access
+   */
+  uint64_t l2_transactions;
+
+  /**
+   * The minimum number of L2 transactions possible based on the access pattern.
+   */
+  uint64_t theoreticalL2Transactions;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * when at least one of thread among warp is active with predicate and condition code
+   * evaluating to true.
+   */
+  uint32_t executed;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityGlobalAccess2;
+
+/**
+ * \brief The activity record for source level result
+ * branch. (deprecated)
+ *
+ * This activity record the locations of the branches in the
+ * source (CUPTI_ACTIVITY_KIND_BRANCH).
+ * Branch activities are now reported using the
+ * CUpti_ActivityBranch2 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_BRANCH.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The pc offset for the branch.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * The number of times this instruction was executed per warp. It will be incremented
+   * regardless of predicate or condition code.
+   */
+  uint32_t executed;
+
+  /**
+   * Number of times this branch diverged
+   */
+  uint32_t diverged;
+
+  /**
+   * This increments each time when this instruction is executed by number
+   * of threads that executed this instruction
+   */
+  uint64_t threadsExecuted;
+} CUpti_ActivityBranch;
+
+/**
+ * \brief The activity record for PC sampling. (deprecated in CUDA 8.0)
+ *
+ * This activity records information obtained by sampling PC
+ * (CUPTI_ACTIVITY_KIND_PC_SAMPLING).
+ * PC sampling activities are now reported using the
+ * CUpti_ActivityPCSampling2 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_PC_SAMPLING.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this instruction.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the instruction.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * Number of times the PC was sampled with the stallReason in the record.
+   * The same PC can be sampled with different stall reasons.
+   */
+  uint32_t samples;
+
+  /**
+   * Current stall reason. Includes one of the reasons from
+   * \ref CUpti_ActivityPCSamplingStallReason
+   */
+  CUpti_ActivityPCSamplingStallReason stallReason;
+} CUpti_ActivityPCSampling;
+
+/**
+ * \brief The activity record for PC sampling. (deprecated in CUDA 9.0)
+ *
+ * This activity records information obtained by sampling PC
+ * (CUPTI_ACTIVITY_KIND_PC_SAMPLING).
+ * PC sampling activities are now reported using the
+ * CUpti_ActivityPCSampling3 activity record.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_PC_SAMPLING.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The properties of this instruction.
+   */
+  CUpti_ActivityFlag flags;
+
+  /**
+   * The ID for source locator.
+   */
+  uint32_t sourceLocatorId;
+
+  /**
+   * The correlation ID of the kernel to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+  * Correlation ID with global/device function name
+  */
+  uint32_t functionId;
+
+  /**
+   * The pc offset for the instruction.
+   */
+  uint32_t pcOffset;
+
+  /**
+   * Number of times the PC was sampled with the stallReason in the record.
+   * These samples indicate that no instruction was issued in that cycle from
+   * the warp scheduler from where the warp was sampled.
+   * Field is valid for devices with compute capability 6.0 and higher
+   */
+  uint32_t latencySamples;
+
+  /**
+   * Number of times the PC was sampled with the stallReason in the record.
+   * The same PC can be sampled with different stall reasons. The count includes
+   * latencySamples.
+   */
+  uint32_t samples;
+
+  /**
+   * Current stall reason. Includes one of the reasons from
+   * \ref CUpti_ActivityPCSamplingStallReason
+   */
+  CUpti_ActivityPCSamplingStallReason stallReason;
+
+  uint32_t pad;
+} CUpti_ActivityPCSampling2;
+
+/**
+ * \brief The activity record for Unified Memory counters (deprecated in CUDA 7.0)
+ *
+ * This activity record represents a Unified Memory counter
+ * (CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The Unified Memory counter kind. See \ref CUpti_ActivityUnifiedMemoryCounterKind
+   */
+  CUpti_ActivityUnifiedMemoryCounterKind counterKind;
+
+  /**
+   * Scope of the Unified Memory counter. See \ref CUpti_ActivityUnifiedMemoryCounterScope
+   */
+  CUpti_ActivityUnifiedMemoryCounterScope scope;
+
+  /**
+   * The ID of the device involved in the memory transfer operation.
+   * It is not relevant if the scope of the counter is global (all devices).
+   */
+  uint32_t deviceId;
+
+  /**
+   * Value of the counter
+   *
+   */
+  uint64_t value;
+
+  /**
+   * The timestamp when this sample was retrieved, in ns. A value of 0
+   * indicates that timestamp information could not be collected
+   */
+  uint64_t timestamp;
+
+  /**
+   * The ID of the process to which this record belongs to. In case of
+   * global scope, processId is undefined.
+   */
+  uint32_t processId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityUnifiedMemoryCounter;
+
+/**
+ * \brief The activity record for Unified Memory counters (deprecated in 12.8)
+ *
+ * This activity record represents a Unified Memory counter
+ * (CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER).
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_UNIFIED_MEMORY_COUNTER
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The Unified Memory counter kind
+   */
+  CUpti_ActivityUnifiedMemoryCounterKind counterKind;
+
+  /**
+   * Value of the counter
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD,
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH,
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THREASHING and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP, it is the size of the
+   * memory region in bytes.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT, it
+   * is the number of page fault groups for the same page.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT,
+   * it is the program counter for the instruction that caused fault.
+   */
+  uint64_t value;
+
+  /**
+   * The start timestamp of the counter, in ns.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH, timestamp is
+   * captured when activity starts on GPU.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT, timestamp is
+   * captured when CUDA driver started processing the fault.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING, timestamp
+   * is captured when CUDA driver detected thrashing of memory region.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING,
+   * timestamp is captured when throttling operation was started by CUDA driver.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP,
+   * timestamp is captured when CUDA driver has pushed all required operations
+   * to the processor specified by dstId.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp of the counter, in ns.
+   * Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD and
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_DTOH, timestamp is
+   * captured when activity finishes on GPU.
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT, timestamp is
+   * captured when CUDA driver queues the replay of faulting memory accesses on the GPU
+   * For counterKind CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING, timestamp
+   * is captured when throttling operation was finished by CUDA driver
+   */
+  uint64_t end;
+
+  /**
+   * This is the virtual base address of the page/s being transferred. For cpu and
+   * gpu faults, the virtual address for the page that faulted.
+   */
+  uint64_t address;
+
+  /**
+   * The ID of the source CPU/device involved in the memory transfer, page fault, thrashing,
+   * throttling or remote map operation. For counterKind
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING, it is a bitwise ORing of the
+   * device IDs fighting for the memory region. Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT
+   */
+  uint32_t srcId;
+
+  /**
+   * The ID of the destination CPU/device involved in the memory transfer or remote map
+   * operation. Ignore this field if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_CPU_PAGE_FAULT_COUNT or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING
+   */
+  uint32_t dstId;
+
+  /**
+   * The ID of the stream causing the transfer.
+   * This value of this field is invalid.
+   */
+  uint32_t streamId;
+
+  /**
+   * The ID of the process to which this record belongs to.
+   */
+  uint32_t processId;
+
+  /**
+   * The flags associated with this record. See enums \ref CUpti_ActivityUnifiedMemoryAccessType
+   * if counterKind is CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_GPU_PAGE_FAULT
+   * and \ref CUpti_ActivityUnifiedMemoryMigrationCause if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_BYTES_TRANSFER_HTOD
+   * and \ref CUpti_ActivityUnifiedMemoryRemoteMapCause if counterKind is
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_REMOTE_MAP and \ref CUpti_ActivityFlag
+   * if counterKind is CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THRASHING or
+   * CUPTI_ACTIVITY_UNIFIED_MEMORY_COUNTER_KIND_THROTTLING
+   */
+  uint32_t flags;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityUnifiedMemoryCounter2;
+
+/**
+* \brief NVLink information. (deprecated in CUDA 9.0)
+*
+* This structure gives capabilities of each logical NVLink connection between two devices,
+* gpu<->gpu or gpu<->CPU which can be used to understand the topology.
+* NVLink information are now reported using the
+* CUpti_ActivityNvLink2 activity record.
+*/
+typedef struct PACKED_ALIGNMENT {
+  /**
+  * The activity record kind, must be CUPTI_ACTIVITY_KIND_NVLINK.
+  */
+  CUpti_ActivityKind kind;
+
+  /**
+  * NVLink version.
+  */
+  uint32_t nvlinkVersion;
+
+  /**
+  * Type of device 0 \ref CUpti_DevType
+  */
+  CUpti_DevType typeDev0;
+
+  /**
+  * Type of device 1 \ref CUpti_DevType
+  */
+  CUpti_DevType typeDev1;
+
+  /**
+  * If typeDev0 is CUPTI_DEV_TYPE_GPU, UUID for device 0. \ref CUpti_ActivityDevice5.
+  * If typeDev0 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+      * Index of the NPU. First index will always be zero.
+      */
+      uint32_t index;
+
+      /**
+      * Domain ID of NPU. On Linux, this can be queried using lspci.
+      */
+      uint32_t domainId;
+    } npu;
+  } idDev0;
+
+  /**
+  * If typeDev1 is CUPTI_DEV_TYPE_GPU, UUID for device 1. \ref CUpti_ActivityDevice5.
+  * If typeDev1 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+      * Index of the NPU. First index will always be zero.
+      */
+      uint32_t index;
+
+      /**
+      * Domain ID of NPU. On Linux, this can be queried using lspci.
+      */
+      uint32_t domainId;
+    } npu;
+  } idDev1;
+
+  /**
+  * Flag gives capabilities of the link \see CUpti_LinkFlag
+  */
+  uint32_t flag;
+
+  /**
+  * Number of physical NVLinks present between two devices.
+  */
+  uint32_t physicalNvLinkCount;
+
+  /**
+  * Port numbers for maximum 4 NVLinks connected to device 0.
+  * If typeDev0 is CUPTI_DEV_TYPE_NPU, ignore this field.
+  * In case of invalid/unknown port number, this field will be set
+  * to value CUPTI_NVLINK_INVALID_PORT.
+  * This will be used to correlate the metric values to individual
+  * physical link and attribute traffic to the logical NVLink in
+  * the topology.
+  */
+  int8_t portDev0[4];
+
+  /**
+  * Port numbers for maximum 4 NVLinks connected to device 1.
+  * If typeDev1 is CUPTI_DEV_TYPE_NPU, ignore this field.
+  * In case of invalid/unknown port number, this field will be set
+  * to value CUPTI_NVLINK_INVALID_PORT.
+  * This will be used to correlate the metric values to individual
+  * physical link and attribute traffic to the logical NVLink in
+  * the topology.
+  */
+  int8_t portDev1[4];
+
+  /**
+  * Bandwidth of NVLink in kbytes/sec
+  */
+  uint64_t bandwidth;
+} CUpti_ActivityNvLink;
+
+/**
+* \brief NVLink information. (deprecated in CUDA 10.0)
+*
+* This structure gives capabilities of each logical NVLink connection between two devices,
+* gpu<->gpu or gpu<->CPU which can be used to understand the topology.
+* NvLink information are now reported using the
+* CUpti_ActivityNvLink4 activity record.
+*/
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_NVLINK.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * NvLink version.
+   */
+  uint32_t nvlinkVersion;
+
+  /**
+   * Type of device 0 \ref CUpti_DevType
+   */
+  CUpti_DevType typeDev0;
+
+  /**
+   * Type of device 1 \ref CUpti_DevType
+   */
+  CUpti_DevType typeDev1;
+
+  /**
+  * If typeDev0 is CUPTI_DEV_TYPE_GPU, UUID for device 0. \ref CUpti_ActivityDevice5.
+  * If typeDev0 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+       * Index of the NPU. First index will always be zero.
+       */
+      uint32_t index;
+
+      /**
+       * Domain ID of NPU. On Linux, this can be queried using lspci.
+       */
+      uint32_t domainId;
+    } npu;
+  } idDev0;
+
+  /**
+  * If typeDev1 is CUPTI_DEV_TYPE_GPU, UUID for device 1. \ref CUpti_ActivityDevice5.
+  * If typeDev1 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+       * Index of the NPU. First index will always be zero.
+       */
+      uint32_t index;
+
+      /**
+       * Domain ID of NPU. On Linux, this can be queried using lspci.
+       */
+      uint32_t domainId;
+    } npu;
+  } idDev1;
+
+  /**
+   * Flag gives capabilities of the link \see CUpti_LinkFlag
+   */
+  uint32_t flag;
+
+  /**
+   * Number of physical NVLinks present between two devices.
+   */
+  uint32_t physicalNvLinkCount;
+
+  /**
+   * Port numbers for maximum 16 NVLinks connected to device 0.
+   * If typeDev0 is CUPTI_DEV_TYPE_NPU, ignore this field.
+   * In case of invalid/unknown port number, this field will be set
+   * to value CUPTI_NVLINK_INVALID_PORT.
+   * This will be used to correlate the metric values to individual
+   * physical link and attribute traffic to the logical NVLink in
+   * the topology.
+   */
+  int8_t portDev0[CUPTI_MAX_NVLINK_PORTS];
+
+  /**
+   * Port numbers for maximum 16 NVLinks connected to device 1.
+   * If typeDev1 is CUPTI_DEV_TYPE_NPU, ignore this field.
+   * In case of invalid/unknown port number, this field will be set
+   * to value CUPTI_NVLINK_INVALID_PORT.
+   * This will be used to correlate the metric values to individual
+   * physical link and attribute traffic to the logical NVLink in
+   * the topology.
+   */
+  int8_t portDev1[CUPTI_MAX_NVLINK_PORTS];
+
+  /**
+   * Bandwidth of NVLink in kbytes/sec
+   */
+  uint64_t  bandwidth;
+} CUpti_ActivityNvLink2;
+
+/**
+* \brief NVLink information.
+*
+* This structure gives capabilities of each logical NVLink connection between two devices,
+* gpu<->gpu or gpu<->CPU which can be used to understand the topology.
+* NvLink information are now reported using the
+* CUpti_ActivityNvLink4 activity record.
+*/
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_NVLINK.
+   */
+  CUpti_ActivityKind kind;
+  /**
+   * NvLink version.
+   */
+  uint32_t nvlinkVersion;
+
+  /**
+   * Type of device 0 \ref CUpti_DevType
+   */
+  CUpti_DevType typeDev0;
+
+  /**
+   * Type of device 1 \ref CUpti_DevType
+   */
+  CUpti_DevType typeDev1;
+
+  /**
+  * If typeDev0 is CUPTI_DEV_TYPE_GPU, UUID for device 0. \ref CUpti_ActivityDevice5.
+  * If typeDev0 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+       * Index of the NPU. First index will always be zero.
+       */
+      uint32_t index;
+
+      /**
+       * Domain ID of NPU. On Linux, this can be queried using lspci.
+       */
+      uint32_t domainId;
+    } npu;
+  } idDev0;
+
+  /**
+  * If typeDev1 is CUPTI_DEV_TYPE_GPU, UUID for device 1. \ref CUpti_ActivityDevice5.
+  * If typeDev1 is CUPTI_DEV_TYPE_NPU, struct npu for NPU.
+  */
+  union {
+    CUuuid uuidDev;
+    struct {
+      /**
+       * Index of the NPU. First index will always be zero.
+       */
+      uint32_t index;
+
+      /**
+       * Domain ID of NPU. On Linux, this can be queried using lspci.
+       */
+      uint32_t domainId;
+    } npu;
+  } idDev1;
+
+  /**
+   * Flag gives capabilities of the link \see CUpti_LinkFlag
+   */
+  uint32_t flag;
+
+  /**
+   * Number of physical NVLinks present between two devices.
+   */
+  uint32_t physicalNvLinkCount;
+
+  /**
+   * Port numbers for maximum 16 NVLinks connected to device 0.
+   * If typeDev0 is CUPTI_DEV_TYPE_NPU, ignore this field.
+   * In case of invalid/unknown port number, this field will be set
+   * to value CUPTI_NVLINK_INVALID_PORT.
+   * This will be used to correlate the metric values to individual
+   * physical link and attribute traffic to the logical NVLink in
+   * the topology.
+   */
+  int8_t portDev0[CUPTI_MAX_NVLINK_PORTS];
+
+  /**
+   * Port numbers for maximum 16 NVLinks connected to device 1.
+   * If typeDev1 is CUPTI_DEV_TYPE_NPU, ignore this field.
+   * In case of invalid/unknown port number, this field will be set
+   * to value CUPTI_NVLINK_INVALID_PORT.
+   * This will be used to correlate the metric values to individual
+   * physical link and attribute traffic to the logical NVLink in
+   * the topology.
+   */
+  int8_t portDev1[CUPTI_MAX_NVLINK_PORTS];
+
+  /**
+   * Bandwidth of NVLink in kbytes/sec
+   */
+  uint64_t bandwidth;
+
+  /**
+   * NVSwitch is connected as an intermediate node.
+   */
+  uint8_t nvswitchConnected;
+
+  /**
+   * Undefined. reserved for internal use
+   */
+  uint8_t pad[7];
+} CUpti_ActivityNvLink3;
+
+/**
+ * \brief The activity record for trace of graph execution.
+ *
+ * This activity record represents execution for a graph without giving visibility
+ * about the execution of its nodes. This is intended to reduce overheads in tracing
+ * each node. The activity kind is CUPTI_ACTIVITY_KIND_GRAPH_TRACE
+ * Graph trace activity is now reported using CUpti_ActivityGraphTrace2 record.
+ */
+typedef struct {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_GRAPH_TRACE
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The correlation ID of the graph launch. Each graph launch is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the graph.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The start timestamp for the graph execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the graph.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the graph execution, in ns. A value of 0
+   * for both the start and end timestamps indicates that timestamp
+   * information could not be collected for the graph.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the graph execution is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The unique ID of the graph that is launched.
+   */
+  uint32_t graphId;
+
+  /**
+   * The ID of the context where the graph is being launched.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the graph is being launched.
+   */
+  uint32_t streamId;
+
+  /**
+   * This field is reserved for internal use
+   */
+  void *reserved;
+} CUpti_ActivityGraphTrace;
+
+/**
+ * \brief The activity record for a context.
+ *
+ * This activity record represents information about a context
+ * (CUPTI_ACTIVITY_KIND_CONTEXT).
+ * Context activity is now reported using CUpti_ActivityContext3 record
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_CONTEXT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The context ID.
+   */
+  uint32_t contextId;
+
+  /**
+   * The device ID.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The compute API kind. \see CUpti_ActivityComputeApiKind
+   */
+  uint16_t computeApiKind;
+
+  /**
+   * The ID for the NULL stream in this context
+   */
+  uint16_t nullStreamId;
+} CUpti_ActivityContext;
+
+/**
+ * \brief The activity record for a context.
+ *
+ * This activity record represents information about a context
+ * (CUPTI_ACTIVITY_KIND_CONTEXT).
+ * Context activity is now reported using CUpti_ActivityContext3 record
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_CONTEXT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The context ID.
+   */
+  uint32_t contextId;
+
+  /**
+   * The device ID.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The compute API kind. \see CUpti_ActivityComputeApiKind
+   */
+  uint16_t computeApiKind;
+
+  /**
+   * The ID for the NULL stream in this context
+   */
+  uint16_t nullStreamId;
+
+  /**
+   * The ID of the parent context. It would be 0 if
+   * context does not have parent
+   */
+  uint32_t parentContextId;
+
+  /**
+   * This field indicates whether the context is a green context
+   */
+  uint8_t isGreenContext;
+
+  uint8_t padding;
+
+  /**
+   * Number of multiprocessors assigned to the green context
+   * Invalid if the field 'isGreenContext' is 0
+   */
+  uint16_t numMultiprocessors;
+} CUpti_ActivityContext2;
+
+/**
+ * \brief The activity record for JIT operations.
+ * This activity represents the JIT operations (compile, load, store) of a CUmodule
+ * from the Compute Cache.
+ * Gives the exact hashed path of where the cached module is loaded from,
+ * or where the module will be stored after Just-In-Time (JIT) compilation.
+ *
+ * JIT activity is now reported using CUpti_ActivityJit2 record
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind must be CUPTI_ACTIVITY_KIND_JIT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+    * The JIT entry type.
+    */
+  CUpti_ActivityJitEntryType jitEntryType;
+
+  /**
+   * The JIT operation type.
+   */
+  CUpti_ActivityJitOperationType jitOperationType;
+
+  /**
+   * The device ID.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The start timestamp for the JIT operation, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the JIT operation.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the JIT operation, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the JIT operation.
+   */
+  uint64_t end;
+
+  /**
+   * The correlation ID of the JIT operation to which
+   * records belong to. Each JIT operation is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver or runtime API activity record that
+   * launched the JIT operation.
+   */
+  uint32_t correlationId;
+
+  /**
+   * Internal use.
+   */
+  uint32_t padding;
+
+  /**
+   * The correlation ID to correlate JIT compilation, load and store operations.
+   * Each JIT compilation unit is assigned a unique correlation ID
+   * at the time of the JIT compilation. This correlation id can be used
+   * to find the matching JIT cache load/store records.
+   */
+  uint64_t jitOperationCorrelationId;
+
+  /**
+   * The size of compute cache.
+   */
+  uint64_t cacheSize;
+
+  /**
+   * The path where the fat binary is cached.
+   */
+  const char* cachePath;
+} CUpti_ActivityJit;
+
+/**
+ * \brief The activity record for CUDA event.
+ *
+ * This activity is used to track recorded events.
+ * (CUPTI_ACTIVITY_KIND_CUDA_EVENT).
+ *
+ * Structure deprecated in CUDA 12.8: Refer to CUpti_ActivityCudaEvent2
+ * for the latest structure.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_CUDA_EVENT.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The correlation ID of the API to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The ID of the context where the event was recorded.
+   */
+  uint32_t contextId;
+
+  /**
+   * The compute stream where the event was recorded.
+   */
+  uint32_t streamId;
+
+  /**
+   * A unique event ID to identify the event record.
+   */
+  uint32_t eventId;
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+} CUpti_ActivityCudaEvent;
+
+/**
+ * \brief The activity record for synchronization management.
+ *
+ * This activity is used to track various CUDA synchronization APIs.
+ * (CUPTI_ACTIVITY_KIND_SYNCHRONIZATION).
+ *
+ * Structure deprecated in CUDA 12.8: Refer to CUpti_ActivitySynchronization2
+ * for the latest structure.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_SYNCHRONIZATION.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The type of record.
+   */
+  CUpti_ActivitySynchronizationType type;
+
+  /**
+   * The start timestamp for the function, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the function.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the function, in ns. A value of 0 for both
+   * the start and end timestamps indicates that timestamp information
+   * could not be collected for the function.
+   */
+  uint64_t end;
+
+  /**
+   * The correlation ID of the API to which this result is associated.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The ID of the context for which the synchronization API is called.
+   * In case of context synchronization API it is the context id for which the API is called.
+   * In case of stream/event synchronization it is the ID of the context where the stream/event was created.
+   */
+  uint32_t contextId;
+
+  /**
+   * The compute stream for which the synchronization API is called.
+   * A CUPTI_SYNCHRONIZATION_INVALID_VALUE value indicate the field is not applicable for this record.
+   * Not valid for cuCtxSynchronize, cuEventSynchronize.
+   */
+  uint32_t streamId;
+
+  /**
+   * The event ID for which the synchronization API is called.
+   * A CUPTI_SYNCHRONIZATION_INVALID_VALUE value indicate the field is not applicable for this record.
+   * Not valid for cuCtxSynchronize, cuStreamSynchronize.
+   */
+  uint32_t cudaEventId;
+} CUpti_ActivitySynchronization;
+
+/**
+ * \brief The activity record for memory copies.
+ *
+ * This activity record represents a memory copy
+ * (CUPTI_ACTIVITY_KIND_MEMCPY).
+ *
+ * Structure deprecated in CUDA 12.8: Refer to CUpti_ActivityMemcpy6
+ * for the latest structure.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * The activity record kind, must be CUPTI_ACTIVITY_KIND_MEMCPY.
+   */
+  CUpti_ActivityKind kind;
+
+  /**
+   * The kind of the memory copy, stored as a byte to reduce record
+   * size. \see CUpti_ActivityMemcpyKind
+   */
+  uint8_t copyKind;
+
+  /**
+   * The source memory kind read by the memory copy, stored as a byte
+   * to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t srcKind;
+
+  /**
+   * The destination memory kind read by the memory copy, stored as a
+   * byte to reduce record size. \see CUpti_ActivityMemoryKind
+   */
+  uint8_t dstKind;
+
+  /**
+   * The flags associated with the memory copy. \see CUpti_ActivityFlag
+   */
+  uint8_t flags;
+
+  /**
+   * The number of bytes transferred by the memory copy.
+   */
+  uint64_t bytes;
+
+  /**
+   * The start timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t start;
+
+  /**
+   * The end timestamp for the memory copy, in ns. A value of 0 for
+   * both the start and end timestamps indicates that timestamp
+   * information could not be collected for the memory copy.
+   */
+  uint64_t end;
+
+  /**
+   * The ID of the device where the memory copy is occurring.
+   */
+  uint32_t deviceId;
+
+  /**
+   * The ID of the context where the memory copy is occurring.
+   */
+  uint32_t contextId;
+
+  /**
+   * The ID of the stream where the memory copy is occurring.
+   */
+  uint32_t streamId;
+
+  /**
+   * The correlation ID of the memory copy. Each memory copy is
+   * assigned a unique correlation ID that is identical to the
+   * correlation ID in the driver API activity record that launched
+   * the memory copy.
+   */
+  uint32_t correlationId;
+
+  /**
+   * The runtime correlation ID of the memory copy. Each memory copy
+   * is assigned a unique runtime correlation ID that is identical to
+   * the correlation ID in the runtime API activity record that
+   * launched the memory copy.
+   */
+  uint32_t runtimeCorrelationId;
+
+#ifdef CUPTILP64
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  uint32_t pad;
+#endif
+
+  /**
+   * Undefined. Reserved for internal use.
+   */
+  void *reserved0;
+
+  /**
+   * The unique ID of the graph node that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint64_t graphNodeId;
+
+  /**
+   * The unique ID of the graph that executed this memcpy through graph launch.
+   * This field will be 0 if the memcpy is not done through graph launch.
+   */
+  uint32_t graphId;
+
+  /**
+   * The ID of the HW channel on which the memory copy is occurring.
+   */
+  uint32_t channelID;
+
+  /**
+   * The type of the channel
+   */
+  CUpti_ChannelType channelType;
+
+  /**
+   *  Reserved for internal use.
+   */
+  uint32_t pad2;
+} CUpti_ActivityMemcpy5;
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_ACTIVITY_DEPRECATED_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_callbacks.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_callbacks.h
new file mode 100644
index 0000000000000000000000000000000000000000..7dc1c94b2a6dc2cbab63af058ccec71f822cf63b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_callbacks.h
@@ -0,0 +1,863 @@
+/*
+ * Copyright 2010-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUPTI_CALLBACKS_H__)
+#define __CUPTI_CALLBACKS_H__
+
+#include <cuda.h>
+#include <builtin_types.h>
+#include <string.h>
+#include <cuda_stdint.h>
+#include <cupti_result.h>
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_CALLBACK_API CUPTI Callback API
+ * Functions, types, and enums that implement the CUPTI Callback API.
+ * @{
+ */
+
+/**
+ * \brief Specifies the point in an API call that a callback is issued.
+ *
+ * Specifies the point in an API call that a callback is issued. This
+ * value is communicated to the callback function via \ref
+ * CUpti_CallbackData::callbackSite.
+ */
+typedef enum {
+  /**
+   * The callback is at the entry of the API call.
+   */
+  CUPTI_API_ENTER                 = 0,
+  /**
+   * The callback is at the exit of the API call.
+   */
+  CUPTI_API_EXIT                  = 1,
+  CUPTI_API_CBSITE_FORCE_INT     = 0x7fffffff
+} CUpti_ApiCallbackSite;
+
+/**
+ * \brief Callback domains.
+ *
+ * Callback domains. Each domain represents callback points for a
+ * group of related API functions or CUDA driver activity.
+ */
+typedef enum {
+  /**
+   * Invalid domain.
+   */
+  CUPTI_CB_DOMAIN_INVALID           = 0,
+  /**
+   * Domain containing callback points for all driver API functions.
+   */
+  CUPTI_CB_DOMAIN_DRIVER_API        = 1,
+  /**
+   * Domain containing callback points for all runtime API
+   * functions.
+   */
+  CUPTI_CB_DOMAIN_RUNTIME_API       = 2,
+  /**
+   * Domain containing callback points for CUDA resource tracking.
+   */
+  CUPTI_CB_DOMAIN_RESOURCE          = 3,
+  /**
+   * Domain containing callback points for CUDA synchronization.
+   */
+  CUPTI_CB_DOMAIN_SYNCHRONIZE       = 4,
+  /**
+   * Domain containing callback points for NVTX API functions.
+   */
+  CUPTI_CB_DOMAIN_NVTX              = 5,
+  /**
+   * Domain containing callback points for various states.
+   */
+  CUPTI_CB_DOMAIN_STATE             = 6,
+
+  CUPTI_CB_DOMAIN_SIZE,
+
+  CUPTI_CB_DOMAIN_FORCE_INT         = 0x7fffffff
+} CUpti_CallbackDomain;
+
+/**
+ * \brief Callback IDs for resource domain.
+ *
+ * Callback IDs for resource domain, CUPTI_CB_DOMAIN_RESOURCE.  This
+ * value is communicated to the callback function via the \p cbid
+ * parameter.
+ */
+typedef enum {
+  /**
+   * Invalid resource callback ID.
+   */
+  CUPTI_CBID_RESOURCE_INVALID                               = 0,
+  /**
+   * A new context has been created.
+   */
+  CUPTI_CBID_RESOURCE_CONTEXT_CREATED                       = 1,
+  /**
+   * A context is about to be destroyed.
+   */
+  CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING              = 2,
+  /**
+   * A new stream has been created.
+   */
+  CUPTI_CBID_RESOURCE_STREAM_CREATED                        = 3,
+  /**
+   * A stream is about to be destroyed.
+   */
+  CUPTI_CBID_RESOURCE_STREAM_DESTROY_STARTING               = 4,
+  /**
+   * The driver has finished initializing.
+   */
+  CUPTI_CBID_RESOURCE_CU_INIT_FINISHED                      = 5,
+  /**
+   * A module has been loaded.
+   */
+  CUPTI_CBID_RESOURCE_MODULE_LOADED                         = 6,
+  /**
+   * A module is about to be unloaded.
+   */
+  CUPTI_CBID_RESOURCE_MODULE_UNLOAD_STARTING                = 7,
+  /**
+   * The current module which is being profiled.
+   */
+  CUPTI_CBID_RESOURCE_MODULE_PROFILED                       = 8,
+  /**
+   * CUDA graph has been created.
+   */
+  CUPTI_CBID_RESOURCE_GRAPH_CREATED                         = 9,
+  /**
+   * CUDA graph is about to be destroyed.
+   */
+  CUPTI_CBID_RESOURCE_GRAPH_DESTROY_STARTING                = 10,
+  /**
+   * CUDA graph is cloned.
+   */
+  CUPTI_CBID_RESOURCE_GRAPH_CLONED                          = 11,
+  /**
+   * CUDA graph node is about to be created
+   */
+  CUPTI_CBID_RESOURCE_GRAPHNODE_CREATE_STARTING             = 12,
+  /**
+   * CUDA graph node is created.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHNODE_CREATED                     = 13,
+  /**
+   * CUDA graph node is about to be destroyed.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHNODE_DESTROY_STARTING            = 14,
+  /**
+   * Dependency on a CUDA graph node is created.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHNODE_DEPENDENCY_CREATED          = 15,
+  /**
+   * Dependency on a CUDA graph node is destroyed.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHNODE_DEPENDENCY_DESTROY_STARTING = 16,
+  /**
+   * An executable CUDA graph is about to be created.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHEXEC_CREATE_STARTING             = 17,
+  /**
+   * An executable CUDA graph is created.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHEXEC_CREATED                     = 18,
+  /**
+   * An executable CUDA graph is about to be destroyed.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHEXEC_DESTROY_STARTING            = 19,
+  /**
+   * CUDA graph node is cloned.
+   */
+  CUPTI_CBID_RESOURCE_GRAPHNODE_CLONED                      = 20,
+  /**
+   * CUDA stream attribute is changed.
+   */
+  CUPTI_CBID_RESOURCE_STREAM_ATTRIBUTE_CHANGED              = 21,
+
+  CUPTI_CBID_RESOURCE_SIZE,
+  CUPTI_CBID_RESOURCE_FORCE_INT                   = 0x7fffffff
+} CUpti_CallbackIdResource;
+
+/**
+ * \brief Callback IDs for synchronization domain.
+ *
+ * Callback IDs for synchronization domain,
+ * CUPTI_CB_DOMAIN_SYNCHRONIZE.  This value is communicated to the
+ * callback function via the \p cbid parameter.
+ */
+typedef enum {
+  /**
+   * Invalid synchronize callback ID.
+   */
+  CUPTI_CBID_SYNCHRONIZE_INVALID                  = 0,
+  /**
+   * Stream synchronization has completed for the stream.
+   */
+  CUPTI_CBID_SYNCHRONIZE_STREAM_SYNCHRONIZED      = 1,
+  /**
+   * Context synchronization has completed for the context.
+   */
+  CUPTI_CBID_SYNCHRONIZE_CONTEXT_SYNCHRONIZED     = 2,
+  CUPTI_CBID_SYNCHRONIZE_SIZE,
+  CUPTI_CBID_SYNCHRONIZE_FORCE_INT                = 0x7fffffff
+} CUpti_CallbackIdSync;
+
+
+/**
+ * \brief Callback IDs for state domain.
+ *
+ * Callback IDs for state domain,
+ * CUPTI_CB_DOMAIN_STATE. This value is communicated to the
+ * callback function via the \p cbid parameter.
+ */
+typedef enum {
+  /**
+   * Invalid state callback ID.
+   */
+  CUPTI_CBID_STATE_INVALID                        = 0,
+  /**
+   * Notification of fatal errors - high impact, non-recoverable
+   * When encountered, CUPTI automatically invokes cuptiFinalize()
+   * User can control behavior of the application in future from 
+   * receiving this callback - such as continuing without profiling, or
+   * terminating the whole application.
+   */
+  CUPTI_CBID_STATE_FATAL_ERROR                    = 1,
+  /**
+   * Notification of non fatal errors - high impact, but recoverable
+   * This notification is not issued in the current release.
+   */
+  CUPTI_CBID_STATE_ERROR                          = 2,
+  /**
+   * Notification of warnings - low impact, recoverable.
+   */
+  CUPTI_CBID_STATE_WARNING                        = 3,
+
+  CUPTI_CBID_STATE_SIZE,
+  CUPTI_CBID_STATE_FORCE_INT         = 0x7fffffff
+} CUpti_CallbackIdState;
+
+
+/**
+ * \brief Data passed into a runtime or driver API callback function.
+ *
+ * Data passed into a runtime or driver API callback function as the
+ * \p cbdata argument to \ref CUpti_CallbackFunc. The \p cbdata will
+ * be this type for \p domain equal to CUPTI_CB_DOMAIN_DRIVER_API or
+ * CUPTI_CB_DOMAIN_RUNTIME_API. The callback data is valid only within
+ * the invocation of the callback function that is passed the data. If
+ * you need to retain some data for use outside of the callback, you
+ * must make a copy of that data. For example, if you make a shallow
+ * copy of CUpti_CallbackData within a callback, you cannot
+ * dereference \p functionParams outside of that callback to access
+ * the function parameters. \p functionName is an exception: the
+ * string pointed to by \p functionName is a global constant and so
+ * may be accessed outside of the callback.
+ */
+typedef struct {
+  /**
+   * Point in the runtime or driver function from where the callback
+   * was issued.
+   */
+  CUpti_ApiCallbackSite callbackSite;
+
+  /**
+   * Name of the runtime or driver API function which issued the
+   * callback. This string is a global constant and so may be
+   * accessed outside of the callback.
+   */
+  const char *functionName;
+
+  /**
+   * Pointer to the arguments passed to the runtime or driver API
+   * call. See generated_cuda_runtime_api_meta.h and
+   * generated_cuda_meta.h for structure definitions for the
+   * parameters for each runtime and driver API function.
+   */
+  const void *functionParams;
+
+  /**
+   * Pointer to the return value of the runtime or driver API
+   * call. This field is only valid within the exit::CUPTI_API_EXIT
+   * callback. For a runtime API \p functionReturnValue points to a
+   * \p cudaError_t. For a driver API \p functionReturnValue points
+   * to a \p CUresult.
+   */
+  void *functionReturnValue;
+
+  /**
+   * Name of the symbol operated on by the runtime or driver API
+   * function which issued the callback. This entry is valid only for
+   * driver and runtime launch callbacks, where it returns the name of
+   * the kernel.
+   */
+  const char *symbolName;
+
+  /**
+   * Driver context current to the thread, or null if no context is
+   * current. This value can change from the entry to exit callback
+   * of a runtime API function if the runtime initializes a context.
+   */
+  CUcontext context;
+
+  /**
+   * Unique ID for the CUDA context associated with the thread. The
+   * UIDs are assigned sequentially as contexts are created and are
+   * unique within a process.
+   */
+  uint32_t contextUid;
+
+  /**
+   * Pointer to data shared between the entry and exit callbacks of
+   * a given runtime or drive API function invocation. This field
+   * can be used to pass 64-bit values from the entry callback to
+   * the corresponding exit callback.
+   */
+  uint64_t *correlationData;
+
+  /**
+   * The activity record correlation ID for this callback. For a
+   * driver domain callback (i.e. \p domain
+   * CUPTI_CB_DOMAIN_DRIVER_API) this ID will equal the correlation ID
+   * in the CUpti_ActivityAPI record corresponding to the CUDA driver
+   * function call. For a runtime domain callback (i.e. \p domain
+   * CUPTI_CB_DOMAIN_RUNTIME_API) this ID will equal the correlation
+   * ID in the CUpti_ActivityAPI record corresponding to the CUDA
+   * runtime function call. Within the callback, this ID can be
+   * recorded to correlate user data with the activity record. This
+   * field is new in 4.1.
+   */
+  uint32_t correlationId;
+
+} CUpti_CallbackData;
+
+/**
+ * \brief Data passed into a resource callback function.
+ *
+ * Data passed into a resource callback function as the \p cbdata
+ * argument to \ref CUpti_CallbackFunc. The \p cbdata will be this
+ * type for \p domain equal to CUPTI_CB_DOMAIN_RESOURCE. The callback
+ * data is valid only within the invocation of the callback function
+ * that is passed the data. If you need to retain some data for use
+ * outside of the callback, you must make a copy of that data.
+ */
+typedef struct {
+  /**
+   * For CUPTI_CBID_RESOURCE_CONTEXT_CREATED and
+   * CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING, the context being
+   * created or destroyed. For CUPTI_CBID_RESOURCE_STREAM_CREATED and
+   * CUPTI_CBID_RESOURCE_STREAM_DESTROY_STARTING, the context
+   * containing the stream being created or destroyed.
+   */
+  CUcontext context;
+
+  union {
+    /**
+     * For CUPTI_CBID_RESOURCE_STREAM_CREATED and
+     * CUPTI_CBID_RESOURCE_STREAM_DESTROY_STARTING, the stream being
+     * created or destroyed.
+     */
+    CUstream stream;
+  } resourceHandle;
+
+  /**
+   * Reserved for future use.
+   */
+  void *resourceDescriptor;
+} CUpti_ResourceData;
+
+
+/**
+ * \brief Module data passed into a resource callback function.
+ *
+ * CUDA module data passed into a resource callback function as the \p cbdata
+ * argument to \ref CUpti_CallbackFunc. The \p cbdata will be this
+ * type for \p domain equal to CUPTI_CB_DOMAIN_RESOURCE. The module
+ * data is valid only within the invocation of the callback function
+ * that is passed the data. If you need to retain some data for use
+ * outside of the callback, you must make a copy of that data.
+ */
+
+typedef struct {
+  /**
+   * Identifier to associate with the CUDA module.
+   */
+    uint32_t moduleId;
+
+  /**
+   * The size of the cubin.
+   */
+    size_t cubinSize;
+
+  /**
+   * Pointer to the associated cubin.
+   */
+    const char *pCubin;
+} CUpti_ModuleResourceData;
+
+/**
+ * \brief CUDA graphs data passed into a resource callback function.
+ *
+ * CUDA graphs data passed into a resource callback function as the \p cbdata
+ * argument to \ref CUpti_CallbackFunc. The \p cbdata will be this
+ * type for \p domain equal to CUPTI_CB_DOMAIN_RESOURCE. The graph
+ * data is valid only within the invocation of the callback function
+ * that is passed the data. If you need to retain some data for use
+ * outside of the callback, you must make a copy of that data.
+ */
+
+typedef struct {
+  /**
+   * CUDA graph
+   */
+    CUgraph graph;
+  /**
+   * The original CUDA graph from which \param graph is cloned
+   */
+    CUgraph originalGraph;
+  /**
+   * CUDA graph node
+   */
+    CUgraphNode node;
+  /**
+   * The original CUDA graph node from which \param node is cloned
+   */
+    CUgraphNode originalNode;
+  /**
+   * Type of the \param node
+   */
+    CUgraphNodeType nodeType;
+  /**
+   * The dependent graph node
+   * The size of the array is \param numDependencies.
+   */
+    CUgraphNode dependency;
+  /**
+   * CUDA executable graph
+   */
+    CUgraphExec graphExec;
+} CUpti_GraphData;
+
+/**
+ * \brief Data passed into a synchronize callback function.
+ *
+ * Data passed into a synchronize callback function as the \p cbdata
+ * argument to \ref CUpti_CallbackFunc. The \p cbdata will be this
+ * type for \p domain equal to CUPTI_CB_DOMAIN_SYNCHRONIZE. The
+ * callback data is valid only within the invocation of the callback
+ * function that is passed the data. If you need to retain some data
+ * for use outside of the callback, you must make a copy of that data.
+ */
+typedef struct {
+  /**
+   * The context of the stream being synchronized.
+   */
+  CUcontext context;
+  /**
+   * The stream being synchronized.
+   */
+  CUstream  stream;
+} CUpti_SynchronizeData;
+
+/**
+ * \brief Data passed into a NVTX callback function.
+ *
+ * Data passed into a NVTX callback function as the \p cbdata argument
+ * to \ref CUpti_CallbackFunc. The \p cbdata will be this type for \p
+ * domain equal to CUPTI_CB_DOMAIN_NVTX. Unless otherwise notes, the
+ * callback data is valid only within the invocation of the callback
+ * function that is passed the data. If you need to retain some data
+ * for use outside of the callback, you must make a copy of that data.
+ */
+typedef struct {
+  /**
+   * Name of the NVTX API function which issued the callback. This
+   * string is a global constant and so may be accessed outside of the
+   * callback.
+   */
+  const char *functionName;
+
+  /**
+   * Pointer to the arguments passed to the NVTX API call. See
+   * generated_nvtx_meta.h for structure definitions for the
+   * parameters for each NVTX API function.
+   */
+  const void *functionParams;
+
+  /**
+   * Pointer to the return value of the NVTX API call. See
+   * nvToolsExt.h for each NVTX API function's return value.
+   */
+  const void *functionReturnValue;
+} CUpti_NvtxData;
+
+/**
+ * \brief Stream attribute data passed into a resource callback function
+ * for CUPTI_CBID_RESOURCE_STREAM_ATTRIBUTE_CHANGED callback
+
+ * Data passed into a resource callback function as the \p cbdata
+ * argument to \ref CUpti_CallbackFunc. The \p cbdata will be this
+ * type for \p domain equal to CUPTI_CB_DOMAIN_RESOURCE. The
+ * stream attribute data is valid only within the invocation of the callback
+ * function that is passed the data. If you need to retain some data
+ * for use outside of the callback, you must make a copy of that data.
+ */
+typedef struct {
+  /**
+   * The CUDA stream handle for the attribute
+   */
+  CUstream stream;
+
+  /**
+   * The type of the CUDA stream attribute
+   */
+  CUstreamAttrID attr;
+
+  /**
+   * The value of the CUDA stream attribute
+   */
+  const CUstreamAttrValue *value;
+} CUpti_StreamAttrData;
+
+/**
+ * \brief Data passed into a State callback function.
+ *
+ * Data passed into a State callback function as the \p cbdata argument
+ * to \ref CUpti_CallbackFunc. The \p cbdata will be this type for \p
+ * domain equal to CUPTI_CB_DOMAIN_STATE and callback Ids belonging to CUpti_CallbackIdState. 
+ * Unless otherwise noted, the callback data is valid only within the invocation of the callback
+ * function that is passed the data. If you need to retain some data
+ * for use outside of the callback, you must make a copy of that data.
+ */
+typedef struct {
+  union {
+    /**
+     * Data passed along with the callback Ids 
+     * Enum CUpti_CallbackIdState used to denote callback ids
+     */
+    struct {
+      /**
+       * Error code
+       */
+      CUptiResult result;
+      /**
+       * String containing more details. It can be NULL.
+       */
+      const char *message;
+    } notification;
+  };
+} CUpti_StateData;
+
+/**
+ * \brief An ID for a driver API, runtime API, resource or
+ * synchronization callback.
+ *
+ * An ID for a driver API, runtime API, resource or synchronization
+ * callback. Within a driver API callback this should be interpreted
+ * as a CUpti_driver_api_trace_cbid value (these values are defined in
+ * cupti_driver_cbid.h). Within a runtime API callback this should be
+ * interpreted as a CUpti_runtime_api_trace_cbid value (these values
+ * are defined in cupti_runtime_cbid.h). Within a resource API
+ * callback this should be interpreted as a \ref
+ * CUpti_CallbackIdResource value. Within a synchronize API callback
+ * this should be interpreted as a \ref CUpti_CallbackIdSync value.
+ */
+typedef uint32_t CUpti_CallbackId;
+
+/**
+ * \brief Function type for a callback.
+ *
+ * Function type for a callback. The type of the data passed to the
+ * callback in \p cbdata depends on the \p domain. If \p domain is
+ * CUPTI_CB_DOMAIN_DRIVER_API or CUPTI_CB_DOMAIN_RUNTIME_API the type
+ * of \p cbdata will be CUpti_CallbackData. If \p domain is
+ * CUPTI_CB_DOMAIN_RESOURCE the type of \p cbdata will be
+ * CUpti_ResourceData. If \p domain is CUPTI_CB_DOMAIN_SYNCHRONIZE the
+ * type of \p cbdata will be CUpti_SynchronizeData. If \p domain is
+ * CUPTI_CB_DOMAIN_NVTX the type of \p cbdata will be CUpti_NvtxData.
+ *
+ * \param userdata User data supplied at subscription of the callback
+ * \param domain The domain of the callback
+ * \param cbid The ID of the callback
+ * \param cbdata Data passed to the callback.
+ */
+typedef void (CUPTIAPI *CUpti_CallbackFunc)(
+    void *userdata,
+    CUpti_CallbackDomain domain,
+    CUpti_CallbackId cbid,
+    const void *cbdata);
+
+/**
+ * \brief A callback subscriber.
+ */
+typedef struct CUpti_Subscriber_st *CUpti_SubscriberHandle;
+
+/**
+ * \brief Pointer to an array of callback domains.
+ */
+typedef CUpti_CallbackDomain *CUpti_DomainTable;
+
+/**
+ * \brief Get the available callback domains.
+ *
+ * Returns in \p *domainTable an array of size \p *domainCount of all
+ * the available callback domains.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param domainCount Returns number of callback domains
+ * \param domainTable Returns pointer to array of available callback domains
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialize CUPTI
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p domainCount or \p domainTable are NULL
+ */
+CUptiResult CUPTIAPI cuptiSupportedDomains(size_t *domainCount,
+                                           CUpti_DomainTable *domainTable);
+
+/**
+ * \brief Initialize a callback subscriber with a callback function
+ * and user data.
+ *
+ * Initializes a callback subscriber with a callback function and
+ * (optionally) a pointer to user data. The returned subscriber handle
+ * can be used to enable and disable the callback for specific domains
+ * and callback IDs.
+ * \note Only a single subscriber can be registered at a time. To ensure
+ * that no other CUPTI client interrupts the profiling session, it's the
+ * responsibility of all the CUPTI clients to call this function before
+ * starting the profling session. In case profiling session is already
+ * started by another CUPTI client, this function returns the error code
+ * CUPTI_ERROR_MULTIPLE_SUBSCRIBERS_NOT_SUPPORTED.
+ * Note that this function returns the same error when application is
+ * launched using NVIDIA tools like nvprof, Visual Profiler, Nsight Systems,
+ * Nsight Compute, cuda-gdb and cuda-memcheck.
+ * \note This function does not enable any callbacks.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param subscriber Returns handle to initialize subscriber
+ * \param callback The callback function
+ * \param userdata A pointer to user data. This data will be passed to
+ * the callback function via the \p userdata parameter.
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialize CUPTI
+ * \retval CUPTI_ERROR_MULTIPLE_SUBSCRIBERS_NOT_SUPPORTED if there is already a CUPTI subscriber
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p subscriber is NULL
+ */
+CUptiResult CUPTIAPI cuptiSubscribe(CUpti_SubscriberHandle *subscriber,
+                                    CUpti_CallbackFunc callback,
+                                    void *userdata);
+
+/**
+ * \brief Unregister a callback subscriber.
+ *
+ * Removes a callback subscriber so that no future callbacks will be
+ * issued to that subscriber.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param subscriber Handle to the initialize subscriber
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialized CUPTI
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p subscriber is NULL or not initialized
+ */
+CUptiResult CUPTIAPI cuptiUnsubscribe(CUpti_SubscriberHandle subscriber);
+
+/**
+ * \brief Get the current enabled/disabled state of a callback for a specific
+ * domain and function ID.
+ *
+ * Returns non-zero in \p *enable if the callback for a domain and
+ * callback ID is enabled, and zero if not enabled.
+ *
+ * \note \b Thread-safety: a subscriber must serialize access to
+ * cuptiGetCallbackState, cuptiEnableCallback, cuptiEnableDomain, and
+ * cuptiEnableAllDomains. For example, if cuptiGetCallbackState(sub,
+ * d, c) and cuptiEnableCallback(sub, d, c) are called concurrently,
+ * the results are undefined.
+ *
+ * \param enable Returns non-zero if callback enabled, zero if not enabled
+ * \param subscriber Handle to the initialize subscriber
+ * \param domain The domain of the callback
+ * \param cbid The ID of the callback
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialized CUPTI
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p enabled is NULL, or if \p
+ * subscriber, \p domain or \p cbid is invalid.
+ */
+CUptiResult CUPTIAPI cuptiGetCallbackState(uint32_t *enable,
+                                           CUpti_SubscriberHandle subscriber,
+                                           CUpti_CallbackDomain domain,
+                                           CUpti_CallbackId cbid);
+
+/**
+ * \brief Enable or disabled callbacks for a specific domain and
+ * callback ID.
+ *
+ * Enable or disabled callbacks for a subscriber for a specific domain
+ * and callback ID.
+ *
+ * \note \b Thread-safety: a subscriber must serialize access to
+ * cuptiGetCallbackState, cuptiEnableCallback, cuptiEnableDomain, and
+ * cuptiEnableAllDomains. For example, if cuptiGetCallbackState(sub,
+ * d, c) and cuptiEnableCallback(sub, d, c) are called concurrently,
+ * the results are undefined.
+ *
+ * \param enable New enable state for the callback. Zero disables the
+ * callback, non-zero enables the callback.
+ * \param subscriber - Handle to callback subscription
+ * \param domain The domain of the callback
+ * \param cbid The ID of the callback
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialized CUPTI
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p subscriber, \p domain or \p
+ * cbid is invalid.
+ */
+CUptiResult CUPTIAPI cuptiEnableCallback(uint32_t enable,
+                                         CUpti_SubscriberHandle subscriber,
+                                         CUpti_CallbackDomain domain,
+                                         CUpti_CallbackId cbid);
+
+/**
+ * \brief Enable or disabled all callbacks for a specific domain.
+ *
+ * Enable or disabled all callbacks for a specific domain.
+ *
+ * \note \b Thread-safety: a subscriber must serialize access to
+ * cuptiGetCallbackState, cuptiEnableCallback, cuptiEnableDomain, and
+ * cuptiEnableAllDomains. For example, if cuptiGetCallbackEnabled(sub,
+ * d, *) and cuptiEnableDomain(sub, d) are called concurrently, the
+ * results are undefined.
+ *
+ * \param enable New enable state for all callbacks in the
+ * domain. Zero disables all callbacks, non-zero enables all
+ * callbacks.
+ * \param subscriber - Handle to callback subscription
+ * \param domain The domain of the callback
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialized CUPTI
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p subscriber or \p domain is invalid
+ */
+CUptiResult CUPTIAPI cuptiEnableDomain(uint32_t enable,
+                                       CUpti_SubscriberHandle subscriber,
+                                       CUpti_CallbackDomain domain);
+
+/**
+ * \brief Enable or disable all callbacks in all domains.
+ *
+ * Enable or disable all callbacks in all domains.
+ *
+ * \note \b Thread-safety: a subscriber must serialize access to
+ * cuptiGetCallbackState, cuptiEnableCallback, cuptiEnableDomain, and
+ * cuptiEnableAllDomains. For example, if cuptiGetCallbackState(sub,
+ * d, *) and cuptiEnableAllDomains(sub) are called concurrently, the
+ * results are undefined.
+ *
+ * \param enable New enable state for all callbacks in all
+ * domain. Zero disables all callbacks, non-zero enables all
+ * callbacks.
+ * \param subscriber - Handle to callback subscription
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if unable to initialized CUPTI
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p subscriber is invalid
+ */
+CUptiResult CUPTIAPI cuptiEnableAllDomains(uint32_t enable,
+                                           CUpti_SubscriberHandle subscriber);
+
+/**
+ * \brief Get the name of a callback for a specific domain and callback ID.
+ *
+ * Returns a pointer to the name c_string in \p **name.
+ *
+ * \note \b Names are available only for the DRIVER and RUNTIME domains.
+ *
+ * \param domain The domain of the callback
+ * \param cbid The ID of the callback
+ * \param name Returns pointer to the name string on success, NULL otherwise
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p name is NULL, or if
+ * \p domain or \p cbid is invalid.
+ */
+CUptiResult CUPTIAPI cuptiGetCallbackName(CUpti_CallbackDomain domain,
+                                          uint32_t cbid,
+                                          const char **name);
+
+/** @} */ /* END CUPTI_CALLBACK_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif  // file guard
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_checkpoint.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_checkpoint.h
new file mode 100644
index 0000000000000000000000000000000000000000..36eeddc4e2b7bfd1902ce313d71f173db70beaef
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_checkpoint.h
@@ -0,0 +1,127 @@
+#pragma once
+
+#include <cuda.h>
+#include <cupti_result.h>
+
+#include <stddef.h>
+#include <stdint.h>
+
+namespace NV { namespace Cupti { namespace Checkpoint {
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * \defgroup CUPTI_CHECKPOINT_API CUPTI Checkpoint API
+ * Functions, types, and enums that implement the CUPTI Checkpoint API.
+ * @{
+ */
+
+/**
+ * \brief Specifies optimization options for a checkpoint, may be OR'd together to specify multiple options.
+ */
+typedef enum
+{
+    CUPTI_CHECKPOINT_OPT_NONE     = 0, //!< Default behavior
+    CUPTI_CHECKPOINT_OPT_TRANSFER = 1, //!< Determine which mem blocks have changed, and only restore those. This optimization is cached, which means cuptiCheckpointRestore must always be called at the same point in the application when this option is enabled, or the result may be incorrect.
+} CUpti_CheckpointOptimizations;
+
+/**
+ * \brief Configuration and handle for a CUPTI Checkpoint
+ *
+ * A CUptiCheckpoint object should be initialized with desired options prior to passing into any
+ * CUPTI Checkpoint API function.  The first call into a Checkpoint API function will initialize internal
+ * state based on these options.  Subsequent changes to these options will not have any effect.
+ *
+ * Checkpoint data is saved in device, host, and filesystem space.  There are options to reserve memory
+ * at each level (device, host, filesystem) which are intended to allow a guarantee that a certain amount
+ * of memory will remain free for use after the checkpoint is saved.
+ * Note, however, that falling back to slower levels of memory (host, and then filesystem) to save the checkpoint
+ * will result in performance degradation.
+ * Currently, the filesystem limitation is not implemented.  Note that falling back to filesystem storage may
+ * significantly impact the performance for saving and restoring a checkpoint.
+ */
+typedef struct
+{
+   size_t structSize;      //!< [in] Must be set to CUpti_Checkpoint_STRUCT_SIZE
+
+   CUcontext ctx;          //!< [in] Set to context to save from, or will use current context if NULL
+
+   size_t reserveDeviceMB; //!< [in] Restrict checkpoint from using last N MB of device memory (-1 = use no device memory)
+   size_t reserveHostMB;   //!< [in] Restrict checkpoint from using last N MB of host memory (-1 = use no host memory)
+   uint8_t allowOverwrite; //!< [in] Boolean, Allow checkpoint to save over existing checkpoint
+   uint8_t optimizations;  //!< [in] Mask of CUpti_CheckpointOptimizations flags for this checkpoint
+
+   void * pPriv;           //!< [in] Assign to NULL
+} CUpti_Checkpoint;
+
+#define CUpti_Checkpoint_STRUCT_SIZE  \
+(offsetof(CUpti_Checkpoint, pPriv) +  \
+sizeof(((CUpti_Checkpoint*)(nullptr))->pPriv))
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \brief Initialize and save a checkpoint of the device state associated with the handle context
+ *
+ * Uses the handle options to configure and save a checkpoint of the device state associated with the specified context.
+ *
+ * \param handle A pointer to a CUpti_Checkpoint object
+ *
+ * \retval CUPTI_SUCCESS if a checkpoint was successfully initialized and saved
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p handle does not appear to refer to a valid CUpti_Checkpoint
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_DEVICE if device associated with context is not compatible with checkpoint API
+ * \retval CUPTI_ERROR_INVALID_OPERATION if Save is requested over an existing checkpoint, but \p allowOverwrite was not originally specified
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY if as configured, not enough backing storage space to save the checkpoint
+ */
+CUptiResult cuptiCheckpointSave(CUpti_Checkpoint * const handle);
+
+/**
+ * \brief Restore a checkpoint to the device associated with its context
+ *
+ * Restores device, pinned, and allocated memory to the state when the checkpoint was saved
+ *
+ * \param handle A pointer to a previously saved CUpti_Checkpoint object
+ *
+ * \retval CUTPI_SUCCESS if the checkpoint was successfully restored
+ * \retval CUPTI_ERROR_NOT_INITIALIZED if the checkpoint was not previously initialized
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if the handle appears invalid
+ * \retval CUPTI_ERROR_UNKNOWN if the restore or optimization operation fails
+ */
+CUptiResult cuptiCheckpointRestore(CUpti_Checkpoint * const handle);
+
+/**
+ * \brief Free the backing data for a checkpoint
+ *
+ * Frees all associated device, host memory and filesystem storage used for this context.
+ * After freeing a handle, it may be re-used as if it was new - options may be re-configured and will
+ * take effect on the next call to \p cuptiCheckpointSave.
+ *
+ * \param handle A pointer to a previously saved CUpti_Checkpoint object
+ *
+ * \retval CUPTI_SUCCESS if the handle was successfully freed
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if the handle was already freed or appears invalid
+ * \retval CUPTI_ERROR_INVALID_CONTEXT if the context is no longer valid
+ */
+CUptiResult cuptiCheckpointFree(CUpti_Checkpoint * const handle);
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
+
+// Exit namespace NV::Cupti::Checkpoint
+}}}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_common.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_common.h
new file mode 100644
index 0000000000000000000000000000000000000000..96d228c4df3c1f090a4979bfe10132e080042fef
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_common.h
@@ -0,0 +1,93 @@
+/*
+ * Copyright 2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+ 
+#if !defined(__CUPTI_COMMON_H__)
+#define __CUPTI_COMMON_H__
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#ifndef CUPTIUTILAPI
+#ifdef _WIN32
+#define CUPTIUTILAPI __stdcall
+#else
+#define CUPTIUTILAPI
+#endif
+#endif
+
+#if defined(__LP64__)
+#define CUPTILP64 1
+#elif defined(_WIN64)
+#define CUPTILP64 1
+#else
+#undef CUPTILP64
+#endif
+
+#define ACTIVITY_RECORD_ALIGNMENT 8
+#if defined(_WIN32) // Windows 32- and 64-bit
+#define START_PACKED_ALIGNMENT __pragma(pack(push,1)) // exact fit - no padding
+#define PACKED_ALIGNMENT __declspec(align(ACTIVITY_RECORD_ALIGNMENT))
+#define END_PACKED_ALIGNMENT __pragma(pack(pop))
+#elif defined(__GNUC__) // GCC
+#define START_PACKED_ALIGNMENT
+#define PACKED_ALIGNMENT __attribute__ ((__packed__)) __attribute__ ((aligned (ACTIVITY_RECORD_ALIGNMENT)))
+#define END_PACKED_ALIGNMENT
+#else // all other compilers
+#define START_PACKED_ALIGNMENT
+#define PACKED_ALIGNMENT
+#define END_PACKED_ALIGNMENT
+#endif
+
+#endif /*__CUPTI_COMMON_H__*/
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_driver_cbid.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_driver_cbid.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b23832372a3a69c7bfbf0aa188b0417d9270be6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_driver_cbid.h
@@ -0,0 +1,799 @@
+
+// *************************************************************************
+//      Definitions of indices for API functions, unique across entire API
+// *************************************************************************
+
+// This file is generated.  Any changes you make will be lost during the next clean build.
+// CUDA public interface, for type definitions and cu* function prototypes
+
+#if !defined(_CUPTI_DRIVER_CBID_H_)
+#define _CUPTI_DRIVER_CBID_H_
+
+typedef enum CUpti_driver_api_trace_cbid_enum {
+    CUPTI_DRIVER_TRACE_CBID_INVALID                                                        = 0,
+    CUPTI_DRIVER_TRACE_CBID_cuInit                                                         = 1,
+    CUPTI_DRIVER_TRACE_CBID_cuDriverGetVersion                                             = 2,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGet                                                    = 3,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetCount                                               = 4,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetName                                                = 5,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceComputeCapability                                      = 6,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceTotalMem                                               = 7,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetProperties                                          = 8,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetAttribute                                           = 9,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxCreate                                                    = 10,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxDestroy                                                   = 11,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxAttach                                                    = 12,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxDetach                                                    = 13,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxPushCurrent                                               = 14,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxPopCurrent                                                = 15,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetDevice                                                 = 16,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxSynchronize                                               = 17,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleLoad                                                   = 18,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleLoadData                                               = 19,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleLoadDataEx                                             = 20,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleLoadFatBinary                                          = 21,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleUnload                                                 = 22,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetFunction                                            = 23,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetGlobal                                              = 24,
+    CUPTI_DRIVER_TRACE_CBID_cu64ModuleGetGlobal                                            = 25,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetTexRef                                              = 26,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetInfo                                                   = 27,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemGetInfo                                                 = 28,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAlloc                                                     = 29,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemAlloc                                                   = 30,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocPitch                                                = 31,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemAllocPitch                                              = 32,
+    CUPTI_DRIVER_TRACE_CBID_cuMemFree                                                      = 33,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemFree                                                    = 34,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetAddressRange                                           = 35,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemGetAddressRange                                         = 36,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocHost                                                 = 37,
+    CUPTI_DRIVER_TRACE_CBID_cuMemFreeHost                                                  = 38,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostAlloc                                                 = 39,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostGetDevicePointer                                      = 40,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemHostGetDevicePointer                                    = 41,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostGetFlags                                              = 42,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoD                                                   = 43,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyHtoD                                                 = 44,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoH                                                   = 45,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyDtoH                                                 = 46,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoD                                                   = 47,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyDtoD                                                 = 48,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoA                                                   = 49,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyDtoA                                                 = 50,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoD                                                   = 51,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyAtoD                                                 = 52,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoA                                                   = 53,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoH                                                   = 54,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoA                                                   = 55,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2D                                                     = 56,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2DUnaligned                                            = 57,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3D                                                     = 58,
+    CUPTI_DRIVER_TRACE_CBID_cu64Memcpy3D                                                   = 59,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoDAsync                                              = 60,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyHtoDAsync                                            = 61,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoHAsync                                              = 62,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyDtoHAsync                                            = 63,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoDAsync                                              = 64,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemcpyDtoDAsync                                            = 65,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoAAsync                                              = 66,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoHAsync                                              = 67,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2DAsync                                                = 68,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DAsync                                                = 69,
+    CUPTI_DRIVER_TRACE_CBID_cu64Memcpy3DAsync                                              = 70,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD8                                                     = 71,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD8                                                   = 72,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD16                                                    = 73,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD16                                                  = 74,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD32                                                    = 75,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD32                                                  = 76,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D8                                                   = 77,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD2D8                                                 = 78,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D16                                                  = 79,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD2D16                                                = 80,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D32                                                  = 81,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD2D32                                                = 82,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncSetBlockShape                                            = 83,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncSetSharedSize                                            = 84,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncGetAttribute                                             = 85,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncSetCacheConfig                                           = 86,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayCreate                                                  = 87,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayGetDescriptor                                           = 88,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayDestroy                                                 = 89,
+    CUPTI_DRIVER_TRACE_CBID_cuArray3DCreate                                                = 90,
+    CUPTI_DRIVER_TRACE_CBID_cuArray3DGetDescriptor                                         = 91,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefCreate                                                 = 92,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefDestroy                                                = 93,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetArray                                               = 94,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetAddress                                             = 95,
+    CUPTI_DRIVER_TRACE_CBID_cu64TexRefSetAddress                                           = 96,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetAddress2D                                           = 97,
+    CUPTI_DRIVER_TRACE_CBID_cu64TexRefSetAddress2D                                         = 98,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetFormat                                              = 99,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetAddressMode                                         = 100,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetFilterMode                                          = 101,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetFlags                                               = 102,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetAddress                                             = 103,
+    CUPTI_DRIVER_TRACE_CBID_cu64TexRefGetAddress                                           = 104,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetArray                                               = 105,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetAddressMode                                         = 106,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetFilterMode                                          = 107,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetFormat                                              = 108,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetFlags                                               = 109,
+    CUPTI_DRIVER_TRACE_CBID_cuParamSetSize                                                 = 110,
+    CUPTI_DRIVER_TRACE_CBID_cuParamSeti                                                    = 111,
+    CUPTI_DRIVER_TRACE_CBID_cuParamSetf                                                    = 112,
+    CUPTI_DRIVER_TRACE_CBID_cuParamSetv                                                    = 113,
+    CUPTI_DRIVER_TRACE_CBID_cuParamSetTexRef                                               = 114,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunch                                                       = 115,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchGrid                                                   = 116,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchGridAsync                                              = 117,
+    CUPTI_DRIVER_TRACE_CBID_cuEventCreate                                                  = 118,
+    CUPTI_DRIVER_TRACE_CBID_cuEventRecord                                                  = 119,
+    CUPTI_DRIVER_TRACE_CBID_cuEventQuery                                                   = 120,
+    CUPTI_DRIVER_TRACE_CBID_cuEventSynchronize                                             = 121,
+    CUPTI_DRIVER_TRACE_CBID_cuEventDestroy                                                 = 122,
+    CUPTI_DRIVER_TRACE_CBID_cuEventElapsedTime                                             = 123,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamCreate                                                 = 124,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamQuery                                                  = 125,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamSynchronize                                            = 126,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamDestroy                                                = 127,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsUnregisterResource                                   = 128,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsSubResourceGetMappedArray                            = 129,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsResourceGetMappedPointer                             = 130,
+    CUPTI_DRIVER_TRACE_CBID_cu64GraphicsResourceGetMappedPointer                           = 131,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsResourceSetMapFlags                                  = 132,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsMapResources                                         = 133,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsUnmapResources                                       = 134,
+    CUPTI_DRIVER_TRACE_CBID_cuGetExportTable                                               = 135,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxSetLimit                                                  = 136,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetLimit                                                  = 137,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10GetDevice                                               = 138,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10CtxCreate                                               = 139,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsD3D10RegisterResource                                = 140,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10RegisterResource                                        = 141,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10UnregisterResource                                      = 142,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10MapResources                                            = 143,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10UnmapResources                                          = 144,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceSetMapFlags                                     = 145,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedArray                                  = 146,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedPointer                                = 147,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedSize                                   = 148,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedPitch                                  = 149,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetSurfaceDimensions                            = 150,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D11GetDevice                                               = 151,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D11CtxCreate                                               = 152,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsD3D11RegisterResource                                = 153,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9GetDevice                                                = 154,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9CtxCreate                                                = 155,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsD3D9RegisterResource                                 = 156,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9GetDirect3DDevice                                        = 157,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9RegisterResource                                         = 158,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9UnregisterResource                                       = 159,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9MapResources                                             = 160,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9UnmapResources                                           = 161,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceSetMapFlags                                      = 162,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetSurfaceDimensions                             = 163,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedArray                                   = 164,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedPointer                                 = 165,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedSize                                    = 166,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedPitch                                   = 167,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9Begin                                                    = 168,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9End                                                      = 169,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9RegisterVertexBuffer                                     = 170,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9MapVertexBuffer                                          = 171,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9UnmapVertexBuffer                                        = 172,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9UnregisterVertexBuffer                                   = 173,
+    CUPTI_DRIVER_TRACE_CBID_cuGLCtxCreate                                                  = 174,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsGLRegisterBuffer                                     = 175,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsGLRegisterImage                                      = 176,
+    CUPTI_DRIVER_TRACE_CBID_cuWGLGetDevice                                                 = 177,
+    CUPTI_DRIVER_TRACE_CBID_cuGLInit                                                       = 178,
+    CUPTI_DRIVER_TRACE_CBID_cuGLRegisterBufferObject                                       = 179,
+    CUPTI_DRIVER_TRACE_CBID_cuGLMapBufferObject                                            = 180,
+    CUPTI_DRIVER_TRACE_CBID_cuGLUnmapBufferObject                                          = 181,
+    CUPTI_DRIVER_TRACE_CBID_cuGLUnregisterBufferObject                                     = 182,
+    CUPTI_DRIVER_TRACE_CBID_cuGLSetBufferObjectMapFlags                                    = 183,
+    CUPTI_DRIVER_TRACE_CBID_cuGLMapBufferObjectAsync                                       = 184,
+    CUPTI_DRIVER_TRACE_CBID_cuGLUnmapBufferObjectAsync                                     = 185,
+    CUPTI_DRIVER_TRACE_CBID_cuVDPAUGetDevice                                               = 186,
+    CUPTI_DRIVER_TRACE_CBID_cuVDPAUCtxCreate                                               = 187,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsVDPAURegisterVideoSurface                            = 188,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsVDPAURegisterOutputSurface                           = 189,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetSurfRef                                             = 190,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfRefCreate                                                = 191,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfRefDestroy                                               = 192,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfRefSetFormat                                             = 193,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfRefSetArray                                              = 194,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfRefGetFormat                                             = 195,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfRefGetArray                                              = 196,
+    CUPTI_DRIVER_TRACE_CBID_cu64DeviceTotalMem                                             = 197,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D10ResourceGetMappedPointer                              = 198,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D10ResourceGetMappedSize                                 = 199,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D10ResourceGetMappedPitch                                = 200,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D10ResourceGetSurfaceDimensions                          = 201,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D9ResourceGetSurfaceDimensions                           = 202,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D9ResourceGetMappedPointer                               = 203,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D9ResourceGetMappedSize                                  = 204,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D9ResourceGetMappedPitch                                 = 205,
+    CUPTI_DRIVER_TRACE_CBID_cu64D3D9MapVertexBuffer                                        = 206,
+    CUPTI_DRIVER_TRACE_CBID_cu64GLMapBufferObject                                          = 207,
+    CUPTI_DRIVER_TRACE_CBID_cu64GLMapBufferObjectAsync                                     = 208,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D11GetDevices                                              = 209,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D11CtxCreateOnDevice                                       = 210,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10GetDevices                                              = 211,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10CtxCreateOnDevice                                       = 212,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9GetDevices                                               = 213,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9CtxCreateOnDevice                                        = 214,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemHostAlloc                                               = 215,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD8Async                                                = 216,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD8Async                                              = 217,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD16Async                                               = 218,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD16Async                                             = 219,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD32Async                                               = 220,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD32Async                                             = 221,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D8Async                                              = 222,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD2D8Async                                            = 223,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D16Async                                             = 224,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD2D16Async                                           = 225,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D32Async                                             = 226,
+    CUPTI_DRIVER_TRACE_CBID_cu64MemsetD2D32Async                                           = 227,
+    CUPTI_DRIVER_TRACE_CBID_cu64ArrayCreate                                                = 228,
+    CUPTI_DRIVER_TRACE_CBID_cu64ArrayGetDescriptor                                         = 229,
+    CUPTI_DRIVER_TRACE_CBID_cu64Array3DCreate                                              = 230,
+    CUPTI_DRIVER_TRACE_CBID_cu64Array3DGetDescriptor                                       = 231,
+    CUPTI_DRIVER_TRACE_CBID_cu64Memcpy2D                                                   = 232,
+    CUPTI_DRIVER_TRACE_CBID_cu64Memcpy2DUnaligned                                          = 233,
+    CUPTI_DRIVER_TRACE_CBID_cu64Memcpy2DAsync                                              = 234,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxCreate_v2                                                 = 235,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10CtxCreate_v2                                            = 236,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D11CtxCreate_v2                                            = 237,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9CtxCreate_v2                                             = 238,
+    CUPTI_DRIVER_TRACE_CBID_cuGLCtxCreate_v2                                               = 239,
+    CUPTI_DRIVER_TRACE_CBID_cuVDPAUCtxCreate_v2                                            = 240,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetGlobal_v2                                           = 241,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetInfo_v2                                                = 242,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAlloc_v2                                                  = 243,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocPitch_v2                                             = 244,
+    CUPTI_DRIVER_TRACE_CBID_cuMemFree_v2                                                   = 245,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetAddressRange_v2                                        = 246,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostGetDevicePointer_v2                                   = 247,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy_v2                                                    = 248,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD8_v2                                                  = 249,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD16_v2                                                 = 250,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD32_v2                                                 = 251,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D8_v2                                                = 252,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D16_v2                                               = 253,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D32_v2                                               = 254,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetAddress_v2                                          = 255,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetAddress2D_v2                                        = 256,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetAddress_v2                                          = 257,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsResourceGetMappedPointer_v2                          = 258,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceTotalMem_v2                                            = 259,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedPointer_v2                             = 260,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedSize_v2                                = 261,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetMappedPitch_v2                               = 262,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10ResourceGetSurfaceDimensions_v2                         = 263,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetSurfaceDimensions_v2                          = 264,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedPointer_v2                              = 265,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedSize_v2                                 = 266,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9ResourceGetMappedPitch_v2                                = 267,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D9MapVertexBuffer_v2                                       = 268,
+    CUPTI_DRIVER_TRACE_CBID_cuGLMapBufferObject_v2                                         = 269,
+    CUPTI_DRIVER_TRACE_CBID_cuGLMapBufferObjectAsync_v2                                    = 270,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostAlloc_v2                                              = 271,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayCreate_v2                                               = 272,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayGetDescriptor_v2                                        = 273,
+    CUPTI_DRIVER_TRACE_CBID_cuArray3DCreate_v2                                             = 274,
+    CUPTI_DRIVER_TRACE_CBID_cuArray3DGetDescriptor_v2                                      = 275,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoD_v2                                                = 276,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoDAsync_v2                                           = 277,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoH_v2                                                = 278,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoHAsync_v2                                           = 279,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoD_v2                                                = 280,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoDAsync_v2                                           = 281,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoH_v2                                                = 282,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoHAsync_v2                                           = 283,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoD_v2                                                = 284,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoA_v2                                                = 285,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoA_v2                                                = 286,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2D_v2                                                  = 287,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2DUnaligned_v2                                         = 288,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2DAsync_v2                                             = 289,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3D_v2                                                  = 290,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DAsync_v2                                             = 291,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoA_v2                                                = 292,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoAAsync_v2                                           = 293,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocHost_v2                                              = 294,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitEvent                                              = 295,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetApiVersion                                             = 296,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D10GetDirect3DDevice                                       = 297,
+    CUPTI_DRIVER_TRACE_CBID_cuD3D11GetDirect3DDevice                                       = 298,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetCacheConfig                                            = 299,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxSetCacheConfig                                            = 300,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostRegister                                              = 301,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostUnregister                                            = 302,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxSetCurrent                                                = 303,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetCurrent                                                = 304,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy                                                       = 305,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAsync                                                  = 306,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchKernel                                                 = 307,
+    CUPTI_DRIVER_TRACE_CBID_cuProfilerStart                                                = 308,
+    CUPTI_DRIVER_TRACE_CBID_cuProfilerStop                                                 = 309,
+    CUPTI_DRIVER_TRACE_CBID_cuPointerGetAttribute                                          = 310,
+    CUPTI_DRIVER_TRACE_CBID_cuProfilerInitialize                                           = 311,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceCanAccessPeer                                          = 312,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxEnablePeerAccess                                          = 313,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxDisablePeerAccess                                         = 314,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPeerRegister                                              = 315,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPeerUnregister                                            = 316,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPeerGetDevicePointer                                      = 317,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyPeer                                                   = 318,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyPeerAsync                                              = 319,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DPeer                                                 = 320,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DPeerAsync                                            = 321,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxDestroy_v2                                                = 322,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxPushCurrent_v2                                            = 323,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxPopCurrent_v2                                             = 324,
+    CUPTI_DRIVER_TRACE_CBID_cuEventDestroy_v2                                              = 325,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamDestroy_v2                                             = 326,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetAddress2D_v3                                        = 327,
+    CUPTI_DRIVER_TRACE_CBID_cuIpcGetMemHandle                                              = 328,
+    CUPTI_DRIVER_TRACE_CBID_cuIpcOpenMemHandle                                             = 329,
+    CUPTI_DRIVER_TRACE_CBID_cuIpcCloseMemHandle                                            = 330,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetByPCIBusId                                          = 331,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetPCIBusId                                            = 332,
+    CUPTI_DRIVER_TRACE_CBID_cuGLGetDevices                                                 = 333,
+    CUPTI_DRIVER_TRACE_CBID_cuIpcGetEventHandle                                            = 334,
+    CUPTI_DRIVER_TRACE_CBID_cuIpcOpenEventHandle                                           = 335,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxSetSharedMemConfig                                        = 336,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetSharedMemConfig                                        = 337,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncSetSharedMemConfig                                       = 338,
+    CUPTI_DRIVER_TRACE_CBID_cuTexObjectCreate                                              = 339,
+    CUPTI_DRIVER_TRACE_CBID_cuTexObjectDestroy                                             = 340,
+    CUPTI_DRIVER_TRACE_CBID_cuTexObjectGetResourceDesc                                     = 341,
+    CUPTI_DRIVER_TRACE_CBID_cuTexObjectGetTextureDesc                                      = 342,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfObjectCreate                                             = 343,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfObjectDestroy                                            = 344,
+    CUPTI_DRIVER_TRACE_CBID_cuSurfObjectGetResourceDesc                                    = 345,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamAddCallback                                            = 346,
+    CUPTI_DRIVER_TRACE_CBID_cuMipmappedArrayCreate                                         = 347,
+    CUPTI_DRIVER_TRACE_CBID_cuMipmappedArrayGetLevel                                       = 348,
+    CUPTI_DRIVER_TRACE_CBID_cuMipmappedArrayDestroy                                        = 349,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetMipmappedArray                                      = 350,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetMipmapFilterMode                                    = 351,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetMipmapLevelBias                                     = 352,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetMipmapLevelClamp                                    = 353,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetMaxAnisotropy                                       = 354,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetMipmappedArray                                      = 355,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetMipmapFilterMode                                    = 356,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetMipmapLevelBias                                     = 357,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetMipmapLevelClamp                                    = 358,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetMaxAnisotropy                                       = 359,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsResourceGetMappedMipmappedArray                      = 360,
+    CUPTI_DRIVER_TRACE_CBID_cuTexObjectGetResourceViewDesc                                 = 361,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkCreate                                                   = 362,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkAddData                                                  = 363,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkAddFile                                                  = 364,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkComplete                                                 = 365,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkDestroy                                                  = 366,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamCreateWithPriority                                     = 367,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetPriority                                            = 368,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetFlags                                               = 369,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetStreamPriorityRange                                    = 370,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocManaged                                              = 371,
+    CUPTI_DRIVER_TRACE_CBID_cuGetErrorString                                               = 372,
+    CUPTI_DRIVER_TRACE_CBID_cuGetErrorName                                                 = 373,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyMaxActiveBlocksPerMultiprocessor                    = 374,
+    CUPTI_DRIVER_TRACE_CBID_cuCompilePtx                                                   = 375,
+    CUPTI_DRIVER_TRACE_CBID_cuBinaryFree                                                   = 376,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamAttachMemAsync                                         = 377,
+    CUPTI_DRIVER_TRACE_CBID_cuPointerSetAttribute                                          = 378,
+    CUPTI_DRIVER_TRACE_CBID_cuMemHostRegister_v2                                           = 379,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsResourceSetMapFlags_v2                               = 380,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkCreate_v2                                                = 381,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkAddData_v2                                               = 382,
+    CUPTI_DRIVER_TRACE_CBID_cuLinkAddFile_v2                                               = 383,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyMaxPotentialBlockSize                               = 384,
+    CUPTI_DRIVER_TRACE_CBID_cuGLGetDevices_v2                                              = 385,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxRetain                                       = 386,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxRelease                                      = 387,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxSetFlags                                     = 388,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxReset                                        = 389,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsEGLRegisterImage                                     = 390,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetFlags                                                  = 391,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxGetState                                     = 392,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamConsumerConnect                                     = 393,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamConsumerDisconnect                                  = 394,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamConsumerAcquireFrame                                = 395,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamConsumerReleaseFrame                                = 396,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoD_v2_ptds                                           = 397,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoH_v2_ptds                                           = 398,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoD_v2_ptds                                           = 399,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoA_v2_ptds                                           = 400,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoD_v2_ptds                                           = 401,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoA_v2_ptds                                           = 402,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoH_v2_ptds                                           = 403,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoA_v2_ptds                                           = 404,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2D_v2_ptds                                             = 405,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2DUnaligned_v2_ptds                                    = 406,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3D_v2_ptds                                             = 407,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy_ptds                                                  = 408,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyPeer_ptds                                              = 409,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DPeer_ptds                                            = 410,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD8_v2_ptds                                             = 411,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD16_v2_ptds                                            = 412,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD32_v2_ptds                                            = 413,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D8_v2_ptds                                           = 414,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D16_v2_ptds                                          = 415,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D32_v2_ptds                                          = 416,
+    CUPTI_DRIVER_TRACE_CBID_cuGLMapBufferObject_v2_ptds                                    = 417,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAsync_ptsz                                             = 418,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoAAsync_v2_ptsz                                      = 419,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyAtoHAsync_v2_ptsz                                      = 420,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyHtoDAsync_v2_ptsz                                      = 421,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoHAsync_v2_ptsz                                      = 422,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyDtoDAsync_v2_ptsz                                      = 423,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy2DAsync_v2_ptsz                                        = 424,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DAsync_v2_ptsz                                        = 425,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyPeerAsync_ptsz                                         = 426,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DPeerAsync_ptsz                                       = 427,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD8Async_ptsz                                           = 428,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD16Async_ptsz                                          = 429,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD32Async_ptsz                                          = 430,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D8Async_ptsz                                         = 431,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D16Async_ptsz                                        = 432,
+    CUPTI_DRIVER_TRACE_CBID_cuMemsetD2D32Async_ptsz                                        = 433,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetPriority_ptsz                                       = 434,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetFlags_ptsz                                          = 435,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitEvent_ptsz                                         = 436,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamAddCallback_ptsz                                       = 437,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamAttachMemAsync_ptsz                                    = 438,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamQuery_ptsz                                             = 439,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamSynchronize_ptsz                                       = 440,
+    CUPTI_DRIVER_TRACE_CBID_cuEventRecord_ptsz                                             = 441,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchKernel_ptsz                                            = 442,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsMapResources_ptsz                                    = 443,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsUnmapResources_ptsz                                  = 444,
+    CUPTI_DRIVER_TRACE_CBID_cuGLMapBufferObjectAsync_v2_ptsz                               = 445,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamProducerConnect                                     = 446,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamProducerDisconnect                                  = 447,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamProducerPresentFrame                                = 448,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphicsResourceGetMappedEglFrame                            = 449,
+    CUPTI_DRIVER_TRACE_CBID_cuPointerGetAttributes                                         = 450,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags           = 451,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyMaxPotentialBlockSizeWithFlags                      = 452,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamProducerReturnFrame                                 = 453,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetP2PAttribute                                        = 454,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefSetBorderColor                                         = 455,
+    CUPTI_DRIVER_TRACE_CBID_cuTexRefGetBorderColor                                         = 456,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAdvise                                                    = 457,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue32                                            = 458,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue32_ptsz                                       = 459,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue32                                           = 460,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue32_ptsz                                      = 461,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBatchMemOp                                             = 462,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBatchMemOp_ptsz                                        = 463,
+    CUPTI_DRIVER_TRACE_CBID_cuNVNbufferGetPointer                                          = 464,
+    CUPTI_DRIVER_TRACE_CBID_cuNVNtextureGetArray                                           = 465,
+    CUPTI_DRIVER_TRACE_CBID_cuNNSetAllocator                                               = 466,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPrefetchAsync                                             = 467,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPrefetchAsync_ptsz                                        = 468,
+    CUPTI_DRIVER_TRACE_CBID_cuEventCreateFromNVNSync                                       = 469,
+    CUPTI_DRIVER_TRACE_CBID_cuEGLStreamConsumerConnectWithFlags                            = 470,
+    CUPTI_DRIVER_TRACE_CBID_cuMemRangeGetAttribute                                         = 471,
+    CUPTI_DRIVER_TRACE_CBID_cuMemRangeGetAttributes                                        = 472,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue64                                            = 473,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue64_ptsz                                       = 474,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue64                                           = 475,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue64_ptsz                                      = 476,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchCooperativeKernel                                      = 477,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchCooperativeKernel_ptsz                                 = 478,
+    CUPTI_DRIVER_TRACE_CBID_cuEventCreateFromEGLSync                                       = 479,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchCooperativeKernelMultiDevice                           = 480,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncSetAttribute                                             = 481,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetUuid                                                = 482,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCtx                                                 = 483,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCtx_ptsz                                            = 484,
+    CUPTI_DRIVER_TRACE_CBID_cuImportExternalMemory                                         = 485,
+    CUPTI_DRIVER_TRACE_CBID_cuExternalMemoryGetMappedBuffer                                = 486,
+    CUPTI_DRIVER_TRACE_CBID_cuExternalMemoryGetMappedMipmappedArray                        = 487,
+    CUPTI_DRIVER_TRACE_CBID_cuDestroyExternalMemory                                        = 488,
+    CUPTI_DRIVER_TRACE_CBID_cuImportExternalSemaphore                                      = 489,
+    CUPTI_DRIVER_TRACE_CBID_cuSignalExternalSemaphoresAsync                                = 490,
+    CUPTI_DRIVER_TRACE_CBID_cuSignalExternalSemaphoresAsync_ptsz                           = 491,
+    CUPTI_DRIVER_TRACE_CBID_cuWaitExternalSemaphoresAsync                                  = 492,
+    CUPTI_DRIVER_TRACE_CBID_cuWaitExternalSemaphoresAsync_ptsz                             = 493,
+    CUPTI_DRIVER_TRACE_CBID_cuDestroyExternalSemaphore                                     = 494,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBeginCapture                                           = 495,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBeginCapture_ptsz                                      = 496,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamEndCapture                                             = 497,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamEndCapture_ptsz                                        = 498,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamIsCapturing                                            = 499,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamIsCapturing_ptsz                                       = 500,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphCreate                                                  = 501,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddKernelNode                                           = 502,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeGetParams                                     = 503,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddMemcpyNode                                           = 504,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphMemcpyNodeGetParams                                     = 505,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddMemsetNode                                           = 506,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphMemsetNodeGetParams                                     = 507,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphMemsetNodeSetParams                                     = 508,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeGetType                                             = 509,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphGetRootNodes                                            = 510,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeGetDependencies                                     = 511,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeGetDependentNodes                                   = 512,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphInstantiate                                             = 513,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphLaunch                                                  = 514,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphLaunch_ptsz                                             = 515,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecDestroy                                             = 516,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphDestroy                                                 = 517,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddDependencies                                         = 518,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphRemoveDependencies                                      = 519,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphMemcpyNodeSetParams                                     = 520,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeSetParams                                     = 521,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphDestroyNode                                             = 522,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphClone                                                   = 523,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeFindInClone                                         = 524,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddChildGraphNode                                       = 525,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddEmptyNode                                            = 526,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchHostFunc                                               = 527,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchHostFunc_ptsz                                          = 528,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphChildGraphNodeGetGraph                                  = 529,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddHostNode                                             = 530,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphHostNodeGetParams                                       = 531,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetLuid                                                = 532,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphHostNodeSetParams                                       = 533,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphGetNodes                                                = 534,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphGetEdges                                                = 535,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCaptureInfo                                         = 536,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCaptureInfo_ptsz                                    = 537,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecKernelNodeSetParams                                 = 538,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBeginCapture_v2                                        = 539,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBeginCapture_v2_ptsz                                   = 540,
+    CUPTI_DRIVER_TRACE_CBID_cuThreadExchangeStreamCaptureMode                              = 541,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetNvSciSyncAttributes                                 = 542,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyAvailableDynamicSMemPerBlock                        = 543,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxRelease_v2                                   = 544,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxReset_v2                                     = 545,
+    CUPTI_DRIVER_TRACE_CBID_cuDevicePrimaryCtxSetFlags_v2                                  = 546,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAddressReserve                                            = 547,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAddressFree                                               = 548,
+    CUPTI_DRIVER_TRACE_CBID_cuMemCreate                                                    = 549,
+    CUPTI_DRIVER_TRACE_CBID_cuMemRelease                                                   = 550,
+    CUPTI_DRIVER_TRACE_CBID_cuMemMap                                                       = 551,
+    CUPTI_DRIVER_TRACE_CBID_cuMemUnmap                                                     = 552,
+    CUPTI_DRIVER_TRACE_CBID_cuMemSetAccess                                                 = 553,
+    CUPTI_DRIVER_TRACE_CBID_cuMemExportToShareableHandle                                   = 554,
+    CUPTI_DRIVER_TRACE_CBID_cuMemImportFromShareableHandle                                 = 555,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetAllocationGranularity                                  = 556,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetAllocationPropertiesFromHandle                         = 557,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetAccess                                                 = 558,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamSetFlags                                               = 559,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamSetFlags_ptsz                                          = 560,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecUpdate                                              = 561,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecMemcpyNodeSetParams                                 = 562,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecMemsetNodeSetParams                                 = 563,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecHostNodeSetParams                                   = 564,
+    CUPTI_DRIVER_TRACE_CBID_cuMemRetainAllocationHandle                                    = 565,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncGetModule                                                = 566,
+    CUPTI_DRIVER_TRACE_CBID_cuIpcOpenMemHandle_v2                                          = 567,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxResetPersistingL2Cache                                    = 568,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeCopyAttributes                                = 569,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeGetAttribute                                  = 570,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeSetAttribute                                  = 571,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamCopyAttributes                                         = 572,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamCopyAttributes_ptsz                                    = 573,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetAttribute                                           = 574,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetAttribute_ptsz                                      = 575,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamSetAttribute                                           = 576,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamSetAttribute_ptsz                                      = 577,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphInstantiate_v2                                          = 578,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetTexture1DLinearMaxWidth                             = 579,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphUpload                                                  = 580,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphUpload_ptsz                                             = 581,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayGetSparseProperties                                     = 582,
+    CUPTI_DRIVER_TRACE_CBID_cuMipmappedArrayGetSparseProperties                            = 583,
+    CUPTI_DRIVER_TRACE_CBID_cuMemMapArrayAsync                                             = 584,
+    CUPTI_DRIVER_TRACE_CBID_cuMemMapArrayAsync_ptsz                                        = 585,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecChildGraphNodeSetParams                             = 586,
+    CUPTI_DRIVER_TRACE_CBID_cuEventRecordWithFlags                                         = 587,
+    CUPTI_DRIVER_TRACE_CBID_cuEventRecordWithFlags_ptsz                                    = 588,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddEventRecordNode                                      = 589,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddEventWaitNode                                        = 590,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphEventRecordNodeGetEvent                                 = 591,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphEventWaitNodeGetEvent                                   = 592,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphEventRecordNodeSetEvent                                 = 593,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphEventWaitNodeSetEvent                                   = 594,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecEventRecordNodeSetEvent                             = 595,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecEventWaitNodeSetEvent                               = 596,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayGetPlane                                                = 597,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocAsync                                                = 598,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocAsync_ptsz                                           = 599,
+    CUPTI_DRIVER_TRACE_CBID_cuMemFreeAsync                                                 = 600,
+    CUPTI_DRIVER_TRACE_CBID_cuMemFreeAsync_ptsz                                            = 601,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolTrimTo                                                = 602,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolSetAttribute                                          = 603,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolGetAttribute                                          = 604,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolSetAccess                                             = 605,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetDefaultMemPool                                      = 606,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolCreate                                                = 607,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolDestroy                                               = 608,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceSetMemPool                                             = 609,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetMemPool                                             = 610,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocFromPoolAsync                                        = 611,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAllocFromPoolAsync_ptsz                                   = 612,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolExportToShareableHandle                               = 613,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolImportFromShareableHandle                             = 614,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolExportPointer                                         = 615,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolImportPointer                                         = 616,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPoolGetAccess                                             = 617,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddExternalSemaphoresSignalNode                         = 618,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExternalSemaphoresSignalNodeGetParams                   = 619,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExternalSemaphoresSignalNodeSetParams                   = 620,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddExternalSemaphoresWaitNode                           = 621,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExternalSemaphoresWaitNodeGetParams                     = 622,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExternalSemaphoresWaitNodeSetParams                     = 623,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecExternalSemaphoresSignalNodeSetParams               = 624,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecExternalSemaphoresWaitNodeSetParams                 = 625,
+    CUPTI_DRIVER_TRACE_CBID_cuGetProcAddress                                               = 626,
+    CUPTI_DRIVER_TRACE_CBID_cuFlushGPUDirectRDMAWrites                                     = 627,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphDebugDotPrint                                           = 628,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCaptureInfo_v2                                      = 629,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCaptureInfo_v2_ptsz                                 = 630,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamUpdateCaptureDependencies                              = 631,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamUpdateCaptureDependencies_ptsz                         = 632,
+    CUPTI_DRIVER_TRACE_CBID_cuUserObjectCreate                                             = 633,
+    CUPTI_DRIVER_TRACE_CBID_cuUserObjectRetain                                             = 634,
+    CUPTI_DRIVER_TRACE_CBID_cuUserObjectRelease                                            = 635,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphRetainUserObject                                        = 636,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphReleaseUserObject                                       = 637,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddMemAllocNode                                         = 638,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddMemFreeNode                                          = 639,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGraphMemTrim                                           = 640,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetGraphMemAttribute                                   = 641,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceSetGraphMemAttribute                                   = 642,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphInstantiateWithFlags                                    = 643,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetExecAffinitySupport                                 = 644,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxCreate_v3                                                 = 645,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetExecAffinity                                           = 646,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetUuid_v2                                             = 647,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphMemAllocNodeGetParams                                   = 648,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphMemFreeNodeGetParams                                    = 649,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeSetEnabled                                          = 650,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeGetEnabled                                          = 651,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchKernelEx                                               = 652,
+    CUPTI_DRIVER_TRACE_CBID_cuLaunchKernelEx_ptsz                                          = 653,
+    CUPTI_DRIVER_TRACE_CBID_cuArrayGetMemoryRequirements                                   = 654,
+    CUPTI_DRIVER_TRACE_CBID_cuMipmappedArrayGetMemoryRequirements                          = 655,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphInstantiateWithParams                                   = 656,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphInstantiateWithParams_ptsz                              = 657,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecGetFlags                                            = 658,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue32_v2                                         = 659,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue32_v2_ptsz                                    = 660,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue64_v2                                         = 661,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWaitValue64_v2_ptsz                                    = 662,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue32_v2                                        = 663,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue32_v2_ptsz                                   = 664,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue64_v2                                        = 665,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamWriteValue64_v2_ptsz                                   = 666,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBatchMemOp_v2                                          = 667,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBatchMemOp_v2_ptsz                                     = 668,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddBatchMemOpNode                                       = 669,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphBatchMemOpNodeGetParams                                 = 670,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphBatchMemOpNodeSetParams                                 = 671,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecBatchMemOpNodeSetParams                             = 672,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetLoadingMode                                         = 673,
+    CUPTI_DRIVER_TRACE_CBID_cuMemGetHandleForAddressRange                                  = 674,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyMaxPotentialClusterSize                             = 675,
+    CUPTI_DRIVER_TRACE_CBID_cuOccupancyMaxActiveClusters                                   = 676,
+    CUPTI_DRIVER_TRACE_CBID_cuGetProcAddress_v2                                            = 677,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryLoadData                                              = 678,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryLoadFromFile                                          = 679,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryUnload                                                = 680,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryGetKernel                                             = 681,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryGetModule                                             = 682,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelGetFunction                                            = 683,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryGetGlobal                                             = 684,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryGetManaged                                            = 685,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelGetAttribute                                           = 686,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelSetAttribute                                           = 687,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelSetCacheConfig                                         = 688,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddKernelNode_v2                                        = 689,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeGetParams_v2                                  = 690,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphKernelNodeSetParams_v2                                  = 691,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecKernelNodeSetParams_v2                              = 692,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetId                                                  = 693,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetId_ptsz                                             = 694,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetId                                                     = 695,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecUpdate_v2                                           = 696,
+    CUPTI_DRIVER_TRACE_CBID_cuTensorMapEncodeTiled                                         = 697,
+    CUPTI_DRIVER_TRACE_CBID_cuTensorMapEncodeIm2col                                        = 698,
+    CUPTI_DRIVER_TRACE_CBID_cuTensorMapReplaceAddress                                      = 699,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryGetUnifiedFunction                                    = 700,
+    CUPTI_DRIVER_TRACE_CBID_cuCoredumpGetAttribute                                         = 701,
+    CUPTI_DRIVER_TRACE_CBID_cuCoredumpGetAttributeGlobal                                   = 702,
+    CUPTI_DRIVER_TRACE_CBID_cuCoredumpSetAttribute                                         = 703,
+    CUPTI_DRIVER_TRACE_CBID_cuCoredumpSetAttributeGlobal                                   = 704,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxSetFlags                                                  = 705,
+    CUPTI_DRIVER_TRACE_CBID_cuMulticastCreate                                              = 706,
+    CUPTI_DRIVER_TRACE_CBID_cuMulticastAddDevice                                           = 707,
+    CUPTI_DRIVER_TRACE_CBID_cuMulticastBindMem                                             = 708,
+    CUPTI_DRIVER_TRACE_CBID_cuMulticastBindAddr                                            = 709,
+    CUPTI_DRIVER_TRACE_CBID_cuMulticastUnbind                                              = 710,
+    CUPTI_DRIVER_TRACE_CBID_cuMulticastGetGranularity                                      = 711,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddNode                                                 = 712,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeSetParams                                           = 713,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphExecNodeSetParams                                       = 714,
+    CUPTI_DRIVER_TRACE_CBID_cuMemAdvise_v2                                                 = 715,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPrefetchAsync_v2                                          = 716,
+    CUPTI_DRIVER_TRACE_CBID_cuMemPrefetchAsync_v2_ptsz                                     = 717,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncGetName                                                  = 718,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelGetName                                                = 719,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBeginCaptureToGraph                                    = 720,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamBeginCaptureToGraph_ptsz                               = 721,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphConditionalHandleCreate                                 = 722,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddNode_v2                                              = 723,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphGetEdges_v2                                             = 724,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeGetDependencies_v2                                  = 725,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphNodeGetDependentNodes_v2                                = 726,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphAddDependencies_v2                                      = 727,
+    CUPTI_DRIVER_TRACE_CBID_cuGraphRemoveDependencies_v2                                   = 728,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCaptureInfo_v3                                      = 729,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCaptureInfo_v3_ptsz                                 = 730,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamUpdateCaptureDependencies_v2                           = 731,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamUpdateCaptureDependencies_v2_ptsz                      = 732,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncGetParamInfo                                             = 733,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelGetParamInfo                                           = 734,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceRegisterAsyncNotification                              = 735,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceUnregisterAsyncNotification                            = 736,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleGetFunctionCount                                       = 737,
+    CUPTI_DRIVER_TRACE_CBID_cuModuleEnumerateFunctions                                     = 738,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryGetKernelCount                                        = 739,
+    CUPTI_DRIVER_TRACE_CBID_cuLibraryEnumerateKernels                                      = 740,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncIsLoaded                                                 = 741,
+    CUPTI_DRIVER_TRACE_CBID_cuFuncLoad                                                     = 742,
+    CUPTI_DRIVER_TRACE_CBID_cuGreenCtxCreate                                               = 743,
+    CUPTI_DRIVER_TRACE_CBID_cuGreenCtxDestroy                                              = 744,
+    CUPTI_DRIVER_TRACE_CBID_cuDeviceGetDevResource                                         = 745,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxGetDevResource                                            = 746,
+    CUPTI_DRIVER_TRACE_CBID_cuGreenCtxGetDevResource                                       = 747,
+    CUPTI_DRIVER_TRACE_CBID_cuDevResourceGenerateDesc                                      = 748,
+    CUPTI_DRIVER_TRACE_CBID_cuGreenCtxRecordEvent                                          = 749,
+    CUPTI_DRIVER_TRACE_CBID_cuGreenCtxWaitEvent                                            = 750,
+    CUPTI_DRIVER_TRACE_CBID_cuDevSmResourceSplitByCount                                    = 751,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetGreenCtx                                            = 752,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxFromGreenCtx                                              = 753,
+    CUPTI_DRIVER_TRACE_CBID_cuKernelGetLibrary                                             = 754,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxRecordEvent                                               = 755,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxWaitEvent                                                 = 756,
+    CUPTI_DRIVER_TRACE_CBID_cuCtxCreate_v4                                                 = 757,
+    CUPTI_DRIVER_TRACE_CBID_cuGreenCtxStreamCreate                                         = 758,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCtx_v2                                              = 759,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetCtx_v2_ptsz                                         = 760,
+    CUPTI_DRIVER_TRACE_CBID_cuMemBatchDecompressAsync                                      = 761,
+    CUPTI_DRIVER_TRACE_CBID_cuMemBatchDecompressAsync_ptsz                                 = 762,
+    CUPTI_DRIVER_TRACE_CBID_cuLogsRegisterCallback                                         = 763,
+    CUPTI_DRIVER_TRACE_CBID_cuLogsUnregisterCallback                                       = 764,
+    CUPTI_DRIVER_TRACE_CBID_cuLogsCurrent                                                  = 765,
+    CUPTI_DRIVER_TRACE_CBID_cuLogsDumpToFile                                               = 766,
+    CUPTI_DRIVER_TRACE_CBID_cuLogsDumpToMemory                                             = 767,
+    CUPTI_DRIVER_TRACE_CBID_cuCheckpointProcessGetRestoreThreadId                          = 768,
+    CUPTI_DRIVER_TRACE_CBID_cuCheckpointProcessGetState                                    = 769,
+    CUPTI_DRIVER_TRACE_CBID_cuCheckpointProcessLock                                        = 770,
+    CUPTI_DRIVER_TRACE_CBID_cuCheckpointProcessCheckpoint                                  = 771,
+    CUPTI_DRIVER_TRACE_CBID_cuCheckpointProcessRestore                                     = 772,
+    CUPTI_DRIVER_TRACE_CBID_cuCheckpointProcessUnlock                                      = 773,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetDevice                                              = 774,
+    CUPTI_DRIVER_TRACE_CBID_cuStreamGetDevice_ptsz                                         = 775,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyBatchAsync                                             = 776,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpyBatchAsync_ptsz                                        = 777,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DBatchAsync                                           = 778,
+    CUPTI_DRIVER_TRACE_CBID_cuMemcpy3DBatchAsync_ptsz                                      = 779,
+    CUPTI_DRIVER_TRACE_CBID_cuEventElapsedTime_v2                                          = 780,
+    CUPTI_DRIVER_TRACE_CBID_cuTensorMapEncodeIm2colWide                                    = 781,
+    CUPTI_DRIVER_TRACE_CBID_SIZE                                                           = 782,
+    CUPTI_DRIVER_TRACE_CBID_FORCE_INT                                                      = 0x7fffffff
+} CUpti_driver_api_trace_cbid;
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_events.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_events.h
new file mode 100644
index 0000000000000000000000000000000000000000..2e4aebc2a1389e8693f02df9b6e3be1e90490870
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_events.h
@@ -0,0 +1,1349 @@
+/*
+ * Copyright 2010-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_EVENTS_H_)
+#define _CUPTI_EVENTS_H_
+
+#include <cuda.h>
+#include <string.h>
+#include <cuda_stdint.h>
+#include <cupti_result.h>
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_EVENT_API CUPTI Event API
+ * Functions, types, and enums that implement the CUPTI Event API.
+ *
+ * \note The CUPTI event API from the header cupti_events.h is not supported on devices
+ * with compute capability 7.5 and higher (i.e. Turing and later GPU architectures).
+ * This API is deprecated in CUDA 12.8 release and will be removed in a future CUDA release.
+ * This is replaced by the host profiling API in the header cupti_profiler_host.h and
+ * target profiling API in the header cupti_range_profiler.h which are supported on
+ * devices with compute capability 7.0 and higher (i.e. Volta and later GPU architectures).
+ *
+ * @{
+ */
+
+/**
+ * \brief ID for an event.
+ *
+ * An event represents a countable activity, action, or occurrence on
+ * the device.
+ */
+typedef uint32_t CUpti_EventID;
+
+/**
+ * \brief ID for an event domain.
+ *
+ * ID for an event domain. An event domain represents a group of
+ * related events. A device may have multiple instances of a domain,
+ * indicating that the device can simultaneously record multiple
+ * instances of each event within that domain.
+ */
+typedef uint32_t CUpti_EventDomainID;
+
+/**
+ * \brief A group of events.
+ *
+ * An event group is a collection of events that are managed
+ * together. All events in an event group must belong to the same
+ * domain.
+ */
+typedef void *CUpti_EventGroup;
+
+/**
+ * \brief Device class.
+ *
+ * Enumeration of device classes for device attribute
+ * CUPTI_DEVICE_ATTR_DEVICE_CLASS.
+ */
+typedef enum {
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_TESLA              = 0,
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_QUADRO             = 1,
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_GEFORCE            = 2,
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_TEGRA              = 3,
+} CUpti_DeviceAttributeDeviceClass;
+
+/**
+ * \brief Device attributes.
+ *
+ * CUPTI device attributes. These attributes can be read using \ref
+ * cuptiDeviceGetAttribute.
+ */
+typedef enum {
+  /**
+   * Number of event IDs for a device. Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_EVENT_ID                            = 1,
+  /**
+   * Number of event domain IDs for a device. Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_EVENT_DOMAIN_ID                     = 2,
+  /**
+   * Get global memory bandwidth in Kbytes/sec. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_GLOBAL_MEMORY_BANDWIDTH                 = 3,
+  /**
+   * Get theoretical maximum number of instructions per cycle. Value
+   * is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_INSTRUCTION_PER_CYCLE                   = 4,
+  /**
+   * Get theoretical maximum number of single precision instructions
+   * that can be executed per second. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_INSTRUCTION_THROUGHPUT_SINGLE_PRECISION = 5,
+  /**
+   * Get number of frame buffers for device.  Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_FRAME_BUFFERS                       = 6,
+  /**
+   * Get PCIE link rate in Mega bits/sec for device. Return 0 if bus-type
+   * is non-PCIE. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_PCIE_LINK_RATE                          = 7,
+  /**
+   * Get PCIE link width for device. Return 0 if bus-type
+   * is non-PCIE. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_PCIE_LINK_WIDTH                         = 8,
+  /**
+   * Get PCIE generation for device. Return 0 if bus-type
+   * is non-PCIE. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_PCIE_GEN                                = 9,
+  /**
+   * Get the class for the device. Value is a
+   * CUpti_DeviceAttributeDeviceClass.
+   */
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS                            = 10,
+  /**
+   * Get the peak single precision flop per cycle. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_FLOP_SP_PER_CYCLE                       = 11,
+  /**
+   * Get the peak double precision flop per cycle. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_FLOP_DP_PER_CYCLE                       = 12,
+  /**
+   * Get number of L2 units. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_L2_UNITS                           = 13,
+  /**
+   * Get the maximum shared memory for the CU_FUNC_CACHE_PREFER_SHARED
+   * preference. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_SHARED_MEMORY_CACHE_CONFIG_PREFER_SHARED = 14,
+  /**
+   * Get the maximum shared memory for the CU_FUNC_CACHE_PREFER_L1
+   * preference. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_SHARED_MEMORY_CACHE_CONFIG_PREFER_L1 = 15,
+  /**
+   * Get the maximum shared memory for the CU_FUNC_CACHE_PREFER_EQUAL
+   * preference. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_SHARED_MEMORY_CACHE_CONFIG_PREFER_EQUAL = 16,
+  /**
+   * Get the peak half precision flop per cycle. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_FLOP_HP_PER_CYCLE                       = 17,
+  /**
+   * Check if Nvlink is connected to device. Returns 1, if at least one
+   * Nvlink is connected to the device, returns 0 otherwise.
+   * Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_NVLINK_PRESENT                          = 18,
+    /**
+   * Check if Nvlink is present between GPU and CPU. Returns Bandwidth,
+   * in Bytes/sec, if Nvlink is present, returns 0 otherwise.
+   * Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_GPU_CPU_NVLINK_BW                       = 19,
+  /**
+   * Check if NVSwitch is present in the underlying topology.
+   * Returns 1, if present, returns 0 otherwise.
+   * Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_NVSWITCH_PRESENT                        = 20,
+  CUPTI_DEVICE_ATTR_FORCE_INT                               = 0x7fffffff,
+} CUpti_DeviceAttribute;
+
+/**
+ * \brief Event domain attributes.
+ *
+ * Event domain attributes. Except where noted, all the attributes can
+ * be read using either \ref cuptiDeviceGetEventDomainAttribute or
+ * \ref cuptiEventDomainGetAttribute.
+ */
+typedef enum {
+  /**
+   * Event domain name. Value is a null terminated const c-string.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_NAME                 = 0,
+  /**
+   * Number of instances of the domain for which event counts will be
+   * collected.  The domain may have additional instances that cannot
+   * be profiled (see CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT).
+   * Can be read only with \ref
+   * cuptiDeviceGetEventDomainAttribute. Value is a uint32_t.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_INSTANCE_COUNT       = 1,
+  /**
+   * Total number of instances of the domain, including instances that
+   * cannot be profiled.  Use CUPTI_EVENT_DOMAIN_ATTR_INSTANCE_COUNT
+   * to get the number of instances that can be profiled. Can be read
+   * only with \ref cuptiDeviceGetEventDomainAttribute. Value is a
+   * uint32_t.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT = 3,
+  /**
+   * Collection method used for events contained in the event domain.
+   * Value is a \ref CUpti_EventCollectionMethod.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_COLLECTION_METHOD    = 4,
+
+  CUPTI_EVENT_DOMAIN_ATTR_FORCE_INT      = 0x7fffffff,
+} CUpti_EventDomainAttribute;
+
+/**
+ * \brief The collection method used for an event.
+ *
+ * The collection method indicates how an event is collected.
+ */
+typedef enum {
+  /**
+   * Event is collected using a hardware global performance monitor.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_PM                  = 0,
+  /**
+   * Event is collected using a hardware SM performance monitor.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_SM                  = 1,
+  /**
+   * Event is collected using software instrumentation.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_INSTRUMENTED        = 2,
+  /**
+   * Event is collected using NvLink throughput counter method.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_NVLINK_TC           = 3,
+  CUPTI_EVENT_COLLECTION_METHOD_FORCE_INT           = 0x7fffffff
+} CUpti_EventCollectionMethod;
+
+/**
+ * \brief Event group attributes.
+ *
+ * Event group attributes. These attributes can be read using \ref
+ * cuptiEventGroupGetAttribute. Attributes marked [rw] can also be
+ * written using \ref cuptiEventGroupSetAttribute.
+ */
+typedef enum {
+  /**
+   * The domain to which the event group is bound. This attribute is
+   * set when the first event is added to the group.  Value is a
+   * CUpti_EventDomainID.
+   */
+  CUPTI_EVENT_GROUP_ATTR_EVENT_DOMAIN_ID              = 0,
+  /**
+   * [rw] Profile all the instances of the domain for this
+   * eventgroup. This feature can be used to get load balancing
+   * across all instances of a domain. Value is an integer.
+   */
+  CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES = 1,
+  /**
+   * [rw] Reserved for user data.
+   */
+  CUPTI_EVENT_GROUP_ATTR_USER_DATA                    = 2,
+  /**
+   * Number of events in the group. Value is a uint32_t.
+   */
+  CUPTI_EVENT_GROUP_ATTR_NUM_EVENTS                   = 3,
+  /**
+   * Enumerates events in the group. Value is a pointer to buffer of
+   * size sizeof(CUpti_EventID) * num_of_events in the eventgroup.
+   * num_of_events can be queried using
+   * CUPTI_EVENT_GROUP_ATTR_NUM_EVENTS.
+   */
+  CUPTI_EVENT_GROUP_ATTR_EVENTS                       = 4,
+  /**
+   * Number of instances of the domain bound to this event group that
+   * will be counted.  Value is a uint32_t.
+   */
+  CUPTI_EVENT_GROUP_ATTR_INSTANCE_COUNT               = 5,
+  /**
+   * Event group scope can be set to CUPTI_EVENT_PROFILING_SCOPE_DEVICE or
+   * CUPTI_EVENT_PROFILING_SCOPE_CONTEXT for an eventGroup, before
+   * adding any event.
+   * Sets the scope of eventgroup as CUPTI_EVENT_PROFILING_SCOPE_DEVICE or
+   * CUPTI_EVENT_PROFILING_SCOPE_CONTEXT when the scope of the events
+   * that will be added is CUPTI_EVENT_PROFILING_SCOPE_BOTH.
+   * If profiling scope of event is either
+   * CUPTI_EVENT_PROFILING_SCOPE_DEVICE or CUPTI_EVENT_PROFILING_SCOPE_CONTEXT
+   * then setting this attribute will not affect the default scope.
+   * It is not allowed to add events of different scope to same eventgroup.
+   * Value is a uint32_t.
+   */
+  CUPTI_EVENT_GROUP_ATTR_PROFILING_SCOPE               = 6,
+  CUPTI_EVENT_GROUP_ATTR_FORCE_INT                     = 0x7fffffff,
+} CUpti_EventGroupAttribute;
+
+/**
+* \brief Profiling scope for event.
+*
+* Profiling scope of event indicates if the event can be collected at context
+* scope or device scope or both i.e. it can be collected at any of context or
+* device scope.
+*/
+typedef enum {
+  /**
+   * Event is collected at context scope.
+   */
+  CUPTI_EVENT_PROFILING_SCOPE_CONTEXT                 = 0,
+  /**
+   * Event is collected at device scope.
+   */
+  CUPTI_EVENT_PROFILING_SCOPE_DEVICE                  = 1,
+  /**
+   * Event can be collected at device or context scope.
+   * The scope can be set using \ref cuptiEventGroupSetAttribute API.
+   */
+  CUPTI_EVENT_PROFILING_SCOPE_BOTH                    = 2,
+  CUPTI_EVENT_PROFILING_SCOPE_FORCE_INT               = 0x7fffffff
+} CUpti_EventProfilingScope;
+
+/**
+ * \brief Event attributes.
+ *
+ * Event attributes. These attributes can be read using \ref
+ * cuptiEventGetAttribute.
+ */
+typedef enum {
+  /**
+   * Event name. Value is a null terminated const c-string.
+   */
+  CUPTI_EVENT_ATTR_NAME              = 0,
+  /**
+   * Short description of event. Value is a null terminated const
+   * c-string.
+   */
+  CUPTI_EVENT_ATTR_SHORT_DESCRIPTION = 1,
+  /**
+   * Long description of event. Value is a null terminated const
+   * c-string.
+   */
+  CUPTI_EVENT_ATTR_LONG_DESCRIPTION  = 2,
+  /**
+   * Category of event. Value is CUpti_EventCategory.
+   */
+  CUPTI_EVENT_ATTR_CATEGORY          = 3,
+  /**
+   * Profiling scope of the events. It can be either device or context or both.
+   * Value is a \ref CUpti_EventProfilingScope.
+   */
+  CUPTI_EVENT_ATTR_PROFILING_SCOPE   = 5,
+
+  CUPTI_EVENT_ATTR_FORCE_INT         = 0x7fffffff,
+} CUpti_EventAttribute;
+
+/**
+ * \brief Event collection modes.
+ *
+ * The event collection mode determines the period over which the
+ * events within the enabled event groups will be collected.
+ */
+typedef enum {
+  /**
+   * Events are collected for the entire duration between the
+   * cuptiEventGroupEnable and cuptiEventGroupDisable calls.
+   * Event values are reset when the events are read.
+   * For CUDA toolkit v6.0 and older this was the default mode.
+   */
+  CUPTI_EVENT_COLLECTION_MODE_CONTINUOUS          = 0,
+  /**
+   * Events are collected only for the durations of kernel executions
+   * that occur between the cuptiEventGroupEnable and
+   * cuptiEventGroupDisable calls. Event collection begins when a
+   * kernel execution begins, and stops when kernel execution
+   * completes. Event values are reset to zero when each kernel
+   * execution begins. If multiple kernel executions occur between the
+   * cuptiEventGroupEnable and cuptiEventGroupDisable calls then the
+   * event values must be read after each kernel launch if those
+   * events need to be associated with the specific kernel launch.
+   * Note that collection in this mode may significantly change the
+   * overall performance characteristics of the application because
+   * kernel executions that occur between the cuptiEventGroupEnable and
+   * cuptiEventGroupDisable calls are serialized on the GPU.
+   * This is the default mode from CUDA toolkit v6.5
+   */
+  CUPTI_EVENT_COLLECTION_MODE_KERNEL              = 1,
+  CUPTI_EVENT_COLLECTION_MODE_FORCE_INT           = 0x7fffffff
+} CUpti_EventCollectionMode;
+
+/**
+ * \brief An event category.
+ *
+ * Each event is assigned to a category that represents the general
+ * type of the event. A event's category is accessed using \ref
+ * cuptiEventGetAttribute and the CUPTI_EVENT_ATTR_CATEGORY attribute.
+ */
+typedef enum {
+  /**
+   * An instruction related event.
+   */
+  CUPTI_EVENT_CATEGORY_INSTRUCTION     = 0,
+  /**
+   * A memory related event.
+   */
+  CUPTI_EVENT_CATEGORY_MEMORY          = 1,
+  /**
+   * A cache related event.
+   */
+  CUPTI_EVENT_CATEGORY_CACHE           = 2,
+  /**
+   * A profile-trigger event.
+   */
+  CUPTI_EVENT_CATEGORY_PROFILE_TRIGGER = 3,
+  /**
+   * A system event.
+   */
+  CUPTI_EVENT_CATEGORY_SYSTEM  = 4,
+  CUPTI_EVENT_CATEGORY_FORCE_INT       = 0x7fffffff
+} CUpti_EventCategory;
+
+/**
+ * \brief The overflow value for a CUPTI event.
+ *
+ * The CUPTI event value that indicates an overflow.
+ */
+#define CUPTI_EVENT_OVERFLOW ((uint64_t)0xFFFFFFFFFFFFFFFFULL)
+
+/**
+ * \brief The value that indicates the event value is invalid
+ */
+#define CUPTI_EVENT_INVALID ((uint64_t)0xFFFFFFFFFFFFFFFEULL)
+
+/**
+ * \brief Flags for cuptiEventGroupReadEvent an
+ * cuptiEventGroupReadAllEvents.
+ *
+ * Flags for \ref cuptiEventGroupReadEvent an \ref
+ * cuptiEventGroupReadAllEvents.
+ */
+typedef enum {
+  /**
+   * No flags.
+   */
+  CUPTI_EVENT_READ_FLAG_NONE          = 0,
+  CUPTI_EVENT_READ_FLAG_FORCE_INT     = 0x7fffffff,
+} CUpti_ReadEventFlags;
+
+
+/**
+ * \brief A set of event groups.
+ *
+ * A set of event groups. When returned by \ref
+ * cuptiEventGroupSetsCreate and \ref cuptiMetricCreateEventGroupSets
+ * a set indicates that event groups that can be enabled at the same
+ * time (i.e. all the events in the set can be collected
+ * simultaneously).
+ */
+typedef struct {
+  /**
+   * The number of event groups in the set.
+   */
+  uint32_t numEventGroups;
+  /**
+   * An array of \p numEventGroups event groups.
+   */
+  CUpti_EventGroup *eventGroups;
+} CUpti_EventGroupSet;
+
+/**
+ * \brief A set of event group sets.
+ *
+ * A set of event group sets. When returned by \ref
+ * cuptiEventGroupSetsCreate and \ref cuptiMetricCreateEventGroupSets
+ * a CUpti_EventGroupSets indicates the number of passes required to
+ * collect all the events, and the event groups that should be
+ * collected during each pass.
+ */
+typedef struct {
+  /**
+   * Number of event group sets.
+   */
+  uint32_t numSets;
+  /**
+   * An array of \p numSets event group sets.
+   */
+  CUpti_EventGroupSet *sets;
+} CUpti_EventGroupSets;
+
+/**
+ * \brief Set the event collection mode.
+ *
+ * Set the event collection mode for a \p context.  The \p mode
+ * controls the event collection behavior of all events in event
+ * groups created in the \p context. This API is invalid in kernel
+ * replay mode.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context
+ * \param mode The event collection mode
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_OPERATION if called when replay mode is enabled
+ * \retval CUPTI_ERROR_NOT_SUPPORTED if mode is not supported on the device
+ */
+
+CUptiResult CUPTIAPI cuptiSetEventCollectionMode(CUcontext context,
+                                                 CUpti_EventCollectionMode mode);
+
+/**
+ * \brief Read a device attribute.
+ *
+ * Read a device attribute and return it in \p *value.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param attrib The attribute to read
+ * \param valueSize Size of buffer pointed by the value, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the value of the attribute
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not a device attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetAttribute(CUdevice device,
+                                             CUpti_DeviceAttribute attrib,
+                                             size_t *valueSize,
+                                             void *value);
+
+/**
+ * \brief Get the number of domains for a device.
+ *
+ * Returns the number of domains in \p numDomains for a device.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param numDomains Returns the number of domains
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numDomains is NULL
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetNumEventDomains(CUdevice device,
+                                                   uint32_t *numDomains);
+
+/**
+ * \brief Get the event domains for a device.
+ *
+ * Returns the event domains IDs in \p domainArray for a device.  The
+ * size of the \p domainArray buffer is given by \p
+ * *arraySizeBytes. The size of the \p domainArray buffer must be at
+ * least \p numdomains * sizeof(CUpti_EventDomainID) or else all
+ * domains will not be returned. The value returned in \p
+ * *arraySizeBytes contains the number of bytes returned in \p
+ * domainArray.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param arraySizeBytes The size of \p domainArray in bytes, and
+ * returns the number of bytes written to \p domainArray
+ * \param domainArray Returns the IDs of the event domains for the device
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or
+ * \p domainArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiDeviceEnumEventDomains(CUdevice device,
+                                                 size_t *arraySizeBytes,
+                                                 CUpti_EventDomainID *domainArray);
+
+/**
+ * \brief Read an event domain attribute.
+ *
+ * Returns an event domain attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param eventDomain ID of the event domain
+ * \param attrib The event domain attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event domain attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetEventDomainAttribute(CUdevice device,
+                                                        CUpti_EventDomainID eventDomain,
+                                                        CUpti_EventDomainAttribute attrib,
+                                                        size_t *valueSize,
+                                                        void *value);
+
+/**
+ * \brief Get the number of event domains available on any device.
+ *
+ * Returns the total number of event domains available on any
+ * CUDA-capable device.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param numDomains Returns the number of domains
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numDomains is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetNumEventDomains(uint32_t *numDomains);
+
+/**
+ * \brief Get the event domains available on any device.
+ *
+ * Returns all the event domains available on any CUDA-capable device.
+ * Event domain IDs are returned in \p domainArray. The size of the \p
+ * domainArray buffer is given by \p *arraySizeBytes. The size of the
+ * \p domainArray buffer must be at least \p numDomains *
+ * sizeof(CUpti_EventDomainID) or all domains will not be
+ * returned. The value returned in \p *arraySizeBytes contains the
+ * number of bytes returned in \p domainArray.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param arraySizeBytes The size of \p domainArray in bytes, and
+ * returns the number of bytes written to \p domainArray
+ * \param domainArray Returns all the event domains
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or
+ * \p domainArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiEnumEventDomains(size_t *arraySizeBytes,
+                                           CUpti_EventDomainID *domainArray);
+
+/**
+ * \brief Read an event domain attribute.
+ *
+ * Returns an event domain attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventDomain ID of the event domain
+ * \param attrib The event domain attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event domain attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventDomainGetAttribute(CUpti_EventDomainID eventDomain,
+                                                  CUpti_EventDomainAttribute attrib,
+                                                  size_t *valueSize,
+                                                  void *value);
+
+/**
+ * \brief Get number of events in a domain.
+ *
+ * Returns the number of events in \p numEvents for a domain.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventDomain ID of the event domain
+ * \param numEvents Returns the number of events in the domain
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numEvents is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventDomainGetNumEvents(CUpti_EventDomainID eventDomain,
+                                                  uint32_t *numEvents);
+
+/**
+ * \brief Get the events in a domain.
+ *
+ * Returns the event IDs in \p eventArray for a domain.  The size of
+ * the \p eventArray buffer is given by \p *arraySizeBytes. The size
+ * of the \p eventArray buffer must be at least \p numdomainevents *
+ * sizeof(CUpti_EventID) or else all events will not be returned. The
+ * value returned in \p *arraySizeBytes contains the number of bytes
+ * returned in \p eventArray.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventDomain ID of the event domain
+ * \param arraySizeBytes The size of \p eventArray in bytes, and
+ * returns the number of bytes written to \p eventArray
+ * \param eventArray Returns the IDs of the events in the domain
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or \p
+ * eventArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiEventDomainEnumEvents(CUpti_EventDomainID eventDomain,
+                                                size_t *arraySizeBytes,
+                                                CUpti_EventID *eventArray);
+
+/**
+ * \brief Get an event attribute.
+ *
+ * Returns an event attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param event ID of the event
+ * \param attrib The event attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventGetAttribute(CUpti_EventID event,
+                                            CUpti_EventAttribute attrib,
+                                            size_t *valueSize,
+                                            void *value);
+
+/**
+ * \brief Find an event by name.
+ *
+ * Find an event by name and return the event ID in \p *event.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param eventName The name of the event to find
+ * \param event Returns the ID of the found event or undefined if
+ * unable to find the event
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_EVENT_NAME if unable to find an event
+ * with name \p eventName. In this case \p *event is undefined
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventName or \p event are NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGetIdFromName(CUdevice device,
+                                             const char *eventName,
+                                             CUpti_EventID *event);
+
+/**
+ * \brief Create a new event group for a context.
+ *
+ * Creates a new event group for \p context and returns the new group
+ * in \p *eventGroup.
+ * \note \p flags are reserved for future use and should be set to zero.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context for the event group
+ * \param eventGroup Returns the new event group
+ * \param flags Reserved - must be zero
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupCreate(CUcontext context,
+                                           CUpti_EventGroup *eventGroup,
+                                           uint32_t flags);
+
+/**
+ * \brief Destroy an event group.
+ *
+ * Destroy an \p eventGroup and free its resources. An event group
+ * cannot be destroyed if it is enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group to destroy
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if the event group is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupDestroy(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Read an event group attribute.
+ *
+ * Read an event group attribute and return it in \p *value.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.).
+ *
+ * \param eventGroup The event group
+ * \param attrib The attribute to read
+ * \param valueSize Size of buffer pointed by the value, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the value of the attribute
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an eventgroup attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventGroupGetAttribute(CUpti_EventGroup eventGroup,
+                                                 CUpti_EventGroupAttribute attrib,
+                                                 size_t *valueSize,
+                                                 void *value);
+
+/**
+ * \brief Write an event group attribute.
+ *
+ * Write an event group attribute.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ * \param attrib The attribute to write
+ * \param valueSize The size, in bytes, of the value
+ * \param value The attribute value to write
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event group attribute, or if
+ * \p attrib is not a writable attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT Indicates that
+ * the \p value buffer is too small to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetAttribute(CUpti_EventGroup eventGroup,
+                                                 CUpti_EventGroupAttribute attrib,
+                                                 size_t valueSize,
+                                                 void *value);
+
+/**
+ * \brief Add an event to an event group.
+ *
+ * Add an event to an event group. The event add can fail for a number of reasons:
+ * \li The event group is enabled
+ * \li The event does not belong to the same event domain as the
+ * events that are already in the event group
+ * \li Device limitations on the events that can belong to the same group
+ * \li The event group is full
+ *
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ * \param event The event to add to the group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is enabled
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if \p event belongs to a
+ * different event domain than the events already in \p eventGroup, or
+ * if a device limitation prevents \p event from being collected at
+ * the same time as the events already in \p eventGroup
+ * \retval CUPTI_ERROR_MAX_LIMIT_REACHED if \p eventGroup is full
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupAddEvent(CUpti_EventGroup eventGroup,
+                                             CUpti_EventID event);
+
+/**
+ * \brief Remove an event from an event group.
+ *
+ * Remove \p event from the an event group. The event cannot be
+ * removed if the event group is enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ * \param event The event to remove from the group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupRemoveEvent(CUpti_EventGroup eventGroup,
+                                                CUpti_EventID event);
+
+/**
+ * \brief Remove all events from an event group.
+ *
+ * Remove all events from an event group. Events cannot be removed if
+ * the event group is enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupRemoveAllEvents(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Zero all the event counts in an event group.
+ *
+ * Zero all the event counts in an event group.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.).
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupResetAllEvents(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Enable an event group.
+ *
+ * Enable an event group. Enabling an event group zeros the value of
+ * all the events in the group and then starts collection of those
+ * events.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_NOT_READY if \p eventGroup does not contain any events
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if \p eventGroup cannot be
+ * enabled due to other already enabled event groups
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ * \retval CUPTI_ERROR_HARDWARE_BUSY if another client is profiling
+ * and hardware is busy
+ */
+CUptiResult CUPTIAPI cuptiEventGroupEnable(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Disable an event group.
+ *
+ * Disable an event group. Disabling an event group stops collection
+ * of events contained in the group.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupDisable(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Read the value for an event in an event group.
+ *
+ * Read the value for an event in an event group. The event value is
+ * returned in the \p eventValueBuffer buffer. \p
+ * eventValueBufferSizeBytes indicates the size of the \p
+ * eventValueBuffer buffer. The buffer must be at least sizeof(uint64)
+ * if ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is not set
+ * on the group containing the event.  The buffer must be at least
+ * (sizeof(uint64) * number of domain instances) if
+ * ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is set on the
+ * group.
+ *
+ * If any instance of an event counter overflows, the value returned
+ * for that event instance will be ::CUPTI_EVENT_OVERFLOW.
+ *
+ * The only allowed value for \p flags is ::CUPTI_EVENT_READ_FLAG_NONE.
+ *
+ * Reading an event from a disabled event group is not allowed. After
+ * being read, an event's value is reset to zero.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.). If \ref cuptiEventGroupResetAllEvents is
+ * called simultaneously with this function, then returned event
+ * values are undefined.
+ *
+ * \param eventGroup The event group
+ * \param flags Flags controlling the reading mode
+ * \param event The event to read
+ * \param eventValueBufferSizeBytes The size of \p eventValueBuffer
+ * in bytes, and returns the number of bytes written to \p
+ * eventValueBuffer
+ * \param eventValueBuffer Returns the event value(s)
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is disabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup, \p
+ * eventValueBufferSizeBytes or \p eventValueBuffer is NULL
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT if size of \p eventValueBuffer
+ * is not sufficient
+ */
+CUptiResult CUPTIAPI cuptiEventGroupReadEvent(CUpti_EventGroup eventGroup,
+                                              CUpti_ReadEventFlags flags,
+                                              CUpti_EventID event,
+                                              size_t *eventValueBufferSizeBytes,
+                                              uint64_t *eventValueBuffer);
+
+/**
+ * \brief Read the values for all the events in an event group.
+ *
+ * Read the values for all the events in an event group. The event
+ * values are returned in the \p eventValueBuffer buffer. \p
+ * eventValueBufferSizeBytes indicates the size of \p
+ * eventValueBuffer.  The buffer must be at least (sizeof(uint64) *
+ * number of events in group) if
+ * ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is not set on
+ * the group containing the events.  The buffer must be at least
+ * (sizeof(uint64) * number of domain instances * number of events in
+ * group) if ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is
+ * set on the group.
+ *
+ * The data format returned in \p eventValueBuffer is:
+ *    - domain instance 0: event0 event1 ... eventN
+ *    - domain instance 1: event0 event1 ... eventN
+ *    - ...
+ *    - domain instance M: event0 event1 ... eventN
+ *
+ * The event order in \p eventValueBuffer is returned in \p
+ * eventIdArray. The size of \p eventIdArray is specified in \p
+ * eventIdArraySizeBytes. The size should be at least
+ * (sizeof(CUpti_EventID) * number of events in group).
+ *
+ * If any instance of any event counter overflows, the value returned
+ * for that event instance will be ::CUPTI_EVENT_OVERFLOW.
+ *
+ * The only allowed value for \p flags is ::CUPTI_EVENT_READ_FLAG_NONE.
+ *
+ * Reading events from a disabled event group is not allowed. After
+ * being read, an event's value is reset to zero.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.). If \ref cuptiEventGroupResetAllEvents is
+ * called simultaneously with this function, then returned event
+ * values are undefined.
+ *
+ * \param eventGroup The event group
+ * \param flags Flags controlling the reading mode
+ * \param eventValueBufferSizeBytes The size of \p eventValueBuffer in
+ * bytes, and returns the number of bytes written to \p
+ * eventValueBuffer
+ * \param eventValueBuffer Returns the event values
+ * \param eventIdArraySizeBytes The size of \p eventIdArray in bytes,
+ * and returns the number of bytes written to \p eventIdArray
+ * \param eventIdArray Returns the IDs of the events in the same order
+ * as the values return in eventValueBuffer.
+ * \param numEventIdsRead Returns the number of event IDs returned
+ * in \p eventIdArray
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is disabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup, \p
+ * eventValueBufferSizeBytes, \p eventValueBuffer, \p
+ * eventIdArraySizeBytes, \p eventIdArray or \p numEventIdsRead is
+ * NULL
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT if size of \p eventValueBuffer
+ * or \p eventIdArray is not sufficient
+ */
+CUptiResult CUPTIAPI cuptiEventGroupReadAllEvents(CUpti_EventGroup       eventGroup,
+                                                  CUpti_ReadEventFlags   flags,
+                                                  size_t                 *eventValueBufferSizeBytes,
+                                                  uint64_t               *eventValueBuffer,
+                                                  size_t                 *eventIdArraySizeBytes,
+                                                  CUpti_EventID          *eventIdArray,
+                                                  size_t                 *numEventIdsRead);
+
+/**
+ * \brief For a set of events, get the grouping that indicates the
+ * number of passes and the event groups necessary to collect the
+ * events.
+ *
+ * The number of events that can be collected simultaneously varies by
+ * device and by the type of the events. When events can be collected
+ * simultaneously, they may need to be grouped into multiple event
+ * groups because they are from different event domains. This function
+ * takes a set of events and determines how many passes are required
+ * to collect all those events, and which events can be collected
+ * simultaneously in each pass.
+ *
+ * The CUpti_EventGroupSets returned in \p eventGroupPasses indicates
+ * how many passes are required to collect the events with the \p
+ * numSets field. Within each event group set, the \p sets array
+ * indicates the event groups that should be collected on each pass.
+ * \note \b Thread-safety: this function is thread safe, but client
+ * must guard against another thread simultaneously destroying \p
+ * context.
+ *
+ * \param context The context for event collection
+ * \param eventIdArraySizeBytes Size of \p eventIdArray in bytes
+ * \param eventIdArray Array of event IDs that need to be grouped
+ * \param eventGroupPasses Returns a CUpti_EventGroupSets object that
+ * indicates the number of passes required to collect the events and
+ * the events to collect on each pass
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventIdArray or
+ * \p eventGroupPasses is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetsCreate(CUcontext context,
+                                               size_t eventIdArraySizeBytes,
+                                               CUpti_EventID *eventIdArray,
+                                               CUpti_EventGroupSets **eventGroupPasses);
+
+/**
+ * \brief Destroy a event group sets object.
+ *
+ * Destroy a CUpti_EventGroupSets object.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroupSets The object to destroy
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if any of the event groups
+ * contained in the sets is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroupSets is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetsDestroy(CUpti_EventGroupSets *eventGroupSets);
+
+
+/**
+ * \brief Enable an event group set.
+ *
+ * Enable a set of event groups. Enabling a set of event groups zeros the value of
+ * all the events in all the groups and then starts collection of those events.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroupSet The pointer to the event group set
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_NOT_READY if \p eventGroup does not contain any events
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if \p eventGroup cannot be
+ * enabled due to other already enabled event groups
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroupSet is NULL
+ * \retval CUPTI_ERROR_HARDWARE_BUSY if other client is profiling and hardware is
+ * busy
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetEnable(CUpti_EventGroupSet *eventGroupSet);
+
+/**
+ * \brief Disable an event group set.
+ *
+ * Disable a set of event groups. Disabling a set of event groups
+ * stops collection of events contained in the groups.
+ * \note \b Thread-safety: this function is thread safe.
+ * \note \b If this call fails, some of the event groups in the set may be disabled
+ * and other event groups may remain enabled.
+ *
+ * \param eventGroupSet The pointer to the event group set
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroupSet is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetDisable(CUpti_EventGroupSet *eventGroupSet);
+
+/**
+ * \brief Enable kernel replay mode.
+ *
+ * Set profiling mode for the context to replay mode. In this mode,
+ * any number of events can be collected in one run of the kernel. The
+ * event collection mode will automatically switch to
+ * CUPTI_EVENT_COLLECTION_MODE_KERNEL.  In this mode, \ref
+ * cuptiSetEventCollectionMode will return
+ * CUPTI_ERROR_INVALID_OPERATION.
+ * \note \b Kernels might take longer to run if many events are enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context
+ * \retval CUPTI_SUCCESS
+ */
+CUptiResult CUPTIAPI cuptiEnableKernelReplayMode(CUcontext context);
+
+/**
+ * \brief Disable kernel replay mode.
+ *
+ * Set profiling mode for the context to non-replay (default)
+ * mode. Event collection mode will be set to
+ * CUPTI_EVENT_COLLECTION_MODE_KERNEL.  All previously enabled
+ * event groups and event group sets will be disabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context
+ * \retval CUPTI_SUCCESS
+ */
+CUptiResult CUPTIAPI cuptiDisableKernelReplayMode(CUcontext context);
+
+/**
+ * \brief Function type for getting updates on kernel replay.
+ *
+ * \param kernelName The mangled kernel name
+ * \param numReplaysDone Number of replays done so far
+ * \param customData Pointer of any custom data passed in when subscribing
+ */
+typedef void (CUPTIAPI *CUpti_KernelReplayUpdateFunc)(
+    const char *kernelName,
+    int numReplaysDone,
+    void *customData);
+
+/**
+ * \brief Subscribe to kernel replay updates.
+ *
+ * When subscribed, the function pointer passed in will be called each time a
+ * kernel run is finished during kernel replay. Previously subscribed function
+ * pointer will be replaced. Pass in NULL as the function pointer unsubscribes
+ * the update.
+ *
+ * \param updateFunc The update function pointer
+ * \param customData Pointer to any custom data
+ * \retval CUPTI_SUCCESS
+ */
+CUptiResult CUPTIAPI cuptiKernelReplaySubscribeUpdate(CUpti_KernelReplayUpdateFunc updateFunc, void *customData);
+
+/** @} */ /* END CUPTI_EVENT_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_EVENTS_H_*/
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_metrics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_metrics.h
new file mode 100644
index 0000000000000000000000000000000000000000..64b7f2d14580320f1ec938da5ea356add191ec3c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_metrics.h
@@ -0,0 +1,824 @@
+/*
+ * Copyright 2011-2024   NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_METRIC_H_)
+#define _CUPTI_METRIC_H_
+
+#include <cuda.h>
+#include <string.h>
+#include <cuda_stdint.h>
+#include <cupti_result.h>
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_METRIC_API CUPTI Metric API
+ * Functions, types, and enums that implement the CUPTI Metric API.
+ *
+ * \note The CUPTI metric API from the header cupti_metrics.h is not supported on devices
+ * with compute capability 7.5 and higher (i.e. Turing and later GPU architectures).
+ * This API is deprecated in CUDA 12.8 release and will be removed in a future CUDA release.
+ * This is replaced by the host profiling API in the header cupti_profiler_host.h and
+ * target profiling API in the header cupti_range_profiler.h which are supported on
+ * devices with compute capability 7.0 and higher (i.e. Volta and later GPU architectures).
+ *
+ * @{
+ */
+
+/**
+ * \brief ID for a metric.
+ *
+ * A metric provides a measure of some aspect of the device.
+ */
+typedef uint32_t CUpti_MetricID;
+
+/**
+ * \brief A metric category.
+ *
+ * Each metric is assigned to a category that represents the general
+ * type of the metric. A metric's category is accessed using \ref
+ * cuptiMetricGetAttribute and the CUPTI_METRIC_ATTR_CATEGORY
+ * attribute.
+ */
+typedef enum {
+  /**
+   * A memory related metric.
+   */
+  CUPTI_METRIC_CATEGORY_MEMORY          = 0,
+  /**
+   * An instruction related metric.
+   */
+  CUPTI_METRIC_CATEGORY_INSTRUCTION     = 1,
+  /**
+   * A multiprocessor related metric.
+   */
+  CUPTI_METRIC_CATEGORY_MULTIPROCESSOR  = 2,
+  /**
+   * A cache related metric.
+   */
+  CUPTI_METRIC_CATEGORY_CACHE           = 3,
+  /**
+   * A texture related metric.
+   */
+  CUPTI_METRIC_CATEGORY_TEXTURE         = 4,
+  /**
+   *A Nvlink related metric.
+   */
+  CUPTI_METRIC_CATEGORY_NVLINK          = 5,
+  /**
+   *A PCIe related metric.
+   */
+  CUPTI_METRIC_CATEGORY_PCIE           = 6,
+  CUPTI_METRIC_CATEGORY_FORCE_INT                         = 0x7fffffff,
+} CUpti_MetricCategory;
+
+/**
+ * \brief A metric evaluation mode.
+ *
+ * A metric can be evaluated per hardware instance to know the load balancing
+ * across instances of a domain or the metric can be evaluated in aggregate mode
+ * when the events involved in metric evaluation are from different event
+ * domains. It might be possible to evaluate some metrics in both
+ * modes for convenience. A metric's evaluation mode is accessed using \ref
+ * CUpti_MetricEvaluationMode and the CUPTI_METRIC_ATTR_EVALUATION_MODE
+ * attribute.
+ */
+typedef enum {
+  /**
+   * If this bit is set, the metric can be profiled for each instance of the
+   * domain. The event values passed to \ref cuptiMetricGetValue can contain
+   * values for one instance of the domain. And \ref cuptiMetricGetValue can
+   * be called for each instance.
+   */
+  CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE         = 1,
+  /**
+   * If this bit is set, the metric can be profiled over all instances. The
+   * event values passed to \ref cuptiMetricGetValue can be aggregated values
+   * of events for all instances of the domain.
+   */
+  CUPTI_METRIC_EVALUATION_MODE_AGGREGATE            = 1 << 1,
+  CUPTI_METRIC_EVALUATION_MODE_FORCE_INT            = 0x7fffffff,
+} CUpti_MetricEvaluationMode;
+
+/**
+ * \brief Kinds of metric values.
+ *
+ * Metric values can be one of several different kinds. Corresponding
+ * to each kind is a member of the CUpti_MetricValue union. The metric
+ * value returned by \ref cuptiMetricGetValue should be accessed using
+ * the appropriate member of that union based on its value kind.
+ */
+typedef enum {
+  /**
+   * The metric value is a 64-bit double.
+   */
+  CUPTI_METRIC_VALUE_KIND_DOUBLE            = 0,
+  /**
+   * The metric value is a 64-bit unsigned integer.
+   */
+  CUPTI_METRIC_VALUE_KIND_UINT64            = 1,
+  /**
+   * The metric value is a percentage represented by a 64-bit
+   * double. For example, 57.5% is represented by the value 57.5.
+   */
+  CUPTI_METRIC_VALUE_KIND_PERCENT           = 2,
+  /**
+   * The metric value is a throughput represented by a 64-bit
+   * integer. The unit for throughput values is bytes/second.
+   */
+  CUPTI_METRIC_VALUE_KIND_THROUGHPUT        = 3,
+  /**
+   * The metric value is a 64-bit signed integer.
+   */
+  CUPTI_METRIC_VALUE_KIND_INT64             = 4,
+  /**
+   * The metric value is a utilization level, as represented by
+   * CUpti_MetricValueUtilizationLevel.
+   */
+  CUPTI_METRIC_VALUE_KIND_UTILIZATION_LEVEL = 5,
+
+  CUPTI_METRIC_VALUE_KIND_FORCE_INT  = 0x7fffffff
+} CUpti_MetricValueKind;
+
+/**
+ * \brief Enumeration of utilization levels for metrics values of kind
+ * CUPTI_METRIC_VALUE_KIND_UTILIZATION_LEVEL. Utilization values can
+ * vary from IDLE (0) to MAX (10) but the enumeration only provides
+ * specific names for a few values.
+ */
+typedef enum {
+  CUPTI_METRIC_VALUE_UTILIZATION_IDLE      = 0,
+  CUPTI_METRIC_VALUE_UTILIZATION_LOW       = 2,
+  CUPTI_METRIC_VALUE_UTILIZATION_MID       = 5,
+  CUPTI_METRIC_VALUE_UTILIZATION_HIGH      = 8,
+  CUPTI_METRIC_VALUE_UTILIZATION_MAX       = 10,
+  CUPTI_METRIC_VALUE_UTILIZATION_FORCE_INT = 0x7fffffff
+} CUpti_MetricValueUtilizationLevel;
+
+/**
+ * \brief Metric attributes.
+ *
+ * Metric attributes describe properties of a metric. These attributes
+ * can be read using \ref cuptiMetricGetAttribute.
+ */
+typedef enum {
+  /**
+   * Metric name. Value is a null terminated const c-string.
+   */
+  CUPTI_METRIC_ATTR_NAME              = 0,
+  /**
+   * Short description of metric. Value is a null terminated const c-string.
+   */
+  CUPTI_METRIC_ATTR_SHORT_DESCRIPTION = 1,
+  /**
+   * Long description of metric. Value is a null terminated const c-string.
+   */
+  CUPTI_METRIC_ATTR_LONG_DESCRIPTION  = 2,
+  /**
+   * Category of the metric. Value is of type CUpti_MetricCategory.
+   */
+  CUPTI_METRIC_ATTR_CATEGORY          = 3,
+  /**
+   * Value type of the metric. Value is of type CUpti_MetricValueKind.
+   */
+  CUPTI_METRIC_ATTR_VALUE_KIND          = 4,
+  /**
+   * Metric evaluation mode. Value is of type CUpti_MetricEvaluationMode.
+   */
+  CUPTI_METRIC_ATTR_EVALUATION_MODE     = 5,
+  CUPTI_METRIC_ATTR_FORCE_INT         = 0x7fffffff,
+} CUpti_MetricAttribute;
+
+/**
+ * \brief A metric value.
+ *
+ * Metric values can be one of several different kinds. Corresponding
+ * to each kind is a member of the CUpti_MetricValue union. The metric
+ * value returned by \ref cuptiMetricGetValue should be accessed using
+ * the appropriate member of that union based on its value kind.
+ */
+typedef union {
+  /*
+   * Value for CUPTI_METRIC_VALUE_KIND_DOUBLE.
+   */
+  double metricValueDouble;
+  /*
+   * Value for CUPTI_METRIC_VALUE_KIND_UINT64.
+   */
+  uint64_t metricValueUint64;
+  /*
+   * Value for CUPTI_METRIC_VALUE_KIND_INT64.
+   */
+  int64_t metricValueInt64;
+  /*
+   * Value for CUPTI_METRIC_VALUE_KIND_PERCENT. For example, 57.5% is
+   * represented by the value 57.5.
+   */
+  double metricValuePercent;
+  /*
+   * Value for CUPTI_METRIC_VALUE_KIND_THROUGHPUT.  The unit for
+   * throughput values is bytes/second.
+   */
+  uint64_t metricValueThroughput;
+  /*
+   * Value for CUPTI_METRIC_VALUE_KIND_UTILIZATION_LEVEL.
+   */
+  CUpti_MetricValueUtilizationLevel metricValueUtilizationLevel;
+} CUpti_MetricValue;
+
+/**
+ * \brief Device class.
+ *
+ * Enumeration of device classes for metric property
+ * CUPTI_METRIC_PROPERTY_DEVICE_CLASS.
+ */
+typedef enum {
+  CUPTI_METRIC_PROPERTY_DEVICE_CLASS_TESLA          = 0,
+  CUPTI_METRIC_PROPERTY_DEVICE_CLASS_QUADRO         = 1,
+  CUPTI_METRIC_PROPERTY_DEVICE_CLASS_GEFORCE        = 2,
+  CUPTI_METRIC_PROPERTY_DEVICE_CLASS_TEGRA          = 3,
+} CUpti_MetricPropertyDeviceClass;
+
+/**
+ * \brief Metric device properties.
+ *
+ * Metric device properties describe device properties which are needed for a metric.
+ * Some of these properties can be collected using cuDeviceGetAttribute.
+ */
+typedef enum {
+  /*
+   * Number of multiprocessors on a device.  This can be collected
+   * using value of \param CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT of
+   * cuDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_MULTIPROCESSOR_COUNT,
+  /*
+   * Maximum number of warps on a multiprocessor. This can be
+   * collected using ratio of value of \param
+   * CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR and \param
+   * CU_DEVICE_ATTRIBUTE_WARP_SIZE of cuDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_WARPS_PER_MULTIPROCESSOR,
+  /*
+   * GPU Time for kernel in ns. This should be profiled using CUPTI
+   * Activity API.
+   */
+  CUPTI_METRIC_PROPERTY_KERNEL_GPU_TIME,
+  /*
+   * Clock rate for device in KHz.  This should be collected using
+   * value of \param CU_DEVICE_ATTRIBUTE_CLOCK_RATE of
+   * cuDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_CLOCK_RATE,
+  /*
+   * Number of Frame buffer units for device. This should be collected
+   * using value of \param CUPTI_DEVICE_ATTRIBUTE_MAX_FRAME_BUFFERS of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_FRAME_BUFFER_COUNT,
+  /*
+   * Global memory bandwidth in KBytes/sec. This should be collected
+   * using value of \param CUPTI_DEVICE_ATTR_GLOBAL_MEMORY_BANDWIDTH
+   * of cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_GLOBAL_MEMORY_BANDWIDTH,
+  /*
+   * PCIE link rate in Mega bits/sec. This should be collected using
+   * value of \param CUPTI_DEVICE_ATTR_PCIE_LINK_RATE of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_PCIE_LINK_RATE,
+  /*
+   * PCIE link width for device. This should be collected using
+   * value of \param CUPTI_DEVICE_ATTR_PCIE_LINK_WIDTH of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_PCIE_LINK_WIDTH,
+  /*
+   * PCIE generation for device. This should be collected using
+   * value of \param CUPTI_DEVICE_ATTR_PCIE_GEN of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_PCIE_GEN,
+  /*
+   * The device class. This should be collected using
+   * value of \param CUPTI_DEVICE_ATTR_DEVICE_CLASS of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_DEVICE_CLASS,
+  /*
+   * Peak single precision floating point operations that
+   * can be performed in one cycle by the device.
+   * This should be collected using value of
+   * \param CUPTI_DEVICE_ATTR_FLOP_SP_PER_CYCLE of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_FLOP_SP_PER_CYCLE,
+  /*
+   * Peak double precision floating point operations that
+   * can be performed in one cycle by the device.
+   * This should be collected using value of
+   * \param CUPTI_DEVICE_ATTR_FLOP_DP_PER_CYCLE of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_FLOP_DP_PER_CYCLE,
+  /*
+   * Number of L2 units on a device. This can be collected
+   * using value of \param CUPTI_DEVICE_ATTR_MAX_L2_UNITS of
+   * cuDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_L2_UNITS,
+  /*
+   * Whether ECC support is enabled on the device. This can be
+   * collected using value of \param CU_DEVICE_ATTRIBUTE_ECC_ENABLED of
+   * cuDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_ECC_ENABLED,
+  /*
+   * Peak half precision floating point operations that
+   * can be performed in one cycle by the device.
+   * This should be collected using value of
+   * \param CUPTI_DEVICE_ATTR_FLOP_HP_PER_CYCLE of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_FLOP_HP_PER_CYCLE,
+  /*
+   * NVLINK Bandwitdh for device. This should be collected
+   * using value of \param CUPTI_DEVICE_ATTR_GPU_CPU_NVLINK_BW of
+   * cuptiDeviceGetAttribute.
+   */
+  CUPTI_METRIC_PROPERTY_GPU_CPU_NVLINK_BANDWIDTH,
+} CUpti_MetricPropertyID;
+
+/**
+ * \brief Get the total number of metrics available on any device.
+ *
+ * Returns the total number of metrics available on any CUDA-capable
+ * devices.
+ *
+ * \param numMetrics Returns the number of metrics
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numMetrics is NULL
+*/
+CUptiResult CUPTIAPI cuptiGetNumMetrics(uint32_t *numMetrics);
+
+/**
+ * \brief Get all the metrics available on any device.
+ *
+ * Returns the metric IDs in \p metricArray for all CUDA-capable
+ * devices.  The size of the \p metricArray buffer is given by \p
+ * *arraySizeBytes. The size of the \p metricArray buffer must be at
+ * least \p numMetrics * sizeof(CUpti_MetricID) or all metric IDs will
+ * not be returned. The value returned in \p *arraySizeBytes contains
+ * the number of bytes returned in \p metricArray.
+ *
+ * \param arraySizeBytes The size of \p metricArray in bytes, and
+ * returns the number of bytes written to \p metricArray
+ * \param metricArray Returns the IDs of the metrics
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or
+ * \p metricArray are NULL
+*/
+CUptiResult CUPTIAPI cuptiEnumMetrics(size_t *arraySizeBytes,
+                                      CUpti_MetricID *metricArray);
+
+/**
+ * \brief Get the number of metrics for a device.
+ *
+ * Returns the number of metrics available for a device.
+ *
+ * \param device The CUDA device
+ * \param numMetrics Returns the number of metrics available for the
+ * device
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numMetrics is NULL
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetNumMetrics(CUdevice device,
+                                              uint32_t *numMetrics);
+
+/**
+ * \brief Get the metrics for a device.
+ *
+ * Returns the metric IDs in \p metricArray for a device.  The size of
+ * the \p metricArray buffer is given by \p *arraySizeBytes. The size
+ * of the \p metricArray buffer must be at least \p numMetrics *
+ * sizeof(CUpti_MetricID) or else all metric IDs will not be
+ * returned. The value returned in \p *arraySizeBytes contains the
+ * number of bytes returned in \p metricArray.
+ *
+ * \param device The CUDA device
+ * \param arraySizeBytes The size of \p metricArray in bytes, and
+ * returns the number of bytes written to \p metricArray
+ * \param metricArray Returns the IDs of the metrics for the device
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or
+ * \p metricArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiDeviceEnumMetrics(CUdevice device,
+                                            size_t *arraySizeBytes,
+                                            CUpti_MetricID *metricArray);
+
+/**
+ * \brief Get a metric attribute.
+ *
+ * Returns a metric attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ *
+ * \param metric ID of the metric
+ * \param attrib The metric attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not a metric attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiMetricGetAttribute(CUpti_MetricID metric,
+                                             CUpti_MetricAttribute attrib,
+                                             size_t *valueSize,
+                                             void *value);
+
+/**
+ * \brief Find an metric by name.
+ *
+ * Find a metric by name and return the metric ID in \p *metric.
+ *
+ * \param device The CUDA device
+ * \param metricName The name of metric to find
+ * \param metric Returns the ID of the found metric or undefined if
+ * unable to find the metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_METRIC_NAME if unable to find a metric
+ * with name \p metricName. In this case \p *metric is undefined
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p metricName or \p
+ * metric are NULL.
+ */
+CUptiResult CUPTIAPI cuptiMetricGetIdFromName(CUdevice device,
+                                              const char *metricName,
+                                              CUpti_MetricID *metric);
+
+/**
+ * \brief Get number of events required to calculate a metric.
+ *
+ * Returns the number of events in \p numEvents that are required to
+ * calculate a metric.
+ *
+ * \param metric ID of the metric
+ * \param numEvents Returns the number of events required for the metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numEvents is NULL
+ */
+CUptiResult CUPTIAPI cuptiMetricGetNumEvents(CUpti_MetricID metric,
+                                             uint32_t *numEvents);
+
+/**
+ * \brief Get the events required to calculating a metric.
+ *
+ * Gets the event IDs in \p eventIdArray required to calculate a \p
+ * metric. The size of the \p eventIdArray buffer is given by \p
+ * *eventIdArraySizeBytes and must be at least \p numEvents *
+ * sizeof(CUpti_EventID) or all events will not be returned. The value
+ * returned in \p *eventIdArraySizeBytes contains the number of bytes
+ * returned in \p eventIdArray.
+ *
+ * \param metric ID of the metric
+ * \param eventIdArraySizeBytes The size of \p eventIdArray in bytes,
+ * and returns the number of bytes written to \p eventIdArray
+ * \param eventIdArray Returns the IDs of the events required to
+ * calculate \p metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventIdArraySizeBytes or \p
+ * eventIdArray are NULL.
+ */
+CUptiResult CUPTIAPI cuptiMetricEnumEvents(CUpti_MetricID metric,
+                                           size_t *eventIdArraySizeBytes,
+                                           CUpti_EventID *eventIdArray);
+
+/**
+ * \brief Get number of properties required to calculate a metric.
+ *
+ * Returns the number of properties in \p numProp that are required to
+ * calculate a metric.
+ *
+ * \param metric ID of the metric
+ * \param numProp Returns the number of properties required for the
+ * metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numProp is NULL
+ */
+CUptiResult CUPTIAPI cuptiMetricGetNumProperties(CUpti_MetricID metric,
+                                                 uint32_t *numProp);
+
+/**
+ * \brief Get the properties required to calculating a metric.
+ *
+ * Gets the property IDs in \p propIdArray required to calculate a \p
+ * metric. The size of the \p propIdArray buffer is given by \p
+ * *propIdArraySizeBytes and must be at least \p numProp *
+ * sizeof(CUpti_DeviceAttribute) or all properties will not be
+ * returned. The value returned in \p *propIdArraySizeBytes contains
+ * the number of bytes returned in \p propIdArray.
+ *
+ * \param metric ID of the metric
+ * \param propIdArraySizeBytes The size of \p propIdArray in bytes,
+ * and returns the number of bytes written to \p propIdArray
+ * \param propIdArray Returns the IDs of the properties required to
+ * calculate \p metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p propIdArraySizeBytes or \p
+ * propIdArray are NULL.
+ */
+CUptiResult CUPTIAPI cuptiMetricEnumProperties(CUpti_MetricID metric,
+                                               size_t *propIdArraySizeBytes,
+                                               CUpti_MetricPropertyID *propIdArray);
+
+
+/**
+ * \brief For a metric get the groups of events that must be collected
+ * in the same pass.
+ *
+ * For a metric get the groups of events that must be collected in the
+ * same pass to ensure that the metric is calculated correctly. If the
+ * events are not collected as specified then the metric value may be
+ * inaccurate.
+ *
+ * The function returns NULL if a metric does not have any required
+ * event group. In this case the events needed for the metric can be
+ * grouped in any manner for collection.
+ *
+ * \param context The context for event collection
+ * \param metric The metric ID
+ * \param eventGroupSets Returns a CUpti_EventGroupSets object that
+ * indicates the events that must be collected in the same pass to
+ * ensure the metric is calculated correctly.  Returns NULL if no
+ * grouping is required for metric
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ */
+CUptiResult CUPTIAPI cuptiMetricGetRequiredEventGroupSets(CUcontext context,
+                                                          CUpti_MetricID metric,
+                                                          CUpti_EventGroupSets **eventGroupSets);
+
+/**
+ * \brief For a set of metrics, get the grouping that indicates the
+ * number of passes and the event groups necessary to collect the
+ * events required for those metrics.
+ *
+ * For a set of metrics, get the grouping that indicates the number of
+ * passes and the event groups necessary to collect the events
+ * required for those metrics.
+ *
+ * \see cuptiEventGroupSetsCreate for details on event group set
+ * creation.
+ *
+ * \param context The context for event collection
+ * \param metricIdArraySizeBytes Size of the metricIdArray in bytes
+ * \param metricIdArray Array of metric IDs
+ * \param eventGroupPasses Returns a CUpti_EventGroupSets object that
+ * indicates the number of passes required to collect the events and
+ * the events to collect on each pass
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p metricIdArray or
+ * \p eventGroupPasses is NULL
+ */
+CUptiResult CUPTIAPI cuptiMetricCreateEventGroupSets(CUcontext context,
+                                                     size_t metricIdArraySizeBytes,
+                                                     CUpti_MetricID *metricIdArray,
+                                                     CUpti_EventGroupSets **eventGroupPasses);
+
+/**
+ * \brief Calculate the value for a metric.
+ *
+ * Use the events collected for a metric to calculate the metric
+ * value. Metric value evaluation depends on the evaluation mode
+ * \ref CUpti_MetricEvaluationMode that the metric supports.
+ * If a metric has evaluation mode as CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE,
+ * then it assumes that the input event value is for one domain instance.
+ * If a metric has evaluation mode as CUPTI_METRIC_EVALUATION_MODE_AGGREGATE,
+ * it assumes that input event values are
+ * normalized to represent all domain instances on a device. For the
+ * most accurate metric collection, the events required for the metric
+ * should be collected for all profiled domain instances. For example,
+ * to collect all instances of an event, set the
+ * CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES attribute on
+ * the group containing the event to 1. The normalized value for the
+ * event is then: (\p sum_event_values * \p totalInstanceCount) / \p
+ * instanceCount, where \p sum_event_values is the summation of the
+ * event values across all profiled domain instances, \p
+ * totalInstanceCount is obtained from querying
+ * CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT and \p instanceCount
+ * is obtained from querying CUPTI_EVENT_GROUP_ATTR_INSTANCE_COUNT (or
+ * CUPTI_EVENT_DOMAIN_ATTR_INSTANCE_COUNT).
+ *
+ * \param device The CUDA device that the metric is being calculated for
+ * \param metric The metric ID
+ * \param eventIdArraySizeBytes The size of \p eventIdArray in bytes
+ * \param eventIdArray The event IDs required to calculate \p metric
+ * \param eventValueArraySizeBytes The size of \p eventValueArray in bytes
+ * \param eventValueArray The normalized event values required to
+ * calculate \p metric. The values must be order to match the order of
+ * events in \p eventIdArray
+ * \param timeDuration The duration over which the events were
+ * collected, in ns
+ * \param metricValue Returns the value for the metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_OPERATION
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT if the
+ * eventIdArray does not contain all the events needed for metric
+ * \retval CUPTI_ERROR_INVALID_EVENT_VALUE if any of the
+ * event values required for the metric is CUPTI_EVENT_OVERFLOW
+ * \retval CUPTI_ERROR_INVALID_METRIC_VALUE if the computed metric value
+ * cannot be represented in the metric's value type. For example,
+ * if the metric value type is unsigned and the computed metric value is negative
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p metricValue,
+ * \p eventIdArray or \p eventValueArray is NULL
+ */
+CUptiResult CUPTIAPI cuptiMetricGetValue(CUdevice device,
+                                         CUpti_MetricID metric,
+                                         size_t eventIdArraySizeBytes,
+                                         CUpti_EventID *eventIdArray,
+                                         size_t eventValueArraySizeBytes,
+                                         uint64_t *eventValueArray,
+                                         uint64_t timeDuration,
+                                         CUpti_MetricValue *metricValue);
+
+/**
+ * \brief Calculate the value for a metric.
+ *
+ * Use the events and properties collected for a metric to calculate
+ * the metric value. Metric value evaluation depends on the evaluation
+ * mode \ref CUpti_MetricEvaluationMode that the metric supports.  If
+ * a metric has evaluation mode as
+ * CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE, then it assumes that the
+ * input event value is for one domain instance.  If a metric has
+ * evaluation mode as CUPTI_METRIC_EVALUATION_MODE_AGGREGATE, it
+ * assumes that input event values are normalized to represent all
+ * domain instances on a device. For the most accurate metric
+ * collection, the events required for the metric should be collected
+ * for all profiled domain instances. For example, to collect all
+ * instances of an event, set the
+ * CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES attribute on
+ * the group containing the event to 1. The normalized value for the
+ * event is then: (\p sum_event_values * \p totalInstanceCount) / \p
+ * instanceCount, where \p sum_event_values is the summation of the
+ * event values across all profiled domain instances, \p
+ * totalInstanceCount is obtained from querying
+ * CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT and \p instanceCount
+ * is obtained from querying CUPTI_EVENT_GROUP_ATTR_INSTANCE_COUNT (or
+ * CUPTI_EVENT_DOMAIN_ATTR_INSTANCE_COUNT).
+ *
+ * \param metric The metric ID
+ * \param eventIdArraySizeBytes The size of \p eventIdArray in bytes
+ * \param eventIdArray The event IDs required to calculate \p metric
+ * \param eventValueArraySizeBytes The size of \p eventValueArray in bytes
+ * \param eventValueArray The normalized event values required to
+ * calculate \p metric. The values must be order to match the order of
+ * events in \p eventIdArray
+ * \param propIdArraySizeBytes The size of \p propIdArray in bytes
+ * \param propIdArray The metric property IDs required to calculate \p metric
+ * \param propValueArraySizeBytes The size of \p propValueArray in bytes
+ * \param propValueArray The metric property values required to
+ * calculate \p metric. The values must be order to match the order of
+ * metric properties in \p propIdArray
+ * \param metricValue Returns the value for the metric
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_METRIC_ID
+ * \retval CUPTI_ERROR_INVALID_OPERATION
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT if the
+ * eventIdArray does not contain all the events needed for metric
+ * \retval CUPTI_ERROR_INVALID_EVENT_VALUE if any of the
+ * event values required for the metric is CUPTI_EVENT_OVERFLOW
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if the computed metric value
+ * cannot be represented in the metric's value type. For example,
+ * if the metric value type is unsigned and the computed metric value is negative
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p metricValue,
+ * \p eventIdArray or \p eventValueArray is NULL
+ */
+CUptiResult CUPTIAPI cuptiMetricGetValue2(CUpti_MetricID metric,
+                                          size_t eventIdArraySizeBytes,
+                                          CUpti_EventID *eventIdArray,
+                                          size_t eventValueArraySizeBytes,
+                                          uint64_t *eventValueArray,
+                                          size_t propIdArraySizeBytes,
+                                          CUpti_MetricPropertyID *propIdArray,
+                                          size_t propValueArraySizeBytes,
+                                          uint64_t *propValueArray,
+                                          CUpti_MetricValue *metricValue);
+
+/** @} */ /* END CUPTI_METRIC_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_METRIC_H_*/
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_nvtx_cbid.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_nvtx_cbid.h
new file mode 100644
index 0000000000000000000000000000000000000000..5ad8c85e6e674b9a016580be88d3c5a2d2619990
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_nvtx_cbid.h
@@ -0,0 +1,111 @@
+/*
+ * Copyright 2013-2017 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+typedef enum {
+  CUPTI_CBID_NVTX_INVALID                               = 0,
+  CUPTI_CBID_NVTX_nvtxMarkA                             = 1,
+  CUPTI_CBID_NVTX_nvtxMarkW                             = 2,
+  CUPTI_CBID_NVTX_nvtxMarkEx                            = 3,
+  CUPTI_CBID_NVTX_nvtxRangeStartA                       = 4,
+  CUPTI_CBID_NVTX_nvtxRangeStartW                       = 5,
+  CUPTI_CBID_NVTX_nvtxRangeStartEx                      = 6,
+  CUPTI_CBID_NVTX_nvtxRangeEnd                          = 7,
+  CUPTI_CBID_NVTX_nvtxRangePushA                        = 8,
+  CUPTI_CBID_NVTX_nvtxRangePushW                        = 9,
+  CUPTI_CBID_NVTX_nvtxRangePushEx                       = 10,
+  CUPTI_CBID_NVTX_nvtxRangePop                          = 11,
+  CUPTI_CBID_NVTX_nvtxNameCategoryA                     = 12,
+  CUPTI_CBID_NVTX_nvtxNameCategoryW                     = 13,
+  CUPTI_CBID_NVTX_nvtxNameOsThreadA                     = 14,
+  CUPTI_CBID_NVTX_nvtxNameOsThreadW                     = 15,
+  CUPTI_CBID_NVTX_nvtxNameCuDeviceA                     = 16,
+  CUPTI_CBID_NVTX_nvtxNameCuDeviceW                     = 17,
+  CUPTI_CBID_NVTX_nvtxNameCuContextA                    = 18,
+  CUPTI_CBID_NVTX_nvtxNameCuContextW                    = 19,
+  CUPTI_CBID_NVTX_nvtxNameCuStreamA                     = 20,
+  CUPTI_CBID_NVTX_nvtxNameCuStreamW                     = 21,
+  CUPTI_CBID_NVTX_nvtxNameCuEventA                      = 22,
+  CUPTI_CBID_NVTX_nvtxNameCuEventW                      = 23,
+  CUPTI_CBID_NVTX_nvtxNameCudaDeviceA                   = 24,
+  CUPTI_CBID_NVTX_nvtxNameCudaDeviceW                   = 25,
+  CUPTI_CBID_NVTX_nvtxNameCudaStreamA                   = 26,
+  CUPTI_CBID_NVTX_nvtxNameCudaStreamW                   = 27,
+  CUPTI_CBID_NVTX_nvtxNameCudaEventA                    = 28,
+  CUPTI_CBID_NVTX_nvtxNameCudaEventW                    = 29,
+  CUPTI_CBID_NVTX_nvtxDomainMarkEx                      = 30,
+  CUPTI_CBID_NVTX_nvtxDomainRangeStartEx                = 31,
+  CUPTI_CBID_NVTX_nvtxDomainRangeEnd                    = 32,
+  CUPTI_CBID_NVTX_nvtxDomainRangePushEx                 = 33,
+  CUPTI_CBID_NVTX_nvtxDomainRangePop                    = 34,
+  CUPTI_CBID_NVTX_nvtxDomainResourceCreate              = 35,
+  CUPTI_CBID_NVTX_nvtxDomainResourceDestroy             = 36,
+  CUPTI_CBID_NVTX_nvtxDomainNameCategoryA               = 37,
+  CUPTI_CBID_NVTX_nvtxDomainNameCategoryW               = 38,
+  CUPTI_CBID_NVTX_nvtxDomainRegisterStringA             = 39,
+  CUPTI_CBID_NVTX_nvtxDomainRegisterStringW             = 40,
+  CUPTI_CBID_NVTX_nvtxDomainCreateA                     = 41,
+  CUPTI_CBID_NVTX_nvtxDomainCreateW                     = 42,
+  CUPTI_CBID_NVTX_nvtxDomainDestroy                     = 43,
+  CUPTI_CBID_NVTX_nvtxDomainSyncUserCreate              = 44,
+  CUPTI_CBID_NVTX_nvtxDomainSyncUserDestroy             = 45,
+  CUPTI_CBID_NVTX_nvtxDomainSyncUserAcquireStart        = 46,
+  CUPTI_CBID_NVTX_nvtxDomainSyncUserAcquireFailed       = 47,
+  CUPTI_CBID_NVTX_nvtxDomainSyncUserAcquireSuccess      = 48,
+  CUPTI_CBID_NVTX_nvtxDomainSyncUserReleasing           = 49,
+  CUPTI_CBID_NVTX_SIZE,
+  CUPTI_CBID_NVTX_FORCE_INT                             = 0x7fffffff
+} CUpti_nvtx_api_trace_cbid;
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif    
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pcsampling.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pcsampling.h
new file mode 100644
index 0000000000000000000000000000000000000000..97f42d14b938204b3b79c4ca1356b88896bcae35
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pcsampling.h
@@ -0,0 +1,936 @@
+/*
+ * Copyright 2020-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_PCSAMPLING_H_)
+#define _CUPTI_PCSAMPLING_H_
+
+#include <cuda.h>
+#include <stdint.h>
+#include <stddef.h>
+#include "cupti_result.h"
+#include "cupti_common.h"
+
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_PCSAMPLING_API CUPTI PC Sampling API
+ * Functions, types, and enums that implement the CUPTI PC Sampling API.
+ * @{
+ */
+
+#ifndef CUPTI_PCSAMPLING_STRUCT_SIZE
+#define CUPTI_PCSAMPLING_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+#ifndef CUPTI_STALL_REASON_STRING_SIZE
+#define CUPTI_STALL_REASON_STRING_SIZE                                            128
+#endif
+
+/**
+ * \brief PC Sampling collection mode
+ */
+typedef enum
+{
+  /**
+   * INVALID Value
+   */
+  CUPTI_PC_SAMPLING_COLLECTION_MODE_INVALID                   = 0,
+  /**
+   * Continuous mode. Kernels are not serialized in this mode.
+   */
+  CUPTI_PC_SAMPLING_COLLECTION_MODE_CONTINUOUS                = 1,
+  /**
+   * Serialized mode. Kernels are serialized in this mode.
+   */
+  CUPTI_PC_SAMPLING_COLLECTION_MODE_KERNEL_SERIALIZED         = 2,
+} CUpti_PCSamplingCollectionMode;
+
+/**
+ * \brief PC Sampling stall reasons
+ */
+typedef struct PACKED_ALIGNMENT
+{
+  /**
+   * [r] Collected stall reason index
+   */
+  uint32_t pcSamplingStallReasonIndex;
+  /**
+   * [r] Number of times the PC was sampled with the stallReason.
+   */
+  uint32_t samples;
+} CUpti_PCSamplingStallReason;
+
+/**
+ * \brief PC Sampling data
+ */
+typedef struct PACKED_ALIGNMENT
+{
+  /**
+   * [w] Size of the data structure.
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [r] Unique cubin id
+   */
+  uint64_t cubinCrc;
+  /**
+   * [r] PC offset
+   */
+  uint64_t pcOffset;
+  /**
+   * The function's unique symbol index in the module.
+   */
+  uint32_t functionIndex;
+  /**
+   * Padding
+   */
+  uint32_t pad;
+  /**
+   * [r] The function name. This name string might be shared across all the records
+   * including records from activity APIs representing the same function, and so it should not be
+   * modified or freed until post processing of all the records is done. Once done, it is user’s responsibility to
+   * free the memory using free() function.
+   */
+  char* functionName;
+  /**
+   * [r] Collected stall reason count
+   */
+  size_t stallReasonCount;
+  /**
+   * [r] Stall reason id
+   * Total samples
+   */
+  CUpti_PCSamplingStallReason *stallReason;
+  /**
+   * The correlation ID of the kernel to which this result is associated. Only valid for serialized mode of pc sampling collection.
+   * For continous mode of collection the correlationId will be set to 0.
+   */
+  uint32_t correlationId;
+} CUpti_PCSamplingPCData;
+
+/**
+ * \brief PC Sampling output data format
+ */
+typedef enum
+{
+    CUPTI_PC_SAMPLING_OUTPUT_DATA_FORMAT_INVALID          = 0,
+  /**
+   * HW buffer data will be parsed during collection of data
+   */
+    CUPTI_PC_SAMPLING_OUTPUT_DATA_FORMAT_PARSED           = 1,
+} CUpti_PCSamplingOutputDataFormat;
+
+/**
+ * \brief Collected PC Sampling data
+ *
+ */
+typedef struct PACKED_ALIGNMENT
+{
+  /**
+   * [w] Size of the data structure.
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Number of PCs to be collected
+   */
+  size_t collectNumPcs;
+  /**
+   * [r] Number of samples collected across all PCs.
+   * It includes samples for user modules, samples for non-user kernels and dropped samples.
+   * It includes counts for all non selected stall reasons.
+   * CUPTI does not provide PC records for non-user kernels.
+   * CUPTI does not provide PC records for instructions for which all selected stall reason metrics counts are zero.
+   */
+  uint64_t totalSamples;
+  /**
+   * [r] Number of samples that were dropped by hardware due to backpressure/overflow.
+   */
+  uint64_t droppedSamples;
+  /**
+   * [r] Number of PCs collected
+   */
+  size_t totalNumPcs;
+  /**
+   * [r] Number of PCs available for collection
+   */
+  size_t remainingNumPcs;
+  /**
+   * [r] Unique identifier for each range.
+   * Data collected across multiple ranges in multiple buffers can be identified using range id.
+   */
+  uint64_t rangeId;
+  /**
+   * [r] Profiled PC data
+   * This data struct should have enough memory to collect number of PCs mentioned in \brief collectNumPcs
+   */
+  CUpti_PCSamplingPCData *pPcData;
+  /**
+   * [r] Number of samples collected across all non user kernels PCs.
+   * It includes samples for non-user kernels.
+   * It includes counts for all non selected stall reasons as well.
+   * CUPTI does not provide PC records for non-user kernels.
+   */
+  uint64_t nonUsrKernelsTotalSamples;
+
+  /**
+   * [r] Status of the hardware buffer.
+   * CUPTI returns the error code CUPTI_ERROR_OUT_OF_MEMORY when hardware buffer is full.
+   * When hardware buffer is full, user will get pc data as 0. To mitigate this issue, one or more of the below options can be tried:
+   * 1. Increase the hardware buffer size using the attribute CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_HARDWARE_BUFFER_SIZE
+   * 2. Decrease the thread sleep span using the attribute CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_WORKER_THREAD_PERIODIC_SLEEP_SPAN
+   * 3. Decrease the sampling frequency using the attribute CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SAMPLING_PERIOD
+   */
+  uint8_t hardwareBufferFull;
+} CUpti_PCSamplingData;
+
+/**
+ * \brief PC Sampling configuration attributes
+ *
+ * PC Sampling configuration attribute types. These attributes can be read
+ * using \ref cuptiPCSamplingGetConfigurationAttribute and can be written
+ * using \ref cuptiPCSamplingSetConfigurationAttribute. Attributes marked
+ * [r] can only be read using \ref cuptiPCSamplingGetConfigurationAttribute
+ * [w] can only be written using \ref cuptiPCSamplingSetConfigurationAttribute
+ * [rw] can be read using \ref cuptiPCSamplingGetConfigurationAttribute and
+ * written using \ref cuptiPCSamplingSetConfigurationAttribute
+ */
+typedef enum
+{
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_INVALID                            = 0,
+  /**
+   * [rw] Sampling period for PC Sampling.
+   * DEFAULT - CUPTI defined value based on number of SMs
+   * Valid values for the sampling
+   * periods are between 5 to 31 both inclusive. This will set the
+   * sampling period to (2^samplingPeriod) cycles.
+   * For e.g. for sampling period = 5 to 31, cycles = 32, 64, 128,..., 2^31
+   * Value is a uint32_t
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SAMPLING_PERIOD                    = 1,
+  /**
+   * [w] Number of stall reasons to collect.
+   * DEFAULT - All stall reasons will be collected
+   * Value is a size_t
+   * [w] Stall reasons to collect
+   * DEFAULT - All stall reasons will be collected
+   * Input value should be a pointer pointing to array of stall reason indexes
+   * containing all the stall reason indexes to collect.
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_STALL_REASON                       = 2,
+  /**
+   * [rw] Size of SW buffer for raw PC counter data downloaded from HW buffer
+   * DEFAULT - 1 MB, which can accommodate approximately 5500 PCs
+   * with all stall reasons
+   * Approximately it takes 16 Bytes (and some fixed size memory)
+   * to accommodate one PC with one stall reason
+   * For e.g. 1 PC with 1 stall reason = 32 Bytes
+   *          1 PC with 2 stall reason = 48 Bytes
+   *          1 PC with 4 stall reason = 96 Bytes
+   * Value is a size_t
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SCRATCH_BUFFER_SIZE                = 3,
+  /**
+   * [rw] Size of HW buffer in bytes
+   * DEFAULT - 512 MB
+   * If sampling period is too less, HW buffer can overflow
+   * and drop PC data
+   * Value is a size_t
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_HARDWARE_BUFFER_SIZE               = 4,
+  /**
+   * [rw] PC Sampling collection mode
+   * DEFAULT - CUPTI_PC_SAMPLING_COLLECTION_MODE_CONTINUOUS
+   * Input value should be of type \ref CUpti_PCSamplingCollectionMode.
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_COLLECTION_MODE                    = 5,
+  /**
+   * [rw] Control over PC Sampling data collection range
+   * Default - 0
+   * 1 - Allows user to start and stop PC Sampling using APIs -
+   * \ref cuptiPCSamplingStart() - Start PC Sampling
+   * \ref cuptiPCSamplingStop() - Stop PC Sampling
+   * Value is a uint32_t
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_ENABLE_START_STOP_CONTROL          = 6,
+  /**
+   * [w] Value for output data format
+   * Default - CUPTI_PC_SAMPLING_OUTPUT_DATA_FORMAT_PARSED
+   * Input value should be of type \ref CUpti_PCSamplingOutputDataFormat.
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_OUTPUT_DATA_FORMAT                 = 7,
+  /**
+   * [w] Data buffer to hold collected PC Sampling data PARSED_DATA
+   * Default - none.
+   * Buffer type is void * which can point to PARSED_DATA
+   * Refer \ref CUpti_PCSamplingData for buffer format for PARSED_DATA
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SAMPLING_DATA_BUFFER               = 8,
+  /**
+   * [rw] Control sleep time of the worker threads created by CUPTI for various PC sampling operations.
+   * CUPTI creates multiple worker threads to offload certain operations to these threads. This includes decoding of HW data to
+   * the CUPTI PC sampling data and correlating PC data to SASS instructions. CUPTI wakes up these threads periodically.
+   * Default - 100 milliseconds.
+   * Value is a uint32_t
+   */
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_WORKER_THREAD_PERIODIC_SLEEP_SPAN  = 9,
+  CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_FORCE_INT                          = 0x7fffffff,
+} CUpti_PCSamplingConfigurationAttributeType;
+
+/**
+ * \brief PC sampling configuration information structure
+ *
+ * This structure provides \ref CUpti_PCSamplingConfigurationAttributeType which can be configured
+ * or queried for PC sampling configuration
+ */
+typedef struct
+{
+  /**
+   * Refer \ref CUpti_PCSamplingConfigurationAttributeType for all supported attribute types
+   */
+  CUpti_PCSamplingConfigurationAttributeType attributeType;
+  /*
+   * Configure or query status for \p attributeType
+   * CUPTI_SUCCESS for valid \p attributeType and \p attributeData
+   * CUPTI_ERROR_INVALID_OPERATION if \p attributeData is not valid
+   * CUPTI_ERROR_INVALID_PARAMETER if \p attributeType is not valid
+   */
+  CUptiResult attributeStatus;
+  union
+  {
+    /**
+     * Invalid Value
+     */
+    struct
+    {
+      uint64_t data[3];
+    } invalidData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SAMPLING_PERIOD
+     */
+    struct
+    {
+      uint32_t samplingPeriod;
+    } samplingPeriodData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_STALL_REASON
+     */
+    struct
+    {
+      size_t stallReasonCount;
+      uint32_t *pStallReasonIndex;
+    } stallReasonData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SCRATCH_BUFFER_SIZE
+     */
+    struct
+    {
+      size_t scratchBufferSize;
+    } scratchBufferSizeData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_HARDWARE_BUFFER_SIZE
+     */
+    struct
+    {
+      size_t hardwareBufferSize;
+    } hardwareBufferSizeData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_COLLECTION_MODE
+     */
+    struct
+    {
+      CUpti_PCSamplingCollectionMode collectionMode;
+    } collectionModeData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_ENABLE_START_STOP_CONTROL
+     */
+    struct
+    {
+      uint32_t enableStartStopControl;
+    } enableStartStopControlData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_OUTPUT_DATA_FORMAT
+     */
+    struct
+    {
+      CUpti_PCSamplingOutputDataFormat outputDataFormat;
+    } outputDataFormatData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_SAMPLING_DATA_BUFFER
+     */
+    struct
+    {
+      void *samplingDataBuffer;
+    } samplingDataBufferData;
+    /**
+     * Refer \ref CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_WORKER_THREAD_PERIODIC_SLEEP_SPAN
+     */
+    struct
+    {
+      uint32_t workerThreadPeriodicSleepSpan;
+    } workerThreadPeriodicSleepSpanData;
+    
+  } attributeData;
+} CUpti_PCSamplingConfigurationInfo;
+
+/**
+ * \brief PC sampling configuration structure
+ *
+ * This structure configures PC sampling using \ref cuptiPCSamplingSetConfigurationAttribute
+ * and queries PC sampling default configuration using \ref cuptiPCSamplingGetConfigurationAttribute
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingConfigurationInfoParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+  /**
+   * [w] Number of attributes to configure using \ref cuptiPCSamplingSetConfigurationAttribute or query
+   * using \ref cuptiPCSamplingGetConfigurationAttribute
+   */
+  size_t numAttributes;
+  /**
+   * Refer \ref CUpti_PCSamplingConfigurationInfo
+   */
+  CUpti_PCSamplingConfigurationInfo *pPCSamplingConfigurationInfo;
+} CUpti_PCSamplingConfigurationInfoParams;
+#define CUpti_PCSamplingConfigurationInfoParamsSize                 CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingConfigurationInfoParams,pPCSamplingConfigurationInfo)
+
+/**
+ * \brief Write PC Sampling configuration attribute.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingConfigurationInfoParams
+ * containing PC sampling configuration.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called with
+ * some invalid \p attrib.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if attribute \p value is not valid
+ * or any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingSetConfigurationAttribute(CUpti_PCSamplingConfigurationInfoParams *pParams);
+
+/**
+ * \brief Read PC Sampling configuration attribute.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingConfigurationInfoParams
+ * containing PC sampling configuration.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called with
+ * some invalid attribute.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p attrib is not valid
+ * or any \p pParams is not valid
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT indicates that
+ * the \p value buffer is too small to hold the attribute value
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingGetConfigurationAttribute(CUpti_PCSamplingConfigurationInfoParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingEnable
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingGetDataParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+  /**
+   * \param pcSamplingData Data buffer to hold collected PC Sampling data PARSED_DATA
+   * Buffer type is void * which can point to PARSED_DATA
+   * Refer \ref CUpti_PCSamplingData for buffer format for PARSED_DATA
+   */
+  void *pcSamplingData;
+} CUpti_PCSamplingGetDataParams;
+#define CUpti_PCSamplingGetDataParamsSize                           CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingGetDataParams, pcSamplingData)
+/**
+ * \brief Flush GPU PC sampling data periodically.
+ *
+ * Flushing of GPU PC Sampling data is required at following point to maintain uniqueness of PCs:
+ * For \brief CUPTI_PC_SAMPLING_COLLECTION_MODE_CONTINUOUS, after every module load-unload-load
+ * For \brief CUPTI_PC_SAMPLING_COLLECTION_MODE_KERNEL_SERIALIZED, after every kernel ends
+ * If configuration option \brief CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_ENABLE_START_STOP_CONTROL
+ * is enabled, then after every range end i.e. \brief cuptiPCSamplingStop()
+ *
+ * If application is profiled in \brief CUPTI_PC_SAMPLING_COLLECTION_MODE_CONTINUOUS, with disabled
+ * \brief CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_ENABLE_START_STOP_CONTROL, and there is no module unload,
+ * user can collect data in two ways:
+ * Use \brief cuptiPCSamplingGetData() API periodically
+ * Use \brief cuptiPCSamplingDisable() on application exit and read GPU PC sampling data from sampling
+ * data buffer passed during configuration.
+ * Note: In case, \brief cuptiPCSamplingGetData() API is not called periodically, then sampling data buffer
+ * passed during configuration should be large enough to hold all PCs data.
+ *       \brief cuptiPCSamplingGetData() API never does device synchronization.
+ *       It is possible that when the API is called there is some unconsumed data from the HW buffer. In this case
+ * CUPTI provides only the data available with it at that moment.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingGetDataParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called without
+ * enabling PC sampling.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY indicates that the HW buffer is full
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingGetData(CUpti_PCSamplingGetDataParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingEnable
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingEnableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+} CUpti_PCSamplingEnableParams;
+#define CUpti_PCSamplingEnableParamsSize                           CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingEnableParams, ctx)
+
+/**
+ * \brief Enable PC sampling.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingEnableParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingEnable(CUpti_PCSamplingEnableParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingDisable
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingDisableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+} CUpti_PCSamplingDisableParams;
+#define CUpti_PCSamplingDisableParamsSize                           CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingDisableParams, ctx)
+
+/**
+ * \brief Disable PC sampling.
+ *
+ * For application which doesn't destroy the CUDA context explicitly,
+ * this API does the PC Sampling tear-down, joins threads and copies PC records in the buffer provided
+ * during the PC sampling configuration. PC records which can't be accommodated in the buffer are discarded.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingDisableParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingDisable(CUpti_PCSamplingDisableParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingStart
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingStartParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+} CUpti_PCSamplingStartParams;
+#define CUpti_PCSamplingStartParamsSize                             CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingStartParams, ctx)
+
+/**
+ * \brief Start PC sampling.
+ *
+ * User can collect PC Sampling data for user-defined range specified by Start/Stop APIs.
+ * This API can be used to mark starting of range. Set configuration option
+ * \brief CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_ENABLE_START_STOP_CONTROL to use this API.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingStartParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called with
+ * incorrect PC Sampling configuration.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingStart(CUpti_PCSamplingStartParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingStop
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingStopParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+} CUpti_PCSamplingStopParams;
+#define CUpti_PCSamplingStopParamsSize                              CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingStopParams, ctx)
+
+/**
+ * \brief Stop PC sampling.
+ *
+ * User can collect PC Sampling data for user-defined range specified by Start/Stop APIs.
+ * This API can be used to mark end of range. Set configuration option
+ * \brief CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_ENABLE_START_STOP_CONTROL to use this API.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingStopParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called with
+ * incorrect PC Sampling configuration.
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingStop(CUpti_PCSamplingStopParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingGetNumStallReasons
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingGetNumStallReasonsParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+  /**
+   * [r] Number of stall reasons
+   */
+  size_t *numStallReasons;
+} CUpti_PCSamplingGetNumStallReasonsParams;
+#define CUpti_PCSamplingGetNumStallReasonsParamsSize                CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingGetNumStallReasonsParams, numStallReasons)
+
+/**
+ * \brief Get PC sampling stall reason count.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingGetNumStallReasonsParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingGetNumStallReasons(CUpti_PCSamplingGetNumStallReasonsParams *pParams);
+
+/**
+ * \brief Params for cuptiPCSamplingGetStallReasons
+ */
+typedef struct
+{
+  /**
+   * [w] Size of the data structure i.e. CUpti_PCSamplingGetStallReasonsParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Assign to NULL
+   */
+  void* pPriv;
+  /**
+   * [w] CUcontext
+   */
+  CUcontext ctx;
+  /**
+   * [w] Number of stall reasons
+   */
+  size_t numStallReasons;
+  /**
+   * [r] Stall reason index
+   */
+  uint32_t *stallReasonIndex;
+  /**
+   * [r] Stall reasons name
+   */
+  char **stallReasons;
+} CUpti_PCSamplingGetStallReasonsParams;
+#define CUpti_PCSamplingGetStallReasonsParamsSize                   CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_PCSamplingGetStallReasonsParams, stallReasons)
+
+/**
+ * \brief Get PC sampling stall reasons.
+ *
+ * \param pParams A pointer to \ref CUpti_PCSamplingGetStallReasonsParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device
+ * does not support the API
+ */
+CUptiResult CUPTIAPI cuptiPCSamplingGetStallReasons(CUpti_PCSamplingGetStallReasonsParams *pParams);
+
+
+/**
+ * \brief Params for cuptiGetSassToSourceCorrelation
+ */
+typedef struct CUpti_GetSassToSourceCorrelationParams {
+  /**
+   * [w] Size of the data structure i.e. CUpti_GetSassToSourceCorrelationParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Pointer to cubin binary where function belongs.
+   */
+  const void* cubin;
+  /**
+   * [w] Function name to which PC belongs.
+   */
+  const char *functionName;
+  /**
+   * [w] Size of cubin binary.
+   */
+  size_t cubinSize;
+  /**
+   * [r] Line number in the source code.
+   */
+  uint32_t lineNumber;
+  /**
+   * [w] PC offset
+   */
+  uint64_t pcOffset;
+  /**
+   * [r] Path for the source file.
+   */
+  char *fileName;
+  /**
+   * [r] Path for the directory of source file.
+   */
+  char *dirName;
+} CUpti_GetSassToSourceCorrelationParams;
+
+#define CUpti_GetSassToSourceCorrelationParamsSize     CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_GetSassToSourceCorrelationParams, dirName)
+
+/**
+ * \brief SASS to Source correlation.
+ *
+ * \param pParams A pointer to \ref CUpti_GetSassToSourceCorrelationParams
+ *
+ * It is expected from user to free allocated memory for fileName and dirName after use.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if either of the parameters cubin or functionName
+ * is NULL or cubinSize is zero or size field is not set correctly.
+ * \retval CUPTI_ERROR_INVALID_MODULE provided cubin is invalid.
+ * \retval CUPTI_ERROR_UNKNOWN an internal error occurred.
+ * This error code is also used for cases when the function is not present in the module.
+ * A better error code will be returned in the future release.
+ */
+CUptiResult CUPTIAPI cuptiGetSassToSourceCorrelation(CUpti_GetSassToSourceCorrelationParams *pParams);
+
+/**
+ * \brief Params for cuptiGetCubinCrc
+ */
+typedef struct {
+  /**
+   * [w] Size of configuration structure.
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * [w] Size of cubin binary.
+   */
+  size_t cubinSize;
+  /**
+   * [w] Pointer to cubin binary
+   */
+  const void* cubin;
+  /**
+   * [r] Computed CRC will be stored in it.
+   */
+  uint64_t cubinCrc;
+} CUpti_GetCubinCrcParams;
+#define CUpti_GetCubinCrcParamsSize     CUPTI_PCSAMPLING_STRUCT_SIZE(CUpti_GetCubinCrcParams, cubinCrc)
+
+/**
+ * \brief Get the CRC of cubin.
+ *
+ * This function returns the CRC of provided cubin binary.
+ *
+ * \param pParams A pointer to \ref CUpti_GetCubinCrcParams
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if parameter cubin is NULL or
+ * provided cubinSize is zero or size field is not set.
+ */
+CUptiResult CUPTIAPI cuptiGetCubinCrc(CUpti_GetCubinCrcParams *pParams);
+
+/**
+ * \brief Function type for callback used by CUPTI to request crc of
+ * loaded module.
+ *
+ * This callback function ask for crc of provided module in function.
+ * The provided crc will be stored in PC sampling records i.e. in the field 'cubinCrc' of the PC sampling
+ * struct CUpti_PCSamplingPCData. The CRC is uses during the offline source correlation to uniquely identify the module.
+ *
+ * \param cubin The pointer to cubin binary
+ * \param cubinSize The size of cubin binary.
+ * \param cubinCrc Returns the computed crc of cubin.
+ */
+typedef void (CUPTIAPI *CUpti_ComputeCrcCallbackFunc)(
+    const void* cubin,
+    size_t cubinSize,
+    uint64_t *cubinCrc);
+
+/**
+ * \brief Register callback function with CUPTI to use
+ * your own algorithm to compute cubin crc.
+ *
+ * This function registers a callback function and it gets called
+ * from CUPTI when a CUDA module is loaded.
+ *
+ * \param funcComputeCubinCrc callback is invoked when a CUDA module
+ * is loaded.
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p funcComputeCubinCrc is NULL.
+ */
+CUptiResult CUPTIAPI cuptiRegisterComputeCrcCallback(CUpti_ComputeCrcCallbackFunc funcComputeCubinCrc);
+
+/** @} */ /* END CUPTI_PCSAMPLING_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_PCSAMPLING_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pcsampling_util.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pcsampling_util.h
new file mode 100644
index 0000000000000000000000000000000000000000..595d6028fbf2ff9a3bbffaafe90ec80f7d512533
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pcsampling_util.h
@@ -0,0 +1,402 @@
+#if !defined(_CUPTI_PCSAMPLING_UTIL_H_)
+#define _CUPTI_PCSAMPLING_UTIL_H_
+
+#include <cupti_pcsampling.h>
+#include <fstream>
+
+#include <cupti_common.h>
+
+#ifndef CUPTI_UTIL_STRUCT_SIZE
+#define CUPTI_UTIL_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+#ifndef CHECK_PC_SAMPLING_STRUCT_FIELD_EXISTS
+#define CHECK_PC_SAMPLING_STRUCT_FIELD_EXISTS(type, member, structSize)    \
+    (offsetof(type, member) < structSize)
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__)
+    #pragma GCC visibility push(default)
+#endif
+
+namespace CUPTI { namespace PcSamplingUtil {
+
+/**
+ * \defgroup CUPTI_PCSAMPLING_UTILITY CUPTI PC Sampling Utility API
+ * Functions, types, and enums that implement the CUPTI PC Sampling Utility API.
+ * @{
+ */
+
+/**
+ * \brief Header info will be stored in file.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * Version of file format.
+   */
+  uint32_t version;
+  /**
+   * Total number of buffers present in the file.
+   */
+  uint32_t totalBuffers;
+} Header;
+
+/**
+ * \brief BufferInfo will be stored in the file for every buffer
+ *  i.e for every call of UtilDumpPcSamplingBufferInFile() API.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * Total number of PC records.
+   */
+  uint64_t recordCount;
+  /**
+   * Count of all stall reasons supported on the GPU
+   */
+  size_t numStallReasons;
+  /**
+   * Total number of stall reasons in single record.
+   */
+  uint64_t numSelectedStallReasons;
+  /**
+   * Buffer size in Bytes.
+   */
+  uint64_t bufferByteSize;
+} BufferInfo;
+
+/**
+ * \brief All available stall reasons name and respective indexes
+ * will be stored in it.
+ */
+typedef struct PACKED_ALIGNMENT {
+  /**
+   * Number of all available stall reasons
+   */
+  size_t numStallReasons;
+  /**
+   * Stall reasons names of all available stall reasons
+   */
+  char **stallReasons;
+  /**
+   * Stall reason index of all available stall reasons
+   */
+  uint32_t *stallReasonIndex;
+} PcSamplingStallReasons;
+
+/**
+ * \brief CUPTI PC sampling buffer types.
+ *
+ */
+typedef enum {
+  /**
+   * Invalid buffer type.
+   */
+  PC_SAMPLING_BUFFER_INVALID             = 0,
+  /**
+   * Refers to CUpti_PCSamplingData buffer.
+   */
+  PC_SAMPLING_BUFFER_PC_TO_COUNTER_DATA  = 1
+} PcSamplingBufferType;
+
+/**
+ * \brief CUPTI PC sampling utility API result codes.
+ *
+ * Error and result codes returned by CUPTI PC sampling utility API.
+ */
+typedef enum {
+  /**
+   * No error
+   */
+  CUPTI_UTIL_SUCCESS                                       = 0,
+  /**
+   * One or more of the parameters are invalid.
+   */
+  CUPTI_UTIL_ERROR_INVALID_PARAMETER                       = 1,
+  /**
+   * Unable to create a new file
+   */
+  CUPTI_UTIL_ERROR_UNABLE_TO_CREATE_FILE                   = 2,
+  /**
+   * Unable to open a file
+   */
+  CUPTI_UTIL_ERROR_UNABLE_TO_OPEN_FILE                     = 3,
+  /**
+   * Read or write operation failed
+   */
+  CUPTI_UTIL_ERROR_READ_WRITE_OPERATION_FAILED             = 4,
+  /**
+   * Provided file handle is corrupted.
+   */
+  CUPTI_UTIL_ERROR_FILE_HANDLE_CORRUPTED                   = 5,
+  /**
+   * seek operation failed.
+   */
+  CUPTI_UTIL_ERROR_SEEK_OPERATION_FAILED                   = 6,
+  /**
+   * Unable to allocate enough memory to perform the requested
+   * operation.
+   */
+  CUPTI_UTIL_ERROR_OUT_OF_MEMORY                           = 7,
+  /**
+   * An unknown internal error has occurred.
+   */
+  CUPTI_UTIL_ERROR_UNKNOWN                                 = 999,
+  CUPTI_UTIL_ERROR_FORCE_INT                               = 0x7fffffff
+} CUptiUtilResult;
+
+/**
+ * \brief Params for \ref CuptiUtilPutPcSampData
+ */
+typedef struct {
+  /**
+   * Size of the data structure i.e. CUpti_PCSamplingDisableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * Type of buffer to store in file
+   */
+  PcSamplingBufferType bufferType;
+  /**
+   * PC sampling buffer.
+   */
+  void *pSamplingData;
+  /**
+   * Number of configured attributes
+   */
+  size_t numAttributes;
+  /**
+   * Refer \ref CUpti_PCSamplingConfigurationInfo
+   * It is expected to provide configuration details of at least
+   * CUPTI_PC_SAMPLING_CONFIGURATION_ATTR_TYPE_STALL_REASON attribute.
+   */
+  CUpti_PCSamplingConfigurationInfo *pPCSamplingConfigurationInfo;
+  /**
+   * Refer \ref PcSamplingStallReasons.
+   */
+  PcSamplingStallReasons *pPcSamplingStallReasons;
+  /**
+   * File name to store buffer into it.
+   */
+  const char* fileName;
+} CUptiUtil_PutPcSampDataParams;
+#define CUptiUtil_PutPcSampDataParamsSize                   CUPTI_UTIL_STRUCT_SIZE(CUptiUtil_PutPcSampDataParams, fileName)
+
+/**
+ * \brief Dump PC sampling data into the file.
+ *
+ * This API can be called multiple times.
+ * It will append buffer in the file.
+ * For every buffer it will store BufferInfo
+ * so that before retrieving data it will help to allocate buffer
+ * to store retrieved data.
+ * This API creates file if file does not present.
+ * If stallReasonIndex or stallReasons pointer of \ref CUptiUtil_PutPcSampDataParams is NULL
+ * then stall reasons data  will not be stored in file.
+ * It is expected to store all available stall reason data at least once to refer it during
+ * offline correlation.
+ *
+ * \retval CUPTI_UTIL_SUCCESS
+ * \retval CUPTI_UTIL_ERROR_INVALID_PARAMETER error out if buffer type is invalid
+ * or if either of pSamplingData, pParams pointer is NULL or stall reason configuration details not provided
+ * or filename is empty.
+ * \retval CUPTI_UTIL_ERROR_UNABLE_TO_CREATE_FILE
+ * \retval CUPTI_UTIL_ERROR_UNABLE_TO_OPEN_FILE
+ * \retval CUPTI_UTIL_ERROR_READ_WRITE_OPERATION_FAILED
+ */
+CUptiUtilResult CUPTIUTILAPI CuptiUtilPutPcSampData(CUptiUtil_PutPcSampDataParams *pParams);
+
+/**
+ * \brief Params for \ref CuptiUtilGetHeaderData
+ */
+typedef struct {
+  /**
+   * Size of the data structure i.e. CUpti_PCSamplingDisableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * File handle.
+   */
+  std::ifstream *fileHandler;
+  /**
+   * Header Info.
+   */
+  Header headerInfo;
+
+} CUptiUtil_GetHeaderDataParams;
+#define CUptiUtil_GetHeaderDataParamsSize                   CUPTI_UTIL_STRUCT_SIZE(CUptiUtil_GetHeaderDataParams, headerInfo)
+
+/**
+ * \brief Get header data of file.
+ *
+ * This API must be called once initially while retrieving data from file.
+ * \ref Header structure, it gives info about total number
+ * of buffers present in the file.
+ *
+ * \retval CUPTI_UTIL_SUCCESS
+ * \retval CUPTI_UTIL_ERROR_INVALID_PARAMETER error out if either of pParam or fileHandle is NULL or param struct size is incorrect.
+ * \retval CUPTI_UTIL_ERROR_FILE_HANDLE_CORRUPTED file handle is not in good state to read data from file
+ * \retval CUPTI_UTIL_ERROR_READ_WRITE_OPERATION_FAILED  failed to read data from file.
+ */
+CUptiUtilResult CUPTIUTILAPI CuptiUtilGetHeaderData(CUptiUtil_GetHeaderDataParams *pParams);
+
+/**
+ * \brief Params for \ref CuptiUtilGetBufferInfo
+ */
+typedef struct {
+  /**
+   * Size of the data structure i.e. CUpti_PCSamplingDisableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * File handle.
+   */
+  std::ifstream *fileHandler;
+  /**
+   * Buffer Info.
+   */
+  BufferInfo bufferInfoData;
+} CUptiUtil_GetBufferInfoParams;
+#define CUptiUtil_GetBufferInfoParamsSize                   CUPTI_UTIL_STRUCT_SIZE(CUptiUtil_GetBufferInfoParams, bufferInfoData)
+
+/**
+ * \brief Get buffer info data of file.
+ *
+ * This API must be called every time before calling CuptiUtilGetPcSampData API.
+ * \ref BufferInfo structure, it gives info about recordCount and stallReasonCount
+ * of every record in the buffer. This will help to allocate exact buffer to retrieve data into it.
+ *
+ * \retval CUPTI_UTIL_SUCCESS
+ * \retval CUPTI_UTIL_ERROR_INVALID_PARAMETER error out if either of pParam or fileHandle is NULL or param struct size is incorrect.
+ * \retval CUPTI_UTIL_ERROR_FILE_HANDLE_CORRUPTED file handle is not in good state to read data from file.
+ * \retval CUPTI_UTIL_ERROR_READ_WRITE_OPERATION_FAILED failed to read data from file.
+ */
+CUptiUtilResult CUPTIUTILAPI CuptiUtilGetBufferInfo(CUptiUtil_GetBufferInfoParams *pParams);
+
+/**
+ * \brief Params for \ref CuptiUtilGetPcSampData
+ */
+typedef struct {
+  /**
+   * Size of the data structure i.e. CUpti_PCSamplingDisableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * File handle.
+   */
+  std::ifstream *fileHandler;
+  /**
+   * Type of buffer to store in file
+   */
+  PcSamplingBufferType bufferType;
+  /**
+   * Pointer to collected buffer info using \ref CuptiUtilGetBufferInfo
+   */
+  BufferInfo *pBufferInfoData;
+  /**
+   * Pointer to allocated memory to store retrieved data from file.
+   */
+  void *pSamplingData;
+  /**
+   * Number of configuration attributes
+   */
+  size_t numAttributes;
+  /**
+   * Refer \ref CUpti_PCSamplingConfigurationInfo
+   */
+  CUpti_PCSamplingConfigurationInfo *pPCSamplingConfigurationInfo;
+  /**
+   * Refer \ref PcSamplingStallReasons.
+   * For stallReasons field of \ref PcSamplingStallReasons it is expected to
+   * allocate memory for each string element of array.
+   */
+  PcSamplingStallReasons *pPcSamplingStallReasons;
+} CUptiUtil_GetPcSampDataParams;
+#define CUptiUtil_GetPcSampDataParamsSize                   CUPTI_UTIL_STRUCT_SIZE(CUptiUtil_GetPcSampDataParams, pPcSamplingStallReasons)
+
+/**
+ * \brief Retrieve PC sampling data from file into allocated buffer.
+ *
+ * This API must be called after CuptiUtilGetBufferInfo API.
+ * It will retrieve data from file into allocated buffer.
+ *
+ * \retval CUPTI_UTIL_SUCCESS
+ * \retval CUPTI_UTIL_ERROR_INVALID_PARAMETER error out if buffer type is invalid
+ * or if either of pSampData, pParams is NULL. If pPcSamplingStallReasons is not NULL then
+ * error out if either of stallReasonIndex, stallReasons or stallReasons array element pointer is NULL.
+ * or filename is empty.
+ * \retval CUPTI_UTIL_ERROR_READ_WRITE_OPERATION_FAILED
+ * \retval CUPTI_UTIL_ERROR_FILE_HANDLE_CORRUPTED file handle is not in good state to read data from file.
+ */
+CUptiUtilResult CUPTIUTILAPI CuptiUtilGetPcSampData(CUptiUtil_GetPcSampDataParams *pParams);
+
+/**
+ * \brief Params for \ref CuptiUtilMergePcSampData
+ */
+typedef struct
+{
+  /**
+   * Size of the data structure i.e. CUpti_PCSamplingDisableParamsSize
+   * CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+   * available in the structure. Used to preserve backward compatibility.
+   */
+  size_t size;
+  /**
+   * Number of buffers to merge.
+   */
+  size_t numberOfBuffers;
+  /**
+   * Pointer to array of buffers to merge
+   */
+  CUpti_PCSamplingData *PcSampDataBuffer;
+  /**
+   * Pointer to array of merged buffers as per the range id.
+   */
+  CUpti_PCSamplingData **MergedPcSampDataBuffers;
+  /**
+   * Number of merged buffers.
+   */
+  size_t *numMergedBuffer;
+} CUptiUtil_MergePcSampDataParams;
+#define CUptiUtil_MergePcSampDataParamsSize                   CUPTI_UTIL_STRUCT_SIZE(CUptiUtil_MergePcSampDataParams, numMergedBuffer)
+
+/**
+ * \brief Merge PC sampling data range id wise.
+ *
+ * This API merge PC sampling data range id wise.
+ * It allocates memory for merged data and fill data in it
+ * and provide buffer pointer in MergedPcSampDataBuffers field.
+ * It is expected from user to free merge data buffers after use.
+ *
+ * \retval CUPTI_UTIL_SUCCESS
+ * \retval CUPTI_UTIL_ERROR_INVALID_PARAMETER error out if param struct size is invalid
+ * or count of buffers to merge is invalid i.e less than 1
+ * or either of PcSampDataBuffer, MergedPcSampDataBuffers, numMergedBuffer is NULL
+ * \retval CUPTI_UTIL_ERROR_OUT_OF_MEMORY Unable to allocate memory for merged buffer.
+ */
+CUptiUtilResult CUPTIUTILAPI CuptiUtilMergePcSampData(CUptiUtil_MergePcSampDataParams *pParams);
+
+/** @} */ /* END CUPTI_PCSAMPLING_UTILITY */
+
+} }
+
+#if defined(__GNUC__)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pmsampling.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pmsampling.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba4171b6710564b56bc7e8e64e46c3674fe6c58c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_pmsampling.h
@@ -0,0 +1,490 @@
+/*
+ * Copyright 2024 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_PMSAMPLING_H_)
+#define _CUPTI_PMSAMPLING_H_
+
+#include <cuda.h>
+#include <cupti_result.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+#ifndef CUPTI_PROFILER_STRUCT_SIZE
+#define CUPTI_PROFILER_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+/* CUPTI PM sampling APIs */
+/**
+ * \defgroup CUPTI_PM_SAMPLING_API CUPTI PM Sampling API
+ * Functions to enable, disable, start, stop, and decode PM sampling.
+ * @{
+ */
+typedef struct CUpti_PmSampling_Object CUpti_PmSampling_Object;
+
+typedef enum CUpti_PmSampling_TriggerMode
+{
+    /// The trigger is based off of the SYSCLK frequency, note SYS frequency by default is variable.
+    /// the sample interval (set in the struct CUpti_PmSampling_SetConfig_Params) is in terms of clocks.
+    CUPTI_PM_SAMPLING_TRIGGER_MODE_GPU_SYSCLK_INTERVAL = 0,
+    /// The trigger is based off of a fixed frequency source.
+    /// The sample interval (set in the struct CUpti_PmSampling_SetConfig_Params) is in terms of nanoseconds.
+    /// Note: This trigger mode is not supported on Turing GPU architecture and GA100 GPU.
+    /// It is supported on Ampere GA10x and later GPU architectures.
+    CUPTI_PM_SAMPLING_TRIGGER_MODE_GPU_TIME_INTERVAL = 1,
+    CUPTI_PM_SAMPLING_TRIGGER_MODE_COUNT
+} CUpti_PmSampling_TriggerMode;
+
+typedef enum CUpti_PmSampling_DecodeStopReason
+{
+    CUPTI_PM_SAMPLING_DECODE_STOP_REASON_OTHER = 0,
+    /// Counter data image is full.
+    CUPTI_PM_SAMPLING_DECODE_STOP_REASON_COUNTER_DATA_FULL,
+    /// All the records in the hardware buffer is decoded.
+    CUPTI_PM_SAMPLING_DECODE_STOP_REASON_END_OF_RECORDS,
+    CUPTI_PM_SAMPLING_DECODE_STOP_REASON_COUNT
+} CUpti_PmSampling_DecodeStopReason;
+
+typedef enum CUpti_PmSampling_HardwareBuffer_AppendMode
+{
+    /// Keep the oldest records in the hardware buffer.
+    /// CUPTI will report error for overflow in case hardware buffer is getting filled up.
+    CUPTI_PM_SAMPLING_HARDWARE_BUFFER_APPEND_MODE_KEEP_OLDEST = 0,
+    /// Keep the latest records in the hardware buffer.
+    /// Note: This mode is not supported on Turing GPU architecture.
+    /// It is supported on Ampere and later GPU architectures.
+    CUPTI_PM_SAMPLING_HARDWARE_BUFFER_APPEND_MODE_KEEP_LATEST = 1
+} CUpti_PmSampling_HardwareBuffer_AppendMode;
+
+/**
+ * \brief Params for cuptiPmSamplingSetConfig
+ */
+typedef struct CUpti_PmSampling_SetConfig_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+    /// [in] Size of the config image.
+    size_t configSize;
+    /// [in] Config image.
+    const uint8_t* pConfig;
+    /// [in] The hardware buffer size in which raw PM sampling data
+    /// will be stored. These samples will be decoded to counter data
+    /// image with \ref cuptiPmSamplingDecodeData call.
+    size_t hardwareBufferSize;
+    /// [in] For the trigger mode `CUPTI_PM_SAMPLING_TRIGGER_MODE_GPU_SYSCLK_INTERVAL`, sampling interval
+    /// is the number of sys clock cycles. For the trigger mode `CUPTI_PM_SAMPLING_TRIGGER_MODE_GPU_TIME_INTERVAL`,
+    /// sampling interval is in nanoseconds.
+    uint64_t samplingInterval;
+    /// [in] Trigger mode.
+    /// Note: CUPTI_PM_SAMPLING_TRIGGER_MODE_GPU_TIME_INTERVAL is not supported in Turing and GA100.
+    /// Supported from GA10x onwards.
+    CUpti_PmSampling_TriggerMode triggerMode;
+    /// [in] Append mode for the records in hardware buffer.
+    /// For KEEP_OLDEST mode, all the records will be kept in the buffer and in case hardware buffer is getting filled up.
+    /// overflow will be set to 1 in \ref CUpti_PmSampling_DecodeData_Params. For KEEP_LATEST mode, the new records will
+    /// overwrite the oldest records in the buffer in case of filled buffer.
+    CUpti_PmSampling_HardwareBuffer_AppendMode hwBufferAppendMode;
+} CUpti_PmSampling_SetConfig_Params;
+
+#define CUpti_PmSampling_SetConfig_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_SetConfig_Params, hwBufferAppendMode)
+
+/**
+ * \brief Set the configuration for PM sampling like sampling interval, maximum number of samples
+ * filled in HW buffer, trigger mode and the config image which has scheduling info for metric collection.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_SetConfig_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED for config image which require multiple passes for data collection
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingSetConfig(CUpti_PmSampling_SetConfig_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingEnable
+ */
+typedef struct CUpti_PmSampling_Enable_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Device index.
+    size_t deviceIndex;
+    /// [out] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+} CUpti_PmSampling_Enable_Params;
+
+#define CUpti_PmSampling_Enable_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_Enable_Params, pPmSamplingObject)
+
+/**
+ * \brief Create a PM sampling object and enable PM sampling on the CUDA device.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_Enable_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY if memory allocation fails while creating the PM sampling object
+ * \retval CUPTI_ERROR_INVALID_OPERATION if PM sampling is already enabled on the device
+ * \retval CUPTI_ERROR_INSUFFICIENT_PRIVILEGES if the user does not have sufficient privileges to perform the operation
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingEnable(CUpti_PmSampling_Enable_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingDisable
+ */
+typedef struct CUpti_PmSampling_Disable_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+} CUpti_PmSampling_Disable_Params;
+
+#define CUpti_PmSampling_Disable_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_Disable_Params, pPmSamplingObject)
+
+/**
+ * \brief Disable PM sampling on the CUDA device and destroy the PM sampling object.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_Disable_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingDisable(CUpti_PmSampling_Disable_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingStart
+ */
+typedef struct CUpti_PmSampling_Start_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+} CUpti_PmSampling_Start_Params;
+
+#define CUpti_PmSampling_Start_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_Start_Params, pPmSamplingObject)
+
+/**
+ * \brief Start the PM sampling. The GPU will start collecting the metrics data
+ * periodically based on trigger type and sampling interval passed in CUpti_PmSampling_SetConfig_Params.
+ * The collected data will be stored in the hardware buffer.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_Start_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if PM sampling Start is called without enabling PM sampling,
+ * and PM sampling is already started
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingStart(CUpti_PmSampling_Start_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingStop
+ */
+typedef struct CUpti_PmSampling_Stop_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+} CUpti_PmSampling_Stop_Params;
+
+#define CUpti_PmSampling_Stop_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_Stop_Params, pPmSamplingObject)
+
+/**
+ * \brief Stop the PM sampling. The GPU will stop collecting the metrics data.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_Stop_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if PM sampling Stop is called without enabling PM sampling,
+ * and PM sampling is already stopped
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingStop(CUpti_PmSampling_Stop_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingDecodeData
+ */
+typedef struct CUpti_PmSampling_DecodeData_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+    /// [in] Counter data image.
+    uint8_t* pCounterDataImage;
+    /// [in] Size of the counter data image.
+    size_t counterDataImageSize;
+    /// [out] decode stop reason
+    CUpti_PmSampling_DecodeStopReason decodeStopReason;
+    /// [out] overflow status for hardware buffer.
+    /// To avoid overflow, either increase the maxSamples values in
+    /// \ref CUpti_PmSampling_SetConfig_Params or reduce the sampling interval.
+    uint8_t overflow;
+} CUpti_PmSampling_DecodeData_Params;
+
+#define CUpti_PmSampling_DecodeData_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_DecodeData_Params, overflow)
+
+/**
+ * \brief Decode the metrics data stored in the hardware buffer to the counter data image.
+ *
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_DecodeData_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if PM sampling DecodeData is called without enabling PM sampling
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY if there is record overflow in the hardware buffer
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingDecodeData(CUpti_PmSampling_DecodeData_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingGetCounterData
+ */
+typedef struct CUpti_PmSampling_GetCounterAvailability_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Device index.
+    size_t deviceIndex;
+    /// [inout] Size of the counter availability image. When pCounterAvailabilityImage is NULL,
+    /// this field is used to return the size of the counter availability image.
+    size_t counterAvailabilityImageSize;
+    /// [out] Counter availability image.
+    uint8_t* pCounterAvailabilityImage;
+} CUpti_PmSampling_GetCounterAvailability_Params;
+#define CUpti_PmSampling_GetCounterAvailability_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_GetCounterAvailability_Params, pCounterAvailabilityImage)
+
+/**
+ * \brief Query counter availibility information in a buffer which can be used to filter unavailable raw metrics on host.
+ * Note: This API may fail, if any profiling or sampling session is active on the specified device.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_GetCounterAvailability_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INSUFFICIENT_PRIVILEGES if the user does not have sufficient privileges to perform the operation
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingGetCounterAvailability(CUpti_PmSampling_GetCounterAvailability_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingGetCounterDataSize
+ */
+typedef struct CUpti_PmSampling_GetCounterDataSize_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+    /// [in] Names of the metrics to be collected.
+    const char** pMetricNames;
+    /// [in] Number of metrics to be collected.
+    size_t numMetrics;
+    /// [in] Maximum number of samples to be stored in the counter data image.
+    uint32_t maxSamples;
+    /// [out] Size of the counter data image.
+    size_t counterDataSize;
+} CUpti_PmSampling_GetCounterDataSize_Params;
+#define CUpti_PmSampling_GetCounterDataSize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_GetCounterDataSize_Params, counterDataSize)
+
+/**
+ * \brief Query the size of the counter data image which will be used to store the metrics data.
+ * User need to allocate the memory for the counter data image based on the size returned by this API.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_GetCounterDataSize_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if PM sampling GetCounterDataSize is called without enabling PM sampling
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingGetCounterDataSize(CUpti_PmSampling_GetCounterDataSize_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingCounterDataImageInitialize
+ */
+typedef struct CUpti_PmSampling_CounterDataImage_Initialize_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+    /// [in] Size of the counter data image.
+    size_t counterDataSize;
+    /// [in] Counter data image.
+    uint8_t* pCounterData;
+} CUpti_PmSampling_CounterDataImage_Initialize_Params;
+#define CUpti_PmSampling_CounterDataImage_Initialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_CounterDataImage_Initialize_Params, pCounterData)
+
+/**
+ * \brief Initialize the counter data to CUPTI record format for storing the metric data.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_CounterDataImage_Initialize_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if PM sampling CounterDataInitialize is called without enabling PM sampling
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingCounterDataImageInitialize(CUpti_PmSampling_CounterDataImage_Initialize_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingGetCounterDataInfo
+ */
+typedef struct CUpti_PmSampling_GetCounterDataInfo_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Counter data image.
+    const uint8_t* pCounterDataImage;
+    /// [in] Size of the counter data image.
+    size_t counterDataImageSize;
+    /// [out] Number of samples in the counter data image.
+    size_t numTotalSamples;
+    /// [out] Number of populated samples.
+    size_t numPopulatedSamples;
+    /// [out] Number of samples that have been completed.
+    size_t numCompletedSamples;
+} CUpti_PmSampling_GetCounterDataInfo_Params;
+#define CUpti_PmSampling_GetCounterDataInfo_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_GetCounterDataInfo_Params, numCompletedSamples)
+
+/**
+ * \brief Get the counter data info like number of samples, number of populated
+ * samples and number of completed samples in a counter data image.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_GetCounterDataInfo_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingGetCounterDataInfo(CUpti_PmSampling_GetCounterDataInfo_Params* pParams);
+
+/**
+ * \brief Params for cuptiPmSamplingCounterDataGetSampleInfo
+ */
+typedef struct CUpti_PmSampling_CounterData_GetSampleInfo_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] PM sampling object.
+    CUpti_PmSampling_Object* pPmSamplingObject;
+    /// [in] Counter data image.
+    const uint8_t* pCounterDataImage;
+    /// [in] Size of the counter data image.
+    size_t counterDataImageSize;
+    /// [in] Index of the sample.
+    size_t sampleIndex;
+    /// [out] Start time of the sample.
+    uint64_t startTimestamp;
+    /// [out] End time of the sample.
+    uint64_t endTimestamp;
+} CUpti_PmSampling_CounterData_GetSampleInfo_Params;
+#define CUpti_PmSampling_CounterData_GetSampleInfo_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_PmSampling_CounterData_GetSampleInfo_Params, endTimestamp)
+
+/**
+ * \brief Get the sample info (start and end time stamp) for the given sample index.
+ * Each sample is distinguished by the start and end time stamp.
+ *
+ * \param pParams A pointer to \ref CUpti_PmSampling_CounterData_GetSampleInfo_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiPmSamplingCounterDataGetSampleInfo(CUpti_PmSampling_CounterData_GetSampleInfo_Params* pParams);
+
+/** @} */ /* END CUPTI_PMSAMPLING_API */
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif // _CUPTI_PMSAMPLING_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_host.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_host.h
new file mode 100644
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_host.h
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+/*
+ * Copyright 2024 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_PROFILER_HOST_H_)
+#define _CUPTI_PROFILER_HOST_H_
+
+/*
+CUPTI profiler host API's
+This file contains the CUPTI profiling host API's.
+*/
+#include <cupti_result.h>
+#include <stdint.h>
+#include <stddef.h>
+#include <string>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_PROFILER_HOST_API CUPTI Profiler Host API
+ * Functions, types, and enums that implement the CUPTI Profiler Host API.
+ * @{
+ */
+#ifndef CUPTI_PROFILER_STRUCT_SIZE
+#define CUPTI_PROFILER_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+typedef enum CUpti_MetricType
+{
+    CUPTI_METRIC_TYPE_COUNTER = 0,
+    CUPTI_METRIC_TYPE_RATIO,
+    CUPTI_METRIC_TYPE_THROUGHPUT,
+    CUPTI_METRIC_TYPE__COUNT
+} CUpti_MetricType;
+
+typedef enum CUpti_ProfilerType
+{
+    CUPTI_PROFILER_TYPE_RANGE_PROFILER,
+    CUPTI_PROFILER_TYPE_PM_SAMPLING,
+    CUPTI_PROFILER_TYPE_PROFILER_INVALID
+} CUpti_ProfilerType;
+
+typedef struct CUpti_Profiler_Host_Object CUpti_Profiler_Host_Object;
+
+/**
+ * \brief Params for cuptiProfilerHostInitialize
+ */
+typedef struct CUpti_Profiler_Host_Initialize_Params
+{
+    /// [in] Size of the data structure.
+    /// CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+    /// available in the structure. Used to preserve backward compatibility.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] the profiler kind one from CUpti_ProfilerType
+    CUpti_ProfilerType profilerType;
+    /// [in] accepted for chips supported at the time-of-release.
+    const char* pChipName;
+    /// [in] buffer with counter availability image - required for future chip support
+    const uint8_t* pCounterAvailabilityImage;
+    /// [out] binary blob allocated by CUPTI and operations associated with this object.
+    CUpti_Profiler_Host_Object* pHostObject;
+} CUpti_Profiler_Host_Initialize_Params;
+
+#define CUpti_Profiler_Host_Initialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_Initialize_Params, pHostObject)
+
+/**
+ * \brief Create and initialize the profiler host object (CUpti_Profiler_Host_Object).
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_Initialize_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostInitialize(CUpti_Profiler_Host_Initialize_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostDeinitialize
+ */
+typedef struct CUpti_Profiler_Host_Deinitialize_Params
+{
+    /// [in] Size of the data structure.
+    /// CUPTI client should set the size of the structure. It will be used in CUPTI to check what fields are
+    /// available in the structure. Used to preserve backward compatibility.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    struct CUpti_Profiler_Host_Object* pHostObject;
+} CUpti_Profiler_Host_Deinitialize_Params;
+
+#define CUpti_Profiler_Host_Deinitialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_Deinitialize_Params, pHostObject)
+
+/**
+ * \brief Deinitialize and destroy the profiler host object (CUpti_Profiler_Host_Object).
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_Deinitialize_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostDeinitialize(CUpti_Profiler_Host_Deinitialize_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetSupportedChips
+ */
+typedef struct CUpti_Profiler_Host_GetSupportedChips_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [out] number of supported chips
+    size_t numChips;
+    /// [out] list of supported chips
+    const char* const* ppChipNames;
+} CUpti_Profiler_Host_GetSupportedChips_Params;
+
+#define CUpti_Profiler_Host_GetSupportedChips_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetSupportedChips_Params, ppChipNames)
+
+/**
+ * \brief Get the list of supported chips.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetSupportedChips_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetSupportedChips(CUpti_Profiler_Host_GetSupportedChips_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetSupportedMetrics
+ */
+typedef struct CUpti_Profiler_Host_GetBaseMetrics_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    struct CUpti_Profiler_Host_Object* pHostObject;
+    /// [in] metric type (counter, ratio, throughput)
+    CUpti_MetricType metricType;
+    /// [out] list of base metrics supported of queried metric type for the chip
+    const char** ppMetricNames;
+    /// [out] number of metrics
+    size_t numMetrics;
+} CUpti_Profiler_Host_GetBaseMetrics_Params;
+
+#define CUpti_Profiler_Host_GetBaseMetrics_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetBaseMetrics_Params, numMetrics)
+
+/**
+ * \brief Get the list of supported base metrics for the chip.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetBaseMetrics_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetBaseMetrics(CUpti_Profiler_Host_GetBaseMetrics_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetSubMetrics
+ */
+typedef struct CUpti_Profiler_Host_GetSubMetrics_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    CUpti_Profiler_Host_Object* pHostObject;
+    /// [in] the metric type for queried metric
+    CUpti_MetricType metricType;
+    /// [in] metric name for which sub-metric will be listed
+    const char* pMetricName;
+    /// [out] number of submetrics supported
+    size_t numOfSubmetrics;
+    /// [out] list of submetrics supported for the metric.
+    const char** ppSubMetrics;
+} CUpti_Profiler_Host_GetSubMetrics_Params;
+
+#define CUpti_Profiler_Host_GetSubMetrics_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetSubMetrics_Params, ppSubMetrics)
+
+/**
+ * \brief Get the list of supported sub-metrics for the metric.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetSubMetrics_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_METRIC_NAME if the metric name is not valid or not supported for the chip
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetSubMetrics(CUpti_Profiler_Host_GetSubMetrics_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetMetricProperties
+ */
+typedef struct CUpti_Profiler_Host_GetMetricProperties_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    CUpti_Profiler_Host_Object* pHostObject;
+    /// [in] metric name for which its properties will be listed
+    const char* pMetricName;
+    /// [out] a short description about the metric
+    const char* pDescription;
+    /// [out] associated hw unit for the metric
+    const char* pHwUnit;
+    /// [out] the dimension of the metric values
+    const char* pDimUnit;
+    /// [out] the metric type (counter, ratio or throughput)
+    CUpti_MetricType metricType;
+} CUpti_Profiler_Host_GetMetricProperties_Params;
+
+#define CUpti_Profiler_Host_GetMetricProperties_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetMetricProperties_Params, metricType)
+
+/**
+ * \brief Get the properties of the metric.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetMetricProperties_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_METRIC_NAME if the metric name is not valid or not supported for the chip
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetMetricProperties(CUpti_Profiler_Host_GetMetricProperties_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetRangeName
+ */
+typedef struct CUpti_Profiler_Host_GetRangeName_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] the counter data image where profiling data has been decoded
+    const uint8_t* pCounterDataImage;
+    /// [in] size of counter data image
+    size_t counterDataImageSize;
+    /// [in] range index for which the range name will be queried
+    size_t rangeIndex;
+    /// [in] used in case of nested ranges, default="/". Range1<delimiter>Range2 
+    const char* delimiter;
+    /// [out] the range name.
+    /// Note: that the CUPTI allocate the memory internal and
+    /// its user responsibility to free up the allocated memory
+    const char* pRangeName;
+} CUpti_Profiler_Host_GetRangeName_Params;
+
+#define CUpti_Profiler_Host_GetRangeName_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetRangeName_Params, pRangeName)
+
+/**
+ * \brief Get the range name for the range index stored in the counter data.
+ * In Range profiler, for Auto range mode the range name will be numeric value
+ * assigned to the kernel based on execution order. For user range mode, the 
+ * name of range will be based on the range name provided by the user using
+ * Push range API.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetRangeName_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetRangeName(CUpti_Profiler_Host_GetRangeName_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostEvaluateToGpuValues
+ */
+typedef struct CUpti_Profiler_Host_EvaluateToGpuValues_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    CUpti_Profiler_Host_Object* pHostObject;
+    /// [in] the counter data image where profiling data has been decoded
+    const uint8_t* pCounterDataImage;
+    /// [in] size of counter data image
+    size_t counterDataImageSize;
+    /// [in] range index for which the range name will be queried
+    size_t rangeIndex;
+    /// [in] the metrics for which GPU values will be evaluated for the range
+    const char** ppMetricNames;
+    /// [in] number of metrics
+    size_t numMetrics;
+    /// [out] output value for given metric and range index
+    double* pMetricValues;
+} CUpti_Profiler_Host_EvaluateToGpuValues_Params;
+
+#define CUpti_Profiler_Host_EvaluateToGpuValues_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_EvaluateToGpuValues_Params, pMetricValues)
+
+/**
+ * \brief Evaluate the metric values for the range index stored in the counter data.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_EvaluateToGpuValues_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_METRIC_NAME if the metric name is not valid or not supported for the chip
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostEvaluateToGpuValues(CUpti_Profiler_Host_EvaluateToGpuValues_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostConfigAddMetrics
+ */
+typedef struct CUpti_Profiler_Host_ConfigAddMetrics_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    struct CUpti_Profiler_Host_Object* pHostObject;
+    /// [in] metric names for which config image will be generated
+    const char** ppMetricNames;
+    /// [in] number of metrics
+    size_t numMetrics;
+} CUpti_Profiler_Host_ConfigAddMetrics_Params;
+
+#define CUpti_Profiler_Host_ConfigAddMetrics_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_ConfigAddMetrics_Params, numMetrics)
+
+/**
+ * \brief Add the metrics to the profiler host object for generating the config image.
+ * The config image will have the required information to schedule the metrics for
+ * collecting the profiling data. 
+ * Note: PM sampling only supports single pass config image.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_ConfigAddMetrics_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_METRIC_NAME if the metric name is not valid or not supported for the chip
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostConfigAddMetrics(CUpti_Profiler_Host_ConfigAddMetrics_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetConfigImageSize
+ */
+typedef struct CUpti_Profiler_Host_GetConfigImageSize_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    CUpti_Profiler_Host_Object* pHostObject;
+    /// [out] the size of config image, users need to allocate the buffer for storing
+    size_t configImageSize;
+} CUpti_Profiler_Host_GetConfigImageSize_Params;
+
+#define CUpti_Profiler_Host_GetConfigImageSize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetConfigImageSize_Params, configImageSize)
+
+/**
+ * \brief Get the size of the config image for the metrics added to the profiler host object.
+ * Users need to allocate the buffer for storing the config image.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetConfigImageSize_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetConfigImageSize(CUpti_Profiler_Host_GetConfigImageSize_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetConfigImage
+ */
+typedef struct CUpti_Profiler_Host_GetConfigImage_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] reference to the profiler host object allocated by CUPTI in cuptiProfilerHostInitialize
+    CUpti_Profiler_Host_Object* pHostObject;
+    /// [in] Number of bytes allocated for pBuffer
+    size_t configImageSize;
+    /// [out] Buffer receiving the config image
+    uint8_t* pConfigImage;
+} CUpti_Profiler_Host_GetConfigImage_Params;
+
+#define CUpti_Profiler_Host_GetConfigImage_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetConfigImage_Params, pConfigImage)
+
+/**
+ * \brief Get the config image for the metrics added to the profiler host object.
+ * User will pass the allocated buffer to store the config image.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetConfigImage_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetConfigImage(CUpti_Profiler_Host_GetConfigImage_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetNumOfPasses
+ */
+typedef struct CUpti_Profiler_Host_GetNumOfPasses_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] Number of bytes allocated for pConfigImage
+    size_t configImageSize;
+    /// [in] the config image buffer
+    uint8_t* pConfigImage;
+    /// [out] number of passes required for profiling scheduled metrics in the config image
+    size_t numOfPasses;
+} CUpti_Profiler_Host_GetNumOfPasses_Params;
+
+#define CUpti_Profiler_Host_GetNumOfPasses_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetNumOfPasses_Params, numOfPasses)
+
+/**
+ * \brief Get the number of passes required for profiling the scheduled metrics in the config image.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetNumOfPasses_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetNumOfPasses(CUpti_Profiler_Host_GetNumOfPasses_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerHostGetMaxNumHardwareMetricsPerPass
+ */
+typedef struct CUpti_Profiler_Host_GetMaxNumHardwareMetricsPerPass_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Assign to NULL
+    void* pPriv;
+    /// [in] the profiler kind one from CUpti_ProfilerType
+    CUpti_ProfilerType profilerType;
+    /// [in] accepted for chips supported at the time-of-release.
+    const char* pChipName;
+    /// [in] buffer with counter availability image - required for future chip support
+    uint8_t* pCounterAvailabilityImage;
+    /// [out] maximum number of metrics that can be scheduled in a pass
+    size_t maxMetricsPerPass;
+} CUpti_Profiler_Host_GetMaxNumHardwareMetricsPerPass_Params;
+
+#define CUpti_Profiler_Host_GetMaxNumHardwareMetricsPerPass_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Host_GetMaxNumHardwareMetricsPerPass_Params, maxMetricsPerPass)
+
+/**
+ * \brief Get the maximum number of hardware metrics (metric names which doesn't include _sass_ keyword)
+ * that can be scheduled in a single pass for a chip. While this represents a theoretical upper limit,
+ * practical constraints may prevent reaching this threshold for a specific set of metrics. Furthermore,
+ * the maximum achievable value is contingent upon the characteristics and architecture of the chip in question.
+ * 
+ * Use cuptiProfilerHostGetNumOfPasses API for getting the actual number of passes required for the
+ * for collecting the profiling data for the scheduled metrics in a config image.
+ * 
+ * \param pParams A pointer to \ref CUpti_Profiler_Host_GetMaxNumHardwareMetricsPerPass_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiProfilerHostGetMaxNumHardwareMetricsPerPass(CUpti_Profiler_Host_GetMaxNumHardwareMetricsPerPass_Params* pParams);
+
+/** @} */ /* END CUPTI_METRIC_API */
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif
\ No newline at end of file
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_target.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_target.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8fc197073dcb3bdec1a7349d136ac03434dc932
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_target.h
@@ -0,0 +1,602 @@
+/*
+ * Copyright 2011-2023   NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_PROFILER_TARGET_H_)
+#define _CUPTI_PROFILER_TARGET_H_
+
+#include <cuda.h>
+#include <cupti_result.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_PROFILER_API CUPTI Profiling API
+ * Functions, types, and enums that implement the CUPTI Profiling API.
+ * @{
+ */
+#ifndef CUPTI_PROFILER_STRUCT_SIZE
+#define CUPTI_PROFILER_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+/**
+ * \brief Profiler range attribute
+ *
+ * A metric enabled in the session's configuration is collected separately per unique range-stack in the pass.
+ * This is an attribute to collect metrics around each kernel in a profiling session or in an user defined range.
+ */
+typedef enum
+{
+    /**
+     * Invalid value
+     */
+    CUPTI_Range_INVALID,
+    /**
+     * Ranges are auto defined around each kernel in a profiling session
+     */
+    CUPTI_AutoRange,
+    /**
+     * A range in which metric data to be collected is defined by the user
+     */
+    CUPTI_UserRange,
+    /**
+     * Range count
+     */
+    CUPTI_Range_COUNT,
+} CUpti_ProfilerRange;
+
+/**
+ * \brief Profiler replay attribute
+ *
+ * For metrics which require multipass collection, a replay of the GPU kernel(s) is required.
+ * This is an attribute which specify how the replay of the kernel(s) to be measured is done.
+ */
+typedef enum
+{
+    /**
+     * Invalid Value
+     */
+    CUPTI_Replay_INVALID,
+    /**
+     * Replay is done by CUPTI user around the process
+     */
+    CUPTI_ApplicationReplay,
+    /**
+     * Replay is done around kernel implicitly by CUPTI
+     */
+    CUPTI_KernelReplay,
+    /**
+     * Replay is done by CUPTI user within a process
+     */
+    CUPTI_UserReplay,
+    /**
+     * Replay count
+     */
+    CUPTI_Replay_COUNT,
+} CUpti_ProfilerReplayMode;
+
+/**
+ * \brief Default parameter for cuptiProfilerInitialize
+ */
+typedef struct CUpti_Profiler_Initialize_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_Initialize_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+} CUpti_Profiler_Initialize_Params;
+#define CUpti_Profiler_Initialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Initialize_Params, pPriv)
+
+/**
+ * \brief Default parameter for cuptiProfilerDeInitialize
+ */
+typedef struct CUpti_Profiler_DeInitialize_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+} CUpti_Profiler_DeInitialize_Params;
+#define CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_DeInitialize_Params, pPriv)
+
+/**
+ * \brief Initializes the profiler interface
+ *
+ * Loads the required libraries in the process address space.
+ * Sets up the hooks with the CUDA driver.
+ */
+CUptiResult CUPTIAPI cuptiProfilerInitialize(CUpti_Profiler_Initialize_Params *pParams);
+
+/**
+ * \brief DeInitializes the profiler interface
+ */
+CUptiResult CUPTIAPI cuptiProfilerDeInitialize(CUpti_Profiler_DeInitialize_Params *pParams);
+
+/**
+ * \brief Input parameter to define the counterDataImage
+ */
+typedef struct CUpti_Profiler_CounterDataImageOptions
+{
+    size_t structSize;                                          //!< [in] CUpti_Profiler_CounterDataImageOptions_Params_STRUCT_SIZE
+    void* pPriv;                                                //!< [in] assign to NULL
+
+    const uint8_t* pCounterDataPrefix;                          /**< [in] Address of CounterDataPrefix generated from NVPW_CounterDataBuilder_GetCounterDataPrefix().
+                                                                    Must be align(8).*/
+    size_t counterDataPrefixSize;                               //!< [in] Size of CounterDataPrefix generated from NVPW_CounterDataBuilder_GetCounterDataPrefix().
+    uint32_t maxNumRanges;                                      //!< [in] Maximum number of ranges that can be profiled
+    uint32_t maxNumRangeTreeNodes;                              //!< [in] Maximum number of RangeTree nodes; must be >= maxNumRanges
+    uint32_t maxRangeNameLength;                                //!< [in] Maximum string length of each RangeName, including the trailing NULL character
+} CUpti_Profiler_CounterDataImageOptions;
+#define CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE                       CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImageOptions, maxRangeNameLength)
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageCalculateSize
+ */
+typedef struct CUpti_Profiler_CounterDataImage_CalculateSize_Params
+{
+    size_t structSize;                                          //!< [in] CUpti_Profiler_CounterDataImage_CalculateSize_Params_STRUCT_SIZE
+    void* pPriv;                                                //!< [in] assign to NULL
+
+    size_t sizeofCounterDataImageOptions;                       //!< [in] CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE
+    const CUpti_Profiler_CounterDataImageOptions* pOptions;     //!< [in] Pointer to Counter Data Image Options
+    size_t counterDataImageSize;                                //!< [out]
+} CUpti_Profiler_CounterDataImage_CalculateSize_Params;
+#define CUpti_Profiler_CounterDataImage_CalculateSize_Params_STRUCT_SIZE         CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_CalculateSize_Params, counterDataImageSize)
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageInitialize
+ */
+typedef struct CUpti_Profiler_CounterDataImage_Initialize_Params
+{
+    size_t structSize;                                          //!< [in] CUpti_Profiler_CounterDataImage_Initialize_Params_STRUCT_SIZE
+    void* pPriv;                                                //!< [in] assign to NULL
+
+    size_t sizeofCounterDataImageOptions;                       //!< [in] CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE
+    const CUpti_Profiler_CounterDataImageOptions* pOptions;     //!< [in] Pointer to Counter Data Image Options
+    size_t counterDataImageSize;                                //!< [in] Size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                                 //!< [in] The buffer to be initialized.
+} CUpti_Profiler_CounterDataImage_Initialize_Params;
+#define CUpti_Profiler_CounterDataImage_Initialize_Params_STRUCT_SIZE            CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_Initialize_Params, pCounterDataImage)
+
+/**
+ * \brief A CounterData image allocates space for values for each counter for each range.
+ *
+ * User borne the resposibility of managing the counterDataImage allocations.
+ * CounterDataPrefix contains meta data about the metrics that will be stored in counterDataImage.
+ * Use these APIs to calculate the allocation size and initialize counterData image.
+ */
+CUptiResult CUPTIAPI cuptiProfilerCounterDataImageCalculateSize(CUpti_Profiler_CounterDataImage_CalculateSize_Params* pParams);
+CUptiResult CUPTIAPI cuptiProfilerCounterDataImageInitialize(CUpti_Profiler_CounterDataImage_Initialize_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageCalculateScratchBufferSize
+ */
+typedef struct CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    size_t counterDataImageSize;                            //!< [in] size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                             //!< [in]
+    size_t counterDataScratchBufferSize;                    //!< [out]
+} CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params;
+#define CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params_STRUCT_SIZE    CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params, counterDataScratchBufferSize)
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageInitializeScratchBuffer
+ */
+typedef struct CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    size_t counterDataImageSize;                            //!< [in] size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                             //!< [in]
+    size_t counterDataScratchBufferSize;                    //!< [in] size calculated using cuptiProfilerCounterDataImageCalculateScratchBufferSize
+    uint8_t* pCounterDataScratchBuffer;                     //!< [in] the scratch buffer to be initialized.
+} CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params;
+#define CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params_STRUCT_SIZE       CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params, pCounterDataScratchBuffer)
+
+/**
+ * \brief A temporary storage for CounterData image needed for internal operations
+ *
+ * Use these APIs to calculate the allocation size and initialize counterData image scratch buffer.
+ */
+CUptiResult CUPTIAPI cuptiProfilerCounterDataImageCalculateScratchBufferSize(CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params* pParams);
+CUptiResult CUPTIAPI cuptiProfilerCounterDataImageInitializeScratchBuffer(CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerBeginSession
+ */
+typedef struct CUpti_Profiler_BeginSession_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_BeginSession_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    size_t counterDataImageSize;                            //!< [in] size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                             //!< [in] address of CounterDataImage
+    size_t counterDataScratchBufferSize;                    //!< [in] size calculated from cuptiProfilerCounterDataImageInitializeScratchBuffer
+    uint8_t* pCounterDataScratchBuffer;                     //!< [in] address of CounterDataImage scratch buffer
+    uint8_t bDumpCounterDataInFile;                          //!< [in] [optional]
+    const char* pCounterDataFilePath;                        //!< [in] [optional]
+    CUpti_ProfilerRange range;                               //!< [in] CUpti_ProfilerRange
+    CUpti_ProfilerReplayMode replayMode;                     //!< [in] CUpti_ProfilerReplayMode
+    /* Replay options, required when replay is done by cupti user */
+    size_t maxRangesPerPass;                                //!< [in] Maximum number of ranges that can be recorded in a single pass.
+    size_t maxLaunchesPerPass;                              //!< [in] Maximum number of kernel launches that can be recorded in a single pass; must be >= maxRangesPerPass.
+
+} CUpti_Profiler_BeginSession_Params;
+#define CUpti_Profiler_BeginSession_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_BeginSession_Params, maxLaunchesPerPass)
+/**
+ * \brief Params for cuptiProfilerEndSession
+ */
+typedef struct CUpti_Profiler_EndSession_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_EndSession_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_EndSession_Params;
+#define CUpti_Profiler_EndSession_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_EndSession_Params, ctx)
+
+/**
+ * \brief Begin profiling session sets up the profiling on the device
+ *
+ * Although, it doesn't start the profiling but GPU resources needed for profiling are allocated.
+ * Outside of a session, the GPU will return to its normal operating state.
+ */
+CUptiResult CUPTIAPI cuptiProfilerBeginSession(CUpti_Profiler_BeginSession_Params* pParams);
+/**
+ * \brief Ends profiling session
+ *
+ * Frees up the GPU resources acquired for profiling.
+ * Outside of a session, the GPU will return to it's normal operating state.
+ */
+CUptiResult CUPTIAPI cuptiProfilerEndSession(CUpti_Profiler_EndSession_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerSetConfig
+ */
+typedef struct CUpti_Profiler_SetConfig_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_SetConfig_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    const uint8_t* pConfig;                                 //!< [in] Config created by NVPW_RawMetricsConfig_GetConfigImage(). Must be align(8).
+    size_t configSize;                                      //!< [in] size of config
+    uint16_t minNestingLevel;                               //!< [in] the lowest nesting level to be profiled; must be >= 1
+    uint16_t numNestingLevels;                              //!< [in] the number of nesting levels to profile; must be >= 1
+    size_t passIndex;                                       //!< [in] Set this to zero for in-app replay; set this to the output of EndPass() for application replay
+    uint16_t targetNestingLevel;                            //!< [in] Set this to minNestingLevel for in-app replay; set this to the output of EndPass() for application
+} CUpti_Profiler_SetConfig_Params;
+
+#define CUpti_Profiler_SetConfig_Params_STRUCT_SIZE                    CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_SetConfig_Params, targetNestingLevel)
+
+/**
+ * \brief Params for cuptiProfilerUnsetConfig
+ */
+typedef struct CUpti_Profiler_UnsetConfig_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_UnsetConfig_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_UnsetConfig_Params;
+#define CUpti_Profiler_UnsetConfig_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_UnsetConfig_Params, ctx)
+
+/**
+ * \brief Set metrics configuration to be profiled
+ *
+ * Use these APIs to set the config to profile in a session. It can be used for advanced cases such as where multiple
+ * configurations are collected into a single CounterData Image on the need basis, without restarting the session.
+ */
+CUptiResult CUPTIAPI cuptiProfilerSetConfig(CUpti_Profiler_SetConfig_Params* pParams);
+/**
+ * \brief Unset metrics configuration profiled
+ *
+ */
+CUptiResult CUPTIAPI cuptiProfilerUnsetConfig(CUpti_Profiler_UnsetConfig_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerBeginPass
+ */
+typedef struct CUpti_Profiler_BeginPass_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_BeginPass_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_BeginPass_Params;
+#define CUpti_Profiler_BeginPass_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_BeginPass_Params, ctx)
+
+/**
+ * \brief Params for cuptiProfilerEndPass
+ */
+typedef struct CUpti_Profiler_EndPass_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_EndPass_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    uint16_t targetNestingLevel;                            //!  [out] The targetNestingLevel that will be collected by the *next* BeginPass.
+    size_t passIndex;                                       //!< [out] The passIndex that will be collected by the *next* BeginPass
+    uint8_t allPassesSubmitted;                             //!< [out] becomes true when the last pass has been queued to the GPU
+} CUpti_Profiler_EndPass_Params;
+#define CUpti_Profiler_EndPass_Params_STRUCT_SIZE                    CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_EndPass_Params, allPassesSubmitted)
+
+/**
+ * \brief Replay API: used for multipass collection.
+
+ * These APIs are used if user chooses to replay by itself \ref CUPTI_UserReplay or \ref CUPTI_ApplicationReplay
+ * for multipass collection of the metrics configurations.
+ * It's a no-op in case of \ref CUPTI_KernelReplay.
+ */
+CUptiResult CUPTIAPI cuptiProfilerBeginPass(CUpti_Profiler_BeginPass_Params* pParams);
+
+/**
+ * \brief Replay API: used for multipass collection.
+
+ * These APIs are used if user chooses to replay by itself \ref CUPTI_UserReplay or \ref CUPTI_ApplicationReplay
+ * for multipass collection of the metrics configurations.
+ * Its a no-op in case of \ref CUPTI_KernelReplay.
+ * Returns information for next pass.
+ */
+CUptiResult CUPTIAPI cuptiProfilerEndPass(CUpti_Profiler_EndPass_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerEnableProfiling
+ */
+typedef struct CUpti_Profiler_EnableProfiling_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_EnableProfiling_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_EnableProfiling_Params;
+#define CUpti_Profiler_EnableProfiling_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_EnableProfiling_Params, ctx)
+
+/**
+ * \brief Params for cuptiProfilerDisableProfiling
+ */
+typedef struct CUpti_Profiler_DisableProfiling_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_DisableProfiling_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_DisableProfiling_Params;
+#define CUpti_Profiler_DisableProfiling_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_DisableProfiling_Params, ctx)
+
+/**
+ * \brief Enables Profiling
+ *
+ * In \ref CUPTI_AutoRange, these APIs are used to enable/disable profiling for the kernels to be executed in
+ * a profiling session.
+ */
+CUptiResult CUPTIAPI cuptiProfilerEnableProfiling(CUpti_Profiler_EnableProfiling_Params* pParams);
+
+/**
+ * \brief Disable Profiling
+ *
+ * In \ref CUPTI_AutoRange, these APIs are used to enable/disable profiling for the kernels to be executed in
+ * a profiling session.
+ */
+CUptiResult CUPTIAPI cuptiProfilerDisableProfiling(CUpti_Profiler_DisableProfiling_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerIsPassCollected
+ */
+typedef struct CUpti_Profiler_IsPassCollected_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_IsPassCollected_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    size_t numRangesDropped;                                //!< [out] number of ranges whose data was dropped in the processed pass
+    size_t numTraceBytesDropped;                            //!< [out] number of bytes not written to TraceBuffer due to buffer full
+    uint8_t onePassCollected;                               //!< [out] true if a pass was successfully decoded
+    uint8_t allPassesCollected;                             //!< [out] becomes true when the last pass has been decoded
+} CUpti_Profiler_IsPassCollected_Params;
+#define CUpti_Profiler_IsPassCollected_Params_STRUCT_SIZE            CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_IsPassCollected_Params, allPassesCollected)
+
+/**
+ * \brief Asynchronous call to query if the submitted pass to GPU is collected
+ *
+ */
+CUptiResult CUPTIAPI cuptiProfilerIsPassCollected(CUpti_Profiler_IsPassCollected_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerFlushCounterData
+ */
+typedef struct CUpti_Profiler_FlushCounterData_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_FlushCounterData_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    size_t numRangesDropped;                                //!< [out] number of ranges whose data was dropped in the processed passes
+    size_t numTraceBytesDropped;                            //!< [out] number of bytes not written to TraceBuffer due to buffer full
+} CUpti_Profiler_FlushCounterData_Params;
+#define CUpti_Profiler_FlushCounterData_Params_STRUCT_SIZE           CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_FlushCounterData_Params, numTraceBytesDropped)
+
+/**
+ * \brief Decode all the submitted passes
+ *
+ * Flush Counter data API to ensure every pass is decoded into the counterDataImage passed at beginSession.
+ * This will cause the CPU/GPU sync to collect all the undecoded pass.
+ */
+CUptiResult CUPTIAPI cuptiProfilerFlushCounterData(CUpti_Profiler_FlushCounterData_Params* pParams);
+
+typedef struct CUpti_Profiler_PushRange_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_PushRange_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    const char* pRangeName;                                 //!< [in] specifies the range for subsequent launches; must not be NULL
+    size_t rangeNameLength;                                 //!< [in] assign to strlen(pRangeName) if known; if set to zero, the library will call strlen()
+} CUpti_Profiler_PushRange_Params;
+#define CUpti_Profiler_PushRange_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_PushRange_Params, rangeNameLength)
+
+typedef struct CUpti_Profiler_PopRange_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_PopRange_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_PopRange_Params;
+#define CUpti_Profiler_PopRange_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_PopRange_Params, ctx)
+
+
+/**
+ * \brief Range API's : Push user range
+ *
+ * Counter data is collected per unique range-stack. Identified by a string label passsed by the user.
+ * It's an invalid operation in case of \ref CUPTI_AutoRange.
+ */
+CUptiResult CUPTIAPI cuptiProfilerPushRange(CUpti_Profiler_PushRange_Params *pParams);
+
+/**
+ * \brief Range API's : Pop user range
+ *
+ * Counter data is collected per unique range-stack. Identified by a string label passsed by the user.
+ * It's an invalid operation in case of \ref CUPTI_AutoRange.
+ */
+CUptiResult CUPTIAPI cuptiProfilerPopRange(CUpti_Profiler_PopRange_Params *pParams);
+
+/**
+ * \brief Params for cuptiProfilerGetCounterAvailability
+ */
+typedef struct CUpti_Profiler_GetCounterAvailability_Params
+{
+    size_t structSize;                                  //!< [in] CUpti_Profiler_GetCounterAvailability_Params_STRUCT_SIZE
+    void* pPriv;                                        //!< [in] assign to NULL
+    CUcontext ctx;                                      //!< [in] if NULL, the current CUcontext is used
+    size_t counterAvailabilityImageSize;                //!< [in/out] If `pCounterAvailabilityImage` is NULL, then the required size is returned in
+                                                        //!< `counterAvailabilityImageSize`, otherwise `counterAvailabilityImageSize` should be set to the size of
+                                                        //!< `pCounterAvailabilityImage`, and on return it would be overwritten with number of actual bytes copied
+    uint8_t* pCounterAvailabilityImage;                 //!< [in] buffer receiving counter availability image, may be NULL
+} CUpti_Profiler_GetCounterAvailability_Params;
+#define CUpti_Profiler_GetCounterAvailability_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_GetCounterAvailability_Params, pCounterAvailabilityImage)
+
+/**
+ * \brief Query counter availibility
+ *
+ * Use this API to query counter availability information in a buffer which can be used to filter unavailable raw metrics on host.
+ * Note: This API may fail, if any profiling or sampling session is active on the specified context or its device.
+ */
+CUptiResult CUPTIAPI cuptiProfilerGetCounterAvailability(CUpti_Profiler_GetCounterAvailability_Params *pParams);
+
+/// Generic support level enum for CUPTI
+typedef enum
+{
+    CUPTI_PROFILER_CONFIGURATION_UNKNOWN = 0, //!< Configuration support level unknown - either detection code errored out before setting this value, or unable to determine it
+    CUPTI_PROFILER_CONFIGURATION_UNSUPPORTED, //!< Profiling is unavailable.  For specific feature fields, this means that the current configuration of this feature does not work with profiling.  For instance, SLI-enabled devices do not support profiling, and this value would be returned for SLI on an SLI-enabled device.
+    CUPTI_PROFILER_CONFIGURATION_DISABLED,    //!< Profiling would be available for this configuration, but was disabled by the system
+    CUPTI_PROFILER_CONFIGURATION_SUPPORTED    //!< Profiling is supported.  For specific feature fields, this means that the current configuration of this feature works with profiling.  For instance, SLI-enabled devices do not support profiling, and this value would only be returned for devices which are not SLI-enabled.
+} CUpti_Profiler_Support_Level;
+
+/**
+ * \brief Profiler API types
+ */
+typedef enum
+{
+    CUPTI_PROFILER_RANGE_PROFILING = 0,       //!< CUPTI APIs for range based profiling (cuptiProfiler*)
+    CUPTI_PROFILER_PC_SAMPLING,               //!< CUPTI APIs collecting pc sampling data (cuptiPcSampling*)
+    CUPTI_PROFILER_SASS_METRICS,              //!< CUPTI APIs collecting SASS metrics data (cuptiSassMetrics*)
+    CUPTI_PROFILER_PM_SAMPLING,               //!< CUPTI APIs collecting PM Sampling data (cuptiPmSampling*)
+    CUPTI_PROFILER_UNKNOWN
+} CUpti_Profiler_API;
+
+/**
+ * \brief Params for cuptiProfilerDeviceSupported
+ */
+typedef struct
+{
+    size_t structSize;                                //!< [in] Must be CUpti_Profiler_DeviceSupported_Params_STRUCT_SIZE
+    void *pPriv;                                      //!< [in] assign to NULL
+    CUdevice cuDevice;                                //!< [in] if NULL, the current CUcontext is used
+
+    CUpti_Profiler_Support_Level isSupported;         //!< [out] overall SUPPORTED / UNSUPPORTED flag representing whether Profiling and PC Sampling APIs work on the given device and configuration. SUPPORTED if all following flags are SUPPORTED, UNSUPPORTED otherwise.
+
+    CUpti_Profiler_Support_Level architecture;        //!< [out] SUPPORTED if the device architecture level supports the Profiling API (Compute Capability >= 7.0), UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level sli;                 //!< [out] SUPPORTED if SLI is not enabled, UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level vGpu;                //!< [out] SUPPORTED if vGPU is supported and profiling is enabled, DISABLED if profiling is supported but not enabled, UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level confidentialCompute; //!< [out] SUPPORTED if confidential compute is not enabled, UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level cmp;                 //!< [out] SUPPORTED if not NVIDIA Crypto Mining Processors (CMP), UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level wsl;                 //!< [out] SUPPORTED if WSL supported, UNSUPPORTED otherwise
+    CUpti_Profiler_API     api;                       //!< [in] the CUPTI API type for which device support will be checked
+} CUpti_Profiler_DeviceSupported_Params;
+#define CUpti_Profiler_DeviceSupported_Params_STRUCT_SIZE CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_DeviceSupported_Params, api)
+
+/**
+ * \brief Query device compatibility with Profiling API
+ *
+ * Use this call to determine whether a compute device and configuration are compatible with the Profiling API.
+ * If the configuration does not support profiling, one of several flags will indicate why.
+ */
+CUptiResult CUPTIAPI cuptiProfilerDeviceSupported(CUpti_Profiler_DeviceSupported_Params *pParams);
+
+/** @} */ /* END CUPTI_METRIC_API */
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /*_CUPTI_PROFILER_TARGET_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_range_profiler.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_range_profiler.h
new file mode 100644
index 0000000000000000000000000000000000000000..ebcb25c0921bf473df943d63f476b877fdec2d66
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_range_profiler.h
@@ -0,0 +1,465 @@
+/*
+ * Copyright 2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_RANGE_PROFILER_H_)
+#define _CUPTI_RANGE_PROFILER_H_
+
+#include <cuda.h>
+#include <cupti_result.h>
+#include <cupti_profiler_target.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_RANGE_PROFILER_API CUPTI Range Profiling API
+ * Functions, types, and enums that implement the CUPTI Range Profiling API.
+ * @{
+ */
+#ifndef CUPTI_PROFILER_STRUCT_SIZE
+#define CUPTI_PROFILER_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+
+typedef struct CUpti_RangeProfiler_Object CUpti_RangeProfiler_Object;
+
+/**
+ * \brief Params for cuptiRangeProfilerSetConfig
+ */
+typedef struct CUpti_RangeProfiler_SetConfig_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+    /// [in] Size of the config image.
+    size_t configSize;
+    /// [in] Config image.
+    const uint8_t* pConfig;
+    /// [in] Size of the counter data image.
+    size_t counterDataImageSize;
+    /// [in] Counter data image.
+    uint8_t* pCounterDataImage;
+    /// [in] Profiling Range mode.
+    CUpti_ProfilerRange range;
+    /// [in] Replay mode.
+    CUpti_ProfilerReplayMode replayMode;
+    /// [in] Maximum number of ranges that can be profiled in a pass.
+    size_t maxRangesPerPass;
+    /// [in] number of nesting level to be profiled. For Auto range mode, this should be set to 1.
+    uint16_t numNestingLevels;
+    /// [in] minimum nesting level to be profiled.
+    uint16_t minNestingLevel;
+    /// [in] Pass index for the replay session.
+    size_t passIndex;
+    /// [in] Target nesting level for the replay session.
+    uint16_t targetNestingLevel;
+} CUpti_RangeProfiler_SetConfig_Params;
+
+#define CUpti_RangeProfiler_SetConfig_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_SetConfig_Params, targetNestingLevel)
+
+/**
+ * \brief Set the configuration for range profiler like maximum number of ranges per pass, number of nesting levels,
+ * range and replay mode and the config image which has scheduling info for metric collection.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_SetConfig_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ */
+CUptiResult CUPTIAPI cuptiRangeProfilerSetConfig(CUpti_RangeProfiler_SetConfig_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerEnable
+ */
+typedef struct CUpti_RangeProfiler_Enable_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Context to be used for profiling.
+    CUcontext ctx;
+    /// [out] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+} CUpti_RangeProfiler_Enable_Params;
+#define CUpti_RangeProfiler_Enable_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_Enable_Params, pRangeProfilerObject)
+
+/**
+ * \brief Create a range profiler object and enable range profiling on the CUDA context.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_Enable_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY if memory allocation fails while creating the PM sampling object
+ * \retval CUPTI_ERROR_INSUFFICIENT_PRIVILEGES if the user does not have sufficient privileges to perform the operation
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiRangeProfilerEnable(CUpti_RangeProfiler_Enable_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerDisable
+ */
+typedef struct CUpti_RangeProfiler_Disable_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+} CUpti_RangeProfiler_Disable_Params;
+#define CUpti_RangeProfiler_Disable_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_Disable_Params, pRangeProfilerObject)
+
+/**
+ * \brief Disable the range profiler on the CUDA context and destroy the range profiler object.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_Disable_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ */
+CUptiResult CUPTIAPI cuptiRangeProfilerDisable(CUpti_RangeProfiler_Disable_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerStart
+ */
+typedef struct CUpti_RangeProfiler_Start_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+} CUpti_RangeProfiler_Start_Params;
+#define CUpti_RangeProfiler_Start_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_Start_Params, pRangeProfilerObject)
+
+/**
+ * \brief Start the range profiler.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_Start_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler Start is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiRangeProfilerStart(CUpti_RangeProfiler_Start_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerStop
+ */
+typedef struct CUpti_RangeProfiler_Stop_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+    /// [out] pass index for the replay session.
+    size_t passIndex;
+    /// [out] target nesting level for the replay session.
+    size_t targetNestingLevel;
+    /// [out] 1 if all passes are submitted to GPU for collection, 0 otherwise.
+    uint8_t isAllPassSubmitted;
+} CUpti_RangeProfiler_Stop_Params;
+#define CUpti_RangeProfiler_Stop_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_Stop_Params, isAllPassSubmitted)
+
+/**
+ * \brief Stop the range profiler.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_Stop_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler Stop is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+ */
+CUptiResult CUPTIAPI cuptiRangeProfilerStop(CUpti_RangeProfiler_Stop_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerPushRange
+ */
+typedef struct CUpti_RangeProfiler_PushRange_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+    /// [in] Name of the range to be profiled (only valid for User range mode).
+    const char* pRangeName;
+} CUpti_RangeProfiler_PushRange_Params;
+#define CUpti_RangeProfiler_PushRange_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_PushRange_Params, pRangeName)
+
+/**
+ * \brief Add a new range to the Range Profiler with a given range name.
+ * For nested ranges, this API should be called again for the innermost range. For profiling the nested
+ * range, users need to set the values for minNestingLevel and numNestingLevels in the SetConfig API.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_PushRange_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler PushRange is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerPushRange(CUpti_RangeProfiler_PushRange_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerPopRange
+ */
+typedef struct CUpti_RangeProfiler_PopRange_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+} CUpti_RangeProfiler_PopRange_Params;
+#define CUpti_RangeProfiler_PopRange_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_PopRange_Params, pRangeProfilerObject)
+
+/**
+ * \brief pop the current range to the Range Profiler.
+ * The number of pop range API call should be same as number of push ranges in the same order.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_PopRange_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler PopRange is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerPopRange(CUpti_RangeProfiler_PopRange_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerDecodeData
+ */
+typedef struct CUpti_RangeProfiler_DecodeData_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Range Profiler Object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+    /// [out] Number of ranges dropped in the processed passes.
+    size_t numOfRangeDropped;
+} CUpti_RangeProfiler_DecodeData_Params;
+#define CUpti_RangeProfiler_DecodeData_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_DecodeData_Params, numOfRangeDropped)
+
+/**
+ * \brief Decode the profiling data stored in the hardware to the counter data image passed in the
+ * SetConfig API. This API should be called after cuptiRangeProfilerStop. The counter data image
+ * will be updated with the profiling data for the ranges profiled.
+ * 
+ * For the cases where the number of ranges counter data image can store is less than the number of ranges
+ * profiled (= maxRangesPerPass in SetConfig API), the counter data image will report dropped ranges.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_DecodeData_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler DecodeData is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerDecodeData(CUpti_RangeProfiler_DecodeData_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerGetCounterDataSize
+ */
+typedef struct CUpti_RangeProfiler_GetCounterDataSize_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Periodic sampler object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+    /// [in] Names of the metrics to be collected.
+    const char** pMetricNames;
+    /// [in] Number of metrics to be collected.
+    size_t numMetrics;
+    /// [in] Maximum number of ranges to be stored in the counter data image.
+    size_t maxNumOfRanges;
+    /// [in] Maximum number of RangeTree nodes; must be >= maxNumOfRanges
+    uint32_t maxNumRangeTreeNodes;
+    /// [out] Size of the counter data image.
+    size_t counterDataSize;
+} CUpti_RangeProfiler_GetCounterDataSize_Params;
+#define CUpti_RangeProfiler_GetCounterDataSize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_GetCounterDataSize_Params, counterDataSize)
+
+/**
+ * \brief Get the size of the counter data image required to store the profiling data for the ranges profiled.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_GetCounterDataSize_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler GetCounterDataSize is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerGetCounterDataSize(CUpti_RangeProfiler_GetCounterDataSize_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerCounterDataImageInitialize
+ */
+typedef struct CUpti_RangeProfiler_CounterDataImage_Initialize_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Periodic sampler object.
+    CUpti_RangeProfiler_Object* pRangeProfilerObject;
+    /// [in] Size of the counter data image.
+    size_t counterDataSize;
+    /// [in] Counter data image.
+    uint8_t* pCounterData;
+} CUpti_RangeProfiler_CounterDataImage_Initialize_Params;
+#define CUpti_RangeProfiler_CounterDataImage_Initialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_CounterDataImage_Initialize_Params, pCounterData)
+
+/**
+ * \brief Initialize the counter data image with the profiling data for the ranges profiled.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_CounterDataImage_Initialize_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_OPERATION if range profiler CounterDataImageInitialize is called without enabling range profiler
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerCounterDataImageInitialize(CUpti_RangeProfiler_CounterDataImage_Initialize_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerGetCounterDataInfo
+ */
+typedef struct CUpti_RangeProfiler_GetCounterDataInfo_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Counter data image.
+    const uint8_t* pCounterDataImage;
+    /// [in] Size of the counter data image.
+    size_t counterDataImageSize;
+    /// [out] Number of ranges in the counter data image.
+    size_t numTotalRanges;
+} CUpti_RangeProfiler_GetCounterDataInfo_Params;
+#define CUpti_RangeProfiler_GetCounterDataInfo_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_GetCounterDataInfo_Params, numTotalRanges)
+
+/**
+ * \brief Get the number of ranges stored in the counter data image.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_GetCounterDataInfo_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerGetCounterDataInfo(CUpti_RangeProfiler_GetCounterDataInfo_Params* pParams);
+
+/**
+ * \brief Params for cuptiRangeProfilerCounterDataGetRangeInfo
+ */
+typedef struct CUpti_RangeProfiler_CounterData_GetRangeInfo_Params
+{
+    /// [in] Size of the data structure.
+    size_t structSize;
+    /// [in] Set to NULL.
+    void* pPriv;
+    /// [in] Counter data image.
+    const uint8_t* pCounterDataImage;
+    /// [in] Size of the counter data image.
+    size_t counterDataImageSize;
+    /// [in] Index of the sample.
+    size_t rangeIndex;
+    /// [in] range delimiter.
+    const char* rangeDelimiter;
+    /// [out] RangeName;
+    const char* rangeName;
+} CUpti_RangeProfiler_CounterData_GetRangeInfo_Params;
+#define CUpti_RangeProfiler_CounterData_GetRangeInfo_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_RangeProfiler_CounterData_GetRangeInfo_Params, rangeName)
+
+/**
+ * \brief Get the range name for the given range index.
+ * 
+ * \param pParams A pointer to \ref CUpti_RangeProfiler_CounterData_GetRangeInfo_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_UNKNOWN for any internal error
+*/
+CUptiResult CUPTIAPI cuptiRangeProfilerCounterDataGetRangeInfo(CUpti_RangeProfiler_CounterData_GetRangeInfo_Params* pParams);
+
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /*_CUPTI_RANGE_PROFILER_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_result.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_result.h
new file mode 100644
index 0000000000000000000000000000000000000000..10371ac621b2472086a4d68af4dc9bdc91f8e417
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_result.h
@@ -0,0 +1,360 @@
+/*
+ * Copyright 2010-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_RESULT_H_)
+#define _CUPTI_RESULT_H_
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_RESULT_API CUPTI Result Codes
+ * Error and result codes returned by CUPTI functions.
+ * @{
+ */
+
+/**
+ * \brief CUPTI result codes.
+ *
+ * Error and result codes returned by CUPTI functions.
+ */
+typedef enum {
+    /**
+     * No error.
+     */
+    CUPTI_SUCCESS                                       = 0,
+    /**
+     * One or more of the parameters is invalid.
+     */
+    CUPTI_ERROR_INVALID_PARAMETER                       = 1,
+    /**
+     * The device does not correspond to a valid CUDA device.
+     */
+    CUPTI_ERROR_INVALID_DEVICE                          = 2,
+    /**
+     * The context is NULL or not valid.
+     */
+    CUPTI_ERROR_INVALID_CONTEXT                         = 3,
+    /**
+     * The event domain id is invalid.
+     */
+    CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID                 = 4,
+    /**
+     * The event id is invalid.
+     */
+    CUPTI_ERROR_INVALID_EVENT_ID                        = 5,
+    /**
+     * The event name is invalid.
+     */
+    CUPTI_ERROR_INVALID_EVENT_NAME                      = 6,
+    /**
+     * The current operation cannot be performed due to dependency on
+     * other factors.
+     */
+    CUPTI_ERROR_INVALID_OPERATION                       = 7,
+    /**
+     * Unable to allocate enough memory to perform the requested
+     * operation.
+     */
+    CUPTI_ERROR_OUT_OF_MEMORY                           = 8,
+    /**
+     * An error occurred on the performance monitoring hardware.
+     */
+    CUPTI_ERROR_HARDWARE                                = 9,
+    /**
+     * The output buffer size is not sufficient to return all
+     * requested data.
+     */
+    CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT           = 10,
+    /**
+     * API is not implemented.
+     */
+    CUPTI_ERROR_API_NOT_IMPLEMENTED                     = 11,
+    /**
+     * The maximum limit is reached.
+     */
+    CUPTI_ERROR_MAX_LIMIT_REACHED                       = 12,
+    /**
+     * The object is not yet ready to perform the requested operation.
+     */
+    CUPTI_ERROR_NOT_READY                               = 13,
+    /**
+     * The current operation is not compatible with the current state
+     * of the object
+     */
+    CUPTI_ERROR_NOT_COMPATIBLE                          = 14,
+    /**
+     * CUPTI is unable to initialize its connection to the CUDA
+     * driver.
+     */
+    CUPTI_ERROR_NOT_INITIALIZED                         = 15,
+    /**
+     * The metric id is invalid.
+     */
+    CUPTI_ERROR_INVALID_METRIC_ID                        = 16,
+    /**
+     * The metric name is invalid.
+     */
+    CUPTI_ERROR_INVALID_METRIC_NAME                      = 17,
+    /**
+     * The queue is empty.
+     */
+    CUPTI_ERROR_QUEUE_EMPTY                              = 18,
+    /**
+     * Invalid handle (internal?).
+     */
+    CUPTI_ERROR_INVALID_HANDLE                           = 19,
+    /**
+     * Invalid stream.
+     */
+    CUPTI_ERROR_INVALID_STREAM                           = 20,
+    /**
+     * Invalid kind.
+     */
+    CUPTI_ERROR_INVALID_KIND                             = 21,
+    /**
+     * Invalid event value.
+     */
+    CUPTI_ERROR_INVALID_EVENT_VALUE                      = 22,
+    /**
+     * CUPTI is disabled due to conflicts with other enabled profilers
+     */
+    CUPTI_ERROR_DISABLED                                 = 23,
+    /**
+     * Invalid module.
+     */
+    CUPTI_ERROR_INVALID_MODULE                           = 24,
+    /**
+     * Invalid metric value.
+     */
+    CUPTI_ERROR_INVALID_METRIC_VALUE                     = 25,
+    /**
+     * The performance monitoring hardware is in use by other client.
+     */
+    CUPTI_ERROR_HARDWARE_BUSY                            = 26,
+    /**
+     * The attempted operation is not supported on the current
+     * system or device.
+     */
+    CUPTI_ERROR_NOT_SUPPORTED                            = 27,
+    /**
+     * Unified memory profiling is not supported on the system.
+     * Potential reason could be unsupported OS or architecture.
+     */
+    CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED               = 28,
+    /**
+     * Unified memory profiling is not supported on the device
+     */
+    CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED_ON_DEVICE     = 29,
+    /**
+     * Unified memory profiling is not supported on a multi-GPU
+     * configuration without P2P support between any pair of devices
+     */
+    CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED_ON_NON_P2P_DEVICES = 30,
+    /**
+     * Unified memory profiling is not supported under the
+     * Multi-Process Service (MPS) environment. CUDA 7.5 removes this
+     * restriction.
+     */
+    CUPTI_ERROR_UM_PROFILING_NOT_SUPPORTED_WITH_MPS      = 31,
+    /**
+     * In CUDA 9.0, devices with compute capability 7.0 don't
+     * support CDP tracing
+     */
+    CUPTI_ERROR_CDP_TRACING_NOT_SUPPORTED                = 32,
+    /**
+     * Profiling on virtualized GPU is not supported.
+     */
+    CUPTI_ERROR_VIRTUALIZED_DEVICE_NOT_SUPPORTED         = 33,
+    /**
+     * Profiling results might be incorrect for CUDA applications
+     * compiled with nvcc version older than 9.0 for devices with
+     * compute capability 6.0 and 6.1.
+     * Profiling session will continue and CUPTI will notify it using this error code.
+     * User is advised to recompile the application code with nvcc version 9.0 or later.
+     * Ignore this warning if code is already compiled with the recommended nvcc version.
+     */
+    CUPTI_ERROR_CUDA_COMPILER_NOT_COMPATIBLE             = 34,
+    /**
+     * User doesn't have sufficient privileges which are required to
+     * start the profiling session.
+     * One possible reason for this may be that the NVIDIA driver or your system
+     * administrator may have restricted access to the NVIDIA GPU performance counters.
+     * To learn how to resolve this issue and find more information, please visit
+     * https://developer.nvidia.com/CUPTI_ERROR_INSUFFICIENT_PRIVILEGES
+     */
+    CUPTI_ERROR_INSUFFICIENT_PRIVILEGES                  = 35,
+    /**
+     * Legacy CUPTI Profiling API i.e. event API from the header cupti_events.h and
+     * metric API from the header cupti_metrics.h are not compatible with the
+     * Profiling API in the header cupti_profiler_target.h and Perfworks metrics API
+     * in the headers nvperf_host.h and nvperf_target.h.
+     */
+    CUPTI_ERROR_OLD_PROFILER_API_INITIALIZED             = 36,
+    /**
+     * Missing definition of the OpenACC API routine in the linked OpenACC library.
+     *
+     * One possible reason is that OpenACC library is linked statically in the
+     * user application, which might not have the definition of all the OpenACC
+     * API routines needed for the OpenACC profiling, as compiler might ignore
+     * definitions for the functions not used in the application. This issue
+     * can be mitigated by linking the OpenACC library dynamically.
+     */
+    CUPTI_ERROR_OPENACC_UNDEFINED_ROUTINE                = 37,
+    /**
+     * Legacy CUPTI Profiling API i.e. event API from the header cupti_events.h and
+     * metric API from the header cupti_metrics.h are not supported on devices with
+     * compute capability 7.5 and higher (i.e. Turing and later GPU architectures).
+     * These APIs are deprecated in the CUDA 12.8 release and will be removed in a future CUDA release.
+     * These are replaced by the host profiling API in the header cupti_profiler_host.h and
+     * target profiling API in the header cupti_range_profiler.h which are supported on
+     * devices with compute capability 7.0 and higher (i.e. Volta and later GPU
+     * architectures).
+     */
+    CUPTI_ERROR_LEGACY_PROFILER_NOT_SUPPORTED            = 38,
+    /**
+     * CUPTI doesn't allow multiple callback subscribers. Only a single subscriber
+     * can be registered at a time.
+     * Same error code is used when application is launched using NVIDIA tools
+     * like nvprof, Visual Profiler, Nsight Systems, Nsight Compute, cuda-gdb and
+     * cuda-memcheck.
+     */
+    CUPTI_ERROR_MULTIPLE_SUBSCRIBERS_NOT_SUPPORTED       = 39,
+    /**
+     * Profiling on virtualized GPU is not allowed by hypervisor.
+     */
+    CUPTI_ERROR_VIRTUALIZED_DEVICE_INSUFFICIENT_PRIVILEGES = 40,
+    /**
+     * Profiling and tracing are not allowed when confidential computing mode
+     * is enabled.
+     */
+    CUPTI_ERROR_CONFIDENTIAL_COMPUTING_NOT_SUPPORTED = 41,
+    /**
+     * CUPTI does not support NVIDIA Crypto Mining Processors (CMP).
+     * For more information, please visit https://developer.nvidia.com/ERR_NVCMPGPU
+    */
+    CUPTI_ERROR_CMP_DEVICE_NOT_SUPPORTED = 42,
+    /**
+     * Profiling on Multi-instance GPU (MIG) is not supported.
+     */
+    CUPTI_ERROR_MIG_DEVICE_NOT_SUPPORTED = 43,
+    /**
+     * Profiling on SLI device is not supported.
+     */
+    CUPTI_ERROR_SLI_DEVICE_NOT_SUPPORTED = 44,
+    /**
+     * Profiling on WSL device is not supported.
+     */
+    CUPTI_ERROR_WSL_DEVICE_NOT_SUPPORTED = 45,
+    /**
+     * An unknown internal error has occurred.
+     */
+    CUPTI_ERROR_UNKNOWN                                  = 999,
+    CUPTI_ERROR_FORCE_INT                                = 0x7fffffff
+} CUptiResult;
+
+/**
+ * \brief Get the descriptive string for a CUptiResult.
+ *
+ * Return the descriptive string for a CUptiResult in \p *str.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param result The result to get the string for
+ * \param str Returns the string
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p str is NULL or \p
+ * result is not a valid CUptiResult
+ */
+CUptiResult CUPTIAPI cuptiGetResultString(CUptiResult result, const char **str);
+
+/**
+ * @brief Get the descriptive message corresponding to error codes returned
+ * by CUPTI.
+ * 
+ * Return the descriptive error message for a CUptiResult in \p *str.
+ * \note \b Thread-safety: this function is thread safe.
+ * 
+ * \param result The result to get the descriptive error message for
+ * \param str Returns the error message string
+ * 
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p str is NULL or \p
+ * result is not a valid CUptiResult
+ * 
+ */
+
+CUptiResult CUPTIAPI cuptiGetErrorMessage(CUptiResult result, const char **str);
+
+/** @} */ /* END CUPTI_RESULT_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_RESULT_H_*/
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_runtime_cbid.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_runtime_cbid.h
new file mode 100644
index 0000000000000000000000000000000000000000..16b41e475fcfcf76e6507949699cd04c594becc9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_runtime_cbid.h
@@ -0,0 +1,504 @@
+
+// *************************************************************************
+//      Definitions of indices for API functions, unique across entire API
+// *************************************************************************
+
+// This file is generated.  Any changes you make will be lost during the next clean build.
+// CUDA public interface, for type definitions and cu* function prototypes
+
+#if !defined(_CUPTI_RUNTIME_CBID_H)
+#define _CUPTI_RUNTIME_CBID_H
+
+typedef enum CUpti_runtime_api_trace_cbid_enum {
+    CUPTI_RUNTIME_TRACE_CBID_INVALID                                                       = 0,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDriverGetVersion_v3020                                    = 1,
+    CUPTI_RUNTIME_TRACE_CBID_cudaRuntimeGetVersion_v3020                                   = 2,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDeviceCount_v3020                                      = 3,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDeviceProperties_v3020                                 = 4,
+    CUPTI_RUNTIME_TRACE_CBID_cudaChooseDevice_v3020                                        = 5,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetChannelDesc_v3020                                      = 6,
+    CUPTI_RUNTIME_TRACE_CBID_cudaCreateChannelDesc_v3020                                   = 7,
+    CUPTI_RUNTIME_TRACE_CBID_cudaConfigureCall_v3020                                       = 8,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSetupArgument_v3020                                       = 9,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetLastError_v3020                                        = 10,
+    CUPTI_RUNTIME_TRACE_CBID_cudaPeekAtLastError_v3020                                     = 11,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetErrorString_v3020                                      = 12,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunch_v3020                                              = 13,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFuncSetCacheConfig_v3020                                  = 14,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFuncGetAttributes_v3020                                   = 15,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSetDevice_v3020                                           = 16,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDevice_v3020                                           = 17,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSetValidDevices_v3020                                     = 18,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSetDeviceFlags_v3020                                      = 19,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMalloc_v3020                                              = 20,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocPitch_v3020                                         = 21,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFree_v3020                                                = 22,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocArray_v3020                                         = 23,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFreeArray_v3020                                           = 24,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocHost_v3020                                          = 25,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFreeHost_v3020                                            = 26,
+    CUPTI_RUNTIME_TRACE_CBID_cudaHostAlloc_v3020                                           = 27,
+    CUPTI_RUNTIME_TRACE_CBID_cudaHostGetDevicePointer_v3020                                = 28,
+    CUPTI_RUNTIME_TRACE_CBID_cudaHostGetFlags_v3020                                        = 29,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemGetInfo_v3020                                          = 30,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_v3020                                              = 31,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2D_v3020                                            = 32,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToArray_v3020                                       = 33,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DToArray_v3020                                     = 34,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromArray_v3020                                     = 35,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DFromArray_v3020                                   = 36,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyArrayToArray_v3020                                  = 37,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DArrayToArray_v3020                                = 38,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToSymbol_v3020                                      = 39,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromSymbol_v3020                                    = 40,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyAsync_v3020                                         = 41,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToArrayAsync_v3020                                  = 42,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromArrayAsync_v3020                                = 43,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DAsync_v3020                                       = 44,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DToArrayAsync_v3020                                = 45,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DFromArrayAsync_v3020                              = 46,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToSymbolAsync_v3020                                 = 47,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromSymbolAsync_v3020                               = 48,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset_v3020                                              = 49,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset2D_v3020                                            = 50,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemsetAsync_v3020                                         = 51,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset2DAsync_v3020                                       = 52,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetSymbolAddress_v3020                                    = 53,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetSymbolSize_v3020                                       = 54,
+    CUPTI_RUNTIME_TRACE_CBID_cudaBindTexture_v3020                                         = 55,
+    CUPTI_RUNTIME_TRACE_CBID_cudaBindTexture2D_v3020                                       = 56,
+    CUPTI_RUNTIME_TRACE_CBID_cudaBindTextureToArray_v3020                                  = 57,
+    CUPTI_RUNTIME_TRACE_CBID_cudaUnbindTexture_v3020                                       = 58,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetTextureAlignmentOffset_v3020                           = 59,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetTextureReference_v3020                                 = 60,
+    CUPTI_RUNTIME_TRACE_CBID_cudaBindSurfaceToArray_v3020                                  = 61,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetSurfaceReference_v3020                                 = 62,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLSetGLDevice_v3020                                       = 63,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLRegisterBufferObject_v3020                              = 64,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLMapBufferObject_v3020                                   = 65,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLUnmapBufferObject_v3020                                 = 66,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLUnregisterBufferObject_v3020                            = 67,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLSetBufferObjectMapFlags_v3020                           = 68,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLMapBufferObjectAsync_v3020                              = 69,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLUnmapBufferObjectAsync_v3020                            = 70,
+    CUPTI_RUNTIME_TRACE_CBID_cudaWGLGetDevice_v3020                                        = 71,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsGLRegisterImage_v3020                             = 72,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsGLRegisterBuffer_v3020                            = 73,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsUnregisterResource_v3020                          = 74,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsResourceSetMapFlags_v3020                         = 75,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsMapResources_v3020                                = 76,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsUnmapResources_v3020                              = 77,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsResourceGetMappedPointer_v3020                    = 78,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsSubResourceGetMappedArray_v3020                   = 79,
+    CUPTI_RUNTIME_TRACE_CBID_cudaVDPAUGetDevice_v3020                                      = 80,
+    CUPTI_RUNTIME_TRACE_CBID_cudaVDPAUSetVDPAUDevice_v3020                                 = 81,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsVDPAURegisterVideoSurface_v3020                   = 82,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsVDPAURegisterOutputSurface_v3020                  = 83,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D11GetDevice_v3020                                      = 84,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D11GetDevices_v3020                                     = 85,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D11SetDirect3DDevice_v3020                              = 86,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsD3D11RegisterResource_v3020                       = 87,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10GetDevice_v3020                                      = 88,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10GetDevices_v3020                                     = 89,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10SetDirect3DDevice_v3020                              = 90,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsD3D10RegisterResource_v3020                       = 91,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10RegisterResource_v3020                               = 92,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10UnregisterResource_v3020                             = 93,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10MapResources_v3020                                   = 94,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10UnmapResources_v3020                                 = 95,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10ResourceSetMapFlags_v3020                            = 96,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10ResourceGetSurfaceDimensions_v3020                   = 97,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10ResourceGetMappedArray_v3020                         = 98,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10ResourceGetMappedPointer_v3020                       = 99,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10ResourceGetMappedSize_v3020                          = 100,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10ResourceGetMappedPitch_v3020                         = 101,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9GetDevice_v3020                                       = 102,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9GetDevices_v3020                                      = 103,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9SetDirect3DDevice_v3020                               = 104,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9GetDirect3DDevice_v3020                               = 105,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsD3D9RegisterResource_v3020                        = 106,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9RegisterResource_v3020                                = 107,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9UnregisterResource_v3020                              = 108,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9MapResources_v3020                                    = 109,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9UnmapResources_v3020                                  = 110,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9ResourceSetMapFlags_v3020                             = 111,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9ResourceGetSurfaceDimensions_v3020                    = 112,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9ResourceGetMappedArray_v3020                          = 113,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9ResourceGetMappedPointer_v3020                        = 114,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9ResourceGetMappedSize_v3020                           = 115,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9ResourceGetMappedPitch_v3020                          = 116,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9Begin_v3020                                           = 117,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9End_v3020                                             = 118,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9RegisterVertexBuffer_v3020                            = 119,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9UnregisterVertexBuffer_v3020                          = 120,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9MapVertexBuffer_v3020                                 = 121,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D9UnmapVertexBuffer_v3020                               = 122,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadExit_v3020                                          = 123,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSetDoubleForDevice_v3020                                  = 124,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSetDoubleForHost_v3020                                    = 125,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadSynchronize_v3020                                   = 126,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadGetLimit_v3020                                      = 127,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadSetLimit_v3020                                      = 128,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamCreate_v3020                                        = 129,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamDestroy_v3020                                       = 130,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamSynchronize_v3020                                   = 131,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamQuery_v3020                                         = 132,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventCreate_v3020                                         = 133,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventCreateWithFlags_v3020                                = 134,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventRecord_v3020                                         = 135,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventDestroy_v3020                                        = 136,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventSynchronize_v3020                                    = 137,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventQuery_v3020                                          = 138,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventElapsedTime_v3020                                    = 139,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMalloc3D_v3020                                            = 140,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMalloc3DArray_v3020                                       = 141,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset3D_v3020                                            = 142,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset3DAsync_v3020                                       = 143,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3D_v3020                                            = 144,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DAsync_v3020                                       = 145,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadSetCacheConfig_v3020                                = 146,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamWaitEvent_v3020                                     = 147,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D11GetDirect3DDevice_v3020                              = 148,
+    CUPTI_RUNTIME_TRACE_CBID_cudaD3D10GetDirect3DDevice_v3020                              = 149,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadGetCacheConfig_v3020                                = 150,
+    CUPTI_RUNTIME_TRACE_CBID_cudaPointerGetAttributes_v4000                                = 151,
+    CUPTI_RUNTIME_TRACE_CBID_cudaHostRegister_v4000                                        = 152,
+    CUPTI_RUNTIME_TRACE_CBID_cudaHostUnregister_v4000                                      = 153,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceCanAccessPeer_v4000                                 = 154,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceEnablePeerAccess_v4000                              = 155,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceDisablePeerAccess_v4000                             = 156,
+    CUPTI_RUNTIME_TRACE_CBID_cudaPeerRegister_v4000                                        = 157,
+    CUPTI_RUNTIME_TRACE_CBID_cudaPeerUnregister_v4000                                      = 158,
+    CUPTI_RUNTIME_TRACE_CBID_cudaPeerGetDevicePointer_v4000                                = 159,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyPeer_v4000                                          = 160,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyPeerAsync_v4000                                     = 161,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DPeer_v4000                                        = 162,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DPeerAsync_v4000                                   = 163,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceReset_v3020                                         = 164,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSynchronize_v3020                                   = 165,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetLimit_v3020                                      = 166,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSetLimit_v3020                                      = 167,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetCacheConfig_v3020                                = 168,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSetCacheConfig_v3020                                = 169,
+    CUPTI_RUNTIME_TRACE_CBID_cudaProfilerInitialize_v4000                                  = 170,
+    CUPTI_RUNTIME_TRACE_CBID_cudaProfilerStart_v4000                                       = 171,
+    CUPTI_RUNTIME_TRACE_CBID_cudaProfilerStop_v4000                                        = 172,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetByPCIBusId_v4010                                 = 173,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetPCIBusId_v4010                                   = 174,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGLGetDevices_v4010                                        = 175,
+    CUPTI_RUNTIME_TRACE_CBID_cudaIpcGetEventHandle_v4010                                   = 176,
+    CUPTI_RUNTIME_TRACE_CBID_cudaIpcOpenEventHandle_v4010                                  = 177,
+    CUPTI_RUNTIME_TRACE_CBID_cudaIpcGetMemHandle_v4010                                     = 178,
+    CUPTI_RUNTIME_TRACE_CBID_cudaIpcOpenMemHandle_v4010                                    = 179,
+    CUPTI_RUNTIME_TRACE_CBID_cudaIpcCloseMemHandle_v4010                                   = 180,
+    CUPTI_RUNTIME_TRACE_CBID_cudaArrayGetInfo_v4010                                        = 181,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFuncSetSharedMemConfig_v4020                              = 182,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetSharedMemConfig_v4020                            = 183,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSetSharedMemConfig_v4020                            = 184,
+    CUPTI_RUNTIME_TRACE_CBID_cudaCreateTextureObject_v5000                                 = 185,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDestroyTextureObject_v5000                                = 186,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetTextureObjectResourceDesc_v5000                        = 187,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetTextureObjectTextureDesc_v5000                         = 188,
+    CUPTI_RUNTIME_TRACE_CBID_cudaCreateSurfaceObject_v5000                                 = 189,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDestroySurfaceObject_v5000                                = 190,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetSurfaceObjectResourceDesc_v5000                        = 191,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocMipmappedArray_v5000                                = 192,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetMipmappedArrayLevel_v5000                              = 193,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFreeMipmappedArray_v5000                                  = 194,
+    CUPTI_RUNTIME_TRACE_CBID_cudaBindTextureToMipmappedArray_v5000                         = 195,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsResourceGetMappedMipmappedArray_v5000             = 196,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamAddCallback_v5000                                   = 197,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamCreateWithFlags_v5000                               = 198,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetTextureObjectResourceViewDesc_v5000                    = 199,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetAttribute_v5000                                  = 200,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamDestroy_v5050                                       = 201,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamCreateWithPriority_v5050                            = 202,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetPriority_v5050                                   = 203,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetFlags_v5050                                      = 204,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetStreamPriorityRange_v5050                        = 205,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocManaged_v6000                                       = 206,
+    CUPTI_RUNTIME_TRACE_CBID_cudaOccupancyMaxActiveBlocksPerMultiprocessor_v6000           = 207,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamAttachMemAsync_v6000                                = 208,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetErrorName_v6050                                        = 209,
+    CUPTI_RUNTIME_TRACE_CBID_cudaOccupancyMaxActiveBlocksPerMultiprocessor_v6050           = 210,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000                                        = 211,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDeviceFlags_v7000                                      = 212,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunch_ptsz_v7000                                         = 213,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_ptsz_v7000                                   = 214,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_ptds_v7000                                         = 215,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2D_ptds_v7000                                       = 216,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToArray_ptds_v7000                                  = 217,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DToArray_ptds_v7000                                = 218,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromArray_ptds_v7000                                = 219,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DFromArray_ptds_v7000                              = 220,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyArrayToArray_ptds_v7000                             = 221,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DArrayToArray_ptds_v7000                           = 222,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToSymbol_ptds_v7000                                 = 223,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromSymbol_ptds_v7000                               = 224,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyAsync_ptsz_v7000                                    = 225,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToArrayAsync_ptsz_v7000                             = 226,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromArrayAsync_ptsz_v7000                           = 227,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DAsync_ptsz_v7000                                  = 228,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DToArrayAsync_ptsz_v7000                           = 229,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy2DFromArrayAsync_ptsz_v7000                         = 230,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyToSymbolAsync_ptsz_v7000                            = 231,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyFromSymbolAsync_ptsz_v7000                          = 232,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset_ptds_v7000                                         = 233,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset2D_ptds_v7000                                       = 234,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemsetAsync_ptsz_v7000                                    = 235,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset2DAsync_ptsz_v7000                                  = 236,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetPriority_ptsz_v7000                              = 237,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetFlags_ptsz_v7000                                 = 238,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamSynchronize_ptsz_v7000                              = 239,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamQuery_ptsz_v7000                                    = 240,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamAttachMemAsync_ptsz_v7000                           = 241,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventRecord_ptsz_v7000                                    = 242,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset3D_ptds_v7000                                       = 243,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemset3DAsync_ptsz_v7000                                  = 244,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3D_ptds_v7000                                       = 245,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DAsync_ptsz_v7000                                  = 246,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamWaitEvent_ptsz_v7000                                = 247,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamAddCallback_ptsz_v7000                              = 248,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DPeer_ptds_v7000                                   = 249,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DPeerAsync_ptsz_v7000                              = 250,
+    CUPTI_RUNTIME_TRACE_CBID_cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_v7000  = 251,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPrefetchAsync_v8000                                    = 252,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPrefetchAsync_ptsz_v8000                               = 253,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemAdvise_v8000                                           = 254,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetP2PAttribute_v8000                               = 255,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsEGLRegisterImage_v7000                            = 256,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamConsumerConnect_v7000                            = 257,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamConsumerDisconnect_v7000                         = 258,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamConsumerAcquireFrame_v7000                       = 259,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamConsumerReleaseFrame_v7000                       = 260,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamProducerConnect_v7000                            = 261,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamProducerDisconnect_v7000                         = 262,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamProducerPresentFrame_v7000                       = 263,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamProducerReturnFrame_v7000                        = 264,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphicsResourceGetMappedEglFrame_v7000                   = 265,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemRangeGetAttribute_v8000                                = 266,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemRangeGetAttributes_v8000                               = 267,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEGLStreamConsumerConnectWithFlags_v7000                   = 268,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchCooperativeKernel_v9000                             = 269,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchCooperativeKernel_ptsz_v9000                        = 270,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventCreateFromEGLSync_v9000                              = 271,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchCooperativeKernelMultiDevice_v9000                  = 272,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFuncSetAttribute_v9000                                    = 273,
+    CUPTI_RUNTIME_TRACE_CBID_cudaImportExternalMemory_v10000                               = 274,
+    CUPTI_RUNTIME_TRACE_CBID_cudaExternalMemoryGetMappedBuffer_v10000                      = 275,
+    CUPTI_RUNTIME_TRACE_CBID_cudaExternalMemoryGetMappedMipmappedArray_v10000              = 276,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDestroyExternalMemory_v10000                              = 277,
+    CUPTI_RUNTIME_TRACE_CBID_cudaImportExternalSemaphore_v10000                            = 278,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSignalExternalSemaphoresAsync_v10000                      = 279,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSignalExternalSemaphoresAsync_ptsz_v10000                 = 280,
+    CUPTI_RUNTIME_TRACE_CBID_cudaWaitExternalSemaphoresAsync_v10000                        = 281,
+    CUPTI_RUNTIME_TRACE_CBID_cudaWaitExternalSemaphoresAsync_ptsz_v10000                   = 282,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDestroyExternalSemaphore_v10000                           = 283,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchHostFunc_v10000                                     = 284,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchHostFunc_ptsz_v10000                                = 285,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphCreate_v10000                                        = 286,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphKernelNodeGetParams_v10000                           = 287,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphKernelNodeSetParams_v10000                           = 288,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddKernelNode_v10000                                 = 289,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemcpyNode_v10000                                 = 290,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemcpyNodeGetParams_v10000                           = 291,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemcpyNodeSetParams_v10000                           = 292,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemsetNode_v10000                                 = 293,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemsetNodeGetParams_v10000                           = 294,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemsetNodeSetParams_v10000                           = 295,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddHostNode_v10000                                   = 296,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphHostNodeGetParams_v10000                             = 297,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddChildGraphNode_v10000                             = 298,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphChildGraphNodeGetGraph_v10000                        = 299,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddEmptyNode_v10000                                  = 300,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphClone_v10000                                         = 301,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeFindInClone_v10000                               = 302,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeGetType_v10000                                   = 303,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphGetRootNodes_v10000                                  = 304,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeGetDependencies_v10000                           = 305,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeGetDependentNodes_v10000                         = 306,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddDependencies_v10000                               = 307,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphRemoveDependencies_v10000                            = 308,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphDestroyNode_v10000                                   = 309,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphInstantiate_v10000                                   = 310,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphLaunch_v10000                                        = 311,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphLaunch_ptsz_v10000                                   = 312,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecDestroy_v10000                                   = 313,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphDestroy_v10000                                       = 314,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamBeginCapture_v10000                                 = 315,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamBeginCapture_ptsz_v10000                            = 316,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamIsCapturing_v10000                                  = 317,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamIsCapturing_ptsz_v10000                             = 318,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamEndCapture_v10000                                   = 319,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamEndCapture_ptsz_v10000                              = 320,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphHostNodeSetParams_v10000                             = 321,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphGetNodes_v10000                                      = 322,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphGetEdges_v10000                                      = 323,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetCaptureInfo_v10010                               = 324,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetCaptureInfo_ptsz_v10010                          = 325,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecKernelNodeSetParams_v10010                       = 326,
+    CUPTI_RUNTIME_TRACE_CBID_cudaThreadExchangeStreamCaptureMode_v10010                    = 327,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetNvSciSyncAttributes_v10020                       = 328,
+    CUPTI_RUNTIME_TRACE_CBID_cudaOccupancyAvailableDynamicSMemPerBlock_v10200              = 329,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamSetFlags_v10200                                     = 330,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamSetFlags_ptsz_v10200                                = 331,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecMemcpyNodeSetParams_v10020                       = 332,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecMemsetNodeSetParams_v10020                       = 333,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecHostNodeSetParams_v10020                         = 334,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecUpdate_v10020                                    = 335,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetFuncBySymbol_v11000                                    = 336,
+    CUPTI_RUNTIME_TRACE_CBID_cudaCtxResetPersistingL2Cache_v11000                          = 337,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphKernelNodeCopyAttributes_v11000                      = 338,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphKernelNodeGetAttribute_v11000                        = 339,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphKernelNodeSetAttribute_v11000                        = 340,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamCopyAttributes_v11000                               = 341,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamCopyAttributes_ptsz_v11000                          = 342,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetAttribute_v11000                                 = 343,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetAttribute_ptsz_v11000                            = 344,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamSetAttribute_v11000                                 = 345,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamSetAttribute_ptsz_v11000                            = 346,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetTexture1DLinearMaxWidth_v11010                   = 347,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphUpload_v10000                                        = 348,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphUpload_ptsz_v10000                                   = 349,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemcpyNodeToSymbol_v11010                         = 350,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemcpyNodeFromSymbol_v11010                       = 351,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemcpyNode1D_v11010                               = 352,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemcpyNodeSetParamsToSymbol_v11010                   = 353,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemcpyNodeSetParamsFromSymbol_v11010                 = 354,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemcpyNodeSetParams1D_v11010                         = 355,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecMemcpyNodeSetParamsToSymbol_v11010               = 356,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecMemcpyNodeSetParamsFromSymbol_v11010             = 357,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecMemcpyNodeSetParams1D_v11010                     = 358,
+    CUPTI_RUNTIME_TRACE_CBID_cudaArrayGetSparseProperties_v11010                           = 359,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMipmappedArrayGetSparseProperties_v11010                  = 360,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecChildGraphNodeSetParams_v11010                   = 361,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddEventRecordNode_v11010                            = 362,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphEventRecordNodeGetEvent_v11010                       = 363,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphEventRecordNodeSetEvent_v11010                       = 364,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddEventWaitNode_v11010                              = 365,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphEventWaitNodeGetEvent_v11010                         = 366,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphEventWaitNodeSetEvent_v11010                         = 367,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecEventRecordNodeSetEvent_v11010                   = 368,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecEventWaitNodeSetEvent_v11010                     = 369,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventRecordWithFlags_v11010                               = 370,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventRecordWithFlags_ptsz_v11010                          = 371,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetDefaultMemPool_v11020                            = 372,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocAsync_v11020                                        = 373,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocAsync_ptsz_v11020                                   = 374,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFreeAsync_v11020                                          = 375,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFreeAsync_ptsz_v11020                                     = 376,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolTrimTo_v11020                                      = 377,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolSetAttribute_v11020                                = 378,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolGetAttribute_v11020                                = 379,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolSetAccess_v11020                                   = 380,
+    CUPTI_RUNTIME_TRACE_CBID_cudaArrayGetPlane_v11020                                      = 381,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolGetAccess_v11020                                   = 382,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolCreate_v11020                                      = 383,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolDestroy_v11020                                     = 384,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSetMemPool_v11020                                   = 385,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetMemPool_v11020                                   = 386,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolExportToShareableHandle_v11020                     = 387,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolImportFromShareableHandle_v11020                   = 388,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolExportPointer_v11020                               = 389,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPoolImportPointer_v11020                               = 390,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocFromPoolAsync_v11020                                = 391,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMallocFromPoolAsync_ptsz_v11020                           = 392,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSignalExternalSemaphoresAsync_v2_v11020                   = 393,
+    CUPTI_RUNTIME_TRACE_CBID_cudaSignalExternalSemaphoresAsync_v2_ptsz_v11020              = 394,
+    CUPTI_RUNTIME_TRACE_CBID_cudaWaitExternalSemaphoresAsync_v2_v11020                     = 395,
+    CUPTI_RUNTIME_TRACE_CBID_cudaWaitExternalSemaphoresAsync_v2_ptsz_v11020                = 396,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddExternalSemaphoresSignalNode_v11020               = 397,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExternalSemaphoresSignalNodeGetParams_v11020         = 398,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExternalSemaphoresSignalNodeSetParams_v11020         = 399,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddExternalSemaphoresWaitNode_v11020                 = 400,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExternalSemaphoresWaitNodeGetParams_v11020           = 401,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExternalSemaphoresWaitNodeSetParams_v11020           = 402,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecExternalSemaphoresSignalNodeSetParams_v11020     = 403,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecExternalSemaphoresWaitNodeSetParams_v11020       = 404,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceFlushGPUDirectRDMAWrites_v11030                     = 405,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDriverEntryPoint_v11030                                = 406,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDriverEntryPoint_ptsz_v11030                           = 407,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphDebugDotPrint_v11030                                 = 408,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetCaptureInfo_v2_v11030                            = 409,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetCaptureInfo_v2_ptsz_v11030                       = 410,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamUpdateCaptureDependencies_v11030                    = 411,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamUpdateCaptureDependencies_ptsz_v11030               = 412,
+    CUPTI_RUNTIME_TRACE_CBID_cudaUserObjectCreate_v11030                                   = 413,
+    CUPTI_RUNTIME_TRACE_CBID_cudaUserObjectRetain_v11030                                   = 414,
+    CUPTI_RUNTIME_TRACE_CBID_cudaUserObjectRelease_v11030                                  = 415,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphRetainUserObject_v11030                              = 416,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphReleaseUserObject_v11030                             = 417,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphInstantiateWithFlags_v11040                          = 418,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemAllocNode_v11040                               = 419,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemAllocNodeGetParams_v11040                         = 420,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddMemFreeNode_v11040                                = 421,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphMemFreeNodeGetParams_v11040                          = 422,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGraphMemTrim_v11040                                 = 423,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceGetGraphMemAttribute_v11040                         = 424,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSetGraphMemAttribute_v11040                         = 425,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeSetEnabled_v11060                                = 426,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeGetEnabled_v11060                                = 427,
+    CUPTI_RUNTIME_TRACE_CBID_cudaArrayGetMemoryRequirements_v11060                         = 428,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMipmappedArrayGetMemoryRequirements_v11060                = 429,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernelExC_v11060                                    = 430,
+    CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernelExC_ptsz_v11060                               = 431,
+    CUPTI_RUNTIME_TRACE_CBID_cudaOccupancyMaxPotentialClusterSize_v11070                   = 432,
+    CUPTI_RUNTIME_TRACE_CBID_cudaOccupancyMaxActiveClusters_v11070                         = 433,
+    CUPTI_RUNTIME_TRACE_CBID_cudaCreateTextureObject_v2_v11080                             = 434,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetTextureObjectTextureDesc_v2_v11080                     = 435,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphInstantiateWithParams_v12000                         = 436,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphInstantiateWithParams_ptsz_v12000                    = 437,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecGetFlags_v12000                                  = 438,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetKernel_v12000                                          = 439,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDeviceProperties_v2_v12000                             = 440,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetId_v12000                                        = 441,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetId_ptsz_v12000                                   = 442,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphInstantiate_v12000                                   = 443,
+    CUPTI_RUNTIME_TRACE_CBID_cudaInitDevice_v12000                                         = 444,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddNode_v12020                                       = 445,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeSetParams_v12020                                 = 446,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphExecNodeSetParams_v12020                             = 447,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemAdvise_v2_v12020                                       = 448,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPrefetchAsync_v2_v12020                                = 449,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemPrefetchAsync_v2_ptsz_v12020                           = 450,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFuncGetName_v12030                                        = 451,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamBeginCaptureToGraph_v12030                          = 452,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamBeginCaptureToGraph_ptsz_v12030                     = 453,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphConditionalHandleCreate_v12030                       = 454,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphGetEdges_v2_v12030                                   = 455,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeGetDependencies_v2_v12030                        = 456,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphNodeGetDependentNodes_v2_v12030                      = 457,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddDependencies_v2_v12030                            = 458,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphRemoveDependencies_v2_v12030                         = 459,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGraphAddNode_v2_v12030                                    = 460,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetCaptureInfo_v3_v12030                            = 461,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetCaptureInfo_v3_ptsz_v12030                       = 462,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamUpdateCaptureDependencies_v2_v12030                 = 463,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamUpdateCaptureDependencies_v2_ptsz_v12030            = 464,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceRegisterAsyncNotification_v12040                    = 465,
+    CUPTI_RUNTIME_TRACE_CBID_cudaDeviceUnregisterAsyncNotification_v12040                  = 466,
+    CUPTI_RUNTIME_TRACE_CBID_cudaFuncGetParamInfo_v12040                                   = 467,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDriverEntryPointByVersion_v12050                       = 468,
+    CUPTI_RUNTIME_TRACE_CBID_cudaGetDriverEntryPointByVersion_ptsz_v12050                  = 469,
+    CUPTI_RUNTIME_TRACE_CBID_cuda470_v12060                                                = 470,
+    CUPTI_RUNTIME_TRACE_CBID_cuda471_v12060                                                = 471,
+    CUPTI_RUNTIME_TRACE_CBID_cuda472_v12060                                                = 472,
+    CUPTI_RUNTIME_TRACE_CBID_cuda473_v12060                                                = 473,
+    CUPTI_RUNTIME_TRACE_CBID_cuda474_v12060                                                = 474,
+    CUPTI_RUNTIME_TRACE_CBID_cuda475_v12060                                                = 475,
+    CUPTI_RUNTIME_TRACE_CBID_cuda476_v12060                                                = 476,
+    CUPTI_RUNTIME_TRACE_CBID_cuda477_v12060                                                = 477,
+    CUPTI_RUNTIME_TRACE_CBID_cuda478_v12060                                                = 478,
+    CUPTI_RUNTIME_TRACE_CBID_cuda479_v12060                                                = 479,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetDevice_v12080                                    = 480,
+    CUPTI_RUNTIME_TRACE_CBID_cudaStreamGetDevice_ptsz_v12080                               = 481,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyBatchAsync_v12080                                   = 482,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyBatchAsync_ptsz_v12080                              = 483,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DBatchAsync_v12080                                 = 484,
+    CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy3DBatchAsync_ptsz_v12080                            = 485,
+    CUPTI_RUNTIME_TRACE_CBID_cudaEventElapsedTime_v2_v12080                                = 486,
+    CUPTI_RUNTIME_TRACE_CBID_SIZE                                                          = 487,
+    CUPTI_RUNTIME_TRACE_CBID_FORCE_INT                                                     = 0x7fffffff
+} CUpti_runtime_api_trace_cbid;
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_sass_metrics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_sass_metrics.h
new file mode 100644
index 0000000000000000000000000000000000000000..acb59cf8e5882a5ff13b4a1b0fdc6bc7b0ec47f7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_sass_metrics.h
@@ -0,0 +1,436 @@
+/*
+ * Copyright 2023 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_SASS_METRICS_H_)
+#define _CUPTI_SASS_METRICS_H_
+
+#include <cuda.h>
+#include <cupti_result.h>
+#include <cupti_profiler_target.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_SASS_METRICS_API CUPTI SASS Metrics API
+ * Functions, types, and enums that implement the CUPTI SASS Metrics API.
+ * @{
+ */
+
+typedef enum
+{
+    /// SASS metric data will be collected at GPU level. 
+    /// In CUpti_SassMetricsGetDataProperties_Params struct the numOfInstances will be equal to 1
+    CUPTI_SASS_METRICS_OUTPUT_GRANULARITY_GPU = 0,
+
+    /// SASS metric data will be collected at SM level
+    /// In CUpti_SassMetricsGetDataProperties_Params struct the numOfInstances will be equal to number of SMs in the GPU
+    CUPTI_SASS_METRICS_OUTPUT_GRANULARITY_SM = 1,
+
+    /// SASS metric data will be collected at SM sub-partition level
+    /// In CUpti_SassMetricsGetDataProperties_Params struct the numOfInstances will be equal to number of SM sub-partitions in the GPU
+    CUPTI_SASS_METRICS_OUTPUT_GRANULARITY_SMSP = 2,
+
+    CUPTI_SASS_METRICS_OUTPUT_GRANULARITY_INVALID
+} CUpti_SassMetrics_OutputGranularity;
+
+typedef struct CUpti_SassMetrics_MetricDetails
+{
+    /// unique ID for the SASS metric
+    uint64_t metricId;
+    /// metric name
+    const char* pMetricName;
+    /// metric description
+    const char* pMetricDescription;
+} CUpti_SassMetrics_MetricDetails;
+
+/**
+ * \brief Params for cuptiSassMetricsGetNumOfMetrics
+ */
+typedef struct CUpti_SassMetrics_GetNumOfMetrics_Params
+{
+    /// [in] should be equal to CUpti_SassMetrics_GetNumOfMetrics_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] chip name for which metrics will be queried
+    const char* pChipName;
+    /// [out] number of metrics supported for the queried chip
+    size_t numOfMetrics;
+} CUpti_SassMetrics_GetNumOfMetrics_Params;
+
+#define CUpti_SassMetrics_GetNumOfMetrics_Params_STRUCT_SIZE                 CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetrics_GetNumOfMetrics_Params, numOfMetrics)
+
+/**
+ * \brief Get the number of supported SASS metrics for the chip.
+ * 
+ * \param pParams A pointer to \ref CUpti_SassMetrics_GetNumOfMetrics_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric collection
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsGetNumOfMetrics(CUpti_SassMetrics_GetNumOfMetrics_Params* pParams);
+
+/**
+ * \brief Params for cuptiSassMetricsGetMetrics
+ */
+typedef struct CUpti_SassMetrics_GetMetrics_Params
+{
+    /// [in] should be equal to CUpti_SassMetrics_GetMetrics_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] chip name for which metrics will be queried
+    const char* pChipName;
+    /// [in] number of metrics supported for the queried chip (can be queried using cuptiSassMetricsGetNumOfMetrics())
+    size_t numOfMetrics;
+    /// [out] list of metrics supported for queried chip
+    CUpti_SassMetrics_MetricDetails* pMetricsList;
+} CUpti_SassMetrics_GetMetrics_Params;
+#define CUpti_SassMetrics_GetMetrics_Params_STRUCT_SIZE                 CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetrics_GetMetrics_Params, pMetricsList)
+
+/**
+ * \brief Get the list of all supported SASS metrics for the chip.
+ * 
+ * \param pParams A pointer to \ref CUpti_SassMetrics_GetMetrics_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric collection
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsGetMetrics(CUpti_SassMetrics_GetMetrics_Params* pParams);
+
+/**
+ * \brief Params for cuptiSassMetricsGetProperties
+ */
+typedef struct CUpti_SassMetrics_GetProperties_Params
+{
+    /// [in] should be equal to CUpti_SassMetrics_GetProperties_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] chip name for which metric will be queried
+    const char* pChipName;
+    /// [in] metric name
+    const char* pMetricName;
+    /// [out] returns the metric ID and the metric description
+    CUpti_SassMetrics_MetricDetails metric;
+} CUpti_SassMetrics_GetProperties_Params;
+#define CUpti_SassMetrics_GetProperties_Params_STRUCT_SIZE        CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetrics_GetProperties_Params, metric)
+
+/**
+ * \brief Get metric properties for the queried metric.
+ * For a given metric the results will be put in CUpti_SassMetrics_MetricDetails which
+ * stores metric ID, description of the metric.
+ * 
+ * \param pParams A pointer to \ref CUpti_SassMetrics_GetProperties_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsGetProperties(CUpti_SassMetrics_GetProperties_Params *pParams);
+
+typedef struct CUpti_SassMetrics_Config
+{
+    /// [in] unique id for the SASS metric, can be queried using cuptiSassMetricsGetProperties()
+    uint64_t metricId;
+    /// [in] CUpti_SassMetrics_OutputGranularity
+    uint8_t outputGranularity;
+} CUpti_SassMetrics_Config;
+
+/**
+ * \brief Params for cuptiSassMetricsSetConfig
+ */
+typedef struct CUpti_SassMetricsSetConfig_Params
+{
+    /// [in] equal to CUpti_SassMetricsSetConfig_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] num of metric configs, will be equal to number of metrics queried
+    size_t numOfMetricConfig;
+    /// [in] list of metric config generated for given sass metrics
+    CUpti_SassMetrics_Config* pConfigs;
+    /// [in] device index for which config will be set, user can call this once for
+    /// the device on which the the SASS metric data will be collected
+    uint32_t deviceIndex;
+} CUpti_SassMetricsSetConfig_Params;
+#define CUpti_SassMetricsSetConfig_Params_STRUCT_SIZE                    CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetricsSetConfig_Params, deviceIndex)
+
+/**
+ * \brief Set config for the SASS metric data collection for a device.
+ * User need to call this API before calling any of the SASS metric data collection APIs.
+ * Each set config API call need to be followed by cuptiSassPatchingUnSetConfig API
+ * before calling the cuptiSassMetricsSetConfig() API again for the same device.
+ * 
+ * \param pParams A pointer to \ref CUpti_SassMetricsSetConfig_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_CONTEXT if any cuda context has not been created prior to this API call
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this is called multiple times for the device without calling unset config API
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsSetConfig(CUpti_SassMetricsSetConfig_Params *pParams);
+
+/**
+ * \brief Params for cuptiSassMetricsUnsetConfig
+ */
+typedef struct CUpti_SassMetricsUnsetConfig_Params
+{
+    /// [in] equal to CUpti_SassMetricsUnsetConfig_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] device index for which SASS metric data collection config will get reset, user need to call this API for
+    /// all the devices on which the the SASS metric data collection have been configured.
+    uint32_t deviceIndex;
+} CUpti_SassMetricsUnsetConfig_Params;
+#define CUpti_SassMetricsUnsetConfig_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetricsUnsetConfig_Params, deviceIndex)
+
+/**
+ * \brief Unset config API will reset the SASS metric data collection configuration for the device.
+ * Once this API called CUPTI will deallocate all the memory allocated and remove all
+ * the configuration for SASS metric data collection. User can only call this API for a device where
+ * cuptiSassMetricsSetConfig() API has been called earlier for the device.
+ * 
+ * \param pParams A pointer to \ref CUpti_SassMetricsSetConfig_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_INVALID_CONTEXT if any cuda context has not been created prior to this API call
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this is called multiple times for the device without calling set config API
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsUnsetConfig(CUpti_SassMetricsUnsetConfig_Params *pParams);
+
+/**
+ * \brief Params for cuptiSassMetricsEnable
+ */
+typedef struct CUpti_SassMetricsEnable_Params
+{
+    /// [in] equal to CUpti_SassMetricsEnable_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] CUDA context on which SASS metric data collection will be enabled.
+    /// If set NULL, default context will be consider for SASS metric data collection.
+    CUcontext ctx;
+    /// [in] if false, all the functions will patched regardless of their execution with cuptiSassMetricsEnable() API call.
+    /// when this parameter is set to true, metric data collection for the function will be done at the very first execution in the enable/disble
+    /// range.
+    uint8_t enableLazyPatching;
+} CUpti_SassMetricsEnable_Params;
+#define CUpti_SassMetricsEnable_Params_STRUCT_SIZE                       CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetricsEnable_Params, enableLazyPatching)
+
+/**
+ * \brief Sass metric data collection enable API will mark the start of a range, between which kernel
+ *  will be profiled for SASS metrics.
+ *
+ * \param pParams A pointer to \ref CUpti_SassMetricsEnable_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection
+ * \retval CUPTI_ERROR_INVALID_CONTEXT if any cuda context has not been created prior to this API call
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called multiple times for a cuda context without calling 
+ * cuptiSassMetricsDisable() API or called before cuptiSassMetricsSetConfig() API call.
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsEnable(CUpti_SassMetricsEnable_Params* pParams);
+
+/**
+ * \brief Params for cuptiSassMetricsDisable
+ */
+typedef struct CUpti_SassMetricsDisable_Params
+{
+    /// [in] equal to CUpti_SassMetricsDisable_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] CUDA context on which SASS metric data collection will be disabled.
+    /// If set NULL, default context will be consider for SASS metric data collection.
+    CUcontext ctx;
+    /// [out] Num of dropped SASS records will be equal to numOfPatchedInstructions * numOfInstances.
+    /// Number of dropped records will be zero when data is flushed prior to calling the disable API.
+    size_t numOfDroppedRecords;
+} CUpti_SassMetricsDisable_Params;
+#define CUpti_SassMetricsDisable_Params_STRUCT_SIZE                      CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetricsDisable_Params, numOfDroppedRecords)
+
+/**
+ * \brief SASS metric data collection disable API will mark the end of a range, any kernel launched after this
+ * API call will not be profiled for the SASS metrics.
+ *
+ * \param pParams A pointer to \ref CUpti_SassMetricsDisable_Params
+ * 
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection
+ * \retval CUPTI_ERROR_INVALID_CONTEXT if any cuda context has not been created prior to this API call
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called multiple times for a cuda context without calling 
+ * cuptiSassMetricsEnable() API or called before cuptiSassMetricsSetConfig() API call.
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsDisable(CUpti_SassMetricsDisable_Params* pParams);
+
+/**
+ * \brief Params for cuptiSassMetricsGetDataProperties
+ */
+typedef struct CUpti_SassMetricsGetDataProperties_Params
+{
+    /// [in] equal to CUpti_SassMetricsGetDataProperties_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] CUDA context on which SASS metric data collection was enabled.
+    /// If set NULL, default context will be consider for SASS metric data collection.
+    CUcontext ctx;
+    /// [out] total number of SASS records has been collected
+    size_t numOfPatchedInstructionRecords;
+    /// [out] number of instances for each metric value per instruction.
+    /// This will depend on CUpti_SassPatching_OutputGranularity level set for the metric config.
+    size_t numOfInstances;
+} CUpti_SassMetricsGetDataProperties_Params;
+
+#define CUpti_SassMetricsGetDataProperties_Params_STRUCT_SIZE           CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetricsGetDataProperties_Params, numOfInstances)
+/**
+ * \brief SASS metric data properties API will give the data regarding number of instances of a metric
+ * value and number of SASS instruction data has been collected. The number of instances of a metric
+ * will vary as per user set the output granularity level with CUpti_SassMetrics_OutputGranularity value.
+ * User need to allocate memory for retriving the SASS data using cuptiSassMetricsFlushData() API.
+ * 
+ * \param pParams A pointer to \ref CUpti_SassMetricsGetDataProperties_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called outside the enable/disable range.
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsGetDataProperties(CUpti_SassMetricsGetDataProperties_Params* pParams);
+
+typedef struct CUpti_SassMetrics_InstanceValue
+{
+    // unique id of the metric
+    uint64_t metricId;
+    // metric value 
+    uint64_t value;
+} CUpti_SassMetrics_InstanceValue;
+#define CUpti_SassMetrics_InstanceValue_STRUCT_SIZE                      CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetrics_InstanceValue, value)
+
+typedef struct CUpti_SassMetrics_Data
+{
+    /// [in] equal to CUpti_SassMetricsFlushData_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [out] Unique cubin id
+    uint32_t cubinCrc;
+    /// [out] function's unique symbol index in the module.
+    uint32_t functionIndex;
+    /// [out] The function name
+    const char* functionName;
+    /// [out] pc offset for the function in a module
+    uint32_t pcOffset;
+    /// [out] array of size equal to number of instances per metric, which contains the metric ID and metric value.
+    CUpti_SassMetrics_InstanceValue* pInstanceValues;
+} CUpti_SassMetrics_Data;
+
+/**
+ * \brief Params for cuptiSassMetricsFlushData
+ */
+typedef struct CUpti_SassMetricsFlushData_Params
+{
+    /// [in] equal to CUpti_SassMetricsFlushData_Params_STRUCT_SIZE
+    size_t structSize;
+    /// [in] assign to NULL
+    void* pPriv;
+    /// [in] CUDA context on which SASS metric data collection was enabled.
+    /// If set NULL, default context will be consider for SASS metric data collection.
+    CUcontext ctx;
+    /// [in] number of patched instruction record will be retrived, user can call cuptiSassMetricsGetDataProperties()
+    /// for getting total number of records available.
+    size_t numOfPatchedInstructionRecords;
+    /// [in] number of patched instruction record instances for a metric, user can call cuptiSassMetricsGetDataProperties()
+    /// for getting total number of instances for each record per metric available.
+    size_t numOfInstances;
+    /// [out] 
+    CUpti_SassMetrics_Data* pMetricsData;
+} CUpti_SassMetricsFlushData_Params;
+#define CUpti_SassMetricsFlushData_Params_STRUCT_SIZE                      CUPTI_PROFILER_STRUCT_SIZE(CUpti_SassMetricsFlushData_Params, numOfInstances)
+
+/**
+ * \brief Flush SASS metrics data from CUPTI internal buffer to the user buffer.
+ * User needs to allocate the buffer for retrieving the data. The number of records collected
+ * can be queried using the API cuptiSassMetricsGetDataProperties().
+ *
+ * \param pParams A pointer to \ref CUpti_SassMetricsFlushData_Params
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if any \p pParams is not valid
+ * \retval CUPTI_ERROR_NOT_SUPPORTED indicates that the system/device doesn't support SASS metric data collection.
+ * \retval CUPTI_ERROR_INVALID_OPERATION if this API is called outside the enable/disable range.
+ */
+CUptiResult CUPTIAPI cuptiSassMetricsFlushData(CUpti_SassMetricsFlushData_Params* pParams);
+
+/** @} */ /* END CUPTI_SASS_METRICS_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif // _CUPTI_SASS_METRICS_H_
\ No newline at end of file
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_target.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_target.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4b625d45c65288fa2ea7dc05819ee4dfc4cbdd3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_target.h
@@ -0,0 +1,43 @@
+#if !defined(_CUPTI_TARGET_H_)
+#define _CUPTI_TARGET_H_
+
+/*
+CUPTI profiler target API's
+This file contains the CUPTI profiling API's.
+*/
+#include <cupti_result.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+#ifndef CUPTI_PROFILER_STRUCT_SIZE
+#define CUPTI_PROFILER_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+typedef struct CUpti_Device_GetChipName_Params
+{
+    size_t structSize;                                      //!< [in]
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    size_t deviceIndex;                                     //!< [in]
+    const char* pChipName;                                  //!< [out]
+} CUpti_Device_GetChipName_Params;
+
+#define CUpti_Device_GetChipName_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Device_GetChipName_Params, pChipName)
+CUptiResult CUPTIAPI cuptiDeviceGetChipName(CUpti_Device_GetChipName_Params *pParams);
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_version.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_version.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a8808ea022b4116a1177e6f78d34d0f39604344
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_version.h
@@ -0,0 +1,137 @@
+/*
+ * Copyright 2010-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_VERSION_H_)
+#define _CUPTI_VERSION_H_
+
+#include <cuda_stdint.h>
+#include <cupti_result.h>
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_VERSION_API CUPTI Version
+ * Function and macro to determine the CUPTI version.
+ * @{
+ */
+
+/**
+ * \brief The API version for this implementation of CUPTI.
+ *
+ * The API version for this implementation of CUPTI. This define along
+ * with \ref cuptiGetVersion can be used to dynamically detect if the
+ * version of CUPTI compiled against matches the version of the loaded
+ * CUPTI library.
+ *
+ * v1 : CUDAToolsSDK 4.0
+ * v2 : CUDAToolsSDK 4.1
+ * v3 : CUDA Toolkit 5.0
+ * v4 : CUDA Toolkit 5.5
+ * v5 : CUDA Toolkit 6.0
+ * v6 : CUDA Toolkit 6.5
+ * v7 : CUDA Toolkit 6.5(with sm_52 support)
+ * v8 : CUDA Toolkit 7.0
+ * v9 : CUDA Toolkit 8.0
+ * v10 : CUDA Toolkit 9.0
+ * v11 : CUDA Toolkit 9.1
+ * v12 : CUDA Toolkit 10.0, 10.1 and 10.2
+ * v13 : CUDA Toolkit 11.0
+ * v14 : CUDA Toolkit 11.1
+ * v15 : CUDA Toolkit 11.2, 11.3 and 11.4
+ * v16 : CUDA Toolkit 11.5
+ * v17 : CUDA Toolkit 11.6
+ * v18 : CUDA Toolkit 11.8
+ * v19 : CUDA Toolkit 12.0
+ * v20 : CUDA Toolkit 12.2
+ * v21 : CUDA Toolkit 12.3
+ * v22 : CUDA Toolkit 12.4
+ * v23 : CUDA Toolkit 12.5
+ * v24 : CUDA Toolkit 12.6
+ * v26 : CUDA Toolkit 12.8
+ */
+#define CUPTI_API_VERSION 26
+
+/**
+ * \brief Get the CUPTI API version.
+ *
+ * Return the API version in \p *version.
+ *
+ * \param version Returns the version
+ *
+ * \retval CUPTI_SUCCESS on success
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p version is NULL
+ * \sa CUPTI_API_VERSION
+ */
+CUptiResult CUPTIAPI cuptiGetVersion(uint32_t *version);
+
+/** @} */ /* END CUPTI_VERSION_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_VERSION_H_*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudaGL_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudaGL_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a52e194b265d32f61d47bd3081f4958755bff46
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudaGL_meta.h
@@ -0,0 +1,116 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// Dependent includes
+#ifdef __APPLE__
+#include <OpenGL/gl.h>
+#else
+#include <GL/gl.h>
+#endif
+
+// CUDA public interface, for type definitions and cu* function prototypes
+#include "cudaGL.h"
+
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+typedef struct cuGraphicsGLRegisterBuffer_params_st {
+    CUgraphicsResource *pCudaResource;
+    GLuint buffer;
+    unsigned int Flags;
+} cuGraphicsGLRegisterBuffer_params;
+
+typedef struct cuGraphicsGLRegisterImage_params_st {
+    CUgraphicsResource *pCudaResource;
+    GLuint image;
+    GLenum target;
+    unsigned int Flags;
+} cuGraphicsGLRegisterImage_params;
+
+typedef struct cuGLGetDevices_v2_params_st {
+    unsigned int *pCudaDeviceCount;
+    CUdevice *pCudaDevices;
+    unsigned int cudaDeviceCount;
+    CUGLDeviceList deviceList;
+} cuGLGetDevices_v2_params;
+
+typedef struct cuGLCtxCreate_v2_params_st {
+    CUcontext *pCtx;
+    unsigned int Flags;
+    CUdevice device;
+} cuGLCtxCreate_v2_params;
+
+typedef struct cuGLRegisterBufferObject_params_st {
+    GLuint buffer;
+} cuGLRegisterBufferObject_params;
+
+typedef struct cuGLMapBufferObject_v2_ptds_params_st {
+    CUdeviceptr *dptr;
+    size_t *size;
+    GLuint buffer;
+} cuGLMapBufferObject_v2_ptds_params;
+
+typedef struct cuGLUnmapBufferObject_params_st {
+    GLuint buffer;
+} cuGLUnmapBufferObject_params;
+
+typedef struct cuGLUnregisterBufferObject_params_st {
+    GLuint buffer;
+} cuGLUnregisterBufferObject_params;
+
+typedef struct cuGLSetBufferObjectMapFlags_params_st {
+    GLuint buffer;
+    unsigned int Flags;
+} cuGLSetBufferObjectMapFlags_params;
+
+typedef struct cuGLMapBufferObjectAsync_v2_ptsz_params_st {
+    CUdeviceptr *dptr;
+    size_t *size;
+    GLuint buffer;
+    CUstream hStream;
+} cuGLMapBufferObjectAsync_v2_ptsz_params;
+
+typedef struct cuGLUnmapBufferObjectAsync_params_st {
+    GLuint buffer;
+    CUstream hStream;
+} cuGLUnmapBufferObjectAsync_params;
+
+typedef struct cuGLGetDevices_params_st {
+    unsigned int *pCudaDeviceCount;
+    CUdevice *pCudaDevices;
+    unsigned int cudaDeviceCount;
+    CUGLDeviceList deviceList;
+} cuGLGetDevices_params;
+
+typedef struct cuGLMapBufferObject_v2_params_st {
+    CUdeviceptr *dptr;
+    size_t *size;
+    GLuint buffer;
+} cuGLMapBufferObject_v2_params;
+
+typedef struct cuGLMapBufferObjectAsync_v2_params_st {
+    CUdeviceptr *dptr;
+    size_t *size;
+    GLuint buffer;
+    CUstream hStream;
+} cuGLMapBufferObjectAsync_v2_params;
+
+typedef struct cuGLCtxCreate_params_st {
+    CUcontext *pCtx;
+    unsigned int Flags;
+    CUdevice device;
+} cuGLCtxCreate_params;
+
+typedef struct cuGLMapBufferObject_params_st {
+    CUdeviceptr_v1 *dptr;
+    unsigned int *size;
+    GLuint buffer;
+} cuGLMapBufferObject_params;
+
+typedef struct cuGLMapBufferObjectAsync_params_st {
+    CUdeviceptr_v1 *dptr;
+    unsigned int *size;
+    GLuint buffer;
+    CUstream hStream;
+} cuGLMapBufferObjectAsync_params;
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudaVDPAU_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudaVDPAU_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..abc603c8d9be21e012a9b1641330c2e203d623b2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudaVDPAU_meta.h
@@ -0,0 +1,46 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// Dependent includes
+#include <vdpau/vdpau.h>
+
+// CUDA public interface, for type definitions and cu* function prototypes
+#include "cudaVDPAU.h"
+
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+typedef struct cuVDPAUGetDevice_params_st {
+    CUdevice *pDevice;
+    VdpDevice vdpDevice;
+    VdpGetProcAddress *vdpGetProcAddress;
+} cuVDPAUGetDevice_params;
+
+typedef struct cuVDPAUCtxCreate_v2_params_st {
+    CUcontext *pCtx;
+    unsigned int flags;
+    CUdevice device;
+    VdpDevice vdpDevice;
+    VdpGetProcAddress *vdpGetProcAddress;
+} cuVDPAUCtxCreate_v2_params;
+
+typedef struct cuGraphicsVDPAURegisterVideoSurface_params_st {
+    CUgraphicsResource *pCudaResource;
+    VdpVideoSurface vdpSurface;
+    unsigned int flags;
+} cuGraphicsVDPAURegisterVideoSurface_params;
+
+typedef struct cuGraphicsVDPAURegisterOutputSurface_params_st {
+    CUgraphicsResource *pCudaResource;
+    VdpOutputSurface vdpSurface;
+    unsigned int flags;
+} cuGraphicsVDPAURegisterOutputSurface_params;
+
+typedef struct cuVDPAUCtxCreate_params_st {
+    CUcontext *pCtx;
+    unsigned int flags;
+    CUdevice device;
+    VdpDevice vdpDevice;
+    VdpGetProcAddress *vdpGetProcAddress;
+} cuVDPAUCtxCreate_params;
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_gl_interop_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_gl_interop_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..eaba3ac5a760e338f1edc191609f6fa2a32adee7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_gl_interop_meta.h
@@ -0,0 +1,71 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// CUDA public interface, for type definitions and api function prototypes
+#include "cuda_gl_interop.h"
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+// Currently used parameter trace structures
+typedef struct cudaGLGetDevices_v4010_params_st {
+    unsigned int *pCudaDeviceCount;
+    int *pCudaDevices;
+    unsigned int cudaDeviceCount;
+    enum cudaGLDeviceList deviceList;
+} cudaGLGetDevices_v4010_params;
+
+typedef struct cudaGraphicsGLRegisterImage_v3020_params_st {
+    struct cudaGraphicsResource **resource;
+    GLuint image;
+    GLenum target;
+    unsigned int flags;
+} cudaGraphicsGLRegisterImage_v3020_params;
+
+typedef struct cudaGraphicsGLRegisterBuffer_v3020_params_st {
+    struct cudaGraphicsResource **resource;
+    GLuint buffer;
+    unsigned int flags;
+} cudaGraphicsGLRegisterBuffer_v3020_params;
+
+typedef struct cudaGLSetGLDevice_v3020_params_st {
+    int device;
+} cudaGLSetGLDevice_v3020_params;
+
+typedef struct cudaGLRegisterBufferObject_v3020_params_st {
+    GLuint bufObj;
+} cudaGLRegisterBufferObject_v3020_params;
+
+typedef struct cudaGLMapBufferObject_v3020_params_st {
+    void **devPtr;
+    GLuint bufObj;
+} cudaGLMapBufferObject_v3020_params;
+
+typedef struct cudaGLUnmapBufferObject_v3020_params_st {
+    GLuint bufObj;
+} cudaGLUnmapBufferObject_v3020_params;
+
+typedef struct cudaGLUnregisterBufferObject_v3020_params_st {
+    GLuint bufObj;
+} cudaGLUnregisterBufferObject_v3020_params;
+
+typedef struct cudaGLSetBufferObjectMapFlags_v3020_params_st {
+    GLuint bufObj;
+    unsigned int flags;
+} cudaGLSetBufferObjectMapFlags_v3020_params;
+
+typedef struct cudaGLMapBufferObjectAsync_v3020_params_st {
+    void **devPtr;
+    GLuint bufObj;
+    cudaStream_t stream;
+} cudaGLMapBufferObjectAsync_v3020_params;
+
+typedef struct cudaGLUnmapBufferObjectAsync_v3020_params_st {
+    GLuint bufObj;
+    cudaStream_t stream;
+} cudaGLUnmapBufferObjectAsync_v3020_params;
+
+// Parameter trace structures for removed functions
+
+
+// End of parameter trace structures
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..954db0ad73e2eb029918f595ddee452aa9afd0e3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_meta.h
@@ -0,0 +1,3718 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// No dependent includes
+
+// CUDA public interface, for type definitions and cu* function prototypes
+#include "cuda.h"
+
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+typedef struct cuGetErrorString_params_st {
+    CUresult error;
+    const char **pStr;
+} cuGetErrorString_params;
+
+typedef struct cuGetErrorName_params_st {
+    CUresult error;
+    const char **pStr;
+} cuGetErrorName_params;
+
+typedef struct cuInit_params_st {
+    unsigned int Flags;
+} cuInit_params;
+
+typedef struct cuDriverGetVersion_params_st {
+    int *driverVersion;
+} cuDriverGetVersion_params;
+
+typedef struct cuDeviceGet_params_st {
+    CUdevice *device;
+    int ordinal;
+} cuDeviceGet_params;
+
+typedef struct cuDeviceGetCount_params_st {
+    int *count;
+} cuDeviceGetCount_params;
+
+typedef struct cuDeviceGetName_params_st {
+    char *name;
+    int len;
+    CUdevice dev;
+} cuDeviceGetName_params;
+
+typedef struct cuDeviceGetUuid_params_st {
+    CUuuid *uuid;
+    CUdevice dev;
+} cuDeviceGetUuid_params;
+
+typedef struct cuDeviceGetUuid_v2_params_st {
+    CUuuid *uuid;
+    CUdevice dev;
+} cuDeviceGetUuid_v2_params;
+
+typedef struct cuDeviceGetLuid_params_st {
+    char *luid;
+    unsigned int *deviceNodeMask;
+    CUdevice dev;
+} cuDeviceGetLuid_params;
+
+typedef struct cuDeviceTotalMem_v2_params_st {
+    size_t *bytes;
+    CUdevice dev;
+} cuDeviceTotalMem_v2_params;
+
+typedef struct cuDeviceGetTexture1DLinearMaxWidth_params_st {
+    size_t *maxWidthInElements;
+    CUarray_format format;
+    unsigned numChannels;
+    CUdevice dev;
+} cuDeviceGetTexture1DLinearMaxWidth_params;
+
+typedef struct cuDeviceGetAttribute_params_st {
+    int *pi;
+    CUdevice_attribute attrib;
+    CUdevice dev;
+} cuDeviceGetAttribute_params;
+
+typedef struct cuDeviceGetNvSciSyncAttributes_params_st {
+    void *nvSciSyncAttrList;
+    CUdevice dev;
+    int flags;
+} cuDeviceGetNvSciSyncAttributes_params;
+
+typedef struct cuDeviceSetMemPool_params_st {
+    CUdevice dev;
+    CUmemoryPool pool;
+} cuDeviceSetMemPool_params;
+
+typedef struct cuDeviceGetMemPool_params_st {
+    CUmemoryPool *pool;
+    CUdevice dev;
+} cuDeviceGetMemPool_params;
+
+typedef struct cuDeviceGetDefaultMemPool_params_st {
+    CUmemoryPool *pool_out;
+    CUdevice dev;
+} cuDeviceGetDefaultMemPool_params;
+
+typedef struct cuDeviceGetExecAffinitySupport_params_st {
+    int *pi;
+    CUexecAffinityType type;
+    CUdevice dev;
+} cuDeviceGetExecAffinitySupport_params;
+
+typedef struct cuFlushGPUDirectRDMAWrites_params_st {
+    CUflushGPUDirectRDMAWritesTarget target;
+    CUflushGPUDirectRDMAWritesScope scope;
+} cuFlushGPUDirectRDMAWrites_params;
+
+typedef struct cuDeviceGetProperties_params_st {
+    CUdevprop *prop;
+    CUdevice dev;
+} cuDeviceGetProperties_params;
+
+typedef struct cuDeviceComputeCapability_params_st {
+    int *major;
+    int *minor;
+    CUdevice dev;
+} cuDeviceComputeCapability_params;
+
+typedef struct cuDevicePrimaryCtxRetain_params_st {
+    CUcontext *pctx;
+    CUdevice dev;
+} cuDevicePrimaryCtxRetain_params;
+
+typedef struct cuDevicePrimaryCtxRelease_v2_params_st {
+    CUdevice dev;
+} cuDevicePrimaryCtxRelease_v2_params;
+
+typedef struct cuDevicePrimaryCtxSetFlags_v2_params_st {
+    CUdevice dev;
+    unsigned int flags;
+} cuDevicePrimaryCtxSetFlags_v2_params;
+
+typedef struct cuDevicePrimaryCtxGetState_params_st {
+    CUdevice dev;
+    unsigned int *flags;
+    int *active;
+} cuDevicePrimaryCtxGetState_params;
+
+typedef struct cuDevicePrimaryCtxReset_v2_params_st {
+    CUdevice dev;
+} cuDevicePrimaryCtxReset_v2_params;
+
+typedef struct cuCtxCreate_v2_params_st {
+    CUcontext *pctx;
+    unsigned int flags;
+    CUdevice dev;
+} cuCtxCreate_v2_params;
+
+typedef struct cuCtxCreate_v3_params_st {
+    CUcontext *pctx;
+    CUexecAffinityParam *paramsArray;
+    int numParams;
+    unsigned int flags;
+    CUdevice dev;
+} cuCtxCreate_v3_params;
+
+typedef struct cuCtxCreate_v4_params_st {
+    CUcontext *pctx;
+    CUctxCreateParams *ctxCreateParams;
+    unsigned int flags;
+    CUdevice dev;
+} cuCtxCreate_v4_params;
+
+typedef struct cuCtxDestroy_v2_params_st {
+    CUcontext ctx;
+} cuCtxDestroy_v2_params;
+
+typedef struct cuCtxPushCurrent_v2_params_st {
+    CUcontext ctx;
+} cuCtxPushCurrent_v2_params;
+
+typedef struct cuCtxPopCurrent_v2_params_st {
+    CUcontext *pctx;
+} cuCtxPopCurrent_v2_params;
+
+typedef struct cuCtxSetCurrent_params_st {
+    CUcontext ctx;
+} cuCtxSetCurrent_params;
+
+typedef struct cuCtxGetCurrent_params_st {
+    CUcontext *pctx;
+} cuCtxGetCurrent_params;
+
+typedef struct cuCtxGetDevice_params_st {
+    CUdevice *device;
+} cuCtxGetDevice_params;
+
+typedef struct cuCtxGetFlags_params_st {
+    unsigned int *flags;
+} cuCtxGetFlags_params;
+
+typedef struct cuCtxSetFlags_params_st {
+    unsigned int flags;
+} cuCtxSetFlags_params;
+
+typedef struct cuCtxGetId_params_st {
+    CUcontext ctx;
+    unsigned long long *ctxId;
+} cuCtxGetId_params;
+
+typedef struct cuCtxSetLimit_params_st {
+    CUlimit limit;
+    size_t value;
+} cuCtxSetLimit_params;
+
+typedef struct cuCtxGetLimit_params_st {
+    size_t *pvalue;
+    CUlimit limit;
+} cuCtxGetLimit_params;
+
+typedef struct cuCtxGetCacheConfig_params_st {
+    CUfunc_cache *pconfig;
+} cuCtxGetCacheConfig_params;
+
+typedef struct cuCtxSetCacheConfig_params_st {
+    CUfunc_cache config;
+} cuCtxSetCacheConfig_params;
+
+typedef struct cuCtxGetApiVersion_params_st {
+    CUcontext ctx;
+    unsigned int *version;
+} cuCtxGetApiVersion_params;
+
+typedef struct cuCtxGetStreamPriorityRange_params_st {
+    int *leastPriority;
+    int *greatestPriority;
+} cuCtxGetStreamPriorityRange_params;
+
+typedef struct cuCtxGetExecAffinity_params_st {
+    CUexecAffinityParam *pExecAffinity;
+    CUexecAffinityType type;
+} cuCtxGetExecAffinity_params;
+
+typedef struct cuCtxRecordEvent_params_st {
+    CUcontext hCtx;
+    CUevent hEvent;
+} cuCtxRecordEvent_params;
+
+typedef struct cuCtxWaitEvent_params_st {
+    CUcontext hCtx;
+    CUevent hEvent;
+} cuCtxWaitEvent_params;
+
+typedef struct cuCtxAttach_params_st {
+    CUcontext *pctx;
+    unsigned int flags;
+} cuCtxAttach_params;
+
+typedef struct cuCtxDetach_params_st {
+    CUcontext ctx;
+} cuCtxDetach_params;
+
+typedef struct cuCtxGetSharedMemConfig_params_st {
+    CUsharedconfig *pConfig;
+} cuCtxGetSharedMemConfig_params;
+
+typedef struct cuCtxSetSharedMemConfig_params_st {
+    CUsharedconfig config;
+} cuCtxSetSharedMemConfig_params;
+
+typedef struct cuModuleLoad_params_st {
+    CUmodule *module;
+    const char *fname;
+} cuModuleLoad_params;
+
+typedef struct cuModuleLoadData_params_st {
+    CUmodule *module;
+    const void *image;
+} cuModuleLoadData_params;
+
+typedef struct cuModuleLoadDataEx_params_st {
+    CUmodule *module;
+    const void *image;
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+} cuModuleLoadDataEx_params;
+
+typedef struct cuModuleLoadFatBinary_params_st {
+    CUmodule *module;
+    const void *fatCubin;
+} cuModuleLoadFatBinary_params;
+
+typedef struct cuModuleUnload_params_st {
+    CUmodule hmod;
+} cuModuleUnload_params;
+
+typedef struct cuModuleGetLoadingMode_params_st {
+    CUmoduleLoadingMode *mode;
+} cuModuleGetLoadingMode_params;
+
+typedef struct cuModuleGetFunction_params_st {
+    CUfunction *hfunc;
+    CUmodule hmod;
+    const char *name;
+} cuModuleGetFunction_params;
+
+typedef struct cuModuleGetFunctionCount_params_st {
+    unsigned int *count;
+    CUmodule mod;
+} cuModuleGetFunctionCount_params;
+
+typedef struct cuModuleEnumerateFunctions_params_st {
+    CUfunction *functions;
+    unsigned int numFunctions;
+    CUmodule mod;
+} cuModuleEnumerateFunctions_params;
+
+typedef struct cuModuleGetGlobal_v2_params_st {
+    CUdeviceptr *dptr;
+    size_t *bytes;
+    CUmodule hmod;
+    const char *name;
+} cuModuleGetGlobal_v2_params;
+
+typedef struct cuLinkCreate_v2_params_st {
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+    CUlinkState *stateOut;
+} cuLinkCreate_v2_params;
+
+typedef struct cuLinkAddData_v2_params_st {
+    CUlinkState state;
+    CUjitInputType type;
+    void *data;
+    size_t size;
+    const char *name;
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+} cuLinkAddData_v2_params;
+
+typedef struct cuLinkAddFile_v2_params_st {
+    CUlinkState state;
+    CUjitInputType type;
+    const char *path;
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+} cuLinkAddFile_v2_params;
+
+typedef struct cuLinkComplete_params_st {
+    CUlinkState state;
+    void **cubinOut;
+    size_t *sizeOut;
+} cuLinkComplete_params;
+
+typedef struct cuLinkDestroy_params_st {
+    CUlinkState state;
+} cuLinkDestroy_params;
+
+typedef struct cuModuleGetTexRef_params_st {
+    CUtexref *pTexRef;
+    CUmodule hmod;
+    const char *name;
+} cuModuleGetTexRef_params;
+
+typedef struct cuModuleGetSurfRef_params_st {
+    CUsurfref *pSurfRef;
+    CUmodule hmod;
+    const char *name;
+} cuModuleGetSurfRef_params;
+
+typedef struct cuLibraryLoadData_params_st {
+    CUlibrary *library;
+    const void *code;
+    CUjit_option *jitOptions;
+    void **jitOptionsValues;
+    unsigned int numJitOptions;
+    CUlibraryOption *libraryOptions;
+    void **libraryOptionValues;
+    unsigned int numLibraryOptions;
+} cuLibraryLoadData_params;
+
+typedef struct cuLibraryLoadFromFile_params_st {
+    CUlibrary *library;
+    const char *fileName;
+    CUjit_option *jitOptions;
+    void **jitOptionsValues;
+    unsigned int numJitOptions;
+    CUlibraryOption *libraryOptions;
+    void **libraryOptionValues;
+    unsigned int numLibraryOptions;
+} cuLibraryLoadFromFile_params;
+
+typedef struct cuLibraryUnload_params_st {
+    CUlibrary library;
+} cuLibraryUnload_params;
+
+typedef struct cuLibraryGetKernel_params_st {
+    CUkernel *pKernel;
+    CUlibrary library;
+    const char *name;
+} cuLibraryGetKernel_params;
+
+typedef struct cuLibraryGetKernelCount_params_st {
+    unsigned int *count;
+    CUlibrary lib;
+} cuLibraryGetKernelCount_params;
+
+typedef struct cuLibraryEnumerateKernels_params_st {
+    CUkernel *kernels;
+    unsigned int numKernels;
+    CUlibrary lib;
+} cuLibraryEnumerateKernels_params;
+
+typedef struct cuLibraryGetModule_params_st {
+    CUmodule *pMod;
+    CUlibrary library;
+} cuLibraryGetModule_params;
+
+typedef struct cuKernelGetFunction_params_st {
+    CUfunction *pFunc;
+    CUkernel kernel;
+} cuKernelGetFunction_params;
+
+typedef struct cuKernelGetLibrary_params_st {
+    CUlibrary *pLib;
+    CUkernel kernel;
+} cuKernelGetLibrary_params;
+
+typedef struct cuLibraryGetGlobal_params_st {
+    CUdeviceptr *dptr;
+    size_t *bytes;
+    CUlibrary library;
+    const char *name;
+} cuLibraryGetGlobal_params;
+
+typedef struct cuLibraryGetManaged_params_st {
+    CUdeviceptr *dptr;
+    size_t *bytes;
+    CUlibrary library;
+    const char *name;
+} cuLibraryGetManaged_params;
+
+typedef struct cuLibraryGetUnifiedFunction_params_st {
+    void **fptr;
+    CUlibrary library;
+    const char *symbol;
+} cuLibraryGetUnifiedFunction_params;
+
+typedef struct cuKernelGetAttribute_params_st {
+    int *pi;
+    CUfunction_attribute attrib;
+    CUkernel kernel;
+    CUdevice dev;
+} cuKernelGetAttribute_params;
+
+typedef struct cuKernelSetAttribute_params_st {
+    CUfunction_attribute attrib;
+    int val;
+    CUkernel kernel;
+    CUdevice dev;
+} cuKernelSetAttribute_params;
+
+typedef struct cuKernelSetCacheConfig_params_st {
+    CUkernel kernel;
+    CUfunc_cache config;
+    CUdevice dev;
+} cuKernelSetCacheConfig_params;
+
+typedef struct cuKernelGetName_params_st {
+    const char **name;
+    CUkernel hfunc;
+} cuKernelGetName_params;
+
+typedef struct cuKernelGetParamInfo_params_st {
+    CUkernel kernel;
+    size_t paramIndex;
+    size_t *paramOffset;
+    size_t *paramSize;
+} cuKernelGetParamInfo_params;
+
+typedef struct cuMemGetInfo_v2_params_st {
+    size_t *free;
+    size_t *total;
+} cuMemGetInfo_v2_params;
+
+typedef struct cuMemAlloc_v2_params_st {
+    CUdeviceptr *dptr;
+    size_t bytesize;
+} cuMemAlloc_v2_params;
+
+typedef struct cuMemAllocPitch_v2_params_st {
+    CUdeviceptr *dptr;
+    size_t *pPitch;
+    size_t WidthInBytes;
+    size_t Height;
+    unsigned int ElementSizeBytes;
+} cuMemAllocPitch_v2_params;
+
+typedef struct cuMemFree_v2_params_st {
+    CUdeviceptr dptr;
+} cuMemFree_v2_params;
+
+typedef struct cuMemGetAddressRange_v2_params_st {
+    CUdeviceptr *pbase;
+    size_t *psize;
+    CUdeviceptr dptr;
+} cuMemGetAddressRange_v2_params;
+
+typedef struct cuMemAllocHost_v2_params_st {
+    void **pp;
+    size_t bytesize;
+} cuMemAllocHost_v2_params;
+
+typedef struct cuMemFreeHost_params_st {
+    void *p;
+} cuMemFreeHost_params;
+
+typedef struct cuMemHostAlloc_params_st {
+    void **pp;
+    size_t bytesize;
+    unsigned int Flags;
+} cuMemHostAlloc_params;
+
+typedef struct cuMemHostGetDevicePointer_v2_params_st {
+    CUdeviceptr *pdptr;
+    void *p;
+    unsigned int Flags;
+} cuMemHostGetDevicePointer_v2_params;
+
+typedef struct cuMemHostGetFlags_params_st {
+    unsigned int *pFlags;
+    void *p;
+} cuMemHostGetFlags_params;
+
+typedef struct cuMemAllocManaged_params_st {
+    CUdeviceptr *dptr;
+    size_t bytesize;
+    unsigned int flags;
+} cuMemAllocManaged_params;
+
+typedef struct cuDeviceRegisterAsyncNotification_params_st {
+    CUdevice device;
+    CUasyncCallback callbackFunc;
+    void *userData;
+    CUasyncCallbackHandle *callback;
+} cuDeviceRegisterAsyncNotification_params;
+
+typedef struct cuDeviceUnregisterAsyncNotification_params_st {
+    CUdevice device;
+    CUasyncCallbackHandle callback;
+} cuDeviceUnregisterAsyncNotification_params;
+
+typedef struct cuDeviceGetByPCIBusId_params_st {
+    CUdevice *dev;
+    const char *pciBusId;
+} cuDeviceGetByPCIBusId_params;
+
+typedef struct cuDeviceGetPCIBusId_params_st {
+    char *pciBusId;
+    int len;
+    CUdevice dev;
+} cuDeviceGetPCIBusId_params;
+
+typedef struct cuIpcGetEventHandle_params_st {
+    CUipcEventHandle *pHandle;
+    CUevent event;
+} cuIpcGetEventHandle_params;
+
+typedef struct cuIpcOpenEventHandle_params_st {
+    CUevent *phEvent;
+    CUipcEventHandle handle;
+} cuIpcOpenEventHandle_params;
+
+typedef struct cuIpcGetMemHandle_params_st {
+    CUipcMemHandle *pHandle;
+    CUdeviceptr dptr;
+} cuIpcGetMemHandle_params;
+
+typedef struct cuIpcOpenMemHandle_v2_params_st {
+    CUdeviceptr *pdptr;
+    CUipcMemHandle handle;
+    unsigned int Flags;
+} cuIpcOpenMemHandle_v2_params;
+
+typedef struct cuIpcCloseMemHandle_params_st {
+    CUdeviceptr dptr;
+} cuIpcCloseMemHandle_params;
+
+typedef struct cuMemHostRegister_v2_params_st {
+    void *p;
+    size_t bytesize;
+    unsigned int Flags;
+} cuMemHostRegister_v2_params;
+
+typedef struct cuMemHostUnregister_params_st {
+    void *p;
+} cuMemHostUnregister_params;
+
+typedef struct cuMemcpy_ptds_params_st {
+    CUdeviceptr dst;
+    CUdeviceptr src;
+    size_t ByteCount;
+} cuMemcpy_ptds_params;
+
+typedef struct cuMemcpyPeer_ptds_params_st {
+    CUdeviceptr dstDevice;
+    CUcontext dstContext;
+    CUdeviceptr srcDevice;
+    CUcontext srcContext;
+    size_t ByteCount;
+} cuMemcpyPeer_ptds_params;
+
+typedef struct cuMemcpyHtoD_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    const void *srcHost;
+    size_t ByteCount;
+} cuMemcpyHtoD_v2_ptds_params;
+
+typedef struct cuMemcpyDtoH_v2_ptds_params_st {
+    void *dstHost;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+} cuMemcpyDtoH_v2_ptds_params;
+
+typedef struct cuMemcpyDtoD_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+} cuMemcpyDtoD_v2_ptds_params;
+
+typedef struct cuMemcpyDtoA_v2_ptds_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+} cuMemcpyDtoA_v2_ptds_params;
+
+typedef struct cuMemcpyAtoD_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+} cuMemcpyAtoD_v2_ptds_params;
+
+typedef struct cuMemcpyHtoA_v2_ptds_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    const void *srcHost;
+    size_t ByteCount;
+} cuMemcpyHtoA_v2_ptds_params;
+
+typedef struct cuMemcpyAtoH_v2_ptds_params_st {
+    void *dstHost;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+} cuMemcpyAtoH_v2_ptds_params;
+
+typedef struct cuMemcpyAtoA_v2_ptds_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+} cuMemcpyAtoA_v2_ptds_params;
+
+typedef struct cuMemcpy2D_v2_ptds_params_st {
+    const CUDA_MEMCPY2D *pCopy;
+} cuMemcpy2D_v2_ptds_params;
+
+typedef struct cuMemcpy2DUnaligned_v2_ptds_params_st {
+    const CUDA_MEMCPY2D *pCopy;
+} cuMemcpy2DUnaligned_v2_ptds_params;
+
+typedef struct cuMemcpy3D_v2_ptds_params_st {
+    const CUDA_MEMCPY3D *pCopy;
+} cuMemcpy3D_v2_ptds_params;
+
+typedef struct cuMemcpy3DPeer_ptds_params_st {
+    const CUDA_MEMCPY3D_PEER *pCopy;
+} cuMemcpy3DPeer_ptds_params;
+
+typedef struct cuMemcpyAsync_ptsz_params_st {
+    CUdeviceptr dst;
+    CUdeviceptr src;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyAsync_ptsz_params;
+
+typedef struct cuMemcpyPeerAsync_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    CUcontext dstContext;
+    CUdeviceptr srcDevice;
+    CUcontext srcContext;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyPeerAsync_ptsz_params;
+
+typedef struct cuMemcpyHtoDAsync_v2_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    const void *srcHost;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyHtoDAsync_v2_ptsz_params;
+
+typedef struct cuMemcpyDtoHAsync_v2_ptsz_params_st {
+    void *dstHost;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyDtoHAsync_v2_ptsz_params;
+
+typedef struct cuMemcpyDtoDAsync_v2_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyDtoDAsync_v2_ptsz_params;
+
+typedef struct cuMemcpyHtoAAsync_v2_ptsz_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    const void *srcHost;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyHtoAAsync_v2_ptsz_params;
+
+typedef struct cuMemcpyAtoHAsync_v2_ptsz_params_st {
+    void *dstHost;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyAtoHAsync_v2_ptsz_params;
+
+typedef struct cuMemcpy2DAsync_v2_ptsz_params_st {
+    const CUDA_MEMCPY2D *pCopy;
+    CUstream hStream;
+} cuMemcpy2DAsync_v2_ptsz_params;
+
+typedef struct cuMemcpy3DAsync_v2_ptsz_params_st {
+    const CUDA_MEMCPY3D *pCopy;
+    CUstream hStream;
+} cuMemcpy3DAsync_v2_ptsz_params;
+
+typedef struct cuMemcpy3DPeerAsync_ptsz_params_st {
+    const CUDA_MEMCPY3D_PEER *pCopy;
+    CUstream hStream;
+} cuMemcpy3DPeerAsync_ptsz_params;
+
+typedef struct cuMemcpyBatchAsync_ptsz_params_st {
+    CUdeviceptr *dsts;
+    CUdeviceptr *srcs;
+    size_t *sizes;
+    size_t count;
+    CUmemcpyAttributes *attrs;
+    size_t *attrsIdxs;
+    size_t numAttrs;
+    size_t *failIdx;
+    CUstream hStream;
+} cuMemcpyBatchAsync_ptsz_params;
+
+typedef struct cuMemcpy3DBatchAsync_ptsz_params_st {
+    size_t numOps;
+    CUDA_MEMCPY3D_BATCH_OP *opList;
+    size_t *failIdx;
+    unsigned long long flags;
+    CUstream hStream;
+} cuMemcpy3DBatchAsync_ptsz_params;
+
+typedef struct cuMemsetD8_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    unsigned char uc;
+    size_t N;
+} cuMemsetD8_v2_ptds_params;
+
+typedef struct cuMemsetD16_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    unsigned short us;
+    size_t N;
+} cuMemsetD16_v2_ptds_params;
+
+typedef struct cuMemsetD32_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    unsigned int ui;
+    size_t N;
+} cuMemsetD32_v2_ptds_params;
+
+typedef struct cuMemsetD2D8_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned char uc;
+    size_t Width;
+    size_t Height;
+} cuMemsetD2D8_v2_ptds_params;
+
+typedef struct cuMemsetD2D16_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned short us;
+    size_t Width;
+    size_t Height;
+} cuMemsetD2D16_v2_ptds_params;
+
+typedef struct cuMemsetD2D32_v2_ptds_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned int ui;
+    size_t Width;
+    size_t Height;
+} cuMemsetD2D32_v2_ptds_params;
+
+typedef struct cuMemsetD8Async_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    unsigned char uc;
+    size_t N;
+    CUstream hStream;
+} cuMemsetD8Async_ptsz_params;
+
+typedef struct cuMemsetD16Async_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    unsigned short us;
+    size_t N;
+    CUstream hStream;
+} cuMemsetD16Async_ptsz_params;
+
+typedef struct cuMemsetD32Async_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    unsigned int ui;
+    size_t N;
+    CUstream hStream;
+} cuMemsetD32Async_ptsz_params;
+
+typedef struct cuMemsetD2D8Async_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned char uc;
+    size_t Width;
+    size_t Height;
+    CUstream hStream;
+} cuMemsetD2D8Async_ptsz_params;
+
+typedef struct cuMemsetD2D16Async_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned short us;
+    size_t Width;
+    size_t Height;
+    CUstream hStream;
+} cuMemsetD2D16Async_ptsz_params;
+
+typedef struct cuMemsetD2D32Async_ptsz_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned int ui;
+    size_t Width;
+    size_t Height;
+    CUstream hStream;
+} cuMemsetD2D32Async_ptsz_params;
+
+typedef struct cuArrayCreate_v2_params_st {
+    CUarray *pHandle;
+    const CUDA_ARRAY_DESCRIPTOR *pAllocateArray;
+} cuArrayCreate_v2_params;
+
+typedef struct cuArrayGetDescriptor_v2_params_st {
+    CUDA_ARRAY_DESCRIPTOR *pArrayDescriptor;
+    CUarray hArray;
+} cuArrayGetDescriptor_v2_params;
+
+typedef struct cuArrayGetSparseProperties_params_st {
+    CUDA_ARRAY_SPARSE_PROPERTIES *sparseProperties;
+    CUarray array;
+} cuArrayGetSparseProperties_params;
+
+typedef struct cuMipmappedArrayGetSparseProperties_params_st {
+    CUDA_ARRAY_SPARSE_PROPERTIES *sparseProperties;
+    CUmipmappedArray mipmap;
+} cuMipmappedArrayGetSparseProperties_params;
+
+typedef struct cuArrayGetMemoryRequirements_params_st {
+    CUDA_ARRAY_MEMORY_REQUIREMENTS *memoryRequirements;
+    CUarray array;
+    CUdevice device;
+} cuArrayGetMemoryRequirements_params;
+
+typedef struct cuMipmappedArrayGetMemoryRequirements_params_st {
+    CUDA_ARRAY_MEMORY_REQUIREMENTS *memoryRequirements;
+    CUmipmappedArray mipmap;
+    CUdevice device;
+} cuMipmappedArrayGetMemoryRequirements_params;
+
+typedef struct cuArrayGetPlane_params_st {
+    CUarray *pPlaneArray;
+    CUarray hArray;
+    unsigned int planeIdx;
+} cuArrayGetPlane_params;
+
+typedef struct cuArrayDestroy_params_st {
+    CUarray hArray;
+} cuArrayDestroy_params;
+
+typedef struct cuArray3DCreate_v2_params_st {
+    CUarray *pHandle;
+    const CUDA_ARRAY3D_DESCRIPTOR *pAllocateArray;
+} cuArray3DCreate_v2_params;
+
+typedef struct cuArray3DGetDescriptor_v2_params_st {
+    CUDA_ARRAY3D_DESCRIPTOR *pArrayDescriptor;
+    CUarray hArray;
+} cuArray3DGetDescriptor_v2_params;
+
+typedef struct cuMipmappedArrayCreate_params_st {
+    CUmipmappedArray *pHandle;
+    const CUDA_ARRAY3D_DESCRIPTOR *pMipmappedArrayDesc;
+    unsigned int numMipmapLevels;
+} cuMipmappedArrayCreate_params;
+
+typedef struct cuMipmappedArrayGetLevel_params_st {
+    CUarray *pLevelArray;
+    CUmipmappedArray hMipmappedArray;
+    unsigned int level;
+} cuMipmappedArrayGetLevel_params;
+
+typedef struct cuMipmappedArrayDestroy_params_st {
+    CUmipmappedArray hMipmappedArray;
+} cuMipmappedArrayDestroy_params;
+
+typedef struct cuMemGetHandleForAddressRange_params_st {
+    void *handle;
+    CUdeviceptr dptr;
+    size_t size;
+    CUmemRangeHandleType handleType;
+    unsigned long long flags;
+} cuMemGetHandleForAddressRange_params;
+
+typedef struct cuMemBatchDecompressAsync_ptsz_params_st {
+    CUmemDecompressParams *paramsArray;
+    size_t count;
+    unsigned int flags;
+    size_t *errorIndex;
+    CUstream stream;
+} cuMemBatchDecompressAsync_ptsz_params;
+
+typedef struct cuMemAddressReserve_params_st {
+    CUdeviceptr *ptr;
+    size_t size;
+    size_t alignment;
+    CUdeviceptr addr;
+    unsigned long long flags;
+} cuMemAddressReserve_params;
+
+typedef struct cuMemAddressFree_params_st {
+    CUdeviceptr ptr;
+    size_t size;
+} cuMemAddressFree_params;
+
+typedef struct cuMemCreate_params_st {
+    CUmemGenericAllocationHandle *handle;
+    size_t size;
+    const CUmemAllocationProp *prop;
+    unsigned long long flags;
+} cuMemCreate_params;
+
+typedef struct cuMemRelease_params_st {
+    CUmemGenericAllocationHandle handle;
+} cuMemRelease_params;
+
+typedef struct cuMemMap_params_st {
+    CUdeviceptr ptr;
+    size_t size;
+    size_t offset;
+    CUmemGenericAllocationHandle handle;
+    unsigned long long flags;
+} cuMemMap_params;
+
+typedef struct cuMemMapArrayAsync_ptsz_params_st {
+    CUarrayMapInfo *mapInfoList;
+    unsigned int count;
+    CUstream hStream;
+} cuMemMapArrayAsync_ptsz_params;
+
+typedef struct cuMemUnmap_params_st {
+    CUdeviceptr ptr;
+    size_t size;
+} cuMemUnmap_params;
+
+typedef struct cuMemSetAccess_params_st {
+    CUdeviceptr ptr;
+    size_t size;
+    const CUmemAccessDesc *desc;
+    size_t count;
+} cuMemSetAccess_params;
+
+typedef struct cuMemGetAccess_params_st {
+    unsigned long long *flags;
+    const CUmemLocation *location;
+    CUdeviceptr ptr;
+} cuMemGetAccess_params;
+
+typedef struct cuMemExportToShareableHandle_params_st {
+    void *shareableHandle;
+    CUmemGenericAllocationHandle handle;
+    CUmemAllocationHandleType handleType;
+    unsigned long long flags;
+} cuMemExportToShareableHandle_params;
+
+typedef struct cuMemImportFromShareableHandle_params_st {
+    CUmemGenericAllocationHandle *handle;
+    void *osHandle;
+    CUmemAllocationHandleType shHandleType;
+} cuMemImportFromShareableHandle_params;
+
+typedef struct cuMemGetAllocationGranularity_params_st {
+    size_t *granularity;
+    const CUmemAllocationProp *prop;
+    CUmemAllocationGranularity_flags option;
+} cuMemGetAllocationGranularity_params;
+
+typedef struct cuMemGetAllocationPropertiesFromHandle_params_st {
+    CUmemAllocationProp *prop;
+    CUmemGenericAllocationHandle handle;
+} cuMemGetAllocationPropertiesFromHandle_params;
+
+typedef struct cuMemRetainAllocationHandle_params_st {
+    CUmemGenericAllocationHandle *handle;
+    void *addr;
+} cuMemRetainAllocationHandle_params;
+
+typedef struct cuMemFreeAsync_ptsz_params_st {
+    CUdeviceptr dptr;
+    CUstream hStream;
+} cuMemFreeAsync_ptsz_params;
+
+typedef struct cuMemAllocAsync_ptsz_params_st {
+    CUdeviceptr *dptr;
+    size_t bytesize;
+    CUstream hStream;
+} cuMemAllocAsync_ptsz_params;
+
+typedef struct cuMemPoolTrimTo_params_st {
+    CUmemoryPool pool;
+    size_t minBytesToKeep;
+} cuMemPoolTrimTo_params;
+
+typedef struct cuMemPoolSetAttribute_params_st {
+    CUmemoryPool pool;
+    CUmemPool_attribute attr;
+    void *value;
+} cuMemPoolSetAttribute_params;
+
+typedef struct cuMemPoolGetAttribute_params_st {
+    CUmemoryPool pool;
+    CUmemPool_attribute attr;
+    void *value;
+} cuMemPoolGetAttribute_params;
+
+typedef struct cuMemPoolSetAccess_params_st {
+    CUmemoryPool pool;
+    const CUmemAccessDesc *map;
+    size_t count;
+} cuMemPoolSetAccess_params;
+
+typedef struct cuMemPoolGetAccess_params_st {
+    CUmemAccess_flags *flags;
+    CUmemoryPool memPool;
+    CUmemLocation *location;
+} cuMemPoolGetAccess_params;
+
+typedef struct cuMemPoolCreate_params_st {
+    CUmemoryPool *pool;
+    const CUmemPoolProps *poolProps;
+} cuMemPoolCreate_params;
+
+typedef struct cuMemPoolDestroy_params_st {
+    CUmemoryPool pool;
+} cuMemPoolDestroy_params;
+
+typedef struct cuMemAllocFromPoolAsync_ptsz_params_st {
+    CUdeviceptr *dptr;
+    size_t bytesize;
+    CUmemoryPool pool;
+    CUstream hStream;
+} cuMemAllocFromPoolAsync_ptsz_params;
+
+typedef struct cuMemPoolExportToShareableHandle_params_st {
+    void *handle_out;
+    CUmemoryPool pool;
+    CUmemAllocationHandleType handleType;
+    unsigned long long flags;
+} cuMemPoolExportToShareableHandle_params;
+
+typedef struct cuMemPoolImportFromShareableHandle_params_st {
+    CUmemoryPool *pool_out;
+    void *handle;
+    CUmemAllocationHandleType handleType;
+    unsigned long long flags;
+} cuMemPoolImportFromShareableHandle_params;
+
+typedef struct cuMemPoolExportPointer_params_st {
+    CUmemPoolPtrExportData *shareData_out;
+    CUdeviceptr ptr;
+} cuMemPoolExportPointer_params;
+
+typedef struct cuMemPoolImportPointer_params_st {
+    CUdeviceptr *ptr_out;
+    CUmemoryPool pool;
+    CUmemPoolPtrExportData *shareData;
+} cuMemPoolImportPointer_params;
+
+typedef struct cuMulticastCreate_params_st {
+    CUmemGenericAllocationHandle *mcHandle;
+    const CUmulticastObjectProp *prop;
+} cuMulticastCreate_params;
+
+typedef struct cuMulticastAddDevice_params_st {
+    CUmemGenericAllocationHandle mcHandle;
+    CUdevice dev;
+} cuMulticastAddDevice_params;
+
+typedef struct cuMulticastBindMem_params_st {
+    CUmemGenericAllocationHandle mcHandle;
+    size_t mcOffset;
+    CUmemGenericAllocationHandle memHandle;
+    size_t memOffset;
+    size_t size;
+    unsigned long long flags;
+} cuMulticastBindMem_params;
+
+typedef struct cuMulticastBindAddr_params_st {
+    CUmemGenericAllocationHandle mcHandle;
+    size_t mcOffset;
+    CUdeviceptr memptr;
+    size_t size;
+    unsigned long long flags;
+} cuMulticastBindAddr_params;
+
+typedef struct cuMulticastUnbind_params_st {
+    CUmemGenericAllocationHandle mcHandle;
+    CUdevice dev;
+    size_t mcOffset;
+    size_t size;
+} cuMulticastUnbind_params;
+
+typedef struct cuMulticastGetGranularity_params_st {
+    size_t *granularity;
+    const CUmulticastObjectProp *prop;
+    CUmulticastGranularity_flags option;
+} cuMulticastGetGranularity_params;
+
+typedef struct cuPointerGetAttribute_params_st {
+    void *data;
+    CUpointer_attribute attribute;
+    CUdeviceptr ptr;
+} cuPointerGetAttribute_params;
+
+typedef struct cuMemPrefetchAsync_ptsz_params_st {
+    CUdeviceptr devPtr;
+    size_t count;
+    CUdevice dstDevice;
+    CUstream hStream;
+} cuMemPrefetchAsync_ptsz_params;
+
+typedef struct cuMemPrefetchAsync_v2_ptsz_params_st {
+    CUdeviceptr devPtr;
+    size_t count;
+    CUmemLocation location;
+    unsigned int flags;
+    CUstream hStream;
+} cuMemPrefetchAsync_v2_ptsz_params;
+
+typedef struct cuMemAdvise_params_st {
+    CUdeviceptr devPtr;
+    size_t count;
+    CUmem_advise advice;
+    CUdevice device;
+} cuMemAdvise_params;
+
+typedef struct cuMemAdvise_v2_params_st {
+    CUdeviceptr devPtr;
+    size_t count;
+    CUmem_advise advice;
+    CUmemLocation location;
+} cuMemAdvise_v2_params;
+
+typedef struct cuMemRangeGetAttribute_params_st {
+    void *data;
+    size_t dataSize;
+    CUmem_range_attribute attribute;
+    CUdeviceptr devPtr;
+    size_t count;
+} cuMemRangeGetAttribute_params;
+
+typedef struct cuMemRangeGetAttributes_params_st {
+    void **data;
+    size_t *dataSizes;
+    CUmem_range_attribute *attributes;
+    size_t numAttributes;
+    CUdeviceptr devPtr;
+    size_t count;
+} cuMemRangeGetAttributes_params;
+
+typedef struct cuPointerSetAttribute_params_st {
+    const void *value;
+    CUpointer_attribute attribute;
+    CUdeviceptr ptr;
+} cuPointerSetAttribute_params;
+
+typedef struct cuPointerGetAttributes_params_st {
+    unsigned int numAttributes;
+    CUpointer_attribute *attributes;
+    void **data;
+    CUdeviceptr ptr;
+} cuPointerGetAttributes_params;
+
+typedef struct cuStreamCreate_params_st {
+    CUstream *phStream;
+    unsigned int Flags;
+} cuStreamCreate_params;
+
+typedef struct cuStreamCreateWithPriority_params_st {
+    CUstream *phStream;
+    unsigned int flags;
+    int priority;
+} cuStreamCreateWithPriority_params;
+
+typedef struct cuStreamGetPriority_ptsz_params_st {
+    CUstream hStream;
+    int *priority;
+} cuStreamGetPriority_ptsz_params;
+
+typedef struct cuStreamGetDevice_ptsz_params_st {
+    CUstream hStream;
+    CUdevice *device;
+} cuStreamGetDevice_ptsz_params;
+
+typedef struct cuStreamGetFlags_ptsz_params_st {
+    CUstream hStream;
+    unsigned int *flags;
+} cuStreamGetFlags_ptsz_params;
+
+typedef struct cuStreamGetId_ptsz_params_st {
+    CUstream hStream;
+    unsigned long long *streamId;
+} cuStreamGetId_ptsz_params;
+
+typedef struct cuStreamGetCtx_ptsz_params_st {
+    CUstream hStream;
+    CUcontext *pctx;
+} cuStreamGetCtx_ptsz_params;
+
+typedef struct cuStreamGetCtx_v2_ptsz_params_st {
+    CUstream hStream;
+    CUcontext *pCtx;
+    CUgreenCtx *pGreenCtx;
+} cuStreamGetCtx_v2_ptsz_params;
+
+typedef struct cuStreamWaitEvent_ptsz_params_st {
+    CUstream hStream;
+    CUevent hEvent;
+    unsigned int Flags;
+} cuStreamWaitEvent_ptsz_params;
+
+typedef struct cuStreamAddCallback_ptsz_params_st {
+    CUstream hStream;
+    CUstreamCallback callback;
+    void *userData;
+    unsigned int flags;
+} cuStreamAddCallback_ptsz_params;
+
+typedef struct cuStreamBeginCapture_v2_ptsz_params_st {
+    CUstream hStream;
+    CUstreamCaptureMode mode;
+} cuStreamBeginCapture_v2_ptsz_params;
+
+typedef struct cuStreamBeginCaptureToGraph_ptsz_params_st {
+    CUstream hStream;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    const CUgraphEdgeData *dependencyData;
+    size_t numDependencies;
+    CUstreamCaptureMode mode;
+} cuStreamBeginCaptureToGraph_ptsz_params;
+
+typedef struct cuThreadExchangeStreamCaptureMode_params_st {
+    CUstreamCaptureMode *mode;
+} cuThreadExchangeStreamCaptureMode_params;
+
+typedef struct cuStreamEndCapture_ptsz_params_st {
+    CUstream hStream;
+    CUgraph *phGraph;
+} cuStreamEndCapture_ptsz_params;
+
+typedef struct cuStreamIsCapturing_ptsz_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus;
+} cuStreamIsCapturing_ptsz_params;
+
+typedef struct cuStreamGetCaptureInfo_v2_ptsz_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus_out;
+    cuuint64_t *id_out;
+    CUgraph *graph_out;
+    const CUgraphNode **dependencies_out;
+    size_t *numDependencies_out;
+} cuStreamGetCaptureInfo_v2_ptsz_params;
+
+typedef struct cuStreamGetCaptureInfo_v3_ptsz_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus_out;
+    cuuint64_t *id_out;
+    CUgraph *graph_out;
+    const CUgraphNode **dependencies_out;
+    const CUgraphEdgeData **edgeData_out;
+    size_t *numDependencies_out;
+} cuStreamGetCaptureInfo_v3_ptsz_params;
+
+typedef struct cuStreamUpdateCaptureDependencies_ptsz_params_st {
+    CUstream hStream;
+    CUgraphNode *dependencies;
+    size_t numDependencies;
+    unsigned int flags;
+} cuStreamUpdateCaptureDependencies_ptsz_params;
+
+typedef struct cuStreamUpdateCaptureDependencies_v2_ptsz_params_st {
+    CUstream hStream;
+    CUgraphNode *dependencies;
+    const CUgraphEdgeData *dependencyData;
+    size_t numDependencies;
+    unsigned int flags;
+} cuStreamUpdateCaptureDependencies_v2_ptsz_params;
+
+typedef struct cuStreamAttachMemAsync_ptsz_params_st {
+    CUstream hStream;
+    CUdeviceptr dptr;
+    size_t length;
+    unsigned int flags;
+} cuStreamAttachMemAsync_ptsz_params;
+
+typedef struct cuStreamQuery_ptsz_params_st {
+    CUstream hStream;
+} cuStreamQuery_ptsz_params;
+
+typedef struct cuStreamSynchronize_ptsz_params_st {
+    CUstream hStream;
+} cuStreamSynchronize_ptsz_params;
+
+typedef struct cuStreamDestroy_v2_params_st {
+    CUstream hStream;
+} cuStreamDestroy_v2_params;
+
+typedef struct cuStreamCopyAttributes_ptsz_params_st {
+    CUstream dst;
+    CUstream src;
+} cuStreamCopyAttributes_ptsz_params;
+
+typedef struct cuStreamGetAttribute_ptsz_params_st {
+    CUstream hStream;
+    CUstreamAttrID attr;
+    CUstreamAttrValue *value_out;
+} cuStreamGetAttribute_ptsz_params;
+
+typedef struct cuStreamSetAttribute_ptsz_params_st {
+    CUstream hStream;
+    CUstreamAttrID attr;
+    const CUstreamAttrValue *value;
+} cuStreamSetAttribute_ptsz_params;
+
+typedef struct cuEventCreate_params_st {
+    CUevent *phEvent;
+    unsigned int Flags;
+} cuEventCreate_params;
+
+typedef struct cuEventRecord_ptsz_params_st {
+    CUevent hEvent;
+    CUstream hStream;
+} cuEventRecord_ptsz_params;
+
+typedef struct cuEventRecordWithFlags_ptsz_params_st {
+    CUevent hEvent;
+    CUstream hStream;
+    unsigned int flags;
+} cuEventRecordWithFlags_ptsz_params;
+
+typedef struct cuEventQuery_params_st {
+    CUevent hEvent;
+} cuEventQuery_params;
+
+typedef struct cuEventSynchronize_params_st {
+    CUevent hEvent;
+} cuEventSynchronize_params;
+
+typedef struct cuEventDestroy_v2_params_st {
+    CUevent hEvent;
+} cuEventDestroy_v2_params;
+
+typedef struct cuEventElapsedTime_params_st {
+    float *pMilliseconds;
+    CUevent hStart;
+    CUevent hEnd;
+} cuEventElapsedTime_params;
+
+typedef struct cuEventElapsedTime_v2_params_st {
+    float *pMilliseconds;
+    CUevent hStart;
+    CUevent hEnd;
+} cuEventElapsedTime_v2_params;
+
+typedef struct cuImportExternalMemory_params_st {
+    CUexternalMemory *extMem_out;
+    const CUDA_EXTERNAL_MEMORY_HANDLE_DESC *memHandleDesc;
+} cuImportExternalMemory_params;
+
+typedef struct cuExternalMemoryGetMappedBuffer_params_st {
+    CUdeviceptr *devPtr;
+    CUexternalMemory extMem;
+    const CUDA_EXTERNAL_MEMORY_BUFFER_DESC *bufferDesc;
+} cuExternalMemoryGetMappedBuffer_params;
+
+typedef struct cuExternalMemoryGetMappedMipmappedArray_params_st {
+    CUmipmappedArray *mipmap;
+    CUexternalMemory extMem;
+    const CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC *mipmapDesc;
+} cuExternalMemoryGetMappedMipmappedArray_params;
+
+typedef struct cuDestroyExternalMemory_params_st {
+    CUexternalMemory extMem;
+} cuDestroyExternalMemory_params;
+
+typedef struct cuImportExternalSemaphore_params_st {
+    CUexternalSemaphore *extSem_out;
+    const CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC *semHandleDesc;
+} cuImportExternalSemaphore_params;
+
+typedef struct cuSignalExternalSemaphoresAsync_ptsz_params_st {
+    const CUexternalSemaphore *extSemArray;
+    const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS *paramsArray;
+    unsigned int numExtSems;
+    CUstream stream;
+} cuSignalExternalSemaphoresAsync_ptsz_params;
+
+typedef struct cuWaitExternalSemaphoresAsync_ptsz_params_st {
+    const CUexternalSemaphore *extSemArray;
+    const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS *paramsArray;
+    unsigned int numExtSems;
+    CUstream stream;
+} cuWaitExternalSemaphoresAsync_ptsz_params;
+
+typedef struct cuDestroyExternalSemaphore_params_st {
+    CUexternalSemaphore extSem;
+} cuDestroyExternalSemaphore_params;
+
+typedef struct cuStreamWaitValue32_v2_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWaitValue32_v2_ptsz_params;
+
+typedef struct cuStreamWaitValue64_v2_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWaitValue64_v2_ptsz_params;
+
+typedef struct cuStreamWriteValue32_v2_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWriteValue32_v2_ptsz_params;
+
+typedef struct cuStreamWriteValue64_v2_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWriteValue64_v2_ptsz_params;
+
+typedef struct cuStreamBatchMemOp_v2_ptsz_params_st {
+    CUstream stream;
+    unsigned int count;
+    CUstreamBatchMemOpParams *paramArray;
+    unsigned int flags;
+} cuStreamBatchMemOp_v2_ptsz_params;
+
+typedef struct cuFuncGetAttribute_params_st {
+    int *pi;
+    CUfunction_attribute attrib;
+    CUfunction hfunc;
+} cuFuncGetAttribute_params;
+
+typedef struct cuFuncSetAttribute_params_st {
+    CUfunction hfunc;
+    CUfunction_attribute attrib;
+    int value;
+} cuFuncSetAttribute_params;
+
+typedef struct cuFuncSetCacheConfig_params_st {
+    CUfunction hfunc;
+    CUfunc_cache config;
+} cuFuncSetCacheConfig_params;
+
+typedef struct cuFuncGetModule_params_st {
+    CUmodule *hmod;
+    CUfunction hfunc;
+} cuFuncGetModule_params;
+
+typedef struct cuFuncGetName_params_st {
+    const char **name;
+    CUfunction hfunc;
+} cuFuncGetName_params;
+
+typedef struct cuFuncGetParamInfo_params_st {
+    CUfunction func;
+    size_t paramIndex;
+    size_t *paramOffset;
+    size_t *paramSize;
+} cuFuncGetParamInfo_params;
+
+typedef struct cuFuncIsLoaded_params_st {
+    CUfunctionLoadingState *state;
+    CUfunction function;
+} cuFuncIsLoaded_params;
+
+typedef struct cuFuncLoad_params_st {
+    CUfunction function;
+} cuFuncLoad_params;
+
+typedef struct cuLaunchKernel_ptsz_params_st {
+    CUfunction f;
+    unsigned int gridDimX;
+    unsigned int gridDimY;
+    unsigned int gridDimZ;
+    unsigned int blockDimX;
+    unsigned int blockDimY;
+    unsigned int blockDimZ;
+    unsigned int sharedMemBytes;
+    CUstream hStream;
+    void **kernelParams;
+    void **extra;
+} cuLaunchKernel_ptsz_params;
+
+typedef struct cuLaunchKernelEx_ptsz_params_st {
+    const CUlaunchConfig *config;
+    CUfunction f;
+    void **kernelParams;
+    void **extra;
+} cuLaunchKernelEx_ptsz_params;
+
+typedef struct cuLaunchCooperativeKernel_ptsz_params_st {
+    CUfunction f;
+    unsigned int gridDimX;
+    unsigned int gridDimY;
+    unsigned int gridDimZ;
+    unsigned int blockDimX;
+    unsigned int blockDimY;
+    unsigned int blockDimZ;
+    unsigned int sharedMemBytes;
+    CUstream hStream;
+    void **kernelParams;
+} cuLaunchCooperativeKernel_ptsz_params;
+
+typedef struct cuLaunchCooperativeKernelMultiDevice_params_st {
+    CUDA_LAUNCH_PARAMS *launchParamsList;
+    unsigned int numDevices;
+    unsigned int flags;
+} cuLaunchCooperativeKernelMultiDevice_params;
+
+typedef struct cuLaunchHostFunc_ptsz_params_st {
+    CUstream hStream;
+    CUhostFn fn;
+    void *userData;
+} cuLaunchHostFunc_ptsz_params;
+
+typedef struct cuFuncSetBlockShape_params_st {
+    CUfunction hfunc;
+    int x;
+    int y;
+    int z;
+} cuFuncSetBlockShape_params;
+
+typedef struct cuFuncSetSharedSize_params_st {
+    CUfunction hfunc;
+    unsigned int bytes;
+} cuFuncSetSharedSize_params;
+
+typedef struct cuParamSetSize_params_st {
+    CUfunction hfunc;
+    unsigned int numbytes;
+} cuParamSetSize_params;
+
+typedef struct cuParamSeti_params_st {
+    CUfunction hfunc;
+    int offset;
+    unsigned int value;
+} cuParamSeti_params;
+
+typedef struct cuParamSetf_params_st {
+    CUfunction hfunc;
+    int offset;
+    float value;
+} cuParamSetf_params;
+
+typedef struct cuParamSetv_params_st {
+    CUfunction hfunc;
+    int offset;
+    void *ptr;
+    unsigned int numbytes;
+} cuParamSetv_params;
+
+typedef struct cuLaunch_params_st {
+    CUfunction f;
+} cuLaunch_params;
+
+typedef struct cuLaunchGrid_params_st {
+    CUfunction f;
+    int grid_width;
+    int grid_height;
+} cuLaunchGrid_params;
+
+typedef struct cuLaunchGridAsync_params_st {
+    CUfunction f;
+    int grid_width;
+    int grid_height;
+    CUstream hStream;
+} cuLaunchGridAsync_params;
+
+typedef struct cuParamSetTexRef_params_st {
+    CUfunction hfunc;
+    int texunit;
+    CUtexref hTexRef;
+} cuParamSetTexRef_params;
+
+typedef struct cuFuncSetSharedMemConfig_params_st {
+    CUfunction hfunc;
+    CUsharedconfig config;
+} cuFuncSetSharedMemConfig_params;
+
+typedef struct cuGraphCreate_params_st {
+    CUgraph *phGraph;
+    unsigned int flags;
+} cuGraphCreate_params;
+
+typedef struct cuGraphAddKernelNode_v2_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_KERNEL_NODE_PARAMS *nodeParams;
+} cuGraphAddKernelNode_v2_params;
+
+typedef struct cuGraphKernelNodeGetParams_v2_params_st {
+    CUgraphNode hNode;
+    CUDA_KERNEL_NODE_PARAMS *nodeParams;
+} cuGraphKernelNodeGetParams_v2_params;
+
+typedef struct cuGraphKernelNodeSetParams_v2_params_st {
+    CUgraphNode hNode;
+    const CUDA_KERNEL_NODE_PARAMS *nodeParams;
+} cuGraphKernelNodeSetParams_v2_params;
+
+typedef struct cuGraphAddMemcpyNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_MEMCPY3D *copyParams;
+    CUcontext ctx;
+} cuGraphAddMemcpyNode_params;
+
+typedef struct cuGraphMemcpyNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_MEMCPY3D *nodeParams;
+} cuGraphMemcpyNodeGetParams_params;
+
+typedef struct cuGraphMemcpyNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_MEMCPY3D *nodeParams;
+} cuGraphMemcpyNodeSetParams_params;
+
+typedef struct cuGraphAddMemsetNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_MEMSET_NODE_PARAMS *memsetParams;
+    CUcontext ctx;
+} cuGraphAddMemsetNode_params;
+
+typedef struct cuGraphMemsetNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_MEMSET_NODE_PARAMS *nodeParams;
+} cuGraphMemsetNodeGetParams_params;
+
+typedef struct cuGraphMemsetNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_MEMSET_NODE_PARAMS *nodeParams;
+} cuGraphMemsetNodeSetParams_params;
+
+typedef struct cuGraphAddHostNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_HOST_NODE_PARAMS *nodeParams;
+} cuGraphAddHostNode_params;
+
+typedef struct cuGraphHostNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_HOST_NODE_PARAMS *nodeParams;
+} cuGraphHostNodeGetParams_params;
+
+typedef struct cuGraphHostNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_HOST_NODE_PARAMS *nodeParams;
+} cuGraphHostNodeSetParams_params;
+
+typedef struct cuGraphAddChildGraphNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    CUgraph childGraph;
+} cuGraphAddChildGraphNode_params;
+
+typedef struct cuGraphChildGraphNodeGetGraph_params_st {
+    CUgraphNode hNode;
+    CUgraph *phGraph;
+} cuGraphChildGraphNodeGetGraph_params;
+
+typedef struct cuGraphAddEmptyNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+} cuGraphAddEmptyNode_params;
+
+typedef struct cuGraphAddEventRecordNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    CUevent event;
+} cuGraphAddEventRecordNode_params;
+
+typedef struct cuGraphEventRecordNodeGetEvent_params_st {
+    CUgraphNode hNode;
+    CUevent *event_out;
+} cuGraphEventRecordNodeGetEvent_params;
+
+typedef struct cuGraphEventRecordNodeSetEvent_params_st {
+    CUgraphNode hNode;
+    CUevent event;
+} cuGraphEventRecordNodeSetEvent_params;
+
+typedef struct cuGraphAddEventWaitNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    CUevent event;
+} cuGraphAddEventWaitNode_params;
+
+typedef struct cuGraphEventWaitNodeGetEvent_params_st {
+    CUgraphNode hNode;
+    CUevent *event_out;
+} cuGraphEventWaitNodeGetEvent_params;
+
+typedef struct cuGraphEventWaitNodeSetEvent_params_st {
+    CUgraphNode hNode;
+    CUevent event;
+} cuGraphEventWaitNodeSetEvent_params;
+
+typedef struct cuGraphAddExternalSemaphoresSignalNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams;
+} cuGraphAddExternalSemaphoresSignalNode_params;
+
+typedef struct cuGraphExternalSemaphoresSignalNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *params_out;
+} cuGraphExternalSemaphoresSignalNodeGetParams_params;
+
+typedef struct cuGraphExternalSemaphoresSignalNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams;
+} cuGraphExternalSemaphoresSignalNodeSetParams_params;
+
+typedef struct cuGraphAddExternalSemaphoresWaitNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams;
+} cuGraphAddExternalSemaphoresWaitNode_params;
+
+typedef struct cuGraphExternalSemaphoresWaitNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_EXT_SEM_WAIT_NODE_PARAMS *params_out;
+} cuGraphExternalSemaphoresWaitNodeGetParams_params;
+
+typedef struct cuGraphExternalSemaphoresWaitNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams;
+} cuGraphExternalSemaphoresWaitNodeSetParams_params;
+
+typedef struct cuGraphAddBatchMemOpNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams;
+} cuGraphAddBatchMemOpNode_params;
+
+typedef struct cuGraphBatchMemOpNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams_out;
+} cuGraphBatchMemOpNodeGetParams_params;
+
+typedef struct cuGraphBatchMemOpNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams;
+} cuGraphBatchMemOpNodeSetParams_params;
+
+typedef struct cuGraphExecBatchMemOpNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams;
+} cuGraphExecBatchMemOpNodeSetParams_params;
+
+typedef struct cuGraphAddMemAllocNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    CUDA_MEM_ALLOC_NODE_PARAMS *nodeParams;
+} cuGraphAddMemAllocNode_params;
+
+typedef struct cuGraphMemAllocNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_MEM_ALLOC_NODE_PARAMS *params_out;
+} cuGraphMemAllocNodeGetParams_params;
+
+typedef struct cuGraphAddMemFreeNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    CUdeviceptr dptr;
+} cuGraphAddMemFreeNode_params;
+
+typedef struct cuGraphMemFreeNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUdeviceptr *dptr_out;
+} cuGraphMemFreeNodeGetParams_params;
+
+typedef struct cuDeviceGraphMemTrim_params_st {
+    CUdevice device;
+} cuDeviceGraphMemTrim_params;
+
+typedef struct cuDeviceGetGraphMemAttribute_params_st {
+    CUdevice device;
+    CUgraphMem_attribute attr;
+    void *value;
+} cuDeviceGetGraphMemAttribute_params;
+
+typedef struct cuDeviceSetGraphMemAttribute_params_st {
+    CUdevice device;
+    CUgraphMem_attribute attr;
+    void *value;
+} cuDeviceSetGraphMemAttribute_params;
+
+typedef struct cuGraphClone_params_st {
+    CUgraph *phGraphClone;
+    CUgraph originalGraph;
+} cuGraphClone_params;
+
+typedef struct cuGraphNodeFindInClone_params_st {
+    CUgraphNode *phNode;
+    CUgraphNode hOriginalNode;
+    CUgraph hClonedGraph;
+} cuGraphNodeFindInClone_params;
+
+typedef struct cuGraphNodeGetType_params_st {
+    CUgraphNode hNode;
+    CUgraphNodeType *type;
+} cuGraphNodeGetType_params;
+
+typedef struct cuGraphGetNodes_params_st {
+    CUgraph hGraph;
+    CUgraphNode *nodes;
+    size_t *numNodes;
+} cuGraphGetNodes_params;
+
+typedef struct cuGraphGetRootNodes_params_st {
+    CUgraph hGraph;
+    CUgraphNode *rootNodes;
+    size_t *numRootNodes;
+} cuGraphGetRootNodes_params;
+
+typedef struct cuGraphGetEdges_params_st {
+    CUgraph hGraph;
+    CUgraphNode *from;
+    CUgraphNode *to;
+    size_t *numEdges;
+} cuGraphGetEdges_params;
+
+typedef struct cuGraphGetEdges_v2_params_st {
+    CUgraph hGraph;
+    CUgraphNode *from;
+    CUgraphNode *to;
+    CUgraphEdgeData *edgeData;
+    size_t *numEdges;
+} cuGraphGetEdges_v2_params;
+
+typedef struct cuGraphNodeGetDependencies_params_st {
+    CUgraphNode hNode;
+    CUgraphNode *dependencies;
+    size_t *numDependencies;
+} cuGraphNodeGetDependencies_params;
+
+typedef struct cuGraphNodeGetDependencies_v2_params_st {
+    CUgraphNode hNode;
+    CUgraphNode *dependencies;
+    CUgraphEdgeData *edgeData;
+    size_t *numDependencies;
+} cuGraphNodeGetDependencies_v2_params;
+
+typedef struct cuGraphNodeGetDependentNodes_params_st {
+    CUgraphNode hNode;
+    CUgraphNode *dependentNodes;
+    size_t *numDependentNodes;
+} cuGraphNodeGetDependentNodes_params;
+
+typedef struct cuGraphNodeGetDependentNodes_v2_params_st {
+    CUgraphNode hNode;
+    CUgraphNode *dependentNodes;
+    CUgraphEdgeData *edgeData;
+    size_t *numDependentNodes;
+} cuGraphNodeGetDependentNodes_v2_params;
+
+typedef struct cuGraphAddDependencies_params_st {
+    CUgraph hGraph;
+    const CUgraphNode *from;
+    const CUgraphNode *to;
+    size_t numDependencies;
+} cuGraphAddDependencies_params;
+
+typedef struct cuGraphAddDependencies_v2_params_st {
+    CUgraph hGraph;
+    const CUgraphNode *from;
+    const CUgraphNode *to;
+    const CUgraphEdgeData *edgeData;
+    size_t numDependencies;
+} cuGraphAddDependencies_v2_params;
+
+typedef struct cuGraphRemoveDependencies_params_st {
+    CUgraph hGraph;
+    const CUgraphNode *from;
+    const CUgraphNode *to;
+    size_t numDependencies;
+} cuGraphRemoveDependencies_params;
+
+typedef struct cuGraphRemoveDependencies_v2_params_st {
+    CUgraph hGraph;
+    const CUgraphNode *from;
+    const CUgraphNode *to;
+    const CUgraphEdgeData *edgeData;
+    size_t numDependencies;
+} cuGraphRemoveDependencies_v2_params;
+
+typedef struct cuGraphDestroyNode_params_st {
+    CUgraphNode hNode;
+} cuGraphDestroyNode_params;
+
+typedef struct cuGraphInstantiateWithFlags_params_st {
+    CUgraphExec *phGraphExec;
+    CUgraph hGraph;
+    unsigned long long flags;
+} cuGraphInstantiateWithFlags_params;
+
+typedef struct cuGraphInstantiateWithParams_ptsz_params_st {
+    CUgraphExec *phGraphExec;
+    CUgraph hGraph;
+    CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams;
+} cuGraphInstantiateWithParams_ptsz_params;
+
+typedef struct cuGraphExecGetFlags_params_st {
+    CUgraphExec hGraphExec;
+    cuuint64_t *flags;
+} cuGraphExecGetFlags_params;
+
+typedef struct cuGraphExecKernelNodeSetParams_v2_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_KERNEL_NODE_PARAMS *nodeParams;
+} cuGraphExecKernelNodeSetParams_v2_params;
+
+typedef struct cuGraphExecMemcpyNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_MEMCPY3D *copyParams;
+    CUcontext ctx;
+} cuGraphExecMemcpyNodeSetParams_params;
+
+typedef struct cuGraphExecMemsetNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_MEMSET_NODE_PARAMS *memsetParams;
+    CUcontext ctx;
+} cuGraphExecMemsetNodeSetParams_params;
+
+typedef struct cuGraphExecHostNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_HOST_NODE_PARAMS *nodeParams;
+} cuGraphExecHostNodeSetParams_params;
+
+typedef struct cuGraphExecChildGraphNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    CUgraph childGraph;
+} cuGraphExecChildGraphNodeSetParams_params;
+
+typedef struct cuGraphExecEventRecordNodeSetEvent_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    CUevent event;
+} cuGraphExecEventRecordNodeSetEvent_params;
+
+typedef struct cuGraphExecEventWaitNodeSetEvent_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    CUevent event;
+} cuGraphExecEventWaitNodeSetEvent_params;
+
+typedef struct cuGraphExecExternalSemaphoresSignalNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams;
+} cuGraphExecExternalSemaphoresSignalNodeSetParams_params;
+
+typedef struct cuGraphExecExternalSemaphoresWaitNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams;
+} cuGraphExecExternalSemaphoresWaitNodeSetParams_params;
+
+typedef struct cuGraphNodeSetEnabled_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    unsigned int isEnabled;
+} cuGraphNodeSetEnabled_params;
+
+typedef struct cuGraphNodeGetEnabled_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    unsigned int *isEnabled;
+} cuGraphNodeGetEnabled_params;
+
+typedef struct cuGraphUpload_ptsz_params_st {
+    CUgraphExec hGraphExec;
+    CUstream hStream;
+} cuGraphUpload_ptsz_params;
+
+typedef struct cuGraphLaunch_ptsz_params_st {
+    CUgraphExec hGraphExec;
+    CUstream hStream;
+} cuGraphLaunch_ptsz_params;
+
+typedef struct cuGraphExecDestroy_params_st {
+    CUgraphExec hGraphExec;
+} cuGraphExecDestroy_params;
+
+typedef struct cuGraphDestroy_params_st {
+    CUgraph hGraph;
+} cuGraphDestroy_params;
+
+typedef struct cuGraphExecUpdate_v2_params_st {
+    CUgraphExec hGraphExec;
+    CUgraph hGraph;
+    CUgraphExecUpdateResultInfo *resultInfo;
+} cuGraphExecUpdate_v2_params;
+
+typedef struct cuGraphKernelNodeCopyAttributes_params_st {
+    CUgraphNode dst;
+    CUgraphNode src;
+} cuGraphKernelNodeCopyAttributes_params;
+
+typedef struct cuGraphKernelNodeGetAttribute_params_st {
+    CUgraphNode hNode;
+    CUkernelNodeAttrID attr;
+    CUkernelNodeAttrValue *value_out;
+} cuGraphKernelNodeGetAttribute_params;
+
+typedef struct cuGraphKernelNodeSetAttribute_params_st {
+    CUgraphNode hNode;
+    CUkernelNodeAttrID attr;
+    const CUkernelNodeAttrValue *value;
+} cuGraphKernelNodeSetAttribute_params;
+
+typedef struct cuGraphDebugDotPrint_params_st {
+    CUgraph hGraph;
+    const char *path;
+    unsigned int flags;
+} cuGraphDebugDotPrint_params;
+
+typedef struct cuUserObjectCreate_params_st {
+    CUuserObject *object_out;
+    void *ptr;
+    CUhostFn destroy;
+    unsigned int initialRefcount;
+    unsigned int flags;
+} cuUserObjectCreate_params;
+
+typedef struct cuUserObjectRetain_params_st {
+    CUuserObject object;
+    unsigned int count;
+} cuUserObjectRetain_params;
+
+typedef struct cuUserObjectRelease_params_st {
+    CUuserObject object;
+    unsigned int count;
+} cuUserObjectRelease_params;
+
+typedef struct cuGraphRetainUserObject_params_st {
+    CUgraph graph;
+    CUuserObject object;
+    unsigned int count;
+    unsigned int flags;
+} cuGraphRetainUserObject_params;
+
+typedef struct cuGraphReleaseUserObject_params_st {
+    CUgraph graph;
+    CUuserObject object;
+    unsigned int count;
+} cuGraphReleaseUserObject_params;
+
+typedef struct cuGraphAddNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    CUgraphNodeParams *nodeParams;
+} cuGraphAddNode_params;
+
+typedef struct cuGraphAddNode_v2_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    const CUgraphEdgeData *dependencyData;
+    size_t numDependencies;
+    CUgraphNodeParams *nodeParams;
+} cuGraphAddNode_v2_params;
+
+typedef struct cuGraphNodeSetParams_params_st {
+    CUgraphNode hNode;
+    CUgraphNodeParams *nodeParams;
+} cuGraphNodeSetParams_params;
+
+typedef struct cuGraphExecNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    CUgraphNodeParams *nodeParams;
+} cuGraphExecNodeSetParams_params;
+
+typedef struct cuGraphConditionalHandleCreate_params_st {
+    CUgraphConditionalHandle *pHandle_out;
+    CUgraph hGraph;
+    CUcontext ctx;
+    unsigned int defaultLaunchValue;
+    unsigned int flags;
+} cuGraphConditionalHandleCreate_params;
+
+typedef struct cuOccupancyMaxActiveBlocksPerMultiprocessor_params_st {
+    int *numBlocks;
+    CUfunction func;
+    int blockSize;
+    size_t dynamicSMemSize;
+} cuOccupancyMaxActiveBlocksPerMultiprocessor_params;
+
+typedef struct cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_params_st {
+    int *numBlocks;
+    CUfunction func;
+    int blockSize;
+    size_t dynamicSMemSize;
+    unsigned int flags;
+} cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_params;
+
+typedef struct cuOccupancyMaxPotentialBlockSize_params_st {
+    int *minGridSize;
+    int *blockSize;
+    CUfunction func;
+    CUoccupancyB2DSize blockSizeToDynamicSMemSize;
+    size_t dynamicSMemSize;
+    int blockSizeLimit;
+} cuOccupancyMaxPotentialBlockSize_params;
+
+typedef struct cuOccupancyMaxPotentialBlockSizeWithFlags_params_st {
+    int *minGridSize;
+    int *blockSize;
+    CUfunction func;
+    CUoccupancyB2DSize blockSizeToDynamicSMemSize;
+    size_t dynamicSMemSize;
+    int blockSizeLimit;
+    unsigned int flags;
+} cuOccupancyMaxPotentialBlockSizeWithFlags_params;
+
+typedef struct cuOccupancyAvailableDynamicSMemPerBlock_params_st {
+    size_t *dynamicSmemSize;
+    CUfunction func;
+    int numBlocks;
+    int blockSize;
+} cuOccupancyAvailableDynamicSMemPerBlock_params;
+
+typedef struct cuOccupancyMaxPotentialClusterSize_params_st {
+    int *clusterSize;
+    CUfunction func;
+    const CUlaunchConfig *config;
+} cuOccupancyMaxPotentialClusterSize_params;
+
+typedef struct cuOccupancyMaxActiveClusters_params_st {
+    int *numClusters;
+    CUfunction func;
+    const CUlaunchConfig *config;
+} cuOccupancyMaxActiveClusters_params;
+
+typedef struct cuTexRefSetArray_params_st {
+    CUtexref hTexRef;
+    CUarray hArray;
+    unsigned int Flags;
+} cuTexRefSetArray_params;
+
+typedef struct cuTexRefSetMipmappedArray_params_st {
+    CUtexref hTexRef;
+    CUmipmappedArray hMipmappedArray;
+    unsigned int Flags;
+} cuTexRefSetMipmappedArray_params;
+
+typedef struct cuTexRefSetAddress_v2_params_st {
+    size_t *ByteOffset;
+    CUtexref hTexRef;
+    CUdeviceptr dptr;
+    size_t bytes;
+} cuTexRefSetAddress_v2_params;
+
+typedef struct cuTexRefSetAddress2D_v3_params_st {
+    CUtexref hTexRef;
+    const CUDA_ARRAY_DESCRIPTOR *desc;
+    CUdeviceptr dptr;
+    size_t Pitch;
+} cuTexRefSetAddress2D_v3_params;
+
+typedef struct cuTexRefSetFormat_params_st {
+    CUtexref hTexRef;
+    CUarray_format fmt;
+    int NumPackedComponents;
+} cuTexRefSetFormat_params;
+
+typedef struct cuTexRefSetAddressMode_params_st {
+    CUtexref hTexRef;
+    int dim;
+    CUaddress_mode am;
+} cuTexRefSetAddressMode_params;
+
+typedef struct cuTexRefSetFilterMode_params_st {
+    CUtexref hTexRef;
+    CUfilter_mode fm;
+} cuTexRefSetFilterMode_params;
+
+typedef struct cuTexRefSetMipmapFilterMode_params_st {
+    CUtexref hTexRef;
+    CUfilter_mode fm;
+} cuTexRefSetMipmapFilterMode_params;
+
+typedef struct cuTexRefSetMipmapLevelBias_params_st {
+    CUtexref hTexRef;
+    float bias;
+} cuTexRefSetMipmapLevelBias_params;
+
+typedef struct cuTexRefSetMipmapLevelClamp_params_st {
+    CUtexref hTexRef;
+    float minMipmapLevelClamp;
+    float maxMipmapLevelClamp;
+} cuTexRefSetMipmapLevelClamp_params;
+
+typedef struct cuTexRefSetMaxAnisotropy_params_st {
+    CUtexref hTexRef;
+    unsigned int maxAniso;
+} cuTexRefSetMaxAnisotropy_params;
+
+typedef struct cuTexRefSetBorderColor_params_st {
+    CUtexref hTexRef;
+    float *pBorderColor;
+} cuTexRefSetBorderColor_params;
+
+typedef struct cuTexRefSetFlags_params_st {
+    CUtexref hTexRef;
+    unsigned int Flags;
+} cuTexRefSetFlags_params;
+
+typedef struct cuTexRefGetAddress_v2_params_st {
+    CUdeviceptr *pdptr;
+    CUtexref hTexRef;
+} cuTexRefGetAddress_v2_params;
+
+typedef struct cuTexRefGetArray_params_st {
+    CUarray *phArray;
+    CUtexref hTexRef;
+} cuTexRefGetArray_params;
+
+typedef struct cuTexRefGetMipmappedArray_params_st {
+    CUmipmappedArray *phMipmappedArray;
+    CUtexref hTexRef;
+} cuTexRefGetMipmappedArray_params;
+
+typedef struct cuTexRefGetAddressMode_params_st {
+    CUaddress_mode *pam;
+    CUtexref hTexRef;
+    int dim;
+} cuTexRefGetAddressMode_params;
+
+typedef struct cuTexRefGetFilterMode_params_st {
+    CUfilter_mode *pfm;
+    CUtexref hTexRef;
+} cuTexRefGetFilterMode_params;
+
+typedef struct cuTexRefGetFormat_params_st {
+    CUarray_format *pFormat;
+    int *pNumChannels;
+    CUtexref hTexRef;
+} cuTexRefGetFormat_params;
+
+typedef struct cuTexRefGetMipmapFilterMode_params_st {
+    CUfilter_mode *pfm;
+    CUtexref hTexRef;
+} cuTexRefGetMipmapFilterMode_params;
+
+typedef struct cuTexRefGetMipmapLevelBias_params_st {
+    float *pbias;
+    CUtexref hTexRef;
+} cuTexRefGetMipmapLevelBias_params;
+
+typedef struct cuTexRefGetMipmapLevelClamp_params_st {
+    float *pminMipmapLevelClamp;
+    float *pmaxMipmapLevelClamp;
+    CUtexref hTexRef;
+} cuTexRefGetMipmapLevelClamp_params;
+
+typedef struct cuTexRefGetMaxAnisotropy_params_st {
+    int *pmaxAniso;
+    CUtexref hTexRef;
+} cuTexRefGetMaxAnisotropy_params;
+
+typedef struct cuTexRefGetBorderColor_params_st {
+    float *pBorderColor;
+    CUtexref hTexRef;
+} cuTexRefGetBorderColor_params;
+
+typedef struct cuTexRefGetFlags_params_st {
+    unsigned int *pFlags;
+    CUtexref hTexRef;
+} cuTexRefGetFlags_params;
+
+typedef struct cuTexRefCreate_params_st {
+    CUtexref *pTexRef;
+} cuTexRefCreate_params;
+
+typedef struct cuTexRefDestroy_params_st {
+    CUtexref hTexRef;
+} cuTexRefDestroy_params;
+
+typedef struct cuSurfRefSetArray_params_st {
+    CUsurfref hSurfRef;
+    CUarray hArray;
+    unsigned int Flags;
+} cuSurfRefSetArray_params;
+
+typedef struct cuSurfRefGetArray_params_st {
+    CUarray *phArray;
+    CUsurfref hSurfRef;
+} cuSurfRefGetArray_params;
+
+typedef struct cuTexObjectCreate_params_st {
+    CUtexObject *pTexObject;
+    const CUDA_RESOURCE_DESC *pResDesc;
+    const CUDA_TEXTURE_DESC *pTexDesc;
+    const CUDA_RESOURCE_VIEW_DESC *pResViewDesc;
+} cuTexObjectCreate_params;
+
+typedef struct cuTexObjectDestroy_params_st {
+    CUtexObject texObject;
+} cuTexObjectDestroy_params;
+
+typedef struct cuTexObjectGetResourceDesc_params_st {
+    CUDA_RESOURCE_DESC *pResDesc;
+    CUtexObject texObject;
+} cuTexObjectGetResourceDesc_params;
+
+typedef struct cuTexObjectGetTextureDesc_params_st {
+    CUDA_TEXTURE_DESC *pTexDesc;
+    CUtexObject texObject;
+} cuTexObjectGetTextureDesc_params;
+
+typedef struct cuTexObjectGetResourceViewDesc_params_st {
+    CUDA_RESOURCE_VIEW_DESC *pResViewDesc;
+    CUtexObject texObject;
+} cuTexObjectGetResourceViewDesc_params;
+
+typedef struct cuSurfObjectCreate_params_st {
+    CUsurfObject *pSurfObject;
+    const CUDA_RESOURCE_DESC *pResDesc;
+} cuSurfObjectCreate_params;
+
+typedef struct cuSurfObjectDestroy_params_st {
+    CUsurfObject surfObject;
+} cuSurfObjectDestroy_params;
+
+typedef struct cuSurfObjectGetResourceDesc_params_st {
+    CUDA_RESOURCE_DESC *pResDesc;
+    CUsurfObject surfObject;
+} cuSurfObjectGetResourceDesc_params;
+
+typedef struct cuTensorMapEncodeTiled_params_st {
+    CUtensorMap *tensorMap;
+    CUtensorMapDataType tensorDataType;
+    cuuint32_t tensorRank;
+    void *globalAddress;
+    const cuuint64_t *globalDim;
+    const cuuint64_t *globalStrides;
+    const cuuint32_t *boxDim;
+    const cuuint32_t *elementStrides;
+    CUtensorMapInterleave interleave;
+    CUtensorMapSwizzle swizzle;
+    CUtensorMapL2promotion l2Promotion;
+    CUtensorMapFloatOOBfill oobFill;
+} cuTensorMapEncodeTiled_params;
+
+typedef struct cuTensorMapEncodeIm2col_params_st {
+    CUtensorMap *tensorMap;
+    CUtensorMapDataType tensorDataType;
+    cuuint32_t tensorRank;
+    void *globalAddress;
+    const cuuint64_t *globalDim;
+    const cuuint64_t *globalStrides;
+    const int *pixelBoxLowerCorner;
+    const int *pixelBoxUpperCorner;
+    cuuint32_t channelsPerPixel;
+    cuuint32_t pixelsPerColumn;
+    const cuuint32_t *elementStrides;
+    CUtensorMapInterleave interleave;
+    CUtensorMapSwizzle swizzle;
+    CUtensorMapL2promotion l2Promotion;
+    CUtensorMapFloatOOBfill oobFill;
+} cuTensorMapEncodeIm2col_params;
+
+typedef struct cuTensorMapReplaceAddress_params_st {
+    CUtensorMap *tensorMap;
+    void *globalAddress;
+} cuTensorMapReplaceAddress_params;
+
+typedef struct cuDeviceCanAccessPeer_params_st {
+    int *canAccessPeer;
+    CUdevice dev;
+    CUdevice peerDev;
+} cuDeviceCanAccessPeer_params;
+
+typedef struct cuCtxEnablePeerAccess_params_st {
+    CUcontext peerContext;
+    unsigned int Flags;
+} cuCtxEnablePeerAccess_params;
+
+typedef struct cuCtxDisablePeerAccess_params_st {
+    CUcontext peerContext;
+} cuCtxDisablePeerAccess_params;
+
+typedef struct cuDeviceGetP2PAttribute_params_st {
+    int *value;
+    CUdevice_P2PAttribute attrib;
+    CUdevice srcDevice;
+    CUdevice dstDevice;
+} cuDeviceGetP2PAttribute_params;
+
+typedef struct cuGraphicsUnregisterResource_params_st {
+    CUgraphicsResource resource;
+} cuGraphicsUnregisterResource_params;
+
+typedef struct cuGraphicsSubResourceGetMappedArray_params_st {
+    CUarray *pArray;
+    CUgraphicsResource resource;
+    unsigned int arrayIndex;
+    unsigned int mipLevel;
+} cuGraphicsSubResourceGetMappedArray_params;
+
+typedef struct cuGraphicsResourceGetMappedMipmappedArray_params_st {
+    CUmipmappedArray *pMipmappedArray;
+    CUgraphicsResource resource;
+} cuGraphicsResourceGetMappedMipmappedArray_params;
+
+typedef struct cuGraphicsResourceGetMappedPointer_v2_params_st {
+    CUdeviceptr *pDevPtr;
+    size_t *pSize;
+    CUgraphicsResource resource;
+} cuGraphicsResourceGetMappedPointer_v2_params;
+
+typedef struct cuGraphicsResourceSetMapFlags_v2_params_st {
+    CUgraphicsResource resource;
+    unsigned int flags;
+} cuGraphicsResourceSetMapFlags_v2_params;
+
+typedef struct cuGraphicsMapResources_ptsz_params_st {
+    unsigned int count;
+    CUgraphicsResource *resources;
+    CUstream hStream;
+} cuGraphicsMapResources_ptsz_params;
+
+typedef struct cuGraphicsUnmapResources_ptsz_params_st {
+    unsigned int count;
+    CUgraphicsResource *resources;
+    CUstream hStream;
+} cuGraphicsUnmapResources_ptsz_params;
+
+typedef struct cuGetProcAddress_v2_params_st {
+    const char *symbol;
+    void **pfn;
+    int cudaVersion;
+    cuuint64_t flags;
+    CUdriverProcAddressQueryResult *symbolStatus;
+} cuGetProcAddress_v2_params;
+
+typedef struct cuCoredumpGetAttribute_params_st {
+    CUcoredumpSettings attrib;
+    void *value;
+    size_t *size;
+} cuCoredumpGetAttribute_params;
+
+typedef struct cuCoredumpGetAttributeGlobal_params_st {
+    CUcoredumpSettings attrib;
+    void *value;
+    size_t *size;
+} cuCoredumpGetAttributeGlobal_params;
+
+typedef struct cuCoredumpSetAttribute_params_st {
+    CUcoredumpSettings attrib;
+    void *value;
+    size_t *size;
+} cuCoredumpSetAttribute_params;
+
+typedef struct cuCoredumpSetAttributeGlobal_params_st {
+    CUcoredumpSettings attrib;
+    void *value;
+    size_t *size;
+} cuCoredumpSetAttributeGlobal_params;
+
+typedef struct cuGetExportTable_params_st {
+    const void **ppExportTable;
+    const CUuuid *pExportTableId;
+} cuGetExportTable_params;
+
+typedef struct cuGreenCtxCreate_params_st {
+    CUgreenCtx *phCtx;
+    CUdevResourceDesc desc;
+    CUdevice dev;
+    unsigned int flags;
+} cuGreenCtxCreate_params;
+
+typedef struct cuGreenCtxDestroy_params_st {
+    CUgreenCtx hCtx;
+} cuGreenCtxDestroy_params;
+
+typedef struct cuCtxFromGreenCtx_params_st {
+    CUcontext *pContext;
+    CUgreenCtx hCtx;
+} cuCtxFromGreenCtx_params;
+
+typedef struct cuDeviceGetDevResource_params_st {
+    CUdevice device;
+    CUdevResource *resource;
+    CUdevResourceType type;
+} cuDeviceGetDevResource_params;
+
+typedef struct cuCtxGetDevResource_params_st {
+    CUcontext hCtx;
+    CUdevResource *resource;
+    CUdevResourceType type;
+} cuCtxGetDevResource_params;
+
+typedef struct cuGreenCtxGetDevResource_params_st {
+    CUgreenCtx hCtx;
+    CUdevResource *resource;
+    CUdevResourceType type;
+} cuGreenCtxGetDevResource_params;
+
+typedef struct cuDevSmResourceSplitByCount_params_st {
+    CUdevResource *result;
+    unsigned int *nbGroups;
+    const CUdevResource *input;
+    CUdevResource *remaining;
+    unsigned int useFlags;
+    unsigned int minCount;
+} cuDevSmResourceSplitByCount_params;
+
+typedef struct cuDevResourceGenerateDesc_params_st {
+    CUdevResourceDesc *phDesc;
+    CUdevResource *resources;
+    unsigned int nbResources;
+} cuDevResourceGenerateDesc_params;
+
+typedef struct cuGreenCtxRecordEvent_params_st {
+    CUgreenCtx hCtx;
+    CUevent hEvent;
+} cuGreenCtxRecordEvent_params;
+
+typedef struct cuGreenCtxWaitEvent_params_st {
+    CUgreenCtx hCtx;
+    CUevent hEvent;
+} cuGreenCtxWaitEvent_params;
+
+typedef struct cuStreamGetGreenCtx_params_st {
+    CUstream hStream;
+    CUgreenCtx *phCtx;
+} cuStreamGetGreenCtx_params;
+
+typedef struct cuGreenCtxStreamCreate_params_st {
+    CUstream *phStream;
+    CUgreenCtx greenCtx;
+    unsigned int flags;
+    int priority;
+} cuGreenCtxStreamCreate_params;
+
+typedef struct cuMemHostRegister_params_st {
+    void *p;
+    size_t bytesize;
+    unsigned int Flags;
+} cuMemHostRegister_params;
+
+typedef struct cuGraphicsResourceSetMapFlags_params_st {
+    CUgraphicsResource resource;
+    unsigned int flags;
+} cuGraphicsResourceSetMapFlags_params;
+
+typedef struct cuLinkCreate_params_st {
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+    CUlinkState *stateOut;
+} cuLinkCreate_params;
+
+typedef struct cuLinkAddData_params_st {
+    CUlinkState state;
+    CUjitInputType type;
+    void *data;
+    size_t size;
+    const char *name;
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+} cuLinkAddData_params;
+
+typedef struct cuLinkAddFile_params_st {
+    CUlinkState state;
+    CUjitInputType type;
+    const char *path;
+    unsigned int numOptions;
+    CUjit_option *options;
+    void **optionValues;
+} cuLinkAddFile_params;
+
+typedef struct cuTexRefSetAddress2D_v2_params_st {
+    CUtexref hTexRef;
+    const CUDA_ARRAY_DESCRIPTOR *desc;
+    CUdeviceptr dptr;
+    size_t Pitch;
+} cuTexRefSetAddress2D_v2_params;
+
+typedef struct cuDeviceTotalMem_params_st {
+    unsigned int *bytes;
+    CUdevice dev;
+} cuDeviceTotalMem_params;
+
+typedef struct cuCtxCreate_params_st {
+    CUcontext *pctx;
+    unsigned int flags;
+    CUdevice dev;
+} cuCtxCreate_params;
+
+typedef struct cuModuleGetGlobal_params_st {
+    CUdeviceptr_v1 *dptr;
+    unsigned int *bytes;
+    CUmodule hmod;
+    const char *name;
+} cuModuleGetGlobal_params;
+
+typedef struct cuMemGetInfo_params_st {
+    unsigned int *free;
+    unsigned int *total;
+} cuMemGetInfo_params;
+
+typedef struct cuMemAlloc_params_st {
+    CUdeviceptr_v1 *dptr;
+    unsigned int bytesize;
+} cuMemAlloc_params;
+
+typedef struct cuMemAllocPitch_params_st {
+    CUdeviceptr_v1 *dptr;
+    unsigned int *pPitch;
+    unsigned int WidthInBytes;
+    unsigned int Height;
+    unsigned int ElementSizeBytes;
+} cuMemAllocPitch_params;
+
+typedef struct cuMemFree_params_st {
+    CUdeviceptr_v1 dptr;
+} cuMemFree_params;
+
+typedef struct cuMemGetAddressRange_params_st {
+    CUdeviceptr_v1 *pbase;
+    unsigned int *psize;
+    CUdeviceptr_v1 dptr;
+} cuMemGetAddressRange_params;
+
+typedef struct cuMemAllocHost_params_st {
+    void **pp;
+    unsigned int bytesize;
+} cuMemAllocHost_params;
+
+typedef struct cuMemHostGetDevicePointer_params_st {
+    CUdeviceptr_v1 *pdptr;
+    void *p;
+    unsigned int Flags;
+} cuMemHostGetDevicePointer_params;
+
+typedef struct cuMemcpyHtoD_params_st {
+    CUdeviceptr_v1 dstDevice;
+    const void *srcHost;
+    unsigned int ByteCount;
+} cuMemcpyHtoD_params;
+
+typedef struct cuMemcpyDtoH_params_st {
+    void *dstHost;
+    CUdeviceptr_v1 srcDevice;
+    unsigned int ByteCount;
+} cuMemcpyDtoH_params;
+
+typedef struct cuMemcpyDtoD_params_st {
+    CUdeviceptr_v1 dstDevice;
+    CUdeviceptr_v1 srcDevice;
+    unsigned int ByteCount;
+} cuMemcpyDtoD_params;
+
+typedef struct cuMemcpyDtoA_params_st {
+    CUarray dstArray;
+    unsigned int dstOffset;
+    CUdeviceptr_v1 srcDevice;
+    unsigned int ByteCount;
+} cuMemcpyDtoA_params;
+
+typedef struct cuMemcpyAtoD_params_st {
+    CUdeviceptr_v1 dstDevice;
+    CUarray srcArray;
+    unsigned int srcOffset;
+    unsigned int ByteCount;
+} cuMemcpyAtoD_params;
+
+typedef struct cuMemcpyHtoA_params_st {
+    CUarray dstArray;
+    unsigned int dstOffset;
+    const void *srcHost;
+    unsigned int ByteCount;
+} cuMemcpyHtoA_params;
+
+typedef struct cuMemcpyAtoH_params_st {
+    void *dstHost;
+    CUarray srcArray;
+    unsigned int srcOffset;
+    unsigned int ByteCount;
+} cuMemcpyAtoH_params;
+
+typedef struct cuMemcpyAtoA_params_st {
+    CUarray dstArray;
+    unsigned int dstOffset;
+    CUarray srcArray;
+    unsigned int srcOffset;
+    unsigned int ByteCount;
+} cuMemcpyAtoA_params;
+
+typedef struct cuMemcpyHtoAAsync_params_st {
+    CUarray dstArray;
+    unsigned int dstOffset;
+    const void *srcHost;
+    unsigned int ByteCount;
+    CUstream hStream;
+} cuMemcpyHtoAAsync_params;
+
+typedef struct cuMemcpyAtoHAsync_params_st {
+    void *dstHost;
+    CUarray srcArray;
+    unsigned int srcOffset;
+    unsigned int ByteCount;
+    CUstream hStream;
+} cuMemcpyAtoHAsync_params;
+
+typedef struct cuMemcpy2D_params_st {
+    const CUDA_MEMCPY2D_v1 *pCopy;
+} cuMemcpy2D_params;
+
+typedef struct cuMemcpy2DUnaligned_params_st {
+    const CUDA_MEMCPY2D_v1 *pCopy;
+} cuMemcpy2DUnaligned_params;
+
+typedef struct cuMemcpy3D_params_st {
+    const CUDA_MEMCPY3D_v1 *pCopy;
+} cuMemcpy3D_params;
+
+typedef struct cuMemcpyHtoDAsync_params_st {
+    CUdeviceptr_v1 dstDevice;
+    const void *srcHost;
+    unsigned int ByteCount;
+    CUstream hStream;
+} cuMemcpyHtoDAsync_params;
+
+typedef struct cuMemcpyDtoHAsync_params_st {
+    void *dstHost;
+    CUdeviceptr_v1 srcDevice;
+    unsigned int ByteCount;
+    CUstream hStream;
+} cuMemcpyDtoHAsync_params;
+
+typedef struct cuMemcpyDtoDAsync_params_st {
+    CUdeviceptr_v1 dstDevice;
+    CUdeviceptr_v1 srcDevice;
+    unsigned int ByteCount;
+    CUstream hStream;
+} cuMemcpyDtoDAsync_params;
+
+typedef struct cuMemcpy2DAsync_params_st {
+    const CUDA_MEMCPY2D_v1 *pCopy;
+    CUstream hStream;
+} cuMemcpy2DAsync_params;
+
+typedef struct cuMemcpy3DAsync_params_st {
+    const CUDA_MEMCPY3D_v1 *pCopy;
+    CUstream hStream;
+} cuMemcpy3DAsync_params;
+
+typedef struct cuMemsetD8_params_st {
+    CUdeviceptr_v1 dstDevice;
+    unsigned char uc;
+    unsigned int N;
+} cuMemsetD8_params;
+
+typedef struct cuMemsetD16_params_st {
+    CUdeviceptr_v1 dstDevice;
+    unsigned short us;
+    unsigned int N;
+} cuMemsetD16_params;
+
+typedef struct cuMemsetD32_params_st {
+    CUdeviceptr_v1 dstDevice;
+    unsigned int ui;
+    unsigned int N;
+} cuMemsetD32_params;
+
+typedef struct cuMemsetD2D8_params_st {
+    CUdeviceptr_v1 dstDevice;
+    unsigned int dstPitch;
+    unsigned char uc;
+    unsigned int Width;
+    unsigned int Height;
+} cuMemsetD2D8_params;
+
+typedef struct cuMemsetD2D16_params_st {
+    CUdeviceptr_v1 dstDevice;
+    unsigned int dstPitch;
+    unsigned short us;
+    unsigned int Width;
+    unsigned int Height;
+} cuMemsetD2D16_params;
+
+typedef struct cuMemsetD2D32_params_st {
+    CUdeviceptr_v1 dstDevice;
+    unsigned int dstPitch;
+    unsigned int ui;
+    unsigned int Width;
+    unsigned int Height;
+} cuMemsetD2D32_params;
+
+typedef struct cuArrayCreate_params_st {
+    CUarray *pHandle;
+    const CUDA_ARRAY_DESCRIPTOR_v1 *pAllocateArray;
+} cuArrayCreate_params;
+
+typedef struct cuArrayGetDescriptor_params_st {
+    CUDA_ARRAY_DESCRIPTOR_v1 *pArrayDescriptor;
+    CUarray hArray;
+} cuArrayGetDescriptor_params;
+
+typedef struct cuArray3DCreate_params_st {
+    CUarray *pHandle;
+    const CUDA_ARRAY3D_DESCRIPTOR_v1 *pAllocateArray;
+} cuArray3DCreate_params;
+
+typedef struct cuArray3DGetDescriptor_params_st {
+    CUDA_ARRAY3D_DESCRIPTOR_v1 *pArrayDescriptor;
+    CUarray hArray;
+} cuArray3DGetDescriptor_params;
+
+typedef struct cuTexRefSetAddress_params_st {
+    unsigned int *ByteOffset;
+    CUtexref hTexRef;
+    CUdeviceptr_v1 dptr;
+    unsigned int bytes;
+} cuTexRefSetAddress_params;
+
+typedef struct cuTexRefSetAddress2D_params_st {
+    CUtexref hTexRef;
+    const CUDA_ARRAY_DESCRIPTOR_v1 *desc;
+    CUdeviceptr_v1 dptr;
+    unsigned int Pitch;
+} cuTexRefSetAddress2D_params;
+
+typedef struct cuTexRefGetAddress_params_st {
+    CUdeviceptr_v1 *pdptr;
+    CUtexref hTexRef;
+} cuTexRefGetAddress_params;
+
+typedef struct cuGraphicsResourceGetMappedPointer_params_st {
+    CUdeviceptr_v1 *pDevPtr;
+    unsigned int *pSize;
+    CUgraphicsResource resource;
+} cuGraphicsResourceGetMappedPointer_params;
+
+typedef struct cuCtxDestroy_params_st {
+    CUcontext ctx;
+} cuCtxDestroy_params;
+
+typedef struct cuCtxPopCurrent_params_st {
+    CUcontext *pctx;
+} cuCtxPopCurrent_params;
+
+typedef struct cuCtxPushCurrent_params_st {
+    CUcontext ctx;
+} cuCtxPushCurrent_params;
+
+typedef struct cuStreamDestroy_params_st {
+    CUstream hStream;
+} cuStreamDestroy_params;
+
+typedef struct cuEventDestroy_params_st {
+    CUevent hEvent;
+} cuEventDestroy_params;
+
+typedef struct cuDevicePrimaryCtxRelease_params_st {
+    CUdevice dev;
+} cuDevicePrimaryCtxRelease_params;
+
+typedef struct cuDevicePrimaryCtxReset_params_st {
+    CUdevice dev;
+} cuDevicePrimaryCtxReset_params;
+
+typedef struct cuDevicePrimaryCtxSetFlags_params_st {
+    CUdevice dev;
+    unsigned int flags;
+} cuDevicePrimaryCtxSetFlags_params;
+
+typedef struct cuMemcpyHtoD_v2_params_st {
+    CUdeviceptr dstDevice;
+    const void *srcHost;
+    size_t ByteCount;
+} cuMemcpyHtoD_v2_params;
+
+typedef struct cuMemcpyDtoH_v2_params_st {
+    void *dstHost;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+} cuMemcpyDtoH_v2_params;
+
+typedef struct cuMemcpyDtoD_v2_params_st {
+    CUdeviceptr dstDevice;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+} cuMemcpyDtoD_v2_params;
+
+typedef struct cuMemcpyDtoA_v2_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+} cuMemcpyDtoA_v2_params;
+
+typedef struct cuMemcpyAtoD_v2_params_st {
+    CUdeviceptr dstDevice;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+} cuMemcpyAtoD_v2_params;
+
+typedef struct cuMemcpyHtoA_v2_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    const void *srcHost;
+    size_t ByteCount;
+} cuMemcpyHtoA_v2_params;
+
+typedef struct cuMemcpyAtoH_v2_params_st {
+    void *dstHost;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+} cuMemcpyAtoH_v2_params;
+
+typedef struct cuMemcpyAtoA_v2_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+} cuMemcpyAtoA_v2_params;
+
+typedef struct cuMemcpyHtoAAsync_v2_params_st {
+    CUarray dstArray;
+    size_t dstOffset;
+    const void *srcHost;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyHtoAAsync_v2_params;
+
+typedef struct cuMemcpyAtoHAsync_v2_params_st {
+    void *dstHost;
+    CUarray srcArray;
+    size_t srcOffset;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyAtoHAsync_v2_params;
+
+typedef struct cuMemcpy2D_v2_params_st {
+    const CUDA_MEMCPY2D *pCopy;
+} cuMemcpy2D_v2_params;
+
+typedef struct cuMemcpy2DUnaligned_v2_params_st {
+    const CUDA_MEMCPY2D *pCopy;
+} cuMemcpy2DUnaligned_v2_params;
+
+typedef struct cuMemcpy3D_v2_params_st {
+    const CUDA_MEMCPY3D *pCopy;
+} cuMemcpy3D_v2_params;
+
+typedef struct cuMemcpyHtoDAsync_v2_params_st {
+    CUdeviceptr dstDevice;
+    const void *srcHost;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyHtoDAsync_v2_params;
+
+typedef struct cuMemcpyDtoHAsync_v2_params_st {
+    void *dstHost;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyDtoHAsync_v2_params;
+
+typedef struct cuMemcpyDtoDAsync_v2_params_st {
+    CUdeviceptr dstDevice;
+    CUdeviceptr srcDevice;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyDtoDAsync_v2_params;
+
+typedef struct cuMemcpy2DAsync_v2_params_st {
+    const CUDA_MEMCPY2D *pCopy;
+    CUstream hStream;
+} cuMemcpy2DAsync_v2_params;
+
+typedef struct cuMemcpy3DAsync_v2_params_st {
+    const CUDA_MEMCPY3D *pCopy;
+    CUstream hStream;
+} cuMemcpy3DAsync_v2_params;
+
+typedef struct cuMemsetD8_v2_params_st {
+    CUdeviceptr dstDevice;
+    unsigned char uc;
+    size_t N;
+} cuMemsetD8_v2_params;
+
+typedef struct cuMemsetD16_v2_params_st {
+    CUdeviceptr dstDevice;
+    unsigned short us;
+    size_t N;
+} cuMemsetD16_v2_params;
+
+typedef struct cuMemsetD32_v2_params_st {
+    CUdeviceptr dstDevice;
+    unsigned int ui;
+    size_t N;
+} cuMemsetD32_v2_params;
+
+typedef struct cuMemsetD2D8_v2_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned char uc;
+    size_t Width;
+    size_t Height;
+} cuMemsetD2D8_v2_params;
+
+typedef struct cuMemsetD2D16_v2_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned short us;
+    size_t Width;
+    size_t Height;
+} cuMemsetD2D16_v2_params;
+
+typedef struct cuMemsetD2D32_v2_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned int ui;
+    size_t Width;
+    size_t Height;
+} cuMemsetD2D32_v2_params;
+
+typedef struct cuMemcpy_params_st {
+    CUdeviceptr dst;
+    CUdeviceptr src;
+    size_t ByteCount;
+} cuMemcpy_params;
+
+typedef struct cuMemcpyAsync_params_st {
+    CUdeviceptr dst;
+    CUdeviceptr src;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyAsync_params;
+
+typedef struct cuMemcpyPeer_params_st {
+    CUdeviceptr dstDevice;
+    CUcontext dstContext;
+    CUdeviceptr srcDevice;
+    CUcontext srcContext;
+    size_t ByteCount;
+} cuMemcpyPeer_params;
+
+typedef struct cuMemcpyPeerAsync_params_st {
+    CUdeviceptr dstDevice;
+    CUcontext dstContext;
+    CUdeviceptr srcDevice;
+    CUcontext srcContext;
+    size_t ByteCount;
+    CUstream hStream;
+} cuMemcpyPeerAsync_params;
+
+typedef struct cuMemcpy3DPeer_params_st {
+    const CUDA_MEMCPY3D_PEER *pCopy;
+} cuMemcpy3DPeer_params;
+
+typedef struct cuMemcpy3DPeerAsync_params_st {
+    const CUDA_MEMCPY3D_PEER *pCopy;
+    CUstream hStream;
+} cuMemcpy3DPeerAsync_params;
+
+typedef struct cuMemcpyBatchAsync_params_st {
+    CUdeviceptr *dsts;
+    CUdeviceptr *srcs;
+    size_t *sizes;
+    size_t count;
+    CUmemcpyAttributes *attrs;
+    size_t *attrsIdxs;
+    size_t numAttrs;
+    size_t *failIdx;
+    CUstream hStream;
+} cuMemcpyBatchAsync_params;
+
+typedef struct cuMemcpy3DBatchAsync_params_st {
+    size_t numOps;
+    CUDA_MEMCPY3D_BATCH_OP *opList;
+    size_t *failIdx;
+    unsigned long long flags;
+    CUstream hStream;
+} cuMemcpy3DBatchAsync_params;
+
+typedef struct cuMemsetD8Async_params_st {
+    CUdeviceptr dstDevice;
+    unsigned char uc;
+    size_t N;
+    CUstream hStream;
+} cuMemsetD8Async_params;
+
+typedef struct cuMemsetD16Async_params_st {
+    CUdeviceptr dstDevice;
+    unsigned short us;
+    size_t N;
+    CUstream hStream;
+} cuMemsetD16Async_params;
+
+typedef struct cuMemsetD32Async_params_st {
+    CUdeviceptr dstDevice;
+    unsigned int ui;
+    size_t N;
+    CUstream hStream;
+} cuMemsetD32Async_params;
+
+typedef struct cuMemsetD2D8Async_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned char uc;
+    size_t Width;
+    size_t Height;
+    CUstream hStream;
+} cuMemsetD2D8Async_params;
+
+typedef struct cuMemsetD2D16Async_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned short us;
+    size_t Width;
+    size_t Height;
+    CUstream hStream;
+} cuMemsetD2D16Async_params;
+
+typedef struct cuMemsetD2D32Async_params_st {
+    CUdeviceptr dstDevice;
+    size_t dstPitch;
+    unsigned int ui;
+    size_t Width;
+    size_t Height;
+    CUstream hStream;
+} cuMemsetD2D32Async_params;
+
+typedef struct cuStreamGetPriority_params_st {
+    CUstream hStream;
+    int *priority;
+} cuStreamGetPriority_params;
+
+typedef struct cuStreamGetId_params_st {
+    CUstream hStream;
+    unsigned long long *streamId;
+} cuStreamGetId_params;
+
+typedef struct cuStreamGetFlags_params_st {
+    CUstream hStream;
+    unsigned int *flags;
+} cuStreamGetFlags_params;
+
+typedef struct cuStreamGetDevice_params_st {
+    CUstream hStream;
+    CUdevice *device;
+} cuStreamGetDevice_params;
+
+typedef struct cuStreamGetCtx_params_st {
+    CUstream hStream;
+    CUcontext *pctx;
+} cuStreamGetCtx_params;
+
+typedef struct cuStreamGetCtx_v2_params_st {
+    CUstream hStream;
+    CUcontext *pCtx;
+    CUgreenCtx *pGreenCtx;
+} cuStreamGetCtx_v2_params;
+
+typedef struct cuStreamWaitEvent_params_st {
+    CUstream hStream;
+    CUevent hEvent;
+    unsigned int Flags;
+} cuStreamWaitEvent_params;
+
+typedef struct cuStreamAddCallback_params_st {
+    CUstream hStream;
+    CUstreamCallback callback;
+    void *userData;
+    unsigned int flags;
+} cuStreamAddCallback_params;
+
+typedef struct cuStreamAttachMemAsync_params_st {
+    CUstream hStream;
+    CUdeviceptr dptr;
+    size_t length;
+    unsigned int flags;
+} cuStreamAttachMemAsync_params;
+
+typedef struct cuStreamQuery_params_st {
+    CUstream hStream;
+} cuStreamQuery_params;
+
+typedef struct cuStreamSynchronize_params_st {
+    CUstream hStream;
+} cuStreamSynchronize_params;
+
+typedef struct cuEventRecord_params_st {
+    CUevent hEvent;
+    CUstream hStream;
+} cuEventRecord_params;
+
+typedef struct cuEventRecordWithFlags_params_st {
+    CUevent hEvent;
+    CUstream hStream;
+    unsigned int flags;
+} cuEventRecordWithFlags_params;
+
+typedef struct cuLaunchKernel_params_st {
+    CUfunction f;
+    unsigned int gridDimX;
+    unsigned int gridDimY;
+    unsigned int gridDimZ;
+    unsigned int blockDimX;
+    unsigned int blockDimY;
+    unsigned int blockDimZ;
+    unsigned int sharedMemBytes;
+    CUstream hStream;
+    void **kernelParams;
+    void **extra;
+} cuLaunchKernel_params;
+
+typedef struct cuLaunchKernelEx_params_st {
+    const CUlaunchConfig *config;
+    CUfunction f;
+    void **kernelParams;
+    void **extra;
+} cuLaunchKernelEx_params;
+
+typedef struct cuLaunchHostFunc_params_st {
+    CUstream hStream;
+    CUhostFn fn;
+    void *userData;
+} cuLaunchHostFunc_params;
+
+typedef struct cuGraphicsMapResources_params_st {
+    unsigned int count;
+    CUgraphicsResource *resources;
+    CUstream hStream;
+} cuGraphicsMapResources_params;
+
+typedef struct cuGraphicsUnmapResources_params_st {
+    unsigned int count;
+    CUgraphicsResource *resources;
+    CUstream hStream;
+} cuGraphicsUnmapResources_params;
+
+typedef struct cuStreamWriteValue32_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWriteValue32_params;
+
+typedef struct cuStreamWaitValue32_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWaitValue32_params;
+
+typedef struct cuStreamWriteValue64_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWriteValue64_params;
+
+typedef struct cuStreamWaitValue64_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWaitValue64_params;
+
+typedef struct cuStreamBatchMemOp_params_st {
+    CUstream stream;
+    unsigned int count;
+    CUstreamBatchMemOpParams *paramArray;
+    unsigned int flags;
+} cuStreamBatchMemOp_params;
+
+typedef struct cuStreamWriteValue32_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWriteValue32_ptsz_params;
+
+typedef struct cuStreamWaitValue32_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWaitValue32_ptsz_params;
+
+typedef struct cuStreamWriteValue64_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWriteValue64_ptsz_params;
+
+typedef struct cuStreamWaitValue64_ptsz_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWaitValue64_ptsz_params;
+
+typedef struct cuStreamBatchMemOp_ptsz_params_st {
+    CUstream stream;
+    unsigned int count;
+    CUstreamBatchMemOpParams *paramArray;
+    unsigned int flags;
+} cuStreamBatchMemOp_ptsz_params;
+
+typedef struct cuStreamWriteValue32_v2_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWriteValue32_v2_params;
+
+typedef struct cuStreamWaitValue32_v2_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint32_t value;
+    unsigned int flags;
+} cuStreamWaitValue32_v2_params;
+
+typedef struct cuStreamWriteValue64_v2_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWriteValue64_v2_params;
+
+typedef struct cuStreamWaitValue64_v2_params_st {
+    CUstream stream;
+    CUdeviceptr addr;
+    cuuint64_t value;
+    unsigned int flags;
+} cuStreamWaitValue64_v2_params;
+
+typedef struct cuStreamBatchMemOp_v2_params_st {
+    CUstream stream;
+    unsigned int count;
+    CUstreamBatchMemOpParams *paramArray;
+    unsigned int flags;
+} cuStreamBatchMemOp_v2_params;
+
+typedef struct cuMemPrefetchAsync_params_st {
+    CUdeviceptr devPtr;
+    size_t count;
+    CUdevice dstDevice;
+    CUstream hStream;
+} cuMemPrefetchAsync_params;
+
+typedef struct cuMemPrefetchAsync_v2_params_st {
+    CUdeviceptr devPtr;
+    size_t count;
+    CUmemLocation location;
+    unsigned int flags;
+    CUstream hStream;
+} cuMemPrefetchAsync_v2_params;
+
+typedef struct cuLaunchCooperativeKernel_params_st {
+    CUfunction f;
+    unsigned int gridDimX;
+    unsigned int gridDimY;
+    unsigned int gridDimZ;
+    unsigned int blockDimX;
+    unsigned int blockDimY;
+    unsigned int blockDimZ;
+    unsigned int sharedMemBytes;
+    CUstream hStream;
+    void **kernelParams;
+} cuLaunchCooperativeKernel_params;
+
+typedef struct cuSignalExternalSemaphoresAsync_params_st {
+    const CUexternalSemaphore *extSemArray;
+    const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS *paramsArray;
+    unsigned int numExtSems;
+    CUstream stream;
+} cuSignalExternalSemaphoresAsync_params;
+
+typedef struct cuWaitExternalSemaphoresAsync_params_st {
+    const CUexternalSemaphore *extSemArray;
+    const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS *paramsArray;
+    unsigned int numExtSems;
+    CUstream stream;
+} cuWaitExternalSemaphoresAsync_params;
+
+typedef struct cuStreamBeginCapture_params_st {
+    CUstream hStream;
+} cuStreamBeginCapture_params;
+
+typedef struct cuStreamBeginCapture_ptsz_params_st {
+    CUstream hStream;
+} cuStreamBeginCapture_ptsz_params;
+
+typedef struct cuStreamBeginCapture_v2_params_st {
+    CUstream hStream;
+    CUstreamCaptureMode mode;
+} cuStreamBeginCapture_v2_params;
+
+typedef struct cuStreamBeginCaptureToGraph_params_st {
+    CUstream hStream;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    const CUgraphEdgeData *dependencyData;
+    size_t numDependencies;
+    CUstreamCaptureMode mode;
+} cuStreamBeginCaptureToGraph_params;
+
+typedef struct cuStreamEndCapture_params_st {
+    CUstream hStream;
+    CUgraph *phGraph;
+} cuStreamEndCapture_params;
+
+typedef struct cuStreamIsCapturing_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus;
+} cuStreamIsCapturing_params;
+
+typedef struct cuStreamGetCaptureInfo_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus_out;
+    cuuint64_t *id_out;
+} cuStreamGetCaptureInfo_params;
+
+typedef struct cuStreamGetCaptureInfo_ptsz_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus_out;
+    cuuint64_t *id_out;
+} cuStreamGetCaptureInfo_ptsz_params;
+
+typedef struct cuStreamGetCaptureInfo_v2_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus_out;
+    cuuint64_t *id_out;
+    CUgraph *graph_out;
+    const CUgraphNode **dependencies_out;
+    size_t *numDependencies_out;
+} cuStreamGetCaptureInfo_v2_params;
+
+typedef struct cuStreamGetCaptureInfo_v3_params_st {
+    CUstream hStream;
+    CUstreamCaptureStatus *captureStatus_out;
+    cuuint64_t *id_out;
+    CUgraph *graph_out;
+    const CUgraphNode **dependencies_out;
+    const CUgraphEdgeData **edgeData_out;
+    size_t *numDependencies_out;
+} cuStreamGetCaptureInfo_v3_params;
+
+typedef struct cuGraphAddKernelNode_params_st {
+    CUgraphNode *phGraphNode;
+    CUgraph hGraph;
+    const CUgraphNode *dependencies;
+    size_t numDependencies;
+    const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams;
+} cuGraphAddKernelNode_params;
+
+typedef struct cuGraphKernelNodeGetParams_params_st {
+    CUgraphNode hNode;
+    CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams;
+} cuGraphKernelNodeGetParams_params;
+
+typedef struct cuGraphKernelNodeSetParams_params_st {
+    CUgraphNode hNode;
+    const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams;
+} cuGraphKernelNodeSetParams_params;
+
+typedef struct cuGraphExecKernelNodeSetParams_params_st {
+    CUgraphExec hGraphExec;
+    CUgraphNode hNode;
+    const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams;
+} cuGraphExecKernelNodeSetParams_params;
+
+typedef struct cuGraphInstantiateWithParams_params_st {
+    CUgraphExec *phGraphExec;
+    CUgraph hGraph;
+    CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams;
+} cuGraphInstantiateWithParams_params;
+
+typedef struct cuGraphExecUpdate_params_st {
+    CUgraphExec hGraphExec;
+    CUgraph hGraph;
+    CUgraphNode *hErrorNode_out;
+    CUgraphExecUpdateResult *updateResult_out;
+} cuGraphExecUpdate_params;
+
+typedef struct cuGraphUpload_params_st {
+    CUgraphExec hGraph;
+    CUstream hStream;
+} cuGraphUpload_params;
+
+typedef struct cuGraphLaunch_params_st {
+    CUgraphExec hGraph;
+    CUstream hStream;
+} cuGraphLaunch_params;
+
+typedef struct cuStreamCopyAttributes_params_st {
+    CUstream dstStream;
+    CUstream srcStream;
+} cuStreamCopyAttributes_params;
+
+typedef struct cuStreamGetAttribute_params_st {
+    CUstream hStream;
+    CUstreamAttrID attr;
+    CUstreamAttrValue *value;
+} cuStreamGetAttribute_params;
+
+typedef struct cuStreamSetAttribute_params_st {
+    CUstream hStream;
+    CUstreamAttrID attr;
+    const CUstreamAttrValue *param;
+} cuStreamSetAttribute_params;
+
+typedef struct cuIpcOpenMemHandle_params_st {
+    CUdeviceptr *pdptr;
+    CUipcMemHandle handle;
+    unsigned int Flags;
+} cuIpcOpenMemHandle_params;
+
+typedef struct cuGraphInstantiate_params_st {
+    CUgraphExec *phGraphExec;
+    CUgraph hGraph;
+    CUgraphNode *phErrorNode;
+    char *logBuffer;
+    size_t bufferSize;
+} cuGraphInstantiate_params;
+
+typedef struct cuGraphInstantiate_v2_params_st {
+    CUgraphExec *phGraphExec;
+    CUgraph hGraph;
+    CUgraphNode *phErrorNode;
+    char *logBuffer;
+    size_t bufferSize;
+} cuGraphInstantiate_v2_params;
+
+typedef struct cuMemMapArrayAsync_params_st {
+    CUarrayMapInfo *mapInfoList;
+    unsigned int count;
+    CUstream hStream;
+} cuMemMapArrayAsync_params;
+
+typedef struct cuMemFreeAsync_params_st {
+    CUdeviceptr dptr;
+    CUstream hStream;
+} cuMemFreeAsync_params;
+
+typedef struct cuMemAllocAsync_params_st {
+    CUdeviceptr *dptr;
+    size_t bytesize;
+    CUstream hStream;
+} cuMemAllocAsync_params;
+
+typedef struct cuMemAllocFromPoolAsync_params_st {
+    CUdeviceptr *dptr;
+    size_t bytesize;
+    CUmemoryPool pool;
+    CUstream hStream;
+} cuMemAllocFromPoolAsync_params;
+
+typedef struct cuStreamUpdateCaptureDependencies_params_st {
+    CUstream hStream;
+    CUgraphNode *dependencies;
+    size_t numDependencies;
+    unsigned int flags;
+} cuStreamUpdateCaptureDependencies_params;
+
+typedef struct cuStreamUpdateCaptureDependencies_v2_params_st {
+    CUstream hStream;
+    CUgraphNode *dependencies;
+    const CUgraphEdgeData *dependencyData;
+    size_t numDependencies;
+    unsigned int flags;
+} cuStreamUpdateCaptureDependencies_v2_params;
+
+typedef struct cuMemBatchDecompressAsync_params_st {
+    CUmemDecompressParams *paramsArray;
+    size_t count;
+    unsigned int flags;
+    size_t *errorIndex;
+    CUstream stream;
+} cuMemBatchDecompressAsync_params;
+
+typedef struct cuGetProcAddress_params_st {
+    const char *symbol;
+    void **pfn;
+    int cudaVersion;
+    cuuint64_t flags;
+} cuGetProcAddress_params;
+
+typedef struct cuCheckpointProcessGetRestoreThreadId_params_st {
+    int pid;
+    int *tid;
+} cuCheckpointProcessGetRestoreThreadId_params;
+
+typedef struct cuCheckpointProcessGetState_params_st {
+    int pid;
+    CUprocessState *state;
+} cuCheckpointProcessGetState_params;
+
+typedef struct cuCheckpointProcessLock_params_st {
+    int pid;
+    CUcheckpointLockArgs *args;
+} cuCheckpointProcessLock_params;
+
+typedef struct cuCheckpointProcessCheckpoint_params_st {
+    int pid;
+    CUcheckpointCheckpointArgs *args;
+} cuCheckpointProcessCheckpoint_params;
+
+typedef struct cuCheckpointProcessRestore_params_st {
+    int pid;
+    CUcheckpointRestoreArgs *args;
+} cuCheckpointProcessRestore_params;
+
+typedef struct cuCheckpointProcessUnlock_params_st {
+    int pid;
+    CUcheckpointUnlockArgs *args;
+} cuCheckpointProcessUnlock_params;
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_runtime_api_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_runtime_api_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..52321905dd0a82e550332f5d67b03fd4612860e7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_runtime_api_meta.h
@@ -0,0 +1,2372 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// CUDA public interface, for type definitions and api function prototypes
+#include "cuda_runtime_api.h"
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+// Currently used parameter trace structures
+typedef struct cudaDeviceSetLimit_v3020_params_st {
+    enum cudaLimit limit;
+    size_t value;
+} cudaDeviceSetLimit_v3020_params;
+
+typedef struct cudaDeviceGetLimit_v3020_params_st {
+    size_t *pValue;
+    enum cudaLimit limit;
+} cudaDeviceGetLimit_v3020_params;
+
+typedef struct cudaDeviceGetTexture1DLinearMaxWidth_v11010_params_st {
+    size_t *maxWidthInElements;
+    const struct cudaChannelFormatDesc *fmtDesc;
+    int device;
+} cudaDeviceGetTexture1DLinearMaxWidth_v11010_params;
+
+typedef struct cudaDeviceGetCacheConfig_v3020_params_st {
+    enum cudaFuncCache *pCacheConfig;
+} cudaDeviceGetCacheConfig_v3020_params;
+
+typedef struct cudaDeviceGetStreamPriorityRange_v5050_params_st {
+    int *leastPriority;
+    int *greatestPriority;
+} cudaDeviceGetStreamPriorityRange_v5050_params;
+
+typedef struct cudaDeviceSetCacheConfig_v3020_params_st {
+    enum cudaFuncCache cacheConfig;
+} cudaDeviceSetCacheConfig_v3020_params;
+
+typedef struct cudaDeviceGetByPCIBusId_v4010_params_st {
+    int *device;
+    const char *pciBusId;
+} cudaDeviceGetByPCIBusId_v4010_params;
+
+typedef struct cudaDeviceGetPCIBusId_v4010_params_st {
+    char *pciBusId;
+    int len;
+    int device;
+} cudaDeviceGetPCIBusId_v4010_params;
+
+typedef struct cudaIpcGetEventHandle_v4010_params_st {
+    cudaIpcEventHandle_t *handle;
+    cudaEvent_t event;
+} cudaIpcGetEventHandle_v4010_params;
+
+typedef struct cudaIpcOpenEventHandle_v4010_params_st {
+    cudaEvent_t *event;
+    cudaIpcEventHandle_t handle;
+} cudaIpcOpenEventHandle_v4010_params;
+
+typedef struct cudaIpcGetMemHandle_v4010_params_st {
+    cudaIpcMemHandle_t *handle;
+    void *devPtr;
+} cudaIpcGetMemHandle_v4010_params;
+
+typedef struct cudaIpcOpenMemHandle_v4010_params_st {
+    void **devPtr;
+    cudaIpcMemHandle_t handle;
+    unsigned int flags;
+} cudaIpcOpenMemHandle_v4010_params;
+
+typedef struct cudaIpcCloseMemHandle_v4010_params_st {
+    void *devPtr;
+} cudaIpcCloseMemHandle_v4010_params;
+
+typedef struct cudaDeviceFlushGPUDirectRDMAWrites_v11030_params_st {
+    enum cudaFlushGPUDirectRDMAWritesTarget target;
+    enum cudaFlushGPUDirectRDMAWritesScope scope;
+} cudaDeviceFlushGPUDirectRDMAWrites_v11030_params;
+
+typedef struct cudaDeviceRegisterAsyncNotification_v12040_params_st {
+    int device;
+    cudaAsyncCallback callbackFunc;
+    void *userData;
+    cudaAsyncCallbackHandle_t *callback;
+} cudaDeviceRegisterAsyncNotification_v12040_params;
+
+typedef struct cudaDeviceUnregisterAsyncNotification_v12040_params_st {
+    int device;
+    cudaAsyncCallbackHandle_t callback;
+} cudaDeviceUnregisterAsyncNotification_v12040_params;
+
+typedef struct cudaDeviceGetSharedMemConfig_v4020_params_st {
+    enum cudaSharedMemConfig *pConfig;
+} cudaDeviceGetSharedMemConfig_v4020_params;
+
+typedef struct cudaDeviceSetSharedMemConfig_v4020_params_st {
+    enum cudaSharedMemConfig config;
+} cudaDeviceSetSharedMemConfig_v4020_params;
+
+typedef struct cudaGetErrorName_v6050_params_st {
+    cudaError_t error;
+} cudaGetErrorName_v6050_params;
+
+typedef struct cudaGetErrorString_v3020_params_st {
+    cudaError_t error;
+} cudaGetErrorString_v3020_params;
+
+typedef struct cudaGetDeviceCount_v3020_params_st {
+    int *count;
+} cudaGetDeviceCount_v3020_params;
+
+typedef struct cudaGetDeviceProperties_v2_v12000_params_st {
+    struct cudaDeviceProp *prop;
+    int device;
+} cudaGetDeviceProperties_v2_v12000_params;
+
+typedef struct cudaDeviceGetAttribute_v5000_params_st {
+    int *value;
+    enum cudaDeviceAttr attr;
+    int device;
+} cudaDeviceGetAttribute_v5000_params;
+
+typedef struct cudaDeviceGetDefaultMemPool_v11020_params_st {
+    cudaMemPool_t *memPool;
+    int device;
+} cudaDeviceGetDefaultMemPool_v11020_params;
+
+typedef struct cudaDeviceSetMemPool_v11020_params_st {
+    int device;
+    cudaMemPool_t memPool;
+} cudaDeviceSetMemPool_v11020_params;
+
+typedef struct cudaDeviceGetMemPool_v11020_params_st {
+    cudaMemPool_t *memPool;
+    int device;
+} cudaDeviceGetMemPool_v11020_params;
+
+typedef struct cudaDeviceGetNvSciSyncAttributes_v10020_params_st {
+    void *nvSciSyncAttrList;
+    int device;
+    int flags;
+} cudaDeviceGetNvSciSyncAttributes_v10020_params;
+
+typedef struct cudaDeviceGetP2PAttribute_v8000_params_st {
+    int *value;
+    enum cudaDeviceP2PAttr attr;
+    int srcDevice;
+    int dstDevice;
+} cudaDeviceGetP2PAttribute_v8000_params;
+
+typedef struct cudaChooseDevice_v3020_params_st {
+    int *device;
+    const struct cudaDeviceProp *prop;
+} cudaChooseDevice_v3020_params;
+
+typedef struct cudaInitDevice_v12000_params_st {
+    int device;
+    unsigned int deviceFlags;
+    unsigned int flags;
+} cudaInitDevice_v12000_params;
+
+typedef struct cudaSetDevice_v3020_params_st {
+    int device;
+} cudaSetDevice_v3020_params;
+
+typedef struct cudaGetDevice_v3020_params_st {
+    int *device;
+} cudaGetDevice_v3020_params;
+
+typedef struct cudaSetValidDevices_v3020_params_st {
+    int *device_arr;
+    int len;
+} cudaSetValidDevices_v3020_params;
+
+typedef struct cudaSetDeviceFlags_v3020_params_st {
+    unsigned int flags;
+} cudaSetDeviceFlags_v3020_params;
+
+typedef struct cudaGetDeviceFlags_v7000_params_st {
+    unsigned int *flags;
+} cudaGetDeviceFlags_v7000_params;
+
+typedef struct cudaStreamCreate_v3020_params_st {
+    cudaStream_t *pStream;
+} cudaStreamCreate_v3020_params;
+
+typedef struct cudaStreamCreateWithFlags_v5000_params_st {
+    cudaStream_t *pStream;
+    unsigned int flags;
+} cudaStreamCreateWithFlags_v5000_params;
+
+typedef struct cudaStreamCreateWithPriority_v5050_params_st {
+    cudaStream_t *pStream;
+    unsigned int flags;
+    int priority;
+} cudaStreamCreateWithPriority_v5050_params;
+
+typedef struct cudaStreamGetPriority_ptsz_v7000_params_st {
+    cudaStream_t hStream;
+    int *priority;
+} cudaStreamGetPriority_ptsz_v7000_params;
+
+typedef struct cudaStreamGetFlags_ptsz_v7000_params_st {
+    cudaStream_t hStream;
+    unsigned int *flags;
+} cudaStreamGetFlags_ptsz_v7000_params;
+
+typedef struct cudaStreamGetId_ptsz_v12000_params_st {
+    cudaStream_t hStream;
+    unsigned long long *streamId;
+} cudaStreamGetId_ptsz_v12000_params;
+
+typedef struct cudaStreamGetDevice_ptsz_v12080_params_st {
+    cudaStream_t hStream;
+    int *device;
+} cudaStreamGetDevice_ptsz_v12080_params;
+
+typedef struct cudaStreamCopyAttributes_ptsz_v11000_params_st {
+    cudaStream_t dst;
+    cudaStream_t src;
+} cudaStreamCopyAttributes_ptsz_v11000_params;
+
+typedef struct cudaStreamGetAttribute_ptsz_v11000_params_st {
+    cudaStream_t hStream;
+    cudaStreamAttrID attr;
+    cudaStreamAttrValue *value_out;
+} cudaStreamGetAttribute_ptsz_v11000_params;
+
+typedef struct cudaStreamSetAttribute_ptsz_v11000_params_st {
+    cudaStream_t hStream;
+    cudaStreamAttrID attr;
+    const cudaStreamAttrValue *value;
+} cudaStreamSetAttribute_ptsz_v11000_params;
+
+typedef struct cudaStreamDestroy_v5050_params_st {
+    cudaStream_t stream;
+} cudaStreamDestroy_v5050_params;
+
+typedef struct cudaStreamWaitEvent_ptsz_v7000_params_st {
+    cudaStream_t stream;
+    cudaEvent_t event;
+    unsigned int flags;
+} cudaStreamWaitEvent_ptsz_v7000_params;
+
+typedef struct cudaStreamAddCallback_ptsz_v7000_params_st {
+    cudaStream_t stream;
+    cudaStreamCallback_t callback;
+    void *userData;
+    unsigned int flags;
+} cudaStreamAddCallback_ptsz_v7000_params;
+
+typedef struct cudaStreamSynchronize_ptsz_v7000_params_st {
+    cudaStream_t stream;
+} cudaStreamSynchronize_ptsz_v7000_params;
+
+typedef struct cudaStreamQuery_ptsz_v7000_params_st {
+    cudaStream_t stream;
+} cudaStreamQuery_ptsz_v7000_params;
+
+typedef struct cudaStreamAttachMemAsync_ptsz_v7000_params_st {
+    cudaStream_t stream;
+    void *devPtr;
+    size_t length;
+    unsigned int flags;
+} cudaStreamAttachMemAsync_ptsz_v7000_params;
+
+typedef struct cudaStreamBeginCapture_ptsz_v10000_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureMode mode;
+} cudaStreamBeginCapture_ptsz_v10000_params;
+
+typedef struct cudaStreamBeginCaptureToGraph_ptsz_v12030_params_st {
+    cudaStream_t stream;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *dependencies;
+    const cudaGraphEdgeData *dependencyData;
+    size_t numDependencies;
+    enum cudaStreamCaptureMode mode;
+} cudaStreamBeginCaptureToGraph_ptsz_v12030_params;
+
+typedef struct cudaThreadExchangeStreamCaptureMode_v10010_params_st {
+    enum cudaStreamCaptureMode *mode;
+} cudaThreadExchangeStreamCaptureMode_v10010_params;
+
+typedef struct cudaStreamEndCapture_ptsz_v10000_params_st {
+    cudaStream_t stream;
+    cudaGraph_t *pGraph;
+} cudaStreamEndCapture_ptsz_v10000_params;
+
+typedef struct cudaStreamIsCapturing_ptsz_v10000_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *pCaptureStatus;
+} cudaStreamIsCapturing_ptsz_v10000_params;
+
+typedef struct cudaStreamGetCaptureInfo_v2_ptsz_v11030_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *captureStatus_out;
+    unsigned long long *id_out;
+    cudaGraph_t *graph_out;
+    const cudaGraphNode_t **dependencies_out;
+    size_t *numDependencies_out;
+} cudaStreamGetCaptureInfo_v2_ptsz_v11030_params;
+
+typedef struct cudaStreamGetCaptureInfo_v3_ptsz_v12030_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *captureStatus_out;
+    unsigned long long *id_out;
+    cudaGraph_t *graph_out;
+    const cudaGraphNode_t **dependencies_out;
+    const cudaGraphEdgeData **edgeData_out;
+    size_t *numDependencies_out;
+} cudaStreamGetCaptureInfo_v3_ptsz_v12030_params;
+
+typedef struct cudaStreamUpdateCaptureDependencies_ptsz_v11030_params_st {
+    cudaStream_t stream;
+    cudaGraphNode_t *dependencies;
+    size_t numDependencies;
+    unsigned int flags;
+} cudaStreamUpdateCaptureDependencies_ptsz_v11030_params;
+
+typedef struct cudaStreamUpdateCaptureDependencies_v2_ptsz_v12030_params_st {
+    cudaStream_t stream;
+    cudaGraphNode_t *dependencies;
+    const cudaGraphEdgeData *dependencyData;
+    size_t numDependencies;
+    unsigned int flags;
+} cudaStreamUpdateCaptureDependencies_v2_ptsz_v12030_params;
+
+typedef struct cudaEventCreate_v3020_params_st {
+    cudaEvent_t *event;
+} cudaEventCreate_v3020_params;
+
+typedef struct cudaEventCreateWithFlags_v3020_params_st {
+    cudaEvent_t *event;
+    unsigned int flags;
+} cudaEventCreateWithFlags_v3020_params;
+
+typedef struct cudaEventRecord_ptsz_v7000_params_st {
+    cudaEvent_t event;
+    cudaStream_t stream;
+} cudaEventRecord_ptsz_v7000_params;
+
+typedef struct cudaEventRecordWithFlags_ptsz_v11010_params_st {
+    cudaEvent_t event;
+    cudaStream_t stream;
+    unsigned int flags;
+} cudaEventRecordWithFlags_ptsz_v11010_params;
+
+typedef struct cudaEventQuery_v3020_params_st {
+    cudaEvent_t event;
+} cudaEventQuery_v3020_params;
+
+typedef struct cudaEventSynchronize_v3020_params_st {
+    cudaEvent_t event;
+} cudaEventSynchronize_v3020_params;
+
+typedef struct cudaEventDestroy_v3020_params_st {
+    cudaEvent_t event;
+} cudaEventDestroy_v3020_params;
+
+typedef struct cudaEventElapsedTime_v3020_params_st {
+    float *ms;
+    cudaEvent_t start;
+    cudaEvent_t end;
+} cudaEventElapsedTime_v3020_params;
+
+typedef struct cudaEventElapsedTime_v2_v12080_params_st {
+    float *ms;
+    cudaEvent_t start;
+    cudaEvent_t end;
+} cudaEventElapsedTime_v2_v12080_params;
+
+typedef struct cudaImportExternalMemory_v10000_params_st {
+    cudaExternalMemory_t *extMem_out;
+    const struct cudaExternalMemoryHandleDesc *memHandleDesc;
+} cudaImportExternalMemory_v10000_params;
+
+typedef struct cudaExternalMemoryGetMappedBuffer_v10000_params_st {
+    void **devPtr;
+    cudaExternalMemory_t extMem;
+    const struct cudaExternalMemoryBufferDesc *bufferDesc;
+} cudaExternalMemoryGetMappedBuffer_v10000_params;
+
+typedef struct cudaExternalMemoryGetMappedMipmappedArray_v10000_params_st {
+    cudaMipmappedArray_t *mipmap;
+    cudaExternalMemory_t extMem;
+    const struct cudaExternalMemoryMipmappedArrayDesc *mipmapDesc;
+} cudaExternalMemoryGetMappedMipmappedArray_v10000_params;
+
+typedef struct cudaDestroyExternalMemory_v10000_params_st {
+    cudaExternalMemory_t extMem;
+} cudaDestroyExternalMemory_v10000_params;
+
+typedef struct cudaImportExternalSemaphore_v10000_params_st {
+    cudaExternalSemaphore_t *extSem_out;
+    const struct cudaExternalSemaphoreHandleDesc *semHandleDesc;
+} cudaImportExternalSemaphore_v10000_params;
+
+typedef struct cudaSignalExternalSemaphoresAsync_v2_ptsz_v11020_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreSignalParams *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaSignalExternalSemaphoresAsync_v2_ptsz_v11020_params;
+
+typedef struct cudaWaitExternalSemaphoresAsync_v2_ptsz_v11020_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreWaitParams *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaWaitExternalSemaphoresAsync_v2_ptsz_v11020_params;
+
+typedef struct cudaDestroyExternalSemaphore_v10000_params_st {
+    cudaExternalSemaphore_t extSem;
+} cudaDestroyExternalSemaphore_v10000_params;
+
+typedef struct cudaLaunchKernel_ptsz_v7000_params_st {
+    const void *func;
+    dim3 gridDim;
+    dim3 blockDim;
+    void **args;
+    size_t sharedMem;
+    cudaStream_t stream;
+} cudaLaunchKernel_ptsz_v7000_params;
+
+typedef struct cudaLaunchKernelExC_ptsz_v11060_params_st {
+    const cudaLaunchConfig_t *config;
+    const void *func;
+    void **args;
+} cudaLaunchKernelExC_ptsz_v11060_params;
+
+typedef struct cudaLaunchCooperativeKernel_ptsz_v9000_params_st {
+    const void *func;
+    dim3 gridDim;
+    dim3 blockDim;
+    void **args;
+    size_t sharedMem;
+    cudaStream_t stream;
+} cudaLaunchCooperativeKernel_ptsz_v9000_params;
+
+typedef struct cudaLaunchCooperativeKernelMultiDevice_v9000_params_st {
+    struct cudaLaunchParams *launchParamsList;
+    unsigned int numDevices;
+    unsigned int flags;
+} cudaLaunchCooperativeKernelMultiDevice_v9000_params;
+
+typedef struct cudaFuncSetCacheConfig_v3020_params_st {
+    const void *func;
+    enum cudaFuncCache cacheConfig;
+} cudaFuncSetCacheConfig_v3020_params;
+
+typedef struct cudaFuncGetAttributes_v3020_params_st {
+    struct cudaFuncAttributes *attr;
+    const void *func;
+} cudaFuncGetAttributes_v3020_params;
+
+typedef struct cudaFuncSetAttribute_v9000_params_st {
+    const void *func;
+    enum cudaFuncAttribute attr;
+    int value;
+} cudaFuncSetAttribute_v9000_params;
+
+typedef struct cudaFuncGetName_v12030_params_st {
+    const char **name;
+    const void *func;
+} cudaFuncGetName_v12030_params;
+
+typedef struct cudaFuncGetParamInfo_v12040_params_st {
+    const void *func;
+    size_t paramIndex;
+    size_t *paramOffset;
+    size_t *paramSize;
+} cudaFuncGetParamInfo_v12040_params;
+
+typedef struct cudaLaunchHostFunc_ptsz_v10000_params_st {
+    cudaStream_t stream;
+    cudaHostFn_t fn;
+    void *userData;
+} cudaLaunchHostFunc_ptsz_v10000_params;
+
+typedef struct cudaFuncSetSharedMemConfig_v4020_params_st {
+    const void *func;
+    enum cudaSharedMemConfig config;
+} cudaFuncSetSharedMemConfig_v4020_params;
+
+typedef struct cudaOccupancyMaxActiveBlocksPerMultiprocessor_v6050_params_st {
+    int *numBlocks;
+    const void *func;
+    int blockSize;
+    size_t dynamicSMemSize;
+} cudaOccupancyMaxActiveBlocksPerMultiprocessor_v6050_params;
+
+typedef struct cudaOccupancyAvailableDynamicSMemPerBlock_v10200_params_st {
+    size_t *dynamicSmemSize;
+    const void *func;
+    int numBlocks;
+    int blockSize;
+} cudaOccupancyAvailableDynamicSMemPerBlock_v10200_params;
+
+typedef struct cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_v7000_params_st {
+    int *numBlocks;
+    const void *func;
+    int blockSize;
+    size_t dynamicSMemSize;
+    unsigned int flags;
+} cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_v7000_params;
+
+typedef struct cudaOccupancyMaxPotentialClusterSize_v11070_params_st {
+    int *clusterSize;
+    const void *func;
+    const cudaLaunchConfig_t *launchConfig;
+} cudaOccupancyMaxPotentialClusterSize_v11070_params;
+
+typedef struct cudaOccupancyMaxActiveClusters_v11070_params_st {
+    int *numClusters;
+    const void *func;
+    const cudaLaunchConfig_t *launchConfig;
+} cudaOccupancyMaxActiveClusters_v11070_params;
+
+typedef struct cudaMallocManaged_v6000_params_st {
+    void **devPtr;
+    size_t size;
+    unsigned int flags;
+} cudaMallocManaged_v6000_params;
+
+typedef struct cudaMalloc_v3020_params_st {
+    void **devPtr;
+    size_t size;
+} cudaMalloc_v3020_params;
+
+typedef struct cudaMallocHost_v3020_params_st {
+    void **ptr;
+    size_t size;
+} cudaMallocHost_v3020_params;
+
+typedef struct cudaMallocPitch_v3020_params_st {
+    void **devPtr;
+    size_t *pitch;
+    size_t width;
+    size_t height;
+} cudaMallocPitch_v3020_params;
+
+typedef struct cudaMallocArray_v3020_params_st {
+    cudaArray_t *array;
+    const struct cudaChannelFormatDesc *desc;
+    size_t width;
+    size_t height;
+    unsigned int flags;
+} cudaMallocArray_v3020_params;
+
+typedef struct cudaFree_v3020_params_st {
+    void *devPtr;
+} cudaFree_v3020_params;
+
+typedef struct cudaFreeHost_v3020_params_st {
+    void *ptr;
+} cudaFreeHost_v3020_params;
+
+typedef struct cudaFreeArray_v3020_params_st {
+    cudaArray_t array;
+} cudaFreeArray_v3020_params;
+
+typedef struct cudaFreeMipmappedArray_v5000_params_st {
+    cudaMipmappedArray_t mipmappedArray;
+} cudaFreeMipmappedArray_v5000_params;
+
+typedef struct cudaHostAlloc_v3020_params_st {
+    void **pHost;
+    size_t size;
+    unsigned int flags;
+} cudaHostAlloc_v3020_params;
+
+typedef struct cudaHostRegister_v4000_params_st {
+    void *ptr;
+    size_t size;
+    unsigned int flags;
+} cudaHostRegister_v4000_params;
+
+typedef struct cudaHostUnregister_v4000_params_st {
+    void *ptr;
+} cudaHostUnregister_v4000_params;
+
+typedef struct cudaHostGetDevicePointer_v3020_params_st {
+    void **pDevice;
+    void *pHost;
+    unsigned int flags;
+} cudaHostGetDevicePointer_v3020_params;
+
+typedef struct cudaHostGetFlags_v3020_params_st {
+    unsigned int *pFlags;
+    void *pHost;
+} cudaHostGetFlags_v3020_params;
+
+typedef struct cudaMalloc3D_v3020_params_st {
+    struct cudaPitchedPtr *pitchedDevPtr;
+    struct cudaExtent extent;
+} cudaMalloc3D_v3020_params;
+
+typedef struct cudaMalloc3DArray_v3020_params_st {
+    cudaArray_t *array;
+    const struct cudaChannelFormatDesc *desc;
+    struct cudaExtent extent;
+    unsigned int flags;
+} cudaMalloc3DArray_v3020_params;
+
+typedef struct cudaMallocMipmappedArray_v5000_params_st {
+    cudaMipmappedArray_t *mipmappedArray;
+    const struct cudaChannelFormatDesc *desc;
+    struct cudaExtent extent;
+    unsigned int numLevels;
+    unsigned int flags;
+} cudaMallocMipmappedArray_v5000_params;
+
+typedef struct cudaGetMipmappedArrayLevel_v5000_params_st {
+    cudaArray_t *levelArray;
+    cudaMipmappedArray_const_t mipmappedArray;
+    unsigned int level;
+} cudaGetMipmappedArrayLevel_v5000_params;
+
+typedef struct cudaMemcpy3D_ptds_v7000_params_st {
+    const struct cudaMemcpy3DParms *p;
+} cudaMemcpy3D_ptds_v7000_params;
+
+typedef struct cudaMemcpy3DPeer_ptds_v7000_params_st {
+    const struct cudaMemcpy3DPeerParms *p;
+} cudaMemcpy3DPeer_ptds_v7000_params;
+
+typedef struct cudaMemcpy3DAsync_ptsz_v7000_params_st {
+    const struct cudaMemcpy3DParms *p;
+    cudaStream_t stream;
+} cudaMemcpy3DAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpy3DPeerAsync_ptsz_v7000_params_st {
+    const struct cudaMemcpy3DPeerParms *p;
+    cudaStream_t stream;
+} cudaMemcpy3DPeerAsync_ptsz_v7000_params;
+
+typedef struct cudaMemGetInfo_v3020_params_st {
+    size_t *free;
+    size_t *total;
+} cudaMemGetInfo_v3020_params;
+
+typedef struct cudaArrayGetInfo_v4010_params_st {
+    struct cudaChannelFormatDesc *desc;
+    struct cudaExtent *extent;
+    unsigned int *flags;
+    cudaArray_t array;
+} cudaArrayGetInfo_v4010_params;
+
+typedef struct cudaArrayGetPlane_v11020_params_st {
+    cudaArray_t *pPlaneArray;
+    cudaArray_t hArray;
+    unsigned int planeIdx;
+} cudaArrayGetPlane_v11020_params;
+
+typedef struct cudaArrayGetMemoryRequirements_v11060_params_st {
+    struct cudaArrayMemoryRequirements *memoryRequirements;
+    cudaArray_t array;
+    int device;
+} cudaArrayGetMemoryRequirements_v11060_params;
+
+typedef struct cudaMipmappedArrayGetMemoryRequirements_v11060_params_st {
+    struct cudaArrayMemoryRequirements *memoryRequirements;
+    cudaMipmappedArray_t mipmap;
+    int device;
+} cudaMipmappedArrayGetMemoryRequirements_v11060_params;
+
+typedef struct cudaArrayGetSparseProperties_v11010_params_st {
+    struct cudaArraySparseProperties *sparseProperties;
+    cudaArray_t array;
+} cudaArrayGetSparseProperties_v11010_params;
+
+typedef struct cudaMipmappedArrayGetSparseProperties_v11010_params_st {
+    struct cudaArraySparseProperties *sparseProperties;
+    cudaMipmappedArray_t mipmap;
+} cudaMipmappedArrayGetSparseProperties_v11010_params;
+
+typedef struct cudaMemcpy_ptds_v7000_params_st {
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy_ptds_v7000_params;
+
+typedef struct cudaMemcpyPeer_v4000_params_st {
+    void *dst;
+    int dstDevice;
+    const void *src;
+    int srcDevice;
+    size_t count;
+} cudaMemcpyPeer_v4000_params;
+
+typedef struct cudaMemcpy2D_ptds_v7000_params_st {
+    void *dst;
+    size_t dpitch;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2D_ptds_v7000_params;
+
+typedef struct cudaMemcpy2DToArray_ptds_v7000_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2DToArray_ptds_v7000_params;
+
+typedef struct cudaMemcpy2DFromArray_ptds_v7000_params_st {
+    void *dst;
+    size_t dpitch;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2DFromArray_ptds_v7000_params;
+
+typedef struct cudaMemcpy2DArrayToArray_ptds_v7000_params_st {
+    cudaArray_t dst;
+    size_t wOffsetDst;
+    size_t hOffsetDst;
+    cudaArray_const_t src;
+    size_t wOffsetSrc;
+    size_t hOffsetSrc;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2DArrayToArray_ptds_v7000_params;
+
+typedef struct cudaMemcpyToSymbol_ptds_v7000_params_st {
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyToSymbol_ptds_v7000_params;
+
+typedef struct cudaMemcpyFromSymbol_ptds_v7000_params_st {
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyFromSymbol_ptds_v7000_params;
+
+typedef struct cudaMemcpyAsync_ptsz_v7000_params_st {
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpyPeerAsync_v4000_params_st {
+    void *dst;
+    int dstDevice;
+    const void *src;
+    int srcDevice;
+    size_t count;
+    cudaStream_t stream;
+} cudaMemcpyPeerAsync_v4000_params;
+
+typedef struct cudaMemcpyBatchAsync_ptsz_v12080_params_st {
+    void **dsts;
+    void **srcs;
+    size_t *sizes;
+    size_t count;
+    struct cudaMemcpyAttributes *attrs;
+    size_t *attrsIdxs;
+    size_t numAttrs;
+    size_t *failIdx;
+    cudaStream_t stream;
+} cudaMemcpyBatchAsync_ptsz_v12080_params;
+
+typedef struct cudaMemcpy3DBatchAsync_ptsz_v12080_params_st {
+    size_t numOps;
+    struct cudaMemcpy3DBatchOp *opList;
+    size_t *failIdx;
+    unsigned long long flags;
+    cudaStream_t stream;
+} cudaMemcpy3DBatchAsync_ptsz_v12080_params;
+
+typedef struct cudaMemcpy2DAsync_ptsz_v7000_params_st {
+    void *dst;
+    size_t dpitch;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpy2DAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpy2DToArrayAsync_ptsz_v7000_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpy2DToArrayAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpy2DFromArrayAsync_ptsz_v7000_params_st {
+    void *dst;
+    size_t dpitch;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpy2DFromArrayAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpyToSymbolAsync_ptsz_v7000_params_st {
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyToSymbolAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpyFromSymbolAsync_ptsz_v7000_params_st {
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyFromSymbolAsync_ptsz_v7000_params;
+
+typedef struct cudaMemset_ptds_v7000_params_st {
+    void *devPtr;
+    int value;
+    size_t count;
+} cudaMemset_ptds_v7000_params;
+
+typedef struct cudaMemset2D_ptds_v7000_params_st {
+    void *devPtr;
+    size_t pitch;
+    int value;
+    size_t width;
+    size_t height;
+} cudaMemset2D_ptds_v7000_params;
+
+typedef struct cudaMemset3D_ptds_v7000_params_st {
+    struct cudaPitchedPtr pitchedDevPtr;
+    int value;
+    struct cudaExtent extent;
+} cudaMemset3D_ptds_v7000_params;
+
+typedef struct cudaMemsetAsync_ptsz_v7000_params_st {
+    void *devPtr;
+    int value;
+    size_t count;
+    cudaStream_t stream;
+} cudaMemsetAsync_ptsz_v7000_params;
+
+typedef struct cudaMemset2DAsync_ptsz_v7000_params_st {
+    void *devPtr;
+    size_t pitch;
+    int value;
+    size_t width;
+    size_t height;
+    cudaStream_t stream;
+} cudaMemset2DAsync_ptsz_v7000_params;
+
+typedef struct cudaMemset3DAsync_ptsz_v7000_params_st {
+    struct cudaPitchedPtr pitchedDevPtr;
+    int value;
+    struct cudaExtent extent;
+    cudaStream_t stream;
+} cudaMemset3DAsync_ptsz_v7000_params;
+
+typedef struct cudaGetSymbolAddress_v3020_params_st {
+    void **devPtr;
+    const void *symbol;
+} cudaGetSymbolAddress_v3020_params;
+
+typedef struct cudaGetSymbolSize_v3020_params_st {
+    size_t *size;
+    const void *symbol;
+} cudaGetSymbolSize_v3020_params;
+
+typedef struct cudaMemPrefetchAsync_ptsz_v8000_params_st {
+    const void *devPtr;
+    size_t count;
+    int dstDevice;
+    cudaStream_t stream;
+} cudaMemPrefetchAsync_ptsz_v8000_params;
+
+typedef struct cudaMemPrefetchAsync_v2_ptsz_v12020_params_st {
+    const void *devPtr;
+    size_t count;
+    struct cudaMemLocation location;
+    unsigned int flags;
+    cudaStream_t stream;
+} cudaMemPrefetchAsync_v2_ptsz_v12020_params;
+
+typedef struct cudaMemAdvise_v8000_params_st {
+    const void *devPtr;
+    size_t count;
+    enum cudaMemoryAdvise advice;
+    int device;
+} cudaMemAdvise_v8000_params;
+
+typedef struct cudaMemAdvise_v2_v12020_params_st {
+    const void *devPtr;
+    size_t count;
+    enum cudaMemoryAdvise advice;
+    struct cudaMemLocation location;
+} cudaMemAdvise_v2_v12020_params;
+
+typedef struct cudaMemRangeGetAttribute_v8000_params_st {
+    void *data;
+    size_t dataSize;
+    enum cudaMemRangeAttribute attribute;
+    const void *devPtr;
+    size_t count;
+} cudaMemRangeGetAttribute_v8000_params;
+
+typedef struct cudaMemRangeGetAttributes_v8000_params_st {
+    void **data;
+    size_t *dataSizes;
+    enum cudaMemRangeAttribute *attributes;
+    size_t numAttributes;
+    const void *devPtr;
+    size_t count;
+} cudaMemRangeGetAttributes_v8000_params;
+
+typedef struct cudaMemcpyToArray_ptds_v7000_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyToArray_ptds_v7000_params;
+
+typedef struct cudaMemcpyFromArray_ptds_v7000_params_st {
+    void *dst;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyFromArray_ptds_v7000_params;
+
+typedef struct cudaMemcpyArrayToArray_ptds_v7000_params_st {
+    cudaArray_t dst;
+    size_t wOffsetDst;
+    size_t hOffsetDst;
+    cudaArray_const_t src;
+    size_t wOffsetSrc;
+    size_t hOffsetSrc;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyArrayToArray_ptds_v7000_params;
+
+typedef struct cudaMemcpyToArrayAsync_ptsz_v7000_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyToArrayAsync_ptsz_v7000_params;
+
+typedef struct cudaMemcpyFromArrayAsync_ptsz_v7000_params_st {
+    void *dst;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t count;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyFromArrayAsync_ptsz_v7000_params;
+
+typedef struct cudaMallocAsync_ptsz_v11020_params_st {
+    void **devPtr;
+    size_t size;
+    cudaStream_t hStream;
+} cudaMallocAsync_ptsz_v11020_params;
+
+typedef struct cudaFreeAsync_ptsz_v11020_params_st {
+    void *devPtr;
+    cudaStream_t hStream;
+} cudaFreeAsync_ptsz_v11020_params;
+
+typedef struct cudaMemPoolTrimTo_v11020_params_st {
+    cudaMemPool_t memPool;
+    size_t minBytesToKeep;
+} cudaMemPoolTrimTo_v11020_params;
+
+typedef struct cudaMemPoolSetAttribute_v11020_params_st {
+    cudaMemPool_t memPool;
+    enum cudaMemPoolAttr attr;
+    void *value;
+} cudaMemPoolSetAttribute_v11020_params;
+
+typedef struct cudaMemPoolGetAttribute_v11020_params_st {
+    cudaMemPool_t memPool;
+    enum cudaMemPoolAttr attr;
+    void *value;
+} cudaMemPoolGetAttribute_v11020_params;
+
+typedef struct cudaMemPoolSetAccess_v11020_params_st {
+    cudaMemPool_t memPool;
+    const struct cudaMemAccessDesc *descList;
+    size_t count;
+} cudaMemPoolSetAccess_v11020_params;
+
+typedef struct cudaMemPoolGetAccess_v11020_params_st {
+    enum cudaMemAccessFlags *flags;
+    cudaMemPool_t memPool;
+    struct cudaMemLocation *location;
+} cudaMemPoolGetAccess_v11020_params;
+
+typedef struct cudaMemPoolCreate_v11020_params_st {
+    cudaMemPool_t *memPool;
+    const struct cudaMemPoolProps *poolProps;
+} cudaMemPoolCreate_v11020_params;
+
+typedef struct cudaMemPoolDestroy_v11020_params_st {
+    cudaMemPool_t memPool;
+} cudaMemPoolDestroy_v11020_params;
+
+typedef struct cudaMallocFromPoolAsync_ptsz_v11020_params_st {
+    void **ptr;
+    size_t size;
+    cudaMemPool_t memPool;
+    cudaStream_t stream;
+} cudaMallocFromPoolAsync_ptsz_v11020_params;
+
+typedef struct cudaMemPoolExportToShareableHandle_v11020_params_st {
+    void *shareableHandle;
+    cudaMemPool_t memPool;
+    enum cudaMemAllocationHandleType handleType;
+    unsigned int flags;
+} cudaMemPoolExportToShareableHandle_v11020_params;
+
+typedef struct cudaMemPoolImportFromShareableHandle_v11020_params_st {
+    cudaMemPool_t *memPool;
+    void *shareableHandle;
+    enum cudaMemAllocationHandleType handleType;
+    unsigned int flags;
+} cudaMemPoolImportFromShareableHandle_v11020_params;
+
+typedef struct cudaMemPoolExportPointer_v11020_params_st {
+    struct cudaMemPoolPtrExportData *exportData;
+    void *ptr;
+} cudaMemPoolExportPointer_v11020_params;
+
+typedef struct cudaMemPoolImportPointer_v11020_params_st {
+    void **ptr;
+    cudaMemPool_t memPool;
+    struct cudaMemPoolPtrExportData *exportData;
+} cudaMemPoolImportPointer_v11020_params;
+
+typedef struct cudaPointerGetAttributes_v4000_params_st {
+    struct cudaPointerAttributes *attributes;
+    const void *ptr;
+} cudaPointerGetAttributes_v4000_params;
+
+typedef struct cudaDeviceCanAccessPeer_v4000_params_st {
+    int *canAccessPeer;
+    int device;
+    int peerDevice;
+} cudaDeviceCanAccessPeer_v4000_params;
+
+typedef struct cudaDeviceEnablePeerAccess_v4000_params_st {
+    int peerDevice;
+    unsigned int flags;
+} cudaDeviceEnablePeerAccess_v4000_params;
+
+typedef struct cudaDeviceDisablePeerAccess_v4000_params_st {
+    int peerDevice;
+} cudaDeviceDisablePeerAccess_v4000_params;
+
+typedef struct cudaGraphicsUnregisterResource_v3020_params_st {
+    cudaGraphicsResource_t resource;
+} cudaGraphicsUnregisterResource_v3020_params;
+
+typedef struct cudaGraphicsResourceSetMapFlags_v3020_params_st {
+    cudaGraphicsResource_t resource;
+    unsigned int flags;
+} cudaGraphicsResourceSetMapFlags_v3020_params;
+
+typedef struct cudaGraphicsMapResources_v3020_params_st {
+    int count;
+    cudaGraphicsResource_t *resources;
+    cudaStream_t stream;
+} cudaGraphicsMapResources_v3020_params;
+
+typedef struct cudaGraphicsUnmapResources_v3020_params_st {
+    int count;
+    cudaGraphicsResource_t *resources;
+    cudaStream_t stream;
+} cudaGraphicsUnmapResources_v3020_params;
+
+typedef struct cudaGraphicsResourceGetMappedPointer_v3020_params_st {
+    void **devPtr;
+    size_t *size;
+    cudaGraphicsResource_t resource;
+} cudaGraphicsResourceGetMappedPointer_v3020_params;
+
+typedef struct cudaGraphicsSubResourceGetMappedArray_v3020_params_st {
+    cudaArray_t *array;
+    cudaGraphicsResource_t resource;
+    unsigned int arrayIndex;
+    unsigned int mipLevel;
+} cudaGraphicsSubResourceGetMappedArray_v3020_params;
+
+typedef struct cudaGraphicsResourceGetMappedMipmappedArray_v5000_params_st {
+    cudaMipmappedArray_t *mipmappedArray;
+    cudaGraphicsResource_t resource;
+} cudaGraphicsResourceGetMappedMipmappedArray_v5000_params;
+
+typedef struct cudaGetChannelDesc_v3020_params_st {
+    struct cudaChannelFormatDesc *desc;
+    cudaArray_const_t array;
+} cudaGetChannelDesc_v3020_params;
+
+typedef struct cudaCreateChannelDesc_v3020_params_st {
+    int x;
+    int y;
+    int z;
+    int w;
+    enum cudaChannelFormatKind f;
+} cudaCreateChannelDesc_v3020_params;
+
+typedef struct cudaCreateTextureObject_v5000_params_st {
+    cudaTextureObject_t *pTexObject;
+    const struct cudaResourceDesc *pResDesc;
+    const struct cudaTextureDesc *pTexDesc;
+    const struct cudaResourceViewDesc *pResViewDesc;
+} cudaCreateTextureObject_v5000_params;
+
+typedef struct cudaDestroyTextureObject_v5000_params_st {
+    cudaTextureObject_t texObject;
+} cudaDestroyTextureObject_v5000_params;
+
+typedef struct cudaGetTextureObjectResourceDesc_v5000_params_st {
+    struct cudaResourceDesc *pResDesc;
+    cudaTextureObject_t texObject;
+} cudaGetTextureObjectResourceDesc_v5000_params;
+
+typedef struct cudaGetTextureObjectTextureDesc_v5000_params_st {
+    struct cudaTextureDesc *pTexDesc;
+    cudaTextureObject_t texObject;
+} cudaGetTextureObjectTextureDesc_v5000_params;
+
+typedef struct cudaGetTextureObjectResourceViewDesc_v5000_params_st {
+    struct cudaResourceViewDesc *pResViewDesc;
+    cudaTextureObject_t texObject;
+} cudaGetTextureObjectResourceViewDesc_v5000_params;
+
+typedef struct cudaCreateSurfaceObject_v5000_params_st {
+    cudaSurfaceObject_t *pSurfObject;
+    const struct cudaResourceDesc *pResDesc;
+} cudaCreateSurfaceObject_v5000_params;
+
+typedef struct cudaDestroySurfaceObject_v5000_params_st {
+    cudaSurfaceObject_t surfObject;
+} cudaDestroySurfaceObject_v5000_params;
+
+typedef struct cudaGetSurfaceObjectResourceDesc_v5000_params_st {
+    struct cudaResourceDesc *pResDesc;
+    cudaSurfaceObject_t surfObject;
+} cudaGetSurfaceObjectResourceDesc_v5000_params;
+
+typedef struct cudaDriverGetVersion_v3020_params_st {
+    int *driverVersion;
+} cudaDriverGetVersion_v3020_params;
+
+typedef struct cudaRuntimeGetVersion_v3020_params_st {
+    int *runtimeVersion;
+} cudaRuntimeGetVersion_v3020_params;
+
+typedef struct cudaGraphCreate_v10000_params_st {
+    cudaGraph_t *pGraph;
+    unsigned int flags;
+} cudaGraphCreate_v10000_params;
+
+typedef struct cudaGraphAddKernelNode_v10000_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const struct cudaKernelNodeParams *pNodeParams;
+} cudaGraphAddKernelNode_v10000_params;
+
+typedef struct cudaGraphKernelNodeGetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    struct cudaKernelNodeParams *pNodeParams;
+} cudaGraphKernelNodeGetParams_v10000_params;
+
+typedef struct cudaGraphKernelNodeSetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    const struct cudaKernelNodeParams *pNodeParams;
+} cudaGraphKernelNodeSetParams_v10000_params;
+
+typedef struct cudaGraphKernelNodeCopyAttributes_v11000_params_st {
+    cudaGraphNode_t hSrc;
+    cudaGraphNode_t hDst;
+} cudaGraphKernelNodeCopyAttributes_v11000_params;
+
+typedef struct cudaGraphKernelNodeGetAttribute_v11000_params_st {
+    cudaGraphNode_t hNode;
+    cudaKernelNodeAttrID attr;
+    cudaKernelNodeAttrValue *value_out;
+} cudaGraphKernelNodeGetAttribute_v11000_params;
+
+typedef struct cudaGraphKernelNodeSetAttribute_v11000_params_st {
+    cudaGraphNode_t hNode;
+    cudaKernelNodeAttrID attr;
+    const cudaKernelNodeAttrValue *value;
+} cudaGraphKernelNodeSetAttribute_v11000_params;
+
+typedef struct cudaGraphAddMemcpyNode_v10000_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const struct cudaMemcpy3DParms *pCopyParams;
+} cudaGraphAddMemcpyNode_v10000_params;
+
+typedef struct cudaGraphAddMemcpyNodeToSymbol_v11010_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaGraphAddMemcpyNodeToSymbol_v11010_params;
+
+typedef struct cudaGraphAddMemcpyNodeFromSymbol_v11010_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaGraphAddMemcpyNodeFromSymbol_v11010_params;
+
+typedef struct cudaGraphAddMemcpyNode1D_v11010_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaGraphAddMemcpyNode1D_v11010_params;
+
+typedef struct cudaGraphMemcpyNodeGetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    struct cudaMemcpy3DParms *pNodeParams;
+} cudaGraphMemcpyNodeGetParams_v10000_params;
+
+typedef struct cudaGraphMemcpyNodeSetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    const struct cudaMemcpy3DParms *pNodeParams;
+} cudaGraphMemcpyNodeSetParams_v10000_params;
+
+typedef struct cudaGraphMemcpyNodeSetParamsToSymbol_v11010_params_st {
+    cudaGraphNode_t node;
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaGraphMemcpyNodeSetParamsToSymbol_v11010_params;
+
+typedef struct cudaGraphMemcpyNodeSetParamsFromSymbol_v11010_params_st {
+    cudaGraphNode_t node;
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaGraphMemcpyNodeSetParamsFromSymbol_v11010_params;
+
+typedef struct cudaGraphMemcpyNodeSetParams1D_v11010_params_st {
+    cudaGraphNode_t node;
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaGraphMemcpyNodeSetParams1D_v11010_params;
+
+typedef struct cudaGraphAddMemsetNode_v10000_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const struct cudaMemsetParams *pMemsetParams;
+} cudaGraphAddMemsetNode_v10000_params;
+
+typedef struct cudaGraphMemsetNodeGetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    struct cudaMemsetParams *pNodeParams;
+} cudaGraphMemsetNodeGetParams_v10000_params;
+
+typedef struct cudaGraphMemsetNodeSetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    const struct cudaMemsetParams *pNodeParams;
+} cudaGraphMemsetNodeSetParams_v10000_params;
+
+typedef struct cudaGraphAddHostNode_v10000_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const struct cudaHostNodeParams *pNodeParams;
+} cudaGraphAddHostNode_v10000_params;
+
+typedef struct cudaGraphHostNodeGetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    struct cudaHostNodeParams *pNodeParams;
+} cudaGraphHostNodeGetParams_v10000_params;
+
+typedef struct cudaGraphHostNodeSetParams_v10000_params_st {
+    cudaGraphNode_t node;
+    const struct cudaHostNodeParams *pNodeParams;
+} cudaGraphHostNodeSetParams_v10000_params;
+
+typedef struct cudaGraphAddChildGraphNode_v10000_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    cudaGraph_t childGraph;
+} cudaGraphAddChildGraphNode_v10000_params;
+
+typedef struct cudaGraphChildGraphNodeGetGraph_v10000_params_st {
+    cudaGraphNode_t node;
+    cudaGraph_t *pGraph;
+} cudaGraphChildGraphNodeGetGraph_v10000_params;
+
+typedef struct cudaGraphAddEmptyNode_v10000_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+} cudaGraphAddEmptyNode_v10000_params;
+
+typedef struct cudaGraphAddEventRecordNode_v11010_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    cudaEvent_t event;
+} cudaGraphAddEventRecordNode_v11010_params;
+
+typedef struct cudaGraphEventRecordNodeGetEvent_v11010_params_st {
+    cudaGraphNode_t node;
+    cudaEvent_t *event_out;
+} cudaGraphEventRecordNodeGetEvent_v11010_params;
+
+typedef struct cudaGraphEventRecordNodeSetEvent_v11010_params_st {
+    cudaGraphNode_t node;
+    cudaEvent_t event;
+} cudaGraphEventRecordNodeSetEvent_v11010_params;
+
+typedef struct cudaGraphAddEventWaitNode_v11010_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    cudaEvent_t event;
+} cudaGraphAddEventWaitNode_v11010_params;
+
+typedef struct cudaGraphEventWaitNodeGetEvent_v11010_params_st {
+    cudaGraphNode_t node;
+    cudaEvent_t *event_out;
+} cudaGraphEventWaitNodeGetEvent_v11010_params;
+
+typedef struct cudaGraphEventWaitNodeSetEvent_v11010_params_st {
+    cudaGraphNode_t node;
+    cudaEvent_t event;
+} cudaGraphEventWaitNodeSetEvent_v11010_params;
+
+typedef struct cudaGraphAddExternalSemaphoresSignalNode_v11020_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const struct cudaExternalSemaphoreSignalNodeParams *nodeParams;
+} cudaGraphAddExternalSemaphoresSignalNode_v11020_params;
+
+typedef struct cudaGraphExternalSemaphoresSignalNodeGetParams_v11020_params_st {
+    cudaGraphNode_t hNode;
+    struct cudaExternalSemaphoreSignalNodeParams *params_out;
+} cudaGraphExternalSemaphoresSignalNodeGetParams_v11020_params;
+
+typedef struct cudaGraphExternalSemaphoresSignalNodeSetParams_v11020_params_st {
+    cudaGraphNode_t hNode;
+    const struct cudaExternalSemaphoreSignalNodeParams *nodeParams;
+} cudaGraphExternalSemaphoresSignalNodeSetParams_v11020_params;
+
+typedef struct cudaGraphAddExternalSemaphoresWaitNode_v11020_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    const struct cudaExternalSemaphoreWaitNodeParams *nodeParams;
+} cudaGraphAddExternalSemaphoresWaitNode_v11020_params;
+
+typedef struct cudaGraphExternalSemaphoresWaitNodeGetParams_v11020_params_st {
+    cudaGraphNode_t hNode;
+    struct cudaExternalSemaphoreWaitNodeParams *params_out;
+} cudaGraphExternalSemaphoresWaitNodeGetParams_v11020_params;
+
+typedef struct cudaGraphExternalSemaphoresWaitNodeSetParams_v11020_params_st {
+    cudaGraphNode_t hNode;
+    const struct cudaExternalSemaphoreWaitNodeParams *nodeParams;
+} cudaGraphExternalSemaphoresWaitNodeSetParams_v11020_params;
+
+typedef struct cudaGraphAddMemAllocNode_v11040_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    struct cudaMemAllocNodeParams *nodeParams;
+} cudaGraphAddMemAllocNode_v11040_params;
+
+typedef struct cudaGraphMemAllocNodeGetParams_v11040_params_st {
+    cudaGraphNode_t node;
+    struct cudaMemAllocNodeParams *params_out;
+} cudaGraphMemAllocNodeGetParams_v11040_params;
+
+typedef struct cudaGraphAddMemFreeNode_v11040_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    void *dptr;
+} cudaGraphAddMemFreeNode_v11040_params;
+
+typedef struct cudaGraphMemFreeNodeGetParams_v11040_params_st {
+    cudaGraphNode_t node;
+    void *dptr_out;
+} cudaGraphMemFreeNodeGetParams_v11040_params;
+
+typedef struct cudaDeviceGraphMemTrim_v11040_params_st {
+    int device;
+} cudaDeviceGraphMemTrim_v11040_params;
+
+typedef struct cudaDeviceGetGraphMemAttribute_v11040_params_st {
+    int device;
+    enum cudaGraphMemAttributeType attr;
+    void *value;
+} cudaDeviceGetGraphMemAttribute_v11040_params;
+
+typedef struct cudaDeviceSetGraphMemAttribute_v11040_params_st {
+    int device;
+    enum cudaGraphMemAttributeType attr;
+    void *value;
+} cudaDeviceSetGraphMemAttribute_v11040_params;
+
+typedef struct cudaGraphClone_v10000_params_st {
+    cudaGraph_t *pGraphClone;
+    cudaGraph_t originalGraph;
+} cudaGraphClone_v10000_params;
+
+typedef struct cudaGraphNodeFindInClone_v10000_params_st {
+    cudaGraphNode_t *pNode;
+    cudaGraphNode_t originalNode;
+    cudaGraph_t clonedGraph;
+} cudaGraphNodeFindInClone_v10000_params;
+
+typedef struct cudaGraphNodeGetType_v10000_params_st {
+    cudaGraphNode_t node;
+    enum cudaGraphNodeType *pType;
+} cudaGraphNodeGetType_v10000_params;
+
+typedef struct cudaGraphGetNodes_v10000_params_st {
+    cudaGraph_t graph;
+    cudaGraphNode_t *nodes;
+    size_t *numNodes;
+} cudaGraphGetNodes_v10000_params;
+
+typedef struct cudaGraphGetRootNodes_v10000_params_st {
+    cudaGraph_t graph;
+    cudaGraphNode_t *pRootNodes;
+    size_t *pNumRootNodes;
+} cudaGraphGetRootNodes_v10000_params;
+
+typedef struct cudaGraphGetEdges_v10000_params_st {
+    cudaGraph_t graph;
+    cudaGraphNode_t *from;
+    cudaGraphNode_t *to;
+    size_t *numEdges;
+} cudaGraphGetEdges_v10000_params;
+
+typedef struct cudaGraphGetEdges_v2_v12030_params_st {
+    cudaGraph_t graph;
+    cudaGraphNode_t *from;
+    cudaGraphNode_t *to;
+    cudaGraphEdgeData *edgeData;
+    size_t *numEdges;
+} cudaGraphGetEdges_v2_v12030_params;
+
+typedef struct cudaGraphNodeGetDependencies_v10000_params_st {
+    cudaGraphNode_t node;
+    cudaGraphNode_t *pDependencies;
+    size_t *pNumDependencies;
+} cudaGraphNodeGetDependencies_v10000_params;
+
+typedef struct cudaGraphNodeGetDependencies_v2_v12030_params_st {
+    cudaGraphNode_t node;
+    cudaGraphNode_t *pDependencies;
+    cudaGraphEdgeData *edgeData;
+    size_t *pNumDependencies;
+} cudaGraphNodeGetDependencies_v2_v12030_params;
+
+typedef struct cudaGraphNodeGetDependentNodes_v10000_params_st {
+    cudaGraphNode_t node;
+    cudaGraphNode_t *pDependentNodes;
+    size_t *pNumDependentNodes;
+} cudaGraphNodeGetDependentNodes_v10000_params;
+
+typedef struct cudaGraphNodeGetDependentNodes_v2_v12030_params_st {
+    cudaGraphNode_t node;
+    cudaGraphNode_t *pDependentNodes;
+    cudaGraphEdgeData *edgeData;
+    size_t *pNumDependentNodes;
+} cudaGraphNodeGetDependentNodes_v2_v12030_params;
+
+typedef struct cudaGraphAddDependencies_v10000_params_st {
+    cudaGraph_t graph;
+    const cudaGraphNode_t *from;
+    const cudaGraphNode_t *to;
+    size_t numDependencies;
+} cudaGraphAddDependencies_v10000_params;
+
+typedef struct cudaGraphAddDependencies_v2_v12030_params_st {
+    cudaGraph_t graph;
+    const cudaGraphNode_t *from;
+    const cudaGraphNode_t *to;
+    const cudaGraphEdgeData *edgeData;
+    size_t numDependencies;
+} cudaGraphAddDependencies_v2_v12030_params;
+
+typedef struct cudaGraphRemoveDependencies_v10000_params_st {
+    cudaGraph_t graph;
+    const cudaGraphNode_t *from;
+    const cudaGraphNode_t *to;
+    size_t numDependencies;
+} cudaGraphRemoveDependencies_v10000_params;
+
+typedef struct cudaGraphRemoveDependencies_v2_v12030_params_st {
+    cudaGraph_t graph;
+    const cudaGraphNode_t *from;
+    const cudaGraphNode_t *to;
+    const cudaGraphEdgeData *edgeData;
+    size_t numDependencies;
+} cudaGraphRemoveDependencies_v2_v12030_params;
+
+typedef struct cudaGraphDestroyNode_v10000_params_st {
+    cudaGraphNode_t node;
+} cudaGraphDestroyNode_v10000_params;
+
+typedef struct cudaGraphInstantiate_v12000_params_st {
+    cudaGraphExec_t *pGraphExec;
+    cudaGraph_t graph;
+    unsigned long long flags;
+} cudaGraphInstantiate_v12000_params;
+
+typedef struct cudaGraphInstantiateWithFlags_v11040_params_st {
+    cudaGraphExec_t *pGraphExec;
+    cudaGraph_t graph;
+    unsigned long long flags;
+} cudaGraphInstantiateWithFlags_v11040_params;
+
+typedef struct cudaGraphInstantiateWithParams_ptsz_v12000_params_st {
+    cudaGraphExec_t *pGraphExec;
+    cudaGraph_t graph;
+    cudaGraphInstantiateParams *instantiateParams;
+} cudaGraphInstantiateWithParams_ptsz_v12000_params;
+
+typedef struct cudaGraphExecGetFlags_v12000_params_st {
+    cudaGraphExec_t graphExec;
+    unsigned long long *flags;
+} cudaGraphExecGetFlags_v12000_params;
+
+typedef struct cudaGraphExecKernelNodeSetParams_v10010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    const struct cudaKernelNodeParams *pNodeParams;
+} cudaGraphExecKernelNodeSetParams_v10010_params;
+
+typedef struct cudaGraphExecMemcpyNodeSetParams_v10020_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    const struct cudaMemcpy3DParms *pNodeParams;
+} cudaGraphExecMemcpyNodeSetParams_v10020_params;
+
+typedef struct cudaGraphExecMemcpyNodeSetParamsToSymbol_v11010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaGraphExecMemcpyNodeSetParamsToSymbol_v11010_params;
+
+typedef struct cudaGraphExecMemcpyNodeSetParamsFromSymbol_v11010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaGraphExecMemcpyNodeSetParamsFromSymbol_v11010_params;
+
+typedef struct cudaGraphExecMemcpyNodeSetParams1D_v11010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaGraphExecMemcpyNodeSetParams1D_v11010_params;
+
+typedef struct cudaGraphExecMemsetNodeSetParams_v10020_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    const struct cudaMemsetParams *pNodeParams;
+} cudaGraphExecMemsetNodeSetParams_v10020_params;
+
+typedef struct cudaGraphExecHostNodeSetParams_v10020_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    const struct cudaHostNodeParams *pNodeParams;
+} cudaGraphExecHostNodeSetParams_v10020_params;
+
+typedef struct cudaGraphExecChildGraphNodeSetParams_v11010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t node;
+    cudaGraph_t childGraph;
+} cudaGraphExecChildGraphNodeSetParams_v11010_params;
+
+typedef struct cudaGraphExecEventRecordNodeSetEvent_v11010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t hNode;
+    cudaEvent_t event;
+} cudaGraphExecEventRecordNodeSetEvent_v11010_params;
+
+typedef struct cudaGraphExecEventWaitNodeSetEvent_v11010_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t hNode;
+    cudaEvent_t event;
+} cudaGraphExecEventWaitNodeSetEvent_v11010_params;
+
+typedef struct cudaGraphExecExternalSemaphoresSignalNodeSetParams_v11020_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t hNode;
+    const struct cudaExternalSemaphoreSignalNodeParams *nodeParams;
+} cudaGraphExecExternalSemaphoresSignalNodeSetParams_v11020_params;
+
+typedef struct cudaGraphExecExternalSemaphoresWaitNodeSetParams_v11020_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t hNode;
+    const struct cudaExternalSemaphoreWaitNodeParams *nodeParams;
+} cudaGraphExecExternalSemaphoresWaitNodeSetParams_v11020_params;
+
+typedef struct cudaGraphNodeSetEnabled_v11060_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t hNode;
+    unsigned int isEnabled;
+} cudaGraphNodeSetEnabled_v11060_params;
+
+typedef struct cudaGraphNodeGetEnabled_v11060_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraphNode_t hNode;
+    unsigned int *isEnabled;
+} cudaGraphNodeGetEnabled_v11060_params;
+
+typedef struct cudaGraphExecUpdate_v10020_params_st {
+    cudaGraphExec_t hGraphExec;
+    cudaGraph_t hGraph;
+    cudaGraphExecUpdateResultInfo *resultInfo;
+} cudaGraphExecUpdate_v10020_params;
+
+typedef struct cudaGraphUpload_ptsz_v10000_params_st {
+    cudaGraphExec_t graphExec;
+    cudaStream_t stream;
+} cudaGraphUpload_ptsz_v10000_params;
+
+typedef struct cudaGraphLaunch_ptsz_v10000_params_st {
+    cudaGraphExec_t graphExec;
+    cudaStream_t stream;
+} cudaGraphLaunch_ptsz_v10000_params;
+
+typedef struct cudaGraphExecDestroy_v10000_params_st {
+    cudaGraphExec_t graphExec;
+} cudaGraphExecDestroy_v10000_params;
+
+typedef struct cudaGraphDestroy_v10000_params_st {
+    cudaGraph_t graph;
+} cudaGraphDestroy_v10000_params;
+
+typedef struct cudaGraphDebugDotPrint_v11030_params_st {
+    cudaGraph_t graph;
+    const char *path;
+    unsigned int flags;
+} cudaGraphDebugDotPrint_v11030_params;
+
+typedef struct cudaUserObjectCreate_v11030_params_st {
+    cudaUserObject_t *object_out;
+    void *ptr;
+    cudaHostFn_t destroy;
+    unsigned int initialRefcount;
+    unsigned int flags;
+} cudaUserObjectCreate_v11030_params;
+
+typedef struct cudaUserObjectRetain_v11030_params_st {
+    cudaUserObject_t object;
+    unsigned int count;
+} cudaUserObjectRetain_v11030_params;
+
+typedef struct cudaUserObjectRelease_v11030_params_st {
+    cudaUserObject_t object;
+    unsigned int count;
+} cudaUserObjectRelease_v11030_params;
+
+typedef struct cudaGraphRetainUserObject_v11030_params_st {
+    cudaGraph_t graph;
+    cudaUserObject_t object;
+    unsigned int count;
+    unsigned int flags;
+} cudaGraphRetainUserObject_v11030_params;
+
+typedef struct cudaGraphReleaseUserObject_v11030_params_st {
+    cudaGraph_t graph;
+    cudaUserObject_t object;
+    unsigned int count;
+} cudaGraphReleaseUserObject_v11030_params;
+
+typedef struct cudaGraphAddNode_v12020_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    size_t numDependencies;
+    struct cudaGraphNodeParams *nodeParams;
+} cudaGraphAddNode_v12020_params;
+
+typedef struct cudaGraphAddNode_v2_v12030_params_st {
+    cudaGraphNode_t *pGraphNode;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *pDependencies;
+    const cudaGraphEdgeData *dependencyData;
+    size_t numDependencies;
+    struct cudaGraphNodeParams *nodeParams;
+} cudaGraphAddNode_v2_v12030_params;
+
+typedef struct cudaGraphNodeSetParams_v12020_params_st {
+    cudaGraphNode_t node;
+    struct cudaGraphNodeParams *nodeParams;
+} cudaGraphNodeSetParams_v12020_params;
+
+typedef struct cudaGraphExecNodeSetParams_v12020_params_st {
+    cudaGraphExec_t graphExec;
+    cudaGraphNode_t node;
+    struct cudaGraphNodeParams *nodeParams;
+} cudaGraphExecNodeSetParams_v12020_params;
+
+typedef struct cudaGraphConditionalHandleCreate_v12030_params_st {
+    cudaGraphConditionalHandle *pHandle_out;
+    cudaGraph_t graph;
+    unsigned int defaultLaunchValue;
+    unsigned int flags;
+} cudaGraphConditionalHandleCreate_v12030_params;
+
+typedef struct cudaGetDriverEntryPoint_ptsz_v11030_params_st {
+    const char *symbol;
+    void **funcPtr;
+    unsigned long long flags;
+    enum cudaDriverEntryPointQueryResult *driverStatus;
+} cudaGetDriverEntryPoint_ptsz_v11030_params;
+
+typedef struct cudaGetDriverEntryPointByVersion_ptsz_v12050_params_st {
+    const char *symbol;
+    void **funcPtr;
+    unsigned int cudaVersion;
+    unsigned long long flags;
+    enum cudaDriverEntryPointQueryResult *driverStatus;
+} cudaGetDriverEntryPointByVersion_ptsz_v12050_params;
+
+typedef struct cudaGetFuncBySymbol_v11000_params_st {
+    cudaFunction_t *functionPtr;
+    const void *symbolPtr;
+} cudaGetFuncBySymbol_v11000_params;
+
+typedef struct cudaGetKernel_v12000_params_st {
+    cudaKernel_t *kernelPtr;
+    const void *entryFuncAddr;
+} cudaGetKernel_v12000_params;
+
+typedef struct cudaMemcpy_v3020_params_st {
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy_v3020_params;
+
+typedef struct cudaMemcpyToSymbol_v3020_params_st {
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyToSymbol_v3020_params;
+
+typedef struct cudaMemcpyFromSymbol_v3020_params_st {
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyFromSymbol_v3020_params;
+
+typedef struct cudaMemcpy2D_v3020_params_st {
+    void *dst;
+    size_t dpitch;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2D_v3020_params;
+
+typedef struct cudaMemcpyToArray_v3020_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyToArray_v3020_params;
+
+typedef struct cudaMemcpy2DToArray_v3020_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2DToArray_v3020_params;
+
+typedef struct cudaMemcpyFromArray_v3020_params_st {
+    void *dst;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyFromArray_v3020_params;
+
+typedef struct cudaMemcpy2DFromArray_v3020_params_st {
+    void *dst;
+    size_t dpitch;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2DFromArray_v3020_params;
+
+typedef struct cudaMemcpyArrayToArray_v3020_params_st {
+    cudaArray_t dst;
+    size_t wOffsetDst;
+    size_t hOffsetDst;
+    cudaArray_const_t src;
+    size_t wOffsetSrc;
+    size_t hOffsetSrc;
+    size_t count;
+    enum cudaMemcpyKind kind;
+} cudaMemcpyArrayToArray_v3020_params;
+
+typedef struct cudaMemcpy2DArrayToArray_v3020_params_st {
+    cudaArray_t dst;
+    size_t wOffsetDst;
+    size_t hOffsetDst;
+    cudaArray_const_t src;
+    size_t wOffsetSrc;
+    size_t hOffsetSrc;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+} cudaMemcpy2DArrayToArray_v3020_params;
+
+typedef struct cudaMemcpy3D_v3020_params_st {
+    const struct cudaMemcpy3DParms *p;
+} cudaMemcpy3D_v3020_params;
+
+typedef struct cudaMemcpy3DPeer_v4000_params_st {
+    const struct cudaMemcpy3DPeerParms *p;
+} cudaMemcpy3DPeer_v4000_params;
+
+typedef struct cudaMemcpyBatchAsync_v12080_params_st {
+    void **dsts;
+    void **srcs;
+    size_t *sizes;
+    size_t count;
+    struct cudaMemcpyAttributes *attrs;
+    size_t *attrsIdxs;
+    size_t numAttrs;
+    size_t *failIdx;
+    cudaStream_t stream;
+} cudaMemcpyBatchAsync_v12080_params;
+
+typedef struct cudaMemcpy3DBatchAsync_v12080_params_st {
+    size_t numOps;
+    struct cudaMemcpy3DBatchOp *opList;
+    size_t *failIdx;
+    unsigned long long flags;
+    cudaStream_t stream;
+} cudaMemcpy3DBatchAsync_v12080_params;
+
+typedef struct cudaMemset_v3020_params_st {
+    void *devPtr;
+    int value;
+    size_t count;
+} cudaMemset_v3020_params;
+
+typedef struct cudaMemset2D_v3020_params_st {
+    void *devPtr;
+    size_t pitch;
+    int value;
+    size_t width;
+    size_t height;
+} cudaMemset2D_v3020_params;
+
+typedef struct cudaMemset3D_v3020_params_st {
+    struct cudaPitchedPtr pitchedDevPtr;
+    int value;
+    struct cudaExtent extent;
+} cudaMemset3D_v3020_params;
+
+typedef struct cudaMemcpyAsync_v3020_params_st {
+    void *dst;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyAsync_v3020_params;
+
+typedef struct cudaMemcpyToSymbolAsync_v3020_params_st {
+    const void *symbol;
+    const void *src;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyToSymbolAsync_v3020_params;
+
+typedef struct cudaMemcpyFromSymbolAsync_v3020_params_st {
+    void *dst;
+    const void *symbol;
+    size_t count;
+    size_t offset;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyFromSymbolAsync_v3020_params;
+
+typedef struct cudaMemcpy2DAsync_v3020_params_st {
+    void *dst;
+    size_t dpitch;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpy2DAsync_v3020_params;
+
+typedef struct cudaMemcpyToArrayAsync_v3020_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t count;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyToArrayAsync_v3020_params;
+
+typedef struct cudaMemcpy2DToArrayAsync_v3020_params_st {
+    cudaArray_t dst;
+    size_t wOffset;
+    size_t hOffset;
+    const void *src;
+    size_t spitch;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpy2DToArrayAsync_v3020_params;
+
+typedef struct cudaMemcpyFromArrayAsync_v3020_params_st {
+    void *dst;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t count;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpyFromArrayAsync_v3020_params;
+
+typedef struct cudaMemcpy2DFromArrayAsync_v3020_params_st {
+    void *dst;
+    size_t dpitch;
+    cudaArray_const_t src;
+    size_t wOffset;
+    size_t hOffset;
+    size_t width;
+    size_t height;
+    enum cudaMemcpyKind kind;
+    cudaStream_t stream;
+} cudaMemcpy2DFromArrayAsync_v3020_params;
+
+typedef struct cudaMemcpy3DAsync_v3020_params_st {
+    const struct cudaMemcpy3DParms *p;
+    cudaStream_t stream;
+} cudaMemcpy3DAsync_v3020_params;
+
+typedef struct cudaMemcpy3DPeerAsync_v4000_params_st {
+    const struct cudaMemcpy3DPeerParms *p;
+    cudaStream_t stream;
+} cudaMemcpy3DPeerAsync_v4000_params;
+
+typedef struct cudaMemsetAsync_v3020_params_st {
+    void *devPtr;
+    int value;
+    size_t count;
+    cudaStream_t stream;
+} cudaMemsetAsync_v3020_params;
+
+typedef struct cudaMemset2DAsync_v3020_params_st {
+    void *devPtr;
+    size_t pitch;
+    int value;
+    size_t width;
+    size_t height;
+    cudaStream_t stream;
+} cudaMemset2DAsync_v3020_params;
+
+typedef struct cudaMemset3DAsync_v3020_params_st {
+    struct cudaPitchedPtr pitchedDevPtr;
+    int value;
+    struct cudaExtent extent;
+    cudaStream_t stream;
+} cudaMemset3DAsync_v3020_params;
+
+typedef struct cudaStreamQuery_v3020_params_st {
+    cudaStream_t stream;
+} cudaStreamQuery_v3020_params;
+
+typedef struct cudaStreamGetDevice_v12080_params_st {
+    cudaStream_t hStream;
+    int *device;
+} cudaStreamGetDevice_v12080_params;
+
+typedef struct cudaStreamGetFlags_v5050_params_st {
+    cudaStream_t hStream;
+    unsigned int *flags;
+} cudaStreamGetFlags_v5050_params;
+
+typedef struct cudaStreamGetId_v12000_params_st {
+    cudaStream_t hStream;
+    unsigned long long *streamId;
+} cudaStreamGetId_v12000_params;
+
+typedef struct cudaStreamGetPriority_v5050_params_st {
+    cudaStream_t hStream;
+    int *priority;
+} cudaStreamGetPriority_v5050_params;
+
+typedef struct cudaEventRecord_v3020_params_st {
+    cudaEvent_t event;
+    cudaStream_t stream;
+} cudaEventRecord_v3020_params;
+
+typedef struct cudaEventRecordWithFlags_v11010_params_st {
+    cudaEvent_t event;
+    cudaStream_t stream;
+    unsigned int flags;
+} cudaEventRecordWithFlags_v11010_params;
+
+typedef struct cudaStreamWaitEvent_v3020_params_st {
+    cudaStream_t stream;
+    cudaEvent_t event;
+    unsigned int flags;
+} cudaStreamWaitEvent_v3020_params;
+
+typedef struct cudaStreamAddCallback_v5000_params_st {
+    cudaStream_t stream;
+    cudaStreamCallback_t callback;
+    void *userData;
+    unsigned int flags;
+} cudaStreamAddCallback_v5000_params;
+
+typedef struct cudaStreamAttachMemAsync_v6000_params_st {
+    cudaStream_t stream;
+    void *devPtr;
+    size_t length;
+    unsigned int flags;
+} cudaStreamAttachMemAsync_v6000_params;
+
+typedef struct cudaStreamSynchronize_v3020_params_st {
+    cudaStream_t stream;
+} cudaStreamSynchronize_v3020_params;
+
+typedef struct cudaLaunchKernel_v7000_params_st {
+    const void *func;
+    dim3 gridDim;
+    dim3 blockDim;
+    void **args;
+    size_t sharedMem;
+    cudaStream_t stream;
+} cudaLaunchKernel_v7000_params;
+
+typedef struct cudaLaunchKernelExC_v11060_params_st {
+    const cudaLaunchConfig_t *config;
+    const void *func;
+    void **args;
+} cudaLaunchKernelExC_v11060_params;
+
+typedef struct cudaLaunchCooperativeKernel_v9000_params_st {
+    const void *func;
+    dim3 gridDim;
+    dim3 blockDim;
+    void **args;
+    size_t sharedMem;
+    cudaStream_t stream;
+} cudaLaunchCooperativeKernel_v9000_params;
+
+typedef struct cudaLaunchHostFunc_v10000_params_st {
+    cudaStream_t stream;
+    cudaHostFn_t fn;
+    void *userData;
+} cudaLaunchHostFunc_v10000_params;
+
+typedef struct cudaMemPrefetchAsync_v8000_params_st {
+    const void *devPtr;
+    size_t count;
+    int dstDevice;
+    cudaStream_t stream;
+} cudaMemPrefetchAsync_v8000_params;
+
+typedef struct cudaMemPrefetchAsync_v2_v12020_params_st {
+    const void *devPtr;
+    size_t count;
+    struct cudaMemLocation location;
+    unsigned int flags;
+    cudaStream_t stream;
+} cudaMemPrefetchAsync_v2_v12020_params;
+
+typedef struct cudaSignalExternalSemaphoresAsync_v10000_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreSignalParams_v1 *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaSignalExternalSemaphoresAsync_v10000_params;
+
+typedef struct cudaSignalExternalSemaphoresAsync_ptsz_v10000_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreSignalParams_v1 *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaSignalExternalSemaphoresAsync_ptsz_v10000_params;
+
+typedef struct cudaSignalExternalSemaphoresAsync_v2_v11020_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreSignalParams *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaSignalExternalSemaphoresAsync_v2_v11020_params;
+
+typedef struct cudaWaitExternalSemaphoresAsync_v10000_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreWaitParams_v1 *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaWaitExternalSemaphoresAsync_v10000_params;
+
+typedef struct cudaWaitExternalSemaphoresAsync_ptsz_v10000_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreWaitParams_v1 *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaWaitExternalSemaphoresAsync_ptsz_v10000_params;
+
+typedef struct cudaWaitExternalSemaphoresAsync_v2_v11020_params_st {
+    const cudaExternalSemaphore_t *extSemArray;
+    const struct cudaExternalSemaphoreWaitParams *paramsArray;
+    unsigned int numExtSems;
+    cudaStream_t stream;
+} cudaWaitExternalSemaphoresAsync_v2_v11020_params;
+
+typedef struct cudaGraphInstantiateWithParams_v12000_params_st {
+    cudaGraphExec_t *pGraphExec;
+    cudaGraph_t graph;
+    cudaGraphInstantiateParams *instantiateParams;
+} cudaGraphInstantiateWithParams_v12000_params;
+
+typedef struct cudaGraphUpload_v10000_params_st {
+    cudaGraphExec_t graphExec;
+    cudaStream_t stream;
+} cudaGraphUpload_v10000_params;
+
+typedef struct cudaGraphLaunch_v10000_params_st {
+    cudaGraphExec_t graphExec;
+    cudaStream_t stream;
+} cudaGraphLaunch_v10000_params;
+
+typedef struct cudaStreamBeginCapture_v10000_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureMode mode;
+} cudaStreamBeginCapture_v10000_params;
+
+typedef struct cudaStreamBeginCaptureToGraph_v12030_params_st {
+    cudaStream_t stream;
+    cudaGraph_t graph;
+    const cudaGraphNode_t *dependencies;
+    const cudaGraphEdgeData *dependencyData;
+    size_t numDependencies;
+    enum cudaStreamCaptureMode mode;
+} cudaStreamBeginCaptureToGraph_v12030_params;
+
+typedef struct cudaStreamEndCapture_v10000_params_st {
+    cudaStream_t stream;
+    cudaGraph_t *pGraph;
+} cudaStreamEndCapture_v10000_params;
+
+typedef struct cudaStreamIsCapturing_v10000_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *pCaptureStatus;
+} cudaStreamIsCapturing_v10000_params;
+
+typedef struct cudaStreamGetCaptureInfo_v10010_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *captureStatus_out;
+    unsigned long long *id_out;
+} cudaStreamGetCaptureInfo_v10010_params;
+
+typedef struct cudaStreamGetCaptureInfo_ptsz_v10010_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *captureStatus_out;
+    unsigned long long *id_out;
+} cudaStreamGetCaptureInfo_ptsz_v10010_params;
+
+typedef struct cudaStreamGetCaptureInfo_v2_v11030_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *captureStatus_out;
+    unsigned long long *id_out;
+    cudaGraph_t *graph_out;
+    const cudaGraphNode_t **dependencies_out;
+    size_t *numDependencies_out;
+} cudaStreamGetCaptureInfo_v2_v11030_params;
+
+typedef struct cudaStreamGetCaptureInfo_v3_v12030_params_st {
+    cudaStream_t stream;
+    enum cudaStreamCaptureStatus *captureStatus_out;
+    unsigned long long *id_out;
+    cudaGraph_t *graph_out;
+    const cudaGraphNode_t **dependencies_out;
+    const cudaGraphEdgeData **edgeData_out;
+    size_t *numDependencies_out;
+} cudaStreamGetCaptureInfo_v3_v12030_params;
+
+typedef struct cudaStreamUpdateCaptureDependencies_v11030_params_st {
+    cudaStream_t stream;
+    cudaGraphNode_t *dependencies;
+    size_t numDependencies;
+    unsigned int flags;
+} cudaStreamUpdateCaptureDependencies_v11030_params;
+
+typedef struct cudaStreamUpdateCaptureDependencies_v2_v12030_params_st {
+    cudaStream_t stream;
+    cudaGraphNode_t *dependencies;
+    const cudaGraphEdgeData *dependencyData;
+    size_t numDependencies;
+    unsigned int flags;
+} cudaStreamUpdateCaptureDependencies_v2_v12030_params;
+
+typedef struct cudaStreamCopyAttributes_v11000_params_st {
+    cudaStream_t dstStream;
+    cudaStream_t srcStream;
+} cudaStreamCopyAttributes_v11000_params;
+
+typedef struct cudaStreamGetAttribute_v11000_params_st {
+    cudaStream_t stream;
+    cudaStreamAttrID attr;
+    cudaStreamAttrValue *value;
+} cudaStreamGetAttribute_v11000_params;
+
+typedef struct cudaStreamSetAttribute_v11000_params_st {
+    cudaStream_t stream;
+    cudaStreamAttrID attr;
+    const cudaStreamAttrValue *param;
+} cudaStreamSetAttribute_v11000_params;
+
+typedef struct cudaMallocAsync_v11020_params_st {
+    void **devPtr;
+    size_t size;
+    cudaStream_t hStream;
+} cudaMallocAsync_v11020_params;
+
+typedef struct cudaFreeAsync_v11020_params_st {
+    void *devPtr;
+    cudaStream_t hStream;
+} cudaFreeAsync_v11020_params;
+
+typedef struct cudaMallocFromPoolAsync_v11020_params_st {
+    void **ptr;
+    size_t size;
+    cudaMemPool_t memPool;
+    cudaStream_t stream;
+} cudaMallocFromPoolAsync_v11020_params;
+
+typedef struct cudaGetDriverEntryPoint_v11030_params_st {
+    const char *symbol;
+    void **funcPtr;
+    unsigned long long flags;
+    enum cudaDriverEntryPointQueryResult *driverStatus;
+} cudaGetDriverEntryPoint_v11030_params;
+
+typedef struct cudaGetDriverEntryPointByVersion_v12050_params_st {
+    const char *symbol;
+    void **funcPtr;
+    unsigned int cudaVersion;
+    unsigned long long flags;
+    enum cudaDriverEntryPointQueryResult *driverStatus;
+} cudaGetDriverEntryPointByVersion_v12050_params;
+
+typedef struct cudaGetDeviceProperties_v3020_params_st {
+    struct cudaDeviceProp *prop;
+    int device;
+} cudaGetDeviceProperties_v3020_params;
+
+// Parameter trace structures for removed functions
+
+
+// End of parameter trace structures
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_vdpau_interop_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_vdpau_interop_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..88e79d1957925c4bbacd381e9461d5072de88f24
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cuda_vdpau_interop_meta.h
@@ -0,0 +1,38 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// CUDA public interface, for type definitions and api function prototypes
+#include "cuda_vdpau_interop.h"
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+// Currently used parameter trace structures
+typedef struct cudaVDPAUGetDevice_v3020_params_st {
+    int *device;
+    VdpDevice vdpDevice;
+    VdpGetProcAddress *vdpGetProcAddress;
+} cudaVDPAUGetDevice_v3020_params;
+
+typedef struct cudaVDPAUSetVDPAUDevice_v3020_params_st {
+    int device;
+    VdpDevice vdpDevice;
+    VdpGetProcAddress *vdpGetProcAddress;
+} cudaVDPAUSetVDPAUDevice_v3020_params;
+
+typedef struct cudaGraphicsVDPAURegisterVideoSurface_v3020_params_st {
+    struct cudaGraphicsResource **resource;
+    VdpVideoSurface vdpSurface;
+    unsigned int flags;
+} cudaGraphicsVDPAURegisterVideoSurface_v3020_params;
+
+typedef struct cudaGraphicsVDPAURegisterOutputSurface_v3020_params_st {
+    struct cudaGraphicsResource **resource;
+    VdpOutputSurface vdpSurface;
+    unsigned int flags;
+} cudaGraphicsVDPAURegisterOutputSurface_v3020_params;
+
+// Parameter trace structures for removed functions
+
+
+// End of parameter trace structures
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudart_removed_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudart_removed_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..a0fc27a71bb3fc883db9fe7562eea3f28145430d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_cudart_removed_meta.h
@@ -0,0 +1,162 @@
+// This file is generated.  Any changes you make will be lost during the next clean build.
+
+// CUDA public interface, for type definitions and api function prototypes
+#include "cudart_removed.h"
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+// Currently used parameter trace structures
+typedef struct cudaStreamDestroy_v3020_params_st {
+    cudaStream_t stream;
+} cudaStreamDestroy_v3020_params;
+
+typedef struct cudaOccupancyMaxActiveBlocksPerMultiprocessor_v6000_params_st {
+    int *numBlocks;
+    const void *func;
+    size_t numDynamicSmemBytes;
+} cudaOccupancyMaxActiveBlocksPerMultiprocessor_v6000_params;
+
+typedef struct cudaConfigureCall_v3020_params_st {
+    dim3 gridDim;
+    dim3 blockDim;
+    size_t sharedMem  __dv;
+    cudaStream_t stream  __dv;
+} cudaConfigureCall_v3020_params;
+
+typedef struct cudaSetupArgument_v3020_params_st {
+    const void *arg;
+    size_t size;
+    size_t offset;
+} cudaSetupArgument_v3020_params;
+
+typedef struct cudaLaunch_v3020_params_st {
+    const void *func;
+} cudaLaunch_v3020_params;
+
+typedef struct cudaLaunch_ptsz_v7000_params_st {
+    const void *func;
+} cudaLaunch_ptsz_v7000_params;
+
+typedef struct cudaStreamSetFlags_v10200_params_st {
+    cudaStream_t hStream;
+    unsigned int flags;
+} cudaStreamSetFlags_v10200_params;
+
+typedef struct cudaStreamSetFlags_ptsz_v10200_params_st {
+    cudaStream_t hStream;
+    unsigned int flags;
+} cudaStreamSetFlags_ptsz_v10200_params;
+
+typedef struct cudaProfilerInitialize_v4000_params_st {
+    const char *configFile;
+    const char *outputFile;
+    cudaOutputMode_t outputMode;
+} cudaProfilerInitialize_v4000_params;
+
+typedef struct cudaThreadSetLimit_v3020_params_st {
+    enum cudaLimit limit;
+    size_t value;
+} cudaThreadSetLimit_v3020_params;
+
+typedef struct cudaThreadGetLimit_v3020_params_st {
+    size_t *pValue;
+    enum cudaLimit limit;
+} cudaThreadGetLimit_v3020_params;
+
+typedef struct cudaThreadGetCacheConfig_v3020_params_st {
+    enum cudaFuncCache *pCacheConfig;
+} cudaThreadGetCacheConfig_v3020_params;
+
+typedef struct cudaThreadSetCacheConfig_v3020_params_st {
+    enum cudaFuncCache cacheConfig;
+} cudaThreadSetCacheConfig_v3020_params;
+
+typedef struct cudaSetDoubleForDevice_v3020_params_st {
+    double *d;
+} cudaSetDoubleForDevice_v3020_params;
+
+typedef struct cudaSetDoubleForHost_v3020_params_st {
+    double *d;
+} cudaSetDoubleForHost_v3020_params;
+
+typedef struct cudaCreateTextureObject_v2_v11080_params_st {
+    cudaTextureObject_t *pTexObject;
+    const struct cudaResourceDesc *pResDesc;
+    const struct cudaTextureDesc *pTexDesc;
+    const struct cudaResourceViewDesc *pResViewDesc;
+} cudaCreateTextureObject_v2_v11080_params;
+
+typedef struct cudaGetTextureObjectTextureDesc_v2_v11080_params_st {
+    struct cudaTextureDesc *pTexDesc;
+    cudaTextureObject_t texObject;
+} cudaGetTextureObjectTextureDesc_v2_v11080_params;
+
+typedef struct cudaBindTexture_v3020_params_st {
+    size_t *offset;
+    const struct textureReference *texref;
+    const void *devPtr;
+    const struct cudaChannelFormatDesc *desc;
+    size_t size  __dv;
+} cudaBindTexture_v3020_params;
+
+typedef struct cudaBindTexture2D_v3020_params_st {
+    size_t *offset;
+    const struct textureReference *texref;
+    const void *devPtr;
+    const struct cudaChannelFormatDesc *desc;
+    size_t width;
+    size_t height;
+    size_t pitch;
+} cudaBindTexture2D_v3020_params;
+
+typedef struct cudaBindTextureToArray_v3020_params_st {
+    const struct textureReference *texref;
+    cudaArray_const_t array;
+    const struct cudaChannelFormatDesc *desc;
+} cudaBindTextureToArray_v3020_params;
+
+typedef struct cudaBindTextureToMipmappedArray_v5000_params_st {
+    const struct textureReference *texref;
+    cudaMipmappedArray_const_t mipmappedArray;
+    const struct cudaChannelFormatDesc *desc;
+} cudaBindTextureToMipmappedArray_v5000_params;
+
+typedef struct cudaUnbindTexture_v3020_params_st {
+    const struct textureReference *texref;
+} cudaUnbindTexture_v3020_params;
+
+typedef struct cudaGetTextureAlignmentOffset_v3020_params_st {
+    size_t *offset;
+    const struct textureReference *texref;
+} cudaGetTextureAlignmentOffset_v3020_params;
+
+typedef struct cudaGetTextureReference_v3020_params_st {
+    const struct textureReference **texref;
+    const void *symbol;
+} cudaGetTextureReference_v3020_params;
+
+typedef struct cudaBindSurfaceToArray_v3020_params_st {
+    const struct surfaceReference *surfref;
+    cudaArray_const_t array;
+    const struct cudaChannelFormatDesc *desc;
+} cudaBindSurfaceToArray_v3020_params;
+
+typedef struct cudaGetSurfaceReference_v3020_params_st {
+    const struct surfaceReference **surfref;
+    const void *symbol;
+} cudaGetSurfaceReference_v3020_params;
+
+typedef struct cudaGraphInstantiate_v10000_params_st {
+    cudaGraphExec_t *pGraphExec;
+    cudaGraph_t graph;
+    cudaGraphNode_t *pErrorNode;
+    char *pLogBuffer;
+    size_t bufferSize;
+} cudaGraphInstantiate_v10000_params;
+
+// Parameter trace structures for removed functions
+
+
+// End of parameter trace structures
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_nvtx_meta.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_nvtx_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..ed8877e21f0651fe1564151090850694eb495cfb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/generated_nvtx_meta.h
@@ -0,0 +1,247 @@
+/*
+ * Copyright 2013-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+// *************************************************************************
+//      Definitions of structs to hold parameters for each function
+// *************************************************************************
+
+typedef struct nvtxMarkEx_params_st {
+  const nvtxEventAttributes_t* eventAttrib;
+} nvtxMarkEx_params;
+
+typedef struct nvtxMarkA_params_st {
+  const char* message;
+} nvtxMarkA_params;
+
+typedef struct nvtxMarkW_params_st {
+  const wchar_t* message;
+} nvtxMarkW_params;
+
+typedef struct nvtxRangeStartEx_params_st {
+  const nvtxEventAttributes_t* eventAttrib;
+} nvtxRangeStartEx_params;
+
+typedef struct nvtxRangeStartA_params_st {
+  const char* message;
+} nvtxRangeStartA_params;
+
+typedef struct nvtxRangeStartW_params_st {
+  const wchar_t* message;
+} nvtxRangeStartW_params;
+
+typedef struct nvtxRangeEnd_params_st {
+  nvtxRangeId_t id;
+} nvtxRangeEnd_params;
+
+typedef struct nvtxRangePushEx_params_st {
+  const nvtxEventAttributes_t* eventAttrib;
+} nvtxRangePushEx_params;
+
+typedef struct nvtxRangePushA_params_st {
+  const char* message;
+} nvtxRangePushA_params;
+
+typedef struct nvtxRangePushW_params_st {
+  const wchar_t* message;
+} nvtxRangePushW_params;
+
+typedef struct nvtxRangePop_params_st {
+  /* WAR: Windows compiler doesn't allow empty structs */
+  /* This field shouldn't be used */
+  void *dummy;
+} nvtxRangePop_params;
+
+typedef struct nvtxNameCategoryA_params_st {
+  uint32_t category;
+  const char* name;
+} nvtxNameCategoryA_params;
+
+typedef struct nvtxNameCategoryW_params_st {
+  uint32_t category;
+  const wchar_t* name;
+} nvtxNameCategoryW_params;
+
+typedef struct nvtxNameOsThreadA_params_st {
+  uint32_t threadId;
+  const char* name;
+} nvtxNameOsThreadA_params;
+
+typedef struct nvtxNameOsThreadW_params_st {
+  uint32_t threadId;
+  const wchar_t* name;
+} nvtxNameOsThreadW_params;
+
+typedef struct nvtxNameCuDeviceA_params_st {
+  CUdevice device;
+  const char* name;
+} nvtxNameCuDeviceA_params;
+
+typedef struct nvtxNameCuDeviceW_params_st {
+  CUdevice device;
+  const wchar_t* name;
+} nvtxNameCuDeviceW_params;
+
+typedef struct nvtxNameCuContextA_params_st {
+  CUcontext context;
+  const char* name;
+} nvtxNameCuContextA_params;
+
+typedef struct nvtxNameCuContextW_params_st {
+  CUcontext context;
+  const wchar_t* name;
+} nvtxNameCuContextW_params;
+
+typedef struct nvtxNameCuStreamA_params_st {
+  CUstream stream;
+  const char* name;
+} nvtxNameCuStreamA_params;
+
+typedef struct nvtxNameCuStreamW_params_st {
+  CUstream stream;
+  const wchar_t* name;
+} nvtxNameCuStreamW_params;
+
+typedef struct nvtxNameCuEventA_params_st {
+  CUevent event;
+  const char* name;
+} nvtxNameCuEventA_params;
+
+typedef struct nvtxNameCuEventW_params_st {
+  CUevent event;
+  const wchar_t* name;
+} nvtxNameCuEventW_params;
+
+typedef struct nvtxNameCudaDeviceA_params_st {
+  int device;
+  const char* name;
+} nvtxNameCudaDeviceA_params;
+
+typedef struct nvtxNameCudaDeviceW_params_st {
+  int device;
+  const wchar_t* name;
+} nvtxNameCudaDeviceW_params;
+
+typedef struct nvtxNameCudaStreamA_params_st {
+  cudaStream_t stream;
+  const char* name;
+} nvtxNameCudaStreamA_params;
+
+typedef struct nvtxNameCudaStreamW_params_st {
+  cudaStream_t stream;
+  const wchar_t* name;
+} nvtxNameCudaStreamW_params;
+
+typedef struct nvtxNameCudaEventA_params_st {
+  cudaEvent_t event;
+  const char* name;
+} nvtxNameCudaEventA_params;
+
+typedef struct nvtxNameCudaEventW_params_st {
+  cudaEvent_t event;
+  const wchar_t* name;
+} nvtxNameCudaEventW_params;
+
+typedef struct nvtxDomainCreateA_params_st {
+  const char* name;
+} nvtxDomainCreateA_params;
+
+typedef struct nvtxDomainDestroy_params_st {
+  nvtxDomainHandle_t domain;
+} nvtxDomainDestroy_params;
+
+typedef struct nvtxDomainMarkEx_params_st {
+  nvtxDomainHandle_t domain;
+  nvtxMarkEx_params core;
+} nvtxDomainMarkEx_params;
+
+typedef struct nvtxDomainRangeStartEx_params_st {
+  nvtxDomainHandle_t domain;
+  nvtxRangeStartEx_params core;
+} nvtxDomainRangeStartEx_params;
+
+typedef struct nvtxDomainRangeEnd_params_st {
+  nvtxDomainHandle_t domain;
+  nvtxRangeEnd_params core;
+} nvtxDomainRangeEnd_params;
+
+typedef struct nvtxDomainRangePushEx_params_st {
+  nvtxDomainHandle_t domain;
+  nvtxRangePushEx_params core;
+} nvtxDomainRangePushEx_params;
+
+typedef struct nvtxDomainRangePop_params_st {
+  nvtxDomainHandle_t domain;
+} nvtxDomainRangePop_params;
+
+typedef struct nvtxSyncUserCreate_params_st {
+  nvtxDomainHandle_t domain;
+  const nvtxSyncUserAttributes_t* attribs;
+} nvtxSyncUserCreate_params;
+
+typedef struct nvtxSyncUserCommon_params_st {
+  nvtxSyncUser_t handle;
+} nvtxSyncUserCommon_params;
+
+typedef struct nvtxDomainRegisterStringA_params_st {
+    nvtxDomainHandle_t domain;
+    const char* string;
+} nvtxDomainRegisterStringA_params;
+
+typedef struct nvtxDomainRegisterStringW_params_st {
+    nvtxDomainHandle_t domain;
+    const char* string;
+} nvtxDomainRegisterStringW_params;
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_common.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_common.h
new file mode 100644
index 0000000000000000000000000000000000000000..0ed01f7bc2851f43678e58efe34fc5579cca3a35
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_common.h
@@ -0,0 +1,393 @@
+#ifndef NVPERF_COMMON_H
+#define NVPERF_COMMON_H
+
+/*
+ * Copyright 2014-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO USER:
+ *
+ * This source code is subject to NVIDIA ownership rights under U.S. and
+ * international Copyright laws.
+ *
+ * This software and the information contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+ * of a form of NVIDIA software license agreement.
+ *
+ * NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+ * CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+ * IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+ * REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+ * MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+ * OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+ * OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+ * OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+ * OR PERFORMANCE OF THIS SOURCE CODE.
+ *
+ * U.S. Government End Users.   This source code is a "commercial item" as
+ * that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+ * "commercial computer  software"  and "commercial computer software
+ * documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+ * and is provided to the U.S. Government only as a commercial end item.
+ * Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+ * 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+ * source code with only those rights set forth herein.
+ *
+ * Any use of this source code in individual and commercial software must
+ * include, in the user documentation and internal comments to the code,
+ * the above Disclaimer and U.S. Government End Users Notice.
+ */
+
+#include <stddef.h>
+#include <stdint.h>
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility push(default)
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL __attribute__ ((visibility ("hidden")))
+    #endif
+#else
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL
+    #endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ *  @file   nvperf_common.h
+ */
+
+#ifndef NVPERF_NVPA_STATUS_DEFINED
+#define NVPERF_NVPA_STATUS_DEFINED
+
+    /// Error codes.
+    typedef enum NVPA_Status
+    {
+        /// Success
+        NVPA_STATUS_SUCCESS = 0,
+        /// Generic error.
+        NVPA_STATUS_ERROR = 1,
+        /// Internal error.  Please file a bug!
+        NVPA_STATUS_INTERNAL_ERROR = 2,
+        /// NVPW_InitializeTarget() or NVPW_InitializeHost() has not been called yet.
+        NVPA_STATUS_NOT_INITIALIZED = 3,
+        /// The NvPerf DLL/DSO could not be loaded during NVPW_Initialize*(). Please ensure they are placed in the
+        /// appropriate location that can be founder by a dynamic linker. And on Linux systems, confirm that the
+        /// LD_LIBRARY_PATH environment variable is set correctly. Alternatively, you may utilize
+        /// NVPW_SetLibraryLoadPaths() to define additional library search paths.
+        NVPA_STATUS_NOT_LOADED = 4,
+        /// The function was not found in this version of the NvPerf DLL/DSO. Or if you are directly calling
+        /// NVPA_GetProcAddress(), please ensure the function name is spelled correctly.
+        NVPA_STATUS_FUNCTION_NOT_FOUND = 5,
+        /// The request was intentionally not supported.
+        NVPA_STATUS_NOT_SUPPORTED = 6,
+        /// The request was not implemented by this version.
+        NVPA_STATUS_NOT_IMPLEMENTED = 7,
+        /// Invalid argument.
+        NVPA_STATUS_INVALID_ARGUMENT = 8,
+        /// UNUSED
+        NVPA_STATUS_INVALID_METRIC_ID = 9,
+        /// No driver has been loaded via NVPW_*_LoadDriver().
+        NVPA_STATUS_DRIVER_NOT_LOADED = 10,
+        /// Failed memory allocation.
+        NVPA_STATUS_OUT_OF_MEMORY = 11,
+        /// UNUSED
+        NVPA_STATUS_INVALID_THREAD_STATE = 12,
+        /// UNUSED
+        NVPA_STATUS_FAILED_CONTEXT_ALLOC = 13,
+        /// The specified GPU is not supported. It is recommended to call IsGpuSupported() for more information
+        NVPA_STATUS_UNSUPPORTED_GPU = 14,
+        /// The installed NVIDIA driver is too old.
+        NVPA_STATUS_INSUFFICIENT_DRIVER_VERSION = 15,
+        /// UNUSED
+        NVPA_STATUS_OBJECT_NOT_REGISTERED = 16,
+        /// Profiling permission not granted; see https://developer.nvidia.com/nvidia-development-tools-solutions-
+        /// ERR_NVGPUCTRPERM-permission-issue-performance-counters
+        NVPA_STATUS_INSUFFICIENT_PRIVILEGE = 17,
+        /// UNUSED
+        NVPA_STATUS_INVALID_CONTEXT_STATE = 18,
+        /// UNUSED
+        NVPA_STATUS_INVALID_OBJECT_STATE = 19,
+        /// The request could not be fulfilled because a system resource is already in use.
+        NVPA_STATUS_RESOURCE_UNAVAILABLE = 20,
+        /// UNUSED
+        NVPA_STATUS_DRIVER_LOADED_TOO_LATE = 21,
+        /// The provided buffer is not large enough.
+        NVPA_STATUS_INSUFFICIENT_SPACE = 22,
+        /// UNUSED
+        NVPA_STATUS_OBJECT_MISMATCH = 23,
+        /// Virtualized GPU (vGPU) is not supported.
+        NVPA_STATUS_VIRTUALIZED_DEVICE_NOT_SUPPORTED = 24,
+        /// Profiling permission was not granted or the device was disabled.
+        NVPA_STATUS_PROFILING_NOT_ALLOWED = 25,
+        NVPA_STATUS__COUNT
+    } NVPA_Status;
+
+
+    inline void NVPW_NVPAStatusToString(NVPA_Status status, const char** ppStatusStr, const char** ppCommentStr)
+    {
+        switch (status)
+        {
+            case NVPA_STATUS_SUCCESS:
+                *ppStatusStr = "NVPA_STATUS_SUCCESS";
+                *ppCommentStr = "Success";
+                return;
+            case NVPA_STATUS_ERROR:
+                *ppStatusStr = "NVPA_STATUS_ERROR";
+                *ppCommentStr = "Generic error.";
+                return;
+            case NVPA_STATUS_INTERNAL_ERROR:
+                *ppStatusStr = "NVPA_STATUS_INTERNAL_ERROR";
+                *ppCommentStr = "Internal error.  Please file a bug!";
+                return;
+            case NVPA_STATUS_NOT_INITIALIZED:
+                *ppStatusStr = "NVPA_STATUS_NOT_INITIALIZED";
+                *ppCommentStr = "NVPW_InitializeTarget() or NVPW_InitializeHost() has not been called yet.";
+                return;
+            case NVPA_STATUS_NOT_LOADED:
+                *ppStatusStr = "NVPA_STATUS_NOT_LOADED";
+                *ppCommentStr = "The NvPerf DLL/DSO could not be loaded during NVPW_Initialize*(). Please ensure they are placed in the appropriate location that can be founder by a dynamic linker. And on Linux systems, confirm that the LD_LIBRARY_PATH environment variable is set correctly. Alternatively, you may utilize NVPW_SetLibraryLoadPaths() to define additional library search paths.";
+                return;
+            case NVPA_STATUS_FUNCTION_NOT_FOUND:
+                *ppStatusStr = "NVPA_STATUS_FUNCTION_NOT_FOUND";
+                *ppCommentStr = "The function was not found in this version of the NvPerf DLL/DSO. Or if you are directly calling NVPA_GetProcAddress(), please ensure the function name is spelled correctly.";
+                return;
+            case NVPA_STATUS_NOT_SUPPORTED:
+                *ppStatusStr = "NVPA_STATUS_NOT_SUPPORTED";
+                *ppCommentStr = "The request was intentionally not supported.";
+                return;
+            case NVPA_STATUS_NOT_IMPLEMENTED:
+                *ppStatusStr = "NVPA_STATUS_NOT_IMPLEMENTED";
+                *ppCommentStr = "The request was not implemented by this version.";
+                return;
+            case NVPA_STATUS_INVALID_ARGUMENT:
+                *ppStatusStr = "NVPA_STATUS_INVALID_ARGUMENT";
+                *ppCommentStr = "Invalid argument.";
+                return;
+            case NVPA_STATUS_INVALID_METRIC_ID:
+                *ppStatusStr = "NVPA_STATUS_INVALID_METRIC_ID";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_DRIVER_NOT_LOADED:
+                *ppStatusStr = "NVPA_STATUS_DRIVER_NOT_LOADED";
+                *ppCommentStr = "No driver has been loaded via NVPW_*_LoadDriver().";
+                return;
+            case NVPA_STATUS_OUT_OF_MEMORY:
+                *ppStatusStr = "NVPA_STATUS_OUT_OF_MEMORY";
+                *ppCommentStr = "Failed memory allocation.";
+                return;
+            case NVPA_STATUS_INVALID_THREAD_STATE:
+                *ppStatusStr = "NVPA_STATUS_INVALID_THREAD_STATE";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_FAILED_CONTEXT_ALLOC:
+                *ppStatusStr = "NVPA_STATUS_FAILED_CONTEXT_ALLOC";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_UNSUPPORTED_GPU:
+                *ppStatusStr = "NVPA_STATUS_UNSUPPORTED_GPU";
+                *ppCommentStr = "The specified GPU is not supported. It is recommended to call IsGpuSupported() for more information";
+                return;
+            case NVPA_STATUS_INSUFFICIENT_DRIVER_VERSION:
+                *ppStatusStr = "NVPA_STATUS_INSUFFICIENT_DRIVER_VERSION";
+                *ppCommentStr = "The installed NVIDIA driver is too old.";
+                return;
+            case NVPA_STATUS_OBJECT_NOT_REGISTERED:
+                *ppStatusStr = "NVPA_STATUS_OBJECT_NOT_REGISTERED";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_INSUFFICIENT_PRIVILEGE:
+                *ppStatusStr = "NVPA_STATUS_INSUFFICIENT_PRIVILEGE";
+                *ppCommentStr = "Profiling permission not granted; see https://developer.nvidia.com/nvidia-development-tools-solutions-ERR_NVGPUCTRPERM-permission-issue-performance-counters";
+                return;
+            case NVPA_STATUS_INVALID_CONTEXT_STATE:
+                *ppStatusStr = "NVPA_STATUS_INVALID_CONTEXT_STATE";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_INVALID_OBJECT_STATE:
+                *ppStatusStr = "NVPA_STATUS_INVALID_OBJECT_STATE";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_RESOURCE_UNAVAILABLE:
+                *ppStatusStr = "NVPA_STATUS_RESOURCE_UNAVAILABLE";
+                *ppCommentStr = "The request could not be fulfilled because a system resource is already in use.";
+                return;
+            case NVPA_STATUS_DRIVER_LOADED_TOO_LATE:
+                *ppStatusStr = "NVPA_STATUS_DRIVER_LOADED_TOO_LATE";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_INSUFFICIENT_SPACE:
+                *ppStatusStr = "NVPA_STATUS_INSUFFICIENT_SPACE";
+                *ppCommentStr = "The provided buffer is not large enough.";
+                return;
+            case NVPA_STATUS_OBJECT_MISMATCH:
+                *ppStatusStr = "NVPA_STATUS_OBJECT_MISMATCH";
+                *ppCommentStr = "UNUSED";
+                return;
+            case NVPA_STATUS_VIRTUALIZED_DEVICE_NOT_SUPPORTED:
+                *ppStatusStr = "NVPA_STATUS_VIRTUALIZED_DEVICE_NOT_SUPPORTED";
+                *ppCommentStr = "Virtualized GPU (vGPU) is not supported.";
+                return;
+            case NVPA_STATUS_PROFILING_NOT_ALLOWED:
+                *ppStatusStr = "NVPA_STATUS_PROFILING_NOT_ALLOWED";
+                *ppCommentStr = "Profiling permission was not granted or the device was disabled.";
+                return;
+            default:
+                *ppStatusStr = "Unrecognized status";
+                *ppCommentStr = "This status is unrecognized. Is it coming from a newer version of NvPerf library?";
+                return;
+        }
+    }
+
+
+#endif // NVPERF_NVPA_STATUS_DEFINED
+
+
+#ifndef NVPERF_NVPA_ACTIVITY_KIND_DEFINED
+#define NVPERF_NVPA_ACTIVITY_KIND_DEFINED
+
+    /// The configuration's activity-kind dictates which types of data may be collected.
+    typedef enum NVPA_ActivityKind
+    {
+        /// Invalid value.
+        NVPA_ACTIVITY_KIND_INVALID = 0,
+        /// A workload-centric activity for serialized and pipelined collection.
+        /// 
+        /// Profiler is capable of collecting both serialized and pipelined metrics.  The library introduces any
+        /// synchronization required to collect serialized metrics.
+        NVPA_ACTIVITY_KIND_PROFILER,
+        /// A realtime activity for sampling counters from the CPU or GPU.
+        NVPA_ACTIVITY_KIND_REALTIME_SAMPLED,
+        /// A realtime activity for profiling counters from the CPU or GPU without CPU/GPU synchronizations.
+        NVPA_ACTIVITY_KIND_REALTIME_PROFILER,
+        NVPA_ACTIVITY_KIND__COUNT
+    } NVPA_ActivityKind;
+
+
+#endif // NVPERF_NVPA_ACTIVITY_KIND_DEFINED
+
+
+#ifndef NVPERF_NVPA_BOOL_DEFINED
+#define NVPERF_NVPA_BOOL_DEFINED
+    /// The type used for boolean values.
+    typedef uint8_t NVPA_Bool;
+#endif // NVPERF_NVPA_BOOL_DEFINED
+
+#ifndef NVPA_STRUCT_SIZE
+#define NVPA_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif // NVPA_STRUCT_SIZE
+
+#ifndef NVPW_FIELD_EXISTS
+#define NVPW_FIELD_EXISTS(pParams_, name_) \
+    ((pParams_)->structSize >= (size_t)((const uint8_t*)(&(pParams_)->name_) + sizeof(pParams_)->name_ - (const uint8_t*)(pParams_)))
+#endif // NVPW_FIELD_EXISTS
+
+
+#ifndef NVPERF_NVPA_GETPROCADDRESS_DEFINED
+#define NVPERF_NVPA_GETPROCADDRESS_DEFINED
+
+typedef NVPA_Status (*NVPA_GenericFn)(void);
+
+
+    /// 
+    /// Gets the address of an NvPerf API function.
+    /// 
+    /// \return A function pointer to the function, or NULL if the function is not available.
+    /// 
+    /// \param pFunctionName [in] Name of the function to retrieve.
+    NVPA_GenericFn NVPA_GetProcAddress(const char* pFunctionName);
+
+#endif
+
+#ifndef NVPERF_NVPW_SETLIBRARYLOADPATHS_DEFINED
+#define NVPERF_NVPW_SETLIBRARYLOADPATHS_DEFINED
+
+
+    typedef struct NVPW_SetLibraryLoadPaths_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] number of paths in ppPaths
+        size_t numPaths;
+        /// [in] array of null-terminated paths
+        const char** ppPaths;
+    } NVPW_SetLibraryLoadPaths_Params;
+#define NVPW_SetLibraryLoadPaths_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_SetLibraryLoadPaths_Params, ppPaths)
+
+    /// Sets library search path for \ref NVPW_InitializeHost() and \ref NVPW_InitializeTarget().
+    /// \ref NVPW_InitializeHost() and \ref NVPW_InitializeTarget load the NvPerf DLL/DSO.  This function sets
+    /// ordered paths that will be searched with the LoadLibrary() or dlopen() call.
+    /// If load paths are set by this function, the default set of load paths
+    /// will not be attempted.
+    /// Each path must point at a directory (not a file name).
+    /// This function is not thread-safe.
+    /// Example Usage:
+    /// \code
+    ///     const char* paths[] = {
+    ///         "path1", "path2", etc
+    ///     };
+    ///     NVPW_SetLibraryLoadPaths_Params params{NVPW_SetLibraryLoadPaths_Params_STRUCT_SIZE};
+    ///     params.numPaths = sizeof(paths)/sizeof(paths[0]);
+    ///     params.ppPaths = paths;
+    ///     NVPW_SetLibraryLoadPaths(&params);
+    ///     NVPW_InitializeHost();
+    ///     params.numPaths = 0;
+    ///     params.ppPaths = NULL;
+    ///     NVPW_SetLibraryLoadPaths(&params);
+    /// \endcode
+    NVPA_Status NVPW_SetLibraryLoadPaths(NVPW_SetLibraryLoadPaths_Params* pParams);
+
+    typedef struct NVPW_SetLibraryLoadPathsW_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] number of paths in ppwPaths
+        size_t numPaths;
+        /// [in] array of null-terminated paths
+        const wchar_t** ppwPaths;
+    } NVPW_SetLibraryLoadPathsW_Params;
+#define NVPW_SetLibraryLoadPathsW_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_SetLibraryLoadPathsW_Params, ppwPaths)
+
+    /// Sets library search path for \ref NVPW_InitializeHost() and \ref NVPW_InitializeTarget().
+    /// \ref NVPW_InitializeHost() and \ref NVPW_InitializeTarget load the NvPerf DLL/DSO.  This function sets
+    /// ordered paths that will be searched with the LoadLibrary() or dlopen() call.
+    /// If load paths are set by this function, the default set of load paths
+    /// will not be attempted.
+    /// Each path must point at a directory (not a file name).
+    /// This function is not thread-safe.
+    /// Example Usage:
+    /// \code
+    ///     const wchar_t* wpaths[] = {
+    ///         L"path1", L"path2", etc
+    ///     };
+    ///     NVPW_SetLibraryLoadPathsW_Params params{NVPW_SetLibraryLoadPathsW_Params_STRUCT_SIZE};
+    ///     params.numPaths = sizeof(wpaths)/sizeof(wpaths[0]);
+    ///     params.ppwPaths = wpaths;
+    ///     NVPW_SetLibraryLoadPathsW(&params);
+    ///     NVPW_InitializeHost();
+    ///     params.numPaths = 0;
+    ///     params.ppwPaths = NULL;
+    ///     NVPW_SetLibraryLoadPathsW(&params);
+    /// \endcode
+    NVPA_Status NVPW_SetLibraryLoadPathsW(NVPW_SetLibraryLoadPathsW_Params* pParams);
+
+#endif
+
+
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#endif // NVPERF_COMMON_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_cuda_host.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_cuda_host.h
new file mode 100644
index 0000000000000000000000000000000000000000..9b4533b25148b7cd28e0ed30be022893514415a5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_cuda_host.h
@@ -0,0 +1,179 @@
+#ifndef NVPERF_CUDA_HOST_H
+#define NVPERF_CUDA_HOST_H
+
+/*
+ * Copyright 2014-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO USER:
+ *
+ * This source code is subject to NVIDIA ownership rights under U.S. and
+ * international Copyright laws.
+ *
+ * This software and the information contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+ * of a form of NVIDIA software license agreement.
+ *
+ * NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+ * CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+ * IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+ * REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+ * MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+ * OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+ * OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+ * OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+ * OR PERFORMANCE OF THIS SOURCE CODE.
+ *
+ * U.S. Government End Users.   This source code is a "commercial item" as
+ * that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+ * "commercial computer  software"  and "commercial computer software
+ * documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+ * and is provided to the U.S. Government only as a commercial end item.
+ * Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+ * 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+ * source code with only those rights set forth herein.
+ *
+ * Any use of this source code in individual and commercial software must
+ * include, in the user documentation and internal comments to the code,
+ * the above Disclaimer and U.S. Government End Users Notice.
+ */
+
+#include <stddef.h>
+#include <stdint.h>
+#include "nvperf_common.h"
+#include "nvperf_host.h"
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility push(default)
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL __attribute__ ((visibility ("hidden")))
+    #endif
+#else
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL
+    #endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ *  @file   nvperf_cuda_host.h
+ */
+
+    typedef struct NVPW_CUDA_RawMetricsConfig_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        NVPA_ActivityKind activityKind;
+        /// [in]
+        const char* pChipName;
+        /// [out] new NVPA_RawMetricsConfig object
+        struct NVPA_RawMetricsConfig* pRawMetricsConfig;
+    } NVPW_CUDA_RawMetricsConfig_Create_Params;
+#define NVPW_CUDA_RawMetricsConfig_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_RawMetricsConfig_Create_Params, pRawMetricsConfig)
+
+    NVPA_Status NVPW_CUDA_RawMetricsConfig_Create(NVPW_CUDA_RawMetricsConfig_Create_Params* pParams);
+
+    typedef struct NVPW_CUDA_RawMetricsConfig_Create_V2_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        NVPA_ActivityKind activityKind;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [out] new NVPA_RawMetricsConfig object
+        struct NVPA_RawMetricsConfig* pRawMetricsConfig;
+    } NVPW_CUDA_RawMetricsConfig_Create_V2_Params;
+#define NVPW_CUDA_RawMetricsConfig_Create_V2_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_RawMetricsConfig_Create_V2_Params, pRawMetricsConfig)
+
+    /// Use either 'pChipName' or 'pCounterAvailabilityImage'.
+    NVPA_Status NVPW_CUDA_RawMetricsConfig_Create_V2(NVPW_CUDA_RawMetricsConfig_Create_V2_Params* pParams);
+
+    typedef struct NVPW_CUDA_CounterDataBuilder_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [out] new NVPA_CounterDataBuilder object
+        struct NVPA_CounterDataBuilder* pCounterDataBuilder;
+    } NVPW_CUDA_CounterDataBuilder_Create_Params;
+#define NVPW_CUDA_CounterDataBuilder_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_CounterDataBuilder_Create_Params, pCounterDataBuilder)
+
+    /// Use either 'pChipName' or 'pCounterAvailabilityImage'.
+    NVPA_Status NVPW_CUDA_CounterDataBuilder_Create(NVPW_CUDA_CounterDataBuilder_Create_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator NVPW_MetricsEvaluator;
+
+    typedef struct NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [out]
+        size_t scratchBufferSize;
+    } NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params;
+#define NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params, scratchBufferSize)
+
+    /// Use either 'pChipName' or 'pCounterAvailabilityImage'.
+    NVPA_Status NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params* pParams);
+
+    typedef struct NVPW_CUDA_MetricsEvaluator_Initialize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        uint8_t* pScratchBuffer;
+        /// [in] the size of the 'pScratchBuffer' array, should be at least the size of the 'scratchBufferSize' returned
+        /// by 'NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize'
+        size_t scratchBufferSize;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in] must be provided if 'pCounterDataImage' is not NULL
+        size_t counterDataImageSize;
+        /// [out]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+    } NVPW_CUDA_MetricsEvaluator_Initialize_Params;
+#define NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_MetricsEvaluator_Initialize_Params, pMetricsEvaluator)
+
+    /// Use one of 'pChipName', 'pCounterAvailabilityImage', or 'pCounterDataImage'. 'pChipName' or
+    /// 'pCounterAvailabilityImage' will create a metrics evaluator based on a virtual device while 'pCounterDataImage'
+    /// will create a metrics evaluator based on the actual device.
+    NVPA_Status NVPW_CUDA_MetricsEvaluator_Initialize(NVPW_CUDA_MetricsEvaluator_Initialize_Params* pParams);
+
+
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#endif // NVPERF_CUDA_HOST_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_host.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_host.h
new file mode 100644
index 0000000000000000000000000000000000000000..62a53528b64d6b3da8daf7058cec21781ae0e8cb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_host.h
@@ -0,0 +1,1178 @@
+#ifndef NVPERF_HOST_H
+#define NVPERF_HOST_H
+
+/*
+ * Copyright 2014-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO USER:
+ *
+ * This source code is subject to NVIDIA ownership rights under U.S. and
+ * international Copyright laws.
+ *
+ * This software and the information contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+ * of a form of NVIDIA software license agreement.
+ *
+ * NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+ * CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+ * IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+ * REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+ * MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+ * OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+ * OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+ * OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+ * OR PERFORMANCE OF THIS SOURCE CODE.
+ *
+ * U.S. Government End Users.   This source code is a "commercial item" as
+ * that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+ * "commercial computer  software"  and "commercial computer software
+ * documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+ * and is provided to the U.S. Government only as a commercial end item.
+ * Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+ * 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+ * source code with only those rights set forth herein.
+ *
+ * Any use of this source code in individual and commercial software must
+ * include, in the user documentation and internal comments to the code,
+ * the above Disclaimer and U.S. Government End Users Notice.
+ */
+
+#include <stddef.h>
+#include <stdint.h>
+#include "nvperf_common.h"
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility push(default)
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL __attribute__ ((visibility ("hidden")))
+    #endif
+#else
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL
+    #endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ *  @file   nvperf_host.h
+ */
+
+
+// Guard against multiple definition of NvPerf host types
+#ifndef NVPERF_HOST_API_DEFINED
+#define NVPERF_HOST_API_DEFINED
+
+
+/***************************************************************************//**
+ *  @name   Host Configuration
+ *  @{
+ */
+
+    typedef struct NVPW_InitializeHost_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+    } NVPW_InitializeHost_Params;
+#define NVPW_InitializeHost_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_InitializeHost_Params, pPriv)
+
+    /// Load the host library.
+    NVPA_Status NVPW_InitializeHost(NVPW_InitializeHost_Params* pParams);
+
+    typedef struct NVPW_CounterData_CalculateCounterDataImageCopySize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// The CounterDataPrefix generated from e.g.    nvperf2 initdata   or
+        /// NVPW_CounterDataBuilder_GetCounterDataPrefix().  Must be align(8).
+        const uint8_t* pCounterDataPrefix;
+        size_t counterDataPrefixSize;
+        /// max number of ranges that can be profiled
+        uint32_t maxNumRanges;
+        /// max number of RangeTree nodes; must be >= maxNumRanges
+        uint32_t maxNumRangeTreeNodes;
+        /// max string length of each RangeName, including the trailing NUL character
+        uint32_t maxRangeNameLength;
+        const uint8_t* pCounterDataSrc;
+        /// [out] required size of the copy buffer
+        size_t copyDataImageCounterSize;
+    } NVPW_CounterData_CalculateCounterDataImageCopySize_Params;
+#define NVPW_CounterData_CalculateCounterDataImageCopySize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterData_CalculateCounterDataImageCopySize_Params, copyDataImageCounterSize)
+
+    NVPA_Status NVPW_CounterData_CalculateCounterDataImageCopySize(NVPW_CounterData_CalculateCounterDataImageCopySize_Params* pParams);
+
+    typedef struct NVPW_CounterData_InitializeCounterDataImageCopy_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// The CounterDataPrefix generated from e.g.    nvperf2 initdata   or
+        /// NVPW_CounterDataBuilder_GetCounterDataPrefix().  Must be align(8).
+        const uint8_t* pCounterDataPrefix;
+        size_t counterDataPrefixSize;
+        /// max number of ranges that can be profiled
+        uint32_t maxNumRanges;
+        /// max number of RangeTree nodes; must be >= maxNumRanges
+        uint32_t maxNumRangeTreeNodes;
+        /// max string length of each RangeName, including the trailing NUL character
+        uint32_t maxRangeNameLength;
+        const uint8_t* pCounterDataSrc;
+        uint8_t* pCounterDataDst;
+    } NVPW_CounterData_InitializeCounterDataImageCopy_Params;
+#define NVPW_CounterData_InitializeCounterDataImageCopy_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterData_InitializeCounterDataImageCopy_Params, pCounterDataDst)
+
+    NVPA_Status NVPW_CounterData_InitializeCounterDataImageCopy(NVPW_CounterData_InitializeCounterDataImageCopy_Params* pParams);
+
+    typedef struct NVPW_CounterData_ExtractCounterDataPrefix_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// The source buffer to extract the prefix from.
+        const uint8_t* pCounterDataSrc;
+        size_t counterDataSrcSize;
+        /// [in] If not NULL, the prefix will be copied into this buffer.
+        uint8_t* pCounterDataPrefix;
+        /// [inout] if 'pCounterDataPrefix' is NULL, size of counter data prefix will be returned; otherwise it should
+        /// be set to the size of buffer allocated for 'pCounterDataPrefix'.
+        size_t counterDataPrefixSize;
+    } NVPW_CounterData_ExtractCounterDataPrefix_Params;
+#define NVPW_CounterData_ExtractCounterDataPrefix_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterData_ExtractCounterDataPrefix_Params, counterDataPrefixSize)
+
+    NVPA_Status NVPW_CounterData_ExtractCounterDataPrefix(NVPW_CounterData_ExtractCounterDataPrefix_Params* pParams);
+
+    typedef struct NVPA_CounterDataCombiner NVPA_CounterDataCombiner;
+
+    typedef struct NVPW_CounterDataCombiner_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// The destination counter data into which the source datas will be combined
+        uint8_t* pCounterDataDst;
+        /// [out] The created counter data combiner
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+    } NVPW_CounterDataCombiner_Create_Params;
+#define NVPW_CounterDataCombiner_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_Create_Params, pCounterDataCombiner)
+
+    NVPA_Status NVPW_CounterDataCombiner_Create(NVPW_CounterDataCombiner_Create_Params* pParams);
+
+    typedef struct NVPW_CounterDataCombiner_Destroy_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+    } NVPW_CounterDataCombiner_Destroy_Params;
+#define NVPW_CounterDataCombiner_Destroy_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_Destroy_Params, pCounterDataCombiner)
+
+    NVPA_Status NVPW_CounterDataCombiner_Destroy(NVPW_CounterDataCombiner_Destroy_Params* pParams);
+
+    typedef struct NVPW_CounterDataCombiner_CreateRange_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+        size_t numDescriptions;
+        const char* const* ppDescriptions;
+        /// [out]
+        size_t rangeIndexDst;
+    } NVPW_CounterDataCombiner_CreateRange_Params;
+#define NVPW_CounterDataCombiner_CreateRange_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_CreateRange_Params, rangeIndexDst)
+
+    NVPA_Status NVPW_CounterDataCombiner_CreateRange(NVPW_CounterDataCombiner_CreateRange_Params* pParams);
+
+    typedef struct NVPW_CounterDataCombiner_CopyIntoRange_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+        /// [in]
+        size_t rangeIndexDst;
+        /// [in]
+        const uint8_t* pCounterDataSrc;
+        /// [in]
+        size_t rangeIndexSrc;
+    } NVPW_CounterDataCombiner_CopyIntoRange_Params;
+#define NVPW_CounterDataCombiner_CopyIntoRange_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_CopyIntoRange_Params, rangeIndexSrc)
+
+    /// In order to use this API, the source counter data and the destination counter data must have identical counters
+    NVPA_Status NVPW_CounterDataCombiner_CopyIntoRange(NVPW_CounterDataCombiner_CopyIntoRange_Params* pParams);
+
+    typedef struct NVPW_CounterDataCombiner_AccumulateIntoRange_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+        size_t rangeIndexDst;
+        uint32_t dstMultiplier;
+        const uint8_t* pCounterDataSrc;
+        size_t rangeIndexSrc;
+        uint32_t srcMultiplier;
+    } NVPW_CounterDataCombiner_AccumulateIntoRange_Params;
+#define NVPW_CounterDataCombiner_AccumulateIntoRange_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_AccumulateIntoRange_Params, srcMultiplier)
+
+    NVPA_Status NVPW_CounterDataCombiner_AccumulateIntoRange(NVPW_CounterDataCombiner_AccumulateIntoRange_Params* pParams);
+
+    typedef struct NVPW_CounterDataCombiner_SumIntoRange_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+        size_t rangeIndexDst;
+        const uint8_t* pCounterDataSrc;
+        size_t rangeIndexSrc;
+    } NVPW_CounterDataCombiner_SumIntoRange_Params;
+#define NVPW_CounterDataCombiner_SumIntoRange_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_SumIntoRange_Params, rangeIndexSrc)
+
+    NVPA_Status NVPW_CounterDataCombiner_SumIntoRange(NVPW_CounterDataCombiner_SumIntoRange_Params* pParams);
+
+    typedef struct NVPW_CounterDataCombiner_WeightedSumIntoRange_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataCombiner* pCounterDataCombiner;
+        size_t rangeIndexDst;
+        double dstMultiplier;
+        const uint8_t* pCounterDataSrc;
+        size_t rangeIndexSrc;
+        double srcMultiplier;
+    } NVPW_CounterDataCombiner_WeightedSumIntoRange_Params;
+#define NVPW_CounterDataCombiner_WeightedSumIntoRange_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataCombiner_WeightedSumIntoRange_Params, srcMultiplier)
+
+    NVPA_Status NVPW_CounterDataCombiner_WeightedSumIntoRange(NVPW_CounterDataCombiner_WeightedSumIntoRange_Params* pParams);
+
+/**
+ *  @}
+ ******************************************************************************/
+ 
+/***************************************************************************//**
+ *  @name   Metrics Configuration
+ *  @{
+ */
+
+    typedef struct NVPA_RawMetricsConfig NVPA_RawMetricsConfig;
+
+    typedef struct NVPA_RawMetricRequest
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// in
+        const char* pMetricName;
+        /// in
+        NVPA_Bool isolated;
+        /// in; ignored by AddMetric but observed by CounterData initialization
+        NVPA_Bool keepInstances;
+    } NVPA_RawMetricRequest;
+#define NVPA_RAW_METRIC_REQUEST_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPA_RawMetricRequest, keepInstances)
+
+    typedef struct NVPW_GetSupportedChipNames_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [out]
+        const char* const* ppChipNames;
+        /// [out]
+        size_t numChipNames;
+    } NVPW_GetSupportedChipNames_Params;
+#define NVPW_GetSupportedChipNames_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_GetSupportedChipNames_Params, numChipNames)
+
+    NVPA_Status NVPW_GetSupportedChipNames(NVPW_GetSupportedChipNames_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_Destroy_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_RawMetricsConfig* pRawMetricsConfig;
+    } NVPW_RawMetricsConfig_Destroy_Params;
+#define NVPW_RawMetricsConfig_Destroy_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_Destroy_Params, pRawMetricsConfig)
+
+    NVPA_Status NVPW_RawMetricsConfig_Destroy(NVPW_RawMetricsConfig_Destroy_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_SetCounterAvailability_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_RawMetricsConfig* pRawMetricsConfig;
+        /// [in] buffer with counter availability image
+        const uint8_t* pCounterAvailabilityImage;
+    } NVPW_RawMetricsConfig_SetCounterAvailability_Params;
+#define NVPW_RawMetricsConfig_SetCounterAvailability_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_SetCounterAvailability_Params, pCounterAvailabilityImage)
+
+    NVPA_Status NVPW_RawMetricsConfig_SetCounterAvailability(NVPW_RawMetricsConfig_SetCounterAvailability_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_BeginPassGroup_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_RawMetricsConfig* pRawMetricsConfig;
+        size_t maxPassCount;
+    } NVPW_RawMetricsConfig_BeginPassGroup_Params;
+#define NVPW_RawMetricsConfig_BeginPassGroup_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_BeginPassGroup_Params, maxPassCount)
+
+    NVPA_Status NVPW_RawMetricsConfig_BeginPassGroup(NVPW_RawMetricsConfig_BeginPassGroup_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_EndPassGroup_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_RawMetricsConfig* pRawMetricsConfig;
+    } NVPW_RawMetricsConfig_EndPassGroup_Params;
+#define NVPW_RawMetricsConfig_EndPassGroup_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_EndPassGroup_Params, pRawMetricsConfig)
+
+    NVPA_Status NVPW_RawMetricsConfig_EndPassGroup(NVPW_RawMetricsConfig_EndPassGroup_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GetNumMetrics_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        /// [out]
+        size_t numMetrics;
+    } NVPW_RawMetricsConfig_GetNumMetrics_Params;
+#define NVPW_RawMetricsConfig_GetNumMetrics_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GetNumMetrics_Params, numMetrics)
+
+    NVPA_Status NVPW_RawMetricsConfig_GetNumMetrics(NVPW_RawMetricsConfig_GetNumMetrics_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GetMetricProperties_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        size_t metricIndex;
+        /// [out]
+        const char* pMetricName;
+        /// [out]
+        NVPA_Bool supportsPipelined;
+        /// [out]
+        NVPA_Bool supportsIsolated;
+    } NVPW_RawMetricsConfig_GetMetricProperties_Params;
+#define NVPW_RawMetricsConfig_GetMetricProperties_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GetMetricProperties_Params, supportsIsolated)
+
+    NVPA_Status NVPW_RawMetricsConfig_GetMetricProperties(NVPW_RawMetricsConfig_GetMetricProperties_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GetMetricProperties_V2_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        size_t metricIndex;
+        /// [out]
+        const char* pMetricName;
+    } NVPW_RawMetricsConfig_GetMetricProperties_V2_Params;
+#define NVPW_RawMetricsConfig_GetMetricProperties_V2_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GetMetricProperties_V2_Params, pMetricName)
+
+    NVPA_Status NVPW_RawMetricsConfig_GetMetricProperties_V2(NVPW_RawMetricsConfig_GetMetricProperties_V2_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_AddMetrics_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_RawMetricsConfig* pRawMetricsConfig;
+        const NVPA_RawMetricRequest* pRawMetricRequests;
+        size_t numMetricRequests;
+    } NVPW_RawMetricsConfig_AddMetrics_Params;
+#define NVPW_RawMetricsConfig_AddMetrics_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_AddMetrics_Params, numMetricRequests)
+
+    NVPA_Status NVPW_RawMetricsConfig_AddMetrics(NVPW_RawMetricsConfig_AddMetrics_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_IsAddMetricsPossible_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        const NVPA_RawMetricRequest* pRawMetricRequests;
+        size_t numMetricRequests;
+        /// [out]
+        NVPA_Bool isPossible;
+    } NVPW_RawMetricsConfig_IsAddMetricsPossible_Params;
+#define NVPW_RawMetricsConfig_IsAddMetricsPossible_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_IsAddMetricsPossible_Params, isPossible)
+
+    NVPA_Status NVPW_RawMetricsConfig_IsAddMetricsPossible(NVPW_RawMetricsConfig_IsAddMetricsPossible_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GenerateConfigImage_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_RawMetricsConfig* pRawMetricsConfig;
+        /// [in] If true, all existing pass groups may be merged to reduce number of passes.
+        /// If merge was successful, distribution of counters in passes may be updated as a side-effect. The effects
+        /// will be persistent in pRawMetricsConfig.
+        NVPA_Bool mergeAllPassGroups;
+    } NVPW_RawMetricsConfig_GenerateConfigImage_Params;
+#define NVPW_RawMetricsConfig_GenerateConfigImage_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GenerateConfigImage_Params, mergeAllPassGroups)
+
+    /// This API may fail if called inside a pass group with `mergeAllPassGroups` = true.
+    NVPA_Status NVPW_RawMetricsConfig_GenerateConfigImage(NVPW_RawMetricsConfig_GenerateConfigImage_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GetConfigImage_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        /// [in] Number of bytes allocated for pBuffer
+        size_t bytesAllocated;
+        /// [out] [optional] Buffer receiving the config image
+        uint8_t* pBuffer;
+        /// [out] Count of bytes that would be copied into pBuffer
+        size_t bytesCopied;
+    } NVPW_RawMetricsConfig_GetConfigImage_Params;
+#define NVPW_RawMetricsConfig_GetConfigImage_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GetConfigImage_Params, bytesCopied)
+
+    NVPA_Status NVPW_RawMetricsConfig_GetConfigImage(NVPW_RawMetricsConfig_GetConfigImage_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GetNumPasses_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        /// [out]
+        size_t numPipelinedPasses;
+        /// [out]
+        size_t numIsolatedPasses;
+    } NVPW_RawMetricsConfig_GetNumPasses_Params;
+#define NVPW_RawMetricsConfig_GetNumPasses_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GetNumPasses_Params, numIsolatedPasses)
+
+    /// Total num passes = numPipelinedPasses + numIsolatedPasses * numNestingLevels
+    NVPA_Status NVPW_RawMetricsConfig_GetNumPasses(NVPW_RawMetricsConfig_GetNumPasses_Params* pParams);
+
+    typedef struct NVPW_RawMetricsConfig_GetNumPasses_V2_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const NVPA_RawMetricsConfig* pRawMetricsConfig;
+        /// [out]
+        size_t numPasses;
+    } NVPW_RawMetricsConfig_GetNumPasses_V2_Params;
+#define NVPW_RawMetricsConfig_GetNumPasses_V2_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_RawMetricsConfig_GetNumPasses_V2_Params, numPasses)
+
+    /// Total num passes = numPasses * numNestingLevels
+    NVPA_Status NVPW_RawMetricsConfig_GetNumPasses_V2(NVPW_RawMetricsConfig_GetNumPasses_V2_Params* pParams);
+
+    typedef struct NVPW_PeriodicSampler_Config_GetSocEstimatedSampleSize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] Typically created by e.g. NVPW_RawMetricsConfig_GetConfigImage(), must be align(8).
+        const uint8_t* pConfig;
+        /// [in]
+        size_t configSize;
+        /// [out]
+        size_t sampleSize;
+    } NVPW_PeriodicSampler_Config_GetSocEstimatedSampleSize_Params;
+#define NVPW_PeriodicSampler_Config_GetSocEstimatedSampleSize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_Config_GetSocEstimatedSampleSize_Params, sampleSize)
+
+    /// Estimate per sample records size based on a virtual device
+    NVPA_Status NVPW_PeriodicSampler_Config_GetSocEstimatedSampleSize(NVPW_PeriodicSampler_Config_GetSocEstimatedSampleSize_Params* pParams);
+
+    typedef struct NVPW_PeriodicSampler_Config_GetGpuEstimatedSampleSize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] Typically created by e.g. NVPW_RawMetricsConfig_GetConfigImage(), must be align(8).
+        const uint8_t* pConfig;
+        /// [in]
+        size_t configSize;
+        /// [out]
+        size_t sampleSize;
+    } NVPW_PeriodicSampler_Config_GetGpuEstimatedSampleSize_Params;
+#define NVPW_PeriodicSampler_Config_GetGpuEstimatedSampleSize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_Config_GetGpuEstimatedSampleSize_Params, sampleSize)
+
+    /// Estimate per sample records size based on a virtual device
+    NVPA_Status NVPW_PeriodicSampler_Config_GetGpuEstimatedSampleSize(NVPW_PeriodicSampler_Config_GetGpuEstimatedSampleSize_Params* pParams);
+
+/**
+ *  @}
+ ******************************************************************************/
+ 
+    typedef struct NVPW_Config_GetRawCounterInfo_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pConfig;
+        /// [in]
+        size_t configSize;
+        /// [in]
+        const char* pRawCounterName;
+        /// [inout] array containing indices of passes the counter resides in. 'pPassIndices' is in, '*pPassIndices' is
+        /// out.
+        size_t* pPassIndices;
+        /// [inout] if 'pPassIndices' is NULL, the count of passes this counter resides in will be returned; otherwise
+        /// it should be set to the capacity of 'pPassIndices' array, and on return, it will be overwritten to reflect
+        /// the actual count filled into 'pPassIndices'
+        size_t numPassIndices;
+    } NVPW_Config_GetRawCounterInfo_Params;
+#define NVPW_Config_GetRawCounterInfo_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Config_GetRawCounterInfo_Params, numPassIndices)
+
+    NVPA_Status NVPW_Config_GetRawCounterInfo(NVPW_Config_GetRawCounterInfo_Params* pParams);
+
+    typedef struct NVPW_Config_GetRawCounters_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pConfig;
+        /// [in]
+        size_t configSize;
+        /// [in]
+        size_t passIndex;
+        /// [inout] array containing raw counter names. 'ppRawCounterNames' is in, '*ppRawCounterNames' is out.
+        const char** ppRawCounterNames;
+        /// [inout] if 'ppRawCounterNames' is NULL, the count of raw counters will be returned; otherwise it should be
+        /// set to the capacity of 'ppRawCounterNames' array, and on return, it will be overwritten to reflect the
+        /// actual count filled into 'ppRawCounterNames'
+        size_t numRawCounters;
+    } NVPW_Config_GetRawCounters_Params;
+#define NVPW_Config_GetRawCounters_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Config_GetRawCounters_Params, numRawCounters)
+
+    NVPA_Status NVPW_Config_GetRawCounters(NVPW_Config_GetRawCounters_Params* pParams);
+
+/***************************************************************************//**
+ *  @name   CounterData Creation
+ *  @{
+ */
+
+    typedef struct NVPA_CounterDataBuilder NVPA_CounterDataBuilder;
+
+    typedef struct NVPW_CounterDataBuilder_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [out]
+        NVPA_CounterDataBuilder* pCounterDataBuilder;
+        const char* pChipName;
+    } NVPW_CounterDataBuilder_Create_Params;
+#define NVPW_CounterDataBuilder_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataBuilder_Create_Params, pChipName)
+
+    NVPA_Status NVPW_CounterDataBuilder_Create(NVPW_CounterDataBuilder_Create_Params* pParams);
+
+    typedef struct NVPW_CounterDataBuilder_Destroy_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataBuilder* pCounterDataBuilder;
+    } NVPW_CounterDataBuilder_Destroy_Params;
+#define NVPW_CounterDataBuilder_Destroy_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataBuilder_Destroy_Params, pCounterDataBuilder)
+
+    NVPA_Status NVPW_CounterDataBuilder_Destroy(NVPW_CounterDataBuilder_Destroy_Params* pParams);
+
+    typedef struct NVPW_CounterDataBuilder_AddMetrics_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataBuilder* pCounterDataBuilder;
+        const NVPA_RawMetricRequest* pRawMetricRequests;
+        size_t numMetricRequests;
+    } NVPW_CounterDataBuilder_AddMetrics_Params;
+#define NVPW_CounterDataBuilder_AddMetrics_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataBuilder_AddMetrics_Params, numMetricRequests)
+
+    NVPA_Status NVPW_CounterDataBuilder_AddMetrics(NVPW_CounterDataBuilder_AddMetrics_Params* pParams);
+
+    typedef struct NVPW_CounterDataBuilder_GetCounterDataPrefix_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        NVPA_CounterDataBuilder* pCounterDataBuilder;
+        /// [in] Number of bytes allocated for pBuffer
+        size_t bytesAllocated;
+        /// [out] [optional] Buffer receiving the counter data prefix
+        uint8_t* pBuffer;
+        /// [out] Count of bytes that would be copied to pBuffer
+        size_t bytesCopied;
+    } NVPW_CounterDataBuilder_GetCounterDataPrefix_Params;
+#define NVPW_CounterDataBuilder_GetCounterDataPrefix_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterDataBuilder_GetCounterDataPrefix_Params, bytesCopied)
+
+    NVPA_Status NVPW_CounterDataBuilder_GetCounterDataPrefix(NVPW_CounterDataBuilder_GetCounterDataPrefix_Params* pParams);
+
+/**
+ *  @}
+ ******************************************************************************/
+ 
+/***************************************************************************//**
+ *  @name   Metrics Evaluator
+ *  @{
+ */
+
+    typedef struct NVPW_MetricsEvaluator NVPW_MetricsEvaluator;
+
+#ifndef NVPW_DIM_UNIT_DEFINED
+#define NVPW_DIM_UNIT_DEFINED
+    typedef enum NVPW_DimUnitName
+    {
+        NVPW_DIM_UNIT_INVALID = 3518299157,
+        NVPW_DIM_UNIT_UNITLESS = 2126137902,
+        NVPW_DIM_UNIT_ATTRIBUTES = 3776338729,
+        NVPW_DIM_UNIT_BYTES = 3797850191,
+        NVPW_DIM_UNIT_CTAS = 1960564139,
+        NVPW_DIM_UNIT_CTC_CYCLES = 2224883873,
+        NVPW_DIM_UNIT_DRAM_CYCLES = 2650981327,
+        NVPW_DIM_UNIT_FBP_CYCLES = 1785238957,
+        NVPW_DIM_UNIT_FE_OPS = 2919159083,
+        NVPW_DIM_UNIT_GPC_CYCLES = 1222631184,
+        NVPW_DIM_UNIT_IDC_REQUESTS = 2012649669,
+        NVPW_DIM_UNIT_INSTRUCTIONS = 1418625543,
+        NVPW_DIM_UNIT_KILOBYTES = 1335980302,
+        NVPW_DIM_UNIT_L1DATA_BANK_ACCESSES = 1479493682,
+        NVPW_DIM_UNIT_L1DATA_BANK_CONFLICTS = 3433170787,
+        NVPW_DIM_UNIT_L1TEX_REQUESTS = 1306473767,
+        NVPW_DIM_UNIT_L1TEX_TAGS = 26573010,
+        NVPW_DIM_UNIT_L1TEX_WAVEFRONTS = 129373765,
+        NVPW_DIM_UNIT_L2_REQUESTS = 1143695106,
+        NVPW_DIM_UNIT_L2_SECTORS = 3424101564,
+        NVPW_DIM_UNIT_L2_TAGS = 3755612781,
+        NVPW_DIM_UNIT_LRC_REQUESTS = 2280914327,
+        NVPW_DIM_UNIT_LRC_SECTORS = 7212034,
+        NVPW_DIM_UNIT_MCC_CYCLES = 1826685787,
+        NVPW_DIM_UNIT_NANOSECONDS = 3047500672,
+        NVPW_DIM_UNIT_NVDLA_CYCLES = 3374059789,
+        NVPW_DIM_UNIT_NVENC_CYCLES = 2267185244,
+        NVPW_DIM_UNIT_NVLRX_CYCLES = 4059934930,
+        NVPW_DIM_UNIT_NVLTX_CYCLES = 1814350488,
+        NVPW_DIM_UNIT_OFA_CYCLES = 4290210307,
+        NVPW_DIM_UNIT_PCIE_CYCLES = 1230450943,
+        NVPW_DIM_UNIT_PERCENT = 1284354694,
+        NVPW_DIM_UNIT_PIXELS = 4227616663,
+        NVPW_DIM_UNIT_PIXEL_SHADER_BARRIERS = 3705502518,
+        NVPW_DIM_UNIT_PRIMITIVES = 2373084002,
+        NVPW_DIM_UNIT_PVAVPU_CYCLES = 2238259366,
+        NVPW_DIM_UNIT_PVA_CYCLES = 202044173,
+        NVPW_DIM_UNIT_QUADS = 1539753497,
+        NVPW_DIM_UNIT_REGISTERS = 2837260947,
+        NVPW_DIM_UNIT_SAMPLES = 746046551,
+        NVPW_DIM_UNIT_SECONDS = 1164825258,
+        NVPW_DIM_UNIT_SYSL2_REQUESTS = 2165109286,
+        NVPW_DIM_UNIT_SYSL2_SECTORS = 2268734175,
+        NVPW_DIM_UNIT_SYSL2_TAGS = 3308651352,
+        NVPW_DIM_UNIT_SYSLRC_REQUESTS = 3328245480,
+        NVPW_DIM_UNIT_SYSLRC_SECTORS = 1190477493,
+        NVPW_DIM_UNIT_SYS_CYCLES = 3310821688,
+        NVPW_DIM_UNIT_TEXELS = 1293214069,
+        NVPW_DIM_UNIT_THREADS = 164261907,
+        NVPW_DIM_UNIT_TMEM_ACCESSES = 3742902067,
+        NVPW_DIM_UNIT_VERTICES = 1873662209,
+        NVPW_DIM_UNIT_VIC_CYCLES = 103143588,
+        NVPW_DIM_UNIT_WARPS = 97951949,
+        NVPW_DIM_UNIT_WORKIDS = 1971113483,
+        NVPW_DIM_UNIT_WORKLOADS = 1728142656
+    } NVPW_DimUnitName;
+#endif //NVPW_DIM_UNIT_DEFINED
+
+#ifndef NVPW_HW_UNIT_DEFINED
+#define NVPW_HW_UNIT_DEFINED
+    typedef enum NVPW_HwUnit
+    {
+        NVPW_HW_UNIT_INVALID = 3498035701,
+        NVPW_HW_UNIT_CROP = 2872137846,
+        NVPW_HW_UNIT_CTC = 4123164475,
+        NVPW_HW_UNIT_DRAM = 1662616918,
+        NVPW_HW_UNIT_DRAMC = 1401232876,
+        NVPW_HW_UNIT_FBP = 2947194306,
+        NVPW_HW_UNIT_FBPA = 690045803,
+        NVPW_HW_UNIT_FE = 2204924321,
+        NVPW_HW_UNIT_GPC = 1911735839,
+        NVPW_HW_UNIT_GPU = 1014363534,
+        NVPW_HW_UNIT_GR = 2933618517,
+        NVPW_HW_UNIT_IDC = 842765289,
+        NVPW_HW_UNIT_L1TEX = 893940957,
+        NVPW_HW_UNIT_LRC = 4004756136,
+        NVPW_HW_UNIT_LTS = 2333266697,
+        NVPW_HW_UNIT_MCC = 3980130194,
+        NVPW_HW_UNIT_NVDLA = 4201167892,
+        NVPW_HW_UNIT_NVENC = 207708260,
+        NVPW_HW_UNIT_NVLRX = 3091684901,
+        NVPW_HW_UNIT_NVLTX = 869679659,
+        NVPW_HW_UNIT_OFA = 70307371,
+        NVPW_HW_UNIT_PCIE = 3433264174,
+        NVPW_HW_UNIT_PDA = 345193251,
+        NVPW_HW_UNIT_PES = 804128425,
+        NVPW_HW_UNIT_PROP = 3339255507,
+        NVPW_HW_UNIT_PVA = 2565499490,
+        NVPW_HW_UNIT_PVAVPU = 1656645655,
+        NVPW_HW_UNIT_RASTER = 187932504,
+        NVPW_HW_UNIT_SM = 724224710,
+        NVPW_HW_UNIT_SMSP = 2837616917,
+        NVPW_HW_UNIT_SYS = 768990063,
+        NVPW_HW_UNIT_SYSLRC = 3247626950,
+        NVPW_HW_UNIT_SYSLTS = 4137740217,
+        NVPW_HW_UNIT_TPC = 1889024613,
+        NVPW_HW_UNIT_VAF = 753670509,
+        NVPW_HW_UNIT_VIC = 322439594,
+        NVPW_HW_UNIT_VPC = 275561583,
+        NVPW_HW_UNIT_ZCULL = 2401248356,
+        NVPW_HW_UNIT_ZROP = 979500456
+    } NVPW_HwUnit;
+#endif //NVPW_HW_UNIT_DEFINED
+
+    typedef enum NVPW_RollupOp
+    {
+        NVPW_ROLLUP_OP_AVG = 0,
+        NVPW_ROLLUP_OP_MAX,
+        NVPW_ROLLUP_OP_MIN,
+        NVPW_ROLLUP_OP_SUM,
+        NVPW_ROLLUP_OP__COUNT
+    } NVPW_RollupOp;
+
+    typedef enum NVPW_MetricType
+    {
+        NVPW_METRIC_TYPE_COUNTER = 0,
+        NVPW_METRIC_TYPE_RATIO,
+        NVPW_METRIC_TYPE_THROUGHPUT,
+        NVPW_METRIC_TYPE__COUNT
+    } NVPW_MetricType;
+
+    typedef enum NVPW_Submetric
+    {
+        NVPW_SUBMETRIC_NONE = 0,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED = 1,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_ACTIVE = 2,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_ACTIVE_PER_SECOND = 3,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_ELAPSED = 4,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_ELAPSED_PER_SECOND = 5,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_FRAME = 6,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_FRAME_PER_SECOND = 7,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_REGION = 8,
+        NVPW_SUBMETRIC_PEAK_SUSTAINED_REGION_PER_SECOND = 9,
+        NVPW_SUBMETRIC_PER_CYCLE_ACTIVE = 10,
+        NVPW_SUBMETRIC_PER_CYCLE_ELAPSED = 11,
+        NVPW_SUBMETRIC_PER_CYCLE_IN_FRAME = 12,
+        NVPW_SUBMETRIC_PER_CYCLE_IN_REGION = 13,
+        NVPW_SUBMETRIC_PER_SECOND = 14,
+        NVPW_SUBMETRIC_PCT_OF_PEAK_SUSTAINED_ACTIVE = 15,
+        NVPW_SUBMETRIC_PCT_OF_PEAK_SUSTAINED_ELAPSED = 16,
+        NVPW_SUBMETRIC_PCT_OF_PEAK_SUSTAINED_FRAME = 17,
+        NVPW_SUBMETRIC_PCT_OF_PEAK_SUSTAINED_REGION = 18,
+        NVPW_SUBMETRIC_MAX_RATE = 19,
+        NVPW_SUBMETRIC_PCT = 20,
+        NVPW_SUBMETRIC_RATIO = 21,
+        NVPW_SUBMETRIC__COUNT
+    } NVPW_Submetric;
+
+    typedef struct NVPW_MetricEvalRequest
+    {
+        /// the metric index as in 'NVPW_MetricsEvaluator_GetMetricNames'
+        size_t metricIndex;
+        /// one of 'NVPW_MetricType'
+        uint8_t metricType;
+        /// one of 'NVPW_RollupOp', required for Counter and Throughput, doesn't apply to Ratio
+        uint8_t rollupOp;
+        /// one of 'NVPW_Submetric', required for Ratio and Throughput, optional for Counter
+        uint16_t submetric;
+    } NVPW_MetricEvalRequest;
+#define NVPW_MetricEvalRequest_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricEvalRequest, submetric)
+
+    typedef struct NVPW_DimUnitFactor
+    {
+        /// one of 'NVPW_DimUnitName'
+        uint32_t dimUnit;
+        int8_t exponent;
+    } NVPW_DimUnitFactor;
+#define NVPW_DimUnitFactor_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_DimUnitFactor, exponent)
+
+    typedef struct NVPW_MetricsEvaluator_Destroy_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+    } NVPW_MetricsEvaluator_Destroy_Params;
+#define NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_Destroy_Params, pMetricsEvaluator)
+
+    NVPA_Status NVPW_MetricsEvaluator_Destroy(NVPW_MetricsEvaluator_Destroy_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetMetricNames_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] one of 'NVPW_MetricType'
+        uint8_t metricType;
+        /// [out]
+        const char* pMetricNames;
+        /// [out]
+        const size_t* pMetricNameBeginIndices;
+        /// [out]
+        size_t numMetrics;
+    } NVPW_MetricsEvaluator_GetMetricNames_Params;
+#define NVPW_MetricsEvaluator_GetMetricNames_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetMetricNames_Params, numMetrics)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetMetricNames(NVPW_MetricsEvaluator_GetMetricNames_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetMetricTypeAndIndex_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] can be either a base metric or a metric
+        const char* pMetricName;
+        /// [out] one of 'NVPW_MetricType'
+        uint8_t metricType;
+        /// [out] the metric index as in 'NVPW_MetricsEvaluator_GetMetricNames'
+        size_t metricIndex;
+    } NVPW_MetricsEvaluator_GetMetricTypeAndIndex_Params;
+#define NVPW_MetricsEvaluator_GetMetricTypeAndIndex_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetMetricTypeAndIndex_Params, metricIndex)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetMetricTypeAndIndex(NVPW_MetricsEvaluator_GetMetricTypeAndIndex_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in]
+        const char* pMetricName;
+        /// [inout] 'pMetricEvalRequest' is in, '*pMetricEvalRequest' is out
+        struct NVPW_MetricEvalRequest* pMetricEvalRequest;
+        /// [in] set to 'NVPW_MetricEvalRequest_STRUCT_SIZE'
+        size_t metricEvalRequestStructSize;
+    } NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params;
+#define NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params, metricEvalRequestStructSize)
+
+    NVPA_Status NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_HwUnitToString_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] one of 'NVPW_HwUnit'
+        uint32_t hwUnit;
+        /// [out]
+        const char* pHwUnitName;
+    } NVPW_MetricsEvaluator_HwUnitToString_Params;
+#define NVPW_MetricsEvaluator_HwUnitToString_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_HwUnitToString_Params, pHwUnitName)
+
+    NVPA_Status NVPW_MetricsEvaluator_HwUnitToString(NVPW_MetricsEvaluator_HwUnitToString_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetCounterProperties_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] the metric index as in 'NVPW_MetricsEvaluator_GetMetricNames'
+        size_t counterIndex;
+        /// [out]
+        const char* pDescription;
+        /// [out] one of 'NVPW_HwUnit'
+        uint32_t hwUnit;
+    } NVPW_MetricsEvaluator_GetCounterProperties_Params;
+#define NVPW_MetricsEvaluator_GetCounterProperties_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetCounterProperties_Params, hwUnit)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetCounterProperties(NVPW_MetricsEvaluator_GetCounterProperties_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetRatioMetricProperties_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] the metric index as in 'NVPW_MetricsEvaluator_GetMetricNames'
+        size_t ratioMetricIndex;
+        /// [out]
+        const char* pDescription;
+        /// [out]
+        uint64_t hwUnit;
+    } NVPW_MetricsEvaluator_GetRatioMetricProperties_Params;
+#define NVPW_MetricsEvaluator_GetRatioMetricProperties_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetRatioMetricProperties_Params, hwUnit)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetRatioMetricProperties(NVPW_MetricsEvaluator_GetRatioMetricProperties_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetThroughputMetricProperties_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] the metric index as in 'NVPW_MetricsEvaluator_GetMetricNames'
+        size_t throughputMetricIndex;
+        /// [out]
+        const char* pDescription;
+        /// [out]
+        uint32_t hwUnit;
+        /// [out] number of constituent counters for the throughput metric
+        size_t numCounters;
+        /// [out] metric indices as in 'NVPW_MetricsEvaluator_GetMetricNames', valid if 'numCounters' > 0, otherwise
+        /// returned as nullptr
+        const size_t* pCounterIndices;
+        /// [out] number of constituent sub-throughputs for the throughput metric
+        size_t numSubThroughputs;
+        /// [out] metric indices as in 'NVPW_MetricsEvaluator_GetMetricNames', valid if 'numSubThroughputs' > 0,
+        /// otherwise returned as nullptr
+        const size_t* pSubThroughputIndices;
+    } NVPW_MetricsEvaluator_GetThroughputMetricProperties_Params;
+#define NVPW_MetricsEvaluator_GetThroughputMetricProperties_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetThroughputMetricProperties_Params, pSubThroughputIndices)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetThroughputMetricProperties(NVPW_MetricsEvaluator_GetThroughputMetricProperties_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetSupportedSubmetrics_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] one of 'NVPW_MetricType'
+        uint8_t metricType;
+        /// [out] an array of 'NVPW_Submetric'
+        const uint16_t* pSupportedSubmetrics;
+        /// [out]
+        size_t numSupportedSubmetrics;
+    } NVPW_MetricsEvaluator_GetSupportedSubmetrics_Params;
+#define NVPW_MetricsEvaluator_GetSupportedSubmetrics_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetSupportedSubmetrics_Params, numSupportedSubmetrics)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetSupportedSubmetrics(NVPW_MetricsEvaluator_GetSupportedSubmetrics_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetMetricRawDependencies_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in]
+        const struct NVPW_MetricEvalRequest* pMetricEvalRequests;
+        /// [in]
+        size_t numMetricEvalRequests;
+        /// [in] set to 'NVPW_MetricEvalRequest_STRUCT_SIZE'
+        size_t metricEvalRequestStructSize;
+        /// [in] set to sizeof('NVPW_MetricEvalRequest')
+        size_t metricEvalRequestStrideSize;
+        /// [inout] 'ppRawDependencies' is in, '*ppRawDependencies' is out
+        const char** ppRawDependencies;
+        /// [inout] if 'ppRawDependencies' is NULL, number of raw dependencies available will be returned; otherwise it
+        /// should be set to the number of elements allocated for 'ppRawDependencies', and on return, it will be
+        /// overwritten by number of elements copied to 'ppRawDependencies'
+        size_t numRawDependencies;
+        /// [inout] 'ppOptionalRawDependencies' is in, '*ppOptionalRawDependencies' is out
+        const char** ppOptionalRawDependencies;
+        /// [inout] if 'ppOptionalRawDependencies' is NULL, number of optional raw dependencies available will be
+        /// returned; otherwise it should be set to the number of elements allocated for 'ppOptionalRawDependencies',
+        /// and on return, it will be overwritten by number of elements copied to 'ppOptionalRawDependencies'
+        size_t numOptionalRawDependencies;
+    } NVPW_MetricsEvaluator_GetMetricRawDependencies_Params;
+#define NVPW_MetricsEvaluator_GetMetricRawDependencies_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetMetricRawDependencies_Params, numOptionalRawDependencies)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetMetricRawDependencies(NVPW_MetricsEvaluator_GetMetricRawDependencies_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_DimUnitToString_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] one of 'NVPW_DimUnitName'
+        uint32_t dimUnit;
+        /// [out]
+        const char* pSingularName;
+        /// [out]
+        const char* pPluralName;
+    } NVPW_MetricsEvaluator_DimUnitToString_Params;
+#define NVPW_MetricsEvaluator_DimUnitToString_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_DimUnitToString_Params, pPluralName)
+
+    NVPA_Status NVPW_MetricsEvaluator_DimUnitToString(NVPW_MetricsEvaluator_DimUnitToString_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_GetMetricDimUnits_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in]
+        const struct NVPW_MetricEvalRequest* pMetricEvalRequest;
+        /// [in] set to 'NVPW_MetricEvalRequest_STRUCT_SIZE'
+        size_t metricEvalRequestStructSize;
+        /// [inout] 'pDimUnits' is in, '*pDimUnits' is out
+        NVPW_DimUnitFactor* pDimUnits;
+        /// [inout] if 'pDimUnits' is NULL, number of dim-units available will be returned; otherwise it should be set
+        /// to the number of elements allocated for 'pDimUnits', and on return, it will be overwritten by number of
+        /// elements copied to 'pDimUnits'
+        size_t numDimUnits;
+        /// [in] set to 'NVPW_DimUnitFactor_STRUCT_SIZE'
+        size_t dimUnitFactorStructSize;
+    } NVPW_MetricsEvaluator_GetMetricDimUnits_Params;
+#define NVPW_MetricsEvaluator_GetMetricDimUnits_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_GetMetricDimUnits_Params, dimUnitFactorStructSize)
+
+    NVPA_Status NVPW_MetricsEvaluator_GetMetricDimUnits(NVPW_MetricsEvaluator_GetMetricDimUnits_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_SetUserData_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in] duration in ns of user defined frame
+        double frameDuration;
+        /// [in] duration in ns of user defined region
+        double regionDuration;
+        /// [in]
+        NVPA_Bool isolated;
+    } NVPW_MetricsEvaluator_SetUserData_Params;
+#define NVPW_MetricsEvaluator_SetUserData_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_SetUserData_Params, isolated)
+
+    NVPA_Status NVPW_MetricsEvaluator_SetUserData(NVPW_MetricsEvaluator_SetUserData_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_EvaluateToGpuValues_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in]
+        const struct NVPW_MetricEvalRequest* pMetricEvalRequests;
+        /// [in]
+        size_t numMetricEvalRequests;
+        /// [in] set to 'NVPW_MetricEvalRequest_STRUCT_SIZE'
+        size_t metricEvalRequestStructSize;
+        /// [in] set to sizeof('NVPW_MetricEvalRequest')
+        size_t metricEvalRequestStrideSize;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+        /// [in]
+        size_t rangeIndex;
+        /// [in]
+        NVPA_Bool isolated;
+        /// [inout] 'pMetricValues' is in, '*pMetricValues' is out
+        double* pMetricValues;
+    } NVPW_MetricsEvaluator_EvaluateToGpuValues_Params;
+#define NVPW_MetricsEvaluator_EvaluateToGpuValues_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_EvaluateToGpuValues_Params, pMetricValues)
+
+    NVPA_Status NVPW_MetricsEvaluator_EvaluateToGpuValues(NVPW_MetricsEvaluator_EvaluateToGpuValues_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator_SetDeviceAttributes_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+    } NVPW_MetricsEvaluator_SetDeviceAttributes_Params;
+#define NVPW_MetricsEvaluator_SetDeviceAttributes_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_MetricsEvaluator_SetDeviceAttributes_Params, counterDataImageSize)
+
+    NVPA_Status NVPW_MetricsEvaluator_SetDeviceAttributes(NVPW_MetricsEvaluator_SetDeviceAttributes_Params* pParams);
+
+/**
+ *  @}
+ ******************************************************************************/
+ 
+
+#endif // NVPERF_HOST_API_DEFINED
+
+
+
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#endif // NVPERF_HOST_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_target.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_target.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1c5c85b403c5ebb16d66882aa26c1f1db1d5089
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_target.h
@@ -0,0 +1,626 @@
+#ifndef NVPERF_TARGET_H
+#define NVPERF_TARGET_H
+
+/*
+ * Copyright 2014-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO USER:
+ *
+ * This source code is subject to NVIDIA ownership rights under U.S. and
+ * international Copyright laws.
+ *
+ * This software and the information contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+ * of a form of NVIDIA software license agreement.
+ *
+ * NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+ * CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+ * IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+ * REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+ * MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+ * OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+ * OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+ * OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+ * OR PERFORMANCE OF THIS SOURCE CODE.
+ *
+ * U.S. Government End Users.   This source code is a "commercial item" as
+ * that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+ * "commercial computer  software"  and "commercial computer software
+ * documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+ * and is provided to the U.S. Government only as a commercial end item.
+ * Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+ * 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+ * source code with only those rights set forth herein.
+ *
+ * Any use of this source code in individual and commercial software must
+ * include, in the user documentation and internal comments to the code,
+ * the above Disclaimer and U.S. Government End Users Notice.
+ */
+
+#include <stddef.h>
+#include <stdint.h>
+#include "nvperf_common.h"
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility push(default)
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL __attribute__ ((visibility ("hidden")))
+    #endif
+#else
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL
+    #endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ *  @file   nvperf_target.h
+ */
+
+#ifndef NVPW_GPU_ARCHITECTURE_SUPPORT_LEVEL_DEFINED
+#define NVPW_GPU_ARCHITECTURE_SUPPORT_LEVEL_DEFINED
+    /// GPU architecture support level
+    typedef enum NVPW_GpuArchitectureSupportLevel
+    {
+        NVPW_GPU_ARCHITECTURE_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_GPU_ARCHITECTURE_SUPPORT_LEVEL_UNSUPPORTED,
+        NVPW_GPU_ARCHITECTURE_SUPPORT_LEVEL_SUPPORTED
+    } NVPW_GpuArchitectureSupportLevel;
+#endif //NVPW_GPU_ARCHITECTURE_SUPPORT_LEVEL_DEFINED
+
+#ifndef NVPW_SLI_SUPPORT_LEVEL_DEFINED
+#define NVPW_SLI_SUPPORT_LEVEL_DEFINED
+    /// SLI configuration support level
+    typedef enum NVPW_SliSupportLevel
+    {
+        NVPW_SLI_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_SLI_SUPPORT_LEVEL_UNSUPPORTED,
+        /// Only Non-SLI configurations are supported.
+        NVPW_SLI_SUPPORT_LEVEL_SUPPORTED_NON_SLI_CONFIGURATION
+    } NVPW_SliSupportLevel;
+#endif //NVPW_SLI_SUPPORT_LEVEL_DEFINED
+
+#ifndef NVPW_VGPU_SUPPORT_LEVEL_DEFINED
+#define NVPW_VGPU_SUPPORT_LEVEL_DEFINED
+    /// Virtualized GPU configuration support level
+    typedef enum NVPW_VGpuSupportLevel
+    {
+        NVPW_VGPU_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_VGPU_SUPPORT_LEVEL_UNSUPPORTED,
+        /// Supported but not allowed by system admin.
+        NVPW_VGPU_SUPPORT_LEVEL_SUPPORTED_DISALLOWED,
+        NVPW_VGPU_SUPPORT_LEVEL_SUPPORTED_ALLOWED,
+        NVPW_VGPU_SUPPORT_LEVEL_SUPPORTED_NON_VGPU_CONFIGURATION
+    } NVPW_VGpuSupportLevel;
+#endif //NVPW_VGPU_SUPPORT_LEVEL_DEFINED
+
+#ifndef NVPW_CONF_COMPUTE_SUPPORT_LEVEL_DEFINED
+#define NVPW_CONF_COMPUTE_SUPPORT_LEVEL_DEFINED
+    /// Confidential Compute mode support level
+    typedef enum NVPW_ConfidentialComputeSupportLevel
+    {
+        NVPW_CONF_COMPUTE_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_CONF_COMPUTE_SUPPORT_LEVEL_UNSUPPORTED,
+        NVPW_CONF_COMPUTE_SUPPORT_LEVEL_SUPPORTED_NON_CONF_COMPUTE_CONFIGURATION,
+        NVPW_CONF_COMPUTE_SUPPORT_LEVEL_SUPPORTED_CONF_COMPUTE_DEVTOOLS_MODE
+    } NVPW_ConfidentialComputeSupportLevel;
+#endif //NVPW_CONF_COMPUTE_SUPPORT_LEVEL_DEFINED
+
+#ifndef NVPW_CMP_SUPPORT_LEVEL_DEFINED
+#define NVPW_CMP_SUPPORT_LEVEL_DEFINED
+    /// CMP support level
+    typedef enum NVPW_CmpSupportLevel
+    {
+        NVPW_CMP_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_CMP_SUPPORT_LEVEL_UNSUPPORTED,
+        NVPW_CMP_SUPPORT_LEVEL_SUPPORTED_NON_CMP_CONFIGURATON
+    } NVPW_CmpSupportLevel;
+#endif //NVPW_CMP_SUPPORT_LEVEL_DEFINED
+
+#ifndef NVPW_WSL_SUPPORT_LEVEL_DEFINED
+#define NVPW_WSL_SUPPORT_LEVEL_DEFINED
+    /// WSL support level
+    typedef enum NVPW_WslSupportLevel
+    {
+        NVPW_WSL_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_WSL_SUPPORT_LEVEL_UNSUPPORTED_INSUFFICIENT_DRIVER_VERSION,
+        NVPW_WSL_SUPPORT_LEVEL_SUPPORTED,
+        NVPW_WSL_SUPPORT_LEVEL_SUPPORTED_NON_WSL_CONFIGURATION
+    } NVPW_WslSupportLevel;
+#endif //NVPW_WSL_SUPPORT_LEVEL_DEFINED
+
+#ifndef NVPW_MIG_SUPPORT_LEVEL_DEFINED
+#define NVPW_MIG_SUPPORT_LEVEL_DEFINED
+    /// MIG support level
+    typedef enum NVPW_MigSupportLevel
+    {
+        NVPW_MIG_SUPPORT_LEVEL_UNKNOWN = 0,
+        NVPW_MIG_SUPPORT_LEVEL_UNSUPPORTED,
+        NVPW_MIG_SUPPORT_LEVEL_SUPPORTED,
+        NVPW_MIG_SUPPORT_LEVEL_SUPPORTED_NON_MIG_CONFIGURATION
+    } NVPW_MigSupportLevel;
+#endif //NVPW_MIG_SUPPORT_LEVEL_DEFINED
+
+    typedef struct NVPW_InitializeTarget_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+    } NVPW_InitializeTarget_Params;
+#define NVPW_InitializeTarget_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_InitializeTarget_Params, pPriv)
+
+    /// Load the target library.
+    NVPA_Status NVPW_InitializeTarget(NVPW_InitializeTarget_Params* pParams);
+
+    typedef struct NVPW_GetDeviceCount_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        size_t numDevices;
+    } NVPW_GetDeviceCount_Params;
+#define NVPW_GetDeviceCount_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_GetDeviceCount_Params, numDevices)
+
+    NVPA_Status NVPW_GetDeviceCount(NVPW_GetDeviceCount_Params* pParams);
+
+    typedef struct NVPW_Device_GetNames_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        size_t deviceIndex;
+        const char* pDeviceName;
+        const char* pChipName;
+    } NVPW_Device_GetNames_Params;
+#define NVPW_Device_GetNames_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Device_GetNames_Params, pChipName)
+
+    NVPA_Status NVPW_Device_GetNames(NVPW_Device_GetNames_Params* pParams);
+
+    typedef struct NVPW_PciBusId
+    {
+        /// The PCI domain on which the device bus resides.
+        uint32_t domain;
+        ///  The bus on which the device resides.
+        uint16_t bus;
+        /// device ID.
+        uint16_t device;
+    } NVPW_PciBusId;
+#define NVPW_PciBusId_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PciBusId, device)
+
+    typedef struct NVPW_Device_GetPciBusIds_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] caller-allocated array of NVPW_PciBusId, indexed by NVPW deviceIndex
+        NVPW_PciBusId* pBusIds;
+        /// [in] size of the pBusIDs array; use result from NVPW_GetDeviceCount
+        size_t numDevices;
+    } NVPW_Device_GetPciBusIds_Params;
+#define NVPW_Device_GetPciBusIds_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Device_GetPciBusIds_Params, numDevices)
+
+    NVPA_Status NVPW_Device_GetPciBusIds(NVPW_Device_GetPciBusIds_Params* pParams);
+
+
+#define NVPW_DEVICE_MIG_GPU_INSTANCE_ID_INVALID     0xFFFFFFFFu
+#define NVPW_DEVICE_MIG_GPU_INSTANCE_ID_FULLCHIP    0xFFFFFFFEu
+
+
+    typedef struct NVPW_Device_GetMigAttributes_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        size_t deviceIndex;
+        /// [out]
+        NVPA_Bool isMigPartition;
+        /// [out]
+        uint32_t gpuInstanceId;
+        /// [out]
+        uint32_t computeInstanceId;
+    } NVPW_Device_GetMigAttributes_Params;
+#define NVPW_Device_GetMigAttributes_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Device_GetMigAttributes_Params, computeInstanceId)
+
+    NVPA_Status NVPW_Device_GetMigAttributes(NVPW_Device_GetMigAttributes_Params* pParams);
+
+    typedef struct NVPW_Adapter_GetDeviceIndex_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        struct IDXGIAdapter* pAdapter;
+        /// [in]
+        size_t sliIndex;
+        /// [out]
+        size_t deviceIndex;
+    } NVPW_Adapter_GetDeviceIndex_Params;
+#define NVPW_Adapter_GetDeviceIndex_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Adapter_GetDeviceIndex_Params, deviceIndex)
+
+    NVPA_Status NVPW_Adapter_GetDeviceIndex(NVPW_Adapter_GetDeviceIndex_Params* pParams);
+
+    typedef struct NVPW_CounterData_GetNumRanges_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const uint8_t* pCounterDataImage;
+        size_t numRanges;
+    } NVPW_CounterData_GetNumRanges_Params;
+#define NVPW_CounterData_GetNumRanges_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterData_GetNumRanges_Params, numRanges)
+
+    NVPA_Status NVPW_CounterData_GetNumRanges(NVPW_CounterData_GetNumRanges_Params* pParams);
+
+    typedef struct NVPW_CounterData_GetChipName_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+        /// [out]
+        const char* pChipName;
+    } NVPW_CounterData_GetChipName_Params;
+#define NVPW_CounterData_GetChipName_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterData_GetChipName_Params, pChipName)
+
+    NVPA_Status NVPW_CounterData_GetChipName(NVPW_CounterData_GetChipName_Params* pParams);
+
+    typedef struct NVPW_Config_GetNumPasses_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pConfig;
+        /// [out]
+        size_t numPipelinedPasses;
+        /// [out]
+        size_t numIsolatedPasses;
+    } NVPW_Config_GetNumPasses_Params;
+#define NVPW_Config_GetNumPasses_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Config_GetNumPasses_Params, numIsolatedPasses)
+
+    /// Total num passes = numPipelinedPasses + numIsolatedPasses * numNestingLevels
+    NVPA_Status NVPW_Config_GetNumPasses(NVPW_Config_GetNumPasses_Params* pParams);
+
+    typedef struct NVPW_Config_GetNumPasses_V2_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pConfig;
+        /// [out]
+        size_t numPasses;
+    } NVPW_Config_GetNumPasses_V2_Params;
+#define NVPW_Config_GetNumPasses_V2_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Config_GetNumPasses_V2_Params, numPasses)
+
+    /// Total num passes = numPasses * numNestingLevels
+    NVPA_Status NVPW_Config_GetNumPasses_V2(NVPW_Config_GetNumPasses_V2_Params* pParams);
+
+#define NVPW_API_SET_CUDA_PROFILER             0x18209d0775b2f89dULL
+
+#define NVPW_API_SET_D3D11_PROFILER            0xca55c6738445db2bULL
+
+#define NVPW_API_SET_D3D12_PROFILER            0xc0c2d46dd7c7ad78ULL
+
+#define NVPW_API_SET_EGL_PROFILER              0x3c3747dae1f9565cULL
+
+#define NVPW_API_SET_GPU_PERIODICSAMPLER       0x9f4c2571fc0b2e8aULL
+
+#define NVPW_API_SET_METRICSEVALUATOR          0x0368a8768d811af9ULL
+
+#define NVPW_API_SET_METRICS_AD10X_COMP        0xbe57278e12cb5288ULL
+
+#define NVPW_API_SET_METRICS_AD10X_GRFX        0x5cbf0774f81bf491ULL
+
+#define NVPW_API_SET_METRICS_GA100_COMP        0x16b7d8c20d8b4915ULL
+
+#define NVPW_API_SET_METRICS_GA100_GRFX        0xc94eaabec04a94faULL
+
+#define NVPW_API_SET_METRICS_GA10X_COMP        0xb5d6391c2e299ab5ULL
+
+#define NVPW_API_SET_METRICS_GA10X_GRFX        0x6ebc121178b5ce0bULL
+
+#define NVPW_API_SET_METRICS_GV100_COMP        0x863705cc57919f72ULL
+
+#define NVPW_API_SET_METRICS_GV100_GRFX        0x9900da75d164fecfULL
+
+#define NVPW_API_SET_METRICS_GV11B_COMP        0xd3f79a859235848fULL
+
+#define NVPW_API_SET_METRICS_GV11B_GRFX        0xeb8e26220106e227ULL
+
+#define NVPW_API_SET_METRICS_TU10X_COMP        0x70f40be0afd35da8ULL
+
+#define NVPW_API_SET_METRICS_TU10X_GRFX        0xdf219cb838db6968ULL
+
+#define NVPW_API_SET_METRICS_TU11X_COMP        0xeb0069d7d0956678ULL
+
+#define NVPW_API_SET_METRICS_TU11X_GRFX        0x0977d9342bd62743ULL
+
+#define NVPW_API_SET_OPENGL_PROFILER           0xe4cd9ea40f2ee777ULL
+
+#define NVPW_API_SET_VULKAN_PROFILER           0x8c56b6a03d779689ULL
+
+#define NVPW_SDK_VERSION               0x1e128b6f001423fcULL
+
+    typedef struct NVPW_QueryVersionNumber_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        uint64_t apiSet;
+        /// [out]
+        uint32_t major;
+        /// [out]
+        uint32_t minor;
+        /// [out]
+        uint32_t patch;
+        /// [out]
+        uint32_t relMajor;
+        /// [out]
+        uint32_t relMinor;
+        /// [out]
+        uint32_t relPatch;
+    } NVPW_QueryVersionNumber_Params;
+#define NVPW_QueryVersionNumber_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_QueryVersionNumber_Params, relPatch)
+
+    /// Query version number of an API set
+    NVPA_Status NVPW_QueryVersionNumber(NVPW_QueryVersionNumber_Params* pParams);
+
+    typedef enum NVPW_Device_ClockStatus
+    {
+        /// clock status is unknown
+        NVPW_DEVICE_CLOCK_STATUS_UNKNOWN,
+        /// clocks are locked to rated tdp values - Deprecated, use NVPW_DEVICE_CLOCK_STATUS_LOCKED instead
+        NVPW_DEVICE_CLOCK_STATUS_LOCKED_TO_RATED_TDP,
+        /// clocks are not locked and can boost above rated tdp
+        NVPW_DEVICE_CLOCK_STATUS_BOOST_ENABLED,
+        /// clocks are not locked and will not go above rated tdp
+        NVPW_DEVICE_CLOCK_STATUS_BOOST_DISABLED,
+        /// clocks are locked
+        NVPW_DEVICE_CLOCK_STATUS_LOCKED,
+        /// clocks are not locked
+        NVPW_DEVICE_CLOCK_STATUS_UNLOCKED,
+        NVPW_DEVICE_CLOCK_STATUS__COUNT
+    } NVPW_Device_ClockStatus;
+
+    typedef enum NVPW_Device_ClockLevel
+    {
+        /// clock level is invalid
+        NVPW_DEVICE_CLOCK_LEVEL_INVALID,
+        /// clock level is at rated tdp
+        NVPW_DEVICE_CLOCK_LEVEL_RATED_TDP,
+        /// clock level is at turbo boost
+        NVPW_DEVICE_CLOCK_LEVEL_TURBO_BOOST,
+        NVPW_DEVICE_CLOCK_LEVEL__COUNT
+    } NVPW_Device_ClockLevel;
+
+    typedef struct NVPW_Device_GetClockStatus_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        size_t deviceIndex;
+        /// [in]
+        NVPW_Device_ClockStatus clockStatus;
+        /// [in]
+        NVPW_Device_ClockLevel clockLevel;
+    } NVPW_Device_GetClockStatus_Params;
+#define NVPW_Device_GetClockStatus_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Device_GetClockStatus_Params, clockLevel)
+
+    NVPA_Status NVPW_Device_GetClockStatus(NVPW_Device_GetClockStatus_Params* pParams);
+
+    typedef enum NVPW_Device_ClockSetting
+    {
+        /// invalid op, specify valid clocks operation during profiling
+        NVPW_DEVICE_CLOCK_SETTING_INVALID,
+        /// default to driver/application config (normally unlocked and not boosted, but could be unlocked boosted, or
+        /// locked to rated TDP)
+        NVPW_DEVICE_CLOCK_SETTING_DEFAULT,
+        /// lock clocks at rated tdp base values
+        NVPW_DEVICE_CLOCK_SETTING_LOCK_TO_RATED_TDP,
+        /// lock clocks at turbo boost values
+        NVPW_DEVICE_CLOCK_SETTING_LOCK_TO_TURBO_BOOST,
+        NVPW_DEVICE_CLOCK_SETTING__COUNT
+    } NVPW_Device_ClockSetting;
+
+    typedef struct NVPW_Device_SetClockSetting_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        size_t deviceIndex;
+        /// [in]
+        NVPW_Device_ClockSetting clockSetting;
+    } NVPW_Device_SetClockSetting_Params;
+#define NVPW_Device_SetClockSetting_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Device_SetClockSetting_Params, clockSetting)
+
+    NVPA_Status NVPW_Device_SetClockSetting(NVPW_Device_SetClockSetting_Params* pParams);
+
+    typedef struct NVPW_CounterData_GetRangeDescriptions_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const uint8_t* pCounterDataImage;
+        size_t rangeIndex;
+        /// [inout] Number of descriptions allocated in ppDescriptions
+        size_t numDescriptions;
+        const char** ppDescriptions;
+    } NVPW_CounterData_GetRangeDescriptions_Params;
+#define NVPW_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CounterData_GetRangeDescriptions_Params, ppDescriptions)
+
+    NVPA_Status NVPW_CounterData_GetRangeDescriptions(NVPW_CounterData_GetRangeDescriptions_Params* pParams);
+
+    typedef struct NVPW_Profiler_CounterData_GetRangeDescriptions_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        const uint8_t* pCounterDataImage;
+        size_t rangeIndex;
+        /// [inout] Number of descriptions allocated in ppDescriptions
+        size_t numDescriptions;
+        const char** ppDescriptions;
+    } NVPW_Profiler_CounterData_GetRangeDescriptions_Params;
+#define NVPW_Profiler_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_Profiler_CounterData_GetRangeDescriptions_Params, ppDescriptions)
+
+    NVPA_Status NVPW_Profiler_CounterData_GetRangeDescriptions(NVPW_Profiler_CounterData_GetRangeDescriptions_Params* pParams);
+
+#ifndef NVPW_PERIODIC_SAMPLER_COUNTER_DATA_APPEND_MODE_DEFINED
+#define NVPW_PERIODIC_SAMPLER_COUNTER_DATA_APPEND_MODE_DEFINED
+    typedef enum NVPW_PeriodicSampler_CounterData_AppendMode
+    {
+        NVPW_PERIODIC_SAMPLER_COUNTER_DATA_APPEND_MODE_LINEAR = 0,
+        NVPW_PERIODIC_SAMPLER_COUNTER_DATA_APPEND_MODE_CIRCULAR = 1,
+        NVPW_PERIODIC_SAMPLER_COUNTER_DATA_APPEND_MODE__COUNT
+    } NVPW_PeriodicSampler_CounterData_AppendMode;
+#endif //NVPW_PERIODIC_SAMPLER_COUNTER_DATA_APPEND_MODE_DEFINED
+
+    typedef struct NVPW_PeriodicSampler_CounterData_GetSampleTime_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t rangeIndex;
+        /// [out]
+        uint64_t timestampStart;
+        /// [out]
+        uint64_t timestampEnd;
+    } NVPW_PeriodicSampler_CounterData_GetSampleTime_Params;
+#define NVPW_PeriodicSampler_CounterData_GetSampleTime_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_CounterData_GetSampleTime_Params, timestampEnd)
+
+    NVPA_Status NVPW_PeriodicSampler_CounterData_GetSampleTime(NVPW_PeriodicSampler_CounterData_GetSampleTime_Params* pParams);
+
+    typedef struct NVPW_PeriodicSampler_CounterData_TrimInPlace_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+        /// [out]
+        size_t counterDataImageTrimmedSize;
+    } NVPW_PeriodicSampler_CounterData_TrimInPlace_Params;
+#define NVPW_PeriodicSampler_CounterData_TrimInPlace_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_CounterData_TrimInPlace_Params, counterDataImageTrimmedSize)
+
+    NVPA_Status NVPW_PeriodicSampler_CounterData_TrimInPlace(NVPW_PeriodicSampler_CounterData_TrimInPlace_Params* pParams);
+
+    typedef struct NVPW_PeriodicSampler_CounterData_GetInfo_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+        /// [out] total number of ranges in the counter data
+        size_t numTotalRanges;
+        /// [out] if in "linear" mode, this API returns the number of "populated" ranges; if it's in "circular" mode,
+        /// then it returns the last "populated" range index + 1, when there is no such range, it returns 0.
+        size_t numPopulatedRanges;
+        /// [out] if in "linear" mode, this API returns the number of "completed" ranges; if it's in "circular" mode,
+        /// then it returns the last "completed" range index + 1, when there is no such range, it returns 0.
+        size_t numCompletedRanges;
+    } NVPW_PeriodicSampler_CounterData_GetInfo_Params;
+#define NVPW_PeriodicSampler_CounterData_GetInfo_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_CounterData_GetInfo_Params, numCompletedRanges)
+
+    /// In periodic sampler, a range in counter data stores exactly one sample's data. For better performance, periodic
+    /// sampler may operate in an out-of-order fashion when populating sample data, i.e. it may not fully populate all
+    /// counters of a sample/range before starting to populate the next sample/range. As a result, we have two concepts
+    /// here, "populated" & "completed": a range is considered "populated" even if only partial counters have been
+    /// written; on the other hand, a range is only considered "completed" if all the collecting counters have been
+    /// written.
+    NVPA_Status NVPW_PeriodicSampler_CounterData_GetInfo(NVPW_PeriodicSampler_CounterData_GetInfo_Params* pParams);
+
+    typedef struct NVPW_PeriodicSampler_CounterData_GetTriggerCount_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+        /// [in]
+        size_t rangeIndex;
+        /// [out]
+        uint32_t triggerCount;
+    } NVPW_PeriodicSampler_CounterData_GetTriggerCount_Params;
+#define NVPW_PeriodicSampler_CounterData_GetTriggerCount_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_CounterData_GetTriggerCount_Params, triggerCount)
+
+    NVPA_Status NVPW_PeriodicSampler_CounterData_GetTriggerCount(NVPW_PeriodicSampler_CounterData_GetTriggerCount_Params* pParams);
+
+    typedef struct NVPW_PeriodicSampler_CounterData_IsDataComplete_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in]
+        size_t counterDataImageSize;
+        /// [in]
+        size_t rangeIndex;
+        /// [out]
+        NVPA_Bool isComplete;
+    } NVPW_PeriodicSampler_CounterData_IsDataComplete_Params;
+#define NVPW_PeriodicSampler_CounterData_IsDataComplete_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_PeriodicSampler_CounterData_IsDataComplete_Params, isComplete)
+
+    /// Checks whether a given sample's data is complete. See also 'NVPW_PeriodicSampler_CounterData_GetInfo'
+    NVPA_Status NVPW_PeriodicSampler_CounterData_IsDataComplete(NVPW_PeriodicSampler_CounterData_IsDataComplete_Params* pParams);
+
+
+    typedef struct NVPW_TimestampReport
+    {
+        uint32_t payload;
+        uint8_t reserved0004[4];
+        uint64_t timestamp;
+    } NVPW_TimestampReport;
+
+
+
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#endif // NVPERF_TARGET_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/__init__.py
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/libcheckpoint.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/libcheckpoint.so
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+size 1644872
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/libcupti.so.12 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/libcupti.so.12
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+version https://git-lfs.github.com/spec/v1
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/libpcsamplingutil.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_cupti/lib/libpcsamplingutil.so
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/__init__.py
new file mode 100644
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/include/__init__.py
new file mode 100644
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/include/nvrtc.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/include/nvrtc.h
new file mode 100644
index 0000000000000000000000000000000000000000..48cc9638fded05ae260b79c511cb4f42c11cb07a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/include/nvrtc.h
@@ -0,0 +1,1141 @@
+//
+// NVIDIA_COPYRIGHT_BEGIN
+//
+// Copyright (c) 2014-2024, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+//
+// NVIDIA_COPYRIGHT_END
+//
+
+#ifndef __NVRTC_H__
+#define __NVRTC_H__
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+#include <stdlib.h>
+
+
+/*************************************************************************//**
+ *
+ * \defgroup error Error Handling
+ *
+ * NVRTC defines the following enumeration type and function for API call
+ * error handling.
+ *
+ ****************************************************************************/
+
+
+/**
+ * \ingroup error
+ * \brief   The enumerated type nvrtcResult defines API call result codes.
+ *          NVRTC API functions return nvrtcResult to indicate the call
+ *          result.
+ */
+typedef enum {
+  NVRTC_SUCCESS = 0,
+  NVRTC_ERROR_OUT_OF_MEMORY = 1,
+  NVRTC_ERROR_PROGRAM_CREATION_FAILURE = 2,
+  NVRTC_ERROR_INVALID_INPUT = 3,
+  NVRTC_ERROR_INVALID_PROGRAM = 4,
+  NVRTC_ERROR_INVALID_OPTION = 5,
+  NVRTC_ERROR_COMPILATION = 6,
+  NVRTC_ERROR_BUILTIN_OPERATION_FAILURE = 7,
+  NVRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION = 8,
+  NVRTC_ERROR_NO_LOWERED_NAMES_BEFORE_COMPILATION = 9,
+  NVRTC_ERROR_NAME_EXPRESSION_NOT_VALID = 10,
+  NVRTC_ERROR_INTERNAL_ERROR = 11,
+  NVRTC_ERROR_TIME_FILE_WRITE_FAILED = 12,
+  NVRTC_ERROR_NO_PCH_CREATE_ATTEMPTED = 13,
+  NVRTC_ERROR_PCH_CREATE_HEAP_EXHAUSTED = 14,
+  NVRTC_ERROR_PCH_CREATE = 15,
+  NVRTC_ERROR_CANCELLED = 16
+} nvrtcResult;
+
+
+/**
+ * \ingroup error
+ * \brief   nvrtcGetErrorString is a helper function that returns a string
+ *          describing the given nvrtcResult code, e.g., NVRTC_SUCCESS to
+ *          \c "NVRTC_SUCCESS".
+ *          For unrecognized enumeration values, it returns
+ *          \c "NVRTC_ERROR unknown".
+ *
+ * \param   [in] result CUDA Runtime Compilation API result code.
+ * \return  Message string for the given #nvrtcResult code.
+ */
+const char *nvrtcGetErrorString(nvrtcResult result);
+
+
+/*************************************************************************//**
+ *
+ * \defgroup query General Information Query
+ *
+ * NVRTC defines the following function for general information query.
+ *
+ ****************************************************************************/
+
+
+/**
+ * \ingroup query
+ * \brief   nvrtcVersion sets the output parameters \p major and \p minor
+ *          with the CUDA Runtime Compilation version number.
+ *
+ * \param   [out] major CUDA Runtime Compilation major version number.
+ * \param   [out] minor CUDA Runtime Compilation minor version number.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *
+ */
+nvrtcResult nvrtcVersion(int *major, int *minor);
+
+
+/**
+ * \ingroup query
+ * \brief   nvrtcGetNumSupportedArchs sets the output parameter \p numArchs 
+ *          with the number of architectures supported by NVRTC. This can 
+ *          then be used to pass an array to ::nvrtcGetSupportedArchs to
+ *          get the supported architectures.
+ *
+ * \param   [out] numArchs number of supported architectures.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *
+ * see    ::nvrtcGetSupportedArchs
+ */
+nvrtcResult nvrtcGetNumSupportedArchs(int* numArchs);
+
+
+/**
+ * \ingroup query
+ * \brief   nvrtcGetSupportedArchs populates the array passed via the output parameter 
+ *          \p supportedArchs with the architectures supported by NVRTC. The array is
+ *          sorted in the ascending order. The size of the array to be passed can be
+ *          determined using ::nvrtcGetNumSupportedArchs.
+ *
+ * \param   [out] supportedArchs sorted array of supported architectures.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *
+ * see    ::nvrtcGetNumSupportedArchs
+ */
+nvrtcResult nvrtcGetSupportedArchs(int* supportedArchs);
+
+
+/*************************************************************************//**
+ *
+ * \defgroup compilation Compilation
+ *
+ * NVRTC defines the following type and functions for actual compilation.
+ *
+ ****************************************************************************/
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcProgram is the unit of compilation, and an opaque handle for
+ *          a program.
+ *
+ * To compile a CUDA program string, an instance of nvrtcProgram must be
+ * created first with ::nvrtcCreateProgram, then compiled with
+ * ::nvrtcCompileProgram.
+ */
+typedef struct _nvrtcProgram *nvrtcProgram;
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcCreateProgram creates an instance of nvrtcProgram with the
+ *          given input parameters, and sets the output parameter \p prog with
+ *          it.
+ *
+ * \param   [out] prog         CUDA Runtime Compilation program.
+ * \param   [in]  src          CUDA program source.
+ * \param   [in]  name         CUDA program name.\n
+ *                             \p name can be \c NULL; \c "default_program" is
+ *                             used when \p name is \c NULL or "".
+ * \param   [in]  numHeaders   Number of headers used.\n
+ *                             \p numHeaders must be greater than or equal to 0.
+ * \param   [in]  headers      Sources of the headers.\n
+ *                             \p headers can be \c NULL when \p numHeaders is
+ *                             0.
+ * \param   [in]  includeNames Name of each header by which they can be
+ *                             included in the CUDA program source.\n
+ *                             \p includeNames can be \c NULL when \p numHeaders
+ *                             is 0. These headers must be included with the exact
+ *                             names specified here.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_OUT_OF_MEMORY \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_PROGRAM_CREATION_FAILURE \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcDestroyProgram
+ */
+nvrtcResult nvrtcCreateProgram(nvrtcProgram *prog,
+                               const char *src,
+                               const char *name,
+                               int numHeaders,
+                               const char * const *headers,
+                               const char * const *includeNames);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcDestroyProgram destroys the given program.
+ *
+ * \param    [in] prog CUDA Runtime Compilation program.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcCreateProgram
+ */
+nvrtcResult nvrtcDestroyProgram(nvrtcProgram *prog);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcCompileProgram compiles the given program.
+ *
+ * \param   [in] prog       CUDA Runtime Compilation program.
+ * \param   [in] numOptions Number of compiler options passed.
+ * \param   [in] options    Compiler options in the form of C string array.\n
+ *                          \p options can be \c NULL when \p numOptions is 0.
+ *
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_OUT_OF_MEMORY \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_OPTION \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_COMPILATION \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_BUILTIN_OPERATION_FAILURE \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_TIME_FILE_WRITE_FAILED \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_CANCELLED \endlink
+ *
+ * It supports compile options listed in \ref options.
+ */
+nvrtcResult nvrtcCompileProgram(nvrtcProgram prog,
+                                int numOptions, const char * const *options);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetPTXSize sets the value of \p ptxSizeRet with the size of the PTX
+ *          generated by the previous compilation of \p prog (including the
+ *          trailing \c NULL).
+ *
+ * \param   [in]  prog       CUDA Runtime Compilation program.
+ * \param   [out] ptxSizeRet Size of the generated PTX (including the trailing
+ *                           \c NULL).
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetPTX
+ */
+nvrtcResult nvrtcGetPTXSize(nvrtcProgram prog, size_t *ptxSizeRet);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetPTX stores the PTX generated by the previous compilation
+ *          of \p prog in the memory pointed by \p ptx.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [out] ptx  Compiled result.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetPTXSize
+ */
+nvrtcResult nvrtcGetPTX(nvrtcProgram prog, char *ptx);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetCUBINSize sets the value of \p cubinSizeRet with the size of the cubin
+ *          generated by the previous compilation of \p prog. The value of
+ *          cubinSizeRet is set to 0 if the value specified to \c -arch is a
+ *          virtual architecture instead of an actual architecture.
+ *
+ * \param   [in]  prog       CUDA Runtime Compilation program.
+ * \param   [out] cubinSizeRet Size of the generated cubin.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetCUBIN
+ */
+nvrtcResult nvrtcGetCUBINSize(nvrtcProgram prog, size_t *cubinSizeRet);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetCUBIN stores the cubin generated by the previous compilation
+ *          of \p prog in the memory pointed by \p cubin. No cubin is available
+ *          if the value specified to \c -arch is a virtual architecture instead
+ *          of an actual architecture.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [out] cubin  Compiled and assembled result.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetCUBINSize
+ */
+nvrtcResult nvrtcGetCUBIN(nvrtcProgram prog, char *cubin);
+
+
+#if defined(_WIN32)
+# define __DEPRECATED__(msg) __declspec(deprecated(msg))
+#elif (defined(__GNUC__) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 5 && !defined(__clang__))))
+# define __DEPRECATED__(msg) __attribute__((deprecated))
+#elif (defined(__GNUC__))
+# define __DEPRECATED__(msg) __attribute__((deprecated(msg)))
+#else
+# define __DEPRECATED__(msg)
+#endif
+
+/**
+ * \ingroup compilation
+ * \brief   
+ * DEPRECATION NOTICE: This function will be removed in a future release. Please use
+ * nvrtcGetLTOIRSize (and nvrtcGetLTOIR) instead.
+ */
+__DEPRECATED__("This function will be removed in a future release. Please use nvrtcGetLTOIRSize instead")
+nvrtcResult nvrtcGetNVVMSize(nvrtcProgram prog, size_t *nvvmSizeRet);
+
+/**
+ * \ingroup compilation
+ * \brief   
+ * DEPRECATION NOTICE: This function will be removed in a future release. Please use
+ * nvrtcGetLTOIR (and nvrtcGetLTOIRSize) instead.
+ */
+__DEPRECATED__("This function will be removed in a future release. Please use nvrtcGetLTOIR instead")
+nvrtcResult nvrtcGetNVVM(nvrtcProgram prog, char *nvvm);
+
+#undef __DEPRECATED__
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetLTOIRSize sets the value of \p LTOIRSizeRet with the size of the LTO IR
+ *          generated by the previous compilation of \p prog. The value of
+ *          LTOIRSizeRet is set to 0 if the program was not compiled with 
+ *          \c -dlto.
+ *
+ * \param   [in]  prog       CUDA Runtime Compilation program.
+ * \param   [out] LTOIRSizeRet Size of the generated LTO IR.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetLTOIR
+ */
+nvrtcResult nvrtcGetLTOIRSize(nvrtcProgram prog, size_t *LTOIRSizeRet);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetLTOIR stores the LTO IR generated by the previous compilation
+ *          of \p prog in the memory pointed by \p LTOIR. No LTO IR is available
+ *          if the program was compiled without \c -dlto.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [out] LTOIR Compiled result.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetLTOIRSize
+ */
+nvrtcResult nvrtcGetLTOIR(nvrtcProgram prog, char *LTOIR);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetOptiXIRSize sets the value of \p optixirSizeRet with the size of the OptiX IR
+ *          generated by the previous compilation of \p prog. The value of
+ *          nvrtcGetOptiXIRSize is set to 0 if the program was compiled with 
+ *          options incompatible with OptiX IR generation.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [out] optixirSizeRet Size of the generated LTO IR.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetOptiXIR
+ */
+nvrtcResult nvrtcGetOptiXIRSize(nvrtcProgram prog, size_t *optixirSizeRet);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetOptiXIR stores the OptiX IR generated by the previous compilation
+ *          of \p prog in the memory pointed by \p optixir. No OptiX IR is available
+ *          if the program was compiled with options incompatible with OptiX IR generation.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [out] optixir Optix IR Compiled result.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetOptiXIRSize
+ */
+nvrtcResult nvrtcGetOptiXIR(nvrtcProgram prog, char *optixir);
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetProgramLogSize sets \p logSizeRet with the size of the
+ *          log generated by the previous compilation of \p prog (including the
+ *          trailing \c NULL).
+ *
+ * Note that compilation log may be generated with warnings and informative
+ * messages, even when the compilation of \p prog succeeds.
+ *
+ * \param   [in]  prog       CUDA Runtime Compilation program.
+ * \param   [out] logSizeRet Size of the compilation log
+ *                           (including the trailing \c NULL).
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetProgramLog
+ */
+nvrtcResult nvrtcGetProgramLogSize(nvrtcProgram prog, size_t *logSizeRet);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetProgramLog stores the log generated by the previous
+ *          compilation of \p prog in the memory pointed by \p log.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [out] log  Compilation log.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *
+ * \see     ::nvrtcGetProgramLogSize
+ */
+nvrtcResult nvrtcGetProgramLog(nvrtcProgram prog, char *log);
+
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcAddNameExpression notes the given name expression
+ *          denoting the address of a __global__ function 
+ *          or __device__/__constant__ variable.
+ *
+ * The identical name expression string must be provided on a subsequent
+ * call to nvrtcGetLoweredName to extract the lowered name.
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [in] name_expression constant expression denoting the address of
+ *               a __global__ function or __device__/__constant__ variable.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION \endlink
+ *
+ * \see     ::nvrtcGetLoweredName
+ */
+nvrtcResult nvrtcAddNameExpression(nvrtcProgram prog,
+                                   const char * const name_expression);
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcGetLoweredName extracts the lowered (mangled) name
+ *          for a __global__ function or __device__/__constant__ variable,
+ *          and updates *lowered_name to point to it. The memory containing
+ *          the name is released when the NVRTC program is destroyed by 
+ *          nvrtcDestroyProgram.
+ *          The identical name expression must have been previously
+ *          provided to nvrtcAddNameExpression.
+ *
+ * \param   [in]  prog CUDA Runtime Compilation program.
+ * \param   [in] name_expression constant expression denoting the address of 
+ *               a __global__ function or __device__/__constant__ variable.
+ * \param   [out] lowered_name initialized by the function to point to a
+ *               C string containing the lowered (mangled)
+ *               name corresponding to the provided name expression.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_NO_LOWERED_NAMES_BEFORE_COMPILATION \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_NAME_EXPRESSION_NOT_VALID \endlink
+ *
+ * \see     ::nvrtcAddNameExpression
+ */
+nvrtcResult nvrtcGetLoweredName(nvrtcProgram prog,
+                                const char *const name_expression,
+                                const char** lowered_name);
+
+
+/*************************************************************************//**
+ *
+ * \defgroup precompiled_header Precompiled header (PCH) (CUDA 12.8+)
+ *
+ * NVRTC defines the following function related to PCH. Also see PCH related
+ * flags passed to nvrtcCompileProgram.
+ ****************************************************************************/
+
+
+/**
+ * \ingroup precompiled_header
+ * \brief   retrieve the current size of the PCH Heap.
+ *
+ * \param   [out] ret pointer to location where the size of the PCH Heap
+ *                 will be stored
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ *
+ */
+nvrtcResult nvrtcGetPCHHeapSize(size_t* ret);
+
+/**
+ * \ingroup precompiled_header
+ * \brief   set the size of the PCH Heap.
+ *
+ * \param   [in] size requested size of the PCH Heap, in bytes
+ * 
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ * 
+ * The requested size may be rounded up to a platform dependent
+ * alignment (e.g. page size). If the PCH Heap has already been allocated,
+ * the heap memory will be freed and a new PCH Heap will be allocated. 
+ */
+nvrtcResult nvrtcSetPCHHeapSize(size_t size);
+
+/**
+ * \ingroup precompiled_header
+ * \brief   returns the PCH creation status.
+ *
+ * \param   [in] prog CUDA Runtime Compilation program.
+ *
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_NO_PCH_CREATE_ATTEMPTED \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_PCH_CREATE \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_PCH_CREATE_HEAP_EXHAUSTED \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ * 
+ * NVRTC_SUCCESS indicates that the PCH was successfully created.
+ * NVRTC_ERROR_NO_PCH_CREATE_ATTEMPTED indicates that no PCH creation 
+ * was attempted, either because PCH functionality was not requested during
+ * the preceding nvrtcCompileProgram call, or automatic PCH processing was
+ * requested, and compiler chose not to create a PCH file.
+ * NVRTC_ERROR_PCH_CREATE_HEAP_EXHAUSTED indicates that a PCH file could 
+ * potentially have been created, but the compiler ran out space in the PCH
+ * heap. In this scenario, the nvrtcGetPCHHeapSizeRequired() can be used to 
+ * query the required heap size, the heap can be reallocated for this size with
+ * nvrtcSetPCHHeapSize() and PCH creation may be reattempted again invoking
+ * nvrtcCompileProgram() with a new NVRTC program instance.
+ * NVRTC_ERROR_PCH_CREATE indicates that an error condition prevented the 
+ * PCH file from being created. 
+ */
+nvrtcResult nvrtcGetPCHCreateStatus(nvrtcProgram prog);
+
+/**
+ * \ingroup precompiled_header
+ * \brief   retrieve the required size of the PCH heap required to compile
+ *          the given program.
+ *
+ * \param   [in] prog CUDA Runtime Compilation program.
+ * \param   [out] size pointer to location where the required size of the PCH Heap
+ *                will be stored
+ *
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ * The size retrieved using this function is only valid if nvrtcGetPCHCreateStatus()
+ * returned NVRTC_SUCCESS or NVRTC_ERROR_PCH_CREATE_HEAP_EXHAUSTED
+ */
+nvrtcResult nvrtcGetPCHHeapSizeRequired(nvrtcProgram prog, size_t* size);
+
+/**
+ * \ingroup compilation
+ * \brief   nvrtcSetFlowCallback registers a callback function that the compiler 
+ *          will invoke at different points during a call to nvrtcCompileProgram, 
+ *          and the callback function can decide whether to cancel compilation by
+ *          returning specific values.
+ * 
+ * The callback function must satisfy the following constraints:
+ * 
+ * (1) Its signature should be:
+ *     @code
+ *     int callback(void* param1, void* param2);
+ *     @endcode
+ *     When invoking the callback, the compiler will always pass \p payload to 
+ *     param1 so that the callback may make decisions based on \p payload . It'll
+ *     always pass NULL to param2 for now which is reserved for future extensions.
+ * 
+ * (2) It must return 1 to cancel compilation or 0 to continue. 
+ *     Other return values are reserved for future use. 
+ * 
+ * (3) It must return consistent values. Once it returns 1 at one point, it must
+ *     return 1 in all following invocations during the current nvrtcCompileProgram
+ *     call in progress.
+ * 
+ * (4) It must be thread-safe.
+ * 
+ * (5) It must not invoke any nvrtc/libnvvm/ptx APIs.
+ *  
+ * \param   [in] prog CUDA Runtime Compilation program.
+ * \param   [in] callback the callback that issues cancellation signal.
+ * \param   [in] payload to be passed as a parameter when invoking the callback.
+ * \return
+ *   - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_PROGRAM \endlink
+ *   - \link #nvrtcResult NVRTC_ERROR_INVALID_INPUT \endlink
+ */
+nvrtcResult nvrtcSetFlowCallback(nvrtcProgram prog, int (*callback)(void*, void*), void *payload);
+
+/**
+ * \defgroup options Supported Compile Options
+ *
+ * NVRTC supports the compile options below.
+ * Option names with two preceding dashs (\c --) are long option names and
+ * option names with one preceding dash (\c -) are short option names.
+ * Short option names can be used instead of long option names.
+ * When a compile option takes an argument, an assignment operator (\c =)
+ * is used to separate the compile option argument from the compile option
+ * name, e.g., \c "--gpu-architecture=compute_60".
+ * Alternatively, the compile option name and the argument can be specified in
+ * separate strings without an assignment operator, .e.g,
+ * \c "--gpu-architecture" \c "compute_60".
+ * Single-character short option names, such as \c -D, \c -U, and \c -I, do
+ * not require an assignment operator, and the compile option name and the
+ * argument can be present in the same string with or without spaces between
+ * them.
+ * For instance, \c "-D=<def>", \c "-D<def>", and \c "-D <def>" are all
+ * supported.
+ *
+ * The valid compiler options are:
+ *
+ *   - Compilation targets
+ *     - \c --gpu-architecture=\<arch\> (\c -arch)
+ * 
+ *       Specify the name of the class of GPU architectures for which the
+ *       input must be compiled.\n
+ *       - Valid <c>\<arch\></c>s:
+ *         - \c compute_50
+ *         - \c compute_52
+ *         - \c compute_53
+ *         - \c compute_60
+ *         - \c compute_61
+ *         - \c compute_62
+ *         - \c compute_70
+ *         - \c compute_72
+ *         - \c compute_75
+ *         - \c compute_80
+ *         - \c compute_87
+ *         - \c compute_89
+ *         - \c compute_90
+ *         - \c compute_90a
+ *         - \c compute_100
+ *         - \c compute_100a 
+ *         - \c sm_50
+ *         - \c sm_52
+ *         - \c sm_53
+ *         - \c sm_60
+ *         - \c sm_61
+ *         - \c sm_62
+ *         - \c sm_70
+ *         - \c sm_72
+ *         - \c sm_75
+ *         - \c sm_80
+ *         - \c sm_87
+ *         - \c sm_89
+ *         - \c sm_90
+ *         - \c sm_90a
+ *         - \c sm_100
+ *         - \c sm_100a
+ *       - Default: \c compute_52
+ *   - Separate compilation / whole-program compilation
+ *     - \c --device-c (\c -dc)
+ *
+ *       Generate relocatable code that can be linked with other relocatable
+ *       device code.  It is equivalent to \c --relocatable-device-code=true.
+ *     - \c --device-w (\c -dw)
+ * 
+ *       Generate non-relocatable code.  It is equivalent to \c --relocatable-device-code=false.
+ *     - \c --relocatable-device-code={true|false} (\c -rdc)
+ * 
+ *       Enable (disable) the generation of relocatable device code.
+ *       - Default: \c false
+ *     - \c --extensible-whole-program (\c -ewp)
+ * 
+ *       Do extensible whole program compilation of device code.
+ *       - Default: \c false
+ *   - Debugging support
+ *     - \c --device-debug (\c -G)
+ * 
+ *       Generate debug information. If \c --dopt is not specified, then turns off all optimizations.
+ *     - \c --generate-line-info (\c -lineinfo)
+ * 
+ *       Generate line-number information.
+ *   - Code generation
+ *     - \c --dopt \c on (\c -dopt)
+ *
+ *     - \c --dopt=on
+ * 
+ *       Enable device code optimization. When specified along with \c -G, enables
+ *       limited debug information generation for optimized device code (currently,
+ *       only line number information).  When \c -G is not specified, \c -dopt=on is implicit.
+ * 
+ *     - \c --ptxas-options \<options\> (\c -Xptxas)
+ * 
+ *     - \c --ptxas-options=\<options\>
+ * 
+ *       Specify options directly to ptxas, the PTX optimizing assembler.
+ *     - \c --maxrregcount=\<N\> (\c -maxrregcount)
+ * 
+ *       Specify the maximum amount of registers that GPU functions can use.
+ *       Until a function-specific limit, a higher value will generally
+ *       increase the performance of individual GPU threads that execute this
+ *       function.  However, because thread registers are allocated from a
+ *       global register pool on each GPU, a higher value of this option will
+ *       also reduce the maximum thread block size, thereby reducing the amount
+ *       of thread parallelism.  Hence, a good maxrregcount value is the result
+ *       of a trade-off.  If this option is not specified, then no maximum is
+ *       assumed.  Value less than the minimum registers required by ABI will
+ *       be bumped up by the compiler to ABI minimum limit.
+ * 
+ *     - \c --ftz={true|false} (\c -ftz)
+ *  
+ *       When performing single-precision floating-point operations, flush
+ *       denormal values to zero or preserve denormal values.
+ * 
+ *       \c --use_fast_math implies \c --ftz=true.
+ *       - Default: \c false
+ * 
+ *     - \c --prec-sqrt={true|false} (\c -prec-sqrt)
+ * 
+ *       For single-precision floating-point square root, use IEEE
+ *       round-to-nearest mode or use a faster approximation.
+ *       \c --use_fast_math implies \c --prec-sqrt=false.
+ *       - Default: \c true
+ * 
+ *     - \c --prec-div={true|false} (\c -prec-div)
+ *       For single-precision floating-point division and reciprocals, use IEEE
+ *       round-to-nearest mode or use a faster approximation. 
+ *       \c --use_fast_math implies \c --prec-div=false.
+ *       - Default: \c true
+ *  
+ *     - \c --fmad={true|false} (\c -fmad)
+ * 
+ *       Enables (disables) the contraction of floating-point multiplies and
+ *       adds/subtracts into floating-point multiply-add operations (FMAD,
+ *       FFMA, or DFMA).  \c --use_fast_math implies \c --fmad=true.
+ *       - Default: \c true
+ * 
+ *     - \c --use_fast_math (\c -use_fast_math)
+ * 
+ *       Make use of fast math operations.
+ *       \c --use_fast_math implies \c --ftz=true \c --prec-div=false
+ *       \c --prec-sqrt=false \c --fmad=true.
+ * 
+ *     - \c --extra-device-vectorization (\c -extra-device-vectorization)
+ * 
+ *       Enables more aggressive device code vectorization in the NVVM optimizer.
+ * 
+ *     - \c --modify-stack-limit={true|false} (\c -modify-stack-limit)
+ * 
+ *       On Linux, during compilation, use \c setrlimit() to increase stack size 
+ *       to maximum allowed. The limit is reset to the previous value at the
+ *       end of compilation.
+ *       Note: \c setrlimit() changes the value for the entire process.
+ *       - Default: \c true
+ * 
+ *     - \c --dlink-time-opt (\c -dlto)
+ * 
+ *       Generate intermediate code for later link-time optimization.
+ *       It implies \c -rdc=true. 
+ *       Note: when this option is used the \c nvrtcGetLTOIR API should be used, 
+ *       as PTX or Cubin will not be generated.
+ * 
+ *     - \c --gen-opt-lto (\c -gen-opt-lto)
+ * 
+ *       Run the optimizer passes before generating the LTO IR.
+ *
+ *     - \c --optix-ir (\c -optix-ir)
+ * 
+ *       Generate OptiX IR. The Optix IR is only intended for consumption by OptiX
+ *       through appropriate APIs. This feature is not supported with 
+ *       link-time-optimization (\c -dlto).
+ * 
+ *       Note: when this option is used the nvrtcGetOptiX API should be used, 
+ *       as PTX or Cubin will not be generated.
+ * 
+ *     - \c --jump-table-density=[0-101] (\c -jtd)
+ * 
+ *       Specify the case density percentage in switch statements, and use it as
+ *       a minimal threshold to determine whether jump table(brx.idx instruction)
+ *       will be used to implement a switch statement. Default value is 101. The
+ *       percentage ranges from 0 to 101 inclusively.
+ * 
+ *     - \c --device-stack-protector={true|false} (\c -device-stack-protector)
+ * 
+ *       Enable (disable) the generation of stack canaries in device code.
+ * 
+ *       - Default: \c false
+ * 
+ *   - Preprocessing
+ *     - \c --define-macro=\<def\> (\c -D)
+ * 
+ *       \c \<def\> can be either \c \<name\> or \c \<name=definitions\>.
+ *       - \c \<name\>
+ * 
+ *         Predefine \c \<name\> as a macro with definition \c 1.
+ *       - \c \<name\>=\<definition\>
+ * 
+ *         The contents of \c \<definition\> are tokenized and preprocessed
+ *         as if they appeared during translation phase three in a \c \#define
+ *         directive.  In particular, the definition will be truncated by
+ *         embedded new line characters.
+ * 
+ *     - \c --undefine-macro=\<def\> (\c -U)
+ * 
+ *       Cancel any previous definition of \c \<def\>.
+ * 
+ *     - \c --include-path=\<dir\> (\c -I)
+ * 
+ *       Add the directory \c \<dir\> to the list of directories to be
+ *       searched for headers.  These paths are searched after the list of
+ *       headers given to ::nvrtcCreateProgram.
+ * 
+ *     - \c --pre-include=\<header\> (\c -include)
+ * 
+ *       Preinclude \c \<header\> during preprocessing.
+ * 
+ *     - \c --no-source-include (\c -no-source-include)
+ * 
+ *       The preprocessor by default adds the directory of each input sources
+ *       to the include path. This option disables this feature and only
+ *       considers the path specified explicitly.
+ *
+ *   - Language Dialect
+ *     - \c --std={c++03|c++11|c++14|c++17|c++20} (\c -std)
+ * 
+ *       Set language dialect to C++03, C++11, C++14, C++17 or C++20
+ *       - Default: \c c++17
+ * 
+ *     - \c --builtin-move-forward={true|false} (\c -builtin-move-forward)
+ * 
+ *       Provide builtin definitions of \c std::move and \c std::forward,
+ *       when C++11 or later language dialect is selected.
+ *       - Default: \c true
+ * 
+ *     - \c --builtin-initializer-list={true|false}
+ *       (\c -builtin-initializer-list)
+ * 
+ *       Provide builtin definitions of \c std::initializer_list class and
+ *       member functions when C++11 or later language dialect is selected.
+ *       - Default: \c true
+ * 
+ *   - Precompiled header support (CUDA 12.8+)
+ *     - \c --pch (\c -pch)
+ * 
+ *       Enable automatic PCH processing.
+ * 
+ *     - \c --create-pch=<file-name> (\c -create-pch)
+ * 
+ *       Create a PCH file.
+ * 
+ *     - \c --use-pch=<file-name> (\c -use-pch)
+ * 
+ *       Use the specified PCH file.
+ * 
+ *     - \c --pch-dir=<directory-name> (\c -pch-dir)
+ * 
+ *       When using automatic PCH (\c -pch), look for and create PCH files in the 
+ *       specified directory. When using explicit PCH (\c -create-pch or \c -use-pch),
+ *       the directory name is prefixed before the specified file name, unless
+ *       the file name is an absolute path name. 
+ * 
+ *     - \c --pch-verbose={true|false} (\c -pch-verbose)
+ * 
+ *       In automatic PCH mode, for each PCH file that could not be used in current
+ *       compilation, print the reason in the compilation log.
+ *       - Default: \c true 
+ * 
+ *     - \c --pch-messages={true|false} (\c -pch-messages)
+ * 
+ *       Print a message in the compilation log, if a PCH file was created or used
+ *       in the current compilation.
+ *       - Default: \c true 
+ * 
+ *     - \c --instantiate-templates-in-pch={true|false} (\c -instantiate-templates-in-pch)
+ * 
+ *       Enable or disable instantiatiation of templates before PCH creation. Instantiating
+ *       templates may increase the size of the PCH file, while reducing the compilation 
+ *       cost when using the PCH file (since some template instantiations can be skipped).
+ *       - Default: \c true 
+ * 
+ *   - Misc.
+ *     - \c --disable-warnings (\c -w)
+ * 
+ *       Inhibit all warning messages.
+ *
+ *     - \c --restrict (\c -restrict)
+ * 
+ *       Programmer assertion that all kernel pointer parameters are restrict
+ *       pointers.
+ * 
+ *     - \c --device-as-default-execution-space
+ *       (\c -default-device)
+ * 
+ *       Treat entities with no execution space annotation as \c __device__
+ *       entities.
+ * 
+ *     - \c --device-int128 (\c -device-int128)
+ * 
+ *       Allow the \c __int128 type in device code. Also causes the macro \c __CUDACC_RTC_INT128__
+ *       to be defined.
+ * 
+ *     - \c --device-float128 (\c -device-float128)
+ * 
+ *       Allow the \c __float128 and \c _Float128 types in device code. Also
+ *       causes the macro \c D__CUDACC_RTC_FLOAT128__ to be defined.
+ * 
+ *     - \c --optimization-info=\<kind\> (\c -opt-info)
+ * 
+ *       Provide optimization reports for the specified kind of optimization.
+ *       The following kind tags are supported:
+ *         - \c inline : emit a remark when a function is inlined.
+ *
+ *     - \c --display-error-number (\c -err-no)
+ * 
+ *       Display diagnostic number for warning messages. (Default)
+ * 
+ *     - \c --no-display-error-number (\c -no-err-no)
+ * 
+ *       Disables the display of a diagnostic number for warning messages.
+ * 
+ *     - \c --diag-error=<error-number>,... (\c -diag-error)
+ * 
+ *       Emit error for specified diagnostic message number(s). Message numbers can be separated by comma.
+ * 
+ *     - \c --diag-suppress=<error-number>,... (\c -diag-suppress)
+ * 
+ *       Suppress specified diagnostic message number(s). Message numbers can be separated by comma.
+ * 
+ *     - \c --diag-warn=<error-number>,... (\c -diag-warn)
+ * 
+ *       Emit warning for specified diagnostic message number(s). Message numbers can be separated by comma.
+ * 
+ *     - \c --brief-diagnostics={true|false}  (\c -brief-diag)
+ * 
+ *       This option disables or enables showing source line and column info 
+ *       in a diagnostic.
+ *       The \c --brief-diagnostics=true will not show the source line and column info.
+ *       - Default: \c false
+ * 
+ *     - \c --time=<file-name> (\c -time)
+ * 
+ *        Generate a comma separated value table with the time taken by each compilation
+ *        phase, and append it at the end of the file given as the option argument.
+ *       If the file does not exist, the column headings are generated in the first row
+ *       of the table. If the file name is '-', the timing data is written to the compilation log.
+ * 
+ *     - \c --split-compile=<number-of-threads> (\c -split-compile=<number-of-threads>)
+ * 
+ *       Perform compiler optimizations in parallel.
+ *       Split compilation attempts to reduce compile time by enabling the compiler to run certain
+ *       optimization passes concurrently. This option accepts a numerical value that specifies the
+ *       maximum number of threads the compiler can use. One can also allow the compiler to use the maximum
+ *       threads available on the system by setting \c --split-compile=0.
+ *       Setting \c --split-compile=1 will cause this option to be ignored.
+ * 
+ *     - \c --fdevice-syntax-only (\c -fdevice-syntax-only)
+ * 
+ *       Ends device compilation after front-end syntax checking. This option does not generate valid
+ *       device code.
+ *
+ *     - \c --minimal  (\c -minimal)
+ * 
+ *        Omit certain language features to reduce compile time for small programs. 
+ *        In particular, the following are omitted: 
+ *            - Texture and surface functions and associated types, e.g., \c cudaTextureObject_t.
+ *            - CUDA Runtime Functions that are provided by the cudadevrt device code library, 
+ *              typically named with prefix "cuda", e.g., \c cudaMalloc.
+ *            - Kernel launch from device code.
+ *            - Types and macros associated with CUDA Runtime and Driver APIs, 
+ *              provided by \c cuda/tools/cudart/driver_types.h, typically named with prefix "cuda", e.g., \c cudaError_t.
+ * 
+ *     - \c --device-stack-protector (\c -device-stack-protector)
+ * 
+ *      Enable stack canaries in device code.
+ *      Stack canaries make it more difficult to exploit certain types of memory safety bugs involving
+ *      stack-local variables. The compiler uses heuristics to assess the risk of such a bug in each function.
+ *      Only those functions which are deemed high-risk make use of a stack canary.
+ * 
+ *     - \c --fdevice-time-trace=<file-name> (\c -fdevice-time-trace=<file-name>)
+ *      Enables the time profiler, outputting a JSON file based on given <file-name>. Results can be analyzed on
+ *      chrome://tracing for a flamegraph visualization.
+ * 
+ */
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+
+/* The utility function 'nvrtcGetTypeName' is not available by default. Define
+   the macro 'NVRTC_GET_TYPE_NAME' to a non-zero value to make it available.
+*/
+   
+#if NVRTC_GET_TYPE_NAME || __DOXYGEN_ONLY__
+
+#if NVRTC_USE_CXXABI || __clang__ || __GNUC__ || __DOXYGEN_ONLY__
+#include <cxxabi.h>
+#include <cstdlib>
+
+#elif defined(_WIN32)
+#include <Windows.h>
+#include <DbgHelp.h>
+#endif /* NVRTC_USE_CXXABI || __clang__ || __GNUC__ */
+
+
+#include <string>
+#include <typeinfo>
+
+template <typename T> struct __nvrtcGetTypeName_helper_t { };
+
+/*************************************************************************//**
+ *
+ * \defgroup hosthelper Host Helper
+ *
+ * NVRTC defines the following functions for easier interaction with host code.
+ *
+ ****************************************************************************/
+
+/**
+ * \ingroup hosthelper
+ * \brief   nvrtcGetTypeName stores the source level name of a type in the given 
+ *          std::string location. 
+ *
+ * This function is only provided when the macro NVRTC_GET_TYPE_NAME is
+ * defined with a non-zero value. It uses abi::__cxa_demangle or UnDecorateSymbolName
+ * function calls to extract the type name, when using gcc/clang or cl.exe compilers,
+ * respectively. If the name extraction fails, it will return NVRTC_INTERNAL_ERROR,
+ * otherwise *result is initialized with the extracted name.
+ * 
+ * Windows-specific notes:
+ * - nvrtcGetTypeName() is not multi-thread safe because it calls UnDecorateSymbolName(), 
+ *   which is not multi-thread safe.
+ * - The returned string may contain Microsoft-specific keywords such as __ptr64 and __cdecl.
+ *
+ * \param   [in] tinfo: reference to object of type std::type_info for a given type.
+ * \param   [in] result: pointer to std::string in which to store the type name.
+ * \return
+ *  - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *  - \link #nvrtcResult NVRTC_ERROR_INTERNAL_ERROR \endlink
+ *
+ */
+inline nvrtcResult nvrtcGetTypeName(const std::type_info &tinfo, std::string *result)
+{
+#if USE_CXXABI || __clang__ || __GNUC__
+  const char *name = tinfo.name();
+  int status;
+  char *undecorated_name = abi::__cxa_demangle(name, 0, 0, &status);
+  if (status == 0) {
+    *result = undecorated_name;
+    free(undecorated_name);
+    return NVRTC_SUCCESS;
+  }
+#elif defined(_WIN32)
+  const char *name = tinfo.raw_name();
+  if (!name || *name != '.') {
+    return NVRTC_ERROR_INTERNAL_ERROR;
+  }
+  char undecorated_name[4096];
+  //name+1 skips over the '.' prefix
+  if(UnDecorateSymbolName(name+1, undecorated_name,
+                          sizeof(undecorated_name) / sizeof(*undecorated_name),
+                           //note: doesn't seem to work correctly without UNDNAME_NO_ARGUMENTS.
+                           UNDNAME_NO_ARGUMENTS | UNDNAME_NAME_ONLY ) ) {
+    *result = undecorated_name;
+    return NVRTC_SUCCESS;
+  }
+#endif  /* USE_CXXABI || __clang__ || __GNUC__ */
+
+  return NVRTC_ERROR_INTERNAL_ERROR;
+}
+
+/**
+ * \ingroup hosthelper
+ * \brief   nvrtcGetTypeName stores the source level name of the template type argument
+ *          T in the given std::string location.
+ *
+ * This function is only provided when the macro NVRTC_GET_TYPE_NAME is
+ * defined with a non-zero value. It uses abi::__cxa_demangle or UnDecorateSymbolName
+ * function calls to extract the type name, when using gcc/clang or cl.exe compilers,
+ * respectively. If the name extraction fails, it will return NVRTC_INTERNAL_ERROR,
+ * otherwise *result is initialized with the extracted name.
+ * 
+ * Windows-specific notes:
+ * - nvrtcGetTypeName() is not multi-thread safe because it calls UnDecorateSymbolName(), 
+ *   which is not multi-thread safe.
+ * - The returned string may contain Microsoft-specific keywords such as __ptr64 and __cdecl.
+ *
+ * \param   [in] result: pointer to std::string in which to store the type name.
+ * \return
+ *  - \link #nvrtcResult NVRTC_SUCCESS \endlink
+ *  - \link #nvrtcResult NVRTC_ERROR_INTERNAL_ERROR \endlink
+ *
+ */
+ 
+template <typename T>
+nvrtcResult nvrtcGetTypeName(std::string *result)
+{
+  nvrtcResult res = nvrtcGetTypeName(typeid(__nvrtcGetTypeName_helper_t<T>), 
+                                     result);
+  if (res != NVRTC_SUCCESS) 
+    return res;
+
+  std::string repr = *result;
+  std::size_t idx = repr.find("__nvrtcGetTypeName_helper_t");
+  idx = (idx != std::string::npos) ? repr.find("<", idx) : idx;
+  std::size_t last_idx = repr.find_last_of('>');
+  if (idx == std::string::npos || last_idx == std::string::npos) {
+    return NVRTC_ERROR_INTERNAL_ERROR;
+  }
+  ++idx;
+  *result = repr.substr(idx, last_idx - idx);
+  return NVRTC_SUCCESS;
+}
+
+#endif  /* NVRTC_GET_TYPE_NAME */
+
+#endif /* __NVRTC_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc-builtins.alt.so.12.8 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc-builtins.alt.so.12.8
new file mode 100644
index 0000000000000000000000000000000000000000..42c9ab575e23eb0427a5262923021b25afbc0a1b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc-builtins.alt.so.12.8
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e971f2dcced2628f327ddb037d02c41b834f2c3dbfbff03c9d2c3177f1b9b6d
+size 6309832
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/builtin_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/builtin_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..5247c40807f0dd36a886513ab1bff5d2977364db
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/builtin_types.h
@@ -0,0 +1,64 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "device_types.h"
+#if !defined(__CUDACC_RTC__)
+#define EXCLUDE_FROM_RTC
+#include "driver_types.h"
+#undef EXCLUDE_FROM_RTC
+#endif /* !__CUDACC_RTC__ */
+#include "surface_types.h"
+#include "texture_types.h"
+#include "vector_types.h"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/channel_descriptor.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/channel_descriptor.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4fba89435ec69efeddaaaacfe2b6e2f4144dd34
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/channel_descriptor.h
@@ -0,0 +1,597 @@
+/*
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CHANNEL_DESCRIPTOR_H__)
+#define __CHANNEL_DESCRIPTOR_H__
+
+#if defined(__cplusplus)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+/**
+ * \addtogroup CUDART_HIGHLEVEL
+ *
+ * @{
+ */
+
+/**
+ * \brief \hl Returns a channel descriptor using the specified format
+ *
+ * Returns a channel descriptor with format \p f and number of bits of each
+ * component \p x, \p y, \p z, and \p w.  The ::cudaChannelFormatDesc is
+ * defined as:
+ * \code
+  struct cudaChannelFormatDesc {
+    int x, y, z, w;
+    enum cudaChannelFormatKind f;
+  };
+ * \endcode
+ *
+ * where ::cudaChannelFormatKind is one of ::cudaChannelFormatKindSigned,
+ * ::cudaChannelFormatKindUnsigned, cudaChannelFormatKindFloat,
+ * ::cudaChannelFormatKindSignedNormalized8X1, ::cudaChannelFormatKindSignedNormalized8X2,
+ * ::cudaChannelFormatKindSignedNormalized8X4,
+ * ::cudaChannelFormatKindUnsignedNormalized8X1, ::cudaChannelFormatKindUnsignedNormalized8X2,
+ * ::cudaChannelFormatKindUnsignedNormalized8X4,
+ * ::cudaChannelFormatKindSignedNormalized16X1, ::cudaChannelFormatKindSignedNormalized16X2,
+ * ::cudaChannelFormatKindSignedNormalized16X4,
+ * ::cudaChannelFormatKindUnsignedNormalized16X1, ::cudaChannelFormatKindUnsignedNormalized16X2,
+ * ::cudaChannelFormatKindUnsignedNormalized16X4, 
+ * ::cudaChannelFormatKindUnsignedNormalized1010102
+ * or ::cudaChannelFormatKindNV12.
+ *
+ * The format is specified by the template specialization.
+ *
+ * The template function specializes for the following scalar types:
+ * char, signed char, unsigned char, short, unsigned short, int, unsigned int, long, unsigned long, and float.
+ * The template function specializes for the following vector types:
+ * char{1|2|4}, uchar{1|2|4}, short{1|2|4}, ushort{1|2|4}, int{1|2|4}, uint{1|2|4}, long{1|2|4}, ulong{1|2|4}, float{1|2|4}.
+ * The template function specializes for following cudaChannelFormatKind enum values:
+ * ::cudaChannelFormatKind{Uns|S}ignedNormalized{8|16}X{1|2|4}, 
+ * ::cudaChannelFormatKindUnsignedNormalized1010102
+ * and ::cudaChannelFormatKindNV12.
+ *
+ * Invoking the function on a type without a specialization defaults to creating a channel format of kind ::cudaChannelFormatKindNone
+ *
+ * \return
+ * Channel descriptor with format \p f
+ *
+ * \sa \ref ::cudaCreateChannelDesc(int,int,int,int,cudaChannelFormatKind) "cudaCreateChannelDesc (Low level)",
+ * ::cudaGetChannelDesc, 
+ */
+template<class T> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc(void)
+{
+  return cudaCreateChannelDesc(0, 0, 0, 0, cudaChannelFormatKindNone);
+}
+
+static __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDescHalf(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindFloat);
+}
+
+static __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDescHalf1(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindFloat);
+}
+
+static __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDescHalf2(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindFloat);
+}
+
+static __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDescHalf4(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindFloat);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<char>(void)
+{
+  int e = (int)sizeof(char) * 8;
+
+#if defined(_CHAR_UNSIGNED) || defined(__CHAR_UNSIGNED__)
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+#else /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+#endif /* _CHAR_UNSIGNED || __CHAR_UNSIGNED__ */
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<signed char>(void)
+{
+  int e = (int)sizeof(signed char) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<unsigned char>(void)
+{
+  int e = (int)sizeof(unsigned char) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<char1>(void)
+{
+  int e = (int)sizeof(signed char) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<uchar1>(void)
+{
+  int e = (int)sizeof(unsigned char) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<char2>(void)
+{
+  int e = (int)sizeof(signed char) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<uchar2>(void)
+{
+  int e = (int)sizeof(unsigned char) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<char4>(void)
+{
+  int e = (int)sizeof(signed char) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<uchar4>(void)
+{
+  int e = (int)sizeof(unsigned char) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<short>(void)
+{
+  int e = (int)sizeof(short) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<unsigned short>(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<short1>(void)
+{
+  int e = (int)sizeof(short) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<ushort1>(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<short2>(void)
+{
+  int e = (int)sizeof(short) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<ushort2>(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<short4>(void)
+{
+  int e = (int)sizeof(short) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<ushort4>(void)
+{
+  int e = (int)sizeof(unsigned short) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<int>(void)
+{
+  int e = (int)sizeof(int) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<unsigned int>(void)
+{
+  int e = (int)sizeof(unsigned int) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<int1>(void)
+{
+  int e = (int)sizeof(int) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<uint1>(void)
+{
+  int e = (int)sizeof(unsigned int) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<int2>(void)
+{
+  int e = (int)sizeof(int) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<uint2>(void)
+{
+  int e = (int)sizeof(unsigned int) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<int4>(void)
+{
+  int e = (int)sizeof(int) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<uint4>(void)
+{
+  int e = (int)sizeof(unsigned int) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindUnsigned);
+}
+
+#if !defined(__LP64__)
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<long>(void)
+{
+  int e = (int)sizeof(long) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<unsigned long>(void)
+{
+  int e = (int)sizeof(unsigned long) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<long1>(void)
+{
+  int e = (int)sizeof(long) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<ulong1>(void)
+{
+  int e = (int)sizeof(unsigned long) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<long2>(void)
+{
+  int e = (int)sizeof(long) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<ulong2>(void)
+{
+  int e = (int)sizeof(unsigned long) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindUnsigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<long4>(void)
+{
+  int e = (int)sizeof(long) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindSigned);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<ulong4>(void)
+{
+  int e = (int)sizeof(unsigned long) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindUnsigned);
+}
+
+#endif /* !__LP64__ */
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<float>(void)
+{
+  int e = (int)sizeof(float) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindFloat);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<float1>(void)
+{
+  int e = (int)sizeof(float) * 8;
+
+  return cudaCreateChannelDesc(e, 0, 0, 0, cudaChannelFormatKindFloat);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<float2>(void)
+{
+  int e = (int)sizeof(float) * 8;
+
+  return cudaCreateChannelDesc(e, e, 0, 0, cudaChannelFormatKindFloat);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<float4>(void)
+{
+  int e = (int)sizeof(float) * 8;
+
+  return cudaCreateChannelDesc(e, e, e, e, cudaChannelFormatKindFloat);
+}
+
+static __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDescNV12(void)
+{
+    int e = (int)sizeof(char) * 8;
+
+    return cudaCreateChannelDesc(e, e, e, 0, cudaChannelFormatKindNV12);
+}
+
+template<cudaChannelFormatKind> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc(void)
+{
+    return cudaCreateChannelDesc(0, 0, 0, 0, cudaChannelFormatKindNone);
+}
+
+/* Signed 8-bit normalized integer formats */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedNormalized8X1>(void)
+{
+    return cudaCreateChannelDesc(8, 0, 0, 0, cudaChannelFormatKindSignedNormalized8X1);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedNormalized8X2>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 0, 0, cudaChannelFormatKindSignedNormalized8X2);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedNormalized8X4>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindSignedNormalized8X4);
+}
+
+/* Unsigned 8-bit normalized integer formats */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized8X1>(void)
+{
+    return cudaCreateChannelDesc(8, 0, 0, 0, cudaChannelFormatKindUnsignedNormalized8X1);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized8X2>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 0, 0, cudaChannelFormatKindUnsignedNormalized8X2);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized8X4>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedNormalized8X4);
+}
+
+/* Signed 16-bit normalized integer formats */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedNormalized16X1>(void)
+{
+    return cudaCreateChannelDesc(16, 0, 0, 0, cudaChannelFormatKindSignedNormalized16X1);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedNormalized16X2>(void)
+{
+    return cudaCreateChannelDesc(16, 16, 0, 0, cudaChannelFormatKindSignedNormalized16X2);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedNormalized16X4>(void)
+{
+    return cudaCreateChannelDesc(16, 16, 16, 16, cudaChannelFormatKindSignedNormalized16X4);
+}
+
+/* Unsigned 16-bit normalized integer formats */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized16X1>(void)
+{
+    return cudaCreateChannelDesc(16, 0, 0, 0, cudaChannelFormatKindUnsignedNormalized16X1);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized16X2>(void)
+{
+    return cudaCreateChannelDesc(16, 16, 0, 0, cudaChannelFormatKindUnsignedNormalized16X2);
+}
+
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized16X4>(void)
+{
+    return cudaCreateChannelDesc(16, 16, 16, 16, cudaChannelFormatKindUnsignedNormalized16X4);
+}
+
+/* NV12 format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindNV12>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 0, cudaChannelFormatKindNV12);
+}
+
+/* Int101010 format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedNormalized1010102>(void)
+{
+    return cudaCreateChannelDesc(10, 10, 10, 2, cudaChannelFormatKindUnsignedNormalized1010102);
+}
+
+/* BC1 format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed1>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed1);
+}
+
+/* BC1sRGB format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed1SRGB>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed1SRGB);
+}
+
+/* BC2 format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed2>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed2);
+}
+
+/* BC2sRGB format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed2SRGB>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed2SRGB);
+}
+
+/* BC3 format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed3>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed3);
+}
+
+/* BC3sRGB format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed3SRGB>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed3SRGB);
+}
+
+/* BC4 unsigned format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed4>(void)
+{
+    return cudaCreateChannelDesc(8, 0, 0, 0, cudaChannelFormatKindUnsignedBlockCompressed4);
+}
+
+/* BC4 signed format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedBlockCompressed4>(void)
+{
+    return cudaCreateChannelDesc(8, 0, 0, 0, cudaChannelFormatKindSignedBlockCompressed4);
+}
+
+/* BC5 unsigned format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed5>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 0, 0, cudaChannelFormatKindUnsignedBlockCompressed5);
+}
+
+/* BC5 signed format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedBlockCompressed5>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 0, 0, cudaChannelFormatKindSignedBlockCompressed5);
+}
+
+/* BC6H unsigned format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed6H>(void)
+{
+    return cudaCreateChannelDesc(16, 16, 16, 0, cudaChannelFormatKindUnsignedBlockCompressed6H);
+}
+
+/* BC6H signed format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindSignedBlockCompressed6H>(void)
+{
+    return cudaCreateChannelDesc(16, 16, 16, 0, cudaChannelFormatKindSignedBlockCompressed6H);
+}
+
+/* BC7 format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed7>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed7);
+}
+
+/* BC7sRGB format */
+template<> __inline__ __host__ cudaChannelFormatDesc cudaCreateChannelDesc<cudaChannelFormatKindUnsignedBlockCompressed7SRGB>(void)
+{
+    return cudaCreateChannelDesc(8, 8, 8, 8, cudaChannelFormatKindUnsignedBlockCompressed7SRGB);
+}
+
+#endif /* __cplusplus */
+
+/** @} */
+/** @} */ /* END CUDART_TEXTURE_HL */
+
+#endif /* !__CHANNEL_DESCRIPTOR_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/common_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/common_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..5f8ea3d242640f2196b789c7da6c05d2ed1bed3e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/common_functions.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#if defined(_MSC_VER)
+#pragma message("common_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.")
+#else
+#warning "common_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead."
+#endif
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_COMMON_FUNCTIONS_H_WRAPPER__
+#endif
+
+#include "crt/common_functions.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_COMMON_FUNCTIONS_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_COMMON_FUNCTIONS_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups.h
new file mode 100644
index 0000000000000000000000000000000000000000..0532a97bbaba37b6aa8540426d9d89adef6f4612
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups.h
@@ -0,0 +1,1743 @@
+/*
+ * Copyright 1993-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _COOPERATIVE_GROUPS_H_
+#define _COOPERATIVE_GROUPS_H_
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#include "cooperative_groups/details/info.h"
+#include "cooperative_groups/details/driver_abi.h"
+#include "cooperative_groups/details/helpers.h"
+#include "cooperative_groups/details/memory.h"
+
+#if defined(_CG_HAS_STL_ATOMICS)
+#include <cuda/atomic>
+#define _CG_THREAD_SCOPE(scope) _CG_STATIC_CONST_DECL cuda::thread_scope thread_scope = scope;
+#else
+#define _CG_THREAD_SCOPE(scope)
+#endif
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+    _CG_CONST_DECL unsigned int coalesced_group_id = 1;
+    _CG_CONST_DECL unsigned int multi_grid_group_id = 2;
+    _CG_CONST_DECL unsigned int grid_group_id = 3;
+    _CG_CONST_DECL unsigned int thread_block_id = 4;
+    _CG_CONST_DECL unsigned int multi_tile_group_id = 5;
+    _CG_CONST_DECL unsigned int cluster_group_id = 6;
+}
+
+/**
+ * class thread_group;
+ *
+ * Generic thread group type, into which all groups are convertible.
+ * It acts as a container for all storage necessary for the derived groups,
+ * and will dispatch the API calls to the correct derived group. This means
+ * that all derived groups must implement the same interface as thread_group.
+ */
+class thread_group
+{
+protected:
+    struct group_data {
+        unsigned int _unused : 1;
+        unsigned int type : 7, : 0;
+    };
+
+    struct gg_data  {
+        details::grid_workspace *gridWs;
+    };
+
+#if defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+    struct mg_data  {
+        unsigned long long _unused : 1;
+        unsigned long long type    : 7;
+        unsigned long long handle  : 56;
+        const details::multi_grid::multi_grid_functions *functions;
+    };
+#endif
+
+    struct tg_data {
+        unsigned int is_tiled : 1;
+        unsigned int type : 7;
+        unsigned int size : 24;
+        // packed to 4b
+        unsigned int metaGroupSize : 16;
+        unsigned int metaGroupRank : 16;
+        // packed to 8b
+        unsigned int mask;
+        // packed to 12b
+        unsigned int _res;
+    };
+
+    friend _CG_QUALIFIER thread_group tiled_partition(const thread_group& parent, unsigned int tilesz);
+    friend class thread_block;
+
+    union __align__(8) {
+        group_data  group;
+        tg_data     coalesced;
+        gg_data     grid;
+#if defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+        mg_data     multi_grid;
+#endif
+    } _data;
+
+    _CG_QUALIFIER thread_group operator=(const thread_group& src);
+
+    _CG_QUALIFIER thread_group(unsigned int type) {
+        _data.group.type = type;
+        _data.group._unused = false;
+    }
+
+#ifdef _CG_CPP11_FEATURES
+    static_assert(sizeof(tg_data) <= 16, "Failed size check");
+    static_assert(sizeof(gg_data) <= 16, "Failed size check");
+#  ifdef _CG_ABI_EXPERIMENTAL
+    static_assert(sizeof(mg_data) <= 16, "Failed size check");
+#  endif
+#endif
+
+public:
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_device)
+
+    _CG_QUALIFIER unsigned long long size() const;
+    _CG_QUALIFIER unsigned long long num_threads() const;
+    _CG_QUALIFIER unsigned long long thread_rank() const;
+    _CG_QUALIFIER void sync() const;
+    _CG_QUALIFIER unsigned int get_type() const {
+        return _data.group.type;
+    }
+
+};
+
+template <unsigned int TyId>
+struct thread_group_base : public thread_group {
+    _CG_QUALIFIER thread_group_base() : thread_group(TyId) {}
+    _CG_STATIC_CONST_DECL unsigned int id = TyId;
+};
+
+#if defined(_CG_HAS_MULTI_GRID_GROUP)
+
+/**
+ * class multi_grid_group;
+ *
+ * Threads within this this group are guaranteed to be co-resident on the
+ * same system, on multiple devices within the same launched kernels.
+ * To use this group, the kernel must have been launched with
+ * cuLaunchCooperativeKernelMultiDevice (or the CUDA Runtime equivalent),
+ * and the device must support it (queryable device attribute).
+ *
+ * Constructed via this_multi_grid();
+ */
+
+
+# if defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+class multi_grid_group;
+
+// Multi grid group requires these functions to be templated to prevent ptxas from trying to use CG syscalls
+template <typename = void>
+__device__ _CG_DEPRECATED multi_grid_group this_multi_grid();
+
+class multi_grid_group : public thread_group_base<details::multi_grid_group_id>
+{
+private:
+    template <typename = void>
+    _CG_QUALIFIER multi_grid_group() {
+        _data.multi_grid.functions = details::multi_grid::load_grid_intrinsics();
+        _data.multi_grid.handle = _data.multi_grid.functions->get_intrinsic_handle();
+    }
+
+    friend multi_grid_group this_multi_grid<void>();
+
+public:
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_system)
+
+    _CG_QUALIFIER bool is_valid() const {
+        return (_data.multi_grid.handle != 0);
+    }
+
+    _CG_QUALIFIER void sync() const {
+        if (!is_valid()) {
+            _CG_ABORT();
+        }
+        _data.multi_grid.functions->sync(_data.multi_grid.handle);
+    }
+
+    _CG_QUALIFIER unsigned long long num_threads() const {
+        _CG_ASSERT(is_valid());
+        return _data.multi_grid.functions->size(_data.multi_grid.handle);
+    }
+
+    _CG_QUALIFIER unsigned long long size() const {
+        return num_threads();
+    }
+
+    _CG_QUALIFIER unsigned long long thread_rank() const {
+        _CG_ASSERT(is_valid());
+        return _data.multi_grid.functions->thread_rank(_data.multi_grid.handle);
+    }
+
+    _CG_QUALIFIER unsigned int grid_rank() const {
+        _CG_ASSERT(is_valid());
+        return (_data.multi_grid.functions->grid_rank(_data.multi_grid.handle));
+    }
+
+    _CG_QUALIFIER unsigned int num_grids() const {
+        _CG_ASSERT(is_valid());
+        return (_data.multi_grid.functions->num_grids(_data.multi_grid.handle));
+    }
+};
+# else
+class multi_grid_group
+{
+private:
+    unsigned long long _handle;
+    unsigned int _size;
+    unsigned int _rank;
+
+    friend _CG_QUALIFIER multi_grid_group this_multi_grid();
+
+    _CG_QUALIFIER multi_grid_group() {
+        _handle = details::multi_grid::get_intrinsic_handle();
+        _size = details::multi_grid::size(_handle);
+        _rank = details::multi_grid::thread_rank(_handle);
+    }
+
+public:
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_system)
+
+    _CG_QUALIFIER _CG_DEPRECATED bool is_valid() const {
+        return (_handle != 0);
+    }
+
+    _CG_QUALIFIER _CG_DEPRECATED void sync() const {
+        if (!is_valid()) {
+            _CG_ABORT();
+        }
+        details::multi_grid::sync(_handle);
+    }
+
+    _CG_QUALIFIER _CG_DEPRECATED unsigned long long num_threads() const {
+        _CG_ASSERT(is_valid());
+        return _size;
+    }
+
+    _CG_QUALIFIER _CG_DEPRECATED unsigned long long size() const {
+        return num_threads();
+    }
+
+    _CG_QUALIFIER _CG_DEPRECATED unsigned long long thread_rank() const {
+        _CG_ASSERT(is_valid());
+        return _rank;
+    }
+
+    _CG_QUALIFIER _CG_DEPRECATED unsigned int grid_rank() const {
+        _CG_ASSERT(is_valid());
+        return (details::multi_grid::grid_rank(_handle));
+    }
+
+    _CG_QUALIFIER _CG_DEPRECATED unsigned int num_grids() const {
+        _CG_ASSERT(is_valid());
+        return (details::multi_grid::num_grids(_handle));
+    }
+};
+# endif
+
+/**
+ * multi_grid_group this_multi_grid()
+ *
+ * Constructs a multi_grid_group
+ */
+# if defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+template <typename>
+__device__
+#else
+_CG_QUALIFIER
+# endif
+_CG_DEPRECATED
+multi_grid_group this_multi_grid()
+{
+    return multi_grid_group();
+}
+#endif
+
+/**
+ * class grid_group;
+ *
+ * Threads within this this group are guaranteed to be co-resident on the
+ * same device within the same launched kernel. To use this group, the kernel
+ * must have been launched with cuLaunchCooperativeKernel (or the CUDA Runtime equivalent),
+ * and the device must support it (queryable device attribute).
+ *
+ * Constructed via this_grid();
+ */
+class grid_group : public thread_group_base<details::grid_group_id>
+{
+    _CG_STATIC_CONST_DECL unsigned int _group_id = details::grid_group_id;
+    friend _CG_QUALIFIER grid_group this_grid();
+
+private:
+    _CG_QUALIFIER grid_group(details::grid_workspace *gridWs) {
+        _data.grid.gridWs = gridWs;
+    }
+
+ public:
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_device)
+
+    _CG_QUALIFIER bool is_valid() const {
+        return (_data.grid.gridWs != NULL);
+    }
+
+    _CG_QUALIFIER void sync() const {
+        if (!is_valid()) {
+            _CG_ABORT();
+        }
+        details::grid::sync(&_data.grid.gridWs->barrier);
+    }
+
+#if defined(_CG_CPP11_FEATURES)
+    using arrival_token = unsigned int;
+
+    _CG_QUALIFIER arrival_token barrier_arrive() const {
+        if (!is_valid()) {
+            _CG_ABORT();
+        }
+        return details::grid::barrier_arrive(&_data.grid.gridWs->barrier);
+    }
+
+    _CG_QUALIFIER void barrier_wait(arrival_token&& token) const {
+        details::grid::barrier_wait(token, &_data.grid.gridWs->barrier);
+    }
+#endif
+
+    _CG_STATIC_QUALIFIER unsigned long long size() {
+        return details::grid::size();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 group_dim() {
+        return details::grid::grid_dim();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 dim_threads() {
+        return details::grid::dim_threads();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned long long num_threads() {
+        return details::grid::num_threads();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 thread_index() {
+        return details::grid::thread_index();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned long long thread_rank() {
+        return details::grid::thread_rank();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 dim_blocks() {
+        return details::grid::dim_blocks();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned long long num_blocks() {
+        return details::grid::num_blocks();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 block_index() {
+        return details::grid::block_index();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned long long block_rank() {
+        return details::grid::block_rank();
+    }
+
+# if defined(_CG_HAS_CLUSTER_GROUP)
+    _CG_STATIC_QUALIFIER dim3 dim_clusters() {
+        return details::grid::dim_clusters();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned long long num_clusters() {
+        return details::grid::num_clusters();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 cluster_index() {
+        return details::grid::cluster_index();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned long long cluster_rank() {
+        return details::grid::cluster_rank();
+    }
+# endif
+};
+
+_CG_QUALIFIER grid_group this_grid() {
+    // Load a workspace from the driver
+    grid_group gg(details::get_grid_workspace());
+#ifdef _CG_DEBUG
+    // *all* threads must be available to synchronize
+    gg.sync();
+#endif // _CG_DEBUG
+    return gg;
+}
+
+#if defined(_CG_HAS_CLUSTER_GROUP)
+/**
+ * class cluster_group
+ *
+ * Every GPU kernel is executed by a grid of thread blocks. A grid can be evenly
+ * divided along all dimensions to form groups of blocks, each group of which is
+ * a block cluster. Clustered grids are subject to various restrictions and
+ * limitations. Primarily, a cluster consists of at most 8 blocks by default
+ * (although the user is allowed to opt-in to non-standard sizes,) and clustered
+ * grids are subject to additional occupancy limitations due to per-cluster
+ * hardware resource consumption. In exchange, a block cluster is guaranteed to
+ * be a cooperative group, with access to all cooperative group capabilities, as
+ * well as cluster specific capabilities and accelerations. A cluster_group
+ * represents a block cluster.
+ *
+ * Constructed via this_cluster_group();
+ */
+class cluster_group : public thread_group_base<details::cluster_group_id>
+{
+    // Friends
+    friend _CG_QUALIFIER cluster_group this_cluster();
+
+    // Disable constructor
+    _CG_QUALIFIER cluster_group()
+    {
+    }
+
+ public:
+    //_CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_cluster)
+
+    using arrival_token = struct {};
+
+    // Functionality exposed by the group
+    _CG_STATIC_QUALIFIER void sync()
+    {
+        return details::cluster::sync();
+    }
+
+    _CG_STATIC_QUALIFIER arrival_token barrier_arrive()
+    {
+        details::cluster::barrier_arrive();
+        return arrival_token();
+    }
+
+    _CG_STATIC_QUALIFIER void barrier_wait()
+    {
+        return details::cluster::barrier_wait();
+    }
+
+    _CG_STATIC_QUALIFIER void barrier_wait(arrival_token&&)
+    {
+        return details::cluster::barrier_wait();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int query_shared_rank(const void *addr)
+    {
+        return details::cluster::query_shared_rank(addr);
+    }
+
+    template <typename T>
+    _CG_STATIC_QUALIFIER T* map_shared_rank(T *addr, int rank)
+    {
+        return details::cluster::map_shared_rank(addr, rank);
+    }
+
+    _CG_STATIC_QUALIFIER dim3 block_index()
+    {
+        return details::cluster::block_index();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int block_rank()
+    {
+        return details::cluster::block_rank();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 thread_index()
+    {
+        return details::cluster::thread_index();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int thread_rank()
+    {
+        return details::cluster::thread_rank();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 dim_blocks()
+    {
+        return details::cluster::dim_blocks();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int num_blocks()
+    {
+        return details::cluster::num_blocks();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 dim_threads()
+    {
+        return details::cluster::dim_threads();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int num_threads()
+    {
+        return details::cluster::num_threads();
+    }
+
+    // Legacy aliases
+    _CG_STATIC_QUALIFIER unsigned int size()
+    {
+        return num_threads();
+    }
+};
+
+/*
+ * cluster_group this_cluster()
+ *
+ * Constructs a cluster_group
+ */
+_CG_QUALIFIER cluster_group this_cluster()
+{
+    cluster_group cg;
+#ifdef _CG_DEBUG
+    cg.sync();
+#endif
+    return cg;
+}
+#endif
+
+#if defined(_CG_CPP11_FEATURES)
+class thread_block;
+template <unsigned int MaxBlockSize>
+_CG_QUALIFIER thread_block this_thread_block(block_tile_memory<MaxBlockSize>& scratch);
+#endif
+
+/**
+ * class thread_block
+ *
+ * Every GPU kernel is executed by a grid of thread blocks, and threads within
+ * each block are guaranteed to reside on the same streaming multiprocessor.
+ * A thread_block represents a thread block whose dimensions are not known until runtime.
+ *
+ * Constructed via this_thread_block();
+ */
+class thread_block : public thread_group_base<details::thread_block_id>
+{
+    // Friends
+    friend _CG_QUALIFIER thread_block this_thread_block();
+    friend _CG_QUALIFIER thread_group tiled_partition(const thread_group& parent, unsigned int tilesz);
+    friend _CG_QUALIFIER thread_group tiled_partition(const thread_block& parent, unsigned int tilesz);
+
+#if defined(_CG_CPP11_FEATURES)
+    template <unsigned int MaxBlockSize>
+    friend _CG_QUALIFIER thread_block this_thread_block(block_tile_memory<MaxBlockSize>& scratch);
+    template <unsigned int Size>
+    friend class __static_size_multi_warp_tile_base;
+
+    details::multi_warp_scratch* const tile_memory;
+
+    template <unsigned int MaxBlockSize>
+    _CG_QUALIFIER thread_block(block_tile_memory<MaxBlockSize>& scratch) :
+        tile_memory(details::get_scratch_ptr(&scratch)) {
+#ifdef _CG_DEBUG
+        if (num_threads() > MaxBlockSize) {
+            details::abort();
+        }
+#endif
+
+
+#if defined(_CG_USER_PROVIDED_SHARED_MEMORY)
+#define _CG_SKIP_BARRIER_INIT_TARGET NV_NO_TARGET
+#else
+#define _CG_SKIP_BARRIER_INIT_TARGET NV_PROVIDES_SM_80
+#endif
+        NV_IF_ELSE_TARGET(
+            _CG_SKIP_BARRIER_INIT_TARGET,
+            // skip if clause
+        ,
+            (tile_memory->init_barriers(thread_rank());
+            sync();)
+        )
+    }
+#endif
+#undef _CG_SKIP_BARRIER_INIT_TARGET
+
+    // Disable constructor
+    _CG_QUALIFIER thread_block()
+#if defined(_CG_CPP11_FEATURES)
+    : tile_memory(details::get_scratch_ptr(NULL))
+#endif
+    { }
+
+    // Internal Use
+    _CG_QUALIFIER thread_group _get_tiled_threads(unsigned int tilesz) const {
+        const bool pow2_tilesz = ((tilesz & (tilesz - 1)) == 0);
+
+        // Invalid, immediately fail
+        if (tilesz == 0 || (tilesz > 32) || !pow2_tilesz) {
+            details::abort();
+            return (thread_block());
+        }
+
+        unsigned int mask;
+        unsigned int base_offset = thread_rank() & (~(tilesz - 1));
+        unsigned int masklength = min((unsigned int)size() - base_offset, tilesz);
+
+        mask = (unsigned int)(-1) >> (32 - masklength);
+        mask <<= (details::laneid() & ~(tilesz - 1));
+        thread_group tile = thread_group(details::coalesced_group_id);
+        tile._data.coalesced.mask = mask;
+        tile._data.coalesced.size = __popc(mask);
+        tile._data.coalesced.metaGroupSize = (details::cta::size() + tilesz - 1) / tilesz;
+        tile._data.coalesced.metaGroupRank = details::cta::thread_rank() / tilesz;
+        tile._data.coalesced.is_tiled = true;
+        return (tile);
+    }
+
+ public:
+    _CG_STATIC_CONST_DECL unsigned int _group_id = details::thread_block_id;
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_block)
+
+    _CG_STATIC_QUALIFIER void sync() {
+        details::cta::sync();
+    }
+
+#if defined(_CG_CPP11_FEATURES)
+    struct arrival_token {};
+
+    _CG_QUALIFIER arrival_token barrier_arrive() const {
+        return arrival_token();
+    }
+
+    _CG_QUALIFIER void barrier_wait(arrival_token&&) const {
+        details::cta::sync();
+    }
+#endif
+
+    _CG_STATIC_QUALIFIER unsigned int size() {
+        return details::cta::size();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int thread_rank() {
+        return details::cta::thread_rank();
+    }
+
+    // Additional functionality exposed by the group
+    _CG_STATIC_QUALIFIER dim3 group_index() {
+        return details::cta::group_index();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 thread_index() {
+        return details::cta::thread_index();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 group_dim() {
+        return details::cta::block_dim();
+    }
+
+    _CG_STATIC_QUALIFIER dim3 dim_threads() {
+        return details::cta::dim_threads();
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int num_threads() {
+        return details::cta::num_threads();
+    }
+
+};
+
+/**
+ * thread_block this_thread_block()
+ *
+ * Constructs a thread_block group
+ */
+_CG_QUALIFIER thread_block this_thread_block()
+{
+    return (thread_block());
+}
+
+#if defined(_CG_CPP11_FEATURES)
+template <unsigned int MaxBlockSize>
+_CG_QUALIFIER thread_block this_thread_block(block_tile_memory<MaxBlockSize>& scratch) {
+    return (thread_block(scratch));
+}
+#endif
+
+/**
+ * class coalesced_group
+ *
+ * A group representing the current set of converged threads in a warp.
+ * The size of the group is not guaranteed and it may return a group of
+ * only one thread (itself).
+ *
+ * This group exposes warp-synchronous builtins.
+ * Constructed via coalesced_threads();
+ */
+class coalesced_group : public thread_group_base<details::coalesced_group_id>
+{
+private:
+    friend _CG_QUALIFIER coalesced_group coalesced_threads();
+    friend _CG_QUALIFIER thread_group tiled_partition(const thread_group& parent, unsigned int tilesz);
+    friend _CG_QUALIFIER coalesced_group tiled_partition(const coalesced_group& parent, unsigned int tilesz);
+    friend class details::_coalesced_group_data_access;
+
+    _CG_QUALIFIER unsigned int _packLanes(unsigned laneMask) const {
+        unsigned int member_pack = 0;
+        unsigned int member_rank = 0;
+        for (int bit_idx = 0; bit_idx < 32; bit_idx++) {
+            unsigned int lane_bit = _data.coalesced.mask & (1 << bit_idx);
+            if (lane_bit) {
+                if (laneMask & lane_bit)
+                    member_pack |= 1 << member_rank;
+                member_rank++;
+            }
+        }
+        return (member_pack);
+    }
+
+    // Internal Use
+    _CG_QUALIFIER coalesced_group _get_tiled_threads(unsigned int tilesz) const {
+        const bool pow2_tilesz = ((tilesz & (tilesz - 1)) == 0);
+
+        // Invalid, immediately fail
+        if (tilesz == 0 || (tilesz > 32) || !pow2_tilesz) {
+            details::abort();
+            return (coalesced_group(0));
+        }
+        if (size() <= tilesz) {
+            return (*this);
+        }
+
+        if ((_data.coalesced.is_tiled == true) && pow2_tilesz) {
+            unsigned int base_offset = (thread_rank() & (~(tilesz - 1)));
+            unsigned int masklength = min((unsigned int)size() - base_offset, tilesz);
+            unsigned int mask = (unsigned int)(-1) >> (32 - masklength);
+
+            mask <<= (details::laneid() & ~(tilesz - 1));
+            coalesced_group coalesced_tile = coalesced_group(mask);
+            coalesced_tile._data.coalesced.metaGroupSize = size() / tilesz;
+            coalesced_tile._data.coalesced.metaGroupRank = thread_rank() / tilesz;
+            coalesced_tile._data.coalesced.is_tiled = true;
+            return (coalesced_tile);
+        }
+        else if ((_data.coalesced.is_tiled == false) && pow2_tilesz) {
+            unsigned int mask = 0;
+            unsigned int member_rank = 0;
+            int seen_lanes = (thread_rank() / tilesz) * tilesz;
+            for (unsigned int bit_idx = 0; bit_idx < 32; bit_idx++) {
+                unsigned int lane_bit = _data.coalesced.mask & (1 << bit_idx);
+                if (lane_bit) {
+                    if (seen_lanes <= 0 && member_rank < tilesz) {
+                        mask |= lane_bit;
+                        member_rank++;
+                    }
+                    seen_lanes--;
+                }
+            }
+            coalesced_group coalesced_tile = coalesced_group(mask);
+            // Override parent with the size of this group
+            coalesced_tile._data.coalesced.metaGroupSize = (size() + tilesz - 1) / tilesz;
+            coalesced_tile._data.coalesced.metaGroupRank = thread_rank() / tilesz;
+            return coalesced_tile;
+        }
+        else {
+            // None in _CG_VERSION 1000
+            details::abort();
+        }
+
+        return (coalesced_group(0));
+    }
+
+ protected:
+    _CG_QUALIFIER coalesced_group(unsigned int mask) {
+        _data.coalesced.mask = mask;
+        _data.coalesced.size = __popc(mask);
+        _data.coalesced.metaGroupRank = 0;
+        _data.coalesced.metaGroupSize = 1;
+        _data.coalesced.is_tiled = false;
+    }
+
+    _CG_QUALIFIER unsigned int get_mask() const {
+        return (_data.coalesced.mask);
+    }
+
+ public:
+    _CG_STATIC_CONST_DECL unsigned int _group_id = details::coalesced_group_id;
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_block)
+
+    _CG_QUALIFIER unsigned int num_threads() const {
+        return _data.coalesced.size;
+    }
+
+    _CG_QUALIFIER unsigned int size() const {
+        return num_threads();
+    }
+
+    _CG_QUALIFIER unsigned int thread_rank() const {
+        return (__popc(_data.coalesced.mask & details::lanemask32_lt()));
+    }
+
+    // Rank of this group in the upper level of the hierarchy
+    _CG_QUALIFIER unsigned int meta_group_rank() const {
+        return _data.coalesced.metaGroupRank;
+    }
+
+    // Total num partitions created out of all CTAs when the group was created
+    _CG_QUALIFIER unsigned int meta_group_size() const {
+        return _data.coalesced.metaGroupSize;
+    }
+
+    _CG_QUALIFIER void sync() const {
+        __syncwarp(_data.coalesced.mask);
+    }
+
+#ifdef _CG_CPP11_FEATURES
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl(TyElem&& elem, int srcRank) const {
+        unsigned int lane = (srcRank == 0) ? __ffs(_data.coalesced.mask) - 1 :
+            (size() == 32) ? srcRank : __fns(_data.coalesced.mask, 0, (srcRank + 1));
+
+        return details::tile::shuffle_dispatch<TyElem>::shfl(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), _data.coalesced.mask, lane, 32);
+    }
+
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl_down(TyElem&& elem, unsigned int delta) const {
+        if (size() == 32) {
+            return details::tile::shuffle_dispatch<TyElem>::shfl_down(
+                _CG_STL_NAMESPACE::forward<TyElem>(elem), 0xFFFFFFFF, delta, 32);
+        }
+
+        unsigned int lane = __fns(_data.coalesced.mask, details::laneid(), delta + 1);
+
+        if (lane >= 32)
+            lane = details::laneid();
+
+        return details::tile::shuffle_dispatch<TyElem>::shfl(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), _data.coalesced.mask, lane, 32);
+    }
+
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl_up(TyElem&& elem, int delta) const {
+        if (size() == 32) {
+            return details::tile::shuffle_dispatch<TyElem>::shfl_up(
+                _CG_STL_NAMESPACE::forward<TyElem>(elem), 0xFFFFFFFF, delta, 32);
+        }
+
+        unsigned lane = __fns(_data.coalesced.mask, details::laneid(), -(delta + 1));
+        if (lane >= 32)
+            lane = details::laneid();
+
+        return details::tile::shuffle_dispatch<TyElem>::shfl(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), _data.coalesced.mask, lane, 32);
+    }
+#else
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl(TyIntegral var, unsigned int src_rank) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        unsigned int lane = (src_rank == 0) ? __ffs(_data.coalesced.mask) - 1 :
+            (size() == 32) ? src_rank : __fns(_data.coalesced.mask, 0, (src_rank + 1));
+        return (__shfl_sync(_data.coalesced.mask, var, lane, 32));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl_up(TyIntegral var, int delta) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        if (size() == 32) {
+            return (__shfl_up_sync(0xFFFFFFFF, var, delta, 32));
+        }
+        unsigned lane = __fns(_data.coalesced.mask, details::laneid(), -(delta + 1));
+        if (lane >= 32) lane = details::laneid();
+        return (__shfl_sync(_data.coalesced.mask, var, lane, 32));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl_down(TyIntegral var, int delta) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        if (size() == 32) {
+            return (__shfl_down_sync(0xFFFFFFFF, var, delta, 32));
+        }
+        unsigned int lane = __fns(_data.coalesced.mask, details::laneid(), delta + 1);
+        if (lane >= 32) lane = details::laneid();
+        return (__shfl_sync(_data.coalesced.mask, var, lane, 32));
+    }
+#endif
+
+    _CG_QUALIFIER int any(int predicate) const {
+        return (__ballot_sync(_data.coalesced.mask, predicate) != 0);
+    }
+    _CG_QUALIFIER int all(int predicate) const {
+        return (__ballot_sync(_data.coalesced.mask, predicate) == _data.coalesced.mask);
+    }
+    _CG_QUALIFIER unsigned int ballot(int predicate) const {
+        if (size() == 32) {
+            return (__ballot_sync(0xFFFFFFFF, predicate));
+        }
+        unsigned int lane_ballot = __ballot_sync(_data.coalesced.mask, predicate);
+        return (_packLanes(lane_ballot));
+    }
+
+#ifdef _CG_HAS_MATCH_COLLECTIVE
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER unsigned int match_any(TyIntegral val) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        if (size() == 32) {
+            return (__match_any_sync(0xFFFFFFFF, val));
+        }
+        unsigned int lane_match = __match_any_sync(_data.coalesced.mask, val);
+        return (_packLanes(lane_match));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER unsigned int match_all(TyIntegral val, int &pred) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        if (size() == 32) {
+            return (__match_all_sync(0xFFFFFFFF, val, &pred));
+        }
+        unsigned int lane_match = __match_all_sync(_data.coalesced.mask, val, &pred);
+        return (_packLanes(lane_match));
+    }
+
+#endif /* !_CG_HAS_MATCH_COLLECTIVE */
+
+};
+
+_CG_QUALIFIER coalesced_group coalesced_threads()
+{
+    return (coalesced_group(__activemask()));
+}
+
+namespace details {
+    template <unsigned int Size> struct verify_thread_block_tile_size;
+    template <> struct verify_thread_block_tile_size<32> { typedef void OK; };
+    template <> struct verify_thread_block_tile_size<16> { typedef void OK; };
+    template <> struct verify_thread_block_tile_size<8>  { typedef void OK; };
+    template <> struct verify_thread_block_tile_size<4>  { typedef void OK; };
+    template <> struct verify_thread_block_tile_size<2>  { typedef void OK; };
+    template <> struct verify_thread_block_tile_size<1>  { typedef void OK; };
+
+#ifdef _CG_CPP11_FEATURES
+    template <unsigned int Size>
+    using _is_power_of_2 = _CG_STL_NAMESPACE::integral_constant<bool, (Size & (Size - 1)) == 0>;
+
+    template <unsigned int Size>
+    using _is_single_warp = _CG_STL_NAMESPACE::integral_constant<bool, Size <= 32>;
+    template <unsigned int Size>
+    using _is_multi_warp =
+    _CG_STL_NAMESPACE::integral_constant<bool, (Size > 32) && (Size <= 1024)>;
+
+    template <unsigned int Size>
+    using _is_valid_single_warp_tile =
+        _CG_STL_NAMESPACE::integral_constant<bool, _is_power_of_2<Size>::value && _is_single_warp<Size>::value>;
+    template <unsigned int Size>
+    using _is_valid_multi_warp_tile =
+        _CG_STL_NAMESPACE::integral_constant<bool, _is_power_of_2<Size>::value && _is_multi_warp<Size>::value>;
+#else
+    template <unsigned int Size>
+    struct _is_multi_warp {
+        static const bool value = false;
+    };
+#endif
+}
+
+template <unsigned int Size>
+class __static_size_tile_base
+{
+protected:
+    _CG_STATIC_CONST_DECL unsigned int numThreads = Size;
+
+public:
+    _CG_THREAD_SCOPE(cuda::thread_scope::thread_scope_block)
+
+    // Rank of thread within tile
+    _CG_STATIC_QUALIFIER unsigned int thread_rank() {
+        return (details::cta::thread_rank() & (numThreads - 1));
+    }
+
+    // Number of threads within tile
+    _CG_STATIC_CONSTEXPR_QUALIFIER unsigned int num_threads() {
+        return numThreads;
+    }
+
+    _CG_STATIC_CONSTEXPR_QUALIFIER unsigned int size() {
+        return num_threads();
+    }
+};
+
+template <unsigned int Size>
+class __static_size_thread_block_tile_base : public __static_size_tile_base<Size>
+{
+    friend class details::_coalesced_group_data_access;
+    typedef details::tile::tile_helpers<Size> th;
+
+#ifdef _CG_CPP11_FEATURES
+    static_assert(details::_is_valid_single_warp_tile<Size>::value, "Size must be one of 1/2/4/8/16/32");
+#else
+    typedef typename details::verify_thread_block_tile_size<Size>::OK valid;
+#endif
+    using __static_size_tile_base<Size>::numThreads;
+    _CG_STATIC_CONST_DECL unsigned int fullMask = 0xFFFFFFFF;
+
+ protected:
+    _CG_STATIC_QUALIFIER unsigned int build_mask() {
+        unsigned int mask = fullMask;
+        if (numThreads != 32) {
+            // [0,31] representing the current active thread in the warp
+            unsigned int laneId = details::laneid();
+            // shift mask according to the partition it belongs to
+            mask = th::tileMask << (laneId & ~(th::laneMask));
+        }
+        return (mask);
+    }
+
+public:
+    _CG_STATIC_CONST_DECL unsigned int _group_id = details::coalesced_group_id;
+
+    _CG_STATIC_QUALIFIER void sync() {
+        __syncwarp(build_mask());
+    }
+
+#ifdef _CG_CPP11_FEATURES
+    // PTX supported collectives
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl(TyElem&& elem, int srcRank) const {
+        return details::tile::shuffle_dispatch<TyElem>::shfl(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), build_mask(), srcRank, numThreads);
+    }
+
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl_down(TyElem&& elem, unsigned int delta) const {
+        return details::tile::shuffle_dispatch<TyElem>::shfl_down(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), build_mask(), delta, numThreads);
+    }
+
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl_up(TyElem&& elem, unsigned int delta) const {
+        return details::tile::shuffle_dispatch<TyElem>::shfl_up(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), build_mask(), delta, numThreads);
+    }
+
+    template <typename TyElem, typename TyRet = details::remove_qual<TyElem>>
+    _CG_QUALIFIER TyRet shfl_xor(TyElem&& elem, unsigned int laneMask) const {
+        return details::tile::shuffle_dispatch<TyElem>::shfl_xor(
+            _CG_STL_NAMESPACE::forward<TyElem>(elem), build_mask(), laneMask, numThreads);
+    }
+#else
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl(TyIntegral var, int srcRank) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        return (__shfl_sync(build_mask(), var, srcRank, numThreads));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl_down(TyIntegral var, unsigned int delta) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        return (__shfl_down_sync(build_mask(), var, delta, numThreads));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl_up(TyIntegral var, unsigned int delta) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        return (__shfl_up_sync(build_mask(), var, delta, numThreads));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER TyIntegral shfl_xor(TyIntegral var, unsigned int laneMask) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        return (__shfl_xor_sync(build_mask(), var, laneMask, numThreads));
+    }
+#endif //_CG_CPP11_FEATURES
+
+    _CG_QUALIFIER int any(int predicate) const {
+        unsigned int lane_ballot = __ballot_sync(build_mask(), predicate);
+        return (lane_ballot != 0);
+    }
+    _CG_QUALIFIER int all(int predicate) const {
+        unsigned int lane_ballot = __ballot_sync(build_mask(), predicate);
+        return (lane_ballot == build_mask());
+    }
+    _CG_QUALIFIER unsigned int ballot(int predicate) const {
+        unsigned int lane_ballot = __ballot_sync(build_mask(), predicate);
+        return (lane_ballot >> (details::laneid() & (~(th::laneMask))));
+    }
+
+#ifdef _CG_HAS_MATCH_COLLECTIVE
+    template <typename TyIntegral>
+    _CG_QUALIFIER unsigned int match_any(TyIntegral val) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        unsigned int lane_match = __match_any_sync(build_mask(), val);
+        return (lane_match >> (details::laneid() & (~(th::laneMask))));
+    }
+
+    template <typename TyIntegral>
+    _CG_QUALIFIER unsigned int match_all(TyIntegral val, int &pred) const {
+        details::assert_if_not_arithmetic<TyIntegral>();
+        unsigned int lane_match = __match_all_sync(build_mask(), val, &pred);
+        return (lane_match >> (details::laneid() & (~(th::laneMask))));
+    }
+#endif
+
+};
+
+template <unsigned int Size, typename ParentT>
+class __static_parent_thread_block_tile_base
+{
+public:
+    // Rank of this group in the upper level of the hierarchy
+    _CG_STATIC_QUALIFIER unsigned int meta_group_rank() {
+        return ParentT::thread_rank() / Size;
+    }
+
+    // Total num partitions created out of all CTAs when the group was created
+    _CG_STATIC_QUALIFIER unsigned int meta_group_size() {
+        return (ParentT::size() + Size - 1) / Size;
+    }
+};
+
+/**
+ * class thread_block_tile<unsigned int Size, ParentT = void>
+ *
+ * Statically-sized group type, representing one tile of a thread block.
+ * The only specializations currently supported are those with native
+ * hardware support (1/2/4/8/16/32)
+ *
+ * This group exposes warp-synchronous builtins.
+ * Can only be constructed via tiled_partition<Size>(ParentT&)
+ */
+
+template <unsigned int Size, typename ParentT = void>
+class __single_warp_thread_block_tile :
+    public __static_size_thread_block_tile_base<Size>,
+    public __static_parent_thread_block_tile_base<Size, ParentT>
+{
+    typedef __static_parent_thread_block_tile_base<Size, ParentT> staticParentBaseT;
+    friend class details::_coalesced_group_data_access;
+
+protected:
+    _CG_QUALIFIER __single_warp_thread_block_tile() { };
+    _CG_QUALIFIER __single_warp_thread_block_tile(unsigned int, unsigned int) { };
+
+    _CG_STATIC_QUALIFIER unsigned int get_mask() {
+        return __static_size_thread_block_tile_base<Size>::build_mask();
+    }
+};
+
+template <unsigned int Size>
+class __single_warp_thread_block_tile<Size, void> :
+    public __static_size_thread_block_tile_base<Size>,
+    public thread_group_base<details::coalesced_group_id>
+{
+    _CG_STATIC_CONST_DECL unsigned int numThreads = Size;
+
+    template <unsigned int, typename ParentT> friend class __single_warp_thread_block_tile;
+    friend class details::_coalesced_group_data_access;
+
+    typedef __static_size_thread_block_tile_base<numThreads> staticSizeBaseT;
+
+protected:
+    _CG_QUALIFIER __single_warp_thread_block_tile(unsigned int meta_group_rank = 0, unsigned int meta_group_size = 1) {
+        _data.coalesced.mask = staticSizeBaseT::build_mask();
+        _data.coalesced.size = numThreads;
+        _data.coalesced.metaGroupRank = meta_group_rank;
+        _data.coalesced.metaGroupSize = meta_group_size;
+        _data.coalesced.is_tiled = true;
+    }
+
+    _CG_QUALIFIER unsigned int get_mask() const {
+        return (_data.coalesced.mask);
+    }
+
+public:
+    using staticSizeBaseT::sync;
+    using staticSizeBaseT::size;
+    using staticSizeBaseT::num_threads;
+    using staticSizeBaseT::thread_rank;
+
+    _CG_QUALIFIER unsigned int meta_group_rank() const {
+        return _data.coalesced.metaGroupRank;
+    }
+
+    _CG_QUALIFIER unsigned int meta_group_size() const {
+        return _data.coalesced.metaGroupSize;
+    }
+};
+
+/**
+ * Outer level API calls
+ * void sync(GroupT) - see <group_type>.sync()
+ * void thread_rank(GroupT) - see <group_type>.thread_rank()
+ * void group_size(GroupT) - see <group_type>.size()
+ */
+template <class GroupT>
+_CG_QUALIFIER void sync(GroupT const &g)
+{
+    g.sync();
+}
+
+// TODO: Use a static dispatch to determine appropriate return type
+// C++03 is stuck with unsigned long long for now
+#ifdef _CG_CPP11_FEATURES
+template <class GroupT>
+_CG_QUALIFIER auto thread_rank(GroupT const& g) -> decltype(g.thread_rank()) {
+    return g.thread_rank();
+}
+
+
+template <class GroupT>
+_CG_QUALIFIER auto group_size(GroupT const &g) -> decltype(g.num_threads()) {
+    return g.num_threads();
+}
+#else
+template <class GroupT>
+_CG_QUALIFIER unsigned long long thread_rank(GroupT const& g) {
+    return static_cast<unsigned long long>(g.thread_rank());
+}
+
+
+template <class GroupT>
+_CG_QUALIFIER unsigned long long group_size(GroupT const &g) {
+    return static_cast<unsigned long long>(g.num_threads());
+}
+#endif
+
+
+/**
+ * tiled_partition
+ *
+ * The tiled_partition(parent, tilesz) method is a collective operation that
+ * partitions the parent group into a one-dimensional, row-major, tiling of subgroups.
+ *
+ * A total of ((size(parent)+tilesz-1)/tilesz) subgroups will
+ * be created where threads having identical k = (thread_rank(parent)/tilesz)
+ * will be members of the same subgroup.
+ *
+ * The implementation may cause the calling thread to wait until all the members
+ * of the parent group have invoked the operation before resuming execution.
+ *
+ * Functionality is limited to power-of-two sized subgorup instances of at most
+ * 32 threads. Only thread_block, thread_block_tile<>, and their subgroups can be
+ * tiled_partition() in _CG_VERSION 1000.
+ */
+_CG_QUALIFIER thread_group tiled_partition(const thread_group& parent, unsigned int tilesz)
+{
+    if (parent.get_type() == details::coalesced_group_id) {
+        const coalesced_group *_cg = static_cast<const coalesced_group*>(&parent);
+        return _cg->_get_tiled_threads(tilesz);
+    }
+    else {
+        const thread_block *_tb = static_cast<const thread_block*>(&parent);
+        return _tb->_get_tiled_threads(tilesz);
+    }
+}
+
+// Thread block type overload: returns a basic thread_group for now (may be specialized later)
+_CG_QUALIFIER thread_group tiled_partition(const thread_block& parent, unsigned int tilesz)
+{
+    return (parent._get_tiled_threads(tilesz));
+}
+
+// Coalesced group type overload: retains its ability to stay coalesced
+_CG_QUALIFIER coalesced_group tiled_partition(const coalesced_group& parent, unsigned int tilesz)
+{
+    return (parent._get_tiled_threads(tilesz));
+}
+
+namespace details {
+    template <unsigned int Size, typename ParentT>
+    class internal_thread_block_tile : public __single_warp_thread_block_tile<Size, ParentT> {};
+
+    template <unsigned int Size, typename ParentT>
+    _CG_QUALIFIER internal_thread_block_tile<Size, ParentT> tiled_partition_internal() {
+        return internal_thread_block_tile<Size, ParentT>();
+    }
+
+    template <typename TyVal, typename GroupT, typename WarpLambda, typename InterWarpLambda>
+    _CG_QUALIFIER TyVal multi_warp_collectives_helper(
+            const GroupT& group,
+            WarpLambda warp_lambda,
+            InterWarpLambda inter_warp_lambda) {
+                return group.template collectives_scheme<TyVal>(warp_lambda, inter_warp_lambda);
+            }
+
+    template <typename T, typename GroupT>
+    _CG_QUALIFIER T* multi_warp_scratch_location_getter(const GroupT& group, unsigned int warp_id) {
+        return group.template get_scratch_location<T>(warp_id);
+    }
+
+    template <typename GroupT>
+    _CG_QUALIFIER details::barrier_t* multi_warp_sync_location_getter(const GroupT& group) {
+        return group.get_sync_location();
+    }
+
+}
+/**
+ * tiled_partition<tilesz>
+ *
+ * The tiled_partition<tilesz>(parent) method is a collective operation that
+ * partitions the parent group into a one-dimensional, row-major, tiling of subgroups.
+ *
+ * A total of ((size(parent)/tilesz) subgroups will be created,
+ * therefore the parent group size must be evenly divisible by the tilesz.
+ * The allow parent groups are thread_block or thread_block_tile<size>.
+ *
+ * The implementation may cause the calling thread to wait until all the members
+ * of the parent group have invoked the operation before resuming execution.
+ *
+ * Functionality is limited to native hardware sizes, 1/2/4/8/16/32.
+ * The size(parent) must be greater than the template Size parameter
+ * otherwise the results are undefined.
+ */
+
+#if defined(_CG_CPP11_FEATURES)
+template <unsigned int Size>
+class __static_size_multi_warp_tile_base : public __static_size_tile_base<Size>
+{
+    static_assert(details::_is_valid_multi_warp_tile<Size>::value, "Size must be one of 64/128/256/512");
+
+    template <typename TyVal, typename GroupT, typename WarpLambda, typename InterWarpLambda>
+    friend __device__ TyVal details::multi_warp_collectives_helper(
+            const GroupT& group,
+            WarpLambda warp_lambda,
+            InterWarpLambda inter_warp_lambda);
+    template <typename T, typename GroupT>
+    friend __device__ T* details::multi_warp_scratch_location_getter(const GroupT& group, unsigned int warp_id);
+    template <typename GroupT>
+    friend __device__ details::barrier_t* details::multi_warp_sync_location_getter(const GroupT& group);
+    template <unsigned int OtherSize>
+    friend class __static_size_multi_warp_tile_base;
+    using WarpType = details::internal_thread_block_tile<32, __static_size_multi_warp_tile_base<Size>>;
+    using ThisType = __static_size_multi_warp_tile_base<Size>;
+    _CG_STATIC_CONST_DECL int numWarps = Size / 32;
+
+protected:
+    details::multi_warp_scratch* const tile_memory;
+
+    template <typename GroupT>
+    _CG_QUALIFIER __static_size_multi_warp_tile_base(const GroupT& g) : tile_memory(g.tile_memory) {
+#if !defined(_CG_USER_PROVIDED_SHARED_MEMORY)
+        NV_IF_TARGET(NV_PROVIDES_SM_80,
+            details::sync_warps_reset(get_sync_location(), details::cta::thread_rank());
+            g.sync();
+        )
+#endif
+    }
+
+
+private:
+    _CG_QUALIFIER details::barrier_t* get_sync_location() const {
+        // Different group sizes use different barriers, all groups of a given size share one barrier.
+        unsigned int sync_id = details::log2(Size / 64);
+        return &tile_memory->barriers[sync_id];
+    }
+
+    template <typename T>
+    _CG_QUALIFIER T* get_scratch_location(unsigned int warp_id) const {
+        unsigned int scratch_id = (details::cta::thread_rank() - thread_rank()) / 32 + warp_id;
+        return reinterpret_cast<T*>(&tile_memory->communication_memory[scratch_id]);
+    }
+
+    template <typename T>
+    _CG_QUALIFIER T* get_scratch_location() const {
+        unsigned int scratch_id = details::cta::thread_rank() / 32;
+        return reinterpret_cast<T*>(&tile_memory->communication_memory[scratch_id]);
+    }
+
+    template <typename TyVal>
+    _CG_QUALIFIER TyVal shfl_impl(TyVal val, unsigned int src) const {
+        unsigned int src_warp = src / 32;
+        auto warp = details::tiled_partition_internal<32, ThisType>();
+        details::barrier_t* sync_location = get_sync_location();
+
+        // Get warp slot of the source threads warp.
+        TyVal* warp_scratch_location = get_scratch_location<TyVal>(src_warp);
+
+        if (warp.meta_group_rank() == src_warp) {
+            warp.sync();
+            // Put shuffled value into my warp slot and let my warp arrive at the barrier.
+            if (thread_rank() == src) {
+                *warp_scratch_location = val;
+            }
+            details::sync_warps_arrive(sync_location, details::cta::thread_rank(), numWarps);
+            TyVal result = *warp_scratch_location;
+            details::sync_warps_wait(sync_location, details::cta::thread_rank());
+            return result;
+        }
+        else {
+            // Wait for the source warp to arrive on the barrier.
+            details::sync_warps_wait_for_specific_warp(sync_location,
+                    (details::cta::thread_rank() / 32 - warp.meta_group_rank() + src_warp));
+            TyVal result = *warp_scratch_location;
+            details::sync_warps(sync_location, details::cta::thread_rank(), numWarps);
+            return result;
+        }
+    }
+
+    template <typename TyVal, typename WarpLambda, typename InterWarpLambda>
+    _CG_QUALIFIER TyVal collectives_scheme(const WarpLambda& warp_lambda, const InterWarpLambda& inter_warp_lambda) const {
+        static_assert(sizeof(TyVal) <= details::multi_warp_scratch::communication_size,
+                      "Collectives with tiles larger than 32 threads are limited to types smaller then 8 bytes");
+        auto warp = details::tiled_partition_internal<32, ThisType>();
+        details::barrier_t* sync_location = get_sync_location();
+        TyVal* warp_scratch_location = get_scratch_location<TyVal>();
+
+        warp_lambda(warp, warp_scratch_location);
+
+        if (details::sync_warps_last_releases(sync_location, details::cta::thread_rank(), numWarps)) {
+            auto subwarp = details::tiled_partition_internal<numWarps, decltype(warp)>();
+            if (subwarp.meta_group_rank() == 0) {
+                TyVal* thread_scratch_location = get_scratch_location<TyVal>(subwarp.thread_rank());
+                inter_warp_lambda(subwarp, thread_scratch_location);
+            }
+            warp.sync();
+            details::sync_warps_release(sync_location, warp.thread_rank() == 0, details::cta::thread_rank(), numWarps);
+        }
+        TyVal result = *warp_scratch_location;
+        return result;
+    }
+
+public:
+    _CG_STATIC_CONST_DECL unsigned int _group_id = details::multi_tile_group_id;
+
+    using __static_size_tile_base<Size>::thread_rank;
+
+    template <typename TyVal>
+    _CG_QUALIFIER TyVal shfl(TyVal val, unsigned int src) const {
+        static_assert(sizeof(TyVal) <= details::multi_warp_scratch::communication_size,
+                      "Collectives with tiles larger than 32 threads are limited to types smaller then 8 bytes");
+        return shfl_impl(val, src);
+    }
+
+    _CG_QUALIFIER void sync() const {
+        details::sync_warps(get_sync_location(), details::cta::thread_rank(), numWarps);
+    }
+
+    _CG_QUALIFIER int any(int predicate) const {
+        auto warp_lambda = [=] (WarpType& warp, int* warp_scratch_location) {
+                *warp_scratch_location = __any_sync(0xFFFFFFFF, predicate);
+        };
+        auto inter_warp_lambda =
+            [] (details::internal_thread_block_tile<numWarps, WarpType>& subwarp, int* thread_scratch_location) {
+                *thread_scratch_location = __any_sync(0xFFFFFFFFU >> (32 - numWarps), *thread_scratch_location);
+        };
+        return collectives_scheme<int>(warp_lambda, inter_warp_lambda);
+    }
+
+    _CG_QUALIFIER int all(int predicate) const {
+        auto warp_lambda = [=] (WarpType& warp, int* warp_scratch_location) {
+                *warp_scratch_location = __all_sync(0xFFFFFFFF, predicate);
+        };
+        auto inter_warp_lambda =
+            [] (details::internal_thread_block_tile<numWarps, WarpType>& subwarp, int* thread_scratch_location) {
+                *thread_scratch_location = __all_sync(0xFFFFFFFFU >> (32 - numWarps), *thread_scratch_location);
+        };
+        return collectives_scheme<int>(warp_lambda, inter_warp_lambda);
+    }
+};
+
+
+template <unsigned int Size, typename ParentT = void>
+class __multi_warp_thread_block_tile :
+    public __static_size_multi_warp_tile_base<Size>,
+    public __static_parent_thread_block_tile_base<Size, ParentT>
+{
+    typedef __static_parent_thread_block_tile_base<Size, ParentT> staticParentBaseT;
+    typedef __static_size_multi_warp_tile_base<Size> staticTileBaseT;
+protected:
+    _CG_QUALIFIER __multi_warp_thread_block_tile(const ParentT& g) :
+        __static_size_multi_warp_tile_base<Size>(g) {}
+};
+
+template <unsigned int Size>
+class __multi_warp_thread_block_tile<Size, void> : public __static_size_multi_warp_tile_base<Size>
+{
+    const unsigned int metaGroupRank;
+    const unsigned int metaGroupSize;
+
+protected:
+    template <unsigned int OtherSize, typename ParentT>
+    _CG_QUALIFIER __multi_warp_thread_block_tile(const __multi_warp_thread_block_tile<OtherSize, ParentT>& g) :
+        __static_size_multi_warp_tile_base<Size>(g), metaGroupRank(g.meta_group_rank()), metaGroupSize(g.meta_group_size()) {}
+
+public:
+    _CG_QUALIFIER unsigned int meta_group_rank() const {
+        return metaGroupRank;
+    }
+
+    _CG_QUALIFIER unsigned int meta_group_size() const {
+        return metaGroupSize;
+    }
+};
+#endif
+
+template <unsigned int Size, typename ParentT = void>
+class thread_block_tile;
+
+namespace details {
+    template <unsigned int Size, typename ParentT, bool IsMultiWarp>
+    class thread_block_tile_impl;
+
+    template <unsigned int Size, typename ParentT>
+    class thread_block_tile_impl<Size, ParentT, false>: public __single_warp_thread_block_tile<Size, ParentT>
+    {
+    protected:
+        template <unsigned int OtherSize, typename OtherParentT, bool OtherIsMultiWarp>
+        _CG_QUALIFIER thread_block_tile_impl(const thread_block_tile_impl<OtherSize, OtherParentT, OtherIsMultiWarp>& g) :
+            __single_warp_thread_block_tile<Size, ParentT>(g.meta_group_rank(), g.meta_group_size()) {}
+
+        _CG_QUALIFIER thread_block_tile_impl(const thread_block& g) :
+            __single_warp_thread_block_tile<Size, ParentT>() {}
+    };
+
+#if defined(_CG_CPP11_FEATURES)
+    template <unsigned int Size, typename ParentT>
+    class thread_block_tile_impl<Size, ParentT, true> : public __multi_warp_thread_block_tile<Size, ParentT>
+    {
+        protected:
+        template <typename GroupT>
+        _CG_QUALIFIER thread_block_tile_impl(const GroupT& g) :
+            __multi_warp_thread_block_tile<Size, ParentT>(g) {}
+    };
+#else
+    template <unsigned int Size, typename ParentT>
+    class thread_block_tile_impl<Size, ParentT, true>
+    {
+        protected:
+        template <typename GroupT>
+        _CG_QUALIFIER thread_block_tile_impl(const GroupT& g) {}
+    };
+#endif
+}
+
+template <unsigned int Size, typename ParentT>
+class thread_block_tile : public details::thread_block_tile_impl<Size, ParentT, details::_is_multi_warp<Size>::value>
+{
+    friend _CG_QUALIFIER thread_block_tile<1, void> this_thread();
+
+protected:
+    _CG_QUALIFIER thread_block_tile(const ParentT& g) :
+        details::thread_block_tile_impl<Size, ParentT, details::_is_multi_warp<Size>::value>(g) {}
+
+public:
+    _CG_QUALIFIER operator thread_block_tile<Size, void>() const {
+        return thread_block_tile<Size, void>(*this);
+    }
+};
+
+template <unsigned int Size>
+class thread_block_tile<Size, void> : public details::thread_block_tile_impl<Size, void, details::_is_multi_warp<Size>::value>
+{
+    template <unsigned int, typename ParentT>
+    friend class thread_block_tile;
+
+protected:
+    template <unsigned int OtherSize, typename OtherParentT>
+    _CG_QUALIFIER thread_block_tile(const thread_block_tile<OtherSize, OtherParentT>& g) :
+        details::thread_block_tile_impl<Size, void, details::_is_multi_warp<Size>::value>(g) {}
+
+public:
+    template <typename ParentT>
+    _CG_QUALIFIER thread_block_tile(const thread_block_tile<Size, ParentT>& g) :
+        details::thread_block_tile_impl<Size, void, details::_is_multi_warp<Size>::value>(g) {}
+};
+
+namespace details {
+    template <unsigned int Size, typename ParentT>
+    struct tiled_partition_impl;
+
+    template <unsigned int Size>
+    struct tiled_partition_impl<Size, thread_block> : public thread_block_tile<Size, thread_block> {
+        _CG_QUALIFIER tiled_partition_impl(const thread_block& g) :
+            thread_block_tile<Size, thread_block>(g) {}
+    };
+
+    // ParentT = static thread_block_tile<ParentSize, GrandParent> specialization
+    template <unsigned int Size, unsigned int ParentSize, typename GrandParent>
+    struct tiled_partition_impl<Size, thread_block_tile<ParentSize, GrandParent> > :
+        public thread_block_tile<Size, thread_block_tile<ParentSize, GrandParent> > {
+#ifdef _CG_CPP11_FEATURES
+        static_assert(Size < ParentSize, "Tile size bigger or equal to the parent group size");
+#endif
+        _CG_QUALIFIER tiled_partition_impl(const thread_block_tile<ParentSize, GrandParent>& g) :
+            thread_block_tile<Size, thread_block_tile<ParentSize, GrandParent> >(g) {}
+    };
+
+}
+
+template <unsigned int Size, typename ParentT>
+_CG_QUALIFIER thread_block_tile<Size, ParentT> tiled_partition(const ParentT& g)
+{
+    return details::tiled_partition_impl<Size, ParentT>(g);
+}
+
+/**
+ * thread_group this_thread()
+ *
+ * Constructs a generic thread_group containing only the calling thread
+ */
+_CG_QUALIFIER thread_block_tile<1, void> this_thread()
+{
+    // Make thread_block_tile<1, thread_block> parent of the returned group, so it will have its
+    // meta group rank and size set to 0 and 1 respectively.
+    return thread_block_tile<1, thread_block_tile<1, thread_block> >(this_thread_block());
+}
+
+/**
+ * <group_type>.sync()
+ *
+ * Executes a barrier across the group
+ *
+ * Implements both a compiler fence and an architectural fence to prevent,
+ * memory reordering around the barrier.
+ */
+_CG_QUALIFIER void thread_group::sync() const
+{
+    switch (_data.group.type) {
+    case details::coalesced_group_id:
+        cooperative_groups::sync(*static_cast<const coalesced_group*>(this));
+        break;
+    case details::thread_block_id:
+        cooperative_groups::sync(*static_cast<const thread_block*>(this));
+        break;
+    case details::grid_group_id:
+        cooperative_groups::sync(*static_cast<const grid_group*>(this));
+        break;
+#if defined(_CG_HAS_MULTI_GRID_GROUP) && defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+    case details::multi_grid_group_id:
+        cooperative_groups::sync(*static_cast<const multi_grid_group*>(this));
+        break;
+#endif
+#if defined(_CG_HAS_CLUSTER_GROUP)
+    case details::cluster_group_id:
+        cooperative_groups::sync(*static_cast<const cluster_group*>(this));
+        break;
+#endif
+    default:
+        break;
+    }
+}
+
+/**
+ * <group_type>.size()
+ *
+ * Returns the total number of threads in the group.
+ */
+_CG_QUALIFIER unsigned long long thread_group::size() const
+{
+    unsigned long long size = 0;
+    switch (_data.group.type) {
+    case details::coalesced_group_id:
+        size = cooperative_groups::group_size(*static_cast<const coalesced_group*>(this));
+        break;
+    case details::thread_block_id:
+        size = cooperative_groups::group_size(*static_cast<const thread_block*>(this));
+        break;
+    case details::grid_group_id:
+        size = cooperative_groups::group_size(*static_cast<const grid_group*>(this));
+        break;
+#if defined(_CG_HAS_MULTI_GRID_GROUP) && defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+    case details::multi_grid_group_id:
+        size = cooperative_groups::group_size(*static_cast<const multi_grid_group*>(this));
+        break;
+#endif
+#if defined(_CG_HAS_CLUSTER_GROUP)
+    case details::cluster_group_id:
+        size = cooperative_groups::group_size(*static_cast<const cluster_group*>(this));
+        break;
+#endif
+    default:
+        break;
+    }
+    return size;
+}
+
+/**
+ * <group_type>.thread_rank()
+ *
+ * Returns the linearized rank of the calling thread along the interval [0, size()).
+ */
+_CG_QUALIFIER unsigned long long thread_group::thread_rank() const
+{
+    unsigned long long rank = 0;
+    switch (_data.group.type) {
+    case details::coalesced_group_id:
+        rank = cooperative_groups::thread_rank(*static_cast<const coalesced_group*>(this));
+        break;
+    case details::thread_block_id:
+        rank = cooperative_groups::thread_rank(*static_cast<const thread_block*>(this));
+        break;
+    case details::grid_group_id:
+        rank = cooperative_groups::thread_rank(*static_cast<const grid_group*>(this));
+        break;
+#if defined(_CG_HAS_MULTI_GRID_GROUP) && defined(_CG_CPP11_FEATURES) && defined(_CG_ABI_EXPERIMENTAL)
+    case details::multi_grid_group_id:
+        rank = cooperative_groups::thread_rank(*static_cast<const multi_grid_group*>(this));
+        break;
+#endif
+#if defined(_CG_HAS_CLUSTER_GROUP)
+    case details::cluster_group_id:
+        rank = cooperative_groups::thread_rank(*static_cast<const cluster_group*>(this));
+        break;
+#endif
+    default:
+        break;
+    }
+    return rank;
+}
+
+_CG_END_NAMESPACE
+
+#include <cooperative_groups/details/partitioning.h>
+#if (!defined(_MSC_VER) || defined(_WIN64))
+# include <cooperative_groups/details/invoke.h>
+#endif
+
+# endif /* ! (__cplusplus, __CUDACC__) */
+
+#endif /* !_COOPERATIVE_GROUPS_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/async.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/async.h
new file mode 100644
index 0000000000000000000000000000000000000000..1b7dcb2433f2cb7d1ef61290995ac871a901b1e8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/async.h
@@ -0,0 +1,452 @@
+/* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * The source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * The Licensed Deliverables contained herein are PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CG_ASYNC_H
+#define _CG_ASYNC_H
+
+#include "helpers.h"
+#include "info.h"
+
+#include <cuda_pipeline.h>
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+// Groups supported by memcpy_async
+template <class TyGroup>
+struct _async_copy_group_supported : public _CG_STL_NAMESPACE::false_type {};
+
+template <unsigned int Sz, typename TyPar>
+struct _async_copy_group_supported<cooperative_groups::thread_block_tile<Sz, TyPar>>
+    : public _CG_STL_NAMESPACE::true_type {};
+template <>
+struct _async_copy_group_supported<cooperative_groups::coalesced_group> : public _CG_STL_NAMESPACE::true_type {};
+template <>
+struct _async_copy_group_supported<cooperative_groups::thread_block> : public _CG_STL_NAMESPACE::true_type {};
+
+template <class TyGroup>
+using async_copy_group_supported = _async_copy_group_supported<details::remove_qual<TyGroup>>;
+
+// Groups that require optimization
+template <class TyGroup>
+struct _async_copy_optimize_tile : public _CG_STL_NAMESPACE::false_type {};
+
+template <typename TyPar>
+struct _async_copy_optimize_tile<cooperative_groups::thread_block_tile<1, TyPar>>
+    : public _CG_STL_NAMESPACE::false_type {};
+
+template <unsigned int Sz, typename TyPar>
+struct _async_copy_optimize_tile<cooperative_groups::thread_block_tile<Sz, TyPar>>
+    : public _CG_STL_NAMESPACE::true_type {};
+
+template <class TyGroup>
+using async_copy_optimize_tile = _async_copy_optimize_tile<details::remove_qual<TyGroup>>;
+
+// SFINAE helpers for tile optimizations
+template <class TyGroup>
+using enable_tile_optimization =
+    typename _CG_STL_NAMESPACE::enable_if<async_copy_optimize_tile<TyGroup>::value, void *>::type;
+
+template <class TyGroup>
+using disable_tile_optimization =
+    typename _CG_STL_NAMESPACE::enable_if<!async_copy_optimize_tile<TyGroup>::value, void *>::type;
+
+// Segment for punning to aligned types
+template <unsigned int N>
+struct _Segment {
+    int _seg[N];
+};
+
+// Trivial layout guaranteed-aligned copy-async compatible segments
+template <unsigned int N>
+struct Segment;
+template <>
+struct __align__(4) Segment<1> : public _Segment<1>{};
+template <>
+struct __align__(8) Segment<2> : public _Segment<2>{};
+template <>
+struct __align__(16) Segment<4> : public _Segment<4>{};
+
+// Interleaved element by element copies from source to dest
+template <typename TyGroup, typename TyElem>
+_CG_STATIC_QUALIFIER void inline_copy(TyGroup &group, TyElem *__restrict__ dst, const TyElem *__restrict__ src,
+                                      size_t count) {
+    const unsigned int rank = group.thread_rank();
+    const unsigned int stride = group.size();
+
+    for (size_t idx = rank; idx < count; idx += stride) {
+        dst[idx] = src[idx];
+    }
+}
+
+template <typename TyGroup, typename TyElem, enable_tile_optimization<TyGroup> = nullptr>
+_CG_STATIC_QUALIFIER void accelerated_async_copy(TyGroup &group, TyElem *__restrict__ dst,
+                                                 const TyElem *__restrict__ src, size_t count) {
+    static_assert(async_copy_group_supported<TyGroup>::value,
+                  "Async copy is only supported for groups that represent private shared memory");
+
+    if (count == 0) {
+        return;
+    }
+
+    const bool dstIsNotShared = !__isShared(dst);
+    const bool srcIsNotGlobal = !__isGlobal(src);
+
+    if (dstIsNotShared || srcIsNotGlobal) {
+        inline_copy(group, dst, src, count);
+        return;
+    }
+
+    const unsigned int stride = group.size();
+    const unsigned int rank = group.thread_rank();
+    // Efficient copies require warps to operate on the same amount of work at each step.
+    // remainders are handled in a separate stage to prevent branching
+    const unsigned int subWarpMask = (stride - 1);
+    const unsigned int subwarpCopies = (subWarpMask & (unsigned int)count);
+    const unsigned int maxSubwarpRank = min(rank, subwarpCopies - 1);
+
+    const size_t warpCopies = (count & (~subWarpMask));
+
+    for (size_t idx = 0; idx < warpCopies; idx += stride) {
+        size_t _srcIdx = rank + idx;
+        size_t _dstIdx = rank + idx;
+        __pipeline_memcpy_async(dst + _dstIdx, src + _srcIdx, sizeof(TyElem));
+    }
+
+    if (subwarpCopies) {
+        size_t _srcIdx = warpCopies + maxSubwarpRank;
+        size_t _dstIdx = warpCopies + maxSubwarpRank;
+        __pipeline_memcpy_async(dst + _dstIdx, src + _srcIdx, sizeof(TyElem));
+    }
+}
+
+template <typename TyGroup, typename TyElem, disable_tile_optimization<TyGroup> = nullptr>
+_CG_STATIC_QUALIFIER void accelerated_async_copy(TyGroup &group, TyElem *__restrict__ dst,
+                                                 const TyElem *__restrict__ src, size_t count) {
+    static_assert(async_copy_group_supported<TyGroup>::value,
+                  "Async copy is only supported for groups that represent private shared memory");
+
+    const bool dstIsNotShared = !__isShared(dst);
+    const bool srcIsNotGlobal = !__isGlobal(src);
+
+    if (dstIsNotShared || srcIsNotGlobal) {
+        inline_copy(group, dst, src, count);
+        return;
+    }
+
+    unsigned int stride = group.size();
+    unsigned int rank = group.thread_rank();
+
+    for (size_t idx = rank; idx < count; idx += stride) {
+        size_t _srcIdx = idx;
+        size_t _dstIdx = idx;
+        __pipeline_memcpy_async(dst + _dstIdx, src + _srcIdx, sizeof(TyElem));
+    }
+}
+
+// Determine best possible alignment given an input and initial conditions
+// Attempts to generate as little code as possible, most likely should only be used with 1 and 2 byte alignments
+template <unsigned int MinAlignment, unsigned int MaxAlignment>
+_CG_STATIC_QUALIFIER uint32_t find_best_alignment(void *__restrict__ dst, const void *__restrict__ src) {
+    // Narrowing conversion intentional
+    uint32_t base1 = (uint32_t) reinterpret_cast<uintptr_t>(src);
+    uint32_t base2 = (uint32_t) reinterpret_cast<uintptr_t>(dst);
+
+    uint32_t diff = ((base1) ^ (base2)) & (MaxAlignment - 1);
+
+    // range [MaxAlignment, alignof(elem)], step: x >> 1
+    // over range of possible alignments, choose best available out of range
+    uint32_t out = MaxAlignment;
+#pragma unroll
+    for (uint32_t alignment = (MaxAlignment >> 1); alignment >= MinAlignment; alignment >>= 1) {
+        if (alignment & diff)
+            out = alignment;
+    }
+
+    return out;
+}
+
+// Determine best possible alignment given an input and initial conditions
+// Attempts to generate as little code as possible, most likely should only be used with 1 and 2 byte alignments
+template <typename TyType, typename TyGroup>
+_CG_STATIC_QUALIFIER void copy_like(const TyGroup &group, void *__restrict__ _dst, const void *__restrict__ _src,
+                                    size_t count) {
+    const char *src = reinterpret_cast<const char *>(_src);
+    char *dst = reinterpret_cast<char *>(_dst);
+
+    constexpr uint32_t targetAlignment = (uint32_t)alignof(TyType);
+
+    uint32_t base = (uint32_t) reinterpret_cast<uintptr_t>(src);
+    uint32_t alignOffset = ((~base) + 1) & (targetAlignment - 1);
+
+    inline_copy(group, dst, src, alignOffset);
+    count -= alignOffset;
+    src += alignOffset;
+    dst += alignOffset;
+
+    // Copy using the best available alignment, async_copy expects n-datums, not bytes
+    size_t asyncCount = count / sizeof(TyType);
+    accelerated_async_copy(group, reinterpret_cast<TyType *>(dst), reinterpret_cast<const TyType *>(src), asyncCount);
+    asyncCount *= sizeof(TyType);
+
+    count -= asyncCount;
+    src += asyncCount;
+    dst += asyncCount;
+    inline_copy(group, dst, src, count);
+}
+
+// We must determine alignment and manually align src/dst ourselves
+template <size_t AlignHint>
+struct _memcpy_async_align_dispatch {
+    template <typename TyGroup>
+    _CG_STATIC_QUALIFIER void copy(TyGroup &group, void *__restrict__ dst, const void *__restrict__ src, size_t count) {
+        uint32_t alignment = find_best_alignment<AlignHint, 16>(dst, src);
+
+        // Avoid copying the extra bytes if desired copy count is smaller
+        alignment = count < alignment ? AlignHint : alignment;
+
+        switch (alignment) {
+        default:
+        case 1:
+            inline_copy(group, reinterpret_cast<char *>(dst), reinterpret_cast<const char *>(src), count);
+            break;
+        case 2:
+            inline_copy(group, reinterpret_cast<short *>(dst), reinterpret_cast<const short *>(src), count >> 1);
+            break;
+        case 4:
+            copy_like<Segment<1>>(group, dst, src, count);
+            break;
+        case 8:
+            copy_like<Segment<2>>(group, dst, src, count);
+            break;
+        case 16:
+            copy_like<Segment<4>>(group, dst, src, count);
+            break;
+        }
+    }
+};
+
+// Specialization for 4 byte alignments
+template <>
+struct _memcpy_async_align_dispatch<4> {
+    template <typename TyGroup>
+    _CG_STATIC_QUALIFIER void copy(TyGroup &group, void *__restrict__ _dst, const void *__restrict__ _src,
+                                   size_t count) {
+        const Segment<1> *src = reinterpret_cast<const Segment<1> *>(_src);
+        Segment<1> *dst = reinterpret_cast<Segment<1> *>(_dst);
+
+        // Dispatch straight to aligned LDGSTS calls
+        accelerated_async_copy(group, dst, src, count / sizeof(*dst));
+    }
+};
+
+// Specialization for 8 byte alignments
+template <>
+struct _memcpy_async_align_dispatch<8> {
+    template <typename TyGroup>
+    _CG_STATIC_QUALIFIER void copy(TyGroup &group, void *__restrict__ _dst, const void *__restrict__ _src,
+                                   size_t count) {
+        const Segment<2> *src = reinterpret_cast<const Segment<2> *>(_src);
+        Segment<2> *dst = reinterpret_cast<Segment<2> *>(_dst);
+
+        // Dispatch straight to aligned LDGSTS calls
+        accelerated_async_copy(group, dst, src, count / sizeof(*dst));
+    }
+};
+
+// Alignments over 16 are truncated to 16 and bypass alignment
+// This is the highest performing memcpy available
+template <>
+struct _memcpy_async_align_dispatch<16> {
+    template <typename TyGroup>
+    _CG_STATIC_QUALIFIER void copy(TyGroup &group, void *__restrict__ _dst, const void *__restrict__ _src,
+                                   size_t count) {
+        const Segment<4> *src = reinterpret_cast<const Segment<4> *>(_src);
+        Segment<4> *dst = reinterpret_cast<Segment<4> *>(_dst);
+
+        // Dispatch straight to aligned LDGSTS calls
+        accelerated_async_copy(group, dst, src, count / sizeof(*dst));
+    }
+};
+
+// byte-wide API
+template <size_t Alignment, class TyGroup>
+_CG_STATIC_QUALIFIER void _memcpy_async_dispatch_to_aligned_copy(const TyGroup &group, void *__restrict__ _dst,
+                                                                 const void *__restrict__ _src, size_t count) {
+    static_assert(!(Alignment & (Alignment - 1)), "Known static alignment dispatch must be a power of 2");
+    details::_memcpy_async_align_dispatch<Alignment>::copy(group, _dst, _src, count);
+}
+
+// Internal dispatch APIs
+// These deduce the alignments and sizes necessary to invoke the underlying copy engine
+template <typename Ty>
+using is_void = _CG_STL_NAMESPACE::is_same<Ty, void>;
+
+template <typename Ty>
+using enable_if_not_void = typename _CG_STL_NAMESPACE::enable_if<!is_void<Ty>::value, void *>::type;
+
+template <typename Ty>
+using enable_if_void = typename _CG_STL_NAMESPACE::enable_if<is_void<Ty>::value, void *>::type;
+
+template <typename Ty>
+using enable_if_integral =
+    typename _CG_STL_NAMESPACE::enable_if<_CG_STL_NAMESPACE::is_integral<Ty>::value, void *>::type;
+
+// byte-wide API using aligned_sized_t
+template <class TyGroup, template <size_t> typename Alignment, size_t Hint>
+_CG_STATIC_QUALIFIER void _memcpy_async_bytes(const TyGroup &group, void *__restrict__ _dst,
+                                              const void *__restrict__ _src, const Alignment<Hint> &count) {
+    constexpr size_t _align = (Hint > 16) ? 16 : Hint;
+
+    details::_memcpy_async_dispatch_to_aligned_copy<_align>(group, _dst, _src, (size_t)count);
+}
+
+// byte-wide API using type for aligment
+template <class TyGroup, typename TyElem, typename TySize, size_t Hint = alignof(TyElem),
+          enable_if_not_void<TyElem> = nullptr, enable_if_integral<TySize> = nullptr>
+_CG_STATIC_QUALIFIER void _memcpy_async_bytes(const TyGroup &group, TyElem *__restrict__ _dst,
+                                              const TyElem *__restrict__ _src, const TySize& count) {
+    constexpr size_t _align = (Hint > 16) ? 16 : Hint;
+
+    details::_memcpy_async_dispatch_to_aligned_copy<_align>(group, _dst, _src, count);
+}
+
+// byte-wide API with full alignment deduction required
+template <class TyGroup, typename TyElem, typename TySize, enable_if_void<TyElem> = nullptr,
+          enable_if_integral<TySize> = nullptr>
+_CG_STATIC_QUALIFIER void _memcpy_async_bytes(const TyGroup &group, TyElem *__restrict__ _dst,
+                                              const TyElem *__restrict__ _src, const TySize& count) {
+    details::_memcpy_async_dispatch_to_aligned_copy<1>(group, _dst, _src, count);
+}
+
+// 1d-datum API
+template <class TyGroup, typename TyElem, size_t Hint = alignof(TyElem)>
+_CG_STATIC_QUALIFIER void _memcpy_async_datum(const TyGroup &group, TyElem *__restrict__ dst, const size_t dstCount,
+                                              const TyElem *__restrict__ src, const size_t srcCount) {
+    constexpr unsigned int _align = Hint;
+    const size_t totalCount = min(dstCount, srcCount) * sizeof(TyElem);
+
+    details::_memcpy_async_dispatch_to_aligned_copy<_align>(group, dst, src, totalCount);
+}
+
+// 1d-datum API using aligned_size_t
+template <class TyGroup, typename TyElem, template <size_t> typename Alignment, size_t Hint>
+_CG_STATIC_QUALIFIER void _memcpy_async_datum(const TyGroup &group, TyElem *__restrict__ dst, const Alignment<Hint> &dstCount,
+                                              const TyElem *__restrict__ src, const Alignment<Hint> &srcCount) {
+    constexpr unsigned int _align = Hint;
+    const size_t totalCount = min((size_t)dstCount, (size_t)srcCount) * sizeof(TyElem);
+
+    details::_memcpy_async_dispatch_to_aligned_copy<_align>(group, dst, src, totalCount);
+}
+
+} // namespace details
+
+/*
+ * Group submit batch of async-copy to cover contiguous 1D array
+ * and commit that batch to eventually wait for completion.
+ */
+template <class TyGroup, typename TyElem, typename TySizeT>
+_CG_STATIC_QUALIFIER void memcpy_async(const TyGroup &group, TyElem *__restrict__ _dst, const TyElem *__restrict__ _src,
+                                       const TySizeT &count) {
+    details::_memcpy_async_bytes(group, _dst, _src, count);
+    __pipeline_commit();
+}
+
+/*
+ * Group submit batch of async-copy to cover contiguous 1D array
+ * and commit that batch to eventually wait for completion.
+ * Object counts are in datum sized chunks, not bytes.
+ */
+template <class TyGroup, class TyElem, typename DstLayout, typename SrcLayout>
+_CG_STATIC_QUALIFIER void memcpy_async(const TyGroup &group, TyElem *__restrict__ dst, const DstLayout &dstLayout,
+                                       const TyElem *__restrict__ src, const SrcLayout &srcLayout) {
+    details::_memcpy_async_datum(group, dst, dstLayout, src, srcLayout);
+    __pipeline_commit();
+}
+
+/* Group wait for prior Nth stage of memcpy_async to complete. */
+template <unsigned int Stage, class TyGroup>
+_CG_STATIC_QUALIFIER void wait_prior(const TyGroup &group) {
+    __pipeline_wait_prior(Stage);
+    group.sync();
+}
+
+/* Group wait all previously submitted memcpy_async to complete. */
+template <class TyGroup>
+_CG_STATIC_QUALIFIER void wait(const TyGroup &group) {
+    __pipeline_wait_prior(0);
+    group.sync();
+}
+
+/***************** CG APIs including pipeline are deprecated *****************/
+
+/* Group submit batch of async-copy to cover of contiguous 1D array
+   to a pipeline and commit the batch*/
+template <class TyGroup, class TyElem>
+_CG_DEPRECATED _CG_STATIC_QUALIFIER void memcpy_async(TyGroup &group, TyElem *dst, size_t dstCount, const TyElem *src, size_t srcCount,
+                                       nvcuda::experimental::pipeline &pipe) {
+    details::_memcpy_async_datum(group, dst, dstCount, src, srcCount);
+    pipe.commit();
+}
+
+/* Group wait for prior Nth stage of memcpy_async to complete. */
+template <unsigned int Stage, class TyGroup>
+_CG_DEPRECATED _CG_STATIC_QUALIFIER void wait_prior(TyGroup &group, nvcuda::experimental::pipeline &pipe) {
+    pipe.wait_prior<Stage>();
+    group.sync();
+}
+
+/* Group wait for stage-S of memcpy_async to complete. */
+template <class TyGroup>
+_CG_DEPRECATED _CG_STATIC_QUALIFIER void wait(TyGroup &group, nvcuda::experimental::pipeline &pipe, size_t stage) {
+    pipe.wait(stage);
+    group.sync();
+}
+_CG_END_NAMESPACE
+
+#endif // _CG_ASYNC_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/coalesced_reduce.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/coalesced_reduce.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ba03fc9e4d0c78f07e3e5e1f97aff03e7a3d6f8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/coalesced_reduce.h
@@ -0,0 +1,95 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_COALESCED_REDUCE_H_
+#define _CG_COALESCED_REDUCE_H_
+
+#include "info.h"
+#include "helpers.h"
+#include "cooperative_groups.h"
+#include "partitioning.h"
+#include "coalesced_scan.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+
+template <typename TyVal, typename TyOp, unsigned int TySize, typename ParentT>
+_CG_QUALIFIER auto coalesced_reduce(const __single_warp_thread_block_tile<TySize, ParentT>& group, 
+                                    TyVal&& val,
+                                    TyOp&& op) -> decltype(op(val, val)) {
+    auto out = val;
+    for (int mask = TySize >> 1; mask > 0; mask >>= 1) {
+        out = op(out, group.shfl_xor(out, mask));
+    }
+
+    return out;
+}
+
+template <typename TyVal, typename TyOp>
+_CG_QUALIFIER auto coalesced_reduce(const coalesced_group& group, TyVal&& val, TyOp&& op) -> decltype(op(val, val)) {
+    if (group.size() == 32) {
+        // Full coalesced group can go through faster path by being treated as a tile of size 32
+        auto tile = details::tiled_partition_internal<32, void>();
+        return coalesced_reduce(tile, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+    }
+    else {
+        auto scan_result =
+            inclusive_scan_non_contiguous(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+        unsigned int group_mask = _coalesced_group_data_access::get_mask(group);
+        unsigned int last_thread_id = 31 - __clz(group_mask);
+        return details::tile::shuffle_dispatch<TyVal>::shfl(
+            _CG_STL_NAMESPACE::forward<TyVal>(scan_result), group_mask, last_thread_id, 32);
+    }
+}
+
+} // details
+
+_CG_END_NAMESPACE
+
+#endif // _CG_COALESCED_REDUCE_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/coalesced_scan.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/coalesced_scan.h
new file mode 100644
index 0000000000000000000000000000000000000000..383f4bde059dd8daad7d1c56e99152ea7ee28a08
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/coalesced_scan.h
@@ -0,0 +1,174 @@
+/* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_COALESCED_SCAN_H_
+#define _CG_COALESCED_SCAN_H_
+
+#include "info.h"
+#include "helpers.h"
+#include "cooperative_groups.h"
+#include "partitioning.h"
+#include "functional.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+
+template <typename TyGroup, typename TyVal, typename TyOp>
+_CG_QUALIFIER auto inclusive_scan_contiguous(const TyGroup& group, TyVal&& val, TyOp&& op) -> decltype(op(val, val)) {
+    auto out = val;
+    for (int mask = 1; mask < group.size(); mask <<= 1) {
+        auto tmp = group.shfl_up(out, mask);
+        if (mask <= group.thread_rank()) {
+            out = op(out, tmp);
+        }
+    }
+
+    return out;
+}
+
+template <typename TyGroup, typename TyVal, typename TyOp>
+_CG_QUALIFIER auto inclusive_scan_non_contiguous(const TyGroup& group, TyVal&& val, TyOp&& op) -> decltype(op(val, val)) {
+    const unsigned int groupSize = group.size();
+    auto out = val;
+
+    const unsigned int mask = details::_coalesced_group_data_access::get_mask(group);
+    unsigned int lanemask = details::lanemask32_lt() & mask;
+    unsigned int srcLane = details::laneid();
+
+    const unsigned int base = __ffs(mask)-1; /* lane with rank == 0 */
+    const unsigned int rank = __popc(lanemask);
+
+    for (unsigned int i = 1, j = 1; i < groupSize; i <<= 1) {
+        if (i <= rank) {
+            srcLane -= j;
+            j = i; /* maximum possible lane */
+
+            unsigned int begLane = base + rank - i; /* minimum possible lane */
+
+            /*  Next source lane is in the range [ begLane .. srcLane ]
+                *  If begLane < srcLane then do a binary search.
+                */
+            while (begLane < srcLane) {
+                const unsigned int halfLane = (begLane + srcLane) >> 1;
+                const unsigned int halfMask = lanemask >> halfLane;
+                const unsigned int d = __popc(halfMask);
+                if (d < i) {
+                    srcLane = halfLane - 1; /* halfLane too large */
+                }
+                else if ((i < d) || !(halfMask & 0x01)) {
+                    begLane = halfLane + 1; /* halfLane too small */
+                }
+                else {
+                    begLane = srcLane = halfLane; /* happen to hit */
+                }
+            }
+        }
+
+        auto tmp = details::tile::shuffle_dispatch<TyVal>::shfl(out, mask, srcLane, 32);
+        if (i <= rank) {
+            out = op(out, tmp);
+        }
+    }
+    return out;
+}
+
+template <unsigned int TySize, typename ParentT, typename TyVal, typename TyOp>
+_CG_QUALIFIER auto coalesced_inclusive_scan(const __single_warp_thread_block_tile<TySize, ParentT>& group,
+                                            TyVal&& val,
+                                            TyOp&& op) -> decltype(op(val, val)) {
+    return inclusive_scan_contiguous(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+}
+
+template <typename TyVal, typename TyOp>
+_CG_QUALIFIER auto coalesced_inclusive_scan(const coalesced_group& group, TyVal&& val, TyOp&& op) -> decltype(op(val, val)) {
+    if (group.size() == 32) {
+        return inclusive_scan_contiguous(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+    }
+    else {
+        return inclusive_scan_non_contiguous(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+    }
+}
+
+template <bool IntegralOptimized>
+struct scan_choose_convertion;
+
+template<>
+struct scan_choose_convertion<true> {
+    template <typename TyGroup, typename TyRes, typename TyVal>
+    _CG_STATIC_QUALIFIER details::remove_qual<TyVal> convert_inclusive_to_exclusive(const TyGroup& group, TyRes& result, TyVal&& val) {
+        return result - val;
+    }
+};
+
+template<>
+struct scan_choose_convertion<false> {
+    template <typename TyGroup, typename TyRes, typename TyVal>
+    _CG_STATIC_QUALIFIER details::remove_qual<TyVal> convert_inclusive_to_exclusive(const TyGroup& group, TyRes& result, TyVal&& val) {
+        auto ret = group.shfl_up(result, 1);
+        if (group.thread_rank() == 0) {
+            return {};
+        }
+        else {
+            return ret;
+        }
+    }
+};
+
+template <typename TyGroup, typename TyRes, typename TyVal, typename TyFn>
+_CG_QUALIFIER auto convert_inclusive_to_exclusive(const TyGroup& group, TyRes& result, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    using conversion = scan_choose_convertion<_CG_STL_NAMESPACE::is_same<remove_qual<TyFn>, cooperative_groups::plus<remove_qual<TyVal>>>::value
+                                 && _CG_STL_NAMESPACE::is_integral<remove_qual<TyVal>>::value>;
+    return conversion::convert_inclusive_to_exclusive(group, result, _CG_STL_NAMESPACE::forward<TyVal>(val));
+}
+
+} // details
+
+_CG_END_NAMESPACE
+
+#endif // _CG_COALESCED_SCAN_H_
\ No newline at end of file
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/driver_abi.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/driver_abi.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c866fcf740beb709a106057d28e8a2a1ac37924
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/driver_abi.h
@@ -0,0 +1,99 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_DRIVER_API_H
+#define _CG_DRIVER_API_H
+
+#include "info.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+    template <unsigned int RegId>
+    _CG_QUALIFIER unsigned int load_env_reg() {
+        // Abort by default
+        _CG_ABORT();
+        return 0;
+    }
+
+    template <unsigned int HiReg, unsigned int LoReg>
+    _CG_QUALIFIER unsigned long long load_env_reg64() {
+        unsigned long long registerLo = load_env_reg<LoReg>();
+        unsigned long long registerHi = load_env_reg<HiReg>();
+
+        return (registerHi << 32) | registerLo;
+    }
+
+// inline PTX for accessing registers requires an immediate for the special reg
+# define LOAD_ENVREG(NUMBER) \
+    template <> _CG_QUALIFIER unsigned int load_env_reg<NUMBER>() { \
+        unsigned int r; \
+        asm ("mov.u32 %0, %%envreg" #NUMBER ";" : "=r"(r)); \
+        return r; \
+    }
+
+    // Instantiate loaders for registers used
+    LOAD_ENVREG(0);
+    LOAD_ENVREG(1);
+    LOAD_ENVREG(2);
+# undef LOAD_ENVREG
+
+    struct grid_workspace {
+        unsigned int wsSize;
+        unsigned int barrier;
+    };
+
+    _CG_QUALIFIER grid_workspace* get_grid_workspace() {
+        unsigned long long gridWsAbiAddress = load_env_reg64<1, 2>();
+        // Interpret the address from envreg 1 and 2 as the driver's grid workspace
+        return (reinterpret_cast<grid_workspace*>(gridWsAbiAddress));
+    }
+}
+_CG_END_NAMESPACE
+
+#endif // _CG_DRIVER_API_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/functional.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/functional.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f151fe2c270421ba56e22935e84c4bf93790eff
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/functional.h
@@ -0,0 +1,212 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_FUNCTIONAL_H
+#define _CG_FUNCTIONAL_H
+
+#include "info.h"
+#include "helpers.h"
+
+#ifdef _CG_CPP11_FEATURES
+#ifdef _CG_USE_CUDA_STL
+# include <cuda/std/functional>
+#endif
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+#ifdef _CG_USE_CUDA_STL
+    using cuda::std::plus;
+    using cuda::std::bit_and;
+    using cuda::std::bit_xor;
+    using cuda::std::bit_or;
+#else
+    template <typename Ty> struct plus {__device__ __forceinline__ Ty operator()(Ty arg1, Ty arg2) const {return arg1 + arg2;}};
+    template <typename Ty> struct bit_and {__device__ __forceinline__ Ty operator()(Ty arg1, Ty arg2) const {return arg1 & arg2;}};
+    template <typename Ty> struct bit_xor {__device__ __forceinline__ Ty operator()(Ty arg1, Ty arg2) const {return arg1 ^ arg2;}};
+    template <typename Ty> struct bit_or {__device__ __forceinline__ Ty operator()(Ty arg1, Ty arg2) const {return arg1 | arg2;}};
+#endif // _CG_USE_PLATFORM_STL
+} // details
+
+template <typename Ty>
+struct plus : public details::plus<Ty> {};
+
+template <typename Ty>
+struct less {
+    __device__ __forceinline__ Ty operator()(Ty arg1, Ty arg2) const {
+        return (arg2 < arg1) ? arg2 : arg1;
+    }
+};
+
+template <typename Ty>
+struct greater {
+    __device__ __forceinline__ Ty operator()(Ty arg1, Ty arg2) const {
+        return (arg1 < arg2) ? arg2 : arg1;
+    }
+};
+
+template <typename Ty>
+struct bit_and : public details::bit_and<Ty> {};
+
+template <typename Ty>
+struct bit_xor : public details::bit_xor<Ty> {};
+
+template <typename Ty>
+struct bit_or : public details::bit_or<Ty> {};
+
+#if defined(_CG_HAS_STL_ATOMICS)
+namespace details {
+    template <class Ty>
+    using _atomic_is_type_supported = _CG_STL_NAMESPACE::integral_constant<bool,
+            _CG_STL_NAMESPACE::is_integral<Ty>::value && (sizeof(Ty) == 4 || sizeof(Ty) == 8)>;
+
+    template <typename TyOp> struct _atomic_op_supported                                : public _CG_STL_NAMESPACE::false_type {};
+    template <typename Ty> struct _atomic_op_supported<cooperative_groups::plus<Ty>>    : public _atomic_is_type_supported<Ty> {};
+    template <typename Ty> struct _atomic_op_supported<cooperative_groups::less<Ty>>    : public _atomic_is_type_supported<Ty> {};
+    template <typename Ty> struct _atomic_op_supported<cooperative_groups::greater<Ty>> : public _atomic_is_type_supported<Ty> {};
+    template <typename Ty> struct _atomic_op_supported<cooperative_groups::bit_and<Ty>> : public _atomic_is_type_supported<Ty> {};
+    template <typename Ty> struct _atomic_op_supported<cooperative_groups::bit_or<Ty>>  : public _atomic_is_type_supported<Ty> {};
+    template <typename Ty> struct _atomic_op_supported<cooperative_groups::bit_xor<Ty>> : public _atomic_is_type_supported<Ty> {};
+
+    template<typename TyAtomic, typename TyVal, typename TyOp>
+    _CG_QUALIFIER remove_qual<TyVal> atomic_cas_fallback(TyAtomic&& atomic, TyVal&& val, TyOp&& op) {
+        auto old = atomic.load(cuda::std::memory_order_relaxed);
+        while(!atomic.compare_exchange_weak(old, op(old, val), cuda::std::memory_order_relaxed));
+        return old;
+    }
+
+    template<typename TyOp>
+    struct op_picker;
+
+    template<typename TyVal>
+    struct op_picker<cooperative_groups::plus<TyVal>> {
+        template<typename TyAtomic>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val) {
+            return atomic.fetch_add(val, cuda::std::memory_order_relaxed);
+        }
+    };
+
+    template<typename TyVal>
+    struct op_picker<cooperative_groups::less<TyVal>> {
+        template<typename TyAtomic>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val) {
+            return atomic.fetch_min(val, cuda::std::memory_order_relaxed);
+        }
+    };
+
+    template<typename TyVal>
+    struct op_picker<cooperative_groups::greater<TyVal>> {
+        template<typename TyAtomic>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val) {
+            return atomic.fetch_max(val, cuda::std::memory_order_relaxed);
+        }
+    };
+
+    template<typename TyVal>
+    struct op_picker<cooperative_groups::bit_and<TyVal>> {
+        template<typename TyAtomic>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val) {
+            return atomic.fetch_and(val, cuda::std::memory_order_relaxed);
+        }
+    };
+
+    template<typename TyVal>
+    struct op_picker<cooperative_groups::bit_xor<TyVal>> {
+        template<typename TyAtomic>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val) {
+            return atomic.fetch_xor(val, cuda::std::memory_order_relaxed);
+        }
+    };
+
+    template<typename TyVal>
+    struct op_picker<cooperative_groups::bit_or<TyVal>> {
+        template<typename TyAtomic>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val) {
+            return atomic.fetch_or(val, cuda::std::memory_order_relaxed);
+        }
+    };
+
+    template<bool atomic_supported>
+    struct atomic_update_dispatch {};
+
+    template<>
+    struct atomic_update_dispatch<false> {
+        template<typename TyAtomic, typename TyVal, typename TyOp>
+        _CG_STATIC_QUALIFIER remove_qual<TyVal> atomic_update(TyAtomic& atomic, TyVal&& val, TyOp&& op) {
+            return atomic_cas_fallback(atomic, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+        }
+    };
+
+    template<>
+    struct atomic_update_dispatch<true> {
+        template<typename TyAtomic, typename TyVal, typename TyOp>
+        _CG_STATIC_QUALIFIER TyVal atomic_update(TyAtomic& atomic, TyVal val, TyOp&& op) {
+            using dispatch = op_picker<details::remove_qual<TyOp>>;
+
+            return dispatch::atomic_update(atomic, val);
+        }
+    };
+
+    template<typename TyAtomic, typename TyVal, typename TyOp>
+    _CG_QUALIFIER remove_qual<TyVal> atomic_update(TyAtomic& atomic, TyVal&& val, TyOp&& op) {
+        using dispatch = atomic_update_dispatch<_atomic_op_supported<details::remove_qual<TyOp>>::value>;
+
+        return dispatch::atomic_update(atomic, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+    }
+
+    template<typename TyAtomic, typename TyVal>
+    _CG_QUALIFIER void atomic_store(TyAtomic& atomic, TyVal&& val) {
+        atomic.store(val, cuda::std::memory_order_relaxed);
+    }
+}
+#endif
+
+_CG_END_NAMESPACE
+
+#endif
+#endif //_CG_FUNCTIONAL_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/helpers.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/helpers.h
new file mode 100644
index 0000000000000000000000000000000000000000..1485d9f503daa8d518af75775f7a7a415cb031d4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/helpers.h
@@ -0,0 +1,693 @@
+ /* Copyright 1993-2021 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _COOPERATIVE_GROUPS_HELPERS_H_
+# define _COOPERATIVE_GROUPS_HELPERS_H_
+
+#include "info.h"
+#include "sync.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+#ifdef _CG_CPP11_FEATURES
+    template <typename Ty> struct _is_float_or_half          : public _CG_STL_NAMESPACE::is_floating_point<Ty> {};
+# ifdef _CG_HAS_FP16_COLLECTIVE
+    template <>            struct _is_float_or_half<__half>  : public _CG_STL_NAMESPACE::true_type {};
+    template <>            struct _is_float_or_half<__half2> : public _CG_STL_NAMESPACE::true_type {};
+# endif
+    template <typename Ty>
+    using  is_float_or_half = _is_float_or_half<typename _CG_STL_NAMESPACE::remove_cv<Ty>::type>;
+
+    // Non-STL utility templates 
+    template <typename Ty>
+    using remove_qual = typename _CG_STL_NAMESPACE::remove_cv<typename _CG_STL_NAMESPACE::remove_reference<Ty>::type>::type;
+
+    template <typename TyLhs, typename TyRhs>
+    using is_op_type_same = _CG_STL_NAMESPACE::is_same<remove_qual<TyLhs>, remove_qual<TyRhs>
+    >;
+#endif
+
+    template <typename TyTrunc>
+    _CG_STATIC_QUALIFIER TyTrunc vec3_to_linear(dim3 index, dim3 nIndex) {
+        return ((TyTrunc)index.z * nIndex.y * nIndex.x) +
+               ((TyTrunc)index.y * nIndex.x) +
+                (TyTrunc)index.x;
+    }
+
+    namespace cta {
+
+        _CG_STATIC_QUALIFIER void sync()
+        {
+            __barrier_sync(0);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int num_threads()
+        {
+            return static_cast<unsigned int>(blockDim.x * blockDim.y * blockDim.z);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int thread_rank()
+        {
+            return vec3_to_linear<unsigned int>(threadIdx, blockDim);
+        }
+
+        _CG_STATIC_QUALIFIER dim3 group_index()
+        {
+            return dim3(blockIdx.x, blockIdx.y, blockIdx.z);
+        }
+
+        _CG_STATIC_QUALIFIER dim3 thread_index()
+        {
+            return dim3(threadIdx.x, threadIdx.y, threadIdx.z);
+        }
+
+        _CG_STATIC_QUALIFIER dim3 dim_threads()
+        {
+            return dim3(blockDim.x, blockDim.y, blockDim.z);
+        }
+
+        // Legacy aliases
+        _CG_STATIC_QUALIFIER unsigned int size()
+        {
+            return num_threads();
+        }
+
+        _CG_STATIC_QUALIFIER dim3 block_dim()
+        {
+            return dim_threads();
+        }
+
+    };
+
+    class _coalesced_group_data_access {
+    public:
+        // Retrieve mask of coalesced groups and tiles
+        template <typename TyGroup>
+        _CG_STATIC_QUALIFIER unsigned int get_mask(const TyGroup &group) {
+            return group.get_mask();
+        }
+
+        template <typename TyGroup>
+        _CG_STATIC_QUALIFIER TyGroup construct_from_mask(unsigned int mask) {
+            return TyGroup(mask);
+        }
+
+        template <typename TyGroup>
+        _CG_STATIC_QUALIFIER void modify_meta_group(TyGroup &group, unsigned int mgRank, unsigned int mgSize) {
+            group._data.coalesced.metaGroupRank = mgRank;
+            group._data.coalesced.metaGroupSize = mgSize;
+        }
+    };
+
+    namespace tile {
+        template <unsigned int TileCount, unsigned int TileMask, unsigned int LaneMask, unsigned int ShiftCount>
+        struct _tile_helpers{
+            _CG_STATIC_CONST_DECL unsigned int tileCount = TileCount;
+            _CG_STATIC_CONST_DECL unsigned int tileMask = TileMask;
+            _CG_STATIC_CONST_DECL unsigned int laneMask = LaneMask;
+            _CG_STATIC_CONST_DECL unsigned int shiftCount = ShiftCount;
+        };
+
+        template <unsigned int> struct tile_helpers;
+        template <> struct tile_helpers<32> : public _tile_helpers<1,  0xFFFFFFFF, 0x1F, 5> {};
+        template <> struct tile_helpers<16> : public _tile_helpers<2,  0x0000FFFF, 0x0F, 4> {};
+        template <> struct tile_helpers<8>  : public _tile_helpers<4,  0x000000FF, 0x07, 3> {};
+        template <> struct tile_helpers<4>  : public _tile_helpers<8,  0x0000000F, 0x03, 2> {};
+        template <> struct tile_helpers<2>  : public _tile_helpers<16, 0x00000003, 0x01, 1> {};
+        template <> struct tile_helpers<1>  : public _tile_helpers<32, 0x00000001, 0x00, 0> {};
+
+#ifdef _CG_CPP11_FEATURES
+        namespace shfl {
+            /***********************************************************************************
+             * Recursively Sliced Shuffle
+             *  Purpose:
+             *      Slices an input type a number of times into integral types so that shuffles
+             *      are well defined
+             *  Expectations:
+             *      This object *should not* be used from a reinterpret_cast pointer unless
+             *      some alignment guarantees can be met. Use a memcpy to guarantee that loads
+             *      from the integral types stored within are aligned and correct.
+             **********************************************************************************/
+            template <unsigned int count, bool intSized = (count <= sizeof(int))>
+            struct recursive_sliced_shuffle_helper;
+
+            template <unsigned int count>
+            struct recursive_sliced_shuffle_helper<count, true> {
+                int val;
+
+                template <typename TyFn>
+                _CG_QUALIFIER void invoke_shuffle(const TyFn &shfl) {
+                    val = shfl(val);
+                }
+            };
+
+            template <unsigned int count>
+            struct recursive_sliced_shuffle_helper<count, false> {
+                int val;
+                recursive_sliced_shuffle_helper<count - sizeof(int)> next;
+
+                template <typename TyFn>
+                _CG_QUALIFIER void invoke_shuffle(const TyFn &shfl) {
+                    val = shfl(val);
+                    next.invoke_shuffle(shfl);
+                }
+            };
+        }
+
+        struct _memory_shuffle {
+            template <typename TyElem, typename TyShflFn>
+            _CG_STATIC_QUALIFIER TyElem _shfl_internal(TyElem elem, const TyShflFn& fn) {
+                static_assert(sizeof(TyElem) <= 32, "Cooperative groups collectives are limited to types smaller than 32B");
+                return TyElem{};
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl(TyElem&& elem, unsigned int gMask, unsigned int srcRank, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return 0;
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl_down(TyElem&& elem, unsigned int gMask, unsigned int delta, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return 0;
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl_up(TyElem&& elem, unsigned int gMask, unsigned int delta, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return 0;
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl_xor(TyElem&& elem, unsigned int gMask, unsigned int lMask, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return 0;
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+        };
+
+        /***********************************************************************************
+         * Intrinsic Device Function Shuffle
+         *  Purpose:
+         *      Uses a shuffle helper that has characteristics best suited for moving
+         *      elements between threads
+         *  Expectations:
+         *      Object given will be forced into an l-value type so that it can be used
+         *      with a helper structure that reinterprets the data into intrinsic compatible
+         *      types
+         *  Notes:
+         *      !! TyRet is required so that objects are returned by value and not as
+         *      dangling references depending on the value category of the passed object
+         **********************************************************************************/
+        struct _intrinsic_compat_shuffle {
+            template <unsigned int count>
+            using shfl_helper = shfl::recursive_sliced_shuffle_helper<count>;
+
+            template <typename TyElem, typename TyShflFn>
+            _CG_STATIC_QUALIFIER TyElem _shfl_internal(TyElem elem, const TyShflFn& fn) {
+                static_assert(__is_trivially_copyable(TyElem), "Type is not compatible with device shuffle");
+                shfl_helper<sizeof(TyElem)> helper;
+                memcpy(&helper, &elem, sizeof(TyElem));
+                helper.invoke_shuffle(fn);
+                memcpy(&elem, &helper, sizeof(TyElem));
+                return elem;
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl(TyElem&& elem, unsigned int gMask, unsigned int srcRank, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return __shfl_sync(gMask, val, srcRank, threads);
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl_down(TyElem&& elem, unsigned int gMask, unsigned int delta, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return __shfl_down_sync(gMask, val, delta, threads);
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl_up(TyElem&& elem, unsigned int gMask, unsigned int delta, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return __shfl_up_sync(gMask, val, delta, threads);
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+
+            template <typename TyElem, typename TyRet = remove_qual<TyElem>>
+            _CG_STATIC_QUALIFIER TyRet shfl_xor(TyElem&& elem, unsigned int gMask, unsigned int lMask, unsigned int threads) {
+                auto shfl = [=](int val) -> int {
+                    return __shfl_xor_sync(gMask, val, lMask, threads);
+                };
+
+                return _shfl_internal<TyRet>(_CG_STL_NAMESPACE::forward<TyElem>(elem), shfl);
+            }
+        };
+
+        struct _native_shuffle {
+            template <typename TyElem>
+            _CG_STATIC_QUALIFIER TyElem shfl(
+                    TyElem elem, unsigned int gMask, unsigned int srcRank, unsigned int threads) {
+                return static_cast<TyElem>(__shfl_sync(gMask, elem, srcRank, threads));
+            }
+
+            template <typename TyElem>
+            _CG_STATIC_QUALIFIER TyElem shfl_down(
+                    TyElem elem, unsigned int gMask, unsigned int delta, unsigned int threads) {
+                return static_cast<TyElem>(__shfl_down_sync(gMask, elem, delta, threads));
+            }
+
+            template <typename TyElem>
+            _CG_STATIC_QUALIFIER TyElem shfl_up(
+                    TyElem elem, unsigned int gMask, unsigned int delta, unsigned int threads) {
+                return static_cast<TyElem>(__shfl_up_sync(gMask, elem, delta, threads));
+            }
+
+            template <typename TyElem>
+            _CG_STATIC_QUALIFIER TyElem shfl_xor(
+                    TyElem elem, unsigned int gMask, unsigned int lMask, unsigned int threads) {
+                return static_cast<TyElem>(__shfl_xor_sync(gMask, elem, lMask, threads));
+            }
+        };
+
+        // Almost all arithmetic types are supported by native shuffle
+        // Vector types are the exception
+        template <typename TyElem>
+        using use_native_shuffle = _CG_STL_NAMESPACE::integral_constant<
+            bool,
+            _CG_STL_NAMESPACE::is_integral<
+                remove_qual<TyElem>>::value ||
+            details::is_float_or_half<
+                remove_qual<TyElem>>::value
+        >;
+
+        constexpr unsigned long long _MemoryShuffleCutoff = 32;
+
+        template <typename TyElem,
+                  bool IsNative = use_native_shuffle<TyElem>::value,
+                  bool InMem = (sizeof(TyElem) > _MemoryShuffleCutoff)>
+        struct shuffle_dispatch;
+
+        template <typename TyElem>
+        struct shuffle_dispatch<TyElem, true, false> :  public _native_shuffle {};
+
+        template <typename TyElem>
+        struct shuffle_dispatch<TyElem, false, false> : public _intrinsic_compat_shuffle {};
+
+        template <typename TyElem>
+        struct shuffle_dispatch<TyElem, false, true> :  public _memory_shuffle {};
+
+#endif //_CG_CPP11_FEATURES
+    };
+
+    namespace multi_grid {
+        struct multi_grid_functions;
+    };
+
+    namespace grid {
+        _CG_STATIC_QUALIFIER unsigned int barrier_arrive(unsigned int *bar) {
+            return details::sync_grids_arrive(bar);
+        }
+
+        _CG_STATIC_QUALIFIER void barrier_wait(unsigned int token, unsigned int *bar) {
+            details::sync_grids_wait(token, bar);
+        }
+
+        _CG_STATIC_QUALIFIER void sync(unsigned int *bar) {
+            unsigned int token = details::sync_grids_arrive(bar);
+            details::sync_grids_wait(token, bar);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned long long num_blocks()
+        {
+            // grid.y * grid.z -> [max(65535) * max(65535)] fits within 4b, promote after multiplication
+            // grid.x * (grid.y * grid.z) -> [max(2^31-1) * max(65535 * 65535)]  exceeds 4b, promote before multiplication
+            return (unsigned long long)gridDim.x * (gridDim.y * gridDim.z);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned long long num_threads()
+        {
+            return num_blocks() * cta::num_threads();
+        }
+
+        _CG_STATIC_QUALIFIER unsigned long long block_rank()
+        {
+            return vec3_to_linear<unsigned long long>(blockIdx, gridDim);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned long long thread_rank()
+        {
+            return block_rank() * cta::num_threads() + cta::thread_rank();
+        }
+
+        _CG_STATIC_QUALIFIER dim3 dim_blocks()
+        {
+            return dim3(gridDim.x, gridDim.y, gridDim.z);
+        }
+
+        _CG_STATIC_QUALIFIER dim3 block_index()
+        {
+            return dim3(blockIdx.x, blockIdx.y, blockIdx.z);
+        }
+
+        _CG_STATIC_QUALIFIER dim3 dim_threads()
+        {
+            return dim3(gridDim.x * blockDim.x, gridDim.y * blockDim.y, gridDim.z * blockDim.z);
+        }
+
+        _CG_STATIC_QUALIFIER dim3 thread_index()
+        {
+            return dim3(blockIdx.x * blockDim.x + threadIdx.x,
+                        blockIdx.y * blockDim.y + threadIdx.y,
+                        blockIdx.z * blockDim.z + threadIdx.z);
+        }
+
+#if defined(_CG_HAS_CLUSTER_GROUP)
+        _CG_STATIC_QUALIFIER dim3 dim_clusters() {
+            return __clusterGridDimInClusters();
+        }
+
+        _CG_STATIC_QUALIFIER unsigned long long num_clusters() {
+            const dim3 dimClusters = dim_clusters();
+            return dimClusters.x * dimClusters.y * dimClusters.z;
+        }
+
+        _CG_STATIC_QUALIFIER dim3 cluster_index() {
+            return __clusterIdx();
+        }
+
+        _CG_STATIC_QUALIFIER unsigned long long cluster_rank() {
+            return vec3_to_linear<unsigned long long>(cluster_index(), dim_clusters());
+        }
+#endif
+
+        // Legacy aliases
+        _CG_STATIC_QUALIFIER unsigned long long size()
+        {
+            return num_threads();
+        }
+
+        _CG_STATIC_QUALIFIER dim3 grid_dim()
+        {
+            return dim_blocks();
+        }
+    };
+
+
+#if defined(_CG_HAS_MULTI_GRID_GROUP)
+
+    namespace multi_grid {
+        _CG_STATIC_QUALIFIER unsigned long long get_intrinsic_handle()
+        {
+#if defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__)
+            //this function is defined in device runtime library
+            //which requires separate compilation mode (__CUDACC_RDC__)
+            //or extended whole program mode (__CUDACC_EWP__)
+            return (cudaCGGetIntrinsicHandle(cudaCGScopeMultiGrid));
+#else   /* !(__CUDACC_RDC__ || __CUDACC_EWP__) */
+            return 0;
+#endif  /* __CUDACC_RDC__ || __CUDACC_EWP__ */
+        }
+
+        _CG_STATIC_QUALIFIER void sync(const unsigned long long handle)
+        {
+#if defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__)
+            //this function is defined in device runtime library
+            //which requires separate compilation mode (__CUDACC_RDC__)
+            //or extended whole program mode (__CUDACC_EWP__)
+            cudaError_t err = cudaCGSynchronize(handle, 0);
+#endif  /* __CUDACC_RDC__ || __CUDACC_EWP__ */
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int size(const unsigned long long handle)
+        {
+            unsigned int numThreads = 0;
+#if defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__)
+            //this function is defined in device runtime library
+            //which requires separate compilation mode (__CUDACC_RDC__)
+            //or extended whole program mode (__CUDACC_EWP__)
+            cudaCGGetSize(&numThreads, NULL, handle);
+#endif  /* __CUDACC_RDC__ || __CUDACC_EWP__ */
+            return numThreads;
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int thread_rank(const unsigned long long handle)
+        {
+            unsigned int threadRank = 0;
+#if defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__)
+            //this function is defined in device runtime library
+            //which requires separate compilation mode (__CUDACC_RDC__)
+            //or extended whole program mode (__CUDACC_EWP__)
+            cudaCGGetRank(&threadRank, NULL, handle);
+#endif  /* __CUDACC_RDC__ || __CUDACC_EWP__ */
+            return threadRank;
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int grid_rank(const unsigned long long handle)
+        {
+            unsigned int gridRank = 0;
+#if defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__)
+            //this function is defined in device runtime library
+            //which requires separate compilation mode (__CUDACC_RDC__)
+            //or extended whole program mode (__CUDACC_EWP__)
+            cudaCGGetRank(NULL, &gridRank, handle);
+#endif  /* __CUDACC_RDC__ || __CUDACC_EWP__ */
+            return gridRank;
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int num_grids(const unsigned long long handle)
+        {
+            unsigned int numGrids = 0;
+#if defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__)
+            //this function is defined in device runtime library
+            //which requires separate compilation mode (__CUDACC_RDC__)
+            //or extended whole program mode (__CUDACC_EWP__)
+            cudaCGGetSize(NULL, &numGrids, handle);
+#endif  /* __CUDACC_RDC__ || __CUDACC_EWP__ */
+            return numGrids;
+        }
+
+# ifdef _CG_CPP11_FEATURES
+        struct multi_grid_functions {
+            decltype(multi_grid::get_intrinsic_handle) *get_intrinsic_handle;
+            decltype(multi_grid::sync) *sync;
+            decltype(multi_grid::size) *size;
+            decltype(multi_grid::thread_rank) *thread_rank;
+            decltype(multi_grid::grid_rank) *grid_rank;
+            decltype(multi_grid::num_grids) *num_grids;
+        };
+
+        template <typename = void>
+        _CG_STATIC_QUALIFIER const multi_grid_functions* load_grid_intrinsics() {
+            __constant__ static const multi_grid_functions mgf {
+                &multi_grid::get_intrinsic_handle,
+                &multi_grid::sync,
+                &multi_grid::size,
+                &multi_grid::thread_rank,
+                &multi_grid::grid_rank,
+                &multi_grid::num_grids
+            };
+
+            return &mgf;
+        }
+# endif
+    };
+#endif
+
+#if defined(_CG_HAS_CLUSTER_GROUP)
+    namespace cluster {
+
+        _CG_STATIC_QUALIFIER bool isReal()
+        {
+            return __clusterDimIsSpecified();
+        }
+
+        _CG_STATIC_QUALIFIER void barrier_arrive()
+        {
+            __cluster_barrier_arrive();
+        }
+
+        _CG_STATIC_QUALIFIER void barrier_wait()
+        {
+            __cluster_barrier_wait();
+        }
+
+        _CG_STATIC_QUALIFIER void sync()
+        {
+            barrier_arrive();
+            barrier_wait();
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int query_shared_rank(const void *addr)
+        {
+            return __cluster_query_shared_rank(addr);
+        }
+
+        template <typename T>
+        _CG_STATIC_QUALIFIER T* map_shared_rank(T *addr, int rank)
+        {
+            return static_cast<T*>(__cluster_map_shared_rank(addr, rank));
+        }
+
+        _CG_STATIC_QUALIFIER dim3 block_index()
+        {
+            return __clusterRelativeBlockIdx();
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int block_rank()
+        {
+            return __clusterRelativeBlockRank();
+        }
+
+        _CG_STATIC_QUALIFIER dim3 thread_index()
+        {
+            const dim3 blockIndex = block_index();
+            return dim3(blockIndex.x * blockDim.x + threadIdx.x,
+                        blockIndex.y * blockDim.y + threadIdx.y,
+                        blockIndex.z * blockDim.z + threadIdx.z);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int thread_rank()
+        {
+            return block_rank() * cta::num_threads() + cta::thread_rank();
+        }
+
+        _CG_STATIC_QUALIFIER dim3 dim_blocks()
+        {
+            return __clusterDim();
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int num_blocks()
+        {
+            return __clusterSizeInBlocks();
+        }
+
+        _CG_STATIC_QUALIFIER dim3 dim_threads()
+        {
+            const dim3 dimBlocks = dim_blocks();
+            const unsigned int x = dimBlocks.x * blockDim.x;
+            const unsigned int y = dimBlocks.y * blockDim.y;
+            const unsigned int z = dimBlocks.z * blockDim.z;
+            return dim3(x, y, z);
+        }
+
+        _CG_STATIC_QUALIFIER unsigned int num_threads()
+        {
+            return num_blocks() * cta::num_threads();
+        }
+
+    };
+#endif
+
+    _CG_STATIC_QUALIFIER unsigned int laneid()
+    {
+        unsigned int laneid;
+        asm ("mov.u32 %0, %%laneid;" : "=r"(laneid));
+        return laneid;
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int lanemask32_eq()
+    {
+        unsigned int lanemask32_eq;
+        asm ("mov.u32 %0, %%lanemask_eq;" : "=r"(lanemask32_eq));
+        return (lanemask32_eq);
+    }
+
+    _CG_STATIC_QUALIFIER unsigned int lanemask32_lt()
+    {
+        unsigned int lanemask32_lt;
+        asm ("mov.u32 %0, %%lanemask_lt;" : "=r"(lanemask32_lt));
+        return (lanemask32_lt);
+    }
+
+    _CG_STATIC_QUALIFIER void abort()
+    {
+        _CG_ABORT();
+    }
+
+    template <typename Ty>
+    _CG_QUALIFIER void assert_if_not_arithmetic() {
+#ifdef _CG_CPP11_FEATURES
+        static_assert(
+            _CG_STL_NAMESPACE::is_integral<Ty>::value ||
+            details::is_float_or_half<Ty>::value,
+            "Error: Ty is neither integer or float"
+        );
+#endif //_CG_CPP11_FEATURES
+    }
+
+#ifdef _CG_CPP11_FEATURES
+    _CG_STATIC_QUALIFIER constexpr unsigned int log2(unsigned int x) {
+        return x == 1 ? 0 : 1 + log2(x / 2);
+    }
+#endif //_CG_CPP11_FEATURES
+
+}; // !Namespace internal
+
+_CG_END_NAMESPACE
+
+#endif /* !_COOPERATIVE_GROUPS_HELPERS_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/info.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/info.h
new file mode 100644
index 0000000000000000000000000000000000000000..6abbea1e1ec5b0a92c18baabec7485929fe91c0d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/info.h
@@ -0,0 +1,345 @@
+ /* Copyright 1993-2021 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#include <nv/target>
+
+#ifndef _CG_INFO_H_
+#define _CG_INFO_H_
+/*
+** Define: _CG_VERSION
+*/
+#define _CG_VERSION 1000
+
+/*
+** Define: _CG_ABI_VERSION
+*/
+#ifndef _CG_ABI_VERSION
+# define _CG_ABI_VERSION 1
+#endif
+
+/*
+** Define: _CG_ABI_EXPERIMENTAL
+** Desc: If enabled, sets all features enabled (ABI-breaking or experimental)
+*/
+#if defined(_CG_ABI_EXPERIMENTAL)
+#endif
+
+#define _CG_CONCAT_INNER(x, y) x ## y
+#define _CG_CONCAT_OUTER(x, y) _CG_CONCAT_INNER(x, y)
+#define _CG_NAMESPACE _CG_CONCAT_OUTER(__v, _CG_ABI_VERSION)
+
+#define _CG_BEGIN_NAMESPACE \
+    namespace cooperative_groups { namespace _CG_NAMESPACE {
+#define _CG_END_NAMESPACE \
+    }; using namespace _CG_NAMESPACE; };
+
+#if (defined(__cplusplus) && (__cplusplus >= 201103L)) || (defined(_MSC_VER) && (_MSC_VER >= 1900))
+# define _CG_CPP11_FEATURES
+#endif
+
+#if !defined(_CG_QUALIFIER)
+# define _CG_QUALIFIER __forceinline__ __device__
+#endif
+#if !defined(_CG_STATIC_QUALIFIER)
+# define _CG_STATIC_QUALIFIER static __forceinline__ __device__
+#endif
+#if !defined(_CG_CONSTEXPR_QUALIFIER)
+# if defined(_CG_CPP11_FEATURES)
+#  define _CG_CONSTEXPR_QUALIFIER constexpr __forceinline__ __device__
+# else
+#  define _CG_CONSTEXPR_QUALIFIER _CG_QUALIFIER
+# endif
+#endif
+#if !defined(_CG_STATIC_CONSTEXPR_QUALIFIER)
+# if defined(_CG_CPP11_FEATURES)
+#  define _CG_STATIC_CONSTEXPR_QUALIFIER static constexpr __forceinline__ __device__
+# else
+#  define _CG_STATIC_CONSTEXPR_QUALIFIER _CG_STATIC_QUALIFIER
+# endif
+#endif
+
+#if defined(_MSC_VER)
+# define _CG_DEPRECATED __declspec(deprecated)
+#else
+# define _CG_DEPRECATED __attribute__((deprecated))
+#endif
+
+#if defined(__CUDA_MINIMUM_ARCH__)
+# define _CG_CUDA_ARCH __CUDA_MINIMUM_ARCH__
+#elif defined(__CUDA_ARCH__)
+# define _CG_CUDA_ARCH __CUDA_ARCH__
+#endif
+
+#if (_CG_CUDA_ARCH >= 600) || !defined(_CG_CUDA_ARCH)
+# define _CG_HAS_GRID_GROUP
+#endif
+#if (_CG_CUDA_ARCH >= 600) || !defined(_CG_CUDA_ARCH)
+# define _CG_HAS_MULTI_GRID_GROUP
+#endif
+#if (_CG_CUDA_ARCH >= 700) || !defined(_CG_CUDA_ARCH)
+# define _CG_HAS_MATCH_COLLECTIVE
+#endif
+
+#if ((_CG_CUDA_ARCH >= 800) || !defined(_CG_CUDA_ARCH)) && !defined(_CG_USER_PROVIDED_SHARED_MEMORY)
+# define _CG_HAS_RESERVED_SHARED
+#endif
+
+#if ((_CG_CUDA_ARCH >= 900) || !defined(_CG_CUDA_ARCH)) && \
+    (defined(__NVCC__) || defined(__CUDACC_RTC__) || defined(_CG_CLUSTER_INTRINSICS_AVAILABLE)) && \
+    defined(_CG_CPP11_FEATURES)
+# define _CG_HAS_CLUSTER_GROUP
+#endif
+
+#if (_CG_CUDA_ARCH >= 900) || !defined(_CG_CUDA_ARCH)
+# define _CG_HAS_INSTR_ELECT
+#endif
+
+// Has __half and __half2
+// Only usable if you include the cuda_fp16.h extension, and
+// _before_ including cooperative_groups.h
+#ifdef __CUDA_FP16_TYPES_EXIST__
+# define _CG_HAS_FP16_COLLECTIVE
+#endif
+
+// Include libcu++ where supported.
+#if defined(_CG_CPP11_FEATURES) && !defined(__ibmxl__) && (!defined(_MSC_VER) || defined(_WIN64)) && \
+    !defined(_CG_LIMIT_INCLUDED_DEPENDENCIES)
+# define _CG_USE_CUDA_STL
+#else
+# define _CG_USE_OWN_TRAITS
+#endif
+
+#if defined(_CG_USE_CUDA_STL) && !defined(__QNX__) && (!defined(__CUDA_ARCH__) || \
+    ((!defined(_MSC_VER) && __CUDA_ARCH__ >= 600) || (defined(_MSC_VER) && __CUDA_ARCH__ >= 700)))
+# define _CG_HAS_STL_ATOMICS
+#endif
+
+#ifdef _CG_CPP11_FEATURES
+// Use cuda::std:: for type_traits
+# if defined(_CG_USE_CUDA_STL)
+#  define _CG_STL_NAMESPACE cuda::std
+#  include <cuda/std/type_traits>
+// Use CG's implementation of type traits
+# else
+#  define _CG_STL_NAMESPACE cooperative_groups::details::templates
+# endif
+#endif
+
+#ifdef _CG_CPP11_FEATURES
+# define _CG_STATIC_CONST_DECL static constexpr
+# define _CG_CONST_DECL constexpr
+#else
+# define _CG_STATIC_CONST_DECL static const
+# define _CG_CONST_DECL const
+#endif
+
+#if (defined(_MSC_VER) && !defined(_WIN64)) || defined(__arm__)
+# define _CG_ASM_PTR_CONSTRAINT "r"
+#else
+#  define _CG_ASM_PTR_CONSTRAINT "l"
+#endif
+
+/*
+** Define: CG_DEBUG
+** What: Enables various runtime safety checks
+*/
+#if defined(__CUDACC_DEBUG__) && defined(CG_DEBUG) && !defined(NDEBUG)
+# define _CG_DEBUG
+#endif
+
+#if defined(_CG_DEBUG)
+# include <assert.h>
+# define _CG_ASSERT(x) assert((x));
+# define _CG_ABORT() assert(0);
+#else
+# define _CG_ASSERT(x)
+# define _CG_ABORT() __trap();
+#endif
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+    _CG_STATIC_CONST_DECL unsigned int default_max_block_size = 1024;
+
+#if defined(_CG_CPP11_FEATURES) && !defined(_CG_USE_CUDA_STL)
+namespace templates {
+
+/**
+ * Integral constants
+ **/
+template <typename Ty, Ty Val>
+struct integral_constant {
+    static constexpr Ty value = Val;
+    typedef Ty type;
+
+    _CG_QUALIFIER constexpr operator type() const noexcept { return value; }
+    _CG_QUALIFIER constexpr type operator()() const noexcept { return value; }
+};
+
+typedef integral_constant<bool, true>  true_type;
+typedef integral_constant<bool, false> false_type;
+
+/**
+ * CV Qualifiers
+ **/
+template <class Ty> struct is_lvalue_reference       : public details::templates::false_type {};
+template <class Ty> struct is_lvalue_reference<Ty&>  : public details::templates::true_type {};
+
+template <class Ty> struct remove_reference       {typedef Ty type;};
+template <class Ty> struct remove_reference<Ty&>  {typedef Ty type;};
+template <class Ty> struct remove_reference<Ty&&> {typedef Ty type;};
+
+template <class Ty>
+using remove_reference_t = typename details::templates::remove_reference<Ty>::type;
+
+template <class Ty> struct remove_const           {typedef Ty type;};
+template <class Ty> struct remove_const<const Ty> {typedef Ty type;};
+
+template <class Ty> struct remove_volatile              {typedef Ty type;};
+template <class Ty> struct remove_volatile<volatile Ty> {typedef Ty type;};
+
+template <class Ty> struct remove_cv {typedef typename details::templates::remove_volatile<typename details::templates::remove_const<Ty>::type>::type type;};
+
+template <class Ty>
+using remove_cv_t = typename details::templates::remove_cv<Ty>::type;
+
+template <class Ty>
+_CG_QUALIFIER Ty&& forward(remove_reference_t<Ty> &t) noexcept {
+    return static_cast<Ty&&>(t);
+}
+
+template <class Ty>
+_CG_QUALIFIER Ty&& forward(remove_reference_t<Ty> &&t) noexcept {
+    static_assert(!details::templates::is_lvalue_reference<Ty>::value, "Forwarding an rvalue as an lvalue is not allowed.");
+    return static_cast<Ty&&>(t);
+}
+
+/**
+ * is_integral
+ **/
+template <class Ty> struct _is_integral                     : public details::templates::false_type {};
+template <>         struct _is_integral<bool>               : public details::templates::true_type {};
+template <>         struct _is_integral<char>               : public details::templates::true_type {};
+template <>         struct _is_integral<unsigned char>      : public details::templates::true_type {};
+template <>         struct _is_integral<short>              : public details::templates::true_type {};
+template <>         struct _is_integral<unsigned short>     : public details::templates::true_type {};
+template <>         struct _is_integral<int>                : public details::templates::true_type {};
+template <>         struct _is_integral<unsigned int>       : public details::templates::true_type {};
+template <>         struct _is_integral<long>               : public details::templates::true_type {};
+template <>         struct _is_integral<long long>          : public details::templates::true_type {};
+template <>         struct _is_integral<unsigned long>      : public details::templates::true_type {};
+template <>         struct _is_integral<unsigned long long> : public details::templates::true_type {};
+//Vector type support?
+
+template <typename Ty>
+struct is_integral : public details::templates::_is_integral<typename details::templates::remove_cv<Ty>::type> {};
+
+/**
+ * is_floating_point
+ **/
+template <class Ty> struct _is_floating_point              : public details::templates::false_type {};
+template <>         struct _is_floating_point<float>       : public details::templates::true_type {};
+template <>         struct _is_floating_point<double>      : public details::templates::true_type {};
+template <>         struct _is_floating_point<long double> : public details::templates::true_type {};
+# ifdef __CUDA_FP16_TYPES_EXIST__
+template <>         struct _is_floating_point<__half>      : public details::templates::true_type {};
+template <>         struct _is_floating_point<__half2>     : public details::templates::true_type {};
+# endif
+//Vector type support?
+
+template <typename Ty>
+struct is_floating_point : public details::templates::_is_floating_point<typename details::templates::remove_cv<Ty>::type> {};
+
+template <class T>
+struct is_arithmetic : details::templates::integral_constant<
+    bool,
+    details::templates::is_integral<T>::value ||
+    details::templates::is_floating_point<T>::value> {};
+
+template <typename Ty, bool = details::templates::is_arithmetic<Ty>::value>
+struct _is_unsigned : details::templates::integral_constant<bool, Ty(0) < Ty(-1)> {};
+
+template <typename Ty>
+struct _is_unsigned<Ty,false> : details::templates::false_type {};
+
+template <typename Ty>
+struct is_unsigned : _is_unsigned<typename details::templates::remove_cv<Ty>::type> {};
+
+template <typename Ty> struct _is_pointer      : public details::templates::false_type {};
+template <typename Ty> struct _is_pointer<Ty*> : public details::templates::true_type {};
+
+template <typename Ty>
+struct is_pointer : _is_pointer<typename details::templates::remove_cv<Ty>::type> {};
+
+/**
+ * programmatic type traits
+ **/
+template<bool B, class Ty = void>
+struct enable_if {};
+
+template<class Ty>
+struct enable_if<true, Ty> { typedef Ty type; };
+
+template<bool Cond, typename Ty = void>
+using enable_if_t = typename details::templates::enable_if<Cond, Ty>::type;
+
+template<class Ty1, class Ty2>
+struct is_same : details::templates::false_type {};
+
+template<class Ty>
+struct is_same<Ty, Ty> : details::templates::true_type {};
+
+} // templates
+#endif // _CG_CPP11_FEATURES
+
+} // details
+_CG_END_NAMESPACE
+
+
+#endif // _CG_INFO_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/invoke.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/invoke.h
new file mode 100644
index 0000000000000000000000000000000000000000..f00314ce140e390be90a1ab3c328fd73d73c0d46
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/invoke.h
@@ -0,0 +1,189 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CG_INVOKE_H
+#define _CG_INVOKE_H
+
+#include "info.h"
+#include "helpers.h"
+
+#if defined(_CG_CPP11_FEATURES)
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+
+    template <typename Group>
+    struct _elect_group_supported : _CG_STL_NAMESPACE::false_type {};
+#ifdef _CG_HAS_INSTR_ELECT
+    template<>
+    struct _elect_group_supported<coalesced_group> : _CG_STL_NAMESPACE::true_type {};
+    template<unsigned int Size, typename Parent>
+    struct _elect_group_supported<thread_block_tile<Size, Parent>> :
+        _CG_STL_NAMESPACE::integral_constant<bool, (Size <= 32)> {};
+#endif
+
+    template <typename Group>
+    struct elect_group_supported : public _elect_group_supported<details::remove_qual<Group>> {};
+
+    template<typename Group>
+    _CG_STATIC_QUALIFIER bool elect_one(const Group& group, unsigned int mask, unsigned int& leader_lane) {
+        int is_leader = 0;
+#ifdef _CG_HAS_INSTR_ELECT
+        asm("{\n\t"
+          " .reg .pred p;\n\t"
+          "  elect.sync %0|p, %2;\n\t"
+          " @p mov.s32 %1, 1;\n\t"
+          "}"
+          : "+r"(leader_lane), "+r"(is_leader) : "r" (mask));
+#endif
+        return is_leader;
+    }
+
+    template<bool UseElect>
+    struct invoke_one_impl {};
+
+    template<>
+    struct invoke_one_impl<true> {
+        template<typename Group, typename Fn, typename... Args>
+        _CG_STATIC_QUALIFIER void invoke_one(const Group& group, Fn&& fn, Args&&... args) {
+            auto mask = details::_coalesced_group_data_access::get_mask(group);
+            unsigned int leader_lane = 0;
+
+            if (elect_one(group, mask, leader_lane)) {
+                _CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...);
+            }
+        }
+
+        template<typename Group, typename Fn, typename... Args>
+        _CG_STATIC_QUALIFIER auto invoke_one_broadcast(const Group& group, Fn&& fn, Args&&... args)
+                -> typename _CG_STL_NAMESPACE::remove_reference<
+                    decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...))>::type {
+
+            using ResultType = decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...));
+            details::remove_qual<ResultType> result;
+            auto mask = details::_coalesced_group_data_access::get_mask(group);
+            unsigned int leader_lane = 0;
+
+            if (elect_one(group, mask, leader_lane)) {
+                result = _CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...);
+            }
+
+            // Need to use low level api instead of group.shfl, because elect_one returns lane id, not group rank.
+            return tile::shuffle_dispatch<ResultType>::shfl(result, mask, leader_lane, 32);
+        }
+    };
+
+    template<>
+    struct invoke_one_impl<false> {
+        template<typename Group, typename Fn, typename... Args>
+        _CG_STATIC_QUALIFIER void invoke_one(const Group& group, Fn&& fn, Args&&... args) {
+            if (group.thread_rank() == 0) {
+                _CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...);
+            }
+        }
+
+        template<typename Group, typename Fn, typename... Args>
+        _CG_STATIC_QUALIFIER auto invoke_one_broadcast(const Group& group, Fn&& fn, Args&&... args)
+                -> typename _CG_STL_NAMESPACE::remove_reference<
+                    decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...))>::type {
+
+            using ResultType = decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...));
+            details::remove_qual<ResultType> result;
+
+            if (group.thread_rank() == 0) {
+                result = _CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...);
+            }
+
+            return group.shfl(result, 0);
+        }
+    };
+
+
+}; // namespace details
+
+template<typename Group, typename Fn, typename... Args>
+_CG_QUALIFIER void invoke_one(const Group& group, Fn&& fn, Args&&... args) {
+    using impl = details::invoke_one_impl<details::elect_group_supported<Group>::value>;
+    impl::invoke_one(group, _CG_STL_NAMESPACE::forward<Fn>(fn), _CG_STL_NAMESPACE::forward<Args>(args)...);
+}
+
+template<typename Fn, typename... Args>
+_CG_QUALIFIER auto invoke_one_broadcast(const coalesced_group& group, Fn&& fn, Args&&... args)
+        -> typename _CG_STL_NAMESPACE::remove_reference<
+            decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...))>::type {
+
+    using ResultType = decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...));
+    static_assert(!_CG_STL_NAMESPACE::is_same<ResultType, void>::value,
+                  "For invocables returning void invoke_one should be used instead");
+    using impl = details::invoke_one_impl<details::elect_group_supported<coalesced_group>::value>;
+    return impl::invoke_one_broadcast(group,
+                                      _CG_STL_NAMESPACE::forward<Fn>(fn),
+                                      _CG_STL_NAMESPACE::forward<Args>(args)...);
+}
+
+template<unsigned int Size, typename Parent, typename Fn, typename... Args>
+_CG_QUALIFIER auto invoke_one_broadcast(const thread_block_tile<Size, Parent>& group, Fn&& fn, Args&&... args)
+        -> typename _CG_STL_NAMESPACE::remove_reference<
+            decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...))>::type {
+
+    using ResultType = decltype(_CG_STL_NAMESPACE::forward<Fn>(fn)(_CG_STL_NAMESPACE::forward<Args>(args)...));
+    static_assert(!_CG_STL_NAMESPACE::is_same<ResultType, void>::value,
+                  "For invocables returning void invoke_one should be used instead");
+    using impl = details::invoke_one_impl<details::elect_group_supported<thread_block_tile<Size, Parent>>::value>;
+    return impl::invoke_one_broadcast(group,
+                                      _CG_STL_NAMESPACE::forward<Fn>(fn),
+                                      _CG_STL_NAMESPACE::forward<Args>(args)...);
+}
+
+_CG_END_NAMESPACE
+
+#endif //_CG_CPP11_FEATURES
+
+#endif // _CG_INVOKE_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/memory.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/memory.h
new file mode 100644
index 0000000000000000000000000000000000000000..25fdc7646932889a4992b8d3a6249a610c76709a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/memory.h
@@ -0,0 +1,136 @@
+/* Copyright 1993-2022 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _COOPERATIVE_GROUPS_MEMORY_H_
+# define _COOPERATIVE_GROUPS_MEMORY_H_
+
+#include "info.h"
+
+_CG_BEGIN_NAMESPACE
+
+#if defined(_CG_CPP11_FEATURES)
+namespace details {
+    _CG_STATIC_CONST_DECL int scratch_num_reserved_bytes = 12;
+
+    // Should only be called for SM80+
+    _CG_STATIC_QUALIFIER void* reserved_shared_ptr()
+    {
+        unsigned long long ptr = 0;
+        NV_IF_TARGET(NV_PROVIDES_SM_80,
+        (asm ("{\n\t"
+             " .reg .u32 start;\n\t"
+             " .reg .u64 extended;\n\t"
+             " mov.u32 start, %%reserved_smem_offset_1;\n\t"
+             " cvt.u64.u32 extended, start;\n\t"
+             " cvta.shared.u64 %0, extended;\n\t"
+             "}"
+             : "=l"(ptr));)
+        )
+        return reinterpret_cast<void*>(ptr);
+    }
+
+    struct multi_warp_scratch {
+        // One barrier per possible size of the group.
+        _CG_STATIC_CONST_DECL unsigned int memory_barriers_count = 5;
+        _CG_STATIC_CONST_DECL size_t sync_memory_size = memory_barriers_count * sizeof(barrier_t);
+
+        using communication_type = unsigned long long;
+        _CG_STATIC_CONST_DECL size_t communication_size = sizeof(communication_type);
+
+        // Layout of the scratch space:
+        barrier_t barriers[memory_barriers_count];
+        char reserved[scratch_num_reserved_bytes]; // Reserve 12 bytes for future use
+        communication_type communication_memory[default_max_block_size / 32];
+
+        _CG_STATIC_CONSTEXPR_QUALIFIER unsigned int scratch_size_needed(unsigned int max_block_size) {
+            // One slot of collectives memory per warp.
+            return scratch_num_reserved_bytes + (unsigned int)sync_memory_size + max_block_size / 32 * (unsigned int)communication_size;
+        }
+
+        _CG_QUALIFIER void init_barriers(unsigned int thread_rank) {
+            if (thread_rank < memory_barriers_count) {
+                barriers[thread_rank] = 0;
+            }
+        }
+    };
+
+#if defined(_CG_HAS_RESERVED_SHARED)
+    // CG can expect at least 288 bytes available in reserved shared
+    static_assert(sizeof(multi_warp_scratch) <= 288, "multi-warp scratch size is too large");
+#endif
+
+    // Make sure the structure can fit into the user provided memory
+    static_assert(sizeof(multi_warp_scratch) <= multi_warp_scratch::scratch_size_needed(default_max_block_size),
+                  "multi-warp scratch size is too large");
+
+
+    _CG_QUALIFIER multi_warp_scratch* get_scratch_ptr(void* user_scratch) {
+        void *ptr;
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+            (ptr = reserved_shared_ptr();)
+        ,
+            (ptr = user_scratch;)
+        )
+        return static_cast<multi_warp_scratch*>(ptr);
+
+    }
+
+}
+
+template <unsigned int MaxBlockSize = details::default_max_block_size>
+struct __align__(details::multi_warp_scratch::communication_size) block_tile_memory {
+private:
+#if !defined(_CG_HAS_RESERVED_SHARED)
+    char scratch[details::multi_warp_scratch::scratch_size_needed(MaxBlockSize)];
+#endif
+};
+#endif
+
+_CG_END_NAMESPACE
+
+#endif /* !_COOPERATIVE_GROUPS_MEMORY_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/partitioning.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/partitioning.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe66fb926876c045b49a2c8f0b83379dae7599fa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/partitioning.h
@@ -0,0 +1,160 @@
+/*
+ * Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CG_PARTITIONING_H
+#define _CG_PARTITIONING_H
+
+#include "info.h"
+#include "helpers.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+
+    template <typename TyGroup>
+    _CG_STATIC_QUALIFIER coalesced_group _binary_partition(const TyGroup &tile, bool pred) {
+        const unsigned int fullMask = ~0u;
+
+        unsigned int thisMask = _coalesced_group_data_access::get_mask(tile);
+        unsigned int predMask = pred ? 0 : fullMask;
+        unsigned int setMask = __ballot_sync(thisMask, pred);
+
+        if (setMask == thisMask || setMask == 0) {
+            coalesced_group subTile = _coalesced_group_data_access::construct_from_mask<coalesced_group>(thisMask);
+            _coalesced_group_data_access::modify_meta_group(subTile, 0, 1);
+            return subTile;
+        }
+        else {
+            unsigned int subMask = thisMask & (setMask ^ predMask);
+            coalesced_group subTile = _coalesced_group_data_access::construct_from_mask<coalesced_group>(subMask);
+            _coalesced_group_data_access::modify_meta_group(subTile, pred, 2);
+            return subTile;
+        }
+    }
+
+#if defined(_CG_HAS_MATCH_COLLECTIVE) && defined(_CG_CPP11_FEATURES)
+    template <typename TyPredicate>
+    struct _labeled_partition_dispatch {
+        template <typename TyGroup>
+        _CG_QUALIFIER coalesced_group operator()(const TyGroup &tile, TyPredicate pred) {
+            unsigned int thisMask = _coalesced_group_data_access::get_mask(tile);
+            unsigned int subMask  = __match_any_sync(thisMask, pred);
+            unsigned int laneId   = details::laneid();
+
+            coalesced_group subTile = _coalesced_group_data_access::construct_from_mask<coalesced_group>(subMask);
+
+            int leaderLaneId = __ffs(subMask) - 1;
+            bool isLeader = leaderLaneId == laneId;
+            unsigned int leaderMask = __ballot_sync(thisMask, isLeader);
+
+            // Count leaders with lower laneid, that will be the meta rank of this tile
+            unsigned int tileRank = __popc(leaderMask & ((1 << leaderLaneId) - 1));
+
+            _coalesced_group_data_access::modify_meta_group(subTile, tileRank, __popc(leaderMask));
+
+            return subTile;
+        }
+    };
+
+    template <>
+    struct _labeled_partition_dispatch<bool> {
+        template <typename TyGroup>
+        _CG_QUALIFIER coalesced_group operator()(const TyGroup &tile, bool pred) {
+            return _binary_partition(tile, pred);
+        }
+    };
+
+    template <typename TyPredicate>
+    struct _labeled_partition_dispatch<TyPredicate*> {
+        template <typename TyGroup>
+        _CG_QUALIFIER coalesced_group operator()(const TyGroup &tile, TyPredicate* pred) {
+            auto impl = _labeled_partition_dispatch<unsigned long long>();
+            return impl(tile, reinterpret_cast<unsigned long long>(pred));
+        }
+    };
+#endif
+}; // namespace details
+
+_CG_STATIC_QUALIFIER coalesced_group binary_partition(const coalesced_group &tile, bool pred) {
+    return details::_binary_partition(tile, pred);
+}
+
+template <unsigned int Size, typename ParentT>
+_CG_STATIC_QUALIFIER coalesced_group binary_partition(const thread_block_tile<Size, ParentT> &tile, bool pred) {
+#ifdef _CG_CPP11_FEATURES
+    static_assert(Size <= 32, "Binary partition is available only for tiles of size smaller or equal to 32");
+#endif
+    return details::_binary_partition(tile, pred);
+}
+
+
+#if defined(_CG_HAS_MATCH_COLLECTIVE) && defined(_CG_CPP11_FEATURES)
+template <typename TyPredicate>
+_CG_STATIC_QUALIFIER coalesced_group labeled_partition(const coalesced_group &tile, TyPredicate pred) {
+    static_assert(_CG_STL_NAMESPACE::is_integral<TyPredicate>::value ||
+                  _CG_STL_NAMESPACE::is_pointer<TyPredicate>::value,
+                  "labeled_partition predicate must be an integral or pointer type");
+    auto dispatch = details::_labeled_partition_dispatch<details::remove_qual<TyPredicate>>();
+    return dispatch(tile, pred);
+}
+
+template <typename TyPredicate, unsigned int Size, typename ParentT>
+_CG_STATIC_QUALIFIER coalesced_group labeled_partition(const thread_block_tile<Size, ParentT> &tile, TyPredicate pred) {
+    static_assert(_CG_STL_NAMESPACE::is_integral<TyPredicate>::value ||
+                  _CG_STL_NAMESPACE::is_pointer<TyPredicate>::value,
+                  "labeled_partition predicate must be an integral or pointer type");
+    static_assert(Size <= 32, "Labeled partition is available only for tiles of size smaller or equal to 32");
+    auto dispatch = details::_labeled_partition_dispatch<details::remove_qual<TyPredicate>>();
+    return dispatch(tile, pred);
+}
+#endif
+
+_CG_END_NAMESPACE
+
+#endif // _CG_PARTITIONING_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/reduce.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/reduce.h
new file mode 100644
index 0000000000000000000000000000000000000000..b89d7fbaf340c1febd6c8a7de13f346daa1a1371
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/reduce.h
@@ -0,0 +1,424 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_REDUCE_H_
+#define _CG_REDUCE_H_
+
+#include "info.h"
+#include "helpers.h"
+#include "coalesced_reduce.h"
+#include "functional.h"
+#include "cooperative_groups.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+
+    template <class Ty>
+    using _redux_is_add_supported = _CG_STL_NAMESPACE::integral_constant<
+            bool,
+            _CG_STL_NAMESPACE::is_integral<Ty>::value && (sizeof(Ty) <= 4)>;
+
+    template <class Ty>
+    using redux_is_add_supported = _redux_is_add_supported<Ty>;
+
+    // A specialization for 64 bit logical operations is possible
+    // but for now only accelerate 32 bit bitwise ops
+    template <class Ty>
+    using redux_is_logical_supported = redux_is_add_supported<Ty>;
+
+    // Base operator support case
+    template <class TyOp, class Ty> struct _redux_op_supported                 : public _CG_STL_NAMESPACE::false_type {};
+    template <class Ty> struct _redux_op_supported<cooperative_groups::plus<Ty>,    Ty> : public redux_is_add_supported<Ty> {};
+    template <class Ty> struct _redux_op_supported<cooperative_groups::less<Ty>,    Ty> : public redux_is_add_supported<Ty> {};
+    template <class Ty> struct _redux_op_supported<cooperative_groups::greater<Ty>, Ty> : public redux_is_add_supported<Ty> {};
+    template <class Ty> struct _redux_op_supported<cooperative_groups::bit_and<Ty>, Ty> : public redux_is_logical_supported<Ty> {};
+    template <class Ty> struct _redux_op_supported<cooperative_groups::bit_or<Ty>,  Ty> : public redux_is_logical_supported<Ty> {};
+    template <class Ty> struct _redux_op_supported<cooperative_groups::bit_xor<Ty>, Ty> : public redux_is_logical_supported<Ty> {};
+
+    template <class Ty, template <class> class TyOp>
+    using redux_op_supported = _redux_op_supported<
+            typename details::remove_qual<TyOp<Ty>>,
+            Ty>;
+
+    // Groups smaller than 16 actually have worse performance characteristics when used with redux
+    // tiles of size 16 and 32 perform the same or better and have better code generation profiles
+    template <class TyGroup> struct _redux_group_optimized : public _CG_STL_NAMESPACE::false_type {};
+
+    template <unsigned int Sz, typename TyPar>
+    struct _redux_group_optimized<cooperative_groups::thread_block_tile<Sz, TyPar>> : public _CG_STL_NAMESPACE::integral_constant<
+                                                                                            bool,
+                                                                                            (Sz >= 16)> {};
+    template <unsigned int Sz, typename TyPar>
+    struct _redux_group_optimized<internal_thread_block_tile<Sz, TyPar>>            : public _CG_STL_NAMESPACE::integral_constant<
+                                                                                            bool,
+                                                                                            (Sz >= 16)> {};
+    template <>
+    struct _redux_group_optimized<cooperative_groups::coalesced_group>              : public _CG_STL_NAMESPACE::true_type  {};
+
+    template <typename TyGroup>
+    using redux_group_optimized = _redux_group_optimized<details::remove_qual<TyGroup>>;
+
+    template <template <class> class TyOp>
+    _CG_STATIC_QUALIFIER int pick_redux(int mask, int val);
+    template <template <class> class TyOp>
+    _CG_STATIC_QUALIFIER unsigned int pick_redux(int mask, unsigned int val);
+
+    template <> _CG_QUALIFIER int pick_redux<cooperative_groups::plus>(int mask, int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_add_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER int pick_redux<cooperative_groups::less>(int mask, int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_min_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER int pick_redux<cooperative_groups::greater>(int mask, int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_max_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER int pick_redux<cooperative_groups::bit_and>(int mask, int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return static_cast<int>(__reduce_and_sync(mask, val));), return 0;)
+    }
+    template <> _CG_QUALIFIER int pick_redux<cooperative_groups::bit_xor>(int mask, int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return static_cast<int>(__reduce_xor_sync(mask, val));), return 0;)
+    }
+    template <> _CG_QUALIFIER int pick_redux<cooperative_groups::bit_or>(int mask, int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return static_cast<int>(__reduce_or_sync(mask, val));), return 0;)
+    }
+
+    template <> _CG_QUALIFIER unsigned int pick_redux<cooperative_groups::plus>(int mask, unsigned int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_add_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER unsigned int pick_redux<cooperative_groups::less>(int mask, unsigned int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_min_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER unsigned int pick_redux<cooperative_groups::greater>(int mask, unsigned int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_max_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER unsigned int pick_redux<cooperative_groups::bit_and>(int mask, unsigned int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_and_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER unsigned int pick_redux<cooperative_groups::bit_xor>(int mask, unsigned int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_xor_sync(mask, val);), return 0;)
+    }
+    template <> _CG_QUALIFIER unsigned int pick_redux<cooperative_groups::bit_or>(int mask, unsigned int val) {
+        NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80, (return __reduce_or_sync(mask, val);), return 0;)
+    }
+
+    template <typename TyVal, bool = _CG_STL_NAMESPACE::is_unsigned<TyVal>::value>
+    struct _accelerated_op;
+
+    // Signed type redux intrinsic dispatch
+    template <typename TyVal>
+    struct _accelerated_op<TyVal, false> {
+        template <template <class> class TyOp>
+        _CG_STATIC_QUALIFIER TyVal redux(int mask, TyVal val) {
+            return static_cast<TyVal>(pick_redux<TyOp>(mask, static_cast<int>(val)));
+        }
+    };
+
+    // Unsigned type redux intrinsic dispatch
+    template <typename TyVal>
+    struct _accelerated_op<TyVal, true> {
+        template <template <class> class TyOp>
+        _CG_STATIC_QUALIFIER TyVal redux(int mask, TyVal val) {
+            return static_cast<TyVal>(pick_redux<TyOp>(mask, static_cast<unsigned int>(val)));
+        }
+    };
+
+    template <typename TyVal>
+    using accelerated_op = _accelerated_op<TyVal>;
+
+
+    template <typename TyVal, typename TyFnInput, typename TyGroup>
+    class _redux_dispatch {
+        template <class Ty, template <class> class TyOp>
+        using _redux_is_usable = _CG_STL_NAMESPACE::integral_constant<bool,
+            redux_op_supported<Ty, TyOp>::value &&
+            redux_group_optimized<TyGroup>::value>;
+
+        template <class Ty, template <class> class TyOp>
+        using redux_is_usable = typename _CG_STL_NAMESPACE::enable_if<_redux_is_usable<Ty, TyOp>::value, void>::type*;
+
+        template <class Ty, template <class> class TyOp>
+        using redux_is_not_usable = typename _CG_STL_NAMESPACE::enable_if<!_redux_is_usable<Ty, TyOp>::value, void>::type*;
+
+    public:
+        // Dispatch to redux if the combination of op and args are supported
+        template<
+            template <class> class TyOp,
+            redux_is_usable<TyFnInput, TyOp> = nullptr>
+        _CG_STATIC_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyOp<TyFnInput>&& op) -> decltype(op(val, val)) {
+            NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+                // Retrieve the mask for the group and dispatch to redux
+                return accelerated_op<TyFnInput>::template redux<TyOp>(_coalesced_group_data_access::get_mask(group), _CG_STL_NAMESPACE::forward<TyVal>(val));
+            ,
+                // Arch does not support redux, fallback to shuffles
+                return coalesced_reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp<TyFnInput>>(op));
+            )
+        }
+
+        template<
+            template <class> class TyOp,
+            redux_is_usable<TyFnInput, TyOp> = nullptr>
+        _CG_STATIC_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyOp<TyFnInput>& op) -> decltype(op(val, val)) {
+            NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+                // Retrieve the mask for the group and dispatch to redux
+                return accelerated_op<TyFnInput>::template redux<TyOp>(_coalesced_group_data_access::get_mask(group), _CG_STL_NAMESPACE::forward<TyVal>(val));
+            ,
+                // Arch does not support redux, fallback to shuffles
+                return coalesced_reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp<TyFnInput>>(op));
+            )
+        }
+
+        // Fallback shuffle sync reduction
+        template <
+            template <class> class TyOp,
+            redux_is_not_usable<TyFnInput, TyOp> = nullptr>
+        _CG_STATIC_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyOp<TyFnInput>&& op) -> decltype(op(val, val)) {
+            //Dispatch to fallback shuffle sync accelerated reduction
+            return coalesced_reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp<TyFnInput>>(op));
+        }
+
+    };
+
+    // Group support for reduce.
+    template <class TyGroup> struct _reduce_group_supported : public _CG_STL_NAMESPACE::false_type {};
+
+    template <unsigned int Sz, typename TyPar>
+    struct _reduce_group_supported<cooperative_groups::thread_block_tile<Sz, TyPar>> : public _CG_STL_NAMESPACE::true_type {};
+    template <unsigned int Sz, typename TyPar>
+    struct _reduce_group_supported<internal_thread_block_tile<Sz, TyPar>>            : public _CG_STL_NAMESPACE::true_type {};
+    template <>
+    struct _reduce_group_supported<cooperative_groups::coalesced_group>              : public _CG_STL_NAMESPACE::true_type {};
+
+    template <typename TyGroup>
+    using reduce_group_supported = _reduce_group_supported<details::remove_qual<TyGroup>>;
+
+    template <typename TyVal, typename TyFnInput, template <class> class TyOp, typename TyGroup>
+    _CG_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyOp<TyFnInput>&& op) -> decltype(op(val, val)) {
+        static_assert(details::is_op_type_same<TyFnInput, TyVal>::value, "Operator and argument types differ");
+
+        using dispatch = details::_redux_dispatch<TyVal, TyFnInput, TyGroup>;
+        return dispatch::reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp<TyFnInput>>(op));
+    }
+
+    template <typename TyVal, typename TyFnInput, template <class> class TyOp, typename TyGroup>
+    _CG_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyOp<TyFnInput>& op) -> decltype(op(val, val)) {
+        static_assert(details::is_op_type_same<TyFnInput, TyVal>::value, "Operator and argument types differ");
+
+        using dispatch = details::_redux_dispatch<TyVal, TyFnInput, TyGroup>;
+        return dispatch::reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp<TyFnInput>>(op));
+    }
+
+
+    template <typename TyVal, typename TyOp, typename TyGroup>
+    _CG_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyOp&& op) -> decltype(op(val, val)) {
+        return details::coalesced_reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyOp>(op));
+    }
+
+    template <unsigned int GroupId>
+    struct tile_reduce_dispatch;
+
+    template <>
+    struct tile_reduce_dispatch<details::coalesced_group_id> {
+        template <typename TyGroup, typename TyVal, typename TyFn>
+        _CG_STATIC_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+            return details::reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+        }
+    };
+
+#if defined(_CG_CPP11_FEATURES)
+    template <>
+    struct tile_reduce_dispatch<details::multi_tile_group_id> {
+        template <unsigned int Size, typename ParentT, typename TyVal, typename TyFn>
+        _CG_STATIC_QUALIFIER auto reduce(const thread_block_tile<Size, ParentT>& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+            using warpType = details::internal_thread_block_tile<32, __static_size_multi_warp_tile_base<Size>>;
+            using TyRet = details::remove_qual<TyVal>;
+            const unsigned int num_warps = Size / 32;
+
+            auto warp_lambda = [&] (const warpType& warp, TyRet* warp_scratch_location) {
+                    *warp_scratch_location =
+                        details::reduce(warp, _CG_STL_NAMESPACE::forward<TyVal>(val), op);
+            };
+            auto inter_warp_lambda =
+                [&] (const details::internal_thread_block_tile<num_warps, warpType>& subwarp, TyRet* thread_scratch_location) {
+                    *thread_scratch_location =
+                        details::reduce(subwarp, *thread_scratch_location, _CG_STL_NAMESPACE::forward<TyFn>(op));
+            };
+            return details::multi_warp_collectives_helper<TyRet>(group, warp_lambda, inter_warp_lambda);
+        }
+    };
+
+    template <unsigned int GroupId>
+    struct tile_async_reduce_dispatch;
+
+    template <>
+    struct tile_async_reduce_dispatch<details::coalesced_group_id> {
+        template <typename GroupT, typename TyDst, typename TyVal, typename TyFn, typename TyResHandler>
+        _CG_STATIC_QUALIFIER void reduce(const GroupT& group, TyDst& dst, TyVal&& val, TyFn&& op, TyResHandler& res_handler) {
+            // Do regular, in group reduction
+            auto result = details::reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), op);
+
+            // One thread stores/updates the destination
+            if (group.thread_rank() == 0) {
+                res_handler(result);
+            }
+        }
+    };
+
+    template <>
+    struct tile_async_reduce_dispatch<details::multi_tile_group_id> {
+        template <unsigned int TySize, typename ParentT, typename TyDst, typename TyInputVal, typename TyFn, typename TyResHandler>
+        _CG_STATIC_QUALIFIER void reduce(const thread_block_tile<TySize, ParentT>& group, TyDst& dst, TyInputVal&& val, TyFn&& op, TyResHandler& res_handler) {
+            using TyVal = remove_qual<TyInputVal>;
+            const unsigned int num_warps = TySize / 32;
+            details::barrier_t* sync_location = multi_warp_sync_location_getter(group);
+            auto warp_scratch_location = multi_warp_scratch_location_getter<TyVal>(group, group.thread_rank() / 32);
+
+            // Do in warp reduce
+            auto warp = details::tiled_partition_internal<32, thread_block_tile<TySize, ParentT>>();
+            *warp_scratch_location = details::reduce(warp, _CG_STL_NAMESPACE::forward<TyInputVal>(val), op);
+
+            // Tile of size num_warps from the last warp to arrive does final reduction step
+            if (details::sync_warps_last_releases(sync_location, details::cta::thread_rank(), num_warps)) {
+                auto subwarp = details::tiled_partition_internal<num_warps, decltype(warp)>();
+                if (subwarp.meta_group_rank() == 0) {
+                    auto thread_scratch_location = multi_warp_scratch_location_getter<TyVal>(group, subwarp.thread_rank());
+                    auto thread_val = *thread_scratch_location;
+                    // Release other warps, we read their contribution already.
+                    subwarp.sync();
+                    details::sync_warps_release(sync_location, subwarp.thread_rank() == 0, details::cta::thread_rank(), num_warps);
+                    TyVal result = details::reduce(subwarp, thread_val, op);
+                    // One thread stores the result or updates the atomic
+                    if (subwarp.thread_rank() == 0) {
+                        res_handler(result);
+                    }
+                }
+                warp.sync();
+            }
+        }
+    };
+#endif
+
+    template <typename TyGroup, typename TyInputVal, typename TyRetVal>
+    _CG_QUALIFIER void check_reduce_params() {
+        static_assert(details::is_op_type_same<TyInputVal, TyRetVal>::value, "Operator input and output types differ");
+        static_assert(details::reduce_group_supported<TyGroup>::value, "This group does not exclusively represent a tile");
+    };
+
+    template <typename TyGroup, typename TyDstVal, typename TyInputVal, typename TyRetVal>
+    _CG_QUALIFIER void check_async_reduce_params() {
+        check_reduce_params<TyGroup, TyInputVal, TyRetVal>();
+        static_assert(details::is_op_type_same<TyDstVal, TyInputVal>::value, "Destination and input types differ");
+    }
+} // details
+
+template <typename TyGroup, typename TyVal, typename TyFn>
+_CG_QUALIFIER auto reduce(const TyGroup& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_reduce_params<TyGroup, details::remove_qual<TyVal>, decltype(op(val, val))>();
+
+    using dispatch = details::tile_reduce_dispatch<TyGroup::_group_id>;
+    return dispatch::reduce(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+#if defined(_CG_CPP11_FEATURES)
+
+# if defined(_CG_HAS_STL_ATOMICS)
+template<typename TyGroup, typename TyVal, cuda::thread_scope Sco, typename TyInputVal, typename TyFn>
+void _CG_QUALIFIER reduce_update_async(const TyGroup& group, cuda::atomic<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) {
+    details::check_async_reduce_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+    auto update_lambda = [&] (TyVal& result) {
+        details::atomic_update(dst, result, op);
+    };
+    using dispatch = details::tile_async_reduce_dispatch<TyGroup::_group_id>;
+    dispatch::reduce(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op), update_lambda);
+}
+
+template<typename TyGroup, typename TyVal, cuda::thread_scope Sco, typename TyInputVal, typename TyFn>
+void _CG_QUALIFIER reduce_update_async(const TyGroup& group, const cuda::atomic_ref<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) {
+    details::check_async_reduce_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+    auto update_lambda = [&] (TyVal& result) {
+        details::atomic_update(dst, result, op);
+    };
+    using dispatch = details::tile_async_reduce_dispatch<TyGroup::_group_id>;
+    dispatch::reduce(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op), update_lambda);
+}
+
+template<typename TyGroup, typename TyVal, cuda::thread_scope Sco, typename TyInputVal, typename TyFn>
+void _CG_QUALIFIER reduce_store_async(const TyGroup& group, cuda::atomic<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) {
+    details::check_async_reduce_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+    auto store_lambda = [&] (TyVal& result) {
+        details::atomic_store(dst, result);
+    };
+    using dispatch = details::tile_async_reduce_dispatch<TyGroup::_group_id>;
+    dispatch::reduce(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op), store_lambda);
+}
+
+template<typename TyGroup, typename TyVal, cuda::thread_scope Sco, typename TyInputVal, typename TyFn>
+void _CG_QUALIFIER reduce_store_async(const TyGroup& group, const cuda::atomic_ref<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) {
+    details::check_async_reduce_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+    auto store_lambda = [&] (TyVal& result) {
+        details::atomic_store(dst, result);
+    };
+    using dispatch = details::tile_async_reduce_dispatch<TyGroup::_group_id>;
+    dispatch::reduce(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op), store_lambda);
+}
+# endif
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, typename TyFn>
+void _CG_QUALIFIER reduce_store_async(const TyGroup& group, TyVal* dst, TyInputVal&& val, TyFn&& op) {
+    details::check_async_reduce_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+    auto store_lambda = [&] (TyVal& result) {
+        *dst = result;
+    };
+    using dispatch = details::tile_async_reduce_dispatch<TyGroup::_group_id>;
+    dispatch::reduce(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op), store_lambda);
+}
+#endif
+
+_CG_END_NAMESPACE
+
+#endif // _CG_REDUCE_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/scan.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/scan.h
new file mode 100644
index 0000000000000000000000000000000000000000..96d68350e48307d120289e22872abc66f5188115
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/scan.h
@@ -0,0 +1,320 @@
+/* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_SCAN_H_
+#define _CG_SCAN_H_
+
+#include "info.h"
+#include "helpers.h"
+#include "functional.h"
+#include "coalesced_scan.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details {
+
+    // Group support for scan.
+    template <class TyGroup> struct _scan_group_supported : public _CG_STL_NAMESPACE::false_type {};
+
+    template <unsigned int Sz, typename TyPar>
+    struct _scan_group_supported<cooperative_groups::thread_block_tile<Sz, TyPar>> : public _CG_STL_NAMESPACE::true_type {};
+    template <unsigned int Sz, typename TyPar>
+    struct _scan_group_supported<internal_thread_block_tile<Sz, TyPar>>            : public _CG_STL_NAMESPACE::true_type {};
+    template <>
+    struct _scan_group_supported<cooperative_groups::coalesced_group>              : public _CG_STL_NAMESPACE::true_type {};
+
+    template <typename TyGroup>
+    using scan_group_supported = _scan_group_supported<details::remove_qual<TyGroup>>;
+
+    template <bool IsIntegralPlus>
+    struct integral_optimized_scan;
+
+    enum class ScanType { exclusive, inclusive };
+
+    template <unsigned int GroupId,  ScanType TyScan>
+    struct scan_dispatch;
+
+    template <ScanType TyScan>
+    struct scan_dispatch<details::coalesced_group_id, TyScan> {
+        template <typename TyGroup, typename TyVal, typename TyFn>
+        _CG_STATIC_QUALIFIER auto scan(const TyGroup& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+            auto scan_result = coalesced_inclusive_scan(group, val, op);
+            if (TyScan == ScanType::exclusive) {
+                scan_result = convert_inclusive_to_exclusive(group,
+                                                             scan_result,
+                                                             _CG_STL_NAMESPACE::forward<TyVal>(val),
+                                                             _CG_STL_NAMESPACE::forward<TyFn>(op));
+            }
+            return scan_result;
+        }
+    };
+
+#if defined(_CG_CPP11_FEATURES)
+    template <ScanType TyScan>
+    struct scan_dispatch<details::multi_tile_group_id, TyScan> {
+        template <unsigned int Size, typename ParentT, typename TyVal, typename TyFn>
+        _CG_STATIC_QUALIFIER auto scan(const thread_block_tile<Size, ParentT>& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+            using warpType = details::internal_thread_block_tile<32, __static_size_multi_warp_tile_base<Size>>;
+            using TyRet = details::remove_qual<TyVal>;
+            const unsigned int num_warps = Size / 32;
+            // In warp scan result, calculated in warp_lambda
+            TyRet warp_scan;
+
+            // In warp scan, put sum in the warp_scratch_location
+            auto warp_lambda = [&] (const warpType& warp, TyRet* warp_scratch_location) {
+                warp_scan = 
+                    details::coalesced_inclusive_scan(warp, _CG_STL_NAMESPACE::forward<TyVal>(val), op);
+                if (warp.thread_rank() + 1 == warp.size()) {
+                    *warp_scratch_location = warp_scan;
+                }
+                if (TyScan == ScanType::exclusive) {
+                    warp_scan = warp.shfl_up(warp_scan, 1);
+                }
+            };
+
+            // Tile of size num_warps performing the final scan part (exclusive scan of warp sums), other threads will add it
+            // to its in-warp scan result
+            auto inter_warp_lambda =
+                [&] (const details::internal_thread_block_tile<num_warps, warpType>& subwarp, TyRet* thread_scratch_location) {
+                    auto thread_val = *thread_scratch_location;
+                    auto result = coalesced_inclusive_scan(subwarp, thread_val, op);
+                    *thread_scratch_location = convert_inclusive_to_exclusive(subwarp, result, thread_val, op);
+            };
+
+            TyRet previous_warps_sum = details::multi_warp_collectives_helper<TyRet>(group, warp_lambda, inter_warp_lambda);
+            if (TyScan == ScanType::exclusive && warpType::thread_rank() == 0) {
+                return previous_warps_sum;
+            }
+            if (warpType::meta_group_rank() == 0) {
+                return warp_scan;
+            }
+            else {
+                return op(warp_scan, previous_warps_sum);
+            }
+        }
+    };
+
+#if defined(_CG_HAS_STL_ATOMICS)
+    template <unsigned int GroupId,  ScanType TyScan>
+    struct scan_update_dispatch;
+
+    template <ScanType TyScan>
+    struct scan_update_dispatch<details::coalesced_group_id, TyScan> {
+        template <typename TyGroup, typename TyAtomic, typename TyVal, typename TyFn>
+        _CG_STATIC_QUALIFIER auto scan(const TyGroup& group, TyAtomic& dst, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+            details::remove_qual<TyVal> old;
+
+            // Do regular in group scan
+            auto scan_result = details::coalesced_inclusive_scan(group, val, op);
+
+            // Last thread updates the atomic and distributes its old value to other threads
+            if (group.thread_rank() == group.size() - 1) {                                                
+                old = atomic_update(dst, scan_result, _CG_STL_NAMESPACE::forward<TyFn>(op));
+            }
+            old = group.shfl(old, group.size() - 1);
+            if (TyScan == ScanType::exclusive) {
+                scan_result = convert_inclusive_to_exclusive(group, scan_result, _CG_STL_NAMESPACE::forward<TyVal>(val), op);
+            }
+            scan_result = op(old, scan_result);
+            return scan_result;
+        }
+    };
+
+    template <ScanType TyScan>
+    struct scan_update_dispatch<details::multi_tile_group_id, TyScan> {
+        template <unsigned int Size, typename ParentT, typename TyAtomic, typename TyVal, typename TyFn>
+        _CG_STATIC_QUALIFIER auto scan(const thread_block_tile<Size, ParentT>& group, TyAtomic& dst, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+            using warpType = details::internal_thread_block_tile<32, __static_size_multi_warp_tile_base<Size>>;
+            using TyRet = details::remove_qual<TyVal>;
+            const unsigned int num_warps = Size / 32;
+            // In warp scan result, calculated in warp_lambda
+            TyRet warp_scan;
+
+            // In warp scan, put sum in the warp_scratch_location
+            auto warp_lambda = [&] (const warpType& warp, TyRet* warp_scratch_location) {
+                warp_scan = 
+                    details::coalesced_inclusive_scan(warp, _CG_STL_NAMESPACE::forward<TyVal>(val), op);
+                if (warp.thread_rank() + 1 == warp.size()) {
+                    *warp_scratch_location = warp_scan;
+                }
+                if (TyScan == ScanType::exclusive) {
+                    warp_scan = warp.shfl_up(warp_scan, 1);
+                }
+            };
+
+            // Tile of size num_warps performing the final scan part (exclusive scan of warp sums), other threads will add it
+            // to its in-warp scan result
+            auto inter_warp_lambda =
+                [&] (const details::internal_thread_block_tile<num_warps, warpType>& subwarp, TyRet* thread_scratch_location) {
+                    auto thread_val = *thread_scratch_location;
+                    auto scan_result = details::coalesced_inclusive_scan(subwarp, thread_val, op);
+                    TyRet offset;
+                    // Single thread does the atomic update with sum of all contributions and reads the old value.
+                    if (subwarp.thread_rank() == subwarp.size() - 1) {
+                        offset = details::atomic_update(dst, scan_result, op);
+                    }
+                    offset = subwarp.shfl(offset, subwarp.size() - 1);
+                    scan_result = convert_inclusive_to_exclusive(subwarp, scan_result, thread_val, op);
+                    // Add offset read from the atomic to the scanned warp sum.
+                    // Skipping first thread, since it got defautly constructed value from the conversion,
+                    // it should just return the offset received from the thread that did the atomic update.
+                    if (subwarp.thread_rank() != 0) {
+                        offset = op(scan_result, offset);
+                    }
+                    *thread_scratch_location = offset;
+            };
+
+            TyRet previous_warps_sum = details::multi_warp_collectives_helper<TyRet>(group, warp_lambda, inter_warp_lambda);
+            if (TyScan == ScanType::exclusive && warpType::thread_rank() == 0) {
+                return previous_warps_sum;
+            }
+            return op(warp_scan, previous_warps_sum);
+        }
+    };
+#endif
+#endif
+
+    template <typename TyGroup, typename TyInputVal, typename TyRetVal>
+    _CG_QUALIFIER void check_scan_params() {
+        static_assert(details::is_op_type_same<TyInputVal, TyRetVal>::value, "Operator input and output types differ");
+        static_assert(details::scan_group_supported<TyGroup>::value, "This group does not exclusively represent a tile");
+    }
+
+#if defined(_CG_HAS_STL_ATOMICS)
+    template <typename TyGroup, typename TyDstVal, typename TyInputVal, typename TyRetVal>
+    _CG_QUALIFIER void check_scan_update_params() {
+        check_scan_params<TyGroup, TyInputVal, TyRetVal>();
+        static_assert(details::is_op_type_same<TyDstVal, TyInputVal>::value, "Destination and input types differ");
+    }
+#endif
+
+} // details
+
+template <typename TyGroup, typename TyVal, typename TyFn>
+_CG_QUALIFIER auto inclusive_scan(const TyGroup& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_scan_params<TyGroup, TyVal, decltype(op(val, val))>();
+
+    using dispatch = details::scan_dispatch<TyGroup::_group_id, details::ScanType::inclusive>;
+    return dispatch::scan(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+template <typename TyGroup, typename TyVal>
+_CG_QUALIFIER details::remove_qual<TyVal> inclusive_scan(const TyGroup& group, TyVal&& val) {
+    return inclusive_scan(group, _CG_STL_NAMESPACE::forward<TyVal>(val), cooperative_groups::plus<details::remove_qual<TyVal>>());
+}
+
+template <typename TyGroup, typename TyVal, typename TyFn>
+_CG_QUALIFIER auto exclusive_scan(const TyGroup& group, TyVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_scan_params<TyGroup, TyVal, decltype(op(val, val))>();
+
+    using dispatch = details::scan_dispatch<TyGroup::_group_id, details::ScanType::exclusive>;
+    return dispatch::scan(group, _CG_STL_NAMESPACE::forward<TyVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+template <typename TyGroup, typename TyVal>
+_CG_QUALIFIER details::remove_qual<TyVal> exclusive_scan(const TyGroup& group, TyVal&& val) {
+    return exclusive_scan(group, _CG_STL_NAMESPACE::forward<TyVal>(val), cooperative_groups::plus<details::remove_qual<TyVal>>());
+}
+
+#if defined(_CG_HAS_STL_ATOMICS)
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco, typename TyFn>
+_CG_QUALIFIER auto inclusive_scan_update(const TyGroup& group, cuda::atomic<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_scan_update_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+
+    using dispatch = details::scan_update_dispatch<TyGroup::_group_id, details::ScanType::inclusive>;
+    return dispatch::scan(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco>
+_CG_QUALIFIER TyVal inclusive_scan_update(const TyGroup& group, cuda::atomic<TyVal, Sco> & dst, TyInputVal&& val) {
+    return inclusive_scan_update(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), cooperative_groups::plus<TyVal>());
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco, typename TyFn>
+_CG_QUALIFIER auto exclusive_scan_update(const TyGroup& group, cuda::atomic<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_scan_update_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+
+    using dispatch = details::scan_update_dispatch<TyGroup::_group_id, details::ScanType::exclusive>;
+    return dispatch::scan(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco>
+_CG_QUALIFIER TyVal exclusive_scan_update(const TyGroup& group, cuda::atomic<TyVal, Sco>& dst, TyInputVal&& val) {
+    return exclusive_scan_update(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), cooperative_groups::plus<TyVal>());
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco, typename TyFn>
+_CG_QUALIFIER auto inclusive_scan_update(const TyGroup& group, const cuda::atomic_ref<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_scan_update_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+
+    using dispatch = details::scan_update_dispatch<TyGroup::_group_id, details::ScanType::inclusive>;
+    return dispatch::scan(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco>
+_CG_QUALIFIER TyVal inclusive_scan_update(const TyGroup& group, const cuda::atomic_ref<TyVal, Sco> & dst, TyInputVal&& val) {
+    return inclusive_scan_update(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), cooperative_groups::plus<TyVal>());
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco, typename TyFn>
+_CG_QUALIFIER auto exclusive_scan_update(const TyGroup& group, const cuda::atomic_ref<TyVal, Sco>& dst, TyInputVal&& val, TyFn&& op) -> decltype(op(val, val)) {
+    details::check_scan_update_params<TyGroup, TyVal, details::remove_qual<TyInputVal>, decltype(op(val, val))>();
+
+    using dispatch = details::scan_update_dispatch<TyGroup::_group_id, details::ScanType::exclusive>;
+    return dispatch::scan(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), _CG_STL_NAMESPACE::forward<TyFn>(op));
+}
+
+template<typename TyGroup, typename TyVal, typename TyInputVal, cuda::thread_scope Sco>
+_CG_QUALIFIER TyVal exclusive_scan_update(const TyGroup& group, const cuda::atomic_ref<TyVal, Sco>& dst, TyInputVal&& val) {
+    return exclusive_scan_update(group, dst, _CG_STL_NAMESPACE::forward<TyInputVal>(val), cooperative_groups::plus<TyVal>());
+}
+#endif
+
+_CG_END_NAMESPACE
+
+#endif // _CG_SCAN_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/sync.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/sync.h
new file mode 100644
index 0000000000000000000000000000000000000000..07ec3736c71fd6095b661dc0a66602d8e384f83e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/details/sync.h
@@ -0,0 +1,281 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _CG_GRID_H
+#define _CG_GRID_H
+
+#include "info.h"
+
+_CG_BEGIN_NAMESPACE
+
+namespace details
+{
+typedef unsigned int barrier_t;
+
+_CG_STATIC_QUALIFIER bool bar_has_flipped(unsigned int old_arrive, unsigned int current_arrive) {
+    return (((old_arrive ^ current_arrive) & 0x80000000) != 0);
+}
+
+_CG_STATIC_QUALIFIER bool is_cta_master() {
+    return (threadIdx.x + threadIdx.y + threadIdx.z == 0);
+}
+
+_CG_STATIC_QUALIFIER unsigned int sync_grids_arrive(volatile barrier_t *arrived) {
+    unsigned int oldArrive = 0;
+
+    __barrier_sync(0);
+
+    if (is_cta_master()) {
+        unsigned int expected = gridDim.x * gridDim.y * gridDim.z;
+        bool gpu_master = (blockIdx.x + blockIdx.y + blockIdx.z == 0);
+        unsigned int nb = 1;
+
+        if (gpu_master) {
+            nb = 0x80000000 - (expected - 1);
+        }
+
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+        // Barrier update with release; polling with acquire
+        asm volatile("atom.add.release.gpu.u32 %0,[%1],%2;" : "=r"(oldArrive) : _CG_ASM_PTR_CONSTRAINT((unsigned int*)arrived), "r"(nb) : "memory");
+        ,
+        // Fence; barrier update; volatile polling; fence
+        __threadfence();
+        oldArrive = atomicAdd((unsigned int*)arrived, nb);
+        );
+    }
+
+    return oldArrive;
+}
+
+
+_CG_STATIC_QUALIFIER void sync_grids_wait(unsigned int oldArrive, volatile barrier_t *arrived) {
+    if (is_cta_master()) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+        unsigned int current_arrive;
+        do {
+            asm volatile("ld.acquire.gpu.u32 %0,[%1];" : "=r"(current_arrive) : _CG_ASM_PTR_CONSTRAINT((unsigned int *)arrived) : "memory");
+        } while (!bar_has_flipped(oldArrive, current_arrive));
+        ,
+        while (!bar_has_flipped(oldArrive, *arrived));
+        __threadfence();
+        );
+    }
+
+    __barrier_sync(0);
+}
+
+/* - Multi warp groups synchronization routines - */
+
+#ifdef _CG_CPP11_FEATURES
+// Need both acquire and release for the last warp, since it won't be able to acquire with red.and
+_CG_STATIC_QUALIFIER unsigned int atom_or_acq_rel_cta(unsigned int *addr, unsigned int val) {
+    unsigned int old;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+    (asm volatile("atom.or.acq_rel.cta.b32 %0,[%1],%2;" : "=r"(old) : _CG_ASM_PTR_CONSTRAINT(addr), "r"(val) : "memory");)
+    ,
+    (__threadfence_block();
+    old = atomicOr(addr, val);)
+    );
+    return old;
+}
+
+// Special case where barrier is arrived, but not waited on
+_CG_STATIC_QUALIFIER void red_or_release_cta(unsigned int *addr, unsigned int val) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+    (asm volatile("red.or.release.cta.b32 [%0],%1;" :: _CG_ASM_PTR_CONSTRAINT(addr), "r"(val) : "memory");)
+    ,
+    (__threadfence_block();
+    atomicOr(addr, val);)
+    );
+}
+
+// Usually called by last arriving warp to released other warps, can be relaxed, since or was already acq_rel
+_CG_STATIC_QUALIFIER void red_and_relaxed_cta(unsigned int *addr, unsigned int val) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+    (asm volatile("red.and.relaxed.cta.b32 [%0],%1;" :: _CG_ASM_PTR_CONSTRAINT(addr), "r"(val) : "memory");)
+    ,
+    (atomicAnd(addr, val);)
+    );
+}
+
+// Special case of release, where last warp was doing extra work before releasing others, need to be release
+//  to ensure that extra work is visible
+_CG_STATIC_QUALIFIER void red_and_release_cta(unsigned int *addr, unsigned int val) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+    (asm volatile("red.and.release.cta.b32 [%0],%1;" :: _CG_ASM_PTR_CONSTRAINT(addr), "r"(val) : "memory");)
+    ,
+    (__threadfence_block();
+    atomicAnd(addr, val);)
+    );
+}
+
+// Read the barrier, acquire to ensure all memory operations following the sync are correctly performed after it is released
+_CG_STATIC_QUALIFIER unsigned int ld_acquire_cta(unsigned int *addr) {
+    unsigned int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_70,
+    (asm volatile("ld.acquire.cta.u32 %0,[%1];" : "=r"(val) : _CG_ASM_PTR_CONSTRAINT(addr) : "memory");)
+    ,
+    (val = *((volatile unsigned int*) addr);
+    __threadfence_block();)
+    );
+    return val;
+}
+
+// Get synchronization bit mask of my thread_block_tile of size num_warps. Thread ranks 0..31 have the first bit assigned to them,
+// thread ranks 32..63 second etc 
+// Bit masks are unique for each group, groups of the same size will have the same number of bits set, but on different positions 
+_CG_STATIC_QUALIFIER unsigned int get_group_mask(unsigned int thread_rank, unsigned int num_warps) {
+    return num_warps == 32 ? ~0 : ((1 << num_warps) - 1) << (num_warps * (thread_rank / (num_warps * 32)));
+}
+
+_CG_STATIC_QUALIFIER void barrier_wait(barrier_t *arrived, unsigned int warp_bit) {
+    while(ld_acquire_cta(arrived) & warp_bit);
+}
+
+// Default blocking sync.
+_CG_STATIC_QUALIFIER void sync_warps(barrier_t *arrived, unsigned int thread_rank, unsigned int num_warps) {
+    unsigned int warp_id = thread_rank / 32;
+    bool warp_master = (thread_rank % 32 == 0);
+    unsigned int warp_bit = 1 << warp_id;
+    unsigned int group_mask = get_group_mask(thread_rank, num_warps);
+
+    __syncwarp(0xFFFFFFFF);
+
+    if (warp_master) {
+        unsigned int old = atom_or_acq_rel_cta(arrived, warp_bit);
+        if (((old | warp_bit) & group_mask) == group_mask) {
+            red_and_relaxed_cta(arrived, ~group_mask);
+        }
+        else {
+            barrier_wait(arrived, warp_bit);
+        }
+    }
+
+    __syncwarp(0xFFFFFFFF);
+}
+
+// Blocking sync, except the last arriving warp, that releases other warps, returns to do other stuff first.
+// Warp returning true from this function needs to call sync_warps_release.
+_CG_STATIC_QUALIFIER bool sync_warps_last_releases(barrier_t *arrived, unsigned int thread_rank, unsigned int num_warps) {
+    unsigned int warp_id = thread_rank / 32;
+    bool warp_master = (thread_rank % 32 == 0);
+    unsigned int warp_bit = 1 << warp_id;
+    unsigned int group_mask = get_group_mask(thread_rank, num_warps);
+
+    __syncwarp(0xFFFFFFFF);
+
+    unsigned int old = 0;
+    if (warp_master) {
+        old = atom_or_acq_rel_cta(arrived, warp_bit);
+    }
+    old = __shfl_sync(0xFFFFFFFF, old, 0);
+    if (((old | warp_bit) & group_mask) == group_mask) {
+        return true;
+    }
+    barrier_wait(arrived, warp_bit);
+
+    return false;
+}
+
+// Release my group from the barrier.
+_CG_STATIC_QUALIFIER void sync_warps_release(barrier_t *arrived, bool is_master, unsigned int thread_rank, unsigned int num_warps) {
+    unsigned int group_mask = get_group_mask(thread_rank, num_warps);
+    if (is_master) {
+        red_and_release_cta(arrived, ~group_mask);
+    }
+}
+
+// Arrive at my group barrier, but don't block or release the barrier, even if every one arrives.
+// sync_warps_release needs to be called by some warp after this one to reset the barrier.
+_CG_STATIC_QUALIFIER void sync_warps_arrive(barrier_t *arrived, unsigned int thread_rank, unsigned int num_warps) {
+    unsigned int warp_id = thread_rank / 32;
+    bool warp_master = (thread_rank % 32 == 0);
+    unsigned int warp_bit = 1 << warp_id;
+    unsigned int group_mask = get_group_mask(thread_rank, num_warps);
+
+    __syncwarp(0xFFFFFFFF);
+
+    if (warp_master) {
+        red_or_release_cta(arrived, warp_bit);
+    }
+}
+
+// Wait for my warp to be released from the barrier. Warp must have arrived first.
+_CG_STATIC_QUALIFIER void sync_warps_wait(barrier_t *arrived, unsigned int thread_rank) {
+    unsigned int warp_id = thread_rank / 32;
+    unsigned int warp_bit = 1 << warp_id;
+
+    barrier_wait(arrived, warp_bit);
+}
+
+// Wait for specific warp to arrive at the barrier
+_CG_QUALIFIER void sync_warps_wait_for_specific_warp(barrier_t *arrived, unsigned int wait_warp_id) {
+    unsigned int wait_mask = 1 << wait_warp_id;
+    while((ld_acquire_cta(arrived) & wait_mask) != wait_mask);
+}
+
+// Initialize the bit corresponding to my warp in the barrier
+_CG_QUALIFIER void sync_warps_reset(barrier_t *arrived, unsigned int thread_rank) {
+    unsigned int warp_id = thread_rank / 32;
+    unsigned int warp_bit = 1 << warp_id;
+
+    __syncwarp(0xFFFFFFFF);
+
+    if (thread_rank % 32 == 0) {
+        red_and_release_cta(arrived, ~warp_bit);
+    }
+    // No need to sync after the atomic, there will be a sync of the group that is being partitioned right after this.
+}
+
+#endif
+
+} // details
+
+_CG_END_NAMESPACE
+
+#endif // _CG_GRID_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/memcpy_async.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/memcpy_async.h
new file mode 100644
index 0000000000000000000000000000000000000000..50b907d9a1fe45cdc411891a20d8fd035118e5be
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/memcpy_async.h
@@ -0,0 +1,62 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _COOPERATIVE_GROUPS_MEMCPY_ASYNC
+#define _COOPERATIVE_GROUPS_MEMCPY_ASYNC
+
+#include "../cooperative_groups.h"
+#include "details/info.h"
+
+#ifdef _CG_CPP11_FEATURES
+# include "details/async.h"
+#else
+# error This file requires compiler support for the ISO C++ 2011 standard. This support must be enabled with the \
+         -std=c++11 compiler option.
+#endif
+
+#endif // _COOPERATIVE_GROUPS_MEMCPY_ASYNC
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/reduce.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/reduce.h
new file mode 100644
index 0000000000000000000000000000000000000000..3c87d780db0b437f1ae06e0ef8d60137233795c0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/reduce.h
@@ -0,0 +1,63 @@
+ /* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _COOPERATIVE_GROUPS_REDUCE_H
+#define _COOPERATIVE_GROUPS_REDUCE_H
+
+#include "../cooperative_groups.h"
+#include "details/info.h"
+
+#ifdef _CG_CPP11_FEATURES
+# include "details/reduce.h"
+#else
+# error This file requires compiler support for the ISO C++ 2011 standard. This support must be enabled with the \
+         -std=c++11 compiler option.
+#endif
+
+
+#endif //_COOPERATIVE_GROUPS_REDUCE_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/scan.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/scan.h
new file mode 100644
index 0000000000000000000000000000000000000000..9bc27078028318ada00cbcccd052e0d6cc930cfe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cooperative_groups/scan.h
@@ -0,0 +1,63 @@
+/* Copyright 1993-2016 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#ifndef _COOPERATIVE_GROUPS_SCAN_H
+#define _COOPERATIVE_GROUPS_SCAN_H
+
+#include "../cooperative_groups.h"
+#include "details/info.h"
+
+#ifdef _CG_CPP11_FEATURES
+# include "details/scan.h"
+#else
+# error This file requires compiler support for the ISO C++ 2011 standard. This support must be enabled with the \
+         -std=c++11 compiler option.
+#endif
+
+
+#endif //_COOPERATIVE_GROUPS_SCAN_H
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuComplex.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuComplex.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b167111b0b387a5279da6749d946560e1c42c1b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuComplex.h
@@ -0,0 +1,348 @@
+/*
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(CU_COMPLEX_H_)
+#define CU_COMPLEX_H_
+
+#if !defined(__CUDACC_RTC__)
+#if defined(__GNUC__)
+#if defined(__clang__) || (!defined(__PGIC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 2)))
+#pragma GCC diagnostic ignored "-Wunused-function"
+#endif
+#endif
+#endif
+
+/* When trying to include C header file in C++ Code extern "C" is required
+ * But the Standard QNX headers already have ifdef extern in them when compiling C++ Code
+ * extern "C" cannot be nested
+ * Hence keep the header out of extern "C" block
+ */
+
+#if !defined(__CUDACC__)
+#include <math.h>       /* import fabsf, sqrt */
+#endif /* !defined(__CUDACC__) */
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+#include "vector_types.h"
+
+typedef float2 cuFloatComplex;
+
+__host__ __device__ static __inline__ float cuCrealf (cuFloatComplex x) 
+{ 
+    return x.x; 
+}
+
+__host__ __device__ static __inline__ float cuCimagf (cuFloatComplex x) 
+{ 
+    return x.y; 
+}
+
+__host__ __device__ static __inline__ cuFloatComplex make_cuFloatComplex 
+                                                             (float r, float i)
+{
+    cuFloatComplex res;
+    res.x = r;
+    res.y = i;
+    return res;
+}
+
+__host__ __device__ static __inline__ cuFloatComplex cuConjf (cuFloatComplex x)
+{
+    return make_cuFloatComplex (cuCrealf(x), -cuCimagf(x));
+}
+__host__ __device__ static __inline__ cuFloatComplex cuCaddf (cuFloatComplex x,
+                                                              cuFloatComplex y)
+{
+    return make_cuFloatComplex (cuCrealf(x) + cuCrealf(y), 
+                                cuCimagf(x) + cuCimagf(y));
+}
+
+__host__ __device__ static __inline__ cuFloatComplex cuCsubf (cuFloatComplex x,
+                                                              cuFloatComplex y)
+{
+        return make_cuFloatComplex (cuCrealf(x) - cuCrealf(y), 
+                                    cuCimagf(x) - cuCimagf(y));
+}
+
+/* This implementation could suffer from intermediate overflow even though
+ * the final result would be in range. However, various implementations do
+ * not guard against this (presumably to avoid losing performance), so we 
+ * don't do it either to stay competitive.
+ */
+__host__ __device__ static __inline__ cuFloatComplex cuCmulf (cuFloatComplex x,
+                                                              cuFloatComplex y)
+{
+    cuFloatComplex prod;
+    prod = make_cuFloatComplex  ((cuCrealf(x) * cuCrealf(y)) - 
+                                 (cuCimagf(x) * cuCimagf(y)),
+                                 (cuCrealf(x) * cuCimagf(y)) + 
+                                 (cuCimagf(x) * cuCrealf(y)));
+    return prod;
+}
+
+/* This implementation guards against intermediate underflow and overflow
+ * by scaling. Such guarded implementations are usually the default for
+ * complex library implementations, with some also offering an unguarded,
+ * faster version.
+ */
+__host__ __device__ static __inline__ cuFloatComplex cuCdivf (cuFloatComplex x,
+                                                              cuFloatComplex y)
+{
+    cuFloatComplex quot;
+    float s = fabsf(cuCrealf(y)) + fabsf(cuCimagf(y));
+    float oos = 1.0f / s;
+    float ars = cuCrealf(x) * oos;
+    float ais = cuCimagf(x) * oos;
+    float brs = cuCrealf(y) * oos;
+    float bis = cuCimagf(y) * oos;
+    s = (brs * brs) + (bis * bis);
+    oos = 1.0f / s;
+    quot = make_cuFloatComplex (((ars * brs) + (ais * bis)) * oos,
+                                ((ais * brs) - (ars * bis)) * oos);
+    return quot;
+}
+
+/* 
+ * We would like to call hypotf(), but it's not available on all platforms.
+ * This discrete implementation guards against intermediate underflow and 
+ * overflow by scaling. Otherwise we would lose half the exponent range. 
+ * There are various ways of doing guarded computation. For now chose the 
+ * simplest and fastest solution, however this may suffer from inaccuracies 
+ * if sqrt and division are not IEEE compliant. 
+ */
+__host__ __device__ static __inline__ float cuCabsf (cuFloatComplex x)
+{
+    float a = cuCrealf(x);
+    float b = cuCimagf(x);
+    float v, w, t;
+    a = fabsf(a);
+    b = fabsf(b);
+    if (a > b) {
+        v = a;
+        w = b; 
+    } else {
+        v = b;
+        w = a;
+    }
+    t = w / v;
+    t = 1.0f + t * t;
+    t = v * sqrtf(t);
+    if ((v == 0.0f) || (v > 3.402823466e38f) || (w > 3.402823466e38f)) {
+        t = v + w;
+    }
+    return t;
+}
+
+/* Double precision */
+typedef double2 cuDoubleComplex;
+
+__host__ __device__ static __inline__ double cuCreal (cuDoubleComplex x) 
+{ 
+    return x.x; 
+}
+
+__host__ __device__ static __inline__ double cuCimag (cuDoubleComplex x) 
+{ 
+    return x.y; 
+}
+
+__host__ __device__ static __inline__ cuDoubleComplex make_cuDoubleComplex 
+                                                           (double r, double i)
+{
+    cuDoubleComplex res;
+    res.x = r;
+    res.y = i;
+    return res;
+}
+
+__host__ __device__ static __inline__ cuDoubleComplex cuConj(cuDoubleComplex x)
+{
+    return make_cuDoubleComplex (cuCreal(x), -cuCimag(x));
+}
+
+__host__ __device__ static __inline__ cuDoubleComplex cuCadd(cuDoubleComplex x,
+                                                             cuDoubleComplex y)
+{
+    return make_cuDoubleComplex (cuCreal(x) + cuCreal(y), 
+                                 cuCimag(x) + cuCimag(y));
+}
+
+__host__ __device__ static __inline__ cuDoubleComplex cuCsub(cuDoubleComplex x,
+                                                             cuDoubleComplex y)
+{
+    return make_cuDoubleComplex (cuCreal(x) - cuCreal(y), 
+                                 cuCimag(x) - cuCimag(y));
+}
+
+/* This implementation could suffer from intermediate overflow even though
+ * the final result would be in range. However, various implementations do
+ * not guard against this (presumably to avoid losing performance), so we 
+ * don't do it either to stay competitive.
+ */
+__host__ __device__ static __inline__ cuDoubleComplex cuCmul(cuDoubleComplex x,
+                                                             cuDoubleComplex y)
+{
+    cuDoubleComplex prod;
+    prod = make_cuDoubleComplex ((cuCreal(x) * cuCreal(y)) - 
+                                 (cuCimag(x) * cuCimag(y)),
+                                 (cuCreal(x) * cuCimag(y)) + 
+                                 (cuCimag(x) * cuCreal(y)));
+    return prod;
+}
+
+/* This implementation guards against intermediate underflow and overflow
+ * by scaling. Such guarded implementations are usually the default for
+ * complex library implementations, with some also offering an unguarded,
+ * faster version.
+ */
+__host__ __device__ static __inline__ cuDoubleComplex cuCdiv(cuDoubleComplex x,
+                                                             cuDoubleComplex y)
+{
+    cuDoubleComplex quot;
+    double s = (fabs(cuCreal(y))) + (fabs(cuCimag(y)));
+    double oos = 1.0 / s;
+    double ars = cuCreal(x) * oos;
+    double ais = cuCimag(x) * oos;
+    double brs = cuCreal(y) * oos;
+    double bis = cuCimag(y) * oos;
+    s = (brs * brs) + (bis * bis);
+    oos = 1.0 / s;
+    quot = make_cuDoubleComplex (((ars * brs) + (ais * bis)) * oos,
+                                 ((ais * brs) - (ars * bis)) * oos);
+    return quot;
+}
+
+/* This implementation guards against intermediate underflow and overflow
+ * by scaling. Otherwise we would lose half the exponent range. There are
+ * various ways of doing guarded computation. For now chose the simplest
+ * and fastest solution, however this may suffer from inaccuracies if sqrt
+ * and division are not IEEE compliant.
+ */
+__host__ __device__ static __inline__ double cuCabs (cuDoubleComplex x)
+{
+    double a = cuCreal(x);
+    double b = cuCimag(x);
+    double v, w, t;
+    a = fabs(a);
+    b = fabs(b);
+    if (a > b) {
+        v = a;
+        w = b; 
+    } else {
+        v = b;
+        w = a;
+    }
+    t = w / v;
+    t = 1.0 + t * t;
+    t = v * sqrt(t);
+    if ((v == 0.0) || 
+        (v > 1.79769313486231570e+308) || (w > 1.79769313486231570e+308)) {
+        t = v + w;
+    }
+    return t;
+}
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+/* aliases */
+typedef cuFloatComplex cuComplex;
+__host__ __device__ static __inline__ cuComplex make_cuComplex (float x, 
+                                                                float y) 
+{ 
+    return make_cuFloatComplex (x, y); 
+}
+
+/* float-to-double promotion */
+__host__ __device__ static __inline__ cuDoubleComplex cuComplexFloatToDouble
+                                                      (cuFloatComplex c)
+{
+    return make_cuDoubleComplex ((double)cuCrealf(c), (double)cuCimagf(c));
+}
+
+__host__ __device__ static __inline__ cuFloatComplex cuComplexDoubleToFloat
+(cuDoubleComplex c)
+{
+	return make_cuFloatComplex ((float)cuCreal(c), (float)cuCimag(c));
+}
+
+
+__host__ __device__ static __inline__  cuComplex cuCfmaf( cuComplex x, cuComplex y, cuComplex d)
+{
+    float real_res;
+    float imag_res;
+    
+    real_res = (cuCrealf(x) *  cuCrealf(y)) + cuCrealf(d);
+    imag_res = (cuCrealf(x) *  cuCimagf(y)) + cuCimagf(d);
+            
+    real_res = -(cuCimagf(x) * cuCimagf(y))  + real_res;  
+    imag_res =  (cuCimagf(x) *  cuCrealf(y)) + imag_res;          
+     
+    return make_cuComplex(real_res, imag_res);
+}
+
+__host__ __device__ static __inline__  cuDoubleComplex cuCfma( cuDoubleComplex x, cuDoubleComplex y, cuDoubleComplex d)
+{
+    double real_res;
+    double imag_res;
+    
+    real_res = (cuCreal(x) *  cuCreal(y)) + cuCreal(d);
+    imag_res = (cuCreal(x) *  cuCimag(y)) + cuCimag(d);
+            
+    real_res = -(cuCimag(x) * cuCimag(y))  + real_res;  
+    imag_res =  (cuCimag(x) *  cuCreal(y)) + imag_res;     
+     
+    return make_cuDoubleComplex(real_res, imag_res);
+}
+
+#endif /* !defined(CU_COMPLEX_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..66c74d8c48d2a80fdfbccb3dca0c992c59c1d0ff
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda.h
@@ -0,0 +1,26280 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef __cuda_cuda_h__
+#define __cuda_cuda_h__
+
+
+
+
+#include <stdlib.h>
+#ifdef _MSC_VER
+typedef unsigned __int32 cuuint32_t;
+typedef unsigned __int64 cuuint64_t;
+#else
+#include <stdint.h>
+typedef uint32_t cuuint32_t;
+typedef uint64_t cuuint64_t;
+#endif
+
+#if defined(__CUDA_API_VERSION_INTERNAL) || defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+#define __CUDA_DEPRECATED
+#elif defined(_MSC_VER)
+#define __CUDA_DEPRECATED __declspec(deprecated)
+#elif defined(__GNUC__)
+#define __CUDA_DEPRECATED __attribute__((deprecated))
+#else
+#define __CUDA_DEPRECATED
+#endif
+
+#if defined(CUDA_FORCE_API_VERSION)
+#error "CUDA_FORCE_API_VERSION is no longer supported."
+#endif
+
+#if defined(__CUDA_API_VERSION_INTERNAL) || defined(CUDA_API_PER_THREAD_DEFAULT_STREAM)
+    #define __CUDA_API_PER_THREAD_DEFAULT_STREAM
+    #define __CUDA_API_PTDS(api) api ## _ptds
+    #define __CUDA_API_PTSZ(api) api ## _ptsz
+#else
+    #define __CUDA_API_PTDS(api) api
+    #define __CUDA_API_PTSZ(api) api
+#endif
+
+#define cuDeviceTotalMem                    cuDeviceTotalMem_v2
+#define cuCtxCreate                         cuCtxCreate_v2
+#define cuCtxCreate_v3                      cuCtxCreate_v3
+#define cuCtxCreate_v4                      cuCtxCreate_v4
+#define cuModuleGetGlobal                   cuModuleGetGlobal_v2
+#define cuMemGetInfo                        cuMemGetInfo_v2
+#define cuMemAlloc                          cuMemAlloc_v2
+#define cuMemAllocPitch                     cuMemAllocPitch_v2
+#define cuMemFree                           cuMemFree_v2
+#define cuMemGetAddressRange                cuMemGetAddressRange_v2
+#define cuMemAllocHost                      cuMemAllocHost_v2
+#define cuMemHostGetDevicePointer           cuMemHostGetDevicePointer_v2
+#define cuMemcpyHtoD                        __CUDA_API_PTDS(cuMemcpyHtoD_v2)
+#define cuMemcpyDtoH                        __CUDA_API_PTDS(cuMemcpyDtoH_v2)
+#define cuMemcpyDtoD                        __CUDA_API_PTDS(cuMemcpyDtoD_v2)
+#define cuMemcpyDtoA                        __CUDA_API_PTDS(cuMemcpyDtoA_v2)
+#define cuMemcpyAtoD                        __CUDA_API_PTDS(cuMemcpyAtoD_v2)
+#define cuMemcpyHtoA                        __CUDA_API_PTDS(cuMemcpyHtoA_v2)
+#define cuMemcpyAtoH                        __CUDA_API_PTDS(cuMemcpyAtoH_v2)
+#define cuMemcpyAtoA                        __CUDA_API_PTDS(cuMemcpyAtoA_v2)
+#define cuMemcpyHtoAAsync                   __CUDA_API_PTSZ(cuMemcpyHtoAAsync_v2)
+#define cuMemcpyAtoHAsync                   __CUDA_API_PTSZ(cuMemcpyAtoHAsync_v2)
+#define cuMemcpy2D                          __CUDA_API_PTDS(cuMemcpy2D_v2)
+#define cuMemcpy2DUnaligned                 __CUDA_API_PTDS(cuMemcpy2DUnaligned_v2)
+#define cuMemcpy3D                          __CUDA_API_PTDS(cuMemcpy3D_v2)
+#define cuMemcpyHtoDAsync                   __CUDA_API_PTSZ(cuMemcpyHtoDAsync_v2)
+#define cuMemcpyDtoHAsync                   __CUDA_API_PTSZ(cuMemcpyDtoHAsync_v2)
+#define cuMemcpyDtoDAsync                   __CUDA_API_PTSZ(cuMemcpyDtoDAsync_v2)
+#define cuMemcpy2DAsync                     __CUDA_API_PTSZ(cuMemcpy2DAsync_v2)
+#define cuMemcpy3DAsync                     __CUDA_API_PTSZ(cuMemcpy3DAsync_v2)
+#define cuMemcpyBatchAsync                  __CUDA_API_PTSZ(cuMemcpyBatchAsync)
+#define cuMemcpy3DBatchAsync                __CUDA_API_PTSZ(cuMemcpy3DBatchAsync)
+#define cuMemsetD8                          __CUDA_API_PTDS(cuMemsetD8_v2)
+#define cuMemsetD16                         __CUDA_API_PTDS(cuMemsetD16_v2)
+#define cuMemsetD32                         __CUDA_API_PTDS(cuMemsetD32_v2)
+#define cuMemsetD2D8                        __CUDA_API_PTDS(cuMemsetD2D8_v2)
+#define cuMemsetD2D16                       __CUDA_API_PTDS(cuMemsetD2D16_v2)
+#define cuMemsetD2D32                       __CUDA_API_PTDS(cuMemsetD2D32_v2)
+#define cuArrayCreate                       cuArrayCreate_v2
+#define cuArrayGetDescriptor                cuArrayGetDescriptor_v2
+#define cuArray3DCreate                     cuArray3DCreate_v2
+#define cuArray3DGetDescriptor              cuArray3DGetDescriptor_v2
+#define cuTexRefSetAddress                  cuTexRefSetAddress_v2
+#define cuTexRefGetAddress                  cuTexRefGetAddress_v2
+#define cuGraphicsResourceGetMappedPointer  cuGraphicsResourceGetMappedPointer_v2
+#define cuCtxDestroy                        cuCtxDestroy_v2
+#define cuCtxPopCurrent                     cuCtxPopCurrent_v2
+#define cuCtxPushCurrent                    cuCtxPushCurrent_v2
+#define cuStreamDestroy                     cuStreamDestroy_v2
+#define cuEventDestroy                      cuEventDestroy_v2
+#define cuTexRefSetAddress2D                cuTexRefSetAddress2D_v3
+#define cuLinkCreate                        cuLinkCreate_v2
+#define cuLinkAddData                       cuLinkAddData_v2
+#define cuLinkAddFile                       cuLinkAddFile_v2
+#define cuMemHostRegister                   cuMemHostRegister_v2
+#define cuGraphicsResourceSetMapFlags       cuGraphicsResourceSetMapFlags_v2
+#define cuStreamBeginCapture                __CUDA_API_PTSZ(cuStreamBeginCapture_v2)
+#define cuDevicePrimaryCtxRelease           cuDevicePrimaryCtxRelease_v2
+#define cuDevicePrimaryCtxReset             cuDevicePrimaryCtxReset_v2
+#define cuDevicePrimaryCtxSetFlags          cuDevicePrimaryCtxSetFlags_v2
+#define cuDeviceGetUuid_v2                  cuDeviceGetUuid_v2
+#define cuIpcOpenMemHandle                  cuIpcOpenMemHandle_v2
+
+#define cuGraphInstantiate                  cuGraphInstantiateWithFlags
+
+#define cuGraphExecUpdate                   cuGraphExecUpdate_v2 
+#define cuGetProcAddress                    cuGetProcAddress_v2
+#define cuGraphAddKernelNode                cuGraphAddKernelNode_v2
+#define cuGraphKernelNodeGetParams          cuGraphKernelNodeGetParams_v2
+#define cuGraphKernelNodeSetParams          cuGraphKernelNodeSetParams_v2
+#define cuGraphExecKernelNodeSetParams      cuGraphExecKernelNodeSetParams_v2
+
+#define cuStreamWriteValue32                __CUDA_API_PTSZ(cuStreamWriteValue32_v2)
+#define cuStreamWaitValue32                 __CUDA_API_PTSZ(cuStreamWaitValue32_v2)
+#define cuStreamWriteValue64                __CUDA_API_PTSZ(cuStreamWriteValue64_v2)
+#define cuStreamWaitValue64                 __CUDA_API_PTSZ(cuStreamWaitValue64_v2)
+#define cuStreamBatchMemOp                  __CUDA_API_PTSZ(cuStreamBatchMemOp_v2)
+#define cuStreamGetCaptureInfo              __CUDA_API_PTSZ(cuStreamGetCaptureInfo_v2)
+#define cuStreamGetCaptureInfo_v2           __CUDA_API_PTSZ(cuStreamGetCaptureInfo_v2)
+
+#if defined(__CUDA_API_PER_THREAD_DEFAULT_STREAM)
+    #define cuMemcpy                            __CUDA_API_PTDS(cuMemcpy)
+    #define cuMemcpyAsync                       __CUDA_API_PTSZ(cuMemcpyAsync)
+    #define cuMemcpyPeer                        __CUDA_API_PTDS(cuMemcpyPeer)
+    #define cuMemcpyPeerAsync                   __CUDA_API_PTSZ(cuMemcpyPeerAsync)
+    #define cuMemcpy3DPeer                      __CUDA_API_PTDS(cuMemcpy3DPeer)
+    #define cuMemcpy3DPeerAsync                 __CUDA_API_PTSZ(cuMemcpy3DPeerAsync)
+    #define cuMemPrefetchAsync                  __CUDA_API_PTSZ(cuMemPrefetchAsync)
+    #define cuMemPrefetchAsync_v2               __CUDA_API_PTSZ(cuMemPrefetchAsync_v2)
+
+    #define cuMemsetD8Async                     __CUDA_API_PTSZ(cuMemsetD8Async)
+    #define cuMemsetD16Async                    __CUDA_API_PTSZ(cuMemsetD16Async)
+    #define cuMemsetD32Async                    __CUDA_API_PTSZ(cuMemsetD32Async)
+    #define cuMemsetD2D8Async                   __CUDA_API_PTSZ(cuMemsetD2D8Async)
+    #define cuMemsetD2D16Async                  __CUDA_API_PTSZ(cuMemsetD2D16Async)
+    #define cuMemsetD2D32Async                  __CUDA_API_PTSZ(cuMemsetD2D32Async)
+
+    #define cuStreamGetPriority                 __CUDA_API_PTSZ(cuStreamGetPriority)
+    #define cuStreamGetId                       __CUDA_API_PTSZ(cuStreamGetId)
+    #define cuStreamGetFlags                    __CUDA_API_PTSZ(cuStreamGetFlags)
+    #define cuStreamGetDevice                   __CUDA_API_PTSZ(cuStreamGetDevice)
+    #define cuStreamGetCtx                      __CUDA_API_PTSZ(cuStreamGetCtx)
+    #define cuStreamGetCtx_v2                   __CUDA_API_PTSZ(cuStreamGetCtx_v2)
+    #define cuStreamWaitEvent                   __CUDA_API_PTSZ(cuStreamWaitEvent)
+    #define cuStreamEndCapture                  __CUDA_API_PTSZ(cuStreamEndCapture)
+    #define cuStreamIsCapturing                 __CUDA_API_PTSZ(cuStreamIsCapturing)
+    #define cuStreamGetCaptureInfo_v3           __CUDA_API_PTSZ(cuStreamGetCaptureInfo_v3)
+    #define cuStreamUpdateCaptureDependencies   __CUDA_API_PTSZ(cuStreamUpdateCaptureDependencies)
+    #define cuStreamUpdateCaptureDependencies_v2 __CUDA_API_PTSZ(cuStreamUpdateCaptureDependencies_v2)
+    #define cuStreamAddCallback                 __CUDA_API_PTSZ(cuStreamAddCallback)
+    #define cuStreamAttachMemAsync              __CUDA_API_PTSZ(cuStreamAttachMemAsync)
+    #define cuStreamQuery                       __CUDA_API_PTSZ(cuStreamQuery)
+    #define cuStreamSynchronize                 __CUDA_API_PTSZ(cuStreamSynchronize)
+    #define cuEventRecord                       __CUDA_API_PTSZ(cuEventRecord)
+    #define cuEventRecordWithFlags              __CUDA_API_PTSZ(cuEventRecordWithFlags)
+    #define cuLaunchKernel                      __CUDA_API_PTSZ(cuLaunchKernel)
+    #define cuLaunchKernelEx                    __CUDA_API_PTSZ(cuLaunchKernelEx)
+    #define cuLaunchHostFunc                    __CUDA_API_PTSZ(cuLaunchHostFunc)
+    #define cuGraphicsMapResources              __CUDA_API_PTSZ(cuGraphicsMapResources)
+    #define cuGraphicsUnmapResources            __CUDA_API_PTSZ(cuGraphicsUnmapResources)
+
+
+    #define cuLaunchCooperativeKernel           __CUDA_API_PTSZ(cuLaunchCooperativeKernel)
+
+    #define cuSignalExternalSemaphoresAsync     __CUDA_API_PTSZ(cuSignalExternalSemaphoresAsync)
+    #define cuWaitExternalSemaphoresAsync       __CUDA_API_PTSZ(cuWaitExternalSemaphoresAsync)
+
+    #define cuGraphInstantiateWithParams        __CUDA_API_PTSZ(cuGraphInstantiateWithParams)
+    #define cuGraphUpload                       __CUDA_API_PTSZ(cuGraphUpload)
+    #define cuGraphLaunch                       __CUDA_API_PTSZ(cuGraphLaunch)
+    #define cuStreamCopyAttributes              __CUDA_API_PTSZ(cuStreamCopyAttributes)
+    #define cuStreamGetAttribute                __CUDA_API_PTSZ(cuStreamGetAttribute)
+    #define cuStreamSetAttribute                __CUDA_API_PTSZ(cuStreamSetAttribute)
+    #define cuMemMapArrayAsync                  __CUDA_API_PTSZ(cuMemMapArrayAsync)
+
+    #define cuMemFreeAsync                      __CUDA_API_PTSZ(cuMemFreeAsync)
+    #define cuMemAllocAsync                     __CUDA_API_PTSZ(cuMemAllocAsync)
+    #define cuMemAllocFromPoolAsync             __CUDA_API_PTSZ(cuMemAllocFromPoolAsync)
+
+    #define cuStreamBeginCaptureToGraph         __CUDA_API_PTSZ(cuStreamBeginCaptureToGraph)
+
+#endif
+
+#define cuMemBatchDecompressAsync               __CUDA_API_PTSZ(cuMemBatchDecompressAsync)
+
+/**
+ * \file cuda.h
+ * \brief Header file for the CUDA Toolkit application programming interface.
+ *
+ * \file cudaGL.h
+ * \brief Header file for the OpenGL interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * \file cudaD3D9.h
+ * \brief Header file for the Direct3D 9 interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ */
+
+/**
+ * \defgroup CUDA_TYPES Data types used by CUDA driver
+ * @{
+ */
+
+/**
+ * CUDA API version number
+ */
+#define CUDA_VERSION 12080
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * CUDA device pointer
+ * CUdeviceptr is defined as an unsigned integer type whose size matches the size of a pointer on the target platform.
+ */
+#if defined(_WIN64) || defined(__LP64__)
+typedef unsigned long long CUdeviceptr_v2;
+#else
+typedef unsigned int CUdeviceptr_v2;
+#endif
+typedef CUdeviceptr_v2 CUdeviceptr;                          /**< CUDA device pointer */
+
+typedef int CUdevice_v1;                                     /**< CUDA device */
+typedef CUdevice_v1 CUdevice;                                /**< CUDA device */
+typedef struct CUctx_st *CUcontext;                          /**< A regular context handle */
+typedef struct CUmod_st *CUmodule;                           /**< CUDA module */
+typedef struct CUfunc_st *CUfunction;                        /**< CUDA function */
+typedef struct CUlib_st *CUlibrary;                          /**< CUDA library */
+typedef struct CUkern_st *CUkernel;                          /**< CUDA kernel */
+typedef struct CUarray_st *CUarray;                          /**< CUDA array */
+typedef struct CUmipmappedArray_st *CUmipmappedArray;        /**< CUDA mipmapped array */
+typedef struct CUtexref_st *CUtexref;                        /**< CUDA texture reference */
+typedef struct CUsurfref_st *CUsurfref;                      /**< CUDA surface reference */
+typedef struct CUevent_st *CUevent;                          /**< CUDA event */
+typedef struct CUstream_st *CUstream;                        /**< CUDA stream */
+typedef struct CUgraphicsResource_st *CUgraphicsResource;    /**< CUDA graphics interop resource */
+typedef unsigned long long CUtexObject_v1;                   /**< An opaque value that represents a CUDA texture object */
+typedef CUtexObject_v1 CUtexObject;                          /**< An opaque value that represents a CUDA texture object */
+typedef unsigned long long CUsurfObject_v1;                  /**< An opaque value that represents a CUDA surface object */
+typedef CUsurfObject_v1 CUsurfObject;                        /**< An opaque value that represents a CUDA surface object */ 
+typedef struct CUextMemory_st *CUexternalMemory;             /**< CUDA external memory */
+typedef struct CUextSemaphore_st *CUexternalSemaphore;       /**< CUDA external semaphore */
+typedef struct CUgraph_st *CUgraph;                          /**< CUDA graph */
+typedef struct CUgraphNode_st *CUgraphNode;                  /**< CUDA graph node */
+typedef struct CUgraphExec_st *CUgraphExec;                  /**< CUDA executable graph */
+typedef struct CUmemPoolHandle_st *CUmemoryPool;             /**< CUDA memory pool */
+typedef struct CUuserObject_st *CUuserObject;                /**< CUDA user object for graphs */
+typedef cuuint64_t CUgraphConditionalHandle; /**< CUDA graph conditional handle */
+typedef struct CUgraphDeviceUpdatableNode_st *CUgraphDeviceNode; /**< CUDA graph device node handle */
+typedef struct CUasyncCallbackEntry_st *CUasyncCallbackHandle;            /**< CUDA async notification callback handle */
+/*!
+ * \typedef typedef struct CUgreenCtx_st* CUgreenCtx
+ * A green context handle. This handle can be used safely from only one CPU thread at a time.
+ * Created via ::cuGreenCtxCreate
+ */
+typedef struct CUgreenCtx_st *CUgreenCtx;
+
+#ifndef CU_UUID_HAS_BEEN_DEFINED
+#define CU_UUID_HAS_BEEN_DEFINED
+typedef struct CUuuid_st {                                /**< CUDA definition of UUID */
+    char bytes[16];
+} CUuuid;
+#endif
+
+/**
+ * CUDA IPC handle size
+ */
+#define CU_IPC_HANDLE_SIZE 64
+
+/**
+ * Fabric handle - An opaque handle representing a memory allocation
+ * that can be exported to processes in same or different nodes. For IPC
+ * between processes on different nodes they must be connected via the
+ * NVSwitch fabric.
+ */
+typedef struct CUmemFabricHandle_st {
+    unsigned char data[CU_IPC_HANDLE_SIZE];
+} CUmemFabricHandle_v1;
+typedef CUmemFabricHandle_v1 CUmemFabricHandle;
+
+/**
+ * CUDA IPC event handle
+ */
+typedef struct CUipcEventHandle_st {
+    char reserved[CU_IPC_HANDLE_SIZE];
+} CUipcEventHandle_v1;
+typedef CUipcEventHandle_v1 CUipcEventHandle;
+
+/**
+ * CUDA IPC mem handle
+ */
+typedef struct CUipcMemHandle_st {
+    char reserved[CU_IPC_HANDLE_SIZE];
+} CUipcMemHandle_v1;
+typedef CUipcMemHandle_v1 CUipcMemHandle;
+
+/**
+ * CUDA Ipc Mem Flags
+ */
+typedef enum CUipcMem_flags_enum {
+    CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS = 0x1 /**< Automatically enable peer access between remote devices as needed */
+} CUipcMem_flags;
+
+
+/**
+ * CUDA Mem Attach Flags
+ */
+typedef enum CUmemAttach_flags_enum {
+    CU_MEM_ATTACH_GLOBAL = 0x1, /**< Memory can be accessed by any stream on any device */
+    CU_MEM_ATTACH_HOST   = 0x2, /**< Memory cannot be accessed by any stream on any device */
+    CU_MEM_ATTACH_SINGLE = 0x4  /**< Memory can only be accessed by a single stream on the associated device */
+} CUmemAttach_flags;
+
+/**
+ * Context creation flags
+ */
+typedef enum CUctx_flags_enum {
+    CU_CTX_SCHED_AUTO          = 0x00, /**< Automatic scheduling */
+    CU_CTX_SCHED_SPIN          = 0x01, /**< Set spin as default scheduling */
+    CU_CTX_SCHED_YIELD         = 0x02, /**< Set yield as default scheduling */
+    CU_CTX_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */
+    CU_CTX_BLOCKING_SYNC       = 0x04, /**< Set blocking synchronization as default scheduling
+                                         *  \deprecated This flag was deprecated as of CUDA 4.0
+                                         *  and was replaced with ::CU_CTX_SCHED_BLOCKING_SYNC. */
+    CU_CTX_SCHED_MASK          = 0x07,
+    CU_CTX_MAP_HOST            = 0x08, /**< \deprecated This flag was deprecated as of CUDA 11.0 
+                                         *  and it no longer has any effect. All contexts 
+                                         *  as of CUDA 3.2 behave as though the flag is enabled. */
+    CU_CTX_LMEM_RESIZE_TO_MAX  = 0x10, /**< Keep local memory allocation after launch */
+    CU_CTX_COREDUMP_ENABLE     = 0x20, /**< Trigger coredumps from exceptions in this context */
+    CU_CTX_USER_COREDUMP_ENABLE= 0x40, /**< Enable user pipe to trigger coredumps in this context */
+    CU_CTX_SYNC_MEMOPS         = 0x80, /**< Ensure synchronous memory operations on this context will synchronize */
+    CU_CTX_FLAGS_MASK          = 0xFF
+} CUctx_flags;
+
+/**
+ * Event sched flags
+ */
+typedef enum CUevent_sched_flags_enum {
+    CU_EVENT_SCHED_AUTO = 0x00, /**< Automatic scheduling */
+    CU_EVENT_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */
+    CU_EVENT_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */
+    CU_EVENT_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */
+} CUevent_sched_flags;
+
+/**
+ * NVCL event scheduling flags
+ */
+typedef enum cl_event_flags_enum {
+    NVCL_EVENT_SCHED_AUTO = 0x00, /**< Automatic scheduling */
+    NVCL_EVENT_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */
+    NVCL_EVENT_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */
+    NVCL_EVENT_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */
+} cl_event_flags;
+
+/**
+ * NVCL context scheduling flags
+ */
+typedef enum cl_context_flags_enum {
+    NVCL_CTX_SCHED_AUTO = 0x00, /**< Automatic scheduling */
+    NVCL_CTX_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */
+    NVCL_CTX_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */
+    NVCL_CTX_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */
+} cl_context_flags;
+
+
+/**
+ * Stream creation flags
+ */
+typedef enum CUstream_flags_enum {
+    CU_STREAM_DEFAULT             = 0x0, /**< Default stream flag */
+    CU_STREAM_NON_BLOCKING        = 0x1  /**< Stream does not synchronize with stream 0 (the NULL stream) */
+} CUstream_flags;
+
+/**
+ * Legacy stream handle
+ *
+ * Stream handle that can be passed as a CUstream to use an implicit stream
+ * with legacy synchronization behavior.
+ *
+ * See details of the \link_sync_behavior
+ */
+#define CU_STREAM_LEGACY     ((CUstream)0x1)
+
+/**
+ * Per-thread stream handle
+ *
+ * Stream handle that can be passed as a CUstream to use an implicit stream
+ * with per-thread synchronization behavior.
+ *
+ * See details of the \link_sync_behavior
+ */
+#define CU_STREAM_PER_THREAD ((CUstream)0x2)
+
+/**
+ * Event creation flags
+ */
+typedef enum CUevent_flags_enum {
+    CU_EVENT_DEFAULT        = 0x0, /**< Default event flag */
+    CU_EVENT_BLOCKING_SYNC  = 0x1, /**< Event uses blocking synchronization */
+    CU_EVENT_DISABLE_TIMING = 0x2, /**< Event will not record timing data */
+    CU_EVENT_INTERPROCESS   = 0x4  /**< Event is suitable for interprocess use. CU_EVENT_DISABLE_TIMING must be set */
+} CUevent_flags;
+
+/**
+ * Event record flags
+ */
+typedef enum CUevent_record_flags_enum {
+    CU_EVENT_RECORD_DEFAULT  = 0x0, /**< Default event record flag */
+    CU_EVENT_RECORD_EXTERNAL = 0x1  /**< When using stream capture, create an event record node
+                                      *  instead of the default behavior.  This flag is invalid
+                                      *  when used outside of capture. */
+} CUevent_record_flags;
+
+/**
+ * Event wait flags
+ */
+typedef enum CUevent_wait_flags_enum {
+    CU_EVENT_WAIT_DEFAULT  = 0x0, /**< Default event wait flag */
+    CU_EVENT_WAIT_EXTERNAL = 0x1  /**< When using stream capture, create an event wait node
+                                    *  instead of the default behavior.  This flag is invalid
+                                    *  when used outside of capture.*/
+} CUevent_wait_flags;
+
+/**
+ * Flags for ::cuStreamWaitValue32 and ::cuStreamWaitValue64
+ */
+typedef enum CUstreamWaitValue_flags_enum {
+    CU_STREAM_WAIT_VALUE_GEQ   = 0x0,   /**< Wait until (int32_t)(*addr - value) >= 0 (or int64_t for 64 bit
+                                             values). Note this is a cyclic comparison which ignores wraparound.
+                                             (Default behavior.) */
+    CU_STREAM_WAIT_VALUE_EQ    = 0x1,   /**< Wait until *addr == value. */
+    CU_STREAM_WAIT_VALUE_AND   = 0x2,   /**< Wait until (*addr & value) != 0. */
+    CU_STREAM_WAIT_VALUE_NOR   = 0x3,   /**< Wait until ~(*addr | value) != 0. Support for this operation can be
+                                             queried with ::cuDeviceGetAttribute() and
+                                             ::CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR.*/
+    CU_STREAM_WAIT_VALUE_FLUSH = 1<<30  /**< Follow the wait operation with a flush of outstanding remote writes. This
+                                             means that, if a remote write operation is guaranteed to have reached the
+                                             device before the wait can be satisfied, that write is guaranteed to be
+                                             visible to downstream device work. The device is permitted to reorder
+                                             remote writes internally. For example, this flag would be required if
+                                             two remote writes arrive in a defined order, the wait is satisfied by the
+                                             second write, and downstream work needs to observe the first write.
+                                             Support for this operation is restricted to selected platforms and can be
+                                             queried with ::CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES.*/
+} CUstreamWaitValue_flags;
+
+/**
+ * Flags for ::cuStreamWriteValue32
+ */
+typedef enum CUstreamWriteValue_flags_enum {
+    CU_STREAM_WRITE_VALUE_DEFAULT           = 0x0, /**< Default behavior */
+    CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER = 0x1  /**< Permits the write to be reordered with writes which were issued
+                                                        before it, as a performance optimization. Normally,
+                                                        ::cuStreamWriteValue32 will provide a memory fence before the
+                                                        write, which has similar semantics to
+                                                        __threadfence_system() but is scoped to the stream
+                                                        rather than a CUDA thread.
+                                                        This flag is not supported in the v2 API. */
+} CUstreamWriteValue_flags;
+
+/**
+ * Operations for ::cuStreamBatchMemOp
+ */
+typedef enum CUstreamBatchMemOpType_enum {
+    CU_STREAM_MEM_OP_WAIT_VALUE_32  = 1,     /**< Represents a ::cuStreamWaitValue32 operation */
+    CU_STREAM_MEM_OP_WRITE_VALUE_32 = 2,     /**< Represents a ::cuStreamWriteValue32 operation */
+    CU_STREAM_MEM_OP_WAIT_VALUE_64  = 4,     /**< Represents a ::cuStreamWaitValue64 operation */
+    CU_STREAM_MEM_OP_WRITE_VALUE_64 = 5,     /**< Represents a ::cuStreamWriteValue64 operation */
+    CU_STREAM_MEM_OP_BARRIER = 6,            /**< Insert a memory barrier of the specified type */ 
+    CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES = 3 /**< This has the same effect as ::CU_STREAM_WAIT_VALUE_FLUSH, but as a
+                                                  standalone operation. */
+} CUstreamBatchMemOpType;
+
+/**
+ * Flags for ::cuStreamMemoryBarrier
+ */
+typedef enum CUstreamMemoryBarrier_flags_enum {
+    CU_STREAM_MEMORY_BARRIER_TYPE_SYS = 0x0, /**< System-wide memory barrier. */
+    CU_STREAM_MEMORY_BARRIER_TYPE_GPU = 0x1 /**< Limit memory barrier scope to the GPU. */
+} CUstreamMemoryBarrier_flags;
+
+/**
+ * Per-operation parameters for ::cuStreamBatchMemOp
+ */
+typedef union CUstreamBatchMemOpParams_union {
+    CUstreamBatchMemOpType operation;
+    struct CUstreamMemOpWaitValueParams_st {
+        CUstreamBatchMemOpType operation;
+        CUdeviceptr address;
+        union {
+            cuuint32_t value;
+            cuuint64_t value64;
+        };
+        unsigned int flags;
+        CUdeviceptr alias; /**< For driver internal use. Initial value is unimportant. */
+    } waitValue;
+    struct CUstreamMemOpWriteValueParams_st {
+        CUstreamBatchMemOpType operation;
+        CUdeviceptr address;
+        union {
+            cuuint32_t value;
+            cuuint64_t value64;
+        };
+        unsigned int flags;
+        CUdeviceptr alias; /**< For driver internal use. Initial value is unimportant. */
+    } writeValue;
+    struct CUstreamMemOpFlushRemoteWritesParams_st {
+        CUstreamBatchMemOpType operation;
+        unsigned int flags;
+    } flushRemoteWrites;
+    struct CUstreamMemOpMemoryBarrierParams_st { /**< Only supported in the _v2 API */
+        CUstreamBatchMemOpType operation;
+        unsigned int flags;
+    } memoryBarrier;
+    cuuint64_t pad[6];
+} CUstreamBatchMemOpParams_v1;
+typedef CUstreamBatchMemOpParams_v1 CUstreamBatchMemOpParams;
+
+typedef struct CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st {
+    CUcontext ctx;
+    unsigned int count;
+    CUstreamBatchMemOpParams *paramArray;
+    unsigned int flags;
+} CUDA_BATCH_MEM_OP_NODE_PARAMS_v1;
+typedef CUDA_BATCH_MEM_OP_NODE_PARAMS_v1 CUDA_BATCH_MEM_OP_NODE_PARAMS;
+
+/**
+ * Batch memory operation node parameters
+ */
+typedef struct CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st {
+    CUcontext ctx;                        /**< Context to use for the operations. */
+    unsigned int count;                   /**< Number of operations in paramArray. */
+    CUstreamBatchMemOpParams *paramArray; /**< Array of batch memory operations. */
+    unsigned int flags;                   /**< Flags to control the node. */
+} CUDA_BATCH_MEM_OP_NODE_PARAMS_v2;
+
+/**
+ * Occupancy calculator flag
+ */
+typedef enum CUoccupancy_flags_enum {
+    CU_OCCUPANCY_DEFAULT                  = 0x0, /**< Default behavior */
+    CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE = 0x1  /**< Assume global caching is enabled and cannot be automatically turned off */
+} CUoccupancy_flags;
+
+/**
+ * Flags for ::cuStreamUpdateCaptureDependencies
+ */
+typedef enum CUstreamUpdateCaptureDependencies_flags_enum {
+    CU_STREAM_ADD_CAPTURE_DEPENDENCIES = 0x0, /**< Add new nodes to the dependency set */
+    CU_STREAM_SET_CAPTURE_DEPENDENCIES = 0x1  /**< Replace the dependency set with the new nodes */
+} CUstreamUpdateCaptureDependencies_flags;
+
+/**
+* Types of async notification that can be sent
+*/
+typedef enum CUasyncNotificationType_enum {
+    CU_ASYNC_NOTIFICATION_TYPE_OVER_BUDGET = 0x1
+} CUasyncNotificationType;
+
+/**
+* Information passed to the user via the async notification callback
+*/
+typedef struct CUasyncNotificationInfo_st {
+    CUasyncNotificationType type;
+    union {
+        struct {
+            unsigned long long bytesOverBudget;
+        } overBudget;
+    } info;
+} CUasyncNotificationInfo;
+
+/**
+ * CUDA async notification callback
+ * \param info Information describing what actions to take as a result of this trim notification.
+ * \param userData Pointer to user defined data provided at registration.
+ * \param callback The callback handle associated with this specific callback.
+ */
+typedef void (*CUasyncCallback)(CUasyncNotificationInfo *info, void *userData, CUasyncCallbackHandle callback);
+
+/**
+ * Array formats
+ */
+typedef enum CUarray_format_enum {
+    CU_AD_FORMAT_UNSIGNED_INT8            = 0x01, /**< Unsigned 8-bit integers */
+    CU_AD_FORMAT_UNSIGNED_INT16           = 0x02, /**< Unsigned 16-bit integers */
+    CU_AD_FORMAT_UNSIGNED_INT32           = 0x03, /**< Unsigned 32-bit integers */
+    CU_AD_FORMAT_SIGNED_INT8              = 0x08, /**< Signed 8-bit integers */
+    CU_AD_FORMAT_SIGNED_INT16             = 0x09, /**< Signed 16-bit integers */
+    CU_AD_FORMAT_SIGNED_INT32             = 0x0a, /**< Signed 32-bit integers */
+    CU_AD_FORMAT_HALF                     = 0x10, /**< 16-bit floating point */
+    CU_AD_FORMAT_FLOAT                    = 0x20, /**< 32-bit floating point */
+    CU_AD_FORMAT_NV12                     = 0xb0, /**< 8-bit YUV planar format, with 4:2:0 sampling */
+    CU_AD_FORMAT_UNORM_INT8X1             = 0xc0, /**< 1 channel unsigned 8-bit normalized integer */
+    CU_AD_FORMAT_UNORM_INT8X2             = 0xc1, /**< 2 channel unsigned 8-bit normalized integer */
+    CU_AD_FORMAT_UNORM_INT8X4             = 0xc2, /**< 4 channel unsigned 8-bit normalized integer */
+    CU_AD_FORMAT_UNORM_INT16X1            = 0xc3, /**< 1 channel unsigned 16-bit normalized integer */
+    CU_AD_FORMAT_UNORM_INT16X2            = 0xc4, /**< 2 channel unsigned 16-bit normalized integer */
+    CU_AD_FORMAT_UNORM_INT16X4            = 0xc5, /**< 4 channel unsigned 16-bit normalized integer */
+    CU_AD_FORMAT_SNORM_INT8X1             = 0xc6, /**< 1 channel signed 8-bit normalized integer */
+    CU_AD_FORMAT_SNORM_INT8X2             = 0xc7, /**< 2 channel signed 8-bit normalized integer */
+    CU_AD_FORMAT_SNORM_INT8X4             = 0xc8, /**< 4 channel signed 8-bit normalized integer */
+    CU_AD_FORMAT_SNORM_INT16X1            = 0xc9, /**< 1 channel signed 16-bit normalized integer */
+    CU_AD_FORMAT_SNORM_INT16X2            = 0xca, /**< 2 channel signed 16-bit normalized integer */
+    CU_AD_FORMAT_SNORM_INT16X4            = 0xcb, /**< 4 channel signed 16-bit normalized integer */
+    CU_AD_FORMAT_BC1_UNORM                = 0x91, /**< 4 channel unsigned normalized block-compressed (BC1 compression) format */
+    CU_AD_FORMAT_BC1_UNORM_SRGB           = 0x92, /**< 4 channel unsigned normalized block-compressed (BC1 compression) format with sRGB encoding*/
+    CU_AD_FORMAT_BC2_UNORM                = 0x93, /**< 4 channel unsigned normalized block-compressed (BC2 compression) format */
+    CU_AD_FORMAT_BC2_UNORM_SRGB           = 0x94, /**< 4 channel unsigned normalized block-compressed (BC2 compression) format with sRGB encoding*/
+    CU_AD_FORMAT_BC3_UNORM                = 0x95, /**< 4 channel unsigned normalized block-compressed (BC3 compression) format */
+    CU_AD_FORMAT_BC3_UNORM_SRGB           = 0x96, /**< 4 channel unsigned normalized block-compressed (BC3 compression) format with sRGB encoding*/
+    CU_AD_FORMAT_BC4_UNORM                = 0x97, /**< 1 channel unsigned normalized block-compressed (BC4 compression) format */
+    CU_AD_FORMAT_BC4_SNORM                = 0x98, /**< 1 channel signed normalized block-compressed (BC4 compression) format */
+    CU_AD_FORMAT_BC5_UNORM                = 0x99, /**< 2 channel unsigned normalized block-compressed (BC5 compression) format */
+    CU_AD_FORMAT_BC5_SNORM                = 0x9a, /**< 2 channel signed normalized block-compressed (BC5 compression) format */
+    CU_AD_FORMAT_BC6H_UF16                = 0x9b, /**< 3 channel unsigned half-float block-compressed (BC6H compression) format */
+    CU_AD_FORMAT_BC6H_SF16                = 0x9c, /**< 3 channel signed half-float block-compressed (BC6H compression) format */
+    CU_AD_FORMAT_BC7_UNORM                = 0x9d, /**< 4 channel unsigned normalized block-compressed (BC7 compression) format */
+    CU_AD_FORMAT_BC7_UNORM_SRGB           = 0x9e, /**< 4 channel unsigned normalized block-compressed (BC7 compression) format with sRGB encoding */
+    CU_AD_FORMAT_P010                     = 0x9f, /**< 10-bit YUV planar format, with 4:2:0 sampling */
+    CU_AD_FORMAT_P016                     = 0xa1, /**< 16-bit YUV planar format, with 4:2:0 sampling */
+    CU_AD_FORMAT_NV16                     = 0xa2, /**< 8-bit YUV planar format, with 4:2:2 sampling */
+    CU_AD_FORMAT_P210                     = 0xa3, /**< 10-bit YUV planar format, with 4:2:2 sampling */
+    CU_AD_FORMAT_P216                     = 0xa4, /**< 16-bit YUV planar format, with 4:2:2 sampling */
+    CU_AD_FORMAT_YUY2                     = 0xa5, /**< 2 channel, 8-bit YUV packed planar format, with 4:2:2 sampling */
+    CU_AD_FORMAT_Y210                     = 0xa6, /**< 2 channel, 10-bit YUV packed planar format, with 4:2:2 sampling */
+    CU_AD_FORMAT_Y216                     = 0xa7, /**< 2 channel, 16-bit YUV packed planar format, with 4:2:2 sampling */
+    CU_AD_FORMAT_AYUV                     = 0xa8, /**< 4 channel, 8-bit YUV packed planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_Y410                     = 0xa9, /**< 10-bit YUV packed planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_Y416                     = 0xb1, /**< 4 channel, 12-bit YUV packed planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_Y444_PLANAR8             = 0xb2, /**< 3 channel 8-bit YUV planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_Y444_PLANAR10            = 0xb3, /**< 3 channel 10-bit YUV planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_YUV444_8bit_SemiPlanar   = 0xb4, /**< 3 channel 8-bit YUV semi-planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_YUV444_16bit_SemiPlanar  = 0xb5, /**< 3 channel 16-bit YUV semi-planar format, with 4:4:4 sampling */
+    CU_AD_FORMAT_UNORM_INT_101010_2       = 0x50, /**< 4 channel unorm R10G10B10A2 RGB format */
+    CU_AD_FORMAT_MAX                      = 0x7FFFFFFF
+} CUarray_format;
+
+/**
+ * Texture reference addressing modes
+ */
+typedef enum CUaddress_mode_enum {
+    CU_TR_ADDRESS_MODE_WRAP   = 0, /**< Wrapping address mode */
+    CU_TR_ADDRESS_MODE_CLAMP  = 1, /**< Clamp to edge address mode */
+    CU_TR_ADDRESS_MODE_MIRROR = 2, /**< Mirror address mode */
+    CU_TR_ADDRESS_MODE_BORDER = 3  /**< Border address mode */
+} CUaddress_mode;
+
+/**
+ * Texture reference filtering modes
+ */
+typedef enum CUfilter_mode_enum {
+    CU_TR_FILTER_MODE_POINT  = 0, /**< Point filter mode */
+    CU_TR_FILTER_MODE_LINEAR = 1  /**< Linear filter mode */
+} CUfilter_mode;
+
+/**
+ * Device properties
+ */
+typedef enum CUdevice_attribute_enum {
+    CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 1,                          /**< Maximum number of threads per block */
+    CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X = 2,                                /**< Maximum block dimension X */
+    CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y = 3,                                /**< Maximum block dimension Y */
+    CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z = 4,                                /**< Maximum block dimension Z */
+    CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X = 5,                                 /**< Maximum grid dimension X */
+    CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y = 6,                                 /**< Maximum grid dimension Y */
+    CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7,                                 /**< Maximum grid dimension Z */
+    CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8,                    /**< Maximum shared memory available per block in bytes */
+    CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK = 8,                        /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK */
+    CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9,                          /**< Memory available on device for __constant__ variables in a CUDA C kernel in bytes */
+    CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10,                                     /**< Warp size in threads */
+    CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11,                                     /**< Maximum pitch in bytes allowed by memory copies */
+    CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12,                       /**< Maximum number of 32-bit registers available per block */
+    CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK = 12,                           /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK */
+    CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13,                                    /**< Typical clock frequency in kilohertz */
+    CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14,                             /**< Alignment requirement for textures */
+    CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15,                                   /**< Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT. */
+    CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16,                          /**< Number of multiprocessors on device */
+    CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17,                           /**< Specifies whether there is a run time limit on kernels */
+    CU_DEVICE_ATTRIBUTE_INTEGRATED = 18,                                    /**< Device is integrated with host memory */
+    CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19,                           /**< Device can map host memory into CUDA address space */
+    CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20,                                  /**< Compute mode (See ::CUcomputemode for details) */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH = 21,                       /**< Maximum 1D texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH = 22,                       /**< Maximum 2D texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT = 23,                      /**< Maximum 2D texture height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH = 24,                       /**< Maximum 3D texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT = 25,                      /**< Maximum 3D texture height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH = 26,                       /**< Maximum 3D texture depth */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH = 27,               /**< Maximum 2D layered texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT = 28,              /**< Maximum 2D layered texture height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS = 29,              /**< Maximum layers in a 2D layered texture */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH = 27,                 /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT = 28,                /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES = 29,             /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS */
+    CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30,                             /**< Alignment requirement for surfaces */
+    CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31,                            /**< Device can possibly execute multiple kernels concurrently */
+    CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32,                                   /**< Device has ECC support enabled */
+    CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33,                                    /**< PCI bus ID of the device */
+    CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34,                                 /**< PCI device ID of the device */
+    CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35,                                    /**< Device is using TCC driver model */
+    CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36,                             /**< Peak memory clock frequency in kilohertz */
+    CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH = 37,                       /**< Global memory bus width in bits */
+    CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE = 38,                                 /**< Size of L2 cache in bytes */
+    CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39,                /**< Maximum resident threads per multiprocessor */
+    CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT = 40,                            /**< Number of asynchronous engines */
+    CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING = 41,                            /**< Device shares a unified address space with the host */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH = 42,               /**< Maximum 1D layered texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS = 43,              /**< Maximum layers in a 1D layered texture */
+    CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER = 44,                              /**< Deprecated, do not use. */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH = 45,                /**< Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT = 46,               /**< Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE = 47,             /**< Alternate maximum 3D texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE = 48,            /**< Alternate maximum 3D texture height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE = 49,             /**< Alternate maximum 3D texture depth */
+    CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID = 50,                                 /**< PCI domain ID of the device */
+    CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT = 51,                       /**< Pitch alignment requirement for textures */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH = 52,                  /**< Maximum cubemap texture width/height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH = 53,          /**< Maximum cubemap layered texture width/height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS = 54,         /**< Maximum layers in a cubemap layered texture */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH = 55,                       /**< Maximum 1D surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH = 56,                       /**< Maximum 2D surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT = 57,                      /**< Maximum 2D surface height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH = 58,                       /**< Maximum 3D surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT = 59,                      /**< Maximum 3D surface height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH = 60,                       /**< Maximum 3D surface depth */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH = 61,               /**< Maximum 1D layered surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS = 62,              /**< Maximum layers in a 1D layered surface */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH = 63,               /**< Maximum 2D layered surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT = 64,              /**< Maximum 2D layered surface height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS = 65,              /**< Maximum layers in a 2D layered surface */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH = 66,                  /**< Maximum cubemap surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH = 67,          /**< Maximum cubemap layered surface width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS = 68,         /**< Maximum layers in a cubemap layered surface */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH = 69,                /**< Deprecated, do not use. Use cudaDeviceGetTexture1DLinearMaxWidth() or cuDeviceGetTexture1DLinearMaxWidth() instead. */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH = 70,                /**< Maximum 2D linear texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT = 71,               /**< Maximum 2D linear texture height */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH = 72,                /**< Maximum 2D linear texture pitch in bytes */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH = 73,             /**< Maximum mipmapped 2D texture width */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT = 74,            /**< Maximum mipmapped 2D texture height */
+    CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR = 75,                      /**< Major compute capability version number */
+    CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR = 76,                      /**< Minor compute capability version number */
+    CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH = 77,             /**< Maximum mipmapped 1D texture width */
+    CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED = 78,                   /**< Device supports stream priorities */
+    CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED = 79,                     /**< Device supports caching globals in L1 */
+    CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED = 80,                      /**< Device supports caching locals in L1 */
+    CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR = 81,          /**< Maximum shared memory available per multiprocessor in bytes */
+    CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR = 82,              /**< Maximum number of 32-bit registers available per multiprocessor */
+    CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY = 83,                                /**< Device can allocate managed memory on this system */
+    CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD = 84,                               /**< Device is on a multi-GPU board */
+    CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID = 85,                      /**< Unique id for a group of devices on the same multi-GPU board */
+    CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED = 86,                  /**< Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)*/
+    CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO = 87,         /**< Ratio of single precision performance (in floating-point operations per second) to double precision performance */
+    CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS = 88,                        /**< Device supports coherently accessing pageable memory without calling cudaHostRegister on it */
+    CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS = 89,                     /**< Device can coherently access managed memory concurrently with the CPU */
+    CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED = 90,                  /**< Device supports compute preemption. */
+    CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM = 91,       /**< Device can access host registered memory at the same virtual address as the CPU */
+    CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS_V1 = 92,                     /**< Deprecated, along with v1 MemOps API, ::cuStreamBatchMemOp and related APIs are supported. */
+    CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS_V1 = 93,              /**< Deprecated, along with v1 MemOps API, 64-bit operations are supported in ::cuStreamBatchMemOp and related APIs. */
+    CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V1 = 94,              /**< Deprecated, along with v1 MemOps API, ::CU_STREAM_WAIT_VALUE_NOR is supported. */
+    CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH = 95,                            /**< Device supports launching cooperative kernels via ::cuLaunchCooperativeKernel */
+    CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH = 96,               /**< Deprecated, ::cuLaunchCooperativeKernelMultiDevice is deprecated. */
+    CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN = 97,             /**< Maximum optin shared memory per block */
+    CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES = 98,                       /**< The ::CU_STREAM_WAIT_VALUE_FLUSH flag and the ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device. See \ref CUDA_MEMOP for additional details. */
+    CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED = 99,                       /**< Device supports host memory registration via ::cudaHostRegister. */
+    CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES = 100, /**< Device accesses pageable memory via the host's page tables. */
+    CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST = 101,          /**< The host can directly access managed memory on the device without migration. */
+    CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED = 102,         /**< Deprecated, Use CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED*/
+    CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED = 102,         /**< Device supports virtual memory management APIs like ::cuMemAddressReserve, ::cuMemCreate, ::cuMemMap and related APIs */
+    CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED = 103,  /**< Device supports exporting memory to a posix file descriptor with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate */
+    CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED = 104,           /**< Device supports exporting memory to a Win32 NT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate */
+    CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED = 105,       /**< Device supports exporting memory to a Win32 KMT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate */
+    CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR = 106,                /**< Maximum number of blocks per multiprocessor */
+    CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED = 107,                /**< Device supports compression of memory */
+    CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE = 108,                 /**< Maximum L2 persisting lines capacity setting in bytes. */
+    CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE = 109,                /**< Maximum value of CUaccessPolicyWindow::num_bytes. */
+    CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED = 110,      /**< Device supports specifying the GPUDirect RDMA flag with ::cuMemCreate */
+    CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK = 111,             /**< Shared memory reserved by CUDA driver per block in bytes */
+    CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED = 112,                  /**< Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays */
+    CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED = 113,            /**< Device supports using the ::cuMemHostRegister flag ::CU_MEMHOSTERGISTER_READ_ONLY to register memory that must be mapped as read-only to the GPU */
+    CU_DEVICE_ATTRIBUTE_TIMELINE_SEMAPHORE_INTEROP_SUPPORTED = 114,         /**< External timeline semaphore interop is supported on the device */
+    CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED = 115,                       /**< Device supports using the ::cuMemAllocAsync and ::cuMemPool family of APIs */
+    CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED = 116,                    /**< Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information) */
+    CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS = 117,         /**< The returned attribute shall be interpreted as a bitmask, where the individual bits are described by the ::CUflushGPUDirectRDMAWritesOptions enum */
+    CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING = 118,              /**< GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See ::CUGPUDirectRDMAWritesOrdering for the numerical values returned here. */
+    CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES = 119,               /**< Handle types supported with mempool based IPC */
+    CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH = 120,                               /**< Indicates device supports cluster launch */
+    CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED = 121,        /**< Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays */
+    CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS = 122,                /**< 64-bit operations are supported in ::cuStreamBatchMemOp and related MemOp APIs. */
+    CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR = 123,                /**< ::CU_STREAM_WAIT_VALUE_NOR is supported by MemOp APIs. */
+    CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED = 124,                            /**< Device supports buffer sharing with dma_buf mechanism. */ 
+    CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED = 125,                          /**< Device supports IPC Events. */ 
+    CU_DEVICE_ATTRIBUTE_MEM_SYNC_DOMAIN_COUNT = 126,                        /**< Number of memory domains the device supports. */
+    CU_DEVICE_ATTRIBUTE_TENSOR_MAP_ACCESS_SUPPORTED = 127,                  /**< Device supports accessing memory using Tensor Map. */
+    CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_FABRIC_SUPPORTED = 128,                 /**< Device supports exporting memory to a fabric handle with cuMemExportToShareableHandle() or requested with cuMemCreate() */
+    CU_DEVICE_ATTRIBUTE_UNIFIED_FUNCTION_POINTERS = 129,                    /**< Device supports unified function pointers. */
+    CU_DEVICE_ATTRIBUTE_NUMA_CONFIG = 130,                                  /**< NUMA configuration of a device: value is of type ::CUdeviceNumaConfig enum */
+    CU_DEVICE_ATTRIBUTE_NUMA_ID = 131,                                      /**< NUMA node ID of the GPU memory */
+    CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED = 132,                          /**< Device supports switch multicast and reduction operations. */
+    CU_DEVICE_ATTRIBUTE_MPS_ENABLED = 133,                                  /**< Indicates if contexts created on this device will be shared via MPS */
+    CU_DEVICE_ATTRIBUTE_HOST_NUMA_ID = 134,                                 /**< NUMA ID of the host node closest to the device. Returns -1 when system does not support NUMA. */
+    CU_DEVICE_ATTRIBUTE_D3D12_CIG_SUPPORTED = 135,                          /**< Device supports CIG with D3D12. */
+    CU_DEVICE_ATTRIBUTE_MEM_DECOMPRESS_ALGORITHM_MASK = 136,                /**< The returned valued shall be interpreted as a bitmask, where the individual bits are described by the ::CUmemDecompressAlgorithm enum. */
+    CU_DEVICE_ATTRIBUTE_MEM_DECOMPRESS_MAXIMUM_LENGTH = 137,                /**< The returned valued is the maximum length in bytes of a single decompress operation that is allowed. */
+    CU_DEVICE_ATTRIBUTE_GPU_PCI_DEVICE_ID    = 139, /**< The combined 16-bit PCI device ID and 16-bit PCI vendor ID. */
+    CU_DEVICE_ATTRIBUTE_GPU_PCI_SUBSYSTEM_ID = 140, /**< The combined 16-bit PCI subsystem ID and 16-bit PCI subsystem vendor ID. */
+    CU_DEVICE_ATTRIBUTE_HOST_NUMA_MULTINODE_IPC_SUPPORTED = 143,             /**< Device supports HOST_NUMA location IPC between nodes in a multi-node system. */
+    CU_DEVICE_ATTRIBUTE_MAX
+} CUdevice_attribute;
+
+/**
+ * Legacy device properties
+ */
+typedef struct CUdevprop_st {
+    int maxThreadsPerBlock;     /**< Maximum number of threads per block */
+    int maxThreadsDim[3];       /**< Maximum size of each dimension of a block */
+    int maxGridSize[3];         /**< Maximum size of each dimension of a grid */
+    int sharedMemPerBlock;      /**< Shared memory available per block in bytes */
+    int totalConstantMemory;    /**< Constant memory available on device in bytes */
+    int SIMDWidth;              /**< Warp size in threads */
+    int memPitch;               /**< Maximum pitch in bytes allowed by memory copies */
+    int regsPerBlock;           /**< 32-bit registers available per block */
+    int clockRate;              /**< Clock frequency in kilohertz */
+    int textureAlign;           /**< Alignment requirement for textures */
+} CUdevprop_v1;
+typedef CUdevprop_v1 CUdevprop;
+
+/**
+ * Pointer information
+ */
+typedef enum CUpointer_attribute_enum {
+    CU_POINTER_ATTRIBUTE_CONTEXT = 1,                     /**< The ::CUcontext on which a pointer was allocated or registered */
+    CU_POINTER_ATTRIBUTE_MEMORY_TYPE = 2,                 /**< The ::CUmemorytype describing the physical location of a pointer */
+    CU_POINTER_ATTRIBUTE_DEVICE_POINTER = 3,              /**< The address at which a pointer's memory may be accessed on the device */
+    CU_POINTER_ATTRIBUTE_HOST_POINTER = 4,                /**< The address at which a pointer's memory may be accessed on the host */
+    CU_POINTER_ATTRIBUTE_P2P_TOKENS = 5,                  /**< A pair of tokens for use with the nv-p2p.h Linux kernel interface */
+    CU_POINTER_ATTRIBUTE_SYNC_MEMOPS = 6,                 /**< Synchronize every synchronous memory operation initiated on this region */
+    CU_POINTER_ATTRIBUTE_BUFFER_ID = 7,                   /**< A process-wide unique ID for an allocated memory region*/
+    CU_POINTER_ATTRIBUTE_IS_MANAGED = 8,                  /**< Indicates if the pointer points to managed memory */
+    CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL = 9,              /**< A device ordinal of a device on which a pointer was allocated or registered */
+    CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE = 10, /**< 1 if this pointer maps to an allocation that is suitable for ::cudaIpcGetMemHandle, 0 otherwise **/
+    CU_POINTER_ATTRIBUTE_RANGE_START_ADDR = 11,           /**< Starting address for this requested pointer */
+    CU_POINTER_ATTRIBUTE_RANGE_SIZE = 12,                 /**< Size of the address range for this requested pointer */
+    CU_POINTER_ATTRIBUTE_MAPPED = 13,                     /**< 1 if this pointer is in a valid address range that is mapped to a backing allocation, 0 otherwise **/
+    CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES = 14,       /**< Bitmask of allowed ::CUmemAllocationHandleType for this allocation **/
+    CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE = 15, /**< 1 if the memory this pointer is referencing can be used with the GPUDirect RDMA API **/
+    CU_POINTER_ATTRIBUTE_ACCESS_FLAGS = 16,               /**< Returns the access flags the device associated with the current context has on the corresponding memory referenced by the pointer given */
+    CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE = 17,             /**< Returns the mempool handle for the allocation if it was allocated from a mempool. Otherwise returns NULL. **/
+    CU_POINTER_ATTRIBUTE_MAPPING_SIZE = 18,               /**< Size of the actual underlying mapping that the pointer belongs to **/
+    CU_POINTER_ATTRIBUTE_MAPPING_BASE_ADDR = 19,          /**< The start address of the mapping that the pointer belongs to **/
+    CU_POINTER_ATTRIBUTE_MEMORY_BLOCK_ID = 20             /**< A process-wide unique id corresponding to the physical allocation the pointer belongs to **/
+  , CU_POINTER_ATTRIBUTE_IS_HW_DECOMPRESS_CAPABLE = 21    /**< Returns in \p *data a boolean that indicates whether the pointer points to memory that is capable to be used for hardware accelerated decompression. */
+} CUpointer_attribute;
+
+/**
+ * Function properties
+ */
+typedef enum CUfunction_attribute_enum {
+    /**
+     * The maximum number of threads per block, beyond which a launch of the
+     * function would fail. This number depends on both the function and the
+     * device on which the function is currently loaded.
+     */
+    CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0,
+
+    /**
+     * The size in bytes of statically-allocated shared memory required by
+     * this function. This does not include dynamically-allocated shared
+     * memory requested by the user at runtime.
+     */
+    CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES = 1,
+
+    /**
+     * The size in bytes of user-allocated constant memory required by this
+     * function.
+     */
+    CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES = 2,
+
+    /**
+     * The size in bytes of local memory used by each thread of this function.
+     */
+    CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES = 3,
+
+    /**
+     * The number of registers used by each thread of this function.
+     */
+    CU_FUNC_ATTRIBUTE_NUM_REGS = 4,
+
+    /**
+     * The PTX virtual architecture version for which the function was
+     * compiled. This value is the major PTX version * 10 + the minor PTX
+     * version, so a PTX version 1.3 function would return the value 13.
+     * Note that this may return the undefined value of 0 for cubins
+     * compiled prior to CUDA 3.0.
+     */
+    CU_FUNC_ATTRIBUTE_PTX_VERSION = 5,
+
+    /**
+     * The binary architecture version for which the function was compiled.
+     * This value is the major binary version * 10 + the minor binary version,
+     * so a binary version 1.3 function would return the value 13. Note that
+     * this will return a value of 10 for legacy cubins that do not have a
+     * properly-encoded binary architecture version.
+     */
+    CU_FUNC_ATTRIBUTE_BINARY_VERSION = 6,
+
+    /**
+     * The attribute to indicate whether the function has been compiled with
+     * user specified option "-Xptxas --dlcm=ca" set .
+     */
+    CU_FUNC_ATTRIBUTE_CACHE_MODE_CA = 7,
+
+    /**
+     * The maximum size in bytes of dynamically-allocated shared memory that can be used by
+     * this function. If the user-specified dynamic shared memory size is larger than this
+     * value, the launch will fail.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES = 8,
+
+    /**
+     * On devices where the L1 cache and shared memory use the same hardware resources, 
+     * this sets the shared memory carveout preference, in percent of the total shared memory.
+     * Refer to ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR.
+     * This is only a hint, and the driver can choose a different ratio if required to execute the function.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT = 9,
+
+    /**
+     * If this attribute is set, the kernel must launch with a valid cluster
+     * size specified.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET = 10,
+
+    /**
+     * The required cluster width in blocks. The values must either all be 0 or
+     * all be positive. The validity of the cluster dimensions is otherwise
+     * checked at launch time.
+     *
+     * If the value is set during compile time, it cannot be set at runtime.
+     * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH = 11,
+
+    /**
+     * The required cluster height in blocks. The values must either all be 0 or
+     * all be positive. The validity of the cluster dimensions is otherwise
+     * checked at launch time.
+     *
+     * If the value is set during compile time, it cannot be set at runtime.
+     * Setting it at runtime should return CUDA_ERROR_NOT_PERMITTED.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT = 12,
+
+    /**
+     * The required cluster depth in blocks. The values must either all be 0 or
+     * all be positive. The validity of the cluster dimensions is otherwise
+     * checked at launch time.
+     *
+     * If the value is set during compile time, it cannot be set at runtime.
+     * Setting it at runtime should return CUDA_ERROR_NOT_PERMITTED.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH = 13,
+
+    /**
+     * Whether the function can be launched with non-portable cluster size. 1 is
+     * allowed, 0 is disallowed. A non-portable cluster size may only function
+     * on the specific SKUs the program is tested on. The launch might fail if
+     * the program is run on a different hardware platform.
+     *
+     * CUDA API provides cudaOccupancyMaxActiveClusters to assist with checking
+     * whether the desired size can be launched on the current device.
+     *
+     * Portable Cluster Size
+     *
+     * A portable cluster size is guaranteed to be functional on all compute
+     * capabilities higher than the target compute capability. The portable
+     * cluster size for sm_90 is 8 blocks per cluster. This value may increase
+     * for future compute capabilities.
+     *
+     * The specific hardware unit may support higher cluster sizes that’s not
+     * guaranteed to be portable.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED = 14,
+
+    /**
+     * The block scheduling policy of a function. The value type is
+     * CUclusterSchedulingPolicy / cudaClusterSchedulingPolicy.
+     * See ::cuFuncSetAttribute, ::cuKernelSetAttribute
+     */
+    CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 15,
+
+    CU_FUNC_ATTRIBUTE_MAX
+} CUfunction_attribute;
+
+/**
+ * Function cache configurations
+ */
+typedef enum CUfunc_cache_enum {
+    CU_FUNC_CACHE_PREFER_NONE    = 0x00, /**< no preference for shared memory or L1 (default) */
+    CU_FUNC_CACHE_PREFER_SHARED  = 0x01, /**< prefer larger shared memory and smaller L1 cache */
+    CU_FUNC_CACHE_PREFER_L1      = 0x02, /**< prefer larger L1 cache and smaller shared memory */
+    CU_FUNC_CACHE_PREFER_EQUAL   = 0x03  /**< prefer equal sized L1 cache and shared memory */
+} CUfunc_cache;
+
+/**
+ * \deprecated
+ *
+ * Shared memory configurations
+ */
+typedef enum CUsharedconfig_enum {
+    CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE    = 0x00, /**< set default shared memory bank size */
+    CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE  = 0x01, /**< set shared memory bank width to four bytes */
+    CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE = 0x02  /**< set shared memory bank width to eight bytes */
+} CUsharedconfig;
+
+/**
+ * Shared memory carveout configurations. These may be passed to ::cuFuncSetAttribute or ::cuKernelSetAttribute
+ */
+typedef enum CUshared_carveout_enum {
+    CU_SHAREDMEM_CARVEOUT_DEFAULT       = -1,  /**< No preference for shared memory or L1 (default) */
+    CU_SHAREDMEM_CARVEOUT_MAX_SHARED    = 100, /**< Prefer maximum available shared memory, minimum L1 cache */
+    CU_SHAREDMEM_CARVEOUT_MAX_L1        = 0    /**< Prefer maximum available L1 cache, minimum shared memory */
+} CUshared_carveout;
+
+/**
+ * Memory types
+ */
+typedef enum CUmemorytype_enum {
+    CU_MEMORYTYPE_HOST    = 0x01,    /**< Host memory */
+    CU_MEMORYTYPE_DEVICE  = 0x02,    /**< Device memory */
+    CU_MEMORYTYPE_ARRAY   = 0x03,    /**< Array memory */
+    CU_MEMORYTYPE_UNIFIED = 0x04     /**< Unified device or host memory */
+} CUmemorytype;
+
+/**
+ * Compute Modes
+ */
+typedef enum CUcomputemode_enum {
+    CU_COMPUTEMODE_DEFAULT           = 0, /**< Default compute mode (Multiple contexts allowed per device) */
+    CU_COMPUTEMODE_PROHIBITED        = 2, /**< Compute-prohibited mode (No contexts can be created on this device at this time) */
+    CU_COMPUTEMODE_EXCLUSIVE_PROCESS = 3  /**< Compute-exclusive-process mode (Only one context used by a single process can be present on this device at a time) */
+} CUcomputemode;
+
+/**
+ * Memory advise values
+ */
+typedef enum CUmem_advise_enum {
+    CU_MEM_ADVISE_SET_READ_MOSTLY          = 1, /**< Data will mostly be read and only occasionally be written to */
+    CU_MEM_ADVISE_UNSET_READ_MOSTLY        = 2, /**< Undo the effect of ::CU_MEM_ADVISE_SET_READ_MOSTLY */
+    CU_MEM_ADVISE_SET_PREFERRED_LOCATION   = 3, /**< Set the preferred location for the data as the specified device */
+    CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION = 4, /**< Clear the preferred location for the data */
+    CU_MEM_ADVISE_SET_ACCESSED_BY          = 5, /**< Data will be accessed by the specified device, so prevent page faults as much as possible */
+    CU_MEM_ADVISE_UNSET_ACCESSED_BY        = 6  /**< Let the Unified Memory subsystem decide on the page faulting policy for the specified device */
+} CUmem_advise;
+
+typedef enum CUmem_range_attribute_enum {
+    CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY                 = 1, /**< Whether the range will mostly be read and only occasionally be written to */
+    CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION          = 2, /**< The preferred location of the range */
+    CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY                 = 3, /**< Memory range has ::CU_MEM_ADVISE_SET_ACCESSED_BY set for specified device */
+    CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION      = 4  /**< The last location to which the range was prefetched */
+    , CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_TYPE     = 5 /**< The preferred location type of the range */
+    , CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_ID       = 6 /**< The preferred location id of the range */
+    , CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_TYPE = 7 /**< The last location type to which the range was prefetched */
+    , CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_ID   = 8 /**< The last location id to which the range was prefetched */
+} CUmem_range_attribute;
+
+/**
+ * Online compiler and linker options
+ */
+typedef enum CUjit_option_enum
+{
+    /**
+     * Max number of registers that a thread may use.\n
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+     */
+    CU_JIT_MAX_REGISTERS = 0,
+
+    /**
+     * IN: Specifies minimum number of threads per block to target compilation
+     * for\n
+     * OUT: Returns the number of threads the compiler actually targeted.
+     * This restricts the resource utilization of the compiler (e.g. max
+     * registers) such that a block with the given number of threads should be
+     * able to launch based on register limitations. Note, this option does not
+     * currently take into account any other resource limitations, such as
+     * shared memory utilization.\n
+     * Cannot be combined with ::CU_JIT_TARGET.\n
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+     */
+    CU_JIT_THREADS_PER_BLOCK = 1,
+
+    /**
+     * Overwrites the option value with the total wall clock time, in
+     * milliseconds, spent in the compiler and linker\n
+     * Option type: float\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_WALL_TIME = 2,
+
+    /**
+     * Pointer to a buffer in which to print any log messages
+     * that are informational in nature (the buffer size is specified via
+     * option ::CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES)\n
+     * Option type: char *\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_INFO_LOG_BUFFER = 3,
+
+    /**
+     * IN: Log buffer size in bytes.  Log messages will be capped at this size
+     * (including null terminator)\n
+     * OUT: Amount of log buffer filled with messages\n
+     * Option type: unsigned int\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES = 4,
+
+    /**
+     * Pointer to a buffer in which to print any log messages that
+     * reflect errors (the buffer size is specified via option
+     * ::CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES)\n
+     * Option type: char *\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_ERROR_LOG_BUFFER = 5,
+
+    /**
+     * IN: Log buffer size in bytes.  Log messages will be capped at this size
+     * (including null terminator)\n
+     * OUT: Amount of log buffer filled with messages\n
+     * Option type: unsigned int\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES = 6,
+
+    /**
+     * Level of optimizations to apply to generated code (0 - 4), with 4
+     * being the default and highest level of optimizations.\n
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+     */
+    CU_JIT_OPTIMIZATION_LEVEL = 7,
+
+    /**
+     * No option value required. Determines the target based on the current
+     * attached context (default)\n
+     * Option type: No option value needed\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_TARGET_FROM_CUCONTEXT = 8,
+
+    /**
+     * Target is chosen based on supplied ::CUjit_target.  Cannot be
+     * combined with ::CU_JIT_THREADS_PER_BLOCK.\n
+     * Option type: unsigned int for enumerated type ::CUjit_target\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_TARGET = 9,
+
+    /**
+     * Specifies choice of fallback strategy if matching cubin is not found.
+     * Choice is based on supplied ::CUjit_fallback.  This option cannot be
+     * used with cuLink* APIs as the linker requires exact matches.\n
+     * Option type: unsigned int for enumerated type ::CUjit_fallback\n
+     * Applies to: compiler only
+     */
+    CU_JIT_FALLBACK_STRATEGY = 10,
+
+    /**
+     * Specifies whether to create debug information in output (-g)
+     * (0: false, default)\n
+     * Option type: int\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_GENERATE_DEBUG_INFO = 11,
+
+    /**
+     * Generate verbose log messages (0: false, default)\n
+     * Option type: int\n
+     * Applies to: compiler and linker
+     */
+    CU_JIT_LOG_VERBOSE = 12,
+
+    /**
+     * Generate line number information (-lineinfo) (0: false, default)\n
+     * Option type: int\n
+     * Applies to: compiler only
+     */
+    CU_JIT_GENERATE_LINE_INFO = 13,
+
+    /**
+     * Specifies whether to enable caching explicitly (-dlcm) \n
+     * Choice is based on supplied ::CUjit_cacheMode_enum.\n
+     * Option type: unsigned int for enumerated type ::CUjit_cacheMode_enum\n
+     * Applies to: compiler only
+     */
+    CU_JIT_CACHE_MODE = 14,
+
+    /**
+     * \deprecated
+     * This jit option is deprecated and should not be used.
+     */
+    CU_JIT_NEW_SM3X_OPT = 15,
+
+    /**
+     * This jit option is used for internal purpose only.
+     */
+    CU_JIT_FAST_COMPILE = 16,
+
+    /**
+     * Array of device symbol names that will be relocated to the corresponding
+     * host addresses stored in ::CU_JIT_GLOBAL_SYMBOL_ADDRESSES.\n
+     * Must contain ::CU_JIT_GLOBAL_SYMBOL_COUNT entries.\n
+     * When loading a device module, driver will relocate all encountered
+     * unresolved symbols to the host addresses.\n
+     * It is only allowed to register symbols that correspond to unresolved
+     * global variables.\n
+     * It is illegal to register the same device symbol at multiple addresses.\n
+     * Option type: const char **\n
+     * Applies to: dynamic linker only
+     */
+    CU_JIT_GLOBAL_SYMBOL_NAMES = 17,
+
+    /**
+     * Array of host addresses that will be used to relocate corresponding
+     * device symbols stored in ::CU_JIT_GLOBAL_SYMBOL_NAMES.\n
+     * Must contain ::CU_JIT_GLOBAL_SYMBOL_COUNT entries.\n
+     * Option type: void **\n
+     * Applies to: dynamic linker only
+     */
+    CU_JIT_GLOBAL_SYMBOL_ADDRESSES = 18,
+
+    /**
+     * Number of entries in ::CU_JIT_GLOBAL_SYMBOL_NAMES and
+     * ::CU_JIT_GLOBAL_SYMBOL_ADDRESSES arrays.\n
+     * Option type: unsigned int\n
+     * Applies to: dynamic linker only
+     */
+    CU_JIT_GLOBAL_SYMBOL_COUNT = 19,
+
+    /**
+     * \deprecated
+     * Enable link-time optimization (-dlto) for device code (Disabled by default).\n
+     * This option is not supported on 32-bit platforms.\n
+     * Option type: int\n
+     * Applies to: compiler and linker
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_LTO = 20,
+
+    /**
+     * \deprecated
+     * Control single-precision denormals (-ftz) support (0: false, default).
+     * 1 : flushes denormal values to zero
+     * 0 : preserves denormal values
+     * Option type: int\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_FTZ = 21,
+
+    /**
+     * \deprecated
+     * Control single-precision floating-point division and reciprocals
+     * (-prec-div) support (1: true, default).
+     * 1 : Enables the IEEE round-to-nearest mode
+     * 0 : Enables the fast approximation mode
+     * Option type: int\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_PREC_DIV = 22,
+
+    /**
+     * \deprecated
+     * Control single-precision floating-point square root
+     * (-prec-sqrt) support (1: true, default).
+     * 1 : Enables the IEEE round-to-nearest mode
+     * 0 : Enables the fast approximation mode
+     * Option type: int\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_PREC_SQRT = 23,
+
+    /**
+     * \deprecated
+     * Enable/Disable the contraction of floating-point multiplies
+     * and adds/subtracts into floating-point multiply-add (-fma)
+     * operations (1: Enable, default; 0: Disable).
+     * Option type: int\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_FMA = 24,
+
+    /**
+     * \deprecated
+     * Array of kernel names that should be preserved at link time while others
+     * can be removed.\n
+     * Must contain ::CU_JIT_REFERENCED_KERNEL_COUNT entries.\n
+     * Note that kernel names can be mangled by the compiler in which case the
+     * mangled name needs to be specified.\n
+     * Wildcard "*" can be used to represent zero or more characters instead of
+     * specifying the full or mangled name.\n
+     * It is important to note that the wildcard "*" is also added implicitly.
+     * For example, specifying "foo" will match "foobaz", "barfoo", "barfoobaz" and
+     * thus preserve all kernels with those names. This can be avoided by providing
+     * a more specific name like "barfoobaz".\n
+     * Option type: const char **\n
+     * Applies to: dynamic linker only
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_REFERENCED_KERNEL_NAMES = 25,
+
+    /**
+     * \deprecated
+     * Number of entries in ::CU_JIT_REFERENCED_KERNEL_NAMES array.\n
+     * Option type: unsigned int\n
+     * Applies to: dynamic linker only
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_REFERENCED_KERNEL_COUNT = 26,
+
+    /**
+     * \deprecated
+     * Array of variable names (__device__ and/or __constant__) that should be
+     * preserved at link time while others can be removed.\n
+     * Must contain ::CU_JIT_REFERENCED_VARIABLE_COUNT entries.\n
+     * Note that variable names can be mangled by the compiler in which case the
+     * mangled name needs to be specified.\n
+     * Wildcard "*" can be used to represent zero or more characters instead of
+     * specifying the full or mangled name.\n
+     * It is important to note that the wildcard "*" is also added implicitly.
+     * For example, specifying "foo" will match "foobaz", "barfoo", "barfoobaz" and
+     * thus preserve all variables with those names. This can be avoided by providing
+     * a more specific name like "barfoobaz".\n
+     * Option type: const char **\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_REFERENCED_VARIABLE_NAMES = 27,
+
+    /**
+     * \deprecated
+     * Number of entries in ::CU_JIT_REFERENCED_VARIABLE_NAMES array.\n
+     * Option type: unsigned int\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_REFERENCED_VARIABLE_COUNT = 28,
+
+    /**
+     * \deprecated
+     * This option serves as a hint to enable the JIT compiler/linker
+     * to remove constant (__constant__) and device (__device__) variables
+     * unreferenced in device code (Disabled by default).\n
+     * Note that host references to constant and device variables using APIs like
+     * ::cuModuleGetGlobal() with this option specified may result in undefined behavior unless
+     * the variables are explicitly specified using ::CU_JIT_REFERENCED_VARIABLE_NAMES.\n
+     * Option type: int\n
+     * Applies to: link-time optimization specified with CU_JIT_LTO
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_OPTIMIZE_UNUSED_DEVICE_VARIABLES = 29,
+
+    /**
+     * Generate position independent code (0: false)\n
+     * Option type: int\n
+     * Applies to: compiler only
+     */
+    CU_JIT_POSITION_INDEPENDENT_CODE = 30,
+
+    /**
+     * This option hints to the JIT compiler the minimum number of CTAs from the
+     * kernel’s grid to be mapped to a SM. This option is ignored when used together
+     * with ::CU_JIT_MAX_REGISTERS or ::CU_JIT_THREADS_PER_BLOCK.
+     * Optimizations based on this option need ::CU_JIT_MAX_THREADS_PER_BLOCK to
+     * be specified as well. For kernels already using PTX directive .minnctapersm,
+     * this option will be ignored by default. Use ::CU_JIT_OVERRIDE_DIRECTIVE_VALUES
+     * to let this option take precedence over the PTX directive.
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+    */
+    CU_JIT_MIN_CTA_PER_SM = 31,
+
+     /**
+     * Maximum number threads in a thread block, computed as the product of
+     * the maximum extent specifed for each dimension of the block. This limit
+     * is guaranteed not to be exeeded in any invocation of the kernel. Exceeding
+     * the the maximum number of threads results in runtime error or kernel launch
+     * failure. For kernels already using PTX directive .maxntid, this option will
+     * be ignored by default. Use ::CU_JIT_OVERRIDE_DIRECTIVE_VALUES to let this
+     * option take precedence over the PTX directive.
+     * Option type: int\n
+     * Applies to: compiler only
+    */
+    CU_JIT_MAX_THREADS_PER_BLOCK = 32,
+
+    /**
+     * This option lets the values specified using ::CU_JIT_MAX_REGISTERS,
+     * ::CU_JIT_THREADS_PER_BLOCK, ::CU_JIT_MAX_THREADS_PER_BLOCK and
+     * ::CU_JIT_MIN_CTA_PER_SM take precedence over any PTX directives.
+     * (0: Disable, default; 1: Enable)
+     * Option type: int\n
+     * Applies to: compiler only
+    */
+    CU_JIT_OVERRIDE_DIRECTIVE_VALUES = 33,
+    CU_JIT_NUM_OPTIONS
+
+} CUjit_option;
+
+/*
+ * Indicates that compute device class supports accelerated features.
+ */
+#define CU_COMPUTE_ACCELERATED_TARGET_BASE   0x10000
+
+/**
+ * Online compilation targets
+ */
+typedef enum CUjit_target_enum
+{
+    CU_TARGET_COMPUTE_30 = 30, /**< Compute device class 3.0 */
+    CU_TARGET_COMPUTE_32 = 32, /**< Compute device class 3.2 */
+    CU_TARGET_COMPUTE_35 = 35, /**< Compute device class 3.5 */
+    CU_TARGET_COMPUTE_37 = 37, /**< Compute device class 3.7 */
+    CU_TARGET_COMPUTE_50 = 50, /**< Compute device class 5.0 */
+    CU_TARGET_COMPUTE_52 = 52, /**< Compute device class 5.2 */
+    CU_TARGET_COMPUTE_53 = 53, /**< Compute device class 5.3 */
+    CU_TARGET_COMPUTE_60 = 60, /**< Compute device class 6.0.*/
+    CU_TARGET_COMPUTE_61 = 61, /**< Compute device class 6.1.*/
+    CU_TARGET_COMPUTE_62 = 62, /**< Compute device class 6.2.*/
+    CU_TARGET_COMPUTE_70 = 70, /**< Compute device class 7.0.*/
+    CU_TARGET_COMPUTE_72 = 72, /**< Compute device class 7.2.*/
+    CU_TARGET_COMPUTE_75 = 75, /**< Compute device class 7.5.*/
+    CU_TARGET_COMPUTE_80 = 80, /**< Compute device class 8.0.*/
+    CU_TARGET_COMPUTE_86 = 86, /**< Compute device class 8.6.*/
+    CU_TARGET_COMPUTE_87 = 87, /**< Compute device class 8.7.*/
+    CU_TARGET_COMPUTE_89 = 89, /**< Compute device class 8.9.*/
+    CU_TARGET_COMPUTE_90 = 90, /**< Compute device class 9.0.*/
+    CU_TARGET_COMPUTE_100 = 100, /**< Compute device class 10.0.*/
+    CU_TARGET_COMPUTE_101 = 101,       /**< Compute device class 10.1.*/
+    CU_TARGET_COMPUTE_120 = 120, /**< Compute device class 12.0.*/
+
+    /**< Compute device class 9.0. with accelerated features.*/
+    CU_TARGET_COMPUTE_90A = CU_COMPUTE_ACCELERATED_TARGET_BASE + CU_TARGET_COMPUTE_90,
+    /**< Compute device class 10.0. with accelerated features.*/
+    CU_TARGET_COMPUTE_100A = CU_COMPUTE_ACCELERATED_TARGET_BASE + CU_TARGET_COMPUTE_100,
+    /**< Compute device class 10.1 with accelerated features.*/
+    CU_TARGET_COMPUTE_101A = CU_COMPUTE_ACCELERATED_TARGET_BASE + CU_TARGET_COMPUTE_101,
+    /**< Compute device class 12.0. with accelerated features.*/
+    CU_TARGET_COMPUTE_120A = CU_COMPUTE_ACCELERATED_TARGET_BASE + CU_TARGET_COMPUTE_120,
+} CUjit_target;
+
+/**
+ * Cubin matching fallback strategies
+ */
+typedef enum CUjit_fallback_enum
+{
+    CU_PREFER_PTX = 0,  /**< Prefer to compile ptx if exact binary match not found */
+
+    CU_PREFER_BINARY    /**< Prefer to fall back to compatible binary code if exact match not found */
+
+} CUjit_fallback;
+
+/**
+ * Caching modes for dlcm
+ */
+typedef enum CUjit_cacheMode_enum
+{
+    CU_JIT_CACHE_OPTION_NONE = 0, /**< Compile with no -dlcm flag specified */
+    CU_JIT_CACHE_OPTION_CG,       /**< Compile with L1 cache disabled */
+    CU_JIT_CACHE_OPTION_CA        /**< Compile with L1 cache enabled */
+} CUjit_cacheMode;
+
+/**
+ * Device code formats
+ */
+typedef enum CUjitInputType_enum
+{
+    /**
+     * Compiled device-class-specific device code\n
+     * Applicable options: none
+     */
+    CU_JIT_INPUT_CUBIN = 0,
+
+    /**
+     * PTX source code\n
+     * Applicable options: PTX compiler options
+     */
+    CU_JIT_INPUT_PTX = 1,
+
+    /**
+     * Bundle of multiple cubins and/or PTX of some device code\n
+     * Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY
+     */
+    CU_JIT_INPUT_FATBINARY = 2,
+
+    /**
+     * Host object with embedded device code\n
+     * Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY
+     */
+    CU_JIT_INPUT_OBJECT = 3,
+
+    /**
+     * Archive of host objects with embedded device code\n
+     * Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY
+     */
+    CU_JIT_INPUT_LIBRARY = 4,
+
+    /**
+     * \deprecated
+     * High-level intermediate code for link-time optimization\n
+     * Applicable options: NVVM compiler options, PTX compiler options
+     *
+     * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0
+     */
+    CU_JIT_INPUT_NVVM = 5,
+
+    CU_JIT_NUM_INPUT_TYPES = 6
+} CUjitInputType;
+
+typedef struct CUlinkState_st *CUlinkState;
+
+/**
+ * Flags to register a graphics resource
+ */
+typedef enum CUgraphicsRegisterFlags_enum {
+    CU_GRAPHICS_REGISTER_FLAGS_NONE           = 0x00,
+    CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY      = 0x01,
+    CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD  = 0x02,
+    CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST   = 0x04,
+    CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER = 0x08
+} CUgraphicsRegisterFlags;
+
+/**
+ * Flags for mapping and unmapping interop resources
+ */
+typedef enum CUgraphicsMapResourceFlags_enum {
+    CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE          = 0x00,
+    CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY     = 0x01,
+    CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 0x02
+} CUgraphicsMapResourceFlags;
+
+/**
+ * Array indices for cube faces
+ */
+typedef enum CUarray_cubemap_face_enum {
+    CU_CUBEMAP_FACE_POSITIVE_X  = 0x00, /**< Positive X face of cubemap */
+    CU_CUBEMAP_FACE_NEGATIVE_X  = 0x01, /**< Negative X face of cubemap */
+    CU_CUBEMAP_FACE_POSITIVE_Y  = 0x02, /**< Positive Y face of cubemap */
+    CU_CUBEMAP_FACE_NEGATIVE_Y  = 0x03, /**< Negative Y face of cubemap */
+    CU_CUBEMAP_FACE_POSITIVE_Z  = 0x04, /**< Positive Z face of cubemap */
+    CU_CUBEMAP_FACE_NEGATIVE_Z  = 0x05  /**< Negative Z face of cubemap */
+} CUarray_cubemap_face;
+
+/**
+ * Limits
+ */
+typedef enum CUlimit_enum {
+    CU_LIMIT_STACK_SIZE                       = 0x00, /**< GPU thread stack size */
+    CU_LIMIT_PRINTF_FIFO_SIZE                 = 0x01, /**< GPU printf FIFO size */
+    CU_LIMIT_MALLOC_HEAP_SIZE                 = 0x02, /**< GPU malloc heap size */
+    CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH           = 0x03, /**< GPU device runtime launch synchronize depth */
+    CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT = 0x04, /**< GPU device runtime pending launch count */
+    CU_LIMIT_MAX_L2_FETCH_GRANULARITY         = 0x05, /**< A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint */
+    CU_LIMIT_PERSISTING_L2_CACHE_SIZE         = 0x06, /**< A size in bytes for L2 persisting lines cache size */
+    CU_LIMIT_SHMEM_SIZE                       = 0x07, /**< A maximum size in bytes of shared memory available to CUDA kernels on a CIG context. Can only be queried, cannot be set */
+    CU_LIMIT_CIG_ENABLED                      = 0x08, /**< A non-zero value indicates this CUDA context is a CIG-enabled context. Can only be queried, cannot be set */
+    CU_LIMIT_CIG_SHMEM_FALLBACK_ENABLED       = 0x09, /**< When set to zero, CUDA will fail to launch a kernel on a CIG context, instead of using the fallback path, if the kernel uses more shared memory than available */
+    CU_LIMIT_MAX
+} CUlimit;
+
+/**
+ * Resource types
+ */
+typedef enum CUresourcetype_enum {
+    CU_RESOURCE_TYPE_ARRAY           = 0x00, /**< Array resource */
+    CU_RESOURCE_TYPE_MIPMAPPED_ARRAY = 0x01, /**< Mipmapped array resource */
+    CU_RESOURCE_TYPE_LINEAR          = 0x02, /**< Linear resource */
+    CU_RESOURCE_TYPE_PITCH2D         = 0x03  /**< Pitch 2D resource */
+} CUresourcetype;
+
+#ifdef _WIN32
+#define CUDA_CB __stdcall
+#else
+#define CUDA_CB
+#endif
+
+/**
+ * CUDA host function
+ * \param userData Argument value passed to the function
+ */
+typedef void (CUDA_CB *CUhostFn)(void *userData);
+
+/**
+ * Specifies performance hint with ::CUaccessPolicyWindow for hitProp and missProp members.
+ */
+typedef enum CUaccessProperty_enum {
+    CU_ACCESS_PROPERTY_NORMAL           = 0,    /**< Normal cache persistence. */
+    CU_ACCESS_PROPERTY_STREAMING        = 1,    /**< Streaming access is less likely to persit from cache. */
+    CU_ACCESS_PROPERTY_PERSISTING       = 2     /**< Persisting access is more likely to persist in cache.*/
+} CUaccessProperty;
+
+/**
+ * Specifies an access policy for a window, a contiguous extent of memory
+ * beginning at base_ptr and ending at base_ptr + num_bytes.
+ * num_bytes is limited by CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE.
+ * Partition into many segments and assign segments such that:
+ * sum of "hit segments" / window == approx. ratio.
+ * sum of "miss segments" / window == approx 1-ratio.
+ * Segments and ratio specifications are fitted to the capabilities of
+ * the architecture.
+ * Accesses in a hit segment apply the hitProp access policy.
+ * Accesses in a miss segment apply the missProp access policy.
+ */
+typedef struct CUaccessPolicyWindow_st {
+    void *base_ptr;                     /**< Starting address of the access policy window. CUDA driver may align it. */
+    size_t num_bytes;                   /**< Size in bytes of the window policy. CUDA driver may restrict the maximum size and alignment. */
+    float hitRatio;                     /**< hitRatio specifies percentage of lines assigned hitProp, rest are assigned missProp. */
+    CUaccessProperty hitProp;           /**< ::CUaccessProperty set for hit. */
+    CUaccessProperty missProp;          /**< ::CUaccessProperty set for miss. Must be either NORMAL or STREAMING */
+} CUaccessPolicyWindow_v1;
+/**
+ * Access policy window
+ */
+typedef CUaccessPolicyWindow_v1 CUaccessPolicyWindow;
+
+/**
+ * GPU kernel node parameters
+ */
+typedef struct CUDA_KERNEL_NODE_PARAMS_st {
+    CUfunction func;             /**< Kernel to launch */
+    unsigned int gridDimX;       /**< Width of grid in blocks */
+    unsigned int gridDimY;       /**< Height of grid in blocks */
+    unsigned int gridDimZ;       /**< Depth of grid in blocks */
+    unsigned int blockDimX;      /**< X dimension of each thread block */
+    unsigned int blockDimY;      /**< Y dimension of each thread block */
+    unsigned int blockDimZ;      /**< Z dimension of each thread block */
+    unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */
+    void **kernelParams;         /**< Array of pointers to kernel parameters */
+    void **extra;                /**< Extra options */
+} CUDA_KERNEL_NODE_PARAMS_v1;
+
+/**
+ * GPU kernel node parameters
+ */
+typedef struct CUDA_KERNEL_NODE_PARAMS_v2_st {
+    CUfunction func;             /**< Kernel to launch */
+    unsigned int gridDimX;       /**< Width of grid in blocks */
+    unsigned int gridDimY;       /**< Height of grid in blocks */
+    unsigned int gridDimZ;       /**< Depth of grid in blocks */
+    unsigned int blockDimX;      /**< X dimension of each thread block */
+    unsigned int blockDimY;      /**< Y dimension of each thread block */
+    unsigned int blockDimZ;      /**< Z dimension of each thread block */
+    unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */
+    void **kernelParams;         /**< Array of pointers to kernel parameters */
+    void **extra;                /**< Extra options */
+    CUkernel kern;               /**< Kernel to launch, will only be referenced if func is NULL */
+    CUcontext ctx;               /**< Context for the kernel task to run in. The value NULL will indicate the current context should be used by the api. This field is ignored if func is set. */
+} CUDA_KERNEL_NODE_PARAMS_v2;
+typedef CUDA_KERNEL_NODE_PARAMS_v2 CUDA_KERNEL_NODE_PARAMS;
+
+/**
+ * GPU kernel node parameters
+ */
+typedef struct CUDA_KERNEL_NODE_PARAMS_v3_st {
+    CUfunction func;             /**< Kernel to launch */
+    unsigned int gridDimX;       /**< Width of grid in blocks */
+    unsigned int gridDimY;       /**< Height of grid in blocks */
+    unsigned int gridDimZ;       /**< Depth of grid in blocks */
+    unsigned int blockDimX;      /**< X dimension of each thread block */
+    unsigned int blockDimY;      /**< Y dimension of each thread block */
+    unsigned int blockDimZ;      /**< Z dimension of each thread block */
+    unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */
+    void **kernelParams;         /**< Array of pointers to kernel parameters */
+    void **extra;                /**< Extra options */
+    CUkernel kern;               /**< Kernel to launch, will only be referenced if func is NULL */
+    CUcontext ctx;               /**< Context for the kernel task to run in. The value NULL will indicate the current context should be used by the api. This field is ignored if func is set. */
+} CUDA_KERNEL_NODE_PARAMS_v3;
+
+/**
+ * Memset node parameters
+ */
+typedef struct CUDA_MEMSET_NODE_PARAMS_st {
+    CUdeviceptr dst;                        /**< Destination device pointer */
+    size_t pitch;                           /**< Pitch of destination device pointer. Unused if height is 1 */
+    unsigned int value;                     /**< Value to be set */
+    unsigned int elementSize;               /**< Size of each element in bytes. Must be 1, 2, or 4. */
+    size_t width;                           /**< Width of the row in elements */
+    size_t height;                          /**< Number of rows */
+} CUDA_MEMSET_NODE_PARAMS_v1;
+typedef CUDA_MEMSET_NODE_PARAMS_v1 CUDA_MEMSET_NODE_PARAMS;
+
+/**
+ * Memset node parameters
+ */
+typedef struct CUDA_MEMSET_NODE_PARAMS_v2_st {
+    CUdeviceptr dst;                        /**< Destination device pointer */
+    size_t pitch;                           /**< Pitch of destination device pointer. Unused if height is 1 */
+    unsigned int value;                     /**< Value to be set */
+    unsigned int elementSize;               /**< Size of each element in bytes. Must be 1, 2, or 4. */
+    size_t width;                           /**< Width of the row in elements */
+    size_t height;                          /**< Number of rows */
+    CUcontext ctx;                          /**< Context on which to run the node */
+} CUDA_MEMSET_NODE_PARAMS_v2;
+
+/**
+ * Host node parameters
+ */
+typedef struct CUDA_HOST_NODE_PARAMS_st {
+    CUhostFn fn;    /**< The function to call when the node executes */
+    void* userData; /**< Argument to pass to the function */
+} CUDA_HOST_NODE_PARAMS_v1;
+typedef CUDA_HOST_NODE_PARAMS_v1 CUDA_HOST_NODE_PARAMS;
+
+/**
+ * Host node parameters
+ */
+typedef struct CUDA_HOST_NODE_PARAMS_v2_st {
+    CUhostFn fn;    /**< The function to call when the node executes */
+    void* userData; /**< Argument to pass to the function */
+} CUDA_HOST_NODE_PARAMS_v2;
+
+/**
+ * Conditional node handle flags
+ */
+#define CU_GRAPH_COND_ASSIGN_DEFAULT   0x1 /**< Default value is applied when graph is launched. */
+
+/**
+ * Conditional node types
+ */
+typedef enum CUgraphConditionalNodeType_enum {
+     CU_GRAPH_COND_TYPE_IF = 0,     /**< Conditional 'if/else' Node. Body[0] executed if condition is non-zero.  If \p size == 2, an optional ELSE graph is created and this is executed if the condition is zero. */
+     CU_GRAPH_COND_TYPE_WHILE = 1,  /**< Conditional 'while' Node. Body executed repeatedly while condition value is non-zero. */
+     CU_GRAPH_COND_TYPE_SWITCH = 2, /**< Conditional 'switch' Node. Body[n] is executed once, where 'n' is the value of the condition. If the condition does not match a body index, no body is launched. */
+} CUgraphConditionalNodeType;
+
+/**
+ * Conditional node parameters
+ */
+typedef struct CUDA_CONDITIONAL_NODE_PARAMS {
+    CUgraphConditionalHandle handle;   /**< Conditional node handle.
+                                            Handles must be created in advance of creating the node
+                                            using ::cuGraphConditionalHandleCreate. */
+    CUgraphConditionalNodeType type;   /**< Type of conditional node. */
+    unsigned int size;                 /**< Size of graph output array.  Allowed values are 1 for CU_GRAPH_COND_TYPE_WHILE, 1 or 2
+                                            for CU_GRAPH_COND_TYPE_IF, or any value greater than zero for CU_GRAPH_COND_TYPE_SWITCH. */
+    CUgraph *phGraph_out;              /**< CUDA-owned array populated with conditional node child graphs during creation of the node.
+                                            Valid for the lifetime of the conditional node.
+                                            The contents of the graph(s) are subject to the following constraints:
+
+                                            - Allowed node types are kernel nodes, empty nodes, child graphs, memsets,
+                                              memcopies, and conditionals. This applies recursively to child graphs and conditional bodies.
+                                            - All kernels, including kernels in nested conditionals or child graphs at any level,
+                                              must belong to the same CUDA context.
+
+                                            These graphs may be populated using graph node creation APIs or ::cuStreamBeginCaptureToGraph.
+
+                                            CU_GRAPH_COND_TYPE_IF:
+                                            phGraph_out[0] is executed when the condition is non-zero.  If \p size == 2, phGraph_out[1] will
+                                            be executed when the condition is zero.
+                                            CU_GRAPH_COND_TYPE_WHILE:
+                                            phGraph_out[0] is executed as long as the condition is non-zero.
+                                            CU_GRAPH_COND_TYPE_SWITCH:
+                                            phGraph_out[n] is executed when the condition is equal to n.  If the condition >= \p size,
+                                            no body graph is executed.
+                                         */
+    CUcontext ctx;                     /**< Context on which to run the node.  Must match context used to create the handle and all body nodes. */
+} CUDA_CONDITIONAL_NODE_PARAMS;
+
+/**
+ * Graph node types
+ */
+typedef enum CUgraphNodeType_enum {
+    CU_GRAPH_NODE_TYPE_KERNEL           = 0, /**< GPU kernel node */
+    CU_GRAPH_NODE_TYPE_MEMCPY           = 1, /**< Memcpy node */
+    CU_GRAPH_NODE_TYPE_MEMSET           = 2, /**< Memset node */
+    CU_GRAPH_NODE_TYPE_HOST             = 3, /**< Host (executable) node */
+    CU_GRAPH_NODE_TYPE_GRAPH            = 4, /**< Node which executes an embedded graph */
+    CU_GRAPH_NODE_TYPE_EMPTY            = 5, /**< Empty (no-op) node */
+    CU_GRAPH_NODE_TYPE_WAIT_EVENT       = 6, /**< External event wait node */
+    CU_GRAPH_NODE_TYPE_EVENT_RECORD     = 7, /**< External event record node */
+    CU_GRAPH_NODE_TYPE_EXT_SEMAS_SIGNAL = 8, /**< External semaphore signal node */
+    CU_GRAPH_NODE_TYPE_EXT_SEMAS_WAIT   = 9, /**< External semaphore wait node */
+    CU_GRAPH_NODE_TYPE_MEM_ALLOC        = 10,/**< Memory Allocation Node */
+    CU_GRAPH_NODE_TYPE_MEM_FREE         = 11,/**< Memory Free Node */
+    CU_GRAPH_NODE_TYPE_BATCH_MEM_OP     = 12,/**< Batch MemOp Node */
+    CU_GRAPH_NODE_TYPE_CONDITIONAL      = 13 /**< Conditional Node
+
+                                                  May be used to implement a conditional execution path or loop
+                                                  inside of a graph. The graph(s) contained within the body of the conditional node
+                                                  can be selectively executed or iterated upon based on the value of a conditional
+                                                  variable.
+
+                                                  Handles must be created in advance of creating the node
+                                                  using ::cuGraphConditionalHandleCreate.
+
+                                                  The following restrictions apply to graphs which contain conditional nodes:
+                                                   The graph cannot be used in a child node.
+                                                   Only one instantiation of the graph may exist at any point in time.
+                                                   The graph cannot be cloned.
+
+                                                  To set the control value, supply a default value when creating the handle and/or
+                                                  call ::cudaGraphSetConditional from device code.*/
+} CUgraphNodeType;
+
+/**
+ * Type annotations that can be applied to graph edges as part of ::CUgraphEdgeData.
+ */
+typedef enum CUgraphDependencyType_enum {
+    CU_GRAPH_DEPENDENCY_TYPE_DEFAULT = 0, /**< This is an ordinary dependency. */
+    CU_GRAPH_DEPENDENCY_TYPE_PROGRAMMATIC = 1  /**< This dependency type allows the downstream node to
+                                                    use \c cudaGridDependencySynchronize(). It may only be used
+                                                    between kernel nodes, and must be used with either the
+                                                    ::CU_GRAPH_KERNEL_NODE_PORT_PROGRAMMATIC or
+                                                    ::CU_GRAPH_KERNEL_NODE_PORT_LAUNCH_ORDER outgoing port. */
+} CUgraphDependencyType;
+
+/**
+ * This port activates when the kernel has finished executing.
+ */
+#define CU_GRAPH_KERNEL_NODE_PORT_DEFAULT 0
+/**
+ * This port activates when all blocks of the kernel have performed cudaTriggerProgrammaticLaunchCompletion()
+ * or have terminated. It must be used with edge type ::CU_GRAPH_DEPENDENCY_TYPE_PROGRAMMATIC. See also
+ * ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT.
+ */
+#define CU_GRAPH_KERNEL_NODE_PORT_PROGRAMMATIC 1
+/**
+ * This port activates when all blocks of the kernel have begun execution. See also
+ * ::CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT.
+ */
+#define CU_GRAPH_KERNEL_NODE_PORT_LAUNCH_ORDER 2
+
+/**
+ * Optional annotation for edges in a CUDA graph. Note, all edges implicitly have annotations and
+ * default to a zero-initialized value if not specified. A zero-initialized struct indicates a
+ * standard full serialization of two nodes with memory visibility.
+ */
+typedef struct CUgraphEdgeData_st {
+    unsigned char from_port; /**< This indicates when the dependency is triggered from the upstream
+                                  node on the edge. The meaning is specfic to the node type. A value
+                                  of 0 in all cases means full completion of the upstream node, with
+                                  memory visibility to the downstream node or portion thereof
+                                  (indicated by \c to_port).
+                                  <br>
+                                  Only kernel nodes define non-zero ports. A kernel node
+                                  can use the following output port types:
+                                  ::CU_GRAPH_KERNEL_NODE_PORT_DEFAULT, ::CU_GRAPH_KERNEL_NODE_PORT_PROGRAMMATIC,
+                                  or ::CU_GRAPH_KERNEL_NODE_PORT_LAUNCH_ORDER. */
+    unsigned char to_port; /**< This indicates what portion of the downstream node is dependent on
+                                the upstream node or portion thereof (indicated by \c from_port). The
+                                meaning is specific to the node type. A value of 0 in all cases means
+                                the entirety of the downstream node is dependent on the upstream work.
+                                <br>
+                                Currently no node types define non-zero ports. Accordingly, this field
+                                must be set to zero. */
+    unsigned char type; /**< This should be populated with a value from ::CUgraphDependencyType. (It
+                             is typed as char due to compiler-specific layout of bitfields.) See
+                             ::CUgraphDependencyType. */
+    unsigned char reserved[5]; /**< These bytes are unused and must be zeroed. This ensures
+                                    compatibility if additional fields are added in the future. */
+} CUgraphEdgeData;
+
+/**
+ * Graph instantiation results
+*/
+typedef enum CUgraphInstantiateResult_enum
+{
+    CUDA_GRAPH_INSTANTIATE_SUCCESS = 0,                          /**< Instantiation succeeded */
+    CUDA_GRAPH_INSTANTIATE_ERROR = 1,                            /**< Instantiation failed for an unexpected reason which is described in the return value of the function */
+    CUDA_GRAPH_INSTANTIATE_INVALID_STRUCTURE = 2,                /**< Instantiation failed due to invalid structure, such as cycles */
+    CUDA_GRAPH_INSTANTIATE_NODE_OPERATION_NOT_SUPPORTED = 3,     /**< Instantiation for device launch failed because the graph contained an unsupported operation */
+    CUDA_GRAPH_INSTANTIATE_MULTIPLE_CTXS_NOT_SUPPORTED = 4,      /**< Instantiation for device launch failed due to the nodes belonging to different contexts */
+    CUDA_GRAPH_INSTANTIATE_CONDITIONAL_HANDLE_UNUSED = 5,        /**< One or more conditional handles are not associated with conditional nodes */
+} CUgraphInstantiateResult;
+
+/**
+ * Graph instantiation parameters
+ */
+typedef struct CUDA_GRAPH_INSTANTIATE_PARAMS_st
+{
+	cuuint64_t flags;                    /**< Instantiation flags */
+	CUstream hUploadStream;              /**< Upload stream */
+	CUgraphNode hErrNode_out;            /**< The node which caused instantiation to fail, if any */
+	CUgraphInstantiateResult result_out; /**< Whether instantiation was successful.  If it failed, the reason why */
+} CUDA_GRAPH_INSTANTIATE_PARAMS;
+
+typedef enum CUsynchronizationPolicy_enum {
+    CU_SYNC_POLICY_AUTO = 1,
+    CU_SYNC_POLICY_SPIN = 2,
+    CU_SYNC_POLICY_YIELD = 3,
+    CU_SYNC_POLICY_BLOCKING_SYNC = 4
+} CUsynchronizationPolicy;
+
+/**
+ * Cluster scheduling policies. These may be passed to ::cuFuncSetAttribute or ::cuKernelSetAttribute
+ */
+typedef enum CUclusterSchedulingPolicy_enum {
+    CU_CLUSTER_SCHEDULING_POLICY_DEFAULT        = 0, /**< the default policy */
+    CU_CLUSTER_SCHEDULING_POLICY_SPREAD         = 1, /**< spread the blocks within a cluster to the SMs */
+    CU_CLUSTER_SCHEDULING_POLICY_LOAD_BALANCING = 2  /**< allow the hardware to load-balance the blocks in a cluster to the SMs */
+} CUclusterSchedulingPolicy;
+
+/**
+ * Memory Synchronization Domain
+ *
+ * A kernel can be launched in a specified memory synchronization domain that affects all memory operations issued by
+ * that kernel. A memory barrier issued in one domain will only order memory operations in that domain, thus eliminating
+ * latency increase from memory barriers ordering unrelated traffic.
+ *
+ * By default, kernels are launched in domain 0. Kernel launched with ::CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE will have a
+ * different domain ID. User may also alter the domain ID with ::CUlaunchMemSyncDomainMap for a specific stream /
+ * graph node / kernel launch. See ::CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN, ::cuStreamSetAttribute, ::cuLaunchKernelEx,
+ * ::cuGraphKernelNodeSetAttribute.
+ *
+ * Memory operations done in kernels launched in different domains are considered system-scope distanced. In other
+ * words, a GPU scoped memory synchronization is not sufficient for memory order to be observed by kernels in another
+ * memory synchronization domain even if they are on the same GPU.
+ */
+typedef enum CUlaunchMemSyncDomain_enum {
+    CU_LAUNCH_MEM_SYNC_DOMAIN_DEFAULT = 0,    /**< Launch kernels in the default domain */
+    CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE  = 1     /**< Launch kernels in the remote domain */
+} CUlaunchMemSyncDomain;
+
+/**
+ * Memory Synchronization Domain map
+ *
+ * See ::cudaLaunchMemSyncDomain.
+ *
+ * By default, kernels are launched in domain 0. Kernel launched with ::CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE will have a
+ * different domain ID. User may also alter the domain ID with ::CUlaunchMemSyncDomainMap for a specific stream /
+ * graph node / kernel launch. See ::CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+ *
+ * Domain ID range is available through ::CU_DEVICE_ATTRIBUTE_MEM_SYNC_DOMAIN_COUNT.
+ */
+typedef struct CUlaunchMemSyncDomainMap_st {
+    unsigned char default_;     /**< The default domain ID to use for designated kernels */
+    unsigned char remote;       /**< The remote domain ID to use for designated kernels */
+} CUlaunchMemSyncDomainMap;
+
+/**
+ * Launch attributes enum; used as id field of ::CUlaunchAttribute
+ */
+typedef enum CUlaunchAttributeID_enum {
+    CU_LAUNCH_ATTRIBUTE_IGNORE = 0 /**< Ignored entry, for convenient composition */
+  , CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW   = 1 /**< Valid for streams, graph nodes, launches. See
+                                                      ::CUlaunchAttributeValue::accessPolicyWindow. */
+  , CU_LAUNCH_ATTRIBUTE_COOPERATIVE            = 2 /**< Valid for graph nodes, launches. See
+                                                      ::CUlaunchAttributeValue::cooperative. */
+  , CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY = 3 /**< Valid for streams. See
+                                                      ::CUlaunchAttributeValue::syncPolicy. */
+  , CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION                    = 4 /**< Valid for graph nodes, launches. See ::CUlaunchAttributeValue::clusterDim. */
+  , CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 5 /**< Valid for graph nodes, launches. See ::CUlaunchAttributeValue::clusterSchedulingPolicyPreference. */
+  , CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION    = 6 /**< Valid for launches. Setting
+                                                                  ::CUlaunchAttributeValue::programmaticStreamSerializationAllowed
+                                                                  to non-0 signals that the kernel will use programmatic
+                                                                  means to resolve its stream dependency, so that the
+                                                                  CUDA runtime should opportunistically allow the grid's
+                                                                  execution to overlap with the previous kernel in the
+                                                                  stream, if that kernel requests the overlap. The
+                                                                  dependent launches can choose to wait on the
+                                                                  dependency using the programmatic sync
+                                                                  (cudaGridDependencySynchronize() or equivalent PTX
+                                                                  instructions). */
+  , CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT                   = 7 /**< Valid for launches. Set
+                                                                      ::CUlaunchAttributeValue::programmaticEvent to
+                                                                      record the event. Event recorded through this
+                                                                      launch attribute is guaranteed to only trigger
+                                                                      after all block in the associated kernel trigger
+                                                                      the event. A block can trigger the event through
+                                                                      PTX launchdep.release or CUDA builtin function
+                                                                      cudaTriggerProgrammaticLaunchCompletion(). A
+                                                                      trigger can also be inserted at the beginning of
+                                                                      each block's execution if triggerAtBlockStart is
+                                                                      set to non-0. The dependent launches can choose to
+                                                                      wait on the dependency using the programmatic sync
+                                                                      (cudaGridDependencySynchronize() or equivalent PTX
+                                                                      instructions). Note that dependents (including the
+                                                                      CPU thread calling cuEventSynchronize()) are not
+                                                                      guaranteed to observe the release precisely when
+                                                                      it is released.  For example, cuEventSynchronize()
+                                                                      may only observe the event trigger long after the
+                                                                      associated kernel has completed. This recording
+                                                                      type is primarily meant for establishing
+                                                                      programmatic dependency between device tasks. Note
+                                                                      also this type of dependency allows, but does not
+                                                                      guarantee, concurrent execution of tasks.
+                                                                      <br>
+                                                                      The event supplied must not be an interprocess or
+                                                                      interop event. The event must disable timing (i.e.
+                                                                      must be created with the ::CU_EVENT_DISABLE_TIMING
+                                                                      flag set).
+                                                                      */
+  , CU_LAUNCH_ATTRIBUTE_PRIORITY               = 8 /**< Valid for streams, graph nodes, launches. See
+                                                        ::CUlaunchAttributeValue::priority. */
+  , CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP    = 9 /**< Valid for streams, graph nodes, launches. See
+                                                      ::CUlaunchAttributeValue::memSyncDomainMap. */
+  , CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN        = 10 /**< Valid for streams, graph nodes, launches. See
+                                                       ::CUlaunchAttributeValue::memSyncDomain. */
+  , CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION = 11 /**< Valid for graph nodes, launches. Set
+                                                              ::CUlaunchAttributeValue::preferredClusterDim
+                                                              to allow the kernel launch to specify a preferred substitute
+                                                              cluster dimension. Blocks may be grouped according to either
+                                                              the dimensions specified with this attribute (grouped into a
+                                                              "preferred substitute cluster"), or the one specified with
+                                                              ::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION attribute (grouped
+                                                              into a "regular cluster"). The cluster dimensions of a
+                                                              "preferred substitute cluster" shall be an integer multiple
+                                                              greater than zero of the regular cluster dimensions. The
+                                                              device will attempt - on a best-effort basis - to group
+                                                              thread blocks into preferred clusters over grouping them
+                                                              into regular clusters. When it deems necessary (primarily
+                                                              when the device temporarily runs out of physical resources
+                                                              to launch the larger preferred clusters), the device may
+                                                              switch to launch the regular clusters instead to attempt to
+                                                              utilize as much of the physical device resources as possible.
+                                                              <br>
+                                                              Each type of cluster will have its enumeration / coordinate
+                                                              setup as if the grid consists solely of its type of cluster.
+                                                              For example, if the preferred substitute cluster dimensions
+                                                              double the regular cluster dimensions, there might be
+                                                              simultaneously a regular cluster indexed at (1,0,0), and a
+                                                              preferred cluster indexed at (1,0,0). In this example, the
+                                                              preferred substitute cluster (1,0,0) replaces regular
+                                                              clusters (2,0,0) and (3,0,0) and groups their blocks.
+                                                              <br>
+                                                              This attribute will only take effect when a regular cluster
+                                                              dimension has been specified. The preferred substitute
+                                                              cluster dimension must be an integer multiple greater than
+                                                              zero of the regular cluster dimension and must divide the
+                                                              grid. It must also be no more than `maxBlocksPerCluster`, if
+                                                              it is set in the kernel's `__launch_bounds__`. Otherwise it
+                                                              must be less than the maximum value the driver can support.
+                                                              Otherwise, setting this attribute to a value physically
+                                                              unable to fit on any particular device is permitted. */
+  , CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT = 12 /**< Valid for launches. Set
+                                                          ::CUlaunchAttributeValue::launchCompletionEvent to record the
+                                                          event.
+                                                          <br>
+                                                          Nominally, the event is triggered once all blocks of the kernel
+                                                          have begun execution. Currently this is a best effort. If a kernel
+                                                          B has a launch completion dependency on a kernel A, B may wait
+                                                          until A is complete. Alternatively, blocks of B may begin before
+                                                          all blocks of A have begun, for example if B can claim execution
+                                                          resources unavailable to A (e.g. they run on different GPUs) or
+                                                          if B is a higher priority than A.
+                                                          Exercise caution if such an ordering inversion could lead
+                                                          to deadlock.
+                                                          <br>
+                                                          A launch completion event is nominally similar to a programmatic
+                                                          event with \c triggerAtBlockStart set except that it is not
+                                                          visible to \c cudaGridDependencySynchronize() and can be used with
+                                                          compute capability less than 9.0.
+                                                          <br>
+                                                          The event supplied must not be an interprocess or interop
+                                                          event. The event must disable timing (i.e. must be created
+                                                          with the ::CU_EVENT_DISABLE_TIMING flag set). */
+  , CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE = 13 /**< Valid for graph nodes, launches. This attribute is graphs-only,
+                                                               and passing it to a launch in a non-capturing stream will result
+                                                               in an error.
+                                                               <br>
+                                                               ::CUlaunchAttributeValue::deviceUpdatableKernelNode::deviceUpdatable can 
+                                                               only be set to 0 or 1. Setting the field to 1 indicates that the
+                                                               corresponding kernel node should be device-updatable. On success, a handle
+                                                               will be returned via
+                                                               ::CUlaunchAttributeValue::deviceUpdatableKernelNode::devNode which can be
+                                                               passed to the various device-side update functions to update the node's
+                                                               kernel parameters from within another kernel. For more information on the
+                                                               types of device updates that can be made, as well as the relevant limitations
+                                                               thereof, see ::cudaGraphKernelNodeUpdatesApply.
+                                                               <br>
+                                                               Nodes which are device-updatable have additional restrictions compared to
+                                                               regular kernel nodes. Firstly, device-updatable nodes cannot be removed
+                                                               from their graph via ::cuGraphDestroyNode. Additionally, once opted-in
+                                                               to this functionality, a node cannot opt out, and any attempt to set the
+                                                               deviceUpdatable attribute to 0 will result in an error. Device-updatable
+                                                               kernel nodes also cannot have their attributes copied to/from another kernel
+                                                               node via ::cuGraphKernelNodeCopyAttributes. Graphs containing one or more
+                                                               device-updatable nodes also do not allow multiple instantiation, and neither
+                                                               the graph nor its instantiated version can be passed to ::cuGraphExecUpdate.
+                                                               <br>
+                                                               If a graph contains device-updatable nodes and updates those nodes from the device
+                                                               from within the graph, the graph must be uploaded with ::cuGraphUpload before it
+                                                               is launched. For such a graph, if host-side executable graph updates are made to the
+                                                               device-updatable nodes, the graph must be uploaded before it is launched again. */
+  , CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT = 14 /**< Valid for launches. On devices where the L1 cache and shared memory use the
+                                                                   same hardware resources, setting ::CUlaunchAttributeValue::sharedMemCarveout to a 
+                                                                   percentage between 0-100 signals the CUDA driver to set the shared memory carveout 
+                                                                   preference, in percent of the total shared memory for that kernel launch. 
+                                                                   This attribute takes precedence over ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+                                                                   This is only a hint, and the CUDA driver can choose a different configuration if
+                                                                   required for the launch. */
+#if defined(__CUDA_API_VERSION_INTERNAL) && !defined(__CUDA_API_VERSION_INTERNAL_ODR)
+  , CU_LAUNCH_ATTRIBUTE_MAX
+#endif
+} CUlaunchAttributeID;
+
+/**
+ * Launch attributes union; used as value field of ::CUlaunchAttribute
+ */
+typedef union CUlaunchAttributeValue_union {
+    char pad[64]; /* Pad to 64 bytes */
+    CUaccessPolicyWindow accessPolicyWindow; /**< Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW. */
+    int cooperative; /**< Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero indicates a cooperative
+                        kernel (see ::cuLaunchCooperativeKernel). */
+    CUsynchronizationPolicy syncPolicy; /**< Value of launch attribute
+                                           ::CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY. ::CUsynchronizationPolicy for
+                                           work queued up in this stream */
+
+    /**
+     *  Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION that
+     *  represents the desired cluster dimensions for the kernel. Opaque type
+     *  with the following fields:
+     *      - \p x - The X dimension of the cluster, in blocks. Must be a divisor
+     *               of the grid X dimension.
+     *      - \p y - The Y dimension of the cluster, in blocks. Must be a divisor
+     *               of the grid Y dimension.
+     *      - \p z - The Z dimension of the cluster, in blocks. Must be a divisor
+     *               of the grid Z dimension.
+     */
+    struct {
+        unsigned int x;
+        unsigned int y;
+        unsigned int z;
+    } clusterDim;
+    CUclusterSchedulingPolicy clusterSchedulingPolicyPreference; /**< Value of launch attribute
+                                                                    ::CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+                                                                    scheduling policy preference for the kernel. */
+    int programmaticStreamSerializationAllowed;  /**< Value of launch attribute
+                                                   ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION. */
+    /**
+     *  Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+     *  with the following fields:
+     *      - \p CUevent event - Event to fire when all blocks trigger it.
+     *      - \p Event record flags, see ::cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+     *      - \p triggerAtBlockStart - If this is set to non-0, each block launch will automatically trigger the event.
+     */
+    struct {
+        CUevent event;
+        int flags;
+        int triggerAtBlockStart;
+    } programmaticEvent;
+    /**
+     * Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT
+     * with the following fields:
+     *     - \p CUevent event - Event to fire when the last block launches
+     *     - \p int flags; - Event record flags, see ::cuEventRecordWithFlags. Does not accept ::CU_EVENT_RECORD_EXTERNAL.
+     */ 
+    struct {
+        CUevent event;
+        int flags;
+    } launchCompletionEvent;
+    int priority; /**< Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution priority of the kernel. */
+    CUlaunchMemSyncDomainMap memSyncDomainMap; /**< Value of launch attribute
+                                                  ::CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP. See
+                                                  ::CUlaunchMemSyncDomainMap. */
+    CUlaunchMemSyncDomain memSyncDomain;       /**< Value of launch attribute
+                                                  ::CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN. See::CUlaunchMemSyncDomain */
+    /**
+     *  Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION
+     *  that represents the desired preferred cluster dimensions for the kernel.
+     *  Opaque type with the following fields:
+     *      - \p x - The X dimension of the preferred cluster, in blocks. Must
+     *               be a divisor of the grid X dimension, and must be a
+     *               multiple of the \p x field of ::CUlaunchAttributeValue::clusterDim.
+     *      - \p y - The Y dimension of the preferred cluster, in blocks. Must
+     *               be a divisor of the grid Y dimension, and must be a
+     *               multiple of the \p y field of ::CUlaunchAttributeValue::clusterDim.
+     *      - \p z - The Z dimension of the preferred cluster, in blocks. Must be
+     *               equal to the \p z field of ::CUlaunchAttributeValue::clusterDim.
+     */
+    struct {
+        unsigned int x;
+        unsigned int y;
+        unsigned int z;
+    } preferredClusterDim;
+
+    /**
+     *  Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE.
+     *  with the following fields:
+     *      - \p int deviceUpdatable - Whether or not the resulting kernel node should be device-updatable.
+     *      - \p CUgraphDeviceNode devNode - Returns a handle to pass to the various device-side update functions.
+     */
+    struct {
+        int deviceUpdatable;
+        CUgraphDeviceNode devNode;
+    } deviceUpdatableKernelNode;
+    unsigned int sharedMemCarveout; /**< Value of launch attribute ::CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT. */
+} CUlaunchAttributeValue;
+
+/**
+ * Launch attribute
+ */
+typedef struct CUlaunchAttribute_st {
+    CUlaunchAttributeID id; /**< Attribute to set */
+    char pad[8 - sizeof(CUlaunchAttributeID)];
+    CUlaunchAttributeValue value; /**< Value of the attribute */
+} CUlaunchAttribute;
+
+/**
+ * CUDA extensible launch configuration
+ */
+typedef struct CUlaunchConfig_st {
+    unsigned int gridDimX;       /**< Width of grid in blocks */
+    unsigned int gridDimY;       /**< Height of grid in blocks */
+    unsigned int gridDimZ;       /**< Depth of grid in blocks */
+    unsigned int blockDimX;      /**< X dimension of each thread block */
+    unsigned int blockDimY;      /**< Y dimension of each thread block */
+    unsigned int blockDimZ;      /**< Z dimension of each thread block */
+    unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */
+    CUstream hStream;            /**< Stream identifier */
+    CUlaunchAttribute *attrs;    /**< List of attributes; nullable if ::CUlaunchConfig::numAttrs == 0 */
+    unsigned int numAttrs;       /**< Number of attributes populated in ::CUlaunchConfig::attrs */
+} CUlaunchConfig;
+
+typedef CUlaunchAttributeID CUkernelNodeAttrID;
+#define CU_KERNEL_NODE_ATTRIBUTE_ACCESS_POLICY_WINDOW CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW
+#define CU_KERNEL_NODE_ATTRIBUTE_COOPERATIVE          CU_LAUNCH_ATTRIBUTE_COOPERATIVE
+#define CU_KERNEL_NODE_ATTRIBUTE_CLUSTER_DIMENSION                    CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+#define CU_KERNEL_NODE_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE
+#define CU_KERNEL_NODE_ATTRIBUTE_PRIORITY             CU_LAUNCH_ATTRIBUTE_PRIORITY
+#define CU_KERNEL_NODE_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP  CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP
+#define CU_KERNEL_NODE_ATTRIBUTE_MEM_SYNC_DOMAIN      CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN
+#define CU_KERNEL_NODE_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION
+#define CU_KERNEL_NODE_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE
+#define CU_KERNEL_NODE_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT
+
+typedef CUlaunchAttributeValue CUkernelNodeAttrValue_v1;
+typedef CUkernelNodeAttrValue_v1 CUkernelNodeAttrValue;
+
+/**
+ * Possible stream capture statuses returned by ::cuStreamIsCapturing
+ */
+typedef enum CUstreamCaptureStatus_enum {
+    CU_STREAM_CAPTURE_STATUS_NONE        = 0, /**< Stream is not capturing */
+    CU_STREAM_CAPTURE_STATUS_ACTIVE      = 1, /**< Stream is actively capturing */
+    CU_STREAM_CAPTURE_STATUS_INVALIDATED = 2  /**< Stream is part of a capture sequence that
+                                                   has been invalidated, but not terminated */
+} CUstreamCaptureStatus;
+
+/**
+ * Possible modes for stream capture thread interactions. For more details see
+ * ::cuStreamBeginCapture and ::cuThreadExchangeStreamCaptureMode
+ */
+typedef enum CUstreamCaptureMode_enum {
+    CU_STREAM_CAPTURE_MODE_GLOBAL       = 0,
+    CU_STREAM_CAPTURE_MODE_THREAD_LOCAL = 1,
+    CU_STREAM_CAPTURE_MODE_RELAXED      = 2
+} CUstreamCaptureMode;
+
+typedef CUlaunchAttributeID CUstreamAttrID;
+#define CU_STREAM_ATTRIBUTE_ACCESS_POLICY_WINDOW   CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW
+#define CU_STREAM_ATTRIBUTE_SYNCHRONIZATION_POLICY CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY
+#define CU_STREAM_ATTRIBUTE_PRIORITY               CU_LAUNCH_ATTRIBUTE_PRIORITY
+#define CU_STREAM_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP    CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP
+#define CU_STREAM_ATTRIBUTE_MEM_SYNC_DOMAIN        CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN
+
+typedef CUlaunchAttributeValue CUstreamAttrValue_v1;
+typedef CUstreamAttrValue_v1 CUstreamAttrValue;
+
+/**
+ * Flags to specify search options. For more details see ::cuGetProcAddress
+ */
+typedef enum CUdriverProcAddress_flags_enum {
+    CU_GET_PROC_ADDRESS_DEFAULT = 0,                        /**< Default search mode for driver symbols. */
+    CU_GET_PROC_ADDRESS_LEGACY_STREAM = 1 << 0,             /**< Search for legacy versions of driver symbols. */
+    CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM = 1 << 1  /**< Search for per-thread versions of driver symbols. */ 
+} CUdriverProcAddress_flags;
+
+/**
+ * Flags to indicate search status. For more details see ::cuGetProcAddress
+ */
+typedef enum CUdriverProcAddressQueryResult_enum {
+    CU_GET_PROC_ADDRESS_SUCCESS                = 0,  /**< Symbol was succesfully found */
+    CU_GET_PROC_ADDRESS_SYMBOL_NOT_FOUND       = 1,  /**< Symbol was not found in search */
+    CU_GET_PROC_ADDRESS_VERSION_NOT_SUFFICIENT = 2   /**< Symbol was found but version supplied was not sufficient */
+}  CUdriverProcAddressQueryResult;
+
+/**
+ * Execution Affinity Types 
+ */
+typedef enum CUexecAffinityType_enum {
+    CU_EXEC_AFFINITY_TYPE_SM_COUNT = 0,  /**< Create a context with limited SMs. */
+    CU_EXEC_AFFINITY_TYPE_MAX
+} CUexecAffinityType;
+
+/**
+ * Value for ::CU_EXEC_AFFINITY_TYPE_SM_COUNT
+ */
+typedef struct CUexecAffinitySmCount_st {
+    unsigned int val;    /**< The number of SMs the context is limited to use. */
+} CUexecAffinitySmCount_v1;
+typedef CUexecAffinitySmCount_v1 CUexecAffinitySmCount;
+
+/**
+ * Execution Affinity Parameters 
+ */
+typedef struct CUexecAffinityParam_st {
+    CUexecAffinityType type;
+    union {
+        CUexecAffinitySmCount smCount;    /** Value for ::CU_EXEC_AFFINITY_TYPE_SM_COUNT */
+    } param;
+} CUexecAffinityParam_v1;
+/**
+ * Execution Affinity Parameters
+ */
+typedef CUexecAffinityParam_v1 CUexecAffinityParam;
+
+typedef enum CUcigDataType_enum {
+    CIG_DATA_TYPE_D3D12_COMMAND_QUEUE = 0x1,    /** D3D12 Command Queue Handle */
+} CUcigDataType;
+
+/**
+* CIG Context Create Params
+*/
+typedef struct CUctxCigParam_st {
+    CUcigDataType sharedDataType;
+    void* sharedData;
+} CUctxCigParam;
+
+/**
+* Params for creating CUDA context
+* Exactly one of execAffinityParams and cigParams 
+* must be non-NULL.
+*/
+typedef struct CUctxCreateParams_st {
+    CUexecAffinityParam *execAffinityParams;
+    int                  numExecAffinityParams;
+    CUctxCigParam       *cigParams;
+} CUctxCreateParams;
+
+/**
+ * Library options to be specified with ::cuLibraryLoadData() or ::cuLibraryLoadFromFile()
+ */
+typedef enum CUlibraryOption_enum
+{
+    CU_LIBRARY_HOST_UNIVERSAL_FUNCTION_AND_DATA_TABLE = 0,
+
+    /**
+     * Specifes that the argument \p code passed to ::cuLibraryLoadData() will be preserved.
+     * Specifying this option will let the driver know that \p code can be accessed at any point
+     * until ::cuLibraryUnload(). The default behavior is for the driver to allocate and
+     * maintain its own copy of \p code. Note that this is only a memory usage optimization
+     * hint and the driver can choose to ignore it if required.
+     * Specifying this option with ::cuLibraryLoadFromFile() is invalid and
+     * will return ::CUDA_ERROR_INVALID_VALUE.
+     */
+    CU_LIBRARY_BINARY_IS_PRESERVED = 1,
+
+    CU_LIBRARY_NUM_OPTIONS
+} CUlibraryOption;
+
+typedef struct CUlibraryHostUniversalFunctionAndDataTable_st
+{
+    void *functionTable;
+    size_t functionWindowSize;
+    void *dataTable;
+    size_t dataWindowSize;
+} CUlibraryHostUniversalFunctionAndDataTable;
+
+/**
+ * Error codes
+ */
+typedef enum cudaError_enum {
+    /**
+     * The API call returned with no errors. In the case of query calls, this
+     * also means that the operation being queried is complete (see
+     * ::cuEventQuery() and ::cuStreamQuery()).
+     */
+    CUDA_SUCCESS                              = 0,
+
+    /**
+     * This indicates that one or more of the parameters passed to the API call
+     * is not within an acceptable range of values.
+     */
+    CUDA_ERROR_INVALID_VALUE                  = 1,
+
+    /**
+     * The API call failed because it was unable to allocate enough memory or
+     * other resources to perform the requested operation.
+     */
+    CUDA_ERROR_OUT_OF_MEMORY                  = 2,
+
+    /**
+     * This indicates that the CUDA driver has not been initialized with
+     * ::cuInit() or that initialization has failed.
+     */
+    CUDA_ERROR_NOT_INITIALIZED                = 3,
+
+    /**
+     * This indicates that the CUDA driver is in the process of shutting down.
+     */
+    CUDA_ERROR_DEINITIALIZED                  = 4,
+
+    /**
+     * This indicates profiler is not initialized for this run. This can
+     * happen when the application is running with external profiling tools
+     * like visual profiler.
+     */
+    CUDA_ERROR_PROFILER_DISABLED              = 5,
+
+    /**
+     * \deprecated
+     * This error return is deprecated as of CUDA 5.0. It is no longer an error
+     * to attempt to enable/disable the profiling via ::cuProfilerStart or
+     * ::cuProfilerStop without initialization.
+     */
+    CUDA_ERROR_PROFILER_NOT_INITIALIZED       = 6,
+
+    /**
+     * \deprecated
+     * This error return is deprecated as of CUDA 5.0. It is no longer an error
+     * to call cuProfilerStart() when profiling is already enabled.
+     */
+    CUDA_ERROR_PROFILER_ALREADY_STARTED       = 7,
+
+    /**
+     * \deprecated
+     * This error return is deprecated as of CUDA 5.0. It is no longer an error
+     * to call cuProfilerStop() when profiling is already disabled.
+     */
+    CUDA_ERROR_PROFILER_ALREADY_STOPPED       = 8,
+
+    /**
+     * This indicates that the CUDA driver that the application has loaded is a
+     * stub library. Applications that run with the stub rather than a real
+     * driver loaded will result in CUDA API returning this error.
+     */
+    CUDA_ERROR_STUB_LIBRARY                   = 34,
+
+    /**  
+     * This indicates that requested CUDA device is unavailable at the current
+     * time. Devices are often unavailable due to use of
+     * ::CU_COMPUTEMODE_EXCLUSIVE_PROCESS or ::CU_COMPUTEMODE_PROHIBITED.
+     */
+    CUDA_ERROR_DEVICE_UNAVAILABLE            = 46,
+
+    /**
+     * This indicates that no CUDA-capable devices were detected by the installed
+     * CUDA driver.
+     */
+    CUDA_ERROR_NO_DEVICE                      = 100,
+
+    /**
+     * This indicates that the device ordinal supplied by the user does not
+     * correspond to a valid CUDA device or that the action requested is
+     * invalid for the specified device.
+     */
+    CUDA_ERROR_INVALID_DEVICE                 = 101,
+
+    /**
+     * This error indicates that the Grid license is not applied.
+     */
+    CUDA_ERROR_DEVICE_NOT_LICENSED            = 102,
+
+    /**
+     * This indicates that the device kernel image is invalid. This can also
+     * indicate an invalid CUDA module.
+     */
+    CUDA_ERROR_INVALID_IMAGE                  = 200,
+
+    /**
+     * This most frequently indicates that there is no context bound to the
+     * current thread. This can also be returned if the context passed to an
+     * API call is not a valid handle (such as a context that has had
+     * ::cuCtxDestroy() invoked on it). This can also be returned if a user
+     * mixes different API versions (i.e. 3010 context with 3020 API calls).
+     * See ::cuCtxGetApiVersion() for more details.
+     * This can also be returned if the green context passed to an API call
+     * was not converted to a ::CUcontext using ::cuCtxFromGreenCtx API.
+     */
+    CUDA_ERROR_INVALID_CONTEXT                = 201,
+
+    /**
+     * This indicated that the context being supplied as a parameter to the
+     * API call was already the active context.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.2. It is no longer an
+     * error to attempt to push the active context via ::cuCtxPushCurrent().
+     */
+    CUDA_ERROR_CONTEXT_ALREADY_CURRENT        = 202,
+
+    /**
+     * This indicates that a map or register operation has failed.
+     */
+    CUDA_ERROR_MAP_FAILED                     = 205,
+
+    /**
+     * This indicates that an unmap or unregister operation has failed.
+     */
+    CUDA_ERROR_UNMAP_FAILED                   = 206,
+
+    /**
+     * This indicates that the specified array is currently mapped and thus
+     * cannot be destroyed.
+     */
+    CUDA_ERROR_ARRAY_IS_MAPPED                = 207,
+
+    /**
+     * This indicates that the resource is already mapped.
+     */
+    CUDA_ERROR_ALREADY_MAPPED                 = 208,
+
+    /**
+     * This indicates that there is no kernel image available that is suitable
+     * for the device. This can occur when a user specifies code generation
+     * options for a particular CUDA source file that do not include the
+     * corresponding device configuration.
+     */
+    CUDA_ERROR_NO_BINARY_FOR_GPU              = 209,
+
+    /**
+     * This indicates that a resource has already been acquired.
+     */
+    CUDA_ERROR_ALREADY_ACQUIRED               = 210,
+
+    /**
+     * This indicates that a resource is not mapped.
+     */
+    CUDA_ERROR_NOT_MAPPED                     = 211,
+
+    /**
+     * This indicates that a mapped resource is not available for access as an
+     * array.
+     */
+    CUDA_ERROR_NOT_MAPPED_AS_ARRAY            = 212,
+
+    /**
+     * This indicates that a mapped resource is not available for access as a
+     * pointer.
+     */
+    CUDA_ERROR_NOT_MAPPED_AS_POINTER          = 213,
+
+    /**
+     * This indicates that an uncorrectable ECC error was detected during
+     * execution.
+     */
+    CUDA_ERROR_ECC_UNCORRECTABLE              = 214,
+
+    /**
+     * This indicates that the ::CUlimit passed to the API call is not
+     * supported by the active device.
+     */
+    CUDA_ERROR_UNSUPPORTED_LIMIT              = 215,
+
+    /**
+     * This indicates that the ::CUcontext passed to the API call can
+     * only be bound to a single CPU thread at a time but is already
+     * bound to a CPU thread.
+     */
+    CUDA_ERROR_CONTEXT_ALREADY_IN_USE         = 216,
+
+    /**
+     * This indicates that peer access is not supported across the given
+     * devices.
+     */
+    CUDA_ERROR_PEER_ACCESS_UNSUPPORTED        = 217,
+
+    /**
+     * This indicates that a PTX JIT compilation failed.
+     */
+    CUDA_ERROR_INVALID_PTX                    = 218,
+
+    /**
+     * This indicates an error with OpenGL or DirectX context.
+     */
+    CUDA_ERROR_INVALID_GRAPHICS_CONTEXT       = 219,
+
+    /**
+    * This indicates that an uncorrectable NVLink error was detected during the
+    * execution.
+    */
+    CUDA_ERROR_NVLINK_UNCORRECTABLE           = 220,
+
+    /**
+    * This indicates that the PTX JIT compiler library was not found.
+    */
+    CUDA_ERROR_JIT_COMPILER_NOT_FOUND         = 221,
+
+    /**
+     * This indicates that the provided PTX was compiled with an unsupported toolchain.
+     */
+
+    CUDA_ERROR_UNSUPPORTED_PTX_VERSION        = 222,
+
+    /**
+     * This indicates that the PTX JIT compilation was disabled.
+     */
+    CUDA_ERROR_JIT_COMPILATION_DISABLED       = 223,
+
+    /**
+     * This indicates that the ::CUexecAffinityType passed to the API call is not
+     * supported by the active device.
+     */ 
+    CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY      = 224,
+
+    /**
+     * This indicates that the code to be compiled by the PTX JIT contains
+     * unsupported call to cudaDeviceSynchronize.
+     */
+    CUDA_ERROR_UNSUPPORTED_DEVSIDE_SYNC       = 225,
+
+    /**
+     * This indicates that an exception occurred on the device that is now
+     * contained by the GPU's error containment capability. Common causes are -
+     * a. Certain types of invalid accesses of peer GPU memory over nvlink
+     * b. Certain classes of hardware errors
+     * This leaves the process in an inconsistent state and any further CUDA
+     * work will return the same error. To continue using CUDA, the process must
+     * be terminated and relaunched.
+     */
+    CUDA_ERROR_CONTAINED                      = 226,
+
+    /**
+     * This indicates that the device kernel source is invalid. This includes
+     * compilation/linker errors encountered in device code or user error.
+     */
+    CUDA_ERROR_INVALID_SOURCE                 = 300,
+
+    /**
+     * This indicates that the file specified was not found.
+     */
+    CUDA_ERROR_FILE_NOT_FOUND                 = 301,
+
+    /**
+     * This indicates that a link to a shared object failed to resolve.
+     */
+    CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND = 302,
+
+    /**
+     * This indicates that initialization of a shared object failed.
+     */
+    CUDA_ERROR_SHARED_OBJECT_INIT_FAILED      = 303,
+
+    /**
+     * This indicates that an OS call failed.
+     */
+    CUDA_ERROR_OPERATING_SYSTEM               = 304,
+
+    /**
+     * This indicates that a resource handle passed to the API call was not
+     * valid. Resource handles are opaque types like ::CUstream and ::CUevent.
+     */
+    CUDA_ERROR_INVALID_HANDLE                 = 400,
+
+    /**
+     * This indicates that a resource required by the API call is not in a
+     * valid state to perform the requested operation.
+     */
+    CUDA_ERROR_ILLEGAL_STATE                  = 401,
+
+    /**
+     * This indicates an attempt was made to introspect an object in a way that
+     * would discard semantically important information. This is either due to
+     * the object using funtionality newer than the API version used to
+     * introspect it or omission of optional return arguments.
+     */
+    CUDA_ERROR_LOSSY_QUERY                    = 402,
+
+    /**
+     * This indicates that a named symbol was not found. Examples of symbols
+     * are global/constant variable names, driver function names, texture names,
+     * and surface names.
+     */
+    CUDA_ERROR_NOT_FOUND                      = 500,
+
+    /**
+     * This indicates that asynchronous operations issued previously have not
+     * completed yet. This result is not actually an error, but must be indicated
+     * differently than ::CUDA_SUCCESS (which indicates completion). Calls that
+     * may return this value include ::cuEventQuery() and ::cuStreamQuery().
+     */
+    CUDA_ERROR_NOT_READY                      = 600,
+
+    /**
+     * While executing a kernel, the device encountered a
+     * load or store instruction on an invalid memory address.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_ILLEGAL_ADDRESS                = 700,
+
+    /**
+     * This indicates that a launch did not occur because it did not have
+     * appropriate resources. This error usually indicates that the user has
+     * attempted to pass too many arguments to the device kernel, or the
+     * kernel launch specifies too many threads for the kernel's register
+     * count. Passing arguments of the wrong size (i.e. a 64-bit pointer
+     * when a 32-bit int is expected) is equivalent to passing too many
+     * arguments and can also result in this error.
+     */
+    CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES        = 701,
+
+    /**
+     * This indicates that the device kernel took too long to execute. This can
+     * only occur if timeouts are enabled - see the device attribute
+     * ::CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_LAUNCH_TIMEOUT                 = 702,
+
+    /**
+     * This error indicates a kernel launch that uses an incompatible texturing
+     * mode.
+     */
+    CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING  = 703,
+
+    /**
+     * This error indicates that a call to ::cuCtxEnablePeerAccess() is
+     * trying to re-enable peer access to a context which has already
+     * had peer access to it enabled.
+     */
+    CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED    = 704,
+
+    /**
+     * This error indicates that ::cuCtxDisablePeerAccess() is
+     * trying to disable peer access which has not been enabled yet
+     * via ::cuCtxEnablePeerAccess().
+     */
+    CUDA_ERROR_PEER_ACCESS_NOT_ENABLED        = 705,
+
+    /**
+     * This error indicates that the primary context for the specified device
+     * has already been initialized.
+     */
+    CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE         = 708,
+
+    /**
+     * This error indicates that the context current to the calling thread
+     * has been destroyed using ::cuCtxDestroy, or is a primary context which
+     * has not yet been initialized.
+     */
+    CUDA_ERROR_CONTEXT_IS_DESTROYED           = 709,
+
+    /**
+     * A device-side assert triggered during kernel execution. The context
+     * cannot be used anymore, and must be destroyed. All existing device
+     * memory allocations from this context are invalid and must be
+     * reconstructed if the program is to continue using CUDA.
+     */
+    CUDA_ERROR_ASSERT                         = 710,
+
+    /**
+     * This error indicates that the hardware resources required to enable
+     * peer access have been exhausted for one or more of the devices
+     * passed to ::cuCtxEnablePeerAccess().
+     */
+    CUDA_ERROR_TOO_MANY_PEERS                 = 711,
+
+    /**
+     * This error indicates that the memory range passed to ::cuMemHostRegister()
+     * has already been registered.
+     */
+    CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED = 712,
+
+    /**
+     * This error indicates that the pointer passed to ::cuMemHostUnregister()
+     * does not correspond to any currently registered memory region.
+     */
+    CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED     = 713,
+
+    /**
+     * While executing a kernel, the device encountered a stack error.
+     * This can be due to stack corruption or exceeding the stack size limit.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_HARDWARE_STACK_ERROR           = 714,
+
+    /**
+     * While executing a kernel, the device encountered an illegal instruction.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_ILLEGAL_INSTRUCTION            = 715,
+
+    /**
+     * While executing a kernel, the device encountered a load or store instruction
+     * on a memory address which is not aligned.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_MISALIGNED_ADDRESS             = 716,
+
+    /**
+     * While executing a kernel, the device encountered an instruction
+     * which can only operate on memory locations in certain address spaces
+     * (global, shared, or local), but was supplied a memory address not
+     * belonging to an allowed address space.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_INVALID_ADDRESS_SPACE          = 717,
+
+    /**
+     * While executing a kernel, the device program counter wrapped its address space.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_INVALID_PC                     = 718,
+
+    /**
+     * An exception occurred on the device while executing a kernel. Common
+     * causes include dereferencing an invalid device pointer and accessing
+     * out of bounds shared memory. Less common cases can be system specific - more
+     * information about these cases can be found in the system specific user guide.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    CUDA_ERROR_LAUNCH_FAILED                  = 719,
+
+    /**
+     * This error indicates that the number of blocks launched per grid for a kernel that was
+     * launched via either ::cuLaunchCooperativeKernel or ::cuLaunchCooperativeKernelMultiDevice
+     * exceeds the maximum number of blocks as allowed by ::cuOccupancyMaxActiveBlocksPerMultiprocessor
+     * or ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors
+     * as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
+     */
+    CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE   = 720,
+
+    /**
+     * An exception occurred on the device while exiting a kernel using tensor memory: the
+     * tensor memory was not completely deallocated. This leaves the process in an inconsistent
+     * state and any further CUDA work will return the same error. To continue using CUDA, the
+     * process must be terminated and relaunched.
+     */
+    CUDA_ERROR_TENSOR_MEMORY_LEAK             = 721,
+
+    /**
+     * This error indicates that the attempted operation is not permitted.
+     */
+    CUDA_ERROR_NOT_PERMITTED                  = 800,
+
+    /**
+     * This error indicates that the attempted operation is not supported
+     * on the current system or device.
+     */
+    CUDA_ERROR_NOT_SUPPORTED                  = 801,
+
+    /**
+     * This error indicates that the system is not yet ready to start any CUDA
+     * work.  To continue using CUDA, verify the system configuration is in a
+     * valid state and all required driver daemons are actively running.
+     * More information about this error can be found in the system specific
+     * user guide.
+     */
+    CUDA_ERROR_SYSTEM_NOT_READY               = 802,
+
+    /**
+     * This error indicates that there is a mismatch between the versions of
+     * the display driver and the CUDA driver. Refer to the compatibility documentation
+     * for supported versions.
+     */
+    CUDA_ERROR_SYSTEM_DRIVER_MISMATCH         = 803,
+
+    /**
+     * This error indicates that the system was upgraded to run with forward compatibility
+     * but the visible hardware detected by CUDA does not support this configuration.
+     * Refer to the compatibility documentation for the supported hardware matrix or ensure
+     * that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES
+     * environment variable.
+     */
+    CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE = 804,
+
+    /**
+     * This error indicates that the MPS client failed to connect to the MPS control daemon or the MPS server.
+     */
+    CUDA_ERROR_MPS_CONNECTION_FAILED          = 805,
+
+    /**
+     * This error indicates that the remote procedural call between the MPS server and the MPS client failed.
+     */
+    CUDA_ERROR_MPS_RPC_FAILURE                = 806,
+
+    /**
+     * This error indicates that the MPS server is not ready to accept new MPS client requests.
+     * This error can be returned when the MPS server is in the process of recovering from a fatal failure.
+     */
+    CUDA_ERROR_MPS_SERVER_NOT_READY           = 807,
+
+    /**
+     * This error indicates that the hardware resources required to create MPS client have been exhausted.
+     */
+    CUDA_ERROR_MPS_MAX_CLIENTS_REACHED        = 808,
+
+    /**
+     * This error indicates the the hardware resources required to support device connections have been exhausted.
+     */
+    CUDA_ERROR_MPS_MAX_CONNECTIONS_REACHED    = 809,
+
+    /**
+     * This error indicates that the MPS client has been terminated by the server. To continue using CUDA, the process must be terminated and relaunched.
+     */
+    CUDA_ERROR_MPS_CLIENT_TERMINATED          = 810,
+
+    /**
+     * This error indicates that the module is using CUDA Dynamic Parallelism, but the current configuration, like MPS, does not support it.
+     */
+    CUDA_ERROR_CDP_NOT_SUPPORTED              = 811,
+
+    /**
+     * This error indicates that a module contains an unsupported interaction between different versions of CUDA Dynamic Parallelism.
+     */
+    CUDA_ERROR_CDP_VERSION_MISMATCH           = 812,
+
+    /**
+     * This error indicates that the operation is not permitted when
+     * the stream is capturing.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED     = 900,
+
+    /**
+     * This error indicates that the current capture sequence on the stream
+     * has been invalidated due to a previous error.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_INVALIDATED     = 901,
+
+    /**
+     * This error indicates that the operation would have resulted in a merge
+     * of two independent capture sequences.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_MERGE           = 902,
+
+    /**
+     * This error indicates that the capture was not initiated in this stream.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_UNMATCHED       = 903,
+
+    /**
+     * This error indicates that the capture sequence contains a fork that was
+     * not joined to the primary stream.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_UNJOINED        = 904,
+
+    /**
+     * This error indicates that a dependency would have been created which
+     * crosses the capture sequence boundary. Only implicit in-stream ordering
+     * dependencies are allowed to cross the boundary.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_ISOLATION       = 905,
+
+    /**
+     * This error indicates a disallowed implicit dependency on a current capture
+     * sequence from cudaStreamLegacy.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_IMPLICIT        = 906,
+
+    /**
+     * This error indicates that the operation is not permitted on an event which
+     * was last recorded in a capturing stream.
+     */
+    CUDA_ERROR_CAPTURED_EVENT                 = 907,
+
+    /**
+     * A stream capture sequence not initiated with the ::CU_STREAM_CAPTURE_MODE_RELAXED
+     * argument to ::cuStreamBeginCapture was passed to ::cuStreamEndCapture in a
+     * different thread.
+     */
+    CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD    = 908,
+
+    /**
+     * This error indicates that the timeout specified for the wait operation has lapsed.
+     */
+    CUDA_ERROR_TIMEOUT                        = 909,
+
+    /**
+     * This error indicates that the graph update was not performed because it included 
+     * changes which violated constraints specific to instantiated graph update.
+     */
+    CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE      = 910,
+
+    /**
+     * This indicates that an async error has occurred in a device outside of CUDA.
+     * If CUDA was waiting for an external device's signal before consuming shared data,
+     * the external device signaled an error indicating that the data is not valid for
+     * consumption. This leaves the process in an inconsistent state and any further CUDA
+     * work will return the same error. To continue using CUDA, the process must be
+     * terminated and relaunched.
+     */
+    CUDA_ERROR_EXTERNAL_DEVICE               = 911,
+
+    /**
+     * Indicates a kernel launch error due to cluster misconfiguration.
+     */
+    CUDA_ERROR_INVALID_CLUSTER_SIZE           = 912,
+
+    /**
+     * Indiciates a function handle is not loaded when calling an API that requires
+     * a loaded function.
+    */
+    CUDA_ERROR_FUNCTION_NOT_LOADED            = 913,
+
+    /**
+     * This error indicates one or more resources passed in are not valid resource
+     * types for the operation.
+    */
+    CUDA_ERROR_INVALID_RESOURCE_TYPE          = 914,
+
+    /**
+     * This error indicates one or more resources are insufficient or non-applicable for
+     * the operation.
+    */
+    CUDA_ERROR_INVALID_RESOURCE_CONFIGURATION = 915,
+
+    /**
+     * This error indicates that an error happened during the key rotation
+     * sequence.
+    */
+    CUDA_ERROR_KEY_ROTATION                   = 916,
+
+    /**
+     * This indicates that an unknown internal error has occurred.
+     */
+    CUDA_ERROR_UNKNOWN                        = 999
+} CUresult;
+
+/**
+ * P2P Attributes
+ */
+typedef enum CUdevice_P2PAttribute_enum {
+    CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK                     = 0x01,  /**< A relative value indicating the performance of the link between two devices */
+    CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED                     = 0x02,  /**< P2P Access is enable */
+    CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED              = 0x03,  /**< Atomic operation over the link supported */
+    CU_DEVICE_P2P_ATTRIBUTE_ACCESS_ACCESS_SUPPORTED              = 0x04,  /**< \deprecated use CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED instead */
+    CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED          = 0x04   /**< Accessing CUDA arrays over the link supported */
+} CUdevice_P2PAttribute;
+
+/**
+ * CUDA stream callback
+ * \param hStream The stream the callback was added to, as passed to ::cuStreamAddCallback.  May be NULL.
+ * \param status ::CUDA_SUCCESS or any persistent error on the stream.
+ * \param userData User parameter provided at registration.
+ */
+typedef void (CUDA_CB *CUstreamCallback)(CUstream hStream, CUresult status, void *userData);
+
+/**
+ * Block size to per-block dynamic shared memory mapping for a certain
+ * kernel \param blockSize Block size of the kernel.
+ *
+ * \return The dynamic shared memory needed by a block.
+ */
+typedef size_t (CUDA_CB *CUoccupancyB2DSize)(int blockSize);
+
+/**
+ * If set, host memory is portable between CUDA contexts.
+ * Flag for ::cuMemHostAlloc()
+ */
+#define CU_MEMHOSTALLOC_PORTABLE        0x01
+
+/**
+ * If set, host memory is mapped into CUDA address space and
+ * ::cuMemHostGetDevicePointer() may be called on the host pointer.
+ * Flag for ::cuMemHostAlloc()
+ */
+#define CU_MEMHOSTALLOC_DEVICEMAP       0x02
+
+/**
+ * If set, host memory is allocated as write-combined - fast to write,
+ * faster to DMA, slow to read except via SSE4 streaming load instruction
+ * (MOVNTDQA).
+ * Flag for ::cuMemHostAlloc()
+ */
+#define CU_MEMHOSTALLOC_WRITECOMBINED   0x04
+
+/**
+ * If set, host memory is portable between CUDA contexts.
+ * Flag for ::cuMemHostRegister()
+ */
+#define CU_MEMHOSTREGISTER_PORTABLE     0x01
+
+/**
+ * If set, host memory is mapped into CUDA address space and
+ * ::cuMemHostGetDevicePointer() may be called on the host pointer.
+ * Flag for ::cuMemHostRegister()
+ */
+#define CU_MEMHOSTREGISTER_DEVICEMAP    0x02
+
+/**
+ * If set, the passed memory pointer is treated as pointing to some
+ * memory-mapped I/O space, e.g. belonging to a third-party PCIe device.
+ * On Windows the flag is a no-op.
+ * On Linux that memory is marked as non cache-coherent for the GPU and
+ * is expected to be physically contiguous. It may return
+ * ::CUDA_ERROR_NOT_PERMITTED if run as an unprivileged user,
+ * ::CUDA_ERROR_NOT_SUPPORTED on older Linux kernel versions.
+ * On all other platforms, it is not supported and ::CUDA_ERROR_NOT_SUPPORTED
+ * is returned.
+ * Flag for ::cuMemHostRegister()
+ */
+#define CU_MEMHOSTREGISTER_IOMEMORY     0x04
+
+/**
+* If set, the passed memory pointer is treated as pointing to memory that is
+* considered read-only by the device.  On platforms without
+* ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, this flag is
+* required in order to register memory mapped to the CPU as read-only.  Support
+* for the use of this flag can be queried from the device attribute
+* ::CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED.  Using this flag with
+* a current context associated with a device that does not have this attribute
+* set will cause ::cuMemHostRegister to error with ::CUDA_ERROR_NOT_SUPPORTED.
+*/
+#define CU_MEMHOSTREGISTER_READ_ONLY    0x08
+
+/**
+ * 2D memory copy parameters
+ */
+typedef struct CUDA_MEMCPY2D_st {
+    size_t srcXInBytes;         /**< Source X in bytes */
+    size_t srcY;                /**< Source Y */
+
+    CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */
+    const void *srcHost;        /**< Source host pointer */
+    CUdeviceptr srcDevice;      /**< Source device pointer */
+    CUarray srcArray;           /**< Source array reference */
+    size_t srcPitch;            /**< Source pitch (ignored when src is array) */
+
+    size_t dstXInBytes;         /**< Destination X in bytes */
+    size_t dstY;                /**< Destination Y */
+
+    CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */
+    void *dstHost;              /**< Destination host pointer */
+    CUdeviceptr dstDevice;      /**< Destination device pointer */
+    CUarray dstArray;           /**< Destination array reference */
+    size_t dstPitch;            /**< Destination pitch (ignored when dst is array) */
+
+    size_t WidthInBytes;        /**< Width of 2D memory copy in bytes */
+    size_t Height;              /**< Height of 2D memory copy */
+} CUDA_MEMCPY2D_v2;
+typedef CUDA_MEMCPY2D_v2 CUDA_MEMCPY2D;
+
+/**
+ * 3D memory copy parameters
+ */
+typedef struct CUDA_MEMCPY3D_st {
+    size_t srcXInBytes;         /**< Source X in bytes */
+    size_t srcY;                /**< Source Y */
+    size_t srcZ;                /**< Source Z */
+    size_t srcLOD;              /**< Source LOD */
+    CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */
+    const void *srcHost;        /**< Source host pointer */
+    CUdeviceptr srcDevice;      /**< Source device pointer */
+    CUarray srcArray;           /**< Source array reference */
+    void *reserved0;            /**< Must be NULL */
+    size_t srcPitch;            /**< Source pitch (ignored when src is array) */
+    size_t srcHeight;           /**< Source height (ignored when src is array; may be 0 if Depth==1) */
+
+    size_t dstXInBytes;         /**< Destination X in bytes */
+    size_t dstY;                /**< Destination Y */
+    size_t dstZ;                /**< Destination Z */
+    size_t dstLOD;              /**< Destination LOD */
+    CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */
+    void *dstHost;              /**< Destination host pointer */
+    CUdeviceptr dstDevice;      /**< Destination device pointer */
+    CUarray dstArray;           /**< Destination array reference */
+    void *reserved1;            /**< Must be NULL */
+    size_t dstPitch;            /**< Destination pitch (ignored when dst is array) */
+    size_t dstHeight;           /**< Destination height (ignored when dst is array; may be 0 if Depth==1) */
+
+    size_t WidthInBytes;        /**< Width of 3D memory copy in bytes */
+    size_t Height;              /**< Height of 3D memory copy */
+    size_t Depth;               /**< Depth of 3D memory copy */
+} CUDA_MEMCPY3D_v2;
+typedef CUDA_MEMCPY3D_v2 CUDA_MEMCPY3D;
+
+/**
+ * 3D memory cross-context copy parameters
+ */
+typedef struct CUDA_MEMCPY3D_PEER_st {
+    size_t srcXInBytes;         /**< Source X in bytes */
+    size_t srcY;                /**< Source Y */
+    size_t srcZ;                /**< Source Z */
+    size_t srcLOD;              /**< Source LOD */
+    CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */
+    const void *srcHost;        /**< Source host pointer */
+    CUdeviceptr srcDevice;      /**< Source device pointer */
+    CUarray srcArray;           /**< Source array reference */
+    CUcontext srcContext;       /**< Source context (ignored with srcMemoryType is ::CU_MEMORYTYPE_ARRAY) */
+    size_t srcPitch;            /**< Source pitch (ignored when src is array) */
+    size_t srcHeight;           /**< Source height (ignored when src is array; may be 0 if Depth==1) */
+
+    size_t dstXInBytes;         /**< Destination X in bytes */
+    size_t dstY;                /**< Destination Y */
+    size_t dstZ;                /**< Destination Z */
+    size_t dstLOD;              /**< Destination LOD */
+    CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */
+    void *dstHost;              /**< Destination host pointer */
+    CUdeviceptr dstDevice;      /**< Destination device pointer */
+    CUarray dstArray;           /**< Destination array reference */
+    CUcontext dstContext;       /**< Destination context (ignored with dstMemoryType is ::CU_MEMORYTYPE_ARRAY) */
+    size_t dstPitch;            /**< Destination pitch (ignored when dst is array) */
+    size_t dstHeight;           /**< Destination height (ignored when dst is array; may be 0 if Depth==1) */
+
+    size_t WidthInBytes;        /**< Width of 3D memory copy in bytes */
+    size_t Height;              /**< Height of 3D memory copy */
+    size_t Depth;               /**< Depth of 3D memory copy */
+} CUDA_MEMCPY3D_PEER_v1;
+typedef CUDA_MEMCPY3D_PEER_v1 CUDA_MEMCPY3D_PEER;
+
+/**
+ * Memcpy node parameters
+ */
+typedef struct CUDA_MEMCPY_NODE_PARAMS_st {
+    int flags;                 /**< Must be zero */
+    int reserved;              /**< Must be zero */
+    CUcontext copyCtx;         /**< Context on which to run the node */
+    CUDA_MEMCPY3D copyParams;  /**< Parameters for the memory copy */
+} CUDA_MEMCPY_NODE_PARAMS;
+
+/**
+ * Array descriptor
+ */
+typedef struct CUDA_ARRAY_DESCRIPTOR_st
+{
+    size_t Width;             /**< Width of array */
+    size_t Height;            /**< Height of array */
+
+    CUarray_format Format;    /**< Array format */
+    unsigned int NumChannels; /**< Channels per array element */
+} CUDA_ARRAY_DESCRIPTOR_v2;
+typedef CUDA_ARRAY_DESCRIPTOR_v2 CUDA_ARRAY_DESCRIPTOR;
+
+/**
+ * 3D array descriptor
+ */
+typedef struct CUDA_ARRAY3D_DESCRIPTOR_st
+{
+    size_t Width;             /**< Width of 3D array */
+    size_t Height;            /**< Height of 3D array */
+    size_t Depth;             /**< Depth of 3D array */
+
+    CUarray_format Format;    /**< Array format */
+    unsigned int NumChannels; /**< Channels per array element */
+    unsigned int Flags;       /**< Flags */
+} CUDA_ARRAY3D_DESCRIPTOR_v2;
+typedef CUDA_ARRAY3D_DESCRIPTOR_v2 CUDA_ARRAY3D_DESCRIPTOR;
+
+/**
+ * Indicates that the layered sparse CUDA array or CUDA mipmapped array has a single mip tail region for all layers
+ */
+#define CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL 0x1
+
+/**
+ * CUDA array sparse properties
+ */
+typedef struct CUDA_ARRAY_SPARSE_PROPERTIES_st {
+    struct {
+        unsigned int width;     /**< Width of sparse tile in elements */
+        unsigned int height;    /**< Height of sparse tile in elements */
+        unsigned int depth;     /**< Depth of sparse tile in elements */
+    } tileExtent;
+
+    /**
+     * First mip level at which the mip tail begins.
+     */
+    unsigned int miptailFirstLevel;
+    /**
+     * Total size of the mip tail.
+     */
+    unsigned long long miptailSize;
+    /**
+     * Flags will either be zero or ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL
+     */
+    unsigned int flags;
+    unsigned int reserved[4];
+} CUDA_ARRAY_SPARSE_PROPERTIES_v1;
+typedef CUDA_ARRAY_SPARSE_PROPERTIES_v1 CUDA_ARRAY_SPARSE_PROPERTIES;
+
+/**
+ * CUDA array memory requirements
+ */
+typedef struct CUDA_ARRAY_MEMORY_REQUIREMENTS_st {
+    size_t size;                /**< Total required memory size */
+    size_t alignment;           /**< alignment requirement */
+    unsigned int reserved[4];
+} CUDA_ARRAY_MEMORY_REQUIREMENTS_v1;
+typedef CUDA_ARRAY_MEMORY_REQUIREMENTS_v1 CUDA_ARRAY_MEMORY_REQUIREMENTS;
+
+/**
+ * CUDA Resource descriptor
+ */
+typedef struct CUDA_RESOURCE_DESC_st
+{
+    CUresourcetype resType;                   /**< Resource type */
+
+    union {
+        struct {
+            CUarray hArray;                   /**< CUDA array */
+        } array;
+        struct {
+            CUmipmappedArray hMipmappedArray; /**< CUDA mipmapped array */
+        } mipmap;
+        struct {
+            CUdeviceptr devPtr;               /**< Device pointer */
+            CUarray_format format;            /**< Array format */
+            unsigned int numChannels;         /**< Channels per array element */
+            size_t sizeInBytes;               /**< Size in bytes */
+        } linear;
+        struct {
+            CUdeviceptr devPtr;               /**< Device pointer */
+            CUarray_format format;            /**< Array format */
+            unsigned int numChannels;         /**< Channels per array element */
+            size_t width;                     /**< Width of the array in elements */
+            size_t height;                    /**< Height of the array in elements */
+            size_t pitchInBytes;              /**< Pitch between two rows in bytes */
+        } pitch2D;
+        struct {
+            int reserved[32];
+        } reserved;
+    } res;
+
+    unsigned int flags;                       /**< Flags (must be zero) */
+} CUDA_RESOURCE_DESC_v1;
+typedef CUDA_RESOURCE_DESC_v1 CUDA_RESOURCE_DESC;
+
+/**
+ * Texture descriptor
+ */
+typedef struct CUDA_TEXTURE_DESC_st {
+    CUaddress_mode addressMode[3];  /**< Address modes */
+    CUfilter_mode filterMode;       /**< Filter mode */
+    unsigned int flags;             /**< Flags */
+    unsigned int maxAnisotropy;     /**< Maximum anisotropy ratio */
+    CUfilter_mode mipmapFilterMode; /**< Mipmap filter mode */
+    float mipmapLevelBias;          /**< Mipmap level bias */
+    float minMipmapLevelClamp;      /**< Mipmap minimum level clamp */
+    float maxMipmapLevelClamp;      /**< Mipmap maximum level clamp */
+    float borderColor[4];           /**< Border Color */
+    int reserved[12];
+} CUDA_TEXTURE_DESC_v1;
+typedef CUDA_TEXTURE_DESC_v1 CUDA_TEXTURE_DESC;
+
+/**
+ * Resource view format
+ */
+typedef enum CUresourceViewFormat_enum
+{
+    CU_RES_VIEW_FORMAT_NONE          = 0x00, /**< No resource view format (use underlying resource format) */
+    CU_RES_VIEW_FORMAT_UINT_1X8      = 0x01, /**< 1 channel unsigned 8-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_2X8      = 0x02, /**< 2 channel unsigned 8-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_4X8      = 0x03, /**< 4 channel unsigned 8-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_1X8      = 0x04, /**< 1 channel signed 8-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_2X8      = 0x05, /**< 2 channel signed 8-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_4X8      = 0x06, /**< 4 channel signed 8-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_1X16     = 0x07, /**< 1 channel unsigned 16-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_2X16     = 0x08, /**< 2 channel unsigned 16-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_4X16     = 0x09, /**< 4 channel unsigned 16-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_1X16     = 0x0a, /**< 1 channel signed 16-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_2X16     = 0x0b, /**< 2 channel signed 16-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_4X16     = 0x0c, /**< 4 channel signed 16-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_1X32     = 0x0d, /**< 1 channel unsigned 32-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_2X32     = 0x0e, /**< 2 channel unsigned 32-bit integers */
+    CU_RES_VIEW_FORMAT_UINT_4X32     = 0x0f, /**< 4 channel unsigned 32-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_1X32     = 0x10, /**< 1 channel signed 32-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_2X32     = 0x11, /**< 2 channel signed 32-bit integers */
+    CU_RES_VIEW_FORMAT_SINT_4X32     = 0x12, /**< 4 channel signed 32-bit integers */
+    CU_RES_VIEW_FORMAT_FLOAT_1X16    = 0x13, /**< 1 channel 16-bit floating point */
+    CU_RES_VIEW_FORMAT_FLOAT_2X16    = 0x14, /**< 2 channel 16-bit floating point */
+    CU_RES_VIEW_FORMAT_FLOAT_4X16    = 0x15, /**< 4 channel 16-bit floating point */
+    CU_RES_VIEW_FORMAT_FLOAT_1X32    = 0x16, /**< 1 channel 32-bit floating point */
+    CU_RES_VIEW_FORMAT_FLOAT_2X32    = 0x17, /**< 2 channel 32-bit floating point */
+    CU_RES_VIEW_FORMAT_FLOAT_4X32    = 0x18, /**< 4 channel 32-bit floating point */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC1  = 0x19, /**< Block compressed 1 */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC2  = 0x1a, /**< Block compressed 2 */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC3  = 0x1b, /**< Block compressed 3 */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC4  = 0x1c, /**< Block compressed 4 unsigned */
+    CU_RES_VIEW_FORMAT_SIGNED_BC4    = 0x1d, /**< Block compressed 4 signed */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC5  = 0x1e, /**< Block compressed 5 unsigned */
+    CU_RES_VIEW_FORMAT_SIGNED_BC5    = 0x1f, /**< Block compressed 5 signed */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC6H = 0x20, /**< Block compressed 6 unsigned half-float */
+    CU_RES_VIEW_FORMAT_SIGNED_BC6H   = 0x21, /**< Block compressed 6 signed half-float */
+    CU_RES_VIEW_FORMAT_UNSIGNED_BC7  = 0x22  /**< Block compressed 7 */
+} CUresourceViewFormat;
+
+/**
+ * Resource view descriptor
+ */
+typedef struct CUDA_RESOURCE_VIEW_DESC_st
+{
+    CUresourceViewFormat format;   /**< Resource view format */
+    size_t width;                  /**< Width of the resource view */
+    size_t height;                 /**< Height of the resource view */
+    size_t depth;                  /**< Depth of the resource view */
+    unsigned int firstMipmapLevel; /**< First defined mipmap level */
+    unsigned int lastMipmapLevel;  /**< Last defined mipmap level */
+    unsigned int firstLayer;       /**< First layer index */
+    unsigned int lastLayer;        /**< Last layer index */
+    unsigned int reserved[16];
+} CUDA_RESOURCE_VIEW_DESC_v1;
+typedef CUDA_RESOURCE_VIEW_DESC_v1 CUDA_RESOURCE_VIEW_DESC;
+
+/**
+ * Size of tensor map descriptor
+ */
+#define CU_TENSOR_MAP_NUM_QWORDS 16
+
+/**
+ * Tensor map descriptor. Requires compiler support for aligning to 64 bytes.
+ */
+typedef struct CUtensorMap_st {
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+    alignas(64)
+#elif __STDC_VERSION__ >= 201112L
+    _Alignas(64)
+#endif
+    cuuint64_t opaque[CU_TENSOR_MAP_NUM_QWORDS];
+} CUtensorMap;
+
+/**
+ * Tensor map data type
+ */
+typedef enum CUtensorMapDataType_enum {
+    CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0,
+    CU_TENSOR_MAP_DATA_TYPE_UINT16,
+    CU_TENSOR_MAP_DATA_TYPE_UINT32,
+    CU_TENSOR_MAP_DATA_TYPE_INT32,
+    CU_TENSOR_MAP_DATA_TYPE_UINT64,
+    CU_TENSOR_MAP_DATA_TYPE_INT64,
+    CU_TENSOR_MAP_DATA_TYPE_FLOAT16,
+    CU_TENSOR_MAP_DATA_TYPE_FLOAT32,
+    CU_TENSOR_MAP_DATA_TYPE_FLOAT64,
+    CU_TENSOR_MAP_DATA_TYPE_BFLOAT16,
+    CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ,
+    CU_TENSOR_MAP_DATA_TYPE_TFLOAT32,
+    CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ,
+    CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B,
+    CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B,
+    CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B
+} CUtensorMapDataType;
+
+/**
+ * Tensor map interleave layout type
+ */
+typedef enum CUtensorMapInterleave_enum {
+    CU_TENSOR_MAP_INTERLEAVE_NONE = 0,
+    CU_TENSOR_MAP_INTERLEAVE_16B,
+    CU_TENSOR_MAP_INTERLEAVE_32B
+} CUtensorMapInterleave;
+
+/**
+ * Tensor map swizzling mode of shared memory banks
+ */
+typedef enum CUtensorMapSwizzle_enum {
+    CU_TENSOR_MAP_SWIZZLE_NONE = 0,
+    CU_TENSOR_MAP_SWIZZLE_32B,
+    CU_TENSOR_MAP_SWIZZLE_64B,
+    CU_TENSOR_MAP_SWIZZLE_128B,
+    CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B,
+    CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B_FLIP_8B,
+    CU_TENSOR_MAP_SWIZZLE_128B_ATOM_64B
+} CUtensorMapSwizzle;
+
+/**
+ * Tensor map L2 promotion type
+ */
+typedef enum CUtensorMapL2promotion_enum {
+    CU_TENSOR_MAP_L2_PROMOTION_NONE = 0,
+    CU_TENSOR_MAP_L2_PROMOTION_L2_64B,
+    CU_TENSOR_MAP_L2_PROMOTION_L2_128B,
+    CU_TENSOR_MAP_L2_PROMOTION_L2_256B
+} CUtensorMapL2promotion;
+
+/**
+ * Tensor map out-of-bounds fill type
+ */
+typedef enum CUtensorMapFloatOOBfill_enum {
+    CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0,
+    CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA
+} CUtensorMapFloatOOBfill;
+
+/**
+ * Tensor map Im2Col wide mode
+ */
+typedef enum CUtensorMapIm2ColWideMode_enum {
+    CU_TENSOR_MAP_IM2COL_WIDE_MODE_W = 0,
+    CU_TENSOR_MAP_IM2COL_WIDE_MODE_W128
+} CUtensorMapIm2ColWideMode;
+
+/**
+ * GPU Direct v3 tokens
+ */
+typedef struct CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st {
+    unsigned long long p2pToken;
+    unsigned int vaSpaceToken;
+} CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1;
+typedef CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1 CUDA_POINTER_ATTRIBUTE_P2P_TOKENS;
+
+/**
+* Access flags that specify the level of access the current context's device has
+* on the memory referenced.
+*/
+typedef enum CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS_enum {
+    CU_POINTER_ATTRIBUTE_ACCESS_FLAG_NONE      = 0x0,   /**< No access, meaning the device cannot access this memory at all, thus must be staged through accessible memory in order to complete certain operations */
+    CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READ      = 0x1,   /**< Read-only access, meaning writes to this memory are considered invalid accesses and thus return error in that case. */
+    CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READWRITE = 0x3    /**< Read-write access, the device has full read-write access to the memory */
+} CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS;
+
+/**
+ * Kernel launch parameters
+ */
+typedef struct CUDA_LAUNCH_PARAMS_st {
+    CUfunction function;         /**< Kernel to launch */
+    unsigned int gridDimX;       /**< Width of grid in blocks */
+    unsigned int gridDimY;       /**< Height of grid in blocks */
+    unsigned int gridDimZ;       /**< Depth of grid in blocks */
+    unsigned int blockDimX;      /**< X dimension of each thread block */
+    unsigned int blockDimY;      /**< Y dimension of each thread block */
+    unsigned int blockDimZ;      /**< Z dimension of each thread block */
+    unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */
+    CUstream hStream;            /**< Stream identifier */
+    void **kernelParams;         /**< Array of pointers to kernel parameters */
+} CUDA_LAUNCH_PARAMS_v1;
+typedef CUDA_LAUNCH_PARAMS_v1 CUDA_LAUNCH_PARAMS;
+
+/**
+ * External memory handle types
+ */
+typedef enum CUexternalMemoryHandleType_enum {
+    /**
+     * Handle is an opaque file descriptor
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD          = 1,
+    /**
+     * Handle is an opaque shared NT handle
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32       = 2,
+    /**
+     * Handle is an opaque, globally shared handle
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT   = 3,
+    /**
+     * Handle is a D3D12 heap object
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP         = 4,
+    /**
+     * Handle is a D3D12 committed resource
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE     = 5,
+    /**
+     * Handle is a shared NT handle to a D3D11 resource
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE     = 6,
+    /**
+     * Handle is a globally shared handle to a D3D11 resource
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT = 7,
+    /**
+     * Handle is an NvSciBuf object
+     */
+    CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF = 8,
+} CUexternalMemoryHandleType;
+
+/**
+ * Indicates that the external memory object is a dedicated resource
+ */
+#define CUDA_EXTERNAL_MEMORY_DEDICATED   0x1
+
+/** When the \p flags parameter of ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+ * contains this flag, it indicates that signaling an external semaphore object
+ * should skip performing appropriate memory synchronization operations over all
+ * the external memory objects that are imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF,
+ * which otherwise are performed by default to ensure data coherency with other
+ * importers of the same NvSciBuf memory objects.
+ */
+#define CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC 0x01
+
+/** When the \p flags parameter of ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+ * contains this flag, it indicates that waiting on an external semaphore object
+ * should skip performing appropriate memory synchronization operations over all
+ * the external memory objects that are imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF,
+ * which otherwise are performed by default to ensure data coherency with other
+ * importers of the same NvSciBuf memory objects.
+ */
+#define CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC 0x02
+
+/**
+ * When \p flags of ::cuDeviceGetNvSciSyncAttributes is set to this,
+ * it indicates that application needs signaler specific NvSciSyncAttr
+ * to be filled by ::cuDeviceGetNvSciSyncAttributes.
+ */
+#define CUDA_NVSCISYNC_ATTR_SIGNAL 0x1
+
+/**
+ * When \p flags of ::cuDeviceGetNvSciSyncAttributes is set to this,
+ * it indicates that application needs waiter specific NvSciSyncAttr
+ * to be filled by ::cuDeviceGetNvSciSyncAttributes.
+ */
+#define CUDA_NVSCISYNC_ATTR_WAIT 0x2
+/**
+ * External memory handle descriptor
+ */
+typedef struct CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st {
+    /**
+     * Type of the handle
+     */
+    CUexternalMemoryHandleType type;
+    union {
+        /**
+         * File descriptor referencing the memory object. Valid
+         * when type is
+         * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD
+         */
+        int fd;
+        /**
+         * Win32 handle referencing the semaphore object. Valid when
+         * type is one of the following:
+         * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32
+         * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT
+         * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP
+         * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE
+         * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE
+         * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT
+         * Exactly one of 'handle' and 'name' must be non-NULL. If
+         * type is one of the following:
+         * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT
+         * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT
+         * then 'name' must be NULL.
+         */
+        struct {
+            /**
+             * Valid NT handle. Must be NULL if 'name' is non-NULL
+             */
+            void *handle;
+            /**
+             * Name of a valid memory object.
+             * Must be NULL if 'handle' is non-NULL.
+             */
+            const void *name;
+        } win32;
+        /**
+         * A handle representing an NvSciBuf Object. Valid when type
+         * is ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF
+         */
+        const void *nvSciBufObject;
+    } handle;
+    /**
+     * Size of the memory allocation
+     */
+    unsigned long long size;
+    /**
+     * Flags must either be zero or ::CUDA_EXTERNAL_MEMORY_DEDICATED
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+} CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1;
+typedef CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1 CUDA_EXTERNAL_MEMORY_HANDLE_DESC;
+
+/**
+ * External memory buffer descriptor
+ */
+typedef struct CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st {
+    /**
+     * Offset into the memory object where the buffer's base is
+     */
+    unsigned long long offset;
+    /**
+     * Size of the buffer
+     */
+    unsigned long long size;
+    /**
+     * Flags reserved for future use. Must be zero.
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+} CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1;
+typedef CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1 CUDA_EXTERNAL_MEMORY_BUFFER_DESC;
+
+/**
+ * External memory mipmap descriptor
+ */
+typedef struct CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st {
+    /**
+     * Offset into the memory object where the base level of the
+     * mipmap chain is.
+     */
+    unsigned long long offset;
+    /**
+     * Format, dimension and type of base level of the mipmap chain
+     */
+    CUDA_ARRAY3D_DESCRIPTOR arrayDesc;
+    /**
+     * Total number of levels in the mipmap chain
+     */
+    unsigned int numLevels;
+    unsigned int reserved[16];
+} CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1;
+typedef CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1 CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC;
+
+/**
+ * External semaphore handle types
+ */
+typedef enum CUexternalSemaphoreHandleType_enum {
+    /**
+     * Handle is an opaque file descriptor
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD             = 1,
+    /**
+     * Handle is an opaque shared NT handle
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32          = 2,
+    /**
+     * Handle is an opaque, globally shared handle
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT      = 3,
+    /**
+     * Handle is a shared NT handle referencing a D3D12 fence object
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE           = 4,
+    /**
+     * Handle is a shared NT handle referencing a D3D11 fence object
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE           = 5,
+    /**
+     * Opaque handle to NvSciSync Object
+	 */
+	CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC             = 6,
+    /**
+     * Handle is a shared NT handle referencing a D3D11 keyed mutex object
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX     = 7,
+    /**
+     * Handle is a globally shared handle referencing a D3D11 keyed mutex object
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT = 8,
+    /**
+     * Handle is an opaque file descriptor referencing a timeline semaphore
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD = 9,
+    /**
+     * Handle is an opaque shared NT handle referencing a timeline semaphore
+     */
+    CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 = 10
+} CUexternalSemaphoreHandleType;
+
+/**
+ * External semaphore handle descriptor
+ */
+typedef struct CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st {
+    /**
+     * Type of the handle
+     */
+    CUexternalSemaphoreHandleType type;
+    union {
+        /**
+         * File descriptor referencing the semaphore object. Valid
+         * when type is one of the following:
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD
+         */
+        int fd;
+        /**
+         * Win32 handle referencing the semaphore object. Valid when
+         * type is one of the following:
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32
+         * Exactly one of 'handle' and 'name' must be non-NULL. If
+         * type is one of the following:
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT
+         * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT
+         * then 'name' must be NULL.
+         */
+        struct {
+            /**
+             * Valid NT handle. Must be NULL if 'name' is non-NULL
+             */
+            void *handle;
+            /**
+             * Name of a valid synchronization primitive.
+             * Must be NULL if 'handle' is non-NULL.
+             */
+            const void *name;
+        } win32;
+        /**
+         * Valid NvSciSyncObj. Must be non NULL
+         */
+        const void* nvSciSyncObj;
+    } handle;
+    /**
+     * Flags reserved for the future. Must be zero.
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+} CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1;
+typedef CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1 CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC;
+
+/**
+ * External semaphore signal parameters
+ */
+typedef struct CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st {
+    struct {
+        /**
+         * Parameters for fence objects
+         */
+        struct {
+            /**
+             * Value of fence to be signaled
+             */
+            unsigned long long value;
+        } fence;
+        union {
+            /**
+             * Pointer to NvSciSyncFence. Valid if ::CUexternalSemaphoreHandleType
+             * is of type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC.
+             */
+            void *fence;
+            unsigned long long reserved;
+        } nvSciSync;
+        /**
+         * Parameters for keyed mutex objects
+         */
+        struct {
+            /**
+             * Value of key to release the mutex with
+             */
+            unsigned long long key;
+        } keyedMutex;
+        unsigned int reserved[12];
+    } params;
+    /**
+     * Only when ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to
+     * signal a ::CUexternalSemaphore of type
+     * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+     * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which indicates
+     * that while signaling the ::CUexternalSemaphore, no memory synchronization
+     * operations should be performed for any external memory object imported
+     * as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+     * For all other types of ::CUexternalSemaphore, flags must be zero.
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+} CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1;
+typedef CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1 CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS;
+
+/**
+ * External semaphore wait parameters
+ */
+typedef struct CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st {
+    struct {
+        /**
+         * Parameters for fence objects
+         */
+        struct {
+            /**
+             * Value of fence to be waited on
+             */
+            unsigned long long value;
+        } fence;
+        /**
+         * Pointer to NvSciSyncFence. Valid if CUexternalSemaphoreHandleType
+         * is of type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC.
+         */
+        union {
+            void *fence;
+            unsigned long long reserved;
+        } nvSciSync;
+        /**
+         * Parameters for keyed mutex objects
+         */
+        struct {
+            /**
+             * Value of key to acquire the mutex with
+             */
+            unsigned long long key;
+            /**
+             * Timeout in milliseconds to wait to acquire the mutex
+             */
+            unsigned int timeoutMs;
+        } keyedMutex;
+        unsigned int reserved[10];
+    } params;
+    /**
+     * Only when ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on
+     * a ::CUexternalSemaphore of type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC,
+     * the valid flag is ::CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC
+     * which indicates that while waiting for the ::CUexternalSemaphore, no memory
+     * synchronization operations should be performed for any external memory
+     * object imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+     * For all other types of ::CUexternalSemaphore, flags must be zero.
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+} CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1;
+typedef CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1 CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS;
+
+/**
+ * Semaphore signal node parameters
+ */
+typedef struct CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st {
+    CUexternalSemaphore* extSemArray;                         /**< Array of external semaphore handles. */
+    const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray; /**< Array of external semaphore signal parameters. */
+    unsigned int numExtSems;                                  /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+} CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1;
+typedef CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 CUDA_EXT_SEM_SIGNAL_NODE_PARAMS;
+
+/**
+ * Semaphore signal node parameters
+ */
+typedef struct CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st {
+    CUexternalSemaphore* extSemArray;                         /**< Array of external semaphore handles. */
+    const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray; /**< Array of external semaphore signal parameters. */
+    unsigned int numExtSems;                                  /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+} CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2;
+
+/**
+ * Semaphore wait node parameters
+ */
+typedef struct CUDA_EXT_SEM_WAIT_NODE_PARAMS_st {
+    CUexternalSemaphore* extSemArray;                       /**< Array of external semaphore handles. */
+    const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray; /**< Array of external semaphore wait parameters. */
+    unsigned int numExtSems;                                /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+} CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1;
+typedef CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 CUDA_EXT_SEM_WAIT_NODE_PARAMS;
+
+/**
+ * Semaphore wait node parameters
+ */
+typedef struct CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st {
+    CUexternalSemaphore* extSemArray;                       /**< Array of external semaphore handles. */
+    const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray; /**< Array of external semaphore wait parameters. */
+    unsigned int numExtSems;                                /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+} CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2;
+
+typedef unsigned long long CUmemGenericAllocationHandle_v1;
+typedef CUmemGenericAllocationHandle_v1 CUmemGenericAllocationHandle;
+
+/**
+ * Flags for specifying particular handle types
+ */
+typedef enum CUmemAllocationHandleType_enum {
+    CU_MEM_HANDLE_TYPE_NONE                  = 0x0,  /**< Does not allow any export mechanism. > */
+    CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR = 0x1,  /**< Allows a file descriptor to be used for exporting. Permitted only on POSIX systems. (int) */
+    CU_MEM_HANDLE_TYPE_WIN32                 = 0x2,  /**< Allows a Win32 NT handle to be used for exporting. (HANDLE) */
+    CU_MEM_HANDLE_TYPE_WIN32_KMT             = 0x4,  /**< Allows a Win32 KMT handle to be used for exporting. (D3DKMT_HANDLE) */
+    CU_MEM_HANDLE_TYPE_FABRIC                = 0x8,  /**< Allows a fabric handle to be used for exporting. (CUmemFabricHandle)*/
+    CU_MEM_HANDLE_TYPE_MAX                   = 0x7FFFFFFF
+} CUmemAllocationHandleType;
+
+/**
+ * Specifies the memory protection flags for mapping.
+ */
+typedef enum CUmemAccess_flags_enum {
+    CU_MEM_ACCESS_FLAGS_PROT_NONE        = 0x0,  /**< Default, make the address range not accessible */
+    CU_MEM_ACCESS_FLAGS_PROT_READ        = 0x1,  /**< Make the address range read accessible */
+    CU_MEM_ACCESS_FLAGS_PROT_READWRITE   = 0x3,  /**< Make the address range read-write accessible */
+    CU_MEM_ACCESS_FLAGS_PROT_MAX         = 0x7FFFFFFF
+} CUmemAccess_flags;
+
+/**
+ * Specifies the type of location
+ */
+typedef enum CUmemLocationType_enum {
+    CU_MEM_LOCATION_TYPE_INVALID    = 0x0,
+    CU_MEM_LOCATION_TYPE_DEVICE     = 0x1,  /**< Location is a device location, thus id is a device ordinal */
+    CU_MEM_LOCATION_TYPE_HOST       = 0x2,   /**< Location is host, id is ignored */
+    CU_MEM_LOCATION_TYPE_HOST_NUMA  = 0x3,  /**< Location is a host NUMA node, thus id is a host NUMA node id */
+    CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT = 0x4,  /**< Location is a host NUMA node of the current thread, id is ignored */
+    CU_MEM_LOCATION_TYPE_MAX        = 0x7FFFFFFF
+} CUmemLocationType;
+
+/**
+* Defines the allocation types available
+*/
+typedef enum CUmemAllocationType_enum {
+    CU_MEM_ALLOCATION_TYPE_INVALID = 0x0,
+
+    /** This allocation type is 'pinned', i.e. cannot migrate from its current
+      * location while the application is actively using it
+      */
+    CU_MEM_ALLOCATION_TYPE_PINNED  = 0x1,
+    CU_MEM_ALLOCATION_TYPE_MAX     = 0x7FFFFFFF
+} CUmemAllocationType;
+
+/**
+* Flag for requesting different optimal and required granularities for an allocation.
+*/
+typedef enum CUmemAllocationGranularity_flags_enum {
+    CU_MEM_ALLOC_GRANULARITY_MINIMUM     = 0x0,     /**< Minimum required granularity for allocation */
+    CU_MEM_ALLOC_GRANULARITY_RECOMMENDED = 0x1      /**< Recommended granularity for allocation for best performance */
+} CUmemAllocationGranularity_flags;
+
+/**
+* Specifies the handle type for address range
+*/
+typedef enum CUmemRangeHandleType_enum
+{
+    CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD = 0x1,
+    CU_MEM_RANGE_HANDLE_TYPE_MAX        = 0x7FFFFFFF
+} CUmemRangeHandleType;
+
+/**
+* Flag for requesting handle type for address range.
+*/
+typedef enum CUmemRangeFlags_enum {
+    CU_MEM_RANGE_FLAG_DMA_BUF_MAPPING_TYPE_PCIE     = 0x1   /**< Indicates that DMA_BUF handle should be mapped via PCIe BAR1 */
+} CUmemRangeFlags;
+
+/**
+ * Sparse subresource types
+ */
+typedef enum CUarraySparseSubresourceType_enum {
+    CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL = 0,
+    CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL = 1
+} CUarraySparseSubresourceType;
+
+/**
+ * Memory operation types
+ */
+typedef enum CUmemOperationType_enum {
+    CU_MEM_OPERATION_TYPE_MAP = 1,
+    CU_MEM_OPERATION_TYPE_UNMAP = 2
+} CUmemOperationType;
+
+/**
+ * Memory handle types
+ */
+typedef enum CUmemHandleType_enum {
+    CU_MEM_HANDLE_TYPE_GENERIC = 0
+} CUmemHandleType;
+
+/**
+ * Specifies the CUDA array or CUDA mipmapped array memory mapping information
+ */
+typedef struct CUarrayMapInfo_st {    
+    CUresourcetype resourceType;                    /**< Resource type */
+
+    union {
+        CUmipmappedArray mipmap;
+        CUarray array;
+    } resource;
+
+    CUarraySparseSubresourceType subresourceType;   /**< Sparse subresource type */
+
+    union {
+        struct {
+            unsigned int level;                     /**< For CUDA mipmapped arrays must a valid mipmap level. For CUDA arrays must be zero */            
+            unsigned int layer;                     /**< For CUDA layered arrays must be a valid layer index. Otherwise, must be zero */
+            unsigned int offsetX;                   /**< Starting X offset in elements */
+            unsigned int offsetY;                   /**< Starting Y offset in elements */
+            unsigned int offsetZ;                   /**< Starting Z offset in elements */            
+            unsigned int extentWidth;               /**< Width in elements */
+            unsigned int extentHeight;              /**< Height in elements */
+            unsigned int extentDepth;               /**< Depth in elements */
+        } sparseLevel;
+        struct {
+            unsigned int layer;                     /**< For CUDA layered arrays must be a valid layer index. Otherwise, must be zero */
+            unsigned long long offset;              /**< Offset within mip tail */
+            unsigned long long size;                /**< Extent in bytes */
+        } miptail;
+    } subresource;
+    
+    CUmemOperationType memOperationType;            /**< Memory operation type */
+    CUmemHandleType memHandleType;                  /**< Memory handle type */
+
+    union {
+        CUmemGenericAllocationHandle memHandle;
+    } memHandle;
+    
+    unsigned long long offset;                      /**< Offset within the memory */
+    unsigned int deviceBitMask;                     /**< Device ordinal bit mask */
+    unsigned int flags;                             /**< flags for future use, must be zero now. */
+    unsigned int reserved[2];                       /**< Reserved for future use, must be zero now. */
+} CUarrayMapInfo_v1;
+typedef CUarrayMapInfo_v1 CUarrayMapInfo;
+
+/**
+ * Specifies a memory location.
+ */
+typedef struct CUmemLocation_st {
+    CUmemLocationType type; /**< Specifies the location type, which modifies the meaning of id. */
+    int id;                 /**< identifier for a given this location's ::CUmemLocationType. */
+} CUmemLocation_v1;
+typedef CUmemLocation_v1 CUmemLocation;
+
+/**
+ * Specifies compression attribute for an allocation.
+ */
+typedef enum CUmemAllocationCompType_enum {
+    CU_MEM_ALLOCATION_COMP_NONE = 0x0, /**< Allocating non-compressible memory */
+    CU_MEM_ALLOCATION_COMP_GENERIC = 0x1 /**< Allocating  compressible memory */
+} CUmemAllocationCompType;
+
+/**
+ * This flag if set indicates that the memory will be used as a tile pool.
+ */
+#define CU_MEM_CREATE_USAGE_TILE_POOL    0x1
+/**
+ * This flag, if set, indicates that the memory will be used as a buffer for
+ * hardware accelerated decompression.
+ */
+#define CU_MEM_CREATE_USAGE_HW_DECOMPRESS 0x2
+
+/**
+* Specifies the allocation properties for a allocation.
+*/
+typedef struct CUmemAllocationProp_st {
+    /** Allocation type */
+    CUmemAllocationType type;
+    /** requested ::CUmemAllocationHandleType */
+    CUmemAllocationHandleType requestedHandleTypes;
+    /** Location of allocation */
+    CUmemLocation location;
+    /**
+     * Windows-specific POBJECT_ATTRIBUTES required when
+     * ::CU_MEM_HANDLE_TYPE_WIN32 is specified.  This object attributes structure
+     * includes security attributes that define
+     * the scope of which exported allocations may be transferred to other
+     * processes.  In all other cases, this field is required to be zero.
+     */
+    void *win32HandleMetaData;
+    struct {
+         /**
+         * Allocation hint for requesting compressible memory.
+         * On devices that support Compute Data Compression, compressible
+         * memory can be used to accelerate accesses to data with unstructured
+         * sparsity and other compressible data patterns. Applications are 
+         * expected to query allocation property of the handle obtained with 
+         * ::cuMemCreate using ::cuMemGetAllocationPropertiesFromHandle to 
+         * validate if the obtained allocation is compressible or not. Note that 
+         * compressed memory may not be mappable on all devices.
+         */
+         unsigned char compressionType;
+         unsigned char gpuDirectRDMACapable;
+         /** Bitmask indicating intended usage for this allocation */
+         unsigned short usage;
+         unsigned char reserved[4];
+    } allocFlags;
+} CUmemAllocationProp_v1;
+typedef CUmemAllocationProp_v1 CUmemAllocationProp;
+
+/**
+* Flags for querying different granularities for a multicast object
+*/
+typedef enum CUmulticastGranularity_flags_enum {
+    CU_MULTICAST_GRANULARITY_MINIMUM     = 0x0,     /**< Minimum required granularity */
+    CU_MULTICAST_GRANULARITY_RECOMMENDED = 0x1      /**< Recommended granularity for best performance */
+} CUmulticastGranularity_flags;
+
+/**
+* Specifies the properties for a multicast object.
+*/
+typedef struct CUmulticastObjectProp_st {
+    /**
+     * The number of devices in the multicast team that will bind memory to this
+     * object
+     */
+    unsigned int numDevices;
+    /** 
+     * The maximum amount of memory that can be bound to this multicast object
+     * per device
+     */
+    size_t size;
+    /**
+     * Bitmask of exportable handle types (see ::CUmemAllocationHandleType) for
+     * this object
+     */
+    unsigned long long handleTypes;
+    /** 
+     * Flags for future use, must be zero now
+     */
+    unsigned long long flags;
+} CUmulticastObjectProp_v1;
+typedef CUmulticastObjectProp_v1 CUmulticastObjectProp;
+
+/**
+ * Memory access descriptor
+ */
+typedef struct CUmemAccessDesc_st {
+    CUmemLocation location;        /**< Location on which the request is to change it's accessibility */
+    CUmemAccess_flags flags;       /**< ::CUmemProt accessibility flags to set on the request */
+} CUmemAccessDesc_v1;
+typedef CUmemAccessDesc_v1 CUmemAccessDesc;
+
+/**
+ * CUDA Graph Update error types
+ */
+typedef enum CUgraphExecUpdateResult_enum {
+    CU_GRAPH_EXEC_UPDATE_SUCCESS                     = 0x0, /**< The update succeeded */
+    CU_GRAPH_EXEC_UPDATE_ERROR                       = 0x1, /**< The update failed for an unexpected reason which is described in the return value of the function */
+    CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED      = 0x2, /**< The update failed because the topology changed */
+    CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED     = 0x3, /**< The update failed because a node type changed */
+    CU_GRAPH_EXEC_UPDATE_ERROR_FUNCTION_CHANGED      = 0x4, /**< The update failed because the function of a kernel node changed (CUDA driver < 11.2) */
+    CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED    = 0x5, /**< The update failed because the parameters changed in a way that is not supported */
+    CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED         = 0x6, /**< The update failed because something about the node is not supported */
+    CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE = 0x7, /**< The update failed because the function of a kernel node changed in an unsupported way */
+    CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED    = 0x8  /**< The update failed because the node attributes changed in a way that is not supported */
+} CUgraphExecUpdateResult;
+
+/**
+ * Result information returned by cuGraphExecUpdate
+ */
+typedef struct CUgraphExecUpdateResultInfo_st {
+    /**
+     * Gives more specific detail when a cuda graph update fails.
+     */
+    CUgraphExecUpdateResult result;
+
+    /**
+     * The "to node" of the error edge when the topologies do not match.
+     * The error node when the error is associated with a specific node.
+     * NULL when the error is generic.
+     */
+    CUgraphNode errorNode;
+
+    /**
+     * The from node of error edge when the topologies do not match. Otherwise NULL.
+     */
+    CUgraphNode errorFromNode;
+} CUgraphExecUpdateResultInfo_v1; 
+typedef CUgraphExecUpdateResultInfo_v1 CUgraphExecUpdateResultInfo;
+
+/**
+ * CUDA memory pool attributes
+ */
+typedef enum CUmemPool_attribute_enum {
+    /**
+     * (value type = int)
+     * Allow cuMemAllocAsync to use memory asynchronously freed
+     * in another streams as long as a stream ordering dependency
+     * of the allocating stream on the free action exists.
+     * Cuda events and null stream interactions can create the required
+     * stream ordered dependencies. (default enabled)
+     */
+    CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES = 1,
+
+    /**
+     * (value type = int)
+     * Allow reuse of already completed frees when there is no dependency
+     * between the free and allocation. (default enabled)
+     */
+    CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC,
+
+    /**
+     * (value type = int)
+     * Allow cuMemAllocAsync to insert new stream dependencies
+     * in order to establish the stream ordering required to reuse
+     * a piece of memory released by cuFreeAsync (default enabled).
+     */
+    CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of reserved memory in bytes to hold onto before trying
+     * to release memory back to the OS. When more than the release
+     * threshold bytes of memory are held by the memory pool, the
+     * allocator will try to release memory back to the OS on the
+     * next call to stream, event or context synchronize. (default 0)
+     */
+    CU_MEMPOOL_ATTR_RELEASE_THRESHOLD,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of backing memory currently allocated for the mempool.
+     */
+    CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of backing memory allocated for the mempool since the
+     * last time it was reset. High watermark can only be reset to zero.
+     */
+    CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of memory from the pool that is currently in use by the application.
+     */
+    CU_MEMPOOL_ATTR_USED_MEM_CURRENT,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of the amount of memory from the pool that was in use by the application since
+     * the last time it was reset. High watermark can only be reset to zero.
+     */
+    CU_MEMPOOL_ATTR_USED_MEM_HIGH
+} CUmemPool_attribute;
+
+/**
+ * This flag, if set, indicates that the memory will be used as a buffer for
+ * hardware accelerated decompression.
+ */
+#define CU_MEM_POOL_CREATE_USAGE_HW_DECOMPRESS 0x2
+
+/**
+ * Specifies the properties of allocations made from the pool.
+ */
+typedef struct CUmemPoolProps_st {
+    CUmemAllocationType allocType;         /**< Allocation type. Currently must be specified as CU_MEM_ALLOCATION_TYPE_PINNED */
+    CUmemAllocationHandleType handleTypes; /**< Handle types that will be supported by allocations from the pool. */
+    CUmemLocation location;                /**< Location where allocations should reside. */
+    /**
+     * Windows-specific LPSECURITYATTRIBUTES required when
+     * ::CU_MEM_HANDLE_TYPE_WIN32 is specified.  This security attribute defines
+     * the scope of which exported allocations may be transferred to other
+     * processes.  In all other cases, this field is required to be zero.
+     */
+    void *win32SecurityAttributes;
+    size_t maxSize;             /**< Maximum pool size. When set to 0, defaults to a system dependent value. */
+    unsigned short usage;       /**< Bitmask indicating intended usage for the pool. */
+    unsigned char reserved[54]; /**< reserved for future use, must be 0 */
+} CUmemPoolProps_v1;
+typedef CUmemPoolProps_v1 CUmemPoolProps;
+
+/**
+ * Opaque data for exporting a pool allocation
+ */
+typedef struct CUmemPoolPtrExportData_st {
+    unsigned char reserved[64];
+} CUmemPoolPtrExportData_v1;
+typedef CUmemPoolPtrExportData_v1 CUmemPoolPtrExportData;
+
+/**
+ * Memory allocation node parameters
+ */
+typedef struct CUDA_MEM_ALLOC_NODE_PARAMS_v1_st {
+    /**
+    * in: location where the allocation should reside (specified in ::location).
+    * ::handleTypes must be ::CU_MEM_HANDLE_TYPE_NONE. IPC is not supported.
+    */
+    CUmemPoolProps poolProps;
+    const CUmemAccessDesc *accessDescs; /**< in: array of memory access descriptors. Used to describe peer GPU access */
+    size_t accessDescCount; /**< in: number of memory access descriptors.  Must not exceed the number of GPUs. */
+    size_t bytesize; /**< in: size in bytes of the requested allocation */
+    CUdeviceptr dptr; /**< out: address of the allocation returned by CUDA */
+} CUDA_MEM_ALLOC_NODE_PARAMS_v1;
+typedef CUDA_MEM_ALLOC_NODE_PARAMS_v1 CUDA_MEM_ALLOC_NODE_PARAMS;
+
+/**
+ * Memory allocation node parameters
+ */
+typedef struct CUDA_MEM_ALLOC_NODE_PARAMS_v2_st {
+    /**
+    * in: location where the allocation should reside (specified in ::location).
+    * ::handleTypes must be ::CU_MEM_HANDLE_TYPE_NONE. IPC is not supported.
+    */
+    CUmemPoolProps poolProps;
+    const CUmemAccessDesc *accessDescs; /**< in: array of memory access descriptors. Used to describe peer GPU access */
+    size_t accessDescCount; /**< in: number of memory access descriptors.  Must not exceed the number of GPUs. */
+    size_t bytesize; /**< in: size in bytes of the requested allocation */
+    CUdeviceptr dptr; /**< out: address of the allocation returned by CUDA */
+} CUDA_MEM_ALLOC_NODE_PARAMS_v2;
+
+/**
+ * Memory free node parameters
+ */
+typedef struct CUDA_MEM_FREE_NODE_PARAMS_st {
+    CUdeviceptr dptr; /**< in: the pointer to free */
+} CUDA_MEM_FREE_NODE_PARAMS;
+
+typedef enum CUgraphMem_attribute_enum {
+    /**
+     * (value type = cuuint64_t)
+     * Amount of memory, in bytes, currently associated with graphs
+     */
+    CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of memory, in bytes, associated with graphs since the
+     * last time it was reset.  High watermark can only be reset to zero.
+     */
+    CU_GRAPH_MEM_ATTR_USED_MEM_HIGH,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of memory, in bytes, currently allocated for use by
+     * the CUDA graphs asynchronous allocator.
+     */
+    CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of memory, in bytes, currently allocated for use by
+     * the CUDA graphs asynchronous allocator.
+     */
+    CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH
+} CUgraphMem_attribute;
+
+/**
+ * Child graph node parameters
+ */
+typedef struct CUDA_CHILD_GRAPH_NODE_PARAMS_st {
+    CUgraph graph; /**< The child graph to clone into the node for node creation, or
+                        a handle to the graph owned by the node for node query */
+} CUDA_CHILD_GRAPH_NODE_PARAMS;
+
+/**
+ * Event record node parameters
+ */
+typedef struct CUDA_EVENT_RECORD_NODE_PARAMS_st {
+    CUevent event; /**< The event to record when the node executes */
+} CUDA_EVENT_RECORD_NODE_PARAMS;
+
+/**
+ * Event wait node parameters
+ */
+typedef struct CUDA_EVENT_WAIT_NODE_PARAMS_st {
+    CUevent event; /**< The event to wait on from the node */
+} CUDA_EVENT_WAIT_NODE_PARAMS;
+
+/**
+ * Graph node parameters.  See ::cuGraphAddNode.
+ */
+typedef struct CUgraphNodeParams_st {
+    CUgraphNodeType type; /**< Type of the node */
+    int reserved0[3]; /**< Reserved. Must be zero. */
+
+    union {
+        long long                             reserved1[29]; /**< Padding. Unused bytes must be zero. */
+        CUDA_KERNEL_NODE_PARAMS_v3            kernel;        /**< Kernel node parameters. */
+        CUDA_MEMCPY_NODE_PARAMS               memcpy;        /**< Memcpy node parameters. */
+        CUDA_MEMSET_NODE_PARAMS_v2            memset;        /**< Memset node parameters. */
+        CUDA_HOST_NODE_PARAMS_v2              host;          /**< Host node parameters. */
+        CUDA_CHILD_GRAPH_NODE_PARAMS          graph;         /**< Child graph node parameters. */
+        CUDA_EVENT_WAIT_NODE_PARAMS           eventWait;     /**< Event wait node parameters. */
+        CUDA_EVENT_RECORD_NODE_PARAMS         eventRecord;   /**< Event record node parameters. */
+        CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2    extSemSignal;  /**< External semaphore signal node parameters. */
+        CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2      extSemWait;    /**< External semaphore wait node parameters. */
+        CUDA_MEM_ALLOC_NODE_PARAMS_v2         alloc;         /**< Memory allocation node parameters. */
+        CUDA_MEM_FREE_NODE_PARAMS             free;          /**< Memory free node parameters. */
+        CUDA_BATCH_MEM_OP_NODE_PARAMS_v2      memOp;         /**< MemOp node parameters. */
+        CUDA_CONDITIONAL_NODE_PARAMS          conditional;   /**< Conditional node parameters. */
+    };
+
+    long long reserved2; /**< Reserved bytes. Must be zero. */
+} CUgraphNodeParams;
+
+/**
+ * If set, each kernel launched as part of ::cuLaunchCooperativeKernelMultiDevice only
+ * waits for prior work in the stream corresponding to that GPU to complete before the
+ * kernel begins execution.
+ */
+#define CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC   0x01
+
+/**
+ * If set, any subsequent work pushed in a stream that participated in a call to
+ * ::cuLaunchCooperativeKernelMultiDevice will only wait for the kernel launched on
+ * the GPU corresponding to that stream to complete before it begins execution.
+ */
+#define CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC  0x02
+
+/**
+ * If set, the CUDA array is a collection of layers, where each layer is either a 1D
+ * or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number
+ * of layers, not the depth of a 3D array.
+ */
+#define CUDA_ARRAY3D_LAYERED        0x01
+
+/**
+ * Deprecated, use CUDA_ARRAY3D_LAYERED
+ */
+#define CUDA_ARRAY3D_2DARRAY        0x01
+
+/**
+ * This flag must be set in order to bind a surface reference
+ * to the CUDA array
+ */
+#define CUDA_ARRAY3D_SURFACE_LDST   0x02
+
+/**
+ * If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The
+ * width of such a CUDA array must be equal to its height, and Depth must be six.
+ * If ::CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps
+ * and Depth must be a multiple of six.
+ */
+#define CUDA_ARRAY3D_CUBEMAP        0x04
+
+/**
+ * This flag must be set in order to perform texture gather operations
+ * on a CUDA array.
+ */
+#define CUDA_ARRAY3D_TEXTURE_GATHER 0x08
+
+/**
+ * This flag if set indicates that the CUDA
+ * array is a DEPTH_TEXTURE.
+ */
+#define CUDA_ARRAY3D_DEPTH_TEXTURE 0x10
+
+/**
+ * This flag indicates that the CUDA array may be bound as a color target
+ * in an external graphics API
+ */
+#define CUDA_ARRAY3D_COLOR_ATTACHMENT 0x20
+
+/**
+ * This flag if set indicates that the CUDA array or CUDA mipmapped array
+ * is a sparse CUDA array or CUDA mipmapped array respectively
+ */
+#define CUDA_ARRAY3D_SPARSE 0x40
+
+/**
+ * This flag if set indicates that the CUDA array or CUDA mipmapped array
+ * will allow deferred memory mapping
+ */
+#define CUDA_ARRAY3D_DEFERRED_MAPPING 0x80
+
+/**
+ * This flag indicates that the CUDA array will be used for hardware accelerated
+ * video encode/decode operations.
+ */
+#define CUDA_ARRAY3D_VIDEO_ENCODE_DECODE 0x100
+
+/**
+ * Override the texref format with a format inferred from the array.
+ * Flag for ::cuTexRefSetArray()
+ */
+#define CU_TRSA_OVERRIDE_FORMAT 0x01
+
+/**
+ * Read the texture as integers rather than promoting the values to floats
+ * in the range [0,1].
+ * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate()
+ */
+#define CU_TRSF_READ_AS_INTEGER         0x01
+
+/**
+ * Use normalized texture coordinates in the range [0,1) instead of [0,dim).
+ * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate()
+ */
+#define CU_TRSF_NORMALIZED_COORDINATES  0x02
+
+/**
+ * Perform sRGB->linear conversion during texture read.
+ * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate()
+ */
+#define CU_TRSF_SRGB  0x10
+
+ /**
+  * Disable any trilinear filtering optimizations.
+  * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate()
+  */
+#define CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION  0x20
+
+/**
+ * Enable seamless cube map filtering.
+ * Flag for ::cuTexObjectCreate()
+ */
+#define CU_TRSF_SEAMLESS_CUBEMAP  0x40
+
+/**
+ * C++ compile time constant for CU_LAUNCH_PARAM_END
+ */
+#define CU_LAUNCH_PARAM_END_AS_INT     0x00
+
+/**
+ * End of array terminator for the \p extra parameter to
+ * ::cuLaunchKernel
+ */
+#define CU_LAUNCH_PARAM_END            ((void*)CU_LAUNCH_PARAM_END_AS_INT)
+
+/**
+ * C++ compile time constant for CU_LAUNCH_PARAM_BUFFER_POINTER
+ */
+#define CU_LAUNCH_PARAM_BUFFER_POINTER_AS_INT 0x01
+
+/**
+ * Indicator that the next value in the \p extra parameter to
+ * ::cuLaunchKernel will be a pointer to a buffer containing all kernel
+ * parameters used for launching kernel \p f.  This buffer needs to
+ * honor all alignment/padding requirements of the individual parameters.
+ * If ::CU_LAUNCH_PARAM_BUFFER_SIZE is not also specified in the
+ * \p extra array, then ::CU_LAUNCH_PARAM_BUFFER_POINTER will have no
+ * effect.
+ */
+#define CU_LAUNCH_PARAM_BUFFER_POINTER        ((void*)CU_LAUNCH_PARAM_BUFFER_POINTER_AS_INT)
+
+/**
+ * C++ compile time constant for CU_LAUNCH_PARAM_BUFFER_SIZE
+ */
+#define CU_LAUNCH_PARAM_BUFFER_SIZE_AS_INT 0x02
+
+/**
+ * Indicator that the next value in the \p extra parameter to
+ * ::cuLaunchKernel will be a pointer to a size_t which contains the
+ * size of the buffer specified with ::CU_LAUNCH_PARAM_BUFFER_POINTER.
+ * It is required that ::CU_LAUNCH_PARAM_BUFFER_POINTER also be specified
+ * in the \p extra array if the value associated with
+ * ::CU_LAUNCH_PARAM_BUFFER_SIZE is not zero.
+ */
+#define CU_LAUNCH_PARAM_BUFFER_SIZE        ((void*)CU_LAUNCH_PARAM_BUFFER_SIZE_AS_INT)
+
+/**
+ * For texture references loaded into the module, use default texunit from
+ * texture reference.
+ */
+#define CU_PARAM_TR_DEFAULT -1
+
+/**
+ * Device that represents the CPU
+ */
+#define CU_DEVICE_CPU               ((CUdevice)-1)
+
+/**
+ * Device that represents an invalid device
+ */
+#define CU_DEVICE_INVALID           ((CUdevice)-2)
+
+/**
+ * Bitmasks for ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS
+ */
+typedef enum CUflushGPUDirectRDMAWritesOptions_enum {
+    CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_HOST   = 1<<0, /**< ::cuFlushGPUDirectRDMAWrites() and its CUDA Runtime API counterpart are supported on the device. */
+    CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_MEMOPS = 1<<1  /**< The ::CU_STREAM_WAIT_VALUE_FLUSH flag and the ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device. */
+} CUflushGPUDirectRDMAWritesOptions;
+
+/**
+ * Platform native ordering for GPUDirect RDMA writes
+ */
+typedef enum CUGPUDirectRDMAWritesOrdering_enum {
+    CU_GPU_DIRECT_RDMA_WRITES_ORDERING_NONE        = 0,   /**< The device does not natively support ordering of remote writes. ::cuFlushGPUDirectRDMAWrites() can be leveraged if supported. */
+    CU_GPU_DIRECT_RDMA_WRITES_ORDERING_OWNER       = 100, /**< Natively, the device can consistently consume remote writes, although other CUDA devices may not. */
+    CU_GPU_DIRECT_RDMA_WRITES_ORDERING_ALL_DEVICES = 200  /**< Any CUDA device in the system can consistently consume remote writes to this device. */
+} CUGPUDirectRDMAWritesOrdering;
+
+/**
+ * The scopes for ::cuFlushGPUDirectRDMAWrites
+ */
+typedef enum CUflushGPUDirectRDMAWritesScope_enum {
+    CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_OWNER       = 100, /**< Blocks until remote writes are visible to the CUDA device context owning the data. */
+    CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_ALL_DEVICES = 200  /**< Blocks until remote writes are visible to all CUDA device contexts. */
+} CUflushGPUDirectRDMAWritesScope;
+ 
+/**
+ * The targets for ::cuFlushGPUDirectRDMAWrites
+ */
+typedef enum CUflushGPUDirectRDMAWritesTarget_enum {
+    CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TARGET_CURRENT_CTX = 0 /**< Sets the target for ::cuFlushGPUDirectRDMAWrites() to the currently active CUDA device context. */
+} CUflushGPUDirectRDMAWritesTarget;
+
+/**
+ * The additional write options for ::cuGraphDebugDotPrint
+ */
+typedef enum CUgraphDebugDot_flags_enum {
+    CU_GRAPH_DEBUG_DOT_FLAGS_VERBOSE                        = 1<<0,  /**< Output all debug data as if every debug flag is enabled */
+    CU_GRAPH_DEBUG_DOT_FLAGS_RUNTIME_TYPES                  = 1<<1,  /**< Use CUDA Runtime structures for output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_PARAMS             = 1<<2,  /**< Adds CUDA_KERNEL_NODE_PARAMS values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_MEMCPY_NODE_PARAMS             = 1<<3,  /**< Adds CUDA_MEMCPY3D values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_MEMSET_NODE_PARAMS             = 1<<4,  /**< Adds CUDA_MEMSET_NODE_PARAMS values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_HOST_NODE_PARAMS               = 1<<5,  /**< Adds CUDA_HOST_NODE_PARAMS values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_EVENT_NODE_PARAMS              = 1<<6,  /**< Adds CUevent handle from record and wait nodes to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_SIGNAL_NODE_PARAMS   = 1<<7,  /**< Adds CUDA_EXT_SEM_SIGNAL_NODE_PARAMS values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_WAIT_NODE_PARAMS     = 1<<8,  /**< Adds CUDA_EXT_SEM_WAIT_NODE_PARAMS values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_ATTRIBUTES         = 1<<9,  /**< Adds CUkernelNodeAttrValue values to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_HANDLES                        = 1<<10, /**< Adds node handles and every kernel function handle to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_MEM_ALLOC_NODE_PARAMS          = 1<<11, /**< Adds memory alloc node parameters to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_MEM_FREE_NODE_PARAMS           = 1<<12, /**< Adds memory free node parameters to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_BATCH_MEM_OP_NODE_PARAMS       = 1<<13, /**< Adds batch mem op node parameters to output */
+    CU_GRAPH_DEBUG_DOT_FLAGS_EXTRA_TOPO_INFO                = 1<<14, /**< Adds edge numbering information */
+    CU_GRAPH_DEBUG_DOT_FLAGS_CONDITIONAL_NODE_PARAMS        = 1<<15  /**< Adds conditional node parameters to output */
+} CUgraphDebugDot_flags;
+
+/**
+ * Flags for user objects for graphs
+ */
+typedef enum CUuserObject_flags_enum {
+    CU_USER_OBJECT_NO_DESTRUCTOR_SYNC = 1  /**< Indicates the destructor execution is not synchronized by any CUDA handle. */
+} CUuserObject_flags;
+
+/**
+ * Flags for retaining user object references for graphs
+ */
+typedef enum CUuserObjectRetain_flags_enum {
+    CU_GRAPH_USER_OBJECT_MOVE = 1  /**< Transfer references from the caller rather than creating new references. */
+} CUuserObjectRetain_flags;
+
+/**
+ * Flags for instantiating a graph
+ */
+typedef enum CUgraphInstantiate_flags_enum {
+    CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH  = 1 /**< Automatically free memory allocated in a graph before relaunching. */
+  , CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD               = 2 /**< Automatically upload the graph after instantiation. Only supported by
+                                                              ::cuGraphInstantiateWithParams.  The upload will be performed using the
+                                                              stream provided in \p instantiateParams. */
+  , CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH        = 4 /**< Instantiate the graph to be launchable from the device. This flag can only
+                                                              be used on platforms which support unified addressing. This flag cannot be
+                                                              used in conjunction with CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH. */
+  , CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY    = 8 /**< Run the graph using the per-node priority attributes rather than the
+                                                              priority of the stream it is launched into. */
+} CUgraphInstantiate_flags;
+
+/**
+ * CUDA device NUMA configuration
+ */
+typedef enum CUdeviceNumaConfig_enum {
+    CU_DEVICE_NUMA_CONFIG_NONE = 0, /**< The GPU is not a NUMA node */
+    CU_DEVICE_NUMA_CONFIG_NUMA_NODE, /**< The GPU is a NUMA node, CU_DEVICE_ATTRIBUTE_NUMA_ID contains its NUMA ID */
+} CUdeviceNumaConfig;
+
+/**
+ * CUDA Process States
+ */
+typedef enum CUprocessState_enum {
+    CU_PROCESS_STATE_RUNNING = 0,  /**< Default process state */
+    CU_PROCESS_STATE_LOCKED,       /**< CUDA API locks are taken so further CUDA API calls will block */
+    CU_PROCESS_STATE_CHECKPOINTED, /**< Application memory contents have been checkpointed and underlying allocations and device handles have been released */
+    CU_PROCESS_STATE_FAILED,       /**< Application entered an uncorrectable error during the checkpoint/restore process */
+} CUprocessState;
+
+/**
+ * CUDA checkpoint optional lock arguments
+ */
+typedef struct CUcheckpointLockArgs_st {
+    unsigned int timeoutMs; /**< Timeout in milliseconds to attempt to lock the process, 0 indicates no timeout */
+    unsigned int reserved0; /**< Reserved for future use, must be zero */
+    cuuint64_t reserved1[7]; /**< Reserved for future use, must be zeroed */
+} CUcheckpointLockArgs;
+
+/**
+ * CUDA checkpoint optional checkpoint arguments
+ */
+typedef struct CUcheckpointCheckpointArgs_st {
+    cuuint64_t reserved[8]; /**< Reserved for future use, must be zeroed */
+} CUcheckpointCheckpointArgs;
+
+/**
+ * CUDA checkpoint optional restore arguments
+ */
+typedef struct CUcheckpointRestoreArgs_st {
+    cuuint64_t reserved[8]; /**< Reserved for future use, must be zeroed */
+} CUcheckpointRestoreArgs;
+
+/**
+ * CUDA checkpoint optional unlock arguments
+ */
+typedef struct CUcheckpointUnlockArgs_st {
+    cuuint64_t reserved[8]; /**< Reserved for future use, must be zeroed */
+} CUcheckpointUnlockArgs;
+
+/**
+ * Flags to specify for copies within a batch. For more details see ::cuMemcpyBatchAsync.
+ */
+typedef enum CUmemcpyFlags_enum {
+    CU_MEMCPY_FLAG_DEFAULT = 0x0,
+
+    /**
+     * Hint to the driver to try and overlap the copy with compute work on the SMs.
+     */
+    CU_MEMCPY_FLAG_PREFER_OVERLAP_WITH_COMPUTE = 0x1
+} CUmemcpyFlags;
+
+/**
+ * These flags allow applications to convey the source access ordering CUDA must maintain.
+ * The destination will always be accessed in stream order.
+ */
+typedef enum CUmemcpySrcAccessOrder_enum {
+    /**
+     * Default invalid.
+     */
+    CU_MEMCPY_SRC_ACCESS_ORDER_INVALID = 0x0,
+
+    /**
+     * Indicates that access to the source pointer must be in stream order.
+     */
+    CU_MEMCPY_SRC_ACCESS_ORDER_STREAM = 0x1,
+
+    /**
+     * Indicates that access to the source pointer can be out of stream order and
+     * all accesses must be complete before the API call returns. This flag is suited for
+     * ephemeral sources (ex., stack variables) when it's known that no prior operations
+     * in the stream can be accessing the memory and also that the lifetime of the memory
+     * is limited to the scope that the source variable was declared in. Specifying
+     * this flag allows the driver to optimize the copy and removes the need for the user
+     * to synchronize the stream after the API call.
+     */
+    CU_MEMCPY_SRC_ACCESS_ORDER_DURING_API_CALL = 0x2,
+
+    /**
+     * Indicates that access to the source pointer can be out of stream order and the accesses
+     * can happen even after the API call returns. This flag is suited for host pointers
+     * allocated outside CUDA (ex., via malloc) when it's known that no prior operations
+     * in the stream can be accessing the memory. Specifying this flag allows the driver
+     * to optimize the copy on certain platforms.
+     */
+    CU_MEMCPY_SRC_ACCESS_ORDER_ANY = 0x3,
+
+    CU_MEMCPY_SRC_ACCESS_ORDER_MAX = 0x7FFFFFFF
+}  CUmemcpySrcAccessOrder;
+
+/**
+ * Attributes specific to copies within a batch. For more details on usage see ::cuMemcpyBatchAsync.
+ */
+typedef struct CUmemcpyAttributes_st {
+    CUmemcpySrcAccessOrder srcAccessOrder;  /**< Source access ordering to be observed for copies with this attribute. */
+    CUmemLocation srcLocHint;               /**< Hint location for the source operand. Ignored when the pointers are not managed memory or memory allocated outside CUDA. */
+    CUmemLocation dstLocHint;               /**< Hint location for the destination operand. Ignored when the pointers are not managed memory or memory allocated outside CUDA. */
+    unsigned int flags;                     /**< Additional flags for copies with this attribute. See ::CUmemcpyFlags */
+} CUmemcpyAttributes_v1;
+typedef CUmemcpyAttributes_v1 CUmemcpyAttributes;
+
+/**
+ * These flags allow applications to convey the operand type for individual copies specified in ::cuMemcpy3DBatchAsync.
+ */
+typedef enum CUmemcpy3DOperandType_enum {
+    CU_MEMCPY_OPERAND_TYPE_POINTER = 0x1,     /**< Memcpy operand is a valid pointer. */
+    CU_MEMCPY_OPERAND_TYPE_ARRAY = 0x2,       /**< Memcpy operand is a CUarray. */
+    CU_MEMCPY_OPERAND_TYPE_MAX = 0x7FFFFFFF
+} CUmemcpy3DOperandType;
+
+/**
+ * Struct representing offset into a CUarray in elements
+ */
+typedef struct CUoffset3D_st {
+    size_t x;
+    size_t y;
+    size_t z;
+} CUoffset3D_v1;
+typedef CUoffset3D_v1 CUoffset3D;
+
+/**
+ * Struct representing width/height/depth of a CUarray in elements
+ */
+typedef struct CUextent3D_st {
+    size_t width;
+    size_t height;
+    size_t depth;
+} CUextent3D_v1;
+typedef CUextent3D_v1 CUextent3D;
+
+/**
+ * Struct representing an operand for copy with ::cuMemcpy3DBatchAsync
+ */
+typedef struct CUmemcpy3DOperand_st {
+    CUmemcpy3DOperandType type;
+    union {
+        /**
+         * Struct representing an operand when ::CUmemcpy3DOperand::type is ::CU_MEMCPY_OPERAND_TYPE_POINTER
+         */
+        struct {
+            CUdeviceptr ptr;
+            size_t rowLength;        /**< Length of each row in elements. */
+            size_t layerHeight;      /**< Height of each layer in elements. */ 
+            CUmemLocation locHint;   /**< Hint location for the operand. Ignored when the pointers are not managed memory or memory allocated outside CUDA. */
+        } ptr;
+
+        /**
+         * Struct representing an operand when ::CUmemcpy3DOperand::type is ::CU_MEMCPY_OPERAND_TYPE_ARRAY
+         */
+        struct {
+            CUarray array;
+            CUoffset3D offset;
+        } array;
+    } op;
+} CUmemcpy3DOperand_v1;
+typedef CUmemcpy3DOperand_v1 CUmemcpy3DOperand;
+
+typedef struct CUDA_MEMCPY3D_BATCH_OP_st {
+    CUmemcpy3DOperand src;                    /**< Source memcpy operand. */
+    CUmemcpy3DOperand dst;                    /**< Destination memcpy operand. */
+    CUextent3D extent;                        /**< Extents of the memcpy between src and dst. The width, height and depth components must not be 0.*/
+    CUmemcpySrcAccessOrder srcAccessOrder;    /**< Source access ordering to be observed for copy from src to dst. */
+    unsigned int flags;                       /**< Additional flags for copies with this attribute. See ::CUmemcpyFlags */
+} CUDA_MEMCPY3D_BATCH_OP_v1;
+typedef CUDA_MEMCPY3D_BATCH_OP_v1 CUDA_MEMCPY3D_BATCH_OP;
+
+/** @} */ /* END CUDA_TYPES */
+
+#if defined(__GNUC__)
+  #if defined(__CUDA_API_PUSH_VISIBILITY_DEFAULT)
+    #pragma GCC visibility push(default)
+  #endif
+#endif
+
+#ifdef _WIN32
+#define CUDAAPI __stdcall
+#else
+#define CUDAAPI
+#endif
+
+/**
+ * \defgroup CUDA_ERROR Error Handling
+ *
+ * ___MANBRIEF___ error handling functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the error handling functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Gets the string description of an error code
+ *
+ * Sets \p *pStr to the address of a NULL-terminated string description
+ * of the error code \p error.
+ * If the error code is not recognized, ::CUDA_ERROR_INVALID_VALUE
+ * will be returned and \p *pStr will be set to the NULL address.
+ *
+ * \param error - Error code to convert to string
+ * \param pStr - Address of the string pointer.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::CUresult,
+ * ::cudaGetErrorString
+ */
+CUresult CUDAAPI cuGetErrorString(CUresult error, const char **pStr);
+
+/**
+ * \brief Gets the string representation of an error code enum name
+ *
+ * Sets \p *pStr to the address of a NULL-terminated string representation
+ * of the name of the enum error code \p error.
+ * If the error code is not recognized, ::CUDA_ERROR_INVALID_VALUE
+ * will be returned and \p *pStr will be set to the NULL address.
+ *
+ * \param error - Error code to convert to string
+ * \param pStr - Address of the string pointer.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::CUresult,
+ * ::cudaGetErrorName
+ */
+CUresult CUDAAPI cuGetErrorName(CUresult error, const char **pStr);
+
+/** @} */ /* END CUDA_ERROR */
+
+/**
+ * \defgroup CUDA_INITIALIZE Initialization
+ *
+ * ___MANBRIEF___ initialization functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the initialization functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Initialize the CUDA driver API
+ * Initializes the driver API and must be called before any other function from
+ * the driver API in the current process. Currently, the \p Flags parameter must be 0. If ::cuInit()
+ * has not been called, any function from the driver API will return
+ * ::CUDA_ERROR_NOT_INITIALIZED.
+ *
+ * \param Flags - Initialization flag for CUDA.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_SYSTEM_DRIVER_MISMATCH,
+ * ::CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE
+ * \notefnerr
+ */
+CUresult CUDAAPI cuInit(unsigned int Flags);
+
+/** @} */ /* END CUDA_INITIALIZE */
+
+/**
+ * \defgroup CUDA_VERSION Version Management
+ *
+ * ___MANBRIEF___ version management functions of the low-level CUDA driver
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the version management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the latest CUDA version supported by driver
+ *
+ * Returns in \p *driverVersion the version of CUDA supported by
+ * the driver.  The version is returned as
+ * (1000 &times; major + 10 &times; minor). For example, CUDA 9.2
+ * would be represented by 9020.
+ *
+ * This function automatically returns ::CUDA_ERROR_INVALID_VALUE if
+ * \p driverVersion is NULL.
+ *
+ * \param driverVersion - Returns the CUDA driver version
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaDriverGetVersion,
+ * ::cudaRuntimeGetVersion
+ */
+CUresult CUDAAPI cuDriverGetVersion(int *driverVersion);
+
+/** @} */ /* END CUDA_VERSION */
+
+/**
+ * \defgroup CUDA_DEVICE Device Management
+ *
+ * ___MANBRIEF___ device management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the device management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns a handle to a compute device
+ *
+ * Returns in \p *device a device handle given an ordinal in the range <b>[0,
+ * ::cuDeviceGetCount()-1]</b>.
+ *
+ * \param device  - Returned device handle
+ * \param ordinal - Device number to get handle for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGetLuid,
+ * ::cuDeviceTotalMem,
+ * ::cuDeviceGetExecAffinitySupport
+ */
+CUresult CUDAAPI cuDeviceGet(CUdevice *device, int ordinal);
+
+/**
+ * \brief Returns the number of compute-capable devices
+ *
+ * Returns in \p *count the number of devices with compute capability greater
+ * than or equal to 2.0 that are available for execution. If there is no such
+ * device, ::cuDeviceGetCount() returns 0.
+ *
+ * \param count - Returned number of compute-capable devices
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGetLuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem,
+ * ::cuDeviceGetExecAffinitySupport,
+ * ::cudaGetDeviceCount
+ */
+CUresult CUDAAPI cuDeviceGetCount(int *count);
+
+/**
+ * \brief Returns an identifier string for the device
+ *
+ * Returns an ASCII string identifying the device \p dev in the NULL-terminated
+ * string pointed to by \p name. \p len specifies the maximum length of the
+ * string that may be returned.
+ *
+ * \param name - Returned identifier string for the device
+ * \param len  - Maximum length of string to store in \p name
+ * \param dev  - Device to get identifier string for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGetLuid,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem,
+ * ::cuDeviceGetExecAffinitySupport,
+ * ::cudaGetDeviceProperties
+ */
+CUresult CUDAAPI cuDeviceGetName(char *name, int len, CUdevice dev);
+
+/**
+ * \brief Return an UUID for the device
+ *
+ * Note there is a later version of this API, ::cuDeviceGetUuid_v2. It will
+ * supplant this version in 12.0, which is retained for minor version compatibility.
+ *
+ * Returns 16-octets identifying the device \p dev in the structure
+ * pointed by the \p uuid.
+ *
+ * \param uuid - Returned UUID
+ * \param dev  - Device to get identifier string for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetUuid_v2
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetLuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem,
+ * ::cuDeviceGetExecAffinitySupport,
+ * ::cudaGetDeviceProperties
+ */
+CUresult CUDAAPI cuDeviceGetUuid(CUuuid *uuid, CUdevice dev);
+
+/**
+ * \brief Return an UUID for the device (11.4+)
+ *
+ * Returns 16-octets identifying the device \p dev in the structure
+ * pointed by the \p uuid. If the device is in MIG mode, returns its
+ * MIG UUID which uniquely identifies the subscribed MIG compute instance.
+ *
+ * \param uuid - Returned UUID
+ * \param dev  - Device to get identifier string for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetLuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem,
+ * ::cudaGetDeviceProperties
+ */
+CUresult CUDAAPI cuDeviceGetUuid_v2(CUuuid *uuid, CUdevice dev);
+
+/**
+ * \brief Return an LUID and device node mask for the device
+ *
+ * Return identifying information (\p luid and \p deviceNodeMask) to allow
+ * matching device with graphics APIs.
+ *
+ * \param luid - Returned LUID
+ * \param deviceNodeMask - Returned device node mask
+ * \param dev  - Device to get identifier string for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem,
+ * ::cuDeviceGetExecAffinitySupport,
+ * ::cudaGetDeviceProperties
+ */
+CUresult CUDAAPI cuDeviceGetLuid(char *luid, unsigned int *deviceNodeMask, CUdevice dev);
+
+/**
+ * \brief Returns the total amount of memory on the device
+ *
+ * Returns in \p *bytes the total amount of memory available on the device
+ * \p dev in bytes.
+ *
+ * \param bytes - Returned memory available on device in bytes
+ * \param dev   - Device handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceGetExecAffinitySupport,
+ * ::cudaMemGetInfo
+ */
+CUresult CUDAAPI cuDeviceTotalMem(size_t *bytes, CUdevice dev);
+
+/**
+ * \brief Returns the maximum number of elements allocatable in a 1D linear texture for a given texture element size.
+ *
+ * Returns in \p maxWidthInElements the maximum number of texture elements allocatable in a 1D linear texture
+ * for given \p format and \p numChannels.
+ *
+ * \param maxWidthInElements    - Returned maximum number of texture elements allocatable for given \p format and \p numChannels.
+ * \param format                - Texture format.
+ * \param numChannels           - Number of channels per texture element.
+ * \param dev                   - Device handle.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGet,
+ * ::cudaMemGetInfo,
+ * ::cuDeviceTotalMem
+ */
+CUresult CUDAAPI cuDeviceGetTexture1DLinearMaxWidth(size_t *maxWidthInElements, CUarray_format format, unsigned numChannels, CUdevice dev);
+
+/**
+ * \brief Returns information about the device
+ *
+ * Returns in \p *pi the integer value of the attribute \p attrib on device
+ * \p dev. The supported attributes are:
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK: Maximum number of threads per
+ *   block;
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X: Maximum x-dimension of a block
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y: Maximum y-dimension of a block
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z: Maximum z-dimension of a block
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X: Maximum x-dimension of a grid
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y: Maximum y-dimension of a grid
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z: Maximum z-dimension of a grid
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK: Maximum amount of
+ *   shared memory available to a thread block in bytes
+ * - ::CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY: Memory available on device for
+ *   __constant__ variables in a CUDA C kernel in bytes
+ * - ::CU_DEVICE_ATTRIBUTE_WARP_SIZE: Warp size in threads
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_PITCH: Maximum pitch in bytes allowed by the
+ *   memory copy functions that involve memory regions allocated through
+ *   ::cuMemAllocPitch()
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH: Maximum 1D
+ *  texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH: Maximum width
+ *  for a 1D texture bound to linear memory
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH: Maximum
+ *  mipmapped 1D texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH: Maximum 2D
+ *  texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT: Maximum 2D
+ *  texture height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH: Maximum width
+ *  for a 2D texture bound to linear memory
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT: Maximum height
+ *  for a 2D texture bound to linear memory
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH: Maximum pitch
+ *  in bytes for a 2D texture bound to linear memory
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH: Maximum
+ *  mipmapped 2D texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT: Maximum
+ *  mipmapped 2D texture height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH: Maximum 3D
+ *  texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT: Maximum 3D
+ *  texture height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH: Maximum 3D
+ *  texture depth
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE:
+ *  Alternate maximum 3D texture width, 0 if no alternate
+ *  maximum 3D texture size is supported
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE:
+ *  Alternate maximum 3D texture height, 0 if no alternate
+ *  maximum 3D texture size is supported
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE:
+ *  Alternate maximum 3D texture depth, 0 if no alternate
+ *  maximum 3D texture size is supported
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH:
+ *  Maximum cubemap texture width or height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH:
+ *  Maximum 1D layered texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS:
+ *   Maximum layers in a 1D layered texture
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH:
+ *  Maximum 2D layered texture width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT:
+ *   Maximum 2D layered texture height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS:
+ *   Maximum layers in a 2D layered texture
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH:
+ *   Maximum cubemap layered texture width or height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS:
+ *   Maximum layers in a cubemap layered texture
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH:
+ *   Maximum 1D surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH:
+ *   Maximum 2D surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT:
+ *   Maximum 2D surface height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH:
+ *   Maximum 3D surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT:
+ *   Maximum 3D surface height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH:
+ *   Maximum 3D surface depth
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH:
+ *   Maximum 1D layered surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS:
+ *   Maximum layers in a 1D layered surface
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH:
+ *   Maximum 2D layered surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT:
+ *   Maximum 2D layered surface height
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS:
+ *   Maximum layers in a 2D layered surface
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH:
+ *   Maximum cubemap surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH:
+ *   Maximum cubemap layered surface width
+ * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS:
+ *   Maximum layers in a cubemap layered surface
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK: Maximum number of 32-bit
+ *   registers available to a thread block
+ * - ::CU_DEVICE_ATTRIBUTE_CLOCK_RATE: The typical clock frequency in kilohertz
+ * - ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT: Alignment requirement; texture
+ *   base addresses aligned to ::textureAlign bytes do not need an offset
+ *   applied to texture fetches
+ * - ::CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT: Pitch alignment requirement
+ *   for 2D texture references bound to pitched memory
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_OVERLAP: 1 if the device can concurrently copy
+ *   memory between host and device while executing a kernel, or 0 if not
+ * - ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT: Number of multiprocessors on
+ *   the device
+ * - ::CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT: 1 if there is a run time limit
+ *   for kernels executed on the device, or 0 if not
+ * - ::CU_DEVICE_ATTRIBUTE_INTEGRATED: 1 if the device is integrated with the
+ *   memory subsystem, or 0 if not
+ * - ::CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY: 1 if the device can map host
+ *   memory into the CUDA address space, or 0 if not
+ * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE: Compute mode that device is currently
+ *   in. Available modes are as follows:
+ *   - ::CU_COMPUTEMODE_DEFAULT: Default mode - Device is not restricted and
+ *     can have multiple CUDA contexts present at a single time.
+ *   - ::CU_COMPUTEMODE_PROHIBITED: Compute-prohibited mode - Device is
+ *     prohibited from creating new CUDA contexts.
+ *   - ::CU_COMPUTEMODE_EXCLUSIVE_PROCESS:  Compute-exclusive-process mode - Device
+ *     can have only one context used by a single process at a time.
+ * - ::CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS: 1 if the device supports
+ *   executing multiple kernels within the same context simultaneously, or 0 if
+ *   not. It is not guaranteed that multiple kernels will be resident
+ *   on the device concurrently so this feature should not be relied upon for
+ *   correctness.
+ * - ::CU_DEVICE_ATTRIBUTE_ECC_ENABLED: 1 if error correction is enabled on the
+ *    device, 0 if error correction is disabled or not supported by the device
+ * - ::CU_DEVICE_ATTRIBUTE_PCI_BUS_ID: PCI bus identifier of the device
+ * - ::CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID: PCI device (also known as slot) identifier
+ *   of the device
+ * - ::CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID: PCI domain identifier of the device
+ * - ::CU_DEVICE_ATTRIBUTE_TCC_DRIVER: 1 if the device is using a TCC driver. TCC
+ *    is only available on Tesla hardware running Windows Vista or later
+ * - ::CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE: Peak memory clock frequency in kilohertz
+ * - ::CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH: Global memory bus width in bits
+ * - ::CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE: Size of L2 cache in bytes. 0 if the device doesn't have L2 cache
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR: Maximum resident threads per multiprocessor
+ * - ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING: 1 if the device shares a unified address space with
+ *   the host, or 0 if not
+ * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR: Major compute capability version number
+ * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR: Minor compute capability version number
+ * - ::CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED: 1 if device supports caching globals
+ *    in L1 cache, 0 if caching globals in L1 cache is not supported by the device
+ * - ::CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED: 1 if device supports caching locals
+ *    in L1 cache, 0 if caching locals in L1 cache is not supported by the device
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR: Maximum amount of
+ *   shared memory available to a multiprocessor in bytes; this amount is shared
+ *   by all thread blocks simultaneously resident on a multiprocessor
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR: Maximum number of 32-bit
+ *   registers available to a multiprocessor; this number is shared by all thread
+ *   blocks simultaneously resident on a multiprocessor
+ * - ::CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY: 1 if device supports allocating managed memory
+ *   on this system, 0 if allocating managed memory is not supported by the device on this system.
+ * - ::CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD: 1 if device is on a multi-GPU board, 0 if not.
+ * - ::CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID: Unique identifier for a group of devices
+ *   associated with the same board. Devices on the same multi-GPU board will share the same identifier.
+ * - ::CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED: 1 if Link between the device and the host
+ *   supports native atomic operations.
+ * - ::CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO: Ratio of single precision performance
+ *   (in floating-point operations per second) to double precision performance.
+ * - ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS: Device supports coherently accessing
+ *   pageable memory without calling cudaHostRegister on it.
+ * - ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS: Device can coherently access managed memory
+ *   concurrently with the CPU.
+ * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED: Device supports Compute Preemption.
+ * - ::CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM: Device can access host registered
+ *   memory at the same virtual address as the CPU.
+ * -  ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN: The maximum per block shared memory size
+ *    supported on this device. This is the maximum value that can be opted into when using the cuFuncSetAttribute() or cuKernelSetAttribute() call.
+ *    For more details see ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES
+ * - ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES: Device accesses pageable memory via the host's
+ *   page tables.
+ * - ::CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST: The host can directly access managed memory on the device without migration.
+ * - ::CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED:  Device supports virtual memory management APIs like ::cuMemAddressReserve, ::cuMemCreate, ::cuMemMap and related APIs
+ * - ::CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED: Device supports exporting memory to a posix file descriptor with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate
+ * - ::CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED:  Device supports exporting memory to a Win32 NT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate
+ * - ::CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED: Device supports exporting memory to a Win32 KMT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR: Maximum number of thread blocks that can reside on a multiprocessor
+ * - ::CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED: Device supports compressible memory allocation via ::cuMemCreate
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE: Maximum L2 persisting lines capacity setting in bytes
+ * - ::CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE: Maximum value of CUaccessPolicyWindow::num_bytes 
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED: Device supports specifying the GPUDirect RDMA flag with ::cuMemCreate.
+ * - ::CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK: Amount of shared memory per block reserved by CUDA driver in bytes
+ * - ::CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED: Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays. 
+ * - ::CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED: Device supports using the ::cuMemHostRegister flag ::CU_MEMHOSTERGISTER_READ_ONLY to register memory that must be mapped as read-only to the GPU
+ * - ::CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED: Device supports using the ::cuMemAllocAsync and ::cuMemPool family of APIs
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED: Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information)
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS: The returned attribute shall be interpreted as a bitmask, where the individual bits are described by the ::CUflushGPUDirectRDMAWritesOptions enum
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING: GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See ::CUGPUDirectRDMAWritesOrdering for the numerical values returned here.
+ * - ::CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES: Bitmask of handle types supported with mempool based IPC
+ * - ::CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED: Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays.
+ * - ::CU_DEVICE_ATTRIBUTE_NUMA_CONFIG: NUMA configuration of a device: value is of type ::CUdeviceNumaConfig enum
+ * - ::CU_DEVICE_ATTRIBUTE_NUMA_ID: NUMA node ID of the GPU memory
+ * - ::CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED: Device supports switch multicast and reduction operations.
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_PCI_DEVICE_ID: The combined 16-bit PCI device ID and 16-bit PCI vendor ID.
+ * - ::CU_DEVICE_ATTRIBUTE_GPU_PCI_SUBSYSTEM_ID: The combined 16-bit PCI subsystem ID and 16-bit PCI subsystem vendor ID.
+ID.
+ *
+ * \param pi     - Returned device attribute value
+ * \param attrib - Device attribute to query
+ * \param dev    - Device handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem,
+ * ::cuDeviceGetExecAffinitySupport,
+ * ::cudaDeviceGetAttribute,
+ * ::cudaGetDeviceProperties
+ */
+CUresult CUDAAPI cuDeviceGetAttribute(int *pi, CUdevice_attribute attrib, CUdevice dev);
+
+/**
+ * \brief Return NvSciSync attributes that this device can support.
+ *
+ * Returns in \p nvSciSyncAttrList, the properties of NvSciSync that
+ * this CUDA device, \p dev can support. The returned \p nvSciSyncAttrList
+ * can be used to create an NvSciSync object that matches this device's capabilities.
+ * 
+ * If NvSciSyncAttrKey_RequiredPerm field in \p nvSciSyncAttrList is
+ * already set this API will return ::CUDA_ERROR_INVALID_VALUE.
+ * 
+ * The applications should set \p nvSciSyncAttrList to a valid 
+ * NvSciSyncAttrList failing which this API will return
+ * ::CUDA_ERROR_INVALID_HANDLE.
+ * 
+ * The \p flags controls how applications intends to use
+ * the NvSciSync created from the \p nvSciSyncAttrList. The valid flags are:
+ * - ::CUDA_NVSCISYNC_ATTR_SIGNAL, specifies that the applications intends to 
+ * signal an NvSciSync on this CUDA device.
+ * - ::CUDA_NVSCISYNC_ATTR_WAIT, specifies that the applications intends to 
+ * wait on an NvSciSync on this CUDA device.
+ *
+ * At least one of these flags must be set, failing which the API
+ * returns ::CUDA_ERROR_INVALID_VALUE. Both the flags are orthogonal
+ * to one another: a developer may set both these flags that allows to
+ * set both wait and signal specific attributes in the same \p nvSciSyncAttrList.
+ *
+ * Note that this API updates the input \p nvSciSyncAttrList with values equivalent
+ * to the following public attribute key-values:
+ * NvSciSyncAttrKey_RequiredPerm is set to
+ * - NvSciSyncAccessPerm_SignalOnly if ::CUDA_NVSCISYNC_ATTR_SIGNAL is set in \p flags.
+ * - NvSciSyncAccessPerm_WaitOnly if ::CUDA_NVSCISYNC_ATTR_WAIT is set in \p flags.
+ * - NvSciSyncAccessPerm_WaitSignal if both ::CUDA_NVSCISYNC_ATTR_WAIT and
+ * ::CUDA_NVSCISYNC_ATTR_SIGNAL are set in \p flags.
+ * NvSciSyncAttrKey_PrimitiveInfo is set to
+ * - NvSciSyncAttrValPrimitiveType_SysmemSemaphore on any valid \p device.
+ * - NvSciSyncAttrValPrimitiveType_Syncpoint if \p device is a Tegra device.
+ * - NvSciSyncAttrValPrimitiveType_SysmemSemaphorePayload64b if \p device is GA10X+.
+ * NvSciSyncAttrKey_GpuId is set to the same UUID that is returned for this 
+ * \p device from ::cuDeviceGetUuid.
+ *
+ * \param nvSciSyncAttrList     - Return NvSciSync attributes supported.
+ * \param dev                   - Valid Cuda Device to get NvSciSync attributes for.
+ * \param flags                 - flags describing NvSciSync usage.
+ *
+ * \return
+ *
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa
+ * ::cuImportExternalSemaphore,
+ * ::cuDestroyExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuDeviceGetNvSciSyncAttributes(void *nvSciSyncAttrList, CUdevice dev, int flags);
+
+/**
+ * \brief Sets the current memory pool of a device
+ *
+ * The memory pool must be local to the specified device.
+ * ::cuMemAllocAsync allocates from the current mempool of the provided stream's device.
+ * By default, a device's current memory pool is its default memory pool.
+ *
+ * \note Use ::cuMemAllocFromPoolAsync to specify asynchronous allocations from a device different
+ * than the one the stream runs on. 
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuDeviceGetDefaultMemPool, ::cuDeviceGetMemPool, ::cuMemPoolCreate, ::cuMemPoolDestroy, ::cuMemAllocFromPoolAsync
+ */
+CUresult CUDAAPI cuDeviceSetMemPool(CUdevice dev, CUmemoryPool pool);
+
+/**
+ * \brief Gets the current mempool for a device
+ *
+ * Returns the last pool provided to ::cuDeviceSetMemPool for this device
+ * or the device's default memory pool if ::cuDeviceSetMemPool has never been called.
+ * By default the current mempool is the default mempool for a device.
+ * Otherwise the returned pool must have been set with ::cuDeviceSetMemPool.
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuDeviceGetDefaultMemPool, ::cuMemPoolCreate, ::cuDeviceSetMemPool
+ */
+CUresult CUDAAPI cuDeviceGetMemPool(CUmemoryPool *pool, CUdevice dev);
+
+/**
+ * \brief Returns the default mempool of a device
+ *
+ * The default mempool of a device contains device memory from that device.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemPoolTrimTo, ::cuMemPoolGetAttribute, ::cuMemPoolSetAttribute, cuMemPoolSetAccess, ::cuDeviceGetMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuDeviceGetDefaultMemPool(CUmemoryPool *pool_out, CUdevice dev);
+
+/**
+ * \brief Returns information about the execution affinity support of the device.
+ *
+ * Returns in \p *pi whether execution affinity type \p type is supported by device \p dev.
+ * The supported types are:
+ * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT: 1 if context with limited SMs is supported by the device,
+ *   or 0 if not;
+ *
+ * \param pi   - 1 if the execution affinity type \p type is supported by the device, or 0 if not
+ * \param type - Execution affinity type to query
+ * \param dev  - Device handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem
+ */
+CUresult CUDAAPI cuDeviceGetExecAffinitySupport(int *pi, CUexecAffinityType type, CUdevice dev);
+
+/**
+ * \brief Blocks until remote writes are visible to the specified scope
+ *
+ * Blocks until GPUDirect RDMA writes to the target context via mappings
+ * created through APIs like nvidia_p2p_get_pages (see
+ * https://docs.nvidia.com/cuda/gpudirect-rdma for more information), are
+ * visible to the specified scope.
+ *
+ * If the scope equals or lies within the scope indicated by
+ * ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING, the call
+ * will be a no-op and can be safely omitted for performance. This can be
+ * determined by comparing the numerical values between the two enums, with
+ * smaller scopes having smaller values.
+ *
+ * On platforms that support GPUDirect RDMA writes via more than one path in
+ * hardware (see ::CU_MEM_RANGE_FLAG_DMA_BUF_MAPPING_TYPE_PCIE), the user should
+ * consider those paths as belonging to separate ordering domains. Note that in
+ * such cases CUDA driver will report both RDMA writes ordering and RDMA write
+ * scope as ALL_DEVICES and a call to cuFlushGPUDirectRDMA will be a no-op,
+ * but when these multiple paths are used simultaneously, it is the user's
+ * responsibility to ensure ordering by using mechanisms outside the scope of
+ * CUDA.
+ *
+ * Users may query support for this API via
+ * ::CU_DEVICE_ATTRIBUTE_FLUSH_FLUSH_GPU_DIRECT_RDMA_OPTIONS.
+ *
+ * \param target - The target of the operation, see ::CUflushGPUDirectRDMAWritesTarget
+ * \param scope  - The scope of the operation, see ::CUflushGPUDirectRDMAWritesScope
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ */
+CUresult CUDAAPI cuFlushGPUDirectRDMAWrites(CUflushGPUDirectRDMAWritesTarget target, CUflushGPUDirectRDMAWritesScope scope);
+
+/** @} */ /* END CUDA_DEVICE */
+
+/**
+ * \defgroup CUDA_DEVICE_DEPRECATED Device Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated device management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the device management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns properties for a selected device
+ *
+ * \deprecated
+ *
+ * This function was deprecated as of CUDA 5.0 and replaced by ::cuDeviceGetAttribute().
+ *
+ * Returns in \p *prop the properties of device \p dev. The ::CUdevprop
+ * structure is defined as:
+ *
+ * \code
+     typedef struct CUdevprop_st {
+     int maxThreadsPerBlock;
+     int maxThreadsDim[3];
+     int maxGridSize[3];
+     int sharedMemPerBlock;
+     int totalConstantMemory;
+     int SIMDWidth;
+     int memPitch;
+     int regsPerBlock;
+     int clockRate;
+     int textureAlign
+  } CUdevprop;
+ * \endcode
+ * where:
+ *
+ * - ::maxThreadsPerBlock is the maximum number of threads per block;
+ * - ::maxThreadsDim[3] is the maximum sizes of each dimension of a block;
+ * - ::maxGridSize[3] is the maximum sizes of each dimension of a grid;
+ * - ::sharedMemPerBlock is the total amount of shared memory available per
+ *   block in bytes;
+ * - ::totalConstantMemory is the total amount of constant memory available on
+ *   the device in bytes;
+ * - ::SIMDWidth is the warp size;
+ * - ::memPitch is the maximum pitch allowed by the memory copy functions that
+ *   involve memory regions allocated through ::cuMemAllocPitch();
+ * - ::regsPerBlock is the total number of registers available per block;
+ * - ::clockRate is the clock frequency in kilohertz;
+ * - ::textureAlign is the alignment requirement; texture base addresses that
+ *   are aligned to ::textureAlign bytes do not need an offset applied to
+ *   texture fetches.
+ *
+ * \param prop - Returned properties of device
+ * \param dev  - Device to get properties for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuDeviceGetProperties(CUdevprop *prop, CUdevice dev);
+
+/**
+ * \brief Returns the compute capability of the device
+ *
+ * \deprecated
+ *
+ * This function was deprecated as of CUDA 5.0 and its functionality superseded
+ * by ::cuDeviceGetAttribute().
+ *
+ * Returns in \p *major and \p *minor the major and minor revision numbers that
+ * define the compute capability of the device \p dev.
+ *
+ * \param major - Major revision number
+ * \param minor - Minor revision number
+ * \param dev   - Device handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetCount,
+ * ::cuDeviceGetName,
+ * ::cuDeviceGetUuid,
+ * ::cuDeviceGet,
+ * ::cuDeviceTotalMem
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuDeviceComputeCapability(int *major, int *minor, CUdevice dev);
+
+/** @} */ /* END CUDA_DEVICE_DEPRECATED */
+
+/**
+ * \defgroup CUDA_PRIMARY_CTX Primary Context Management
+ *
+ * ___MANBRIEF___ primary context management functions of the low-level CUDA driver
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the primary context management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * The primary context is unique per device and shared with the CUDA runtime API.
+ * These functions allow integration with other libraries using CUDA.
+ *
+ * @{
+ */
+
+/**
+ * \brief Retain the primary context on the GPU
+ *
+ * Retains the primary context on the device.
+ * Once the user successfully retains the primary context, the primary context
+ * will be active and available to the user until the user releases it
+ * with ::cuDevicePrimaryCtxRelease() or resets it with ::cuDevicePrimaryCtxReset().
+ * Unlike ::cuCtxCreate() the newly retained context is not pushed onto the stack.
+ *
+ * Retaining the primary context for the first time will fail with ::CUDA_ERROR_UNKNOWN
+ * if the compute mode of the device is ::CU_COMPUTEMODE_PROHIBITED. The function
+ * ::cuDeviceGetAttribute() can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to
+ * determine the compute mode  of the device.
+ * The <i>nvidia-smi</i> tool can be used to set the compute mode for
+ * devices. Documentation for <i>nvidia-smi</i> can be obtained by passing a
+ * -h option to it.
+ *
+ * Please note that the primary context always supports pinned allocations. Other
+ * flags can be specified by ::cuDevicePrimaryCtxSetFlags().
+ *
+ * \param pctx  - Returned context handle of the new context
+ * \param dev   - Device for which primary context is requested
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuDevicePrimaryCtxRelease,
+ * ::cuDevicePrimaryCtxSetFlags,
+ * ::cuCtxCreate,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuDevicePrimaryCtxRetain(CUcontext *pctx, CUdevice dev);
+
+/**
+ * \brief Release the primary context on the GPU
+ *
+ * Releases the primary context interop on the device.
+ * A retained context should always be released once the user is done using
+ * it. The context is automatically reset once the last reference to it is
+ * released. This behavior is different when the primary context was retained
+ * by the CUDA runtime from CUDA 4.0 and earlier. In this case, the primary
+ * context remains always active.
+ *
+ * Releasing a primary context that has not been previously retained will
+ * fail with ::CUDA_ERROR_INVALID_CONTEXT.
+ *
+ * Please note that unlike ::cuCtxDestroy() this method does not pop the context
+ * from stack in any circumstances.
+ *
+ * \param dev - Device which primary context is released
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuDevicePrimaryCtxRetain,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuDevicePrimaryCtxRelease(CUdevice dev);
+
+/**
+ * \brief Set flags for the primary context
+ *
+ * Sets the flags for the primary context on the device overwriting perviously
+ * set ones.
+ *
+ * The three LSBs of the \p flags parameter can be used to control how the OS
+ * thread, which owns the CUDA context at the time of an API call, interacts
+ * with the OS scheduler when waiting for results from the GPU. Only one of
+ * the scheduling flags can be set when creating a context.
+ *
+ * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
+ * results from the GPU. This can decrease latency when waiting for the GPU,
+ * but may lower the performance of CPU threads if they are performing work in
+ * parallel with the CUDA thread.
+ *
+ * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for
+ * results from the GPU. This can increase latency when waiting for the GPU,
+ * but can increase the performance of CPU threads performing work in parallel
+ * with the GPU.
+ *
+ * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work.
+ *
+ * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work. <br>
+ * <b>Deprecated:</b> This flag was deprecated as of CUDA 4.0 and was
+ * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC.
+ *
+ * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero,
+ * uses a heuristic based on the number of active CUDA contexts in the
+ * process \e C and the number of logical processors in the system \e P. If
+ * \e C > \e P, then CUDA will yield to other OS threads when waiting for
+ * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while
+ * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN).
+ * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on
+ * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC
+ * for low-powered devices.
+ *
+ * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory
+ * after resizing local memory for a kernel. This can prevent thrashing by
+ * local memory allocations when launching many kernels with high local
+ * memory usage at the cost of potentially increased memory usage. <br>
+ * <b>Deprecated:</b> This flag is deprecated and the behavior enabled
+ * by this flag is now the default and cannot be disabled.
+ *
+ * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally
+ * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can
+ * be set during context creation to instruct CUDA to create a coredump if
+ * this context raises an exception during execution. These environment variables
+ * are described in the CUDA-GDB user guide under the "GPU core dump support"
+ * section.
+ * The initial settings will be taken from the global settings at the time of
+ * context creation. The other settings that control coredump output can be 
+ * modified by calling ::cuCoredumpSetAttribute from the created context after
+ * it becomes current.
+ *
+ * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not
+ * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment 
+ * variables, this flag can be set during context creation to instruct CUDA to
+ * create a coredump if data is written to a certain pipe that is present in the
+ * OS space. These environment variables are described in the CUDA-GDB user
+ * guide under the "GPU core dump support" section.
+ * It is important to note that the pipe name *must* be set with
+ * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is
+ * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set.
+ * The initial settings will be taken from the global settings at the time of
+ * context creation. The other settings that control coredump output can be 
+ * modified by calling ::cuCoredumpSetAttribute from the created context after 
+ * it becomes current.
+ *
+ * - ::CU_CTX_SYNC_MEMOPS: Ensures that synchronous memory operations initiated
+ * on this context will always synchronize. See further documentation in the
+ * section titled "API Synchronization behavior" to learn more about cases when
+ * synchronous memory operations can exhibit asynchronous behavior.
+ *
+ * \param dev   - Device for which the primary context flags are set
+ * \param flags - New flags for the device
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa ::cuDevicePrimaryCtxRetain,
+ * ::cuDevicePrimaryCtxGetState,
+ * ::cuCtxCreate,
+ * ::cuCtxGetFlags,
+ * ::cuCtxSetFlags,
+ * ::cudaSetDeviceFlags
+ */
+CUresult CUDAAPI cuDevicePrimaryCtxSetFlags(CUdevice dev, unsigned int flags);
+
+/**
+ * \brief Get the state of the primary context
+ *
+ * Returns in \p *flags the flags for the primary context of \p dev, and in
+ * \p *active whether it is active.  See ::cuDevicePrimaryCtxSetFlags for flag
+ * values.
+ *
+ * \param dev    - Device to get primary context flags for
+ * \param flags  - Pointer to store flags
+ * \param active - Pointer to store context state; 0 = inactive, 1 = active
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDevicePrimaryCtxSetFlags,
+ * ::cuCtxGetFlags,
+ * ::cuCtxSetFlags,
+ * ::cudaGetDeviceFlags
+ */
+CUresult CUDAAPI cuDevicePrimaryCtxGetState(CUdevice dev, unsigned int *flags, int *active);
+
+/**
+ * \brief Destroy all allocations and reset all state on the primary context
+ *
+ * Explicitly destroys and cleans up all resources associated with the current
+ * device in the current process.
+ *
+ * Note that it is responsibility of the calling function to ensure that no
+ * other module in the process is using the device any more. For that reason
+ * it is recommended to use ::cuDevicePrimaryCtxRelease() in most cases.
+ * However it is safe for other modules to call ::cuDevicePrimaryCtxRelease()
+ * even after resetting the device.
+ * Resetting the primary context does not release it, an application that has
+ * retained the primary context should explicitly release its usage.
+ *
+ * \param dev - Device for which primary context is destroyed
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE
+ * \notefnerr
+ *
+ * \sa ::cuDevicePrimaryCtxRetain,
+ * ::cuDevicePrimaryCtxRelease,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cudaDeviceReset
+ */
+CUresult CUDAAPI cuDevicePrimaryCtxReset(CUdevice dev);
+
+/** @} */ /* END CUDA_PRIMARY_CTX */
+
+/**
+ * \defgroup CUDA_CTX Context Management
+ *
+ * ___MANBRIEF___ context management functions of the low-level CUDA driver
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the context management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * Please note that some functions are described in
+ * \ref CUDA_PRIMARY_CTX "Primary Context Management" section.
+ *
+ * @{
+ */
+
+/**
+ * \brief Create a CUDA context
+ *
+ * \note In most cases it is recommended to use ::cuDevicePrimaryCtxRetain.
+ *
+ * Creates a new CUDA context and associates it with the calling thread. The
+ * \p flags parameter is described below. The context is created with a usage
+ * count of 1 and the caller of ::cuCtxCreate() must call ::cuCtxDestroy()
+ * when done using the context. If a context is already current to the thread,
+ * it is supplanted by the newly created context and may be restored by a subsequent
+ * call to ::cuCtxPopCurrent().
+ *
+ * The three LSBs of the \p flags parameter can be used to control how the OS
+ * thread, which owns the CUDA context at the time of an API call, interacts
+ * with the OS scheduler when waiting for results from the GPU. Only one of
+ * the scheduling flags can be set when creating a context.
+ *
+ * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
+ * results from the GPU. This can decrease latency when waiting for the GPU,
+ * but may lower the performance of CPU threads if they are performing work in
+ * parallel with the CUDA thread.
+ *
+ * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for
+ * results from the GPU. This can increase latency when waiting for the GPU,
+ * but can increase the performance of CPU threads performing work in parallel
+ * with the GPU.
+ *
+ * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work.
+ *
+ * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work. <br>
+ * <b>Deprecated:</b> This flag was deprecated as of CUDA 4.0 and was
+ * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC.
+ *
+ * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero,
+ * uses a heuristic based on the number of active CUDA contexts in the
+ * process \e C and the number of logical processors in the system \e P. If
+ * \e C > \e P, then CUDA will yield to other OS threads when waiting for
+ * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while
+ * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN).
+ * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on
+ * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC
+ * for low-powered devices.
+ *
+ * - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations.
+ * This flag must be set in order to allocate pinned host memory that is
+ * accessible to the GPU.
+ *
+ * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory
+ * after resizing local memory for a kernel. This can prevent thrashing by
+ * local memory allocations when launching many kernels with high local
+ * memory usage at the cost of potentially increased memory usage. <br>
+ * <b>Deprecated:</b> This flag is deprecated and the behavior enabled
+ * by this flag is now the default and cannot be disabled.
+ * Instead, the per-thread stack size can be controlled with ::cuCtxSetLimit().
+ *
+ * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally
+ * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can
+ * be set during context creation to instruct CUDA to create a coredump if
+ * this context raises an exception during execution. These environment variables
+ * are described in the CUDA-GDB user guide under the "GPU core dump support"
+ * section.
+ * The initial attributes will be taken from the global attributes at the time of
+ * context creation. The other attributes that control coredump output can be 
+ * modified by calling ::cuCoredumpSetAttribute from the created context after
+ * it becomes current.
+ *
+ * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not
+ * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment 
+ * variables, this flag can be set during context creation to instruct CUDA to
+ * create a coredump if data is written to a certain pipe that is present in the
+ * OS space. These environment variables are described in the CUDA-GDB user
+ * guide under the "GPU core dump support" section.
+ * It is important to note that the pipe name *must* be set with
+ * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is
+ * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set.
+ * The initial attributes will be taken from the global attributes at the time of
+ * context creation. The other attributes that control coredump output can be 
+ * modified by calling ::cuCoredumpSetAttribute from the created context after 
+ * it becomes current.
+ * Setting this flag on any context creation is equivalent to setting the 
+ * ::CU_COREDUMP_ENABLE_USER_TRIGGER attribute to \p true globally.
+ *
+ * - ::CU_CTX_SYNC_MEMOPS: Ensures that synchronous memory operations initiated
+ * on this context will always synchronize. See further documentation in the
+ * section titled "API Synchronization behavior" to learn more about cases when
+ * synchronous memory operations can exhibit asynchronous behavior.
+ *
+ * Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of
+ * the device is ::CU_COMPUTEMODE_PROHIBITED. The function ::cuDeviceGetAttribute()
+ * can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the
+ * compute mode of the device. The <i>nvidia-smi</i> tool can be used to set
+ * the compute mode for * devices.
+ * Documentation for <i>nvidia-smi</i> can be obtained by passing a
+ * -h option to it.
+ *
+ * \param pctx  - Returned context handle of the new context
+ * \param flags - Context creation flags
+ * \param dev   - Device to create context on
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCoredumpSetAttributeGlobal,
+ * ::cuCoredumpSetAttribute,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuCtxCreate(CUcontext *pctx, unsigned int flags, CUdevice dev);
+
+/**
+ * \brief Create a CUDA context with execution affinity
+ *
+ * Creates a new CUDA context with execution affinity and associates it with
+ * the calling thread. The \p paramsArray and \p flags parameter are described below.
+ * The context is created with a usage count of 1 and the caller of ::cuCtxCreate() must
+ * call ::cuCtxDestroy() when done using the context. If a context is already
+ * current to the thread, it is supplanted by the newly created context and may
+ * be restored by a subsequent call to ::cuCtxPopCurrent().
+ *
+ * The type and the amount of execution resource the context can use is limited by \p paramsArray
+ * and \p numParams. The \p paramsArray is an array of \p CUexecAffinityParam and the \p numParams
+ * describes the size of the array. If two \p CUexecAffinityParam in the array have the same type,
+ * the latter execution affinity parameter overrides the former execution affinity parameter.
+ * The supported execution affinity types are:
+ * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT limits the portion of SMs that the context can use. The portion
+ *   of SMs is specified as the number of SMs via \p CUexecAffinitySmCount. This limit will be internally
+ *   rounded up to the next hardware-supported amount. Hence, it is imperative to query the actual execution
+ *   affinity of the context via \p cuCtxGetExecAffinity after context creation. Currently, this attribute
+ *   is only supported under Volta+ MPS.
+ *
+ * The three LSBs of the \p flags parameter can be used to control how the OS
+ * thread, which owns the CUDA context at the time of an API call, interacts
+ * with the OS scheduler when waiting for results from the GPU. Only one of
+ * the scheduling flags can be set when creating a context.
+ *
+ * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
+ * results from the GPU. This can decrease latency when waiting for the GPU,
+ * but may lower the performance of CPU threads if they are performing work in
+ * parallel with the CUDA thread.
+ *
+ * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for
+ * results from the GPU. This can increase latency when waiting for the GPU,
+ * but can increase the performance of CPU threads performing work in parallel
+ * with the GPU.
+ *
+ * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work.
+ *
+ * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work. <br>
+ * <b>Deprecated:</b> This flag was deprecated as of CUDA 4.0 and was
+ * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC.
+ *
+ * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero,
+ * uses a heuristic based on the number of active CUDA contexts in the
+ * process \e C and the number of logical processors in the system \e P. If
+ * \e C > \e P, then CUDA will yield to other OS threads when waiting for
+ * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while
+ * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN).
+ * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on
+ * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC
+ * for low-powered devices.
+ *
+ * - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations.
+ * This flag must be set in order to allocate pinned host memory that is
+ * accessible to the GPU.
+ *
+ * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory
+ * after resizing local memory for a kernel. This can prevent thrashing by
+ * local memory allocations when launching many kernels with high local
+ * memory usage at the cost of potentially increased memory usage. <br>
+ * <b>Deprecated:</b> This flag is deprecated and the behavior enabled
+ * by this flag is now the default and cannot be disabled.
+ * Instead, the per-thread stack size can be controlled with ::cuCtxSetLimit().
+ *
+ * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally
+ * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can
+ * be set during context creation to instruct CUDA to create a coredump if
+ * this context raises an exception during execution. These environment variables
+ * are described in the CUDA-GDB user guide under the "GPU core dump support"
+ * section.
+ * The initial attributes will be taken from the global attributes at the time of
+ * context creation. The other attributes that control coredump output can be 
+ * modified by calling ::cuCoredumpSetAttribute from the created context after
+ * it becomes current.
+ *
+ * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not
+ * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment 
+ * variables, this flag can be set during context creation to instruct CUDA to
+ * create a coredump if data is written to a certain pipe that is present in the
+ * OS space. These environment variables are described in the CUDA-GDB user
+ * guide under the "GPU core dump support" section.
+ * It is important to note that the pipe name *must* be set with
+ * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is
+ * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set.
+ * The initial attributes will be taken from the global attributes at the time of
+ * context creation. The other attributes that control coredump output can be 
+ * modified by calling ::cuCoredumpSetAttribute from the created context after 
+ * it becomes current.
+ * Setting this flag on any context creation is equivalent to setting the 
+ * ::CU_COREDUMP_ENABLE_USER_TRIGGER attribute to \p true globally.
+ *
+ * Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of
+ * the device is ::CU_COMPUTEMODE_PROHIBITED. The function ::cuDeviceGetAttribute()
+ * can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the
+ * compute mode of the device. The <i>nvidia-smi</i> tool can be used to set
+ * the compute mode for * devices.
+ * Documentation for <i>nvidia-smi</i> can be obtained by passing a
+ * -h option to it.
+ *
+ * \param pctx        - Returned context handle of the new context
+ * \param paramsArray - Execution affinity parameters
+ * \param numParams   - Number of execution affinity parameters
+ * \param flags       - Context creation flags
+ * \param dev         - Device to create context on
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cuCoredumpSetAttributeGlobal,
+ * ::cuCoredumpSetAttribute,
+ * ::CUexecAffinityParam
+ */
+CUresult CUDAAPI cuCtxCreate_v3(CUcontext *pctx, CUexecAffinityParam *paramsArray, int numParams, unsigned int flags, CUdevice dev);
+
+/**
+ * \brief Create a CUDA context
+ *
+ * Creates a new CUDA context and associates it with the calling thread. The
+ * \p flags parameter is described below. The context is created with a usage
+ * count of 1 and the caller of ::cuCtxCreate() must call ::cuCtxDestroy()
+ * when done using the context. If a context is already current to the thread,
+ * it is supplanted by the newly created context and may be restored by a subsequent
+ * call to ::cuCtxPopCurrent().
+ * 
+ * CUDA context can be created with execution affinity. The type and the amount of 
+   execution resource the context can use is limited by \p paramsArray and \p numExecAffinityParams
+   in \p execAffinity. The \p paramsArray is an array of \p CUexecAffinityParam and the \p numExecAffinityParams
+ * describes the size of the paramsArray. If two \p CUexecAffinityParam in the array have the same type,
+ * the latter execution affinity parameter overrides the former execution affinity parameter.
+ * The supported execution affinity types are:
+ * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT limits the portion of SMs that the context can use. The portion
+ *   of SMs is specified as the number of SMs via \p CUexecAffinitySmCount. This limit will be internally
+ *   rounded up to the next hardware-supported amount. Hence, it is imperative to query the actual execution
+ *   affinity of the context via \p cuCtxGetExecAffinity after context creation. Currently, this attribute
+ *   is only supported under Volta+ MPS.
+ * 
+ * CUDA context can be created in CIG(CUDA in Graphics) mode by setting \p cigParams. 
+ * Data from graphics client is shared with CUDA via the \p sharedData in \p cigParams. 
+ * Support for D3D12 graphics client can be determined using ::cuDeviceGetAttribute() with 
+ * ::CU_DEVICE_ATTRIBUTE_D3D12_CIG_SUPPORTED. \p sharedData is a ID3D12CommandQueue handle.
+ * Either \p execAffinityParams or \p cigParams can be set to a non-null value. Setting both to a 
+ * non-null value will result in an undefined behavior.
+ * 
+ * The three LSBs of the \p flags parameter can be used to control how the OS
+ * thread, which owns the CUDA context at the time of an API call, interacts
+ * with the OS scheduler when waiting for results from the GPU. Only one of
+ * the scheduling flags can be set when creating a context.
+ *
+ * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for
+ * results from the GPU. This can decrease latency when waiting for the GPU,
+ * but may lower the performance of CPU threads if they are performing work in
+ * parallel with the CUDA thread.
+ *
+ * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for
+ * results from the GPU. This can increase latency when waiting for the GPU,
+ * but can increase the performance of CPU threads performing work in parallel
+ * with the GPU.
+ *
+ * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work.
+ *
+ * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a
+ * synchronization primitive when waiting for the GPU to finish work. <br>
+ * <b>Deprecated:</b> This flag was deprecated as of CUDA 4.0 and was
+ * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC.
+ *
+ * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero,
+ * uses a heuristic based on the number of active CUDA contexts in the
+ * process \e C and the number of logical processors in the system \e P. If
+ * \e C > \e P, then CUDA will yield to other OS threads when waiting for
+ * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while
+ * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN).
+ * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on
+ * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC
+ * for low-powered devices.
+ *
+ * - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations.
+ * This flag must be set in order to allocate pinned host memory that is
+ * accessible to the GPU.
+ *
+ * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory
+ * after resizing local memory for a kernel. This can prevent thrashing by
+ * local memory allocations when launching many kernels with high local
+ * memory usage at the cost of potentially increased memory usage. <br>
+ * <b>Deprecated:</b> This flag is deprecated and the behavior enabled
+ * by this flag is now the default and cannot be disabled.
+ * Instead, the per-thread stack size can be controlled with ::cuCtxSetLimit().
+ *
+ * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally
+ * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can
+ * be set during context creation to instruct CUDA to create a coredump if
+ * this context raises an exception during execution. These environment variables
+ * are described in the CUDA-GDB user guide under the "GPU core dump support"
+ * section.
+ * The initial attributes will be taken from the global attributes at the time of
+ * context creation. The other attributes that control coredump output can be
+ * modified by calling ::cuCoredumpSetAttribute from the created context after
+ * it becomes current. This flag is not supported when CUDA context is created in
+ * CIG(CUDA in Graphics) mode.
+ *
+ * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not
+ * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment
+ * variables, this flag can be set during context creation to instruct CUDA to
+ * create a coredump if data is written to a certain pipe that is present in the
+ * OS space. These environment variables are described in the CUDA-GDB user
+ * guide under the "GPU core dump support" section.
+ * It is important to note that the pipe name *must* be set with
+ * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is
+ * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set.
+ * The initial attributes will be taken from the global attributes at the time of
+ * context creation. The other attributes that control coredump output can be
+ * modified by calling ::cuCoredumpSetAttribute from the created context after
+ * it becomes current.
+ * Setting this flag on any context creation is equivalent to setting the
+ * ::CU_COREDUMP_ENABLE_USER_TRIGGER attribute to \p true globally.
+ * This flag is not supported when CUDA context is created in
+ * CIG(CUDA in Graphics) mode.
+ *
+ * - ::CU_CTX_SYNC_MEMOPS: Ensures that synchronous memory operations initiated
+ * on this context will always synchronize. See further documentation in the
+ * section titled "API Synchronization behavior" to learn more about cases when
+ * synchronous memory operations can exhibit asynchronous behavior.
+ *
+ * Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of
+ * the device is ::CU_COMPUTEMODE_PROHIBITED. The function ::cuDeviceGetAttribute()
+ * can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the
+ * compute mode of the device. The <i>nvidia-smi</i> tool can be used to set
+ * the compute mode for * devices.
+ * Documentation for <i>nvidia-smi</i> can be obtained by passing a
+ * -h option to it.
+ *
+ * Context creation will fail with :: CUDA_ERROR_INVALID_VALUE if invalid parameter was
+ * passed by client to create the CUDA context.
+ * 
+ * Context creation in CIG mode will fail with ::CUDA_ERROR_NOT_SUPPORTED if CIG is not supported
+ * by the device or the driver.
+ * \param pctx              - Returned context handle of the new context
+ * \param ctxCreateParams   - Context creation parameters
+ * \param flags             - Context creation flags
+ * \param dev               - Device to create context on
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCoredumpSetAttributeGlobal,
+ * ::cuCoredumpSetAttribute,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuCtxCreate_v4(CUcontext *pctx, CUctxCreateParams *ctxCreateParams, unsigned int flags, CUdevice dev);
+
+/**
+ * \brief Destroy a CUDA context
+ *
+ * Destroys the CUDA context specified by \p ctx.  The context \p ctx will be
+ * destroyed regardless of how many threads it is current to.
+ * It is the responsibility of the calling function to ensure that no API
+ * call issues using \p ctx while ::cuCtxDestroy() is executing.
+ *
+ * Destroys and cleans up all resources associated with the context.
+ * It is the caller's responsibility to ensure that the context or its resources
+ * are not accessed or passed in subsequent API calls and doing so will result in undefined behavior.
+ * These resources include CUDA types ::CUmodule, ::CUfunction, ::CUstream, ::CUevent,
+ * ::CUarray, ::CUmipmappedArray, ::CUtexObject, ::CUsurfObject, ::CUtexref, ::CUsurfref,
+ * ::CUgraphicsResource, ::CUlinkState, ::CUexternalMemory and ::CUexternalSemaphore.
+ * These resources also include memory allocations by ::cuMemAlloc(), ::cuMemAllocHost(),
+ * ::cuMemAllocManaged() and ::cuMemAllocPitch().
+ *
+ * If \p ctx is current to the calling thread then \p ctx will also be
+ * popped from the current thread's context stack (as though ::cuCtxPopCurrent()
+ * were called).  If \p ctx is current to other threads, then \p ctx will
+ * remain current to those threads, and attempting to access \p ctx from
+ * those threads will result in the error ::CUDA_ERROR_CONTEXT_IS_DESTROYED.
+ *
+ * \note ::cuCtxDestroy() will not destroy memory allocations by ::cuMemCreate(), ::cuMemAllocAsync() and
+ * ::cuMemAllocFromPoolAsync(). These memory allocations are not associated with any CUDA context and need to
+ * be destroyed explicitly.
+ *
+ * \param ctx - Context to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuCtxDestroy(CUcontext ctx);
+
+/**
+ * \brief Pushes a context on the current CPU thread
+ *
+ * Pushes the given context \p ctx onto the CPU thread's stack of current
+ * contexts. The specified context becomes the CPU thread's current context, so
+ * all CUDA functions that operate on the current context are affected.
+ *
+ * The previous current context may be made current again by calling
+ * ::cuCtxDestroy() or ::cuCtxPopCurrent().
+ *
+ * \param ctx - Context to push
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx);
+
+/**
+ * \brief Pops the current CUDA context from the current CPU thread.
+ *
+ * Pops the current CUDA context from the CPU thread and passes back the
+ * old context handle in \p *pctx. That context may then be made current
+ * to a different CPU thread by calling ::cuCtxPushCurrent().
+ *
+ * If a context was current to the CPU thread before ::cuCtxCreate() or
+ * ::cuCtxPushCurrent() was called, this function makes that context current to
+ * the CPU thread again.
+ *
+ * \param pctx - Returned popped context handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx);
+
+/**
+ * \brief Binds the specified CUDA context to the calling CPU thread
+ *
+ * Binds the specified CUDA context to the calling CPU thread.
+ * If \p ctx is NULL then the CUDA context previously bound to the
+ * calling CPU thread is unbound and ::CUDA_SUCCESS is returned.
+ *
+ * If there exists a CUDA context stack on the calling CPU thread, this
+ * will replace the top of that stack with \p ctx.
+ * If \p ctx is NULL then this will be equivalent to popping the top
+ * of the calling CPU thread's CUDA context stack (or a no-op if the
+ * calling CPU thread's CUDA context stack is empty).
+ *
+ * \param ctx - Context to bind to the calling CPU thread
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa
+ * ::cuCtxGetCurrent,
+ * ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cudaSetDevice
+ */
+CUresult CUDAAPI cuCtxSetCurrent(CUcontext ctx);
+
+/**
+ * \brief Returns the CUDA context bound to the calling CPU thread.
+ *
+ * Returns in \p *pctx the CUDA context bound to the calling CPU thread.
+ * If no context is bound to the calling CPU thread then \p *pctx is
+ * set to NULL and ::CUDA_SUCCESS is returned.
+ *
+ * \param pctx - Returned context handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * \notefnerr
+ *
+ * \sa
+ * ::cuCtxSetCurrent,
+ * ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cudaGetDevice
+ */
+CUresult CUDAAPI cuCtxGetCurrent(CUcontext *pctx);
+
+/**
+ * \brief Returns the device handle for the current context
+ *
+ * Returns in \p *device the handle of the current context's device.
+ *
+ * \param device - Returned device handle for the current context
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cudaGetDevice
+ */
+CUresult CUDAAPI cuCtxGetDevice(CUdevice *device);
+
+/**
+ * \brief Returns the flags for the current context
+ *
+ * Returns in \p *flags the flags of the current context. See ::cuCtxCreate
+ * for flag values.
+ *
+ * \param flags - Pointer to store flags of current context
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetCurrent,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetLimit,
+ * ::cuCtxGetSharedMemConfig,
+ * ::cuCtxGetStreamPriorityRange,
+ * ::cuCtxSetFlags,
+ * ::cudaGetDeviceFlags
+ */
+CUresult CUDAAPI cuCtxGetFlags(unsigned int *flags);
+
+/**
+ * \brief Sets the flags for the current context
+ *
+ * Sets the flags for the current context overwriting previously set ones. See
+ * ::cuDevicePrimaryCtxSetFlags for flag values.
+ *
+ * \param flags - Flags to set on the current context
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetCurrent,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetLimit,
+ * ::cuCtxGetSharedMemConfig,
+ * ::cuCtxGetStreamPriorityRange,
+ * ::cuCtxGetFlags,
+ * ::cudaGetDeviceFlags,
+ * ::cuDevicePrimaryCtxSetFlags,
+ */
+CUresult CUDAAPI cuCtxSetFlags(unsigned int flags);
+
+/**
+ * \brief Returns the unique Id associated with the context supplied
+ *
+ * Returns in \p ctxId the unique Id which is associated with a given context.
+ * The Id is unique for the life of the program for this instance of CUDA.
+ * If context is supplied as NULL and there is one current, the Id of the
+ * current context is returned.
+ *
+ * \param ctx - Context for which to obtain the Id
+ * \param ctxId - Pointer to store the Id of the context
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPushCurrent
+ */
+CUresult CUDAAPI cuCtxGetId(CUcontext ctx, unsigned long long *ctxId);
+
+/**
+ * \brief Block for the current context's tasks to complete
+ *
+ * Blocks until the current context has completed all preceding requested tasks.
+ * If the current context is the primary context, green contexts that have been
+ * created will also be synchronized.
+ * ::cuCtxSynchronize() returns an error if one of the preceding tasks failed.
+ * If the context was created with the ::CU_CTX_SCHED_BLOCKING_SYNC flag, the
+ * CPU thread will block until the GPU context has finished its work.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cudaDeviceSynchronize
+ */
+CUresult CUDAAPI cuCtxSynchronize(void);
+
+/**
+ * \brief Set resource limits
+ *
+ * Setting \p limit to \p value is a request by the application to update
+ * the current limit maintained by the context. The driver is free to
+ * modify the requested value to meet h/w requirements (this could be
+ * clamping to minimum or maximum values, rounding up to nearest element
+ * size, etc). The application can use ::cuCtxGetLimit() to find out exactly
+ * what the limit has been set to.
+ *
+ * Setting each ::CUlimit has its own specific restrictions, so each is
+ * discussed here.
+ *
+ * - ::CU_LIMIT_STACK_SIZE controls the stack size in bytes of each GPU thread.
+ *   The driver automatically increases the per-thread stack size
+ *   for each kernel launch as needed. This size isn't reset back to the
+ *   original value after each launch. Setting this value will take effect 
+ *   immediately, and if necessary, the device will block until all preceding 
+ *   requested tasks are complete.
+ *
+ * - ::CU_LIMIT_PRINTF_FIFO_SIZE controls the size in bytes of the FIFO used
+ *   by the ::printf() device system call. Setting ::CU_LIMIT_PRINTF_FIFO_SIZE
+ *   must be performed before launching any kernel that uses the ::printf()
+ *   device system call, otherwise ::CUDA_ERROR_INVALID_VALUE will be returned.
+ *
+ * - ::CU_LIMIT_MALLOC_HEAP_SIZE controls the size in bytes of the heap used
+ *   by the ::malloc() and ::free() device system calls. Setting
+ *   ::CU_LIMIT_MALLOC_HEAP_SIZE must be performed before launching any kernel
+ *   that uses the ::malloc() or ::free() device system calls, otherwise
+ *   ::CUDA_ERROR_INVALID_VALUE will be returned.
+ *
+ * - ::CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH controls the maximum nesting depth of
+ *   a grid at which a thread can safely call ::cudaDeviceSynchronize(). Setting
+ *   this limit must be performed before any launch of a kernel that uses the
+ *   device runtime and calls ::cudaDeviceSynchronize() above the default sync
+ *   depth, two levels of grids. Calls to ::cudaDeviceSynchronize() will fail
+ *   with error code ::cudaErrorSyncDepthExceeded if the limitation is
+ *   violated. This limit can be set smaller than the default or up the maximum
+ *   launch depth of 24. When setting this limit, keep in mind that additional
+ *   levels of sync depth require the driver to reserve large amounts of device
+ *   memory which can no longer be used for user allocations. If these
+ *   reservations of device memory fail, ::cuCtxSetLimit() will return
+ *   ::CUDA_ERROR_OUT_OF_MEMORY, and the limit can be reset to a lower value.
+ *   This limit is only applicable to devices of compute capability < 9.0.
+ *   Attempting to set this limit on devices of other compute capability
+ *   versions will result in the error ::CUDA_ERROR_UNSUPPORTED_LIMIT being
+ *   returned.
+ *
+ * - ::CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT controls the maximum number of
+ *   outstanding device runtime launches that can be made from the current
+ *   context. A grid is outstanding from the point of launch up until the grid
+ *   is known to have been completed. Device runtime launches which violate
+ *   this limitation fail and return ::cudaErrorLaunchPendingCountExceeded when
+ *   ::cudaGetLastError() is called after launch. If more pending launches than
+ *   the default (2048 launches) are needed for a module using the device
+ *   runtime, this limit can be increased. Keep in mind that being able to
+ *   sustain additional pending launches will require the driver to reserve
+ *   larger amounts of device memory upfront which can no longer be used for
+ *   allocations. If these reservations fail, ::cuCtxSetLimit() will return
+ *   ::CUDA_ERROR_OUT_OF_MEMORY, and the limit can be reset to a lower value.
+ *   This limit is only applicable to devices of compute capability 3.5 and
+ *   higher. Attempting to set this limit on devices of compute capability less
+ *   than 3.5 will result in the error ::CUDA_ERROR_UNSUPPORTED_LIMIT being
+ *   returned.
+ *
+ * - ::CU_LIMIT_MAX_L2_FETCH_GRANULARITY controls the L2 cache fetch granularity.
+ *   Values can range from 0B to 128B. This is purely a performance hint and
+ *   it can be ignored or clamped depending on the platform.
+ *
+ * - ::CU_LIMIT_PERSISTING_L2_CACHE_SIZE controls size in bytes available for
+ *   persisting L2 cache. This is purely a performance hint and it can be
+ *   ignored or clamped depending on the platform.
+ *
+ * \param limit - Limit to set
+ * \param value - Size of limit
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNSUPPORTED_LIMIT,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSynchronize,
+ * ::cudaDeviceSetLimit
+ */
+CUresult CUDAAPI cuCtxSetLimit(CUlimit limit, size_t value);
+
+/**
+ * \brief Returns resource limits
+ *
+ * Returns in \p *pvalue the current size of \p limit.  The supported
+ * ::CUlimit values are:
+ * - ::CU_LIMIT_STACK_SIZE: stack size in bytes of each GPU thread.
+ * - ::CU_LIMIT_PRINTF_FIFO_SIZE: size in bytes of the FIFO used by the
+ *   ::printf() device system call.
+ * - ::CU_LIMIT_MALLOC_HEAP_SIZE: size in bytes of the heap used by the
+ *   ::malloc() and ::free() device system calls.
+ * - ::CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH: maximum grid depth at which a thread
+ *   can issue the device runtime call ::cudaDeviceSynchronize() to wait on
+ *   child grid launches to complete.
+ * - ::CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT: maximum number of outstanding
+ *   device runtime launches that can be made from this context.
+ * - ::CU_LIMIT_MAX_L2_FETCH_GRANULARITY: L2 cache fetch granularity.
+ * - ::CU_LIMIT_PERSISTING_L2_CACHE_SIZE: Persisting L2 cache size in bytes
+ *
+ * \param limit  - Limit to query
+ * \param pvalue - Returned size of limit
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNSUPPORTED_LIMIT
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cudaDeviceGetLimit
+ */
+CUresult CUDAAPI cuCtxGetLimit(size_t *pvalue, CUlimit limit);
+
+/**
+ * \brief Returns the preferred cache configuration for the current context.
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this function returns through \p pconfig the preferred cache configuration
+ * for the current context. This is only a preference. The driver will use
+ * the requested configuration if possible, but it is free to choose a different
+ * configuration if required to execute functions.
+ *
+ * This will return a \p pconfig of ::CU_FUNC_CACHE_PREFER_NONE on devices
+ * where the size of the L1 cache and shared memory are fixed.
+ *
+ * The supported cache configurations are:
+ * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default)
+ * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache
+ * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory
+ * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
+ *
+ * \param pconfig - Returned cache configuration
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cuFuncSetCacheConfig,
+ * ::cudaDeviceGetCacheConfig
+ */
+CUresult CUDAAPI cuCtxGetCacheConfig(CUfunc_cache *pconfig);
+
+/**
+ * \brief Sets the preferred cache configuration for the current context.
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p config the preferred cache configuration for
+ * the current context. This is only a preference. The driver will use
+ * the requested configuration if possible, but it is free to choose a different
+ * configuration if required to execute the function. Any function preference
+ * set via ::cuFuncSetCacheConfig() or ::cuKernelSetCacheConfig() will be preferred over this context-wide
+ * setting. Setting the context-wide cache configuration to
+ * ::CU_FUNC_CACHE_PREFER_NONE will cause subsequent kernel launches to prefer
+ * to not change the cache configuration unless required to launch the kernel.
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ * The supported cache configurations are:
+ * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default)
+ * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache
+ * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory
+ * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
+ *
+ * \param config - Requested cache configuration
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cuFuncSetCacheConfig,
+ * ::cudaDeviceSetCacheConfig,
+ * ::cuKernelSetCacheConfig
+ */
+CUresult CUDAAPI cuCtxSetCacheConfig(CUfunc_cache config);
+
+/**
+ * \brief Gets the context's API version.
+ *
+ * Returns a version number in \p version corresponding to the capabilities of
+ * the context (e.g. 3010 or 3020), which library developers can use to direct
+ * callers to a specific API version. If \p ctx is NULL, returns the API version
+ * used to create the currently bound context.
+ *
+ * Note that new API versions are only introduced when context capabilities are
+ * changed that break binary compatibility, so the API version and driver version
+ * may be different. For example, it is valid for the API version to be 3020 while
+ * the driver version is 4020.
+ *
+ * \param ctx     - Context to check
+ * \param version - Pointer to version
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+CUresult CUDAAPI cuCtxGetApiVersion(CUcontext ctx, unsigned int *version);
+
+/**
+ * \brief Returns numerical values that correspond to the least and
+ * greatest stream priorities.
+ *
+ * Returns in \p *leastPriority and \p *greatestPriority the numerical values that correspond
+ * to the least and greatest stream priorities respectively. Stream priorities
+ * follow a convention where lower numbers imply greater priorities. The range of
+ * meaningful stream priorities is given by [\p *greatestPriority, \p *leastPriority].
+ * If the user attempts to create a stream with a priority value that is
+ * outside the meaningful range as specified by this API, the priority is
+ * automatically clamped down or up to either \p *leastPriority or \p *greatestPriority
+ * respectively. See ::cuStreamCreateWithPriority for details on creating a
+ * priority stream.
+ * A NULL may be passed in for \p *leastPriority or \p *greatestPriority if the value
+ * is not desired.
+ *
+ * This function will return '0' in both \p *leastPriority and \p *greatestPriority if
+ * the current context's device does not support stream priorities
+ * (see ::cuDeviceGetAttribute).
+ *
+ * \param leastPriority    - Pointer to an int in which the numerical value for least
+ *                           stream priority is returned
+ * \param greatestPriority - Pointer to an int in which the numerical value for greatest
+ *                           stream priority is returned
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreateWithPriority,
+ * ::cuStreamGetPriority,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cudaDeviceGetStreamPriorityRange
+ */
+CUresult CUDAAPI cuCtxGetStreamPriorityRange(int *leastPriority, int *greatestPriority);
+
+/**
+ * \brief Resets all persisting lines in cache to normal status.
+ *
+ * ::cuCtxResetPersistingL2Cache Resets all persisting lines in cache to normal
+ * status. Takes effect on function return.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuCtxResetPersistingL2Cache(void);
+
+/**
+ * \brief Returns the execution affinity setting for the current context.
+ *
+ * Returns in \p *pExecAffinity the current value of \p type. The supported
+ * ::CUexecAffinityType values are:
+ * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT: number of SMs the context is limited to use.
+ *
+ * \param type          - Execution affinity type to query
+ * \param pExecAffinity - Returned execution affinity
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY
+ * \notefnerr
+ *
+ * \sa
+ * ::CUexecAffinityParam
+ */
+CUresult CUDAAPI cuCtxGetExecAffinity(CUexecAffinityParam *pExecAffinity, CUexecAffinityType type);
+
+/**
+ * \brief Records an event.
+ *
+ * Captures in \p hEvent all the activities of the context \p hCtx
+ * at the time of this call. \p hEvent and \p hCtx must be from the same
+ * CUDA context, otherwise ::CUDA_ERROR_INVALID_HANDLE will be returned.
+ * Calls such as ::cuEventQuery() or ::cuCtxWaitEvent() will then examine
+ * or wait for completion of the work that was captured.
+ * Uses of \p hCtx after this call do not modify \p hEvent.
+ * If the context passed to \p hCtx is the primary context, \p hEvent will
+ * capture all the activities of the primary context and its green contexts.
+ * If the context passed to \p hCtx is a context converted from green context
+ * via ::cuCtxFromGreenCtx(), \p hEvent will capture only the activities of the green context.
+ *
+ * \note The API will return ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED if the
+ * specified context \p hCtx has a stream in the capture mode. In such a case,
+ * the call will invalidate all the conflicting captures.
+ * 
+ * \param hCtx - Context to record event for
+ * \param hEvent - Event to record
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED
+ *
+ * \sa
+ * ::cuCtxWaitEvent,
+ * ::cuGreenCtxRecordEvent,
+ * ::cuGreenCtxWaitEvent,
+ * ::cuEventRecord
+ */
+CUresult CUDAAPI cuCtxRecordEvent(CUcontext hCtx, CUevent hEvent);
+
+/**
+ * \brief Make a context wait on an event
+ *
+ * Makes all future work submitted to context \p hCtx wait for all work
+ * captured in \p hEvent. The synchronization will be performed on the device
+ * and will not block the calling CPU thread. See ::cuCtxRecordEvent()
+ * for details on what is captured by an event.
+ * If the context passed to \p hCtx is the primary context, the primary context
+ * and its green contexts will wait for \p hEvent.
+ * If the context passed to \p hCtx is a context converted from green context
+ * via ::cuCtxFromGreenCtx(), the green context will wait for \p hEvent.
+ *
+ * \note \p hEvent may be from a different context or device than \p hCtx.
+ *
+ * \note The API will return ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED and
+ * invalidate the capture if the specified event \p hEvent is part of an ongoing
+ * capture sequence or if the specified context \p hCtx has a stream in the capture mode.
+ *
+ * \param hCtx    - Context to wait
+ * \param hEvent  - Event to wait on
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED
+ *
+ * \sa
+ * ::cuCtxRecordEvent,
+ * ::cuGreenCtxRecordEvent,
+ * ::cuGreenCtxWaitEvent,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuCtxWaitEvent(CUcontext hCtx, CUevent hEvent);
+
+/** @} */ /* END CUDA_CTX */
+
+/**
+ * \defgroup CUDA_CTX_DEPRECATED Context Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated context management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the deprecated context management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Increment a context's usage-count
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated and should not be used.
+ *
+ * Increments the usage count of the context and passes back a context handle
+ * in \p *pctx that must be passed to ::cuCtxDetach() when the application is
+ * done with the context. ::cuCtxAttach() fails if there is no context current
+ * to the thread.
+ *
+ * Currently, the \p flags parameter must be 0.
+ *
+ * \param pctx  - Returned context handle of the current context
+ * \param flags - Context attach flags (must be 0)
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxDetach,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuCtxAttach(CUcontext *pctx, unsigned int flags);
+
+/**
+ * \brief Decrement a context's usage-count
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated and should not be used.
+ *
+ * Decrements the usage count of the context \p ctx, and destroys the context
+ * if the usage count goes to 0. The context must be a handle that was passed
+ * back by ::cuCtxCreate() or ::cuCtxAttach(), and must be current to the
+ * calling thread.
+ *
+ * \param ctx - Context to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuCtxDetach(CUcontext ctx);
+
+
+/**
+ * \brief Returns the current shared memory configuration for the current context.
+ *
+ * \deprecated
+ *
+ * This function will return in \p pConfig the current size of shared memory banks
+ * in the current context. On devices with configurable shared memory banks,
+ * ::cuCtxSetSharedMemConfig can be used to change this setting, so that all
+ * subsequent kernel launches will by default use the new bank size. When
+ * ::cuCtxGetSharedMemConfig is called on devices without configurable shared
+ * memory, it will return the fixed bank size of the hardware.
+ *
+ * The returned bank configurations can be either:
+ * - ::CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE:  shared memory bank width is
+ *   four bytes.
+ * - ::CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: shared memory bank width will
+ *   eight bytes.
+ *
+ * \param pConfig - returned shared memory configuration
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cuCtxGetSharedMemConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cudaDeviceGetSharedMemConfig
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuCtxGetSharedMemConfig(CUsharedconfig *pConfig);
+
+/**
+ * \brief Sets the shared memory configuration for the current context.
+ *
+ * \deprecated
+ *
+ * On devices with configurable shared memory banks, this function will set
+ * the context's shared memory bank size which is used for subsequent kernel
+ * launches.
+ *
+ * Changed the shared memory configuration between launches may insert a device
+ * side synchronization point between those launches.
+ *
+ * Changing the shared memory bank size will not increase shared memory usage
+ * or affect occupancy of kernels, but may have major effects on performance.
+ * Larger bank sizes will allow for greater potential bandwidth to shared memory,
+ * but will change what kinds of accesses to shared memory will result in bank
+ * conflicts.
+ *
+ * This function will do nothing on devices with fixed shared memory bank size.
+ *
+ * The supported bank configurations are:
+ * - ::CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE: set bank width to the default initial
+ *   setting (currently, four bytes).
+ * - ::CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: set shared memory bank width to
+ *   be natively four bytes.
+ * - ::CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: set shared memory bank width to
+ *   be natively eight bytes.
+ *
+ * \param config - requested shared memory configuration
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate,
+ * ::cuCtxDestroy,
+ * ::cuCtxGetApiVersion,
+ * ::cuCtxGetCacheConfig,
+ * ::cuCtxGetDevice,
+ * ::cuCtxGetFlags,
+ * ::cuCtxGetLimit,
+ * ::cuCtxPopCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuCtxSetLimit,
+ * ::cuCtxSynchronize,
+ * ::cuCtxGetSharedMemConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cudaDeviceSetSharedMemConfig
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuCtxSetSharedMemConfig(CUsharedconfig config);
+
+/** @} */ /* END CUDA_CTX_DEPRECATED */
+
+
+/**
+ * \defgroup CUDA_MODULE Module Management
+ *
+ * ___MANBRIEF___ module management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the module management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Loads a compute module
+ *
+ * Takes a filename \p fname and loads the corresponding module \p module into
+ * the current context. The CUDA driver API does not attempt to lazily
+ * allocate the resources needed by a module; if the memory for functions and
+ * data (constant and global) needed by the module cannot be allocated,
+ * ::cuModuleLoad() fails. The file should be a \e cubin file as output by
+ * \b nvcc, or a \e PTX file either as output by \b nvcc or handwritten, or
+ * a \e fatbin file as output by \b nvcc from toolchain 4.0 or later.
+ *
+ * \param module - Returned module
+ * \param fname  - Filename of module to load
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_NOT_FOUND,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_FILE_NOT_FOUND,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU,
+ * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload
+ */
+CUresult CUDAAPI cuModuleLoad(CUmodule *module, const char *fname);
+
+/**
+ * \brief Load a module's data
+ *
+ * Takes a pointer \p image and loads the corresponding module \p module into
+ * the current context. The \p image may be a \e cubin or \e fatbin
+ * as output by \b nvcc, or a NULL-terminated \e PTX, either as output by \b nvcc
+ * or hand-written.
+ *
+ * \param module - Returned module
+ * \param image  - Module data to load
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU,
+ * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload
+ */
+CUresult CUDAAPI cuModuleLoadData(CUmodule *module, const void *image);
+
+/**
+ * \brief Load a module's data with options
+ *
+ * Takes a pointer \p image and loads the corresponding module \p module into
+ * the current context. The \p image may be a \e cubin or \e fatbin
+ * as output by \b nvcc, or a NULL-terminated \e PTX, either as output by \b nvcc
+ * or hand-written.
+ *
+ * \param module       - Returned module
+ * \param image        - Module data to load
+ * \param numOptions   - Number of options
+ * \param options      - Options for JIT
+ * \param optionValues - Option values for JIT
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU,
+ * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload
+ */
+CUresult CUDAAPI cuModuleLoadDataEx(CUmodule *module, const void *image, unsigned int numOptions, CUjit_option *options, void **optionValues);
+
+/**
+ * \brief Load a module's data
+ *
+ * Takes a pointer \p fatCubin and loads the corresponding module \p module
+ * into the current context. The pointer represents a <i>fat binary</i> object,
+ * which is a collection of different \e cubin and/or \e PTX files, all
+ * representing the same device code, but compiled and optimized for different
+ * architectures.
+ *
+ * Prior to CUDA 4.0, there was no documented API for constructing and using
+ * fat binary objects by programmers.  Starting with CUDA 4.0, fat binary
+ * objects can be constructed by providing the <i>-fatbin option</i> to \b nvcc.
+ * More information can be found in the \b nvcc document.
+ *
+ * \param module   - Returned module
+ * \param fatCubin - Fat binary to load
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_NOT_FOUND,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU,
+ * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleUnload
+ */
+CUresult CUDAAPI cuModuleLoadFatBinary(CUmodule *module, const void *fatCubin);
+
+/**
+ * \brief Unloads a module
+ *
+ * Unloads a module \p hmod from the current context. Attempting to unload
+ * a module which was obtained from the Library Management API such as
+ * ::cuLibraryGetModule will return ::CUDA_ERROR_NOT_PERMITTED.
+ *
+ * \param hmod - Module to unload
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_PERMITTED
+ * \notefnerr
+ * \note_destroy_ub
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary
+ */
+CUresult CUDAAPI cuModuleUnload(CUmodule hmod);
+
+/**
+ * CUDA Lazy Loading status
+ */
+typedef enum CUmoduleLoadingMode_enum {
+    CU_MODULE_EAGER_LOADING = 0x1, /**< Lazy Kernel Loading is not enabled */
+    CU_MODULE_LAZY_LOADING  = 0x2, /**< Lazy Kernel Loading is enabled */
+} CUmoduleLoadingMode;
+
+/**
+ * \brief Query lazy loading mode
+ *
+ * Returns lazy loading mode
+ * Module loading mode is controlled by CUDA_MODULE_LOADING env variable
+ *
+ * \param mode      - Returns the lazy loading mode
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ * \sa
+ * ::cuModuleLoad,
+ */
+CUresult CUDAAPI cuModuleGetLoadingMode(CUmoduleLoadingMode *mode);
+
+/**
+ * \brief Returns a function handle
+ *
+ * Returns in \p *hfunc the handle of the function of name \p name located in
+ * module \p hmod. If no function of that name exists, ::cuModuleGetFunction()
+ * returns ::CUDA_ERROR_NOT_FOUND.
+ *
+ * \param hfunc - Returned function handle
+ * \param hmod  - Module to retrieve function from
+ * \param name  - Name of function to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload
+ */
+CUresult CUDAAPI cuModuleGetFunction(CUfunction *hfunc, CUmodule hmod, const char *name);
+
+/**
+ * \brief Returns the number of functions within a module
+ *
+ * Returns in \p count the number of functions in \p mod.
+ *
+ * \param count - Number of functions found within the module
+ * \param mod - Module to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ */
+CUresult CUDAAPI cuModuleGetFunctionCount(unsigned int *count, CUmodule mod);
+
+/**
+ * \brief Returns the function handles within a module.
+ *
+ * Returns in \p functions a maximum number of \p numFunctions function handles within \p mod. When
+ * function loading mode is set to LAZY the function retrieved may be partially loaded. The loading
+ * state of a function can be queried using ::cuFunctionIsLoaded. CUDA APIs may load the function
+ * automatically when called with partially loaded function handle which may incur additional
+ * latency. Alternatively, ::cuFunctionLoad can be used to explicitly load a function. The returned
+ * function handles become invalid when the module is unloaded.
+ *
+ * \param functions - Buffer where the function handles are returned to
+ * \param numFunctions - Maximum number of function handles may be returned to the buffer
+ * \param mod - Module to query from
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetFunctionCount,
+ * ::cuFuncIsLoaded,
+ * ::cuFuncLoad
+ */
+CUresult CUDAAPI cuModuleEnumerateFunctions(CUfunction *functions, unsigned int numFunctions, CUmodule mod);
+
+/**
+ * \brief Returns a global pointer from a module
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the
+ * global of name \p name located in module \p hmod. If no variable of that name
+ * exists, ::cuModuleGetGlobal() returns ::CUDA_ERROR_NOT_FOUND.
+ * One of the parameters \p dptr or \p bytes (not both) can be NULL in which
+ * case it is ignored.
+ *
+ * \param dptr  - Returned global device pointer
+ * \param bytes - Returned global size in bytes
+ * \param hmod  - Module to retrieve global from
+ * \param name  - Name of global to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuModuleGetFunction,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload,
+ * ::cudaGetSymbolAddress,
+ * ::cudaGetSymbolSize
+ */
+CUresult CUDAAPI cuModuleGetGlobal(CUdeviceptr *dptr, size_t *bytes, CUmodule hmod, const char *name);
+
+/**
+ * \brief Creates a pending JIT linker invocation.
+ *
+ * If the call is successful, the caller owns the returned CUlinkState, which
+ * should eventually be destroyed with ::cuLinkDestroy.  The
+ * device code machine size (32 or 64 bit) will match the calling application.
+ *
+ * Both linker and compiler options may be specified.  Compiler options will
+ * be applied to inputs to this linker action which must be compiled from PTX.
+ * The options ::CU_JIT_WALL_TIME,
+ * ::CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES, and ::CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES
+ * will accumulate data until the CUlinkState is destroyed.
+ *
+ * The data passed in via ::cuLinkAddData and ::cuLinkAddFile will be treated
+ * as relocatable (-rdc=true to nvcc) when linking the final cubin during 
+ * ::cuLinkComplete and will have similar consequences as offline relocatable 
+ * device code linking.
+ *
+ * \p optionValues must remain valid for the life of the CUlinkState if output
+ * options are used.  No other references to inputs are maintained after this
+ * call returns.
+ *
+ * \note For LTO-IR input, only LTO-IR compiled with toolkits prior to CUDA 12.0 will be accepted
+ *
+ * \param numOptions   Size of options arrays
+ * \param options      Array of linker and compiler options
+ * \param optionValues Array of option values, each cast to void *
+ * \param stateOut     On success, this will contain a CUlinkState to specify
+ *                     and complete this action
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa ::cuLinkAddData,
+ * ::cuLinkAddFile,
+ * ::cuLinkComplete,
+ * ::cuLinkDestroy
+ */
+CUresult CUDAAPI
+cuLinkCreate(unsigned int numOptions, CUjit_option *options, void **optionValues, CUlinkState *stateOut);
+
+/**
+ * \brief Add an input to a pending linker invocation
+ *
+ * Ownership of \p data is retained by the caller.  No reference is retained to any
+ * inputs after this call returns.
+ *
+ * This method accepts only compiler options, which are used if the data must
+ * be compiled from PTX, and does not accept any of
+ * ::CU_JIT_WALL_TIME, ::CU_JIT_INFO_LOG_BUFFER, ::CU_JIT_ERROR_LOG_BUFFER,
+ * ::CU_JIT_TARGET_FROM_CUCONTEXT, or ::CU_JIT_TARGET.
+ *
+ * \note For LTO-IR input, only LTO-IR compiled with toolkits prior to CUDA 12.0 will be accepted
+ *
+ * \param state        A pending linker action.
+ * \param type         The type of the input data.
+ * \param data         The input data.  PTX must be NULL-terminated.
+ * \param size         The length of the input data.
+ * \param name         An optional name for this input in log messages.
+ * \param numOptions   Size of options.
+ * \param options      Options to be applied only for this input (overrides options from ::cuLinkCreate).
+ * \param optionValues Array of option values, each cast to void *.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_IMAGE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU
+ *
+ * \sa ::cuLinkCreate,
+ * ::cuLinkAddFile,
+ * ::cuLinkComplete,
+ * ::cuLinkDestroy
+ */
+CUresult CUDAAPI
+cuLinkAddData(CUlinkState state, CUjitInputType type, void *data, size_t size, const char *name,
+    unsigned int numOptions, CUjit_option *options, void **optionValues);
+
+/**
+ * \brief Add a file input to a pending linker invocation
+ *
+ * No reference is retained to any inputs after this call returns.
+ *
+ * This method accepts only compiler options, which are used if the input
+ * must be compiled from PTX, and does not accept any of
+ * ::CU_JIT_WALL_TIME, ::CU_JIT_INFO_LOG_BUFFER, ::CU_JIT_ERROR_LOG_BUFFER,
+ * ::CU_JIT_TARGET_FROM_CUCONTEXT, or ::CU_JIT_TARGET.
+ *
+ * This method is equivalent to invoking ::cuLinkAddData on the contents
+ * of the file.
+ *
+ * \note For LTO-IR input, only LTO-IR compiled with toolkits prior to CUDA 12.0 will be accepted
+ *
+ * \param state        A pending linker action
+ * \param type         The type of the input data
+ * \param path         Path to the input file
+ * \param numOptions   Size of options
+ * \param options      Options to be applied only for this input (overrides options from ::cuLinkCreate)
+ * \param optionValues Array of option values, each cast to void *
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_FILE_NOT_FOUND
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_IMAGE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU
+ *
+ * \sa ::cuLinkCreate,
+ * ::cuLinkAddData,
+ * ::cuLinkComplete,
+ * ::cuLinkDestroy
+ */
+CUresult CUDAAPI
+cuLinkAddFile(CUlinkState state, CUjitInputType type, const char *path,
+    unsigned int numOptions, CUjit_option *options, void **optionValues);
+
+/**
+ * \brief Complete a pending linker invocation
+ *
+ * Completes the pending linker action and returns the cubin image for the linked
+ * device code, which can be used with ::cuModuleLoadData.  The cubin is owned by
+ * \p state, so it should be loaded before \p state is destroyed via ::cuLinkDestroy.
+ * This call does not destroy \p state.
+ *
+ * \param state    A pending linker invocation
+ * \param cubinOut On success, this will point to the output image
+ * \param sizeOut  Optional parameter to receive the size of the generated image
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuLinkCreate,
+ * ::cuLinkAddData,
+ * ::cuLinkAddFile,
+ * ::cuLinkDestroy,
+ * ::cuModuleLoadData
+ */
+CUresult CUDAAPI
+cuLinkComplete(CUlinkState state, void **cubinOut, size_t *sizeOut);
+
+/**
+ * \brief Destroys state for a JIT linker invocation.
+ *
+ * \param state State object for the linker invocation
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ *
+ * \sa ::cuLinkCreate
+ */
+CUresult CUDAAPI
+cuLinkDestroy(CUlinkState state);
+
+/** @} */ /* END CUDA_MODULE */
+
+/**
+ * \defgroup CUDA_MODULE_DEPRECATED Module Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated module management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the deprecated module management functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns a handle to a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pTexRef the handle of the texture reference of name \p name
+ * in the module \p hmod. If no texture reference of that name exists,
+ * ::cuModuleGetTexRef() returns ::CUDA_ERROR_NOT_FOUND. This texture reference
+ * handle should not be destroyed, since it will be destroyed when the module
+ * is unloaded.
+ *
+ * \param pTexRef  - Returned texture reference
+ * \param hmod     - Module to retrieve texture reference from
+ * \param name     - Name of texture reference to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa
+ * ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetSurfRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuModuleGetTexRef(CUtexref *pTexRef, CUmodule hmod, const char *name);
+
+/**
+ * \brief Returns a handle to a surface reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pSurfRef the handle of the surface reference of name \p name
+ * in the module \p hmod. If no surface reference of that name exists,
+ * ::cuModuleGetSurfRef() returns ::CUDA_ERROR_NOT_FOUND.
+ *
+ * \param pSurfRef  - Returned surface reference
+ * \param hmod     - Module to retrieve surface reference from
+ * \param name     - Name of surface reference to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \notefnerr
+ *
+ * \sa
+ * ::cuModuleGetFunction,
+ * ::cuModuleGetGlobal,
+ * ::cuModuleGetTexRef,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx,
+ * ::cuModuleLoadFatBinary,
+ * ::cuModuleUnload
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuModuleGetSurfRef(CUsurfref *pSurfRef, CUmodule hmod, const char *name);
+
+/** @} */ /* END CUDA_MODULE_DEPRECATED */
+
+/**
+ * \defgroup CUDA_LIBRARY Library Management
+ *
+ * ___MANBRIEF___ library management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the library management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Load a library with specified code and options
+ *
+ * Takes a pointer \p code and loads the corresponding library \p library based on
+ * the application defined library loading mode:
+ * - If module loading is set to EAGER, via the environment variables described in "Module loading",
+ *   \p library is loaded eagerly into all contexts at the time of the call and future contexts
+ *   at the time of creation until the library is unloaded with ::cuLibraryUnload().
+ * - If the environment variables are set to LAZY, \p library
+ *   is not immediately loaded onto all existent contexts and will only be
+ *   loaded when a function is needed for that context, such as a kernel launch.
+ *
+ * These environment variables are described in the CUDA programming guide under the 
+ * "CUDA environment variables" section.
+ *
+ * The \p code may be a \e cubin or \e fatbin as output by \b nvcc,
+ * or a NULL-terminated \e PTX, either as output by \b nvcc or hand-written.
+ * A fatbin should also contain relocatable code when doing separate compilation.
+ *
+ * Options are passed as an array via \p jitOptions and any corresponding parameters are passed in
+ * \p jitOptionsValues. The number of total JIT options is supplied via \p numJitOptions.
+ * Any outputs will be returned via \p jitOptionsValues.
+ *
+ * Library load options are passed as an array via \p libraryOptions and any corresponding parameters are passed in
+ * \p libraryOptionValues. The number of total library load options is supplied via \p numLibraryOptions.
+ *
+ * \note If the library contains managed variables and no device in the system
+ * supports managed variables this call is expected to return ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \param library             - Returned library
+ * \param code                - Code to load
+ * \param jitOptions          - Options for JIT
+ * \param jitOptionsValues    - Option values for JIT
+ * \param numJitOptions       - Number of options
+ * \param libraryOptions      - Options for loading
+ * \param libraryOptionValues - Option values for loading
+ * \param numLibraryOptions   - Number of options for loading
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU,
+ * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx
+ */
+CUresult CUDAAPI cuLibraryLoadData(CUlibrary *library, const void *code,
+                                   CUjit_option *jitOptions, void **jitOptionsValues, unsigned int numJitOptions,
+                                   CUlibraryOption *libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions);
+
+/**
+ * \brief Load a library with specified file and options
+ *
+ * Takes a pointer \p code and loads the corresponding library \p library based on
+ * the application defined library loading mode:
+ * - If module loading is set to EAGER, via the environment variables described in "Module loading",
+ *   \p library is loaded eagerly into all contexts at the time of the call and future contexts
+ *   at the time of creation until the library is unloaded with ::cuLibraryUnload().
+ * - If the environment variables are set to LAZY, \p library
+ *   is not immediately loaded onto all existent contexts and will only be
+ *   loaded when a function is needed for that context, such as a kernel launch.
+ *
+ * These environment variables are described in the CUDA programming guide under the 
+ * "CUDA environment variables" section.
+ *
+ * The file should be a \e cubin file as output by \b nvcc, or a \e PTX file either
+ * as output by \b nvcc or handwritten, or a \e fatbin file as output by \b nvcc.
+ * A fatbin should also contain relocatable code when doing separate compilation.
+ *
+ * Options are passed as an array via \p jitOptions and any corresponding parameters are
+ * passed in \p jitOptionsValues. The number of total options is supplied via \p numJitOptions.
+ * Any outputs will be returned via \p jitOptionsValues.
+ *
+ * Library load options are passed as an array via \p libraryOptions and any corresponding parameters are passed in
+ * \p libraryOptionValues. The number of total library load options is supplied via \p numLibraryOptions.
+ *
+ * \note If the library contains managed variables and no device in the system
+ * supports managed variables this call is expected to return ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \param library             - Returned library
+ * \param fileName            - File to load from
+ * \param jitOptions          - Options for JIT
+ * \param jitOptionsValues    - Option values for JIT
+ * \param numJitOptions       - Number of options
+ * \param libraryOptions      - Options for loading
+ * \param libraryOptionValues - Option values for loading
+ * \param numLibraryOptions   - Number of options for loading
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_PTX,
+ * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NO_BINARY_FOR_GPU,
+ * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryUnload,
+ * ::cuModuleLoad,
+ * ::cuModuleLoadData,
+ * ::cuModuleLoadDataEx
+ */
+CUresult CUDAAPI cuLibraryLoadFromFile(CUlibrary *library, const char *fileName,
+                                       CUjit_option *jitOptions, void **jitOptionsValues, unsigned int numJitOptions,
+                                       CUlibraryOption *libraryOptions, void **libraryOptionValues, unsigned int numLibraryOptions);
+
+/**
+ * \brief Unloads a library
+ *
+ * Unloads the library specified with \p library
+ *
+ * \param library - Library to unload
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuModuleUnload
+ */
+CUresult CUDAAPI cuLibraryUnload(CUlibrary library);
+
+/**
+ * \brief Returns a kernel handle
+ *
+ * Returns in \p pKernel the handle of the kernel with name \p name located in library \p library.
+ * If kernel handle is not found, the call returns ::CUDA_ERROR_NOT_FOUND.
+ *
+ * \param pKernel - Returned kernel handle
+ * \param library - Library to retrieve kernel from
+ * \param name - Name of kernel to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuKernelGetFunction,
+ * ::cuLibraryGetModule,
+ * ::cuModuleGetFunction
+ */
+CUresult CUDAAPI cuLibraryGetKernel(CUkernel *pKernel, CUlibrary library, const char *name);
+
+/**
+ * \brief Returns the number of kernels within a library
+ *
+ * Returns in \p count the number of kernels in \p lib.
+ *
+ * \param count - Number of kernels found within the library
+ * \param lib - Library to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ */
+CUresult CUDAAPI cuLibraryGetKernelCount(unsigned int *count, CUlibrary lib);
+ 
+/**
+ * \brief Retrieve the kernel handles within a library.
+ *
+ * Returns in \p kernels a maximum number of \p numKernels kernel handles within \p lib.
+ * The returned kernel handle becomes invalid when the library is unloaded.
+ *
+ * \param kernels - Buffer where the kernel handles are returned to
+ * \param numKernels - Maximum number of kernel handles may be returned to the buffer
+ * \param lib - Library to query from
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuLibraryGetKernelCount
+ */
+CUresult CUDAAPI cuLibraryEnumerateKernels(CUkernel *kernels, unsigned int numKernels, CUlibrary lib);
+
+/**
+ * \brief Returns a module handle
+ *
+ * Returns in \p pMod the module handle associated with the current context located in
+ * library \p library. If module handle is not found, the call returns ::CUDA_ERROR_NOT_FOUND.
+ *
+ * \param pMod - Returned module handle
+ * \param library - Library to retrieve module from
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuModuleGetFunction
+ */
+CUresult CUDAAPI cuLibraryGetModule(CUmodule *pMod, CUlibrary library);
+
+/**
+ * \brief Returns a function handle
+ *
+ * Returns in \p pFunc the handle of the function for the requested kernel \p kernel and
+ * the current context. If function handle is not found, the call returns ::CUDA_ERROR_NOT_FOUND.
+ *
+ * \param pFunc - Returned function handle
+ * \param kernel - Kernel to retrieve function for the requested context
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuLibraryGetKernel,
+ * ::cuLibraryGetModule,
+ * ::cuModuleGetFunction
+ */
+CUresult CUDAAPI cuKernelGetFunction(CUfunction *pFunc, CUkernel kernel);
+
+/**
+ * \brief Returns a library handle
+ *
+ * Returns in \p pLib the handle of the library for the requested kernel \p kernel
+ *
+ * \param pLib - Returned library handle
+ * \param kernel - Kernel to retrieve library handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuLibraryGetKernel
+ */
+CUresult CUDAAPI cuKernelGetLibrary(CUlibrary *pLib, CUkernel kernel);
+
+/**
+ * \brief Returns a global device pointer
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the global with
+ * name \p name for the requested library \p library and the current context.
+ * If no global for the requested name \p name exists, the call returns ::CUDA_ERROR_NOT_FOUND.
+ * One of the parameters \p dptr or \p bytes (not both) can be NULL in which
+ * case it is ignored.
+ *
+ * \param dptr - Returned global device pointer for the requested context
+ * \param bytes - Returned global size in bytes
+ * \param library - Library to retrieve global from
+ * \param name - Name of global to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuLibraryGetModule,
+ * cuModuleGetGlobal
+ */
+CUresult CUDAAPI cuLibraryGetGlobal(CUdeviceptr *dptr, size_t *bytes, CUlibrary library, const char *name);
+
+/**
+ * \brief Returns a pointer to managed memory
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the managed memory with
+ * name \p name for the requested library \p library. If no managed memory with the
+ * requested name \p name exists, the call returns ::CUDA_ERROR_NOT_FOUND. One of the parameters
+ * \p dptr or \p bytes (not both) can be NULL in which case it is ignored.
+ * Note that managed memory for library \p library is shared across devices and is registered
+ * when the library is loaded into atleast one context.
+ *
+ * \param dptr - Returned pointer to the managed memory
+ * \param bytes - Returned memory size in bytes
+ * \param library - Library to retrieve managed memory from
+ * \param name - Name of managed memory to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload
+ */
+CUresult CUDAAPI cuLibraryGetManaged(CUdeviceptr *dptr, size_t *bytes, CUlibrary library, const char *name);
+
+/**
+ * \brief Returns a pointer to a unified function
+ *
+ * Returns in \p *fptr the function pointer to a unified function denoted by \p symbol.
+ * If no unified function with name \p symbol exists, the call returns ::CUDA_ERROR_NOT_FOUND.
+ * If there is no device with attribute ::CU_DEVICE_ATTRIBUTE_UNIFIED_FUNCTION_POINTERS present in the system,
+ * the call may return ::CUDA_ERROR_NOT_FOUND.
+ *
+ * \param fptr - Returned pointer to a unified function
+ * \param library - Library to retrieve function pointer memory from
+ * \param symbol - Name of function pointer to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_FOUND
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload
+ */
+CUresult CUDAAPI cuLibraryGetUnifiedFunction(void **fptr, CUlibrary library, const char *symbol);
+
+/**
+ * \brief Returns information about a kernel
+ *
+ * Returns in \p *pi the integer value of the attribute \p attrib for the kernel
+ * \p kernel for the requested device \p dev. The supported attributes are:
+ * - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threads
+ *   per block, beyond which a launch of the kernel would fail. This number
+ *   depends on both the kernel and the requested device.
+ * - ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of
+ *   statically-allocated shared memory per block required by this kernel.
+ *   This does not include dynamically-allocated shared memory requested by
+ *   the user at runtime.
+ * - ::CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES: The size in bytes of user-allocated
+ *   constant memory required by this kernel.
+ * - ::CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES: The size in bytes of local memory
+ *   used by each thread of this kernel.
+ * - ::CU_FUNC_ATTRIBUTE_NUM_REGS: The number of registers used by each thread
+ *   of this kernel.
+ * - ::CU_FUNC_ATTRIBUTE_PTX_VERSION: The PTX virtual architecture version for
+ *   which the kernel was compiled. This value is the major PTX version * 10
+ *   + the minor PTX version, so a PTX version 1.3 function would return the
+ *   value 13. Note that this may return the undefined value of 0 for cubins
+ *   compiled prior to CUDA 3.0.
+ * - ::CU_FUNC_ATTRIBUTE_BINARY_VERSION: The binary architecture version for
+ *   which the kernel was compiled. This value is the major binary
+ *   version * 10 + the minor binary version, so a binary version 1.3 function
+ *   would return the value 13. Note that this will return a value of 10 for
+ *   legacy cubins that do not have a properly-encoded binary architecture
+ *   version.
+ * - ::CU_FUNC_CACHE_MODE_CA: The attribute to indicate whether the kernel has
+ *   been compiled with user specified option "-Xptxas --dlcm=ca" set.
+ * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: The maximum size in bytes of
+ *   dynamically-allocated shared memory.
+ * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: Preferred shared memory-L1
+ *   cache split ratio in percent of total shared memory.
+ * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET: If this attribute is set, the
+ *   kernel must launch with a valid cluster size specified.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in
+ *   blocks.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in
+ *   blocks.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in
+ *   blocks.
+ * - ::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether
+ *   the function can be launched with non-portable cluster size. 1 is allowed,
+ *   0 is disallowed. A non-portable cluster size may only function on the
+ *   specific SKUs the program is tested on. The launch might fail if the
+ *   program is run on a different hardware platform. CUDA API provides
+ *   cudaOccupancyMaxActiveClusters to assist with checking whether the desired
+ *   size can be launched on the current device. A portable cluster size is
+ *   guaranteed to be functional on all compute capabilities higher than the
+ *   target compute capability. The portable cluster size for sm_90 is 8 blocks
+ *   per cluster. This value may increase for future compute capabilities. The
+ *   specific hardware unit may support higher cluster sizes that’s not
+ *   guaranteed to be portable.
+ * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block
+ *   scheduling policy of a function. The value type is CUclusterSchedulingPolicy.
+ *
+ * \note If another thread is trying to set the same attribute on the same device using
+ * ::cuKernelSetAttribute() simultaneously, the attribute query will give the old or new
+ * value depending on the interleavings chosen by the OS scheduler and memory consistency.
+ *
+ * \param pi     - Returned attribute value
+ * \param attrib - Attribute requested
+ * \param kernel  - Kernel to query attribute of
+ * \param dev - Device to query attribute of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuKernelSetAttribute,
+ * ::cuLibraryGetKernel,
+ * ::cuLaunchKernel,
+ * ::cuKernelGetFunction,
+ * ::cuLibraryGetModule,
+ * ::cuModuleGetFunction,
+ * ::cuFuncGetAttribute
+ */
+CUresult CUDAAPI cuKernelGetAttribute(int *pi, CUfunction_attribute attrib, CUkernel kernel, CUdevice dev);
+
+/**
+ * \brief Sets information about a kernel
+ *
+ * This call sets the value of a specified attribute \p attrib on the kernel \p kernel
+ * for the requested device \p dev to an integer value specified by \p val.
+ * This function returns CUDA_SUCCESS if the new value of the attribute could be
+ * successfully set. If the set fails, this call will return an error.
+ * Not all attributes can have values set. Attempting to set a value on a read-only
+ * attribute will result in an error (CUDA_ERROR_INVALID_VALUE)
+ *
+ * Note that attributes set using ::cuFuncSetAttribute() will override the attribute
+ * set by this API irrespective of whether the call to ::cuFuncSetAttribute() is made
+ * before or after this API call. However, ::cuKernelGetAttribute() will always
+ * return the attribute value set by this API.
+ *
+ * Supported attributes are:
+ * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: This is the maximum size in bytes of
+ *   dynamically-allocated shared memory. The value should contain the requested
+ *   maximum size of dynamically-allocated shared memory. The sum of this value and
+ *   the function attribute ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES cannot exceed the
+ *   device attribute ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN.
+ *   The maximal size of requestable dynamic shared memory may differ by GPU
+ *   architecture.
+ * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: On devices where the L1
+ *   cache and shared memory use the same hardware resources, this sets the shared memory
+ *   carveout preference, in percent of the total shared memory.
+ *   See ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR
+ *   This is only a hint, and the driver can choose a different ratio if required to execute the function.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+ * - ::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether
+ *   the function can be launched with non-portable cluster size. 1 is allowed,
+ *   0 is disallowed.
+ * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block
+ *   scheduling policy of a function. The value type is CUclusterSchedulingPolicy.
+ *
+ * \note The API has stricter locking requirements in comparison to its legacy counterpart
+ * ::cuFuncSetAttribute() due to device-wide semantics. If multiple threads are trying to
+ * set the same attribute on the same device simultaneously, the attribute setting will depend
+ * on the interleavings chosen by the OS scheduler and memory consistency.
+ *
+ * \param attrib - Attribute requested
+ * \param val - Value to set
+ * \param kernel  - Kernel to set attribute of
+ * \param dev - Device to set attribute of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuKernelGetAttribute,
+ * ::cuLibraryGetKernel,
+ * ::cuLaunchKernel,
+ * ::cuKernelGetFunction,
+ * ::cuLibraryGetModule,
+ * ::cuModuleGetFunction,
+ * ::cuFuncSetAttribute
+ */
+CUresult CUDAAPI cuKernelSetAttribute(CUfunction_attribute attrib, int val, CUkernel kernel, CUdevice dev);
+
+/**
+ * \brief Sets the preferred cache configuration for a device kernel.
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p config the preferred cache configuration for
+ * the device kernel \p kernel on the requested device \p dev. This is only a preference.
+ * The driver will use the requested configuration if possible, but it is free to choose a different
+ * configuration if required to execute \p kernel.  Any context-wide preference
+ * set via ::cuCtxSetCacheConfig() will be overridden by this per-kernel
+ * setting.
+ *
+ * Note that attributes set using ::cuFuncSetCacheConfig() will override the attribute
+ * set by this API irrespective of whether the call to ::cuFuncSetCacheConfig() is made
+ * before or after this API call.
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ *
+ * The supported cache configurations are:
+ * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default)
+ * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache
+ * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory
+ * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
+ *
+ * \note The API has stricter locking requirements in comparison to its legacy counterpart
+ * ::cuFuncSetCacheConfig() due to device-wide semantics. If multiple threads are trying to
+ * set a config on the same device simultaneously, the cache config setting will depend
+ * on the interleavings chosen by the OS scheduler and memory consistency.
+ *
+ * \param kernel  - Kernel to configure cache for
+ * \param config - Requested cache configuration
+ * \param dev - Device to set attribute of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuLibraryLoadData,
+ * ::cuLibraryLoadFromFile,
+ * ::cuLibraryUnload,
+ * ::cuLibraryGetKernel,
+ * ::cuKernelGetFunction,
+ * ::cuLibraryGetModule,
+ * ::cuModuleGetFunction,
+ * ::cuFuncSetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuLaunchKernel
+ */
+CUresult CUDAAPI cuKernelSetCacheConfig(CUkernel kernel, CUfunc_cache config, CUdevice dev);
+
+/**
+ * \brief Returns the function name for a ::CUkernel handle
+ *
+ * Returns in \p **name the function name associated with the kernel handle \p hfunc .
+ * The function name is returned as a null-terminated string. The returned name is only 
+ * valid when the kernel handle is valid. If the library is unloaded or reloaded, one 
+ * must call the API again to get the updated name. This API may return a mangled name if
+ * the function is not declared as having C linkage. If either \p **name or \p hfunc 
+ * is NULL, ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * \param name - The returned name of the function
+ * \param hfunc - The function handle to retrieve the name for 
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ */
+CUresult CUDAAPI cuKernelGetName(const char **name, CUkernel hfunc);
+
+/**
+ * \brief Returns the offset and size of a kernel parameter in the device-side parameter layout
+ *
+ * Queries the kernel parameter at \p paramIndex into \p kernel's list of parameters, and returns
+ * in \p paramOffset and \p paramSize the offset and size, respectively, where the parameter
+ * will reside in the device-side parameter layout. This information can be used to update kernel
+ * node parameters from the device via ::cudaGraphKernelNodeSetParam() and
+ * ::cudaGraphKernelNodeUpdatesApply(). \p paramIndex must be less than the number of parameters
+ * that \p kernel takes. \p paramSize can be set to NULL if only the parameter offset is desired.
+ *
+ * \param kernel      - The kernel to query
+ * \param paramIndex  - The parameter index to query
+ * \param paramOffset - Returns the offset into the device-side parameter layout at which the parameter resides
+ * \param paramSize   - Optionally returns the size of the parameter in the device-side parameter layout
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+* \sa ::cuFuncGetParamInfo
+ */
+CUresult CUDAAPI cuKernelGetParamInfo(CUkernel kernel, size_t paramIndex, size_t *paramOffset, size_t *paramSize);
+/** @} */ /* END CUDA_LIBRARY */
+
+/**
+ * \defgroup CUDA_MEM Memory Management
+ *
+ * ___MANBRIEF___ memory management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the memory management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Gets free and total memory
+ *
+ * Returns in \p *total the total amount of memory available to the the current context.
+ * Returns in \p *free the amount of memory on the device that is free according to the OS.
+ * CUDA is not guaranteed to be able to allocate all of the memory that the OS reports as free.
+ * In a multi-tenet situation, free estimate returned is prone to race condition where
+ * a new allocation/free done by a different process or a different thread in the same
+ * process between the time when free memory was estimated and reported, will result in
+ * deviation in free value reported and actual free memory.
+ *
+ * The integrated GPU on Tegra shares memory with CPU and other component
+ * of the SoC. The free and total values returned by the API excludes
+ * the SWAP memory space maintained by the OS on some platforms.
+ * The OS may move some of the memory pages into swap area as the GPU or
+ * CPU allocate or access memory. See Tegra app note on how to calculate
+ * total and free memory on Tegra.
+ *
+ * \param free  - Returned free memory in bytes
+ * \param total - Returned total memory in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemGetInfo
+ */
+CUresult CUDAAPI cuMemGetInfo(size_t *free, size_t *total);
+
+/**
+ * \brief Allocates device memory
+ *
+ * Allocates \p bytesize bytes of linear memory on the device and returns in
+ * \p *dptr a pointer to the allocated memory. The allocated memory is suitably
+ * aligned for any kind of variable. The memory is not cleared. If \p bytesize
+ * is 0, ::cuMemAlloc() returns ::CUDA_ERROR_INVALID_VALUE.
+ *
+ * \param dptr     - Returned device pointer
+ * \param bytesize - Requested allocation size in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMalloc
+ */
+CUresult CUDAAPI cuMemAlloc(CUdeviceptr *dptr, size_t bytesize);
+
+/**
+ * \brief Allocates pitched device memory
+ *
+ * Allocates at least \p WidthInBytes * \p Height bytes of linear memory on
+ * the device and returns in \p *dptr a pointer to the allocated memory. The
+ * function may pad the allocation to ensure that corresponding pointers in
+ * any given row will continue to meet the alignment requirements for
+ * coalescing as the address is updated from row to row. \p ElementSizeBytes
+ * specifies the size of the largest reads and writes that will be performed
+ * on the memory range. \p ElementSizeBytes may be 4, 8 or 16 (since coalesced
+ * memory transactions are not possible on other data sizes). If
+ * \p ElementSizeBytes is smaller than the actual read/write size of a kernel,
+ * the kernel will run correctly, but possibly at reduced speed. The pitch
+ * returned in \p *pPitch by ::cuMemAllocPitch() is the width in bytes of the
+ * allocation. The intended usage of pitch is as a separate parameter of the
+ * allocation, used to compute addresses within the 2D array. Given the row
+ * and column of an array element of type \b T, the address is computed as:
+ * \code
+   T* pElement = (T*)((char*)BaseAddress + Row * Pitch) + Column;
+ * \endcode
+ *
+ * The pitch returned by ::cuMemAllocPitch() is guaranteed to work with
+ * ::cuMemcpy2D() under all circumstances. For allocations of 2D arrays, it is
+ * recommended that programmers consider performing pitch allocations using
+ * ::cuMemAllocPitch(). Due to alignment restrictions in the hardware, this is
+ * especially true if the application will be performing 2D memory copies
+ * between different regions of device memory (whether linear memory or CUDA
+ * arrays).
+ *
+ * The byte alignment of the pitch returned by ::cuMemAllocPitch() is guaranteed
+ * to match or exceed the alignment requirement for texture binding with
+ * ::cuTexRefSetAddress2D().
+ *
+ * \param dptr             - Returned device pointer
+ * \param pPitch           - Returned pitch of allocation in bytes
+ * \param WidthInBytes     - Requested allocation width in bytes
+ * \param Height           - Requested allocation height in rows
+ * \param ElementSizeBytes - Size of largest reads/writes for range
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMallocPitch
+ */
+CUresult CUDAAPI cuMemAllocPitch(CUdeviceptr *dptr, size_t *pPitch, size_t WidthInBytes, size_t Height, unsigned int ElementSizeBytes);
+
+/**
+ * \brief Frees device memory
+ *
+ * Frees the memory space pointed to by \p dptr, which must have been returned
+ * by a previous call to one of the following memory allocation APIs - ::cuMemAlloc(), 
+ * ::cuMemAllocPitch(), ::cuMemAllocManaged(), ::cuMemAllocAsync(), ::cuMemAllocFromPoolAsync()
+ *
+ * Note - This API will not perform any implict synchronization when the pointer was allocated with
+ * ::cuMemAllocAsync or ::cuMemAllocFromPoolAsync. Callers must ensure that all accesses to these
+ * pointer have completed before invoking ::cuMemFree. For best performance and memory reuse, users
+ * should use ::cuMemFreeAsync to free memory allocated via the stream ordered memory allocator.
+ * For all other pointers, this API may perform implicit synchronization.
+ * 
+ * \param dptr - Pointer to memory to free
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemAllocManaged, ::cuMemAllocAsync, ::cuMemAllocFromPoolAsync, 
+ * ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, ::cuMemcpy3D, ::cuMemcpy3DAsync,
+ * ::cuMemcpyAtoA, ::cuMemcpyAtoD, ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA,
+ * ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA,
+ * ::cuMemcpyHtoAAsync, ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, ::cuMemFreeAsync,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaFree
+ */
+CUresult CUDAAPI cuMemFree(CUdeviceptr dptr);
+
+/**
+ * \brief Get information on memory allocations
+ *
+ * Returns the base address in \p *pbase and size in \p *psize of the
+ * allocation by ::cuMemAlloc() or ::cuMemAllocPitch() that contains the input
+ * pointer \p dptr. Both parameters \p pbase and \p psize are optional. If one
+ * of them is NULL, it is ignored.
+ *
+ * \param pbase - Returned base address
+ * \param psize - Returned size of device memory allocation
+ * \param dptr  - Device pointer to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_NOT_FOUND,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32
+ */
+CUresult CUDAAPI cuMemGetAddressRange(CUdeviceptr *pbase, size_t *psize, CUdeviceptr dptr);
+
+/**
+ * \brief Allocates page-locked host memory
+ *
+ * Allocates \p bytesize bytes of host memory that is page-locked and
+ * accessible to the device. The driver tracks the virtual memory ranges
+ * allocated with this function and automatically accelerates calls to
+ * functions such as ::cuMemcpy(). Since the memory can be accessed directly by
+ * the device, it can be read or written with much higher bandwidth than
+ * pageable memory obtained with functions such as ::malloc(). 
+ *
+ * On systems where ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES 
+ * is true, ::cuMemAllocHost may not page-lock the allocated memory.
+ *
+ * Page-locking excessive amounts of memory with ::cuMemAllocHost() may degrade system
+ * performance, since it reduces the amount of memory available to the system
+ * for paging. As a result, this function is best used sparingly to allocate
+ * staging areas for data exchange between host and device.
+ *
+ * Note all host memory allocated using ::cuMemAllocHost() will automatically
+ * be immediately accessible to all contexts on all devices which support unified
+ * addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING).
+ * The device pointer that may be used to access this host memory from those
+ * contexts is always equal to the returned host pointer \p *pp.
+ * See \ref CUDA_UNIFIED for additional details.
+ *
+ * \param pp       - Returned pointer to host memory
+ * \param bytesize - Requested allocation size in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMallocHost
+ */
+CUresult CUDAAPI cuMemAllocHost(void **pp, size_t bytesize);
+
+/**
+ * \brief Frees page-locked host memory
+ *
+ * Frees the memory space pointed to by \p p, which must have been returned by
+ * a previous call to ::cuMemAllocHost().
+ *
+ * \param p - Pointer to memory to free
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaFreeHost
+ */
+CUresult CUDAAPI cuMemFreeHost(void *p);
+
+/**
+ * \brief Allocates page-locked host memory
+ *
+ * Allocates \p bytesize bytes of host memory that is page-locked and accessible
+ * to the device. The driver tracks the virtual memory ranges allocated with
+ * this function and automatically accelerates calls to functions such as
+ * ::cuMemcpyHtoD(). Since the memory can be accessed directly by the device,
+ * it can be read or written with much higher bandwidth than pageable memory
+ * obtained with functions such as ::malloc(). 
+ *
+ * On systems where ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES 
+ * is true, ::cuMemHostAlloc may not page-lock the allocated memory.
+ *
+ * Page-locking excessive amounts of memory may degrade system performance, 
+ * since it reduces the amount of memory available to the system for paging. 
+ * As a result, this function is best used sparingly to allocate staging areas 
+ * for data exchange between host and device.
+ *
+ * The \p Flags parameter enables different options to be specified that
+ * affect the allocation, as follows.
+ *
+ * - ::CU_MEMHOSTALLOC_PORTABLE: The memory returned by this call will be
+ *   considered as pinned memory by all CUDA contexts, not just the one that
+ *   performed the allocation.
+ *
+ * - ::CU_MEMHOSTALLOC_DEVICEMAP: Maps the allocation into the CUDA address
+ *   space. The device pointer to the memory may be obtained by calling
+ *   ::cuMemHostGetDevicePointer().
+ *
+ * - ::CU_MEMHOSTALLOC_WRITECOMBINED: Allocates the memory as write-combined
+ *   (WC). WC memory can be transferred across the PCI Express bus more
+ *   quickly on some system configurations, but cannot be read efficiently by
+ *   most CPUs. WC memory is a good option for buffers that will be written by
+ *   the CPU and read by the GPU via mapped pinned memory or host->device
+ *   transfers.
+ *
+ * All of these flags are orthogonal to one another: a developer may allocate
+ * memory that is portable, mapped and/or write-combined with no restrictions.
+ *
+ * The ::CU_MEMHOSTALLOC_DEVICEMAP flag may be specified on CUDA contexts for
+ * devices that do not support mapped pinned memory. The failure is deferred
+ * to ::cuMemHostGetDevicePointer() because the memory may be mapped into
+ * other CUDA contexts via the ::CU_MEMHOSTALLOC_PORTABLE flag.
+ *
+ * The memory allocated by this function must be freed with ::cuMemFreeHost().
+ *
+ * Note all host memory allocated using ::cuMemHostAlloc() will automatically
+ * be immediately accessible to all contexts on all devices which support unified
+ * addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING).
+ * Unless the flag ::CU_MEMHOSTALLOC_WRITECOMBINED is specified, the device pointer
+ * that may be used to access this host memory from those contexts is always equal
+ * to the returned host pointer \p *pp.  If the flag ::CU_MEMHOSTALLOC_WRITECOMBINED
+ * is specified, then the function ::cuMemHostGetDevicePointer() must be used
+ * to query the device pointer, even if the context supports unified addressing.
+ * See \ref CUDA_UNIFIED for additional details.
+ *
+ * \param pp       - Returned pointer to host memory
+ * \param bytesize - Requested allocation size in bytes
+ * \param Flags    - Flags for allocation request
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaHostAlloc
+ */
+CUresult CUDAAPI cuMemHostAlloc(void **pp, size_t bytesize, unsigned int Flags);
+
+/**
+ * \brief Passes back device pointer of mapped pinned memory
+ *
+ * Passes back the device pointer \p pdptr corresponding to the mapped, pinned
+ * host buffer \p p allocated by ::cuMemHostAlloc.
+ *
+ * ::cuMemHostGetDevicePointer() will fail if the ::CU_MEMHOSTALLOC_DEVICEMAP
+ * flag was not specified at the time the memory was allocated, or if the
+ * function is called on a GPU that does not support mapped pinned memory.
+ *
+ * For devices that have a non-zero value for the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM, the memory
+ * can also be accessed from the device using the host pointer \p p.
+ * The device pointer returned by ::cuMemHostGetDevicePointer() may or may not
+ * match the original host pointer \p p and depends on the devices visible to the
+ * application. If all devices visible to the application have a non-zero value for the
+ * device attribute, the device pointer returned by ::cuMemHostGetDevicePointer()
+ * will match the original pointer \p p. If any device visible to the application
+ * has a zero value for the device attribute, the device pointer returned by
+ * ::cuMemHostGetDevicePointer() will not match the original host pointer \p p,
+ * but it will be suitable for use on all devices provided Unified Virtual Addressing
+ * is enabled. In such systems, it is valid to access the memory using either pointer
+ * on devices that have a non-zero value for the device attribute. Note however that
+ * such devices should access the memory using only one of the two pointers and not both.
+ *
+ * \p Flags provides for future releases. For now, it must be set to 0.
+ *
+ * \param pdptr - Returned device pointer
+ * \param p     - Host pointer
+ * \param Flags - Options (must be 0)
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaHostGetDevicePointer
+ */
+CUresult CUDAAPI cuMemHostGetDevicePointer(CUdeviceptr *pdptr, void *p, unsigned int Flags);
+
+/**
+ * \brief Passes back flags that were used for a pinned allocation
+ *
+ * Passes back the flags \p pFlags that were specified when allocating
+ * the pinned host buffer \p p allocated by ::cuMemHostAlloc.
+ *
+ * ::cuMemHostGetFlags() will fail if the pointer does not reside in
+ * an allocation performed by ::cuMemAllocHost() or ::cuMemHostAlloc().
+ *
+ * \param pFlags - Returned flags word
+ * \param p     - Host pointer
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMemAllocHost,
+ * ::cuMemHostAlloc,
+ * ::cudaHostGetFlags
+ */
+CUresult CUDAAPI cuMemHostGetFlags(unsigned int *pFlags, void *p);
+
+/**
+ * \brief Allocates memory that will be automatically managed by the Unified Memory system
+ *
+ * Allocates \p bytesize bytes of managed memory on the device and returns in
+ * \p *dptr a pointer to the allocated memory. If the device doesn't support
+ * allocating managed memory, ::CUDA_ERROR_NOT_SUPPORTED is returned. Support
+ * for managed memory can be queried using the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY. The allocated memory is suitably
+ * aligned for any kind of variable. The memory is not cleared. If \p bytesize
+ * is 0, ::cuMemAllocManaged returns ::CUDA_ERROR_INVALID_VALUE. The pointer
+ * is valid on the CPU and on all GPUs in the system that support managed memory.
+ * All accesses to this pointer must obey the Unified Memory programming model.
+ *
+ * \p flags specifies the default stream association for this allocation.
+ * \p flags must be one of ::CU_MEM_ATTACH_GLOBAL or ::CU_MEM_ATTACH_HOST. If
+ * ::CU_MEM_ATTACH_GLOBAL is specified, then this memory is accessible from
+ * any stream on any device. If ::CU_MEM_ATTACH_HOST is specified, then the
+ * allocation should not be accessed from devices that have a zero value for the
+ * device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS; an explicit call to
+ * ::cuStreamAttachMemAsync will be required to enable access on such devices.
+ *
+ * If the association is later changed via ::cuStreamAttachMemAsync to
+ * a single stream, the default association as specified during ::cuMemAllocManaged
+ * is restored when that stream is destroyed. For __managed__ variables, the
+ * default association is always ::CU_MEM_ATTACH_GLOBAL. Note that destroying a
+ * stream is an asynchronous operation, and as a result, the change to default
+ * association won't happen until all work in the stream has completed.
+ *
+ * Memory allocated with ::cuMemAllocManaged should be released with ::cuMemFree.
+ *
+ * Device memory oversubscription is possible for GPUs that have a non-zero value for the
+ * device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Managed memory on
+ * such GPUs may be evicted from device memory to host memory at any time by the Unified
+ * Memory driver in order to make room for other allocations.
+ *
+ * In a system where all GPUs have a non-zero value for the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS, managed memory may not be populated when this
+ * API returns and instead may be populated on access. In such systems, managed memory can
+ * migrate to any processor's memory at any time. The Unified Memory driver will employ heuristics to
+ * maintain data locality and prevent excessive page faults to the extent possible. The application
+ * can also guide the driver about memory usage patterns via ::cuMemAdvise. The application
+ * can also explicitly migrate memory to a desired processor's memory via
+ * ::cuMemPrefetchAsync.
+ *
+ * In a multi-GPU system where all of the GPUs have a zero value for the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS and all the GPUs have peer-to-peer support
+ * with each other, the physical storage for managed memory is created on the GPU which is active
+ * at the time ::cuMemAllocManaged is called. All other GPUs will reference the data at reduced
+ * bandwidth via peer mappings over the PCIe bus. The Unified Memory driver does not migrate
+ * memory among such GPUs.
+ *
+ * In a multi-GPU system where not all GPUs have peer-to-peer support with each other and
+ * where the value of the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS
+ * is zero for at least one of those GPUs, the location chosen for physical storage of managed
+ * memory is system-dependent.
+ * - On Linux, the location chosen will be device memory as long as the current set of active
+ * contexts are on devices that either have peer-to-peer support with each other or have a
+ * non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ * If there is an active context on a GPU that does not have a non-zero value for that device
+ * attribute and it does not have peer-to-peer support with the other devices that have active
+ * contexts on them, then the location for physical storage will be 'zero-copy' or host memory.
+ * Note that this means that managed memory that is located in device memory is migrated to
+ * host memory if a new context is created on a GPU that doesn't have a non-zero value for
+ * the device attribute and does not support peer-to-peer with at least one of the other devices
+ * that has an active context. This in turn implies that context creation may fail if there is
+ * insufficient host memory to migrate all managed allocations.
+ * - On Windows, the physical storage is always created in 'zero-copy' or host memory.
+ * All GPUs will reference the data at reduced bandwidth over the PCIe bus. In these
+ * circumstances, use of the environment variable CUDA_VISIBLE_DEVICES is recommended to
+ * restrict CUDA to only use those GPUs that have peer-to-peer support.
+ * Alternatively, users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to a
+ * non-zero value to force the driver to always use device memory for physical storage.
+ * When this environment variable is set to a non-zero value, all contexts created in
+ * that process on devices that support managed memory have to be peer-to-peer compatible
+ * with each other. Context creation will fail if a context is created on a device that
+ * supports managed memory and is not peer-to-peer compatible with any of the other
+ * managed memory supporting devices on which contexts were previously created, even if
+ * those contexts have been destroyed. These environment variables are described
+ * in the CUDA programming guide under the "CUDA environment variables" section.
+ * - On ARM, managed memory is not available on discrete gpu with Drive PX-2.
+ *
+ * \param dptr     - Returned device pointer
+ * \param bytesize - Requested allocation size in bytes
+ * \param flags    - Must be one of ::CU_MEM_ATTACH_GLOBAL or ::CU_MEM_ATTACH_HOST
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cuDeviceGetAttribute, ::cuStreamAttachMemAsync,
+ * ::cudaMallocManaged
+ */
+CUresult CUDAAPI cuMemAllocManaged(CUdeviceptr *dptr, size_t bytesize, unsigned int flags);
+
+/**
+* \brief Registers a callback function to receive async notifications
+* 
+* Registers \p callbackFunc to receive async notifications.
+* 
+* The \p userData parameter is passed to the callback function at async notification time.  
+* Likewise, \p callback is also passed to the callback function to distinguish between
+* multiple registered callbacks.
+* 
+* The callback function being registered should be designed to return quickly (~10ms).  
+* Any long running tasks should be queued for execution on an application thread.
+* 
+* Callbacks may not call cuDeviceRegisterAsyncNotification or cuDeviceUnregisterAsyncNotification.
+* Doing so will result in ::CUDA_ERROR_NOT_PERMITTED. Async notification callbacks execute
+* in an undefined order and may be serialized.
+* 
+* Returns in \p *callback a handle representing the registered callback instance.
+* 
+* \param device - The device on which to register the callback
+* \param callbackFunc - The function to register as a callback
+* \param userData - A generic pointer to user data. This is passed into the callback function.
+* \param callback - A handle representing the registered callback instance
+* 
+* \return
+* ::CUDA_SUCCESS
+* ::CUDA_ERROR_NOT_SUPPORTED
+* ::CUDA_ERROR_INVALID_DEVICE
+* ::CUDA_ERROR_INVALID_VALUE
+* ::CUDA_ERROR_NOT_PERMITTED
+* ::CUDA_ERROR_UNKNOWN
+* \notefnerr
+* 
+* \sa
+* ::cuDeviceUnregisterAsyncNotification
+*/
+CUresult CUDAAPI cuDeviceRegisterAsyncNotification(CUdevice device, CUasyncCallback callbackFunc, void *userData, CUasyncCallbackHandle *callback);
+
+/**
+* \brief Unregisters an async notification callback
+* 
+* Unregisters \p callback so that the corresponding callback function will stop receiving
+* async notifications.
+* 
+* \param device - The device from which to remove \p callback.
+* \param callback - The callback instance to unregister from receiving async notifications.
+* 
+* \return
+* ::CUDA_SUCCESS
+* ::CUDA_ERROR_NOT_SUPPORTED
+* ::CUDA_ERROR_INVALID_DEVICE
+* ::CUDA_ERROR_INVALID_VALUE
+* ::CUDA_ERROR_NOT_PERMITTED
+* ::CUDA_ERROR_UNKNOWN
+* \notefnerr
+* 
+* \sa
+* ::cuDeviceRegisterAsyncNotification
+*/
+CUresult CUDAAPI cuDeviceUnregisterAsyncNotification(CUdevice device, CUasyncCallbackHandle callback);
+
+/**
+ * \brief Returns a handle to a compute device
+ *
+ * Returns in \p *device a device handle given a PCI bus ID string.
+ *
+ * \param dev      - Returned device handle
+ *
+ * \param pciBusId - String in one of the following forms:
+ * [domain]:[bus]:[device].[function]
+ * [domain]:[bus]:[device]
+ * [bus]:[device].[function]
+ * where \p domain, \p bus, \p device, and \p function are all hexadecimal values
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGet,
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetPCIBusId,
+ * ::cudaDeviceGetByPCIBusId
+ */
+CUresult CUDAAPI cuDeviceGetByPCIBusId(CUdevice *dev, const char *pciBusId);
+
+/**
+ * \brief Returns a PCI Bus Id string for the device
+ *
+ * Returns an ASCII string identifying the device \p dev in the NULL-terminated
+ * string pointed to by \p pciBusId. \p len specifies the maximum length of the
+ * string that may be returned.
+ *
+ * \param pciBusId - Returned identifier string for the device in the following format
+ * [domain]:[bus]:[device].[function]
+ * where \p domain, \p bus, \p device, and \p function are all hexadecimal values.
+ * pciBusId should be large enough to store 13 characters including the NULL-terminator.
+ *
+ * \param len      - Maximum length of string to store in \p name
+ *
+ * \param dev      - Device to get identifier string for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceGet,
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetByPCIBusId,
+ * ::cudaDeviceGetPCIBusId
+ */
+CUresult CUDAAPI cuDeviceGetPCIBusId(char *pciBusId, int len, CUdevice dev);
+
+/**
+ * \brief Gets an interprocess handle for a previously allocated event
+ *
+ * Takes as input a previously allocated event. This event must have been
+ * created with the ::CU_EVENT_INTERPROCESS and ::CU_EVENT_DISABLE_TIMING
+ * flags set. This opaque handle may be copied into other processes and
+ * opened with ::cuIpcOpenEventHandle to allow efficient hardware
+ * synchronization between GPU work in different processes.
+ *
+ * After the event has been opened in the importing process,
+ * ::cuEventRecord, ::cuEventSynchronize, ::cuStreamWaitEvent and
+ * ::cuEventQuery may be used in either process. Performing operations
+ * on the imported event after the exported event has been freed
+ * with ::cuEventDestroy will result in undefined behavior.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED
+ *
+ * \param pHandle - Pointer to a user allocated CUipcEventHandle
+ *                    in which to return the opaque event handle
+ * \param event   - Event allocated with ::CU_EVENT_INTERPROCESS and
+ *                    ::CU_EVENT_DISABLE_TIMING flags.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_MAP_FAILED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuEventCreate,
+ * ::cuEventDestroy,
+ * ::cuEventSynchronize,
+ * ::cuEventQuery,
+ * ::cuStreamWaitEvent,
+ * ::cuIpcOpenEventHandle,
+ * ::cuIpcGetMemHandle,
+ * ::cuIpcOpenMemHandle,
+ * ::cuIpcCloseMemHandle,
+ * ::cudaIpcGetEventHandle
+ */
+CUresult CUDAAPI cuIpcGetEventHandle(CUipcEventHandle *pHandle, CUevent event);
+
+/**
+ * \brief Opens an interprocess event handle for use in the current process
+ *
+ * Opens an interprocess event handle exported from another process with
+ * ::cuIpcGetEventHandle. This function returns a ::CUevent that behaves like
+ * a locally created event with the ::CU_EVENT_DISABLE_TIMING flag specified.
+ * This event must be freed with ::cuEventDestroy.
+ *
+ * Performing operations on the imported event after the exported event has
+ * been freed with ::cuEventDestroy will result in undefined behavior.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED
+ *
+ * \param phEvent - Returns the imported event
+ * \param handle  - Interprocess handle to open
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_MAP_FAILED,
+ * ::CUDA_ERROR_PEER_ACCESS_UNSUPPORTED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuEventCreate,
+ * ::cuEventDestroy,
+ * ::cuEventSynchronize,
+ * ::cuEventQuery,
+ * ::cuStreamWaitEvent,
+ * ::cuIpcGetEventHandle,
+ * ::cuIpcGetMemHandle,
+ * ::cuIpcOpenMemHandle,
+ * ::cuIpcCloseMemHandle,
+ * ::cudaIpcOpenEventHandle
+ */
+CUresult CUDAAPI cuIpcOpenEventHandle(CUevent *phEvent, CUipcEventHandle handle);
+
+/**
+ * \brief Gets an interprocess memory handle for an existing device memory
+ * allocation
+ *
+ * Takes a pointer to the base of an existing device memory allocation created
+ * with ::cuMemAlloc and exports it for use in another process. This is a
+ * lightweight operation and may be called multiple times on an allocation
+ * without adverse effects.
+ *
+ * If a region of memory is freed with ::cuMemFree and a subsequent call
+ * to ::cuMemAlloc returns memory with the same device address,
+ * ::cuIpcGetMemHandle will return a unique handle for the
+ * new memory.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED
+ *
+ * \param pHandle - Pointer to user allocated ::CUipcMemHandle to return
+ *                    the handle in.
+ * \param dptr    - Base pointer to previously allocated device memory
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_MAP_FAILED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuMemAlloc,
+ * ::cuMemFree,
+ * ::cuIpcGetEventHandle,
+ * ::cuIpcOpenEventHandle,
+ * ::cuIpcOpenMemHandle,
+ * ::cuIpcCloseMemHandle,
+ * ::cudaIpcGetMemHandle
+ */
+CUresult CUDAAPI cuIpcGetMemHandle(CUipcMemHandle *pHandle, CUdeviceptr dptr);
+
+/**
+ * \brief Opens an interprocess memory handle exported from another process
+ * and returns a device pointer usable in the local process.
+ *
+ * Maps memory exported from another process with ::cuIpcGetMemHandle into
+ * the current device address space. For contexts on different devices
+ * ::cuIpcOpenMemHandle can attempt to enable peer access between the
+ * devices as if the user called ::cuCtxEnablePeerAccess. This behavior is
+ * controlled by the ::CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS flag.
+ * ::cuDeviceCanAccessPeer can determine if a mapping is possible.
+ *
+ * Contexts that may open ::CUipcMemHandles are restricted in the following way.
+ * ::CUipcMemHandles from each ::CUdevice in a given process may only be opened
+ * by one ::CUcontext per ::CUdevice per other process.
+ *
+ * If the memory handle has already been opened by the current context, the
+ * reference count on the handle is incremented by 1 and the existing device pointer
+ * is returned.
+ *
+ * Memory returned from ::cuIpcOpenMemHandle must be freed with
+ * ::cuIpcCloseMemHandle.
+ *
+ * Calling ::cuMemFree on an exported memory region before calling
+ * ::cuIpcCloseMemHandle in the importing context will result in undefined
+ * behavior.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED
+ *
+ * \param pdptr  - Returned device pointer
+ * \param handle - ::CUipcMemHandle to open
+ * \param Flags  - Flags for this operation. Must be specified as ::CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_MAP_FAILED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_TOO_MANY_PEERS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \note No guarantees are made about the address returned in \p *pdptr.
+ * In particular, multiple processes may not receive the same address for the same \p handle.
+ *
+ * \sa
+ * ::cuMemAlloc,
+ * ::cuMemFree,
+ * ::cuIpcGetEventHandle,
+ * ::cuIpcOpenEventHandle,
+ * ::cuIpcGetMemHandle,
+ * ::cuIpcCloseMemHandle,
+ * ::cuCtxEnablePeerAccess,
+ * ::cuDeviceCanAccessPeer,
+ * ::cudaIpcOpenMemHandle
+ */
+CUresult CUDAAPI cuIpcOpenMemHandle(CUdeviceptr *pdptr, CUipcMemHandle handle, unsigned int Flags);
+
+/**
+ * \brief Attempts to close memory mapped with ::cuIpcOpenMemHandle
+ *
+ * Decrements the reference count of the memory returned by ::cuIpcOpenMemHandle by 1.
+ * When the reference count reaches 0, this API unmaps the memory. The original allocation
+ * in the exporting process as well as imported mappings in other processes
+ * will be unaffected.
+ *
+ * Any resources used to enable peer access will be freed if this is the
+ * last mapping using them.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED
+ *
+ * \param dptr - Device pointer returned by ::cuIpcOpenMemHandle
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_MAP_FAILED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \sa
+ * ::cuMemAlloc,
+ * ::cuMemFree,
+ * ::cuIpcGetEventHandle,
+ * ::cuIpcOpenEventHandle,
+ * ::cuIpcGetMemHandle,
+ * ::cuIpcOpenMemHandle,
+ * ::cudaIpcCloseMemHandle
+ */
+CUresult CUDAAPI cuIpcCloseMemHandle(CUdeviceptr dptr);
+
+/**
+ * \brief Registers an existing host memory range for use by CUDA
+ *
+ * Page-locks the memory range specified by \p p and \p bytesize and maps it
+ * for the device(s) as specified by \p Flags. This memory range also is added
+ * to the same tracking mechanism as ::cuMemHostAlloc to automatically accelerate
+ * calls to functions such as ::cuMemcpyHtoD(). Since the memory can be accessed
+ * directly by the device, it can be read or written with much higher bandwidth
+ * than pageable memory that has not been registered.  Page-locking excessive
+ * amounts of memory may degrade system performance, since it reduces the amount
+ * of memory available to the system for paging. As a result, this function is
+ * best used sparingly to register staging areas for data exchange between
+ * host and device.
+ *
+ * On systems where ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES 
+ * is true, ::cuMemHostRegister will not page-lock the memory range specified 
+ * by \p ptr but only populate unpopulated pages.
+ *
+ * The \p Flags parameter enables different options to be specified that
+ * affect the allocation, as follows.
+ *
+ * - ::CU_MEMHOSTREGISTER_PORTABLE: The memory returned by this call will be
+ *   considered as pinned memory by all CUDA contexts, not just the one that
+ *   performed the allocation.
+ *
+ * - ::CU_MEMHOSTREGISTER_DEVICEMAP: Maps the allocation into the CUDA address
+ *   space. The device pointer to the memory may be obtained by calling
+ *   ::cuMemHostGetDevicePointer().
+ *
+ * - ::CU_MEMHOSTREGISTER_IOMEMORY: The pointer is treated as pointing to some
+ *   I/O memory space, e.g. the PCI Express resource of a 3rd party device.
+ *
+ * - ::CU_MEMHOSTREGISTER_READ_ONLY: The pointer is treated as pointing to memory
+ *   that is considered read-only by the device.  On platforms without
+ *   ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, this flag is
+ *   required in order to register memory mapped to the CPU as read-only.  Support
+ *   for the use of this flag can be queried from the device attribute
+ *   ::CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED.  Using this flag with
+ *   a current context associated with a device that does not have this attribute
+ *   set will cause ::cuMemHostRegister to error with CUDA_ERROR_NOT_SUPPORTED.
+ *
+ * All of these flags are orthogonal to one another: a developer may page-lock
+ * memory that is portable or mapped with no restrictions.
+ *
+ * The ::CU_MEMHOSTREGISTER_DEVICEMAP flag may be specified on CUDA contexts for
+ * devices that do not support mapped pinned memory. The failure is deferred
+ * to ::cuMemHostGetDevicePointer() because the memory may be mapped into
+ * other CUDA contexts via the ::CU_MEMHOSTREGISTER_PORTABLE flag.
+ *
+ * For devices that have a non-zero value for the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM, the memory
+ * can also be accessed from the device using the host pointer \p p.
+ * The device pointer returned by ::cuMemHostGetDevicePointer() may or may not
+ * match the original host pointer \p ptr and depends on the devices visible to the
+ * application. If all devices visible to the application have a non-zero value for the
+ * device attribute, the device pointer returned by ::cuMemHostGetDevicePointer()
+ * will match the original pointer \p ptr. If any device visible to the application
+ * has a zero value for the device attribute, the device pointer returned by
+ * ::cuMemHostGetDevicePointer() will not match the original host pointer \p ptr,
+ * but it will be suitable for use on all devices provided Unified Virtual Addressing
+ * is enabled. In such systems, it is valid to access the memory using either pointer
+ * on devices that have a non-zero value for the device attribute. Note however that
+ * such devices should access the memory using only of the two pointers and not both.
+ *
+ * The memory page-locked by this function must be unregistered with
+ * ::cuMemHostUnregister().
+ *
+ * \param p        - Host pointer to memory to page-lock
+ * \param bytesize - Size in bytes of the address range to page-lock
+ * \param Flags    - Flags for allocation request
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMemHostUnregister,
+ * ::cuMemHostGetFlags,
+ * ::cuMemHostGetDevicePointer,
+ * ::cudaHostRegister
+ */
+CUresult CUDAAPI cuMemHostRegister(void *p, size_t bytesize, unsigned int Flags);
+
+/**
+ * \brief Unregisters a memory range that was registered with cuMemHostRegister.
+ *
+ * Unmaps the memory range whose base address is specified by \p p, and makes
+ * it pageable again.
+ *
+ * The base address must be the same one specified to ::cuMemHostRegister().
+ *
+ * \param p - Host pointer to memory to unregister
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED,
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMemHostRegister,
+ * ::cudaHostUnregister
+ */
+CUresult CUDAAPI cuMemHostUnregister(void *p);
+
+/**
+ * \brief Copies memory
+ *
+ * Copies data between two pointers.
+ * \p dst and \p src are base pointers of the destination and source, respectively.
+ * \p ByteCount specifies the number of bytes to copy.
+ * Note that this function infers the type of the transfer (host to host, host to
+ *   device, device to device, or device to host) from the pointer values.  This
+ *   function is only allowed in contexts which support unified addressing.
+ *
+ * \param dst - Destination unified virtual address space pointer
+ * \param src - Source unified virtual address space pointer
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy,
+ * ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol
+ */
+CUresult CUDAAPI cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount);
+
+/**
+ * \brief Copies device memory between two contexts
+ *
+ * Copies from device memory in one context to device memory in another
+ * context. \p dstDevice is the base device pointer of the destination memory
+ * and \p dstContext is the destination context.  \p srcDevice is the base
+ * device pointer of the source memory and \p srcContext is the source pointer.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstDevice  - Destination device pointer
+ * \param dstContext - Destination context
+ * \param srcDevice  - Source device pointer
+ * \param srcContext - Source context
+ * \param ByteCount  - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuMemcpyDtoD, ::cuMemcpy3DPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerAsync,
+ * ::cuMemcpy3DPeerAsync,
+ * ::cudaMemcpyPeer
+ */
+CUresult CUDAAPI cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Host to Device
+ *
+ * Copies from host memory to device memory. \p dstDevice and \p srcHost are
+ * the base addresses of the destination and source, respectively. \p ByteCount
+ * specifies the number of bytes to copy.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param srcHost   - Source host pointer
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy,
+ * ::cudaMemcpyToSymbol
+ */
+CUresult CUDAAPI cuMemcpyHtoD(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Device to Host
+ *
+ * Copies from device to host memory. \p dstHost and \p srcDevice specify the
+ * base pointers of the destination and source, respectively. \p ByteCount
+ * specifies the number of bytes to copy.
+ *
+ * \param dstHost   - Destination host pointer
+ * \param srcDevice - Source device pointer
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy,
+ * ::cudaMemcpyFromSymbol
+ */
+CUresult CUDAAPI cuMemcpyDtoH(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Device to Device
+ *
+ * Copies from device memory to device memory. \p dstDevice and \p srcDevice
+ * are the base pointers of the destination and source, respectively.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param srcDevice - Source device pointer
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy,
+ * ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol
+ */
+CUresult CUDAAPI cuMemcpyDtoD(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Device to Array
+ *
+ * Copies from device memory to a 1D CUDA array. \p dstArray and \p dstOffset
+ * specify the CUDA array handle and starting index of the destination data.
+ * \p srcDevice specifies the base pointer of the source. \p ByteCount
+ * specifies the number of bytes to copy.
+ *
+ * \param dstArray  - Destination array
+ * \param dstOffset - Offset in bytes of destination array
+ * \param srcDevice - Source device pointer
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpyToArray
+ */
+CUresult CUDAAPI cuMemcpyDtoA(CUarray dstArray, size_t dstOffset, CUdeviceptr srcDevice, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Array to Device
+ *
+ * Copies from one 1D CUDA array to device memory. \p dstDevice specifies the
+ * base pointer of the destination and must be naturally aligned with the CUDA
+ * array elements. \p srcArray and \p srcOffset specify the CUDA array handle
+ * and the offset in bytes into the array where the copy is to begin.
+ * \p ByteCount specifies the number of bytes to copy and must be evenly
+ * divisible by the array element size.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param srcArray  - Source array
+ * \param srcOffset - Offset in bytes of source array
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpyFromArray
+ */
+CUresult CUDAAPI cuMemcpyAtoD(CUdeviceptr dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Host to Array
+ *
+ * Copies from host memory to a 1D CUDA array. \p dstArray and \p dstOffset
+ * specify the CUDA array handle and starting offset in bytes of the destination
+ * data.  \p pSrc specifies the base address of the source. \p ByteCount specifies
+ * the number of bytes to copy.
+ *
+ * \param dstArray  - Destination array
+ * \param dstOffset - Offset in bytes of destination array
+ * \param srcHost   - Source host pointer
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpyToArray
+ */
+CUresult CUDAAPI cuMemcpyHtoA(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Array to Host
+ *
+ * Copies from one 1D CUDA array to host memory. \p dstHost specifies the base
+ * pointer of the destination. \p srcArray and \p srcOffset specify the CUDA
+ * array handle and starting offset in bytes of the source data.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstHost   - Destination device pointer
+ * \param srcArray  - Source array
+ * \param srcOffset - Offset in bytes of source array
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpyFromArray
+ */
+CUresult CUDAAPI cuMemcpyAtoH(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+
+/**
+ * \brief Copies memory from Array to Array
+ *
+ * Copies from one 1D CUDA array to another. \p dstArray and \p srcArray
+ * specify the handles of the destination and source CUDA arrays for the copy,
+ * respectively. \p dstOffset and \p srcOffset specify the destination and
+ * source offsets in bytes into the CUDA arrays. \p ByteCount is the number of
+ * bytes to be copied. The size of the elements in the CUDA arrays need not be
+ * the same format, but the elements must be the same size; and count must be
+ * evenly divisible by that size.
+ *
+ * \param dstArray  - Destination array
+ * \param dstOffset - Offset in bytes of destination array
+ * \param srcArray  - Source array
+ * \param srcOffset - Offset in bytes of source array
+ * \param ByteCount - Size of memory copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpyArrayToArray
+ */
+CUresult CUDAAPI cuMemcpyAtoA(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+
+/**
+ * \brief Copies memory for 2D arrays
+ *
+ * Perform a 2D memory copy according to the parameters specified in \p pCopy.
+ * The ::CUDA_MEMCPY2D structure is defined as:
+ *
+ * \code
+   typedef struct CUDA_MEMCPY2D_st {
+      unsigned int srcXInBytes, srcY;
+      CUmemorytype srcMemoryType;
+          const void *srcHost;
+          CUdeviceptr srcDevice;
+          CUarray srcArray;
+          unsigned int srcPitch;
+
+      unsigned int dstXInBytes, dstY;
+      CUmemorytype dstMemoryType;
+          void *dstHost;
+          CUdeviceptr dstDevice;
+          CUarray dstArray;
+          unsigned int dstPitch;
+
+      unsigned int WidthInBytes;
+      unsigned int Height;
+   } CUDA_MEMCPY2D;
+ * \endcode
+ * where:
+ * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
+ *   source and destination, respectively; ::CUmemorytype_enum is defined as:
+ *
+ * \code
+   typedef enum CUmemorytype_enum {
+      CU_MEMORYTYPE_HOST = 0x01,
+      CU_MEMORYTYPE_DEVICE = 0x02,
+      CU_MEMORYTYPE_ARRAY = 0x03,
+      CU_MEMORYTYPE_UNIFIED = 0x04
+   } CUmemorytype;
+ * \endcode
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::srcArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch
+ * specify the (host) base address of the source data and the bytes per row to
+ * apply. ::srcArray is ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch
+ * specify the (device) base address of the source data and the bytes per row
+ * to apply. ::srcArray is ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
+ * handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are
+ * ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
+ * specify the (host) base address of the destination data and the bytes per
+ * row to apply. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::dstArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
+ * specify the (device) base address of the destination data and the bytes per
+ * row to apply. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the
+ * handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch are
+ * ignored.
+ *
+ * - ::srcXInBytes and ::srcY specify the base address of the source data for
+ *   the copy.
+ *
+ * \par
+ * For host pointers, the starting address is
+ * \code
+  void* Start = (void*)((char*)srcHost+srcY*srcPitch + srcXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr Start = srcDevice+srcY*srcPitch+srcXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::dstXInBytes and ::dstY specify the base address of the destination data
+ *   for the copy.
+ *
+ * \par
+ * For host pointers, the base address is
+ * \code
+  void* dstStart = (void*)((char*)dstHost+dstY*dstPitch + dstXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr dstStart = dstDevice+dstY*dstPitch+dstXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::WidthInBytes and ::Height specify the width (in bytes) and height of
+ *   the 2D copy being performed.
+ * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes +
+ *   ::srcXInBytes, and ::dstPitch must be greater than or equal to
+ *   ::WidthInBytes + dstXInBytes.
+ *
+ * \par
+ * ::cuMemcpy2D() returns an error if any pitch is greater than the maximum
+ * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes back
+ * pitches that always work with ::cuMemcpy2D(). On intra-device memory copies
+ * (device to device, CUDA array to device, CUDA array to CUDA array),
+ * ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().
+ * ::cuMemcpy2DUnaligned() does not have this restriction, but may run
+ * significantly slower in the cases where ::cuMemcpy2D() would have returned
+ * an error code.
+ *
+ * \param pCopy - Parameters for the memory copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray,
+ * ::cudaMemcpy2DFromArray
+ */
+CUresult CUDAAPI cuMemcpy2D(const CUDA_MEMCPY2D *pCopy);
+
+/**
+ * \brief Copies memory for 2D arrays
+ *
+ * Perform a 2D memory copy according to the parameters specified in \p pCopy.
+ * The ::CUDA_MEMCPY2D structure is defined as:
+ *
+ * \code
+   typedef struct CUDA_MEMCPY2D_st {
+      unsigned int srcXInBytes, srcY;
+      CUmemorytype srcMemoryType;
+      const void *srcHost;
+      CUdeviceptr srcDevice;
+      CUarray srcArray;
+      unsigned int srcPitch;
+      unsigned int dstXInBytes, dstY;
+      CUmemorytype dstMemoryType;
+      void *dstHost;
+      CUdeviceptr dstDevice;
+      CUarray dstArray;
+      unsigned int dstPitch;
+      unsigned int WidthInBytes;
+      unsigned int Height;
+   } CUDA_MEMCPY2D;
+ * \endcode
+ * where:
+ * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
+ *   source and destination, respectively; ::CUmemorytype_enum is defined as:
+ *
+ * \code
+   typedef enum CUmemorytype_enum {
+      CU_MEMORYTYPE_HOST = 0x01,
+      CU_MEMORYTYPE_DEVICE = 0x02,
+      CU_MEMORYTYPE_ARRAY = 0x03,
+      CU_MEMORYTYPE_UNIFIED = 0x04
+   } CUmemorytype;
+ * \endcode
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::srcArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch
+ * specify the (host) base address of the source data and the bytes per row to
+ * apply. ::srcArray is ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch
+ * specify the (device) base address of the source data and the bytes per row
+ * to apply. ::srcArray is ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
+ * handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are
+ * ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::dstArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
+ * specify the (host) base address of the destination data and the bytes per
+ * row to apply. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
+ * specify the (device) base address of the destination data and the bytes per
+ * row to apply. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the
+ * handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch are
+ * ignored.
+ *
+ * - ::srcXInBytes and ::srcY specify the base address of the source data for
+ *   the copy.
+ *
+ * \par
+ * For host pointers, the starting address is
+ * \code
+  void* Start = (void*)((char*)srcHost+srcY*srcPitch + srcXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr Start = srcDevice+srcY*srcPitch+srcXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::dstXInBytes and ::dstY specify the base address of the destination data
+ *   for the copy.
+ *
+ * \par
+ * For host pointers, the base address is
+ * \code
+  void* dstStart = (void*)((char*)dstHost+dstY*dstPitch + dstXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr dstStart = dstDevice+dstY*dstPitch+dstXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::WidthInBytes and ::Height specify the width (in bytes) and height of
+ *   the 2D copy being performed.
+ * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes +
+ *   ::srcXInBytes, and ::dstPitch must be greater than or equal to
+ *   ::WidthInBytes + dstXInBytes.
+ *
+ * \par
+ * ::cuMemcpy2D() returns an error if any pitch is greater than the maximum
+ * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes back
+ * pitches that always work with ::cuMemcpy2D(). On intra-device memory copies
+ * (device to device, CUDA array to device, CUDA array to CUDA array),
+ * ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch().
+ * ::cuMemcpy2DUnaligned() does not have this restriction, but may run
+ * significantly slower in the cases where ::cuMemcpy2D() would have returned
+ * an error code.
+ *
+ * \param pCopy - Parameters for the memory copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray,
+ * ::cudaMemcpy2DFromArray
+ */
+CUresult CUDAAPI cuMemcpy2DUnaligned(const CUDA_MEMCPY2D *pCopy);
+
+/**
+ * \brief Copies memory for 3D arrays
+ *
+ * Perform a 3D memory copy according to the parameters specified in
+ * \p pCopy. The ::CUDA_MEMCPY3D structure is defined as:
+ *
+ * \code
+        typedef struct CUDA_MEMCPY3D_st {
+
+            unsigned int srcXInBytes, srcY, srcZ;
+            unsigned int srcLOD;
+            CUmemorytype srcMemoryType;
+                const void *srcHost;
+                CUdeviceptr srcDevice;
+                CUarray srcArray;
+                unsigned int srcPitch;  // ignored when src is array
+                unsigned int srcHeight; // ignored when src is array; may be 0 if Depth==1
+
+            unsigned int dstXInBytes, dstY, dstZ;
+            unsigned int dstLOD;
+            CUmemorytype dstMemoryType;
+                void *dstHost;
+                CUdeviceptr dstDevice;
+                CUarray dstArray;
+                unsigned int dstPitch;  // ignored when dst is array
+                unsigned int dstHeight; // ignored when dst is array; may be 0 if Depth==1
+
+            unsigned int WidthInBytes;
+            unsigned int Height;
+            unsigned int Depth;
+        } CUDA_MEMCPY3D;
+ * \endcode
+ * where:
+ * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
+ *   source and destination, respectively; ::CUmemorytype_enum is defined as:
+ *
+ * \code
+   typedef enum CUmemorytype_enum {
+      CU_MEMORYTYPE_HOST = 0x01,
+      CU_MEMORYTYPE_DEVICE = 0x02,
+      CU_MEMORYTYPE_ARRAY = 0x03,
+      CU_MEMORYTYPE_UNIFIED = 0x04
+   } CUmemorytype;
+ * \endcode
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::srcArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and
+ * ::srcHeight specify the (host) base address of the source data, the bytes
+ * per row, and the height of each 2D slice of the 3D array. ::srcArray is
+ * ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch and
+ * ::srcHeight specify the (device) base address of the source data, the bytes
+ * per row, and the height of each 2D slice of the 3D array. ::srcArray is
+ * ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
+ * handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and
+ * ::srcHeight are ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::dstArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
+ * specify the (host) base address of the destination data, the bytes per row,
+ * and the height of each 2D slice of the 3D array. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
+ * specify the (device) base address of the destination data, the bytes per
+ * row, and the height of each 2D slice of the 3D array. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the
+ * handle of the destination data. ::dstHost, ::dstDevice, ::dstPitch and
+ * ::dstHeight are ignored.
+ *
+ * - ::srcXInBytes, ::srcY and ::srcZ specify the base address of the source
+ *   data for the copy.
+ *
+ * \par
+ * For host pointers, the starting address is
+ * \code
+  void* Start = (void*)((char*)srcHost+(srcZ*srcHeight+srcY)*srcPitch + srcXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr Start = srcDevice+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - dstXInBytes, ::dstY and ::dstZ specify the base address of the
+ *   destination data for the copy.
+ *
+ * \par
+ * For host pointers, the base address is
+ * \code
+  void* dstStart = (void*)((char*)dstHost+(dstZ*dstHeight+dstY)*dstPitch + dstXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr dstStart = dstDevice+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::WidthInBytes, ::Height and ::Depth specify the width (in bytes), height
+ *   and depth of the 3D copy being performed.
+ * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes +
+ *   ::srcXInBytes, and ::dstPitch must be greater than or equal to
+ *   ::WidthInBytes + dstXInBytes.
+ * - If specified, ::srcHeight must be greater than or equal to ::Height +
+ *   ::srcY, and ::dstHeight must be greater than or equal to ::Height + ::dstY.
+ *
+ * \par
+ * ::cuMemcpy3D() returns an error if any pitch is greater than the maximum
+ * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH).
+ *
+ * The ::srcLOD and ::dstLOD members of the ::CUDA_MEMCPY3D structure must be
+ * set to 0.
+ *
+ * \param pCopy - Parameters for the memory copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMemcpy3D
+ */
+CUresult CUDAAPI cuMemcpy3D(const CUDA_MEMCPY3D *pCopy);
+
+/**
+ * \brief Copies memory between contexts
+ *
+ * Perform a 3D memory copy according to the parameters specified in
+ * \p pCopy.  See the definition of the ::CUDA_MEMCPY3D_PEER structure
+ * for documentation of its parameters.
+ *
+ * \param pCopy - Parameters for the memory copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_sync
+ *
+ * \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerAsync,
+ * ::cuMemcpy3DPeerAsync,
+ * ::cudaMemcpy3DPeer
+ */
+CUresult CUDAAPI cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER *pCopy);
+
+/**
+ * \brief Copies memory asynchronously
+ *
+ * Copies data between two pointers.
+ * \p dst and \p src are base pointers of the destination and source, respectively.
+ * \p ByteCount specifies the number of bytes to copy.
+ * Note that this function infers the type of the transfer (host to host, host to
+ *   device, device to device, or device to host) from the pointer values.  This
+ *   function is only allowed in contexts which support unified addressing.
+ *
+ * \param dst       - Destination unified virtual address space pointer
+ * \param src       - Source unified virtual address space pointer
+ * \param ByteCount - Size of memory copy in bytes
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpyAsync,
+ * ::cudaMemcpyToSymbolAsync,
+ * ::cudaMemcpyFromSymbolAsync
+ */
+CUresult CUDAAPI cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies device memory between two contexts asynchronously.
+ *
+ * Copies from device memory in one context to device memory in another
+ * context. \p dstDevice is the base device pointer of the destination memory
+ * and \p dstContext is the destination context.  \p srcDevice is the base
+ * device pointer of the source memory and \p srcContext is the source pointer.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstDevice  - Destination device pointer
+ * \param dstContext - Destination context
+ * \param srcDevice  - Source device pointer
+ * \param srcContext - Source context
+ * \param ByteCount  - Size of memory copy in bytes
+ * \param hStream    - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpy3DPeer, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpy3DPeerAsync,
+ * ::cudaMemcpyPeerAsync
+ */
+CUresult CUDAAPI cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies memory from Host to Device
+ *
+ * Copies from host memory to device memory. \p dstDevice and \p srcHost are
+ * the base addresses of the destination and source, respectively. \p ByteCount
+ * specifies the number of bytes to copy.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param srcHost   - Source host pointer
+ * \param ByteCount - Size of memory copy in bytes
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpyAsync,
+ * ::cudaMemcpyToSymbolAsync
+ */
+CUresult CUDAAPI cuMemcpyHtoDAsync(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies memory from Device to Host
+ *
+ * Copies from device to host memory. \p dstHost and \p srcDevice specify the
+ * base pointers of the destination and source, respectively. \p ByteCount
+ * specifies the number of bytes to copy.
+ *
+ * \param dstHost   - Destination host pointer
+ * \param srcDevice - Source device pointer
+ * \param ByteCount - Size of memory copy in bytes
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpyAsync,
+ * ::cudaMemcpyFromSymbolAsync
+ */
+CUresult CUDAAPI cuMemcpyDtoHAsync(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies memory from Device to Device
+ *
+ * Copies from device memory to device memory. \p dstDevice and \p srcDevice
+ * are the base pointers of the destination and source, respectively.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param srcDevice - Source device pointer
+ * \param ByteCount - Size of memory copy in bytes
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpyAsync,
+ * ::cudaMemcpyToSymbolAsync,
+ * ::cudaMemcpyFromSymbolAsync
+ */
+CUresult CUDAAPI cuMemcpyDtoDAsync(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies memory from Host to Array
+ *
+ * Copies from host memory to a 1D CUDA array. \p dstArray and \p dstOffset
+ * specify the CUDA array handle and starting offset in bytes of the
+ * destination data. \p srcHost specifies the base address of the source.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstArray  - Destination array
+ * \param dstOffset - Offset in bytes of destination array
+ * \param srcHost   - Source host pointer
+ * \param ByteCount - Size of memory copy in bytes
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpyToArrayAsync
+ */
+CUresult CUDAAPI cuMemcpyHtoAAsync(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies memory from Array to Host
+ *
+ * Copies from one 1D CUDA array to host memory. \p dstHost specifies the base
+ * pointer of the destination. \p srcArray and \p srcOffset specify the CUDA
+ * array handle and starting offset in bytes of the source data.
+ * \p ByteCount specifies the number of bytes to copy.
+ *
+ * \param dstHost   - Destination pointer
+ * \param srcArray  - Source array
+ * \param srcOffset - Offset in bytes of source array
+ * \param ByteCount - Size of memory copy in bytes
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_memcpy
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpyFromArrayAsync
+ */
+CUresult CUDAAPI cuMemcpyAtoHAsync(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream);
+
+/**
+ * \brief Copies memory for 2D arrays
+ *
+ * Perform a 2D memory copy according to the parameters specified in \p pCopy.
+ * The ::CUDA_MEMCPY2D structure is defined as:
+ *
+ * \code
+   typedef struct CUDA_MEMCPY2D_st {
+      unsigned int srcXInBytes, srcY;
+      CUmemorytype srcMemoryType;
+      const void *srcHost;
+      CUdeviceptr srcDevice;
+      CUarray srcArray;
+      unsigned int srcPitch;
+      unsigned int dstXInBytes, dstY;
+      CUmemorytype dstMemoryType;
+      void *dstHost;
+      CUdeviceptr dstDevice;
+      CUarray dstArray;
+      unsigned int dstPitch;
+      unsigned int WidthInBytes;
+      unsigned int Height;
+   } CUDA_MEMCPY2D;
+ * \endcode
+ * where:
+ * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
+ *   source and destination, respectively; ::CUmemorytype_enum is defined as:
+ *
+ * \code
+   typedef enum CUmemorytype_enum {
+      CU_MEMORYTYPE_HOST = 0x01,
+      CU_MEMORYTYPE_DEVICE = 0x02,
+      CU_MEMORYTYPE_ARRAY = 0x03,
+      CU_MEMORYTYPE_UNIFIED = 0x04
+   } CUmemorytype;
+ * \endcode
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch
+ * specify the (host) base address of the source data and the bytes per row to
+ * apply. ::srcArray is ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::srcArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch
+ * specify the (device) base address of the source data and the bytes per row
+ * to apply. ::srcArray is ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
+ * handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are
+ * ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::dstArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
+ * specify the (host) base address of the destination data and the bytes per
+ * row to apply. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
+ * specify the (device) base address of the destination data and the bytes per
+ * row to apply. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the
+ * handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch are
+ * ignored.
+ *
+ * - ::srcXInBytes and ::srcY specify the base address of the source data for
+ *   the copy.
+ *
+ * \par
+ * For host pointers, the starting address is
+ * \code
+  void* Start = (void*)((char*)srcHost+srcY*srcPitch + srcXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr Start = srcDevice+srcY*srcPitch+srcXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::dstXInBytes and ::dstY specify the base address of the destination data
+ *   for the copy.
+ *
+ * \par
+ * For host pointers, the base address is
+ * \code
+  void* dstStart = (void*)((char*)dstHost+dstY*dstPitch + dstXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr dstStart = dstDevice+dstY*dstPitch+dstXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::WidthInBytes and ::Height specify the width (in bytes) and height of
+ *   the 2D copy being performed.
+ * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes +
+ *   ::srcXInBytes, and ::dstPitch must be greater than or equal to
+ *   ::WidthInBytes + dstXInBytes.
+ * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes +
+ *   ::srcXInBytes, and ::dstPitch must be greater than or equal to
+ *   ::WidthInBytes + dstXInBytes.
+ * - If specified, ::srcHeight must be greater than or equal to ::Height +
+ *   ::srcY, and ::dstHeight must be greater than or equal to ::Height + ::dstY.
+ *
+ * \par
+ * ::cuMemcpy2DAsync() returns an error if any pitch is greater than the maximum
+ * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes back
+ * pitches that always work with ::cuMemcpy2D(). On intra-device memory copies
+ * (device to device, CUDA array to device, CUDA array to CUDA array),
+ * ::cuMemcpy2DAsync() may fail for pitches not computed by ::cuMemAllocPitch().
+ *
+ * \param pCopy   - Parameters for the memory copy
+ * \param hStream - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync
+ */
+CUresult CUDAAPI cuMemcpy2DAsync(const CUDA_MEMCPY2D *pCopy, CUstream hStream);
+
+/**
+ * \brief Copies memory for 3D arrays
+ *
+ * Perform a 3D memory copy according to the parameters specified in
+ * \p pCopy. The ::CUDA_MEMCPY3D structure is defined as:
+ *
+ * \code
+        typedef struct CUDA_MEMCPY3D_st {
+
+            unsigned int srcXInBytes, srcY, srcZ;
+            unsigned int srcLOD;
+            CUmemorytype srcMemoryType;
+                const void *srcHost;
+                CUdeviceptr srcDevice;
+                CUarray srcArray;
+                unsigned int srcPitch;  // ignored when src is array
+                unsigned int srcHeight; // ignored when src is array; may be 0 if Depth==1
+
+            unsigned int dstXInBytes, dstY, dstZ;
+            unsigned int dstLOD;
+            CUmemorytype dstMemoryType;
+                void *dstHost;
+                CUdeviceptr dstDevice;
+                CUarray dstArray;
+                unsigned int dstPitch;  // ignored when dst is array
+                unsigned int dstHeight; // ignored when dst is array; may be 0 if Depth==1
+
+            unsigned int WidthInBytes;
+            unsigned int Height;
+            unsigned int Depth;
+        } CUDA_MEMCPY3D;
+ * \endcode
+ * where:
+ * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the
+ *   source and destination, respectively; ::CUmemorytype_enum is defined as:
+ *
+ * \code
+   typedef enum CUmemorytype_enum {
+      CU_MEMORYTYPE_HOST = 0x01,
+      CU_MEMORYTYPE_DEVICE = 0x02,
+      CU_MEMORYTYPE_ARRAY = 0x03,
+      CU_MEMORYTYPE_UNIFIED = 0x04
+   } CUmemorytype;
+ * \endcode
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::srcArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and
+ * ::srcHeight specify the (host) base address of the source data, the bytes
+ * per row, and the height of each 2D slice of the 3D array. ::srcArray is
+ * ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch and
+ * ::srcHeight specify the (device) base address of the source data, the bytes
+ * per row, and the height of each 2D slice of the 3D array. ::srcArray is
+ * ignored.
+ *
+ * \par
+ * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the
+ * handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and
+ * ::srcHeight are ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch
+ *   specify the (unified virtual address space) base address of the source data
+ *   and the bytes per row to apply.  ::dstArray is ignored.
+ * This value may be used only if unified addressing is supported in the calling
+ *   context.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch
+ * specify the (host) base address of the destination data, the bytes per row,
+ * and the height of each 2D slice of the 3D array. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch
+ * specify the (device) base address of the destination data, the bytes per
+ * row, and the height of each 2D slice of the 3D array. ::dstArray is ignored.
+ *
+ * \par
+ * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the
+ * handle of the destination data. ::dstHost, ::dstDevice, ::dstPitch and
+ * ::dstHeight are ignored.
+ *
+ * - ::srcXInBytes, ::srcY and ::srcZ specify the base address of the source
+ *   data for the copy.
+ *
+ * \par
+ * For host pointers, the starting address is
+ * \code
+  void* Start = (void*)((char*)srcHost+(srcZ*srcHeight+srcY)*srcPitch + srcXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr Start = srcDevice+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - dstXInBytes, ::dstY and ::dstZ specify the base address of the
+ *   destination data for the copy.
+ *
+ * \par
+ * For host pointers, the base address is
+ * \code
+  void* dstStart = (void*)((char*)dstHost+(dstZ*dstHeight+dstY)*dstPitch + dstXInBytes);
+ * \endcode
+ *
+ * \par
+ * For device pointers, the starting address is
+ * \code
+  CUdeviceptr dstStart = dstDevice+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes;
+ * \endcode
+ *
+ * \par
+ * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array
+ * element size.
+ *
+ * - ::WidthInBytes, ::Height and ::Depth specify the width (in bytes), height
+ *   and depth of the 3D copy being performed.
+ * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes +
+ *   ::srcXInBytes, and ::dstPitch must be greater than or equal to
+ *   ::WidthInBytes + dstXInBytes.
+ * - If specified, ::srcHeight must be greater than or equal to ::Height +
+ *   ::srcY, and ::dstHeight must be greater than or equal to ::Height + ::dstY.
+ *
+ * \par
+ * ::cuMemcpy3DAsync() returns an error if any pitch is greater than the maximum
+ * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH).
+ *
+ * The ::srcLOD and ::dstLOD members of the ::CUDA_MEMCPY3D structure must be
+ * set to 0.
+ *
+ * \param pCopy - Parameters for the memory copy
+ * \param hStream - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemcpy3DAsync
+ */
+CUresult CUDAAPI cuMemcpy3DAsync(const CUDA_MEMCPY3D *pCopy, CUstream hStream);
+
+/**
+ * \brief Copies memory between contexts asynchronously.
+ *
+ * Perform a 3D memory copy according to the parameters specified in
+ * \p pCopy.  See the definition of the ::CUDA_MEMCPY3D_PEER structure
+ * for documentation of its parameters.
+ *
+ * \param pCopy - Parameters for the memory copy
+ * \param hStream - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerAsync,
+ * ::cuMemcpy3DPeerAsync,
+ * ::cudaMemcpy3DPeerAsync
+ */
+CUresult CUDAAPI cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER *pCopy, CUstream hStream);
+
+/**
+ * \brief Performs a batch of memory copies asynchronously.
+ *
+ * Performs a batch of memory copies. The batch as a whole executes in stream order but copies within a
+ * batch are not guaranteed to execute in any specific order. This API only supports pointer-to-pointer copies.
+ * For copies involving CUDA arrays, please see ::cuMemcpy3DBatchAsync.
+ *
+ * Performs memory copies from source buffers specified in \p srcs to destination buffers specified in \p dsts.
+ * The size of each copy is specified in \p sizes. All three arrays must be of the same length as specified
+ * by \p count. Since there are no ordering guarantees for copies within a batch, specifying any dependent copies
+ * within a batch will result in undefined behavior.
+ *
+ * Every copy in the batch has to be associated with a set of attributes specified in the \p attrs array.
+ * Each entry in this array can apply to more than one copy. This can be done by specifying in the \p attrsIdxs array,
+ * the index of the first copy that the corresponding entry in the \p attrs array applies to. Both \p attrs and
+ * \p attrsIdxs must be of the same length as specified by \p numAttrs. For example, if a batch has 10 copies listed
+ * in dst/src/sizes, the first 6 of which have one set of attributes and the remaining 4 another, then \p numAttrs
+ * will be 2, \p attrsIdxs will be {0, 6} and \p attrs will contains the two sets of attributes. Note that the first entry
+ * in \p attrsIdxs must always be 0. Also, each entry must be greater than the previous entry and the last entry should be
+ * less than \p count. Furthermore, \p numAttrs must be lesser than or equal to \p count.
+ *
+ * The ::CUmemcpyAttributes::srcAccessOrder indicates the source access ordering to be observed for copies associated
+ * with the attribute. If the source access order is set to ::CU_MEMCPY_SRC_ACCESS_ORDER_STREAM, then the source will
+ * be accessed in stream order. If the source access order is set to ::CU_MEMCPY_SRC_ACCESS_ORDER_DURING_API_CALL then
+ * it indicates that access to the source pointer can be out of stream order and all accesses must be complete before
+ * the API call returns. This flag is suited for ephemeral sources (ex., stack variables) when it's known that no prior
+ * operations in the stream can be accessing the memory and also that the lifetime of the memory is limited to the scope
+ * that the source variable was declared in. Specifying this flag allows the driver to optimize the copy and removes the
+ * need for the user to synchronize the stream after the API call. If the source access order is set to
+ * ::CU_MEMCPY_SRC_ACCESS_ORDER_ANY then it indicates that access to the source pointer can be out of stream order and the
+ * accesses can happen even after the API call returns. This flag is suited for host pointers allocated
+ * outside CUDA (ex., via malloc) when it's known that no prior operations in the stream can be accessing the memory.
+ * Specifying this flag allows the driver to optimize the copy on certain platforms. Each memcpy operation in the batch must
+ * have a valid ::CUmemcpyAttributes corresponding to it including the appropriate srcAccessOrder setting, otherwise the API
+ * will return ::CUDA_ERROR_INVALID_VALUE.
+ *
+ * The ::CUmemcpyAttributes::srcLocHint and ::CUmemcpyAttributes::dstLocHint allows applications to specify hint locations
+ * for operands of a copy when the operand doesn't have a fixed location. That is, these hints are
+ * only applicable for managed memory pointers on devices where ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS is true or
+ * system-allocated pageable memory on devices where ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS is true.
+ * For other cases, these hints are ignored.
+ *
+ * The ::CUmemcpyAttributes::flags field can be used to specify certain flags for copies. Setting the
+ * ::CU_MEMCPY_FLAG_PREFER_OVERLAP_WITH_COMPUTE flag indicates that the associated copies should preferably overlap with
+ * any compute work. Note that this flag is a hint and can be ignored depending on the platform and other parameters of the copy.
+ *
+ * If any error is encountered while parsing the batch, the index within the batch where the error was encountered
+ * will be returned in \p failIdx. 
+ *
+ * \param dsts          - Array of destination pointers.
+ * \param srcs          - Array of memcpy source pointers.
+ * \param sizes         - Array of sizes for memcpy operations.
+ * \param count         - Size of \p dsts, \p srcs and \p sizes arrays
+ * \param attrs         - Array of memcpy attributes. 
+ * \param attrsIdxs     - Array of indices to specify which copies each entry in the \p attrs array applies to.
+                          The attributes specified in attrs[k] will be applied to copies starting from attrsIdxs[k]
+                          through attrsIdxs[k+1] - 1. Also attrs[numAttrs-1] will apply to copies starting from
+                          attrsIdxs[numAttrs-1] through count - 1.
+ * \param numAttrs      - Size of \p attrs and \p attrsIdxs arrays.
+ * \param failIdx       - Pointer to a location to return the index of the copy where a failure was encountered.
+                          The value will be SIZE_MAX if the error doesn't pertain to any specific copy.
+ * \param hStream       - The stream to enqueue the operations in. Must not be legacy NULL stream.
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_DEINITIALIZED
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_async
+ * \note_memcpy
+ */
+CUresult CUDAAPI cuMemcpyBatchAsync(CUdeviceptr *dsts, CUdeviceptr *srcs, size_t *sizes, size_t count,
+                                    CUmemcpyAttributes *attrs, size_t *attrsIdxs, size_t numAttrs,
+                                    size_t *failIdx, CUstream hStream);
+
+/**
+ * \brief Performs a batch of 3D memory copies asynchronously.
+ *
+ * Performs a batch of memory copies. The batch as a whole executes in stream order but copies within a
+ * batch are not guaranteed to execute in any specific order. Note that this means specifying any dependent
+ * copies within a batch will result in undefined behavior.
+ *
+ * Performs memory copies as specified in the \p opList array. The length of this array is specified in \p numOps.
+ * Each entry in this array describes a copy operation. This includes among other things, the source and destination
+ * operands for the copy as specified in ::CUDA_MEMCPY3D_BATCH_OP::src and ::CUDA_MEMCPY3D_BATCH_OP::dst respectively.
+ * The source and destination operands of a copy can either be a pointer or a CUDA array. The width, height and depth
+ * of a copy is specified in ::CUDA_MEMCPY3D_BATCH_OP::extent. The width, height and depth of a copy are specified in
+ * elements and must not be zero. For pointer-to-pointer copies, the element size is considered to be 1. For pointer
+ * to CUDA array or vice versa copies, the element size is determined by the CUDA array. For CUDA array to CUDA array copies,
+ * the element size of the two CUDA arrays must match.
+ *
+ * For a given operand, if ::CUmemcpy3DOperand::type is specified as ::CU_MEMCPY_OPERAND_TYPE_POINTER, then
+ * ::CUmemcpy3DOperand::op::ptr will be used. The ::CUmemcpy3DOperand::op::ptr::ptr field must contain the pointer where
+ * the copy should begin. The ::CUmemcpy3DOperand::op::ptr::rowLength field specifies the length of each row in elements and
+ * must either be zero or be greater than or equal to the width of the copy specified in ::CUDA_MEMCPY3D_BATCH_OP::extent::width.
+ * The ::CUmemcpy3DOperand::op::ptr::layerHeight field specifies the height of each layer and must either be zero or be greater than
+ * or equal to the height of the copy specified in ::CUDA_MEMCPY3D_BATCH_OP::extent::height. When either of these values is zero,
+ * that aspect of the operand is considered to be tightly packed according to the copy extent. For managed memory pointers on devices where
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS is true or system-allocated pageable memory on devices where
+ * ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS is true, the ::CUmemcpy3DOperand::op::ptr::locHint field can be used to hint
+ * the location of the operand.
+ *
+ * If an operand's type is specified as ::CU_MEMCPY_OPERAND_TYPE_ARRAY, then ::CUmemcpy3DOperand::op::array will be used.
+ * The ::CUmemcpy3DOperand::op::array::array field specifies the CUDA array and ::CUmemcpy3DOperand::op::array::offset specifies
+ * the 3D offset into that array where the copy begins.
+ *
+ * The ::CUmemcpyAttributes::srcAccessOrder indicates the source access ordering to be observed for copies associated
+ * with the attribute. If the source access order is set to ::CU_MEMCPY_SRC_ACCESS_ORDER_STREAM, then the source will
+ * be accessed in stream order. If the source access order is set to ::CU_MEMCPY_SRC_ACCESS_ORDER_DURING_API_CALL then
+ * it indicates that access to the source pointer can be out of stream order and all accesses must be complete before
+ * the API call returns. This flag is suited for ephemeral sources (ex., stack variables) when it's known that no prior
+ * operations in the stream can be accessing the memory and also that the lifetime of the memory is limited to the scope
+ * that the source variable was declared in. Specifying this flag allows the driver to optimize the copy and removes the
+ * need for the user to synchronize the stream after the API call. If the source access order is set to
+ * ::CU_MEMCPY_SRC_ACCESS_ORDER_ANY then it indicates that access to the source pointer can be out of stream order and the
+ * accesses can happen even after the API call returns. This flag is suited for host pointers allocated
+ * outside CUDA (ex., via malloc) when it's known that no prior operations in the stream can be accessing the memory.
+ * Specifying this flag allows the driver to optimize the copy on certain platforms. Each memcopy operation in \p opList must
+ * have a valid srcAccessOrder setting, otherwise this API will return ::CUDA_ERROR_INVALID_VALUE.
+ *
+ * The ::CUmemcpyAttributes::flags field can be used to specify certain flags for copies. Setting the
+ * ::CU_MEMCPY_FLAG_PREFER_OVERLAP_WITH_COMPUTE flag indicates that the associated copies should preferably overlap with
+ * any compute work. Note that this flag is a hint and can be ignored depending on the platform and other parameters of the copy.
+ *
+ * If any error is encountered while parsing the batch, the index within the batch where the error was encountered
+ * will be returned in \p failIdx. 
+ *
+ * \param numOps     - Total number of memcpy operations. 
+ * \param opList     - Array of size \p numOps containing the actual memcpy operations. 
+ * \param failIdx    - Pointer to a location to return the index of the copy where a failure was encountered.
+ *                     The value will be SIZE_MAX if the error doesn't pertain to any specific copy.
+ * \param flags      - Flags for future use, must be zero now.
+ * \param hStream    - The stream to enqueue the operations in. Must not be default NULL stream.
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_DEINITIALIZED
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_async
+ * \note_memcpy
+ */
+CUresult CUDAAPI cuMemcpy3DBatchAsync(size_t numOps, CUDA_MEMCPY3D_BATCH_OP *opList,
+                                      size_t *failIdx, unsigned long long flags, CUstream hStream);
+
+/**
+ * \brief Initializes device memory
+ *
+ * Sets the memory range of \p N 8-bit values to the specified value
+ * \p uc.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param uc        - Value to set
+ * \param N         - Number of elements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset
+ */
+CUresult CUDAAPI cuMemsetD8(CUdeviceptr dstDevice, unsigned char uc, size_t N);
+
+/**
+ * \brief Initializes device memory
+ *
+ * Sets the memory range of \p N 16-bit values to the specified value
+ * \p us. The \p dstDevice pointer must be two byte aligned.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param us        - Value to set
+ * \param N         - Number of elements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset
+ */
+CUresult CUDAAPI cuMemsetD16(CUdeviceptr dstDevice, unsigned short us, size_t N);
+
+/**
+ * \brief Initializes device memory
+ *
+ * Sets the memory range of \p N 32-bit values to the specified value
+ * \p ui. The \p dstDevice pointer must be four byte aligned.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param ui        - Value to set
+ * \param N         - Number of elements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32Async,
+ * ::cudaMemset
+ */
+CUresult CUDAAPI cuMemsetD32(CUdeviceptr dstDevice, unsigned int ui, size_t N);
+
+/**
+ * \brief Initializes device memory
+ *
+ * Sets the 2D memory range of \p Width 8-bit values to the specified value
+ * \p uc. \p Height specifies the number of rows to set, and \p dstPitch
+ * specifies the number of bytes between each row. This function performs
+ * fastest when the pitch is one that has been passed back by
+ * ::cuMemAllocPitch().
+ *
+ * \param dstDevice - Destination device pointer
+ * \param dstPitch  - Pitch of destination device pointer(Unused if \p Height is 1)
+ * \param uc        - Value to set
+ * \param Width     - Width of row
+ * \param Height    - Number of rows
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset2D
+ */
+CUresult CUDAAPI cuMemsetD2D8(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height);
+
+/**
+ * \brief Initializes device memory
+ *
+ * Sets the 2D memory range of \p Width 16-bit values to the specified value
+ * \p us. \p Height specifies the number of rows to set, and \p dstPitch
+ * specifies the number of bytes between each row. The \p dstDevice pointer
+ * and \p dstPitch offset must be two byte aligned. This function performs
+ * fastest when the pitch is one that has been passed back by
+ * ::cuMemAllocPitch().
+ *
+ * \param dstDevice - Destination device pointer
+ * \param dstPitch  - Pitch of destination device pointer(Unused if \p Height is 1)
+ * \param us        - Value to set
+ * \param Width     - Width of row
+ * \param Height    - Number of rows
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset2D
+ */
+CUresult CUDAAPI cuMemsetD2D16(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height);
+
+/**
+ * \brief Initializes device memory
+ *
+ * Sets the 2D memory range of \p Width 32-bit values to the specified value
+ * \p ui. \p Height specifies the number of rows to set, and \p dstPitch
+ * specifies the number of bytes between each row. The \p dstDevice pointer
+ * and \p dstPitch offset must be four byte aligned. This function performs
+ * fastest when the pitch is one that has been passed back by
+ * ::cuMemAllocPitch().
+ *
+ * \param dstDevice - Destination device pointer
+ * \param dstPitch  - Pitch of destination device pointer(Unused if \p Height is 1)
+ * \param ui        - Value to set
+ * \param Width     - Width of row
+ * \param Height    - Number of rows
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset2D
+ */
+CUresult CUDAAPI cuMemsetD2D32(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height);
+
+/**
+ * \brief Sets device memory
+ *
+ * Sets the memory range of \p N 8-bit values to the specified value
+ * \p uc.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param uc        - Value to set
+ * \param N         - Number of elements
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemsetAsync
+ */
+CUresult CUDAAPI cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc, size_t N, CUstream hStream);
+
+/**
+ * \brief Sets device memory
+ *
+ * Sets the memory range of \p N 16-bit values to the specified value
+ * \p us. The \p dstDevice pointer must be two byte aligned.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param us        - Value to set
+ * \param N         - Number of elements
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemsetAsync
+ */
+CUresult CUDAAPI cuMemsetD16Async(CUdeviceptr dstDevice, unsigned short us, size_t N, CUstream hStream);
+
+/**
+ * \brief Sets device memory
+ *
+ * Sets the memory range of \p N 32-bit values to the specified value
+ * \p ui. The \p dstDevice pointer must be four byte aligned.
+ *
+ * \param dstDevice - Destination device pointer
+ * \param ui        - Value to set
+ * \param N         - Number of elements
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, ::cuMemsetD32,
+ * ::cudaMemsetAsync
+ */
+CUresult CUDAAPI cuMemsetD32Async(CUdeviceptr dstDevice, unsigned int ui, size_t N, CUstream hStream);
+
+/**
+ * \brief Sets device memory
+ *
+ * Sets the 2D memory range of \p Width 8-bit values to the specified value
+ * \p uc. \p Height specifies the number of rows to set, and \p dstPitch
+ * specifies the number of bytes between each row. This function performs
+ * fastest when the pitch is one that has been passed back by
+ * ::cuMemAllocPitch().
+ *
+ * \param dstDevice - Destination device pointer
+ * \param dstPitch  - Pitch of destination device pointer(Unused if \p Height is 1)
+ * \param uc        - Value to set
+ * \param Width     - Width of row
+ * \param Height    - Number of rows
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset2DAsync
+ */
+CUresult CUDAAPI cuMemsetD2D8Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream);
+
+/**
+ * \brief Sets device memory
+ *
+ * Sets the 2D memory range of \p Width 16-bit values to the specified value
+ * \p us. \p Height specifies the number of rows to set, and \p dstPitch
+ * specifies the number of bytes between each row. The \p dstDevice pointer
+ * and \p dstPitch offset must be two byte aligned. This function performs
+ * fastest when the pitch is one that has been passed back by
+ * ::cuMemAllocPitch().
+ *
+ * \param dstDevice - Destination device pointer
+ * \param dstPitch  - Pitch of destination device pointer(Unused if \p Height is 1)
+ * \param us        - Value to set
+ * \param Width     - Width of row
+ * \param Height    - Number of rows
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D32, ::cuMemsetD2D32Async,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset2DAsync
+ */
+CUresult CUDAAPI cuMemsetD2D16Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream);
+
+/**
+ * \brief Sets device memory
+ *
+ * Sets the 2D memory range of \p Width 32-bit values to the specified value
+ * \p ui. \p Height specifies the number of rows to set, and \p dstPitch
+ * specifies the number of bytes between each row. The \p dstDevice pointer
+ * and \p dstPitch offset must be four byte aligned. This function performs
+ * fastest when the pitch is one that has been passed back by
+ * ::cuMemAllocPitch().
+ *
+ * \param dstDevice - Destination device pointer
+ * \param dstPitch  - Pitch of destination device pointer(Unused if \p Height is 1)
+ * \param ui        - Value to set
+ * \param Width     - Width of row
+ * \param Height    - Number of rows
+ * \param hStream   - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32,
+ * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async,
+ * ::cuMemsetD32, ::cuMemsetD32Async,
+ * ::cudaMemset2DAsync
+ */
+CUresult CUDAAPI cuMemsetD2D32Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream);
+
+/**
+ * \brief Creates a 1D or 2D CUDA array
+ *
+ * Creates a CUDA array according to the ::CUDA_ARRAY_DESCRIPTOR structure
+ * \p pAllocateArray and returns a handle to the new CUDA array in \p *pHandle.
+ * The ::CUDA_ARRAY_DESCRIPTOR is defined as:
+ *
+ * \code
+    typedef struct {
+        unsigned int Width;
+        unsigned int Height;
+        CUarray_format Format;
+        unsigned int NumChannels;
+    } CUDA_ARRAY_DESCRIPTOR;
+ * \endcode
+ * where:
+ *
+ * - \p Width, and \p Height are the width, and height of the CUDA array (in
+ * elements); the CUDA array is one-dimensional if height is 0, two-dimensional
+ * otherwise;
+ * - ::Format specifies the format of the elements; ::CUarray_format is
+ * defined as:
+ * \code
+    typedef enum CUarray_format_enum {
+        CU_AD_FORMAT_UNSIGNED_INT8 = 0x01,
+        CU_AD_FORMAT_UNSIGNED_INT16 = 0x02,
+        CU_AD_FORMAT_UNSIGNED_INT32 = 0x03,
+        CU_AD_FORMAT_SIGNED_INT8 = 0x08,
+        CU_AD_FORMAT_SIGNED_INT16 = 0x09,
+        CU_AD_FORMAT_SIGNED_INT32 = 0x0a,
+        CU_AD_FORMAT_HALF = 0x10,
+        CU_AD_FORMAT_FLOAT = 0x20,
+        CU_AD_FORMAT_NV12 = 0xb0, 
+        CU_AD_FORMAT_UNORM_INT8X1 = 0xc0, 
+        CU_AD_FORMAT_UNORM_INT8X2 = 0xc1, 
+        CU_AD_FORMAT_UNORM_INT8X4 = 0xc2, 
+        CU_AD_FORMAT_UNORM_INT16X1 = 0xc3, 
+        CU_AD_FORMAT_UNORM_INT16X2 = 0xc4, 
+        CU_AD_FORMAT_UNORM_INT16X4 = 0xc5, 
+        CU_AD_FORMAT_SNORM_INT8X1 = 0xc6, 
+        CU_AD_FORMAT_SNORM_INT8X2 = 0xc7, 
+        CU_AD_FORMAT_SNORM_INT8X4 = 0xc8, 
+        CU_AD_FORMAT_SNORM_INT16X1 = 0xc9,
+        CU_AD_FORMAT_SNORM_INT16X2 = 0xca,
+        CU_AD_FORMAT_SNORM_INT16X4 = 0xcb,
+        CU_AD_FORMAT_BC1_UNORM = 0x91,
+        CU_AD_FORMAT_BC1_UNORM_SRGB = 0x92,
+        CU_AD_FORMAT_BC2_UNORM = 0x93,
+        CU_AD_FORMAT_BC2_UNORM_SRGB = 0x94,
+        CU_AD_FORMAT_BC3_UNORM = 0x95,
+        CU_AD_FORMAT_BC3_UNORM_SRGB = 0x96,
+        CU_AD_FORMAT_BC4_UNORM = 0x97,
+        CU_AD_FORMAT_BC4_SNORM = 0x98,
+        CU_AD_FORMAT_BC5_UNORM = 0x99,
+        CU_AD_FORMAT_BC5_SNORM = 0x9a,
+        CU_AD_FORMAT_BC6H_UF16 = 0x9b,
+        CU_AD_FORMAT_BC6H_SF16 = 0x9c,
+        CU_AD_FORMAT_BC7_UNORM = 0x9d,
+        CU_AD_FORMAT_BC7_UNORM_SRGB = 0x9e,
+        CU_AD_FORMAT_P010 = 0x9f,
+        CU_AD_FORMAT_P016 = 0xa1,
+        CU_AD_FORMAT_NV16 = 0xa2,
+        CU_AD_FORMAT_P210 = 0xa3,
+        CU_AD_FORMAT_P216 = 0xa4,
+        CU_AD_FORMAT_YUY2 = 0xa5,
+        CU_AD_FORMAT_Y210 = 0xa6,
+        CU_AD_FORMAT_Y216 = 0xa7,
+        CU_AD_FORMAT_AYUV = 0xa8,
+        CU_AD_FORMAT_Y410 = 0xa9,
+        CU_AD_FORMAT_Y416 = 0xb1,
+        CU_AD_FORMAT_Y444_PLANAR8 = 0xb2,
+        CU_AD_FORMAT_Y444_PLANAR10 = 0xb3,
+        CU_AD_FORMAT_YUV444_8bit_SemiPlanar = 0xb4,
+        CU_AD_FORMAT_YUV444_16bit_SemiPlanar = 0xb5,
+        CU_AD_FORMAT_UNORM_INT_101010_2 = 0x50,
+   } CUarray_format;
+ *  \endcode
+ * - \p NumChannels specifies the number of packed components per CUDA array
+ * element; it may be 1, 2, or 4;
+ *
+ * Here are examples of CUDA array descriptions:
+ *
+ * Description for a CUDA array of 2048 floats:
+ * \code
+    CUDA_ARRAY_DESCRIPTOR desc;
+    desc.Format = CU_AD_FORMAT_FLOAT;
+    desc.NumChannels = 1;
+    desc.Width = 2048;
+    desc.Height = 1;
+ * \endcode
+ *
+ * Description for a 64 x 64 CUDA array of floats:
+ * \code
+    CUDA_ARRAY_DESCRIPTOR desc;
+    desc.Format = CU_AD_FORMAT_FLOAT;
+    desc.NumChannels = 1;
+    desc.Width = 64;
+    desc.Height = 64;
+ * \endcode
+ *
+ * Description for a \p width x \p height CUDA array of 64-bit, 4x16-bit
+ * float16's:
+ * \code
+    CUDA_ARRAY_DESCRIPTOR desc;
+    desc.Format = CU_AD_FORMAT_HALF;
+    desc.NumChannels = 4;
+    desc.Width = width;
+    desc.Height = height;
+ * \endcode
+ *
+ * Description for a \p width x \p height CUDA array of 16-bit elements, each
+ * of which is two 8-bit unsigned chars:
+ * \code
+    CUDA_ARRAY_DESCRIPTOR arrayDesc;
+    desc.Format = CU_AD_FORMAT_UNSIGNED_INT8;
+    desc.NumChannels = 2;
+    desc.Width = width;
+    desc.Height = height;
+ * \endcode
+ *
+ * \param pHandle        - Returned array
+ * \param pAllocateArray - Array descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMallocArray
+ */
+CUresult CUDAAPI cuArrayCreate(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTOR *pAllocateArray);
+
+/**
+ * \brief Get a 1D or 2D CUDA array descriptor
+ *
+ * Returns in \p *pArrayDescriptor a descriptor containing information on the
+ * format and dimensions of the CUDA array \p hArray. It is useful for
+ * subroutines that have been passed a CUDA array, but need to know the CUDA
+ * array parameters for validation or other purposes.
+ *
+ * \param pArrayDescriptor - Returned array descriptor
+ * \param hArray           - Array to get descriptor of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaArrayGetInfo
+ */
+CUresult CUDAAPI cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR *pArrayDescriptor, CUarray hArray);
+
+/**
+ * \brief Returns the layout properties of a sparse CUDA array
+ *
+ * Returns the layout properties of a sparse CUDA array in \p sparseProperties
+ * If the CUDA array is not allocated with flag ::CUDA_ARRAY3D_SPARSE 
+ * ::CUDA_ERROR_INVALID_VALUE will be returned.
+ *
+ * If the returned value in ::CUDA_ARRAY_SPARSE_PROPERTIES::flags contains ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL,
+ * then ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize represents the total size of the array. Otherwise, it will be zero.
+ * Also, the returned value in ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailFirstLevel is always zero.
+ * Note that the \p array must have been allocated using ::cuArrayCreate or ::cuArray3DCreate. For CUDA arrays obtained
+ * using ::cuMipmappedArrayGetLevel, ::CUDA_ERROR_INVALID_VALUE will be returned. Instead, ::cuMipmappedArrayGetSparseProperties 
+ * must be used to obtain the sparse properties of the entire CUDA mipmapped array to which \p array belongs to.
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \param[out] sparseProperties - Pointer to ::CUDA_ARRAY_SPARSE_PROPERTIES
+ * \param[in] array - CUDA array to get the sparse properties of
+ * \sa ::cuMipmappedArrayGetSparseProperties, ::cuMemMapArrayAsync
+ */
+CUresult CUDAAPI cuArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES *sparseProperties, CUarray array);
+
+/**
+ * \brief Returns the layout properties of a sparse CUDA mipmapped array
+ *
+ * Returns the sparse array layout properties in \p sparseProperties
+ * If the CUDA mipmapped array is not allocated with flag ::CUDA_ARRAY3D_SPARSE 
+ * ::CUDA_ERROR_INVALID_VALUE will be returned.
+ *
+ * For non-layered CUDA mipmapped arrays, ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize returns the
+ * size of the mip tail region. The mip tail region includes all mip levels whose width, height or depth
+ * is less than that of the tile.
+ * For layered CUDA mipmapped arrays, if ::CUDA_ARRAY_SPARSE_PROPERTIES::flags contains ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL,
+ * then ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize specifies the size of the mip tail of all layers combined. 
+ * Otherwise, ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize specifies mip tail size per layer.
+ * The returned value of ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailFirstLevel is valid only if ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize is non-zero.
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \param[out] sparseProperties - Pointer to ::CUDA_ARRAY_SPARSE_PROPERTIES
+ * \param[in] mipmap - CUDA mipmapped array to get the sparse properties of
+ * \sa ::cuArrayGetSparseProperties, ::cuMemMapArrayAsync
+ */
+CUresult CUDAAPI cuMipmappedArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES *sparseProperties, CUmipmappedArray mipmap);
+
+/**
+ * \brief Returns the memory requirements of a CUDA array
+ *
+ * Returns the memory requirements of a CUDA array in \p memoryRequirements
+ * If the CUDA array is not allocated with flag ::CUDA_ARRAY3D_DEFERRED_MAPPING
+ * ::CUDA_ERROR_INVALID_VALUE will be returned.
+ *
+ * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::size 
+ * represents the total size of the CUDA array.
+ * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::alignment 
+ * represents the alignment necessary for mapping the CUDA array.
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \param[out] memoryRequirements - Pointer to ::CUDA_ARRAY_MEMORY_REQUIREMENTS
+ * \param[in] array - CUDA array to get the memory requirements of
+ * \param[in] device - Device to get the memory requirements for
+ * \sa ::cuMipmappedArrayGetMemoryRequirements, ::cuMemMapArrayAsync
+ */
+CUresult CUDAAPI cuArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS *memoryRequirements, CUarray array, CUdevice device);
+ 
+/**
+ * \brief Returns the memory requirements of a CUDA mipmapped array
+ *
+ * Returns the memory requirements of a CUDA mipmapped array in \p memoryRequirements
+ * If the CUDA mipmapped array is not allocated with flag ::CUDA_ARRAY3D_DEFERRED_MAPPING
+ * ::CUDA_ERROR_INVALID_VALUE will be returned.
+ *
+ * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::size 
+ * represents the total size of the CUDA mipmapped array.
+ * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::alignment 
+ * represents the alignment necessary for mapping the CUDA mipmapped  
+ * array.
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \param[out] memoryRequirements - Pointer to ::CUDA_ARRAY_MEMORY_REQUIREMENTS
+ * \param[in] mipmap - CUDA mipmapped array to get the memory requirements of
+ * \param[in] device - Device to get the memory requirements for
+ * \sa ::cuArrayGetMemoryRequirements, ::cuMemMapArrayAsync
+ */
+CUresult CUDAAPI cuMipmappedArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS *memoryRequirements, CUmipmappedArray mipmap, CUdevice device);
+
+/**
+ * \brief Gets a CUDA array plane from a CUDA array
+ *
+ * Returns in \p pPlaneArray a CUDA array that represents a single format plane
+ * of the CUDA array \p hArray.
+ *
+ * If \p planeIdx is greater than the maximum number of planes in this array or if the array does
+ * not have a multi-planar format e.g: ::CU_AD_FORMAT_NV12, then ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * Note that if the \p hArray has format ::CU_AD_FORMAT_NV12, then passing in 0 for \p planeIdx returns
+ * a CUDA array of the same size as \p hArray but with one channel and ::CU_AD_FORMAT_UNSIGNED_INT8 as its format.
+ * If 1 is passed for \p planeIdx, then the returned CUDA array has half the height and width
+ * of \p hArray with two channels and ::CU_AD_FORMAT_UNSIGNED_INT8 as its format.
+ *
+ * \param pPlaneArray   - Returned CUDA array referenced by the \p planeIdx
+ * \param hArray        - Multiplanar CUDA array
+ * \param planeIdx      - Plane index
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuArrayCreate,
+ * ::cudaArrayGetPlane
+ */
+CUresult CUDAAPI cuArrayGetPlane(CUarray *pPlaneArray, CUarray hArray, unsigned int planeIdx);
+
+/**
+ * \brief Destroys a CUDA array
+ *
+ * Destroys the CUDA array \p hArray.
+ *
+ * \param hArray - Array to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ARRAY_IS_MAPPED,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaFreeArray
+ */
+CUresult CUDAAPI cuArrayDestroy(CUarray hArray);
+
+/**
+ * \brief Creates a 3D CUDA array
+ *
+ * Creates a CUDA array according to the ::CUDA_ARRAY3D_DESCRIPTOR structure
+ * \p pAllocateArray and returns a handle to the new CUDA array in \p *pHandle.
+ * The ::CUDA_ARRAY3D_DESCRIPTOR is defined as:
+ *
+ * \code
+    typedef struct {
+        unsigned int Width;
+        unsigned int Height;
+        unsigned int Depth;
+        CUarray_format Format;
+        unsigned int NumChannels;
+        unsigned int Flags;
+    } CUDA_ARRAY3D_DESCRIPTOR;
+ * \endcode
+ * where:
+ *
+ * - \p Width, \p Height, and \p Depth are the width, height, and depth of the
+ * CUDA array (in elements); the following types of CUDA arrays can be allocated:
+ *     - A 1D array is allocated if \p Height and \p Depth extents are both zero.
+ *     - A 2D array is allocated if only \p Depth extent is zero.
+ *     - A 3D array is allocated if all three extents are non-zero.
+ *     - A 1D layered CUDA array is allocated if only \p Height is zero and the
+ *       ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 1D array. The number
+ *       of layers is determined by the depth extent.
+ *     - A 2D layered CUDA array is allocated if all three extents are non-zero and
+ *       the ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 2D array. The number
+ *       of layers is determined by the depth extent.
+ *     - A cubemap CUDA array is allocated if all three extents are non-zero and the
+ *       ::CUDA_ARRAY3D_CUBEMAP flag is set. \p Width must be equal to \p Height, and
+ *       \p Depth must be six. A cubemap is a special type of 2D layered CUDA array,
+ *       where the six layers represent the six faces of a cube. The order of the six
+ *       layers in memory is the same as that listed in ::CUarray_cubemap_face.
+ *     - A cubemap layered CUDA array is allocated if all three extents are non-zero,
+ *       and both, ::CUDA_ARRAY3D_CUBEMAP and ::CUDA_ARRAY3D_LAYERED flags are set.
+ *       \p Width must be equal to \p Height, and \p Depth must be a multiple of six.
+ *       A cubemap layered CUDA array is a special type of 2D layered CUDA array that
+ *       consists of a collection of cubemaps. The first six layers represent the first
+ *       cubemap, the next six layers form the second cubemap, and so on.
+ *
+ * - ::Format specifies the format of the elements; ::CUarray_format is
+ * defined as:
+ * \code
+    typedef enum CUarray_format_enum {
+        CU_AD_FORMAT_UNSIGNED_INT8 = 0x01,
+        CU_AD_FORMAT_UNSIGNED_INT16 = 0x02,
+        CU_AD_FORMAT_UNSIGNED_INT32 = 0x03,
+        CU_AD_FORMAT_SIGNED_INT8 = 0x08,
+        CU_AD_FORMAT_SIGNED_INT16 = 0x09,
+        CU_AD_FORMAT_SIGNED_INT32 = 0x0a,
+        CU_AD_FORMAT_HALF = 0x10,
+        CU_AD_FORMAT_FLOAT = 0x20,
+        CU_AD_FORMAT_NV12 = 0xb0, 
+        CU_AD_FORMAT_UNORM_INT8X1 = 0xc0, 
+        CU_AD_FORMAT_UNORM_INT8X2 = 0xc1, 
+        CU_AD_FORMAT_UNORM_INT8X4 = 0xc2, 
+        CU_AD_FORMAT_UNORM_INT16X1 = 0xc3, 
+        CU_AD_FORMAT_UNORM_INT16X2 = 0xc4, 
+        CU_AD_FORMAT_UNORM_INT16X4 = 0xc5, 
+        CU_AD_FORMAT_SNORM_INT8X1 = 0xc6, 
+        CU_AD_FORMAT_SNORM_INT8X2 = 0xc7, 
+        CU_AD_FORMAT_SNORM_INT8X4 = 0xc8, 
+        CU_AD_FORMAT_SNORM_INT16X1 = 0xc9,
+        CU_AD_FORMAT_SNORM_INT16X2 = 0xca,
+        CU_AD_FORMAT_SNORM_INT16X4 = 0xcb,
+        CU_AD_FORMAT_BC1_UNORM = 0x91,
+        CU_AD_FORMAT_BC1_UNORM_SRGB = 0x92,
+        CU_AD_FORMAT_BC2_UNORM = 0x93,
+        CU_AD_FORMAT_BC2_UNORM_SRGB = 0x94,
+        CU_AD_FORMAT_BC3_UNORM = 0x95,
+        CU_AD_FORMAT_BC3_UNORM_SRGB = 0x96,
+        CU_AD_FORMAT_BC4_UNORM = 0x97,
+        CU_AD_FORMAT_BC4_SNORM = 0x98,
+        CU_AD_FORMAT_BC5_UNORM = 0x99,
+        CU_AD_FORMAT_BC5_SNORM = 0x9a,
+        CU_AD_FORMAT_BC6H_UF16 = 0x9b,
+        CU_AD_FORMAT_BC6H_SF16 = 0x9c,
+        CU_AD_FORMAT_BC7_UNORM = 0x9d,
+        CU_AD_FORMAT_BC7_UNORM_SRGB = 0x9e,
+        CU_AD_FORMAT_P010 = 0x9f,
+        CU_AD_FORMAT_P016 = 0xa1,
+        CU_AD_FORMAT_NV16 = 0xa2,
+        CU_AD_FORMAT_P210 = 0xa3,
+        CU_AD_FORMAT_P216 = 0xa4,
+        CU_AD_FORMAT_YUY2 = 0xa5,
+        CU_AD_FORMAT_Y210 = 0xa6,
+        CU_AD_FORMAT_Y216 = 0xa7,
+        CU_AD_FORMAT_AYUV = 0xa8,
+        CU_AD_FORMAT_Y410 = 0xa9,
+        CU_AD_FORMAT_Y416 = 0xb1,
+        CU_AD_FORMAT_Y444_PLANAR8 = 0xb2,
+        CU_AD_FORMAT_Y444_PLANAR10 = 0xb3,
+        CU_AD_FORMAT_YUV444_8bit_SemiPlanar = 0xb4,
+        CU_AD_FORMAT_YUV444_16bit_SemiPlanar = 0xb5,
+        CU_AD_FORMAT_UNORM_INT_101010_2 = 0x50,
+    } CUarray_format;
+ *  \endcode
+ *
+ * - \p NumChannels specifies the number of packed components per CUDA array
+ * element; it may be 1, 2, or 4;
+ *
+ * - ::Flags may be set to
+ *   - ::CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA arrays. If this flag is set,
+ *     \p Depth specifies the number of layers, not the depth of a 3D array.
+ *   - ::CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound to the CUDA array.
+ *     If this flag is not set, ::cuSurfRefSetArray will fail when attempting to bind the CUDA array
+ *     to a surface reference.
+ *   - ::CUDA_ARRAY3D_CUBEMAP to enable creation of cubemaps. If this flag is set, \p Width must be
+ *     equal to \p Height, and \p Depth must be six. If the ::CUDA_ARRAY3D_LAYERED flag is also set,
+ *     then \p Depth must be a multiple of six.
+ *   - ::CUDA_ARRAY3D_TEXTURE_GATHER to indicate that the CUDA array will be used for texture gather.
+ *     Texture gather can only be performed on 2D CUDA arrays.
+ *
+ * \p Width, \p Height and \p Depth must meet certain size requirements as listed in the following table.
+ * All values are specified in elements. Note that for brevity's sake, the full name of the device attribute
+ * is not specified. For ex., TEXTURE1D_WIDTH refers to the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH.
+ *
+ * Note that 2D CUDA arrays have different size requirements if the ::CUDA_ARRAY3D_TEXTURE_GATHER flag
+ * is set. \p Width and \p Height must not be greater than ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH
+ * and ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT respectively, in that case.
+ *
+ * <table>
+ * <tr><td><b>CUDA array type</b></td>
+ * <td><b>Valid extents that must always be met<br>{(width range in elements), (height range),
+ * (depth range)}</b></td>
+ * <td><b>Valid extents with CUDA_ARRAY3D_SURFACE_LDST set<br>
+ * {(width range in elements), (height range), (depth range)}</b></td></tr>
+ * <tr><td>1D</td>
+ * <td><small>{ (1,TEXTURE1D_WIDTH), 0, 0 }</small></td>
+ * <td><small>{ (1,SURFACE1D_WIDTH), 0, 0 }</small></td></tr>
+ * <tr><td>2D</td>
+ * <td><small>{ (1,TEXTURE2D_WIDTH), (1,TEXTURE2D_HEIGHT), 0 }</small></td>
+ * <td><small>{ (1,SURFACE2D_WIDTH), (1,SURFACE2D_HEIGHT), 0 }</small></td></tr>
+ * <tr><td>3D</td>
+ * <td><small>{ (1,TEXTURE3D_WIDTH), (1,TEXTURE3D_HEIGHT), (1,TEXTURE3D_DEPTH) }
+ * <br>OR<br>{ (1,TEXTURE3D_WIDTH_ALTERNATE), (1,TEXTURE3D_HEIGHT_ALTERNATE),
+ * (1,TEXTURE3D_DEPTH_ALTERNATE) }</small></td>
+ * <td><small>{ (1,SURFACE3D_WIDTH), (1,SURFACE3D_HEIGHT),
+ * (1,SURFACE3D_DEPTH) }</small></td></tr>
+ * <tr><td>1D Layered</td>
+ * <td><small>{ (1,TEXTURE1D_LAYERED_WIDTH), 0,
+ * (1,TEXTURE1D_LAYERED_LAYERS) }</small></td>
+ * <td><small>{ (1,SURFACE1D_LAYERED_WIDTH), 0,
+ * (1,SURFACE1D_LAYERED_LAYERS) }</small></td></tr>
+ * <tr><td>2D Layered</td>
+ * <td><small>{ (1,TEXTURE2D_LAYERED_WIDTH), (1,TEXTURE2D_LAYERED_HEIGHT),
+ * (1,TEXTURE2D_LAYERED_LAYERS) }</small></td>
+ * <td><small>{ (1,SURFACE2D_LAYERED_WIDTH), (1,SURFACE2D_LAYERED_HEIGHT),
+ * (1,SURFACE2D_LAYERED_LAYERS) }</small></td></tr>
+ * <tr><td>Cubemap</td>
+ * <td><small>{ (1,TEXTURECUBEMAP_WIDTH), (1,TEXTURECUBEMAP_WIDTH), 6 }</small></td>
+ * <td><small>{ (1,SURFACECUBEMAP_WIDTH),
+ * (1,SURFACECUBEMAP_WIDTH), 6 }</small></td></tr>
+ * <tr><td>Cubemap Layered</td>
+ * <td><small>{ (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_WIDTH),
+ * (1,TEXTURECUBEMAP_LAYERED_LAYERS) }</small></td>
+ * <td><small>{ (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_WIDTH),
+ * (1,SURFACECUBEMAP_LAYERED_LAYERS) }</small></td></tr>
+ * </table>
+ *
+ * Here are examples of CUDA array descriptions:
+ *
+ * Description for a CUDA array of 2048 floats:
+ * \code
+    CUDA_ARRAY3D_DESCRIPTOR desc;
+    desc.Format = CU_AD_FORMAT_FLOAT;
+    desc.NumChannels = 1;
+    desc.Width = 2048;
+    desc.Height = 0;
+    desc.Depth = 0;
+ * \endcode
+ *
+ * Description for a 64 x 64 CUDA array of floats:
+ * \code
+    CUDA_ARRAY3D_DESCRIPTOR desc;
+    desc.Format = CU_AD_FORMAT_FLOAT;
+    desc.NumChannels = 1;
+    desc.Width = 64;
+    desc.Height = 64;
+    desc.Depth = 0;
+ * \endcode
+ *
+ * Description for a \p width x \p height x \p depth CUDA array of 64-bit,
+ * 4x16-bit float16's:
+ * \code
+    CUDA_ARRAY3D_DESCRIPTOR desc;
+    desc.Format = CU_AD_FORMAT_HALF;
+    desc.NumChannels = 4;
+    desc.Width = width;
+    desc.Height = height;
+    desc.Depth = depth;
+ * \endcode
+ *
+ * \param pHandle        - Returned array
+ * \param pAllocateArray - 3D array descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuArray3DGetDescriptor, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaMalloc3DArray
+ */
+CUresult CUDAAPI cuArray3DCreate(CUarray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR *pAllocateArray);
+
+/**
+ * \brief Get a 3D CUDA array descriptor
+ *
+ * Returns in \p *pArrayDescriptor a descriptor containing information on the
+ * format and dimensions of the CUDA array \p hArray. It is useful for
+ * subroutines that have been passed a CUDA array, but need to know the CUDA
+ * array parameters for validation or other purposes.
+ *
+ * This function may be called on 1D and 2D arrays, in which case the \p Height
+ * and/or \p Depth members of the descriptor struct will be set to 0.
+ *
+ * \param pArrayDescriptor - Returned 3D array descriptor
+ * \param hArray           - 3D array to get descriptor of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ * \notefnerr
+ *
+ * \sa ::cuArray3DCreate, ::cuArrayCreate,
+ * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost,
+ * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned,
+ * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD,
+ * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync,
+ * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync,
+ * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost,
+ * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc,
+ * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16,
+ * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32,
+ * ::cudaArrayGetInfo
+ */
+CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR *pArrayDescriptor, CUarray hArray);
+
+/**
+ * \brief Creates a CUDA mipmapped array
+ *
+ * Creates a CUDA mipmapped array according to the ::CUDA_ARRAY3D_DESCRIPTOR structure
+ * \p pMipmappedArrayDesc and returns a handle to the new CUDA mipmapped array in \p *pHandle.
+ * \p numMipmapLevels specifies the number of mipmap levels to be allocated. This value is
+ * clamped to the range [1, 1 + floor(log2(max(width, height, depth)))].
+ *
+ * The ::CUDA_ARRAY3D_DESCRIPTOR is defined as:
+ *
+ * \code
+    typedef struct {
+        unsigned int Width;
+        unsigned int Height;
+        unsigned int Depth;
+        CUarray_format Format;
+        unsigned int NumChannels;
+        unsigned int Flags;
+    } CUDA_ARRAY3D_DESCRIPTOR;
+ * \endcode
+ * where:
+ *
+ * - \p Width, \p Height, and \p Depth are the width, height, and depth of the
+ * CUDA array (in elements); the following types of CUDA arrays can be allocated:
+ *     - A 1D mipmapped array is allocated if \p Height and \p Depth extents are both zero.
+ *     - A 2D mipmapped array is allocated if only \p Depth extent is zero.
+ *     - A 3D mipmapped array is allocated if all three extents are non-zero.
+ *     - A 1D layered CUDA mipmapped array is allocated if only \p Height is zero and the
+ *       ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 1D array. The number
+ *       of layers is determined by the depth extent.
+ *     - A 2D layered CUDA mipmapped array is allocated if all three extents are non-zero and
+ *       the ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 2D array. The number
+ *       of layers is determined by the depth extent.
+ *     - A cubemap CUDA mipmapped array is allocated if all three extents are non-zero and the
+ *       ::CUDA_ARRAY3D_CUBEMAP flag is set. \p Width must be equal to \p Height, and
+ *       \p Depth must be six. A cubemap is a special type of 2D layered CUDA array,
+ *       where the six layers represent the six faces of a cube. The order of the six
+ *       layers in memory is the same as that listed in ::CUarray_cubemap_face.
+ *     - A cubemap layered CUDA mipmapped array is allocated if all three extents are non-zero,
+ *       and both, ::CUDA_ARRAY3D_CUBEMAP and ::CUDA_ARRAY3D_LAYERED flags are set.
+ *       \p Width must be equal to \p Height, and \p Depth must be a multiple of six.
+ *       A cubemap layered CUDA array is a special type of 2D layered CUDA array that
+ *       consists of a collection of cubemaps. The first six layers represent the first
+ *       cubemap, the next six layers form the second cubemap, and so on.
+ *
+ * - ::Format specifies the format of the elements; ::CUarray_format is
+ * defined as:
+ * \code
+    typedef enum CUarray_format_enum {
+        CU_AD_FORMAT_UNSIGNED_INT8 = 0x01,
+        CU_AD_FORMAT_UNSIGNED_INT16 = 0x02,
+        CU_AD_FORMAT_UNSIGNED_INT32 = 0x03,
+        CU_AD_FORMAT_SIGNED_INT8 = 0x08,
+        CU_AD_FORMAT_SIGNED_INT16 = 0x09,
+        CU_AD_FORMAT_SIGNED_INT32 = 0x0a,
+        CU_AD_FORMAT_HALF = 0x10,
+        CU_AD_FORMAT_FLOAT = 0x20,
+        CU_AD_FORMAT_NV12 = 0xb0, 
+        CU_AD_FORMAT_UNORM_INT8X1 = 0xc0, 
+        CU_AD_FORMAT_UNORM_INT8X2 = 0xc1, 
+        CU_AD_FORMAT_UNORM_INT8X4 = 0xc2, 
+        CU_AD_FORMAT_UNORM_INT16X1 = 0xc3, 
+        CU_AD_FORMAT_UNORM_INT16X2 = 0xc4, 
+        CU_AD_FORMAT_UNORM_INT16X4 = 0xc5, 
+        CU_AD_FORMAT_SNORM_INT8X1 = 0xc6, 
+        CU_AD_FORMAT_SNORM_INT8X2 = 0xc7, 
+        CU_AD_FORMAT_SNORM_INT8X4 = 0xc8, 
+        CU_AD_FORMAT_SNORM_INT16X1 = 0xc9,
+        CU_AD_FORMAT_SNORM_INT16X2 = 0xca,
+        CU_AD_FORMAT_SNORM_INT16X4 = 0xcb,
+        CU_AD_FORMAT_BC1_UNORM = 0x91,
+        CU_AD_FORMAT_BC1_UNORM_SRGB = 0x92,
+        CU_AD_FORMAT_BC2_UNORM = 0x93,
+        CU_AD_FORMAT_BC2_UNORM_SRGB = 0x94,
+        CU_AD_FORMAT_BC3_UNORM = 0x95,
+        CU_AD_FORMAT_BC3_UNORM_SRGB = 0x96,
+        CU_AD_FORMAT_BC4_UNORM = 0x97,
+        CU_AD_FORMAT_BC4_SNORM = 0x98,
+        CU_AD_FORMAT_BC5_UNORM = 0x99,
+        CU_AD_FORMAT_BC5_SNORM = 0x9a,
+        CU_AD_FORMAT_BC6H_UF16 = 0x9b,
+        CU_AD_FORMAT_BC6H_SF16 = 0x9c,
+        CU_AD_FORMAT_BC7_UNORM = 0x9d,
+        CU_AD_FORMAT_BC7_UNORM_SRGB = 0x9e,
+        CU_AD_FORMAT_P010 = 0x9f,
+        CU_AD_FORMAT_P016 = 0xa1,
+        CU_AD_FORMAT_NV16 = 0xa2,
+        CU_AD_FORMAT_P210 = 0xa3,
+        CU_AD_FORMAT_P216 = 0xa4,
+        CU_AD_FORMAT_YUY2 = 0xa5,
+        CU_AD_FORMAT_Y210 = 0xa6,
+        CU_AD_FORMAT_Y216 = 0xa7,
+        CU_AD_FORMAT_AYUV = 0xa8,
+        CU_AD_FORMAT_Y410 = 0xa9,
+        CU_AD_FORMAT_Y416 = 0xb1,
+        CU_AD_FORMAT_Y444_PLANAR8 = 0xb2,
+        CU_AD_FORMAT_Y444_PLANAR10 = 0xb3,
+        CU_AD_FORMAT_YUV444_8bit_SemiPlanar = 0xb4,
+        CU_AD_FORMAT_YUV444_16bit_SemiPlanar = 0xb5,
+        CU_AD_FORMAT_UNORM_INT_101010_2 = 0x50,
+    } CUarray_format;
+ *  \endcode
+ *
+ * - \p NumChannels specifies the number of packed components per CUDA array
+ * element; it may be 1, 2, or 4;
+ *
+ * - ::Flags may be set to
+ *   - ::CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA mipmapped arrays. If this flag is set,
+ *     \p Depth specifies the number of layers, not the depth of a 3D array.
+ *   - ::CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound to individual mipmap levels of
+ *     the CUDA mipmapped array. If this flag is not set, ::cuSurfRefSetArray will fail when attempting to
+ *     bind a mipmap level of the CUDA mipmapped array to a surface reference.
+  *   - ::CUDA_ARRAY3D_CUBEMAP to enable creation of mipmapped cubemaps. If this flag is set, \p Width must be
+ *     equal to \p Height, and \p Depth must be six. If the ::CUDA_ARRAY3D_LAYERED flag is also set,
+ *     then \p Depth must be a multiple of six.
+ *   - ::CUDA_ARRAY3D_TEXTURE_GATHER to indicate that the CUDA mipmapped array will be used for texture gather.
+ *     Texture gather can only be performed on 2D CUDA mipmapped arrays.
+ *
+ * \p Width, \p Height and \p Depth must meet certain size requirements as listed in the following table.
+ * All values are specified in elements. Note that for brevity's sake, the full name of the device attribute
+ * is not specified. For ex., TEXTURE1D_MIPMAPPED_WIDTH refers to the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH.
+ *
+ * <table>
+ * <tr><td><b>CUDA array type</b></td>
+ * <td><b>Valid extents that must always be met<br>{(width range in elements), (height range),
+ * (depth range)}</b></td>
+ * <td><b>Valid extents with CUDA_ARRAY3D_SURFACE_LDST set<br>
+ * {(width range in elements), (height range), (depth range)}</b></td></tr>
+ * <tr><td>1D</td>
+ * <td><small>{ (1,TEXTURE1D_MIPMAPPED_WIDTH), 0, 0 }</small></td>
+ * <td><small>{ (1,SURFACE1D_WIDTH), 0, 0 }</small></td></tr>
+ * <tr><td>2D</td>
+ * <td><small>{ (1,TEXTURE2D_MIPMAPPED_WIDTH), (1,TEXTURE2D_MIPMAPPED_HEIGHT), 0 }</small></td>
+ * <td><small>{ (1,SURFACE2D_WIDTH), (1,SURFACE2D_HEIGHT), 0 }</small></td></tr>
+ * <tr><td>3D</td>
+ * <td><small>{ (1,TEXTURE3D_WIDTH), (1,TEXTURE3D_HEIGHT), (1,TEXTURE3D_DEPTH) }
+ * <br>OR<br>{ (1,TEXTURE3D_WIDTH_ALTERNATE), (1,TEXTURE3D_HEIGHT_ALTERNATE),
+ * (1,TEXTURE3D_DEPTH_ALTERNATE) }</small></td>
+ * <td><small>{ (1,SURFACE3D_WIDTH), (1,SURFACE3D_HEIGHT),
+ * (1,SURFACE3D_DEPTH) }</small></td></tr>
+ * <tr><td>1D Layered</td>
+ * <td><small>{ (1,TEXTURE1D_LAYERED_WIDTH), 0,
+ * (1,TEXTURE1D_LAYERED_LAYERS) }</small></td>
+ * <td><small>{ (1,SURFACE1D_LAYERED_WIDTH), 0,
+ * (1,SURFACE1D_LAYERED_LAYERS) }</small></td></tr>
+ * <tr><td>2D Layered</td>
+ * <td><small>{ (1,TEXTURE2D_LAYERED_WIDTH), (1,TEXTURE2D_LAYERED_HEIGHT),
+ * (1,TEXTURE2D_LAYERED_LAYERS) }</small></td>
+ * <td><small>{ (1,SURFACE2D_LAYERED_WIDTH), (1,SURFACE2D_LAYERED_HEIGHT),
+ * (1,SURFACE2D_LAYERED_LAYERS) }</small></td></tr>
+ * <tr><td>Cubemap</td>
+ * <td><small>{ (1,TEXTURECUBEMAP_WIDTH), (1,TEXTURECUBEMAP_WIDTH), 6 }</small></td>
+ * <td><small>{ (1,SURFACECUBEMAP_WIDTH),
+ * (1,SURFACECUBEMAP_WIDTH), 6 }</small></td></tr>
+ * <tr><td>Cubemap Layered</td>
+ * <td><small>{ (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_WIDTH),
+ * (1,TEXTURECUBEMAP_LAYERED_LAYERS) }</small></td>
+ * <td><small>{ (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_WIDTH),
+ * (1,SURFACECUBEMAP_LAYERED_LAYERS) }</small></td></tr>
+ * </table>
+ *
+ *
+ * \param pHandle             - Returned mipmapped array
+ * \param pMipmappedArrayDesc - mipmapped array descriptor
+ * \param numMipmapLevels     - Number of mipmap levels
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMipmappedArrayDestroy,
+ * ::cuMipmappedArrayGetLevel,
+ * ::cuArrayCreate,
+ * ::cudaMallocMipmappedArray
+ */
+CUresult CUDAAPI cuMipmappedArrayCreate(CUmipmappedArray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR *pMipmappedArrayDesc, unsigned int numMipmapLevels);
+
+/**
+ * \brief Gets a mipmap level of a CUDA mipmapped array
+ *
+ * Returns in \p *pLevelArray a CUDA array that represents a single mipmap level
+ * of the CUDA mipmapped array \p hMipmappedArray.
+ *
+ * If \p level is greater than the maximum number of levels in this mipmapped array,
+ * ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * \param pLevelArray     - Returned mipmap level CUDA array
+ * \param hMipmappedArray - CUDA mipmapped array
+ * \param level           - Mipmap level
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMipmappedArrayCreate,
+ * ::cuMipmappedArrayDestroy,
+ * ::cuArrayCreate,
+ * ::cudaGetMipmappedArrayLevel
+ */
+CUresult CUDAAPI cuMipmappedArrayGetLevel(CUarray *pLevelArray, CUmipmappedArray hMipmappedArray, unsigned int level);
+
+/**
+ * \brief Destroys a CUDA mipmapped array
+ *
+ * Destroys the CUDA mipmapped array \p hMipmappedArray.
+ *
+ * \param hMipmappedArray - Mipmapped array to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ARRAY_IS_MAPPED,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMipmappedArrayCreate,
+ * ::cuMipmappedArrayGetLevel,
+ * ::cuArrayCreate,
+ * ::cudaFreeMipmappedArray
+ */
+CUresult CUDAAPI cuMipmappedArrayDestroy(CUmipmappedArray hMipmappedArray);
+
+/** 
+* \brief Retrieve handle for an address range 
+* 
+* Get a handle of the specified type to an address range. The address range
+* must have been obtained by a prior call to either ::cuMemAlloc or ::cuMemAddressReserve.
+* If the address range was obtained via ::cuMemAddressReserve, it must also be fully mapped via ::cuMemMap.
+* The address range must have been obtained by a prior call to either ::cuMemAllocHost or
+* ::cuMemHostAlloc on Tegra.
+* 
+* Users must ensure the \p dptr and \p size are aligned to the host page size.
+* 
+* When requesting CUmemRangeHandleType::CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD,
+* users are expected to query for dma_buf support for the platform
+* by using ::CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED device attribute before calling
+* this API. The \p handle will be interpreted as a pointer to an integer to store the dma_buf file descriptor.
+* Users must ensure the entire address range is backed and mapped when
+* the address range is allocated by ::cuMemAddressReserve. All the physical
+* allocations backing the address range must be resident on the same device and
+* have identical allocation properties. Users are also expected to retrieve a
+* new handle every time the underlying physical allocation(s) corresponding
+* to a previously queried VA range are changed.
+*
+* For CUmemRangeHandleType::CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD, users may set
+* flags to ::CU_MEM_RANGE_FLAG_DMA_BUF_MAPPING_TYPE_PCIE. Which when set on a
+* supported platform, will give a DMA_BUF handle mapped via PCIE BAR1 or will
+* return an error otherwise.
+* 
+* \param[out] handle     - Pointer to the location where the returned handle will be stored. 
+* \param[in] dptr        - Pointer to a valid CUDA device allocation. Must be aligned to host page size.
+* \param[in] size        - Length of the address range. Must be aligned to host page size.
+* \param[in] handleType  - Type of handle requested (defines type and size of the \p handle output parameter)
+* \param[in] flags       - When requesting CUmemRangeHandleType::CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD the value could be
+*                          ::CU_MEM_RANGE_FLAG_DMA_BUF_MAPPING_TYPE_PCIE, otherwise 0.
+* 
+* \return
+* CUDA_SUCCESS 
+* CUDA_ERROR_INVALID_VALUE 
+* CUDA_ERROR_NOT_SUPPORTED 
+*/
+CUresult CUDAAPI cuMemGetHandleForAddressRange(void *handle, CUdeviceptr dptr, size_t size, CUmemRangeHandleType handleType, unsigned long long flags);
+
+/**
+ * \brief Bitmasks for CU_DEVICE_ATTRIBUTE_MEM_DECOMPRESS_ALGORITHM_MASK.
+ */
+typedef enum CUmemDecompressAlgorithm_enum {
+    CU_MEM_DECOMPRESS_UNSUPPORTED       = 0,    /**< Decompression is unsupported. */
+    CU_MEM_DECOMPRESS_ALGORITHM_DEFLATE = 1<<0, /**< Deflate is supported. */
+    CU_MEM_DECOMPRESS_ALGORITHM_SNAPPY  = 1<<1  /**< Snappy is supported. */
+} CUmemDecompressAlgorithm;
+
+/**
+ * \brief Structure describing the parameters that compose a single
+ *        decompression operation.
+ */
+typedef struct CUmemDecompressParams_st {
+    /** The number of bytes to be read and decompressed from
+     *  ::CUmemDecompressParams_st.src. */
+    size_t srcNumBytes;
+    /** The number of bytes that the decompression operation will be expected to
+     *  write to ::CUmemDecompressParams_st.dst. This value is optional; if
+     *  present, it may be used by the CUDA driver as a heuristic for scheduling
+     *  the individual decompression operations. */
+    size_t dstNumBytes;
+    /** After the decompression operation has completed, the actual number of
+     * bytes written to ::CUmemDecompressParams.dst will be recorded as a 32-bit
+     * unsigned integer in the memory at this address. */
+    cuuint32_t *dstActBytes;
+    /** Pointer to a buffer of at least ::CUmemDecompressParams_st.srcNumBytes
+      * compressed bytes. */
+    const void *src;
+    /** Pointer to a buffer where the decompressed data will be written. The
+      * number of bytes written to this location will be recorded in the memory
+      * pointed to by ::CUmemDecompressParams_st.dstActBytes */
+    void *dst;
+    /** The decompression algorithm to use. */
+    CUmemDecompressAlgorithm algo;
+    /*  These bytes are unused and must be zeroed. This ensures compatibility if
+     *  additional fields are added in the future. */
+    unsigned char padding[20];
+} CUmemDecompressParams;
+
+/**
+ * \brief   Submit a batch of \p count independent decompression operations.
+ *
+ * \details Each of the \p count decompression operations is described by a
+ *          single entry in the \p paramsArray array. Once the batch has been
+ *          submitted, the function will return, and decompression will happen
+ *          asynchronously w.r.t. the CPU. To the work completion tracking
+ *          mechanisms in the CUDA driver, the batch will be considered a single
+ *          unit of work and processed according to stream semantics, i.e., it
+ *          is not possible to query the completion of individual decompression
+ *          operations within a batch.
+ *
+ *          The memory pointed to by each of ::CUmemDecompressParams.src,
+ *          ::CUmemDecompressParams.dst, and ::CUmemDecompressParams.dstActBytes,
+ *          must be capable of usage with the hardware decompress feature. That
+ *          is, for each of said pointers, the pointer attribute
+ *          ::CU_POINTER_ATTRIBUTE_IS_MEM_DECOMPRESS_CAPABLE should give a
+ *          non-zero value. To ensure this, the memory backing the pointers
+ *          should have been allocated using one of the following CUDA memory
+ *          allocators:
+ *          * ::cuMemAlloc()
+ *          * ::cuMemCreate() with the usage flag ::CU_MEM_CREATE_USAGE_HW_DECOMPRESS
+ *          * ::cuMemAllocFromPoolAsync() from a pool that was created with
+ *            the usage flag ::CU_MEM_POOL_CREATE_USAGE_HW_DECOMPRESS
+ *          Additionally, ::CUmemDecompressParams.src, ::CUmemDecompressParams.dst,
+ *          and ::CUmemDecompressParams.dstActBytes, must all be accessible from
+ *          the device associated with the context where \p stream was created.
+ *          For information on how to ensure this, see the documentation for the
+ *          allocator of interest.
+ *
+ * \param[in]  paramsArray  The array of structures describing the independent
+ *                          decompression operations.
+ * \param[in]  count        The number of entries in \p paramsArray array.
+ * \param[in]  flags        Must be 0.
+ * \param[out] errorIndex   The index into \p paramsArray of the decompression
+ *                          operation for which the error returned by this
+ *                          function pertains to. If \p index is SIZE_MAX and
+ *                          the value returned is not ::CUDA_SUCCESS, then the
+ *                          error returned by this function should be considered
+ *                          a general error that does not pertain to a
+ *                          particular decompression operation. May be \p NULL,
+ *                          in which case, no index will be recorded in the
+ *                          event of error.
+ * \param[in]  stream       The stream where the work will be enqueued.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemAlloc, ::cuMemPoolCreate, ::cuMemAllocFromPoolAsync
+ */
+CUresult CUDAAPI cuMemBatchDecompressAsync(
+    CUmemDecompressParams *paramsArray,
+    size_t count,
+    unsigned int flags,
+    size_t *errorIndex,
+    CUstream stream
+);
+
+/** @} */ /* END CUDA_MEM */
+
+/**
+ * \defgroup CUDA_VA Virtual Memory Management
+ *
+ * ___MANBRIEF___ virtual memory management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the virtual memory management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+* \brief Allocate an address range reservation. 
+* 
+* Reserves a virtual address range based on the given parameters, giving
+* the starting address of the range in \p ptr.  This API requires a system that
+* supports UVA.  The size and address parameters must be a multiple of the
+* host page size and the alignment must be a power of two or zero for default
+* alignment.
+*
+* \param[out] ptr       - Resulting pointer to start of virtual address range allocated
+* \param[in]  size      - Size of the reserved virtual address range requested
+* \param[in]  alignment - Alignment of the reserved virtual address range requested
+* \param[in]  addr      - Fixed starting address range requested
+* \param[in]  flags     - Currently unused, must be zero
+* \return
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_OUT_OF_MEMORY,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemAddressFree
+*/
+CUresult CUDAAPI cuMemAddressReserve(CUdeviceptr *ptr, size_t size, size_t alignment, CUdeviceptr addr, unsigned long long flags);
+
+/**
+* \brief Free an address range reservation.
+* 
+* Frees a virtual address range reserved by cuMemAddressReserve.  The size
+* must match what was given to memAddressReserve and the ptr given must
+* match what was returned from memAddressReserve.
+*
+* \param[in] ptr  - Starting address of the virtual address range to free
+* \param[in] size - Size of the virtual address region to free
+* \return
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemAddressReserve
+*/
+CUresult CUDAAPI cuMemAddressFree(CUdeviceptr ptr, size_t size);
+
+/**
+* \brief Create a CUDA memory handle representing a memory allocation of a given size described by the given properties
+*
+* This creates a memory allocation on the target device specified through the
+* \p prop structure. The created allocation will not have any device or host
+* mappings. The generic memory \p handle for the allocation can be
+* mapped to the address space of calling process via ::cuMemMap. This handle
+* cannot be transmitted directly to other processes (see
+* ::cuMemExportToShareableHandle).  On Windows, the caller must also pass
+* an LPSECURITYATTRIBUTE in \p prop to be associated with this handle which
+* limits or allows access to this handle for a recipient process (see
+* ::CUmemAllocationProp::win32HandleMetaData for more).  The \p size of this
+* allocation must be a multiple of the the value given via
+* ::cuMemGetAllocationGranularity with the ::CU_MEM_ALLOC_GRANULARITY_MINIMUM
+* flag.
+* To create a CPU allocation targeting a specific host NUMA node, applications must
+* set ::CUmemAllocationProp::CUmemLocation::type to ::CU_MEM_LOCATION_TYPE_HOST_NUMA and
+* ::CUmemAllocationProp::CUmemLocation::id must specify the NUMA ID of the CPU.
+* On systems where NUMA is not available ::CUmemAllocationProp::CUmemLocation::id must be set to 0.
+* Specifying ::CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT or ::CU_MEM_LOCATION_TYPE_HOST as the
+* ::CUmemLocation::type will result in ::CUDA_ERROR_INVALID_VALUE.
+*
+* Applications that intend to use ::CU_MEM_HANDLE_TYPE_FABRIC based memory sharing must ensure:
+* (1) `nvidia-caps-imex-channels` character device is created by the driver and is listed under /proc/devices 
+* (2) have at least one IMEX channel file accessible by the user launching the application.
+*
+* When exporter and importer CUDA processes have been granted access to the same IMEX channel, they can securely
+* share memory.
+*
+* The IMEX channel security model works on a per user basis. Which means all processes under a user can share
+* memory if the user has access to a valid IMEX channel. When multi-user isolation is desired, a separate IMEX
+* channel is required for each user.
+*
+* These channel files exist in /dev/nvidia-caps-imex-channels/channel* and can be created using standard OS
+* native calls like mknod on Linux. For example: To create channel0 with the major number from /proc/devices
+* users can execute the following command: `mknod /dev/nvidia-caps-imex-channels/channel0 c <major number> 0`
+*
+* If ::CUmemAllocationProp::allocFlags::usage contains ::CU_MEM_CREATE_USAGE_TILE_POOL flag then
+* the memory allocation is intended only to be used as backing tile pool for sparse CUDA arrays
+* and sparse CUDA mipmapped arrays.
+* (see ::cuMemMapArrayAsync).
+*
+* \param[out] handle - Value of handle returned. All operations on this allocation are to be performed using this handle.
+* \param[in]  size   - Size of the allocation requested
+* \param[in]  prop   - Properties of the allocation to create.
+* \param[in]  flags  - flags for future use, must be zero now.
+* \return
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_OUT_OF_MEMORY,
+* ::CUDA_ERROR_INVALID_DEVICE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+* \notefnerr
+*
+* \sa ::cuMemRelease, ::cuMemExportToShareableHandle, ::cuMemImportFromShareableHandle
+*/
+CUresult CUDAAPI cuMemCreate(CUmemGenericAllocationHandle *handle, size_t size, const CUmemAllocationProp *prop, unsigned long long flags);
+
+/**
+* \brief Release a memory handle representing a memory allocation which was previously allocated through cuMemCreate.
+* 
+* Frees the memory that was allocated on a device through cuMemCreate.
+*
+* The memory allocation will be freed when all outstanding mappings to the memory
+* are unmapped and when all outstanding references to the handle (including it's
+* shareable counterparts) are also released. The generic memory handle can be
+* freed when there are still outstanding mappings made with this handle. Each
+* time a recipient process imports a shareable handle, it needs to pair it with
+* ::cuMemRelease for the handle to be freed.  If \p handle is not a valid handle
+* the behavior is undefined. 
+*
+* \param[in] handle Value of handle which was returned previously by cuMemCreate.
+* \return
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+* \notefnerr
+*
+* \sa ::cuMemCreate
+*/
+CUresult CUDAAPI cuMemRelease(CUmemGenericAllocationHandle handle);
+
+/**
+* \brief Maps an allocation handle to a reserved virtual address range.
+*
+* Maps bytes of memory represented by \p handle starting from byte \p offset to
+* \p size to address range [\p addr, \p addr + \p size]. This range must be an
+* address reservation previously reserved with ::cuMemAddressReserve, and
+* \p offset + \p size must be less than the size of the memory allocation.
+* Both \p ptr, \p size, and \p offset must be a multiple of the value given via
+* ::cuMemGetAllocationGranularity with the ::CU_MEM_ALLOC_GRANULARITY_MINIMUM flag.
+* If \p handle represents a multicast object, \p ptr, \p size and \p offset must
+* be aligned to the value returned by ::cuMulticastGetGranularity with the flag
+* ::CU_MULTICAST_MINIMUM_GRANULARITY. For best performance however, it is
+* recommended that \p ptr, \p size and \p offset be aligned to the value
+* returned by ::cuMulticastGetGranularity with the flag
+* ::CU_MULTICAST_RECOMMENDED_GRANULARITY.
+* 
+* Please note calling ::cuMemMap does not make the address accessible,
+* the caller needs to update accessibility of a contiguous mapped VA
+* range by calling ::cuMemSetAccess.
+* 
+* Once a recipient process obtains a shareable memory handle
+* from ::cuMemImportFromShareableHandle, the process must
+* use ::cuMemMap to map the memory into its address ranges before
+* setting accessibility with ::cuMemSetAccess.
+*  
+* ::cuMemMap can only create mappings on VA range reservations 
+* that are not currently mapped.
+* 
+* \param[in] ptr    - Address where memory will be mapped. 
+* \param[in] size   - Size of the memory mapping. 
+* \param[in] offset - Offset into the memory represented by 
+*                   - \p handle from which to start mapping
+*                   - Note: currently must be zero.
+* \param[in] handle - Handle to a shareable memory 
+* \param[in] flags  - flags for future use, must be zero now. 
+* \return
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_INVALID_DEVICE,
+* ::CUDA_ERROR_OUT_OF_MEMORY,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+* \notefnerr
+*
+* \sa ::cuMemUnmap, ::cuMemSetAccess, ::cuMemCreate, ::cuMemAddressReserve, ::cuMemImportFromShareableHandle
+*/
+CUresult CUDAAPI cuMemMap(CUdeviceptr ptr, size_t size, size_t offset, CUmemGenericAllocationHandle handle, unsigned long long flags);
+
+/**
+ * \brief Maps or unmaps subregions of sparse CUDA arrays and sparse CUDA mipmapped arrays
+ *
+ * Performs map or unmap operations on subregions of sparse CUDA arrays and sparse CUDA mipmapped arrays.
+ * Each operation is specified by a ::CUarrayMapInfo entry in the \p mapInfoList array of size \p count.
+ * The structure ::CUarrayMapInfo is defined as follow:
+ \code
+     typedef struct CUarrayMapInfo_st {
+        CUresourcetype resourceType;                   
+        union {
+            CUmipmappedArray mipmap;
+            CUarray array;
+        } resource;
+
+        CUarraySparseSubresourceType subresourceType;   
+        union {
+            struct {
+                unsigned int level;                     
+                unsigned int layer;                     
+                unsigned int offsetX;                   
+                unsigned int offsetY;                   
+                unsigned int offsetZ;                   
+                unsigned int extentWidth;               
+                unsigned int extentHeight;              
+                unsigned int extentDepth;               
+            } sparseLevel;
+            struct {
+                unsigned int layer;
+                unsigned long long offset;              
+                unsigned long long size;                
+            } miptail;
+        } subresource;
+
+        CUmemOperationType memOperationType;
+        
+        CUmemHandleType memHandleType;                  
+        union {
+            CUmemGenericAllocationHandle memHandle;
+        } memHandle;
+
+        unsigned long long offset;                      
+        unsigned int deviceBitMask;                     
+        unsigned int flags;                             
+        unsigned int reserved[2];                       
+    } CUarrayMapInfo;
+ \endcode
+ *
+ * where ::CUarrayMapInfo::resourceType specifies the type of resource to be operated on.
+ * If ::CUarrayMapInfo::resourceType is set to ::CUresourcetype::CU_RESOURCE_TYPE_ARRAY then 
+ * ::CUarrayMapInfo::resource::array must be set to a valid sparse CUDA array handle.
+ * The CUDA array must be either a 2D, 2D layered or 3D CUDA array and must have been allocated using
+ * ::cuArrayCreate or ::cuArray3DCreate with the flag ::CUDA_ARRAY3D_SPARSE
+ * or ::CUDA_ARRAY3D_DEFERRED_MAPPING.
+ * For CUDA arrays obtained using ::cuMipmappedArrayGetLevel, ::CUDA_ERROR_INVALID_VALUE will be returned.
+ * If ::CUarrayMapInfo::resourceType is set to ::CUresourcetype::CU_RESOURCE_TYPE_MIPMAPPED_ARRAY 
+ * then ::CUarrayMapInfo::resource::mipmap must be set to a valid sparse CUDA mipmapped array handle.
+ * The CUDA mipmapped array must be either a 2D, 2D layered or 3D CUDA mipmapped array and must have been
+ * allocated using ::cuMipmappedArrayCreate with the flag ::CUDA_ARRAY3D_SPARSE
+ * or ::CUDA_ARRAY3D_DEFERRED_MAPPING.
+ *
+ * ::CUarrayMapInfo::subresourceType specifies the type of subresource within the resource. 
+ * ::CUarraySparseSubresourceType_enum is defined as:
+ \code
+    typedef enum CUarraySparseSubresourceType_enum {
+        CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL = 0,
+        CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL = 1
+    } CUarraySparseSubresourceType;
+ \endcode
+ *
+ * where ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL indicates a
+ * sparse-miplevel which spans at least one tile in every dimension. The remaining miplevels which
+ * are too small to span at least one tile in any dimension constitute the mip tail region as indicated by 
+ * ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL subresource type.
+ *
+ * If ::CUarrayMapInfo::subresourceType is set to ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL
+ * then ::CUarrayMapInfo::subresource::sparseLevel struct must contain valid array subregion offsets and extents.
+ * The ::CUarrayMapInfo::subresource::sparseLevel::offsetX, ::CUarrayMapInfo::subresource::sparseLevel::offsetY
+ * and ::CUarrayMapInfo::subresource::sparseLevel::offsetZ must specify valid X, Y and Z offsets respectively.
+ * The ::CUarrayMapInfo::subresource::sparseLevel::extentWidth, ::CUarrayMapInfo::subresource::sparseLevel::extentHeight
+ * and ::CUarrayMapInfo::subresource::sparseLevel::extentDepth must specify valid width, height and depth extents respectively.
+ * These offsets and extents must be aligned to the corresponding tile dimension.
+ * For CUDA mipmapped arrays ::CUarrayMapInfo::subresource::sparseLevel::level must specify a valid mip level index. Otherwise,
+ * must be zero.
+ * For layered CUDA arrays and layered CUDA mipmapped arrays ::CUarrayMapInfo::subresource::sparseLevel::layer must specify a valid layer index. Otherwise,
+ * must be zero.
+ * ::CUarrayMapInfo::subresource::sparseLevel::offsetZ must be zero and ::CUarrayMapInfo::subresource::sparseLevel::extentDepth
+ * must be set to 1 for 2D and 2D layered CUDA arrays and CUDA mipmapped arrays.
+ * Tile extents can be obtained by calling ::cuArrayGetSparseProperties and ::cuMipmappedArrayGetSparseProperties
+ *
+ * If ::CUarrayMapInfo::subresourceType is set to ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL
+ * then ::CUarrayMapInfo::subresource::miptail struct must contain valid mip tail offset in 
+ * ::CUarrayMapInfo::subresource::miptail::offset and size in ::CUarrayMapInfo::subresource::miptail::size.
+ * Both, mip tail offset and mip tail size must be aligned to the tile size. 
+ * For layered CUDA mipmapped arrays which don't have the flag ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL set in ::CUDA_ARRAY_SPARSE_PROPERTIES::flags
+ * as returned by ::cuMipmappedArrayGetSparseProperties, ::CUarrayMapInfo::subresource::miptail::layer must specify a valid layer index.
+ * Otherwise, must be zero.
+ *
+ * If ::CUarrayMapInfo::resource::array or ::CUarrayMapInfo::resource::mipmap was created with ::CUDA_ARRAY3D_DEFERRED_MAPPING
+ * flag set the ::CUarrayMapInfo::subresourceType and the contents of ::CUarrayMapInfo::subresource will be ignored.
+ *
+ * ::CUarrayMapInfo::memOperationType specifies the type of operation. ::CUmemOperationType is defined as:
+ \code
+    typedef enum CUmemOperationType_enum {
+        CU_MEM_OPERATION_TYPE_MAP = 1,
+        CU_MEM_OPERATION_TYPE_UNMAP = 2
+    } CUmemOperationType;
+ \endcode
+ * If ::CUarrayMapInfo::memOperationType is set to ::CUmemOperationType::CU_MEM_OPERATION_TYPE_MAP then the subresource 
+ * will be mapped onto the tile pool memory specified by ::CUarrayMapInfo::memHandle at offset ::CUarrayMapInfo::offset. 
+ * The tile pool allocation has to be created by specifying the ::CU_MEM_CREATE_USAGE_TILE_POOL flag when calling ::cuMemCreate. Also, 
+ * ::CUarrayMapInfo::memHandleType must be set to ::CUmemHandleType::CU_MEM_HANDLE_TYPE_GENERIC.
+ * 
+ * If ::CUarrayMapInfo::memOperationType is set to ::CUmemOperationType::CU_MEM_OPERATION_TYPE_UNMAP then an unmapping operation
+ * is performed. ::CUarrayMapInfo::memHandle must be NULL.
+ *
+ * ::CUarrayMapInfo::deviceBitMask specifies the list of devices that must map or unmap physical memory. 
+ * Currently, this mask must have exactly one bit set, and the corresponding device must match the device associated with the stream. 
+ * If ::CUarrayMapInfo::memOperationType is set to ::CUmemOperationType::CU_MEM_OPERATION_TYPE_MAP, the device must also match 
+ * the device associated with the tile pool memory allocation as specified by ::CUarrayMapInfo::memHandle.
+ *
+ * ::CUarrayMapInfo::flags and ::CUarrayMapInfo::reserved[] are unused and must be set to zero.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ *
+ * \param[in] mapInfoList - List of ::CUarrayMapInfo
+ * \param[in] count       - Count of ::CUarrayMapInfo  in \p mapInfoList
+ * \param[in] hStream     - Stream identifier for the stream to use for map or unmap operations
+ *
+ * \sa ::cuMipmappedArrayCreate, ::cuArrayCreate, ::cuArray3DCreate, ::cuMemCreate, ::cuArrayGetSparseProperties, ::cuMipmappedArrayGetSparseProperties
+ */
+CUresult CUDAAPI cuMemMapArrayAsync(CUarrayMapInfo  *mapInfoList, unsigned int count, CUstream hStream);
+
+/**
+* \brief Unmap the backing memory of a given address range.
+*
+* The range must be the entire contiguous address range that was mapped to.  In
+* other words, ::cuMemUnmap cannot unmap a sub-range of an address range mapped
+* by ::cuMemCreate / ::cuMemMap.  Any backing memory allocations will be freed
+* if there are no existing mappings and there are no unreleased memory handles.
+*
+* When ::cuMemUnmap returns successfully the address range is converted to an
+* address reservation and can be used for a future calls to ::cuMemMap.  Any new
+* mapping to this virtual address will need to have access granted through
+* ::cuMemSetAccess, as all mappings start with no accessibility setup.
+*
+* \param[in] ptr  - Starting address for the virtual address range to unmap
+* \param[in] size - Size of the virtual address range to unmap
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+* \notefnerr
+* \note_sync
+*
+* \sa ::cuMemCreate, ::cuMemAddressReserve
+*/
+CUresult CUDAAPI cuMemUnmap(CUdeviceptr ptr, size_t size);
+
+/**
+* \brief Set the access flags for each location specified in \p desc for the given virtual address range
+* 
+* Given the virtual address range via \p ptr and \p size, and the locations
+* in the array given by \p desc and \p count, set the access flags for the
+* target locations.  The range must be a fully mapped address range
+* containing all allocations created by ::cuMemMap / ::cuMemCreate.
+* Users cannot specify ::CU_MEM_LOCATION_TYPE_HOST_NUMA accessibility for allocations created on with other location types.
+* Note: When ::CUmemAccessDesc::CUmemLocation::type is ::CU_MEM_LOCATION_TYPE_HOST_NUMA, ::CUmemAccessDesc::CUmemLocation::id
+* is ignored.
+* When setting the access flags for a virtual address range mapping a multicast
+* object, \p ptr and \p size must be aligned to the value returned by
+* ::cuMulticastGetGranularity with the flag ::CU_MULTICAST_MINIMUM_GRANULARITY.
+* For best performance however, it is recommended that \p ptr and \p size be
+* aligned to the value returned by ::cuMulticastGetGranularity with the flag
+* ::CU_MULTICAST_RECOMMENDED_GRANULARITY.
+*
+* \param[in] ptr   - Starting address for the virtual address range
+* \param[in] size  - Length of the virtual address range
+* \param[in] desc  - Array of ::CUmemAccessDesc that describe how to change the
+*                  - mapping for each location specified
+* \param[in] count - Number of ::CUmemAccessDesc in \p desc
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_INVALID_DEVICE,
+* ::CUDA_ERROR_NOT_SUPPORTED
+* \notefnerr
+* \note_sync
+*
+* \sa ::cuMemSetAccess, ::cuMemCreate, :cuMemMap
+*/
+CUresult CUDAAPI cuMemSetAccess(CUdeviceptr ptr, size_t size, const CUmemAccessDesc *desc, size_t count);
+
+/**
+* \brief Get the access \p flags set for the given \p location and \p ptr
+*
+* \param[out] flags   - Flags set for this location
+* \param[in] location - Location in which to check the flags for
+* \param[in] ptr      - Address in which to check the access flags for
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_INVALID_DEVICE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemSetAccess
+*/
+CUresult CUDAAPI cuMemGetAccess(unsigned long long *flags, const CUmemLocation *location, CUdeviceptr ptr);
+
+/**
+* \brief Exports an allocation to a requested shareable handle type
+*
+* Given a CUDA memory handle, create a shareable memory
+* allocation handle that can be used to share the memory with other
+* processes. The recipient process can convert the shareable handle back into a
+* CUDA memory handle using ::cuMemImportFromShareableHandle and map
+* it with ::cuMemMap. The implementation of what this handle is and how it
+* can be transferred is defined by the requested handle type in \p handleType
+*
+* Once all shareable handles are closed and the allocation is released, the allocated
+* memory referenced will be released back to the OS and uses of the CUDA handle afterward
+* will lead to undefined behavior.
+*
+* This API can also be used in conjunction with other APIs (e.g. Vulkan, OpenGL)
+* that support importing memory from the shareable type
+*
+* \param[out] shareableHandle - Pointer to the location in which to store the requested handle type
+* \param[in] handle           - CUDA handle for the memory allocation
+* \param[in] handleType       - Type of shareable handle requested (defines type and size of the \p shareableHandle output parameter)
+* \param[in] flags            - Reserved, must be zero
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemImportFromShareableHandle
+*/
+CUresult CUDAAPI cuMemExportToShareableHandle(void *shareableHandle, CUmemGenericAllocationHandle handle, CUmemAllocationHandleType handleType, unsigned long long flags);
+
+/**
+* \brief Imports an allocation from a requested shareable handle type.
+*
+* If the current process cannot support the memory described by this shareable
+* handle, this API will error as ::CUDA_ERROR_NOT_SUPPORTED.
+*
+* If \p shHandleType is ::CU_MEM_HANDLE_TYPE_FABRIC and the importer process has not been
+* granted access to the same IMEX channel as the exporter process, this API will error
+* as ::CUDA_ERROR_NOT_PERMITTED.
+*
+* \note Importing shareable handles exported from some graphics APIs(VUlkan, OpenGL, etc)
+* created on devices under an SLI group may not be supported, and thus this API will
+* return CUDA_ERROR_NOT_SUPPORTED.
+* There is no guarantee that the contents of \p handle will be the same CUDA memory handle
+* for the same given OS shareable handle, or the same underlying allocation.
+*
+* \param[out] handle       - CUDA Memory handle for the memory allocation.
+* \param[in]  osHandle     - Shareable Handle representing the memory allocation that is to be imported. 
+* \param[in]  shHandleType - handle type of the exported handle ::CUmemAllocationHandleType.
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemExportToShareableHandle, ::cuMemMap, ::cuMemRelease
+*/
+CUresult CUDAAPI cuMemImportFromShareableHandle(CUmemGenericAllocationHandle *handle, void *osHandle, CUmemAllocationHandleType shHandleType);
+
+/**
+* \brief Calculates either the minimal or recommended granularity 
+*
+* Calculates either the minimal or recommended granularity
+* for a given allocation specification and returns it in granularity.  This
+* granularity can be used as a multiple for alignment, size, or address mapping.
+*
+* \param[out] granularity Returned granularity.
+* \param[in]  prop Property for which to determine the granularity for
+* \param[in]  option Determines which granularity to return
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemCreate, ::cuMemMap
+*/
+CUresult CUDAAPI cuMemGetAllocationGranularity(size_t *granularity, const CUmemAllocationProp *prop, CUmemAllocationGranularity_flags option);
+
+/**
+* \brief Retrieve the contents of the property structure defining properties for this handle
+*
+* \param[out] prop  - Pointer to a properties structure which will hold the information about this handle
+* \param[in] handle - Handle which to perform the query on
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemCreate, ::cuMemImportFromShareableHandle
+*/
+CUresult CUDAAPI cuMemGetAllocationPropertiesFromHandle(CUmemAllocationProp *prop, CUmemGenericAllocationHandle handle);
+
+/**
+* \brief Given an address \p addr, returns the allocation handle of the backing memory allocation.
+*
+* The handle is guaranteed to be the same handle value used to map the memory. If the address
+* requested is not mapped, the function will fail. The returned handle must be released with
+* corresponding number of calls to ::cuMemRelease.
+*
+* \note The address \p addr, can be any address in a range previously mapped
+* by ::cuMemMap, and not necessarily the start address.
+*
+* \param[out] handle CUDA Memory handle for the backing memory allocation.
+* \param[in] addr Memory address to query, that has been mapped previously.
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMemCreate, ::cuMemRelease, ::cuMemMap
+*/
+CUresult CUDAAPI cuMemRetainAllocationHandle(CUmemGenericAllocationHandle *handle, void *addr);
+
+/** @} */ /* END CUDA_VA */
+
+/**
+ * \defgroup CUDA_MALLOC_ASYNC Stream Ordered Memory Allocator
+ *
+ * ___MANBRIEF___ Functions for performing allocation and free operations in stream order.
+ *                Functions for controlling the behavior of the underlying allocator.
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the stream ordered memory allocator exposed by the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ *
+ * \section CUDA_MALLOC_ASYNC_overview overview
+ *
+ * The asynchronous allocator allows the user to allocate and free in stream order.
+ * All asynchronous accesses of the allocation must happen between
+ * the stream executions of the allocation and the free. If the memory is accessed
+ * outside of the promised stream order, a use before allocation / use after free error
+ * will cause undefined behavior.
+ *
+ * The allocator is free to reallocate the memory as long as it can guarantee
+ * that compliant memory accesses will not overlap temporally.
+ * The allocator may refer to internal stream ordering as well as inter-stream dependencies
+ * (such as CUDA events and null stream dependencies) when establishing the temporal guarantee.
+ * The allocator may also insert inter-stream dependencies to establish the temporal guarantee. 
+ *
+ * \section CUDA_MALLOC_ASYNC_support Supported Platforms
+ *
+ * Whether or not a device supports the integrated stream ordered memory allocator
+ * may be queried by calling ::cuDeviceGetAttribute() with the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED
+ */
+
+/**
+ * \brief Frees memory with stream ordered semantics
+ *
+ * Inserts a free operation into \p hStream.
+ * The allocation must not be accessed after stream execution reaches the free.
+ * After this API returns, accessing the memory from any subsequent work launched on the GPU
+ * or querying its pointer attributes results in undefined behavior.
+ *
+ * \note During stream capture, this function results in the creation of a free node and
+ *       must therefore be passed the address of a graph allocation.
+ * 
+ * \param dptr - memory to free
+ * \param hStream - The stream establishing the stream ordering contract. 
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context),
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ */
+CUresult CUDAAPI cuMemFreeAsync(CUdeviceptr dptr, CUstream hStream);
+
+/**
+ * \brief Allocates memory with stream ordered semantics
+ *
+ * Inserts an allocation operation into \p hStream.
+ * A pointer to the allocated memory is returned immediately in *dptr.
+ * The allocation must not be accessed until the the allocation operation completes.
+ * The allocation comes from the memory pool current to the stream's device.
+ *
+ * \note The default memory pool of a device contains device memory from that device.
+ * \note Basic stream ordering allows future work submitted into the same stream to use the allocation.
+ *       Stream query, stream synchronize, and CUDA events can be used to guarantee that the allocation
+ *       operation completes before work submitted in a separate stream runs. 
+ * \note During stream capture, this function results in the creation of an allocation node.  In this case,
+ *       the allocation is owned by the graph instead of the memory pool. The memory pool's properties
+ *       are used to set the node's creation parameters.
+ *
+ * \param[out] dptr    - Returned device pointer
+ * \param[in] bytesize - Number of bytes to allocate
+ * \param[in] hStream  - The stream establishing the stream ordering contract and the memory pool to allocate from
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context),
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemAllocFromPoolAsync, ::cuMemFreeAsync, ::cuDeviceSetMemPool,
+ *     ::cuDeviceGetDefaultMemPool, ::cuDeviceGetMemPool, ::cuMemPoolCreate,
+ *     ::cuMemPoolSetAccess, ::cuMemPoolSetAttribute
+ */
+CUresult CUDAAPI cuMemAllocAsync(CUdeviceptr *dptr, size_t bytesize, CUstream hStream);
+
+/**
+ * \brief Tries to release memory back to the OS
+ *
+ * Releases memory back to the OS until the pool contains fewer than minBytesToKeep
+ * reserved bytes, or there is no more memory that the allocator can safely release.
+ * The allocator cannot release OS allocations that back outstanding asynchronous allocations.
+ * The OS allocations may happen at different granularity from the user allocations.
+ *
+ * \note: Allocations that have not been freed count as outstanding. 
+ * \note: Allocations that have been asynchronously freed but whose completion has
+ *        not been observed on the host (eg. by a synchronize) can count as outstanding.
+ *
+ * \param[in] pool           - The memory pool to trim
+ * \param[in] minBytesToKeep - If the pool has less than minBytesToKeep reserved,
+ * the TrimTo operation is a no-op.  Otherwise the pool will be guaranteed to have
+ * at least minBytesToKeep bytes reserved after the operation.
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool,
+ *     ::cuDeviceGetMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuMemPoolTrimTo(CUmemoryPool pool, size_t minBytesToKeep);
+
+/**
+ * \brief Sets attributes of a memory pool
+ *
+ * Supported attributes are:
+ * - ::CU_MEMPOOL_ATTR_RELEASE_THRESHOLD: (value type = cuuint64_t)
+ *                    Amount of reserved memory in bytes to hold onto before trying
+ *                    to release memory back to the OS. When more than the release
+ *                    threshold bytes of memory are held by the memory pool, the
+ *                    allocator will try to release memory back to the OS on the
+ *                    next call to stream, event or context synchronize. (default 0)
+ * - ::CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES: (value type = int)
+ *                    Allow ::cuMemAllocAsync to use memory asynchronously freed
+ *                    in another stream as long as a stream ordering dependency
+ *                    of the allocating stream on the free action exists.
+ *                    Cuda events and null stream interactions can create the required
+ *                    stream ordered dependencies. (default enabled)
+ * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC: (value type = int)
+ *                    Allow reuse of already completed frees when there is no dependency
+ *                    between the free and allocation. (default enabled)
+ * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES: (value type = int)
+ *                    Allow ::cuMemAllocAsync to insert new stream dependencies
+ *                    in order to establish the stream ordering required to reuse
+ *                    a piece of memory released by ::cuMemFreeAsync (default enabled).
+ * - ::CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH: (value type = cuuint64_t)
+ *                    Reset the high watermark that tracks the amount of backing memory that was
+ *                    allocated for the memory pool. It is illegal to set this attribute to a non-zero value.
+ * - ::CU_MEMPOOL_ATTR_USED_MEM_HIGH: (value type = cuuint64_t)
+ *                    Reset the high watermark that tracks the amount of used memory that was
+ *                    allocated for the memory pool.
+ *
+ * \param[in] pool  - The memory pool to modify
+ * \param[in] attr  - The attribute to modify
+ * \param[in] value - Pointer to the value to assign
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool,
+ *     ::cuDeviceGetMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuMemPoolSetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void *value);
+
+/**
+ * \brief Gets attributes of a memory pool
+ *
+ * Supported attributes are:
+ * - ::CU_MEMPOOL_ATTR_RELEASE_THRESHOLD: (value type = cuuint64_t)
+ *                    Amount of reserved memory in bytes to hold onto before trying
+ *                    to release memory back to the OS. When more than the release
+ *                    threshold bytes of memory are held by the memory pool, the
+ *                    allocator will try to release memory back to the OS on the
+ *                    next call to stream, event or context synchronize. (default 0)
+ * - ::CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES: (value type = int)
+ *                    Allow ::cuMemAllocAsync to use memory asynchronously freed
+ *                    in another stream as long as a stream ordering dependency
+ *                    of the allocating stream on the free action exists.
+ *                    Cuda events and null stream interactions can create the required
+ *                    stream ordered dependencies. (default enabled)
+ * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC: (value type = int)
+ *                    Allow reuse of already completed frees when there is no dependency
+ *                    between the free and allocation. (default enabled)
+ * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES: (value type = int)
+ *                    Allow ::cuMemAllocAsync to insert new stream dependencies
+ *                    in order to establish the stream ordering required to reuse
+ *                    a piece of memory released by ::cuMemFreeAsync (default enabled).
+ * - ::CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT: (value type = cuuint64_t)
+ *                    Amount of backing memory currently allocated for the mempool
+ * - ::CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH: (value type = cuuint64_t)
+ *                    High watermark of backing memory allocated for the mempool since the
+ *                    last time it was reset.
+ * - ::CU_MEMPOOL_ATTR_USED_MEM_CURRENT: (value type = cuuint64_t)
+ *                    Amount of memory from the pool that is currently in use by the application.
+ * - ::CU_MEMPOOL_ATTR_USED_MEM_HIGH: (value type = cuuint64_t)
+ *                    High watermark of the amount of memory from the pool that was in use by the application.
+ *
+ * \param[in] pool   - The memory pool to get attributes of
+ * \param[in] attr   - The attribute to get 
+ * \param[out] value - Retrieved value
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool,
+ *     ::cuDeviceGetMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuMemPoolGetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void *value);
+
+/**
+ * \brief Controls visibility of pools between devices
+ *
+ * \param[in] pool  - The pool being modified
+ * \param[in] map   - Array of access descriptors. Each descriptor instructs the access to enable for a single gpu.
+ * \param[in] count - Number of descriptors in the map array.
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool,
+ *     ::cuDeviceGetMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuMemPoolSetAccess(CUmemoryPool pool, const CUmemAccessDesc *map, size_t count);
+
+/**
+ * \brief Returns the accessibility of a pool from a device
+ *
+ * Returns the accessibility of the pool's memory from the specified location. 
+ *
+ * \param[out] flags   - the accessibility of the pool from the specified location
+ * \param[in] memPool  - the pool being queried
+ * \param[in] location - the location accessing the pool
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool,
+ *     ::cuDeviceGetMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuMemPoolGetAccess(CUmemAccess_flags *flags, CUmemoryPool memPool, CUmemLocation *location);
+
+/**
+ * \brief Creates a memory pool
+ *
+ * Creates a CUDA memory pool and returns the handle in \p pool.  The \p poolProps determines
+ * the properties of the pool such as the backing device and IPC capabilities. 
+ *
+ * To create a memory pool targeting a specific host NUMA node, applications must
+ * set ::CUmemPoolProps::CUmemLocation::type to ::CU_MEM_LOCATION_TYPE_HOST_NUMA and
+ * ::CUmemPoolProps::CUmemLocation::id must specify the NUMA ID of the host memory node.
+ * Specifying ::CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT or ::CU_MEM_LOCATION_TYPE_HOST as the
+ * ::CUmemPoolProps::CUmemLocation::type will result in ::CUDA_ERROR_INVALID_VALUE.
+* By default, the pool's memory will be accessible from the device it is allocated on.
+ * In the case of pools created with ::CU_MEM_LOCATION_TYPE_HOST_NUMA, their default accessibility
+ * will be from the host CPU.
+ * Applications can control the maximum size of the pool by specifying a non-zero value for ::CUmemPoolProps::maxSize.
+ * If set to 0, the maximum size of the pool will default to a system dependent value.
+ *
+ * Applications that intend to use ::CU_MEM_HANDLE_TYPE_FABRIC based memory sharing must ensure:
+ * (1) `nvidia-caps-imex-channels` character device is created by the driver and is listed under /proc/devices 
+ * (2) have at least one IMEX channel file accessible by the user launching the application.
+ *
+ * When exporter and importer CUDA processes have been granted access to the same IMEX channel, they can securely
+ * share memory.
+ *
+ * The IMEX channel security model works on a per user basis. Which means all processes under a user can share
+ * memory if the user has access to a valid IMEX channel. When multi-user isolation is desired, a separate IMEX
+ * channel is required for each user.
+ *
+ * These channel files exist in /dev/nvidia-caps-imex-channels/channel* and can be created using standard OS
+ * native calls like mknod on Linux. For example: To create channel0 with the major number from /proc/devices
+ * users can execute the following command: `mknod /dev/nvidia-caps-imex-channels/channel0 c <major number> 0`
+ *
+ * \note Specifying CU_MEM_HANDLE_TYPE_NONE creates a memory pool that will not support IPC.
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_NOT_PERMITTED
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa ::cuDeviceSetMemPool, ::cuDeviceGetMemPool, ::cuDeviceGetDefaultMemPool,
+ *     ::cuMemAllocFromPoolAsync, ::cuMemPoolExportToShareableHandle
+ */
+CUresult CUDAAPI cuMemPoolCreate(CUmemoryPool *pool, const CUmemPoolProps *poolProps);
+
+/**
+ * \brief Destroys the specified memory pool
+ *
+ * If any pointers obtained from this pool haven't been freed or
+ * the pool has free operations that haven't completed
+ * when ::cuMemPoolDestroy is invoked, the function will return immediately and the
+ * resources associated with the pool will be released automatically
+ * once there are no more outstanding allocations. 
+ *
+ * Destroying the current mempool of a device sets the default mempool of
+ * that device as the current mempool for that device.
+ *
+ * \note A device's default memory pool cannot be destroyed.
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuMemFreeAsync, ::cuDeviceSetMemPool, ::cuDeviceGetMemPool,
+ *     ::cuDeviceGetDefaultMemPool, ::cuMemPoolCreate
+ */
+CUresult CUDAAPI cuMemPoolDestroy(CUmemoryPool pool);
+
+/**
+ * \brief Allocates memory from a specified pool with stream ordered semantics.
+ *
+ * Inserts an allocation operation into \p hStream.
+ * A pointer to the allocated memory is returned immediately in *dptr.
+ * The allocation must not be accessed until the the allocation operation completes.
+ * The allocation comes from the specified memory pool.
+ *
+ * \note
+ *    -  The specified memory pool may be from a device different than that of the specified \p hStream. 
+ * 
+ *    -  Basic stream ordering allows future work submitted into the same stream to use the allocation.
+ *       Stream query, stream synchronize, and CUDA events can be used to guarantee that the allocation
+ *       operation completes before work submitted in a separate stream runs. 
+ *
+ * \note During stream capture, this function results in the creation of an allocation node.  In this case,
+ *       the allocation is owned by the graph instead of the memory pool. The memory pool's properties
+ *       are used to set the node's creation parameters.
+ *
+ * \param[out] dptr    - Returned device pointer
+ * \param[in] bytesize - Number of bytes to allocate
+ * \param[in] pool     - The pool to allocate from 
+ * \param[in] hStream  - The stream establishing the stream ordering semantic
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context),
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool,
+ *     ::cuDeviceGetMemPool, ::cuMemPoolCreate, ::cuMemPoolSetAccess,
+ *     ::cuMemPoolSetAttribute
+ */
+CUresult CUDAAPI cuMemAllocFromPoolAsync(CUdeviceptr *dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream);
+
+/**
+ * \brief Exports a memory pool to the requested handle type.
+ *
+ * Given an IPC capable mempool, create an OS handle to share the pool with another process.
+ * A recipient process can convert the shareable handle into a mempool with ::cuMemPoolImportFromShareableHandle.
+ * Individual pointers can then be shared with the ::cuMemPoolExportPointer and ::cuMemPoolImportPointer APIs.
+ * The implementation of what the shareable handle is and how it can be transferred is defined by the requested
+ * handle type.
+ *
+ * \note: To create an IPC capable mempool, create a mempool with a CUmemAllocationHandleType other than CU_MEM_HANDLE_TYPE_NONE.
+ *
+ * \param[out] handle_out  - Returned OS handle 
+ * \param[in] pool         - pool to export 
+ * \param[in] handleType   - the type of handle to create 
+ * \param[in] flags        - must be 0 
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemPoolImportFromShareableHandle, ::cuMemPoolExportPointer,
+ *     ::cuMemPoolImportPointer, ::cuMemAllocAsync, ::cuMemFreeAsync,
+ *     ::cuDeviceGetDefaultMemPool, ::cuDeviceGetMemPool, ::cuMemPoolCreate,
+ *     ::cuMemPoolSetAccess, ::cuMemPoolSetAttribute
+ */
+CUresult CUDAAPI cuMemPoolExportToShareableHandle(void *handle_out, CUmemoryPool pool, CUmemAllocationHandleType handleType, unsigned long long flags);
+
+/**
+ * \brief imports a memory pool from a shared handle.
+ *
+ * Specific allocations can be imported from the imported pool with cuMemPoolImportPointer.
+ *
+ * If \p handleType is ::CU_MEM_HANDLE_TYPE_FABRIC and the importer process has not been
+ * granted access to the same IMEX channel as the exporter process, this API will error
+ * as ::CUDA_ERROR_NOT_PERMITTED.
+ * 
+ *
+ * \note Imported memory pools do not support creating new allocations.
+ *       As such imported memory pools may not be used in cuDeviceSetMemPool
+ *       or ::cuMemAllocFromPoolAsync calls.
+ *
+ * \param[out] pool_out    - Returned memory pool
+ * \param[in] handle       - OS handle of the pool to open 
+ * \param[in] handleType   - The type of handle being imported 
+ * \param[in] flags        - must be 0 
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemPoolExportToShareableHandle, ::cuMemPoolExportPointer, ::cuMemPoolImportPointer
+ */
+CUresult CUDAAPI cuMemPoolImportFromShareableHandle(
+        CUmemoryPool *pool_out,
+        void *handle,
+        CUmemAllocationHandleType handleType,
+        unsigned long long flags);
+
+/**
+ * \brief Export data to share a memory pool allocation between processes.
+ *
+ * Constructs \p shareData_out for sharing a specific allocation from an already shared memory pool.
+ * The recipient process can import the allocation with the ::cuMemPoolImportPointer api.
+ * The data is not a handle and may be shared through any IPC mechanism.
+ *
+ * \param[out] shareData_out - Returned export data  
+ * \param[in] ptr            - pointer to memory being exported
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemPoolExportToShareableHandle, ::cuMemPoolImportFromShareableHandle, ::cuMemPoolImportPointer
+ */
+CUresult CUDAAPI cuMemPoolExportPointer(CUmemPoolPtrExportData *shareData_out, CUdeviceptr ptr);
+
+/**
+ * \brief Import a memory pool allocation from another process.
+ *
+ * Returns in \p ptr_out a pointer to the imported memory.
+ * The imported memory must not be accessed before the allocation operation completes
+ * in the exporting process. The imported memory must be freed from all importing processes before
+ * being freed in the exporting process. The pointer may be freed with cuMemFree
+ * or cuMemFreeAsync.  If cuMemFreeAsync is used, the free must be completed
+ * on the importing process before the free operation on the exporting process.
+ *
+ * \note The cuMemFreeAsync api may be used in the exporting process before
+ *       the cuMemFreeAsync operation completes in its stream as long as the
+ *       cuMemFreeAsync in the exporting process specifies a stream with
+ *       a stream dependency on the importing process's cuMemFreeAsync.
+ *
+ * \param[out] ptr_out  - pointer to imported memory
+ * \param[in] pool      - pool from which to import
+ * \param[in] shareData - data specifying the memory to import
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemPoolExportToShareableHandle, ::cuMemPoolImportFromShareableHandle, ::cuMemPoolExportPointer
+ */
+CUresult CUDAAPI cuMemPoolImportPointer(CUdeviceptr *ptr_out, CUmemoryPool pool, CUmemPoolPtrExportData *shareData);
+
+/** @} */ /* END CUDA_MALLOC_ASYNC */
+
+/**
+ * \defgroup CUDA_MULTICAST Multicast Object Management
+ *
+ * ___MANBRIEF___ Functions for creating multicast objects, adding devices to them and binding/unbinding memory
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the CUDA multicast object operations exposed by the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ *
+ * \section CUDA_MULTICAST_overview overview
+ *
+ * A multicast object created via ::cuMulticastCreate enables certain memory
+ * operations to be broadcast to a team of devices. Devices can be added to a
+ * multicast object via ::cuMulticastAddDevice. Memory can be bound on each
+ * participating device via either ::cuMulticastBindMem or ::cuMulticastBindAddr.
+ * Multicast objects can be mapped into a device's virtual address space using
+ * the virtual memmory management APIs (see ::cuMemMap and ::cuMemSetAccess).
+ *
+ * \section CUDA_MULTICAST_support Supported Platforms
+ *
+ * Support for multicast on a specific device can be queried using the device
+ * attribute ::CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED
+ */
+
+/**
+ * \brief Create a generic allocation handle representing a multicast object described by the given properties.
+ *
+ * This creates a multicast object as described by \p prop. The number of
+ * participating devices is specified by ::CUmulticastObjectProp::numDevices.
+ * Devices can be added to the multicast object via ::cuMulticastAddDevice.
+ * All participating devices must be added to the multicast object before memory
+ * can be bound to it. Memory is bound to the multicast object via either
+ * ::cuMulticastBindMem or ::cuMulticastBindAddr, and can be unbound via
+ * ::cuMulticastUnbind. The total amount of memory that can be bound per device
+ * is specified by :CUmulticastObjectProp::size. This size must be a multiple of
+ * the value returned by ::cuMulticastGetGranularity with the flag
+ * ::CU_MULTICAST_GRANULARITY_MINIMUM. For best performance however, the size
+ * should be aligned to the value returned by ::cuMulticastGetGranularity with
+ * the flag ::CU_MULTICAST_GRANULARITY_RECOMMENDED.
+ *
+ * After all participating devices have been added, multicast objects can also
+ * be mapped to a device's virtual address space using the virtual memory
+ * management APIs (see ::cuMemMap and ::cuMemSetAccess). Multicast objects can
+ * also be shared with other processes by requesting a shareable handle via
+ * ::cuMemExportToShareableHandle. Note that the desired types of shareable
+ * handles must be specified in the bitmask ::CUmulticastObjectProp::handleTypes.
+ * Multicast objects can be released using the virtual memory management API
+ * ::cuMemRelease.
+ *
+ * \param[out] mcHandle     Value of handle returned.
+ * \param[in]  prop         Properties of the multicast object to create.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa ::cuMulticastAddDevice, ::cuMulticastBindMem, ::cuMulticastBindAddr, ::cuMulticastUnbind
+ * \sa ::cuMemCreate, ::cuMemRelease, ::cuMemExportToShareableHandle, ::cuMemImportFromShareableHandle
+ */
+CUresult CUDAAPI cuMulticastCreate(CUmemGenericAllocationHandle *mcHandle, const CUmulticastObjectProp *prop);
+
+/**
+ * \brief Associate a device to a multicast object.
+ *
+ * Associates a device to a multicast object. The added device will be a part of
+ * the multicast team of size specified by CUmulticastObjectProp::numDevices
+ * during ::cuMulticastCreate.
+ * The association of the device to the multicast object is permanent during
+ * the life time of the multicast object.
+ * All devices must be added to the multicast team before any memory can be
+ * bound to any device in the team. Any calls to ::cuMulticastBindMem or
+ * ::cuMulticastBindAddr will block until all devices have been added.
+ * Similarly all devices must be added to the multicast team before a virtual
+ * address range can be mapped to the multicast object. A call to ::cuMemMap
+ * will block until all devices have been added.
+ *
+ * \param[in] mcHandle     Handle representing a multicast object.
+ * \param[in] dev          Device that will be associated to the multicast
+ *                         object.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa ::cuMulticastCreate, ::cuMulticastBindMem, ::cuMulticastBindAddr
+ */
+CUresult CUDAAPI cuMulticastAddDevice(CUmemGenericAllocationHandle mcHandle, CUdevice dev);
+
+/**
+ * \brief Bind a memory allocation represented by a handle to a multicast object.
+ *
+ * Binds a memory allocation specified by \p memHandle and created via
+ * ::cuMemCreate to a multicast object represented by \p mcHandle and created
+ * via ::cuMulticastCreate. The intended \p size of the bind, the offset in the
+ * multicast range \p mcOffset as well as the offset in the memory \p memOffset
+ * must be a multiple of the value returned by ::cuMulticastGetGranularity with
+ * the flag ::CU_MULTICAST_GRANULARITY_MINIMUM. For best performance however,
+ * \p size, \p mcOffset and \p memOffset should be aligned to the granularity of
+ * the memory allocation(see ::cuMemGetAllocationGranularity) or to the value
+ * returned by ::cuMulticastGetGranularity with the flag
+ * ::CU_MULTICAST_GRANULARITY_RECOMMENDED.
+ *
+ * The \p size + \p memOffset cannot be larger than the size of the allocated
+ * memory. Similarly the \p size + \p mcOffset cannot be larger than the size
+ * of the multicast object.
+ * The memory allocation must have beeen created on one of the devices
+ * that was added to the multicast team via ::cuMulticastAddDevice.
+ * Externally shareable as well as imported multicast objects can be bound only
+ * to externally shareable memory.
+ * Note that this call will return CUDA_ERROR_OUT_OF_MEMORY if there are
+ * insufficient resources required to perform the bind. This call may also
+ * return CUDA_ERROR_SYSTEM_NOT_READY if the necessary system software is not
+ * initialized or running.
+ *
+ * \param[in]  mcHandle     Handle representing a multicast object.
+ * \param[in]  mcOffset     Offset into the multicast object for attachment.
+ * \param[in]  memHandle    Handle representing a memory allocation.
+ * \param[in]  memOffset    Offset into the memory for attachment.
+ * \param[in]  size         Size of the memory that will be bound to the
+ *                          multicast object.
+ * \param[in]  flags        Flags for future use, must be zero for now.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_SYSTEM_NOT_READY
+ *
+ * \sa ::cuMulticastCreate, ::cuMulticastAddDevice, ::cuMemCreate
+ */
+CUresult CUDAAPI cuMulticastBindMem(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags);
+
+/**
+ * \brief Bind a memory allocation represented by a virtual address to a multicast object.
+ *
+ * Binds a memory allocation specified by its mapped address \p memptr to a
+ * multicast object represented by \p mcHandle.
+ * The memory must have been allocated via ::cuMemCreate or ::cudaMallocAsync.
+ * The intended \p size of the bind, the offset in the multicast range
+ * \p mcOffset and \p memptr must be a multiple of the value returned by
+ * ::cuMulticastGetGranularity with the flag ::CU_MULTICAST_GRANULARITY_MINIMUM.
+ * For best performance however, \p size, \p mcOffset and \p memptr should be
+ * aligned to the value returned by ::cuMulticastGetGranularity with the flag
+ * ::CU_MULTICAST_GRANULARITY_RECOMMENDED.
+ *
+ * The \p size cannot be larger than the size of the allocated memory.
+ * Similarly the \p size + \p mcOffset cannot be larger than the total size
+ * of the multicast object.
+ * The memory allocation must have beeen created on one of the devices
+ * that was added to the multicast team via ::cuMulticastAddDevice.
+ * Externally shareable as well as imported multicast objects can be bound only
+ * to externally shareable memory.
+ * Note that this call will return CUDA_ERROR_OUT_OF_MEMORY if there are
+ * insufficient resources required to perform the bind. This call may also
+ * return CUDA_ERROR_SYSTEM_NOT_READY if the necessary system software is not
+ * initialized or running.
+ *
+ * \param[in]  mcHandle     Handle representing a multicast object.
+ * \param[in]  mcOffset     Offset into multicast va range for attachment.
+ * \param[in]  memptr       Virtual address of the memory allocation.
+ * \param[in]  size         Size of memory that will be bound to the
+ *                          multicast object.
+ * \param[in]  flags        Flags for future use, must be zero now.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY,
+ * ::CUDA_ERROR_SYSTEM_NOT_READY
+ *
+ * \sa ::cuMulticastCreate, ::cuMulticastAddDevice, ::cuMemCreate
+ */
+CUresult CUDAAPI cuMulticastBindAddr(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags);
+
+/**
+ * \brief Unbind any memory allocations bound to a multicast object at a given offset and upto a given size.
+ *
+ * Unbinds any memory allocations hosted on \p dev and bound to a multicast
+ * object at \p mcOffset and upto a given \p size.
+ * The intended \p size of the unbind and the offset in the multicast range
+ * ( \p mcOffset ) must be a multiple of the value returned by
+ * ::cuMulticastGetGranularity flag ::CU_MULTICAST_GRANULARITY_MINIMUM.
+ * The \p size + \p mcOffset cannot be larger than the total size of the
+ * multicast object.
+ *
+ * \note 
+ * Warning:
+ * The \p mcOffset and the \p size must match the corresponding values specified
+ * during the bind call. Any other values may result in undefined behavior.
+ *
+ * \param[in]  mcHandle     Handle representing a multicast object.
+ * \param[in]  dev          Device that hosts the memory allocation.
+ * \param[in]  mcOffset     Offset into the multicast object.
+ * \param[in]  size         Desired size to unbind.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa ::cuMulticastBindMem, ::cuMulticastBindAddr
+ */
+CUresult CUDAAPI cuMulticastUnbind(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, size_t size);
+
+/**
+* \brief Calculates either the minimal or recommended granularity for multicast object
+*
+* Calculates either the minimal or recommended granularity for a given set of
+* multicast object properties and returns it in granularity.  This granularity
+* can be used as a multiple for size, bind offsets and address mappings of the
+* multicast object.
+*
+* \param[out] granularity Returned granularity.
+* \param[in]  prop        Properties of the multicast object.
+* \param[in]  option      Determines which granularity to return.
+*
+* \returns
+* ::CUDA_SUCCESS,
+* ::CUDA_ERROR_INVALID_VALUE,
+* ::CUDA_ERROR_NOT_INITIALIZED,
+* ::CUDA_ERROR_DEINITIALIZED,
+* ::CUDA_ERROR_NOT_PERMITTED,
+* ::CUDA_ERROR_NOT_SUPPORTED
+*
+* \sa ::cuMulticastCreate, ::cuMulticastBindMem, ::cuMulticastBindAddr, ::cuMulticastUnbind
+*/
+CUresult CUDAAPI cuMulticastGetGranularity(size_t *granularity, const CUmulticastObjectProp *prop, CUmulticastGranularity_flags option);
+
+/** @} */ /* END CUDA_MULTICAST */
+
+/**
+ * \defgroup CUDA_UNIFIED Unified Addressing
+ *
+ * ___MANBRIEF___ unified addressing functions of the low-level CUDA driver
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the unified addressing functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ *
+ * \section CUDA_UNIFIED_overview Overview
+ *
+ * CUDA devices can share a unified address space with the host.
+ * For these devices there is no distinction between a device
+ * pointer and a host pointer -- the same pointer value may be
+ * used to access memory from the host program and from a kernel
+ * running on the device (with exceptions enumerated below).
+ *
+ * \section CUDA_UNIFIED_support Supported Platforms
+ *
+ * Whether or not a device supports unified addressing may be
+ * queried by calling ::cuDeviceGetAttribute() with the device
+ * attribute ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING.
+ *
+ * Unified addressing is automatically enabled in 64-bit processes
+ *
+ * \section CUDA_UNIFIED_lookup Looking Up Information from Pointer Values
+ *
+ * It is possible to look up information about the memory which backs a
+ * pointer value.  For instance, one may want to know if a pointer points
+ * to host or device memory.  As another example, in the case of device
+ * memory, one may want to know on which CUDA device the memory
+ * resides.  These properties may be queried using the function
+ * ::cuPointerGetAttribute()
+ *
+ * Since pointers are unique, it is not necessary to specify information
+ * about the pointers specified to the various copy functions in the
+ * CUDA API.  The function ::cuMemcpy() may be used to perform a copy
+ * between two pointers, ignoring whether they point to host or device
+ * memory (making ::cuMemcpyHtoD(), ::cuMemcpyDtoD(), and ::cuMemcpyDtoH()
+ * unnecessary for devices supporting unified addressing).  For
+ * multidimensional copies, the memory type ::CU_MEMORYTYPE_UNIFIED may be
+ * used to specify that the CUDA driver should infer the location of the
+ * pointer from its value.
+ *
+ * \section CUDA_UNIFIED_automaphost Automatic Mapping of Host Allocated Host Memory
+ *
+ * All host memory allocated in all contexts using ::cuMemAllocHost() and
+ * ::cuMemHostAlloc() is always directly accessible from all contexts on
+ * all devices that support unified addressing.  This is the case regardless
+ * of whether or not the flags ::CU_MEMHOSTALLOC_PORTABLE and
+ * ::CU_MEMHOSTALLOC_DEVICEMAP are specified.
+ *
+ * The pointer value through which allocated host memory may be accessed
+ * in kernels on all devices that support unified addressing is the same
+ * as the pointer value through which that memory is accessed on the host,
+ * so it is not necessary to call ::cuMemHostGetDevicePointer() to get the device
+ * pointer for these allocations.
+ *
+ * Note that this is not the case for memory allocated using the flag
+ * ::CU_MEMHOSTALLOC_WRITECOMBINED, as discussed below.
+ *
+ * \section CUDA_UNIFIED_autopeerregister Automatic Registration of Peer Memory
+ *
+ * Upon enabling direct access from a context that supports unified addressing
+ * to another peer context that supports unified addressing using
+ * ::cuCtxEnablePeerAccess() all memory allocated in the peer context using
+ * ::cuMemAlloc() and ::cuMemAllocPitch() will immediately be accessible
+ * by the current context.  The device pointer value through
+ * which any peer memory may be accessed in the current context
+ * is the same pointer value through which that memory may be
+ * accessed in the peer context.
+ *
+ * \section CUDA_UNIFIED_exceptions Exceptions, Disjoint Addressing
+ *
+ * Not all memory may be accessed on devices through the same pointer
+ * value through which they are accessed on the host.  These exceptions
+ * are host memory registered using ::cuMemHostRegister() and host memory
+ * allocated using the flag ::CU_MEMHOSTALLOC_WRITECOMBINED.  For these
+ * exceptions, there exists a distinct host and device address for the
+ * memory.  The device address is guaranteed to not overlap any valid host
+ * pointer range and is guaranteed to have the same value across all
+ * contexts that support unified addressing.
+ *
+ * This device address may be queried using ::cuMemHostGetDevicePointer()
+ * when a context using unified addressing is current.  Either the host
+ * or the unified device pointer value may be used to refer to this memory
+ * through ::cuMemcpy() and similar functions using the
+ * ::CU_MEMORYTYPE_UNIFIED memory type.
+ *
+ */
+
+/**
+ * \brief Returns information about a pointer
+ *
+ * The supported attributes are:
+ *
+ * - ::CU_POINTER_ATTRIBUTE_CONTEXT:
+ *
+ *      Returns in \p *data the ::CUcontext in which \p ptr was allocated or
+ *      registered.
+ *      The type of \p data must be ::CUcontext *.
+ *
+ *      If \p ptr was not allocated by, mapped by, or registered with
+ *      a ::CUcontext which uses unified virtual addressing then
+ *      ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_MEMORY_TYPE:
+ *
+ *      Returns in \p *data the physical memory type of the memory that
+ *      \p ptr addresses as a ::CUmemorytype enumerated value.
+ *      The type of \p data must be unsigned int.
+ *
+ *      If \p ptr addresses device memory then \p *data is set to
+ *      ::CU_MEMORYTYPE_DEVICE.  The particular ::CUdevice on which the
+ *      memory resides is the ::CUdevice of the ::CUcontext returned by the
+ *      ::CU_POINTER_ATTRIBUTE_CONTEXT attribute of \p ptr.
+ *
+ *      If \p ptr addresses host memory then \p *data is set to
+ *      ::CU_MEMORYTYPE_HOST.
+ *
+ *      If \p ptr was not allocated by, mapped by, or registered with
+ *      a ::CUcontext which uses unified virtual addressing then
+ *      ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ *      If the current ::CUcontext does not support unified virtual
+ *      addressing then ::CUDA_ERROR_INVALID_CONTEXT is returned.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER:
+ *
+ *      Returns in \p *data the device pointer value through which
+ *      \p ptr may be accessed by kernels running in the current
+ *      ::CUcontext.
+ *      The type of \p data must be CUdeviceptr *.
+ *
+ *      If there exists no device pointer value through which
+ *      kernels running in the current ::CUcontext may access
+ *      \p ptr then ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ *      If there is no current ::CUcontext then
+ *      ::CUDA_ERROR_INVALID_CONTEXT is returned.
+ *
+ *      Except in the exceptional disjoint addressing cases discussed
+ *      below, the value returned in \p *data will equal the input
+ *      value \p ptr.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_HOST_POINTER:
+ *
+ *      Returns in \p *data the host pointer value through which
+ *      \p ptr may be accessed by by the host program.
+ *      The type of \p data must be void **.
+ *      If there exists no host pointer value through which
+ *      the host program may directly access \p ptr then
+ *      ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ *      Except in the exceptional disjoint addressing cases discussed
+ *      below, the value returned in \p *data will equal the input
+ *      value \p ptr.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_P2P_TOKENS:
+ *
+ *      Returns in \p *data two tokens for use with the nv-p2p.h Linux
+ *      kernel interface. \p data must be a struct of type
+ *      CUDA_POINTER_ATTRIBUTE_P2P_TOKENS.
+ *
+ *      \p ptr must be a pointer to memory obtained from :cuMemAlloc().
+ *      Note that p2pToken and vaSpaceToken are only valid for the
+ *      lifetime of the source allocation. A subsequent allocation at
+ *      the same address may return completely different tokens.
+ *      Querying this attribute has a side effect of setting the attribute
+ *      ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS for the region of memory that
+ *      \p ptr points to.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS:
+ *
+ *      A boolean attribute which when set, ensures that synchronous memory operations
+ *      initiated on the region of memory that \p ptr points to will always synchronize.
+ *      See further documentation in the section titled "API synchronization behavior"
+ *      to learn more about cases when synchronous memory operations can
+ *      exhibit asynchronous behavior.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_BUFFER_ID:
+ *
+ *      Returns in \p *data a buffer ID which is guaranteed to be unique within the process.
+ *      \p data must point to an unsigned long long.
+ *
+ *      \p ptr must be a pointer to memory obtained from a CUDA memory allocation API.
+ *      Every memory allocation from any of the CUDA memory allocation APIs will
+ *      have a unique ID over a process lifetime. Subsequent allocations do not reuse IDs
+ *      from previous freed allocations. IDs are only unique within a single process.
+ *
+ *
+ * - ::CU_POINTER_ATTRIBUTE_IS_MANAGED:
+ *
+ *      Returns in \p *data a boolean that indicates whether the pointer points to
+ *      managed memory or not.
+ *
+ *      If \p ptr is not a valid CUDA pointer then ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL:
+ *
+ *      Returns in \p *data an integer representing a device ordinal of a device against
+ *      which the memory was allocated or registered.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE:
+ *
+ *      Returns in \p *data a boolean that indicates if this pointer maps to
+ *      an allocation that is suitable for ::cudaIpcGetMemHandle.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_RANGE_START_ADDR:
+ *
+ *      Returns in \p *data the starting address for the allocation referenced
+ *      by the device pointer \p ptr.  Note that this is not necessarily the
+ *      address of the mapped region, but the address of the mappable address
+ *      range \p ptr references (e.g. from ::cuMemAddressReserve).
+ *
+ * - ::CU_POINTER_ATTRIBUTE_RANGE_SIZE:
+ *
+ *      Returns in \p *data the size for the allocation referenced by the device
+ *      pointer \p ptr.  Note that this is not necessarily the size of the mapped
+ *      region, but the size of the mappable address range \p ptr references
+ *      (e.g. from ::cuMemAddressReserve).  To retrieve the size of the mapped
+ *      region, see ::cuMemGetAddressRange
+ *
+ * - ::CU_POINTER_ATTRIBUTE_MAPPED:
+ *
+ *      Returns in \p *data a boolean that indicates if this pointer is in a
+ *      valid address range that is mapped to a backing allocation.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES:
+ *
+ *      Returns a bitmask of the allowed handle types for an allocation that may
+ *      be passed to ::cuMemExportToShareableHandle.
+ * 
+ * - ::CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE:
+ * 
+ *      Returns in \p *data the handle to the mempool that the allocation was obtained from.
+ *
+ * - ::CU_POINTER_ATTRIBUTE_IS_HW_DECOMPRESS_CAPABLE:
+ *
+ *      Returns in \p *data a boolean that indicates whether the pointer points
+ *      to memory that is capable to be used for hardware accelerated
+ *      decompression.
+ *
+ * \par
+ *
+ * Note that for most allocations in the unified virtual address space
+ * the host and device pointer for accessing the allocation will be the
+ * same.  The exceptions to this are
+ *  - user memory registered using ::cuMemHostRegister
+ *  - host memory allocated using ::cuMemHostAlloc with the
+ *    ::CU_MEMHOSTALLOC_WRITECOMBINED flag
+ * For these types of allocation there will exist separate, disjoint host
+ * and device addresses for accessing the allocation.  In particular
+ *  - The host address will correspond to an invalid unmapped device address
+ *    (which will result in an exception if accessed from the device)
+ *  - The device address will correspond to an invalid unmapped host address
+ *    (which will result in an exception if accessed from the host).
+ * For these types of allocations, querying ::CU_POINTER_ATTRIBUTE_HOST_POINTER
+ * and ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER may be used to retrieve the host
+ * and device addresses from either address.
+ *
+ * \param data      - Returned pointer attribute value
+ * \param attribute - Pointer attribute to query
+ * \param ptr       - Pointer
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuPointerSetAttribute,
+ * ::cuMemAlloc,
+ * ::cuMemFree,
+ * ::cuMemAllocHost,
+ * ::cuMemFreeHost,
+ * ::cuMemHostAlloc,
+ * ::cuMemHostRegister,
+ * ::cuMemHostUnregister,
+ * ::cudaPointerGetAttributes
+ */
+CUresult CUDAAPI cuPointerGetAttribute(void *data, CUpointer_attribute attribute, CUdeviceptr ptr);
+
+/**
+ * \brief Prefetches memory to the specified destination device
+ * 
+ * Note there is a later version of this API, ::cuMemPrefetchAsync_v2. It will
+ * supplant this version in 13.0, which is retained for minor version compatibility.
+ *
+ * Prefetches memory to the specified destination device.  \p devPtr is the
+ * base device pointer of the memory to be prefetched and \p dstDevice is the
+ * destination device. \p count specifies the number of bytes to copy. \p hStream
+ * is the stream in which the operation is enqueued. The memory range must refer
+ * to managed memory allocated via ::cuMemAllocManaged or declared via __managed__ variables
+ * or it may also refer to system-allocated memory on systems with non-zero 
+ * CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
+ *
+ * Passing in CU_DEVICE_CPU for \p dstDevice will prefetch the data to host memory. If
+ * \p dstDevice is a GPU, then the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS
+ * must be non-zero. Additionally, \p hStream must be associated with a device that has a
+ * non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ *
+ * The start address and end address of the memory range will be rounded down and rounded up
+ * respectively to be aligned to CPU page size before the prefetch operation is enqueued
+ * in the stream.
+ *
+ * If no physical memory has been allocated for this region, then this memory region
+ * will be populated and mapped on the destination device. If there's insufficient
+ * memory to prefetch the desired region, the Unified Memory driver may evict pages from other
+ * ::cuMemAllocManaged allocations to host memory in order to make room. Device memory
+ * allocated using ::cuMemAlloc or ::cuArrayCreate will not be evicted.
+ *
+ * By default, any mappings to the previous location of the migrated pages are removed and
+ * mappings for the new location are only setup on \p dstDevice. The exact behavior however
+ * also depends on the settings applied to this memory range via ::cuMemAdvise as described
+ * below:
+ *
+ * If ::CU_MEM_ADVISE_SET_READ_MOSTLY was set on any subset of this memory range,
+ * then that subset will create a read-only copy of the pages on \p dstDevice.
+ *
+ * If ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION was called on any subset of this memory
+ * range, then the pages will be migrated to \p dstDevice even if \p dstDevice is not the
+ * preferred location of any pages in the memory range.
+ *
+ * If ::CU_MEM_ADVISE_SET_ACCESSED_BY was called on any subset of this memory range,
+ * then mappings to those pages from all the appropriate processors are updated to
+ * refer to the new location if establishing such a mapping is possible. Otherwise,
+ * those mappings are cleared.
+ *
+ * Note that this API is not required for functionality and only serves to improve performance
+ * by allowing the application to migrate data to a suitable location before it is accessed.
+ * Memory accesses to this range are always coherent and are allowed even when the data is
+ * actively being migrated.
+ *
+ * Note that this function is asynchronous with respect to the host and all work
+ * on other devices.
+ *
+ * \param devPtr    - Pointer to be prefetched
+ * \param count     - Size in bytes
+ * \param dstDevice - Destination device to prefetch to
+ * \param hStream    - Stream to enqueue prefetch operation
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemcpy, ::cuMemcpyPeer, ::cuMemcpyAsync,
+ * ::cuMemcpy3DPeerAsync, ::cuMemAdvise, ::cuMemPrefetchAsync
+ * ::cudaMemPrefetchAsync_v2
+ */
+CUresult CUDAAPI cuMemPrefetchAsync(CUdeviceptr devPtr, size_t count, CUdevice dstDevice, CUstream hStream);
+
+/**
+ * \brief Prefetches memory to the specified destination location
+ *
+ * Prefetches memory to the specified destination location.  \p devPtr is the
+ * base device pointer of the memory to be prefetched and \p location specifies the
+ * destination location. \p count specifies the number of bytes to copy. \p hStream
+ * is the stream in which the operation is enqueued. The memory range must refer
+ * to managed memory allocated via ::cuMemAllocManaged or declared via __managed__ variables.
+ *
+ * Specifying ::CU_MEM_LOCATION_TYPE_DEVICE for ::CUmemLocation::type will prefetch memory to GPU
+ * specified by device ordinal ::CUmemLocation::id which must have non-zero value for the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Additionally, \p hStream must be associated with a device
+ * that has a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ * Specifying ::CU_MEM_LOCATION_TYPE_HOST as ::CUmemLocation::type will prefetch data to host memory.
+ * Applications can request prefetching memory to a specific host NUMA node by specifying
+ * ::CU_MEM_LOCATION_TYPE_HOST_NUMA for ::CUmemLocation::type and a valid host NUMA node id in ::CUmemLocation::id
+ * Users can also request prefetching memory to the host NUMA node closest to the current thread's CPU by specifying
+ * ::CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT for ::CUmemLocation::type. Note when ::CUmemLocation::type is etiher
+ * ::CU_MEM_LOCATION_TYPE_HOST OR ::CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT, ::CUmemLocation::id will be ignored.
+ *
+ * The start address and end address of the memory range will be rounded down and rounded up
+ * respectively to be aligned to CPU page size before the prefetch operation is enqueued
+ * in the stream.
+ *
+ * If no physical memory has been allocated for this region, then this memory region
+ * will be populated and mapped on the destination device. If there's insufficient
+ * memory to prefetch the desired region, the Unified Memory driver may evict pages from other
+ * ::cuMemAllocManaged allocations to host memory in order to make room. Device memory
+ * allocated using ::cuMemAlloc or ::cuArrayCreate will not be evicted.
+ *
+ * By default, any mappings to the previous location of the migrated pages are removed and
+ * mappings for the new location are only setup on the destination location. The exact behavior however
+ * also depends on the settings applied to this memory range via ::cuMemAdvise as described
+ * below:
+ *
+ * If ::CU_MEM_ADVISE_SET_READ_MOSTLY was set on any subset of this memory range,
+ * then that subset will create a read-only copy of the pages on destination location.
+ * If however the destination location is a host NUMA node, then any pages of that subset
+ * that are already in another host NUMA node will be transferred to the destination.
+ *
+ * If ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION was called on any subset of this memory
+ * range, then the pages will be migrated to \p location even if \p location is not the
+ * preferred location of any pages in the memory range.
+ *
+ * If ::CU_MEM_ADVISE_SET_ACCESSED_BY was called on any subset of this memory range,
+ * then mappings to those pages from all the appropriate processors are updated to
+ * refer to the new location if establishing such a mapping is possible. Otherwise,
+ * those mappings are cleared.
+ *
+ * Note that this API is not required for functionality and only serves to improve performance
+ * by allowing the application to migrate data to a suitable location before it is accessed.
+ * Memory accesses to this range are always coherent and are allowed even when the data is
+ * actively being migrated.
+ *
+ * Note that this function is asynchronous with respect to the host and all work
+ * on other devices.
+ *
+ * \param devPtr    - Pointer to be prefetched
+ * \param count     - Size in bytes
+ * \param dstDevice - Destination device to prefetch to
+ * \param flags     - flags for future use, must be zero now. 
+ * \param hStream   - Stream to enqueue prefetch operation
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemcpy, ::cuMemcpyPeer, ::cuMemcpyAsync,
+ * ::cuMemcpy3DPeerAsync, ::cuMemAdvise, ::cuMemPrefetchAsync
+ * ::cudaMemPrefetchAsync_v2
+ */
+CUresult CUDAAPI cuMemPrefetchAsync_v2(CUdeviceptr devPtr, size_t count, CUmemLocation location, unsigned int flags, CUstream hStream);
+
+/**
+ * \brief Advise about the usage of a given memory range
+ *
+ * Note there is a later version of this API, ::cuMemAdvise_v2. It will
+ * supplant this version in 13.0, which is retained for minor version compatibility.
+ * 
+ * Advise the Unified Memory subsystem about the usage pattern for the memory range
+ * starting at \p devPtr with a size of \p count bytes. The start address and end address of the memory
+ * range will be rounded down and rounded up respectively to be aligned to CPU page size before the
+ * advice is applied. The memory range must refer to managed memory allocated via ::cuMemAllocManaged
+ * or declared via __managed__ variables. The memory range could also refer to system-allocated pageable
+ * memory provided it represents a valid, host-accessible region of memory and all additional constraints
+ * imposed by \p advice as outlined below are also satisfied. Specifying an invalid system-allocated pageable
+ * memory range results in an error being returned.
+ *
+ * The \p advice parameter can take the following values:
+ * - ::CU_MEM_ADVISE_SET_READ_MOSTLY: This implies that the data is mostly going to be read
+ * from and only occasionally written to. Any read accesses from any processor to this region will create a
+ * read-only copy of at least the accessed pages in that processor's memory. Additionally, if ::cuMemPrefetchAsync
+ * is called on this region, it will create a read-only copy of the data on the destination processor.
+ * If any processor writes to this region, all copies of the corresponding page will be invalidated
+ * except for the one where the write occurred. The \p device argument is ignored for this advice.
+ * Note that for a page to be read-duplicated, the accessing processor must either be the CPU or a GPU
+ * that has a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ * Also, if a context is created on a device that does not have the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS set, then read-duplication will not occur until
+ * all such contexts are destroyed.
+ * If the memory region refers to valid system-allocated pageable memory, then the accessing device must
+ * have a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS for a read-only
+ * copy to be created on that device. Note however that if the accessing device also has a non-zero value for the
+ * device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then setting this advice
+ * will not create a read-only copy when that device accesses this memory region.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_READ_MOSTLY:  Undoes the effect of ::CU_MEM_ADVISE_SET_READ_MOSTLY and also prevents the
+ * Unified Memory driver from attempting heuristic read-duplication on the memory range. Any read-duplicated
+ * copies of the data will be collapsed into a single copy. The location for the collapsed
+ * copy will be the preferred location if the page has a preferred location and one of the read-duplicated
+ * copies was resident at that location. Otherwise, the location chosen is arbitrary.
+ *
+ * - ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION: This advice sets the preferred location for the
+ * data to be the memory belonging to \p device. Passing in CU_DEVICE_CPU for \p device sets the
+ * preferred location as host memory. If \p device is a GPU, then it must have a non-zero value for the
+ * device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Setting the preferred location
+ * does not cause data to migrate to that location immediately. Instead, it guides the migration policy
+ * when a fault occurs on that memory region. If the data is already in its preferred location and the
+ * faulting processor can establish a mapping without requiring the data to be migrated, then
+ * data migration will be avoided. On the other hand, if the data is not in its preferred location
+ * or if a direct mapping cannot be established, then it will be migrated to the processor accessing
+ * it. It is important to note that setting the preferred location does not prevent data prefetching
+ * done using ::cuMemPrefetchAsync.
+ * Having a preferred location can override the page thrash detection and resolution logic in the Unified
+ * Memory driver. Normally, if a page is detected to be constantly thrashing between for example host and device
+ * memory, the page may eventually be pinned to host memory by the Unified Memory driver. But
+ * if the preferred location is set as device memory, then the page will continue to thrash indefinitely.
+ * If ::CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice, unless read accesses from
+ * \p device will not result in a read-only copy being created on that device as outlined in description for
+ * the advice ::CU_MEM_ADVISE_SET_READ_MOSTLY.
+ * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero
+ * value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION: Undoes the effect of ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION
+ * and changes the preferred location to none.
+ *
+ * - ::CU_MEM_ADVISE_SET_ACCESSED_BY: This advice implies that the data will be accessed by \p device.
+ * Passing in ::CU_DEVICE_CPU for \p device will set the advice for the CPU. If \p device is a GPU, then
+ * the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS must be non-zero.
+ * This advice does not cause data migration and has no impact on the location of the data per se. Instead,
+ * it causes the data to always be mapped in the specified processor's page tables, as long as the
+ * location of the data permits a mapping to be established. If the data gets migrated for any reason,
+ * the mappings are updated accordingly.
+ * This advice is recommended in scenarios where data locality is not important, but avoiding faults is.
+ * Consider for example a system containing multiple GPUs with peer-to-peer access enabled, where the
+ * data located on one GPU is occasionally accessed by peer GPUs. In such scenarios, migrating data
+ * over to the other GPUs is not as important because the accesses are infrequent and the overhead of
+ * migration may be too high. But preventing faults can still help improve performance, and so having
+ * a mapping set up in advance is useful. Note that on CPU access of this data, the data may be migrated
+ * to host memory because the CPU typically cannot access device memory directly. Any GPU that had the
+ * ::CU_MEM_ADVISE_SET_ACCESSED_BY flag set for this data will now have its mapping updated to point to the
+ * page in host memory.
+ * If ::CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice. Additionally, if the
+ * preferred location of this memory region or any subset of it is also \p device, then the policies
+ * associated with ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION will override the policies of this advice.
+ * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero
+ * value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if \p device has
+ * a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES,
+ * then this call has no effect.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_ACCESSED_BY: Undoes the effect of ::CU_MEM_ADVISE_SET_ACCESSED_BY. Any mappings to
+ * the data from \p device may be removed at any time causing accesses to result in non-fatal page faults.
+ * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero
+ * value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if \p device has
+ * a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES,
+ * then this call has no effect.
+ *
+ * \param devPtr - Pointer to memory to set the advice for
+ * \param count  - Size in bytes of the memory range
+ * \param advice - Advice to be applied for the specified memory range
+ * \param device - Device to apply the advice for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemcpy, ::cuMemcpyPeer, ::cuMemcpyAsync,
+ * ::cuMemcpy3DPeerAsync, ::cuMemPrefetchAsync, ::cuMemAdvise_v2
+ * ::cudaMemAdvise
+ */
+CUresult CUDAAPI cuMemAdvise(CUdeviceptr devPtr, size_t count, CUmem_advise advice, CUdevice device);
+
+/**
+ * \brief Advise about the usage of a given memory range
+ *
+ * Advise the Unified Memory subsystem about the usage pattern for the memory range
+ * starting at \p devPtr with a size of \p count bytes. The start address and end address of the memory
+ * range will be rounded down and rounded up respectively to be aligned to CPU page size before the
+ * advice is applied. The memory range must refer to managed memory allocated via ::cuMemAllocManaged
+ * or declared via __managed__ variables. The memory range could also refer to system-allocated pageable
+ * memory provided it represents a valid, host-accessible region of memory and all additional constraints
+ * imposed by \p advice as outlined below are also satisfied. Specifying an invalid system-allocated pageable
+ * memory range results in an error being returned.
+ *
+ * The \p advice parameter can take the following values:
+ * - ::CU_MEM_ADVISE_SET_READ_MOSTLY: This implies that the data is mostly going to be read
+ * from and only occasionally written to. Any read accesses from any processor to this region will create a
+ * read-only copy of at least the accessed pages in that processor's memory. Additionally, if ::cuMemPrefetchAsync
+ * or ::cuMemPrefetchAsync_v2 is called on this region, it will create a read-only copy of the data on the destination processor.
+ * If the target location for ::cuMemPrefetchAsync_v2 is a host NUMA node and a read-only copy already exists on
+ * another host NUMA node, that copy will be migrated to the targeted host NUMA node.
+ * If any processor writes to this region, all copies of the corresponding page will be invalidated
+ * except for the one where the write occurred. If the writing processor is the CPU and the preferred location of
+ * the page is a host NUMA node, then the page will also be migrated to that host NUMA node. The \p location argument is ignored for this advice.
+ * Note that for a page to be read-duplicated, the accessing processor must either be the CPU or a GPU
+ * that has a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ * Also, if a context is created on a device that does not have the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS set, then read-duplication will not occur until
+ * all such contexts are destroyed.
+ * If the memory region refers to valid system-allocated pageable memory, then the accessing device must
+ * have a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS for a read-only
+ * copy to be created on that device. Note however that if the accessing device also has a non-zero value for the
+ * device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then setting this advice
+ * will not create a read-only copy when that device accesses this memory region.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_READ_MOSTLY:  Undoes the effect of ::CU_MEM_ADVISE_SET_READ_MOSTLY and also prevents the
+ * Unified Memory driver from attempting heuristic read-duplication on the memory range. Any read-duplicated
+ * copies of the data will be collapsed into a single copy. The location for the collapsed
+ * copy will be the preferred location if the page has a preferred location and one of the read-duplicated
+ * copies was resident at that location. Otherwise, the location chosen is arbitrary.
+ * Note: The \p location argument is ignored for this advice.
+ *
+ * - ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION: This advice sets the preferred location for the
+ * data to be the memory belonging to \p location. When ::CUmemLocation::type is ::CU_MEM_LOCATION_TYPE_HOST,
+ * ::CUmemLocation::id is ignored and the preferred location is set to be host memory. To set the preferred location
+ * to a specific host NUMA node, applications must set ::CUmemLocation::type to ::CU_MEM_LOCATION_TYPE_HOST_NUMA and
+ * ::CUmemLocation::id must specify the NUMA ID of the host NUMA node. If ::CUmemLocation::type is set to ::CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT,
+ * ::CUmemLocation::id will be ignored and the the host NUMA node closest to the calling thread's CPU will be used as the preferred location.
+ * If ::CUmemLocation::type is a ::CU_MEM_LOCATION_TYPE_DEVICE, then ::CUmemLocation::id must be a valid device ordinal
+ * and the device must have a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ * Setting the preferred location does not cause data to migrate to that location immediately. Instead, it guides the migration policy
+ * when a fault occurs on that memory region. If the data is already in its preferred location and the
+ * faulting processor can establish a mapping without requiring the data to be migrated, then
+ * data migration will be avoided. On the other hand, if the data is not in its preferred location
+ * or if a direct mapping cannot be established, then it will be migrated to the processor accessing
+ * it. It is important to note that setting the preferred location does not prevent data prefetching
+ * done using ::cuMemPrefetchAsync.
+ * Having a preferred location can override the page thrash detection and resolution logic in the Unified
+ * Memory driver. Normally, if a page is detected to be constantly thrashing between for example host and device
+ * memory, the page may eventually be pinned to host memory by the Unified Memory driver. But
+ * if the preferred location is set as device memory, then the page will continue to thrash indefinitely.
+ * If ::CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice, unless read accesses from
+ * \p location will not result in a read-only copy being created on that procesor as outlined in description for
+ * the advice ::CU_MEM_ADVISE_SET_READ_MOSTLY.
+ * If the memory region refers to valid system-allocated pageable memory, and ::CUmemLocation::type is CU_MEM_LOCATION_TYPE_DEVICE
+ * then ::CUmemLocation::id must be a valid device that has a non-zero alue for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION: Undoes the effect of ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION
+ * and changes the preferred location to none. The \p location argument is ignored for this advice.
+ *
+ * - ::CU_MEM_ADVISE_SET_ACCESSED_BY: This advice implies that the data will be accessed by processor \p location.
+ * The ::CUmemLocation::type must be either ::CU_MEM_LOCATION_TYPE_DEVICE with ::CUmemLocation::id representing a valid device
+ * ordinal or ::CU_MEM_LOCATION_TYPE_HOST and ::CUmemLocation::id will be ignored. All other location types are invalid.
+ * If ::CUmemLocation::id is a GPU, then the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS must be non-zero.
+ * This advice does not cause data migration and has no impact on the location of the data per se. Instead,
+ * it causes the data to always be mapped in the specified processor's page tables, as long as the
+ * location of the data permits a mapping to be established. If the data gets migrated for any reason,
+ * the mappings are updated accordingly.
+ * This advice is recommended in scenarios where data locality is not important, but avoiding faults is.
+ * Consider for example a system containing multiple GPUs with peer-to-peer access enabled, where the
+ * data located on one GPU is occasionally accessed by peer GPUs. In such scenarios, migrating data
+ * over to the other GPUs is not as important because the accesses are infrequent and the overhead of
+ * migration may be too high. But preventing faults can still help improve performance, and so having
+ * a mapping set up in advance is useful. Note that on CPU access of this data, the data may be migrated
+ * to host memory because the CPU typically cannot access device memory directly. Any GPU that had the
+ * ::CU_MEM_ADVISE_SET_ACCESSED_BY flag set for this data will now have its mapping updated to point to the
+ * page in host memory.
+ * If ::CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice. Additionally, if the
+ * preferred location of this memory region or any subset of it is also \p location, then the policies
+ * associated with ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION will override the policies of this advice.
+ * If the memory region refers to valid system-allocated pageable memory, and ::CUmemLocation::type is ::CU_MEM_LOCATION_TYPE_DEVICE
+ * then device in ::CUmemLocation::id must have a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
+ * Additionally, if ::CUmemLocation::id has a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES,
+ * then this call has no effect.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_ACCESSED_BY: Undoes the effect of ::CU_MEM_ADVISE_SET_ACCESSED_BY. Any mappings to
+ * the data from \p location may be removed at any time causing accesses to result in non-fatal page faults.
+ * If the memory region refers to valid system-allocated pageable memory, and ::CUmemLocation::type is ::CU_MEM_LOCATION_TYPE_DEVICE
+ * then device in ::CUmemLocation::id must have a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
+ * Additionally, if ::CUmemLocation::id has a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES,
+ * then this call has no effect.
+ *
+ * \param devPtr   - Pointer to memory to set the advice for
+ * \param count    - Size in bytes of the memory range
+ * \param advice   - Advice to be applied for the specified memory range
+ * \param location - location to apply the advice for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemcpy, ::cuMemcpyPeer, ::cuMemcpyAsync,
+ * ::cuMemcpy3DPeerAsync, ::cuMemPrefetchAsync, ::cuMemAdvise
+ * ::cudaMemAdvise
+ */
+CUresult CUDAAPI cuMemAdvise_v2(CUdeviceptr devPtr, size_t count, CUmem_advise advice, CUmemLocation location);
+
+/**
+ * \brief Query an attribute of a given memory range
+ *
+ * Query an attribute about the memory range starting at \p devPtr with a size of \p count bytes. The
+ * memory range must refer to managed memory allocated via ::cuMemAllocManaged or declared via
+ * __managed__ variables.
+ *
+ * The \p attribute parameter can take the following values:
+ * - ::CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY: If this attribute is specified, \p data will be interpreted
+ * as a 32-bit integer, and \p dataSize must be 4. The result returned will be 1 if all pages in the given
+ * memory range have read-duplication enabled, or 0 otherwise.
+ * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION: If this attribute is specified, \p data will be
+ * interpreted as a 32-bit integer, and \p dataSize must be 4. The result returned will be a GPU device
+ * id if all pages in the memory range have that GPU as their preferred location, or it will be CU_DEVICE_CPU
+ * if all pages in the memory range have the CPU as their preferred location, or it will be CU_DEVICE_INVALID
+ * if either all the pages don't have the same preferred location or some of the pages don't have a
+ * preferred location at all. Note that the actual location of the pages in the memory range at the time of
+ * the query may be different from the preferred location.
+ * - ::CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY: If this attribute is specified, \p data will be interpreted
+ * as an array of 32-bit integers, and \p dataSize must be a non-zero multiple of 4. The result returned
+ * will be a list of device ids that had ::CU_MEM_ADVISE_SET_ACCESSED_BY set for that entire memory range.
+ * If any device does not have that advice set for the entire memory range, that device will not be included.
+ * If \p data is larger than the number of devices that have that advice set for that memory range,
+ * CU_DEVICE_INVALID will be returned in all the extra space provided. For ex., if \p dataSize is 12
+ * (i.e. \p data has 3 elements) and only device 0 has the advice set, then the result returned will be
+ * { 0, CU_DEVICE_INVALID, CU_DEVICE_INVALID }. If \p data is smaller than the number of devices that have
+ * that advice set, then only as many devices will be returned as can fit in the array. There is no
+ * guarantee on which specific devices will be returned, however.
+ * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION: If this attribute is specified, \p data will be
+ * interpreted as a 32-bit integer, and \p dataSize must be 4. The result returned will be the last location
+ * to which all pages in the memory range were prefetched explicitly via ::cuMemPrefetchAsync. This will either be
+ * a GPU id or CU_DEVICE_CPU depending on whether the last location for prefetch was a GPU or the CPU
+ * respectively. If any page in the memory range was never explicitly prefetched or if all pages were not
+ * prefetched to the same location, CU_DEVICE_INVALID will be returned. Note that this simply returns the
+ * last location that the application requested to prefetch the memory range to. It gives no indication as to
+ * whether the prefetch operation to that location has completed or even begun.
+ * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_TYPE: If this attribute is specified, \p data will be
+ * interpreted as a ::CUmemLocationType, and \p dataSize must be sizeof(CUmemLocationType). The ::CUmemLocationType returned will be
+ * ::CU_MEM_LOCATION_TYPE_DEVICE if all pages in the memory range have the same GPU as their preferred location, or ::CUmemLocationType
+ * will be ::CU_MEM_LOCATION_TYPE_HOST if all pages in the memory range have the CPU as their preferred location, or it will be ::CU_MEM_LOCATION_TYPE_HOST_NUMA
+ * if all the pages in the memory range have the same host NUMA node ID as their preferred location or it will be ::CU_MEM_LOCATION_TYPE_INVALID
+ * if either all the pages don't have the same preferred location or some of the pages don't have a preferred location at all.
+ * Note that the actual location type of the pages in the memory range at the time of the query may be different from the preferred location type.
+ *  - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_ID: If this attribute is specified, \p data will be
+ * interpreted as a 32-bit integer, and \p dataSize must be 4. If the ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_TYPE query for the same address range
+ * returns ::CU_MEM_LOCATION_TYPE_DEVICE, it will be a valid device ordinal or if it returns ::CU_MEM_LOCATION_TYPE_HOST_NUMA, it will be a valid host NUMA node ID
+ * or if it returns any other location type, the id should be ignored.
+ * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_TYPE: If this attribute is specified, \p data will be
+ * interpreted as a ::CUmemLocationType, and \p dataSize must be sizeof(CUmemLocationType). The result returned will be the last location
+ * to which all pages in the memory range were prefetched explicitly via ::cuMemPrefetchAsync. The ::CUmemLocationType returned
+ * will be ::CU_MEM_LOCATION_TYPE_DEVICE if the last prefetch location was a GPU or ::CU_MEM_LOCATION_TYPE_HOST if it was the CPU or ::CU_MEM_LOCATION_TYPE_HOST_NUMA if
+ * the last prefetch location was a specific host NUMA node. If any page in the memory range was never explicitly prefetched or if all pages were not
+ * prefetched to the same location, ::CUmemLocationType will be ::CU_MEM_LOCATION_TYPE_INVALID.
+ * Note that this simply returns the last location type that the application requested to prefetch the memory range to. It gives no indication as to
+ * whether the prefetch operation to that location has completed or even begun.
+ *  - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_ID: If this attribute is specified, \p data will be
+ * interpreted as a 32-bit integer, and \p dataSize must be 4. If the ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_TYPE query for the same address range
+ * returns ::CU_MEM_LOCATION_TYPE_DEVICE, it will be a valid device ordinal or if it returns ::CU_MEM_LOCATION_TYPE_HOST_NUMA, it will be a valid host NUMA node ID
+ * or if it returns any other location type, the id should be ignored.
+ *
+ * \param data      - A pointers to a memory location where the result
+ *                    of each attribute query will be written to.
+ * \param dataSize  - Array containing the size of data
+ * \param attribute - The attribute to query
+ * \param devPtr    - Start of the range to query
+ * \param count     - Size of the range to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ *
+ * \sa ::cuMemRangeGetAttributes, ::cuMemPrefetchAsync,
+ * ::cuMemAdvise,
+ * ::cudaMemRangeGetAttribute
+ */
+CUresult CUDAAPI cuMemRangeGetAttribute(void *data, size_t dataSize, CUmem_range_attribute attribute, CUdeviceptr devPtr, size_t count);
+
+/**
+ * \brief Query attributes of a given memory range.
+ *
+ * Query attributes of the memory range starting at \p devPtr with a size of \p count bytes. The
+ * memory range must refer to managed memory allocated via ::cuMemAllocManaged or declared via
+ * __managed__ variables. The \p attributes array will be interpreted to have \p numAttributes
+ * entries. The \p dataSizes array will also be interpreted to have \p numAttributes entries.
+ * The results of the query will be stored in \p data.
+ *
+ * The list of supported attributes are given below. Please refer to ::cuMemRangeGetAttribute for
+ * attribute descriptions and restrictions.
+ *
+ * - ::CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY
+ * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION
+ * - ::CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY
+ * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION
+ * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_TYPE
+ * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_ID
+ * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_TYPE
+ * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_ID
+ *
+ * \param data          - A two-dimensional array containing pointers to memory
+ *                        locations where the result of each attribute query will be written to.
+ * \param dataSizes     - Array containing the sizes of each result
+ * \param attributes    - An array of attributes to query
+ *                        (numAttributes and the number of attributes in this array should match)
+ * \param numAttributes - Number of attributes to query
+ * \param devPtr        - Start of the range to query
+ * \param count         - Size of the range to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa ::cuMemRangeGetAttribute, ::cuMemAdvise,
+ * ::cuMemPrefetchAsync,
+ * ::cudaMemRangeGetAttributes
+ */
+CUresult CUDAAPI cuMemRangeGetAttributes(void **data, size_t *dataSizes, CUmem_range_attribute *attributes, size_t numAttributes, CUdeviceptr devPtr, size_t count);
+
+/**
+ * \brief Set attributes on a previously allocated memory region
+ *
+ * The supported attributes are:
+ *
+ * - ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS:
+ *
+ *      A boolean attribute that can either be set (1) or unset (0). When set,
+ *      the region of memory that \p ptr points to is guaranteed to always synchronize
+ *      memory operations that are synchronous. If there are some previously initiated
+ *      synchronous memory operations that are pending when this attribute is set, the
+ *      function does not return until those memory operations are complete.
+ *      See further documentation in the section titled "API synchronization behavior"
+ *      to learn more about cases when synchronous memory operations can
+ *      exhibit asynchronous behavior.
+ *      \p value will be considered as a pointer to an unsigned integer to which this attribute is to be set.
+ *
+ * \param value     - Pointer to memory containing the value to be set
+ * \param attribute - Pointer attribute to set
+ * \param ptr       - Pointer to a memory region allocated using CUDA memory allocation APIs
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa ::cuPointerGetAttribute,
+ * ::cuPointerGetAttributes,
+ * ::cuMemAlloc,
+ * ::cuMemFree,
+ * ::cuMemAllocHost,
+ * ::cuMemFreeHost,
+ * ::cuMemHostAlloc,
+ * ::cuMemHostRegister,
+ * ::cuMemHostUnregister
+ */
+CUresult CUDAAPI cuPointerSetAttribute(const void *value, CUpointer_attribute attribute, CUdeviceptr ptr);
+
+/**
+ * \brief Returns information about a pointer.
+ *
+ * The supported attributes are (refer to ::cuPointerGetAttribute for attribute descriptions and restrictions):
+ *
+ * - ::CU_POINTER_ATTRIBUTE_CONTEXT
+ * - ::CU_POINTER_ATTRIBUTE_MEMORY_TYPE
+ * - ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER
+ * - ::CU_POINTER_ATTRIBUTE_HOST_POINTER
+ * - ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS
+ * - ::CU_POINTER_ATTRIBUTE_BUFFER_ID
+ * - ::CU_POINTER_ATTRIBUTE_IS_MANAGED
+ * - ::CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL
+ * - ::CU_POINTER_ATTRIBUTE_RANGE_START_ADDR
+ * - ::CU_POINTER_ATTRIBUTE_RANGE_SIZE
+ * - ::CU_POINTER_ATTRIBUTE_MAPPED
+ * - ::CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE
+ * - ::CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES
+ * - ::CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE
+ * - ::CU_POINTER_ATTRIBUTE_IS_HW_DECOMPRESS_CAPABLE
+ *
+ * \param numAttributes - Number of attributes to query
+ * \param attributes    - An array of attributes to query
+ *                      (numAttributes and the number of attributes in this array should match)
+ * \param data          - A two-dimensional array containing pointers to memory
+ *                      locations where the result of each attribute query will be written to.
+ * \param ptr           - Pointer to query
+ *
+ * Unlike ::cuPointerGetAttribute, this function will not return an error when the \p ptr
+ * encountered is not a valid CUDA pointer. Instead, the attributes are assigned default NULL values
+ * and CUDA_SUCCESS is returned.
+ *
+ * If \p ptr was not allocated by, mapped by, or registered with a ::CUcontext which uses UVA
+ * (Unified Virtual Addressing), ::CUDA_ERROR_INVALID_CONTEXT is returned.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuPointerGetAttribute,
+ * ::cuPointerSetAttribute,
+ * ::cudaPointerGetAttributes
+ */
+CUresult CUDAAPI cuPointerGetAttributes(unsigned int numAttributes, CUpointer_attribute *attributes, void **data, CUdeviceptr ptr);
+
+/** @} */ /* END CUDA_UNIFIED */
+
+/**
+ * \defgroup CUDA_STREAM Stream Management
+ *
+ * ___MANBRIEF___ stream management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the stream management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Create a stream
+ *
+ * Creates a stream and returns a handle in \p phStream.  The \p Flags argument
+ * determines behaviors of the stream.
+ *
+ * Valid values for \p Flags are:
+ * - ::CU_STREAM_DEFAULT: Default stream creation flag.
+ * - ::CU_STREAM_NON_BLOCKING: Specifies that work running in the created
+ *   stream may run concurrently with work in stream 0 (the NULL stream), and that
+ *   the created stream should perform no implicit synchronization with stream 0.
+ *
+ * \param phStream - Returned newly created stream
+ * \param Flags    - Parameters for stream creation
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreateWithPriority,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamCreate,
+ * ::cudaStreamCreateWithFlags
+ */
+CUresult CUDAAPI cuStreamCreate(CUstream *phStream, unsigned int Flags);
+
+/**
+ * \brief Create a stream with the given priority
+ *
+ * Creates a stream with the specified priority and returns a handle in \p phStream.
+ * This affects the scheduling priority of work in the stream. Priorities provide a
+ * hint to preferentially run work with higher priority when possible, but do
+ * not preempt already-running work or provide any other functional guarantee on
+ * execution order.
+ *
+ * \p priority follows a convention where lower numbers represent higher priorities.
+ * '0' represents default priority. The range of meaningful numerical priorities can
+ * be queried using ::cuCtxGetStreamPriorityRange. If the specified priority is
+ * outside the numerical range returned by ::cuCtxGetStreamPriorityRange,
+ * it will automatically be clamped to the lowest or the highest number in the range.
+ *
+ * \param phStream    - Returned newly created stream
+ * \param flags       - Flags for stream creation. See ::cuStreamCreate for a list of
+ *                      valid flags
+ * \param priority    - Stream priority. Lower numbers represent higher priorities.
+ *                      See ::cuCtxGetStreamPriorityRange for more information about
+ *                      meaningful stream priorities that can be passed.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \note Stream priorities are supported only on GPUs
+ * with compute capability 3.5 or higher.
+ *
+ * \note In the current implementation, only compute kernels launched in
+ * priority streams are affected by the stream's priority. Stream priorities have
+ * no effect on host-to-device and device-to-host memory operations.
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreate,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cuCtxGetStreamPriorityRange,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamCreateWithPriority
+ */
+CUresult CUDAAPI cuStreamCreateWithPriority(CUstream *phStream, unsigned int flags, int priority);
+
+
+/**
+ * \brief Query the priority of a given stream
+ *
+ * Query the priority of a stream created using ::cuStreamCreate, ::cuStreamCreateWithPriority or ::cuGreenCtxStreamCreate
+ * and return the priority in \p priority. Note that if the stream was created with a
+ * priority outside the numerical range returned by ::cuCtxGetStreamPriorityRange,
+ * this function returns the clamped priority.
+ * See ::cuStreamCreateWithPriority for details about priority clamping.
+ *
+ * \param hStream    - Handle to the stream to be queried
+ * \param priority   - Pointer to a signed integer in which the stream's priority is returned
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreate,
+ * ::cuStreamCreateWithPriority,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuCtxGetStreamPriorityRange,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cudaStreamGetPriority
+ */
+CUresult CUDAAPI cuStreamGetPriority(CUstream hStream, int *priority);
+
+/**
+ * \brief Returns the device handle of the stream
+ *
+ * Returns in \p *device the device handle of the stream
+ *
+ * \param hStream - Handle to the stream to be queried
+ * \param device - Returns the device to which a stream belongs 
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamDestroy,
+ * ::cuStreamCreate,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuStreamGetFlags
+ */
+CUresult CUDAAPI cuStreamGetDevice(CUstream hStream, CUdevice *device);
+
+/**
+ * \brief Query the flags of a given stream
+ *
+ * Query the flags of a stream created using ::cuStreamCreate, ::cuStreamCreateWithPriority or ::cuGreenCtxStreamCreate
+ * and return the flags in \p flags.
+ *
+ * \param hStream    - Handle to the stream to be queried
+ * \param flags      - Pointer to an unsigned integer in which the stream's flags are returned
+ *                     The value returned in \p flags is a logical 'OR' of all flags that
+ *                     were used while creating this stream. See ::cuStreamCreate for the list
+ *                     of valid flags
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreate,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cudaStreamGetFlags
+ * ::cuStreamGetDevice
+ */
+CUresult CUDAAPI cuStreamGetFlags(CUstream hStream, unsigned int *flags);
+
+/**
+ * \brief Returns the unique Id associated with the stream handle supplied
+ *
+ * Returns in \p streamId the unique Id which is associated with the given stream handle.
+ * The Id is unique for the life of the program.
+ * 
+ * The stream handle \p hStream can refer to any of the following:
+ * <ul>
+ *   <li>a stream created via any of the CUDA driver APIs such as ::cuStreamCreate
+ *   and ::cuStreamCreateWithPriority, or their runtime API equivalents such as
+ *   ::cudaStreamCreate, ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority.
+ *   Passing an invalid handle will result in undefined behavior.</li>
+ *   <li>any of the special streams such as the NULL stream, ::CU_STREAM_LEGACY and
+ *   ::CU_STREAM_PER_THREAD. The runtime API equivalents of these are also accepted,
+ *   which are NULL, ::cudaStreamLegacy and ::cudaStreamPerThread respectively.</li>
+ * </ul>
+ *
+ * \param hStream    - Handle to the stream to be queried
+ * \param streamId   - Pointer to store the Id of the stream
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cudaStreamGetId
+ */
+CUresult CUDAAPI cuStreamGetId(CUstream hStream, unsigned long long *streamId);
+
+/**
+ * \brief Query the context associated with a stream
+ *
+ * Returns the CUDA context that the stream is associated with.
+ *
+ * Note there is a later version of this API, ::cuStreamGetCtx_v2. It will
+ * supplant this version in CUDA 13.0. It is recommended to use ::cuStreamGetCtx_v2
+ * till then as this version will return ::CUDA_ERROR_NOT_SUPPORTED for streams created via the API ::cuGreenCtxStreamCreate.
+ *
+ * The stream handle \p hStream can refer to any of the following:
+ * <ul>
+ *   <li>a stream created via any of the CUDA driver APIs such as ::cuStreamCreate
+ *   and ::cuStreamCreateWithPriority, or their runtime API equivalents such as
+ *   ::cudaStreamCreate, ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority.
+ *   The returned context is the context that was active in the calling thread when the
+ *   stream was created. Passing an invalid handle will result in undefined behavior.</li>
+ *   <li>any of the special streams such as the NULL stream, ::CU_STREAM_LEGACY and
+ *   ::CU_STREAM_PER_THREAD. The runtime API equivalents of these are also accepted,
+ *   which are NULL, ::cudaStreamLegacy and ::cudaStreamPerThread respectively.
+ *   Specifying any of the special handles will return the context current to the
+ *   calling thread. If no context is current to the calling thread,
+ *   ::CUDA_ERROR_INVALID_CONTEXT is returned.</li>
+ * </ul>
+ *
+ * \param hStream - Handle to the stream to be queried
+ * \param pctx    - Returned context associated with the stream
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreateWithPriority,
+ * ::cuStreamGetPriority,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamCreate,
+ * ::cuStreamGetCtx_v2,
+ * ::cudaStreamCreateWithFlags
+ */
+CUresult CUDAAPI cuStreamGetCtx(CUstream hStream, CUcontext *pctx);
+
+/**
+ * \brief Query the contexts associated with a stream
+ *
+ * Returns the contexts that the stream is associated with.
+ *
+ * If the stream is associated with a green context, the API returns the green context in \p pGreenCtx
+ * and the primary context of the associated device in \p pCtx.
+ *
+ * If the stream is associated with a regular context, the API returns the regular context in \p pCtx
+ * and NULL in \p pGreenCtx.
+ *
+ * The stream handle \p hStream can refer to any of the following:
+ * <ul>
+ *   <li>a stream created via any of the CUDA driver APIs such as ::cuStreamCreate,
+ *   ::cuStreamCreateWithPriority and ::cuGreenCtxStreamCreate, or their runtime API equivalents such as
+ *   ::cudaStreamCreate, ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority.
+ *   Passing an invalid handle will result in undefined behavior.</li>
+ *   <li>any of the special streams such as the NULL stream, ::CU_STREAM_LEGACY and
+ *   ::CU_STREAM_PER_THREAD. The runtime API equivalents of these are also accepted,
+ *   which are NULL, ::cudaStreamLegacy and ::cudaStreamPerThread respectively.
+ *   If any of the special handles are specified, the API will operate on the context current to the
+ *   calling thread. If a green context (that was converted via ::cuCtxFromGreenCtx() before setting it current)
+ *   is current to the calling thread, the API will return the green context in \p pGreenCtx
+ *   and the primary context of the associated device in \p pCtx. If a regular context is current,
+ *   the API returns the regular context in \p pCtx and NULL in \p pGreenCtx.
+ *   Note that specifying ::CU_STREAM_PER_THREAD or ::cudaStreamPerThread will return ::CUDA_ERROR_INVALID_HANDLE
+ *   if a green context is current to the calling thread.
+ *   If no context is current to the calling thread, ::CUDA_ERROR_INVALID_CONTEXT is returned.</li>
+ * </ul>
+ *
+ * \param hStream   - Handle to the stream to be queried
+ * \param pCtx      - Returned regular context associated with the stream
+ * \param pGreenCtx - Returned green context if the stream is associated with a green context or NULL if not
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreate
+ * ::cuStreamCreateWithPriority,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamCreate,
+ * ::cudaStreamCreateWithFlags,
+ */
+CUresult CUDAAPI cuStreamGetCtx_v2(CUstream hStream, CUcontext *pCtx, CUgreenCtx *pGreenCtx);
+
+/**
+ * \brief Make a compute stream wait on an event
+ *
+ * Makes all future work submitted to \p hStream wait for all work captured in
+ * \p hEvent.  See ::cuEventRecord() for details on what is captured by an event.
+ * The synchronization will be performed efficiently on the device when applicable.
+ * \p hEvent may be from a different context or device than \p hStream.
+ *
+ * flags include:
+ * - ::CU_EVENT_WAIT_DEFAULT: Default event creation flag.
+ * - ::CU_EVENT_WAIT_EXTERNAL: Event is captured in the graph as an external
+ *   event node when performing stream capture. This flag is invalid outside
+ *   of stream capture.
+ *
+ * \param hStream - Stream to wait
+ * \param hEvent  - Event to wait on (may not be NULL)
+ * \param Flags   - See ::CUevent_capture_flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuEventRecord,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cuStreamDestroy,
+ * ::cudaStreamWaitEvent
+ */
+CUresult CUDAAPI cuStreamWaitEvent(CUstream hStream, CUevent hEvent, unsigned int Flags);
+
+/**
+ * \brief Add a callback to a compute stream
+ *
+ * \note This function is slated for eventual deprecation and removal. If
+ * you do not require the callback to execute in case of a device error,
+ * consider using ::cuLaunchHostFunc. Additionally, this function is not
+ * supported with ::cuStreamBeginCapture and ::cuStreamEndCapture, unlike
+ * ::cuLaunchHostFunc.
+ *
+ * Adds a callback to be called on the host after all currently enqueued
+ * items in the stream have completed.  For each
+ * cuStreamAddCallback call, the callback will be executed exactly once.
+ * The callback will block later work in the stream until it is finished.
+ *
+ * The callback may be passed ::CUDA_SUCCESS or an error code.  In the event
+ * of a device error, all subsequently executed callbacks will receive an
+ * appropriate ::CUresult.
+ *
+ * Callbacks must not make any CUDA API calls.  Attempting to use a CUDA API
+ * will result in ::CUDA_ERROR_NOT_PERMITTED.  Callbacks must not perform any
+ * synchronization that may depend on outstanding device work or other callbacks
+ * that are not mandated to run earlier.  Callbacks without a mandated order
+ * (in independent streams) execute in undefined order and may be serialized.
+ *
+ * For the purposes of Unified Memory, callback execution makes a number of
+ * guarantees:
+ * <ul>
+ *   <li>The callback stream is considered idle for the duration of the
+ *   callback.  Thus, for example, a callback may always use memory attached
+ *   to the callback stream.</li>
+ *   <li>The start of execution of a callback has the same effect as
+ *   synchronizing an event recorded in the same stream immediately prior to
+ *   the callback.  It thus synchronizes streams which have been "joined"
+ *   prior to the callback.</li>
+ *   <li>Adding device work to any stream does not have the effect of making
+ *   the stream active until all preceding host functions and stream callbacks
+ *   have executed.  Thus, for
+ *   example, a callback might use global attached memory even if work has
+ *   been added to another stream, if the work has been ordered behind the
+ *   callback with an event.</li>
+ *   <li>Completion of a callback does not cause a stream to become
+ *   active except as described above.  The callback stream will remain idle
+ *   if no device work follows the callback, and will remain idle across
+ *   consecutive callbacks without device work in between.  Thus, for example,
+ *   stream synchronization can be done by signaling from a callback at the
+ *   end of the stream.</li>
+ * </ul>
+ *
+ * \param hStream  - Stream to add callback to
+ * \param callback - The function to call once preceding stream operations are complete
+ * \param userData - User specified data to be passed to the callback function
+ * \param flags    - Reserved for future use, must be 0
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamWaitEvent,
+ * ::cuStreamDestroy,
+ * ::cuMemAllocManaged,
+ * ::cuStreamAttachMemAsync,
+ * ::cuLaunchHostFunc,
+ * ::cudaStreamAddCallback
+ */
+CUresult CUDAAPI cuStreamAddCallback(CUstream hStream, CUstreamCallback callback, void *userData, unsigned int flags);
+
+/**
+ * \brief Begins graph capture on a stream
+ *
+ * Begin graph capture on \p hStream. When a stream is in capture mode, all operations
+ * pushed into the stream will not be executed, but will instead be captured into
+ * a graph, which will be returned via ::cuStreamEndCapture. Capture may not be initiated
+ * if \p stream is CU_STREAM_LEGACY. Capture must be ended on the same stream in which
+ * it was initiated, and it may only be initiated if the stream is not already in capture
+ * mode. The capture mode may be queried via ::cuStreamIsCapturing. A unique id
+ * representing the capture sequence may be queried via ::cuStreamGetCaptureInfo.
+ *
+ * If \p mode is not ::CU_STREAM_CAPTURE_MODE_RELAXED, ::cuStreamEndCapture must be
+ * called on this stream from the same thread.
+ *
+ * \param hStream - Stream in which to initiate capture
+ * \param mode    - Controls the interaction of this capture sequence with other API
+ *                  calls that are potentially unsafe. For more details see
+ *                  ::cuThreadExchangeStreamCaptureMode.
+ *
+ * \note Kernels captured using this API must not use texture and surface references.
+ *       Reading or writing through any texture or surface reference is undefined
+ *       behavior. This restriction does not apply to texture and surface objects.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamCreate,
+ * ::cuStreamIsCapturing,
+ * ::cuStreamEndCapture,
+ * ::cuThreadExchangeStreamCaptureMode
+ */
+CUresult CUDAAPI cuStreamBeginCapture(CUstream hStream, CUstreamCaptureMode mode);
+
+/**
+ * \brief Begins graph capture on a stream to an existing graph
+ *
+ * Begin graph capture on \p hStream, placing new nodes into an existing graph. When a stream is 
+ * in capture mode, all operations pushed into the stream will not be executed, but will instead 
+ * be captured into \p hGraph. The graph will not be instantiable until the user calls 
+ * ::cuStreamEndCapture. 
+ *  
+ * Capture may not be initiated if \p stream is CU_STREAM_LEGACY. Capture must be ended on the 
+ * same stream in which it was initiated, and it may only be initiated if the stream is not 
+ * already in capture mode. The capture mode may be queried via ::cuStreamIsCapturing. A unique id
+ * representing the capture sequence may be queried via ::cuStreamGetCaptureInfo.
+ *
+ * If \p mode is not ::CU_STREAM_CAPTURE_MODE_RELAXED, ::cuStreamEndCapture must be
+ * called on this stream from the same thread.
+ *
+ * \param hStream         - Stream in which to initiate capture.
+ * \param hGraph          - Graph to capture into.
+ * \param dependencies    - Dependencies of the first node captured in the stream.  Can be NULL if numDependencies is 0.
+ * \param dependencyData  - Optional array of data associated with each dependency.
+ * \param numDependencies - Number of dependencies.
+ * \param mode            - Controls the interaction of this capture sequence with other API
+ *                          calls that are potentially unsafe. For more details see
+ *                          ::cuThreadExchangeStreamCaptureMode.
+ *
+ * \note Kernels captured using this API must not use texture and surface references.
+ *       Reading or writing through any texture or surface reference is undefined
+ *       behavior. This restriction does not apply to texture and surface objects.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamBeginCapture,
+ * ::cuStreamCreate,
+ * ::cuStreamIsCapturing,
+ * ::cuStreamEndCapture,
+ * ::cuThreadExchangeStreamCaptureMode,
+ * ::cuGraphAddNode,
+ */
+CUresult CUDAAPI cuStreamBeginCaptureToGraph(CUstream hStream, CUgraph hGraph, const CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, CUstreamCaptureMode mode);
+
+/**
+ * \brief Swaps the stream capture interaction mode for a thread
+ *
+ * Sets the calling thread's stream capture interaction mode to the value contained
+ * in \p *mode, and overwrites \p *mode with the previous mode for the thread. To
+ * facilitate deterministic behavior across function or module boundaries, callers
+ * are encouraged to use this API in a push-pop fashion: \code
+     CUstreamCaptureMode mode = desiredMode;
+     cuThreadExchangeStreamCaptureMode(&mode);
+     ...
+     cuThreadExchangeStreamCaptureMode(&mode); // restore previous mode
+ * \endcode
+ *
+ * During stream capture (see ::cuStreamBeginCapture), some actions, such as a call
+ * to ::cudaMalloc, may be unsafe. In the case of ::cudaMalloc, the operation is
+ * not enqueued asynchronously to a stream, and is not observed by stream capture.
+ * Therefore, if the sequence of operations captured via ::cuStreamBeginCapture
+ * depended on the allocation being replayed whenever the graph is launched, the
+ * captured graph would be invalid.
+ *
+ * Therefore, stream capture places restrictions on API calls that can be made within
+ * or concurrently to a ::cuStreamBeginCapture-::cuStreamEndCapture sequence. This
+ * behavior can be controlled via this API and flags to ::cuStreamBeginCapture.
+ *
+ * A thread's mode is one of the following:
+ * - \p CU_STREAM_CAPTURE_MODE_GLOBAL: This is the default mode. If the local thread has
+ *   an ongoing capture sequence that was not initiated with
+ *   \p CU_STREAM_CAPTURE_MODE_RELAXED at \p cuStreamBeginCapture, or if any other thread
+ *   has a concurrent capture sequence initiated with \p CU_STREAM_CAPTURE_MODE_GLOBAL,
+ *   this thread is prohibited from potentially unsafe API calls.
+ * - \p CU_STREAM_CAPTURE_MODE_THREAD_LOCAL: If the local thread has an ongoing capture
+ *   sequence not initiated with \p CU_STREAM_CAPTURE_MODE_RELAXED, it is prohibited
+ *   from potentially unsafe API calls. Concurrent capture sequences in other threads
+ *   are ignored.
+ * - \p CU_STREAM_CAPTURE_MODE_RELAXED: The local thread is not prohibited from potentially
+ *   unsafe API calls. Note that the thread is still prohibited from API calls which
+ *   necessarily conflict with stream capture, for example, attempting ::cuEventQuery
+ *   on an event that was last recorded inside a capture sequence.
+ *
+ * \param mode - Pointer to mode value to swap with the current mode
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamBeginCapture
+ */
+CUresult CUDAAPI cuThreadExchangeStreamCaptureMode(CUstreamCaptureMode *mode);
+
+/**
+ * \brief Ends capture on a stream, returning the captured graph
+ *
+ * End capture on \p hStream, returning the captured graph via \p phGraph.
+ * Capture must have been initiated on \p hStream via a call to ::cuStreamBeginCapture.
+ * If capture was invalidated, due to a violation of the rules of stream capture, then
+ * a NULL graph will be returned.
+ *
+ * If the \p mode argument to ::cuStreamBeginCapture was not
+ * ::CU_STREAM_CAPTURE_MODE_RELAXED, this call must be from the same thread as
+ * ::cuStreamBeginCapture.
+ *
+ * \param hStream - Stream to query
+ * \param phGraph - The captured graph
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamCreate,
+ * ::cuStreamBeginCapture,
+ * ::cuStreamIsCapturing,
+ * ::cuGraphDestroy
+ */
+CUresult CUDAAPI cuStreamEndCapture(CUstream hStream, CUgraph *phGraph);
+
+/**
+ * \brief Returns a stream's capture status
+ *
+ * Return the capture status of \p hStream via \p captureStatus. After a successful
+ * call, \p *captureStatus will contain one of the following:
+ * - ::CU_STREAM_CAPTURE_STATUS_NONE: The stream is not capturing.
+ * - ::CU_STREAM_CAPTURE_STATUS_ACTIVE: The stream is capturing.
+ * - ::CU_STREAM_CAPTURE_STATUS_INVALIDATED: The stream was capturing but an error
+ *   has invalidated the capture sequence. The capture sequence must be terminated
+ *   with ::cuStreamEndCapture on the stream where it was initiated in order to
+ *   continue using \p hStream.
+ *
+ * Note that, if this is called on ::CU_STREAM_LEGACY (the "null stream") while
+ * a blocking stream in the same context is capturing, it will return
+ * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT and \p *captureStatus is unspecified
+ * after the call. The blocking stream capture is not invalidated.
+ *
+ * When a blocking stream is capturing, the legacy stream is in an
+ * unusable state until the blocking stream capture is terminated. The legacy
+ * stream is not supported for stream capture, but attempted use would have an
+ * implicit dependency on the capturing stream(s).
+ *
+ * \param hStream       - Stream to query
+ * \param captureStatus - Returns the stream's capture status
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamCreate,
+ * ::cuStreamBeginCapture,
+ * ::cuStreamEndCapture
+ */
+CUresult CUDAAPI cuStreamIsCapturing(CUstream hStream, CUstreamCaptureStatus *captureStatus);
+
+
+/**
+ * \brief Query a stream's capture state
+ *
+ * Query stream state related to stream capture.
+ *
+ * If called on ::CU_STREAM_LEGACY (the "null stream") while a stream not created 
+ * with ::CU_STREAM_NON_BLOCKING is capturing, returns ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT.
+ *
+ * Valid data (other than capture status) is returned only if both of the following are true:
+ * - the call returns CUDA_SUCCESS
+ * - the returned capture status is ::CU_STREAM_CAPTURE_STATUS_ACTIVE
+ *
+ * \param hStream - The stream to query
+ * \param captureStatus_out - Location to return the capture status of the stream; required
+ * \param id_out - Optional location to return an id for the capture sequence, which is
+ *           unique over the lifetime of the process
+ * \param graph_out - Optional location to return the graph being captured into. All
+ *           operations other than destroy and node removal are permitted on the graph
+ *           while the capture sequence is in progress. This API does not transfer
+ *           ownership of the graph, which is transferred or destroyed at
+ *           ::cuStreamEndCapture. Note that the graph handle may be invalidated before
+ *           end of capture for certain errors. Nodes that are or become
+ *           unreachable from the original stream at ::cuStreamEndCapture due to direct
+ *           actions on the graph do not trigger ::CUDA_ERROR_STREAM_CAPTURE_UNJOINED.
+ * \param dependencies_out - Optional location to store a pointer to an array of nodes.
+ *           The next node to be captured in the stream will depend on this set of nodes,
+ *           absent operations such as event wait which modify this set. The array pointer
+ *           is valid until the next API call which operates on the stream or until the
+ *           capture is terminated. The node handles may be copied out and are valid until
+ *           they or the graph is destroyed. The driver-owned array may also be passed
+ *           directly to APIs that operate on the graph (not the stream) without copying.
+ * \param numDependencies_out - Optional location to store the size of the array
+ *           returned in dependencies_out.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamGetCaptureInfo_v3
+ * ::cuStreamBeginCapture,
+ * ::cuStreamIsCapturing,
+ * ::cuStreamUpdateCaptureDependencies
+ */
+CUresult CUDAAPI cuStreamGetCaptureInfo(CUstream hStream, CUstreamCaptureStatus *captureStatus_out,
+        cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, size_t *numDependencies_out);
+
+/**
+ * \brief Query a stream's capture state (12.3+)
+ *
+ * Query stream state related to stream capture.
+ *
+ * If called on ::CU_STREAM_LEGACY (the "null stream") while a stream not created 
+ * with ::CU_STREAM_NON_BLOCKING is capturing, returns ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT.
+ *
+ * Valid data (other than capture status) is returned only if both of the following are true:
+ * - the call returns CUDA_SUCCESS
+ * - the returned capture status is ::CU_STREAM_CAPTURE_STATUS_ACTIVE
+ *
+ * If \p edgeData_out is non-NULL then \p dependencies_out must be as well. If
+ * \p dependencies_out is non-NULL and \p edgeData_out is NULL, but there is non-zero edge
+ * data for one or more of the current stream dependencies, the call will return
+ * ::CUDA_ERROR_LOSSY_QUERY.
+ *
+ * \param hStream - The stream to query
+ * \param captureStatus_out - Location to return the capture status of the stream; required
+ * \param id_out - Optional location to return an id for the capture sequence, which is
+ *           unique over the lifetime of the process
+ * \param graph_out - Optional location to return the graph being captured into. All
+ *           operations other than destroy and node removal are permitted on the graph
+ *           while the capture sequence is in progress. This API does not transfer
+ *           ownership of the graph, which is transferred or destroyed at
+ *           ::cuStreamEndCapture. Note that the graph handle may be invalidated before
+ *           end of capture for certain errors. Nodes that are or become
+ *           unreachable from the original stream at ::cuStreamEndCapture due to direct
+ *           actions on the graph do not trigger ::CUDA_ERROR_STREAM_CAPTURE_UNJOINED.
+ * \param dependencies_out - Optional location to store a pointer to an array of nodes.
+ *           The next node to be captured in the stream will depend on this set of nodes,
+ *           absent operations such as event wait which modify this set. The array pointer
+ *           is valid until the next API call which operates on the stream or until the
+ *           capture is terminated. The node handles may be copied out and are valid until
+ *           they or the graph is destroyed. The driver-owned array may also be passed
+ *           directly to APIs that operate on the graph (not the stream) without copying.
+ * \param edgeData_out - Optional location to store a pointer to an array of graph edge
+ *           data. This array parallels \c dependencies_out; the next node to be added
+ *           has an edge to \c dependencies_out[i] with annotation \c edgeData_out[i] for
+ *           each \c i. The array pointer is valid until the next API call which operates
+ *           on the stream or until the capture is terminated.
+ * \param numDependencies_out - Optional location to store the size of the array
+ *           returned in dependencies_out.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT,
+ * ::CUDA_ERROR_LOSSY_QUERY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamGetCaptureInfo
+ * ::cuStreamBeginCapture,
+ * ::cuStreamIsCapturing,
+ * ::cuStreamUpdateCaptureDependencies
+ */
+CUresult CUDAAPI cuStreamGetCaptureInfo_v3(CUstream hStream, CUstreamCaptureStatus *captureStatus_out,
+        cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out,
+        const CUgraphEdgeData **edgeData_out, size_t *numDependencies_out);
+
+/**
+ * \brief Update the set of dependencies in a capturing stream (11.3+)
+ *
+ * Modifies the dependency set of a capturing stream. The dependency set is the set
+ * of nodes that the next captured node in the stream will depend on.
+ *
+ * Valid flags are ::CU_STREAM_ADD_CAPTURE_DEPENDENCIES and
+ * ::CU_STREAM_SET_CAPTURE_DEPENDENCIES. These control whether the set passed to
+ * the API is added to the existing set or replaces it. A flags value of 0 defaults
+ * to ::CU_STREAM_ADD_CAPTURE_DEPENDENCIES.
+ *
+ * Nodes that are removed from the dependency set via this API do not result in
+ * ::CUDA_ERROR_STREAM_CAPTURE_UNJOINED if they are unreachable from the stream at
+ * ::cuStreamEndCapture.
+ *
+ * Returns ::CUDA_ERROR_ILLEGAL_STATE if the stream is not capturing.
+ *
+ * This API is new in CUDA 11.3. Developers requiring compatibility across minor
+ * versions to CUDA 11.0 should not use this API or provide a fallback.
+ *
+ * \param hStream - The stream to update
+ * \param dependencies - The set of dependencies to add
+ * \param numDependencies - The size of the dependencies array
+ * \param flags - See above
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_ILLEGAL_STATE
+ *
+ * \sa
+ * ::cuStreamBeginCapture,
+ * ::cuStreamGetCaptureInfo,
+ */
+CUresult CUDAAPI cuStreamUpdateCaptureDependencies(CUstream hStream, CUgraphNode *dependencies, size_t numDependencies, unsigned int flags);
+
+/**
+ * \brief Update the set of dependencies in a capturing stream (12.3+)
+ *
+ * Modifies the dependency set of a capturing stream. The dependency set is the set
+ * of nodes that the next captured node in the stream will depend on along with the
+ * edge data for those dependencies.
+ *
+ * Valid flags are ::CU_STREAM_ADD_CAPTURE_DEPENDENCIES and
+ * ::CU_STREAM_SET_CAPTURE_DEPENDENCIES. These control whether the set passed to
+ * the API is added to the existing set or replaces it. A flags value of 0 defaults
+ * to ::CU_STREAM_ADD_CAPTURE_DEPENDENCIES.
+ *
+ * Nodes that are removed from the dependency set via this API do not result in
+ * ::CUDA_ERROR_STREAM_CAPTURE_UNJOINED if they are unreachable from the stream at
+ * ::cuStreamEndCapture.
+ *
+ * Returns ::CUDA_ERROR_ILLEGAL_STATE if the stream is not capturing.
+ *
+ * \param hStream - The stream to update
+ * \param dependencies - The set of dependencies to add
+ * \param dependencyData - Optional array of data associated with each dependency.
+ * \param numDependencies - The size of the dependencies array
+ * \param flags - See above
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_ILLEGAL_STATE
+ *
+ * \sa
+ * ::cuStreamBeginCapture,
+ * ::cuStreamGetCaptureInfo,
+ */
+CUresult CUDAAPI cuStreamUpdateCaptureDependencies_v2(CUstream hStream, CUgraphNode *dependencies,
+    const CUgraphEdgeData *dependencyData, size_t numDependencies, unsigned int flags);
+
+/**
+ * \brief Attach memory to a stream asynchronously
+ *
+ * Enqueues an operation in \p hStream to specify stream association of
+ * \p length bytes of memory starting from \p dptr. This function is a
+ * stream-ordered operation, meaning that it is dependent on, and will
+ * only take effect when, previous work in stream has completed. Any
+ * previous association is automatically replaced.
+ *
+ * \p dptr must point to one of the following types of memories:
+ * - managed memory declared using the __managed__ keyword or allocated with
+ *   ::cuMemAllocManaged.
+ * - a valid host-accessible region of system-allocated pageable memory. This
+ *   type of memory may only be specified if the device associated with the
+ *   stream reports a non-zero value for the device attribute
+ *   ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS.
+ *
+ * For managed allocations, \p length must be either zero or the entire
+ * allocation's size. Both indicate that the entire allocation's stream
+ * association is being changed. Currently, it is not possible to change stream
+ * association for a portion of a managed allocation.
+ *
+ * For pageable host allocations, \p length must be non-zero.
+ *
+ * The stream association is specified using \p flags which must be
+ * one of ::CUmemAttach_flags.
+ * If the ::CU_MEM_ATTACH_GLOBAL flag is specified, the memory can be accessed
+ * by any stream on any device.
+ * If the ::CU_MEM_ATTACH_HOST flag is specified, the program makes a guarantee
+ * that it won't access the memory on the device from any stream on a device that
+ * has a zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS.
+ * If the ::CU_MEM_ATTACH_SINGLE flag is specified and \p hStream is associated with
+ * a device that has a zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS,
+ * the program makes a guarantee that it will only access the memory on the device
+ * from \p hStream. It is illegal to attach singly to the NULL stream, because the
+ * NULL stream is a virtual global stream and not a specific stream. An error will
+ * be returned in this case.
+ *
+ * When memory is associated with a single stream, the Unified Memory system will
+ * allow CPU access to this memory region so long as all operations in \p hStream
+ * have completed, regardless of whether other streams are active. In effect,
+ * this constrains exclusive ownership of the managed memory region by
+ * an active GPU to per-stream activity instead of whole-GPU activity.
+ *
+ * Accessing memory on the device from streams that are not associated with
+ * it will produce undefined results. No error checking is performed by the
+ * Unified Memory system to ensure that kernels launched into other streams
+ * do not access this region.
+ *
+ * It is a program's responsibility to order calls to ::cuStreamAttachMemAsync
+ * via events, synchronization or other means to ensure legal access to memory
+ * at all times. Data visibility and coherency will be changed appropriately
+ * for all kernels which follow a stream-association change.
+ *
+ * If \p hStream is destroyed while data is associated with it, the association is
+ * removed and the association reverts to the default visibility of the allocation
+ * as specified at ::cuMemAllocManaged. For __managed__ variables, the default
+ * association is always ::CU_MEM_ATTACH_GLOBAL. Note that destroying a stream is an
+ * asynchronous operation, and as a result, the change to default association won't
+ * happen until all work in the stream has completed.
+ *
+ * \param hStream - Stream in which to enqueue the attach operation
+ * \param dptr    - Pointer to memory (must be a pointer to managed memory or
+ *                  to a valid host-accessible region of system-allocated
+ *                  pageable memory)
+ * \param length  - Length of memory
+ * \param flags   - Must be one of ::CUmemAttach_flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamWaitEvent,
+ * ::cuStreamDestroy,
+ * ::cuMemAllocManaged,
+ * ::cudaStreamAttachMemAsync
+ */
+CUresult CUDAAPI cuStreamAttachMemAsync(CUstream hStream, CUdeviceptr dptr, size_t length, unsigned int flags);
+
+/**
+ * \brief Determine status of a compute stream
+ *
+ * Returns ::CUDA_SUCCESS if all operations in the stream specified by
+ * \p hStream have completed, or ::CUDA_ERROR_NOT_READY if not.
+ *
+ * For the purposes of Unified Memory, a return value of ::CUDA_SUCCESS
+ * is equivalent to having called ::cuStreamSynchronize().
+ *
+ * \param hStream - Stream to query status of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_READY
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuStreamWaitEvent,
+ * ::cuStreamDestroy,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamQuery
+ */
+CUresult CUDAAPI cuStreamQuery(CUstream hStream);
+
+/**
+ * \brief Wait until a stream's tasks are completed
+ *
+ * Waits until the device has completed all operations in the stream specified
+ * by \p hStream. If the context was created with the
+ * ::CU_CTX_SCHED_BLOCKING_SYNC flag, the CPU thread will block until the
+ * stream is finished with all of its tasks.
+ *
+ * \param hStream - Stream to wait for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE
+
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuStreamDestroy,
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamSynchronize
+ */
+CUresult CUDAAPI cuStreamSynchronize(CUstream hStream);
+
+/**
+ * \brief Destroys a stream
+ *
+ * Destroys the stream specified by \p hStream.
+ *
+ * In case the device is still doing work in the stream \p hStream
+ * when ::cuStreamDestroy() is called, the function will return immediately
+ * and the resources associated with \p hStream will be released automatically
+ * once the device has completed all work in \p hStream.
+ *
+ * \param hStream - Stream to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamDestroy
+ */
+CUresult CUDAAPI cuStreamDestroy(CUstream hStream);
+
+/**
+ * \brief Copies attributes from source stream to destination stream.
+ *
+ * Copies attributes from source stream \p src to destination stream \p dst.
+ * Both streams must have the same context.
+ *
+ * \param[out] dst Destination stream
+ * \param[in] src Source stream
+ * For list of attributes see ::CUstreamAttrID
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuStreamCopyAttributes(CUstream dst, CUstream src);
+
+/**
+ * \brief Queries stream attribute.
+ *
+ * Queries attribute \p attr from \p hStream and stores it in corresponding
+ * member of \p value_out.
+ *
+ * \param[in] hStream
+ * \param[in] attr
+ * \param[out] value_out
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuStreamGetAttribute(CUstream hStream, CUstreamAttrID attr,
+                                      CUstreamAttrValue *value_out);
+
+/**
+ * \brief Sets stream attribute.
+ *
+ * Sets attribute \p attr on \p hStream from corresponding attribute of
+ * \p value. The updated attribute will be applied to subsequent work
+ * submitted to the stream. It will not affect previously submitted work.
+ *
+ * \param[out] hStream
+ * \param[in] attr
+ * \param[in] value
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuStreamSetAttribute(CUstream hStream, CUstreamAttrID attr,
+                                      const CUstreamAttrValue *value);
+
+/** @} */ /* END CUDA_STREAM */
+
+
+/**
+ * \defgroup CUDA_EVENT Event Management
+ *
+ * ___MANBRIEF___ event management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the event management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates an event
+ *
+ * Creates an event *phEvent for the current context with the flags specified via
+ * \p Flags. Valid flags include:
+ * - ::CU_EVENT_DEFAULT: Default event creation flag.
+ * - ::CU_EVENT_BLOCKING_SYNC: Specifies that the created event should use blocking
+ *   synchronization.  A CPU thread that uses ::cuEventSynchronize() to wait on
+ *   an event created with this flag will block until the event has actually
+ *   been recorded.
+ * - ::CU_EVENT_DISABLE_TIMING: Specifies that the created event does not need
+ *   to record timing data.  Events created with this flag specified and
+ *   the ::CU_EVENT_BLOCKING_SYNC flag not specified will provide the best
+ *   performance when used with ::cuStreamWaitEvent() and ::cuEventQuery().
+ * - ::CU_EVENT_INTERPROCESS: Specifies that the created event may be used as an
+ *   interprocess event by ::cuIpcGetEventHandle(). ::CU_EVENT_INTERPROCESS must
+ *   be specified along with ::CU_EVENT_DISABLE_TIMING.
+ *
+ * \param phEvent - Returns newly created event
+ * \param Flags   - Event creation flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa
+ * ::cuEventRecord,
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuEventDestroy,
+ * ::cuEventElapsedTime,
+ * ::cudaEventCreate,
+ * ::cudaEventCreateWithFlags
+ */
+CUresult CUDAAPI cuEventCreate(CUevent *phEvent, unsigned int Flags);
+
+/**
+ * \brief Records an event
+ *
+ * Captures in \p hEvent the contents of \p hStream at the time of this call.
+ * \p hEvent and \p hStream must be from the same context otherwise
+ * ::CUDA_ERROR_INVALID_HANDLE is returned.
+ * Calls such as ::cuEventQuery() or ::cuStreamWaitEvent() will then
+ * examine or wait for completion of the work that was captured. Uses of
+ * \p hStream after this call do not modify \p hEvent. See note on default
+ * stream behavior for what is captured in the default case.
+ *
+ * ::cuEventRecord() can be called multiple times on the same event and
+ * will overwrite the previously captured state. Other APIs such as
+ * ::cuStreamWaitEvent() use the most recently captured state at the time
+ * of the API call, and are not affected by later calls to
+ * ::cuEventRecord(). Before the first call to ::cuEventRecord(), an
+ * event represents an empty set of work, so for example ::cuEventQuery()
+ * would return ::CUDA_SUCCESS.
+ *
+ * \param hEvent  - Event to record
+ * \param hStream - Stream to record event for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuStreamWaitEvent,
+ * ::cuEventDestroy,
+ * ::cuEventElapsedTime,
+ * ::cudaEventRecord,
+ * ::cuEventRecordWithFlags
+ */
+CUresult CUDAAPI cuEventRecord(CUevent hEvent, CUstream hStream);
+
+/**
+ * \brief Records an event
+ *
+ * Captures in \p hEvent the contents of \p hStream at the time of this call.
+ * \p hEvent and \p hStream must be from the same context otherwise
+ * ::CUDA_ERROR_INVALID_HANDLE is returned.
+ * Calls such as ::cuEventQuery() or ::cuStreamWaitEvent() will then
+ * examine or wait for completion of the work that was captured. Uses of
+ * \p hStream after this call do not modify \p hEvent. See note on default
+ * stream behavior for what is captured in the default case.
+ *
+ * ::cuEventRecordWithFlags() can be called multiple times on the same event and
+ * will overwrite the previously captured state. Other APIs such as
+ * ::cuStreamWaitEvent() use the most recently captured state at the time
+ * of the API call, and are not affected by later calls to
+ * ::cuEventRecordWithFlags(). Before the first call to ::cuEventRecordWithFlags(), an
+ * event represents an empty set of work, so for example ::cuEventQuery()
+ * would return ::CUDA_SUCCESS.
+ *
+ * flags include:
+ * - ::CU_EVENT_RECORD_DEFAULT: Default event creation flag.
+ * - ::CU_EVENT_RECORD_EXTERNAL: Event is captured in the graph as an external
+ *   event node when performing stream capture. This flag is invalid outside
+ *   of stream capture.
+ *
+ * \param hEvent  - Event to record
+ * \param hStream - Stream to record event for
+ * \param flags   - See ::CUevent_capture_flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuStreamWaitEvent,
+ * ::cuEventDestroy,
+ * ::cuEventElapsedTime,
+ * ::cuEventRecord,
+ * ::cudaEventRecord
+ */
+CUresult CUDAAPI cuEventRecordWithFlags(CUevent hEvent, CUstream hStream, unsigned int flags);
+
+/**
+ * \brief Queries an event's status
+ *
+ * Queries the status of all work currently captured by \p hEvent. See
+ * ::cuEventRecord() for details on what is captured by an event.
+ *
+ * Returns ::CUDA_SUCCESS if all captured work has been completed, or
+ * ::CUDA_ERROR_NOT_READY if any captured work is incomplete.
+ *
+ * For the purposes of Unified Memory, a return value of ::CUDA_SUCCESS
+ * is equivalent to having called ::cuEventSynchronize().
+ *
+ * \param hEvent - Event to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_READY
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventRecord,
+ * ::cuEventSynchronize,
+ * ::cuEventDestroy,
+ * ::cuEventElapsedTime,
+ * ::cudaEventQuery
+ */
+CUresult CUDAAPI cuEventQuery(CUevent hEvent);
+
+/**
+ * \brief Waits for an event to complete
+ *
+ * Waits until the completion of all work currently captured in \p hEvent.
+ * See ::cuEventRecord() for details on what is captured by an event.
+ *
+ * Waiting for an event that was created with the ::CU_EVENT_BLOCKING_SYNC
+ * flag will cause the calling CPU thread to block until the event has
+ * been completed by the device.  If the ::CU_EVENT_BLOCKING_SYNC flag has
+ * not been set, then the CPU thread will busy-wait until the event has
+ * been completed by the device.
+ *
+ * \param hEvent - Event to wait for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventRecord,
+ * ::cuEventQuery,
+ * ::cuEventDestroy,
+ * ::cuEventElapsedTime,
+ * ::cudaEventSynchronize
+ */
+CUresult CUDAAPI cuEventSynchronize(CUevent hEvent);
+
+/**
+ * \brief Destroys an event
+ *
+ * Destroys the event specified by \p hEvent.
+ *
+ * An event may be destroyed before it is complete (i.e., while
+ * ::cuEventQuery() would return ::CUDA_ERROR_NOT_READY). In this case, the
+ * call does not block on completion of the event, and any associated
+ * resources will automatically be released asynchronously at completion.
+ *
+ * \param hEvent - Event to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventRecord,
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuEventElapsedTime,
+ * ::cudaEventDestroy
+ */
+CUresult CUDAAPI cuEventDestroy(CUevent hEvent);
+
+/**
+ * \brief Computes the elapsed time between two events
+ *
+ * Computes the elapsed time between two events (in milliseconds with a
+ * resolution of around 0.5 microseconds).
+ *
+ * If either event was last recorded in a non-NULL stream, the resulting time
+ * may be greater than expected (even if both used the same stream handle). This
+ * happens because the ::cuEventRecord() operation takes place asynchronously
+ * and there is no guarantee that the measured latency is actually just between
+ * the two events. Any number of other different stream operations could execute
+ * in between the two measured events, thus altering the timing in a significant
+ * way.
+ *
+ * If ::cuEventRecord() has not been called on either event then
+ * ::CUDA_ERROR_INVALID_HANDLE is returned. If ::cuEventRecord() has been called
+ * on both events but one or both of them has not yet been completed (that is,
+ * ::cuEventQuery() would return ::CUDA_ERROR_NOT_READY on at least one of the
+ * events), ::CUDA_ERROR_NOT_READY is returned. If either event was created with
+ * the ::CU_EVENT_DISABLE_TIMING flag, then this function will return
+ * ::CUDA_ERROR_INVALID_HANDLE.
+ * 
+ * Note there is a later version of this API, ::cuEventElapsedTime_v2. It will
+ * supplant this version in CUDA 13.0, which is retained for minor version compatibility.
+ *
+ * \param pMilliseconds - Time between \p hStart and \p hEnd in ms
+ * \param hStart        - Starting event
+ * \param hEnd          - Ending event
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_READY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventRecord,
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuEventDestroy,
+ * ::cudaEventElapsedTime
+ */
+CUresult CUDAAPI cuEventElapsedTime(float *pMilliseconds, CUevent hStart, CUevent hEnd);
+
+/**
+ * \brief Computes the elapsed time between two events
+ *
+ * Computes the elapsed time between two events (in milliseconds with a
+ * resolution of around 0.5 microseconds). Note this API is not guaranteed
+ * to return the latest errors for pending work. As such this API is intended to
+ * serve as an elapsed time calculation only and any polling for completion on the
+ * events to be compared should be done with ::cuEventQuery instead.
+ *
+ * If either event was last recorded in a non-NULL stream, the resulting time
+ * may be greater than expected (even if both used the same stream handle). This
+ * happens because the ::cuEventRecord() operation takes place asynchronously
+ * and there is no guarantee that the measured latency is actually just between
+ * the two events. Any number of other different stream operations could execute
+ * in between the two measured events, thus altering the timing in a significant
+ * way.
+ *
+ * If ::cuEventRecord() has not been called on either event then
+ * ::CUDA_ERROR_INVALID_HANDLE is returned. If ::cuEventRecord() has been called
+ * on both events but one or both of them has not yet been completed (that is,
+ * ::cuEventQuery() would return ::CUDA_ERROR_NOT_READY on at least one of the
+ * events), ::CUDA_ERROR_NOT_READY is returned. If either event was created with
+ * the ::CU_EVENT_DISABLE_TIMING flag, then this function will return
+ * ::CUDA_ERROR_INVALID_HANDLE.
+ *
+ * \param pMilliseconds - Time between \p hStart and \p hEnd in ms
+ * \param hStart        - Starting event
+ * \param hEnd          - Ending event
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_READY,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuEventCreate,
+ * ::cuEventRecord,
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuEventDestroy,
+ * ::cudaEventElapsedTime
+ */
+CUresult CUDAAPI cuEventElapsedTime_v2(float *pMilliseconds, CUevent hStart, CUevent hEnd);
+
+/** @} */ /* END CUDA_EVENT */
+
+/**
+ * \defgroup CUDA_EXTRES_INTEROP External Resource Interoperability
+ *
+ * ___MANBRIEF___ External resource interoperability functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the external resource interoperability functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+ /**
+ * \brief Imports an external memory object
+ *
+ * Imports an externally allocated memory object and returns
+ * a handle to that in \p extMem_out.
+ *
+ * The properties of the handle being imported must be described in
+ * \p memHandleDesc. The ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC structure
+ * is defined as follows:
+ *
+ * \code
+        typedef struct CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st {
+            CUexternalMemoryHandleType type;
+            union {
+                int fd;
+                struct {
+                    void *handle;
+                    const void *name;
+                } win32;
+                const void *nvSciBufObject;
+            } handle;
+            unsigned long long size;
+            unsigned int flags;
+        } CUDA_EXTERNAL_MEMORY_HANDLE_DESC;
+ * \endcode
+ *
+ * where ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type specifies the type
+ * of handle being imported. ::CUexternalMemoryHandleType is
+ * defined as:
+ *
+ * \code
+        typedef enum CUexternalMemoryHandleType_enum {
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD          = 1,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32       = 2,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT   = 3,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP         = 4,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE     = 5,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE     = 6,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT = 7,
+            CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF           = 8,
+        } CUexternalMemoryHandleType;
+ * \endcode
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD, then
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::fd must be a valid
+ * file descriptor referencing a memory object. Ownership of
+ * the file descriptor is transferred to the CUDA driver when the
+ * handle is imported successfully. Performing any operations on the
+ * file descriptor after it is imported results in undefined behavior.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32, then exactly one
+ * of ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be
+ * NULL. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * references a memory object. Ownership of this handle is
+ * not transferred to CUDA after the import operation, so the
+ * application must release the handle using the appropriate system
+ * call. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a memory object.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT, then
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must
+ * be non-NULL and
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name
+ * must be NULL. The handle specified must be a globally shared KMT
+ * handle. This handle does not hold a reference to the underlying
+ * object, and thus will be invalid when all references to the
+ * memory object are destroyed.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP, then exactly one
+ * of ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be
+ * NULL. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D12Device::CreateSharedHandle when referring to a
+ * ID3D12Heap object. This handle holds a reference to the underlying
+ * object. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a ID3D12Heap object.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE, then exactly one
+ * of ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be
+ * NULL. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D12Device::CreateSharedHandle when referring to a
+ * ID3D12Resource object. This handle holds a reference to the
+ * underlying object. If
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a ID3D12Resource object.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE, then
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must
+ * represent a valid shared NT handle that is returned by
+ * IDXGIResource1::CreateSharedHandle when referring to a
+ * ID3D11Resource object. If
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a ID3D11Resource object.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT, then
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must
+ * represent a valid shared KMT handle that is returned by
+ * IDXGIResource::GetSharedHandle when referring to a
+ * ID3D11Resource object and
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name
+ * must be NULL.
+ *
+ * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, then
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::nvSciBufObject must be non-NULL
+ * and reference a valid NvSciBuf object.
+ * If the NvSciBuf object imported into CUDA is also mapped by other drivers, then the
+ * application must use ::cuWaitExternalSemaphoresAsync or ::cuSignalExternalSemaphoresAsync
+ * as appropriate barriers to maintain coherence between CUDA and the other drivers.
+ * See ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC and ::CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC
+ * for memory synchronization.
+ *
+ *
+ * The size of the memory object must be specified in
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::size.
+ *
+ * Specifying the flag ::CUDA_EXTERNAL_MEMORY_DEDICATED in
+ * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::flags indicates that the
+ * resource is a dedicated resource. The definition of what a
+ * dedicated resource is outside the scope of this extension.
+ * This flag must be set if ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type
+ * is one of the following:
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT
+ *
+ * \param extMem_out    - Returned handle to an external memory object
+ * \param memHandleDesc - Memory import handle descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OPERATING_SYSTEM
+ * \notefnerr
+ *
+ * \note If the Vulkan memory imported into CUDA is mapped on the CPU then the
+ * application must use vkInvalidateMappedMemoryRanges/vkFlushMappedMemoryRanges
+ * as well as appropriate Vulkan pipeline barriers to maintain coherence between
+ * CPU and GPU. For more information on these APIs, please refer to "Synchronization
+ * and Cache Control" chapter from Vulkan specification.
+ *
+ * \sa ::cuDestroyExternalMemory,
+ * ::cuExternalMemoryGetMappedBuffer,
+ * ::cuExternalMemoryGetMappedMipmappedArray
+ */
+CUresult CUDAAPI cuImportExternalMemory(CUexternalMemory *extMem_out, const CUDA_EXTERNAL_MEMORY_HANDLE_DESC *memHandleDesc);
+
+/**
+ * \brief Maps a buffer onto an imported memory object
+ *
+ * Maps a buffer onto an imported memory object and returns a device
+ * pointer in \p devPtr.
+ *
+ * The properties of the buffer being mapped must be described in
+ * \p bufferDesc. The ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC structure is
+ * defined as follows:
+ *
+ * \code
+        typedef struct CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st {
+            unsigned long long offset;
+            unsigned long long size;
+            unsigned int flags;
+        } CUDA_EXTERNAL_MEMORY_BUFFER_DESC;
+ * \endcode
+ *
+ * where ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC::offset is the offset in
+ * the memory object where the buffer's base address is.
+ * ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC::size is the size of the buffer.
+ * ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC::flags must be zero.
+ *
+ * The offset and size have to be suitably aligned to match the
+ * requirements of the external API. Mapping two buffers whose ranges
+ * overlap may or may not result in the same virtual address being
+ * returned for the overlapped portion. In such cases, the application
+ * must ensure that all accesses to that region from the GPU are
+ * volatile. Otherwise writes made via one address are not guaranteed
+ * to be visible via the other address, even if they're issued by the
+ * same thread. It is recommended that applications map the combined
+ * range instead of mapping separate buffers and then apply the
+ * appropriate offsets to the returned pointer to derive the
+ * individual buffers.
+ *
+ * The returned pointer \p devPtr must be freed using ::cuMemFree.
+ *
+ * \param devPtr     - Returned device pointer to buffer
+ * \param extMem     - Handle to external memory object
+ * \param bufferDesc - Buffer descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuImportExternalMemory,
+ * ::cuDestroyExternalMemory,
+ * ::cuExternalMemoryGetMappedMipmappedArray
+ */
+CUresult CUDAAPI cuExternalMemoryGetMappedBuffer(CUdeviceptr *devPtr, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_BUFFER_DESC *bufferDesc);
+
+/**
+ * \brief Maps a CUDA mipmapped array onto an external memory object
+ *
+ * Maps a CUDA mipmapped array onto an external object and returns a
+ * handle to it in \p mipmap.
+ *
+ * The properties of the CUDA mipmapped array being mapped must be
+ * described in \p mipmapDesc. The structure
+ * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC is defined as follows:
+ *
+ * \code
+        typedef struct CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st {
+            unsigned long long offset;
+            CUDA_ARRAY3D_DESCRIPTOR arrayDesc;
+            unsigned int numLevels;
+        } CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC;
+ * \endcode
+ *
+ * where ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::offset is the
+ * offset in the memory object where the base level of the mipmap
+ * chain is.
+ * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::arrayDesc describes
+ * the format, dimensions and type of the base level of the mipmap
+ * chain. For further details on these parameters, please refer to the
+ * documentation for ::cuMipmappedArrayCreate. Note that if the mipmapped
+ * array is bound as a color target in the graphics API, then the flag
+ * ::CUDA_ARRAY3D_COLOR_ATTACHMENT must be specified in
+ * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::arrayDesc::Flags.
+ * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::numLevels specifies
+ * the total number of levels in the mipmap chain.
+ *
+ * If \p extMem was imported from a handle of type ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, then
+ * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::numLevels must be equal to 1.
+ *
+ *
+ * The returned CUDA mipmapped array must be freed using ::cuMipmappedArrayDestroy.
+ *
+ * \param mipmap     - Returned CUDA mipmapped array
+ * \param extMem     - Handle to external memory object
+ * \param mipmapDesc - CUDA array descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuImportExternalMemory,
+ * ::cuDestroyExternalMemory,
+ * ::cuExternalMemoryGetMappedBuffer
+ */
+CUresult CUDAAPI cuExternalMemoryGetMappedMipmappedArray(CUmipmappedArray *mipmap, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC *mipmapDesc);
+
+/**
+ * \brief Destroys an external memory object.
+ *
+ * Destroys the specified external memory object. Any existing buffers
+ * and CUDA mipmapped arrays mapped onto this object must no longer be
+ * used and must be explicitly freed using ::cuMemFree and
+ * ::cuMipmappedArrayDestroy respectively.
+ *
+ * \param extMem - External memory object to be destroyed
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuImportExternalMemory,
+ * ::cuExternalMemoryGetMappedBuffer,
+ * ::cuExternalMemoryGetMappedMipmappedArray
+ */
+CUresult CUDAAPI cuDestroyExternalMemory(CUexternalMemory extMem);
+
+/**
+ * \brief Imports an external semaphore
+ *
+ * Imports an externally allocated synchronization object and returns
+ * a handle to that in \p extSem_out.
+ *
+ * The properties of the handle being imported must be described in
+ * \p semHandleDesc. The ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC is
+ * defined as follows:
+ *
+ * \code
+        typedef struct CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st {
+            CUexternalSemaphoreHandleType type;
+            union {
+                int fd;
+                struct {
+                    void *handle;
+                    const void *name;
+                } win32;
+                const void* NvSciSyncObj;
+            } handle;
+            unsigned int flags;
+        } CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC;
+ * \endcode
+ *
+ * where ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type specifies the type of
+ * handle being imported. ::CUexternalSemaphoreHandleType is defined
+ * as:
+ *
+ * \code
+        typedef enum CUexternalSemaphoreHandleType_enum {
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD                = 1,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32             = 2,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT         = 3,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE              = 4,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE              = 5,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC                = 6,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX        = 7,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT    = 8,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD    = 9,
+            CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 = 10
+        } CUexternalSemaphoreHandleType;
+ * \endcode
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::fd must be a valid
+ * file descriptor referencing a synchronization object. Ownership of
+ * the file descriptor is transferred to the CUDA driver when the
+ * handle is imported successfully. Performing any operations on the
+ * file descriptor after it is imported results in undefined behavior.
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, then exactly one
+ * of ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be
+ * NULL. If
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * references a synchronization object. Ownership of this handle is
+ * not transferred to CUDA after the import operation, so the
+ * application must release the handle using the appropriate system
+ * call. If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object.
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle must
+ * be non-NULL and
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name
+ * must be NULL. The handle specified must be a globally shared KMT
+ * handle. This handle does not hold a reference to the underlying
+ * object, and thus will be invalid when all references to the
+ * synchronization object are destroyed.
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, then exactly one
+ * of ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be
+ * NULL. If
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D12Device::CreateSharedHandle when referring to a
+ * ID3D12Fence object. This handle holds a reference to the underlying
+ * object. If
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object that
+ * refers to a valid ID3D12Fence object.
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle
+ * represents a valid shared NT handle that is returned by
+ * ID3D11Fence::CreateSharedHandle. If
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object that
+ * refers to a valid ID3D11Fence object.
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::nvSciSyncObj
+ * represents a valid NvSciSyncObj.
+ *
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle
+ * represents a valid shared NT handle that
+ * is returned by IDXGIResource1::CreateSharedHandle when referring to
+ * a IDXGIKeyedMutex object. If
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object that
+ * refers to a valid IDXGIKeyedMutex object.
+ *
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle
+ * represents a valid shared KMT handle that
+ * is returned by IDXGIResource::GetSharedHandle when referring to
+ * a IDXGIKeyedMutex object and
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must be NULL.
+ * 
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, then
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::fd must be a valid
+ * file descriptor referencing a synchronization object. Ownership of
+ * the file descriptor is transferred to the CUDA driver when the
+ * handle is imported successfully. Performing any operations on the
+ * file descriptor after it is imported results in undefined behavior.
+ * 
+ * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32, then exactly one
+ * of ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be
+ * NULL. If
+ * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * references a synchronization object. Ownership of this handle is
+ * not transferred to CUDA after the import operation, so the
+ * application must release the handle using the appropriate system
+ * call. If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object.
+ *
+ * \param extSem_out    - Returned handle to an external semaphore
+ * \param semHandleDesc - Semaphore import handle descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OPERATING_SYSTEM
+ * \notefnerr
+ *
+ * \sa ::cuDestroyExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuImportExternalSemaphore(CUexternalSemaphore *extSem_out, const CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC *semHandleDesc);
+
+/**
+ * \brief Signals a set of external semaphore objects
+ *
+ * Enqueues a signal operation on a set of externally allocated
+ * semaphore object in the specified stream. The operations will be
+ * executed when all prior operations in the stream complete.
+ *
+ * The exact semantics of signaling a semaphore depends on the type of
+ * the object.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT
+ * then signaling the semaphore will set it to the signaled state.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32
+ * then the semaphore will be set to the value specified in
+ * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::fence::value.
+ *
+ * If the semaphore object is of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC
+ * this API sets ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence
+ * to a value that can be used by subsequent waiters of the same NvSciSync object
+ * to order operations with those currently submitted in \p stream. Such an update
+ * will overwrite previous contents of
+ * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence. By default,
+ * signaling such an external semaphore object causes appropriate memory synchronization
+ * operations to be performed over all external memory objects that are imported as
+ * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. This ensures that any subsequent accesses
+ * made by other importers of the same set of NvSciBuf memory object(s) are coherent.
+ * These operations can be skipped by specifying the flag
+ * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC, which can be used as a
+ * performance optimization when data coherency is not required. But specifying this
+ * flag in scenarios where data coherency is required results in undefined behavior.
+ * Also, for semaphore object of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC,
+ * if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in
+ * ::cuDeviceGetNvSciSyncAttributes to CUDA_NVSCISYNC_ATTR_SIGNAL, this API will return
+ * CUDA_ERROR_NOT_SUPPORTED.
+ * NvSciSyncFence associated with semaphore object of the type 
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC can be deterministic. For this the 
+ * NvSciSyncAttrList used to create the semaphore object must have value of 
+ * NvSciSyncAttrKey_RequireDeterministicFences key set to true. Deterministic fences 
+ * allow users to enqueue a wait over the semaphore object even before corresponding
+ * signal is enqueued. For such a semaphore object, CUDA guarantees that each signal 
+ * operation will increment the fence value by '1'. Users are expected to track count 
+ * of signals enqueued on the semaphore object and insert waits accordingly. When such 
+ * a semaphore object is signaled from multiple streams, due to concurrent stream 
+ * execution, it is possible that the order in which the semaphore gets signaled is 
+ * indeterministic. This could lead to waiters of the semaphore getting unblocked 
+ * incorrectly. Users are expected to handle such situations, either by not using the 
+ * same semaphore object with deterministic fence support enabled in different streams 
+ * or by adding explicit dependency amongst such streams so that the semaphore is 
+ * signaled in order.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT
+ * then the keyed mutex will be released with the key specified in
+ * ::CUDA_EXTERNAL_SEMAPHORE_PARAMS::params::keyedmutex::key.
+ *
+ * \param extSemArray - Set of external semaphores to be signaled
+ * \param paramsArray - Array of semaphore parameters
+ * \param numExtSems  - Number of semaphores to signal
+ * \param stream      - Stream to enqueue the signal operations in
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuImportExternalSemaphore,
+ * ::cuDestroyExternalSemaphore,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuSignalExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream);
+
+/**
+ * \brief Waits on a set of external semaphore objects
+ *
+ * Enqueues a wait operation on a set of externally allocated
+ * semaphore object in the specified stream. The operations will be
+ * executed when all prior operations in the stream complete.
+ *
+ * The exact semantics of waiting on a semaphore depends on the type
+ * of the object.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT
+ * then waiting on the semaphore will wait until the semaphore reaches
+ * the signaled state. The semaphore will then be reset to the
+ * unsignaled state. Therefore for every signal operation, there can
+ * only be one wait operation.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32
+ * then waiting on the semaphore will wait until the value of the
+ * semaphore is greater than or equal to
+ * ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::fence::value.
+ *
+ * If the semaphore object is of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC
+ * then, waiting on the semaphore will wait until the
+ * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence is signaled by the
+ * signaler of the NvSciSyncObj that was associated with this semaphore object.
+ * By default, waiting on such an external semaphore object causes appropriate
+ * memory synchronization operations to be performed over all external memory objects
+ * that are imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. This ensures that
+ * any subsequent accesses made by other importers of the same set of NvSciBuf memory
+ * object(s) are coherent. These operations can be skipped by specifying the flag
+ * ::CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC, which can be used as a
+ * performance optimization when data coherency is not required. But specifying this
+ * flag in scenarios where data coherency is required results in undefined behavior.
+ * Also, for semaphore object of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC,
+ * if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in
+ * ::cuDeviceGetNvSciSyncAttributes to CUDA_NVSCISYNC_ATTR_WAIT, this API will return
+ * CUDA_ERROR_NOT_SUPPORTED.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX,
+ * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT
+ * then the keyed mutex will be acquired when it is released with the key 
+ * specified in ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::keyedmutex::key 
+ * or until the timeout specified by
+ * ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::keyedmutex::timeoutMs
+ * has lapsed. The timeout interval can either be a finite value
+ * specified in milliseconds or an infinite value. In case an infinite
+ * value is specified the timeout never elapses. The windows INFINITE
+ * macro must be used to specify infinite timeout.
+ *
+ * \param extSemArray - External semaphores to be waited on
+ * \param paramsArray - Array of semaphore parameters
+ * \param numExtSems  - Number of semaphores to wait on
+ * \param stream      - Stream to enqueue the wait operations in
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_TIMEOUT
+ * \notefnerr
+ *
+ * \sa ::cuImportExternalSemaphore,
+ * ::cuDestroyExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuWaitExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream);
+
+/**
+ * \brief Destroys an external semaphore
+ *
+ * Destroys an external semaphore object and releases any references
+ * to the underlying resource. Any outstanding signals or waits must
+ * have completed before the semaphore is destroyed.
+ *
+ * \param extSem - External semaphore to be destroyed
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa ::cuImportExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuDestroyExternalSemaphore(CUexternalSemaphore extSem);
+
+/** @} */ /* END CUDA_EXTRES_INTEROP */
+
+/**
+ * \defgroup CUDA_MEMOP Stream Memory Operations
+ *
+ * ___MANBRIEF___ Stream memory operations of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the stream memory operations of the low-level CUDA
+ * driver application programming interface.
+ *
+ * Support for the ::CU_STREAM_WAIT_VALUE_NOR flag can be queried with
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V2.
+ *
+ * Support for the ::cuStreamWriteValue64() and ::cuStreamWaitValue64()
+ * functions, as well as for the ::CU_STREAM_MEM_OP_WAIT_VALUE_64 and
+ * ::CU_STREAM_MEM_OP_WRITE_VALUE_64 flags, can be queried with
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS.
+ *
+ * Support for both ::CU_STREAM_WAIT_VALUE_FLUSH and
+ * ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES requires dedicated platform
+ * hardware features and can be queried with ::cuDeviceGetAttribute() and
+ * ::CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES.
+ *
+ * Note that all memory pointers passed as parameters to these operations
+ * are device pointers. Where necessary a device pointer should be
+ * obtained, for example with ::cuMemHostGetDevicePointer().
+ *
+ * None of the operations accepts pointers to managed memory buffers
+ * (::cuMemAllocManaged).
+ *
+ * \note
+ * Warning:
+ * Improper use of these APIs may deadlock the application. Synchronization 
+ * ordering established through these APIs is not visible to CUDA. CUDA tasks 
+ * that are (even indirectly) ordered by these APIs should also have that order
+ * expressed with CUDA-visible dependencies such as events. This ensures that 
+ * the scheduler does not serialize them in an improper order.
+ *
+ * @{
+ */
+
+/**
+ * \brief Wait on a memory location
+ *
+ * Enqueues a synchronization of the stream on the given memory location. Work
+ * ordered after the operation will block until the given condition on the
+ * memory is satisfied. By default, the condition is to wait for
+ * (int32_t)(*addr - value) >= 0, a cyclic greater-or-equal.
+ * Other condition types can be specified via \p flags.
+ *
+ * If the memory was registered via ::cuMemHostRegister(), the device pointer
+ * should be obtained with ::cuMemHostGetDevicePointer(). This function cannot
+ * be used with managed memory (::cuMemAllocManaged).
+ *
+ * Support for CU_STREAM_WAIT_VALUE_NOR can be queried with ::cuDeviceGetAttribute() and
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V2.
+ *
+ * \note
+ * Warning:
+ * Improper use of this API may deadlock the application. Synchronization 
+ * ordering established through this API is not visible to CUDA. CUDA tasks 
+ * that are (even indirectly) ordered by this API should also have that order
+ * expressed with CUDA-visible dependencies such as events. This ensures that 
+ * the scheduler does not serialize them in an improper order.
+ *
+ * \param stream The stream to synchronize on the memory location.
+ * \param addr The memory location to wait on.
+ * \param value The value to compare with the memory location.
+ * \param flags See ::CUstreamWaitValue_flags.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuStreamWaitValue64,
+ * ::cuStreamWriteValue32,
+ * ::cuStreamWriteValue64,
+ * ::cuStreamBatchMemOp,
+ * ::cuMemHostRegister,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuStreamWaitValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+
+/**
+ * \brief Wait on a memory location
+ *
+ * Enqueues a synchronization of the stream on the given memory location. Work
+ * ordered after the operation will block until the given condition on the
+ * memory is satisfied. By default, the condition is to wait for
+ * (int64_t)(*addr - value) >= 0, a cyclic greater-or-equal.
+ * Other condition types can be specified via \p flags.
+ *
+ * If the memory was registered via ::cuMemHostRegister(), the device pointer
+ * should be obtained with ::cuMemHostGetDevicePointer().
+ *
+ * Support for this can be queried with ::cuDeviceGetAttribute() and
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS.
+ *
+ * \note
+ * Warning:
+ * Improper use of this API may deadlock the application. Synchronization 
+ * ordering established through this API is not visible to CUDA. CUDA tasks 
+ * that are (even indirectly) ordered by this API should also have that order
+ * expressed with CUDA-visible dependencies such as events. This ensures that 
+ * the scheduler does not serialize them in an improper order.
+ *
+ * \param stream The stream to synchronize on the memory location.
+ * \param addr The memory location to wait on.
+ * \param value The value to compare with the memory location.
+ * \param flags See ::CUstreamWaitValue_flags.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuStreamWaitValue32,
+ * ::cuStreamWriteValue32,
+ * ::cuStreamWriteValue64,
+ * ::cuStreamBatchMemOp,
+ * ::cuMemHostRegister,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuStreamWaitValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+
+/**
+ * \brief Write a value to memory
+ *
+ * Write a value to memory.
+ *
+ * If the memory was registered via ::cuMemHostRegister(), the device pointer
+ * should be obtained with ::cuMemHostGetDevicePointer(). This function cannot
+ * be used with managed memory (::cuMemAllocManaged).
+ *
+ * \param stream The stream to do the write in.
+ * \param addr The device address to write to.
+ * \param value The value to write.
+ * \param flags See ::CUstreamWriteValue_flags.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuStreamWriteValue64,
+ * ::cuStreamWaitValue32,
+ * ::cuStreamWaitValue64,
+ * ::cuStreamBatchMemOp,
+ * ::cuMemHostRegister,
+ * ::cuEventRecord
+ */
+CUresult CUDAAPI cuStreamWriteValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+
+/**
+ * \brief Write a value to memory
+ *
+ * Write a value to memory.
+ *
+ * If the memory was registered via ::cuMemHostRegister(), the device pointer
+ * should be obtained with ::cuMemHostGetDevicePointer().
+ *
+ * Support for this can be queried with ::cuDeviceGetAttribute() and
+ * ::CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS.
+ *
+ * \param stream The stream to do the write in.
+ * \param addr The device address to write to.
+ * \param value The value to write.
+ * \param flags See ::CUstreamWriteValue_flags.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuStreamWriteValue32,
+ * ::cuStreamWaitValue32,
+ * ::cuStreamWaitValue64,
+ * ::cuStreamBatchMemOp,
+ * ::cuMemHostRegister,
+ * ::cuEventRecord
+ */
+CUresult CUDAAPI cuStreamWriteValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+
+/**
+ * \brief Batch operations to synchronize the stream via memory operations
+ *
+ * This is a batch version of ::cuStreamWaitValue32() and ::cuStreamWriteValue32().
+ * Batching operations may avoid some performance overhead in both the API call
+ * and the device execution versus adding them to the stream in separate API
+ * calls. The operations are enqueued in the order they appear in the array.
+ *
+ * See ::CUstreamBatchMemOpType for the full set of supported operations, and
+ * ::cuStreamWaitValue32(), ::cuStreamWaitValue64(), ::cuStreamWriteValue32(),
+ * and ::cuStreamWriteValue64() for details of specific operations.
+ *
+ * See related APIs for details on querying support for specific operations.
+ *
+ * \note
+ * Warning:
+ * Improper use of this API may deadlock the application. Synchronization 
+ * ordering established through this API is not visible to CUDA. CUDA tasks 
+ * that are (even indirectly) ordered by this API should also have that order
+ * expressed with CUDA-visible dependencies such as events. This ensures that 
+ * the scheduler does not serialize them in an improper order. For more 
+ * information, see the Stream Memory Operations section in the programming 
+ * guide(https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html).
+ *
+ * \param stream The stream to enqueue the operations in.
+ * \param count The number of operations in the array. Must be less than 256.
+ * \param paramArray The types and parameters of the individual operations.
+ * \param flags Reserved for future expansion; must be 0.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \notefnerr
+ *
+ * \sa ::cuStreamWaitValue32,
+ * ::cuStreamWaitValue64,
+ * ::cuStreamWriteValue32,
+ * ::cuStreamWriteValue64,
+ * ::cuMemHostRegister
+ */
+CUresult CUDAAPI cuStreamBatchMemOp(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+
+/** @} */ /* END CUDA_MEMOP */
+
+/**
+ * \defgroup CUDA_EXEC Execution Control
+ *
+ * ___MANBRIEF___ execution control functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the execution control functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns information about a function
+ *
+ * Returns in \p *pi the integer value of the attribute \p attrib on the kernel
+ * given by \p hfunc. The supported attributes are:
+ * - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threads
+ *   per block, beyond which a launch of the function would fail. This number
+ *   depends on both the function and the device on which the function is
+ *   currently loaded.
+ * - ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of
+ *   statically-allocated shared memory per block required by this function.
+ *   This does not include dynamically-allocated shared memory requested by
+ *   the user at runtime.
+ * - ::CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES: The size in bytes of user-allocated
+ *   constant memory required by this function.
+ * - ::CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES: The size in bytes of local memory
+ *   used by each thread of this function.
+ * - ::CU_FUNC_ATTRIBUTE_NUM_REGS: The number of registers used by each thread
+ *   of this function.
+ * - ::CU_FUNC_ATTRIBUTE_PTX_VERSION: The PTX virtual architecture version for
+ *   which the function was compiled. This value is the major PTX version * 10
+ *   + the minor PTX version, so a PTX version 1.3 function would return the
+ *   value 13. Note that this may return the undefined value of 0 for cubins
+ *   compiled prior to CUDA 3.0.
+ * - ::CU_FUNC_ATTRIBUTE_BINARY_VERSION: The binary architecture version for
+ *   which the function was compiled. This value is the major binary
+ *   version * 10 + the minor binary version, so a binary version 1.3 function
+ *   would return the value 13. Note that this will return a value of 10 for
+ *   legacy cubins that do not have a properly-encoded binary architecture
+ *   version.
+ * - ::CU_FUNC_CACHE_MODE_CA: The attribute to indicate whether the function has
+ *   been compiled with user specified option "-Xptxas --dlcm=ca" set .
+ * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: The maximum size in bytes of
+ *   dynamically-allocated shared memory.
+ * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: Preferred shared memory-L1
+ *   cache split ratio in percent of total shared memory.
+ * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET: If this attribute is set, the
+ *   kernel must launch with a valid cluster size specified.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in
+ *   blocks.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in
+ *   blocks.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in
+ *   blocks.
+ * - ::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether
+ *   the function can be launched with non-portable cluster size. 1 is allowed,
+ *   0 is disallowed. A non-portable cluster size may only function on the
+ *   specific SKUs the program is tested on. The launch might fail if the
+ *   program is run on a different hardware platform. CUDA API provides
+ *   cudaOccupancyMaxActiveClusters to assist with checking whether the desired
+ *   size can be launched on the current device. A portable cluster size is
+ *   guaranteed to be functional on all compute capabilities higher than the
+ *   target compute capability. The portable cluster size for sm_90 is 8 blocks
+ *   per cluster. This value may increase for future compute capabilities. The
+ *   specific hardware unit may support higher cluster sizes that’s not
+ *   guaranteed to be portable.
+ * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block
+ *   scheduling policy of a function. The value type is CUclusterSchedulingPolicy.
+ *
+ * With a few execeptions, function attributes may also be queried on unloaded
+ * function handles returned from ::cuModuleEnumerateFunctions.
+ * ::CUDA_ERROR_FUNCTION_NOT_LOADED is returned if the attribute requires a fully
+ * loaded function but the function is not loaded. The loading state of a function
+ * may be queried using ::cuFuncIsloaded. ::cuFuncLoad may be called to explicitly
+ * load a function before querying the following attributes that require the function
+ * to be loaded:
+ * - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK
+ * - ::CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES
+ * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES
+ *
+ * \param pi     - Returned attribute value
+ * \param attrib - Attribute requested
+ * \param hfunc  - Function to query attribute of
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_FUNCTION_NOT_LOADED
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cuLaunchKernel,
+ * ::cudaFuncGetAttributes,
+ * ::cudaFuncSetAttribute,
+ * ::cuFuncIsLoaded,
+ * ::cuFuncLoad,
+ * ::cuKernelGetAttribute
+ */
+CUresult CUDAAPI cuFuncGetAttribute(int *pi, CUfunction_attribute attrib, CUfunction hfunc);
+
+/**
+ * \brief Sets information about a function
+ *
+ * This call sets the value of a specified attribute \p attrib on the kernel given
+ * by \p hfunc to an integer value specified by \p val
+ * This function returns CUDA_SUCCESS if the new value of the attribute could be
+ * successfully set. If the set fails, this call will return an error.
+ * Not all attributes can have values set. Attempting to set a value on a read-only
+ * attribute will result in an error (CUDA_ERROR_INVALID_VALUE)
+ *
+ * Supported attributes for the cuFuncSetAttribute call are:
+ * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: This maximum size in bytes of
+ *   dynamically-allocated shared memory. The value should contain the requested
+ *   maximum size of dynamically-allocated shared memory. The sum of this value and
+ *   the function attribute ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES cannot exceed the
+ *   device attribute ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN.
+ *   The maximal size of requestable dynamic shared memory may differ by GPU
+ *   architecture.
+ * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: On devices where the L1
+ *   cache and shared memory use the same hardware resources, this sets the shared memory
+ *   carveout preference, in percent of the total shared memory. 
+ *   See ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR
+ *   This is only a hint, and the driver can choose a different ratio if required to execute the function.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+ * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED.
+ * - ::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether
+ *   the function can be launched with non-portable cluster size. 1 is allowed,
+ *   0 is disallowed.
+ * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block
+ *   scheduling policy of a function. The value type is CUclusterSchedulingPolicy.
+ *
+ * \param hfunc  - Function to query attribute of
+ * \param attrib - Attribute requested
+ * \param value   - The value to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cuLaunchKernel,
+ * ::cudaFuncGetAttributes,
+ * ::cudaFuncSetAttribute,
+ * ::cuKernelSetAttribute
+ */
+CUresult CUDAAPI cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value);
+
+/**
+ * \brief Sets the preferred cache configuration for a device function
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p config the preferred cache configuration for
+ * the device function \p hfunc. This is only a preference. The driver will use
+ * the requested configuration if possible, but it is free to choose a different
+ * configuration if required to execute \p hfunc.  Any context-wide preference
+ * set via ::cuCtxSetCacheConfig() will be overridden by this per-function
+ * setting unless the per-function setting is ::CU_FUNC_CACHE_PREFER_NONE. In
+ * that case, the current context-wide setting will be used.
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ *
+ * The supported cache configurations are:
+ * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default)
+ * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache
+ * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory
+ * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory
+ *
+ * \param hfunc  - Kernel to configure cache for
+ * \param config - Requested cache configuration
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cuLaunchKernel,
+ * ::cudaFuncSetCacheConfig,
+ * ::cuKernelSetCacheConfig
+ */
+CUresult CUDAAPI cuFuncSetCacheConfig(CUfunction hfunc, CUfunc_cache config);
+
+
+/**
+ * \brief Returns a module handle
+ *
+ * Returns in \p *hmod the handle of the module that function \p hfunc
+ * is located in. The lifetime of the module corresponds to the lifetime of
+ * the context it was loaded in or until the module is explicitly unloaded.
+ *
+ * The CUDA runtime manages its own modules loaded into the primary context.
+ * If the handle returned by this API refers to a module loaded by the CUDA runtime,
+ * calling ::cuModuleUnload() on that module will result in undefined behavior.
+ *
+ * \param hmod - Returned module handle
+ * \param hfunc   - Function to retrieve module for
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \notefnerr
+ *
+ */
+CUresult CUDAAPI cuFuncGetModule(CUmodule *hmod, CUfunction hfunc);
+
+/**
+ * \brief Returns the function name for a ::CUfunction handle
+ *
+ * Returns in \p **name the function name associated with the function handle \p hfunc .
+ * The function name is returned as a null-terminated string. The returned name is only 
+ * valid when the function handle is valid. If the module is unloaded or reloaded, one 
+ * must call the API again to get the updated name. This API may return a mangled name if
+ * the function is not declared as having C linkage. If either \p **name or \p hfunc 
+ * is NULL, ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * \param name - The returned name of the function
+ * \param hfunc - The function handle to retrieve the name for 
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+ */
+CUresult CUDAAPI cuFuncGetName(const char **name, CUfunction hfunc);
+
+/**
+ * \brief Returns the offset and size of a kernel parameter in the device-side parameter layout
+ *
+ * Queries the kernel parameter at \p paramIndex into \p func's list of parameters, and returns
+ * in \p paramOffset and \p paramSize the offset and size, respectively, where the parameter
+ * will reside in the device-side parameter layout. This information can be used to update kernel
+ * node parameters from the device via ::cudaGraphKernelNodeSetParam() and
+ * ::cudaGraphKernelNodeUpdatesApply(). \p paramIndex must be less than the number of parameters
+ * that \p func takes. \p paramSize can be set to NULL if only the parameter offset is desired.
+ *
+ * \param func        - The function to query
+ * \param paramIndex  - The parameter index to query
+ * \param paramOffset - Returns the offset into the device-side parameter layout at which the parameter resides
+ * \param paramSize   - Optionally returns the size of the parameter in the device-side parameter layout
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ *
+* \sa ::cuKernelGetParamInfo
+ */
+CUresult CUDAAPI cuFuncGetParamInfo(CUfunction func, size_t paramIndex, size_t *paramOffset, size_t *paramSize);
+
+typedef enum CUfunctionLoadingState_enum {
+    CU_FUNCTION_LOADING_STATE_UNLOADED = 0,
+    CU_FUNCTION_LOADING_STATE_LOADED = 1,
+    CU_FUNCTION_LOADING_STATE_MAX
+} CUfunctionLoadingState;
+
+/**
+ * \brief Returns if the function is loaded
+ *
+ * Returns in \p state the loading state of \p function.
+ *
+ * \param state - returned loading state
+ * \param function - the function to check
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuFuncLoad,
+ * ::cuModuleEnumerateFunctions
+ */
+CUresult CUDAAPI cuFuncIsLoaded(CUfunctionLoadingState *state, CUfunction function);
+
+/**
+ * \brief Loads a function
+ *
+ * Finalizes function loading for \p function. Calling this API with a
+ * fully loaded function has no effect.
+ *
+ * \param function - the function to load
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuModuleEnumerateFunctions,
+ * ::cuFuncIsLoaded
+ */
+CUresult CUDAAPI cuFuncLoad(CUfunction function);
+
+/**
+ * \brief Launches a CUDA function ::CUfunction or a CUDA kernel ::CUkernel
+ *
+ * Invokes the function ::CUfunction or the kernel ::CUkernel \p f
+ * on a \p gridDimX x \p gridDimY x \p gridDimZ grid of blocks.
+ * Each block contains \p blockDimX x \p blockDimY x
+ * \p blockDimZ threads.
+ *
+ * \p sharedMemBytes sets the amount of dynamic shared memory that will be
+ * available to each thread block.
+ *
+ * Kernel parameters to \p f can be specified in one of two ways:
+ *
+ * 1) Kernel parameters can be specified via \p kernelParams.  If \p f
+ * has N parameters, then \p kernelParams needs to be an array of N
+ * pointers.  Each of \p kernelParams[0] through \p kernelParams[N-1]
+ * must point to a region of memory from which the actual kernel
+ * parameter will be copied.  The number of kernel parameters and their
+ * offsets and sizes do not need to be specified as that information is
+ * retrieved directly from the kernel's image.
+ *
+ * 2) Kernel parameters can also be packaged by the application into
+ * a single buffer that is passed in via the \p extra parameter.
+ * This places the burden on the application of knowing each kernel
+ * parameter's size and alignment/padding within the buffer.  Here is
+ * an example of using the \p extra parameter in this manner:
+ * \code
+    size_t argBufferSize;
+    char argBuffer[256];
+
+    // populate argBuffer and argBufferSize
+
+    void *config[] = {
+        CU_LAUNCH_PARAM_BUFFER_POINTER, argBuffer,
+        CU_LAUNCH_PARAM_BUFFER_SIZE,    &argBufferSize,
+        CU_LAUNCH_PARAM_END
+    };
+    status = cuLaunchKernel(f, gx, gy, gz, bx, by, bz, sh, s, NULL, config);
+ * \endcode
+ *
+ * The \p extra parameter exists to allow ::cuLaunchKernel to take
+ * additional less commonly used arguments.  \p extra specifies a list of
+ * names of extra settings and their corresponding values.  Each extra
+ * setting name is immediately followed by the corresponding value.  The
+ * list must be terminated with either NULL or ::CU_LAUNCH_PARAM_END.
+ *
+ * - ::CU_LAUNCH_PARAM_END, which indicates the end of the \p extra
+ *   array;
+ * - ::CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next
+ *   value in \p extra will be a pointer to a buffer containing all
+ *   the kernel parameters for launching kernel \p f;
+ * - ::CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next
+ *   value in \p extra will be a pointer to a size_t containing the
+ *   size of the buffer specified with ::CU_LAUNCH_PARAM_BUFFER_POINTER;
+ *
+ * The error ::CUDA_ERROR_INVALID_VALUE will be returned if kernel
+ * parameters are specified with both \p kernelParams and \p extra
+ * (i.e. both \p kernelParams and \p extra are non-NULL).
+ *
+ * Calling ::cuLaunchKernel() invalidates the persistent function state
+ * set through the following deprecated APIs:
+ *  ::cuFuncSetBlockShape(),
+ *  ::cuFuncSetSharedSize(),
+ *  ::cuParamSetSize(),
+ *  ::cuParamSeti(),
+ *  ::cuParamSetf(),
+ *  ::cuParamSetv().
+ *
+ * Note that to use ::cuLaunchKernel(), the kernel \p f must either have
+ * been compiled with toolchain version 3.2 or later so that it will
+ * contain kernel parameter information, or have no kernel parameters.
+ * If either of these conditions is not met, then ::cuLaunchKernel() will
+ * return ::CUDA_ERROR_INVALID_IMAGE.
+ *
+ * Note that the API can also be used to launch context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to launch
+ * the kernel on will either be taken from the specified stream \p hStream
+ * or the current context in case of NULL stream.
+ *
+ * \param f              - Function ::CUfunction or Kernel ::CUkernel to launch
+ * \param gridDimX       - Width of grid in blocks
+ * \param gridDimY       - Height of grid in blocks
+ * \param gridDimZ       - Depth of grid in blocks
+ * \param blockDimX      - X dimension of each thread block
+ * \param blockDimY      - Y dimension of each thread block
+ * \param blockDimZ      - Z dimension of each thread block
+ * \param sharedMemBytes - Dynamic shared-memory size per thread block in bytes
+ * \param hStream        - Stream identifier
+ * \param kernelParams   - Array of pointers to kernel parameters
+ * \param extra          - Extra options
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_IMAGE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cudaLaunchKernel,
+ * ::cuLibraryGetKernel,
+ * ::cuKernelSetCacheConfig,
+ * ::cuKernelGetAttribute,
+ * ::cuKernelSetAttribute
+ */
+CUresult CUDAAPI cuLaunchKernel(CUfunction f,
+                                unsigned int gridDimX,
+                                unsigned int gridDimY,
+                                unsigned int gridDimZ,
+                                unsigned int blockDimX,
+                                unsigned int blockDimY,
+                                unsigned int blockDimZ,
+                                unsigned int sharedMemBytes,
+                                CUstream hStream,
+                                void **kernelParams,
+                                void **extra);
+
+/**
+ * \brief Launches a CUDA function ::CUfunction or a CUDA kernel ::CUkernel with launch-time configuration
+ *
+ * Invokes the function ::CUfunction or the kernel ::CUkernel \p f with the specified launch-time configuration
+ * \p config.
+ *
+ * The ::CUlaunchConfig structure is defined as:
+ *
+ * \code
+ *       typedef struct CUlaunchConfig_st {
+ *     unsigned int gridDimX;
+ *     unsigned int gridDimY;
+ *     unsigned int gridDimZ;
+ *     unsigned int blockDimX;
+ *     unsigned int blockDimY;
+ *     unsigned int blockDimZ;
+ *     unsigned int sharedMemBytes;
+ *     CUstream hStream;
+ *     CUlaunchAttribute *attrs;
+ *     unsigned int numAttrs;
+ * } CUlaunchConfig;
+ * \endcode
+ *
+ * where:
+ * - ::CUlaunchConfig::gridDimX is the width of the grid in blocks.
+ * - ::CUlaunchConfig::gridDimY is the height of the grid in blocks.
+ * - ::CUlaunchConfig::gridDimZ is the depth of the grid in blocks.
+ * - ::CUlaunchConfig::blockDimX is the X dimension of each thread block.
+ * - ::CUlaunchConfig::blockDimX is the Y dimension of each thread block.
+ * - ::CUlaunchConfig::blockDimZ is the Z dimension of each thread block.
+ * - ::CUlaunchConfig::sharedMemBytes is the dynamic shared-memory size per
+ *   thread block in bytes.
+ * - ::CUlaunchConfig::hStream is the handle to the stream to perform the launch
+ *   in. The CUDA context associated with this stream must match that associated
+ *   with function f.
+ * - ::CUlaunchConfig::attrs is an array of ::CUlaunchConfig::numAttrs
+ *   continguous ::CUlaunchAttribute elements. The value of this pointer is not
+ *   considered if ::CUlaunchConfig::numAttrs is zero. However, in that case, it
+ *   is recommended to set the pointer to NULL.
+ * - ::CUlaunchConfig::numAttrs is the number of attributes populating the
+ *   first ::CUlaunchConfig::numAttrs positions of the ::CUlaunchConfig::attrs
+ *   array.
+ *
+ * Launch-time configuration is specified by adding entries to
+ * ::CUlaunchConfig::attrs. Each entry is an attribute ID and a corresponding
+ * attribute value.
+ *
+ * The ::CUlaunchAttribute structure is defined as:
+ * \code
+ *       typedef struct CUlaunchAttribute_st {
+ *     CUlaunchAttributeID id;
+ *     CUlaunchAttributeValue value;
+ * } CUlaunchAttribute;
+ * \endcode
+ * where:
+ * - ::CUlaunchAttribute::id is a unique enum identifying the attribute.
+ * - ::CUlaunchAttribute::value is a union that hold the attribute value.
+ *
+ * An example of using the \p config parameter:
+ * \code
+ *       CUlaunchAttribute coopAttr = {.id = CU_LAUNCH_ATTRIBUTE_COOPERATIVE,
+ *                               .value = 1};
+ * CUlaunchConfig config = {... // set block and grid dimensions
+ *                        .attrs = &coopAttr,
+ *                        .numAttrs = 1};
+ *
+ * cuLaunchKernelEx(&config, kernel, NULL, NULL);
+ * \endcode
+ *
+ * The ::CUlaunchAttributeID enum is defined as:
+ * \code
+ *       typedef enum CUlaunchAttributeID_enum {
+ *     CU_LAUNCH_ATTRIBUTE_IGNORE = 0,
+ *     CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW   = 1,
+ *     CU_LAUNCH_ATTRIBUTE_COOPERATIVE            = 2,
+ *     CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY = 3,
+ *     CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION                    = 4,
+ *     CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 5,
+ *     CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION    = 6,
+ *     CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT                   = 7,
+ *     CU_LAUNCH_ATTRIBUTE_PRIORITY               = 8,
+ *     CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP    = 9,
+ *     CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN        = 10,
+ *     CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION = 11,
+ *     CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT = 12,
+ *     CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE = 13,
+ * } CUlaunchAttributeID;
+ * \endcode
+ *
+ * and the corresponding ::CUlaunchAttributeValue union as :
+ * \code
+ *       typedef union CUlaunchAttributeValue_union {
+ *     CUaccessPolicyWindow accessPolicyWindow;
+ *     int cooperative;
+ *     CUsynchronizationPolicy syncPolicy;
+ *     struct {
+ *         unsigned int x;
+ *         unsigned int y;
+ *         unsigned int z;
+ *     } clusterDim;
+ *     CUclusterSchedulingPolicy clusterSchedulingPolicyPreference;
+ *     int programmaticStreamSerializationAllowed;
+ *     struct {
+ *         CUevent event;
+ *         int flags;
+ *         int triggerAtBlockStart;
+ *     } programmaticEvent;
+ *     int priority;
+ *     CUlaunchMemSyncDomainMap memSyncDomainMap;
+ *     CUlaunchMemSyncDomain memSyncDomain;
+ *     struct {
+ *         unsigned int x;
+ *         unsigned int y;
+ *         unsigned int z;
+ *     } preferredClusterDim;
+ *     struct {
+ *         CUevent event;
+ *         int flags;
+ *     } launchCompletionEvent;
+ *     struct {
+ *         int deviceUpdatable;
+ *         CUgraphDeviceNode devNode;
+ *     } deviceUpdatableKernelNode;
+ * } CUlaunchAttributeValue;
+ * \endcode
+ *
+ * Setting ::CU_LAUNCH_ATTRIBUTE_COOPERATIVE to a non-zero value causes the
+ * kernel launch to be a cooperative launch, with exactly the same usage and
+ * semantics of ::cuLaunchCooperativeKernel.
+ *
+ * Setting ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION to a non-zero
+ * values causes the kernel to use programmatic means to resolve its stream
+ * dependency -- enabling the CUDA runtime to opportunistically allow the grid's
+ * execution to overlap with the previous kernel in the stream, if that kernel
+ * requests the overlap.
+ *
+ * ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT records an event along with the
+ * kernel launch. Event recorded through this launch attribute is guaranteed to
+ * only trigger after all block in the associated kernel trigger the event. A
+ * block can trigger the event through PTX launchdep.release or CUDA builtin
+ * function cudaTriggerProgrammaticLaunchCompletion(). A trigger can also be
+ * inserted at the beginning of each block's execution if triggerAtBlockStart is
+ * set to non-0. Note that dependents (including the CPU thread calling
+ * cuEventSynchronize()) are not guaranteed to observe the release precisely
+ * when it is released. For example, cuEventSynchronize() may only observe the
+ * event trigger long after the associated kernel has completed. This recording
+ * type is primarily meant for establishing programmatic dependency between
+ * device tasks. The event supplied must not be an interprocess or interop
+ * event. The event must disable timing (i.e. created with
+ * ::CU_EVENT_DISABLE_TIMING flag set).
+ *
+ * ::CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT records an event along with
+ * the kernel launch. Nominally, the event is triggered once all blocks of the
+ * kernel have begun execution. Currently this is a best effort. If a kernel B
+ * has a launch completion dependency on a kernel A, B may wait until A is
+ * complete. Alternatively, blocks of B may begin before all blocks of A have
+ * begun, for example:
+ *
+ *  - If B can claim execution resources unavaiable to A, for example if they
+ *    run on different GPUs.
+ *  - If B is a higher priority than A.
+ *
+ * Exercise caution if such an ordering inversion could lead to deadlock. The
+ * event supplied must not be an interprocess or interop event. The event must
+ * disable timing (i.e. must be created with the ::CU_EVENT_DISABLE_TIMING flag
+ * set).
+ *
+ * Setting ::CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE to 1
+ * on a captured launch causes the resulting kernel node to be device-updatable.
+ * This attribute is specific to graphs, and passing it to a launch in a
+ * non-capturing stream results in an error. Passing a value other than 0 or 1 is
+ * not allowed.
+ *
+ * On success, a handle will be returned via
+ * ::CUlaunchAttributeValue::deviceUpdatableKernelNode::devNode which can be passed
+ * to the various device-side update functions to update the node's kernel parameters
+ * from within another kernel. For more information on the types of device updates
+ * that can be made, as well as the relevant limitations thereof, see
+ * ::cudaGraphKernelNodeUpdatesApply.
+ *
+ * Kernel nodes which are device-updatable have additional restrictions compared to regular
+ * kernel nodes. Firstly, device-updatable nodes cannot be removed from their graph via
+ * ::cuGraphDestroyNode. Additionally, once opted-in to this functionality, a node cannot
+ * opt out, and any attempt to set the attribute to 0 will result in an error. Graphs
+ * containing one or more device-updatable node also do not allow multiple instantiation.
+ *
+ * ::CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION allows the kernel launch to
+ * specify a preferred substitute cluster dimension. Blocks may be grouped
+ * according to either the dimensions specified with this attribute (grouped
+ * into a "preferred substitute cluster"), or the one specified with
+ * ::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION attribute (grouped into a "regular
+ * cluster"). The cluster dimensions of a "preferred substitute cluster" shall
+ * be an integer multiple greater than zero of the regular cluster dimensions.
+ * The device will attempt - on a best-effort basis - to group thread blocks
+ * into preferred clusters over grouping them into regular clusters. When it
+ * deems necessary (primarily when the device temporarily runs out of physical
+ * resources to launch the larger preferred clusters), the device may switch to
+ * launch the regular clusters instead to attempt to utilize as much of the
+ * physical device resources as possible.
+ *
+ * Each type of cluster will have its enumeration / coordinate setup as if the
+ * grid consists solely of its type of cluster. For example, if the preferred
+ * substitute cluster dimensions double the regular cluster dimensions, there
+ * might be simultaneously a regular cluster indexed at (1,0,0), and a preferred
+ * cluster indexed at (1,0,0). In this example, the preferred substitute cluster
+ * (1,0,0) replaces regular clusters (2,0,0) and (3,0,0) and groups their
+ * blocks.
+ *
+ * This attribute will only take effect when a regular cluster dimension has
+ * been specified. The preferred substitute The preferred substitute cluster
+ * dimension must be an integer multiple greater than zero of the regular
+ * cluster dimension and must divide the grid. It must also be no more than
+ * `maxBlocksPerCluster`, if it is set in the kernel's `__launch_bounds__`.
+ * Otherwise it must be less than the maximum value the driver can support.
+ * Otherwise, setting this attribute to a value physically unable to fit on any
+ * particular device is permitted.
+ *
+ * The effect of other attributes is consistent with their effect when set via
+ * persistent APIs.
+ *
+ * See ::cuStreamSetAttribute for
+ * - ::CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW
+ * - ::CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY
+ *
+ * See ::cuFuncSetAttribute for
+ * - ::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+ * - ::CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE
+ *
+ * Kernel parameters to \p f can be specified in the same ways that they can be
+ * using ::cuLaunchKernel.
+ *
+ * Note that the API can also be used to launch context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to launch
+ * the kernel on will either be taken from the specified stream ::CUlaunchConfig::hStream
+ * or the current context in case of NULL stream.
+ *
+ * \param config         - Config to launch
+ * \param f              - Function ::CUfunction or Kernel ::CUkernel to launch
+ * \param kernelParams   - Array of pointers to kernel parameters
+ * \param extra          - Extra options
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_IMAGE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cudaLaunchKernel,
+ * ::cudaLaunchKernelEx,
+ * ::cuLibraryGetKernel,
+ * ::cuKernelSetCacheConfig,
+ * ::cuKernelGetAttribute,
+ * ::cuKernelSetAttribute
+ */
+CUresult CUDAAPI cuLaunchKernelEx(const CUlaunchConfig *config,
+                                  CUfunction f,
+                                  void **kernelParams,
+                                  void **extra);
+
+/**
+ * \brief Launches a CUDA function ::CUfunction or a CUDA kernel ::CUkernel where thread blocks
+ * can cooperate and synchronize as they execute
+ *
+ * Invokes the function ::CUfunction or the kernel ::CUkernel \p f on a \p gridDimX x \p gridDimY x \p gridDimZ
+ * grid of blocks. Each block contains \p blockDimX x \p blockDimY x
+ * \p blockDimZ threads.
+ *
+ * \p sharedMemBytes sets the amount of dynamic shared memory that will be
+ * available to each thread block.
+ *
+ * The device on which this kernel is invoked must have a non-zero value for
+ * the device attribute ::CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH.
+ *
+ * The total number of blocks launched cannot exceed the maximum number of blocks per
+ * multiprocessor as returned by ::cuOccupancyMaxActiveBlocksPerMultiprocessor (or
+ * ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors
+ * as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
+ *
+ * The kernel cannot make use of CUDA dynamic parallelism.
+ *
+ * Kernel parameters must be specified via \p kernelParams.  If \p f
+ * has N parameters, then \p kernelParams needs to be an array of N
+ * pointers.  Each of \p kernelParams[0] through \p kernelParams[N-1]
+ * must point to a region of memory from which the actual kernel
+ * parameter will be copied.  The number of kernel parameters and their
+ * offsets and sizes do not need to be specified as that information is
+ * retrieved directly from the kernel's image.
+ *
+ * Calling ::cuLaunchCooperativeKernel() sets persistent function state that is
+ * the same as function state set through ::cuLaunchKernel API
+ *
+ * When the kernel \p f is launched via ::cuLaunchCooperativeKernel(), the previous
+ * block shape, shared size and parameter info associated with \p f
+ * is overwritten.
+ *
+ * Note that to use ::cuLaunchCooperativeKernel(), the kernel \p f must either have
+ * been compiled with toolchain version 3.2 or later so that it will
+ * contain kernel parameter information, or have no kernel parameters.
+ * If either of these conditions is not met, then ::cuLaunchCooperativeKernel() will
+ * return ::CUDA_ERROR_INVALID_IMAGE.
+ *
+ * Note that the API can also be used to launch context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to launch
+ * the kernel on will either be taken from the specified stream \p hStream
+ * or the current context in case of NULL stream.
+ *
+ * \param f              - Function ::CUfunction or Kernel ::CUkernel to launch
+ * \param gridDimX       - Width of grid in blocks
+ * \param gridDimY       - Height of grid in blocks
+ * \param gridDimZ       - Depth of grid in blocks
+ * \param blockDimX      - X dimension of each thread block
+ * \param blockDimY      - Y dimension of each thread block
+ * \param blockDimZ      - Z dimension of each thread block
+ * \param sharedMemBytes - Dynamic shared-memory size per thread block in bytes
+ * \param hStream        - Stream identifier
+ * \param kernelParams   - Array of pointers to kernel parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_IMAGE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED,
+ * ::CUDA_ERROR_NOT_FOUND
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cuLaunchCooperativeKernelMultiDevice,
+ * ::cudaLaunchCooperativeKernel,
+ * ::cuLibraryGetKernel,
+ * ::cuKernelSetCacheConfig,
+ * ::cuKernelGetAttribute,
+ * ::cuKernelSetAttribute
+ */
+CUresult CUDAAPI cuLaunchCooperativeKernel(CUfunction f,
+                                unsigned int gridDimX,
+                                unsigned int gridDimY,
+                                unsigned int gridDimZ,
+                                unsigned int blockDimX,
+                                unsigned int blockDimY,
+                                unsigned int blockDimZ,
+                                unsigned int sharedMemBytes,
+                                CUstream hStream,
+                                void **kernelParams);
+
+/**
+ * \brief Launches CUDA functions on multiple devices where thread blocks can cooperate and synchronize as they execute
+ *
+ * \deprecated This function is deprecated as of CUDA 11.3.
+ *
+ * Invokes kernels as specified in the \p launchParamsList array where each element
+ * of the array specifies all the parameters required to perform a single kernel launch.
+ * These kernels can cooperate and synchronize as they execute. The size of the array is
+ * specified by \p numDevices.
+ *
+ * No two kernels can be launched on the same device. All the devices targeted by this
+ * multi-device launch must be identical. All devices must have a non-zero value for the
+ * device attribute ::CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH.
+ *
+ * All kernels launched must be identical with respect to the compiled code. Note that
+ * any __device__, __constant__ or __managed__ variables present in the module that owns
+ * the kernel launched on each device, are independently instantiated on every device.
+ * It is the application's responsibility to ensure these variables are initialized and
+ * used appropriately.
+ *
+ * The size of the grids as specified in blocks, the size of the blocks themselves
+ * and the amount of shared memory used by each thread block must also match across
+ * all launched kernels.
+ *
+ * The streams used to launch these kernels must have been created via either ::cuStreamCreate
+ * or ::cuStreamCreateWithPriority. The NULL stream or ::CU_STREAM_LEGACY or ::CU_STREAM_PER_THREAD
+ * cannot be used.
+ *
+ * The total number of blocks launched per kernel cannot exceed the maximum number of blocks
+ * per multiprocessor as returned by ::cuOccupancyMaxActiveBlocksPerMultiprocessor (or
+ * ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors
+ * as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT. Since the
+ * total number of blocks launched per device has to match across all devices, the maximum
+ * number of blocks that can be launched per device will be limited by the device with the
+ * least number of multiprocessors.
+ *
+ * The kernels cannot make use of CUDA dynamic parallelism.
+ *
+ * The ::CUDA_LAUNCH_PARAMS structure is defined as:
+ * \code
+        typedef struct CUDA_LAUNCH_PARAMS_st
+        {
+            CUfunction function;
+            unsigned int gridDimX;
+            unsigned int gridDimY;
+            unsigned int gridDimZ;
+            unsigned int blockDimX;
+            unsigned int blockDimY;
+            unsigned int blockDimZ;
+            unsigned int sharedMemBytes;
+            CUstream hStream;
+            void **kernelParams;
+        } CUDA_LAUNCH_PARAMS;
+ * \endcode
+ * where:
+ * - ::CUDA_LAUNCH_PARAMS::function specifies the kernel to be launched. All functions must
+ *   be identical with respect to the compiled code.
+ *   Note that you can also specify context-less kernel ::CUkernel by querying the handle
+ *   using ::cuLibraryGetKernel() and then casting to ::CUfunction. In this case, the context to
+ *   launch the kernel on be taken from the specified stream ::CUDA_LAUNCH_PARAMS::hStream.
+ * - ::CUDA_LAUNCH_PARAMS::gridDimX is the width of the grid in blocks. This must match across
+ *   all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::gridDimY is the height of the grid in blocks. This must match across
+ *   all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::gridDimZ is the depth of the grid in blocks. This must match across
+ *   all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::blockDimX is the X dimension of each thread block. This must match across
+ *   all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::blockDimX is the Y dimension of each thread block. This must match across
+ *   all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::blockDimZ is the Z dimension of each thread block. This must match across
+ *   all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::sharedMemBytes is the dynamic shared-memory size per thread block in bytes.
+ *   This must match across all kernels launched.
+ * - ::CUDA_LAUNCH_PARAMS::hStream is the handle to the stream to perform the launch in. This cannot
+ *   be the NULL stream or ::CU_STREAM_LEGACY or ::CU_STREAM_PER_THREAD. The CUDA context associated
+ *   with this stream must match that associated with ::CUDA_LAUNCH_PARAMS::function.
+ * - ::CUDA_LAUNCH_PARAMS::kernelParams is an array of pointers to kernel parameters. If
+ *   ::CUDA_LAUNCH_PARAMS::function has N parameters, then ::CUDA_LAUNCH_PARAMS::kernelParams
+ *   needs to be an array of N pointers. Each of ::CUDA_LAUNCH_PARAMS::kernelParams[0] through
+ *   ::CUDA_LAUNCH_PARAMS::kernelParams[N-1] must point to a region of memory from which the actual
+ *   kernel parameter will be copied. The number of kernel parameters and their offsets and sizes
+ *   do not need to be specified as that information is retrieved directly from the kernel's image.
+ *
+ * By default, the kernel won't begin execution on any GPU until all prior work in all the specified
+ * streams has completed. This behavior can be overridden by specifying the flag
+ * ::CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC. When this flag is specified, each kernel
+ * will only wait for prior work in the stream corresponding to that GPU to complete before it begins
+ * execution.
+ *
+ * Similarly, by default, any subsequent work pushed in any of the specified streams will not begin
+ * execution until the kernels on all GPUs have completed. This behavior can be overridden by specifying
+ * the flag ::CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC. When this flag is specified,
+ * any subsequent work pushed in any of the specified streams will only wait for the kernel launched
+ * on the GPU corresponding to that stream to complete before it begins execution.
+ *
+ * Calling ::cuLaunchCooperativeKernelMultiDevice() sets persistent function state that is
+ * the same as function state set through ::cuLaunchKernel API when called individually for each
+ * element in \p launchParamsList.
+ *
+ * When kernels are launched via ::cuLaunchCooperativeKernelMultiDevice(), the previous
+ * block shape, shared size and parameter info associated with each ::CUDA_LAUNCH_PARAMS::function
+ * in \p launchParamsList is overwritten.
+ *
+ * Note that to use ::cuLaunchCooperativeKernelMultiDevice(), the kernels must either have
+ * been compiled with toolchain version 3.2 or later so that it will
+ * contain kernel parameter information, or have no kernel parameters.
+ * If either of these conditions is not met, then ::cuLaunchCooperativeKernelMultiDevice() will
+ * return ::CUDA_ERROR_INVALID_IMAGE.
+ *
+ * \param launchParamsList - List of launch parameters, one per device
+ * \param numDevices       - Size of the \p launchParamsList array
+ * \param flags            - Flags to control launch behavior
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_IMAGE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuFuncSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cuLaunchCooperativeKernel,
+ * ::cudaLaunchCooperativeKernelMultiDevice
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuLaunchCooperativeKernelMultiDevice(CUDA_LAUNCH_PARAMS *launchParamsList, unsigned int numDevices, unsigned int flags);
+
+/**
+ * \brief Enqueues a host function call in a stream
+ *
+ * Enqueues a host function to run in a stream.  The function will be called
+ * after currently enqueued work and will block work added after it.
+ *
+ * The host function must not make any CUDA API calls.  Attempting to use a
+ * CUDA API may result in ::CUDA_ERROR_NOT_PERMITTED, but this is not required.
+ * The host function must not perform any synchronization that may depend on
+ * outstanding CUDA work not mandated to run earlier.  Host functions without a
+ * mandated order (such as in independent streams) execute in undefined order
+ * and may be serialized.
+ *
+ * For the purposes of Unified Memory, execution makes a number of guarantees:
+ * <ul>
+ *   <li>The stream is considered idle for the duration of the function's
+ *   execution.  Thus, for example, the function may always use memory attached
+ *   to the stream it was enqueued in.</li>
+ *   <li>The start of execution of the function has the same effect as
+ *   synchronizing an event recorded in the same stream immediately prior to
+ *   the function.  It thus synchronizes streams which have been "joined"
+ *   prior to the function.</li>
+ *   <li>Adding device work to any stream does not have the effect of making
+ *   the stream active until all preceding host functions and stream callbacks
+ *   have executed.  Thus, for
+ *   example, a function might use global attached memory even if work has
+ *   been added to another stream, if the work has been ordered behind the
+ *   function call with an event.</li>
+ *   <li>Completion of the function does not cause a stream to become
+ *   active except as described above.  The stream will remain idle
+ *   if no device work follows the function, and will remain idle across
+ *   consecutive host functions or stream callbacks without device work in
+ *   between.  Thus, for example,
+ *   stream synchronization can be done by signaling from a host function at the
+ *   end of the stream.</li>
+ * </ul>
+ *
+ * Note that, in contrast to ::cuStreamAddCallback, the function will not be
+ * called in the event of an error in the CUDA context.
+ *
+ * \param hStream  - Stream to enqueue function call in
+ * \param fn       - The function to call once preceding stream operations are complete
+ * \param userData - User-specified data to be passed to the function
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuStreamCreate,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamWaitEvent,
+ * ::cuStreamDestroy,
+ * ::cuMemAllocManaged,
+ * ::cuStreamAttachMemAsync,
+ * ::cuStreamAddCallback
+ */
+CUresult CUDAAPI cuLaunchHostFunc(CUstream hStream, CUhostFn fn, void *userData);
+
+/** @} */ /* END CUDA_EXEC */
+
+/**
+ * \defgroup CUDA_EXEC_DEPRECATED Execution Control [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated execution control functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the deprecated execution control functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Sets the block-dimensions for the function
+ *
+ * \deprecated
+ *
+ * Specifies the \p x, \p y, and \p z dimensions of the thread blocks that are
+ * created when the kernel given by \p hfunc is launched.
+ *
+ * \param hfunc - Kernel to specify dimensions of
+ * \param x     - X dimension
+ * \param y     - Y dimension
+ * \param z     - Z dimension
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetSharedSize,
+ * ::cuFuncSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSeti,
+ * ::cuParamSetf,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuFuncSetBlockShape(CUfunction hfunc, int x, int y, int z);
+
+/**
+ * \brief Sets the dynamic shared-memory size for the function
+ *
+ * \deprecated
+ *
+ * Sets through \p bytes the amount of dynamic shared memory that will be
+ * available to each thread block when the kernel given by \p hfunc is launched.
+ *
+ * \param hfunc - Kernel to specify dynamic shared-memory size for
+ * \param bytes - Dynamic shared-memory size per thread in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetCacheConfig,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSeti,
+ * ::cuParamSetf,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuFuncSetSharedSize(CUfunction hfunc, unsigned int bytes);
+
+/**
+ * \brief Sets the parameter size for the function
+ *
+ * \deprecated
+ *
+ * Sets through \p numbytes the total size in bytes needed by the function
+ * parameters of the kernel corresponding to \p hfunc.
+ *
+ * \param hfunc    - Kernel to set parameter size for
+ * \param numbytes - Size of parameter list in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetf,
+ * ::cuParamSeti,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetSize(CUfunction hfunc, unsigned int numbytes);
+
+/**
+ * \brief Adds an integer parameter to the function's argument list
+ *
+ * \deprecated
+ *
+ * Sets an integer parameter that will be specified the next time the
+ * kernel corresponding to \p hfunc will be invoked. \p offset is a byte offset.
+ *
+ * \param hfunc  - Kernel to add parameter to
+ * \param offset - Offset to add parameter to argument list
+ * \param value  - Value of parameter
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSetf,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuParamSeti(CUfunction hfunc, int offset, unsigned int value);
+
+/**
+ * \brief Adds a floating-point parameter to the function's argument list
+ *
+ * \deprecated
+ *
+ * Sets a floating-point parameter that will be specified the next time the
+ * kernel corresponding to \p hfunc will be invoked. \p offset is a byte offset.
+ *
+ * \param hfunc  - Kernel to add parameter to
+ * \param offset - Offset to add parameter to argument list
+ * \param value  - Value of parameter
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSeti,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetf(CUfunction hfunc, int offset, float value);
+
+/**
+ * \brief Adds arbitrary data to the function's argument list
+ *
+ * \deprecated
+ *
+ * Copies an arbitrary amount of data (specified in \p numbytes) from \p ptr
+ * into the parameter space of the kernel corresponding to \p hfunc. \p offset
+ * is a byte offset.
+ *
+ * \param hfunc    - Kernel to add data to
+ * \param offset   - Offset to add data to argument list
+ * \param ptr      - Pointer to arbitrary data
+ * \param numbytes - Size of data to copy in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSetf,
+ * ::cuParamSeti,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetv(CUfunction hfunc, int offset, void *ptr, unsigned int numbytes);
+
+/**
+ * \brief Launches a CUDA function
+ *
+ * \deprecated
+ *
+ * Invokes the kernel \p f on a 1 x 1 x 1 grid of blocks. The block
+ * contains the number of threads specified by a previous call to
+ * ::cuFuncSetBlockShape().
+ *
+ * The block shape, dynamic shared memory size, and parameter information
+ * must be set using
+ *  ::cuFuncSetBlockShape(),
+ *  ::cuFuncSetSharedSize(),
+ *  ::cuParamSetSize(),
+ *  ::cuParamSeti(),
+ *  ::cuParamSetf(), and
+ *  ::cuParamSetv()
+ * prior to calling this function.
+ *
+ * Launching a function via ::cuLaunchKernel() invalidates the function's
+ * block shape, dynamic shared memory size, and parameter information. After
+ * launching via cuLaunchKernel, this state must be re-initialized prior to
+ * calling this function. Failure to do so results in undefined behavior.
+ *
+ * \param f - Kernel to launch
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSetf,
+ * ::cuParamSeti,
+ * ::cuParamSetv,
+ * ::cuLaunchGrid,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuLaunch(CUfunction f);
+
+/**
+ * \brief Launches a CUDA function
+ *
+ * \deprecated
+ *
+ * Invokes the kernel \p f on a \p grid_width x \p grid_height grid of
+ * blocks. Each block contains the number of threads specified by a previous
+ * call to ::cuFuncSetBlockShape().
+ *
+ * The block shape, dynamic shared memory size, and parameter information
+ * must be set using
+ *  ::cuFuncSetBlockShape(),
+ *  ::cuFuncSetSharedSize(),
+ *  ::cuParamSetSize(),
+ *  ::cuParamSeti(),
+ *  ::cuParamSetf(), and
+ *  ::cuParamSetv()
+ * prior to calling this function.
+ *
+ * Launching a function via ::cuLaunchKernel() invalidates the function's
+ * block shape, dynamic shared memory size, and parameter information. After
+ * launching via cuLaunchKernel, this state must be re-initialized prior to
+ * calling this function. Failure to do so results in undefined behavior.
+ *
+ * \param f           - Kernel to launch
+ * \param grid_width  - Width of grid in blocks
+ * \param grid_height - Height of grid in blocks
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSetf,
+ * ::cuParamSeti,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGridAsync,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuLaunchGrid(CUfunction f, int grid_width, int grid_height);
+
+/**
+ * \brief Launches a CUDA function
+ *
+ * \deprecated
+ *
+ * Invokes the kernel \p f on a \p grid_width x \p grid_height grid of
+ * blocks. Each block contains the number of threads specified by a previous
+ * call to ::cuFuncSetBlockShape().
+ *
+ * The block shape, dynamic shared memory size, and parameter information
+ * must be set using
+ *  ::cuFuncSetBlockShape(),
+ *  ::cuFuncSetSharedSize(),
+ *  ::cuParamSetSize(),
+ *  ::cuParamSeti(),
+ *  ::cuParamSetf(), and
+ *  ::cuParamSetv()
+ * prior to calling this function.
+ *
+ * Launching a function via ::cuLaunchKernel() invalidates the function's
+ * block shape, dynamic shared memory size, and parameter information. After
+ * launching via cuLaunchKernel, this state must be re-initialized prior to
+ * calling this function. Failure to do so results in undefined behavior.
+ *
+ * \param f           - Kernel to launch
+ * \param grid_width  - Width of grid in blocks
+ * \param grid_height - Height of grid in blocks
+ * \param hStream     - Stream identifier
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_LAUNCH_FAILED,
+ * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING,
+ * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED
+ *
+ * \note In certain cases where cubins are created with no ABI (i.e., using \p ptxas \p --abi-compile \p no),
+ *       this function may serialize kernel launches. The CUDA driver retains asynchronous behavior by
+ *       growing the per-thread stack as needed per launch and not shrinking it afterwards.
+ *
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa ::cuFuncSetBlockShape,
+ * ::cuFuncSetSharedSize,
+ * ::cuFuncGetAttribute,
+ * ::cuParamSetSize,
+ * ::cuParamSetf,
+ * ::cuParamSeti,
+ * ::cuParamSetv,
+ * ::cuLaunch,
+ * ::cuLaunchGrid,
+ * ::cuLaunchKernel
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuLaunchGridAsync(CUfunction f, int grid_width, int grid_height, CUstream hStream);
+
+
+/**
+ * \brief Adds a texture-reference to the function's argument list
+ *
+ * \deprecated
+ *
+ * Makes the CUDA array or linear memory bound to the texture reference
+ * \p hTexRef available to a device program as a texture. In this version of
+ * CUDA, the texture-reference must be obtained via ::cuModuleGetTexRef() and
+ * the \p texunit parameter must be set to ::CU_PARAM_TR_DEFAULT.
+ *
+ * \param hfunc   - Kernel to add texture-reference to
+ * \param texunit - Texture unit (must be ::CU_PARAM_TR_DEFAULT)
+ * \param hTexRef - Texture-reference to add to argument list
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetTexRef(CUfunction hfunc, int texunit, CUtexref hTexRef);
+
+/**
+ * \brief Sets the shared memory configuration for a device function.
+ *
+ * \deprecated
+ *
+ * On devices with configurable shared memory banks, this function will
+ * force all subsequent launches of the specified device function to have
+ * the given shared memory bank size configuration. On any given launch of the
+ * function, the shared memory configuration of the device will be temporarily
+ * changed if needed to suit the function's preferred configuration. Changes in
+ * shared memory configuration between subsequent launches of functions,
+ * may introduce a device side synchronization point.
+ *
+ * Any per-function setting of shared memory bank size set via
+ * ::cuFuncSetSharedMemConfig will override the context wide setting set with
+ * ::cuCtxSetSharedMemConfig.
+ *
+ * Changing the shared memory bank size will not increase shared memory usage
+ * or affect occupancy of kernels, but may have major effects on performance.
+ * Larger bank sizes will allow for greater potential bandwidth to shared memory,
+ * but will change what kinds of accesses to shared memory will result in bank
+ * conflicts.
+ *
+ * This function will do nothing on devices with fixed shared memory bank size.
+ *
+ * The supported bank configurations are:
+ * - ::CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE: use the context's shared memory
+ *   configuration when launching this function.
+ * - ::CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: set shared memory bank width to
+ *   be natively four bytes when launching this function.
+ * - ::CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: set shared memory bank width to
+ *   be natively eight bytes when launching this function.
+ *
+ * \param hfunc  - kernel to be given a shared memory config
+ * \param config - requested shared memory configuration
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \notefnerr
+ *
+ * \sa ::cuCtxGetCacheConfig,
+ * ::cuCtxSetCacheConfig,
+ * ::cuCtxGetSharedMemConfig,
+ * ::cuCtxSetSharedMemConfig,
+ * ::cuFuncGetAttribute,
+ * ::cuLaunchKernel,
+ * ::cudaFuncSetSharedMemConfig
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuFuncSetSharedMemConfig(CUfunction hfunc, CUsharedconfig config);
+
+/** @} */ /* END CUDA_EXEC_DEPRECATED */
+
+/**
+ * \defgroup CUDA_GRAPH Graph Management
+ *
+ * ___MANBRIEF___ graph management functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the graph management functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates a graph
+ *
+ * Creates an empty graph, which is returned via \p phGraph.
+ *
+ * \param phGraph - Returns newly created graph
+ * \param flags   - Graph creation flags, must be 0
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode,
+ * ::cuGraphInstantiate,
+ * ::cuGraphDestroy,
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphClone
+ */
+CUresult CUDAAPI cuGraphCreate(CUgraph *phGraph, unsigned int flags);
+
+/**
+ * \brief Creates a kernel execution node and adds it to a graph
+ *
+ * Creates a new kernel execution node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * The CUDA_KERNEL_NODE_PARAMS structure is defined as:
+ *
+ * \code
+ *  typedef struct CUDA_KERNEL_NODE_PARAMS_st {
+ *      CUfunction func;
+ *      unsigned int gridDimX;
+ *      unsigned int gridDimY;
+ *      unsigned int gridDimZ;
+ *      unsigned int blockDimX;
+ *      unsigned int blockDimY;
+ *      unsigned int blockDimZ;
+ *      unsigned int sharedMemBytes;
+ *      void **kernelParams;
+ *      void **extra;
+ *      CUkernel kern;
+ *      CUcontext ctx;
+ *  } CUDA_KERNEL_NODE_PARAMS;
+ * \endcode
+ *
+ * When the graph is launched, the node will invoke kernel \p func on a (\p gridDimX x
+ * \p gridDimY x \p gridDimZ) grid of blocks. Each block contains
+ * (\p blockDimX x \p blockDimY x \p blockDimZ) threads.
+ *
+ * \p sharedMemBytes sets the amount of dynamic shared memory that will be
+ * available to each thread block.
+ *
+ * Kernel parameters to \p func can be specified in one of two ways:
+ *
+ * 1) Kernel parameters can be specified via \p kernelParams. If the kernel has N
+ * parameters, then \p kernelParams needs to be an array of N pointers. Each pointer,
+ * from \p kernelParams[0] to \p kernelParams[N-1], points to the region of memory from which the actual
+ * parameter will be copied. The number of kernel parameters and their offsets and sizes do not need
+ * to be specified as that information is retrieved directly from the kernel's image.
+ *
+ * 2) Kernel parameters for non-cooperative kernels can also be packaged by the application into a single
+ * buffer that is passed in via \p extra. This places the burden on the application of knowing each
+ * kernel parameter's size and alignment/padding within the buffer. The \p extra parameter exists
+ * to allow this function to take additional less commonly used arguments. \p extra specifies
+ * a list of names of extra settings and their corresponding values. Each extra setting name is
+ * immediately followed by the corresponding value. The list must be terminated with either NULL or
+ * CU_LAUNCH_PARAM_END.
+ *
+ * - ::CU_LAUNCH_PARAM_END, which indicates the end of the \p extra
+ *   array;
+ * - ::CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next
+ *   value in \p extra will be a pointer to a buffer
+ *   containing all the kernel parameters for launching kernel
+ *   \p func;
+ * - ::CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next
+ *   value in \p extra will be a pointer to a size_t
+ *   containing the size of the buffer specified with
+ *   ::CU_LAUNCH_PARAM_BUFFER_POINTER;
+ *
+ * The error ::CUDA_ERROR_INVALID_VALUE will be returned if kernel parameters are specified with both
+ * \p kernelParams and \p extra (i.e. both \p kernelParams and \p extra are non-NULL).
+ * ::CUDA_ERROR_INVALID_VALUE will be returned if \p extra is used for a cooperative kernel.
+ *
+ * The \p kernelParams or \p extra array, as well as the argument values it points to,
+ * are copied during this call.
+ *
+ * \note Kernels launched using graphs must not use texture and surface references. Reading or
+ *       writing through any texture or surface reference is undefined behavior.
+ *       This restriction does not apply to texture and surface objects.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the GPU execution node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuLaunchKernel,
+ * ::cuLaunchCooperativeKernel,
+ * ::cuGraphKernelNodeGetParams,
+ * ::cuGraphKernelNodeSetParams,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddKernelNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Returns a kernel node's parameters
+ *
+ * Returns the parameters of kernel node \p hNode in \p nodeParams.
+ * The \p kernelParams or \p extra array returned in \p nodeParams,
+ * as well as the argument values it points to, are owned by the node.
+ * This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cuGraphKernelNodeSetParams to update the
+ * parameters of this node.
+ *
+ * The params will contain either \p kernelParams or \p extra,
+ * according to which of these was most recently set on the node.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param nodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuLaunchKernel,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphKernelNodeSetParams
+ */
+CUresult CUDAAPI cuGraphKernelNodeGetParams(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Sets a kernel node's parameters
+ *
+ * Sets the parameters of kernel node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuLaunchKernel,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphKernelNodeGetParams
+ */
+CUresult CUDAAPI cuGraphKernelNodeSetParams(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Creates a memcpy node and adds it to a graph
+ *
+ * Creates a new memcpy node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * When the graph is launched, the node will perform the memcpy described by \p copyParams.
+ * See ::cuMemcpy3D() for a description of the structure and its restrictions.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. If one or more of the operands refer
+ * to managed memory, then using the memory type ::CU_MEMORYTYPE_UNIFIED is disallowed
+ * for those operand(s). The managed memory will be treated as residing on either the
+ * host or the device, depending on which memory type is specified.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param copyParams      - Parameters for the memory copy
+ * \param ctx             - Context on which to run the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuMemcpy3D,
+ * ::cuGraphMemcpyNodeGetParams,
+ * ::cuGraphMemcpyNodeSetParams,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddMemcpyNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_MEMCPY3D *copyParams, CUcontext ctx);
+
+/**
+ * \brief Returns a memcpy node's parameters
+ *
+ * Returns the parameters of memcpy node \p hNode in \p nodeParams.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param nodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMemcpy3D,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphMemcpyNodeSetParams
+ */
+CUresult CUDAAPI cuGraphMemcpyNodeGetParams(CUgraphNode hNode, CUDA_MEMCPY3D *nodeParams);
+
+/**
+ * \brief Sets a memcpy node's parameters
+ *
+ * Sets the parameters of memcpy node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuMemcpy3D,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphMemcpyNodeGetParams
+ */
+CUresult CUDAAPI cuGraphMemcpyNodeSetParams(CUgraphNode hNode, const CUDA_MEMCPY3D *nodeParams);
+
+/**
+ * \brief Creates a memset node and adds it to a graph
+ *
+ * Creates a new memset node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * The element size must be 1, 2, or 4 bytes.
+ * When the graph is launched, the node will perform the memset described by \p memsetParams.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param memsetParams    - Parameters for the memory set
+ * \param ctx             - Context on which to run the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuMemsetD2D32,
+ * ::cuGraphMemsetNodeGetParams,
+ * ::cuGraphMemsetNodeSetParams,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemcpyNode
+ */
+CUresult CUDAAPI cuGraphAddMemsetNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_MEMSET_NODE_PARAMS *memsetParams, CUcontext ctx);
+
+/**
+ * \brief Returns a memset node's parameters
+ *
+ * Returns the parameters of memset node \p hNode in \p nodeParams.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param nodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuMemsetD2D32,
+ * ::cuGraphAddMemsetNode,
+ * ::cuGraphMemsetNodeSetParams
+ */
+CUresult CUDAAPI cuGraphMemsetNodeGetParams(CUgraphNode hNode, CUDA_MEMSET_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Sets a memset node's parameters
+ *
+ * Sets the parameters of memset node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuMemsetD2D32,
+ * ::cuGraphAddMemsetNode,
+ * ::cuGraphMemsetNodeGetParams
+ */
+CUresult CUDAAPI cuGraphMemsetNodeSetParams(CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Creates a host execution node and adds it to a graph
+ *
+ * Creates a new CPU execution node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * When the graph is launched, the node will invoke the specified CPU function.
+ * Host nodes are not supported under MPS with pre-Volta GPUs.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the host node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuLaunchHostFunc,
+ * ::cuGraphHostNodeGetParams,
+ * ::cuGraphHostNodeSetParams,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddHostNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_HOST_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Returns a host node's parameters
+ *
+ * Returns the parameters of host node \p hNode in \p nodeParams.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param nodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuLaunchHostFunc,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphHostNodeSetParams
+ */
+CUresult CUDAAPI cuGraphHostNodeGetParams(CUgraphNode hNode, CUDA_HOST_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Sets a host node's parameters
+ *
+ * Sets the parameters of host node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuLaunchHostFunc,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphHostNodeGetParams
+ */
+CUresult CUDAAPI cuGraphHostNodeSetParams(CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Creates a child graph node and adds it to a graph
+ *
+ * Creates a new node which executes an embedded graph, and adds it to \p hGraph with
+ * \p numDependencies dependencies specified via \p dependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * If \p hGraph contains allocation or free nodes, this call will return an error.
+ *
+ * The node executes an embedded child graph. The child graph is cloned in this call.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param childGraph      - The graph to clone into this node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphChildGraphNodeGetGraph,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode,
+ * ::cuGraphClone
+ */
+CUresult CUDAAPI cuGraphAddChildGraphNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUgraph childGraph);
+
+/**
+ * \brief Gets a handle to the embedded graph of a child graph node
+ *
+ * Gets a handle to the embedded graph in a child graph node. This call
+ * does not clone the graph. Changes to the graph will be reflected in
+ * the node, and the node retains ownership of the graph.
+ *
+ * Allocation and free nodes cannot be added to the returned graph.
+ * Attempting to do so will return an error.
+ *
+ * \param hNode   - Node to get the embedded graph for
+ * \param phGraph - Location to store a handle to the graph
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphNodeFindInClone
+ */
+CUresult CUDAAPI cuGraphChildGraphNodeGetGraph(CUgraphNode hNode, CUgraph *phGraph);
+
+/**
+ * \brief Creates an empty node and adds it to a graph
+ *
+ * Creates a new node which performs no operation, and adds it to \p hGraph with
+ * \p numDependencies dependencies specified via \p dependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * An empty node performs no operation during execution, but can be used for
+ * transitive ordering. For example, a phased execution graph with 2 groups of n
+ * nodes with a barrier between them can be represented using an empty node and
+ * 2*n dependency edges, rather than no empty node and n^2 dependency edges.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddEmptyNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies);
+
+/**
+ * \brief Creates an event record node and adds it to a graph
+ *
+ * Creates a new event record node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and event specified in \p event.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * Each launch of the graph will record \p event to capture execution of the
+ * node's dependencies.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param event           - Event for the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddEventRecordNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUevent event);
+
+/**
+ * \brief Returns the event associated with an event record node
+ *
+ * Returns the event of event record node \p hNode in \p event_out.
+ *
+ * \param hNode     - Node to get the event for
+ * \param event_out - Pointer to return the event
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphEventRecordNodeSetEvent,
+ * ::cuGraphEventWaitNodeGetEvent,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuGraphEventRecordNodeGetEvent(CUgraphNode hNode, CUevent *event_out);
+
+/**
+ * \brief Sets an event record node's event
+ *
+ * Sets the event of event record node \p hNode to \p event.
+ *
+ * \param hNode - Node to set the event for
+ * \param event - Event to use
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphEventRecordNodeGetEvent,
+ * ::cuGraphEventWaitNodeSetEvent,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuGraphEventRecordNodeSetEvent(CUgraphNode hNode, CUevent event);
+
+/**
+ * \brief Creates an event wait node and adds it to a graph
+ *
+ * Creates a new event wait node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and event specified in \p event.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * The graph node will wait for all work captured in \p event.  See ::cuEventRecord()
+ * for details on what is captured by an event. \p event may be from a different context
+ * or device than the launch stream.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param event           - Event for the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddEventWaitNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUevent event);
+
+/**
+ * \brief Returns the event associated with an event wait node
+ *
+ * Returns the event of event wait node \p hNode in \p event_out.
+ *
+ * \param hNode     - Node to get the event for
+ * \param event_out - Pointer to return the event
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphEventWaitNodeSetEvent,
+ * ::cuGraphEventRecordNodeGetEvent,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuGraphEventWaitNodeGetEvent(CUgraphNode hNode, CUevent *event_out);
+
+/**
+ * \brief Sets an event wait node's event
+ *
+ * Sets the event of event wait node \p hNode to \p event.
+ *
+ * \param hNode - Node to set the event for
+ * \param event - Event to use
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphEventWaitNodeGetEvent,
+ * ::cuGraphEventRecordNodeSetEvent,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent
+ */
+CUresult CUDAAPI cuGraphEventWaitNodeSetEvent(CUgraphNode hNode, CUevent event);
+
+/**
+ * \brief Creates an external semaphore signal node and adds it to a graph
+ *
+ * Creates a new external semaphore signal node and adds it to \p hGraph with \p
+ * numDependencies dependencies specified via \p dependencies and arguments specified
+ * in \p nodeParams. It is possible for \p numDependencies to be 0, in which case the
+ * node will be placed at the root of the graph. \p dependencies may not have any
+ * duplicate entries. A handle to the new node will be returned in \p phGraphNode.
+ *
+ * Performs a signal operation on a set of externally allocated semaphore objects
+ * when the node is launched.  The operation(s) will occur after all of the node's
+ * dependencies have completed.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphExternalSemaphoresSignalNodeGetParams,
+ * ::cuGraphExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuImportExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddExternalSemaphoresSignalNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Returns an external semaphore signal node's parameters
+ *
+ * Returns the parameters of an external semaphore signal node \p hNode in \p params_out.
+ * The \p extSemArray and \p paramsArray returned in \p params_out,
+ * are owned by the node.  This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cuGraphExternalSemaphoresSignalNodeSetParams to update the
+ * parameters of this node.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param params_out - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuLaunchKernel,
+ * ::cuGraphAddExternalSemaphoresSignalNode,
+ * ::cuGraphExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuGraphExternalSemaphoresSignalNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *params_out);
+
+/**
+ * \brief Sets an external semaphore signal node's parameters
+ *
+ * Sets the parameters of an external semaphore signal node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresSignalNode,
+ * ::cuGraphExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuGraphExternalSemaphoresSignalNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Creates an external semaphore wait node and adds it to a graph
+ *
+ * Creates a new external semaphore wait node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries. A handle
+ * to the new node will be returned in \p phGraphNode.
+ *
+ * Performs a wait operation on a set of externally allocated semaphore objects
+ * when the node is launched.  The node's dependencies will not be launched until
+ * the wait operation has completed.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphExternalSemaphoresWaitNodeGetParams,
+ * ::cuGraphExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresSignalNode,
+ * ::cuImportExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddExternalSemaphoresWaitNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Returns an external semaphore wait node's parameters
+ *
+ * Returns the parameters of an external semaphore wait node \p hNode in \p params_out.
+ * The \p extSemArray and \p paramsArray returned in \p params_out,
+ * are owned by the node.  This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cuGraphExternalSemaphoresSignalNodeSetParams to update the
+ * parameters of this node.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param params_out - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuLaunchKernel,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuGraphExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuGraphExternalSemaphoresWaitNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS *params_out);
+
+/**
+ * \brief Sets an external semaphore wait node's parameters
+ *
+ * Sets the parameters of an external semaphore wait node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuGraphExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync
+ */
+CUresult CUDAAPI cuGraphExternalSemaphoresWaitNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Creates a batch memory operation node and adds it to a graph
+ *
+ * Creates a new batch memory operation node and adds it to \p hGraph with \p
+ * numDependencies dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * When the node is added, the paramArray inside \p nodeParams is copied and therefore it can be
+ * freed after the call returns.
+ *
+ * \note
+ * Warning:
+ * Improper use of this API may deadlock the application. Synchronization 
+ * ordering established through this API is not visible to CUDA. CUDA tasks 
+ * that are (even indirectly) ordered by this API should also have that order
+ * expressed with CUDA-visible dependencies such as events. This ensures that 
+ * the scheduler does not serialize them in an improper order. For more 
+ * information, see the Stream Memory Operations section in the programming 
+ * guide(https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html).
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuStreamBatchMemOp,
+ * ::cuStreamWaitValue32,
+ * ::cuStreamWriteValue32,
+ * ::cuStreamWaitValue64,
+ * ::cuStreamWriteValue64,
+ * ::cuGraphBatchMemOpNodeGetParams,
+ * ::cuGraphBatchMemOpNodeSetParams,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddBatchMemOpNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Returns a batch mem op node's parameters
+ *
+ * Returns the parameters of batch mem op node \p hNode in \p nodeParams_out.
+ * The \p paramArray returned in \p nodeParams_out is owned by the node.
+ * This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cuGraphBatchMemOpNodeSetParams to update the
+ * parameters of this node.
+ *
+ * \param hNode          - Node to get the parameters for
+ * \param nodeParams_out - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuStreamBatchMemOp,
+ * ::cuGraphAddBatchMemOpNode,
+ * ::cuGraphBatchMemOpNodeSetParams
+ */
+CUresult CUDAAPI cuGraphBatchMemOpNodeGetParams(CUgraphNode hNode, CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams_out);
+
+/**
+ * \brief Sets a batch mem op node's parameters
+ *
+ * Sets the parameters of batch mem op node \p hNode to \p nodeParams.
+ *
+ * The paramArray inside \p nodeParams is copied and therefore it can be
+ * freed after the call returns.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetParams,
+ * ::cuStreamBatchMemOp,
+ * ::cuGraphAddBatchMemOpNode,
+ * ::cuGraphBatchMemOpNodeGetParams
+ */
+CUresult CUDAAPI cuGraphBatchMemOpNodeSetParams(CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Sets the parameters for a batch mem op node in the given graphExec
+ *
+ * Sets the parameters of a batch mem op node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * The following fields on operations may be modified on an executable graph:
+ *
+ *  op.waitValue.address
+ *  op.waitValue.value[64]
+ *  op.waitValue.flags bits corresponding to wait type (i.e. CU_STREAM_WAIT_VALUE_FLUSH bit cannot be modified)
+ *  op.writeValue.address
+ *  op.writeValue.value[64]
+ *
+ * Other fields, such as the context, count or type of operations, and other types of operations such as membars, 
+ * may not be modified.
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * The paramArray inside \p nodeParams is copied and therefore it can be
+ * freed after the call returns.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Batch mem op node from the graph from which graphExec was instantiated
+ * \param nodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuStreamBatchMemOp,
+ * ::cuGraphAddBatchMemOpNode,
+ * ::cuGraphBatchMemOpNodeGetParams,
+ * ::cuGraphBatchMemOpNodeSetParams,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecBatchMemOpNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Creates an allocation node and adds it to a graph
+ *
+ * Creates a new allocation node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries. A handle
+ * to the new node will be returned in \p phGraphNode.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * When ::cuGraphAddMemAllocNode creates an allocation node, it returns the address of the allocation in
+ * \p nodeParams.dptr.  The allocation's address remains fixed across instantiations and launches.
+ *
+ * If the allocation is freed in the same graph, by creating a free node using ::cuGraphAddMemFreeNode,
+ * the allocation can be accessed by nodes ordered after the allocation node but before the free node.
+ * These allocations cannot be freed outside the owning graph, and they can only be freed once in the
+ * owning graph.
+ *
+ * If the allocation is not freed in the same graph, then it can be accessed not only by nodes in the
+ * graph which are ordered after the allocation node, but also by stream operations ordered after the
+ * graph's execution but before the allocation is freed.
+ *
+ * Allocations which are not freed in the same graph can be freed by:
+ * - passing the allocation to ::cuMemFreeAsync or ::cuMemFree;
+ * - launching a graph with a free node for that allocation; or
+ * - specifying ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH during instantiation, which makes
+ * each launch behave as though it called ::cuMemFreeAsync for every unfreed allocation.
+ * 
+ * It is not possible to free an allocation in both the owning graph and another graph.  If the allocation
+ * is freed in the same graph, a free node cannot be added to another graph.  If the allocation is freed
+ * in another graph, a free node can no longer be added to the owning graph.
+ *
+ * The following restrictions apply to graphs which contain allocation and/or memory free nodes:
+ * - Nodes and edges of the graph cannot be deleted.
+ * - The graph cannot be used in a child node.
+ * - Only one instantiation of the graph may exist at any point in time.
+ * - The graph cannot be cloned.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphAddMemFreeNode,
+ * ::cuGraphMemAllocNodeGetParams,
+ * ::cuDeviceGraphMemTrim,
+ * ::cuDeviceGetGraphMemAttribute,
+ * ::cuDeviceSetGraphMemAttribute,
+ * ::cuMemAllocAsync,
+ * ::cuMemFreeAsync,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphAddExternalSemaphoresSignalNode,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddMemAllocNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUDA_MEM_ALLOC_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Returns a memory alloc node's parameters
+ *
+ * Returns the parameters of a memory alloc node \p hNode in \p params_out.
+ * The \p poolProps and \p accessDescs returned in \p params_out, are owned by the
+ * node.  This memory remains valid until the node is destroyed.  The returned
+ * parameters must not be modified.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param params_out - Pointer to return the parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddMemAllocNode,
+ * ::cuGraphMemFreeNodeGetParams
+ */
+CUresult CUDAAPI cuGraphMemAllocNodeGetParams(CUgraphNode hNode, CUDA_MEM_ALLOC_NODE_PARAMS *params_out);
+
+/**
+ * \brief Creates a memory free node and adds it to a graph
+ *
+ * Creates a new memory free node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries. A handle
+ * to the new node will be returned in \p phGraphNode.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param dptr            - Address of memory to free
+ *
+ * ::cuGraphAddMemFreeNode will return ::CUDA_ERROR_INVALID_VALUE if the user attempts to free:
+ * - an allocation twice in the same graph.
+ * - an address that was not returned by an allocation node.
+ * - an invalid address.
+ *
+ * The following restrictions apply to graphs which contain allocation and/or memory free nodes:
+ * - Nodes and edges of the graph cannot be deleted.
+ * - The graph cannot be used in a child node.
+ * - Only one instantiation of the graph may exist at any point in time.
+ * - The graph cannot be cloned.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphAddMemAllocNode,
+ * ::cuGraphMemFreeNodeGetParams,
+ * ::cuDeviceGraphMemTrim,
+ * ::cuDeviceGetGraphMemAttribute,
+ * ::cuDeviceSetGraphMemAttribute,
+ * ::cuMemAllocAsync,
+ * ::cuMemFreeAsync,
+ * ::cuGraphCreate,
+ * ::cuGraphDestroyNode,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphAddExternalSemaphoresSignalNode,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphAddMemFreeNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUdeviceptr dptr);
+
+/**
+ * \brief Returns a memory free node's parameters
+ *
+ * Returns the address of a memory free node \p hNode in \p dptr_out.
+ *
+ * \param hNode    - Node to get the parameters for
+ * \param dptr_out - Pointer to return the device address
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddMemFreeNode,
+ * ::cuGraphMemAllocNodeGetParams
+ */
+CUresult CUDAAPI cuGraphMemFreeNodeGetParams(CUgraphNode hNode, CUdeviceptr *dptr_out);
+
+/**
+ * \brief Free unused memory that was cached on the specified device for use with graphs back to the OS.
+ *
+ * Blocks which are not in use by a graph that is either currently executing or scheduled to execute are
+ * freed back to the operating system.
+ *
+ * \param device - The device for which cached memory should be freed.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ *
+ * \sa
+ * ::cuGraphAddMemAllocNode,
+ * ::cuGraphAddMemFreeNode,
+ * ::cuDeviceSetGraphMemAttribute,
+ * ::cuDeviceGetGraphMemAttribute
+ */
+CUresult CUDAAPI cuDeviceGraphMemTrim(CUdevice device);
+
+/**
+ * \brief Query asynchronous allocation attributes related to graphs
+ *
+ * Valid attributes are:
+ *
+ * - ::CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT: Amount of memory, in bytes, currently associated with graphs
+ * - ::CU_GRAPH_MEM_ATTR_USED_MEM_HIGH: High watermark of memory, in bytes, associated with graphs since the
+ *   last time it was reset.  High watermark can only be reset to zero.
+ * - ::CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT: Amount of memory, in bytes, currently allocated for use by
+ *   the CUDA graphs asynchronous allocator.
+ * - ::CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH: High watermark of memory, in bytes, currently allocated for use by
+ *   the CUDA graphs asynchronous allocator.
+ *
+ * \param device - Specifies the scope of the query
+ * \param attr - attribute to get
+ * \param value - retrieved value
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ *
+ * \sa
+ * ::cuDeviceSetGraphMemAttribute,
+ * ::cuGraphAddMemAllocNode,
+ * ::cuGraphAddMemFreeNode
+ */
+CUresult CUDAAPI cuDeviceGetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value);
+
+/**
+ * \brief Set asynchronous allocation attributes related to graphs
+ *
+ * Valid attributes are:
+ *
+ * - ::CU_GRAPH_MEM_ATTR_USED_MEM_HIGH: High watermark of memory, in bytes, associated with graphs since the
+ *   last time it was reset.  High watermark can only be reset to zero.
+ * - ::CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH: High watermark of memory, in bytes, currently allocated for use by
+ *   the CUDA graphs asynchronous allocator.
+ *
+ * \param device - Specifies the scope of the query
+ * \param attr - attribute to get
+ * \param value - pointer to value to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ *
+ * \sa
+ * ::cuDeviceGetGraphMemAttribute,
+ * ::cuGraphAddMemAllocNode,
+ * ::cuGraphAddMemFreeNode
+ */
+CUresult CUDAAPI cuDeviceSetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value);
+
+/**
+ * \brief Clones a graph
+ *
+ * This function creates a copy of \p originalGraph and returns it in \p phGraphClone.
+ * All parameters are copied into the cloned graph. The original graph may be modified
+ * after this call without affecting the clone.
+ *
+ * Child graph nodes in the original graph are recursively copied into the clone.
+ *
+ * \param phGraphClone  - Returns newly created cloned graph
+ * \param originalGraph - Graph to clone
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate,
+ * ::cuGraphNodeFindInClone
+ */
+CUresult CUDAAPI cuGraphClone(CUgraph *phGraphClone, CUgraph originalGraph);
+
+/**
+ * \brief Finds a cloned version of a node
+ *
+ * This function returns the node in \p hClonedGraph corresponding to \p hOriginalNode
+ * in the original graph.
+ *
+ * \p hClonedGraph must have been cloned from \p hOriginalGraph via ::cuGraphClone.
+ * \p hOriginalNode must have been in \p hOriginalGraph at the time of the call to
+ * ::cuGraphClone, and the corresponding cloned node in \p hClonedGraph must not have
+ * been removed. The cloned node is then returned via \p phClonedNode.
+ *
+ * \param phNode  - Returns handle to the cloned node
+ * \param hOriginalNode - Handle to the original node
+ * \param hClonedGraph - Cloned graph to query
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphClone
+ */
+CUresult CUDAAPI cuGraphNodeFindInClone(CUgraphNode *phNode, CUgraphNode hOriginalNode, CUgraph hClonedGraph);
+
+/**
+ * \brief Returns a node's type
+ *
+ * Returns the node type of \p hNode in \p type.
+ *
+ * \param hNode - Node to query
+ * \param type  - Pointer to return the node type
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphChildGraphNodeGetGraph,
+ * ::cuGraphKernelNodeGetParams,
+ * ::cuGraphKernelNodeSetParams,
+ * ::cuGraphHostNodeGetParams,
+ * ::cuGraphHostNodeSetParams,
+ * ::cuGraphMemcpyNodeGetParams,
+ * ::cuGraphMemcpyNodeSetParams,
+ * ::cuGraphMemsetNodeGetParams,
+ * ::cuGraphMemsetNodeSetParams
+ */
+CUresult CUDAAPI cuGraphNodeGetType(CUgraphNode hNode, CUgraphNodeType *type);
+
+/**
+ * \brief Returns a graph's nodes
+ *
+ * Returns a list of \p hGraph's nodes. \p nodes may be NULL, in which case this
+ * function will return the number of nodes in \p numNodes. Otherwise,
+ * \p numNodes entries will be filled in. If \p numNodes is higher than the actual
+ * number of nodes, the remaining entries in \p nodes will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p numNodes.
+ *
+ * \param hGraph   - Graph to query
+ * \param nodes    - Pointer to return the nodes
+ * \param numNodes - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphNodeGetType,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphGetNodes(CUgraph hGraph, CUgraphNode *nodes, size_t *numNodes);
+
+/**
+ * \brief Returns a graph's root nodes
+ *
+ * Returns a list of \p hGraph's root nodes. \p rootNodes may be NULL, in which case this
+ * function will return the number of root nodes in \p numRootNodes. Otherwise,
+ * \p numRootNodes entries will be filled in. If \p numRootNodes is higher than the actual
+ * number of root nodes, the remaining entries in \p rootNodes will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p numRootNodes.
+ *
+ * \param hGraph       - Graph to query
+ * \param rootNodes    - Pointer to return the root nodes
+ * \param numRootNodes - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate,
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphNodeGetType,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphGetRootNodes(CUgraph hGraph, CUgraphNode *rootNodes, size_t *numRootNodes);
+
+/**
+ * \brief Returns a graph's dependency edges
+ *
+ * Returns a list of \p hGraph's dependency edges. Edges are returned via corresponding
+ * indices in \p from and \p to; that is, the node in \p to[i] has a dependency on the
+ * node in \p from[i]. \p from and \p to may both be NULL, in which
+ * case this function only returns the number of edges in \p numEdges. Otherwise,
+ * \p numEdges entries will be filled in. If \p numEdges is higher than the actual
+ * number of edges, the remaining entries in \p from and \p to will be set to NULL, and
+ * the number of edges actually returned will be written to \p numEdges.
+ *
+ * \param hGraph   - Graph to get the edges from
+ * \param from     - Location to return edge endpoints
+ * \param to       - Location to return edge endpoints
+ * \param numEdges - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphAddDependencies,
+ * ::cuGraphRemoveDependencies,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphGetEdges(CUgraph hGraph, CUgraphNode *from, CUgraphNode *to, size_t *numEdges);
+
+/**
+ * \brief Returns a graph's dependency edges (12.3+)
+ *
+ * Returns a list of \p hGraph's dependency edges. Edges are returned via corresponding
+ * indices in \p from, \p to and \p edgeData; that is, the node in \p to[i] has a
+ * dependency on the node in \p from[i] with data \p edgeData[i]. \p from and \p to may
+ * both be NULL, in which case this function only returns the number of edges in
+ * \p numEdges. Otherwise, \p numEdges entries will be filled in. If \p numEdges is higher
+ * than the actual number of edges, the remaining entries in \p from and \p to will be
+ * set to NULL, and the number of edges actually returned will be written to \p numEdges.
+ * \p edgeData may alone be NULL, in which case the edges must all have default (zeroed)
+ * edge data. Attempting a lossy query via NULL \p edgeData will result in
+ * ::CUDA_ERROR_LOSSY_QUERY. If \p edgeData is non-NULL then \p from and \p to must be
+ * as well.
+ *
+ * \param hGraph   - Graph to get the edges from
+ * \param from     - Location to return edge endpoints
+ * \param to       - Location to return edge endpoints
+ * \param edgeData - Optional location to return edge data
+ * \param numEdges - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_LOSSY_QUERY,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphAddDependencies,
+ * ::cuGraphRemoveDependencies,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphGetEdges_v2(CUgraph hGraph, CUgraphNode *from, CUgraphNode *to, CUgraphEdgeData *edgeData, size_t *numEdges);
+
+/**
+ * \brief Returns a node's dependencies
+ *
+ * Returns a list of \p node's dependencies. \p dependencies may be NULL, in which case this
+ * function will return the number of dependencies in \p numDependencies. Otherwise,
+ * \p numDependencies entries will be filled in. If \p numDependencies is higher than the actual
+ * number of dependencies, the remaining entries in \p dependencies will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p numDependencies.
+ *
+ * \param hNode           - Node to query
+ * \param dependencies    - Pointer to return the dependencies
+ * \param numDependencies - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeGetDependentNodes,
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphAddDependencies,
+ * ::cuGraphRemoveDependencies
+ */
+CUresult CUDAAPI cuGraphNodeGetDependencies(CUgraphNode hNode, CUgraphNode *dependencies, size_t *numDependencies);
+
+/**
+ * \brief Returns a node's dependencies (12.3+)
+ *
+ * Returns a list of \p node's dependencies. \p dependencies may be NULL, in which case this
+ * function will return the number of dependencies in \p numDependencies. Otherwise,
+ * \p numDependencies entries will be filled in. If \p numDependencies is higher than the actual
+ * number of dependencies, the remaining entries in \p dependencies will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p numDependencies.
+ *
+ * Note that if an edge has non-zero (non-default) edge data and \p edgeData is NULL,
+ * this API will return ::CUDA_ERROR_LOSSY_QUERY. If \p edgeData is non-NULL, then
+ * \p dependencies must be as well.
+ *
+ * \param hNode           - Node to query
+ * \param dependencies    - Pointer to return the dependencies
+ * \param edgeData        - Optional array to return edge data for each dependency
+ * \param numDependencies - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_LOSSY_QUERY,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeGetDependentNodes,
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphAddDependencies,
+ * ::cuGraphRemoveDependencies
+ */
+CUresult CUDAAPI cuGraphNodeGetDependencies_v2(CUgraphNode hNode, CUgraphNode *dependencies, CUgraphEdgeData *edgeData, size_t *numDependencies);
+
+/**
+ * \brief Returns a node's dependent nodes
+ *
+ * Returns a list of \p node's dependent nodes. \p dependentNodes may be NULL, in which
+ * case this function will return the number of dependent nodes in \p numDependentNodes.
+ * Otherwise, \p numDependentNodes entries will be filled in. If \p numDependentNodes is
+ * higher than the actual number of dependent nodes, the remaining entries in
+ * \p dependentNodes will be set to NULL, and the number of nodes actually obtained will
+ * be returned in \p numDependentNodes.
+ *
+ * \param hNode             - Node to query
+ * \param dependentNodes    - Pointer to return the dependent nodes
+ * \param numDependentNodes - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphAddDependencies,
+ * ::cuGraphRemoveDependencies
+ */
+CUresult CUDAAPI cuGraphNodeGetDependentNodes(CUgraphNode hNode, CUgraphNode *dependentNodes, size_t *numDependentNodes);
+
+/**
+ * \brief Returns a node's dependent nodes (12.3+)
+ *
+ * Returns a list of \p node's dependent nodes. \p dependentNodes may be NULL, in which
+ * case this function will return the number of dependent nodes in \p numDependentNodes.
+ * Otherwise, \p numDependentNodes entries will be filled in. If \p numDependentNodes is
+ * higher than the actual number of dependent nodes, the remaining entries in
+ * \p dependentNodes will be set to NULL, and the number of nodes actually obtained will
+ * be returned in \p numDependentNodes.
+ *
+ * Note that if an edge has non-zero (non-default) edge data and \p edgeData is NULL,
+ * this API will return ::CUDA_ERROR_LOSSY_QUERY.  If \p edgeData is non-NULL, then
+ * \p dependentNodes must be as well.
+ *
+ * \param hNode             - Node to query
+ * \param dependentNodes    - Pointer to return the dependent nodes
+ * \param edgeData          - Optional pointer to return edge data for dependent nodes
+ * \param numDependentNodes - See description
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_LOSSY_QUERY,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphGetNodes,
+ * ::cuGraphGetRootNodes,
+ * ::cuGraphGetEdges,
+ * ::cuGraphAddDependencies,
+ * ::cuGraphRemoveDependencies
+ */
+CUresult CUDAAPI cuGraphNodeGetDependentNodes_v2(CUgraphNode hNode, CUgraphNode *dependentNodes, CUgraphEdgeData *edgeData, size_t *numDependentNodes);
+
+/**
+ * \brief Adds dependency edges to a graph
+ *
+ * The number of dependencies to be added is defined by \p numDependencies
+ * Elements in \p from and \p to at corresponding indices define a dependency.
+ * Each node in \p from and \p to must belong to \p hGraph.
+ *
+ * If \p numDependencies is 0, elements in \p from and \p to will be ignored.
+ * Specifying an existing dependency will return an error.
+ *
+ * \param hGraph - Graph to which dependencies are added
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param numDependencies - Number of dependencies to be added
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphRemoveDependencies,
+ * ::cuGraphGetEdges,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphAddDependencies(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, size_t numDependencies);
+
+/**
+ * \brief Adds dependency edges to a graph (12.3+)
+ *
+ * The number of dependencies to be added is defined by \p numDependencies
+ * Elements in \p from and \p to at corresponding indices define a dependency.
+ * Each node in \p from and \p to must belong to \p hGraph.
+ *
+ * If \p numDependencies is 0, elements in \p from and \p to will be ignored.
+ * Specifying an existing dependency will return an error.
+ *
+ * \param hGraph - Graph to which dependencies are added
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param edgeData - Optional array of edge data. If NULL, default (zeroed) edge data is assumed.
+ * \param numDependencies - Number of dependencies to be added
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphRemoveDependencies,
+ * ::cuGraphGetEdges,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphAddDependencies_v2(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, const CUgraphEdgeData *edgeData, size_t numDependencies);
+
+/**
+ * \brief Removes dependency edges from a graph
+ *
+ * The number of \p dependencies to be removed is defined by \p numDependencies.
+ * Elements in \p from and \p to at corresponding indices define a dependency.
+ * Each node in \p from and \p to must belong to \p hGraph.
+ *
+ * If \p numDependencies is 0, elements in \p from and \p to will be ignored.
+ * Specifying a non-existing dependency will return an error.
+ *
+ * Dependencies cannot be removed from graphs which contain allocation or free nodes.
+ * Any attempt to do so will return an error.
+ *
+ * \param hGraph - Graph from which to remove dependencies
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param numDependencies - Number of dependencies to be removed
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddDependencies,
+ * ::cuGraphGetEdges,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphRemoveDependencies(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, size_t numDependencies);
+
+/**
+ * \brief Removes dependency edges from a graph (12.3+)
+ *
+ * The number of \p dependencies to be removed is defined by \p numDependencies.
+ * Elements in \p from and \p to at corresponding indices define a dependency.
+ * Each node in \p from and \p to must belong to \p hGraph.
+ *
+ * If \p numDependencies is 0, elements in \p from and \p to will be ignored.
+ * Specifying an edge that does not exist in the graph, with data matching
+ * \p edgeData, results in an error. \p edgeData is nullable, which is equivalent
+ * to passing default (zeroed) data for each edge.
+ *
+ * Dependencies cannot be removed from graphs which contain allocation or free nodes.
+ * Any attempt to do so will return an error.
+ *
+ * \param hGraph - Graph from which to remove dependencies
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param edgeData - Optional array of edge data. If NULL, edge data is assumed to
+ *                   be default (zeroed).
+ * \param numDependencies - Number of dependencies to be removed
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddDependencies,
+ * ::cuGraphGetEdges,
+ * ::cuGraphNodeGetDependencies,
+ * ::cuGraphNodeGetDependentNodes
+ */
+CUresult CUDAAPI cuGraphRemoveDependencies_v2(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, const CUgraphEdgeData *edgeData, size_t numDependencies);
+
+/**
+ * \brief Remove a node from the graph
+ *
+ * Removes \p hNode from its graph. This operation also severs any dependencies of other nodes
+ * on \p hNode and vice versa.
+ *
+ * Nodes which belong to a graph which contains allocation or free nodes cannot be destroyed.
+ * Any attempt to do so will return an error.
+ *
+ * \param hNode  - Node to remove
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphAddEmptyNode,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphAddMemsetNode
+ */
+CUresult CUDAAPI cuGraphDestroyNode(CUgraphNode hNode);
+
+/**
+ * \brief Creates an executable graph from a graph
+ *
+ * Instantiates \p hGraph as an executable graph. The graph is validated for any
+ * structural constraints or intra-node constraints which were not previously
+ * validated. If instantiation is successful, a handle to the instantiated graph
+ * is returned in \p phGraphExec.
+ *
+ * The \p flags parameter controls the behavior of instantiation and subsequent
+ * graph launches.  Valid flags are:
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, which configures a
+ * graph containing memory allocation nodes to automatically free any
+ * unfreed memory allocations before the graph is relaunched.
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH, which configures the graph for launch
+ * from the device. If this flag is passed, the executable graph handle returned can be
+ * used to launch the graph from both the host and device. This flag can only be used
+ * on platforms which support unified addressing. This flag cannot be used in
+ * conjunction with ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH.
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY, which causes the graph
+ * to use the priorities from the per-node attributes rather than the priority
+ * of the launch stream during execution. Note that priorities are only available
+ * on kernel nodes, and are copied from stream priority during stream capture.
+ *
+ * If \p hGraph contains any allocation or free nodes, there can be at most one
+ * executable graph in existence for that graph at a time. An attempt to instantiate
+ * a second executable graph before destroying the first with ::cuGraphExecDestroy
+ * will result in an error.
+ * The same also applies if \p hGraph contains any device-updatable kernel nodes.
+ *
+ * If \p hGraph contains kernels which call device-side cudaGraphLaunch() from multiple
+ * contexts, this will result in an error.
+ *
+ * Graphs instantiated for launch on the device have additional restrictions which do not
+ * apply to host graphs:
+ *
+ * - The graph's nodes must reside on a single context.
+ * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes.
+ * - The graph cannot be empty and must contain at least one kernel, memcpy, or memset node.
+ *   Operation-specific restrictions are outlined below.
+ * - Kernel nodes:
+ *   - Use of CUDA Dynamic Parallelism is not permitted.
+ *   - Cooperative launches are permitted as long as MPS is not in use.
+ * - Memcpy nodes:
+ *   - Only copies involving device memory and/or pinned device-mapped host memory are permitted.
+ *   - Copies involving CUDA arrays are not permitted.
+ *   - Both operands must be accessible from the current context, and the current context must
+ *     match the context of other nodes in the graph.
+ *
+ * \param phGraphExec - Returns instantiated graph
+ * \param hGraph      - Graph to instantiate
+ * \param flags       - Flags to control instantiation.  See ::CUgraphInstantiate_flags.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphInstantiate,
+ * ::cuGraphCreate,
+ * ::cuGraphUpload,
+ * ::cuGraphLaunch,
+ * ::cuGraphExecDestroy
+ */
+CUresult CUDAAPI cuGraphInstantiate(CUgraphExec *phGraphExec, CUgraph hGraph, unsigned long long flags);
+
+/**
+ * \brief Creates an executable graph from a graph
+ *
+ * Instantiates \p hGraph as an executable graph according to the \p instantiateParams structure.
+ * The graph is validated for any structural constraints or intra-node constraints
+ * which were not previously validated. If instantiation is successful, a handle to
+ * the instantiated graph is returned in \p phGraphExec.
+ *
+ * \p instantiateParams controls the behavior of instantiation and subsequent
+ * graph launches, as well as returning more detailed information in the event of an error.
+ * ::CUDA_GRAPH_INSTANTIATE_PARAMS is defined as:
+ *
+ * \code
+    typedef struct {
+        cuuint64_t flags;
+        CUstream hUploadStream;
+        CUgraphNode hErrNode_out;
+        CUgraphInstantiateResult result_out;
+    } CUDA_GRAPH_INSTANTIATE_PARAMS;
+ * \endcode
+ *
+ * The \p flags field controls the behavior of instantiation and subsequent
+ * graph launches. Valid flags are:
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, which configures a
+ * graph containing memory allocation nodes to automatically free any
+ * unfreed memory allocations before the graph is relaunched.
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD, which will perform an upload of the graph
+ * into \p hUploadStream once the graph has been instantiated.
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH, which configures the graph for launch
+ * from the device. If this flag is passed, the executable graph handle returned can be
+ * used to launch the graph from both the host and device. This flag can only be used
+ * on platforms which support unified addressing. This flag cannot be used in
+ * conjunction with ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH.
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY, which causes the graph
+ * to use the priorities from the per-node attributes rather than the priority
+ * of the launch stream during execution. Note that priorities are only available
+ * on kernel nodes, and are copied from stream priority during stream capture.
+ *
+ * If \p hGraph contains any allocation or free nodes, there can be at most one
+ * executable graph in existence for that graph at a time. An attempt to instantiate a
+ * second executable graph before destroying the first with ::cuGraphExecDestroy will
+ * result in an error.
+ * The same also applies if \p hGraph contains any device-updatable kernel nodes.
+ *
+ * If \p hGraph contains kernels which call device-side cudaGraphLaunch() from multiple
+ * contexts, this will result in an error.
+ *
+ * Graphs instantiated for launch on the device have additional restrictions which do not
+ * apply to host graphs:
+ *
+ * - The graph's nodes must reside on a single context.
+ * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes.
+ * - The graph cannot be empty and must contain at least one kernel, memcpy, or memset node.
+ *   Operation-specific restrictions are outlined below.
+ * - Kernel nodes:
+ *   - Use of CUDA Dynamic Parallelism is not permitted.
+ *   - Cooperative launches are permitted as long as MPS is not in use.
+ * - Memcpy nodes:
+ *   - Only copies involving device memory and/or pinned device-mapped host memory are permitted.
+ *   - Copies involving CUDA arrays are not permitted.
+ *   - Both operands must be accessible from the current context, and the current context must
+ *     match the context of other nodes in the graph.
+ *
+ * In the event of an error, the \p result_out and \p hErrNode_out fields will contain more
+ * information about the nature of the error. Possible error reporting includes:
+ *
+ * - ::CUDA_GRAPH_INSTANTIATE_ERROR, if passed an invalid value or if an unexpected error occurred
+ *   which is described by the return value of the function. \p hErrNode_out will be set to NULL.
+ * - ::CUDA_GRAPH_INSTANTIATE_INVALID_STRUCTURE, if the graph structure is invalid. \p hErrNode_out
+ *   will be set to one of the offending nodes.
+ * - ::CUDA_GRAPH_INSTANTIATE_NODE_OPERATION_NOT_SUPPORTED, if the graph is instantiated for device
+ *   launch but contains a node of an unsupported node type, or a node which performs unsupported
+ *   operations, such as use of CUDA dynamic parallelism within a kernel node. \p hErrNode_out will
+ *   be set to this node.
+ * - ::CUDA_GRAPH_INSTANTIATE_MULTIPLE_CTXS_NOT_SUPPORTED, if the graph is instantiated for device
+ *   launch but a node’s context differs from that of another node. This error can also be returned
+ *   if a graph is not instantiated for device launch and it contains kernels which call device-side
+ *   cudaGraphLaunch() from multiple contexts. \p hErrNode_out will be set to this node.
+ *
+ * If instantiation is successful, \p result_out will be set to ::CUDA_GRAPH_INSTANTIATE_SUCCESS,
+ * and \p hErrNode_out will be set to NULL.
+ *
+ * \param phGraphExec       - Returns instantiated graph
+ * \param hGraph            - Graph to instantiate
+ * \param instantiateParams - Instantiation parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate,
+ * ::cuGraphInstantiate,
+ * ::cuGraphExecDestroy
+ */
+CUresult CUDAAPI cuGraphInstantiateWithParams(CUgraphExec *phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams);
+
+/**
+ * \brief Query the instantiation flags of an executable graph
+ *
+ * Returns the flags that were passed to instantiation for the given executable graph.
+ * ::CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD will not be returned by this API as it does
+ * not affect the resulting executable graph.
+ *
+ * \param hGraphExec - The executable graph to query
+ * \param flags      - Returns the instantiation flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphInstantiate,
+ * ::cuGraphInstantiateWithParams
+ */
+CUresult CUDAAPI cuGraphExecGetFlags(CUgraphExec hGraphExec, cuuint64_t *flags);
+
+/**
+ * \brief Sets the parameters for a kernel node in the given graphExec
+ *
+ * Sets the parameters of a kernel node in an executable graph \p hGraphExec. 
+ * The node is identified by the corresponding node \p hNode in the 
+ * non-executable graph, from which the executable graph was instantiated. 
+ *
+ * \p hNode must not have been removed from the original graph. All \p nodeParams 
+ * fields may change, but the following restrictions apply to \p func updates: 
+ *
+ *   - The owning context of the function cannot change.
+ *   - A node whose function originally did not use CUDA dynamic parallelism cannot be updated
+ *     to a function which uses CDP
+ *   - A node whose function originally did not make device-side update calls cannot be updated
+ *     to a function which makes device-side update calls.
+ *   - If \p hGraphExec was not instantiated for device launch, a node whose function originally
+ *     did not use device-side cudaGraphLaunch() cannot be updated to a function which uses
+ *     device-side cudaGraphLaunch() unless the node resides on the same context as nodes which
+ *     contained such calls at instantiate-time. If no such calls were present at instantiation,
+ *     these updates cannot be performed at all.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already 
+ * enqueued or running launches of \p hGraphExec are not affected by this call. 
+ * \p hNode is also not modified by this call.
+ *
+ * If \p hNode is a device-updatable kernel node, the next upload/launch of \p hGraphExec
+ * will overwrite any previous device-side updates. Additionally, applying host updates to a
+ * device-updatable kernel node while it is being updated from the device will result in
+ * undefined behavior.
+ * 
+ * \param hGraphExec  - The executable graph in which to set the specified node
+ * \param hNode       - kernel node from the graph from which graphExec was instantiated
+ * \param nodeParams  - Updated Parameters to set
+ * 
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddKernelNode,
+ * ::cuGraphKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecKernelNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec.
+ *
+ * Updates the work represented by \p hNode in \p hGraphExec as though \p hNode had 
+ * contained \p copyParams at instantiation.  hNode must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from hNode are ignored.
+ *
+ * The source and destination memory in \p copyParams must be allocated from the same 
+ * contexts as the original source and destination memory.  Both the instantiation-time 
+ * memory operands and the memory operands in \p copyParams must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  hNode is also 
+ * not modified by this call.
+ *
+ * Returns CUDA_ERROR_INVALID_VALUE if the memory operands' mappings changed or
+ * either the original or new memory operands are multidimensional.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Memcpy node from the graph which was used to instantiate graphExec
+ * \param copyParams - The updated parameters to set
+ * \param ctx        - Context on which to run the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddMemcpyNode,
+ * ::cuGraphMemcpyNodeSetParams,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecMemcpyNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMCPY3D *copyParams, CUcontext ctx);
+
+/**
+ * \brief Sets the parameters for a memset node in the given graphExec.
+ *
+ * Updates the work represented by \p hNode in \p hGraphExec as though \p hNode had 
+ * contained \p memsetParams at instantiation.  hNode must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from hNode are ignored.
+ *
+ * Zero sized operations are not supported.
+ *
+ * The new destination pointer in memsetParams must be to the same kind of allocation
+ * as the original destination pointer and have the same context association and device mapping
+ * as the original destination pointer.
+ *
+ * Both the value and pointer address may be updated.  
+ * Changing other aspects of the memset (width, height, element size or pitch) may cause the update to be rejected.
+ * Specifically, for 2d memsets, all dimension changes are rejected.
+ * For 1d memsets, changes in height are explicitly rejected and other changes are oportunistically allowed
+ * if the resulting work maps onto the work resources already allocated for the node.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  hNode is also 
+ * not modified by this call.
+ *
+ * \param hGraphExec   - The executable graph in which to set the specified node
+ * \param hNode        - Memset node from the graph which was used to instantiate graphExec
+ * \param memsetParams - The updated parameters to set
+ * \param ctx          - Context on which to run the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddMemsetNode,
+ * ::cuGraphMemsetNodeSetParams,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecMemsetNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS *memsetParams, CUcontext ctx);
+
+/**
+ * \brief Sets the parameters for a host node in the given graphExec.
+ *
+ * Updates the work represented by \p hNode in \p hGraphExec as though \p hNode had 
+ * contained \p nodeParams at instantiation.  hNode must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from hNode are ignored.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  hNode is also 
+ * not modified by this call.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Host node from the graph which was used to instantiate graphExec
+ * \param nodeParams - The updated parameters to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddHostNode,
+ * ::cuGraphHostNodeSetParams,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecHostNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Updates node parameters in the child graph node in the given graphExec.
+ *
+ * Updates the work represented by \p hNode in \p hGraphExec as though the nodes contained
+ * in \p hNode's graph had the parameters contained in \p childGraph's nodes at instantiation.
+ * \p hNode must remain in the graph which was used to instantiate \p hGraphExec.
+ * Changed edges to and from \p hNode are ignored.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p hNode is also 
+ * not modified by this call.
+ *
+ * The topology of \p childGraph, as well as the node insertion order,  must match that
+ * of the graph contained in \p hNode.  See ::cuGraphExecUpdate() for a list of restrictions
+ * on what can be updated in an instantiated graph.  The update is recursive, so child graph
+ * nodes contained within the top level child graph will also be updated.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Host node from the graph which was used to instantiate graphExec
+ * \param childGraph - The graph supplying the updated parameters
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddChildGraphNode,
+ * ::cuGraphChildGraphNodeGetGraph,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecChildGraphNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraph childGraph);
+
+/**
+ * \brief Sets the event for an event record node in the given graphExec
+ *
+ * Sets the event of an event record node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - event record node from the graph from which graphExec was instantiated
+ * \param event      - Updated event to use
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddEventRecordNode,
+ * ::cuGraphEventRecordNodeGetEvent,
+ * ::cuGraphEventWaitNodeSetEvent,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecEventRecordNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event);
+
+/**
+ * \brief Sets the event for an event wait node in the given graphExec
+ *
+ * Sets the event of an event wait node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - event wait node from the graph from which graphExec was instantiated
+ * \param event      - Updated event to use
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddEventWaitNode,
+ * ::cuGraphEventWaitNodeGetEvent,
+ * ::cuGraphEventRecordNodeSetEvent,
+ * ::cuEventRecordWithFlags,
+ * ::cuStreamWaitEvent,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecEventWaitNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event);
+
+/**
+ * \brief Sets the parameters for an external semaphore signal node in the given graphExec
+ *
+ * Sets the parameters of an external semaphore signal node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * Changing \p nodeParams->numExtSems is not supported.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - semaphore signal node from the graph from which graphExec was instantiated
+ * \param nodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresSignalNode,
+ * ::cuImportExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecExternalSemaphoresSignalNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Sets the parameters for an external semaphore wait node in the given graphExec
+ *
+ * Sets the parameters of an external semaphore wait node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * Changing \p nodeParams->numExtSems is not supported.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - semaphore wait node from the graph from which graphExec was instantiated
+ * \param nodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphExecNodeSetParams,
+ * ::cuGraphAddExternalSemaphoresWaitNode,
+ * ::cuImportExternalSemaphore,
+ * ::cuSignalExternalSemaphoresAsync,
+ * ::cuWaitExternalSemaphoresAsync,
+ * ::cuGraphExecKernelNodeSetParams,
+ * ::cuGraphExecMemcpyNodeSetParams,
+ * ::cuGraphExecMemsetNodeSetParams,
+ * ::cuGraphExecHostNodeSetParams,
+ * ::cuGraphExecChildGraphNodeSetParams,
+ * ::cuGraphExecEventRecordNodeSetEvent,
+ * ::cuGraphExecEventWaitNodeSetEvent,
+ * ::cuGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecExternalSemaphoresWaitNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams);
+
+/**
+ * \brief Enables or disables the specified node in the given graphExec
+ *
+ * Sets \p hNode to be either enabled or disabled. Disabled nodes are functionally equivalent 
+ * to empty nodes until they are reenabled. Existing node parameters are not affected by 
+ * disabling/enabling the node.
+ *  
+ * The node is identified by the corresponding node \p hNode in the non-executable 
+ * graph, from which the executable graph was instantiated.   
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * If \p hNode is a device-updatable kernel node, the next upload/launch of \p hGraphExec
+ * will overwrite any previous device-side updates. Additionally, applying host updates to a
+ * device-updatable kernel node while it is being updated from the device will result in
+ * undefined behavior.
+ *
+ * \note Currently only kernel, memset and memcpy nodes are supported. 
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Node from the graph from which graphExec was instantiated
+ * \param isEnabled  - Node is enabled if != 0, otherwise the node is disabled
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeGetEnabled,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ * ::cuGraphLaunch
+ */
+CUresult CUDAAPI cuGraphNodeSetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int isEnabled);
+
+/**
+ * \brief Query whether a node in the given graphExec is enabled
+ *
+ * Sets isEnabled to 1 if \p hNode is enabled, or 0 if \p hNode is disabled.
+ *
+ * The node is identified by the corresponding node \p hNode in the non-executable 
+ * graph, from which the executable graph was instantiated.   
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * \note Currently only kernel, memset and memcpy nodes are supported. 
+ * \note This function will not reflect device-side updates for device-updatable kernel nodes.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Node from the graph from which graphExec was instantiated
+ * \param isEnabled  - Location to return the enabled status of the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphNodeSetEnabled,
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ * ::cuGraphLaunch
+ */
+CUresult CUDAAPI cuGraphNodeGetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int *isEnabled);
+
+/**
+ * \brief Uploads an executable graph in a stream
+ *
+ * Uploads \p hGraphExec to the device in \p hStream without executing it. Uploads of
+ * the same \p hGraphExec will be serialized. Each upload is ordered behind both any
+ * previous work in \p hStream and any previous launches of \p hGraphExec.
+ * Uses memory cached by \p stream to back the allocations owned by \p hGraphExec.
+ *
+ * \param hGraphExec - Executable graph to upload
+ * \param hStream    - Stream in which to upload the graph
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphInstantiate,
+ * ::cuGraphLaunch,
+ * ::cuGraphExecDestroy
+ */
+CUresult CUDAAPI cuGraphUpload(CUgraphExec hGraphExec, CUstream hStream);
+
+/**
+ * \brief Launches an executable graph in a stream
+ *
+ * Executes \p hGraphExec in \p hStream. Only one instance of \p hGraphExec may be executing
+ * at a time. Each launch is ordered behind both any previous work in \p hStream
+ * and any previous launches of \p hGraphExec. To execute a graph concurrently, it must be
+ * instantiated multiple times into multiple executable graphs.
+ *
+ * If any allocations created by \p hGraphExec remain unfreed (from a previous launch) and
+ * \p hGraphExec was not instantiated with ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH,
+ * the launch will fail with ::CUDA_ERROR_INVALID_VALUE.
+ *
+ * \param hGraphExec - Executable graph to launch
+ * \param hStream    - Stream in which to launch the graph
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphInstantiate,
+ * ::cuGraphUpload,
+ * ::cuGraphExecDestroy
+ */
+CUresult CUDAAPI cuGraphLaunch(CUgraphExec hGraphExec, CUstream hStream);
+
+/**
+ * \brief Destroys an executable graph
+ *
+ * Destroys the executable graph specified by \p hGraphExec, as well
+ * as all of its executable nodes. If the executable graph is
+ * in-flight, it will not be terminated, but rather freed
+ * asynchronously on completion.
+ *
+ * \param hGraphExec - Executable graph to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphInstantiate,
+ * ::cuGraphUpload,
+ * ::cuGraphLaunch
+ */
+CUresult CUDAAPI cuGraphExecDestroy(CUgraphExec hGraphExec);
+
+/**
+ * \brief Destroys a graph
+ *
+ * Destroys the graph specified by \p hGraph, as well as all of its nodes.
+ *
+ * \param hGraph - Graph to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate
+ */
+CUresult CUDAAPI cuGraphDestroy(CUgraph hGraph);
+
+/**
+ * \brief Check whether an executable graph can be updated with a graph and perform the update if possible
+ *
+ * Updates the node parameters in the instantiated graph specified by \p hGraphExec with the
+ * node parameters in a topologically identical graph specified by \p hGraph.
+ *
+ * Limitations:
+ *
+ * - Kernel nodes:
+ *   - The owning context of the function cannot change.
+ *   - A node whose function originally did not use CUDA dynamic parallelism cannot be updated
+ *     to a function which uses CDP.
+ *   - A node whose function originally did not make device-side update calls cannot be updated
+ *     to a function which makes device-side update calls.
+ *   - A cooperative node cannot be updated to a non-cooperative node, and vice-versa.
+ *   - If the graph was instantiated with CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY, the
+ *     priority attribute cannot change. Equality is checked on the originally requested
+ *     priority values, before they are clamped to the device's supported range.
+ *   - If \p hGraphExec was not instantiated for device launch, a node whose function originally
+ *     did not use device-side cudaGraphLaunch() cannot be updated to a function which uses
+ *     device-side cudaGraphLaunch() unless the node resides on the same context as nodes which
+ *     contained such calls at instantiate-time. If no such calls were present at instantiation,
+ *     these updates cannot be performed at all.
+ *   - Neither \p hGraph nor \p hGraphExec may contain device-updatable kernel nodes.
+ * - Memset and memcpy nodes:
+ *   - The CUDA device(s) to which the operand(s) was allocated/mapped cannot change.
+ *   - The source/destination memory must be allocated from the same contexts as the original
+ *     source/destination memory.
+ *   - For 2d memsets, only address and assinged value may be updated.
+ *   - For 1d memsets, updating dimensions is also allowed, but may fail if the resulting operation doesn't
+ *     map onto the work resources already allocated for the node. 
+ * - Additional memcpy node restrictions:
+ *   - Changing either the source or destination memory type(i.e. CU_MEMORYTYPE_DEVICE,
+ *     CU_MEMORYTYPE_ARRAY, etc.) is not supported.
+ * - External semaphore wait nodes and record nodes:
+ *   - Changing the number of semaphores is not supported.
+ * - Conditional nodes:
+ *   - Changing node parameters is not supported.
+ *   - Changeing parameters of nodes within the conditional body graph is subject to the rules above.
+ *   - Conditional handle flags and default values are updated as part of the graph update.
+ *
+ * Note:  The API may add further restrictions in future releases.  The return code should always be checked.
+ *
+ * cuGraphExecUpdate sets the result member of \p resultInfo to CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED
+ * under the following conditions:
+ * - The count of nodes directly in \p hGraphExec and \p hGraph differ, in which case resultInfo->errorNode
+ *   is set to NULL.
+ * - \p hGraph has more exit nodes than \p hGraph, in which case resultInfo->errorNode is set to one of
+ *   the exit nodes in hGraph. 
+ * - A node in \p hGraph has a different number of dependencies than the node from \p hGraphExec it is paired with,
+ *   in which case resultInfo->errorNode is set to the node from \p hGraph.
+ * - A node in \p hGraph has a dependency that does not match with the corresponding dependency of the paired node
+ *   from \p hGraphExec. resultInfo->errorNode will be set to the node from \p hGraph. resultInfo->errorFromNode
+ *   will be set to the mismatched dependency. The dependencies are paired based on edge order and a dependency
+ *   does not match when the nodes are already paired based on other edges examined in the graph.
+ *
+ * cuGraphExecUpdate sets the result member of \p resultInfo to: 
+ * - CU_GRAPH_EXEC_UPDATE_ERROR if passed an invalid value.
+ * - CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED if the graph topology changed
+ * - CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED if the type of a node changed, in which case
+ *   \p hErrorNode_out is set to the node from \p hGraph.
+ * - CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE if the function changed in an unsupported
+ *   way(see note above), in which case \p hErrorNode_out is set to the node from \p hGraph
+ * - CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED if any parameters to a node changed in a way 
+ *   that is not supported, in which case \p hErrorNode_out is set to the node from \p hGraph.
+ * - CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED if any attributes of a node changed in a way
+ *   that is not supported, in which case \p hErrorNode_out is set to the node from \p hGraph.
+ * - CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED if something about a node is unsupported, like 
+ *   the node's type or configuration, in which case \p hErrorNode_out is set to the node from \p hGraph
+ *
+ * If the update fails for a reason not listed above, the result member of \p resultInfo will be set
+ * to CU_GRAPH_EXEC_UPDATE_ERROR. If the update succeeds, the result member will be set to CU_GRAPH_EXEC_UPDATE_SUCCESS.
+ *
+ * cuGraphExecUpdate returns CUDA_SUCCESS when the updated was performed successfully.  It returns
+ * CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE if the graph update was not performed because it included 
+ * changes which violated constraints specific to instantiated graph update.
+ *
+ * \param hGraphExec The instantiated graph to be updated
+ * \param hGraph The graph containing the updated parameters
+ * \param resultInfo the error info structure 
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE,
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecUpdate(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphExecUpdateResultInfo *resultInfo);
+
+/**
+ * \brief Copies attributes from source node to destination node.
+ *
+ * Copies attributes from source node \p src to destination node \p dst.
+ * Both node must have the same context.
+ *
+ * \param[out] dst Destination node
+ * \param[in] src Source node
+ * For list of attributes see ::CUkernelNodeAttrID
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuGraphKernelNodeCopyAttributes(CUgraphNode dst, CUgraphNode src);
+
+/**
+ * \brief Queries node attribute.
+ * 
+ * Queries attribute \p attr from node \p hNode and stores it in corresponding
+ * member of \p value_out.
+ *
+ * \param[in] hNode
+ * \param[in] attr
+ * \param[out] value_out 
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *  
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuGraphKernelNodeGetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr,
+                                      CUkernelNodeAttrValue *value_out);
+ 
+/**
+ * \brief Sets node attribute.
+ * 
+ * Sets attribute \p attr on node \p hNode from corresponding attribute of
+ * \p value.
+ *
+ * \param[out] hNode
+ * \param[in] attr
+ * \param[out] value
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE
+ * \notefnerr
+ *
+ * \sa
+ * ::CUaccessPolicyWindow
+ */
+CUresult CUDAAPI cuGraphKernelNodeSetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr,
+                                      const CUkernelNodeAttrValue *value);
+
+/**
+ * \brief Write a DOT file describing graph structure
+ *
+ * Using the provided \p hGraph, write to \p path a DOT formatted description of the graph.
+ * By default this includes the graph topology, node types, node id, kernel names and memcpy direction.
+ * \p flags can be specified to write more detailed information about each node type such as
+ * parameter values, kernel attributes, node and function handles.
+ *
+ * \param hGraph - The graph to create a DOT file from
+ * \param path   - The path to write the DOT file to
+ * \param flags  - Flags from CUgraphDebugDot_flags for specifying which additional node information to write
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OPERATING_SYSTEM
+ */
+CUresult CUDAAPI cuGraphDebugDotPrint(CUgraph hGraph, const char *path, unsigned int flags);
+
+/**
+ * \brief Create a user object
+ *
+ * Create a user object with the specified destructor callback and initial reference count. The
+ * initial references are owned by the caller.
+ *
+ * Destructor callbacks cannot make CUDA API calls and should avoid blocking behavior, as they
+ * are executed by a shared internal thread. Another thread may be signaled to perform such
+ * actions, if it does not block forward progress of tasks scheduled through CUDA.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param object_out      - Location to return the user object handle
+ * \param ptr             - The pointer to pass to the destroy function
+ * \param destroy         - Callback to free the user object when it is no longer in use
+ * \param initialRefcount - The initial refcount to create the object with, typically 1. The
+ *                          initial references are owned by the calling thread.
+ * \param flags           - Currently it is required to pass ::CU_USER_OBJECT_NO_DESTRUCTOR_SYNC,
+ *                          which is the only defined flag. This indicates that the destroy
+ *                          callback cannot be waited on by any CUDA API. Users requiring
+ *                          synchronization of the callback should signal its completion
+ *                          manually.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuUserObjectRetain,
+ * ::cuUserObjectRelease,
+ * ::cuGraphRetainUserObject,
+ * ::cuGraphReleaseUserObject,
+ * ::cuGraphCreate
+ */
+CUresult CUDAAPI cuUserObjectCreate(CUuserObject *object_out, void *ptr, CUhostFn destroy,
+                                    unsigned int initialRefcount, unsigned int flags);
+
+/**
+ * \brief Retain a reference to a user object
+ *
+ * Retains new references to a user object. The new references are owned by the caller.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param object - The object to retain
+ * \param count  - The number of references to retain, typically 1. Must be nonzero
+ *                 and not larger than INT_MAX.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuUserObjectCreate,
+ * ::cuUserObjectRelease,
+ * ::cuGraphRetainUserObject,
+ * ::cuGraphReleaseUserObject,
+ * ::cuGraphCreate
+ */
+CUresult CUDAAPI cuUserObjectRetain(CUuserObject object, unsigned int count);
+
+/**
+ * \brief Release a reference to a user object
+ *
+ * Releases user object references owned by the caller. The object's destructor is invoked if
+ * the reference count reaches zero.
+ *
+ * It is undefined behavior to release references not owned by the caller, or to use a user
+ * object handle after all references are released.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param object - The object to release
+ * \param count  - The number of references to release, typically 1. Must be nonzero
+ *                 and not larger than INT_MAX.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuUserObjectCreate,
+ * ::cuUserObjectRetain,
+ * ::cuGraphRetainUserObject,
+ * ::cuGraphReleaseUserObject,
+ * ::cuGraphCreate
+ */
+CUresult CUDAAPI cuUserObjectRelease(CUuserObject object, unsigned int count);
+
+/**
+ * \brief Retain a reference to a user object from a graph
+ *
+ * Creates or moves user object references that will be owned by a CUDA graph.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param graph  - The graph to associate the reference with
+ * \param object - The user object to retain a reference for
+ * \param count  - The number of references to add to the graph, typically 1. Must be
+ *                 nonzero and not larger than INT_MAX.
+ * \param flags  - The optional flag ::CU_GRAPH_USER_OBJECT_MOVE transfers references
+ *                 from the calling thread, rather than create new references. Pass 0
+ *                 to create new references.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuUserObjectCreate,
+ * ::cuUserObjectRetain,
+ * ::cuUserObjectRelease,
+ * ::cuGraphReleaseUserObject,
+ * ::cuGraphCreate
+ */
+CUresult CUDAAPI cuGraphRetainUserObject(CUgraph graph, CUuserObject object, unsigned int count, unsigned int flags);
+
+/**
+ * \brief Release a user object reference from a graph
+ *
+ * Releases user object references owned by a graph.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param graph  - The graph that will release the reference
+ * \param object - The user object to release a reference for
+ * \param count  - The number of references to release, typically 1. Must be nonzero
+ *                 and not larger than INT_MAX.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuUserObjectCreate,
+ * ::cuUserObjectRetain,
+ * ::cuUserObjectRelease,
+ * ::cuGraphRetainUserObject,
+ * ::cuGraphCreate
+ */
+CUresult CUDAAPI cuGraphReleaseUserObject(CUgraph graph, CUuserObject object, unsigned int count);
+
+/**
+ * \brief Adds a node of arbitrary type to a graph
+ *
+ * Creates a new node in \p hGraph described by \p nodeParams with \p numDependencies
+ * dependencies specified via \p dependencies. \p numDependencies may be 0.
+ * \p dependencies may be null if \p numDependencies is 0. \p dependencies may not have
+ * any duplicate entries.
+ *
+ * \p nodeParams is a tagged union. The node type should be specified in the \p type field,
+ * and type-specific parameters in the corresponding union member. All unused bytes - that
+ * is, \p reserved0 and all bytes past the utilized union member - must be set to zero.
+ * It is recommended to use brace initialization or memset to ensure all bytes are
+ * initialized.
+ *
+ * Note that for some node types, \p nodeParams may contain "out parameters" which are
+ * modified during the call, such as \p nodeParams->alloc.dptr.
+ *
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Specification of the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate,
+ * ::cuGraphNodeSetParams,
+ * ::cuGraphExecNodeSetParams
+ */
+CUresult CUDAAPI cuGraphAddNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUgraphNodeParams *nodeParams);
+
+/**
+ * \brief Adds a node of arbitrary type to a graph (12.3+)
+ *
+ * Creates a new node in \p hGraph described by \p nodeParams with \p numDependencies
+ * dependencies specified via \p dependencies. \p numDependencies may be 0.
+ * \p dependencies may be null if \p numDependencies is 0. \p dependencies may not have
+ * any duplicate entries.
+ *
+ * \p nodeParams is a tagged union. The node type should be specified in the \p type field,
+ * and type-specific parameters in the corresponding union member. All unused bytes - that
+ * is, \p reserved0 and all bytes past the utilized union member - must be set to zero.
+ * It is recommended to use brace initialization or memset to ensure all bytes are
+ * initialized.
+ *
+ * Note that for some node types, \p nodeParams may contain "out parameters" which are
+ * modified during the call, such as \p nodeParams->alloc.dptr.
+ *
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param dependencyData  - Optional edge data for the dependencies. If NULL, the data is
+ *                          assumed to be default (zeroed) for all dependencies.
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Specification of the node
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphCreate,
+ * ::cuGraphNodeSetParams,
+ * ::cuGraphExecNodeSetParams
+ */
+CUresult CUDAAPI cuGraphAddNode_v2(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, CUgraphNodeParams *nodeParams);
+
+/**
+ * \brief Update's a graph node's parameters
+ *
+ * Sets the parameters of graph node \p hNode to \p nodeParams. The node type specified by
+ * \p nodeParams->type must match the type of \p hNode. \p nodeParams must be fully
+ * initialized and all unused bytes (reserved, padding) zeroed.
+ *
+ * Modifying parameters is not supported for node types CU_GRAPH_NODE_TYPE_MEM_ALLOC and
+ * CU_GRAPH_NODE_TYPE_MEM_FREE.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphExecNodeSetParams
+ */
+CUresult CUDAAPI cuGraphNodeSetParams(CUgraphNode hNode, CUgraphNodeParams *nodeParams);
+
+/**
+ * \brief Update's a graph node's parameters in an instantiated graph
+ *
+ * Sets the parameters of a node in an executable graph \p hGraphExec. The node is identified
+ * by the corresponding node \p hNode in the non-executable graph from which the executable
+ * graph was instantiated. \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * Allowed changes to parameters on executable graphs are as follows:
+ * <table>
+ *   <tr><th>Node type<th>Allowed changes
+ *   <tr><td>kernel<td>See ::cuGraphExecKernelNodeSetParams
+ *   <tr><td>memcpy<td>Addresses for 1-dimensional copies if allocated in same context; see ::cuGraphExecMemcpyNodeSetParams
+ *   <tr><td>memset<td>Addresses for 1-dimensional memsets if allocated in same context; see ::cuGraphExecMemsetNodeSetParams
+ *   <tr><td>host<td>Unrestricted
+ *   <tr><td>child graph<td>Topology must match and restrictions apply recursively; see ::cuGraphExecUpdate
+ *   <tr><td>event wait<td>Unrestricted
+ *   <tr><td>event record<td>Unrestricted
+ *   <tr><td>external semaphore signal<td>Number of semaphore operations cannot change
+ *   <tr><td>external semaphore wait<td>Number of semaphore operations cannot change
+ *   <tr><td>memory allocation<td>API unsupported
+ *   <tr><td>memory free<td>API unsupported
+ *   <tr><td>batch memops<td>Addresses, values, and operation type for wait operations; see ::cuGraphExecBatchMemOpNodeSetParams
+ * </table>
+ *
+ * \param hGraphExec  - The executable graph in which to update the specified node
+ * \param hNode       - Corresponding node from the graph from which graphExec was instantiated
+ * \param nodeParams  - Updated Parameters to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ * ::cuGraphNodeSetParams
+ * ::cuGraphExecUpdate,
+ * ::cuGraphInstantiate
+ */
+CUresult CUDAAPI cuGraphExecNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraphNodeParams *nodeParams);
+
+/**
+ * \brief Create a conditional handle
+ *
+ * Creates a conditional handle associated with \p hGraph. 
+ *  
+ * The conditional handle must be associated with a conditional node in this graph or one of its children.
+ *  
+ * Handles not associated with a conditional node may cause graph instantiation to fail. 
+ *  
+ * Handles can only be set from the context with which they are associated. 
+ *
+ * \param pHandle_out        - Pointer used to return the handle to the caller.
+ * \param hGraph             - Graph which will contain the conditional node using this handle.
+ * \param ctx                - Context for the handle and associated conditional node.
+ * \param defaultLaunchValue - Optional initial value for the conditional variable.
+ *                             Applied at the beginning of each graph execution if CU_GRAPH_COND_ASSIGN_DEFAULT is set in \p flags.
+ * \param flags              - Currently must be CU_GRAPH_COND_ASSIGN_DEFAULT or 0.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode
+ */
+CUresult CUDAAPI cuGraphConditionalHandleCreate(CUgraphConditionalHandle *pHandle_out, CUgraph hGraph, CUcontext ctx, unsigned int defaultLaunchValue, unsigned int flags);
+
+/** @} */ /* END CUDA_GRAPH */
+
+/**
+ * \defgroup CUDA_OCCUPANCY Occupancy
+ *
+ * ___MANBRIEF___ occupancy calculation functions of the low-level CUDA driver
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the occupancy calculation functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns occupancy of a function
+ *
+ * Returns in \p *numBlocks the number of the maximum active blocks per
+ * streaming multiprocessor.
+ *
+ * Note that the API can also be used with context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will be the current context.
+ *
+ * \param numBlocks       - Returned occupancy
+ * \param func            - Kernel for which occupancy is calculated
+ * \param blockSize       - Block size the kernel is intended to be launched with
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ */
+CUresult CUDAAPI cuOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize);
+
+/**
+ * \brief Returns occupancy of a function
+ *
+ * Returns in \p *numBlocks the number of the maximum active blocks per
+ * streaming multiprocessor.
+ *
+ * The \p Flags parameter controls how special cases are handled. The
+ * valid flags are:
+ *
+ * - ::CU_OCCUPANCY_DEFAULT, which maintains the default behavior as
+ *   ::cuOccupancyMaxActiveBlocksPerMultiprocessor;
+ *
+ * - ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE, which suppresses the
+ *   default behavior on platform where global caching affects
+ *   occupancy. On such platforms, if caching is enabled, but
+ *   per-block SM resource usage would result in zero occupancy, the
+ *   occupancy calculator will calculate the occupancy as if caching
+ *   is disabled. Setting ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE makes
+ *   the occupancy calculator to return 0 in such cases. More information
+ *   can be found about this feature in the "Unified L1/Texture Cache"
+ *   section of the Maxwell tuning guide.
+ *
+ * Note that the API can also be with launch context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will be the current context.
+ *
+ * \param numBlocks       - Returned occupancy
+ * \param func            - Kernel for which occupancy is calculated
+ * \param blockSize       - Block size the kernel is intended to be launched with
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ * \param flags           - Requested behavior for the occupancy calculator
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ */
+CUresult CUDAAPI cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize, unsigned int flags);
+
+/**
+ * \brief Suggest a launch configuration with reasonable occupancy
+ *
+ * Returns in \p *blockSize a reasonable block size that can achieve
+ * the maximum occupancy (or, the maximum number of active warps with
+ * the fewest blocks per multiprocessor), and in \p *minGridSize the
+ * minimum grid size to achieve the maximum occupancy.
+ *
+ * If \p blockSizeLimit is 0, the configurator will use the maximum
+ * block size permitted by the device / function instead.
+ *
+ * If per-block dynamic shared memory allocation is not needed, the
+ * user should leave both \p blockSizeToDynamicSMemSize and \p
+ * dynamicSMemSize as 0.
+ *
+ * If per-block dynamic shared memory allocation is needed, then if
+ * the dynamic shared memory size is constant regardless of block
+ * size, the size should be passed through \p dynamicSMemSize, and \p
+ * blockSizeToDynamicSMemSize should be NULL.
+ *
+ * Otherwise, if the per-block dynamic shared memory size varies with
+ * different block sizes, the user needs to provide a unary function
+ * through \p blockSizeToDynamicSMemSize that computes the dynamic
+ * shared memory needed by \p func for any given block size. \p
+ * dynamicSMemSize is ignored. An example signature is:
+ *
+ * \code
+ *    // Take block size, returns dynamic shared memory needed
+ *    size_t blockToSmem(int blockSize);
+ * \endcode
+ *
+ * Note that the API can also be used with context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will be the current context.
+ *
+ * \param minGridSize - Returned minimum grid size needed to achieve the maximum occupancy
+ * \param blockSize   - Returned maximum block size that can achieve the maximum occupancy
+ * \param func        - Kernel for which launch configuration is calculated
+ * \param blockSizeToDynamicSMemSize - A function that calculates how much per-block dynamic shared memory \p func uses based on the block size
+ * \param dynamicSMemSize - Dynamic shared memory usage intended, in bytes
+ * \param blockSizeLimit  - The maximum block size \p func is designed to handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaOccupancyMaxPotentialBlockSize
+ */
+CUresult CUDAAPI cuOccupancyMaxPotentialBlockSize(int *minGridSize, int *blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit);
+
+/**
+ * \brief Suggest a launch configuration with reasonable occupancy
+ *
+ * An extended version of ::cuOccupancyMaxPotentialBlockSize. In
+ * addition to arguments passed to ::cuOccupancyMaxPotentialBlockSize,
+ * ::cuOccupancyMaxPotentialBlockSizeWithFlags also takes a \p Flags
+ * parameter.
+ *
+ * The \p Flags parameter controls how special cases are handled. The
+ * valid flags are:
+ *
+ * - ::CU_OCCUPANCY_DEFAULT, which maintains the default behavior as
+ *   ::cuOccupancyMaxPotentialBlockSize;
+ *
+ * - ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE, which suppresses the
+ *   default behavior on platform where global caching affects
+ *   occupancy. On such platforms, the launch configurations that
+ *   produces maximal occupancy might not support global
+ *   caching. Setting ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE
+ *   guarantees that the the produced launch configuration is global
+ *   caching compatible at a potential cost of occupancy. More information
+ *   can be found about this feature in the "Unified L1/Texture Cache"
+ *   section of the Maxwell tuning guide.
+ *
+ * Note that the API can also be used with context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will be the current context.
+ *
+ * \param minGridSize - Returned minimum grid size needed to achieve the maximum occupancy
+ * \param blockSize   - Returned maximum block size that can achieve the maximum occupancy
+ * \param func        - Kernel for which launch configuration is calculated
+ * \param blockSizeToDynamicSMemSize - A function that calculates how much per-block dynamic shared memory \p func uses based on the block size
+ * \param dynamicSMemSize - Dynamic shared memory usage intended, in bytes
+ * \param blockSizeLimit  - The maximum block size \p func is designed to handle
+ * \param flags       - Options
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ */
+CUresult CUDAAPI cuOccupancyMaxPotentialBlockSizeWithFlags(int *minGridSize, int *blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit, unsigned int flags);
+
+/**
+ * \brief Returns dynamic shared memory available per block when launching \p numBlocks blocks on SM 
+ *
+ * Returns in \p *dynamicSmemSize the maximum size of dynamic shared memory to allow \p numBlocks blocks per SM. 
+ *
+ * Note that the API can also be used with context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will be the current context.
+ *
+ * \param dynamicSmemSize - Returned maximum dynamic shared memory 
+ * \param func            - Kernel function for which occupancy is calculated
+ * \param numBlocks       - Number of blocks to fit on SM 
+ * \param blockSize       - Size of the blocks
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ */
+CUresult CUDAAPI cuOccupancyAvailableDynamicSMemPerBlock(size_t *dynamicSmemSize, CUfunction func, int numBlocks, int blockSize);
+
+/**
+ * \brief Given the kernel function (\p func) and launch configuration
+ * (\p config), return the maximum cluster size in \p *clusterSize.
+ *
+ * The cluster dimensions in \p config are ignored. If func has a required
+ * cluster size set (see ::cudaFuncGetAttributes / ::cuFuncGetAttribute),\p
+ * *clusterSize will reflect the required cluster size.
+ *
+ * By default this function will always return a value that's portable on
+ * future hardware. A higher value may be returned if the kernel function
+ * allows non-portable cluster sizes.
+ *
+ * This function will respect the compile time launch bounds.
+ *
+ * Note that the API can also be used with context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will either be taken from the specified stream \p config->hStream
+ * or the current context in case of NULL stream.
+ *
+ * \param clusterSize - Returned maximum cluster size that can be launched
+ *                      for the given kernel function and launch configuration
+ * \param func        - Kernel function for which maximum cluster
+ *                      size is calculated
+ * \param config      - Launch configuration for the given kernel function
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaFuncGetAttributes,
+ * ::cuFuncGetAttribute
+ */
+CUresult CUDAAPI cuOccupancyMaxPotentialClusterSize(int *clusterSize, CUfunction func, const CUlaunchConfig *config);
+
+/**
+ * \brief Given the kernel function (\p func) and launch configuration
+ * (\p config), return the maximum number of clusters that could co-exist
+ * on the target device in \p *numClusters.
+ *
+ * If the function has required cluster size already set (see
+ * ::cudaFuncGetAttributes / ::cuFuncGetAttribute), the cluster size
+ * from config must either be unspecified or match the required size.
+ * Without required sizes, the cluster size must be specified in config,
+ * else the function will return an error.
+ *
+ * Note that various attributes of the kernel function may affect occupancy
+ * calculation. Runtime environment may affect how the hardware schedules
+ * the clusters, so the calculated occupancy is not guaranteed to be achievable.
+ *
+ * Note that the API can also be used with context-less kernel ::CUkernel
+ * by querying the handle using ::cuLibraryGetKernel() and then passing it
+ * to the API by casting to ::CUfunction. Here, the context to use for calculations
+ * will either be taken from the specified stream \p config->hStream
+ * or the current context in case of NULL stream.
+ *
+ * \param numClusters - Returned maximum number of clusters that
+ *                      could co-exist on the target device
+ * \param func        - Kernel function for which maximum number
+ *                      of clusters are calculated
+ * \param config      - Launch configuration for the given kernel function
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_CLUSTER_SIZE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaFuncGetAttributes,
+ * ::cuFuncGetAttribute
+ */
+CUresult CUDAAPI cuOccupancyMaxActiveClusters(int *numClusters, CUfunction func, const CUlaunchConfig *config);
+/** @} */ /* END CUDA_OCCUPANCY */
+
+/**
+ * \defgroup CUDA_TEXREF_DEPRECATED Texture Reference Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated texture reference management functions of the
+ * low-level CUDA driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the deprecated texture reference management
+ * functions of the low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Binds an array as a texture reference
+ *
+ * \deprecated
+ *
+ * Binds the CUDA array \p hArray to the texture reference \p hTexRef. Any
+ * previous address or CUDA array state associated with the texture reference
+ * is superseded by this function. \p Flags must be set to
+ * ::CU_TRSA_OVERRIDE_FORMAT. Any CUDA array previously bound to \p hTexRef is
+ * unbound.
+ *
+ * \param hTexRef - Texture reference to bind
+ * \param hArray  - Array to bind
+ * \param Flags   - Options (must be ::CU_TRSA_OVERRIDE_FORMAT)
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetArray(CUtexref hTexRef, CUarray hArray, unsigned int Flags);
+
+/**
+ * \brief Binds a mipmapped array to a texture reference
+ *
+ * \deprecated
+ *
+ * Binds the CUDA mipmapped array \p hMipmappedArray to the texture reference \p hTexRef.
+ * Any previous address or CUDA array state associated with the texture reference
+ * is superseded by this function. \p Flags must be set to ::CU_TRSA_OVERRIDE_FORMAT.
+ * Any CUDA array previously bound to \p hTexRef is unbound.
+ *
+ * \param hTexRef         - Texture reference to bind
+ * \param hMipmappedArray - Mipmapped array to bind
+ * \param Flags           - Options (must be ::CU_TRSA_OVERRIDE_FORMAT)
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmappedArray(CUtexref hTexRef, CUmipmappedArray hMipmappedArray, unsigned int Flags);
+
+/**
+ * \brief Binds an address as a texture reference
+ *
+ * \deprecated
+ *
+ * Binds a linear address range to the texture reference \p hTexRef. Any
+ * previous address or CUDA array state associated with the texture reference
+ * is superseded by this function. Any memory previously bound to \p hTexRef
+ * is unbound.
+ *
+ * Since the hardware enforces an alignment requirement on texture base
+ * addresses, ::cuTexRefSetAddress() passes back a byte offset in
+ * \p *ByteOffset that must be applied to texture fetches in order to read from
+ * the desired memory. This offset must be divided by the texel size and
+ * passed to kernels that read from the texture so they can be applied to the
+ * ::tex1Dfetch() function.
+ *
+ * If the device memory pointer was returned from ::cuMemAlloc(), the offset
+ * is guaranteed to be 0 and NULL may be passed as the \p ByteOffset parameter.
+ *
+ * The total number of elements (or texels) in the linear address range
+ * cannot exceed ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH.
+ * The number of elements is computed as (\p bytes / bytesPerElement),
+ * where bytesPerElement is determined from the data format and number of
+ * components set using ::cuTexRefSetFormat().
+ *
+ * \param ByteOffset - Returned byte offset
+ * \param hTexRef    - Texture reference to bind
+ * \param dptr       - Device pointer to bind
+ * \param bytes      - Size of memory to bind in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetAddress(size_t *ByteOffset, CUtexref hTexRef, CUdeviceptr dptr, size_t bytes);
+
+/**
+ * \brief Binds an address as a 2D texture reference
+ *
+ * \deprecated
+ *
+ * Binds a linear address range to the texture reference \p hTexRef. Any
+ * previous address or CUDA array state associated with the texture reference
+ * is superseded by this function. Any memory previously bound to \p hTexRef
+ * is unbound.
+ *
+ * Using a ::tex2D() function inside a kernel requires a call to either
+ * ::cuTexRefSetArray() to bind the corresponding texture reference to an
+ * array, or ::cuTexRefSetAddress2D() to bind the texture reference to linear
+ * memory.
+ *
+ * Function calls to ::cuTexRefSetFormat() cannot follow calls to
+ * ::cuTexRefSetAddress2D() for the same texture reference.
+ *
+ * It is required that \p dptr be aligned to the appropriate hardware-specific
+ * texture alignment. You can query this value using the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT. If an unaligned \p dptr is
+ * supplied, ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * \p Pitch has to be aligned to the hardware-specific texture pitch alignment.
+ * This value can be queried using the device attribute
+ * ::CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT. If an unaligned \p Pitch is
+ * supplied, ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * Width and Height, which are specified in elements (or texels), cannot exceed
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH and
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT respectively.
+ * \p Pitch, which is specified in bytes, cannot exceed
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH.
+ *
+ * \param hTexRef - Texture reference to bind
+ * \param desc    - Descriptor of CUDA array
+ * \param dptr    - Device pointer to bind
+ * \param Pitch   - Line pitch in bytes
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR *desc, CUdeviceptr dptr, size_t Pitch);
+
+/**
+ * \brief Sets the format for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the format of the data to be read by the texture reference
+ * \p hTexRef. \p fmt and \p NumPackedComponents are exactly analogous to the
+ * ::Format and ::NumChannels members of the ::CUDA_ARRAY_DESCRIPTOR structure:
+ * They specify the format of each component and the number of components per
+ * array element.
+ *
+ * \param hTexRef             - Texture reference
+ * \param fmt                 - Format to set
+ * \param NumPackedComponents - Number of components per array element
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat,
+ * ::cudaCreateChannelDesc
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetFormat(CUtexref hTexRef, CUarray_format fmt, int NumPackedComponents);
+
+/**
+ * \brief Sets the addressing mode for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the addressing mode \p am for the given dimension \p dim of the
+ * texture reference \p hTexRef. If \p dim is zero, the addressing mode is
+ * applied to the first parameter of the functions used to fetch from the
+ * texture; if \p dim is 1, the second, and so on. ::CUaddress_mode is defined
+ * as:
+ * \code
+   typedef enum CUaddress_mode_enum {
+      CU_TR_ADDRESS_MODE_WRAP = 0,
+      CU_TR_ADDRESS_MODE_CLAMP = 1,
+      CU_TR_ADDRESS_MODE_MIRROR = 2,
+      CU_TR_ADDRESS_MODE_BORDER = 3
+   } CUaddress_mode;
+ * \endcode
+ *
+ * Note that this call has no effect if \p hTexRef is bound to linear memory.
+ * Also, if the flag, ::CU_TRSF_NORMALIZED_COORDINATES, is not set, the only
+ * supported address mode is ::CU_TR_ADDRESS_MODE_CLAMP.
+ *
+ * \param hTexRef - Texture reference
+ * \param dim     - Dimension
+ * \param am      - Addressing mode to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetAddressMode(CUtexref hTexRef, int dim, CUaddress_mode am);
+
+/**
+ * \brief Sets the filtering mode for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the filtering mode \p fm to be used when reading memory through
+ * the texture reference \p hTexRef. ::CUfilter_mode_enum is defined as:
+ *
+ * \code
+   typedef enum CUfilter_mode_enum {
+      CU_TR_FILTER_MODE_POINT = 0,
+      CU_TR_FILTER_MODE_LINEAR = 1
+   } CUfilter_mode;
+ * \endcode
+ *
+ * Note that this call has no effect if \p hTexRef is bound to linear memory.
+ *
+ * \param hTexRef - Texture reference
+ * \param fm      - Filtering mode to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetFilterMode(CUtexref hTexRef, CUfilter_mode fm);
+
+/**
+ * \brief Sets the mipmap filtering mode for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the mipmap filtering mode \p fm to be used when reading memory through
+ * the texture reference \p hTexRef. ::CUfilter_mode_enum is defined as:
+ *
+ * \code
+   typedef enum CUfilter_mode_enum {
+      CU_TR_FILTER_MODE_POINT = 0,
+      CU_TR_FILTER_MODE_LINEAR = 1
+   } CUfilter_mode;
+ * \endcode
+ *
+ * Note that this call has no effect if \p hTexRef is not bound to a mipmapped array.
+ *
+ * \param hTexRef - Texture reference
+ * \param fm      - Filtering mode to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmapFilterMode(CUtexref hTexRef, CUfilter_mode fm);
+
+/**
+ * \brief Sets the mipmap level bias for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the mipmap level bias \p bias to be added to the specified mipmap level when
+ * reading memory through the texture reference \p hTexRef.
+ *
+ * Note that this call has no effect if \p hTexRef is not bound to a mipmapped array.
+ *
+ * \param hTexRef - Texture reference
+ * \param bias    - Mipmap level bias
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmapLevelBias(CUtexref hTexRef, float bias);
+
+/**
+ * \brief Sets the mipmap min/max mipmap level clamps for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the min/max mipmap level clamps, \p minMipmapLevelClamp and \p maxMipmapLevelClamp
+ * respectively, to be used when reading memory through the texture reference
+ * \p hTexRef.
+ *
+ * Note that this call has no effect if \p hTexRef is not bound to a mipmapped array.
+ *
+ * \param hTexRef        - Texture reference
+ * \param minMipmapLevelClamp - Mipmap min level clamp
+ * \param maxMipmapLevelClamp - Mipmap max level clamp
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmapLevelClamp(CUtexref hTexRef, float minMipmapLevelClamp, float maxMipmapLevelClamp);
+
+/**
+ * \brief Sets the maximum anisotropy for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the maximum anisotropy \p maxAniso to be used when reading memory through
+ * the texture reference \p hTexRef.
+ *
+ * Note that this call has no effect if \p hTexRef is bound to linear memory.
+ *
+ * \param hTexRef  - Texture reference
+ * \param maxAniso - Maximum anisotropy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMaxAnisotropy(CUtexref hTexRef, unsigned int maxAniso);
+
+/**
+ * \brief Sets the border color for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies the value of the RGBA color via the \p pBorderColor to the texture reference
+ * \p hTexRef. The color value supports only float type and holds color components in
+ * the following sequence:
+ * pBorderColor[0] holds 'R' component
+ * pBorderColor[1] holds 'G' component
+ * pBorderColor[2] holds 'B' component
+ * pBorderColor[3] holds 'A' component
+ *
+ * Note that the color values can be set only when the Address mode is set to
+ * CU_TR_ADDRESS_MODE_BORDER using ::cuTexRefSetAddressMode.
+ * Applications using integer border color values have to "reinterpret_cast" their values to float.
+ *
+ * \param hTexRef       - Texture reference
+ * \param pBorderColor  - RGBA color
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddressMode,
+ * ::cuTexRefGetAddressMode, ::cuTexRefGetBorderColor
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetBorderColor(CUtexref hTexRef, float *pBorderColor);
+
+/**
+ * \brief Sets the flags for a texture reference
+ *
+ * \deprecated
+ *
+ * Specifies optional flags via \p Flags to specify the behavior of data
+ * returned through the texture reference \p hTexRef. The valid flags are:
+ *
+ * - ::CU_TRSF_READ_AS_INTEGER, which suppresses the default behavior of
+ *   having the texture promote integer data to floating point data in the
+ *   range [0, 1]. Note that texture with 32-bit integer format
+ *   would not be promoted, regardless of whether or not this
+ *   flag is specified;
+ * - ::CU_TRSF_NORMALIZED_COORDINATES, which suppresses the
+ *   default behavior of having the texture coordinates range
+ *   from [0, Dim) where Dim is the width or height of the CUDA
+ *   array. Instead, the texture coordinates [0, 1.0) reference
+ *   the entire breadth of the array dimension;
+ * - ::CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION, which disables any trilinear
+ *   filtering optimizations. Trilinear optimizations improve texture filtering
+ *   performance by allowing bilinear filtering on textures in scenarios where
+ *   it can closely approximate the expected results.
+ *
+ * \param hTexRef - Texture reference
+ * \param Flags   - Optional flags to set
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetFlags(CUtexref hTexRef, unsigned int Flags);
+
+/**
+ * \brief Gets the address associated with a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pdptr the base address bound to the texture reference
+ * \p hTexRef, or returns ::CUDA_ERROR_INVALID_VALUE if the texture reference
+ * is not bound to any device memory range.
+ *
+ * \param pdptr   - Returned device address
+ * \param hTexRef - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetAddress(CUdeviceptr *pdptr, CUtexref hTexRef);
+
+/**
+ * \brief Gets the array bound to a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *phArray the CUDA array bound to the texture reference
+ * \p hTexRef, or returns ::CUDA_ERROR_INVALID_VALUE if the texture reference
+ * is not bound to any CUDA array.
+ *
+ * \param phArray - Returned array
+ * \param hTexRef - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetArray(CUarray *phArray, CUtexref hTexRef);
+
+/**
+ * \brief Gets the mipmapped array bound to a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *phMipmappedArray the CUDA mipmapped array bound to the texture
+ * reference \p hTexRef, or returns ::CUDA_ERROR_INVALID_VALUE if the texture reference
+ * is not bound to any CUDA mipmapped array.
+ *
+ * \param phMipmappedArray - Returned mipmapped array
+ * \param hTexRef          - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmappedArray(CUmipmappedArray *phMipmappedArray, CUtexref hTexRef);
+
+/**
+ * \brief Gets the addressing mode used by a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pam the addressing mode corresponding to the
+ * dimension \p dim of the texture reference \p hTexRef. Currently, the only
+ * valid value for \p dim are 0 and 1.
+ *
+ * \param pam     - Returned addressing mode
+ * \param hTexRef - Texture reference
+ * \param dim     - Dimension
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetAddressMode(CUaddress_mode *pam, CUtexref hTexRef, int dim);
+
+/**
+ * \brief Gets the filter-mode used by a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pfm the filtering mode of the texture reference
+ * \p hTexRef.
+ *
+ * \param pfm     - Returned filtering mode
+ * \param hTexRef - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetFilterMode(CUfilter_mode *pfm, CUtexref hTexRef);
+
+/**
+ * \brief Gets the format used by a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pFormat and \p *pNumChannels the format and number
+ * of components of the CUDA array bound to the texture reference \p hTexRef.
+ * If \p pFormat or \p pNumChannels is NULL, it will be ignored.
+ *
+ * \param pFormat      - Returned format
+ * \param pNumChannels - Returned number of components
+ * \param hTexRef      - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetFormat(CUarray_format *pFormat, int *pNumChannels, CUtexref hTexRef);
+
+/**
+ * \brief Gets the mipmap filtering mode for a texture reference
+ *
+ * \deprecated
+ *
+ * Returns the mipmap filtering mode in \p pfm that's used when reading memory through
+ * the texture reference \p hTexRef.
+ *
+ * \param pfm     - Returned mipmap filtering mode
+ * \param hTexRef - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmapFilterMode(CUfilter_mode *pfm, CUtexref hTexRef);
+
+/**
+ * \brief Gets the mipmap level bias for a texture reference
+ *
+ * \deprecated
+ *
+ * Returns the mipmap level bias in \p pBias that's added to the specified mipmap
+ * level when reading memory through the texture reference \p hTexRef.
+ *
+ * \param pbias   - Returned mipmap level bias
+ * \param hTexRef - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmapLevelBias(float *pbias, CUtexref hTexRef);
+
+/**
+ * \brief Gets the min/max mipmap level clamps for a texture reference
+ *
+ * \deprecated
+ *
+ * Returns the min/max mipmap level clamps in \p pminMipmapLevelClamp and \p pmaxMipmapLevelClamp
+ * that's used when reading memory through the texture reference \p hTexRef.
+ *
+ * \param pminMipmapLevelClamp - Returned mipmap min level clamp
+ * \param pmaxMipmapLevelClamp - Returned mipmap max level clamp
+ * \param hTexRef              - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmapLevelClamp(float *pminMipmapLevelClamp, float *pmaxMipmapLevelClamp, CUtexref hTexRef);
+
+/**
+ * \brief Gets the maximum anisotropy for a texture reference
+ *
+ * \deprecated
+ *
+ * Returns the maximum anisotropy in \p pmaxAniso that's used when reading memory through
+ * the texture reference \p hTexRef.
+ *
+ * \param pmaxAniso - Returned maximum anisotropy
+ * \param hTexRef   - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMaxAnisotropy(int *pmaxAniso, CUtexref hTexRef);
+
+/**
+ * \brief Gets the border color used by a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p pBorderColor, values of the RGBA color used by
+ * the texture reference \p hTexRef.
+ * The color value is of type float and holds color components in
+ * the following sequence:
+ * pBorderColor[0] holds 'R' component
+ * pBorderColor[1] holds 'G' component
+ * pBorderColor[2] holds 'B' component
+ * pBorderColor[3] holds 'A' component
+ *
+ * \param hTexRef  - Texture reference
+ * \param pBorderColor   - Returned Type and Value of RGBA color
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddressMode,
+ * ::cuTexRefSetAddressMode, ::cuTexRefSetBorderColor
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetBorderColor(float *pBorderColor, CUtexref hTexRef);
+
+/**
+ * \brief Gets the flags used by a texture reference
+ *
+ * \deprecated
+ *
+ * Returns in \p *pFlags the flags of the texture reference \p hTexRef.
+ *
+ * \param pFlags  - Returned flags
+ * \param hTexRef - Texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefSetAddress,
+ * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray,
+ * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat,
+ * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray,
+ * ::cuTexRefGetFilterMode, ::cuTexRefGetFormat
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetFlags(unsigned int *pFlags, CUtexref hTexRef);
+
+/**
+ * \brief Creates a texture reference
+ *
+ * \deprecated
+ *
+ * Creates a texture reference and returns its handle in \p *pTexRef. Once
+ * created, the application must call ::cuTexRefSetArray() or
+ * ::cuTexRefSetAddress() to associate the reference with allocated memory.
+ * Other texture reference functions are used to specify the format and
+ * interpretation (addressing, filtering, etc.) to be used when the memory is
+ * read through this texture reference.
+ *
+ * \param pTexRef - Returned texture reference
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefDestroy
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefCreate(CUtexref *pTexRef);
+
+/**
+ * \brief Destroys a texture reference
+ *
+ * \deprecated
+ *
+ * Destroys the texture reference specified by \p hTexRef.
+ *
+ * \param hTexRef - Texture reference to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuTexRefCreate
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefDestroy(CUtexref hTexRef);
+
+/** @} */ /* END CUDA_TEXREF_DEPRECATED */
+
+
+/**
+ * \defgroup CUDA_SURFREF_DEPRECATED Surface Reference Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ surface reference management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the surface reference management functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Sets the CUDA array for a surface reference.
+ *
+ * \deprecated
+ *
+ * Sets the CUDA array \p hArray to be read and written by the surface reference
+ * \p hSurfRef.  Any previous CUDA array state associated with the surface
+ * reference is superseded by this function.  \p Flags must be set to 0.
+ * The ::CUDA_ARRAY3D_SURFACE_LDST flag must have been set for the CUDA array.
+ * Any CUDA array previously bound to \p hSurfRef is unbound.
+
+ * \param hSurfRef - Surface reference handle
+ * \param hArray - CUDA array handle
+ * \param Flags - set to 0
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuModuleGetSurfRef,
+ * ::cuSurfRefGetArray
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuSurfRefSetArray(CUsurfref hSurfRef, CUarray hArray, unsigned int Flags);
+
+/**
+ * \brief Passes back the CUDA array bound to a surface reference.
+ *
+ * \deprecated
+ *
+ * Returns in \p *phArray the CUDA array bound to the surface reference
+ * \p hSurfRef, or returns ::CUDA_ERROR_INVALID_VALUE if the surface reference
+ * is not bound to any CUDA array.
+
+ * \param phArray - Surface reference handle
+ * \param hSurfRef - Surface reference handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa ::cuModuleGetSurfRef, ::cuSurfRefSetArray
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuSurfRefGetArray(CUarray *phArray, CUsurfref hSurfRef);
+
+/** @} */ /* END CUDA_SURFREF_DEPRECATED */
+
+/**
+ * \defgroup CUDA_TEXOBJECT Texture Object Management
+ *
+ * ___MANBRIEF___ texture object management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the texture object management functions of the
+ * low-level CUDA driver application programming interface. The texture
+ * object API is only supported on devices of compute capability 3.0 or higher.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates a texture object
+ *
+ * Creates a texture object and returns it in \p pTexObject. \p pResDesc describes
+ * the data to texture from. \p pTexDesc describes how the data should be sampled.
+ * \p pResViewDesc is an optional argument that specifies an alternate format for
+ * the data described by \p pResDesc, and also describes the subresource region
+ * to restrict access to when texturing. \p pResViewDesc can only be specified if
+ * the type of resource is a CUDA array or a CUDA mipmapped array not in a block
+ * compressed format.
+ *
+ * Texture objects are only supported on devices of compute capability 3.0 or higher.
+ * Additionally, a texture object is an opaque value, and, as such, should only be
+ * accessed through CUDA API calls.
+ *
+ * The ::CUDA_RESOURCE_DESC structure is defined as:
+ * \code
+        typedef struct CUDA_RESOURCE_DESC_st
+        {
+            CUresourcetype resType;
+
+            union {
+                struct {
+                    CUarray hArray;
+                } array;
+                struct {
+                    CUmipmappedArray hMipmappedArray;
+                } mipmap;
+                struct {
+                    CUdeviceptr devPtr;
+                    CUarray_format format;
+                    unsigned int numChannels;
+                    size_t sizeInBytes;
+                } linear;
+                struct {
+                    CUdeviceptr devPtr;
+                    CUarray_format format;
+                    unsigned int numChannels;
+                    size_t width;
+                    size_t height;
+                    size_t pitchInBytes;
+                } pitch2D;
+            } res;
+
+            unsigned int flags;
+        } CUDA_RESOURCE_DESC;
+
+ * \endcode
+ * where:
+ * - ::CUDA_RESOURCE_DESC::resType specifies the type of resource to texture from.
+ * CUresourceType is defined as:
+ * \code
+        typedef enum CUresourcetype_enum {
+            CU_RESOURCE_TYPE_ARRAY           = 0x00,
+            CU_RESOURCE_TYPE_MIPMAPPED_ARRAY = 0x01,
+            CU_RESOURCE_TYPE_LINEAR          = 0x02,
+            CU_RESOURCE_TYPE_PITCH2D         = 0x03
+        } CUresourcetype;
+ * \endcode
+ *
+ * \par
+ * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_ARRAY, ::CUDA_RESOURCE_DESC::res::array::hArray
+ * must be set to a valid CUDA array handle.
+ *
+ * \par
+ * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_MIPMAPPED_ARRAY, ::CUDA_RESOURCE_DESC::res::mipmap::hMipmappedArray
+ * must be set to a valid CUDA mipmapped array handle.
+ *
+ * \par
+ * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_LINEAR, ::CUDA_RESOURCE_DESC::res::linear::devPtr
+ * must be set to a valid device pointer, that is aligned to ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT.
+ * ::CUDA_RESOURCE_DESC::res::linear::format and ::CUDA_RESOURCE_DESC::res::linear::numChannels
+ * describe the format of each component and the number of components per array element. ::CUDA_RESOURCE_DESC::res::linear::sizeInBytes
+ * specifies the size of the array in bytes. The total number of elements in the linear address range cannot exceed
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH. The number of elements is computed as (sizeInBytes / (sizeof(format) * numChannels)).
+ *
+ * \par
+ * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_PITCH2D, ::CUDA_RESOURCE_DESC::res::pitch2D::devPtr
+ * must be set to a valid device pointer, that is aligned to ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT.
+ * ::CUDA_RESOURCE_DESC::res::pitch2D::format and ::CUDA_RESOURCE_DESC::res::pitch2D::numChannels
+ * describe the format of each component and the number of components per array element. ::CUDA_RESOURCE_DESC::res::pitch2D::width
+ * and ::CUDA_RESOURCE_DESC::res::pitch2D::height specify the width and height of the array in elements, and cannot exceed
+ * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH and ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT respectively.
+ * ::CUDA_RESOURCE_DESC::res::pitch2D::pitchInBytes specifies the pitch between two rows in bytes and has to be aligned to
+ * ::CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT. Pitch cannot exceed ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH.
+ *
+ * - ::flags must be set to zero.
+ *
+ *
+ * The ::CUDA_TEXTURE_DESC struct is defined as
+ * \code
+        typedef struct CUDA_TEXTURE_DESC_st {
+            CUaddress_mode addressMode[3];
+            CUfilter_mode filterMode;
+            unsigned int flags;
+            unsigned int maxAnisotropy;
+            CUfilter_mode mipmapFilterMode;
+            float mipmapLevelBias;
+            float minMipmapLevelClamp;
+            float maxMipmapLevelClamp;
+        } CUDA_TEXTURE_DESC;
+ * \endcode
+ * where
+ * - ::CUDA_TEXTURE_DESC::addressMode specifies the addressing mode for each dimension of the texture data. ::CUaddress_mode is defined as:
+ *   \code
+        typedef enum CUaddress_mode_enum {
+            CU_TR_ADDRESS_MODE_WRAP = 0,
+            CU_TR_ADDRESS_MODE_CLAMP = 1,
+            CU_TR_ADDRESS_MODE_MIRROR = 2,
+            CU_TR_ADDRESS_MODE_BORDER = 3
+        } CUaddress_mode;
+ *   \endcode
+ *   This is ignored if ::CUDA_RESOURCE_DESC::resType is ::CU_RESOURCE_TYPE_LINEAR. Also, if the flag, ::CU_TRSF_NORMALIZED_COORDINATES
+ *   is not set, the only supported address mode is ::CU_TR_ADDRESS_MODE_CLAMP.
+ *
+ * - ::CUDA_TEXTURE_DESC::filterMode specifies the filtering mode to be used when fetching from the texture. CUfilter_mode is defined as:
+ *   \code
+        typedef enum CUfilter_mode_enum {
+            CU_TR_FILTER_MODE_POINT = 0,
+            CU_TR_FILTER_MODE_LINEAR = 1
+        } CUfilter_mode;
+ *   \endcode
+ *   This is ignored if ::CUDA_RESOURCE_DESC::resType is ::CU_RESOURCE_TYPE_LINEAR.
+ *
+ * - ::CUDA_TEXTURE_DESC::flags can be any combination of the following:
+ *   - ::CU_TRSF_READ_AS_INTEGER, which suppresses the default behavior of
+ *   having the texture promote integer data to floating point data in the
+ *   range [0, 1]. Note that texture with 32-bit integer format would not be 
+ *   promoted, regardless of whether or not this flag is specified.
+ *   - ::CU_TRSF_NORMALIZED_COORDINATES, which suppresses the default behavior
+ *   of having the texture coordinates range from [0, Dim) where Dim is the 
+ *   width or height of the CUDA array. Instead, the texture coordinates 
+ *   [0, 1.0) reference the entire breadth of the array dimension; Note that
+ *   for CUDA mipmapped arrays, this flag has to be set.
+ *   - ::CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION, which disables any trilinear
+ *   filtering optimizations. Trilinear optimizations improve texture filtering
+ *   performance by allowing bilinear filtering on textures in scenarios where
+ *   it can closely approximate the expected results.
+ *   - ::CU_TRSF_SEAMLESS_CUBEMAP, which enables seamless cube map filtering. 
+ *   This flag can only be specified if the underlying resource is a CUDA array 
+ *   or a CUDA mipmapped array that was created with the flag ::CUDA_ARRAY3D_CUBEMAP.
+ *   When seamless cube map filtering is enabled, texture address modes specified 
+ *   by ::CUDA_TEXTURE_DESC::addressMode are ignored. Instead, if the ::CUDA_TEXTURE_DESC::filterMode 
+ *   is set to ::CU_TR_FILTER_MODE_POINT the address mode ::CU_TR_ADDRESS_MODE_CLAMP 
+ *   will be applied for all dimensions. If the ::CUDA_TEXTURE_DESC::filterMode is 
+ *   set to ::CU_TR_FILTER_MODE_LINEAR seamless cube map filtering will be performed
+ *   when sampling along the cube face borders.
+ *
+ * - ::CUDA_TEXTURE_DESC::maxAnisotropy specifies the maximum anisotropy ratio to be used when doing anisotropic filtering. This value will be
+ *   clamped to the range [1,16].
+ *
+ * - ::CUDA_TEXTURE_DESC::mipmapFilterMode specifies the filter mode when the calculated mipmap level lies between two defined mipmap levels.
+ *
+ * - ::CUDA_TEXTURE_DESC::mipmapLevelBias specifies the offset to be applied to the calculated mipmap level.
+ *
+ * - ::CUDA_TEXTURE_DESC::minMipmapLevelClamp specifies the lower end of the mipmap level range to clamp access to.
+ *
+ * - ::CUDA_TEXTURE_DESC::maxMipmapLevelClamp specifies the upper end of the mipmap level range to clamp access to.
+ *
+ *
+ * The ::CUDA_RESOURCE_VIEW_DESC struct is defined as
+ * \code
+        typedef struct CUDA_RESOURCE_VIEW_DESC_st
+        {
+            CUresourceViewFormat format;
+            size_t width;
+            size_t height;
+            size_t depth;
+            unsigned int firstMipmapLevel;
+            unsigned int lastMipmapLevel;
+            unsigned int firstLayer;
+            unsigned int lastLayer;
+        } CUDA_RESOURCE_VIEW_DESC;
+ * \endcode
+ * where:
+ * - ::CUDA_RESOURCE_VIEW_DESC::format specifies how the data contained in the CUDA array or CUDA mipmapped array should
+ *   be interpreted. Note that this can incur a change in size of the texture data. If the resource view format is a block
+ *   compressed format, then the underlying CUDA array or CUDA mipmapped array has to have a base of format ::CU_AD_FORMAT_UNSIGNED_INT32.
+ *   with 2 or 4 channels, depending on the block compressed format. For ex., BC1 and BC4 require the underlying CUDA array to have
+ *   a format of ::CU_AD_FORMAT_UNSIGNED_INT32 with 2 channels. The other BC formats require the underlying resource to have the same base
+ *   format but with 4 channels.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::width specifies the new width of the texture data. If the resource view format is a block
+ *   compressed format, this value has to be 4 times the original width of the resource. For non block compressed formats,
+ *   this value has to be equal to that of the original resource.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::height specifies the new height of the texture data. If the resource view format is a block
+ *   compressed format, this value has to be 4 times the original height of the resource. For non block compressed formats,
+ *   this value has to be equal to that of the original resource.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::depth specifies the new depth of the texture data. This value has to be equal to that of the
+ *   original resource.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::firstMipmapLevel specifies the most detailed mipmap level. This will be the new mipmap level zero.
+ *   For non-mipmapped resources, this value has to be zero.::CUDA_TEXTURE_DESC::minMipmapLevelClamp and ::CUDA_TEXTURE_DESC::maxMipmapLevelClamp
+ *   will be relative to this value. For ex., if the firstMipmapLevel is set to 2, and a minMipmapLevelClamp of 1.2 is specified,
+ *   then the actual minimum mipmap level clamp will be 3.2.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::lastMipmapLevel specifies the least detailed mipmap level. For non-mipmapped resources, this value
+ *   has to be zero.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::firstLayer specifies the first layer index for layered textures. This will be the new layer zero.
+ *   For non-layered resources, this value has to be zero.
+ *
+ * - ::CUDA_RESOURCE_VIEW_DESC::lastLayer specifies the last layer index for layered textures. For non-layered resources,
+ *   this value has to be zero.
+ *
+ *
+ * \param pTexObject   - Texture object to create
+ * \param pResDesc     - Resource descriptor
+ * \param pTexDesc     - Texture descriptor
+ * \param pResViewDesc - Resource view descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexObjectDestroy,
+ * ::cudaCreateTextureObject
+ */
+CUresult CUDAAPI cuTexObjectCreate(CUtexObject *pTexObject, const CUDA_RESOURCE_DESC *pResDesc, const CUDA_TEXTURE_DESC *pTexDesc, const CUDA_RESOURCE_VIEW_DESC *pResViewDesc);
+
+/**
+ * \brief Destroys a texture object
+ *
+ * Destroys the texture object specified by \p texObject.
+ *
+ * \param texObject - Texture object to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexObjectCreate,
+ * ::cudaDestroyTextureObject
+ */
+CUresult CUDAAPI cuTexObjectDestroy(CUtexObject texObject);
+
+/**
+ * \brief Returns a texture object's resource descriptor
+ *
+ * Returns the resource descriptor for the texture object specified by \p texObject.
+ *
+ * \param pResDesc  - Resource descriptor
+ * \param texObject - Texture object
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexObjectCreate,
+ * ::cudaGetTextureObjectResourceDesc,
+ */
+CUresult CUDAAPI cuTexObjectGetResourceDesc(CUDA_RESOURCE_DESC *pResDesc, CUtexObject texObject);
+
+/**
+ * \brief Returns a texture object's texture descriptor
+ *
+ * Returns the texture descriptor for the texture object specified by \p texObject.
+ *
+ * \param pTexDesc  - Texture descriptor
+ * \param texObject - Texture object
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexObjectCreate,
+ * ::cudaGetTextureObjectTextureDesc
+ */
+CUresult CUDAAPI cuTexObjectGetTextureDesc(CUDA_TEXTURE_DESC *pTexDesc, CUtexObject texObject);
+
+/**
+ * \brief Returns a texture object's resource view descriptor
+ *
+ * Returns the resource view descriptor for the texture object specified by \p texObject.
+ * If no resource view was set for \p texObject, the ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * \param pResViewDesc - Resource view descriptor
+ * \param texObject    - Texture object
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTexObjectCreate,
+ * ::cudaGetTextureObjectResourceViewDesc
+ */
+CUresult CUDAAPI cuTexObjectGetResourceViewDesc(CUDA_RESOURCE_VIEW_DESC *pResViewDesc, CUtexObject texObject);
+
+/** @} */ /* END CUDA_TEXOBJECT */
+
+/**
+ * \defgroup CUDA_SURFOBJECT Surface Object Management
+ *
+ * ___MANBRIEF___ surface object management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the surface object management functions of the
+ * low-level CUDA driver application programming interface. The surface
+ * object API is only supported on devices of compute capability 3.0 or higher.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates a surface object
+ *
+ * Creates a surface object and returns it in \p pSurfObject. \p pResDesc describes
+ * the data to perform surface load/stores on. ::CUDA_RESOURCE_DESC::resType must be
+ * ::CU_RESOURCE_TYPE_ARRAY and  ::CUDA_RESOURCE_DESC::res::array::hArray
+ * must be set to a valid CUDA array handle. ::CUDA_RESOURCE_DESC::flags must be set to zero.
+ *
+ * Surface objects are only supported on devices of compute capability 3.0 or higher.
+ * Additionally, a surface object is an opaque value, and, as such, should only be
+ * accessed through CUDA API calls.
+ *
+ * \param pSurfObject - Surface object to create
+ * \param pResDesc    - Resource descriptor
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuSurfObjectDestroy,
+ * ::cudaCreateSurfaceObject
+ */
+CUresult CUDAAPI cuSurfObjectCreate(CUsurfObject *pSurfObject, const CUDA_RESOURCE_DESC *pResDesc);
+
+/**
+ * \brief Destroys a surface object
+ *
+ * Destroys the surface object specified by \p surfObject.
+ *
+ * \param surfObject - Surface object to destroy
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuSurfObjectCreate,
+ * ::cudaDestroySurfaceObject
+ */
+CUresult CUDAAPI cuSurfObjectDestroy(CUsurfObject surfObject);
+
+/**
+ * \brief Returns a surface object's resource descriptor
+ *
+ * Returns the resource descriptor for the surface object specified by \p surfObject.
+ *
+ * \param pResDesc   - Resource descriptor
+ * \param surfObject - Surface object
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuSurfObjectCreate,
+ * ::cudaGetSurfaceObjectResourceDesc
+ */
+CUresult CUDAAPI cuSurfObjectGetResourceDesc(CUDA_RESOURCE_DESC *pResDesc, CUsurfObject surfObject);
+
+/** @} */ /* END CUDA_SURFOBJECT */
+
+/**
+ * \defgroup CUDA_TENSOR_MEMORY Tensor Map Object Managment
+ *
+ * ___MANBRIEF___ tensor map object management functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the tensor map object management functions of the
+ * low-level CUDA driver application programming interface. The tensor
+ * core API is only supported on devices of compute capability 9.0 or higher.
+ *
+ * @{
+ */
+
+/**
+ * \brief Create a tensor map descriptor object representing tiled memory region
+ *
+ * Creates a descriptor for Tensor Memory Access (TMA) object specified
+ * by the parameters describing a tiled region and returns it in \p tensorMap.
+ *
+ * Tensor map objects are only supported on devices of compute capability 9.0 or higher.
+ * Additionally, a tensor map object is an opaque value, and, as such, should only be
+ * accessed through CUDA APIs and PTX.
+ *
+ * The parameters passed are bound to the following requirements:
+ *
+ * - \p tensorMap address must be aligned to 64 bytes.
+ *
+ * - \p tensorDataType has to be an enum from ::CUtensorMapDataType which is defined as:
+ * \code
+    typedef enum CUtensorMapDataType_enum {
+        CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0,       // 1 byte
+        CU_TENSOR_MAP_DATA_TYPE_UINT16,          // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_UINT32,          // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_INT32,           // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_UINT64,          // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_INT64,           // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT16,         // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32,         // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT64,         // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_BFLOAT16,        // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ,     // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32,        // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ,    // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B,    // 4 bits
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B,   // 4 bits
+        CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B    // 6 bits
+    } CUtensorMapDataType;
+ * \endcode
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B copies '16 x U4' packed values to memory aligned as 8 bytes. There are no gaps between packed values.
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B copies '16 x U4' packed values to memory aligned as 16 bytes. There are 8 byte gaps between every 8 byte chunk of packed values.
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B copies '16 x U6' packed values to memory aligned as 16 bytes. There are 4 byte gaps between every 12 byte chunk of packed values.
+ *
+ * - \p tensorRank must be non-zero and less than or equal to the maximum supported dimensionality of 5. If \p interleave is not
+ * ::CU_TENSOR_MAP_INTERLEAVE_NONE, then \p tensorRank must additionally be greater than or equal to 3.
+ *
+ * - \p globalAddress, which specifies the starting address of the memory region described, must be 16 byte aligned. The following requirements need to also be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p globalAddress must be 32 byte aligned.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, \p globalAddress must be 32 byte aligned.
+ *
+  * - \p globalDim array, which specifies tensor size of each of the \p tensorRank dimensions, must be non-zero and less than or
+ * equal to 2^32. Additionally, the following requirements need to be met for the packed data types:
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, globalDim[0] must be a multiple of 128.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B, \p globalDim[0] must be a multiple of 2.
+ *    - Dimension for the packed data types must reflect the number of individual U# values.
+ *
+ * - \p globalStrides array, which specifies tensor stride of each of the lower \p tensorRank - 1 dimensions in bytes, must be a
+ * multiple of 16 and less than 2^40. Additionally, the following requirements need to be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, the strides must be a multiple of 32.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, the strides must be a multiple of 32.
+ * Each following dimension specified includes previous dimension stride:
+ * \code
+    globalStrides[0] = globalDim[0] * elementSizeInBytes(tensorDataType) + padding[0];
+    for (i = 1; i < tensorRank - 1; i++)
+        globalStrides[i] = globalStrides[i – 1] * (globalDim[i] + padding[i]);
+        assert(globalStrides[i] >= globalDim[i]);
+ * \endcode
+ *
+ * - \p boxDim array, which specifies number of elements to be traversed along each of the \p tensorRank dimensions, must be non-zero
+ * and less than or equal to 256. Additionally, the following requirements need to be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, { \p boxDim[0] * elementSizeInBytes( \p tensorDataType ) } must be a multiple of 16 bytes.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, boxDim[0] must be 128.
+ *
+ * - \p elementStrides array, which specifies the iteration step along each of the \p tensorRank dimensions, must be non-zero and less
+ * than or equal to 8. Note that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, the first element of this array is ignored since
+ * TMA doesn’t support the stride for dimension zero.
+ * When all elements of \p elementStrides array is one, \p boxDim specifies the number of elements to load. However, if the \p elementStrides[i]
+ * is not equal to one, then TMA loads ceil( \p boxDim[i] / \p elementStrides[i]) number of elements along i-th dimension. To load N elements along
+ * i-th dimension, \p boxDim[i] must be set to N * \p elementStrides[i].
+ *
+ * - \p interleave specifies the interleaved layout of type ::CUtensorMapInterleave, which is defined as:
+ * \code
+    typedef enum CUtensorMapInterleave_enum {
+        CU_TENSOR_MAP_INTERLEAVE_NONE = 0,
+        CU_TENSOR_MAP_INTERLEAVE_16B,
+        CU_TENSOR_MAP_INTERLEAVE_32B
+    } CUtensorMapInterleave;
+ * \endcode
+ * TMA supports interleaved layouts like NC/8HWC8 where C8 utilizes 16 bytes in memory assuming 2 byte per channel or NC/16HWC16 where C16
+ * uses 32 bytes.
+ * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE and \p swizzle is not ::CU_TENSOR_MAP_SWIZZLE_NONE, the bounding box inner dimension
+ * (computed as \p boxDim[0] multiplied by element size derived from \p tensorDataType) must be less than or equal to the swizzle size.
+ *    - CU_TENSOR_MAP_SWIZZLE_32B requires the bounding box inner dimension to be <= 32.
+ *    - CU_TENSOR_MAP_SWIZZLE_64B requires the bounding box inner dimension to be <= 64.
+ *    - CU_TENSOR_MAP_SWIZZLE_128B* require the bounding box inner dimension to be <= 128.
+ * Additionally, \p tensorDataType of ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B requires \p interleave to be ::CU_TENSOR_MAP_INTERLEAVE_NONE.
+ *
+ * - \p swizzle, which specifies the shared memory bank swizzling pattern, has to be of type ::CUtensorMapSwizzle which is defined as:
+ * \code
+    typedef enum CUtensorMapSwizzle_enum {
+        CU_TENSOR_MAP_SWIZZLE_NONE = 0,
+        CU_TENSOR_MAP_SWIZZLE_32B,                   // Swizzle 16B chunks within 32B  span
+        CU_TENSOR_MAP_SWIZZLE_64B,                   // Swizzle 16B chunks within 64B  span
+        CU_TENSOR_MAP_SWIZZLE_128B,                  // Swizzle 16B chunks within 128B span
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B,         // Swizzle 32B chunks within 128B span
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B_FLIP_8B, // Swizzle 32B chunks within 128B span, additionally swap lower 8B with upper 8B within each 16B for every alternate row
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_64B          // Swizzle 64B chunks within 128B span
+    } CUtensorMapSwizzle;
+ * \endcode
+ * Data are organized in a specific order in global memory; however, this may not match the order in which the application accesses data
+ * in shared memory. This difference in data organization may cause bank conflicts when shared memory is accessed. In order to avoid this
+ * problem, data can be loaded to shared memory with shuffling across shared memory banks.
+ * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p swizzle must be ::CU_TENSOR_MAP_SWIZZLE_32B.
+ * Other interleave modes can have any swizzling pattern.
+ * When the \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B, only the following swizzle modes are supported:
+ *    - CU_TENSOR_MAP_SWIZZLE_NONE (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_64B (Store only)
+ * When the \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, only the following swizzle modes are supported:
+ *    - CU_TENSOR_MAP_SWIZZLE_NONE (Load only)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B (Load only)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B (Load only)
+ *
+ * - \p l2Promotion specifies L2 fetch size which indicates the byte granurality at which L2 requests is filled from DRAM. It must be of
+ * type ::CUtensorMapL2promotion, which is defined as:
+ * \code
+    typedef enum CUtensorMapL2promotion_enum {
+        CU_TENSOR_MAP_L2_PROMOTION_NONE = 0,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_64B,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_128B,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_256B
+    } CUtensorMapL2promotion;
+ * \endcode
+ *
+ * - \p oobFill, which indicates whether zero or a special NaN constant should be used to fill out-of-bound elements, must be of type
+ * ::CUtensorMapFloatOOBfill which is defined as:
+ * \code
+    typedef enum CUtensorMapFloatOOBfill_enum {
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0,
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA
+    } CUtensorMapFloatOOBfill;
+ * \endcode
+ * Note that ::CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA can only be used when \p tensorDataType represents a floating-point data type,
+ * and when \p tensorDataType is not ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B, ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, and ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B.
+ *
+ * \param tensorMap         - Tensor map object to create
+ * \param tensorDataType    - Tensor data type
+ * \param tensorRank        - Dimensionality of tensor
+ * \param globalAddress     - Starting address of memory region described by tensor
+ * \param globalDim         - Array containing tensor size (number of elements) along each of the \p tensorRank dimensions
+ * \param globalStrides     - Array containing stride size (in bytes) along each of the \p tensorRank - 1 dimensions
+ * \param boxDim            - Array containing traversal box size (number of elments) along each of the \p tensorRank dimensions. Specifies how many elements to be traversed along each tensor dimension.
+ * \param elementStrides    - Array containing traversal stride in each of the \p tensorRank dimensions
+ * \param interleave        - Type of interleaved layout the tensor addresses
+ * \param swizzle           - Bank swizzling pattern inside shared memory
+ * \param l2Promotion       - L2 promotion size
+ * \param oobFill           - Indicate whether zero or special NaN constant must be used to fill out-of-bound elements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTensorMapEncodeIm2col,
+ * ::cuTensorMapEncodeIm2colWide,
+ * ::cuTensorMapReplaceAddress
+ */
+CUresult CUDAAPI cuTensorMapEncodeTiled(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, const cuuint32_t *boxDim, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill);
+
+/**
+ * \brief Create a tensor map descriptor object representing im2col memory region
+ *
+ * Creates a descriptor for Tensor Memory Access (TMA) object specified
+ * by the parameters describing a im2col memory layout and returns it in \p tensorMap.
+ *
+ * Tensor map objects are only supported on devices of compute capability 9.0 or higher.
+ * Additionally, a tensor map object is an opaque value, and, as such, should only be
+ * accessed through CUDA APIs and PTX.
+ *
+ * The parameters passed are bound to the following requirements:
+ *
+ * - \p tensorMap address must be aligned to 64 bytes.
+ *
+ * - \p tensorDataType has to be an enum from ::CUtensorMapDataType which is defined as:
+ * \code
+    typedef enum CUtensorMapDataType_enum {
+        CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0,       // 1 byte
+        CU_TENSOR_MAP_DATA_TYPE_UINT16,          // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_UINT32,          // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_INT32,           // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_UINT64,          // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_INT64,           // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT16,         // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32,         // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT64,         // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_BFLOAT16,        // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ,     // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32,        // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ     // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B,    // 4 bits
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B,   // 4 bits
+        CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B    // 6 bits
+    } CUtensorMapDataType;
+ * \endcode
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B copies '16 x U4' packed values to memory aligned as 8 bytes. There are no gaps between packed values.
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B copies '16 x U4' packed values to memory aligned as 16 bytes. There are 8 byte gaps between every 8 byte chunk of packed values.
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B copies '16 x U6' packed values to memory aligned as 16 bytes. There are 4 byte gaps between every 12 byte chunk of packed values.
+ *
+ * - \p tensorRank, which specifies the number of tensor dimensions, must be 3, 4, or 5.
+ *
+ * - \p globalAddress, which specifies the starting address of the memory region described, must be 16 byte aligned. The following requirements need to also be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p globalAddress must be 32 byte aligned.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, \p globalAddress must be 32 byte aligned.
+ *
+ * - \p globalDim array, which specifies tensor size of each of the \p tensorRank dimensions, must be non-zero and less than or
+ * equal to 2^32. Additionally, the following requirements need to be met for the packed data types:
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, globalDim[0] must be a multiple of 128.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B, \p globalDim[0] must be a multiple of 2.
+ *    - Dimension for the packed data types must reflect the number of individual U# values.
+ *
+ * - \p globalStrides array, which specifies tensor stride of each of the lower \p tensorRank - 1 dimensions in bytes, must be a
+ * multiple of 16 and less than 2^40. Additionally, the following requirements need to be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, the strides must be a multiple of 32.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, the strides must be a multiple of 32.
+ * Each following dimension specified includes previous dimension stride:
+ * \code
+    globalStrides[0] = globalDim[0] * elementSizeInBytes(tensorDataType) + padding[0];
+    for (i = 1; i < tensorRank - 1; i++)
+        globalStrides[i] = globalStrides[i – 1] * (globalDim[i] + padding[i]);
+        assert(globalStrides[i] >= globalDim[i]);
+ * \endcode
+ *
+ * - \p pixelBoxLowerCorner array specifies the coordinate offsets {D, H, W} of the bounding box from top/left/front corner. The number of
+ * offsets and their precision depend on the tensor dimensionality:
+ *    - When \p tensorRank is 3, one signed offset within range [-32768, 32767] is supported.
+ *    - When \p tensorRank is 4, two signed offsets each within range [-128, 127] are supported.
+ *    - When \p tensorRank is 5, three offsets each within range [-16, 15] are supported.
+ *
+ * - \p pixelBoxUpperCorner array specifies the coordinate offsets {D, H, W} of the bounding box from bottom/right/back corner. The number of
+ * offsets and their precision depend on the tensor dimensionality:
+ *    - When \p tensorRank is 3, one signed offset within range [-32768, 32767] is supported.
+ *    - When \p tensorRank is 4, two signed offsets each within range [-128, 127] are supported.
+ *    - When \p tensorRank is 5, three offsets each within range [-16, 15] are supported.
+ * The bounding box specified by \p pixelBoxLowerCorner and \p pixelBoxUpperCorner must have non-zero area.
+ *
+ * - \p channelsPerPixel, which specifies the number of elements which must be accessed along C dimension, must be less than or equal to 256.
+ * Additionally, when \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, \p channelsPerPixel must be 128.
+ *
+ * - \p pixelsPerColumn, which specifies the number of elements that must be accessed along the {N, D, H, W} dimensions, must be less than or
+ * equal to 1024.
+ *
+ * - \p elementStrides array, which specifies the iteration step along each of the \p tensorRank dimensions, must be non-zero and less
+ * than or equal to 8. Note that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, the first element of this array is ignored since
+ * TMA doesn’t support the stride for dimension zero.
+ * When all elements of the \p elementStrides array are one, \p boxDim specifies the number of elements to load. However, if \p elementStrides[i]
+ * is not equal to one for some \p i, then TMA loads ceil( \p boxDim[i] / \p elementStrides[i]) number of elements along i-th dimension.
+ * To load N elements along i-th dimension, \p boxDim[i] must be set to N * \p elementStrides[i].
+ *
+ * - \p interleave specifies the interleaved layout of type ::CUtensorMapInterleave, which is defined as:
+ * \code
+    typedef enum CUtensorMapInterleave_enum {
+        CU_TENSOR_MAP_INTERLEAVE_NONE = 0,
+        CU_TENSOR_MAP_INTERLEAVE_16B,
+        CU_TENSOR_MAP_INTERLEAVE_32B
+    } CUtensorMapInterleave;
+ * \endcode
+ * TMA supports interleaved layouts like NC/8HWC8 where C8 utilizes 16 bytes in memory assuming 2 byte per channel or NC/16HWC16 where C16
+ * uses 32 bytes.
+ * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE and \p swizzle is not ::CU_TENSOR_MAP_SWIZZLE_NONE, the bounding box inner dimension
+ * (computed as \p channelsPerPixel multiplied by element size in bytes derived from \p tensorDataType) must be less than or equal to the swizzle size.
+ *    - CU_TENSOR_MAP_SWIZZLE_32B requires the bounding box inner dimension to be <= 32.
+ *    - CU_TENSOR_MAP_SWIZZLE_64B requires the bounding box inner dimension to be <= 64.
+ *    - CU_TENSOR_MAP_SWIZZLE_128B* require the bounding box inner dimension to be <= 128.
+ * Additionally, \p tensorDataType of ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B requires \p interleave to be ::CU_TENSOR_MAP_INTERLEAVE_NONE.
+ *
+ * - \p swizzle, which specifies the shared memory bank swizzling pattern, has to be of type ::CUtensorMapSwizzle which is defined as:
+ * \code
+    typedef enum CUtensorMapSwizzle_enum {
+        CU_TENSOR_MAP_SWIZZLE_NONE = 0,
+        CU_TENSOR_MAP_SWIZZLE_32B,                   // Swizzle 16B chunks within 32B  span
+        CU_TENSOR_MAP_SWIZZLE_64B,                   // Swizzle 16B chunks within 64B  span
+        CU_TENSOR_MAP_SWIZZLE_128B,                  // Swizzle 16B chunks within 128B span
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B,         // Swizzle 32B chunks within 128B span
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B_FLIP_8B, // Swizzle 32B chunks within 128B span, additionally swap lower 8B with upper 8B within each 16B for every alternate row
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_64B          // Swizzle 64B chunks within 128B span
+    } CUtensorMapSwizzle;
+ * \endcode
+ * Data are organized in a specific order in global memory; however, this may not match the order in which the application accesses data
+ * in shared memory. This difference in data organization may cause bank conflicts when shared memory is accessed. In order to avoid this
+ * problem, data can be loaded to shared memory with shuffling across shared memory banks.
+ * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p swizzle must be ::CU_TENSOR_MAP_SWIZZLE_32B.
+ * Other interleave modes can have any swizzling pattern.
+ * When the \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B, only the following swizzle modes are supported:
+ *    - CU_TENSOR_MAP_SWIZZLE_NONE (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_64B (Store only)
+ * When the \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, only the following swizzle modes are supported:
+ *    - CU_TENSOR_MAP_SWIZZLE_NONE (Load only)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B (Load only)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B (Load only)
+ *
+ * - \p l2Promotion specifies L2 fetch size which indicates the byte granularity at which L2 requests are filled from DRAM. It must be of
+ * type ::CUtensorMapL2promotion, which is defined as:
+ * \code
+    typedef enum CUtensorMapL2promotion_enum {
+        CU_TENSOR_MAP_L2_PROMOTION_NONE = 0,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_64B,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_128B,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_256B
+    } CUtensorMapL2promotion;
+ * \endcode
+ *
+ * - \p oobFill, which indicates whether zero or a special NaN constant should be used to fill out-of-bound elements, must be of type
+ * ::CUtensorMapFloatOOBfill which is defined as:
+ * \code
+    typedef enum CUtensorMapFloatOOBfill_enum {
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0,
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA
+    } CUtensorMapFloatOOBfill;
+ * \endcode
+ * Note that ::CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA can only be used when \p tensorDataType represents a floating-point data type,
+ * and when \p tensorDataType is not ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B, ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, and ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B.
+ *
+ * \param tensorMap             - Tensor map object to create
+ * \param tensorDataType        - Tensor data type
+ * \param tensorRank            - Dimensionality of tensor; must be at least 3
+ * \param globalAddress         - Starting address of memory region described by tensor
+ * \param globalDim             - Array containing tensor size (number of elements) along each of the \p tensorRank dimensions
+ * \param globalStrides         - Array containing stride size (in bytes) along each of the \p tensorRank - 1 dimensions
+ * \param pixelBoxLowerCorner   - Array containing DHW dimensions of lower box corner
+ * \param pixelBoxUpperCorner   - Array containing DHW dimensions of upper box corner
+ * \param channelsPerPixel      - Number of channels per pixel
+ * \param pixelsPerColumn       - Number of pixels per column
+ * \param elementStrides        - Array containing traversal stride in each of the \p tensorRank dimensions
+ * \param interleave            - Type of interleaved layout the tensor addresses
+ * \param swizzle               - Bank swizzling pattern inside shared memory
+ * \param l2Promotion           - L2 promotion size
+ * \param oobFill               - Indicate whether zero or special NaN constant will be used to fill out-of-bound elements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTensorMapEncodeTiled,
+ * ::cuTensorMapEncodeIm2colWide,
+ * ::cuTensorMapReplaceAddress
+ */
+CUresult CUDAAPI cuTensorMapEncodeIm2col(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, const int *pixelBoxLowerCorner, const int *pixelBoxUpperCorner, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill);
+
+/**
+ * \brief Create a tensor map descriptor object representing im2col memory region, but where
+ * the elements are exclusively loaded along the W dimension.
+ *
+ * Creates a descriptor for Tensor Memory Access (TMA) object specified by the parameters
+ * describing a im2col memory layout and where the row is always loaded along the W dimensuin
+ * and returns it in \p tensorMap. This assumes the tensor layout in memory is either NDHWC,
+ * NHWC, or NWC.
+ *
+ * This API is only supported on devices of compute capability 10.0 or higher.
+ * Additionally, a tensor map object is an opaque value, and, as such, should only be
+ * accessed through CUDA APIs and PTX.
+ *
+ * The parameters passed are bound to the following requirements:
+ *
+ * - \p tensorMap address must be aligned to 64 bytes.
+ *
+ * - \p tensorDataType has to be an enum from ::CUtensorMapDataType which is defined as:
+ * \code
+    typedef enum CUtensorMapDataType_enum {
+        CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0,       // 1 byte
+        CU_TENSOR_MAP_DATA_TYPE_UINT16,          // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_UINT32,          // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_INT32,           // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_UINT64,          // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_INT64,           // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT16,         // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32,         // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT64,         // 8 bytes
+        CU_TENSOR_MAP_DATA_TYPE_BFLOAT16,        // 2 bytes
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ,     // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32,        // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ     // 4 bytes
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B,    // 4 bits
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B,   // 4 bits
+        CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B    // 6 bits
+    } CUtensorMapDataType;
+ * \endcode
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B copies '16 x U4' packed values to memory aligned as 8 bytes. There are no gaps between packed values.
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B copies '16 x U4' packed values to memory aligned as 16 bytes. There are 8 byte gaps between every 8 byte chunk of packed values.
+ *  ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B copies '16 x U6' packed values to memory aligned as 16 bytes. There are 4 byte gaps between every 12 byte chunk of packed values.
+ *
+ * - \p tensorRank, which specifies the number of tensor dimensions, must be 3, 4, or 5.
+ *
+ * - \p globalAddress, which specifies the starting address of the memory region described, must be 16 byte aligned. The following requirements need to also be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p globalAddress must be 32 byte aligned.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, \p globalAddress must be 32 byte aligned.
+ *
+  * - \p globalDim array, which specifies tensor size of each of the \p tensorRank dimensions, must be non-zero and less than or
+ * equal to 2^32. Additionally, the following requirements need to be met for the packed data types:
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, globalDim[0] must be a multiple of 128.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B, \p globalDim[0] must be a multiple of 2.
+ *    - Dimension for the packed data types must reflect the number of individual U# values.
+ *
+ * - \p globalStrides array, which specifies tensor stride of each of the lower \p tensorRank - 1 dimensions in bytes, must be a
+ * multiple of 16 and less than 2^40. Additionally, the following requirements need to be met:
+ *    - When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, the strides must be a multiple of 32.
+ *    - When \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, the strides must be a multiple of 32.
+ * Each following dimension specified includes previous dimension stride:
+ * \code
+    globalStrides[0] = globalDim[0] * elementSizeInBytes(tensorDataType) + padding[0];
+    for (i = 1; i < tensorRank - 1; i++)
+        globalStrides[i] = globalStrides[i – 1] * (globalDim[i] + padding[i]);
+        assert(globalStrides[i] >= globalDim[i]);
+ * \endcode
+ *
+ * - \p pixelBoxLowerCornerWidth specifies the coordinate offset W of the bounding box from left corner. The offset must be
+ * within range [-32768, 32767].
+ *
+ * - \p pixelBoxUpperCornerWidth specifies the coordinate offset W of the bounding box from right corner. The offset must be
+ * within range [-32768, 32767].
+ *
+ * The bounding box specified by \p pixelBoxLowerCornerWidth and \p pixelBoxUpperCornerWidth must have non-zero area. Note
+ * that the size of the box along D and H dimensions is always equal to one.
+ *
+ * - \p channelsPerPixel, which specifies the number of elements which must be accessed along C dimension, must be less than or equal to 256.
+ * Additionally, when \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B or ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, \p channelsPerPixel must be 128.
+ *
+ * - \p pixelsPerColumn, which specifies the number of elements that must be accessed along the W dimension, must be less than or
+ * equal to 1024. This field is ignored when \p mode is ::CU_TENSOR_MAP_IM2COL_WIDE_MODE_W128.
+ *
+ * - \p elementStrides array, which specifies the iteration step along each of the \p tensorRank dimensions, must be non-zero and less
+ * than or equal to 8. Note that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, the first element of this array is ignored since
+ * TMA doesn’t support the stride for dimension zero.
+ * When all elements of the \p elementStrides array are one, \p boxDim specifies the number of elements to load. However, if \p elementStrides[i]
+ * is not equal to one for some \p i, then TMA loads ceil( \p boxDim[i] / \p elementStrides[i]) number of elements along i-th dimension.
+ * To load N elements along i-th dimension, \p boxDim[i] must be set to N * \p elementStrides[i].
+ *
+ * - \p interleave specifies the interleaved layout of type ::CUtensorMapInterleave, which is defined as:
+ * \code
+    typedef enum CUtensorMapInterleave_enum {
+        CU_TENSOR_MAP_INTERLEAVE_NONE = 0,
+        CU_TENSOR_MAP_INTERLEAVE_16B,
+        CU_TENSOR_MAP_INTERLEAVE_32B
+    } CUtensorMapInterleave;
+ * \endcode
+ * TMA supports interleaved layouts like NC/8HWC8 where C8 utilizes 16 bytes in memory assuming 2 byte per channel or NC/16HWC16 where C16
+ * uses 32 bytes.
+ * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, the bounding box inner dimension (computed as \p channelsPerPixel multiplied by
+ * element size in bytes derived from \p tensorDataType) must be less than or equal to the swizzle size.
+ *    - CU_TENSOR_MAP_SWIZZLE_64B requires the bounding box inner dimension to be <= 64.
+ *    - CU_TENSOR_MAP_SWIZZLE_128B* require the bounding box inner dimension to be <= 128.
+ * Additionally, \p tensorDataType of ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B requires \p interleave to be ::CU_TENSOR_MAP_INTERLEAVE_NONE.
+ *
+ * - \p mode, which describes loading of elements loaded along the W dimension, has to be one of the following ::CUtensorMapIm2ColWideMode types:
+ * \code
+ *          CU_TENSOR_MAP_IM2COL_WIDE_MODE_W,
+ *          CU_TENSOR_MAP_IM2COL_WIDE_MODE_W128
+ * \endcode
+ * ::CU_TENSOR_MAP_IM2COL_WIDE_MODE_W allows the number of elements loaded along the W dimension to be specified
+ * via the \p pixelsPerColumn field.
+ *
+ * - \p swizzle, which specifies the shared memory bank swizzling pattern, must be one of the following
+ * ::CUtensorMapSwizzle modes (other swizzle modes are not supported):
+ * \code
+    typedef enum CUtensorMapSwizzle_enum {
+        CU_TENSOR_MAP_SWIZZLE_64B,                   // Swizzle 16B chunks within 64B  span
+        CU_TENSOR_MAP_SWIZZLE_128B,                  // Swizzle 16B chunks within 128B span
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B,         // Swizzle 32B chunks within 128B span
+    } CUtensorMapSwizzle;
+ * \endcode
+ * Data are organized in a specific order in global memory; however, this may not match the order in which the application accesses data
+ * in shared memory. This difference in data organization may cause bank conflicts when shared memory is accessed. In order to avoid this
+ * problem, data can be loaded to shared memory with shuffling across shared memory banks.
+ * When the \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B, only the following swizzle modes are supported:
+ *    - CU_TENSOR_MAP_SWIZZLE_128B (Load & Store)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B (Load & Store)
+ * When the \p tensorDataType is ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, only the following swizzle modes are supported:
+ *    - CU_TENSOR_MAP_SWIZZLE_128B (Load only)
+ *    - CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B (Load only)
+ *
+ * - \p l2Promotion specifies L2 fetch size which indicates the byte granularity at which L2 requests are filled from DRAM. It must be of
+ * type ::CUtensorMapL2promotion, which is defined as:
+ * \code
+    typedef enum CUtensorMapL2promotion_enum {
+        CU_TENSOR_MAP_L2_PROMOTION_NONE = 0,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_64B,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_128B,
+        CU_TENSOR_MAP_L2_PROMOTION_L2_256B
+    } CUtensorMapL2promotion;
+ * \endcode
+ *
+ * - \p oobFill, which indicates whether zero or a special NaN constant should be used to fill out-of-bound elements, must be of type
+ * ::CUtensorMapFloatOOBfill which is defined as:
+ * \code
+    typedef enum CUtensorMapFloatOOBfill_enum {
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0,
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA
+    } CUtensorMapFloatOOBfill;
+ * \endcode
+ * Note that ::CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA can only be used when \p tensorDataType represents a floating-point data type,
+ * and when \p tensorDataType is not ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B, ::CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B, and ::CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B.
+ *
+ * \param tensorMap                - Tensor map object to create
+ * \param tensorDataType           - Tensor data type
+ * \param tensorRank               - Dimensionality of tensor; must be at least 3
+ * \param globalAddress            - Starting address of memory region described by tensor
+ * \param globalDim                - Array containing tensor size (number of elements) along each of the \p tensorRank dimensions
+ * \param globalStrides            - Array containing stride size (in bytes) along each of the \p tensorRank - 1 dimensions
+ * \param pixelBoxLowerCornerWidth - Width offset of left box corner
+ * \param pixelBoxUpperCornerWidth - Width offset of right box corner
+ * \param channelsPerPixel         - Number of channels per pixel
+ * \param pixelsPerColumn          - Number of pixels per column
+ * \param elementStrides           - Array containing traversal stride in each of the \p tensorRank dimensions
+ * \param interleave               - Type of interleaved layout the tensor addresses
+ * \param mode                     - W or W128 mode
+ * \param swizzle                  - Bank swizzling pattern inside shared memory
+ * \param l2Promotion              - L2 promotion size
+ * \param oobFill                  - Indicate whether zero or special NaN constant will be used to fill out-of-bound elements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTensorMapEncodeTiled,
+ * ::cuTensorMapEncodeIm2col,
+ * ::cuTensorMapReplaceAddress
+ */
+CUresult CUDAAPI cuTensorMapEncodeIm2colWide(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, int pixelBoxLowerCornerWidth, int pixelBoxUpperCornerWidth, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapIm2ColWideMode mode, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill);
+
+/**
+ * \brief Modify an existing tensor map descriptor with an updated global address
+ *
+ * Modifies the descriptor for Tensor Memory Access (TMA) object passed in \p tensorMap with
+ * an updated \p globalAddress.
+ *
+ * Tensor map objects are only supported on devices of compute capability 9.0 or higher.
+ * Additionally, a tensor map object is an opaque value, and, as such, should only be
+ * accessed through CUDA API calls.
+ *
+ * \param tensorMap             - Tensor map object to modify
+ * \param globalAddress         - Starting address of memory region described by tensor, must follow previous alignment requirements
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuTensorMapEncodeTiled,
+ * ::cuTensorMapEncodeIm2col
+ * ::cuTensorMapEncodeIm2colWide
+ */
+CUresult CUDAAPI cuTensorMapReplaceAddress(CUtensorMap *tensorMap, void *globalAddress);
+
+/** @} */
+/* END CUDA_TENSOR_MEMORY */
+
+/**
+ * \defgroup CUDA_PEER_ACCESS Peer Context Memory Access
+ *
+ * ___MANBRIEF___ direct peer context memory access functions of the low-level
+ * CUDA driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the direct peer context memory access functions
+ * of the low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Queries if a device may directly access a peer device's memory.
+ *
+ * Returns in \p *canAccessPeer a value of 1 if contexts on \p dev are capable of
+ * directly accessing memory from contexts on \p peerDev and 0 otherwise.
+ * If direct access of \p peerDev from \p dev is possible, then access may be
+ * enabled on two specific contexts by calling ::cuCtxEnablePeerAccess().
+ *
+ * \param canAccessPeer - Returned access capability
+ * \param dev           - Device from which allocations on \p peerDev are to
+ *                        be directly accessed.
+ * \param peerDev       - Device on which the allocations to be directly accessed
+ *                        by \p dev reside.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuCtxEnablePeerAccess,
+ * ::cuCtxDisablePeerAccess,
+ * ::cudaDeviceCanAccessPeer
+ */
+CUresult CUDAAPI cuDeviceCanAccessPeer(int *canAccessPeer, CUdevice dev, CUdevice peerDev);
+
+/**
+ * \brief Enables direct access to memory allocations in a peer context.
+ *
+ * If both the current context and \p peerContext are on devices which support unified
+ * addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING) and same
+ * major compute capability, then on success all allocations from \p peerContext will
+ * immediately be accessible by the current context.  See \ref CUDA_UNIFIED for additional
+ * details.
+ *
+ * Note that access granted by this call is unidirectional and that in order to access
+ * memory from the current context in \p peerContext, a separate symmetric call
+ * to ::cuCtxEnablePeerAccess() is required.
+ *
+ * Note that there are both device-wide and system-wide limitations per system
+ * configuration, as noted in the CUDA Programming Guide under the section
+ * "Peer-to-Peer Memory Access".
+ *
+ * Returns ::CUDA_ERROR_PEER_ACCESS_UNSUPPORTED if ::cuDeviceCanAccessPeer() indicates
+ * that the ::CUdevice of the current context cannot directly access memory
+ * from the ::CUdevice of \p peerContext.
+ *
+ * Returns ::CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED if direct access of
+ * \p peerContext from the current context has already been enabled.
+ *
+ * Returns ::CUDA_ERROR_TOO_MANY_PEERS if direct peer access is not possible
+ * because hardware resources required for peer access have been exhausted.
+ *
+ * Returns ::CUDA_ERROR_INVALID_CONTEXT if there is no current context, \p peerContext
+ * is not a valid context, or if the current context is \p peerContext.
+ *
+ * Returns ::CUDA_ERROR_INVALID_VALUE if \p Flags is not 0.
+ *
+ * \param peerContext - Peer context to enable direct access to from the current context
+ * \param Flags       - Reserved for future use and must be set to 0
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED,
+ * ::CUDA_ERROR_TOO_MANY_PEERS,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_PEER_ACCESS_UNSUPPORTED,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceCanAccessPeer,
+ * ::cuCtxDisablePeerAccess,
+ * ::cudaDeviceEnablePeerAccess
+ */
+CUresult CUDAAPI cuCtxEnablePeerAccess(CUcontext peerContext, unsigned int Flags);
+
+/**
+ * \brief Disables direct access to memory allocations in a peer context and
+ * unregisters any registered allocations.
+ *
+  Returns ::CUDA_ERROR_PEER_ACCESS_NOT_ENABLED if direct peer access has
+ * not yet been enabled from \p peerContext to the current context.
+ *
+ * Returns ::CUDA_ERROR_INVALID_CONTEXT if there is no current context, or if
+ * \p peerContext is not a valid context.
+ *
+ * \param peerContext - Peer context to disable direct access to
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_PEER_ACCESS_NOT_ENABLED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * \notefnerr
+ *
+ * \sa
+ * ::cuDeviceCanAccessPeer,
+ * ::cuCtxEnablePeerAccess,
+ * ::cudaDeviceDisablePeerAccess
+ */
+CUresult CUDAAPI cuCtxDisablePeerAccess(CUcontext peerContext);
+
+/**
+ * \brief Queries attributes of the link between two devices.
+ *
+ * Returns in \p *value the value of the requested attribute \p attrib of the
+ * link between \p srcDevice and \p dstDevice. The supported attributes are:
+ * - ::CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK: A relative value indicating the
+ *   performance of the link between two devices.
+ * - ::CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED P2P: 1 if P2P Access is enable.
+ * - ::CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED: 1 if Atomic operations over
+ *   the link are supported.
+ * - ::CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED: 1 if cudaArray can
+ *   be accessed over the link.
+ *
+ * Returns ::CUDA_ERROR_INVALID_DEVICE if \p srcDevice or \p dstDevice are not valid
+ * or if they represent the same device.
+ *
+ * Returns ::CUDA_ERROR_INVALID_VALUE if \p attrib is not valid or if \p value is
+ * a null pointer.
+ *
+ * \param value         - Returned value of the requested attribute
+ * \param attrib        - The requested attribute of the link between \p srcDevice and \p dstDevice.
+ * \param srcDevice     - The source device of the target link.
+ * \param dstDevice     - The destination device of the target link.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa
+ * ::cuCtxEnablePeerAccess,
+ * ::cuCtxDisablePeerAccess,
+ * ::cuDeviceCanAccessPeer,
+ * ::cudaDeviceGetP2PAttribute
+ */
+CUresult CUDAAPI cuDeviceGetP2PAttribute(int* value, CUdevice_P2PAttribute attrib, CUdevice srcDevice, CUdevice dstDevice);
+
+/** @} */ /* END CUDA_PEER_ACCESS */
+
+/**
+ * \defgroup CUDA_GRAPHICS Graphics Interoperability
+ *
+ * ___MANBRIEF___ graphics interoperability functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the graphics interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Unregisters a graphics resource for access by CUDA
+ *
+ * Unregisters the graphics resource \p resource so it is not accessible by
+ * CUDA unless registered again.
+ *
+ * If \p resource is invalid then ::CUDA_ERROR_INVALID_HANDLE is
+ * returned.
+ *
+ * \param resource - Resource to unregister
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsD3D9RegisterResource,
+ * ::cuGraphicsD3D10RegisterResource,
+ * ::cuGraphicsD3D11RegisterResource,
+ * ::cuGraphicsGLRegisterBuffer,
+ * ::cuGraphicsGLRegisterImage,
+ * ::cudaGraphicsUnregisterResource
+ */
+CUresult CUDAAPI cuGraphicsUnregisterResource(CUgraphicsResource resource);
+
+/**
+ * \brief Get an array through which to access a subresource of a mapped graphics resource.
+ *
+ * Returns in \p *pArray an array through which the subresource of the mapped
+ * graphics resource \p resource which corresponds to array index \p arrayIndex
+ * and mipmap level \p mipLevel may be accessed.  The value set in \p *pArray may
+ * change every time that \p resource is mapped.
+ *
+ * If \p resource is not a texture then it cannot be accessed via an array and
+ * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY is returned.
+ * If \p arrayIndex is not a valid array index for \p resource then
+ * ::CUDA_ERROR_INVALID_VALUE is returned.
+ * If \p mipLevel is not a valid mipmap level for \p resource then
+ * ::CUDA_ERROR_INVALID_VALUE is returned.
+ * If \p resource is not mapped then ::CUDA_ERROR_NOT_MAPPED is returned.
+ *
+ * \param pArray      - Returned array through which a subresource of \p resource may be accessed
+ * \param resource    - Mapped resource to access
+ * \param arrayIndex  - Array index for array textures or cubemap face
+ *                      index as defined by ::CUarray_cubemap_face for
+ *                      cubemap textures for the subresource to access
+ * \param mipLevel    - Mipmap level for the subresource to access
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_MAPPED,
+ * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsResourceGetMappedPointer,
+ * ::cudaGraphicsSubResourceGetMappedArray
+ */
+CUresult CUDAAPI cuGraphicsSubResourceGetMappedArray(CUarray *pArray, CUgraphicsResource resource, unsigned int arrayIndex, unsigned int mipLevel);
+
+/**
+ * \brief Get a mipmapped array through which to access a mapped graphics resource.
+ *
+ * Returns in \p *pMipmappedArray a mipmapped array through which the mapped graphics
+ * resource \p resource. The value set in \p *pMipmappedArray may change every time
+ * that \p resource is mapped.
+ *
+ * If \p resource is not a texture then it cannot be accessed via a mipmapped array and
+ * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY is returned.
+ * If \p resource is not mapped then ::CUDA_ERROR_NOT_MAPPED is returned.
+ *
+ * \param pMipmappedArray - Returned mipmapped array through which \p resource may be accessed
+ * \param resource        - Mapped resource to access
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_MAPPED,
+ * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsResourceGetMappedPointer,
+ * ::cudaGraphicsResourceGetMappedMipmappedArray
+ */
+CUresult CUDAAPI cuGraphicsResourceGetMappedMipmappedArray(CUmipmappedArray *pMipmappedArray, CUgraphicsResource resource);
+
+/**
+ * \brief Get a device pointer through which to access a mapped graphics resource.
+ *
+ * Returns in \p *pDevPtr a pointer through which the mapped graphics resource
+ * \p resource may be accessed.
+ * Returns in \p pSize the size of the memory in bytes which may be accessed from that pointer.
+ * The value set in \p pPointer may change every time that \p resource is mapped.
+ *
+ * If \p resource is not a buffer then it cannot be accessed via a pointer and
+ * ::CUDA_ERROR_NOT_MAPPED_AS_POINTER is returned.
+ * If \p resource is not mapped then ::CUDA_ERROR_NOT_MAPPED is returned.
+ * *
+ * \param pDevPtr    - Returned pointer through which \p resource may be accessed
+ * \param pSize      - Returned size of the buffer accessible starting at \p *pPointer
+ * \param resource   - Mapped resource to access
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_MAPPED,
+ * ::CUDA_ERROR_NOT_MAPPED_AS_POINTER
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsMapResources,
+ * ::cuGraphicsSubResourceGetMappedArray,
+ * ::cudaGraphicsResourceGetMappedPointer
+ */
+CUresult CUDAAPI cuGraphicsResourceGetMappedPointer(CUdeviceptr *pDevPtr, size_t *pSize, CUgraphicsResource resource);
+
+/**
+ * \brief Set usage flags for mapping a graphics resource
+ *
+ * Set \p flags for mapping the graphics resource \p resource.
+ *
+ * Changes to \p flags will take effect the next time \p resource is mapped.
+ * The \p flags argument may be any of the following:
+
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA kernels.  This is the default value.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READONLY: Specifies that CUDA kernels which
+ *   access this resource will not write to this resource.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITEDISCARD: Specifies that CUDA kernels
+ *   which access this resource will not read from this resource and will
+ *   write over the entire contents of the resource, so none of the data
+ *   previously stored in the resource will be preserved.
+ *
+ * If \p resource is presently mapped for access by CUDA then
+ * ::CUDA_ERROR_ALREADY_MAPPED is returned.
+ * If \p flags is not one of the above values then ::CUDA_ERROR_INVALID_VALUE is returned.
+ *
+ * \param resource - Registered resource to set flags for
+ * \param flags    - Parameters for resource mapping
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsMapResources,
+ * ::cudaGraphicsResourceSetMapFlags
+ */
+CUresult CUDAAPI cuGraphicsResourceSetMapFlags(CUgraphicsResource resource, unsigned int flags);
+
+/**
+ * \brief Map graphics resources for access by CUDA
+ *
+ * Maps the \p count graphics resources in \p resources for access by CUDA.
+ *
+ * The resources in \p resources may be accessed by CUDA until they
+ * are unmapped. The graphics API from which \p resources were registered
+ * should not access any resources while they are mapped by CUDA. If an
+ * application does so, the results are undefined.
+ *
+ * This function provides the synchronization guarantee that any graphics calls
+ * issued before ::cuGraphicsMapResources() will complete before any subsequent CUDA
+ * work issued in \p stream begins.
+ *
+ * If \p resources includes any duplicate entries then ::CUDA_ERROR_INVALID_HANDLE is returned.
+ * If any of \p resources are presently mapped for access by CUDA then ::CUDA_ERROR_ALREADY_MAPPED is returned.
+ *
+ * \param count      - Number of resources to map
+ * \param resources  - Resources to map for CUDA usage
+ * \param hStream    - Stream with which to synchronize
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_UNKNOWN
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsResourceGetMappedPointer,
+ * ::cuGraphicsSubResourceGetMappedArray,
+ * ::cuGraphicsUnmapResources,
+ * ::cudaGraphicsMapResources
+ */
+CUresult CUDAAPI cuGraphicsMapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+
+/**
+ * \brief Unmap graphics resources.
+ *
+ * Unmaps the \p count graphics resources in \p resources.
+ *
+ * Once unmapped, the resources in \p resources may not be accessed by CUDA
+ * until they are mapped again.
+ *
+ * This function provides the synchronization guarantee that any CUDA work issued
+ * in \p stream before ::cuGraphicsUnmapResources() will complete before any
+ * subsequently issued graphics work begins.
+ *
+ *
+ * If \p resources includes any duplicate entries then ::CUDA_ERROR_INVALID_HANDLE is returned.
+ * If any of \p resources are not presently mapped for access by CUDA then ::CUDA_ERROR_NOT_MAPPED is returned.
+ *
+ * \param count      - Number of resources to unmap
+ * \param resources  - Resources to unmap
+ * \param hStream    - Stream with which to synchronize
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_MAPPED,
+ * ::CUDA_ERROR_UNKNOWN
+ * \note_null_stream
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphicsMapResources,
+ * ::cudaGraphicsUnmapResources
+ */
+CUresult CUDAAPI cuGraphicsUnmapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+
+/** @} */ /* END CUDA_GRAPHICS */
+
+/**
+ * \defgroup CUDA_DRIVER_ENTRY_POINT Driver Entry Point Access 
+ *
+ * ___MANBRIEF___ driver entry point access functions of the low-level CUDA driver API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the driver entry point access functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the requested driver API function pointer
+ *
+ * Returns in \p **pfn the address of the CUDA driver function for the requested
+ * CUDA version and flags.
+ *
+ * The CUDA version is specified as (1000 * major + 10 * minor), so CUDA 11.2
+ * should be specified as 11020. For a requested driver symbol, if the specified
+ * CUDA version is greater than or equal to the CUDA version in which the driver symbol
+ * was introduced, this API will return the function pointer to the corresponding
+ * versioned function.
+ *
+ * The pointer returned by the API should be cast to a function pointer matching the
+ * requested driver function's definition in the API header file. The function pointer
+ * typedef can be picked up from the corresponding typedefs header file. For example,
+ * cudaTypedefs.h consists of function pointer typedefs for driver APIs defined in cuda.h.
+ *
+ * The API will return ::CUDA_SUCCESS and set the returned \p pfn to NULL if the 
+ * requested driver function is not supported on the platform, no ABI 
+ * compatible driver function exists for the specified \p cudaVersion or if the 
+ * driver symbol is invalid.
+ *
+ * It will also set the optional \p symbolStatus to one of the values in
+ * ::CUdriverProcAddressQueryResult with the following meanings:
+ * - ::CU_GET_PROC_ADDRESS_SUCCESS - The requested symbol was succesfully found based
+ *   on input arguments and \p pfn is valid
+ * - ::CU_GET_PROC_ADDRESS_SYMBOL_NOT_FOUND - The requested symbol was not found
+ * - ::CU_GET_PROC_ADDRESS_VERSION_NOT_SUFFICIENT - The requested symbol was found but is
+ *   not supported by cudaVersion specified
+ *
+ * The requested flags can be:
+ * - ::CU_GET_PROC_ADDRESS_DEFAULT: This is the default mode. This is equivalent to
+ *   ::CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM if the code is compiled with
+ *   --default-stream per-thread compilation flag or the macro CUDA_API_PER_THREAD_DEFAULT_STREAM
+ *   is defined; ::CU_GET_PROC_ADDRESS_LEGACY_STREAM otherwise.
+ * - ::CU_GET_PROC_ADDRESS_LEGACY_STREAM: This will enable the search for all driver symbols
+ *   that match the requested driver symbol name except the corresponding per-thread versions.
+ * - ::CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM: This will enable the search for all
+ *   driver symbols that match the requested driver symbol name including the per-thread
+ *   versions. If a per-thread version is not found, the API will return the legacy version
+ *   of the driver function.
+ *
+ * \param symbol - The base name of the driver API function to look for. As an example,
+ *                 for the driver API ::cuMemAlloc_v2, \p symbol would be cuMemAlloc and
+ *                 \p cudaVersion would be the ABI compatible CUDA version for the _v2 variant. 
+ * \param pfn - Location to return the function pointer to the requested driver function
+ * \param cudaVersion - The CUDA version to look for the requested driver symbol 
+ * \param flags -  Flags to specify search options.
+ * \param symbolStatus - Optional location to store the status of the search for
+ *                       \p symbol based on \p cudaVersion. See ::CUdriverProcAddressQueryResult
+ *                       for possible values.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_version_mixing
+ *
+ * \sa
+ * ::cudaGetDriverEntryPoint
+ */
+CUresult CUDAAPI cuGetProcAddress(const char *symbol, void **pfn, int cudaVersion, cuuint64_t flags, CUdriverProcAddressQueryResult *symbolStatus);
+
+/** @} */ /* END CUDA_DRIVER_ENTRY_POINT */
+
+/**
+ * \defgroup CUDA_COREDUMP Coredump Attributes Control API
+ *
+ * ___MANBRIEF___ coredump attribute control functions for the low-level CUDA API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the coredump attribute control functions of the low-level CUDA
+ * driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * Flags for choosing a coredump attribute to get/set
+ */
+typedef enum CUcoredumpSettings_enum {
+    CU_COREDUMP_ENABLE_ON_EXCEPTION = 1,
+    CU_COREDUMP_TRIGGER_HOST,
+    CU_COREDUMP_LIGHTWEIGHT,
+    CU_COREDUMP_ENABLE_USER_TRIGGER,
+    CU_COREDUMP_FILE,
+    CU_COREDUMP_PIPE,
+    CU_COREDUMP_GENERATION_FLAGS,
+    CU_COREDUMP_MAX
+} CUcoredumpSettings;
+
+/**
+ * Flags for controlling coredump contents
+ */
+typedef enum CUCoredumpGenerationFlags {
+    CU_COREDUMP_DEFAULT_FLAGS                = 0,
+    CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES = (1 << 0),
+    CU_COREDUMP_SKIP_GLOBAL_MEMORY           = (1 << 1),
+    CU_COREDUMP_SKIP_SHARED_MEMORY           = (1 << 2),
+    CU_COREDUMP_SKIP_LOCAL_MEMORY            = (1 << 3),
+    CU_COREDUMP_SKIP_ABORT                   = (1 << 4),
+    CU_COREDUMP_SKIP_CONSTBANK_MEMORY        = (1 << 5),
+
+    CU_COREDUMP_LIGHTWEIGHT_FLAGS = CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES
+                                     | CU_COREDUMP_SKIP_GLOBAL_MEMORY
+                                     | CU_COREDUMP_SKIP_SHARED_MEMORY
+                                     | CU_COREDUMP_SKIP_LOCAL_MEMORY
+                                     | CU_COREDUMP_SKIP_CONSTBANK_MEMORY
+} CUCoredumpGenerationFlags;
+
+/**
+ * \brief Allows caller to fetch a coredump attribute value for the current context
+ *
+ * Returns in \p *value the requested value specified by \p attrib. It is up to the caller
+ * to ensure that the data type and size of \p *value matches the request.
+ *
+ * If the caller calls this function with \p *value equal to NULL, the size of the memory
+ * region (in bytes) expected for \p attrib will be placed in \p size.
+ *
+ * The supported attributes are:
+ * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from
+ *      this context will create a coredump at the location specified by ::CU_COREDUMP_FILE.
+ *      The default value is ::false unless set to ::true globally or locally, or the
+ *      CU_CTX_USER_COREDUMP_ENABLE flag was set during context creation.
+ * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will
+ *      also create a coredump. The default value is ::true unless set to ::false globally or
+ *      or locally. This value is deprecated as of CUDA 12.5 - raise the ::CU_COREDUMP_SKIP_ABORT
+ *      flag to disable host device abort() if needed.
+ * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps
+ *      will not have a dump of GPU memory or non-reloc ELF images. The default value is
+ *      ::false unless set to ::true globally or locally. This attribute is deprecated as
+ *      of CUDA 12.5, please use ::CU_COREDUMP_GENERATION_FLAGS instead.
+ * - ::CU_COREDUMP_ENABLE_USER_TRIGGER: Bool where ::true means that a coredump can be
+ *      created by writing to the system pipe specified by ::CU_COREDUMP_PIPE. The default
+ *      value is ::false unless set to ::true globally or locally.
+ * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where
+ *      any coredumps generated by this context will be written. The default value is
+ *      ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running
+ *      the CUDA applications and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_PIPE: String of up to 1023 characters that defines the name of the pipe
+ *      that will be monitored if user-triggered coredumps are enabled. The default value is
+ *      ::corepipe.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running
+ *      the CUDA application and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_GENERATION_FLAGS: An integer with values to allow granular control the data
+ *      contained in a coredump specified as a bitwise OR combination of the following values:
+ *      + ::CU_COREDUMP_DEFAULT_FLAGS - if set by itself, coredump generation returns to its
+ *          default settings of including all memory regions that it is able to access
+ *      + ::CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES - Coredump will not include the data from
+ *          CUDA source modules that are not relocated at runtime.
+ *      + ::CU_COREDUMP_SKIP_GLOBAL_MEMORY - Coredump will not include device-side global data
+ *          that does not belong to any context.
+ *      + ::CU_COREDUMP_SKIP_SHARED_MEMORY - Coredump will not include grid-scale shared memory
+ *          for the warp that the dumped kernel belonged to.
+ *      + ::CU_COREDUMP_SKIP_LOCAL_MEMORY - Coredump will not include local memory from the kernel.
+ *      + ::CU_COREDUMP_LIGHTWEIGHT_FLAGS - Enables all of the above options. Equiavlent to setting
+ *          the ::CU_COREDUMP_LIGHTWEIGHT attribute to ::true.
+ *      + ::CU_COREDUMP_SKIP_ABORT - If set, GPU exceptions will not raise an abort() in the host CPU
+ *          process. Same functional goal as ::CU_COREDUMP_TRIGGER_HOST but better reflects the default
+ *          behavior.
+ *
+ * \param attrib - The enum defining which value to fetch.
+ * \param value - void* containing the requested data.
+ * \param size - The size of the memory region \p value points to.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ *
+ * \sa
+ * ::cuCoredumpGetAttributeGlobal,
+ * ::cuCoredumpSetAttribute,
+ * ::cuCoredumpSetAttributeGlobal
+ */
+CUresult CUDAAPI cuCoredumpGetAttribute(CUcoredumpSettings attrib, void* value, size_t *size);
+
+/**
+ * \brief Allows caller to fetch a coredump attribute value for the entire application
+ *
+ * Returns in \p *value the requested value specified by \p attrib. It is up to the caller
+ * to ensure that the data type and size of \p *value matches the request.
+ *
+ * If the caller calls this function with \p *value equal to NULL, the size of the memory
+ * region (in bytes) expected for \p attrib will be placed in \p size.
+ *
+ * The supported attributes are:
+ * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from
+ *      this context will create a coredump at the location specified by ::CU_COREDUMP_FILE.
+ *      The default value is ::false.
+ * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will
+ *      also create a coredump. The default value is ::true unless set to ::false globally or
+ *      or locally. This value is deprecated as of CUDA 12.5 - raise the ::CU_COREDUMP_SKIP_ABORT
+ *      flag to disable host device abort() if needed.
+ * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps
+ *      will not have a dump of GPU memory or non-reloc ELF images. The default value is
+ *      ::false. This attribute is deprecated as of CUDA 12.5, please use ::CU_COREDUMP_GENERATION_FLAGS
+ *      instead.
+ * - ::CU_COREDUMP_ENABLE_USER_TRIGGER: Bool where ::true means that a coredump can be
+ *      created by writing to the system pipe specified by ::CU_COREDUMP_PIPE. The default
+ *      value is ::false.
+ * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where
+ *      any coredumps generated by this context will be written. The default value is
+ *      ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running
+ *      the CUDA applications and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_PIPE: String of up to 1023 characters that defines the name of the pipe
+ *      that will be monitored if user-triggered coredumps are enabled. The default value is
+ *      ::corepipe.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running
+ *      the CUDA application and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_GENERATION_FLAGS: An integer with values to allow granular control the data
+ *      contained in a coredump specified as a bitwise OR combination of the following values:
+ *      + ::CU_COREDUMP_DEFAULT_FLAGS - if set by itself, coredump generation returns to its
+ *          default settings of including all memory regions that it is able to access
+ *      + ::CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES - Coredump will not include the data from
+ *          CUDA source modules that are not relocated at runtime.
+ *      + ::CU_COREDUMP_SKIP_GLOBAL_MEMORY - Coredump will not include device-side global data
+ *          that does not belong to any context.
+ *      + ::CU_COREDUMP_SKIP_SHARED_MEMORY - Coredump will not include grid-scale shared memory
+ *          for the warp that the dumped kernel belonged to.
+ *      + ::CU_COREDUMP_SKIP_LOCAL_MEMORY - Coredump will not include local memory from the kernel.
+ *      + ::CU_COREDUMP_LIGHTWEIGHT_FLAGS - Enables all of the above options. Equiavlent to setting
+ *          the ::CU_COREDUMP_LIGHTWEIGHT attribute to ::true.
+ *      + ::CU_COREDUMP_SKIP_ABORT - If set, GPU exceptions will not raise an abort() in the host CPU
+ *          process. Same functional goal as ::CU_COREDUMP_TRIGGER_HOST but better reflects the default
+ *          behavior.
+ *
+ * \param attrib - The enum defining which value to fetch.
+ * \param value - void* containing the requested data.
+ * \param size - The size of the memory region \p value points to.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuCoredumpGetAttribute,
+ * ::cuCoredumpSetAttribute,
+ * ::cuCoredumpSetAttributeGlobal
+ */
+CUresult CUDAAPI cuCoredumpGetAttributeGlobal(CUcoredumpSettings attrib, void *value, size_t *size);
+
+/**
+ * \brief Allows caller to set a coredump attribute value for the current context
+ *
+ * This function should be considered an alternate interface to the CUDA-GDB environment
+ * variables defined in this document: https://docs.nvidia.com/cuda/cuda-gdb/index.html#gpu-coredump
+ *
+ * An important design decision to note is that any coredump environment variable values
+ * set before CUDA initializes will take permanent precedence over any values set with this
+ * function. This decision was made to ensure no change in behavior for any users that
+ * may be currently using these variables to get coredumps.
+ *
+ * \p *value shall contain the requested value specified by \p set. It is up to the caller
+ * to ensure that the data type and size of \p *value matches the request.
+ *
+ * If the caller calls this function with \p *value equal to NULL, the size of the memory
+ * region (in bytes) expected for \p set will be placed in \p size.
+ *
+ * /note This function will return ::CUDA_ERROR_NOT_SUPPORTED if the caller attempts to set
+ * ::CU_COREDUMP_ENABLE_ON_EXCEPTION on a GPU of with Compute Capability < 6.0. ::cuCoredumpSetAttributeGlobal
+ * works on those platforms as an alternative.
+ *
+ * /note ::CU_COREDUMP_ENABLE_USER_TRIGGER and ::CU_COREDUMP_PIPE cannot be set on a per-context basis.
+ *
+ * The supported attributes are:
+ * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from
+ *      this context will create a coredump at the location specified by ::CU_COREDUMP_FILE.
+ *      The default value is ::false.
+ * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will
+ *      also create a coredump. The default value is ::true unless set to ::false globally or
+ *      or locally. This value is deprecated as of CUDA 12.5 - raise the ::CU_COREDUMP_SKIP_ABORT
+ *      flag to disable host device abort() if needed.
+ * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps
+ *      will not have a dump of GPU memory or non-reloc ELF images. The default value is
+ *      ::false. This attribute is deprecated as of CUDA 12.5, please use ::CU_COREDUMP_GENERATION_FLAGS
+ *      instead.
+ * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where
+ *      any coredumps generated by this context will be written. The default value is
+ *      ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running
+ *      the CUDA applications and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_GENERATION_FLAGS: An integer with values to allow granular control the data
+ *      contained in a coredump specified as a bitwise OR combination of the following values:
+ *      + ::CU_COREDUMP_DEFAULT_FLAGS - if set by itself, coredump generation returns to its
+ *          default settings of including all memory regions that it is able to access
+ *      + ::CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES - Coredump will not include the data from
+ *          CUDA source modules that are not relocated at runtime.
+ *      + ::CU_COREDUMP_SKIP_GLOBAL_MEMORY - Coredump will not include device-side global data
+ *          that does not belong to any context.
+ *      + ::CU_COREDUMP_SKIP_SHARED_MEMORY - Coredump will not include grid-scale shared memory
+ *          for the warp that the dumped kernel belonged to.
+ *      + ::CU_COREDUMP_SKIP_LOCAL_MEMORY - Coredump will not include local memory from the kernel.
+ *      + ::CU_COREDUMP_LIGHTWEIGHT_FLAGS - Enables all of the above options. Equiavlent to setting
+ *          the ::CU_COREDUMP_LIGHTWEIGHT attribute to ::true.
+ *      + ::CU_COREDUMP_SKIP_ABORT - If set, GPU exceptions will not raise an abort() in the host CPU
+ *          process. Same functional goal as ::CU_COREDUMP_TRIGGER_HOST but better reflects the default
+ *          behavior.
+ *
+ * \param attrib - The enum defining which value to set.
+ * \param value - void* containing the requested data.
+ * \param size - The size of the memory region \p value points to.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_PERMITTED,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa
+ * ::cuCoredumpGetAttributeGlobal,
+ * ::cuCoredumpGetAttribute,
+ * ::cuCoredumpSetAttributeGlobal
+ */
+CUresult CUDAAPI cuCoredumpSetAttribute(CUcoredumpSettings attrib, void* value, size_t *size);
+
+/**
+ * \brief Allows caller to set a coredump attribute value globally
+ *
+ * This function should be considered an alternate interface to the CUDA-GDB environment
+ * variables defined in this document: https://docs.nvidia.com/cuda/cuda-gdb/index.html#gpu-coredump
+ *
+ * An important design decision to note is that any coredump environment variable values
+ * set before CUDA initializes will take permanent precedence over any values set with this
+ * function. This decision was made to ensure no change in behavior for any users that
+ * may be currently using these variables to get coredumps.
+ *
+ * \p *value shall contain the requested value specified by \p set. It is up to the caller
+ * to ensure that the data type and size of \p *value matches the request.
+ *
+ * If the caller calls this function with \p *value equal to NULL, the size of the memory
+ * region (in bytes) expected for \p set will be placed in \p size.
+ *
+ * The supported attributes are:
+ * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from
+ *      this context will create a coredump at the location specified by ::CU_COREDUMP_FILE.
+ *      The default value is ::false.
+ * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will
+ *      also create a coredump. The default value is ::true unless set to ::false globally or
+ *      or locally. This value is deprecated as of CUDA 12.5 - raise the ::CU_COREDUMP_SKIP_ABORT
+ *      flag to disable host device abort() if needed.
+ * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps
+ *      will not have a dump of GPU memory or non-reloc ELF images. The default value is
+ *      ::false. This attribute is deprecated as of CUDA 12.5, please use ::CU_COREDUMP_GENERATION_FLAGS
+ *      instead.
+ * - ::CU_COREDUMP_ENABLE_USER_TRIGGER: Bool where ::true means that a coredump can be
+ *      created by writing to the system pipe specified by ::CU_COREDUMP_PIPE. The default
+ *      value is ::false.
+ * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where
+ *      any coredumps generated by this context will be written. The default value is
+ *      ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running
+ *      the CUDA applications and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_PIPE: String of up to 1023 characters that defines the name of the pipe
+ *      that will be monitored if user-triggered coredumps are enabled. This value may not be
+ *      changed after ::CU_COREDUMP_ENABLE_USER_TRIGGER is set to ::true. The default
+ *      value is ::corepipe.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine
+ *      running the CUDA application and ::PID is the process ID of the CUDA application.
+ * - ::CU_COREDUMP_GENERATION_FLAGS: An integer with values to allow granular control the data
+ *      contained in a coredump specified as a bitwise OR combination of the following values:
+ *      + ::CU_COREDUMP_DEFAULT_FLAGS - if set by itself, coredump generation returns to its
+ *          default settings of including all memory regions that it is able to access
+ *      + ::CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES - Coredump will not include the data from
+ *          CUDA source modules that are not relocated at runtime.
+ *      + ::CU_COREDUMP_SKIP_GLOBAL_MEMORY - Coredump will not include device-side global data
+ *          that does not belong to any context.
+ *      + ::CU_COREDUMP_SKIP_SHARED_MEMORY - Coredump will not include grid-scale shared memory
+ *          for the warp that the dumped kernel belonged to.
+ *      + ::CU_COREDUMP_SKIP_LOCAL_MEMORY - Coredump will not include local memory from the kernel.
+ *      + ::CU_COREDUMP_LIGHTWEIGHT_FLAGS - Enables all of the above options. Equiavlent to setting
+ *          the ::CU_COREDUMP_LIGHTWEIGHT attribute to ::true.
+ *      + ::CU_COREDUMP_SKIP_ABORT - If set, GPU exceptions will not raise an abort() in the host CPU
+ *          process. Same functional goal as ::CU_COREDUMP_TRIGGER_HOST but better reflects the default
+ *          behavior.
+ *
+ * \param attrib - The enum defining which value to set.
+ * \param value - void* containing the requested data.
+ * \param size - The size of the memory region \p value points to.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_PERMITTED
+ *
+ * \sa
+ * ::cuCoredumpGetAttribute,
+ * ::cuCoredumpGetAttributeGlobal,
+ * ::cuCoredumpSetAttribute
+ */
+CUresult CUDAAPI cuCoredumpSetAttributeGlobal(CUcoredumpSettings attrib, void *value, size_t *size);
+
+/** @} */ /* END CUDA_COREDUMP */
+
+CUresult CUDAAPI cuGetExportTable(const void **ppExportTable, const CUuuid *pExportTableId);
+
+/*
+** ******************* GREEN CONTEXTS **********************
+*/
+
+/**
+ * \defgroup CUDA_GREEN_CONTEXTS Green Contexts
+ *
+ * ___MANBRIEF___ Driver level API for creation and manipulation of green contexts
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the APIs for creation and manipulation of green contexts in the CUDA
+ * driver. Green contexts are a lightweight alternative to traditional contexts, with the ability
+ * to pass in a set of resources that they should be initialized with. This allows the developer to
+ * represent distinct spatial partitions of the GPU, provision resources for them, and target them
+ * via the same programming model that CUDA exposes (streams, kernel launches, etc.).
+ *
+ * There are 4 main steps to using these new set of APIs.
+ * - (1) Start with an initial set of resources, for example via ::cuDeviceGetDevResource. Only SM type is supported today.
+ * - (2) Partition this set of resources by providing them as input to a partition API, for example: ::cuDevSmResourceSplitByCount.
+ * - (3) Finalize the specification of resources by creating a descriptor via ::cuDevResourceGenerateDesc.
+ * - (4) Provision the resources and create a green context via ::cuGreenCtxCreate.
+ *
+ * For \p CU_DEV_RESOURCE_TYPE_SM, the partitions created have minimum SM count requirements, often rounding up and aligning the
+ * minCount provided to ::cuDevSmResourceSplitByCount. The following is a guideline for each architecture
+ * and may be subject to change:
+ * - On Compute Architecture 6.X: The minimum count is 1 SM.
+ * - On Compute Architecture 7.X: The minimum count is 2 SMs and must be a multiple of 2.
+ * - On Compute Architecture 8.X: The minimum count is 4 SMs and must be a multiple of 2.
+ * - On Compute Architecture 9.0+: The minimum count is 8 SMs and must be a multiple of 8.
+ *
+ * In the future, flags can be provided to tradeoff functional and performance characteristics versus finer grained SM partitions.
+ *
+ * Even if the green contexts have disjoint SM partitions, it is not guaranteed that the kernels launched
+ * in them will run concurrently or have forward progress guarantees. This is due to other resources (like HW connections,
+ * see ::CUDA_DEVICE_MAX_CONNECTIONS) that could cause a dependency. Additionally, in certain scenarios,
+ * it is possible for the workload to run on more SMs than was provisioned (but never less).
+ * The following are two scenarios which can exhibit this behavior:
+ * - On Volta+ MPS: When \p CUDA_MPS_ACTIVE_THREAD_PERCENTAGE is used,
+ * the set of SMs that are used for running kernels can be scaled up to the value of SMs used for the MPS client.
+ * - On Compute Architecture 9.x: When a module with dynamic parallelism (CDP) is loaded, all future
+ * kernels running under green contexts may use and share an additional set of 2 SMs.
+ *
+ * @{
+ */
+
+/*!
+ * \typedef struct CUdevResourceDesc_st* CUdevResourceDesc;
+ * An opaque descriptor handle. The descriptor encapsulates multiple created and configured resources.
+ * Created via ::cuDevResourceGenerateDesc
+ */
+typedef struct CUdevResourceDesc_st *CUdevResourceDesc;
+
+typedef enum {
+    CU_GREEN_CTX_DEFAULT_STREAM = 0x1, /**< Required. Creates a default stream to use inside the green context */
+} CUgreenCtxCreate_flags;
+
+typedef enum {
+    CU_DEV_SM_RESOURCE_SPLIT_IGNORE_SM_COSCHEDULING = 0x1,
+    CU_DEV_SM_RESOURCE_SPLIT_MAX_POTENTIAL_CLUSTER_SIZE = 0x2,
+} CUdevSmResourceSplit_flags;
+
+#define RESOURCE_ABI_VERSION 1
+#define RESOURCE_ABI_EXTERNAL_BYTES 48
+
+#define _CONCAT_INNER(x, y) x ## y
+#define _CONCAT_OUTER(x, y) _CONCAT_INNER(x, y)
+
+/*!
+ * \typedef enum CUdevResourceType
+ * Type of resource
+ */
+typedef enum {
+    CU_DEV_RESOURCE_TYPE_INVALID = 0,
+    CU_DEV_RESOURCE_TYPE_SM = 1, /**< Streaming multiprocessors related information */
+#if defined(__CUDA_API_VERSION_INTERNAL) && !defined(__CUDA_API_VERSION_INTERNAL_ODR)
+    CU_DEV_RESOURCE_TYPE_MAX,
+#endif
+} CUdevResourceType;
+
+/*!
+ * \struct CUdevSmResource
+ * Data for SM-related resources
+ */
+typedef struct CUdevSmResource_st {
+    unsigned int smCount; /**< The amount of streaming multiprocessors available in this resource. This is an output parameter only, do not write to this field. */
+} CUdevSmResource;
+
+/*!
+ * \struct CUdevResource
+ * A tagged union describing different resources identified by the type field. This structure should not be directly modified outside of the API that created it.
+ * \code
+ * struct {
+ *     CUdevResourceType type;
+ *     union {
+ *         CUdevSmResource sm;
+ *     };
+ * };
+ * \endcode
+ * - If \p type is \p CU_DEV_RESOURCE_TYPE_INVALID, this resoure is not valid and cannot be further accessed.
+ * - If \p type is \p CU_DEV_RESOURCE_TYPE_SM, the ::CUdevSmResource structure \p sm is filled in. For example,
+ * \p sm.smCount will reflect the amount of streaming multiprocessors available in this resource.
+ */
+typedef struct CUdevResource_st {
+    CUdevResourceType type; /**< Type of resource, dictates which union field was last set */
+    unsigned char _internal_padding[92];
+    union {
+        CUdevSmResource sm; /**< Resource corresponding to CU_DEV_RESOURCE_TYPE_SM \p. type. */
+        unsigned char _oversize[RESOURCE_ABI_EXTERNAL_BYTES];
+    };
+} _CONCAT_OUTER(CUdevResource_v, RESOURCE_ABI_VERSION);
+typedef _CONCAT_OUTER(CUdevResource_v, RESOURCE_ABI_VERSION) CUdevResource;
+
+#undef _CONCAT_INNER
+#undef _CONCAT_OUTER
+
+#undef ABI_PER_RESOURCE_EXTERNAL_BYTES
+#undef ABI_RESOURCE_VERSION
+
+/**
+ * \brief Creates a green context with a specified set of resources.
+ *
+ * This API creates a green context with the resources specified in the descriptor \p desc and
+ * returns it in the handle represented by \p phCtx. This API will retain the primary context on device \p dev,
+ * which will is released when the green context is destroyed. It is advised to have the primary context active
+ * before calling this API to avoid the heavy cost of triggering primary context initialization and
+ * deinitialization multiple times.
+ *
+ * The API does not set the green context current. In order to set it current, you need to explicitly set it current
+ * by first converting the green context to a CUcontext using ::cuCtxFromGreenCtx and subsequently calling
+ * ::cuCtxSetCurrent / ::cuCtxPushCurrent. It should be noted that a green context can be current to only one
+ * thread at a time. There is no internal synchronization to make API calls accessing the same green context
+ * from multiple threads work.
+ *
+ * Note: The API is not supported on 32-bit platforms.
+ *
+ * \param phCtx - Pointer for the output handle to the green context
+ * \param desc - Descriptor generated via ::cuDevResourceGenerateDesc which contains the set of resources to be used
+ * \param dev - Device on which to create the green context.
+ * \param flags - One of the supported green context creation flags. \p CU_GREEN_CTX_DEFAULT_STREAM is required.
+ *
+ * The supported flags are:
+ * - \p CU_GREEN_CTX_DEFAULT_STREAM : Creates a default stream to use inside the green context. Required.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa
+ * ::cuGreenCtxDestroy,
+ * ::cuCtxFromGreenCtx,
+ * ::cuCtxSetCurrent,
+ * ::cuCtxPushCurrent,
+ * ::cuDevResourceGenerateDesc,
+ * ::cuDevicePrimaryCtxRetain,
+ * ::cuCtxCreate,
+ * ::cuCtxCreate_v3
+ */
+CUresult CUDAAPI cuGreenCtxCreate(CUgreenCtx* phCtx, CUdevResourceDesc desc, CUdevice dev, unsigned int flags);
+
+/**
+ * \brief Destroys a green context
+ *
+ * Destroys the green context, releasing the primary context of the device that this green context was created for.
+ * Any resources provisioned for this green context (that were initially available via the resource descriptor)
+ * are released as well.
+ * \param hCtx - Green context to be destroyed
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_CONTEXT_IS_DESTROYED
+ *
+ * \sa
+ * ::cuGreenCtxCreate,
+ * ::cuCtxDestroy
+ */
+CUresult CUDAAPI cuGreenCtxDestroy(CUgreenCtx hCtx);
+
+/**
+ * \brief Converts a green context into the primary context
+ *
+ * The API converts a green context into the primary context returned in \p pContext. It is important
+ * to note that the converted context \p pContext is a normal primary context but with
+ * the resources of the specified green context \p hCtx. Once converted, it can then
+ * be used to set the context current with ::cuCtxSetCurrent or with any of the CUDA APIs
+ * that accept a CUcontext parameter.
+ *
+ * Users are expected to call this API before calling any CUDA APIs that accept a
+ * CUcontext. Failing to do so will result in the APIs returning ::CUDA_ERROR_INVALID_CONTEXT.
+ *
+ * \param pContext Returned primary context with green context resources
+ * \param hCtx Green context to convert
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuGreenCtxCreate
+ */
+CUresult CUDAAPI cuCtxFromGreenCtx(CUcontext *pContext, CUgreenCtx hCtx);
+
+/**
+ * \brief Get device resources
+ *
+ * Get the \p type resources available to the \p device.
+ * This may often be the starting point for further partitioning or configuring of resources.
+ *
+ * Note: The API is not supported on 32-bit platforms.
+ *
+ * \param device - Device to get resource for
+ * \param resource - Output pointer to a CUdevResource structure
+ * \param type - Type of resource to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_RESOURCE_TYPE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_DEVICE
+ *
+ * \sa
+ * ::cuDevResourceGenerateDesc
+ */
+CUresult CUDAAPI cuDeviceGetDevResource(CUdevice device, CUdevResource* resource, CUdevResourceType type);
+
+/**
+ * \brief Get context resources
+ *
+ * Get the \p type resources available to the context represented by \p hCtx
+ * \param hCtx - Context to get resource for
+ *
+ * Note: The API is not supported on 32-bit platforms.
+ *
+ * \param resource - Output pointer to a CUdevResource structure
+ * \param type - Type of resource to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_RESOURCE_TYPE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_CONTEXT
+ *
+ * \sa
+ * ::cuDevResourceGenerateDesc
+ */
+CUresult CUDAAPI cuCtxGetDevResource(CUcontext hCtx, CUdevResource* resource, CUdevResourceType type);
+
+/**
+ * \brief Get green context resources
+ *
+ * Get the \p type resources available to the green context represented by \p hCtx
+ * \param hCtx - Green context to get resource for
+ * \param resource - Output pointer to a CUdevResource structure
+ * \param type - Type of resource to retrieve
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_RESOURCE_TYPE,
+ * ::CUDA_ERROR_INVALID_VALUE
+ *
+ * \sa
+ * ::cuDevResourceGenerateDesc
+ */
+CUresult CUDAAPI cuGreenCtxGetDevResource(CUgreenCtx hCtx, CUdevResource* resource, CUdevResourceType type);
+
+/**
+ * \brief Splits \p CU_DEV_RESOURCE_TYPE_SM resources.
+ *
+ * Splits \p CU_DEV_RESOURCE_TYPE_SM resources into \p nbGroups, adhering to the minimum SM count specified in \p minCount
+ * and the usage flags in \p useFlags. If \p result is NULL, the API simulates a split and provides the amount of groups that
+ * would be created in \p nbGroups. Otherwise, \p nbGroups must point to the amount of elements in \p result and on return,
+ * the API will overwrite \p nbGroups with the amount actually created. The groups are written to the array in \p result.
+ * \p nbGroups can be less than the total amount if a smaller number of groups is needed.
+ *
+ * This API is used to spatially partition the input resource. The input resource needs to come from one of
+ * ::cuDeviceGetDevResource, ::cuCtxGetDevResource, or ::cuGreenCtxGetDevResource.
+ * A limitation of the API is that the output results cannot be split again without
+ * first creating a descriptor and a green context with that descriptor.
+ *
+ * When creating the groups, the API will take into account the performance and functional characteristics of the
+ * input resource, and guarantee a split that will create a disjoint set of symmetrical partitions. This may lead to fewer groups created
+ * than purely dividing the total SM count by the \p minCount due to cluster requirements or
+ * alignment and granularity requirements for the minCount.
+ *
+ * The \p remainder set does not have the same functional or performance guarantees as the groups in \p result.
+ * Its use should be carefully planned and future partitions of the \p remainder set are discouraged.
+ *
+ * The following flags are supported:
+ * - \p CU_DEV_SM_RESOURCE_SPLIT_IGNORE_SM_COSCHEDULING : Lower the minimum SM count and alignment, and treat each SM independent of its hierarchy.
+ *  This allows more fine grained partitions but at the cost of advanced features (such as large clusters on compute capability 9.0+).
+ * - \p CU_DEV_SM_RESOURCE_SPLIT_MAX_POTENTIAL_CLUSTER_SIZE : Compute Capability 9.0+ only. Attempt to create groups that may allow
+ *  for maximally sized thread clusters. This can be queried post green context creation using ::cuOccupancyMaxPotentialClusterSize.
+ *
+ * A successful API call must either have:
+ * - A valid array of \p result pointers of size passed in \p nbGroups, with \p input of type \p CU_DEV_RESOURCE_TYPE_SM.
+ * Value of \p minCount must be between 0 and the SM count specified in \p input. \p remaining may be NULL.
+ * - NULL passed in for \p result, with a valid integer pointer in \p nbGroups and \p input of type \p CU_DEV_RESOURCE_TYPE_SM.
+ * Value of \p minCount must be between 0 and the SM count specified in \p input. \p remaining may be NULL.
+ * This queries the number of groups that would be created by the API.
+ *
+ * Note: The API is not supported on 32-bit platforms.
+ *
+ * \param result - Output array of \p CUdevResource resources. Can be NULL to query the number of groups.
+ * \param nbGroups - This is a pointer, specifying the number of groups that would be or should be created as described below.
+ * \param input - Input SM resource to be split. Must be a valid \p CU_DEV_RESOURCE_TYPE_SM resource.
+ * \param remaining - If the input resource cannot be cleanly split among \p nbGroups, the remaining is placed in here.
+ * Can be ommitted (NULL) if the user does not need the remaining set.
+ * \param useFlags - Flags specifying how these partitions are used or which constraints to abide by when splitting the input. Zero is valid for default behavior.
+ * \param minCount - Minimum number of SMs required
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_RESOURCE_TYPE,
+ * ::CUDA_ERROR_INVALID_RESOURCE_CONFIGURATION
+ *
+ * \sa
+ * ::cuGreenCtxGetDevResource,
+ * ::cuCtxGetDevResource,
+ * ::cuDeviceGetDevResource
+ */
+CUresult CUDAAPI cuDevSmResourceSplitByCount(
+    CUdevResource* result, unsigned int* nbGroups, const CUdevResource* input, CUdevResource* remaining, unsigned int useFlags, unsigned int minCount);
+
+/**
+ * \brief Generate a resource descriptor
+ *
+ * Generates a single resource descriptor with the set of resources specified in \p resources.
+ * The generated resource descriptor is necessary for the creation of green contexts via the ::cuGreenCtxCreate API.
+ * Resources of the same type can be passed in, provided they meet the requirements as noted below.
+ *
+ * A successful API call must have:
+ * - A valid output pointer for the \p phDesc descriptor as well as a valid array of \p resources pointers,
+ * with the array size passed in \p nbResources.
+ * If multiple resources are provided in \p resources, the device they came from must be the same,
+ * otherwise CUDA_ERROR_INVALID_RESOURCE_CONFIGURATION is returned.
+ * If multiple resources are provided in \p resources and they are of type ::CU_DEV_RESOURCE_TYPE_SM,
+ * they must be outputs (whether \p result or \p remaining) from the same split API instance,
+ * otherwise CUDA_ERROR_INVALID_RESOURCE_CONFIGURATION is returned.
+ *
+ * Note: The API is not supported on 32-bit platforms.
+ *
+ * \param phDesc - Output descriptor
+ * \param resources - Array of resources to be included in the descriptor
+ * \param nbResources - Number of resources passed in \p resources
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_RESOURCE_TYPE,
+ * ::CUDA_ERROR_INVALID_RESOURCE_CONFIGURATION
+ *
+ * \sa
+ * ::cuDevSmResourceSplitByCount
+ */
+CUresult CUDAAPI cuDevResourceGenerateDesc(CUdevResourceDesc *phDesc, CUdevResource *resources, unsigned int nbResources);
+
+/**
+ * \brief Records an event.
+ *
+ * Captures in \p hEvent all the activities of the green context of \p hCtx
+ * at the time of this call. \p hEvent and \p hCtx must be from the same
+ * primary context otherwise ::CUDA_ERROR_INVALID_HANDLE is returned.
+ * Calls such as ::cuEventQuery() or ::cuGreenCtxWaitEvent() will
+ * then examine or wait for completion of the work that was captured. Uses of
+ * \p hCtx after this call do not modify \p hEvent.
+ *
+ * \note The API will return ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED if the
+ * specified green context \p hCtx has a stream in the capture mode. In such
+ * a case, the call will invalidate all the conflicting captures.
+ *
+ * \param hCtx - Green context to record event for
+ * \param hEvent  - Event to record
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED
+ *
+ * \sa
+ * ::cuGreenCtxWaitEvent,
+ * ::cuEventRecord,
+ * ::cuCtxRecordEvent,
+ * ::cuCtxWaitEvent
+ */
+CUresult CUDAAPI cuGreenCtxRecordEvent(CUgreenCtx hCtx, CUevent hEvent);
+
+/**
+ * \brief Make a green context wait on an event
+ *
+ * Makes all future work submitted to green context \p hCtx wait for all work
+ * captured in \p hEvent. The synchronization will be performed on the device
+ * and will not block the calling CPU thread. See ::cuGreenCtxRecordEvent()
+ * or ::cuEventRecord(), for details on what is captured by an event.
+ *
+ * \note \p hEvent may be from a different context or device than \p hCtx.
+ *
+ * \note The API will return ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED and
+ * invalidate the capture if the specified event \p hEvent is part of an
+ * ongoing capture sequence or if the specified green context \p hCtx has
+ * a stream in the capture mode.
+ *
+ * \param hCtx    - Green context to wait
+ * \param hEvent  - Event to wait on
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED
+ *
+ * \sa
+ * ::cuGreenCtxRecordEvent,
+ * ::cuStreamWaitEvent
+ * ::cuCtxRecordEvent,
+ * ::cuCtxWaitEvent
+*/
+CUresult CUDAAPI cuGreenCtxWaitEvent(CUgreenCtx hCtx, CUevent hEvent);
+
+/**
+ * \brief Query the green context associated with a stream
+ *
+ * Returns the CUDA green context that the stream is associated with, or NULL if the stream
+ * is not associated with any green context.
+ *
+ * The stream handle \p hStream can refer to any of the following:
+ * <ul>
+ *   <li>
+ *   a stream created via any of the CUDA driver APIs such as ::cuStreamCreate, ::cuStreamCreateWithPriority
+ *   and ::cuGreenCtxStreamCreate, or their runtime API equivalents such as
+ *   ::cudaStreamCreate, ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority.
+ *   If during stream creation the context that was active in the calling thread was obtained
+ *   with cuCtxFromGreenCtx, that green context is returned in \p phCtx.
+ *   Otherwise, \p *phCtx is set to NULL instead.
+ *   </li>
+ *   <li>
+ *   special stream such as the NULL stream or ::CU_STREAM_LEGACY.
+ *   In that case if context that is active in the calling thread was obtained
+ *   with cuCtxFromGreenCtx, that green context is returned.
+ *   Otherwise, \p *phCtx is set to NULL instead.
+ *   </li>
+ * </ul>
+ * Passing an invalid handle will result in undefined behavior.
+ *
+ * \param hStream - Handle to the stream to be queried
+ * \param phCtx   - Returned green context associated with the stream
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * \notefnerr
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuStreamCreate,
+ * ::cuStreamCreateWithPriority,
+ * ::cuStreamGetCtx_v2,
+ * ::cuGreenCtxStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamCreate,
+ * ::cudaStreamCreateWithFlags
+ */
+CUresult CUDAAPI cuStreamGetGreenCtx(CUstream hStream, CUgreenCtx *phCtx);
+
+/**
+ * \brief Create a stream for use in the green context
+ *
+ * Creates a stream for use in the specified green context \p greenCtx and returns a handle in \p phStream.
+ * The stream can be destroyed by calling ::cuStreamDestroy(). Note that the API ignores the context that
+ * is current to the calling thread and creates a stream in the specified green context \p greenCtx.
+ *
+ * The supported values for \p flags are:
+ * - ::CU_STREAM_NON_BLOCKING: This must be specified. It indicates that work running in the created
+ *   stream may run concurrently with work in the default stream, and that
+ *   the created stream should perform no implicit synchronization with the default stream.
+ *
+ * Specifying \p priority affects the scheduling priority of work in the stream. Priorities provide a
+ * hint to preferentially run work with higher priority when possible, but do not preempt
+ * already-running work or provide any other functional guarantee on execution order.
+ * \p priority follows a convention where lower numbers represent higher priorities.
+ * '0' represents default priority. The range of meaningful numerical priorities can
+ * be queried using ::cuCtxGetStreamPriorityRange. If the specified priority is
+ * outside the numerical range returned by ::cuCtxGetStreamPriorityRange,
+ * it will automatically be clamped to the lowest or the highest number in the range.
+ *
+ * \param phStream - Returned newly created stream
+ * \param greenCtx - Green context for which to create the stream for
+ * \param flags    - Flags for stream creation. \p CU_STREAM_NON_BLOCKING must be specified.
+ * \param priority - Stream priority. Lower numbers represent higher priorities.
+ *                   See ::cuCtxGetStreamPriorityRange for more information about
+ *                   meaningful stream priorities that can be passed.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \note In the current implementation, only compute kernels launched in
+ * priority streams are affected by the stream's priority. Stream priorities have
+ * no effect on host-to-device and device-to-host memory operations.
+ *
+ * \sa ::cuStreamDestroy,
+ * ::cuGreenCtxCreate
+ * ::cuStreamCreate,
+ * ::cuStreamGetPriority,
+ * ::cuCtxGetStreamPriorityRange,
+ * ::cuStreamGetFlags,
+ * ::cuStreamGetDevice
+ * ::cuStreamWaitEvent,
+ * ::cuStreamQuery,
+ * ::cuStreamSynchronize,
+ * ::cuStreamAddCallback,
+ * ::cudaStreamCreateWithPriority
+ */
+CUresult CUDAAPI cuGreenCtxStreamCreate(CUstream* phStream, CUgreenCtx greenCtx, unsigned int flags, int priority);
+
+/** @} */
+
+/*
+** *************** END CUDA_GREEN_CONTEXTS *****************
+*/
+
+/**
+ * CUDA API versioning support
+ */
+#if defined(__CUDA_API_VERSION_INTERNAL)
+    #undef cuMemHostRegister
+    #undef cuGraphicsResourceSetMapFlags
+    #undef cuLinkCreate
+    #undef cuLinkAddData
+    #undef cuLinkAddFile
+    #undef cuDeviceTotalMem
+    #undef cuCtxCreate
+    #undef cuModuleGetGlobal
+    #undef cuMemGetInfo
+    #undef cuMemAlloc
+    #undef cuMemAllocPitch
+    #undef cuMemFree
+    #undef cuMemGetAddressRange
+    #undef cuMemAllocHost
+    #undef cuMemHostGetDevicePointer
+    #undef cuMemcpyHtoD
+    #undef cuMemcpyDtoH
+    #undef cuMemcpyDtoD
+    #undef cuMemcpyDtoA
+    #undef cuMemcpyAtoD
+    #undef cuMemcpyHtoA
+    #undef cuMemcpyAtoH
+    #undef cuMemcpyAtoA
+    #undef cuMemcpyHtoAAsync
+    #undef cuMemcpyAtoHAsync
+    #undef cuMemcpy2D
+    #undef cuMemcpy2DUnaligned
+    #undef cuMemcpy3D
+    #undef cuMemcpyHtoDAsync
+    #undef cuMemcpyDtoHAsync
+    #undef cuMemcpyDtoDAsync
+    #undef cuMemcpy2DAsync
+    #undef cuMemcpy3DAsync
+    #undef cuMemcpyBatchAsync
+    #undef cuMemcpy3DBatchAsync
+    #undef cuMemsetD8
+    #undef cuMemsetD16
+    #undef cuMemsetD32
+    #undef cuMemsetD2D8
+    #undef cuMemsetD2D16
+    #undef cuMemsetD2D32
+    #undef cuArrayCreate
+    #undef cuArrayGetDescriptor
+    #undef cuArray3DCreate
+    #undef cuArray3DGetDescriptor
+    #undef cuTexRefSetAddress
+    #undef cuTexRefSetAddress2D
+    #undef cuTexRefGetAddress
+    #undef cuGraphicsResourceGetMappedPointer
+    #undef cuCtxDestroy
+    #undef cuCtxPopCurrent
+    #undef cuCtxPushCurrent
+    #undef cuStreamDestroy
+    #undef cuEventDestroy
+    #undef cuMemcpy
+    #undef cuMemcpyAsync
+    #undef cuMemcpyPeer
+    #undef cuMemcpyPeerAsync
+    #undef cuMemcpy3DPeer
+    #undef cuMemcpy3DPeerAsync
+    #undef cuMemsetD8Async
+    #undef cuMemsetD16Async
+    #undef cuMemsetD32Async
+    #undef cuMemsetD2D8Async
+    #undef cuMemsetD2D16Async
+    #undef cuMemsetD2D32Async
+    #undef cuStreamGetPriority
+    #undef cuStreamGetId
+    #undef cuStreamGetFlags
+    #undef cuStreamGetDevice
+    #undef cuStreamGetCtx
+    #undef cuStreamWaitEvent
+    #undef cuStreamAddCallback
+    #undef cuStreamAttachMemAsync
+    #undef cuStreamQuery
+    #undef cuStreamSynchronize
+    #undef cuEventRecord
+    #undef cuEventRecordWithFlags
+    #undef cuLaunchKernel
+    #undef cuLaunchKernelEx
+    #undef cuLaunchHostFunc
+    #undef cuGraphicsMapResources
+    #undef cuGraphicsUnmapResources
+    #undef cuStreamWriteValue32
+    #undef cuStreamWaitValue32
+    #undef cuStreamWriteValue64
+    #undef cuStreamWaitValue64
+    #undef cuStreamBatchMemOp
+    #undef cuStreamWriteValue32_v2
+    #undef cuStreamWaitValue32_v2
+    #undef cuStreamWriteValue64_v2
+    #undef cuStreamWaitValue64_v2
+    #undef cuStreamBatchMemOp_v2
+    #undef cuMemPrefetchAsync
+    #undef cuMemPrefetchAsync_v2
+    #undef cuLaunchCooperativeKernel
+    #undef cuSignalExternalSemaphoresAsync
+    #undef cuWaitExternalSemaphoresAsync
+    #undef cuStreamBeginCapture
+    #undef cuStreamBeginCaptureToGraph
+    #undef cuStreamEndCapture
+    #undef cuStreamIsCapturing
+    #undef cuStreamGetCaptureInfo
+    #undef cuStreamGetCaptureInfo_v2
+    #undef cuStreamGetCaptureInfo_v3
+    #undef cuGraphInstantiateWithParams
+    #undef cuGraphExecUpdate
+    #undef cuGraphUpload
+    #undef cuGraphLaunch
+    #undef cuDevicePrimaryCtxRelease
+    #undef cuDevicePrimaryCtxReset
+    #undef cuDevicePrimaryCtxSetFlags
+    #undef cuIpcOpenMemHandle
+    #undef cuStreamCopyAttributes
+    #undef cuStreamSetAttribute
+    #undef cuStreamGetAttribute
+    #undef cuGraphInstantiate
+    #undef cuGraphAddKernelNode
+    #undef cuGraphKernelNodeGetParams
+    #undef cuGraphKernelNodeSetParams
+    #undef cuGraphExecKernelNodeSetParams
+    #undef cuMemMapArrayAsync
+    #undef cuMemFreeAsync 
+    #undef cuMemAllocAsync 
+    #undef cuMemAllocFromPoolAsync 
+    #undef cuStreamUpdateCaptureDependencies
+    #undef cuStreamUpdateCaptureDependencies_v2
+    #undef cuGetProcAddress
+    #undef cuStreamGetCtx_v2
+    #undef cuMemBatchDecompressAsync
+
+    CUresult CUDAAPI cuMemHostRegister(void *p, size_t bytesize, unsigned int Flags);
+    CUresult CUDAAPI cuGraphicsResourceSetMapFlags(CUgraphicsResource resource, unsigned int flags);
+    CUresult CUDAAPI cuLinkCreate(unsigned int numOptions, CUjit_option *options, void **optionValues, CUlinkState *stateOut);
+    CUresult CUDAAPI cuLinkAddData(CUlinkState state, CUjitInputType type, void *data, size_t size, const char *name,
+        unsigned int numOptions, CUjit_option *options, void **optionValues);
+    CUresult CUDAAPI cuLinkAddFile(CUlinkState state, CUjitInputType type, const char *path,
+        unsigned int numOptions, CUjit_option *options, void **optionValues);
+    CUresult CUDAAPI cuTexRefSetAddress2D_v2(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR *desc, CUdeviceptr dptr, size_t Pitch);
+
+    typedef unsigned int CUdeviceptr_v1;
+
+    typedef struct CUDA_MEMCPY2D_v1_st
+    {
+        unsigned int srcXInBytes;   /**< Source X in bytes */
+        unsigned int srcY;          /**< Source Y */
+        CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */
+        const void *srcHost;        /**< Source host pointer */
+        CUdeviceptr_v1 srcDevice;   /**< Source device pointer */
+        CUarray srcArray;           /**< Source array reference */
+        unsigned int srcPitch;      /**< Source pitch (ignored when src is array) */
+
+        unsigned int dstXInBytes;   /**< Destination X in bytes */
+        unsigned int dstY;          /**< Destination Y */
+        CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */
+        void *dstHost;              /**< Destination host pointer */
+        CUdeviceptr_v1 dstDevice;   /**< Destination device pointer */
+        CUarray dstArray;           /**< Destination array reference */
+        unsigned int dstPitch;      /**< Destination pitch (ignored when dst is array) */
+
+        unsigned int WidthInBytes;  /**< Width of 2D memory copy in bytes */
+        unsigned int Height;        /**< Height of 2D memory copy */
+    } CUDA_MEMCPY2D_v1;
+
+    typedef struct CUDA_MEMCPY3D_v1_st
+    {
+        unsigned int srcXInBytes;   /**< Source X in bytes */
+        unsigned int srcY;          /**< Source Y */
+        unsigned int srcZ;          /**< Source Z */
+        unsigned int srcLOD;        /**< Source LOD */
+        CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */
+        const void *srcHost;        /**< Source host pointer */
+        CUdeviceptr_v1 srcDevice;   /**< Source device pointer */
+        CUarray srcArray;           /**< Source array reference */
+        void *reserved0;            /**< Must be NULL */
+        unsigned int srcPitch;      /**< Source pitch (ignored when src is array) */
+        unsigned int srcHeight;     /**< Source height (ignored when src is array; may be 0 if Depth==1) */
+
+        unsigned int dstXInBytes;   /**< Destination X in bytes */
+        unsigned int dstY;          /**< Destination Y */
+        unsigned int dstZ;          /**< Destination Z */
+        unsigned int dstLOD;        /**< Destination LOD */
+        CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */
+        void *dstHost;              /**< Destination host pointer */
+        CUdeviceptr_v1 dstDevice;   /**< Destination device pointer */
+        CUarray dstArray;           /**< Destination array reference */
+        void *reserved1;            /**< Must be NULL */
+        unsigned int dstPitch;      /**< Destination pitch (ignored when dst is array) */
+        unsigned int dstHeight;     /**< Destination height (ignored when dst is array; may be 0 if Depth==1) */
+
+        unsigned int WidthInBytes;  /**< Width of 3D memory copy in bytes */
+        unsigned int Height;        /**< Height of 3D memory copy */
+        unsigned int Depth;         /**< Depth of 3D memory copy */
+    } CUDA_MEMCPY3D_v1;
+
+    typedef struct CUDA_ARRAY_DESCRIPTOR_v1_st
+    {
+        unsigned int Width;         /**< Width of array */
+        unsigned int Height;        /**< Height of array */
+
+        CUarray_format Format;      /**< Array format */
+        unsigned int NumChannels;   /**< Channels per array element */
+    } CUDA_ARRAY_DESCRIPTOR_v1;
+
+    typedef struct CUDA_ARRAY3D_DESCRIPTOR_v1_st
+    {
+        unsigned int Width;         /**< Width of 3D array */
+        unsigned int Height;        /**< Height of 3D array */
+        unsigned int Depth;         /**< Depth of 3D array */
+
+        CUarray_format Format;      /**< Array format */
+        unsigned int NumChannels;   /**< Channels per array element */
+        unsigned int Flags;         /**< Flags */
+    } CUDA_ARRAY3D_DESCRIPTOR_v1;
+
+    CUresult CUDAAPI cuDeviceTotalMem(unsigned int *bytes, CUdevice dev);
+    CUresult CUDAAPI cuCtxCreate(CUcontext *pctx, unsigned int flags, CUdevice dev);
+    CUresult CUDAAPI cuModuleGetGlobal(CUdeviceptr_v1 *dptr, unsigned int *bytes, CUmodule hmod, const char *name);
+    CUresult CUDAAPI cuMemGetInfo(unsigned int *free, unsigned int *total);
+    CUresult CUDAAPI cuMemAlloc(CUdeviceptr_v1 *dptr, unsigned int bytesize);
+    CUresult CUDAAPI cuMemAllocPitch(CUdeviceptr_v1 *dptr, unsigned int *pPitch, unsigned int WidthInBytes, unsigned int Height, unsigned int ElementSizeBytes);
+    CUresult CUDAAPI cuMemFree(CUdeviceptr_v1 dptr);
+    CUresult CUDAAPI cuMemGetAddressRange(CUdeviceptr_v1 *pbase, unsigned int *psize, CUdeviceptr_v1 dptr);
+    CUresult CUDAAPI cuMemAllocHost(void **pp, unsigned int bytesize);
+    CUresult CUDAAPI cuMemHostGetDevicePointer(CUdeviceptr_v1 *pdptr, void *p, unsigned int Flags);
+    CUresult CUDAAPI cuMemcpyHtoD(CUdeviceptr_v1 dstDevice, const void *srcHost, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyDtoH(void *dstHost, CUdeviceptr_v1 srcDevice, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyDtoD(CUdeviceptr_v1 dstDevice, CUdeviceptr_v1 srcDevice, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyDtoA(CUarray dstArray, unsigned int dstOffset, CUdeviceptr_v1 srcDevice, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyAtoD(CUdeviceptr_v1 dstDevice, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyHtoA(CUarray dstArray, unsigned int dstOffset, const void *srcHost, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyAtoH(void *dstHost, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyAtoA(CUarray dstArray, unsigned int dstOffset, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount);
+    CUresult CUDAAPI cuMemcpyHtoAAsync(CUarray dstArray, unsigned int dstOffset, const void *srcHost, unsigned int ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyAtoHAsync(void *dstHost, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy2D(const CUDA_MEMCPY2D_v1 *pCopy);
+    CUresult CUDAAPI cuMemcpy2DUnaligned(const CUDA_MEMCPY2D_v1 *pCopy);
+    CUresult CUDAAPI cuMemcpy3D(const CUDA_MEMCPY3D_v1 *pCopy);
+    CUresult CUDAAPI cuMemcpyHtoDAsync(CUdeviceptr_v1 dstDevice, const void *srcHost, unsigned int ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyDtoHAsync(void *dstHost, CUdeviceptr_v1 srcDevice, unsigned int ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyDtoDAsync(CUdeviceptr_v1 dstDevice, CUdeviceptr_v1 srcDevice, unsigned int ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy2DAsync(const CUDA_MEMCPY2D_v1 *pCopy, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy3DAsync(const CUDA_MEMCPY3D_v1 *pCopy, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD8(CUdeviceptr_v1 dstDevice, unsigned char uc, unsigned int N);
+    CUresult CUDAAPI cuMemsetD16(CUdeviceptr_v1 dstDevice, unsigned short us, unsigned int N);
+    CUresult CUDAAPI cuMemsetD32(CUdeviceptr_v1 dstDevice, unsigned int ui, unsigned int N);
+    CUresult CUDAAPI cuMemsetD2D8(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned char uc, unsigned int Width, unsigned int Height);
+    CUresult CUDAAPI cuMemsetD2D16(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned short us, unsigned int Width, unsigned int Height);
+    CUresult CUDAAPI cuMemsetD2D32(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned int ui, unsigned int Width, unsigned int Height);
+    CUresult CUDAAPI cuArrayCreate(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTOR_v1 *pAllocateArray);
+    CUresult CUDAAPI cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR_v1 *pArrayDescriptor, CUarray hArray);
+    CUresult CUDAAPI cuArray3DCreate(CUarray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR_v1 *pAllocateArray);
+    CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR_v1 *pArrayDescriptor, CUarray hArray);
+    CUresult CUDAAPI cuTexRefSetAddress(unsigned int *ByteOffset, CUtexref hTexRef, CUdeviceptr_v1 dptr, unsigned int bytes);
+    CUresult CUDAAPI cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR_v1 *desc, CUdeviceptr_v1 dptr, unsigned int Pitch);
+    CUresult CUDAAPI cuTexRefGetAddress(CUdeviceptr_v1 *pdptr, CUtexref hTexRef);
+    CUresult CUDAAPI cuGraphicsResourceGetMappedPointer(CUdeviceptr_v1 *pDevPtr, unsigned int *pSize, CUgraphicsResource resource);
+
+    CUresult CUDAAPI cuCtxDestroy(CUcontext ctx);
+    CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx);
+    CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx);
+    CUresult CUDAAPI cuStreamDestroy(CUstream hStream);
+    CUresult CUDAAPI cuEventDestroy(CUevent hEvent);
+    CUresult CUDAAPI cuDevicePrimaryCtxRelease(CUdevice dev);
+    CUresult CUDAAPI cuDevicePrimaryCtxReset(CUdevice dev);
+    CUresult CUDAAPI cuDevicePrimaryCtxSetFlags(CUdevice dev, unsigned int flags);
+
+    CUresult CUDAAPI cuMemcpyHtoD_v2(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyDtoH_v2(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyDtoD_v2(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyDtoA_v2(CUarray dstArray, size_t dstOffset, CUdeviceptr srcDevice, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyAtoD_v2(CUdeviceptr dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyHtoA_v2(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyAtoH_v2(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyAtoA_v2(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyHtoAAsync_v2(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyAtoHAsync_v2(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy2D_v2(const CUDA_MEMCPY2D *pCopy);
+    CUresult CUDAAPI cuMemcpy2DUnaligned_v2(const CUDA_MEMCPY2D *pCopy);
+    CUresult CUDAAPI cuMemcpy3D_v2(const CUDA_MEMCPY3D *pCopy);
+    CUresult CUDAAPI cuMemcpyHtoDAsync_v2(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyDtoHAsync_v2(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyDtoDAsync_v2(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy2DAsync_v2(const CUDA_MEMCPY2D *pCopy, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy3DAsync_v2(const CUDA_MEMCPY3D *pCopy, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD8_v2(CUdeviceptr dstDevice, unsigned char uc, size_t N);
+    CUresult CUDAAPI cuMemsetD16_v2(CUdeviceptr dstDevice, unsigned short us, size_t N);
+    CUresult CUDAAPI cuMemsetD32_v2(CUdeviceptr dstDevice, unsigned int ui, size_t N);
+    CUresult CUDAAPI cuMemsetD2D8_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height);
+    CUresult CUDAAPI cuMemsetD2D16_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height);
+    CUresult CUDAAPI cuMemsetD2D32_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height);
+    CUresult CUDAAPI cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount);
+    CUresult CUDAAPI cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER *pCopy);
+    CUresult CUDAAPI cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER *pCopy, CUstream hStream);
+    CUresult CUDAAPI cuMemcpyBatchAsync(CUdeviceptr *dsts, CUdeviceptr *srcs, size_t *sizes, size_t count,
+                                        CUmemcpyAttributes *attrs, size_t *attrsIdxs, size_t numAttrs,
+                                        size_t *failIdx, CUstream hStream);
+    CUresult CUDAAPI cuMemcpy3DBatchAsync(size_t numOps, CUDA_MEMCPY3D_BATCH_OP *opList,
+                                          size_t *failIdx, unsigned long long flags, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc, size_t N, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD16Async(CUdeviceptr dstDevice, unsigned short us, size_t N, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD32Async(CUdeviceptr dstDevice, unsigned int ui, size_t N, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD2D8Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD2D16Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream);
+    CUresult CUDAAPI cuMemsetD2D32Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream);
+
+    CUresult CUDAAPI cuStreamGetPriority(CUstream hStream, int *priority);
+    CUresult CUDAAPI cuStreamGetId(CUstream hStream, unsigned long long *streamId);
+    CUresult CUDAAPI cuStreamGetFlags(CUstream hStream, unsigned int *flags);
+    CUresult CUDAAPI cuStreamGetDevice(CUstream hStream, CUdevice *device);
+    CUresult CUDAAPI cuStreamGetCtx(CUstream hStream, CUcontext *pctx);
+    CUresult CUDAAPI cuStreamGetCtx_v2(CUstream hStream, CUcontext *pCtx, CUgreenCtx *pGreenCtx);
+    CUresult CUDAAPI cuStreamWaitEvent(CUstream hStream, CUevent hEvent, unsigned int Flags);
+    CUresult CUDAAPI cuStreamAddCallback(CUstream hStream, CUstreamCallback callback, void *userData, unsigned int flags);
+    CUresult CUDAAPI cuStreamAttachMemAsync(CUstream hStream, CUdeviceptr dptr, size_t length, unsigned int flags);
+    CUresult CUDAAPI cuStreamQuery(CUstream hStream);
+    CUresult CUDAAPI cuStreamSynchronize(CUstream hStream);
+    CUresult CUDAAPI cuEventRecord(CUevent hEvent, CUstream hStream);
+    CUresult CUDAAPI cuEventRecordWithFlags(CUevent hEvent, CUstream hStream, unsigned int flags);
+    CUresult CUDAAPI cuLaunchKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams, void **extra);
+    CUresult CUDAAPI cuLaunchKernelEx(const CUlaunchConfig *config, CUfunction f, void **kernelParams, void **extra);
+    CUresult CUDAAPI cuLaunchHostFunc(CUstream hStream, CUhostFn fn, void *userData);
+    CUresult CUDAAPI cuGraphicsMapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+    CUresult CUDAAPI cuGraphicsUnmapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+    CUresult CUDAAPI cuStreamWriteValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWaitValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWriteValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWaitValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamBatchMemOp(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+
+    CUresult CUDAAPI cuStreamWriteValue32_ptsz(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWaitValue32_ptsz(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWriteValue64_ptsz(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWaitValue64_ptsz(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamBatchMemOp_ptsz(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+
+    CUresult CUDAAPI cuStreamWriteValue32_v2(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWaitValue32_v2(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWriteValue64_v2(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamWaitValue64_v2(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags);
+    CUresult CUDAAPI cuStreamBatchMemOp_v2(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+    CUresult CUDAAPI cuMemPrefetchAsync(CUdeviceptr devPtr, size_t count, CUdevice dstDevice, CUstream hStream);
+    CUresult CUDAAPI cuMemPrefetchAsync_v2(CUdeviceptr devPtr, size_t count, CUmemLocation location, unsigned int flags, CUstream hStream);
+    CUresult CUDAAPI cuLaunchCooperativeKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams);
+    CUresult CUDAAPI cuSignalExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream);
+    CUresult CUDAAPI cuWaitExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream);
+    CUresult CUDAAPI cuStreamBeginCapture(CUstream hStream);
+    CUresult CUDAAPI cuStreamBeginCapture_ptsz(CUstream hStream);
+    CUresult CUDAAPI cuStreamBeginCapture_v2(CUstream hStream, CUstreamCaptureMode mode);
+    CUresult CUDAAPI cuStreamBeginCaptureToGraph(CUstream hStream, CUgraph hGraph, const CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, CUstreamCaptureMode mode);
+    CUresult CUDAAPI cuStreamEndCapture(CUstream hStream, CUgraph *phGraph);
+    CUresult CUDAAPI cuStreamIsCapturing(CUstream hStream, CUstreamCaptureStatus *captureStatus);
+    CUresult CUDAAPI cuStreamGetCaptureInfo(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out);
+    CUresult CUDAAPI cuStreamGetCaptureInfo_ptsz(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out);
+    CUresult CUDAAPI cuStreamGetCaptureInfo_v2(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, size_t *numDependencies_out);
+    CUresult CUDAAPI cuStreamGetCaptureInfo_v3(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, const CUgraphEdgeData **edgeData_out, size_t *numDependencies_out);
+    CUresult CUDAAPI cuGraphAddKernelNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+    CUresult CUDAAPI cuGraphKernelNodeGetParams(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+    CUresult CUDAAPI cuGraphKernelNodeSetParams(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+    CUresult CUDAAPI cuGraphExecKernelNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+    CUresult CUDAAPI cuGraphInstantiateWithParams(CUgraphExec *phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams);
+    CUresult CUDAAPI cuGraphExecUpdate(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphNode *hErrorNode_out, CUgraphExecUpdateResult *updateResult_out);
+    CUresult CUDAAPI cuGraphUpload(CUgraphExec hGraph, CUstream hStream);
+    CUresult CUDAAPI cuGraphLaunch(CUgraphExec hGraph, CUstream hStream);
+    CUresult CUDAAPI cuStreamCopyAttributes(CUstream dstStream, CUstream srcStream);
+    CUresult CUDAAPI cuStreamGetAttribute(CUstream hStream, CUstreamAttrID attr, CUstreamAttrValue *value);
+    CUresult CUDAAPI cuStreamSetAttribute(CUstream hStream, CUstreamAttrID attr, const CUstreamAttrValue *param);
+
+    CUresult CUDAAPI cuIpcOpenMemHandle(CUdeviceptr *pdptr, CUipcMemHandle handle, unsigned int Flags);
+    CUresult CUDAAPI cuGraphInstantiate(CUgraphExec *phGraphExec, CUgraph hGraph, CUgraphNode *phErrorNode, char *logBuffer, size_t bufferSize);
+    CUresult CUDAAPI cuGraphInstantiate_v2(CUgraphExec *phGraphExec, CUgraph hGraph, CUgraphNode *phErrorNode, char *logBuffer, size_t bufferSize);
+
+    CUresult CUDAAPI cuMemMapArrayAsync(CUarrayMapInfo *mapInfoList, unsigned int count, CUstream hStream);
+
+    CUresult CUDAAPI cuMemFreeAsync(CUdeviceptr dptr, CUstream hStream);
+    CUresult CUDAAPI cuMemAllocAsync(CUdeviceptr *dptr, size_t bytesize, CUstream hStream);
+    CUresult CUDAAPI cuMemAllocFromPoolAsync(CUdeviceptr *dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream);
+
+    CUresult CUDAAPI cuStreamUpdateCaptureDependencies(CUstream hStream, CUgraphNode *dependencies, size_t numDependencies, unsigned int flags);
+    CUresult CUDAAPI cuStreamUpdateCaptureDependencies_v2(CUstream hStream, CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, unsigned int flags);
+
+    CUresult CUDAAPI cuMemBatchDecompressAsync(
+        CUmemDecompressParams *paramsArray,
+        size_t count,
+        unsigned int flags,
+        size_t *errorIndex,
+        CUstream stream
+    );
+
+    CUresult CUDAAPI cuGetProcAddress(const char *symbol, void **pfn, int cudaVersion, cuuint64_t flags);
+
+#elif defined(__CUDA_API_PER_THREAD_DEFAULT_STREAM)
+static inline CUresult cuGetProcAddress_v2_ptsz(const char *symbol, void **funcPtr, int driverVersion, cuuint64_t flags, CUdriverProcAddressQueryResult *symbolStatus) {
+    const int procAddressMask = (CU_GET_PROC_ADDRESS_LEGACY_STREAM|
+                                 CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM);
+    if ((flags & procAddressMask) == 0) {
+        flags |= CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM;
+    }
+    return cuGetProcAddress_v2(symbol, funcPtr, driverVersion, flags, symbolStatus); 
+}
+#define cuGetProcAddress_v2 cuGetProcAddress_v2_ptsz
+#endif
+
+/**
+ * \defgroup CUDA_CHECKPOINT CUDA Checkpointing
+ *
+ * ___MANBRIEF___ CUDA checkpoint and restore functionality of the low-level
+ * CUDA driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This sections describes the checkpoint and restore functions of the low-level
+ * CUDA driver application programming interface.
+ *
+ * The CUDA checkpoint and restore API's provide a way to save and restore GPU
+ * state for full process checkpoints when used with CPU side process
+ * checkpointing solutions. They can also be used to pause GPU work and suspend
+ * a CUDA process to allow other applications to make use of GPU resources.
+ *
+ * Checkpoint and restore capabilities are currently restricted to Linux.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the restore thread ID for a CUDA process
+ *
+ * Returns in \p *tid the thread ID of the CUDA restore thread for the process
+ * specified by \p pid.
+ *
+ * \param pid - The process ID of the CUDA process
+ * \param tid - Returned restore thread ID
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ */
+CUresult CUDAAPI cuCheckpointProcessGetRestoreThreadId(int pid, int *tid);
+
+/**
+ * \brief Returns the process state of a CUDA process
+ *
+ * Returns in \p *state the current state of the CUDA process specified by \p pid.
+ *
+ * \param pid - The process ID of the CUDA process
+ * \param state - Returned CUDA process state
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ */
+CUresult CUDAAPI cuCheckpointProcessGetState(int pid, CUprocessState *state);
+
+/**
+ * \brief Lock a running CUDA process
+ *
+ * Lock the CUDA process specified by \p pid which will block further CUDA API
+ * calls. Process must be in the RUNNING state in order to lock.
+ *
+ * Upon successful return the process will be in the LOCKED state.
+ *
+ * If timeoutMs is specified and the timeout is reached the process will be left
+ * in the RUNNING state upon return.
+ *
+ * \param pid - The process ID of the CUDA process
+ * \param args - Optional lock operation arguments
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_ILLEGAL_STATE
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * ::CUDA_ERROR_NOT_READY
+ */
+CUresult CUDAAPI cuCheckpointProcessLock(int pid, CUcheckpointLockArgs *args);
+
+/**
+ * \brief Checkpoint a CUDA process's GPU memory contents
+ *
+ * Checkpoints a CUDA process specified by \p pid that is in the LOCKED
+ * state. The GPU memory contents will be brought into host memory and all
+ * underlying references will be released. Process must be in the LOCKED state
+ * to checkpoint.
+ *
+ * Upon successful return the process will be in the CHECKPOINTED state.
+ *
+ * \param pid - The process ID of the CUDA process
+ * \param args - Optional checkpoint operation arguments
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_ILLEGAL_STATE
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ */
+CUresult CUDAAPI cuCheckpointProcessCheckpoint(int pid, CUcheckpointCheckpointArgs *args);
+
+/**
+ * \brief Restore a CUDA process's GPU memory contents from its last checkpoint
+ *
+ * Restores a CUDA process specified by \p pid from its last checkpoint. Process
+ * must be in the CHECKPOINTED state to restore.
+ *
+ * Upon successful return the process will be in the LOCKED state.
+ *
+ * CUDA process restore requires persistence mode to be enabled or ::cuInit to
+ * have been called before execution.
+ *
+ * \param pid - The process ID of the CUDA process
+ * \param args - Optional restore operation arguments
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_ILLEGAL_STATE
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ *
+ * \sa
+ * ::cuInit
+ */
+CUresult CUDAAPI cuCheckpointProcessRestore(int pid, CUcheckpointRestoreArgs *args);
+
+/**
+ * \brief Unlock a CUDA process to allow CUDA API calls
+ *
+ * Unlocks a process specified by \p pid allowing it to resume making CUDA API
+ * calls. Process must be in the LOCKED state.
+ *
+ * Upon successful return the process will be in the RUNNING state.
+ *
+ * \param pid - The process ID of the CUDA process
+ * \param args - Optional unlock operation arguments
+ *
+ * \return
+ * ::CUDA_SUCCESS
+ * ::CUDA_ERROR_INVALID_VALUE
+ * ::CUDA_ERROR_NOT_INITIALIZED
+ * ::CUDA_ERROR_ILLEGAL_STATE
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ */
+CUresult CUDAAPI cuCheckpointProcessUnlock(int pid, CUcheckpointUnlockArgs *args);
+
+/** @} */ /* End CUDA_CHECKPOINT */
+
+#ifdef __cplusplus
+}
+#endif
+
+#if defined(__GNUC__)
+  #if defined(__CUDA_API_PUSH_VISIBILITY_DEFAULT)
+    #pragma GCC visibility pop
+  #endif
+#endif
+
+#undef __CUDA_DEPRECATED
+
+#endif /* __cuda_cuda_h__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaEGL.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaEGL.h
new file mode 100644
index 0000000000000000000000000000000000000000..f3578faa0304289cdef811af509eded71691352a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaEGL.h
@@ -0,0 +1,662 @@
+/*
+ * Copyright 2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAEGL_H
+#define CUDAEGL_H
+
+#include "cuda.h"
+#include "EGL/egl.h"
+#include "EGL/eglext.h"
+
+
+#ifdef CUDA_FORCE_API_VERSION
+#error "CUDA_FORCE_API_VERSION is no longer supported."
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+  * \addtogroup CUDA_TYPES
+  * @{
+  */
+
+/**
+ * Maximum number of planes per frame
+ */
+#define MAX_PLANES 3
+
+/**
+  * CUDA EglFrame type - array or pointer
+  */
+typedef enum CUeglFrameType_enum {
+    CU_EGL_FRAME_TYPE_ARRAY = 0,  /**< Frame type CUDA array */
+    CU_EGL_FRAME_TYPE_PITCH = 1,  /**< Frame type pointer */
+} CUeglFrameType;
+
+/**
+ * Indicates that timeout for ::cuEGLStreamConsumerAcquireFrame is infinite.
+ */
+#define CUDA_EGL_INFINITE_TIMEOUT 0xFFFFFFFF
+
+/**
+ * Resource location flags- sysmem or vidmem
+ *
+ * For CUDA context on iGPU, since video and system memory are equivalent -
+ * these flags will not have an effect on the execution.
+ *
+ * For CUDA context on dGPU, applications can use the flag ::CUeglResourceLocationFlags
+ * to give a hint about the desired location.
+ *
+ * ::CU_EGL_RESOURCE_LOCATION_SYSMEM - the frame data is made resident on the system memory
+ * to be accessed by CUDA.
+ *
+ * ::CU_EGL_RESOURCE_LOCATION_VIDMEM - the frame data is made resident on the dedicated
+ * video memory to be accessed by CUDA.
+ *
+ * There may be an additional latency due to new allocation and data migration,
+ * if the frame is produced on a different memory.
+
+  */
+typedef enum CUeglResourceLocationFlags_enum {
+    CU_EGL_RESOURCE_LOCATION_SYSMEM   = 0x00,       /**< Resource location sysmem */
+    CU_EGL_RESOURCE_LOCATION_VIDMEM   = 0x01        /**< Resource location vidmem */
+} CUeglResourceLocationFlags;
+
+/**
+  * CUDA EGL Color Format - The different planar and multiplanar formats currently supported for CUDA_EGL interops.
+  * Three channel formats are currently not supported for ::CU_EGL_FRAME_TYPE_ARRAY
+  */
+typedef enum CUeglColorFormat_enum {
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR              = 0x00,  /**< Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR          = 0x01,  /**< Y, UV in two surfaces (UV as one surface) with VU byte ordering, width, height ratio same as YUV420Planar. */
+    CU_EGL_COLOR_FORMAT_YUV422_PLANAR              = 0x02,  /**< Y, U, V  each in a separate  surface, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR          = 0x03,  /**< Y, UV in two surfaces with VU byte ordering, width, height ratio same as YUV422Planar. */
+    CU_EGL_COLOR_FORMAT_RGB                        = 0x04,  /**< R/G/B three channels in one surface with BGR byte ordering. Only pitch linear format supported. */
+    CU_EGL_COLOR_FORMAT_BGR                        = 0x05,  /**< R/G/B three channels in one surface with RGB byte ordering. Only pitch linear format supported. */
+    CU_EGL_COLOR_FORMAT_ARGB                       = 0x06,  /**< R/G/B/A four channels in one surface with BGRA byte ordering. */
+    CU_EGL_COLOR_FORMAT_RGBA                       = 0x07,  /**< R/G/B/A four channels in one surface with ABGR byte ordering. */
+    CU_EGL_COLOR_FORMAT_L                          = 0x08,  /**< single luminance channel in one surface. */
+    CU_EGL_COLOR_FORMAT_R                          = 0x09,  /**< single color channel in one surface. */
+    CU_EGL_COLOR_FORMAT_YUV444_PLANAR              = 0x0A,  /**< Y, U, V in three surfaces, each in a separate surface, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR          = 0x0B,  /**< Y, UV in two surfaces (UV as one surface) with VU byte ordering, width, height ratio same as YUV444Planar. */
+    CU_EGL_COLOR_FORMAT_YUYV_422                   = 0x0C,  /**< Y, U, V in one surface, interleaved as UYVY in one channel. */
+    CU_EGL_COLOR_FORMAT_UYVY_422                   = 0x0D,  /**< Y, U, V in one surface, interleaved as YUYV in one channel. */
+    CU_EGL_COLOR_FORMAT_ABGR                       = 0x0E,  /**< R/G/B/A four channels in one surface with RGBA byte ordering. */
+    CU_EGL_COLOR_FORMAT_BGRA                       = 0x0F,  /**< R/G/B/A four channels in one surface with ARGB byte ordering. */
+    CU_EGL_COLOR_FORMAT_A                          = 0x10,  /**< Alpha color format - one channel in one surface. */
+    CU_EGL_COLOR_FORMAT_RG                         = 0x11,  /**< R/G color format - two channels in one surface with GR byte ordering */
+    CU_EGL_COLOR_FORMAT_AYUV                       = 0x12,  /**< Y, U, V, A four channels in one surface, interleaved as VUYA. */
+    CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR          = 0x13,  /**< Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR          = 0x14,  /**< Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR          = 0x15,  /**< Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR   = 0x16,  /**< Y10, V10U10 in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR   = 0x17,  /**< Y10, V10U10 in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR   = 0x18,  /**< Y12, V12U12 in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR   = 0x19,  /**< Y12, V12U12 in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_VYUY_ER                    = 0x1A,  /**< Extended Range Y, U, V in one surface, interleaved as YVYU in one channel. */
+    CU_EGL_COLOR_FORMAT_UYVY_ER                    = 0x1B,  /**< Extended Range Y, U, V in one surface, interleaved as YUYV in one channel. */
+    CU_EGL_COLOR_FORMAT_YUYV_ER                    = 0x1C,  /**< Extended Range Y, U, V in one surface, interleaved as UYVY in one channel. */
+    CU_EGL_COLOR_FORMAT_YVYU_ER                    = 0x1D,  /**< Extended Range Y, U, V in one surface, interleaved as VYUY in one channel. */
+    CU_EGL_COLOR_FORMAT_YUV_ER                     = 0x1E,  /**< Extended Range Y, U, V three channels in one surface, interleaved as VUY. Only pitch linear format supported. */
+    CU_EGL_COLOR_FORMAT_YUVA_ER                    = 0x1F,  /**< Extended Range Y, U, V, A four channels in one surface, interleaved as AVUY. */
+    CU_EGL_COLOR_FORMAT_AYUV_ER                    = 0x20,  /**< Extended Range Y, U, V, A four channels in one surface, interleaved as VUYA. */
+    CU_EGL_COLOR_FORMAT_YUV444_PLANAR_ER           = 0x21,  /**< Extended Range Y, U, V in three surfaces, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YUV422_PLANAR_ER           = 0x22,  /**< Extended Range Y, U, V in three surfaces, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR_ER           = 0x23,  /**< Extended Range Y, U, V in three surfaces, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR_ER       = 0x24,  /**< Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR_ER       = 0x25,  /**< Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_ER       = 0x26,  /**< Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YVU444_PLANAR_ER           = 0x27,  /**< Extended Range Y, V, U in three surfaces, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU422_PLANAR_ER           = 0x28,  /**< Extended Range Y, V, U in three surfaces, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR_ER           = 0x29,  /**< Extended Range Y, V, U in three surfaces, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR_ER       = 0x2A,  /**< Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR_ER       = 0x2B,  /**< Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_ER       = 0x2C,  /**< Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_BAYER_RGGB                 = 0x2D,  /**< Bayer format - one channel in one surface with interleaved RGGB ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER_BGGR                 = 0x2E,  /**< Bayer format - one channel in one surface with interleaved BGGR ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER_GRBG                 = 0x2F,  /**< Bayer format - one channel in one surface with interleaved GRBG ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER_GBRG                 = 0x30,  /**< Bayer format - one channel in one surface with interleaved GBRG ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER10_RGGB               = 0x31,  /**< Bayer10 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER10_BGGR               = 0x32,  /**< Bayer10 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER10_GRBG               = 0x33,  /**< Bayer10 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER10_GBRG               = 0x34,  /**< Bayer10 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_RGGB               = 0x35,  /**< Bayer12 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_BGGR               = 0x36,  /**< Bayer12 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_GRBG               = 0x37,  /**< Bayer12 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_GBRG               = 0x38,  /**< Bayer12 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER14_RGGB               = 0x39,  /**< Bayer14 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER14_BGGR               = 0x3A,  /**< Bayer14 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER14_GRBG               = 0x3B,  /**< Bayer14 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER14_GBRG               = 0x3C,  /**< Bayer14 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER20_RGGB               = 0x3D,  /**< Bayer20 format - one channel in one surface with interleaved RGGB ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER20_BGGR               = 0x3E,  /**< Bayer20 format - one channel in one surface with interleaved BGGR ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER20_GRBG               = 0x3F,  /**< Bayer20 format - one channel in one surface with interleaved GRBG ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER20_GBRG               = 0x40,  /**< Bayer20 format - one channel in one surface with interleaved GBRG ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    CU_EGL_COLOR_FORMAT_YVU444_PLANAR              = 0x41,  /**< Y, V, U in three surfaces, each in a separate surface, U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU422_PLANAR              = 0x42,  /**< Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR              = 0x43,  /**< Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_RGGB             = 0x44,  /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved RGGB ordering and mapped to opaque integer datatype. */
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_BGGR             = 0x45,  /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved BGGR ordering and mapped to opaque integer datatype. */
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_GRBG             = 0x46,  /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved GRBG ordering and mapped to opaque integer datatype. */
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_GBRG             = 0x47,  /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved GBRG ordering and mapped to opaque integer datatype. */
+    CU_EGL_COLOR_FORMAT_BAYER_BCCR                 = 0x48,  /**< Bayer format - one channel in one surface with interleaved BCCR ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER_RCCB                 = 0x49,  /**< Bayer format - one channel in one surface with interleaved RCCB ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER_CRBC                 = 0x4A,  /**< Bayer format - one channel in one surface with interleaved CRBC ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER_CBRC                 = 0x4B,  /**< Bayer format - one channel in one surface with interleaved CBRC ordering. */
+    CU_EGL_COLOR_FORMAT_BAYER10_CCCC               = 0x4C,  /**< Bayer10 format - one channel in one surface with interleaved CCCC ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_BCCR               = 0x4D,  /**< Bayer12 format - one channel in one surface with interleaved BCCR ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_RCCB               = 0x4E,  /**< Bayer12 format - one channel in one surface with interleaved RCCB ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_CRBC               = 0x4F,  /**< Bayer12 format - one channel in one surface with interleaved CRBC ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_CBRC               = 0x50,  /**< Bayer12 format - one channel in one surface with interleaved CBRC ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_BAYER12_CCCC               = 0x51,  /**< Bayer12 format - one channel in one surface with interleaved CCCC ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    CU_EGL_COLOR_FORMAT_Y                          = 0x52, /**< Color format for single Y plane. */
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_2020     = 0x53, /**< Y, UV in two surfaces (UV as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_2020     = 0x54, /**< Y, VU in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR_2020         = 0x55, /**< Y, U, V  each in a separate  surface, U/V width = 1/2 Y width, U/V height= 1/2 Y height. */             
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR_2020         = 0x56, /**< Y, V, U each in a separate surface, U/V width = 1/2 Y width, U/V height
+= 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_709      = 0x57, /**< Y, UV in two surfaces (UV as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_709      = 0x58, /**< Y, VU in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR_709          = 0x59, /**< Y, U, V  each in a separate  surface, U/V width = 1/2 Y width, U/V height
+= 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR_709          = 0x5A,  /**< Y, V, U each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709  = 0x5B, /**< Y10, V10U10 in two surfaces (VU as one surface), U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020 = 0x5C, /**< Y10, V10U10 in two surfaces (VU as one surface), U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020 = 0x5D, /**< Y10, V10U10 in two surfaces(VU as one surface) U/V width = 1/2 Y width, U/V height  = Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR      = 0x5E, /**< Y10, V10U10 in two surfaces(VU as one surface) U/V width = 1/2 Y width, U/V height  = Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709  = 0x5F, /**< Y10, V10U10 in two surfaces(VU as one surface) U/V width = 1/2 Y width, U/V height  = Y height. */
+    CU_EGL_COLOR_FORMAT_Y_ER                          = 0x60, /**< Extended Range Color format for single Y plane. */
+    CU_EGL_COLOR_FORMAT_Y_709_ER                      = 0x61, /**< Extended Range Color format for single Y plane. */
+    CU_EGL_COLOR_FORMAT_Y10_ER                        = 0x62, /**< Extended Range Color format for single Y10 plane. */
+    CU_EGL_COLOR_FORMAT_Y10_709_ER                    = 0x63, /**< Extended Range Color format for single Y10 plane. */
+    CU_EGL_COLOR_FORMAT_Y12_ER                        = 0x64, /**< Extended Range Color format for single Y12 plane. */
+    CU_EGL_COLOR_FORMAT_Y12_709_ER                    = 0x65, /**< Extended Range Color format for single Y12 plane. */
+    CU_EGL_COLOR_FORMAT_YUVA                          = 0x66, /**< Y, U, V, A four channels in one surface, interleaved as AVUY. */
+    CU_EGL_COLOR_FORMAT_YUV                           = 0x67, /**< Y, U, V three channels in one surface, interleaved as VUY. Only pitch linear format supported. */
+    CU_EGL_COLOR_FORMAT_YVYU                          = 0x68, /**< Y, U, V in one surface, interleaved as YVYU in one channel. */
+    CU_EGL_COLOR_FORMAT_VYUY                          = 0x69, /**< Y, U, V in one surface, interleaved as VYUY in one channel. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER     = 0x6A, /**< Extended Range Y10, V10U10 in two surfaces(VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER = 0x6B, /**< Extended Range Y10, V10U10 in two surfaces(VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER     = 0x6C, /**< Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */ 
+    CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER = 0x6D, /**< Extended Range Y10, V10U10 in two surfaces (VU as one surface)  U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER     = 0x6E, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */ 
+    CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER = 0x6F, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER     = 0x70, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */ 
+    CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER = 0x71, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */
+    CU_EGL_COLOR_FORMAT_UYVY_709                        = 0x72, /**< Y, U, V in one surface, interleaved as UYVY in one channel. */
+    CU_EGL_COLOR_FORMAT_UYVY_709_ER                     = 0x73, /**< Extended Range Y, U, V in one surface, interleaved as UYVY in one channel. */
+    CU_EGL_COLOR_FORMAT_UYVY_2020                       = 0x74, /**< Y, U, V in one surface, interleaved as UYVY in one channel. */
+    CU_EGL_COLOR_FORMAT_MAX
+} CUeglColorFormat;
+
+/**
+ * CUDA EGLFrame structure Descriptor - structure defining one frame of EGL.
+ *
+ * Each frame may contain one or more planes depending on whether the surface  * is Multiplanar or not.
+ */
+typedef struct CUeglFrame_st {
+    union {
+        CUarray pArray[MAX_PLANES];     /**< Array of CUarray corresponding to each plane*/
+        void*   pPitch[MAX_PLANES];     /**< Array of Pointers corresponding to each plane*/
+    } frame;
+    unsigned int width;                 /**< Width of first plane */
+    unsigned int height;                /**< Height of first plane */
+    unsigned int depth;                 /**< Depth of first plane */
+    unsigned int pitch;                 /**< Pitch of first plane */
+    unsigned int planeCount;            /**< Number of planes */
+    unsigned int numChannels;           /**< Number of channels for the plane */
+    CUeglFrameType frameType;           /**< Array or Pitch */
+    CUeglColorFormat eglColorFormat;    /**< CUDA EGL Color Format*/
+    CUarray_format cuFormat;            /**< CUDA Array Format*/
+} CUeglFrame_v1;
+typedef CUeglFrame_v1 CUeglFrame;
+
+/**
+  * CUDA EGLSream Connection
+  */
+typedef struct CUeglStreamConnection_st* CUeglStreamConnection;
+
+/** @} */ /* END CUDA_TYPES */
+
+/**
+ * \file cudaEGL.h
+ * \brief Header file for the EGL interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ */
+
+/**
+ * \defgroup CUDA_EGL EGL Interoperability
+ * \ingroup CUDA_DRIVER
+ *
+ * ___MANBRIEF___ EGL interoperability functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the EGL interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Registers an EGL image
+ *
+ * Registers the EGLImageKHR specified by \p image for access by
+ * CUDA. A handle to the registered object is returned as \p pCudaResource.
+ * Additional Mapping/Unmapping is not required for the registered resource and
+ * ::cuGraphicsResourceGetMappedEglFrame can be directly called on the \p pCudaResource.
+ *
+ * The application will be responsible for synchronizing access to shared objects.
+ * The application must ensure that any pending operation which access the objects have completed
+ * before passing control to CUDA. This may be accomplished by issuing and waiting for
+ * glFinish command on all GLcontexts (for OpenGL and likewise for other APIs).
+ * The application will be also responsible for ensuring that any pending operation on the
+ * registered CUDA resource has completed prior to executing subsequent commands in other APIs
+ * accesing the same memory objects.
+ * This can be accomplished by calling cuCtxSynchronize or cuEventSynchronize (preferably).
+ *
+ * The surface's intended usage is specified using \p flags, as follows:
+ *
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * The EGLImageKHR is an object which can be used to create EGLImage target resource. It is defined as a void pointer.
+ * typedef void* EGLImageKHR
+ *
+ * \param pCudaResource   - Pointer to the returned object handle
+ * \param image           - An EGLImageKHR image which can be used to create target resource.
+ * \param flags           - Map flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ *
+ * \sa ::cuGraphicsEGLRegisterImage, ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsResourceSetMapFlags, ::cuGraphicsMapResources,
+ * ::cuGraphicsUnmapResources,
+ * ::cudaGraphicsEGLRegisterImage
+ */
+CUresult CUDAAPI cuGraphicsEGLRegisterImage(CUgraphicsResource *pCudaResource, EGLImageKHR image, unsigned int flags);
+
+/**
+ * \brief Connect CUDA to EGLStream as a consumer.
+ *
+ * Connect CUDA as a consumer to EGLStreamKHR specified by \p stream.
+ *
+ * The EGLStreamKHR is an EGL object that transfers a sequence of image frames from one
+ * API to another.
+ *
+ * \param conn            - Pointer to the returned connection handle
+ * \param stream          - EGLStreamKHR handle
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ *
+ * \sa ::cuEGLStreamConsumerConnect, ::cuEGLStreamConsumerDisconnect,
+ * ::cuEGLStreamConsumerAcquireFrame, ::cuEGLStreamConsumerReleaseFrame,
+ * ::cudaEGLStreamConsumerConnect
+ */
+CUresult CUDAAPI cuEGLStreamConsumerConnect(CUeglStreamConnection *conn, EGLStreamKHR stream);
+
+/**
+ * \brief Connect CUDA to EGLStream as a consumer with given flags.
+ *
+ * Connect CUDA as a consumer to EGLStreamKHR specified by \p stream with specified \p flags defined by CUeglResourceLocationFlags.
+ *
+ * The flags specify whether the consumer wants to access frames from system memory or video memory.
+ * Default is ::CU_EGL_RESOURCE_LOCATION_VIDMEM.
+ *
+ * \param conn              - Pointer to the returned connection handle
+ * \param stream            - EGLStreamKHR handle
+ * \param flags             - Flags denote intended location - system or video.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ *
+ * \sa ::cuEGLStreamConsumerConnect, ::cuEGLStreamConsumerDisconnect,
+ * ::cuEGLStreamConsumerAcquireFrame, ::cuEGLStreamConsumerReleaseFrame,
+ * ::cudaEGLStreamConsumerConnectWithFlags
+ */
+
+CUresult CUDAAPI cuEGLStreamConsumerConnectWithFlags(CUeglStreamConnection *conn, EGLStreamKHR stream, unsigned int flags);
+
+/**
+ * \brief Disconnect CUDA as a consumer to EGLStream .
+ *
+ * Disconnect CUDA as a consumer to EGLStreamKHR.
+ *
+ * \param conn            - Conection to disconnect.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ *
+ * \sa ::cuEGLStreamConsumerConnect, ::cuEGLStreamConsumerDisconnect,
+ * ::cuEGLStreamConsumerAcquireFrame, ::cuEGLStreamConsumerReleaseFrame,
+ * ::cudaEGLStreamConsumerDisconnect
+ */
+CUresult CUDAAPI cuEGLStreamConsumerDisconnect(CUeglStreamConnection *conn);
+
+/**
+ * \brief Acquire an image frame from the EGLStream with CUDA as a consumer.
+ *
+ * Acquire an image frame from EGLStreamKHR. This API can also acquire an old frame presented
+ * by the producer unless explicitly disabled by setting EGL_SUPPORT_REUSE_NV flag to EGL_FALSE
+ * during stream initialization. By default, EGLStream is created with this flag set to EGL_TRUE.
+ * ::cuGraphicsResourceGetMappedEglFrame can be called on \p pCudaResource to get
+ * ::CUeglFrame.
+ *
+ * \param conn            - Connection on which to acquire
+ * \param pCudaResource   - CUDA resource on which the stream frame will be mapped for use.
+ * \param pStream         - CUDA stream for synchronization and any data migrations
+ *                          implied by ::CUeglResourceLocationFlags.
+ * \param timeout         - Desired timeout in usec for a new frame to be acquired.
+ *                          If set as ::CUDA_EGL_INFINITE_TIMEOUT, acquire waits infinitely.
+ *                          After timeout occurs CUDA consumer tries to acquire an old frame
+ *                          if available and EGL_SUPPORT_REUSE_NV flag is set.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT,
+ *
+ * \sa ::cuEGLStreamConsumerConnect, ::cuEGLStreamConsumerDisconnect,
+ * ::cuEGLStreamConsumerAcquireFrame, ::cuEGLStreamConsumerReleaseFrame,
+ * ::cudaEGLStreamConsumerAcquireFrame
+ */
+CUresult CUDAAPI cuEGLStreamConsumerAcquireFrame(CUeglStreamConnection *conn,
+                                                  CUgraphicsResource *pCudaResource, CUstream *pStream, unsigned int timeout);
+/**
+ * \brief Releases the last frame acquired from the EGLStream.
+ *
+ * Release the acquired image frame specified by \p pCudaResource to EGLStreamKHR.
+ * If EGL_SUPPORT_REUSE_NV flag is set to EGL_TRUE, at the time of EGL creation
+ * this API doesn't release the last frame acquired on the EGLStream.
+ * By default, EGLStream is created with this flag set to EGL_TRUE.
+ *
+ * \param conn            - Connection on which to release
+ * \param pCudaResource   - CUDA resource whose corresponding frame is to be released
+ * \param pStream         - CUDA stream on which release will be done.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ *
+ * \sa ::cuEGLStreamConsumerConnect, ::cuEGLStreamConsumerDisconnect,
+ * ::cuEGLStreamConsumerAcquireFrame, ::cuEGLStreamConsumerReleaseFrame,
+ * ::cudaEGLStreamConsumerReleaseFrame
+ */
+CUresult CUDAAPI cuEGLStreamConsumerReleaseFrame(CUeglStreamConnection *conn,
+                                                  CUgraphicsResource pCudaResource, CUstream *pStream);
+
+/**
+ * \brief Connect CUDA to EGLStream as a producer.
+ *
+ * Connect CUDA as a producer to EGLStreamKHR specified by \p stream.
+ *
+ * The EGLStreamKHR is an EGL object that transfers a sequence of image frames from one
+ * API to another.
+ *
+ * \param conn   - Pointer to the returned connection handle
+ * \param stream - EGLStreamKHR handle
+ * \param width  - width of the image to be submitted to the stream
+ * \param height - height of the image to be submitted to the stream
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ *
+ * \sa ::cuEGLStreamProducerConnect, ::cuEGLStreamProducerDisconnect,
+ * ::cuEGLStreamProducerPresentFrame,
+ * ::cudaEGLStreamProducerConnect
+ */
+CUresult CUDAAPI cuEGLStreamProducerConnect(CUeglStreamConnection *conn, EGLStreamKHR stream,
+                                             EGLint width, EGLint height);
+
+/**
+ * \brief Disconnect CUDA as a producer  to EGLStream .
+ *
+ * Disconnect CUDA as a producer to EGLStreamKHR.
+ *
+ * \param conn            - Conection to disconnect.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ *
+ * \sa ::cuEGLStreamProducerConnect, ::cuEGLStreamProducerDisconnect,
+ * ::cuEGLStreamProducerPresentFrame,
+ * ::cudaEGLStreamProducerDisconnect
+ */
+CUresult CUDAAPI cuEGLStreamProducerDisconnect(CUeglStreamConnection *conn);
+
+/**
+ * \brief Present a CUDA eglFrame to the EGLStream with CUDA as a producer.
+ *
+ * When a frame is presented by the producer, it gets associated with the EGLStream
+ * and thus it is illegal to free the frame before the producer is disconnected.
+ * If a frame is freed and reused it may lead to undefined behavior.
+ *
+ * If producer and consumer are on different GPUs (iGPU and dGPU) then frametype
+ * ::CU_EGL_FRAME_TYPE_ARRAY is not supported. ::CU_EGL_FRAME_TYPE_PITCH can be used for
+ * such cross-device applications.
+ *
+ * The ::CUeglFrame is defined as:
+ * \code
+ * typedef struct CUeglFrame_st {
+ *     union {
+ *         CUarray pArray[MAX_PLANES];
+ *         void*   pPitch[MAX_PLANES];
+ *     } frame;
+ *     unsigned int width;
+ *     unsigned int height;
+ *     unsigned int depth;
+ *     unsigned int pitch;
+ *     unsigned int planeCount;
+ *     unsigned int numChannels;
+ *     CUeglFrameType frameType;
+ *     CUeglColorFormat eglColorFormat;
+ *     CUarray_format cuFormat;
+ * } CUeglFrame;
+ * \endcode
+ *
+ * For ::CUeglFrame of type ::CU_EGL_FRAME_TYPE_PITCH, the application may present sub-region of a memory
+ * allocation. In that case, the pitched pointer will specify the start address of the sub-region in
+ * the allocation and corresponding ::CUeglFrame fields will specify the dimensions of the sub-region.
+ * 
+ * \param conn            - Connection on which to present the CUDA array
+ * \param eglframe        - CUDA Eglstream Proucer Frame handle to be sent to the consumer over EglStream.
+ * \param pStream         - CUDA stream on which to present the frame.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ *
+ * \sa ::cuEGLStreamProducerConnect, ::cuEGLStreamProducerDisconnect,
+ * ::cuEGLStreamProducerReturnFrame,
+ * ::cudaEGLStreamProducerPresentFrame
+ */
+CUresult CUDAAPI cuEGLStreamProducerPresentFrame(CUeglStreamConnection *conn,
+                                                 CUeglFrame eglframe, CUstream *pStream);
+
+/**
+ * \brief Return the CUDA eglFrame to the EGLStream released by the consumer.
+ *
+ * This API can potentially return CUDA_ERROR_LAUNCH_TIMEOUT if the consumer has not 
+ * returned a frame to EGL stream. If timeout is returned the application can retry.
+ *
+ * \param conn            - Connection on which to return
+ * \param eglframe        - CUDA Eglstream Proucer Frame handle returned from the consumer over EglStream.
+ * \param pStream         - CUDA stream on which to return the frame.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_LAUNCH_TIMEOUT
+ *
+ * \sa ::cuEGLStreamProducerConnect, ::cuEGLStreamProducerDisconnect,
+ * ::cuEGLStreamProducerPresentFrame,
+ * ::cudaEGLStreamProducerReturnFrame
+ */
+CUresult CUDAAPI cuEGLStreamProducerReturnFrame(CUeglStreamConnection *conn,
+                                                CUeglFrame *eglframe, CUstream *pStream);
+
+/**
+ * \brief Get an eglFrame through which to access a registered EGL graphics resource.
+ *
+ * Returns in \p *eglFrame an eglFrame pointer through which the registered graphics resource
+ * \p resource may be accessed.
+ * This API can only be called for registered EGL graphics resources.
+ *
+ * The ::CUeglFrame is defined as:
+ * \code
+ * typedef struct CUeglFrame_st {
+ *     union {
+ *         CUarray pArray[MAX_PLANES];
+ *         void*   pPitch[MAX_PLANES];
+ *     } frame;
+ *     unsigned int width;
+ *     unsigned int height;
+ *     unsigned int depth;
+ *     unsigned int pitch;
+ *     unsigned int planeCount;
+ *     unsigned int numChannels;
+ *     CUeglFrameType frameType;
+ *     CUeglColorFormat eglColorFormat;
+ *     CUarray_format cuFormat;
+ * } CUeglFrame;
+ * \endcode
+ *
+ * If \p resource is not registered then ::CUDA_ERROR_NOT_MAPPED is returned.
+ * *
+ * \param eglFrame   - Returned eglFrame.
+ * \param resource   - Registered resource to access.
+ * \param index      - Index for cubemap surfaces.
+ * \param mipLevel   - Mipmap level for the subresource to access.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_NOT_MAPPED
+ *
+ * \sa
+ * ::cuGraphicsMapResources,
+ * ::cuGraphicsSubResourceGetMappedArray,
+ * ::cuGraphicsResourceGetMappedPointer,
+ * ::cudaGraphicsResourceGetMappedEglFrame
+ */
+CUresult CUDAAPI cuGraphicsResourceGetMappedEglFrame(CUeglFrame* eglFrame, CUgraphicsResource resource, unsigned int index, unsigned int mipLevel);
+
+/**
+ * \brief Creates an event from EGLSync object
+ *
+ * Creates an event *phEvent from an EGLSyncKHR eglSync with the flags specified
+ * via \p flags. Valid flags include:
+ * - ::CU_EVENT_DEFAULT: Default event creation flag.
+ * - ::CU_EVENT_BLOCKING_SYNC: Specifies that the created event should use blocking
+ * synchronization.  A CPU thread that uses ::cuEventSynchronize() to wait on
+ * an event created with this flag will block until the event has actually
+ * been completed.
+ *
+ * Once the \p eglSync gets destroyed, ::cuEventDestroy is the only API
+ * that can be invoked on the event.
+ *
+ * ::cuEventRecord and TimingData are not supported for events created from EGLSync.
+ *
+ * The EGLSyncKHR is an opaque handle to an EGL sync object.
+ * typedef void* EGLSyncKHR
+ *
+ * \param phEvent - Returns newly created event
+ * \param eglSync - Opaque handle to EGLSync object
+ * \param flags   - Event creation flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa
+ * ::cuEventQuery,
+ * ::cuEventSynchronize,
+ * ::cuEventDestroy
+ */
+CUresult CUDAAPI cuEventCreateFromEGLSync(CUevent *phEvent, EGLSyncKHR eglSync, unsigned int flags);
+
+/** @} */ /* END CUDA_EGL */
+
+#ifdef __cplusplus
+};
+#endif
+
+#endif
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaEGLTypedefs.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaEGLTypedefs.h
new file mode 100644
index 0000000000000000000000000000000000000000..61b82337dc4bb280869934b11c2105db62ae20c3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaEGLTypedefs.h
@@ -0,0 +1,96 @@
+/*
+ * Copyright 2020-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAEGLTYPEDEFS_H
+#define CUDAEGLTYPEDEFS_H
+
+#include <cudaEGL.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif // __cplusplus
+
+/*
+ * Macros for the latest version for each driver function in cudaEGL.h
+ */
+#define PFN_cuGraphicsEGLRegisterImage  PFN_cuGraphicsEGLRegisterImage_v7000
+#define PFN_cuEGLStreamConsumerConnect  PFN_cuEGLStreamConsumerConnect_v7000
+#define PFN_cuEGLStreamConsumerConnectWithFlags  PFN_cuEGLStreamConsumerConnectWithFlags_v8000
+#define PFN_cuEGLStreamConsumerDisconnect  PFN_cuEGLStreamConsumerDisconnect_v7000
+#define PFN_cuEGLStreamConsumerAcquireFrame  PFN_cuEGLStreamConsumerAcquireFrame_v7000
+#define PFN_cuEGLStreamConsumerReleaseFrame  PFN_cuEGLStreamConsumerReleaseFrame_v7000
+#define PFN_cuEGLStreamProducerConnect  PFN_cuEGLStreamProducerConnect_v7000
+#define PFN_cuEGLStreamProducerDisconnect  PFN_cuEGLStreamProducerDisconnect_v7000
+#define PFN_cuEGLStreamProducerPresentFrame  PFN_cuEGLStreamProducerPresentFrame_v7000
+#define PFN_cuEGLStreamProducerReturnFrame  PFN_cuEGLStreamProducerReturnFrame_v7000
+#define PFN_cuGraphicsResourceGetMappedEglFrame  PFN_cuGraphicsResourceGetMappedEglFrame_v7000
+#define PFN_cuEventCreateFromEGLSync  PFN_cuEventCreateFromEGLSync_v9000
+
+
+/**
+ * Type definitions for functions defined in cudaEGL.h
+ */
+typedef CUresult (CUDAAPI *PFN_cuGraphicsEGLRegisterImage_v7000)(CUgraphicsResource CUDAAPI *pCudaResource, EGLImageKHR image, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamConsumerConnect_v7000)(CUeglStreamConnection CUDAAPI *conn, EGLStreamKHR stream);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamConsumerConnectWithFlags_v8000)(CUeglStreamConnection CUDAAPI *conn, EGLStreamKHR stream, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamConsumerDisconnect_v7000)(CUeglStreamConnection CUDAAPI *conn);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamConsumerAcquireFrame_v7000)(CUeglStreamConnection CUDAAPI *conn, CUgraphicsResource CUDAAPI *pCudaResource, CUstream CUDAAPI *pStream, unsigned int timeout);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamConsumerReleaseFrame_v7000)(CUeglStreamConnection CUDAAPI *conn, CUgraphicsResource pCudaResource, CUstream CUDAAPI *pStream);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamProducerConnect_v7000)(CUeglStreamConnection CUDAAPI *conn, EGLStreamKHR stream, EGLint width, EGLint height);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamProducerDisconnect_v7000)(CUeglStreamConnection CUDAAPI *conn);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamProducerPresentFrame_v7000)(CUeglStreamConnection CUDAAPI *conn, CUeglFrame_v1 eglframe, CUstream CUDAAPI *pStream);
+typedef CUresult (CUDAAPI *PFN_cuEGLStreamProducerReturnFrame_v7000)(CUeglStreamConnection CUDAAPI *conn, CUeglFrame_v1 CUDAAPI *eglframe, CUstream CUDAAPI *pStream);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsResourceGetMappedEglFrame_v7000)(CUeglFrame_v1 CUDAAPI *eglFrame, CUgraphicsResource resource, unsigned int index, unsigned int mipLevel);
+typedef CUresult (CUDAAPI *PFN_cuEventCreateFromEGLSync_v9000)(CUevent CUDAAPI *phEvent, EGLSyncKHR eglSync, unsigned int flags);
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+
+#endif // file guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaGL.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaGL.h
new file mode 100644
index 0000000000000000000000000000000000000000..1a9c70e881774c8f3cf8b6430e7aa53a98d74669
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaGL.h
@@ -0,0 +1,608 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAGL_H
+#define CUDAGL_H
+
+#include <cuda.h>
+#include <GL/gl.h>
+
+#if defined(__CUDA_API_VERSION_INTERNAL) || defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+#define __CUDA_DEPRECATED
+#elif defined(_MSC_VER)
+#define __CUDA_DEPRECATED __declspec(deprecated)
+#elif defined(__GNUC__)
+#define __CUDA_DEPRECATED __attribute__((deprecated))
+#else
+#define __CUDA_DEPRECATED
+#endif
+
+#ifdef CUDA_FORCE_API_VERSION
+#error "CUDA_FORCE_API_VERSION is no longer supported."
+#endif
+
+#if defined(__CUDA_API_VERSION_INTERNAL) || defined(CUDA_API_PER_THREAD_DEFAULT_STREAM)
+    #define __CUDA_API_PER_THREAD_DEFAULT_STREAM
+    #define __CUDA_API_PTDS(api) api ## _ptds
+    #define __CUDA_API_PTSZ(api) api ## _ptsz
+#else
+    #define __CUDA_API_PTDS(api) api
+    #define __CUDA_API_PTSZ(api) api
+#endif
+
+#define cuGLCtxCreate            cuGLCtxCreate_v2
+#define cuGLMapBufferObject      __CUDA_API_PTDS(cuGLMapBufferObject_v2)
+#define cuGLMapBufferObjectAsync __CUDA_API_PTSZ(cuGLMapBufferObjectAsync_v2)
+#define cuGLGetDevices           cuGLGetDevices_v2
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * \file cudaGL.h
+ * \brief Header file for the OpenGL interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ */
+
+/**
+ * \defgroup CUDA_GL OpenGL Interoperability
+ * \ingroup CUDA_DRIVER
+ *
+ * ___MANBRIEF___ OpenGL interoperability functions of the low-level CUDA
+ * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the OpenGL interoperability functions of the
+ * low-level CUDA driver application programming interface. Note that mapping 
+ * of OpenGL resources is performed with the graphics API agnostic, resource 
+ * mapping interface described in \ref CUDA_GRAPHICS "Graphics Interoperability".
+ *
+ * @{
+ */
+
+#if defined(_WIN32)
+#if !defined(WGL_NV_gpu_affinity)
+typedef void* HGPUNV;
+#endif
+#endif /* _WIN32 */
+
+/**
+ * \brief Registers an OpenGL buffer object
+ *
+ * Registers the buffer object specified by \p buffer for access by
+ * CUDA.  A handle to the registered object is returned as \p
+ * pCudaResource.  The register flags \p Flags specify the intended usage,
+ * as follows:
+ *
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * \param pCudaResource - Pointer to the returned object handle
+ * \param buffer - name of buffer object to be registered
+ * \param Flags - Register flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_OPERATING_SYSTEM
+ * \notefnerr
+ *
+ * \sa 
+ * ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsMapResources,
+ * ::cuGraphicsResourceGetMappedPointer,
+ * ::cudaGraphicsGLRegisterBuffer
+ */
+CUresult CUDAAPI cuGraphicsGLRegisterBuffer(CUgraphicsResource *pCudaResource, GLuint buffer, unsigned int Flags);
+
+/**
+ * \brief Register an OpenGL texture or renderbuffer object
+ *
+ * Registers the texture or renderbuffer object specified by \p image for access by CUDA.  
+ * A handle to the registered object is returned as \p pCudaResource.  
+ *
+ * \p target must match the type of the object, and must be one of ::GL_TEXTURE_2D, 
+ * ::GL_TEXTURE_RECTANGLE, ::GL_TEXTURE_CUBE_MAP, ::GL_TEXTURE_3D, ::GL_TEXTURE_2D_ARRAY, 
+ * or ::GL_RENDERBUFFER.
+ *
+ * The register flags \p Flags specify the intended usage, as follows:
+ *
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST: Specifies that CUDA will
+ *   bind this resource to a surface reference.
+ * - ::CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER: Specifies that CUDA will perform
+ *   texture gather operations on this resource.
+ *
+ * The following image formats are supported. For brevity's sake, the list is abbreviated.
+ * For ex., {GL_R, GL_RG} X {8, 16} would expand to the following 4 formats 
+ * {GL_R8, GL_R16, GL_RG8, GL_RG16} :
+ * - GL_RED, GL_RG, GL_RGBA, GL_LUMINANCE, GL_ALPHA, GL_LUMINANCE_ALPHA, GL_INTENSITY
+ * - {GL_R, GL_RG, GL_RGBA} X {8, 16, 16F, 32F, 8UI, 16UI, 32UI, 8I, 16I, 32I}
+ * - {GL_LUMINANCE, GL_ALPHA, GL_LUMINANCE_ALPHA, GL_INTENSITY} X
+ * {8, 16, 16F_ARB, 32F_ARB, 8UI_EXT, 16UI_EXT, 32UI_EXT, 8I_EXT, 16I_EXT, 32I_EXT}
+ *
+ * The following image classes are currently disallowed:
+ * - Textures with borders
+ * - Multisampled renderbuffers
+ *
+ * \param pCudaResource - Pointer to the returned object handle
+ * \param image - name of texture or renderbuffer object to be registered
+ * \param target - Identifies the type of object specified by \p image
+ * \param Flags - Register flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_OPERATING_SYSTEM
+ * \notefnerr
+ *
+ * \sa 
+ * ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsMapResources,
+ * ::cuGraphicsSubResourceGetMappedArray,
+ * ::cudaGraphicsGLRegisterImage
+ */
+CUresult CUDAAPI cuGraphicsGLRegisterImage(CUgraphicsResource *pCudaResource, GLuint image, GLenum target, unsigned int Flags);
+
+#ifdef _WIN32
+/**
+ * \brief Gets the CUDA device associated with hGpu
+ *
+ * Returns in \p *pDevice the CUDA device associated with a \p hGpu, if
+ * applicable.
+ *
+ * \param pDevice - Device associated with hGpu
+ * \param hGpu    - Handle to a GPU, as queried via ::WGL_NV_gpu_affinity()
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuGLMapBufferObject,
+ * ::cuGLRegisterBufferObject, ::cuGLUnmapBufferObject,
+ * ::cuGLUnregisterBufferObject, ::cuGLUnmapBufferObjectAsync,
+ * ::cuGLSetBufferObjectMapFlags,
+ * ::cudaWGLGetDevice
+ */
+CUresult CUDAAPI cuWGLGetDevice(CUdevice *pDevice, HGPUNV hGpu);
+#endif /* _WIN32 */
+
+/**
+ * CUDA devices corresponding to an OpenGL device
+ */
+typedef enum CUGLDeviceList_enum {
+    CU_GL_DEVICE_LIST_ALL            = 0x01, /**< The CUDA devices for all GPUs used by the current OpenGL context */
+    CU_GL_DEVICE_LIST_CURRENT_FRAME  = 0x02, /**< The CUDA devices for the GPUs used by the current OpenGL context in its currently rendering frame */
+    CU_GL_DEVICE_LIST_NEXT_FRAME     = 0x03, /**< The CUDA devices for the GPUs to be used by the current OpenGL context in the next frame */
+} CUGLDeviceList;
+
+/**
+ * \brief Gets the CUDA devices associated with the current OpenGL context
+ *
+ * Returns in \p *pCudaDeviceCount the number of CUDA-compatible devices 
+ * corresponding to the current OpenGL context. Also returns in \p *pCudaDevices 
+ * at most cudaDeviceCount of the CUDA-compatible devices corresponding to 
+ * the current OpenGL context. If any of the GPUs being used by the current OpenGL
+ * context are not CUDA capable then the call will return CUDA_ERROR_NO_DEVICE.
+ *
+ * The \p deviceList argument may be any of the following:
+ * - ::CU_GL_DEVICE_LIST_ALL: Query all devices used by the current OpenGL context.
+ * - ::CU_GL_DEVICE_LIST_CURRENT_FRAME: Query the devices used by the current OpenGL context to
+ *   render the current frame (in SLI).
+ * - ::CU_GL_DEVICE_LIST_NEXT_FRAME: Query the devices used by the current OpenGL context to
+ *   render the next frame (in SLI). Note that this is a prediction, it can't be guaranteed that
+ *   this is correct in all cases.
+ *
+ * \param pCudaDeviceCount - Returned number of CUDA devices.
+ * \param pCudaDevices     - Returned CUDA devices.
+ * \param cudaDeviceCount  - The size of the output device array pCudaDevices.
+ * \param deviceList       - The set of devices to return.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NO_DEVICE,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_GRAPHICS_CONTEXT,
+ * ::CUDA_ERROR_OPERATING_SYSTEM
+ *
+ * \notefnerr
+ *
+ * \sa
+ * ::cuWGLGetDevice,
+ * ::cudaGLGetDevices
+ */
+CUresult CUDAAPI cuGLGetDevices(unsigned int *pCudaDeviceCount, CUdevice *pCudaDevices, unsigned int cudaDeviceCount, CUGLDeviceList deviceList);
+
+/**
+ * \defgroup CUDA_GL_DEPRECATED OpenGL Interoperability [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated OpenGL interoperability functions of the low-level
+ * CUDA driver API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes deprecated OpenGL interoperability functionality.
+ *
+ * @{
+ */
+
+/** Flags to map or unmap a resource */
+typedef enum CUGLmap_flags_enum {
+    CU_GL_MAP_RESOURCE_FLAGS_NONE          = 0x00,
+    CU_GL_MAP_RESOURCE_FLAGS_READ_ONLY     = 0x01,
+    CU_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 0x02,    
+} CUGLmap_flags;
+
+/**
+ * \brief Create a CUDA context for interoperability with OpenGL
+ *
+ * \deprecated This function is deprecated as of Cuda 5.0. 
+ *
+ * This function is deprecated and should no longer be used.  It is
+ * no longer necessary to associate a CUDA context with an OpenGL
+ * context in order to achieve maximum interoperability performance.
+ *
+ * \param pCtx   - Returned CUDA context
+ * \param Flags  - Options for CUDA context creation
+ * \param device - Device on which to create the context
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate, ::cuGLInit, ::cuGLMapBufferObject,
+ * ::cuGLRegisterBufferObject, ::cuGLUnmapBufferObject,
+ * ::cuGLUnregisterBufferObject, ::cuGLMapBufferObjectAsync,
+ * ::cuGLUnmapBufferObjectAsync, ::cuGLSetBufferObjectMapFlags,
+ * ::cuWGLGetDevice
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLCtxCreate(CUcontext *pCtx, unsigned int Flags, CUdevice device );
+
+/**
+ * \brief Initializes OpenGL interoperability
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Initializes OpenGL interoperability. This function is deprecated
+ * and calling it is no longer required. It may fail if the needed
+ * OpenGL driver facilities are not available.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_UNKNOWN
+ * \notefnerr
+ *
+ * \sa ::cuGLMapBufferObject,
+ * ::cuGLRegisterBufferObject, ::cuGLUnmapBufferObject,
+ * ::cuGLUnregisterBufferObject, ::cuGLMapBufferObjectAsync,
+ * ::cuGLUnmapBufferObjectAsync, ::cuGLSetBufferObjectMapFlags,
+ * ::cuWGLGetDevice
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLInit(void);
+
+/**
+ * \brief Registers an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Registers the buffer object specified by \p buffer for access by
+ * CUDA. This function must be called before CUDA can map the buffer
+ * object.  There must be a valid OpenGL context bound to the current
+ * thread when this function is called, and the buffer name is
+ * resolved by that context.
+ *
+ * \param buffer - The name of the buffer object to register.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_ALREADY_MAPPED
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsGLRegisterBuffer
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLRegisterBufferObject(GLuint buffer);
+
+/**
+ * \brief Maps an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Maps the buffer object specified by \p buffer into the address space of the
+ * current CUDA context and returns in \p *dptr and \p *size the base pointer
+ * and size of the resulting mapping.
+ *
+ * There must be a valid OpenGL context bound to the current thread
+ * when this function is called.  This must be the same context, or a
+ * member of the same shareGroup, as the context that was bound when
+ * the buffer was registered.
+ *
+ * All streams in the current CUDA context are synchronized with the
+ * current GL context.
+ *
+ * \param dptr   - Returned mapped base pointer
+ * \param size   - Returned size of mapping
+ * \param buffer - The name of the buffer object to map
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_MAP_FAILED
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsMapResources
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLMapBufferObject(CUdeviceptr *dptr, size_t *size,  GLuint buffer);  
+
+/**
+ * \brief Unmaps an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Unmaps the buffer object specified by \p buffer for access by CUDA.
+ *
+ * There must be a valid OpenGL context bound to the current thread
+ * when this function is called.  This must be the same context, or a
+ * member of the same shareGroup, as the context that was bound when
+ * the buffer was registered.
+ *
+ * All streams in the current CUDA context are synchronized with the
+ * current GL context.
+ *
+ * \param buffer - Buffer object to unmap
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsUnmapResources
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLUnmapBufferObject(GLuint buffer);
+
+/**
+ * \brief Unregister an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Unregisters the buffer object specified by \p buffer.  This
+ * releases any resources associated with the registered buffer.
+ * After this call, the buffer may no longer be mapped for access by
+ * CUDA.
+ *
+ * There must be a valid OpenGL context bound to the current thread
+ * when this function is called.  This must be the same context, or a
+ * member of the same shareGroup, as the context that was bound when
+ * the buffer was registered.
+ *
+ * \param buffer - Name of the buffer object to unregister
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsUnregisterResource
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLUnregisterBufferObject(GLuint buffer);
+
+/**
+ * \brief Set the map flags for an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Sets the map flags for the buffer object specified by \p buffer.
+ *
+ * Changes to \p Flags will take effect the next time \p buffer is mapped.
+ * The \p Flags argument may be any of the following:
+ * - ::CU_GL_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA kernels. This is the default value.
+ * - ::CU_GL_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA kernels which
+ *   access this resource will not write to this resource.
+ * - ::CU_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that CUDA kernels
+ *   which access this resource will not read from this resource and will
+ *   write over the entire contents of the resource, so none of the data
+ *   previously stored in the resource will be preserved.
+ *
+ * If \p buffer has not been registered for use with CUDA, then
+ * ::CUDA_ERROR_INVALID_HANDLE is returned. If \p buffer is presently
+ * mapped for access by CUDA, then ::CUDA_ERROR_ALREADY_MAPPED is returned.
+ *
+ * There must be a valid OpenGL context bound to the current thread
+ * when this function is called.  This must be the same context, or a
+ * member of the same shareGroup, as the context that was bound when
+ * the buffer was registered.
+ *
+ * \param buffer - Buffer object to unmap
+ * \param Flags  - Map flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsResourceSetMapFlags
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLSetBufferObjectMapFlags(GLuint buffer, unsigned int Flags);
+
+/**
+ * \brief Maps an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Maps the buffer object specified by \p buffer into the address space of the
+ * current CUDA context and returns in \p *dptr and \p *size the base pointer
+ * and size of the resulting mapping.
+ *
+ * There must be a valid OpenGL context bound to the current thread
+ * when this function is called.  This must be the same context, or a
+ * member of the same shareGroup, as the context that was bound when
+ * the buffer was registered.
+ *
+ * Stream \p hStream in the current CUDA context is synchronized with
+ * the current GL context.
+ *
+ * \param dptr    - Returned mapped base pointer
+ * \param size    - Returned size of mapping
+ * \param buffer  - The name of the buffer object to map
+ * \param hStream - Stream to synchronize
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_MAP_FAILED
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsMapResources
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLMapBufferObjectAsync(CUdeviceptr *dptr, size_t *size,  GLuint buffer, CUstream hStream);
+
+/**
+ * \brief Unmaps an OpenGL buffer object
+ *
+ * \deprecated This function is deprecated as of Cuda 3.0. 
+ *
+ * Unmaps the buffer object specified by \p buffer for access by CUDA.
+ *
+ * There must be a valid OpenGL context bound to the current thread
+ * when this function is called.  This must be the same context, or a
+ * member of the same shareGroup, as the context that was bound when
+ * the buffer was registered.
+ *
+ * Stream \p hStream in the current CUDA context is synchronized with
+ * the current GL context.
+ *
+ * \param buffer  - Name of the buffer object to unmap
+ * \param hStream - Stream to synchronize
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuGraphicsUnmapResources
+ */
+__CUDA_DEPRECATED CUresult CUDAAPI cuGLUnmapBufferObjectAsync(GLuint buffer, CUstream hStream);
+
+/** @} */ /* END CUDA_GL_DEPRECATED */
+/** @} */ /* END CUDA_GL */
+
+
+#if defined(__CUDA_API_VERSION_INTERNAL)
+    #undef cuGLCtxCreate
+    #undef cuGLMapBufferObject
+    #undef cuGLMapBufferObjectAsync
+    #undef cuGLGetDevices
+
+    CUresult CUDAAPI cuGLGetDevices(unsigned int *pCudaDeviceCount, CUdevice *pCudaDevices, unsigned int cudaDeviceCount, CUGLDeviceList deviceList);
+    CUresult CUDAAPI cuGLMapBufferObject_v2(CUdeviceptr *dptr, size_t *size,  GLuint buffer);
+    CUresult CUDAAPI cuGLMapBufferObjectAsync_v2(CUdeviceptr *dptr, size_t *size,  GLuint buffer, CUstream hStream);
+    CUresult CUDAAPI cuGLCtxCreate(CUcontext *pCtx, unsigned int Flags, CUdevice device );
+    CUresult CUDAAPI cuGLMapBufferObject(CUdeviceptr_v1 *dptr, unsigned int *size,  GLuint buffer);
+    CUresult CUDAAPI cuGLMapBufferObjectAsync(CUdeviceptr_v1 *dptr, unsigned int *size,  GLuint buffer, CUstream hStream);
+#endif /* __CUDA_API_VERSION_INTERNAL */
+
+#ifdef __cplusplus
+};
+#endif
+
+#undef __CUDA_DEPRECATED
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaGLTypedefs.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaGLTypedefs.h
new file mode 100644
index 0000000000000000000000000000000000000000..81f0d5349e435159647af9af379d1e8e8441221c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaGLTypedefs.h
@@ -0,0 +1,123 @@
+/*
+ * Copyright 2020-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAGLTYPEDEFS_H
+#define CUDAGLTYPEDEFS_H
+
+// Dependent includes for cudagl.h
+#include <GL/gl.h>
+
+#include <cudaGL.h>
+
+#if defined(CUDA_API_PER_THREAD_DEFAULT_STREAM)
+    #define __API_TYPEDEF_PTDS(api, default_version, ptds_version) api ## _v ## ptds_version ## _ptds
+    #define __API_TYPEDEF_PTSZ(api, default_version, ptds_version) api ## _v ## ptds_version ## _ptsz
+#else
+    #define __API_TYPEDEF_PTDS(api, default_version, ptds_version) api ## _v ## default_version
+    #define __API_TYPEDEF_PTSZ(api, default_version, ptds_version) api ## _v ## default_version
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif // __cplusplus
+
+/*
+ * Macros for the latest version for each driver function in cudaGL.h
+ */
+#define PFN_cuGraphicsGLRegisterBuffer  PFN_cuGraphicsGLRegisterBuffer_v3000
+#define PFN_cuGraphicsGLRegisterImage  PFN_cuGraphicsGLRegisterImage_v3000
+#define PFN_cuWGLGetDevice  PFN_cuWGLGetDevice_v2020
+#define PFN_cuGLGetDevices  PFN_cuGLGetDevices_v6050
+#define PFN_cuGLCtxCreate  PFN_cuGLCtxCreate_v3020
+#define PFN_cuGLInit  PFN_cuGLInit_v2000
+#define PFN_cuGLRegisterBufferObject  PFN_cuGLRegisterBufferObject_v2000
+#define PFN_cuGLMapBufferObject  __API_TYPEDEF_PTDS(PFN_cuGLMapBufferObject, 3020, 7000)
+#define PFN_cuGLUnmapBufferObject  PFN_cuGLUnmapBufferObject_v2000
+#define PFN_cuGLUnregisterBufferObject  PFN_cuGLUnregisterBufferObject_v2000
+#define PFN_cuGLSetBufferObjectMapFlags  PFN_cuGLSetBufferObjectMapFlags_v2030
+#define PFN_cuGLMapBufferObjectAsync  __API_TYPEDEF_PTSZ(PFN_cuGLMapBufferObjectAsync, 3020, 7000)
+#define PFN_cuGLUnmapBufferObjectAsync  PFN_cuGLUnmapBufferObjectAsync_v2030
+
+
+/**
+ * Type definitions for functions defined in cudaGL.h
+ */
+typedef CUresult (CUDAAPI *PFN_cuGraphicsGLRegisterBuffer_v3000)(CUgraphicsResource *pCudaResource, GLuint buffer, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsGLRegisterImage_v3000)(CUgraphicsResource *pCudaResource, GLuint image, GLenum target, unsigned int Flags);
+#ifdef _WIN32
+typedef CUresult (CUDAAPI *PFN_cuWGLGetDevice_v2020)(CUdevice_v1 *pDevice, HGPUNV hGpu);
+#endif
+typedef CUresult (CUDAAPI *PFN_cuGLGetDevices_v6050)(unsigned int *pCudaDeviceCount, CUdevice_v1 *pCudaDevices, unsigned int cudaDeviceCount, CUGLDeviceList deviceList);
+typedef CUresult (CUDAAPI *PFN_cuGLCtxCreate_v3020)(CUcontext *pCtx, unsigned int Flags, CUdevice_v1 device);
+typedef CUresult (CUDAAPI *PFN_cuGLInit_v2000)(void);
+typedef CUresult (CUDAAPI *PFN_cuGLRegisterBufferObject_v2000)(GLuint buffer);
+typedef CUresult (CUDAAPI *PFN_cuGLMapBufferObject_v7000_ptds)(CUdeviceptr_v2 *dptr, size_t *size, GLuint buffer);
+typedef CUresult (CUDAAPI *PFN_cuGLUnmapBufferObject_v2000)(GLuint buffer);
+typedef CUresult (CUDAAPI *PFN_cuGLUnregisterBufferObject_v2000)(GLuint buffer);
+typedef CUresult (CUDAAPI *PFN_cuGLSetBufferObjectMapFlags_v2030)(GLuint buffer, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuGLMapBufferObjectAsync_v7000_ptsz)(CUdeviceptr_v2 *dptr, size_t *size, GLuint buffer, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGLUnmapBufferObjectAsync_v2030)(GLuint buffer, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGLMapBufferObject_v3020)(CUdeviceptr_v2 *dptr, size_t *size, GLuint buffer);
+typedef CUresult (CUDAAPI *PFN_cuGLMapBufferObjectAsync_v3020)(CUdeviceptr_v2 *dptr, size_t *size, GLuint buffer, CUstream hStream);
+
+/*
+ * Type definitions for older versioned functions in cuda.h
+ */
+#if defined(__CUDA_API_VERSION_INTERNAL)
+typedef CUresult (CUDAAPI *PFN_cuGLGetDevices_v4010)(unsigned int *pCudaDeviceCount, CUdevice_v1 *pCudaDevices, unsigned int cudaDeviceCount, CUGLDeviceList deviceList);
+typedef CUresult (CUDAAPI *PFN_cuGLMapBufferObject_v2000)(CUdeviceptr_v1 *dptr, unsigned int *size, GLuint buffer);
+typedef CUresult (CUDAAPI *PFN_cuGLMapBufferObjectAsync_v2030)(CUdeviceptr_v1 *dptr, unsigned int *size, GLuint buffer, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGLCtxCreate_v2000)(CUcontext *pCtx, unsigned int Flags, CUdevice_v1 device);
+#endif
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+
+#endif // file guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaProfilerTypedefs.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaProfilerTypedefs.h
new file mode 100644
index 0000000000000000000000000000000000000000..bea7df4573aff2fa5b0d0029ce9d40a7ebe2de46
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaProfilerTypedefs.h
@@ -0,0 +1,78 @@
+/*
+ * Copyright 2020-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAPROFILERTYPEDEFS_H
+#define CUDAPROFILERTYPEDEFS_H
+
+#include <cudaProfiler.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif // __cplusplus
+
+/*
+ * Macros for the latest version for each driver function in cudaProfiler.h
+ */
+#define PFN_cuProfilerInitialize  PFN_cuProfilerInitialize_v4000
+#define PFN_cuProfilerStart  PFN_cuProfilerStart_v4000
+#define PFN_cuProfilerStop  PFN_cuProfilerStop_v4000
+
+
+/**
+ * Type definitions for functions defined in cudaProfiler.h
+ */
+typedef CUresult (CUDAAPI *PFN_cuProfilerInitialize_v4000)(const char *configFile, const char *outputFile, CUoutput_mode outputMode);
+typedef CUresult (CUDAAPI *PFN_cuProfilerStart_v4000)(void);
+typedef CUresult (CUDAAPI *PFN_cuProfilerStop_v4000)(void);
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+
+#endif // file guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaTypedefs.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaTypedefs.h
new file mode 100644
index 0000000000000000000000000000000000000000..4957e9a07cdb10141586620f9e0f4d36ede345be
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaTypedefs.h
@@ -0,0 +1,1144 @@
+/*
+ * Copyright 2020-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDATYPEDEFS_H
+#define CUDATYPEDEFS_H
+
+#include <cuda.h>
+
+#if defined(CUDA_API_PER_THREAD_DEFAULT_STREAM)
+    #define __API_TYPEDEF_PTDS(api, default_version, ptds_version) api ## _v ## ptds_version ## _ptds
+    #define __API_TYPEDEF_PTSZ(api, default_version, ptds_version) api ## _v ## ptds_version ## _ptsz
+#else
+    #define __API_TYPEDEF_PTDS(api, default_version, ptds_version) api ## _v ## default_version
+    #define __API_TYPEDEF_PTSZ(api, default_version, ptds_version) api ## _v ## default_version
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif // __cplusplus
+
+/*
+ * Macros for the latest version for each driver function in cuda.h
+ */
+#define PFN_cuGetErrorString  PFN_cuGetErrorString_v6000
+#define PFN_cuGetErrorName  PFN_cuGetErrorName_v6000
+#define PFN_cuInit  PFN_cuInit_v2000
+#define PFN_cuDriverGetVersion  PFN_cuDriverGetVersion_v2020
+#define PFN_cuDeviceGet  PFN_cuDeviceGet_v2000
+#define PFN_cuDeviceGetCount  PFN_cuDeviceGetCount_v2000
+#define PFN_cuDeviceGetName  PFN_cuDeviceGetName_v2000
+#define PFN_cuDeviceGetUuid  PFN_cuDeviceGetUuid_v11040
+#define PFN_cuDeviceGetLuid  PFN_cuDeviceGetLuid_v10000
+#define PFN_cuDeviceTotalMem  PFN_cuDeviceTotalMem_v3020
+#define PFN_cuDeviceGetTexture1DLinearMaxWidth  PFN_cuDeviceGetTexture1DLinearMaxWidth_v11010
+#define PFN_cuDeviceGetAttribute  PFN_cuDeviceGetAttribute_v2000
+#define PFN_cuDeviceGetNvSciSyncAttributes  PFN_cuDeviceGetNvSciSyncAttributes_v10020
+#define PFN_cuDeviceSetMemPool  PFN_cuDeviceSetMemPool_v11020
+#define PFN_cuDeviceGetMemPool  PFN_cuDeviceGetMemPool_v11020
+#define PFN_cuDeviceGetDefaultMemPool  PFN_cuDeviceGetDefaultMemPool_v11020
+#define PFN_cuDeviceGetProperties  PFN_cuDeviceGetProperties_v2000
+#define PFN_cuDeviceComputeCapability  PFN_cuDeviceComputeCapability_v2000
+#define PFN_cuDevicePrimaryCtxRetain  PFN_cuDevicePrimaryCtxRetain_v7000
+#define PFN_cuDevicePrimaryCtxRelease  PFN_cuDevicePrimaryCtxRelease_v11000
+#define PFN_cuDevicePrimaryCtxSetFlags  PFN_cuDevicePrimaryCtxSetFlags_v11000
+#define PFN_cuDevicePrimaryCtxGetState  PFN_cuDevicePrimaryCtxGetState_v7000
+#define PFN_cuDevicePrimaryCtxReset  PFN_cuDevicePrimaryCtxReset_v11000
+#define PFN_cuDeviceGetExecAffinitySupport  PFN_cuDeviceGetExecAffinitySupport_v11040
+#define PFN_cuCtxCreate  PFN_cuCtxCreate_v11040
+#define PFN_cuCtxGetId  PFN_cuCtxGetId_v12000
+#define PFN_cuCtxDestroy  PFN_cuCtxDestroy_v4000
+#define PFN_cuCtxPushCurrent  PFN_cuCtxPushCurrent_v4000
+#define PFN_cuCtxPopCurrent  PFN_cuCtxPopCurrent_v4000
+#define PFN_cuCtxSetCurrent  PFN_cuCtxSetCurrent_v4000
+#define PFN_cuCtxGetCurrent  PFN_cuCtxGetCurrent_v4000
+#define PFN_cuCtxGetDevice  PFN_cuCtxGetDevice_v2000
+#define PFN_cuCtxGetFlags  PFN_cuCtxGetFlags_v7000
+#define PFN_cuCtxSetFlags  PFN_cuCtxSetFlags_v12010
+#define PFN_cuCtxSynchronize  PFN_cuCtxSynchronize_v2000
+#define PFN_cuCtxSetLimit  PFN_cuCtxSetLimit_v3010
+#define PFN_cuCtxGetLimit  PFN_cuCtxGetLimit_v3010
+#define PFN_cuCtxGetCacheConfig  PFN_cuCtxGetCacheConfig_v3020
+#define PFN_cuCtxSetCacheConfig  PFN_cuCtxSetCacheConfig_v3020
+#define PFN_cuCtxGetSharedMemConfig  PFN_cuCtxGetSharedMemConfig_v4020
+#define PFN_cuCtxSetSharedMemConfig  PFN_cuCtxSetSharedMemConfig_v4020
+#define PFN_cuCtxGetApiVersion  PFN_cuCtxGetApiVersion_v3020
+#define PFN_cuCtxGetStreamPriorityRange  PFN_cuCtxGetStreamPriorityRange_v5050
+#define PFN_cuCtxResetPersistingL2Cache  PFN_cuCtxResetPersistingL2Cache_v11000
+#define PFN_cuCtxAttach  PFN_cuCtxAttach_v2000
+#define PFN_cuCtxDetach  PFN_cuCtxDetach_v2000
+#define PFN_cuCtxGetExecAffinity  PFN_cuCtxGetExecAffinity_v11040
+#define PFN_cuModuleLoad  PFN_cuModuleLoad_v2000
+#define PFN_cuModuleLoadData  PFN_cuModuleLoadData_v2000
+#define PFN_cuModuleLoadDataEx  PFN_cuModuleLoadDataEx_v2010
+#define PFN_cuModuleLoadFatBinary  PFN_cuModuleLoadFatBinary_v2000
+#define PFN_cuModuleUnload  PFN_cuModuleUnload_v2000
+#define PFN_cuModuleGetFunction  PFN_cuModuleGetFunction_v2000
+#define PFN_cuModuleGetGlobal  PFN_cuModuleGetGlobal_v3020
+#define PFN_cuModuleGetTexRef  PFN_cuModuleGetTexRef_v2000
+#define PFN_cuModuleGetSurfRef  PFN_cuModuleGetSurfRef_v3000
+#define PFN_cuModuleGetFunctionCount PFN_cuModuleGetFunctionCount_v12040
+#define PFN_cuModuleEnumerateFunctions PFN_cuModuleEnumerateFunctions_v12040
+#define PFN_cuLinkCreate  PFN_cuLinkCreate_v6050
+#define PFN_cuLinkAddData  PFN_cuLinkAddData_v6050
+#define PFN_cuLinkAddFile  PFN_cuLinkAddFile_v6050
+#define PFN_cuLinkComplete  PFN_cuLinkComplete_v5050
+#define PFN_cuLinkDestroy  PFN_cuLinkDestroy_v5050
+#define PFN_cuMemGetInfo  PFN_cuMemGetInfo_v3020
+#define PFN_cuMemAlloc  PFN_cuMemAlloc_v3020
+#define PFN_cuMemAllocPitch  PFN_cuMemAllocPitch_v3020
+#define PFN_cuMemFree  PFN_cuMemFree_v3020
+#define PFN_cuMemGetAddressRange  PFN_cuMemGetAddressRange_v3020
+#define PFN_cuMemAllocHost  PFN_cuMemAllocHost_v3020
+#define PFN_cuMemFreeHost  PFN_cuMemFreeHost_v2000
+#define PFN_cuMemHostAlloc  PFN_cuMemHostAlloc_v2020
+#define PFN_cuMemHostGetDevicePointer  PFN_cuMemHostGetDevicePointer_v3020
+#define PFN_cuMemHostGetFlags  PFN_cuMemHostGetFlags_v2030
+#define PFN_cuMemAllocManaged  PFN_cuMemAllocManaged_v6000
+#define PFN_cuDeviceGetByPCIBusId  PFN_cuDeviceGetByPCIBusId_v4010
+#define PFN_cuDeviceGetPCIBusId  PFN_cuDeviceGetPCIBusId_v4010
+#define PFN_cuIpcGetEventHandle  PFN_cuIpcGetEventHandle_v4010
+#define PFN_cuIpcOpenEventHandle  PFN_cuIpcOpenEventHandle_v4010
+#define PFN_cuIpcGetMemHandle  PFN_cuIpcGetMemHandle_v4010
+#define PFN_cuIpcOpenMemHandle  PFN_cuIpcOpenMemHandle_v11000
+#define PFN_cuIpcCloseMemHandle  PFN_cuIpcCloseMemHandle_v4010
+#define PFN_cuMemHostRegister  PFN_cuMemHostRegister_v6050
+#define PFN_cuMemHostUnregister  PFN_cuMemHostUnregister_v4000
+#define PFN_cuMemcpy  __API_TYPEDEF_PTDS(PFN_cuMemcpy, 4000, 7000)
+#define PFN_cuMemcpyPeer  __API_TYPEDEF_PTDS(PFN_cuMemcpyPeer, 4000, 7000)
+#define PFN_cuMemcpyHtoD  __API_TYPEDEF_PTDS(PFN_cuMemcpyHtoD, 3020, 7000)
+#define PFN_cuMemcpyDtoH  __API_TYPEDEF_PTDS(PFN_cuMemcpyDtoH, 3020, 7000)
+#define PFN_cuMemcpyDtoD  __API_TYPEDEF_PTDS(PFN_cuMemcpyDtoD, 3020, 7000)
+#define PFN_cuMemcpyDtoA  __API_TYPEDEF_PTDS(PFN_cuMemcpyDtoA, 3020, 7000)
+#define PFN_cuMemcpyAtoD  __API_TYPEDEF_PTDS(PFN_cuMemcpyAtoD, 3020, 7000)
+#define PFN_cuMemcpyHtoA  __API_TYPEDEF_PTDS(PFN_cuMemcpyHtoA, 3020, 7000)
+#define PFN_cuMemcpyAtoH  __API_TYPEDEF_PTDS(PFN_cuMemcpyAtoH, 3020, 7000)
+#define PFN_cuMemcpyAtoA  __API_TYPEDEF_PTDS(PFN_cuMemcpyAtoA, 3020, 7000)
+#define PFN_cuMemcpy2D  __API_TYPEDEF_PTDS(PFN_cuMemcpy2D, 3020, 7000)
+#define PFN_cuMemcpy2DUnaligned  __API_TYPEDEF_PTDS(PFN_cuMemcpy2DUnaligned, 3020, 7000)
+#define PFN_cuMemcpy3D  __API_TYPEDEF_PTDS(PFN_cuMemcpy3D, 3020, 7000)
+#define PFN_cuMemcpy3DPeer  __API_TYPEDEF_PTDS(PFN_cuMemcpy3DPeer, 4000, 7000)
+#define PFN_cuMemcpyAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyAsync, 4000, 7000)
+#define PFN_cuMemcpyPeerAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyPeerAsync, 4000, 7000)
+#define PFN_cuMemcpyHtoDAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyHtoDAsync, 3020, 7000)
+#define PFN_cuMemcpyDtoHAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyDtoHAsync, 3020, 7000)
+#define PFN_cuMemcpyDtoDAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyDtoDAsync, 3020, 7000)
+#define PFN_cuMemcpyHtoAAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyHtoAAsync, 3020, 7000)
+#define PFN_cuMemcpyAtoHAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyAtoHAsync, 3020, 7000)
+#define PFN_cuMemcpy2DAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpy2DAsync, 3020, 7000)
+#define PFN_cuMemcpy3DAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpy3DAsync, 3020, 7000)
+#define PFN_cuMemcpy3DPeerAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpy3DPeerAsync, 4000, 7000)
+#define PFN_cuMemcpyBatchAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpyBatchAsync, 12080, 12080)
+#define PFN_cuMemcpy3DBatchAsync  __API_TYPEDEF_PTSZ(PFN_cuMemcpy3DBatchAsync, 12080, 12080)
+#define PFN_cuMemsetD8  __API_TYPEDEF_PTDS(PFN_cuMemsetD8, 3020, 7000)
+#define PFN_cuMemsetD16  __API_TYPEDEF_PTDS(PFN_cuMemsetD16, 3020, 7000)
+#define PFN_cuMemsetD32  __API_TYPEDEF_PTDS(PFN_cuMemsetD32, 3020, 7000)
+#define PFN_cuMemsetD2D8  __API_TYPEDEF_PTDS(PFN_cuMemsetD2D8, 3020, 7000)
+#define PFN_cuMemsetD2D16  __API_TYPEDEF_PTDS(PFN_cuMemsetD2D16, 3020, 7000)
+#define PFN_cuMemsetD2D32  __API_TYPEDEF_PTDS(PFN_cuMemsetD2D32, 3020, 7000)
+#define PFN_cuMemsetD8Async  __API_TYPEDEF_PTSZ(PFN_cuMemsetD8Async, 3020, 7000)
+#define PFN_cuMemsetD16Async  __API_TYPEDEF_PTSZ(PFN_cuMemsetD16Async, 3020, 7000)
+#define PFN_cuMemsetD32Async  __API_TYPEDEF_PTSZ(PFN_cuMemsetD32Async, 3020, 7000)
+#define PFN_cuMemsetD2D8Async  __API_TYPEDEF_PTSZ(PFN_cuMemsetD2D8Async, 3020, 7000)
+#define PFN_cuMemsetD2D16Async  __API_TYPEDEF_PTSZ(PFN_cuMemsetD2D16Async, 3020, 7000)
+#define PFN_cuMemsetD2D32Async  __API_TYPEDEF_PTSZ(PFN_cuMemsetD2D32Async, 3020, 7000)
+#define PFN_cuArrayCreate  PFN_cuArrayCreate_v3020
+#define PFN_cuArrayGetDescriptor  PFN_cuArrayGetDescriptor_v3020
+#define PFN_cuArrayGetSparseProperties  PFN_cuArrayGetSparseProperties_v11010
+#define PFN_cuMipmappedArrayGetSparseProperties  PFN_cuMipmappedArrayGetSparseProperties_v11010
+#define PFN_cuArrayGetMemoryRequirements  PFN_cuArrayGetMemoryRequirements_v11060
+#define PFN_cuMipmappedArrayGetMemoryRequirements  PFN_cuMipmappedArrayGetMemoryRequirements_v11060
+#define PFN_cuArrayGetPlane  PFN_cuArrayGetPlane_v11020
+#define PFN_cuArrayDestroy  PFN_cuArrayDestroy_v2000
+#define PFN_cuArray3DCreate  PFN_cuArray3DCreate_v3020
+#define PFN_cuArray3DGetDescriptor  PFN_cuArray3DGetDescriptor_v3020
+#define PFN_cuMipmappedArrayCreate  PFN_cuMipmappedArrayCreate_v5000
+#define PFN_cuMipmappedArrayGetLevel  PFN_cuMipmappedArrayGetLevel_v5000
+#define PFN_cuMipmappedArrayDestroy  PFN_cuMipmappedArrayDestroy_v5000
+#define PFN_cuMemAddressReserve  PFN_cuMemAddressReserve_v10020
+#define PFN_cuMemAddressFree  PFN_cuMemAddressFree_v10020
+#define PFN_cuMemCreate  PFN_cuMemCreate_v10020
+#define PFN_cuMemRelease  PFN_cuMemRelease_v10020
+#define PFN_cuMemMap  PFN_cuMemMap_v10020
+#define PFN_cuMemMapArrayAsync  __API_TYPEDEF_PTSZ(PFN_cuMemMapArrayAsync, 11010, 11010)
+#define PFN_cuMemUnmap  PFN_cuMemUnmap_v10020
+#define PFN_cuMemSetAccess  PFN_cuMemSetAccess_v10020
+#define PFN_cuMemGetAccess  PFN_cuMemGetAccess_v10020
+#define PFN_cuMemExportToShareableHandle  PFN_cuMemExportToShareableHandle_v10020
+#define PFN_cuMemImportFromShareableHandle  PFN_cuMemImportFromShareableHandle_v10020
+#define PFN_cuMemGetAllocationGranularity  PFN_cuMemGetAllocationGranularity_v10020
+#define PFN_cuMemGetAllocationPropertiesFromHandle  PFN_cuMemGetAllocationPropertiesFromHandle_v10020
+#define PFN_cuMemRetainAllocationHandle  PFN_cuMemRetainAllocationHandle_v11000
+#define PFN_cuMemFreeAsync  __API_TYPEDEF_PTSZ(PFN_cuMemFreeAsync, 11020, 11020)
+#define PFN_cuMemAllocAsync  __API_TYPEDEF_PTSZ(PFN_cuMemAllocAsync, 11020, 11020)
+#define PFN_cuMemPoolTrimTo  PFN_cuMemPoolTrimTo_v11020
+#define PFN_cuMemPoolSetAttribute  PFN_cuMemPoolSetAttribute_v11020
+#define PFN_cuMemPoolGetAttribute  PFN_cuMemPoolGetAttribute_v11020
+#define PFN_cuMemPoolSetAccess  PFN_cuMemPoolSetAccess_v11020
+#define PFN_cuMemPoolGetAccess  PFN_cuMemPoolGetAccess_v11020
+#define PFN_cuMemPoolCreate  PFN_cuMemPoolCreate_v11020
+#define PFN_cuMemPoolDestroy  PFN_cuMemPoolDestroy_v11020
+#define PFN_cuMemAllocFromPoolAsync  __API_TYPEDEF_PTSZ(PFN_cuMemAllocFromPoolAsync, 11020, 11020)
+#define PFN_cuMemPoolExportToShareableHandle  PFN_cuMemPoolExportToShareableHandle_v11020
+#define PFN_cuMemPoolImportFromShareableHandle  PFN_cuMemPoolImportFromShareableHandle_v11020
+#define PFN_cuMemPoolExportPointer  PFN_cuMemPoolExportPointer_v11020
+#define PFN_cuMemPoolImportPointer  PFN_cuMemPoolImportPointer_v11020
+#define PFN_cuPointerGetAttribute  PFN_cuPointerGetAttribute_v4000
+#define PFN_cuMemPrefetchAsync  __API_TYPEDEF_PTSZ(PFN_cuMemPrefetchAsync, 8000, 8000)
+#define PFN_cuMemAdvise  PFN_cuMemAdvise_v8000
+#define PFN_cuMemAdvise_v2  PFN_cuMemAdvise_v12020
+#define PFN_cuMemPrefetchAsync_v2  __API_TYPEDEF_PTSZ(PFN_cuMemPrefetchAsync, 12020, 12020)
+#define PFN_cuMemRangeGetAttribute  PFN_cuMemRangeGetAttribute_v8000
+#define PFN_cuMemRangeGetAttributes  PFN_cuMemRangeGetAttributes_v8000
+#define PFN_cuMulticastCreate  PFN_cuMulticastCreate_v12010
+#define PFN_cuMulticastAddDevice  PFN_cuMulticastAddDevice_v12010
+#define PFN_cuMulticastBindMem  PFN_cuMulticastBindMem_v12010
+#define PFN_cuMulticastBindAddr  PFN_cuMulticastBindAddr_v12010
+#define PFN_cuMulticastUnbind  PFN_cuMulticastUnbind_v12010
+#define PFN_cuMulticastGetGranularity  PFN_cuMulticastGetGranularity_v12010
+#define PFN_cuPointerSetAttribute  PFN_cuPointerSetAttribute_v6000
+#define PFN_cuPointerGetAttributes  PFN_cuPointerGetAttributes_v7000
+#define PFN_cuStreamCreate  PFN_cuStreamCreate_v2000
+#define PFN_cuStreamCreateWithPriority  PFN_cuStreamCreateWithPriority_v5050
+#define PFN_cuStreamGetId	__API_TYPEDEF_PTSZ(PFN_cuStreamGetId_v12000, 12000, 12000)
+#define PFN_cuStreamGetPriority  __API_TYPEDEF_PTSZ(PFN_cuStreamGetPriority, 5050, 7000)
+#define PFN_cuStreamGetFlags  __API_TYPEDEF_PTSZ(PFN_cuStreamGetFlags, 5050, 7000)
+#define PFN_cuStreamGetDevice __API_TYPEDEF_PTSZ(PFN_cuStreamGetDevice, 12080, 12080)
+#define PFN_cuStreamGetCtx  __API_TYPEDEF_PTSZ(PFN_cuStreamGetCtx, 9020, 9020)
+#define PFN_cuStreamWaitEvent  __API_TYPEDEF_PTSZ(PFN_cuStreamWaitEvent, 3020, 7000)
+#define PFN_cuStreamAddCallback  __API_TYPEDEF_PTSZ(PFN_cuStreamAddCallback, 5000, 7000)
+#define PFN_cuStreamBeginCapture  __API_TYPEDEF_PTSZ(PFN_cuStreamBeginCapture, 10010, 10010)
+#define PFN_cuStreamBeginCaptureToGraph  __API_TYPEDEF_PTSZ(PFN_cuStreamBeginCaptureToGraph, 12030, 12030)
+#define PFN_cuThreadExchangeStreamCaptureMode  PFN_cuThreadExchangeStreamCaptureMode_v10010
+#define PFN_cuStreamEndCapture  __API_TYPEDEF_PTSZ(PFN_cuStreamEndCapture, 10000, 10000)
+#define PFN_cuStreamIsCapturing  __API_TYPEDEF_PTSZ(PFN_cuStreamIsCapturing, 10000, 10000)
+#define PFN_cuStreamGetCaptureInfo  __API_TYPEDEF_PTSZ(PFN_cuStreamGetCaptureInfo, 10010, 10010)
+#define PFN_cuStreamGetCaptureInfo_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamGetCaptureInfo, 11030, 11030)
+#define PFN_cuStreamGetCaptureInfo_v3  __API_TYPEDEF_PTSZ(PFN_cuStreamGetCaptureInfo, 12030, 12030)
+#define PFN_cuStreamUpdateCaptureDependencies  __API_TYPEDEF_PTSZ(PFN_cuStreamUpdateCaptureDependencies, 11030, 11030)
+#define PFN_cuStreamUpdateCaptureDependencies_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamUpdateCaptureDependencies, 12030, 12030)
+#define PFN_cuStreamAttachMemAsync  __API_TYPEDEF_PTSZ(PFN_cuStreamAttachMemAsync, 6000, 7000)
+#define PFN_cuStreamQuery  __API_TYPEDEF_PTSZ(PFN_cuStreamQuery, 2000, 7000)
+#define PFN_cuStreamSynchronize  __API_TYPEDEF_PTSZ(PFN_cuStreamSynchronize, 2000, 7000)
+#define PFN_cuStreamDestroy  PFN_cuStreamDestroy_v4000
+#define PFN_cuStreamCopyAttributes  __API_TYPEDEF_PTSZ(PFN_cuStreamCopyAttributes, 11000, 11000)
+#define PFN_cuStreamGetAttribute  __API_TYPEDEF_PTSZ(PFN_cuStreamGetAttribute, 11000, 11000)
+#define PFN_cuStreamSetAttribute  __API_TYPEDEF_PTSZ(PFN_cuStreamSetAttribute, 11000, 11000)
+#define PFN_cuEventCreate  PFN_cuEventCreate_v2000
+#define PFN_cuEventRecord  __API_TYPEDEF_PTSZ(PFN_cuEventRecord, 2000, 7000)
+#define PFN_cuEventRecordWithFlags  __API_TYPEDEF_PTSZ(PFN_cuEventRecordWithFlags, 11010, 11010)
+#define PFN_cuEventQuery  PFN_cuEventQuery_v2000
+#define PFN_cuEventSynchronize  PFN_cuEventSynchronize_v2000
+#define PFN_cuEventDestroy  PFN_cuEventDestroy_v4000
+#define PFN_cuEventElapsedTime  PFN_cuEventElapsedTime_v2000
+#define PFN_cuEventElapsedTime_v2  PFN_cuEventElapsedTime_v12080
+#define PFN_cuImportExternalMemory  PFN_cuImportExternalMemory_v10000
+#define PFN_cuExternalMemoryGetMappedBuffer  PFN_cuExternalMemoryGetMappedBuffer_v10000
+#define PFN_cuExternalMemoryGetMappedMipmappedArray  PFN_cuExternalMemoryGetMappedMipmappedArray_v10000
+#define PFN_cuDestroyExternalMemory  PFN_cuDestroyExternalMemory_v10000
+#define PFN_cuImportExternalSemaphore  PFN_cuImportExternalSemaphore_v10000
+#define PFN_cuSignalExternalSemaphoresAsync  __API_TYPEDEF_PTSZ(PFN_cuSignalExternalSemaphoresAsync, 10000, 10000)
+#define PFN_cuWaitExternalSemaphoresAsync  __API_TYPEDEF_PTSZ(PFN_cuWaitExternalSemaphoresAsync, 10000, 10000)
+#define PFN_cuDestroyExternalSemaphore  PFN_cuDestroyExternalSemaphore_v10000
+#define PFN_cuStreamWaitValue32  __API_TYPEDEF_PTSZ(PFN_cuStreamWaitValue32, 8000, 8000)
+#define PFN_cuStreamWaitValue64  __API_TYPEDEF_PTSZ(PFN_cuStreamWaitValue64, 9000, 9000)
+#define PFN_cuStreamWriteValue32  __API_TYPEDEF_PTSZ(PFN_cuStreamWriteValue32, 8000, 8000)
+#define PFN_cuStreamWriteValue64  __API_TYPEDEF_PTSZ(PFN_cuStreamWriteValue64, 9000, 9000)
+#define PFN_cuStreamBatchMemOp  __API_TYPEDEF_PTSZ(PFN_cuStreamBatchMemOp, 8000, 8000)
+#define PFN_cuStreamWaitValue32_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamWaitValue32, 11070, 11070)
+#define PFN_cuStreamWaitValue64_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamWaitValue64, 11070, 11070)
+#define PFN_cuStreamWriteValue32_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamWriteValue32, 11070, 11070)
+#define PFN_cuStreamWriteValue64_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamWriteValue64, 11070, 11070)
+#define PFN_cuStreamBatchMemOp_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamBatchMemOp, 11070, 11070)
+#define PFN_cuFuncGetAttribute  PFN_cuFuncGetAttribute_v2020
+#define PFN_cuFuncSetAttribute  PFN_cuFuncSetAttribute_v9000
+#define PFN_cuFuncSetCacheConfig  PFN_cuFuncSetCacheConfig_v3000
+#define PFN_cuFuncSetSharedMemConfig  PFN_cuFuncSetSharedMemConfig_v4020
+#define PFN_cuFuncGetName  PFN_cuFuncGetName_v12030
+#define PFN_cuFuncGetParamInfo  PFN_cuFuncGetParamInfo_v12040
+#define PFN_cuFuncIsLoaded PFN_cuFuncIsLoaded_v12040
+#define PFN_cuFuncLoad PFN_cuFuncLoad_v12040
+#define PFN_cuLaunchKernel  __API_TYPEDEF_PTSZ(PFN_cuLaunchKernel, 4000, 7000)
+#define PFN_cuLaunchKernelEx __API_TYPEDEF_PTSZ(PFN_cuLaunchKernelEx, 11060, 11060)
+#define PFN_cuLaunchCooperativeKernel  __API_TYPEDEF_PTSZ(PFN_cuLaunchCooperativeKernel, 9000, 9000)
+#define PFN_cuLaunchCooperativeKernelMultiDevice  PFN_cuLaunchCooperativeKernelMultiDevice_v9000
+#define PFN_cuLaunchHostFunc  __API_TYPEDEF_PTSZ(PFN_cuLaunchHostFunc, 10000, 10000)
+#define PFN_cuFuncSetBlockShape  PFN_cuFuncSetBlockShape_v2000
+#define PFN_cuFuncSetSharedSize  PFN_cuFuncSetSharedSize_v2000
+#define PFN_cuParamSetSize  PFN_cuParamSetSize_v2000
+#define PFN_cuParamSeti  PFN_cuParamSeti_v2000
+#define PFN_cuParamSetf  PFN_cuParamSetf_v2000
+#define PFN_cuParamSetv  PFN_cuParamSetv_v2000
+#define PFN_cuLaunch  PFN_cuLaunch_v2000
+#define PFN_cuLaunchGrid  PFN_cuLaunchGrid_v2000
+#define PFN_cuLaunchGridAsync  PFN_cuLaunchGridAsync_v2000
+#define PFN_cuParamSetTexRef  PFN_cuParamSetTexRef_v2000
+#define PFN_cuGraphCreate  PFN_cuGraphCreate_v10000
+#define PFN_cuGraphAddKernelNode  PFN_cuGraphAddKernelNode_v12000
+#define PFN_cuGraphKernelNodeGetParams  PFN_cuGraphKernelNodeGetParams_v12000
+#define PFN_cuGraphKernelNodeSetParams  PFN_cuGraphKernelNodeSetParams_v12000
+#define PFN_cuGraphAddMemcpyNode  PFN_cuGraphAddMemcpyNode_v10000
+#define PFN_cuGraphMemcpyNodeGetParams  PFN_cuGraphMemcpyNodeGetParams_v10000
+#define PFN_cuGraphMemcpyNodeSetParams  PFN_cuGraphMemcpyNodeSetParams_v10000
+#define PFN_cuGraphAddMemsetNode  PFN_cuGraphAddMemsetNode_v10000
+#define PFN_cuGraphMemsetNodeGetParams  PFN_cuGraphMemsetNodeGetParams_v10000
+#define PFN_cuGraphMemsetNodeSetParams  PFN_cuGraphMemsetNodeSetParams_v10000
+#define PFN_cuGraphAddHostNode  PFN_cuGraphAddHostNode_v10000
+#define PFN_cuGraphHostNodeGetParams  PFN_cuGraphHostNodeGetParams_v10000
+#define PFN_cuGraphHostNodeSetParams  PFN_cuGraphHostNodeSetParams_v10000
+#define PFN_cuGraphAddChildGraphNode  PFN_cuGraphAddChildGraphNode_v10000
+#define PFN_cuGraphChildGraphNodeGetGraph  PFN_cuGraphChildGraphNodeGetGraph_v10000
+#define PFN_cuGraphAddEmptyNode  PFN_cuGraphAddEmptyNode_v10000
+#define PFN_cuGraphAddEventRecordNode  PFN_cuGraphAddEventRecordNode_v11010
+#define PFN_cuGraphEventRecordNodeGetEvent  PFN_cuGraphEventRecordNodeGetEvent_v11010
+#define PFN_cuGraphEventRecordNodeSetEvent  PFN_cuGraphEventRecordNodeSetEvent_v11010
+#define PFN_cuGraphAddEventWaitNode  PFN_cuGraphAddEventWaitNode_v11010
+#define PFN_cuGraphEventWaitNodeGetEvent  PFN_cuGraphEventWaitNodeGetEvent_v11010
+#define PFN_cuGraphEventWaitNodeSetEvent  PFN_cuGraphEventWaitNodeSetEvent_v11010
+#define PFN_cuGraphAddExternalSemaphoresSignalNode  PFN_cuGraphAddExternalSemaphoresSignalNode_v11020
+#define PFN_cuGraphExternalSemaphoresSignalNodeGetParams  PFN_cuGraphExternalSemaphoresSignalNodeGetParams_v11020
+#define PFN_cuGraphExternalSemaphoresSignalNodeSetParams  PFN_cuGraphExternalSemaphoresSignalNodeSetParams_v11020
+#define PFN_cuGraphAddExternalSemaphoresWaitNode  PFN_cuGraphAddExternalSemaphoresWaitNode_v11020
+#define PFN_cuGraphExternalSemaphoresWaitNodeGetParams  PFN_cuGraphExternalSemaphoresWaitNodeGetParams_v11020
+#define PFN_cuGraphExternalSemaphoresWaitNodeSetParams  PFN_cuGraphExternalSemaphoresWaitNodeSetParams_v11020
+#define PFN_cuGraphAddBatchMemOpNode PFN_cuGraphAddBatchMemOpNode_v11070
+#define PFN_cuGraphBatchMemOpNodeGetParams PFN_cuGraphBatchMemOpNodeGetParams_v11070
+#define PFN_cuGraphBatchMemOpNodeSetParams PFN_cuGraphBatchMemOpNodeSetParams _v11070
+#define PFN_cuGraphExecBatchMemOpNodeSetParams PFN_cuGraphExecBatchMemOpNodeSetParams_v11070
+#define PFN_cuGraphClone  PFN_cuGraphClone_v10000
+#define PFN_cuGraphNodeFindInClone  PFN_cuGraphNodeFindInClone_v10000
+#define PFN_cuGraphNodeGetType  PFN_cuGraphNodeGetType_v10000
+#define PFN_cuGraphGetNodes  PFN_cuGraphGetNodes_v10000
+#define PFN_cuGraphGetRootNodes  PFN_cuGraphGetRootNodes_v10000
+#define PFN_cuGraphGetEdges  PFN_cuGraphGetEdges_v12030
+#define PFN_cuGraphNodeGetDependencies  PFN_cuGraphNodeGetDependencies_v12030
+#define PFN_cuGraphNodeGetDependentNodes  PFN_cuGraphNodeGetDependentNodes_v12030
+#define PFN_cuGraphAddDependencies  PFN_cuGraphAddDependencies_v12030
+#define PFN_cuGraphRemoveDependencies  PFN_cuGraphRemoveDependencies_v12030
+#define PFN_cuGraphDestroyNode  PFN_cuGraphDestroyNode_v10000
+
+#define PFN_cuGraphInstantiate  PFN_cuGraphInstantiateWithFlags_v11040
+
+#define PFN_cuGraphInstantiateWithFlags  PFN_cuGraphInstantiateWithFlags_v11040
+#define PFN_cuGraphInstantiateWithParams  __API_TYPEDEF_PTSZ(PFN_cuGraphInstantiateWithParams, 12000, 12000)
+#define PFN_cuGraphExecGetFlags  PFN_cuGraphExecGetFlags_v12000
+#define PFN_cuGraphExecKernelNodeSetParams  PFN_cuGraphExecKernelNodeSetParams_v12000
+#define PFN_cuGraphExecMemcpyNodeSetParams  PFN_cuGraphExecMemcpyNodeSetParams_v10020
+#define PFN_cuGraphExecMemsetNodeSetParams  PFN_cuGraphExecMemsetNodeSetParams_v10020
+#define PFN_cuGraphExecHostNodeSetParams  PFN_cuGraphExecHostNodeSetParams_v10020
+#define PFN_cuGraphExecChildGraphNodeSetParams  PFN_cuGraphExecChildGraphNodeSetParams_v11010
+#define PFN_cuGraphExecEventRecordNodeSetEvent  PFN_cuGraphExecEventRecordNodeSetEvent_v11010
+#define PFN_cuGraphExecEventWaitNodeSetEvent  PFN_cuGraphExecEventWaitNodeSetEvent_v11010
+#define PFN_cuGraphExecExternalSemaphoresSignalNodeSetParams  PFN_cuGraphExecExternalSemaphoresSignalNodeSetParams_v11020
+#define PFN_cuGraphExecExternalSemaphoresWaitNodeSetParams  PFN_cuGraphExecExternalSemaphoresWaitNodeSetParams_v11020
+#define PFN_cuGraphUpload  __API_TYPEDEF_PTSZ(PFN_cuGraphUpload, 11010, 11010)
+#define PFN_cuGraphLaunch  __API_TYPEDEF_PTSZ(PFN_cuGraphLaunch, 10000, 10000)
+#define PFN_cuGraphExecDestroy  PFN_cuGraphExecDestroy_v10000
+#define PFN_cuGraphDestroy  PFN_cuGraphDestroy_v10000
+#define PFN_cuGraphExecUpdate  PFN_cuGraphExecUpdate_v12000
+#define PFN_cuGraphKernelNodeCopyAttributes  PFN_cuGraphKernelNodeCopyAttributes_v11000
+#define PFN_cuGraphKernelNodeGetAttribute  PFN_cuGraphKernelNodeGetAttribute_v11000
+#define PFN_cuGraphKernelNodeSetAttribute  PFN_cuGraphKernelNodeSetAttribute_v11000
+#define PFN_cuGraphDebugDotPrint  PFN_cuGraphDebugDotPrint_v11030
+#define PFN_cuGraphAddMemAllocNode  PFN_cuGraphAddMemAllocNode_v11040
+#define PFN_cuGraphMemAllocNodeGetParams PFN_cuGraphMemAllocNodeGetParams_v11040
+#define PFN_cuGraphAddMemFreeNode  PFN_cuGraphAddMemFreeNode_v11040
+#define PFN_cuGraphMemFreeNodeGetParams PFN_cuGraphMemFreeNodeGetParams_v11040
+#define PFN_cuGraphNodeSetEnabled PFN_cuGraphNodeSetEnabled_v11060
+#define PFN_cuGraphNodeGetEnabled PFN_cuGraphNodeGetEnabled_v11060
+#define PFN_cuGraphAddNode PFN_cuGraphAddNode_v12030
+#define PFN_cuGraphNodeSetParams PFN_cuGraphNodeSetParams_v12020
+#define PFN_cuGraphExecNodeSetParams PFN_cuGraphExecNodeSetParams_v12020
+#define PFN_GraphConditionalHandleCreate PFN_cuGraphConditionalHandleCreate_v12030
+#define PFN_cuDeviceGraphMemTrim  PFN_cuDeviceGraphMemTrim_v11040
+#define PFN_cuDeviceGetGraphMemAttribute  PFN_cuDeviceGetGraphMemAttribute_v11040
+#define PFN_cuDeviceSetGraphMemAttribute  PFN_cuDeviceSetGraphMemAttribute_v11040
+#define PFN_cuOccupancyMaxActiveBlocksPerMultiprocessor  PFN_cuOccupancyMaxActiveBlocksPerMultiprocessor_v6050
+#define PFN_cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags  PFN_cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_v7000
+#define PFN_cuOccupancyMaxPotentialBlockSize  PFN_cuOccupancyMaxPotentialBlockSize_v6050
+#define PFN_cuOccupancyMaxPotentialBlockSizeWithFlags  PFN_cuOccupancyMaxPotentialBlockSizeWithFlags_v7000
+#define PFN_cuOccupancyAvailableDynamicSMemPerBlock  PFN_cuOccupancyAvailableDynamicSMemPerBlock_v10020
+#define PFN_cuOccupancyMaxPotentialClusterSize  PFN_cuOccupancyMaxPotentialClusterSize_v11070
+#define PFN_cuOccupancyMaxActiveClusters  PFN_cuOccupancyMaxActiveClusters_v11070
+#define PFN_cuTexRefSetArray  PFN_cuTexRefSetArray_v2000
+#define PFN_cuTexRefSetMipmappedArray  PFN_cuTexRefSetMipmappedArray_v5000
+#define PFN_cuTexRefSetAddress  PFN_cuTexRefSetAddress_v3020
+#define PFN_cuTexRefSetAddress2D  PFN_cuTexRefSetAddress2D_v4010
+#define PFN_cuTexRefSetFormat  PFN_cuTexRefSetFormat_v2000
+#define PFN_cuTexRefSetAddressMode  PFN_cuTexRefSetAddressMode_v2000
+#define PFN_cuTexRefSetFilterMode  PFN_cuTexRefSetFilterMode_v2000
+#define PFN_cuTexRefSetMipmapFilterMode  PFN_cuTexRefSetMipmapFilterMode_v5000
+#define PFN_cuTexRefSetMipmapLevelBias  PFN_cuTexRefSetMipmapLevelBias_v5000
+#define PFN_cuTexRefSetMipmapLevelClamp  PFN_cuTexRefSetMipmapLevelClamp_v5000
+#define PFN_cuTexRefSetMaxAnisotropy  PFN_cuTexRefSetMaxAnisotropy_v5000
+#define PFN_cuTexRefSetBorderColor  PFN_cuTexRefSetBorderColor_v8000
+#define PFN_cuTexRefSetFlags  PFN_cuTexRefSetFlags_v2000
+#define PFN_cuTexRefGetAddress  PFN_cuTexRefGetAddress_v3020
+#define PFN_cuTexRefGetArray  PFN_cuTexRefGetArray_v2000
+#define PFN_cuTexRefGetMipmappedArray  PFN_cuTexRefGetMipmappedArray_v5000
+#define PFN_cuTexRefGetAddressMode  PFN_cuTexRefGetAddressMode_v2000
+#define PFN_cuTexRefGetFilterMode  PFN_cuTexRefGetFilterMode_v2000
+#define PFN_cuTexRefGetFormat  PFN_cuTexRefGetFormat_v2000
+#define PFN_cuTexRefGetMipmapFilterMode  PFN_cuTexRefGetMipmapFilterMode_v5000
+#define PFN_cuTexRefGetMipmapLevelBias  PFN_cuTexRefGetMipmapLevelBias_v5000
+#define PFN_cuTexRefGetMipmapLevelClamp  PFN_cuTexRefGetMipmapLevelClamp_v5000
+#define PFN_cuTexRefGetMaxAnisotropy  PFN_cuTexRefGetMaxAnisotropy_v5000
+#define PFN_cuTexRefGetBorderColor  PFN_cuTexRefGetBorderColor_v8000
+#define PFN_cuTexRefGetFlags  PFN_cuTexRefGetFlags_v2000
+#define PFN_cuTexRefCreate  PFN_cuTexRefCreate_v2000
+#define PFN_cuTexRefDestroy  PFN_cuTexRefDestroy_v2000
+#define PFN_cuSurfRefSetArray  PFN_cuSurfRefSetArray_v3000
+#define PFN_cuSurfRefGetArray  PFN_cuSurfRefGetArray_v3000
+#define PFN_cuTexObjectCreate  PFN_cuTexObjectCreate_v5000
+#define PFN_cuTexObjectDestroy  PFN_cuTexObjectDestroy_v5000
+#define PFN_cuTexObjectGetResourceDesc  PFN_cuTexObjectGetResourceDesc_v5000
+#define PFN_cuTexObjectGetTextureDesc  PFN_cuTexObjectGetTextureDesc_v5000
+#define PFN_cuTexObjectGetResourceViewDesc  PFN_cuTexObjectGetResourceViewDesc_v5000
+#define PFN_cuSurfObjectCreate  PFN_cuSurfObjectCreate_v5000
+#define PFN_cuSurfObjectDestroy  PFN_cuSurfObjectDestroy_v5000
+#define PFN_cuSurfObjectGetResourceDesc  PFN_cuSurfObjectGetResourceDesc_v5000
+#define PFN_cuTensorMapEncodeTiled  PFN_cuTensorMapEncodeTiled_v12000
+#define PFN_cuTensorMapEncodeIm2col  PFN_cuTensorMapEncodeIm2col_v12000
+#define PFN_cuTensorMapReplaceAddress  PFN_cuTensorMapReplaceAddress_v12000
+#define PFN_cuTensorMapEncodeIm2colWide  PFN_cuTensorMapEncodeIm2colWide_v12080
+#define PFN_cuDeviceCanAccessPeer  PFN_cuDeviceCanAccessPeer_v4000
+#define PFN_cuCtxEnablePeerAccess  PFN_cuCtxEnablePeerAccess_v4000
+#define PFN_cuCtxDisablePeerAccess  PFN_cuCtxDisablePeerAccess_v4000
+#define PFN_cuDeviceGetP2PAttribute  PFN_cuDeviceGetP2PAttribute_v8000
+#define PFN_cuGraphicsUnregisterResource  PFN_cuGraphicsUnregisterResource_v3000
+#define PFN_cuGraphicsSubResourceGetMappedArray  PFN_cuGraphicsSubResourceGetMappedArray_v3000
+#define PFN_cuGraphicsResourceGetMappedMipmappedArray  PFN_cuGraphicsResourceGetMappedMipmappedArray_v5000
+#define PFN_cuGraphicsResourceGetMappedPointer  PFN_cuGraphicsResourceGetMappedPointer_v3020
+#define PFN_cuGraphicsResourceSetMapFlags  PFN_cuGraphicsResourceSetMapFlags_v6050
+#define PFN_cuGraphicsMapResources  __API_TYPEDEF_PTSZ(PFN_cuGraphicsMapResources, 3000, 7000)
+#define PFN_cuGraphicsUnmapResources  __API_TYPEDEF_PTSZ(PFN_cuGraphicsUnmapResources, 3000, 7000)
+#define PFN_cuGetExportTable  PFN_cuGetExportTable_v3000
+#define PFN_cuFuncGetModule  PFN_cuFuncGetModule_v11000
+#define PFN_cuFlushGPUDirectRDMAWrites PFN_cuFlushGPUDirectRDMAWrites_v11030
+#define PFN_cuGetProcAddress  PFN_cuGetProcAddress_v12000
+#define PFN_cuUserObjectCreate  PFN_cuUserObjectCreate_v11030
+#define PFN_cuUserObjectRetain  PFN_cuUserObjectRetain_v11030
+#define PFN_cuUserObjectRelease  PFN_cuUserObjectRelease_v11030
+#define PFN_cuGraphRetainUserObject  PFN_cuGraphRetainUserObject_v11030
+#define PFN_cuGraphReleaseUserObject  PFN_cuGraphReleaseUserObject_v11030
+#define PFN_cuModuleGetLoadingMode  PFN_cuModuleGetLoadingMode_v11070
+#define PFN_cuMemGetHandleForAddressRange  PFN_cuMemGetHandleForAddressRange_v11070
+#define PFN_cuLibraryLoadData PFN_cuLibraryLoadData_v12000
+#define PFN_cuLibraryLoadFromFile PFN_cuLibraryLoadFromFile_v12000
+#define PFN_cuLibraryUnload PFN_cuLibraryUnload_v12000
+#define PFN_cuLibraryGetKernel PFN_cuLibraryGetKernel_v12000
+#define PFN_cuLibraryGetModule PFN_cuLibraryGetModule_v12000
+#define PFN_cuKernelGetFunction PFN_cuKernelGetFunction_v12000
+#define PFN_cuKernelGetLibrary PFN_cuKernelGetLibrary_v12050
+#define PFN_cuLibraryGetGlobal PFN_cuLibraryGetGlobal_v12000
+#define PFN_cuLibraryGetManaged PFN_cuLibraryGetManaged_v12000
+#define PFN_cuLibraryGetKernelCount PFN_cuLibraryGetKernelCount_v12040
+#define PFN_cuLibraryEnumerateKernels PFN_cuLibraryEnumerateKernels_v12040
+#define PFN_cuKernelGetAttribute PFN_cuKernelGetAttribute_v12000
+#define PFN_cuKernelSetAttribute PFN_cuKernelSetAttribute_v12000
+#define PFN_cuKernelSetCacheConfig PFN_cuKernelSetCacheConfig_v12000
+#define PFN_cuKernelGetName  PFN_cuKernelGetName_v12030
+#define PFN_cuKernelGetParamInfo  PFN_cuKernelGetParamInfo_v12040
+#define PFN_cuLibraryGetUnifiedFunction PFN_cuLibraryGetUnifiedFunction_v12000
+#define PFN_cuCoredumpGetAttribute PFN_cuCoredumpGetAttribute_v12010
+#define PFN_cuCoredumpGetAttributeGlobal PFN_cuCoredumpGetAttributeGlobal_v12010
+#define PFN_cuCoredumpSetAttribute PFN_cuCoredumpSetAttribute_v12010
+#define PFN_cuCoredumpSetAttributeGlobal PFN_cuCoredumpSetAttributeGlobal_v12010
+#define PFN_cuDeviceRegisterAsyncNotification PFN_cuDeviceRegisterAsyncNotification_v12040
+#define PFN_cuDeviceUnregisterAsyncNotification PFN_cuDeviceUnregisterAsyncNotification_v12040
+#define PFN_cuGreenCtxCreate PFN_cuGreenCtxCreate_v12040
+#define PFN_cuGreenCtxDestroy PFN_cuGreenCtxDestroy_v12040
+#define PFN_cuDeviceGetDevResource PFN_cuDeviceGetDevResource_v12040
+#define PFN_cuCtxGetDevResource PFN_cuCtxGetDevResource_v12040
+#define PFN_cuGreenCtxGetDevResource PFN_cuGreenCtxGetDevResource_v12040
+#define PFN_cuGreenCtxRecordEvent PFN_cuGreenCtxRecordEvent_v12040
+#define PFN_cuGreenCtxWaitEvent PFN_cuGreenCtxWaitEvent_v12040
+#define PFN_cuDevResourceGenerateDesc PFN_cuDevResourceGenerateDesc_v12040
+#define PFN_cuDevSmResourceSplitByCount PFN_cuDevSmResourceSplitByCount_v12040
+#define PFN_cuStreamGetGreenCtx PFN_cuStreamGetGreenCtx_v12040
+#define PFN_cuCtxFromGreenCtx PFN_cuCtxFromGreenCtx_v12040
+#define PFN_cuCtxRecordEvent PFN_cuCtxRecordEvent_v12050
+#define PFN_cuCtxWaitEvent PFN_cuCtxWaitEvent_v12050
+#define PFN_cuGreenCtxStreamCreate PFN_cuGreenCtxStreamCreate_v12050
+#define PFN_cuStreamGetCtx_v2  __API_TYPEDEF_PTSZ(PFN_cuStreamGetCtx, 12050, 12050)
+#define PFN_cuMemBatchDecompressAsync __API_TYPEDEF_PTSZ(PFN_cuMemBatchDecompressAsync, 12060, 12060)
+
+#define PFN_cuCheckpointProcessGetRestoreThreadId PFN_cuCheckpointProcessGetRestoreThreadId_v12080
+#define PFN_cuCheckpointProcessGetState PFN_cuCheckpointProcessGetState_v12080
+#define PFN_cuCheckpointProcessLock PFN_cuCheckpointProcessLock_v12080
+#define PFN_cuCheckpointProcessCheckpoint PFN_cuCheckpointProcessCheckpoint_v12080
+#define PFN_cuCheckpointProcessRestore PFN_cuCheckpointProcessRestore_v12080
+#define PFN_cuCheckpointProcessUnlock PFN_cuCheckpointProcessUnlock_v12080
+
+/*
+ * Type definitions for functions defined in cuda.h
+ */
+typedef CUresult (CUDAAPI *PFN_cuGetErrorString_v6000)(CUresult error, const char **pStr);
+typedef CUresult (CUDAAPI *PFN_cuGetErrorName_v6000)(CUresult error, const char **pStr);
+typedef CUresult (CUDAAPI *PFN_cuInit_v2000)(unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuDriverGetVersion_v2020)(int *driverVersion);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGet_v2000)(CUdevice_v1 *device, int ordinal);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetCount_v2000)(int *count);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetName_v2000)(char *name, int len, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetUuid_v9020)(CUuuid *uuid, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetUuid_v11040)(CUuuid *uuid, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetLuid_v10000)(char *luid, unsigned int *deviceNodeMask, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceTotalMem_v3020)(size_t *bytes, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetTexture1DLinearMaxWidth_v11010)(size_t *maxWidthInElements, CUarray_format format, unsigned numChannels, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetAttribute_v2000)(int *pi, CUdevice_attribute attrib, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetNvSciSyncAttributes_v10020)(void *nvSciSyncAttrList, CUdevice_v1 dev, int flags);
+typedef CUresult (CUDAAPI *PFN_cuDeviceSetMemPool_v11020)(CUdevice_v1 dev, CUmemoryPool pool);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetMemPool_v11020)(CUmemoryPool *pool, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetDefaultMemPool_v11020)(CUmemoryPool *pool_out, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetProperties_v2000)(CUdevprop_v1 *prop, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceComputeCapability_v2000)(int *major, int *minor, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxRetain_v7000)(CUcontext *pctx, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxRelease_v11000)(CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxSetFlags_v11000)(CUdevice_v1 dev, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxGetState_v7000)(CUdevice_v1 dev, unsigned int *flags, int *active);
+typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxReset_v11000)(CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetExecAffinitySupport_v11040)(int *pi, CUexecAffinityType type, CUdevice dev);
+typedef CUresult (CUDAAPI *PFN_cuCtxCreate_v3020)(CUcontext *pctx, unsigned int flags, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuCtxCreate_v11040)(CUcontext *pctx, CUexecAffinityParam *paramsArray, int numParams, unsigned int flags, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuCtxCreate_v12050)(CUcontext *pctx, CUctxCreateParams *ctxCreateParams, unsigned int flags, CUdevice dev);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetId_v12000)(CUcontext ctx, unsigned long long *ctxId);
+typedef CUresult (CUDAAPI *PFN_cuCtxDestroy_v4000)(CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuCtxPushCurrent_v4000)(CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuCtxPopCurrent_v4000)(CUcontext *pctx);
+typedef CUresult (CUDAAPI *PFN_cuCtxSetCurrent_v4000)(CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetCurrent_v4000)(CUcontext *pctx);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetDevice_v2000)(CUdevice_v1 *device);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetFlags_v7000)(unsigned int *flags);
+typedef CUresult (CUDAAPI *PFN_cuCtxSetFlags_v12010)(unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuCtxSynchronize_v2000)(void);
+typedef CUresult (CUDAAPI *PFN_cuCtxSetLimit_v3010)(CUlimit limit, size_t value);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetLimit_v3010)(size_t *pvalue, CUlimit limit);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetCacheConfig_v3020)(CUfunc_cache *pconfig);
+typedef CUresult (CUDAAPI *PFN_cuCtxSetCacheConfig_v3020)(CUfunc_cache config);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetSharedMemConfig_v4020)(CUsharedconfig *pConfig);
+typedef CUresult (CUDAAPI *PFN_cuCtxSetSharedMemConfig_v4020)(CUsharedconfig config);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetApiVersion_v3020)(CUcontext ctx, unsigned int *version);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetStreamPriorityRange_v5050)(int *leastPriority, int *greatestPriority);
+typedef CUresult (CUDAAPI *PFN_cuCtxResetPersistingL2Cache_v11000)(void);
+typedef CUresult (CUDAAPI *PFN_cuCtxAttach_v2000)(CUcontext *pctx, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuCtxDetach_v2000)(CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuCtxGetExecAffinity_v11040)(CUexecAffinityParam *pExecAffinity, CUexecAffinityType type);
+typedef CUresult (CUDAAPI *PFN_cuModuleLoad_v2000)(CUmodule *module, const char *fname);
+typedef CUresult (CUDAAPI *PFN_cuModuleLoadData_v2000)(CUmodule *module, const void *image);
+typedef CUresult (CUDAAPI *PFN_cuModuleLoadDataEx_v2010)(CUmodule *module, const void *image, unsigned int numOptions, CUjit_option *options, void **optionValues);
+typedef CUresult (CUDAAPI *PFN_cuModuleLoadFatBinary_v2000)(CUmodule *module, const void *fatCubin);
+typedef CUresult (CUDAAPI *PFN_cuModuleUnload_v2000)(CUmodule hmod);
+typedef CUresult (CUDAAPI *PFN_cuModuleGetFunction_v2000)(CUfunction *hfunc, CUmodule hmod, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuModuleGetGlobal_v3020)(CUdeviceptr_v2 *dptr, size_t *bytes, CUmodule hmod, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuModuleGetTexRef_v2000)(CUtexref *pTexRef, CUmodule hmod, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuModuleGetSurfRef_v3000)(CUsurfref *pSurfRef, CUmodule hmod, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuModuleGetFunctionCount)(unsigned int *count, CUmodule hmod);
+typedef CUresult (CUDAAPI *PFN_cuModuleEnumerateFunctions)(CUfunction *functions, unsigned int numFunctions, CUmodule mod);
+typedef CUresult (CUDAAPI *PFN_cuLinkCreate_v6050)(unsigned int numOptions, CUjit_option *options, void **optionValues, CUlinkState *stateOut);
+typedef CUresult (CUDAAPI *PFN_cuLinkAddData_v6050)(CUlinkState state, CUjitInputType type, void *data, size_t size, const char *name, unsigned int numOptions, CUjit_option *options, void **optionValues);
+typedef CUresult (CUDAAPI *PFN_cuLinkAddFile_v6050)(CUlinkState state, CUjitInputType type, const char *path, unsigned int numOptions, CUjit_option *options, void **optionValues);
+typedef CUresult (CUDAAPI *PFN_cuLinkComplete_v5050)(CUlinkState state, void **cubinOut, size_t *sizeOut);
+typedef CUresult (CUDAAPI *PFN_cuLinkDestroy_v5050)(CUlinkState state);
+typedef CUresult (CUDAAPI *PFN_cuMemGetInfo_v3020)(size_t *free, size_t *total);
+typedef CUresult (CUDAAPI *PFN_cuMemAlloc_v3020)(CUdeviceptr_v2 *dptr, size_t bytesize);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocPitch_v3020)(CUdeviceptr_v2 *dptr, size_t *pPitch, size_t WidthInBytes, size_t Height, unsigned int ElementSizeBytes);
+typedef CUresult (CUDAAPI *PFN_cuMemFree_v3020)(CUdeviceptr_v2 dptr);
+typedef CUresult (CUDAAPI *PFN_cuMemGetAddressRange_v3020)(CUdeviceptr_v2 *pbase, size_t *psize, CUdeviceptr_v2 dptr);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocHost_v3020)(void **pp, size_t bytesize);
+typedef CUresult (CUDAAPI *PFN_cuMemFreeHost_v2000)(void *p);
+typedef CUresult (CUDAAPI *PFN_cuMemHostAlloc_v2020)(void **pp, size_t bytesize, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuMemHostGetDevicePointer_v3020)(CUdeviceptr_v2 *pdptr, void *p, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuMemHostGetFlags_v2030)(unsigned int *pFlags, void *p);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocManaged_v6000)(CUdeviceptr_v2 *dptr, size_t bytesize, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetByPCIBusId_v4010)(CUdevice_v1 *dev, const char *pciBusId);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetPCIBusId_v4010)(char *pciBusId, int len, CUdevice_v1 dev);
+typedef CUresult (CUDAAPI *PFN_cuIpcGetEventHandle_v4010)(CUipcEventHandle_v1 *pHandle, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuIpcOpenEventHandle_v4010)(CUevent *phEvent, CUipcEventHandle_v1 handle);
+typedef CUresult (CUDAAPI *PFN_cuIpcGetMemHandle_v4010)(CUipcMemHandle_v1 *pHandle, CUdeviceptr_v2 dptr);
+typedef CUresult (CUDAAPI *PFN_cuIpcOpenMemHandle_v11000)(CUdeviceptr_v2 *pdptr, CUipcMemHandle_v1 handle, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuIpcCloseMemHandle_v4010)(CUdeviceptr_v2 dptr);
+typedef CUresult (CUDAAPI *PFN_cuMemHostRegister_v6050)(void *p, size_t bytesize, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuMemHostUnregister_v4000)(void *p);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy_v7000_ptds)(CUdeviceptr_v2 dst, CUdeviceptr_v2 src, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyPeer_v7000_ptds)(CUdeviceptr_v2 dstDevice, CUcontext dstContext, CUdeviceptr_v2 srcDevice, CUcontext srcContext, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoD_v7000_ptds)(CUdeviceptr_v2 dstDevice, const void *srcHost, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoH_v7000_ptds)(void *dstHost, CUdeviceptr_v2 srcDevice, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoD_v7000_ptds)(CUdeviceptr_v2 dstDevice, CUdeviceptr_v2 srcDevice, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoA_v7000_ptds)(CUarray dstArray, size_t dstOffset, CUdeviceptr_v2 srcDevice, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoD_v7000_ptds)(CUdeviceptr_v2 dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoA_v7000_ptds)(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoH_v7000_ptds)(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoA_v7000_ptds)(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy2D_v7000_ptds)(const CUDA_MEMCPY2D_v2 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy2DUnaligned_v7000_ptds)(const CUDA_MEMCPY2D_v2 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3D_v7000_ptds)(const CUDA_MEMCPY3D_v2 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DPeer_v7000_ptds)(const CUDA_MEMCPY3D_PEER_v1 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAsync_v7000_ptsz)(CUdeviceptr_v2 dst, CUdeviceptr_v2 src, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyPeerAsync_v7000_ptsz)(CUdeviceptr_v2 dstDevice, CUcontext dstContext, CUdeviceptr_v2 srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoDAsync_v7000_ptsz)(CUdeviceptr_v2 dstDevice, const void *srcHost, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoHAsync_v7000_ptsz)(void *dstHost, CUdeviceptr_v2 srcDevice, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoDAsync_v7000_ptsz)(CUdeviceptr_v2 dstDevice, CUdeviceptr_v2 srcDevice, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoAAsync_v7000_ptsz)(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoHAsync_v7000_ptsz)(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy2DAsync_v7000_ptsz)(const CUDA_MEMCPY2D_v2 *pCopy, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DAsync_v7000_ptsz)(const CUDA_MEMCPY3D_v2 *pCopy, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DPeerAsync_v7000_ptsz)(const CUDA_MEMCPY3D_PEER_v1 *pCopy, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyBatchAsync_v12080_ptsz)(CUdeviceptr_v2 *dsts, CUdeviceptr_v2 *srcs, size_t *sizes, size_t count, CUmemcpyAttributes_v1 *attrs, size_t *attrIdxs, size_t numAttrs, size_t *failIdx, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DBatchAsync_v12080_ptsz)(size_t numParams, CUDA_MEMCPY3D_BATCH_OP_v1 *opList, size_t *failIdx, unsigned long long flags, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD8_v7000_ptds)(CUdeviceptr_v2 dstDevice, unsigned char uc, size_t N);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD16_v7000_ptds)(CUdeviceptr_v2 dstDevice, unsigned short us, size_t N);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD32_v7000_ptds)(CUdeviceptr_v2 dstDevice, unsigned int ui, size_t N);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D8_v7000_ptds)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D16_v7000_ptds)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D32_v7000_ptds)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD8Async_v7000_ptsz)(CUdeviceptr_v2 dstDevice, unsigned char uc, size_t N, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD16Async_v7000_ptsz)(CUdeviceptr_v2 dstDevice, unsigned short us, size_t N, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD32Async_v7000_ptsz)(CUdeviceptr_v2 dstDevice, unsigned int ui, size_t N, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D8Async_v7000_ptsz)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D16Async_v7000_ptsz)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D32Async_v7000_ptsz)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuArrayCreate_v3020)(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTOR_v2 *pAllocateArray);
+typedef CUresult (CUDAAPI *PFN_cuArrayGetDescriptor_v3020)(CUDA_ARRAY_DESCRIPTOR_v2 *pArrayDescriptor, CUarray hArray);
+typedef CUresult (CUDAAPI *PFN_cuArrayGetSparseProperties_v11010)(CUDA_ARRAY_SPARSE_PROPERTIES_v1 *sparseProperties, CUarray array);
+typedef CUresult (CUDAAPI *PFN_cuMipmappedArrayGetSparseProperties_v11010)(CUDA_ARRAY_SPARSE_PROPERTIES_v1 *sparseProperties, CUmipmappedArray mipmap);
+typedef CUresult (CUDAAPI *PFN_cuArrayGetMemoryRequirements_v11060)(CUDA_ARRAY_MEMORY_REQUIREMENTS_v1 *memoryRequirements, CUarray array, CUdevice device);
+typedef CUresult (CUDAAPI *PFN_cuMipmappedArrayGetMemoryRequirements_v11060)(CUDA_ARRAY_MEMORY_REQUIREMENTS_v1 *memoryRequirements, CUmipmappedArray mipmap, CUdevice device);
+typedef CUresult (CUDAAPI *PFN_cuArrayGetPlane_v11020)(CUarray *pPlaneArray, CUarray hArray, unsigned int planeIdx);
+typedef CUresult (CUDAAPI *PFN_cuArrayDestroy_v2000)(CUarray hArray);
+typedef CUresult (CUDAAPI *PFN_cuArray3DCreate_v3020)(CUarray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR_v2 *pAllocateArray);
+typedef CUresult (CUDAAPI *PFN_cuArray3DGetDescriptor_v3020)(CUDA_ARRAY3D_DESCRIPTOR_v2 *pArrayDescriptor, CUarray hArray);
+typedef CUresult (CUDAAPI *PFN_cuMipmappedArrayCreate_v5000)(CUmipmappedArray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR_v2 *pMipmappedArrayDesc, unsigned int numMipmapLevels);
+typedef CUresult (CUDAAPI *PFN_cuMipmappedArrayGetLevel_v5000)(CUarray *pLevelArray, CUmipmappedArray hMipmappedArray, unsigned int level);
+typedef CUresult (CUDAAPI *PFN_cuMipmappedArrayDestroy_v5000)(CUmipmappedArray hMipmappedArray);
+typedef CUresult (CUDAAPI *PFN_cuMemAddressReserve_v10020)(CUdeviceptr_v2 *ptr, size_t size, size_t alignment, CUdeviceptr_v2 addr, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMemAddressFree_v10020)(CUdeviceptr_v2 ptr, size_t size);
+typedef CUresult (CUDAAPI *PFN_cuMemCreate_v10020)(CUmemGenericAllocationHandle_v1 *handle, size_t size, const CUmemAllocationProp_v1 *prop, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMemRelease_v10020)(CUmemGenericAllocationHandle_v1 handle);
+typedef CUresult (CUDAAPI *PFN_cuMemMap_v10020)(CUdeviceptr_v2 ptr, size_t size, size_t offset, CUmemGenericAllocationHandle_v1 handle, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMemMapArrayAsync_v11010_ptsz)(CUarrayMapInfo_v1 *mapInfoList, unsigned int count, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemUnmap_v10020)(CUdeviceptr_v2 ptr, size_t size);
+typedef CUresult (CUDAAPI *PFN_cuMemSetAccess_v10020)(CUdeviceptr_v2 ptr, size_t size, const CUmemAccessDesc_v1 *desc, size_t count);
+typedef CUresult (CUDAAPI *PFN_cuMemGetAccess_v10020)(unsigned long long *flags, const CUmemLocation_v1 *location, CUdeviceptr_v2 ptr);
+typedef CUresult (CUDAAPI *PFN_cuMemExportToShareableHandle_v10020)(void *shareableHandle, CUmemGenericAllocationHandle_v1 handle, CUmemAllocationHandleType handleType, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMemImportFromShareableHandle_v10020)(CUmemGenericAllocationHandle_v1 *handle, void *osHandle, CUmemAllocationHandleType shHandleType);
+typedef CUresult (CUDAAPI *PFN_cuMemGetAllocationGranularity_v10020)(size_t *granularity, const CUmemAllocationProp_v1 *prop, CUmemAllocationGranularity_flags option);
+typedef CUresult (CUDAAPI *PFN_cuMemGetAllocationPropertiesFromHandle_v10020)(CUmemAllocationProp_v1 *prop, CUmemGenericAllocationHandle_v1 handle);
+typedef CUresult (CUDAAPI *PFN_cuMemRetainAllocationHandle_v11000)(CUmemGenericAllocationHandle_v1 *handle, void *addr);
+typedef CUresult (CUDAAPI *PFN_cuMemFreeAsync_v11020_ptsz)(CUdeviceptr_v2 dptr, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocAsync_v11020_ptsz)(CUdeviceptr_v2 *dptr, size_t bytesize, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolTrimTo_v11020)(CUmemoryPool pool, size_t minBytesToKeep);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolSetAttribute_v11020)(CUmemoryPool pool, CUmemPool_attribute attr, void *value);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolGetAttribute_v11020)(CUmemoryPool pool, CUmemPool_attribute attr, void *value);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolSetAccess_v11020)(CUmemoryPool pool, const CUmemAccessDesc_v1 *map, size_t count);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolGetAccess_v11020)(CUmemAccess_flags *flags, CUmemoryPool memPool, CUmemLocation_v1 *location);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolCreate_v11020)(CUmemoryPool *pool, const CUmemPoolProps_v1 *poolProps);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolDestroy_v11020)(CUmemoryPool pool);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocFromPoolAsync_v11020_ptsz)(CUdeviceptr_v2 *dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolExportToShareableHandle_v11020)(void *handle_out, CUmemoryPool pool, CUmemAllocationHandleType handleType, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolImportFromShareableHandle_v11020)(CUmemoryPool *pool_out, void *handle, CUmemAllocationHandleType handleType, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolExportPointer_v11020)(CUmemPoolPtrExportData_v1 *shareData_out, CUdeviceptr_v2 ptr);
+typedef CUresult (CUDAAPI *PFN_cuMemPoolImportPointer_v11020)(CUdeviceptr_v2 *ptr_out, CUmemoryPool pool, CUmemPoolPtrExportData_v1 *shareData);
+typedef CUresult (CUDAAPI *PFN_cuPointerGetAttribute_v4000)(void *data, CUpointer_attribute attribute, CUdeviceptr_v2 ptr);
+typedef CUresult (CUDAAPI *PFN_cuMemPrefetchAsync_v8000_ptsz)(CUdeviceptr_v2 devPtr, size_t count, CUdevice_v1 dstDevice, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemAdvise_v8000)(CUdeviceptr_v2 devPtr, size_t count, CUmem_advise advice, CUdevice_v1 device);
+typedef CUresult (CUDAAPI *PFN_cuMemAdvise_v12020)(CUdeviceptr_v2 devPtr, size_t count, CUmem_advise advice, CUmemLocation_v1 location);
+typedef CUresult (CUDAAPI *PFN_cuMemPrefetchAsync_v12020_ptsz)(CUdeviceptr_v2 devPtr, size_t count, CUmemLocation_v1 location, unsigned int flags, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemRangeGetAttribute_v8000)(void *data, size_t dataSize, CUmem_range_attribute attribute, CUdeviceptr_v2 devPtr, size_t count);
+typedef CUresult (CUDAAPI *PFN_cuMemRangeGetAttributes_v8000)(void **data, size_t *dataSizes, CUmem_range_attribute *attributes, size_t numAttributes, CUdeviceptr_v2 devPtr, size_t count);
+typedef CUresult (CUDAAPI *PFN_cuMulticastCreate_v12010)(CUmemGenericAllocationHandle *mcHandle, const CUmulticastObjectProp *prop);
+typedef CUresult (CUDAAPI *PFN_cuMulticastAddDevice_v12010)(CUmemGenericAllocationHandle mcHandle, CUdevice dev);
+typedef CUresult (CUDAAPI *PFN_cuMulticastBindMem_v12010)(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMulticastBindAddr_v12010)(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuMulticastUnbind_v12010)(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, size_t size);
+typedef CUresult (CUDAAPI *PFN_cuMulticastGetGranularity_v12010)(size_t *granularity, const CUmulticastObjectProp *prop, CUmulticastGranularity_flags option);
+typedef CUresult (CUDAAPI *PFN_cuPointerSetAttribute_v6000)(const void *value, CUpointer_attribute attribute, CUdeviceptr_v2 ptr);
+typedef CUresult (CUDAAPI *PFN_cuPointerGetAttributes_v7000)(unsigned int numAttributes, CUpointer_attribute *attributes, void **data, CUdeviceptr_v2 ptr);
+typedef CUresult (CUDAAPI *PFN_cuStreamCreate_v2000)(CUstream *phStream, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamCreateWithPriority_v5050)(CUstream *phStream, unsigned int flags, int priority);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetId_v12000)(CUstream hStream, unsigned long long *streamId);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetId_v12000_ptsz)(CUstream hStream, unsigned long long *streamId);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetPriority_v7000_ptsz)(CUstream hStream, int *priority);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetFlags_v7000_ptsz)(CUstream hStream, unsigned int *flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCtx_v9020_ptsz)(CUstream hStream, CUcontext *pctx);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitEvent_v7000_ptsz)(CUstream hStream, CUevent hEvent, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamAddCallback_v7000_ptsz)(CUstream hStream, CUstreamCallback callback, void *userData, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamBeginCapture_v10010_ptsz)(CUstream hStream, CUstreamCaptureMode mode);
+typedef CUresult (CUDAAPI *PFN_cuStreamBeginCaptureToGraph_v12030_ptsz)(CUstream hStream, CUgraph hGraph, const CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, CUstreamCaptureMode mode);
+typedef CUresult (CUDAAPI *PFN_cuThreadExchangeStreamCaptureMode_v10010)(CUstreamCaptureMode *mode);
+typedef CUresult (CUDAAPI *PFN_cuStreamEndCapture_v10000_ptsz)(CUstream hStream, CUgraph *phGraph);
+typedef CUresult (CUDAAPI *PFN_cuStreamIsCapturing_v10000_ptsz)(CUstream hStream, CUstreamCaptureStatus *captureStatus);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCaptureInfo_v10010_ptsz)(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCaptureInfo_v11030_ptsz)(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, size_t *numDependencies_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCaptureInfo_v12030_ptsz)(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, const CUgraphEdgeData **edgeData_out, size_t *numDependencies_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamUpdateCaptureDependencies_v11030_ptsz)(CUstream hStream, CUgraphNode *dependencies, size_t numDependencies, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamUpdateCaptureDependencies_v12030_ptsz)(CUstream hStream, CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamAttachMemAsync_v7000_ptsz)(CUstream hStream, CUdeviceptr_v2 dptr, size_t length, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamQuery_v7000_ptsz)(CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamSynchronize_v7000_ptsz)(CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamDestroy_v4000)(CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamCopyAttributes_v11000_ptsz)(CUstream dst, CUstream src);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetAttribute_v11000_ptsz)(CUstream hStream, CUstreamAttrID attr, CUstreamAttrValue_v1 *value_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamSetAttribute_v11000_ptsz)(CUstream hStream, CUstreamAttrID attr, const CUstreamAttrValue_v1 *value);
+typedef CUresult (CUDAAPI *PFN_cuEventCreate_v2000)(CUevent *phEvent, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuEventRecord_v7000_ptsz)(CUevent hEvent, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuEventRecordWithFlags_v11010_ptsz)(CUevent hEvent, CUstream hStream, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuEventQuery_v2000)(CUevent hEvent);
+typedef CUresult (CUDAAPI *PFN_cuEventSynchronize_v2000)(CUevent hEvent);
+typedef CUresult (CUDAAPI *PFN_cuEventDestroy_v4000)(CUevent hEvent);
+typedef CUresult (CUDAAPI *PFN_cuEventElapsedTime_v2000)(float *pMilliseconds, CUevent hStart, CUevent hEnd);
+typedef CUresult (CUDAAPI *PFN_cuEventElapsedTime_v12080)(float *pMilliseconds, CUevent hStart, CUevent hEnd);
+typedef CUresult (CUDAAPI *PFN_cuImportExternalMemory_v10000)(CUexternalMemory *extMem_out, const CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1 *memHandleDesc);
+typedef CUresult (CUDAAPI *PFN_cuExternalMemoryGetMappedBuffer_v10000)(CUdeviceptr_v2 *devPtr, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1 *bufferDesc);
+typedef CUresult (CUDAAPI *PFN_cuExternalMemoryGetMappedMipmappedArray_v10000)(CUmipmappedArray *mipmap, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1 *mipmapDesc);
+typedef CUresult (CUDAAPI *PFN_cuDestroyExternalMemory_v10000)(CUexternalMemory extMem);
+typedef CUresult (CUDAAPI *PFN_cuImportExternalSemaphore_v10000)(CUexternalSemaphore *extSem_out, const CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1 *semHandleDesc);
+typedef CUresult (CUDAAPI *PFN_cuSignalExternalSemaphoresAsync_v10000_ptsz)(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1 *paramsArray, unsigned int numExtSems, CUstream stream);
+typedef CUresult (CUDAAPI *PFN_cuWaitExternalSemaphoresAsync_v10000_ptsz)(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1 *paramsArray, unsigned int numExtSems, CUstream stream);
+typedef CUresult (CUDAAPI *PFN_cuDestroyExternalSemaphore_v10000)(CUexternalSemaphore extSem);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue32_v8000_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue64_v9000_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue32_v8000_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue64_v9000_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamBatchMemOp_v8000_ptsz)(CUstream stream, unsigned int count, CUstreamBatchMemOpParams_v1 *paramArray, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue32_v11070_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue64_v11070_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue32_v11070_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue64_v11070_ptsz)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamBatchMemOp_v11070_ptsz)(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuFuncGetAttribute_v2020)(int *pi, CUfunction_attribute attrib, CUfunction hfunc);
+typedef CUresult (CUDAAPI *PFN_cuFuncSetAttribute_v9000)(CUfunction hfunc, CUfunction_attribute attrib, int value);
+typedef CUresult (CUDAAPI *PFN_cuFuncSetCacheConfig_v3000)(CUfunction hfunc, CUfunc_cache config);
+typedef CUresult (CUDAAPI *PFN_cuFuncSetSharedMemConfig_v4020)(CUfunction hfunc, CUsharedconfig config);
+typedef CUresult (CUDAAPI *PFN_cuFuncGetName_v12030)(const char **name, CUfunction hfunc);
+typedef CUresult (CUDAAPI *PFN_cuFuncGetParamInfo_v12040)(CUfunction func, size_t paramIndex, size_t *paramOffset, size_t *paramSize);
+typedef CUresult (CUDAAPI *PFN_cuFuncIsLoaded_v12040)(CUfunctionLoadingState *state, CUfunction hfunc);
+typedef CUresult (CUDAAPI *PFN_cuFuncLoad_v12040)(CUfunction hfunc);
+typedef CUresult (CUDAAPI *PFN_cuLaunchKernel_v7000_ptsz)(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams, void **extra);
+typedef CUresult (CUDAAPI *PFN_cuLaunchKernelEx_v11060_ptsz)(const CUlaunchConfig *config, CUfunction f, void **kernelParams, void **extra);
+typedef CUresult (CUDAAPI *PFN_cuLaunchCooperativeKernel_v9000_ptsz)(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams);
+typedef CUresult (CUDAAPI *PFN_cuLaunchCooperativeKernelMultiDevice_v9000)(CUDA_LAUNCH_PARAMS_v1 *launchParamsList, unsigned int numDevices, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuLaunchHostFunc_v10000_ptsz)(CUstream hStream, CUhostFn fn, void *userData);
+typedef CUresult (CUDAAPI *PFN_cuFuncSetBlockShape_v2000)(CUfunction hfunc, int x, int y, int z);
+typedef CUresult (CUDAAPI *PFN_cuFuncSetSharedSize_v2000)(CUfunction hfunc, unsigned int bytes);
+typedef CUresult (CUDAAPI *PFN_cuParamSetSize_v2000)(CUfunction hfunc, unsigned int numbytes);
+typedef CUresult (CUDAAPI *PFN_cuParamSeti_v2000)(CUfunction hfunc, int offset, unsigned int value);
+typedef CUresult (CUDAAPI *PFN_cuParamSetf_v2000)(CUfunction hfunc, int offset, float value);
+typedef CUresult (CUDAAPI *PFN_cuParamSetv_v2000)(CUfunction hfunc, int offset, void *ptr, unsigned int numbytes);
+typedef CUresult (CUDAAPI *PFN_cuLaunch_v2000)(CUfunction f);
+typedef CUresult (CUDAAPI *PFN_cuLaunchGrid_v2000)(CUfunction f, int grid_width, int grid_height);
+typedef CUresult (CUDAAPI *PFN_cuLaunchGridAsync_v2000)(CUfunction f, int grid_width, int grid_height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuParamSetTexRef_v2000)(CUfunction hfunc, int texunit, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuGraphCreate_v10000)(CUgraph *phGraph, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddKernelNode_v10000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeGetParams_v10000)(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeSetParams_v10000)(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddKernelNode_v12000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS_v2 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeGetParams_v12000)(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS_v2 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeSetParams_v12000)(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v2 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddMemcpyNode_v10000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_MEMCPY3D_v2 *copyParams, CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuGraphMemcpyNodeGetParams_v10000)(CUgraphNode hNode, CUDA_MEMCPY3D_v2 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphMemcpyNodeSetParams_v10000)(CUgraphNode hNode, const CUDA_MEMCPY3D_v2 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddMemsetNode_v10000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_MEMSET_NODE_PARAMS_v1 *memsetParams, CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuGraphMemsetNodeGetParams_v10000)(CUgraphNode hNode, CUDA_MEMSET_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphMemsetNodeSetParams_v10000)(CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddHostNode_v10000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_HOST_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphHostNodeGetParams_v10000)(CUgraphNode hNode, CUDA_HOST_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphHostNodeSetParams_v10000)(CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddChildGraphNode_v10000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUgraph childGraph);
+typedef CUresult (CUDAAPI *PFN_cuGraphChildGraphNodeGetGraph_v10000)(CUgraphNode hNode, CUgraph *phGraph);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddEmptyNode_v10000)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddEventRecordNode_v11010)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuGraphEventRecordNodeGetEvent_v11010)(CUgraphNode hNode, CUevent *event_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphEventRecordNodeSetEvent_v11010)(CUgraphNode hNode, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddEventWaitNode_v11010)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuGraphEventWaitNodeGetEvent_v11010)(CUgraphNode hNode, CUevent *event_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphEventWaitNodeSetEvent_v11010)(CUgraphNode hNode, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddExternalSemaphoresSignalNode_v11020)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExternalSemaphoresSignalNodeGetParams_v11020)(CUgraphNode hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 *params_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphExternalSemaphoresSignalNodeSetParams_v11020)(CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddExternalSemaphoresWaitNode_v11020)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExternalSemaphoresWaitNodeGetParams_v11020)(CUgraphNode hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 *params_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphExternalSemaphoresWaitNodeSetParams_v11020)(CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddBatchMemOpNode_v11070)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphBatchMemOpNodeGetParams_v11070)(CUgraphNode hNode, CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphBatchMemOpNodeSetParams_v11070)(CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecBatchMemOpNodeSetParams_v11070)(CUgraphExec graphExec, CUgraphNode node, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphClone_v10000)(CUgraph *phGraphClone, CUgraph originalGraph);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeFindInClone_v10000)(CUgraphNode *phNode, CUgraphNode hOriginalNode, CUgraph hClonedGraph);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeGetType_v10000)(CUgraphNode hNode, CUgraphNodeType *type);
+typedef CUresult (CUDAAPI *PFN_cuGraphGetNodes_v10000)(CUgraph hGraph, CUgraphNode *nodes, size_t *numNodes);
+typedef CUresult (CUDAAPI *PFN_cuGraphGetRootNodes_v10000)(CUgraph hGraph, CUgraphNode *rootNodes, size_t *numRootNodes);
+typedef CUresult (CUDAAPI *PFN_cuGraphGetEdges_v10000)(CUgraph hGraph, CUgraphNode *from, CUgraphNode *to, size_t *numEdges);
+typedef CUresult (CUDAAPI *PFN_cuGraphGetEdges_v12030)(CUgraph hGraph, CUgraphNode *from, CUgraphNode *to, CUgraphEdgeData *edgeData, size_t *numEdges);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeGetDependencies_v10000)(CUgraphNode hNode, CUgraphNode *dependencies, size_t *numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeGetDependencies_v12030)(CUgraphNode hNode, CUgraphNode *dependencies, CUgraphEdgeData *edgeData, size_t *numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeGetDependentNodes_v10000)(CUgraphNode hNode, CUgraphNode *dependentNodes, size_t *numDependentNodes);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeGetDependentNodes_v12030)(CUgraphNode hNode, CUgraphNode *dependentNodes, CUgraphEdgeData *edgeData, size_t *numDependentNodes);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddDependencies_v10000)(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, size_t numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddDependencies_v12030)(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, const CUgraphEdgeData *edgeData, size_t numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphRemoveDependencies_v10000)(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, size_t numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphRemoveDependencies_v12030)(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, const CUgraphEdgeData *edgeData, size_t numDependencies);
+typedef CUresult (CUDAAPI *PFN_cuGraphDestroyNode_v10000)(CUgraphNode hNode);
+typedef CUresult (CUDAAPI *PFN_cuGraphInstantiateWithFlags_v11040)(CUgraphExec *phGraphExec, CUgraph hGraph, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphInstantiateWithParams_v12000_ptsz)(CUgraphExec *phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecGetFlags_v12000)(CUgraphExec hGraphExec, cuuint64_t *flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecKernelNodeSetParams_v10010)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecKernelNodeSetParams_v12000)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v2 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecMemcpyNodeSetParams_v10020)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMCPY3D_v2 *copyParams, CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecMemsetNodeSetParams_v10020)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS_v1 *memsetParams, CUcontext ctx);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecHostNodeSetParams_v10020)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecChildGraphNodeSetParams_v11010)(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraph childGraph);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecEventRecordNodeSetEvent_v11010)(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecEventWaitNodeSetEvent_v11010)(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecExternalSemaphoresSignalNodeSetParams_v11020)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecExternalSemaphoresWaitNodeSetParams_v11020)(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphUpload_v11010_ptsz)(CUgraphExec hGraphExec, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGraphLaunch_v10000_ptsz)(CUgraphExec hGraphExec, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecDestroy_v10000)(CUgraphExec hGraphExec);
+typedef CUresult (CUDAAPI *PFN_cuGraphDestroy_v10000)(CUgraph hGraph);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecUpdate_v10020)(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphNode *hErrorNode_out, CUgraphExecUpdateResult *updateResult_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecUpdate_v12000)(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphExecUpdateResultInfo *resultInfo);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeCopyAttributes_v11000)(CUgraphNode dst, CUgraphNode src);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeGetAttribute_v11000)(CUgraphNode hNode, CUkernelNodeAttrID attr, CUkernelNodeAttrValue_v1 *value_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphKernelNodeSetAttribute_v11000)(CUgraphNode hNode, CUkernelNodeAttrID attr, const CUkernelNodeAttrValue_v1 *value);
+typedef CUresult (CUDAAPI *PFN_cuGraphDebugDotPrint_v11030)(CUgraph hGraph, const char *path, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddMemAllocNode_v11040)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUDA_MEM_ALLOC_NODE_PARAMS *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphMemAllocNodeGetParams_v11040)(CUgraphNode hNode, CUDA_MEM_ALLOC_NODE_PARAMS *params_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddMemFreeNode_v11040)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUdeviceptr dptr);
+typedef CUresult (CUDAAPI *PFN_cuGraphMemFreeNodeGetParams_v11040)(CUgraphNode hNode, CUdeviceptr *dptr_out);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeSetEnabled_v11060)(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int isEnabled);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeGetEnabled_v11060)(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int *isEnabled);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddNode_v12020)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUgraphNodeParams *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphAddNode_v12030)(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, CUgraphNodeParams *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphNodeSetParams_v12020)(CUgraphNode hNode, CUgraphNodeParams *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphExecNodeSetParams_v12020)(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraphNodeParams *nodeParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphConditionalHandleCreate_v12030)(CUgraphConditionalHandle *pHandle_out, CUgraph hGraph, CUcontext ctx, unsigned int defaultLaunchValue, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGraphMemTrim_v11040)(CUdevice device);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetGraphMemAttribute_v11040)(CUdevice device, CUgraphMem_attribute attr, void* value);
+typedef CUresult (CUDAAPI *PFN_cuDeviceSetGraphMemAttribute_v11040)(CUdevice device, CUgraphMem_attribute attr, void* value);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyMaxActiveBlocksPerMultiprocessor_v6050)(int *numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags_v7000)(int *numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyMaxPotentialBlockSize_v6050)(int *minGridSize, int *blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyMaxPotentialBlockSizeWithFlags_v7000)(int *minGridSize, int *blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyAvailableDynamicSMemPerBlock_v10020)(size_t *dynamicSmemSize, CUfunction func, int numBlocks, int blockSize);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyMaxPotentialClusterSize_v11070)(int *clusterSize, CUfunction func, const CUlaunchConfig *config);
+typedef CUresult (CUDAAPI *PFN_cuOccupancyMaxActiveClusters_v11070)(int *numClusters, CUfunction func, const CUlaunchConfig *config);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetArray_v2000)(CUtexref hTexRef, CUarray hArray, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetMipmappedArray_v5000)(CUtexref hTexRef, CUmipmappedArray hMipmappedArray, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetAddress_v3020)(size_t *ByteOffset, CUtexref hTexRef, CUdeviceptr_v2 dptr, size_t bytes);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetAddress2D_v4010)(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR_v2 *desc, CUdeviceptr_v2 dptr, size_t Pitch);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetFormat_v2000)(CUtexref hTexRef, CUarray_format fmt, int NumPackedComponents);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetAddressMode_v2000)(CUtexref hTexRef, int dim, CUaddress_mode am);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetFilterMode_v2000)(CUtexref hTexRef, CUfilter_mode fm);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetMipmapFilterMode_v5000)(CUtexref hTexRef, CUfilter_mode fm);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetMipmapLevelBias_v5000)(CUtexref hTexRef, float bias);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetMipmapLevelClamp_v5000)(CUtexref hTexRef, float minMipmapLevelClamp, float maxMipmapLevelClamp);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetMaxAnisotropy_v5000)(CUtexref hTexRef, unsigned int maxAniso);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetBorderColor_v8000)(CUtexref hTexRef, float *pBorderColor);
+typedef CUresult (CUDAAPI *PFN_cuTexRefSetFlags_v2000)(CUtexref hTexRef, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetAddress_v3020)(CUdeviceptr_v2 *pdptr, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetArray_v2000)(CUarray *phArray, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetMipmappedArray_v5000)(CUmipmappedArray *phMipmappedArray, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetAddressMode_v2000)(CUaddress_mode *pam, CUtexref hTexRef, int dim);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetFilterMode_v2000)(CUfilter_mode *pfm, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetFormat_v2000)(CUarray_format *pFormat, int *pNumChannels, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetMipmapFilterMode_v5000)(CUfilter_mode *pfm, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetMipmapLevelBias_v5000)(float *pbias, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetMipmapLevelClamp_v5000)(float *pminMipmapLevelClamp, float *pmaxMipmapLevelClamp, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetMaxAnisotropy_v5000)(int *pmaxAniso, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetBorderColor_v8000)(float *pBorderColor, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefGetFlags_v2000)(unsigned int *pFlags, CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefCreate_v2000)(CUtexref *pTexRef);
+typedef CUresult (CUDAAPI *PFN_cuTexRefDestroy_v2000)(CUtexref hTexRef);
+typedef CUresult (CUDAAPI *PFN_cuSurfRefSetArray_v3000)(CUsurfref hSurfRef, CUarray hArray, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuSurfRefGetArray_v3000)(CUarray *phArray, CUsurfref hSurfRef);
+typedef CUresult (CUDAAPI *PFN_cuTexObjectCreate_v5000)(CUtexObject_v1 *pTexObject, const CUDA_RESOURCE_DESC_v1 *pResDesc, const CUDA_TEXTURE_DESC_v1 *pTexDesc, const CUDA_RESOURCE_VIEW_DESC_v1 *pResViewDesc);
+typedef CUresult (CUDAAPI *PFN_cuTexObjectDestroy_v5000)(CUtexObject_v1 texObject);
+typedef CUresult (CUDAAPI *PFN_cuTexObjectGetResourceDesc_v5000)(CUDA_RESOURCE_DESC_v1 *pResDesc, CUtexObject_v1 texObject);
+typedef CUresult (CUDAAPI *PFN_cuTexObjectGetTextureDesc_v5000)(CUDA_TEXTURE_DESC_v1 *pTexDesc, CUtexObject_v1 texObject);
+typedef CUresult (CUDAAPI *PFN_cuTexObjectGetResourceViewDesc_v5000)(CUDA_RESOURCE_VIEW_DESC_v1 *pResViewDesc, CUtexObject_v1 texObject);
+typedef CUresult (CUDAAPI *PFN_cuSurfObjectCreate_v5000)(CUsurfObject_v1 *pSurfObject, const CUDA_RESOURCE_DESC_v1 *pResDesc);
+typedef CUresult (CUDAAPI *PFN_cuSurfObjectDestroy_v5000)(CUsurfObject_v1 surfObject);
+typedef CUresult (CUDAAPI *PFN_cuSurfObjectGetResourceDesc_v5000)(CUDA_RESOURCE_DESC_v1 *pResDesc, CUsurfObject_v1 surfObject);
+typedef CUresult (CUDAAPI *PFN_cuTensorMapEncodeTiled_v12000)(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, const cuuint32_t *boxDim, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill);
+typedef CUresult (CUDAAPI *PFN_cuTensorMapEncodeIm2col_v12000)(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, const int *pixelBoxLowerCorner, const int *pixelBoxUpperCorner, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill);
+typedef CUresult (CUDAAPI *PFN_cuTensorMapReplaceAddress_v12000)(CUtensorMap *tensorMap, void *globalAddress);
+typedef CUresult (CUDAAPI *PFN_cuTensorMapEncodeIm2colWide_v12080)(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, int pixelBoxLowerCornerWidth, int pixelBoxUpperCornerWidth, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapIm2ColWideMode mode, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill);
+typedef CUresult (CUDAAPI *PFN_cuDeviceCanAccessPeer_v4000)(int *canAccessPeer, CUdevice_v1 dev, CUdevice_v1 peerDev);
+typedef CUresult (CUDAAPI *PFN_cuCtxEnablePeerAccess_v4000)(CUcontext peerContext, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuCtxDisablePeerAccess_v4000)(CUcontext peerContext);
+typedef CUresult (CUDAAPI *PFN_cuDeviceGetP2PAttribute_v8000)(int *value, CUdevice_P2PAttribute attrib, CUdevice_v1 srcDevice, CUdevice_v1 dstDevice);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsUnregisterResource_v3000)(CUgraphicsResource resource);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsSubResourceGetMappedArray_v3000)(CUarray *pArray, CUgraphicsResource resource, unsigned int arrayIndex, unsigned int mipLevel);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsResourceGetMappedMipmappedArray_v5000)(CUmipmappedArray *pMipmappedArray, CUgraphicsResource resource);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsResourceGetMappedPointer_v3020)(CUdeviceptr_v2 *pDevPtr, size_t *pSize, CUgraphicsResource resource);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsResourceSetMapFlags_v6050)(CUgraphicsResource resource, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsMapResources_v7000_ptsz)(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsUnmapResources_v7000_ptsz)(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGetExportTable_v3000)(const void **ppExportTable, const CUuuid *pExportTableId);
+typedef CUresult (CUDAAPI *PFN_cuFuncGetModule_v11000)(CUmodule *hmod, CUfunction hfunc);
+typedef CUresult (CUDAAPI *PFN_cuGetProcAddress_v11030)(const char *symbol, void **pfn, int driverVersion, cuuint64_t flags);
+typedef CUresult (CUDAAPI *PFN_cuGetProcAddress_v12000)(const char *symbol, void **pfn, int driverVersion, cuuint64_t flags, CUdriverProcAddressQueryResult *symbolFound);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoD_v3020)(CUdeviceptr_v2 dstDevice, const void *srcHost, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoH_v3020)(void *dstHost, CUdeviceptr_v2 srcDevice, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoD_v3020)(CUdeviceptr_v2 dstDevice, CUdeviceptr_v2 srcDevice, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoA_v3020)(CUarray dstArray, size_t dstOffset, CUdeviceptr_v2 srcDevice, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoD_v3020)(CUdeviceptr_v2 dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoA_v3020)(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoH_v3020)(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoA_v3020)(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoAAsync_v3020)(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoHAsync_v3020)(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy2D_v3020)(const CUDA_MEMCPY2D_v2 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy2DUnaligned_v3020)(const CUDA_MEMCPY2D_v2 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3D_v3020)(const CUDA_MEMCPY3D_v2 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoDAsync_v3020)(CUdeviceptr_v2 dstDevice, const void *srcHost, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoHAsync_v3020)(void *dstHost, CUdeviceptr_v2 srcDevice, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoDAsync_v3020)(CUdeviceptr_v2 dstDevice, CUdeviceptr_v2 srcDevice, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy2DAsync_v3020)(const CUDA_MEMCPY2D_v2 *pCopy, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DAsync_v3020)(const CUDA_MEMCPY3D_v2 *pCopy, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD8_v3020)(CUdeviceptr_v2 dstDevice, unsigned char uc, size_t N);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD16_v3020)(CUdeviceptr_v2 dstDevice, unsigned short us, size_t N);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD32_v3020)(CUdeviceptr_v2 dstDevice, unsigned int ui, size_t N);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D8_v3020)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D16_v3020)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D32_v3020)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy_v4000)(CUdeviceptr_v2 dst, CUdeviceptr_v2 src, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyAsync_v4000)(CUdeviceptr_v2 dst, CUdeviceptr_v2 src, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyPeer_v4000)(CUdeviceptr_v2 dstDevice, CUcontext dstContext, CUdeviceptr_v2 srcDevice, CUcontext srcContext, size_t ByteCount);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyPeerAsync_v4000)(CUdeviceptr_v2 dstDevice, CUcontext dstContext, CUdeviceptr_v2 srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DPeer_v4000)(const CUDA_MEMCPY3D_PEER_v1 *pCopy);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DPeerAsync_v4000)(const CUDA_MEMCPY3D_PEER_v1 *pCopy, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpyBatchAsync_v12080)(CUdeviceptr_v2 *dsts, CUdeviceptr_v2 *srcs, size_t *sizes, size_t count, CUmemcpyAttributes_v1 *attrs, size_t *attrIdxs, size_t numAttrs, size_t *failIdx, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemcpy3DBatchAsync_v12080)(size_t numParams, CUDA_MEMCPY3D_BATCH_OP_v1 *opList, size_t *failIdx, unsigned long long flags, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD8Async_v3020)(CUdeviceptr_v2 dstDevice, unsigned char uc, size_t N, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD16Async_v3020)(CUdeviceptr_v2 dstDevice, unsigned short us, size_t N, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD32Async_v3020)(CUdeviceptr_v2 dstDevice, unsigned int ui, size_t N, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D8Async_v3020)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D16Async_v3020)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemsetD2D32Async_v3020)(CUdeviceptr_v2 dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetPriority_v5050)(CUstream hStream, int *priority);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetFlags_v5050)(CUstream hStream, unsigned int *flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCtx_v9020)(CUstream hStream, CUcontext *pctx);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetDevice_v12080)(CUstream hStream, CUdevice *device);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetDevice_v12080_ptsz)(CUstream hStream, CUdevice *device);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitEvent_v3020)(CUstream hStream, CUevent hEvent, unsigned int Flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamAddCallback_v5000)(CUstream hStream, CUstreamCallback callback, void *userData, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamAttachMemAsync_v6000)(CUstream hStream, CUdeviceptr_v2 dptr, size_t length, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamQuery_v2000)(CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamSynchronize_v2000)(CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuEventRecord_v2000)(CUevent hEvent, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuEventRecordWithFlags_v11010)(CUevent hEvent, CUstream hStream, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuLaunchKernel_v4000)(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams, void **extra);
+typedef CUresult (CUDAAPI *PFN_cuLaunchKernelEx_v11060)(const CUlaunchConfig *config, CUfunction f, void **kernelParams, void **extra);
+typedef CUresult (CUDAAPI *PFN_cuLaunchHostFunc_v10000)(CUstream hStream, CUhostFn fn, void *userData);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsMapResources_v3000)(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsUnmapResources_v3000)(unsigned int count, CUgraphicsResource *resources, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue32_v8000)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue32_v8000)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue64_v9000)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue64_v9000)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamBatchMemOp_v8000)(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue32_v11070)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue32_v11070)(CUstream stream, CUdeviceptr_v2 addr, cuuint32_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWriteValue64_v11070)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamWaitValue64_v11070)(CUstream stream, CUdeviceptr_v2 addr, cuuint64_t value, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamBatchMemOp_v11070)(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuMemPrefetchAsync_v8000)(CUdeviceptr_v2 devPtr, size_t count, CUdevice_v1 dstDevice, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemPrefetchAsync_v12020)(CUdeviceptr_v2 devPtr, size_t count, CUmemLocation_v1 location, unsigned int flags, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuLaunchCooperativeKernel_v9000)(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams);
+typedef CUresult (CUDAAPI *PFN_cuSignalExternalSemaphoresAsync_v10000)(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1 *paramsArray, unsigned int numExtSems, CUstream stream);
+typedef CUresult (CUDAAPI *PFN_cuWaitExternalSemaphoresAsync_v10000)(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1 *paramsArray, unsigned int numExtSems, CUstream stream);
+typedef CUresult (CUDAAPI *PFN_cuStreamBeginCapture_v10010)(CUstream hStream, CUstreamCaptureMode mode);
+typedef CUresult (CUDAAPI *PFN_cuStreamBeginCaptureToGraph_v12030)(CUstream hStream, CUgraph hGraph, const CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, CUstreamCaptureMode mode);
+typedef CUresult (CUDAAPI *PFN_cuStreamEndCapture_v10000)(CUstream hStream, CUgraph *phGraph);
+typedef CUresult (CUDAAPI *PFN_cuStreamIsCapturing_v10000)(CUstream hStream, CUstreamCaptureStatus *captureStatus);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCaptureInfo_v10010)(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCaptureInfo_v11030)(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, size_t *numDependencies_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCaptureInfo_v12030)(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, const CUgraphEdgeData **edgeData_out, size_t *numDependencies_out);
+typedef CUresult (CUDAAPI *PFN_cuStreamUpdateCaptureDependencies_v11030)(CUstream hStream, CUgraphNode *dependencies, size_t numDependencies, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuStreamUpdateCaptureDependencies_v12030)(CUstream hStream, CUgraphNode *dependencies, const CUgraphEdgeData *dependencyData, size_t numDependencies, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphInstantiateWithParams_v12000)(CUgraphExec *phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams);
+typedef CUresult (CUDAAPI *PFN_cuGraphUpload_v11010)(CUgraphExec hGraph, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuGraphLaunch_v10000)(CUgraphExec hGraph, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamCopyAttributes_v11000)(CUstream dstStream, CUstream srcStream);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetAttribute_v11000)(CUstream hStream, CUstreamAttrID attr, CUstreamAttrValue_v1 *value);
+typedef CUresult (CUDAAPI *PFN_cuStreamSetAttribute_v11000)(CUstream hStream, CUstreamAttrID attr, const CUstreamAttrValue_v1 *param);
+typedef CUresult (CUDAAPI *PFN_cuMemMapArrayAsync_v11010)(CUarrayMapInfo_v1 *mapInfoList, unsigned int count, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemFreeAsync_v11020)(CUdeviceptr_v2 dptr, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocAsync_v11020)(CUdeviceptr_v2 *dptr, size_t bytesize, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuMemAllocFromPoolAsync_v11020)(CUdeviceptr_v2 *dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream);
+typedef CUresult (CUDAAPI *PFN_cuFlushGPUDirectRDMAWrites_v11030)(CUflushGPUDirectRDMAWritesTarget target, CUflushGPUDirectRDMAWritesScope scope);
+typedef CUresult (CUDAAPI *PFN_cuUserObjectCreate_v11030)(CUuserObject *object_out, void *ptr, CUhostFn destroy, unsigned int initialRefcount, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuUserObjectRetain_v11030)(CUuserObject object, unsigned int count);
+typedef CUresult (CUDAAPI *PFN_cuUserObjectRelease_v11030)(CUuserObject object, unsigned int count);
+typedef CUresult (CUDAAPI *PFN_cuGraphRetainUserObject_v11030)(CUgraph graph, CUuserObject object, unsigned int count, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphReleaseUserObject_v11030)(CUgraph graph, CUuserObject object, unsigned int count);
+typedef CUresult (CUDAAPI *PFN_cuModuleGetLoadingMode_v11070)(CUmoduleLoadingMode *mode);
+typedef CUresult (CUDAAPI *PFN_cuMemGetHandleForAddressRange_v11070)(void *handle, CUdeviceptr dptr, size_t size, CUmemRangeHandleType handleType, unsigned long long flags);
+typedef CUresult (CUDAAPI *PFN_cuLibraryLoadData_v12000)(CUlibrary *library, const void *code, CUjit_option *jitOptions, void **jitOptionsValues, unsigned int numJitOptions, CUlibraryOption *libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions);
+typedef CUresult (CUDAAPI *PFN_cuLibraryLoadFromFile_v12000)(CUlibrary *library, const char *fileName, CUjit_option *jitOptions, void **jitOptionsValues, unsigned int numJitOptions, CUlibraryOption *libraryOptions, void **libraryOptionValues, unsigned int numLibraryOptions);
+typedef CUresult (CUDAAPI *PFN_cuLibraryUnload_v12000)(CUlibrary library);
+typedef CUresult (CUDAAPI *PFN_cuLibraryGetKernel_v12000)(CUkernel *pKernel, CUlibrary library, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuLibraryGetModule_v12000)(CUmodule *pMod, CUlibrary library);
+typedef CUresult (CUDAAPI *PFN_cuLibraryGetKernelCount)(unsigned int *count, CUlibrary lib);
+typedef CUresult (CUDAAPI *PFN_cuLibraryEnumerateKernels)(CUkernel *kernels, unsigned int numKernels, CUlibrary lib);
+typedef CUresult (CUDAAPI *PFN_cuKernelGetFunction_v12000)(CUfunction *pFunc, CUkernel kernel);
+typedef CUresult (CUDAAPI *PFN_cuKernelGetLibrary_v12050)(CUlibrary *pLib, CUkernel kernel);
+typedef CUresult (CUDAAPI *PFN_cuLibraryGetGlobal_v12000)(CUdeviceptr *dptr, size_t *bytes, CUlibrary library, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuLibraryGetManaged_v12000)(CUdeviceptr *dptr, size_t *bytes, CUlibrary library, const char *name);
+typedef CUresult (CUDAAPI *PFN_cuKernelGetAttribute_v12000)(int *pi, CUfunction_attribute attrib, CUkernel kernel, CUdevice dev);
+typedef CUresult (CUDAAPI *PFN_cuKernelSetAttribute_v12000)(CUfunction_attribute attrib, int val, CUkernel kernel, CUdevice dev);
+typedef CUresult (CUDAAPI *PFN_cuKernelSetCacheConfig_v12000)(CUkernel kernel, CUfunc_cache config, CUdevice dev);
+typedef CUresult (CUDAAPI *PFN_cuKernelGetName_v12030)(const char **name, CUkernel hfunc);
+typedef CUresult (CUDAAPI *PFN_cuKernelGetParamInfo_v12040)(CUkernel kernel, size_t paramIndex, size_t *paramOffset, size_t *paramSize);
+typedef CUresult (CUDAAPI *PFN_cuLibraryGetUnifiedFunction_v12000)(void **fptr, CUlibrary library, const char *symbol);
+typedef CUresult(CUDAAPI *PFN_cuCoredumpGetAttribute_v12010)(CUcoredumpSettings get, void *value, size_t *size);
+typedef CUresult(CUDAAPI *PFN_cuCoredumpGetAttributeGlobal_v12010)(CUcoredumpSettings get, void *value, size_t *size);
+typedef CUresult(CUDAAPI *PFN_cuCoredumpSetAttribute_v12010)(CUcoredumpSettings set, void *value, size_t *size);
+typedef CUresult(CUDAAPI *PFN_cuCoredumpSetAttributeGlobal_v12010)(CUcoredumpSettings set, void *value, size_t *size);
+typedef CUresult(CUDAAPI *PFN_cuDeviceRegisterAsyncNotification_v12040)(CUdevice device, CUasyncCallback callbackFunc, void *userData, CUasyncCallbackHandle *callback);
+typedef CUresult(CUDAAPI *PFN_cuDeviceUnregisterAsyncNotification_v12040)(CUdevice device, CUasyncCallbackHandle callback);
+typedef CUresult(CUDAAPI *PFN_cuGreenCtxCreate_v12040)(CUgreenCtx* phCtx, CUdevResourceDesc desc, CUdevice dev, unsigned int flags);
+typedef CUresult(CUDAAPI *PFN_cuGreenCtxDestroy_v12040)(CUgreenCtx hCtx);
+typedef CUresult(CUDAAPI *PFN_cuDeviceGetDevResource_v12040)(CUdevice dev, CUdevResource* result, CUdevResourceType type);
+typedef CUresult(CUDAAPI *PFN_cuCtxGetDevResource_v12040)(CUcontext hCtx, CUdevResource* result, CUdevResourceType type);
+typedef CUresult(CUDAAPI *PFN_cuGreenCtxGetDevResource_v12040)(CUgreenCtx hCtx, CUdevResource* result, CUdevResourceType type);
+typedef CUresult(CUDAAPI *PFN_cuGreenCtxRecordEvent_v12040)(CUgreenCtx hCtx, CUevent hEvent);
+typedef CUresult(CUDAAPI *PFN_cuGreenCtxWaitEvent_v12040)(CUgreenCtx hCtx, CUevent hEvent);
+typedef CUresult(CUDAAPI *PFN_cuDevResourceGenerateDesc_v12040)(CUdevResourceDesc* phDesc, CUdevResource* resources, unsigned int nbResources);
+typedef CUresult(CUDAAPI *PFN_cuDevSmResourceSplitByCount_v12040)(CUdevResource* result, unsigned int* nbGroups, const CUdevResource* input, CUdevResource* remaining, unsigned int useFlags, unsigned int minCount);
+typedef CUresult(CUDAAPI *PFN_cuStreamGetGreenCtx_v12040)(CUstream hStream, CUgreenCtx *phCtx);
+typedef CUresult(CUDAAPI *PFN_cuCtxFromGreenCtx_v12040)(CUcontext *pContext, CUgreenCtx hCtx);
+typedef CUresult(CUDAAPI *PFN_cuCtxRecordEvent_v12050)(CUcontext hCtx, CUevent hEvent);
+typedef CUresult(CUDAAPI *PFN_cuCtxWaitEvent_v12050)(CUcontext hCtx, CUevent hEvent);
+typedef CUresult (CUDAAPI *PFN_cuGreenCtxStreamCreate_v12050)(CUstream* phStream, CUgreenCtx greenCtx, unsigned int flags, int priority);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCtx_v12050)(CUstream hStream, CUcontext *pctx, CUgreenCtx *pGreenCtx);
+typedef CUresult (CUDAAPI *PFN_cuStreamGetCtx_v12050_ptsz)(CUstream hStream, CUcontext *pctx, CUgreenCtx *pGreenCtx);
+    typedef CUresult (CUDAAPI *PFN_cuMemBatchDecompressAsync_v12060)(CUmemDecompressParams *paramsArray, size_t count, unsigned int flags, size_t *errorIndex, CUstream stream);
+    typedef CUresult (CUDAAPI *PFN_cuMemBatchDecompressAsync_v12060_ptsz)(CUmemDecompressParams *paramsArray, size_t count, unsigned int flags, size_t *errorIndex, CUstream stream);
+/*
+ * Type definitions for older versioned functions in cuda.h
+ */
+#if defined(__CUDA_API_VERSION_INTERNAL)
+    typedef CUresult (CUDAAPI *PFN_cuMemHostRegister_v4000)(void *p, size_t bytesize, unsigned int Flags);
+    typedef CUresult (CUDAAPI *PFN_cuGraphicsResourceSetMapFlags_v3000)(CUgraphicsResource resource, unsigned int flags);
+    typedef CUresult (CUDAAPI *PFN_cuLinkCreate_v5050)(unsigned int numOptions, CUjit_option *options, void **optionValues, CUlinkState *stateOut);
+    typedef CUresult (CUDAAPI *PFN_cuLinkAddData_v5050)(CUlinkState state, CUjitInputType type, void *data, size_t size, const char *name, unsigned int numOptions, CUjit_option *options, void **optionValues);
+    typedef CUresult (CUDAAPI *PFN_cuLinkAddFile_v5050)(CUlinkState state, CUjitInputType type, const char *path, unsigned int numOptions, CUjit_option *options, void **optionValues);
+    typedef CUresult (CUDAAPI *PFN_cuTexRefSetAddress2D_v3020)(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR_v2 *desc, CUdeviceptr_v2 dptr, size_t Pitch);
+    typedef CUresult (CUDAAPI *PFN_cuDeviceTotalMem_v2000)(unsigned int *bytes, CUdevice_v1 dev);
+    typedef CUresult (CUDAAPI *PFN_cuCtxCreate_v2000)(CUcontext *pctx, unsigned int flags, CUdevice_v1 dev);
+    typedef CUresult (CUDAAPI *PFN_cuModuleGetGlobal_v2000)(CUdeviceptr_v1 *dptr, unsigned int *bytes, CUmodule hmod, const char *name);
+    typedef CUresult (CUDAAPI *PFN_cuMemGetInfo_v2000)(unsigned int *free, unsigned int *total);
+    typedef CUresult (CUDAAPI *PFN_cuMemAlloc_v2000)(CUdeviceptr_v1 *dptr, unsigned int bytesize);
+    typedef CUresult (CUDAAPI *PFN_cuMemAllocPitch_v2000)(CUdeviceptr_v1 *dptr, unsigned int *pPitch, unsigned int WidthInBytes, unsigned int Height, unsigned int ElementSizeBytes);
+    typedef CUresult (CUDAAPI *PFN_cuMemFree_v2000)(CUdeviceptr_v1 dptr);
+    typedef CUresult (CUDAAPI *PFN_cuMemGetAddressRange_v2000)(CUdeviceptr_v1 *pbase, unsigned int *psize, CUdeviceptr_v1 dptr);
+    typedef CUresult (CUDAAPI *PFN_cuMemAllocHost_v2000)(void **pp, unsigned int bytesize);
+    typedef CUresult (CUDAAPI *PFN_cuMemHostGetDevicePointer_v2020)(CUdeviceptr_v1 *pdptr, void *p, unsigned int Flags);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoD_v2000)(CUdeviceptr_v1 dstDevice, const void *srcHost, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoH_v2000)(void *dstHost, CUdeviceptr_v1 srcDevice, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoD_v2000)(CUdeviceptr_v1 dstDevice, CUdeviceptr_v1 srcDevice, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoA_v2000)(CUarray dstArray, unsigned int dstOffset, CUdeviceptr_v1 srcDevice, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoD_v2000)(CUdeviceptr_v1 dstDevice, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoA_v2000)(CUarray dstArray, unsigned int dstOffset, const void *srcHost, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoH_v2000)(void *dstHost, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoA_v2000)(CUarray dstArray, unsigned int dstOffset, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoAAsync_v2000)(CUarray dstArray, unsigned int dstOffset, const void *srcHost, unsigned int ByteCount, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyAtoHAsync_v2000)(void *dstHost, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpy2D_v2000)(const CUDA_MEMCPY2D_v1 *pCopy);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpy2DUnaligned_v2000)(const CUDA_MEMCPY2D_v1 *pCopy);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpy3D_v2000)(const CUDA_MEMCPY3D_v1 *pCopy);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyHtoDAsync_v2000)(CUdeviceptr_v1 dstDevice, const void *srcHost, unsigned int ByteCount, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoHAsync_v2000)(void *dstHost, CUdeviceptr_v1 srcDevice, unsigned int ByteCount, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpyDtoDAsync_v3000)(CUdeviceptr_v1 dstDevice, CUdeviceptr_v1 srcDevice, unsigned int ByteCount, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpy2DAsync_v2000)(const CUDA_MEMCPY2D_v1 *pCopy, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemcpy3DAsync_v2000)(const CUDA_MEMCPY3D_v1 *pCopy, CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuMemsetD8_v2000)(CUdeviceptr_v1 dstDevice, unsigned char uc, unsigned int N);
+    typedef CUresult (CUDAAPI *PFN_cuMemsetD16_v2000)(CUdeviceptr_v1 dstDevice, unsigned short us, unsigned int N);
+    typedef CUresult (CUDAAPI *PFN_cuMemsetD32_v2000)(CUdeviceptr_v1 dstDevice, unsigned int ui, unsigned int N);
+    typedef CUresult (CUDAAPI *PFN_cuMemsetD2D8_v2000)(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned char uc, unsigned int Width, unsigned int Height);
+    typedef CUresult (CUDAAPI *PFN_cuMemsetD2D16_v2000)(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned short us, unsigned int Width, unsigned int Height);
+    typedef CUresult (CUDAAPI *PFN_cuMemsetD2D32_v2000)(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned int ui, unsigned int Width, unsigned int Height);
+    typedef CUresult (CUDAAPI *PFN_cuArrayCreate_v2000)(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTOR_v1 *pAllocateArray);
+    typedef CUresult (CUDAAPI *PFN_cuArrayGetDescriptor_v2000)(CUDA_ARRAY_DESCRIPTOR_v1 *pArrayDescriptor, CUarray hArray);
+    typedef CUresult (CUDAAPI *PFN_cuArray3DCreate_v2000)(CUarray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR_v1 *pAllocateArray);
+    typedef CUresult (CUDAAPI *PFN_cuArray3DGetDescriptor_v2000)(CUDA_ARRAY3D_DESCRIPTOR_v1 *pArrayDescriptor, CUarray hArray);
+    typedef CUresult (CUDAAPI *PFN_cuTexRefSetAddress_v2000)(unsigned int *ByteOffset, CUtexref hTexRef, CUdeviceptr_v1 dptr, unsigned int bytes);
+    typedef CUresult (CUDAAPI *PFN_cuTexRefSetAddress2D_v2020)(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR_v1 *desc, CUdeviceptr_v1 dptr, unsigned int Pitch);
+    typedef CUresult (CUDAAPI *PFN_cuTexRefGetAddress_v2000)(CUdeviceptr_v1 *pdptr, CUtexref hTexRef);
+    typedef CUresult (CUDAAPI *PFN_cuGraphicsResourceGetMappedPointer_v3000)(CUdeviceptr_v1 *pDevPtr, unsigned int *pSize, CUgraphicsResource resource);
+    typedef CUresult (CUDAAPI *PFN_cuCtxDestroy_v2000)(CUcontext ctx);
+    typedef CUresult (CUDAAPI *PFN_cuCtxPopCurrent_v2000)(CUcontext *pctx);
+    typedef CUresult (CUDAAPI *PFN_cuCtxPushCurrent_v2000)(CUcontext ctx);
+    typedef CUresult (CUDAAPI *PFN_cuStreamDestroy_v2000)(CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuEventDestroy_v2000)(CUevent hEvent);
+    typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxRelease_v7000)(CUdevice_v1 dev);
+    typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxReset_v7000)(CUdevice_v1 dev);
+    typedef CUresult (CUDAAPI *PFN_cuDevicePrimaryCtxSetFlags_v7000)(CUdevice_v1 dev, unsigned int flags);
+    typedef CUresult (CUDAAPI *PFN_cuStreamBeginCapture_v10000)(CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuStreamBeginCapture_v10000_ptsz)(CUstream hStream);
+    typedef CUresult (CUDAAPI *PFN_cuIpcOpenMemHandle_v4010)(CUdeviceptr_v2 *pdptr, CUipcMemHandle_v1 handle, unsigned int Flags);
+    typedef CUresult (CUDAAPI *PFN_cuGraphInstantiate_v10000)(CUgraphExec *phGraphExec, CUgraph hGraph, CUgraphNode *phErrorNode, char *logBuffer, size_t bufferSize);
+    typedef CUresult (CUDAAPI *PFN_cuGraphInstantiate_v11000)(CUgraphExec *phGraphExec, CUgraph hGraph, CUgraphNode *phErrorNode, char *logBuffer, size_t bufferSize);
+#endif
+
+    typedef CUresult (CUDAAPI *PFN_cuCheckpointProcessGetRestoreThreadId_v12080)(int pid, int *tid);
+    typedef CUresult (CUDAAPI *PFN_cuCheckpointProcessGetState_v12080)(int pid, CUprocessState *state);
+    typedef CUresult (CUDAAPI *PFN_cuCheckpointProcessLock_v12080)(int pid, CUcheckpointLockArgs *args);
+    typedef CUresult (CUDAAPI *PFN_cuCheckpointProcessCheckpoint_v12080)(int pid, CUcheckpointCheckpointArgs *args);
+    typedef CUresult (CUDAAPI *PFN_cuCheckpointProcessRestore_v12080)(int pid, CUcheckpointRestoreArgs *args);
+    typedef CUresult (CUDAAPI *PFN_cuCheckpointProcessUnlock_v12080)(int pid, CUcheckpointUnlockArgs *args);
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+
+#endif // file guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaVDPAU.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaVDPAU.h
new file mode 100644
index 0000000000000000000000000000000000000000..97de57ae494d62ae176fc02ad3c0c3f4d43e1526
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaVDPAU.h
@@ -0,0 +1,282 @@
+/*
+ * Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAVDPAU_H
+#define CUDAVDPAU_H
+
+#ifdef CUDA_FORCE_API_VERSION
+#error "CUDA_FORCE_API_VERSION is no longer supported."
+#endif
+
+#define cuVDPAUCtxCreate cuVDPAUCtxCreate_v2
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * \defgroup CUDA_VDPAU VDPAU Interoperability
+ * \ingroup CUDA_DRIVER
+ *
+ * ___MANBRIEF___ VDPAU interoperability functions of the low-level CUDA driver
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the VDPAU interoperability functions of the
+ * low-level CUDA driver application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Gets the CUDA device associated with a VDPAU device
+ *
+ * Returns in \p *pDevice the CUDA device associated with a \p vdpDevice, if
+ * applicable.
+ *
+ * \param pDevice           - Device associated with vdpDevice
+ * \param vdpDevice         - A VdpDevice handle
+ * \param vdpGetProcAddress - VDPAU's VdpGetProcAddress function pointer
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate, ::cuVDPAUCtxCreate, ::cuGraphicsVDPAURegisterVideoSurface,
+ * ::cuGraphicsVDPAURegisterOutputSurface, ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsResourceSetMapFlags, ::cuGraphicsMapResources,
+ * ::cuGraphicsUnmapResources, ::cuGraphicsSubResourceGetMappedArray,
+ * ::cudaVDPAUGetDevice
+ */
+CUresult CUDAAPI cuVDPAUGetDevice(CUdevice *pDevice, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+
+/**
+ * \brief Create a CUDA context for interoperability with VDPAU
+ *
+ * Creates a new CUDA context, initializes VDPAU interoperability, and
+ * associates the CUDA context with the calling thread. It must be called
+ * before performing any other VDPAU interoperability operations. It may fail
+ * if the needed VDPAU driver facilities are not available. For usage of the
+ * \p flags parameter, see ::cuCtxCreate().
+ *
+ * \param pCtx              - Returned CUDA context
+ * \param flags             - Options for CUDA context creation
+ * \param device            - Device on which to create the context
+ * \param vdpDevice         - The VdpDevice to interop with
+ * \param vdpGetProcAddress - VDPAU's VdpGetProcAddress function pointer
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_DEINITIALIZED,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate, ::cuGraphicsVDPAURegisterVideoSurface,
+ * ::cuGraphicsVDPAURegisterOutputSurface, ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsResourceSetMapFlags, ::cuGraphicsMapResources,
+ * ::cuGraphicsUnmapResources, ::cuGraphicsSubResourceGetMappedArray,
+ * ::cuVDPAUGetDevice
+ */
+CUresult CUDAAPI cuVDPAUCtxCreate(CUcontext *pCtx, unsigned int flags, CUdevice device, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+
+/**
+ * \brief Registers a VDPAU VdpVideoSurface object
+ *
+ * Registers the VdpVideoSurface specified by \p vdpSurface for access by
+ * CUDA. A handle to the registered object is returned as \p pCudaResource.
+ * The surface's intended usage is specified using \p flags, as follows:
+ *
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * The VdpVideoSurface is presented as an array of subresources that may be
+ * accessed using pointers returned by ::cuGraphicsSubResourceGetMappedArray.
+ * The exact number of valid \p arrayIndex values depends on the VDPAU surface
+ * format. The mapping is shown in the table below. \p mipLevel must be 0.
+ *
+ * \htmlonly
+ * <table>
+ * <tr><th>VdpChromaType                               </th><th>arrayIndex</th><th>Size     </th><th>Format</th><th>Content            </th></tr>
+ * <tr><td rowspan="4" valign="top">VDP_CHROMA_TYPE_420</td><td>0         </td><td>w   x h/2</td><td>R8    </td><td>Top-field luma     </td></tr>
+ * <tr>                                                     <td>1         </td><td>w   x h/2</td><td>R8    </td><td>Bottom-field luma  </td></tr>
+ * <tr>                                                     <td>2         </td><td>w/2 x h/4</td><td>R8G8  </td><td>Top-field chroma   </td></tr>
+ * <tr>                                                     <td>3         </td><td>w/2 x h/4</td><td>R8G8  </td><td>Bottom-field chroma</td></tr>
+ * <tr><td rowspan="4" valign="top">VDP_CHROMA_TYPE_422</td><td>0         </td><td>w   x h/2</td><td>R8    </td><td>Top-field luma     </td></tr>
+ * <tr>                                                     <td>1         </td><td>w   x h/2</td><td>R8    </td><td>Bottom-field luma  </td></tr>
+ * <tr>                                                     <td>2         </td><td>w/2 x h/2</td><td>R8G8  </td><td>Top-field chroma   </td></tr>
+ * <tr>                                                     <td>3         </td><td>w/2 x h/2</td><td>R8G8  </td><td>Bottom-field chroma</td></tr>
+ * </table>
+ * \endhtmlonly
+ *
+ * \latexonly
+ * \begin{tabular}{|l|l|l|l|l|}
+ * \hline
+ * VdpChromaType          & arrayIndex & Size      & Format & Content             \\
+ * \hline
+ * VDP\_CHROMA\_TYPE\_420 & 0          & w x h/2   & R8     & Top-field luma      \\
+ *                        & 1          & w x h/2   & R8     & Bottom-field luma   \\
+ *                        & 2          & w/2 x h/4 & R8G8   & Top-field chroma    \\
+ *                        & 3          & w/2 x h/4 & R8G8   & Bottom-field chroma \\
+ * \hline
+ * VDP\_CHROMA\_TYPE\_422 & 0          & w x h/2   & R8     & Top-field luma      \\
+ *                        & 1          & w x h/2   & R8     & Bottom-field luma   \\
+ *                        & 2          & w/2 x h/2 & R8G8   & Top-field chroma    \\
+ *                        & 3          & w/2 x h/2 & R8G8   & Bottom-field chroma \\
+ * \hline
+ * \end{tabular}
+ * \endlatexonly
+ *
+ * \param pCudaResource - Pointer to the returned object handle
+ * \param vdpSurface    - The VdpVideoSurface to be registered
+ * \param flags         - Map flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate, ::cuVDPAUCtxCreate,
+ * ::cuGraphicsVDPAURegisterOutputSurface, ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsResourceSetMapFlags, ::cuGraphicsMapResources,
+ * ::cuGraphicsUnmapResources, ::cuGraphicsSubResourceGetMappedArray,
+ * ::cuVDPAUGetDevice,
+ * ::cudaGraphicsVDPAURegisterVideoSurface
+ */
+CUresult CUDAAPI cuGraphicsVDPAURegisterVideoSurface(CUgraphicsResource *pCudaResource, VdpVideoSurface vdpSurface, unsigned int flags);
+
+/**
+ * \brief Registers a VDPAU VdpOutputSurface object
+ *
+ * Registers the VdpOutputSurface specified by \p vdpSurface for access by
+ * CUDA. A handle to the registered object is returned as \p pCudaResource.
+ * The surface's intended usage is specified using \p flags, as follows:
+ *
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * The VdpOutputSurface is presented as an array of subresources that may be
+ * accessed using pointers returned by ::cuGraphicsSubResourceGetMappedArray.
+ * The exact number of valid \p arrayIndex values depends on the VDPAU surface
+ * format. The mapping is shown in the table below. \p mipLevel must be 0.
+ *
+ * \htmlonly
+ * <table>
+ * <tr><th>VdpRGBAFormat              </th><th>arrayIndex</th><th>Size </th><th>Format </th><th>Content       </th></tr>
+ * <tr><td>VDP_RGBA_FORMAT_B8G8R8A8   </td><td>0         </td><td>w x h</td><td>ARGB8  </td><td>Entire surface</td></tr>
+ * <tr><td>VDP_RGBA_FORMAT_R10G10B10A2</td><td>0         </td><td>w x h</td><td>A2BGR10</td><td>Entire surface</td></tr>
+ * </table>
+ * \endhtmlonly
+ *
+ * \latexonly
+ * \begin{tabular}{|l|l|l|l|l|}
+ * \hline
+ * VdpRGBAFormat                  & arrayIndex & Size  & Format  & Content        \\
+ * \hline
+ * VDP\_RGBA\_FORMAT\_B8G8R8A8    & 0          & w x h & ARGB8   & Entire surface \\
+ * VDP\_RGBA\_FORMAT\_R10G10B10A2 & 0          & w x h & A2BGR10 & Entire surface \\
+ * \hline
+ * \end{tabular}
+ * \endlatexonly
+ *
+ * \param pCudaResource - Pointer to the returned object handle
+ * \param vdpSurface    - The VdpOutputSurface to be registered
+ * \param flags         - Map flags
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_HANDLE,
+ * ::CUDA_ERROR_ALREADY_MAPPED,
+ * ::CUDA_ERROR_INVALID_CONTEXT,
+ * \notefnerr
+ *
+ * \sa ::cuCtxCreate, ::cuVDPAUCtxCreate,
+ * ::cuGraphicsVDPAURegisterVideoSurface, ::cuGraphicsUnregisterResource,
+ * ::cuGraphicsResourceSetMapFlags, ::cuGraphicsMapResources,
+ * ::cuGraphicsUnmapResources, ::cuGraphicsSubResourceGetMappedArray,
+ * ::cuVDPAUGetDevice,
+ * ::cudaGraphicsVDPAURegisterOutputSurface
+ */
+CUresult CUDAAPI cuGraphicsVDPAURegisterOutputSurface(CUgraphicsResource *pCudaResource, VdpOutputSurface vdpSurface, unsigned int flags);
+
+/** @} */ /* END CUDA_VDPAU */
+
+
+#if defined(__CUDA_API_VERSION_INTERNAL)
+    #undef cuVDPAUCtxCreate
+
+    CUresult CUDAAPI cuVDPAUCtxCreate(CUcontext *pCtx, unsigned int flags, CUdevice device, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+#endif /* __CUDA_API_VERSION_INTERNAL */
+
+#ifdef __cplusplus
+};
+#endif
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaVDPAUTypedefs.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaVDPAUTypedefs.h
new file mode 100644
index 0000000000000000000000000000000000000000..2bfd148632827d222548be49b3a2ffb7caa1c4dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudaVDPAUTypedefs.h
@@ -0,0 +1,90 @@
+/*
+ * Copyright 2020-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CUDAVDPAUTYPEDEFS_H
+#define CUDAVDPAUTYPEDEFS_H
+
+// Dependent includes for cudavdpau.h
+#include <vdpau/vdpau.h>
+
+#include <cudaVDPAU.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif // __cplusplus
+
+/*
+ * Macros for the latest version for each driver function in cudaVDPAU.h
+ */
+#define PFN_cuVDPAUGetDevice  PFN_cuVDPAUGetDevice_v3010
+#define PFN_cuVDPAUCtxCreate  PFN_cuVDPAUCtxCreate_v3020
+#define PFN_cuGraphicsVDPAURegisterVideoSurface  PFN_cuGraphicsVDPAURegisterVideoSurface_v3010
+#define PFN_cuGraphicsVDPAURegisterOutputSurface  PFN_cuGraphicsVDPAURegisterOutputSurface_v3010
+
+
+/**
+ * Type definitions for functions defined in cudaVDPAU.h
+ */
+typedef CUresult (CUDAAPI *PFN_cuVDPAUGetDevice_v3010)(CUdevice_v1 *pDevice, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+typedef CUresult (CUDAAPI *PFN_cuVDPAUCtxCreate_v3020)(CUcontext *pCtx, unsigned int flags, CUdevice_v1 device, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsVDPAURegisterVideoSurface_v3010)(CUgraphicsResource *pCudaResource, VdpVideoSurface vdpSurface, unsigned int flags);
+typedef CUresult (CUDAAPI *PFN_cuGraphicsVDPAURegisterOutputSurface_v3010)(CUgraphicsResource *pCudaResource, VdpOutputSurface vdpSurface, unsigned int flags);
+
+/*
+ * Type definitions for older versioned functions in cudaVDPAU.h
+ */
+#if defined(__CUDA_API_VERSION_INTERNAL)
+typedef CUresult (CUDAAPI *PFN_cuVDPAUCtxCreate_v3010)(CUcontext *pCtx, unsigned int flags, CUdevice_v1 device, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+#endif
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+
+#endif // file guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier.h
new file mode 100644
index 0000000000000000000000000000000000000000..12fd878dd10d9f18ad944a0d62ae1caba123fd06
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier.h
@@ -0,0 +1,280 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDA_AWBARRIER_H_
+# define _CUDA_AWBARRIER_H_
+
+# include "cuda_awbarrier_primitives.h"
+
+# if !defined(_CUDA_AWBARRIER_SM_TARGET)
+#  error This file requires compute capability 7.0 or greater.
+# endif
+
+# if !defined(_CUDA_AWBARRIER_CPLUSPLUS_11_OR_LATER)
+#  error This file requires compiler support for the ISO C++ 2011 standard. This support must be enabled with the \
+             -std=c++11 compiler option.
+# endif
+
+_CUDA_AWBARRIER_BEGIN_NAMESPACE
+
+class awbarrier {
+public:
+    class arrival_token {
+    public:
+        arrival_token() = default;
+        ~arrival_token() = default;
+        _CUDA_AWBARRIER_QUALIFIER uint32_t pending_count() const;
+    private:
+        _CUDA_AWBARRIER_QUALIFIER arrival_token(uint64_t token);
+        uint64_t token;
+        friend awbarrier;
+    };
+    awbarrier() = default;
+    awbarrier(const awbarrier&) = delete;
+    awbarrier& operator=(const awbarrier&) = delete;
+    ~awbarrier() = default;
+
+    _CUDA_AWBARRIER_QUALIFIER arrival_token arrive();
+    _CUDA_AWBARRIER_QUALIFIER arrival_token arrive_and_drop();
+    _CUDA_AWBARRIER_QUALIFIER bool timed_wait(arrival_token token, uint32_t hint_cycles);
+    _CUDA_AWBARRIER_QUALIFIER bool timed_wait_parity(bool phase, uint32_t hint_cycles);
+    _CUDA_AWBARRIER_QUALIFIER void wait(arrival_token token);
+    _CUDA_AWBARRIER_QUALIFIER void arrive_and_wait();
+    _CUDA_AWBARRIER_QUALIFIER bool try_wait(arrival_token token, uint32_t maxSleepNanosec);
+    _CUDA_AWBARRIER_QUALIFIER bool try_wait_parity(bool phase, uint32_t maxSleepNanosec);
+    _CUDA_AWBARRIER_STATIC_QUALIFIER __host__ constexpr uint32_t max();
+
+private:
+    uint64_t barrier;
+    friend _CUDA_AWBARRIER_QUALIFIER void init(awbarrier* barrier, uint32_t expected_count);
+    friend _CUDA_AWBARRIER_QUALIFIER void inval(awbarrier* barrier);
+    friend class pipeline;
+};
+
+_CUDA_AWBARRIER_QUALIFIER
+uint32_t awbarrier::arrival_token::pending_count() const
+{
+    const uint32_t pending_count = _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_token_pending_count(this->token);
+#if (__CUDA_ARCH__ >= 900)
+    return pending_count;
+#else
+    return (pending_count >> 15);
+#endif
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+awbarrier::arrival_token::arrival_token(uint64_t token)
+    : token(token)
+{
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+void init(awbarrier* barrier, uint32_t expected_count)
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+    _CUDA_AWBARRIER_ASSERT(expected_count > 0 && expected_count <= _CUDA_AWBARRIER_MAX_COUNT);
+
+#if (__CUDA_ARCH__ >= 900)
+    const uint32_t init_count = expected_count;
+#else
+    const uint32_t init_count = (expected_count << 15) + expected_count;
+#endif
+
+    _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_init(&barrier->barrier, init_count);
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+void inval(awbarrier* barrier)
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+
+    _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_inval(&barrier->barrier);
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+awbarrier::arrival_token awbarrier::arrive()
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+ #if (__CUDA_ARCH__ < 900)
+    const uint32_t arrive_count = 1 << 15;
+    const uint64_t token = _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_arrive_drop_no_complete<false>(&this->barrier, arrive_count);
+    (void)
+#else
+    const uint64_t token =
+ #endif
+    _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_arrive_drop<false>(&this->barrier);
+
+    return arrival_token(token);
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+awbarrier::arrival_token awbarrier::arrive_and_drop()
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+ #if (__CUDA_ARCH__ < 900)
+    const uint32_t arrive_count = 1 << 15;
+    const uint64_t token = _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_arrive_drop_no_complete<true>(&this->barrier, arrive_count);
+    (void)
+#else
+    const uint64_t token =
+ #endif
+    _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_arrive_drop<true>(&this->barrier);
+
+    return arrival_token(token);
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+bool awbarrier::timed_wait(arrival_token token, uint32_t hint_cycles)
+{
+    constexpr uint64_t max_busy_wait_cycles = 1024;
+    constexpr uint32_t max_sleep_ns = 1 << 20;
+
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+    if (_CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_test_wait(&this->barrier, token.token)) {
+        return true;
+    }
+
+    uint64_t start_cycles = clock64();
+    uint64_t elapsed_cycles = 0;
+    uint32_t sleep_ns = 32;
+    while (elapsed_cycles < hint_cycles) {
+        if (_CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_test_wait(&this->barrier, token.token)) {
+            return true;
+        }
+
+        if (elapsed_cycles > max_busy_wait_cycles) {
+            __nanosleep(sleep_ns);
+            if (sleep_ns < max_sleep_ns) {
+                sleep_ns *= 2;
+            }
+        }
+
+        elapsed_cycles = clock64() - start_cycles;
+    }
+
+    return false;
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+bool awbarrier::timed_wait_parity(bool phase, uint32_t hint_cycles)
+{
+    constexpr uint64_t max_busy_wait_cycles = 1024;
+    constexpr uint32_t max_sleep_ns = 1 << 20;
+
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+    if (_CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_test_wait_parity(&this->barrier, phase)) {
+        return true;
+    }
+
+    uint64_t start_cycles = clock64();
+    uint64_t elapsed_cycles = 0;
+    uint32_t sleep_ns = 32;
+    while (elapsed_cycles < hint_cycles) {
+        if (_CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_test_wait_parity(&this->barrier, phase)) {
+            return true;
+        }
+
+        if (elapsed_cycles > max_busy_wait_cycles) {
+            __nanosleep(sleep_ns);
+            if (sleep_ns < max_sleep_ns) {
+                sleep_ns *= 2;
+            }
+        }
+
+        elapsed_cycles = clock64() - start_cycles;
+    }
+
+    return false;
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+bool awbarrier::try_wait(arrival_token token, uint32_t maxSleepNanosec)
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+    return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_try_wait(&this->barrier, token.token, maxSleepNanosec);
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+bool awbarrier::try_wait_parity(bool phase, uint32_t maxSleepNanosec)
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+    return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_try_wait_parity(&this->barrier, phase, maxSleepNanosec);
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+void awbarrier::wait(arrival_token token)
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+    while (!timed_wait(token, ~0u));
+}
+
+_CUDA_AWBARRIER_QUALIFIER
+void awbarrier::arrive_and_wait()
+{
+    _CUDA_AWBARRIER_ASSERT(__isShared(&this->barrier));
+
+    this->wait(this->arrive());
+}
+
+_CUDA_AWBARRIER_QUALIFIER __host__
+constexpr uint32_t awbarrier::max()
+{
+    return _CUDA_AWBARRIER_MAX_COUNT;
+}
+
+_CUDA_AWBARRIER_END_NAMESPACE
+
+#endif /* !_CUDA_AWBARRIER_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier_helpers.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier_helpers.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c58346fe78c59329aca138ebc92add9015c005c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier_helpers.h
@@ -0,0 +1,365 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDA_AWBARRIER_HELPERS_H_
+#define _CUDA_AWBARRIER_HELPERS_H_
+
+#define _CUDA_AWBARRIER_NAMESPACE       nvcuda::experimental
+#define _CUDA_AWBARRIER_BEGIN_NAMESPACE namespace nvcuda { namespace experimental {
+#define _CUDA_AWBARRIER_END_NAMESPACE   } }
+
+#define _CUDA_AWBARRIER_INTERNAL_NAMESPACE       _CUDA_AWBARRIER_NAMESPACE::__awbarrier_internal
+#define _CUDA_AWBARRIER_BEGIN_INTERNAL_NAMESPACE _CUDA_AWBARRIER_BEGIN_NAMESPACE namespace __awbarrier_internal {
+#define _CUDA_AWBARRIER_END_INTERNAL_NAMESPACE   } _CUDA_AWBARRIER_END_NAMESPACE
+
+# if !defined(_CUDA_AWBARRIER_QUALIFIER)
+#  define _CUDA_AWBARRIER_QUALIFIER inline __device__
+# endif
+# if !defined(_CUDA_AWBARRIER_STATIC_QUALIFIER)
+#  define _CUDA_AWBARRIER_STATIC_QUALIFIER static inline __device__
+#endif
+
+#if defined(__CUDA_ARCH__)
+#if (__CUDA_ARCH__ >= 900)
+# define _CUDA_AWBARRIER_SM_TARGET _CUDA_AWBARRIER_SM_90
+#elif  (__CUDA_ARCH__ >= 800)
+# define _CUDA_AWBARRIER_SM_TARGET _CUDA_AWBARRIER_SM_80
+#elif (__CUDA_ARCH__ >= 700)
+# define _CUDA_AWBARRIER_SM_TARGET _CUDA_AWBARRIER_SM_70
+#endif
+#else
+# define _CUDA_AWBARRIER_SM_TARGET _CUDA_AWBARRIER_SM_70
+#endif
+
+#define _CUDA_AWBARRIER_MAX_COUNT ((1 << 14) - 1)
+
+#if defined(__cplusplus) && ((__cplusplus >= 201103L) || (defined(_MSC_VER) && (_MSC_VER >= 1900)))
+# define _CUDA_AWBARRIER_CPLUSPLUS_11_OR_LATER
+#endif
+
+#if !defined(_CUDA_AWBARRIER_DEBUG)
+# if defined(__CUDACC_DEBUG__)
+#  define _CUDA_AWBARRIER_DEBUG 1
+# else
+#  define _CUDA_AWBARRIER_DEBUG 0
+# endif
+#endif
+
+#if defined(_CUDA_AWBARRIER_DEBUG) && (_CUDA_AWBARRIER_DEBUG == 1) && !defined(NDEBUG)
+# if !defined(__CUDACC_RTC__)
+#  include <cassert>
+# endif
+# define _CUDA_AWBARRIER_ASSERT(x) assert((x));
+# define _CUDA_AWBARRIER_ABORT() assert(0);
+#else
+# define _CUDA_AWBARRIER_ASSERT(x)
+# define _CUDA_AWBARRIER_ABORT() __trap();
+#endif
+
+#if defined(__CUDACC_RTC__)
+typedef unsigned short     uint16_t;
+typedef unsigned int       uint32_t;
+typedef unsigned long long uint64_t;
+typedef uint64_t           uintptr_t;
+#else
+# include <stdint.h>
+#endif
+
+// implicitly provided by NVRTC
+#ifndef __CUDACC_RTC__
+#include <nv/target>
+#endif /* !defined(__CUDACC_RTC__) */
+
+typedef uint64_t __mbarrier_t;
+typedef uint64_t __mbarrier_token_t;
+
+_CUDA_AWBARRIER_BEGIN_INTERNAL_NAMESPACE
+
+extern "C" __device__ uint32_t __nvvm_get_smem_pointer(void *);
+
+union AWBarrier {
+    struct {
+        uint32_t expected;
+        uint32_t pending;
+    } split;
+    uint64_t raw;
+};
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+void awbarrier_init(uint64_t* barrier, uint32_t expected_count) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+    _CUDA_AWBARRIER_ASSERT(expected_count > 0 && expected_count < (1 << 29));
+
+    NV_IF_TARGET(NV_PROVIDES_SM_80,
+        asm volatile ("mbarrier.init.shared.b64 [%0], %1;"
+                :
+                : "r"(__nvvm_get_smem_pointer(barrier)), "r"(expected_count)
+                : "memory");
+        return;
+    )
+    NV_IF_TARGET(NV_PROVIDES_SM_70,
+        AWBarrier* awbarrier = reinterpret_cast<AWBarrier*>(barrier);
+
+        awbarrier->split.expected = 0x40000000 - expected_count;
+        awbarrier->split.pending = 0x80000000 - expected_count;
+        return;
+    )
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+void awbarrier_inval(uint64_t* barrier) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+
+    NV_IF_TARGET(NV_PROVIDES_SM_80,
+        asm volatile ("mbarrier.inval.shared.b64 [%0];"
+                :
+                : "r"(__nvvm_get_smem_pointer(barrier))
+                : "memory");
+        return;
+    )
+    return;
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+uint32_t awbarrier_token_pending_count(uint64_t token) {
+    NV_IF_TARGET(NV_PROVIDES_SM_80,
+        uint32_t __pending_count;
+
+        asm ("mbarrier.pending_count.b64 %0, %1;"
+                : "=r"(__pending_count)
+                : "l"(token));
+        return __pending_count;
+    )
+    NV_IF_TARGET(NV_PROVIDES_SM_70,
+        const uint32_t pending = token >> 32;
+        return 0x80000000 - (pending & 0x7fffffff);
+    )
+}
+
+template<bool _Drop>
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+uint64_t awbarrier_arrive_drop(uint64_t* barrier) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+
+    NV_IF_TARGET(NV_PROVIDES_SM_80,
+        uint64_t token;
+
+        if (_Drop) {
+            asm volatile ("mbarrier.arrive_drop.shared.b64 %0, [%1];"
+                    : "=l"(token)
+                    : "r"(__nvvm_get_smem_pointer(barrier))
+                    : "memory");
+        } else {
+            asm volatile ("mbarrier.arrive.shared.b64 %0, [%1];"
+                    : "=l"(token)
+                    : "r"(__nvvm_get_smem_pointer(barrier))
+                    : "memory");
+        }
+
+        return token;
+    )
+    NV_IF_TARGET(NV_PROVIDES_SM_70,
+        AWBarrier* awbarrier = reinterpret_cast<AWBarrier*>(barrier);
+
+        while ((*reinterpret_cast<volatile uint32_t*>(&awbarrier->split.pending) & 0x7fffffff) == 0);
+
+        if (_Drop) {
+            (void)atomicAdd_block(&awbarrier->split.expected, 1);
+        }
+
+        __threadfence_block();
+
+        const uint32_t old_pending = atomicAdd_block(&awbarrier->split.pending, 1);
+        const uint32_t new_pending = old_pending + 1;
+        const bool reset = (old_pending ^ new_pending) & 0x80000000;
+
+        if (reset) {
+            __threadfence_block();
+
+            uint32_t new_expected = *reinterpret_cast<volatile uint32_t*>(&awbarrier->split.expected);
+            new_expected &= ~0x40000000;
+            if (new_expected & 0x20000000) {
+                new_expected |= 0x40000000;
+            }
+            atomicAdd_block(&awbarrier->split.pending, new_expected);
+        }
+
+        return static_cast<uint64_t>(old_pending) << 32;
+    )
+}
+
+template<bool _Drop>
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+uint64_t awbarrier_arrive_drop_no_complete(uint64_t* barrier, uint32_t count) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+    _CUDA_AWBARRIER_ASSERT(count > 0 && count < (1 << 29));
+
+    NV_IF_TARGET(NV_PROVIDES_SM_80,
+        uint64_t token;
+
+        if (_Drop) {
+            asm volatile ("mbarrier.arrive_drop.noComplete.shared.b64 %0, [%1], %2;"
+                    : "=l"(token)
+                    : "r"(__nvvm_get_smem_pointer(barrier)), "r"(count)
+                    : "memory");
+        } else {
+            asm volatile ("mbarrier.arrive.noComplete.shared.b64 %0, [%1], %2;"
+                    : "=l"(token)
+                    : "r"(__nvvm_get_smem_pointer(barrier)), "r"(count)
+                    : "memory");
+        }
+
+        return token;
+    )
+    NV_IF_TARGET(NV_PROVIDES_SM_70,
+        AWBarrier* awbarrier = reinterpret_cast<AWBarrier*>(barrier);
+
+        while ((*reinterpret_cast<volatile uint32_t*>(&awbarrier->split.pending) & 0x7fffffff) == 0);
+
+        if (_Drop) {
+            (void)atomicAdd_block(&awbarrier->split.expected, count);
+        }
+
+        return static_cast<uint64_t>(atomicAdd_block(&awbarrier->split.pending, count)) << 32;
+    )
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool awbarrier_test_wait(uint64_t* barrier, uint64_t token) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+
+    NV_IF_TARGET(NV_PROVIDES_SM_80,
+        uint32_t __wait_complete;
+
+        asm volatile ("{"
+                "    .reg .pred %%p;"
+                "    mbarrier.test_wait.shared.b64 %%p, [%1], %2;"
+                "    selp.b32 %0, 1, 0, %%p;"
+                "}"
+                : "=r"(__wait_complete)
+                : "r"(__nvvm_get_smem_pointer(barrier)), "l"(token)
+                : "memory");
+        return bool(__wait_complete);
+    )
+    NV_IF_TARGET(NV_PROVIDES_SM_70,
+        volatile AWBarrier* awbarrier = reinterpret_cast<volatile AWBarrier*>(barrier);
+
+        return ((token >> 32) ^ awbarrier->split.pending) & 0x80000000;
+    )
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool awbarrier_test_wait_parity(uint64_t* barrier, bool phase_parity) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+    
+    NV_IF_TARGET(NV_PROVIDES_SM_90,
+        uint32_t __wait_complete = 0;
+
+        asm volatile ("{"
+                    ".reg .pred %%p;"
+                    "mbarrier.test_wait.parity.shared.b64 %%p, [%1], %2;"
+                    "selp.b32 %0, 1, 0, %%p;"
+                    "}"
+                : "=r"(__wait_complete)
+                : "r"(__nvvm_get_smem_pointer(barrier)), "r"(static_cast<uint32_t>(phase_parity))
+                : "memory");
+
+        return __wait_complete;
+    )
+    _CUDA_AWBARRIER_ABORT()
+    return false;
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool awbarrier_try_wait(uint64_t* barrier, uint64_t token, uint32_t max_sleep_nanosec) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+    
+    NV_IF_TARGET(NV_PROVIDES_SM_90,
+        uint32_t __wait_complete = 0;
+
+        asm volatile ("{\n\t"
+                    ".reg .pred p;\n\t"
+                    "mbarrier.try_wait.shared.b64 p, [%1], %2, %3;\n\t"
+                    "selp.b32 %0, 1, 0, p;\n\t"
+                    "}"
+                : "=r"(__wait_complete)
+                : "r"(__nvvm_get_smem_pointer(barrier)), "l"(token), "r"(max_sleep_nanosec)
+                : "memory");
+
+        return __wait_complete;
+    )
+    _CUDA_AWBARRIER_ABORT()
+    return false;
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool awbarrier_try_wait_parity(uint64_t* barrier, bool phase_parity, uint32_t max_sleep_nanosec) {
+    _CUDA_AWBARRIER_ASSERT(__isShared(barrier));
+    
+    NV_IF_TARGET(NV_PROVIDES_SM_90,
+        uint32_t __wait_complete = 0;
+
+        asm volatile ("{\n\t"
+                    ".reg .pred p;\n\t"
+                    "mbarrier.try_wait.parity.shared.b64 p, [%1], %2, %3;\n\t"
+                    "selp.b32 %0, 1, 0, p;\n\t"
+                    "}"
+                : "=r"(__wait_complete)
+                : "r"(__nvvm_get_smem_pointer(barrier)), "r"(static_cast<uint32_t>(phase_parity)), "r"(max_sleep_nanosec)
+                : "memory");
+
+        return __wait_complete;
+    )
+    _CUDA_AWBARRIER_ABORT()
+    return false;
+}
+
+_CUDA_AWBARRIER_END_INTERNAL_NAMESPACE
+
+#endif /* !_CUDA_AWBARRIER_HELPERS_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier_primitives.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier_primitives.h
new file mode 100644
index 0000000000000000000000000000000000000000..5562ef3f6afeb7fce4bad4cb8067b3cb1b9a690f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_awbarrier_primitives.h
@@ -0,0 +1,109 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDA_AWBARRIER_PRIMITIVES_H_
+#define _CUDA_AWBARRIER_PRIMITIVES_H_
+
+#include "cuda_awbarrier_helpers.h"
+
+#if !defined(_CUDA_AWBARRIER_SM_TARGET)
+# error This file requires compute capability 7.0 or greater.
+#endif
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER __host__
+uint32_t __mbarrier_maximum_count() {
+    return _CUDA_AWBARRIER_MAX_COUNT;
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+void __mbarrier_init(__mbarrier_t* barrier, uint32_t expected_count) {
+    _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_init(barrier, expected_count);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+void __mbarrier_inval(__mbarrier_t* barrier) {
+    _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_inval(barrier);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+__mbarrier_token_t __mbarrier_arrive(__mbarrier_t* barrier) {
+    return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_arrive_drop<false>(barrier);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+__mbarrier_token_t __mbarrier_arrive_and_drop(__mbarrier_t* barrier) {
+    return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_arrive_drop<true>(barrier);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool __mbarrier_test_wait(__mbarrier_t* barrier, __mbarrier_token_t token) {
+    return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_test_wait(barrier, token);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+uint32_t __mbarrier_token_pending_count(__mbarrier_token_t token) {
+    return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_token_pending_count(token);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool __mbarrier_test_wait_parity(__mbarrier_t* barrier, bool phase_parity) {
+   return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_test_wait_parity(barrier, phase_parity);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool __mbarrier_try_wait(__mbarrier_t* barrier, __mbarrier_token_t token, uint32_t max_sleep_nanosec) {
+   return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_try_wait(barrier, token, max_sleep_nanosec);
+}
+
+_CUDA_AWBARRIER_STATIC_QUALIFIER
+bool __mbarrier_try_wait_parity(__mbarrier_t* barrier, bool phase_parity, uint32_t max_sleep_nanosec) {
+   return _CUDA_AWBARRIER_INTERNAL_NAMESPACE::awbarrier_try_wait_parity(barrier, phase_parity, max_sleep_nanosec);
+}
+
+#endif /* !_CUDA_AWBARRIER_PRIMITIVES_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_bf16.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_bf16.h
new file mode 100644
index 0000000000000000000000000000000000000000..dd049cafd252175a2c3107d86fcdbb6f8a7d0250
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_bf16.h
@@ -0,0 +1,5118 @@
+/*
+* Copyright 1993-2024 NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO LICENSEE:
+*
+* This source code and/or documentation ("Licensed Deliverables") are
+* subject to NVIDIA intellectual property rights under U.S. and
+* international Copyright laws.
+*
+* These Licensed Deliverables contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and
+* conditions of a form of NVIDIA software license agreement by and
+* between NVIDIA and Licensee ("License Agreement") or electronically
+* accepted by Licensee.  Notwithstanding any terms or conditions to
+* the contrary in the License Agreement, reproduction or disclosure
+* of the Licensed Deliverables to any third party without the express
+* written consent of NVIDIA is prohibited.
+*
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+* OF THESE LICENSED DELIVERABLES.
+*
+* U.S. Government End Users.  These Licensed Deliverables are a
+* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+* 1995), consisting of "commercial computer software" and "commercial
+* computer software documentation" as such terms are used in 48
+* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+* only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+* U.S. Government End Users acquire the Licensed Deliverables with
+* only those rights set forth herein.
+*
+* Any use of the Licensed Deliverables in individual and commercial
+* software must include, in the user documentation and internal
+* comments to the code, the above Disclaimer and U.S. Government End
+* Users Notice.
+*/
+
+/**
+* \defgroup CUDA_MATH_INTRINSIC_BFLOAT16 Bfloat16 Precision Intrinsics
+* This section describes nv_bfloat16 precision intrinsic functions.
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+* All of the functions defined here are available in device code.
+* Some of the functions are also available to host compilers, please
+* refer to respective functions' documentation for details.
+*
+* NOTE: Aggressive floating-point optimizations performed by host or device
+* compilers may affect numeric behavior of the functions implemented in this
+* header. Specific examples are:
+* - hsin(__nv_bfloat16);
+* - hcos(__nv_bfloat16);
+* - h2sin(__nv_bfloat162);
+* - h2cos(__nv_bfloat162);
+*
+* The following macros are available to help users selectively enable/disable
+* various definitions present in the header file:
+* - \p CUDA_NO_BFLOAT16 - If defined, this macro will prevent the definition of
+* additional type aliases in the global namespace, helping to avoid potential
+* conflicts with symbols defined in the user program.
+* - \p __CUDA_NO_BFLOAT16_CONVERSIONS__ - If defined, this macro will prevent
+* the use of the C++ type conversions (converting constructors and conversion
+* operators) that are common for built-in floating-point types, but may be
+* undesirable for \p __nv_bfloat16 which is essentially a user-defined type.
+* - \p __CUDA_NO_BFLOAT16_OPERATORS__ and \p __CUDA_NO_BFLOAT162_OPERATORS__ -
+* If defined, these macros will prevent the inadvertent use of usual arithmetic
+* and comparison operators. This enforces the storage-only type semantics and
+* prevents C++ style computations on \p __nv_bfloat16 and \p __nv_bfloat162 types.
+*/
+
+/**
+* \defgroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS Bfloat16 Arithmetic Constants
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these constants, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT16_ARITHMETIC Bfloat16 Arithmetic Functions
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT162_ARITHMETIC Bfloat162 Arithmetic Functions
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT16_COMPARISON Bfloat16 Comparison Functions
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT162_COMPARISON Bfloat162 Comparison Functions
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT16_MISC Bfloat16 Precision Conversion and Data Movement
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT16_FUNCTIONS Bfloat16 Math Functions
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__BFLOAT162_FUNCTIONS Bfloat162 Math Functions
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+* To use these functions, include the header file \p cuda_bf16.h in your program.
+*/
+
+#ifndef __CUDA_BF16_H__
+#define __CUDA_BF16_H__
+
+/* bring in __half data type and operations, for use in converting constructors */
+#include "cuda_fp16.h"
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+/* bring in float2, double4, etc vector types */
+#include "vector_types.h"
+/* bring in operations on vector types like: make_float2 */
+#include "vector_functions.h"
+#endif  /* !defined(__CUDACC_RTC__) */
+
+#define ___CUDA_BF16_STRINGIFY_INNERMOST(x) #x
+#define __CUDA_BF16_STRINGIFY(x) ___CUDA_BF16_STRINGIFY_INNERMOST(x)
+
+#if defined(__cplusplus)
+
+/* Set up function decorations */
+#if (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))
+#define __CUDA_BF16_DECL__ __device__
+#define __CUDA_HOSTDEVICE_BF16_DECL__ __device__
+#define __CUDA_HOSTDEVICE__ __device__
+#elif defined(__CUDACC__) || defined(_NVHPC_CUDA)
+#define __CUDA_BF16_DECL__ static __device__ __inline__
+#define __CUDA_HOSTDEVICE_BF16_DECL__ static __host__ __device__ __inline__
+#define __CUDA_HOSTDEVICE__ __host__ __device__
+#else /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+#if defined(__GNUC__)
+#define __CUDA_HOSTDEVICE_BF16_DECL__ static __attribute__ ((unused))
+#else
+#define __CUDA_HOSTDEVICE_BF16_DECL__ static
+#endif /* defined(__GNUC__) */
+#define __CUDA_HOSTDEVICE__
+#endif /* (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))) */
+
+#define __CUDA_BF16_TYPES_EXIST__
+
+/* Macros to allow nv_bfloat16 & nv_bfloat162 to be used by inline assembly */
+#define __BFLOAT16_TO_US(var) *(reinterpret_cast<unsigned short *>(&(var)))
+#define __BFLOAT16_TO_CUS(var) *(reinterpret_cast<const unsigned short *>(&(var)))
+#define __BFLOAT162_TO_UI(var) *(reinterpret_cast<unsigned int *>(&(var)))
+#define __BFLOAT162_TO_CUI(var) *(reinterpret_cast<const unsigned int *>(&(var)))
+
+/* Forward-declaration of structures defined in "cuda_bf16.hpp" */
+struct __nv_bfloat16;
+struct __nv_bfloat162;
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts double number to nv_bfloat16 precision in round-to-nearest-even mode
+* and returns \p nv_bfloat16 with converted value.
+*
+* \details Converts double number \p a to nv_bfloat16 precision in round-to-nearest-even mode.
+* \param[in] a - double. Is only being read.
+* \returns nv_bfloat16
+* - \p a converted to \p nv_bfloat16 using round-to-nearest-even mode.
+* - __double2bfloat16 \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __double2bfloat16 \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __double2bfloat16(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __double2bfloat16(const double a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts float number to nv_bfloat16 precision in round-to-nearest-even mode
+* and returns \p nv_bfloat16 with converted value. 
+* 
+* \details Converts float number \p a to nv_bfloat16 precision in round-to-nearest-even mode. 
+* \param[in] a - float. Is only being read. 
+* \returns nv_bfloat16
+* - \p a converted to nv_bfloat16 using round-to-nearest-even mode.
+* 
+* \see __float2bfloat16_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16(const float a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts float number to nv_bfloat16 precision in round-to-nearest-even mode
+* and returns \p nv_bfloat16 with converted value.
+*
+* \details Converts float number \p a to nv_bfloat16 precision in round-to-nearest-even mode.
+* \param[in] a - float. Is only being read. 
+* \returns nv_bfloat16
+* - \p a converted to nv_bfloat16 using round-to-nearest-even mode.
+* - __float2bfloat16_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2bfloat16_rn \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2bfloat16_rn(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_rn(const float a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts float number to nv_bfloat16 precision in round-towards-zero mode
+* and returns \p nv_bfloat16 with converted value.
+* 
+* \details Converts float number \p a to nv_bfloat16 precision in round-towards-zero mode.
+* \param[in] a - float. Is only being read. 
+* \returns nv_bfloat16
+* - \p a converted to nv_bfloat16 using round-towards-zero mode.
+* - __float2bfloat16_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2bfloat16_rz \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2bfloat16_rz(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_rz(const float a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts float number to nv_bfloat16 precision in round-down mode
+* and returns \p nv_bfloat16 with converted value.
+* 
+* \details Converts float number \p a to nv_bfloat16 precision in round-down mode.
+* \param[in] a - float. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p a converted to nv_bfloat16 using round-down mode.
+* - __float2bfloat16_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2bfloat16_rd \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2bfloat16_rd(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_rd(const float a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts float number to nv_bfloat16 precision in round-up mode
+* and returns \p nv_bfloat16 with converted value.
+* 
+* \details Converts float number \p a to nv_bfloat16 precision in round-up mode.
+* \param[in] a - float. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p a converted to nv_bfloat16 using round-up mode.
+* - __float2bfloat16_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2bfloat16_ru \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2bfloat16_ru(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_ru(const float a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts \p nv_bfloat16 number to float.
+* 
+* \details Converts nv_bfloat16 number \p a to float.
+* \param[in] a - float. Is only being read. 
+* 
+* \returns float
+* - \p a converted to float. 
+* - __bfloat162float \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __bfloat162float \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __bfloat162float(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ float __bfloat162float(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts input to nv_bfloat16 precision in round-to-nearest-even mode and
+* populates both halves of \p nv_bfloat162 with converted value.
+*
+* \details Converts input \p a to nv_bfloat16 precision in round-to-nearest-even mode and
+* populates both halves of \p nv_bfloat162 with converted value.
+* \param[in] a - float. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The \p nv_bfloat162 value with both halves equal to the converted nv_bfloat16
+* precision number.
+* 
+* \see __float2bfloat16_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __float2bfloat162_rn(const float a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts both input floats to nv_bfloat16 precision in round-to-nearest-even
+* mode and returns \p nv_bfloat162 with converted values.
+*
+* \details Converts both input floats to nv_bfloat16 precision in round-to-nearest-even mode
+* and combines the results into one \p nv_bfloat162 number. Low 16 bits of the return
+* value correspond to the input \p a, high 16 bits correspond to the input \p
+* b.
+* \param[in] a - float. Is only being read. 
+* \param[in] b - float. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The \p nv_bfloat162 value with corresponding halves equal to the
+* converted input floats.
+* 
+* \see __float2bfloat16_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __floats2bfloat162_rn(const float a, const float b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts low 16 bits of \p nv_bfloat162 to float and returns the result
+* 
+* \details Converts low 16 bits of \p nv_bfloat162 input \p a to 32-bit floating-point number
+* and returns the result.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns float
+* - The low 16 bits of \p a converted to float.
+* 
+* \see __bfloat162float(__nv_bfloat16) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ float __low2float(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts high 16 bits of \p nv_bfloat162 to float and returns the result
+* 
+* \details Converts high 16 bits of \p nv_bfloat162 input \p a to 32-bit floating-point number
+* and returns the result.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns float
+* - The high 16 bits of \p a converted to float.
+* 
+* \see __bfloat162float(__nv_bfloat16) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ float __high2float(const __nv_bfloat162 a);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts both components of float2 number to nv_bfloat16 precision in
+* round-to-nearest-even mode and returns \p nv_bfloat162 with converted values.
+* 
+* \details Converts both components of float2 to nv_bfloat16 precision in round-to-nearest-even
+* mode and combines the results into one \p nv_bfloat162 number. Low 16 bits of the
+* return value correspond to \p a.x and high 16 bits of the return value
+* correspond to \p a.y.
+* \param[in] a - float2. Is only being read. 
+*  
+* \returns nv_bfloat162
+* - The \p nv_bfloat162 which has corresponding halves equal to the
+* converted float2 components.
+* 
+* \see __float2bfloat16_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __float22bfloat162_rn(const float2 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Converts both halves of \p nv_bfloat162 to float2 and returns the result.
+* 
+* \details Converts both halves of \p nv_bfloat162 input \p a to float and returns the
+* result as a \p float2 packed value.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns float2
+* - \p a converted to float2.
+* 
+* \see __bfloat162float(__nv_bfloat16) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ float2 __bfloat1622float2(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed char in round-towards-zero mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed
+* char in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns signed char
+* - \p h converted to a signed char using round-towards-zero mode.
+* - __bfloat162char_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162char_rz \cuda_math_formula (x), x > 127\end_cuda_math_formula returns SCHAR_MAX = \p 0x7F.
+* - __bfloat162char_rz \cuda_math_formula (x), x < -128\end_cuda_math_formula returns SCHAR_MIN = \p 0x80.
+* - __bfloat162char_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ signed char __bfloat162char_rz(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned char in round-towards-zero mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned
+* char in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned char
+* - \p h converted to an unsigned char using round-towards-zero mode.
+* - __bfloat162uchar_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162uchar_rz \cuda_math_formula (x), x > 255\end_cuda_math_formula returns UCHAR_MAX = \p 0xFF.
+* - __bfloat162uchar_rz \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __bfloat162uchar_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned char __bfloat162uchar_rz(const __nv_bfloat16 h);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed integer in round-to-nearest-even mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed integer in
+* round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-to-nearest-even mode.
+* - __bfloat162int_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162int_rn \cuda_math_formula (x), x > INT_MAX\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __bfloat162int_rn \cuda_math_formula (x), x < INT_MIN\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __bfloat162int_rn(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ int __bfloat162int_rn(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed integer in round-towards-zero mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed integer in
+* round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-towards-zero mode.
+* - __bfloat162int_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162int_rz \cuda_math_formula (x), x > INT_MAX\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __bfloat162int_rz \cuda_math_formula (x), x < INT_MIN\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __bfloat162int_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ int __bfloat162int_rz(const __nv_bfloat16 h);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed integer in round-down mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed integer in
+* round-down mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-down mode.
+* - __bfloat162int_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162int_rd \cuda_math_formula (x), x > INT_MAX\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __bfloat162int_rd \cuda_math_formula (x), x < INT_MIN\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __bfloat162int_rd(NaN) returns 0.* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ int __bfloat162int_rd(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed integer in round-up mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed integer in
+* round-up mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-up mode.
+* - __bfloat162int_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162int_ru \cuda_math_formula (x), x > INT_MAX\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __bfloat162int_ru \cuda_math_formula (x), x < INT_MIN\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __bfloat162int_ru(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ int __bfloat162int_ru(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed integer to a nv_bfloat16 in round-to-nearest-even mode.
+* 
+* \details Convert the signed integer value \p i to a nv_bfloat16 floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __int2bfloat16_rn(const int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed integer to a nv_bfloat16 in round-towards-zero mode.
+* 
+* \details Convert the signed integer value \p i to a nv_bfloat16 floating-point
+* value in round-towards-zero mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __int2bfloat16_rz(const int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed integer to a nv_bfloat16 in round-down mode.
+* 
+* \details Convert the signed integer value \p i to a nv_bfloat16 floating-point
+* value in round-down mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __int2bfloat16_rd(const int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed integer to a nv_bfloat16 in round-up mode.
+* 
+* \details Convert the signed integer value \p i to a nv_bfloat16 floating-point
+* value in round-up mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __int2bfloat16_ru(const int i);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed short integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed short
+* integer in round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-to-nearest-even mode.
+* - __bfloat162short_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162short_rn \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __bfloat162short_rn \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __bfloat162short_rn(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ short int __bfloat162short_rn(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed short integer in round-towards-zero mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed short
+* integer in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-towards-zero mode.
+* - __bfloat162short_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162short_rz \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __bfloat162short_rz \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __bfloat162short_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ short int __bfloat162short_rz(const __nv_bfloat16 h);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed short integer in round-down mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed short
+* integer in round-down mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-down mode.
+* - __bfloat162short_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162short_rd \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __bfloat162short_rd \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __bfloat162short_rd(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ short int __bfloat162short_rd(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed short integer in round-up mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed short
+* integer in round-up mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-up mode.
+* - __bfloat162short_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __bfloat162short_ru \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __bfloat162short_ru \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __bfloat162short_ru(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ short int __bfloat162short_ru(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed short integer to a nv_bfloat16 in round-to-nearest-even
+* mode.
+* 
+* \details Convert the signed short integer value \p i to a nv_bfloat16 floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __short2bfloat16_rn(const short int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed short integer to a nv_bfloat16 in round-towards-zero mode.
+* 
+* \details Convert the signed short integer value \p i to a nv_bfloat16 floating-point
+* value in round-towards-zero mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __short2bfloat16_rz(const short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed short integer to a nv_bfloat16 in round-down mode.
+* 
+* \details Convert the signed short integer value \p i to a nv_bfloat16 floating-point
+* value in round-down mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __short2bfloat16_rd(const short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed short integer to a nv_bfloat16 in round-up mode.
+* 
+* \details Convert the signed short integer value \p i to a nv_bfloat16 floating-point
+* value in round-up mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __short2bfloat16_ru(const short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned integer in round-to-nearest-even mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned integer
+* in round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned int
+* - \p h converted to an unsigned integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned int __bfloat162uint_rn(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned integer in round-towards-zero mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned integer
+* in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned int
+* - \p h converted to an unsigned integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __bfloat162uint_rz(const __nv_bfloat16 h);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned integer in round-down mode.
+*
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned integer
+* in round-down mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+*
+* \returns unsigned int
+* - \p h converted to an unsigned integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned int __bfloat162uint_rd(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned integer in round-up mode.
+*
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned integer
+* in round-up mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+*
+* \returns unsigned int
+* - \p h converted to an unsigned integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned int __bfloat162uint_ru(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned integer to a nv_bfloat16 in round-to-nearest-even mode.
+* 
+* \details Convert the unsigned integer value \p i to a nv_bfloat16 floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_rn(const unsigned int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned integer to a nv_bfloat16 in round-towards-zero mode.
+* 
+* \details Convert the unsigned integer value \p i to a nv_bfloat16 floating-point
+* value in round-towards-zero mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16.  
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_rz(const unsigned int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned integer to a nv_bfloat16 in round-down mode.
+* 
+* \details Convert the unsigned integer value \p i to a nv_bfloat16 floating-point
+* value in round-down mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_rd(const unsigned int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned integer to a nv_bfloat16 in round-up mode.
+* 
+* \details Convert the unsigned integer value \p i to a nv_bfloat16 floating-point
+* value in round-up mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_ru(const unsigned int i);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned short integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned short
+* integer in round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned short int __bfloat162ushort_rn(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned short integer in round-towards-zero
+* mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned short
+* integer in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned short int __bfloat162ushort_rz(const __nv_bfloat16 h);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned short integer in round-down mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned short
+* integer in round-down mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer. 
+*/
+__CUDA_BF16_DECL__ unsigned short int __bfloat162ushort_rd(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned short integer in round-up mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned short
+* integer in round-up mode. NaN inputs are converted to 0.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer. 
+*/
+__CUDA_BF16_DECL__ unsigned short int __bfloat162ushort_ru(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned short integer to a nv_bfloat16 in round-to-nearest-even
+* mode.
+* 
+* \details Convert the unsigned short integer value \p i to a nv_bfloat16 floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_rn(const unsigned short int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned short integer to a nv_bfloat16 in round-towards-zero
+* mode.
+* 
+* \details Convert the unsigned short integer value \p i to a nv_bfloat16 floating-point
+* value in round-towards-zero mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_rz(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned short integer to a nv_bfloat16 in round-down mode.
+* 
+* \details Convert the unsigned short integer value \p i to a nv_bfloat16 floating-point
+* value in round-down mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_rd(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned short integer to a nv_bfloat16 in round-up mode.
+* 
+* \details Convert the unsigned short integer value \p i to a nv_bfloat16 floating-point
+* value in round-up mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_ru(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned 64-bit integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned 64-bit
+* integer in round-to-nearest-even mode. NaN inputs return 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned long long int __bfloat162ull_rn(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned 64-bit integer in round-towards-zero
+* mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned 64-bit
+* integer in round-towards-zero mode. NaN inputs return 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned long long int __bfloat162ull_rz(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Vector function, combines two \p nv_bfloat16 numbers into one \p nv_bfloat162 number.
+* 
+* \details Combines two input \p nv_bfloat16 number \p x and \p y into one \p nv_bfloat162 number.
+* Input \p x is stored in low 16 bits of the return value, input \p y is stored
+* in high 16 bits of the return value.
+* \param[in] x - nv_bfloat16. Is only being read. 
+* \param[in] y - nv_bfloat16. Is only being read. 
+* 
+* \returns __nv_bfloat162
+* - The \p __nv_bfloat162 vector with one half equal to \p x and the other to \p y. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 make_bfloat162(const __nv_bfloat16 x, const __nv_bfloat16 y);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned 64-bit integer in round-down mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned 64-bit
+* integer in round-down mode. NaN inputs return 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned long long int __bfloat162ull_rd(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to an unsigned 64-bit integer in round-up mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to an unsigned 64-bit
+* integer in round-up mode. NaN inputs return 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ unsigned long long int __bfloat162ull_ru(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned 64-bit integer to a nv_bfloat16 in round-to-nearest-even
+* mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_rn(const unsigned long long int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned 64-bit integer to a nv_bfloat16 in round-towards-zero
+* mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-towards-zero mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_rz(const unsigned long long int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned 64-bit integer to a nv_bfloat16 in round-down mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-down mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16.  
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_rd(const unsigned long long int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert an unsigned 64-bit integer to a nv_bfloat16 in round-up mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-up mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_ru(const unsigned long long int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed 64-bit integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed 64-bit
+* integer in round-to-nearest-even mode. NaN inputs return a long long int with hex value of 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ long long int __bfloat162ll_rn(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed 64-bit integer in round-towards-zero mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed 64-bit
+* integer in round-towards-zero mode. NaN inputs return a long long int with hex value of 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ long long int __bfloat162ll_rz(const __nv_bfloat16 h);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed 64-bit integer in round-down mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed 64-bit
+* integer in round-down mode. NaN inputs return a long long int with hex value of 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ long long int __bfloat162ll_rd(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a nv_bfloat16 to a signed 64-bit integer in round-up mode.
+* 
+* \details Convert the nv_bfloat16 floating-point value \p h to a signed 64-bit
+* integer in round-up mode. NaN inputs return a long long int with hex value of 0x8000000000000000.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ long long int __bfloat162ll_ru(const __nv_bfloat16 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed 64-bit integer to a nv_bfloat16 in round-to-nearest-even
+* mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_rn(const long long int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed 64-bit integer to a nv_bfloat16 in round-towards-zero mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-towards-zero mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_rz(const long long int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed 64-bit integer to a nv_bfloat16 in round-down mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-down mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_rd(const long long int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Convert a signed 64-bit integer to a nv_bfloat16 in round-up mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a nv_bfloat16 floating-point
+* value in round-up mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - \p i converted to nv_bfloat16. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_ru(const long long int i);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Truncate input argument to the integral part.
+* 
+* \details Round \p h to the nearest integer value that does not exceed \p h in
+* magnitude.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The truncated integer value. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 htrunc(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculate ceiling of the input argument.
+* 
+* \details Compute the smallest integer value not less than \p h.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The smallest integer value not less than \p h. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hceil(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculate the largest integer less than or equal to \p h.
+* 
+* \details Calculate the largest integer value which is less than or equal to \p h.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The largest integer value which is less than or equal to \p h. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hfloor(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Round input to nearest integer value in nv_bfloat16 floating-point
+* number.
+* 
+* \details Round \p h to the nearest integer value in nv_bfloat16 floating-point
+* format, with bfloat16way cases rounded to the nearest even integer value.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The nearest integer to \p h. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hrint(const __nv_bfloat16 h);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Truncate \p nv_bfloat162 vector input argument to the integral part.
+* 
+* \details Round each component of vector \p h to the nearest integer value that does
+* not exceed \p h in magnitude.
+* \param[in] h - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The truncated \p h. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2trunc(const __nv_bfloat162 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculate \p nv_bfloat162 vector ceiling of the input argument.
+* 
+* \details For each component of vector \p h compute the smallest integer value not less
+* than \p h.
+* \param[in] h - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector of smallest integers not less than \p h. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2ceil(const __nv_bfloat162 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculate the largest integer less than or equal to \p h.
+* 
+* \details For each component of vector \p h calculate the largest integer value which
+* is less than or equal to \p h.
+* \param[in] h - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector of largest integers which is less than or equal to \p h. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2floor(const __nv_bfloat162 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Round input to nearest integer value in nv_bfloat16 floating-point
+* number.
+* 
+* \details Round each component of \p nv_bfloat162 vector \p h to the nearest integer value in
+* nv_bfloat16 floating-point format, with bfloat16way cases rounded to the
+* nearest even integer value.
+* \param[in] h - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector of rounded integer values. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2rint(const __nv_bfloat162 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Returns \p nv_bfloat162 with both halves equal to the input value.
+* 
+* \details Returns \p nv_bfloat162 number with both halves equal to the input \p a \p nv_bfloat16
+* number.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector which has both its halves equal to the input \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __bfloat162bfloat162(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Swaps both halves of the \p nv_bfloat162 input.
+* 
+* \details Swaps both halves of the \p nv_bfloat162 input and returns a new \p nv_bfloat162 number
+* with swapped halves.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - \p a with its halves being swapped. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __lowhigh2highlow(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Extracts low 16 bits from each of the two \p nv_bfloat162 inputs and combines
+* into one \p nv_bfloat162 number. 
+* 
+* \details Extracts low 16 bits from each of the two \p nv_bfloat162 inputs and combines into
+* one \p nv_bfloat162 number. Low 16 bits from input \p a is stored in low 16 bits of
+* the return value, low 16 bits from input \p b is stored in high 16 bits of
+* the return value. 
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The low 16 bits of \p a and of \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __lows2bfloat162(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Extracts high 16 bits from each of the two \p nv_bfloat162 inputs and
+* combines into one \p nv_bfloat162 number.
+* 
+* \details Extracts high 16 bits from each of the two \p nv_bfloat162 inputs and combines into
+* one \p nv_bfloat162 number. High 16 bits from input \p a is stored in low 16 bits of
+* the return value, high 16 bits from input \p b is stored in high 16 bits of
+* the return value.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The high 16 bits of \p a and of \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __highs2bfloat162(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Returns high 16 bits of \p nv_bfloat162 input.
+*
+* \details Returns high 16 bits of \p nv_bfloat162 input \p a.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The high 16 bits of the input. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __high2bfloat16(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Returns low 16 bits of \p nv_bfloat162 input.
+*
+* \details Returns low 16 bits of \p nv_bfloat162 input \p a.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat16
+* - Returns \p nv_bfloat16 which contains low 16 bits of the input \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __low2bfloat16(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Checks if the input \p nv_bfloat16 number is infinite.
+* 
+* \details Checks if the input \p nv_bfloat16 number \p a is infinite. 
+* \param[in] a - nv_bfloat16. Is only being read. 
+* 
+* \returns int 
+* - -1 if \p a is equal to negative infinity, 
+* - 1 if \p a is equal to positive infinity, 
+* - 0 otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ int __hisinf(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Combines two \p nv_bfloat16 numbers into one \p nv_bfloat162 number.
+* 
+* \details Combines two input \p nv_bfloat16 number \p a and \p b into one \p nv_bfloat162 number.
+* Input \p a is stored in low 16 bits of the return value, input \p b is stored
+* in high 16 bits of the return value.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The nv_bfloat162 with one nv_bfloat16 equal to \p a and the other to \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __halves2bfloat162(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Extracts low 16 bits from \p nv_bfloat162 input.
+* 
+* \details Extracts low 16 bits from \p nv_bfloat162 input \p a and returns a new \p nv_bfloat162
+* number which has both halves equal to the extracted bits.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The nv_bfloat162 with both halves equal to the low 16 bits of the input. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __low2bfloat162(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Extracts high 16 bits from \p nv_bfloat162 input.
+* 
+* \details Extracts high 16 bits from \p nv_bfloat162 input \p a and returns a new \p nv_bfloat162
+* number which has both halves equal to the extracted bits.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The nv_bfloat162 with both halves equal to the high 16 bits of the input. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __high2bfloat162(const __nv_bfloat162 a);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Reinterprets bits in a \p nv_bfloat16 as a signed short integer.
+* 
+* \details Reinterprets the bits in the nv_bfloat16 floating-point number \p h
+* as a signed short integer. 
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns short int
+* - The reinterpreted value. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ short int __bfloat16_as_short(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Reinterprets bits in a \p nv_bfloat16 as an unsigned short integer.
+* 
+* \details Reinterprets the bits in the nv_bfloat16 floating-point \p h
+* as an unsigned short number.
+* \param[in] h - nv_bfloat16. Is only being read. 
+* 
+* \returns unsigned short int
+* - The reinterpreted value.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned short int __bfloat16_as_ushort(const __nv_bfloat16 h);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Reinterprets bits in a signed short integer as a \p nv_bfloat16.
+* 
+* \details Reinterprets the bits in the signed short integer \p i as a
+* nv_bfloat16 floating-point number.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The reinterpreted value.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __short_as_bfloat16(const short int i);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Reinterprets bits in an unsigned short integer as a \p nv_bfloat16.
+* 
+* \details Reinterprets the bits in the unsigned short integer \p i as a
+* nv_bfloat16 floating-point number.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The reinterpreted value.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ushort_as_bfloat16(const unsigned short int i);
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300))) || defined(_NVHPC_CUDA)
+
+#if !defined warpSize && !defined __local_warpSize
+#define warpSize    32
+#define __local_warpSize
+#endif
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Direct copy from indexed thread. 
+* 
+* \details Returns the value of \p var held by the thread whose ID is given by \p srcLane. 
+* If the \p width is less than \p warpSize, then each subsection of the warp behaves as a separate 
+* entity with a starting logical thread ID of 0. If \p srcLane is outside the range \p [0:width-1], 
+* the value returned corresponds to the value of \p var held by the \p srcLane modulo \p width (i.e. 
+* within the same subsection). \p width must have a value which is a power of 2; 
+* results are undefined if \p width is not a power of 2, or is a number greater than 
+* \p warpSize. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat162. Is only being read. 
+* \param[in] srcLane - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p nv_bfloat162. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_sync(const unsigned int mask, const __nv_bfloat162 var, const int srcLane, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with lower ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by subtracting \p delta from the caller's lane ID. 
+* The value of \p var held by the resulting lane ID is returned: in effect, \p var is shifted up 
+* the warp by \p delta threads. If the \p width is less than \p warpSize, then each subsection of the warp 
+* behaves as a separate entity with a starting logical thread ID of 0. The source thread index 
+* will not wrap around the value of \p width, so effectively the lower \p delta threads will be unchanged. 
+* \p width must have a value which is a power of 2; results are undefined if \p width is not a power of 2, 
+* or is a number greater than \p warpSize. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat162. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p nv_bfloat162. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_up_sync(const unsigned int mask, const __nv_bfloat162 var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with higher ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by adding \p delta to the caller's thread ID. 
+* The value of \p var held by the resulting thread ID is returned: this has the effect 
+* of shifting \p var down the warp by \p delta threads. If the \p width is less than \p warpSize, then 
+* each subsection of the warp behaves as a separate entity with a starting logical 
+* thread ID of 0. Similarly to the __shfl_up_sync(), the ID number of the source thread 
+* will not wrap around the value of \p width and the upper \p delta threads 
+* will remain unchanged. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat162. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p nv_bfloat162. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_down_sync(const unsigned int mask, const __nv_bfloat162 var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread based on bitwise XOR of own thread ID. 
+* 
+* \details Calculates a source thread ID by performing a bitwise XOR of the caller's thread ID with \p laneMask: 
+* the value of \p var held by the resulting thread ID is returned. If the \p width is less than \p warpSize, then each 
+* group of \p width consecutive threads are able to access elements from earlier groups of threads, 
+* however if they attempt to access elements from later groups of threads their own value of \p var 
+* will be returned. This mode implements a butterfly addressing pattern such as is used in tree 
+* reduction and broadcast. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat162. Is only being read. 
+* \param[in] laneMask - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p nv_bfloat162. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_xor_sync(const unsigned int mask, const __nv_bfloat162 var, const int laneMask, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Direct copy from indexed thread. 
+* 
+* \details Returns the value of \p var held by the thread whose ID is given by \p srcLane. 
+* If the \p width is less than \p warpSize, then each subsection of the warp behaves as a separate 
+* entity with a starting logical thread ID of 0. If \p srcLane is outside the range \p [0:width-1], 
+* the value returned corresponds to the value of \p var held by the \p srcLane modulo \p width (i.e. 
+* within the same subsection). \p width must have a value which is a power of 2; 
+* results are undefined if \p width is not a power of 2, or is a number greater than 
+* \p warpSize. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat16. Is only being read. 
+* \param[in] srcLane - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p nv_bfloat16. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_sync(const unsigned int mask, const __nv_bfloat16 var, const int srcLane, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with lower ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by subtracting \p delta from the caller's lane ID. 
+* The value of \p var held by the resulting lane ID is returned: in effect, \p var is shifted up 
+* the warp by \p delta threads. If the \p width is less than \p warpSize, then each subsection of the warp 
+* behaves as a separate entity with a starting logical thread ID of 0. The source thread index 
+* will not wrap around the value of \p width, so effectively the lower \p delta threads will be unchanged. 
+* \p width must have a value which is a power of 2; results are undefined if \p width is not a power of 2, 
+* or is a number greater than \p warpSize. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat16. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p nv_bfloat16. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_up_sync(const unsigned int mask, const __nv_bfloat16 var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with higher ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by adding \p delta to the caller's thread ID. 
+* The value of \p var held by the resulting thread ID is returned: this has the effect 
+* of shifting \p var down the warp by \p delta threads. If the \p width is less than \p warpSize, then 
+* each subsection of the warp behaves as a separate entity with a starting logical 
+* thread ID of 0. Similarly to the __shfl_up_sync(), the ID number of the source thread 
+* will not wrap around the value of \p width and the upper \p delta threads 
+* will remain unchanged. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat16. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p nv_bfloat16. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_down_sync(const unsigned int mask, const __nv_bfloat16 var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread based on bitwise XOR of own thread ID. 
+* 
+* \details Calculates a source thread ID by performing a bitwise XOR of the caller's thread ID with \p laneMask: 
+* the value of \p var held by the resulting thread ID is returned. If the \p width is less than \p warpSize, then each 
+* group of \p width consecutive threads are able to access elements from earlier groups of threads, 
+* however if they attempt to access elements from later groups of threads their own value of \p var 
+* will be returned. This mode implements a butterfly addressing pattern such as is used in tree 
+* reduction and broadcast. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - nv_bfloat16. Is only being read. 
+* \param[in] laneMask - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p nv_bfloat16. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_xor_sync(const unsigned int mask, const __nv_bfloat16 var, const int laneMask, const int width = warpSize);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300))) || defined(_NVHPC_CUDA) */
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 320))) || defined(_NVHPC_CUDA)
+#if defined(__local_warpSize)
+#undef warpSize
+#undef __local_warpSize
+#endif
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.nc` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldg(const  __nv_bfloat162 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.nc` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldg(const __nv_bfloat16 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.cg` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldcg(const  __nv_bfloat162 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.cg` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldcg(const __nv_bfloat16 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.ca` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldca(const  __nv_bfloat162 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.ca` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldca(const __nv_bfloat16 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.cs` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldcs(const  __nv_bfloat162 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.cs` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldcs(const __nv_bfloat16 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.lu` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldlu(const  __nv_bfloat162 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.lu` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldlu(const __nv_bfloat16 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.cv` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldcv(const  __nv_bfloat162 *const ptr);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `ld.global.cv` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldcv(const __nv_bfloat16 *const ptr);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.wb` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stwb(__nv_bfloat162 *const ptr, const __nv_bfloat162 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.wb` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stwb(__nv_bfloat16 *const ptr, const __nv_bfloat16 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.cg` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stcg(__nv_bfloat162 *const ptr, const __nv_bfloat162 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.cg` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stcg(__nv_bfloat16 *const ptr, const __nv_bfloat16 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.cs` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stcs(__nv_bfloat162 *const ptr, const __nv_bfloat162 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.cs` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stcs(__nv_bfloat16 *const ptr, const __nv_bfloat16 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.wt` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stwt(__nv_bfloat162 *const ptr, const __nv_bfloat162 value);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_MISC
+* \brief Generates a `st.global.wt` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_BF16_DECL__ void __stwt(__nv_bfloat16 *const ptr, const __nv_bfloat16 value);
+
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 320))) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs nv_bfloat162 vector if-equal comparison.
+* 
+* \details Performs \p nv_bfloat162 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector result of if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __heq2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector not-equal comparison.
+* 
+* \details Performs \p nv_bfloat162 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector result of not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hne2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector less-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The \p nv_bfloat162 result of less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hle2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector greater-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The vector result of greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hge2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector less-than comparison.
+*
+* \details Performs \p nv_bfloat162 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The nv_bfloat162 vector result of less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hlt2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector greater-than comparison.
+* 
+* \details Performs \p nv_bfloat162 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector result of greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hgt2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered if-equal comparison.
+* 
+* \details Performs \p nv_bfloat162 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The vector result of unordered if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hequ2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered not-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The vector result of unordered not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hneu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered less-equal comparison.
+*
+* Performs \p nv_bfloat162 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The vector result of unordered less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hleu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered greater-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The \p nv_bfloat162 vector result of unordered greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hgeu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered less-than comparison.
+*
+* \details Performs \p nv_bfloat162 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The vector result of unordered less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hltu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered greater-than comparison.
+*
+* \details Performs \p nv_bfloat162 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p nv_bfloat16 results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The \p nv_bfloat162 vector result of unordered greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hgtu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs nv_bfloat162 vector if-equal comparison.
+* 
+* \details Performs \p nv_bfloat162 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __heq2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector not-equal comparison.
+* 
+* \details Performs \p nv_bfloat162 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hne2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector less-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hle2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector greater-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hge2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector less-than comparison.
+*
+* \details Performs \p nv_bfloat162 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hlt2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector greater-than comparison.
+* 
+* \details Performs \p nv_bfloat162 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hgt2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered if-equal comparison.
+* 
+* \details Performs \p nv_bfloat162 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of unordered if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hequ2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered not-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hneu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered less-equal comparison.
+*
+* Performs \p nv_bfloat162 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hleu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered greater-equal comparison.
+*
+* \details Performs \p nv_bfloat162 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hgeu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered less-than comparison.
+*
+* \details Performs \p nv_bfloat162 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hltu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered greater-than comparison.
+*
+* \details Performs \p nv_bfloat162 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to 0xFFFF for true, or 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hgtu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Determine whether \p nv_bfloat162 argument is a NaN.
+*
+* \details Determine whether each nv_bfloat16 of input \p nv_bfloat162 number \p a is a NaN.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The nv_bfloat162 with the corresponding \p nv_bfloat16 results set to
+* 1.0 for NaN, 0.0 otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hisnan2(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector addition in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat162 vector add of inputs \p a and \p b, in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-95
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The sum of vectors \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hadd2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p nv_bfloat162 input vector \p b from input vector \p a in
+* round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-104
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The subtraction of vector \p b from \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hsub2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat162 vector multiplication of inputs \p a and \p b, in
+* round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-102
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The result of elementwise multiplying the vectors \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmul2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector addition in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat162 vector add of inputs \p a and \p b, in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+add into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-95
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The sum of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hadd2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p nv_bfloat162 input vector \p b from input vector \p a in
+* round-to-nearest-even mode. Prevents floating-point contractions of mul+sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-104
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The subtraction of vector \p b from \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hsub2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat162 vector multiplication of inputs \p a and \p b, in
+* round-to-nearest-even mode. Prevents floating-point contractions of mul+add
+* or sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-102
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of elementwise multiplying the vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmul2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector division in round-to-nearest-even mode.
+*
+* \details Divides \p nv_bfloat162 input vector \p a by input vector \p b in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-103
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise division of \p a with \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __h2div(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Calculates the absolute value of both halves of the input \p nv_bfloat162 number and
+* returns the result.
+*
+* \details Calculates the absolute value of both halves of the input \p nv_bfloat162 number and
+* returns the result.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns bfloat2
+* - Returns \p a with the absolute value of both halves. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __habs2(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector addition in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Performs \p nv_bfloat162 vector add of inputs \p a and \p b, in round-to-nearest-even
+* mode, and clamps the results to range [0.0, 1.0]. NaN results are flushed to
+* +0.0.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The sum of \p a and \p b, with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hadd2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector subtraction in round-to-nearest-even mode,
+* with saturation to [0.0, 1.0].
+*
+* \details Subtracts \p nv_bfloat162 input vector \p b from input vector \p a in
+* round-to-nearest-even mode, and clamps the results to range [0.0, 1.0]. NaN
+* results are flushed to +0.0.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The subtraction of vector \p b from \p a, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hsub2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector multiplication in round-to-nearest-even mode,
+* with saturation to [0.0, 1.0].
+*
+* \details Performs \p nv_bfloat162 vector multiplication of inputs \p a and \p b, in
+* round-to-nearest-even mode, and clamps the results to range [0.0, 1.0]. NaN
+* results are flushed to +0.0.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The result of elementwise multiplication of vectors \p a and \p b, 
+* with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmul2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b);
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector fused multiply-add in round-to-nearest-even
+* mode.
+*
+* \details Performs \p nv_bfloat162 vector multiply on inputs \p a and \p b,
+* then performs a \p nv_bfloat162 vector add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-105
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* \param[in] c - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The result of elementwise fused multiply-add operation on vectors \p a, \p b, and \p c. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __hfma2(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector fused multiply-add in round-to-nearest-even
+* mode, with saturation to [0.0, 1.0].
+*
+* \details Performs \p nv_bfloat162 vector multiply on inputs \p a and \p b,
+* then performs a \p nv_bfloat162 vector add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode, and clamps the
+* results to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* \param[in] c - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The result of elementwise fused multiply-add operation on vectors \p a, \p b, and \p c, 
+* with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __hfma2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Negates both halves of the input \p nv_bfloat162 number and returns the
+* result.
+*
+* \details Negates both halves of the input \p nv_bfloat162 number \p a and returns the result.
+* \internal
+* \req DEEPLEARN-SRM_REQ-101
+* \endinternal
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - Returns \p a with both halves negated. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hneg2(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Calculates the absolute value of input \p nv_bfloat16 number and returns the result.
+*
+* \details Calculates the absolute value of input \p nv_bfloat16 number and returns the result.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The absolute value of a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __habs(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 addition in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat16 addition of inputs \p a and \p b, in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-94
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The sum of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hadd(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p nv_bfloat16 input \p b from input \p a in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-97
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The result of subtracting \p b from \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hsub(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat16 multiplication of inputs \p a and \p b, in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-99
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The result of multiplying \p a and \p b. 
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmul(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 addition in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat16 addition of inputs \p a and \p b, in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+add into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-94
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* - The sum of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hadd_rn(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p nv_bfloat16 input \p b from input \p a in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-97
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* - The result of subtracting \p b from \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hsub_rn(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat16 multiplication of inputs \p a and \p b, in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+add or sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-99
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* - The result of multiplying \p a and \p b.
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmul_rn(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 division in round-to-nearest-even mode.
+* 
+* \details Divides \p nv_bfloat16 input \p a by input \p b in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-98
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+* 
+* \returns nv_bfloat16
+* - The result of dividing \p a by \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__  __nv_bfloat16 __hdiv(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 addition in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Performs \p nv_bfloat16 add of inputs \p a and \p b, in round-to-nearest-even mode,
+* and clamps the result to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The sum of \p a and \p b, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hadd_sat(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 subtraction in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Subtracts \p nv_bfloat16 input \p b from input \p a in round-to-nearest-even
+* mode,
+* and clamps the result to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The result of subtraction of \p b from \p a, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hsub_sat(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 multiplication in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Performs \p nv_bfloat16 multiplication of inputs \p a and \p b, in round-to-nearest-even
+* mode, and clamps the result to range [0.0, 1.0]. NaN results are flushed to
+* +0.0.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The result of multiplying \p a and \p b, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmul_sat(const __nv_bfloat16 a, const __nv_bfloat16 b);
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 fused multiply-add in round-to-nearest-even mode.
+*
+* \details Performs \p nv_bfloat16 multiply on inputs \p a and \p b,
+* then performs a \p nv_bfloat16 add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-96
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+* \param[in] c - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The result of fused multiply-add operation on \p
+* a, \p b, and \p c. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __hfma(const __nv_bfloat16 a, const __nv_bfloat16 b, const __nv_bfloat16 c);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 fused multiply-add in round-to-nearest-even mode,
+* with saturation to [0.0, 1.0].
+*
+* \details Performs \p nv_bfloat16 multiply on inputs \p a and \p b,
+* then performs a \p nv_bfloat16 add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode, and clamps the result
+* to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+* \param[in] c - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The result of fused multiply-add operation on \p
+* a, \p b, and \p c, with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __hfma_sat(const __nv_bfloat16 a, const __nv_bfloat16 b, const __nv_bfloat16 c);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Negates input \p nv_bfloat16 number and returns the result.
+*
+* \details Negates input \p nv_bfloat16 number and returns the result.
+* \internal
+* \req DEEPLEARN-SRM_REQ-100
+* \endinternal
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - minus a
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hneg(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector if-equal comparison and returns boolean true
+* if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector if-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 if-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of if-equal comparison
+* of vectors \p a and \p b are true;
+* - false otherwise.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbeq2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector not-equal comparison and returns boolean
+* true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector not-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 not-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of not-equal comparison
+* of vectors \p a and \p b are true, 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbne2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector less-equal comparison and returns boolean
+* true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector less-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 less-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of less-equal comparison
+* of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hble2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector greater-equal comparison and returns boolean
+* true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector greater-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 greater-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of greater-equal
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbge2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector less-than comparison and returns boolean
+* true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector less-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 less-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of less-than comparison
+* of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hblt2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector greater-than comparison and returns boolean
+* true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector greater-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 greater-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+* 
+* \returns bool 
+* - true if both \p nv_bfloat16 results of greater-than
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbgt2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered if-equal comparison and returns
+* boolean true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector if-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 if-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of unordered if-equal
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbequ2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered not-equal comparison and returns
+* boolean true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector not-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 not-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of unordered not-equal
+* comparison of vectors \p a and \p b are true;
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbneu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered less-equal comparison and returns
+* boolean true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector less-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 less-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of unordered less-equal
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbleu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered greater-equal comparison and
+* returns boolean true if both \p nv_bfloat16 results are true, boolean false
+* otherwise.
+*
+* \details Performs \p nv_bfloat162 vector greater-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 greater-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of unordered
+* greater-equal comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbgeu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered less-than comparison and returns
+* boolean true if both \p nv_bfloat16 results are true, boolean false otherwise.
+*
+* \details Performs \p nv_bfloat162 vector less-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 less-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of unordered less-than comparison of 
+* vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbltu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Performs \p nv_bfloat162 vector unordered greater-than comparison and
+* returns boolean true if both \p nv_bfloat16 results are true, boolean false
+* otherwise.
+*
+* \details Performs \p nv_bfloat162 vector greater-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p nv_bfloat16 greater-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \param[in] b - nv_bfloat162. Is only being read. 
+*
+* \returns bool
+* - true if both \p nv_bfloat16 results of unordered
+* greater-than comparison of vectors \p a and \p b are true;
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbgtu2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 if-equal comparison.
+*
+* \details Performs \p nv_bfloat16 if-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of if-equal comparison of \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __heq(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 not-equal comparison.
+*
+* \details Performs \p nv_bfloat16 not-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of not-equal comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hne(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 less-equal comparison.
+*
+* \details Performs \p nv_bfloat16 less-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of less-equal comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hle(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 greater-equal comparison.
+*
+* \details Performs \p nv_bfloat16 greater-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of greater-equal comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hge(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 less-than comparison.
+*
+* \details Performs \p nv_bfloat16 less-than comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of less-than comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hlt(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 greater-than comparison.
+*
+* \details Performs \p nv_bfloat16 greater-than comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of greater-than comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hgt(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 unordered if-equal comparison.
+*
+* \details Performs \p nv_bfloat16 if-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered if-equal comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hequ(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 unordered not-equal comparison.
+*
+* \details Performs \p nv_bfloat16 not-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered not-equal comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hneu(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 unordered less-equal comparison.
+*
+* \details Performs \p nv_bfloat16 less-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered less-equal comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hleu(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 unordered greater-equal comparison.
+*
+* \details Performs \p nv_bfloat16 greater-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered greater-equal comparison of \p a
+* and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hgeu(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 unordered less-than comparison.
+*
+* \details Performs \p nv_bfloat16 less-than comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered less-than comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hltu(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Performs \p nv_bfloat16 unordered greater-than comparison.
+*
+* \details Performs \p nv_bfloat16 greater-than comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - nv_bfloat16. Is only being read. 
+* \param[in] b - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered greater-than comparison of \p a
+* and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hgtu(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Determine whether \p nv_bfloat16 argument is a NaN.
+*
+* \details Determine whether \p nv_bfloat16 value \p a is a NaN.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns bool
+* - true if argument is NaN. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hisnan(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Calculates \p nv_bfloat16 maximum of two input values.
+*
+* \details Calculates \p nv_bfloat16 max(\p a, \p b)
+* defined as (\p a > \p b) ? \p a : \p b. 
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmax(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Calculates \p nv_bfloat16 minimum of two input values.
+*
+* \details Calculates \p nv_bfloat16 min(\p a, \p b)
+* defined as (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmin(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Calculates \p nv_bfloat16 maximum of two input values, NaNs pass through.
+*
+* \details Calculates \p nv_bfloat16 max(\p a, \p b)
+* defined as (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmax_nan(const __nv_bfloat16 a, const __nv_bfloat16 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+* \brief Calculates \p nv_bfloat16 minimum of two input values, NaNs pass through.
+*
+* \details Calculates \p nv_bfloat16 min(\p a, \p b)
+* defined as (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmin_nan(const __nv_bfloat16 a, const __nv_bfloat16 b);
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Performs \p nv_bfloat16 fused multiply-add in round-to-nearest-even mode with relu saturation.
+*
+* \details Performs \p nv_bfloat16 multiply on inputs \p a and \p b,
+* then performs a \p nv_bfloat16 add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* Then negative result is clamped to 0.
+* NaN result is converted to canonical NaN.
+* \param[in] a - nv_bfloat16. Is only being read.
+* \param[in] b - nv_bfloat16. Is only being read.
+* \param[in] c - nv_bfloat16. Is only being read.
+*
+* \returns nv_bfloat16
+* - The result of fused multiply-add operation on \p
+* a, \p b, and \p c with relu saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 __hfma_relu(const __nv_bfloat16 a, const __nv_bfloat16 b, const __nv_bfloat16 c);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Calculates \p nv_bfloat162 vector maximum of two inputs.
+*
+* \details Calculates \p nv_bfloat162 vector max(\p a, \p b).
+* Elementwise \p nv_bfloat16 operation is defined as
+* (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of elementwise maximum of vectors \p a  and \p b
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmax2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Calculates \p nv_bfloat162 vector minimum of two inputs.
+*
+* \details Calculates \p nv_bfloat162 vector min(\p a, \p b).
+* Elementwise \p nv_bfloat16 operation is defined as
+* (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of elementwise minimum of vectors \p a  and \p b
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmin2(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Calculates \p nv_bfloat162 vector maximum of two inputs, NaNs pass through.
+*
+* \details Calculates \p nv_bfloat162 vector max(\p a, \p b).
+* Elementwise \p nv_bfloat16 operation is defined as
+* (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of elementwise maximum of vectors \p a  and \p b, with NaNs pass through
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmax2_nan(const __nv_bfloat162 a, const __nv_bfloat162 b);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+* \brief Calculates \p nv_bfloat162 vector minimum of two inputs, NaNs pass through.
+*
+* \details Calculates \p nv_bfloat162 vector min(\p a, \p b).
+* Elementwise \p nv_bfloat16 operation is defined as
+* (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of elementwise minimum of vectors \p a  and \p b, with NaNs pass through
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmin2_nan(const __nv_bfloat162 a, const __nv_bfloat162 b);
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs \p nv_bfloat162 vector fused multiply-add in round-to-nearest-even
+* mode with relu saturation.
+*
+* \details Performs \p nv_bfloat162 vector multiply on inputs \p a and \p b,
+* then performs a \p nv_bfloat162 vector add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* Then negative result is clamped to 0.
+* NaN result is converted to canonical NaN.
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+* \param[in] c - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of elementwise fused multiply-add operation on vectors \p a, \p b, and \p c with relu saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __hfma2_relu(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Performs fast complex multiply-accumulate
+*
+* \details Interprets vector \p nv_bfloat162 input pairs \p a, \p b, and \p c as
+* complex numbers in \p nv_bfloat16 precision and performs
+* complex multiply-accumulate operation: a*b + c
+* \param[in] a - nv_bfloat162. Is only being read.
+* \param[in] b - nv_bfloat162. Is only being read.
+* \param[in] c - nv_bfloat162. Is only being read.
+*
+* \returns nv_bfloat162
+* - The result of complex multiply-accumulate operation on complex numbers \p a, \p b, and \p c
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __hcmadd(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 square root in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 square root of input \p a in round-to-nearest-even mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The square root of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hsqrt(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 reciprocal square root in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat16 reciprocal square root of input \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The reciprocal square root of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hrsqrt(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 reciprocal in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 reciprocal of input \p a in round-to-nearest-even mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The reciprocal of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hrcp(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 natural logarithm in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 natural logarithm of input \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The natural logarithm of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hlog(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 binary logarithm in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 binary logarithm of input \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The binary logarithm of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hlog2(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 decimal logarithm in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 decimal logarithm of input \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The decimal logarithm of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hlog10(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 natural exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat16 natural exponential function of input \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The natural exponential function on \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hexp(const __nv_bfloat16 a);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates approximate \p nv_bfloat16 hyperbolic tangent function.
+*
+* \details Calculates approximate \p nv_bfloat16 hyperbolic tangent function: \cuda_math_formula \tanh(a)\end_cuda_math_formula.
+* This operation uses HW acceleration on devices of compute capability 9.x and higher.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The approximate hyperbolic tangent function of \p a.
+* - htanh_approx \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula (\pm 0)\end_cuda_math_formula.
+* - htanh_approx \cuda_math_formula (\pm\infty)\end_cuda_math_formula returns \cuda_math_formula (\pm 1)\end_cuda_math_formula.
+* - htanh_approx(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 htanh_approx(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector approximate hyperbolic tangent function.
+*
+* \details Calculates \p nv_bfloat162 approximate hyperbolic tangent function of input vector \p a.
+* This operation uses HW acceleration on devices of compute capability 9.x and higher.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise approximate hyperbolic tangent function on vector \p a.
+* 
+* \see htanh_approx(__nv_bfloat16) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2tanh_approx(const __nv_bfloat162 a);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 hyperbolic tangent function in
+* round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 hyperbolic tangent function: \cuda_math_formula \tanh(a)\end_cuda_math_formula in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The hyperbolic tangent function of \p a.
+* - htanh \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula (\pm 0)\end_cuda_math_formula.
+* - htanh \cuda_math_formula (\pm\infty)\end_cuda_math_formula returns \cuda_math_formula (\pm 1)\end_cuda_math_formula.
+* - htanh(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 htanh(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector hyperbolic tangent function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat162 hyperbolic tangent function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise hyperbolic tangent function on vector \p a.
+* 
+* \see htanh(__nv_bfloat16) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2tanh(const __nv_bfloat162 a);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 binary exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat16 binary exponential function of input \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The binary exponential function on \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hexp2(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 decimal exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat16 decimal exponential function of input \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The decimal exponential function on \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hexp10(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 cosine in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 cosine of input \p a in round-to-nearest-even mode.
+*
+* NOTE: this function's implementation calls cosf(float) function and is exposed
+* to compiler optimizations. Specifically, \p --use_fast_math flag changes cosf(float)
+* into an intrinsic __cosf(float), which has less accurate numeric behavior.
+*
+* \param[in] a - nv_bfloat16. Is only being read.
+* \returns nv_bfloat16
+* - The cosine of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hcos(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT16_FUNCTIONS
+* \brief Calculates \p nv_bfloat16 sine in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat16 sine of input \p a in round-to-nearest-even mode.
+*
+* NOTE: this function's implementation calls sinf(float) function and is exposed
+* to compiler optimizations. Specifically, \p --use_fast_math flag changes sinf(float)
+* into an intrinsic __sinf(float), which has less accurate numeric behavior.
+*
+* \param[in] a - nv_bfloat16. Is only being read. 
+*
+* \returns nv_bfloat16
+* - The sine of \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 hsin(const __nv_bfloat16 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector square root in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat162 square root of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise square root on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2sqrt(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector reciprocal square root in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat162 reciprocal square root of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise reciprocal square root on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2rsqrt(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector reciprocal in round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat162 reciprocal of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise reciprocal on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2rcp(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector natural logarithm in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat162 natural logarithm of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise natural logarithm on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2log(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector binary logarithm in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat162 binary logarithm of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise binary logarithm on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2log2(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector decimal logarithm in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat162 decimal logarithm of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise decimal logarithm on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2log10(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p nv_bfloat162 exponential function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise exponential function on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2exp(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector binary exponential function in
+* round-to-nearest-even mode.
+*
+* \details Calculates \p nv_bfloat162 binary exponential function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+*
+* \returns nv_bfloat162
+* - The elementwise binary exponential function on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2exp2(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector decimal exponential function in
+* round-to-nearest-even mode.
+* 
+* \details Calculates \p nv_bfloat162 decimal exponential function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - nv_bfloat162. Is only being read. 
+* 
+* \returns nv_bfloat162
+* - The elementwise decimal exponential function on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2exp10(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector cosine in round-to-nearest-even mode.
+* 
+* \details Calculates \p nv_bfloat162 cosine of input vector \p a in round-to-nearest-even
+* mode.
+*
+* NOTE: this function's implementation calls cosf(float) function and is exposed
+* to compiler optimizations. Specifically, \p --use_fast_math flag changes cosf(float)
+* into an intrinsic __cosf(float), which has less accurate numeric behavior.
+*
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \returns nv_bfloat162
+* - The elementwise cosine on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2cos(const __nv_bfloat162 a);
+/**
+* \ingroup CUDA_MATH__BFLOAT162_FUNCTIONS
+* \brief Calculates \p nv_bfloat162 vector sine in round-to-nearest-even mode.
+* 
+* \details Calculates \p nv_bfloat162 sine of input vector \p a in round-to-nearest-even mode.
+*
+* NOTE: this function's implementation calls sinf(float) function and is exposed
+* to compiler optimizations. Specifically, \p --use_fast_math flag changes sinf(float)
+* into an intrinsic __sinf(float), which has less accurate numeric behavior.
+*
+* \param[in] a - nv_bfloat162. Is only being read. 
+* \returns nv_bfloat162
+* - The elementwise sine on vector \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 h2sin(const __nv_bfloat162 a);
+
+/**
+* \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+* \brief Vector add \p val to the value stored at \p address in global or shared memory, and writes this
+* value back to \p address. The atomicity of the add operation is guaranteed separately for each of the
+* two nv_bfloat16 elements; the entire __nv_bfloat162 is not guaranteed to be atomic as a single 32-bit access.
+* 
+* \details The location of \p address must be in global or shared memory. This operation has undefined
+* behavior otherwise. This operation is natively supported by devices of compute capability 9.x and higher,
+* older devices use emulation path.
+* 
+* \param[in] address - __nv_bfloat162*. An address in global or shared memory.
+* \param[in] val - __nv_bfloat162. The value to be added.
+* 
+* \returns __nv_bfloat162
+* - The old value read from \p address.
+* 
+* \note_ref_guide_atomic
+*/
+__CUDA_BF16_DECL__ __nv_bfloat162 atomicAdd(__nv_bfloat162 *const address, const __nv_bfloat162 val);
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+* \brief Adds \p val to the value stored at \p address in global or shared memory, and writes this value
+* back to \p address. This operation is performed in one atomic operation.
+* 
+* \details The location of \p address must be in global or shared memory. This operation has undefined
+* behavior otherwise. This operation is natively supported by devices of compute capability 9.x and higher,
+* older devices of compute capability 7.x and 8.x use emulation path.
+* 
+* \param[in] address - __nv_bfloat16*. An address in global or shared memory.
+* \param[in] val - __nv_bfloat16. The value to be added.
+* 
+* \returns __nv_bfloat16
+* - The old value read from \p address.
+* 
+* \note_ref_guide_atomic
+*/
+__CUDA_BF16_DECL__ __nv_bfloat16 atomicAdd(__nv_bfloat16 *const address, const __nv_bfloat16 val);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA) */
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+
+#endif /* defined(__cplusplus) */
+
+#if !defined(_MSC_VER) && __cplusplus >= 201103L
+#   define __CPP_VERSION_AT_LEAST_11_BF16
+#elif _MSC_FULL_VER >= 190024210 && _MSVC_LANG >= 201103L
+#   define __CPP_VERSION_AT_LEAST_11_BF16
+#endif
+
+/* C++11 header for std::move. 
+ * In RTC mode, std::move is provided implicitly; don't include the header
+ */
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16) && !defined(__CUDACC_RTC__)
+#include <utility>
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_BF16) && !defined(__CUDACC_RTC__) */
+
+/* C++ header for std::memcpy (used for type punning in host-side implementations).
+ * When compiling as a CUDA source file memcpy is provided implicitly.
+ * !defined(__CUDACC__) implies !defined(__CUDACC_RTC__).
+ */
+#if defined(__cplusplus) && !defined(__CUDACC__)
+#include <cstring>
+#endif /* defined(__cplusplus) && !defined(__CUDACC__) */
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+#include <nv/target>
+#endif  /* !defined(__CUDACC_RTC__) */
+
+#if (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))
+#define __CUDA_BF16_INLINE__
+#define __CUDA_BF16_FORCEINLINE__
+#else
+#define __CUDA_BF16_INLINE__ inline
+#define __CUDA_BF16_FORCEINLINE__ __forceinline__
+#endif /* #if (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3)))) */
+
+/* Set up structure-alignment attribute */
+#if defined(__CUDACC__)
+#define __CUDA_ALIGN__(align) __align__(align)
+#else
+/* Define alignment macro based on compiler type (cannot assume C11 "_Alignas" is available) */
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+#define __CUDA_ALIGN__(n) alignas(n)    /* C++11 kindly gives us a keyword for this */
+#else /* defined(__CPP_VERSION_AT_LEAST_11_BF16)*/
+#if defined(__GNUC__)
+#define __CUDA_ALIGN__(n) __attribute__ ((aligned(n)))
+#elif defined(_MSC_VER)
+#define __CUDA_ALIGN__(n) __declspec(align(n))
+#else
+#define __CUDA_ALIGN__(n)
+#endif /* defined(__GNUC__) */
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+#endif /* defined(__CUDACC__) */
+
+// define __CUDA_BF16_CONSTEXPR__ in order to
+// use constexpr where possible, with supporting C++ dialects
+// undef after use
+#if (defined __CPP_VERSION_AT_LEAST_11_BF16)
+#define __CUDA_BF16_CONSTEXPR__   constexpr
+#else
+#define __CUDA_BF16_CONSTEXPR__
+#endif
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+ * \brief __nv_bfloat16_raw data type
+ * \details Type allows static initialization of \p nv_bfloat16 until it becomes
+ * a built-in type.
+ * 
+ * - Note: this initialization is as a bit-field representation of \p nv_bfloat16,
+ * and not a conversion from \p short to \p nv_bfloat16.
+ * Such representation will be deprecated in a future version of CUDA.
+ * 
+ * - Note: this is visible to non-nvcc compilers, including C-only compilations
+ */
+typedef struct __CUDA_ALIGN__(2) {
+    /**
+     * Storage field contains bits representation of the \p nv_bfloat16 floating-point number.
+     */
+    unsigned short x;
+} __nv_bfloat16_raw;
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+ * \brief __nv_bfloat162_raw data type
+ * \details Type allows static initialization of \p nv_bfloat162 until it becomes
+ * a built-in type.
+ * 
+ * - Note: this initialization is as a bit-field representation of \p nv_bfloat162,
+ * and not a conversion from \p short2 to \p nv_bfloat162.
+ * Such representation will be deprecated in a future version of CUDA.
+ * 
+ * - Note: this is visible to non-nvcc compilers, including C-only compilations
+ */
+typedef struct __CUDA_ALIGN__(4) {
+    /**
+     * Storage field contains bits of the lower \p nv_bfloat16 part.
+     */
+    unsigned short x;
+    /**
+     * Storage field contains bits of the upper \p nv_bfloat16 part.
+     */
+    unsigned short y;
+} __nv_bfloat162_raw;
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+
+/* Hide GCC member initialization list warnings because of host/device in-function init requirement */
+#if defined(__GNUC__)
+#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Weffc++"
+#endif /* __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6) */
+#endif /* defined(__GNUC__) */
+
+/* class' : multiple assignment operators specified
+   The class has multiple assignment operators of a single type. This warning is informational */
+#if defined(_MSC_VER) && _MSC_VER >= 1500
+#pragma warning( push )
+#pragma warning( disable:4522 )
+#endif /* defined(__GNUC__) */
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+ * \brief nv_bfloat16 datatype 
+ * 
+ * \details This structure implements the datatype for storing 
+ * nv_bfloat16 floating-point numbers. The structure implements 
+ * assignment operators and type conversions. 16 bits are being 
+ * used in total: 1 sign bit, 8 bits for the exponent, and 
+ * the significand is being stored in 7 bits. The total 
+ * precision is 8 bits.
+ * 
+ */
+struct __CUDA_ALIGN__(2) __nv_bfloat16 {
+protected:
+    /**
+     * Protected storage variable contains the bits of floating-point data.
+     */
+    unsigned short __x;
+
+public:
+
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * \brief Constructor by default.
+     * \details Emtpy default constructor, result is uninitialized.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+    __nv_bfloat16() = default;
+#else
+    __CUDA_HOSTDEVICE__ __nv_bfloat16() { }
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+
+    /* Convert to/from __nv_bfloat16_raw */
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Constructor from \p __nv_bfloat16_raw.
+     */
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_CONSTEXPR__ __nv_bfloat16(const __nv_bfloat16_raw &hr) : __x(hr.x) { }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Assignment operator from \p __nv_bfloat16_raw.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(const __nv_bfloat16_raw &hr);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Assignment operator from \p __nv_bfloat16_raw to \p volatile \p __nv_bfloat16.
+     */
+    __CUDA_HOSTDEVICE__ volatile __nv_bfloat16 &operator=(const __nv_bfloat16_raw &hr) volatile;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Assignment operator from \p volatile \p __nv_bfloat16_raw to \p volatile \p __nv_bfloat16.
+     */
+    __CUDA_HOSTDEVICE__ volatile __nv_bfloat16 &operator=(const volatile __nv_bfloat16_raw &hr) volatile;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast to \p __nv_bfloat16_raw operator.
+     */
+    __CUDA_HOSTDEVICE__ operator __nv_bfloat16_raw() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast to \p __nv_bfloat16_raw operator with \p volatile input.
+     */
+    __CUDA_HOSTDEVICE__ operator __nv_bfloat16_raw() const volatile;
+
+#if !defined(__CUDA_NO_BFLOAT16_CONVERSIONS__)
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p __half input using default round-to-nearest-even rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE__ __nv_bfloat16(const __half f)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{  cvt.rn.bf16.f16 %0, %1;}\n" : "=h"(__x) : "h"(__BFLOAT16_TO_CUS(f)));
+,
+    __x = __float2bfloat16(__half2float(f)).__x;
+)
+}
+#endif /* #if defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+
+    /* Construct from float/double */
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p float input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(const float f) { __x = __float2bfloat16(f).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p double input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(const double f) { __x = __double2bfloat16(f).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast to \p float operator.
+     */
+    __CUDA_HOSTDEVICE__ operator float() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast to \p __nv_bfloat16 assignment operator from \p float input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(const float f);
+
+    /* We omit "cast to double" operator, so as to not be ambiguous about up-cast */
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast to \p __nv_bfloat16 assignment operator from \p double input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(const double f);
+
+/*
+ * Implicit type conversions to/from integer types were only available to nvcc compilation.
+ * Introducing them for all compilers is a potentially breaking change that may affect
+ * overloads resolution and will require users to update their code.
+ * Define __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__ to opt-out.
+ */
+#if !(defined __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__)
+    /* Allow automatic construction from types supported natively in hardware */
+    /* Note we do avoid constructor init-list because of special host/device compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p short integer input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(short val) { __x = __short2bfloat16_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p unsigned \p short integer input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(unsigned short val) { __x = __ushort2bfloat16_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p int input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(int val) { __x = __int2bfloat16_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p unsigned \p int input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(unsigned int val) { __x = __uint2bfloat16_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(const long val) {
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            __x = __ll2bfloat16_rn(static_cast<long long>(val)).__x;
+        } else {
+            __x = __int2bfloat16_rn(static_cast<int>(val)).__x;
+        }
+    }
+
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p unsigned \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(const unsigned long val) {
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            __x = __ull2bfloat16_rn(static_cast<unsigned long long>(val)).__x;
+        } else {
+            __x = __uint2bfloat16_rn(static_cast<unsigned int>(val)).__x;
+        }
+    }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p long \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(long long val) { __x = __ll2bfloat16_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Construct \p __nv_bfloat16 from \p unsigned \p long \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16(unsigned long long val) { __x = __ull2bfloat16_rn(val).__x; }
+
+    /* Allow automatic casts to supported built-in types, matching all that are permitted with float */
+
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p signed \p char data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162char_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator signed char() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162uchar_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned char() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+     */
+    __CUDA_HOSTDEVICE__ operator char() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p short data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162short_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator short() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p unsigned \p short data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162ushort_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned short() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p int data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162int_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator int() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162uint_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned int() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p long data type.
+     * Using round-toward-zero rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ operator long() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p unsigned \p long data type.
+     * Using round-toward-zero rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned long() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p long \p long data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162ll_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator long long() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p unsigned \p long \p long data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * See __bfloat162ull_rz(__nv_bfloat16) for further details
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned long long() const;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast from \p short assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(short val);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast from \p unsigned \p short assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(unsigned short val);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast from \p int assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(int val);
+   /**
+    * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast from \p unsigned \p int assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(unsigned int val);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast from \p long \p long assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(long long val);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Type cast from \p unsigned \p long \p long assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat16 &operator=(unsigned long long val);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_CONSTEXPR__ operator bool() const { return (__x & 0x7FFFU) != 0U; }
+#endif /* !(defined __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__) */
+#endif /* !defined(__CUDA_NO_BFLOAT16_CONVERSIONS__) */
+};
+
+#if !defined(__CUDA_NO_BFLOAT16_OPERATORS__)
+/* Some basic arithmetic operations expected of a built-in */
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 addition operation.
+ * See also __hadd(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator+(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 subtraction operation.
+ * See also __hsub(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator-(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 multiplication operation.
+ * See also __hmul(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator*(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 division operation.
+ * See also __hdiv(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator/(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 compound assignment with addition operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator+=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 compound assignment with subtraction operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator-=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 compound assignment with multiplication operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator*=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 compound assignment with division operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator/=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+
+/* Note for increment and decrement we use the raw value 0x3F80U equating to nv_bfloat16(1.0F), to avoid the extra conversion */
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 prefix increment operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator++(__nv_bfloat16 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 prefix decrement operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator--(__nv_bfloat16 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 postfix increment operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16  operator++(__nv_bfloat16 &h, const int ignored);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Performs \p nv_bfloat16 postfix decrement operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16  operator--(__nv_bfloat16 &h, const int ignored);
+/* Unary plus and inverse operators */
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Implements \p nv_bfloat16 unary plus operator, returns input value.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator+(const __nv_bfloat16 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_ARITHMETIC
+ * Implements \p nv_bfloat16 unary minus operator.
+ * See also __hneg(__nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator-(const __nv_bfloat16 &h);
+
+/* Some basic comparison operations to make it look like a built-in */
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+ * Performs \p nv_bfloat16 ordered compare equal operation.
+ * See also __heq(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator==(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+ * Performs \p nv_bfloat16 unordered compare not-equal operation.
+ * See also __hneu(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator!=(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+ * Performs \p nv_bfloat16 ordered greater-than compare operation.
+ * See also __hgt(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator> (const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+ * Performs \p nv_bfloat16 ordered less-than compare operation.
+ * See also __hlt(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator< (const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+ * Performs \p nv_bfloat16 ordered greater-or-equal compare operation.
+ * See also __hge(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator>=(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT16_COMPARISON
+ * Performs \p nv_bfloat16 ordered less-or-equal compare operation.
+ * See also __hle(__nv_bfloat16, __nv_bfloat16)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator<=(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh);
+#endif /* !defined(__CUDA_NO_BFLOAT16_OPERATORS__) */
+
+/**
+* \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+ * \brief nv_bfloat162 datatype
+ * \details This structure implements the datatype for storing two 
+ * nv_bfloat16 floating-point numbers. 
+ * The structure implements assignment, arithmetic and comparison
+ * operators, and type conversions. 
+ * 
+ * - NOTE: __nv_bfloat162 is visible to non-nvcc host compilers
+ */
+struct __CUDA_ALIGN__(4) __nv_bfloat162 {
+    /**
+     * Storage field holding lower \p __nv_bfloat16 part.
+     */
+    __nv_bfloat16 x;
+    /**
+     * Storage field holding upper \p __nv_bfloat16 part.
+     */
+    __nv_bfloat16 y;
+
+    // All construct/copy/assign/move
+public:
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * \brief Constructor by default.
+     * \details Emtpy default constructor, result is uninitialized.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+    __nv_bfloat162() = default;
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Move constructor, available for \p C++11 and later dialects
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat162(__nv_bfloat162 &&src);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Move assignment operator, available for \p C++11 and later dialects
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat162 &operator=(__nv_bfloat162 &&src);
+#else
+    __CUDA_HOSTDEVICE__ __nv_bfloat162();
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Constructor from two \p __nv_bfloat16 variables
+     */
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_CONSTEXPR__ __nv_bfloat162(const __nv_bfloat16 &a, const __nv_bfloat16 &b) : x(a), y(b) { }
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Copy constructor
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat162(const __nv_bfloat162 &src);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Copy assignment operator
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat162 &operator=(const __nv_bfloat162 &src);
+
+    /* Convert to/from __nv_bfloat162_raw */
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Constructor from \p __nv_bfloat162_raw
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat162(const __nv_bfloat162_raw &h2r );
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Assignment operator from \p __nv_bfloat162_raw
+     */
+    __CUDA_HOSTDEVICE__ __nv_bfloat162 &operator=(const __nv_bfloat162_raw &h2r);
+    /**
+     * \ingroup CUDA_MATH__BFLOAT16_MISC
+     * Conversion operator to \p __nv_bfloat162_raw
+     */
+    __CUDA_HOSTDEVICE__ operator __nv_bfloat162_raw() const;
+};
+
+#if !defined(__CUDA_NO_BFLOAT162_OPERATORS__)
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 addition operation.
+ * See also __hadd2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator+(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 subtraction operation.
+ * See also __hsub2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator-(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 multiplication operation.
+ * See also __hmul2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator*(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 division operation.
+ * See also __h2div(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator/(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 compound assignment with addition operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator+=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 compound assignment with subtraction operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator-=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 compound assignment with multiplication operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator*=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 compound assignment with division operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator/=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 prefix increment operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 &operator++(__nv_bfloat162 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 prefix decrement operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 &operator--(__nv_bfloat162 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 postfix increment operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162  operator++(__nv_bfloat162 &h, const int ignored);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Performs packed \p nv_bfloat16 postfix decrement operation.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162  operator--(__nv_bfloat162 &h, const int ignored);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Implements packed \p nv_bfloat16 unary plus operator, returns input value.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator+(const __nv_bfloat162 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_ARITHMETIC
+ * Implements packed \p nv_bfloat16 unary minus operator.
+ * See also __hneg2(__nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator-(const __nv_bfloat162 &h);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+ * Performs packed \p nv_bfloat16 ordered compare equal operation.
+ * See also __hbeq2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator==(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+ * Performs packed \p nv_bfloat16 unordered compare not-equal operation.
+ * See also __hbneu2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator!=(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+ * Performs packed \p nv_bfloat16 ordered greater-than compare operation.
+ * See also __hbgt2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator>(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+ * Performs packed \p nv_bfloat16 ordered less-than compare operation.
+ * See also __hblt2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator<(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+ * Performs packed \p nv_bfloat16 ordered greater-or-equal compare operation.
+ * See also __hbge2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator>=(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+/**
+ * \ingroup CUDA_MATH__BFLOAT162_COMPARISON
+ * Performs packed \p nv_bfloat16 ordered less-or-equal compare operation.
+ * See also __hble2(__nv_bfloat162, __nv_bfloat162)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator<=(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh);
+
+#endif /* !defined(__CUDA_NO_BFLOAT162_OPERATORS__) */
+
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+#if !defined(__CUDA_NO_HALF_CONVERSIONS__)
+__CUDA_HOSTDEVICE__ 
+#ifdef __CUDACC_RTC__
+inline
+#else
+__CUDA_BF16_FORCEINLINE__ 
+#endif
+__half::__half(const __nv_bfloat16 f)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{  cvt.rn.f16.bf16 %0, %1;}\n" : "=h"(__x) : "h"(__BFLOAT16_TO_CUS(f)));
+,
+    __x = __float2half_rn(__bfloat162float(f)).__x;
+)
+}
+#endif
+#endif /* #if defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+
+#endif /* defined(__cplusplus) */
+
+#if (defined(__FORCE_INCLUDE_CUDA_BF16_HPP_FROM_BF16_H__) || \
+    !(defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3)))))
+/* Note the .hpp file is included to capture the "nv_bfloat16" & "nv_bfloat162" built-in function definitions. For NVRTC, the built-in
+   function definitions are compiled at NVRTC library build-time and are available through the NVRTC built-ins library at
+   link time.
+*/
+#include "cuda_bf16.hpp"
+#endif /* (defined(__FORCE_INCLUDE_CUDA_BF16_HPP_FROM_BF16_H__) || \
+          !(defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))) */
+
+/* Define first-class types "nv_bfloat16" and "nv_bfloat162", unless user specifies otherwise via "#define CUDA_NO_BFLOAT16" */
+/* C cannot ever have these types defined here, because __nv_bfloat16 and __nv_bfloat162 are C++ classes */
+#if defined(__cplusplus) && !defined(CUDA_NO_BFLOAT16)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+ * \brief This datatype is meant to be the first-class or fundamental
+ * implementation of the bfloat16 numbers format.
+ * 
+ * \details Should be implemented in the compiler in the future.
+ * Current implementation is a simple typedef to a respective
+ * user-level type with underscores.
+ */
+typedef __nv_bfloat16  nv_bfloat16;
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16
+ * \brief This datatype is meant to be the first-class or fundamental
+ * implementation of type for pairs of bfloat16 numbers.
+ * 
+ * \details Should be implemented in the compiler in the future.
+ * Current implementation is a simple typedef to a respective
+ * user-level type with underscores.
+ */
+typedef __nv_bfloat162 nv_bfloat162;
+
+#endif /* defined(__cplusplus) && !defined(CUDA_NO_BFLOAT16) */
+
+#undef __CUDA_BF16_DECL__
+#undef __CUDA_HOSTDEVICE_BF16_DECL__
+#undef __CUDA_HOSTDEVICE__
+#undef __CUDA_BF16_INLINE__
+#undef __CUDA_BF16_FORCEINLINE__
+#undef ___CUDA_BF16_STRINGIFY_INNERMOST
+#undef __CUDA_BF16_STRINGIFY
+
+#endif /* end of include guard: __CUDA_BF16_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_bf16.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_bf16.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..55568d4e4dd07a42b616593c621637f56a030f69
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_bf16.hpp
@@ -0,0 +1,3865 @@
+/*
+* Copyright 1993-2024 NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO LICENSEE:
+*
+* This source code and/or documentation ("Licensed Deliverables") are
+* subject to NVIDIA intellectual property rights under U.S. and
+* international Copyright laws.
+*
+* These Licensed Deliverables contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and
+* conditions of a form of NVIDIA software license agreement by and
+* between NVIDIA and Licensee ("License Agreement") or electronically
+* accepted by Licensee.  Notwithstanding any terms or conditions to
+* the contrary in the License Agreement, reproduction or disclosure
+* of the Licensed Deliverables to any third party without the express
+* written consent of NVIDIA is prohibited.
+*
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+* OF THESE LICENSED DELIVERABLES.
+*
+* U.S. Government End Users.  These Licensed Deliverables are a
+* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+* 1995), consisting of "commercial computer software" and "commercial
+* computer software documentation" as such terms are used in 48
+* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+* only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+* U.S. Government End Users acquire the Licensed Deliverables with
+* only those rights set forth herein.
+*
+* Any use of the Licensed Deliverables in individual and commercial
+* software must include, in the user documentation and internal
+* comments to the code, the above Disclaimer and U.S. Government End
+* Users Notice.
+*/
+
+#if !defined(__CUDA_BF16_HPP__)
+#define __CUDA_BF16_HPP__
+
+#if !defined(__CUDA_BF16_H__)
+#error "Do not include this file directly. Instead, include cuda_bf16.h."
+#endif
+
+#if !defined(IF_DEVICE_OR_CUDACC)
+#if defined(__CUDACC__)
+    #define IF_DEVICE_OR_CUDACC(d, c, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, d, c)
+#else
+    #define IF_DEVICE_OR_CUDACC(d, c, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, d, f)
+#endif
+#endif
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines floating-point positive infinity value for the \p nv_bfloat16 data type
+ */
+#define CUDART_INF_BF16            __ushort_as_bfloat16((unsigned short)0x7F80U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines canonical NaN value for the \p nv_bfloat16 data type
+ */
+#define CUDART_NAN_BF16            __ushort_as_bfloat16((unsigned short)0x7FFFU)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines a minimum representable (denormalized) value for the \p nv_bfloat16 data type
+ */
+#define CUDART_MIN_DENORM_BF16     __ushort_as_bfloat16((unsigned short)0x0001U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines a maximum representable value for the \p nv_bfloat16 data type
+ */
+#define CUDART_MAX_NORMAL_BF16     __ushort_as_bfloat16((unsigned short)0x7F7FU)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines a negative zero value for the \p nv_bfloat16 data type
+ */
+#define CUDART_NEG_ZERO_BF16       __ushort_as_bfloat16((unsigned short)0x8000U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines a positive zero value for the \p nv_bfloat16 data type
+ */
+#define CUDART_ZERO_BF16           __ushort_as_bfloat16((unsigned short)0x0000U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_BFLOAT16_CONSTANTS
+ * \brief Defines a value of 1.0 for the \p nv_bfloat16 data type
+ */
+#define CUDART_ONE_BF16            __ushort_as_bfloat16((unsigned short)0x3F80U)
+
+#if !(defined __DOXYGEN_ONLY__)
+
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(const __nv_bfloat16_raw &hr) { __x = hr.x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ volatile __nv_bfloat16 &__nv_bfloat16::operator=(const __nv_bfloat16_raw &hr) volatile { __x = hr.x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ volatile __nv_bfloat16 &__nv_bfloat16::operator=(const volatile __nv_bfloat16_raw &hr) volatile { __x = hr.x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator __nv_bfloat16_raw() const { __nv_bfloat16_raw ret; ret.x = __x; return ret; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator __nv_bfloat16_raw() const volatile { __nv_bfloat16_raw ret; ret.x = __x; return ret; }
+
+#if !defined(__CUDA_NO_BFLOAT16_CONVERSIONS__)
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator float() const { return __bfloat162float(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(const float f) { __x = __float2bfloat16(f).__x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(const double f) { __x = __double2bfloat16(f).__x; return *this; }
+
+/*
+ * Implicit type conversions to/from integer types were only available to nvcc compilation.
+ * Introducing them for all compilers is a potentially breaking change that may affect
+ * overloads resolution and will require users to update their code.
+ * Define __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__ to opt-out.
+ */
+#if !(defined __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__)
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator signed char() const { return __bfloat162char_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator unsigned char() const { return __bfloat162uchar_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(__bfloat162char_rz(*this));
+        }
+        else
+        {
+            value = static_cast<char>(__bfloat162uchar_rz(*this));
+        }
+        return value;
+    }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator short() const { return __bfloat162short_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator unsigned short() const { return __bfloat162ushort_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator int() const { return __bfloat162int_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator unsigned int() const { return __bfloat162uint_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator long() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__bfloat162ll_rz(*this));
+        }
+        else
+        {
+            retval = static_cast<long>(__bfloat162int_rz(*this));
+        }
+        return retval;
+    }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator unsigned long() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__bfloat162ull_rz(*this));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__bfloat162uint_rz(*this));
+        }
+        return retval;
+    }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator long long() const { return __bfloat162ll_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16::operator unsigned long long() const { return __bfloat162ull_rz(*this); }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(short val) { __x = __short2bfloat16_rn(val).__x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(unsigned short val) { __x = __ushort2bfloat16_rn(val).__x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(int val) { __x = __int2bfloat16_rn(val).__x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(unsigned int val) { __x = __uint2bfloat16_rn(val).__x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(long long val) { __x = __ll2bfloat16_rn(val).__x; return *this; }
+    __CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat16 &__nv_bfloat16::operator=(unsigned long long val) { __x = __ull2bfloat16_rn(val).__x; return *this; }
+#endif /* !(defined __CUDA_BF16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__) */
+#endif /* !defined(__CUDA_NO_BFLOAT16_CONVERSIONS__) */
+
+
+#if !defined(__CUDA_NO_BFLOAT16_OPERATORS__)
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator+(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hadd(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator-(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hsub(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator*(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hmul(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator/(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hdiv(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator+=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh) { lh = __hadd(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator-=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh) { lh = __hsub(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator*=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh) { lh = __hmul(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator/=(__nv_bfloat16 &lh, const __nv_bfloat16 &rh) { lh = __hdiv(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator++(__nv_bfloat16 &h)      { __nv_bfloat16_raw one; one.x = 0x3F80U; h += one; return h; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 &operator--(__nv_bfloat16 &h)      { __nv_bfloat16_raw one; one.x = 0x3F80U; h -= one; return h; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16  operator++(__nv_bfloat16 &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __nv_bfloat16 ret = h;
+    __nv_bfloat16_raw one;
+    one.x = 0x3F80U;
+    h += one;
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16  operator--(__nv_bfloat16 &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __nv_bfloat16 ret = h;
+    __nv_bfloat16_raw one;
+    one.x = 0x3F80U;
+    h -= one;
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator+(const __nv_bfloat16 &h) { return h; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat16 operator-(const __nv_bfloat16 &h) { return __hneg(h); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator==(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __heq(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator!=(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hneu(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator> (const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hgt(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator< (const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hlt(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator>=(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hge(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator<=(const __nv_bfloat16 &lh, const __nv_bfloat16 &rh) { return __hle(lh, rh); }
+#endif /* !defined(__CUDA_NO_BFLOAT16_OPERATORS__) */
+
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162::__nv_bfloat162(__nv_bfloat162 &&src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __BFLOAT162_TO_UI(*this) = std::move(__BFLOAT162_TO_CUI(src));
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162 &__nv_bfloat162::operator=(__nv_bfloat162 &&src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __BFLOAT162_TO_UI(*this) = std::move(__BFLOAT162_TO_CUI(src));
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+    return *this;
+}
+#else
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162::__nv_bfloat162() { }
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162::__nv_bfloat162(const __nv_bfloat162 &src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+   __BFLOAT162_TO_UI(*this) = __BFLOAT162_TO_CUI(src);
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162 &__nv_bfloat162::operator=(const __nv_bfloat162 &src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+   __BFLOAT162_TO_UI(*this) = __BFLOAT162_TO_CUI(src);
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+    return *this;
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162::__nv_bfloat162(const __nv_bfloat162_raw &h2r ) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __BFLOAT162_TO_UI(*this) = __BFLOAT162_TO_CUI(h2r);
+,
+    __nv_bfloat16_raw tr;
+    tr.x = h2r.x;
+    this->x = static_cast<__nv_bfloat16>(tr);
+    tr.x = h2r.y;
+    this->y = static_cast<__nv_bfloat16>(tr);
+)
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162 &__nv_bfloat162::operator=(const __nv_bfloat162_raw &h2r) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __BFLOAT162_TO_UI(*this) = __BFLOAT162_TO_CUI(h2r);
+,
+    __nv_bfloat16_raw tr;
+    tr.x = h2r.x;
+    this->x = static_cast<__nv_bfloat16>(tr);
+    tr.x = h2r.y;
+    this->y = static_cast<__nv_bfloat16>(tr);
+)
+    return *this;
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_INLINE__ __nv_bfloat162::operator __nv_bfloat162_raw() const {
+    __nv_bfloat162_raw ret;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    ret.x = 0U;
+    ret.y = 0U;
+    __BFLOAT162_TO_UI(ret) = __BFLOAT162_TO_CUI(*this);
+,
+    ret.x = static_cast<__nv_bfloat16_raw>(this->x).x;
+    ret.y = static_cast<__nv_bfloat16_raw>(this->y).x;
+)
+    return ret;
+}
+
+#if !defined(__CUDA_NO_BFLOAT162_OPERATORS__)
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator+(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hadd2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator-(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hsub2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator*(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hmul2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator/(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __h2div(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator+=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh) { lh = __hadd2(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator-=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh) { lh = __hsub2(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator*=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh) { lh = __hmul2(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162& operator/=(__nv_bfloat162 &lh, const __nv_bfloat162 &rh) { lh = __h2div(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 &operator++(__nv_bfloat162 &h)      { __nv_bfloat162_raw one; one.x = 0x3F80U; one.y = 0x3F80U; h = __hadd2(h, one); return h; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 &operator--(__nv_bfloat162 &h)      { __nv_bfloat162_raw one; one.x = 0x3F80U; one.y = 0x3F80U; h = __hsub2(h, one); return h; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162  operator++(__nv_bfloat162 &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __nv_bfloat162 ret = h;
+    __nv_bfloat162_raw one;
+    one.x = 0x3F80U;
+    one.y = 0x3F80U;
+    h = __hadd2(h, one);
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162  operator--(__nv_bfloat162 &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __nv_bfloat162 ret = h;
+    __nv_bfloat162_raw one;
+    one.x = 0x3F80U;
+    one.y = 0x3F80U;
+    h = __hsub2(h, one);
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator+(const __nv_bfloat162 &h) { return h; }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ __nv_bfloat162 operator-(const __nv_bfloat162 &h) { return __hneg2(h); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator==(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hbeq2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator!=(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hbneu2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator>(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hbgt2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator<(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hblt2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator>=(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hbge2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_BF16_FORCEINLINE__ bool operator<=(const __nv_bfloat162 &lh, const __nv_bfloat162 &rh) { return __hble2(lh, rh); }
+#endif /* !defined(__CUDA_NO_BFLOAT162_OPERATORS__) */
+
+/* Restore warning for multiple assignment operators */
+#if defined(_MSC_VER) && _MSC_VER >= 1500
+#pragma warning( pop )
+#endif /* defined(_MSC_VER) && _MSC_VER >= 1500 */
+
+/* Restore -Weffc++ warnings from here on */
+#if defined(__GNUC__)
+#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)
+#pragma GCC diagnostic pop
+#endif /* __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6) */
+#endif /* defined(__GNUC__) */
+
+#undef __CUDA_HOSTDEVICE__
+#undef __CUDA_ALIGN__
+
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __internal_float_as_uint(const float f)
+{
+    unsigned int u;
+IF_DEVICE_OR_CUDACC(
+    u = __float_as_uint(f);
+,
+    memcpy(&u, &f, sizeof(f));
+,
+    std::memcpy(&u, &f, sizeof(f));
+)
+    return u;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ float __internal_uint_as_float(const unsigned int u)
+{
+    float f;
+IF_DEVICE_OR_CUDACC(
+    f = __uint_as_float(u);
+,
+    memcpy(&f, &u, sizeof(u));
+,
+    std::memcpy(&f, &u, sizeof(u));
+)
+    return f;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned short __internal_float2bfloat16(const float f, unsigned int &sign, unsigned int &remainder)
+{
+    unsigned int x;
+
+    x = __internal_float_as_uint(f);
+
+    if ((x & 0x7fffffffU) > 0x7f800000U) {
+        sign = 0U;
+        remainder = 0U;
+        return static_cast<unsigned short>(0x7fffU);
+    }
+    sign = x >> 31U;
+    remainder = x << 16U;
+    return static_cast<unsigned short>(x >> 16U);
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ float __internal_double2float_rn(const double x)
+{
+    float r;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f32.f64 %0, %1;" : "=f"(r) : "d"(x));
+,
+    r = static_cast<float>(x);
+)
+    return r;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ double __internal_float2double(const float x)
+{
+    double r;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.f64.f32 %0, %1;" : "=d"(r) : "f"(x));
+,
+    r = static_cast<double>(x);
+)
+    return r;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __double2bfloat16(const double x)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("{  cvt.rn.bf16.f64 %0, %1;}\n" : "=h"(__BFLOAT16_TO_US(val)) : "d"(x));
+    return val;
+,
+    float f = __internal_double2float_rn(x);
+    const double d = __internal_float2double(f);
+    unsigned int u = __internal_float_as_uint(f);
+
+    bool x_is_not_nan = ((u << (unsigned)1U) <= (unsigned)0xFF000000U);
+
+
+    if ((x > 0.0) && (d > x)) {
+        u--;
+    }
+    if ((x < 0.0) && (d < x)) {
+        u--;
+    }
+    if ((d != x) && x_is_not_nan) {
+        u |= 1U;
+    }
+
+    f = __internal_uint_as_float(u);
+
+    return __float2bfloat16(f);
+)
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16(const float a)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{  cvt.rn.bf16.f32 %0, %1;}\n" : "=h"(__BFLOAT16_TO_US(val)) : "f"(a));
+,
+    __nv_bfloat16_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2bfloat16(a, sign, remainder);
+    if ((remainder > 0x80000000U) || ((remainder == 0x80000000U) && ((r.x & 0x1U) != 0U))) {
+        r.x++;
+    }
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_rn(const float a)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{  cvt.rn.bf16.f32 %0, %1;}\n" : "=h"(__BFLOAT16_TO_US(val)) : "f"(a));
+,
+    __nv_bfloat16_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2bfloat16(a, sign, remainder);
+    if ((remainder > 0x80000000U) || ((remainder == 0x80000000U) && ((r.x & 0x1U) != 0U))) {
+        r.x++;
+    }
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_rz(const float a)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{  cvt.rz.bf16.f32 %0, %1;}\n" : "=h"(__BFLOAT16_TO_US(val)) : "f"(a));
+,
+    __nv_bfloat16_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2bfloat16(a, sign, remainder);
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_rd(const float a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("{  cvt.rm.bf16.f32 %0, %1;}\n" : "=h"(__BFLOAT16_TO_US(val)) : "f"(a));
+    return val;
+,
+    __nv_bfloat16 val;
+    __nv_bfloat16_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2bfloat16(a, sign, remainder);
+    if ((remainder != 0U) && (sign != 0U)) {
+        r.x++;
+    }
+    val = r;
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __float2bfloat16_ru(const float a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("{  cvt.rp.bf16.f32 %0, %1;}\n" : "=h"(__BFLOAT16_TO_US(val)) : "f"(a));
+    return val;
+,
+    __nv_bfloat16 val;
+    __nv_bfloat16_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2bfloat16(a, sign, remainder);
+    if ((remainder != 0U) && (sign == 0U)) {
+        r.x++;
+    }
+    val = r;
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __float2bfloat162_rn(const float a)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{.reg .b16 low;\n"
+        "  cvt.rn.bf16.f32 low, %1;\n"
+        "  mov.b32 %0, {low,low};}\n" : "=r"(__BFLOAT162_TO_UI(val)) : "f"(a));
+,
+    val = __nv_bfloat162(__float2bfloat16_rn(a), __float2bfloat16_rn(a));
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __floats2bfloat162_rn(const float a, const float b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{ cvt.rn.bf16x2.f32 %0, %2, %1;}\n"
+        : "=r"(__BFLOAT162_TO_UI(val)) : "f"(a), "f"(b));
+,
+    val = __nv_bfloat162(__float2bfloat16_rn(a), __float2bfloat16_rn(b));
+)
+    return val;
+}
+
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ float __internal_device_bfloat162float(const unsigned short h)
+{
+    float f;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{ cvt.f32.bf16 %0, %1;}\n" : "=f"(f) : "h"(h));
+,
+    asm("{ mov.b32 %0, {0,%1};}\n" : "=f"(f) : "h"(h));
+)
+    return f;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+__CUDA_HOSTDEVICE_BF16_DECL__ float __internal_bfloat162float(const unsigned short h)
+{
+    float f;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    f = __internal_device_bfloat162float(h);
+,
+    unsigned int u = static_cast<unsigned int>(h) << 16;
+    f = __internal_uint_as_float(u);
+)
+    return f;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ float __bfloat162float(const __nv_bfloat16 a)
+{
+    return __internal_bfloat162float(static_cast<__nv_bfloat16_raw>(a).x);
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ float __low2float(const __nv_bfloat162 a)
+{
+    return __internal_bfloat162float(static_cast<__nv_bfloat162_raw>(a).x);
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ float __high2float(const __nv_bfloat162 a)
+{
+    return __internal_bfloat162float(static_cast<__nv_bfloat162_raw>(a).y);
+}
+
+/* CUDA vector-types compatible vector creation function (note returns __nv_bfloat162, not nv_bfloat162) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 make_bfloat162(const __nv_bfloat16 x, const __nv_bfloat16 y)
+{
+    __nv_bfloat162 t; t.x = x; t.y = y; return t;
+}
+
+/* Definitions of intrinsics */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __float22bfloat162_rn(const float2 a)
+{
+    __nv_bfloat162 val = __floats2bfloat162_rn(a.x, a.y);
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ float2 __bfloat1622float2(const __nv_bfloat162 a)
+{
+    float hi_float;
+    float lo_float;
+    lo_float = __internal_bfloat162float(((__nv_bfloat162_raw)a).x);
+    hi_float = __internal_bfloat162float(((__nv_bfloat162_raw)a).y);
+    return make_float2(lo_float, hi_float);
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ int __bfloat162int_rn(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    int val;
+    asm("{  cvt.rni.s32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+    return val;
+,
+    return __float2int_rn(__bfloat162float(h));
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+__CUDA_HOSTDEVICE_BF16_DECL__ int __internal_bfloat162int_rz(const __nv_bfloat16 h)
+{
+    const float f = __bfloat162float(h);
+    int   i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    i = __float2int_rz(f);
+,
+    const int max_val = (int)0x7fffffffU;
+    const int min_val = (int)0x80000000U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        i = 0;
+    } else if (f >= static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        i = static_cast<int>(f);
+    }
+)
+    return i;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ int __bfloat162int_rz(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    int val;
+    asm("{  cvt.rzi.s32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+    return val;
+,
+    return __internal_bfloat162int_rz(h);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ int __bfloat162int_rd(const __nv_bfloat16 h)
+{
+    int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{  cvt.rmi.s32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    asm("cvt.rmi.s32.f32 %0, %1;" : "=r"(val) : "f"(f));
+)
+    return val;
+}
+__CUDA_BF16_DECL__ int __bfloat162int_ru(const __nv_bfloat16 h)
+{
+    int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{  cvt.rpi.s32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    asm("cvt.rpi.s32.f32 %0, %1;" : "=r"(val) : "f"(f));
+)
+    return val;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_int2bfloat16_rn(const int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+        __nv_bfloat16 val;
+       asm("cvt.rn.bf16.s32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+       return val;
+,
+        const float ru = __int2float_ru(i);
+        const float rd = __int2float_rd(i);
+        float rz = __int2float_rz(i);
+        if (ru != rd) {
+            rz = __uint_as_float(__float_as_uint(rz) | 1U);
+        }
+        return __float2bfloat16_rn(rz);
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __int2bfloat16_rn(const int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_int2bfloat16_rn(i);
+,
+    const double d = static_cast<double>(i);
+    return __double2bfloat16(d);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ signed char __bfloat162char_rz(const __nv_bfloat16 h)
+{
+    signed char i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned short tmp = 0;
+    asm("{ .reg.b8 myreg;\n"
+        "  cvt.rzi.s8.bf16 myreg, %1;\n"
+        "  mov.b16 %0, {myreg, 0};\n}"
+         :"=h"(tmp) : "h"(__BFLOAT16_TO_CUS(h)));
+    const unsigned char u = static_cast<unsigned char>(tmp);
+    i = static_cast<signed char>(u);
+,
+    const float f = __bfloat162float(h);
+    const signed char max_val = (signed char)0x7fU;
+    const signed char min_val = (signed char)0x80U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        i = 0;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<signed char>(f);
+    }
+)
+    return i;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned char __bfloat162uchar_rz(const __nv_bfloat16 h)
+{
+    unsigned char i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned short tmp = 0;
+    asm("{ .reg.b8 myreg;\n"
+        "  cvt.rzi.u8.bf16 myreg, %1;\n"
+        "  mov.b16 %0, {myreg, 0};\n}"
+         :"=h"(tmp) : "h"(__BFLOAT16_TO_CUS(h)));
+    i = static_cast<unsigned char>(tmp);
+,
+    const float f = __bfloat162float(h);
+    const unsigned char max_val = 0xffU;
+    const unsigned char min_val = 0U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        i = 0U;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<unsigned char>(f);
+    }
+)
+    return i;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __int2bfloat16_rz(const int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+     __nv_bfloat16 val;
+    asm("cvt.rz.bf16.s32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    return __float2bfloat16_rz(__int2float_rz(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __int2bfloat16_rd(const int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+     __nv_bfloat16 val;
+    asm("cvt.rm.bf16.s32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    return __float2bfloat16_rd(__int2float_rd(i));
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 __int2bfloat16_ru(const int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+     __nv_bfloat16 val;
+    asm("cvt.rp.bf16.s32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    return __float2bfloat16_ru(__int2float_ru(i));
+)
+}
+
+__CUDA_BF16_DECL__ short int __bfloat162short_rn(const __nv_bfloat16 h)
+{
+   short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rni.s16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rni.s16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+
+__CUDA_BF16_DECL__ short int __internal_device_bfloat162short_rz(const __nv_bfloat16 h)
+{
+    short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("cvt.rzi.s16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    asm("{ .reg.f32 f;\n"
+        "  mov.b32 f, {0,%1};\n"
+        "  cvt.rzi.s16.f32 %0,f;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+    return val;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ short int __bfloat162short_rz(const __nv_bfloat16 h)
+{
+    short int val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    val = __internal_device_bfloat162short_rz(h);
+,
+    const float f = __bfloat162float(h);
+    const short int max_val = (short int)0x7fffU;
+    const short int min_val = (short int)0x8000U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        val = 0;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        val = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        val = min_val;
+    } else {
+        val = static_cast<short int>(f);
+    }
+)
+   return val;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ short int __bfloat162short_rd(const __nv_bfloat16 h)
+{
+   short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rmi.s16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rmi.s16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ short int __bfloat162short_ru(const __nv_bfloat16 h)
+{
+   short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rpi.s16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rpi.s16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __short2bfloat16_rn(const short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rn.bf16.s16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    const float f = static_cast<float>(i);
+    return __float2bfloat16_rn(f);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __short2bfloat16_rz(const short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rz.bf16.s16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    return __float2bfloat16_rz(__int2float_rz(static_cast<int>(i)));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __short2bfloat16_rd(const short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rm.bf16.s16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    return __float2bfloat16_rd(__int2float_rd(static_cast<int>(i)));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __short2bfloat16_ru(const short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rp.bf16.s16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    return __float2bfloat16_ru(__int2float_ru(static_cast<int>(i)));
+)
+}
+
+__CUDA_BF16_DECL__ unsigned int __bfloat162uint_rn(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned int val;
+    asm("{  cvt.rni.u32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+    return val;
+,
+    return __float2uint_rn(__bfloat162float(h));
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __internal_bfloat162uint_rz(const __nv_bfloat16 h)
+{
+    const float f = __bfloat162float(h);
+    unsigned int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    i = __float2uint_rz(f);
+,
+    const unsigned int max_val = 0xffffffffU;
+    const unsigned int min_val = 0U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        i = 0U;
+    } else if (f >= static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        i = static_cast<unsigned int>(f);
+    }
+)
+    return i;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __bfloat162uint_rz(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned int val;
+    asm("{  cvt.rzi.u32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+    return val;
+,
+    return __internal_bfloat162uint_rz(h);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ unsigned int __bfloat162uint_rd(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned int val;
+    asm("{  cvt.rmi.u32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+    return val;
+,
+    return __float2uint_rd(__bfloat162float(h));
+)
+}
+__CUDA_BF16_DECL__ unsigned int __bfloat162uint_ru(const __nv_bfloat16 h)
+{
+    unsigned int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{  cvt.rpi.u32.bf16 %0, %1;}\n" : "=r"(val) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    asm("cvt.rpi.u32.f32 %0, %1;" : "=r"(val) : "f"(f));
+)
+    return val;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_uint2bfloat16_rn(const unsigned int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rn.bf16.u32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    const float ru = __uint2float_ru(i);
+    const float rd = __uint2float_rd(i);
+    float rz = __uint2float_rz(i);
+    if (ru != rd) {
+        rz = __uint_as_float(__float_as_uint(rz) | 1U);
+    }
+    return __float2bfloat16_rn(rz);
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_rn(const unsigned int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_uint2bfloat16_rn(i);
+,
+    const double d = static_cast<double>(i);
+    return __double2bfloat16(d);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_rz(const unsigned int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+     __nv_bfloat16 val;
+    asm("cvt.rz.bf16.u32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    return __float2bfloat16_rz(__uint2float_rz(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_rd(const unsigned int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+     __nv_bfloat16 val;
+    asm("cvt.rm.bf16.u32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    return __float2bfloat16_rd(__uint2float_rd(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __uint2bfloat16_ru(const unsigned int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+     __nv_bfloat16 val;
+    asm("cvt.rp.bf16.u32 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "r"(i));
+    return val;
+,
+    return __float2bfloat16_ru(__uint2float_ru(i));
+)
+}
+
+__CUDA_BF16_DECL__ unsigned short int __bfloat162ushort_rn(const __nv_bfloat16 h)
+{
+   unsigned short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rni.u16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rni.u16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+
+__CUDA_BF16_DECL__ unsigned short int __internal_device_bfloat162ushort_rz(const __nv_bfloat16 h)
+{
+   unsigned short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rzi.u16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rzi.u16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned short int __bfloat162ushort_rz(const __nv_bfloat16 h)
+{
+   unsigned short int val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+   val = __internal_device_bfloat162ushort_rz(h);
+,
+    const float f = __bfloat162float(h);
+    const unsigned short int max_val = 0xffffU;
+    const unsigned short int min_val = 0U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        val = 0U;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        val = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        val = min_val;
+    } else {
+        val = static_cast<unsigned short int>(f);
+    }
+)
+   return val;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ unsigned short int __bfloat162ushort_rd(const __nv_bfloat16 h)
+{
+   unsigned short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rmi.u16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rmi.u16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ unsigned short int __bfloat162ushort_ru(const __nv_bfloat16 h)
+{
+   unsigned short int val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm("cvt.rpi.u16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+   asm("{ .reg.f32 f;\n"
+       "  mov.b32 f, {0,%1};\n"
+       "  cvt.rpi.u16.f32 %0,f;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(h)));
+)
+   return val;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_rn(const unsigned short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rn.bf16.u16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    const float f = static_cast<float>(i);
+    return __float2bfloat16_rn(f);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_rz(const unsigned short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rz.bf16.u16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    return __float2bfloat16_rz(__uint2float_rz(static_cast<unsigned int>(i)));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_rd(const unsigned short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rm.bf16.u16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    return __float2bfloat16_rd(__uint2float_rd(static_cast<unsigned int>(i)));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ushort2bfloat16_ru(const unsigned short int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 val;
+    asm("cvt.rp.bf16.u16 %0, %1;" : "=h"(__BFLOAT16_TO_US(val)) : "h"(i));
+    return val;
+,
+    return __float2bfloat16_ru(__uint2float_ru(static_cast<unsigned int>(i)));
+)
+}
+
+__CUDA_BF16_DECL__ unsigned long long int __bfloat162ull_rn(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned long long int i;
+    asm("cvt.rni.u64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+    return i;
+,
+    return __float2ull_rn(__bfloat162float(h));
+)
+}
+
+__CUDA_BF16_DECL__ unsigned long long int __internal_device_bfloat162ull_rz(const __nv_bfloat16 h)
+{
+    unsigned long long int i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("cvt.rzi.u64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    i = __float2ull_rz(f);
+)
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned long long int __bfloat162ull_rz(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_bfloat162ull_rz(h);
+,
+    const float f = __bfloat162float(h);
+    unsigned long long int i;
+    const unsigned long long int max_val = 0xffffffffffffffffULL;
+    const unsigned long long int min_val = 0ULL;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        i = 0x8000000000000000ULL;
+    } else if (f >= static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        i = static_cast<unsigned long long int>(f);
+    }
+    return i;
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ unsigned long long int __bfloat162ull_rd(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    unsigned long long int i;
+    asm("cvt.rmi.u64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+    return i;
+,
+    return __float2ull_rd(__bfloat162float(h));
+)
+}
+__CUDA_BF16_DECL__ unsigned long long int __bfloat162ull_ru(const __nv_bfloat16 h)
+{
+    unsigned long long int i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("cvt.rpi.u64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    asm("cvt.rpi.u64.f32 %0, %1;" : "=l"(i) : "f"(f));
+)
+    return i;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_ull2bfloat16_rn(const unsigned long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rn.bf16.u64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    const float ru = __ull2float_ru(i);
+    const float rd = __ull2float_rd(i);
+    float rz = __ull2float_rz(i);
+    if (ru != rd) {
+        rz = __uint_as_float(__float_as_uint(rz) | 1U);
+    }
+    return __float2bfloat16_rn(rz);
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_rn(const unsigned long long int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_ull2bfloat16_rn(i);
+,
+    float f = static_cast<float>(i);
+    const unsigned long long int uf = static_cast<unsigned long long int>(f);
+    unsigned int u = __internal_float_as_uint(f);
+    // round up happened here
+    // note: no need to handle round up to f == 0x1.p64 specially
+    if (uf > i) {
+        u--;
+    }
+    if (uf != i) {
+        u |= 1U;
+    }
+    f = __internal_uint_as_float(u);
+    return __float2bfloat16_rn(f);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_rz(const unsigned long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rz.bf16.u64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    return __float2bfloat16_rz(__ull2float_rz(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_rd(const unsigned long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rm.bf16.u64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    return __float2bfloat16_rd(__ull2float_rd(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ull2bfloat16_ru(const unsigned long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rp.bf16.u64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    return __float2bfloat16_ru(__ull2float_ru(i));
+)
+}
+__CUDA_BF16_DECL__ long long int __bfloat162ll_rn(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    long long int i;
+    asm("cvt.rni.s64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+    return i;
+,
+    return __float2ll_rn(__bfloat162float(h));
+)
+}
+
+__CUDA_BF16_DECL__ long long int __internal_device_bfloat162ll_rz(const __nv_bfloat16 h)
+{
+    long long int i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("cvt.rzi.s64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    i = __float2ll_rz(f);
+)
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ long long int __bfloat162ll_rz(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_bfloat162ll_rz(h);
+,
+    long long int i;
+    const float f = __bfloat162float(h);
+    const long long int max_val = (long long int)0x7fffffffffffffffULL;
+    const long long int min_val = (long long int)0x8000000000000000ULL;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__nv_bfloat16_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xFF00U) {
+        // NaN
+        i = min_val;
+    } else if (f >= static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        i = static_cast<long long int>(f);
+    }
+    return i;
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ long long int __bfloat162ll_rd(const __nv_bfloat16 h)
+{
+    long long int i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("cvt.rmi.s64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    asm("cvt.rmi.s64.f32 %0, %1;" : "=l"(i) : "f"(f));
+)
+    return i;
+}
+__CUDA_BF16_DECL__ long long int __bfloat162ll_ru(const __nv_bfloat16 h)
+{
+    long long int i;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("cvt.rpi.s64.bf16 %0, %1;" : "=l"(i) : "h"(__BFLOAT16_TO_CUS(h)));
+,
+    const float f = __bfloat162float(h);
+    asm("cvt.rpi.s64.f32 %0, %1;" : "=l"(i) : "f"(f));
+)
+    return i;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_ll2bfloat16_rn(const long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rn.bf16.s64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    const float ru = __ll2float_ru(i);
+    const float rd = __ll2float_rd(i);
+    float rz = __ll2float_rz(i);
+    if (ru != rd) {
+        rz = __uint_as_float(__float_as_uint(rz) | 1U);
+    }
+    return __float2bfloat16_rn(rz);
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_rn(const long long int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_ll2bfloat16_rn(i);
+,
+    float f = static_cast<float>(i);
+    const long long int lf = static_cast<long long int>(f);
+    unsigned int u = __internal_float_as_uint(f);
+
+    if ((f > 0.0f) && (lf > i)) {
+        u--;
+    }
+    if ((f < 0.0f) && (lf < i)) {
+        u--;
+    }
+    if (lf != i) {
+        u |= 1U;
+    }
+
+    f = __internal_uint_as_float(u);
+    return __float2bfloat16_rn(f);
+)
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_rz(const long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rz.bf16.s64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    return __float2bfloat16_rz(__ll2float_rz(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_rd(const long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rm.bf16.s64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    return __float2bfloat16_rd(__ll2float_rd(i));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ll2bfloat16_ru(const long long int i)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 h;
+    asm("cvt.rp.bf16.s64 %0, %1;" : "=h"(__BFLOAT16_TO_US(h)) : "l"(i));
+    return h;
+,
+    return __float2bfloat16_ru(__ll2float_ru(i));
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 htrunc(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 r;
+    asm("cvt.rzi.bf16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(h)));
+    return r;
+,
+    return __float2bfloat16_rz(truncf(__bfloat162float(h)));
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hceil(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 r;
+    asm("cvt.rpi.bf16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(h)));
+    return r;
+,
+    float fh = __bfloat162float(h);
+    asm( "{ cvt.rpi.f32.f32 %0, %0; }\n"
+        :"+f"(fh));
+    return __float2bfloat16_rz(fh);
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hfloor(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 r;
+    asm("cvt.rmi.bf16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(h)));
+    return r;
+,
+    float fh = __bfloat162float(h);
+    asm( "{ cvt.rmi.f32.f32 %0, %0; }\n"
+        :"+f"(fh));
+    return __float2bfloat16_rz(fh);
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hrint(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 r;
+    asm("cvt.rni.bf16.bf16 %0, %1;" : "=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(h)));
+    return r;
+,
+    return __float2bfloat16_rz(rintf(__bfloat162float(h)));
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat162 h2trunc(const __nv_bfloat162 h)
+{
+    const __nv_bfloat16 low  = htrunc(h.x);
+    const __nv_bfloat16 high = htrunc(h.y);
+    return __nv_bfloat162(low, high);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2ceil(const __nv_bfloat162 h)
+{
+    const __nv_bfloat16 low  = hceil(h.x);
+    const __nv_bfloat16 high = hceil(h.y);
+    return __nv_bfloat162(low, high);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2floor(const __nv_bfloat162 h)
+{
+    const __nv_bfloat16 low  = hfloor(h.x);
+    const __nv_bfloat16 high = hfloor(h.y);
+    return __nv_bfloat162(low, high);
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat162 h2rint(const __nv_bfloat162 h)
+{
+    return __halves2bfloat162(hrint(__low2bfloat16(h)), hrint(__high2bfloat16(h)));
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __lows2bfloat162(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 alow,ahigh,blow,bhigh;\n"
+        "  mov.b32 {alow,ahigh}, %1;\n"
+        "  mov.b32 {blow,bhigh}, %2;\n"
+        "  mov.b32 %0, {alow,blow};}\n" : "=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)), "r"(__BFLOAT162_TO_CUI(b)));
+,
+    val.x = a.x;
+    val.y = b.x;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __highs2bfloat162(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 alow,ahigh,blow,bhigh;\n"
+        "  mov.b32 {alow,ahigh}, %1;\n"
+        "  mov.b32 {blow,bhigh}, %2;\n"
+        "  mov.b32 %0, {ahigh,bhigh};}\n" : "=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)), "r"(__BFLOAT162_TO_CUI(b)));
+,
+    val.x = a.y;
+    val.y = b.y;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __low2bfloat16(const __nv_bfloat162 a)
+{
+    __nv_bfloat16 ret;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 low,high;\n"
+        " mov.b32 {low,high}, %1;\n"
+        " mov.b16 %0, low;}" : "=h"(__BFLOAT16_TO_US(ret)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    ret = a.x;
+)
+    return ret;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ int __hisinf(const __nv_bfloat16 a)
+{
+    int retval;
+    const __nv_bfloat16_raw araw = __nv_bfloat16_raw(a);
+    if (araw.x == 0xFF80U) {
+        retval = -1;
+    } else if (araw.x == 0x7F80U) {
+        retval = 1;
+    } else {
+        retval = 0;
+    }
+    return retval;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __low2bfloat162(const __nv_bfloat162 a)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  mov.b32 %0, {low,low};}\n" : "=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    val.x = a.x;
+    val.y = a.x;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __high2bfloat162(const __nv_bfloat162 a)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  mov.b32 %0, {high,high};}\n" : "=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    val.x = a.y;
+    val.y = a.y;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __high2bfloat16(const __nv_bfloat162 a)
+{
+    __nv_bfloat16 ret;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 low,high;\n"
+        " mov.b32 {low,high}, %1;\n"
+        " mov.b16 %0, high;}" : "=h"(__BFLOAT16_TO_US(ret)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    ret = a.y;
+)
+    return ret;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __halves2bfloat162(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  mov.b32 %0, {%1,%2};}\n"
+        : "=r"(__BFLOAT162_TO_UI(val)) : "h"(__BFLOAT16_TO_CUS(a)), "h"(__BFLOAT16_TO_CUS(b)));
+,
+    val.x = a;
+    val.y = b;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __bfloat162bfloat162(const __nv_bfloat16 a)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  mov.b32 %0, {%1,%1};}\n"
+        : "=r"(__BFLOAT162_TO_UI(val)) : "h"(__BFLOAT16_TO_CUS(a)));
+,
+    val.x = a;
+    val.y = a;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __lowhigh2highlow(const __nv_bfloat162 a)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .b16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  mov.b32 %0, {high,low};}\n" : "=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    val.x = a.y;
+    val.y = a.x;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ short int __bfloat16_as_short(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return static_cast<short int>(__BFLOAT16_TO_CUS(h));
+,
+    return static_cast<short int>(__nv_bfloat16_raw(h).x);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned short int __bfloat16_as_ushort(const __nv_bfloat16 h)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __BFLOAT16_TO_CUS(h);
+,
+    return __nv_bfloat16_raw(h).x;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __short_as_bfloat16(const short int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __nv_bfloat16 h;
+    __BFLOAT16_TO_US(h) = static_cast<unsigned short int>(i);
+    return h;
+,
+    __nv_bfloat16_raw hr;
+    hr.x = static_cast<unsigned short int>(i);
+    return __nv_bfloat16(hr);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __ushort_as_bfloat16(const unsigned short int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __nv_bfloat16 h;
+    __BFLOAT16_TO_US(h) = i;
+    return h;
+,
+    __nv_bfloat16_raw hr;
+    hr.x = i;
+    return __nv_bfloat16(hr);
+)
+}
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300))) || defined(_NVHPC_CUDA)
+/******************************************************************************
+*                           __nv_bfloat16, __nv_bfloat162 warp shuffle        *
+******************************************************************************/
+#define __SHUFFLE_SYNC_BFLOAT162_MACRO(name, var, delta, c, mask) /* do */ {\
+   __nv_bfloat162 r; \
+   asm volatile ("{" __CUDA_BF16_STRINGIFY(name) " %0,%1,%2,%3,%4;\n}" \
+       :"=r"(__BFLOAT162_TO_UI(r)): "r"(__BFLOAT162_TO_CUI(var)), "r"(delta), "r"(c), "r"(mask)); \
+   return r; \
+} /* while(0) */
+
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_sync(const unsigned int mask, const __nv_bfloat162 var, const int srcLane, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_SYNC_BFLOAT162_MACRO(shfl.sync.idx.b32, var, srcLane, c, mask)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_up_sync(const unsigned int mask, const __nv_bfloat162 var, const unsigned int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = (warp_size - static_cast<unsigned>(width)) << 8U;
+    __SHUFFLE_SYNC_BFLOAT162_MACRO(shfl.sync.up.b32, var, delta, c, mask)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_down_sync(const unsigned int mask, const __nv_bfloat162 var, const unsigned int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_SYNC_BFLOAT162_MACRO(shfl.sync.down.b32, var, delta, c, mask)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __shfl_xor_sync(const unsigned int mask, const __nv_bfloat162 var, const int laneMask, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_SYNC_BFLOAT162_MACRO(shfl.sync.bfly.b32, var, laneMask, c, mask)
+}
+
+#undef __SHUFFLE_SYNC_BFLOAT162_MACRO
+
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_sync(const unsigned int mask, const __nv_bfloat16 var, const int srcLane, const int width)
+{
+    const __nv_bfloat162 temp1 = __halves2bfloat162(var, var);
+    const __nv_bfloat162 temp2 = __shfl_sync(mask, temp1, srcLane, width);
+    return __low2bfloat16(temp2);
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_up_sync(const unsigned int mask, const __nv_bfloat16 var, const unsigned int delta, const int width)
+{
+    const __nv_bfloat162 temp1 = __halves2bfloat162(var, var);
+    const __nv_bfloat162 temp2 = __shfl_up_sync(mask, temp1, delta, width);
+    return __low2bfloat16(temp2);
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_down_sync(const unsigned int mask, const __nv_bfloat16 var, const unsigned int delta, const int width)
+{
+    const __nv_bfloat162 temp1 = __halves2bfloat162(var, var);
+    const __nv_bfloat162 temp2 = __shfl_down_sync(mask, temp1, delta, width);
+    return __low2bfloat16(temp2);
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __shfl_xor_sync(const unsigned int mask, const __nv_bfloat16 var, const int laneMask, const int width)
+{
+    const __nv_bfloat162 temp1 = __halves2bfloat162(var, var);
+    const __nv_bfloat162 temp2 = __shfl_xor_sync(mask, temp1, laneMask, width);
+    return __low2bfloat16(temp2);
+}
+
+/******************************************************************************
+*               __nv_bfloat16 and __nv_bfloat162 __ldg,__ldcg,__ldca,__ldcs   *
+******************************************************************************/
+
+#if (defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)
+#define __LDG_PTR   "l"
+#else
+#define __LDG_PTR   "r"
+#endif /*(defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)*/
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldg(const  __nv_bfloat162 *const ptr)
+{
+    __nv_bfloat162 ret;
+    asm ("ld.global.nc.b32 %0, [%1];"  : "=r"(__BFLOAT162_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldg(const __nv_bfloat16 *const ptr)
+{
+    __nv_bfloat16 ret;
+    asm ("ld.global.nc.b16 %0, [%1];"  : "=h"(__BFLOAT16_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldcg(const  __nv_bfloat162 *const ptr)
+{
+    __nv_bfloat162 ret;
+    asm ("ld.global.cg.b32 %0, [%1];"  : "=r"(__BFLOAT162_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldcg(const __nv_bfloat16 *const ptr)
+{
+    __nv_bfloat16 ret;
+    asm ("ld.global.cg.b16 %0, [%1];"  : "=h"(__BFLOAT16_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldca(const  __nv_bfloat162 *const ptr)
+{
+    __nv_bfloat162 ret;
+    asm ("ld.global.ca.b32 %0, [%1];"  : "=r"(__BFLOAT162_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldca(const __nv_bfloat16 *const ptr)
+{
+    __nv_bfloat16 ret;
+    asm ("ld.global.ca.b16 %0, [%1];"  : "=h"(__BFLOAT16_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldcs(const  __nv_bfloat162 *const ptr)
+{
+    __nv_bfloat162 ret;
+    asm ("ld.global.cs.b32 %0, [%1];"  : "=r"(__BFLOAT162_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldcs(const __nv_bfloat16 *const ptr)
+{
+    __nv_bfloat16 ret;
+    asm ("ld.global.cs.b16 %0, [%1];"  : "=h"(__BFLOAT16_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldlu(const  __nv_bfloat162 *const ptr)
+{
+    __nv_bfloat162 ret;
+    asm ("ld.global.lu.b32 %0, [%1];"  : "=r"(__BFLOAT162_TO_UI(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldlu(const __nv_bfloat16 *const ptr)
+{
+    __nv_bfloat16 ret;
+    asm ("ld.global.lu.b16 %0, [%1];"  : "=h"(__BFLOAT16_TO_US(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __ldcv(const  __nv_bfloat162 *const ptr)
+{
+    __nv_bfloat162 ret;
+    asm ("ld.global.cv.b32 %0, [%1];"  : "=r"(__BFLOAT162_TO_UI(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __ldcv(const __nv_bfloat16 *const ptr)
+{
+    __nv_bfloat16 ret;
+    asm ("ld.global.cv.b16 %0, [%1];"  : "=h"(__BFLOAT16_TO_US(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+
+__CUDA_BF16_DECL__ void __stwb(__nv_bfloat162 *const ptr, const __nv_bfloat162 value)
+{
+    asm ("st.global.wb.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__BFLOAT162_TO_CUI(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stwb(__nv_bfloat16 *const ptr, const __nv_bfloat16 value)
+{
+    asm ("st.global.wb.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__BFLOAT16_TO_CUS(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stcg(__nv_bfloat162 *const ptr, const __nv_bfloat162 value)
+{
+    asm ("st.global.cg.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__BFLOAT162_TO_CUI(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stcg(__nv_bfloat16 *const ptr, const __nv_bfloat16 value)
+{
+    asm ("st.global.cg.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__BFLOAT16_TO_CUS(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stcs(__nv_bfloat162 *const ptr, const __nv_bfloat162 value)
+{
+    asm ("st.global.cs.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__BFLOAT162_TO_CUI(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stcs(__nv_bfloat16 *const ptr, const __nv_bfloat16 value)
+{
+    asm ("st.global.cs.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__BFLOAT16_TO_CUS(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stwt(__nv_bfloat162 *const ptr, const __nv_bfloat162 value)
+{
+    asm ("st.global.wt.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__BFLOAT162_TO_CUI(value)) : "memory");
+}
+__CUDA_BF16_DECL__ void __stwt(__nv_bfloat16 *const ptr, const __nv_bfloat16 value)
+{
+    asm ("st.global.wt.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__BFLOAT16_TO_CUS(value)) : "memory");
+}
+
+#undef __LDG_PTR
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300))) || defined(_NVHPC_CUDA) */
+/******************************************************************************
+*                             __nv_bfloat162 comparison                       *
+******************************************************************************/
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+#define __COMPARISON_OP_BFLOAT162_MACRO(name) {\
+   __nv_bfloat162 val; \
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,\
+   asm( "{ " __CUDA_BF16_STRINGIFY(name) ".bf16x2.bf16x2 %0,%1,%2;\n}" \
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b))); \
+,\
+   asm( "{.reg .b32 low_a,low_b,high_a,high_b,high_res,low_res;\n"\
+        "  and.b32 high_a, %1, 0xffff0000U;\n"\
+        "  and.b32 high_b, %2, 0xffff0000U;\n"\
+        "  shl.b32 low_a, %1, 16;\n"\
+        "  shl.b32 low_b, %2, 16;\n"\
+        "  " __CUDA_BF16_STRINGIFY(name) ".f32.f32 low_res, low_a, low_b;\n"\
+        "  " __CUDA_BF16_STRINGIFY(name) ".f32.f32 high_res, high_a, high_b;\n"\
+        "  shr.u32 low_res, low_res, 16;\n"\
+        "  or.b32  %0, high_res, low_res;}\n"\
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b))); \
+)\
+   return val; \
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_heq2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.eq)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hne2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.ne)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hle2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.le)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hge2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.ge)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hlt2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.lt)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hgt2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.gt)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hequ2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.equ)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hneu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.neu)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hleu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.leu)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hgeu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.geu)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hltu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.ltu)
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hgtu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __COMPARISON_OP_BFLOAT162_MACRO(set.gtu)
+}
+#undef __COMPARISON_OP_BFLOAT162_MACRO
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __heq2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_heq2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __heq(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __heq(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hne2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hne2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hne(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hne(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hle2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hle2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hle(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hle(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hge2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hge2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hge(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hge(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hlt2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hlt2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hlt(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hlt(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hgt2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hgt2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hgt(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hgt(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hequ2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hequ2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hequ(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hequ(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hneu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hneu2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hneu(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hneu(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hleu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hleu2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hleu(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hleu(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hgeu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hgeu2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hgeu(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hgeu(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hltu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hltu2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hltu(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hltu(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hgtu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hgtu2(a, b);
+,
+    __nv_bfloat162_raw val;
+    val.x = __hgtu(a.x, b.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hgtu(a.y, b.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    return __nv_bfloat162(val);
+)
+}
+
+/******************************************************************************
+*                __nv_bfloat162 comparison with mask output                   *
+******************************************************************************/
+#define __COMPARISON_OP_BFLOAT162_MACRO_MASK(name) {\
+   unsigned val; \
+   asm( "{ " __CUDA_BF16_STRINGIFY(name) ".u32.bf16x2 %0,%1,%2;\n}" \
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b))); \
+   return val; \
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __heq2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.eq)
+,
+    const unsigned short px = __heq(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __heq(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hne2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.ne)
+,
+    const unsigned short px = __hne(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hne(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hle2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.le)
+,
+    const unsigned short px = __hle(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hle(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hge2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.ge)
+,
+    const unsigned short px = __hge(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hge(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hlt2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.lt)
+,
+    const unsigned short px = __hlt(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hlt(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hgt2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.gt)
+,
+    const unsigned short px = __hgt(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hgt(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hequ2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.equ)
+,
+    const unsigned short px = __hequ(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hequ(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hneu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.neu)
+,
+    const unsigned short px = __hneu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hneu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hleu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.leu)
+,
+    const unsigned short px = __hleu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hleu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hgeu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.geu)
+,
+    const unsigned short px = __hgeu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hgeu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hltu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.ltu)
+,
+    const unsigned short px = __hltu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hltu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ unsigned int __hgtu2_mask(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __COMPARISON_OP_BFLOAT162_MACRO_MASK(set.gtu)
+,
+    const unsigned short px = __hgtu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hgtu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+#undef __COMPARISON_OP_BFLOAT162_MACRO_MASK
+
+#define __BOOL_COMPARISON_OP_BFLOAT162_MACRO(name) {\
+   unsigned int val; \
+   bool retval; \
+   asm( "{ " __CUDA_BF16_STRINGIFY(name) ".bf16x2.bf16x2 %0,%1,%2;\n}" \
+        :"=r"(val) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b))); \
+   if (val == 0x3F803F80U) {\
+      retval = true; \
+   } else { \
+      retval = false; \
+   }\
+   return retval;\
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbeq2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.eq)
+,
+    return (__heq(a.x, b.x) && __heq(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbne2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.ne)
+,
+    return (__hne(a.x, b.x) && __hne(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hble2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.le)
+,
+    return (__hle(a.x, b.x) && __hle(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbge2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.ge)
+,
+    return (__hge(a.x, b.x) && __hge(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hblt2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.lt)
+,
+    return (__hlt(a.x, b.x) && __hlt(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbgt2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.gt)
+,
+    return (__hgt(a.x, b.x) && __hgt(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbequ2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.equ)
+,
+    return (__hequ(a.x, b.x) && __hequ(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbneu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.neu)
+,
+    return (__hneu(a.x, b.x) && __hneu(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbleu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.leu)
+,
+    return (__hleu(a.x, b.x) && __hleu(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbgeu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.geu)
+,
+    return (__hgeu(a.x, b.x) && __hgeu(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbltu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.ltu)
+,
+    return (__hltu(a.x, b.x) && __hltu(a.y, b.y));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hbgtu2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __BOOL_COMPARISON_OP_BFLOAT162_MACRO(set.gtu)
+,
+    return (__hgtu(a.x, b.x) && __hgtu(a.y, b.y));
+)
+}
+#undef __BOOL_COMPARISON_OP_BFLOAT162_MACRO
+/******************************************************************************
+*                             __nv_bfloat16 comparison                              *
+******************************************************************************/
+#define __COMPARISON_OP_BFLOAT16_MACRO(name) {\
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,\
+   unsigned short val; \
+   asm( "{ .reg .pred __$temp3;\n" \
+        "  setp." __CUDA_BF16_STRINGIFY(name) ".bf16  __$temp3, %1, %2;\n" \
+        "  selp.u16 %0, 1, 0, __$temp3;}" \
+        : "=h"(val) : "h"(__BFLOAT16_TO_CUS(a)), "h"(__BFLOAT16_TO_CUS(b))); \
+   return (val != 0U) ? true : false; \
+,\
+   unsigned int val; \
+   asm( "{.reg .b32 a,b;\n"\
+        "  mov.b32 a, {0, %1};\n"\
+        "  mov.b32 b, {0, %2};\n"\
+        "  set." __CUDA_BF16_STRINGIFY(name) ".f32.f32 %0, a, b;}\n"\
+        :"=r"(val) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b))); \
+   return (val != 0U) ? true : false; \
+)\
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __heq(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(eq)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa == fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hne(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(ne)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa != fb) && (!__hisnan(a)) && (!__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hle(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(le)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa <= fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hge(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(ge)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa >= fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hlt(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(lt)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa < fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hgt(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(gt)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa > fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hequ(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(equ)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa == fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hneu(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(neu)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa != fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hleu(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(leu)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa <= fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hgeu(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(geu)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa >= fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hltu(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(ltu)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa < fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hgtu(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __COMPARISON_OP_BFLOAT16_MACRO(gtu)
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return (fa > fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+#undef __COMPARISON_OP_BFLOAT16_MACRO
+/******************************************************************************
+*                            __nv_bfloat162 arithmetic                        *
+******************************************************************************/
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hadd2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+   __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ add.bf16x2 %0,%1,%2; }\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+   asm( "{.reg .b32 c;\n"
+        "  mov.b32 c, 0x3f803f80U;\n"
+        "  fma.rn.bf16x2 %0,%1,c,%2;}\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+)
+   return val;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hsub2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+   __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ sub.bf16x2 %0,%1,%2; }\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+   asm( "{.reg .b32 c;\n"
+        "  mov.b32 c, 0xbf80bf80U;\n"
+        "  fma.rn.bf16x2 %0,%2,c,%1;}\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hmul2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+   __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ mul.bf16x2 %0,%1,%2; }\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+   asm( "{.reg .b32 c;\n"
+        "  mov.b32 c, 0x80008000U;\n"
+        "  fma.rn.bf16x2 %0,%1,%2,c;}\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hadd2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+   __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ add.rn.bf16x2 %0,%1,%2; }\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+   asm( "{.reg .b32 c;\n"
+        "  mov.b32 c, 0x3f803f80U;\n"
+        "  fma.rn.bf16x2 %0,%1,c,%2;}\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hsub2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+   __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ sub.rn.bf16x2 %0,%1,%2; }\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+   asm( "{.reg .b32 c;\n"
+        "  mov.b32 c, 0xbf80bf80U;\n"
+        "  fma.rn.bf16x2 %0,%2,c,%1;}\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __internal_device_hmul2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+   __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ mul.rn.bf16x2 %0,%1,%2; }\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+   asm( "{.reg .b32 c;\n"
+        "  mov.b32 c, 0x80008000U;\n"
+        "  fma.rn.bf16x2 %0,%1,%2,c;}\n"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+)
+   return val;
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hadd2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_device_hadd2(a, b);
+,
+    val.x = __hadd(a.x, b.x);
+    val.y = __hadd(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hsub2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_device_hsub2(a, b);
+,
+    val.x = __hsub(a.x, b.x);
+    val.y = __hsub(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmul2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_device_hmul2(a, b);
+,
+    val.x = __hmul(a.x, b.x);
+    val.y = __hmul(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hadd2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_device_hadd2_rn(a, b);
+,
+    val.x = __hadd_rn(a.x, b.x);
+    val.y = __hadd_rn(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hsub2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_device_hsub2_rn(a, b);
+,
+    val.x = __hsub_rn(a.x, b.x);
+    val.y = __hsub_rn(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmul2_rn(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_device_hmul2_rn(a, b);
+,
+    val.x = __hmul_rn(a.x, b.x);
+    val.y = __hmul_rn(a.y, b.y);
+)
+    return val;
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hadd2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm( "{.reg .b32 f, one, zero;\n"
+        "  mov.b32 one, 0x3f803f80U;\n"
+        "  mov.b32 zero, 0;\n"
+        "  fma.rn.bf16x2 f,%1,one,%2;\n"
+        "  max.bf16x2 f, f, zero;\n"
+        "  min.bf16x2 %0, f, one;\n}"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+    val.x = __hadd_sat(a.x, b.x);
+    val.y = __hadd_sat(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hsub2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm( "{.reg .b32 f, one, zero, mone;\n"
+        "  mov.b32 one, 0x3f803f80U;\n"
+        "  mov.b32 zero, 0;\n"
+        "  mov.b32 mone, 0xbf80bf80U;\n"
+        "  fma.rn.bf16x2 f,%2,mone,%1;\n"
+        "  max.bf16x2 f, f, zero;\n"
+        "  min.bf16x2 %0, f, one;\n}"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+    val.x = __hsub_sat(a.x, b.x);
+    val.y = __hsub_sat(a.y, b.y);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmul2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+    __nv_bfloat162 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm( "{.reg .b32 f, one, zero, mzero;\n"
+        "  mov.b32 one, 0x3f803f80U;\n"
+        "  mov.b32 zero, 0;\n"
+        "  mov.b32 mzero, 0x80008000U;\n"
+        "  fma.rn.bf16x2 f,%1,%2,mzero;\n"
+        "  max.bf16x2 f, f, zero;\n"
+        "  min.bf16x2 %0, f, one;\n}"
+        :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+,
+    val.x = __hmul_sat(a.x, b.x);
+    val.y = __hmul_sat(a.y, b.y);
+)
+    return val;
+}
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat162 __hfma2(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c)
+{
+    __nv_bfloat162 val;
+    asm( "{fma.rn.bf16x2 %0,%1,%2,%3;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)),"r"(__BFLOAT162_TO_CUI(c)));
+    return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 __hfma2_sat(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c)
+{
+    __nv_bfloat162 val;
+    asm( "{ .reg .b32 f, one, zero;\n"
+         "  mov.b32 one, 0x3f803f80U;\n"
+         "  mov.b32 zero, 0;\n"
+         "  fma.rn.bf16x2 f, %1, %2, %3;\n"
+         "  max.bf16x2 f, f, zero;\n"
+         "  min.bf16x2 %0, f, one;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)),"r"(__BFLOAT162_TO_CUI(c)));
+    return val;
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __h2div(const __nv_bfloat162 a, const __nv_bfloat162 b) {
+    __nv_bfloat16 ha, hb;
+
+    ha = __low2bfloat16(a);
+    hb = __low2bfloat16(b);
+
+    const __nv_bfloat16 v1 = __hdiv(ha, hb);
+
+    ha = __high2bfloat16(a);
+    hb = __high2bfloat16(b);
+
+    const __nv_bfloat16 v2 = __hdiv(ha, hb);
+
+    return __halves2bfloat162(v1, v2);
+}
+/******************************************************************************
+*                             __nv_bfloat16 arithmetic                        *
+******************************************************************************/
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_sm80_device_hadd(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ add.bf16 %0,%1,%2; }\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+    asm( "{.reg .b16 c;\n"
+        "  mov.b16 c, 0x3f80U;\n"
+        "  fma.rn.bf16 %0,%1,c,%2;}\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+)
+    return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_sm80_device_hsub(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+   __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ sub.bf16 %0,%1,%2; }\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+   asm( "{.reg .b16 c;\n"
+        "  mov.b16 c, 0xbf80U;\n"
+        "  fma.rn.bf16 %0,%2,c,%1;}\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_sm80_device_hmul(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+   __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ mul.bf16 %0,%1,%2; }\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+   asm( "{.reg .b16 c;\n"
+        "  mov.b16 c, 0x8000U;\n"
+        "  fma.rn.bf16 %0,%1,%2,c;}\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_sm80_device_hadd_rn(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+   __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ add.rn.bf16 %0,%1,%2; }\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+   asm( "{.reg .b16 c;\n"
+        "  mov.b16 c, 0x3f80U;\n"
+        "  fma.rn.bf16 %0,%1,c,%2;}\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_sm80_device_hsub_rn(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+   __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ sub.rn.bf16 %0,%1,%2; }\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+   asm( "{.reg .b16 c;\n"
+        "  mov.b16 c, 0xbf80U;\n"
+        "  fma.rn.bf16 %0,%2,c,%1;}\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+)
+   return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_sm80_device_hmul_rn(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+   __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+   asm( "{ mul.rn.bf16 %0,%1,%2; }\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+   asm( "{.reg .b16 c;\n"
+        "  mov.b16 c, 0x8000U;\n"
+        "  fma.rn.bf16 %0,%1,%2,c;}\n"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+)
+   return val;
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_hadd(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_sm80_device_hadd(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    // avoid ftz in device code
+    val = __float2bfloat16(__fmaf_ieee_rn(fa, 1.0f, fb));
+)
+    return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_hsub(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_sm80_device_hsub(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    // avoid ftz in device code
+    val = __float2bfloat16(__fmaf_ieee_rn(fb, -1.0f, fa));
+)
+    return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_hmul(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    val = __internal_sm80_device_hmul(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    // avoid ftz in device code
+    val = __float2bfloat16(__fmaf_ieee_rn(fa, fb, -0.0f));
+)
+    return val;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hadd(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hadd(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return __float2bfloat16(fa + fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hsub(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hsub(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return __float2bfloat16(fa - fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmul(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hmul(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return __float2bfloat16(fa * fb);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hadd_rn(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    return __internal_sm80_device_hadd_rn(a, b);
+,
+    return __hadd(a, b);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hsub_rn(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    return __internal_sm80_device_hsub_rn(a, b);
+,
+    return __hsub(a, b);
+
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmul_rn(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    return __internal_sm80_device_hmul_rn(a, b);
+,
+    return __hmul(a, b);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hadd_sat(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm( "{ .reg .b16 f, one, zero;\n"
+         "  mov.b16 one, 0x3f80U;\n"
+         "  mov.b16 zero, 0;\n"
+         "  fma.rn.bf16 f, %1, one, %2;\n"
+         "  max.bf16 f, f, zero;\n"
+         "  min.bf16 %0, f, one;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+    val = __hmin(__hmax(__hadd(a, b), CUDART_ZERO_BF16), CUDART_ONE_BF16);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hsub_sat(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm( "{ .reg .b16 f, one, zero, mone;\n"
+         "  mov.b16 one, 0x3f80U;\n"
+         "  mov.b16 zero, 0;\n"
+         "  mov.b16 mone, 0xbf80U;\n"
+         "  fma.rn.bf16 f, %2, mone, %1;\n"
+         "  max.bf16 f, f, zero;\n"
+         "  min.bf16 %0, f, one;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+    val = __hmin(__hmax(__hsub(a, b), CUDART_ZERO_BF16), CUDART_ONE_BF16);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmul_sat(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+    __nv_bfloat16 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm( "{ .reg .b16 f, one, zero, mzero;\n"
+         "  mov.b16 one, 0x3f80U;\n"
+         "  mov.b16 zero, 0;\n"
+         "  mov.b16 mzero, 0x8000U;\n"
+         "  fma.rn.bf16 f, %1, %2, mzero;\n"
+         "  max.bf16 f, f, zero;\n"
+         "  min.bf16 %0, f, one;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+,
+    val = __hmin(__hmax(__hmul(a, b), CUDART_ZERO_BF16), CUDART_ONE_BF16);
+)
+    return val;
+}
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __hfma(const __nv_bfloat16 a, const __nv_bfloat16 b, const __nv_bfloat16 c)
+{
+    __nv_bfloat16 val;
+    asm( "{fma.rn.bf16 %0,%1,%2,%3;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)),"h"(__BFLOAT16_TO_CUS(c)));
+    return val;
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __hfma_sat(const __nv_bfloat16 a, const __nv_bfloat16 b, const __nv_bfloat16 c)
+{
+    __nv_bfloat16 val;
+    asm( "{ .reg .b16 f, one, zero;\n"
+         "  mov.b16 one, 0x3f80U;\n"
+         "  mov.b16 zero, 0;\n"
+         "  fma.rn.bf16 f, %1, %2, %3;\n"
+         "  max.bf16 f, f, zero;\n"
+         "  min.bf16 %0, f, one;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)),"h"(__BFLOAT16_TO_CUS(c)));
+    return val;
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+#define __BINARY_OP_BFLOAT16_MACRO(name) /* do */ {\
+   __nv_bfloat16 val; \
+   asm( "{.reg .b32 a,b,res;\n"\
+        "  mov.b32 a, {0,%1};\n"\
+        "  mov.b32 b, {0,%2};\n"\
+        "  " __CUDA_BF16_STRINGIFY(name) ".f32 res, a, b;\n"\
+        "  cvt.rn.bf16.f32 %0, res;}\n"\
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b))); \
+   return val; \
+} /* while(0) */
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_hdiv(const __nv_bfloat16 a, const __nv_bfloat16 b) {
+    const float two_126 =  __uint_as_float(0x7E800000U) ; //2^126
+    const float a_f = __bfloat162float(a);
+    float b_f = __bfloat162float(b);
+    float ans;
+    bool b_big = (fabsf(b_f) >= two_126);
+    if(b_big){b_f *= 0.25f;}
+
+    // f32 div approximation. Good enough for c-r bfloat div.
+    asm("{ div.approx.f32 %0, %1, %2; }" : "=f"(ans) : "f"(a_f), "f"(b_f));
+
+    // Prevent ftz:
+    if(b_big){ans = __fmaf_ieee_rn(ans, 0.25f, -0.0f);} 
+    return __float2bfloat16(ans);
+}
+
+#undef __BINARY_OP_BFLOAT16_MACRO
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hdiv(const __nv_bfloat16 a, const __nv_bfloat16 b) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hdiv(a, b);
+,
+    const float fa = __bfloat162float(a);
+    const float fb = __bfloat162float(b);
+    return __float2bfloat16(fa / fb);
+)
+}
+
+/******************************************************************************
+*                             __nv_bfloat162 functions                        *
+******************************************************************************/
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __hsin_internal(const __nv_bfloat16 a) {
+    float f = __bfloat162float(a);
+    float r = sinf(f);
+    // Detect compile-time FTZ setting:
+    // if subnormal constant is not flushed to zero at compile-time, then
+    // ftz=off, and it is safe to return result of sinf()
+    // Otherwise, ftz=on, then sinf() result is valid for non-flushed
+    // values, and subnormal input is returned unchanged via else
+    // branch.
+    if ((__uint_as_float(0x00000001U) > 0.0f) || (f != 0.0f))
+    {
+        f = r;
+    }
+    return __float2bfloat16_rn(f);
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hsin(const __nv_bfloat16 a) {
+    return __hsin_internal(a);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2sin(const __nv_bfloat162 a) {
+    const __nv_bfloat16 l = __low2bfloat16(a);
+    const __nv_bfloat16 h = __high2bfloat16(a);
+    return __halves2bfloat162(__hsin_internal(l), __hsin_internal(h));
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 __hcos_internal(const __nv_bfloat16 a) {
+    float f = __bfloat162float(a);
+    f = cosf(f);
+    return __float2bfloat16_rn(f);
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hcos(const __nv_bfloat16 a) {
+    return __hcos_internal(a);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2cos(const __nv_bfloat162 a) {
+    const __nv_bfloat16 l = __low2bfloat16(a);
+    const __nv_bfloat16 h = __high2bfloat16(a);
+    return __halves2bfloat162(__hcos_internal(l), __hcos_internal(h));
+}
+
+__CUDA_BF16_DECL__ float __internal_device_fast_bf16exp(const float x)
+{
+    const float log2e_up = __uint_as_float(0x3FB8AA3CU);
+    float fa = x * log2e_up;
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fa));
+    return fa;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 hexp(const __nv_bfloat16 a) {
+    float fa = __bfloat162float(a);
+    fa = __internal_device_fast_bf16exp(fa);
+    return __float2bfloat16_rn(fa);
+}
+
+#define __APPROX_FCAST2(fun) /* do */ {\
+   __nv_bfloat162 val;\
+   asm("{.reg.b16         hl, hu;         \n"\
+                " .reg.b32         fl, fu;         \n"\
+                "  mov.b32         {hl, hu}, %1;   \n"\
+                "  mov.b32         fl, {0,hl};     \n"\
+                "  mov.b32         fu, {0,hu};     \n"\
+                "  " __CUDA_BF16_STRINGIFY(fun) ".approx.f32   fl, fl;     \n"\
+                "  " __CUDA_BF16_STRINGIFY(fun) ".approx.f32   fu, fu;     \n"\
+                "  cvt.rn.bf16.f32    hl, fl;     \n"\
+                "  cvt.rn.bf16.f32    hu, fu;     \n"\
+                "  mov.b32         %0, {hl, hu};   \n"\
+                "}":"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));       \
+   return val;\
+} /* while(0) */
+#define __BF16_SPEC_CASE2(i,r, spc, ulp) \
+   "{.reg.b32 spc, ulp, p;\n"\
+   "  mov.b32 spc," __CUDA_BF16_STRINGIFY(spc) ";\n"\
+   "  mov.b32 ulp," __CUDA_BF16_STRINGIFY(ulp) ";\n"\
+   "  set.eq.f16x2.f16x2 p," __CUDA_BF16_STRINGIFY(i) ", spc;\n"\
+   "  fma.rn.bf16x2 " __CUDA_BF16_STRINGIFY(r) ",p,ulp," __CUDA_BF16_STRINGIFY(r) ";\n}\n"
+
+__CUDA_BF16_DECL__ __nv_bfloat162 h2exp(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm("{.reg.b16         hl, hu;         \n"
+        " .reg.b32         h,r,fl,fu, C;   \n"
+        "  mov.b32         {hl, hu}, %1;   \n"
+        "  mov.b32         h, %1;          \n"
+        "  mov.b32         fl, {0,hl};     \n"
+        "  mov.b32         fu, {0,hu};     \n"
+        "  mov.b32         C, 0x3FB8AA3CU;  \n"
+        "  mul.f32         fl,fl,C;        \n"
+        "  mul.f32         fu,fu,C;        \n"
+        "  ex2.approx.f32      fl, fl;     \n"
+        "  ex2.approx.f32      fu, fu;     \n"
+        "  cvt.rn.bf16.f32    hl, fl;     \n"
+        "  cvt.rn.bf16.f32    hu, fu;     \n"
+        "  mov.b32         r, {hl, hu};    \n"
+        "  mov.b32         %0, r;  \n"
+        "}":"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));
+    return val;
+,
+    return __floats2bfloat162_rn( __internal_device_fast_bf16exp(__low2float(a)), __internal_device_fast_bf16exp(__high2float(a)) );
+)
+}
+
+__CUDA_BF16_DECL__ float __internal_device_tanhf_noftz(const float x)
+{
+    float f = x;
+    float r = tanhf(x);
+    // Detect compile-time FTZ setting:
+    // if subnormal constant is not flushed to zero at compile-time, then
+    // ftz=off, and it is safe to return result of tanhf()
+    // Otherwise, ftz=on, then tanhf() result is valid for non-flushed
+    // values, and subnormal input is returned unchanged via else
+    // branch.
+    if ((__uint_as_float(0x00000001U) > 0.0f) || (f != 0.0f))
+    {
+        f = r;
+    }
+    return f;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 htanh(const __nv_bfloat16 a) {
+    float f = __bfloat162float(a);
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_75,
+    asm("{ tanh.approx.f32 %0, %0; }" : "+f"(f));
+,
+    f = __internal_device_tanhf_noftz(f);
+)
+    __nv_bfloat16 h = __float2bfloat16_rn(f);
+    return h;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2tanh(const __nv_bfloat162 a) {
+    float2 f = __bfloat1622float2(a);
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_75,
+    asm("{ tanh.approx.f32 %0, %0; }" : "+f"(f.x));
+    asm("{ tanh.approx.f32 %0, %0; }" : "+f"(f.y));
+,
+    f.x = __internal_device_tanhf_noftz(f.x);
+    f.y = __internal_device_tanhf_noftz(f.y);
+)
+    __nv_bfloat162 h = __float22bfloat162_rn(f);
+    return h;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 htanh_approx(const __nv_bfloat16 a) {
+    __nv_bfloat16 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16_raw hr = (__nv_bfloat16_raw)a;
+    asm("tanh.approx.bf16 %0, %0;" : "+h"(hr.x));
+    r = (__nv_bfloat16)hr;
+,
+    r = htanh(a);
+)
+    return r;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2tanh_approx(const __nv_bfloat162 a) {
+    __nv_bfloat162 res;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("tanh.approx.bf16x2 %0, %1;" : "=r"(__BFLOAT162_TO_UI(res)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    res = h2tanh(a);
+)
+    return res;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 hexp2(const __nv_bfloat16 a) {
+    float fa = __bfloat162float(a);
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fa));
+    return __float2bfloat16_rn(fa);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2exp2(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __APPROX_FCAST2(ex2)
+,
+    float fl = __low2float(a);
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fl));
+    float fh = __high2float(a);
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fh));
+    return __floats2bfloat162_rn( fl, fh );
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 hexp10(const __nv_bfloat16 a) {
+    const float log10_2 = __uint_as_float(0x40549A78U);
+    float fa = __bfloat162float(a) * log10_2;
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fa));
+    __nv_bfloat16 r = __float2bfloat16_rn(fa);
+    __nv_bfloat16_raw araw = static_cast<__nv_bfloat16_raw>(a);
+    if (araw.x == (unsigned short)0xBC95U)
+    {
+        araw.x = 0x3f75U;
+        r = static_cast<__nv_bfloat16>(araw);
+    }
+    return r;
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2exp10(const __nv_bfloat162 a) {
+    __nv_bfloat162 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{.reg.b16         hl, hu;         \n"
+        " .reg.b32         h,r,fl,fu, C;   \n"
+        "  mov.b32         {hl, hu}, %1;   \n"
+        "  mov.b32         fl, {0,hl};     \n"
+        "  mov.b32         fu, {0,hu};     \n"
+        "  mov.b32         C, 0x40549A78U;  \n"
+        "  mul.f32         fl,fl,C;        \n"
+        "  mul.f32         fu,fu,C;        \n"
+        "  ex2.approx.f32      fl, fl;     \n"
+        "  ex2.approx.f32      fu, fu;     \n"
+        "  cvt.rn.bf16.f32    hl, fl;     \n"
+        "  cvt.rn.bf16.f32    hu, fu;     \n"
+        "  mov.b32         r, {hl, hu};    \n"
+        __BF16_SPEC_CASE2(%1, r, 0xBC95BC95U,0xBF00BF00U)
+        "  mov.b32         %0, r;  \n"
+        "}":"=r"(__BFLOAT162_TO_UI(r)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    const float log10_2 = __uint_as_float(0x40549A78U);
+    float fl = __low2float(a) * log10_2;
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fl));
+
+    float fh = __high2float(a) * log10_2;
+    asm("{ ex2.approx.f32 %0, %0; }" : "+f"(fh));
+
+    r = __floats2bfloat162_rn( fl, fh );
+
+    const __nv_bfloat162_raw araw = static_cast<__nv_bfloat162_raw>(a);
+    if (araw.x == (unsigned short)0xBC95U)
+    {
+        __nv_bfloat16_raw raw_fix;
+        raw_fix.x = (unsigned short)0x3f75U;
+        r.x = static_cast<__nv_bfloat16>(raw_fix);
+    }
+    if (araw.y == (unsigned short)0xBC95U)
+    {
+        __nv_bfloat16_raw raw_fix;
+        raw_fix.x = (unsigned short)0x3f75U;
+        r.y = static_cast<__nv_bfloat16>(raw_fix);
+    }
+)
+    return r;
+}
+
+__CUDA_BF16_DECL__ float __internal_device_fast_bf16log2(float x)
+{
+    asm("{ lg2.approx.f32 %0, %0; }" : "+f"(x));
+    return x;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 hlog2(const __nv_bfloat16 a) {
+    float fa = __bfloat162float(a);
+    fa = __internal_device_fast_bf16log2(fa);
+    return __float2bfloat16_rn(fa);
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat162 h2log2(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __APPROX_FCAST2(lg2)
+,
+    float fl = __low2float(a);
+    fl = __internal_device_fast_bf16log2(fl);
+    float fh = __high2float(a);
+    fh = __internal_device_fast_bf16log2(fh);
+    return __floats2bfloat162_rn( fl, fh );
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 hlog(const __nv_bfloat16 a) {
+    const float flt_ln2 = __uint_as_float(0x3f317218U);
+    float fa = __bfloat162float(a);
+    fa = __internal_device_fast_bf16log2(fa);
+    fa = fa * flt_ln2;
+    return __float2bfloat16_rn(fa);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2log(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm("{.reg.b16         hl, hu;             \n"
+        " .reg.b32         r, fl, fu, C, h;    \n"
+        "  mov.b32         {hl, hu}, %1;       \n"
+        "  mov.b32         h, %1;              \n"
+        "  mov.b32         fl, {0,hl};         \n"
+        "  mov.b32         fu, {0,hu};         \n"
+        "  lg2.approx.f32      fl, fl;         \n"
+        "  lg2.approx.f32      fu, fu;         \n"
+        "  mov.b32         C, 0x3f317218U;     \n"
+        "  mul.f32         fl,fl,C;            \n"
+        "  mul.f32         fu,fu,C;            \n"
+        "  cvt.rn.bf16.f32    hl, fl;         \n"
+        "  cvt.rn.bf16.f32    hu, fu;         \n"
+        "  mov.b32         r, {hl, hu};        \n"
+        "  mov.b32         %0, r;              \n"
+        "}":"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));
+    return val;
+,
+    const float flt_ln2 = __uint_as_float(0x3f317218U);
+
+    float fl = __low2float(a);
+    fl = __internal_device_fast_bf16log2(fl);
+    fl = fl * flt_ln2;
+
+    float fh = __high2float(a);
+    fh = __internal_device_fast_bf16log2(fh);
+    fh = fh * flt_ln2;
+
+    return __floats2bfloat162_rn( fl, fh );
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat16 hlog10(const __nv_bfloat16 a) {
+    const float flt_log10_2 = __uint_as_float(0x3E9A209BU);
+    float fa = __bfloat162float(a);
+    fa = __internal_device_fast_bf16log2(fa);
+    fa = fa * flt_log10_2;
+    return __float2bfloat16_rn(fa);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2log10(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm("{.reg.b16         hl, hu;             \n"
+        " .reg.b32         r, fl, fu, C, h;    \n"
+        "  mov.b32         {hl, hu}, %1;       \n"
+        "  mov.b32         h, %1;              \n"
+        "  mov.b32         fl, {0,hl};         \n"
+        "  mov.b32         fu, {0,hu};         \n"
+        "  lg2.approx.f32      fl, fl;         \n"
+        "  lg2.approx.f32      fu, fu;         \n"
+        "  mov.b32         C, 0x3E9A209BU;      \n"
+        "  mul.f32         fl,fl,C;            \n"
+        "  mul.f32         fu,fu,C;            \n"
+        "  cvt.rn.bf16.f32    hl, fl;         \n"
+        "  cvt.rn.bf16.f32    hu, fu;         \n"
+        "  mov.b32         r, {hl, hu};        \n"
+        "  mov.b32         %0, r;              \n"
+        "}":"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)));
+    return val;
+,
+    const float flt_log10_2 = __uint_as_float(0x3E9A209BU);
+
+    float fl = __low2float(a);
+    fl = __internal_device_fast_bf16log2(fl);
+    fl = fl * flt_log10_2;
+
+    float fh = __high2float(a);
+    fh = __internal_device_fast_bf16log2(fh);
+    fh = fh * flt_log10_2;
+
+    return __floats2bfloat162_rn( fl, fh );
+)
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat162 h2rcp(const __nv_bfloat162 a) {
+    float fl = __low2float(a);
+    asm("{ rcp.approx.f32 %0, %0; }" : "+f"(fl));
+    float fh = __high2float(a);
+    asm("{ rcp.approx.f32 %0, %0; }" : "+f"(fh));
+    return __floats2bfloat162_rn( fl, fh );
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hrcp(const __nv_bfloat16 a) {
+    float fa = __bfloat162float(a);
+    asm("{ rcp.approx.f32 %0, %0; }" : "+f"(fa));
+    return __float2bfloat16_rn(fa);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2rsqrt(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __APPROX_FCAST2(rsqrt)
+,
+    float fl = __low2float(a);
+    asm("{ rsqrt.approx.f32 %0, %0; }" : "+f"(fl));
+    float fh = __high2float(a);
+    asm("{ rsqrt.approx.f32 %0, %0; }" : "+f"(fh));
+    return __floats2bfloat162_rn( fl, fh );
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hrsqrt(const __nv_bfloat16 a) {
+    float fa = __bfloat162float(a);
+    asm("{ rsqrt.approx.f32 %0, %0; }" : "+f"(fa));
+    return __float2bfloat16_rn(fa);
+}
+__CUDA_BF16_DECL__ __nv_bfloat162 h2sqrt(const __nv_bfloat162 a) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __APPROX_FCAST2(sqrt)
+,
+    float fl = __low2float(a);
+    asm("{ sqrt.approx.f32 %0, %0; }" : "+f"(fl));
+    float fh = __high2float(a);
+    asm("{ sqrt.approx.f32 %0, %0; }" : "+f"(fh));
+    return __floats2bfloat162_rn( fl, fh );
+)
+}
+__CUDA_BF16_DECL__ __nv_bfloat16 hsqrt(const __nv_bfloat16 a) {
+    float fa = __bfloat162float(a);
+    asm("{ sqrt.approx.f32 %0, %0; }" : "+f"(fa));
+    return __float2bfloat16_rn(fa);
+}
+#undef __APPROX_FCAST2
+#undef __BF16_SPEC_CASE2
+
+__CUDA_BF16_DECL__ bool __internal_device_hisnan(const __nv_bfloat16 a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 r;
+    asm("{set.nan.bf16.bf16 %0,%1,%1;\n}"
+        :"=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(a)));
+    return __BFLOAT16_TO_CUS(r) != 0U;
+,
+    unsigned int r;
+    asm( "{.reg .b32 a;\n"
+         "  mov.b32 a, {0,%1};\n"
+         "  set.nan.f32.f32 %0, a, a;}\n"
+         :"=r"(r) : "h"(__BFLOAT16_TO_CUS(a)));
+    return r != 0U;
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hisnan2(const __nv_bfloat162 a)
+{
+    __nv_bfloat162 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{set.nan.bf16x2.bf16x2 %0,%1,%1;\n}"
+        :"=r"(__BFLOAT162_TO_UI(r)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    __nv_bfloat162_raw val;
+    val.x = __hisnan(a.x) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    val.y = __hisnan(a.y) ? (unsigned short)0x3F80U : (unsigned short)0U;
+    r = __nv_bfloat162(val);
+)
+    return r;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ bool __hisnan(const __nv_bfloat16 a)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hisnan(a);
+,
+    const __nv_bfloat16_raw hr = static_cast<__nv_bfloat16_raw>(a);
+    return ((hr.x & 0x7FFFU) > 0x7F80U);
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hneg2(const __nv_bfloat162 a)
+{
+    __nv_bfloat162 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{neg.bf16x2 %0,%1;\n}"
+        :"=r"(__BFLOAT162_TO_UI(r)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    r.x = __hneg(a.x);
+    r.y = __hneg(a.y);
+)
+    return r;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __internal_device_hneg(const __nv_bfloat16 a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat16 r;
+    asm("{neg.bf16 %0,%1;\n}"
+        :"=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(a)));
+    return r;
+,
+    const float fa = __bfloat162float(a);
+    return __float2bfloat16(__fmaf_ieee_rn(fa, -1.0f, -0.0f));
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hneg(const __nv_bfloat16 a)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hneg(a);
+,
+    const float fa = __bfloat162float(a);
+    return __float2bfloat16(-fa);
+)
+}
+
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __habs2(const __nv_bfloat162 a)
+{
+    __nv_bfloat162 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{abs.bf16x2 %0,%1;\n}"
+        :"=r"(__BFLOAT162_TO_UI(r)) : "r"(__BFLOAT162_TO_CUI(a)));
+,
+    r.x = __habs(a.x);
+    r.y = __habs(a.y);
+)
+    return r;
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __habs(const __nv_bfloat16 a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat16 r;
+    asm("{abs.bf16 %0,%1;\n}"
+        :"=h"(__BFLOAT16_TO_US(r)) : "h"(__BFLOAT16_TO_CUS(a)));
+    return r;
+,
+    __nv_bfloat16_raw abs_a_raw = static_cast<__nv_bfloat16_raw>(a);
+    abs_a_raw.x &= (unsigned short)0x7FFFU;
+    if (abs_a_raw.x > (unsigned short)0x7F80U)
+    {
+        // return canonical NaN
+        abs_a_raw.x = (unsigned short)0x7FFFU;
+    }
+    return static_cast<__nv_bfloat16>(abs_a_raw);
+)
+}
+
+/******************************************************************************
+*                             __nv_bfloat16 arithmetic                             *
+******************************************************************************/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmax(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat16 val;
+    asm( "{ max.bf16 %0,%1,%2;\n}"
+        :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+    return val;
+,
+    __nv_bfloat16 maxval;
+
+    maxval = (__hge(a, b) || __hisnan(b)) ? a : b;
+
+    if (__hisnan(maxval))
+    {
+        // if both inputs are NaN, return canonical NaN
+        maxval = CUDART_NAN_BF16;
+    }
+    else if (__heq(a, b))
+    {
+        // hmax(+0.0, -0.0) = +0.0
+        // unsigned compare 0x8000U > 0x0000U
+        __nv_bfloat16_raw ra = __nv_bfloat16_raw(a);
+        __nv_bfloat16_raw rb = __nv_bfloat16_raw(b);
+        maxval = (ra.x > rb.x) ? b : a;
+    }
+
+    return maxval;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmin(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat16 val;
+    asm( "{ min.bf16 %0,%1,%2;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+    return val;
+,
+    __nv_bfloat16 minval;
+
+    minval = (__hle(a, b) || __hisnan(b)) ? a : b;
+
+    if (__hisnan(minval))
+    {
+        // if both inputs are NaN, return canonical NaN
+        minval = CUDART_NAN_BF16;
+    }
+    else if (__heq(a, b))
+    {
+        // hmin(+0.0, -0.0) = -0.0
+        // unsigned compare 0x8000U > 0x0000U
+        __nv_bfloat16_raw ra = __nv_bfloat16_raw(a);
+        __nv_bfloat16_raw rb = __nv_bfloat16_raw(b);
+        minval = (ra.x > rb.x) ? a : b;
+    }
+
+    return minval;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmax_nan(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat16 val;
+    asm( "{ max.NaN.bf16 %0,%1,%2;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+    return val;
+,
+    __nv_bfloat16 maxval;
+
+    if (__hisnan(a) || __hisnan(b))
+    {
+        // if either input is NaN, return canonical NaN
+        maxval = CUDART_NAN_BF16;
+    }
+    else
+    {
+        maxval = __hge(a, b) ? a : b;
+    }
+
+    return maxval;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat16 __hmin_nan(const __nv_bfloat16 a, const __nv_bfloat16 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat16 val;
+    asm( "{ min.NaN.bf16 %0,%1,%2;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)));
+    return val;
+,
+    __nv_bfloat16 minval;
+
+    if (__hisnan(a) || __hisnan(b))
+    {
+        // if either input is NaN, return canonical NaN
+        minval = CUDART_NAN_BF16;
+    }
+    else
+    {
+        minval = __hle(a, b) ? a : b;
+    }
+
+    return minval;
+)
+}
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 __hfma_relu(const __nv_bfloat16 a, const __nv_bfloat16 b, const __nv_bfloat16 c)
+{
+    __nv_bfloat16 val;
+    asm( "{ fma.rn.relu.bf16 %0,%1,%2,%3;\n}"
+         :"=h"(__BFLOAT16_TO_US(val)) : "h"(__BFLOAT16_TO_CUS(a)),"h"(__BFLOAT16_TO_CUS(b)),"h"(__BFLOAT16_TO_CUS(c)));
+    return val;
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+/******************************************************************************
+*                            __nv_bfloat162 arithmetic                             *
+******************************************************************************/
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmax2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm( "{ max.bf16x2 %0,%1,%2;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+    return val;
+,
+    __nv_bfloat162 val;
+    val.x = __hmax(a.x, b.x);
+    val.y = __hmax(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmin2(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm( "{ min.bf16x2 %0,%1,%2;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+    return val;
+,
+    __nv_bfloat162 val;
+    val.x = __hmin(a.x, b.x);
+    val.y = __hmin(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmax2_nan(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm( "{ max.NaN.bf16x2 %0,%1,%2;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+    return val;
+,
+    __nv_bfloat162 val;
+    val.x = __hmax_nan(a.x, b.x);
+    val.y = __hmax_nan(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_BF16_DECL__ __nv_bfloat162 __hmin2_nan(const __nv_bfloat162 a, const __nv_bfloat162 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __nv_bfloat162 val;
+    asm( "{ min.NaN.bf16x2 %0,%1,%2;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)));
+    return val;
+,
+    __nv_bfloat162 val;
+    val.x = __hmin_nan(a.x, b.x);
+    val.y = __hmin_nan(a.y, b.y);
+    return val;
+)
+}
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat162 __hfma2_relu(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c)
+{
+    __nv_bfloat162 val;
+    asm( "{ fma.rn.relu.bf16x2 %0,%1,%2,%3;\n}"
+         :"=r"(__BFLOAT162_TO_UI(val)) : "r"(__BFLOAT162_TO_CUI(a)),"r"(__BFLOAT162_TO_CUI(b)),"r"(__BFLOAT162_TO_CUI(c)));
+    return val;
+}
+
+__CUDA_BF16_DECL__ __nv_bfloat162 __hcmadd(const __nv_bfloat162 a, const __nv_bfloat162 b, const __nv_bfloat162 c)
+{
+    // fast version of complex multiply-accumulate
+    // (a.re, a.im) * (b.re, b.im) + (c.re, c.im)
+    // acc.re = (c.re + a.re*b.re) - a.im*b.im
+    // acc.im = (c.im + a.re*b.im) + a.im*b.re
+    __nv_bfloat16 real_tmp = __hfma(a.x, b.x, c.x);
+    __nv_bfloat16 img_tmp  = __hfma(a.x, b.y, c.y);
+    real_tmp = __hfma(__hneg(a.y), b.y, real_tmp);
+    img_tmp  = __hfma(a.y,         b.x, img_tmp);
+    return make_bfloat162(real_tmp, img_tmp);
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 800))) || defined(_NVHPC_CUDA) */
+
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/* Define __PTR for atomicAdd prototypes below, undef after done */
+#if (defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)
+#define __PTR   "l"
+#else
+#define __PTR   "r"
+#endif /*(defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)*/
+
+__CUDA_BF16_DECL__ __nv_bfloat162 atomicAdd(__nv_bfloat162 *const address, const __nv_bfloat162 val)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat162 r;
+    asm volatile ("{ atom.add.noftz.bf16x2 %0,[%1],%2; }\n"
+                  : "=r"(__BFLOAT162_TO_UI(r)) : __PTR(address), "r"(__BFLOAT162_TO_CUI(val))
+                  : "memory");
+    return r;
+,
+    unsigned int* address_as_uint = (unsigned int*)address;
+    unsigned int old = *address_as_uint;
+    unsigned int assumed;
+    do {
+        assumed = old;
+        __nv_bfloat162 new_val = __hadd2(val, *(__nv_bfloat162*)&assumed);
+        old = atomicCAS(address_as_uint, assumed, *(unsigned int*)&new_val);
+    } while (assumed != old);
+    return *(__nv_bfloat162*)&old;
+)
+}
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA)
+__CUDA_BF16_DECL__ __nv_bfloat16 atomicAdd(__nv_bfloat16 *const address, const __nv_bfloat16 val)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    __nv_bfloat16 r;
+    asm volatile ("{ atom.add.noftz.bf16 %0,[%1],%2; }\n"
+                  : "=h"(__BFLOAT16_TO_US(r))
+                  : __PTR(address), "h"(__BFLOAT16_TO_CUS(val))
+                  : "memory");
+    return r;
+,
+    unsigned short int* address_as_us = (unsigned short int*)address;
+    unsigned short int old = *address_as_us;
+    unsigned short int assumed;
+    do {
+        assumed = old;
+        old = atomicCAS(address_as_us, assumed,
+            __bfloat16_as_ushort(__hadd(val, __ushort_as_bfloat16(assumed))));
+    } while (assumed != old);
+    return __ushort_as_bfloat16(old);
+)
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA) */
+
+#undef __PTR
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+#endif /* !(defined __DOXYGEN_ONLY__) */
+
+#endif /* defined(__cplusplus) */
+
+#undef __CUDA_HOSTDEVICE_BF16_DECL__
+#undef __CUDA_BF16_DECL__
+#undef __CUDA_BF16_CONSTEXPR__
+ 
+#if defined(__CPP_VERSION_AT_LEAST_11_BF16)
+#undef __CPP_VERSION_AT_LEAST_11_BF16
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_BF16) */
+
+#undef ___CUDA_BF16_STRINGIFY_INNERMOST
+#undef __CUDA_BF16_STRINGIFY
+
+#endif /* end of include guard: __CUDA_BF16_HPP__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_device_runtime_api.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_device_runtime_api.h
new file mode 100644
index 0000000000000000000000000000000000000000..07b7ec75b43325ea76b50f718822b1caf82cc9da
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_device_runtime_api.h
@@ -0,0 +1,914 @@
+/*
+ * Copyright 1993-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_DEVICE_RUNTIME_API_H__)
+#define __CUDA_DEVICE_RUNTIME_API_H__
+
+#if defined(__CUDACC__) && !defined(__CUDACC_RTC__)
+#include <stdlib.h>
+#endif
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined(CUDA_FORCE_CDP1_IF_SUPPORTED) && !defined(__CUDADEVRT_INTERNAL__) && !defined(_NVHPC_CUDA) && !(defined(_WIN32) && !defined(_WIN64))
+#define __CUDA_INTERNAL_USE_CDP2
+#endif
+
+#if !defined(__CUDACC_RTC__)
+
+#if !defined(__CUDACC_INTERNAL_NO_STUBS__) && !defined(__CUDACC_RDC__) && !defined(__CUDACC_EWP__) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350) && !defined(__CUDADEVRT_INTERNAL__)
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudaFuncAttributes;
+
+// Bug 4398304
+// WAR for doxgyen processing duplicate entries causing warnings to be listed in the documentation
+/** \cond impl_private */
+
+#ifndef __CUDA_INTERNAL_USE_CDP2
+inline __device__  cudaError_t CUDARTAPI cudaMalloc(void **p, size_t s)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI cudaFuncGetAttributes(struct cudaFuncAttributes *p, const void *c)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI cudaDeviceGetAttribute(int *value, enum cudaDeviceAttr attr, int device)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI cudaGetDevice(int *device)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize, unsigned int flags)
+{
+  return cudaErrorUnknown;
+}
+#else // __CUDA_INTERNAL_USE_CDP2
+inline __device__  cudaError_t CUDARTAPI __cudaCDP2Malloc(void **p, size_t s)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI __cudaCDP2FuncGetAttributes(struct cudaFuncAttributes *p, const void *c)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI __cudaCDP2DeviceGetAttribute(int *value, enum cudaDeviceAttr attr, int device)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI __cudaCDP2GetDevice(int *device)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize)
+{
+  return cudaErrorUnknown;
+}
+
+inline __device__  cudaError_t CUDARTAPI __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize, unsigned int flags)
+{
+  return cudaErrorUnknown;
+}
+#endif // __CUDA_INTERNAL_USE_CDP2
+
+/** \endcond  */
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* !defined(__CUDACC_INTERNAL_NO_STUBS__) && !defined(__CUDACC_RDC__) &&  !defined(__CUDACC_EWP__) && defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350) && !defined(__CUDADEVRT_INTERNAL__) */
+
+#endif /* !defined(__CUDACC_RTC__) */
+
+#if defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+# define __DEPRECATED__(msg)
+#elif defined(_WIN32)
+# define __DEPRECATED__(msg) __declspec(deprecated(msg))
+#elif (defined(__GNUC__) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 5 && !defined(__clang__))))
+# define __DEPRECATED__(msg) __attribute__((deprecated))
+#else
+# define __DEPRECATED__(msg) __attribute__((deprecated(msg)))
+#endif
+
+#if defined(__CUDA_ARCH__) && !defined(__CDPRT_SUPPRESS_SYNC_DEPRECATION_WARNING)
+# define __CDPRT_DEPRECATED(func_name) __DEPRECATED__("Use of "#func_name" from device code is deprecated. Moreover, such use will cause this module to fail to load on sm_90+ devices. If calls to "#func_name" from device code cannot be removed for older devices at this time, you may guard them with __CUDA_ARCH__ macros to remove them only for sm_90+ devices, making sure to generate code for compute_90 for the macros to take effect. Note that this mitigation will no longer work when support for "#func_name" from device code is eventually dropped for all devices. Disable this warning with -D__CDPRT_SUPPRESS_SYNC_DEPRECATION_WARNING.")
+#else
+# define __CDPRT_DEPRECATED(func_name)
+#endif
+
+#if defined(__cplusplus) && defined(__CUDACC__)         /* Visible to nvcc front-end only */
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 350)   // Visible to SM>=3.5 and "__host__ __device__" only
+
+#include "driver_types.h"
+#include "crt/host_defines.h"
+
+#define cudaStreamGraphTailLaunch             (cudaStream_t)0x0100000000000000
+#define cudaStreamGraphFireAndForget          (cudaStream_t)0x0200000000000000
+#define cudaStreamGraphFireAndForgetAsSibling (cudaStream_t)0x0300000000000000
+
+#ifdef __CUDA_INTERNAL_USE_CDP2
+#define cudaStreamTailLaunch                ((cudaStream_t)0x3) /**< Per-grid stream with a tail launch semantics. Only applicable when used with CUDA Dynamic Parallelism. */
+#define cudaStreamFireAndForget             ((cudaStream_t)0x4) /**< Per-grid stream with a fire-and-forget synchronization behavior. Only applicable when used with CUDA Dynamic Parallelism. */
+#endif
+
+extern "C"
+{
+
+// Symbols beginning with __cudaCDP* should not be used outside
+// this header file. Instead, compile with -DCUDA_FORCE_CDP1_IF_SUPPORTED if
+// CDP1 support is required.
+
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaDeviceSynchronizeDeprecationAvoidance(void);
+
+// Bug 4398304
+// WAR for doxgyen processing duplicate entries causing warnings to be listed in the documentation
+/** \cond impl_private */
+
+#ifndef __CUDA_INTERNAL_USE_CDP2
+//// CDP1 endpoints
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetAttribute(int *value, enum cudaDeviceAttr attr, int device);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetLimit(size_t *pValue, enum cudaLimit limit);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetCacheConfig(enum cudaFuncCache *pCacheConfig);
+extern __DEPRECATED__("cudaDeviceGetSharedMemConfig deprecated") __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetSharedMemConfig(enum cudaSharedMemConfig *pConfig);
+#if (__CUDA_ARCH__ < 900) && (defined(CUDA_FORCE_CDP1_IF_SUPPORTED) || (defined(_WIN32) && !defined(_WIN64)))
+// cudaDeviceSynchronize is removed on sm_90+
+extern __device__ __cudart_builtin__ __CDPRT_DEPRECATED(cudaDeviceSynchronize) cudaError_t CUDARTAPI cudaDeviceSynchronize(void);
+#endif
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetLastError(void);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaPeekAtLastError(void);
+extern __device__ __cudart_builtin__ const char* CUDARTAPI cudaGetErrorString(cudaError_t error);
+extern __device__ __cudart_builtin__ const char* CUDARTAPI cudaGetErrorName(cudaError_t error);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDeviceCount(int *count);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDevice(int *device);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamCreateWithFlags(cudaStream_t *pStream, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamDestroy(cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamWaitEvent_ptsz(cudaStream_t stream, cudaEvent_t event, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventCreateWithFlags(cudaEvent_t *event, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecord(cudaEvent_t event, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecord_ptsz(cudaEvent_t event, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecordWithFlags_ptsz(cudaEvent_t event, cudaStream_t stream, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventDestroy(cudaEvent_t event);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncGetAttributes(struct cudaFuncAttributes *attr, const void *func);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFree(void *devPtr);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMalloc(void **devPtr, size_t size);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpyAsync(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpyAsync_ptsz(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy2DAsync(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy2DAsync_ptsz(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy3DAsync(const struct cudaMemcpy3DParms *p, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy3DAsync_ptsz(const struct cudaMemcpy3DParms *p, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemsetAsync(void *devPtr, int value, size_t count, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemsetAsync_ptsz(void *devPtr, int value, size_t count, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset2DAsync(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset2DAsync_ptsz(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset3DAsync(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset3DAsync_ptsz(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaRuntimeGetVersion(int *runtimeVersion);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize, unsigned int flags);
+#endif // __CUDA_INTERNAL_USE_CDP2
+
+//// CDP2 endpoints
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2DeviceGetAttribute(int *value, enum cudaDeviceAttr attr, int device);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2DeviceGetLimit(size_t *pValue, enum cudaLimit limit);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2DeviceGetCacheConfig(enum cudaFuncCache *pCacheConfig);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2DeviceGetSharedMemConfig(enum cudaSharedMemConfig *pConfig);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2GetLastError(void);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2PeekAtLastError(void);
+extern __device__ __cudart_builtin__ const char* CUDARTAPI __cudaCDP2GetErrorString(cudaError_t error);
+extern __device__ __cudart_builtin__ const char* CUDARTAPI __cudaCDP2GetErrorName(cudaError_t error);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2GetDeviceCount(int *count);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2GetDevice(int *device);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2StreamCreateWithFlags(cudaStream_t *pStream, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2StreamDestroy(cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2StreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2StreamWaitEvent_ptsz(cudaStream_t stream, cudaEvent_t event, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2EventCreateWithFlags(cudaEvent_t *event, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2EventRecord(cudaEvent_t event, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2EventRecord_ptsz(cudaEvent_t event, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2EventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2EventRecordWithFlags_ptsz(cudaEvent_t event, cudaStream_t stream, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2EventDestroy(cudaEvent_t event);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2FuncGetAttributes(struct cudaFuncAttributes *attr, const void *func);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Free(void *devPtr);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Malloc(void **devPtr, size_t size);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2MemcpyAsync(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2MemcpyAsync_ptsz(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memcpy2DAsync(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memcpy2DAsync_ptsz(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memcpy3DAsync(const struct cudaMemcpy3DParms *p, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memcpy3DAsync_ptsz(const struct cudaMemcpy3DParms *p, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2MemsetAsync(void *devPtr, int value, size_t count, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2MemsetAsync_ptsz(void *devPtr, int value, size_t count, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memset2DAsync(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memset2DAsync_ptsz(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memset3DAsync(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2Memset3DAsync_ptsz(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2RuntimeGetVersion(int *runtimeVersion);
+extern __device__ __cudart_builtin__ void * CUDARTAPI __cudaCDP2GetParameterBuffer(size_t alignment, size_t size);
+extern __device__ __cudart_builtin__ void * CUDARTAPI __cudaCDP2GetParameterBufferV2(void *func, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2LaunchDevice_ptsz(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2LaunchDeviceV2_ptsz(void *parameterBuffer, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2LaunchDevice(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2LaunchDeviceV2(void *parameterBuffer, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize, unsigned int flags);
+
+
+extern  __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream);
+#if defined(CUDA_API_PER_THREAD_DEFAULT_STREAM) 
+static inline  __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphLaunch_ptsz(cudaGraphExec_t graphExec, cudaStream_t stream)
+{
+    if (stream == 0) {
+        stream = cudaStreamPerThread;
+    }
+    return  cudaGraphLaunch(graphExec, stream);
+}
+#endif
+
+/** \endcond */
+
+/**
+  * \ingroup CUDART_GRAPH
+  * \brief Get the currently running device graph id.
+  *
+  * Get the currently running device graph id.
+  * \return Returns the current device graph id, 0 if the call is outside of a device graph.
+  * \sa cudaGraphLaunch
+  */
+static inline __device__ __cudart_builtin__ cudaGraphExec_t CUDARTAPI cudaGetCurrentGraphExec(void)
+{
+    unsigned long long current_graph_exec;
+    asm ("mov.u64 %0, %%current_graph_exec;" : "=l"(current_graph_exec));
+    return (cudaGraphExec_t)current_graph_exec;
+}
+
+/**
+ * \ingroup CUDART_GRAPH
+ * \brief Updates the kernel parameters of the given kernel node
+ *
+ * Updates \p size bytes in the kernel parameters of \p node at \p offset to
+ * the contents of \p value. \p node must be device-updatable, and must reside upon the same
+ * device as the calling kernel.
+ *
+ * If this function is called for the node's immediate dependent and that dependent is configured
+ * for programmatic dependent launch, then a memory fence must be invoked via __threadfence() before
+ * kickoff of the dependent is triggered via ::cudaTriggerProgrammaticLaunchCompletion() to ensure
+ * that the update is visible to that dependent node before it is launched.
+ *
+ * \param node      - The node to update
+ * \param offset    - The offset into the params at which to make the update
+ * \param value     - Buffer containing the params to write
+ * \param size      - Size in bytes to update
+ *
+ * \return
+ * cudaSucces,
+ * cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaGraphKernelNodeSetEnabled,
+ * ::cudaGraphKernelNodeSetGridDim,
+ * ::cudaGraphKernelNodeUpdatesApply
+ */
+extern  __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphKernelNodeSetParam(cudaGraphDeviceNode_t node, size_t offset, const void *value , size_t size);
+
+/**
+ * \ingroup CUDART_GRAPH
+ * \brief Enables or disables the given kernel node
+ *
+ * Enables or disables \p node based upon \p enable. If \p enable is true, the node will be enabled;
+ * if it is false, the node will be disabled. Disabled nodes will act as a NOP during execution.
+ * \p node must be device-updatable, and must reside upon the same device as the calling kernel.
+ *
+ * If this function is called for the node's immediate dependent and that dependent is configured
+ * for programmatic dependent launch, then a memory fence must be invoked via __threadfence() before
+ * kickoff of the dependent is triggered via ::cudaTriggerProgrammaticLaunchCompletion() to ensure
+ * that the update is visible to that dependent node before it is launched.
+ *
+ * \param node      - The node to update
+ * \param enable    - Whether to enable or disable the node
+ *
+ * \return
+ * cudaSucces,
+ * cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaGraphKernelNodeSetParam,
+ * ::cudaGraphKernelNodeSetGridDim,
+ * ::cudaGraphKernelNodeUpdatesApply
+ */
+extern  __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphKernelNodeSetEnabled(cudaGraphDeviceNode_t node, bool enable);
+
+/**
+ * \ingroup CUDART_GRAPH
+ * \brief Updates the grid dimensions of the given kernel node
+ *
+ * Sets the grid dimensions of \p node to \p gridDim. \p node must be device-updatable,
+ * and must reside upon the same device as thecalling kernel.
+ *
+ * If this function is called for the node's immediate dependent and that dependent is configured
+ * for programmatic dependent launch, then a memory fence must be invoked via __threadfence() before
+ * kickoff of the dependent is triggered via ::cudaTriggerProgrammaticLaunchCompletion() to ensure
+ * that the update is visible to that dependent node before it is launched.
+ *
+ * \param node      - The node to update
+ * \param gridDim   - The grid dimensions to set
+ *
+ * \return
+ * cudaSucces,
+ * cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaGraphKernelNodeSetParam,
+ * ::cudaGraphKernelNodeSetEnabled,
+ * ::cudaGraphKernelNodeUpdatesApply
+ */
+extern  __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphKernelNodeSetGridDim(cudaGraphDeviceNode_t node, dim3 gridDim);
+
+/**
+ * \ingroup CUDART_GRAPH
+ * \brief Batch applies multiple kernel node updates
+ *
+ * Batch applies one or more kernel node updates based on the information provided in \p updates.
+ * \p updateCount specifies the number of updates to apply. Each entry in \p updates must specify
+ * a node to update, the type of update to apply, and the parameters for that type of update. See
+ * the documentation for ::cudaGraphKernelNodeUpdate for more detail.
+ *
+ * If this function is called for the node's immediate dependent and that dependent is configured
+ * for programmatic dependent launch, then a memory fence must be invoked via __threadfence() before
+ * kickoff of the dependent is triggered via ::cudaTriggerProgrammaticLaunchCompletion() to ensure
+ * that the update is visible to that dependent node before it is launched.
+ *
+ * \param updates     - The updates to apply
+ * \param updateCount - The number of updates to apply
+ *
+ * \return
+ * cudaSucces,
+ * cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaGraphKernelNodeSetParam,
+ * ::cudaGraphKernelNodeSetEnabled,
+ * ::cudaGraphKernelNodeSetGridDim
+ */
+extern  __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphKernelNodeUpdatesApply(const cudaGraphKernelNodeUpdate *updates, size_t updateCount);
+
+/**
+  * \ingroup CUDART_EXECUTION
+  * \brief Programmatic dependency trigger
+  *
+  * This device function ensures the programmatic launch completion edges /
+  * events are fulfilled. See
+  * ::cudaLaunchAttributeID::cudaLaunchAttributeProgrammaticStreamSerialization
+  * and ::cudaLaunchAttributeID::cudaLaunchAttributeProgrammaticEvent for more
+  * information. The event / edge kick off only happens when every CTAs
+  * in the grid has either exited or called this function at least once,
+  * otherwise the kick off happens automatically after all warps finishes
+  * execution but before the grid completes. The kick off only enables
+  * scheduling of the secondary kernel. It provides no memory visibility
+  * guarantee itself. The user could enforce memory visibility by inserting a
+  * memory fence of the correct scope.
+  */
+static inline __device__ __cudart_builtin__ void CUDARTAPI cudaTriggerProgrammaticLaunchCompletion(void)
+{
+    asm volatile("griddepcontrol.launch_dependents;":::);
+}
+
+/**
+  * \ingroup CUDART_EXECUTION
+  * \brief Programmatic grid dependency synchronization
+  *
+  * This device function will block the thread until all direct grid
+  * dependencies have completed. This API is intended to use in conjuncture with
+  * programmatic / launch event / dependency. See
+  * ::cudaLaunchAttributeID::cudaLaunchAttributeProgrammaticStreamSerialization
+  * and ::cudaLaunchAttributeID::cudaLaunchAttributeProgrammaticEvent for more
+  * information.
+  */
+static inline __device__ __cudart_builtin__ void CUDARTAPI cudaGridDependencySynchronize(void)
+{
+    asm volatile("griddepcontrol.wait;":::"memory");
+}
+
+/**
+  * \ingroup CUDART_GRAPH
+  * \brief Sets the condition value associated with a conditional node.
+  *
+  * Sets the condition value associated with a conditional node.
+  *
+  * Note: \p handle must be associated with the same context as the kernel calling this function.
+  *
+  * \sa cudaGraphConditionalHandleCreate
+  */
+extern __device__ __cudart_builtin__ void CUDARTAPI cudaGraphSetConditional(cudaGraphConditionalHandle handle, unsigned int value);
+
+//// CG API
+extern __device__ __cudart_builtin__ unsigned long long CUDARTAPI cudaCGGetIntrinsicHandle(enum cudaCGScope scope);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaCGSynchronize(unsigned long long handle, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaCGSynchronizeGrid(unsigned long long handle, unsigned int flags);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaCGGetSize(unsigned int *numThreads, unsigned int *numGrids, unsigned long long handle);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaCGGetRank(unsigned int *threadRank, unsigned int *gridRank, unsigned long long handle);
+
+
+//// CDP API
+
+#ifdef __CUDA_ARCH__
+
+// Bug 4398304
+// WAR for doxgyen processing duplicate entries causing warnings to be listed in the documentation
+/** \cond impl_private */
+
+#ifdef __CUDA_INTERNAL_USE_CDP2
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetAttribute(int *value, enum cudaDeviceAttr attr, int device)
+{
+    return __cudaCDP2DeviceGetAttribute(value, attr, device);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetLimit(size_t *pValue, enum cudaLimit limit)
+{
+    return __cudaCDP2DeviceGetLimit(pValue, limit);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetCacheConfig(enum cudaFuncCache *pCacheConfig)
+{
+    return __cudaCDP2DeviceGetCacheConfig(pCacheConfig);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetSharedMemConfig(enum cudaSharedMemConfig *pConfig)
+{
+    return __cudaCDP2DeviceGetSharedMemConfig(pConfig);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetLastError(void)
+{
+    return __cudaCDP2GetLastError();
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaPeekAtLastError(void)
+{
+    return __cudaCDP2PeekAtLastError();
+}
+
+static __inline__ __device__ __cudart_builtin__ const char* CUDARTAPI cudaGetErrorString(cudaError_t error)
+{
+    return __cudaCDP2GetErrorString(error);
+}
+
+static __inline__ __device__ __cudart_builtin__ const char* CUDARTAPI cudaGetErrorName(cudaError_t error)
+{
+    return __cudaCDP2GetErrorName(error);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDeviceCount(int *count)
+{
+    return __cudaCDP2GetDeviceCount(count);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDevice(int *device)
+{
+    return __cudaCDP2GetDevice(device);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamCreateWithFlags(cudaStream_t *pStream, unsigned int flags)
+{
+    return __cudaCDP2StreamCreateWithFlags(pStream, flags);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamDestroy(cudaStream_t stream)
+{
+    return __cudaCDP2StreamDestroy(stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags)
+{
+    return __cudaCDP2StreamWaitEvent(stream, event, flags);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamWaitEvent_ptsz(cudaStream_t stream, cudaEvent_t event, unsigned int flags)
+{
+    return __cudaCDP2StreamWaitEvent_ptsz(stream, event, flags);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventCreateWithFlags(cudaEvent_t *event, unsigned int flags)
+{
+    return __cudaCDP2EventCreateWithFlags(event, flags);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecord(cudaEvent_t event, cudaStream_t stream)
+{
+    return __cudaCDP2EventRecord(event, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecord_ptsz(cudaEvent_t event, cudaStream_t stream)
+{
+    return __cudaCDP2EventRecord_ptsz(event, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags)
+{
+    return __cudaCDP2EventRecordWithFlags(event, stream, flags);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecordWithFlags_ptsz(cudaEvent_t event, cudaStream_t stream, unsigned int flags)
+{
+    return __cudaCDP2EventRecordWithFlags_ptsz(event, stream, flags);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventDestroy(cudaEvent_t event)
+{
+    return __cudaCDP2EventDestroy(event);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncGetAttributes(struct cudaFuncAttributes *attr, const void *func)
+{
+    return __cudaCDP2FuncGetAttributes(attr, func);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFree(void *devPtr)
+{
+    return __cudaCDP2Free(devPtr);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMalloc(void **devPtr, size_t size)
+{
+    return __cudaCDP2Malloc(devPtr, size);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpyAsync(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream)
+{
+    return __cudaCDP2MemcpyAsync(dst, src, count, kind, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpyAsync_ptsz(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream)
+{
+    return __cudaCDP2MemcpyAsync_ptsz(dst, src, count, kind, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy2DAsync(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream)
+{
+    return __cudaCDP2Memcpy2DAsync(dst, dpitch, src, spitch, width, height, kind, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy2DAsync_ptsz(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream)
+{
+    return __cudaCDP2Memcpy2DAsync_ptsz(dst, dpitch, src, spitch, width, height, kind, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy3DAsync(const struct cudaMemcpy3DParms *p, cudaStream_t stream)
+{
+    return __cudaCDP2Memcpy3DAsync(p, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy3DAsync_ptsz(const struct cudaMemcpy3DParms *p, cudaStream_t stream)
+{
+    return __cudaCDP2Memcpy3DAsync_ptsz(p, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemsetAsync(void *devPtr, int value, size_t count, cudaStream_t stream)
+{
+    return __cudaCDP2MemsetAsync(devPtr, value, count, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemsetAsync_ptsz(void *devPtr, int value, size_t count, cudaStream_t stream)
+{
+    return __cudaCDP2MemsetAsync_ptsz(devPtr, value, count, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset2DAsync(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream)
+{
+    return __cudaCDP2Memset2DAsync(devPtr, pitch, value, width, height, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset2DAsync_ptsz(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream)
+{
+    return __cudaCDP2Memset2DAsync_ptsz(devPtr, pitch, value, width, height, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset3DAsync(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream)
+{
+    return __cudaCDP2Memset3DAsync(pitchedDevPtr, value, extent, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset3DAsync_ptsz(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream)
+{
+    return __cudaCDP2Memset3DAsync_ptsz(pitchedDevPtr, value, extent, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaRuntimeGetVersion(int *runtimeVersion)
+{
+    return __cudaCDP2RuntimeGetVersion(runtimeVersion);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize)
+{
+    return __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessor(numBlocks, func, blockSize, dynamicSmemSize);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSmemSize, unsigned int flags)
+{
+    return __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessorWithFlags(numBlocks, func, blockSize, dynamicSmemSize, flags);
+}
+#endif // __CUDA_INTERNAL_USE_CDP2
+
+/** \endcond */
+
+#endif // __CUDA_ARCH__
+
+
+/**
+ * \ingroup CUDART_EXECUTION
+ * \brief Obtains a parameter buffer
+ *
+ * Obtains a parameter buffer which can be filled with parameters for a kernel launch.
+ * Parameters passed to ::cudaLaunchDevice must be allocated via this function.
+ *
+ * This is a low level API and can only be accessed from Parallel Thread Execution (PTX).
+ * CUDA user code should use <<< >>> to launch kernels.
+ *
+ * \param alignment - Specifies alignment requirement of the parameter buffer
+ * \param size      - Specifies size requirement in bytes
+ *
+ * \return
+ * Returns pointer to the allocated parameterBuffer
+ * \notefnerr
+ *
+ * \sa cudaLaunchDevice
+ */
+#ifdef __CUDA_INTERNAL_USE_CDP2
+static __inline__ __device__ __cudart_builtin__ void * CUDARTAPI cudaGetParameterBuffer(size_t alignment, size_t size)
+{
+    return __cudaCDP2GetParameterBuffer(alignment, size);
+}
+#else
+extern __device__ __cudart_builtin__ void * CUDARTAPI cudaGetParameterBuffer(size_t alignment, size_t size);
+#endif
+
+
+#ifdef __CUDA_INTERNAL_USE_CDP2
+static __inline__ __device__ __cudart_builtin__ void * CUDARTAPI cudaGetParameterBufferV2(void *func, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize)
+{
+    return __cudaCDP2GetParameterBufferV2(func, gridDimension, blockDimension, sharedMemSize);
+}
+#else
+extern __device__ __cudart_builtin__ void * CUDARTAPI cudaGetParameterBufferV2(void *func, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize);
+#endif
+
+
+#ifdef __CUDA_INTERNAL_USE_CDP2
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDevice_ptsz(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream)
+{
+    return __cudaCDP2LaunchDevice_ptsz(func, parameterBuffer, gridDimension, blockDimension, sharedMemSize, stream);
+}
+
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDeviceV2_ptsz(void *parameterBuffer, cudaStream_t stream)
+{
+    return __cudaCDP2LaunchDeviceV2_ptsz(parameterBuffer, stream);
+}
+#else
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDevice_ptsz(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDeviceV2_ptsz(void *parameterBuffer, cudaStream_t stream);
+#endif
+
+
+/**
+ * \ingroup CUDART_EXECUTION
+ * \brief Launches a specified kernel
+ *
+ * Launches a specified kernel with the specified parameter buffer. A parameter buffer can be obtained
+ * by calling ::cudaGetParameterBuffer().
+ *
+ * This is a low level API and can only be accessed from Parallel Thread Execution (PTX).
+ * CUDA user code should use <<< >>> to launch the kernels.
+ *
+ * \param func            - Pointer to the kernel to be launched
+ * \param parameterBuffer - Holds the parameters to the launched kernel. parameterBuffer can be NULL. (Optional)
+ * \param gridDimension   - Specifies grid dimensions
+ * \param blockDimension  - Specifies block dimensions
+ * \param sharedMemSize   - Specifies size of shared memory
+ * \param stream          - Specifies the stream to be used
+ *
+ * \return
+ * ::cudaSuccess, ::cudaErrorInvalidDevice, ::cudaErrorLaunchMaxDepthExceeded, ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorStartupFailure, ::cudaErrorLaunchPendingCountExceeded, ::cudaErrorLaunchOutOfResources
+ * \notefnerr
+ * \n Please refer to Execution Configuration and Parameter Buffer Layout from the CUDA Programming
+ * Guide for the detailed descriptions of launch configuration and parameter layout respectively.
+ *
+ * \sa cudaGetParameterBuffer
+ */
+#if defined(CUDA_API_PER_THREAD_DEFAULT_STREAM) && defined(__CUDA_ARCH__)
+    // When compiling for the device and per thread default stream is enabled, add
+    // a static inline redirect to the per thread stream entry points.
+
+    static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI
+    cudaLaunchDevice(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream)
+    {
+#ifdef __CUDA_INTERNAL_USE_CDP2
+        return __cudaCDP2LaunchDevice_ptsz(func, parameterBuffer, gridDimension, blockDimension, sharedMemSize, stream);
+#else
+        return cudaLaunchDevice_ptsz(func, parameterBuffer, gridDimension, blockDimension, sharedMemSize, stream);
+#endif
+    }
+
+    static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI
+    cudaLaunchDeviceV2(void *parameterBuffer, cudaStream_t stream)
+    {
+#ifdef __CUDA_INTERNAL_USE_CDP2
+        return __cudaCDP2LaunchDeviceV2_ptsz(parameterBuffer, stream);
+#else
+        return cudaLaunchDeviceV2_ptsz(parameterBuffer, stream);
+#endif
+    }
+#else // defined(CUDA_API_PER_THREAD_DEFAULT_STREAM) && defined(__CUDA_ARCH__)
+#ifdef __CUDA_INTERNAL_USE_CDP2
+    static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDevice(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream)
+    {
+        return __cudaCDP2LaunchDevice(func, parameterBuffer, gridDimension, blockDimension, sharedMemSize, stream);
+    }
+
+    static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDeviceV2(void *parameterBuffer, cudaStream_t stream)
+    {
+        return __cudaCDP2LaunchDeviceV2(parameterBuffer, stream);
+    }
+#else // __CUDA_INTERNAL_USE_CDP2
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDevice(void *func, void *parameterBuffer, dim3 gridDimension, dim3 blockDimension, unsigned int sharedMemSize, cudaStream_t stream);
+extern __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaLaunchDeviceV2(void *parameterBuffer, cudaStream_t stream);
+#endif // __CUDA_INTERNAL_USE_CDP2
+#endif // defined(CUDA_API_PER_THREAD_DEFAULT_STREAM) && defined(__CUDA_ARCH__)
+
+
+// These symbols should not be used outside of this header file.
+#define __cudaCDP2DeviceGetAttribute
+#define __cudaCDP2DeviceGetLimit
+#define __cudaCDP2DeviceGetCacheConfig
+#define __cudaCDP2DeviceGetSharedMemConfig
+#define __cudaCDP2GetLastError
+#define __cudaCDP2PeekAtLastError
+#define __cudaCDP2GetErrorString
+#define __cudaCDP2GetErrorName
+#define __cudaCDP2GetDeviceCount
+#define __cudaCDP2GetDevice
+#define __cudaCDP2StreamCreateWithFlags
+#define __cudaCDP2StreamDestroy
+#define __cudaCDP2StreamWaitEvent
+#define __cudaCDP2StreamWaitEvent_ptsz
+#define __cudaCDP2EventCreateWithFlags
+#define __cudaCDP2EventRecord
+#define __cudaCDP2EventRecord_ptsz
+#define __cudaCDP2EventRecordWithFlags
+#define __cudaCDP2EventRecordWithFlags_ptsz
+#define __cudaCDP2EventDestroy
+#define __cudaCDP2FuncGetAttributes
+#define __cudaCDP2Free
+#define __cudaCDP2Malloc
+#define __cudaCDP2MemcpyAsync
+#define __cudaCDP2MemcpyAsync_ptsz
+#define __cudaCDP2Memcpy2DAsync
+#define __cudaCDP2Memcpy2DAsync_ptsz
+#define __cudaCDP2Memcpy3DAsync
+#define __cudaCDP2Memcpy3DAsync_ptsz
+#define __cudaCDP2MemsetAsync
+#define __cudaCDP2MemsetAsync_ptsz
+#define __cudaCDP2Memset2DAsync
+#define __cudaCDP2Memset2DAsync_ptsz
+#define __cudaCDP2Memset3DAsync
+#define __cudaCDP2Memset3DAsync_ptsz
+#define __cudaCDP2RuntimeGetVersion
+#define __cudaCDP2GetParameterBuffer
+#define __cudaCDP2GetParameterBufferV2
+#define __cudaCDP2LaunchDevice_ptsz
+#define __cudaCDP2LaunchDeviceV2_ptsz
+#define __cudaCDP2LaunchDevice
+#define __cudaCDP2LaunchDeviceV2
+#define __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessor
+#define __cudaCDP2OccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+
+}
+
+// Bug 4398304
+// WAR for doxgyen processing duplicate entries causing warnings to be listed in the documentation
+/** \cond impl_private */
+
+template <typename T> static __inline__ __device__ __cudart_builtin__ cudaError_t cudaMalloc(T **devPtr, size_t size);
+template <typename T> static __inline__ __device__ __cudart_builtin__ cudaError_t cudaFuncGetAttributes(struct cudaFuncAttributes *attr, T *entry);
+template <typename T> static __inline__ __device__ __cudart_builtin__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, T func, int blockSize, size_t dynamicSmemSize);
+template <typename T> static __inline__ __device__ __cudart_builtin__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, T func, int blockSize, size_t dynamicSmemSize, unsigned int flags);
+
+/** \endcond */
+
+/**
+ * \ingroup CUDART_GRAPH
+ * \brief Updates the kernel parameters of the given kernel node
+ *
+ * Updates the kernel parameters of \p node at \p offset to \p value. \p node must be
+ * device-updatable, and must reside upon the same device as the calling kernel.
+ *
+ * If this function is called for the node's immediate dependent and that dependent is configured
+ * for programmatic dependent launch, then a memory fence must be invoked via __threadfence() before
+ * kickoff of the dependent is triggered via ::cudaTriggerProgrammaticLaunchCompletion() to ensure
+ * that the update is visible to that dependent node before it is launched.
+ *
+ * \param node      - The node to update
+ * \param offset    - The offset into the params at which to make the update
+ * \param value     - Parameter value to write
+ *
+ * \return
+ * cudaSucces,
+ * cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::etblGraphKernelNodeSetEnabled,
+ * ::etblGraphKernelNodeSetGridDim,
+ * ::etblGraphKernelNodeUpdatesApply
+ */
+template <typename T>
+static __inline__ __device__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGraphKernelNodeSetParam(cudaGraphDeviceNode_t node, size_t offset, const T &value)
+{
+    return cudaGraphKernelNodeSetParam(node, offset, &value, sizeof(T));
+}
+
+#endif // !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 350)
+#endif /* defined(__cplusplus) && defined(__CUDACC__) */
+
+#undef __DEPRECATED__
+#undef __CDPRT_DEPRECATED
+#undef __CUDA_INTERNAL_USE_CDP2
+
+#endif /* !__CUDA_DEVICE_RUNTIME_API_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_egl_interop.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_egl_interop.h
new file mode 100644
index 0000000000000000000000000000000000000000..a87d7dc81e34291f526b2b02baf3a47066c3730e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_egl_interop.h
@@ -0,0 +1,645 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_EGL_INTEROP_H__)
+#define __CUDA_EGL_INTEROP_H__
+
+#include "cuda_runtime_api.h"
+#include "cuda_runtime.h"
+#include "cudart_platform.h"
+#include "EGL/egl.h"
+#include "EGL/eglext.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * \addtogroup CUDART_TYPES
+ * @{
+ */
+
+ /**
+ * Maximum number of planes per frame
+ */
+#define CUDA_EGL_MAX_PLANES 3
+
+/**
+ * CUDA EglFrame type - array or pointer
+ */
+typedef enum cudaEglFrameType_enum
+{
+    cudaEglFrameTypeArray = 0,  /**< Frame type CUDA array */
+    cudaEglFrameTypePitch = 1,  /**< Frame type CUDA pointer */
+} cudaEglFrameType;
+
+/**
+ * Resource location flags- sysmem or vidmem
+ *
+ * For CUDA context on iGPU, since video and system memory are equivalent -
+ * these flags will not have an effect on the execution.
+ *
+ * For CUDA context on dGPU, applications can use the flag ::cudaEglResourceLocationFlags
+ * to give a hint about the desired location.
+ *
+ * ::cudaEglResourceLocationSysmem - the frame data is made resident on the system memory
+ * to be accessed by CUDA.
+ *
+ * ::cudaEglResourceLocationVidmem - the frame data is made resident on the dedicated
+ * video memory to be accessed by CUDA.
+ *
+ * There may be an additional latency due to new allocation and data migration,
+ * if the frame is produced on a different memory.
+ */
+typedef enum cudaEglResourceLocationFlags_enum {
+    cudaEglResourceLocationSysmem   = 0x00,       /**< Resource location sysmem */
+    cudaEglResourceLocationVidmem   = 0x01,       /**< Resource location vidmem */
+} cudaEglResourceLocationFlags;
+
+/**
+ * CUDA EGL Color Format - The different planar and multiplanar formats currently supported for CUDA_EGL interops.
+ */
+typedef enum cudaEglColorFormat_enum {
+    cudaEglColorFormatYUV420Planar            = 0,  /**< Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYUV420SemiPlanar        = 1,  /**< Y, UV in two surfaces (UV as one surface) with VU byte ordering, width, height ratio same as YUV420Planar. */
+    cudaEglColorFormatYUV422Planar            = 2,  /**< Y, U, V  each in a separate  surface, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYUV422SemiPlanar        = 3,  /**< Y, UV in two surfaces with VU byte ordering, width, height ratio same as YUV422Planar. */
+    cudaEglColorFormatARGB                    = 6,  /**< R/G/B/A four channels in one surface with BGRA byte ordering. */
+    cudaEglColorFormatRGBA                    = 7,  /**< R/G/B/A four channels in one surface with ABGR byte ordering. */
+    cudaEglColorFormatL                       = 8,  /**< single luminance channel in one surface. */
+    cudaEglColorFormatR                       = 9,  /**< single color channel in one surface. */
+    cudaEglColorFormatYUV444Planar            = 10, /**< Y, U, V in three surfaces, each in a separate surface, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYUV444SemiPlanar        = 11, /**< Y, UV in two surfaces (UV as one surface) with VU byte ordering, width, height ratio same as YUV444Planar. */
+    cudaEglColorFormatYUYV422                 = 12, /**< Y, U, V in one surface, interleaved as UYVY in one channel. */
+    cudaEglColorFormatUYVY422                 = 13, /**< Y, U, V in one surface, interleaved as YUYV in one channel. */
+    cudaEglColorFormatABGR                    = 14, /**< R/G/B/A four channels in one surface with RGBA byte ordering. */
+    cudaEglColorFormatBGRA                    = 15, /**< R/G/B/A four channels in one surface with ARGB byte ordering. */
+    cudaEglColorFormatA                       = 16, /**< Alpha color format - one channel in one surface. */
+    cudaEglColorFormatRG                      = 17, /**< R/G color format - two channels in one surface with GR byte ordering */
+    cudaEglColorFormatAYUV                    = 18, /**< Y, U, V, A four channels in one surface, interleaved as VUYA. */
+    cudaEglColorFormatYVU444SemiPlanar        = 19, /**< Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU422SemiPlanar        = 20, /**< Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU420SemiPlanar        = 21, /**< Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY10V10U10_444SemiPlanar = 22, /**< Y10, V10U10 in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatY10V10U10_420SemiPlanar = 23, /**< Y10, V10U10 in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY12V12U12_444SemiPlanar = 24, /**< Y12, V12U12 in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatY12V12U12_420SemiPlanar = 25, /**< Y12, V12U12 in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatVYUY_ER                 = 26, /**< Extended Range Y, U, V in one surface, interleaved as YVYU in one channel. */
+    cudaEglColorFormatUYVY_ER                 = 27, /**< Extended Range Y, U, V in one surface, interleaved as YUYV in one channel. */
+    cudaEglColorFormatYUYV_ER                 = 28, /**< Extended Range Y, U, V in one surface, interleaved as UYVY in one channel. */
+    cudaEglColorFormatYVYU_ER                 = 29, /**< Extended Range Y, U, V in one surface, interleaved as VYUY in one channel. */
+    cudaEglColorFormatYUVA_ER                 = 31, /**< Extended Range Y, U, V, A four channels in one surface, interleaved as AVUY. */
+    cudaEglColorFormatAYUV_ER                 = 32, /**< Extended Range Y, U, V, A four channels in one surface, interleaved as VUYA. */
+    cudaEglColorFormatYUV444Planar_ER         = 33, /**< Extended Range Y, U, V in three surfaces, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYUV422Planar_ER         = 34, /**< Extended Range Y, U, V in three surfaces, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYUV420Planar_ER         = 35, /**< Extended Range Y, U, V in three surfaces, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYUV444SemiPlanar_ER     = 36, /**< Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYUV422SemiPlanar_ER     = 37, /**< Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYUV420SemiPlanar_ER     = 38, /**< Extended Range Y, UV in two surfaces (UV as one surface) with VU byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYVU444Planar_ER         = 39, /**< Extended Range Y, V, U in three surfaces, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU422Planar_ER         = 40, /**< Extended Range Y, V, U in three surfaces, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU420Planar_ER         = 41, /**< Extended Range Y, V, U in three surfaces, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYVU444SemiPlanar_ER     = 42, /**< Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU422SemiPlanar_ER     = 43, /**< Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU420SemiPlanar_ER     = 44, /**< Extended Range Y, VU in two surfaces (VU as one surface) with UV byte ordering, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatBayerRGGB               = 45, /**< Bayer format - one channel in one surface with interleaved RGGB ordering. */
+    cudaEglColorFormatBayerBGGR               = 46, /**< Bayer format - one channel in one surface with interleaved BGGR ordering. */
+    cudaEglColorFormatBayerGRBG               = 47, /**< Bayer format - one channel in one surface with interleaved GRBG ordering. */
+    cudaEglColorFormatBayerGBRG               = 48, /**< Bayer format - one channel in one surface with interleaved GBRG ordering. */
+    cudaEglColorFormatBayer10RGGB             = 49, /**< Bayer10 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    cudaEglColorFormatBayer10BGGR             = 50, /**< Bayer10 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    cudaEglColorFormatBayer10GRBG             = 51, /**< Bayer10 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    cudaEglColorFormatBayer10GBRG             = 52, /**< Bayer10 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    cudaEglColorFormatBayer12RGGB             = 53, /**< Bayer12 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12BGGR             = 54, /**< Bayer12 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12GRBG             = 55, /**< Bayer12 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12GBRG             = 56, /**< Bayer12 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer14RGGB             = 57, /**< Bayer14 format - one channel in one surface with interleaved RGGB ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    cudaEglColorFormatBayer14BGGR             = 58, /**< Bayer14 format - one channel in one surface with interleaved BGGR ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    cudaEglColorFormatBayer14GRBG             = 59, /**< Bayer14 format - one channel in one surface with interleaved GRBG ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    cudaEglColorFormatBayer14GBRG             = 60, /**< Bayer14 format - one channel in one surface with interleaved GBRG ordering. Out of 16 bits, 14 bits used 2 bits No-op. */
+    cudaEglColorFormatBayer20RGGB             = 61, /**< Bayer20 format - one channel in one surface with interleaved RGGB ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    cudaEglColorFormatBayer20BGGR             = 62, /**< Bayer20 format - one channel in one surface with interleaved BGGR ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    cudaEglColorFormatBayer20GRBG             = 63, /**< Bayer20 format - one channel in one surface with interleaved GRBG ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    cudaEglColorFormatBayer20GBRG             = 64, /**< Bayer20 format - one channel in one surface with interleaved GBRG ordering. Out of 32 bits, 20 bits used 12 bits No-op. */
+    cudaEglColorFormatYVU444Planar            = 65, /**< Y, V, U in three surfaces, each in a separate surface, U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU422Planar            = 66, /**< Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = Y height. */
+    cudaEglColorFormatYVU420Planar            = 67, /**< Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatBayerIspRGGB            = 68, /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved RGGB ordering and mapped to opaque integer datatype. */
+    cudaEglColorFormatBayerIspBGGR            = 69, /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved BGGR ordering and mapped to opaque integer datatype. */
+    cudaEglColorFormatBayerIspGRBG            = 70, /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved GRBG ordering and mapped to opaque integer datatype. */
+    cudaEglColorFormatBayerIspGBRG            = 71, /**< Nvidia proprietary Bayer ISP format - one channel in one surface with interleaved GBRG ordering and mapped to opaque integer datatype. */
+    cudaEglColorFormatBayerBCCR               = 72, /**< Bayer format - one channel in one surface with interleaved BCCR ordering. */
+    cudaEglColorFormatBayerRCCB               = 73, /**< Bayer format - one channel in one surface with interleaved RCCB ordering. */
+    cudaEglColorFormatBayerCRBC               = 74, /**< Bayer format - one channel in one surface with interleaved CRBC ordering. */
+    cudaEglColorFormatBayerCBRC               = 75, /**< Bayer format - one channel in one surface with interleaved CBRC ordering. */
+    cudaEglColorFormatBayer10CCCC             = 76, /**< Bayer10 format - one channel in one surface with interleaved CCCC ordering. Out of 16 bits, 10 bits used 6 bits No-op. */
+    cudaEglColorFormatBayer12BCCR             = 77, /**< Bayer12 format - one channel in one surface with interleaved BCCR ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12RCCB             = 78, /**< Bayer12 format - one channel in one surface with interleaved RCCB ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12CRBC             = 79, /**< Bayer12 format - one channel in one surface with interleaved CRBC ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12CBRC             = 80, /**< Bayer12 format - one channel in one surface with interleaved CBRC ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatBayer12CCCC             = 81, /**< Bayer12 format - one channel in one surface with interleaved CCCC ordering. Out of 16 bits, 12 bits used 4 bits No-op. */
+    cudaEglColorFormatY                       = 82, /**< Color format for single Y plane. */
+    cudaEglColorFormatYUV420SemiPlanar_2020   = 83, /**< Y, UV in two surfaces (UV as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYVU420SemiPlanar_2020   = 84, /**< Y, VU in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYUV420Planar_2020       = 85, /**< Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYVU420Planar_2020       = 86, /**< Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYUV420SemiPlanar_709    = 87, /**< Y, UV in two surfaces (UV as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYVU420SemiPlanar_709    = 88, /**< Y, VU in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYUV420Planar_709        = 89, /**< Y, U, V in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatYVU420Planar_709        = 90, /**< Y, V, U in three surfaces, each in a separate surface, U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY10V10U10_420SemiPlanar_709  = 91, /**< Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY10V10U10_420SemiPlanar_2020 = 92, /**< Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY10V10U10_422SemiPlanar_2020 = 93, /**< Y10, V10U10  in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height =  Y height. */
+    cudaEglColorFormatY10V10U10_422SemiPlanar      = 94, /**< Y10, V10U10  in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height =  Y height. */
+    cudaEglColorFormatY10V10U10_422SemiPlanar_709  = 95, /**< Y10, V10U10  in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height =  Y height. */
+    cudaEglColorFormatY_ER                         = 96, /**< Extended Range Color format for single Y plane. */
+    cudaEglColorFormatY_709_ER                     = 97, /**< Extended Range Color format for single Y plane. */
+    cudaEglColorFormatY10_ER                       = 98, /**< Extended Range Color format for single Y10 plane. */
+    cudaEglColorFormatY10_709_ER                   = 99, /**< Extended Range Color format for single Y10 plane. */
+    cudaEglColorFormatY12_ER                       = 100, /**< Extended Range Color format for single Y12 plane. */
+    cudaEglColorFormatY12_709_ER                   = 101, /**< Extended Range Color format for single Y12 plane. */
+    cudaEglColorFormatYUVA                         = 102, /**< Y, U, V, A four channels in one surface, interleaved as AVUY. */
+    cudaEglColorFormatYVYU                         = 104, /**< Y, U, V in one surface, interleaved as YVYU in one channel. */
+    cudaEglColorFormatVYUY                         = 105, /**< Y, U, V in one surface, interleaved as VYUY in one channel. */
+    cudaEglColorFormatY10V10U10_420SemiPlanar_ER     = 106, /**< Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY10V10U10_420SemiPlanar_709_ER = 107, /**< Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY10V10U10_444SemiPlanar_ER     = 108, /**< Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */ 
+    cudaEglColorFormatY10V10U10_444SemiPlanar_709_ER = 109, /**< Extended Range Y10, V10U10 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatY12V12U12_420SemiPlanar_ER     = 110, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY12V12U12_420SemiPlanar_709_ER = 111, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = 1/2 Y width, U/V height = 1/2 Y height. */
+    cudaEglColorFormatY12V12U12_444SemiPlanar_ER     = 112, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatY12V12U12_444SemiPlanar_709_ER = 113, /**< Extended Range Y12, V12U12 in two surfaces (VU as one surface) U/V width = Y width, U/V height = Y height. */
+    cudaEglColorFormatUYVY709                        = 114, /**< Y, U, V in one surface, interleaved as UYVY in one channel. */
+    cudaEglColorFormatUYVY709_ER                     = 115, /**< Extended Range Y, U, V in one surface, interleaved as UYVY in one channel. */
+    cudaEglColorFormatUYVY2020                       = 116,  /**< Y, U, V in one surface, interleaved as UYVY in one channel. */
+} cudaEglColorFormat;
+
+/**
+ * CUDA EGL Plane Descriptor - structure defining each plane of a CUDA EGLFrame
+ */
+typedef struct cudaEglPlaneDesc_st {
+    unsigned int width;                         /**< Width of plane */
+    unsigned int height;                        /**< Height of plane */
+    unsigned int depth;                         /**< Depth of plane */
+    unsigned int pitch;                         /**< Pitch of plane */
+    unsigned int numChannels;                   /**< Number of channels for the plane */
+    struct cudaChannelFormatDesc channelDesc;   /**< Channel Format Descriptor */
+    unsigned int reserved[4];                   /**< Reserved for future use */
+} cudaEglPlaneDesc;
+
+/**
+ * CUDA EGLFrame Descriptor - structure defining one frame of EGL.
+ *
+ * Each frame may contain one or more planes depending on whether the surface is Multiplanar or not.
+ * Each plane of EGLFrame is represented by ::cudaEglPlaneDesc which is defined as:
+ * \code
+ * typedef struct cudaEglPlaneDesc_st {
+ *     unsigned int width;
+ *     unsigned int height;
+ *     unsigned int depth;
+ *     unsigned int pitch;
+ *     unsigned int numChannels;
+ *     struct cudaChannelFormatDesc channelDesc;
+ *     unsigned int reserved[4];
+ * } cudaEglPlaneDesc;
+ * \endcode
+
+*/
+typedef struct cudaEglFrame_st {
+   union {
+       cudaArray_t            pArray[CUDA_EGL_MAX_PLANES];     /**< Array of CUDA arrays corresponding to each plane*/
+       struct cudaPitchedPtr  pPitch[CUDA_EGL_MAX_PLANES];     /**< Array of Pointers corresponding to each plane*/
+   } frame;
+   cudaEglPlaneDesc planeDesc[CUDA_EGL_MAX_PLANES];     /**< CUDA EGL Plane Descriptor ::cudaEglPlaneDesc*/
+   unsigned int planeCount;                             /**< Number of planes */
+   cudaEglFrameType frameType;                          /**< Array or Pitch */
+   cudaEglColorFormat eglColorFormat;                   /**< CUDA EGL Color Format*/
+} cudaEglFrame;
+
+/**
+ * CUDA EGLSream Connection
+ */
+typedef struct  CUeglStreamConnection_st *cudaEglStreamConnection;
+
+/** @} */ /* END CUDART_TYPES */
+
+/**
+ * \addtogroup CUDART_EGL EGL Interoperability
+ * This section describes the EGL interoperability functions of the CUDA
+ * runtime application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Registers an EGL image
+ *
+ * Registers the EGLImageKHR specified by \p image for access by
+ * CUDA. A handle to the registered object is returned as \p pCudaResource.
+ * Additional Mapping/Unmapping is not required for the registered resource and
+ * ::cudaGraphicsResourceGetMappedEglFrame can be directly called on the \p pCudaResource.
+ *
+ * The application will be responsible for synchronizing access to shared objects.
+ * The application must ensure that any pending operation which access the objects have completed
+ * before passing control to CUDA. This may be accomplished by issuing and waiting for
+ * glFinish command on all GLcontexts (for OpenGL and likewise for other APIs).
+ * The application will be also responsible for ensuring that any pending operation on the
+ * registered CUDA resource has completed prior to executing subsequent commands in other APIs
+ * accesing the same memory objects.
+ * This can be accomplished by calling cuCtxSynchronize or cuEventSynchronize (preferably).
+ *
+ * The surface's intended usage is specified using \p flags, as follows:
+ *
+ * - ::cudaGraphicsRegisterFlagsNone: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::cudaGraphicsRegisterFlagsReadOnly: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::cudaGraphicsRegisterFlagsWriteDiscard: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * The EGLImageKHR is an object which can be used to create EGLImage target resource. It is defined as a void pointer.
+ * typedef void* EGLImageKHR
+ *
+ * \param pCudaResource   - Pointer to the returned object handle
+ * \param image           - An EGLImageKHR image which can be used to create target resource.
+ * \param flags           - Map flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaGraphicsUnregisterResource,
+ * ::cudaGraphicsResourceGetMappedEglFrame,
+ * ::cuGraphicsEGLRegisterImage
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsEGLRegisterImage(struct cudaGraphicsResource **pCudaResource, EGLImageKHR image, unsigned int flags);
+
+/**
+ * \brief Connect CUDA to EGLStream as a consumer.
+ *
+ * Connect CUDA as a consumer to EGLStreamKHR specified by \p eglStream.
+ *
+ * The EGLStreamKHR is an EGL object that transfers a sequence of image frames from one
+ * API to another.
+ *
+ * \param conn              - Pointer to the returned connection handle
+ * \param eglStream         - EGLStreamKHR handle
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamConsumerDisconnect,
+ * ::cudaEGLStreamConsumerAcquireFrame,
+ * ::cudaEGLStreamConsumerReleaseFrame,
+ * ::cuEGLStreamConsumerConnect
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamConsumerConnect(cudaEglStreamConnection *conn, EGLStreamKHR eglStream);
+
+/**
+ * \brief Connect CUDA to EGLStream as a consumer with given flags.
+ *
+ * Connect CUDA as a consumer to EGLStreamKHR specified by \p stream with specified \p flags defined by
+ * ::cudaEglResourceLocationFlags.
+ *
+ * The flags specify whether the consumer wants to access frames from system memory or video memory.
+ * Default is ::cudaEglResourceLocationVidmem.
+ *
+ * \param conn              - Pointer to the returned connection handle
+ * \param eglStream         - EGLStreamKHR handle
+ * \param flags             - Flags denote intended location - system or video.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamConsumerDisconnect,
+ * ::cudaEGLStreamConsumerAcquireFrame,
+ * ::cudaEGLStreamConsumerReleaseFrame,
+ * ::cuEGLStreamConsumerConnectWithFlags
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamConsumerConnectWithFlags(cudaEglStreamConnection *conn, EGLStreamKHR eglStream, unsigned int flags);
+
+/**
+ * \brief Disconnect CUDA as a consumer to EGLStream .
+ *
+ * Disconnect CUDA as a consumer to EGLStreamKHR.
+ *
+ * \param conn            - Conection to disconnect.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamConsumerConnect,
+ * ::cudaEGLStreamConsumerAcquireFrame,
+ * ::cudaEGLStreamConsumerReleaseFrame,
+ * ::cuEGLStreamConsumerDisconnect
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamConsumerDisconnect(cudaEglStreamConnection *conn);
+
+/**
+ * \brief Acquire an image frame from the EGLStream with CUDA as a consumer.
+ *
+ * Acquire an image frame from EGLStreamKHR.
+ * ::cudaGraphicsResourceGetMappedEglFrame can be called on \p pCudaResource to get
+ * ::cudaEglFrame.
+ *
+ * \param conn            - Connection on which to acquire
+ * \param pCudaResource   - CUDA resource on which the EGLStream frame will be mapped for use.
+ * \param pStream         - CUDA stream for synchronization and any data migrations
+ * implied by ::cudaEglResourceLocationFlags.
+ * \param timeout         - Desired timeout in usec.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * ::cudaErrorLaunchTimeout
+ *
+ * \sa
+ * ::cudaEGLStreamConsumerConnect,
+ * ::cudaEGLStreamConsumerDisconnect,
+ * ::cudaEGLStreamConsumerReleaseFrame,
+ * ::cuEGLStreamConsumerAcquireFrame
+ */
+
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamConsumerAcquireFrame(cudaEglStreamConnection *conn,
+        cudaGraphicsResource_t *pCudaResource, cudaStream_t *pStream, unsigned int timeout);
+/**
+ * \brief Releases the last frame acquired from the EGLStream.
+ *
+ * Release the acquired image frame specified by \p pCudaResource to EGLStreamKHR.
+ *
+ * \param conn            - Connection on which to release
+ * \param pCudaResource   - CUDA resource whose corresponding frame is to be released
+ * \param pStream         - CUDA stream on which release will be done.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamConsumerConnect,
+ * ::cudaEGLStreamConsumerDisconnect,
+ * ::cudaEGLStreamConsumerAcquireFrame,
+ * ::cuEGLStreamConsumerReleaseFrame
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamConsumerReleaseFrame(cudaEglStreamConnection *conn,
+                                                  cudaGraphicsResource_t pCudaResource, cudaStream_t *pStream);
+
+/**
+ * \brief Connect CUDA to EGLStream as a producer.
+ *
+ * Connect CUDA as a producer to EGLStreamKHR specified by \p stream.
+ *
+ * The EGLStreamKHR is an EGL object that transfers a sequence of image frames from one
+ * API to another.
+ *
+ * \param conn   - Pointer to the returned connection handle
+ * \param eglStream - EGLStreamKHR handle
+ * \param width  - width of the image to be submitted to the stream
+ * \param height - height of the image to be submitted to the stream
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamProducerDisconnect,
+ * ::cudaEGLStreamProducerPresentFrame,
+ * ::cudaEGLStreamProducerReturnFrame,
+ * ::cuEGLStreamProducerConnect
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamProducerConnect(cudaEglStreamConnection *conn,
+                                                EGLStreamKHR eglStream, EGLint width, EGLint height);
+
+/**
+ * \brief Disconnect CUDA as a producer  to EGLStream .
+ *
+ * Disconnect CUDA as a producer to EGLStreamKHR.
+ *
+ * \param conn            - Conection to disconnect.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamProducerConnect,
+ * ::cudaEGLStreamProducerPresentFrame,
+ * ::cudaEGLStreamProducerReturnFrame,
+ * ::cuEGLStreamProducerDisconnect
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamProducerDisconnect(cudaEglStreamConnection *conn);
+
+/**
+ * \brief Present a CUDA eglFrame to the EGLStream with CUDA as a producer.
+ *
+ * The ::cudaEglFrame is defined as:
+ * \code
+ * typedef struct cudaEglFrame_st {
+ *     union {
+ *         cudaArray_t            pArray[CUDA_EGL_MAX_PLANES];
+ *         struct cudaPitchedPtr  pPitch[CUDA_EGL_MAX_PLANES];
+ *     } frame;
+ *     cudaEglPlaneDesc planeDesc[CUDA_EGL_MAX_PLANES];
+ *     unsigned int planeCount;
+ *     cudaEglFrameType frameType;
+ *     cudaEglColorFormat eglColorFormat;
+ * } cudaEglFrame;
+ * \endcode
+ *
+ * For ::cudaEglFrame of type ::cudaEglFrameTypePitch, the application may present sub-region of a memory
+ * allocation. In that case, ::cudaPitchedPtr::ptr will specify the start address of the sub-region in
+ * the allocation and ::cudaEglPlaneDesc will specify the dimensions of the sub-region.
+ *
+ * \param conn            - Connection on which to present the CUDA array
+ * \param eglframe        - CUDA Eglstream Proucer Frame handle to be sent to the consumer over EglStream.
+ * \param pStream         - CUDA stream on which to present the frame.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamProducerConnect,
+ * ::cudaEGLStreamProducerDisconnect,
+ * ::cudaEGLStreamProducerReturnFrame,
+ * ::cuEGLStreamProducerPresentFrame
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamProducerPresentFrame(cudaEglStreamConnection *conn,
+                                                 cudaEglFrame eglframe, cudaStream_t *pStream);
+
+/**
+ * \brief Return the CUDA eglFrame to the EGLStream last released by the consumer.
+ * 
+ * This API can potentially return cudaErrorLaunchTimeout if the consumer has not 
+ * returned a frame to EGL stream. If timeout is returned the application can retry.
+ *
+ * \param conn            - Connection on which to present the CUDA array
+ * \param eglframe        - CUDA Eglstream Proucer Frame handle returned from the consumer over EglStream.
+ * \param pStream         - CUDA stream on which to return the frame.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \sa
+ * ::cudaEGLStreamProducerConnect,
+ * ::cudaEGLStreamProducerDisconnect,
+ * ::cudaEGLStreamProducerPresentFrame,
+ * ::cuEGLStreamProducerReturnFrame
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEGLStreamProducerReturnFrame(cudaEglStreamConnection *conn,
+                                                cudaEglFrame *eglframe, cudaStream_t *pStream);
+
+/**
+ * \brief Get an eglFrame through which to access a registered EGL graphics resource.
+ *
+ * Returns in \p *eglFrame an eglFrame pointer through which the registered graphics resource
+ * \p resource may be accessed.
+ * This API can only be called for EGL graphics resources.
+ *
+ * The ::cudaEglFrame is defined as
+ * \code
+ * typedef struct cudaEglFrame_st {
+ *     union {
+ *         cudaArray_t             pArray[CUDA_EGL_MAX_PLANES];
+ *         struct cudaPitchedPtr   pPitch[CUDA_EGL_MAX_PLANES];
+ *     } frame;
+ *     cudaEglPlaneDesc planeDesc[CUDA_EGL_MAX_PLANES];
+ *     unsigned int planeCount;
+ *     cudaEglFrameType frameType;
+ *     cudaEglColorFormat eglColorFormat;
+ * } cudaEglFrame;
+ * \endcode
+ *
+ *
+ * \param eglFrame   - Returned eglFrame.
+ * \param resource   - Registered resource to access.
+ * \param index      - Index for cubemap surfaces.
+ * \param mipLevel   - Mipmap level for the subresource to access.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown
+ *
+ * \note Note that in case of multiplanar \p *eglFrame, pitch of only first plane (unsigned int cudaEglPlaneDesc::pitch) is to be considered by the application.
+ *
+ * \sa
+ * ::cudaGraphicsSubResourceGetMappedArray,
+ * ::cudaGraphicsResourceGetMappedPointer,
+ * ::cuGraphicsResourceGetMappedEglFrame
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsResourceGetMappedEglFrame(cudaEglFrame* eglFrame,
+                                        cudaGraphicsResource_t resource, unsigned int index, unsigned int mipLevel);
+
+/**
+ * \brief Creates an event from EGLSync object
+ *
+ * Creates an event *phEvent from an EGLSyncKHR eglSync with the flages specified
+ * via \p flags. Valid flags include:
+ * - ::cudaEventDefault: Default event creation flag.
+ * - ::cudaEventBlockingSync: Specifies that the created event should use blocking
+ * synchronization.  A CPU thread that uses ::cudaEventSynchronize() to wait on
+ * an event created with this flag will block until the event has actually
+ * been completed.
+ *
+ * ::cudaEventRecord and TimingData are not supported for events created from EGLSync.
+ *
+ * The EGLSyncKHR is an opaque handle to an EGL sync object.
+ * typedef void* EGLSyncKHR
+ *
+ * \param phEvent - Returns newly created event
+ * \param eglSync - Opaque handle to EGLSync object
+ * \param flags   - Event creation flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorMemoryAllocation
+ *
+ * \sa
+ * ::cudaEventQuery,
+ * ::cudaEventSynchronize,
+ * ::cudaEventDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEventCreateFromEGLSync(cudaEvent_t *phEvent, EGLSyncKHR eglSync, unsigned int flags);
+
+/** @} */ /* END CUDART_EGL */
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* __CUDA_EGL_INTEROP_H__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp16.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp16.h
new file mode 100644
index 0000000000000000000000000000000000000000..ecfa4584a4cb31d3a75f418cf2e60034b51806b3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp16.h
@@ -0,0 +1,5363 @@
+/*
+* Copyright 1993-2024 NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO LICENSEE:
+*
+* This source code and/or documentation ("Licensed Deliverables") are
+* subject to NVIDIA intellectual property rights under U.S. and
+* international Copyright laws.
+*
+* These Licensed Deliverables contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and
+* conditions of a form of NVIDIA software license agreement by and
+* between NVIDIA and Licensee ("License Agreement") or electronically
+* accepted by Licensee.  Notwithstanding any terms or conditions to
+* the contrary in the License Agreement, reproduction or disclosure
+* of the Licensed Deliverables to any third party without the express
+* written consent of NVIDIA is prohibited.
+*
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+* OF THESE LICENSED DELIVERABLES.
+*
+* U.S. Government End Users.  These Licensed Deliverables are a
+* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+* 1995), consisting of "commercial computer software" and "commercial
+* computer software documentation" as such terms are used in 48
+* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+* only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+* U.S. Government End Users acquire the Licensed Deliverables with
+* only those rights set forth herein.
+*
+* Any use of the Licensed Deliverables in individual and commercial
+* software must include, in the user documentation and internal
+* comments to the code, the above Disclaimer and U.S. Government End
+* Users Notice.
+*/
+
+/**
+* \defgroup CUDA_MATH_INTRINSIC_HALF Half Precision Intrinsics
+* This section describes half precision intrinsic functions.
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+* All of the functions defined here are available in device code.
+* Some of the functions are also available to host compilers, please
+* refer to respective functions' documentation for details.
+*
+* NOTE: Aggressive floating-point optimizations performed by host or device
+* compilers may affect numeric behavior of the functions implemented in this
+* header.
+*
+* The following macros are available to help users selectively enable/disable
+* various definitions present in the header file:
+* - \p CUDA_NO_HALF - If defined, this macro will prevent the definition of
+* additional type aliases in the global namespace, helping to avoid potential
+* conflicts with symbols defined in the user program.
+* - \p __CUDA_NO_HALF_CONVERSIONS__ - If defined, this macro will prevent the
+* use of the C++ type conversions (converting constructors and conversion
+* operators) that are common for built-in floating-point types, but may be
+* undesirable for \p half which is essentially a user-defined type.
+* - \p __CUDA_NO_HALF_OPERATORS__ and \p __CUDA_NO_HALF2_OPERATORS__ - If
+* defined, these macros will prevent the inadvertent use of usual arithmetic
+* and comparison operators. This enforces the storage-only type semantics and
+* prevents C++ style computations on \p half and \p half2 types.
+*/
+
+/**
+* \defgroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS Half Arithmetic Constants
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these constants, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF_ARITHMETIC Half Arithmetic Functions
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF2_ARITHMETIC Half2 Arithmetic Functions
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF_COMPARISON Half Comparison Functions
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF2_COMPARISON Half2 Comparison Functions
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF_MISC Half Precision Conversion and Data Movement
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF_FUNCTIONS Half Math Functions
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+/**
+* \defgroup CUDA_MATH__HALF2_FUNCTIONS Half2 Math Functions
+* \ingroup CUDA_MATH_INTRINSIC_HALF
+* To use these functions, include the header file \p cuda_fp16.h in your program.
+*/
+
+#ifndef __CUDA_FP16_H__
+#define __CUDA_FP16_H__
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+/* bring in float2, double4, etc vector types */
+#include "vector_types.h"
+/* bring in operations on vector types like: make_float2 */
+#include "vector_functions.h"
+#endif  /* !defined(__CUDACC_RTC__) */
+
+#define ___CUDA_FP16_STRINGIFY_INNERMOST(x) #x
+#define __CUDA_FP16_STRINGIFY(x) ___CUDA_FP16_STRINGIFY_INNERMOST(x)
+
+#if defined(__cplusplus)
+
+/* Set up function decorations */
+#if (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))
+#define __CUDA_FP16_DECL__ __device__
+#define __CUDA_HOSTDEVICE_FP16_DECL__ __device__
+#define __CUDA_HOSTDEVICE__ __device__
+#elif defined(__CUDACC__) || defined(_NVHPC_CUDA)
+#define __CUDA_FP16_DECL__ static __device__ __inline__
+#define __CUDA_HOSTDEVICE_FP16_DECL__ static __host__ __device__ __inline__
+#define __CUDA_HOSTDEVICE__ __host__ __device__
+#else /* !defined(__CUDACC__) */
+#if defined(__GNUC__)
+#define __CUDA_HOSTDEVICE_FP16_DECL__ static __attribute__ ((unused))
+#else
+#define __CUDA_HOSTDEVICE_FP16_DECL__ static
+#endif /* defined(__GNUC__) */
+#define __CUDA_HOSTDEVICE__
+#endif /* (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3)))) */
+
+#define __CUDA_FP16_TYPES_EXIST__
+
+/* Macros to allow half & half2 to be used by inline assembly */
+#define __HALF_TO_US(var) *(reinterpret_cast<unsigned short *>(&(var)))
+#define __HALF_TO_CUS(var) *(reinterpret_cast<const unsigned short *>(&(var)))
+#define __HALF2_TO_UI(var) *(reinterpret_cast<unsigned int *>(&(var)))
+#define __HALF2_TO_CUI(var) *(reinterpret_cast<const unsigned int *>(&(var)))
+
+/* Forward-declaration of structures defined in "cuda_fp16.hpp" */
+struct __half;
+struct __half2;
+
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts double number to half precision in round-to-nearest-even mode
+* and returns \p half with converted value.
+*
+* \details Converts double number \p a to half precision in round-to-nearest-even mode.
+* \param[in] a - double. Is only being read.
+* \returns half
+* - \p a converted to half precision using round-to-nearest-even mode.
+* - __double2half \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __double2half \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __double2half(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __double2half(const double a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts float number to half precision in round-to-nearest-even mode
+* and returns \p half with converted value. 
+* 
+* \details Converts float number \p a to half precision in round-to-nearest-even mode. 
+* \param[in] a - float. Is only being read. 
+* \returns half
+* - \p a converted to half precision using round-to-nearest-even mode.
+* 
+* \see __float2half_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half(const float a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts float number to half precision in round-to-nearest-even mode
+* and returns \p half with converted value.
+*
+* \details Converts float number \p a to half precision in round-to-nearest-even mode.
+* \param[in] a - float. Is only being read. 
+* \returns half
+* - \p a converted to half precision using round-to-nearest-even mode.
+* - __float2half_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2half_rn \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2half_rn(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_rn(const float a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts float number to half precision in round-towards-zero mode
+* and returns \p half with converted value.
+* 
+* \details Converts float number \p a to half precision in round-towards-zero mode.
+* \param[in] a - float. Is only being read. 
+* \returns half
+* - \p a converted to half precision using round-towards-zero mode.
+* - __float2half_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2half_rz \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2half_rz(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_rz(const float a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts float number to half precision in round-down mode
+* and returns \p half with converted value.
+* 
+* \details Converts float number \p a to half precision in round-down mode.
+* \param[in] a - float. Is only being read. 
+* 
+* \returns half
+* - \p a converted to half precision using round-down mode.
+* - __float2half_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2half_rd \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2half_rd(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_rd(const float a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts float number to half precision in round-up mode
+* and returns \p half with converted value.
+* 
+* \details Converts float number \p a to half precision in round-up mode.
+* \param[in] a - float. Is only being read. 
+* 
+* \returns half
+* - \p a converted to half precision using round-up mode.
+* - __float2half_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __float2half_ru \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __float2half_ru(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_ru(const float a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts \p half number to float.
+* 
+* \details Converts half number \p a to float.
+* \param[in] a - float. Is only being read. 
+* 
+* \returns float
+* - \p a converted to float. 
+* - __half2float \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - __half2float \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - __half2float(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ float __half2float(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts input to half precision in round-to-nearest-even mode and
+* populates both halves of \p half2 with converted value.
+*
+* \details Converts input \p a to half precision in round-to-nearest-even mode and
+* populates both halves of \p half2 with converted value.
+* \param[in] a - float. Is only being read. 
+*
+* \returns half2
+* - The \p half2 value with both halves equal to the converted half
+* precision number.
+* 
+* \see __float2half_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __float2half2_rn(const float a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts both input floats to half precision in round-to-nearest-even
+* mode and returns \p half2 with converted values.
+*
+* \details Converts both input floats to half precision in round-to-nearest-even mode
+* and combines the results into one \p half2 number. Low 16 bits of the return
+* value correspond to the input \p a, high 16 bits correspond to the input \p
+* b.
+* \param[in] a - float. Is only being read. 
+* \param[in] b - float. Is only being read. 
+* 
+* \returns half2
+* - The \p half2 value with corresponding halves equal to the
+* converted input floats.
+* 
+* \see __float2half_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __floats2half2_rn(const float a, const float b);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts low 16 bits of \p half2 to float and returns the result
+* 
+* \details Converts low 16 bits of \p half2 input \p a to 32-bit floating-point number
+* and returns the result.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns float
+* - The low 16 bits of \p a converted to float.
+* 
+* \see __half2float(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ float __low2float(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts high 16 bits of \p half2 to float and returns the result
+* 
+* \details Converts high 16 bits of \p half2 input \p a to 32-bit floating-point number
+* and returns the result.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns float
+* - The high 16 bits of \p a converted to float.
+* 
+* \see __half2float(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ float __high2float(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed char in round-towards-zero mode.
+*
+* \details Convert the half-precision floating-point value \p h to a signed char
+* integer in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read.
+*
+* \returns signed char
+* - \p h converted to a signed char using round-towards-zero mode.
+* - __half2char_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2char_rz \cuda_math_formula (x), x > 127\end_cuda_math_formula returns SCHAR_MAX = \p 0x7F.
+* - __half2char_rz \cuda_math_formula (x), x < -128\end_cuda_math_formula returns SCHAR_MIN = \p 0x80.
+* - __half2char_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ signed char __half2char_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned char in round-towards-zero
+* mode.
+*
+* \details Convert the half-precision floating-point value \p h to an unsigned
+* char in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read.
+*
+* \returns unsigned char
+* - \p h converted to an unsigned char using round-towards-zero mode.
+* - __half2uchar_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2uchar_rz \cuda_math_formula (x), x > 255\end_cuda_math_formula returns UCHAR_MAX = \p 0xFF.
+* - __half2uchar_rz \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2uchar_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned char __half2uchar_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed short integer in round-towards-zero mode.
+*
+* \details Convert the half-precision floating-point value \p h to a signed short
+* integer in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read.
+*
+* \returns short int
+* - \p h converted to a signed short integer using round-towards-zero mode.
+* - __half2short_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2short_rz \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __half2short_rz \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __half2short_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ short int __half2short_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned short integer in round-towards-zero
+* mode.
+*
+* \details Convert the half-precision floating-point value \p h to an unsigned short
+* integer in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read.
+*
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer using round-towards-zero mode.
+* - __half2ushort_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ushort_rz \cuda_math_formula (+\infty)\end_cuda_math_formula returns USHRT_MAX = \p 0xFFFF.
+* - __half2ushort_rz \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ushort_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned short int __half2ushort_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed integer in round-towards-zero mode.
+*
+* \details Convert the half-precision floating-point value \p h to a signed integer in
+* round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read.
+*
+* \returns int
+* - \p h converted to a signed integer using round-towards-zero mode.
+* - __half2int_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2int_rz \cuda_math_formula (+\infty)\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __half2int_rz \cuda_math_formula (-\infty)\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __half2int_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ int __half2int_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned integer in round-towards-zero mode.
+*
+* \details Convert the half-precision floating-point value \p h to an unsigned integer
+* in round-towards-zero mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read.
+*
+* \returns unsigned int
+* - \p h converted to an unsigned integer using round-towards-zero mode.
+* - __half2uint_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2uint_rz \cuda_math_formula (+\infty)\end_cuda_math_formula returns UINT_MAX = \p 0xFFFFFFFF.
+* - __half2uint_rz \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2uint_rz(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __half2uint_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed 64-bit integer in round-towards-zero mode.
+*
+* \details Convert the half-precision floating-point value \p h to a signed 64-bit
+* integer in round-towards-zero mode. NaN inputs return a long long int with hex value of \p 0x8000000000000000.
+* \param[in] h - half. Is only being read.
+*
+* \returns long long int
+* - \p h converted to a signed 64-bit integer using round-towards-zero mode.
+* - __half2ll_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ll_rz \cuda_math_formula (+\infty)\end_cuda_math_formula returns LLONG_MAX = \p 0x7FFFFFFFFFFFFFFF.
+* - __half2ll_rz \cuda_math_formula (-\infty)\end_cuda_math_formula returns LLONG_MIN = \p 0x8000000000000000.
+* - __half2ll_rz(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ long long int __half2ll_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned 64-bit integer in round-towards-zero
+* mode.
+*
+* \details Convert the half-precision floating-point value \p h to an unsigned 64-bit
+* integer in round-towards-zero mode. NaN inputs return \p 0x8000000000000000.
+* \param[in] h - half. Is only being read.
+*
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer using round-towards-zero mode.
+* - __half2ull_rz \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ull_rz \cuda_math_formula (+\infty)\end_cuda_math_formula returns ULLONG_MAX = \p 0xFFFFFFFFFFFFFFFF.
+* - __half2ull_rz \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ull_rz(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned long long int __half2ull_rz(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Vector function, combines two \p __half numbers into one \p __half2 number.
+* 
+* \details Combines two input \p __half number \p x and \p y into one \p __half2 number.
+* Input \p x is stored in low 16 bits of the return value, input \p y is stored
+* in high 16 bits of the return value.
+* \param[in] x - half. Is only being read. 
+* \param[in] y - half. Is only being read. 
+* 
+* \returns __half2
+* - The \p __half2 vector with one half equal to \p x and the other to \p y. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 make_half2(const __half x, const __half y);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts both components of \p float2 number to half precision in
+* round-to-nearest-even mode and returns \p half2 with converted values.
+* 
+* \details Converts both components of \p float2 to half precision in round-to-nearest-even
+* mode and combines the results into one \p half2 number. Low 16 bits of the
+* return value correspond to \p a.x and high 16 bits of the return value
+* correspond to \p a.y.
+* \param[in] a - float2. Is only being read. 
+*  
+* \returns half2
+* - The \p half2 which has corresponding halves equal to the
+* converted \p float2 components.
+* 
+* \see __float2half_rn(float) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __float22half2_rn(const float2 a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Converts both halves of \p half2 to \p float2 and returns the result.
+* 
+* \details Converts both halves of \p half2 input \p a to \p float2 and returns the
+* result.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns float2
+* - \p a converted to \p float2.
+* 
+* \see __half2float(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ float2 __half22float2(const __half2 a);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed integer in round-to-nearest-even mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed integer in
+* round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-to-nearest-even mode.
+* - __half2int_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2int_rn \cuda_math_formula (+\infty)\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __half2int_rn \cuda_math_formula (-\infty)\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __half2int_rn(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ int __half2int_rn(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed integer in round-down mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed integer in
+* round-down mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-down mode.
+* - __half2int_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2int_rd \cuda_math_formula (+\infty)\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __half2int_rd \cuda_math_formula (-\infty)\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __half2int_rd(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ int __half2int_rd(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed integer in round-up mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed integer in
+* round-up mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns int
+* - \p h converted to a signed integer using round-up mode.
+* - __half2int_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2int_ru \cuda_math_formula (+\infty)\end_cuda_math_formula returns INT_MAX = \p 0x7FFFFFFF.
+* - __half2int_ru \cuda_math_formula (-\infty)\end_cuda_math_formula returns INT_MIN = \p 0x80000000.
+* - __half2int_ru(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ int __half2int_ru(const __half h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed integer to a half in round-to-nearest-even mode.
+* 
+* \details Convert the signed integer value \p i to a half-precision floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_rn(const int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed integer to a half in round-towards-zero mode.
+* 
+* \details Convert the signed integer value \p i to a half-precision floating-point
+* value in round-towards-zero mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_rz(const int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed integer to a half in round-down mode.
+* 
+* \details Convert the signed integer value \p i to a half-precision floating-point
+* value in round-down mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_rd(const int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed integer to a half in round-up mode.
+* 
+* \details Convert the signed integer value \p i to a half-precision floating-point
+* value in round-up mode.
+* \param[in] i - int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_ru(const int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed short integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed short
+* integer in round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-to-nearest-even mode.
+* - __half2short_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2short_rn \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __half2short_rn \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __half2short_rn(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ short int __half2short_rn(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed short integer in round-down mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed short
+* integer in round-down mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-down mode.
+* - __half2short_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2short_rd \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __half2short_rd \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __half2short_rd(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ short int __half2short_rd(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed short integer in round-up mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed short
+* integer in round-up mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns short int
+* - \p h converted to a signed short integer using round-up mode.
+* - __half2short_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2short_ru \cuda_math_formula (x), x > 32767\end_cuda_math_formula returns SHRT_MAX = \p 0x7FFF.
+* - __half2short_ru \cuda_math_formula (x), x < -32768\end_cuda_math_formula returns SHRT_MIN = \p 0x8000.
+* - __half2short_ru(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ short int __half2short_ru(const __half h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed short integer to a half in round-to-nearest-even
+* mode.
+* 
+* \details Convert the signed short integer value \p i to a half-precision floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_rn(const short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed short integer to a half in round-towards-zero mode.
+* 
+* \details Convert the signed short integer value \p i to a half-precision floating-point
+* value in round-towards-zero mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_rz(const short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed short integer to a half in round-down mode.
+* 
+* \details Convert the signed short integer value \p i to a half-precision floating-point
+* value in round-down mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_rd(const short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed short integer to a half in round-up mode.
+* 
+* \details Convert the signed short integer value \p i to a half-precision floating-point
+* value in round-up mode.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_ru(const short int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned integer in round-to-nearest-even mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned integer
+* in round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned int
+* - \p h converted to an unsigned integer using round-to-nearest-even mode.
+* - __half2uint_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2uint_rn \cuda_math_formula (+\infty)\end_cuda_math_formula returns UINT_MAX = \p 0xFFFFFFFF.
+* - __half2uint_rn \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2uint_rn(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned int __half2uint_rn(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned integer in round-down mode.
+*
+* \details Convert the half-precision floating-point value \p h to an unsigned integer
+* in round-down mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+*
+* \returns unsigned int
+* - \p h converted to an unsigned integer using round-down mode.
+* - __half2uint_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2uint_rd \cuda_math_formula (+\infty)\end_cuda_math_formula returns UINT_MAX = \p 0xFFFFFFFF.
+* - __half2uint_rd \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2uint_rd(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned int __half2uint_rd(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned integer in round-up mode.
+*
+* \details Convert the half-precision floating-point value \p h to an unsigned integer
+* in round-up mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+*
+* \returns unsigned int
+* - \p h converted to an unsigned integer using round-up mode.
+* - __half2uint_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2uint_ru \cuda_math_formula (+\infty)\end_cuda_math_formula returns UINT_MAX = \p 0xFFFFFFFF.
+* - __half2uint_ru \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2uint_ru(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned int __half2uint_ru(const __half h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned integer to a half in round-to-nearest-even mode.
+* 
+* \details Convert the unsigned integer value \p i to a half-precision floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_rn(const unsigned int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned integer to a half in round-towards-zero mode.
+* 
+* \details Convert the unsigned integer value \p i to a half-precision floating-point
+* value in round-towards-zero mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half.  
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_rz(const unsigned int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned integer to a half in round-down mode.
+* 
+* \details Convert the unsigned integer value \p i to a half-precision floating-point
+* value in round-down mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_rd(const unsigned int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned integer to a half in round-up mode.
+* 
+* \details Convert the unsigned integer value \p i to a half-precision floating-point
+* value in round-up mode.
+* \param[in] i - unsigned int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_ru(const unsigned int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned short integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned short
+* integer in round-to-nearest-even mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer using round-to-nearest-even mode.
+* - __half2ushort_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ushort_rn \cuda_math_formula (+\infty)\end_cuda_math_formula returns USHRT_MAX = \p 0xFFFF.
+* - __half2ushort_rn \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ushort_rn(NaN) returns 0.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned short int __half2ushort_rn(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned short integer in round-down mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned short
+* integer in round-down mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer using round-down mode.
+* - __half2ushort_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ushort_rd \cuda_math_formula (+\infty)\end_cuda_math_formula returns USHRT_MAX = \p 0xFFFF.
+* - __half2ushort_rd \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ushort_rd(NaN) returns 0.
+*/
+__CUDA_FP16_DECL__ unsigned short int __half2ushort_rd(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned short integer in round-up mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned short
+* integer in round-up mode. NaN inputs are converted to 0.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned short int
+* - \p h converted to an unsigned short integer using round-up mode.
+* - __half2ushort_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ushort_ru \cuda_math_formula (+\infty)\end_cuda_math_formula returns USHRT_MAX = \p 0xFFFF.
+* - __half2ushort_ru \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ushort_ru(NaN) returns 0.
+*/
+__CUDA_FP16_DECL__ unsigned short int __half2ushort_ru(const __half h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned short integer to a half in round-to-nearest-even
+* mode.
+* 
+* \details Convert the unsigned short integer value \p i to a half-precision floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_rn(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned short integer to a half in round-towards-zero
+* mode.
+* 
+* \details Convert the unsigned short integer value \p i to a half-precision floating-point
+* value in round-towards-zero mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_rz(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned short integer to a half in round-down mode.
+* 
+* \details Convert the unsigned short integer value \p i to a half-precision floating-point
+* value in round-down mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_rd(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned short integer to a half in round-up mode.
+* 
+* \details Convert the unsigned short integer value \p i to a half-precision floating-point
+* value in round-up mode.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_ru(const unsigned short int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned 64-bit integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned 64-bit
+* integer in round-to-nearest-even mode. NaN inputs return \p 0x8000000000000000.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer using round-to-nearest-even mode.
+* - __half2ull_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ull_rn \cuda_math_formula (+\infty)\end_cuda_math_formula returns ULLONG_MAX = \p 0xFFFFFFFFFFFFFFFF.
+* - __half2ull_rn \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ull_rn(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned long long int __half2ull_rn(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned 64-bit integer in round-down mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned 64-bit
+* integer in round-down mode. NaN inputs return \p 0x8000000000000000.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer using round-down mode.
+* - __half2ull_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ull_rd \cuda_math_formula (+\infty)\end_cuda_math_formula returns ULLONG_MAX = \p 0xFFFFFFFFFFFFFFFF.
+* - __half2ull_rd \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ull_rd(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned long long int __half2ull_rd(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to an unsigned 64-bit integer in round-up mode.
+* 
+* \details Convert the half-precision floating-point value \p h to an unsigned 64-bit
+* integer in round-up mode. NaN inputs return \p 0x8000000000000000.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned long long int
+* - \p h converted to an unsigned 64-bit integer using round-up mode.
+* - __half2ull_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ull_ru \cuda_math_formula (+\infty)\end_cuda_math_formula returns ULLONG_MAX = \p 0xFFFFFFFFFFFFFFFF.
+* - __half2ull_ru \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns 0.
+* - __half2ull_ru(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ unsigned long long int __half2ull_ru(const __half h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned 64-bit integer to a half in round-to-nearest-even
+* mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a half-precision floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_rn(const unsigned long long int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned 64-bit integer to a half in round-towards-zero
+* mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a half-precision floating-point
+* value in round-towards-zero mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_rz(const unsigned long long int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned 64-bit integer to a half in round-down mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a half-precision floating-point
+* value in round-down mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half.  
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_rd(const unsigned long long int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert an unsigned 64-bit integer to a half in round-up mode.
+* 
+* \details Convert the unsigned 64-bit integer value \p i to a half-precision floating-point
+* value in round-up mode.
+* \param[in] i - unsigned long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_ru(const unsigned long long int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed 64-bit integer in round-to-nearest-even
+* mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed 64-bit
+* integer in round-to-nearest-even mode. NaN inputs return a long long int with hex value of \p 0x8000000000000000.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer using round-to-nearest-even mode.
+* - __half2ll_rn \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ll_rn \cuda_math_formula (+\infty)\end_cuda_math_formula returns LLONG_MAX = \p 0x7FFFFFFFFFFFFFFF.
+* - __half2ll_rn \cuda_math_formula (-\infty)\end_cuda_math_formula returns LLONG_MIN = \p 0x8000000000000000.
+* - __half2ll_rn(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ long long int __half2ll_rn(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed 64-bit integer in round-down mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed 64-bit
+* integer in round-down mode. NaN inputs return a long long int with hex value of \p 0x8000000000000000.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer using round-down mode.
+* - __half2ll_rd \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ll_rd \cuda_math_formula (+\infty)\end_cuda_math_formula returns LLONG_MAX = \p 0x7FFFFFFFFFFFFFFF.
+* - __half2ll_rd \cuda_math_formula (-\infty)\end_cuda_math_formula returns LLONG_MIN = \p 0x8000000000000000.
+* - __half2ll_rd(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ long long int __half2ll_rd(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a half to a signed 64-bit integer in round-up mode.
+* 
+* \details Convert the half-precision floating-point value \p h to a signed 64-bit
+* integer in round-up mode. NaN inputs return a long long int with hex value of \p 0x8000000000000000.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns long long int
+* - \p h converted to a signed 64-bit integer using round-up mode.
+* - __half2ll_ru \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 0.
+* - __half2ll_ru \cuda_math_formula (+\infty)\end_cuda_math_formula returns LLONG_MAX = \p 0x7FFFFFFFFFFFFFFF.
+* - __half2ll_ru \cuda_math_formula (-\infty)\end_cuda_math_formula returns LLONG_MIN = \p 0x8000000000000000.
+* - __half2ll_ru(NaN) returns \p 0x8000000000000000.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ long long int __half2ll_ru(const __half h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed 64-bit integer to a half in round-to-nearest-even
+* mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a half-precision floating-point
+* value in round-to-nearest-even mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_rn(const long long int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed 64-bit integer to a half in round-towards-zero mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a half-precision floating-point
+* value in round-towards-zero mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_rz(const long long int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed 64-bit integer to a half in round-down mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a half-precision floating-point
+* value in round-down mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_rd(const long long int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Convert a signed 64-bit integer to a half in round-up mode.
+* 
+* \details Convert the signed 64-bit integer value \p i to a half-precision floating-point
+* value in round-up mode.
+* \param[in] i - long long int. Is only being read. 
+* 
+* \returns half
+* - \p i converted to half. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_ru(const long long int i);
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Truncate input argument to the integral part.
+* 
+* \details Round \p h to the largest integer value that does not exceed \p h in
+* magnitude.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns half
+* - The truncated value. 
+* - htrunc(
+* \cuda_math_formula \pm 0 \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - htrunc(
+* \cuda_math_formula \pm \infty \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - htrunc(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half htrunc(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculate ceiling of the input argument.
+* 
+* \details Compute the smallest integer value not less than \p h.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns half
+* - The smallest integer value not less than \p h. 
+* - hceil(
+* \cuda_math_formula \pm 0 \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - hceil(
+* \cuda_math_formula \pm \infty \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - hceil(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hceil(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculate the largest integer less than or equal to \p h.
+* 
+* \details Calculate the largest integer value which is less than or equal to \p h.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns half
+* - The largest integer value which is less than or equal to \p h. 
+* - hfloor(
+* \cuda_math_formula \pm 0 \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - hfloor(
+* \cuda_math_formula \pm \infty \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - hfloor(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hfloor(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Round input to nearest integer value in half-precision floating-point
+* number.
+* 
+* \details Round \p h to the nearest integer value in half-precision floating-point
+* format, with halfway cases rounded to the nearest even integer value.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns half
+* - The nearest integer to \p h. 
+* - hrint(
+* \cuda_math_formula \pm 0 \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - hrint(
+* \cuda_math_formula \pm \infty \end_cuda_math_formula
+* ) returns 
+* \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - hrint(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hrint(const __half h);
+
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Truncate \p half2 vector input argument to the integral part.
+* 
+* \details Round each component of vector \p h to the largest integer value that does
+* not exceed \p h in magnitude.
+* \param[in] h - half2. Is only being read. 
+* 
+* \returns half2
+* - The truncated \p h. 
+*
+* \see htrunc(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2trunc(const __half2 h);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculate \p half2 vector ceiling of the input argument.
+* 
+* \details For each component of vector \p h compute the smallest integer value not less
+* than \p h.
+* \param[in] h - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector of smallest integers not less than \p h. 
+*
+* \see hceil(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2ceil(const __half2 h);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculate the largest integer less than or equal to \p h.
+* 
+* \details For each component of vector \p h calculate the largest integer value which
+* is less than or equal to \p h.
+* \param[in] h - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector of largest integers which is less than or equal to \p h. 
+*
+* \see hfloor(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2floor(const __half2 h);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Round input to nearest integer value in half-precision floating-point
+* number.
+* 
+* \details Round each component of \p half2 vector \p h to the nearest integer value in
+* half-precision floating-point format, with halfway cases rounded to the
+* nearest even integer value.
+* \param[in] h - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector of rounded integer values. 
+*
+* \see hrint(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2rint(const __half2 h);
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Returns \p half2 with both halves equal to the input value.
+* 
+* \details Returns \p half2 number with both halves equal to the input \p a \p half
+* number.
+* \param[in] a - half. Is only being read. 
+* 
+* \returns half2
+* - The vector which has both its halves equal to the input \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __half2half2(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Swaps both halves of the \p half2 input.
+* 
+* \details Swaps both halves of the \p half2 input and returns a new \p half2 number
+* with swapped halves.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns half2
+* - \p a with its halves being swapped. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __lowhigh2highlow(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Extracts low 16 bits from each of the two \p half2 inputs and combines
+* into one \p half2 number. 
+* 
+* \details Extracts low 16 bits from each of the two \p half2 inputs and combines into
+* one \p half2 number. Low 16 bits from input \p a is stored in low 16 bits of
+* the return value, low 16 bits from input \p b is stored in high 16 bits of
+* the return value. 
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns half2
+* - The low 16 bits of \p a and of \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __lows2half2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Extracts high 16 bits from each of the two \p half2 inputs and
+* combines into one \p half2 number.
+* 
+* \details Extracts high 16 bits from each of the two \p half2 inputs and combines into
+* one \p half2 number. High 16 bits from input \p a is stored in low 16 bits of
+* the return value, high 16 bits from input \p b is stored in high 16 bits of
+* the return value.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns half2
+* - The high 16 bits of \p a and of \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __highs2half2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Returns high 16 bits of \p half2 input.
+*
+* \details Returns high 16 bits of \p half2 input \p a.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half
+* - The high 16 bits of the input. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __high2half(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Returns low 16 bits of \p half2 input.
+*
+* \details Returns low 16 bits of \p half2 input \p a.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half
+* - Returns \p half which contains low 16 bits of the input \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __low2half(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Checks if the input \p half number is infinite.
+* 
+* \details Checks if the input \p half number \p a is infinite. 
+* \param[in] a - half. Is only being read. 
+* 
+* \returns int 
+* - -1 if \p a is equal to negative infinity, 
+* - 1 if \p a is equal to positive infinity, 
+* - 0 otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ int __hisinf(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Combines two \p half numbers into one \p half2 number.
+* 
+* \details Combines two input \p half number \p a and \p b into one \p half2 number.
+* Input \p a is stored in low 16 bits of the return value, input \p b is stored
+* in high 16 bits of the return value.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+* 
+* \returns half2
+* - The half2 with one half equal to \p a and the other to \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __halves2half2(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Extracts low 16 bits from \p half2 input.
+* 
+* \details Extracts low 16 bits from \p half2 input \p a and returns a new \p half2
+* number which has both halves equal to the extracted bits.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns half2
+* - The half2 with both halves equal to the low 16 bits of the input. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __low2half2(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Extracts high 16 bits from \p half2 input.
+* 
+* \details Extracts high 16 bits from \p half2 input \p a and returns a new \p half2
+* number which has both halves equal to the extracted bits.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns half2
+* - The half2 with both halves equal to the high 16 bits of the input. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __high2half2(const __half2 a);
+
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Reinterprets bits in a \p half as a signed short integer.
+* 
+* \details Reinterprets the bits in the half-precision floating-point number \p h
+* as a signed short integer. 
+* \param[in] h - half. Is only being read. 
+* 
+* \returns short int
+* - The reinterpreted value. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ short int __half_as_short(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Reinterprets bits in a \p half as an unsigned short integer.
+* 
+* \details Reinterprets the bits in the half-precision floating-point \p h
+* as an unsigned short number.
+* \param[in] h - half. Is only being read. 
+* 
+* \returns unsigned short int
+* - The reinterpreted value.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned short int __half_as_ushort(const __half h);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Reinterprets bits in a signed short integer as a \p half.
+* 
+* \details Reinterprets the bits in the signed short integer \p i as a
+* half-precision floating-point number.
+* \param[in] i - short int. Is only being read. 
+* 
+* \returns half
+* - The reinterpreted value.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short_as_half(const short int i);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Reinterprets bits in an unsigned short integer as a \p half.
+* 
+* \details Reinterprets the bits in the unsigned short integer \p i as a
+* half-precision floating-point number.
+* \param[in] i - unsigned short int. Is only being read. 
+* 
+* \returns half
+* - The reinterpreted value.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort_as_half(const unsigned short int i);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Calculates \p half maximum of two input values.
+*
+* \details Calculates \p half max(\p a, \p b)
+* defined as (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmax(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Calculates \p half minimum of two input values.
+*
+* \details Calculates \p half min(\p a, \p b)
+* defined as (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmin(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Calculates \p half2 vector maximum of two inputs.
+*
+* \details Calculates \p half2 vector max(\p a, \p b).
+* Elementwise \p half operation is defined as
+* (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The result of elementwise maximum of vectors \p a  and \p b
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmax2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Calculates \p half2 vector minimum of two inputs.
+*
+* \details Calculates \p half2 vector min(\p a, \p b).
+* Elementwise \p half operation is defined as
+* (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, the other input is returned.
+* - If both inputs are NaNs, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The result of elementwise minimum of vectors \p a  and \p b
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmin2(const __half2 a, const __half2 b);
+
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300)
+#if !defined warpSize && !defined __local_warpSize
+#define warpSize    32
+#define __local_warpSize
+#endif
+
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 700)
+
+#if defined(_WIN32)
+# define __CUDA_FP16_DEPRECATED__(msg) __declspec(deprecated(msg))
+#elif (defined(__GNUC__) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 5 && !defined(__clang__))))
+# define __CUDA_FP16_DEPRECATED__(msg) __attribute__((deprecated))
+#else
+# define __CUDA_FP16_DEPRECATED__(msg) __attribute__((deprecated(msg)))
+#endif
+
+#if defined(_NVHPC_CUDA)
+#define __CUDA_FP16_WSB_DEPRECATION_MESSAGE(x) __CUDA_FP16_STRINGIFY(x) "() is deprecated in favor of " __CUDA_FP16_STRINGIFY(x) "_sync() and may be removed in a future release."
+#else
+#define __CUDA_FP16_WSB_DEPRECATION_MESSAGE(x) __CUDA_FP16_STRINGIFY(x) "() is deprecated in favor of " __CUDA_FP16_STRINGIFY(x) "_sync() and may be removed in a future release (Use -Wno-deprecated-declarations to suppress this warning)."
+#endif
+
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl)) __half2 __shfl(const __half2 var, const int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl_up)) __half2 __shfl_up(const __half2 var, const unsigned int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl_down))__half2 __shfl_down(const __half2 var, const unsigned int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl_xor)) __half2 __shfl_xor(const __half2 var, const int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl)) __half __shfl(const __half var, const int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl_up)) __half __shfl_up(const __half var, const unsigned int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl_down)) __half __shfl_down(const __half var, const unsigned int delta, const int width = warpSize);
+__CUDA_FP16_DECL__ __CUDA_FP16_DEPRECATED__(__CUDA_FP16_WSB_DEPRECATION_MESSAGE(__shfl_xor)) __half __shfl_xor(const __half var, const int delta, const int width = warpSize);
+
+#undef __CUDA_FP16_WSB_DEPRECATION_MESSAGE
+#undef __CUDA_FP16_DEPRECATED__
+#endif /* !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700 */
+
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Direct copy from indexed thread. 
+* 
+* \details Returns the value of \p var held by the thread whose ID is given by \p srcLane. 
+* If the \p width is less than \p warpSize, then each subsection of the warp behaves as a separate 
+* entity with a starting logical thread ID of 0. If \p srcLane is outside the range \p [0:width-1], 
+* the value returned corresponds to the value of \p var held by the \p srcLane modulo \p width (i.e. 
+* within the same subsection). \p width must have a value which is a power of 2; 
+* results are undefined if \p width is not a power of 2, or is a number greater than 
+* \p warpSize.
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half2. Is only being read. 
+* \param[in] srcLane - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p half2. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __shfl_sync(const unsigned int mask, const __half2 var, const int srcLane, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with lower ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by subtracting \p delta from the caller's lane ID. 
+* The value of \p var held by the resulting lane ID is returned: in effect, \p var is shifted up 
+* the warp by \p delta threads. If the \p width is less than \p warpSize, then each subsection of the warp 
+* behaves as a separate entity with a starting logical thread ID of 0. The source thread index 
+* will not wrap around the value of \p width, so effectively the lower \p delta threads will be unchanged. 
+* \p width must have a value which is a power of 2; results are undefined if \p width is not a power of 2, 
+* or is a number greater than \p warpSize. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read. 
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half2. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p half2. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __shfl_up_sync(const unsigned int mask, const __half2 var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with higher ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by adding \p delta to the caller's thread ID. 
+* The value of \p var held by the resulting thread ID is returned: this has the effect 
+* of shifting \p var down the warp by \p delta threads. If the \p width is less than \p warpSize, then 
+* each subsection of the warp behaves as a separate entity with a starting logical 
+* thread ID of 0. Similarly to the __shfl_up_sync(), the ID number of the source thread 
+* will not wrap around the value of \p width and the upper \p delta threads 
+* will remain unchanged. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read.
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half2. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p half2. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __shfl_down_sync(const unsigned int mask, const __half2 var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread based on bitwise XOR of own thread ID. 
+* 
+* \details Calculates a source thread ID by performing a bitwise XOR of the caller's thread ID with \p laneMask: 
+* the value of \p var held by the resulting thread ID is returned. If the \p width is less than \p warpSize, then each 
+* group of \p width consecutive threads are able to access elements from earlier groups of threads, 
+* however if they attempt to access elements from later groups of threads their own value of \p var 
+* will be returned. This mode implements a butterfly addressing pattern such as is used in tree 
+* reduction and broadcast. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read.
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half2. Is only being read. 
+* \param[in] laneMask - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 4-byte word referenced by \p var from the source thread ID as \p half2. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __shfl_xor_sync(const unsigned int mask, const __half2 var, const int laneMask, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Direct copy from indexed thread. 
+* 
+* \details Returns the value of \p var held by the thread whose ID is given by \p srcLane. 
+* If the \p width is less than \p warpSize, then each subsection of the warp behaves as a separate 
+* entity with a starting logical thread ID of 0. If \p srcLane is outside the range \p [0:width-1], 
+* the value returned corresponds to the value of \p var held by the \p srcLane modulo \p width (i.e. 
+* within the same subsection). \p width must have a value which is a power of 2; 
+* results are undefined if \p width is not a power of 2, or is a number greater than 
+* \p warpSize.
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read.
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half. Is only being read. 
+* \param[in] srcLane - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p half. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __shfl_sync(const unsigned int mask, const __half var, const int srcLane, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with lower ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by subtracting \p delta from the caller's lane ID. 
+* The value of \p var held by the resulting lane ID is returned: in effect, \p var is shifted up 
+* the warp by \p delta threads. If the \p width is less than \p warpSize, then each subsection of the warp 
+* behaves as a separate entity with a starting logical thread ID of 0. The source thread index 
+* will not wrap around the value of \p width, so effectively the lower \p delta threads will be unchanged. 
+* \p width must have a value which is a power of 2; results are undefined if \p width is not a power of 2, 
+* or is a number greater than \p warpSize. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read.
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p half. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __shfl_up_sync(const unsigned int mask, const __half var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread with higher ID relative to the caller. 
+* 
+* \details Calculates a source thread ID by adding \p delta to the caller's thread ID. 
+* The value of \p var held by the resulting thread ID is returned: this has the effect 
+* of shifting \p var down the warp by \p delta threads. If the \p width is less than \p warpSize, then 
+* each subsection of the warp behaves as a separate entity with a starting logical 
+* thread ID of 0. Similarly to the __shfl_up_sync(), the ID number of the source thread 
+* will not wrap around the value of \p width and the upper \p delta threads 
+* will remain unchanged. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read.
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half. Is only being read. 
+* \param[in] delta - unsigned int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p half. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __shfl_down_sync(const unsigned int mask, const __half var, const unsigned int delta, const int width = warpSize);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Exchange a variable between threads within a warp. Copy from a thread based on bitwise XOR of own thread ID. 
+* 
+* \details Calculates a source thread ID by performing a bitwise XOR of the caller's thread ID with \p laneMask: 
+* the value of \p var held by the resulting thread ID is returned. If the \p width is less than \p warpSize, then each 
+* group of \p width consecutive threads are able to access elements from earlier groups of threads, 
+* however if they attempt to access elements from later groups of threads their own value of \p var 
+* will be returned. This mode implements a butterfly addressing pattern such as is used in tree 
+* reduction and broadcast. 
+* Threads may only read data from another thread which is actively participating in the
+* \p __shfl_*sync() command. If the target thread is inactive, the retrieved value is undefined.
+* \param[in] mask - unsigned int. Is only being read.
+*  - Indicates the threads participating in the call.
+*  - A bit, representing the thread's lane ID, must be set for each participating thread
+*    to ensure they are properly converged before the intrinsic is executed by the hardware.
+*  - Each calling thread must have its own bit set in the \p mask and all non-exited threads
+*    named in \p mask must execute the same intrinsic with the same \p mask, or the result is undefined.
+* \param[in] var - half. Is only being read. 
+* \param[in] laneMask - int. Is only being read. 
+* \param[in] width - int. Is only being read. 
+* 
+* \returns Returns the 2-byte word referenced by \p var from the source thread ID as \p half. 
+* \note_ref_guide_warp_shuffle
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior not reentrant, not thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __shfl_xor_sync(const unsigned int mask, const __half var, const int laneMask, const int width = warpSize);
+
+#if defined(__local_warpSize)
+#undef warpSize
+#undef __local_warpSize
+#endif
+#endif /*!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300) */
+
+#if defined(__cplusplus) && ( !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 320) )
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.nc` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half2 __ldg(const  __half2 *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.nc` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half __ldg(const __half *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.cg` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half2 __ldcg(const  __half2 *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.cg` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half __ldcg(const __half *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.ca` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half2 __ldca(const  __half2 *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.ca` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half __ldca(const __half *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.cs` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half2 __ldcs(const  __half2 *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.cs` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half __ldcs(const __half *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.lu` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half2 __ldlu(const  __half2 *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.lu` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half __ldlu(const __half *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.cv` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half2 __ldcv(const  __half2 *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `ld.global.cv` load instruction.
+* \param[in] ptr - memory location
+* \returns The value pointed by `ptr`
+*/
+__CUDA_FP16_DECL__ __half __ldcv(const __half *const ptr);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.wb` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stwb(__half2 *const ptr, const __half2 value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.wb` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stwb(__half *const ptr, const __half value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.cg` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stcg(__half2 *const ptr, const __half2 value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.cg` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stcg(__half *const ptr, const __half value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.cs` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stcs(__half2 *const ptr, const __half2 value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.cs` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stcs(__half *const ptr, const __half value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.wt` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stwt(__half2 *const ptr, const __half2 value);
+/**
+* \ingroup CUDA_MATH__HALF_MISC
+* \brief Generates a `st.global.wt` store instruction.
+* \param[out] ptr - memory location
+* \param[in] value - the value to be stored
+*/
+__CUDA_FP16_DECL__ void __stwt(__half *const ptr, const __half value);
+#endif /*defined(__cplusplus) && ( !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 320) )*/
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs half2 vector if-equal comparison.
+* 
+* \details Performs \p half2 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector result of if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __heq2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector not-equal comparison.
+* 
+* \details Performs \p half2 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector result of not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hne2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector less-equal comparison.
+*
+* \details Performs \p half2 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The \p half2 result of less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hle2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector greater-equal comparison.
+*
+* \details Performs \p half2 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The vector result of greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hge2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector less-than comparison.
+*
+* \details Performs \p half2 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The half2 vector result of less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hlt2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector greater-than comparison.
+* 
+* \details Performs \p half2 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector result of greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hgt2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered if-equal comparison.
+* 
+* \details Performs \p half2 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns half2
+* - The vector result of unordered if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hequ2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered not-equal comparison.
+*
+* \details Performs \p half2 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The vector result of unordered not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hneu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered less-equal comparison.
+*
+* Performs \p half2 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The vector result of unordered less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hleu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered greater-equal comparison.
+*
+* \details Performs \p half2 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The \p half2 vector result of unordered greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hgeu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered less-than comparison.
+*
+* \details Performs \p half2 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The vector result of unordered less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hltu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered greater-than comparison.
+*
+* \details Performs \p half2 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p half results are set to 1.0 for true, or 0.0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The \p half2 vector result of unordered greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hgtu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs half2 vector if-equal comparison.
+* 
+* \details Performs \p half2 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __heq2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector not-equal comparison.
+* 
+* \details Performs \p half2 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hne2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector less-equal comparison.
+*
+* \details Performs \p half2 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hle2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector greater-equal comparison.
+*
+* \details Performs \p half2 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hge2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector less-than comparison.
+*
+* \details Performs \p half2 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hlt2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector greater-than comparison.
+* 
+* \details Performs \p half2 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hgt2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered if-equal comparison.
+* 
+* \details Performs \p half2 vector if-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns unsigned int
+* - The vector mask result of unordered if-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hequ2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered not-equal comparison.
+*
+* \details Performs \p half2 vector not-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered not-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hneu2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered less-equal comparison.
+*
+* Performs \p half2 vector less-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered less-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hleu2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered greater-equal comparison.
+*
+* \details Performs \p half2 vector greater-equal comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered greater-equal comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hgeu2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered less-than comparison.
+*
+* \details Performs \p half2 vector less-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered less-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hltu2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered greater-than comparison.
+*
+* \details Performs \p half2 vector greater-than comparison of inputs \p a and \p b.
+* The corresponding \p unsigned bits are set to \p 0xFFFF for true, or \p 0x0 for false.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns unsigned int
+* - The vector mask result of unordered greater-than comparison of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hgtu2_mask(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Determine whether \p half2 argument is a NaN.
+*
+* \details Determine whether each half of input \p half2 number \p a is a NaN.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The half2 with the corresponding \p half results set to
+* 1.0 for NaN, 0.0 otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hisnan2(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector addition in round-to-nearest-even mode.
+*
+* \details Performs \p half2 vector add of inputs \p a and \p b, in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-95
+* \endinternal
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The sum of vectors \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hadd2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p half2 input vector \p b from input vector \p a in
+* round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-104
+* \endinternal
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The subtraction of vector \p b from \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hsub2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p half2 vector multiplication of inputs \p a and \p b, in
+* round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-102
+* \endinternal
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The result of elementwise multiplying the vectors \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmul2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector addition in round-to-nearest-even mode.
+*
+* \details Performs \p half2 vector add of inputs \p a and \p b, in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+add into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-95
+* \endinternal
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The sum of vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hadd2_rn(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p half2 input vector \p b from input vector \p a in
+* round-to-nearest-even mode. Prevents floating-point contractions of mul+sub
+* into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-104
+* \endinternal
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The subtraction of vector \p b from \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hsub2_rn(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p half2 vector multiplication of inputs \p a and \p b, in
+* round-to-nearest-even mode. Prevents floating-point contractions of
+* mul+add or sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-102
+* \endinternal
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The result of elementwise multiplying the vectors \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmul2_rn(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector division in round-to-nearest-even mode.
+*
+* \details Divides \p half2 input vector \p a by input vector \p b in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-103
+* \endinternal
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise division of \p a with \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __h2div(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Calculates the absolute value of both halves of the input \p half2 number and
+* returns the result.
+*
+* \details Calculates the absolute value of both halves of the input \p half2 number and
+* returns the result.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - Returns \p a with the absolute value of both halves. 
+*
+* \see __habs(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __habs2(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector addition in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Performs \p half2 vector add of inputs \p a and \p b, in round-to-nearest-even
+* mode, and clamps the results to range [0.0, 1.0]. NaN results are flushed to
+* +0.0.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The sum of \p a and \p b, with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hadd2_sat(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector subtraction in round-to-nearest-even mode,
+* with saturation to [0.0, 1.0].
+*
+* \details Subtracts \p half2 input vector \p b from input vector \p a in
+* round-to-nearest-even mode, and clamps the results to range [0.0, 1.0]. NaN
+* results are flushed to +0.0.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The subtraction of vector \p b from \p a, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hsub2_sat(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector multiplication in round-to-nearest-even mode,
+* with saturation to [0.0, 1.0].
+*
+* \details Performs \p half2 vector multiplication of inputs \p a and \p b, in
+* round-to-nearest-even mode, and clamps the results to range [0.0, 1.0]. NaN
+* results are flushed to +0.0.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns half2
+* - The result of elementwise multiplication of vectors \p a and \p b, 
+* with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmul2_sat(const __half2 a, const __half2 b);
+
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector fused multiply-add in round-to-nearest-even
+* mode.
+*
+* \details Performs \p half2 vector multiply on inputs \p a and \p b,
+* then performs a \p half2 vector add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-105
+* \endinternal
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* \param[in] c - half2. Is only being read. 
+*
+* \returns half2
+* - The result of elementwise fused multiply-add operation on vectors \p a, \p b, and \p c. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __hfma2(const __half2 a, const __half2 b, const __half2 c);
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector fused multiply-add in round-to-nearest-even
+* mode, with saturation to [0.0, 1.0].
+*
+* \details Performs \p half2 vector multiply on inputs \p a and \p b,
+* then performs a \p half2 vector add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode, and clamps the
+* results to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* \param[in] c - half2. Is only being read. 
+*
+* \returns half2
+* - The result of elementwise fused multiply-add operation on vectors \p a, \p b, and \p c, 
+* with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __hfma2_sat(const __half2 a, const __half2 b, const __half2 c);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Negates both halves of the input \p half2 number and returns the
+* result.
+*
+* \details Negates both halves of the input \p half2 number \p a and returns the result.
+* \internal
+* \req DEEPLEARN-SRM_REQ-101
+* \endinternal
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - Returns \p a with both halves negated. 
+* 
+* \see __hneg(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hneg2(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Calculates the absolute value of input \p half number and returns the result.
+*
+* \details Calculates the absolute value of input \p half number and returns the result.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The absolute value of \p a.
+* - __habs \cuda_math_formula (\pm 0)\end_cuda_math_formula returns +0.
+* - __habs \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - __habs(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __habs(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half addition in round-to-nearest-even mode.
+*
+* \details Performs \p half addition of inputs \p a and \p b, in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-94
+* \endinternal
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns half
+* - The sum of \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hadd(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p half input \p b from input \p a in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-97
+* \endinternal
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns half
+* - The result of subtracting \p b from \p a. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hsub(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p half multiplication of inputs \p a and \p b, in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-99
+* \endinternal
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns half
+* - The result of multiplying \p a and \p b. 
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmul(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half addition in round-to-nearest-even mode.
+*
+* \details Performs \p half addition of inputs \p a and \p b, in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+add into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-94
+* \endinternal
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* - The sum of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hadd_rn(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half subtraction in round-to-nearest-even mode.
+*
+* \details Subtracts \p half input \p b from input \p a in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-97
+* \endinternal
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* - The result of subtracting \p b from \p a.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hsub_rn(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half multiplication in round-to-nearest-even mode.
+*
+* \details Performs \p half multiplication of inputs \p a and \p b, in round-to-nearest-even
+* mode. Prevents floating-point contractions of mul+add or sub into fma.
+* \internal
+* \req DEEPLEARN-SRM_REQ-99
+* \endinternal
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* - The result of multiplying \p a and \p b.
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmul_rn(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half division in round-to-nearest-even mode.
+* 
+* \details Divides \p half input \p a by input \p b in round-to-nearest-even
+* mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-98
+* \endinternal
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+* 
+* \returns half
+* - The result of dividing \p a by \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__  __half __hdiv(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half addition in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Performs \p half add of inputs \p a and \p b, in round-to-nearest-even mode,
+* and clamps the result to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns half
+* - The sum of \p a and \p b, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hadd_sat(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half subtraction in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Subtracts \p half input \p b from input \p a in round-to-nearest-even
+* mode,
+* and clamps the result to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns half
+* - The result of subtraction of \p b from \p a, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hsub_sat(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half multiplication in round-to-nearest-even mode, with
+* saturation to [0.0, 1.0].
+*
+* \details Performs \p half multiplication of inputs \p a and \p b, in round-to-nearest-even
+* mode, and clamps the result to range [0.0, 1.0]. NaN results are flushed to
+* +0.0.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns half
+* - The result of multiplying \p a and \p b, with respect to saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmul_sat(const __half a, const __half b);
+
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half fused multiply-add in round-to-nearest-even mode.
+*
+* \details Performs \p half multiply on inputs \p a and \p b,
+* then performs a \p half add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* \internal
+* \req DEEPLEARN-SRM_REQ-96
+* \endinternal
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+* \param[in] c - half. Is only being read. 
+*
+* \returns half
+* - The result of fused multiply-add operation on \p
+* a, \p b, and \p c. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __hfma(const __half a, const __half b, const __half c);
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half fused multiply-add in round-to-nearest-even mode,
+* with saturation to [0.0, 1.0].
+*
+* \details Performs \p half multiply on inputs \p a and \p b,
+* then performs a \p half add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode, and clamps the result
+* to range [0.0, 1.0]. NaN results are flushed to +0.0.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+* \param[in] c - half. Is only being read. 
+*
+* \returns half
+* - The result of fused multiply-add operation on \p
+* a, \p b, and \p c, with respect to saturation. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __hfma_sat(const __half a, const __half b, const __half c);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Negates input \p half number and returns the result.
+*
+* \details Negates input \p half number and returns the result.
+* \internal
+* \req DEEPLEARN-SRM_REQ-100
+* \endinternal
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - Negated input \p a.
+* - __hneg \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \mp 0 \end_cuda_math_formula.
+* - __hneg \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \mp \infty \end_cuda_math_formula.
+* - __hneg(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hneg(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector if-equal comparison and returns boolean true
+* if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector if-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half if-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of if-equal comparison
+* of vectors \p a and \p b are true;
+* - false otherwise.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbeq2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector not-equal comparison and returns boolean
+* true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector not-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half not-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of not-equal comparison
+* of vectors \p a and \p b are true, 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbne2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector less-equal comparison and returns boolean
+* true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector less-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half less-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of less-equal comparison
+* of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hble2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector greater-equal comparison and returns boolean
+* true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector greater-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half greater-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of greater-equal
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbge2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector less-than comparison and returns boolean
+* true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector less-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half less-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of less-than comparison
+* of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hblt2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector greater-than comparison and returns boolean
+* true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector greater-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half greater-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate false results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+* 
+* \returns bool 
+* - true if both \p half results of greater-than
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbgt2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered if-equal comparison and returns
+* boolean true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector if-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half if-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of unordered if-equal
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbequ2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered not-equal comparison and returns
+* boolean true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector not-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half not-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of unordered not-equal
+* comparison of vectors \p a and \p b are true;
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbneu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered less-equal comparison and returns
+* boolean true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector less-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half less-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of unordered less-equal
+* comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbleu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered greater-equal comparison and
+* returns boolean true if both \p half results are true, boolean false
+* otherwise.
+*
+* \details Performs \p half2 vector greater-equal comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half greater-equal comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of unordered
+* greater-equal comparison of vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbgeu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered less-than comparison and returns
+* boolean true if both \p half results are true, boolean false otherwise.
+*
+* \details Performs \p half2 vector less-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half less-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of unordered less-than comparison of 
+* vectors \p a and \p b are true; 
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbltu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Performs \p half2 vector unordered greater-than comparison and
+* returns boolean true if both \p half results are true, boolean false
+* otherwise.
+*
+* \details Performs \p half2 vector greater-than comparison of inputs \p a and \p b.
+* The bool result is set to true only if both \p half greater-than comparisons
+* evaluate to true, or false otherwise.
+* NaN inputs generate true results.
+* \param[in] a - half2. Is only being read. 
+* \param[in] b - half2. Is only being read. 
+*
+* \returns bool
+* - true if both \p half results of unordered
+* greater-than comparison of vectors \p a and \p b are true;
+* - false otherwise. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbgtu2(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half if-equal comparison.
+*
+* \details Performs \p half if-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of if-equal comparison of \p a and \p b. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __heq(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half not-equal comparison.
+*
+* \details Performs \p half not-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of not-equal comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hne(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half less-equal comparison.
+*
+* \details Performs \p half less-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of less-equal comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hle(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half greater-equal comparison.
+*
+* \details Performs \p half greater-equal comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of greater-equal comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hge(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half less-than comparison.
+*
+* \details Performs \p half less-than comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of less-than comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hlt(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half greater-than comparison.
+*
+* \details Performs \p half greater-than comparison of inputs \p a and \p b.
+* NaN inputs generate false results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of greater-than comparison of \p a and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hgt(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half unordered if-equal comparison.
+*
+* \details Performs \p half if-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered if-equal comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hequ(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half unordered not-equal comparison.
+*
+* \details Performs \p half not-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered not-equal comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hneu(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half unordered less-equal comparison.
+*
+* \details Performs \p half less-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered less-equal comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hleu(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half unordered greater-equal comparison.
+*
+* \details Performs \p half greater-equal comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered greater-equal comparison of \p a
+* and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hgeu(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half unordered less-than comparison.
+*
+* \details Performs \p half less-than comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered less-than comparison of \p a and
+* \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hltu(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Performs \p half unordered greater-than comparison.
+*
+* \details Performs \p half greater-than comparison of inputs \p a and \p b.
+* NaN inputs generate true results.
+* \param[in] a - half. Is only being read. 
+* \param[in] b - half. Is only being read. 
+*
+* \returns bool
+* - The boolean result of unordered greater-than comparison of \p a
+* and \p b.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hgtu(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Determine whether \p half argument is a NaN.
+*
+* \details Determine whether \p half value \p a is a NaN.
+* \param[in] a - half. Is only being read. 
+*
+* \returns bool
+* - true if argument is NaN. 
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hisnan(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Calculates \p half maximum of two input values, NaNs pass through.
+*
+* \details Calculates \p half max(\p a, \p b)
+* defined as (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmax_nan(const __half a, const __half b);
+/**
+* \ingroup CUDA_MATH__HALF_COMPARISON
+* \brief Calculates \p half minimum of two input values, NaNs pass through.
+*
+* \details Calculates \p half min(\p a, \p b)
+* defined as (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+*
+* \returns half
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmin_nan(const __half a, const __half b);
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Performs \p half fused multiply-add in round-to-nearest-even mode with relu saturation.
+*
+* \details Performs \p half multiply on inputs \p a and \p b,
+* then performs a \p half add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* Then negative result is clamped to 0.
+* NaN result is converted to canonical NaN.
+* \param[in] a - half. Is only being read.
+* \param[in] b - half. Is only being read.
+* \param[in] c - half. Is only being read.
+*
+* \returns half
+* - The result of fused multiply-add operation on \p
+* a, \p b, and \p c with relu saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half __hfma_relu(const __half a, const __half b, const __half c);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Calculates \p half2 vector maximum of two inputs, NaNs pass through.
+*
+* \details Calculates \p half2 vector max(\p a, \p b).
+* Elementwise \p half operation is defined as
+* (\p a > \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The result of elementwise maximum of vectors \p a  and \p b, with NaNs pass through
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmax2_nan(const __half2 a, const __half2 b);
+/**
+* \ingroup CUDA_MATH__HALF2_COMPARISON
+* \brief Calculates \p half2 vector minimum of two inputs, NaNs pass through.
+*
+* \details Calculates \p half2 vector min(\p a, \p b).
+* Elementwise \p half operation is defined as
+* (\p a < \p b) ? \p a : \p b.
+* - If either of inputs is NaN, then canonical NaN is returned.
+* - If values of both inputs are 0.0, then +0.0 > -0.0
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+*
+* \returns half2
+* - The result of elementwise minimum of vectors \p a  and \p b, with NaNs pass through
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmin2_nan(const __half2 a, const __half2 b);
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs \p half2 vector fused multiply-add in round-to-nearest-even
+* mode with relu saturation.
+*
+* \details Performs \p half2 vector multiply on inputs \p a and \p b,
+* then performs a \p half2 vector add of the result with \p c,
+* rounding the result once in round-to-nearest-even mode.
+* Then negative result is clamped to 0.
+* NaN result is converted to canonical NaN.
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+* \param[in] c - half2. Is only being read.
+*
+* \returns half2
+* - The result of elementwise fused multiply-add operation on vectors \p a, \p b, and \p c with relu saturation.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __hfma2_relu(const __half2 a, const __half2 b, const __half2 c);
+
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Performs fast complex multiply-accumulate
+*
+* \details Interprets vector \p half2 input pairs \p a, \p b, and \p c as
+* complex numbers in \p half precision: (a.x + I*a.y), (b.x + I*b.y), (c.x + I*c.y)
+* and performs complex multiply-accumulate operation: a*b + c in a simple way:
+* ((a.x*b.x + c.x) - a.y*b.y) + I*((a.x*b.y + c.y) + a.y*b.x)
+* \param[in] a - half2. Is only being read.
+* \param[in] b - half2. Is only being read.
+* \param[in] c - half2. Is only being read.
+*
+* \returns half2
+* - The result of complex multiply-accumulate operation on complex numbers \p a, \p b, and \p c
+* - __half2 result = __hcmadd(a, b, c) is numerically in agreement with:
+* - result.x = __hfma(-a.y, b.y, __hfma(a.x, b.x, c.x))
+* - result.y = __hfma( a.y, b.x, __hfma(a.x, b.y, c.y))
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 __hcmadd(const __half2 a, const __half2 b, const __half2 c);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half square root in round-to-nearest-even mode.
+*
+* \details Calculates \p half square root of input: \cuda_math_formula \sqrt{a} \end_cuda_math_formula in round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The square root of \p a.
+* - hsqrt \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hsqrt \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - hsqrt \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns NaN.
+* - hsqrt(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hsqrt(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half reciprocal square root in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half reciprocal square root of input: \cuda_math_formula \frac{1}{\sqrt{a}}\end_cuda_math_formula in round-to-nearest-even
+* mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The reciprocal square root of \p a.
+* - hrsqrt \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - hrsqrt \cuda_math_formula (+\infty)\end_cuda_math_formula returns +0.
+* - hrsqrt \cuda_math_formula (x), x < 0.0\end_cuda_math_formula returns NaN.
+* - hrsqrt(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hrsqrt(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half reciprocal in round-to-nearest-even mode.
+*
+* \details Calculates \p half reciprocal of input: \cuda_math_formula \frac{1}{a}\end_cuda_math_formula in round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The reciprocal of \p a.
+* - hrcp \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula \pm \infty \end_cuda_math_formula.
+* - hrcp \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+* - hrcp(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hrcp(const __half a);
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half natural logarithm in round-to-nearest-even mode.
+*
+* \details Calculates \p half natural logarithm of input: \cuda_math_formula \ln(a)\end_cuda_math_formula in round-to-nearest-even
+* mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The natural logarithm of \p a.
+* - hlog \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula -\infty \end_cuda_math_formula.
+* - hlog(1) returns +0.
+* - hlog(x), x < 0 returns NaN.
+* - hlog \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hlog(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hlog(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half binary logarithm in round-to-nearest-even mode.
+*
+* \details Calculates \p half binary logarithm of input: \cuda_math_formula \log_{2}(a)\end_cuda_math_formula in round-to-nearest-even
+* mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The binary logarithm of \p a.
+* - hlog2 \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula -\infty \end_cuda_math_formula.
+* - hlog2(1) returns +0.
+* - hlog2(x), x < 0 returns NaN.
+* - hlog2 \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hlog2(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hlog2(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half decimal logarithm in round-to-nearest-even mode.
+*
+* \details Calculates \p half decimal logarithm of input: \cuda_math_formula \log_{10}(a)\end_cuda_math_formula in round-to-nearest-even
+* mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The decimal logarithm of \p a.
+* - hlog10 \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula -\infty \end_cuda_math_formula.
+* - hlog10(1) returns +0.
+* - hlog10(x), x < 0 returns NaN.
+* - hlog10 \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hlog10(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hlog10(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half natural exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half natural exponential function of input: \cuda_math_formula e^{a}\end_cuda_math_formula in
+* round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The natural exponential function on \p a.
+* - hexp \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 1.
+* - hexp \cuda_math_formula (-\infty)\end_cuda_math_formula returns +0.
+* - hexp \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hexp(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hexp(const __half a);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates approximate \p half hyperbolic tangent function.
+*
+* \details Calculates approximate \p half hyperbolic tangent function: \cuda_math_formula \tanh(a)\end_cuda_math_formula.
+* This operation uses HW acceleration on devices of compute capability 7.5 and higher.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The approximate hyperbolic tangent function of \p a.
+* - htanh_approx \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula (\pm 0)\end_cuda_math_formula.
+* - htanh_approx \cuda_math_formula (\pm\infty)\end_cuda_math_formula returns \cuda_math_formula (\pm 1)\end_cuda_math_formula.
+* - htanh_approx(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half htanh_approx(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector approximate hyperbolic tangent function.
+*
+* \details Calculates \p half2 approximate hyperbolic tangent function of input vector \p a.
+* This operation uses HW acceleration on devices of compute capability 7.5 and higher.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise approximate hyperbolic tangent function on vector \p a.
+* 
+* \see htanh_approx(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2tanh_approx(const __half2 a);
+
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half hyperbolic tangent function in
+* round-to-nearest-even mode.
+*
+* \details Calculates \p half hyperbolic tangent function: \cuda_math_formula \tanh(a)\end_cuda_math_formula in
+* round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The hyperbolic tangent function of \p a.
+* - htanh \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula (\pm 0)\end_cuda_math_formula.
+* - htanh \cuda_math_formula (\pm\infty)\end_cuda_math_formula returns \cuda_math_formula (\pm 1)\end_cuda_math_formula.
+* - htanh(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half htanh(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector hyperbolic tangent function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half2 hyperbolic tangent function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise hyperbolic tangent function on vector \p a.
+* 
+* \see htanh(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2tanh(const __half2 a);
+
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half binary exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half binary exponential function of input: \cuda_math_formula 2^{a}\end_cuda_math_formula in
+* round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The binary exponential function on \p a.
+* - hexp2 \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 1.
+* - hexp2 \cuda_math_formula (-\infty)\end_cuda_math_formula returns +0.
+* - hexp2 \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hexp2(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hexp2(const __half a);
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half decimal exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half decimal exponential function of input: \cuda_math_formula 10^{a}\end_cuda_math_formula in
+* round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The decimal exponential function on \p a.
+* - hexp10 \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 1.
+* - hexp10 \cuda_math_formula (-\infty)\end_cuda_math_formula returns +0.
+* - hexp10 \cuda_math_formula (+\infty)\end_cuda_math_formula returns \cuda_math_formula +\infty \end_cuda_math_formula.
+* - hexp10(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hexp10(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half cosine in round-to-nearest-even mode.
+*
+* \details Calculates \p half cosine of input \p a in round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The cosine of \p a.
+* - hcos \cuda_math_formula (\pm 0)\end_cuda_math_formula returns 1.
+* - hcos \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns NaN.
+* - hcos(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hcos(const __half a);
+/**
+* \ingroup CUDA_MATH__HALF_FUNCTIONS
+* \brief Calculates \p half sine in round-to-nearest-even mode.
+*
+* \details Calculates \p half sine of input \p a in round-to-nearest-even mode.
+* \param[in] a - half. Is only being read. 
+*
+* \returns half
+* - The sine of \p a.
+* - hsin \cuda_math_formula (\pm 0)\end_cuda_math_formula returns \cuda_math_formula (\pm 0)\end_cuda_math_formula.
+* - hsin \cuda_math_formula (\pm \infty)\end_cuda_math_formula returns NaN.
+* - hsin(NaN) returns NaN.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half hsin(const __half a);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector square root in round-to-nearest-even mode.
+*
+* \details Calculates \p half2 square root of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise square root on vector \p a.
+* 
+* \see hsqrt(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2sqrt(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector reciprocal square root in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half2 reciprocal square root of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise reciprocal square root on vector \p a.
+* 
+* \see hrsqrt(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2rsqrt(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector reciprocal in round-to-nearest-even mode.
+*
+* \details Calculates \p half2 reciprocal of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise reciprocal on vector \p a.
+* 
+* \see hrcp(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2rcp(const __half2 a);
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector natural logarithm in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half2 natural logarithm of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise natural logarithm on vector \p a.
+* 
+* \see hlog(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2log(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector binary logarithm in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half2 binary logarithm of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise binary logarithm on vector \p a.
+* 
+* \see hlog2(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2log2(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector decimal logarithm in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half2 decimal logarithm of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise decimal logarithm on vector \p a.
+* 
+* \see hlog10(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2log10(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector exponential function in round-to-nearest-even
+* mode.
+*
+* \details Calculates \p half2 exponential function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise exponential function on vector \p a.
+* 
+* \see hexp(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2exp(const __half2 a);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector binary exponential function in
+* round-to-nearest-even mode.
+*
+* \details Calculates \p half2 binary exponential function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+*
+* \returns half2
+* - The elementwise binary exponential function on vector \p a.
+* 
+* \see hexp2(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2exp2(const __half2 a);
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector decimal exponential function in
+* round-to-nearest-even mode.
+* 
+* \details Calculates \p half2 decimal exponential function of input vector \p a in
+* round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns half2
+* - The elementwise decimal exponential function on vector \p a.
+* 
+* \see hexp10(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2exp10(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector cosine in round-to-nearest-even mode.
+* 
+* \details Calculates \p half2 cosine of input vector \p a in round-to-nearest-even
+* mode.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns half2
+* - The elementwise cosine on vector \p a.
+* 
+* \see hcos(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2cos(const __half2 a);
+/**
+* \ingroup CUDA_MATH__HALF2_FUNCTIONS
+* \brief Calculates \p half2 vector sine in round-to-nearest-even mode.
+* 
+* \details Calculates \p half2 sine of input vector \p a in round-to-nearest-even mode.
+* \param[in] a - half2. Is only being read. 
+* 
+* \returns half2
+* - The elementwise sine on vector \p a.
+* 
+* \see hsin(__half) for further details.
+* \internal
+* \exception-guarantee no-throw guarantee
+* \behavior reentrant, thread safe
+* \endinternal
+*/
+__CUDA_FP16_DECL__ __half2 h2sin(const __half2 a);
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+
+/**
+* \ingroup CUDA_MATH__HALF2_ARITHMETIC
+* \brief Vector add \p val to the value stored at \p address in global or shared memory, and writes this
+* value back to \p address. The atomicity of the add operation is guaranteed separately for each of the
+* two \p __half elements; the entire \p __half2 is not guaranteed to be atomic as a single 32-bit access.
+* 
+* \details The location of \p address must be in global or shared memory. This operation has undefined
+* behavior otherwise. This operation is natively supported by devices of compute capability 6.x and higher,
+* older devices use emulation path.
+* 
+* \param[in] address - half2*. An address in global or shared memory.
+* \param[in] val - half2. The value to be added.
+* 
+* \returns half2
+* - The old value read from \p address.
+*
+* \note_ref_guide_atomic
+*/
+__CUDA_FP16_DECL__ __half2 atomicAdd(__half2 *const address, const __half2 val);
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA)
+/**
+* \ingroup CUDA_MATH__HALF_ARITHMETIC
+* \brief Adds \p val to the value stored at \p address in global or shared memory, and writes this value
+* back to \p address. This operation is performed in one atomic operation.
+* 
+* \details The location of \p address must be in global or shared memory. This operation has undefined
+* behavior otherwise. This operation is only supported by devices of compute capability 7.x and higher.
+* 
+* \param[in] address - half*. An address in global or shared memory.
+* \param[in] val - half. The value to be added.
+* 
+* \returns half
+* - The old value read from \p address.
+* 
+* \note_ref_guide_atomic
+*/
+__CUDA_FP16_DECL__ __half atomicAdd(__half *const address, const __half val);
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA) */
+#endif /*defined(__CUDACC__) || defined(_NVHPC_CUDA)*/
+
+
+#endif /* defined(__cplusplus) */
+
+#if !defined(_MSC_VER) && __cplusplus >= 201103L
+#   define __CPP_VERSION_AT_LEAST_11_FP16
+#elif _MSC_FULL_VER >= 190024210 && _MSVC_LANG >= 201103L
+#   define __CPP_VERSION_AT_LEAST_11_FP16
+#endif
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+#include <nv/target>
+#endif  /* !defined(__CUDACC_RTC__) */
+
+/* C++11 header for std::move. 
+ * In RTC mode, std::move is provided implicitly; don't include the header
+ */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP16) && !defined(__CUDACC_RTC__)
+#include <utility>
+#endif /* __cplusplus >= 201103L && !defined(__CUDACC_RTC__) */
+
+/* C++ header for std::memcpy (used for type punning in host-side implementations).
+ * When compiling as a CUDA source file memcpy is provided implicitly.
+ * !defined(__CUDACC__) implies !defined(__CUDACC_RTC__).
+ */
+#if defined(__cplusplus) && !defined(__CUDACC__)
+#include <cstring>
+#endif /* defined(__cplusplus) && !defined(__CUDACC__) */
+
+#if (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))
+#define __CUDA_FP16_INLINE__
+#define __CUDA_FP16_FORCEINLINE__
+#else
+#define __CUDA_FP16_INLINE__ inline
+#define __CUDA_FP16_FORCEINLINE__ __forceinline__
+#endif /* (defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3)))) */
+
+/* Set up structure-alignment attribute */
+#if defined(__CUDACC__)
+#define __CUDA_ALIGN__(align) __align__(align)
+#else
+/* Define alignment macro based on compiler type (cannot assume C11 "_Alignas" is available) */
+#if __cplusplus >= 201103L
+#define __CUDA_ALIGN__(n) alignas(n)    /* C++11 kindly gives us a keyword for this */
+#else /* !defined(__CPP_VERSION_AT_LEAST_11_FP16)*/
+#if defined(__GNUC__)
+#define __CUDA_ALIGN__(n) __attribute__ ((aligned(n)))
+#elif defined(_MSC_VER)
+#define __CUDA_ALIGN__(n) __declspec(align(n))
+#else
+#define __CUDA_ALIGN__(n)
+#endif /* defined(__GNUC__) */
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP16) */
+#endif /* defined(__CUDACC__) */
+
+// define __CUDA_FP16_CONSTEXPR__ in order to
+// use constexpr where possible, with supporting C++ dialects
+// undef after use
+#if (defined __CPP_VERSION_AT_LEAST_11_FP16)
+#define __CUDA_FP16_CONSTEXPR__   constexpr
+#else
+#define __CUDA_FP16_CONSTEXPR__
+#endif
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief __half_raw data type
+ * \details Type allows static initialization of \p half until it becomes
+ * a built-in type.
+ * 
+ * - Note: this initialization is as a bit-field representation of \p half,
+ * and not a conversion from \p short to \p half.
+ * Such representation will be deprecated in a future version of CUDA.
+ * 
+ * - Note: this is visible to non-nvcc compilers, including C-only compilations
+ */
+typedef struct __CUDA_ALIGN__(2) {
+    /**
+     * Storage field contains bits representation of the \p half floating-point number.
+     */
+    unsigned short x;
+} __half_raw;
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief __half2_raw data type
+ * \details Type allows static initialization of \p half2 until it becomes
+ * a built-in type.
+ * 
+ * - Note: this initialization is as a bit-field representation of \p half2,
+ * and not a conversion from \p short2 to \p half2.
+ * Such representation will be deprecated in a future version of CUDA.
+ * 
+ * - Note: this is visible to non-nvcc compilers, including C-only compilations
+ */
+typedef struct __CUDA_ALIGN__(4) {
+    /**
+     * Storage field contains bits of the lower \p half part.
+     */
+    unsigned short x;
+    /**
+     * Storage field contains bits of the upper \p half part.
+     */
+    unsigned short y;
+} __half2_raw;
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+
+/* Hide GCC member initialization list warnings because of host/device in-function init requirement */
+#if defined(__GNUC__)
+#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Weffc++"
+#endif /* __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6) */
+#endif /* defined(__GNUC__) */
+
+/* class' : multiple assignment operators specified
+   The class has multiple assignment operators of a single type. This warning is informational */
+#if defined(_MSC_VER) && _MSC_VER >= 1500
+#pragma warning( push )
+#pragma warning( disable:4522 )
+#endif /* defined(_MSC_VER) && _MSC_VER >= 1500 */
+
+// forward-declaration of bfloat type to be used in converting constructor
+struct __nv_bfloat16;
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief __half data type
+ * \details This structure implements the datatype for storing 
+ * half-precision floating-point numbers. The structure implements 
+ * assignment, arithmetic and comparison operators, and type conversions. 
+ * 16 bits are being used in total: 1 sign bit, 5 bits for the exponent, 
+ * and the significand is being stored in 10 bits. 
+ * The total precision is 11 bits. There are 15361 representable 
+ * numbers within the interval [0.0, 1.0], endpoints included. 
+ * On average we have log10(2**11) ~ 3.311 decimal digits. 
+ * 
+ * The objective here is to provide IEEE754-compliant implementation
+ * of \p binary16 type and arithmetic with limitations due to
+ * device HW not supporting floating-point exceptions.
+ */
+struct __CUDA_ALIGN__(2) __half {
+protected:
+    /**
+     * Protected storage variable contains the bits of floating-point data.
+     */
+    unsigned short __x;
+
+public:
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * \brief Constructor by default.
+     * \details Emtpy default constructor, result is uninitialized.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP16)
+    __half() = default;
+#else
+    __CUDA_HOSTDEVICE__ __half() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP16) */
+
+    /* Convert to/from __half_raw */
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Constructor from \p __half_raw.
+     */
+    __CUDA_HOSTDEVICE__ __CUDA_FP16_CONSTEXPR__ __half(const __half_raw &hr) : __x(hr.x) { }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Assignment operator from \p __half_raw.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const __half_raw &hr);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Assignment operator from \p __half_raw to \p volatile \p __half.
+     */
+    __CUDA_HOSTDEVICE__ volatile __half &operator=(const __half_raw &hr) volatile;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Assignment operator from \p volatile \p __half_raw to \p volatile \p __half.
+     */
+    __CUDA_HOSTDEVICE__ volatile __half &operator=(const volatile __half_raw &hr) volatile;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast to \p __half_raw operator.
+     */
+    __CUDA_HOSTDEVICE__ operator __half_raw() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast to \p __half_raw operator with \p volatile input.
+     */
+    __CUDA_HOSTDEVICE__ operator __half_raw() const volatile;
+#if !defined(__CUDA_NO_HALF_CONVERSIONS__)
+#if defined(__CPP_VERSION_AT_LEAST_11_FP16)
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p __nv_bfloat16 input using default round-to-nearest-even rounding mode.
+     * Need to include the header file \p cuda_bf16.h
+     */
+    explicit __CUDA_HOSTDEVICE__ __half(const __nv_bfloat16 f); //forward declaration only, implemented in cuda_bf16.hpp
+#endif /* #if defined(__CPP_VERSION_AT_LEAST_11_FP16) */
+    /* Construct from float/double */
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p float input using default round-to-nearest-even rounding mode.
+     *
+     * \see __float2half(float) for further details.
+     */
+    __CUDA_HOSTDEVICE__ __half(const float f) { __x = __float2half(f).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p double input using default round-to-nearest-even rounding mode.
+     *
+     * \see __double2half(double) for further details.
+     */
+    __CUDA_HOSTDEVICE__ __half(const double f) { __x = __double2half(f).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast to \p float operator.
+     */
+    __CUDA_HOSTDEVICE__ operator float() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast to \p __half assignment operator from \p float input using default round-to-nearest-even rounding mode.
+     *
+     * \see __float2half(float) for further details.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const float f);
+
+    /* We omit "cast to double" operator, so as to not be ambiguous about up-cast */
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast to \p __half assignment operator from \p double input using default round-to-nearest-even rounding mode.
+     *
+     * \see __double2half(double) for further details.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const double f);
+
+/*
+ * Implicit type conversions to/from integer types were only available to nvcc compilation.
+ * Introducing them for all compilers is a potentially breaking change that may affect
+ * overloads resolution and will require users to update their code.
+ * Define __CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__ to opt-out.
+ */
+#if !(defined __CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__)
+    /* Allow automatic construction from types supported natively in hardware */
+    /* Note we do avoid constructor init-list because of special host/device compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p short integer input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const short val) { __x = __short2half_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p unsigned \p short integer input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const unsigned short val) { __x = __ushort2half_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p int input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const int val) { __x = __int2half_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p unsigned \p int input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const unsigned int val) { __x = __uint2half_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const long val) {
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (default: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            __x = __ll2half_rn(static_cast<long long>(val)).__x;
+        } else {
+            __x = __int2half_rn(static_cast<int>(val)).__x;
+        }
+    }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p unsigned \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const unsigned long val) {
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (default: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            __x = __ull2half_rn(static_cast<unsigned long long>(val)).__x;
+        } else {
+            __x = __uint2half_rn(static_cast<unsigned int>(val)).__x;
+        }
+    }
+
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p long \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const long long val) { __x = __ll2half_rn(val).__x; }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Construct \p __half from \p unsigned \p long \p long input using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half(const unsigned long long val) { __x = __ull2half_rn(val).__x; }
+
+    /* Allow automatic casts to supported built-in types, matching all that are permitted with float */
+
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p signed \p char data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2char_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator signed char() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2uchar_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned char() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in __half2char_rz(__half) and __half2uchar_rz(__half).
+     */
+    __CUDA_HOSTDEVICE__ operator char() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p short data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2short_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator short() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p unsigned \p short data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2ushort_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned short() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p int data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2int_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator int() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2uint_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned int() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p long data type.
+     * Using round-toward-zero rounding mode.
+     *
+     * Detects size of the \p long type and proceeds accordingly, see
+     * further details in __half2int_rz(__half) and __half2ll_rz(__half).
+     */
+    __CUDA_HOSTDEVICE__ operator long() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p unsigned \p long data type.
+     * Using round-toward-zero rounding mode.
+     *
+     * Detects size of the \p unsigned \p long type and proceeds
+     * accordingly, see further details in __half2uint_rz(__half) and __half2ull_rz(__half).
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned long() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p long \p long data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2ll_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator long long() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p unsigned \p long \p long data type.
+     * Using round-toward-zero rounding mode.
+     * 
+     * \see __half2ull_rz(__half) for further details.
+     */
+    __CUDA_HOSTDEVICE__ operator unsigned long long() const;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast from \p short assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const short val);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast from \p unsigned \p short assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const unsigned short val);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast from \p int assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const int val);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast from \p unsigned \p int assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const unsigned int val);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast from \p long \p long assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const long long val);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Type cast from \p unsigned \p long \p long assignment operator, using default round-to-nearest-even rounding mode.
+     */
+    __CUDA_HOSTDEVICE__ __half &operator=(const unsigned long long val);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    __CUDA_HOSTDEVICE__ __CUDA_FP16_CONSTEXPR__ operator bool() const { return (__x & 0x7FFFU) != 0U; }
+#endif /* #if !(defined __CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__) */
+#endif /* !defined(__CUDA_NO_HALF_CONVERSIONS__) */
+};
+
+#if !defined(__CUDA_NO_HALF_OPERATORS__)
+/* Some basic arithmetic operations expected of a built-in */
+
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half addition operation.
+ * \see __hadd(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator+(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half subtraction operation.
+ * \see __hsub(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator-(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half multiplication operation.
+ * \see __hmul(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator*(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half division operation.
+ * \see __hdiv(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator/(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half compound assignment with addition operation.
+ * \see __hadd(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator+=(__half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half compound assignment with subtraction operation.
+ * \see __hsub(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator-=(__half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half compound assignment with multiplication operation.
+ * \see __hmul(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator*=(__half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half compound assignment with division operation.
+ * \see __hdiv(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator/=(__half &lh, const __half &rh);
+/* Note for increment and decrement we use the raw value 0x3C00U equating to half(1.0F), to avoid the extra conversion */
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half prefix increment operation.
+ * \see __hadd(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator++(__half &h);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half prefix decrement operation.
+ * \see __hsub(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator--(__half &h);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half postfix increment operation.
+ * \see __hadd(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half  operator++(__half &h, const int ignored);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Performs \p half postfix decrement operation.
+ * \see __hsub(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half  operator--(__half &h, const int ignored);
+
+/* Unary plus and inverse operators */
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Implements \p half unary plus operator, returns input value.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator+(const __half &h);
+/**
+ * \ingroup CUDA_MATH__HALF_ARITHMETIC
+ * Implements \p half unary minus operator.
+ * \see __hneg(__half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator-(const __half &h);
+/* Some basic comparison operations to make it look like a built-in */
+/**
+ * \ingroup CUDA_MATH__HALF_COMPARISON
+ * Performs \p half ordered compare equal operation.
+ * \see __heq(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator==(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_COMPARISON
+ * Performs \p half unordered compare not-equal operation.
+ * \see __hneu(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator!=(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_COMPARISON
+ * Performs \p half ordered greater-than compare operation.
+ * \see __hgt(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator> (const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_COMPARISON
+ * Performs \p half ordered less-than compare operation.
+ * \see __hlt(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator< (const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_COMPARISON
+ * Performs \p half ordered greater-or-equal compare operation.
+ * \see __hge(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator>=(const __half &lh, const __half &rh);
+/**
+ * \ingroup CUDA_MATH__HALF_COMPARISON
+ * Performs \p half ordered less-or-equal compare operation.
+ * \see __hle(__half, __half)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator<=(const __half &lh, const __half &rh);
+#endif /* !defined(__CUDA_NO_HALF_OPERATORS__) */
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief __half2 data type
+ * \details This structure implements the datatype for storing two 
+ * half-precision floating-point numbers. 
+ * The structure implements assignment, arithmetic and comparison
+ * operators, and type conversions. 
+ * 
+ * - NOTE: __half2 is visible to non-nvcc host compilers
+ */
+struct __CUDA_ALIGN__(4) __half2 {
+    /**
+     * Storage field holding lower \p __half part.
+     */
+    __half x;
+    /**
+     * Storage field holding upper \p __half part.
+     */
+    __half y;
+
+    // All construct/copy/assign/move
+public:
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * \brief Constructor by default.
+     * \details Emtpy default constructor, result is uninitialized.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP16)
+    __half2() = default;
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Move constructor, available for \p C++11 and later dialects
+     */
+    __CUDA_HOSTDEVICE__ __half2(const __half2 &&src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __HALF2_TO_UI(*this) = std::move(__HALF2_TO_CUI(src));
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+}
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Move assignment operator, available for \p C++11 and later dialects
+     */
+    __CUDA_HOSTDEVICE__ __half2 &operator=(const __half2 &&src);
+#else
+    __CUDA_HOSTDEVICE__ __half2() { }
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP16) */
+
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Constructor from two \p __half variables
+     */
+    __CUDA_HOSTDEVICE__ __CUDA_FP16_CONSTEXPR__ __half2(const __half &a, const __half &b) : x(a), y(b) { }
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Copy constructor
+     */
+    __CUDA_HOSTDEVICE__ __half2(const __half2 &src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __HALF2_TO_UI(*this) = __HALF2_TO_CUI(src);
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+}    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Copy assignment operator
+     */
+    __CUDA_HOSTDEVICE__ __half2 &operator=(const __half2 &src);
+
+    /* Convert to/from __half2_raw */
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Constructor from \p __half2_raw
+     */
+    __CUDA_HOSTDEVICE__ __half2(const __half2_raw &h2r ) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __HALF2_TO_UI(*this) = __HALF2_TO_CUI(h2r);
+,
+    __half_raw tr;
+    tr.x = h2r.x;
+    this->x = static_cast<__half>(tr);
+    tr.x = h2r.y;
+    this->y = static_cast<__half>(tr);
+)
+}
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Assignment operator from \p __half2_raw
+     */
+    __CUDA_HOSTDEVICE__ __half2 &operator=(const __half2_raw &h2r);
+    /**
+     * \ingroup CUDA_MATH__HALF_MISC
+     * Conversion operator to \p __half2_raw
+     */
+    __CUDA_HOSTDEVICE__ operator __half2_raw() const;
+};
+
+#if !defined(__CUDA_NO_HALF2_OPERATORS__)
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half addition operation.
+ * \see __hadd2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator+(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half subtraction operation.
+ * \see __hsub2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator-(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half multiplication operation.
+ * \see __hmul2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator*(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half division operation.
+ * \see __h2div(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator/(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half compound assignment with addition operation.
+ * \see __hadd2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator+=(__half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half compound assignment with subtraction operation.
+ * \see __hsub2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator-=(__half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half compound assignment with multiplication operation.
+ * \see __hmul2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator*=(__half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half compound assignment with division operation.
+ * \see __h2div(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator/=(__half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half prefix increment operation.
+ * \see __hadd2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 &operator++(__half2 &h);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half prefix decrement operation.
+ * \see __hsub2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 &operator--(__half2 &h);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half postfix increment operation.
+ * \see __hadd2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2  operator++(__half2 &h, const int ignored);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Performs packed \p half postfix decrement operation.
+ * \see __hsub2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2  operator--(__half2 &h, const int ignored);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Implements packed \p half unary plus operator, returns input value.
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator+(const __half2 &h);
+/**
+ * \ingroup CUDA_MATH__HALF2_ARITHMETIC
+ * Implements packed \p half unary minus operator.
+ * \see __hneg2(__half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator-(const __half2 &h);
+/**
+ * \ingroup CUDA_MATH__HALF2_COMPARISON
+ * Performs packed \p half ordered compare equal operation.
+ * \see __hbeq2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator==(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_COMPARISON
+ * Performs packed \p half unordered compare not-equal operation.
+ * \see __hbneu2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator!=(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_COMPARISON
+ * Performs packed \p half ordered greater-than compare operation.
+ * \see __hbgt2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator>(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_COMPARISON
+ * Performs packed \p half ordered less-than compare operation.
+ * \see __hblt2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator<(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_COMPARISON
+ * Performs packed \p half ordered greater-or-equal compare operation.
+ * \see __hbge2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator>=(const __half2 &lh, const __half2 &rh);
+/**
+ * \ingroup CUDA_MATH__HALF2_COMPARISON
+ * Performs packed \p half ordered less-or-equal compare operation.
+ * \see __hble2(__half2, __half2)
+ */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator<=(const __half2 &lh, const __half2 &rh);
+
+#endif /* !defined(__CUDA_NO_HALF2_OPERATORS__) */
+#endif /* defined(__cplusplus) */
+
+#if (defined(__FORCE_INCLUDE_CUDA_FP16_HPP_FROM_FP16_H__) || \
+    !(defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3)))))
+
+/* Note the .hpp file is included to capture the "half" & "half2" built-in function definitions. For NVRTC, the built-in
+   function definitions are compiled at NVRTC library build-time and are available through the NVRTC built-ins library at
+   link time.
+*/
+#include "cuda_fp16.hpp"
+#endif /* (defined(__FORCE_INCLUDE_CUDA_FP16_HPP_FROM_FP16_H__) || \
+          !(defined(__CUDACC_RTC__) && ((__CUDACC_VER_MAJOR__ > 12) || ((__CUDACC_VER_MAJOR__ == 12) && (__CUDACC_VER_MINOR__ >= 3))))) */
+
+/* Define first-class types "half" and "half2", unless user specifies otherwise via "#define CUDA_NO_HALF" */
+/* C cannot ever have these types defined here, because __half and __half2 are C++ classes */
+#if defined(__cplusplus) && !defined(CUDA_NO_HALF)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is meant to be the first-class or fundamental
+ * implementation of the half-precision numbers format.
+ * 
+ * \details Should be implemented in the compiler in the future.
+ * Current implementation is a simple typedef to a respective
+ * user-level type with underscores.
+ */
+typedef __half half;
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is meant to be the first-class or fundamental
+ * implementation of type for pairs of half-precision numbers.
+ * 
+ * \details Should be implemented in the compiler in the future.
+ * Current implementation is a simple typedef to a respective
+ * user-level type with underscores.
+ */
+typedef __half2 half2;
+// for consistency with __nv_bfloat16
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is an \p __nv_ prefixed alias
+ */
+typedef __half      __nv_half;
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is an \p __nv_ prefixed alias
+ */
+typedef __half2     __nv_half2;
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is an \p __nv_ prefixed alias
+ */
+typedef __half_raw  __nv_half_raw;
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is an \p __nv_ prefixed alias
+ */
+typedef __half2_raw __nv_half2_raw;
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is an \p nv_ prefixed alias
+ */
+typedef __half        nv_half;
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF
+ * \brief This datatype is an \p nv_ prefixed alias
+ */
+typedef __half2       nv_half2;
+#endif /* defined(__cplusplus) && !defined(CUDA_NO_HALF) */
+
+#undef __CUDA_FP16_DECL__
+#undef __CUDA_HOSTDEVICE_FP16_DECL__
+#undef __CUDA_HOSTDEVICE__
+#undef __CUDA_FP16_INLINE__
+#undef __CUDA_FP16_FORCEINLINE__
+#undef ___CUDA_FP16_STRINGIFY_INNERMOST
+#undef __CUDA_FP16_STRINGIFY
+
+#endif /* end of include guard: __CUDA_FP16_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp16.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp16.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..f0eb872db456a661133a1a90bfed36864ff47ebf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp16.hpp
@@ -0,0 +1,3483 @@
+/*
+* Copyright 1993-2024 NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO LICENSEE:
+*
+* This source code and/or documentation ("Licensed Deliverables") are
+* subject to NVIDIA intellectual property rights under U.S. and
+* international Copyright laws.
+*
+* These Licensed Deliverables contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and
+* conditions of a form of NVIDIA software license agreement by and
+* between NVIDIA and Licensee ("License Agreement") or electronically
+* accepted by Licensee.  Notwithstanding any terms or conditions to
+* the contrary in the License Agreement, reproduction or disclosure
+* of the Licensed Deliverables to any third party without the express
+* written consent of NVIDIA is prohibited.
+*
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+* SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+* PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+* NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+* DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+* NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+* LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+* SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+* DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+* WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+* ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+* OF THESE LICENSED DELIVERABLES.
+*
+* U.S. Government End Users.  These Licensed Deliverables are a
+* "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+* 1995), consisting of "commercial computer software" and "commercial
+* computer software documentation" as such terms are used in 48
+* C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+* only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+* 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+* U.S. Government End Users acquire the Licensed Deliverables with
+* only those rights set forth herein.
+*
+* Any use of the Licensed Deliverables in individual and commercial
+* software must include, in the user documentation and internal
+* comments to the code, the above Disclaimer and U.S. Government End
+* Users Notice.
+*/
+
+#if !defined(__CUDA_FP16_HPP__)
+#define __CUDA_FP16_HPP__
+
+#if !defined(__CUDA_FP16_H__)
+#error "Do not include this file directly. Instead, include cuda_fp16.h."
+#endif
+
+#if !defined(IF_DEVICE_OR_CUDACC)
+#if defined(__CUDACC__)
+    #define IF_DEVICE_OR_CUDACC(d, c, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, d, c)
+#else
+    #define IF_DEVICE_OR_CUDACC(d, c, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, d, f)
+#endif
+#endif
+
+/* Macros for half & half2 binary arithmetic */
+#define __BINARY_OP_HALF_MACRO(name) /* do */ {\
+   __half val; \
+   asm( "{" __CUDA_FP16_STRINGIFY(name) ".f16 %0,%1,%2;\n}" \
+        :"=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)),"h"(__HALF_TO_CUS(b))); \
+   return val; \
+} /* while(0) */
+#define __BINARY_OP_HALF2_MACRO(name) /* do */ {\
+   __half2 val; \
+   asm( "{" __CUDA_FP16_STRINGIFY(name) ".f16x2 %0,%1,%2;\n}" \
+        :"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)),"r"(__HALF2_TO_CUI(b))); \
+   return val; \
+} /* while(0) */
+#define __TERNARY_OP_HALF_MACRO(name) /* do */ {\
+   __half val; \
+   asm( "{" __CUDA_FP16_STRINGIFY(name) ".f16 %0,%1,%2,%3;\n}" \
+        :"=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)),"h"(__HALF_TO_CUS(b)),"h"(__HALF_TO_CUS(c))); \
+   return val; \
+} /* while(0) */
+#define __TERNARY_OP_HALF2_MACRO(name) /* do */ {\
+   __half2 val; \
+   asm( "{" __CUDA_FP16_STRINGIFY(name) ".f16x2 %0,%1,%2,%3;\n}" \
+        :"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)),"r"(__HALF2_TO_CUI(b)),"r"(__HALF2_TO_CUI(c))); \
+   return val; \
+} /* while(0) */
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines floating-point positive infinity value for the \p half data type
+ */
+#define CUDART_INF_FP16            __ushort_as_half((unsigned short)0x7C00U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines canonical NaN value for the \p half data type
+ */
+#define CUDART_NAN_FP16            __ushort_as_half((unsigned short)0x7FFFU)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines a minimum representable (denormalized) value for the \p half data type
+ */
+#define CUDART_MIN_DENORM_FP16     __ushort_as_half((unsigned short)0x0001U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines a maximum representable value for the \p half data type
+ */
+#define CUDART_MAX_NORMAL_FP16     __ushort_as_half((unsigned short)0x7BFFU)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines a negative zero value for the \p half data type
+ */
+#define CUDART_NEG_ZERO_FP16       __ushort_as_half((unsigned short)0x8000U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines a positive zero value for the \p half data type
+ */
+#define CUDART_ZERO_FP16           __ushort_as_half((unsigned short)0x0000U)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_HALF_CONSTANTS
+ * \brief Defines a value of 1.0 for the \p half data type
+ */
+#define CUDART_ONE_FP16            __ushort_as_half((unsigned short)0x3C00U)
+
+#if !(defined __DOXYGEN_ONLY__)
+
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const __half_raw &hr) { __x = hr.x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ volatile __half &__half::operator=(const __half_raw &hr) volatile { __x = hr.x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ volatile __half &__half::operator=(const volatile __half_raw &hr) volatile { __x = hr.x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator __half_raw() const { __half_raw ret; ret.x = __x; return ret; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator __half_raw() const volatile { __half_raw ret; ret.x = __x; return ret; }
+#if !defined(__CUDA_NO_HALF_CONVERSIONS__)
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator float() const { return __half2float(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const float f) { __x = __float2half(f).__x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const double f) { __x = __double2half(f).__x; return *this; }
+#if !(defined __CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__)
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator signed char() const { return __half2char_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator unsigned char() const { return __half2uchar_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator char() const {
+    char value;
+    /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+    if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+    {
+        value = static_cast<char>(__half2char_rz(*this));
+    }
+    else
+    {
+        value = static_cast<char>(__half2uchar_rz(*this));
+    }
+    return value;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator short() const { return __half2short_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator unsigned short() const { return __half2ushort_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator int() const { return __half2int_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator unsigned int() const { return __half2uint_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator long() const {
+    long retval;
+    /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+    if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+    {
+        retval = static_cast<long>(__half2ll_rz(*this));
+    }
+    else
+    {
+        retval = static_cast<long>(__half2int_rz(*this));
+    }
+    return retval;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator unsigned long() const {
+    unsigned long retval;
+    /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+    if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+    {
+        retval = static_cast<unsigned long>(__half2ull_rz(*this));
+    }
+    else
+    {
+        retval = static_cast<unsigned long>(__half2uint_rz(*this));
+    }
+    return retval;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator long long() const { return __half2ll_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half::operator unsigned long long() const { return __half2ull_rz(*this); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const short val) { __x = __short2half_rn(val).__x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const unsigned short val) { __x = __ushort2half_rn(val).__x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const int val) { __x = __int2half_rn(val).__x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const unsigned int val) { __x = __uint2half_rn(val).__x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const long long val) { __x = __ll2half_rn(val).__x; return *this; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half &__half::operator=(const unsigned long long val) { __x = __ull2half_rn(val).__x; return *this; }
+
+#endif /* #if !(defined __CUDA_FP16_DISABLE_IMPLICIT_INTEGER_CONVERTS_FOR_HOST_COMPILERS__) || (defined __CUDACC__) */
+#endif /* !defined(__CUDA_NO_HALF_CONVERSIONS__) */
+#if !defined(__CUDA_NO_HALF_OPERATORS__)
+/* Some basic arithmetic operations expected of a built-in */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator+(const __half &lh, const __half &rh) { return __hadd(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator-(const __half &lh, const __half &rh) { return __hsub(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator*(const __half &lh, const __half &rh) { return __hmul(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator/(const __half &lh, const __half &rh) { return __hdiv(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator+=(__half &lh, const __half &rh) { lh = __hadd(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator-=(__half &lh, const __half &rh) { lh = __hsub(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator*=(__half &lh, const __half &rh) { lh = __hmul(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator/=(__half &lh, const __half &rh) { lh = __hdiv(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator++(__half &h)      { __half_raw one; one.x = 0x3C00U; h += one; return h; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half &operator--(__half &h)      { __half_raw one; one.x = 0x3C00U; h -= one; return h; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half  operator++(__half &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __half ret = h;
+    __half_raw one;
+    one.x = 0x3C00U;
+    h += one;
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half  operator--(__half &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __half ret = h;
+    __half_raw one;
+    one.x = 0x3C00U;
+    h -= one;
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator+(const __half &h) { return h; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half operator-(const __half &h) { return __hneg(h); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator==(const __half &lh, const __half &rh) { return __heq(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator!=(const __half &lh, const __half &rh) { return __hneu(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator> (const __half &lh, const __half &rh) { return __hgt(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator< (const __half &lh, const __half &rh) { return __hlt(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator>=(const __half &lh, const __half &rh) { return __hge(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator<=(const __half &lh, const __half &rh) { return __hle(lh, rh); }
+#endif /* !defined(__CUDA_NO_HALF_OPERATORS__) */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP16)
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half2 &__half2::operator=(const __half2 &&src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __HALF2_TO_UI(*this) = std::move(__HALF2_TO_CUI(src));
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+    return *this;
+}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP16) */
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half2 &__half2::operator=(const __half2 &src) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __HALF2_TO_UI(*this) = __HALF2_TO_CUI(src);
+,
+    this->x = src.x;
+    this->y = src.y;
+)
+    return *this;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half2 &__half2::operator=(const __half2_raw &h2r) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __HALF2_TO_UI(*this) = __HALF2_TO_CUI(h2r);
+,
+    __half_raw tr;
+    tr.x = h2r.x;
+    this->x = static_cast<__half>(tr);
+    tr.x = h2r.y;
+    this->y = static_cast<__half>(tr);
+)
+    return *this;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_INLINE__ __half2::operator __half2_raw() const {
+    __half2_raw ret;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    ret.x = 0U;
+    ret.y = 0U;
+    __HALF2_TO_UI(ret) = __HALF2_TO_CUI(*this);
+,
+    ret.x = static_cast<__half_raw>(this->x).x;
+    ret.y = static_cast<__half_raw>(this->y).x;
+)
+    return ret;
+}
+#if !defined(__CUDA_NO_HALF2_OPERATORS__)
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator+(const __half2 &lh, const __half2 &rh) { return __hadd2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator-(const __half2 &lh, const __half2 &rh) { return __hsub2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator*(const __half2 &lh, const __half2 &rh) { return __hmul2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator/(const __half2 &lh, const __half2 &rh) { return __h2div(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator+=(__half2 &lh, const __half2 &rh) { lh = __hadd2(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator-=(__half2 &lh, const __half2 &rh) { lh = __hsub2(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator*=(__half2 &lh, const __half2 &rh) { lh = __hmul2(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2& operator/=(__half2 &lh, const __half2 &rh) { lh = __h2div(lh, rh); return lh; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 &operator++(__half2 &h)      { __half2_raw one; one.x = 0x3C00U; one.y = 0x3C00U; h = __hadd2(h, one); return h; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 &operator--(__half2 &h)      { __half2_raw one; one.x = 0x3C00U; one.y = 0x3C00U; h = __hsub2(h, one); return h; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2  operator++(__half2 &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __half2 ret = h;
+    __half2_raw one;
+    one.x = 0x3C00U;
+    one.y = 0x3C00U;
+    h = __hadd2(h, one);
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2  operator--(__half2 &h, const int ignored)
+{
+    // ignored on purpose. Parameter only needed to distinguish the function declaration from other types of operators.
+    static_cast<void>(ignored);
+
+    const __half2 ret = h;
+    __half2_raw one;
+    one.x = 0x3C00U;
+    one.y = 0x3C00U;
+    h = __hsub2(h, one);
+    return ret;
+}
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator+(const __half2 &h) { return h; }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ __half2 operator-(const __half2 &h) { return __hneg2(h); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator==(const __half2 &lh, const __half2 &rh) { return __hbeq2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator!=(const __half2 &lh, const __half2 &rh) { return __hbneu2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator>(const __half2 &lh, const __half2 &rh) { return __hbgt2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator<(const __half2 &lh, const __half2 &rh) { return __hblt2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator>=(const __half2 &lh, const __half2 &rh) { return __hbge2(lh, rh); }
+__CUDA_HOSTDEVICE__ __CUDA_FP16_FORCEINLINE__ bool operator<=(const __half2 &lh, const __half2 &rh) { return __hble2(lh, rh); }
+#endif /* !defined(__CUDA_NO_HALF2_OPERATORS__) */
+
+/* Restore warning for multiple assignment operators */
+#if defined(_MSC_VER) && _MSC_VER >= 1500
+#pragma warning( pop )
+#endif /* defined(_MSC_VER) && _MSC_VER >= 1500 */
+
+/* Restore -Weffc++ warnings from here on */
+#if defined(__GNUC__)
+#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)
+#pragma GCC diagnostic pop
+#endif /* __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6) */
+#endif /* defined(__GNUC__) */
+
+#undef __CUDA_HOSTDEVICE__
+#undef __CUDA_ALIGN__
+
+#ifndef __CUDACC_RTC__  /* no host functions in NVRTC mode */
+static inline unsigned short __internal_float2half(const float f, unsigned int &sign, unsigned int &remainder)
+{
+    unsigned int x;
+    unsigned int u;
+    unsigned int result;
+#if defined(__CUDACC__)
+    (void)memcpy(&x, &f, sizeof(f));
+#else
+    (void)std::memcpy(&x, &f, sizeof(f));
+#endif
+    u = (x & 0x7fffffffU);
+    sign = ((x >> 16U) & 0x8000U);
+    // NaN/+Inf/-Inf
+    if (u >= 0x7f800000U) {
+        remainder = 0U;
+        result = ((u == 0x7f800000U) ? (sign | 0x7c00U) : 0x7fffU);
+    } else if (u > 0x477fefffU) { // Overflows
+        remainder = 0x80000000U;
+        result = (sign | 0x7bffU);
+    } else if (u >= 0x38800000U) { // Normal numbers
+        remainder = u << 19U;
+        u -= 0x38000000U;
+        result = (sign | (u >> 13U));
+    } else if (u < 0x33000001U) { // +0/-0
+        remainder = u;
+        result = sign;
+    } else { // Denormal numbers
+        const unsigned int exponent = u >> 23U;
+        const unsigned int shift = 0x7eU - exponent;
+        unsigned int mantissa = (u & 0x7fffffU);
+        mantissa |= 0x800000U;
+        remainder = mantissa << (32U - shift);
+        result = (sign | (mantissa >> shift));
+        result &= 0x0000FFFFU;
+    }
+    return static_cast<unsigned short>(result);
+}
+#endif  /* #if !defined(__CUDACC_RTC__) */
+
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __double2half(const double a)
+{
+IF_DEVICE_OR_CUDACC(
+    __half val;
+    asm("{  cvt.rn.f16.f64 %0, %1;}\n" : "=h"(__HALF_TO_US(val)) : "d"(a));
+    return val;
+,
+    __half result;
+    // Perform rounding to 11 bits of precision, convert value
+    // to float and call existing float to half conversion.
+    // By pre-rounding to 11 bits we avoid additional rounding
+    // in float to half conversion.
+    unsigned long long int absa;
+    unsigned long long int ua;
+    (void)memcpy(&ua, &a, sizeof(a));
+    absa = (ua & 0x7fffffffffffffffULL);
+    if ((absa >= 0x40f0000000000000ULL) || (absa <= 0x3e60000000000000ULL))
+    {
+        // |a| >= 2^16 or NaN or |a| <= 2^(-25)
+        // double-rounding is not a problem
+        result = __float2half(static_cast<float>(a));
+    }
+    else
+    {
+        // here 2^(-25) < |a| < 2^16
+        // prepare shifter value such that a + shifter
+        // done in double precision performs round-to-nearest-even
+        // and (a + shifter) - shifter results in a rounded to
+        // 11 bits of precision. Shifter needs to have exponent of
+        // a plus 53 - 11 = 42 and a leading bit in mantissa to guard
+        // against negative values.
+        // So need to have |a| capped to avoid overflow in exponent.
+        // For inputs that are smaller than half precision minnorm
+        // we prepare fixed shifter exponent.
+        unsigned long long shifterBits;
+        if (absa >= 0x3f10000000000000ULL)
+        {   // Here if |a| >= 2^(-14)
+            // add 42 to exponent bits
+            shifterBits  = (ua & 0x7ff0000000000000ULL) + 0x02A0000000000000ULL;
+        }
+        else
+        {   // 2^(-25) < |a| < 2^(-14), potentially results in denormal
+            // set exponent bits to 42 - 14 + bias
+            shifterBits = 0x41B0000000000000ULL;
+        }
+        // set leading mantissa bit to protect against negative inputs
+        shifterBits |= 0x0008000000000000ULL;
+        double shifter;
+        (void)memcpy(&shifter, &shifterBits, sizeof(shifterBits));
+        double aShiftRound = a + shifter;
+
+        // Prevent the compiler from optimizing away a + shifter - shifter
+        // by doing intermediate memcopy and harmless bitwize operation
+        unsigned long long int aShiftRoundBits;
+        (void)memcpy(&aShiftRoundBits, &aShiftRound, sizeof(aShiftRound));
+
+        // the value is positive, so this operation doesn't change anything
+        aShiftRoundBits &= 0x7fffffffffffffffULL;
+
+        (void)memcpy(&aShiftRound, &aShiftRoundBits, sizeof(aShiftRound));
+
+        result = __float2half(static_cast<float>(aShiftRound - shifter));
+    }
+
+    return result;
+,
+    __half result;
+    /*
+    // Perform rounding to 11 bits of precision, convert value
+    // to float and call existing float to half conversion.
+    // By pre-rounding to 11 bits we avoid additional rounding
+    // in float to half conversion.
+    */
+    unsigned long long int absa;
+    unsigned long long int ua;
+    (void)std::memcpy(&ua, &a, sizeof(a));
+    absa = (ua & 0x7fffffffffffffffULL);
+    if ((absa >= 0x40f0000000000000ULL) || (absa <= 0x3e60000000000000ULL))
+    {
+        /*
+        // |a| >= 2^16 or NaN or |a| <= 2^(-25)
+        // double-rounding is not a problem
+        */
+        result = __float2half(static_cast<float>(a));
+    }
+    else
+    {
+        /*
+        // here 2^(-25) < |a| < 2^16
+        // prepare shifter value such that a + shifter
+        // done in double precision performs round-to-nearest-even
+        // and (a + shifter) - shifter results in a rounded to
+        // 11 bits of precision. Shifter needs to have exponent of
+        // a plus 53 - 11 = 42 and a leading bit in mantissa to guard
+        // against negative values.
+        // So need to have |a| capped to avoid overflow in exponent.
+        // For inputs that are smaller than half precision minnorm
+        // we prepare fixed shifter exponent.
+        */
+        unsigned long long shifterBits;
+        if (absa >= 0x3f10000000000000ULL)
+        {
+            /*
+            // Here if |a| >= 2^(-14)
+            // add 42 to exponent bits
+            */
+            shifterBits  = (ua & 0x7ff0000000000000ULL) + 0x02A0000000000000ULL;
+        }
+        else
+        {
+            /*
+            // 2^(-25) < |a| < 2^(-14), potentially results in denormal
+            // set exponent bits to 42 - 14 + bias
+            */
+            shifterBits = 0x41B0000000000000ULL;
+        }
+        // set leading mantissa bit to protect against negative inputs
+        shifterBits |= 0x0008000000000000ULL;
+        double shifter;
+        (void)std::memcpy(&shifter, &shifterBits, sizeof(shifterBits));
+        double aShiftRound = a + shifter;
+
+        /*
+        // Prevent the compiler from optimizing away a + shifter - shifter
+        // by doing intermediate memcopy and harmless bitwize operation
+        */
+        unsigned long long int aShiftRoundBits;
+        (void)std::memcpy(&aShiftRoundBits, &aShiftRound, sizeof(aShiftRound));
+
+        // the value is positive, so this operation doesn't change anything
+        aShiftRoundBits &= 0x7fffffffffffffffULL;
+
+        (void)std::memcpy(&aShiftRound, &aShiftRoundBits, sizeof(aShiftRound));
+
+        result = __float2half(static_cast<float>(aShiftRound - shifter));
+    }
+
+    return result;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half(const float a)
+{
+    __half val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  cvt.rn.f16.f32 %0, %1;}\n" : "=h"(__HALF_TO_US(val)) : "f"(a));
+,
+    __half_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2half(a, sign, remainder);
+    if ((remainder > 0x80000000U) || ((remainder == 0x80000000U) && ((r.x & 0x1U) != 0U))) {
+        r.x++;
+    }
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_rn(const float a)
+{
+    __half val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  cvt.rn.f16.f32 %0, %1;}\n" : "=h"(__HALF_TO_US(val)) : "f"(a));
+,
+    __half_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2half(a, sign, remainder);
+    if ((remainder > 0x80000000U) || ((remainder == 0x80000000U) && ((r.x & 0x1U) != 0U))) {
+        r.x++;
+    }
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_rz(const float a)
+{
+    __half val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  cvt.rz.f16.f32 %0, %1;}\n" : "=h"(__HALF_TO_US(val)) : "f"(a));
+,
+    __half_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2half(a, sign, remainder);
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_rd(const float a)
+{
+    __half val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  cvt.rm.f16.f32 %0, %1;}\n" : "=h"(__HALF_TO_US(val)) : "f"(a));
+,
+    __half_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2half(a, sign, remainder);
+    if ((remainder != 0U) && (sign != 0U)) {
+        r.x++;
+    }
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __float2half_ru(const float a)
+{
+    __half val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  cvt.rp.f16.f32 %0, %1;}\n" : "=h"(__HALF_TO_US(val)) : "f"(a));
+,
+    __half_raw r;
+    unsigned int sign = 0U;
+    unsigned int remainder = 0U;
+    r.x = __internal_float2half(a, sign, remainder);
+    if ((remainder != 0U) && (sign == 0U)) {
+        r.x++;
+    }
+    val = r;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __float2half2_rn(const float a)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low;\n"
+        "  cvt.rn.f16.f32 low, %1;\n"
+        "  mov.b32 %0, {low,low};}\n" : "=r"(__HALF2_TO_UI(val)) : "f"(a));
+,
+    val = __half2(__float2half_rn(a), __float2half_rn(a));
+)
+    return val;
+}
+
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half2 __internal_device_float2_to_half2_rn(const float a, const float b) {
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{ cvt.rn.f16x2.f32 %0, %2, %1; }\n"
+        : "=r"(__HALF2_TO_UI(val)) : "f"(a), "f"(b));
+,
+    asm("{.reg .f16 low,high;\n"
+        "  cvt.rn.f16.f32 low, %1;\n"
+        "  cvt.rn.f16.f32 high, %2;\n"
+        "  mov.b32 %0, {low,high};}\n" : "=r"(__HALF2_TO_UI(val)) : "f"(a), "f"(b));
+)
+    return val;
+}
+
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __floats2half2_rn(const float a, const float b)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    val = __internal_device_float2_to_half2_rn(a,b);
+,
+    val = __half2(__float2half_rn(a), __float2half_rn(b));
+)
+    return val;
+}
+
+#ifndef __CUDACC_RTC__  /* no host functions in NVRTC mode */
+static inline float __internal_half2float(const unsigned short h)
+{
+    unsigned int sign = ((static_cast<unsigned int>(h) >> 15U) & 1U);
+    unsigned int exponent = ((static_cast<unsigned int>(h) >> 10U) & 0x1fU);
+    unsigned int mantissa = ((static_cast<unsigned int>(h) & 0x3ffU) << 13U);
+    float f;
+    if (exponent == 0x1fU) { /* NaN or Inf */
+        /* discard sign of a NaN */
+        sign = ((mantissa != 0U) ? (sign >> 1U) : sign);
+        mantissa = ((mantissa != 0U) ? 0x7fffffU : 0U);
+        exponent = 0xffU;
+    } else if (exponent == 0U) { /* Denorm or Zero */
+        if (mantissa != 0U) {
+            unsigned int msb;
+            exponent = 0x71U;
+            do {
+                msb = (mantissa & 0x400000U);
+                mantissa <<= 1U; /* normalize */
+                --exponent;
+            } while (msb == 0U);
+            mantissa &= 0x7fffffU; /* 1.mantissa is implicit */
+        }
+    } else {
+        exponent += 0x70U;
+    }
+    const unsigned int u = ((sign << 31U) | (exponent << 23U) | mantissa);
+#if defined(__CUDACC__)
+    (void)memcpy(&f, &u, sizeof(u));
+#else
+    (void)std::memcpy(&f, &u, sizeof(u));
+#endif
+    return f;
+}
+#endif  /* !defined(__CUDACC_RTC__) */
+
+__CUDA_HOSTDEVICE_FP16_DECL__ float __half2float(const __half a)
+{
+    float val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  cvt.f32.f16 %0, %1;}\n" : "=f"(val) : "h"(__HALF_TO_CUS(a)));
+,
+    val = __internal_half2float(static_cast<__half_raw>(a).x);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ float __low2float(const __half2 a)
+{
+    float val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high},%1;\n"
+        "  cvt.f32.f16 %0, low;}\n" : "=f"(val) : "r"(__HALF2_TO_CUI(a)));
+,
+    val = __internal_half2float(static_cast<__half2_raw>(a).x);
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ float __high2float(const __half2 a)
+{
+    float val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high},%1;\n"
+        "  cvt.f32.f16 %0, high;}\n" : "=f"(val) : "r"(__HALF2_TO_CUI(a)));
+,
+    val = __internal_half2float(static_cast<__half2_raw>(a).y);
+)
+    return val;
+}
+
+__CUDA_HOSTDEVICE_FP16_DECL__ signed char __half2char_rz(const __half h)
+{
+    signed char i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    unsigned int tmp;
+    asm("cvt.rzi.s8.f16 %0, %1;" : "=r"(tmp) : "h"(__HALF_TO_CUS(h)));
+    const unsigned char u = static_cast<unsigned char>(tmp);
+    i = static_cast<signed char>(u);
+,
+    const float f = __half2float(h);
+    const signed char max_val = (signed char)0x7fU;
+    const signed char min_val = (signed char)0x80U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<signed char>(f);
+    }
+)
+    return i;
+}
+
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned char __half2uchar_rz(const __half h)
+{
+    unsigned char i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    unsigned int tmp;
+    asm("cvt.rzi.u8.f16 %0, %1;" : "=r"(tmp) : "h"(__HALF_TO_CUS(h)));
+    i = static_cast<unsigned char>(tmp);
+,
+    const float f = __half2float(h);
+    const unsigned char max_val = 0xffU;
+    const unsigned char min_val = 0U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0U;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<unsigned char>(f);
+    }
+)
+    return i;
+}
+
+__CUDA_HOSTDEVICE_FP16_DECL__ short int __half2short_rz(const __half h)
+{
+    short int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rzi.s16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+,
+    const float f = __half2float(h);
+    const short int max_val = (short int)0x7fffU;
+    const short int min_val = (short int)0x8000U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<short int>(f);
+    }
+)
+    return i;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned short int __half2ushort_rz(const __half h)
+{
+    unsigned short int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rzi.u16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+,
+    const float f = __half2float(h);
+    const unsigned short int max_val = 0xffffU;
+    const unsigned short int min_val = 0U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0U;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<unsigned short int>(f);
+    }
+)
+    return i;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ int __half2int_rz(const __half h)
+{
+    int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rzi.s32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+,
+    const float f = __half2float(h);
+    const int max_val = (int)0x7fffffffU;
+    const int min_val = (int)0x80000000U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<int>(f);
+    }
+)
+    return i;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __half2uint_rz(const __half h)
+{
+    unsigned int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rzi.u32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+,
+    const float f = __half2float(h);
+    const unsigned int max_val = 0xffffffffU;
+    const unsigned int min_val = 0U;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0U;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<unsigned int>(f);
+    }
+)
+    return i;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ long long int __half2ll_rz(const __half h)
+{
+    long long int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rzi.s64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+,
+    const float f = __half2float(h);
+    const long long int max_val = (long long int)0x7fffffffffffffffULL;
+    const long long int min_val = (long long int)0x8000000000000000ULL;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = min_val;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<long long int>(f);
+    }
+)
+    return i;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned long long int __half2ull_rz(const __half h)
+{
+    unsigned long long int i;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rzi.u64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+,
+    const float f = __half2float(h);
+    const unsigned long long int max_val = 0xffffffffffffffffULL;
+    const unsigned long long int min_val = 0ULL;
+    const unsigned short bits = static_cast<unsigned short>(static_cast<__half_raw>(h).x << 1U);
+    // saturation fixup
+    if (bits > (unsigned short)0xF800U) {
+        // NaN
+        i = 0x8000000000000000ULL;
+    } else if (f > static_cast<float>(max_val)) {
+        // saturate maximum
+        i = max_val;
+    } else if (f < static_cast<float>(min_val)) {
+        // saturate minimum
+        i = min_val;
+    } else {
+        // normal value, conversion is well-defined
+        i = static_cast<unsigned long long int>(f);
+    }
+)
+    return i;
+}
+/* CUDA vector-types compatible vector creation function (note returns __half2, not half2) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 make_half2(const __half x, const __half y)
+{
+    __half2 t; t.x = x; t.y = y; return t;
+}
+
+
+/* Definitions of intrinsics */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __float22half2_rn(const float2 a)
+{
+    const __half2 val = __floats2half2_rn(a.x, a.y);
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ float2 __half22float2(const __half2 a)
+{
+    float hi_float;
+    float lo_float;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high},%1;\n"
+        "  cvt.f32.f16 %0, low;}\n" : "=f"(lo_float) : "r"(__HALF2_TO_CUI(a)));
+
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high},%1;\n"
+        "  cvt.f32.f16 %0, high;}\n" : "=f"(hi_float) : "r"(__HALF2_TO_CUI(a)));
+,
+    lo_float = __internal_half2float(((__half2_raw)a).x);
+    hi_float = __internal_half2float(((__half2_raw)a).y);
+)
+    return make_float2(lo_float, hi_float);
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ int __half2int_rn(const __half h)
+{
+    int i;
+    asm("cvt.rni.s32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ int __half2int_rd(const __half h)
+{
+    int i;
+    asm("cvt.rmi.s32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ int __half2int_ru(const __half h)
+{
+    int i;
+    asm("cvt.rpi.s32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_rn(const int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f16.s32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    // double-rounding is not a problem here: if integer
+    // has more than 24 bits, it is already too large to
+    // be represented in half precision, and result will
+    // be infinity.
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rn(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_rz(const int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rz.f16.s32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rz(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_rd(const int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rm.f16.s32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rd(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __int2half_ru(const int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rp.f16.s32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_ru(f);
+)
+    return h;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ short int __half2short_rn(const __half h)
+{
+    short int i;
+    asm("cvt.rni.s16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ short int __half2short_rd(const __half h)
+{
+    short int i;
+    asm("cvt.rmi.s16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ short int __half2short_ru(const __half h)
+{
+    short int i;
+    asm("cvt.rpi.s16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_rn(const short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f16.s16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rn(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_rz(const short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rz.f16.s16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rz(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_rd(const short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rm.f16.s16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rd(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short2half_ru(const short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rp.f16.s16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_ru(f);
+)
+    return h;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ unsigned int __half2uint_rn(const __half h)
+{
+    unsigned int i;
+    asm("cvt.rni.u32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ unsigned int __half2uint_rd(const __half h)
+{
+    unsigned int i;
+    asm("cvt.rmi.u32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ unsigned int __half2uint_ru(const __half h)
+{
+    unsigned int i;
+    asm("cvt.rpi.u32.f16 %0, %1;" : "=r"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_rn(const unsigned int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f16.u32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    // double-rounding is not a problem here: if integer
+    // has more than 24 bits, it is already too large to
+    // be represented in half precision, and result will
+    // be infinity.
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rn(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_rz(const unsigned int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rz.f16.u32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rz(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_rd(const unsigned int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rm.f16.u32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rd(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __uint2half_ru(const unsigned int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rp.f16.u32 %0, %1;" : "=h"(__HALF_TO_US(h)) : "r"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_ru(f);
+)
+    return h;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ unsigned short int __half2ushort_rn(const __half h)
+{
+    unsigned short int i;
+    asm("cvt.rni.u16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ unsigned short int __half2ushort_rd(const __half h)
+{
+    unsigned short int i;
+    asm("cvt.rmi.u16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ unsigned short int __half2ushort_ru(const __half h)
+{
+    unsigned short int i;
+    asm("cvt.rpi.u16.f16 %0, %1;" : "=h"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_rn(const unsigned short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f16.u16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rn(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_rz(const unsigned short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rz.f16.u16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rz(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_rd(const unsigned short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rm.f16.u16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rd(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort2half_ru(const unsigned short int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rp.f16.u16 %0, %1;" : "=h"(__HALF_TO_US(h)) : "h"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_ru(f);
+)
+    return h;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ unsigned long long int __half2ull_rn(const __half h)
+{
+    unsigned long long int i;
+    asm("cvt.rni.u64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ unsigned long long int __half2ull_rd(const __half h)
+{
+    unsigned long long int i;
+    asm("cvt.rmi.u64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ unsigned long long int __half2ull_ru(const __half h)
+{
+    unsigned long long int i;
+    asm("cvt.rpi.u64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_rn(const unsigned long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f16.u64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    // double-rounding is not a problem here: if integer
+    // has more than 24 bits, it is already too large to
+    // be represented in half precision, and result will
+    // be infinity.
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rn(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_rz(const unsigned long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rz.f16.u64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rz(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_rd(const unsigned long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rm.f16.u64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rd(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ull2half_ru(const unsigned long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rp.f16.u64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_ru(f);
+)
+    return h;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ long long int __half2ll_rn(const __half h)
+{
+    long long int i;
+    asm("cvt.rni.s64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ long long int __half2ll_rd(const __half h)
+{
+    long long int i;
+    asm("cvt.rmi.s64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+__CUDA_FP16_DECL__ long long int __half2ll_ru(const __half h)
+{
+    long long int i;
+    asm("cvt.rpi.s64.f16 %0, %1;" : "=l"(i) : "h"(__HALF_TO_CUS(h)));
+    return i;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_rn(const long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rn.f16.s64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    // double-rounding is not a problem here: if integer
+    // has more than 24 bits, it is already too large to
+    // be represented in half precision, and result will
+    // be infinity.
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rn(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_rz(const long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rz.f16.s64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rz(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_rd(const long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rm.f16.s64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_rd(f);
+)
+    return h;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ll2half_ru(const long long int i)
+{
+    __half h;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("cvt.rp.f16.s64 %0, %1;" : "=h"(__HALF_TO_US(h)) : "l"(i));
+,
+    const float  f = static_cast<float>(i);
+                 h = __float2half_ru(f);
+)
+    return h;
+}
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half htrunc(const __half h)
+{
+    __half r;
+    asm("cvt.rzi.f16.f16 %0, %1;" : "=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(h)));
+    return r;
+}
+__CUDA_FP16_DECL__ __half hceil(const __half h)
+{
+    __half r;
+    asm("cvt.rpi.f16.f16 %0, %1;" : "=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(h)));
+    return r;
+}
+__CUDA_FP16_DECL__ __half hfloor(const __half h)
+{
+    __half r;
+    asm("cvt.rmi.f16.f16 %0, %1;" : "=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(h)));
+    return r;
+}
+__CUDA_FP16_DECL__ __half hrint(const __half h)
+{
+    __half r;
+    asm("cvt.rni.f16.f16 %0, %1;" : "=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(h)));
+    return r;
+}
+
+__CUDA_FP16_DECL__ __half2 h2trunc(const __half2 h)
+{
+    __half2 val;
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  cvt.rzi.f16.f16 low, low;\n"
+        "  cvt.rzi.f16.f16 high, high;\n"
+        "  mov.b32 %0, {low,high};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(h)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2ceil(const __half2 h)
+{
+    __half2 val;
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  cvt.rpi.f16.f16 low, low;\n"
+        "  cvt.rpi.f16.f16 high, high;\n"
+        "  mov.b32 %0, {low,high};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(h)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2floor(const __half2 h)
+{
+    __half2 val;
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  cvt.rmi.f16.f16 low, low;\n"
+        "  cvt.rmi.f16.f16 high, high;\n"
+        "  mov.b32 %0, {low,high};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(h)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2rint(const __half2 h)
+{
+    __half2 val;
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  cvt.rni.f16.f16 low, low;\n"
+        "  cvt.rni.f16.f16 high, high;\n"
+        "  mov.b32 %0, {low,high};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(h)));
+    return val;
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __lows2half2(const __half2 a, const __half2 b)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 alow,ahigh,blow,bhigh;\n"
+        "  mov.b32 {alow,ahigh}, %1;\n"
+        "  mov.b32 {blow,bhigh}, %2;\n"
+        "  mov.b32 %0, {alow,blow};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)), "r"(__HALF2_TO_CUI(b)));
+,
+    val.x = a.x;
+    val.y = b.x;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __highs2half2(const __half2 a, const __half2 b)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 alow,ahigh,blow,bhigh;\n"
+        "  mov.b32 {alow,ahigh}, %1;\n"
+        "  mov.b32 {blow,bhigh}, %2;\n"
+        "  mov.b32 %0, {ahigh,bhigh};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)), "r"(__HALF2_TO_CUI(b)));
+,
+    val.x = a.y;
+    val.y = b.y;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __low2half(const __half2 a)
+{
+    __half ret;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        " mov.b32 {low,high}, %1;\n"
+        " mov.b16 %0, low;}" : "=h"(__HALF_TO_US(ret)) : "r"(__HALF2_TO_CUI(a)));
+,
+    ret = a.x;
+)
+    return ret;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ int __hisinf(const __half a)
+{
+    int retval;
+    const __half_raw araw = __half_raw(a);
+    if (araw.x == 0xFC00U) {
+        retval = -1;
+    } else if (araw.x == 0x7C00U) {
+        retval = 1;
+    } else {
+        retval = 0;
+    }
+    return retval;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __low2half2(const __half2 a)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  mov.b32 %0, {low,low};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+,
+    val.x = a.x;
+    val.y = a.x;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __high2half2(const __half2 a)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  mov.b32 %0, {high,high};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+,
+    val.x = a.y;
+    val.y = a.y;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __high2half(const __half2 a)
+{
+    __half ret;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        " mov.b32 {low,high}, %1;\n"
+        " mov.b16 %0, high;}" : "=h"(__HALF_TO_US(ret)) : "r"(__HALF2_TO_CUI(a)));
+,
+    ret = a.y;
+)
+    return ret;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __halves2half2(const __half a, const __half b)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  mov.b32 %0, {%1,%2};}\n"
+        : "=r"(__HALF2_TO_UI(val)) : "h"(__HALF_TO_CUS(a)), "h"(__HALF_TO_CUS(b)));
+,
+    val.x = a;
+    val.y = b;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __half2half2(const __half a)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{  mov.b32 %0, {%1,%1};}\n"
+        : "=r"(__HALF2_TO_UI(val)) : "h"(__HALF_TO_CUS(a)));
+,
+    val.x = a;
+    val.y = a;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __lowhigh2highlow(const __half2 a)
+{
+    __half2 val;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm("{.reg .f16 low,high;\n"
+        "  mov.b32 {low,high}, %1;\n"
+        "  mov.b32 %0, {high,low};}\n" : "=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+,
+    val.x = a.y;
+    val.y = a.x;
+)
+    return val;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ short int __half_as_short(const __half h)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return static_cast<short int>(__HALF_TO_CUS(h));
+,
+    return static_cast<short int>(__half_raw(h).x);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned short int __half_as_ushort(const __half h)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __HALF_TO_CUS(h);
+,
+    return __half_raw(h).x;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __short_as_half(const short int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __half h;
+    __HALF_TO_US(h) = static_cast<unsigned short int>(i);
+    return h;
+,
+    __half_raw hr;
+    hr.x = static_cast<unsigned short int>(i);
+    return __half(hr);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __ushort_as_half(const unsigned short int i)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __half h;
+    __HALF_TO_US(h) = i;
+    return h;
+,
+    __half_raw hr;
+    hr.x = i;
+    return __half(hr);)
+}
+
+/******************************************************************************
+*                             __half arithmetic                             *
+******************************************************************************/
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half __internal_device_hmax(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF_MACRO(max)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    float fr;
+    asm("{max.f32 %0,%1,%2;\n}"
+        :"=f"(fr) : "f"(fa), "f"(fb));
+    const __half hr = __float2half(fr);
+    return hr;
+)
+}
+__CUDA_FP16_DECL__ __half __internal_device_hmin(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF_MACRO(min)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    float fr;
+    asm("{min.f32 %0,%1,%2;\n}"
+        :"=f"(fr) : "f"(fa), "f"(fb));
+    const __half hr = __float2half(fr);
+    return hr;
+)
+}
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmax(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hmax(a, b);
+,
+    __half maxval;
+
+    maxval = (__hge(a, b) || __hisnan(b)) ? a : b;
+
+    if (__hisnan(maxval))
+    {
+        // if both inputs are NaN, return canonical NaN
+        maxval = CUDART_NAN_FP16;
+    }
+    else if (__heq(a, b))
+    {
+        // hmax(+0.0, -0.0) = +0.0
+        // unsigned compare 0x8000U > 0x0000U
+        __half_raw ra = __half_raw(a);
+        __half_raw rb = __half_raw(b);
+        maxval = (ra.x > rb.x) ? b : a;
+    }
+    return maxval;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmin(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    return __internal_device_hmin(a, b);
+,
+    __half minval;
+
+    minval = (__hle(a, b) || __hisnan(b)) ? a : b;
+
+    if (__hisnan(minval))
+    {
+        // if both inputs are NaN, return canonical NaN
+        minval = CUDART_NAN_FP16;
+    }
+    else if (__heq(a, b))
+    {
+        // hmin(+0.0, -0.0) = -0.0
+        // unsigned compare 0x8000U > 0x0000U
+        __half_raw ra = __half_raw(a);
+        __half_raw rb = __half_raw(b);
+        minval = (ra.x > rb.x) ? a : b;
+    }
+
+    return minval;
+)
+}
+
+
+/******************************************************************************
+*                            __half2 arithmetic                             *
+******************************************************************************/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmax2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF2_MACRO(max)
+,
+    __half2 val;
+    val.x = __hmax(a.x, b.x);
+    val.y = __hmax(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmin2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF2_MACRO(min)
+,
+    __half2 val;
+    val.x = __hmin(a.x, b.x);
+    val.y = __hmin(a.y, b.y);
+    return val;
+)
+}
+
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300) || defined(_NVHPC_CUDA)
+/******************************************************************************
+*                           __half, __half2 warp shuffle                     *
+******************************************************************************/
+#define __SHUFFLE_HALF2_MACRO(name) /* do */ {\
+   __half2 r; \
+   asm volatile ("{" __CUDA_FP16_STRINGIFY(name) " %0,%1,%2,%3;\n}" \
+       :"=r"(__HALF2_TO_UI(r)): "r"(__HALF2_TO_CUI(var)), "r"(delta), "r"(c)); \
+   return r; \
+} /* while(0) */
+
+#define __SHUFFLE_SYNC_HALF2_MACRO(name, var, delta, c, mask) /* do */ {\
+   __half2 r; \
+   asm volatile ("{" __CUDA_FP16_STRINGIFY(name) " %0,%1,%2,%3,%4;\n}" \
+       :"=r"(__HALF2_TO_UI(r)): "r"(__HALF2_TO_CUI(var)), "r"(delta), "r"(c), "r"(mask)); \
+   return r; \
+} /* while(0) */
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 700)
+
+__CUDA_FP16_DECL__ __half2 __shfl(const __half2 var, const int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_HALF2_MACRO(shfl.idx.b32)
+}
+__CUDA_FP16_DECL__ __half2 __shfl_up(const __half2 var, const unsigned int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = (warp_size - static_cast<unsigned>(width)) << 8U;
+    __SHUFFLE_HALF2_MACRO(shfl.up.b32)
+}
+__CUDA_FP16_DECL__ __half2 __shfl_down(const __half2 var, const unsigned int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_HALF2_MACRO(shfl.down.b32)
+}
+__CUDA_FP16_DECL__ __half2 __shfl_xor(const __half2 var, const int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_HALF2_MACRO(shfl.bfly.b32)
+}
+
+#endif /* defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 700) */
+
+__CUDA_FP16_DECL__ __half2 __shfl_sync(const unsigned int mask, const __half2 var, const int srcLane, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_SYNC_HALF2_MACRO(shfl.sync.idx.b32, var, srcLane, c, mask)
+}
+__CUDA_FP16_DECL__ __half2 __shfl_up_sync(const unsigned int mask, const __half2 var, const unsigned int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = (warp_size - static_cast<unsigned>(width)) << 8U;
+    __SHUFFLE_SYNC_HALF2_MACRO(shfl.sync.up.b32, var, delta, c, mask)
+}
+__CUDA_FP16_DECL__ __half2 __shfl_down_sync(const unsigned int mask, const __half2 var, const unsigned int delta, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_SYNC_HALF2_MACRO(shfl.sync.down.b32, var, delta, c, mask)
+}
+__CUDA_FP16_DECL__ __half2 __shfl_xor_sync(const unsigned int mask, const __half2 var, const int laneMask, const int width)
+{
+    unsigned int warp_size;
+    asm("{mov.u32 %0, WARP_SZ;\n}" : "=r"(warp_size));
+    const unsigned int c = ((warp_size - static_cast<unsigned>(width)) << 8U) | 0x1fU;
+    __SHUFFLE_SYNC_HALF2_MACRO(shfl.sync.bfly.b32, var, laneMask, c, mask)
+}
+
+#undef __SHUFFLE_HALF2_MACRO
+#undef __SHUFFLE_SYNC_HALF2_MACRO
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 700)
+
+__CUDA_FP16_DECL__ __half __shfl(const __half var, const int delta, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl(temp1, delta, width);
+    return __low2half(temp2);
+}
+__CUDA_FP16_DECL__ __half __shfl_up(const __half var, const unsigned int delta, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_up(temp1, delta, width);
+    return __low2half(temp2);
+}
+__CUDA_FP16_DECL__ __half __shfl_down(const __half var, const unsigned int delta, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_down(temp1, delta, width);
+    return __low2half(temp2);
+}
+__CUDA_FP16_DECL__ __half __shfl_xor(const __half var, const int delta, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_xor(temp1, delta, width);
+    return __low2half(temp2);
+}
+
+#endif /* defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 700) */
+
+__CUDA_FP16_DECL__ __half __shfl_sync(const unsigned int mask, const __half var, const int srcLane, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_sync(mask, temp1, srcLane, width);
+    return __low2half(temp2);
+}
+__CUDA_FP16_DECL__ __half __shfl_up_sync(const unsigned int mask, const __half var, const unsigned int delta, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_up_sync(mask, temp1, delta, width);
+    return __low2half(temp2);
+}
+__CUDA_FP16_DECL__ __half __shfl_down_sync(const unsigned int mask, const __half var, const unsigned int delta, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_down_sync(mask, temp1, delta, width);
+    return __low2half(temp2);
+}
+__CUDA_FP16_DECL__ __half __shfl_xor_sync(const unsigned int mask, const __half var, const int laneMask, const int width)
+{
+    const __half2 temp1 = __halves2half2(var, var);
+    const __half2 temp2 = __shfl_xor_sync(mask, temp1, laneMask, width);
+    return __low2half(temp2);
+}
+
+#endif /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 300) || defined(_NVHPC_CUDA) */
+/******************************************************************************
+*               __half and __half2 __ldg,__ldcg,__ldca,__ldcs                *
+******************************************************************************/
+
+#if defined(__cplusplus) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 320) || defined(_NVHPC_CUDA))
+#if (defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)
+#define __LDG_PTR   "l"
+#else
+#define __LDG_PTR   "r"
+#endif /*(defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)*/
+__CUDA_FP16_DECL__ __half2 __ldg(const  __half2 *const ptr)
+{
+    __half2 ret;
+    asm ("ld.global.nc.b32 %0, [%1];"  : "=r"(__HALF2_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half __ldg(const __half *const ptr)
+{
+    __half ret;
+    asm ("ld.global.nc.b16 %0, [%1];"  : "=h"(__HALF_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half2 __ldcg(const  __half2 *const ptr)
+{
+    __half2 ret;
+    asm ("ld.global.cg.b32 %0, [%1];"  : "=r"(__HALF2_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half __ldcg(const __half *const ptr)
+{
+    __half ret;
+    asm ("ld.global.cg.b16 %0, [%1];"  : "=h"(__HALF_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half2 __ldca(const  __half2 *const ptr)
+{
+    __half2 ret;
+    asm ("ld.global.ca.b32 %0, [%1];"  : "=r"(__HALF2_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half __ldca(const __half *const ptr)
+{
+    __half ret;
+    asm ("ld.global.ca.b16 %0, [%1];"  : "=h"(__HALF_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half2 __ldcs(const  __half2 *const ptr)
+{
+    __half2 ret;
+    asm ("ld.global.cs.b32 %0, [%1];"  : "=r"(__HALF2_TO_UI(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half __ldcs(const __half *const ptr)
+{
+    __half ret;
+    asm ("ld.global.cs.b16 %0, [%1];"  : "=h"(__HALF_TO_US(ret)) : __LDG_PTR(ptr));
+    return ret;
+}
+__CUDA_FP16_DECL__ __half2 __ldlu(const  __half2 *const ptr)
+{
+    __half2 ret;
+    asm ("ld.global.lu.b32 %0, [%1];"  : "=r"(__HALF2_TO_UI(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_FP16_DECL__ __half __ldlu(const __half *const ptr)
+{
+    __half ret;
+    asm ("ld.global.lu.b16 %0, [%1];"  : "=h"(__HALF_TO_US(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_FP16_DECL__ __half2 __ldcv(const  __half2 *const ptr)
+{
+    __half2 ret;
+    asm ("ld.global.cv.b32 %0, [%1];"  : "=r"(__HALF2_TO_UI(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_FP16_DECL__ __half __ldcv(const __half *const ptr)
+{
+    __half ret;
+    asm ("ld.global.cv.b16 %0, [%1];"  : "=h"(__HALF_TO_US(ret)) : __LDG_PTR(ptr) : "memory");
+    return ret;
+}
+__CUDA_FP16_DECL__ void __stwb(__half2 *const ptr, const __half2 value)
+{
+    asm ("st.global.wb.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__HALF2_TO_CUI(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stwb(__half *const ptr, const __half value)
+{
+    asm ("st.global.wb.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__HALF_TO_CUS(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stcg(__half2 *const ptr, const __half2 value)
+{
+    asm ("st.global.cg.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__HALF2_TO_CUI(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stcg(__half *const ptr, const __half value)
+{
+    asm ("st.global.cg.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__HALF_TO_CUS(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stcs(__half2 *const ptr, const __half2 value)
+{
+    asm ("st.global.cs.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__HALF2_TO_CUI(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stcs(__half *const ptr, const __half value)
+{
+    asm ("st.global.cs.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__HALF_TO_CUS(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stwt(__half2 *const ptr, const __half2 value)
+{
+    asm ("st.global.wt.b32 [%0], %1;"  :: __LDG_PTR(ptr), "r"(__HALF2_TO_CUI(value)) : "memory");
+}
+__CUDA_FP16_DECL__ void __stwt(__half *const ptr, const __half value)
+{
+    asm ("st.global.wt.b16 [%0], %1;"  :: __LDG_PTR(ptr),  "h"(__HALF_TO_CUS(value)) : "memory");
+}
+#undef __LDG_PTR
+#endif /* defined(__cplusplus) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 320) || defined(_NVHPC_CUDA)) */
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+
+/******************************************************************************
+*                             __half2 comparison                             *
+******************************************************************************/
+#define __COMPARISON_OP_HALF2_MACRO(name) /* do */ {\
+   __half2 val; \
+   asm( "{ " __CUDA_FP16_STRINGIFY(name) ".f16x2.f16x2 %0,%1,%2;\n}" \
+        :"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)),"r"(__HALF2_TO_CUI(b))); \
+   return val; \
+} /* while(0) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __heq2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.eq)
+,
+    __half2_raw val;
+    val.x = __heq(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __heq(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hne2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.ne)
+,
+    __half2_raw val;
+    val.x = __hne(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hne(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hle2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.le)
+,
+    __half2_raw val;
+    val.x = __hle(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hle(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hge2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.ge)
+,
+    __half2_raw val;
+    val.x = __hge(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hge(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hlt2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.lt)
+,
+    __half2_raw val;
+    val.x = __hlt(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hlt(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hgt2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.gt)
+,
+    __half2_raw val;
+    val.x = __hgt(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hgt(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hequ2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.equ)
+,
+    __half2_raw val;
+    val.x = __hequ(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hequ(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hneu2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.neu)
+,
+    __half2_raw val;
+    val.x = __hneu(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hneu(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hleu2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.leu)
+,
+    __half2_raw val;
+    val.x = __hleu(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hleu(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hgeu2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.geu)
+,
+    __half2_raw val;
+    val.x = __hgeu(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hgeu(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hltu2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.ltu)
+,
+    __half2_raw val;
+    val.x = __hltu(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hltu(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hgtu2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO(set.gtu)
+,
+    __half2_raw val;
+    val.x = __hgtu(a.x, b.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hgtu(a.y, b.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    return __half2(val);
+)
+}
+#undef __COMPARISON_OP_HALF2_MACRO
+/******************************************************************************
+*                 __half2 comparison with mask output                        *
+******************************************************************************/
+#define __COMPARISON_OP_HALF2_MACRO_MASK(name) /* do */ {\
+   unsigned val; \
+   asm( "{ " __CUDA_FP16_STRINGIFY(name) ".u32.f16x2 %0,%1,%2;\n}" \
+        :"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)),"r"(__HALF2_TO_CUI(b))); \
+   return val; \
+} /* while(0) */
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __heq2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.eq)
+,
+    const unsigned short px = __heq(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __heq(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hne2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.ne)
+,
+    const unsigned short px = __hne(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hne(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hle2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.le)
+,
+    const unsigned short px = __hle(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hle(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hge2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.ge)
+,
+    const unsigned short px = __hge(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hge(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hlt2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.lt)
+,
+    const unsigned short px = __hlt(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hlt(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hgt2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.gt)
+,
+    const unsigned short px = __hgt(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hgt(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hequ2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.equ)
+,
+    const unsigned short px = __hequ(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hequ(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hneu2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.neu)
+,
+    const unsigned short px = __hneu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hneu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hleu2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.leu)
+,
+    const unsigned short px = __hleu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hleu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hgeu2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.geu)
+,
+    const unsigned short px = __hgeu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hgeu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hltu2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.ltu)
+,
+    const unsigned short px = __hltu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hltu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ unsigned int __hgtu2_mask(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF2_MACRO_MASK(set.gtu)
+,
+    const unsigned short px = __hgtu(a.x, b.x) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    const unsigned short py = __hgtu(a.y, b.y) ? (unsigned short)0xFFFFU : (unsigned short)0U;
+    unsigned ur = (unsigned)py;
+             ur <<= (unsigned)16U;
+             ur |= (unsigned)px;
+    return ur;
+)
+}
+#undef __COMPARISON_OP_HALF2_MACRO_MASK
+
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbeq2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __heq2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbne2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hne2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hble2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hle2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbge2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hge2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hblt2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hlt2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbgt2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hgt2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbequ2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hequ2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbneu2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hneu2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbleu2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hleu2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbgeu2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hgeu2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbltu2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hltu2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hbgtu2(const __half2 a, const __half2 b)
+{
+    const unsigned mask = __hgtu2_mask(a, b);
+    return (mask == 0xFFFFFFFFU);
+}
+/******************************************************************************
+*                             __half comparison                              *
+******************************************************************************/
+#define __COMPARISON_OP_HALF_MACRO(name) /* do */ {\
+   unsigned short val; \
+   asm( "{ .reg .pred __$temp3;\n" \
+        "  setp." __CUDA_FP16_STRINGIFY(name) ".f16  __$temp3, %1, %2;\n" \
+        "  selp.u16 %0, 1, 0, __$temp3;}" \
+        : "=h"(val) : "h"(__HALF_TO_CUS(a)), "h"(__HALF_TO_CUS(b))); \
+   return (val != 0U) ? true : false; \
+} /* while(0) */
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __heq(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(eq)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa == fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hne(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(ne)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa != fb) && (!__hisnan(a)) && (!__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hle(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(le)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa <= fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hge(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(ge)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa >= fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hlt(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(lt)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa < fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hgt(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(gt)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa > fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hequ(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(equ)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa == fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hneu(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(neu)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa != fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hleu(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(leu)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa <= fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hgeu(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(geu)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa >= fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hltu(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(ltu)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa < fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hgtu(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __COMPARISON_OP_HALF_MACRO(gtu)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return (fa > fb) || (__hisnan(a)) || (__hisnan(b));
+)
+}
+#undef __COMPARISON_OP_HALF_MACRO
+/******************************************************************************
+*                            __half2 arithmetic                             *
+******************************************************************************/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hadd2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(add)
+,
+    __half2 val;
+    val.x = __hadd(a.x, b.x);
+    val.y = __hadd(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hsub2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(sub)
+,
+    __half2 val;
+    val.x = __hsub(a.x, b.x);
+    val.y = __hsub(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmul2(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(mul)
+,
+    __half2 val;
+    val.x = __hmul(a.x, b.x);
+    val.y = __hmul(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hadd2_sat(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(add.sat)
+,
+    __half2 val;
+    val.x = __hadd_sat(a.x, b.x);
+    val.y = __hadd_sat(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hsub2_sat(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(sub.sat)
+,
+    __half2 val;
+    val.x = __hsub_sat(a.x, b.x);
+    val.y = __hsub_sat(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmul2_sat(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(mul.sat)
+,
+    __half2 val;
+    val.x = __hmul_sat(a.x, b.x);
+    val.y = __hmul_sat(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hadd2_rn(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(add.rn)
+,
+    __half2 val;
+    val.x = __hadd_rn(a.x, b.x);
+    val.y = __hadd_rn(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hsub2_rn(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(sub.rn)
+,
+    __half2 val;
+    val.x = __hsub_rn(a.x, b.x);
+    val.y = __hsub_rn(a.y, b.y);
+    return val;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmul2_rn(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF2_MACRO(mul.rn)
+,
+    __half2 val;
+    val.x = __hmul_rn(a.x, b.x);
+    val.y = __hmul_rn(a.y, b.y);
+    return val;
+)
+}
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half2 __hfma2(const __half2 a, const __half2 b, const __half2 c)
+{
+    __TERNARY_OP_HALF2_MACRO(fma.rn)
+}
+__CUDA_FP16_DECL__ __half2 __hfma2_sat(const __half2 a, const __half2 b, const __half2 c)
+{
+    __TERNARY_OP_HALF2_MACRO(fma.rn.sat)
+}
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __h2div(const __half2 a, const __half2 b) {
+    __half ha = __low2half(a);
+    __half hb = __low2half(b);
+
+    const __half v1 = __hdiv(ha, hb);
+
+    ha = __high2half(a);
+    hb = __high2half(b);
+
+    const __half v2 = __hdiv(ha, hb);
+
+    return __halves2half2(v1, v2);
+}
+
+/******************************************************************************
+*                             __half arithmetic                             *
+******************************************************************************/
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hadd(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(add)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa + fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hsub(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(sub)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa - fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmul(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(mul)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa * fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hadd_sat(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(add.sat)
+,
+    return __hmin(__hmax(__hadd(a, b), CUDART_ZERO_FP16), CUDART_ONE_FP16);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hsub_sat(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(sub.sat)
+,
+    return __hmin(__hmax(__hsub(a, b), CUDART_ZERO_FP16), CUDART_ONE_FP16);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmul_sat(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(mul.sat)
+,
+    return __hmin(__hmax(__hmul(a, b), CUDART_ZERO_FP16), CUDART_ONE_FP16);
+)
+}
+
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hadd_rn(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(add.rn)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa + fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hsub_rn(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(sub.rn)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa - fb);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmul_rn(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __BINARY_OP_HALF_MACRO(mul.rn)
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa * fb);
+)
+}
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half __hfma(const __half a, const __half b, const __half c)
+{
+    __TERNARY_OP_HALF_MACRO(fma.rn)
+}
+__CUDA_FP16_DECL__ __half __hfma_sat(const __half a, const __half b, const __half c)
+{
+    __TERNARY_OP_HALF_MACRO(fma.rn.sat)
+}
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hdiv(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    __half v;
+    __half abs;
+    __half den;
+    __HALF_TO_US(den) = 0x008FU;
+
+    float rcp;
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+
+    asm("{rcp.approx.ftz.f32 %0, %1;\n}" :"=f"(rcp) : "f"(fb));
+
+    float fv = rcp * fa;
+
+    v = __float2half(fv);
+    abs = __habs(v);
+    if (__hlt(abs, den) && __hlt(__float2half(0.0f), abs))  {
+        const float err = __fmaf_rn(-fb, fv, fa);
+        fv = __fmaf_rn(rcp, err, fv);
+        v = __float2half(fv);
+    }
+    return v;
+,
+    const float fa = __half2float(a);
+    const float fb = __half2float(b);
+    return __float2half(fa / fb);
+)
+}
+
+/******************************************************************************
+*                             __half2 functions                  *
+******************************************************************************/
+#if defined(_NVHPC_CUDA) || defined(__CUDACC__)
+#define __APPROX_FCAST(fun) /* do */ {\
+   __half val;\
+   asm("{.reg.b32         f;        \n"\
+                " .reg.b16         r;        \n"\
+                "  mov.b16         r,%1;     \n"\
+                "  cvt.f32.f16     f,r;      \n"\
+                "  " __CUDA_FP16_STRINGIFY(fun) ".approx.ftz.f32   f,f;  \n"\
+                "  cvt.rn.f16.f32      r,f;  \n"\
+                "  mov.b16         %0,r;     \n"\
+                "}": "=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));\
+   return val;\
+} /* while(0) */
+#define __APPROX_FCAST2(fun) /* do */ {\
+   __half2 val;\
+   asm("{.reg.b16         hl, hu;         \n"\
+                " .reg.b32         fl, fu;         \n"\
+                "  mov.b32         {hl, hu}, %1;   \n"\
+                "  cvt.f32.f16     fl, hl;         \n"\
+                "  cvt.f32.f16     fu, hu;         \n"\
+                "  " __CUDA_FP16_STRINGIFY(fun) ".approx.ftz.f32   fl, fl;     \n"\
+                "  " __CUDA_FP16_STRINGIFY(fun) ".approx.ftz.f32   fu, fu;     \n"\
+                "  cvt.rn.f16.f32      hl, fl;     \n"\
+                "  cvt.rn.f16.f32      hu, fu;     \n"\
+                "  mov.b32         %0, {hl, hu};   \n"\
+                "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));       \
+   return val;\
+} /* while(0) */
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530) || defined(_NVHPC_CUDA)
+#define __SPEC_CASE2(i,r, spc, ulp) \
+   "{.reg.b32 spc, ulp, p;\n"\
+   "  mov.b32 spc," __CUDA_FP16_STRINGIFY(spc) ";\n"\
+   "  mov.b32 ulp," __CUDA_FP16_STRINGIFY(ulp) ";\n"\
+   "  set.eq.f16x2.f16x2 p," __CUDA_FP16_STRINGIFY(i) ", spc;\n"\
+   "  fma.rn.f16x2 " __CUDA_FP16_STRINGIFY(r) ",p,ulp," __CUDA_FP16_STRINGIFY(r) ";\n}\n"
+#define __SPEC_CASE(i,r, spc, ulp) \
+   "{.reg.b16 spc, ulp, p;\n"\
+   "  mov.b16 spc," __CUDA_FP16_STRINGIFY(spc) ";\n"\
+   "  mov.b16 ulp," __CUDA_FP16_STRINGIFY(ulp) ";\n"\
+   "  set.eq.f16.f16 p," __CUDA_FP16_STRINGIFY(i) ", spc;\n"\
+   "  fma.rn.f16 " __CUDA_FP16_STRINGIFY(r) ",p,ulp," __CUDA_FP16_STRINGIFY(r) ";\n}\n"
+static __device__ __forceinline__ float __float_simpl_sinf(float a);
+static __device__ __forceinline__ float __float_simpl_cosf(float a);
+__CUDA_FP16_DECL__ __half hsin(const __half a) {
+    const float sl = __float_simpl_sinf(__half2float(a));
+    __half r = __float2half_rn(sl);
+    asm("{\n\t"
+        "  .reg.b16 i,r,t;     \n\t"
+        "  mov.b16 r, %0;      \n\t"
+        "  mov.b16 i, %1;      \n\t"
+        "  and.b16 t, r, 0x8000U; \n\t"
+        "  abs.f16 r, r;   \n\t"
+        "  abs.f16 i, i;   \n\t"
+        __SPEC_CASE(i, r, 0X32B3U, 0x0800U)
+        __SPEC_CASE(i, r, 0X5CB0U, 0x9000U)
+        "  or.b16  r,r,t;      \n\t"
+        "  mov.b16 %0, r;      \n"
+        "}\n" : "+h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(a)));
+    return r;
+}
+__CUDA_FP16_DECL__ __half2 h2sin(const __half2 a) {
+    const float sl = __float_simpl_sinf(__half2float(a.x));
+    const float sh = __float_simpl_sinf(__half2float(a.y));
+    __half2 r = __floats2half2_rn(sl, sh);
+    asm("{\n\t"
+        "  .reg.b32 i,r,t;             \n\t"
+        "  mov.b32 r, %0;              \n\t"
+        "  mov.b32 i, %1;              \n\t"
+        "  and.b32 t, r, 0x80008000U;   \n\t"
+        "  abs.f16x2 r, r;   \n\t"
+        "  abs.f16x2 i, i;   \n\t"
+        __SPEC_CASE2(i, r, 0X32B332B3U, 0x08000800U)
+        __SPEC_CASE2(i, r, 0X5CB05CB0U, 0x90009000U)
+        "  or.b32  r, r, t;            \n\t"
+        "  mov.b32 %0, r;              \n"
+        "}\n" : "+r"(__HALF2_TO_UI(r)) : "r"(__HALF2_TO_CUI(a)));
+    return r;
+}
+__CUDA_FP16_DECL__ __half hcos(const __half a) {
+    const float cl = __float_simpl_cosf(__half2float(a));
+    __half r = __float2half_rn(cl);
+    asm("{\n\t"
+        "  .reg.b16 i,r;        \n\t"
+        "  mov.b16 r, %0;       \n\t"
+        "  mov.b16 i, %1;       \n\t"
+        "  abs.f16 i, i;        \n\t"
+        __SPEC_CASE(i, r, 0X2B7CU, 0x1000U)
+        "  mov.b16 %0, r;       \n"
+        "}\n" : "+h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(a)));
+    return r;
+}
+__CUDA_FP16_DECL__ __half2 h2cos(const __half2 a) {
+    const float cl = __float_simpl_cosf(__half2float(a.x));
+    const float ch = __float_simpl_cosf(__half2float(a.y));
+    __half2 r = __floats2half2_rn(cl, ch);
+    asm("{\n\t"
+        "  .reg.b32 i,r;   \n\t"
+        "  mov.b32 r, %0;  \n\t"
+        "  mov.b32 i, %1;  \n\t"
+        "  abs.f16x2 i, i; \n\t"
+        __SPEC_CASE2(i, r, 0X2B7C2B7CU, 0x10001000U)
+        "  mov.b32 %0, r;  \n"
+        "}\n" : "+r"(__HALF2_TO_UI(r)) : "r"(__HALF2_TO_CUI(a)));
+    return r;
+}
+static __device__ __forceinline__ float __internal_trig_reduction_kernel(const float a, unsigned int *const quadrant)
+{
+    const float ar = __fmaf_rn(a, 0.636619772F, 12582912.0F);
+    const unsigned q = __float_as_uint(ar);
+    const float j = __fsub_rn(ar, 12582912.0F);
+    float t = __fmaf_rn(j, -1.5707962512969971e+000F, a);
+    t = __fmaf_rn(j, -7.5497894158615964e-008F, t);
+    *quadrant = q;
+    return t;
+}
+static __device__ __forceinline__ float __internal_sin_cos_kernel(const float x, const unsigned int i)
+{
+    float z;
+    const float x2 = x*x;
+    float a8;
+    float a6;
+    float a4;
+    float a2;
+    float a1;
+    float a0;
+
+    if ((i & 1U) != 0U) {
+        // cos
+        a8 =  2.44331571e-5F;
+        a6 = -1.38873163e-3F;
+        a4 =  4.16666457e-2F;
+        a2 = -5.00000000e-1F;
+        a1 = x2;
+        a0 = 1.0F;
+    }
+    else {
+        // sin
+        a8 = -1.95152959e-4F;
+        a6 =  8.33216087e-3F;
+        a4 = -1.66666546e-1F;
+        a2 = 0.0F;
+        a1 = x;
+        a0 = x;
+    }
+
+    z = __fmaf_rn(a8, x2, a6);
+    z = __fmaf_rn(z, x2, a4);
+    z = __fmaf_rn(z, x2, a2);
+    z = __fmaf_rn(z, a1, a0);
+
+    if ((i & 2U) != 0U) {
+        z = -z;
+    }
+    return z;
+}
+static __device__ __forceinline__ float __float_simpl_sinf(float a)
+{
+    float z;
+    unsigned i;
+    a = __internal_trig_reduction_kernel(a, &i);
+    z = __internal_sin_cos_kernel(a, i);
+    return z;
+}
+static __device__ __forceinline__ float __float_simpl_cosf(float a)
+{
+    float z;
+    unsigned i;
+    a = __internal_trig_reduction_kernel(a, &i);
+    z = __internal_sin_cos_kernel(a, (i & 0x3U) + 1U);
+    return z;
+}
+
+__CUDA_FP16_DECL__ __half hexp(const __half a) {
+    __half val;
+    asm("{.reg.b32         f, C, nZ;       \n"
+        " .reg.b16         h,r;            \n"
+        "  mov.b16         h,%1;           \n"
+        "  cvt.f32.f16     f,h;            \n"
+        "  mov.b32         C, 0x3fb8aa3bU; \n"
+        "  mov.b32         nZ, 0x80000000U;\n"
+        "  fma.rn.f32      f,f,C,nZ;       \n"
+        "  ex2.approx.ftz.f32  f,f;        \n"
+        "  cvt.rn.f16.f32      r,f;        \n"
+        __SPEC_CASE(h, r, 0X1F79U, 0x9400U)
+        __SPEC_CASE(h, r, 0X25CFU, 0x9400U)
+        __SPEC_CASE(h, r, 0XC13BU, 0x0400U)
+        __SPEC_CASE(h, r, 0XC1EFU, 0x0200U)
+        "  mov.b16         %0,r;           \n"
+        "}": "=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2exp(const __half2 a) {
+    __half2 val;
+    asm("{.reg.b16         hl, hu;         \n"
+        " .reg.b32         h,r,fl,fu,C,nZ; \n"
+        "  mov.b32         {hl, hu}, %1;   \n"
+        "  mov.b32         h, %1;          \n"
+        "  cvt.f32.f16     fl, hl;         \n"
+        "  cvt.f32.f16     fu, hu;         \n"
+        "  mov.b32         C, 0x3fb8aa3bU; \n"
+        "  mov.b32         nZ, 0x80000000U;\n"
+        "  fma.rn.f32      fl,fl,C,nZ;     \n"
+        "  fma.rn.f32      fu,fu,C,nZ;     \n"
+        "  ex2.approx.ftz.f32  fl, fl;     \n"
+        "  ex2.approx.ftz.f32  fu, fu;     \n"
+        "  cvt.rn.f16.f32      hl, fl;     \n"
+        "  cvt.rn.f16.f32      hu, fu;     \n"
+        "  mov.b32         r, {hl, hu};    \n"
+        __SPEC_CASE2(h, r, 0X1F791F79U, 0x94009400U)
+        __SPEC_CASE2(h, r, 0X25CF25CFU, 0x94009400U)
+        __SPEC_CASE2(h, r, 0XC13BC13BU, 0x04000400U)
+        __SPEC_CASE2(h, r, 0XC1EFC1EFU, 0x02000200U)
+        "  mov.b32         %0, r;  \n"
+        "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+    return val;
+}
+#endif /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530) || defined(_NVHPC_CUDA) */
+
+__CUDA_FP16_DECL__ __half htanh(const __half a) {
+    float f = __half2float(a);
+    f = tanhf(f);
+    __half h = __float2half_rn(f);
+    return h;
+}
+__CUDA_FP16_DECL__ __half2 h2tanh(const __half2 a) {
+    float2 f = __half22float2(a);
+    f.x = tanhf(f.x);
+    f.y = tanhf(f.y);
+    __half2 h = __float22half2_rn(f);
+    return h;
+}
+
+__CUDA_FP16_DECL__ __half htanh_approx(const __half a) {
+    __half r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_75,
+    __half_raw hr = (__half_raw)a;
+    asm("tanh.approx.f16 %0, %0;" : "+h"(hr.x));
+    r = (__half)hr;
+,
+    r = htanh(a);
+)
+    return r;
+}
+__CUDA_FP16_DECL__ __half2 h2tanh_approx(const __half2 a) {
+    __half2 res;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_75,
+    asm("tanh.approx.f16x2 %0, %1;" : "=r"(__HALF2_TO_UI(res)) : "r"(__HALF2_TO_CUI(a)));
+,
+    res = h2tanh(a);
+)
+    return res;
+}
+
+__CUDA_FP16_DECL__ __half hexp2(const __half a) {
+    __half val;
+    asm("{.reg.b32         f, ULP;         \n"
+        " .reg.b16         r;              \n"
+        "  mov.b16         r,%1;           \n"
+        "  cvt.f32.f16     f,r;            \n"
+        "  ex2.approx.ftz.f32      f,f;    \n"
+        "  mov.b32         ULP, 0x33800000U;\n"
+        "  fma.rn.f32      f,f,ULP,f;      \n"
+        "  cvt.rn.f16.f32      r,f;        \n"
+        "  mov.b16         %0,r;           \n"
+        "}": "=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2exp2(const __half2 a) {
+    __half2 val;
+    asm("{.reg.b16         hl, hu;         \n"
+        " .reg.b32         fl, fu, ULP;    \n"
+        "  mov.b32         {hl, hu}, %1;   \n"
+        "  cvt.f32.f16     fl, hl;         \n"
+        "  cvt.f32.f16     fu, hu;         \n"
+        "  ex2.approx.ftz.f32  fl, fl;     \n"
+        "  ex2.approx.ftz.f32  fu, fu;     \n"
+        "  mov.b32         ULP, 0x33800000U;\n"
+        "  fma.rn.f32      fl,fl,ULP,fl;   \n"
+        "  fma.rn.f32      fu,fu,ULP,fu;   \n"
+        "  cvt.rn.f16.f32      hl, fl;     \n"
+        "  cvt.rn.f16.f32      hu, fu;     \n"
+        "  mov.b32         %0, {hl, hu};   \n"
+        "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+    return val;
+}
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half hexp10(const __half a) {
+    __half val;
+    asm("{.reg.b16         h,r;            \n"
+        " .reg.b32         f, C, nZ;       \n"
+        "  mov.b16         h, %1;          \n"
+        "  cvt.f32.f16     f, h;           \n"
+        "  mov.b32         C, 0x40549A78U; \n"
+        "  mov.b32         nZ, 0x80000000U;\n"
+        "  fma.rn.f32      f,f,C,nZ;       \n"
+        "  ex2.approx.ftz.f32  f, f;       \n"
+        "  cvt.rn.f16.f32      r, f;       \n"
+        __SPEC_CASE(h, r, 0x34DEU, 0x9800U)
+        __SPEC_CASE(h, r, 0x9766U, 0x9000U)
+        __SPEC_CASE(h, r, 0x9972U, 0x1000U)
+        __SPEC_CASE(h, r, 0xA5C4U, 0x1000U)
+        __SPEC_CASE(h, r, 0xBF0AU, 0x8100U)
+        "  mov.b16         %0, r;          \n"
+        "}":"=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2exp10(const __half2 a) {
+    __half2 val;
+    asm("{.reg.b16         hl, hu;         \n"
+        " .reg.b32         h,r,fl,fu,C,nZ; \n"
+        "  mov.b32         {hl, hu}, %1;   \n"
+        "  mov.b32         h, %1;          \n"
+        "  cvt.f32.f16     fl, hl;         \n"
+        "  cvt.f32.f16     fu, hu;         \n"
+        "  mov.b32         C, 0x40549A78U; \n"
+        "  mov.b32         nZ, 0x80000000U;\n"
+        "  fma.rn.f32      fl,fl,C,nZ;     \n"
+        "  fma.rn.f32      fu,fu,C,nZ;     \n"
+        "  ex2.approx.ftz.f32  fl, fl;     \n"
+        "  ex2.approx.ftz.f32  fu, fu;     \n"
+        "  cvt.rn.f16.f32      hl, fl;     \n"
+        "  cvt.rn.f16.f32      hu, fu;     \n"
+        "  mov.b32         r, {hl, hu};    \n"
+        __SPEC_CASE2(h, r, 0x34DE34DEU, 0x98009800U)
+        __SPEC_CASE2(h, r, 0x97669766U, 0x90009000U)
+        __SPEC_CASE2(h, r, 0x99729972U, 0x10001000U)
+        __SPEC_CASE2(h, r, 0xA5C4A5C4U, 0x10001000U)
+        __SPEC_CASE2(h, r, 0xBF0ABF0AU, 0x81008100U)
+        "  mov.b32         %0, r;  \n"
+        "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half hlog2(const __half a) {
+    __half val;
+    asm("{.reg.b16         h, r;           \n"
+        " .reg.b32         f;              \n"
+        "  mov.b16         h, %1;          \n"
+        "  cvt.f32.f16     f, h;           \n"
+        "  lg2.approx.ftz.f32  f, f;       \n"
+        "  cvt.rn.f16.f32      r, f;       \n"
+        __SPEC_CASE(r, r, 0xA2E2U, 0x8080U)
+        __SPEC_CASE(r, r, 0xBF46U, 0x9400U)
+        "  mov.b16         %0, r;          \n"
+        "}":"=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2log2(const __half2 a) {
+    __half2 val;
+    asm("{.reg.b16         hl, hu;         \n"
+        " .reg.b32         fl, fu, r, p;   \n"
+        "  mov.b32         {hl, hu}, %1;   \n"
+        "  cvt.f32.f16     fl, hl;         \n"
+        "  cvt.f32.f16     fu, hu;         \n"
+        "  lg2.approx.ftz.f32  fl, fl;     \n"
+        "  lg2.approx.ftz.f32  fu, fu;     \n"
+        "  cvt.rn.f16.f32      hl, fl;     \n"
+        "  cvt.rn.f16.f32      hu, fu;     \n"
+        "  mov.b32         r, {hl, hu};    \n"
+        __SPEC_CASE2(r, r, 0xA2E2A2E2U, 0x80808080U)
+        __SPEC_CASE2(r, r, 0xBF46BF46U, 0x94009400U)
+        "  mov.b32         %0, r;          \n"
+        "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half hlog(const __half a) {
+    __half val;
+    asm("{.reg.b32         f, C;           \n"
+        " .reg.b16         r,h;            \n"
+        "  mov.b16         h,%1;           \n"
+        "  cvt.f32.f16     f,h;            \n"
+        "  lg2.approx.ftz.f32  f,f;        \n"
+        "  mov.b32         C, 0x3f317218U;  \n"
+        "  mul.f32         f,f,C;          \n"
+        "  cvt.rn.f16.f32      r,f;        \n"
+        __SPEC_CASE(h, r, 0X160DU, 0x9C00U)
+        __SPEC_CASE(h, r, 0X3BFEU, 0x8010U)
+        __SPEC_CASE(h, r, 0X3C0BU, 0x8080U)
+        __SPEC_CASE(h, r, 0X6051U, 0x1C00U)
+        "  mov.b16         %0,r;           \n"
+        "}": "=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2log(const __half2 a) {
+    __half2 val;
+    asm("{.reg.b16         hl, hu;             \n"
+        " .reg.b32         r, fl, fu, C, h;    \n"
+        "  mov.b32         {hl, hu}, %1;       \n"
+        "  mov.b32         h, %1;              \n"
+        "  cvt.f32.f16     fl, hl;             \n"
+        "  cvt.f32.f16     fu, hu;             \n"
+        "  lg2.approx.ftz.f32  fl, fl;         \n"
+        "  lg2.approx.ftz.f32  fu, fu;         \n"
+        "  mov.b32         C, 0x3f317218U;     \n"
+        "  mul.f32         fl,fl,C;            \n"
+        "  mul.f32         fu,fu,C;            \n"
+        "  cvt.rn.f16.f32      hl, fl;         \n"
+        "  cvt.rn.f16.f32      hu, fu;         \n"
+        "  mov.b32         r, {hl, hu};        \n"
+        __SPEC_CASE2(h, r, 0X160D160DU, 0x9C009C00U)
+        __SPEC_CASE2(h, r, 0X3BFE3BFEU, 0x80108010U)
+        __SPEC_CASE2(h, r, 0X3C0B3C0BU, 0x80808080U)
+        __SPEC_CASE2(h, r, 0X60516051U, 0x1C001C00U)
+        "  mov.b32         %0, r;              \n"
+        "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half hlog10(const __half a) {
+    __half val;
+    asm("{.reg.b16         h, r;           \n"
+        " .reg.b32         f, C;           \n"
+        "  mov.b16         h, %1;          \n"
+        "  cvt.f32.f16     f, h;           \n"
+        "  lg2.approx.ftz.f32  f, f;       \n"
+        "  mov.b32         C, 0x3E9A209BU; \n"
+        "  mul.f32         f,f,C;          \n"
+        "  cvt.rn.f16.f32      r, f;       \n"
+        __SPEC_CASE(h, r, 0x338FU, 0x1000U)
+        __SPEC_CASE(h, r, 0x33F8U, 0x9000U)
+        __SPEC_CASE(h, r, 0x57E1U, 0x9800U)
+        __SPEC_CASE(h, r, 0x719DU, 0x9C00U)
+        "  mov.b16         %0, r;          \n"
+        "}":"=h"(__HALF_TO_US(val)) : "h"(__HALF_TO_CUS(a)));
+    return val;
+}
+__CUDA_FP16_DECL__ __half2 h2log10(const __half2 a) {
+    __half2 val;
+    asm("{.reg.b16         hl, hu;             \n"
+        " .reg.b32         r, fl, fu, C, h;    \n"
+        "  mov.b32         {hl, hu}, %1;       \n"
+        "  mov.b32         h, %1;              \n"
+        "  cvt.f32.f16     fl, hl;             \n"
+        "  cvt.f32.f16     fu, hu;             \n"
+        "  lg2.approx.ftz.f32  fl, fl;         \n"
+        "  lg2.approx.ftz.f32  fu, fu;         \n"
+        "  mov.b32         C, 0x3E9A209BU;     \n"
+        "  mul.f32         fl,fl,C;            \n"
+        "  mul.f32         fu,fu,C;            \n"
+        "  cvt.rn.f16.f32      hl, fl;         \n"
+        "  cvt.rn.f16.f32      hu, fu;         \n"
+        "  mov.b32         r, {hl, hu};        \n"
+        __SPEC_CASE2(h, r, 0x338F338FU, 0x10001000U)
+        __SPEC_CASE2(h, r, 0x33F833F8U, 0x90009000U)
+        __SPEC_CASE2(h, r, 0x57E157E1U, 0x98009800U)
+        __SPEC_CASE2(h, r, 0x719D719DU, 0x9C009C00U)
+        "  mov.b32         %0, r;              \n"
+        "}":"=r"(__HALF2_TO_UI(val)) : "r"(__HALF2_TO_CUI(a)));
+    return val;
+}
+#undef __SPEC_CASE2
+#undef __SPEC_CASE
+#endif /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530) || defined(_NVHPC_CUDA) */
+__CUDA_FP16_DECL__ __half2 h2rcp(const __half2 a) {
+    __APPROX_FCAST2(rcp)
+}
+__CUDA_FP16_DECL__ __half hrcp(const __half a) {
+    __APPROX_FCAST(rcp)
+}
+__CUDA_FP16_DECL__ __half2 h2rsqrt(const __half2 a) {
+    __APPROX_FCAST2(rsqrt)
+}
+__CUDA_FP16_DECL__ __half hrsqrt(const __half a) {
+    __APPROX_FCAST(rsqrt)
+}
+__CUDA_FP16_DECL__ __half2 h2sqrt(const __half2 a) {
+    __APPROX_FCAST2(sqrt)
+}
+__CUDA_FP16_DECL__ __half hsqrt(const __half a) {
+    __APPROX_FCAST(sqrt)
+}
+#undef __APPROX_FCAST
+#undef __APPROX_FCAST2
+#endif /* defined(_NVHPC_CUDA) || defined(__CUDACC__) */
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hisnan2(const __half2 a)
+{
+    __half2 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    asm("{set.nan.f16x2.f16x2 %0,%1,%2;\n}"
+        :"=r"(__HALF2_TO_UI(r)) : "r"(__HALF2_TO_CUI(a)), "r"(__HALF2_TO_CUI(a)));
+,
+    __half2_raw val;
+    val.x = __hisnan(a.x) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    val.y = __hisnan(a.y) ? (unsigned short)0x3C00U : (unsigned short)0U;
+    r = __half2(val);
+)
+    return r;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ bool __hisnan(const __half a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __half r;
+    asm("{set.nan.f16.f16 %0,%1,%2;\n}"
+        :"=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(a)), "h"(__HALF_TO_CUS(a)));
+    return __HALF_TO_CUS(r) != 0U;
+,
+    const __half_raw hr = static_cast<__half_raw>(a);
+    return ((hr.x & (unsigned short)0x7FFFU) > (unsigned short)0x7C00U);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hneg2(const __half2 a)
+{
+    __half2 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    asm("{neg.f16x2 %0,%1;\n}"
+        :"=r"(__HALF2_TO_UI(r)) : "r"(__HALF2_TO_CUI(a)));
+,
+    r.x = __hneg(a.x);
+    r.y = __hneg(a.y);
+)
+    return r;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hneg(const __half a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __half r;
+    asm("{neg.f16 %0,%1;\n}"
+        :"=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(a)));
+    return r;
+,
+    const float fa = __half2float(a);
+    return __float2half(-fa);
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __habs2(const __half2 a)
+{
+    __half2 r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    asm("{abs.f16x2 %0,%1;\n}"
+        :"=r"(__HALF2_TO_UI(r)) : "r"(__HALF2_TO_CUI(a)));
+,
+    r.x = __habs(a.x);
+    r.y = __habs(a.y);
+)
+    return r;
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __habs(const __half a)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
+    __half r;
+    asm("{abs.f16 %0,%1;\n}"
+        :"=h"(__HALF_TO_US(r)) : "h"(__HALF_TO_CUS(a)));
+    return r;
+,
+    __half_raw abs_a_raw = static_cast<__half_raw>(a);
+    abs_a_raw.x &= (unsigned short)0x7FFFU;
+    if (abs_a_raw.x > (unsigned short)0x7C00U)
+    {
+        // return canonical NaN
+        abs_a_raw.x = (unsigned short)0x7FFFU;
+    }
+    return static_cast<__half>(abs_a_raw);
+)
+}
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half2 __hcmadd(const __half2 a, const __half2 b, const __half2 c)
+{
+    // fast version of complex multiply-accumulate
+    // (a.re, a.im) * (b.re, b.im) + (c.re, c.im)
+    // acc.re = (c.re + a.re*b.re) - a.im*b.im
+    // acc.im = (c.im + a.re*b.im) + a.im*b.re
+    __half real_tmp =  __hfma(a.x, b.x, c.x);
+    __half img_tmp  =  __hfma(a.x, b.y, c.y);
+    real_tmp = __hfma(__hneg(a.y), b.y, real_tmp);
+    img_tmp  = __hfma(a.y,         b.x, img_tmp);
+    return make_half2(real_tmp, img_tmp);
+}
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmax_nan(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF_MACRO(max.NaN)
+,
+    __half maxval;
+    if (__hisnan(a) || __hisnan(b))
+    {
+        maxval = CUDART_NAN_FP16;
+    }
+    else
+    {
+        maxval = __hmax(a, b);
+    }
+    return maxval;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half __hmin_nan(const __half a, const __half b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF_MACRO(min.NaN)
+,
+    __half minval;
+    if (__hisnan(a) || __hisnan(b))
+    {
+        minval = CUDART_NAN_FP16;
+    }
+    else
+    {
+        minval = __hmin(a, b);
+    }
+    return minval;
+)
+}
+
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half __hfma_relu(const __half a, const __half b, const __half c)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __TERNARY_OP_HALF_MACRO(fma.rn.relu)
+,
+    return __hmax_nan(__hfma(a, b, c), CUDART_ZERO_FP16);
+)
+}
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmax2_nan(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF2_MACRO(max.NaN)
+,
+    __half2 result = __hmax2(a, b);
+    if (__hisnan(a.x) || __hisnan(b.x))
+    {
+        result.x = CUDART_NAN_FP16;
+    }
+    if (__hisnan(a.y) || __hisnan(b.y))
+    {
+        result.y = CUDART_NAN_FP16;
+    }
+    return result;
+)
+}
+__CUDA_HOSTDEVICE_FP16_DECL__ __half2 __hmin2_nan(const __half2 a, const __half2 b)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __BINARY_OP_HALF2_MACRO(min.NaN)
+,
+    __half2 result = __hmin2(a, b);
+    if (__hisnan(a.x) || __hisnan(b.x))
+    {
+        result.x = CUDART_NAN_FP16;
+    }
+    if (__hisnan(a.y) || __hisnan(b.y))
+    {
+        result.y = CUDART_NAN_FP16;
+    }
+    return result;
+)
+}
+#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__ __half2 __hfma2_relu(const __half2 a, const __half2 b, const __half2 c)
+{
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    __TERNARY_OP_HALF2_MACRO(fma.rn.relu)
+,
+    __half2_raw hzero;
+    hzero.x = (unsigned short)0U;
+    hzero.y = (unsigned short)0U;
+    return __hmax2_nan(__hfma2(a, b, c), __half2(hzero));
+)
+}
+#endif /* defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)) || defined(_NVHPC_CUDA) */
+
+#if defined(__CUDACC__) || defined(_NVHPC_CUDA)
+/* Define __PTR for atomicAdd prototypes below, undef after done */
+#if (defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)
+#define __PTR   "l"
+#else
+#define __PTR   "r"
+#endif /*(defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)*/
+
+__CUDA_FP16_DECL__  __half2 atomicAdd(__half2 *const address, const __half2 val) {
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_60,
+    __half2 r;
+    asm volatile ("{ atom.add.noftz.f16x2 %0,[%1],%2; }\n"
+                  : "=r"(__HALF2_TO_UI(r)) : __PTR(address), "r"(__HALF2_TO_CUI(val))
+                  : "memory");
+    return r;
+,
+    unsigned int* address_as_uint = (unsigned int*)address;
+    unsigned int old = *address_as_uint;
+    unsigned int assumed;
+    do {
+        assumed = old;
+        __half2 new_val = __hadd2(val, *(__half2*)&assumed);
+        old = atomicCAS(address_as_uint, assumed, *(unsigned int*)&new_val);
+    } while (assumed != old);
+    return *(__half2*)&old;
+)
+}
+
+#if (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA)
+__CUDA_FP16_DECL__  __half atomicAdd(__half *const address, const __half val) {
+    __half r;
+    asm volatile ("{ atom.add.noftz.f16 %0,[%1],%2; }\n"
+                  : "=h"(__HALF_TO_US(r))
+                  : __PTR(address), "h"(__HALF_TO_CUS(val))
+                  : "memory");
+    return r;
+}
+#endif /* (defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700))) || defined(_NVHPC_CUDA) */
+
+#undef __PTR
+#endif /* defined(__CUDACC__) || defined(_NVHPC_CUDA) */
+#endif /* !(defined __DOXYGEN_ONLY__) */
+#endif /* defined(__cplusplus) */
+
+#undef __TERNARY_OP_HALF2_MACRO
+#undef __TERNARY_OP_HALF_MACRO
+#undef __BINARY_OP_HALF2_MACRO
+#undef __BINARY_OP_HALF_MACRO
+
+#undef __CUDA_HOSTDEVICE_FP16_DECL__
+#undef __CUDA_FP16_DECL__
+
+#undef __HALF_TO_US
+#undef __HALF_TO_CUS
+#undef __HALF2_TO_UI
+#undef __HALF2_TO_CUI
+#undef __CUDA_FP16_CONSTEXPR__
+
+#if defined(__CPP_VERSION_AT_LEAST_11_FP16)
+#undef __CPP_VERSION_AT_LEAST_11_FP16
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP16) */
+
+#undef ___CUDA_FP16_STRINGIFY_INNERMOST
+#undef __CUDA_FP16_STRINGIFY
+
+#endif /* end of include guard: __CUDA_FP16_HPP__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp4.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp4.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b291e7b78610171e9068b39ed5c503b35f45ad4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp4.h
@@ -0,0 +1,357 @@
+/*
+ * Copyright 2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef __CUDA_FP4_H__
+#define __CUDA_FP4_H__
+
+/* Set up function decorations */
+#if defined(__CUDACC__)
+#define __CUDA_FP4_DECL__ static __device__ __inline__
+#define __CUDA_HOSTDEVICE_FP4__ __host__ __device__
+#define __CUDA_HOSTDEVICE_FP4_DECL__ static __host__ __device__ __inline__
+#else /* !defined(__CUDACC__) */
+#if defined(__GNUC__)
+#define __CUDA_HOSTDEVICE_FP4_DECL__ static __attribute__((unused))
+#else
+#define __CUDA_HOSTDEVICE_FP4_DECL__ static
+#endif /* defined(__GNUC__) */
+#define __CUDA_HOSTDEVICE_FP4__
+#endif /* defined(__CUDACC_) */
+
+#if !defined(_MSC_VER) && __cplusplus >= 201103L
+#define __CPP_VERSION_AT_LEAST_11_FP4
+#elif _MSC_FULL_VER >= 190024210 && _MSVC_LANG >= 201103L
+#define __CPP_VERSION_AT_LEAST_11_FP4
+#endif
+
+/* bring in fp6 types infrastructure and dependencies */
+#include "cuda_fp6.h"
+
+/**
+ * \defgroup CUDA_MATH_INTRINSIC_FP4 FP4 Intrinsics
+ * This section describes fp4 intrinsic functions.
+ * To use these functions, include the header file \p cuda_fp4.h in your
+ * program.
+ *
+ * \note Most of the operations defined here benefit from native HW support
+ * when compiled for specific GPU targets (e.g. devices of compute capability 10.0a),
+ * other targets use emulation path.
+ *
+ * The following macros are available to help users selectively enable/disable
+ * various definitions present in the header file:
+ * - \p __CUDA_NO_FP4_CONVERSIONS__ - If defined, this macro will prevent any
+ * use of the C++ type conversions (converting constructors and conversion
+ * operators) defined in the header.
+ * - \p __CUDA_NO_FP4_CONVERSION_OPERATORS__ - If defined, this macro will
+ * prevent any use of the  C++ conversion operators from \p fp4 to other types.
+ */
+
+/**
+ * \defgroup CUDA_MATH_FP4_MISC FP4 Conversion and Data Movement
+ * \ingroup CUDA_MATH_INTRINSIC_FP4
+ * To use these functions, include the header file \p cuda_fp4.h in your
+ * program.
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief 8-bit \p unsigned \p integer
+ * type abstraction used for \p fp4 floating-point
+ * numbers storage.
+ */
+typedef __nv_fp8_storage_t __nv_fp4_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief 8-bit \p unsigned \p integer
+ * type abstraction used for storage of pairs of
+ * \p fp4 floating-point numbers.
+ */
+typedef __nv_fp8_storage_t __nv_fp4x2_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief 16-bit \p unsigned \p integer
+ * type abstraction used for storage of tetrads of
+ * \p fp4 floating-point numbers.
+ */
+typedef __nv_fp8x2_storage_t __nv_fp4x4_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Enumerates the possible
+ * interpretations of the 4-bit values when referring to them as
+ * \p fp4 types.
+ */
+typedef enum __nv_fp4_interpretation_t {
+    __NV_E2M1, /**< Stands for \p fp4 numbers of \p e2m1 kind. */
+} __nv_fp4_interpretation_t;
+
+/* Forward-declaration of C-style APIs */
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input \p double precision \p x to \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp4 type of the kind specified by
+ * \p fp4_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t
+__nv_cvt_double_to_fp4(const double x,
+                       const __nv_fp4_interpretation_t fp4_interpretation,
+                       const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input vector of two \p double precision numbers packed
+ * in \p double2 \p x into a vector of two values of \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp4 values of the
+ * kind specified by \p fp4_interpretation parameter, using
+ * rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t
+__nv_cvt_double2_to_fp4x2(const double2 x,
+                          const __nv_fp4_interpretation_t fp4_interpretation,
+                          const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input \p single precision \p x to \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp4 type of the kind specified by
+ * \p fp4_interpretation parameter, using
+ * rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t
+__nv_cvt_float_to_fp4(const float x,
+                      const __nv_fp4_interpretation_t fp4_interpretation,
+                      const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input vector of two \p single precision numbers packed
+ * in \p float2 \p x into a vector of two values of \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp4 values of the
+ * kind specified by \p fp4_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t
+__nv_cvt_float2_to_fp4x2(const float2 x,
+                         const __nv_fp4_interpretation_t fp4_interpretation,
+                         const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input \p half precision \p x to \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp4 type of the kind specified by
+ * \p fp4_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t
+__nv_cvt_halfraw_to_fp4(const __half_raw x,
+                        const __nv_fp4_interpretation_t fp4_interpretation,
+                        const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input vector of two \p half precision numbers packed
+ * in \p __half2_raw \p x into a vector of two values of \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp4 values of the
+ * kind specified by \p fp4_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t __nv_cvt_halfraw2_to_fp4x2(
+    const __half2_raw x,
+    const __nv_fp4_interpretation_t fp4_interpretation,
+    const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input \p nv_bfloat16 precision \p x to \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp4 type of the kind specified by
+ * \p fp4_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t __nv_cvt_bfloat16raw_to_fp4(
+    const __nv_bfloat16_raw x,
+    const __nv_fp4_interpretation_t fp4_interpretation,
+    const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input vector of two \p nv_bfloat16 precision numbers packed
+ * in \p __nv_bfloat162_raw \p x into a vector of two values of \p fp4 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp4 values of the
+ * kind specified by \p fp4_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp4x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t
+__nv_cvt_bfloat16raw2_to_fp4x2(
+    const __nv_bfloat162_raw x,
+    const __nv_fp4_interpretation_t fp4_interpretation,
+    const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input \p fp4 \p x of the specified kind
+ * to \p half precision.
+ *
+ * \details Converts input \p x of \p fp4 type of the kind specified by
+ * \p fp4_interpretation parameter
+ * to \p half precision.
+ *
+ * \returns
+ * - The \p __half_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __half_raw
+__nv_cvt_fp4_to_halfraw(const __nv_fp4_storage_t x,
+                        const __nv_fp4_interpretation_t fp4_interpretation);
+/**
+ * \ingroup CUDA_MATH_FP4_MISC
+ * \brief Converts input vector of two \p fp4 values of the specified kind
+ * to a vector of two \p half precision values packed in \p __half2_raw
+ * structure.
+ *
+ * \details Converts input vector \p x of \p fp4 type of the kind specified by
+ * \p fp4_interpretation parameter
+ * to a vector of two \p half precision values and returns as \p __half2_raw
+ * structure.
+ *
+ * \returns
+ * - The \p __half2_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP4_DECL__ __half2_raw
+__nv_cvt_fp4x2_to_halfraw2(const __nv_fp4x2_storage_t x,
+                           const __nv_fp4_interpretation_t fp4_interpretation);
+
+#if defined(__cplusplus)
+
+#define __CUDA_FP4_TYPES_EXIST__
+
+/* Forward-declaration of structures defined in "cuda_fp4.hpp" */
+struct __nv_fp4_e2m1;
+struct __nv_fp4x2_e2m1;
+struct __nv_fp4x4_e2m1;
+
+#endif /* defined(__cplusplus) */
+
+#include "cuda_fp4.hpp"
+
+#undef __CUDA_FP4_DECL__
+#undef __CUDA_HOSTDEVICE_FP4__
+#undef __CUDA_HOSTDEVICE_FP4_DECL__
+
+#if defined(__CPP_VERSION_AT_LEAST_11_FP4)
+#undef __CPP_VERSION_AT_LEAST_11_FP4
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP4) */
+
+#endif /* end of include guard: __CUDA_FP4_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp4.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp4.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..97716a433d797c49961ee0ba33b4fb84e9b3bb1d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp4.hpp
@@ -0,0 +1,953 @@
+/*
+ * Copyright 2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_FP4_HPP__)
+#define __CUDA_FP4_HPP__
+
+#if !defined(__CUDA_FP4_H__)
+#error "Do not include this file directly. Instead, include cuda_fp4.h."
+#endif
+
+/* C++ header for std::memcpy (used for type punning in host-side
+ * implementations). When compiling as a CUDA source file memcpy is provided
+ * implicitly. !defined(__CUDACC__) implies !defined(__CUDACC_RTC__).
+ */
+#if defined(__cplusplus) && !defined(__CUDACC__)
+#include <cstring>
+#elif !defined(__cplusplus) && !defined(__CUDACC__)
+#include <string.h>
+#endif /* defined(__cplusplus) && !defined(__CUDACC__) */
+
+/*
+ * Bring in the standard assertions header to enforce the subset
+ * of rounding modes supported by the APIs defined here.
+ * NOTE: NVRTC defines its own assert
+ */
+#if !defined (__CUDACC_RTC__)
+#include <assert.h>
+#endif
+
+/* Set up structure-alignment attribute */
+#if !(defined __CUDA_ALIGN__)
+#if defined(__CUDACC__)
+#define __CUDA_ALIGN__(align) __align__(align)
+#else
+/* Define alignment macro based on compiler type (cannot assume C11 "_Alignas"
+ * is available) */
+#if __cplusplus >= 201103L
+#define __CUDA_ALIGN__(n)                                                      \
+    alignas(n) /* C++11 kindly gives us a keyword for this */
+#else          /* !defined(__CPP_VERSION_AT_LEAST_11_FP4)*/
+#if defined(__GNUC__)
+#define __CUDA_ALIGN__(n) __attribute__((aligned(n)))
+#elif defined(_MSC_VER)
+#define __CUDA_ALIGN__(n) __declspec(align(n))
+#else
+#define __CUDA_ALIGN__(n)
+#endif /* defined(__GNUC__) */
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP4) */
+#endif /* defined(__CUDACC__) */
+#endif /* !(defined __CUDA_ALIGN__) */
+
+#if !(defined __CPP_VERSION_AT_LEAST_11_FP4)
+/* need c++11 for explicit operators */
+#define __CUDA_NO_FP4_CONVERSION_OPERATORS__
+#endif
+
+#if !(defined __DOXYGEN_ONLY__)
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t
+__nv_cvt_double_to_fp4(const double x,
+                       const __nv_fp4_interpretation_t fp4_interpretation,
+                       const enum cudaRoundMode rounding) {
+    unsigned char res;
+    unsigned long long int xbits;
+
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&xbits, &x, sizeof(x));
+#else
+    (void)std::memcpy(&xbits, &x, sizeof(x));
+#endif
+    unsigned char FP4_MAXNORM;
+    unsigned char FP4_MANTISSA_MASK;
+    unsigned short int FP4_EXP_BIAS;
+    unsigned long long int FP4_SIGNIFICAND_BITS;
+    unsigned long long int FP4_MINDENORM_O2;
+    unsigned long long int FP4_OVERFLOW_THRESHOLD;
+    unsigned long long int FP4_MINNORM;
+    const unsigned long long int DP_INF_BITS = 0x7FF0000000000000ULL;
+
+    // fp4_interpretation == __NV_E2M1
+    FP4_EXP_BIAS = 1U;
+    FP4_SIGNIFICAND_BITS = 2ULL;
+    FP4_MANTISSA_MASK = 0x1U;
+    FP4_MINDENORM_O2 = 0x3FD0000000000000ULL; // mindenorm/2 = 2^-2
+    FP4_OVERFLOW_THRESHOLD =
+        0x4018000000000000ULL; // maxnorm = 6.0
+    FP4_MAXNORM = 0x7U;
+    FP4_MINNORM = 0x3FF0000000000000ULL; // minnorm = 2^0
+
+    // 1/2 LSB of the target format, positioned in double precision mantissa
+    // helpful in midpoints detection during round-to-nearest-even step
+    const unsigned long long int FP4_DP_HALF_ULP =
+        (unsigned long long int)1ULL << (53ULL - FP4_SIGNIFICAND_BITS - 1ULL);
+    // prepare sign bit in target format
+    unsigned char sign = (unsigned char)((xbits >> 63ULL) << 3U);
+    // prepare exponent field in target format
+    unsigned char exp =
+        (unsigned char)((((unsigned short int)(xbits >> 52ULL)) & 0x7FFU) -
+                        1023U + FP4_EXP_BIAS);
+    // round mantissa to target format width, rounding towards zero
+    unsigned char mantissa =
+        (unsigned char)(xbits >> (53ULL - FP4_SIGNIFICAND_BITS)) &
+        FP4_MANTISSA_MASK;
+    unsigned long long int absx = xbits & 0x7FFFFFFFFFFFFFFFULL;
+
+    if (absx <= FP4_MINDENORM_O2) {
+        // zero or underflow
+        res = 0U;
+    } else if (absx > FP4_OVERFLOW_THRESHOLD) {
+        // overflow or NaN
+        if (absx > DP_INF_BITS)
+        {
+            // NaN converts to positive FP4_MAXNORM
+            sign = 0U;
+        }
+        res = FP4_MAXNORM;
+    } else if (absx >= FP4_MINNORM) {
+        res = (unsigned char)((exp << (FP4_SIGNIFICAND_BITS - 1U)) | mantissa);
+        // rounded-off bits
+        unsigned long long int round =
+            xbits & ((FP4_DP_HALF_ULP << 1ULL) - 1ULL);
+        if (rounding == cudaRoundNearest)
+        {
+            // round-to-nearest-even adjustment
+            if ((round > FP4_DP_HALF_ULP) ||
+                ((round == FP4_DP_HALF_ULP) && (mantissa & 1U))) {
+                res = (unsigned char)(res + 1U);
+            }
+        } else {
+            assert(rounding == cudaRoundZero);
+        }
+    } else // Denormal range
+    {
+        unsigned char shift = (unsigned char)(1U - exp);
+        // add implicit leading bit
+        mantissa |= (unsigned char)(1U << (FP4_SIGNIFICAND_BITS - 1U));
+        // additional round-off due to denormalization
+        res = (unsigned char)(mantissa >> shift);
+
+        if (rounding == cudaRoundNearest)
+        {
+            // rounded-off bits, including implicit leading bit
+            unsigned long long int round =
+                (xbits | ((unsigned long long int)1ULL << (53ULL - 1ULL))) &
+                ((FP4_DP_HALF_ULP << (shift + 1ULL)) - 1ULL);
+            // round-to-nearest-even adjustment
+            if ((round > (FP4_DP_HALF_ULP << shift)) ||
+                ((round == (FP4_DP_HALF_ULP << shift)) && (res & 1U))) {
+                res = (unsigned char)(res + 1U);
+            }
+        } else {
+            assert(rounding == cudaRoundZero);
+        }
+    }
+
+    res |= sign;
+
+    return (__nv_fp4_storage_t)res;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t
+__nv_cvt_double2_to_fp4x2(const double2 x,
+                          const __nv_fp4_interpretation_t fp4_interpretation,
+                          const enum cudaRoundMode rounding) {
+    __nv_fp4x2_storage_t storage = (__nv_fp4x2_storage_t)__nv_cvt_double_to_fp4(
+        x.y, fp4_interpretation, rounding);
+    storage = (__nv_fp4x2_storage_t)(storage << 4U);
+    storage = (__nv_fp4x2_storage_t)(storage |
+                                     __nv_cvt_double_to_fp4(
+                                         x.x, fp4_interpretation, rounding));
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t
+__nv_cvt_float_to_fp4(const float x,
+                      const __nv_fp4_interpretation_t fp4_interpretation,
+                      const enum cudaRoundMode rounding) {
+    __nv_fp4_storage_t res = 0U;
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    if (rounding == cudaRoundNearest)
+    {
+        unsigned short storage;
+        // fp4_interpretation == __NV_E2M1
+        asm("{ .reg .b8 __$temp1;                           \n"
+            " cvt.rn.satfinite.e2m1x2.f32 __$temp1, %2, %1; \n"
+            " mov.b16 %0, {__$temp1, 0};                   }\n"
+            : "=h"(storage)
+            : "f"(x), "f"(0.0f));
+        res = (__nv_fp4_storage_t)storage;
+    } else
+#endif
+    {
+        res = __nv_cvt_double_to_fp4((double)x, fp4_interpretation, rounding);
+    }
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t
+__nv_cvt_float2_to_fp4x2(const float2 x,
+                         const __nv_fp4_interpretation_t fp4_interpretation,
+                         const enum cudaRoundMode rounding) {
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+    unsigned short storage;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    if (rounding == cudaRoundNearest) {
+        // fp4_interpretation == __NV_E2M1
+        asm("{ .reg .b8 __$temp1;                           \n"
+            " cvt.rn.satfinite.e2m1x2.f32 __$temp1, %2, %1; \n"
+            " mov.b16 %0, {__$temp1, 0};                   }\n"
+            : "=h"(storage)
+            : "f"(x.x), "f"(x.y));
+    } else
+#endif
+    {
+        storage = (__nv_fp4x2_storage_t)__nv_cvt_float_to_fp4(
+            x.y, fp4_interpretation, rounding);
+        storage = (__nv_fp4x2_storage_t)(storage << 4U);
+        storage = (__nv_fp4x2_storage_t)(storage | __nv_cvt_float_to_fp4(
+                                                       x.x,
+                                                       fp4_interpretation, rounding));
+    }
+    return (__nv_fp4x2_storage_t)storage;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t
+__nv_cvt_halfraw_to_fp4(const __half_raw x,
+                        const __nv_fp4_interpretation_t fp4_interpretation,
+                        const enum cudaRoundMode rounding) {
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+    __nv_fp4_storage_t res = 0U;
+    float fx = __internal_halfraw_to_float(x);
+    res = __nv_cvt_float_to_fp4(fx, fp4_interpretation, rounding);
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t __nv_cvt_halfraw2_to_fp4x2(
+    const __half2_raw x,
+    const __nv_fp4_interpretation_t fp4_interpretation,
+    const enum cudaRoundMode rounding) {
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+    unsigned short tmp;
+    __half_raw raw;
+    raw.x = x.x;
+    __nv_fp4_storage_t lo =
+        __nv_cvt_halfraw_to_fp4(raw, fp4_interpretation, rounding);
+    raw.x = x.y;
+    __nv_fp4_storage_t hi =
+        __nv_cvt_halfraw_to_fp4(raw, fp4_interpretation, rounding);
+    tmp = hi;
+    tmp = (__nv_fp4x2_storage_t)(tmp << 4U);
+    tmp = (__nv_fp4x2_storage_t)(tmp | lo);
+    return (__nv_fp4x2_storage_t)tmp;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4_storage_t __nv_cvt_bfloat16raw_to_fp4(
+    const __nv_bfloat16_raw x,
+    const __nv_fp4_interpretation_t fp4_interpretation,
+    const enum cudaRoundMode rounding) {
+    const float fx = __internal_bf16raw_to_float(x);
+    const __nv_fp4_storage_t res =
+        __nv_cvt_float_to_fp4(fx, fp4_interpretation, rounding);
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __nv_fp4x2_storage_t
+__nv_cvt_bfloat16raw2_to_fp4x2(
+    const __nv_bfloat162_raw x,
+    const __nv_fp4_interpretation_t fp4_interpretation,
+    const enum cudaRoundMode rounding) {
+    __nv_bfloat16_raw raw;
+    raw.x = x.y;
+    __nv_fp4x2_storage_t storage =
+        (__nv_fp4x2_storage_t)__nv_cvt_bfloat16raw_to_fp4(raw,
+                                        fp4_interpretation, rounding);
+    storage = (__nv_fp4x2_storage_t)(storage << 4U);
+    raw.x = x.x;
+    storage = (__nv_fp4x2_storage_t)(storage |
+                                     __nv_cvt_bfloat16raw_to_fp4(raw,
+                                        fp4_interpretation, rounding));
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __half2_raw
+__nv_cvt_fp4x2_to_halfraw2(const __nv_fp4x2_storage_t x,
+                           const __nv_fp4_interpretation_t fp4_interpretation);
+__CUDA_HOSTDEVICE_FP4_DECL__ __half_raw
+__nv_cvt_fp4_to_halfraw(const __nv_fp4_storage_t x,
+                        const __nv_fp4_interpretation_t fp4_interpretation) {
+    __half_raw res;
+    res.x = 0U;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    res.x =
+        __nv_cvt_fp4x2_to_halfraw2((__nv_fp4x2_storage_t)x, fp4_interpretation)
+            .x;
+#else
+    {
+        // fp4_interpretation == __NV_E2M1
+        // convert to e2m3 first
+        __nv_fp6_storage_t fp6e2m3 = (x & 0xFU) << 2U;
+        res = __nv_cvt_fp6_to_halfraw(fp6e2m3, __NV_E2M3);
+    }
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ __half2_raw
+__nv_cvt_fp4x2_to_halfraw2(const __nv_fp4x2_storage_t x,
+                           const __nv_fp4_interpretation_t fp4_interpretation) {
+    __half2_raw res;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    unsigned int half2_storage;
+    unsigned short tmp = (unsigned short)x;
+    asm("{ .reg .b8 __$temp1, __$tempz;                 \n"
+        " mov.b16 {__$temp1, __$tempz}, %1;             \n"
+        " cvt.rn.f16x2.e2m1x2 %0, __$temp1;            }\n"
+        : "=r"(half2_storage)
+        : "h"(tmp));
+    (void)memcpy(&res, &half2_storage, sizeof(half2_storage));
+#else
+    res.x =
+        __nv_cvt_fp4_to_halfraw((__nv_fp4_storage_t)x, fp4_interpretation).x;
+    res.y = __nv_cvt_fp4_to_halfraw((__nv_fp4_storage_t)(x >> 4U),
+                                    fp4_interpretation)
+                .x;
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP4_DECL__ unsigned short int
+__internal_pack_u8x2_to_u16(const unsigned char src_lo,
+                            const unsigned char src_hi) {
+    return (((unsigned short int)src_hi) << 8U) |
+            ((unsigned short int)src_lo);
+}
+
+#endif /* !(defined __DOXYGEN_ONLY__) */
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+
+/**
+ * \defgroup CUDA_MATH_FP4_E2M1_STRUCT C++ struct for handling fp4 data type of e2m1 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP4
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP4_E2M1_STRUCT
+ * \brief __nv_fp4_e2m1 datatype
+ *
+ * \details This structure implements the datatype for handling
+ * \p fp4 floating-point numbers of \p e2m1 kind:
+ * with 1 sign, 2 exponent, 1 implicit and 1 explicit mantissa bits.
+ * This encoding does not support Inf/NaN.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp4_e2m1 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP4_E2M1_STRUCT
+     * Storage variable contains the \p fp4 floating-point data.
+     */
+    __nv_fp4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP4)
+    __nv_fp4_e2m1() = default;
+#else
+    __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP4) */
+
+#if !defined(__CUDA_NO_FP4_CONVERSIONS__)
+
+    /* Construct from wider FP types */
+    /* Note we do avoid constructor init-list because of special host/device
+     * compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p __half data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const __half f) {
+        __x = __nv_cvt_halfraw_to_fp4(static_cast<__half_raw>(f),
+                                      __NV_E2M1, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p __nv_bfloat16 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const __nv_bfloat16 f) {
+        __x = __nv_cvt_bfloat16raw_to_fp4(static_cast<__nv_bfloat16_raw>(f),
+                                          __NV_E2M1, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p float data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const float f) {
+        __x = __nv_cvt_float_to_fp4(f, __NV_E2M1, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p double data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const double f) {
+        __x = __nv_cvt_double_to_fp4(f, __NV_E2M1, cudaRoundNearest);
+    }
+
+    /* Converts from integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p unsigned \p short \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__
+    __nv_fp4_e2m1(const unsigned short int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p unsigned \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const unsigned int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p unsigned \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const unsigned long int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p unsigned \p long \p long \p int data type, relies on
+     * \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__
+    __nv_fp4_e2m1(const unsigned long long int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const short int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p long \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const long int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p long \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4_e2m1(const long long int val) {
+        __x = static_cast<__nv_fp4_e2m1>(static_cast<float>(val)).__x;
+    }
+
+#if !defined(__CUDA_NO_FP4_CONVERSION_OPERATORS__)
+    /* Widening FP converts */
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p __half data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator __half() const {
+        return static_cast<__half>(__nv_cvt_fp4_to_halfraw(__x, __NV_E2M1));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p float data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator float() const {
+        return __internal_halfraw_to_float(
+            __nv_cvt_fp4_to_halfraw(__x, __NV_E2M1));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p __nv_bfloat16 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator __nv_bfloat16() const {
+        return __float2bfloat16_rz(float(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p double data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator double() const {
+        return static_cast<double>(float(*this));
+    }
+
+    /* Convert to integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator unsigned char() const {
+        unsigned char i;
+        const float f = float(*this);
+
+        if (f < 0.0f) {
+            // saturate minimum
+            i = 0U;
+        } else {
+            // normal value
+            i = static_cast<unsigned char>(f);
+        }
+        return i;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p unsigned \p short \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator unsigned short int() const {
+        return __half2ushort_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator unsigned int() const {
+        return __half2uint_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p unsigned \p long \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator unsigned long int() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__half2ull_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__half2uint_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p unsigned \p long \p long \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator unsigned long long int() const {
+        return __half2ull_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p signed \p char data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator signed char() const {
+        const float f = float(*this);
+        return static_cast<signed char>(f);
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+     * 
+     * Clamps inputs to the output range.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(static_cast<signed char>(*this));
+        }
+        else
+        {
+            value = static_cast<char>(static_cast<unsigned char>(*this));
+        }
+        return value;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator short int() const {
+        return __half2short_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator int() const {
+        return __half2int_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p long \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator long int() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__half2ll_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<long>(__half2int_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p long \p long \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator long long int() const {
+        return __half2ll_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator bool() const {
+        return (__x & 0x7U) != 0U;
+    }
+#endif /* !defined(__CUDA_NO_FP4_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP4_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP4X2_E2M1_STRUCT C++ struct for handling vector type of two fp4 values of e2m1 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP4
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP4X2_E2M1_STRUCT
+ * \brief __nv_fp4x2_e2m1 datatype
+ *
+ * \details This structure implements the datatype for handling two
+ * \p fp4 floating-point numbers of \p e2m1 kind each.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp4x2_e2m1 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP4X2_E2M1_STRUCT
+     * Storage variable contains the vector of two \p fp4 floating-point data
+     * values.
+     */
+    __nv_fp4x2_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP4)
+    __nv_fp4x2_e2m1() = default;
+#else
+    __CUDA_HOSTDEVICE_FP4__ __nv_fp4x2_e2m1() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP4) */
+
+#if !defined(__CUDA_NO_FP4_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p __half2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x2_e2m1(const __half2 f) {
+        __x = __nv_cvt_halfraw2_to_fp4x2(static_cast<__half2_raw>(f),
+                                         __NV_E2M1, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p __nv_bfloat162 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x2_e2m1(const __nv_bfloat162 f) {
+        __x = __nv_cvt_bfloat16raw2_to_fp4x2(static_cast<__nv_bfloat162_raw>(f),
+                                             __NV_E2M1, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p float2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x2_e2m1(const float2 f) {
+        __x = __nv_cvt_float2_to_fp4x2(f, __NV_E2M1, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p double2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x2_e2m1(const double2 f) {
+        __x = __nv_cvt_double2_to_fp4x2(f, __NV_E2M1, cudaRoundNearest);
+    }
+
+#if !defined(__CUDA_NO_FP4_CONVERSION_OPERATORS__)
+    /* Widening converts */
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p __half2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator __half2() const {
+        return static_cast<__half2>(__nv_cvt_fp4x2_to_halfraw2(__x, __NV_E2M1));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p float2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator float2() const {
+        return __internal_halfraw2_to_float2(
+            __nv_cvt_fp4x2_to_halfraw2(__x, __NV_E2M1));
+    }
+#endif /* !defined(__CUDA_NO_FP4_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP4_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP4X4_E2M1_STRUCT C++ struct for handling vector type of four fp4 values of e2m1 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP4
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP4X4_E2M1_STRUCT
+ * \brief __nv_fp4x4_e2m1 datatype
+ *
+ * \details This structure implements the datatype for handling four
+ * \p fp4 floating-point numbers of \p e2m1 kind each.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(2) __nv_fp4x4_e2m1 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP4X4_E2M1_STRUCT
+     * Storage variable contains the vector of four \p fp4 floating-point data
+     * values.
+     */
+    __nv_fp4x4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP4)
+    __nv_fp4x4_e2m1() = default;
+#else
+    __CUDA_HOSTDEVICE_FP4__ __nv_fp4x4_e2m1() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP4) */
+
+#if !defined(__CUDA_NO_FP4_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from a pair of \p __half2 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x4_e2m1(const __half2 flo,
+                                                     const __half2 fhi) {
+        const __nv_fp4x2_storage_t rlo = __nv_cvt_halfraw2_to_fp4x2(
+            static_cast<__half2_raw>(flo), __NV_E2M1, cudaRoundNearest);
+        const __nv_fp4x2_storage_t rhi = __nv_cvt_halfraw2_to_fp4x2(
+            static_cast<__half2_raw>(fhi), __NV_E2M1, cudaRoundNearest);
+        __x = __internal_pack_u8x2_to_u16(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from a pair of \p __nv_bfloat162 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x4_e2m1(const __nv_bfloat162 flo,
+                                                     const __nv_bfloat162 fhi) {
+        const __nv_fp4x2_storage_t rlo = __nv_cvt_bfloat16raw2_to_fp4x2(
+            static_cast<__nv_bfloat162_raw>(flo), __NV_E2M1, cudaRoundNearest);
+        const __nv_fp4x2_storage_t rhi = __nv_cvt_bfloat16raw2_to_fp4x2(
+            static_cast<__nv_bfloat162_raw>(fhi), __NV_E2M1, cudaRoundNearest);
+        __x = __internal_pack_u8x2_to_u16(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p float4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x4_e2m1(const float4 f) {
+        const float2 flo = {f.x, f.y};
+        const float2 fhi = {f.z, f.w};
+        const __nv_fp4x2_storage_t rlo =
+            __nv_cvt_float2_to_fp4x2(flo, __NV_E2M1, cudaRoundNearest);
+        const __nv_fp4x2_storage_t rhi =
+            __nv_cvt_float2_to_fp4x2(fhi, __NV_E2M1, cudaRoundNearest);
+        __x = __internal_pack_u8x2_to_u16(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Constructor from \p double4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ __nv_fp4x4_e2m1(const double4 f) {
+        const double2 flo = {f.x, f.y};
+        const double2 fhi = {f.z, f.w};
+        const __nv_fp4x2_storage_t rlo =
+            __nv_cvt_double2_to_fp4x2(flo, __NV_E2M1, cudaRoundNearest);
+        const __nv_fp4x2_storage_t rhi =
+            __nv_cvt_double2_to_fp4x2(fhi, __NV_E2M1, cudaRoundNearest);
+        __x = __internal_pack_u8x2_to_u16(rlo, rhi);
+    }
+
+#if !defined(__CUDA_NO_FP4_CONVERSION_OPERATORS__)
+    /* Widening converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP4_MISC
+     * Conversion operator to \p float4 vector data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP4__ operator float4() const {
+        const __nv_fp4x2_storage_t slo = static_cast<__nv_fp4x2_storage_t>(__x);
+        const __nv_fp4x2_storage_t shi =
+            static_cast<__nv_fp4x2_storage_t>(__x >> 8U);
+        float2 rlo = __internal_halfraw2_to_float2(
+            __nv_cvt_fp4x2_to_halfraw2(slo, __NV_E2M1));
+        float2 rhi = __internal_halfraw2_to_float2(
+            __nv_cvt_fp4x2_to_halfraw2(shi, __NV_E2M1));
+        float4 res = {rlo.x, rlo.y, rhi.x, rhi.y};
+        return res;
+    }
+#endif /* !defined(__CUDA_NO_FP4_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP4_CONVERSIONS__) */
+};
+
+#endif /* defined(__cplusplus) */
+
+#endif /* end of include guard: __CUDA_FP4_HPP__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp6.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp6.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b0d6d5abf0781f4e378877d7395818898fa9ce0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp6.h
@@ -0,0 +1,362 @@
+/*
+ * Copyright 2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef __CUDA_FP6_H__
+#define __CUDA_FP6_H__
+
+/* Set up function decorations */
+#if defined(__CUDACC__)
+#define __CUDA_FP6_DECL__ static __device__ __inline__
+#define __CUDA_HOSTDEVICE_FP6__ __host__ __device__
+#define __CUDA_HOSTDEVICE_FP6_DECL__ static __host__ __device__ __inline__
+#else /* !defined(__CUDACC__) */
+#if defined(__GNUC__)
+#define __CUDA_HOSTDEVICE_FP6_DECL__ static __attribute__((unused))
+#else
+#define __CUDA_HOSTDEVICE_FP6_DECL__ static
+#endif /* defined(__GNUC__) */
+#define __CUDA_HOSTDEVICE_FP6__
+#endif /* defined(__CUDACC_) */
+
+#if !defined(_MSC_VER) && __cplusplus >= 201103L
+#define __CPP_VERSION_AT_LEAST_11_FP6
+#elif _MSC_FULL_VER >= 190024210 && _MSVC_LANG >= 201103L
+#define __CPP_VERSION_AT_LEAST_11_FP6
+#endif
+
+/* bring in fp8 types infrastructure and dependencies */
+#include "cuda_fp8.h"
+
+/**
+ * \defgroup CUDA_MATH_INTRINSIC_FP6 FP6 Intrinsics
+ * This section describes fp6 intrinsic functions.
+ * To use these functions, include the header file \p cuda_fp6.h in your
+ * program.
+ *
+ * \note Most of the operations defined here benefit from native HW support
+ * when compiled for specific GPU targets (e.g. devices of compute capability 10.0a),
+ * other targets use emulation path.
+ *
+ * The following macros are available to help users selectively enable/disable
+ * various definitions present in the header file:
+ * - \p __CUDA_NO_FP6_CONVERSIONS__ - If defined, this macro will prevent any
+ * use of the C++ type conversions (converting constructors and conversion
+ * operators) defined in the header.
+ * - \p __CUDA_NO_FP6_CONVERSION_OPERATORS__ - If defined, this macro will
+ * prevent any use of the  C++ conversion operators from \p fp6 to other types.
+ */
+
+/**
+ * \defgroup CUDA_MATH_FP6_MISC FP6 Conversion and Data Movement
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ * To use these functions, include the header file \p cuda_fp6.h in your
+ * program.
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief 8-bit \p unsigned \p integer
+ * type abstraction used for \p fp6 floating-point
+ * numbers storage.
+ */
+typedef __nv_fp8_storage_t __nv_fp6_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief 16-bit \p unsigned \p integer
+ * type abstraction used for storage of pairs of
+ * \p fp6 floating-point numbers.
+ */
+typedef __nv_fp8x2_storage_t __nv_fp6x2_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief 32-bit \p unsigned \p integer
+ * type abstraction used for storage of tetrads of
+ * \p fp6 floating-point numbers.
+ */
+typedef __nv_fp8x4_storage_t __nv_fp6x4_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Enumerates the possible
+ * interpretations of the 8-bit values when referring to them as
+ * \p fp6 types.
+ */
+typedef enum __nv_fp6_interpretation_t {
+    __NV_E2M3, /**< Stands for \p fp6 numbers of \p e2m3 kind. */
+    __NV_E3M2, /**< Stands for \p fp6 numbers of \p e3m2 kind. */
+} __nv_fp6_interpretation_t;
+
+/* Forward-declaration of C-style APIs */
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input \p double precision \p x to \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp6 type of the kind specified by
+ * \p fp6_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t
+__nv_cvt_double_to_fp6(const double x,
+                       const __nv_fp6_interpretation_t fp6_interpretation,
+                       const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input vector of two \p double precision numbers packed
+ * in \p double2 \p x into a vector of two values of \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp6 values of the
+ * kind specified by \p fp6_interpretation parameter, using
+ * rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t
+__nv_cvt_double2_to_fp6x2(const double2 x,
+                          const __nv_fp6_interpretation_t fp6_interpretation,
+                          const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input \p single precision \p x to \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp6 type of the kind specified by
+ * \p fp6_interpretation parameter, using
+ * rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t
+__nv_cvt_float_to_fp6(const float x,
+                      const __nv_fp6_interpretation_t fp6_interpretation,
+                      const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input vector of two \p single precision numbers packed
+ * in \p float2 \p x into a vector of two values of \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp6 values of the
+ * kind specified by \p fp6_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t
+__nv_cvt_float2_to_fp6x2(const float2 x,
+                         const __nv_fp6_interpretation_t fp6_interpretation,
+                         const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input \p half precision \p x to \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp6 type of the kind specified by
+ * \p fp6_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t
+__nv_cvt_halfraw_to_fp6(const __half_raw x,
+                        const __nv_fp6_interpretation_t fp6_interpretation,
+                        const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input vector of two \p half precision numbers packed
+ * in \p __half2_raw \p x into a vector of two values of \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp6 values of the
+ * kind specified by \p fp6_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t __nv_cvt_halfraw2_to_fp6x2(
+    const __half2_raw x,
+    const __nv_fp6_interpretation_t fp6_interpretation,
+    const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input \p nv_bfloat16 precision \p x to \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input \p x to \p fp6 type of the kind specified by
+ * \p fp6_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t __nv_cvt_bfloat16raw_to_fp6(
+    const __nv_bfloat16_raw x,
+    const __nv_fp6_interpretation_t fp6_interpretation,
+    const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input vector of two \p nv_bfloat16 precision numbers packed
+ * in \p __nv_bfloat162_raw \p x into a vector of two values of \p fp6 type of the
+ * requested kind using specified rounding mode and saturating
+ * the out-of-range values.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp6 values of the
+ * kind specified by \p fp6_interpretation parameter,
+ * using rounding mode specified by \p rounding parameter.
+ * Large out-of-range values saturate to MAXNORM of the same sign.
+ * \p NaN input values result in positive MAXNORM.
+ *
+ * \returns
+ * - The \p __nv_fp6x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t
+__nv_cvt_bfloat16raw2_to_fp6x2(
+    const __nv_bfloat162_raw x,
+    const __nv_fp6_interpretation_t fp6_interpretation,
+    const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input \p fp6 \p x of the specified kind
+ * to \p half precision.
+ *
+ * \details Converts input \p x of \p fp6 type of the kind specified by
+ * \p fp6_interpretation parameter
+ * to \p half precision.
+ *
+ * \returns
+ * - The \p __half_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __half_raw
+__nv_cvt_fp6_to_halfraw(const __nv_fp6_storage_t x,
+                        const __nv_fp6_interpretation_t fp6_interpretation);
+/**
+ * \ingroup CUDA_MATH_FP6_MISC
+ * \brief Converts input vector of two \p fp6 values of the specified kind
+ * to a vector of two \p half precision values packed in \p __half2_raw
+ * structure.
+ *
+ * \details Converts input vector \p x of \p fp6 type of the kind specified by
+ * \p fp6_interpretation parameter
+ * to a vector of two \p half precision values and returns as \p __half2_raw
+ * structure.
+ *
+ * \returns
+ * - The \p __half2_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP6_DECL__ __half2_raw
+__nv_cvt_fp6x2_to_halfraw2(const __nv_fp6x2_storage_t x,
+                           const __nv_fp6_interpretation_t fp6_interpretation);
+
+#if defined(__cplusplus)
+
+#define __CUDA_FP6_TYPES_EXIST__
+
+/* Forward-declaration of structures defined in "cuda_fp6.hpp" */
+struct __nv_fp6_e3m2;
+struct __nv_fp6x2_e3m2;
+struct __nv_fp6x4_e3m2;
+
+struct __nv_fp6_e2m3;
+struct __nv_fp6x2_e2m3;
+struct __nv_fp6x4_e2m3;
+
+#endif /* defined(__cplusplus) */
+
+#include "cuda_fp6.hpp"
+
+#undef __CUDA_FP6_DECL__
+#undef __CUDA_HOSTDEVICE_FP6__
+#undef __CUDA_HOSTDEVICE_FP6_DECL__
+
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+#undef __CPP_VERSION_AT_LEAST_11_FP6
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#endif /* end of include guard: __CUDA_FP6_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp6.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp6.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..f3848cebcc7629972a58c8a330a25195c5c565d4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp6.hpp
@@ -0,0 +1,1549 @@
+/*
+ * Copyright 2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_FP6_HPP__)
+#define __CUDA_FP6_HPP__
+
+#if !defined(__CUDA_FP6_H__)
+#error "Do not include this file directly. Instead, include cuda_fp6.h."
+#endif
+
+/* C++ header for std::memcpy (used for type punning in host-side
+ * implementations). When compiling as a CUDA source file memcpy is provided
+ * implicitly. !defined(__CUDACC__) implies !defined(__CUDACC_RTC__).
+ */
+#if defined(__cplusplus) && !defined(__CUDACC__)
+#include <cstring>
+#elif !defined(__cplusplus) && !defined(__CUDACC__)
+#include <string.h>
+#endif /* defined(__cplusplus) && !defined(__CUDACC__) */
+
+/*
+ * Bring in the standard assertions header to enforce the subset
+ * of rounding modes supported by the APIs defined here.
+ * NOTE: NVRTC defines its own assert
+ */
+#if !defined (__CUDACC_RTC__)
+#include <assert.h>
+#endif
+
+/* Set up structure-alignment attribute */
+#if !(defined __CUDA_ALIGN__)
+#if defined(__CUDACC__)
+#define __CUDA_ALIGN__(align) __align__(align)
+#else
+/* Define alignment macro based on compiler type (cannot assume C11 "_Alignas"
+ * is available) */
+#if __cplusplus >= 201103L
+#define __CUDA_ALIGN__(n)                                                      \
+    alignas(n) /* C++11 kindly gives us a keyword for this */
+#else          /* !defined(__CPP_VERSION_AT_LEAST_11_FP6)*/
+#if defined(__GNUC__)
+#define __CUDA_ALIGN__(n) __attribute__((aligned(n)))
+#elif defined(_MSC_VER)
+#define __CUDA_ALIGN__(n) __declspec(align(n))
+#else
+#define __CUDA_ALIGN__(n)
+#endif /* defined(__GNUC__) */
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+#endif /* defined(__CUDACC__) */
+#endif /* !(defined __CUDA_ALIGN__) */
+
+#if !(defined __CPP_VERSION_AT_LEAST_11_FP6)
+/* need c++11 for explicit operators */
+#define __CUDA_NO_FP6_CONVERSION_OPERATORS__
+#endif
+
+#if !(defined __DOXYGEN_ONLY__)
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t
+__nv_cvt_double_to_fp6(const double x,
+                       const __nv_fp6_interpretation_t fp6_interpretation,
+                       const enum cudaRoundMode rounding) {
+    unsigned char res;
+    unsigned long long int xbits;
+
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&xbits, &x, sizeof(x));
+#else
+    (void)std::memcpy(&xbits, &x, sizeof(x));
+#endif
+    unsigned char FP6_MAXNORM;
+    unsigned char FP6_MANTISSA_MASK;
+    unsigned short int FP6_EXP_BIAS;
+    unsigned long long int FP6_SIGNIFICAND_BITS;
+    unsigned long long int FP6_MINDENORM_O2;
+    unsigned long long int FP6_OVERFLOW_THRESHOLD;
+    unsigned long long int FP6_MINNORM;
+    const unsigned long long int DP_INF_BITS = 0x7FF0000000000000ULL;
+
+    switch (fp6_interpretation)
+    {
+        case __NV_E2M3:
+            FP6_EXP_BIAS = 1U;
+            FP6_SIGNIFICAND_BITS = 4ULL;
+            FP6_MANTISSA_MASK = 0x7U;
+            FP6_MINDENORM_O2 = 0x3FB0000000000000ULL; // mindenorm/2 = 2^-4
+            FP6_OVERFLOW_THRESHOLD =
+                0x401E000000000000ULL; // maxnorm = 7.5
+            FP6_MAXNORM = 0x1FU;
+            FP6_MINNORM = 0x3FF0000000000000ULL; // minnorm = 2^0
+            break;
+        case __NV_E3M2:
+        default:
+            FP6_EXP_BIAS = 3U;
+            FP6_SIGNIFICAND_BITS = 3ULL;
+            FP6_MANTISSA_MASK = 0x3U;
+            FP6_MINDENORM_O2 = 0x3FA0000000000000ULL; // mindenorm/2 = 2^-5
+            FP6_OVERFLOW_THRESHOLD =
+                0x403C000000000000ULL; // maxnorm = 28
+            FP6_MAXNORM = 0x1FU;
+            FP6_MINNORM = 0x3FD0000000000000ULL; // minnorm = 2^-2
+            break;
+    }
+
+    // 1/2 LSB of the target format, positioned in double precision mantissa
+    // helpful in midpoints detection during round-to-nearest-even step
+    const unsigned long long int FP6_DP_HALF_ULP =
+        (unsigned long long int)1ULL << (53ULL - FP6_SIGNIFICAND_BITS - 1ULL);
+    // prepare sign bit in target format
+    unsigned char sign = (unsigned char)((xbits >> 63ULL) << 5U);
+    // prepare exponent field in target format
+    unsigned char exp =
+        (unsigned char)((((unsigned short int)(xbits >> 52ULL)) & 0x7FFU) -
+                        1023U + FP6_EXP_BIAS);
+    // round mantissa to target format width, rounding towards zero
+    unsigned char mantissa =
+        (unsigned char)(xbits >> (53ULL - FP6_SIGNIFICAND_BITS)) &
+        FP6_MANTISSA_MASK;
+    unsigned long long int absx = xbits & 0x7FFFFFFFFFFFFFFFULL;
+
+    if (absx <= FP6_MINDENORM_O2) {
+        // zero or underflow
+        res = 0U;
+    } else if (absx > FP6_OVERFLOW_THRESHOLD) {
+        // overflow or NaN
+        if (absx > DP_INF_BITS)
+        {
+            // NaN converts to positive FP6_MAXNORM
+            sign = 0U;
+        }
+        res = FP6_MAXNORM;
+    } else if (absx >= FP6_MINNORM) {
+        res = (unsigned char)((exp << (FP6_SIGNIFICAND_BITS - 1U)) | mantissa);
+        // rounded-off bits
+        unsigned long long int round =
+            xbits & ((FP6_DP_HALF_ULP << 1ULL) - 1ULL);
+        if (rounding == cudaRoundNearest)
+        {
+            // round-to-nearest-even adjustment
+            if ((round > FP6_DP_HALF_ULP) ||
+                ((round == FP6_DP_HALF_ULP) && (mantissa & 1U))) {
+                res = (unsigned char)(res + 1U);
+            }
+        } else {
+            assert(rounding == cudaRoundZero);
+        }
+    } else // Denormal range
+    {
+        unsigned char shift = (unsigned char)(1U - exp);
+        // add implicit leading bit
+        mantissa |= (unsigned char)(1U << (FP6_SIGNIFICAND_BITS - 1U));
+        // additional round-off due to denormalization
+        res = (unsigned char)(mantissa >> shift);
+
+        if (rounding == cudaRoundNearest)
+        {
+            // rounded-off bits, including implicit leading bit
+            unsigned long long int round =
+                (xbits | ((unsigned long long int)1ULL << (53ULL - 1ULL))) &
+                ((FP6_DP_HALF_ULP << (shift + 1ULL)) - 1ULL);
+            // round-to-nearest-even adjustment
+            if ((round > (FP6_DP_HALF_ULP << shift)) ||
+                ((round == (FP6_DP_HALF_ULP << shift)) && (res & 1U))) {
+                res = (unsigned char)(res + 1U);
+            }
+        } else {
+            assert(rounding == cudaRoundZero);
+        }
+    }
+
+    res |= sign;
+
+    return (__nv_fp6_storage_t)res;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t
+__nv_cvt_double2_to_fp6x2(const double2 x,
+                          const __nv_fp6_interpretation_t fp6_interpretation,
+                          const enum cudaRoundMode rounding) {
+    __nv_fp6x2_storage_t storage = (__nv_fp6x2_storage_t)__nv_cvt_double_to_fp6(
+        x.y, fp6_interpretation, rounding);
+    storage = (__nv_fp6x2_storage_t)(storage << 8U);
+    storage = (__nv_fp6x2_storage_t)(storage |
+                                     __nv_cvt_double_to_fp6(
+                                         x.x, fp6_interpretation, rounding));
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t
+__nv_cvt_float_to_fp6(const float x,
+                      const __nv_fp6_interpretation_t fp6_interpretation,
+                      const enum cudaRoundMode rounding) {
+    __nv_fp6_storage_t res = 0U;
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    if (rounding == cudaRoundNearest)
+    {
+        __nv_fp6x2_storage_t storage;
+        if (fp6_interpretation == __NV_E3M2) {
+            asm("{cvt.rn.satfinite.e3m2x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x), "f"(0.0f));
+        } else {
+            asm("{cvt.rn.satfinite.e2m3x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x), "f"(0.0f));
+        }
+        res = (__nv_fp6_storage_t)storage;
+    } else
+#endif
+    {
+        res = __nv_cvt_double_to_fp6((double)x, fp6_interpretation, rounding);
+    }
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t
+__nv_cvt_float2_to_fp6x2(const float2 x,
+                         const __nv_fp6_interpretation_t fp6_interpretation,
+                         const enum cudaRoundMode rounding) {
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+    __nv_fp6x2_storage_t storage;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    if (rounding == cudaRoundNearest) {
+        if (fp6_interpretation == __NV_E3M2) {
+            asm("{cvt.rn.satfinite.e3m2x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x.x), "f"(x.y));
+        } else {
+            asm("{cvt.rn.satfinite.e2m3x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x.x), "f"(x.y));
+        }
+    } else
+#endif
+    {
+        storage = (__nv_fp6x2_storage_t)__nv_cvt_float_to_fp6(
+            x.y, fp6_interpretation, rounding);
+        storage = (__nv_fp6x2_storage_t)(storage << 8U);
+        storage = (__nv_fp6x2_storage_t)(storage | __nv_cvt_float_to_fp6(
+                                                       x.x,
+                                                       fp6_interpretation, rounding));
+    }
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t
+__nv_cvt_halfraw_to_fp6(const __half_raw x,
+                        const __nv_fp6_interpretation_t fp6_interpretation,
+                        const enum cudaRoundMode rounding) {
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+    __nv_fp6_storage_t res = 0U;
+    float fx = __internal_halfraw_to_float(x);
+    res = __nv_cvt_float_to_fp6(fx, fp6_interpretation, rounding);
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t __nv_cvt_halfraw2_to_fp6x2(
+    const __half2_raw x,
+    const __nv_fp6_interpretation_t fp6_interpretation,
+    const enum cudaRoundMode rounding) {
+    assert((rounding == cudaRoundNearest) || (rounding == cudaRoundZero));
+    __nv_fp6x2_storage_t tmp;
+    __half_raw raw;
+    raw.x = x.x;
+    __nv_fp6_storage_t lo =
+        __nv_cvt_halfraw_to_fp6(raw, fp6_interpretation, rounding);
+    raw.x = x.y;
+    __nv_fp6_storage_t hi =
+        __nv_cvt_halfraw_to_fp6(raw, fp6_interpretation, rounding);
+    tmp = hi;
+    tmp = (__nv_fp6x2_storage_t)(tmp << 8U);
+    tmp = (__nv_fp6x2_storage_t)(tmp | lo);
+    return tmp;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6_storage_t __nv_cvt_bfloat16raw_to_fp6(
+    const __nv_bfloat16_raw x,
+    const __nv_fp6_interpretation_t fp6_interpretation,
+    const enum cudaRoundMode rounding) {
+    const float fx = __internal_bf16raw_to_float(x);
+    const __nv_fp6_storage_t res =
+        __nv_cvt_float_to_fp6(fx, fp6_interpretation, rounding);
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __nv_fp6x2_storage_t
+__nv_cvt_bfloat16raw2_to_fp6x2(
+    const __nv_bfloat162_raw x,
+    const __nv_fp6_interpretation_t fp6_interpretation,
+    const enum cudaRoundMode rounding) {
+    __nv_bfloat16_raw raw;
+    raw.x = x.y;
+    __nv_fp6x2_storage_t storage =
+        (__nv_fp6x2_storage_t)__nv_cvt_bfloat16raw_to_fp6(raw,
+                                        fp6_interpretation, rounding);
+    storage = (__nv_fp6x2_storage_t)(storage << 8U);
+    raw.x = x.x;
+    storage = (__nv_fp6x2_storage_t)(storage |
+                                     __nv_cvt_bfloat16raw_to_fp6(raw,
+                                        fp6_interpretation, rounding));
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __half2_raw
+__nv_cvt_fp6x2_to_halfraw2(const __nv_fp6x2_storage_t x,
+                           const __nv_fp6_interpretation_t fp6_interpretation);
+__CUDA_HOSTDEVICE_FP6_DECL__ __half_raw
+__nv_cvt_fp6_to_halfraw(const __nv_fp6_storage_t x,
+                        const __nv_fp6_interpretation_t fp6_interpretation) {
+    __half_raw res;
+    res.x = 0U;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    res.x =
+        __nv_cvt_fp6x2_to_halfraw2((__nv_fp6x2_storage_t)x, fp6_interpretation)
+            .x;
+#else
+    {
+        unsigned short int ur = (unsigned short int)x;
+        ur = (unsigned short int)(ur << 10U);
+
+        unsigned short int sign = ur & 0x8000U;
+        unsigned short int exponent;
+        unsigned short int bias_difference;
+        unsigned short int mantissa;
+
+        if (fp6_interpretation == __NV_E3M2) {
+            bias_difference = (unsigned short int)(15 - 3) << 10U;
+            exponent = (unsigned short int)(((ur & 0x7000U) >> 2U) + bias_difference);
+            mantissa = (ur & 0x0C00U) >> 2U;
+        } else {
+            //__NV_E2M3
+            bias_difference = (unsigned short int)(15 - 1) << 10U;
+            exponent = (unsigned short int)(((ur & 0x6000U) >> 3U) + bias_difference);
+            mantissa = (ur & 0x1C00U) >> 3U;
+        }
+
+        if (exponent == bias_difference) {
+            // zero or denormal
+            if (mantissa != 0U) {
+                // normalize
+                mantissa = (unsigned short int)(mantissa << 1U);
+                while ((mantissa & 0x0400U) == 0U) {
+                    mantissa = (unsigned short int)(mantissa << 1U);
+                    exponent = (unsigned short int)(exponent - 0x0400U);
+                }
+                // discard implicit leading bit
+                mantissa &= 0x03FFU;
+            } else { // Zero
+                exponent = 0U;
+            }
+        }
+
+        res.x = (sign | exponent) | mantissa;
+    }
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP6_DECL__ __half2_raw
+__nv_cvt_fp6x2_to_halfraw2(const __nv_fp6x2_storage_t x,
+                           const __nv_fp6_interpretation_t fp6_interpretation) {
+    __half2_raw res;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    unsigned int half2_storage;
+    if (fp6_interpretation == __NV_E3M2) {
+        asm("{cvt.rn.f16x2.e3m2x2 %0, %1;}\n" : "=r"(half2_storage) : "h"(x));
+    } else {
+        asm("{cvt.rn.f16x2.e2m3x2 %0, %1;}\n" : "=r"(half2_storage) : "h"(x));
+    }
+    (void)memcpy(&res, &half2_storage, sizeof(half2_storage));
+#else
+    res.x =
+        __nv_cvt_fp6_to_halfraw((__nv_fp6_storage_t)x, fp6_interpretation).x;
+    res.y = __nv_cvt_fp6_to_halfraw((__nv_fp6_storage_t)(x >> 8U),
+                                    fp6_interpretation)
+                .x;
+#endif
+    return res;
+}
+
+#endif /* !(defined __DOXYGEN_ONLY__) */
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+
+/**
+ * \defgroup CUDA_MATH_FP6_E3M2_STRUCT C++ struct for handling fp6 data type of e3m2 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6_E3M2_STRUCT
+ * \brief __nv_fp6_e3m2 datatype
+ *
+ * \details This structure implements the datatype for handling
+ * \p fp6 floating-point numbers of \p e3m2 kind:
+ * with 1 sign, 3 exponent, 1 implicit and 2 explicit mantissa bits.
+ * This encoding does not support Inf/NaN.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp6_e3m2 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP6_E3M2_STRUCT
+     * Storage variable contains the \p fp6 floating-point data.
+     */
+    __nv_fp6_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+    __nv_fp6_e3m2() = default;
+#else
+    __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#if !defined(__CUDA_NO_FP6_CONVERSIONS__)
+
+    /* Construct from wider FP types */
+    /* Note we do avoid constructor init-list because of special host/device
+     * compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __half data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const __half f) {
+        __x = __nv_cvt_halfraw_to_fp6(static_cast<__half_raw>(f),
+                                      __NV_E3M2, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __nv_bfloat16 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const __nv_bfloat16 f) {
+        __x = __nv_cvt_bfloat16raw_to_fp6(static_cast<__nv_bfloat16_raw>(f),
+                                          __NV_E3M2, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p float data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const float f) {
+        __x = __nv_cvt_float_to_fp6(f, __NV_E3M2, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p double data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const double f) {
+        __x = __nv_cvt_double_to_fp6(f, __NV_E3M2, cudaRoundNearest);
+    }
+
+    /* Converts from integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p short \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__
+    __nv_fp6_e3m2(const unsigned short int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const unsigned int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const unsigned long int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p long \p long \p int data type, relies on
+     * \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__
+    __nv_fp6_e3m2(const unsigned long long int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const short int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p long \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const long int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p long \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e3m2(const long long int val) {
+        __x = static_cast<__nv_fp6_e3m2>(static_cast<float>(val)).__x;
+    }
+
+#if !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__)
+    /* Widening FP converts */
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p __half data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator __half() const {
+        return static_cast<__half>(__nv_cvt_fp6_to_halfraw(__x, __NV_E3M2));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p float data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator float() const {
+        return __internal_halfraw_to_float(
+            __nv_cvt_fp6_to_halfraw(__x, __NV_E3M2));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p __nv_bfloat16 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator __nv_bfloat16() const {
+        return __float2bfloat16_rz(float(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p double data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator double() const {
+        return static_cast<double>(float(*this));
+    }
+
+    /* Convert to integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned char() const {
+        unsigned char i;
+        const float f = float(*this);
+
+        if (f < 0.0f) {
+            // saturate minimum
+            i = 0U;
+        } else {
+            // normal value
+            i = static_cast<unsigned char>(f);
+        }
+        return i;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p short \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned short int() const {
+        return __half2ushort_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned int() const {
+        return __half2uint_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p long \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned long int() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__half2ull_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__half2uint_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p long \p long \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned long long int() const {
+        return __half2ull_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p signed \p char data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator signed char() const {
+        const float f = float(*this);
+        return static_cast<signed char>(f);
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+     * 
+     * Clamps inputs to the output range.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(static_cast<signed char>(*this));
+        }
+        else
+        {
+            value = static_cast<char>(static_cast<unsigned char>(*this));
+        }
+        return value;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator short int() const {
+        return __half2short_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator int() const {
+        return __half2int_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p long \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator long int() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__half2ll_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<long>(__half2int_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p long \p long \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator long long int() const {
+        return __half2ll_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator bool() const {
+        return (__x & 0x1FU) != 0U;
+    }
+#endif /* !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP6_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP6X2_E3M2_STRUCT C++ struct for handling vector type of two fp6 values of e3m2 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6X2_E3M2_STRUCT
+ * \brief __nv_fp6x2_e3m2 datatype
+ *
+ * \details This structure implements the datatype for handling two
+ * \p fp6 floating-point numbers of \p e3m2 kind each.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(2) __nv_fp6x2_e3m2 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP6X2_E3M2_STRUCT
+     * Storage variable contains the vector of two \p fp6 floating-point data
+     * values.
+     */
+    __nv_fp6x2_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+    __nv_fp6x2_e3m2() = default;
+#else
+    __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e3m2() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#if !defined(__CUDA_NO_FP6_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __half2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e3m2(const __half2 f) {
+        __x = __nv_cvt_halfraw2_to_fp6x2(static_cast<__half2_raw>(f),
+                                         __NV_E3M2, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __nv_bfloat162 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e3m2(const __nv_bfloat162 f) {
+        __x = __nv_cvt_bfloat16raw2_to_fp6x2(static_cast<__nv_bfloat162_raw>(f),
+                                             __NV_E3M2, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p float2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e3m2(const float2 f) {
+        __x = __nv_cvt_float2_to_fp6x2(f, __NV_E3M2, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p double2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e3m2(const double2 f) {
+        __x = __nv_cvt_double2_to_fp6x2(f, __NV_E3M2, cudaRoundNearest);
+    }
+
+#if !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__)
+    /* Widening converts */
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p __half2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator __half2() const {
+        return static_cast<__half2>(__nv_cvt_fp6x2_to_halfraw2(__x, __NV_E3M2));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p float2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator float2() const {
+        return __internal_halfraw2_to_float2(
+            __nv_cvt_fp6x2_to_halfraw2(__x, __NV_E3M2));
+    }
+#endif /* !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP6_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP6X4_E3M2_STRUCT C++ struct for handling vector type of four fp6 values of e3m2 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6X4_E3M2_STRUCT
+ * \brief __nv_fp6x4_e3m2 datatype
+ *
+ * \details This structure implements the datatype for handling four
+ * \p fp6 floating-point numbers of \p e3m2 kind each.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(4) __nv_fp6x4_e3m2 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP6X4_E3M2_STRUCT
+     * Storage variable contains the vector of four \p fp6 floating-point data
+     * values.
+     */
+    __nv_fp6x4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+    __nv_fp6x4_e3m2() = default;
+#else
+    __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e3m2() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#if !defined(__CUDA_NO_FP6_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from a pair of \p __half2 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e3m2(const __half2 flo,
+                                                     const __half2 fhi) {
+        const __nv_fp6x2_storage_t rlo = __nv_cvt_halfraw2_to_fp6x2(
+            static_cast<__half2_raw>(flo), __NV_E3M2, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi = __nv_cvt_halfraw2_to_fp6x2(
+            static_cast<__half2_raw>(fhi), __NV_E3M2, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from a pair of \p __nv_bfloat162 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e3m2(const __nv_bfloat162 flo,
+                                                     const __nv_bfloat162 fhi) {
+        const __nv_fp6x2_storage_t rlo = __nv_cvt_bfloat16raw2_to_fp6x2(
+            static_cast<__nv_bfloat162_raw>(flo), __NV_E3M2, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi = __nv_cvt_bfloat16raw2_to_fp6x2(
+            static_cast<__nv_bfloat162_raw>(fhi), __NV_E3M2, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p float4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e3m2(const float4 f) {
+        const float2 flo = {f.x, f.y};
+        const float2 fhi = {f.z, f.w};
+        const __nv_fp6x2_storage_t rlo =
+            __nv_cvt_float2_to_fp6x2(flo, __NV_E3M2, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi =
+            __nv_cvt_float2_to_fp6x2(fhi, __NV_E3M2, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p double4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e3m2(const double4 f) {
+        const double2 flo = {f.x, f.y};
+        const double2 fhi = {f.z, f.w};
+        const __nv_fp6x2_storage_t rlo =
+            __nv_cvt_double2_to_fp6x2(flo, __NV_E3M2, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi =
+            __nv_cvt_double2_to_fp6x2(fhi, __NV_E3M2, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+
+#if !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__)
+    /* Widening converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p float4 vector data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator float4() const {
+        const __nv_fp6x2_storage_t slo = static_cast<__nv_fp6x2_storage_t>(__x);
+        const __nv_fp6x2_storage_t shi =
+            static_cast<__nv_fp6x2_storage_t>(__x >> 16U);
+        float2 rlo = __internal_halfraw2_to_float2(
+            __nv_cvt_fp6x2_to_halfraw2(slo, __NV_E3M2));
+        float2 rhi = __internal_halfraw2_to_float2(
+            __nv_cvt_fp6x2_to_halfraw2(shi, __NV_E3M2));
+        float4 res = {rlo.x, rlo.y, rhi.x, rhi.y};
+        return res;
+    }
+#endif /* !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP6_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP6_E2M3_STRUCT C++ struct for handling fp6 data type of e2m3 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6_E2M3_STRUCT
+ * \brief __nv_fp6_e2m3 datatype
+ *
+ * \details This structure implements the datatype for storing
+ * \p fp6 floating-point numbers of \p e2m3 kind:
+ * with 1 sign, 2 exponent, 1 implicit and 3 explicit mantissa bits.
+ * This encoding does not support Inf/NaN.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp6_e2m3 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP6_E2M3_STRUCT
+     * Storage variable contains the \p fp6 floating-point data.
+     */
+    __nv_fp6_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+    __nv_fp6_e2m3() = default;
+#else
+    __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#if !defined(__CUDA_NO_FP6_CONVERSIONS__)
+
+    /* Construct from wider FP types */
+    /* Note we do avoid constructor init-list because of special host/device
+     * compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __half data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const __half f) {
+        __x = __nv_cvt_halfraw_to_fp6(static_cast<__half_raw>(f),
+                                      __NV_E2M3, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __nv_bfloat16 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const __nv_bfloat16 f) {
+        __x = __nv_cvt_bfloat16raw_to_fp6(static_cast<__nv_bfloat16_raw>(f),
+                                          __NV_E2M3, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p float data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const float f) {
+        __x = __nv_cvt_float_to_fp6(f, __NV_E2M3, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p double data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values and \p cudaRoundNearest rounding mode.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const double f) {
+        __x = __nv_cvt_double_to_fp6(f, __NV_E2M3, cudaRoundNearest);
+    }
+
+    /* Converts from integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p short \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__
+    __nv_fp6_e2m3(const unsigned short int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const unsigned int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const unsigned long int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p unsigned \p long \p long \p int data type, relies on
+     * \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__
+    __nv_fp6_e2m3(const unsigned long long int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const short int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p long \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const long int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p long \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6_e2m3(const long long int val) {
+        __x = static_cast<__nv_fp6_e2m3>(static_cast<float>(val)).__x;
+    }
+
+#if !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__)
+    /* Widening FP converts */
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p __half data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator __half() const {
+        return static_cast<__half>(__nv_cvt_fp6_to_halfraw(__x, __NV_E2M3));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p float data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator float() const {
+        return __internal_halfraw_to_float(
+            __nv_cvt_fp6_to_halfraw(__x, __NV_E2M3));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p __nv_bfloat16 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator __nv_bfloat16() const {
+        return __float2bfloat16_rz(float(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p double data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator double() const {
+        return static_cast<double>(float(*this));
+    }
+
+    /* Convert to integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned char() const {
+        unsigned char i;
+        const float f = float(*this);
+
+        if (f < 0.0f) {
+            // saturate minimum
+            i = 0U;
+        } else {
+            // normal value
+            i = static_cast<unsigned char>(f);
+        }
+        return i;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p short \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned short int() const {
+        return __half2ushort_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned int() const {
+        return __half2uint_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p long \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned long int() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__half2ull_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__half2uint_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p unsigned \p long \p long \p int data type.
+     * Clamps negative inputs to zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator unsigned long long int() const {
+        return __half2ull_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p signed \p char data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator signed char() const {
+        const float f = float(*this);
+        return static_cast<signed char>(f);
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+     * 
+     * Clamps inputs to the output range.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(static_cast<signed char>(*this));
+        }
+        else
+        {
+            value = static_cast<char>(static_cast<unsigned char>(*this));
+        }
+        return value;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator short int() const {
+        return __half2short_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator int() const {
+        return __half2int_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p long \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator long int() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__half2ll_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<long>(__half2int_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p long \p long \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator long long int() const {
+        return __half2ll_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator bool() const {
+        return (__x & 0x1FU) != 0U;
+    }
+#endif /* !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP6_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP6X2_E2M3_STRUCT C++ struct for handling vector type of two fp6 values of e2m3 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6X2_E2M3_STRUCT
+ * \brief __nv_fp6x2_e2m3 datatype
+ *
+ * \details This structure implements the datatype for handling two
+ * \p fp6 floating-point numbers of \p e2m3 kind each.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(2) __nv_fp6x2_e2m3 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP6X2_E2M3_STRUCT
+     * Storage variable contains the vector of two \p fp6 floating-point data
+     * values.
+     */
+    __nv_fp6x2_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+    __nv_fp6x2_e2m3() = default;
+#else
+    __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e2m3() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#if !defined(__CUDA_NO_FP6_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __half2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e2m3(const __half2 f) {
+        __x = __nv_cvt_halfraw2_to_fp6x2(static_cast<__half2_raw>(f),
+                                         __NV_E2M3, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p __nv_bfloat162 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e2m3(const __nv_bfloat162 f) {
+        __x = __nv_cvt_bfloat16raw2_to_fp6x2(static_cast<__nv_bfloat162_raw>(f),
+                                             __NV_E2M3, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p float2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e2m3(const float2 f) {
+        __x = __nv_cvt_float2_to_fp6x2(f, __NV_E2M3, cudaRoundNearest);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p double2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x2_e2m3(const double2 f) {
+        __x = __nv_cvt_double2_to_fp6x2(f, __NV_E2M3, cudaRoundNearest);
+    }
+
+#if !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__)
+    /* Widening converts */
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p __half2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator __half2() const {
+        return static_cast<__half2>(__nv_cvt_fp6x2_to_halfraw2(__x, __NV_E2M3));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p float2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator float2() const {
+        return __internal_halfraw2_to_float2(
+            __nv_cvt_fp6x2_to_halfraw2(__x, __NV_E2M3));
+    }
+#endif /* !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP6_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP6X4_E2M3_STRUCT C++ struct for handling vector type of four fp6 values of e2m3 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP6
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP6X4_E2M3_STRUCT
+ * \brief __nv_fp6x4_e2m3 datatype
+ *
+ * \details This structure implements the datatype for handling four
+ * \p fp6 floating-point numbers of \p e2m3 kind each.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(4) __nv_fp6x4_e2m3 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP6X4_E2M3_STRUCT
+     * Storage variable contains the vector of four \p fp6 floating-point data
+     * values.
+     */
+    __nv_fp6x4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP6)
+    __nv_fp6x4_e2m3() = default;
+#else
+    __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e2m3() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP6) */
+
+#if !defined(__CUDA_NO_FP6_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from a pair of \p __half2 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e2m3(const __half2 flo,
+                                                     const __half2 fhi) {
+        const __nv_fp6x2_storage_t rlo = __nv_cvt_halfraw2_to_fp6x2(
+            static_cast<__half2_raw>(flo), __NV_E2M3, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi = __nv_cvt_halfraw2_to_fp6x2(
+            static_cast<__half2_raw>(fhi), __NV_E2M3, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from a pair of \p __nv_bfloat162 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e2m3(const __nv_bfloat162 flo,
+                                                     const __nv_bfloat162 fhi) {
+        const __nv_fp6x2_storage_t rlo = __nv_cvt_bfloat16raw2_to_fp6x2(
+            static_cast<__nv_bfloat162_raw>(flo), __NV_E2M3, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi = __nv_cvt_bfloat16raw2_to_fp6x2(
+            static_cast<__nv_bfloat162_raw>(fhi), __NV_E2M3, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p float4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e2m3(const float4 f) {
+        const float2 flo = {f.x, f.y};
+        const float2 fhi = {f.z, f.w};
+        const __nv_fp6x2_storage_t rlo =
+            __nv_cvt_float2_to_fp6x2(flo, __NV_E2M3, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi =
+            __nv_cvt_float2_to_fp6x2(fhi, __NV_E2M3, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Constructor from \p double4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ __nv_fp6x4_e2m3(const double4 f) {
+        const double2 flo = {f.x, f.y};
+        const double2 fhi = {f.z, f.w};
+        const __nv_fp6x2_storage_t rlo =
+            __nv_cvt_double2_to_fp6x2(flo, __NV_E2M3, cudaRoundNearest);
+        const __nv_fp6x2_storage_t rhi =
+            __nv_cvt_double2_to_fp6x2(fhi, __NV_E2M3, cudaRoundNearest);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+
+#if !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__)
+    /* Widening converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP6_MISC
+     * Conversion operator to \p float4 vector data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP6__ operator float4() const {
+        const __nv_fp6x2_storage_t slo = static_cast<__nv_fp6x2_storage_t>(__x);
+        const __nv_fp6x2_storage_t shi =
+            static_cast<__nv_fp6x2_storage_t>(__x >> 16U);
+        float2 rlo = __internal_halfraw2_to_float2(
+            __nv_cvt_fp6x2_to_halfraw2(slo, __NV_E2M3));
+        float2 rhi = __internal_halfraw2_to_float2(
+            __nv_cvt_fp6x2_to_halfraw2(shi, __NV_E2M3));
+        float4 res = {rlo.x, rlo.y, rhi.x, rhi.y};
+        return res;
+    }
+#endif /* !defined(__CUDA_NO_FP6_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP6_CONVERSIONS__) */
+};
+
+#endif /* defined(__cplusplus) */
+
+#endif /* end of include guard: __CUDA_FP6_HPP__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp8.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp8.h
new file mode 100644
index 0000000000000000000000000000000000000000..6761a4a353140f1b96ae0b0f7f7c439be3aeedae
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp8.h
@@ -0,0 +1,475 @@
+/*
+ * Copyright 2022-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef __CUDA_FP8_H__
+#define __CUDA_FP8_H__
+
+/* Set up function decorations */
+#if defined(__CUDACC__)
+#define __CUDA_FP8_DECL__ static __device__ __inline__
+#define __CUDA_HOSTDEVICE_FP8__ __host__ __device__
+#define __CUDA_HOSTDEVICE_FP8_DECL__ static __host__ __device__ __inline__
+#else /* !defined(__CUDACC__) */
+#if defined(__GNUC__)
+#define __CUDA_HOSTDEVICE_FP8_DECL__ static __attribute__((unused))
+#else
+#define __CUDA_HOSTDEVICE_FP8_DECL__ static
+#endif /* defined(__GNUC__) */
+#define __CUDA_HOSTDEVICE_FP8__
+#endif /* defined(__CUDACC_) */
+
+#if !defined(_MSC_VER) && __cplusplus >= 201103L
+#define __CPP_VERSION_AT_LEAST_11_FP8
+#elif _MSC_FULL_VER >= 190024210 && _MSVC_LANG >= 201103L
+#define __CPP_VERSION_AT_LEAST_11_FP8
+#endif
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+/* bring in enum cudaRoundMode */
+#include "device_types.h"
+#endif  /* !defined(__CUDACC_RTC__) */
+
+/* bring in __half_raw data type */
+#include "cuda_fp16.h"
+/* bring in __nv_bfloat16_raw data type */
+#include "cuda_bf16.h"
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+/* bring in float2, double4, etc vector types */
+#include "vector_types.h"
+#endif  /* !defined(__CUDACC_RTC__) */
+
+/**
+ * \defgroup CUDA_MATH_INTRINSIC_FP8 FP8 Intrinsics
+ * This section describes fp8 intrinsic functions.
+ * To use these functions, include the header file \p cuda_fp8.h in your
+ * program.
+ * The following macros are available to help users selectively enable/disable
+ * various definitions present in the header file:
+ * - \p __CUDA_NO_FP8_CONVERSIONS__ - If defined, this macro will prevent any
+ * use of the C++ type conversions (converting constructors and conversion
+ * operators) defined in the header.
+ * - \p __CUDA_NO_FP8_CONVERSION_OPERATORS__ - If defined, this macro will
+ * prevent any use of the  C++ conversion operators from \p fp8 to other types.
+ */
+
+/**
+ * \defgroup CUDA_MATH_FP8_MISC FP8 Conversion and Data Movement
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ * To use these functions, include the header file \p cuda_fp8.h in your
+ * program.
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief 8-bit \p unsigned \p integer
+ * type abstraction used for \p fp8 floating-point
+ * numbers storage.
+ */
+typedef unsigned char __nv_fp8_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief 16-bit \p unsigned \p integer
+ * type abstraction used for storage of pairs of
+ * \p fp8 floating-point numbers.
+ */
+typedef unsigned short int __nv_fp8x2_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief 32-bit \p unsigned \p integer
+ * type abstraction used for storage of tetrads of
+ * \p fp8 floating-point numbers.
+ */
+typedef unsigned int __nv_fp8x4_storage_t;
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Enumerates the modes applicable when
+ * performing a narrowing conversion to \p fp8 destination types.
+ */
+typedef enum __nv_saturation_t {
+    /**
+     * Means no saturation to finite is performed when conversion
+     * results in rounding values outside the range of destination
+     * type.
+     * NOTE: for fp8 type of e4m3 kind, the results that are larger
+     * than the maximum representable finite number of the target
+     * format become NaN.
+     */
+    __NV_NOSAT,
+    /**
+     * Means input larger than the maximum representable
+     * finite number MAXNORM of the target format round to the
+     * MAXNORM of the same sign as input.
+     */
+    __NV_SATFINITE,
+} __nv_saturation_t;
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Enumerates the possible
+ * interpretations of the 8-bit values when referring to them as
+ * \p fp8 types.
+ */
+typedef enum __nv_fp8_interpretation_t {
+    __NV_E4M3, /**< Stands for \p fp8 numbers of \p e4m3 kind. */
+    __NV_E5M2, /**< Stands for \p fp8 numbers of \p e5m2 kind. */
+} __nv_fp8_interpretation_t;
+
+/* Forward-declaration of C-style APIs */
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p double precision \p x to \p fp8 type of the
+ * requested kind using round-to-nearest-even rounding and requested saturation
+ * mode.
+ *
+ * \details Converts input \p x to \p fp8 type of the kind specified by
+ * \p fp8_interpretation parameter,
+ * using round-to-nearest-even rounding and
+ * saturation mode specified by \p saturate parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t
+__nv_cvt_double_to_fp8(const double x, const __nv_saturation_t saturate,
+                       const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input vector of two \p double precision numbers packed
+ * in \p double2 \p x into a vector of two values of \p fp8 type of
+ * the requested kind using round-to-nearest-even rounding and requested
+ * saturation mode.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp8 values of the
+ * kind specified by \p fp8_interpretation parameter, using
+ * round-to-nearest-even rounding and saturation mode specified by \p saturate
+ * parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t
+__nv_cvt_double2_to_fp8x2(const double2 x, const __nv_saturation_t saturate,
+                          const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p single precision \p x to \p fp8 type of the
+ * requested kind using round-to-nearest-even rounding and requested saturation
+ * mode.
+ *
+ * \details Converts input \p x to \p fp8 type of the kind specified by
+ * \p fp8_interpretation parameter,
+ * using round-to-nearest-even rounding and
+ * saturation mode specified by \p saturate parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t
+__nv_cvt_float_to_fp8(const float x, const __nv_saturation_t saturate,
+                      const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input vector of two \p single precision numbers packed
+ * in \p float2 \p x into a vector of two values of \p fp8 type of
+ * the requested kind using round-to-nearest-even rounding and requested
+ * saturation mode.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp8 values of the
+ * kind specified by \p fp8_interpretation parameter, using
+ * round-to-nearest-even rounding and saturation mode specified by \p saturate
+ * parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t
+__nv_cvt_float2_to_fp8x2(const float2 x, const __nv_saturation_t saturate,
+                         const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p half precision \p x to \p fp8 type of the requested
+ * kind using round-to-nearest-even rounding and requested saturation mode.
+ *
+ * \details Converts input \p x to \p fp8 type of the kind specified by
+ * \p fp8_interpretation parameter,
+ * using round-to-nearest-even rounding and
+ * saturation mode specified by \p saturate parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t
+__nv_cvt_halfraw_to_fp8(const __half_raw x, const __nv_saturation_t saturate,
+                        const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input vector of two \p half precision numbers packed
+ * in \p __half2_raw \p x into a vector of two values of \p fp8 type of
+ * the requested kind using round-to-nearest-even rounding and requested
+ * saturation mode.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp8 values of the
+ * kind specified by \p fp8_interpretation parameter, using
+ * round-to-nearest-even rounding and saturation mode specified by \p saturate
+ * parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t __nv_cvt_halfraw2_to_fp8x2(
+    const __half2_raw x, const __nv_saturation_t saturate,
+    const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p nv_bfloat16 precision \p x to \p fp8 type of the
+ * requested kind using round-to-nearest-even rounding and requested saturation
+ * mode.
+ *
+ * \details Converts input \p x to \p fp8 type of the kind specified by
+ * \p fp8_interpretation parameter,
+ * using round-to-nearest-even rounding and
+ * saturation mode specified by \p saturate parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t __nv_cvt_bfloat16raw_to_fp8(
+    const __nv_bfloat16_raw x, const __nv_saturation_t saturate,
+    const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input vector of two \p nv_bfloat16 precision numbers packed
+ * in \p __nv_bfloat162_raw \p x into a vector of two values of \p fp8 type of
+ * the requested kind using round-to-nearest-even rounding and requested
+ * saturation mode.
+ *
+ * \details Converts input vector \p x to a vector of two \p fp8 values of the
+ * kind specified by \p fp8_interpretation parameter, using
+ * round-to-nearest-even rounding and saturation mode specified by \p saturate
+ * parameter.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t
+__nv_cvt_bfloat16raw2_to_fp8x2(
+    const __nv_bfloat162_raw x, const __nv_saturation_t saturate,
+    const __nv_fp8_interpretation_t fp8_interpretation);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p fp8 \p x of the specified kind
+ * to \p half precision.
+ *
+ * \details Converts input \p x of \p fp8 type of the kind specified by
+ * \p fp8_interpretation parameter
+ * to \p half precision.
+ *
+ * \returns
+ * - The \p __half_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __half_raw
+__nv_cvt_fp8_to_halfraw(const __nv_fp8_storage_t x,
+                        const __nv_fp8_interpretation_t fp8_interpretation);
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input vector of two \p fp8 values of the specified kind
+ * to a vector of two \p half precision values packed in \p __half2_raw
+ * structure.
+ *
+ * \details Converts input vector \p x of \p fp8 type of the kind specified by
+ * \p fp8_interpretation parameter
+ * to a vector of two \p half precision values and returns as \p __half2_raw
+ * structure.
+ *
+ * \returns
+ * - The \p __half2_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __half2_raw
+__nv_cvt_fp8x2_to_halfraw2(const __nv_fp8x2_storage_t x,
+                           const __nv_fp8_interpretation_t fp8_interpretation);
+
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p bfloat16 input into a scaling factor of \p e8m0 kind.
+ *
+ * \details Input number's absolute value is rounded to the closest power of two in the
+ * direction specified via \p rounding parameter. Rounded results that are
+ * smaller than the smallest representable target format number 2^-127 are then
+ * clipped to 2^-127. Results that are larger than the largest representable
+ * target format number 2^127 are either clipped to 2^127 if \p saturate equals
+ * to \p __NV_SATFINITE, or convert to \p NaN otherwise. \p NaN inputs convert
+ * into \p NaN output, encoded as \p 0xFF in the target format.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t __nv_cvt_bfloat16raw_to_e8m0(const __nv_bfloat16_raw x, const __nv_saturation_t saturate, const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts a pair of \p bfloat16 values into a pair of scaling factors of \p e8m0 kind.
+ *
+ * \see __nv_cvt_bfloat16raw_to_e8m0() for details of conversion.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t __nv_cvt_bfloat162raw_to_e8m0x2(const __nv_bfloat162_raw x, const __nv_saturation_t saturate, const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p float value into a scaling factor of \p e8m0 kind.
+ *
+ * \see __nv_cvt_bfloat16raw_to_e8m0() for details of conversion.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t __nv_cvt_float_to_e8m0(const float x, const __nv_saturation_t saturate, const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts a pair of \p float values into a pair of scaling factors of \p e8m0 kind.
+ *
+ * \see __nv_cvt_bfloat16raw_to_e8m0() for details of conversion.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t __nv_cvt_float2_to_e8m0x2(const float2 x, const __nv_saturation_t saturate, const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input \p double value into a scaling factor of \p e8m0 kind.
+ *
+ * \see __nv_cvt_bfloat16raw_to_e8m0() for details of conversion.
+ *
+ * \returns
+ * - The \p __nv_fp8_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t __nv_cvt_double_to_e8m0(const double x, const __nv_saturation_t saturate, const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts a pair of \p double values into a pair of scaling factors of \p e8m0 kind.
+ *
+ * \see __nv_cvt_bfloat16raw_to_e8m0() for details of conversion.
+ *
+ * \returns
+ * - The \p __nv_fp8x2_storage_t value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t __nv_cvt_double2_to_e8m0x2(const double2 x, const __nv_saturation_t saturate, const enum cudaRoundMode rounding);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input scaling factor value of \p e8m0 kind into \p bfloat16.
+ *
+ * \details Input scales are exact powers of two or a \p NaN value,
+ * also representable in the target format.
+ *
+ * \returns
+ * - The \p __nv_bfloat16_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_bfloat16_raw __nv_cvt_e8m0_to_bf16raw(const __nv_fp8_storage_t x);
+
+/**
+ * \ingroup CUDA_MATH_FP8_MISC
+ * \brief Converts input pair of scaling factors of \p e8m0 kind into a pair of \p bfloat16 values.
+ *
+ * \returns
+ * - The \p __nv_bfloat162_raw value holds the result of conversion.
+ */
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_bfloat162_raw __nv_cvt_e8m0x2_to_bf162raw(const __nv_fp8x2_storage_t x);
+
+#if defined(__cplusplus)
+
+#define __CUDA_FP8_TYPES_EXIST__
+
+/* Forward-declaration of structures defined in "cuda_fp8.hpp" */
+struct __nv_fp8_e5m2;
+struct __nv_fp8x2_e5m2;
+struct __nv_fp8x4_e5m2;
+
+struct __nv_fp8_e4m3;
+struct __nv_fp8x2_e4m3;
+struct __nv_fp8x4_e4m3;
+
+struct __nv_fp8_e8m0;
+struct __nv_fp8x2_e8m0;
+struct __nv_fp8x4_e8m0;
+
+#endif /* defined(__cplusplus) */
+
+#include "cuda_fp8.hpp"
+
+#undef __CUDA_FP8_DECL__
+#undef __CUDA_HOSTDEVICE_FP8__
+#undef __CUDA_HOSTDEVICE_FP8_DECL__
+
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+#undef __CPP_VERSION_AT_LEAST_11_FP8
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#endif /* end of include guard: __CUDA_FP8_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp8.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp8.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..06b741ef49a29a9d8aaceda085549f198710bc20
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_fp8.hpp
@@ -0,0 +1,2728 @@
+/*
+ * Copyright 2022-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_FP8_HPP__)
+#define __CUDA_FP8_HPP__
+
+#if !defined(__CUDA_FP8_H__)
+#error "Do not include this file directly. Instead, include cuda_fp8.h."
+#endif
+
+/* C++ header for std::memcpy (used for type punning in host-side
+ * implementations). When compiling as a CUDA source file memcpy is provided
+ * implicitly. !defined(__CUDACC__) implies !defined(__CUDACC_RTC__).
+ */
+#if defined(__cplusplus) && !defined(__CUDACC__)
+#include <cstring>
+#elif !defined(__cplusplus) && !defined(__CUDACC__)
+#include <string.h>
+#endif /* defined(__cplusplus) && !defined(__CUDACC__) */
+
+// implicitly provided by NVRTC
+#if !defined(__CUDACC_RTC__)
+#include <nv/target>
+#endif  /* !defined(__CUDACC_RTC__) */
+
+#if !defined(IF_DEVICE_OR_CUDACC)
+#if defined(__CUDACC__)
+    #define IF_DEVICE_OR_CUDACC(d, c, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, d, c)
+#else
+    #define IF_DEVICE_OR_CUDACC(d, c, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, d, f)
+#endif
+#endif
+
+/*
+ * Bring in the standard assertions header to enforce the subset
+ * of rounding modes supported by the APIs defined here.
+ * NOTE: NVRTC defines its own assert
+ */
+#if !defined (__CUDACC_RTC__)
+#include <assert.h>
+#endif
+
+/* Set up structure-alignment attribute */
+#if !(defined __CUDA_ALIGN__)
+#if defined(__CUDACC__)
+#define __CUDA_ALIGN__(align) __align__(align)
+#else
+/* Define alignment macro based on compiler type (cannot assume C11 "_Alignas"
+ * is available) */
+#if __cplusplus >= 201103L
+#define __CUDA_ALIGN__(n)                                                      \
+    alignas(n) /* C++11 kindly gives us a keyword for this */
+#else          /* !defined(__CPP_VERSION_AT_LEAST_11_FP8)*/
+#if defined(__GNUC__)
+#define __CUDA_ALIGN__(n) __attribute__((aligned(n)))
+#elif defined(_MSC_VER)
+#define __CUDA_ALIGN__(n) __declspec(align(n))
+#else
+#define __CUDA_ALIGN__(n)
+#endif /* defined(__GNUC__) */
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+#endif /* defined(__CUDACC__) */
+#endif /* !(defined __CUDA_ALIGN__) */
+
+#if !(defined __CPP_VERSION_AT_LEAST_11_FP8)
+/* need c++11 for explicit operators */
+#define __CUDA_NO_FP8_CONVERSION_OPERATORS__
+#endif
+
+#if !(defined __DOXYGEN_ONLY__)
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t
+__nv_cvt_double_to_fp8(const double x, const __nv_saturation_t saturate,
+                       const __nv_fp8_interpretation_t fp8_interpretation) {
+    unsigned char res;
+    unsigned long long int xbits;
+
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&xbits, &x, sizeof(x));
+#else
+    (void)std::memcpy(&xbits, &x, sizeof(x));
+#endif
+    unsigned char FP8_MAXNORM;
+    unsigned char FP8_MANTISSA_MASK;
+    unsigned short int FP8_EXP_BIAS;
+    unsigned long long int FP8_SIGNIFICAND_BITS;
+    const unsigned long long int DP_INF_BITS = 0x7FF0000000000000ULL;
+    unsigned long long int FP8_MINDENORM_O2;
+    unsigned long long int FP8_OVERFLOW_THRESHOLD;
+    unsigned long long int FP8_MINNORM;
+
+    if (fp8_interpretation == __NV_E4M3) {
+        FP8_EXP_BIAS = 7U;
+        FP8_SIGNIFICAND_BITS = 4ULL;
+        FP8_MANTISSA_MASK = 0x7U;
+        FP8_MINDENORM_O2 = 0x3F50000000000000ULL; // mindenorm/2 = 2^-10
+        FP8_OVERFLOW_THRESHOLD =
+            0x407D000000000000ULL; // maxnorm + 1/2ulp = 0x1.Cp+8 + 0x1p+4
+        FP8_MAXNORM = 0x7EU;
+        FP8_MINNORM = 0x3F90000000000000ULL; // minnorm = 2^-6
+    } else {                                 //__NV_E5M2
+        FP8_EXP_BIAS = 15U;
+        FP8_SIGNIFICAND_BITS = 3ULL;
+        FP8_MANTISSA_MASK = 0x3U;
+        FP8_MINDENORM_O2 = 0x3EE0000000000000ULL; // mindenorm/2 = 2^-17
+        FP8_OVERFLOW_THRESHOLD =
+            0x40EE000000000000ULL -
+            1ULL; // maxnorm + 1/2ulp = 0x1.Ep+15, and -1 to have common code
+        FP8_MAXNORM = 0x7BU;
+        FP8_MINNORM = 0x3F10000000000000ULL; // minnorm = 2^-14
+    }
+
+    // 1/2 LSB of the target format, positioned in double precision mantissa
+    // helpful in midpoints detection during round-to-nearest-even step
+    const unsigned long long int FP8_DP_HALF_ULP =
+        (unsigned long long int)1ULL << (53ULL - FP8_SIGNIFICAND_BITS - 1ULL);
+    // prepare sign bit in target format
+    unsigned char sign = (unsigned char)((xbits >> 63ULL) << 7U);
+    // prepare exponent field in target format
+    unsigned char exp =
+        (unsigned char)((((unsigned short int)(xbits >> 52ULL)) & 0x7FFU) -
+                        1023U + FP8_EXP_BIAS);
+    // round mantissa to target format width, rounding towards zero
+    unsigned char mantissa =
+        (unsigned char)(xbits >> (53ULL - FP8_SIGNIFICAND_BITS)) &
+        FP8_MANTISSA_MASK;
+    unsigned long long int absx = xbits & 0x7FFFFFFFFFFFFFFFULL;
+
+    if (absx <= FP8_MINDENORM_O2) {
+        // zero or underflow
+        res = 0U;
+    } else if (absx > DP_INF_BITS) {
+        // NaN
+        if (fp8_interpretation == __NV_E4M3) {
+            res = 0x7FU;
+        } else {
+            // NaN --> QNaN
+            res = 0x7EU | mantissa;
+        }
+    } else if (absx > FP8_OVERFLOW_THRESHOLD) {
+        if (saturate == __NV_SATFINITE) {
+            res = FP8_MAXNORM;
+        } else {
+            // __NV_NOSAT
+            if (fp8_interpretation == __NV_E4M3) {
+                // no Inf in E4M3
+                res = 0x7FU; // NaN
+            } else {
+                res = 0x7CU; // Inf in E5M2
+            }
+        }
+    } else if (absx >= FP8_MINNORM) {
+        res = (unsigned char)((exp << (FP8_SIGNIFICAND_BITS - 1U)) | mantissa);
+        // rounded-off bits
+        unsigned long long int round =
+            xbits & ((FP8_DP_HALF_ULP << 1ULL) - 1ULL);
+        // round-to-nearest-even adjustment
+        if ((round > FP8_DP_HALF_ULP) ||
+            ((round == FP8_DP_HALF_ULP) && (mantissa & 1U))) {
+            res = (unsigned char)(res + 1U);
+        }
+    } else // Denormal range
+    {
+        unsigned char shift = (unsigned char)(1U - exp);
+        // add implicit leading bit
+        mantissa |= (unsigned char)(1U << (FP8_SIGNIFICAND_BITS - 1U));
+        // additional round-off due to denormalization
+        res = (unsigned char)(mantissa >> shift);
+
+        // rounded-off bits, including implicit leading bit
+        unsigned long long int round =
+            (xbits | ((unsigned long long int)1ULL << (53ULL - 1ULL))) &
+            ((FP8_DP_HALF_ULP << (shift + 1ULL)) - 1ULL);
+        // round-to-nearest-even adjustment
+        if ((round > (FP8_DP_HALF_ULP << shift)) ||
+            ((round == (FP8_DP_HALF_ULP << shift)) && (res & 1U))) {
+            res = (unsigned char)(res + 1U);
+        }
+    }
+
+    res |= sign;
+
+    return (__nv_fp8_storage_t)res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t
+__nv_cvt_double2_to_fp8x2(const double2 x, const __nv_saturation_t saturate,
+                          const __nv_fp8_interpretation_t fp8_interpretation) {
+    __nv_fp8x2_storage_t storage = (__nv_fp8x2_storage_t)__nv_cvt_double_to_fp8(
+        x.y, saturate, fp8_interpretation);
+    storage = (__nv_fp8x2_storage_t)(storage << 8U);
+    storage = (__nv_fp8x2_storage_t)(storage |
+                                     __nv_cvt_double_to_fp8(
+                                         x.x, saturate, fp8_interpretation));
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t
+__nv_cvt_float_to_fp8(const float x, const __nv_saturation_t saturate,
+                      const __nv_fp8_interpretation_t fp8_interpretation) {
+    __nv_fp8_storage_t res = 0U;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 890)
+    if (saturate == __NV_SATFINITE) {
+        __nv_fp8x2_storage_t storage;
+        if (fp8_interpretation == __NV_E5M2) {
+            asm("{cvt.rn.satfinite.e5m2x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x), "f"(0.0f));
+        } else {
+            asm("{cvt.rn.satfinite.e4m3x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x), "f"(0.0f));
+        }
+        res = (__nv_fp8_storage_t)storage;
+    } else
+#endif
+    {
+        unsigned int xbits;
+#if defined(__CUDACC__) || (!defined __cplusplus)
+        (void)memcpy(&xbits, &x, sizeof(x));
+#else
+        (void)std::memcpy(&xbits, &x, sizeof(x));
+#endif
+
+        // isnan
+        if ((xbits & 0x7FFFFFFFU) > 0x7F800000U) {
+            // Canonical NaN
+            xbits = 0x7FFFFFFFU;
+        }
+
+        float fx;
+#if defined(__CUDACC__) || (!defined __cplusplus)
+        (void)memcpy(&fx, &xbits, sizeof(xbits));
+#else
+        (void)std::memcpy(&fx, &xbits, sizeof(xbits));
+#endif
+
+        const double dx = (double)fx;
+        res = __nv_cvt_double_to_fp8(dx, saturate, fp8_interpretation);
+    }
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t
+__nv_cvt_float2_to_fp8x2(const float2 x, const __nv_saturation_t saturate,
+                         const __nv_fp8_interpretation_t fp8_interpretation) {
+    __nv_fp8x2_storage_t storage;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 890)
+    if (saturate == __NV_SATFINITE) {
+        if (fp8_interpretation == __NV_E5M2) {
+            asm("{cvt.rn.satfinite.e5m2x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x.x), "f"(x.y));
+        } else {
+            asm("{cvt.rn.satfinite.e4m3x2.f32 %0, %2, %1;}\n"
+                : "=h"(storage)
+                : "f"(x.x), "f"(x.y));
+        }
+    } else
+#endif
+    {
+        storage = (__nv_fp8x2_storage_t)__nv_cvt_float_to_fp8(
+            x.y, saturate, fp8_interpretation);
+        storage = (__nv_fp8x2_storage_t)(storage << 8U);
+        storage = (__nv_fp8x2_storage_t)(storage | __nv_cvt_float_to_fp8(
+                                                       x.x, saturate,
+                                                       fp8_interpretation));
+    }
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ float
+__internal_halfraw_to_float(const __half_raw x) {
+    float f;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 530)
+    asm("{cvt.f32.f16 %0, %1;}\n" : "=f"(f) : "h"(x.x));
+#else
+    const unsigned int ux = (unsigned int)x.x;
+    unsigned int sign = (ux >> 15U) & 1U;
+    unsigned int exponent = (ux >> 10U) & 0x1fU;
+    unsigned int mantissa = (ux & 0x3ffU) << 13U;
+    if (exponent == 0x1fU) { /* NaN or Inf */
+        /* discard sign of a NaN */
+        sign = ((mantissa != 0U) ? (sign >> 1U) : sign);
+        mantissa = ((mantissa != 0U) ? 0x7fffffU : 0U);
+        exponent = 0xffU;
+    } else if (exponent == 0U) { /* Denorm or Zero */
+        if (mantissa != 0U) {
+            unsigned int msb;
+            exponent = 0x71U;
+            do {
+                msb = (mantissa & 0x400000U);
+                mantissa <<= 1U; /* normalize */
+                --exponent;
+            } while (msb == 0U);
+            mantissa &= 0x7fffffU; /* 1.mantissa is implicit */
+        }
+    } else {
+        exponent += 0x70U;
+    }
+    const unsigned int u = ((sign << 31U) | (exponent << 23U) | mantissa);
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&f, &u, sizeof(u));
+#else
+    (void)std::memcpy(&f, &u, sizeof(u));
+#endif
+#endif /* (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 530) */
+    return f;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ float2
+__internal_halfraw2_to_float2(const __half2_raw x) {
+    __half_raw raw;
+    float2 res;
+    raw.x = x.x;
+    res.x = __internal_halfraw_to_float(raw);
+    raw.x = x.y;
+    res.y = __internal_halfraw_to_float(raw);
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t
+__nv_cvt_halfraw_to_fp8(const __half_raw x, const __nv_saturation_t saturate,
+                        const __nv_fp8_interpretation_t fp8_interpretation) {
+    __nv_fp8_storage_t res = 0U;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 890)
+    if (saturate == __NV_SATFINITE) {
+        unsigned int half2_storage = (unsigned int)(x.x);
+        __nv_fp8x2_storage_t tmp;
+        if (fp8_interpretation == __NV_E5M2) {
+            asm("{cvt.rn.satfinite.e5m2x2.f16x2 %0, %1;}\n"
+                : "=h"(tmp)
+                : "r"(half2_storage));
+        } else {
+            asm("{cvt.rn.satfinite.e4m3x2.f16x2 %0, %1;}\n"
+                : "=h"(tmp)
+                : "r"(half2_storage));
+        }
+        res = (__nv_fp8_storage_t)tmp;
+    } else
+#endif
+    {
+        float fx = __internal_halfraw_to_float(x);
+        res = __nv_cvt_float_to_fp8(fx, saturate, fp8_interpretation);
+    }
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t __nv_cvt_halfraw2_to_fp8x2(
+    const __half2_raw x, const __nv_saturation_t saturate,
+    const __nv_fp8_interpretation_t fp8_interpretation) {
+    __nv_fp8x2_storage_t tmp;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 890)
+    if (saturate == __NV_SATFINITE) {
+        unsigned int half2_storage;
+        (void)memcpy(&half2_storage, &x, sizeof(x));
+
+        if (fp8_interpretation == __NV_E5M2) {
+            asm("{cvt.rn.satfinite.e5m2x2.f16x2 %0, %1;}\n"
+                : "=h"(tmp)
+                : "r"(half2_storage));
+        } else {
+            asm("{cvt.rn.satfinite.e4m3x2.f16x2 %0, %1;}\n"
+                : "=h"(tmp)
+                : "r"(half2_storage));
+        }
+    } else
+#endif
+    {
+        __half_raw raw;
+        raw.x = x.x;
+        __nv_fp8_storage_t lo =
+            __nv_cvt_halfraw_to_fp8(raw, saturate, fp8_interpretation);
+        raw.x = x.y;
+        __nv_fp8_storage_t hi =
+            __nv_cvt_halfraw_to_fp8(raw, saturate, fp8_interpretation);
+        tmp = hi;
+        tmp = (__nv_fp8x2_storage_t)(tmp << 8U);
+        tmp = (__nv_fp8x2_storage_t)(tmp | lo);
+    }
+    return tmp;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ float
+__internal_bf16raw_to_float(const __nv_bfloat16_raw x) {
+    const unsigned int ux = ((unsigned int)x.x) << 16U;
+    float fx;
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&fx, &ux, sizeof(ux));
+#else
+    (void)std::memcpy(&fx, &ux, sizeof(ux));
+#endif
+    return fx;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8_storage_t __nv_cvt_bfloat16raw_to_fp8(
+    const __nv_bfloat16_raw x, const __nv_saturation_t saturate,
+    const __nv_fp8_interpretation_t fp8_interpretation) {
+    const float fx = __internal_bf16raw_to_float(x);
+    const __nv_fp8_storage_t res =
+        __nv_cvt_float_to_fp8(fx, saturate, fp8_interpretation);
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_fp8x2_storage_t
+__nv_cvt_bfloat16raw2_to_fp8x2(
+    const __nv_bfloat162_raw x, const __nv_saturation_t saturate,
+    const __nv_fp8_interpretation_t fp8_interpretation) {
+    __nv_bfloat16_raw raw;
+    raw.x = x.y;
+    __nv_fp8x2_storage_t storage =
+        (__nv_fp8x2_storage_t)__nv_cvt_bfloat16raw_to_fp8(raw, saturate,
+                                                          fp8_interpretation);
+    storage = (__nv_fp8x2_storage_t)(storage << 8U);
+    raw.x = x.x;
+    storage = (__nv_fp8x2_storage_t)(storage |
+                                     __nv_cvt_bfloat16raw_to_fp8(
+                                         raw, saturate, fp8_interpretation));
+    return storage;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __half2_raw
+__nv_cvt_fp8x2_to_halfraw2(const __nv_fp8x2_storage_t x,
+                           const __nv_fp8_interpretation_t fp8_interpretation);
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __half_raw
+__nv_cvt_fp8_to_halfraw(const __nv_fp8_storage_t x,
+                        const __nv_fp8_interpretation_t fp8_interpretation) {
+    __half_raw res;
+    res.x = 0U;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 890)
+    res.x =
+        __nv_cvt_fp8x2_to_halfraw2((__nv_fp8x2_storage_t)x, fp8_interpretation)
+            .x;
+#else
+    unsigned short int ur = (unsigned short int)x;
+    ur = (unsigned short int)(ur << 8U);
+
+    if (fp8_interpretation == __NV_E5M2) {
+        if ((ur & 0x7FFFU) > 0x7C00U) {
+            /* If NaN, return canonical NaN */
+            ur = 0x7FFFU;
+        }
+    } else { // __NV_E4M3
+        unsigned short int sign = ur & 0x8000U;
+        unsigned short int exponent =
+            (unsigned short int)(((ur & 0x7800U) >> 1U) + 0x2000U);
+        unsigned short int mantissa = (ur & 0x0700U) >> 1U;
+        unsigned char absx = 0x7FU & (unsigned char)x;
+
+        if (absx == 0x7FU) // NaN
+        {
+            ur = 0x7FFFU; // fp16 canonical NaN, discard sign
+        } else if (exponent == 0x2000U) {
+            // zero or denormal
+            if (mantissa != 0U) {
+                // normalize
+                mantissa = (unsigned short int)(mantissa << 1U);
+                while ((mantissa & 0x0400U) == 0U) {
+                    mantissa = (unsigned short int)(mantissa << 1U);
+                    exponent = (unsigned short int)(exponent - 0x0400U);
+                }
+                // discard implicit leading bit
+                mantissa &= 0x03FFU;
+            } else { // Zero
+                exponent = 0U;
+            }
+
+            ur = (sign | exponent) | mantissa;
+        } else {
+            ur = (sign | exponent) | mantissa;
+        }
+    }
+    res.x = ur;
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __half2_raw
+__nv_cvt_fp8x2_to_halfraw2(const __nv_fp8x2_storage_t x,
+                           const __nv_fp8_interpretation_t fp8_interpretation) {
+    __half2_raw res;
+#if (defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 890)
+    unsigned int half2_storage;
+    if (fp8_interpretation == __NV_E5M2) {
+        asm("{cvt.rn.f16x2.e5m2x2 %0, %1;}\n" : "=r"(half2_storage) : "h"(x));
+    } else {
+        asm("{cvt.rn.f16x2.e4m3x2 %0, %1;}\n" : "=r"(half2_storage) : "h"(x));
+    }
+    (void)memcpy(&res, &half2_storage, sizeof(half2_storage));
+#else
+    res.x =
+        __nv_cvt_fp8_to_halfraw((__nv_fp8_storage_t)x, fp8_interpretation).x;
+    res.y = __nv_cvt_fp8_to_halfraw((__nv_fp8_storage_t)(x >> 8U),
+                                    fp8_interpretation)
+                .x;
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_fp8_storage_t __nv_cvt_bfloat16raw_to_e8m0(const __nv_bfloat16_raw x,
+    const __nv_saturation_t saturate, const enum cudaRoundMode rounding)
+{
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    unsigned short ures = 0U;
+    unsigned in = (unsigned)(x.x);
+    if ((rounding == cudaRoundZero) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rz.satfinite.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    } else if ((rounding == cudaRoundZero) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rz.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rp.satfinite.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rp.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    }
+    return (__nv_fp8_storage_t)ures;
+#else
+    // extract exponent bits, provides non-saturated result in RZ
+    __nv_fp8_storage_t res = (unsigned char)(x.x >> 7U);
+
+    if (rounding == cudaRoundPosInf) {
+        // round-up if mantissa non-zero and |x| > 2^-127 and finite
+        if ((x.x & 0x007FU) && ((x.x & 0x7FFFU) > 0x0040U) && ((x.x & 0x7FFFU) < 0x7F80U)) res++;
+    }
+
+    // Handle saturation of non-NaN large inputs to finite
+    if (saturate == __NV_SATFINITE) {
+        // non-NaN, Overflow --> Max
+        if (((x.x & 0x7FFFU) <= 0x7F80U) && (res == 0xFFU))
+        {
+            res--;
+        }
+    }
+    return res;
+#endif
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_fp8x2_storage_t __nv_cvt_bfloat162raw_to_e8m0x2(const __nv_bfloat162_raw x,
+    const __nv_saturation_t saturate, const enum cudaRoundMode rounding)
+{
+    assert((rounding == cudaRoundZero) || (rounding == cudaRoundPosInf));
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    __nv_fp8x2_storage_t ures = 0U;
+    unsigned in = (unsigned)(x.x) | ((unsigned)(x.y) << (unsigned)16U);
+    if ((rounding == cudaRoundZero) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rz.satfinite.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    } else if ((rounding == cudaRoundZero) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rz.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rp.satfinite.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rp.ue8m0x2.bf16x2 %0, %1;}\n"
+                : "=h"(ures)
+                : "r"(in));
+    }
+    return ures;
+#else
+    __nv_bfloat16_raw lo, hi;
+    lo.x = x.x;
+    hi.x = x.y;
+    __nv_fp8x2_storage_t ures = __nv_cvt_bfloat16raw_to_e8m0(hi, saturate, rounding);
+    ures <<= (unsigned short)8U;
+    ures |= __nv_cvt_bfloat16raw_to_e8m0(lo, saturate, rounding);
+    return ures;
+#endif
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ unsigned int __internal_fp8_float_as_uint(const float f)
+{
+    unsigned int u;
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&u, &f, sizeof(f));
+#else
+    (void)::std::memcpy(&u, &f, sizeof(f));
+#endif
+    return u;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ float __internal_fp8_uint_as_float(const unsigned int u)
+{
+    float f;
+#if defined(__CUDACC__) || (!defined __cplusplus)
+    (void)memcpy(&f, &u, sizeof(u));
+#else
+    (void)::std::memcpy(&f, &u, sizeof(u));
+#endif
+    return f;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_bfloat16_raw
+__internal_float_to_bf16raw_rz(const float x) {
+    __nv_bfloat16_raw r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
+    asm("{  cvt.rz.bf16.f32 %0, %1;}\n" : "=h"(r.x) : "f"(x));
+,
+    unsigned int ux = __internal_fp8_float_as_uint(x);
+    if ((ux & 0x7FFFFFFFU) > 0x7f800000U)
+    {
+        // NaN
+        r.x = (unsigned short int)0x7FFFU;
+    }
+    else
+    {
+        r.x = (unsigned short int)(ux >> 16U);
+    }
+)
+    return r;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__ __nv_bfloat16_raw
+__internal_float_to_bf16raw_ru(const float x) {
+    __nv_bfloat16_raw r;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_90,
+    asm("{  cvt.rp.bf16.f32 %0, %1;}\n" : "=h"(r.x) : "f"(x));
+,
+    unsigned int ux = __internal_fp8_float_as_uint(x);
+    if ((ux & 0x7FFFFFFFU) > 0x7f800000U)
+    {
+        // NaN
+        r.x = (unsigned short int)0x7FFFU;
+    }
+    else if ((ux < 0x7f800000U) && ((ux & 0x0000FFFFU) != 0))
+    {
+        // 0 <= x < +inf, round-up
+        r.x = (unsigned short int)((ux >> 16U) + 1U);
+    }
+    else {
+        // truncate others
+        r.x = (unsigned short int)(ux >> 16U);
+    }
+)
+    return r;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_fp8_storage_t __nv_cvt_float_to_e8m0(const float x,
+    const __nv_saturation_t saturate, const enum cudaRoundMode rounding)
+{
+    assert((rounding == cudaRoundZero) || (rounding == cudaRoundPosInf));
+    __nv_fp8_storage_t res = 0U;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    unsigned short ures = 0U;
+    if ((rounding == cudaRoundZero) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rz.satfinite.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(ures)
+                : "f"(x), "f"(0.0f));
+    } else if ((rounding == cudaRoundZero) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rz.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(ures)
+                : "f"(x), "f"(0.0f));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rp.satfinite.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(ures)
+                : "f"(x), "f"(0.0f));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rp.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(ures)
+                : "f"(x), "f"(0.0f));
+    }
+    res = (__nv_fp8_storage_t)ures;
+#else
+    if (rounding == cudaRoundZero)
+    {
+        res = __nv_cvt_bfloat16raw_to_e8m0(__internal_float_to_bf16raw_rz(x), saturate, rounding);
+    }
+    else
+    {   //cudaRoundPosInf
+        float absx = __internal_fp8_uint_as_float((__internal_fp8_float_as_uint(x) << (unsigned)1U) >> (unsigned)1U);
+        res = __nv_cvt_bfloat16raw_to_e8m0(__internal_float_to_bf16raw_ru(absx), saturate, rounding);
+    }
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_fp8x2_storage_t __nv_cvt_float2_to_e8m0x2(const float2 x,
+    const __nv_saturation_t saturate, const enum cudaRoundMode rounding)
+{
+    assert((rounding == cudaRoundZero) || (rounding == cudaRoundPosInf));
+    __nv_fp8x2_storage_t res = 0U;
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    if ((rounding == cudaRoundZero) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rz.satfinite.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(res)
+                : "f"(x.x), "f"(x.y));
+    } else if ((rounding == cudaRoundZero) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rz.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(res)
+                : "f"(x.x), "f"(x.y));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_SATFINITE)) {
+        asm("{cvt.rp.satfinite.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(res)
+                : "f"(x.x), "f"(x.y));
+    } else if ((rounding == cudaRoundPosInf) && (saturate == __NV_NOSAT)) {
+        asm("{cvt.rp.ue8m0x2.f32 %0, %2, %1;}\n"
+                : "=h"(res)
+                : "f"(x.x), "f"(x.y));
+    }
+#else
+    res = __nv_cvt_float_to_e8m0(x.y, saturate, rounding);
+    res <<= (unsigned short)8U;
+    res |= __nv_cvt_float_to_e8m0(x.x, saturate, rounding);
+#endif
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+float __internal_double_to_float_with_sticky(double x)
+{
+#if (defined __CUDA_ARCH__)
+    // protect from ftz in device code
+    float f;
+    double d;
+    asm("{  cvt.rn.f32.f64 %0, %1;}\n" : "=f"(f) : "d"(x));
+    asm("{  cvt.f64.f32 %0, %1;}\n" : "=d"(d) : "f"(f));
+#else
+    const float f = (float)x;
+    const double d = (double)f;
+#endif
+    unsigned int u = __internal_fp8_float_as_uint(f);
+    int x_is_not_nan = ((u << (unsigned)1U) <= (unsigned)0xFF000000U) ? 1 : 0;
+
+    if ((x > 0.0) && (d > x)) {
+        u--;
+    }
+    if ((x < 0.0) && (d < x)) {
+        u--;
+    }
+    if ((d != x) && (x_is_not_nan == 1)) {
+        u |= 1U;
+    }
+    return __internal_fp8_uint_as_float(u);
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_fp8_storage_t __nv_cvt_double_to_e8m0(const double x,
+    const __nv_saturation_t saturate, const enum cudaRoundMode rounding)
+{
+    float fx_with_sticky = __internal_double_to_float_with_sticky(x);
+    __nv_fp8_storage_t res = __nv_cvt_float_to_e8m0(fx_with_sticky, saturate, rounding);
+
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_fp8x2_storage_t __nv_cvt_double2_to_e8m0x2(const double2 x,
+    const __nv_saturation_t saturate, const enum cudaRoundMode rounding)
+{
+    float2 f;
+    f.x = __internal_double_to_float_with_sticky(x.x);
+    f.y = __internal_double_to_float_with_sticky(x.y);
+    return __nv_cvt_float2_to_e8m0x2(f, saturate, rounding);
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+unsigned short __internal_e8m0_to_bf16(const __nv_fp8_storage_t x)
+{
+    unsigned short res;
+    // shift bias exponent bits into place
+    res = ((unsigned short)x) << 7U;
+
+    if (x == 0xFFU) {
+        res = 0x7FFFU; // NaN --> Canonical QNaN
+    } else if (x == 0U) {
+        res = 0x0040U; // 2^-127
+    }
+
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_bfloat16_raw __nv_cvt_e8m0_to_bf16raw(const __nv_fp8_storage_t x)
+{
+    __nv_bfloat16_raw res;
+
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    unsigned short in = (unsigned short)x;
+    unsigned hr = 0U;
+    asm("{cvt.rn.bf16x2.ue8m0x2 %0, %1;}\n"
+                : "=r"(hr)
+                : "h"(in));
+
+    res.x = (unsigned short)hr;
+#else
+    res.x = __internal_e8m0_to_bf16(x);
+#endif
+
+    return res;
+}
+
+__CUDA_HOSTDEVICE_FP8_DECL__
+__nv_bfloat162_raw __nv_cvt_e8m0x2_to_bf162raw(const __nv_fp8x2_storage_t x)
+{
+    __nv_bfloat162_raw res;
+
+#if ((defined __CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && \
+     ((__CUDA_ARCH_HAS_FEATURE__(SM100_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM101_ALL)) || \
+      (__CUDA_ARCH_HAS_FEATURE__(SM120_ALL))))
+    unsigned short in = (unsigned short)x;
+    unsigned hr = 0U;
+    asm("{cvt.rn.bf16x2.ue8m0x2 %0, %1;}\n"
+                : "=r"(hr)
+                : "h"(in));
+
+    res.x = (unsigned short)hr;
+    res.y = (unsigned short)(hr >> (unsigned)16U);
+#else
+
+    res.x = __internal_e8m0_to_bf16((__nv_fp8_storage_t)x);
+    res.y = __internal_e8m0_to_bf16((__nv_fp8_storage_t)(x >> (unsigned short)8U));
+
+#endif
+
+    return res;
+}
+
+#endif /* !(defined __DOXYGEN_ONLY__) */
+
+/* All other definitions in this file are only visible to C++ compilers */
+#if defined(__cplusplus)
+
+/**
+ * \defgroup CUDA_MATH_FP8_E5M2_STRUCT C++ struct for handling fp8 data type of e5m2 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8_E5M2_STRUCT
+ * \brief __nv_fp8_e5m2 datatype
+ *
+ * \details This structure implements the datatype for handling
+ * \p fp8 floating-point numbers of \p e5m2 kind:
+ * with 1 sign, 5 exponent, 1 implicit and 2 explicit mantissa bits.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp8_e5m2 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8_E5M2_STRUCT
+     * Storage variable contains the \p fp8 floating-point data.
+     */
+    __nv_fp8_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8_e5m2() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider FP types */
+    /* Note we do avoid constructor init-list because of special host/device
+     * compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __half data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const __half f) {
+        __x = __nv_cvt_halfraw_to_fp8(static_cast<__half_raw>(f),
+                                      __NV_SATFINITE, __NV_E5M2);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __nv_bfloat16 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const __nv_bfloat16 f) {
+        __x = __nv_cvt_bfloat16raw_to_fp8(static_cast<__nv_bfloat16_raw>(f),
+                                          __NV_SATFINITE, __NV_E5M2);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const float f) {
+        __x = __nv_cvt_float_to_fp8(f, __NV_SATFINITE, __NV_E5M2);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const double f) {
+        __x = __nv_cvt_double_to_fp8(f, __NV_SATFINITE, __NV_E5M2);
+    }
+
+    /* Converts from integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p short \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__
+    __nv_fp8_e5m2(const unsigned short int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const unsigned int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const unsigned long int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p long \p long \p int data type, relies on
+     * \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__
+    __nv_fp8_e5m2(const unsigned long long int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p short \p int data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const short int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p long \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const long int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p long \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e5m2(const long long int val) {
+        __x = static_cast<__nv_fp8_e5m2>(static_cast<float>(val)).__x;
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening FP converts */
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __half data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __half() const {
+        return static_cast<__half>(__nv_cvt_fp8_to_halfraw(__x, __NV_E5M2));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float() const {
+        return __internal_halfraw_to_float(
+            __nv_cvt_fp8_to_halfraw(__x, __NV_E5M2));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __nv_bfloat16 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __nv_bfloat16() const {
+        return __float2bfloat16_rz(float(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p double data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator double() const {
+        return static_cast<double>(float(*this));
+    }
+
+    /* Convert to integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned char() const {
+        unsigned char i;
+        const float f = float(*this);
+        const unsigned char max_val = 0xFFU;
+        const unsigned char min_val = 0U;
+        const unsigned char bits = (*this).__x;
+        // saturation fixup
+        if ((bits & 0x7FU) > 0x7CU) {
+            // NaN
+            i = 0;
+        } else if (f > static_cast<float>(max_val)) {
+            // saturate maximum
+            i = max_val;
+        } else if (f < static_cast<float>(min_val)) {
+            // saturate minimum
+            i = min_val;
+        } else {
+            // normal value
+            i = static_cast<unsigned char>(f);
+        }
+        return i;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p short \p int data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned short int() const {
+        return __half2ushort_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned int() const {
+        return __half2uint_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p long \p int data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p zero if output type is 32-bit.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL if output type is 64-bit.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned long int() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__half2ull_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__half2uint_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p long \p long \p int data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned long long int() const {
+        return __half2ull_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p signed \p char data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator signed char() const {
+        signed char i;
+        const float f = float(*this);
+        const signed char max_val = (signed char)0x7FU;
+        const signed char min_val = (signed char)0x80U;
+        const unsigned char bits = (*this).__x;
+        // saturation fixup
+        if ((bits & 0x7FU) > 0x7CU) {
+            // NaN
+            i = 0;
+        } else if (f > static_cast<float>(max_val)) {
+            // saturate maximum
+            i = max_val;
+        } else if (f < static_cast<float>(min_val)) {
+            // saturate minimum
+            i = min_val;
+        } else {
+            // normal value
+            i = static_cast<signed char>(f);
+        }
+        return i;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+
+     * Clamps inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(static_cast<signed char>(*this));
+        }
+        else
+        {
+            value = static_cast<char>(static_cast<unsigned char>(*this));
+        }
+        return value;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p short \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator short int() const {
+        return __half2short_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator int() const {
+        return __half2int_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero if output type is 32-bit.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL if output type is 64-bit.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator long int() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__half2ll_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<long>(__half2int_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p long \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p 0x8000000000000000LL.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator long long int() const {
+        return __half2ll_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator bool() const {
+        return (__x & 0x7FU) != 0U;
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP8X2_E5M2_STRUCT C++ struct for handling vector type of two fp8 values of e5m2 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8X2_E5M2_STRUCT
+ * \brief __nv_fp8x2_e5m2 datatype
+ *
+ * \details This structure implements the datatype for handling two
+ * \p fp8 floating-point numbers of \p e5m2 kind each:
+ * with 1 sign, 5 exponent, 1 implicit and 2 explicit mantissa bits.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(2) __nv_fp8x2_e5m2 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8X2_E5M2_STRUCT
+     * Storage variable contains the vector of two \p fp8 floating-point data
+     * values.
+     */
+    __nv_fp8x2_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8x2_e5m2() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e5m2() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __half2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e5m2(const __half2 f) {
+        __x = __nv_cvt_halfraw2_to_fp8x2(static_cast<__half2_raw>(f),
+                                         __NV_SATFINITE, __NV_E5M2);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __nv_bfloat162 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e5m2(const __nv_bfloat162 f) {
+        __x = __nv_cvt_bfloat16raw2_to_fp8x2(static_cast<__nv_bfloat162_raw>(f),
+                                             __NV_SATFINITE, __NV_E5M2);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e5m2(const float2 f) {
+        __x = __nv_cvt_float2_to_fp8x2(f, __NV_SATFINITE, __NV_E5M2);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e5m2(const double2 f) {
+        __x = __nv_cvt_double2_to_fp8x2(f, __NV_SATFINITE, __NV_E5M2);
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening converts */
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __half2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __half2() const {
+        return static_cast<__half2>(__nv_cvt_fp8x2_to_halfraw2(__x, __NV_E5M2));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float2() const {
+        return __internal_halfraw2_to_float2(
+            __nv_cvt_fp8x2_to_halfraw2(__x, __NV_E5M2));
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+__CUDA_HOSTDEVICE_FP8_DECL__ unsigned int
+__internal_pack_u16x2_to_u32(const unsigned short int src_lo,
+                             const unsigned short int src_hi) {
+    unsigned int dst;
+#if (defined __CUDACC__) && (defined __CUDA_ARCH__)
+    asm("{  mov.b32 %0, {%1,%2};}\n" : "=r"(dst) : "h"(src_lo), "h"(src_hi));
+#else
+    dst = (static_cast<unsigned int>(src_hi) << 16U) |
+          static_cast<unsigned int>(src_lo);
+#endif
+    return dst;
+}
+
+/**
+ * \defgroup CUDA_MATH_FP8X4_E5M2_STRUCT C++ struct for handling vector type of four fp8 values of e5m2 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8X4_E5M2_STRUCT
+ * \brief __nv_fp8x4_e5m2 datatype
+ *
+ * \details This structure implements the datatype for handling four
+ * \p fp8 floating-point numbers of \p e5m2 kind each:
+ * with 1 sign, 5 exponent, 1 implicit and 2 explicit mantissa bits.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(4) __nv_fp8x4_e5m2 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8X4_E5M2_STRUCT
+     * Storage variable contains the vector of four \p fp8 floating-point data
+     * values.
+     */
+    __nv_fp8x4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8x4_e5m2() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e5m2() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from a pair of \p __half2 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e5m2(const __half2 flo,
+                                                     const __half2 fhi) {
+        const __nv_fp8x2_storage_t rlo = __nv_cvt_halfraw2_to_fp8x2(
+            static_cast<__half2_raw>(flo), __NV_SATFINITE, __NV_E5M2);
+        const __nv_fp8x2_storage_t rhi = __nv_cvt_halfraw2_to_fp8x2(
+            static_cast<__half2_raw>(fhi), __NV_SATFINITE, __NV_E5M2);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from a pair of \p __nv_bfloat162 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e5m2(const __nv_bfloat162 flo,
+                                                     const __nv_bfloat162 fhi) {
+        const __nv_fp8x2_storage_t rlo = __nv_cvt_bfloat16raw2_to_fp8x2(
+            static_cast<__nv_bfloat162_raw>(flo), __NV_SATFINITE, __NV_E5M2);
+        const __nv_fp8x2_storage_t rhi = __nv_cvt_bfloat16raw2_to_fp8x2(
+            static_cast<__nv_bfloat162_raw>(fhi), __NV_SATFINITE, __NV_E5M2);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e5m2(const float4 f) {
+        const float2 flo = {f.x, f.y};
+        const float2 fhi = {f.z, f.w};
+        const __nv_fp8x2_storage_t rlo =
+            __nv_cvt_float2_to_fp8x2(flo, __NV_SATFINITE, __NV_E5M2);
+        const __nv_fp8x2_storage_t rhi =
+            __nv_cvt_float2_to_fp8x2(fhi, __NV_SATFINITE, __NV_E5M2);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e5m2(const double4 f) {
+        const double2 flo = {f.x, f.y};
+        const double2 fhi = {f.z, f.w};
+        const __nv_fp8x2_storage_t rlo =
+            __nv_cvt_double2_to_fp8x2(flo, __NV_SATFINITE, __NV_E5M2);
+        const __nv_fp8x2_storage_t rhi =
+            __nv_cvt_double2_to_fp8x2(fhi, __NV_SATFINITE, __NV_E5M2);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float4 vector data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float4() const {
+        const __nv_fp8x2_storage_t slo = static_cast<__nv_fp8x2_storage_t>(__x);
+        const __nv_fp8x2_storage_t shi =
+            static_cast<__nv_fp8x2_storage_t>(__x >> 16U);
+        float2 rlo = __internal_halfraw2_to_float2(
+            __nv_cvt_fp8x2_to_halfraw2(slo, __NV_E5M2));
+        float2 rhi = __internal_halfraw2_to_float2(
+            __nv_cvt_fp8x2_to_halfraw2(shi, __NV_E5M2));
+        float4 res = {rlo.x, rlo.y, rhi.x, rhi.y};
+        return res;
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP8_E4M3_STRUCT C++ struct for handling fp8 data type of e4m3 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8_E4M3_STRUCT
+ * \brief __nv_fp8_e4m3 datatype
+ *
+ * \details This structure implements the datatype for storing
+ * \p fp8 floating-point numbers of \p e4m3 kind:
+ * with 1 sign, 4 exponent, 1 implicit and 3 explicit mantissa bits.
+ * The encoding doesn't support Infinity.
+ * NaNs are limited to 0x7F and 0xFF values.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp8_e4m3 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8_E4M3_STRUCT
+     * Storage variable contains the \p fp8 floating-point data.
+     */
+    __nv_fp8_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8_e4m3() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider FP types */
+    /* Note we do avoid constructor init-list because of special host/device
+     * compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __half data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const __half f) {
+        __x = __nv_cvt_halfraw_to_fp8(static_cast<__half_raw>(f),
+                                      __NV_SATFINITE, __NV_E4M3);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __nv_bfloat16 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const __nv_bfloat16 f) {
+        __x = __nv_cvt_bfloat16raw_to_fp8(static_cast<__nv_bfloat16_raw>(f),
+                                          __NV_SATFINITE, __NV_E4M3);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const float f) {
+        __x = __nv_cvt_float_to_fp8(f, __NV_SATFINITE, __NV_E4M3);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const double f) {
+        __x = __nv_cvt_double_to_fp8(f, __NV_SATFINITE, __NV_E4M3);
+    }
+
+    /* Converts from integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p short \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__
+    __nv_fp8_e4m3(const unsigned short int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const unsigned int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const unsigned long int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p long \p long \p int data type, relies on
+     * \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__
+    __nv_fp8_e4m3(const unsigned long long int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p short \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const short int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p int data type, relies on \p __NV_SATFINITE behavior
+     * for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const long int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p long \p long \p int data type, relies on \p
+     * __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e4m3(const long long int val) {
+        __x = static_cast<__nv_fp8_e4m3>(static_cast<float>(val)).__x;
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening FP converts */
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __half data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __half() const {
+        return static_cast<__half>(__nv_cvt_fp8_to_halfraw(__x, __NV_E4M3));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float() const {
+        return __internal_halfraw_to_float(
+            __nv_cvt_fp8_to_halfraw(__x, __NV_E4M3));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __nv_bfloat16 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __nv_bfloat16() const {
+        return __float2bfloat16_rz(float(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p double data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator double() const {
+        return static_cast<double>(float(*this));
+    }
+
+    /* Convert to integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned char() const {
+        unsigned char i;
+        const float f = float(*this);
+        const unsigned char max_val = 0xFFU;
+        const unsigned char min_val = 0U;
+        const unsigned char bits = (*this).__x;
+        // saturation fixup
+        if ((bits & 0x7FU) == 0x7FU) {
+            // NaN
+            i = 0;
+        } else if (f > static_cast<float>(max_val)) {
+            // saturate maximum
+            i = max_val;
+        } else if (f < static_cast<float>(min_val)) {
+            // saturate minimum
+            i = min_val;
+        } else {
+            // normal value
+            i = static_cast<unsigned char>(f);
+        }
+        return i;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p short \p int data type.
+     * Clamps negative inputs to zero.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned short int() const {
+        return __half2ushort_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Clamps negative inputs to zero.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned int() const {
+        return __half2uint_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p long \p int data type.
+     * Clamps negative and too large inputs to the output range.
+     * \p NaN inputs convert to \p zero if output type is 32-bit.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL if output type is 64-bit.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned long int() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__half2ull_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__half2uint_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p long \p long \p int data type.
+     * Clamps negative inputs to zero.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned long long int() const {
+        return __half2ull_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p signed \p char data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator signed char() const {
+        signed char i;
+        const float f = float(*this);
+        const signed char max_val = (signed char)0x7FU;
+        const signed char min_val = (signed char)0x80U;
+        const unsigned char bits = (*this).__x;
+        // saturation fixup
+        if ((bits & 0x7FU) == 0x7FU) {
+            // NaN
+            i = 0;
+        } else if (f > static_cast<float>(max_val)) {
+            // saturate maximum
+            i = max_val;
+        } else if (f < static_cast<float>(min_val)) {
+            // saturate minimum
+            i = min_val;
+        } else {
+            // normal value
+            i = static_cast<signed char>(f);
+        }
+        return i;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+
+     * Clamps inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(static_cast<signed char>(*this));
+        }
+        else
+        {
+            value = static_cast<char>(static_cast<unsigned char>(*this));
+        }
+        return value;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p short \p int data type.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator short int() const {
+        return __half2short_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p int data type.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator int() const {
+        return __half2int_rz(__half(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero if output type is 32-bit.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL if output type is 64-bit.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator long int() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__half2ll_rz(__half(*this)));
+        }
+        else
+        {
+            retval = static_cast<long>(__half2int_rz(__half(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p long \p long \p int data type.
+     * \p NaN inputs convert to \p 0x8000000000000000LL.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator long long int() const {
+        return __half2ll_rz(__half(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p bool data type.
+     * +0 and -0 inputs convert to \p false.
+     * Non-zero inputs convert to \p true.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator bool() const {
+        return (__x & 0x7FU) != 0U;
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP8X2_E4M3_STRUCT C++ struct for handling vector type of two fp8 values of e4m3 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8X2_E4M3_STRUCT
+ * \brief __nv_fp8x2_e4m3 datatype
+ *
+ * \details This structure implements the datatype for storage
+ * and operations on the vector of two \p fp8 values of \p e4m3 kind each:
+ * with 1 sign, 4 exponent, 1 implicit and 3 explicit mantissa bits.
+ * The encoding doesn't support Infinity.
+ * NaNs are limited to 0x7F and 0xFF values.
+ */
+struct __CUDA_ALIGN__(2) __nv_fp8x2_e4m3 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8X2_E4M3_STRUCT
+     * Storage variable contains the vector of two \p fp8 floating-point data
+     * values.
+     */
+    __nv_fp8x2_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8x2_e4m3() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e4m3() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __half2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e4m3(const __half2 f) {
+        __x = __nv_cvt_halfraw2_to_fp8x2(static_cast<__half2_raw>(f),
+                                         __NV_SATFINITE, __NV_E4M3);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __nv_bfloat162 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e4m3(const __nv_bfloat162 f) {
+        __x = __nv_cvt_bfloat16raw2_to_fp8x2(static_cast<__nv_bfloat162_raw>(f),
+                                             __NV_SATFINITE, __NV_E4M3);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e4m3(const float2 f) {
+        __x = __nv_cvt_float2_to_fp8x2(f, __NV_SATFINITE, __NV_E4M3);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e4m3(const double2 f) {
+        __x = __nv_cvt_double2_to_fp8x2(f, __NV_SATFINITE, __NV_E4M3);
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening converts */
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __half2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __half2() const {
+        return static_cast<__half2>(__nv_cvt_fp8x2_to_halfraw2(__x, __NV_E4M3));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float2() const {
+        return __internal_halfraw2_to_float2(
+            __nv_cvt_fp8x2_to_halfraw2(__x, __NV_E4M3));
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP8X4_E4M3_STRUCT C++ struct for handling vector type of four fp8 values of e4m3 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8X4_E4M3_STRUCT
+ * \brief __nv_fp8x4_e4m3 datatype
+ *
+ * \details This structure implements the datatype for storage
+ * and operations on the vector of four \p fp8 values of \p e4m3 kind each:
+ * with 1 sign, 4 exponent, 1 implicit and 3 explicit mantissa bits.
+ * The encoding doesn't support Infinity.
+ * NaNs are limited to 0x7F and 0xFF values.
+ */
+struct __CUDA_ALIGN__(4) __nv_fp8x4_e4m3 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8X4_E4M3_STRUCT
+     * Storage variable contains the vector of four \p fp8 floating-point data
+     * values.
+     */
+    __nv_fp8x4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8x4_e4m3() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e4m3() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from a pair of \p __half2 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e4m3(const __half2 flo,
+                                                     const __half2 fhi) {
+        const __nv_fp8x2_storage_t rlo = __nv_cvt_halfraw2_to_fp8x2(
+            static_cast<__half2_raw>(flo), __NV_SATFINITE, __NV_E4M3);
+        const __nv_fp8x2_storage_t rhi = __nv_cvt_halfraw2_to_fp8x2(
+            static_cast<__half2_raw>(fhi), __NV_SATFINITE, __NV_E4M3);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from a pair of \p __nv_bfloat162 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e4m3(const __nv_bfloat162 flo,
+                                                     const __nv_bfloat162 fhi) {
+        const __nv_fp8x2_storage_t rlo = __nv_cvt_bfloat16raw2_to_fp8x2(
+            static_cast<__nv_bfloat162_raw>(flo), __NV_SATFINITE, __NV_E4M3);
+        const __nv_fp8x2_storage_t rhi = __nv_cvt_bfloat16raw2_to_fp8x2(
+            static_cast<__nv_bfloat162_raw>(fhi), __NV_SATFINITE, __NV_E4M3);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e4m3(const float4 f) {
+        const float2 flo = {f.x, f.y};
+        const float2 fhi = {f.z, f.w};
+        const __nv_fp8x2_storage_t rlo =
+            __nv_cvt_float2_to_fp8x2(flo, __NV_SATFINITE, __NV_E4M3);
+        const __nv_fp8x2_storage_t rhi =
+            __nv_cvt_float2_to_fp8x2(fhi, __NV_SATFINITE, __NV_E4M3);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e4m3(const double4 f) {
+        const double2 flo = {f.x, f.y};
+        const double2 fhi = {f.z, f.w};
+        const __nv_fp8x2_storage_t rlo =
+            __nv_cvt_double2_to_fp8x2(flo, __NV_SATFINITE, __NV_E4M3);
+        const __nv_fp8x2_storage_t rhi =
+            __nv_cvt_double2_to_fp8x2(fhi, __NV_SATFINITE, __NV_E4M3);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float4 vector data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float4() const {
+        const __nv_fp8x2_storage_t slo = static_cast<__nv_fp8x2_storage_t>(__x);
+        const __nv_fp8x2_storage_t shi =
+            static_cast<__nv_fp8x2_storage_t>(__x >> 16U);
+        float2 rlo = __internal_halfraw2_to_float2(
+            __nv_cvt_fp8x2_to_halfraw2(slo, __NV_E4M3));
+        float2 rhi = __internal_halfraw2_to_float2(
+            __nv_cvt_fp8x2_to_halfraw2(shi, __NV_E4M3));
+        float4 res = {rlo.x, rlo.y, rhi.x, rhi.y};
+        return res;
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \ingroup CUDA_MATH_FP8_E8M0_STRUCT
+ * \brief __nv_fp8_e8m0 datatype
+ *
+ * \details This structure implements the datatype for handling
+ * 8-bit scale factors of \p e8m0 kind: interpreted as powers of two
+ * with biased exponent. Bias equals to 127, so numbers 0 through 254
+ * represent 2^-127 through 2^127. Number \p 0xFF = 255 is reserved
+ * for NaN.
+ *
+ * The structure implements converting constructors and operators.
+ */
+struct __CUDA_ALIGN__(1) __nv_fp8_e8m0 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8_E8M0_STRUCT
+     * Storage variable contains the 8-bit scale data.
+     */
+    __nv_fp8_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8_e8m0() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider FP types */
+    /* Note we do avoid constructor init-list because of special host/device
+     * compilation rules */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __half data type, relies on \p __NV_SATFINITE
+     * behavior for large input values and \p cudaRoundPosInf for
+     * rounding.
+     * \see __nv_cvt_float_to_e8m0 for further details
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const __half f) {
+        __x = __nv_cvt_float_to_e8m0(__internal_halfraw_to_float(static_cast<__half_raw>(f)),
+                                     __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __nv_bfloat16 data type, relies on \p __NV_SATFINITE
+     * behavior for large input values and \p cudaRoundPosInf for
+     * rounding.
+     * \see __nv_cvt_bfloat16raw_to_e8m0 for further details
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const __nv_bfloat16 f) {
+        __x = __nv_cvt_bfloat16raw_to_e8m0(static_cast<__nv_bfloat16_raw>(f),
+                                           __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float data type, relies on \p __NV_SATFINITE behavior
+     * behavior for large input values and \p cudaRoundPosInf for
+     * rounding.
+     * \see __nv_cvt_float_to_e8m0 for further details
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const float f) {
+        __x = __nv_cvt_float_to_e8m0(f, __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double data type, relies on \p __NV_SATFINITE
+     * behavior for large input values and \p cudaRoundPosInf for
+     * rounding.
+     * \see __nv_cvt_double_to_e8m0 for further details
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const double f) {
+        __x = __nv_cvt_double_to_e8m0(f, __NV_SATFINITE, cudaRoundPosInf);
+    }
+
+    /* Converts from integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p short \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__
+    __nv_fp8_e8m0(const unsigned short int val) {
+        __x = static_cast<__nv_fp8_e8m0>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const unsigned int val) {
+        __x = static_cast<__nv_fp8_e8m0>(static_cast<double>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p long \p long \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__
+    __nv_fp8_e8m0(const unsigned long long int val) {
+        __nv_bfloat16 rn = __ull2bfloat16_rn(val);
+        __nv_bfloat16_raw rn_raw = static_cast<__nv_bfloat16_raw>(rn);
+        unsigned long long int back_int = __bfloat162ull_rz(rn);
+        if (back_int < val)
+        {
+            rn_raw.x++;
+        }
+        __x = __nv_cvt_bfloat16raw_to_e8m0(rn_raw, __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p unsigned \p long \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const unsigned long int val) {
+        __x = static_cast<__nv_fp8_e8m0>(static_cast<unsigned long long int>(val)).__x;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p short \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const short int val) {
+        __x = static_cast<__nv_fp8_e8m0>(static_cast<float>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p int data type, relies on
+    * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const int val) {
+        __x = static_cast<__nv_fp8_e8m0>(static_cast<double>(val)).__x;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p long \p long \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const long long int val) {
+        __nv_bfloat16 rn = __ll2bfloat16_rn(val);
+        __nv_bfloat16_raw rn_raw = static_cast<__nv_bfloat16_raw>(rn);
+        long long int back_int = __bfloat162ll_rz(rn);
+        if (((val > 0) && (back_int < val)) || ((val < 0) && (back_int > val)))
+        {
+            rn_raw.x++;
+        }
+        __x = __nv_cvt_bfloat16raw_to_e8m0(rn_raw, __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p long \p int data type, relies on
+     * \p cudaRoundPosInf rounding.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8_e8m0(const long int val) {
+        __x = static_cast<__nv_fp8_e8m0>(static_cast<long long int>(val)).__x;
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening FP converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float() const {
+        return __internal_bf16raw_to_float(__nv_cvt_e8m0_to_bf16raw((*this).__x));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __nv_bfloat16 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __nv_bfloat16() const {
+        return static_cast<__nv_bfloat16>(
+            __nv_cvt_e8m0_to_bf16raw((*this).__x));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p double data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator double() const {
+        const float f = float(*this);
+        double d;
+#if (defined __CUDA_ARCH__)
+        // protect from ftz in device code
+        asm("{  cvt.f64.f32 %0, %1;}\n" : "=d"(d) : "f"(f));
+#else
+        d = static_cast<double>(f);
+#endif
+        return d;
+    }
+
+    /* rounding conversion to half */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __half data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __half() const {
+        return __float2half_rn(float(*this));
+    }
+
+    /* Convert to integral */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p char data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned char() const {
+        unsigned char i;
+        const float f = float(*this);
+        const unsigned char max_val = 0xFFU;
+        const unsigned char bits = (*this).__x;
+        // saturation fixup
+        if (bits == 0xFFU) {
+            // NaN
+            i = 0;
+        } else if (f > static_cast<float>(max_val)) {
+            // saturate maximum
+            i = max_val;
+        } else {
+            // normal value
+            i = static_cast<unsigned char>(f);
+        }
+        return i;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p short \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned short int() const {
+        return __bfloat162ushort_rz(__nv_bfloat16(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned int() const {
+        return __bfloat162uint_rz(__nv_bfloat16(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero if output type is 32-bit.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL if output type is 64-bit.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned long int() const {
+        unsigned long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(unsigned long) == sizeof(unsigned long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<unsigned long>(__bfloat162ull_rz(__nv_bfloat16(*this)));
+        }
+        else
+        {
+            retval = static_cast<unsigned long>(__bfloat162uint_rz(__nv_bfloat16(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p unsigned \p long \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator unsigned long long int() const {
+        return __bfloat162ull_rz(__nv_bfloat16(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p signed \p char data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator signed char() const {
+        signed char i;
+        const float f = float(*this);
+        const signed char max_val = (signed char)0x7FU;
+        const unsigned char bits = (*this).__x;
+        // saturation fixup
+        if (bits == 0xFFU) {
+            // NaN
+            i = 0;
+        } else if (f > static_cast<float>(max_val)) {
+            // saturate maximum
+            i = max_val;
+        } else {
+            // normal value
+            i = static_cast<signed char>(f);
+        }
+        return i;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to an implementation defined \p char data type.
+     * 
+     * Detects signedness of the \p char type and proceeds accordingly, see
+     * further details in signed and unsigned char operators.
+
+     * Clamps inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator char() const {
+        char value;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (((char)-1) < (char)0)
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            value = static_cast<char>(static_cast<signed char>(*this));
+        }
+        else
+        {
+            value = static_cast<char>(static_cast<unsigned char>(*this));
+        }
+        return value;
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p short \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator short int() const {
+        return __bfloat162short_rz(__nv_bfloat16(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator int() const {
+        return __bfloat162int_rz(__nv_bfloat16(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p zero if output type is 32-bit.
+     * \p NaN inputs convert to \p 0x8000000000000000ULL if output type is 64-bit.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator long int() const {
+        long retval;
+        /* Suppress VS warning: warning C4127: conditional expression is constant */
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (push)
+#pragma warning (disable: 4127)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        if (sizeof(long) == sizeof(long long))
+#if defined(_MSC_VER) && !defined(__CUDA_ARCH__)
+#pragma warning (pop)
+#endif /* _MSC_VER && !defined(__CUDA_ARCH__) */
+        {
+            retval = static_cast<long>(__bfloat162ll_rz(__nv_bfloat16(*this)));
+        }
+        else
+        {
+            retval = static_cast<long>(__bfloat162int_rz(__nv_bfloat16(*this)));
+        }
+        return retval;
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p long \p long \p int data type.
+     * Clamps too large inputs to the output range.
+     * \p NaN inputs convert to \p 0x8000000000000000LL.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator long long int() const {
+        return __bfloat162ll_rz(__nv_bfloat16(*this));
+    }
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p bool data type.
+     * All values in input range are non-zero, so result is always \p true.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator bool() const {
+        return true;
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP8X2_E8M0_STRUCT C++ struct for handling vector type of two scale factors of e8m0 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8X2_E8M0_STRUCT
+ * \brief __nv_fp8x2_e8m0 datatype
+ *
+ * \details This structure implements the datatype for storage
+ * and operations on the vector of two scale factors of \p e8m0 kind each.
+ */
+struct __CUDA_ALIGN__(2) __nv_fp8x2_e8m0 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8X2_E8M0_STRUCT
+     * Storage variable contains the vector of two scale factor
+     * values.
+     */
+    __nv_fp8x2_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8x2_e8m0() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e8m0() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __half2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e8m0(const __half2 f) {
+        __x = __nv_cvt_float2_to_e8m0x2(__half22float2(f),
+                                __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p __nv_bfloat162 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e8m0(const __nv_bfloat162 f) {
+        __x = __nv_cvt_bfloat162raw_to_e8m0x2(static_cast<__nv_bfloat162_raw>(f),
+                                             __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e8m0(const float2 f) {
+        __x = __nv_cvt_float2_to_e8m0x2(f,
+                                __NV_SATFINITE, cudaRoundPosInf);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double2 data type, relies on \p __NV_SATFINITE
+     * behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x2_e8m0(const double2 f) {
+        __x = __nv_cvt_double2_to_e8m0x2(f, __NV_SATFINITE, cudaRoundPosInf);
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening converts */
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __nv_bfloat162 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __nv_bfloat162() const {
+        return static_cast<__nv_bfloat162>(__nv_cvt_e8m0x2_to_bf162raw((*this).__x));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float2() const {
+        return __bfloat1622float2(static_cast<__nv_bfloat162>(*this));
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p __half2 data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator __half2() const {
+        return __float22half2_rn(static_cast<float2>(*this));
+    }
+
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+/**
+ * \defgroup CUDA_MATH_FP8X4_E8M0_STRUCT C++ struct for handling vector type of four scale factors of e8m0 kind.
+ * \ingroup CUDA_MATH_INTRINSIC_FP8
+ */
+
+/**
+ * \ingroup CUDA_MATH_FP8X4_E8M0_STRUCT
+ * \brief __nv_fp8x4_e8m0 datatype
+ *
+ * \details This structure implements the datatype for storage
+ * and operations on the vector of scale factors of \p e8m0 kind each.
+ */
+struct __CUDA_ALIGN__(4) __nv_fp8x4_e8m0 {
+  public:
+    /**
+     * \ingroup CUDA_MATH_FP8X4_E8M0_STRUCT
+     * Storage variable contains the vector of four scale factor
+     * values.
+     */
+    __nv_fp8x4_storage_t __x;
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor by default.
+     */
+#if defined(__CPP_VERSION_AT_LEAST_11_FP8)
+    __nv_fp8x4_e8m0() = default;
+#else
+    __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e8m0() {}
+#endif /* defined(__CPP_VERSION_AT_LEAST_11_FP8) */
+
+#if !defined(__CUDA_NO_FP8_CONVERSIONS__)
+
+    /* Construct from wider types */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from a pair of \p __half2 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e8m0(const __half2 flo,
+                                                     const __half2 fhi) {
+        const __nv_fp8x2_storage_t rlo = static_cast<__nv_fp8x2_e8m0>(flo).__x;
+        const __nv_fp8x2_storage_t rhi = static_cast<__nv_fp8x2_e8m0>(fhi).__x;
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from a pair of \p __nv_bfloat162 data type values,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e8m0(const __nv_bfloat162 flo,
+                                                     const __nv_bfloat162 fhi) {
+        const __nv_fp8x2_storage_t rlo = static_cast<__nv_fp8x2_e8m0>(flo).__x;
+        const __nv_fp8x2_storage_t rhi = static_cast<__nv_fp8x2_e8m0>(fhi).__x;
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p float4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e8m0(const float4 f) {
+        const float2 flo = {f.x, f.y};
+        const float2 fhi = {f.z, f.w};
+        const __nv_fp8x2_storage_t rlo =
+            __nv_cvt_float2_to_e8m0x2(flo, __NV_SATFINITE, cudaRoundPosInf);
+        const __nv_fp8x2_storage_t rhi =
+            __nv_cvt_float2_to_e8m0x2(fhi, __NV_SATFINITE, cudaRoundPosInf);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Constructor from \p double4 vector data type,
+     * relies on \p __NV_SATFINITE behavior for out-of-range values.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ __nv_fp8x4_e8m0(const double4 f) {
+        const double2 flo = {f.x, f.y};
+        const double2 fhi = {f.z, f.w};
+        const __nv_fp8x2_storage_t rlo =
+            __nv_cvt_double2_to_e8m0x2(flo, __NV_SATFINITE, cudaRoundPosInf);
+        const __nv_fp8x2_storage_t rhi =
+            __nv_cvt_double2_to_e8m0x2(fhi, __NV_SATFINITE, cudaRoundPosInf);
+        __x = __internal_pack_u16x2_to_u32(rlo, rhi);
+    }
+
+#if !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__)
+    /* Widening converts */
+
+    /**
+     * \ingroup CUDA_MATH_FP8_MISC
+     * Conversion operator to \p float4 vector data type.
+     */
+    explicit __CUDA_HOSTDEVICE_FP8__ operator float4() const {
+        __nv_fp8x2_e8m0 lo;
+        lo.__x = static_cast<__nv_fp8x2_storage_t>(__x);
+        __nv_fp8x2_e8m0 hi;
+        hi.__x = static_cast<__nv_fp8x2_storage_t>(__x >> 16U);
+
+        float2 rlo = static_cast<float2>(lo);
+        float2 rhi = static_cast<float2>(hi);
+        float4 res = {rlo.x, rlo.y, rhi.x, rhi.y};
+        return res;
+    }
+#endif /* !defined(__CUDA_NO_FP8_CONVERSION_OPERATORS__) */
+#endif /* !defined(__CUDA_NO_FP8_CONVERSIONS__) */
+};
+
+#endif /* defined(__cplusplus) */
+
+#endif /* end of include guard: __CUDA_FP8_HPP__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_gl_interop.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_gl_interop.h
new file mode 100644
index 0000000000000000000000000000000000000000..df64a8afa14f695bb05810266ac40b227c078cc5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_gl_interop.h
@@ -0,0 +1,514 @@
+/*
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_GL_INTEROP_H__)
+#define __CUDA_GL_INTEROP_H__
+
+#include "cuda_runtime_api.h"
+
+#if defined(__APPLE__)
+
+#include <OpenGL/gl.h>
+
+#else /* __APPLE__ */
+
+#if defined(__arm__) || defined(__aarch64__)
+#ifndef GL_VERSION
+#error Please include the appropriate gl headers before including cuda_gl_interop.h
+#endif
+#else
+#include <GL/gl.h>
+#endif
+
+#endif /* __APPLE__ */
+
+/** \cond impl_private */
+#if defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+#define __CUDA_DEPRECATED
+#elif defined(_MSC_VER)
+#define __CUDA_DEPRECATED __declspec(deprecated)
+#elif defined(__GNUC__)
+#define __CUDA_DEPRECATED __attribute__((deprecated))
+#else
+#define __CUDA_DEPRECATED
+#endif
+/** \endcond impl_private */
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * \addtogroup CUDART_OPENGL OpenGL Interoperability
+ * This section describes the OpenGL interoperability functions of the CUDA
+ * runtime application programming interface. Note that mapping of OpenGL
+ * resources is performed with the graphics API agnostic, resource mapping 
+ * interface described in \ref CUDART_INTEROP "Graphics Interopability".
+ *
+ * @{
+ */
+
+/**
+ * CUDA devices corresponding to the current OpenGL context
+ */
+enum cudaGLDeviceList
+{
+  cudaGLDeviceListAll           = 1, /**< The CUDA devices for all GPUs used by the current OpenGL context */
+  cudaGLDeviceListCurrentFrame  = 2, /**< The CUDA devices for the GPUs used by the current OpenGL context in its currently rendering frame */
+  cudaGLDeviceListNextFrame     = 3  /**< The CUDA devices for the GPUs to be used by the current OpenGL context in the next frame  */
+};
+
+/**
+ * \brief Gets the CUDA devices associated with the current OpenGL context
+ *
+ * Returns in \p *pCudaDeviceCount the number of CUDA-compatible devices 
+ * corresponding to the current OpenGL context. Also returns in \p *pCudaDevices 
+ * at most \p cudaDeviceCount of the CUDA-compatible devices corresponding to 
+ * the current OpenGL context. If any of the GPUs being used by the current OpenGL
+ * context are not CUDA capable then the call will return ::cudaErrorNoDevice.
+ *
+ * \param pCudaDeviceCount - Returned number of CUDA devices corresponding to the 
+ *                           current OpenGL context
+ * \param pCudaDevices     - Returned CUDA devices corresponding to the current 
+ *                           OpenGL context
+ * \param cudaDeviceCount  - The size of the output device array \p pCudaDevices
+ * \param deviceList       - The set of devices to return.  This set may be
+ *                           ::cudaGLDeviceListAll for all devices, 
+ *                           ::cudaGLDeviceListCurrentFrame for the devices used to
+ *                           render the current frame (in SLI), or
+ *                           ::cudaGLDeviceListNextFrame for the devices used to
+ *                           render the next frame (in SLI).
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNoDevice,
+ * ::cudaErrorInvalidGraphicsContext,
+ * ::cudaErrorOperatingSystem,
+ * ::cudaErrorUnknown
+ *
+ * \note This function is not supported on Mac OS X.
+ * \notefnerr
+ *
+ * \sa 
+ * ::cudaGraphicsUnregisterResource,
+ * ::cudaGraphicsMapResources, 
+ * ::cudaGraphicsSubResourceGetMappedArray, 
+ * ::cudaGraphicsResourceGetMappedPointer,
+ * ::cuGLGetDevices 
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGLGetDevices(unsigned int *pCudaDeviceCount, int *pCudaDevices, unsigned int cudaDeviceCount, enum cudaGLDeviceList deviceList);
+
+/**
+ * \brief Register an OpenGL texture or renderbuffer object
+ *
+ * Registers the texture or renderbuffer object specified by \p image for access by CUDA.
+ * A handle to the registered object is returned as \p resource.
+ *
+ * \p target must match the type of the object, and must be one of ::GL_TEXTURE_2D, 
+ * ::GL_TEXTURE_RECTANGLE, ::GL_TEXTURE_CUBE_MAP, ::GL_TEXTURE_3D, ::GL_TEXTURE_2D_ARRAY, 
+ * or ::GL_RENDERBUFFER.
+ *
+ * The register flags \p flags specify the intended usage, as follows: 
+ * - ::cudaGraphicsRegisterFlagsNone: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::cudaGraphicsRegisterFlagsReadOnly: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::cudaGraphicsRegisterFlagsWriteDiscard: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ * - ::cudaGraphicsRegisterFlagsSurfaceLoadStore: Specifies that CUDA will
+ *   bind this resource to a surface reference.
+ * - ::cudaGraphicsRegisterFlagsTextureGather: Specifies that CUDA will perform
+ *   texture gather operations on this resource.
+ *
+ * The following image formats are supported. For brevity's sake, the list is abbreviated.
+ * For ex., {GL_R, GL_RG} X {8, 16} would expand to the following 4 formats 
+ * {GL_R8, GL_R16, GL_RG8, GL_RG16} :
+ * - GL_RED, GL_RG, GL_RGBA, GL_LUMINANCE, GL_ALPHA, GL_LUMINANCE_ALPHA, GL_INTENSITY
+ * - {GL_R, GL_RG, GL_RGBA} X {8, 16, 16F, 32F, 8UI, 16UI, 32UI, 8I, 16I, 32I}
+ * - {GL_LUMINANCE, GL_ALPHA, GL_LUMINANCE_ALPHA, GL_INTENSITY} X
+ * {8, 16, 16F_ARB, 32F_ARB, 8UI_EXT, 16UI_EXT, 32UI_EXT, 8I_EXT, 16I_EXT, 32I_EXT}
+ *
+ * The following image classes are currently disallowed:
+ * - Textures with borders
+ * - Multisampled renderbuffers
+ *
+ * \param resource - Pointer to the returned object handle
+ * \param image    - name of texture or renderbuffer object to be registered
+ * \param target   - Identifies the type of object specified by \p image 
+ * \param flags    - Register flags
+ * 
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorOperatingSystem,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ *
+ * \sa 
+ * ::cudaGraphicsUnregisterResource,
+ * ::cudaGraphicsMapResources, 
+ * ::cudaGraphicsSubResourceGetMappedArray,
+ * ::cuGraphicsGLRegisterImage
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsGLRegisterImage(struct cudaGraphicsResource **resource, GLuint image, GLenum target, unsigned int flags);
+
+/**
+ * \brief Registers an OpenGL buffer object
+ *
+ * Registers the buffer object specified by \p buffer for access by
+ * CUDA.  A handle to the registered object is returned as \p
+ * resource.  The register flags \p flags specify the intended usage,
+ * as follows:
+ *
+ * - ::cudaGraphicsRegisterFlagsNone: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::cudaGraphicsRegisterFlagsReadOnly: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::cudaGraphicsRegisterFlagsWriteDiscard: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * \param resource - Pointer to the returned object handle
+ * \param buffer   - name of buffer object to be registered
+ * \param flags    - Register flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorOperatingSystem,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ *
+ * \sa 
+ * ::cudaGraphicsUnregisterResource,
+ * ::cudaGraphicsMapResources,
+ * ::cudaGraphicsResourceGetMappedPointer,
+ * ::cuGraphicsGLRegisterBuffer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsGLRegisterBuffer(struct cudaGraphicsResource **resource, GLuint buffer, unsigned int flags);
+
+#ifdef _WIN32
+#ifndef WGL_NV_gpu_affinity
+typedef void* HGPUNV;
+#endif
+
+/**
+ * \brief Gets the CUDA device associated with hGpu
+ *
+ * Returns the CUDA device associated with a hGpu, if applicable.
+ *
+ * \param device - Returns the device associated with hGpu, or -1 if hGpu is
+ * not a compute device.
+ * \param hGpu   - Handle to a GPU, as queried via WGL_NV_gpu_affinity
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ *
+ * \sa
+ * ::WGL_NV_gpu_affinity,
+ * ::cuWGLGetDevice
+ */
+extern __host__ cudaError_t CUDARTAPI cudaWGLGetDevice(int *device, HGPUNV hGpu);
+#endif
+
+/** @} */ /* END CUDART_OPENGL */
+
+/**
+ * \addtogroup CUDART_OPENGL_DEPRECATED OpenGL Interoperability [DEPRECATED]
+ * This section describes deprecated OpenGL interoperability functionality.
+ *
+ * @{
+ */
+
+/**
+ * CUDA GL Map Flags
+ */
+enum cudaGLMapFlags
+{
+  cudaGLMapFlagsNone         = 0,  /**< Default; Assume resource can be read/written */
+  cudaGLMapFlagsReadOnly     = 1,  /**< CUDA kernels will not write to this resource */
+  cudaGLMapFlagsWriteDiscard = 2   /**< CUDA kernels will only write to and will not read from this resource */
+};
+
+/**
+ * \brief Sets a CUDA device to use OpenGL interoperability
+ *
+ * \deprecated This function is deprecated as of CUDA 5.0. 
+ *
+ * This function is deprecated and should no longer be used.  It is
+ * no longer necessary to associate a CUDA device with an OpenGL
+ * context in order to achieve maximum interoperability performance.
+ *
+ * This function will immediately initialize the primary context on 
+ * \p device if needed.
+ *
+ * \param device - Device to use for OpenGL interoperability
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorSetOnActiveProcess
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsGLRegisterBuffer, ::cudaGraphicsGLRegisterImage
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLSetGLDevice(int device);
+
+/**
+ * \brief Registers a buffer object for access by CUDA
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Registers the buffer object of ID \p bufObj for access by
+ * CUDA. This function must be called before CUDA can map the buffer
+ * object.  The OpenGL context used to create the buffer, or another
+ * context from the same share group, must be bound to the current
+ * thread when this is called.
+ *
+ * \param bufObj - Buffer object ID to register
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInitializationError
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsGLRegisterBuffer
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLRegisterBufferObject(GLuint bufObj);
+
+/**
+ * \brief Maps a buffer object for access by CUDA
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Maps the buffer object of ID \p bufObj into the address space of
+ * CUDA and returns in \p *devPtr the base pointer of the resulting
+ * mapping.  The buffer must have previously been registered by
+ * calling ::cudaGLRegisterBufferObject().  While a buffer is mapped
+ * by CUDA, any OpenGL operation which references the buffer will
+ * result in undefined behavior.  The OpenGL context used to create
+ * the buffer, or another context from the same share group, must be
+ * bound to the current thread when this is called.
+ *
+ * All streams in the current thread are synchronized with the current
+ * GL context.
+ *
+ * \param devPtr - Returned device pointer to CUDA object
+ * \param bufObj - Buffer object ID to map
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMapBufferObjectFailed
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsMapResources
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLMapBufferObject(void **devPtr, GLuint bufObj);
+
+/**
+ * \brief Unmaps a buffer object for access by CUDA
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Unmaps the buffer object of ID \p bufObj for access by CUDA.  When
+ * a buffer is unmapped, the base address returned by
+ * ::cudaGLMapBufferObject() is invalid and subsequent references to
+ * the address result in undefined behavior.  The OpenGL context used
+ * to create the buffer, or another context from the same share group,
+ * must be bound to the current thread when this is called.
+ *
+ * All streams in the current thread are synchronized with the current
+ * GL context.
+ *
+ * \param bufObj - Buffer object to unmap
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnmapBufferObjectFailed
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsUnmapResources
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLUnmapBufferObject(GLuint bufObj);
+
+/**
+ * \brief Unregisters a buffer object for access by CUDA
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Unregisters the buffer object of ID \p bufObj for access by CUDA
+ * and releases any CUDA resources associated with the buffer.  Once a
+ * buffer is unregistered, it may no longer be mapped by CUDA.  The GL
+ * context used to create the buffer, or another context from the
+ * same share group, must be bound to the current thread when this is
+ * called.
+ *
+ * \param bufObj - Buffer object to unregister
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsUnregisterResource
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLUnregisterBufferObject(GLuint bufObj);
+
+/**
+ * \brief Set usage flags for mapping an OpenGL buffer
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Set flags for mapping the OpenGL buffer \p bufObj
+ *
+ * Changes to flags will take effect the next time \p bufObj is mapped.
+ * The \p flags argument may be any of the following:
+ *
+ * - ::cudaGLMapFlagsNone: Specifies no hints about how this buffer will
+ * be used. It is therefore assumed that this buffer will be read from and
+ * written to by CUDA kernels. This is the default value.
+ * - ::cudaGLMapFlagsReadOnly: Specifies that CUDA kernels which access this
+ * buffer will not write to the buffer.
+ * - ::cudaGLMapFlagsWriteDiscard: Specifies that CUDA kernels which access
+ * this buffer will not read from the buffer and will write over the
+ * entire contents of the buffer, so none of the data previously stored in
+ * the buffer will be preserved.
+ *
+ * If \p bufObj has not been registered for use with CUDA, then
+ * ::cudaErrorInvalidResourceHandle is returned. If \p bufObj is presently
+ * mapped for access by CUDA, then ::cudaErrorUnknown is returned.
+ *
+ * \param bufObj    - Registered buffer object to set flags for
+ * \param flags     - Parameters for buffer mapping
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsResourceSetMapFlags
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLSetBufferObjectMapFlags(GLuint bufObj, unsigned int flags); 
+
+/**
+ * \brief Maps a buffer object for access by CUDA
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Maps the buffer object of ID \p bufObj into the address space of
+ * CUDA and returns in \p *devPtr the base pointer of the resulting
+ * mapping.  The buffer must have previously been registered by
+ * calling ::cudaGLRegisterBufferObject().  While a buffer is mapped
+ * by CUDA, any OpenGL operation which references the buffer will
+ * result in undefined behavior.  The OpenGL context used to create
+ * the buffer, or another context from the same share group, must be
+ * bound to the current thread when this is called.
+ *
+ * Stream /p stream is synchronized with the current GL context.
+ *
+ * \param devPtr - Returned device pointer to CUDA object
+ * \param bufObj - Buffer object ID to map
+ * \param stream - Stream to synchronize
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMapBufferObjectFailed
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsMapResources
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLMapBufferObjectAsync(void **devPtr, GLuint bufObj, cudaStream_t stream);
+
+/**
+ * \brief Unmaps a buffer object for access by CUDA
+ *
+ * \deprecated This function is deprecated as of CUDA 3.0. 
+ *
+ * Unmaps the buffer object of ID \p bufObj for access by CUDA.  When
+ * a buffer is unmapped, the base address returned by
+ * ::cudaGLMapBufferObject() is invalid and subsequent references to
+ * the address result in undefined behavior.  The OpenGL context used
+ * to create the buffer, or another context from the same share group,
+ * must be bound to the current thread when this is called.
+ *
+ * Stream /p stream is synchronized with the current GL context.
+ *
+ * \param bufObj - Buffer object to unmap
+ * \param stream - Stream to synchronize
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnmapBufferObjectFailed
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsUnmapResources
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaGLUnmapBufferObjectAsync(GLuint bufObj, cudaStream_t stream);
+
+/** @} */ /* END CUDART_OPENGL_DEPRECATED */
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#undef __CUDA_DEPRECATED
+
+#endif /* __CUDA_GL_INTEROP_H__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_occupancy.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_occupancy.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb543adff62f0212ef5423f1e710dcb0d70108a6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_occupancy.h
@@ -0,0 +1,2094 @@
+/*
+ * Copyright 1993-2017 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/**
+ * CUDA Occupancy Calculator
+ *
+ * NAME
+ *
+ *   cudaOccMaxActiveBlocksPerMultiprocessor,
+ *   cudaOccMaxPotentialOccupancyBlockSize,
+ *   cudaOccMaxPotentialOccupancyBlockSizeVariableSMem
+ *   cudaOccAvailableDynamicSMemPerBlock
+ *
+ * DESCRIPTION
+ *
+ *   The CUDA occupancy calculator provides a standalone, programmatical
+ *   interface to compute the occupancy of a function on a device. It can also
+ *   provide occupancy-oriented launch configuration suggestions.
+ *
+ *   The function and device are defined by the user through
+ *   cudaOccFuncAttributes, cudaOccDeviceProp, and cudaOccDeviceState
+ *   structures. All APIs require all 3 of them.
+ *
+ *   See the structure definition for more details about the device / function
+ *   descriptors.
+ *
+ *   See each API's prototype for API usage.
+ *
+ * COMPATIBILITY
+ *
+ *   The occupancy calculator will be updated on each major CUDA toolkit
+ *   release. It does not provide forward compatibility, i.e. new hardwares
+ *   released after this implementation's release will not be supported.
+ *
+ * NOTE
+ *
+ *   If there is access to CUDA runtime, and the sole intent is to calculate
+ *   occupancy related values on one of the accessible CUDA devices, using CUDA
+ *   runtime's occupancy calculation APIs is recommended.
+ *
+ */
+
+#ifndef __cuda_occupancy_h__
+#define __cuda_occupancy_h__
+
+#include <stddef.h>
+#include <limits.h>
+#include <string.h>
+
+
+// __OCC_INLINE will be undefined at the end of this header
+//
+#ifdef __CUDACC__
+#define __OCC_INLINE inline __host__ __device__
+#elif defined _MSC_VER
+#define __OCC_INLINE __inline
+#else // GNUCC assumed
+#define __OCC_INLINE inline
+#endif
+
+enum cudaOccError_enum {
+    CUDA_OCC_SUCCESS              = 0,  // no error encountered
+    CUDA_OCC_ERROR_INVALID_INPUT  = 1,  // input parameter is invalid
+    CUDA_OCC_ERROR_UNKNOWN_DEVICE = 2,  // requested device is not supported in
+                                        // current implementation or device is
+                                        // invalid
+};
+typedef enum cudaOccError_enum       cudaOccError;
+
+typedef struct cudaOccResult         cudaOccResult;
+typedef struct cudaOccDeviceProp     cudaOccDeviceProp;
+typedef struct cudaOccFuncAttributes cudaOccFuncAttributes;
+typedef struct cudaOccDeviceState    cudaOccDeviceState;
+
+/**
+ * The CUDA occupancy calculator computes the occupancy of the function
+ * described by attributes with the given block size (blockSize), static device
+ * properties (properties), dynamic device states (states) and per-block dynamic
+ * shared memory allocation (dynamicSMemSize) in bytes, and output it through
+ * result along with other useful information. The occupancy is computed in
+ * terms of the maximum number of active blocks per multiprocessor. The user can
+ * then convert it to other metrics, such as number of active warps.
+ *
+ * RETURN VALUE
+ *
+ * The occupancy and related information is returned through result.
+ *
+ * If result->activeBlocksPerMultiprocessor is 0, then the given parameter
+ * combination cannot run on the device.
+ *
+ * ERRORS
+ *
+ *     CUDA_OCC_ERROR_INVALID_INPUT   input parameter is invalid.
+ *     CUDA_OCC_ERROR_UNKNOWN_DEVICE  requested device is not supported in
+ *     current implementation or device is invalid
+ */
+static __OCC_INLINE
+cudaOccError cudaOccMaxActiveBlocksPerMultiprocessor(
+    cudaOccResult               *result,           // out
+    const cudaOccDeviceProp     *properties,       // in
+    const cudaOccFuncAttributes *attributes,       // in
+    const cudaOccDeviceState    *state,            // in
+    int                          blockSize,        // in
+    size_t                       dynamicSmemSize); // in
+
+/**
+ * The CUDA launch configurator C API suggests a grid / block size pair (in
+ * minGridSize and blockSize) that achieves the best potential occupancy
+ * (i.e. maximum number of active warps with the smallest number of blocks) for
+ * the given function described by attributes, on a device described by
+ * properties with settings in state.
+ *
+ * If per-block dynamic shared memory allocation is not needed, the user should
+ * leave both blockSizeToDynamicSMemSize and dynamicSMemSize as 0.
+ *
+ * If per-block dynamic shared memory allocation is needed, then if the dynamic
+ * shared memory size is constant regardless of block size, the size should be
+ * passed through dynamicSMemSize, and blockSizeToDynamicSMemSize should be
+ * NULL.
+ *
+ * Otherwise, if the per-block dynamic shared memory size varies with different
+ * block sizes, the user needs to provide a pointer to an unary function through
+ * blockSizeToDynamicSMemSize that computes the dynamic shared memory needed by
+ * a block of the function for any given block size. dynamicSMemSize is
+ * ignored. An example signature is:
+ *
+ *    // Take block size, returns dynamic shared memory needed
+ *    size_t blockToSmem(int blockSize);
+ *
+ * RETURN VALUE
+ *
+ * The suggested block size and the minimum number of blocks needed to achieve
+ * the maximum occupancy are returned through blockSize and minGridSize.
+ *
+ * If *blockSize is 0, then the given combination cannot run on the device.
+ *
+ * ERRORS
+ *
+ *     CUDA_OCC_ERROR_INVALID_INPUT   input parameter is invalid.
+ *     CUDA_OCC_ERROR_UNKNOWN_DEVICE  requested device is not supported in
+ *     current implementation or device is invalid
+ *
+ */
+static __OCC_INLINE
+cudaOccError cudaOccMaxPotentialOccupancyBlockSize(
+    int                         *minGridSize,      // out
+    int                         *blockSize,        // out
+    const cudaOccDeviceProp     *properties,       // in
+    const cudaOccFuncAttributes *attributes,       // in
+    const cudaOccDeviceState    *state,            // in
+    size_t                     (*blockSizeToDynamicSMemSize)(int), // in
+    size_t                       dynamicSMemSize); // in
+
+/**
+ * The CUDA launch configurator C++ API suggests a grid / block size pair (in
+ * minGridSize and blockSize) that achieves the best potential occupancy
+ * (i.e. the maximum number of active warps with the smallest number of blocks)
+ * for the given function described by attributes, on a device described by
+ * properties with settings in state.
+ *
+ * If per-block dynamic shared memory allocation is 0 or constant regardless of
+ * block size, the user can use cudaOccMaxPotentialOccupancyBlockSize to
+ * configure the launch. A constant dynamic shared memory allocation size in
+ * bytes can be passed through dynamicSMemSize.
+ *
+ * Otherwise, if the per-block dynamic shared memory size varies with different
+ * block sizes, the user needs to use
+ * cudaOccMaxPotentialOccupancyBlockSizeVariableSmem instead, and provide a
+ * functor / pointer to an unary function (blockSizeToDynamicSMemSize) that
+ * computes the dynamic shared memory needed by func for any given block
+ * size. An example signature is:
+ *
+ *  // Take block size, returns per-block dynamic shared memory needed
+ *  size_t blockToSmem(int blockSize);
+ *
+ * RETURN VALUE
+ *
+ * The suggested block size and the minimum number of blocks needed to achieve
+ * the maximum occupancy are returned through blockSize and minGridSize.
+ *
+ * If *blockSize is 0, then the given combination cannot run on the device.
+ *
+ * ERRORS
+ *
+ *     CUDA_OCC_ERROR_INVALID_INPUT   input parameter is invalid.
+ *     CUDA_OCC_ERROR_UNKNOWN_DEVICE  requested device is not supported in
+ *     current implementation or device is invalid
+ *
+ */
+
+#if defined(__cplusplus)
+namespace {
+
+__OCC_INLINE
+cudaOccError cudaOccMaxPotentialOccupancyBlockSize(
+    int                         *minGridSize,          // out
+    int                         *blockSize,            // out
+    const cudaOccDeviceProp     *properties,           // in
+    const cudaOccFuncAttributes *attributes,           // in
+    const cudaOccDeviceState    *state,                // in
+    size_t                       dynamicSMemSize = 0); // in
+
+template <typename UnaryFunction>
+__OCC_INLINE
+cudaOccError cudaOccMaxPotentialOccupancyBlockSizeVariableSMem(
+    int                         *minGridSize,          // out
+    int                         *blockSize,            // out
+    const cudaOccDeviceProp     *properties,           // in
+    const cudaOccFuncAttributes *attributes,           // in
+    const cudaOccDeviceState    *state,                // in
+    UnaryFunction                blockSizeToDynamicSMemSize); // in
+
+} // namespace anonymous
+#endif // defined(__cplusplus)
+
+/**
+ *
+ * The CUDA dynamic shared memory calculator computes the maximum size of 
+ * per-block dynamic shared memory if we want to place numBlocks blocks
+ * on an SM.
+ *
+ * RETURN VALUE
+ *
+ * Returns in *dynamicSmemSize the maximum size of dynamic shared memory to allow 
+ * numBlocks blocks per SM.
+ *
+ * ERRORS
+ *
+ *     CUDA_OCC_ERROR_INVALID_INPUT   input parameter is invalid.
+ *     CUDA_OCC_ERROR_UNKNOWN_DEVICE  requested device is not supported in
+ *     current implementation or device is invalid
+ *
+ */
+static __OCC_INLINE
+cudaOccError cudaOccAvailableDynamicSMemPerBlock(
+    size_t                      *dynamicSmemSize,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    int                         numBlocks,
+    int                         blockSize);
+
+/**
+ * Data structures
+ *
+ * These structures are subject to change for future architecture and CUDA
+ * releases. C users should initialize the structure as {0}.
+ *
+ */
+
+/**
+ * Device descriptor
+ *
+ * This structure describes a device.
+ */
+struct cudaOccDeviceProp {
+    int    computeMajor;                // Compute capability major version
+    int    computeMinor;                // Compute capability minor
+                                        // version. None supported minor version
+                                        // may cause error
+    int    maxThreadsPerBlock;          // Maximum number of threads per block
+    int    maxThreadsPerMultiprocessor; // Maximum number of threads per SM
+                                        // i.e. (Max. number of warps) x (warp
+                                        // size)
+    int    regsPerBlock;                // Maximum number of registers per block
+    int    regsPerMultiprocessor;       // Maximum number of registers per SM
+    int    warpSize;                    // Warp size
+    size_t sharedMemPerBlock;           // Maximum shared memory size per block
+    size_t sharedMemPerMultiprocessor;  // Maximum shared memory size per SM
+    int    numSms;                      // Number of SMs available
+    size_t sharedMemPerBlockOptin;      // Maximum optin shared memory size per block
+    size_t reservedSharedMemPerBlock;   // Shared memory per block reserved by driver
+
+#ifdef __cplusplus
+    // This structure can be converted from a cudaDeviceProp structure for users
+    // that use this header in their CUDA applications.
+    //
+    // If the application have access to the CUDA Runtime API, the application
+    // can obtain the device properties of a CUDA device through
+    // cudaGetDeviceProperties, and initialize a cudaOccDeviceProp with the
+    // cudaDeviceProp structure.
+    //
+    // Example:
+    /*
+     {
+         cudaDeviceProp prop;
+
+         cudaGetDeviceProperties(&prop, ...);
+
+         cudaOccDeviceProp occProp = prop;
+
+         ...
+
+         cudaOccMaxPotentialOccupancyBlockSize(..., &occProp, ...);
+     }
+     */
+    //
+    template<typename DeviceProp>
+    __OCC_INLINE
+    cudaOccDeviceProp(const DeviceProp &props)
+    :   computeMajor                (props.major),
+        computeMinor                (props.minor),
+        maxThreadsPerBlock          (props.maxThreadsPerBlock),
+        maxThreadsPerMultiprocessor (props.maxThreadsPerMultiProcessor),
+        regsPerBlock                (props.regsPerBlock),
+        regsPerMultiprocessor       (props.regsPerMultiprocessor),
+        warpSize                    (props.warpSize),
+        sharedMemPerBlock           (props.sharedMemPerBlock),
+        sharedMemPerMultiprocessor  (props.sharedMemPerMultiprocessor),
+        numSms                      (props.multiProcessorCount),
+        sharedMemPerBlockOptin      (props.sharedMemPerBlockOptin),
+        reservedSharedMemPerBlock   (props.reservedSharedMemPerBlock)
+    {}
+
+    __OCC_INLINE
+    cudaOccDeviceProp()
+    :   computeMajor                (0),
+        computeMinor                (0),
+        maxThreadsPerBlock          (0),
+        maxThreadsPerMultiprocessor (0),
+        regsPerBlock                (0),
+        regsPerMultiprocessor       (0),
+        warpSize                    (0),
+        sharedMemPerBlock           (0),
+        sharedMemPerMultiprocessor  (0),
+        numSms                      (0),
+        sharedMemPerBlockOptin      (0),
+        reservedSharedMemPerBlock   (0)
+    {}
+#endif // __cplusplus
+};
+
+/**
+ * Partitioned global caching option
+ */
+typedef enum cudaOccPartitionedGCConfig_enum {
+    PARTITIONED_GC_OFF,        // Disable partitioned global caching
+    PARTITIONED_GC_ON,         // Prefer partitioned global caching
+    PARTITIONED_GC_ON_STRICT   // Force partitioned global caching
+} cudaOccPartitionedGCConfig;
+
+/**
+ * Per function opt in maximum dynamic shared memory limit
+ */
+typedef enum cudaOccFuncShmemConfig_enum {
+    FUNC_SHMEM_LIMIT_DEFAULT,   // Default shmem limit
+    FUNC_SHMEM_LIMIT_OPTIN,     // Use the optin shmem limit
+} cudaOccFuncShmemConfig;
+
+/**
+ * Function descriptor
+ *
+ * This structure describes a CUDA function.
+ */
+struct cudaOccFuncAttributes {
+    int maxThreadsPerBlock; // Maximum block size the function can work with. If
+                            // unlimited, use INT_MAX or any value greater than
+                            // or equal to maxThreadsPerBlock of the device
+    int numRegs;            // Number of registers used. When the function is
+                            // launched on device, the register count may change
+                            // due to internal tools requirements.
+    size_t sharedSizeBytes; // Number of static shared memory used
+
+    cudaOccPartitionedGCConfig partitionedGCConfig; 
+                            // Partitioned global caching is required to enable
+                            // caching on certain chips, such as sm_52
+                            // devices. Partitioned global caching can be
+                            // automatically disabled if the occupancy
+                            // requirement of the launch cannot support caching.
+                            //
+                            // To override this behavior with caching on and
+                            // calculate occupancy strictly according to the
+                            // preference, set partitionedGCConfig to
+                            // PARTITIONED_GC_ON_STRICT. This is especially
+                            // useful for experimenting and finding launch
+                            // configurations (MaxPotentialOccupancyBlockSize)
+                            // that allow global caching to take effect.
+                            //
+                            // This flag only affects the occupancy calculation.
+
+    cudaOccFuncShmemConfig shmemLimitConfig;
+                            // Certain chips like sm_70 allow a user to opt into
+                            // a higher per block limit of dynamic shared memory
+                            // This optin is performed on a per function basis
+                            // using the cuFuncSetAttribute function
+
+    size_t maxDynamicSharedSizeBytes;
+                            // User set limit on maximum dynamic shared memory
+                            // usable by the kernel
+                            // This limit is set using the cuFuncSetAttribute
+                            // function.
+
+    int numBlockBarriers;   // Number of block barriers used (default to 1)
+#ifdef __cplusplus
+    // This structure can be converted from a cudaFuncAttributes structure for
+    // users that use this header in their CUDA applications.
+    //
+    // If the application have access to the CUDA Runtime API, the application
+    // can obtain the function attributes of a CUDA kernel function through
+    // cudaFuncGetAttributes, and initialize a cudaOccFuncAttributes with the
+    // cudaFuncAttributes structure.
+    //
+    // Example:
+    /*
+      __global__ void foo() {...}
+
+      ...
+
+      {
+          cudaFuncAttributes attr;
+
+          cudaFuncGetAttributes(&attr, foo);
+
+          cudaOccFuncAttributes occAttr = attr;
+
+          ...
+
+          cudaOccMaxPotentialOccupancyBlockSize(..., &occAttr, ...);
+      }
+     */
+    //
+    template<typename FuncAttributes>
+    __OCC_INLINE
+    cudaOccFuncAttributes(const FuncAttributes &attr)
+    :   maxThreadsPerBlock  (attr.maxThreadsPerBlock),
+        numRegs             (attr.numRegs),
+        sharedSizeBytes     (attr.sharedSizeBytes),
+        partitionedGCConfig (PARTITIONED_GC_OFF),
+        shmemLimitConfig    (FUNC_SHMEM_LIMIT_OPTIN),
+        maxDynamicSharedSizeBytes (attr.maxDynamicSharedSizeBytes),
+        numBlockBarriers    (1)
+    {}
+
+    __OCC_INLINE
+    cudaOccFuncAttributes()
+    :   maxThreadsPerBlock  (0),
+        numRegs             (0),
+        sharedSizeBytes     (0),
+        partitionedGCConfig (PARTITIONED_GC_OFF),
+        shmemLimitConfig    (FUNC_SHMEM_LIMIT_DEFAULT),
+        maxDynamicSharedSizeBytes (0),
+        numBlockBarriers    (0)
+    {}
+#endif
+};
+
+typedef enum cudaOccCacheConfig_enum {
+    CACHE_PREFER_NONE   = 0x00, // no preference for shared memory or L1 (default)
+    CACHE_PREFER_SHARED = 0x01, // prefer larger shared memory and smaller L1 cache
+    CACHE_PREFER_L1     = 0x02, // prefer larger L1 cache and smaller shared memory
+    CACHE_PREFER_EQUAL  = 0x03  // prefer equal sized L1 cache and shared memory
+} cudaOccCacheConfig;
+
+typedef enum cudaOccCarveoutConfig_enum {
+    SHAREDMEM_CARVEOUT_DEFAULT       = -1,  // no preference for shared memory or L1 (default)
+    SHAREDMEM_CARVEOUT_MAX_SHARED    = 100, // prefer maximum available shared memory, minimum L1 cache
+    SHAREDMEM_CARVEOUT_MAX_L1        = 0,    // prefer maximum available L1 cache, minimum shared memory
+    SHAREDMEM_CARVEOUT_HALF          = 50   // prefer half of maximum available shared memory, with the rest as L1 cache
+} cudaOccCarveoutConfig;
+
+/**
+ * Device state descriptor
+ *
+ * This structure describes device settings that affect occupancy calculation.
+ */
+struct cudaOccDeviceState
+{
+    // Cache / shared memory split preference. Deprecated on Volta 
+    cudaOccCacheConfig cacheConfig; 
+    // Shared memory / L1 split preference. Supported on only Volta
+    int carveoutConfig;
+
+#ifdef __cplusplus
+    __OCC_INLINE
+    cudaOccDeviceState()
+    :   cacheConfig     (CACHE_PREFER_NONE),
+        carveoutConfig  (SHAREDMEM_CARVEOUT_DEFAULT)
+    {}
+#endif
+};
+
+typedef enum cudaOccLimitingFactor_enum {
+                                    // Occupancy limited due to:
+    OCC_LIMIT_WARPS         = 0x01, // - warps available
+    OCC_LIMIT_REGISTERS     = 0x02, // - registers available
+    OCC_LIMIT_SHARED_MEMORY = 0x04, // - shared memory available
+    OCC_LIMIT_BLOCKS        = 0x08, // - blocks available
+    OCC_LIMIT_BARRIERS      = 0x10  // - barrier available
+} cudaOccLimitingFactor;
+
+/**
+ * Occupancy output
+ *
+ * This structure contains occupancy calculator's output.
+ */
+struct cudaOccResult {
+    int activeBlocksPerMultiprocessor; // Occupancy
+    unsigned int limitingFactors;      // Factors that limited occupancy. A bit
+                                       // field that counts the limiting
+                                       // factors, see cudaOccLimitingFactor
+    int blockLimitRegs;                // Occupancy due to register
+                                       // usage, INT_MAX if the kernel does not
+                                       // use any register.
+    int blockLimitSharedMem;           // Occupancy due to shared memory
+                                       // usage, INT_MAX if the kernel does not
+                                       // use shared memory.
+    int blockLimitWarps;               // Occupancy due to block size limit
+    int blockLimitBlocks;              // Occupancy due to maximum number of blocks
+                                       // managable per SM
+    int blockLimitBarriers;            // Occupancy due to block barrier usage
+    int allocatedRegistersPerBlock;    // Actual number of registers allocated per
+                                       // block
+    size_t allocatedSharedMemPerBlock; // Actual size of shared memory allocated
+                                       // per block
+    cudaOccPartitionedGCConfig partitionedGCConfig;
+                                       // Report if partitioned global caching
+                                       // is actually enabled.
+};
+
+/**
+ * Partitioned global caching support
+ *
+ * See cudaOccPartitionedGlobalCachingModeSupport
+ */
+typedef enum cudaOccPartitionedGCSupport_enum {
+    PARTITIONED_GC_NOT_SUPPORTED,  // Partitioned global caching is not supported
+    PARTITIONED_GC_SUPPORTED,      // Partitioned global caching is supported
+} cudaOccPartitionedGCSupport;
+
+/**
+ * Implementation
+ */
+
+/**
+ * Max compute capability supported
+ */
+
+#define __CUDA_OCC_MAJOR__ 12
+#define __CUDA_OCC_MINOR__ 0
+
+//////////////////////////////////////////
+//    Mathematical Helper Functions     //
+//////////////////////////////////////////
+
+static __OCC_INLINE int __occMin(int lhs, int rhs)
+{
+    return rhs < lhs ? rhs : lhs;
+}
+
+static __OCC_INLINE int __occDivideRoundUp(int x, int y)
+{
+    return (x + (y - 1)) / y;
+}
+
+static __OCC_INLINE int __occRoundUp(int x, int y)
+{
+    return y * __occDivideRoundUp(x, y);
+}
+
+//////////////////////////////////////////
+//      Architectural Properties        //
+//////////////////////////////////////////
+
+/**
+ * Granularity of shared memory allocation
+ */
+static __OCC_INLINE cudaOccError cudaOccSMemAllocationGranularity(int *limit, const cudaOccDeviceProp *properties)
+{
+    int value;
+
+    switch(properties->computeMajor) {
+        case 3:
+        case 5:
+        case 6:
+        case 7:
+            value = 256;
+            break;
+        case 8:
+        case 9:
+        case 10:
+        case 12:
+            value = 128;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    *limit = value;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/**
+ * Maximum number of registers per thread
+ */
+static __OCC_INLINE cudaOccError cudaOccRegAllocationMaxPerThread(int *limit, const cudaOccDeviceProp *properties)
+{
+    int value;
+
+    switch(properties->computeMajor) {
+        case 3:
+        case 5:
+        case 6:
+            value = 255;
+            break;
+        case 7:
+        case 8:
+        case 9:
+        case 10:
+        case 12:
+            value = 256;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    *limit = value;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/**
+ * Granularity of register allocation
+ */
+static __OCC_INLINE cudaOccError cudaOccRegAllocationGranularity(int *limit, const cudaOccDeviceProp *properties)
+{
+    int value;
+
+    switch(properties->computeMajor) {
+        case 3:
+        case 5:
+        case 6:
+        case 7:
+        case 8:
+        case 9:
+        case 10:
+        case 12:
+            value = 256;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    *limit = value;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/**
+ * Number of sub-partitions
+ */
+static __OCC_INLINE cudaOccError cudaOccSubPartitionsPerMultiprocessor(int *limit, const cudaOccDeviceProp *properties)
+{
+    int value;
+
+    switch(properties->computeMajor) {
+        case 3:
+        case 5:
+        case 7:
+        case 8:
+        case 9:
+        case 10:
+        case 12:
+            value = 4;
+            break;
+        case 6:
+            value = properties->computeMinor ? 4 : 2;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    *limit = value;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+
+/**
+ * Maximum number of blocks that can run simultaneously on a multiprocessor
+ */
+static __OCC_INLINE cudaOccError cudaOccMaxBlocksPerMultiprocessor(int* limit, const cudaOccDeviceProp *properties)
+{
+    int value;
+
+    switch(properties->computeMajor) {
+        case 3:
+            value = 16;
+            break;
+        case 5:
+        case 6:
+            value = 32;
+            break;
+        case 7: {
+            int isTuring = properties->computeMinor == 5;
+            value = (isTuring) ? 16 : 32;
+            break;
+        }
+        case 8:
+            if (properties->computeMinor == 0) {
+                value = 32;
+            }
+            else if (properties->computeMinor == 9) {
+                value = 24;
+            }
+            else {
+                value = 16;
+            }
+            break;
+        case 9:
+            value = 32;
+            break;
+        case 10:
+            switch(properties->computeMinor) {
+                case 1 :
+                    value = 24;
+                    break;
+                case 0 : /* explicitly added to avoid build failure in WDDM driver components */
+                default :
+                    value = 32;
+            }
+            break;
+        case 12:
+            value = 24;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    *limit = value;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/** 
+ * Align up shared memory based on compute major configurations
+ */
+static __OCC_INLINE cudaOccError cudaOccAlignUpShmemSizeVoltaPlus(size_t *shMemSize, const cudaOccDeviceProp *properties)
+{
+    // Volta and Turing have shared L1 cache / shared memory, and support cache
+    // configuration to trade one for the other. These values are needed to
+    // map carveout config ratio to the next available architecture size
+    size_t size = *shMemSize;
+
+    switch (properties->computeMajor) {
+    case 7: {
+        // Turing supports 32KB and 64KB shared mem.
+        int isTuring = properties->computeMinor == 5;
+        if (isTuring) {
+            if      (size <= 32 * 1024) {
+                *shMemSize = 32 * 1024;
+            }
+            else if (size <= 64 * 1024) {
+                *shMemSize = 64 * 1024;
+            }
+            else {
+                return CUDA_OCC_ERROR_INVALID_INPUT;
+            }
+        }
+        // Volta supports 0KB, 8KB, 16KB, 32KB, 64KB, and 96KB shared mem.
+        else {
+            if      (size == 0) {
+                *shMemSize = 0;
+            }
+            else if (size <= 8 * 1024) {
+                *shMemSize = 8 * 1024;
+            }
+            else if (size <= 16 * 1024) {
+                *shMemSize = 16 * 1024;
+            }
+            else if (size <= 32 * 1024) {
+                *shMemSize = 32 * 1024;
+            }
+            else if (size <= 64 * 1024) {
+                *shMemSize = 64 * 1024;
+            }
+            else if (size <= 96 * 1024) {
+                *shMemSize = 96 * 1024;
+            }
+            else {
+                return CUDA_OCC_ERROR_INVALID_INPUT;
+            }
+        }
+        break;
+    }
+    case 8:
+        if (properties->computeMinor == 0 || properties->computeMinor == 7) {
+            if      (size == 0) {
+                *shMemSize = 0;
+            }
+            else if (size <= 8 * 1024) {
+                *shMemSize = 8 * 1024;
+            }
+            else if (size <= 16 * 1024) {
+                *shMemSize = 16 * 1024;
+            }
+            else if (size <= 32 * 1024) {
+                *shMemSize = 32 * 1024;
+            }
+            else if (size <= 64 * 1024) {
+                *shMemSize = 64 * 1024;
+            }
+            else if (size <= 100 * 1024) {
+                *shMemSize = 100 * 1024;
+            }
+            else if (size <= 132 * 1024) {
+                *shMemSize = 132 * 1024;
+            }
+            else if (size <= 164 * 1024) {
+                *shMemSize = 164 * 1024;
+            }
+            else {
+                return CUDA_OCC_ERROR_INVALID_INPUT;
+            }
+        }
+        else {
+            if      (size == 0) {
+                *shMemSize = 0;
+            }
+            else if (size <= 8 * 1024) {
+                *shMemSize = 8 * 1024;
+            }
+            else if (size <= 16 * 1024) {
+                *shMemSize = 16 * 1024;
+            }
+            else if (size <= 32 * 1024) {
+                *shMemSize = 32 * 1024;
+            }
+            else if (size <= 64 * 1024) {
+                *shMemSize = 64 * 1024;
+            }
+            else if (size <= 100 * 1024) {
+                *shMemSize = 100 * 1024;
+            }
+            else {
+                return CUDA_OCC_ERROR_INVALID_INPUT;
+            }
+        }
+        break;
+    case 9: {
+        if      (size == 0) {
+            *shMemSize = 0;
+        }
+        else if (size <= 8 * 1024) {
+            *shMemSize = 8 * 1024;
+        }
+        else if (size <= 16 * 1024) {
+            *shMemSize = 16 * 1024;
+        }
+        else if (size <= 32 * 1024) {
+            *shMemSize = 32 * 1024;
+        }
+        else if (size <= 64 * 1024) {
+            *shMemSize = 64 * 1024;
+        }
+        else if (size <= 100 * 1024) {
+            *shMemSize = 100 * 1024;
+        }
+        else if (size <= 132 * 1024) {
+            *shMemSize = 132 * 1024;
+        }
+        else if (size <= 164 * 1024) {
+            *shMemSize = 164 * 1024;
+        }
+        else if (size <= 196 * 1024) {
+            *shMemSize = 196 * 1024;
+        }
+        else if (size <= 228 * 1024) {
+            *shMemSize = 228 * 1024;
+        }
+        else {
+            return CUDA_OCC_ERROR_INVALID_INPUT;
+        }
+        break;
+    }
+    case 10: {
+        switch (properties->computeMinor) {
+    // GB10x GPUs in Blackwell family have the below compute minors and corresponding
+    // shared memory configs
+            case 0:
+            case 1:
+                if      (size == 0) {
+                    *shMemSize = 0;
+                }
+                else if (size <= 8 * 1024) {
+                    *shMemSize = 8 * 1024;
+                }
+                else if (size <= 16 * 1024) {
+                    *shMemSize = 16 * 1024;
+                }
+                else if (size <= 32 * 1024) {
+                    *shMemSize = 32 * 1024;
+                }
+                else if (size <= 64 * 1024) {
+                    *shMemSize = 64 * 1024;
+                }
+                else if (size <= 100 * 1024) {
+                    *shMemSize = 100 * 1024;
+                }
+                else if (size <= 132 * 1024) {
+                    *shMemSize = 132 * 1024;
+                }
+                else if (size <= 164 * 1024) {
+                    *shMemSize = 164 * 1024;
+                }
+                else if (size <= 196 * 1024) {
+                    *shMemSize = 196 * 1024;
+                }
+                else if (size <= 228 * 1024) {
+                    *shMemSize = 228 * 1024;
+                }
+                else {
+                    return CUDA_OCC_ERROR_INVALID_INPUT;
+                }
+                break;
+            default:
+                return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+        }
+        break;
+    }
+    case 12: {
+        switch (properties->computeMinor) {
+            case 0:
+                if      (size == 0) {
+                    *shMemSize = 0;
+                }
+                else if (size <= 8 * 1024) {
+                    *shMemSize = 8 * 1024;
+                }
+                else if (size <= 16 * 1024) {
+                    *shMemSize = 16 * 1024;
+                }
+                else if (size <= 32 * 1024) {
+                    *shMemSize = 32 * 1024;
+                }
+                else if (size <= 64 * 1024) {
+                    *shMemSize = 64 * 1024;
+                }
+                else if (size <= 100 * 1024) {
+                    *shMemSize = 100 * 1024;
+                }
+                else {
+                    return CUDA_OCC_ERROR_INVALID_INPUT;
+                }
+                break;
+            default:
+                return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+        }
+        break;
+    }
+    break;
+    default:
+        return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/**
+ * Shared memory based on the new carveoutConfig API introduced with Volta
+ */
+static __OCC_INLINE cudaOccError cudaOccSMemPreferenceVoltaPlus(size_t *limit, const cudaOccDeviceProp *properties, const cudaOccDeviceState *state)
+{
+    cudaOccError status = CUDA_OCC_SUCCESS;
+    size_t preferenceShmemSize;
+
+    // CUDA 9.0 introduces a new API to set shared memory - L1 configuration on supported
+    // devices. This preference will take precedence over the older cacheConfig setting.
+    // Map cacheConfig to its effective preference value.
+    int effectivePreference = state->carveoutConfig;
+    if ((effectivePreference < SHAREDMEM_CARVEOUT_DEFAULT) || (effectivePreference > SHAREDMEM_CARVEOUT_MAX_SHARED)) {
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+    
+    if (effectivePreference == SHAREDMEM_CARVEOUT_DEFAULT) {
+        switch (state->cacheConfig)
+        {
+        case CACHE_PREFER_L1:
+            effectivePreference = SHAREDMEM_CARVEOUT_MAX_L1;
+            break;
+        case CACHE_PREFER_SHARED:
+            effectivePreference = SHAREDMEM_CARVEOUT_MAX_SHARED;
+            break;
+        case CACHE_PREFER_EQUAL:
+            effectivePreference = SHAREDMEM_CARVEOUT_HALF;
+            break;
+        default:
+            effectivePreference = SHAREDMEM_CARVEOUT_DEFAULT;
+            break;
+        }
+    }
+
+    if (effectivePreference == SHAREDMEM_CARVEOUT_DEFAULT) {
+        preferenceShmemSize = properties->sharedMemPerMultiprocessor;
+    }
+    else {
+        preferenceShmemSize = (size_t) (effectivePreference * properties->sharedMemPerMultiprocessor) / 100;
+    }
+
+    status = cudaOccAlignUpShmemSizeVoltaPlus(&preferenceShmemSize, properties);
+    *limit = preferenceShmemSize;
+    return status;
+}
+
+/**
+ * Shared memory based on the cacheConfig
+ */
+static __OCC_INLINE cudaOccError cudaOccSMemPreference(size_t *limit, const cudaOccDeviceProp *properties, const cudaOccDeviceState *state)
+{
+    size_t bytes                          = 0;
+    size_t sharedMemPerMultiprocessorHigh = properties->sharedMemPerMultiprocessor;
+    cudaOccCacheConfig cacheConfig        = state->cacheConfig;
+
+    // Kepler has shared L1 cache / shared memory, and support cache
+    // configuration to trade one for the other. These values are needed to
+    // calculate the correct shared memory size for user requested cache
+    // configuration.
+    //
+    size_t minCacheSize                   = 16384;
+    size_t maxCacheSize                   = 49152;
+    size_t cacheAndSharedTotal            = sharedMemPerMultiprocessorHigh + minCacheSize;
+    size_t sharedMemPerMultiprocessorLow  = cacheAndSharedTotal - maxCacheSize;
+
+    switch (properties->computeMajor) {
+        case 3:
+            // Kepler supports 16KB, 32KB, or 48KB partitions for L1. The rest
+            // is shared memory.
+            //
+            switch (cacheConfig) {
+                default :
+                case CACHE_PREFER_NONE:
+                case CACHE_PREFER_SHARED:
+                    bytes = sharedMemPerMultiprocessorHigh;
+                    break;
+                case CACHE_PREFER_L1:
+                    bytes = sharedMemPerMultiprocessorLow;
+                    break;
+                case CACHE_PREFER_EQUAL:
+                    // Equal is the mid-point between high and low. It should be
+                    // equivalent to low + 16KB.
+                    //
+                    bytes = (sharedMemPerMultiprocessorHigh + sharedMemPerMultiprocessorLow) / 2;
+                    break;
+            }
+            break;
+        case 5:
+        case 6:
+            // Maxwell and Pascal have dedicated shared memory.
+            //
+            bytes = sharedMemPerMultiprocessorHigh;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    *limit = bytes;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/**
+ * Shared memory based on config requested by User
+ */
+static __OCC_INLINE cudaOccError cudaOccSMemPerMultiprocessor(size_t *limit, const cudaOccDeviceProp *properties, const cudaOccDeviceState *state)
+{
+    // Volta introduces a new API that allows for shared memory carveout preference. Because it is a shared memory preference,
+    // it is handled separately from the cache config preference.
+    if (properties->computeMajor >= 7) {
+        return cudaOccSMemPreferenceVoltaPlus(limit, properties, state);
+    }
+    return cudaOccSMemPreference(limit, properties, state);
+}
+
+/**
+ * Return the per block shared memory limit based on function config
+ */
+static __OCC_INLINE cudaOccError cudaOccSMemPerBlock(size_t *limit, const cudaOccDeviceProp *properties, cudaOccFuncShmemConfig shmemLimitConfig, size_t smemPerCta)
+{
+    switch (properties->computeMajor) {
+        case 2:
+        case 3:
+        case 4:
+        case 5:
+        case 6:
+            *limit = properties->sharedMemPerBlock;
+            break;
+        case 7:
+        case 8:
+        case 9:
+        case 10:
+        case 12:
+            switch (shmemLimitConfig) {
+                default:
+                case FUNC_SHMEM_LIMIT_DEFAULT:
+                    *limit = properties->sharedMemPerBlock;
+                    break;
+                case FUNC_SHMEM_LIMIT_OPTIN:
+                    if (smemPerCta > properties->sharedMemPerBlock) {
+                        *limit = properties->sharedMemPerBlockOptin;
+                    }
+                    else {
+                        *limit = properties->sharedMemPerBlock;
+                    }
+                    break;
+            }
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    // Starting Ampere, CUDA driver reserves additional shared memory per block
+    if (properties->computeMajor >= 8) {
+        *limit += properties->reservedSharedMemPerBlock;
+    }
+
+    return CUDA_OCC_SUCCESS;
+}
+
+/**
+ * Partitioned global caching mode support
+ */
+static __OCC_INLINE cudaOccError cudaOccPartitionedGlobalCachingModeSupport(cudaOccPartitionedGCSupport *limit, const cudaOccDeviceProp *properties)
+{
+    *limit = PARTITIONED_GC_NOT_SUPPORTED;
+
+    if ((properties->computeMajor == 5 && (properties->computeMinor == 2 || properties->computeMinor == 3)) ||
+        properties->computeMajor == 6) {
+        *limit = PARTITIONED_GC_SUPPORTED;
+    }
+
+    if (properties->computeMajor == 6 && properties->computeMinor == 0) {
+        *limit = PARTITIONED_GC_NOT_SUPPORTED;
+    }
+
+    return CUDA_OCC_SUCCESS;
+}
+
+///////////////////////////////////////////////
+//            User Input Sanity              //
+///////////////////////////////////////////////
+
+static __OCC_INLINE cudaOccError cudaOccDevicePropCheck(const cudaOccDeviceProp *properties)
+{
+    // Verify device properties
+    //
+    // Each of these limits must be a positive number.
+    //
+    // Compute capacity is checked during the occupancy calculation
+    //
+    if (properties->maxThreadsPerBlock          <= 0 ||
+        properties->maxThreadsPerMultiprocessor <= 0 ||
+        properties->regsPerBlock                <= 0 ||
+        properties->regsPerMultiprocessor       <= 0 ||
+        properties->warpSize                    <= 0 ||
+        properties->sharedMemPerBlock           <= 0 ||
+        properties->sharedMemPerMultiprocessor  <= 0 ||
+        properties->numSms                      <= 0) {
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+
+    return CUDA_OCC_SUCCESS;
+}
+
+static __OCC_INLINE cudaOccError cudaOccFuncAttributesCheck(const cudaOccFuncAttributes *attributes)
+{
+    // Verify function attributes
+    //
+    if (attributes->maxThreadsPerBlock <= 0 ||
+        attributes->numRegs < 0) {            // Compiler may choose not to use
+                                              // any register (empty kernels,
+                                              // etc.)
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+
+    return CUDA_OCC_SUCCESS;
+}
+
+static __OCC_INLINE cudaOccError cudaOccDeviceStateCheck(const cudaOccDeviceState *state)
+{
+    (void)state;   // silence unused-variable warning
+    // Placeholder
+    //
+
+    return CUDA_OCC_SUCCESS;
+}
+
+static __OCC_INLINE cudaOccError cudaOccInputCheck(
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state)
+{
+    cudaOccError status = CUDA_OCC_SUCCESS;
+
+    status = cudaOccDevicePropCheck(properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    status = cudaOccFuncAttributesCheck(attributes);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    status = cudaOccDeviceStateCheck(state);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    return status;
+}
+
+///////////////////////////////////////////////
+//    Occupancy calculation Functions        //
+///////////////////////////////////////////////
+
+static __OCC_INLINE cudaOccPartitionedGCConfig cudaOccPartitionedGCExpected(
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes)
+{
+    cudaOccPartitionedGCSupport gcSupport;
+    cudaOccPartitionedGCConfig gcConfig;
+
+    cudaOccPartitionedGlobalCachingModeSupport(&gcSupport, properties);
+
+    gcConfig = attributes->partitionedGCConfig;
+
+    if (gcSupport == PARTITIONED_GC_NOT_SUPPORTED) {
+        gcConfig = PARTITIONED_GC_OFF;
+    }
+
+    return gcConfig;
+}
+
+// Warp limit
+//
+static __OCC_INLINE cudaOccError cudaOccMaxBlocksPerSMWarpsLimit(
+    int                         *limit,
+    cudaOccPartitionedGCConfig   gcConfig,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    int                          blockSize)
+{
+    cudaOccError status = CUDA_OCC_SUCCESS;
+    int maxWarpsPerSm;
+    int warpsAllocatedPerCTA;
+    int maxBlocks;
+    (void)attributes;   // silence unused-variable warning
+
+    if (blockSize > properties->maxThreadsPerBlock) {
+        maxBlocks = 0;
+    }
+    else {
+        maxWarpsPerSm = properties->maxThreadsPerMultiprocessor / properties->warpSize;
+        warpsAllocatedPerCTA = __occDivideRoundUp(blockSize, properties->warpSize);
+        maxBlocks = 0;
+
+        if (gcConfig != PARTITIONED_GC_OFF) {
+            int maxBlocksPerSmPartition;
+            int maxWarpsPerSmPartition;
+
+            // If partitioned global caching is on, then a CTA can only use a SM
+            // partition (a half SM), and thus a half of the warp slots
+            // available per SM
+            //
+            maxWarpsPerSmPartition  = maxWarpsPerSm / 2;
+            maxBlocksPerSmPartition = maxWarpsPerSmPartition / warpsAllocatedPerCTA;
+            maxBlocks               = maxBlocksPerSmPartition * 2;
+        }
+        // On hardware that supports partitioned global caching, each half SM is
+        // guaranteed to support at least 32 warps (maximum number of warps of a
+        // CTA), so caching will not cause 0 occupancy due to insufficient warp
+        // allocation slots.
+        //
+        else {
+            maxBlocks = maxWarpsPerSm / warpsAllocatedPerCTA;
+        }
+    }
+
+    *limit = maxBlocks;
+
+    return status;
+}
+
+// Shared memory limit
+//
+static __OCC_INLINE cudaOccError cudaOccMaxBlocksPerSMSmemLimit(
+    int                         *limit,
+    cudaOccResult               *result,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    int                          blockSize,
+    size_t                       dynamicSmemSize)
+{
+    cudaOccError status = CUDA_OCC_SUCCESS;
+    int allocationGranularity;
+    size_t userSmemPreference = 0;
+    size_t totalSmemUsagePerCTA;
+    size_t maxSmemUsagePerCTA;
+    size_t smemAllocatedPerCTA;
+    size_t staticSmemSize;
+    size_t sharedMemPerMultiprocessor;
+    size_t smemLimitPerCTA;
+    int maxBlocks;
+    int dynamicSmemSizeExceeded = 0;
+    int totalSmemSizeExceeded = 0;
+    (void)blockSize;   // silence unused-variable warning
+
+    status = cudaOccSMemAllocationGranularity(&allocationGranularity, properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    // Obtain the user preferred shared memory size. This setting is ignored if
+    // user requests more shared memory than preferred.
+    //
+    status = cudaOccSMemPerMultiprocessor(&userSmemPreference, properties, state);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    staticSmemSize = attributes->sharedSizeBytes + properties->reservedSharedMemPerBlock;
+    totalSmemUsagePerCTA = staticSmemSize + dynamicSmemSize;
+    smemAllocatedPerCTA = __occRoundUp((int)totalSmemUsagePerCTA, (int)allocationGranularity);
+
+    maxSmemUsagePerCTA = staticSmemSize + attributes->maxDynamicSharedSizeBytes;
+
+    dynamicSmemSizeExceeded = 0;
+    totalSmemSizeExceeded   = 0;
+
+    // Obtain the user set maximum dynamic size if it exists
+    // If so, the current launch dynamic shared memory must not
+    // exceed the set limit
+    if (attributes->shmemLimitConfig != FUNC_SHMEM_LIMIT_DEFAULT &&
+        dynamicSmemSize > attributes->maxDynamicSharedSizeBytes) {
+        dynamicSmemSizeExceeded = 1;
+    }
+
+    status = cudaOccSMemPerBlock(&smemLimitPerCTA, properties, attributes->shmemLimitConfig, maxSmemUsagePerCTA);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    if (smemAllocatedPerCTA > smemLimitPerCTA) {
+        totalSmemSizeExceeded = 1;
+    }
+
+    if (dynamicSmemSizeExceeded || totalSmemSizeExceeded) {
+        maxBlocks = 0;
+    }
+    else {
+        // User requested shared memory limit is used as long as it is greater
+        // than the total shared memory used per CTA, i.e. as long as at least
+        // one CTA can be launched.
+        if (userSmemPreference >= smemAllocatedPerCTA) {
+            sharedMemPerMultiprocessor = userSmemPreference;
+        }
+        else {
+            // On Volta+, user requested shared memory will limit occupancy
+            // if it's less than shared memory per CTA. Otherwise, the
+            // maximum shared memory limit is used.
+            if (properties->computeMajor >= 7) {
+                sharedMemPerMultiprocessor = smemAllocatedPerCTA;
+                status = cudaOccAlignUpShmemSizeVoltaPlus(&sharedMemPerMultiprocessor, properties);
+                if (status != CUDA_OCC_SUCCESS) {
+                    return status;
+                }
+            }
+            else {
+                sharedMemPerMultiprocessor = properties->sharedMemPerMultiprocessor;
+            }
+        }
+
+        if (smemAllocatedPerCTA > 0) {
+            maxBlocks = (int)(sharedMemPerMultiprocessor / smemAllocatedPerCTA);
+        }
+        else {
+            maxBlocks = INT_MAX;
+        }
+    }
+
+    result->allocatedSharedMemPerBlock = smemAllocatedPerCTA;
+
+    *limit = maxBlocks;
+
+    return status;
+}
+
+static __OCC_INLINE
+cudaOccError cudaOccMaxBlocksPerSMRegsLimit(
+    int                         *limit,
+    cudaOccPartitionedGCConfig  *gcConfig,
+    cudaOccResult               *result,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    int                          blockSize)
+{
+    cudaOccError status = CUDA_OCC_SUCCESS;
+    int allocationGranularity;
+    int warpsAllocatedPerCTA;
+    int regsAllocatedPerCTA;
+    int regsAssumedPerCTA;
+    int regsPerWarp;
+    int regsAllocatedPerWarp;
+    int numSubPartitions;
+    int numRegsPerSubPartition;
+    int numWarpsPerSubPartition;
+    int numWarpsPerSM;
+    int maxBlocks;
+    int maxRegsPerThread;
+
+    status = cudaOccRegAllocationGranularity(
+        &allocationGranularity,
+        properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    status = cudaOccRegAllocationMaxPerThread(
+        &maxRegsPerThread,
+        properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    status = cudaOccSubPartitionsPerMultiprocessor(&numSubPartitions, properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    warpsAllocatedPerCTA = __occDivideRoundUp(blockSize, properties->warpSize);
+
+    // GPUs of compute capability 2.x and higher allocate registers to warps
+    //
+    // Number of regs per warp is regs per thread x warp size, rounded up to
+    // register allocation granularity
+    //
+    regsPerWarp          = attributes->numRegs * properties->warpSize;
+    regsAllocatedPerWarp = __occRoundUp(regsPerWarp, allocationGranularity);
+    regsAllocatedPerCTA  = regsAllocatedPerWarp * warpsAllocatedPerCTA;
+
+    // Hardware verifies if a launch fits the per-CTA register limit. For
+    // historical reasons, the verification logic assumes register
+    // allocations are made to all partitions simultaneously. Therefore, to
+    // simulate the hardware check, the warp allocation needs to be rounded
+    // up to the number of partitions.
+    //
+    regsAssumedPerCTA = regsAllocatedPerWarp * __occRoundUp(warpsAllocatedPerCTA, numSubPartitions);
+
+    if (properties->regsPerBlock < regsAssumedPerCTA ||   // Hardware check
+        properties->regsPerBlock < regsAllocatedPerCTA || // Software check
+        attributes->numRegs > maxRegsPerThread) {         // Per thread limit check
+        maxBlocks = 0;
+    }
+    else {
+        if (regsAllocatedPerWarp > 0) {
+            // Registers are allocated in each sub-partition. The max number
+            // of warps that can fit on an SM is equal to the max number of
+            // warps per sub-partition x number of sub-partitions.
+            //
+            numRegsPerSubPartition  = properties->regsPerMultiprocessor / numSubPartitions;
+            numWarpsPerSubPartition = numRegsPerSubPartition / regsAllocatedPerWarp;
+
+            maxBlocks = 0;
+
+            if (*gcConfig != PARTITIONED_GC_OFF) {
+                int numSubPartitionsPerSmPartition;
+                int numWarpsPerSmPartition;
+                int maxBlocksPerSmPartition;
+
+                // If partitioned global caching is on, then a CTA can only
+                // use a half SM, and thus a half of the registers available
+                // per SM
+                //
+                numSubPartitionsPerSmPartition = numSubPartitions / 2;
+                numWarpsPerSmPartition         = numWarpsPerSubPartition * numSubPartitionsPerSmPartition;
+                maxBlocksPerSmPartition        = numWarpsPerSmPartition / warpsAllocatedPerCTA;
+                maxBlocks                      = maxBlocksPerSmPartition * 2;
+            }
+
+            // Try again if partitioned global caching is not enabled, or if
+            // the CTA cannot fit on the SM with caching on (maxBlocks == 0).  In the latter
+            // case, the device will automatically turn off caching, except
+            // if the user forces enablement via PARTITIONED_GC_ON_STRICT to calculate
+            // occupancy and launch configuration.
+            //
+            if (maxBlocks == 0 && *gcConfig != PARTITIONED_GC_ON_STRICT) {
+               // In case *gcConfig was PARTITIONED_GC_ON flip it OFF since
+               // this is what it will be if we spread CTA across partitions.
+               //
+               *gcConfig = PARTITIONED_GC_OFF;
+               numWarpsPerSM = numWarpsPerSubPartition * numSubPartitions;
+               maxBlocks     = numWarpsPerSM / warpsAllocatedPerCTA;
+            }
+        }
+        else {
+            maxBlocks = INT_MAX;
+        }
+    }
+
+
+    result->allocatedRegistersPerBlock = regsAllocatedPerCTA;
+
+    *limit = maxBlocks;
+
+    return status;
+}
+
+// Barrier limit
+//
+static __OCC_INLINE cudaOccError cudaOccMaxBlocksPerSMBlockBarrierLimit(
+    int                         *limit,
+    int                          ctaLimitBlocks,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes)
+{
+    cudaOccError status = CUDA_OCC_SUCCESS;
+    int numBarriersAvailable = 0;
+    int numBarriersUsed = attributes->numBlockBarriers;
+    int maxBlocks = INT_MAX;
+
+    switch(properties->computeMajor) {
+        case 5:
+        case 6:
+        case 7:
+            numBarriersAvailable = ctaLimitBlocks * 2;
+            break;
+        case 8:
+            if (properties->computeMinor == 0) {
+                numBarriersAvailable = ctaLimitBlocks * 2;
+            }
+            else {
+                numBarriersAvailable = ctaLimitBlocks;
+            }
+            break;
+        case 9:
+            numBarriersAvailable = ctaLimitBlocks * 2;
+            break;
+        case 10:
+            switch(properties->computeMinor) {
+                case 1 :
+                    numBarriersAvailable = ctaLimitBlocks;
+                    break;
+                case 0 : /* explicitly added to avoid build failure in WDDM driver components. */
+                default :
+                    numBarriersAvailable = ctaLimitBlocks * 2;
+            }
+
+            break;
+        case 12:
+            numBarriersAvailable = ctaLimitBlocks;
+            break;
+        default:
+            return CUDA_OCC_ERROR_UNKNOWN_DEVICE;
+    }
+
+    if (numBarriersUsed) {
+        maxBlocks = numBarriersAvailable / numBarriersUsed;
+    }
+
+    *limit = maxBlocks;
+
+    return status;
+}
+
+///////////////////////////////////
+//      API Implementations      //
+///////////////////////////////////
+
+static __OCC_INLINE
+cudaOccError cudaOccMaxActiveBlocksPerMultiprocessor(
+    cudaOccResult               *result,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    int                          blockSize,
+    size_t                       dynamicSmemSize)
+{
+    cudaOccError status          = CUDA_OCC_SUCCESS;
+    int          ctaLimitWarps   = 0;
+    int          ctaLimitBlocks  = 0;
+    int          ctaLimitSMem    = 0;
+    int          ctaLimitRegs    = 0;
+    int          ctaLimitBars    = 0;
+    int          ctaLimit        = 0;
+    unsigned int limitingFactors = 0;
+    
+    cudaOccPartitionedGCConfig gcConfig = PARTITIONED_GC_OFF;
+
+    if (!result || !properties || !attributes || !state || blockSize <= 0) {
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+
+    ///////////////////////////
+    // Check user input
+    ///////////////////////////
+
+    status = cudaOccInputCheck(properties, attributes, state);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    ///////////////////////////
+    // Initialization
+    ///////////////////////////
+
+    gcConfig = cudaOccPartitionedGCExpected(properties, attributes);
+
+    ///////////////////////////
+    // Compute occupancy
+    ///////////////////////////
+
+    // Limits due to registers/SM
+    // Also compute if partitioned global caching has to be turned off
+    //
+    status = cudaOccMaxBlocksPerSMRegsLimit(&ctaLimitRegs, &gcConfig, result, properties, attributes, blockSize);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    // SMs on GP100 (6.0) have 2 subpartitions, while those on GP10x have 4.
+    // As a result, an SM on GP100 may be able to run more CTAs than the one on GP10x.
+    // For forward compatibility within Pascal family, if a function cannot run on GP10x (maxBlock == 0),
+    // we do not let it run on any Pascal processor, even though it may be able to run on GP100.
+    // Therefore, we check the occupancy on GP10x when it can run on GP100
+    //
+    if (properties->computeMajor == 6 && properties->computeMinor == 0 && ctaLimitRegs) {
+        cudaOccDeviceProp propertiesGP10x;
+        cudaOccPartitionedGCConfig gcConfigGP10x = gcConfig;
+        int ctaLimitRegsGP10x = 0;
+
+        // Set up properties for GP10x
+        memcpy(&propertiesGP10x, properties, sizeof(propertiesGP10x));
+        propertiesGP10x.computeMinor = 1;
+
+        status = cudaOccMaxBlocksPerSMRegsLimit(&ctaLimitRegsGP10x, &gcConfigGP10x, result, &propertiesGP10x, attributes, blockSize);
+        if (status != CUDA_OCC_SUCCESS) {
+            return status;
+        }
+
+        if (ctaLimitRegsGP10x == 0) {
+            ctaLimitRegs = 0;
+        }
+    }
+
+    // Limits due to warps/SM
+    //
+    status = cudaOccMaxBlocksPerSMWarpsLimit(&ctaLimitWarps, gcConfig, properties, attributes, blockSize);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    // Limits due to blocks/SM
+    //
+    status = cudaOccMaxBlocksPerMultiprocessor(&ctaLimitBlocks, properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    // Limits due to shared memory/SM
+    //
+    status = cudaOccMaxBlocksPerSMSmemLimit(&ctaLimitSMem, result, properties, attributes, state, blockSize, dynamicSmemSize);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    ///////////////////////////
+    // Overall occupancy
+    ///////////////////////////
+
+    // Overall limit is min() of limits due to above reasons
+    //
+    ctaLimit = __occMin(ctaLimitRegs, __occMin(ctaLimitSMem, __occMin(ctaLimitWarps, ctaLimitBlocks)));
+
+    // Determine occupancy limiting factors
+    //
+    if (ctaLimit == ctaLimitWarps) {
+        limitingFactors |= OCC_LIMIT_WARPS;
+    }
+    if (ctaLimit == ctaLimitRegs) {
+        limitingFactors |= OCC_LIMIT_REGISTERS;
+    }
+    if (ctaLimit == ctaLimitSMem) {
+        limitingFactors |= OCC_LIMIT_SHARED_MEMORY;
+    }
+    if (ctaLimit == ctaLimitBlocks) {
+        limitingFactors |= OCC_LIMIT_BLOCKS;
+    }
+
+    // For Hopper onwards compute the limits to occupancy based on block barrier count
+    //
+    if (properties->computeMajor >= 9 && attributes->numBlockBarriers > 0) {
+        // Limits due to barrier/SM
+        //
+        status = cudaOccMaxBlocksPerSMBlockBarrierLimit(&ctaLimitBars, ctaLimitBlocks, properties, attributes);
+        if (status != CUDA_OCC_SUCCESS) {
+            return status;
+        }
+
+        // Recompute overall limit based on barrier/SM
+        //
+        ctaLimit = __occMin(ctaLimitBars, ctaLimit);
+
+        // Determine if this is occupancy limiting factor
+        //
+        if (ctaLimit == ctaLimitBars) {
+            limitingFactors |= OCC_LIMIT_BARRIERS;
+        }
+    }
+    else {
+        ctaLimitBars = INT_MAX;
+    }
+
+    // Fill in the return values
+    //
+    result->limitingFactors = limitingFactors;
+
+    result->blockLimitRegs      = ctaLimitRegs;
+    result->blockLimitSharedMem = ctaLimitSMem;
+    result->blockLimitWarps     = ctaLimitWarps;
+    result->blockLimitBlocks    = ctaLimitBlocks;
+    result->blockLimitBarriers  = ctaLimitBars;
+    result->partitionedGCConfig = gcConfig;
+
+    // Final occupancy
+    result->activeBlocksPerMultiprocessor = ctaLimit;
+
+    return CUDA_OCC_SUCCESS;
+}
+
+static __OCC_INLINE
+cudaOccError cudaOccAvailableDynamicSMemPerBlock(
+    size_t                      *bytesAvailable,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    int                         numBlocks,
+    int                         blockSize)
+{
+    int allocationGranularity;
+    size_t smemLimitPerBlock;
+    size_t smemAvailableForDynamic;
+    size_t userSmemPreference = 0;
+    size_t sharedMemPerMultiprocessor;
+    cudaOccResult result;
+    cudaOccError status = CUDA_OCC_SUCCESS;
+
+    if (numBlocks <= 0)
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+
+    // First compute occupancy of potential kernel launch.
+    //
+    status = cudaOccMaxActiveBlocksPerMultiprocessor(&result, properties, attributes, state, blockSize, 0);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+    // Check if occupancy is achievable given user requested number of blocks. 
+    //
+    if (result.activeBlocksPerMultiprocessor < numBlocks) {
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+
+    status = cudaOccSMemAllocationGranularity(&allocationGranularity, properties);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    // Return the per block shared memory limit based on function config.
+    //
+    status = cudaOccSMemPerBlock(&smemLimitPerBlock, properties, attributes->shmemLimitConfig, properties->sharedMemPerMultiprocessor);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    // If there is only a single block needed per SM, then the user preference can be ignored and the fully SW
+    // limit is allowed to be used as shared memory otherwise if more than one block is needed, then the user
+    // preference sets the total limit of available shared memory.
+    //
+    cudaOccSMemPerMultiprocessor(&userSmemPreference, properties, state);
+    if (numBlocks == 1) {
+        sharedMemPerMultiprocessor = smemLimitPerBlock;
+    }
+    else {
+        if (!userSmemPreference) {
+            userSmemPreference = 1 ;
+            status = cudaOccAlignUpShmemSizeVoltaPlus(&userSmemPreference, properties);
+            if (status != CUDA_OCC_SUCCESS) {
+                return status;
+            }
+        }
+        sharedMemPerMultiprocessor = userSmemPreference;
+    }
+
+    // Compute total shared memory available per SM
+    //
+    smemAvailableForDynamic =  sharedMemPerMultiprocessor / numBlocks;
+    smemAvailableForDynamic = (smemAvailableForDynamic / allocationGranularity) * allocationGranularity;
+
+    // Cap shared memory
+    //
+    if (smemAvailableForDynamic > smemLimitPerBlock) {
+        smemAvailableForDynamic = smemLimitPerBlock;
+    }
+
+    // Now compute dynamic shared memory size
+    smemAvailableForDynamic = smemAvailableForDynamic - attributes->sharedSizeBytes; 
+
+    // Cap computed dynamic SM by user requested limit specified via cuFuncSetAttribute()
+    //
+    if (smemAvailableForDynamic > attributes->maxDynamicSharedSizeBytes)
+        smemAvailableForDynamic = attributes->maxDynamicSharedSizeBytes;
+
+    *bytesAvailable = smemAvailableForDynamic;
+    return CUDA_OCC_SUCCESS;
+}
+
+static __OCC_INLINE
+cudaOccError cudaOccMaxPotentialOccupancyBlockSize(
+    int                         *minGridSize,
+    int                         *blockSize,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    size_t                     (*blockSizeToDynamicSMemSize)(int),
+    size_t                       dynamicSMemSize)
+{
+    cudaOccError  status = CUDA_OCC_SUCCESS;
+    cudaOccResult result;
+
+    // Limits
+    int occupancyLimit;
+    int granularity;
+    int blockSizeLimit;
+
+    // Recorded maximum
+    int maxBlockSize = 0;
+    int numBlocks    = 0;
+    int maxOccupancy = 0;
+
+    // Temporary
+    int blockSizeToTryAligned;
+    int blockSizeToTry;
+    int blockSizeLimitAligned;
+    int occupancyInBlocks;
+    int occupancyInThreads;
+
+    ///////////////////////////
+    // Check user input
+    ///////////////////////////
+
+    if (!minGridSize || !blockSize || !properties || !attributes || !state) {
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+
+    status = cudaOccInputCheck(properties, attributes, state);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    /////////////////////////////////////////////////////////////////////////////////
+    // Try each block size, and pick the block size with maximum occupancy
+    /////////////////////////////////////////////////////////////////////////////////
+
+    occupancyLimit = properties->maxThreadsPerMultiprocessor;
+    granularity    = properties->warpSize;
+
+    blockSizeLimit        = __occMin(properties->maxThreadsPerBlock, attributes->maxThreadsPerBlock);
+    blockSizeLimitAligned = __occRoundUp(blockSizeLimit, granularity);
+
+    for (blockSizeToTryAligned = blockSizeLimitAligned; blockSizeToTryAligned > 0; blockSizeToTryAligned -= granularity) {
+        blockSizeToTry = __occMin(blockSizeLimit, blockSizeToTryAligned);
+
+        // Ignore dynamicSMemSize if the user provides a mapping
+        //
+        if (blockSizeToDynamicSMemSize) {
+            dynamicSMemSize = (*blockSizeToDynamicSMemSize)(blockSizeToTry);
+        }
+
+        status = cudaOccMaxActiveBlocksPerMultiprocessor(
+            &result,
+            properties,
+            attributes,
+            state,
+            blockSizeToTry,
+            dynamicSMemSize);
+
+        if (status != CUDA_OCC_SUCCESS) {
+            return status;
+        }
+
+        occupancyInBlocks = result.activeBlocksPerMultiprocessor;
+        occupancyInThreads = blockSizeToTry * occupancyInBlocks;
+
+        if (occupancyInThreads > maxOccupancy) {
+            maxBlockSize = blockSizeToTry;
+            numBlocks    = occupancyInBlocks;
+            maxOccupancy = occupancyInThreads;
+        }
+
+        // Early out if we have reached the maximum
+        //
+        if (occupancyLimit == maxOccupancy) {
+            break;
+        }
+    }
+
+    ///////////////////////////
+    // Return best available
+    ///////////////////////////
+
+    // Suggested min grid size to achieve a full machine launch
+    //
+    *minGridSize = numBlocks * properties->numSms;
+    *blockSize = maxBlockSize;
+
+    return status;
+}
+
+
+#if defined(__cplusplus)
+
+namespace {
+
+__OCC_INLINE
+cudaOccError cudaOccMaxPotentialOccupancyBlockSize(
+    int                         *minGridSize,
+    int                         *blockSize,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    size_t                       dynamicSMemSize)
+{
+    return cudaOccMaxPotentialOccupancyBlockSize(
+        minGridSize,
+        blockSize,
+        properties,
+        attributes,
+        state,
+        NULL,
+        dynamicSMemSize);
+}
+
+template <typename UnaryFunction>
+__OCC_INLINE
+cudaOccError cudaOccMaxPotentialOccupancyBlockSizeVariableSMem(
+    int                         *minGridSize,
+    int                         *blockSize,
+    const cudaOccDeviceProp     *properties,
+    const cudaOccFuncAttributes *attributes,
+    const cudaOccDeviceState    *state,
+    UnaryFunction                blockSizeToDynamicSMemSize)
+{
+    cudaOccError  status = CUDA_OCC_SUCCESS;
+    cudaOccResult result;
+
+    // Limits
+    int occupancyLimit;
+    int granularity;
+    int blockSizeLimit;
+
+    // Recorded maximum
+    int maxBlockSize = 0;
+    int numBlocks    = 0;
+    int maxOccupancy = 0;
+
+    // Temporary
+    int blockSizeToTryAligned;
+    int blockSizeToTry;
+    int blockSizeLimitAligned;
+    int occupancyInBlocks;
+    int occupancyInThreads;
+    size_t dynamicSMemSize;
+
+    ///////////////////////////
+    // Check user input
+    ///////////////////////////
+
+    if (!minGridSize || !blockSize || !properties || !attributes || !state) {
+        return CUDA_OCC_ERROR_INVALID_INPUT;
+    }
+
+    status = cudaOccInputCheck(properties, attributes, state);
+    if (status != CUDA_OCC_SUCCESS) {
+        return status;
+    }
+
+    /////////////////////////////////////////////////////////////////////////////////
+    // Try each block size, and pick the block size with maximum occupancy
+    /////////////////////////////////////////////////////////////////////////////////
+
+    occupancyLimit = properties->maxThreadsPerMultiprocessor;
+    granularity    = properties->warpSize;
+    blockSizeLimit        = __occMin(properties->maxThreadsPerBlock, attributes->maxThreadsPerBlock);
+    blockSizeLimitAligned = __occRoundUp(blockSizeLimit, granularity);
+
+    for (blockSizeToTryAligned = blockSizeLimitAligned; blockSizeToTryAligned > 0; blockSizeToTryAligned -= granularity) {
+        blockSizeToTry = __occMin(blockSizeLimit, blockSizeToTryAligned);
+
+        dynamicSMemSize = blockSizeToDynamicSMemSize(blockSizeToTry);
+
+        status = cudaOccMaxActiveBlocksPerMultiprocessor(
+            &result,
+            properties,
+            attributes,
+            state,
+            blockSizeToTry,
+            dynamicSMemSize);
+
+        if (status != CUDA_OCC_SUCCESS) {
+            return status;
+        }
+
+        occupancyInBlocks = result.activeBlocksPerMultiprocessor;
+
+        occupancyInThreads = blockSizeToTry * occupancyInBlocks;
+
+        if (occupancyInThreads > maxOccupancy) {
+            maxBlockSize = blockSizeToTry;
+            numBlocks    = occupancyInBlocks;
+            maxOccupancy = occupancyInThreads;
+        }
+
+        // Early out if we have reached the maximum
+        //
+        if (occupancyLimit == maxOccupancy) {
+            break;
+        }
+    }
+
+    ///////////////////////////
+    // Return best available
+    ///////////////////////////
+
+    // Suggested min grid size to achieve a full machine launch
+    //
+    *minGridSize = numBlocks * properties->numSms;
+    *blockSize = maxBlockSize;
+
+    return status;
+}
+
+} // namespace anonymous
+
+#endif /*__cplusplus */
+
+#undef __OCC_INLINE
+
+#endif /*__cuda_occupancy_h__*/
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline.h
new file mode 100644
index 0000000000000000000000000000000000000000..46bc89e4499576f1ae58848cd8684ba3e32420cf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline.h
@@ -0,0 +1,224 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDA_PIPELINE_H_
+# define _CUDA_PIPELINE_H_
+
+# include "cuda_pipeline_primitives.h"
+
+# if !defined(_CUDA_PIPELINE_CPLUSPLUS_11_OR_LATER)
+#  error This file requires compiler support for the ISO C++ 2011 standard. This support must be enabled with the \
+         -std=c++11 compiler option.
+# endif
+
+# if defined(_CUDA_PIPELINE_ARCH_700_OR_LATER)
+#  include "cuda_awbarrier.h"
+# endif
+
+// Integration with libcu++'s cuda::barrier<cuda::thread_scope_block>.
+
+# if defined(_CUDA_PIPELINE_ARCH_700_OR_LATER)
+#  if defined(_LIBCUDACXX_CUDA_ABI_VERSION)
+#   define _LIBCUDACXX_PIPELINE_ASSUMED_ABI_VERSION _LIBCUDACXX_CUDA_ABI_VERSION
+#  else
+#   define _LIBCUDACXX_PIPELINE_ASSUMED_ABI_VERSION 4
+#  endif
+
+#  define _LIBCUDACXX_PIPELINE_CONCAT(X, Y) X ## Y
+#  define _LIBCUDACXX_PIPELINE_CONCAT2(X, Y) _LIBCUDACXX_PIPELINE_CONCAT(X, Y)
+#  define _LIBCUDACXX_PIPELINE_INLINE_NAMESPACE _LIBCUDACXX_PIPELINE_CONCAT2(__, _LIBCUDACXX_PIPELINE_ASSUMED_ABI_VERSION)
+
+namespace cuda { inline namespace _LIBCUDACXX_PIPELINE_INLINE_NAMESPACE {
+    struct __block_scope_barrier_base;
+}}
+
+# endif
+
+_CUDA_PIPELINE_BEGIN_NAMESPACE
+
+template<size_t N, typename T>
+_CUDA_PIPELINE_QUALIFIER
+auto segment(T* ptr) -> T(*)[N];
+
+class pipeline {
+public:
+    pipeline(const pipeline&) = delete;
+    pipeline(pipeline&&) = delete;
+    pipeline& operator=(const pipeline&) = delete;
+    pipeline& operator=(pipeline&&) = delete;
+
+    _CUDA_PIPELINE_QUALIFIER pipeline();
+    _CUDA_PIPELINE_QUALIFIER size_t commit();
+    _CUDA_PIPELINE_QUALIFIER void commit_and_wait();
+    _CUDA_PIPELINE_QUALIFIER void wait(size_t batch);
+    template<unsigned N>
+    _CUDA_PIPELINE_QUALIFIER void wait_prior();
+
+# if defined(_CUDA_PIPELINE_ARCH_700_OR_LATER)
+    _CUDA_PIPELINE_QUALIFIER void arrive_on(awbarrier& barrier);
+    _CUDA_PIPELINE_QUALIFIER void arrive_on(cuda::__block_scope_barrier_base& barrier);
+# endif
+
+private:
+    size_t current_batch;
+};
+
+template<class T>
+_CUDA_PIPELINE_QUALIFIER
+void memcpy_async(T& dst, const T& src, pipeline& pipe);
+
+template<class T, size_t DstN, size_t SrcN>
+_CUDA_PIPELINE_QUALIFIER
+void memcpy_async(T(*dst)[DstN], const T(*src)[SrcN], pipeline& pipe);
+
+template<size_t N, typename T>
+_CUDA_PIPELINE_QUALIFIER
+auto segment(T* ptr) -> T(*)[N]
+{
+    return (T(*)[N])ptr;
+}
+
+_CUDA_PIPELINE_QUALIFIER
+pipeline::pipeline()
+    : current_batch(0)
+{
+}
+
+_CUDA_PIPELINE_QUALIFIER
+size_t pipeline::commit()
+{
+    _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_commit();
+    return this->current_batch++;
+}
+
+_CUDA_PIPELINE_QUALIFIER
+void pipeline::commit_and_wait()
+{
+    (void)pipeline::commit();
+    pipeline::wait_prior<0>();
+}
+
+_CUDA_PIPELINE_QUALIFIER
+void pipeline::wait(size_t batch)
+{
+    const size_t prior = this->current_batch > batch ? this->current_batch - batch : 0;
+
+    switch (prior) {
+    case  0 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<0>(); break;
+    case  1 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<1>(); break;
+    case  2 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<2>(); break;
+    case  3 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<3>(); break;
+    case  4 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<4>(); break;
+    case  5 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<5>(); break;
+    case  6 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<6>(); break;
+    case  7 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<7>(); break;
+    default : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<8>(); break;
+    }
+}
+
+template<unsigned N>
+_CUDA_PIPELINE_QUALIFIER
+void pipeline::wait_prior()
+{
+    _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<N>();
+}
+
+# if defined(_CUDA_PIPELINE_ARCH_700_OR_LATER)
+_CUDA_PIPELINE_QUALIFIER
+void pipeline::arrive_on(awbarrier& barrier)
+{
+    _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_arrive_on(&barrier.barrier);
+}
+
+_CUDA_PIPELINE_QUALIFIER
+void pipeline::arrive_on(cuda::__block_scope_barrier_base & barrier)
+{
+    _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_arrive_on(reinterpret_cast<uint64_t *>(&barrier));
+}
+# endif
+
+template<class T>
+_CUDA_PIPELINE_QUALIFIER
+void memcpy_async(T& dst, const T& src, pipeline& pipe)
+{
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(&src) & (alignof(T) - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(&dst) & (alignof(T) - 1)));
+
+    if (__is_trivially_copyable(T)) {
+        _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_copy_relaxed<sizeof(T), alignof(T)>(
+                reinterpret_cast<void*>(&dst), reinterpret_cast<const void*>(&src));
+    } else {
+        dst = src;
+    }
+}
+
+template<class T, size_t DstN, size_t SrcN>
+_CUDA_PIPELINE_QUALIFIER
+void memcpy_async(T(*dst)[DstN], const T(*src)[SrcN], pipeline& pipe)
+{
+    constexpr size_t dst_size = sizeof(*dst);
+    constexpr size_t src_size = sizeof(*src);
+    static_assert(dst_size == 4 || dst_size == 8 || dst_size == 16, "Unsupported copy size.");
+    static_assert(src_size <= dst_size, "Source size must be less than or equal to destination size.");
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (dst_size - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (dst_size - 1)));
+
+    if (__is_trivially_copyable(T)) {
+        _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_copy_strict<sizeof(*dst), sizeof(*src)>(
+                reinterpret_cast<void*>(*dst), reinterpret_cast<const void*>(*src));
+    } else {
+        for (size_t i = 0; i < DstN; ++i) {
+            (*dst)[i] = (i < SrcN) ? (*src)[i] : T();
+        }
+    }
+}
+
+_CUDA_PIPELINE_END_NAMESPACE
+
+#endif /* !_CUDA_PIPELINE_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline_helpers.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline_helpers.h
new file mode 100644
index 0000000000000000000000000000000000000000..01882b6b976347b9bfdf276c3d0adcec1d8a55fa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline_helpers.h
@@ -0,0 +1,373 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDA_PIPELINE_HELPERS_H_
+# define _CUDA_PIPELINE_HELPERS_H_
+
+# define _CUDA_PIPELINE_NAMESPACE       nvcuda::experimental
+# define _CUDA_PIPELINE_BEGIN_NAMESPACE namespace nvcuda { namespace experimental {
+# define _CUDA_PIPELINE_END_NAMESPACE   } }
+
+# define _CUDA_PIPELINE_INTERNAL_NAMESPACE       _CUDA_PIPELINE_NAMESPACE::__pipeline_internal
+# define _CUDA_PIPELINE_BEGIN_INTERNAL_NAMESPACE _CUDA_PIPELINE_BEGIN_NAMESPACE namespace __pipeline_internal {
+# define _CUDA_PIPELINE_END_INTERNAL_NAMESPACE   } _CUDA_PIPELINE_END_NAMESPACE
+
+# if !defined(_CUDA_PIPELINE_QUALIFIER)
+#  define _CUDA_PIPELINE_QUALIFIER inline __device__
+# endif
+# if !defined(_CUDA_PIPELINE_STATIC_QUALIFIER)
+#  define _CUDA_PIPELINE_STATIC_QUALIFIER static inline __device__
+# endif
+
+# if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700)
+#  define _CUDA_PIPELINE_ARCH_700_OR_LATER
+# endif
+
+# if (__CUDA_ARCH__ >= 800)
+#  define _CUDA_PIPELINE_HAS_ASYNC_COPY 1
+# else
+#  define _CUDA_PIPELINE_HAS_ASYNC_COPY 0
+# endif
+
+# if !defined(_CUDA_PIPELINE_MAX_STAGES)
+#  define _CUDA_PIPELINE_MAX_STAGES 8
+# endif
+
+# if defined(__cplusplus) && ((__cplusplus >= 201103L) || (defined(_MSC_VER) && (_MSC_VER >= 1900)))
+#  define _CUDA_PIPELINE_CPLUSPLUS_11_OR_LATER
+# endif
+
+# if !defined(_CUDA_PIPELINE_DEBUG)
+#  if defined(__CUDACC_DEBUG__)
+#   define _CUDA_PIPELINE_DEBUG 1
+#  else
+#   define _CUDA_PIPELINE_DEBUG 0
+#  endif
+# endif
+
+# if defined(_CUDA_PIPELINE_DEBUG) && (_CUDA_PIPELINE_DEBUG == 1) && !defined(NDEBUG)
+#  if !defined(__CUDACC_RTC__)
+#   include <cassert>
+#  endif
+#  define _CUDA_PIPELINE_ASSERT(x) assert((x));
+#  define _CUDA_PIPELINE_ABORT() assert(0);
+# else
+#  define _CUDA_PIPELINE_ASSERT(x)
+#  define _CUDA_PIPELINE_ABORT() __trap();
+# endif
+
+# if defined(_CUDA_PIPELINE_CPLUSPLUS_11_OR_LATER)
+#  define _CUDA_PIPELINE_STATIC_ASSERT(c, m) static_assert(c, m)
+# else
+#  define _CUDA_PIPELINE_STATIC_ASSERT(c, m)
+# endif
+
+# if (defined(_MSC_VER) && !defined(_WIN64)) || defined(__arm__)
+#  define _CUDA_PIPELINE_ASM_PTR_CONSTRAINT "r"
+# else
+#  define _CUDA_PIPELINE_ASM_PTR_CONSTRAINT "l"
+# endif
+
+# if defined(__CUDACC_RTC__)
+typedef unsigned int       uint32_t;
+typedef unsigned long long uint64_t;
+typedef uint64_t           uintptr_t;
+# else
+#  include <stdint.h>
+# endif
+
+_CUDA_PIPELINE_BEGIN_INTERNAL_NAMESPACE
+
+_CUDA_PIPELINE_STATIC_ASSERT(sizeof(short) ==  2, "Size mismatch for type 'short'");
+_CUDA_PIPELINE_STATIC_ASSERT(sizeof(int)   ==  4, "Size mismatch for type 'int'");
+_CUDA_PIPELINE_STATIC_ASSERT(sizeof(int2)  ==  8, "Size mismatch for type 'int2'");
+_CUDA_PIPELINE_STATIC_ASSERT(sizeof(int4)  == 16, "Size mismatch for type 'int4'");
+
+extern "C" __device__ uint32_t __nvvm_get_smem_pointer(void *);
+
+template<size_t CopySize, size_t SourceSize>
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_memcpy_sync(void* __restrict__ dst, const void* __restrict__ src)
+{
+    _CUDA_PIPELINE_STATIC_ASSERT(CopySize == 4 || CopySize == 8 || CopySize == 16, "Unsupported copy size.");
+    _CUDA_PIPELINE_STATIC_ASSERT(SourceSize <= CopySize, "Source size must be less than or equal to copy size");
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (CopySize - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (CopySize - 1)));
+
+    char* const d = reinterpret_cast<char*>(dst);
+    const char* const s = reinterpret_cast<const char*>(src);
+
+    size_t copy_step_size;
+    if (SourceSize == 0) {
+        copy_step_size = CopySize;
+    } else if (SourceSize == 2 || SourceSize == 4 || SourceSize == 8 || SourceSize == 16) {
+        copy_step_size = SourceSize;
+    } else {
+        copy_step_size = 1;
+    }
+
+    for (size_t i = 0; i < CopySize; i += copy_step_size) {
+        const bool copy_source = SourceSize && (i < SourceSize);
+
+        switch (copy_step_size) {
+        case 1:
+            d[i] = copy_source ? s[i] : char();
+            break;
+        case 2:
+            *reinterpret_cast<short*>(d + i) = copy_source ? *reinterpret_cast<const short*>(s + i) : short();
+            break;
+        case 4:
+            *reinterpret_cast<int*>(d + i) = copy_source ? *reinterpret_cast<const int*>(s + i) : int();
+            break;
+        case 8:
+            *reinterpret_cast<int2*>(d + i) = copy_source ? *reinterpret_cast<const int2*>(s + i) : int2();
+            break;
+        case 16:
+            *reinterpret_cast<int4*>(d + i) = copy_source ? *reinterpret_cast<const int4*>(s + i) : int4();
+            break;
+        }
+    }
+}
+
+template<bool UseHwAsyncCopy>
+struct ImplementationChooser;
+
+template<>
+struct ImplementationChooser<true> {
+    template<size_t CopySize, size_t SourceSize>
+    struct CpAsyncChooser {
+        _CUDA_PIPELINE_STATIC_QUALIFIER
+        void cp_async(void* __restrict__ dst, const void* __restrict__ src)
+        {
+            asm volatile ("cp.async.ca.shared.global [%0], [%1], %2, %3;"
+                :
+                : "r"(__nvvm_get_smem_pointer(dst)), _CUDA_PIPELINE_ASM_PTR_CONSTRAINT(src), "n"(CopySize),
+                  "n"(SourceSize)
+                : "memory");
+        }
+    };
+
+    template<size_t SourceSize>
+    struct CpAsyncChooser<16, SourceSize> {
+        _CUDA_PIPELINE_STATIC_QUALIFIER
+        void cp_async(void* __restrict__ dst, const void* __restrict__ src)
+        {
+            asm volatile ("cp.async.cg.shared.global [%0], [%1], %2, %3;"
+                :
+                : "r"(__nvvm_get_smem_pointer(dst)), _CUDA_PIPELINE_ASM_PTR_CONSTRAINT(src), "n"(16), "n"(SourceSize)
+                : "memory");
+        }
+    };
+
+    template<size_t CopySize, size_t SourceSize>
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_memcpy_async(void* __restrict__ dst, const void* __restrict__ src)
+    {
+        _CUDA_PIPELINE_STATIC_ASSERT(CopySize == 4 || CopySize == 8 || CopySize == 16, "Unsupported copy size.");
+        _CUDA_PIPELINE_STATIC_ASSERT(SourceSize <= CopySize, "Source size must be less than or equal to copy size");
+        _CUDA_PIPELINE_ASSERT(__isShared(dst));
+        _CUDA_PIPELINE_ASSERT(__isGlobal(src));
+        _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (CopySize - 1)));
+        _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (CopySize - 1)));
+
+        CpAsyncChooser<CopySize, SourceSize>::cp_async(dst, src);
+    }
+
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_commit()
+    {
+        asm volatile ("cp.async.commit_group;");
+    }
+
+    template<unsigned N>
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_wait_prior()
+    {
+        asm volatile ("cp.async.wait_group %0;"
+            :
+            : "n"(N < _CUDA_PIPELINE_MAX_STAGES ? N : _CUDA_PIPELINE_MAX_STAGES));
+    }
+
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_arrive_on(uint64_t* barrier)
+    {
+        _CUDA_PIPELINE_ASSERT(__isShared(barrier));
+
+        asm volatile ("cp.async.mbarrier.arrive.shared.b64 [%0];"
+            :
+            : "r"(__nvvm_get_smem_pointer(barrier)));
+    }
+};
+
+template<>
+struct ImplementationChooser<false> {
+    template<size_t CopySize, size_t SourceSize>
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_memcpy_async(void* __restrict__ dst, const void* __restrict__ src)
+    {
+        _CUDA_PIPELINE_STATIC_ASSERT(CopySize == 4 || CopySize == 8 || CopySize == 16, "Unsupported copy size.");
+        _CUDA_PIPELINE_STATIC_ASSERT(SourceSize <= CopySize, "Source size must be less than or equal to copy size");
+        _CUDA_PIPELINE_ASSERT(__isShared(dst));
+        _CUDA_PIPELINE_ASSERT(__isGlobal(src));
+        _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (CopySize - 1)));
+        _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (CopySize - 1)));
+
+        pipeline_memcpy_sync<CopySize, SourceSize>(dst, src);
+    }
+
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_commit()
+    {
+    }
+
+    template<unsigned N>
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_wait_prior()
+    {
+    }
+
+    _CUDA_PIPELINE_STATIC_QUALIFIER
+    void pipeline_arrive_on(uint64_t* barrier)
+    {
+    }
+};
+
+template<size_t CopySize, size_t SourceSize>
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_memcpy_async(void* __restrict__ dst, const void* __restrict__ src)
+{
+    _CUDA_PIPELINE_STATIC_ASSERT(CopySize == 4 || CopySize == 8 || CopySize == 16, "Unsupported copy size.");
+    _CUDA_PIPELINE_STATIC_ASSERT(SourceSize <= CopySize, "Source size must be less than or equal to copy size");
+    _CUDA_PIPELINE_ASSERT(__isShared(dst));
+    _CUDA_PIPELINE_ASSERT(__isGlobal(src));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (CopySize - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (CopySize - 1)));
+
+    ImplementationChooser<_CUDA_PIPELINE_HAS_ASYNC_COPY>::pipeline_memcpy_async<CopySize, SourceSize>(dst, src);
+}
+
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_commit()
+{
+    ImplementationChooser<_CUDA_PIPELINE_HAS_ASYNC_COPY>::pipeline_commit();
+}
+
+template<unsigned N>
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_wait_prior()
+{
+    ImplementationChooser<_CUDA_PIPELINE_HAS_ASYNC_COPY>::pipeline_wait_prior<N>();
+}
+
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_arrive_on(uint64_t* barrier)
+{
+    ImplementationChooser<_CUDA_PIPELINE_HAS_ASYNC_COPY>::pipeline_arrive_on(barrier);
+}
+
+template<size_t CopySize, size_t SourceSize>
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_copy_strict(void* __restrict__ dst, const void* __restrict__ src)
+{
+    _CUDA_PIPELINE_STATIC_ASSERT(CopySize == 4 || CopySize == 8 || CopySize == 16, "Unsupported copy size.");
+    _CUDA_PIPELINE_STATIC_ASSERT(SourceSize <= CopySize, "Source size must be less than or equal to copy size.");
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (CopySize - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (CopySize - 1)));
+
+    if (__isGlobal(src) && __isShared(dst)) {
+        pipeline_memcpy_async<CopySize, SourceSize>(dst, src);
+    } else {
+        pipeline_memcpy_sync<CopySize, SourceSize>(dst, src);
+    }
+}
+
+template<size_t CopySize, size_t Align>
+_CUDA_PIPELINE_QUALIFIER
+void pipeline_copy_relaxed(void* __restrict__ dst, const void* __restrict__ src)
+{
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src) & (Align - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst) & (Align - 1)));
+
+    const char* s = reinterpret_cast<const char*>(src);
+    char* d = reinterpret_cast<char*>(dst);
+    size_t remaining = CopySize;
+
+    while (remaining) {
+        if ((Align >= 16) && (remaining >= 16)) {
+            pipeline_copy_strict<16, 16>(dst, src);
+            d += 16;
+            s += 16;
+            remaining -= 16;
+        } else if ((Align >= 8) && (remaining >= 8)) {
+            pipeline_copy_strict<8, 8>(dst, src);
+            d += 8;
+            s += 8;
+            remaining -= 8;
+        } else if ((Align >= 4) && (remaining >= 4)) {
+            pipeline_copy_strict<4, 4>(dst, src);
+            d += 4;
+            s += 4;
+            remaining -= 4;
+        } else if ((Align >= 2) && (remaining >= 2)) {
+            *reinterpret_cast<short*>(d) = *reinterpret_cast<const short*>(s);
+            d += 2;
+            s += 2;
+            remaining -= 2;
+        } else {
+            *d = *s;
+            d += 1;
+            s += 1;
+            remaining -= 1;
+        }
+    }
+}
+
+_CUDA_PIPELINE_END_INTERNAL_NAMESPACE
+
+#endif /* !_CUDA_PIPELINE_HELPERS_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline_primitives.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline_primitives.h
new file mode 100644
index 0000000000000000000000000000000000000000..eaba0cfb5ac9184bec5e837d2ec2f9db11d873ae
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_pipeline_primitives.h
@@ -0,0 +1,148 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDA_PIPELINE_PRIMITIVES_H_
+# define _CUDA_PIPELINE_PRIMITIVES_H_
+
+# include "cuda_pipeline_helpers.h"
+
+_CUDA_PIPELINE_STATIC_QUALIFIER
+void __pipeline_memcpy_async(void* __restrict__ dst_shared, const void* __restrict__ src_global, size_t size_and_align,
+                             size_t zfill = 0)
+{
+    _CUDA_PIPELINE_ASSERT(size_and_align == 4 || size_and_align == 8 || size_and_align == 16);
+    _CUDA_PIPELINE_ASSERT(zfill <= size_and_align);
+    _CUDA_PIPELINE_ASSERT(__isShared(dst_shared));
+    _CUDA_PIPELINE_ASSERT(__isGlobal(src_global));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(dst_shared) & (size_and_align - 1)));
+    _CUDA_PIPELINE_ASSERT(!(reinterpret_cast<uintptr_t>(src_global) & (size_and_align - 1)));
+
+    switch (size_and_align) {
+    case 16:
+        switch (zfill) {
+        case  0: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 16>(dst_shared, src_global); return;
+        case  1: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 15>(dst_shared, src_global); return;
+        case  2: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 14>(dst_shared, src_global); return;
+        case  3: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 13>(dst_shared, src_global); return;
+        case  4: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 12>(dst_shared, src_global); return;
+        case  5: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 11>(dst_shared, src_global); return;
+        case  6: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16, 10>(dst_shared, src_global); return;
+        case  7: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  9>(dst_shared, src_global); return;
+        case  8: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  8>(dst_shared, src_global); return;
+        case  9: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  7>(dst_shared, src_global); return;
+        case 10: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  6>(dst_shared, src_global); return;
+        case 11: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  5>(dst_shared, src_global); return;
+        case 12: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  4>(dst_shared, src_global); return;
+        case 13: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  3>(dst_shared, src_global); return;
+        case 14: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  2>(dst_shared, src_global); return;
+        case 15: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  1>(dst_shared, src_global); return;
+        case 16: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async<16,  0>(dst_shared, src_global); return;
+        default: _CUDA_PIPELINE_ABORT();                                                                   return;
+        }
+    case 8:
+        switch (zfill) {
+        case  0: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  8>(dst_shared, src_global); return;
+        case  1: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  7>(dst_shared, src_global); return;
+        case  2: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  6>(dst_shared, src_global); return;
+        case  3: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  5>(dst_shared, src_global); return;
+        case  4: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  4>(dst_shared, src_global); return;
+        case  5: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  3>(dst_shared, src_global); return;
+        case  6: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  2>(dst_shared, src_global); return;
+        case  7: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  1>(dst_shared, src_global); return;
+        case  8: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 8,  0>(dst_shared, src_global); return;
+        default: _CUDA_PIPELINE_ABORT();                                                                   return;
+        }
+    case 4:
+        switch (zfill) {
+        case  0: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 4,  4>(dst_shared, src_global); return;
+        case  1: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 4,  3>(dst_shared, src_global); return;
+        case  2: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 4,  2>(dst_shared, src_global); return;
+        case  3: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 4,  1>(dst_shared, src_global); return;
+        case  4: _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_memcpy_async< 4,  0>(dst_shared, src_global); return;
+        default: _CUDA_PIPELINE_ABORT();                                                                   return;
+        }
+    default:
+        _CUDA_PIPELINE_ABORT();
+        return;
+    }
+}
+
+_CUDA_PIPELINE_STATIC_QUALIFIER
+void __pipeline_commit()
+{
+    _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_commit();
+}
+
+_CUDA_PIPELINE_STATIC_QUALIFIER
+void __pipeline_wait_prior(size_t prior)
+{
+    switch (prior) {
+    case  0 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<0>(); return;
+    case  1 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<1>(); return;
+    case  2 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<2>(); return;
+    case  3 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<3>(); return;
+    case  4 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<4>(); return;
+    case  5 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<5>(); return;
+    case  6 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<6>(); return;
+    case  7 : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<7>(); return;
+    default : _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_wait_prior<8>(); return;
+    }
+}
+
+# if defined(_CUDA_PIPELINE_ARCH_700_OR_LATER)
+#  include "cuda_awbarrier_primitives.h"
+
+_CUDA_PIPELINE_STATIC_QUALIFIER
+void __pipeline_arrive_on(__mbarrier_t* barrier)
+{
+    _CUDA_PIPELINE_INTERNAL_NAMESPACE::pipeline_arrive_on(barrier);
+}
+# endif
+
+#endif /* !_CUDA_PIPELINE_PRIMITIVES_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_runtime.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_runtime.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc36a6079c30f86fa9df3a011a5a2670861c0fe6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_runtime.h
@@ -0,0 +1,2591 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_RUNTIME_H__)
+#define __CUDA_RUNTIME_H__
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_RUNTIME_H__
+#endif
+
+#define EXCLUDE_FROM_RTC
+#if defined(__GNUC__)
+#if defined(__clang__) || (!defined(__PGIC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)))
+#pragma GCC diagnostic push
+#endif
+#if defined(__clang__) || (!defined(__PGIC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 2)))
+#pragma GCC diagnostic ignored "-Wunused-function"
+#endif
+#elif defined(_MSC_VER)
+#pragma warning(push)
+#pragma warning(disable: 4820)
+#endif
+#ifdef __QNX__
+#if (__GNUC__ == 4 && __GNUC_MINOR__ >= 7)
+typedef unsigned size_t;
+#endif
+#endif
+#undef EXCLUDE_FROM_RTC
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "crt/host_config.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "builtin_types.h"
+#include "library_types.h"
+#if !defined(__CUDACC_RTC__)
+#define EXCLUDE_FROM_RTC
+#include "channel_descriptor.h"
+#include "cuda_runtime_api.h"
+#include "driver_functions.h"
+#undef EXCLUDE_FROM_RTC
+#endif /* !__CUDACC_RTC__ */
+#include "crt/host_defines.h"
+#ifdef __CUDACC_RTC__
+#include "target"
+#endif  /* defined(__CUDACC_RTC__) */
+
+
+#include "vector_functions.h"
+
+#if defined(__CUDACC__)
+
+#if defined(__CUDACC_RTC__)
+#include "nvrtc_device_runtime.h"
+#include "crt/device_functions.h"
+#include "crt/common_functions.h"
+#include "device_launch_parameters.h"
+
+#else /* !__CUDACC_RTC__ */
+#define EXCLUDE_FROM_RTC
+#include "crt/common_functions.h"
+#include "crt/device_functions.h"
+#include "device_launch_parameters.h"
+
+#if defined(__CUDACC_EXTENDED_LAMBDA__)
+#include <functional>
+#include <utility>
+struct  __device_builtin__ __nv_lambda_preheader_injection { };
+#endif /* defined(__CUDACC_EXTENDED_LAMBDA__) */
+
+#undef EXCLUDE_FROM_RTC
+#endif /* __CUDACC_RTC__ */
+
+#endif /* __CUDACC__ */
+
+/** \cond impl_private */
+#if defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+#define __CUDA_DEPRECATED
+#elif defined(_MSC_VER)
+#define __CUDA_DEPRECATED __declspec(deprecated)
+#elif defined(__GNUC__)
+#define __CUDA_DEPRECATED __attribute__((deprecated))
+#else
+#define __CUDA_DEPRECATED
+#endif
+/** \endcond impl_private */
+
+#define EXCLUDE_FROM_RTC
+#if defined(__cplusplus) && !defined(__CUDACC_RTC__)
+
+#if __cplusplus >= 201103L || (defined(_MSC_VER) && (_MSC_VER >= 1900))
+#include <utility>
+#endif
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+/**
+ * \addtogroup CUDART_HIGHLEVEL
+ * @{
+ */
+
+/**
+ *\brief Launches a device function
+ *
+ * The function invokes kernel \p func on \p gridDim (\p gridDim.x &times; \p gridDim.y
+ * &times; \p gridDim.z) grid of blocks. Each block contains \p blockDim (\p blockDim.x &times;
+ * \p blockDim.y &times; \p blockDim.z) threads.
+ *
+ * If the kernel has N parameters the \p args should point to array of N pointers.
+ * Each pointer, from <tt>args[0]</tt> to <tt>args[N - 1]</tt>, point to the region
+ * of memory from which the actual parameter will be copied.
+ *
+ * \p sharedMem sets the amount of dynamic shared memory that will be available to
+ * each thread block.
+ *
+ * \p stream specifies a stream the invocation is associated to.
+ *
+ * \param func        - Device function symbol
+ * \param gridDim     - Grid dimentions
+ * \param blockDim    - Block dimentions
+ * \param args        - Arguments
+ * \param sharedMem   - Shared memory (defaults to 0)
+ * \param stream      - Stream identifier (defaults to NULL)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaLaunchKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C API)"
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaLaunchKernel(
+  T           *func,
+  dim3         gridDim,
+  dim3         blockDim,
+  void       **args,
+  size_t       sharedMem = 0,
+  cudaStream_t stream = 0
+)
+{
+    return ::cudaLaunchKernel((const void *)func, gridDim, blockDim, args, sharedMem, stream);
+}
+
+
+#if __cplusplus >= 201103L || (defined(_MSC_VER) && (_MSC_VER >= 1900)) || defined(__DOXYGEN_ONLY__)
+/**
+ * \brief Launches a CUDA function with launch-time configuration
+ *
+ * Invokes the kernel \p kernel on \p config->gridDim (\p config->gridDim.x
+ * &times; \p config->gridDim.y &times; \p config->gridDim.z) grid of blocks.
+ * Each block contains \p config->blockDim (\p config->blockDim.x &times;
+ * \p config->blockDim.y &times; \p config->blockDim.z) threads.
+ *
+ * \p config->dynamicSmemBytes sets the amount of dynamic shared memory that
+ * will be available to each thread block.
+ *
+ * \p config->stream specifies a stream the invocation is associated to.
+ *
+ * Configuration beyond grid and block dimensions, dynamic shared memory size,
+ * and stream can be provided with the following two fields of \p config:
+ *
+ * \p config->attrs is an array of \p config->numAttrs contiguous
+ * ::cudaLaunchAttribute elements. The value of this pointer is not considered
+ * if \p config->numAttrs is zero. However, in that case, it is recommended to
+ * set the pointer to NULL.
+ * \p config->numAttrs is the number of attributes populating the first
+ * \p config->numAttrs positions of the \p config->attrs array.
+ *
+ * The kernel arguments should be passed as arguments to this function via the
+ * \p args parameter pack.
+ *
+ * The C API version of this function, \p cudaLaunchKernelExC, is also available
+ * for pre-C++11 compilers and for use cases where the ability to pass kernel
+ * parameters via void* array is preferable.
+ *
+ * \param config - Launch configuration
+ * \param kernel - Kernel to launch
+ * \param args   - Parameter pack of kernel parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * \ref ::cudaLaunchKernelExC(const cudaLaunchConfig_t *config, const void *func, void **args) "cudaLaunchKernelEx (C API)",
+ * ::cuLaunchKernelEx
+ */
+template<typename... ExpTypes, typename... ActTypes>
+static __inline__ __host__ cudaError_t cudaLaunchKernelEx(
+  const cudaLaunchConfig_t *config,
+  void (*kernel)(ExpTypes...),
+  ActTypes &&... args
+)
+{
+    return [&](ExpTypes... coercedArgs){
+        void *pArgs[] = { &coercedArgs... };
+        return ::cudaLaunchKernelExC(config, (const void *)kernel, pArgs);
+    }(std::forward<ActTypes>(args)...);
+}
+
+/**
+ * \brief Launches a CUDA function with launch-time configuration
+ *
+ * Invokes the kernel \p kernel on \p config->gridDim (\p config->gridDim.x
+ * &times; \p config->gridDim.y &times; \p config->gridDim.z) grid of blocks.
+ * Each block contains \p config->blockDim (\p config->blockDim.x &times;
+ * \p config->blockDim.y &times; \p config->blockDim.z) threads.
+ *
+ * \p config->dynamicSmemBytes sets the amount of dynamic shared memory that
+ * will be available to each thread block.
+ *
+ * \p config->stream specifies a stream the invocation is associated to.
+ *
+ * Configuration beyond grid and block dimensions, dynamic shared memory size,
+ * and stream can be provided with the following two fields of \p config:
+ *
+ * \p config->attrs is an array of \p config->numAttrs contiguous
+ * ::cudaLaunchAttribute elements. The value of this pointer is not considered
+ * if \p config->numAttrs is zero. However, in that case, it is recommended to
+ * set the pointer to NULL.
+ * \p config->numAttrs is the number of attributes populating the first
+ * \p config->numAttrs positions of the \p config->attrs array.
+ *
+ * The kernel arguments should be passed as arguments to this function via the
+ * \p args parameter pack.
+ *
+ * The C API version of this function, \p cudaLaunchKernelExC, is also available
+ * for pre-C++11 compilers and for use cases where the ability to pass kernel
+ * parameters via void* array is preferable.
+ *
+ * \param config - Launch configuration
+ * \param func   - Kernel to launch
+ * \param args   - Parameter pack of kernel parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * \ref ::cudaLaunchKernelExC(const cudaLaunchConfig_t *config, const void *func, void **args) "cudaLaunchKernelEx (C API)",
+ * ::cuLaunchKernelEx
+ */
+template<typename... ActTypes>
+static __inline__ __host__ cudaError_t cudaLaunchKernelEx(
+  const cudaLaunchConfig_t *config,
+  const cudaKernel_t kernel,
+  ActTypes &&... args
+)
+{
+    void *pArgs[] = { &args... };
+    return ::cudaLaunchKernelExC(config, (const void *)kernel, pArgs);
+}
+#endif
+
+/**
+ *\brief Launches a device function
+ *
+ * The function invokes kernel \p func on \p gridDim (\p gridDim.x &times; \p gridDim.y
+ * &times; \p gridDim.z) grid of blocks. Each block contains \p blockDim (\p blockDim.x &times;
+ * \p blockDim.y &times; \p blockDim.z) threads.
+ *
+ * The device on which this kernel is invoked must have a non-zero value for
+ * the device attribute ::cudaDevAttrCooperativeLaunch.
+ *
+ * The total number of blocks launched cannot exceed the maximum number of blocks per
+ * multiprocessor as returned by ::cudaOccupancyMaxActiveBlocksPerMultiprocessor (or
+ * ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors
+ * as specified by the device attribute ::cudaDevAttrMultiProcessorCount.
+ *
+ * The kernel cannot make use of CUDA dynamic parallelism.
+ *
+ * If the kernel has N parameters the \p args should point to array of N pointers.
+ * Each pointer, from <tt>args[0]</tt> to <tt>args[N - 1]</tt>, point to the region
+ * of memory from which the actual parameter will be copied.
+ *
+ * \p sharedMem sets the amount of dynamic shared memory that will be available to
+ * each thread block.
+ *
+ * \p stream specifies a stream the invocation is associated to.
+ *
+ * \param func        - Device function symbol
+ * \param gridDim     - Grid dimentions
+ * \param blockDim    - Block dimentions
+ * \param args        - Arguments
+ * \param sharedMem   - Shared memory (defaults to 0)
+ * \param stream      - Stream identifier (defaults to NULL)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorSharedObjectInitFailed
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaLaunchCooperativeKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchCooperativeKernel (C API)"
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaLaunchCooperativeKernel(
+            T *func,
+  dim3         gridDim,
+  dim3         blockDim,
+  void       **args,
+  size_t       sharedMem = 0,
+  cudaStream_t stream = 0
+)
+{
+    return ::cudaLaunchCooperativeKernel((const void *)func, gridDim, blockDim, args, sharedMem, stream);
+}
+
+/**
+ * \brief \hl Creates an event object with the specified flags
+ *
+ * Creates an event object with the specified flags. Valid flags include:
+ * - ::cudaEventDefault: Default event creation flag.
+ * - ::cudaEventBlockingSync: Specifies that event should use blocking
+ *   synchronization. A host thread that uses ::cudaEventSynchronize() to wait
+ *   on an event created with this flag will block until the event actually
+ *   completes.
+ * - ::cudaEventDisableTiming: Specifies that the created event does not need
+ *   to record timing data.  Events created with this flag specified and
+ *   the ::cudaEventBlockingSync flag not specified will provide the best
+ *   performance when used with ::cudaStreamWaitEvent() and ::cudaEventQuery().
+ *
+ * \param event - Newly created event
+ * \param flags - Flags for new event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventRecord, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cudaStreamWaitEvent
+ */
+static __inline__ __host__ cudaError_t cudaEventCreate(
+  cudaEvent_t  *event,
+  unsigned int  flags
+)
+{
+  return ::cudaEventCreateWithFlags(event, flags);
+}
+
+/**
+ * \brief Creates an executable graph from a graph
+ *
+ * Instantiates \p graph as an executable graph. The graph is validated for any
+ * structural constraints or intra-node constraints which were not previously
+ * validated. If instantiation is successful, a handle to the instantiated graph
+ * is returned in \p pGraphExec.
+ *
+ * If there are any errors, diagnostic information may be returned in \p pErrorNode and
+ * \p pLogBuffer. This is the primary way to inspect instantiation errors. The output
+ * will be null terminated unless the diagnostics overflow
+ * the buffer. In this case, they will be truncated, and the last byte can be
+ * inspected to determine if truncation occurred.
+ *
+ * \param pGraphExec - Returns instantiated graph
+ * \param graph      - Graph to instantiate
+ * \param pErrorNode - In case of an instantiation error, this may be modified to
+ *                      indicate a node contributing to the error
+ * \param pLogBuffer   - A character buffer to store diagnostic messages
+ * \param bufferSize  - Size of the log buffer in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphInstantiateWithFlags,
+ * ::cudaGraphCreate,
+ * ::cudaGraphUpload,
+ * ::cudaGraphLaunch,
+ * ::cudaGraphExecDestroy
+ */
+static __inline__ __host__ cudaError_t cudaGraphInstantiate(
+  cudaGraphExec_t *pGraphExec,
+  cudaGraph_t graph,
+  cudaGraphNode_t *pErrorNode,
+  char *pLogBuffer,
+  size_t bufferSize
+)
+{
+  (void)pErrorNode;
+  (void)pLogBuffer;
+  (void)bufferSize;
+  return ::cudaGraphInstantiate(pGraphExec, graph, 0);
+}
+
+/**
+ * \brief \hl Allocates page-locked memory on the host
+ *
+ * Allocates \p size bytes of host memory that is page-locked and accessible
+ * to the device. The driver tracks the virtual memory ranges allocated with
+ * this function and automatically accelerates calls to functions such as
+ * ::cudaMemcpy(). Since the memory can be accessed directly by the device, it
+ * can be read or written with much higher bandwidth than pageable memory
+ * obtained with functions such as ::malloc(). Allocating excessive amounts of
+ * pinned memory may degrade system performance, since it reduces the amount
+ * of memory available to the system for paging. As a result, this function is
+ * best used sparingly to allocate staging areas for data exchange between host
+ * and device.
+ *
+ * The \p flags parameter enables different options to be specified that affect
+ * the allocation, as follows.
+ * - ::cudaHostAllocDefault: This flag's value is defined to be 0.
+ * - ::cudaHostAllocPortable: The memory returned by this call will be
+ * considered as pinned memory by all CUDA contexts, not just the one that
+ * performed the allocation.
+ * - ::cudaHostAllocMapped: Maps the allocation into the CUDA address space.
+ * The device pointer to the memory may be obtained by calling
+ * ::cudaHostGetDevicePointer().
+ * - ::cudaHostAllocWriteCombined: Allocates the memory as write-combined (WC).
+ * WC memory can be transferred across the PCI Express bus more quickly on some
+ * system configurations, but cannot be read efficiently by most CPUs.  WC
+ * memory is a good option for buffers that will be written by the CPU and read
+ * by the device via mapped pinned memory or host->device transfers.
+ *
+ * All of these flags are orthogonal to one another: a developer may allocate
+ * memory that is portable, mapped and/or write-combined with no restrictions.
+ *
+ * ::cudaSetDeviceFlags() must have been called with the ::cudaDeviceMapHost
+ * flag in order for the ::cudaHostAllocMapped flag to have any effect.
+ *
+ * The ::cudaHostAllocMapped flag may be specified on CUDA contexts for devices
+ * that do not support mapped pinned memory. The failure is deferred to
+ * ::cudaHostGetDevicePointer() because the memory may be mapped into other
+ * CUDA contexts via the ::cudaHostAllocPortable flag.
+ *
+ * Memory allocated by this function must be freed with ::cudaFreeHost().
+ *
+ * \param ptr   - Device pointer to allocated memory
+ * \param size  - Requested allocation size in bytes
+ * \param flags - Requested properties of allocated memory
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaSetDeviceFlags,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc
+ */
+static __inline__ __host__ cudaError_t cudaMallocHost(
+  void         **ptr,
+  size_t         size,
+  unsigned int   flags
+)
+{
+  return ::cudaHostAlloc(ptr, size, flags);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaHostAlloc(
+  T            **ptr,
+  size_t         size,
+  unsigned int   flags
+)
+{
+  return ::cudaHostAlloc((void**)(void*)ptr, size, flags);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaHostGetDevicePointer(
+  T            **pDevice,
+  void          *pHost,
+  unsigned int   flags
+)
+{
+  return ::cudaHostGetDevicePointer((void**)(void*)pDevice, pHost, flags);
+}
+
+/**
+ * \brief Allocates memory that will be automatically managed by the Unified Memory system
+ *
+ * Allocates \p size bytes of managed memory on the device and returns in
+ * \p *devPtr a pointer to the allocated memory. If the device doesn't support
+ * allocating managed memory, ::cudaErrorNotSupported is returned. Support
+ * for managed memory can be queried using the device attribute
+ * ::cudaDevAttrManagedMemory. The allocated memory is suitably
+ * aligned for any kind of variable. The memory is not cleared. If \p size
+ * is 0, ::cudaMallocManaged returns ::cudaErrorInvalidValue. The pointer
+ * is valid on the CPU and on all GPUs in the system that support managed memory.
+ * All accesses to this pointer must obey the Unified Memory programming model.
+ *
+ * \p flags specifies the default stream association for this allocation.
+ * \p flags must be one of ::cudaMemAttachGlobal or ::cudaMemAttachHost. The
+ * default value for \p flags is ::cudaMemAttachGlobal.
+ * If ::cudaMemAttachGlobal is specified, then this memory is accessible from
+ * any stream on any device. If ::cudaMemAttachHost is specified, then the
+ * allocation should not be accessed from devices that have a zero value for the
+ * device attribute ::cudaDevAttrConcurrentManagedAccess; an explicit call to
+ * ::cudaStreamAttachMemAsync will be required to enable access on such devices.
+ *
+ * If the association is later changed via ::cudaStreamAttachMemAsync to
+ * a single stream, the default association, as specifed during ::cudaMallocManaged,
+ * is restored when that stream is destroyed. For __managed__ variables, the
+ * default association is always ::cudaMemAttachGlobal. Note that destroying a
+ * stream is an asynchronous operation, and as a result, the change to default
+ * association won't happen until all work in the stream has completed.
+ *
+ * Memory allocated with ::cudaMallocManaged should be released with ::cudaFree.
+ *
+ * Device memory oversubscription is possible for GPUs that have a non-zero value for the
+ * device attribute ::cudaDevAttrConcurrentManagedAccess. Managed memory on
+ * such GPUs may be evicted from device memory to host memory at any time by the Unified
+ * Memory driver in order to make room for other allocations.
+ *
+ * In a multi-GPU system where all GPUs have a non-zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess, managed memory may not be populated when this
+ * API returns and instead may be populated on access. In such systems, managed memory can
+ * migrate to any processor's memory at any time. The Unified Memory driver will employ heuristics to
+ * maintain data locality and prevent excessive page faults to the extent possible. The application
+ * can also guide the driver about memory usage patterns via ::cudaMemAdvise. The application
+ * can also explicitly migrate memory to a desired processor's memory via
+ * ::cudaMemPrefetchAsync.
+ *
+ * In a multi-GPU system where all of the GPUs have a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess and all the GPUs have peer-to-peer support
+ * with each other, the physical storage for managed memory is created on the GPU which is active
+ * at the time ::cudaMallocManaged is called. All other GPUs will reference the data at reduced
+ * bandwidth via peer mappings over the PCIe bus. The Unified Memory driver does not migrate
+ * memory among such GPUs.
+ *
+ * In a multi-GPU system where not all GPUs have peer-to-peer support with each other and
+ * where the value of the device attribute ::cudaDevAttrConcurrentManagedAccess
+ * is zero for at least one of those GPUs, the location chosen for physical storage of managed
+ * memory is system-dependent.
+ * - On Linux, the location chosen will be device memory as long as the current set of active
+ * contexts are on devices that either have peer-to-peer support with each other or have a
+ * non-zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * If there is an active context on a GPU that does not have a non-zero value for that device
+ * attribute and it does not have peer-to-peer support with the other devices that have active
+ * contexts on them, then the location for physical storage will be 'zero-copy' or host memory.
+ * Note that this means that managed memory that is located in device memory is migrated to
+ * host memory if a new context is created on a GPU that doesn't have a non-zero value for
+ * the device attribute and does not support peer-to-peer with at least one of the other devices
+ * that has an active context. This in turn implies that context creation may fail if there is
+ * insufficient host memory to migrate all managed allocations.
+ * - On Windows, the physical storage is always created in 'zero-copy' or host memory.
+ * All GPUs will reference the data at reduced bandwidth over the PCIe bus. In these
+ * circumstances, use of the environment variable CUDA_VISIBLE_DEVICES is recommended to
+ * restrict CUDA to only use those GPUs that have peer-to-peer support.
+ * Alternatively, users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to a non-zero
+ * value to force the driver to always use device memory for physical storage.
+ * When this environment variable is set to a non-zero value, all devices used in
+ * that process that support managed memory have to be peer-to-peer compatible
+ * with each other. The error ::cudaErrorInvalidDevice will be returned if a device
+ * that supports managed memory is used and it is not peer-to-peer compatible with
+ * any of the other managed memory supporting devices that were previously used in
+ * that process, even if ::cudaDeviceReset has been called on those devices. These
+ * environment variables are described in the CUDA programming guide under the
+ * "CUDA environment variables" section.
+ * - On ARM, managed memory is not available on discrete gpu with Drive PX-2.
+ *
+ * \param devPtr - Pointer to allocated device memory
+ * \param size   - Requested allocation size in bytes
+ * \param flags  - Must be either ::cudaMemAttachGlobal or ::cudaMemAttachHost (defaults to ::cudaMemAttachGlobal)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMallocPitch, ::cudaFree, ::cudaMallocArray, ::cudaFreeArray,
+ * ::cudaMalloc3D, ::cudaMalloc3DArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc, ::cudaDeviceGetAttribute, ::cudaStreamAttachMemAsync
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaMallocManaged(
+  T            **devPtr,
+  size_t         size,
+  unsigned int   flags = cudaMemAttachGlobal
+)
+{
+  return ::cudaMallocManaged((void**)(void*)devPtr, size, flags);
+}
+
+/**
+ * \brief Advise about the usage of a given memory range.
+ *
+ * This is an alternate spelling for cudaMemAdvise made available through function overloading.
+ *
+ * \sa ::cudaMemAdvise,
+ * \ref ::cudaMemAdvise(const void* devPtr, size_t count, enum cudaMemoryAdvise advice, struct cudaMemLocation location)  "cudaMemAdvise (C API)"
+ */
+template<class T>
+cudaError_t cudaMemAdvise(
+  T                      *devPtr,
+  size_t                 count,
+  enum cudaMemoryAdvise  advice,
+  struct cudaMemLocation location
+)
+{
+  return ::cudaMemAdvise_v2((const void *)devPtr, count, advice, location);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMemPrefetchAsync(
+  T                       *devPtr,
+  size_t                  count,
+  struct cudaMemLocation  location,
+  unsigned int            flags,
+  cudaStream_t            stream = 0
+)
+{
+  return ::cudaMemPrefetchAsync_v2((const void *)devPtr, count, location, flags, stream);
+}
+
+/**
+ * \brief Attach memory to a stream asynchronously
+ *
+ * Enqueues an operation in \p stream to specify stream association of
+ * \p length bytes of memory starting from \p devPtr. This function is a
+ * stream-ordered operation, meaning that it is dependent on, and will
+ * only take effect when, previous work in stream has completed. Any
+ * previous association is automatically replaced.
+ *
+ * \p devPtr must point to an one of the following types of memories:
+ * - managed memory declared using the __managed__ keyword or allocated with
+ *   ::cudaMallocManaged.
+ * - a valid host-accessible region of system-allocated pageable memory. This
+ *   type of memory may only be specified if the device associated with the
+ *   stream reports a non-zero value for the device attribute
+ *   ::cudaDevAttrPageableMemoryAccess.
+ *
+ * For managed allocations, \p length must be either zero or the entire
+ * allocation's size. Both indicate that the entire allocation's stream
+ * association is being changed. Currently, it is not possible to change stream
+ * association for a portion of a managed allocation.
+ *
+ * For pageable allocations, \p length must be non-zero.
+ *
+ * The stream association is specified using \p flags which must be
+ * one of ::cudaMemAttachGlobal, ::cudaMemAttachHost or ::cudaMemAttachSingle.
+ * The default value for \p flags is ::cudaMemAttachSingle
+ * If the ::cudaMemAttachGlobal flag is specified, the memory can be accessed
+ * by any stream on any device.
+ * If the ::cudaMemAttachHost flag is specified, the program makes a guarantee
+ * that it won't access the memory on the device from any stream on a device that
+ * has a zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * If the ::cudaMemAttachSingle flag is specified and \p stream is associated with
+ * a device that has a zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess,
+ * the program makes a guarantee that it will only access the memory on the device
+ * from \p stream. It is illegal to attach singly to the NULL stream, because the
+ * NULL stream is a virtual global stream and not a specific stream. An error will
+ * be returned in this case.
+ *
+ * When memory is associated with a single stream, the Unified Memory system will
+ * allow CPU access to this memory region so long as all operations in \p stream
+ * have completed, regardless of whether other streams are active. In effect,
+ * this constrains exclusive ownership of the managed memory region by
+ * an active GPU to per-stream activity instead of whole-GPU activity.
+ *
+ * Accessing memory on the device from streams that are not associated with
+ * it will produce undefined results. No error checking is performed by the
+ * Unified Memory system to ensure that kernels launched into other streams
+ * do not access this region.
+ *
+ * It is a program's responsibility to order calls to ::cudaStreamAttachMemAsync
+ * via events, synchronization or other means to ensure legal access to memory
+ * at all times. Data visibility and coherency will be changed appropriately
+ * for all kernels which follow a stream-association change.
+ *
+ * If \p stream is destroyed while data is associated with it, the association is
+ * removed and the association reverts to the default visibility of the allocation
+ * as specified at ::cudaMallocManaged. For __managed__ variables, the default
+ * association is always ::cudaMemAttachGlobal. Note that destroying a stream is an
+ * asynchronous operation, and as a result, the change to default association won't
+ * happen until all work in the stream has completed.
+ *
+ * \param stream  - Stream in which to enqueue the attach operation
+ * \param devPtr  - Pointer to memory (must be a pointer to managed memory or
+ *                  to a valid host-accessible region of system-allocated
+ *                  memory)
+ * \param length  - Length of memory (defaults to zero)
+ * \param flags   - Must be one of ::cudaMemAttachGlobal, ::cudaMemAttachHost or ::cudaMemAttachSingle (defaults to ::cudaMemAttachSingle)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotReady,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate, ::cudaStreamCreateWithFlags, ::cudaStreamWaitEvent, ::cudaStreamSynchronize, ::cudaStreamAddCallback, ::cudaStreamDestroy, ::cudaMallocManaged
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaStreamAttachMemAsync(
+  cudaStream_t   stream,
+  T              *devPtr,
+  size_t         length = 0,
+  unsigned int   flags  = cudaMemAttachSingle
+)
+{
+  return ::cudaStreamAttachMemAsync(stream, (void*)devPtr, length, flags);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMalloc(
+  T      **devPtr,
+  size_t   size
+)
+{
+  return ::cudaMalloc((void**)(void*)devPtr, size);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMallocHost(
+  T            **ptr,
+  size_t         size,
+  unsigned int   flags = 0
+)
+{
+  return cudaMallocHost((void**)(void*)ptr, size, flags);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMallocPitch(
+  T      **devPtr,
+  size_t  *pitch,
+  size_t   width,
+  size_t   height
+)
+{
+  return ::cudaMallocPitch((void**)(void*)devPtr, pitch, width, height);
+}
+
+/**
+ * \brief Allocate from a pool
+ *
+ * This is an alternate spelling for cudaMallocFromPoolAsync
+ * made available through function overloading.
+ *
+ * \sa ::cudaMallocFromPoolAsync,
+ * \ref ::cudaMallocAsync(void** ptr, size_t size, cudaStream_t hStream)  "cudaMallocAsync (C API)"
+ */
+static __inline__ __host__ cudaError_t cudaMallocAsync(
+  void        **ptr,
+  size_t        size,
+  cudaMemPool_t memPool,
+  cudaStream_t  stream
+)
+{
+  return ::cudaMallocFromPoolAsync(ptr, size, memPool, stream);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMallocAsync(
+  T           **ptr,
+  size_t        size,
+  cudaMemPool_t memPool,
+  cudaStream_t  stream
+)
+{
+  return ::cudaMallocFromPoolAsync((void**)(void*)ptr, size, memPool, stream);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMallocAsync(
+  T           **ptr,
+  size_t        size,
+  cudaStream_t  stream
+)
+{
+  return ::cudaMallocAsync((void**)(void*)ptr, size, stream);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaMallocFromPoolAsync(
+  T           **ptr,
+  size_t        size,
+  cudaMemPool_t memPool,
+  cudaStream_t  stream
+)
+{
+  return ::cudaMallocFromPoolAsync((void**)(void*)ptr, size, memPool, stream);
+}
+
+/**
+ * \brief Performs a batch of memory copies asynchronously.
+ *
+ * This is an alternate spelling for cudaMemcpyBatchAsync
+ * made available through function overloading.
+ *
+ * \sa ::cudaMemcpyBatchAsync
+ */
+template<typename T, typename U>
+static __inline__ __host__ cudaError_t cudaMemcpyBatchAsync(
+      T **dsts, U **srcs, size_t *sizes, size_t count, struct cudaMemcpyAttributes *attrs,
+      size_t *attrsIdxs, size_t numAttrs, size_t *failIdx, cudaStream_t hStream
+)
+{
+  return ::cudaMemcpyBatchAsync((void **)dsts, (void **)srcs, sizes, count, attrs, attrsIdxs, numAttrs, failIdx, hStream);
+}
+
+/**
+ * \brief Performs a batch of memory copies asynchronously.
+ *
+ * This is an alternate spelling for cudaMemcpyBatchAsync
+ * made available through function overloading.
+ *
+ * The ::cudaMemcpyAttributes specified by \p attr are applicable for all the copies specified in the batch.
+ *
+ * \sa ::cudaMemcpyBatchAsync
+ */
+template<typename T, typename U>
+static __inline__ __host__ cudaError_t cudaMemcpyBatchAsync(
+      T **dsts, U **srcs, size_t *sizes, size_t count, struct cudaMemcpyAttributes attr, size_t *failIdx, cudaStream_t hStream
+)
+{
+  size_t attrsIdxs = 0;
+  return ::cudaMemcpyBatchAsync((void **)dsts, (void **)srcs, sizes, count, &attr, &attrsIdxs, 1, failIdx, hStream);
+}
+
+#if defined(__CUDACC__)
+
+/**
+ * \brief \hl Copies data to the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src
+ * to the memory area \p offset bytes from the start of symbol
+ * \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToDevice.
+ *
+ * \param symbol - Device symbol reference
+ * \param src    - Source memory address
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_sync
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaMemcpyToSymbol(
+  const T                   &symbol,
+  const void                *src,
+        size_t               count,
+        size_t               offset = 0,
+        enum cudaMemcpyKind  kind   = cudaMemcpyHostToDevice
+)
+{
+  return ::cudaMemcpyToSymbol((const void*)&symbol, src, count, offset, kind);
+}
+
+/**
+ * \brief \hl Copies data to the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src
+ * to the memory area \p offset bytes from the start of symbol
+ * \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToDevice.
+ *
+ * ::cudaMemcpyToSymbolAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If
+ * \p kind is ::cudaMemcpyHostToDevice and \p stream is non-zero, the copy
+ * may overlap with operations in other streams.
+ *
+ * \param symbol - Device symbol reference
+ * \param src    - Source memory address
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_async
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyFromSymbolAsync
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaMemcpyToSymbolAsync(
+  const T                   &symbol,
+  const void                *src,
+        size_t               count,
+        size_t               offset = 0,
+        enum cudaMemcpyKind  kind   = cudaMemcpyHostToDevice,
+        cudaStream_t         stream = 0
+)
+{
+  return ::cudaMemcpyToSymbolAsync((const void*)&symbol, src, count, offset, kind, stream);
+}
+
+/**
+ * \brief \hl Copies data from the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area \p offset bytes
+ * from the start of symbol \p symbol to the memory area pointed to by \p dst.
+ * The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost or ::cudaMemcpyDeviceToDevice.
+ *
+ * \param dst    - Destination memory address
+ * \param symbol - Device symbol reference
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_sync
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaMemcpyFromSymbol(
+        void                *dst,
+  const T                   &symbol,
+        size_t               count,
+        size_t               offset = 0,
+        enum cudaMemcpyKind  kind   = cudaMemcpyDeviceToHost
+)
+{
+  return ::cudaMemcpyFromSymbol(dst, (const void*)&symbol, count, offset, kind);
+}
+
+/**
+ * \brief \hl Copies data from the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area \p offset bytes
+ * from the start of symbol \p symbol to the memory area pointed to by \p dst.
+ * The memory areas may not overlap. \p symbol is a variable that resides in
+ * global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost or ::cudaMemcpyDeviceToDevice.
+ *
+ * ::cudaMemcpyFromSymbolAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally be
+ * associated to a stream by passing a non-zero \p stream argument. If \p kind
+ * is ::cudaMemcpyDeviceToHost and \p stream is non-zero, the copy may overlap
+ * with operations in other streams.
+ *
+ * \param dst    - Destination memory address
+ * \param symbol - Device symbol reference
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_async
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaMemcpyFromSymbolAsync(
+        void                *dst,
+  const T                   &symbol,
+        size_t               count,
+        size_t               offset = 0,
+        enum cudaMemcpyKind  kind   = cudaMemcpyDeviceToHost,
+        cudaStream_t         stream = 0
+)
+{
+  return ::cudaMemcpyFromSymbolAsync(dst, (const void*)&symbol, count, offset, kind, stream);
+}
+
+/**
+ * \brief Creates a memcpy node to copy to a symbol on the device and adds it to a graph
+ *
+ * Creates a new memcpy node to copy to \p symbol and adds it to \p graph with
+ * \p numDependencies dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p src to the memory area pointed to by \p offset bytes from the start
+ * of symbol \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of
+ * transfer is inferred from the pointer values. However, ::cudaMemcpyDefault
+ * is only allowed on systems that support unified virtual addressing.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param symbol          - Device symbol address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyToSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeFromSymbol,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemsetNode
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGraphAddMemcpyNodeToSymbol(
+    cudaGraphNode_t *pGraphNode,
+    cudaGraph_t graph,
+    const cudaGraphNode_t *pDependencies,
+    size_t numDependencies,
+    const T &symbol,
+    const void* src,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind)
+{
+  return ::cudaGraphAddMemcpyNodeToSymbol(pGraphNode, graph, pDependencies, numDependencies, (const void*)&symbol, src, count, offset, kind);
+}
+
+/**
+ * \brief Creates a memcpy node to copy from a symbol on the device and adds it to a graph
+ *
+ * Creates a new memcpy node to copy from \p symbol and adds it to \p graph with
+ * \p numDependencies dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p offset bytes from the start of symbol \p symbol to the memory area
+ *  pointed to by \p dst. The memory areas may not overlap. \p symbol is a variable
+ *  that resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of transfer
+ * is inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param dst             - Destination memory address
+ * \param symbol          - Device symbol address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyFromSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeToSymbol,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemsetNode
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGraphAddMemcpyNodeFromSymbol(
+    cudaGraphNode_t* pGraphNode,
+    cudaGraph_t graph,
+    const cudaGraphNode_t* pDependencies,
+    size_t numDependencies,
+    void* dst,
+    const T &symbol,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind)
+{
+  return ::cudaGraphAddMemcpyNodeFromSymbol(pGraphNode, graph, pDependencies, numDependencies, dst, (const void*)&symbol, count, offset, kind);
+}
+
+/**
+ * \brief Sets a memcpy node's parameters to copy to a symbol on the device
+ *
+ * Sets the parameters of memcpy node \p node to the copy described by the provided parameters.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p src to the memory area pointed to by \p offset bytes from the start
+ * of symbol \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of
+ * transfer is inferred from the pointer values. However, ::cudaMemcpyDefault
+ * is only allowed on systems that support unified virtual addressing.
+ *
+ * \param node            - Node to set the parameters for
+ * \param symbol          - Device symbol address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyToSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGraphMemcpyNodeSetParamsToSymbol(
+    cudaGraphNode_t node,
+    const T &symbol,
+    const void* src,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind)
+{
+  return ::cudaGraphMemcpyNodeSetParamsToSymbol(node, (const void*)&symbol, src, count, offset, kind);
+}
+
+/**
+ * \brief Sets a memcpy node's parameters to copy from a symbol on the device
+ *
+ * Sets the parameters of memcpy node \p node to the copy described by the provided parameters.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p offset bytes from the start of symbol \p symbol to the memory area
+ *  pointed to by \p dst. The memory areas may not overlap. \p symbol is a variable
+ *  that resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of transfer
+ * is inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * \param node            - Node to set the parameters for
+ * \param dst             - Destination memory address
+ * \param symbol          - Device symbol address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGraphMemcpyNodeSetParamsFromSymbol(
+    cudaGraphNode_t node,
+    void* dst,
+    const T &symbol,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind)
+{
+  return ::cudaGraphMemcpyNodeSetParamsFromSymbol(node, dst, (const void*)&symbol, count, offset, kind);
+}
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec to copy to a symbol on the device
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained the given params at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * \p src and \p symbol must be allocated from the same contexts as the original source and
+ * destination memory.  The instantiation-time memory operands must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * Returns ::cudaErrorInvalidValue if the memory operands' mappings changed or
+ * the original memory operands are multidimensional.
+ *
+ * \param hGraphExec      - The executable graph in which to set the specified node
+ * \param node            - Memcpy node from the graph which was used to instantiate graphExec
+ * \param symbol          - Device symbol address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeToSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGraphExecMemcpyNodeSetParamsToSymbol(
+    cudaGraphExec_t hGraphExec,
+    cudaGraphNode_t node,
+    const T &symbol,
+    const void* src,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind)
+{
+    return ::cudaGraphExecMemcpyNodeSetParamsToSymbol(hGraphExec, node, (const void*)&symbol, src, count, offset, kind);
+}
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec to copy from a symbol on the device
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained the given params at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * \p symbol and \p dst must be allocated from the same contexts as the original source and
+ * destination memory.  The instantiation-time memory operands must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * Returns ::cudaErrorInvalidValue if the memory operands' mappings changed or
+ * the original memory operands are multidimensional.
+ *
+ * \param hGraphExec      - The executable graph in which to set the specified node
+ * \param node            - Memcpy node from the graph which was used to instantiate graphExec
+ * \param dst             - Destination memory address
+ * \param symbol          - Device symbol address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGraphExecMemcpyNodeSetParamsFromSymbol(
+    cudaGraphExec_t hGraphExec,
+    cudaGraphNode_t node,
+    void* dst,
+    const T &symbol,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind)
+{
+  return ::cudaGraphExecMemcpyNodeSetParamsFromSymbol(hGraphExec, node, dst, (const void*)&symbol, count, offset, kind);
+}
+
+// convenience function to avoid source breakage in c++ code
+static __inline__ __host__ cudaError_t CUDARTAPI cudaGraphExecUpdate(cudaGraphExec_t hGraphExec, cudaGraph_t hGraph, cudaGraphNode_t *hErrorNode_out, enum cudaGraphExecUpdateResult *updateResult_out)
+{
+    cudaGraphExecUpdateResultInfo resultInfo;
+    cudaError_t status = cudaGraphExecUpdate(hGraphExec, hGraph, &resultInfo);
+    if (hErrorNode_out) {
+        *hErrorNode_out = resultInfo.errorNode;
+    }
+    if (updateResult_out) {
+        *updateResult_out = resultInfo.result;
+    }
+    return status;
+}
+
+#if __cplusplus >= 201103L || (defined(_MSC_VER) && (_MSC_VER >= 1900))
+
+/**
+ * \brief Creates a user object by wrapping a C++ object
+ *
+ * TODO detail
+ *
+ * \param object_out      - Location to return the user object handle
+ * \param objectToWrap    - This becomes the \ptr argument to ::cudaUserObjectCreate. A
+ *                          lambda will be passed for the \p destroy argument, which calls
+ *                          delete on this object pointer.
+ * \param initialRefcount - The initial refcount to create the object with, typically 1. The
+ *                          initial references are owned by the calling thread.
+ * \param flags           - Currently it is required to pass cudaUserObjectNoDestructorSync,
+ *                          which is the only defined flag. This indicates that the destroy
+ *                          callback cannot be waited on by any CUDA API. Users requiring
+ *                          synchronization of the callback should signal its completion
+ *                          manually.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa
+ * ::cudaUserObjectCreate
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaUserObjectCreate(
+    cudaUserObject_t *object_out,
+    T *objectToWrap,
+    unsigned int initialRefcount,
+    unsigned int flags)
+{
+    return ::cudaUserObjectCreate(
+            object_out,
+            objectToWrap,
+            [](void *vpObj) { delete reinterpret_cast<T *>(vpObj); },
+            initialRefcount,
+            flags);
+}
+
+template<class T>
+static __inline__ __host__ cudaError_t cudaUserObjectCreate(
+    cudaUserObject_t *object_out,
+    T *objectToWrap,
+    unsigned int initialRefcount,
+    cudaUserObjectFlags flags)
+{
+    return cudaUserObjectCreate(object_out, objectToWrap, initialRefcount, (unsigned int)flags);
+}
+
+#endif
+
+/**
+ * \brief \hl Finds the address associated with a CUDA symbol
+ *
+ * Returns in \p *devPtr the address of symbol \p symbol on the device.
+ * \p symbol can either be a variable that resides in global or constant memory space.
+ * If \p symbol cannot be found, or if \p symbol is not declared
+ * in the global or constant memory space, \p *devPtr is unchanged and the error
+ * ::cudaErrorInvalidSymbol is returned.
+ *
+ * \param devPtr - Return device pointer associated with symbol
+ * \param symbol - Device symbol reference
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa \ref ::cudaGetSymbolAddress(void**, const void*) "cudaGetSymbolAddress (C API)",
+ * \ref ::cudaGetSymbolSize(size_t*, const T&) "cudaGetSymbolSize (C++ API)"
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGetSymbolAddress(
+        void **devPtr,
+  const T     &symbol
+)
+{
+  return ::cudaGetSymbolAddress(devPtr, (const void*)&symbol);
+}
+
+/**
+ * \brief \hl Finds the size of the object associated with a CUDA symbol
+ *
+ * Returns in \p *size the size of symbol \p symbol. \p symbol must be a
+ * variable that resides in global or constant memory space.
+ * If \p symbol cannot be found, or if \p symbol is not declared
+ * in global or constant memory space, \p *size is unchanged and the error
+ * ::cudaErrorInvalidSymbol is returned.
+ *
+ * \param size   - Size of object associated with symbol
+ * \param symbol - Device symbol reference
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa \ref ::cudaGetSymbolAddress(void**, const T&) "cudaGetSymbolAddress (C++ API)",
+ * \ref ::cudaGetSymbolSize(size_t*, const void*) "cudaGetSymbolSize (C API)"
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaGetSymbolSize(
+        size_t *size,
+  const T      &symbol
+)
+{
+  return ::cudaGetSymbolSize(size, (const void*)&symbol);
+}
+
+/**
+ * \brief \hl Sets the preferred cache configuration for a device function
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p cacheConfig the preferred cache configuration
+ * for the function specified via \p func. This is only a preference. The
+ * runtime will use the requested configuration if possible, but it is free to
+ * choose a different configuration if required to execute \p func.
+ *
+ * \p func must be a pointer to a function that executes on the device.
+ * The parameter specified by \p func must be declared as a \p __global__
+ * function. If the specified function does not exist,
+ * then ::cudaErrorInvalidDeviceFunction is returned.
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ * The supported cache configurations are:
+ * - ::cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
+ * - ::cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
+ * - ::cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory
+ *
+ * \param func        - device function pointer
+ * \param cacheConfig - Requested cache configuration
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \ref ::cudaLaunchKernel(T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C++ API)",
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, T*) "cudaFuncGetAttributes (C++ API)",
+ * ::cudaSetDoubleForDevice,
+ * ::cudaSetDoubleForHost,
+ * ::cudaThreadGetCacheConfig,
+ * ::cudaThreadSetCacheConfig
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaFuncSetCacheConfig(
+  T                  *func,
+  enum cudaFuncCache  cacheConfig
+)
+{
+  return ::cudaFuncSetCacheConfig((const void*)func, cacheConfig);
+}
+
+template<class T>
+static __inline__ 
+__CUDA_DEPRECATED 
+__host__ cudaError_t cudaFuncSetSharedMemConfig(
+  T                        *func,
+  enum cudaSharedMemConfig  config
+)
+{
+#if defined(__GNUC__)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+#elif defined(_MSC_VER)
+#pragma warning(suppress: 4996)    
+#endif
+  return ::cudaFuncSetSharedMemConfig((const void*)func, config);
+#if defined(__GNUC__)
+#pragma GCC diagnostic pop
+#endif
+}
+
+#endif // __CUDACC__
+
+/**
+ * \brief Returns occupancy for a device function
+ *
+ * Returns in \p *numBlocks the maximum number of active blocks per
+ * streaming multiprocessor for the device function.
+ *
+ * \param numBlocks       - Returned occupancy
+ * \param func            - Kernel function for which occupancy is calulated
+ * \param blockSize       - Block size the kernel is intended to be launched with
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSize
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ * \sa ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    int   *numBlocks,
+    T      func,
+    int    blockSize,
+    size_t dynamicSMemSize)
+{
+    return ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(numBlocks, (const void*)func, blockSize, dynamicSMemSize, cudaOccupancyDefault);
+}
+
+/**
+ * \brief Returns occupancy for a device function with the specified flags
+ *
+ * Returns in \p *numBlocks the maximum number of active blocks per
+ * streaming multiprocessor for the device function.
+ *
+ * The \p flags parameter controls how special cases are handled. Valid flags include:
+ *
+ * - ::cudaOccupancyDefault: keeps the default behavior as
+ *   ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ *
+ * - ::cudaOccupancyDisableCachingOverride: suppresses the default behavior
+ *   on platform where global caching affects occupancy. On such platforms, if caching
+ *   is enabled, but per-block SM resource usage would result in zero occupancy, the
+ *   occupancy calculator will calculate the occupancy as if caching is disabled.
+ *   Setting this flag makes the occupancy calculator to return 0 in such cases.
+ *   More information can be found about this feature in the "Unified L1/Texture Cache"
+ *   section of the Maxwell tuning guide.
+ *
+ * \param numBlocks       - Returned occupancy
+ * \param func            - Kernel function for which occupancy is calulated
+ * \param blockSize       - Block size the kernel is intended to be launched with
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ * \param flags           - Requested behavior for the occupancy calculator
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ * \sa ::cudaOccupancyMaxPotentialBlockSize
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ * \sa ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(
+    int         *numBlocks,
+    T            func,
+    int          blockSize,
+    size_t       dynamicSMemSize,
+    unsigned int flags)
+{
+    return ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(numBlocks, (const void*)func, blockSize, dynamicSMemSize, flags);
+}
+
+/**
+ * Helper functor for cudaOccupancyMaxPotentialBlockSize
+ */
+class __cudaOccupancyB2DHelper {
+  size_t n;
+public:
+  inline __host__ CUDART_DEVICE __cudaOccupancyB2DHelper(size_t n_) : n(n_) {}
+  inline __host__ CUDART_DEVICE size_t operator()(int)
+  {
+      return n;
+  }
+};
+
+/**
+ * \brief Returns grid and block size that achieves maximum potential occupancy for a device function
+ *
+ * Returns in \p *minGridSize and \p *blocksize a suggested grid /
+ * block size pair that achieves the best potential occupancy
+ * (i.e. the maximum number of active warps with the smallest number
+ * of blocks).
+ *
+ * The \p flags parameter controls how special cases are handled. Valid flags include:
+ *
+ * - ::cudaOccupancyDefault: keeps the default behavior as
+ *   ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ *
+ * - ::cudaOccupancyDisableCachingOverride: This flag suppresses the default behavior
+ *   on platform where global caching affects occupancy. On such platforms, if caching
+ *   is enabled, but per-block SM resource usage would result in zero occupancy, the
+ *   occupancy calculator will calculate the occupancy as if caching is disabled.
+ *   Setting this flag makes the occupancy calculator to return 0 in such cases.
+ *   More information can be found about this feature in the "Unified L1/Texture Cache"
+ *   section of the Maxwell tuning guide.
+ *
+ * \param minGridSize - Returned minimum grid size needed to achieve the best potential occupancy
+ * \param blockSize   - Returned block size
+ * \param func        - Device function symbol
+ * \param blockSizeToDynamicSMemSize - A unary function / functor that takes block size, and returns the size, in bytes, of dynamic shared memory needed for a block
+ * \param blockSizeLimit  - The maximum block size \p func is designed to work with. 0 means no limit.
+ * \param flags       - Requested behavior for the occupancy calculator
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSize
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ * \sa ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+
+template<typename UnaryFunction, class T>
+static __inline__ __host__ CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(
+    int           *minGridSize,
+    int           *blockSize,
+    T              func,
+    UnaryFunction  blockSizeToDynamicSMemSize,
+    int            blockSizeLimit = 0,
+    unsigned int   flags = 0)
+{
+    cudaError_t status;
+
+    // Device and function properties
+    int                       device;
+    struct cudaFuncAttributes attr;
+
+    // Limits
+    int maxThreadsPerMultiProcessor;
+    int warpSize;
+    int devMaxThreadsPerBlock;
+    int multiProcessorCount;
+    int funcMaxThreadsPerBlock;
+    int occupancyLimit;
+    int granularity;
+
+    // Recorded maximum
+    int maxBlockSize = 0;
+    int numBlocks    = 0;
+    int maxOccupancy = 0;
+
+    // Temporary
+    int blockSizeToTryAligned;
+    int blockSizeToTry;
+    int blockSizeLimitAligned;
+    int occupancyInBlocks;
+    int occupancyInThreads;
+    size_t dynamicSMemSize;
+
+    ///////////////////////////
+    // Check user input
+    ///////////////////////////
+
+    if (!minGridSize || !blockSize || !func) {
+        return cudaErrorInvalidValue;
+    }
+
+    //////////////////////////////////////////////
+    // Obtain device and function properties
+    //////////////////////////////////////////////
+
+    status = ::cudaGetDevice(&device);
+    if (status != cudaSuccess) {
+        return status;
+    }
+
+    status = cudaDeviceGetAttribute(
+        &maxThreadsPerMultiProcessor,
+        cudaDevAttrMaxThreadsPerMultiProcessor,
+        device);
+    if (status != cudaSuccess) {
+        return status;
+    }
+
+    status = cudaDeviceGetAttribute(
+        &warpSize,
+        cudaDevAttrWarpSize,
+        device);
+    if (status != cudaSuccess) {
+        return status;
+    }
+
+    status = cudaDeviceGetAttribute(
+        &devMaxThreadsPerBlock,
+        cudaDevAttrMaxThreadsPerBlock,
+        device);
+    if (status != cudaSuccess) {
+        return status;
+    }
+
+    status = cudaDeviceGetAttribute(
+        &multiProcessorCount,
+        cudaDevAttrMultiProcessorCount,
+        device);
+    if (status != cudaSuccess) {
+        return status;
+    }
+
+    status = cudaFuncGetAttributes(&attr, func);
+    if (status != cudaSuccess) {
+        return status;
+    }
+    
+    funcMaxThreadsPerBlock = attr.maxThreadsPerBlock;
+
+    /////////////////////////////////////////////////////////////////////////////////
+    // Try each block size, and pick the block size with maximum occupancy
+    /////////////////////////////////////////////////////////////////////////////////
+
+    occupancyLimit = maxThreadsPerMultiProcessor;
+    granularity    = warpSize;
+
+    if (blockSizeLimit == 0) {
+        blockSizeLimit = devMaxThreadsPerBlock;
+    }
+
+    if (devMaxThreadsPerBlock < blockSizeLimit) {
+        blockSizeLimit = devMaxThreadsPerBlock;
+    }
+
+    if (funcMaxThreadsPerBlock < blockSizeLimit) {
+        blockSizeLimit = funcMaxThreadsPerBlock;
+    }
+
+    blockSizeLimitAligned = ((blockSizeLimit + (granularity - 1)) / granularity) * granularity;
+
+    for (blockSizeToTryAligned = blockSizeLimitAligned; blockSizeToTryAligned > 0; blockSizeToTryAligned -= granularity) {
+        // This is needed for the first iteration, because
+        // blockSizeLimitAligned could be greater than blockSizeLimit
+        //
+        if (blockSizeLimit < blockSizeToTryAligned) {
+            blockSizeToTry = blockSizeLimit;
+        } else {
+            blockSizeToTry = blockSizeToTryAligned;
+        }
+        
+        dynamicSMemSize = blockSizeToDynamicSMemSize(blockSizeToTry);
+
+        status = cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(
+            &occupancyInBlocks,
+            func,
+            blockSizeToTry,
+            dynamicSMemSize,
+            flags);
+
+        if (status != cudaSuccess) {
+            return status;
+        }
+
+        occupancyInThreads = blockSizeToTry * occupancyInBlocks;
+
+        if (occupancyInThreads > maxOccupancy) {
+            maxBlockSize = blockSizeToTry;
+            numBlocks    = occupancyInBlocks;
+            maxOccupancy = occupancyInThreads;
+        }
+
+        // Early out if we have reached the maximum
+        //
+        if (occupancyLimit == maxOccupancy) {
+            break;
+        }
+    }
+
+    ///////////////////////////
+    // Return best available
+    ///////////////////////////
+
+    // Suggested min grid size to achieve a full machine launch
+    //
+    *minGridSize = numBlocks * multiProcessorCount;
+    *blockSize = maxBlockSize;
+
+    return status;
+}
+
+/**
+ * \brief Returns grid and block size that achieves maximum potential occupancy for a device function
+ *
+ * Returns in \p *minGridSize and \p *blocksize a suggested grid /
+ * block size pair that achieves the best potential occupancy
+ * (i.e. the maximum number of active warps with the smallest number
+ * of blocks).
+ *
+ * \param minGridSize - Returned minimum grid size needed to achieve the best potential occupancy
+ * \param blockSize   - Returned block size
+ * \param func        - Device function symbol
+ * \param blockSizeToDynamicSMemSize - A unary function / functor that takes block size, and returns the size, in bytes, of dynamic shared memory needed for a block
+ * \param blockSizeLimit  - The maximum block size \p func is designed to work with. 0 means no limit.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSize
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ * \sa ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+
+template<typename UnaryFunction, class T>
+static __inline__ __host__ CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSizeVariableSMem(
+    int           *minGridSize,
+    int           *blockSize,
+    T              func,
+    UnaryFunction  blockSizeToDynamicSMemSize,
+    int            blockSizeLimit = 0)
+{
+    return cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(minGridSize, blockSize, func, blockSizeToDynamicSMemSize, blockSizeLimit, cudaOccupancyDefault);
+}
+
+/**
+ * \brief Returns grid and block size that achieves maximum potential occupancy for a device function
+ *
+ * Returns in \p *minGridSize and \p *blocksize a suggested grid /
+ * block size pair that achieves the best potential occupancy
+ * (i.e. the maximum number of active warps with the smallest number
+ * of blocks).
+ *
+ * Use \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem if the
+ * amount of per-block dynamic shared memory changes with different
+ * block sizes.
+ *
+ * \param minGridSize - Returned minimum grid size needed to achieve the best potential occupancy
+ * \param blockSize   - Returned block size
+ * \param func        - Device function symbol
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ * \param blockSizeLimit  - The maximum block size \p func is designed to work with. 0 means no limit.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ * \sa ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+template<class T>
+static __inline__ __host__ CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSize(
+    int    *minGridSize,
+    int    *blockSize,
+    T       func,
+    size_t  dynamicSMemSize = 0,
+    int     blockSizeLimit = 0)
+{
+  return cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(minGridSize, blockSize, func, __cudaOccupancyB2DHelper(dynamicSMemSize), blockSizeLimit, cudaOccupancyDefault);
+}
+
+/**
+ * \brief Returns dynamic shared memory available per block when launching \p numBlocks blocks on SM.
+ *
+ * Returns in \p *dynamicSmemSize the maximum size of dynamic shared memory to allow \p numBlocks blocks per SM. 
+ *
+ * \param dynamicSmemSize - Returned maximum dynamic shared memory 
+ * \param func            - Kernel function for which occupancy is calculated
+ * \param numBlocks       - Number of blocks to fit on SM 
+ * \param blockSize       - Size of the block
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaOccupancyMaxPotentialBlockSize
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeWithFlags
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaOccupancyAvailableDynamicSMemPerBlock(
+    size_t *dynamicSmemSize,
+    T      *func,
+    int     numBlocks,
+    int     blockSize)
+{
+    return ::cudaOccupancyAvailableDynamicSMemPerBlock(dynamicSmemSize, (const void*)func, numBlocks, blockSize);
+}
+
+/**
+ * \brief Returns grid and block size that achived maximum potential occupancy for a device function with the specified flags
+ *
+ * Returns in \p *minGridSize and \p *blocksize a suggested grid /
+ * block size pair that achieves the best potential occupancy
+ * (i.e. the maximum number of active warps with the smallest number
+ * of blocks).
+ *
+ * The \p flags parameter controls how special cases are handle. Valid flags include:
+ *
+ * - ::cudaOccupancyDefault: keeps the default behavior as
+ *   ::cudaOccupancyMaxPotentialBlockSize
+ *
+ * - ::cudaOccupancyDisableCachingOverride: This flag suppresses the default behavior
+ *   on platform where global caching affects occupancy. On such platforms, if caching
+ *   is enabled, but per-block SM resource usage would result in zero occupancy, the
+ *   occupancy calculator will calculate the occupancy as if caching is disabled.
+ *   Setting this flag makes the occupancy calculator to return 0 in such cases.
+ *   More information can be found about this feature in the "Unified L1/Texture Cache"
+ *   section of the Maxwell tuning guide.
+ *
+ * Use \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem if the
+ * amount of per-block dynamic shared memory changes with different
+ * block sizes.
+ *
+ * \param minGridSize - Returned minimum grid size needed to achieve the best potential occupancy
+ * \param blockSize   - Returned block size
+ * \param func        - Device function symbol
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ * \param blockSizeLimit  - The maximum block size \p func is designed to work with. 0 means no limit.
+ * \param flags       - Requested behavior for the occupancy calculator
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaOccupancyMaxPotentialBlockSize
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMem
+ * \sa ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
+ * \sa ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+template<class T>
+static __inline__ __host__ CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSizeWithFlags(
+    int    *minGridSize,
+    int    *blockSize,
+    T      func,
+    size_t dynamicSMemSize = 0,
+    int    blockSizeLimit = 0,
+    unsigned int flags = 0)
+{
+    return cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(minGridSize, blockSize, func, __cudaOccupancyB2DHelper(dynamicSMemSize), blockSizeLimit, flags);
+}
+
+/**
+ * \brief Given the kernel function (\p func) and launch configuration
+ * (\p config), return the maximum cluster size in \p *clusterSize.
+ *
+ * The cluster dimensions in \p config are ignored. If func has a required
+ * cluster size set (see ::cudaFuncGetAttributes),\p *clusterSize will reflect 
+ * the required cluster size.
+ *
+ * By default this function will always return a value that's portable on
+ * future hardware. A higher value may be returned if the kernel function
+ * allows non-portable cluster sizes.
+ *
+ * This function will respect the compile time launch bounds.
+ *
+ * \param clusterSize - Returned maximum cluster size that can be launched
+ *                      for the given kernel function and launch configuration
+ * \param func        - Kernel function for which maximum cluster
+ *                      size is calculated
+ * \param config      - Launch configuration for the given kernel function
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * ::cudaFuncGetAttributes
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaOccupancyMaxPotentialClusterSize(
+    int *clusterSize,
+    T *func,
+    const cudaLaunchConfig_t *config)
+{
+    return ::cudaOccupancyMaxPotentialClusterSize(clusterSize, (const void*)func, config);
+}
+
+/**
+ * \brief Given the kernel function (\p func) and launch configuration
+ * (\p config), return the maximum number of clusters that could co-exist
+ * on the target device in \p *numClusters.
+ *
+ * If the function has required cluster size already set (see
+ * ::cudaFuncGetAttributes), the cluster size from config must either be
+ * unspecified or match the required size.
+ * Without required sizes, the cluster size must be specified in config,
+ * else the function will return an error.
+ *
+ * Note that various attributes of the kernel function may affect occupancy
+ * calculation. Runtime environment may affect how the hardware schedules
+ * the clusters, so the calculated occupancy is not guaranteed to be achievable.
+ *
+ * \param numClusters - Returned maximum number of clusters that
+ *                      could co-exist on the target device
+ * \param func        - Kernel function for which maximum number
+ *                      of clusters are calculated
+ * \param config      - Launch configuration for the given kernel function
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidClusterSize,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * ::cudaFuncGetAttributes
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaOccupancyMaxActiveClusters(
+    int *numClusters,
+    T *func,
+    const cudaLaunchConfig_t *config)
+{
+    return ::cudaOccupancyMaxActiveClusters(numClusters, (const void*)func, config);
+}
+
+#if defined __CUDACC__
+
+/**
+ * \brief \hl Find out attributes for a given function
+ *
+ * This function obtains the attributes of a function specified via \p entry.
+ * The parameter \p entry must be a pointer to a function that executes
+ * on the device. The parameter specified by \p entry must be declared as a \p __global__
+ * function. The fetched attributes are placed in \p attr. If the specified
+ * function does not exist, then ::cudaErrorInvalidDeviceFunction is returned.
+ *
+ * Note that some function attributes such as
+ * \ref ::cudaFuncAttributes::maxThreadsPerBlock "maxThreadsPerBlock"
+ * may vary based on the device that is currently being used.
+ *
+ * \param attr  - Return pointer to function's attributes
+ * \param entry - Function to get attributes of
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaLaunchKernel(T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C++ API)",
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const void*) "cudaFuncGetAttributes (C API)",
+ * ::cudaSetDoubleForDevice,
+ * ::cudaSetDoubleForHost
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaFuncGetAttributes(
+  struct cudaFuncAttributes *attr,
+  T                         *entry
+)
+{
+  return ::cudaFuncGetAttributes(attr, (const void*)entry);
+}
+
+/**
+ * \brief \hl Set attributes for a given function
+ *
+ * This function sets the attributes of a function specified via \p entry.
+ * The parameter \p entry must be a pointer to a function that executes
+ * on the device. The parameter specified by \p entry must be declared as a \p __global__
+ * function. The enumeration defined by \p attr is set to the value defined by \p value.
+ * If the specified function does not exist, then ::cudaErrorInvalidDeviceFunction is returned.
+ * If the specified attribute cannot be written, or if the value is incorrect, 
+ * then ::cudaErrorInvalidValue is returned.
+ *
+ * Valid values for \p attr are:
+ * - ::cudaFuncAttributeMaxDynamicSharedMemorySize - The requested maximum size in bytes of dynamically-allocated shared memory. The sum of this value and the function attribute ::sharedSizeBytes
+ *   cannot exceed the device attribute ::cudaDevAttrMaxSharedMemoryPerBlockOptin. The maximal size of requestable dynamic shared memory may differ by GPU architecture.
+ * - ::cudaFuncAttributePreferredSharedMemoryCarveout - On devices where the L1 cache and shared memory use the same hardware resources, 
+ *   this sets the shared memory carveout preference, in percent of the total shared memory. See ::cudaDevAttrMaxSharedMemoryPerMultiprocessor.
+ *   This is only a hint, and the driver can choose a different ratio if required to execute the function.
+ * - ::cudaFuncAttributeRequiredClusterWidth: The required cluster width in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeRequiredClusterHeight: The required cluster height in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeRequiredClusterDepth: The required cluster depth in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeNonPortableClusterSizeAllowed: Indicates whether the
+ *   function can be launched with non-portable cluster size. 1 is allowed, 0 is
+ *   disallowed.
+ * - ::cudaFuncAttributeClusterSchedulingPolicyPreference: The block
+ *   scheduling policy of a function. The value type is cudaClusterSchedulingPolicy.
+ *
+ * \param entry - Function to get attributes of
+ * \param attr  - Attribute to set
+ * \param value - Value to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaLaunchKernel(T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C++ API)",
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const void*) "cudaFuncGetAttributes (C API)",
+ * ::cudaSetDoubleForDevice,
+ * ::cudaSetDoubleForHost
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaFuncSetAttribute(
+  T                        *func,
+  enum cudaFuncAttribute    attr,
+  int                       value
+)
+{
+  return ::cudaFuncSetAttribute((const void*)func, attr, value);
+}
+
+/**
+ * \brief Returns the function name for a device entry function pointer.
+ *
+ * Returns in \p **name the function name associated with the symbol \p func .
+ * The function name is returned as a null-terminated string. This API may
+ * return a mangled name if the function is not declared as having C linkage.
+ * If \p **name is NULL, ::cudaErrorInvalidValue is returned. If \p func is
+ * not a device entry function, ::cudaErrorInvalidDeviceFunction is returned.
+ *
+ * \param name - The returned name of the function
+ * \param func - The function pointer to retrieve name for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaFuncGetName(const char **name, const void *func) "cudaFuncGetName (C API)"
+ */
+template<class T>
+static __inline__ __host__ cudaError_t CUDARTAPI cudaFuncGetName(
+  const char **name,
+            T *func
+)
+{
+  return ::cudaFuncGetName(name, (const void *)func);
+}
+
+/**
+ * \brief Get pointer to device kernel that matches entry function \p entryFuncAddr
+  *
+  * Returns in \p kernelPtr the device kernel corresponding to the entry function \p entryFuncAddr.
+  *
+  * \param kernelPtr          - Returns the device kernel
+  * \param entryFuncAddr      - Address of device entry function to search kernel for
+  *
+  * \return
+  * ::cudaSuccess
+  *
+  * \sa
+  * \ref ::cudaGetKernel(cudaKernel_t *kernelPtr, const void *entryFuncAddr) "cudaGetKernel (C API)"
+  */
+template<class T>
+static  __inline__ __host__ cudaError_t cudaGetKernel(
+  cudaKernel_t *kernelPtr,
+             T *func
+)
+{
+  return ::cudaGetKernel(kernelPtr, (const void *)func);
+}
+
+/**
+ * \brief Returns a global device pointer
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the global with
+ * name \p name for the requested library \p library and the current device.
+ * If no global for the requested name \p name exists, the call returns ::cudaErrorSymbolNotFound.
+ * One of the parameters \p dptr or \p bytes (not both) can be NULL in which
+ * case it is ignored.
+ *
+ * \param dptr - Returned global device pointer for the requested library
+ * \param bytes - Returned global size in bytes
+ * \param library - Library to retrieve global from
+ * \param name - Name of global to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ * ::cudaErrorDeviceUninitialized,
+ * ::cudaErrorContextIsDestroyed
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cudaLibraryGetManaged
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaLibraryGetGlobal(T **dptr, size_t *bytes, cudaLibrary_t library, const char *name)
+{
+    return ::cudaLibraryGetGlobal((void**)(void*)dptr, bytes, library, name);
+}
+
+/**
+ * \brief Returns a pointer to managed memory
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the managed memory with
+ * name \p name for the requested library \p library. If no managed memory with the
+ * requested name \p name exists, the call returns ::cudaErrorSymbolNotFound. One of the parameters
+ * \p dptr or \p bytes (not both) can be NULL in which case it is ignored.
+ * Note that managed memory for library \p library is shared across devices and is registered
+ * when the library is loaded.
+ *
+ * \param dptr - Returned pointer to the managed memory
+ * \param bytes - Returned memory size in bytes
+ * \param library - Library to retrieve managed memory from
+ * \param name - Name of managed memory to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cudaLibraryGetGlobal
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaLibraryGetManaged(T **dptr, size_t *bytes, cudaLibrary_t library, const char *name)
+{
+    return ::cudaLibraryGetManaged((void**)(void*)dptr, bytes, library, name);
+}
+
+/**
+ * \brief Returns a pointer to a unified function
+ *
+ * Returns in \p *fptr the function pointer to a unified function denoted by \p symbol.
+ * If no unified function with name \p symbol exists, the call returns ::cudaErrorSymbolNotFound.
+ * If there is no device with attribute ::cudaDeviceProp::unifiedFunctionPointers present in the system,
+ * the call may return ::cudaErrorSymbolNotFound.
+ *
+ * \param fptr - Returned pointer to a unified function
+ * \param library - Library to retrieve function pointer memory from
+ * \param symbol - Name of function pointer to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload
+ */
+template<class T>
+static __inline__ __host__ cudaError_t cudaLibraryGetUnifiedFunction(T **fptr, cudaLibrary_t library, const char *symbol)
+{
+    return ::cudaLibraryGetUnifiedFunction((void**)(void*)fptr, library, symbol);
+}
+
+/** @} */ /* END CUDART_LIBRARY */
+#endif /* __CUDACC__ */
+
+/** @} */ /* END CUDART_HIGHLEVEL */
+
+#endif /* __cplusplus && !__CUDACC_RTC__ */
+
+#if !defined(__CUDACC_RTC__)
+#if defined(__GNUC__)
+#if defined(__clang__) || (!defined(__PGIC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)))
+#pragma GCC diagnostic pop
+#endif
+#elif defined(_MSC_VER)
+#pragma warning(pop)
+#endif
+#endif
+
+#undef EXCLUDE_FROM_RTC
+#undef __CUDA_DEPRECATED
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_RUNTIME_H__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_RUNTIME_H__
+#endif
+
+#endif /* !__CUDA_RUNTIME_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_runtime_api.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_runtime_api.h
new file mode 100644
index 0000000000000000000000000000000000000000..3328e2cb3f78f2f13eb2018f49c8f7ac0d4a06a2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_runtime_api.h
@@ -0,0 +1,14933 @@
+/*
+ * Copyright 1993-2024 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+
+
+#if !defined(__CUDA_RUNTIME_API_H__)
+#define __CUDA_RUNTIME_API_H__
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_RUNTIME_API_H__
+#endif
+
+/**
+ * \latexonly
+ * \page sync_async API synchronization behavior
+ *
+ * \section memcpy_sync_async_behavior Memcpy
+ * The API provides memcpy/memset functions in both synchronous and asynchronous forms,
+ * the latter having an \e "Async" suffix. This is a misnomer as each function
+ * may exhibit synchronous or asynchronous behavior depending on the arguments
+ * passed to the function. In the reference documentation, each memcpy function is
+ * categorized as \e synchronous or \e asynchronous, corresponding to the definitions
+ * below.
+ * 
+ * \subsection MemcpySynchronousBehavior Synchronous
+ * 
+ * <ol>
+ * <li> For transfers from pageable host memory to device memory, a stream sync is performed
+ * before the copy is initiated. The function will return once the pageable
+ * buffer has been copied to the staging memory for DMA transfer to device memory,
+ * but the DMA to final destination may not have completed.
+ * 
+ * <li> For transfers from pinned host memory to device memory, the function is synchronous
+ * with respect to the host.
+ *
+ * <li> For transfers from device to either pageable or pinned host memory, the function returns
+ * only once the copy has completed.
+ * 
+ * <li> For transfers from device memory to device memory, no host-side synchronization is
+ * performed.
+ *
+ * <li> For transfers from any host memory to any host memory, the function is fully
+ * synchronous with respect to the host.
+ * </ol>
+ * 
+ * \subsection MemcpyAsynchronousBehavior Asynchronous
+ *
+ * <ol>
+ * <li> For transfers between device memory and pageable host memory, the function might 
+ * be synchronous with respect to host.
+ *
+ * <li> For transfers from any host memory to any host memory, the function is fully
+ * synchronous with respect to the host.
+ * 
+ * <li> If pageable memory must first be staged to pinned memory, the driver may
+ * synchronize with the stream and stage the copy into pinned memory.
+ *
+ * <li> For all other transfers, the function should be fully asynchronous.
+ * </ol>
+ *
+ * \section memset_sync_async_behavior Memset
+ * The cudaMemset functions are asynchronous with respect to the host
+ * except when the target memory is pinned host memory. The \e Async
+ * versions are always asynchronous with respect to the host.
+ *
+ * \section kernel_launch_details Kernel Launches
+ * Kernel launches are asynchronous with respect to the host. Details of
+ * concurrent kernel execution and data transfers can be found in the CUDA
+ * Programmers Guide.
+ *
+ * \endlatexonly
+ */
+
+/**
+ * There are two levels for the runtime API.
+ *
+ * The C API (<i>cuda_runtime_api.h</i>) is
+ * a C-style interface that does not require compiling with \p nvcc.
+ *
+ * The \ref CUDART_HIGHLEVEL "C++ API" (<i>cuda_runtime.h</i>) is a
+ * C++-style interface built on top of the C API. It wraps some of the
+ * C API routines, using overloading, references and default arguments.
+ * These wrappers can be used from C++ code and can be compiled with any C++
+ * compiler. The C++ API also has some CUDA-specific wrappers that wrap
+ * C API routines that deal with symbols, textures, and device functions.
+ * These wrappers require the use of \p nvcc because they depend on code being
+ * generated by the compiler. For example, the execution configuration syntax
+ * to invoke kernels is only available in source code compiled with \p nvcc.
+ */
+
+/** CUDA Runtime API Version */
+#define CUDART_VERSION  12080
+
+#if defined(__CUDA_API_VER_MAJOR__) && defined(__CUDA_API_VER_MINOR__)
+# define __CUDART_API_VERSION ((__CUDA_API_VER_MAJOR__ * 1000) + (__CUDA_API_VER_MINOR__ * 10))
+#else
+# define __CUDART_API_VERSION CUDART_VERSION
+#endif
+
+#ifndef __DOXYGEN_ONLY__
+#include "crt/host_defines.h"
+#endif
+#include "builtin_types.h"
+
+#if !defined(__CUDACC_RTC_MINIMAL__) && ((defined(__CUDACC_RDC__)  || defined(__CUDACC_EWP__) || !defined(__CUDACC_RTC__)))
+#include "cuda_device_runtime_api.h"
+#endif /* !defined(__CUDACC_RTC_MINIMAL__) && (defined(__CUDACC_RDC__) || defined(__CUDACC_EWP__) || !defined(__CUDACC_RTC__)) */
+
+
+#ifndef __CUDACC_RTC_MINIMAL__
+#if defined(CUDA_API_PER_THREAD_DEFAULT_STREAM) || defined(__CUDA_API_VERSION_INTERNAL)
+    #define __CUDART_API_PER_THREAD_DEFAULT_STREAM
+    #define __CUDART_API_PTDS(api) api ## _ptds
+    #define __CUDART_API_PTSZ(api) api ## _ptsz
+#else
+    #define __CUDART_API_PTDS(api) api
+    #define __CUDART_API_PTSZ(api) api
+#endif
+
+#define cudaSignalExternalSemaphoresAsync  __CUDART_API_PTSZ(cudaSignalExternalSemaphoresAsync_v2)
+#define cudaWaitExternalSemaphoresAsync    __CUDART_API_PTSZ(cudaWaitExternalSemaphoresAsync_v2)
+
+    #define cudaStreamGetCaptureInfo       __CUDART_API_PTSZ(cudaStreamGetCaptureInfo_v2)
+
+#define cudaGetDeviceProperties cudaGetDeviceProperties_v2
+
+#if defined(__CUDART_API_PER_THREAD_DEFAULT_STREAM)
+    #define cudaMemcpy                     __CUDART_API_PTDS(cudaMemcpy)
+    #define cudaMemcpyToSymbol             __CUDART_API_PTDS(cudaMemcpyToSymbol)
+    #define cudaMemcpyFromSymbol           __CUDART_API_PTDS(cudaMemcpyFromSymbol)
+    #define cudaMemcpy2D                   __CUDART_API_PTDS(cudaMemcpy2D)
+    #define cudaMemcpyToArray              __CUDART_API_PTDS(cudaMemcpyToArray)
+    #define cudaMemcpy2DToArray            __CUDART_API_PTDS(cudaMemcpy2DToArray)
+    #define cudaMemcpyFromArray            __CUDART_API_PTDS(cudaMemcpyFromArray)
+    #define cudaMemcpy2DFromArray          __CUDART_API_PTDS(cudaMemcpy2DFromArray)
+    #define cudaMemcpyArrayToArray         __CUDART_API_PTDS(cudaMemcpyArrayToArray)
+    #define cudaMemcpy2DArrayToArray       __CUDART_API_PTDS(cudaMemcpy2DArrayToArray)
+    #define cudaMemcpy3D                   __CUDART_API_PTDS(cudaMemcpy3D)
+    #define cudaMemcpy3DPeer               __CUDART_API_PTDS(cudaMemcpy3DPeer)
+    #define cudaMemset                     __CUDART_API_PTDS(cudaMemset)
+    #define cudaMemset2D                   __CUDART_API_PTDS(cudaMemset2D)
+    #define cudaMemset3D                   __CUDART_API_PTDS(cudaMemset3D)
+    #define cudaGraphInstantiateWithParams __CUDART_API_PTSZ(cudaGraphInstantiateWithParams)
+    #define cudaGraphUpload                __CUDART_API_PTSZ(cudaGraphUpload)
+    #define cudaGraphLaunch                __CUDART_API_PTSZ(cudaGraphLaunch)
+    #define cudaStreamBeginCapture         __CUDART_API_PTSZ(cudaStreamBeginCapture)
+    #define cudaStreamBeginCaptureToGraph  __CUDART_API_PTSZ(cudaStreamBeginCaptureToGraph)
+    #define cudaStreamEndCapture           __CUDART_API_PTSZ(cudaStreamEndCapture)
+    #define cudaStreamGetCaptureInfo_v3    __CUDART_API_PTSZ(cudaStreamGetCaptureInfo_v3)
+    #define cudaStreamUpdateCaptureDependencies  __CUDART_API_PTSZ(cudaStreamUpdateCaptureDependencies)
+    #define cudaStreamUpdateCaptureDependencies_v2  __CUDART_API_PTSZ(cudaStreamUpdateCaptureDependencies_v2)
+    #define cudaStreamIsCapturing          __CUDART_API_PTSZ(cudaStreamIsCapturing)
+    #define cudaMemcpyAsync                __CUDART_API_PTSZ(cudaMemcpyAsync)
+    #define cudaMemcpyToSymbolAsync        __CUDART_API_PTSZ(cudaMemcpyToSymbolAsync)
+    #define cudaMemcpyFromSymbolAsync      __CUDART_API_PTSZ(cudaMemcpyFromSymbolAsync)
+    #define cudaMemcpy2DAsync              __CUDART_API_PTSZ(cudaMemcpy2DAsync)
+    #define cudaMemcpyToArrayAsync         __CUDART_API_PTSZ(cudaMemcpyToArrayAsync)
+    #define cudaMemcpy2DToArrayAsync       __CUDART_API_PTSZ(cudaMemcpy2DToArrayAsync)
+    #define cudaMemcpyFromArrayAsync       __CUDART_API_PTSZ(cudaMemcpyFromArrayAsync)
+    #define cudaMemcpy2DFromArrayAsync     __CUDART_API_PTSZ(cudaMemcpy2DFromArrayAsync)
+    #define cudaMemcpy3DAsync              __CUDART_API_PTSZ(cudaMemcpy3DAsync)
+    #define cudaMemcpy3DPeerAsync          __CUDART_API_PTSZ(cudaMemcpy3DPeerAsync)
+    #define cudaMemcpyBatchAsync          __CUDART_API_PTSZ(cudaMemcpyBatchAsync)
+    #define cudaMemcpy3DBatchAsync        __CUDART_API_PTSZ(cudaMemcpy3DBatchAsync)
+    #define cudaMemsetAsync                __CUDART_API_PTSZ(cudaMemsetAsync)
+    #define cudaMemset2DAsync              __CUDART_API_PTSZ(cudaMemset2DAsync)
+    #define cudaMemset3DAsync              __CUDART_API_PTSZ(cudaMemset3DAsync)
+    #define cudaStreamQuery                __CUDART_API_PTSZ(cudaStreamQuery)
+    #define cudaStreamGetDevice            __CUDART_API_PTSZ(cudaStreamGetDevice)
+    #define cudaStreamGetFlags             __CUDART_API_PTSZ(cudaStreamGetFlags)
+    #define cudaStreamGetId                __CUDART_API_PTSZ(cudaStreamGetId)
+    #define cudaStreamGetPriority          __CUDART_API_PTSZ(cudaStreamGetPriority)
+    #define cudaEventRecord                __CUDART_API_PTSZ(cudaEventRecord)
+    #define cudaEventRecordWithFlags       __CUDART_API_PTSZ(cudaEventRecordWithFlags)
+    #define cudaStreamWaitEvent            __CUDART_API_PTSZ(cudaStreamWaitEvent)
+    #define cudaStreamAddCallback          __CUDART_API_PTSZ(cudaStreamAddCallback)
+    #define cudaStreamAttachMemAsync       __CUDART_API_PTSZ(cudaStreamAttachMemAsync)
+    #define cudaStreamSynchronize          __CUDART_API_PTSZ(cudaStreamSynchronize)
+    #define cudaLaunchKernel               __CUDART_API_PTSZ(cudaLaunchKernel)
+    #define cudaLaunchKernelExC            __CUDART_API_PTSZ(cudaLaunchKernelExC)
+    #define cudaLaunchHostFunc             __CUDART_API_PTSZ(cudaLaunchHostFunc)
+    #define cudaMemPrefetchAsync           __CUDART_API_PTSZ(cudaMemPrefetchAsync)
+    #define cudaMemPrefetchAsync_v2        __CUDART_API_PTSZ(cudaMemPrefetchAsync_v2)
+    #define cudaLaunchCooperativeKernel    __CUDART_API_PTSZ(cudaLaunchCooperativeKernel)
+    #define cudaStreamCopyAttributes       __CUDART_API_PTSZ(cudaStreamCopyAttributes)
+    #define cudaStreamGetAttribute         __CUDART_API_PTSZ(cudaStreamGetAttribute)
+    #define cudaStreamSetAttribute         __CUDART_API_PTSZ(cudaStreamSetAttribute)
+    #define cudaMallocAsync                __CUDART_API_PTSZ(cudaMallocAsync)
+    #define cudaFreeAsync                  __CUDART_API_PTSZ(cudaFreeAsync)
+    #define cudaMallocFromPoolAsync        __CUDART_API_PTSZ(cudaMallocFromPoolAsync)
+    #define cudaGetDriverEntryPoint        __CUDART_API_PTSZ(cudaGetDriverEntryPoint)
+    #define cudaGetDriverEntryPointByVersion  __CUDART_API_PTSZ(cudaGetDriverEntryPointByVersion)
+#endif
+
+#endif  /* __CUDACC_RTC_MINIMAL__ */
+
+/** \cond impl_private */
+#if !defined(__dv)
+
+#if defined(__cplusplus)
+
+#define __dv(v) \
+        = v
+
+#else /* __cplusplus */
+
+#define __dv(v)
+
+#endif /* __cplusplus */
+
+#endif /* !__dv */
+/** \endcond impl_private */
+
+#if (defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 350))   /** Visible to SM>=3.5 and "__host__ __device__" only **/
+
+#define CUDART_DEVICE __device__ 
+
+#else
+
+#define CUDART_DEVICE
+
+#endif /** CUDART_DEVICE */
+
+#if !defined(__CUDACC_RTC__)
+#define EXCLUDE_FROM_RTC
+
+/** \cond impl_private */
+#if defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+#define __CUDA_DEPRECATED
+#elif defined(_MSC_VER)
+#define __CUDA_DEPRECATED __declspec(deprecated)
+#elif defined(__GNUC__)
+#define __CUDA_DEPRECATED __attribute__((deprecated))
+#else
+#define __CUDA_DEPRECATED
+#endif
+/** \endcond impl_private */
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * \defgroup CUDART_DEVICE Device Management
+ *
+ * ___MANBRIEF___ device management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the device management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Destroy all allocations and reset all state on the current device
+ * in the current process.
+ *
+ * Explicitly destroys and cleans up all resources associated with the current
+ * device in the current process. It is the caller's responsibility to ensure
+ * that the resources are not accessed or passed in subsequent API calls and
+ * doing so will result in undefined behavior. These resources include CUDA types
+ * ::cudaStream_t, ::cudaEvent_t, ::cudaArray_t, ::cudaMipmappedArray_t, ::cudaPitchedPtr,
+ * ::cudaTextureObject_t, ::cudaSurfaceObject_t, ::textureReference, ::surfaceReference,
+ * ::cudaExternalMemory_t, ::cudaExternalSemaphore_t and ::cudaGraphicsResource_t.
+ * These resources also include memory allocations by ::cudaMalloc, ::cudaMallocHost,
+ * ::cudaMallocManaged and ::cudaMallocPitch.
+ * Any subsequent API call to this device will reinitialize the device.
+ *
+ * Note that this function will reset the device immediately.  It is the caller's
+ * responsibility to ensure that the device is not being accessed by any 
+ * other host threads from the process when this function is called.
+ *
+ * \note ::cudaDeviceReset() will not destroy memory allocations by ::cudaMallocAsync() and
+ * ::cudaMallocFromPoolAsync(). These memory allocations need to be destroyed explicitly.
+ * \note If a non-primary ::CUcontext is current to the thread, ::cudaDeviceReset()
+ * will destroy only the internal CUDA RT state for that ::CUcontext.
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSynchronize
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceReset(void);
+
+/**
+ * \brief Wait for compute device to finish
+ *
+ * Blocks until the device has completed all preceding requested tasks.
+ * ::cudaDeviceSynchronize() returns an error if one of the preceding tasks
+ * has failed. If the ::cudaDeviceScheduleBlockingSync flag was set for 
+ * this device, the host thread will block until the device has finished 
+ * its work.
+ *
+ * \return
+ * ::cudaSuccess
+ * \note_device_sync_deprecated
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceReset,
+ * ::cuCtxSynchronize
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceSynchronize(void);
+
+/**
+ * \brief Set resource limits
+ *
+ * Setting \p limit to \p value is a request by the application to update
+ * the current limit maintained by the device.  The driver is free to
+ * modify the requested value to meet h/w requirements (this could be
+ * clamping to minimum or maximum values, rounding up to nearest element
+ * size, etc).  The application can use ::cudaDeviceGetLimit() to find out
+ * exactly what the limit has been set to.
+ *
+ * Setting each ::cudaLimit has its own specific restrictions, so each is
+ * discussed here.
+ *
+ * - ::cudaLimitStackSize controls the stack size in bytes of each GPU thread.
+ *
+ * - ::cudaLimitPrintfFifoSize controls the size in bytes of the shared FIFO
+ *   used by the ::printf() device system call. Setting
+ *   ::cudaLimitPrintfFifoSize must not be performed after launching any kernel
+ *   that uses the ::printf() device system call - in such case
+ *   ::cudaErrorInvalidValue will be returned.
+ *
+ * - ::cudaLimitMallocHeapSize controls the size in bytes of the heap used by
+ *   the ::malloc() and ::free() device system calls. Setting
+ *   ::cudaLimitMallocHeapSize must not be performed after launching any kernel
+ *   that uses the ::malloc() or ::free() device system calls - in such case
+ *   ::cudaErrorInvalidValue will be returned.
+ *
+ * - ::cudaLimitDevRuntimeSyncDepth controls the maximum nesting depth of a
+ *   grid at which a thread can safely call ::cudaDeviceSynchronize(). Setting
+ *   this limit must be performed before any launch of a kernel that uses the
+ *   device runtime and calls ::cudaDeviceSynchronize() above the default sync
+ *   depth, two levels of grids. Calls to ::cudaDeviceSynchronize() will fail
+ *   with error code ::cudaErrorSyncDepthExceeded if the limitation is
+ *   violated. This limit can be set smaller than the default or up the maximum
+ *   launch depth of 24. When setting this limit, keep in mind that additional
+ *   levels of sync depth require the runtime to reserve large amounts of
+ *   device memory which can no longer be used for user allocations. If these
+ *   reservations of device memory fail, ::cudaDeviceSetLimit will return
+ *   ::cudaErrorMemoryAllocation, and the limit can be reset to a lower value.
+ *   This limit is only applicable to devices of compute capability < 9.0.
+ *   Attempting to set this limit on devices of other compute capability will
+ *   results in error ::cudaErrorUnsupportedLimit being returned.
+ *
+ * - ::cudaLimitDevRuntimePendingLaunchCount controls the maximum number of
+ *   outstanding device runtime launches that can be made from the current
+ *   device. A grid is outstanding from the point of launch up until the grid
+ *   is known to have been completed. Device runtime launches which violate
+ *   this limitation fail and return ::cudaErrorLaunchPendingCountExceeded when
+ *   ::cudaGetLastError() is called after launch. If more pending launches than
+ *   the default (2048 launches) are needed for a module using the device
+ *   runtime, this limit can be increased. Keep in mind that being able to
+ *   sustain additional pending launches will require the runtime to reserve
+ *   larger amounts of device memory upfront which can no longer be used for
+ *   allocations. If these reservations fail, ::cudaDeviceSetLimit will return
+ *   ::cudaErrorMemoryAllocation, and the limit can be reset to a lower value.
+ *   This limit is only applicable to devices of compute capability 3.5 and
+ *   higher. Attempting to set this limit on devices of compute capability less
+ *   than 3.5 will result in the error ::cudaErrorUnsupportedLimit being
+ *   returned.
+ *
+ * - ::cudaLimitMaxL2FetchGranularity controls the L2 cache fetch granularity.
+ *   Values can range from 0B to 128B. This is purely a performance hint and
+ *   it can be ignored or clamped depending on the platform.
+ *
+ * - ::cudaLimitPersistingL2CacheSize controls size in bytes available
+ *   for persisting L2 cache. This is purely a performance hint and it
+ *   can be ignored or clamped depending on the platform.
+ *
+ * \param limit - Limit to set
+ * \param value - Size of limit
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnsupportedLimit,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceGetLimit,
+ * ::cuCtxSetLimit
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceSetLimit(enum cudaLimit limit, size_t value);
+
+/**
+ * \brief Return resource limits
+ *
+ * Returns in \p *pValue the current size of \p limit. The following ::cudaLimit values are supported.
+ * - ::cudaLimitStackSize is the stack size in bytes of each GPU thread.
+ * - ::cudaLimitPrintfFifoSize is the size in bytes of the shared FIFO used by the
+ *   ::printf() device system call.
+ * - ::cudaLimitMallocHeapSize is the size in bytes of the heap used by the
+ *   ::malloc() and ::free() device system calls.
+ * - ::cudaLimitDevRuntimeSyncDepth is the maximum grid depth at which a
+ *   thread can isssue the device runtime call ::cudaDeviceSynchronize()
+ *   to wait on child grid launches to complete. This functionality is removed
+ *   for devices of compute capability >= 9.0, and hence will return error
+ *   ::cudaErrorUnsupportedLimit on such devices.
+ * - ::cudaLimitDevRuntimePendingLaunchCount is the maximum number of outstanding
+ *   device runtime launches.
+ * - ::cudaLimitMaxL2FetchGranularity is the L2 cache fetch granularity.
+ * - ::cudaLimitPersistingL2CacheSize is the persisting L2 cache size in bytes.
+ *
+ * \param limit  - Limit to query
+ * \param pValue - Returned size of the limit
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnsupportedLimit,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceSetLimit,
+ * ::cuCtxGetLimit
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetLimit(size_t *pValue, enum cudaLimit limit);
+
+/**
+ * \brief Returns the maximum number of elements allocatable in a 1D linear texture for a given element size.
+ *
+ * Returns in \p maxWidthInElements the maximum number of elements allocatable in a 1D linear texture
+ * for given format descriptor \p fmtDesc.
+ *
+ * \param maxWidthInElements    - Returns maximum number of texture elements allocatable for given \p fmtDesc.
+ * \param fmtDesc               - Texture format description.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnsupportedLimit,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuDeviceGetTexture1DLinearMaxWidth
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetTexture1DLinearMaxWidth(size_t *maxWidthInElements, const struct cudaChannelFormatDesc *fmtDesc, int device);
+#endif
+
+/**
+ * \brief Returns the preferred cache configuration for the current device.
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this returns through \p pCacheConfig the preferred cache
+ * configuration for the current device. This is only a preference. The
+ * runtime will use the requested configuration if possible, but it is free to
+ * choose a different configuration if required to execute functions.
+ *
+ * This will return a \p pCacheConfig of ::cudaFuncCachePreferNone on devices
+ * where the size of the L1 cache and shared memory are fixed.
+ *
+ * The supported cache configurations are:
+ * - ::cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
+ * - ::cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
+ * - ::cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory
+ * - ::cudaFuncCachePreferEqual: prefer equal size L1 cache and shared memory
+ *
+ * \param pCacheConfig - Returned cache configuration
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSetCacheConfig,
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)",
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)",
+ * ::cuCtxGetCacheConfig
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetCacheConfig(enum cudaFuncCache *pCacheConfig);
+
+/**
+ * \brief Returns numerical values that correspond to the least and
+ * greatest stream priorities.
+ *
+ * Returns in \p *leastPriority and \p *greatestPriority the numerical values that correspond
+ * to the least and greatest stream priorities respectively. Stream priorities
+ * follow a convention where lower numbers imply greater priorities. The range of
+ * meaningful stream priorities is given by [\p *greatestPriority, \p *leastPriority].
+ * If the user attempts to create a stream with a priority value that is
+ * outside the the meaningful range as specified by this API, the priority is
+ * automatically clamped down or up to either \p *leastPriority or \p *greatestPriority
+ * respectively. See ::cudaStreamCreateWithPriority for details on creating a
+ * priority stream.
+ * A NULL may be passed in for \p *leastPriority or \p *greatestPriority if the value
+ * is not desired.
+ *
+ * This function will return '0' in both \p *leastPriority and \p *greatestPriority if
+ * the current context's device does not support stream priorities
+ * (see ::cudaDeviceGetAttribute).
+ *
+ * \param leastPriority    - Pointer to an int in which the numerical value for least
+ *                           stream priority is returned
+ * \param greatestPriority - Pointer to an int in which the numerical value for greatest
+ *                           stream priority is returned
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreateWithPriority,
+ * ::cudaStreamGetPriority,
+ * ::cuCtxGetStreamPriorityRange
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetStreamPriorityRange(int *leastPriority, int *greatestPriority);
+
+/**
+ * \brief Sets the preferred cache configuration for the current device.
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p cacheConfig the preferred cache
+ * configuration for the current device. This is only a preference. The
+ * runtime will use the requested configuration if possible, but it is free to
+ * choose a different configuration if required to execute the function. Any
+ * function preference set via
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)"
+ * or
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)"
+ * will be preferred over this device-wide setting. Setting the device-wide
+ * cache configuration to ::cudaFuncCachePreferNone will cause subsequent
+ * kernel launches to prefer to not change the cache configuration unless
+ * required to launch the kernel.
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ * The supported cache configurations are:
+ * - ::cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
+ * - ::cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
+ * - ::cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory
+ * - ::cudaFuncCachePreferEqual: prefer equal size L1 cache and shared memory
+ *
+ * \param cacheConfig - Requested cache configuration
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceGetCacheConfig,
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)",
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)",
+ * ::cuCtxSetCacheConfig
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceSetCacheConfig(enum cudaFuncCache cacheConfig);
+
+/**
+ * \brief Returns a handle to a compute device
+ *
+ * Returns in \p *device a device ordinal given a PCI bus ID string.
+ *
+ * \param device   - Returned device ordinal
+ *
+ * \param pciBusId - String in one of the following forms: 
+ * [domain]:[bus]:[device].[function]
+ * [domain]:[bus]:[device]
+ * [bus]:[device].[function]
+ * where \p domain, \p bus, \p device, and \p function are all hexadecimal values
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceGetPCIBusId,
+ * ::cuDeviceGetByPCIBusId
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGetByPCIBusId(int *device, const char *pciBusId);
+
+/**
+ * \brief Returns a PCI Bus Id string for the device
+ *
+ * Returns an ASCII string identifying the device \p dev in the NULL-terminated
+ * string pointed to by \p pciBusId. \p len specifies the maximum length of the
+ * string that may be returned.
+ *
+ * \param pciBusId - Returned identifier string for the device in the following format
+ * [domain]:[bus]:[device].[function]
+ * where \p domain, \p bus, \p device, and \p function are all hexadecimal values.
+ * pciBusId should be large enough to store 13 characters including the NULL-terminator.
+ *
+ * \param len      - Maximum length of string to store in \p name
+ *
+ * \param device   - Device to get identifier string for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceGetByPCIBusId,
+ * ::cuDeviceGetPCIBusId
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGetPCIBusId(char *pciBusId, int len, int device);
+
+/**
+ * \brief Gets an interprocess handle for a previously allocated event
+ *
+ * Takes as input a previously allocated event. This event must have been 
+ * created with the ::cudaEventInterprocess and ::cudaEventDisableTiming
+ * flags set. This opaque handle may be copied into other processes and
+ * opened with ::cudaIpcOpenEventHandle to allow efficient hardware
+ * synchronization between GPU work in different processes.
+ *
+ * After the event has been been opened in the importing process, 
+ * ::cudaEventRecord, ::cudaEventSynchronize, ::cudaStreamWaitEvent and 
+ * ::cudaEventQuery may be used in either process. Performing operations 
+ * on the imported event after the exported event has been freed 
+ * with ::cudaEventDestroy will result in undefined behavior.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cudaDeviceGetAttribute with ::cudaDevAttrIpcEventSupport
+ *
+ * \param handle - Pointer to a user allocated cudaIpcEventHandle
+ *                    in which to return the opaque event handle
+ * \param event   - Event allocated with ::cudaEventInterprocess and 
+ *                    ::cudaEventDisableTiming flags.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorMapBufferObjectFailed,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaEventCreate,
+ * ::cudaEventDestroy,
+ * ::cudaEventSynchronize,
+ * ::cudaEventQuery,
+ * ::cudaStreamWaitEvent,
+ * ::cudaIpcOpenEventHandle,
+ * ::cudaIpcGetMemHandle,
+ * ::cudaIpcOpenMemHandle,
+ * ::cudaIpcCloseMemHandle,
+ * ::cuIpcGetEventHandle
+ */
+extern __host__ cudaError_t CUDARTAPI cudaIpcGetEventHandle(cudaIpcEventHandle_t *handle, cudaEvent_t event);
+
+/**
+ * \brief Opens an interprocess event handle for use in the current process
+ *
+ * Opens an interprocess event handle exported from another process with 
+ * ::cudaIpcGetEventHandle. This function returns a ::cudaEvent_t that behaves like 
+ * a locally created event with the ::cudaEventDisableTiming flag specified. 
+ * This event must be freed with ::cudaEventDestroy.
+ *
+ * Performing operations on the imported event after the exported event has 
+ * been freed with ::cudaEventDestroy will result in undefined behavior.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cudaDeviceGetAttribute with ::cudaDevAttrIpcEventSupport
+ *
+ * \param event - Returns the imported event
+ * \param handle  - Interprocess handle to open
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorMapBufferObjectFailed,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorDeviceUninitialized
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaEventCreate,
+ * ::cudaEventDestroy,
+ * ::cudaEventSynchronize,
+ * ::cudaEventQuery,
+ * ::cudaStreamWaitEvent,
+ * ::cudaIpcGetEventHandle,
+ * ::cudaIpcGetMemHandle,
+ * ::cudaIpcOpenMemHandle,
+ * ::cudaIpcCloseMemHandle,
+ * ::cuIpcOpenEventHandle
+ */
+extern __host__ cudaError_t CUDARTAPI cudaIpcOpenEventHandle(cudaEvent_t *event, cudaIpcEventHandle_t handle);
+
+/**
+ * \brief Gets an interprocess memory handle for an existing device memory
+ *          allocation
+ *
+ * Takes a pointer to the base of an existing device memory allocation created 
+ * with ::cudaMalloc and exports it for use in another process. This is a 
+ * lightweight operation and may be called multiple times on an allocation
+ * without adverse effects. 
+ *
+ * If a region of memory is freed with ::cudaFree and a subsequent call
+ * to ::cudaMalloc returns memory with the same device address,
+ * ::cudaIpcGetMemHandle will return a unique handle for the
+ * new memory. 
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cudaDeviceGetAttribute with ::cudaDevAttrIpcEventSupport
+ *
+ * \param handle - Pointer to user allocated ::cudaIpcMemHandle to return
+ *                    the handle in.
+ * \param devPtr - Base pointer to previously allocated device memory 
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorMapBufferObjectFailed,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMalloc,
+ * ::cudaFree,
+ * ::cudaIpcGetEventHandle,
+ * ::cudaIpcOpenEventHandle,
+ * ::cudaIpcOpenMemHandle,
+ * ::cudaIpcCloseMemHandle,
+ * ::cuIpcGetMemHandle
+ */
+extern __host__ cudaError_t CUDARTAPI cudaIpcGetMemHandle(cudaIpcMemHandle_t *handle, void *devPtr);
+
+/**
+ * \brief Opens an interprocess memory handle exported from another process
+ *          and returns a device pointer usable in the local process.
+ *
+ * Maps memory exported from another process with ::cudaIpcGetMemHandle into
+ * the current device address space. For contexts on different devices 
+ * ::cudaIpcOpenMemHandle can attempt to enable peer access between the
+ * devices as if the user called ::cudaDeviceEnablePeerAccess. This behavior is 
+ * controlled by the ::cudaIpcMemLazyEnablePeerAccess flag. 
+ * ::cudaDeviceCanAccessPeer can determine if a mapping is possible.
+ *
+ * ::cudaIpcOpenMemHandle can open handles to devices that may not be visible
+ * in the process calling the API.
+ *
+ * Contexts that may open ::cudaIpcMemHandles are restricted in the following way.
+ * ::cudaIpcMemHandles from each device in a given process may only be opened 
+ * by one context per device per other process.
+ *
+ * If the memory handle has already been opened by the current context, the
+ * reference count on the handle is incremented by 1 and the existing device pointer
+ * is returned.
+ *
+ * Memory returned from ::cudaIpcOpenMemHandle must be freed with
+ * ::cudaIpcCloseMemHandle.
+ *
+ * Calling ::cudaFree on an exported memory region before calling
+ * ::cudaIpcCloseMemHandle in the importing context will result in undefined
+ * behavior.
+ * 
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cudaDeviceGetAttribute with ::cudaDevAttrIpcEventSupport
+ *
+ * \param devPtr - Returned device pointer
+ * \param handle - ::cudaIpcMemHandle to open
+ * \param flags  - Flags for this operation. Must be specified as ::cudaIpcMemLazyEnablePeerAccess
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorMapBufferObjectFailed,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorDeviceUninitialized,
+ * ::cudaErrorTooManyPeers,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \note No guarantees are made about the address returned in \p *devPtr.  
+ * In particular, multiple processes may not receive the same address for the same \p handle.
+ *
+ * \sa
+ * ::cudaMalloc,
+ * ::cudaFree,
+ * ::cudaIpcGetEventHandle,
+ * ::cudaIpcOpenEventHandle,
+ * ::cudaIpcGetMemHandle,
+ * ::cudaIpcCloseMemHandle,
+ * ::cudaDeviceEnablePeerAccess,
+ * ::cudaDeviceCanAccessPeer,
+ * ::cuIpcOpenMemHandle
+ */
+extern __host__ cudaError_t CUDARTAPI cudaIpcOpenMemHandle(void **devPtr, cudaIpcMemHandle_t handle, unsigned int flags);
+
+/**
+ * \brief Attempts to close memory mapped with cudaIpcOpenMemHandle
+ * 
+ * Decrements the reference count of the memory returnd by ::cudaIpcOpenMemHandle by 1.
+ * When the reference count reaches 0, this API unmaps the memory. The original allocation
+ * in the exporting process as well as imported mappings in other processes
+ * will be unaffected.
+ *
+ * Any resources used to enable peer access will be freed if this is the
+ * last mapping using them.
+ *
+ * IPC functionality is restricted to devices with support for unified
+ * addressing on Linux and Windows operating systems.
+ * IPC functionality on Windows is supported for compatibility purposes
+ * but not recommended as it comes with performance cost.
+ * Users can test their device for IPC functionality by calling
+ * ::cudaDeviceGetAttribute with ::cudaDevAttrIpcEventSupport
+ *
+ * \param devPtr - Device pointer returned by ::cudaIpcOpenMemHandle
+ * 
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorMapBufferObjectFailed,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMalloc,
+ * ::cudaFree,
+ * ::cudaIpcGetEventHandle,
+ * ::cudaIpcOpenEventHandle,
+ * ::cudaIpcGetMemHandle,
+ * ::cudaIpcOpenMemHandle,
+ * ::cuIpcCloseMemHandle
+ */
+extern __host__ cudaError_t CUDARTAPI cudaIpcCloseMemHandle(void *devPtr);
+
+/**
+ * \brief Blocks until remote writes are visible to the specified scope
+ *
+ * Blocks until remote writes to the target context via mappings created
+ * through GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see
+ * https://docs.nvidia.com/cuda/gpudirect-rdma for more information), are
+ * visible to the specified scope.
+ *
+ * If the scope equals or lies within the scope indicated by
+ * ::cudaDevAttrGPUDirectRDMAWritesOrdering, the call will be a no-op and
+ * can be safely omitted for performance. This can be determined by
+ * comparing the numerical values between the two enums, with smaller
+ * scopes having smaller values.
+ *
+ * Users may query support for this API via ::cudaDevAttrGPUDirectRDMAFlushWritesOptions.
+ *
+ * \param target - The target of the operation, see cudaFlushGPUDirectRDMAWritesTarget
+ * \param scope  - The scope of the operation, see cudaFlushGPUDirectRDMAWritesScope
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotSupported,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuFlushGPUDirectRDMAWrites
+ */
+#if __CUDART_API_VERSION >= 11030
+extern __host__ cudaError_t CUDARTAPI cudaDeviceFlushGPUDirectRDMAWrites(enum cudaFlushGPUDirectRDMAWritesTarget target, enum cudaFlushGPUDirectRDMAWritesScope scope);
+#endif
+
+/**
+* \brief Registers a callback function to receive async notifications
+*
+* Registers \p callbackFunc to receive async notifications.
+*
+* The \p userData parameter is passed to the callback function at async notification time.
+* Likewise, \p callback is also passed to the callback function to distinguish between
+* multiple registered callbacks.
+*
+* The callback function being registered should be designed to return quickly (~10ms).
+* Any long running tasks should be queued for execution on an application thread.
+*
+* Callbacks may not call cudaDeviceRegisterAsyncNotification or cudaDeviceUnregisterAsyncNotification.
+* Doing so will result in ::cudaErrorNotPermitted. Async notification callbacks execute
+* in an undefined order and may be serialized.
+*
+* Returns in \p *callback a handle representing the registered callback instance.
+*
+* \param device - The device on which to register the callback
+* \param callbackFunc - The function to register as a callback
+* \param userData - A generic pointer to user data. This is passed into the callback function.
+* \param callback - A handle representing the registered callback instance
+*
+* \return
+* ::cudaSuccess
+* ::cudaErrorNotSupported
+* ::cudaErrorInvalidDevice
+* ::cudaErrorInvalidValue
+* ::cudaErrorNotPermitted
+* ::cudaErrorUnknown
+* \notefnerr
+*
+* \sa
+* ::cudaDeviceUnregisterAsyncNotification
+*/
+extern __host__ cudaError_t CUDARTAPI cudaDeviceRegisterAsyncNotification(int device, cudaAsyncCallback callbackFunc, void* userData, cudaAsyncCallbackHandle_t* callback);
+
+/**
+* \brief Unregisters an async notification callback
+*
+* Unregisters \p callback so that the corresponding callback function will stop receiving
+* async notifications.
+*
+* \param device - The device from which to remove \p callback.
+* \param callback - The callback instance to unregister from receiving async notifications.
+*
+* \return
+* ::cudaSuccess
+* ::cudaErrorNotSupported
+* ::cudaErrorInvalidDevice
+* ::cudaErrorInvalidValue
+* ::cudaErrorNotPermitted
+* ::cudaErrorUnknown
+* \notefnerr
+*
+* \sa
+* ::cudaDeviceRegisterAsyncNotification
+*/
+extern __host__ cudaError_t CUDARTAPI cudaDeviceUnregisterAsyncNotification(int device, cudaAsyncCallbackHandle_t callback);
+
+/** @} */ /* END CUDART_DEVICE */
+
+/**
+ * \defgroup CUDART_DEVICE_DEPRECATED Device Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated device management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the deprecated device management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the shared memory configuration for the current device.
+ *
+ * \deprecated
+ *
+ * This function will return in \p pConfig the current size of shared memory banks
+ * on the current device. On devices with configurable shared memory banks, 
+ * ::cudaDeviceSetSharedMemConfig can be used to change this setting, so that all 
+ * subsequent kernel launches will by default use the new bank size. When 
+ * ::cudaDeviceGetSharedMemConfig is called on devices without configurable shared 
+ * memory, it will return the fixed bank size of the hardware.
+ *
+ * The returned bank configurations can be either:
+ * - ::cudaSharedMemBankSizeFourByte - shared memory bank width is four bytes.
+ * - ::cudaSharedMemBankSizeEightByte - shared memory bank width is eight bytes.
+ *
+ * \param pConfig - Returned cache configuration
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSetCacheConfig,
+ * ::cudaDeviceGetCacheConfig,
+ * ::cudaDeviceSetSharedMemConfig,
+ * ::cudaFuncSetCacheConfig,
+ * ::cuCtxGetSharedMemConfig
+ */
+extern __CUDA_DEPRECATED __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetSharedMemConfig(enum cudaSharedMemConfig *pConfig);
+
+/**
+ * \brief Sets the shared memory configuration for the current device.
+ *
+ * \deprecated
+ *
+ * On devices with configurable shared memory banks, this function will set
+ * the shared memory bank size which is used for all subsequent kernel launches.
+ * Any per-function setting of shared memory set via ::cudaFuncSetSharedMemConfig
+ * will override the device wide setting.
+ *
+ * Changing the shared memory configuration between launches may introduce
+ * a device side synchronization point.
+ *
+ * Changing the shared memory bank size will not increase shared memory usage
+ * or affect occupancy of kernels, but may have major effects on performance. 
+ * Larger bank sizes will allow for greater potential bandwidth to shared memory,
+ * but will change what kinds of accesses to shared memory will result in bank 
+ * conflicts.
+ *
+ * This function will do nothing on devices with fixed shared memory bank size.
+ *
+ * The supported bank configurations are:
+ * - ::cudaSharedMemBankSizeDefault: set bank width the device default (currently,
+ *   four bytes)
+ * - ::cudaSharedMemBankSizeFourByte: set shared memory bank width to be four bytes
+ *   natively.
+ * - ::cudaSharedMemBankSizeEightByte: set shared memory bank width to be eight 
+ *   bytes natively.
+ *
+ * \param config - Requested cache configuration
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSetCacheConfig,
+ * ::cudaDeviceGetCacheConfig,
+ * ::cudaDeviceGetSharedMemConfig,
+ * ::cudaFuncSetCacheConfig,
+ * ::cuCtxSetSharedMemConfig
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaDeviceSetSharedMemConfig(enum cudaSharedMemConfig config);
+/** @} */ /* END CUDART_DEVICE_DEPRECATED */
+
+/**
+ * \defgroup CUDART_THREAD_DEPRECATED Thread Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated thread management functions of the CUDA runtime
+ * API (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes deprecated thread management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Exit and clean up from CUDA launches
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated because its name does not 
+ * reflect its behavior.  Its functionality is identical to the 
+ * non-deprecated function ::cudaDeviceReset(), which should be used
+ * instead.
+ *
+ * Explicitly destroys all cleans up all resources associated with the current
+ * device in the current process.  Any subsequent API call to this device will 
+ * reinitialize the device.  
+ *
+ * Note that this function will reset the device immediately.  It is the caller's
+ * responsibility to ensure that the device is not being accessed by any 
+ * other host threads from the process when this function is called.
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceReset
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaThreadExit(void);
+
+/**
+ * \brief Wait for compute device to finish
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated because its name does not 
+ * reflect its behavior.  Its functionality is similar to the 
+ * non-deprecated function ::cudaDeviceSynchronize(), which should be used
+ * instead.
+ *
+ * Blocks until the device has completed all preceding requested tasks.
+ * ::cudaThreadSynchronize() returns an error if one of the preceding tasks
+ * has failed. If the ::cudaDeviceScheduleBlockingSync flag was set for 
+ * this device, the host thread will block until the device has finished 
+ * its work.
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSynchronize
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaThreadSynchronize(void);
+
+/**
+ * \brief Set resource limits
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated because its name does not 
+ * reflect its behavior.  Its functionality is identical to the 
+ * non-deprecated function ::cudaDeviceSetLimit(), which should be used
+ * instead.
+ *
+ * Setting \p limit to \p value is a request by the application to update
+ * the current limit maintained by the device.  The driver is free to
+ * modify the requested value to meet h/w requirements (this could be
+ * clamping to minimum or maximum values, rounding up to nearest element
+ * size, etc).  The application can use ::cudaThreadGetLimit() to find out
+ * exactly what the limit has been set to.
+ *
+ * Setting each ::cudaLimit has its own specific restrictions, so each is
+ * discussed here.
+ *
+ * - ::cudaLimitStackSize controls the stack size of each GPU thread.
+ *
+ * - ::cudaLimitPrintfFifoSize controls the size of the shared FIFO
+ *   used by the ::printf() device system call.
+ *   Setting ::cudaLimitPrintfFifoSize must be performed before
+ *   launching any kernel that uses the ::printf() device
+ *   system call, otherwise ::cudaErrorInvalidValue will be returned.
+ *
+ * - ::cudaLimitMallocHeapSize controls the size of the heap used
+ *   by the ::malloc() and ::free() device system calls.  Setting
+ *   ::cudaLimitMallocHeapSize must be performed before launching
+ *   any kernel that uses the ::malloc() or ::free() device system calls,
+ *   otherwise ::cudaErrorInvalidValue will be returned.
+ *
+ * \param limit - Limit to set
+ * \param value - Size in bytes of limit
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnsupportedLimit,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSetLimit
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaThreadSetLimit(enum cudaLimit limit, size_t value);
+
+/**
+ * \brief Returns resource limits
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated because its name does not 
+ * reflect its behavior.  Its functionality is identical to the 
+ * non-deprecated function ::cudaDeviceGetLimit(), which should be used
+ * instead.
+ *
+ * Returns in \p *pValue the current size of \p limit.  The supported
+ * ::cudaLimit values are:
+ * - ::cudaLimitStackSize: stack size of each GPU thread;
+ * - ::cudaLimitPrintfFifoSize: size of the shared FIFO used by the
+ *   ::printf() device system call.
+ * - ::cudaLimitMallocHeapSize: size of the heap used by the
+ *   ::malloc() and ::free() device system calls;
+ *
+ * \param limit  - Limit to query
+ * \param pValue - Returned size in bytes of limit
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorUnsupportedLimit,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceGetLimit
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaThreadGetLimit(size_t *pValue, enum cudaLimit limit);
+
+/**
+ * \brief Returns the preferred cache configuration for the current device.
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated because its name does not 
+ * reflect its behavior.  Its functionality is identical to the 
+ * non-deprecated function ::cudaDeviceGetCacheConfig(), which should be 
+ * used instead.
+ * 
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this returns through \p pCacheConfig the preferred cache
+ * configuration for the current device. This is only a preference. The
+ * runtime will use the requested configuration if possible, but it is free to
+ * choose a different configuration if required to execute functions.
+ *
+ * This will return a \p pCacheConfig of ::cudaFuncCachePreferNone on devices
+ * where the size of the L1 cache and shared memory are fixed.
+ *
+ * The supported cache configurations are:
+ * - ::cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
+ * - ::cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
+ * - ::cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory
+ *
+ * \param pCacheConfig - Returned cache configuration
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceGetCacheConfig
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaThreadGetCacheConfig(enum cudaFuncCache *pCacheConfig);
+
+/**
+ * \brief Sets the preferred cache configuration for the current device.
+ *
+ * \deprecated
+ *
+ * Note that this function is deprecated because its name does not 
+ * reflect its behavior.  Its functionality is identical to the 
+ * non-deprecated function ::cudaDeviceSetCacheConfig(), which should be 
+ * used instead.
+ * 
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p cacheConfig the preferred cache
+ * configuration for the current device. This is only a preference. The
+ * runtime will use the requested configuration if possible, but it is free to
+ * choose a different configuration if required to execute the function. Any
+ * function preference set via
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)"
+ * or
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)"
+ * will be preferred over this device-wide setting. Setting the device-wide
+ * cache configuration to ::cudaFuncCachePreferNone will cause subsequent
+ * kernel launches to prefer to not change the cache configuration unless
+ * required to launch the kernel.
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ * The supported cache configurations are:
+ * - ::cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
+ * - ::cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
+ * - ::cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory
+ *
+ * \param cacheConfig - Requested cache configuration
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSetCacheConfig
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaThreadSetCacheConfig(enum cudaFuncCache cacheConfig);
+
+/** @} */ /* END CUDART_THREAD_DEPRECATED */
+
+/**
+ * \defgroup CUDART_ERROR Error Handling
+ *
+ * ___MANBRIEF___ error handling functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the error handling functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the last error from a runtime call
+ *
+ * Returns the last error that has been produced by any of the runtime calls
+ * in the same instance of the CUDA Runtime library in the host thread and
+ * resets it to ::cudaSuccess.
+ *
+ * Note: Multiple instances of the CUDA Runtime library can be present in an
+ * application when using a library that statically links the CUDA Runtime.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMissingConfiguration,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorUnmapBufferObjectFailed,
+ * ::cudaErrorInvalidDevicePointer,
+ * ::cudaErrorInvalidTexture,
+ * ::cudaErrorInvalidTextureBinding,
+ * ::cudaErrorInvalidChannelDescriptor,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorInvalidFilterSetting,
+ * ::cudaErrorInvalidNormSetting,
+ * ::cudaErrorUnknown,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorInsufficientDriver,
+ * ::cudaErrorNoDevice,
+ * ::cudaErrorSetOnActiveProcess,
+ * ::cudaErrorStartupFailure,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaPeekAtLastError, ::cudaGetErrorName, ::cudaGetErrorString, ::cudaError
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetLastError(void);
+
+/**
+ * \brief Returns the last error from a runtime call
+ *
+ * Returns the last error that has been produced by any of the runtime calls
+ * in the same instance of the CUDA Runtime library in the host thread. This
+ * call does not reset the error to ::cudaSuccess like ::cudaGetLastError().
+ *
+ * Note: Multiple instances of the CUDA Runtime library can be present in an
+ * application when using a library that statically links the CUDA Runtime.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMissingConfiguration,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorUnmapBufferObjectFailed,
+ * ::cudaErrorInvalidDevicePointer,
+ * ::cudaErrorInvalidTexture,
+ * ::cudaErrorInvalidTextureBinding,
+ * ::cudaErrorInvalidChannelDescriptor,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorInvalidFilterSetting,
+ * ::cudaErrorInvalidNormSetting,
+ * ::cudaErrorUnknown,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorInsufficientDriver,
+ * ::cudaErrorNoDevice,
+ * ::cudaErrorSetOnActiveProcess,
+ * ::cudaErrorStartupFailure,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetLastError, ::cudaGetErrorName, ::cudaGetErrorString, ::cudaError
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaPeekAtLastError(void);
+
+/**
+ * \brief Returns the string representation of an error code enum name
+ *
+ * Returns a string containing the name of an error code in the enum.  If the error
+ * code is not recognized, "unrecognized error code" is returned.
+ *
+ * \param error - Error code to convert to string
+ *
+ * \return
+ * \p char* pointer to a NULL-terminated string
+ *
+ * \sa ::cudaGetErrorString, ::cudaGetLastError, ::cudaPeekAtLastError, ::cudaError,
+ * ::cuGetErrorName
+ */
+extern __host__ __cudart_builtin__ const char* CUDARTAPI cudaGetErrorName(cudaError_t error);
+
+/**
+ * \brief Returns the description string for an error code
+ *
+ * Returns the description string for an error code.  If the error
+ * code is not recognized, "unrecognized error code" is returned.
+ *
+ * \param error - Error code to convert to string
+ *
+ * \return
+ * \p char* pointer to a NULL-terminated string
+ *
+ * \sa ::cudaGetErrorName, ::cudaGetLastError, ::cudaPeekAtLastError, ::cudaError,
+ * ::cuGetErrorString
+ */
+extern __host__ __cudart_builtin__ const char* CUDARTAPI cudaGetErrorString(cudaError_t error);
+/** @} */ /* END CUDART_ERROR */
+
+/**
+ * \addtogroup CUDART_DEVICE 
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the number of compute-capable devices
+ *
+ * Returns in \p *count the number of devices with compute capability greater
+ * or equal to 2.0 that are available for execution.
+ *
+ * \param count - Returns the number of devices with compute capability
+ * greater or equal to 2.0
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDevice, ::cudaSetDevice, ::cudaGetDeviceProperties,
+ * ::cudaChooseDevice, 
+ * ::cudaInitDevice,
+ * ::cuDeviceGetCount
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDeviceCount(int *count);
+
+/**
+ * \brief Returns information about the compute-device
+ *
+ * Returns in \p *prop the properties of device \p dev. The ::cudaDeviceProp
+ * structure is defined as:
+ * \code
+    struct cudaDeviceProp {
+        char name[256];
+        cudaUUID_t uuid;
+        size_t totalGlobalMem;
+        size_t sharedMemPerBlock;
+        int regsPerBlock;
+        int warpSize;
+        size_t memPitch;
+        int maxThreadsPerBlock;
+        int maxThreadsDim[3];
+        int maxGridSize[3];
+        int clockRate;
+        size_t totalConstMem;
+        int major;
+        int minor;
+        size_t textureAlignment;
+        size_t texturePitchAlignment;
+        int deviceOverlap;
+        int multiProcessorCount;
+        int kernelExecTimeoutEnabled;
+        int integrated;
+        int canMapHostMemory;
+        int computeMode;
+        int maxTexture1D;
+        int maxTexture1DMipmap;
+        int maxTexture1DLinear;
+        int maxTexture2D[2];
+        int maxTexture2DMipmap[2];
+        int maxTexture2DLinear[3];
+        int maxTexture2DGather[2];
+        int maxTexture3D[3];
+        int maxTexture3DAlt[3];
+        int maxTextureCubemap;
+        int maxTexture1DLayered[2];
+        int maxTexture2DLayered[3];
+        int maxTextureCubemapLayered[2];
+        int maxSurface1D;
+        int maxSurface2D[2];
+        int maxSurface3D[3];
+        int maxSurface1DLayered[2];
+        int maxSurface2DLayered[3];
+        int maxSurfaceCubemap;
+        int maxSurfaceCubemapLayered[2];
+        size_t surfaceAlignment;
+        int concurrentKernels;
+        int ECCEnabled;
+        int pciBusID;
+        int pciDeviceID;
+        int pciDomainID;
+        int tccDriver;
+        int asyncEngineCount;
+        int unifiedAddressing;
+        int memoryClockRate;
+        int memoryBusWidth;
+        int l2CacheSize;
+        int persistingL2CacheMaxSize;
+        int maxThreadsPerMultiProcessor;
+        int streamPrioritiesSupported;
+        int globalL1CacheSupported;
+        int localL1CacheSupported;
+        size_t sharedMemPerMultiprocessor;
+        int regsPerMultiprocessor;
+        int managedMemory;
+        int isMultiGpuBoard;
+        int multiGpuBoardGroupID;
+        int singleToDoublePrecisionPerfRatio;
+        int pageableMemoryAccess;
+        int concurrentManagedAccess;
+        int computePreemptionSupported;
+        int canUseHostPointerForRegisteredMem;
+        int cooperativeLaunch;
+        int cooperativeMultiDeviceLaunch;
+        int pageableMemoryAccessUsesHostPageTables;
+        int directManagedMemAccessFromHost;
+        int accessPolicyMaxWindowSize;
+    }
+ \endcode
+ * where:
+ * - \ref ::cudaDeviceProp::name "name[256]" is an ASCII string identifying
+ *   the device.
+ * - \ref ::cudaDeviceProp::uuid "uuid" is a 16-byte unique identifier.
+ * - \ref ::cudaDeviceProp::totalGlobalMem "totalGlobalMem" is the total
+ *   amount of global memory available on the device in bytes.
+ * - \ref ::cudaDeviceProp::sharedMemPerBlock "sharedMemPerBlock" is the
+ *   maximum amount of shared memory available to a thread block in bytes.
+ * - \ref ::cudaDeviceProp::regsPerBlock "regsPerBlock" is the maximum number
+ *   of 32-bit registers available to a thread block.
+ * - \ref ::cudaDeviceProp::warpSize "warpSize" is the warp size in threads.
+ * - \ref ::cudaDeviceProp::memPitch "memPitch" is the maximum pitch in
+ *   bytes allowed by the memory copy functions that involve memory regions
+ *   allocated through ::cudaMallocPitch().
+ * - \ref ::cudaDeviceProp::maxThreadsPerBlock "maxThreadsPerBlock" is the
+ *   maximum number of threads per block.
+ * - \ref ::cudaDeviceProp::maxThreadsDim "maxThreadsDim[3]" contains the
+ *   maximum size of each dimension of a block.
+ * - \ref ::cudaDeviceProp::maxGridSize "maxGridSize[3]" contains the
+ *   maximum size of each dimension of a grid.
+ * - \ref ::cudaDeviceProp::clockRate "clockRate" is the clock frequency in
+ *   kilohertz.
+ * - \ref ::cudaDeviceProp::totalConstMem "totalConstMem" is the total amount
+ *   of constant memory available on the device in bytes.
+ * - \ref ::cudaDeviceProp::major "major",
+ *   \ref ::cudaDeviceProp::minor "minor" are the major and minor revision
+ *   numbers defining the device's compute capability.
+ * - \ref ::cudaDeviceProp::textureAlignment "textureAlignment" is the
+ *   alignment requirement; texture base addresses that are aligned to
+ *   \ref ::cudaDeviceProp::textureAlignment "textureAlignment" bytes do not
+ *   need an offset applied to texture fetches.
+ * - \ref ::cudaDeviceProp::texturePitchAlignment "texturePitchAlignment" is the
+ *   pitch alignment requirement for 2D texture references that are bound to 
+ *   pitched memory.
+ * - \ref ::cudaDeviceProp::deviceOverlap "deviceOverlap" is 1 if the device
+ *   can concurrently copy memory between host and device while executing a
+ *   kernel, or 0 if not.  Deprecated, use instead asyncEngineCount.
+ * - \ref ::cudaDeviceProp::multiProcessorCount "multiProcessorCount" is the
+ *   number of multiprocessors on the device.
+ * - \ref ::cudaDeviceProp::kernelExecTimeoutEnabled "kernelExecTimeoutEnabled"
+ *   is 1 if there is a run time limit for kernels executed on the device, or
+ *   0 if not.
+ * - \ref ::cudaDeviceProp::integrated "integrated" is 1 if the device is an
+ *   integrated (motherboard) GPU and 0 if it is a discrete (card) component.
+ * - \ref ::cudaDeviceProp::canMapHostMemory "canMapHostMemory" is 1 if the
+ *   device can map host memory into the CUDA address space for use with
+ *   ::cudaHostAlloc()/::cudaHostGetDevicePointer(), or 0 if not.
+ * - \ref ::cudaDeviceProp::computeMode "computeMode" is the compute mode
+ *   that the device is currently in. Available modes are as follows:
+ *   - cudaComputeModeDefault: Default mode - Device is not restricted and
+ *     multiple threads can use ::cudaSetDevice() with this device.
+ *   - cudaComputeModeProhibited: Compute-prohibited mode - No threads can use
+ *     ::cudaSetDevice() with this device.
+ *   - cudaComputeModeExclusiveProcess: Compute-exclusive-process mode - Many 
+ *     threads in one process will be able to use ::cudaSetDevice() with this device.
+ *   <br> When an occupied exclusive mode device is chosen with ::cudaSetDevice,
+ *   all subsequent non-device management runtime functions will return
+ *   ::cudaErrorDevicesUnavailable.
+ * - \ref ::cudaDeviceProp::maxTexture1D "maxTexture1D" is the maximum 1D
+ *   texture size.
+ * - \ref ::cudaDeviceProp::maxTexture1DMipmap "maxTexture1DMipmap" is the maximum
+ *   1D mipmapped texture texture size.
+ * - \ref ::cudaDeviceProp::maxTexture1DLinear "maxTexture1DLinear" is the maximum
+ *   1D texture size for textures bound to linear memory.
+ * - \ref ::cudaDeviceProp::maxTexture2D "maxTexture2D[2]" contains the maximum
+ *   2D texture dimensions.
+ * - \ref ::cudaDeviceProp::maxTexture2DMipmap "maxTexture2DMipmap[2]" contains the
+ *   maximum 2D mipmapped texture dimensions.
+ * - \ref ::cudaDeviceProp::maxTexture2DLinear "maxTexture2DLinear[3]" contains the 
+ *   maximum 2D texture dimensions for 2D textures bound to pitch linear memory.
+ * - \ref ::cudaDeviceProp::maxTexture2DGather "maxTexture2DGather[2]" contains the 
+ *   maximum 2D texture dimensions if texture gather operations have to be performed.
+ * - \ref ::cudaDeviceProp::maxTexture3D "maxTexture3D[3]" contains the maximum
+ *   3D texture dimensions.
+ * - \ref ::cudaDeviceProp::maxTexture3DAlt "maxTexture3DAlt[3]"
+ *   contains the maximum alternate 3D texture dimensions.
+ * - \ref ::cudaDeviceProp::maxTextureCubemap "maxTextureCubemap" is the 
+ *   maximum cubemap texture width or height.
+ * - \ref ::cudaDeviceProp::maxTexture1DLayered "maxTexture1DLayered[2]" contains
+ *   the maximum 1D layered texture dimensions.
+ * - \ref ::cudaDeviceProp::maxTexture2DLayered "maxTexture2DLayered[3]" contains
+ *   the maximum 2D layered texture dimensions.
+ * - \ref ::cudaDeviceProp::maxTextureCubemapLayered "maxTextureCubemapLayered[2]"
+ *   contains the maximum cubemap layered texture dimensions.
+ * - \ref ::cudaDeviceProp::maxSurface1D "maxSurface1D" is the maximum 1D
+ *   surface size.
+ * - \ref ::cudaDeviceProp::maxSurface2D "maxSurface2D[2]" contains the maximum
+ *   2D surface dimensions.
+ * - \ref ::cudaDeviceProp::maxSurface3D "maxSurface3D[3]" contains the maximum
+ *   3D surface dimensions.
+ * - \ref ::cudaDeviceProp::maxSurface1DLayered "maxSurface1DLayered[2]" contains
+ *   the maximum 1D layered surface dimensions.
+ * - \ref ::cudaDeviceProp::maxSurface2DLayered "maxSurface2DLayered[3]" contains
+ *   the maximum 2D layered surface dimensions.
+ * - \ref ::cudaDeviceProp::maxSurfaceCubemap "maxSurfaceCubemap" is the maximum 
+ *   cubemap surface width or height.
+ * - \ref ::cudaDeviceProp::maxSurfaceCubemapLayered "maxSurfaceCubemapLayered[2]"
+ *   contains the maximum cubemap layered surface dimensions.
+ * - \ref ::cudaDeviceProp::surfaceAlignment "surfaceAlignment" specifies the
+ *   alignment requirements for surfaces.
+ * - \ref ::cudaDeviceProp::concurrentKernels "concurrentKernels" is 1 if the
+ *   device supports executing multiple kernels within the same context
+ *   simultaneously, or 0 if not. It is not guaranteed that multiple kernels
+ *   will be resident on the device concurrently so this feature should not be
+ *   relied upon for correctness.
+ * - \ref ::cudaDeviceProp::ECCEnabled "ECCEnabled" is 1 if the device has ECC
+ *   support turned on, or 0 if not.
+ * - \ref ::cudaDeviceProp::pciBusID "pciBusID" is the PCI bus identifier of
+ *   the device.
+ * - \ref ::cudaDeviceProp::pciDeviceID "pciDeviceID" is the PCI device
+ *   (sometimes called slot) identifier of the device.
+ * - \ref ::cudaDeviceProp::pciDomainID "pciDomainID" is the PCI domain identifier
+ *   of the device.
+ * - \ref ::cudaDeviceProp::tccDriver "tccDriver" is 1 if the device is using a
+ *   TCC driver or 0 if not.
+ * - \ref ::cudaDeviceProp::asyncEngineCount "asyncEngineCount" is 1 when the
+ *   device can concurrently copy memory between host and device while executing
+ *   a kernel. It is 2 when the device can concurrently copy memory between host
+ *   and device in both directions and execute a kernel at the same time. It is
+ *   0 if neither of these is supported.
+ * - \ref ::cudaDeviceProp::unifiedAddressing "unifiedAddressing" is 1 if the device 
+ *   shares a unified address space with the host and 0 otherwise.
+ * - \ref ::cudaDeviceProp::memoryClockRate "memoryClockRate" is the peak memory 
+ *   clock frequency in kilohertz.
+ * - \ref ::cudaDeviceProp::memoryBusWidth "memoryBusWidth" is the memory bus width  
+ *   in bits.
+ * - \ref ::cudaDeviceProp::l2CacheSize "l2CacheSize" is L2 cache size in bytes. 
+ * - \ref ::cudaDeviceProp::persistingL2CacheMaxSize "persistingL2CacheMaxSize" is L2 cache's maximum persisting lines size in bytes.
+ * - \ref ::cudaDeviceProp::maxThreadsPerMultiProcessor "maxThreadsPerMultiProcessor"  
+ *   is the number of maximum resident threads per multiprocessor.
+ * - \ref ::cudaDeviceProp::streamPrioritiesSupported "streamPrioritiesSupported"
+ *   is 1 if the device supports stream priorities, or 0 if it is not supported.
+ * - \ref ::cudaDeviceProp::globalL1CacheSupported "globalL1CacheSupported"
+ *   is 1 if the device supports caching of globals in L1 cache, or 0 if it is not supported.
+ * - \ref ::cudaDeviceProp::localL1CacheSupported "localL1CacheSupported"
+ *   is 1 if the device supports caching of locals in L1 cache, or 0 if it is not supported.
+ * - \ref ::cudaDeviceProp::sharedMemPerMultiprocessor "sharedMemPerMultiprocessor" is the
+ *   maximum amount of shared memory available to a multiprocessor in bytes; this amount is
+ *   shared by all thread blocks simultaneously resident on a multiprocessor.
+ * - \ref ::cudaDeviceProp::regsPerMultiprocessor "regsPerMultiprocessor" is the maximum number
+ *   of 32-bit registers available to a multiprocessor; this number is shared
+ *   by all thread blocks simultaneously resident on a multiprocessor.
+ * - \ref ::cudaDeviceProp::managedMemory "managedMemory"
+ *   is 1 if the device supports allocating managed memory on this system, or 0 if it is not supported.
+ * - \ref ::cudaDeviceProp::isMultiGpuBoard "isMultiGpuBoard"
+ *   is 1 if the device is on a multi-GPU board (e.g. Gemini cards), and 0 if not;
+ * - \ref ::cudaDeviceProp::multiGpuBoardGroupID "multiGpuBoardGroupID" is a unique identifier
+ *   for a group of devices associated with the same board.
+ *   Devices on the same multi-GPU board will share the same identifier.
+ * - \ref ::cudaDeviceProp::hostNativeAtomicSupported "hostNativeAtomicSupported"
+ *   is 1 if the link between the device and the host supports native atomic operations, or 0 if it is not supported.
+ * - \ref ::cudaDeviceProp::singleToDoublePrecisionPerfRatio "singleToDoublePrecisionPerfRatio"  
+ *   is the ratio of single precision performance (in floating-point operations per second)
+ *   to double precision performance.
+ * - \ref ::cudaDeviceProp::pageableMemoryAccess "pageableMemoryAccess" is 1 if the device supports
+ *   coherently accessing pageable memory without calling cudaHostRegister on it, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::concurrentManagedAccess "concurrentManagedAccess" is 1 if the device can
+ *   coherently access managed memory concurrently with the CPU, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::computePreemptionSupported "computePreemptionSupported" is 1 if the device
+ *   supports Compute Preemption, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::canUseHostPointerForRegisteredMem "canUseHostPointerForRegisteredMem" is 1 if
+ *   the device can access host registered memory at the same virtual address as the CPU, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::cooperativeLaunch "cooperativeLaunch" is 1 if the device supports launching
+ *   cooperative kernels via ::cudaLaunchCooperativeKernel, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::cooperativeMultiDeviceLaunch "cooperativeMultiDeviceLaunch" is 1 if the device
+ *   supports launching cooperative kernels via ::cudaLaunchCooperativeKernelMultiDevice, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::sharedMemPerBlockOptin "sharedMemPerBlockOptin"
+ *   is the per device maximum shared memory per block usable by special opt in
+ * - \ref ::cudaDeviceProp::pageableMemoryAccessUsesHostPageTables "pageableMemoryAccessUsesHostPageTables" is 1 if the device accesses
+ *   pageable memory via the host's page tables, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::directManagedMemAccessFromHost "directManagedMemAccessFromHost" is 1 if the host can directly access managed
+ *   memory on the device without migration, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::maxBlocksPerMultiProcessor "maxBlocksPerMultiProcessor" is the maximum number of thread blocks
+ *   that can reside on a multiprocessor.
+ * - \ref ::cudaDeviceProp::accessPolicyMaxWindowSize "accessPolicyMaxWindowSize" is
+ *   the maximum value of ::cudaAccessPolicyWindow::num_bytes.
+ * - \ref ::cudaDeviceProp::reservedSharedMemPerBlock "reservedSharedMemPerBlock"
+ *   is the shared memory reserved by CUDA driver per block in bytes
+ * - \ref ::cudaDeviceProp::hostRegisterSupported "hostRegisterSupported"
+ *  is 1 if the device supports host memory registration via ::cudaHostRegister, and 0 otherwise.
+ * - \ref ::cudaDeviceProp::sparseCudaArraySupported "sparseCudaArraySupported"
+ *  is 1 if the device supports sparse CUDA arrays and sparse CUDA mipmapped arrays, 0 otherwise
+ * - \ref ::cudaDeviceProp::hostRegisterReadOnlySupported "hostRegisterReadOnlySupported"
+ *  is 1 if the device supports using the ::cudaHostRegister flag cudaHostRegisterReadOnly to register memory that must be mapped as
+ *  read-only to the GPU
+ * - \ref ::cudaDeviceProp::timelineSemaphoreInteropSupported "timelineSemaphoreInteropSupported"
+ *  is 1 if external timeline semaphore interop is supported on the device, 0 otherwise
+ * - \ref ::cudaDeviceProp::memoryPoolsSupported "memoryPoolsSupported"
+ *  is 1 if the device supports using the cudaMallocAsync and cudaMemPool family of APIs, 0 otherwise
+ * - \ref ::cudaDeviceProp::gpuDirectRDMASupported "gpuDirectRDMASupported"
+ *  is 1 if the device supports GPUDirect RDMA APIs, 0 otherwise
+ * - \ref ::cudaDeviceProp::gpuDirectRDMAFlushWritesOptions "gpuDirectRDMAFlushWritesOptions"
+ *  is a bitmask to be interpreted according to the ::cudaFlushGPUDirectRDMAWritesOptions enum
+ * - \ref ::cudaDeviceProp::gpuDirectRDMAWritesOrdering "gpuDirectRDMAWritesOrdering"
+ *  See the ::cudaGPUDirectRDMAWritesOrdering enum for numerical values
+ * - \ref ::cudaDeviceProp::memoryPoolSupportedHandleTypes "memoryPoolSupportedHandleTypes"
+ *  is a bitmask of handle types supported with mempool-based IPC
+ * - \ref ::cudaDeviceProp::deferredMappingCudaArraySupported "deferredMappingCudaArraySupported"
+ *  is 1 if the device supports deferred mapping CUDA arrays and CUDA mipmapped arrays
+ * - \ref ::cudaDeviceProp::ipcEventSupported "ipcEventSupported"
+ *  is 1 if the device supports IPC Events, and 0 otherwise
+ * - \ref ::cudaDeviceProp::unifiedFunctionPointers "unifiedFunctionPointers"
+ *  is 1 if the device support unified pointers, and 0 otherwise
+ * \param prop   - Properties for the specified device
+ * \param device - Device number to get properties for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaSetDevice, ::cudaChooseDevice,
+ * ::cudaDeviceGetAttribute, 
+ * ::cudaInitDevice,
+ * ::cuDeviceGetAttribute,
+ * ::cuDeviceGetName
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDeviceProperties(struct cudaDeviceProp *prop, int device);
+
+/**
+ * \brief Returns information about the device
+ *
+ * Returns in \p *value the integer value of the attribute \p attr on device
+ * \p device. The supported attributes are:
+ * - ::cudaDevAttrMaxThreadsPerBlock: Maximum number of threads per block
+ * - ::cudaDevAttrMaxBlockDimX: Maximum x-dimension of a block
+ * - ::cudaDevAttrMaxBlockDimY: Maximum y-dimension of a block
+ * - ::cudaDevAttrMaxBlockDimZ: Maximum z-dimension of a block
+ * - ::cudaDevAttrMaxGridDimX: Maximum x-dimension of a grid
+ * - ::cudaDevAttrMaxGridDimY: Maximum y-dimension of a grid
+ * - ::cudaDevAttrMaxGridDimZ: Maximum z-dimension of a grid
+ * - ::cudaDevAttrMaxSharedMemoryPerBlock: Maximum amount of shared memory
+ *   available to a thread block in bytes
+ * - ::cudaDevAttrTotalConstantMemory: Memory available on device for
+ *   __constant__ variables in a CUDA C kernel in bytes
+ * - ::cudaDevAttrWarpSize: Warp size in threads
+ * - ::cudaDevAttrMaxPitch: Maximum pitch in bytes allowed by the memory copy
+ *   functions that involve memory regions allocated through ::cudaMallocPitch()
+ * - ::cudaDevAttrMaxTexture1DWidth: Maximum 1D texture width
+ * - ::cudaDevAttrMaxTexture1DLinearWidth: Maximum width for a 1D texture bound
+ *   to linear memory
+ * - ::cudaDevAttrMaxTexture1DMipmappedWidth: Maximum mipmapped 1D texture width
+ * - ::cudaDevAttrMaxTexture2DWidth: Maximum 2D texture width
+ * - ::cudaDevAttrMaxTexture2DHeight: Maximum 2D texture height
+ * - ::cudaDevAttrMaxTexture2DLinearWidth: Maximum width for a 2D texture
+ *   bound to linear memory
+ * - ::cudaDevAttrMaxTexture2DLinearHeight: Maximum height for a 2D texture
+ *   bound to linear memory
+ * - ::cudaDevAttrMaxTexture2DLinearPitch: Maximum pitch in bytes for a 2D
+ *   texture bound to linear memory
+ * - ::cudaDevAttrMaxTexture2DMipmappedWidth: Maximum mipmapped 2D texture
+ *   width
+ * - ::cudaDevAttrMaxTexture2DMipmappedHeight: Maximum mipmapped 2D texture
+ *   height
+ * - ::cudaDevAttrMaxTexture3DWidth: Maximum 3D texture width
+ * - ::cudaDevAttrMaxTexture3DHeight: Maximum 3D texture height
+ * - ::cudaDevAttrMaxTexture3DDepth: Maximum 3D texture depth
+ * - ::cudaDevAttrMaxTexture3DWidthAlt: Alternate maximum 3D texture width,
+ *   0 if no alternate maximum 3D texture size is supported
+ * - ::cudaDevAttrMaxTexture3DHeightAlt: Alternate maximum 3D texture height,
+ *   0 if no alternate maximum 3D texture size is supported
+ * - ::cudaDevAttrMaxTexture3DDepthAlt: Alternate maximum 3D texture depth,
+ *   0 if no alternate maximum 3D texture size is supported
+ * - ::cudaDevAttrMaxTextureCubemapWidth: Maximum cubemap texture width or
+ *   height
+ * - ::cudaDevAttrMaxTexture1DLayeredWidth: Maximum 1D layered texture width
+ * - ::cudaDevAttrMaxTexture1DLayeredLayers: Maximum layers in a 1D layered
+ *   texture
+ * - ::cudaDevAttrMaxTexture2DLayeredWidth: Maximum 2D layered texture width
+ * - ::cudaDevAttrMaxTexture2DLayeredHeight: Maximum 2D layered texture height
+ * - ::cudaDevAttrMaxTexture2DLayeredLayers: Maximum layers in a 2D layered
+ *   texture
+ * - ::cudaDevAttrMaxTextureCubemapLayeredWidth: Maximum cubemap layered
+ *   texture width or height
+ * - ::cudaDevAttrMaxTextureCubemapLayeredLayers: Maximum layers in a cubemap
+ *   layered texture
+ * - ::cudaDevAttrMaxSurface1DWidth: Maximum 1D surface width
+ * - ::cudaDevAttrMaxSurface2DWidth: Maximum 2D surface width
+ * - ::cudaDevAttrMaxSurface2DHeight: Maximum 2D surface height
+ * - ::cudaDevAttrMaxSurface3DWidth: Maximum 3D surface width
+ * - ::cudaDevAttrMaxSurface3DHeight: Maximum 3D surface height
+ * - ::cudaDevAttrMaxSurface3DDepth: Maximum 3D surface depth
+ * - ::cudaDevAttrMaxSurface1DLayeredWidth: Maximum 1D layered surface width
+ * - ::cudaDevAttrMaxSurface1DLayeredLayers: Maximum layers in a 1D layered
+ *   surface
+ * - ::cudaDevAttrMaxSurface2DLayeredWidth: Maximum 2D layered surface width
+ * - ::cudaDevAttrMaxSurface2DLayeredHeight: Maximum 2D layered surface height
+ * - ::cudaDevAttrMaxSurface2DLayeredLayers: Maximum layers in a 2D layered
+ *   surface
+ * - ::cudaDevAttrMaxSurfaceCubemapWidth: Maximum cubemap surface width
+ * - ::cudaDevAttrMaxSurfaceCubemapLayeredWidth: Maximum cubemap layered
+ *   surface width
+ * - ::cudaDevAttrMaxSurfaceCubemapLayeredLayers: Maximum layers in a cubemap
+ *   layered surface
+ * - ::cudaDevAttrMaxRegistersPerBlock: Maximum number of 32-bit registers 
+ *   available to a thread block
+ * - ::cudaDevAttrClockRate: Peak clock frequency in kilohertz
+ * - ::cudaDevAttrTextureAlignment: Alignment requirement; texture base
+ *   addresses aligned to ::textureAlign bytes do not need an offset applied
+ *   to texture fetches
+ * - ::cudaDevAttrTexturePitchAlignment: Pitch alignment requirement for 2D
+ *   texture references bound to pitched memory
+ * - ::cudaDevAttrGpuOverlap: 1 if the device can concurrently copy memory
+ *   between host and device while executing a kernel, or 0 if not
+ * - ::cudaDevAttrMultiProcessorCount: Number of multiprocessors on the device
+ * - ::cudaDevAttrKernelExecTimeout: 1 if there is a run time limit for kernels
+ *   executed on the device, or 0 if not
+ * - ::cudaDevAttrIntegrated: 1 if the device is integrated with the memory
+ *   subsystem, or 0 if not
+ * - ::cudaDevAttrCanMapHostMemory: 1 if the device can map host memory into
+ *   the CUDA address space, or 0 if not
+ * - ::cudaDevAttrComputeMode: Compute mode is the compute mode that the device
+ *   is currently in. Available modes are as follows:
+ *   - ::cudaComputeModeDefault: Default mode - Device is not restricted and
+ *     multiple threads can use ::cudaSetDevice() with this device.
+ *   - ::cudaComputeModeProhibited: Compute-prohibited mode - No threads can use
+ *     ::cudaSetDevice() with this device.
+ *   - ::cudaComputeModeExclusiveProcess: Compute-exclusive-process mode - Many 
+ *     threads in one process will be able to use ::cudaSetDevice() with this
+ *     device.
+ * - ::cudaDevAttrConcurrentKernels: 1 if the device supports executing
+ *   multiple kernels within the same context simultaneously, or 0 if
+ *   not. It is not guaranteed that multiple kernels will be resident on the
+ *   device concurrently so this feature should not be relied upon for
+ *   correctness.
+ * - ::cudaDevAttrEccEnabled: 1 if error correction is enabled on the device,
+ *   0 if error correction is disabled or not supported by the device
+ * - ::cudaDevAttrPciBusId: PCI bus identifier of the device
+ * - ::cudaDevAttrPciDeviceId: PCI device (also known as slot) identifier of
+ *   the device
+ * - ::cudaDevAttrTccDriver: 1 if the device is using a TCC driver. TCC is only
+ *   available on Tesla hardware running Windows Vista or later.
+ * - ::cudaDevAttrMemoryClockRate: Peak memory clock frequency in kilohertz
+ * - ::cudaDevAttrGlobalMemoryBusWidth: Global memory bus width in bits
+ * - ::cudaDevAttrL2CacheSize: Size of L2 cache in bytes. 0 if the device
+ *   doesn't have L2 cache.
+ * - ::cudaDevAttrMaxThreadsPerMultiProcessor: Maximum resident threads per 
+ *   multiprocessor
+ * - ::cudaDevAttrUnifiedAddressing: 1 if the device shares a unified address
+ *   space with the host, or 0 if not
+ * - ::cudaDevAttrComputeCapabilityMajor: Major compute capability version
+ *   number
+ * - ::cudaDevAttrComputeCapabilityMinor: Minor compute capability version
+ *   number
+ * - ::cudaDevAttrStreamPrioritiesSupported: 1 if the device supports stream
+ *   priorities, or 0 if not
+ * - ::cudaDevAttrGlobalL1CacheSupported: 1 if device supports caching globals 
+ *    in L1 cache, 0 if not
+ * - ::cudaDevAttrLocalL1CacheSupported: 1 if device supports caching locals 
+ *    in L1 cache, 0 if not
+ * - ::cudaDevAttrMaxSharedMemoryPerMultiprocessor: Maximum amount of shared memory
+ *   available to a multiprocessor in bytes; this amount is shared by all 
+ *   thread blocks simultaneously resident on a multiprocessor
+ * - ::cudaDevAttrMaxRegistersPerMultiprocessor: Maximum number of 32-bit registers 
+ *   available to a multiprocessor; this number is shared by all thread blocks
+ *   simultaneously resident on a multiprocessor
+ * - ::cudaDevAttrManagedMemory: 1 if device supports allocating
+ *   managed memory, 0 if not
+ * - ::cudaDevAttrIsMultiGpuBoard: 1 if device is on a multi-GPU board, 0 if not
+ * - ::cudaDevAttrMultiGpuBoardGroupID: Unique identifier for a group of devices on the
+ *   same multi-GPU board
+ * - ::cudaDevAttrHostNativeAtomicSupported: 1 if the link between the device and the
+ *   host supports native atomic operations
+ * - ::cudaDevAttrSingleToDoublePrecisionPerfRatio: Ratio of single precision performance
+ *   (in floating-point operations per second) to double precision performance
+ * - ::cudaDevAttrPageableMemoryAccess: 1 if the device supports coherently accessing
+ *   pageable memory without calling cudaHostRegister on it, and 0 otherwise
+ * - ::cudaDevAttrConcurrentManagedAccess: 1 if the device can coherently access managed
+ *   memory concurrently with the CPU, and 0 otherwise
+ * - ::cudaDevAttrComputePreemptionSupported: 1 if the device supports
+ *   Compute Preemption, 0 if not
+ * - ::cudaDevAttrCanUseHostPointerForRegisteredMem: 1 if the device can access host
+ *   registered memory at the same virtual address as the CPU, and 0 otherwise
+ * - ::cudaDevAttrCooperativeLaunch: 1 if the device supports launching cooperative kernels
+ *   via ::cudaLaunchCooperativeKernel, and 0 otherwise
+ * - ::cudaDevAttrCooperativeMultiDeviceLaunch: 1 if the device supports launching cooperative
+ *   kernels via ::cudaLaunchCooperativeKernelMultiDevice, and 0 otherwise
+ * - ::cudaDevAttrCanFlushRemoteWrites: 1 if the device supports flushing of outstanding 
+ *   remote writes, and 0 otherwise
+ * - ::cudaDevAttrHostRegisterSupported: 1 if the device supports host memory registration
+ *   via ::cudaHostRegister, and 0 otherwise
+ * - ::cudaDevAttrPageableMemoryAccessUsesHostPageTables: 1 if the device accesses pageable memory via the
+ *   host's page tables, and 0 otherwise
+ * - ::cudaDevAttrDirectManagedMemAccessFromHost: 1 if the host can directly access managed memory on the device
+ *   without migration, and 0 otherwise
+ * - ::cudaDevAttrMaxSharedMemoryPerBlockOptin: Maximum per block shared memory size on the device. This value can
+ *   be opted into when using ::cudaFuncSetAttribute
+ * - ::cudaDevAttrMaxBlocksPerMultiprocessor: Maximum number of thread blocks that can reside on a multiprocessor
+ * - ::cudaDevAttrMaxPersistingL2CacheSize: Maximum L2 persisting lines capacity setting in bytes
+ * - ::cudaDevAttrMaxAccessPolicyWindowSize: Maximum value of cudaAccessPolicyWindow::num_bytes
+ * - ::cudaDevAttrReservedSharedMemoryPerBlock: Shared memory reserved by CUDA driver per block in bytes
+ * - ::cudaDevAttrSparseCudaArraySupported: 1 if the device supports sparse CUDA arrays and sparse CUDA mipmapped arrays.
+ * - ::cudaDevAttrHostRegisterReadOnlySupported: Device supports using the ::cudaHostRegister flag cudaHostRegisterReadOnly
+ *   to register memory that must be mapped as read-only to the GPU
+ * - ::cudaDevAttrMemoryPoolsSupported: 1 if the device supports using the cudaMallocAsync and cudaMemPool family of APIs, and 0 otherwise
+ * - ::cudaDevAttrGPUDirectRDMASupported: 1 if the device supports GPUDirect RDMA APIs, and 0 otherwise
+ * - ::cudaDevAttrGPUDirectRDMAFlushWritesOptions: bitmask to be interpreted according to the ::cudaFlushGPUDirectRDMAWritesOptions enum 
+ * - ::cudaDevAttrGPUDirectRDMAWritesOrdering: see the ::cudaGPUDirectRDMAWritesOrdering enum for numerical values
+ * - ::cudaDevAttrMemoryPoolSupportedHandleTypes: Bitmask of handle types supported with mempool based IPC
+ * - ::cudaDevAttrDeferredMappingCudaArraySupported : 1 if the device supports deferred mapping CUDA arrays and CUDA mipmapped arrays.
+ * - ::cudaDevAttrIpcEventSupport: 1 if the device supports IPC Events.
+ * - ::cudaDevAttrNumaConfig: NUMA configuration of a device: value is of type ::cudaDeviceNumaConfig enum
+ * - ::cudaDevAttrNumaId: NUMA node ID of the GPU memory
+ * - ::cudaDevAttrGpuPciDeviceId: The combined 16-bit PCI device ID and 16-bit PCI vendor ID.
+ * - ::cudaDevAttrGpuPciSubsystemId: The combined 16-bit PCI subsystem ID and 16-bit PCI vendor subsystem ID.
+ *
+ * \param value  - Returned device attribute value
+ * \param attr   - Device attribute to query
+ * \param device - Device number to query 
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaSetDevice, ::cudaChooseDevice,
+ * ::cudaGetDeviceProperties, 
+ * ::cudaInitDevice,
+ * ::cuDeviceGetAttribute
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetAttribute(int *value, enum cudaDeviceAttr attr, int device);
+
+/**
+ * \brief Returns the default mempool of a device
+ *
+ * The default mempool of a device contains device memory from that device.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue
+ * ::cudaErrorNotSupported
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cuDeviceGetDefaultMemPool, ::cudaMallocAsync, ::cudaMemPoolTrimTo, ::cudaMemPoolGetAttribute, ::cudaDeviceSetMemPool, ::cudaMemPoolSetAttribute, ::cudaMemPoolSetAccess
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGetDefaultMemPool(cudaMemPool_t *memPool, int device);
+
+
+/**
+ * \brief Sets the current memory pool of a device
+ *
+ * The memory pool must be local to the specified device.
+ * Unless a mempool is specified in the ::cudaMallocAsync call,
+ * ::cudaMallocAsync allocates from the current mempool of the provided stream's device.
+ * By default, a device's current memory pool is its default memory pool.
+ *
+ * \note Use ::cudaMallocFromPoolAsync to specify asynchronous allocations from a device different
+ * than the one the stream runs on.
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * ::cudaErrorInvalidDevice
+ * ::cudaErrorNotSupported
+ * \notefnerr
+ * \note_callback
+ *
+ * \sa ::cuDeviceSetMemPool, ::cudaDeviceGetMemPool, ::cudaDeviceGetDefaultMemPool, ::cudaMemPoolCreate, ::cudaMemPoolDestroy, ::cudaMallocFromPoolAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceSetMemPool(int device, cudaMemPool_t memPool);
+
+/**
+ * \brief Gets the current mempool for a device
+ *
+ * Returns the last pool provided to ::cudaDeviceSetMemPool for this device
+ * or the device's default memory pool if ::cudaDeviceSetMemPool has never been called.
+ * By default the current mempool is the default mempool for a device,
+ * otherwise the returned pool must have been set with ::cuDeviceSetMemPool or ::cudaDeviceSetMemPool.
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * ::cudaErrorNotSupported
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cuDeviceGetMemPool, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceSetMemPool
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGetMemPool(cudaMemPool_t *memPool, int device);
+
+/**
+ * \brief Return NvSciSync attributes that this device can support.
+ *
+ * Returns in \p nvSciSyncAttrList, the properties of NvSciSync that
+ * this CUDA device, \p dev can support. The returned \p nvSciSyncAttrList
+ * can be used to create an NvSciSync that matches this device's capabilities.
+ * 
+ * If NvSciSyncAttrKey_RequiredPerm field in \p nvSciSyncAttrList is
+ * already set this API will return ::cudaErrorInvalidValue.
+ * 
+ * The applications should set \p nvSciSyncAttrList to a valid 
+ * NvSciSyncAttrList failing which this API will return
+ * ::cudaErrorInvalidHandle.
+ * 
+ * The \p flags controls how applications intends to use
+ * the NvSciSync created from the \p nvSciSyncAttrList. The valid flags are:
+ * - ::cudaNvSciSyncAttrSignal, specifies that the applications intends to 
+ * signal an NvSciSync on this CUDA device.
+ * - ::cudaNvSciSyncAttrWait, specifies that the applications intends to 
+ * wait on an NvSciSync on this CUDA device.
+ *
+ * At least one of these flags must be set, failing which the API
+ * returns ::cudaErrorInvalidValue. Both the flags are orthogonal
+ * to one another: a developer may set both these flags that allows to
+ * set both wait and signal specific attributes in the same \p nvSciSyncAttrList.
+ *
+ * Note that this API updates the input \p nvSciSyncAttrList with values equivalent
+ * to the following public attribute key-values:
+ * NvSciSyncAttrKey_RequiredPerm is set to
+ * - NvSciSyncAccessPerm_SignalOnly if ::cudaNvSciSyncAttrSignal is set in \p flags.
+ * - NvSciSyncAccessPerm_WaitOnly if ::cudaNvSciSyncAttrWait is set in \p flags.
+ * - NvSciSyncAccessPerm_WaitSignal if both ::cudaNvSciSyncAttrWait and
+ * ::cudaNvSciSyncAttrSignal are set in \p flags.
+ * NvSciSyncAttrKey_PrimitiveInfo is set to
+ * - NvSciSyncAttrValPrimitiveType_SysmemSemaphore on any valid \p device.
+ * - NvSciSyncAttrValPrimitiveType_Syncpoint if \p device is a Tegra device.
+ * - NvSciSyncAttrValPrimitiveType_SysmemSemaphorePayload64b if \p device is GA10X+.
+ * NvSciSyncAttrKey_GpuId is set to the same UUID that is returned in 
+ * \p cudaDeviceProp.uuid from ::cudaDeviceGetProperties for this \p device.
+ *
+ * \param nvSciSyncAttrList     - Return NvSciSync attributes supported.
+ * \param device                - Valid Cuda Device to get NvSciSync attributes for.
+ * \param flags                 - flags describing NvSciSync usage.
+ *
+ * \return
+ *
+ * ::cudaSuccess,
+ * ::cudaErrorDeviceUninitialized,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidHandle,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorMemoryAllocation
+ *
+ * \sa
+ * ::cudaImportExternalSemaphore,
+ * ::cudaDestroyExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGetNvSciSyncAttributes(void *nvSciSyncAttrList, int device, int flags);
+
+/**
+ * \brief Queries attributes of the link between two devices.
+ *
+ * Returns in \p *value the value of the requested attribute \p attrib of the
+ * link between \p srcDevice and \p dstDevice. The supported attributes are:
+ * - ::cudaDevP2PAttrPerformanceRank: A relative value indicating the
+ *   performance of the link between two devices. Lower value means better
+ *   performance (0 being the value used for most performant link).
+ * - ::cudaDevP2PAttrAccessSupported: 1 if peer access is enabled.
+ * - ::cudaDevP2PAttrNativeAtomicSupported: 1 if native atomic operations over
+ *   the link are supported.
+ * - ::cudaDevP2PAttrCudaArrayAccessSupported: 1 if accessing CUDA arrays over
+ *   the link is supported.
+ *
+ * Returns ::cudaErrorInvalidDevice if \p srcDevice or \p dstDevice are not valid
+ * or if they represent the same device.
+ *
+ * Returns ::cudaErrorInvalidValue if \p attrib is not valid or if \p value is
+ * a null pointer.
+ *
+ * \param value         - Returned value of the requested attribute
+ * \param attrib        - The requested attribute of the link between \p srcDevice and \p dstDevice.
+ * \param srcDevice     - The source device of the target link.
+ * \param dstDevice     - The destination device of the target link.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceEnablePeerAccess,
+ * ::cudaDeviceDisablePeerAccess,
+ * ::cudaDeviceCanAccessPeer,
+ * ::cuDeviceGetP2PAttribute
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetP2PAttribute(int *value, enum cudaDeviceP2PAttr attr, int srcDevice, int dstDevice);
+
+/**
+ * \brief Select compute-device which best matches criteria
+ *
+ * Returns in \p *device the device which has properties that best match
+ * \p *prop.
+ *
+ * \param device - Device with best match
+ * \param prop   - Desired device properties
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaSetDevice,
+ * ::cudaGetDeviceProperties, 
+ * ::cudaInitDevice
+ */
+extern __host__ cudaError_t CUDARTAPI cudaChooseDevice(int *device, const struct cudaDeviceProp *prop);
+/**
+ * \brief Initialize device to be used for GPU executions
+ *
+ * This function will initialize the CUDA Runtime structures and primary context on \p device when called,
+ * but the context will not be made current to \p device.
+ *
+ * When ::cudaInitDeviceFlagsAreValid is set in \p flags, deviceFlags are applied to the requested device.
+ * The values of deviceFlags match those of the flags parameters in ::cudaSetDeviceFlags. 
+ * The effect may be verified by ::cudaGetDeviceFlags.
+ *
+ * This function will return an error if the device is in ::cudaComputeModeExclusiveProcess
+ * and is occupied by another process or if the device is in ::cudaComputeModeProhibited.
+ *
+ * \param device - Device on which the runtime will initialize itself.
+ * \param deviceFlags - Parameters for device operation.
+ * \param flags - Flags for controlling the device initialization.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaGetDeviceProperties,
+ * ::cudaChooseDevice, ::cudaSetDevice
+ * ::cuCtxSetCurrent
+ */
+extern __host__ cudaError_t CUDARTAPI cudaInitDevice(int device, unsigned int deviceFlags, unsigned int flags);
+/**
+ * \brief Set device to be used for GPU executions
+ *
+ * Sets \p device as the current device for the calling host thread.
+ * Valid device id's are 0 to (::cudaGetDeviceCount() - 1).
+ *
+ * Any device memory subsequently allocated from this host thread
+ * using ::cudaMalloc(), ::cudaMallocPitch() or ::cudaMallocArray()
+ * will be physically resident on \p device.  Any host memory allocated
+ * from this host thread using ::cudaMallocHost() or ::cudaHostAlloc() 
+ * or ::cudaHostRegister() will have its lifetime associated  with
+ * \p device.  Any streams or events created from this host thread will 
+ * be associated with \p device.  Any kernels launched from this host
+ * thread using the <<<>>> operator or ::cudaLaunchKernel() will be executed
+ * on \p device.
+ *
+ * This call may be made from any host thread, to any device, and at 
+ * any time.  This function will do no synchronization with the previous 
+ * or new device, 
+ * and should only take significant time when it initializes the runtime's context state.
+ * This call will bind the primary context of the specified device to the calling thread and all the
+ * subsequent memory allocations, stream and event creations, and kernel launches
+ * will be associated with the primary context. 
+ * This function will also immediately initialize the runtime state on the primary context, 
+ * and the context will be current on \p device immediately. This function will return an 
+ * error if the device is in ::cudaComputeModeExclusiveProcess and is occupied by another 
+ * process or if the device is in ::cudaComputeModeProhibited.
+ * 
+ * It is not required to call ::cudaInitDevice before using this function.
+ * \param device - Device on which the active host thread should execute the
+ * device code.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorDeviceUnavailable,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaGetDeviceProperties,
+ * ::cudaChooseDevice,
+ * ::cudaInitDevice,
+ * ::cuCtxSetCurrent
+ */
+extern __host__ cudaError_t CUDARTAPI cudaSetDevice(int device);
+
+/**
+ * \brief Returns which device is currently being used
+ *
+ * Returns in \p *device the current device for the calling host thread.
+ *
+ * \param device - Returns the device on which the active host thread
+ * executes the device code.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorDeviceUnavailable,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaSetDevice, ::cudaGetDeviceProperties,
+ * ::cudaChooseDevice,
+ * ::cuCtxGetCurrent
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDevice(int *device);
+
+/**
+ * \brief Set a list of devices that can be used for CUDA
+ *
+ * Sets a list of devices for CUDA execution in priority order using
+ * \p device_arr. The parameter \p len specifies the number of elements in the
+ * list.  CUDA will try devices from the list sequentially until it finds one
+ * that works.  If this function is not called, or if it is called with a \p len
+ * of 0, then CUDA will go back to its default behavior of trying devices
+ * sequentially from a default list containing all of the available CUDA
+ * devices in the system. If a specified device ID in the list does not exist,
+ * this function will return ::cudaErrorInvalidDevice. If \p len is not 0 and
+ * \p device_arr is NULL or if \p len exceeds the number of devices in
+ * the system, then ::cudaErrorInvalidValue is returned.
+ *
+ * \param device_arr - List of devices to try
+ * \param len        - Number of devices in specified list
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaSetDevice, ::cudaGetDeviceProperties,
+ * ::cudaSetDeviceFlags,
+ * ::cudaChooseDevice
+ */
+extern __host__ cudaError_t CUDARTAPI cudaSetValidDevices(int *device_arr, int len);
+
+/**
+ * \brief Sets flags to be used for device executions
+ * 
+ * Records \p flags as the flags for the current device. If the current device
+ * has been set and that device has already been initialized, the previous flags
+ * are overwritten. If the current device has not been initialized, it is
+ * initialized with the provided flags. If no device has been made current to
+ * the calling thread, a default device is selected and initialized with the
+ * provided flags.
+ * 
+ * The three LSBs of the \p flags parameter can be used to control how the CPU
+ * thread interacts with the OS scheduler when waiting for results from the
+ * device.
+ *
+ * - ::cudaDeviceScheduleAuto: The default value if the \p flags parameter is
+ * zero, uses a heuristic based on the number of active CUDA contexts in the
+ * process \p C and the number of logical processors in the system \p P. If
+ * \p C \> \p P, then CUDA will yield to other OS threads when waiting for the
+ * device, otherwise CUDA will not yield while waiting for results and
+ * actively spin on the processor. Additionally, on Tegra devices,
+ * ::cudaDeviceScheduleAuto uses a heuristic based on the power profile of
+ * the platform and may choose ::cudaDeviceScheduleBlockingSync for low-powered
+ * devices.
+ * - ::cudaDeviceScheduleSpin: Instruct CUDA to actively spin when waiting for
+ * results from the device. This can decrease latency when waiting for the
+ * device, but may lower the performance of CPU threads if they are performing
+ * work in parallel with the CUDA thread.
+ * - ::cudaDeviceScheduleYield: Instruct CUDA to yield its thread when waiting
+ * for results from the device. This can increase latency when waiting for the
+ * device, but can increase the performance of CPU threads performing work in
+ * parallel with the device.
+ * - ::cudaDeviceScheduleBlockingSync: Instruct CUDA to block the CPU thread 
+ * on a synchronization primitive when waiting for the device to finish work.
+ * - ::cudaDeviceBlockingSync: Instruct CUDA to block the CPU thread on a 
+ * synchronization primitive when waiting for the device to finish work. <br>
+ * \ref deprecated "Deprecated:" This flag was deprecated as of CUDA 4.0 and
+ * replaced with ::cudaDeviceScheduleBlockingSync.
+ * - ::cudaDeviceMapHost: This flag enables allocating pinned
+ * host memory that is accessible to the device. It is implicit for the
+ * runtime but may be absent if a context is created using the driver API.
+ * If this flag is not set, ::cudaHostGetDevicePointer() will always return
+ * a failure code.
+ * - ::cudaDeviceLmemResizeToMax: Instruct CUDA to not reduce local memory
+ * after resizing local memory for a kernel. This can prevent thrashing by
+ * local memory allocations when launching many kernels with high local
+ * memory usage at the cost of potentially increased memory usage. <br>
+ * \ref deprecated "Deprecated:" This flag is deprecated and the behavior enabled
+ * by this flag is now the default and cannot be disabled.
+ * - ::cudaDeviceSyncMemops: Ensures that synchronous memory operations initiated
+ * on this context will always synchronize. See further documentation in the
+ * section titled "API Synchronization behavior" to learn more about cases when
+ * synchronous memory operations can exhibit asynchronous behavior.
+ *
+ * \param flags - Parameters for device operation
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceFlags, ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaGetDeviceProperties,
+ * ::cudaSetDevice, ::cudaSetValidDevices,
+ * ::cudaInitDevice,
+ * ::cudaChooseDevice,
+ * ::cuDevicePrimaryCtxSetFlags
+ */
+extern __host__ cudaError_t CUDARTAPI cudaSetDeviceFlags( unsigned int flags );
+
+/**
+ * \brief Gets the flags for the current device
+ *
+ * 
+ * Returns in \p flags the flags for the current device. If there is a current
+ * device for the calling thread, the flags for the device are returned. If
+ * there is no current device, the flags for the first device are returned,
+ * which may be the default flags.  Compare to the behavior of
+ * ::cudaSetDeviceFlags.
+ *
+ * Typically, the flags returned should match the behavior that will be seen
+ * if the calling thread uses a device after this call, without any change to
+ * the flags or current device inbetween by this or another thread.  Note that
+ * if the device is not initialized, it is possible for another thread to
+ * change the flags for the current device before it is initialized.
+ * Additionally, when using exclusive mode, if this thread has not requested a
+ * specific device, it may use a device other than the first device, contrary
+ * to the assumption made by this function.
+ *
+ * If a context has been created via the driver API and is current to the
+ * calling thread, the flags for that context are always returned.
+ *
+ * Flags returned by this function may specifically include ::cudaDeviceMapHost
+ * even though it is not accepted by ::cudaSetDeviceFlags because it is
+ * implicit in runtime API flags.  The reason for this is that the current
+ * context may have been created via the driver API in which case the flag is
+ * not implicit and may be unset.
+ *
+ * \param flags - Pointer to store the device flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDevice, ::cudaGetDeviceProperties,
+ * ::cudaSetDevice, ::cudaSetDeviceFlags,
+ * ::cudaInitDevice,
+ * ::cuCtxGetFlags,
+ * ::cuDevicePrimaryCtxGetState
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetDeviceFlags( unsigned int *flags );
+/** @} */ /* END CUDART_DEVICE */
+
+/**
+ * \defgroup CUDART_STREAM Stream Management
+ *
+ * ___MANBRIEF___ stream management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the stream management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Create an asynchronous stream
+ *
+ * Creates a new asynchronous stream on the context that is current to the calling host thread.
+ * If no context is current to the calling host thread, then the primary context for a device
+ * is selected, made current to the calling thread, and initialized before creating a stream on it.
+ *
+ * \param pStream - Pointer to new stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreateWithPriority,
+ * ::cudaStreamCreateWithFlags,
+ * ::cudaStreamGetPriority,
+ * ::cudaStreamGetFlags,
+ * ::cudaStreamGetDevice,
+ * ::cudaStreamQuery,
+ * ::cudaStreamSynchronize,
+ * ::cudaStreamWaitEvent,
+ * ::cudaStreamAddCallback,
+ * ::cudaSetDevice,
+ * ::cudaStreamDestroy,
+ * ::cuStreamCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamCreate(cudaStream_t *pStream);
+
+/**
+ * \brief Create an asynchronous stream
+ *
+ * Creates a new asynchronous stream on the context that is current to the calling host thread.
+ * If no context is current to the calling host thread, then the primary context for a device
+ * is selected, made current to the calling thread, and initialized before creating a stream on it.
+ * The \p flags argument determines the behaviors of the stream.  Valid values for \p flags are
+ * - ::cudaStreamDefault: Default stream creation flag.
+ * - ::cudaStreamNonBlocking: Specifies that work running in the created 
+ *   stream may run concurrently with work in stream 0 (the NULL stream), and that
+ *   the created stream should perform no implicit synchronization with stream 0.
+ *
+ * \param pStream - Pointer to new stream identifier
+ * \param flags   - Parameters for stream creation
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate,
+ * ::cudaStreamCreateWithPriority,
+ * ::cudaStreamGetFlags,
+ * ::cudaStreamGetDevice,
+ * ::cudaStreamQuery,
+ * ::cudaStreamSynchronize,
+ * ::cudaStreamWaitEvent,
+ * ::cudaStreamAddCallback,
+ * ::cudaSetDevice,
+ * ::cudaStreamDestroy,
+ * ::cuStreamCreate
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamCreateWithFlags(cudaStream_t *pStream, unsigned int flags);
+
+/**
+ * \brief Create an asynchronous stream with the specified priority
+ *
+ * Creates a stream with the specified priority and returns a handle in \p pStream.
+ * The stream is created on the context that is current to the calling host thread.
+ * If no context is current to the calling host thread, then the primary context for a device
+ * is selected, made current to the calling thread, and initialized before creating a stream on it.
+ * This affects the scheduling priority of work in the stream. Priorities provide a
+ * hint to preferentially run work with higher priority when possible, but do
+ * not preempt already-running work or provide any other functional guarantee on
+ * execution order.
+ *
+ * \p priority follows a convention where lower numbers represent higher priorities.
+ * '0' represents default priority. The range of meaningful numerical priorities can
+ * be queried using ::cudaDeviceGetStreamPriorityRange. If the specified priority is
+ * outside the numerical range returned by ::cudaDeviceGetStreamPriorityRange,
+ * it will automatically be clamped to the lowest or the highest number in the range.
+ *
+ * \param pStream  - Pointer to new stream identifier
+ * \param flags    - Flags for stream creation. See ::cudaStreamCreateWithFlags for a list of valid flags that can be passed
+ * \param priority - Priority of the stream. Lower numbers represent higher priorities.
+ *                   See ::cudaDeviceGetStreamPriorityRange for more information about
+ *                   the meaningful stream priorities that can be passed.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \note Stream priorities are supported only on GPUs
+ * with compute capability 3.5 or higher.
+ *
+ * \note In the current implementation, only compute kernels launched in
+ * priority streams are affected by the stream's priority. Stream priorities have
+ * no effect on host-to-device and device-to-host memory operations.
+ *
+ * \sa ::cudaStreamCreate,
+ * ::cudaStreamCreateWithFlags,
+ * ::cudaDeviceGetStreamPriorityRange,
+ * ::cudaStreamGetPriority,
+ * ::cudaStreamQuery,
+ * ::cudaStreamWaitEvent,
+ * ::cudaStreamAddCallback,
+ * ::cudaStreamSynchronize,
+ * ::cudaSetDevice,
+ * ::cudaStreamDestroy,
+ * ::cuStreamCreateWithPriority
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamCreateWithPriority(cudaStream_t *pStream, unsigned int flags, int priority);
+
+/**
+ * \brief Query the priority of a stream
+ *
+ * Query the priority of a stream. The priority is returned in in \p priority.
+ * Note that if the stream was created with a priority outside the meaningful
+ * numerical range returned by ::cudaDeviceGetStreamPriorityRange,
+ * this function returns the clamped priority.
+ * See ::cudaStreamCreateWithPriority for details about priority clamping.
+ *
+ * \param hStream    - Handle to the stream to be queried
+ * \param priority   - Pointer to a signed integer in which the stream's priority is returned
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreateWithPriority,
+ * ::cudaDeviceGetStreamPriorityRange,
+ * ::cudaStreamGetFlags,
+ * ::cudaStreamGetDevice,
+ * ::cuStreamGetPriority
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetPriority(cudaStream_t hStream, int *priority);
+
+/**
+ * \brief Query the flags of a stream
+ *
+ * Query the flags of a stream. The flags are returned in \p flags.
+ * See ::cudaStreamCreateWithFlags for a list of valid flags.
+ *
+ * \param hStream - Handle to the stream to be queried
+ * \param flags   - Pointer to an unsigned integer in which the stream's flags are returned
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreateWithPriority,
+ * ::cudaStreamCreateWithFlags,
+ * ::cudaStreamGetPriority,
+ * ::cudaStreamGetDevice,
+ * ::cuStreamGetFlags
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetFlags(cudaStream_t hStream, unsigned int *flags);
+
+/**
+ * \brief Query the Id of a stream
+ *
+ * Query the Id of a stream. The Id is returned in \p streamId.
+ * The Id is unique for the life of the program.
+ *
+ * The stream handle \p hStream can refer to any of the following:
+ * <ul>
+ *   <li>a stream created via any of the CUDA runtime APIs such as ::cudaStreamCreate, 
+ *   ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority, or their driver 
+ *   API equivalents such as ::cuStreamCreate or ::cuStreamCreateWithPriority.
+ *   Passing an invalid handle will result in undefined behavior.</li>
+ *   <li>any of the special streams such as the NULL stream, ::cudaStreamLegacy 
+ *   and ::cudaStreamPerThread respectively.  The driver API equivalents of these 
+ *   are also accepted which are NULL, ::CU_STREAM_LEGACY and ::CU_STREAM_PER_THREAD.</li>
+ * </ul>
+ * 
+ * \param hStream    - Handle to the stream to be queried
+ * \param streamId   - Pointer to an unsigned long long in which the stream Id is returned
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreateWithPriority,
+ * ::cudaStreamCreateWithFlags,
+ * ::cudaStreamGetPriority,
+ * ::cudaStreamGetFlags,
+ * ::cuStreamGetId
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetId(cudaStream_t hStream, unsigned long long *streamId);
+
+/**
+ * \brief Query the device of a stream
+ *
+ * Returns in \p *device the device of the stream.
+ *
+ * \param hStream - Handle to the stream to be queried
+ * \param device - Returns the device to which the stream belongs
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorDeviceUnavailable,
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaSetDevice,
+ * ::cudaGetDevice,
+ * ::cudaStreamCreate,
+ * ::cudaStreamGetPriority,
+ * ::cudaStreamGetFlags,
+ * ::cuStreamGetId
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetDevice(cudaStream_t hStream, int *device);
+
+/**
+ * \brief Resets all persisting lines in cache to normal status.
+ *
+ * Resets all persisting lines in cache to normal status.
+ * Takes effect on function return.
+ *
+ * \return
+ * ::cudaSuccess,
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ cudaError_t CUDARTAPI cudaCtxResetPersistingL2Cache(void);
+
+/**
+ * \brief Copies attributes from source stream to destination stream.
+ *
+ * Copies attributes from source stream \p src to destination stream \p dst.
+ * Both streams must have the same context.
+ *
+ * \param[out] dst Destination stream
+ * \param[in] src Source stream
+ * For attributes see ::cudaStreamAttrID
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotSupported
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamCopyAttributes(cudaStream_t dst, cudaStream_t src);
+
+ /**
+ * \brief Queries stream attribute.
+ *
+ * Queries attribute \p attr from \p hStream and stores it in corresponding
+ * member of \p value_out.
+ *
+ * \param[in] hStream
+ * \param[in] attr
+ * \param[out] value_out
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetAttribute(
+        cudaStream_t hStream, cudaStreamAttrID attr,
+        cudaStreamAttrValue *value_out);
+
+ /**
+ * \brief Sets stream attribute.
+ *
+ * Sets attribute \p attr on \p hStream from corresponding attribute of
+ * \p value. The updated attribute will be applied to subsequent work
+ * submitted to the stream. It will not affect previously submitted work.
+ *
+ * \param[out] hStream
+ * \param[in] attr
+ * \param[in] value
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamSetAttribute(
+        cudaStream_t hStream, cudaStreamAttrID attr,
+        const cudaStreamAttrValue *value);
+
+ /**
+ * \brief Destroys and cleans up an asynchronous stream
+ *
+ * Destroys and cleans up the asynchronous stream specified by \p stream.
+ *
+ * In case the device is still doing work in the stream \p stream
+ * when ::cudaStreamDestroy() is called, the function will return immediately 
+ * and the resources associated with \p stream will be released automatically 
+ * once the device has completed all work in \p stream.
+ *
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa ::cudaStreamCreate,
+ * ::cudaStreamCreateWithFlags,
+ * ::cudaStreamQuery,
+ * ::cudaStreamWaitEvent,
+ * ::cudaStreamSynchronize,
+ * ::cudaStreamAddCallback,
+ * ::cuStreamDestroy
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamDestroy(cudaStream_t stream);
+
+/**
+ * \brief Make a compute stream wait on an event
+ *
+ * Makes all future work submitted to \p stream wait for all work captured in
+ * \p event.  See ::cudaEventRecord() for details on what is captured by an event.
+ * The synchronization will be performed efficiently on the device when applicable.
+ * \p event may be from a different device than \p stream.
+ *
+ * flags include:
+ * - ::cudaEventWaitDefault: Default event creation flag.
+ * - ::cudaEventWaitExternal: Event is captured in the graph as an external
+ *   event node when performing stream capture.
+ *
+ * \param stream - Stream to wait
+ * \param event  - Event to wait on
+ * \param flags  - Parameters for the operation(See above)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate, ::cudaStreamCreateWithFlags, ::cudaStreamQuery, ::cudaStreamSynchronize, ::cudaStreamAddCallback, ::cudaStreamDestroy,
+ * ::cuStreamWaitEvent
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags __dv(0));
+
+/**
+ * Type of stream callback functions.
+ * \param stream The stream as passed to ::cudaStreamAddCallback, may be NULL.
+ * \param status ::cudaSuccess or any persistent error on the stream.
+ * \param userData User parameter provided at registration.
+ */
+typedef void (CUDART_CB *cudaStreamCallback_t)(cudaStream_t stream, cudaError_t status, void *userData);
+
+/**
+ * \brief Add a callback to a compute stream
+ *
+ * \note This function is slated for eventual deprecation and removal. If
+ * you do not require the callback to execute in case of a device error,
+ * consider using ::cudaLaunchHostFunc. Additionally, this function is not
+ * supported with ::cudaStreamBeginCapture and ::cudaStreamEndCapture, unlike
+ * ::cudaLaunchHostFunc.
+ *
+ * Adds a callback to be called on the host after all currently enqueued
+ * items in the stream have completed.  For each 
+ * cudaStreamAddCallback call, a callback will be executed exactly once.
+ * The callback will block later work in the stream until it is finished.
+ *
+ * The callback may be passed ::cudaSuccess or an error code.  In the event
+ * of a device error, all subsequently executed callbacks will receive an
+ * appropriate ::cudaError_t.
+ *
+ * Callbacks must not make any CUDA API calls.  Attempting to use CUDA APIs
+ * may result in ::cudaErrorNotPermitted.  Callbacks must not perform any
+ * synchronization that may depend on outstanding device work or other callbacks
+ * that are not mandated to run earlier.  Callbacks without a mandated order
+ * (in independent streams) execute in undefined order and may be serialized.
+ *
+ * For the purposes of Unified Memory, callback execution makes a number of
+ * guarantees:
+ * <ul>
+ *   <li>The callback stream is considered idle for the duration of the
+ *   callback.  Thus, for example, a callback may always use memory attached
+ *   to the callback stream.</li>
+ *   <li>The start of execution of a callback has the same effect as
+ *   synchronizing an event recorded in the same stream immediately prior to
+ *   the callback.  It thus synchronizes streams which have been "joined"
+ *   prior to the callback.</li>
+ *   <li>Adding device work to any stream does not have the effect of making
+ *   the stream active until all preceding callbacks have executed.  Thus, for
+ *   example, a callback might use global attached memory even if work has
+ *   been added to another stream, if it has been properly ordered with an
+ *   event.</li>
+ *   <li>Completion of a callback does not cause a stream to become
+ *   active except as described above.  The callback stream will remain idle
+ *   if no device work follows the callback, and will remain idle across
+ *   consecutive callbacks without device work in between.  Thus, for example,
+ *   stream synchronization can be done by signaling from a callback at the
+ *   end of the stream.</li>
+ * </ul>
+ *
+ * \param stream   - Stream to add callback to
+ * \param callback - The function to call once preceding stream operations are complete
+ * \param userData - User specified data to be passed to the callback function
+ * \param flags    - Reserved for future use, must be 0
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate, ::cudaStreamCreateWithFlags, ::cudaStreamQuery, ::cudaStreamSynchronize, ::cudaStreamWaitEvent, ::cudaStreamDestroy, ::cudaMallocManaged, ::cudaStreamAttachMemAsync,
+ * ::cudaLaunchHostFunc, ::cuStreamAddCallback
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamAddCallback(cudaStream_t stream,
+        cudaStreamCallback_t callback, void *userData, unsigned int flags);
+
+/**
+ * \brief Waits for stream tasks to complete
+ *
+ * Blocks until \p stream has completed all operations. If the
+ * ::cudaDeviceScheduleBlockingSync flag was set for this device, 
+ * the host thread will block until the stream is finished with 
+ * all of its tasks.
+ *
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate, ::cudaStreamCreateWithFlags, ::cudaStreamQuery, ::cudaStreamWaitEvent, ::cudaStreamAddCallback, ::cudaStreamDestroy,
+ * ::cuStreamSynchronize
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamSynchronize(cudaStream_t stream);
+
+/**
+ * \brief Queries an asynchronous stream for completion status
+ *
+ * Returns ::cudaSuccess if all operations in \p stream have
+ * completed, or ::cudaErrorNotReady if not.
+ *
+ * For the purposes of Unified Memory, a return value of ::cudaSuccess
+ * is equivalent to having called ::cudaStreamSynchronize().
+ *
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotReady,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate, ::cudaStreamCreateWithFlags, ::cudaStreamWaitEvent, ::cudaStreamSynchronize, ::cudaStreamAddCallback, ::cudaStreamDestroy,
+ * ::cuStreamQuery
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamQuery(cudaStream_t stream);
+
+/**
+ * \brief Attach memory to a stream asynchronously
+ *
+ * Enqueues an operation in \p stream to specify stream association of
+ * \p length bytes of memory starting from \p devPtr. This function is a
+ * stream-ordered operation, meaning that it is dependent on, and will
+ * only take effect when, previous work in stream has completed. Any
+ * previous association is automatically replaced.
+ *
+ * \p devPtr must point to an one of the following types of memories:
+ * - managed memory declared using the __managed__ keyword or allocated with
+ *   ::cudaMallocManaged.
+ * - a valid host-accessible region of system-allocated pageable memory. This
+ *   type of memory may only be specified if the device associated with the
+ *   stream reports a non-zero value for the device attribute
+ *   ::cudaDevAttrPageableMemoryAccess.
+ *
+ * For managed allocations, \p length must be either zero or the entire
+ * allocation's size. Both indicate that the entire allocation's stream
+ * association is being changed. Currently, it is not possible to change stream
+ * association for a portion of a managed allocation.
+ *
+ * For pageable allocations, \p length must be non-zero.
+ *
+ * The stream association is specified using \p flags which must be
+ * one of ::cudaMemAttachGlobal, ::cudaMemAttachHost or ::cudaMemAttachSingle.
+ * The default value for \p flags is ::cudaMemAttachSingle
+ * If the ::cudaMemAttachGlobal flag is specified, the memory can be accessed
+ * by any stream on any device.
+ * If the ::cudaMemAttachHost flag is specified, the program makes a guarantee
+ * that it won't access the memory on the device from any stream on a device that
+ * has a zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * If the ::cudaMemAttachSingle flag is specified and \p stream is associated with
+ * a device that has a zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess,
+ * the program makes a guarantee that it will only access the memory on the device
+ * from \p stream. It is illegal to attach singly to the NULL stream, because the
+ * NULL stream is a virtual global stream and not a specific stream. An error will
+ * be returned in this case.
+ *
+ * When memory is associated with a single stream, the Unified Memory system will
+ * allow CPU access to this memory region so long as all operations in \p stream
+ * have completed, regardless of whether other streams are active. In effect,
+ * this constrains exclusive ownership of the managed memory region by
+ * an active GPU to per-stream activity instead of whole-GPU activity.
+ *
+ * Accessing memory on the device from streams that are not associated with
+ * it will produce undefined results. No error checking is performed by the
+ * Unified Memory system to ensure that kernels launched into other streams
+ * do not access this region. 
+ *
+ * It is a program's responsibility to order calls to ::cudaStreamAttachMemAsync
+ * via events, synchronization or other means to ensure legal access to memory
+ * at all times. Data visibility and coherency will be changed appropriately
+ * for all kernels which follow a stream-association change.
+ *
+ * If \p stream is destroyed while data is associated with it, the association is
+ * removed and the association reverts to the default visibility of the allocation
+ * as specified at ::cudaMallocManaged. For __managed__ variables, the default
+ * association is always ::cudaMemAttachGlobal. Note that destroying a stream is an
+ * asynchronous operation, and as a result, the change to default association won't
+ * happen until all work in the stream has completed.
+ *
+ * \param stream  - Stream in which to enqueue the attach operation
+ * \param devPtr  - Pointer to memory (must be a pointer to managed memory or
+ *                  to a valid host-accessible region of system-allocated
+ *                  memory)
+ * \param length  - Length of memory (defaults to zero)
+ * \param flags   - Must be one of ::cudaMemAttachGlobal, ::cudaMemAttachHost or ::cudaMemAttachSingle (defaults to ::cudaMemAttachSingle)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotReady,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate, ::cudaStreamCreateWithFlags, ::cudaStreamWaitEvent, ::cudaStreamSynchronize, ::cudaStreamAddCallback, ::cudaStreamDestroy, ::cudaMallocManaged,
+ * ::cuStreamAttachMemAsync
+ */
+#if defined(__cplusplus)
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamAttachMemAsync(cudaStream_t stream, void *devPtr, size_t length __dv(0), unsigned int flags = cudaMemAttachSingle);
+#else
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamAttachMemAsync(cudaStream_t stream, void *devPtr, size_t length __dv(0), unsigned int flags);
+#endif
+
+/**
+ * \brief Begins graph capture on a stream
+ *
+ * Begin graph capture on \p stream. When a stream is in capture mode, all operations
+ * pushed into the stream will not be executed, but will instead be captured into
+ * a graph, which will be returned via ::cudaStreamEndCapture. Capture may not be initiated
+ * if \p stream is ::cudaStreamLegacy. Capture must be ended on the same stream in which
+ * it was initiated, and it may only be initiated if the stream is not already in capture
+ * mode. The capture mode may be queried via ::cudaStreamIsCapturing. A unique id
+ * representing the capture sequence may be queried via ::cudaStreamGetCaptureInfo.
+ *
+ * If \p mode is not ::cudaStreamCaptureModeRelaxed, ::cudaStreamEndCapture must be
+ * called on this stream from the same thread.
+ *
+ * \note Kernels captured using this API must not use texture and surface references.
+ *       Reading or writing through any texture or surface reference is undefined
+ *       behavior. This restriction does not apply to texture and surface objects.
+ *
+ * \param stream - Stream in which to initiate capture
+ * \param mode    - Controls the interaction of this capture sequence with other API
+ *                  calls that are potentially unsafe. For more details see
+ *                  ::cudaThreadExchangeStreamCaptureMode.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamCreate,
+ * ::cudaStreamIsCapturing,
+ * ::cudaStreamEndCapture,
+ * ::cudaThreadExchangeStreamCaptureMode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamBeginCapture(cudaStream_t stream, enum cudaStreamCaptureMode mode);
+
+/**
+ * \brief Begins graph capture on a stream to an existing graph
+ *
+ * Begin graph capture on \p stream. When a stream is in capture mode, all operations
+ * pushed into the stream will not be executed, but will instead be captured into
+ * \p graph, which will be returned via ::cudaStreamEndCapture.
+ *
+ * Capture may not be initiated if \p stream is ::cudaStreamLegacy. Capture must be ended on the
+ * same stream in which it was initiated, and it may only be initiated if the stream is not
+ * already in capture mode. The capture mode may be queried via ::cudaStreamIsCapturing. A unique id
+ * representing the capture sequence may be queried via ::cudaStreamGetCaptureInfo.
+ *
+ * If \p mode is not ::cudaStreamCaptureModeRelaxed, ::cudaStreamEndCapture must be
+ * called on this stream from the same thread.
+ *
+ * \note Kernels captured using this API must not use texture and surface references.
+ *       Reading or writing through any texture or surface reference is undefined
+ *       behavior. This restriction does not apply to texture and surface objects.
+ *
+ * \param stream          - Stream in which to initiate capture.
+ * \param graph           - Graph to capture into.
+ * \param dependencies    - Dependencies of the first node captured in the stream.  Can be NULL if numDependencies is 0.
+ * \param dependencyData  - Optional array of data associated with each dependency.
+ * \param numDependencies - Number of dependencies.
+ * \param mode            - Controls the interaction of this capture sequence with other API
+ *                          calls that are potentially unsafe. For more details see
+ *                          ::cudaThreadExchangeStreamCaptureMode.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamCreate,
+ * ::cudaStreamIsCapturing,
+ * ::cudaStreamEndCapture,
+ * ::cudaThreadExchangeStreamCaptureMode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamBeginCaptureToGraph(cudaStream_t stream, cudaGraph_t graph, const cudaGraphNode_t *dependencies, const cudaGraphEdgeData *dependencyData, size_t numDependencies, enum cudaStreamCaptureMode mode);
+
+/**
+ * \brief Swaps the stream capture interaction mode for a thread
+ *
+ * Sets the calling thread's stream capture interaction mode to the value contained
+ * in \p *mode, and overwrites \p *mode with the previous mode for the thread. To
+ * facilitate deterministic behavior across function or module boundaries, callers
+ * are encouraged to use this API in a push-pop fashion: \code
+     cudaStreamCaptureMode mode = desiredMode;
+     cudaThreadExchangeStreamCaptureMode(&mode);
+     ...
+     cudaThreadExchangeStreamCaptureMode(&mode); // restore previous mode
+ * \endcode
+ *
+ * During stream capture (see ::cudaStreamBeginCapture), some actions, such as a call
+ * to ::cudaMalloc, may be unsafe. In the case of ::cudaMalloc, the operation is
+ * not enqueued asynchronously to a stream, and is not observed by stream capture.
+ * Therefore, if the sequence of operations captured via ::cudaStreamBeginCapture
+ * depended on the allocation being replayed whenever the graph is launched, the
+ * captured graph would be invalid.
+ *
+ * Therefore, stream capture places restrictions on API calls that can be made within
+ * or concurrently to a ::cudaStreamBeginCapture-::cudaStreamEndCapture sequence. This
+ * behavior can be controlled via this API and flags to ::cudaStreamBeginCapture.
+ *
+ * A thread's mode is one of the following:
+ * - \p cudaStreamCaptureModeGlobal: This is the default mode. If the local thread has
+ *   an ongoing capture sequence that was not initiated with
+ *   \p cudaStreamCaptureModeRelaxed at \p cuStreamBeginCapture, or if any other thread
+ *   has a concurrent capture sequence initiated with \p cudaStreamCaptureModeGlobal,
+ *   this thread is prohibited from potentially unsafe API calls.
+ * - \p cudaStreamCaptureModeThreadLocal: If the local thread has an ongoing capture
+ *   sequence not initiated with \p cudaStreamCaptureModeRelaxed, it is prohibited
+ *   from potentially unsafe API calls. Concurrent capture sequences in other threads
+ *   are ignored.
+ * - \p cudaStreamCaptureModeRelaxed: The local thread is not prohibited from potentially
+ *   unsafe API calls. Note that the thread is still prohibited from API calls which
+ *   necessarily conflict with stream capture, for example, attempting ::cudaEventQuery
+ *   on an event that was last recorded inside a capture sequence.
+ *
+ * \param mode - Pointer to mode value to swap with the current mode
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamBeginCapture
+ */
+extern __host__ cudaError_t CUDARTAPI cudaThreadExchangeStreamCaptureMode(enum cudaStreamCaptureMode *mode);
+
+/**
+ * \brief Ends capture on a stream, returning the captured graph
+ *
+ * End capture on \p stream, returning the captured graph via \p pGraph.
+ * Capture must have been initiated on \p stream via a call to ::cudaStreamBeginCapture.
+ * If capture was invalidated, due to a violation of the rules of stream capture, then
+ * a NULL graph will be returned.
+ *
+ * If the \p mode argument to ::cudaStreamBeginCapture was not
+ * ::cudaStreamCaptureModeRelaxed, this call must be from the same thread as
+ * ::cudaStreamBeginCapture.
+ *
+ * \param stream - Stream to query
+ * \param pGraph - The captured graph
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorStreamCaptureWrongThread
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamCreate,
+ * ::cudaStreamBeginCapture,
+ * ::cudaStreamIsCapturing,
+ * ::cudaGraphDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamEndCapture(cudaStream_t stream, cudaGraph_t *pGraph);
+
+/**
+ * \brief Returns a stream's capture status
+ *
+ * Return the capture status of \p stream via \p pCaptureStatus. After a successful
+ * call, \p *pCaptureStatus will contain one of the following:
+ * - ::cudaStreamCaptureStatusNone: The stream is not capturing.
+ * - ::cudaStreamCaptureStatusActive: The stream is capturing.
+ * - ::cudaStreamCaptureStatusInvalidated: The stream was capturing but an error
+ *   has invalidated the capture sequence. The capture sequence must be terminated
+ *   with ::cudaStreamEndCapture on the stream where it was initiated in order to
+ *   continue using \p stream.
+ *
+ * Note that, if this is called on ::cudaStreamLegacy (the "null stream") while
+ * a blocking stream on the same device is capturing, it will return
+ * ::cudaErrorStreamCaptureImplicit and \p *pCaptureStatus is unspecified
+ * after the call. The blocking stream capture is not invalidated.
+ *
+ * When a blocking stream is capturing, the legacy stream is in an
+ * unusable state until the blocking stream capture is terminated. The legacy
+ * stream is not supported for stream capture, but attempted use would have an
+ * implicit dependency on the capturing stream(s).
+ *
+ * \param stream         - Stream to query
+ * \param pCaptureStatus - Returns the stream's capture status
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorStreamCaptureImplicit
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamCreate,
+ * ::cudaStreamBeginCapture,
+ * ::cudaStreamEndCapture
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamIsCapturing(cudaStream_t stream, enum cudaStreamCaptureStatus *pCaptureStatus);
+
+
+/**
+ * \brief Query a stream's capture state
+ *
+ * Query stream state related to stream capture.
+ *
+ * If called on ::cudaStreamLegacy (the "null stream") while a stream not created 
+ * with ::cudaStreamNonBlocking is capturing, returns ::cudaErrorStreamCaptureImplicit.
+ *
+ * Valid data (other than capture status) is returned only if both of the following are true:
+ * - the call returns cudaSuccess
+ * - the returned capture status is ::cudaStreamCaptureStatusActive
+ *
+ * \param stream - The stream to query
+ * \param captureStatus_out - Location to return the capture status of the stream; required
+ * \param id_out - Optional location to return an id for the capture sequence, which is
+ *           unique over the lifetime of the process
+ * \param graph_out - Optional location to return the graph being captured into. All
+ *           operations other than destroy and node removal are permitted on the graph
+ *           while the capture sequence is in progress. This API does not transfer
+ *           ownership of the graph, which is transferred or destroyed at
+ *           ::cudaStreamEndCapture. Note that the graph handle may be invalidated before
+ *           end of capture for certain errors. Nodes that are or become
+ *           unreachable from the original stream at ::cudaStreamEndCapture due to direct
+ *           actions on the graph do not trigger ::cudaErrorStreamCaptureUnjoined.
+ * \param dependencies_out - Optional location to store a pointer to an array of nodes.
+ *           The next node to be captured in the stream will depend on this set of nodes,
+ *           absent operations such as event wait which modify this set. The array pointer
+ *           is valid until the next API call which operates on the stream or until the
+ *           capture is terminated. The node handles may be copied out and are valid until
+ *           they or the graph is destroyed. The driver-owned array may also be passed
+ *           directly to APIs that operate on the graph (not the stream) without copying.
+ * \param numDependencies_out - Optional location to store the size of the array
+ *           returned in dependencies_out.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorStreamCaptureImplicit
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamGetCaptureInfo_v3,
+ * ::cudaStreamBeginCapture,
+ * ::cudaStreamIsCapturing,
+ * ::cudaStreamUpdateCaptureDependencies
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamGetCaptureInfo(cudaStream_t stream, enum cudaStreamCaptureStatus *captureStatus_out, unsigned long long *id_out __dv(0), cudaGraph_t *graph_out __dv(0), const cudaGraphNode_t **dependencies_out __dv(0), size_t *numDependencies_out __dv(0));
+
+/**
+ * \brief Query a stream's capture state (12.3+)
+ *
+ * Query stream state related to stream capture.
+ *
+ * If called on ::cudaStreamLegacy (the "null stream") while a stream not created 
+ * with ::cudaStreamNonBlocking is capturing, returns ::cudaErrorStreamCaptureImplicit.
+ *
+ * Valid data (other than capture status) is returned only if both of the following are true:
+ * - the call returns cudaSuccess
+ * - the returned capture status is ::cudaStreamCaptureStatusActive
+ *
+ * If \p edgeData_out is non-NULL then \p dependencies_out must be as well. If
+ * \p dependencies_out is non-NULL and \p edgeData_out is NULL, but there is non-zero edge
+ * data for one or more of the current stream dependencies, the call will return
+ * ::cudaErrorLossyQuery.
+ *
+ * \param stream - The stream to query
+ * \param captureStatus_out - Location to return the capture status of the stream; required
+ * \param id_out - Optional location to return an id for the capture sequence, which is
+ *           unique over the lifetime of the process
+ * \param graph_out - Optional location to return the graph being captured into. All
+ *           operations other than destroy and node removal are permitted on the graph
+ *           while the capture sequence is in progress. This API does not transfer
+ *           ownership of the graph, which is transferred or destroyed at
+ *           ::cudaStreamEndCapture. Note that the graph handle may be invalidated before
+ *           end of capture for certain errors. Nodes that are or become
+ *           unreachable from the original stream at ::cudaStreamEndCapture due to direct
+ *           actions on the graph do not trigger ::cudaErrorStreamCaptureUnjoined.
+ * \param dependencies_out - Optional location to store a pointer to an array of nodes.
+ *           The next node to be captured in the stream will depend on this set of nodes,
+ *           absent operations such as event wait which modify this set. The array pointer
+ *           is valid until the next API call which operates on the stream or until the
+ *           capture is terminated. The node handles may be copied out and are valid until
+ *           they or the graph is destroyed. The driver-owned array may also be passed
+ *           directly to APIs that operate on the graph (not the stream) without copying.
+ * \param edgeData_out - Optional location to store a pointer to an array of graph edge
+ *           data. This array parallels \c dependencies_out; the next node to be added
+ *           has an edge to \c dependencies_out[i] with annotation \c edgeData_out[i] for
+ *           each \c i. The array pointer is valid until the next API call which operates
+ *           on the stream or until the capture is terminated.
+ * \param numDependencies_out - Optional location to store the size of the array
+ *           returned in dependencies_out.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorStreamCaptureImplicit,
+ * ::cudaErrorLossyQuery
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamBeginCapture,
+ * ::cudaStreamIsCapturing,
+ * ::cudaStreamUpdateCaptureDependencies
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamGetCaptureInfo_v3(cudaStream_t stream,
+    enum cudaStreamCaptureStatus *captureStatus_out, unsigned long long *id_out __dv(0),
+    cudaGraph_t *graph_out __dv(0), const cudaGraphNode_t **dependencies_out __dv(0),
+    const cudaGraphEdgeData **edgeData_out __dv(0), size_t *numDependencies_out __dv(0));
+
+/**
+ * \brief Update the set of dependencies in a capturing stream (11.3+)
+ *
+ * Modifies the dependency set of a capturing stream. The dependency set is the set
+ * of nodes that the next captured node in the stream will depend on.
+ *
+ * Valid flags are ::cudaStreamAddCaptureDependencies and
+ * ::cudaStreamSetCaptureDependencies. These control whether the set passed to
+ * the API is added to the existing set or replaces it. A flags value of 0 defaults
+ * to ::cudaStreamAddCaptureDependencies.
+ *
+ * Nodes that are removed from the dependency set via this API do not result in
+ * ::cudaErrorStreamCaptureUnjoined if they are unreachable from the stream at
+ * ::cudaStreamEndCapture.
+ *
+ * Returns ::cudaErrorIllegalState if the stream is not capturing.
+ *
+ * This API is new in CUDA 11.3. Developers requiring compatibility across minor
+ * versions of the CUDA driver to 11.0 should not use this API or provide a fallback.
+ *
+ * \param stream - The stream to update
+ * \param dependencies - The set of dependencies to add
+ * \param numDependencies - The size of the dependencies array
+ * \param flags - See above
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorIllegalState
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamBeginCapture,
+ * ::cudaStreamGetCaptureInfo,
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamUpdateCaptureDependencies(cudaStream_t stream, cudaGraphNode_t *dependencies, size_t numDependencies, unsigned int flags __dv(0));
+
+/**
+ * \brief Update the set of dependencies in a capturing stream (12.3+)
+ *
+ * Modifies the dependency set of a capturing stream. The dependency set is the set
+ * of nodes that the next captured node in the stream will depend on.
+ *
+ * Valid flags are ::cudaStreamAddCaptureDependencies and
+ * ::cudaStreamSetCaptureDependencies. These control whether the set passed to
+ * the API is added to the existing set or replaces it. A flags value of 0 defaults
+ * to ::cudaStreamAddCaptureDependencies.
+ *
+ * Nodes that are removed from the dependency set via this API do not result in
+ * ::cudaErrorStreamCaptureUnjoined if they are unreachable from the stream at
+ * ::cudaStreamEndCapture.
+ *
+ * Returns ::cudaErrorIllegalState if the stream is not capturing.
+ *
+ * \param stream - The stream to update
+ * \param dependencies - The set of dependencies to add
+ * \param dependencyData - Optional array of data associated with each dependency.
+ * \param numDependencies - The size of the dependencies array
+ * \param flags - See above
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorIllegalState
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaStreamBeginCapture,
+ * ::cudaStreamGetCaptureInfo,
+ */
+extern __host__ cudaError_t CUDARTAPI cudaStreamUpdateCaptureDependencies_v2(cudaStream_t stream, cudaGraphNode_t *dependencies, const cudaGraphEdgeData *dependencyData, size_t numDependencies, unsigned int flags __dv(0));
+/** @} */ /* END CUDART_STREAM */
+
+/**
+ * \defgroup CUDART_EVENT Event Management
+ *
+ * ___MANBRIEF___ event management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the event management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates an event object
+ *
+ * Creates an event object for the current device using ::cudaEventDefault.
+ *
+ * \param event - Newly created event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*, unsigned int) "cudaEventCreate (C++ API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventRecord, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cudaStreamWaitEvent,
+ * ::cuEventCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEventCreate(cudaEvent_t *event);
+
+/**
+ * \brief Creates an event object with the specified flags
+ *
+ * Creates an event object for the current device with the specified flags. Valid
+ * flags include:
+ * - ::cudaEventDefault: Default event creation flag.
+ * - ::cudaEventBlockingSync: Specifies that event should use blocking
+ *   synchronization. A host thread that uses ::cudaEventSynchronize() to wait
+ *   on an event created with this flag will block until the event actually
+ *   completes.
+ * - ::cudaEventDisableTiming: Specifies that the created event does not need
+ *   to record timing data.  Events created with this flag specified and
+ *   the ::cudaEventBlockingSync flag not specified will provide the best
+ *   performance when used with ::cudaStreamWaitEvent() and ::cudaEventQuery().
+ * - ::cudaEventInterprocess: Specifies that the created event may be used as an
+ *   interprocess event by ::cudaIpcGetEventHandle(). ::cudaEventInterprocess must
+ *   be specified along with ::cudaEventDisableTiming.
+ *
+ * \param event - Newly created event
+ * \param flags - Flags for new event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cudaStreamWaitEvent,
+ * ::cuEventCreate
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventCreateWithFlags(cudaEvent_t *event, unsigned int flags);
+
+/**
+ * \brief Records an event
+ *
+ * Captures in \p event the contents of \p stream at the time of this call.
+ * \p event and \p stream must be on the same CUDA context.
+ * Calls such as ::cudaEventQuery() or ::cudaStreamWaitEvent() will then
+ * examine or wait for completion of the work that was captured. Uses of
+ * \p stream after this call do not modify \p event. See note on default
+ * stream behavior for what is captured in the default case.
+ *
+ * ::cudaEventRecord() can be called multiple times on the same event and
+ * will overwrite the previously captured state. Other APIs such as
+ * ::cudaStreamWaitEvent() use the most recently captured state at the time
+ * of the API call, and are not affected by later calls to
+ * ::cudaEventRecord(). Before the first call to ::cudaEventRecord(), an
+ * event represents an empty set of work, so for example ::cudaEventQuery()
+ * would return ::cudaSuccess.
+ *
+ * \param event  - Event to record
+ * \param stream - Stream in which to record event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cudaStreamWaitEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cuEventRecord
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecord(cudaEvent_t event, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Records an event
+ *
+ * Captures in \p event the contents of \p stream at the time of this call.
+ * \p event and \p stream must be on the same CUDA context.
+ * Calls such as ::cudaEventQuery() or ::cudaStreamWaitEvent() will then
+ * examine or wait for completion of the work that was captured. Uses of
+ * \p stream after this call do not modify \p event. See note on default
+ * stream behavior for what is captured in the default case.
+ *
+ * ::cudaEventRecordWithFlags() can be called multiple times on the same event and
+ * will overwrite the previously captured state. Other APIs such as
+ * ::cudaStreamWaitEvent() use the most recently captured state at the time
+ * of the API call, and are not affected by later calls to
+ * ::cudaEventRecordWithFlags(). Before the first call to ::cudaEventRecordWithFlags(), an
+ * event represents an empty set of work, so for example ::cudaEventQuery()
+ * would return ::cudaSuccess.
+ *
+ * flags include:
+ * - ::cudaEventRecordDefault: Default event creation flag.
+ * - ::cudaEventRecordExternal: Event is captured in the graph as an external
+ *   event node when performing stream capture.
+ *
+ * \param event  - Event to record
+ * \param stream - Stream in which to record event
+ * \param flags  - Parameters for the operation(See above)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cudaStreamWaitEvent,
+ * ::cudaEventRecord,
+ * ::cuEventRecord,
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream __dv(0), unsigned int flags __dv(0));
+#endif
+
+/**
+ * \brief Queries an event's status
+ *
+ * Queries the status of all work currently captured by \p event. See
+ * ::cudaEventRecord() for details on what is captured by an event.
+ *
+ * Returns ::cudaSuccess if all captured work has been completed, or
+ * ::cudaErrorNotReady if any captured work is incomplete.
+ *
+ * For the purposes of Unified Memory, a return value of ::cudaSuccess
+ * is equivalent to having called ::cudaEventSynchronize().
+ *
+ * \param event - Event to query
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotReady,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventRecord,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cuEventQuery
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEventQuery(cudaEvent_t event);
+
+/**
+ * \brief Waits for an event to complete
+ *
+ * Waits until the completion of all work currently captured in \p event.
+ * See ::cudaEventRecord() for details on what is captured by an event.
+ *
+ * Waiting for an event that was created with the ::cudaEventBlockingSync
+ * flag will cause the calling CPU thread to block until the event has
+ * been completed by the device.  If the ::cudaEventBlockingSync flag has
+ * not been set, then the CPU thread will busy-wait until the event has
+ * been completed by the device.
+ *
+ * \param event - Event to wait for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventRecord,
+ * ::cudaEventQuery, ::cudaEventDestroy, ::cudaEventElapsedTime,
+ * ::cuEventSynchronize
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEventSynchronize(cudaEvent_t event);
+
+/**
+ * \brief Destroys an event object
+ *
+ * Destroys the event specified by \p event.
+ *
+ * An event may be destroyed before it is complete (i.e., while
+ * ::cudaEventQuery() would return ::cudaErrorNotReady). In this case, the
+ * call does not block on completion of the event, and any associated
+ * resources will automatically be released asynchronously at completion.
+ *
+ * \param event - Event to destroy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventRecord, ::cudaEventElapsedTime,
+ * ::cuEventDestroy
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventDestroy(cudaEvent_t event);
+
+/**
+ * \brief Computes the elapsed time between events
+ *
+ * Computes the elapsed time between two events (in milliseconds with a
+ * resolution of around 0.5 microseconds).
+ *
+ * If either event was last recorded in a non-NULL stream, the resulting time
+ * may be greater than expected (even if both used the same stream handle). This
+ * happens because the ::cudaEventRecord() operation takes place asynchronously
+ * and there is no guarantee that the measured latency is actually just between
+ * the two events. Any number of other different stream operations could execute
+ * in between the two measured events, thus altering the timing in a significant
+ * way.
+ *
+ * If ::cudaEventRecord() has not been called on either event, then
+ * ::cudaErrorInvalidResourceHandle is returned. If ::cudaEventRecord() has been
+ * called on both events but one or both of them has not yet been completed
+ * (that is, ::cudaEventQuery() would return ::cudaErrorNotReady on at least one
+ * of the events), ::cudaErrorNotReady is returned. If either event was created
+ * with the ::cudaEventDisableTiming flag, then this function will return
+ * ::cudaErrorInvalidResourceHandle.
+ *
+ * \param ms    - Time between \p start and \p end in ms
+ * \param start - Starting event
+ * \param end   - Ending event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotReady,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventRecord,
+ * ::cuEventElapsedTime
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEventElapsedTime(float *ms, cudaEvent_t start, cudaEvent_t end);
+
+/**
+ * \brief Computes the elapsed time between events
+ *
+ * Computes the elapsed time between two events (in milliseconds with a
+ * resolution of around 0.5 microseconds). Note this API is not guaranteed
+ * to return the latest errors for pending work. As such this API is intended to
+ * serve as a elapsed time calculation only and polling for completion on the
+ * events to be compared should be done with ::cudaEventQuery instead.
+ *
+ * If either event was last recorded in a non-NULL stream, the resulting time
+ * may be greater than expected (even if both used the same stream handle). This
+ * happens because the ::cudaEventRecord() operation takes place asynchronously
+ * and there is no guarantee that the measured latency is actually just between
+ * the two events. Any number of other different stream operations could execute
+ * in between the two measured events, thus altering the timing in a significant
+ * way.
+ *
+ * If ::cudaEventRecord() has not been called on either event, then
+ * ::cudaErrorInvalidResourceHandle is returned. If ::cudaEventRecord() has been
+ * called on both events but one or both of them has not yet been completed
+ * (that is, ::cudaEventQuery() would return ::cudaErrorNotReady on at least one
+ * of the events), ::cudaErrorNotReady is returned. If either event was created
+ * with the ::cudaEventDisableTiming flag, then this function will return
+ * ::cudaErrorInvalidResourceHandle.
+ *
+ * \param ms    - Time between \p start and \p end in ms
+ * \param start - Starting event
+ * \param end   - Ending event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotReady,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_null_event
+ *
+ * \sa \ref ::cudaEventCreate(cudaEvent_t*) "cudaEventCreate (C API)",
+ * ::cudaEventCreateWithFlags, ::cudaEventQuery,
+ * ::cudaEventSynchronize, ::cudaEventDestroy, ::cudaEventRecord,
+ * ::cuEventElapsedTime
+ */
+extern __host__ cudaError_t CUDARTAPI cudaEventElapsedTime_v2(float *ms, cudaEvent_t start, cudaEvent_t end);
+
+/** @} */ /* END CUDART_EVENT */
+
+/**
+ * \defgroup CUDART_EXTRES_INTEROP External Resource Interoperability
+ *
+ * ___MANBRIEF___ External resource interoperability functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the external resource interoperability functions of the CUDA
+ * runtime application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Imports an external memory object
+ *
+ * Imports an externally allocated memory object and returns
+ * a handle to that in \p extMem_out.
+ *
+ * The properties of the handle being imported must be described in
+ * \p memHandleDesc. The ::cudaExternalMemoryHandleDesc structure
+ * is defined as follows:
+ *
+ * \code
+        typedef struct cudaExternalMemoryHandleDesc_st {
+            cudaExternalMemoryHandleType type;
+            union {
+                int fd;
+                struct {
+                    void *handle;
+                    const void *name;
+                } win32;
+                const void *nvSciBufObject;
+            } handle;
+            unsigned long long size;
+            unsigned int flags;
+        } cudaExternalMemoryHandleDesc;
+ * \endcode
+ *
+ * where ::cudaExternalMemoryHandleDesc::type specifies the type
+ * of handle being imported. ::cudaExternalMemoryHandleType is
+ * defined as:
+ *
+ * \code
+        typedef enum cudaExternalMemoryHandleType_enum {
+            cudaExternalMemoryHandleTypeOpaqueFd         = 1,
+            cudaExternalMemoryHandleTypeOpaqueWin32      = 2,
+            cudaExternalMemoryHandleTypeOpaqueWin32Kmt   = 3,
+            cudaExternalMemoryHandleTypeD3D12Heap        = 4,
+            cudaExternalMemoryHandleTypeD3D12Resource    = 5,
+	        cudaExternalMemoryHandleTypeD3D11Resource    = 6,
+		    cudaExternalMemoryHandleTypeD3D11ResourceKmt = 7,
+            cudaExternalMemoryHandleTypeNvSciBuf         = 8
+        } cudaExternalMemoryHandleType;
+ * \endcode
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeOpaqueFd, then
+ * ::cudaExternalMemoryHandleDesc::handle::fd must be a valid
+ * file descriptor referencing a memory object. Ownership of
+ * the file descriptor is transferred to the CUDA driver when the
+ * handle is imported successfully. Performing any operations on the
+ * file descriptor after it is imported results in undefined behavior.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeOpaqueWin32, then exactly one
+ * of ::cudaExternalMemoryHandleDesc::handle::win32::handle and
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalMemoryHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * references a memory object. Ownership of this handle is
+ * not transferred to CUDA after the import operation, so the
+ * application must release the handle using the appropriate system
+ * call. If ::cudaExternalMemoryHandleDesc::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a memory object.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeOpaqueWin32Kmt, then
+ * ::cudaExternalMemoryHandleDesc::handle::win32::handle must
+ * be non-NULL and
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name
+ * must be NULL. The handle specified must be a globally shared KMT
+ * handle. This handle does not hold a reference to the underlying
+ * object, and thus will be invalid when all references to the
+ * memory object are destroyed.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeD3D12Heap, then exactly one
+ * of ::cudaExternalMemoryHandleDesc::handle::win32::handle and
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalMemoryHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D12Device::CreateSharedHandle when referring to a
+ * ID3D12Heap object. This handle holds a reference to the underlying
+ * object. If ::cudaExternalMemoryHandleDesc::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a ID3D12Heap object.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeD3D12Resource, then exactly one
+ * of ::cudaExternalMemoryHandleDesc::handle::win32::handle and
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalMemoryHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D12Device::CreateSharedHandle when referring to a
+ * ID3D12Resource object. This handle holds a reference to the
+ * underlying object. If
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a ID3D12Resource object.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeD3D11Resource,then exactly one
+ * of ::cudaExternalMemoryHandleDesc::handle::win32::handle and
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalMemoryHandleDesc::handle::win32::handle is    
+ * not NULL, then it must represent a valid shared NT handle that is  
+ * returned by  IDXGIResource1::CreateSharedHandle when referring to a 
+ * ID3D11Resource object. If
+ * ::cudaExternalMemoryHandleDesc::handle::win32::name
+ * is not NULL, then it must point to a NULL-terminated array of
+ * UTF-16 characters that refers to a ID3D11Resource object.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeD3D11ResourceKmt, then
+ * ::cudaExternalMemoryHandleDesc::handle::win32::handle must
+ * be non-NULL and ::cudaExternalMemoryHandleDesc::handle::win32::name
+ * must be NULL. The handle specified must be a valid shared KMT
+ * handle that is returned by IDXGIResource::GetSharedHandle when
+ * referring to a ID3D11Resource object.
+ *
+ * If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeNvSciBuf, then
+ * ::cudaExternalMemoryHandleDesc::handle::nvSciBufObject must be NON-NULL
+ * and reference a valid NvSciBuf object.
+ * If the NvSciBuf object imported into CUDA is also mapped by other drivers, then the
+ * application must use ::cudaWaitExternalSemaphoresAsync or ::cudaSignalExternalSemaphoresAsync
+ * as approprriate barriers to maintain coherence between CUDA and the other drivers.
+ * See ::cudaExternalSemaphoreWaitSkipNvSciBufMemSync and ::cudaExternalSemaphoreSignalSkipNvSciBufMemSync 
+ * for memory synchronization.
+ *
+ * The size of the memory object must be specified in
+ * ::cudaExternalMemoryHandleDesc::size.
+ *
+ * Specifying the flag ::cudaExternalMemoryDedicated in
+ * ::cudaExternalMemoryHandleDesc::flags indicates that the
+ * resource is a dedicated resource. The definition of what a
+ * dedicated resource is outside the scope of this extension.
+ * This flag must be set if ::cudaExternalMemoryHandleDesc::type
+ * is one of the following:
+ * ::cudaExternalMemoryHandleTypeD3D12Resource
+ * ::cudaExternalMemoryHandleTypeD3D11Resource
+ * ::cudaExternalMemoryHandleTypeD3D11ResourceKmt
+ *
+ * \param extMem_out    - Returned handle to an external memory object
+ * \param memHandleDesc - Memory import handle descriptor
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorOperatingSystem
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \note If the Vulkan memory imported into CUDA is mapped on the CPU then the
+ * application must use vkInvalidateMappedMemoryRanges/vkFlushMappedMemoryRanges
+ * as well as appropriate Vulkan pipeline barriers to maintain coherence between
+ * CPU and GPU. For more information on these APIs, please refer to "Synchronization
+ * and Cache Control" chapter from Vulkan specification.
+ *
+ *
+ * \sa ::cudaDestroyExternalMemory,
+ * ::cudaExternalMemoryGetMappedBuffer,
+ * ::cudaExternalMemoryGetMappedMipmappedArray
+ */
+extern __host__ cudaError_t CUDARTAPI cudaImportExternalMemory(cudaExternalMemory_t *extMem_out, const struct cudaExternalMemoryHandleDesc *memHandleDesc);
+
+/**
+ * \brief Maps a buffer onto an imported memory object
+ *
+ * Maps a buffer onto an imported memory object and returns a device
+ * pointer in \p devPtr.
+ *
+ * The properties of the buffer being mapped must be described in
+ * \p bufferDesc. The ::cudaExternalMemoryBufferDesc structure is
+ * defined as follows:
+ *
+ * \code
+        typedef struct cudaExternalMemoryBufferDesc_st {
+            unsigned long long offset;
+            unsigned long long size;
+            unsigned int flags;
+        } cudaExternalMemoryBufferDesc;
+ * \endcode
+ *
+ * where ::cudaExternalMemoryBufferDesc::offset is the offset in
+ * the memory object where the buffer's base address is.
+ * ::cudaExternalMemoryBufferDesc::size is the size of the buffer.
+ * ::cudaExternalMemoryBufferDesc::flags must be zero.
+ *
+ * The offset and size have to be suitably aligned to match the
+ * requirements of the external API. Mapping two buffers whose ranges
+ * overlap may or may not result in the same virtual address being
+ * returned for the overlapped portion. In such cases, the application
+ * must ensure that all accesses to that region from the GPU are
+ * volatile. Otherwise writes made via one address are not guaranteed
+ * to be visible via the other address, even if they're issued by the
+ * same thread. It is recommended that applications map the combined
+ * range instead of mapping separate buffers and then apply the
+ * appropriate offsets to the returned pointer to derive the
+ * individual buffers.
+ *
+ * The returned pointer \p devPtr must be freed using ::cudaFree.
+ *
+ * \param devPtr     - Returned device pointer to buffer
+ * \param extMem     - Handle to external memory object
+ * \param bufferDesc - Buffer descriptor
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaImportExternalMemory,
+ * ::cudaDestroyExternalMemory,
+ * ::cudaExternalMemoryGetMappedMipmappedArray
+ */
+extern __host__ cudaError_t CUDARTAPI cudaExternalMemoryGetMappedBuffer(void **devPtr, cudaExternalMemory_t extMem, const struct cudaExternalMemoryBufferDesc *bufferDesc);
+
+/**
+ * \brief Maps a CUDA mipmapped array onto an external memory object
+ *
+ * Maps a CUDA mipmapped array onto an external object and returns a
+ * handle to it in \p mipmap.
+ *
+ * The properties of the CUDA mipmapped array being mapped must be
+ * described in \p mipmapDesc. The structure
+ * ::cudaExternalMemoryMipmappedArrayDesc is defined as follows:
+ *
+ * \code
+        typedef struct cudaExternalMemoryMipmappedArrayDesc_st {
+            unsigned long long offset;
+            cudaChannelFormatDesc formatDesc;
+            cudaExtent extent;
+            unsigned int flags;
+            unsigned int numLevels;
+        } cudaExternalMemoryMipmappedArrayDesc;
+ * \endcode
+ *
+ * where ::cudaExternalMemoryMipmappedArrayDesc::offset is the
+ * offset in the memory object where the base level of the mipmap
+ * chain is.
+ * ::cudaExternalMemoryMipmappedArrayDesc::formatDesc describes the
+ * format of the data.
+ * ::cudaExternalMemoryMipmappedArrayDesc::extent specifies the
+ * dimensions of the base level of the mipmap chain.
+ * ::cudaExternalMemoryMipmappedArrayDesc::flags are flags associated
+ * with CUDA mipmapped arrays. For further details, please refer to
+ * the documentation for ::cudaMalloc3DArray. Note that if the mipmapped
+ * array is bound as a color target in the graphics API, then the flag
+ * ::cudaArrayColorAttachment must be specified in 
+ * ::cudaExternalMemoryMipmappedArrayDesc::flags.
+ * ::cudaExternalMemoryMipmappedArrayDesc::numLevels specifies
+ * the total number of levels in the mipmap chain.
+ *
+ * The returned CUDA mipmapped array must be freed using ::cudaFreeMipmappedArray.
+ *
+ * \param mipmap     - Returned CUDA mipmapped array
+ * \param extMem     - Handle to external memory object
+ * \param mipmapDesc - CUDA array descriptor
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaImportExternalMemory,
+ * ::cudaDestroyExternalMemory,
+ * ::cudaExternalMemoryGetMappedBuffer
+ *
+ * \note If ::cudaExternalMemoryHandleDesc::type is
+ * ::cudaExternalMemoryHandleTypeNvSciBuf, then
+ * ::cudaExternalMemoryMipmappedArrayDesc::numLevels must not be greater than 1.
+ */
+extern __host__ cudaError_t CUDARTAPI cudaExternalMemoryGetMappedMipmappedArray(cudaMipmappedArray_t *mipmap, cudaExternalMemory_t extMem, const struct cudaExternalMemoryMipmappedArrayDesc *mipmapDesc);
+
+/**
+ * \brief Destroys an external memory object.
+ *
+ * Destroys the specified external memory object. Any existing buffers
+ * and CUDA mipmapped arrays mapped onto this object must no longer be
+ * used and must be explicitly freed using ::cudaFree and
+ * ::cudaFreeMipmappedArray respectively.
+ *
+ * \param extMem - External memory object to be destroyed
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa ::cudaImportExternalMemory,
+ * ::cudaExternalMemoryGetMappedBuffer,
+ * ::cudaExternalMemoryGetMappedMipmappedArray
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDestroyExternalMemory(cudaExternalMemory_t extMem);
+
+/**
+ * \brief Imports an external semaphore
+ *
+ * Imports an externally allocated synchronization object and returns
+ * a handle to that in \p extSem_out.
+ *
+ * The properties of the handle being imported must be described in
+ * \p semHandleDesc. The ::cudaExternalSemaphoreHandleDesc is defined
+ * as follows:
+ *
+ * \code
+        typedef struct cudaExternalSemaphoreHandleDesc_st {
+            cudaExternalSemaphoreHandleType type;
+            union {
+                int fd;
+                struct {
+                    void *handle;
+                    const void *name;
+                } win32;
+                const void* NvSciSyncObj;
+            } handle;
+            unsigned int flags;
+        } cudaExternalSemaphoreHandleDesc;
+ * \endcode
+ *
+ * where ::cudaExternalSemaphoreHandleDesc::type specifies the type of
+ * handle being imported. ::cudaExternalSemaphoreHandleType is defined
+ * as:
+ *
+ * \code
+        typedef enum cudaExternalSemaphoreHandleType_enum {
+            cudaExternalSemaphoreHandleTypeOpaqueFd                = 1,
+            cudaExternalSemaphoreHandleTypeOpaqueWin32             = 2,
+            cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt          = 3,
+            cudaExternalSemaphoreHandleTypeD3D12Fence              = 4,
+            cudaExternalSemaphoreHandleTypeD3D11Fence              = 5,
+            cudaExternalSemaphoreHandleTypeNvSciSync               = 6,
+            cudaExternalSemaphoreHandleTypeKeyedMutex              = 7,
+            cudaExternalSemaphoreHandleTypeKeyedMutexKmt           = 8,
+            cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd     = 9,
+            cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32  = 10
+        } cudaExternalSemaphoreHandleType;
+ * \endcode
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeOpaqueFd, then
+ * ::cudaExternalSemaphoreHandleDesc::handle::fd must be a valid file
+ * descriptor referencing a synchronization object. Ownership of the
+ * file descriptor is transferred to the CUDA driver when the handle
+ * is imported successfully. Performing any operations on the file
+ * descriptor after it is imported results in undefined behavior.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeOpaqueWin32, then exactly one of
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle and
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalSemaphoreHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * references a synchronization object. Ownership of this handle is
+ * not transferred to CUDA after the import operation, so the
+ * application must release the handle using the appropriate system
+ * call. If ::cudaExternalSemaphoreHandleDesc::handle::win32::name is
+ * not NULL, then it must name a valid synchronization object.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt, then
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle must be
+ * non-NULL and ::cudaExternalSemaphoreHandleDesc::handle::win32::name
+ * must be NULL. The handle specified must be a globally shared KMT
+ * handle. This handle does not hold a reference to the underlying
+ * object, and thus will be invalid when all references to the
+ * synchronization object are destroyed.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeD3D12Fence, then exactly one of
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle and
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalSemaphoreHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D12Device::CreateSharedHandle when referring to a
+ * ID3D12Fence object. This handle holds a reference to the underlying
+ * object. If ::cudaExternalSemaphoreHandleDesc::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object that
+ * refers to a valid ID3D12Fence object.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeD3D11Fence, then exactly one of
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle and
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalSemaphoreHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * is returned by ID3D11Fence::CreateSharedHandle. If 
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::name
+ * is not NULL, then it must name a valid synchronization object that
+ * refers to a valid ID3D11Fence object.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeNvSciSync, then
+ * ::cudaExternalSemaphoreHandleDesc::handle::nvSciSyncObj
+ * represents a valid NvSciSyncObj.
+ *
+ * ::cudaExternalSemaphoreHandleTypeKeyedMutex, then exactly one of
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle and
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalSemaphoreHandleDesc::handle::win32::handle
+ * is not NULL, then it represent a valid shared NT handle that
+ * is returned by IDXGIResource1::CreateSharedHandle when referring to
+ * a IDXGIKeyedMutex object.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeKeyedMutexKmt, then
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle must be
+ * non-NULL and ::cudaExternalSemaphoreHandleDesc::handle::win32::name
+ * must be NULL. The handle specified must represent a valid KMT
+ * handle that is returned by IDXGIResource::GetSharedHandle when
+ * referring to a IDXGIKeyedMutex object.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd, then
+ * ::cudaExternalSemaphoreHandleDesc::handle::fd must be a valid file
+ * descriptor referencing a synchronization object. Ownership of the
+ * file descriptor is transferred to the CUDA driver when the handle
+ * is imported successfully. Performing any operations on the file
+ * descriptor after it is imported results in undefined behavior.
+ *
+ * If ::cudaExternalSemaphoreHandleDesc::type is
+ * ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32, then exactly one of
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::handle and
+ * ::cudaExternalSemaphoreHandleDesc::handle::win32::name must not be
+ * NULL. If ::cudaExternalSemaphoreHandleDesc::handle::win32::handle
+ * is not NULL, then it must represent a valid shared NT handle that
+ * references a synchronization object. Ownership of this handle is
+ * not transferred to CUDA after the import operation, so the
+ * application must release the handle using the appropriate system
+ * call. If ::cudaExternalSemaphoreHandleDesc::handle::win32::name is
+ * not NULL, then it must name a valid synchronization object.
+ *
+ * \param extSem_out    - Returned handle to an external semaphore
+ * \param semHandleDesc - Semaphore import handle descriptor
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorOperatingSystem
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDestroyExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaImportExternalSemaphore(cudaExternalSemaphore_t *extSem_out, const struct cudaExternalSemaphoreHandleDesc *semHandleDesc);
+
+/**
+ * \brief Signals a set of external semaphore objects
+ *
+ * Enqueues a signal operation on a set of externally allocated
+ * semaphore object in the specified stream. The operations will be
+ * executed when all prior operations in the stream complete.
+ *
+ * The exact semantics of signaling a semaphore depends on the type of
+ * the object.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::cudaExternalSemaphoreHandleTypeOpaqueFd,
+ * ::cudaExternalSemaphoreHandleTypeOpaqueWin32,
+ * ::cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt
+ * then signaling the semaphore will set it to the signaled state.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::cudaExternalSemaphoreHandleTypeD3D12Fence,
+ * ::cudaExternalSemaphoreHandleTypeD3D11Fence,
+ * ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd,
+ * ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32
+ * then the semaphore will be set to the value specified in
+ * ::cudaExternalSemaphoreSignalParams::params::fence::value.
+ *
+ * If the semaphore object is of the type ::cudaExternalSemaphoreHandleTypeNvSciSync
+ * this API sets ::cudaExternalSemaphoreSignalParams::params::nvSciSync::fence to a
+ * value that can be used by subsequent waiters of the same NvSciSync object to
+ * order operations with those currently submitted in \p stream. Such an update
+ * will overwrite previous contents of
+ * ::cudaExternalSemaphoreSignalParams::params::nvSciSync::fence. By default,
+ * signaling such an external semaphore object causes appropriate memory synchronization
+ * operations to be performed over all the external memory objects that are imported as
+ * ::cudaExternalMemoryHandleTypeNvSciBuf. This ensures that any subsequent accesses
+ * made by other importers of the same set of NvSciBuf memory object(s) are coherent.
+ * These operations can be skipped by specifying the flag
+ * ::cudaExternalSemaphoreSignalSkipNvSciBufMemSync, which can be used as a
+ * performance optimization when data coherency is not required. But specifying this
+ * flag in scenarios where data coherency is required results in undefined behavior.
+ * Also, for semaphore object of the type ::cudaExternalSemaphoreHandleTypeNvSciSync,
+ * if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in
+ * ::cudaDeviceGetNvSciSyncAttributes to cudaNvSciSyncAttrSignal, this API will return
+ * cudaErrorNotSupported.
+ * 
+ * ::cudaExternalSemaphoreSignalParams::params::nvSciSync::fence associated with 
+ * semaphore object of the type ::cudaExternalSemaphoreHandleTypeNvSciSync can be 
+ * deterministic. For this the NvSciSyncAttrList used to create the semaphore object 
+ * must have value of NvSciSyncAttrKey_RequireDeterministicFences key set to true. 
+ * Deterministic fences allow users to enqueue a wait over the semaphore object even 
+ * before corresponding signal is enqueued. For such a semaphore object, CUDA guarantees 
+ * that each signal operation will increment the fence value by '1'. Users are expected 
+ * to track count of signals enqueued on the semaphore object and insert waits accordingly. 
+ * When such a semaphore object is signaled from multiple streams, due to concurrent 
+ * stream execution, it is possible that the order in which the semaphore gets signaled 
+ * is indeterministic. This could lead to waiters of the semaphore getting unblocked 
+ * incorrectly. Users are expected to handle such situations, either by not using the 
+ * same semaphore object with deterministic fence support enabled in different streams 
+ * or by adding explicit dependency amongst such streams so that the semaphore is 
+ * signaled in order.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::cudaExternalSemaphoreHandleTypeKeyedMutex,
+ * ::cudaExternalSemaphoreHandleTypeKeyedMutexKmt,
+ * then the keyed mutex will be released with the key specified in
+ * ::cudaExternalSemaphoreSignalParams::params::keyedmutex::key.
+ *
+ * \param extSemArray - Set of external semaphores to be signaled
+ * \param paramsArray - Array of semaphore parameters
+ * \param numExtSems  - Number of semaphores to signal
+ * \param stream     - Stream to enqueue the signal operations in
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaImportExternalSemaphore,
+ * ::cudaDestroyExternalSemaphore,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaSignalExternalSemaphoresAsync(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreSignalParams *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Waits on a set of external semaphore objects
+ *
+ * Enqueues a wait operation on a set of externally allocated
+ * semaphore object in the specified stream. The operations will be
+ * executed when all prior operations in the stream complete.
+ *
+ * The exact semantics of waiting on a semaphore depends on the type
+ * of the object.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::cudaExternalSemaphoreHandleTypeOpaqueFd,
+ * ::cudaExternalSemaphoreHandleTypeOpaqueWin32,
+ * ::cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt
+ * then waiting on the semaphore will wait until the semaphore reaches
+ * the signaled state. The semaphore will then be reset to the
+ * unsignaled state. Therefore for every signal operation, there can
+ * only be one wait operation.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::cudaExternalSemaphoreHandleTypeD3D12Fence,
+ * ::cudaExternalSemaphoreHandleTypeD3D11Fence,
+ * ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd,
+ * ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32
+ * then waiting on the semaphore will wait until the value of the
+ * semaphore is greater than or equal to
+ * ::cudaExternalSemaphoreWaitParams::params::fence::value.
+ *
+ * If the semaphore object is of the type ::cudaExternalSemaphoreHandleTypeNvSciSync
+ * then, waiting on the semaphore will wait until the
+ * ::cudaExternalSemaphoreSignalParams::params::nvSciSync::fence is signaled by the
+ * signaler of the NvSciSyncObj that was associated with this semaphore object.
+ * By default, waiting on such an external semaphore object causes appropriate
+ * memory synchronization operations to be performed over all external memory objects
+ * that are imported as ::cudaExternalMemoryHandleTypeNvSciBuf. This ensures that
+ * any subsequent accesses made by other importers of the same set of NvSciBuf memory
+ * object(s) are coherent. These operations can be skipped by specifying the flag
+ * ::cudaExternalSemaphoreWaitSkipNvSciBufMemSync, which can be used as a
+ * performance optimization when data coherency is not required. But specifying this
+ * flag in scenarios where data coherency is required results in undefined behavior.
+ * Also, for semaphore object of the type ::cudaExternalSemaphoreHandleTypeNvSciSync,
+ * if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in
+ * ::cudaDeviceGetNvSciSyncAttributes to cudaNvSciSyncAttrWait, this API will return
+ * cudaErrorNotSupported.
+ *
+ * If the semaphore object is any one of the following types:
+ * ::cudaExternalSemaphoreHandleTypeKeyedMutex,
+ * ::cudaExternalSemaphoreHandleTypeKeyedMutexKmt,
+ * then the keyed mutex will be acquired when it is released with the key specified 
+ * in ::cudaExternalSemaphoreSignalParams::params::keyedmutex::key or
+ * until the timeout specified by
+ * ::cudaExternalSemaphoreSignalParams::params::keyedmutex::timeoutMs
+ * has lapsed. The timeout interval can either be a finite value
+ * specified in milliseconds or an infinite value. In case an infinite
+ * value is specified the timeout never elapses. The windows INFINITE
+ * macro must be used to specify infinite timeout
+ *
+ * \param extSemArray - External semaphores to be waited on
+ * \param paramsArray - Array of semaphore parameters
+ * \param numExtSems  - Number of semaphores to wait on
+ * \param stream      - Stream to enqueue the wait operations in
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle
+ * ::cudaErrorTimeout
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaImportExternalSemaphore,
+ * ::cudaDestroyExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaWaitExternalSemaphoresAsync(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreWaitParams *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Destroys an external semaphore
+ *
+ * Destroys an external semaphore object and releases any references
+ * to the underlying resource. Any outstanding signals or waits must
+ * have completed before the semaphore is destroyed.
+ *
+ * \param extSem - External semaphore to be destroyed
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa ::cudaImportExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDestroyExternalSemaphore(cudaExternalSemaphore_t extSem);
+
+/** @} */ /* END CUDART_EXTRES_INTEROP */
+
+/**
+ * \defgroup CUDART_EXECUTION Execution Control
+ *
+ * ___MANBRIEF___ execution control functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the execution control functions of the CUDA runtime
+ * application programming interface.
+ *
+ * Some functions have overloaded C++ API template versions documented separately in the
+ * \ref CUDART_HIGHLEVEL "C++ API Routines" module.
+ *
+ * @{
+ */
+
+/**
+ * \brief Launches a device function
+ *
+ * The function invokes kernel \p func on \p gridDim (\p gridDim.x &times; \p gridDim.y
+ * &times; \p gridDim.z) grid of blocks. Each block contains \p blockDim (\p blockDim.x &times;
+ * \p blockDim.y &times; \p blockDim.z) threads.
+ *
+ * If the kernel has N parameters the \p args should point to array of N pointers.
+ * Each pointer, from <tt>args[0]</tt> to <tt>args[N - 1]</tt>, point to the region
+ * of memory from which the actual parameter will be copied.
+ *
+ * For templated functions, pass the function symbol as follows:
+ * func_name<template_arg_0,...,template_arg_N>
+ *
+ * \p sharedMem sets the amount of dynamic shared memory that will be available to
+ * each thread block.
+ *
+ * \p stream specifies a stream the invocation is associated to.
+ *
+ * \param func        - Device function symbol
+ * \param gridDim     - Grid dimentions
+ * \param blockDim    - Block dimentions
+ * \param args        - Arguments
+ * \param sharedMem   - Shared memory
+ * \param stream      - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * \ref ::cudaLaunchKernel(const T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C++ API)",
+ * ::cuLaunchKernel
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLaunchKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream);
+
+/**
+ * \brief Launches a CUDA function with launch-time configuration
+ *
+ * Note that the functionally equivalent variadic template ::cudaLaunchKernelEx
+ * is available for C++11 and newer.
+ *
+ * Invokes the kernel \p func on \p config->gridDim (\p config->gridDim.x
+ * &times; \p config->gridDim.y &times; \p config->gridDim.z) grid of blocks.
+ * Each block contains \p config->blockDim (\p config->blockDim.x &times;
+ * \p config->blockDim.y &times; \p config->blockDim.z) threads.
+ *
+ * \p config->dynamicSmemBytes sets the amount of dynamic shared memory that
+ * will be available to each thread block.
+ *
+ * \p config->stream specifies a stream the invocation is associated to.
+ *
+ * Configuration beyond grid and block dimensions, dynamic shared memory size,
+ * and stream can be provided with the following two fields of \p config:
+ *
+ * \p config->attrs is an array of \p config->numAttrs contiguous
+ * ::cudaLaunchAttribute elements. The value of this pointer is not considered
+ * if \p config->numAttrs is zero. However, in that case, it is recommended to
+ * set the pointer to NULL.                                  
+ * \p config->numAttrs is the number of attributes populating the first
+ * \p config->numAttrs positions of the \p config->attrs array.
+ *
+ * If the kernel has N parameters the \p args should point to array of N
+ * pointers. Each pointer, from <tt>args[0]</tt> to <tt>args[N - 1]</tt>, point
+ * to the region of memory from which the actual parameter will be copied.
+ *
+ * N.B. This function is so named to avoid unintentionally invoking the
+ *      templated version, \p cudaLaunchKernelEx, for kernels taking a single
+ *      void** or void* parameter.
+ *
+ * \param config - Launch configuration
+ * \param func   - Kernel to launch
+ * \param args   - Array of pointers to kernel parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorJitCompilerNotFound,
+ * ::cudaErrorJitCompilationDisabled
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * \ref ::cudaLaunchKernelEx(const cudaLaunchConfig_t *config, void (*kernel)(ExpTypes...), ActTypes &&... args) "cudaLaunchKernelEx (C++ API)",
+ * ::cuLaunchKernelEx
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLaunchKernelExC(const cudaLaunchConfig_t *config, const void *func, void **args);
+
+/**
+ * \brief Launches a device function where thread blocks can cooperate and synchronize as they execute
+ *
+ * The function invokes kernel \p func on \p gridDim (\p gridDim.x &times; \p gridDim.y
+ * &times; \p gridDim.z) grid of blocks. Each block contains \p blockDim (\p blockDim.x &times;
+ * \p blockDim.y &times; \p blockDim.z) threads.
+ *
+ * The device on which this kernel is invoked must have a non-zero value for
+ * the device attribute ::cudaDevAttrCooperativeLaunch.
+ *
+ * The total number of blocks launched cannot exceed the maximum number of blocks per
+ * multiprocessor as returned by ::cudaOccupancyMaxActiveBlocksPerMultiprocessor (or
+ * ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors
+ * as specified by the device attribute ::cudaDevAttrMultiProcessorCount.
+ *
+ * The kernel cannot make use of CUDA dynamic parallelism.
+ *
+ * If the kernel has N parameters the \p args should point to array of N pointers.
+ * Each pointer, from <tt>args[0]</tt> to <tt>args[N - 1]</tt>, point to the region
+ * of memory from which the actual parameter will be copied.
+ *
+ * For templated functions, pass the function symbol as follows:
+ * func_name<template_arg_0,...,template_arg_N>
+ *
+ * \p sharedMem sets the amount of dynamic shared memory that will be available to
+ * each thread block.
+ *
+ * \p stream specifies a stream the invocation is associated to.
+ *
+ * \param func        - Device function symbol
+ * \param gridDim     - Grid dimentions
+ * \param blockDim    - Block dimentions
+ * \param args        - Arguments
+ * \param sharedMem   - Shared memory
+ * \param stream      - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorCooperativeLaunchTooLarge,
+ * ::cudaErrorSharedObjectInitFailed
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * \ref ::cudaLaunchCooperativeKernel(const T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchCooperativeKernel (C++ API)",
+ * ::cudaLaunchCooperativeKernelMultiDevice,
+ * ::cuLaunchCooperativeKernel
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLaunchCooperativeKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream);
+
+/**
+ * \brief Launches device functions on multiple devices where thread blocks can cooperate and synchronize as they execute
+ *
+ * \deprecated This function is deprecated as of CUDA 11.3.
+ *
+ * Invokes kernels as specified in the \p launchParamsList array where each element
+ * of the array specifies all the parameters required to perform a single kernel launch.
+ * These kernels can cooperate and synchronize as they execute. The size of the array is
+ * specified by \p numDevices.
+ *
+ * No two kernels can be launched on the same device. All the devices targeted by this
+ * multi-device launch must be identical. All devices must have a non-zero value for the
+ * device attribute ::cudaDevAttrCooperativeMultiDeviceLaunch.
+ *
+ * The same kernel must be launched on all devices. Note that any __device__ or __constant__
+ * variables are independently instantiated on every device. It is the application's
+ * responsiblity to ensure these variables are initialized and used appropriately.
+ *
+ * The size of the grids as specified in blocks, the size of the blocks themselves and the
+ * amount of shared memory used by each thread block must also match across all launched kernels.
+ *
+ * The streams used to launch these kernels must have been created via either ::cudaStreamCreate
+ * or ::cudaStreamCreateWithPriority or ::cudaStreamCreateWithPriority. The NULL stream or
+ * ::cudaStreamLegacy or ::cudaStreamPerThread cannot be used.
+ *
+ * The total number of blocks launched per kernel cannot exceed the maximum number of blocks
+ * per multiprocessor as returned by ::cudaOccupancyMaxActiveBlocksPerMultiprocessor (or
+ * ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors
+ * as specified by the device attribute ::cudaDevAttrMultiProcessorCount. Since the
+ * total number of blocks launched per device has to match across all devices, the maximum
+ * number of blocks that can be launched per device will be limited by the device with the
+ * least number of multiprocessors.
+ *
+ * The kernel cannot make use of CUDA dynamic parallelism.
+ *
+ * The ::cudaLaunchParams structure is defined as:
+ * \code
+        struct cudaLaunchParams
+        {
+            void *func;
+            dim3 gridDim;
+            dim3 blockDim;
+            void **args;
+            size_t sharedMem;
+            cudaStream_t stream;
+        };
+ * \endcode
+ * where:
+ * - ::cudaLaunchParams::func specifies the kernel to be launched. This same functions must
+ *   be launched on all devices. For templated functions, pass the function symbol as follows:
+ *   func_name<template_arg_0,...,template_arg_N>
+ * - ::cudaLaunchParams::gridDim specifies the width, height and depth of the grid in blocks.
+ *   This must match across all kernels launched.
+ * - ::cudaLaunchParams::blockDim is the width, height and depth of each thread block. This
+ *   must match across all kernels launched.
+ * - ::cudaLaunchParams::args specifies the arguments to the kernel. If the kernel has
+ *   N parameters then ::cudaLaunchParams::args should point to array of N pointers. Each
+ *   pointer, from <tt>::cudaLaunchParams::args[0]</tt> to <tt>::cudaLaunchParams::args[N - 1]</tt>,
+ *   point to the region of memory from which the actual parameter will be copied.
+ * - ::cudaLaunchParams::sharedMem is the dynamic shared-memory size per thread block in bytes.
+ *   This must match across all kernels launched.
+ * - ::cudaLaunchParams::stream is the handle to the stream to perform the launch in. This cannot
+ *   be the NULL stream or ::cudaStreamLegacy or ::cudaStreamPerThread.
+ *
+ * By default, the kernel won't begin execution on any GPU until all prior work in all the specified
+ * streams has completed. This behavior can be overridden by specifying the flag
+ * ::cudaCooperativeLaunchMultiDeviceNoPreSync. When this flag is specified, each kernel
+ * will only wait for prior work in the stream corresponding to that GPU to complete before it begins
+ * execution.
+ *
+ * Similarly, by default, any subsequent work pushed in any of the specified streams will not begin
+ * execution until the kernels on all GPUs have completed. This behavior can be overridden by specifying
+ * the flag ::cudaCooperativeLaunchMultiDeviceNoPostSync. When this flag is specified,
+ * any subsequent work pushed in any of the specified streams will only wait for the kernel launched
+ * on the GPU corresponding to that stream to complete before it begins execution.
+ *
+ * \param launchParamsList - List of launch parameters, one per device
+ * \param numDevices       - Size of the \p launchParamsList array
+ * \param flags            - Flags to control launch behavior
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidConfiguration,
+ * ::cudaErrorLaunchFailure,
+ * ::cudaErrorLaunchTimeout,
+ * ::cudaErrorLaunchOutOfResources,
+ * ::cudaErrorCooperativeLaunchTooLarge,
+ * ::cudaErrorSharedObjectInitFailed
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * \ref ::cudaLaunchCooperativeKernel(const T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchCooperativeKernel (C++ API)",
+ * ::cudaLaunchCooperativeKernel,
+ * ::cuLaunchCooperativeKernelMultiDevice
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaLaunchCooperativeKernelMultiDevice(struct cudaLaunchParams *launchParamsList, unsigned int numDevices, unsigned int flags  __dv(0));
+
+/**
+ * \brief Sets the preferred cache configuration for a device function
+ *
+ * On devices where the L1 cache and shared memory use the same hardware
+ * resources, this sets through \p cacheConfig the preferred cache configuration
+ * for the function specified via \p func. This is only a preference. The
+ * runtime will use the requested configuration if possible, but it is free to
+ * choose a different configuration if required to execute \p func.
+ *
+ * \p func is a device function symbol and must be declared as a
+ * \c __global__ function. If the specified function does not exist,
+ * then ::cudaErrorInvalidDeviceFunction is returned. For templated functions,
+ * pass the function symbol as follows: func_name<template_arg_0,...,template_arg_N>
+ *
+ * This setting does nothing on devices where the size of the L1 cache and
+ * shared memory are fixed.
+ *
+ * Launching a kernel with a different preference than the most recent
+ * preference setting may insert a device-side synchronization point.
+ *
+ * The supported cache configurations are:
+ * - ::cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
+ * - ::cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
+ * - ::cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory
+ * - ::cudaFuncCachePreferEqual: prefer equal size L1 cache and shared memory
+ *
+ * \param func        - Device function symbol
+ * \param cacheConfig - Requested cache configuration
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction
+ * \notefnerr
+ * \note_string_api_deprecation2
+ * \note_init_rt
+ * \note_callback
+ *
+ * \note This API does not accept a ::cudaKernel_t casted as void*. If cache config modification
+ * is required for a ::cudaKernel_t (or a __global__ function), it can be replaced with a call to 
+ * ::cudaFuncSetAttributes with the attribute ::cudaFuncAttributePreferredSharedMemoryCarveout 
+ * to specify a more granular L1 cache and shared memory split configuration.
+ *
+ * \sa 
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const void*) "cudaFuncGetAttributes (C API)",
+ * \ref ::cudaLaunchKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C API)",
+ * ::cuFuncSetCacheConfig
+ */
+extern __host__ cudaError_t CUDARTAPI cudaFuncSetCacheConfig(const void *func, enum cudaFuncCache cacheConfig);
+
+/**
+ * \brief Find out attributes for a given function
+ *
+ * This function obtains the attributes of a function specified via \p func.
+ * \p func is a device function symbol and must be declared as a
+ * \c __global__ function. The fetched attributes are placed in \p attr.
+ * If the specified function does not exist, then it is assumed to 
+ * be a ::cudaKernel_t and used as is.
+ * For templated functions, pass the function symbol as follows: 
+ * func_name<template_arg_0,...,template_arg_N>
+ *
+ * Note that some function attributes such as
+ * \ref ::cudaFuncAttributes::maxThreadsPerBlock "maxThreadsPerBlock"
+ * may vary based on the device that is currently being used.
+ *
+ * \param attr - Return pointer to function's attributes
+ * \param func - Device function symbol
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction
+ * \notefnerr
+ * \note_string_api_deprecation2
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa 
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, T*) "cudaFuncGetAttributes (C++ API)",
+ * \ref ::cudaLaunchKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C API)",
+ * ::cuFuncGetAttribute
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncGetAttributes(struct cudaFuncAttributes *attr, const void *func);
+
+
+/**
+ * \brief Set attributes for a given function
+ *
+ * This function sets the attributes of a function specified via \p func.
+ * The parameter \p func must be a pointer to a function that executes
+ * on the device. The parameter specified by \p func must be declared as a \p __global__
+ * function. The enumeration defined by \p attr is set to the value defined by \p value.
+ * If the specified function does not exist, then it is assumed to 
+ * be a ::cudaKernel_t and used as is.
+ * If the specified attribute cannot be written, or if the value is incorrect, 
+ * then ::cudaErrorInvalidValue is returned.
+ *
+ * Valid values for \p attr are:
+ * - ::cudaFuncAttributeMaxDynamicSharedMemorySize - The requested maximum size in bytes of dynamically-allocated shared memory. The sum of this value and the function attribute ::sharedSizeBytes
+ *   cannot exceed the device attribute ::cudaDevAttrMaxSharedMemoryPerBlockOptin. The maximal size of requestable dynamic shared memory may differ by GPU architecture.
+ * - ::cudaFuncAttributePreferredSharedMemoryCarveout - On devices where the L1 cache and shared memory use the same hardware resources, 
+ *   this sets the shared memory carveout preference, in percent of the total shared memory. See ::cudaDevAttrMaxSharedMemoryPerMultiprocessor.
+ *   This is only a hint, and the driver can choose a different ratio if required to execute the function.
+ * - ::cudaFuncAttributeRequiredClusterWidth: The required cluster width in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeRequiredClusterHeight: The required cluster height in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeRequiredClusterDepth: The required cluster depth in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeNonPortableClusterSizeAllowed: Indicates whether the
+ *   function can be launched with non-portable cluster size. 1 is allowed, 0 is
+ *   disallowed.
+ * - ::cudaFuncAttributeClusterSchedulingPolicyPreference: The block
+ *   scheduling policy of a function. The value type is cudaClusterSchedulingPolicy.
+ *
+ * \param func  - Function to get attributes of
+ * \param attr  - Attribute to set
+ * \param value - Value to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaLaunchKernel(const T *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream) "cudaLaunchKernel (C++ API)",
+ * \ref ::cudaFuncSetCacheConfig(T*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C++ API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const void*) "cudaFuncGetAttributes (C API)",
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncSetAttribute(const void *func, enum cudaFuncAttribute attr, int value);
+
+/**
+ * \brief Returns the function name for a device entry function pointer.
+ *
+ * Returns in \p **name the function name associated with the symbol \p func .
+ * The function name is returned as a null-terminated string. This API may
+ * return a mangled name if the function is not declared as having C linkage.
+ * If \p **name is NULL, ::cudaErrorInvalidValue is returned.
+ * If \p func is not a device entry function, then it is assumed to
+ * be a ::cudaKernel_t and used as is.
+ *
+ * \param name - The returned name of the function
+ * \param func - The function pointer to retrieve name for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \ref ::cudaFuncGetName(const char **name, const T *func) "cudaFuncGetName (C++ API)"
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncGetName(const char **name, const void *func);
+
+/**
+ * \brief Returns the offset and size of a kernel parameter in the device-side parameter layout.
+ *
+ * Queries the kernel parameter at \p paramIndex in \p func's list of parameters and returns
+ * parameter information via \p paramOffset and \p paramSize. \p paramOffset returns the
+ * offset of the parameter in the device-side parameter layout. \p paramSize returns the size
+ * in bytes of the parameter. This information can be used to update kernel node parameters
+ * from the device via ::cudaGraphKernelNodeSetParam() and ::cudaGraphKernelNodeUpdatesApply().
+ * \p paramIndex must be less than the number of parameters that \p func takes.
+ *
+ * \param func        - The function to query
+ * \param paramIndex  - The parameter index to query
+ * \param paramOffset - The offset into the device-side parameter layout at which the parameter resides
+ * \param paramSize   - The size of the parameter in the device-side parameter layout
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * \notefnerr
+ * \note_cudaKernel_t
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncGetParamInfo(const void *func, size_t paramIndex, size_t *paramOffset, size_t *paramSize);
+
+/**
+ * \brief Converts a double argument to be executed on a device
+ *
+ * \param d - Double to convert
+ *
+ * \deprecated This function is deprecated as of CUDA 7.5
+ *
+ * Converts the double value of \p d to an internal float representation if
+ * the device does not support double arithmetic. If the device does natively
+ * support doubles, then this function does nothing.
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const void*) "cudaFuncGetAttributes (C API)",
+ * ::cudaSetDoubleForHost
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaSetDoubleForDevice(double *d);
+
+/**
+ * \brief Converts a double argument after execution on a device
+ *
+ * \deprecated This function is deprecated as of CUDA 7.5
+ *
+ * Converts the double value of \p d from a potentially internal float
+ * representation if the device does not support double arithmetic. If the
+ * device does natively support doubles, then this function does nothing.
+ *
+ * \param d - Double to convert
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * \ref ::cudaFuncSetCacheConfig(const void*, enum cudaFuncCache) "cudaFuncSetCacheConfig (C API)",
+ * \ref ::cudaFuncGetAttributes(struct cudaFuncAttributes*, const void*) "cudaFuncGetAttributes (C API)",
+ * ::cudaSetDoubleForDevice
+ */
+extern __CUDA_DEPRECATED  __host__ cudaError_t CUDARTAPI cudaSetDoubleForHost(double *d);
+
+/**
+ * \brief Enqueues a host function call in a stream
+ *
+ * Enqueues a host function to run in a stream.  The function will be called
+ * after currently enqueued work and will block work added after it.
+ *
+ * The host function must not make any CUDA API calls.  Attempting to use a
+ * CUDA API may result in ::cudaErrorNotPermitted, but this is not required.
+ * The host function must not perform any synchronization that may depend on
+ * outstanding CUDA work not mandated to run earlier.  Host functions without a
+ * mandated order (such as in independent streams) execute in undefined order
+ * and may be serialized.
+ *
+ * For the purposes of Unified Memory, execution makes a number of guarantees:
+ * <ul>
+ *   <li>The stream is considered idle for the duration of the function's
+ *   execution.  Thus, for example, the function may always use memory attached
+ *   to the stream it was enqueued in.</li>
+ *   <li>The start of execution of the function has the same effect as
+ *   synchronizing an event recorded in the same stream immediately prior to
+ *   the function.  It thus synchronizes streams which have been "joined"
+ *   prior to the function.</li>
+ *   <li>Adding device work to any stream does not have the effect of making
+ *   the stream active until all preceding host functions and stream callbacks
+ *   have executed.  Thus, for
+ *   example, a function might use global attached memory even if work has
+ *   been added to another stream, if the work has been ordered behind the
+ *   function call with an event.</li>
+ *   <li>Completion of the function does not cause a stream to become
+ *   active except as described above.  The stream will remain idle
+ *   if no device work follows the function, and will remain idle across
+ *   consecutive host functions or stream callbacks without device work in
+ *   between.  Thus, for example,
+ *   stream synchronization can be done by signaling from a host function at the
+ *   end of the stream.</li>
+ * </ul>
+ *
+ * Note that, in constrast to ::cuStreamAddCallback, the function will not be
+ * called in the event of an error in the CUDA context.
+ *
+ * \param hStream  - Stream to enqueue function call in
+ * \param fn       - The function to call once preceding stream operations are complete
+ * \param userData - User-specified data to be passed to the function
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaStreamCreate,
+ * ::cudaStreamQuery,
+ * ::cudaStreamSynchronize,
+ * ::cudaStreamWaitEvent,
+ * ::cudaStreamDestroy,
+ * ::cudaMallocManaged,
+ * ::cudaStreamAttachMemAsync,
+ * ::cudaStreamAddCallback,
+ * ::cuLaunchHostFunc
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLaunchHostFunc(cudaStream_t stream, cudaHostFn_t fn, void *userData);
+
+/** @} */ /* END CUDART_EXECUTION */
+
+/**
+ * \defgroup CUDART_EXECUTION_DEPRECATED Execution Control [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated execution control functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the deprecated execution control functions of the CUDA runtime
+ * application programming interface.
+ *
+ * Some functions have overloaded C++ API template versions documented separately in the
+ * \ref CUDART_HIGHLEVEL "C++ API Routines" module.
+ *
+ * @{
+ */
+
+/**
+ * \brief Sets the shared memory configuration for a device function
+ *
+ * \deprecated
+ *
+ * On devices with configurable shared memory banks, this function will 
+ * force all subsequent launches of the specified device function to have
+ * the given shared memory bank size configuration. On any given launch of the
+ * function, the shared memory configuration of the device will be temporarily
+ * changed if needed to suit the function's preferred configuration. Changes in
+ * shared memory configuration between subsequent launches of functions, 
+ * may introduce a device side synchronization point.
+ *
+ * Any per-function setting of shared memory bank size set via 
+ * ::cudaFuncSetSharedMemConfig will override the device wide setting set by
+ * ::cudaDeviceSetSharedMemConfig.
+ *
+ * Changing the shared memory bank size will not increase shared memory usage
+ * or affect occupancy of kernels, but may have major effects on performance. 
+ * Larger bank sizes will allow for greater potential bandwidth to shared memory,
+ * but will change what kinds of accesses to shared memory will result in bank 
+ * conflicts.
+ *
+ * This function will do nothing on devices with fixed shared memory bank size.
+ *
+ * For templated functions, pass the function symbol as follows:
+ * func_name<template_arg_0,...,template_arg_N>
+ *
+ * The supported bank configurations are:
+ * - ::cudaSharedMemBankSizeDefault: use the device's shared memory configuration
+ *   when launching this function.
+ * - ::cudaSharedMemBankSizeFourByte: set shared memory bank width to be 
+ *   four bytes natively when launching this function.
+ * - ::cudaSharedMemBankSizeEightByte: set shared memory bank width to be eight 
+ *   bytes natively when launching this function.
+ *
+ * \param func   - Device function symbol
+ * \param config - Requested shared memory configuration
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_string_api_deprecation2
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceSetSharedMemConfig,
+ * ::cudaDeviceGetSharedMemConfig,
+ * ::cudaDeviceSetCacheConfig,
+ * ::cudaDeviceGetCacheConfig,
+ * ::cudaFuncSetCacheConfig,
+ * ::cuFuncSetSharedMemConfig
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaFuncSetSharedMemConfig(const void *func, enum cudaSharedMemConfig config);
+/** @} */ /* END CUDART_EXECUTION_DEPRECATED */
+
+/**
+ * \defgroup CUDART_OCCUPANCY Occupancy
+ *
+ * ___MANBRIEF___ occupancy calculation functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the occupancy calculation functions of the CUDA runtime
+ * application programming interface.
+ *
+ * Besides the occupancy calculator functions
+ * (\ref ::cudaOccupancyMaxActiveBlocksPerMultiprocessor and \ref ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags),
+ * there are also C++ only occupancy-based launch configuration functions documented in
+ * \ref CUDART_HIGHLEVEL "C++ API Routines" module.
+ *
+ * See
+ * \ref ::cudaOccupancyMaxPotentialBlockSize(int*, int*, T, size_t, int) "cudaOccupancyMaxPotentialBlockSize (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeWithFlags(int*, int*, T, size_t, int, unsigned int) "cudaOccupancyMaxPotentialBlockSize (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMem(int*, int*, T, UnaryFunction, int) "cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(int*, int*, T, UnaryFunction, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API)"
+ * \ref ::cudaOccupancyAvailableDynamicSMemPerBlock(size_t*, T, int, int) "cudaOccupancyAvailableDynamicSMemPerBlock (C++ API)",
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns occupancy for a device function
+ *
+ * Returns in \p *numBlocks the maximum number of active blocks per
+ * streaming multiprocessor for the device function.
+ *
+ * \param numBlocks       - Returned occupancy
+ * \param func            - Kernel function for which occupancy is calculated
+ * \param blockSize       - Block size the kernel is intended to be launched with
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags,
+ * \ref ::cudaOccupancyMaxPotentialBlockSize(int*, int*, T, size_t, int) "cudaOccupancyMaxPotentialBlockSize (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeWithFlags(int*, int*, T, size_t, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeWithFlags (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMem(int*, int*, T, UnaryFunction, int) "cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(int*, int*, T, UnaryFunction, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags (C++ API)",
+ * \ref ::cudaOccupancyAvailableDynamicSMemPerBlock(size_t*, T, int, int) "cudaOccupancyAvailableDynamicSMemPerBlock (C++ API)",
+ * ::cuOccupancyMaxActiveBlocksPerMultiprocessor
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSMemSize);
+
+/**
+ * \brief Returns dynamic shared memory available per block when launching \p numBlocks blocks on SM.
+ *
+ * Returns in \p *dynamicSmemSize the maximum size of dynamic shared memory to allow \p numBlocks blocks per SM. 
+ *
+ * \param dynamicSmemSize - Returned maximum dynamic shared memory 
+ * \param func            - Kernel function for which occupancy is calculated
+ * \param numBlocks       - Number of blocks to fit on SM 
+ * \param blockSize       - Size of the block
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags,
+ * \ref ::cudaOccupancyMaxPotentialBlockSize(int*, int*, T, size_t, int) "cudaOccupancyMaxPotentialBlockSize (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeWithFlags(int*, int*, T, size_t, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeWithFlags (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMem(int*, int*, T, UnaryFunction, int) "cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(int*, int*, T, UnaryFunction, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags (C++ API)",
+ * ::cudaOccupancyAvailableDynamicSMemPerBlock
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyAvailableDynamicSMemPerBlock(size_t *dynamicSmemSize, const void *func, int numBlocks, int blockSize);
+
+/**
+ * \brief Returns occupancy for a device function with the specified flags
+ *
+ * Returns in \p *numBlocks the maximum number of active blocks per
+ * streaming multiprocessor for the device function.
+ *
+ * The \p flags parameter controls how special cases are handled. Valid flags include:
+ *
+ * - ::cudaOccupancyDefault: keeps the default behavior as
+ *   ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+ *
+ * - ::cudaOccupancyDisableCachingOverride: This flag suppresses the default behavior
+ *   on platform where global caching affects occupancy. On such platforms, if caching
+ *   is enabled, but per-block SM resource usage would result in zero occupancy, the
+ *   occupancy calculator will calculate the occupancy as if caching is disabled.
+ *   Setting this flag makes the occupancy calculator to return 0 in such cases.
+ *   More information can be found about this feature in the "Unified L1/Texture Cache"
+ *   section of the Maxwell tuning guide.
+ *
+ * \param numBlocks       - Returned occupancy
+ * \param func            - Kernel function for which occupancy is calculated
+ * \param blockSize       - Block size the kernel is intended to be launched with
+ * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes
+ * \param flags           - Requested behavior for the occupancy calculator
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaOccupancyMaxActiveBlocksPerMultiprocessor,
+ * \ref ::cudaOccupancyMaxPotentialBlockSize(int*, int*, T, size_t, int) "cudaOccupancyMaxPotentialBlockSize (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeWithFlags(int*, int*, T, size_t, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeWithFlags (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMem(int*, int*, T, UnaryFunction, int) "cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API)",
+ * \ref ::cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags(int*, int*, T, UnaryFunction, int, unsigned int) "cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags (C++ API)",
+ * \ref ::cudaOccupancyAvailableDynamicSMemPerBlock(size_t*, T, int, int) "cudaOccupancyAvailableDynamicSMemPerBlock (C++ API)",
+ * ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSMemSize, unsigned int flags);
+
+/**
+ * \brief Given the kernel function (\p func) and launch configuration
+ * (\p config), return the maximum cluster size in \p *clusterSize.
+ *
+ * The cluster dimensions in \p config are ignored. If func has a required
+ * cluster size set (see ::cudaFuncGetAttributes),\p *clusterSize will reflect 
+ * the required cluster size.
+ *
+ * By default this function will always return a value that's portable on
+ * future hardware. A higher value may be returned if the kernel function
+ * allows non-portable cluster sizes.
+ *
+ * This function will respect the compile time launch bounds.
+ *
+ * \param clusterSize - Returned maximum cluster size that can be launched
+ *                      for the given kernel function and launch configuration
+ * \param func        - Kernel function for which maximum cluster
+ *                      size is calculated
+ * \param config      - Launch configuration for the given kernel function
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa ::cudaFuncGetAttributes
+ * \ref ::cudaOccupancyMaxPotentialClusterSize(int*, T, const cudaLaunchConfig_t*) "cudaOccupancyMaxPotentialClusterSize (C++ API)",
+ * ::cuOccupancyMaxPotentialClusterSize
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxPotentialClusterSize(int *clusterSize, const void *func, const cudaLaunchConfig_t *launchConfig);
+
+
+/**
+ * \brief Given the kernel function (\p func) and launch configuration
+ * (\p config), return the maximum number of clusters that could co-exist
+ * on the target device in \p *numClusters.
+ *
+ * If the function has required cluster size already set (see
+ * ::cudaFuncGetAttributes), the cluster size from config must either be
+ * unspecified or match the required size.
+ * Without required sizes, the cluster size must be specified in config,
+ * else the function will return an error.
+ *
+ * Note that various attributes of the kernel function may affect occupancy
+ * calculation. Runtime environment may affect how the hardware schedules
+ * the clusters, so the calculated occupancy is not guaranteed to be achievable.
+ *
+ * \param numClusters - Returned maximum number of clusters that
+ *                      could co-exist on the target device
+ * \param func        - Kernel function for which maximum number
+ *                      of clusters are calculated
+ * \param config      - Launch configuration for the given kernel function
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidClusterSize,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * ::cudaFuncGetAttributes
+ * \ref ::cudaOccupancyMaxActiveClusters(int*, T, const cudaLaunchConfig_t*) "cudaOccupancyMaxActiveClusters (C++ API)",
+ * ::cuOccupancyMaxActiveClusters
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaOccupancyMaxActiveClusters(int *numClusters, const void *func, const cudaLaunchConfig_t *launchConfig);
+/** @} */ /* END CUDA_OCCUPANCY */
+
+/**
+ * \defgroup CUDART_MEMORY Memory Management
+ *
+ * ___MANBRIEF___ memory management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the memory management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * Some functions have overloaded C++ API template versions documented separately in the
+ * \ref CUDART_HIGHLEVEL "C++ API Routines" module.
+ *
+ * @{
+ */
+
+/**
+ * \brief Allocates memory that will be automatically managed by the Unified Memory system
+ *
+ * Allocates \p size bytes of managed memory on the device and returns in
+ * \p *devPtr a pointer to the allocated memory. If the device doesn't support
+ * allocating managed memory, ::cudaErrorNotSupported is returned. Support
+ * for managed memory can be queried using the device attribute
+ * ::cudaDevAttrManagedMemory. The allocated memory is suitably
+ * aligned for any kind of variable. The memory is not cleared. If \p size
+ * is 0, ::cudaMallocManaged returns ::cudaErrorInvalidValue. The pointer
+ * is valid on the CPU and on all GPUs in the system that support managed memory.
+ * All accesses to this pointer must obey the Unified Memory programming model.
+ *
+ * \p flags specifies the default stream association for this allocation.
+ * \p flags must be one of ::cudaMemAttachGlobal or ::cudaMemAttachHost. The
+ * default value for \p flags is ::cudaMemAttachGlobal.
+ * If ::cudaMemAttachGlobal is specified, then this memory is accessible from
+ * any stream on any device. If ::cudaMemAttachHost is specified, then the
+ * allocation should not be accessed from devices that have a zero value for the
+ * device attribute ::cudaDevAttrConcurrentManagedAccess; an explicit call to
+ * ::cudaStreamAttachMemAsync will be required to enable access on such devices.
+ *
+ * If the association is later changed via ::cudaStreamAttachMemAsync to
+ * a single stream, the default association, as specifed during ::cudaMallocManaged,
+ * is restored when that stream is destroyed. For __managed__ variables, the
+ * default association is always ::cudaMemAttachGlobal. Note that destroying a
+ * stream is an asynchronous operation, and as a result, the change to default
+ * association won't happen until all work in the stream has completed.
+ *
+ * Memory allocated with ::cudaMallocManaged should be released with ::cudaFree.
+ *
+ * Device memory oversubscription is possible for GPUs that have a non-zero value for the
+ * device attribute ::cudaDevAttrConcurrentManagedAccess. Managed memory on
+ * such GPUs may be evicted from device memory to host memory at any time by the Unified
+ * Memory driver in order to make room for other allocations.
+ *
+ * In a system where all GPUs have a non-zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess, managed memory may not be populated when this
+ * API returns and instead may be populated on access. In such systems, managed memory can
+ * migrate to any processor's memory at any time. The Unified Memory driver will employ heuristics to
+ * maintain data locality and prevent excessive page faults to the extent possible. The application
+ * can also guide the driver about memory usage patterns via ::cudaMemAdvise. The application
+ * can also explicitly migrate memory to a desired processor's memory via
+ * ::cudaMemPrefetchAsync.
+ *
+ * In a multi-GPU system where all of the GPUs have a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess and all the GPUs have peer-to-peer support
+ * with each other, the physical storage for managed memory is created on the GPU which is active
+ * at the time ::cudaMallocManaged is called. All other GPUs will reference the data at reduced
+ * bandwidth via peer mappings over the PCIe bus. The Unified Memory driver does not migrate
+ * memory among such GPUs.
+ *
+ * In a multi-GPU system where not all GPUs have peer-to-peer support with each other and
+ * where the value of the device attribute ::cudaDevAttrConcurrentManagedAccess
+ * is zero for at least one of those GPUs, the location chosen for physical storage of managed
+ * memory is system-dependent.
+ * - On Linux, the location chosen will be device memory as long as the current set of active
+ * contexts are on devices that either have peer-to-peer support with each other or have a
+ * non-zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * If there is an active context on a GPU that does not have a non-zero value for that device
+ * attribute and it does not have peer-to-peer support with the other devices that have active
+ * contexts on them, then the location for physical storage will be 'zero-copy' or host memory.
+ * Note that this means that managed memory that is located in device memory is migrated to
+ * host memory if a new context is created on a GPU that doesn't have a non-zero value for
+ * the device attribute and does not support peer-to-peer with at least one of the other devices
+ * that has an active context. This in turn implies that context creation may fail if there is
+ * insufficient host memory to migrate all managed allocations.
+ * - On Windows, the physical storage is always created in 'zero-copy' or host memory.
+ * All GPUs will reference the data at reduced bandwidth over the PCIe bus. In these
+ * circumstances, use of the environment variable CUDA_VISIBLE_DEVICES is recommended to
+ * restrict CUDA to only use those GPUs that have peer-to-peer support.
+ * Alternatively, users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to a non-zero
+ * value to force the driver to always use device memory for physical storage.
+ * When this environment variable is set to a non-zero value, all devices used in
+ * that process that support managed memory have to be peer-to-peer compatible
+ * with each other. The error ::cudaErrorInvalidDevice will be returned if a device
+ * that supports managed memory is used and it is not peer-to-peer compatible with
+ * any of the other managed memory supporting devices that were previously used in
+ * that process, even if ::cudaDeviceReset has been called on those devices. These
+ * environment variables are described in the CUDA programming guide under the
+ * "CUDA environment variables" section.
+ *
+ * \param devPtr - Pointer to allocated device memory
+ * \param size   - Requested allocation size in bytes
+ * \param flags  - Must be either ::cudaMemAttachGlobal or ::cudaMemAttachHost (defaults to ::cudaMemAttachGlobal)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMallocPitch, ::cudaFree, ::cudaMallocArray, ::cudaFreeArray,
+ * ::cudaMalloc3D, ::cudaMalloc3DArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc, ::cudaDeviceGetAttribute, ::cudaStreamAttachMemAsync,
+ * ::cuMemAllocManaged
+ */
+#if defined(__cplusplus)
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMallocManaged(void **devPtr, size_t size, unsigned int flags = cudaMemAttachGlobal);
+#else
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMallocManaged(void **devPtr, size_t size, unsigned int flags);
+#endif
+
+/**
+ * \brief Allocate memory on the device
+ *
+ * Allocates \p size bytes of linear memory on the device and returns in
+ * \p *devPtr a pointer to the allocated memory. The allocated memory is
+ * suitably aligned for any kind of variable. The memory is not cleared.
+ * ::cudaMalloc() returns ::cudaErrorMemoryAllocation in case of failure.
+ *
+ * The device version of ::cudaFree cannot be used with a \p *devPtr
+ * allocated using the host API, and vice versa.
+ *
+ * \param devPtr - Pointer to allocated device memory
+ * \param size   - Requested allocation size in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMallocPitch, ::cudaFree, ::cudaMallocArray, ::cudaFreeArray,
+ * ::cudaMalloc3D, ::cudaMalloc3DArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::cuMemAlloc
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMalloc(void **devPtr, size_t size);
+
+/**
+ * \brief Allocates page-locked memory on the host
+ *
+ * Allocates \p size bytes of host memory that is page-locked and accessible
+ * to the device. The driver tracks the virtual memory ranges allocated with
+ * this function and automatically accelerates calls to functions such as
+ * ::cudaMemcpy*(). Since the memory can be accessed directly by the device,
+ * it can be read or written with much higher bandwidth than pageable memory
+ * obtained with functions such as ::malloc(). 
+
+ * On systems where ::pageableMemoryAccessUsesHostPageTables
+ * is true, ::cudaMallocHost may not page-lock the allocated memory.
+
+ * Page-locking excessive amounts of memory with ::cudaMallocHost() may degrade 
+ * system performance, since it reduces the amount of memory available to the 
+ * system for paging. As a result, this function is best used sparingly to allocate 
+ * staging areas for data exchange between host and device.
+ *
+ * \param ptr  - Pointer to allocated host memory
+ * \param size - Requested allocation size in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaMallocPitch, ::cudaMallocArray, ::cudaMalloc3D,
+ * ::cudaMalloc3DArray, ::cudaHostAlloc, ::cudaFree, ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t, unsigned int) "cudaMallocHost (C++ API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::cuMemAllocHost
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMallocHost(void **ptr, size_t size);
+
+/**
+ * \brief Allocates pitched memory on the device
+ *
+ * Allocates at least \p width (in bytes) * \p height bytes of linear memory
+ * on the device and returns in \p *devPtr a pointer to the allocated memory.
+ * The function may pad the allocation to ensure that corresponding pointers
+ * in any given row will continue to meet the alignment requirements for
+ * coalescing as the address is updated from row to row. The pitch returned in
+ * \p *pitch by ::cudaMallocPitch() is the width in bytes of the allocation.
+ * The intended usage of \p pitch is as a separate parameter of the allocation,
+ * used to compute addresses within the 2D array. Given the row and column of
+ * an array element of type \p T, the address is computed as:
+ * \code
+    T* pElement = (T*)((char*)BaseAddress + Row * pitch) + Column;
+   \endcode
+ *
+ * For allocations of 2D arrays, it is recommended that programmers consider
+ * performing pitch allocations using ::cudaMallocPitch(). Due to pitch
+ * alignment restrictions in the hardware, this is especially true if the
+ * application will be performing 2D memory copies between different regions
+ * of device memory (whether linear memory or CUDA arrays).
+ *
+ * \param devPtr - Pointer to allocated pitched device memory
+ * \param pitch  - Pitch for allocation
+ * \param width  - Requested pitched allocation width (in bytes)
+ * \param height - Requested pitched allocation height
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaFree, ::cudaMallocArray, ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaMalloc3D, ::cudaMalloc3DArray,
+ * ::cudaHostAlloc,
+ * ::cuMemAllocPitch
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMallocPitch(void **devPtr, size_t *pitch, size_t width, size_t height);
+
+/**
+ * \brief Allocate an array on the device
+ *
+ * Allocates a CUDA array according to the ::cudaChannelFormatDesc structure
+ * \p desc and returns a handle to the new CUDA array in \p *array.
+ *
+ * The ::cudaChannelFormatDesc is defined as:
+ * \code
+    struct cudaChannelFormatDesc {
+        int x, y, z, w;
+    enum cudaChannelFormatKind f;
+    };
+    \endcode
+ * where ::cudaChannelFormatKind is one of ::cudaChannelFormatKindSigned,
+ * ::cudaChannelFormatKindUnsigned, or ::cudaChannelFormatKindFloat.
+ *
+ * The \p flags parameter enables different options to be specified that affect
+ * the allocation, as follows.
+ * - ::cudaArrayDefault: This flag's value is defined to be 0 and provides default array allocation
+ * - ::cudaArraySurfaceLoadStore: Allocates an array that can be read from or written to using a surface reference
+ * - ::cudaArrayTextureGather: This flag indicates that texture gather operations will be performed on the array.
+ * - ::cudaArraySparse: Allocates a CUDA array without physical backing memory. The subregions within this sparse array
+ *   can later be mapped onto a physical memory allocation by calling ::cuMemMapArrayAsync. 
+ *   The physical backing memory must be allocated via ::cuMemCreate.
+ * - ::cudaArrayDeferredMapping: Allocates a CUDA array without physical backing memory. The entire array can 
+ *   later be mapped onto a physical memory allocation by calling ::cuMemMapArrayAsync. 
+ *   The physical backing memory must be allocated via ::cuMemCreate.
+ *
+ * \p width and \p height must meet certain size requirements. See ::cudaMalloc3DArray() for more details.
+ *
+ * \param array  - Pointer to allocated array in device memory
+ * \param desc   - Requested channel format
+ * \param width  - Requested array allocation width
+ * \param height - Requested array allocation height
+ * \param flags  - Requested properties of allocated array
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaMallocPitch, ::cudaFree, ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaMalloc3D, ::cudaMalloc3DArray,
+ * ::cudaHostAlloc,
+ * ::cuArrayCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMallocArray(cudaArray_t *array, const struct cudaChannelFormatDesc *desc, size_t width, size_t height __dv(0), unsigned int flags __dv(0));
+
+/**
+ * \brief Frees memory on the device
+ *
+ * Frees the memory space pointed to by \p devPtr, which must have been
+ * returned by a previous call to one of the following memory allocation APIs -
+ * ::cudaMalloc(), ::cudaMallocPitch(), ::cudaMallocManaged(), ::cudaMallocAsync(),
+ * ::cudaMallocFromPoolAsync().
+ * 
+ * Note - This API will not perform any implicit synchronization when the pointer was
+ * allocated with ::cudaMallocAsync or ::cudaMallocFromPoolAsync. Callers must ensure
+ * that all accesses to these pointer have completed before invoking ::cudaFree. For
+ * best performance and memory reuse, users should use ::cudaFreeAsync to free memory
+ * allocated via the stream ordered memory allocator.
+ * For all other pointers, this API may perform implicit synchronization.
+ * 
+ * If ::cudaFree(\p devPtr) has already been called before,
+ * an error is returned. If \p devPtr is 0, no operation is performed.
+ * ::cudaFree() returns ::cudaErrorValue in case of failure.
+ *
+ * The device version of ::cudaFree cannot be used with a \p *devPtr
+ * allocated using the host API, and vice versa.
+ *
+ * \param devPtr - Device pointer to memory to free
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaMallocPitch, ::cudaMallocManaged, ::cudaMallocArray, ::cudaFreeArray, ::cudaMallocAsync, ::cudaMallocFromPoolAsync
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaMalloc3D, ::cudaMalloc3DArray, ::cudaFreeAsync
+ * ::cudaHostAlloc,
+ * ::cuMemFree
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFree(void *devPtr);
+
+/**
+ * \brief Frees page-locked memory
+ *
+ * Frees the memory space pointed to by \p hostPtr, which must have been
+ * returned by a previous call to ::cudaMallocHost() or ::cudaHostAlloc().
+ *
+ * \param ptr - Pointer to memory to free
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaMallocPitch, ::cudaFree, ::cudaMallocArray,
+ * ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaMalloc3D, ::cudaMalloc3DArray, ::cudaHostAlloc,
+ * ::cuMemFreeHost
+ */
+extern __host__ cudaError_t CUDARTAPI cudaFreeHost(void *ptr);
+
+/**
+ * \brief Frees an array on the device
+ *
+ * Frees the CUDA array \p array, which must have been returned by a
+ * previous call to ::cudaMallocArray(). If \p devPtr is 0,
+ * no operation is performed.
+ *
+ * \param array - Pointer to array to free
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaMallocPitch, ::cudaFree, ::cudaMallocArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::cuArrayDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaFreeArray(cudaArray_t array);
+
+/**
+ * \brief Frees a mipmapped array on the device
+ *
+ * Frees the CUDA mipmapped array \p mipmappedArray, which must have been 
+ * returned by a previous call to ::cudaMallocMipmappedArray(). If \p devPtr
+ * is 0, no operation is performed.
+ *
+ * \param mipmappedArray - Pointer to mipmapped array to free
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc, ::cudaMallocPitch, ::cudaFree, ::cudaMallocArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::cuMipmappedArrayDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaFreeMipmappedArray(cudaMipmappedArray_t mipmappedArray);
+
+
+/**
+ * \brief Allocates page-locked memory on the host
+ *
+ * Allocates \p size bytes of host memory that is page-locked and accessible
+ * to the device. The driver tracks the virtual memory ranges allocated with
+ * this function and automatically accelerates calls to functions such as
+ * ::cudaMemcpy(). Since the memory can be accessed directly by the device, it
+ * can be read or written with much higher bandwidth than pageable memory
+ * obtained with functions such as ::malloc(). Allocating excessive amounts of
+ * pinned memory may degrade system performance, since it reduces the amount
+ * of memory available to the system for paging. As a result, this function is
+ * best used sparingly to allocate staging areas for data exchange between host
+ * and device.
+ *
+ * The \p flags parameter enables different options to be specified that affect
+ * the allocation, as follows.
+ * - ::cudaHostAllocDefault: This flag's value is defined to be 0 and causes
+ * ::cudaHostAlloc() to emulate ::cudaMallocHost().
+ * - ::cudaHostAllocPortable: The memory returned by this call will be
+ * considered as pinned memory by all CUDA contexts, not just the one that
+ * performed the allocation.
+ * - ::cudaHostAllocMapped: Maps the allocation into the CUDA address space.
+ * The device pointer to the memory may be obtained by calling
+ * ::cudaHostGetDevicePointer().
+ * - ::cudaHostAllocWriteCombined: Allocates the memory as write-combined (WC).
+ * WC memory can be transferred across the PCI Express bus more quickly on some
+ * system configurations, but cannot be read efficiently by most CPUs.  WC
+ * memory is a good option for buffers that will be written by the CPU and read
+ * by the device via mapped pinned memory or host->device transfers.
+ *
+ * All of these flags are orthogonal to one another: a developer may allocate
+ * memory that is portable, mapped and/or write-combined with no restrictions.
+ *
+ * In order for the ::cudaHostAllocMapped flag to have any effect, the CUDA context
+ * must support the ::cudaDeviceMapHost flag, which can be checked via
+ * ::cudaGetDeviceFlags(). The ::cudaDeviceMapHost flag is implicitly set for
+ * contexts created via the runtime API.
+ *
+ * The ::cudaHostAllocMapped flag may be specified on CUDA contexts for devices
+ * that do not support mapped pinned memory. The failure is deferred to
+ * ::cudaHostGetDevicePointer() because the memory may be mapped into other
+ * CUDA contexts via the ::cudaHostAllocPortable flag.
+ *
+ * Memory allocated by this function must be freed with ::cudaFreeHost().
+ *
+ * \param pHost - Device pointer to allocated memory
+ * \param size  - Requested allocation size in bytes
+ * \param flags - Requested properties of allocated memory
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaSetDeviceFlags,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost,
+ * ::cudaGetDeviceFlags,
+ * ::cuMemHostAlloc
+ */
+extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
+
+/**
+ * \brief Registers an existing host memory range for use by CUDA
+ *
+ * Page-locks the memory range specified by \p ptr and \p size and maps it
+ * for the device(s) as specified by \p flags. This memory range also is added
+ * to the same tracking mechanism as ::cudaHostAlloc() to automatically accelerate
+ * calls to functions such as ::cudaMemcpy(). Since the memory can be accessed 
+ * directly by the device, it can be read or written with much higher bandwidth 
+ * than pageable memory that has not been registered.  Page-locking excessive
+ * amounts of memory may degrade system performance, since it reduces the amount
+ * of memory available to the system for paging. As a result, this function is
+ * best used sparingly to register staging areas for data exchange between
+ * host and device.
+ * 
+ * On systems where ::pageableMemoryAccessUsesHostPageTables is true, ::cudaHostRegister 
+ * will not page-lock the memory range specified by \p ptr but only populate 
+ * unpopulated pages.
+ *
+ * ::cudaHostRegister is supported only on I/O coherent devices that have a non-zero
+ * value for the device attribute ::cudaDevAttrHostRegisterSupported.
+ *
+ * The \p flags parameter enables different options to be specified that
+ * affect the allocation, as follows.
+ *
+ * - ::cudaHostRegisterDefault: On a system with unified virtual addressing,
+ *   the memory will be both mapped and portable.  On a system with no unified
+ *   virtual addressing, the memory will be neither mapped nor portable.
+ *
+ * - ::cudaHostRegisterPortable: The memory returned by this call will be
+ *   considered as pinned memory by all CUDA contexts, not just the one that
+ *   performed the allocation.
+ *
+ * - ::cudaHostRegisterMapped: Maps the allocation into the CUDA address
+ *   space. The device pointer to the memory may be obtained by calling
+ *   ::cudaHostGetDevicePointer().
+ *
+ * - ::cudaHostRegisterIoMemory: The passed memory pointer is treated as
+ *   pointing to some memory-mapped I/O space, e.g. belonging to a
+ *   third-party PCIe device, and it will marked as non cache-coherent and
+ *   contiguous.
+ *
+ * - ::cudaHostRegisterReadOnly: The passed memory pointer is treated as
+ *   pointing to memory that is considered read-only by the device.  On
+ *   platforms without ::cudaDevAttrPageableMemoryAccessUsesHostPageTables, this
+ *   flag is required in order to register memory mapped to the CPU as
+ *   read-only.  Support for the use of this flag can be queried from the device
+ *   attribute ::cudaDevAttrHostRegisterReadOnlySupported.  Using this flag with
+ *   a current context associated with a device that does not have this attribute
+ *   set will cause ::cudaHostRegister to error with cudaErrorNotSupported.
+ *
+ * All of these flags are orthogonal to one another: a developer may page-lock
+ * memory that is portable or mapped with no restrictions.
+ *
+ * The CUDA context must have been created with the ::cudaMapHost flag in
+ * order for the ::cudaHostRegisterMapped flag to have any effect.
+ *
+ * The ::cudaHostRegisterMapped flag may be specified on CUDA contexts for
+ * devices that do not support mapped pinned memory. The failure is deferred
+ * to ::cudaHostGetDevicePointer() because the memory may be mapped into
+ * other CUDA contexts via the ::cudaHostRegisterPortable flag.
+ *
+ * For devices that have a non-zero value for the device attribute
+ * ::cudaDevAttrCanUseHostPointerForRegisteredMem, the memory
+ * can also be accessed from the device using the host pointer \p ptr.
+ * The device pointer returned by ::cudaHostGetDevicePointer() may or may not
+ * match the original host pointer \p ptr and depends on the devices visible to the
+ * application. If all devices visible to the application have a non-zero value for the
+ * device attribute, the device pointer returned by ::cudaHostGetDevicePointer()
+ * will match the original pointer \p ptr. If any device visible to the application
+ * has a zero value for the device attribute, the device pointer returned by
+ * ::cudaHostGetDevicePointer() will not match the original host pointer \p ptr,
+ * but it will be suitable for use on all devices provided Unified Virtual Addressing
+ * is enabled. In such systems, it is valid to access the memory using either pointer
+ * on devices that have a non-zero value for the device attribute. Note however that
+ * such devices should access the memory using only of the two pointers and not both.
+ *
+ * The memory page-locked by this function must be unregistered with ::cudaHostUnregister().
+ *
+ * \param ptr   - Host pointer to memory to page-lock
+ * \param size  - Size in bytes of the address range to page-lock in bytes
+ * \param flags - Flags for allocation request
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorHostMemoryAlreadyRegistered,
+ * ::cudaErrorNotSupported
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaHostUnregister, ::cudaHostGetFlags, ::cudaHostGetDevicePointer,
+ * ::cuMemHostRegister
+ */
+extern __host__ cudaError_t CUDARTAPI cudaHostRegister(void *ptr, size_t size, unsigned int flags);
+
+/**
+ * \brief Unregisters a memory range that was registered with cudaHostRegister
+ *
+ * Unmaps the memory range whose base address is specified by \p ptr, and makes
+ * it pageable again.
+ *
+ * The base address must be the same one specified to ::cudaHostRegister().
+ *
+ * \param ptr - Host pointer to memory to unregister
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorHostMemoryNotRegistered
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaHostUnregister,
+ * ::cuMemHostUnregister
+ */
+extern __host__ cudaError_t CUDARTAPI cudaHostUnregister(void *ptr);
+
+/**
+ * \brief Passes back device pointer of mapped host memory allocated by
+ * cudaHostAlloc or registered by cudaHostRegister
+ *
+ * Passes back the device pointer corresponding to the mapped, pinned host
+ * buffer allocated by ::cudaHostAlloc() or registered by ::cudaHostRegister().
+ *
+ * ::cudaHostGetDevicePointer() will fail if the ::cudaDeviceMapHost flag was
+ * not specified before deferred context creation occurred, or if called on a
+ * device that does not support mapped, pinned memory.
+ *
+ * For devices that have a non-zero value for the device attribute
+ * ::cudaDevAttrCanUseHostPointerForRegisteredMem, the memory
+ * can also be accessed from the device using the host pointer \p pHost.
+ * The device pointer returned by ::cudaHostGetDevicePointer() may or may not
+ * match the original host pointer \p pHost and depends on the devices visible to the
+ * application. If all devices visible to the application have a non-zero value for the
+ * device attribute, the device pointer returned by ::cudaHostGetDevicePointer()
+ * will match the original pointer \p pHost. If any device visible to the application
+ * has a zero value for the device attribute, the device pointer returned by
+ * ::cudaHostGetDevicePointer() will not match the original host pointer \p pHost,
+ * but it will be suitable for use on all devices provided Unified Virtual Addressing
+ * is enabled. In such systems, it is valid to access the memory using either pointer
+ * on devices that have a non-zero value for the device attribute. Note however that
+ * such devices should access the memory using only of the two pointers and not both.
+ *
+ * \p flags provides for future releases.  For now, it must be set to 0.
+ *
+ * \param pDevice - Returned device pointer for mapped memory
+ * \param pHost   - Requested host pointer mapping
+ * \param flags   - Flags for extensions (must be 0 for now)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaSetDeviceFlags, ::cudaHostAlloc,
+ * ::cuMemHostGetDevicePointer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaHostGetDevicePointer(void **pDevice, void *pHost, unsigned int flags);
+
+/**
+ * \brief Passes back flags used to allocate pinned host memory allocated by
+ * cudaHostAlloc
+ *
+ * ::cudaHostGetFlags() will fail if the input pointer does not
+ * reside in an address range allocated by ::cudaHostAlloc().
+ *
+ * \param pFlags - Returned flags word
+ * \param pHost - Host pointer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaHostAlloc,
+ * ::cuMemHostGetFlags
+ */
+extern __host__ cudaError_t CUDARTAPI cudaHostGetFlags(unsigned int *pFlags, void *pHost);
+
+/**
+ * \brief Allocates logical 1D, 2D, or 3D memory objects on the device
+ *
+ * Allocates at least \p width * \p height * \p depth bytes of linear memory
+ * on the device and returns a ::cudaPitchedPtr in which \p ptr is a pointer
+ * to the allocated memory. The function may pad the allocation to ensure
+ * hardware alignment requirements are met. The pitch returned in the \p pitch
+ * field of \p pitchedDevPtr is the width in bytes of the allocation.
+ *
+ * The returned ::cudaPitchedPtr contains additional fields \p xsize and
+ * \p ysize, the logical width and height of the allocation, which are
+ * equivalent to the \p width and \p height \p extent parameters provided by
+ * the programmer during allocation.
+ *
+ * For allocations of 2D and 3D objects, it is highly recommended that
+ * programmers perform allocations using ::cudaMalloc3D() or
+ * ::cudaMallocPitch(). Due to alignment restrictions in the hardware, this is
+ * especially true if the application will be performing memory copies
+ * involving 2D or 3D objects (whether linear memory or CUDA arrays).
+ *
+ * \param pitchedDevPtr  - Pointer to allocated pitched device memory
+ * \param extent         - Requested allocation size (\p width field in bytes)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMallocPitch, ::cudaFree, ::cudaMemcpy3D, ::cudaMemset3D,
+ * ::cudaMalloc3DArray, ::cudaMallocArray, ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc, ::make_cudaPitchedPtr, ::make_cudaExtent,
+ * ::cuMemAllocPitch
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMalloc3D(struct cudaPitchedPtr* pitchedDevPtr, struct cudaExtent extent);
+
+/**
+ * \brief Allocate an array on the device
+ *
+ * Allocates a CUDA array according to the ::cudaChannelFormatDesc structure
+ * \p desc and returns a handle to the new CUDA array in \p *array.
+ *
+ * The ::cudaChannelFormatDesc is defined as:
+ * \code
+    struct cudaChannelFormatDesc {
+        int x, y, z, w;
+        enum cudaChannelFormatKind f;
+    };
+    \endcode
+ * where ::cudaChannelFormatKind is one of ::cudaChannelFormatKindSigned,
+ * ::cudaChannelFormatKindUnsigned, or ::cudaChannelFormatKindFloat.
+ *
+ * ::cudaMalloc3DArray() can allocate the following:
+ *
+ * - A 1D array is allocated if the height and depth extents are both zero.
+ * - A 2D array is allocated if only the depth extent is zero.
+ * - A 3D array is allocated if all three extents are non-zero.
+ * - A 1D layered CUDA array is allocated if only the height extent is zero and
+ * the cudaArrayLayered flag is set. Each layer is a 1D array. The number of layers is 
+ * determined by the depth extent.
+ * - A 2D layered CUDA array is allocated if all three extents are non-zero and 
+ * the cudaArrayLayered flag is set. Each layer is a 2D array. The number of layers is 
+ * determined by the depth extent.
+ * - A cubemap CUDA array is allocated if all three extents are non-zero and the
+ * cudaArrayCubemap flag is set. Width must be equal to height, and depth must be six. A cubemap is
+ * a special type of 2D layered CUDA array, where the six layers represent the six faces of a cube. 
+ * The order of the six layers in memory is the same as that listed in ::cudaGraphicsCubeFace.
+ * - A cubemap layered CUDA array is allocated if all three extents are non-zero, and both,
+ * cudaArrayCubemap and cudaArrayLayered flags are set. Width must be equal to height, and depth must be 
+ * a multiple of six. A cubemap layered CUDA array is a special type of 2D layered CUDA array that consists 
+ * of a collection of cubemaps. The first six layers represent the first cubemap, the next six layers form 
+ * the second cubemap, and so on.
+ *
+ *
+ * The \p flags parameter enables different options to be specified that affect
+ * the allocation, as follows.
+ * - ::cudaArrayDefault: This flag's value is defined to be 0 and provides default array allocation
+ * - ::cudaArrayLayered: Allocates a layered CUDA array, with the depth extent indicating the number of layers
+ * - ::cudaArrayCubemap: Allocates a cubemap CUDA array. Width must be equal to height, and depth must be six.
+ *   If the cudaArrayLayered flag is also set, depth must be a multiple of six.
+ * - ::cudaArraySurfaceLoadStore: Allocates a CUDA array that could be read from or written to using a surface
+ *   reference.
+ * - ::cudaArrayTextureGather: This flag indicates that texture gather operations will be performed on the CUDA 
+ *   array. Texture gather can only be performed on 2D CUDA arrays.
+ * - ::cudaArraySparse: Allocates a CUDA array without physical backing memory. The subregions within this sparse array 
+ *   can later be mapped onto a physical memory allocation by calling ::cuMemMapArrayAsync. This flag can only be used for 
+ *   creating 2D, 3D or 2D layered sparse CUDA arrays. The physical backing memory must be allocated via ::cuMemCreate.
+ * - ::cudaArrayDeferredMapping: Allocates a CUDA array without physical backing memory. The entire array can
+ *   later be mapped onto a physical memory allocation by calling ::cuMemMapArrayAsync. The physical backing memory must be allocated
+ *   via ::cuMemCreate.
+ *
+ * The width, height and depth extents must meet certain size requirements as listed in the following table.
+ * All values are specified in elements.
+ *
+ * Note that 2D CUDA arrays have different size requirements if the ::cudaArrayTextureGather flag is set. In that
+ * case, the valid range for (width, height, depth) is ((1,maxTexture2DGather[0]), (1,maxTexture2DGather[1]), 0).
+ *
+ * \xmlonly
+ * <table outputclass="xmlonly">
+ * <tgroup cols="3" colsep="1" rowsep="1">
+ * <colspec colname="c1" colwidth="1.0*"/>
+ * <colspec colname="c2" colwidth="3.0*"/>
+ * <colspec colname="c3" colwidth="3.0*"/>
+ * <thead>
+ * <row>
+ * <entry>CUDA array type</entry>
+ * <entry>Valid extents that must always be met {(width range in elements),
+ * (height range), (depth range)}</entry>
+ * <entry>Valid extents with cudaArraySurfaceLoadStore set {(width range in
+ * elements), (height range), (depth range)}</entry>
+ * </row>
+ * </thead>
+ * <tbody>
+ * <row>
+ * <entry>1D</entry>
+ * <entry>{ (1,maxTexture1D), 0, 0 }</entry>
+ * <entry>{ (1,maxSurface1D), 0, 0 }</entry>
+ * </row>
+ * <row>
+ * <entry>2D</entry>
+ * <entry>{ (1,maxTexture2D[0]), (1,maxTexture2D[1]), 0 }</entry>
+ * <entry>{ (1,maxSurface2D[0]), (1,maxSurface2D[1]), 0 }</entry>
+ * </row>
+ * <row>
+ * <entry>3D</entry>
+ * <entry>{ (1,maxTexture3D[0]), (1,maxTexture3D[1]), (1,maxTexture3D[2]) }
+ * OR { (1,maxTexture3DAlt[0]), (1,maxTexture3DAlt[1]),
+ * (1,maxTexture3DAlt[2]) }</entry>
+ * <entry>{ (1,maxSurface3D[0]), (1,maxSurface3D[1]), (1,maxSurface3D[2]) }</entry>
+ * </row>
+ * <row>
+ * <entry>1D Layered</entry>
+ * <entry>{ (1,maxTexture1DLayered[0]), 0, (1,maxTexture1DLayered[1]) }</entry>
+ * <entry>{ (1,maxSurface1DLayered[0]), 0, (1,maxSurface1DLayered[1]) }</entry>
+ * </row>
+ * <row>
+ * <entry>2D Layered</entry>
+ * <entry>{ (1,maxTexture2DLayered[0]), (1,maxTexture2DLayered[1]),
+ * (1,maxTexture2DLayered[2]) }</entry>
+ * <entry>{ (1,maxSurface2DLayered[0]), (1,maxSurface2DLayered[1]),
+ * (1,maxSurface2DLayered[2]) }</entry>
+ * </row>
+ * <row>
+ * <entry>Cubemap</entry>
+ * <entry>{ (1,maxTextureCubemap), (1,maxTextureCubemap), 6 }</entry>
+ * <entry>{ (1,maxSurfaceCubemap), (1,maxSurfaceCubemap), 6 }</entry>
+ * </row>
+ * <row>
+ * <entry>Cubemap Layered</entry>
+ * <entry>{ (1,maxTextureCubemapLayered[0]), (1,maxTextureCubemapLayered[0]),
+ * (1,maxTextureCubemapLayered[1]) }</entry>
+ * <entry>{ (1,maxSurfaceCubemapLayered[0]), (1,maxSurfaceCubemapLayered[0]),
+ * (1,maxSurfaceCubemapLayered[1]) }</entry>
+ * </row>
+ * </tbody>
+ * </tgroup>
+ * </table>
+ * \endxmlonly
+ *
+ * \param array  - Pointer to allocated array in device memory
+ * \param desc   - Requested channel format
+ * \param extent - Requested allocation size (\p width field in elements)
+ * \param flags  - Flags for extensions
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc3D, ::cudaMalloc, ::cudaMallocPitch, ::cudaFree,
+ * ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::make_cudaExtent,
+ * ::cuArray3DCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMalloc3DArray(cudaArray_t *array, const struct cudaChannelFormatDesc* desc, struct cudaExtent extent, unsigned int flags __dv(0));
+
+/**
+ * \brief Allocate a mipmapped array on the device
+ *
+ * Allocates a CUDA mipmapped array according to the ::cudaChannelFormatDesc structure
+ * \p desc and returns a handle to the new CUDA mipmapped array in \p *mipmappedArray.
+ * \p numLevels specifies the number of mipmap levels to be allocated. This value is
+ * clamped to the range [1, 1 + floor(log2(max(width, height, depth)))].
+ *
+ * The ::cudaChannelFormatDesc is defined as:
+ * \code
+    struct cudaChannelFormatDesc {
+        int x, y, z, w;
+        enum cudaChannelFormatKind f;
+    };
+    \endcode
+ * where ::cudaChannelFormatKind is one of ::cudaChannelFormatKindSigned,
+ * ::cudaChannelFormatKindUnsigned, or ::cudaChannelFormatKindFloat.
+ *
+ * ::cudaMallocMipmappedArray() can allocate the following:
+ *
+ * - A 1D mipmapped array is allocated if the height and depth extents are both zero.
+ * - A 2D mipmapped array is allocated if only the depth extent is zero.
+ * - A 3D mipmapped array is allocated if all three extents are non-zero.
+ * - A 1D layered CUDA mipmapped array is allocated if only the height extent is zero and
+ * the cudaArrayLayered flag is set. Each layer is a 1D mipmapped array. The number of layers is 
+ * determined by the depth extent.
+ * - A 2D layered CUDA mipmapped array is allocated if all three extents are non-zero and 
+ * the cudaArrayLayered flag is set. Each layer is a 2D mipmapped array. The number of layers is 
+ * determined by the depth extent.
+ * - A cubemap CUDA mipmapped array is allocated if all three extents are non-zero and the
+ * cudaArrayCubemap flag is set. Width must be equal to height, and depth must be six.
+ * The order of the six layers in memory is the same as that listed in ::cudaGraphicsCubeFace.
+ * - A cubemap layered CUDA mipmapped array is allocated if all three extents are non-zero, and both,
+ * cudaArrayCubemap and cudaArrayLayered flags are set. Width must be equal to height, and depth must be 
+ * a multiple of six. A cubemap layered CUDA mipmapped array is a special type of 2D layered CUDA mipmapped
+ * array that consists of a collection of cubemap mipmapped arrays. The first six layers represent the 
+ * first cubemap mipmapped array, the next six layers form the second cubemap mipmapped array, and so on.
+ *
+ *
+ * The \p flags parameter enables different options to be specified that affect
+ * the allocation, as follows.
+ * - ::cudaArrayDefault: This flag's value is defined to be 0 and provides default mipmapped array allocation
+ * - ::cudaArrayLayered: Allocates a layered CUDA mipmapped array, with the depth extent indicating the number of layers
+ * - ::cudaArrayCubemap: Allocates a cubemap CUDA mipmapped array. Width must be equal to height, and depth must be six.
+ *   If the cudaArrayLayered flag is also set, depth must be a multiple of six.
+ * - ::cudaArraySurfaceLoadStore: This flag indicates that individual mipmap levels of the CUDA mipmapped array 
+ *   will be read from or written to using a surface reference.
+ * - ::cudaArrayTextureGather: This flag indicates that texture gather operations will be performed on the CUDA 
+ *   array. Texture gather can only be performed on 2D CUDA mipmapped arrays, and the gather operations are
+ *   performed only on the most detailed mipmap level.
+ * - ::cudaArraySparse: Allocates a CUDA mipmapped array without physical backing memory. The subregions within this sparse array
+ *   can later be mapped onto a physical memory allocation by calling ::cuMemMapArrayAsync. This flag can only be used for creating 
+ *   2D, 3D or 2D layered sparse CUDA mipmapped arrays. The physical backing memory must be allocated via ::cuMemCreate.
+ * - ::cudaArrayDeferredMapping: Allocates a CUDA mipmapped array without physical backing memory. The entire array can
+ *   later be mapped onto a physical memory allocation by calling ::cuMemMapArrayAsync. The physical backing memory must be allocated
+ *   via ::cuMemCreate.
+ *
+ * The width, height and depth extents must meet certain size requirements as listed in the following table.
+ * All values are specified in elements.
+ *
+ * \xmlonly
+ * <table outputclass="xmlonly">
+ * <tgroup cols="3" colsep="1" rowsep="1">
+ * <colspec colname="c1" colwidth="1.0*"/>
+ * <colspec colname="c2" colwidth="3.0*"/>
+ * <colspec colname="c3" colwidth="3.0*"/>
+ * <thead>
+ * <row>
+ * <entry>CUDA array type</entry>
+ * <entry>Valid extents that must always be met {(width range in elements),
+ * (height range), (depth range)}</entry>
+ * <entry>Valid extents with cudaArraySurfaceLoadStore set {(width range in
+ * elements), (height range), (depth range)}</entry>
+ * </row>
+ * </thead>
+ * <tbody>
+ * <row>
+ * <entry>1D</entry>
+ * <entry>{ (1,maxTexture1DMipmap), 0, 0 }</entry>
+ * <entry>{ (1,maxSurface1D), 0, 0 }</entry>
+ * </row>
+ * <row>
+ * <entry>2D</entry>
+ * <entry>{ (1,maxTexture2DMipmap[0]), (1,maxTexture2DMipmap[1]), 0 }</entry>
+ * <entry>{ (1,maxSurface2D[0]), (1,maxSurface2D[1]), 0 }</entry>
+ * </row>
+ * <row>
+ * <entry>3D</entry>
+ * <entry>{ (1,maxTexture3D[0]), (1,maxTexture3D[1]), (1,maxTexture3D[2]) }
+ * OR { (1,maxTexture3DAlt[0]), (1,maxTexture3DAlt[1]),
+ * (1,maxTexture3DAlt[2]) }</entry>
+ * <entry>{ (1,maxSurface3D[0]), (1,maxSurface3D[1]), (1,maxSurface3D[2]) }</entry>
+ * </row>
+ * <row>
+ * <entry>1D Layered</entry>
+ * <entry>{ (1,maxTexture1DLayered[0]), 0, (1,maxTexture1DLayered[1]) }</entry>
+ * <entry>{ (1,maxSurface1DLayered[0]), 0, (1,maxSurface1DLayered[1]) }</entry>
+ * </row>
+ * <row>
+ * <entry>2D Layered</entry>
+ * <entry>{ (1,maxTexture2DLayered[0]), (1,maxTexture2DLayered[1]),
+ * (1,maxTexture2DLayered[2]) }</entry>
+ * <entry>{ (1,maxSurface2DLayered[0]), (1,maxSurface2DLayered[1]),
+ * (1,maxSurface2DLayered[2]) }</entry>
+ * </row>
+ * <row>
+ * <entry>Cubemap</entry>
+ * <entry>{ (1,maxTextureCubemap), (1,maxTextureCubemap), 6 }</entry>
+ * <entry>{ (1,maxSurfaceCubemap), (1,maxSurfaceCubemap), 6 }</entry>
+ * </row>
+ * <row>
+ * <entry>Cubemap Layered</entry>
+ * <entry>{ (1,maxTextureCubemapLayered[0]), (1,maxTextureCubemapLayered[0]),
+ * (1,maxTextureCubemapLayered[1]) }</entry>
+ * <entry>{ (1,maxSurfaceCubemapLayered[0]), (1,maxSurfaceCubemapLayered[0]),
+ * (1,maxSurfaceCubemapLayered[1]) }</entry>
+ * </row>
+ * </tbody>
+ * </tgroup>
+ * </table>
+ * \endxmlonly
+ *
+ * \param mipmappedArray  - Pointer to allocated mipmapped array in device memory
+ * \param desc            - Requested channel format
+ * \param extent          - Requested allocation size (\p width field in elements)
+ * \param numLevels       - Number of mipmap levels to allocate
+ * \param flags           - Flags for extensions
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc3D, ::cudaMalloc, ::cudaMallocPitch, ::cudaFree,
+ * ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::make_cudaExtent,
+ * ::cuMipmappedArrayCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMallocMipmappedArray(cudaMipmappedArray_t *mipmappedArray, const struct cudaChannelFormatDesc* desc, struct cudaExtent extent, unsigned int numLevels, unsigned int flags __dv(0));
+
+/**
+ * \brief Gets a mipmap level of a CUDA mipmapped array
+ *
+ * Returns in \p *levelArray a CUDA array that represents a single mipmap level
+ * of the CUDA mipmapped array \p mipmappedArray.
+ *
+ * If \p level is greater than the maximum number of levels in this mipmapped array,
+ * ::cudaErrorInvalidValue is returned.
+ *
+ * If \p mipmappedArray is NULL,
+ * ::cudaErrorInvalidResourceHandle is returned.
+ *
+ * \param levelArray     - Returned mipmap level CUDA array
+ * \param mipmappedArray - CUDA mipmapped array
+ * \param level          - Mipmap level
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc3D, ::cudaMalloc, ::cudaMallocPitch, ::cudaFree,
+ * ::cudaFreeArray,
+ * \ref ::cudaMallocHost(void**, size_t) "cudaMallocHost (C API)",
+ * ::cudaFreeHost, ::cudaHostAlloc,
+ * ::make_cudaExtent,
+ * ::cuMipmappedArrayGetLevel
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetMipmappedArrayLevel(cudaArray_t *levelArray, cudaMipmappedArray_const_t mipmappedArray, unsigned int level);
+
+/**
+ * \brief Copies data between 3D objects
+ *
+\code
+struct cudaExtent {
+  size_t width;
+  size_t height;
+  size_t depth;
+};
+struct cudaExtent make_cudaExtent(size_t w, size_t h, size_t d);
+
+struct cudaPos {
+  size_t x;
+  size_t y;
+  size_t z;
+};
+struct cudaPos make_cudaPos(size_t x, size_t y, size_t z);
+
+struct cudaMemcpy3DParms {
+  cudaArray_t           srcArray;
+  struct cudaPos        srcPos;
+  struct cudaPitchedPtr srcPtr;
+  cudaArray_t           dstArray;
+  struct cudaPos        dstPos;
+  struct cudaPitchedPtr dstPtr;
+  struct cudaExtent     extent;
+  enum cudaMemcpyKind   kind;
+};
+\endcode
+ *
+ * ::cudaMemcpy3D() copies data betwen two 3D objects. The source and
+ * destination objects may be in either host memory, device memory, or a CUDA
+ * array. The source, destination, extent, and kind of copy performed is
+ * specified by the ::cudaMemcpy3DParms struct which should be initialized to
+ * zero before use:
+\code
+cudaMemcpy3DParms myParms = {0};
+\endcode
+ *
+ * The struct passed to ::cudaMemcpy3D() must specify one of \p srcArray or
+ * \p srcPtr and one of \p dstArray or \p dstPtr. Passing more than one
+ * non-zero source or destination will cause ::cudaMemcpy3D() to return an
+ * error.
+ *
+ * The \p srcPos and \p dstPos fields are optional offsets into the source and
+ * destination objects and are defined in units of each object's elements. The
+ * element for a host or device pointer is assumed to be <b>unsigned char</b>.
+ *
+ * The \p extent field defines the dimensions of the transferred area in
+ * elements. If a CUDA array is participating in the copy, the extent is
+ * defined in terms of that array's elements. If no CUDA array is
+ * participating in the copy then the extents are defined in elements of
+ * <b>unsigned char</b>.
+ *
+ * The \p kind field defines the direction of the copy. It must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * For ::cudaMemcpyHostToHost or ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost
+ * passed as kind and cudaArray type passed as source or destination, if the kind
+ * implies cudaArray type to be present on the host, ::cudaMemcpy3D() will
+ * disregard that implication and silently correct the kind based on the fact that
+ * cudaArray type can only be present on the device.
+ *
+ * If the source and destination are both arrays, ::cudaMemcpy3D() will return
+ * an error if they do not have the same element size.
+ *
+ * The source and destination object may not overlap. If overlapping source
+ * and destination objects are specified, undefined behavior will result.
+ *
+ * The source object must entirely contain the region defined by \p srcPos
+ * and \p extent. The destination object must entirely contain the region
+ * defined by \p dstPos and \p extent.
+ *
+ * ::cudaMemcpy3D() returns an error if the pitch of \p srcPtr or \p dstPtr
+ * exceeds the maximum allowed. The pitch of a ::cudaPitchedPtr allocated
+ * with ::cudaMalloc3D() will always be valid.
+ *
+ * \param p - 3D memory copy parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc3D, ::cudaMalloc3DArray, ::cudaMemset3D, ::cudaMemcpy3DAsync,
+ * ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::make_cudaExtent, ::make_cudaPos,
+ * ::cuMemcpy3D
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy3D(const struct cudaMemcpy3DParms *p);
+
+/**
+ * \brief Copies memory between devices
+ *
+ * Perform a 3D memory copy according to the parameters specified in
+ * \p p.  See the definition of the ::cudaMemcpy3DPeerParms structure
+ * for documentation of its parameters.
+ *
+ * Note that this function is synchronous with respect to the host only if
+ * the source or destination of the transfer is host memory.  Note also 
+ * that this copy is serialized with respect to all pending and future 
+ * asynchronous work in to the current device, the copy's source device,
+ * and the copy's destination device (use ::cudaMemcpy3DPeerAsync to avoid 
+ * this synchronization).
+ *
+ * \param p - Parameters for the memory copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidPitchValue
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync, ::cudaMemcpyPeerAsync,
+ * ::cudaMemcpy3DPeerAsync,
+ * ::cuMemcpy3DPeer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy3DPeer(const struct cudaMemcpy3DPeerParms *p);
+
+/**
+ * \brief Copies data between 3D objects
+ *
+\code
+struct cudaExtent {
+  size_t width;
+  size_t height;
+  size_t depth;
+};
+struct cudaExtent make_cudaExtent(size_t w, size_t h, size_t d);
+
+struct cudaPos {
+  size_t x;
+  size_t y;
+  size_t z;
+};
+struct cudaPos make_cudaPos(size_t x, size_t y, size_t z);
+
+struct cudaMemcpy3DParms {
+  cudaArray_t           srcArray;
+  struct cudaPos        srcPos;
+  struct cudaPitchedPtr srcPtr;
+  cudaArray_t           dstArray;
+  struct cudaPos        dstPos;
+  struct cudaPitchedPtr dstPtr;
+  struct cudaExtent     extent;
+  enum cudaMemcpyKind   kind;
+};
+\endcode
+ *
+ * ::cudaMemcpy3DAsync() copies data betwen two 3D objects. The source and
+ * destination objects may be in either host memory, device memory, or a CUDA
+ * array. The source, destination, extent, and kind of copy performed is
+ * specified by the ::cudaMemcpy3DParms struct which should be initialized to
+ * zero before use:
+\code
+cudaMemcpy3DParms myParms = {0};
+\endcode
+ *
+ * The struct passed to ::cudaMemcpy3DAsync() must specify one of \p srcArray
+ * or \p srcPtr and one of \p dstArray or \p dstPtr. Passing more than one
+ * non-zero source or destination will cause ::cudaMemcpy3DAsync() to return an
+ * error.
+ *
+ * The \p srcPos and \p dstPos fields are optional offsets into the source and
+ * destination objects and are defined in units of each object's elements. The
+ * element for a host or device pointer is assumed to be <b>unsigned char</b>.
+ * For CUDA arrays, positions must be in the range [0, 2048) for any
+ * dimension.
+ *
+ * The \p extent field defines the dimensions of the transferred area in
+ * elements. If a CUDA array is participating in the copy, the extent is
+ * defined in terms of that array's elements. If no CUDA array is
+ * participating in the copy then the extents are defined in elements of
+ * <b>unsigned char</b>.
+ *
+ * The \p kind field defines the direction of the copy. It must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * For ::cudaMemcpyHostToHost or ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost
+ * passed as kind and cudaArray type passed as source or destination, if the kind
+ * implies cudaArray type to be present on the host, ::cudaMemcpy3DAsync() will
+ * disregard that implication and silently correct the kind based on the fact that
+ * cudaArray type can only be present on the device.
+ *
+ * If the source and destination are both arrays, ::cudaMemcpy3DAsync() will
+ * return an error if they do not have the same element size.
+ *
+ * The source and destination object may not overlap. If overlapping source
+ * and destination objects are specified, undefined behavior will result.
+ *
+ * The source object must lie entirely within the region defined by \p srcPos
+ * and \p extent. The destination object must lie entirely within the region
+ * defined by \p dstPos and \p extent.
+ *
+ * ::cudaMemcpy3DAsync() returns an error if the pitch of \p srcPtr or
+ * \p dstPtr exceeds the maximum allowed. The pitch of a
+ * ::cudaPitchedPtr allocated with ::cudaMalloc3D() will always be valid.
+ *
+ * ::cudaMemcpy3DAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If
+ * \p kind is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and \p stream
+ * is non-zero, the copy may overlap with operations in other streams.
+ *
+ * The device version of this function only handles device to device copies and
+ * cannot be given local or shared pointers.
+ *
+ * \param p      - 3D memory copy parameters
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMalloc3D, ::cudaMalloc3DArray, ::cudaMemset3D, ::cudaMemcpy3D,
+ * ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, :::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::make_cudaExtent, ::make_cudaPos,
+ * ::cuMemcpy3DAsync
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy3DAsync(const struct cudaMemcpy3DParms *p, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies memory between devices asynchronously.
+ *
+ * Perform a 3D memory copy according to the parameters specified in
+ * \p p.  See the definition of the ::cudaMemcpy3DPeerParms structure
+ * for documentation of its parameters.
+ *
+ * \param p      - Parameters for the memory copy
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidPitchValue
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync, ::cudaMemcpyPeerAsync,
+ * ::cudaMemcpy3DPeerAsync,
+ * ::cuMemcpy3DPeerAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy3DPeerAsync(const struct cudaMemcpy3DPeerParms *p, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Gets free and total device memory
+ *
+ * Returns in \p *total the total amount of memory available to the the current context.
+ * Returns in \p *free the amount of memory on the device that is free according to the OS.
+ * CUDA is not guaranteed to be able to allocate all of the memory that the OS reports as free.
+ * In a multi-tenet situation, free estimate returned is prone to race condition where
+ * a new allocation/free done by a different process or a different thread in the same
+ * process between the time when free memory was estimated and reported, will result in
+ * deviation in free value reported and actual free memory.
+ *
+ * The integrated GPU on Tegra shares memory with CPU and other component
+ * of the SoC. The free and total values returned by the API excludes
+ * the SWAP memory space maintained by the OS on some platforms.
+ * The OS may move some of the memory pages into swap area as the GPU or
+ * CPU allocate or access memory. See Tegra app note on how to calculate
+ * total and free memory on Tegra.
+ *
+ * \param free  - Returned free memory in bytes
+ * \param total - Returned total memory in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorLaunchFailure
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuMemGetInfo
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemGetInfo(size_t *free, size_t *total);
+
+/**
+ * \brief Gets info about the specified cudaArray
+ * 
+ * Returns in \p *desc, \p *extent and \p *flags respectively, the type, shape 
+ * and flags of \p array.
+ *
+ * Any of \p *desc, \p *extent and \p *flags may be specified as NULL.
+ *
+ * \param desc   - Returned array type
+ * \param extent - Returned array shape. 2D arrays will have depth of zero
+ * \param flags  - Returned array flags
+ * \param array  - The ::cudaArray to get info for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuArrayGetDescriptor,
+ * ::cuArray3DGetDescriptor
+ */
+extern __host__ cudaError_t CUDARTAPI cudaArrayGetInfo(struct cudaChannelFormatDesc *desc, struct cudaExtent *extent, unsigned int *flags, cudaArray_t array);
+
+/**
+ * \brief Gets a CUDA array plane from a CUDA array
+ *
+ * Returns in \p pPlaneArray a CUDA array that represents a single format plane
+ * of the CUDA array \p hArray.
+ *
+ * If \p planeIdx is greater than the maximum number of planes in this array or if the array does
+ * not have a multi-planar format e.g: ::cudaChannelFormatKindNV12, then ::cudaErrorInvalidValue is returned.
+ *
+ * Note that if the \p hArray has format ::cudaChannelFormatKindNV12, then passing in 0 for \p planeIdx returns
+ * a CUDA array of the same size as \p hArray but with one 8-bit channel and ::cudaChannelFormatKindUnsigned as its format kind.
+ * If 1 is passed for \p planeIdx, then the returned CUDA array has half the height and width
+ * of \p hArray with two 8-bit channels and ::cudaChannelFormatKindUnsigned as its format kind.
+ *
+ * \param pPlaneArray   - Returned CUDA array referenced by the \p planeIdx
+ * \param hArray        - CUDA array
+ * \param planeIdx      - Plane index
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ *
+ * \sa
+ * ::cuArrayGetPlane
+ */
+extern __host__ cudaError_t CUDARTAPI cudaArrayGetPlane(cudaArray_t *pPlaneArray, cudaArray_t hArray, unsigned int planeIdx);
+
+/**
+ * \brief Returns the memory requirements of a CUDA array
+ *
+ * Returns the memory requirements of a CUDA array in \p memoryRequirements
+ * If the CUDA array is not allocated with flag ::cudaArrayDeferredMapping
+ * ::cudaErrorInvalidValue will be returned.
+ *
+ * The returned value in ::cudaArrayMemoryRequirements::size
+ * represents the total size of the CUDA array.
+ * The returned value in ::cudaArrayMemoryRequirements::alignment
+ * represents the alignment necessary for mapping the CUDA array.
+ *
+ * \return
+ * ::cudaSuccess
+ * ::cudaErrorInvalidValue
+ *
+ * \param[out] memoryRequirements - Pointer to ::cudaArrayMemoryRequirements
+ * \param[in] array - CUDA array to get the memory requirements of
+ * \param[in] device - Device to get the memory requirements for
+ * \sa ::cudaMipmappedArrayGetMemoryRequirements
+ */
+extern __host__ cudaError_t CUDARTAPI cudaArrayGetMemoryRequirements(struct cudaArrayMemoryRequirements  *memoryRequirements, cudaArray_t array, int device);
+
+/**
+ * \brief Returns the memory requirements of a CUDA mipmapped array
+ *
+ * Returns the memory requirements of a CUDA mipmapped array in \p memoryRequirements
+ * If the CUDA mipmapped array is not allocated with flag ::cudaArrayDeferredMapping
+ * ::cudaErrorInvalidValue will be returned.
+ *
+ * The returned value in ::cudaArrayMemoryRequirements::size
+ * represents the total size of the CUDA mipmapped array.
+ * The returned value in ::cudaArrayMemoryRequirements::alignment
+ * represents the alignment necessary for mapping the CUDA mipmapped
+ * array.
+ *
+ * \return
+ * ::cudaSuccess
+ * ::cudaErrorInvalidValue
+ *
+ * \param[out] memoryRequirements - Pointer to ::cudaArrayMemoryRequirements
+ * \param[in] mipmap - CUDA mipmapped array to get the memory requirements of
+ * \param[in] device - Device to get the memory requirements for
+ * \sa ::cudaArrayGetMemoryRequirements
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMipmappedArrayGetMemoryRequirements(struct cudaArrayMemoryRequirements *memoryRequirements, cudaMipmappedArray_t mipmap, int device);
+
+/**
+ * \brief Returns the layout properties of a sparse CUDA array
+ *
+ * Returns the layout properties of a sparse CUDA array in \p sparseProperties.
+ * If the CUDA array is not allocated with flag ::cudaArraySparse
+ * ::cudaErrorInvalidValue will be returned.
+ *
+ * If the returned value in ::cudaArraySparseProperties::flags contains ::cudaArraySparsePropertiesSingleMipTail,
+ * then ::cudaArraySparseProperties::miptailSize represents the total size of the array. Otherwise, it will be zero.
+ * Also, the returned value in ::cudaArraySparseProperties::miptailFirstLevel is always zero.
+ * Note that the \p array must have been allocated using ::cudaMallocArray or ::cudaMalloc3DArray. For CUDA arrays obtained
+ * using ::cudaMipmappedArrayGetLevel, ::cudaErrorInvalidValue will be returned. Instead, ::cudaMipmappedArrayGetSparseProperties
+ * must be used to obtain the sparse properties of the entire CUDA mipmapped array to which \p array belongs to.
+ *
+ * \return
+ * ::cudaSuccess
+ * ::cudaErrorInvalidValue
+ *
+ * \param[out] sparseProperties - Pointer to return the ::cudaArraySparseProperties
+ * \param[in] array             - The CUDA array to get the sparse properties of 
+ *
+ * \sa
+ * ::cudaMipmappedArrayGetSparseProperties,
+ * ::cuMemMapArrayAsync
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaArrayGetSparseProperties(struct cudaArraySparseProperties *sparseProperties, cudaArray_t array);
+#endif
+
+/**
+ * \brief Returns the layout properties of a sparse CUDA mipmapped array
+ *
+ * Returns the sparse array layout properties in \p sparseProperties.
+ * If the CUDA mipmapped array is not allocated with flag ::cudaArraySparse
+ * ::cudaErrorInvalidValue will be returned.
+ *
+ * For non-layered CUDA mipmapped arrays, ::cudaArraySparseProperties::miptailSize returns the
+ * size of the mip tail region. The mip tail region includes all mip levels whose width, height or depth
+ * is less than that of the tile.
+ * For layered CUDA mipmapped arrays, if ::cudaArraySparseProperties::flags contains ::cudaArraySparsePropertiesSingleMipTail,
+ * then ::cudaArraySparseProperties::miptailSize specifies the size of the mip tail of all layers combined.
+ * Otherwise, ::cudaArraySparseProperties::miptailSize specifies mip tail size per layer.
+ * The returned value of ::cudaArraySparseProperties::miptailFirstLevel is valid only if ::cudaArraySparseProperties::miptailSize is non-zero.
+ *
+ * \return
+ * ::cudaSuccess
+ * ::cudaErrorInvalidValue
+ *
+ * \param[out] sparseProperties - Pointer to return ::cudaArraySparseProperties
+ * \param[in] mipmap            - The CUDA mipmapped array to get the sparse properties of
+ *
+ * \sa
+ * ::cudaArrayGetSparseProperties,
+ * ::cuMemMapArrayAsync
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaMipmappedArrayGetSparseProperties(struct cudaArraySparseProperties *sparseProperties, cudaMipmappedArray_t mipmap);
+#endif
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src to the
+ * memory area pointed to by \p dst, where \p kind specifies the direction
+ * of the copy, and must be one of ::cudaMemcpyHostToHost,
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing. Calling
+ * ::cudaMemcpy() with dst and src pointers that do not match the direction of
+ * the copy results in an undefined behavior.
+ *
+ * \param dst   - Destination memory address
+ * \param src   - Source memory address
+ * \param count - Size in bytes to copy
+ * \param kind  - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \note_sync
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyDtoH,
+ * ::cuMemcpyHtoD,
+ * ::cuMemcpyDtoD,
+ * ::cuMemcpy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind);
+
+/**
+ * \brief Copies memory between two devices
+ *
+ * Copies memory from one device to memory on another device.  \p dst is the 
+ * base device pointer of the destination memory and \p dstDevice is the 
+ * destination device.  \p src is the base device pointer of the source memory 
+ * and \p srcDevice is the source device.  \p count specifies the number of bytes 
+ * to copy.
+ *
+ * Note that this function is asynchronous with respect to the host, but 
+ * serialized with respect all pending and future asynchronous work in to the 
+ * current device, \p srcDevice, and \p dstDevice (use ::cudaMemcpyPeerAsync 
+ * to avoid this synchronization).
+ *
+ * \param dst       - Destination device pointer
+ * \param dstDevice - Destination device
+ * \param src       - Source device pointer
+ * \param srcDevice - Source device
+ * \param count     - Size of memory copy in bytes
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyAsync, ::cudaMemcpyPeerAsync,
+ * ::cudaMemcpy3DPeerAsync,
+ * ::cuMemcpyPeer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyPeer(void *dst, int dstDevice, const void *src, int srcDevice, size_t count);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the memory
+ * area pointed to by \p src to the memory area pointed to by \p dst, where
+ * \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing. \p dpitch and
+ * \p spitch are the widths in memory in bytes of the 2D arrays pointed to by
+ * \p dst and \p src, including any padding added to the end of each row. The
+ * memory areas may not overlap. \p width must not exceed either \p dpitch or
+ * \p spitch. Calling ::cudaMemcpy2D() with \p dst and \p src pointers that do
+ * not match the direction of the copy results in an undefined behavior.
+ * ::cudaMemcpy2D() returns an error if \p dpitch or \p spitch exceeds
+ * the maximum allowed.
+ *
+ * \param dst    - Destination memory address
+ * \param dpitch - Pitch of destination memory
+ * \param src    - Source memory address
+ * \param spitch - Pitch of source memory
+ * \param width  - Width of matrix transfer (columns in bytes)
+ * \param height - Height of matrix transfer (rows)
+ * \param kind   - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2D,
+ * ::cuMemcpy2DUnaligned
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy2D(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the memory
+ * area pointed to by \p src to the CUDA array \p dst starting at
+ * \p hOffset rows and \p wOffset bytes from the upper left corner,
+ * where \p kind specifies the direction of the copy, and must be one
+ * of ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * \p spitch is the width in memory in bytes of the 2D array pointed to by
+ * \p src, including any padding added to the end of each row. \p wOffset +
+ * \p width must not exceed the width of the CUDA array \p dst. \p width must
+ * not exceed \p spitch. ::cudaMemcpy2DToArray() returns an error if \p spitch
+ * exceeds the maximum allowed.
+ *
+ * \param dst     - Destination memory address
+ * \param wOffset - Destination starting X offset (columns in bytes)
+ * \param hOffset - Destination starting Y offset (rows)
+ * \param src     - Source memory address
+ * \param spitch  - Pitch of source memory
+ * \param width   - Width of matrix transfer (columns in bytes)
+ * \param height  - Height of matrix transfer (rows)
+ * \param kind    - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2D,
+ * ::cuMemcpy2DUnaligned
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the CUDA
+ * array \p src starting at \p hOffset rows and \p wOffset bytes from the
+ * upper left corner to the memory area pointed to by \p dst, where
+ * \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing. \p dpitch is the
+ * width in memory in bytes of the 2D array pointed to by \p dst, including any
+ * padding added to the end of each row. \p wOffset + \p width must not exceed
+ * the width of the CUDA array \p src. \p width must not exceed \p dpitch.
+ * ::cudaMemcpy2DFromArray() returns an error if \p dpitch exceeds the maximum
+ * allowed.
+ *
+ * \param dst     - Destination memory address
+ * \param dpitch  - Pitch of destination memory
+ * \param src     - Source memory address
+ * \param wOffset - Source starting X offset (columns in bytes)
+ * \param hOffset - Source starting Y offset (rows)
+ * \param width   - Width of matrix transfer (columns in bytes)
+ * \param height  - Height of matrix transfer (rows)
+ * \param kind    - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2D,
+ * ::cuMemcpy2DUnaligned
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DFromArray(void *dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, enum cudaMemcpyKind kind);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the CUDA
+ * array \p src starting at \p hOffsetSrc rows and \p wOffsetSrc bytes from the
+ * upper left corner to the CUDA array \p dst starting at \p hOffsetDst rows
+ * and \p wOffsetDst bytes from the upper left corner, where \p kind
+ * specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * \p wOffsetDst + \p width must not exceed the width of the CUDA array \p dst.
+ * \p wOffsetSrc + \p width must not exceed the width of the CUDA array \p src.
+ *
+ * \param dst        - Destination memory address
+ * \param wOffsetDst - Destination starting X offset (columns in bytes)
+ * \param hOffsetDst - Destination starting Y offset (rows)
+ * \param src        - Source memory address
+ * \param wOffsetSrc - Source starting X offset (columns in bytes)
+ * \param hOffsetSrc - Source starting Y offset (rows)
+ * \param width      - Width of matrix transfer (columns in bytes)
+ * \param height     - Height of matrix transfer (rows)
+ * \param kind       - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2D,
+ * ::cuMemcpy2DUnaligned
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t width, size_t height, enum cudaMemcpyKind kind __dv(cudaMemcpyDeviceToDevice));
+
+/**
+ * \brief Copies data to the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src
+ * to the memory area pointed to by \p offset bytes from the start of symbol
+ * \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of
+ * transfer is inferred from the pointer values. However, ::cudaMemcpyDefault
+ * is only allowed on systems that support unified virtual addressing.
+ *
+ * \param symbol - Device symbol address
+ * \param src    - Source memory address
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_sync
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray,  ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy,
+ * ::cuMemcpyHtoD,
+ * ::cuMemcpyDtoD
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyToSymbol(const void *symbol, const void *src, size_t count, size_t offset __dv(0), enum cudaMemcpyKind kind __dv(cudaMemcpyHostToDevice));
+
+
+/**
+ * \brief Copies data from the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p offset bytes
+ * from the start of symbol \p symbol to the memory area pointed to by \p dst.
+ * The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of
+ * transfer is inferred from the pointer values. However, ::cudaMemcpyDefault
+ * is only allowed on systems that support unified virtual addressing.
+ *
+ * \param dst    - Destination memory address
+ * \param symbol - Device symbol address
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_sync
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy,
+ * ::cuMemcpyDtoH,
+ * ::cuMemcpyDtoD
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyFromSymbol(void *dst, const void *symbol, size_t count, size_t offset __dv(0), enum cudaMemcpyKind kind __dv(cudaMemcpyDeviceToHost));
+
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src to the
+ * memory area pointed to by \p dst, where \p kind specifies the
+ * direction of the copy, and must be one of ::cudaMemcpyHostToHost,
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * 
+ * The memory areas may not overlap. Calling ::cudaMemcpyAsync() with \p dst and
+ * \p src pointers that do not match the direction of the copy results in an
+ * undefined behavior.
+ *
+ * ::cudaMemcpyAsync() is asynchronous with respect to the host, so the call
+ * may return before the copy is complete. The copy can optionally be
+ * associated to a stream by passing a non-zero \p stream argument. If \p kind
+ * is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and the \p stream is
+ * non-zero, the copy may overlap with operations in other streams.
+ *
+ * The device version of this function only handles device to device copies and
+ * cannot be given local or shared pointers.
+ *
+ * \param dst    - Destination memory address
+ * \param src    - Source memory address
+ * \param count  - Size in bytes to copy
+ * \param kind   - Type of transfer
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyAsync,
+ * ::cuMemcpyDtoHAsync,
+ * ::cuMemcpyHtoDAsync,
+ * ::cuMemcpyDtoDAsync
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpyAsync(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies memory between two devices asynchronously.
+ *
+ * Copies memory from one device to memory on another device.  \p dst is the 
+ * base device pointer of the destination memory and \p dstDevice is the 
+ * destination device.  \p src is the base device pointer of the source memory 
+ * and \p srcDevice is the source device.  \p count specifies the number of bytes 
+ * to copy.
+ *
+ * Note that this function is asynchronous with respect to the host and all work
+ * on other devices.
+ *
+ * \param dst       - Destination device pointer
+ * \param dstDevice - Destination device
+ * \param src       - Source device pointer
+ * \param srcDevice - Source device
+ * \param count     - Size of memory copy in bytes
+ * \param stream    - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync,
+ * ::cudaMemcpy3DPeerAsync,
+ * ::cuMemcpyPeerAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyPeerAsync(void *dst, int dstDevice, const void *src, int srcDevice, size_t count, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Performs a batch of memory copies asynchronously.
+ *
+ * Performs a batch of memory copies. The batch as a whole executes in stream order but copies within a
+ * batch are not guaranteed to execute in any specific order. This API only supports pointer-to-pointer copies.
+ * For copies involving CUDA arrays, please see ::cudaMemcpy3DBatchAsync.
+ *
+ * Performs memory copies from source buffers specified in \p srcs to destination buffers specified in \p dsts.
+ * The size of each copy is specified in \p sizes. All three arrays must be of the same length as specified
+ * by \p count. Since there are no ordering guarantees for copies within a batch, specifying any dependent copies
+ * within a batch will result in undefined behavior.
+ *
+ * Every copy in the batch has to be associated with a set of attributes specified in the \p attrs array.
+ * Each entry in this array can apply to more than one copy. This can be done by specifying in the \p attrsIdxs array,
+ * the index of the first copy that the corresponding entry in the \p attrs array applies to. Both \p attrs and
+ * \p attrsIdxs must be of the same length as specified by \p numAttrs. For example, if a batch has 10 copies listed
+ * in dst/src/sizes, the first 6 of which have one set of attributes and the remaining 4 another, then \p numAttrs
+ * will be 2, \p attrsIdxs will be {0, 6} and \p attrs will contains the two sets of attributes. Note that the first entry
+ * in \p attrsIdxs must always be 0. Also, each entry must be greater than the previous entry and the last entry should be
+ * less than \p count. Furthermore, \p numAttrs must be lesser than or equal to \p count.
+ *
+ * The ::cudaMemcpyAttributes::srcAccessOrder indicates the source access ordering to be observed for copies associated
+ * with the attribute. If the source access order is set to ::cudaMemcpySrcAccessOrderStream, then the source will
+ * be accessed in stream order. If the source access order is set to ::cudaMemcpySrcAccessOrderDuringApiCall then
+ * it indicates that access to the source pointer can be out of stream order and all accesses must be complete before the
+ * API call returns. This flag is suited for ephemeral sources (ex., stack variables) when it's known that no prior
+ * operations in the stream can be accessing the memory and also that the lifetime of the memory is limited to the scope
+ * that the source variable was declared in. Specifying this flag allows the driver to optimize the copy and removes the
+ * need for the user to synchronize the stream after the API call. If the source access order is set to
+ * ::cudaMemcpySrcAccessOrderAny then it indicates that access to the source pointer can be out of stream order and the
+ * accesses can happen even after the API call returns. This flag is suited for host pointers allocated
+ * outside CUDA (ex., via malloc) when it's known that no prior operations in the stream can be accessing the memory.
+ * Specifying this flag allows the driver to optimize the copy on certain platforms. Each memcpy operation in the batch
+ * must have a valid ::cudaMemcpyAttributes corresponding to it including the appropriate srcAccessOrder setting,
+ * otherwise the API will return ::cudaErrorInvalidValue.
+ *
+ * The ::cudaMemcpyAttributes::srcLocHint and ::cudaMemcpyAttributes::dstLocHint allows applications to specify hint locations
+ * for operands of a copy when the operand doesn't have a fixed location. That is, these hints are
+ * only applicable for managed memory pointers on devices where ::cudaDevAttrConcurrentManagedAccess is true or
+ * system-allocated pageable memory on devices where ::cudaDevAttrPageableMemoryAccess is true.
+ * For other cases, these hints are ignored.
+ *
+ * The ::cudaMemcpyAttributes::flags field can be used to specify certain flags for copies. Setting the
+ * ::cudaMemcpyFlagPreferOverlapWithCompute flag indicates that the associated copies should preferably overlap with
+ * any compute work. Note that this flag is a hint and can be ignored depending on the platform and other parameters of the copy.
+ *
+ * If any error is encountered while parsing the batch, the index within the batch where the error was encountered
+ * will be returned in \p failIdx. 
+ *
+ * \param dsts          - Array of destination pointers.
+ * \param srcs          - Array of memcpy source pointers.
+ * \param sizes         - Array of sizes for memcpy operations.
+ * \param count         - Size of \p dsts, \p srcs and \p sizes arrays
+ * \param attrs         - Array of memcpy attributes. 
+ * \param attrsIdxs     - Array of indices to specify which copies each entry in the \p attrs array applies to.
+ *                        The attributes specified in attrs[k] will be applied to copies starting from attrsIdxs[k]
+ *                        through attrsIdxs[k+1] - 1. Also attrs[numAttrs-1] will apply to copies starting from
+ *                        attrsIdxs[numAttrs-1] through count - 1.
+ * \param numAttrs      - Size of \p attrs and \p attrsIdxs arrays.
+ * \param failIdx       - Pointer to a location to return the index of the copy where a failure was encountered.
+ *                        The value will be SIZE_MAX if the error doesn't pertain to any specific copy.
+ * \param hStream       - The stream to enqueue the operations in. Must not be legacy NULL stream.
+ *
+ * \return
+ * ::cudaSuccess
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_async
+ * \note_memcpy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyBatchAsync(void **dsts, void **srcs, size_t *sizes, size_t count, struct cudaMemcpyAttributes *attrs, size_t *attrsIdxs, size_t numAttrs, size_t *failIdx, cudaStream_t stream);
+
+/**
+ * \brief Performs a batch of 3D memory copies asynchronously.
+ *
+ * Performs a batch of memory copies. The batch as a whole executes in stream order but copies within a
+ * batch are not guaranteed to execute in any specific order. Note that this means specifying any dependent
+ * copies within a batch will result in undefined behavior.
+ *
+* Performs memory copies as specified in the \p opList array. The length of this array is specified in \p numOps.
+ * Each entry in this array describes a copy operation. This includes among other things, the source and destination
+ * operands for the copy as specified in ::cudaMemcpy3DBatchOp::src and ::cudaMemcpy3DBatchOp::dst respectively.
+ * The source and destination operands of a copy can either be a pointer or a CUDA array. The width, height and depth
+ * of a copy is specified in ::cudaMemcpy3DBatchOp::extent. The width, height and depth of a copy are specified in
+ * elements and must not be zero. For pointer-to-pointer copies, the element size is considered to be 1. For pointer
+ * to CUDA array or vice versa copies, the element size is determined by the CUDA array. For CUDA array to CUDA array copies,
+ * the element size of the two CUDA arrays must match.
+ *
+ * For a given operand, if ::cudaMemcpy3DOperand::type is specified as ::cudaMemcpyOperandTypePointer, then
+ * ::cudaMemcpy3DOperand::op::ptr will be used. The ::cudaMemcpy3DOperand::op::ptr::ptr field must contain the pointer where
+ * the copy should begin. The ::cudaMemcpy3DOperand::op::ptr::rowLength field specifies the length of each row in elements and
+ * must either be zero or be greater than or equal to the width of the copy specified in ::cudaMemcpy3DBatchOp::extent::width.
+ * The ::cudaMemcpy3DOperand::op::ptr::layerHeight field specifies the height of each layer and must either be
+ * zero or be greater than or equal to the height of the copy specified in ::cudaMemcpy3DBatchOp::extent::height.
+ * When either of these values is zero, that aspect of the operand is considered to be tightly packed according to the copy extent.
+ * For managed memory pointers on devices where ::cudaDevAttrConcurrentManagedAccess is true or system-allocated pageable memory
+ * on devices where ::cudaDevAttrPageableMemoryAccess is true, the ::cudaMemcpy3DOperand::op::ptr::locHint field can be used to hint
+ * the location of the operand.
+ *
+ * If an operand's type is specified as ::cudaMemcpyOperandTypeArray, then ::cudaMemcpy3DOperand::op::array will be used.
+ * The ::cudaMemcpy3DOperand::op::array::array field specifies the CUDA array and ::cudaMemcpy3DOperand::op::array::offset specifies
+ * the 3D offset into that array where the copy begins.
+ *
+ * The ::cudaMemcpyAttributes::srcAccessOrder indicates the source access ordering to be observed for copies associated
+ * with the attribute. If the source access order is set to ::cudaMemcpySrcAccessOrderStream, then the source will
+ * be accessed in stream order. If the source access order is set to ::cudaMemcpySrcAccessOrderDuringApiCall then
+ * it indicates that access to the source pointer can be out of stream order and all accesses must be complete before the
+ * API call returns. This flag is suited for ephemeral sources (ex., stack variables) when it's known that no prior
+ * operations in the stream can be accessing the memory and also that the lifetime of the memory is limited to the scope
+ * that the source variable was declared in. Specifying this flag allows the driver to optimize the copy and removes the
+ * need for the user to synchronize the stream after the API call. If the source access order is set to
+ * ::cudaMemcpySrcAccessOrderAny then it indicates that access to the source pointer can be out of stream order and the
+ * accesses can happen even after the API call returns. This flag is suited for host pointers allocated
+ * outside CUDA (ex., via malloc) when it's known that no prior operations in the stream can be accessing the memory.
+ * Specifying this flag allows the driver to optimize the copy on certain platforms. Each memcopy operation in \p opList
+ * must have a valid srcAccessOrder setting, otherwise this API will return ::cudaErrorInvalidValue.
+ *
+ * The ::cudaMemcpyAttributes::flags field can be used to specify certain flags for copies. Setting the
+ * ::cudaMemcpyFlagPreferOverlapWithCompute flag indicates that the associated copies should preferably overlap with
+ * any compute work. Note that this flag is a hint and can be ignored depending on the platform and other parameters of the copy.
+ *
+ * If any error is encountered while parsing the batch, the index within the batch where the error was encountered
+ * will be returned in \p failIdx. 
+ *
+ * \param numOps     - Total number of memcpy operations. 
+ * \param opList     - Array of size \p numOps containing the actual memcpy operations. 
+ * \param failIdx    - Pointer to a location to return the index of the copy where a failure was encountered.
+ *                     The value will be SIZE_MAX if the error doesn't pertain to any specific copy.
+ * \param flags      - Flags for future use, must be zero now.
+ * \param hStream    - The stream to enqueue the operations in. Must not be default NULL stream.
+ *
+ * \return
+ * ::cudaSuccess
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_async
+ * \note_memcpy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy3DBatchAsync(size_t numOps, struct cudaMemcpy3DBatchOp *opList, size_t *failIdx, unsigned long long flags, cudaStream_t stream);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the memory
+ * area pointed to by \p src to the memory area pointed to by \p dst, where
+ * \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * \p dpitch and \p spitch are the widths in memory in bytes of the 2D arrays
+ * pointed to by \p dst and \p src, including any padding added to the end of
+ * each row. The memory areas may not overlap. \p width must not exceed either
+ * \p dpitch or \p spitch.
+ *
+ * Calling ::cudaMemcpy2DAsync() with \p dst and \p src pointers that do not
+ * match the direction of the copy results in an undefined behavior.
+ * ::cudaMemcpy2DAsync() returns an error if \p dpitch or \p spitch is greater
+ * than the maximum allowed.
+ *
+ * ::cudaMemcpy2DAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If
+ * \p kind is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and
+ * \p stream is non-zero, the copy may overlap with operations in other
+ * streams.
+ *
+ * The device version of this function only handles device to device copies and
+ * cannot be given local or shared pointers.
+ *
+ * \param dst    - Destination memory address
+ * \param dpitch - Pitch of destination memory
+ * \param src    - Source memory address
+ * \param spitch - Pitch of source memory
+ * \param width  - Width of matrix transfer (columns in bytes)
+ * \param height - Height of matrix transfer (rows)
+ * \param kind   - Type of transfer
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2DAsync
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy2DAsync(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the memory
+ * area pointed to by \p src to the CUDA array \p dst starting at \p hOffset
+ * rows and \p wOffset bytes from the upper left corner, where \p kind specifies
+ * the direction of the copy, and must be one of ::cudaMemcpyHostToHost,
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * \p spitch is the width in memory in bytes of the 2D array pointed to by
+ * \p src, including any padding added to the end of each row. \p wOffset +
+ * \p width must not exceed the width of the CUDA array \p dst. \p width must
+ * not exceed \p spitch. ::cudaMemcpy2DToArrayAsync() returns an error if
+ * \p spitch exceeds the maximum allowed.
+ *
+ * ::cudaMemcpy2DToArrayAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If
+ * \p kind is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and
+ * \p stream is non-zero, the copy may overlap with operations in other
+ * streams.
+ *
+ * \param dst     - Destination memory address
+ * \param wOffset - Destination starting X offset (columns in bytes)
+ * \param hOffset - Destination starting Y offset (rows)
+ * \param src     - Source memory address
+ * \param spitch  - Pitch of source memory
+ * \param width   - Width of matrix transfer (columns in bytes)
+ * \param height  - Height of matrix transfer (rows)
+ * \param kind    - Type of transfer
+ * \param stream  - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ *
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2DAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies data between host and device
+ *
+ * Copies a matrix (\p height rows of \p width bytes each) from the CUDA
+ * array \p src starting at \p hOffset rows and \p wOffset bytes from the
+ * upper left corner to the memory area pointed to by \p dst,
+ * where \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ * \p dpitch is the width in memory in bytes of the 2D
+ * array pointed to by \p dst, including any padding added to the end of each
+ * row. \p wOffset + \p width must not exceed the width of the CUDA array
+ * \p src. \p width must not exceed \p dpitch. ::cudaMemcpy2DFromArrayAsync()
+ * returns an error if \p dpitch exceeds the maximum allowed.
+ *
+ * ::cudaMemcpy2DFromArrayAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally be
+ * associated to a stream by passing a non-zero \p stream argument. If \p kind
+ * is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and \p stream is
+ * non-zero, the copy may overlap with operations in other streams.
+ *
+ * \param dst     - Destination memory address
+ * \param dpitch  - Pitch of destination memory
+ * \param src     - Source memory address
+ * \param wOffset - Source starting X offset (columns in bytes)
+ * \param hOffset - Source starting Y offset (rows)
+ * \param width   - Width of matrix transfer (columns in bytes)
+ * \param height  - Height of matrix transfer (rows)
+ * \param kind    - Type of transfer
+ * \param stream  - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidPitchValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ * \note_memcpy
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ *
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpy2DAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DFromArrayAsync(void *dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies data to the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src
+ * to the memory area pointed to by \p offset bytes from the start of symbol
+ * \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of transfer
+ * is inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * ::cudaMemcpyToSymbolAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If
+ * \p kind is ::cudaMemcpyHostToDevice and \p stream is non-zero, the copy
+ * may overlap with operations in other streams.
+ *
+ * \param symbol - Device symbol address
+ * \param src    - Source memory address
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyAsync,
+ * ::cuMemcpyHtoDAsync,
+ * ::cuMemcpyDtoDAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyToSymbolAsync(const void *symbol, const void *src, size_t count, size_t offset, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies data from the given symbol on the device
+ *
+ * Copies \p count bytes from the memory area pointed to by \p offset bytes
+ * from the start of symbol \p symbol to the memory area pointed to by \p dst.
+ * The memory areas may not overlap. \p symbol is a variable that resides in
+ * global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of transfer
+ * is inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * ::cudaMemcpyFromSymbolAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally be
+ * associated to a stream by passing a non-zero \p stream argument. If \p kind
+ * is ::cudaMemcpyDeviceToHost and \p stream is non-zero, the copy may overlap
+ * with operations in other streams.
+ *
+ * \param dst    - Destination memory address
+ * \param symbol - Device symbol address
+ * \param count  - Size in bytes to copy
+ * \param offset - Offset from start of symbol in bytes
+ * \param kind   - Type of transfer
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorInvalidMemcpyDirection,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync,
+ * ::cuMemcpyAsync,
+ * ::cuMemcpyDtoHAsync,
+ * ::cuMemcpyDtoDAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemcpyFromSymbolAsync(void *dst, const void *symbol, size_t count, size_t offset, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+
+/**
+ * \brief Initializes or sets device memory to a value
+ *
+ * Fills the first \p count bytes of the memory area pointed to by \p devPtr
+ * with the constant byte value \p value.
+ *
+ * Note that this function is asynchronous with respect to the host unless
+ * \p devPtr refers to pinned host memory.
+ *
+ * \param devPtr - Pointer to device memory
+ * \param value  - Value to set for each byte of specified memory
+ * \param count  - Size in bytes to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_memset
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuMemsetD8,
+ * ::cuMemsetD16,
+ * ::cuMemsetD32
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemset(void *devPtr, int value, size_t count);
+
+/**
+ * \brief Initializes or sets device memory to a value
+ *
+ * Sets to the specified value \p value a matrix (\p height rows of \p width
+ * bytes each) pointed to by \p dstPtr. \p pitch is the width in bytes of the
+ * 2D array pointed to by \p dstPtr, including any padding added to the end
+ * of each row. This function performs fastest when the pitch is one that has
+ * been passed back by ::cudaMallocPitch().
+ *
+ * Note that this function is asynchronous with respect to the host unless
+ * \p devPtr refers to pinned host memory.
+ *
+ * \param devPtr - Pointer to 2D device memory
+ * \param pitch  - Pitch in bytes of 2D device memory(Unused if \p height is 1)
+ * \param value  - Value to set for each byte of specified memory
+ * \param width  - Width of matrix set (columns in bytes)
+ * \param height - Height of matrix set (rows)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_memset
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemset, ::cudaMemset3D, ::cudaMemsetAsync,
+ * ::cudaMemset2DAsync, ::cudaMemset3DAsync,
+ * ::cuMemsetD2D8,
+ * ::cuMemsetD2D16,
+ * ::cuMemsetD2D32
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemset2D(void *devPtr, size_t pitch, int value, size_t width, size_t height);
+
+/**
+ * \brief Initializes or sets device memory to a value
+ *
+ * Initializes each element of a 3D array to the specified value \p value.
+ * The object to initialize is defined by \p pitchedDevPtr. The \p pitch field
+ * of \p pitchedDevPtr is the width in memory in bytes of the 3D array pointed
+ * to by \p pitchedDevPtr, including any padding added to the end of each row.
+ * The \p xsize field specifies the logical width of each row in bytes, while
+ * the \p ysize field specifies the height of each 2D slice in rows.
+ * The \p pitch field of \p pitchedDevPtr is ignored when \p height and \p depth 
+ * are both equal to 1. 
+ *
+ * The extents of the initialized region are specified as a \p width in bytes,
+ * a \p height in rows, and a \p depth in slices.
+ *
+ * Extents with \p width greater than or equal to the \p xsize of
+ * \p pitchedDevPtr may perform significantly faster than extents narrower
+ * than the \p xsize. Secondarily, extents with \p height equal to the
+ * \p ysize of \p pitchedDevPtr will perform faster than when the \p height is
+ * shorter than the \p ysize.
+ *
+ * This function performs fastest when the \p pitchedDevPtr has been allocated
+ * by ::cudaMalloc3D().
+ *
+ * Note that this function is asynchronous with respect to the host unless
+ * \p pitchedDevPtr refers to pinned host memory.
+ *
+ * \param pitchedDevPtr - Pointer to pitched device memory
+ * \param value         - Value to set for each byte of specified memory
+ * \param extent        - Size parameters for where to set device memory (\p width field in bytes)
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_memset
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemset, ::cudaMemset2D,
+ * ::cudaMemsetAsync, ::cudaMemset2DAsync, ::cudaMemset3DAsync,
+ * ::cudaMalloc3D, ::make_cudaPitchedPtr,
+ * ::make_cudaExtent
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemset3D(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent);
+
+/**
+ * \brief Initializes or sets device memory to a value
+ *
+ * Fills the first \p count bytes of the memory area pointed to by \p devPtr
+ * with the constant byte value \p value.
+ *
+ * ::cudaMemsetAsync() is asynchronous with respect to the host, so
+ * the call may return before the memset is complete. The operation can optionally
+ * be associated to a stream by passing a non-zero \p stream argument.
+ * If \p stream is non-zero, the operation may overlap with operations in other streams.
+ *
+ * The device version of this function only handles device to device copies and
+ * cannot be given local or shared pointers.
+ *
+ * \param devPtr - Pointer to device memory
+ * \param value  - Value to set for each byte of specified memory
+ * \param count  - Size in bytes to set
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemset, ::cudaMemset2D, ::cudaMemset3D,
+ * ::cudaMemset2DAsync, ::cudaMemset3DAsync,
+ * ::cuMemsetD8Async,
+ * ::cuMemsetD16Async,
+ * ::cuMemsetD32Async
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemsetAsync(void *devPtr, int value, size_t count, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Initializes or sets device memory to a value
+ *
+ * Sets to the specified value \p value a matrix (\p height rows of \p width
+ * bytes each) pointed to by \p dstPtr. \p pitch is the width in bytes of the
+ * 2D array pointed to by \p dstPtr, including any padding added to the end
+ * of each row. This function performs fastest when the pitch is one that has
+ * been passed back by ::cudaMallocPitch().
+ *
+ * ::cudaMemset2DAsync() is asynchronous with respect to the host, so
+ * the call may return before the memset is complete. The operation can optionally
+ * be associated to a stream by passing a non-zero \p stream argument.
+ * If \p stream is non-zero, the operation may overlap with operations in other streams.
+ *
+ * The device version of this function only handles device to device copies and
+ * cannot be given local or shared pointers.
+ *
+ * \param devPtr - Pointer to 2D device memory
+ * \param pitch  - Pitch in bytes of 2D device memory(Unused if \p height is 1)
+ * \param value  - Value to set for each byte of specified memory
+ * \param width  - Width of matrix set (columns in bytes)
+ * \param height - Height of matrix set (rows)
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemset, ::cudaMemset2D, ::cudaMemset3D,
+ * ::cudaMemsetAsync, ::cudaMemset3DAsync,
+ * ::cuMemsetD2D8Async,
+ * ::cuMemsetD2D16Async,
+ * ::cuMemsetD2D32Async
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset2DAsync(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Initializes or sets device memory to a value
+ *
+ * Initializes each element of a 3D array to the specified value \p value.
+ * The object to initialize is defined by \p pitchedDevPtr. The \p pitch field
+ * of \p pitchedDevPtr is the width in memory in bytes of the 3D array pointed
+ * to by \p pitchedDevPtr, including any padding added to the end of each row.
+ * The \p xsize field specifies the logical width of each row in bytes, while
+ * the \p ysize field specifies the height of each 2D slice in rows.
+ * The \p pitch field of \p pitchedDevPtr is ignored when \p height and \p depth 
+ * are both equal to 1. 
+ *
+ * The extents of the initialized region are specified as a \p width in bytes,
+ * a \p height in rows, and a \p depth in slices.
+ *
+ * Extents with \p width greater than or equal to the \p xsize of
+ * \p pitchedDevPtr may perform significantly faster than extents narrower
+ * than the \p xsize. Secondarily, extents with \p height equal to the
+ * \p ysize of \p pitchedDevPtr will perform faster than when the \p height is
+ * shorter than the \p ysize.
+ *
+ * This function performs fastest when the \p pitchedDevPtr has been allocated
+ * by ::cudaMalloc3D().
+ *
+ * ::cudaMemset3DAsync() is asynchronous with respect to the host, so
+ * the call may return before the memset is complete. The operation can optionally
+ * be associated to a stream by passing a non-zero \p stream argument.
+ * If \p stream is non-zero, the operation may overlap with operations in other streams.
+ *
+ * The device version of this function only handles device to device copies and
+ * cannot be given local or shared pointers.
+ *
+ * \param pitchedDevPtr - Pointer to pitched device memory
+ * \param value         - Value to set for each byte of specified memory
+ * \param extent        - Size parameters for where to set device memory (\p width field in bytes)
+ * \param stream - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_memset
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemset, ::cudaMemset2D, ::cudaMemset3D,
+ * ::cudaMemsetAsync, ::cudaMemset2DAsync,
+ * ::cudaMalloc3D, ::make_cudaPitchedPtr,
+ * ::make_cudaExtent
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset3DAsync(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Finds the address associated with a CUDA symbol
+ *
+ * Returns in \p *devPtr the address of symbol \p symbol on the device.
+ * \p symbol is a variable that resides in global or constant memory space.
+ * If \p symbol cannot be found, or if \p symbol is not declared in the
+ * global or constant memory space, \p *devPtr is unchanged and the error
+ * ::cudaErrorInvalidSymbol is returned.
+ *
+ * \param devPtr - Return device pointer associated with symbol
+ * \param symbol - Device symbol address
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * \ref ::cudaGetSymbolAddress(void**, const T&) "cudaGetSymbolAddress (C++ API)",
+ * \ref ::cudaGetSymbolSize(size_t*, const void*) "cudaGetSymbolSize (C API)",
+ * ::cuModuleGetGlobal
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetSymbolAddress(void **devPtr, const void *symbol);
+
+/**
+ * \brief Finds the size of the object associated with a CUDA symbol
+ *
+ * Returns in \p *size the size of symbol \p symbol. \p symbol is a variable that
+ * resides in global or constant memory space. If \p symbol cannot be found, or
+ * if \p symbol is not declared in global or constant memory space, \p *size is
+ * unchanged and the error ::cudaErrorInvalidSymbol is returned.
+ *
+ * \param size   - Size of object associated with symbol
+ * \param symbol - Device symbol address
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidSymbol,
+ * ::cudaErrorNoKernelImageForDevice
+ * \notefnerr
+ * \note_string_api_deprecation
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * \ref ::cudaGetSymbolAddress(void**, const void*) "cudaGetSymbolAddress (C API)",
+ * \ref ::cudaGetSymbolSize(size_t*, const T&) "cudaGetSymbolSize (C++ API)",
+ * ::cuModuleGetGlobal
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetSymbolSize(size_t *size, const void *symbol);
+
+/**
+ * \brief Prefetches memory to the specified destination device
+ *
+ * Prefetches memory to the specified destination device.  \p devPtr is the 
+ * base device pointer of the memory to be prefetched and \p dstDevice is the 
+ * destination device. \p count specifies the number of bytes to copy. \p stream
+ * is the stream in which the operation is enqueued. The memory range must refer
+ * to managed memory allocated via ::cudaMallocManaged or declared via __managed__ variables,
+ * or it may also refer to system-allocated memory on systems with non-zero
+ * cudaDevAttrPageableMemoryAccess.
+ *
+ * Passing in cudaCpuDeviceId for \p dstDevice will prefetch the data to host memory. If
+ * \p dstDevice is a GPU, then the device attribute ::cudaDevAttrConcurrentManagedAccess
+ * must be non-zero. Additionally, \p stream must be associated with a device that has a
+ * non-zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * The start address and end address of the memory range will be rounded down and rounded up
+ * respectively to be aligned to CPU page size before the prefetch operation is enqueued
+ * in the stream.
+ *
+ * If no physical memory has been allocated for this region, then this memory region
+ * will be populated and mapped on the destination device. If there's insufficient
+ * memory to prefetch the desired region, the Unified Memory driver may evict pages from other
+ * ::cudaMallocManaged allocations to host memory in order to make room. Device memory
+ * allocated using ::cudaMalloc or ::cudaMallocArray will not be evicted.
+ *
+ * By default, any mappings to the previous location of the migrated pages are removed and
+ * mappings for the new location are only setup on \p dstDevice. The exact behavior however
+ * also depends on the settings applied to this memory range via ::cudaMemAdvise as described
+ * below:
+ *
+ * If ::cudaMemAdviseSetReadMostly was set on any subset of this memory range,
+ * then that subset will create a read-only copy of the pages on \p dstDevice.
+ *
+ * If ::cudaMemAdviseSetPreferredLocation was called on any subset of this memory
+ * range, then the pages will be migrated to \p dstDevice even if \p dstDevice is not the
+ * preferred location of any pages in the memory range.
+ *
+ * If ::cudaMemAdviseSetAccessedBy was called on any subset of this memory range,
+ * then mappings to those pages from all the appropriate processors are updated to
+ * refer to the new location if establishing such a mapping is possible. Otherwise,
+ * those mappings are cleared.
+ *
+ * Note that this API is not required for functionality and only serves to improve performance
+ * by allowing the application to migrate data to a suitable location before it is accessed.
+ * Memory accesses to this range are always coherent and are allowed even when the data is
+ * actively being migrated.
+ *
+ * Note that this function is asynchronous with respect to the host and all work
+ * on other devices.
+ *
+ * \param devPtr    - Pointer to be prefetched
+ * \param count     - Size in bytes
+ * \param dstDevice - Destination device to prefetch to
+ * \param stream    - Stream to enqueue prefetch operation
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync,
+ * ::cudaMemcpy3DPeerAsync, ::cudaMemAdvise, ::cudaMemAdvise_v2
+ * ::cuMemPrefetchAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPrefetchAsync(const void *devPtr, size_t count, int dstDevice, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Prefetches memory to the specified destination location
+ *
+ * Prefetches memory to the specified destination location.  \p devPtr is the
+ * base device pointer of the memory to be prefetched and \p location specifies the
+ * destination location. \p count specifies the number of bytes to copy. \p stream
+ * is the stream in which the operation is enqueued. The memory range must refer
+ * to managed memory allocated via ::cudaMallocManaged or declared via __managed__ variables, 
+ * or it may also refer to system-allocated memory on systems with non-zero
+ * cudaDevAttrPageableMemoryAccess.
+ * 
+ * Specifying ::cudaMemLocationTypeDevice for ::cudaMemLocation::type will prefetch memory to GPU
+ * specified by device ordinal ::cudaMemLocation::id which must have non-zero value for the device attribute
+ * ::concurrentManagedAccess. Additionally, \p stream must be associated with a device
+ * that has a non-zero value for the device attribute ::concurrentManagedAccess.
+ * Specifying ::cudaMemLocationTypeHost as ::cudaMemLocation::type will prefetch data to host memory.
+ * Applications can request prefetching memory to a specific host NUMA node by specifying
+ * ::cudaMemLocationTypeHostNuma for ::cudaMemLocation::type and a valid host NUMA node id in ::cudaMemLocation::id
+ * Users can also request prefetching memory to the host NUMA node closest to the current thread's CPU by specifying
+ * ::cudaMemLocationTypeHostNumaCurrent for ::cudaMemLocation::type. Note when ::cudaMemLocation::type is etiher
+ * ::cudaMemLocationTypeHost OR ::cudaMemLocationTypeHostNumaCurrent, ::cudaMemLocation::id will be ignored.
+ *
+ * The start address and end address of the memory range will be rounded down and rounded up
+ * respectively to be aligned to CPU page size before the prefetch operation is enqueued
+ * in the stream.
+ *
+ * If no physical memory has been allocated for this region, then this memory region
+ * will be populated and mapped on the destination device. If there's insufficient
+ * memory to prefetch the desired region, the Unified Memory driver may evict pages from other
+ * ::cudaMallocManaged allocations to host memory in order to make room. Device memory
+ * allocated using ::cudaMalloc or ::cudaMallocArray will not be evicted.
+ *
+ * By default, any mappings to the previous location of the migrated pages are removed and
+ * mappings for the new location are only setup on the destination location. The exact behavior however
+ * also depends on the settings applied to this memory range via ::cuMemAdvise as described
+ * below:
+ *
+ * If ::cudaMemAdviseSetReadMostly was set on any subset of this memory range,
+ * then that subset will create a read-only copy of the pages on destination location.
+ * If however the destination location is a host NUMA node, then any pages of that subset
+ * that are already in another host NUMA node will be transferred to the destination.
+ *
+ * If ::cudaMemAdviseSetPreferredLocation was called on any subset of this memory
+ * range, then the pages will be migrated to \p location even if \p location is not the
+ * preferred location of any pages in the memory range.
+ *
+ * If ::cudaMemAdviseSetAccessedBy was called on any subset of this memory range,
+ * then mappings to those pages from all the appropriate processors are updated to
+ * refer to the new location if establishing such a mapping is possible. Otherwise,
+ * those mappings are cleared.
+ *
+ * Note that this API is not required for functionality and only serves to improve performance
+ * by allowing the application to migrate data to a suitable location before it is accessed.
+ * Memory accesses to this range are always coherent and are allowed even when the data is
+ * actively being migrated.
+ *
+ * Note that this function is asynchronous with respect to the host and all work
+ * on other devices.
+ *
+ * \param devPtr    - Pointer to be prefetched
+ * \param count     - Size in bytes
+ * \param location  - location to prefetch to
+ * \param flags     - flags for future use, must be zero now. 
+ * \param stream    - Stream to enqueue prefetch operation
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync,
+ * ::cudaMemcpy3DPeerAsync, ::cudaMemAdvise, ::cudaMemAdvise_v2
+ * ::cuMemPrefetchAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPrefetchAsync_v2(const void *devPtr, size_t count, struct cudaMemLocation location, unsigned int flags, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Advise about the usage of a given memory range
+ *
+ * Advise the Unified Memory subsystem about the usage pattern for the memory range
+ * starting at \p devPtr with a size of \p count bytes. The start address and end address of the memory
+ * range will be rounded down and rounded up respectively to be aligned to CPU page size before the
+ * advice is applied. The memory range must refer to managed memory allocated via ::cudaMallocManaged
+ * or declared via __managed__ variables. The memory range could also refer to system-allocated pageable
+ * memory provided it represents a valid, host-accessible region of memory and all additional constraints
+ * imposed by \p advice as outlined below are also satisfied. Specifying an invalid system-allocated pageable
+ * memory range results in an error being returned.
+ *
+ * The \p advice parameter can take the following values:
+ * - ::cudaMemAdviseSetReadMostly: This implies that the data is mostly going to be read
+ * from and only occasionally written to. Any read accesses from any processor to this region will create a
+ * read-only copy of at least the accessed pages in that processor's memory. Additionally, if ::cudaMemPrefetchAsync
+ * is called on this region, it will create a read-only copy of the data on the destination processor.
+ * If any processor writes to this region, all copies of the corresponding page will be invalidated
+ * except for the one where the write occurred. The \p device argument is ignored for this advice.
+ * Note that for a page to be read-duplicated, the accessing processor must either be the CPU or a GPU
+ * that has a non-zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * Also, if a context is created on a device that does not have the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess set, then read-duplication will not occur until
+ * all such contexts are destroyed.
+ * If the memory region refers to valid system-allocated pageable memory, then the accessing device must
+ * have a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccess for a read-only
+ * copy to be created on that device. Note however that if the accessing device also has a non-zero value for the
+ * device attribute ::cudaDevAttrPageableMemoryAccessUsesHostPageTables, then setting this advice
+ * will not create a read-only copy when that device accesses this memory region.
+ *
+ * - ::cudaMemAdviceUnsetReadMostly: Undoes the effect of ::cudaMemAdviceReadMostly and also prevents the
+ * Unified Memory driver from attempting heuristic read-duplication on the memory range. Any read-duplicated
+ * copies of the data will be collapsed into a single copy. The location for the collapsed
+ * copy will be the preferred location if the page has a preferred location and one of the read-duplicated
+ * copies was resident at that location. Otherwise, the location chosen is arbitrary.
+ *
+ * - ::cudaMemAdviseSetPreferredLocation: This advice sets the preferred location for the
+ * data to be the memory belonging to \p device. Passing in cudaCpuDeviceId for \p device sets the
+ * preferred location as host memory. If \p device is a GPU, then it must have a non-zero value for the
+ * device attribute ::cudaDevAttrConcurrentManagedAccess. Setting the preferred location
+ * does not cause data to migrate to that location immediately. Instead, it guides the migration policy
+ * when a fault occurs on that memory region. If the data is already in its preferred location and the
+ * faulting processor can establish a mapping without requiring the data to be migrated, then
+ * data migration will be avoided. On the other hand, if the data is not in its preferred location
+ * or if a direct mapping cannot be established, then it will be migrated to the processor accessing
+ * it. It is important to note that setting the preferred location does not prevent data prefetching
+ * done using ::cudaMemPrefetchAsync.
+ * Having a preferred location can override the page thrash detection and resolution logic in the Unified
+ * Memory driver. Normally, if a page is detected to be constantly thrashing between for example host and device
+ * memory, the page may eventually be pinned to host memory by the Unified Memory driver. But
+ * if the preferred location is set as device memory, then the page will continue to thrash indefinitely.
+ * If ::cudaMemAdviseSetReadMostly is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice, unless read accesses from
+ * \p device will not result in a read-only copy being created on that device as outlined in description for
+ * the advice ::cudaMemAdviseSetReadMostly.
+ * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero
+ * value for the device attribute ::cudaDevAttrPageableMemoryAccess.
+ *
+ * - ::cudaMemAdviseUnsetPreferredLocation: Undoes the effect of ::cudaMemAdviseSetPreferredLocation
+ * and changes the preferred location to none.
+ *
+ * - ::cudaMemAdviseSetAccessedBy: This advice implies that the data will be accessed by \p device.
+ * Passing in ::cudaCpuDeviceId for \p device will set the advice for the CPU. If \p device is a GPU, then
+ * the device attribute ::cudaDevAttrConcurrentManagedAccess must be non-zero.
+ * This advice does not cause data migration and has no impact on the location of the data per se. Instead,
+ * it causes the data to always be mapped in the specified processor's page tables, as long as the
+ * location of the data permits a mapping to be established. If the data gets migrated for any reason,
+ * the mappings are updated accordingly.
+ * This advice is recommended in scenarios where data locality is not important, but avoiding faults is.
+ * Consider for example a system containing multiple GPUs with peer-to-peer access enabled, where the
+ * data located on one GPU is occasionally accessed by peer GPUs. In such scenarios, migrating data
+ * over to the other GPUs is not as important because the accesses are infrequent and the overhead of
+ * migration may be too high. But preventing faults can still help improve performance, and so having
+ * a mapping set up in advance is useful. Note that on CPU access of this data, the data may be migrated
+ * to host memory because the CPU typically cannot access device memory directly. Any GPU that had the
+ * ::cudaMemAdviceSetAccessedBy flag set for this data will now have its mapping updated to point to the
+ * page in host memory.
+ * If ::cudaMemAdviseSetReadMostly is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice. Additionally, if the
+ * preferred location of this memory region or any subset of it is also \p device, then the policies
+ * associated with ::cudaMemAdviseSetPreferredLocation will override the policies of this advice.
+ * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero
+ * value for the device attribute ::cudaDevAttrPageableMemoryAccess. Additionally, if \p device has
+ * a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccessUsesHostPageTables,
+ * then this call has no effect.
+ *
+ * - ::cudaMemAdviseUnsetAccessedBy: Undoes the effect of ::cudaMemAdviseSetAccessedBy. Any mappings to
+ * the data from \p device may be removed at any time causing accesses to result in non-fatal page faults.
+ * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero
+ * value for the device attribute ::cudaDevAttrPageableMemoryAccess. Additionally, if \p device has
+ * a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccessUsesHostPageTables,
+ * then this call has no effect.
+ *
+ * \param devPtr - Pointer to memory to set the advice for
+ * \param count  - Size in bytes of the memory range
+ * \param advice - Advice to be applied for the specified memory range
+ * \param device - Device to apply the advice for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync,
+ * ::cudaMemcpy3DPeerAsync, ::cudaMemPrefetchAsync,
+ * ::cuMemAdvise
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemAdvise(const void *devPtr, size_t count, enum cudaMemoryAdvise advice, int device);
+
+/**
+ * \brief Advise about the usage of a given memory range
+ *
+ * Advise the Unified Memory subsystem about the usage pattern for the memory range
+ * starting at \p devPtr with a size of \p count bytes. The start address and end address of the memory
+ * range will be rounded down and rounded up respectively to be aligned to CPU page size before the
+ * advice is applied. The memory range must refer to managed memory allocated via ::cudaMallocManaged
+ * or declared via __managed__ variables. The memory range could also refer to system-allocated pageable
+ * memory provided it represents a valid, host-accessible region of memory and all additional constraints
+ * imposed by \p advice as outlined below are also satisfied. Specifying an invalid system-allocated pageable
+ * memory range results in an error being returned.
+ *
+ * The \p advice parameter can take the following values:
+ * - ::cudaMemAdviseSetReadMostly: This implies that the data is mostly going to be read
+ * from and only occasionally written to. Any read accesses from any processor to this region will create a
+ * read-only copy of at least the accessed pages in that processor's memory. Additionally, if ::cudaMemPrefetchAsync
+ * or ::cudaMemPrefetchAsync_v2 is called on this region, it will create a read-only copy of the data on the destination processor.
+ * If the target location for ::cudaMemPrefetchAsync_v2 is a host NUMA node and a read-only copy already exists on
+ * another host NUMA node, that copy will be migrated to the targeted host NUMA node.
+ * If any processor writes to this region, all copies of the corresponding page will be invalidated
+ * except for the one where the write occurred. If the writing processor is the CPU and the preferred location of
+ * the page is a host NUMA node, then the page will also be migrated to that host NUMA node. The \p location argument is ignored for this advice.
+ * Note that for a page to be read-duplicated, the accessing processor must either be the CPU or a GPU
+ * that has a non-zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * Also, if a context is created on a device that does not have the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess set, then read-duplication will not occur until
+ * all such contexts are destroyed.
+ * If the memory region refers to valid system-allocated pageable memory, then the accessing device must
+ * have a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccess for a read-only
+ * copy to be created on that device. Note however that if the accessing device also has a non-zero value for the
+ * device attribute ::cudaDevAttrPageableMemoryAccessUsesHostPageTables, then setting this advice
+ * will not create a read-only copy when that device accesses this memory region.
+ *
+ * - ::cudaMemAdviceUnsetReadMostly:  Undoes the effect of ::cudaMemAdviseSetReadMostly and also prevents the
+ * Unified Memory driver from attempting heuristic read-duplication on the memory range. Any read-duplicated
+ * copies of the data will be collapsed into a single copy. The location for the collapsed
+ * copy will be the preferred location if the page has a preferred location and one of the read-duplicated
+ * copies was resident at that location. Otherwise, the location chosen is arbitrary.
+ * Note: The \p location argument is ignored for this advice.
+ *
+ * - ::cudaMemAdviseSetPreferredLocation: This advice sets the preferred location for the
+ * data to be the memory belonging to \p location. When ::cudaMemLocation::type is ::cudaMemLocationTypeHost,
+ * ::cudaMemLocation::id is ignored and the preferred location is set to be host memory. To set the preferred location
+ * to a specific host NUMA node, applications must set ::cudaMemLocation::type to ::cudaMemLocationTypeHostNuma and
+ * ::cudaMemLocation::id must specify the NUMA ID of the host NUMA node. If ::cudaMemLocation::type is set to ::cudaMemLocationTypeHostNumaCurrent,
+ * ::cudaMemLocation::id will be ignored and the host NUMA node closest to the calling thread's CPU will be used as the preferred location.
+ * If ::cudaMemLocation::type is a ::cudaMemLocationTypeDevice, then ::cudaMemLocation::id must be a valid device ordinal
+ * and the device must have a non-zero value for the device attribute ::cudaDevAttrConcurrentManagedAccess.
+ * Setting the preferred location does not cause data to migrate to that location immediately. Instead, it guides the migration policy
+ * when a fault occurs on that memory region. If the data is already in its preferred location and the
+ * faulting processor can establish a mapping without requiring the data to be migrated, then
+ * data migration will be avoided. On the other hand, if the data is not in its preferred location
+ * or if a direct mapping cannot be established, then it will be migrated to the processor accessing
+ * it. It is important to note that setting the preferred location does not prevent data prefetching
+ * done using ::cudaMemPrefetchAsync.
+ * Having a preferred location can override the page thrash detection and resolution logic in the Unified
+ * Memory driver. Normally, if a page is detected to be constantly thrashing between for example host and device
+ * memory, the page may eventually be pinned to host memory by the Unified Memory driver. But
+ * if the preferred location is set as device memory, then the page will continue to thrash indefinitely.
+ * If ::cudaMemAdviseSetReadMostly is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice, unless read accesses from
+ * \p location will not result in a read-only copy being created on that procesor as outlined in description for
+ * the advice ::cudaMemAdviseSetReadMostly.
+ * If the memory region refers to valid system-allocated pageable memory, and ::cudaMemLocation::type is ::cudaMemLocationTypeDevice
+ * then ::cudaMemLocation::id must be a valid device that has a non-zero alue for the device attribute ::cudaDevAttrPageableMemoryAccess.
+ *
+ * - ::cudaMemAdviseUnsetPreferredLocation: Undoes the effect of ::cudaMemAdviseSetPreferredLocation
+ * and changes the preferred location to none. The \p location argument is ignored for this advice.
+ *
+ * - ::cudaMemAdviseSetAccessedBy: This advice implies that the data will be accessed by processor \p location.
+ * The ::cudaMemLocation::type must be either ::cudaMemLocationTypeDevice with ::cudaMemLocation::id representing a valid device
+ * ordinal or ::cudaMemLocationTypeHost and ::cudaMemLocation::id will be ignored. All other location types are invalid.
+ * If ::cudaMemLocation::id is a GPU, then the device attribute ::cudaDevAttrConcurrentManagedAccess must be non-zero.
+ * This advice does not cause data migration and has no impact on the location of the data per se. Instead,
+ * it causes the data to always be mapped in the specified processor's page tables, as long as the
+ * location of the data permits a mapping to be established. If the data gets migrated for any reason,
+ * the mappings are updated accordingly.
+ * This advice is recommended in scenarios where data locality is not important, but avoiding faults is.
+ * Consider for example a system containing multiple GPUs with peer-to-peer access enabled, where the
+ * data located on one GPU is occasionally accessed by peer GPUs. In such scenarios, migrating data
+ * over to the other GPUs is not as important because the accesses are infrequent and the overhead of
+ * migration may be too high. But preventing faults can still help improve performance, and so having
+ * a mapping set up in advance is useful. Note that on CPU access of this data, the data may be migrated
+ * to host memory because the CPU typically cannot access device memory directly. Any GPU that had the
+ * ::cudaMemAdviseSetAccessedBy flag set for this data will now have its mapping updated to point to the
+ * page in host memory.
+ * If ::cudaMemAdviseSetReadMostly is also set on this memory region or any subset of it, then the
+ * policies associated with that advice will override the policies of this advice. Additionally, if the
+ * preferred location of this memory region or any subset of it is also \p location, then the policies
+ * associated with ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION will override the policies of this advice.
+ * If the memory region refers to valid system-allocated pageable memory, and ::cudaMemLocation::type is ::cudaMemLocationTypeDevice
+ * then device in ::cudaMemLocation::id must have a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccess.
+ * Additionally, if ::cudaMemLocation::id has a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccessUsesHostPageTables,
+ * then this call has no effect.
+ *
+ * - ::CU_MEM_ADVISE_UNSET_ACCESSED_BY: Undoes the effect of ::cudaMemAdviseSetAccessedBy. Any mappings to
+ * the data from \p location may be removed at any time causing accesses to result in non-fatal page faults.
+ * If the memory region refers to valid system-allocated pageable memory, and ::cudaMemLocation::type is ::cudaMemLocationTypeDevice
+ * then device in ::cudaMemLocation::id must have a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccess.
+ * Additionally, if ::cudaMemLocation::id has a non-zero value for the device attribute ::cudaDevAttrPageableMemoryAccessUsesHostPageTables,
+ * then this call has no effect.
+ *
+ * \param devPtr   - Pointer to memory to set the advice for
+ * \param count    - Size in bytes of the memory range
+ * \param advice   - Advice to be applied for the specified memory range
+ * \param location - location to apply the advice for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpyPeer, ::cudaMemcpyAsync,
+ * ::cudaMemcpy3DPeerAsync, ::cudaMemPrefetchAsync,
+ * ::cuMemAdvise, ::cuMemAdvise_v2
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemAdvise_v2(const void *devPtr, size_t count, enum cudaMemoryAdvise advice, struct cudaMemLocation location);
+
+/**
+* \brief Query an attribute of a given memory range
+*
+* Query an attribute about the memory range starting at \p devPtr with a size of \p count bytes. The
+* memory range must refer to managed memory allocated via ::cudaMallocManaged or declared via
+* __managed__ variables.
+*
+* The \p attribute parameter can take the following values:
+* - ::cudaMemRangeAttributeReadMostly: If this attribute is specified, \p data will be interpreted
+* as a 32-bit integer, and \p dataSize must be 4. The result returned will be 1 if all pages in the given
+* memory range have read-duplication enabled, or 0 otherwise.
+* - ::cudaMemRangeAttributePreferredLocation: If this attribute is specified, \p data will be
+* interpreted as a 32-bit integer, and \p dataSize must be 4. The result returned will be a GPU device
+* id if all pages in the memory range have that GPU as their preferred location, or it will be cudaCpuDeviceId
+* if all pages in the memory range have the CPU as their preferred location, or it will be cudaInvalidDeviceId
+* if either all the pages don't have the same preferred location or some of the pages don't have a
+* preferred location at all. Note that the actual location of the pages in the memory range at the time of
+* the query may be different from the preferred location.
+* - ::cudaMemRangeAttributeAccessedBy: If this attribute is specified, \p data will be interpreted
+* as an array of 32-bit integers, and \p dataSize must be a non-zero multiple of 4. The result returned
+* will be a list of device ids that had ::cudaMemAdviceSetAccessedBy set for that entire memory range.
+* If any device does not have that advice set for the entire memory range, that device will not be included.
+* If \p data is larger than the number of devices that have that advice set for that memory range,
+* cudaInvalidDeviceId will be returned in all the extra space provided. For ex., if \p dataSize is 12
+* (i.e. \p data has 3 elements) and only device 0 has the advice set, then the result returned will be
+* { 0, cudaInvalidDeviceId, cudaInvalidDeviceId }. If \p data is smaller than the number of devices that have
+* that advice set, then only as many devices will be returned as can fit in the array. There is no
+* guarantee on which specific devices will be returned, however.
+* - ::cudaMemRangeAttributeLastPrefetchLocation: If this attribute is specified, \p data will be
+* interpreted as a 32-bit integer, and \p dataSize must be 4. The result returned will be the last location
+* to which all pages in the memory range were prefetched explicitly via ::cudaMemPrefetchAsync. This will either be
+* a GPU id or cudaCpuDeviceId depending on whether the last location for prefetch was a GPU or the CPU
+* respectively. If any page in the memory range was never explicitly prefetched or if all pages were not
+* prefetched to the same location, cudaInvalidDeviceId will be returned. Note that this simply returns the
+* last location that the applicaton requested to prefetch the memory range to. It gives no indication as to
+* whether the prefetch operation to that location has completed or even begun.
+ * - ::cudaMemRangeAttributePreferredLocationType: If this attribute is specified, \p data will be
+ * interpreted as a ::cudaMemLocationType, and \p dataSize must be sizeof(cudaMemLocationType). The ::cudaMemLocationType returned will be
+ * ::cudaMemLocationTypeDevice if all pages in the memory range have the same GPU as their preferred location, or ::cudaMemLocationType
+ * will be ::cudaMemLocationTypeHost if all pages in the memory range have the CPU as their preferred location, or or it will be ::cudaMemLocationTypeHostNuma
+ * if all the pages in the memory range have the same host NUMA node ID as their preferred location or it will be ::cudaMemLocationTypeInvalid
+ * if either all the pages don't have the same preferred location or some of the pages don't have a preferred location at all.
+ * Note that the actual location type of the pages in the memory range at the time of the query may be different from the preferred location type.
+ *  - ::cudaMemRangeAttributePreferredLocationId: If this attribute is specified, \p data will be
+ * interpreted as a 32-bit integer, and \p dataSize must be 4. If the ::cudaMemRangeAttributePreferredLocationType query for the same address range
+ * returns ::cudaMemLocationTypeDevice, it will be a valid device ordinal or if it returns ::cudaMemLocationTypeHostNuma, it will be a valid host NUMA node ID
+ * or if it returns any other location type, the id should be ignored.
+ * - ::cudaMemRangeAttributeLastPrefetchLocationType: If this attribute is specified, \p data will be
+ * interpreted as a ::cudaMemLocationType, and \p dataSize must be sizeof(cudaMemLocationType). The result returned will be the last location type
+ * to which all pages in the memory range were prefetched explicitly via ::cuMemPrefetchAsync. The ::cudaMemLocationType returned
+ * will be ::cudaMemLocationTypeDevice if the last prefetch location was the GPU or ::cudaMemLocationTypeHost if it was the CPU or ::cudaMemLocationTypeHostNuma if
+ * the last prefetch location was a specific host NUMA node. If any page in the memory range was never explicitly prefetched or if all pages were not
+ * prefetched to the same location, ::CUmemLocationType will be ::cudaMemLocationTypeInvalid.
+ * Note that this simply returns the last location type that the application requested to prefetch the memory range to. It gives no indication as to
+ * whether the prefetch operation to that location has completed or even begun.
+ *  - ::cudaMemRangeAttributeLastPrefetchLocationId: If this attribute is specified, \p data will be
+ * interpreted as a 32-bit integer, and \p dataSize must be 4. If the ::cudaMemRangeAttributeLastPrefetchLocationType query for the same address range
+ * returns ::cudaMemLocationTypeDevice, it will be a valid device ordinal or if it returns ::cudaMemLocationTypeHostNuma, it will be a valid host NUMA node ID
+ * or if it returns any other location type, the id should be ignored.
+*
+* \param data      - A pointers to a memory location where the result
+*                    of each attribute query will be written to.
+* \param dataSize  - Array containing the size of data
+* \param attribute - The attribute to query
+* \param devPtr    - Start of the range to query
+* \param count     - Size of the range to query
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemRangeGetAttributes, ::cudaMemPrefetchAsync,
+ * ::cudaMemAdvise,
+ * ::cuMemRangeGetAttribute
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemRangeGetAttribute(void *data, size_t dataSize, enum cudaMemRangeAttribute attribute, const void *devPtr, size_t count);
+
+/**
+ * \brief Query attributes of a given memory range.
+ *
+ * Query attributes of the memory range starting at \p devPtr with a size of \p count bytes. The
+ * memory range must refer to managed memory allocated via ::cudaMallocManaged or declared via
+ * __managed__ variables. The \p attributes array will be interpreted to have \p numAttributes
+ * entries. The \p dataSizes array will also be interpreted to have \p numAttributes entries.
+ * The results of the query will be stored in \p data.
+ *
+ * The list of supported attributes are given below. Please refer to ::cudaMemRangeGetAttribute for
+ * attribute descriptions and restrictions.
+ *
+ * - ::cudaMemRangeAttributeReadMostly
+ * - ::cudaMemRangeAttributePreferredLocation
+ * - ::cudaMemRangeAttributeAccessedBy
+ * - ::cudaMemRangeAttributeLastPrefetchLocation
+ * - :: cudaMemRangeAttributePreferredLocationType
+ * - :: cudaMemRangeAttributePreferredLocationId
+ * - :: cudaMemRangeAttributeLastPrefetchLocationType
+ * - :: cudaMemRangeAttributeLastPrefetchLocationId
+ *
+ * \param data          - A two-dimensional array containing pointers to memory
+ *                        locations where the result of each attribute query will be written to.
+ * \param dataSizes     - Array containing the sizes of each result
+ * \param attributes    - An array of attributes to query
+ *                        (numAttributes and the number of attributes in this array should match)
+ * \param numAttributes - Number of attributes to query
+ * \param devPtr        - Start of the range to query
+ * \param count         - Size of the range to query
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemRangeGetAttribute, ::cudaMemAdvise,
+ * ::cudaMemPrefetchAsync,
+ * ::cuMemRangeGetAttributes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemRangeGetAttributes(void **data, size_t *dataSizes, enum cudaMemRangeAttribute *attributes, size_t numAttributes, const void *devPtr, size_t count);
+
+/** @} */ /* END CUDART_MEMORY */
+
+/**
+ * \defgroup CUDART_MEMORY_DEPRECATED Memory Management [DEPRECATED]
+ *
+ * ___MANBRIEF___ deprecated memory management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes deprecated memory management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * Some functions have overloaded C++ API template versions documented separately in the
+ * \ref CUDART_HIGHLEVEL "C++ API Routines" module.
+ *
+ * @{
+ */
+
+/**
+ * \brief Copies data between host and device
+ *
+ * \deprecated
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src to the
+ * CUDA array \p dst starting at \p hOffset rows and \p wOffset bytes from
+ * the upper left corner, where \p kind specifies the direction
+ * of the copy, and must be one of ::cudaMemcpyHostToHost,
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * \param dst     - Destination memory address
+ * \param wOffset - Destination starting X offset (columns in bytes)
+ * \param hOffset - Destination starting Y offset (rows)
+ * \param src     - Source memory address
+ * \param count   - Size in bytes to copy
+ * \param kind    - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpyFromArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpyArrayToArray, ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpyToArrayAsync, ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpyFromArrayAsync, ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyHtoA,
+ * ::cuMemcpyDtoA
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaMemcpyToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t count, enum cudaMemcpyKind kind);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * \deprecated
+ *
+ * Copies \p count bytes from the CUDA array \p src starting at \p hOffset rows
+ * and \p wOffset bytes from the upper left corner to the memory area pointed to
+ * by \p dst, where \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * \param dst     - Destination memory address
+ * \param src     - Source memory address
+ * \param wOffset - Source starting X offset (columns in bytes)
+ * \param hOffset - Source starting Y offset (rows)
+ * \param count   - Size in bytes to copy
+ * \param kind    - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_sync
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D, ::cudaMemcpyToArray,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpyArrayToArray, ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpyToArrayAsync, ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpyFromArrayAsync, ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyAtoH,
+ * ::cuMemcpyAtoD
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaMemcpyFromArray(void *dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, enum cudaMemcpyKind kind);
+
+/**
+ * \brief Copies data between host and device
+ *
+ * \deprecated
+ *
+ * Copies \p count bytes from the CUDA array \p src starting at \p hOffsetSrc
+ * rows and \p wOffsetSrc bytes from the upper left corner to the CUDA array
+ * \p dst starting at \p hOffsetDst rows and \p wOffsetDst bytes from the upper
+ * left corner, where \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * \param dst        - Destination memory address
+ * \param wOffsetDst - Destination starting X offset (columns in bytes)
+ * \param hOffsetDst - Destination starting Y offset (rows)
+ * \param src        - Source memory address
+ * \param wOffsetSrc - Source starting X offset (columns in bytes)
+ * \param hOffsetSrc - Source starting Y offset (rows)
+ * \param count      - Size in bytes to copy
+ * \param kind       - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D, ::cudaMemcpyToArray,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpyFromArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpyToArrayAsync, ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpyFromArrayAsync, ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyAtoA
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaMemcpyArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t count, enum cudaMemcpyKind kind __dv(cudaMemcpyDeviceToDevice));
+
+/**
+ * \brief Copies data between host and device
+ *
+ * \deprecated
+ *
+ * Copies \p count bytes from the memory area pointed to by \p src to the
+ * CUDA array \p dst starting at \p hOffset rows and \p wOffset bytes from
+ * the upper left corner, where \p kind specifies the
+ * direction of the copy, and must be one of ::cudaMemcpyHostToHost,
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * ::cudaMemcpyToArrayAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If \p
+ * kind is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and \p stream
+ * is non-zero, the copy may overlap with operations in other streams.
+ *
+ * \param dst     - Destination memory address
+ * \param wOffset - Destination starting X offset (columns in bytes)
+ * \param hOffset - Destination starting Y offset (rows)
+ * \param src     - Source memory address
+ * \param count   - Size in bytes to copy
+ * \param kind    - Type of transfer
+ * \param stream  - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D, ::cudaMemcpyToArray,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpyFromArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpyArrayToArray, ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpyFromArrayAsync, ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyHtoAAsync,
+ * ::cuMemcpy2DAsync
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaMemcpyToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Copies data between host and device
+ *
+ * \deprecated
+ *
+ * Copies \p count bytes from the CUDA array \p src starting at \p hOffset rows
+ * and \p wOffset bytes from the upper left corner to the memory area pointed to
+ * by \p dst, where \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * ::cudaMemcpyFromArrayAsync() is asynchronous with respect to the host, so
+ * the call may return before the copy is complete. The copy can optionally
+ * be associated to a stream by passing a non-zero \p stream argument. If \p
+ * kind is ::cudaMemcpyHostToDevice or ::cudaMemcpyDeviceToHost and \p stream
+ * is non-zero, the copy may overlap with operations in other streams.
+ *
+ * \param dst     - Destination memory address
+ * \param src     - Source memory address
+ * \param wOffset - Source starting X offset (columns in bytes)
+ * \param hOffset - Source starting Y offset (rows)
+ * \param count   - Size in bytes to copy
+ * \param kind    - Type of transfer
+ * \param stream  - Stream identifier
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidMemcpyDirection
+ * \notefnerr
+ * \note_async
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaMemcpy, ::cudaMemcpy2D, ::cudaMemcpyToArray,
+ * ::cudaMemcpy2DToArray, ::cudaMemcpyFromArray, ::cudaMemcpy2DFromArray,
+ * ::cudaMemcpyArrayToArray, ::cudaMemcpy2DArrayToArray, ::cudaMemcpyToSymbol,
+ * ::cudaMemcpyFromSymbol, ::cudaMemcpyAsync, ::cudaMemcpy2DAsync,
+ * ::cudaMemcpyToArrayAsync, ::cudaMemcpy2DToArrayAsync,
+ * ::cudaMemcpy2DFromArrayAsync,
+ * ::cudaMemcpyToSymbolAsync, ::cudaMemcpyFromSymbolAsync,
+ * ::cuMemcpyAtoHAsync,
+ * ::cuMemcpy2DAsync
+ */
+extern __CUDA_DEPRECATED __host__ cudaError_t CUDARTAPI cudaMemcpyFromArrayAsync(void *dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+
+/** @} */ /* END CUDART_MEMORY_DEPRECATED */
+
+/**
+ * \defgroup CUDART_MEMORY_POOLS Stream Ordered Memory Allocator 
+ *
+ * ___MANBRIEF___ Functions for performing allocation and free operations in stream order.
+ *                Functions for controlling the behavior of the underlying allocator.
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ * 
+ *
+ * @{
+ *
+ * \section CUDART_MEMORY_POOLS_overview overview
+ *
+ * The asynchronous allocator allows the user to allocate and free in stream order.
+ * All asynchronous accesses of the allocation must happen between
+ * the stream executions of the allocation and the free. If the memory is accessed
+ * outside of the promised stream order, a use before allocation / use after free error
+ * will cause undefined behavior.
+ *
+ * The allocator is free to reallocate the memory as long as it can guarantee
+ * that compliant memory accesses will not overlap temporally.
+ * The allocator may refer to internal stream ordering as well as inter-stream dependencies
+ * (such as CUDA events and null stream dependencies) when establishing the temporal guarantee.
+ * The allocator may also insert inter-stream dependencies to establish the temporal guarantee.
+ *
+ * \section CUDART_MEMORY_POOLS_support Supported Platforms
+ *
+ * Whether or not a device supports the integrated stream ordered memory allocator
+ * may be queried by calling ::cudaDeviceGetAttribute() with the device attribute
+ * ::cudaDevAttrMemoryPoolsSupported.
+ */
+
+/**
+ * \brief Allocates memory with stream ordered semantics
+ *
+ * Inserts an allocation operation into \p hStream.
+ * A pointer to the allocated memory is returned immediately in *dptr.
+ * The allocation must not be accessed until the the allocation operation completes.
+ * The allocation comes from the memory pool associated with the stream's device.
+ *
+ * \note The default memory pool of a device contains device memory from that device.
+ * \note Basic stream ordering allows future work submitted into the same stream to use the allocation.
+ *       Stream query, stream synchronize, and CUDA events can be used to guarantee that the allocation
+ *       operation completes before work submitted in a separate stream runs.
+ * \note During stream capture, this function results in the creation of an allocation node.  In this case,
+ *       the allocation is owned by the graph instead of the memory pool. The memory pool's properties
+ *       are used to set the node's creation parameters.
+ *
+ * \param[out] devPtr  - Returned device pointer
+ * \param[in] size     - Number of bytes to allocate
+ * \param[in] hStream  - The stream establishing the stream ordering contract and the memory pool to allocate from
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorOutOfMemory,
+ * \notefnerr
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cuMemAllocAsync,
+ * \ref ::cudaMallocAsync(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream)  "cudaMallocAsync (C++ API)", 
+ * ::cudaMallocFromPoolAsync, ::cudaFreeAsync, ::cudaDeviceSetMemPool, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceGetMemPool, ::cudaMemPoolSetAccess, ::cudaMemPoolSetAttribute, ::cudaMemPoolGetAttribute
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMallocAsync(void **devPtr, size_t size, cudaStream_t hStream);
+
+/**
+ * \brief Frees memory with stream ordered semantics
+ *
+ * Inserts a free operation into \p hStream.
+ * The allocation must not be accessed after stream execution reaches the free.
+ * After this API returns, accessing the memory from any subsequent work launched on the GPU
+ * or querying its pointer attributes results in undefined behavior.
+ *
+ * \note During stream capture, this function results in the creation of a free node and
+ *       must therefore be passed the address of a graph allocation.
+ *
+ * \param dptr - memory to free
+ * \param hStream - The stream establishing the stream ordering promise
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported
+ * \notefnerr
+ * \note_null_stream
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cuMemFreeAsync, ::cudaMallocAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaFreeAsync(void *devPtr, cudaStream_t hStream);
+
+/**
+ * \brief Tries to release memory back to the OS
+ *
+ * Releases memory back to the OS until the pool contains fewer than minBytesToKeep
+ * reserved bytes, or there is no more memory that the allocator can safely release.
+ * The allocator cannot release OS allocations that back outstanding asynchronous allocations.
+ * The OS allocations may happen at different granularity from the user allocations.
+ *
+ * \note: Allocations that have not been freed count as outstanding.
+ * \note: Allocations that have been asynchronously freed but whose completion has
+ *        not been observed on the host (eg. by a synchronize) can count as outstanding.
+ *
+ * \param[in] pool           - The memory pool to trim
+ * \param[in] minBytesToKeep - If the pool has less than minBytesToKeep reserved,
+ * the TrimTo operation is a no-op.  Otherwise the pool will be guaranteed to have
+ * at least minBytesToKeep bytes reserved after the operation.
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_callback
+ *
+ * \sa ::cuMemPoolTrimTo, ::cudaMallocAsync, ::cudaFreeAsync, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceGetMemPool, ::cudaMemPoolCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolTrimTo(cudaMemPool_t memPool, size_t minBytesToKeep);
+
+/**
+ * \brief Sets attributes of a memory pool
+ *
+ * Supported attributes are:
+ * - ::cudaMemPoolAttrReleaseThreshold: (value type = cuuint64_t)
+ *                    Amount of reserved memory in bytes to hold onto before trying
+ *                    to release memory back to the OS. When more than the release
+ *                    threshold bytes of memory are held by the memory pool, the
+ *                    allocator will try to release memory back to the OS on the
+ *                    next call to stream, event or context synchronize. (default 0)
+ * - ::cudaMemPoolReuseFollowEventDependencies: (value type = int)
+ *                    Allow ::cudaMallocAsync to use memory asynchronously freed
+ *                    in another stream as long as a stream ordering dependency
+ *                    of the allocating stream on the free action exists.
+ *                    Cuda events and null stream interactions can create the required
+ *                    stream ordered dependencies. (default enabled)
+ * - ::cudaMemPoolReuseAllowOpportunistic: (value type = int)
+ *                    Allow reuse of already completed frees when there is no dependency
+ *                    between the free and allocation. (default enabled)
+ * - ::cudaMemPoolReuseAllowInternalDependencies: (value type = int)
+ *                    Allow ::cudaMallocAsync to insert new stream dependencies
+ *                    in order to establish the stream ordering required to reuse
+ *                    a piece of memory released by ::cudaFreeAsync (default enabled).
+ * - ::cudaMemPoolAttrReservedMemHigh: (value type = cuuint64_t)
+ *                    Reset the high watermark that tracks the amount of backing memory that was
+ *                    allocated for the memory pool. It is illegal to set this attribute to a non-zero value.
+ * - ::cudaMemPoolAttrUsedMemHigh: (value type = cuuint64_t)
+ *                    Reset the high watermark that tracks the amount of used memory that was
+ *                    allocated for the memory pool. It is illegal to set this attribute to a non-zero value.
+ *
+ * \param[in] pool  - The memory pool to modify
+ * \param[in] attr  - The attribute to modify
+ * \param[in] value - Pointer to the value to assign
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_callback
+ *
+ * \sa ::cuMemPoolSetAttribute, ::cudaMallocAsync, ::cudaFreeAsync, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceGetMemPool, ::cudaMemPoolCreate
+
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolSetAttribute(cudaMemPool_t memPool, enum cudaMemPoolAttr attr, void *value );
+
+/**
+ * \brief Gets attributes of a memory pool
+ *
+ * Supported attributes are:
+ * - ::cudaMemPoolAttrReleaseThreshold: (value type = cuuint64_t)
+ *                    Amount of reserved memory in bytes to hold onto before trying
+ *                    to release memory back to the OS. When more than the release
+ *                    threshold bytes of memory are held by the memory pool, the
+ *                    allocator will try to release memory back to the OS on the
+ *                    next call to stream, event or context synchronize. (default 0)
+ * - ::cudaMemPoolReuseFollowEventDependencies: (value type = int)
+ *                    Allow ::cudaMallocAsync to use memory asynchronously freed
+ *                    in another stream as long as a stream ordering dependency
+ *                    of the allocating stream on the free action exists.
+ *                    Cuda events and null stream interactions can create the required
+ *                    stream ordered dependencies. (default enabled)
+ * - ::cudaMemPoolReuseAllowOpportunistic: (value type = int)
+ *                    Allow reuse of already completed frees when there is no dependency
+ *                    between the free and allocation. (default enabled)
+ * - ::cudaMemPoolReuseAllowInternalDependencies: (value type = int)
+ *                    Allow ::cudaMallocAsync to insert new stream dependencies
+ *                    in order to establish the stream ordering required to reuse
+ *                    a piece of memory released by ::cudaFreeAsync (default enabled).
+ * - ::cudaMemPoolAttrReservedMemCurrent: (value type = cuuint64_t)
+ *                    Amount of backing memory currently allocated for the mempool.
+ * - ::cudaMemPoolAttrReservedMemHigh: (value type = cuuint64_t)
+ *                    High watermark of backing memory allocated for the mempool since
+ *                    the last time it was reset.
+ * - ::cudaMemPoolAttrUsedMemCurrent: (value type = cuuint64_t)
+ *                    Amount of memory from the pool that is currently in use by the application.
+ * - ::cudaMemPoolAttrUsedMemHigh: (value type = cuuint64_t)
+ *                    High watermark of the amount of memory from the pool that was in use by the
+ *                    application since the last time it was reset.
+ *
+ * \param[in] pool  - The memory pool to get attributes of 
+ * \param[in] attr  - The attribute to get
+ * \param[in] value - Retrieved value 
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_callback
+ *
+ * \sa ::cuMemPoolGetAttribute, ::cudaMallocAsync, ::cudaFreeAsync, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceGetMemPool, ::cudaMemPoolCreate
+
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolGetAttribute(cudaMemPool_t memPool, enum cudaMemPoolAttr attr, void *value );
+
+/**
+ * \brief Controls visibility of pools between devices
+ *
+ * \param[in] pool  - The pool being modified
+ * \param[in] map   - Array of access descriptors. Each descriptor instructs the access to enable for a single gpu
+ * \param[in] count - Number of descriptors in the map array.
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa ::cuMemPoolSetAccess, ::cudaMemPoolGetAccess, ::cudaMallocAsync, cudaFreeAsync
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolSetAccess(cudaMemPool_t memPool, const struct cudaMemAccessDesc *descList, size_t count);
+
+/**
+ * \brief Returns the accessibility of a pool from a device
+ *
+ * Returns the accessibility of the pool's memory from the specified location.
+ *
+ * \param[out] flags   - the accessibility of the pool from the specified location
+ * \param[in] memPool  - the pool being queried
+ * \param[in] location - the location accessing the pool
+ *
+ * \sa ::cuMemPoolGetAccess, ::cudaMemPoolSetAccess
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolGetAccess(enum cudaMemAccessFlags *flags, cudaMemPool_t memPool, struct cudaMemLocation *location);
+
+/**
+ * \brief Creates a memory pool
+ *
+ * Creates a CUDA memory pool and returns the handle in \p pool.  The \p poolProps determines
+ * the properties of the pool such as the backing device and IPC capabilities.
+ *
+ * To create a memory pool targeting a specific host NUMA node, applications must
+ * set ::cudaMemPoolProps::cudaMemLocation::type to ::cudaMemLocationTypeHostNuma and
+ * ::cudaMemPoolProps::cudaMemLocation::id must specify the NUMA ID of the host memory node.
+ * Specifying ::cudaMemLocationTypeHostNumaCurrent or ::cudaMemLocationTypeHost as the
+ * ::cudaMemPoolProps::cudaMemLocation::type will result in ::cudaErrorInvalidValue.
+* By default, the pool's memory will be accessible from the device it is allocated on.
+ * In the case of pools created with ::cudaMemLocationTypeHostNuma, their default accessibility
+ * will be from the host CPU.
+ * Applications can control the maximum size of the pool by specifying a non-zero value for ::cudaMemPoolProps::maxSize.
+ * If set to 0, the maximum size of the pool will default to a system dependent value.
+ *
+ * Applications that intend to use ::CU_MEM_HANDLE_TYPE_FABRIC based memory sharing must ensure:
+ * (1) `nvidia-caps-imex-channels` character device is created by the driver and is listed under /proc/devices 
+ * (2) have at least one IMEX channel file accessible by the user launching the application.
+ *
+ * When exporter and importer CUDA processes have been granted access to the same IMEX channel, they can securely
+ * share memory.
+ *
+ * The IMEX channel security model works on a per user basis. Which means all processes under a user can share
+ * memory if the user has access to a valid IMEX channel. When multi-user isolation is desired, a separate IMEX
+ * channel is required for each user.
+ *
+ * These channel files exist in /dev/nvidia-caps-imex-channels/channel* and can be created using standard OS
+ * native calls like mknod on Linux. For example: To create channel0 with the major number from /proc/devices
+ * users can execute the following command: `mknod /dev/nvidia-caps-imex-channels/channel0 c <major number> 0`
+ *
+ * \note Specifying cudaMemHandleTypeNone creates a memory pool that will not support IPC.
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported
+ *
+ * \sa ::cuMemPoolCreate, ::cudaDeviceSetMemPool, ::cudaMallocFromPoolAsync, ::cudaMemPoolExportToShareableHandle, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceGetMemPool
+
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolCreate(cudaMemPool_t *memPool, const struct cudaMemPoolProps *poolProps);
+
+/**
+ * \brief Destroys the specified memory pool 
+ *
+ * If any pointers obtained from this pool haven't been freed or
+ * the pool has free operations that haven't completed
+ * when ::cudaMemPoolDestroy is invoked, the function will return immediately and the
+ * resources associated with the pool will be released automatically
+ * once there are no more outstanding allocations.
+ *
+ * Destroying the current mempool of a device sets the default mempool of
+ * that device as the current mempool for that device.
+ *
+ * \note A device's default memory pool cannot be destroyed.
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa cuMemPoolDestroy, ::cudaFreeAsync, ::cudaDeviceSetMemPool, ::cudaDeviceGetDefaultMemPool, ::cudaDeviceGetMemPool, ::cudaMemPoolCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolDestroy(cudaMemPool_t memPool);
+
+/**
+ * \brief Allocates memory from a specified pool with stream ordered semantics.
+ *
+ * Inserts an allocation operation into \p hStream.
+ * A pointer to the allocated memory is returned immediately in *dptr.
+ * The allocation must not be accessed until the the allocation operation completes.
+ * The allocation comes from the specified memory pool.
+ *
+ * \note
+ *    -  The specified memory pool may be from a device different than that of the specified \p hStream.
+ *
+ *    -  Basic stream ordering allows future work submitted into the same stream to use the allocation.
+ *       Stream query, stream synchronize, and CUDA events can be used to guarantee that the allocation
+ *       operation completes before work submitted in a separate stream runs.
+ *
+ * \note During stream capture, this function results in the creation of an allocation node.  In this case,
+ *       the allocation is owned by the graph instead of the memory pool. The memory pool's properties
+ *       are used to set the node's creation parameters.
+ *
+ * \param[out] ptr     - Returned device pointer
+ * \param[in] bytesize - Number of bytes to allocate
+ * \param[in] memPool  - The pool to allocate from
+ * \param[in] stream   - The stream establishing the stream ordering semantic
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorOutOfMemory
+ *
+ * \sa ::cuMemAllocFromPoolAsync,
+ * \ref ::cudaMallocAsync(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream)  "cudaMallocAsync (C++ API)", 
+ * ::cudaMallocAsync, ::cudaFreeAsync, ::cudaDeviceGetDefaultMemPool, ::cudaMemPoolCreate, ::cudaMemPoolSetAccess, ::cudaMemPoolSetAttribute
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMallocFromPoolAsync(void **ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream);
+
+/**
+ * \brief Exports a memory pool to the requested handle type.
+ *
+ * Given an IPC capable mempool, create an OS handle to share the pool with another process.
+ * A recipient process can convert the shareable handle into a mempool with ::cudaMemPoolImportFromShareableHandle.
+ * Individual pointers can then be shared with the ::cudaMemPoolExportPointer and ::cudaMemPoolImportPointer APIs.
+ * The implementation of what the shareable handle is and how it can be transferred is defined by the requested
+ * handle type.
+ *
+ * \note: To create an IPC capable mempool, create a mempool with a CUmemAllocationHandleType other than cudaMemHandleTypeNone.
+ *
+ * \param[out] handle_out  - pointer to the location in which to store the requested handle 
+ * \param[in] pool         - pool to export
+ * \param[in] handleType   - the type of handle to create
+ * \param[in] flags        - must be 0
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorOutOfMemory
+ *
+ * \sa ::cuMemPoolExportToShareableHandle, ::cudaMemPoolImportFromShareableHandle, ::cudaMemPoolExportPointer, ::cudaMemPoolImportPointer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolExportToShareableHandle(
+    void                            *shareableHandle,
+    cudaMemPool_t                    memPool,
+    enum cudaMemAllocationHandleType handleType,
+    unsigned int                     flags);
+
+/**
+ * \brief imports a memory pool from a shared handle.
+ *
+ * Specific allocations can be imported from the imported pool with ::cudaMemPoolImportPointer.
+ *
+ * \note Imported memory pools do not support creating new allocations.
+ *       As such imported memory pools may not be used in ::cudaDeviceSetMemPool
+ *       or ::cudaMallocFromPoolAsync calls.
+ *
+ * \param[out] pool_out    - Returned memory pool
+ * \param[in] handle       - OS handle of the pool to open
+ * \param[in] handleType   - The type of handle being imported
+ * \param[in] flags        - must be 0
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorOutOfMemory
+ *
+ * \sa ::cuMemPoolImportFromShareableHandle, ::cudaMemPoolExportToShareableHandle, ::cudaMemPoolExportPointer, ::cudaMemPoolImportPointer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolImportFromShareableHandle(
+    cudaMemPool_t                   *memPool,
+    void                            *shareableHandle,
+    enum cudaMemAllocationHandleType handleType,
+    unsigned int                     flags);
+
+/**
+ * \brief Export data to share a memory pool allocation between processes.
+ *
+ * Constructs \p shareData_out for sharing a specific allocation from an already shared memory pool.
+ * The recipient process can import the allocation with the ::cudaMemPoolImportPointer api.
+ * The data is not a handle and may be shared through any IPC mechanism.
+ *
+ * \param[out] shareData_out - Returned export data
+ * \param[in] ptr            - pointer to memory being exported
+ *
+ * \returns
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorOutOfMemory
+ *
+ * \sa ::cuMemPoolExportPointer, ::cudaMemPoolExportToShareableHandle, ::cudaMemPoolImportFromShareableHandle, ::cudaMemPoolImportPointer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolExportPointer(struct cudaMemPoolPtrExportData *exportData, void *ptr);
+
+/**
+ * \brief Import a memory pool allocation from another process.
+ *
+ * Returns in \p ptr_out a pointer to the imported memory.
+ * The imported memory must not be accessed before the allocation operation completes
+ * in the exporting process. The imported memory must be freed from all importing processes before
+ * being freed in the exporting process. The pointer may be freed with cudaFree
+ * or cudaFreeAsync.  If ::cudaFreeAsync is used, the free must be completed
+ * on the importing process before the free operation on the exporting process.
+ *
+ * \note The ::cudaFreeAsync api may be used in the exporting process before
+ *       the ::cudaFreeAsync operation completes in its stream as long as the
+ *       ::cudaFreeAsync in the exporting process specifies a stream with
+ *       a stream dependency on the importing process's ::cudaFreeAsync.
+ *
+ * \param[out] ptr_out  - pointer to imported memory
+ * \param[in] pool      - pool from which to import
+ * \param[in] shareData - data specifying the memory to import
+ *
+ * \returns
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_INITIALIZED,
+ * ::CUDA_ERROR_OUT_OF_MEMORY
+ *
+ * \sa ::cuMemPoolImportPointer, ::cudaMemPoolExportToShareableHandle, ::cudaMemPoolImportFromShareableHandle, ::cudaMemPoolExportPointer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaMemPoolImportPointer(void **ptr, cudaMemPool_t memPool, struct cudaMemPoolPtrExportData *exportData);
+
+/** @} */ /* END CUDART_MEMORY_POOLS */
+
+/**
+ * \defgroup CUDART_UNIFIED Unified Addressing
+ *
+ * ___MANBRIEF___ unified addressing functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the unified addressing functions of the CUDA 
+ * runtime application programming interface.
+ *
+ * @{
+ *
+ * \section CUDART_UNIFIED_overview Overview
+ *
+ * CUDA devices can share a unified address space with the host.  
+ * For these devices there is no distinction between a device
+ * pointer and a host pointer -- the same pointer value may be 
+ * used to access memory from the host program and from a kernel 
+ * running on the device (with exceptions enumerated below).
+ *
+ * \section CUDART_UNIFIED_support Supported Platforms
+ * 
+ * Whether or not a device supports unified addressing may be 
+ * queried by calling ::cudaGetDeviceProperties() with the device 
+ * property ::cudaDeviceProp::unifiedAddressing.
+ *
+ * Unified addressing is automatically enabled in 64-bit processes .
+ *
+ * \section CUDART_UNIFIED_lookup Looking Up Information from Pointer Values
+ *
+ * It is possible to look up information about the memory which backs a 
+ * pointer value.  For instance, one may want to know if a pointer points
+ * to host or device memory.  As another example, in the case of device 
+ * memory, one may want to know on which CUDA device the memory 
+ * resides.  These properties may be queried using the function 
+ * ::cudaPointerGetAttributes()
+ *
+ * Since pointers are unique, it is not necessary to specify information
+ * about the pointers specified to ::cudaMemcpy() and other copy functions.  
+ * The copy direction ::cudaMemcpyDefault may be used to specify that the 
+ * CUDA runtime should infer the location of the pointer from its value.
+ *
+ * \section CUDART_UNIFIED_automaphost Automatic Mapping of Host Allocated Host Memory
+ *
+ * All host memory allocated through all devices using ::cudaMallocHost() and
+ * ::cudaHostAlloc() is always directly accessible from all devices that 
+ * support unified addressing.  This is the case regardless of whether or 
+ * not the flags ::cudaHostAllocPortable and ::cudaHostAllocMapped are 
+ * specified.
+ *
+ * The pointer value through which allocated host memory may be accessed 
+ * in kernels on all devices that support unified addressing is the same 
+ * as the pointer value through which that memory is accessed on the host.
+ * It is not necessary to call ::cudaHostGetDevicePointer() to get the device 
+ * pointer for these allocations.  
+ *
+ * Note that this is not the case for memory allocated using the flag
+ * ::cudaHostAllocWriteCombined, as discussed below.
+ *
+ * \section CUDART_UNIFIED_autopeerregister Direct Access of Peer Memory
+ 
+ * Upon enabling direct access from a device that supports unified addressing 
+ * to another peer device that supports unified addressing using 
+ * ::cudaDeviceEnablePeerAccess() all memory allocated in the peer device using 
+ * ::cudaMalloc() and ::cudaMallocPitch() will immediately be accessible 
+ * by the current device.  The device pointer value through 
+ * which any peer's memory may be accessed in the current device 
+ * is the same pointer value through which that memory may be 
+ * accessed from the peer device. 
+ *
+ * \section CUDART_UNIFIED_exceptions Exceptions, Disjoint Addressing
+ * 
+ * Not all memory may be accessed on devices through the same pointer
+ * value through which they are accessed on the host.  These exceptions
+ * are host memory registered using ::cudaHostRegister() and host memory
+ * allocated using the flag ::cudaHostAllocWriteCombined.  For these 
+ * exceptions, there exists a distinct host and device address for the
+ * memory.  The device address is guaranteed to not overlap any valid host
+ * pointer range and is guaranteed to have the same value across all devices
+ * that support unified addressing.  
+ * 
+ * This device address may be queried using ::cudaHostGetDevicePointer() 
+ * when a device using unified addressing is current.  Either the host 
+ * or the unified device pointer value may be used to refer to this memory 
+ * in ::cudaMemcpy() and similar functions using the ::cudaMemcpyDefault 
+ * memory direction.
+ *
+ */
+
+/**
+ * \brief Returns attributes about a specified pointer
+ *
+ * Returns in \p *attributes the attributes of the pointer \p ptr.
+ * If pointer was not allocated in, mapped by or registered with context
+ * supporting unified addressing ::cudaErrorInvalidValue is returned.
+ *
+ * \note In CUDA 11.0 forward passing host pointer will return ::cudaMemoryTypeUnregistered
+ * in ::cudaPointerAttributes::type and call will return ::cudaSuccess.
+ *
+ * The ::cudaPointerAttributes structure is defined as:
+ * \code
+    struct cudaPointerAttributes {
+        enum cudaMemoryType type;
+        int device;
+        void *devicePointer;
+        void *hostPointer;
+    }
+    \endcode
+ * In this structure, the individual fields mean
+ *
+ * - \ref ::cudaPointerAttributes::type identifies type of memory. It can be
+ *    ::cudaMemoryTypeUnregistered for unregistered host memory,
+ *    ::cudaMemoryTypeHost for registered host memory, ::cudaMemoryTypeDevice for device
+ *    memory or  ::cudaMemoryTypeManaged for managed memory.
+ *
+ * - \ref ::cudaPointerAttributes::device "device" is the device against which
+ *   \p ptr was allocated.  If \p ptr has memory type ::cudaMemoryTypeDevice
+ *   then this identifies the device on which the memory referred to by \p ptr
+ *   physically resides.  If \p ptr has memory type ::cudaMemoryTypeHost then this
+ *   identifies the device which was current when the allocation was made
+ *   (and if that device is deinitialized then this allocation will vanish
+ *   with that device's state).
+ *
+ * - \ref ::cudaPointerAttributes::devicePointer "devicePointer" is
+ *   the device pointer alias through which the memory referred to by \p ptr
+ *   may be accessed on the current device.
+ *   If the memory referred to by \p ptr cannot be accessed directly by the 
+ *   current device then this is NULL.  
+ *
+ * - \ref ::cudaPointerAttributes::hostPointer "hostPointer" is
+ *   the host pointer alias through which the memory referred to by \p ptr
+ *   may be accessed on the host.
+ *   If the memory referred to by \p ptr cannot be accessed directly by the
+ *   host then this is NULL.
+ *
+ * \param attributes - Attributes for the specified pointer
+ * \param ptr        - Pointer to get attributes for
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaGetDeviceCount, ::cudaGetDevice, ::cudaSetDevice,
+ * ::cudaChooseDevice,
+ * ::cudaInitDevice,
+ * ::cuPointerGetAttributes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaPointerGetAttributes(struct cudaPointerAttributes *attributes, const void *ptr);
+
+/** @} */ /* END CUDART_UNIFIED */
+
+/**
+ * \defgroup CUDART_PEER Peer Device Memory Access
+ *
+ * ___MANBRIEF___ peer device memory access functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the peer device memory access functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Queries if a device may directly access a peer device's memory.
+ *
+ * Returns in \p *canAccessPeer a value of 1 if device \p device is capable of
+ * directly accessing memory from \p peerDevice and 0 otherwise.  If direct
+ * access of \p peerDevice from \p device is possible, then access may be
+ * enabled by calling ::cudaDeviceEnablePeerAccess().
+ *
+ * \param canAccessPeer - Returned access capability
+ * \param device        - Device from which allocations on \p peerDevice are to
+ *                        be directly accessed.
+ * \param peerDevice    - Device on which the allocations to be directly accessed 
+ *                        by \p device reside.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceEnablePeerAccess,
+ * ::cudaDeviceDisablePeerAccess,
+ * ::cuDeviceCanAccessPeer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceCanAccessPeer(int *canAccessPeer, int device, int peerDevice);
+
+/**
+ * \brief Enables direct access to memory allocations on a peer device.
+ *
+ * On success, all allocations from \p peerDevice will immediately be accessible by
+ * the current device.  They will remain accessible until access is explicitly
+ * disabled using ::cudaDeviceDisablePeerAccess() or either device is reset using
+ * ::cudaDeviceReset().
+ *
+ * Note that access granted by this call is unidirectional and that in order to access
+ * memory on the current device from \p peerDevice, a separate symmetric call 
+ * to ::cudaDeviceEnablePeerAccess() is required.
+ *
+ * Note that there are both device-wide and system-wide limitations per system
+ * configuration, as noted in the CUDA Programming Guide under the section
+ * "Peer-to-Peer Memory Access".
+ *
+ * Returns ::cudaErrorInvalidDevice if ::cudaDeviceCanAccessPeer() indicates
+ * that the current device cannot directly access memory from \p peerDevice.
+ *
+ * Returns ::cudaErrorPeerAccessAlreadyEnabled if direct access of
+ * \p peerDevice from the current device has already been enabled.
+ *
+ * Returns ::cudaErrorInvalidValue if \p flags is not 0.
+ *
+ * \param peerDevice  - Peer device to enable direct access to from the current device
+ * \param flags       - Reserved for future use and must be set to 0
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorPeerAccessAlreadyEnabled,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceCanAccessPeer,
+ * ::cudaDeviceDisablePeerAccess,
+ * ::cuCtxEnablePeerAccess
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceEnablePeerAccess(int peerDevice, unsigned int flags);
+
+/**
+ * \brief Disables direct access to memory allocations on a peer device.
+ *
+ * Returns ::cudaErrorPeerAccessNotEnabled if direct access to memory on
+ * \p peerDevice has not yet been enabled from the current device.
+ *
+ * \param peerDevice - Peer device to disable direct access to
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorPeerAccessNotEnabled,
+ * ::cudaErrorInvalidDevice
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa ::cudaDeviceCanAccessPeer,
+ * ::cudaDeviceEnablePeerAccess,
+ * ::cuCtxDisablePeerAccess
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDeviceDisablePeerAccess(int peerDevice);
+
+/** @} */ /* END CUDART_PEER */
+
+/** \defgroup CUDART_OPENGL OpenGL Interoperability */
+
+/** \defgroup CUDART_OPENGL_DEPRECATED OpenGL Interoperability [DEPRECATED] */
+
+/** \defgroup CUDART_D3D9 Direct3D 9 Interoperability */
+
+/** \defgroup CUDART_D3D9_DEPRECATED Direct3D 9 Interoperability [DEPRECATED] */
+
+/** \defgroup CUDART_D3D10 Direct3D 10 Interoperability */
+
+/** \defgroup CUDART_D3D10_DEPRECATED Direct3D 10 Interoperability [DEPRECATED] */
+
+/** \defgroup CUDART_D3D11 Direct3D 11 Interoperability */
+
+/** \defgroup CUDART_D3D11_DEPRECATED Direct3D 11 Interoperability [DEPRECATED] */
+
+/** \defgroup CUDART_VDPAU VDPAU Interoperability */
+
+/** \defgroup CUDART_EGL EGL Interoperability */
+
+/**
+ * \defgroup CUDART_INTEROP Graphics Interoperability
+ *
+ * ___MANBRIEF___ graphics interoperability functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the graphics interoperability functions of the CUDA
+ * runtime application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Unregisters a graphics resource for access by CUDA
+ *
+ * Unregisters the graphics resource \p resource so it is not accessible by
+ * CUDA unless registered again.
+ *
+ * If \p resource is invalid then ::cudaErrorInvalidResourceHandle is
+ * returned.
+ *
+ * \param resource - Resource to unregister
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa
+ * ::cudaGraphicsD3D9RegisterResource,
+ * ::cudaGraphicsD3D10RegisterResource,
+ * ::cudaGraphicsD3D11RegisterResource,
+ * ::cudaGraphicsGLRegisterBuffer,
+ * ::cudaGraphicsGLRegisterImage,
+ * ::cuGraphicsUnregisterResource
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsUnregisterResource(cudaGraphicsResource_t resource);
+
+/**
+ * \brief Set usage flags for mapping a graphics resource
+ *
+ * Set \p flags for mapping the graphics resource \p resource.
+ *
+ * Changes to \p flags will take effect the next time \p resource is mapped.
+ * The \p flags argument may be any of the following:
+ * - ::cudaGraphicsMapFlagsNone: Specifies no hints about how \p resource will
+ *     be used. It is therefore assumed that CUDA may read from or write to \p resource.
+ * - ::cudaGraphicsMapFlagsReadOnly: Specifies that CUDA will not write to \p resource.
+ * - ::cudaGraphicsMapFlagsWriteDiscard: Specifies CUDA will not read from \p resource and will
+ *   write over the entire contents of \p resource, so none of the data
+ *   previously stored in \p resource will be preserved.
+ *
+ * If \p resource is presently mapped for access by CUDA then ::cudaErrorUnknown is returned.
+ * If \p flags is not one of the above values then ::cudaErrorInvalidValue is returned.
+ *
+ * \param resource - Registered resource to set flags for
+ * \param flags    - Parameters for resource mapping
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown,
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphicsMapResources,
+ * ::cuGraphicsResourceSetMapFlags
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsResourceSetMapFlags(cudaGraphicsResource_t resource, unsigned int flags);
+
+/**
+ * \brief Map graphics resources for access by CUDA
+ *
+ * Maps the \p count graphics resources in \p resources for access by CUDA.
+ *
+ * The resources in \p resources may be accessed by CUDA until they
+ * are unmapped. The graphics API from which \p resources were registered
+ * should not access any resources while they are mapped by CUDA. If an
+ * application does so, the results are undefined.
+ *
+ * This function provides the synchronization guarantee that any graphics calls
+ * issued before ::cudaGraphicsMapResources() will complete before any subsequent CUDA
+ * work issued in \p stream begins.
+ *
+ * If \p resources contains any duplicate entries then ::cudaErrorInvalidResourceHandle
+ * is returned. If any of \p resources are presently mapped for access by
+ * CUDA then ::cudaErrorUnknown is returned.
+ *
+ * \param count     - Number of resources to map
+ * \param resources - Resources to map for CUDA
+ * \param stream    - Stream for synchronization
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphicsResourceGetMappedPointer,
+ * ::cudaGraphicsSubResourceGetMappedArray,
+ * ::cudaGraphicsUnmapResources,
+ * ::cuGraphicsMapResources
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsMapResources(int count, cudaGraphicsResource_t *resources, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Unmap graphics resources.
+ *
+ * Unmaps the \p count graphics resources in \p resources.
+ *
+ * Once unmapped, the resources in \p resources may not be accessed by CUDA
+ * until they are mapped again.
+ *
+ * This function provides the synchronization guarantee that any CUDA work issued
+ * in \p stream before ::cudaGraphicsUnmapResources() will complete before any
+ * subsequently issued graphics work begins.
+ *
+ * If \p resources contains any duplicate entries then ::cudaErrorInvalidResourceHandle
+ * is returned. If any of \p resources are not presently mapped for access by
+ * CUDA then ::cudaErrorUnknown is returned.
+ *
+ * \param count     - Number of resources to unmap
+ * \param resources - Resources to unmap
+ * \param stream    - Stream for synchronization
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \note_null_stream
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphicsMapResources,
+ * ::cuGraphicsUnmapResources
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsUnmapResources(int count, cudaGraphicsResource_t *resources, cudaStream_t stream __dv(0));
+
+/**
+ * \brief Get an device pointer through which to access a mapped graphics resource.
+ *
+ * Returns in \p *devPtr a pointer through which the mapped graphics resource
+ * \p resource may be accessed.
+ * Returns in \p *size the size of the memory in bytes which may be accessed from that pointer.
+ * The value set in \p devPtr may change every time that \p resource is mapped.
+ *
+ * If \p resource is not a buffer then it cannot be accessed via a pointer and
+ * ::cudaErrorUnknown is returned.
+ * If \p resource is not mapped then ::cudaErrorUnknown is returned.
+ * *
+ * \param devPtr     - Returned pointer through which \p resource may be accessed
+ * \param size       - Returned size of the buffer accessible starting at \p *devPtr
+ * \param resource   - Mapped resource to access
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphicsMapResources,
+ * ::cudaGraphicsSubResourceGetMappedArray,
+ * ::cuGraphicsResourceGetMappedPointer
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsResourceGetMappedPointer(void **devPtr, size_t *size, cudaGraphicsResource_t resource);
+
+/**
+ * \brief Get an array through which to access a subresource of a mapped graphics resource.
+ *
+ * Returns in \p *array an array through which the subresource of the mapped
+ * graphics resource \p resource which corresponds to array index \p arrayIndex
+ * and mipmap level \p mipLevel may be accessed.  The value set in \p array may
+ * change every time that \p resource is mapped.
+ *
+ * If \p resource is not a texture then it cannot be accessed via an array and
+ * ::cudaErrorUnknown is returned.
+ * If \p arrayIndex is not a valid array index for \p resource then
+ * ::cudaErrorInvalidValue is returned.
+ * If \p mipLevel is not a valid mipmap level for \p resource then
+ * ::cudaErrorInvalidValue is returned.
+ * If \p resource is not mapped then ::cudaErrorUnknown is returned.
+ *
+ * \param array       - Returned array through which a subresource of \p resource may be accessed
+ * \param resource    - Mapped resource to access
+ * \param arrayIndex  - Array index for array textures or cubemap face
+ *                      index as defined by ::cudaGraphicsCubeFace for
+ *                      cubemap textures for the subresource to access
+ * \param mipLevel    - Mipmap level for the subresource to access
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphicsResourceGetMappedPointer,
+ * ::cuGraphicsSubResourceGetMappedArray
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsSubResourceGetMappedArray(cudaArray_t *array, cudaGraphicsResource_t resource, unsigned int arrayIndex, unsigned int mipLevel);
+
+/**
+ * \brief Get a mipmapped array through which to access a mapped graphics resource.
+ *
+ * Returns in \p *mipmappedArray a mipmapped array through which the mapped
+ * graphics resource \p resource may be accessed. The value set in \p mipmappedArray may
+ * change every time that \p resource is mapped.
+ *
+ * If \p resource is not a texture then it cannot be accessed via an array and
+ * ::cudaErrorUnknown is returned.
+ * If \p resource is not mapped then ::cudaErrorUnknown is returned.
+ *
+ * \param mipmappedArray - Returned mipmapped array through which \p resource may be accessed
+ * \param resource       - Mapped resource to access
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphicsResourceGetMappedPointer,
+ * ::cuGraphicsResourceGetMappedMipmappedArray
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsResourceGetMappedMipmappedArray(cudaMipmappedArray_t *mipmappedArray, cudaGraphicsResource_t resource);
+
+/** @} */ /* END CUDART_INTEROP */
+
+/**
+ * \defgroup CUDART_TEXTURE_OBJECT Texture Object Management
+ *
+ * ___MANBRIEF___ texture object management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the low level texture object management functions
+ * of the CUDA runtime application programming interface. The texture
+ * object API is only supported on devices of compute capability 3.0 or higher.
+ *
+ * @{
+ */
+
+/**
+ * \brief Get the channel descriptor of an array
+ *
+ * Returns in \p *desc the channel descriptor of the CUDA array \p array.
+ *
+ * \param desc  - Channel format
+ * \param array - Memory array on device
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa \ref ::cudaCreateChannelDesc(int, int, int, int, cudaChannelFormatKind) "cudaCreateChannelDesc (C API)",
+ * ::cudaCreateTextureObject, ::cudaCreateSurfaceObject
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetChannelDesc(struct cudaChannelFormatDesc *desc, cudaArray_const_t array);
+
+/**
+ * \brief Returns a channel descriptor using the specified format
+ *
+ * Returns a channel descriptor with format \p f and number of bits of each
+ * component \p x, \p y, \p z, and \p w.  The ::cudaChannelFormatDesc is
+ * defined as:
+ * \code
+  struct cudaChannelFormatDesc {
+    int x, y, z, w;
+    enum cudaChannelFormatKind f;
+  };
+ * \endcode
+ *
+ * where ::cudaChannelFormatKind is one of ::cudaChannelFormatKindSigned,
+ * ::cudaChannelFormatKindUnsigned, or ::cudaChannelFormatKindFloat.
+ *
+ * \param x - X component
+ * \param y - Y component
+ * \param z - Z component
+ * \param w - W component
+ * \param f - Channel format
+ *
+ * \return
+ * Channel descriptor with format \p f
+ *
+ * \sa \ref ::cudaCreateChannelDesc(void) "cudaCreateChannelDesc (C++ API)",
+ * ::cudaGetChannelDesc, ::cudaCreateTextureObject, ::cudaCreateSurfaceObject
+ */
+extern __host__ struct cudaChannelFormatDesc CUDARTAPI cudaCreateChannelDesc(int x, int y, int z, int w, enum cudaChannelFormatKind f);
+
+/**
+ * \brief Creates a texture object
+ *
+ * Creates a texture object and returns it in \p pTexObject. \p pResDesc describes
+ * the data to texture from. \p pTexDesc describes how the data should be sampled.
+ * \p pResViewDesc is an optional argument that specifies an alternate format for
+ * the data described by \p pResDesc, and also describes the subresource region
+ * to restrict access to when texturing. \p pResViewDesc can only be specified if
+ * the type of resource is a CUDA array or a CUDA mipmapped array not in a block
+ * compressed format.
+ *
+ * Texture objects are only supported on devices of compute capability 3.0 or higher.
+ * Additionally, a texture object is an opaque value, and, as such, should only be
+ * accessed through CUDA API calls.
+ *
+ * The ::cudaResourceDesc structure is defined as:
+ * \code
+        struct cudaResourceDesc {
+            enum cudaResourceType resType;
+            
+            union {
+                struct {
+                    cudaArray_t array;
+                } array;
+                struct {
+                    cudaMipmappedArray_t mipmap;
+                } mipmap;
+                struct {
+                    void *devPtr;
+                    struct cudaChannelFormatDesc desc;
+                    size_t sizeInBytes;
+                } linear;
+                struct {
+                    void *devPtr;
+                    struct cudaChannelFormatDesc desc;
+                    size_t width;
+                    size_t height;
+                    size_t pitchInBytes;
+                } pitch2D;
+            } res;
+        };
+ * \endcode
+ * where:
+ * - ::cudaResourceDesc::resType specifies the type of resource to texture from.
+ * CUresourceType is defined as:
+ * \code
+        enum cudaResourceType {
+            cudaResourceTypeArray          = 0x00,
+            cudaResourceTypeMipmappedArray = 0x01,
+            cudaResourceTypeLinear         = 0x02,
+            cudaResourceTypePitch2D        = 0x03
+        };
+ * \endcode
+ *
+ * \par
+ * If ::cudaResourceDesc::resType is set to ::cudaResourceTypeArray, ::cudaResourceDesc::res::array::array
+ * must be set to a valid CUDA array handle.
+ *
+ * \par
+ * If ::cudaResourceDesc::resType is set to ::cudaResourceTypeMipmappedArray, ::cudaResourceDesc::res::mipmap::mipmap
+ * must be set to a valid CUDA mipmapped array handle and ::cudaTextureDesc::normalizedCoords must be set to true.
+ *
+ * \par
+ * If ::cudaResourceDesc::resType is set to ::cudaResourceTypeLinear, ::cudaResourceDesc::res::linear::devPtr
+ * must be set to a valid device pointer, that is aligned to ::cudaDeviceProp::textureAlignment.
+ * ::cudaResourceDesc::res::linear::desc describes the format and the number of components per array element. ::cudaResourceDesc::res::linear::sizeInBytes
+ * specifies the size of the array in bytes. The total number of elements in the linear address range cannot exceed 
+ * ::cudaDeviceProp::maxTexture1DLinear. The number of elements is computed as (sizeInBytes / sizeof(desc)).
+ *
+ * \par
+ * If ::cudaResourceDesc::resType is set to ::cudaResourceTypePitch2D, ::cudaResourceDesc::res::pitch2D::devPtr
+ * must be set to a valid device pointer, that is aligned to ::cudaDeviceProp::textureAlignment.
+ * ::cudaResourceDesc::res::pitch2D::desc describes the format and the number of components per array element. ::cudaResourceDesc::res::pitch2D::width
+ * and ::cudaResourceDesc::res::pitch2D::height specify the width and height of the array in elements, and cannot exceed
+ * ::cudaDeviceProp::maxTexture2DLinear[0] and ::cudaDeviceProp::maxTexture2DLinear[1] respectively.
+ * ::cudaResourceDesc::res::pitch2D::pitchInBytes specifies the pitch between two rows in bytes and has to be aligned to 
+ * ::cudaDeviceProp::texturePitchAlignment. Pitch cannot exceed ::cudaDeviceProp::maxTexture2DLinear[2].
+ *
+ *
+ * The ::cudaTextureDesc struct is defined as
+ * \code
+        struct cudaTextureDesc {
+            enum cudaTextureAddressMode addressMode[3];
+            enum cudaTextureFilterMode  filterMode;
+            enum cudaTextureReadMode    readMode;
+            int                         sRGB;
+            float                       borderColor[4];
+            int                         normalizedCoords;
+            unsigned int                maxAnisotropy;
+            enum cudaTextureFilterMode  mipmapFilterMode;
+            float                       mipmapLevelBias;
+            float                       minMipmapLevelClamp;
+            float                       maxMipmapLevelClamp;
+            int                         disableTrilinearOptimization;
+            int                         seamlessCubemap;
+        };
+ * \endcode
+ * where
+ * - ::cudaTextureDesc::addressMode specifies the addressing mode for each dimension of the texture data. ::cudaTextureAddressMode is defined as:
+ *   \code
+        enum cudaTextureAddressMode {
+            cudaAddressModeWrap   = 0,
+            cudaAddressModeClamp  = 1,
+            cudaAddressModeMirror = 2,
+            cudaAddressModeBorder = 3
+        };
+ *   \endcode
+ *   This is ignored if ::cudaResourceDesc::resType is ::cudaResourceTypeLinear. Also, if ::cudaTextureDesc::normalizedCoords
+ *   is set to zero, ::cudaAddressModeWrap and ::cudaAddressModeMirror won't be supported and will be switched to ::cudaAddressModeClamp.
+ *
+ * - ::cudaTextureDesc::filterMode specifies the filtering mode to be used when fetching from the texture. ::cudaTextureFilterMode is defined as:
+ *   \code
+        enum cudaTextureFilterMode {
+            cudaFilterModePoint  = 0,
+            cudaFilterModeLinear = 1
+        };
+ *   \endcode
+ *   This is ignored if ::cudaResourceDesc::resType is ::cudaResourceTypeLinear.
+ *
+ * - ::cudaTextureDesc::readMode specifies whether integer data should be converted to floating point or not. ::cudaTextureReadMode is defined as:
+ *   \code
+        enum cudaTextureReadMode {
+            cudaReadModeElementType     = 0,
+            cudaReadModeNormalizedFloat = 1
+        };
+ *   \endcode
+ *   Note that this applies only to 8-bit and 16-bit integer formats. 32-bit integer format would not be promoted, regardless of 
+ *   whether or not this ::cudaTextureDesc::readMode is set ::cudaReadModeNormalizedFloat is specified.
+ *
+ * - ::cudaTextureDesc::sRGB specifies whether sRGB to linear conversion should be performed during texture fetch.
+ *
+ * - ::cudaTextureDesc::borderColor specifies the float values of color. where:
+ *   ::cudaTextureDesc::borderColor[0] contains value of 'R', 
+ *   ::cudaTextureDesc::borderColor[1] contains value of 'G',
+ *   ::cudaTextureDesc::borderColor[2] contains value of 'B', 
+ *   ::cudaTextureDesc::borderColor[3] contains value of 'A'
+ *   Note that application using integer border color values will need to <reinterpret_cast> these values to float.
+ *   The values are set only when the addressing mode specified by ::cudaTextureDesc::addressMode is cudaAddressModeBorder.
+ *
+ * - ::cudaTextureDesc::normalizedCoords specifies whether the texture coordinates will be normalized or not.
+ *
+ * - ::cudaTextureDesc::maxAnisotropy specifies the maximum anistropy ratio to be used when doing anisotropic filtering. This value will be
+ *   clamped to the range [1,16].
+ *
+ * - ::cudaTextureDesc::mipmapFilterMode specifies the filter mode when the calculated mipmap level lies between two defined mipmap levels.
+ *
+ * - ::cudaTextureDesc::mipmapLevelBias specifies the offset to be applied to the calculated mipmap level.
+ *
+ * - ::cudaTextureDesc::minMipmapLevelClamp specifies the lower end of the mipmap level range to clamp access to.
+ *
+ * - ::cudaTextureDesc::maxMipmapLevelClamp specifies the upper end of the mipmap level range to clamp access to.
+ *
+ * - ::cudaTextureDesc::disableTrilinearOptimization specifies whether the trilinear filtering optimizations will be disabled.
+ *
+ * - ::cudaTextureDesc::seamlessCubemap specifies whether seamless cube map filtering is enabled. This flag can only be specified if the 
+ *   underlying resource is a CUDA array or a CUDA mipmapped array that was created with the flag ::cudaArrayCubemap.
+ *   When seamless cube map filtering is enabled, texture address modes specified by ::cudaTextureDesc::addressMode are ignored.
+ *   Instead, if the ::cudaTextureDesc::filterMode is set to ::cudaFilterModePoint the address mode ::cudaAddressModeClamp will be applied for all dimensions.
+ *   If the ::cudaTextureDesc::filterMode is set to ::cudaFilterModeLinear seamless cube map filtering will be performed when sampling along the cube face borders.
+ *
+ * The ::cudaResourceViewDesc struct is defined as
+ * \code
+        struct cudaResourceViewDesc {
+            enum cudaResourceViewFormat format;
+            size_t                      width;
+            size_t                      height;
+            size_t                      depth;
+            unsigned int                firstMipmapLevel;
+            unsigned int                lastMipmapLevel;
+            unsigned int                firstLayer;
+            unsigned int                lastLayer;
+        };
+ * \endcode
+ * where:
+ * - ::cudaResourceViewDesc::format specifies how the data contained in the CUDA array or CUDA mipmapped array should
+ *   be interpreted. Note that this can incur a change in size of the texture data. If the resource view format is a block
+ *   compressed format, then the underlying CUDA array or CUDA mipmapped array has to have a 32-bit unsigned integer format
+ *   with 2 or 4 channels, depending on the block compressed format. For ex., BC1 and BC4 require the underlying CUDA array to have
+ *   a 32-bit unsigned int with 2 channels. The other BC formats require the underlying resource to have the same 32-bit unsigned int
+ *   format but with 4 channels.
+ *
+ * - ::cudaResourceViewDesc::width specifies the new width of the texture data. If the resource view format is a block
+ *   compressed format, this value has to be 4 times the original width of the resource. For non block compressed formats,
+ *   this value has to be equal to that of the original resource.
+ *
+ * - ::cudaResourceViewDesc::height specifies the new height of the texture data. If the resource view format is a block
+ *   compressed format, this value has to be 4 times the original height of the resource. For non block compressed formats,
+ *   this value has to be equal to that of the original resource.
+ *
+ * - ::cudaResourceViewDesc::depth specifies the new depth of the texture data. This value has to be equal to that of the
+ *   original resource.
+ *
+ * - ::cudaResourceViewDesc::firstMipmapLevel specifies the most detailed mipmap level. This will be the new mipmap level zero.
+ *   For non-mipmapped resources, this value has to be zero.::cudaTextureDesc::minMipmapLevelClamp and ::cudaTextureDesc::maxMipmapLevelClamp
+ *   will be relative to this value. For ex., if the firstMipmapLevel is set to 2, and a minMipmapLevelClamp of 1.2 is specified,
+ *   then the actual minimum mipmap level clamp will be 3.2.
+ *
+ * - ::cudaResourceViewDesc::lastMipmapLevel specifies the least detailed mipmap level. For non-mipmapped resources, this value
+ *   has to be zero.
+ *
+ * - ::cudaResourceViewDesc::firstLayer specifies the first layer index for layered textures. This will be the new layer zero.
+ *   For non-layered resources, this value has to be zero.
+ *
+ * - ::cudaResourceViewDesc::lastLayer specifies the last layer index for layered textures. For non-layered resources, 
+ *   this value has to be zero.
+ *
+ *
+ * \param pTexObject   - Texture object to create
+ * \param pResDesc     - Resource descriptor
+ * \param pTexDesc     - Texture descriptor
+ * \param pResViewDesc - Resource view descriptor
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDestroyTextureObject,
+ * ::cuTexObjectCreate
+ */
+
+extern __host__ cudaError_t CUDARTAPI cudaCreateTextureObject(cudaTextureObject_t *pTexObject, const struct cudaResourceDesc *pResDesc, const struct cudaTextureDesc *pTexDesc, const struct cudaResourceViewDesc *pResViewDesc);
+
+/**
+ * \brief Destroys a texture object
+ *
+ * Destroys the texture object specified by \p texObject.
+ *
+ * \param texObject - Texture object to destroy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa
+ * ::cudaCreateTextureObject,
+ * ::cuTexObjectDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDestroyTextureObject(cudaTextureObject_t texObject);
+
+/**
+ * \brief Returns a texture object's resource descriptor
+ *
+ * Returns the resource descriptor for the texture object specified by \p texObject.
+ *
+ * \param pResDesc  - Resource descriptor
+ * \param texObject - Texture object
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaCreateTextureObject,
+ * ::cuTexObjectGetResourceDesc
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetTextureObjectResourceDesc(struct cudaResourceDesc *pResDesc, cudaTextureObject_t texObject);
+
+/**
+ * \brief Returns a texture object's texture descriptor
+ *
+ * Returns the texture descriptor for the texture object specified by \p texObject.
+ *
+ * \param pTexDesc  - Texture descriptor
+ * \param texObject - Texture object
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaCreateTextureObject,
+ * ::cuTexObjectGetTextureDesc
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetTextureObjectTextureDesc(struct cudaTextureDesc *pTexDesc, cudaTextureObject_t texObject);
+
+/**
+ * \brief Returns a texture object's resource view descriptor
+ *
+ * Returns the resource view descriptor for the texture object specified by \p texObject.
+ * If no resource view was specified, ::cudaErrorInvalidValue is returned.
+ *
+ * \param pResViewDesc - Resource view descriptor
+ * \param texObject    - Texture object
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaCreateTextureObject,
+ * ::cuTexObjectGetResourceViewDesc
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetTextureObjectResourceViewDesc(struct cudaResourceViewDesc *pResViewDesc, cudaTextureObject_t texObject);
+
+/** @} */ /* END CUDART_TEXTURE_OBJECT */
+
+/**
+ * \defgroup CUDART_SURFACE_OBJECT Surface Object Management
+ *
+ * ___MANBRIEF___ surface object management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the low level texture object management functions
+ * of the CUDA runtime application programming interface. The surface object 
+ * API is only supported on devices of compute capability 3.0 or higher.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates a surface object
+ *
+ * Creates a surface object and returns it in \p pSurfObject. \p pResDesc describes
+ * the data to perform surface load/stores on. ::cudaResourceDesc::resType must be 
+ * ::cudaResourceTypeArray and  ::cudaResourceDesc::res::array::array
+ * must be set to a valid CUDA array handle.
+ *
+ * Surface objects are only supported on devices of compute capability 3.0 or higher.
+ * Additionally, a surface object is an opaque value, and, as such, should only be
+ * accessed through CUDA API calls.
+ *
+ * \param pSurfObject - Surface object to create
+ * \param pResDesc    - Resource descriptor
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidChannelDescriptor,
+ * ::cudaErrorInvalidResourceHandle
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDestroySurfaceObject,
+ * ::cuSurfObjectCreate
+ */
+
+extern __host__ cudaError_t CUDARTAPI cudaCreateSurfaceObject(cudaSurfaceObject_t *pSurfObject, const struct cudaResourceDesc *pResDesc);
+
+/**
+ * \brief Destroys a surface object
+ *
+ * Destroys the surface object specified by \p surfObject.
+ *
+ * \param surfObject - Surface object to destroy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa
+ * ::cudaCreateSurfaceObject,
+ * ::cuSurfObjectDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDestroySurfaceObject(cudaSurfaceObject_t surfObject);
+
+/**
+ * \brief Returns a surface object's resource descriptor
+ * Returns the resource descriptor for the surface object specified by \p surfObject.
+ *
+ * \param pResDesc   - Resource descriptor
+ * \param surfObject - Surface object
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaCreateSurfaceObject,
+ * ::cuSurfObjectGetResourceDesc
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGetSurfaceObjectResourceDesc(struct cudaResourceDesc *pResDesc, cudaSurfaceObject_t surfObject);
+
+/** @} */ /* END CUDART_SURFACE_OBJECT */
+
+/**
+ * \defgroup CUDART__VERSION Version Management
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the latest version of CUDA supported by the driver
+ *
+ * Returns in \p *driverVersion the latest version of CUDA supported by
+ * the driver. The version is returned as (1000 &times; major + 10 &times; minor).
+ * For example, CUDA 9.2 would be represented by 9020. If no driver is installed,
+ * then 0 is returned as the driver version.
+ *
+ * This function automatically returns ::cudaErrorInvalidValue
+ * if \p driverVersion is NULL.
+ *
+ * \param driverVersion - Returns the CUDA driver version.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaRuntimeGetVersion,
+ * ::cuDriverGetVersion
+ */
+extern __host__ cudaError_t CUDARTAPI cudaDriverGetVersion(int *driverVersion);
+
+/**
+ * \brief Returns the CUDA Runtime version
+ *
+ * Returns in \p *runtimeVersion the version number of the current CUDA
+ * Runtime instance. The version is returned as
+ * (1000 &times; major + 10 &times; minor). For example,
+ * CUDA 9.2 would be represented by 9020.
+ *
+ * As of CUDA 12.0, this function no longer initializes CUDA. The purpose
+ * of this API is solely to return a compile-time constant stating the
+ * CUDA Toolkit version in the above format.
+ *
+ * This function automatically returns ::cudaErrorInvalidValue if
+ * the \p runtimeVersion argument is NULL.
+ *
+ * \param runtimeVersion - Returns the CUDA Runtime version.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDriverGetVersion,
+ * ::cuDriverGetVersion
+ */
+extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaRuntimeGetVersion(int *runtimeVersion);
+
+/** @} */ /* END CUDART__VERSION */
+
+/**
+ * \defgroup CUDART_GRAPH Graph Management
+ *
+ * ___MANBRIEF___ graph management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the graph management functions of CUDA
+ * runtime application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Creates a graph
+ *
+ * Creates an empty graph, which is returned via \p pGraph.
+ *
+ * \param pGraph - Returns newly created graph
+ * \param flags   - Graph creation flags, must be 0
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode,
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphDestroy,
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphClone
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphCreate(cudaGraph_t *pGraph, unsigned int flags);
+
+/**
+ * \brief Creates a kernel execution node and adds it to a graph
+ *
+ * Creates a new kernel execution node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies and arguments specified in \p pNodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * The cudaKernelNodeParams structure is defined as:
+ *
+ * \code
+ *  struct cudaKernelNodeParams
+ *  {
+ *      void* func;
+ *      dim3 gridDim;
+ *      dim3 blockDim;
+ *      unsigned int sharedMemBytes;
+ *      void **kernelParams;
+ *      void **extra;
+ *  };
+ * \endcode
+ *
+ * When the graph is launched, the node will invoke kernel \p func on a (\p gridDim.x x
+ * \p gridDim.y x \p gridDim.z) grid of blocks. Each block contains
+ * (\p blockDim.x x \p blockDim.y x \p blockDim.z) threads.
+ *
+ * \p sharedMem sets the amount of dynamic shared memory that will be
+ * available to each thread block.
+ *
+ * Kernel parameters to \p func can be specified in one of two ways:
+ *
+ * 1) Kernel parameters can be specified via \p kernelParams. If the kernel has N
+ * parameters, then \p kernelParams needs to be an array of N pointers. Each pointer,
+ * from \p kernelParams[0] to \p kernelParams[N-1], points to the region of memory from which the actual
+ * parameter will be copied. The number of kernel parameters and their offsets and sizes do not need
+ * to be specified as that information is retrieved directly from the kernel's image.
+ *
+ * 2) Kernel parameters can also be packaged by the application into a single buffer that is passed in
+ * via \p extra. This places the burden on the application of knowing each kernel
+ * parameter's size and alignment/padding within the buffer. The \p extra parameter exists
+ * to allow this function to take additional less commonly used arguments. \p extra specifies
+ * a list of names of extra settings and their corresponding values. Each extra setting name is
+ * immediately followed by the corresponding value. The list must be terminated with either NULL or
+ * CU_LAUNCH_PARAM_END.
+ *
+ * - ::CU_LAUNCH_PARAM_END, which indicates the end of the \p extra
+ *   array;
+ * - ::CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next
+ *   value in \p extra will be a pointer to a buffer
+ *   containing all the kernel parameters for launching kernel
+ *   \p func;
+ * - ::CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next
+ *   value in \p extra will be a pointer to a size_t
+ *   containing the size of the buffer specified with
+ *   ::CU_LAUNCH_PARAM_BUFFER_POINTER;
+ *
+ * The error ::cudaErrorInvalidValue will be returned if kernel parameters are specified with both
+ * \p kernelParams and \p extra (i.e. both \p kernelParams and
+ * \p extra are non-NULL).
+ *
+ * The \p kernelParams or \p extra array, as well as the argument values it points to,
+ * are copied during this call.
+ *
+ * \note Kernels launched using graphs must not use texture and surface references. Reading or
+ *       writing through any texture or surface reference is undefined behavior.
+ *       This restriction does not apply to texture and surface objects.
+ *
+ * \param pGraphNode     - Returns newly created node
+ * \param graph          - Graph to which to add the node
+ * \param pDependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param pNodeParams      - Parameters for the GPU execution node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaLaunchKernel,
+ * ::cudaGraphKernelNodeGetParams,
+ * ::cudaGraphKernelNodeSetParams,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddKernelNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, const struct cudaKernelNodeParams *pNodeParams);
+
+/**
+ * \brief Returns a kernel node's parameters
+ *
+ * Returns the parameters of kernel node \p node in \p pNodeParams.
+ * The \p kernelParams or \p extra array returned in \p pNodeParams,
+ * as well as the argument values it points to, are owned by the node.
+ * This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cudaGraphKernelNodeSetParams to update the
+ * parameters of this node.
+ *
+ * The params will contain either \p kernelParams or \p extra,
+ * according to which of these was most recently set on the node.
+ *
+ * \param node        - Node to get the parameters for
+ * \param pNodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaLaunchKernel,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphKernelNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphKernelNodeGetParams(cudaGraphNode_t node, struct cudaKernelNodeParams *pNodeParams);
+
+/**
+ * \brief Sets a kernel node's parameters
+ *
+ * Sets the parameters of kernel node \p node to \p pNodeParams.
+ *
+ * \param node        - Node to set the parameters for
+ * \param pNodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorMemoryAllocation
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaLaunchKernel,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphKernelNodeGetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphKernelNodeSetParams(cudaGraphNode_t node, const struct cudaKernelNodeParams *pNodeParams);
+
+/**
+ * \brief Copies attributes from source node to destination node.
+ *
+ * Copies attributes from source node \p src to destination node \p dst.
+ * Both node must have the same context.
+ *
+ * \param[out] dst Destination node
+ * \param[in] src Source node
+ * For list of attributes see ::cudaKernelNodeAttrID
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidContext
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphKernelNodeCopyAttributes(
+        cudaGraphNode_t hSrc,
+        cudaGraphNode_t hDst);
+
+/**
+ * \brief Queries node attribute.
+ *
+ * Queries attribute \p attr from node \p hNode and stores it in corresponding
+ * member of \p value_out.
+ *
+ * \param[in] hNode
+ * \param[in] attr
+ * \param[out] value_out
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphKernelNodeGetAttribute(
+    cudaGraphNode_t hNode,
+    cudaKernelNodeAttrID attr,
+    cudaKernelNodeAttrValue *value_out);
+
+/**
+ * \brief Sets node attribute.
+ *
+ * Sets attribute \p attr on node \p hNode from corresponding attribute of
+ * \p value.
+ *
+ * \param[out] hNode
+ * \param[in] attr
+ * \param[out] value
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaAccessPolicyWindow
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphKernelNodeSetAttribute(
+    cudaGraphNode_t hNode,
+    cudaKernelNodeAttrID attr,
+    const cudaKernelNodeAttrValue *value);
+
+/**
+ * \brief Creates a memcpy node and adds it to a graph
+ *
+ * Creates a new memcpy node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will perform the memcpy described by \p pCopyParams.
+ * See ::cudaMemcpy3D() for a description of the structure and its restrictions.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * \param pGraphNode     - Returns newly created node
+ * \param graph          - Graph to which to add the node
+ * \param pDependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param pCopyParams      - Parameters for the memory copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaMemcpy3D,
+ * ::cudaGraphAddMemcpyNodeToSymbol,
+ * ::cudaGraphAddMemcpyNodeFromSymbol,
+ * ::cudaGraphAddMemcpyNode1D,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemsetNode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemcpyNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, const struct cudaMemcpy3DParms *pCopyParams);
+
+/**
+ * \brief Creates a memcpy node to copy to a symbol on the device and adds it to a graph
+ *
+ * Creates a new memcpy node to copy to \p symbol and adds it to \p graph with
+ * \p numDependencies dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p src to the memory area pointed to by \p offset bytes from the start
+ * of symbol \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of
+ * transfer is inferred from the pointer values. However, ::cudaMemcpyDefault
+ * is only allowed on systems that support unified virtual addressing.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param symbol          - Device symbol address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyToSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeFromSymbol,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemcpyNodeToSymbol(
+    cudaGraphNode_t *pGraphNode,
+    cudaGraph_t graph,
+    const cudaGraphNode_t *pDependencies,
+    size_t numDependencies,
+    const void* symbol,
+    const void* src,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Creates a memcpy node to copy from a symbol on the device and adds it to a graph
+ *
+ * Creates a new memcpy node to copy from \p symbol and adds it to \p graph with
+ * \p numDependencies dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p offset bytes from the start of symbol \p symbol to the memory area
+ *  pointed to by \p dst. The memory areas may not overlap. \p symbol is a variable
+ *  that resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of transfer
+ * is inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param dst             - Destination memory address
+ * \param symbol          - Device symbol address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyFromSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeToSymbol,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemcpyNodeFromSymbol(
+    cudaGraphNode_t* pGraphNode,
+    cudaGraph_t graph,
+    const cudaGraphNode_t* pDependencies,
+    size_t numDependencies,
+    void* dst,
+    const void* symbol,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Creates a 1D memcpy node and adds it to a graph
+ *
+ * Creates a new 1D memcpy node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory
+ * area pointed to by \p src to the memory area pointed to by \p dst, where
+ * \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing. Launching a
+ * memcpy node with dst and src pointers that do not match the direction of
+ * the copy results in an undefined behavior.
+ *
+ * Memcpy nodes have some additional restrictions with regards to managed memory, if the
+ * system contains at least one device which has a zero value for the device attribute
+ * ::cudaDevAttrConcurrentManagedAccess.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param dst             - Destination memory address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpy,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParams1D,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemcpyNode1D(
+    cudaGraphNode_t *pGraphNode,
+    cudaGraph_t graph,
+    const cudaGraphNode_t *pDependencies,
+    size_t numDependencies,
+    void* dst,
+    const void* src,
+    size_t count,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Returns a memcpy node's parameters
+ *
+ * Returns the parameters of memcpy node \p node in \p pNodeParams.
+ *
+ * \param node        - Node to get the parameters for
+ * \param pNodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpy3D,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphMemcpyNodeGetParams(cudaGraphNode_t node, struct cudaMemcpy3DParms *pNodeParams);
+
+/**
+ * \brief Sets a memcpy node's parameters
+ *
+ * Sets the parameters of memcpy node \p node to \p pNodeParams.
+ *
+ * \param node        - Node to set the parameters for
+ * \param pNodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaMemcpy3D,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParams1D,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphMemcpyNodeSetParams(cudaGraphNode_t node, const struct cudaMemcpy3DParms *pNodeParams);
+
+/**
+ * \brief Sets a memcpy node's parameters to copy to a symbol on the device
+ *
+ * Sets the parameters of memcpy node \p node to the copy described by the provided parameters.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p src to the memory area pointed to by \p offset bytes from the start
+ * of symbol \p symbol. The memory areas may not overlap. \p symbol is a variable that
+ * resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of
+ * transfer is inferred from the pointer values. However, ::cudaMemcpyDefault
+ * is only allowed on systems that support unified virtual addressing.
+ *
+ * \param node            - Node to set the parameters for
+ * \param symbol          - Device symbol address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyToSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphMemcpyNodeSetParamsToSymbol(
+    cudaGraphNode_t node,
+    const void* symbol,
+    const void* src,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Sets a memcpy node's parameters to copy from a symbol on the device
+ *
+ * Sets the parameters of memcpy node \p node to the copy described by the provided parameters.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory area
+ * pointed to by \p offset bytes from the start of symbol \p symbol to the memory area
+ *  pointed to by \p dst. The memory areas may not overlap. \p symbol is a variable
+ *  that resides in global or constant memory space. \p kind can be either
+ * ::cudaMemcpyDeviceToHost, ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault.
+ * Passing ::cudaMemcpyDefault is recommended, in which case the type of transfer
+ * is inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing.
+ *
+ * \param node            - Node to set the parameters for
+ * \param dst             - Destination memory address
+ * \param symbol          - Device symbol address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpyFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphMemcpyNodeSetParamsFromSymbol(
+    cudaGraphNode_t node,
+    void* dst,
+    const void* symbol,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Sets a memcpy node's parameters to perform a 1-dimensional copy
+ *
+ * Sets the parameters of memcpy node \p node to the copy described by the provided parameters.
+ *
+ * When the graph is launched, the node will copy \p count bytes from the memory
+ * area pointed to by \p src to the memory area pointed to by \p dst, where
+ * \p kind specifies the direction of the copy, and must be one of
+ * ::cudaMemcpyHostToHost, ::cudaMemcpyHostToDevice, ::cudaMemcpyDeviceToHost,
+ * ::cudaMemcpyDeviceToDevice, or ::cudaMemcpyDefault. Passing
+ * ::cudaMemcpyDefault is recommended, in which case the type of transfer is
+ * inferred from the pointer values. However, ::cudaMemcpyDefault is only
+ * allowed on systems that support unified virtual addressing. Launching a
+ * memcpy node with dst and src pointers that do not match the direction of
+ * the copy results in an undefined behavior.
+ *
+ * \param node            - Node to set the parameters for
+ * \param dst             - Destination memory address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemcpy,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeGetParams
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphMemcpyNodeSetParams1D(
+    cudaGraphNode_t node,
+    void* dst,
+    const void* src,
+    size_t count,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Creates a memset node and adds it to a graph
+ *
+ * Creates a new memset node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * The element size must be 1, 2, or 4 bytes.
+ * When the graph is launched, the node will perform the memset described by \p pMemsetParams.
+ *
+ * \param pGraphNode     - Returns newly created node
+ * \param graph          - Graph to which to add the node
+ * \param pDependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param pMemsetParams    - Parameters for the memory set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDevice
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaMemset2D,
+ * ::cudaGraphMemsetNodeGetParams,
+ * ::cudaGraphMemsetNodeSetParams,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemcpyNode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemsetNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, const struct cudaMemsetParams *pMemsetParams);
+
+/**
+ * \brief Returns a memset node's parameters
+ *
+ * Returns the parameters of memset node \p node in \p pNodeParams.
+ *
+ * \param node        - Node to get the parameters for
+ * \param pNodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaMemset2D,
+ * ::cudaGraphAddMemsetNode,
+ * ::cudaGraphMemsetNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphMemsetNodeGetParams(cudaGraphNode_t node, struct cudaMemsetParams *pNodeParams);
+
+/**
+ * \brief Sets a memset node's parameters
+ *
+ * Sets the parameters of memset node \p node to \p pNodeParams.
+ *
+ * \param node        - Node to set the parameters for
+ * \param pNodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaMemset2D,
+ * ::cudaGraphAddMemsetNode,
+ * ::cudaGraphMemsetNodeGetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphMemsetNodeSetParams(cudaGraphNode_t node, const struct cudaMemsetParams *pNodeParams);
+
+/**
+ * \brief Creates a host execution node and adds it to a graph
+ *
+ * Creates a new CPU execution node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies and arguments specified in \p pNodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * When the graph is launched, the node will invoke the specified CPU function.
+ * Host nodes are not supported under MPS with pre-Volta GPUs.
+ *
+ * \param pGraphNode     - Returns newly created node
+ * \param graph          - Graph to which to add the node
+ * \param pDependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param pNodeParams      - Parameters for the host node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaLaunchHostFunc,
+ * ::cudaGraphHostNodeGetParams,
+ * ::cudaGraphHostNodeSetParams,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddHostNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, const struct cudaHostNodeParams *pNodeParams);
+
+/**
+ * \brief Returns a host node's parameters
+ *
+ * Returns the parameters of host node \p node in \p pNodeParams.
+ *
+ * \param node        - Node to get the parameters for
+ * \param pNodeParams - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaLaunchHostFunc,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphHostNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphHostNodeGetParams(cudaGraphNode_t node, struct cudaHostNodeParams *pNodeParams);
+
+/**
+ * \brief Sets a host node's parameters
+ *
+ * Sets the parameters of host node \p node to \p nodeParams.
+ *
+ * \param node        - Node to set the parameters for
+ * \param pNodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaLaunchHostFunc,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphHostNodeGetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphHostNodeSetParams(cudaGraphNode_t node, const struct cudaHostNodeParams *pNodeParams);
+
+/**
+ * \brief Creates a child graph node and adds it to a graph
+ *
+ * Creates a new node which executes an embedded graph, and adds it to \p graph with
+ * \p numDependencies dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * If \p hGraph contains allocation or free nodes, this call will return an error.
+ *
+ * The node executes an embedded child graph. The child graph is cloned in this call.
+ *
+ * \param pGraphNode     - Returns newly created node
+ * \param graph          - Graph to which to add the node
+ * \param pDependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param childGraph      - The graph to clone into this node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphChildGraphNodeGetGraph,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode,
+ * ::cudaGraphClone
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddChildGraphNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, cudaGraph_t childGraph);
+
+/**
+ * \brief Gets a handle to the embedded graph of a child graph node
+ *
+ * Gets a handle to the embedded graph in a child graph node. This call
+ * does not clone the graph. Changes to the graph will be reflected in
+ * the node, and the node retains ownership of the graph.
+ *
+ * Allocation and free nodes cannot be added to the returned graph.
+ * Attempting to do so will return an error.
+ *
+ * \param node   - Node to get the embedded graph for
+ * \param pGraph - Location to store a handle to the graph
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphNodeFindInClone
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphChildGraphNodeGetGraph(cudaGraphNode_t node, cudaGraph_t *pGraph);
+
+/**
+ * \brief Creates an empty node and adds it to a graph
+ *
+ * Creates a new node which performs no operation, and adds it to \p graph with
+ * \p numDependencies dependencies specified via \p pDependencies.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p pGraphNode.
+ *
+ * An empty node performs no operation during execution, but can be used for
+ * transitive ordering. For example, a phased execution graph with 2 groups of n
+ * nodes with a barrier between them can be represented using an empty node and
+ * 2*n dependency edges, rather than no empty node and n^2 dependency edges.
+ *
+ * \param pGraphNode     - Returns newly created node
+ * \param graph          - Graph to which to add the node
+ * \param pDependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddEmptyNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies);
+
+/**
+ * \brief Creates an event record node and adds it to a graph
+ *
+ * Creates a new event record node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and event specified in \p event.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * Each launch of the graph will record \p event to capture execution of the
+ * node's dependencies.
+ *
+ * These nodes may not be used in loops or conditionals.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param event           - Event for the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphAddEventRecordNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, cudaEvent_t event);
+#endif
+
+/**
+ * \brief Returns the event associated with an event record node
+ *
+ * Returns the event of event record node \p hNode in \p event_out.
+ *
+ * \param hNode     - Node to get the event for
+ * \param event_out - Pointer to return the event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphEventRecordNodeSetEvent,
+ * ::cudaGraphEventWaitNodeGetEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphEventRecordNodeGetEvent(cudaGraphNode_t node, cudaEvent_t *event_out);
+#endif
+
+/**
+ * \brief Sets an event record node's event
+ *
+ * Sets the event of event record node \p hNode to \p event.
+ *
+ * \param hNode - Node to set the event for
+ * \param event - Event to use
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphEventRecordNodeGetEvent,
+ * ::cudaGraphEventWaitNodeSetEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphEventRecordNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event);
+#endif
+
+/**
+ * \brief Creates an event wait node and adds it to a graph
+ *
+ * Creates a new event wait node and adds it to \p hGraph with \p numDependencies
+ * dependencies specified via \p dependencies and event specified in \p event.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries.
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * The graph node will wait for all work captured in \p event.  See ::cuEventRecord()
+ * for details on what is captured by an event.  The synchronization will be performed
+ * efficiently on the device when applicable.  \p event may be from a different context
+ * or device than the launch stream.
+ *
+ * These nodes may not be used in loops or conditionals.
+ *
+ * \param phGraphNode     - Returns newly created node
+ * \param hGraph          - Graph to which to add the node
+ * \param dependencies    - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param event           - Event for the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphAddEventWaitNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, cudaEvent_t event);
+#endif
+
+/**
+ * \brief Returns the event associated with an event wait node
+ *
+ * Returns the event of event wait node \p hNode in \p event_out.
+ *
+ * \param hNode     - Node to get the event for
+ * \param event_out - Pointer to return the event
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphEventWaitNodeSetEvent,
+ * ::cudaGraphEventRecordNodeGetEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphEventWaitNodeGetEvent(cudaGraphNode_t node, cudaEvent_t *event_out);
+#endif
+
+/**
+ * \brief Sets an event wait node's event
+ *
+ * Sets the event of event wait node \p hNode to \p event.
+ *
+ * \param hNode - Node to set the event for
+ * \param event - Event to use
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphEventWaitNodeGetEvent,
+ * ::cudaGraphEventRecordNodeSetEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphEventWaitNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event);
+#endif
+
+/**
+ * \brief Creates an external semaphore signal node and adds it to a graph
+ *
+ * Creates a new external semaphore signal node and adds it to \p graph with \p
+ * numDependencies dependencies specified via \p dependencies and arguments specified
+ * in \p nodeParams. It is possible for \p numDependencies to be 0, in which case the
+ * node will be placed at the root of the graph. \p dependencies may not have any
+ * duplicate entries. A handle to the new node will be returned in \p pGraphNode.
+ *
+ * Performs a signal operation on a set of externally allocated semaphore objects
+ * when the node is launched.  The operation(s) will occur after all of the node's
+ * dependencies have completed.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphExternalSemaphoresSignalNodeGetParams,
+ * ::cudaGraphExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaImportExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddExternalSemaphoresSignalNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, const struct cudaExternalSemaphoreSignalNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Returns an external semaphore signal node's parameters
+ *
+ * Returns the parameters of an external semaphore signal node \p hNode in \p params_out.
+ * The \p extSemArray and \p paramsArray returned in \p params_out,
+ * are owned by the node.  This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cudaGraphExternalSemaphoresSignalNodeSetParams to update the
+ * parameters of this node.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param params_out - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaLaunchKernel,
+ * ::cudaGraphAddExternalSemaphoresSignalNode,
+ * ::cudaGraphExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphExternalSemaphoresSignalNodeGetParams(cudaGraphNode_t hNode, struct cudaExternalSemaphoreSignalNodeParams *params_out);
+#endif
+
+/**
+ * \brief Sets an external semaphore signal node's parameters
+ *
+ * Sets the parameters of an external semaphore signal node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresSignalNode,
+ * ::cudaGraphExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphExternalSemaphoresSignalNodeSetParams(cudaGraphNode_t hNode, const struct cudaExternalSemaphoreSignalNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Creates an external semaphore wait node and adds it to a graph
+ *
+ * Creates a new external semaphore wait node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p dependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p dependencies may not have any duplicate entries. A handle
+ * to the new node will be returned in \p pGraphNode.
+ *
+ * Performs a wait operation on a set of externally allocated semaphore objects
+ * when the node is launched.  The node's dependencies will not be launched until
+ * the wait operation has completed.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphExternalSemaphoresWaitNodeGetParams,
+ * ::cudaGraphExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresSignalNode,
+ * ::cudaImportExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddExternalSemaphoresWaitNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, const struct cudaExternalSemaphoreWaitNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Returns an external semaphore wait node's parameters
+ *
+ * Returns the parameters of an external semaphore wait node \p hNode in \p params_out.
+ * The \p extSemArray and \p paramsArray returned in \p params_out,
+ * are owned by the node.  This memory remains valid until the node is destroyed or its
+ * parameters are modified, and should not be modified
+ * directly. Use ::cudaGraphExternalSemaphoresSignalNodeSetParams to update the
+ * parameters of this node.
+ *
+ * \param hNode      - Node to get the parameters for
+ * \param params_out - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaLaunchKernel,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaGraphExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphExternalSemaphoresWaitNodeGetParams(cudaGraphNode_t hNode, struct cudaExternalSemaphoreWaitNodeParams *params_out);
+#endif
+
+/**
+ * \brief Sets an external semaphore wait node's parameters
+ *
+ * Sets the parameters of an external semaphore wait node \p hNode to \p nodeParams.
+ *
+ * \param hNode      - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaGraphExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphExternalSemaphoresWaitNodeSetParams(cudaGraphNode_t hNode, const struct cudaExternalSemaphoreWaitNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Creates an allocation node and adds it to a graph
+ *
+ * Creates a new allocation node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies and arguments specified in \p nodeParams.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries. A handle
+ * to the new node will be returned in \p pGraphNode.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Parameters for the node
+ *
+ * When ::cudaGraphAddMemAllocNode creates an allocation node, it returns the address of the allocation in
+ * \p nodeParams.dptr.  The allocation's address remains fixed across instantiations and launches.
+ *
+ * If the allocation is freed in the same graph, by creating a free node using ::cudaGraphAddMemFreeNode,
+ * the allocation can be accessed by nodes ordered after the allocation node but before the free node.
+ * These allocations cannot be freed outside the owning graph, and they can only be freed once in the
+ * owning graph.
+ *
+ * If the allocation is not freed in the same graph, then it can be accessed not only by nodes in the
+ * graph which are ordered after the allocation node, but also by stream operations ordered after the
+ * graph's execution but before the allocation is freed.
+ *
+ * Allocations which are not freed in the same graph can be freed by:
+ * - passing the allocation to ::cudaMemFreeAsync or ::cudaMemFree;
+ * - launching a graph with a free node for that allocation; or
+ * - specifying ::cudaGraphInstantiateFlagAutoFreeOnLaunch during instantiation, which makes
+ *   each launch behave as though it called ::cudaMemFreeAsync for every unfreed allocation.
+ *
+ * It is not possible to free an allocation in both the owning graph and another graph.  If the allocation
+ * is freed in the same graph, a free node cannot be added to another graph.  If the allocation is freed
+ * in another graph, a free node can no longer be added to the owning graph.
+ *
+ * The following restrictions apply to graphs which contain allocation and/or memory free nodes:
+ * - Nodes and edges of the graph cannot be deleted.
+ * - The graph cannot be used in a child node.
+ * - Only one instantiation of the graph may exist at any point in time.
+ * - The graph cannot be cloned.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorOutOfMemory
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphAddMemFreeNode,
+ * ::cudaGraphMemAllocNodeGetParams,
+ * ::cudaDeviceGraphMemTrim,
+ * ::cudaDeviceGetGraphMemAttribute,
+ * ::cudaDeviceSetGraphMemAttribute,
+ * ::cudaMallocAsync,
+ * ::cudaFreeAsync,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphAddExternalSemaphoresSignalNode,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemAllocNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, struct cudaMemAllocNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Returns a memory alloc node's parameters
+ *
+ * Returns the parameters of a memory alloc node \p hNode in \p params_out.
+ * The \p poolProps and \p accessDescs returned in \p params_out, are owned by the
+ * node.  This memory remains valid until the node is destroyed.  The returned
+ * parameters must not be modified.
+ *
+ * \param node       - Node to get the parameters for
+ * \param params_out - Pointer to return the parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemAllocNode,
+ * ::cudaGraphMemFreeNodeGetParams
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaGraphMemAllocNodeGetParams(cudaGraphNode_t node, struct cudaMemAllocNodeParams *params_out);
+#endif
+
+/**
+ * \brief Creates a memory free node and adds it to a graph
+ *
+ * Creates a new memory free node and adds it to \p graph with \p numDependencies
+ * dependencies specified via \p pDependencies and address specified in \p dptr.
+ * It is possible for \p numDependencies to be 0, in which case the node will be placed
+ * at the root of the graph. \p pDependencies may not have any duplicate entries. A handle
+ * to the new node will be returned in \p pGraphNode.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param dptr            - Address of memory to free
+ *
+ * ::cudaGraphAddMemFreeNode will return ::cudaErrorInvalidValue if the user attempts to free:
+ * - an allocation twice in the same graph.
+ * - an address that was not returned by an allocation node.
+ * - an invalid address.
+ *
+ * The following restrictions apply to graphs which contain allocation and/or memory free nodes:
+ * - Nodes and edges of the graph cannot be deleted.
+ * - The graph cannot be used in a child node.
+ * - Only one instantiation of the graph may exist at any point in time.
+ * - The graph cannot be cloned.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorNotSupported,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorOutOfMemory
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphAddMemAllocNode,
+ * ::cudaGraphMemFreeNodeGetParams,
+ * ::cudaDeviceGraphMemTrim,
+ * ::cudaDeviceGetGraphMemAttribute,
+ * ::cudaDeviceSetGraphMemAttribute,
+ * ::cudaMallocAsync,
+ * ::cudaFreeAsync,
+ * ::cudaGraphCreate,
+ * ::cudaGraphDestroyNode,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphAddExternalSemaphoresSignalNode,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddMemFreeNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, void *dptr);
+#endif
+
+/**
+ * \brief Returns a memory free node's parameters
+ *
+ * Returns the address of a memory free node \p hNode in \p dptr_out.
+ *
+ * \param node     - Node to get the parameters for
+ * \param dptr_out - Pointer to return the device address
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemFreeNode,
+ * ::cudaGraphMemFreeNodeGetParams
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaGraphMemFreeNodeGetParams(cudaGraphNode_t node, void *dptr_out);
+#endif
+
+/**
+ * \brief Free unused memory that was cached on the specified device for use with graphs back to the OS.
+ *
+ * Blocks which are not in use by a graph that is either currently executing or scheduled to execute are
+ * freed back to the operating system.
+ *
+ * \param device - The device for which cached memory should be freed.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemAllocNode,
+ * ::cudaGraphAddMemFreeNode,
+ * ::cudaDeviceGetGraphMemAttribute,
+ * ::cudaDeviceSetGraphMemAttribute,
+ * ::cudaMallocAsync,
+ * ::cudaFreeAsync
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGraphMemTrim(int device);
+#endif
+
+/**
+ * \brief Query asynchronous allocation attributes related to graphs
+ *
+ * Valid attributes are:
+ *
+ * - ::cudaGraphMemAttrUsedMemCurrent: Amount of memory, in bytes, currently associated with graphs
+ * - ::cudaGraphMemAttrUsedMemHigh: High watermark of memory, in bytes, associated with graphs since the
+ *   last time it was reset.  High watermark can only be reset to zero.
+ * - ::cudaGraphMemAttrReservedMemCurrent: Amount of memory, in bytes, currently allocated for use by
+ *   the CUDA graphs asynchronous allocator.
+ * - ::cudaGraphMemAttrReservedMemHigh: High watermark of memory, in bytes, currently allocated for use by
+ *   the CUDA graphs asynchronous allocator.
+ *
+ * \param device - Specifies the scope of the query
+ * \param attr - attribute to get
+ * \param value - retrieved value
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceSetGraphMemAttribute,
+ * ::cudaGraphAddMemAllocNode,
+ * ::cudaGraphAddMemFreeNode,
+ * ::cudaDeviceGraphMemTrim,
+ * ::cudaMallocAsync,
+ * ::cudaFreeAsync
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaDeviceGetGraphMemAttribute(int device, enum cudaGraphMemAttributeType attr, void* value);
+#endif
+
+/**
+ * \brief Set asynchronous allocation attributes related to graphs
+ *
+ * Valid attributes are:
+ *
+ * - ::cudaGraphMemAttrUsedMemHigh: High watermark of memory, in bytes, associated with graphs since the
+ *   last time it was reset.  High watermark can only be reset to zero.
+ * - ::cudaGraphMemAttrReservedMemHigh: High watermark of memory, in bytes, currently allocated for use by
+ *   the CUDA graphs asynchronous allocator.
+ *
+ * \param device - Specifies the scope of the query
+ * \param attr - attribute to get
+ * \param value - pointer to value to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaDeviceGetGraphMemAttribute,
+ * ::cudaGraphAddMemAllocNode,
+ * ::cudaGraphAddMemFreeNode,
+ * ::cudaDeviceGraphMemTrim,
+ * ::cudaMallocAsync,
+ * ::cudaFreeAsync
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaDeviceSetGraphMemAttribute(int device, enum cudaGraphMemAttributeType attr, void* value);
+#endif
+
+/**
+ * \brief Clones a graph
+ *
+ * This function creates a copy of \p originalGraph and returns it in \p pGraphClone.
+ * All parameters are copied into the cloned graph. The original graph may be modified 
+ * after this call without affecting the clone.
+ *
+ * Child graph nodes in the original graph are recursively copied into the clone.
+ *
+ * \param pGraphClone  - Returns newly created cloned graph
+ * \param originalGraph - Graph to clone
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphCreate,
+ * ::cudaGraphNodeFindInClone
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphClone(cudaGraph_t *pGraphClone, cudaGraph_t originalGraph);
+
+/**
+ * \brief Finds a cloned version of a node
+ *
+ * This function returns the node in \p clonedGraph corresponding to \p originalNode 
+ * in the original graph.
+ *
+ * \p clonedGraph must have been cloned from \p originalGraph via ::cudaGraphClone. 
+ * \p originalNode must have been in \p originalGraph at the time of the call to 
+ * ::cudaGraphClone, and the corresponding cloned node in \p clonedGraph must not have 
+ * been removed. The cloned node is then returned via \p pClonedNode.
+ *
+ * \param pNode  - Returns handle to the cloned node
+ * \param originalNode - Handle to the original node
+ * \param clonedGraph - Cloned graph to query
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphClone
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeFindInClone(cudaGraphNode_t *pNode, cudaGraphNode_t originalNode, cudaGraph_t clonedGraph);
+
+/**
+ * \brief Returns a node's type
+ *
+ * Returns the node type of \p node in \p pType.
+ *
+ * \param node - Node to query
+ * \param pType  - Pointer to return the node type
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphChildGraphNodeGetGraph,
+ * ::cudaGraphKernelNodeGetParams,
+ * ::cudaGraphKernelNodeSetParams,
+ * ::cudaGraphHostNodeGetParams,
+ * ::cudaGraphHostNodeSetParams,
+ * ::cudaGraphMemcpyNodeGetParams,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemsetNodeGetParams,
+ * ::cudaGraphMemsetNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeGetType(cudaGraphNode_t node, enum cudaGraphNodeType *pType);
+
+/**
+ * \brief Returns a graph's nodes
+ *
+ * Returns a list of \p graph's nodes. \p nodes may be NULL, in which case this
+ * function will return the number of nodes in \p numNodes. Otherwise,
+ * \p numNodes entries will be filled in. If \p numNodes is higher than the actual
+ * number of nodes, the remaining entries in \p nodes will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p numNodes.
+ *
+ * \param graph    - Graph to query
+ * \param nodes    - Pointer to return the nodes
+ * \param numNodes - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphCreate,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphNodeGetType,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphGetNodes(cudaGraph_t graph, cudaGraphNode_t *nodes, size_t *numNodes);
+
+/**
+ * \brief Returns a graph's root nodes
+ *
+ * Returns a list of \p graph's root nodes. \p pRootNodes may be NULL, in which case this
+ * function will return the number of root nodes in \p pNumRootNodes. Otherwise,
+ * \p pNumRootNodes entries will be filled in. If \p pNumRootNodes is higher than the actual
+ * number of root nodes, the remaining entries in \p pRootNodes will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p pNumRootNodes.
+ *
+ * \param graph       - Graph to query
+ * \param pRootNodes    - Pointer to return the root nodes
+ * \param pNumRootNodes - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphCreate,
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphNodeGetType,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphGetRootNodes(cudaGraph_t graph, cudaGraphNode_t *pRootNodes, size_t *pNumRootNodes);
+
+/**
+ * \brief Returns a graph's dependency edges
+ *
+ * Returns a list of \p graph's dependency edges. Edges are returned via corresponding
+ * indices in \p from and \p to; that is, the node in \p to[i] has a dependency on the
+ * node in \p from[i]. \p from and \p to may both be NULL, in which
+ * case this function only returns the number of edges in \p numEdges. Otherwise,
+ * \p numEdges entries will be filled in. If \p numEdges is higher than the actual
+ * number of edges, the remaining entries in \p from and \p to will be set to NULL, and
+ * the number of edges actually returned will be written to \p numEdges.
+ *
+ * \param graph    - Graph to get the edges from
+ * \param from     - Location to return edge endpoints
+ * \param to       - Location to return edge endpoints
+ * \param numEdges - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphRemoveDependencies,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphGetEdges(cudaGraph_t graph, cudaGraphNode_t *from, cudaGraphNode_t *to, size_t *numEdges);
+
+/**
+ * \brief Returns a graph's dependency edges (12.3+)
+ *
+ * Returns a list of \p graph's dependency edges. Edges are returned via corresponding
+ * indices in \p from, \p to and \p edgeData; that is, the node in \p to[i] has a
+ * dependency on the node in \p from[i] with data \p edgeData[i]. \p from and \p to may
+ * both be NULL, in which case this function only returns the number of edges in
+ * \p numEdges. Otherwise, \p numEdges entries will be filled in. If \p numEdges is higher
+ * than the actual number of edges, the remaining entries in \p from and \p to will be
+ * set to NULL, and the number of edges actually returned will be written to \p numEdges.
+ * \p edgeData may alone be NULL, in which case the edges must all have default (zeroed)
+ * edge data. Attempting a losst query via NULL \p edgeData will result in
+ * ::cudaErrorLossyQuery. If \p edgeData is non-NULL then \p from and \p to must be as
+ * well.
+ *
+ * \param graph    - Graph to get the edges from
+ * \param from     - Location to return edge endpoints
+ * \param to       - Location to return edge endpoints
+ * \param edgeData - Optional location to return edge data
+ * \param numEdges - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorLossyQuery,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphRemoveDependencies,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphGetEdges_v2(cudaGraph_t graph, cudaGraphNode_t *from, cudaGraphNode_t *to, cudaGraphEdgeData *edgeData, size_t *numEdges);
+
+/**
+ * \brief Returns a node's dependencies
+ *
+ * Returns a list of \p node's dependencies. \p pDependencies may be NULL, in which case this
+ * function will return the number of dependencies in \p pNumDependencies. Otherwise,
+ * \p pNumDependencies entries will be filled in. If \p pNumDependencies is higher than the actual
+ * number of dependencies, the remaining entries in \p pDependencies will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p pNumDependencies.
+ *
+ * \param node           - Node to query
+ * \param pDependencies    - Pointer to return the dependencies
+ * \param pNumDependencies - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeGetDependentNodes,
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphRemoveDependencies
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeGetDependencies(cudaGraphNode_t node, cudaGraphNode_t *pDependencies, size_t *pNumDependencies);
+
+/**
+ * \brief Returns a node's dependencies (12.3+)
+ *
+ * Returns a list of \p node's dependencies. \p pDependencies may be NULL, in which case this
+ * function will return the number of dependencies in \p pNumDependencies. Otherwise,
+ * \p pNumDependencies entries will be filled in. If \p pNumDependencies is higher than the actual
+ * number of dependencies, the remaining entries in \p pDependencies will be set to NULL, and the
+ * number of nodes actually obtained will be returned in \p pNumDependencies.
+ *
+ * Note that if an edge has non-zero (non-default) edge data and \p edgeData is NULL,
+ * this API will return ::cudaErrorLossyQuery. If \p edgeData is non-NULL, then
+ * \p pDependencies must be as well.
+ *
+ * \param node             - Node to query
+ * \param pDependencies    - Pointer to return the dependencies
+ * \param edgeData         - Optional array to return edge data for each dependency
+ * \param pNumDependencies - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorLossyQuery,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeGetDependentNodes,
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphRemoveDependencies
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeGetDependencies_v2(cudaGraphNode_t node, cudaGraphNode_t *pDependencies, cudaGraphEdgeData *edgeData, size_t *pNumDependencies);
+
+/**
+ * \brief Returns a node's dependent nodes
+ *
+ * Returns a list of \p node's dependent nodes. \p pDependentNodes may be NULL, in which
+ * case this function will return the number of dependent nodes in \p pNumDependentNodes.
+ * Otherwise, \p pNumDependentNodes entries will be filled in. If \p pNumDependentNodes is
+ * higher than the actual number of dependent nodes, the remaining entries in
+ * \p pDependentNodes will be set to NULL, and the number of nodes actually obtained will
+ * be returned in \p pNumDependentNodes.
+ *
+ * \param node             - Node to query
+ * \param pDependentNodes    - Pointer to return the dependent nodes
+ * \param pNumDependentNodes - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphRemoveDependencies
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeGetDependentNodes(cudaGraphNode_t node, cudaGraphNode_t *pDependentNodes, size_t *pNumDependentNodes);
+
+/**
+ * \brief Returns a node's dependent nodes (12.3+)
+ *
+ * Returns a list of \p node's dependent nodes. \p pDependentNodes may be NULL, in which
+ * case this function will return the number of dependent nodes in \p pNumDependentNodes.
+ * Otherwise, \p pNumDependentNodes entries will be filled in. If \p pNumDependentNodes is
+ * higher than the actual number of dependent nodes, the remaining entries in
+ * \p pDependentNodes will be set to NULL, and the number of nodes actually obtained will
+ * be returned in \p pNumDependentNodes.
+ *
+ * Note that if an edge has non-zero (non-default) edge data and \p edgeData is NULL,
+ * this API will return ::cudaErrorLossyQuery. If \p edgeData is non-NULL, then
+ * \p pDependentNodes must be as well.
+ *
+ * \param node               - Node to query
+ * \param pDependentNodes    - Pointer to return the dependent nodes
+ * \param edgeData           - Optional pointer to return edge data for dependent nodes
+ * \param pNumDependentNodes - See description
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorLossyQuery,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphGetNodes,
+ * ::cudaGraphGetRootNodes,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphRemoveDependencies
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeGetDependentNodes_v2(cudaGraphNode_t node, cudaGraphNode_t *pDependentNodes, cudaGraphEdgeData *edgeData, size_t *pNumDependentNodes);
+
+/**
+ * \brief Adds dependency edges to a graph.
+ *
+ * The number of dependencies to be added is defined by \p numDependencies
+ * Elements in \p pFrom and \p pTo at corresponding indices define a dependency.
+ * Each node in \p pFrom and \p pTo must belong to \p graph.
+ *
+ * If \p numDependencies is 0, elements in \p pFrom and \p pTo will be ignored.
+ * Specifying an existing dependency will return an error.
+ *
+ * \param graph - Graph to which dependencies are added
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param numDependencies - Number of dependencies to be added
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphRemoveDependencies,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddDependencies(cudaGraph_t graph, const cudaGraphNode_t *from, const cudaGraphNode_t *to, size_t numDependencies);
+
+/**
+ * \brief Adds dependency edges to a graph. (12.3+)
+ *
+ * The number of dependencies to be added is defined by \p numDependencies
+ * Elements in \p pFrom and \p pTo at corresponding indices define a dependency.
+ * Each node in \p pFrom and \p pTo must belong to \p graph.
+ *
+ * If \p numDependencies is 0, elements in \p pFrom and \p pTo will be ignored.
+ * Specifying an existing dependency will return an error.
+ *
+ * \param graph - Graph to which dependencies are added
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param edgeData - Optional array of edge data. If NULL, default (zeroed) edge data is assumed.
+ * \param numDependencies - Number of dependencies to be added
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphRemoveDependencies,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddDependencies_v2(cudaGraph_t graph, const cudaGraphNode_t *from, const cudaGraphNode_t *to, const cudaGraphEdgeData *edgeData, size_t numDependencies);
+
+/**
+ * \brief Removes dependency edges from a graph.
+ *
+ * The number of \p pDependencies to be removed is defined by \p numDependencies.
+ * Elements in \p pFrom and \p pTo at corresponding indices define a dependency.
+ * Each node in \p pFrom and \p pTo must belong to \p graph.
+ *
+ * If \p numDependencies is 0, elements in \p pFrom and \p pTo will be ignored.
+ * Specifying a non-existing dependency will return an error.
+ *
+ * \param graph - Graph from which to remove dependencies
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param numDependencies - Number of dependencies to be removed
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphRemoveDependencies(cudaGraph_t graph, const cudaGraphNode_t *from, const cudaGraphNode_t *to, size_t numDependencies);
+
+/**
+ * \brief Removes dependency edges from a graph. (12.3+)
+ *
+ * The number of \p pDependencies to be removed is defined by \p numDependencies.
+ * Elements in \p pFrom and \p pTo at corresponding indices define a dependency.
+ * Each node in \p pFrom and \p pTo must belong to \p graph.
+ *
+ * If \p numDependencies is 0, elements in \p pFrom and \p pTo will be ignored.
+ * Specifying an edge that does not exist in the graph, with data matching
+ * \p edgeData, results in an error. \p edgeData is nullable, which is equivalent
+ * to passing default (zeroed) data for each edge.
+ *
+ * \param graph - Graph from which to remove dependencies
+ * \param from - Array of nodes that provide the dependencies
+ * \param to - Array of dependent nodes
+ * \param edgeData - Optional array of edge data. If NULL, edge data is assumed to
+ *                   be default (zeroed).
+ * \param numDependencies - Number of dependencies to be removed
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddDependencies,
+ * ::cudaGraphGetEdges,
+ * ::cudaGraphNodeGetDependencies,
+ * ::cudaGraphNodeGetDependentNodes
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphRemoveDependencies_v2(cudaGraph_t graph, const cudaGraphNode_t *from, const cudaGraphNode_t *to, const cudaGraphEdgeData *edgeData, size_t numDependencies);
+
+/**
+ * \brief Remove a node from the graph
+ *
+ * Removes \p node from its graph. This operation also severs any dependencies of other nodes 
+ * on \p node and vice versa.
+ *
+ * Dependencies cannot be removed from graphs which contain allocation or free nodes.
+ * Any attempt to do so will return an error.
+ *
+ * \param node  - Node to remove
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphAddEmptyNode,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemsetNode
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphDestroyNode(cudaGraphNode_t node);
+
+/**
+ * \brief Creates an executable graph from a graph
+ *
+ * Instantiates \p graph as an executable graph. The graph is validated for any
+ * structural constraints or intra-node constraints which were not previously
+ * validated. If instantiation is successful, a handle to the instantiated graph
+ * is returned in \p pGraphExec.
+ *
+ * The \p flags parameter controls the behavior of instantiation and subsequent
+ * graph launches.  Valid flags are:
+ *
+ * - ::cudaGraphInstantiateFlagAutoFreeOnLaunch, which configures a
+ * graph containing memory allocation nodes to automatically free any
+ * unfreed memory allocations before the graph is relaunched.
+ *
+ * - ::cudaGraphInstantiateFlagDeviceLaunch, which configures the graph for launch
+ * from the device. If this flag is passed, the executable graph handle returned can be
+ * used to launch the graph from both the host and device. This flag cannot be used in
+ * conjunction with ::cudaGraphInstantiateFlagAutoFreeOnLaunch.
+ *
+ * - ::cudaGraphInstantiateFlagUseNodePriority, which causes the graph
+ * to use the priorities from the per-node attributes rather than the priority
+ * of the launch stream during execution. Note that priorities are only available
+ * on kernel nodes, and are copied from stream priority during stream capture.
+ *
+ * If \p graph contains any allocation or free nodes, there can be at most one
+ * executable graph in existence for that graph at a time. An attempt to
+ * instantiate a second executable graph before destroying the first with
+ * ::cudaGraphExecDestroy will result in an error.
+ * The same also applies if \p graph contains any device-updatable kernel nodes.
+ * 
+ * Graphs instantiated for launch on the device have additional restrictions which do not
+ * apply to host graphs:
+ *
+ * - The graph's nodes must reside on a single device.
+ * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes.
+ * - The graph cannot be empty and must contain at least one kernel, memcpy, or memset node.
+ *   Operation-specific restrictions are outlined below.
+ * - Kernel nodes:
+ *   - Use of CUDA Dynamic Parallelism is not permitted.
+ *   - Cooperative launches are permitted as long as MPS is not in use.
+ * - Memcpy nodes:
+ *   - Only copies involving device memory and/or pinned device-mapped host memory are permitted.
+ *   - Copies involving CUDA arrays are not permitted.
+ *   - Both operands must be accessible from the current device, and the current device must
+ *     match the device of other nodes in the graph.
+ *
+ * If \p graph is not instantiated for launch on the device but contains kernels which
+ * call device-side cudaGraphLaunch() from multiple devices, this will result in an error.
+ *
+ * \param pGraphExec - Returns instantiated graph
+ * \param graph      - Graph to instantiate
+ * \param flags      - Flags to control instantiation.  See ::CUgraphInstantiate_flags.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphInstantiateWithFlags,
+ * ::cudaGraphCreate,
+ * ::cudaGraphUpload,
+ * ::cudaGraphLaunch,
+ * ::cudaGraphExecDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphInstantiate(cudaGraphExec_t *pGraphExec, cudaGraph_t graph, unsigned long long flags __dv(0));
+
+/**
+ * \brief Creates an executable graph from a graph
+ *
+ * Instantiates \p graph as an executable graph. The graph is validated for any
+ * structural constraints or intra-node constraints which were not previously
+ * validated. If instantiation is successful, a handle to the instantiated graph
+ * is returned in \p pGraphExec.
+ *
+ * The \p flags parameter controls the behavior of instantiation and subsequent
+ * graph launches.  Valid flags are:
+ *
+ * - ::cudaGraphInstantiateFlagAutoFreeOnLaunch, which configures a
+ * graph containing memory allocation nodes to automatically free any
+ * unfreed memory allocations before the graph is relaunched.
+ *
+ * - ::cudaGraphInstantiateFlagDeviceLaunch, which configures the graph for launch
+ * from the device. If this flag is passed, the executable graph handle returned can be
+ * used to launch the graph from both the host and device. This flag can only be used
+ * on platforms which support unified addressing. This flag cannot be used in
+ * conjunction with ::cudaGraphInstantiateFlagAutoFreeOnLaunch.
+ *
+ * - ::cudaGraphInstantiateFlagUseNodePriority, which causes the graph
+ * to use the priorities from the per-node attributes rather than the priority
+ * of the launch stream during execution. Note that priorities are only available
+ * on kernel nodes, and are copied from stream priority during stream capture.
+ *
+ * If \p graph contains any allocation or free nodes, there can be at most one
+ * executable graph in existence for that graph at a time. An attempt to
+ * instantiate a second executable graph before destroying the first with
+ * ::cudaGraphExecDestroy will result in an error.
+ * The same also applies if \p graph contains any device-updatable kernel nodes.
+ *
+ * If \p graph contains kernels which call device-side cudaGraphLaunch() from multiple
+ * devices, this will result in an error.
+ *
+ * Graphs instantiated for launch on the device have additional restrictions which do not
+ * apply to host graphs:
+ *
+ * - The graph's nodes must reside on a single device.
+ * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes.
+ * - The graph cannot be empty and must contain at least one kernel, memcpy, or memset node.
+ *   Operation-specific restrictions are outlined below.
+ * - Kernel nodes:
+ *   - Use of CUDA Dynamic Parallelism is not permitted.
+ *   - Cooperative launches are permitted as long as MPS is not in use.
+ * - Memcpy nodes:
+ *   - Only copies involving device memory and/or pinned device-mapped host memory are permitted.
+ *   - Copies involving CUDA arrays are not permitted.
+ *   - Both operands must be accessible from the current device, and the current device must
+ *     match the device of other nodes in the graph.
+ *
+ * \param pGraphExec - Returns instantiated graph
+ * \param graph      - Graph to instantiate
+ * \param flags      - Flags to control instantiation.  See ::CUgraphInstantiate_flags.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphCreate,
+ * ::cudaGraphUpload,
+ * ::cudaGraphLaunch,
+ * ::cudaGraphExecDestroy
+ */
+#if __CUDART_API_VERSION >= 11040
+extern __host__ cudaError_t CUDARTAPI cudaGraphInstantiateWithFlags(cudaGraphExec_t *pGraphExec, cudaGraph_t graph, unsigned long long flags __dv(0));
+#endif
+
+/**
+ * \brief Creates an executable graph from a graph
+ *
+ * Instantiates \p graph as an executable graph according to the \p instantiateParams structure.
+ * The graph is validated for any structural constraints or intra-node constraints
+ * which were not previously validated. If instantiation is successful, a handle to
+ * the instantiated graph is returned in \p pGraphExec.
+ *
+ * \p instantiateParams controls the behavior of instantiation and subsequent
+ * graph launches, as well as returning more detailed information in the event of an error.
+ * ::cudaGraphInstantiateParams is defined as:
+ *
+ * \code
+    typedef struct {
+        unsigned long long flags;
+        cudaStream_t uploadStream;
+        cudaGraphNode_t errNode_out;
+        cudaGraphInstantiateResult result_out;
+    } cudaGraphInstantiateParams;
+ * \endcode
+ *
+ * The \p flags field controls the behavior of instantiation and subsequent
+ * graph launches. Valid flags are:
+ *
+ * - ::cudaGraphInstantiateFlagAutoFreeOnLaunch, which configures a
+ * graph containing memory allocation nodes to automatically free any
+ * unfreed memory allocations before the graph is relaunched.
+ *
+ * - ::cudaGraphInstantiateFlagUpload, which will perform an upload of the graph
+ * into \p uploadStream once the graph has been instantiated.
+ *
+ * - ::cudaGraphInstantiateFlagDeviceLaunch, which configures the graph for launch
+ * from the device. If this flag is passed, the executable graph handle returned can be
+ * used to launch the graph from both the host and device. This flag can only be used
+ * on platforms which support unified addressing. This flag cannot be used in
+ * conjunction with ::cudaGraphInstantiateFlagAutoFreeOnLaunch.
+ *
+ * - ::cudaGraphInstantiateFlagUseNodePriority, which causes the graph
+ * to use the priorities from the per-node attributes rather than the priority
+ * of the launch stream during execution. Note that priorities are only available
+ * on kernel nodes, and are copied from stream priority during stream capture.
+ *
+ * If \p graph contains any allocation or free nodes, there can be at most one
+ * executable graph in existence for that graph at a time. An attempt to instantiate a
+ * second executable graph before destroying the first with ::cudaGraphExecDestroy will
+ * result in an error.
+ * The same also applies if \p graph contains any device-updatable kernel nodes.
+ *
+ * If \p graph contains kernels which call device-side cudaGraphLaunch() from multiple
+ * devices, this will result in an error.
+ *
+ * Graphs instantiated for launch on the device have additional restrictions which do not
+ * apply to host graphs:
+ *
+ * - The graph's nodes must reside on a single device.
+ * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes.
+ * - The graph cannot be empty and must contain at least one kernel, memcpy, or memset node.
+ *   Operation-specific restrictions are outlined below.
+ * - Kernel nodes:
+ *   - Use of CUDA Dynamic Parallelism is not permitted.
+ *   - Cooperative launches are permitted as long as MPS is not in use.
+ * - Memcpy nodes:
+ *   - Only copies involving device memory and/or pinned device-mapped host memory are permitted.
+ *   - Copies involving CUDA arrays are not permitted.
+ *   - Both operands must be accessible from the current device, and the current device must
+ *     match the device of other nodes in the graph.
+ *
+ * In the event of an error, the \p result_out and \p errNode_out fields will contain more
+ * information about the nature of the error. Possible error reporting includes:
+ *
+ * - ::cudaGraphInstantiateError, if passed an invalid value or if an unexpected error occurred
+ *   which is described by the return value of the function. \p errNode_out will be set to NULL.
+ * - ::cudaGraphInstantiateInvalidStructure, if the graph structure is invalid. \p errNode_out
+ *   will be set to one of the offending nodes.
+ * - ::cudaGraphInstantiateNodeOperationNotSupported, if the graph is instantiated for device
+ *   launch but contains a node of an unsupported node type, or a node which performs unsupported
+ *   operations, such as use of CUDA dynamic parallelism within a kernel node. \p errNode_out will
+ *   be set to this node.
+ * - ::cudaGraphInstantiateMultipleDevicesNotSupported, if the graph is instantiated for device
+ *   launch but a node’s device differs from that of another node. This error can also be returned
+ *   if a graph is not instantiated for device launch and it contains kernels which call device-side
+ *   cudaGraphLaunch() from multiple devices. \p errNode_out will be set to this node.
+ *
+ * If instantiation is successful, \p result_out will be set to ::cudaGraphInstantiateSuccess,
+ * and \p hErrNode_out will be set to NULL.
+ *
+ * \param pGraphExec       - Returns instantiated graph
+ * \param graph            - Graph to instantiate
+ * \param instantiateParams - Instantiation parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphCreate,
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphInstantiateWithFlags,
+ * ::cudaGraphExecDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphInstantiateWithParams(cudaGraphExec_t *pGraphExec, cudaGraph_t graph, cudaGraphInstantiateParams *instantiateParams);
+
+/**
+ * \brief Query the instantiation flags of an executable graph
+ *
+ * Returns the flags that were passed to instantiation for the given executable graph.
+ * ::cudaGraphInstantiateFlagUpload will not be returned by this API as it does
+ * not affect the resulting executable graph.
+ *
+ * \param graphExec - The executable graph to query
+ * \param flags     - Returns the instantiation flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphInstantiateWithFlags,
+ * ::cudaGraphInstantiateWithParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecGetFlags(cudaGraphExec_t graphExec, unsigned long long *flags);
+
+/**
+ * \brief Sets the parameters for a kernel node in the given graphExec
+ *
+ * Sets the parameters of a kernel node in an executable graph \p hGraphExec. 
+ * The node is identified by the corresponding node \p node in the 
+ * non-executable graph, from which the executable graph was instantiated. 
+ *
+ * \p node must not have been removed from the original graph. All \p nodeParams 
+ * fields may change, but the following restrictions apply to \p func updates: 
+ *
+ *   - The owning device of the function cannot change.
+ *   - A node whose function originally did not use CUDA dynamic parallelism cannot be updated
+ *     to a function which uses CDP
+ *   - A node whose function originally did not make device-side update calls cannot be updated
+ *     to a function which makes device-side update calls.
+ *   - If \p hGraphExec was not instantiated for device launch, a node whose function originally
+ *     did not use device-side cudaGraphLaunch() cannot be updated to a function which uses
+ *     device-side cudaGraphLaunch() unless the node resides on the same device as nodes which
+ *     contained such calls at instantiate-time. If no such calls were present at instantiation,
+ *     these updates cannot be performed at all.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already 
+ * enqueued or running launches of \p hGraphExec are not affected by this call. 
+ * \p node is also not modified by this call.
+ *
+ * If \p node is a device-updatable kernel node, the next upload/launch of \p hGraphExec
+ * will overwrite any previous device-side updates. Additionally, applying host updates to a
+ * device-updatable kernel node while it is being updated from the device will result in
+ * undefined behavior.
+ *
+ * \param hGraphExec  - The executable graph in which to set the specified node
+ * \param node        - kernel node from the graph from which graphExec was instantiated
+ * \param pNodeParams - Updated Parameters to set
+ * 
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_cudaKernel_t
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddKernelNode,
+ * ::cudaGraphKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecKernelNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const struct cudaKernelNodeParams *pNodeParams);
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec.
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained \p pNodeParams at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * The source and destination memory in \p pNodeParams must be allocated from the same 
+ * contexts as the original source and destination memory.  Both the instantiation-time 
+ * memory operands and the memory operands in \p pNodeParams must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * Returns ::cudaErrorInvalidValue if the memory operands' mappings changed or
+ * either the original or new memory operands are multidimensional.
+ *
+ * \param hGraphExec  - The executable graph in which to set the specified node
+ * \param node        - Memcpy node from the graph which was used to instantiate graphExec
+ * \param pNodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphExecMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphExecMemcpyNodeSetParams1D,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecMemcpyNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const struct cudaMemcpy3DParms *pNodeParams);
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec to copy to a symbol on the device
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained the given params at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * \p src and \p symbol must be allocated from the same contexts as the original source and
+ * destination memory.  The instantiation-time memory operands must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * Returns ::cudaErrorInvalidValue if the memory operands' mappings changed or
+ * the original memory operands are multidimensional.
+ *
+ * \param hGraphExec      - The executable graph in which to set the specified node
+ * \param node            - Memcpy node from the graph which was used to instantiate graphExec
+ * \param symbol          - Device symbol address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeToSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphExecMemcpyNodeSetParamsToSymbol(
+    cudaGraphExec_t hGraphExec,
+    cudaGraphNode_t node,
+    const void* symbol,
+    const void* src,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec to copy from a symbol on the device
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained the given params at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * \p symbol and \p dst must be allocated from the same contexts as the original source and
+ * destination memory.  The instantiation-time memory operands must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * Returns ::cudaErrorInvalidValue if the memory operands' mappings changed or
+ * the original memory operands are multidimensional.
+ *
+ * \param hGraphExec      - The executable graph in which to set the specified node
+ * \param node            - Memcpy node from the graph which was used to instantiate graphExec
+ * \param dst             - Destination memory address
+ * \param symbol          - Device symbol address
+ * \param count           - Size in bytes to copy
+ * \param offset          - Offset from start of symbol in bytes
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNodeFromSymbol,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParamsFromSymbol,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParamsToSymbol,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphExecMemcpyNodeSetParamsFromSymbol(
+    cudaGraphExec_t hGraphExec,
+    cudaGraphNode_t node,
+    void* dst,
+    const void* symbol,
+    size_t count,
+    size_t offset,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Sets the parameters for a memcpy node in the given graphExec to perform a 1-dimensional copy
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained the given params at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * \p src and \p dst must be allocated from the same contexts as the original source
+ * and destination memory.  The instantiation-time memory operands must be 1-dimensional.
+ * Zero-length operations are not supported.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * Returns ::cudaErrorInvalidValue if the memory operands' mappings changed or
+ * the original memory operands are multidimensional.
+ *
+ * \param hGraphExec      - The executable graph in which to set the specified node
+ * \param node            - Memcpy node from the graph which was used to instantiate graphExec
+ * \param dst             - Destination memory address
+ * \param src             - Source memory address
+ * \param count           - Size in bytes to copy
+ * \param kind            - Type of transfer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddMemcpyNode,
+ * ::cudaGraphAddMemcpyNode1D,
+ * ::cudaGraphMemcpyNodeSetParams,
+ * ::cudaGraphMemcpyNodeSetParams1D,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphExecMemcpyNodeSetParams1D(
+    cudaGraphExec_t hGraphExec,
+    cudaGraphNode_t node,
+    void* dst,
+    const void* src,
+    size_t count,
+    enum cudaMemcpyKind kind);
+#endif
+
+/**
+ * \brief Sets the parameters for a memset node in the given graphExec.
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained \p pNodeParams at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * Zero sized operations are not supported.
+ *
+ * The new destination pointer in \p pNodeParams must be to the same kind of allocation
+ * as the original destination pointer and have the same context association and device mapping
+ * as the original destination pointer.
+ *
+ * Both the value and pointer address may be updated.  
+ * Changing other aspects of the memset (width, height, element size or pitch) may cause the update to be rejected.
+ * Specifically, for 2d memsets, all dimension changes are rejected.
+ * For 1d memsets, changes in height are explicitly rejected and other changes are oportunistically allowed
+ * if the resulting work maps onto the work resources already allocated for the node.
+
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * \param hGraphExec  - The executable graph in which to set the specified node
+ * \param node        - Memset node from the graph which was used to instantiate graphExec
+ * \param pNodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddMemsetNode,
+ * ::cudaGraphMemsetNodeSetParams,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecMemsetNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const struct cudaMemsetParams *pNodeParams);
+
+/**
+ * \brief Sets the parameters for a host node in the given graphExec.
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though \p node had 
+ * contained \p pNodeParams at instantiation.  \p node must remain in the graph which was 
+ * used to instantiate \p hGraphExec.  Changed edges to and from \p node are ignored.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued 
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also 
+ * not modified by this call.
+ *
+ * \param hGraphExec  - The executable graph in which to set the specified node
+ * \param node        - Host node from the graph which was used to instantiate graphExec
+ * \param pNodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddHostNode,
+ * ::cudaGraphHostNodeSetParams,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecHostNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const struct cudaHostNodeParams *pNodeParams);
+
+/**
+ * \brief Updates node parameters in the child graph node in the given graphExec.
+ *
+ * Updates the work represented by \p node in \p hGraphExec as though the nodes contained
+ * in \p node's graph had the parameters contained in \p childGraph's nodes at instantiation.
+ * \p node must remain in the graph which was used to instantiate \p hGraphExec.
+ * Changed edges to and from \p node are ignored.
+ *
+ * The modifications only affect future launches of \p hGraphExec.  Already enqueued
+ * or running launches of \p hGraphExec are not affected by this call.  \p node is also
+ * not modified by this call.
+ *
+ * The topology of \p childGraph, as well as the node insertion order,  must match that
+ * of the graph contained in \p node.  See ::cudaGraphExecUpdate() for a list of restrictions
+ * on what can be updated in an instantiated graph.  The update is recursive, so child graph
+ * nodes contained within the top level child graph will also be updated.
+
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param node       - Host node from the graph which was used to instantiate graphExec
+ * \param childGraph - The graph supplying the updated parameters
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddChildGraphNode,
+ * ::cudaGraphChildGraphNodeGetGraph,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphExecChildGraphNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, cudaGraph_t childGraph);
+#endif
+
+/**
+ * \brief Sets the event for an event record node in the given graphExec
+ *
+ * Sets the event of an event record node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Event record node from the graph from which graphExec was instantiated
+ * \param event      - Updated event to use
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddEventRecordNode,
+ * ::cudaGraphEventRecordNodeGetEvent,
+ * ::cudaGraphEventWaitNodeSetEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphExecEventRecordNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event);
+#endif
+
+/**
+ * \brief Sets the event for an event wait node in the given graphExec
+ *
+ * Sets the event of an event wait node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Event wait node from the graph from which graphExec was instantiated
+ * \param event      - Updated event to use
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddEventWaitNode,
+ * ::cudaGraphEventWaitNodeGetEvent,
+ * ::cudaGraphEventRecordNodeSetEvent,
+ * ::cudaEventRecordWithFlags,
+ * ::cudaStreamWaitEvent,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphExecEventWaitNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event);
+#endif
+
+/**
+ * \brief Sets the parameters for an external semaphore signal node in the given graphExec
+ *
+ * Sets the parameters of an external semaphore signal node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * Changing \p nodeParams->numExtSems is not supported.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - semaphore signal node from the graph from which graphExec was instantiated
+ * \param nodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresSignalNode,
+ * ::cudaImportExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresWaitNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecExternalSemaphoresSignalNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const struct cudaExternalSemaphoreSignalNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Sets the parameters for an external semaphore wait node in the given graphExec
+ *
+ * Sets the parameters of an external semaphore wait node in an executable graph \p hGraphExec.
+ * The node is identified by the corresponding node \p hNode in the
+ * non-executable graph, from which the executable graph was instantiated.
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * Changing \p nodeParams->numExtSems is not supported.
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - semaphore wait node from the graph from which graphExec was instantiated
+ * \param nodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphExecNodeSetParams,
+ * ::cudaGraphAddExternalSemaphoresWaitNode,
+ * ::cudaImportExternalSemaphore,
+ * ::cudaSignalExternalSemaphoresAsync,
+ * ::cudaWaitExternalSemaphoresAsync,
+ * ::cudaGraphExecKernelNodeSetParams,
+ * ::cudaGraphExecMemcpyNodeSetParams,
+ * ::cudaGraphExecMemsetNodeSetParams,
+ * ::cudaGraphExecHostNodeSetParams,
+ * ::cudaGraphExecChildGraphNodeSetParams,
+ * ::cudaGraphExecEventRecordNodeSetEvent,
+ * ::cudaGraphExecEventWaitNodeSetEvent,
+ * ::cudaGraphExecExternalSemaphoresSignalNodeSetParams,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+#if __CUDART_API_VERSION >= 11020
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecExternalSemaphoresWaitNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const struct cudaExternalSemaphoreWaitNodeParams *nodeParams);
+#endif
+
+/**
+ * \brief Enables or disables the specified node in the given graphExec
+ *
+ * Sets \p hNode to be either enabled or disabled. Disabled nodes are functionally equivalent 
+ * to empty nodes until they are reenabled. Existing node parameters are not affected by 
+ * disabling/enabling the node.
+ *  
+ * The node is identified by the corresponding node \p hNode in the non-executable 
+ * graph, from which the executable graph was instantiated.   
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p hGraphExec. Already
+ * enqueued or running launches of \p hGraphExec are not affected by this call.
+ * \p hNode is also not modified by this call.
+ *
+ * \note Currently only kernel, memset and memcpy nodes are supported. 
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Node from the graph from which graphExec was instantiated
+ * \param isEnabled  - Node is enabled if != 0, otherwise the node is disabled
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeGetEnabled,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ * ::cudaGraphLaunch
+ */
+#if __CUDART_API_VERSION >= 11060
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeSetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int isEnabled);
+#endif
+
+/**
+ * \brief Query whether a node in the given graphExec is enabled
+ *
+ * Sets isEnabled to 1 if \p hNode is enabled, or 0 if \p hNode is disabled.
+ *
+ * The node is identified by the corresponding node \p hNode in the non-executable 
+ * graph, from which the executable graph was instantiated.   
+ *
+ * \p hNode must not have been removed from the original graph.
+ *
+ * \note Currently only kernel, memset and memcpy nodes are supported. 
+ *
+ * \param hGraphExec - The executable graph in which to set the specified node
+ * \param hNode      - Node from the graph from which graphExec was instantiated
+ * \param isEnabled  - Location to return the enabled status of the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphNodeSetEnabled,
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ * ::cudaGraphLaunch
+ */
+#if __CUDART_API_VERSION >= 11060
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeGetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int *isEnabled);
+#endif
+
+/**
+ * \brief Check whether an executable graph can be updated with a graph and perform the update if possible
+ *
+ * Updates the node parameters in the instantiated graph specified by \p hGraphExec with the
+ * node parameters in a topologically identical graph specified by \p hGraph.
+ *
+ * Limitations:
+ *
+ * - Kernel nodes:
+ *   - The owning context of the function cannot change.
+ *   - A node whose function originally did not use CUDA dynamic parallelism cannot be updated
+ *     to a function which uses CDP.
+ *   - A node whose function originally did not make device-side update calls cannot be updated
+ *     to a function which makes device-side update calls.
+ *   - A cooperative node cannot be updated to a non-cooperative node, and vice-versa.
+ *   - If the graph was instantiated with cudaGraphInstantiateFlagUseNodePriority, the
+ *     priority attribute cannot change. Equality is checked on the originally requested
+ *     priority values, before they are clamped to the device's supported range.
+ *   - If \p hGraphExec was not instantiated for device launch, a node whose function originally
+ *     did not use device-side cudaGraphLaunch() cannot be updated to a function which uses
+ *     device-side cudaGraphLaunch() unless the node resides on the same device as nodes which
+ *     contained such calls at instantiate-time. If no such calls were present at instantiation,
+ *     these updates cannot be performed at all.
+ *   - Neither \p hGraph nor \p hGraphExec may contain device-updatable kernel nodes.
+ * - Memset and memcpy nodes:
+ *   - The CUDA device(s) to which the operand(s) was allocated/mapped cannot change.
+ *   - The source/destination memory must be allocated from the same contexts as the original
+ *     source/destination memory.
+ *   - For 2d memsets, only address and assinged value may be updated.
+ *   - For 1d memsets, updating dimensions is also allowed, but may fail if the resulting operation doesn't
+ *     map onto the work resources already allocated for the node. 
+ * - Additional memcpy node restrictions:
+ *   - Changing either the source or destination memory type(i.e. CU_MEMORYTYPE_DEVICE,
+ *     CU_MEMORYTYPE_ARRAY, etc.) is not supported.
+ * - Conditional nodes:
+ *   - Changing node parameters is not supported.
+ *   - Changeing parameters of nodes within the conditional body graph is subject to the rules above.
+ *   - Conditional handle flags and default values are updated as part of the graph update.
+ *
+ * Note:  The API may add further restrictions in future releases.  The return code should always be checked.
+ *
+ * cudaGraphExecUpdate sets the result member of \p resultInfo to cudaGraphExecUpdateErrorTopologyChanged
+ * under the following conditions:
+ * - The count of nodes directly in \p hGraphExec and \p hGraph differ, in which case resultInfo->errorNode
+ *   is set to NULL.
+ * - \p hGraph has more exit nodes than \p hGraph, in which case resultInfo->errorNode is set to one of
+ *   the exit nodes in hGraph. 
+ * - A node in \p hGraph has a different number of dependencies than the node from \p hGraphExec it is paired with,
+ *   in which case resultInfo->errorNode is set to the node from \p hGraph.
+ * - A node in \p hGraph has a dependency that does not match with the corresponding dependency of the paired node
+ *   from \p hGraphExec. resultInfo->errorNode will be set to the node from \p hGraph. resultInfo->errorFromNode
+ *   will be set to the mismatched dependency. The dependencies are paired based on edge order and a dependency
+ *   does not match when the nodes are already paired based on other edges examined in the graph.
+ *
+ * cudaGraphExecUpdate sets \p the result member of \p resultInfo to:
+ * - cudaGraphExecUpdateError if passed an invalid value.
+ * - cudaGraphExecUpdateErrorTopologyChanged if the graph topology changed
+ * - cudaGraphExecUpdateErrorNodeTypeChanged if the type of a node changed, in which case
+ *   \p hErrorNode_out is set to the node from \p hGraph.
+ * - cudaGraphExecUpdateErrorFunctionChanged if the function of a kernel node changed (CUDA driver < 11.2)
+ * - cudaGraphExecUpdateErrorUnsupportedFunctionChange if the func field of a kernel changed in an
+ *   unsupported way(see note above), in which case \p hErrorNode_out is set to the node from \p hGraph
+ * - cudaGraphExecUpdateErrorParametersChanged if any parameters to a node changed in a way 
+ *   that is not supported, in which case \p hErrorNode_out is set to the node from \p hGraph
+ * - cudaGraphExecUpdateErrorAttributesChanged if any attributes of a node changed in a way 
+ *   that is not supported, in which case \p hErrorNode_out is set to the node from \p hGraph
+ * - cudaGraphExecUpdateErrorNotSupported if something about a node is unsupported, like 
+ *   the node's type or configuration, in which case \p hErrorNode_out is set to the node from \p hGraph
+ *
+ * If the update fails for a reason not listed above, the result member of \p resultInfo will be set
+ * to cudaGraphExecUpdateError. If the update succeeds, the result member will be set to cudaGraphExecUpdateSuccess.
+ *
+ * cudaGraphExecUpdate returns cudaSuccess when the updated was performed successfully.  It returns
+ * cudaErrorGraphExecUpdateFailure if the graph update was not performed because it included 
+ * changes which violated constraints specific to instantiated graph update.
+ *
+ * \param hGraphExec The instantiated graph to be updated
+ * \param hGraph The graph containing the updated parameters
+   \param resultInfo the error info structure
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorGraphExecUpdateFailure,
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphInstantiate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecUpdate(cudaGraphExec_t hGraphExec, cudaGraph_t hGraph, cudaGraphExecUpdateResultInfo *resultInfo);
+
+/**
+ * \brief Uploads an executable graph in a stream
+ *
+ * Uploads \p hGraphExec to the device in \p hStream without executing it. Uploads of
+ * the same \p hGraphExec will be serialized. Each upload is ordered behind both any
+ * previous work in \p hStream and any previous launches of \p hGraphExec.
+ * Uses memory cached by \p stream to back the allocations owned by \p graphExec.
+ *
+ * \param hGraphExec - Executable graph to upload
+ * \param hStream    - Stream in which to upload the graph
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * \notefnerr
+ * \note_init_rt
+ *
+ * \sa
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphLaunch,
+ * ::cudaGraphExecDestroy
+ */
+#if __CUDART_API_VERSION >= 11010
+ extern __host__ cudaError_t CUDARTAPI cudaGraphUpload(cudaGraphExec_t graphExec, cudaStream_t stream);
+#endif
+
+/**
+ * \brief Launches an executable graph in a stream
+ *
+ * Executes \p graphExec in \p stream. Only one instance of \p graphExec may be executing
+ * at a time. Each launch is ordered behind both any previous work in \p stream
+ * and any previous launches of \p graphExec. To execute a graph concurrently, it must be
+ * instantiated multiple times into multiple executable graphs.
+ *
+ * If any allocations created by \p graphExec remain unfreed (from a previous launch) and
+ * \p graphExec was not instantiated with ::cudaGraphInstantiateFlagAutoFreeOnLaunch,
+ * the launch will fail with ::cudaErrorInvalidValue.
+ *
+ * \param graphExec - Executable graph to launch
+ * \param stream    - Stream in which to launch the graph
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphUpload,
+ * ::cudaGraphExecDestroy
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream);
+
+/**
+ * \brief Destroys an executable graph
+ *
+ * Destroys the executable graph specified by \p graphExec.
+ *
+ * \param graphExec - Executable graph to destroy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa
+ * ::cudaGraphInstantiate,
+ * ::cudaGraphUpload,
+ * ::cudaGraphLaunch
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecDestroy(cudaGraphExec_t graphExec);
+
+/**
+ * \brief Destroys a graph
+ *
+ * Destroys the graph specified by \p graph, as well as all of its nodes.
+ *
+ * \param graph - Graph to destroy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ * \note_destroy_ub
+ *
+ * \sa
+ * ::cudaGraphCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphDestroy(cudaGraph_t graph);
+
+/**
+ * \brief Write a DOT file describing graph structure
+ *
+ * Using the provided \p graph, write to \p path a DOT formatted description of the graph.
+ * By default this includes the graph topology, node types, node id, kernel names and memcpy direction.
+ * \p flags can be specified to write more detailed information about each node type such as
+ * parameter values, kernel attributes, node and function handles.
+ *
+ * \param graph - The graph to create a DOT file from
+ * \param path  - The path to write the DOT file to
+ * \param flags - Flags from cudaGraphDebugDotFlags for specifying which additional node information to write
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorOperatingSystem
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphDebugDotPrint(cudaGraph_t graph, const char *path, unsigned int flags);
+
+/**
+ * \brief Create a user object
+ *
+ * Create a user object with the specified destructor callback and initial reference count. The
+ * initial references are owned by the caller.
+ *
+ * Destructor callbacks cannot make CUDA API calls and should avoid blocking behavior, as they
+ * are executed by a shared internal thread. Another thread may be signaled to perform such
+ * actions, if it does not block forward progress of tasks scheduled through CUDA.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param object_out      - Location to return the user object handle
+ * \param ptr             - The pointer to pass to the destroy function
+ * \param destroy         - Callback to free the user object when it is no longer in use
+ * \param initialRefcount - The initial refcount to create the object with, typically 1. The
+ *                          initial references are owned by the calling thread.
+ * \param flags           - Currently it is required to pass ::cudaUserObjectNoDestructorSync,
+ *                          which is the only defined flag. This indicates that the destroy
+ *                          callback cannot be waited on by any CUDA API. Users requiring
+ *                          synchronization of the callback should signal its completion
+ *                          manually.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa
+ * ::cudaUserObjectRetain,
+ * ::cudaUserObjectRelease,
+ * ::cudaGraphRetainUserObject,
+ * ::cudaGraphReleaseUserObject,
+ * ::cudaGraphCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaUserObjectCreate(cudaUserObject_t *object_out, void *ptr, cudaHostFn_t destroy, unsigned int initialRefcount, unsigned int flags);
+
+/**
+ * \brief Retain a reference to a user object
+ *
+ * Retains new references to a user object. The new references are owned by the caller.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param object - The object to retain
+ * \param count  - The number of references to retain, typically 1. Must be nonzero
+ *                 and not larger than INT_MAX.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa
+ * ::cudaUserObjectCreate,
+ * ::cudaUserObjectRelease,
+ * ::cudaGraphRetainUserObject,
+ * ::cudaGraphReleaseUserObject,
+ * ::cudaGraphCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaUserObjectRetain(cudaUserObject_t object, unsigned int count __dv(1));
+
+/**
+ * \brief Release a reference to a user object
+ *
+ * Releases user object references owned by the caller. The object's destructor is invoked if
+ * the reference count reaches zero.
+ *
+ * It is undefined behavior to release references not owned by the caller, or to use a user
+ * object handle after all references are released.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param object - The object to release
+ * \param count  - The number of references to release, typically 1. Must be nonzero
+ *                 and not larger than INT_MAX.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa
+ * ::cudaUserObjectCreate,
+ * ::cudaUserObjectRetain,
+ * ::cudaGraphRetainUserObject,
+ * ::cudaGraphReleaseUserObject,
+ * ::cudaGraphCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaUserObjectRelease(cudaUserObject_t object, unsigned int count __dv(1));
+
+/**
+ * \brief Retain a reference to a user object from a graph
+ *
+ * Creates or moves user object references that will be owned by a CUDA graph.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param graph  - The graph to associate the reference with
+ * \param object - The user object to retain a reference for
+ * \param count  - The number of references to add to the graph, typically 1. Must be
+ *                 nonzero and not larger than INT_MAX.
+ * \param flags  - The optional flag ::cudaGraphUserObjectMove transfers references
+ *                 from the calling thread, rather than create new references. Pass 0
+ *                 to create new references.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa
+ * ::cudaUserObjectCreate
+ * ::cudaUserObjectRetain,
+ * ::cudaUserObjectRelease,
+ * ::cudaGraphReleaseUserObject,
+ * ::cudaGraphCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphRetainUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count __dv(1), unsigned int flags __dv(0));
+
+/**
+ * \brief Release a user object reference from a graph
+ *
+ * Releases user object references owned by a graph.
+ *
+ * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects.
+ *
+ * \param graph  - The graph that will release the reference
+ * \param object - The user object to release a reference for
+ * \param count  - The number of references to release, typically 1. Must be nonzero
+ *                 and not larger than INT_MAX.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa
+ * ::cudaUserObjectCreate
+ * ::cudaUserObjectRetain,
+ * ::cudaUserObjectRelease,
+ * ::cudaGraphRetainUserObject,
+ * ::cudaGraphCreate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphReleaseUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count __dv(1));
+
+/**
+ * \brief Adds a node of arbitrary type to a graph
+ *
+ * Creates a new node in \p graph described by \p nodeParams with \p numDependencies
+ * dependencies specified via \p pDependencies. \p numDependencies may be 0.
+ * \p pDependencies may be null if \p numDependencies is 0. \p pDependencies may not have
+ * any duplicate entries.
+ *
+ * \p nodeParams is a tagged union. The node type should be specified in the \p type field,
+ * and type-specific parameters in the corresponding union member. All unused bytes - that
+ * is, \p reserved0 and all bytes past the utilized union member - must be set to zero.
+ * It is recommended to use brace initialization or memset to ensure all bytes are
+ * initialized.
+ *
+ * Note that for some node types, \p nodeParams may contain "out parameters" which are
+ * modified during the call, such as \p nodeParams->alloc.dptr.
+ *
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Specification of the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorNotSupported
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphCreate,
+ * ::cudaGraphNodeSetParams,
+ * ::cudaGraphExecNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddNode(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, size_t numDependencies, struct cudaGraphNodeParams *nodeParams);
+
+/**
+ * \brief Adds a node of arbitrary type to a graph (12.3+)
+ *
+ * Creates a new node in \p graph described by \p nodeParams with \p numDependencies
+ * dependencies specified via \p pDependencies. \p numDependencies may be 0.
+ * \p pDependencies may be null if \p numDependencies is 0. \p pDependencies may not have
+ * any duplicate entries.
+ *
+ * \p nodeParams is a tagged union. The node type should be specified in the \p type field,
+ * and type-specific parameters in the corresponding union member. All unused bytes - that
+ * is, \p reserved0 and all bytes past the utilized union member - must be set to zero.
+ * It is recommended to use brace initialization or memset to ensure all bytes are
+ * initialized.
+ *
+ * Note that for some node types, \p nodeParams may contain "out parameters" which are
+ * modified during the call, such as \p nodeParams->alloc.dptr.
+ *
+ * A handle to the new node will be returned in \p phGraphNode.
+ *
+ * \param pGraphNode      - Returns newly created node
+ * \param graph           - Graph to which to add the node
+ * \param pDependencies   - Dependencies of the node
+ * \param dependencyData  - Optional edge data for the dependencies. If NULL, the data is
+ *                          assumed to be default (zeroed) for all dependencies.
+ * \param numDependencies - Number of dependencies
+ * \param nodeParams      - Specification of the node
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorNotSupported
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphCreate,
+ * ::cudaGraphNodeSetParams,
+ * ::cudaGraphExecNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphAddNode_v2(cudaGraphNode_t *pGraphNode, cudaGraph_t graph, const cudaGraphNode_t *pDependencies, const cudaGraphEdgeData *dependencyData, size_t numDependencies, struct cudaGraphNodeParams *nodeParams);
+
+/**
+ * \brief Update's a graph node's parameters
+ *
+ * Sets the parameters of graph node \p node to \p nodeParams. The node type specified by
+ * \p nodeParams->type must match the type of \p node. \p nodeParams must be fully
+ * initialized and all unused bytes (reserved, padding) zeroed.
+ *
+ * Modifying parameters is not supported for node types cudaGraphNodeTypeMemAlloc and
+ * cudaGraphNodeTypeMemFree.
+ *
+ * \param node       - Node to set the parameters for
+ * \param nodeParams - Parameters to copy
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorNotSupported
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphExecNodeSetParams
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphNodeSetParams(cudaGraphNode_t node, struct cudaGraphNodeParams *nodeParams);
+
+/**
+ * \brief Update's a graph node's parameters in an instantiated graph
+ *
+ * Sets the parameters of a node in an executable graph \p graphExec. The node is identified
+ * by the corresponding node \p node in the non-executable graph from which the executable
+ * graph was instantiated. \p node must not have been removed from the original graph.
+ *
+ * The modifications only affect future launches of \p graphExec. Already
+ * enqueued or running launches of \p graphExec are not affected by this call.
+ * \p node is also not modified by this call.
+ *
+ * Allowed changes to parameters on executable graphs are as follows:
+ * <table>
+ *   <tr><th>Node type<th>Allowed changes
+ *   <tr><td>kernel<td>See ::cudaGraphExecKernelNodeSetParams
+ *   <tr><td>memcpy<td>Addresses for 1-dimensional copies if allocated in same context; see ::cudaGraphExecMemcpyNodeSetParams
+ *   <tr><td>memset<td>Addresses for 1-dimensional memsets if allocated in same context; see ::cudaGraphExecMemsetNodeSetParams
+ *   <tr><td>host<td>Unrestricted
+ *   <tr><td>child graph<td>Topology must match and restrictions apply recursively; see ::cudaGraphExecUpdate
+ *   <tr><td>event wait<td>Unrestricted
+ *   <tr><td>event record<td>Unrestricted
+ *   <tr><td>external semaphore signal<td>Number of semaphore operations cannot change
+ *   <tr><td>external semaphore wait<td>Number of semaphore operations cannot change
+ *   <tr><td>memory allocation<td>API unsupported
+ *   <tr><td>memory free<td>API unsupported
+ * </table>
+ *
+ * \param graphExec  - The executable graph in which to update the specified node
+ * \param node       - Corresponding node from the graph from which graphExec was instantiated
+ * \param nodeParams - Updated Parameters to set
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorNotSupported
+ * \note_graph_thread_safety
+ * \notefnerr
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cudaGraphAddNode,
+ * ::cudaGraphNodeSetParams
+ * ::cudaGraphExecUpdate,
+ * ::cudaGraphInstantiate
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphExecNodeSetParams(cudaGraphExec_t graphExec, cudaGraphNode_t node, struct cudaGraphNodeParams *nodeParams);
+
+/**
+ * \brief Create a conditional handle
+ *
+ * Creates a conditional handle associated with \p hGraph.
+ *
+ * The conditional handle must be associated with a conditional node in this graph or one of its children.
+ *  
+ * Handles not associated with a conditional node may cause graph instantiation to fail. 
+ *
+ * \param pHandle_out        - Pointer used to return the handle to the caller.
+ * \param hGraph             - Graph which will contain the conditional node using this handle.
+ * \param defaultLaunchValue - Optional initial value for the conditional variable.
+ *                             Applied at the beginning of each graph execution if cudaGraphCondAssignDefault is set in \p flags.
+ * \param flags              - Currently must be cudaGraphCondAssignDefault or 0.
+ *
+ * \return
+ * ::CUDA_SUCCESS,
+ * ::CUDA_ERROR_INVALID_VALUE,
+ * ::CUDA_ERROR_NOT_SUPPORTED
+ * \note_graph_thread_safety
+ * \notefnerr
+ *
+ * \sa
+ * ::cuGraphAddNode,
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphConditionalHandleCreate(cudaGraphConditionalHandle *pHandle_out, cudaGraph_t graph, unsigned int defaultLaunchValue __dv(0), unsigned int flags __dv(0));
+
+/** @} */ /* END CUDART_GRAPH */
+
+/**
+ * \defgroup CUDART_DRIVER_ENTRY_POINT Driver Entry Point Access
+ *
+ * ___MANBRIEF___ driver entry point access functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the driver entry point access functions of CUDA
+ * runtime application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns the requested driver API function pointer
+ *
+ * Returns in \p **funcPtr the address of the CUDA driver function for the requested flags.
+ *
+ * For a requested driver symbol, if the CUDA version in which the driver symbol was
+ * introduced is less than or equal to the CUDA runtime version, the API will return
+ * the function pointer to the corresponding versioned driver function.
+ *
+ * The pointer returned by the API should be cast to a function pointer matching the
+ * requested driver function's definition in the API header file. The function pointer
+ * typedef can be picked up from the corresponding typedefs header file. For example,
+ * cudaTypedefs.h consists of function pointer typedefs for driver APIs defined in cuda.h.
+ *
+ * The API will return ::cudaSuccess and set the returned \p funcPtr if the
+ * requested driver function is valid and supported on the platform.
+ *
+ * The API will return ::cudaSuccess and set the returned \p funcPtr to NULL if the
+ * requested driver function is not supported on the platform, no ABI
+ * compatible driver function exists for the CUDA runtime version or if the
+ * driver symbol is invalid.
+ *
+ * It will also set the optional \p driverStatus to one of the values in 
+ * ::cudaDriverEntryPointQueryResult with the following meanings:
+ * - ::cudaDriverEntryPointSuccess - The requested symbol was succesfully found based
+ *   on input arguments and \p pfn is valid
+ * - ::cudaDriverEntryPointSymbolNotFound - The requested symbol was not found
+ * - ::cudaDriverEntryPointVersionNotSufficent - The requested symbol was found but is
+ *   not supported by the current runtime version (CUDART_VERSION)
+ *
+ * The requested flags can be:
+ * - ::cudaEnableDefault: This is the default mode. This is equivalent to
+ *   ::cudaEnablePerThreadDefaultStream if the code is compiled with
+ *   --default-stream per-thread compilation flag or the macro CUDA_API_PER_THREAD_DEFAULT_STREAM
+ *   is defined; ::cudaEnableLegacyStream otherwise.
+ * - ::cudaEnableLegacyStream: This will enable the search for all driver symbols
+ *   that match the requested driver symbol name except the corresponding per-thread versions.
+ * - ::cudaEnablePerThreadDefaultStream: This will enable the search for all
+ *   driver symbols that match the requested driver symbol name including the per-thread
+ *   versions. If a per-thread version is not found, the API will return the legacy version
+ *   of the driver function.
+ *
+ * \param symbol - The base name of the driver API function to look for. As an example,
+ *                 for the driver API ::cuMemAlloc_v2, \p symbol would be cuMemAlloc.
+ *                 Note that the API will use the CUDA runtime version to return the
+ *                 address to the most recent ABI compatible driver symbol, ::cuMemAlloc
+ *                 or ::cuMemAlloc_v2.
+ * \param funcPtr - Location to return the function pointer to the requested driver function
+ * \param flags -  Flags to specify search options.
+ * \param driverStatus - Optional location to store the status of finding the symbol from
+ *                       the driver. See ::cudaDriverEntryPointQueryResult for 
+ *                       possible values.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported
+ * \note_version_mixing
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuGetProcAddress
+ */
+#if defined(__cplusplus)
+extern __host__ cudaError_t CUDARTAPI cudaGetDriverEntryPoint(const char *symbol, void **funcPtr, unsigned long long flags, enum cudaDriverEntryPointQueryResult *driverStatus = NULL);
+#else
+extern __host__ cudaError_t CUDARTAPI cudaGetDriverEntryPoint(const char *symbol, void **funcPtr, unsigned long long flags, enum cudaDriverEntryPointQueryResult *driverStatus);
+#endif
+
+/**
+ * \brief Returns the requested driver API function pointer by CUDA version
+ *
+ * Returns in \p **funcPtr the address of the CUDA driver function for the requested flags and CUDA driver version.
+ *
+ * The CUDA version is specified as (1000 * major + 10 * minor), so CUDA 11.2
+ * should be specified as 11020. For a requested driver symbol, if the specified
+ * CUDA version is greater than or equal to the CUDA version in which the driver symbol
+ * was introduced, this API will return the function pointer to the corresponding
+ * versioned function.
+ *
+ * The pointer returned by the API should be cast to a function pointer matching the
+ * requested driver function's definition in the API header file. The function pointer
+ * typedef can be picked up from the corresponding typedefs header file. For example,
+ * cudaTypedefs.h consists of function pointer typedefs for driver APIs defined in cuda.h.
+ *
+ * For the case where the CUDA version requested is greater than the CUDA Toolkit 
+ * installed, there may not be an appropriate function pointer typedef in the
+ * corresponding header file and may need a custom typedef to match the driver
+ * function signature returned. This can be done by getting the typedefs from a later
+ * toolkit or creating appropriately matching custom function typedefs.
+ *
+ * The API will return ::cudaSuccess and set the returned \p funcPtr if the
+ * requested driver function is valid and supported on the platform.
+ *
+ * The API will return ::cudaSuccess and set the returned \p funcPtr to NULL if the
+ * requested driver function is not supported on the platform, no ABI
+ * compatible driver function exists for the requested version or if the
+ * driver symbol is invalid.
+ *
+ * It will also set the optional \p driverStatus to one of the values in 
+ * ::cudaDriverEntryPointQueryResult with the following meanings:
+ * - ::cudaDriverEntryPointSuccess - The requested symbol was succesfully found based
+ *   on input arguments and \p pfn is valid
+ * - ::cudaDriverEntryPointSymbolNotFound - The requested symbol was not found
+ * - ::cudaDriverEntryPointVersionNotSufficent - The requested symbol was found but is
+ *   not supported by the specified version \p cudaVersion
+ *
+ * The requested flags can be:
+ * - ::cudaEnableDefault: This is the default mode. This is equivalent to
+ *   ::cudaEnablePerThreadDefaultStream if the code is compiled with
+ *   --default-stream per-thread compilation flag or the macro CUDA_API_PER_THREAD_DEFAULT_STREAM
+ *   is defined; ::cudaEnableLegacyStream otherwise.
+ * - ::cudaEnableLegacyStream: This will enable the search for all driver symbols
+ *   that match the requested driver symbol name except the corresponding per-thread versions.
+ * - ::cudaEnablePerThreadDefaultStream: This will enable the search for all
+ *   driver symbols that match the requested driver symbol name including the per-thread
+ *   versions. If a per-thread version is not found, the API will return the legacy version
+ *   of the driver function.
+ *
+ * \param symbol - The base name of the driver API function to look for. As an example,
+ *                 for the driver API ::cuMemAlloc_v2, \p symbol would be cuMemAlloc.
+ * \param funcPtr - Location to return the function pointer to the requested driver function
+ * \param cudaVersion - The CUDA version to look for the requested driver symbol 
+ * \param flags -  Flags to specify search options.
+ * \param driverStatus - Optional location to store the status of finding the symbol from
+ *                       the driver. See ::cudaDriverEntryPointQueryResult for 
+ *                       possible values.
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorNotSupported
+ * \note_version_mixing
+ * \note_init_rt
+ * \note_callback
+ *
+ * \sa
+ * ::cuGetProcAddress
+ */
+#if defined(__cplusplus)
+extern __host__ cudaError_t CUDARTAPI cudaGetDriverEntryPointByVersion(const char *symbol, void **funcPtr, unsigned int cudaVersion, unsigned long long flags, enum cudaDriverEntryPointQueryResult *driverStatus = NULL);
+#else
+extern __host__ cudaError_t CUDARTAPI cudaGetDriverEntryPointByVersion(const char *symbol, void **funcPtr, unsigned int cudaVersion, unsigned long long flags, enum cudaDriverEntryPointQueryResult *driverStatus);
+#endif
+
+/** @} */ /* END CUDART_DRIVER_ENTRY_POINT */
+
+/**
+ * \defgroup CUDART_LIBRARY Library Management
+ *
+ * ___MANBRIEF___ library management functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the library management functions of the CUDA runtime
+ * application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Load a library with specified code and options
+ *
+ * Takes a pointer \p code and loads the corresponding library \p library based on
+ * the application defined library loading mode:
+ * - If module loading is set to EAGER, via the environment variables described in "Module loading",
+ *   \p library is loaded eagerly into all contexts at the time of the call and future contexts
+ *   at the time of creation until the library is unloaded with ::cudaLibraryUnload().
+ * - If the environment variables are set to LAZY, \p library
+ *   is not immediately loaded onto all existent contexts and will only be
+ *   loaded when a function is needed for that context, such as a kernel launch.
+ *
+ * These environment variables are described in the CUDA programming guide under the 
+ * "CUDA environment variables" section.
+ *
+ * The \p code may be a \e cubin or \e fatbin as output by \b nvcc,
+ * or a NULL-terminated \e PTX, either as output by \b nvcc or hand-written.
+ * A fatbin should also contain relocatable code when doing separate compilation.
+ * Please also see the documentation for nvrtc (https://docs.nvidia.com/cuda/nvrtc/index.html), 
+ * nvjitlink (https://docs.nvidia.com/cuda/nvjitlink/index.html), and nvfatbin
+ * (https://docs.nvidia.com/cuda/nvfatbin/index.html) for more information on generating
+ * loadable code at runtime.
+ *
+ * Options are passed as an array via \p jitOptions and any corresponding parameters are passed in
+ * \p jitOptionsValues. The number of total JIT options is supplied via \p numJitOptions.
+ * Any outputs will be returned via \p jitOptionsValues.
+ *
+ * Library load options are passed as an array via \p libraryOptions and any corresponding parameters are passed in
+ * \p libraryOptionValues. The number of total library load options is supplied via \p numLibraryOptions.
+ *
+ * \param library             - Returned library
+ * \param code                - Code to load
+ * \param jitOptions          - Options for JIT
+ * \param jitOptionsValues    - Option values for JIT
+ * \param numJitOptions       - Number of options
+ * \param libraryOptions      - Options for loading
+ * \param libraryOptionValues - Option values for loading
+ * \param numLibraryOptions   - Number of options for loading
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorSharedObjectSymbolNotFound,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorJitCompilerNotFound
+ *
+ * \sa ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cuLibraryLoadData
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryLoadData(cudaLibrary_t *library, const void *code,
+                                   enum cudaJitOption *jitOptions, void **jitOptionsValues, unsigned int numJitOptions,
+                                   enum cudaLibraryOption *libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions);
+
+/**
+ * \brief Load a library with specified file and options
+ *
+ * Takes a pointer \p code and loads the corresponding library \p library based on
+ * the application defined library loading mode:
+ * - If module loading is set to EAGER, via the environment variables described in "Module loading",
+ *   \p library is loaded eagerly into all contexts at the time of the call and future contexts
+ *   at the time of creation until the library is unloaded with ::cudaLibraryUnload().
+ * - If the environment variables are set to LAZY, \p library
+ *   is not immediately loaded onto all existent contexts and will only be
+ *   loaded when a function is needed for that context, such as a kernel launch.
+ *
+ * These environment variables are described in the CUDA programming guide under the 
+ * "CUDA environment variables" section.
+ *
+ * The file should be a \e cubin file as output by \b nvcc, or a \e PTX file either
+ * as output by \b nvcc or handwritten, or a \e fatbin file as output by \b nvcc.
+ * A fatbin should also contain relocatable code when doing separate compilation.
+ * Please also see the documentation for nvrtc (https://docs.nvidia.com/cuda/nvrtc/index.html), 
+ * nvjitlink (https://docs.nvidia.com/cuda/nvjitlink/index.html), and nvfatbin
+ * (https://docs.nvidia.com/cuda/nvfatbin/index.html) for more information on generating
+ * loadable code at runtime.
+ *
+ * Options are passed as an array via \p jitOptions and any corresponding parameters are
+ * passed in \p jitOptionsValues. The number of total options is supplied via \p numJitOptions.
+ * Any outputs will be returned via \p jitOptionsValues.
+ *
+ * Library load options are passed as an array via \p libraryOptions and any corresponding parameters are passed in
+ * \p libraryOptionValues. The number of total library load options is supplied via \p numLibraryOptions.
+ *
+ * \param library             - Returned library
+ * \param fileName            - File to load from
+ * \param jitOptions          - Options for JIT
+ * \param jitOptionsValues    - Option values for JIT
+ * \param numJitOptions       - Number of options
+ * \param libraryOptions      - Options for loading
+ * \param libraryOptionValues - Option values for loading
+ * \param numLibraryOptions   - Number of options for loading
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorMemoryAllocation,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInvalidPtx,
+ * ::cudaErrorUnsupportedPtxVersion,
+ * ::cudaErrorNoKernelImageForDevice,
+ * ::cudaErrorSharedObjectSymbolNotFound,
+ * ::cudaErrorSharedObjectInitFailed,
+ * ::cudaErrorJitCompilerNotFound
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryUnload,
+ * ::cuLibraryLoadFromFile
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryLoadFromFile(cudaLibrary_t *library, const char *fileName,
+                                       enum cudaJitOption *jitOptions, void **jitOptionsValues, unsigned int numJitOptions,
+                                       enum cudaLibraryOption *libraryOptions, void **libraryOptionValues, unsigned int numLibraryOptions);
+
+/**
+ * \brief Unloads a library
+ *
+ * Unloads the library specified with \p library
+ *
+ * \param library - Library to unload
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cuLibraryUnload
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryUnload(cudaLibrary_t library);
+
+/**
+ * \brief Returns a kernel handle
+ *
+ * Returns in \p pKernel the handle of the kernel with name \p name located in library \p library.
+ * If kernel handle is not found, the call returns ::cudaErrorSymbolNotFound.
+ *
+ * \param pKernel - Returned kernel handle
+ * \param library - Library to retrieve kernel from
+ * \param name - Name of kernel to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cuLibraryGetKernel
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryGetKernel(cudaKernel_t *pKernel, cudaLibrary_t library, const char *name);
+
+/**
+ * \brief Returns a global device pointer
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the global with
+ * name \p name for the requested library \p library and the current device.
+ * If no global for the requested name \p name exists, the call returns ::cudaErrorSymbolNotFound.
+ * One of the parameters \p dptr or \p bytes (not both) can be NULL in which
+ * case it is ignored. The returned \p dptr cannot be passed to the Symbol APIs
+ * such as ::cudaMemcpyToSymbol, ::cudaMemcpyFromSymbol, ::cudaGetSymbolAddress, or
+ * ::cudaGetSymbolSize.
+ *
+ * \param dptr - Returned global device pointer for the requested library
+ * \param bytes - Returned global size in bytes
+ * \param library - Library to retrieve global from
+ * \param name - Name of global to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ * ::cudaErrorDeviceUninitialized,
+ * ::cudaErrorContextIsDestroyed
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cudaLibraryGetManaged,
+ * ::cuLibraryGetGlobal
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryGetGlobal(void **dptr, size_t *bytes, cudaLibrary_t library, const char *name);
+
+/**
+ * \brief Returns a pointer to managed memory
+ *
+ * Returns in \p *dptr and \p *bytes the base pointer and size of the managed memory with
+ * name \p name for the requested library \p library. If no managed memory with the
+ * requested name \p name exists, the call returns ::cudaErrorSymbolNotFound. One of the parameters
+ * \p dptr or \p bytes (not both) can be NULL in which case it is ignored.
+ * Note that managed memory for library \p library is shared across devices and is registered
+ * when the library is loaded. The returned \p dptr cannot be passed to the Symbol APIs
+ * such as ::cudaMemcpyToSymbol, ::cudaMemcpyFromSymbol, ::cudaGetSymbolAddress, or
+ * ::cudaGetSymbolSize.
+ *
+ * \param dptr - Returned pointer to the managed memory
+ * \param bytes - Returned memory size in bytes
+ * \param library - Library to retrieve managed memory from
+ * \param name - Name of managed memory to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cudaLibraryGetGlobal,
+ * ::cuLibraryGetManaged
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryGetManaged(void **dptr, size_t *bytes, cudaLibrary_t library, const char *name);
+
+/**
+ * \brief Returns a pointer to a unified function
+ *
+ * Returns in \p *fptr the function pointer to a unified function denoted by \p symbol.
+ * If no unified function with name \p symbol exists, the call returns ::cudaErrorSymbolNotFound.
+ * If there is no device with attribute ::cudaDeviceProp::unifiedFunctionPointers present in the system,
+ * the call may return ::cudaErrorSymbolNotFound.
+ *
+ * \param fptr - Returned pointer to a unified function
+ * \param library - Library to retrieve function pointer memory from
+ * \param symbol - Name of function pointer to retrieve
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorSymbolNotFound
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cuLibraryGetUnifiedFunction
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryGetUnifiedFunction(void **fptr, cudaLibrary_t library, const char *symbol);
+
+/**
+ * \brief Returns the number of kernels within a library
+ *
+ * Returns in \p count the number of kernels in \p lib.
+ *
+ * \param count - Number of kernels found within the library
+ * \param lib - Library to query
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ *
+ * \sa ::cudaLibraryEnumerateKernels,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryLoadData,
+ * ::cuLibraryGetKernelCount
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryGetKernelCount(unsigned int *count, cudaLibrary_t lib);
+
+/**
+ * \brief Retrieve the kernel handles within a library.
+ *
+ * Returns in \p kernels a maximum number of \p numKernels kernel handles within \p lib.
+ * The returned kernel handle becomes invalid when the library is unloaded.
+ *
+ * \param kernels - Buffer where the kernel handles are returned to
+ * \param numKernels - Maximum number of kernel handles may be returned to the buffer
+ * \param lib - Library to query from
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorCudartUnloading,
+ * ::cudaErrorInitializationError,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle
+ *
+ * \sa ::cudaLibraryGetKernelCount,
+ * ::cuLibraryEnumerateKernels
+ */
+extern __host__ cudaError_t CUDARTAPI cudaLibraryEnumerateKernels(cudaKernel_t *kernels, unsigned int numKernels, cudaLibrary_t lib);
+
+/**
+ * \brief Sets information about a kernel
+ *
+ * This call sets the value of a specified attribute \p attr on the kernel \p kernel
+ * for the requested device \p device to an integer value specified by \p value.
+ * This function returns ::cudaSuccess if the new value of the attribute could be
+ * successfully set. If the set fails, this call will return an error.
+ * Not all attributes can have values set. Attempting to set a value on a read-only
+ * attribute will result in an error (::cudaErrorInvalidValue)
+ *
+ * Note that attributes set using ::cudaFuncSetAttribute() will override the attribute
+ * set by this API irrespective of whether the call to ::cudaFuncSetAttribute() is made
+ * before or after this API call. Because of this and the stricter locking requirements
+ * mentioned below it is suggested that this call be used during the initialization path
+ * and not on each thread accessing \p kernel such as on kernel launches or on the
+ * critical path.
+ *
+ * Valid values for \p attr are:
+ * - ::cudaFuncAttributeMaxDynamicSharedMemorySize - The requested maximum size in bytes of dynamically-allocated shared memory. The sum of this value and the function attribute ::sharedSizeBytes
+ *   cannot exceed the device attribute ::cudaDevAttrMaxSharedMemoryPerBlockOptin. The maximal size of requestable dynamic shared memory may differ by GPU architecture.
+ * - ::cudaFuncAttributePreferredSharedMemoryCarveout - On devices where the L1 cache and shared memory use the same hardware resources, 
+ *   this sets the shared memory carveout preference, in percent of the total shared memory. See ::cudaDevAttrMaxSharedMemoryPerMultiprocessor.
+ *   This is only a hint, and the driver can choose a different ratio if required to execute the function.
+ * - ::cudaFuncAttributeRequiredClusterWidth: The required cluster width in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeRequiredClusterHeight: The required cluster height in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeRequiredClusterDepth: The required cluster depth in
+ *   blocks. The width, height, and depth values must either all be 0 or all be
+ *   positive. The validity of the cluster dimensions is checked at launch time.
+ *   If the value is set during compile time, it cannot be set at runtime.
+ *   Setting it at runtime will return cudaErrorNotPermitted.
+ * - ::cudaFuncAttributeNonPortableClusterSizeAllowed: Indicates whether the
+ *   function can be launched with non-portable cluster size. 1 is allowed, 0 is
+ *   disallowed.
+ * - ::cudaFuncAttributeClusterSchedulingPolicyPreference: The block
+ *   scheduling policy of a function. The value type is cudaClusterSchedulingPolicy.
+ *
+ * \note The API has stricter locking requirements in comparison to its legacy counterpart
+ * ::cudaFuncSetAttribute() due to device-wide semantics. If multiple threads are trying to
+ * set the same attribute on the same device simultaneously, the attribute setting will depend
+ * on the interleavings chosen by the OS scheduler and memory consistency.
+ *
+ * \param kernel  - Kernel to set attribute of
+ * \param attr - Attribute requested
+ * \param value - Value to set
+ * \param device - Device to set attribute of
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDeviceFunction,
+ * ::cudaErrorInvalidValue
+ *
+ * \sa ::cudaLibraryLoadData,
+ * ::cudaLibraryLoadFromFile,
+ * ::cudaLibraryUnload,
+ * ::cudaLibraryGetKernel,
+ * ::cudaLaunchKernel,
+ * ::cudaFuncSetAttribute,
+ * ::cuKernelSetAttribute
+ */
+extern __host__ cudaError_t CUDARTAPI cudaKernelSetAttributeForDevice(cudaKernel_t kernel, enum cudaFuncAttribute attr, int value, int device);
+
+/** @} */ /* END CUDART_LIBRARY */
+
+/** \cond impl_private */
+extern __host__ cudaError_t CUDARTAPI cudaGetExportTable(const void **ppExportTable, const cudaUUID_t *pExportTableId);
+/** \endcond impl_private */
+
+/**
+ * \defgroup CUDART_HIGHLEVEL C++ API Routines
+ *
+ * ___MANBRIEF___ C++ high level API functions of the CUDA runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the C++ high level API functions of the CUDA runtime
+ * application programming interface. To use these functions, your
+ * application needs to be compiled with the \p nvcc compiler.
+ *
+ * \brief C++-style interface built on top of CUDA runtime API
+ */
+
+/**
+ * \defgroup CUDART_DRIVER Interactions with the CUDA Driver API
+ *
+ * ___MANBRIEF___ interactions between CUDA Driver API and CUDA Runtime API
+ * (___CURRENT_FILE___) ___ENDMANBRIEF___
+ *
+ * This section describes the interactions between the CUDA Driver API and the CUDA Runtime API
+ *
+ * @{
+ *
+ * \section CUDART_CUDA_primary Primary Contexts
+ *
+ * There exists a one to one relationship between CUDA devices in the CUDA Runtime
+ * API and ::CUcontext s in the CUDA Driver API within a process.  The specific
+ * context which the CUDA Runtime API uses for a device is called the device's
+ * primary context.  From the perspective of the CUDA Runtime API, a device and 
+ * its primary context are synonymous.
+ *
+ * \section CUDART_CUDA_init Initialization and Tear-Down
+ *
+ * CUDA Runtime API calls operate on the CUDA Driver API ::CUcontext which is current to
+ * to the calling host thread.
+ * 
+ * The function ::cudaInitDevice() ensures that the primary context is initialized
+ * for the requested device but does not make it current to the calling thread. 
+ *
+ * The function ::cudaSetDevice() initializes the primary context for the
+ * specified device and makes it current to the calling thread by calling ::cuCtxSetCurrent().
+ *
+ * The CUDA Runtime API will automatically initialize the primary context for
+ * a device at the first CUDA Runtime API call which requires an active context.
+ * If no ::CUcontext is current to the calling thread when a CUDA Runtime API call 
+ * which requires an active context is made, then the primary context for a device 
+ * will be selected, made current to the calling thread, and initialized.
+ *
+ * The context which the CUDA Runtime API initializes will be initialized using 
+ * the parameters specified by the CUDA Runtime API functions
+ * ::cudaSetDeviceFlags(), 
+ * ::cudaD3D9SetDirect3DDevice(), 
+ * ::cudaD3D10SetDirect3DDevice(), 
+ * ::cudaD3D11SetDirect3DDevice(), 
+ * ::cudaGLSetGLDevice(), and
+ * ::cudaVDPAUSetVDPAUDevice().
+ * Note that these functions will fail with ::cudaErrorSetOnActiveProcess if they are 
+ * called when the primary context for the specified device has already been initialized,
+ * except for ::cudaSetDeviceFlags() which will simply overwrite the previous settings.
+ *
+ * Primary contexts will remain active until they are explicitly deinitialized 
+ * using ::cudaDeviceReset().  The function ::cudaDeviceReset() will deinitialize the 
+ * primary context for the calling thread's current device immediately.  The context 
+ * will remain current to all of the threads that it was current to.  The next CUDA 
+ * Runtime API call on any thread which requires an active context will trigger the 
+ * reinitialization of that device's primary context.
+ *
+ * Note that primary contexts are shared resources. It is recommended that
+ * the primary context not be reset except just before exit or to recover from an
+ * unspecified launch failure.
+ * 
+ * \section CUDART_CUDA_context Context Interoperability
+ *
+ * Note that the use of multiple ::CUcontext s per device within a single process 
+ * will substantially degrade performance and is strongly discouraged.  Instead,
+ * it is highly recommended that the implicit one-to-one device-to-context mapping
+ * for the process provided by the CUDA Runtime API be used.
+ *
+ * If a non-primary ::CUcontext created by the CUDA Driver API is current to a
+ * thread then the CUDA Runtime API calls to that thread will operate on that 
+ * ::CUcontext, with some exceptions listed below.  Interoperability between data
+ * types is discussed in the following sections.
+ *
+ * The function ::cudaPointerGetAttributes() will return the error 
+ * ::cudaErrorIncompatibleDriverContext if the pointer being queried was allocated by a 
+ * non-primary context.  The function ::cudaDeviceEnablePeerAccess() and the rest of 
+ * the peer access API may not be called when a non-primary ::CUcontext is current.  
+ * To use the pointer query and peer access APIs with a context created using the 
+ * CUDA Driver API, it is necessary that the CUDA Driver API be used to access
+ * these features.
+ *
+ * All CUDA Runtime API state (e.g, global variables' addresses and values) travels
+ * with its underlying ::CUcontext.  In particular, if a ::CUcontext is moved from one 
+ * thread to another then all CUDA Runtime API state will move to that thread as well.
+ *
+ * Please note that attaching to legacy contexts (those with a version of 3010 as returned
+ * by ::cuCtxGetApiVersion()) is not possible. The CUDA Runtime will return
+ * ::cudaErrorIncompatibleDriverContext in such cases.
+ *
+ * \section CUDART_CUDA_stream Interactions between CUstream and cudaStream_t
+ *
+ * The types ::CUstream and ::cudaStream_t are identical and may be used interchangeably.
+ *
+ * \section CUDART_CUDA_event Interactions between CUevent and cudaEvent_t
+ *
+ * The types ::CUevent and ::cudaEvent_t are identical and may be used interchangeably.
+ *
+ * \section CUDART_CUDA_array Interactions between CUarray and cudaArray_t 
+ *
+ * The types ::CUarray and struct ::cudaArray * represent the same data type and may be used
+ * interchangeably by casting the two types between each other.
+ *
+ * In order to use a ::CUarray in a CUDA Runtime API function which takes a struct ::cudaArray *,
+ * it is necessary to explicitly cast the ::CUarray to a struct ::cudaArray *.
+ *
+ * In order to use a struct ::cudaArray * in a CUDA Driver API function which takes a ::CUarray,
+ * it is necessary to explicitly cast the struct ::cudaArray * to a ::CUarray .
+ *
+ * \section CUDART_CUDA_graphicsResource Interactions between CUgraphicsResource and cudaGraphicsResource_t
+ *
+ * The types ::CUgraphicsResource and ::cudaGraphicsResource_t represent the same data type and may be used
+ * interchangeably by casting the two types between each other.
+ *
+ * In order to use a ::CUgraphicsResource in a CUDA Runtime API function which takes a 
+ * ::cudaGraphicsResource_t, it is necessary to explicitly cast the ::CUgraphicsResource 
+ * to a ::cudaGraphicsResource_t.
+ *
+ * In order to use a ::cudaGraphicsResource_t in a CUDA Driver API function which takes a
+ * ::CUgraphicsResource, it is necessary to explicitly cast the ::cudaGraphicsResource_t 
+ * to a ::CUgraphicsResource.
+ *
+ * \section CUDART_CUDA_texture_objects Interactions between CUtexObject and cudaTextureObject_t
+ *
+ * The types ::CUtexObject and ::cudaTextureObject_t represent the same data type and may be used
+ * interchangeably by casting the two types between each other.
+ *
+ * In order to use a ::CUtexObject in a CUDA Runtime API function which takes a ::cudaTextureObject_t,
+ * it is necessary to explicitly cast the ::CUtexObject to a ::cudaTextureObject_t.
+ *
+ * In order to use a ::cudaTextureObject_t in a CUDA Driver API function which takes a ::CUtexObject,
+ * it is necessary to explicitly cast the ::cudaTextureObject_t to a ::CUtexObject.
+ *
+ * \section CUDART_CUDA_surface_objects Interactions between CUsurfObject and cudaSurfaceObject_t
+ *
+ * The types ::CUsurfObject and ::cudaSurfaceObject_t represent the same data type and may be used
+ * interchangeably by casting the two types between each other.
+ *
+ * In order to use a ::CUsurfObject in a CUDA Runtime API function which takes a ::cudaSurfaceObject_t,
+ * it is necessary to explicitly cast the ::CUsurfObject to a ::cudaSurfaceObject_t.
+ *
+ * In order to use a ::cudaSurfaceObject_t in a CUDA Driver API function which takes a ::CUsurfObject,
+ * it is necessary to explicitly cast the ::cudaSurfaceObject_t to a ::CUsurfObject.
+ *
+ * \section CUDART_CUDA_module Interactions between CUfunction and cudaFunction_t
+ *
+ * The types ::CUfunction and ::cudaFunction_t represent the same data type and may be used
+ * interchangeably by casting the two types between each other.
+ *
+ * In order to use a ::cudaFunction_t in a CUDA Driver API function which takes a ::CUfunction,
+ * it is necessary to explicitly cast the ::cudaFunction_t to a ::CUfunction.
+ *
+ * \section CUDART_CUDA_library Interactions between CUkernel and cudaKernel_t
+ *
+ * The types ::CUkernel and ::cudaKernel_t represent the same data type and may be used
+ * interchangeably by casting the two types between each other.
+ *
+ * In order to use a ::cudaKernel_t in a CUDA Driver API function which takes a ::CUkernel,
+ * it is necessary to explicitly cast the ::cudaKernel_t to a ::CUkernel.
+ *
+ */
+
+ /**
+  * \brief Get pointer to device entry function that matches entry function \p symbolPtr
+  *
+  * Returns in \p functionPtr the device entry function corresponding to the symbol \p symbolPtr.
+  *
+  * \param functionPtr     - Returns the device entry function
+  * \param symbolPtr       - Pointer to device entry function to search for
+  *
+  * \return
+  * ::cudaSuccess
+  *
+  */
+extern __host__ cudaError_t cudaGetFuncBySymbol(cudaFunction_t* functionPtr, const void* symbolPtr);
+
+/**
+ * \brief Get pointer to device kernel that matches entry function \p entryFuncAddr
+  *
+  * Returns in \p kernelPtr the device kernel corresponding to the entry function \p entryFuncAddr.
+  *
+  * Note that it is possible that there are multiple symbols belonging to different
+  * translation units with the same \p entryFuncAddr registered with this CUDA Runtime
+  * and so the order which the translation units are loaded and registered with the
+  * CUDA Runtime can lead to differing return pointers in \p kernelPtr .
+  * Suggested methods of ensuring uniqueness are to limit visibility of __global__
+  * device functions by using static or hidden visibility attribute in the
+  * respective translation units.
+  *
+  * \param kernelPtr          - Returns the device kernel
+  * \param entryFuncAddr      - Address of device entry function to search kernel for
+  *
+  * \return
+  * ::cudaSuccess
+  *
+  * \sa
+  * \ref ::cudaGetKernel(cudaKernel_t *kernelPtr, const T *entryFuncAddr) "cudaGetKernel (C++ API)"
+  */
+extern __host__ cudaError_t CUDARTAPI cudaGetKernel(cudaKernel_t *kernelPtr, const void *entryFuncAddr);
+
+/** @} */ /* END CUDART_DRIVER */
+
+#if defined(__CUDA_API_VERSION_INTERNAL)
+    #undef cudaMemcpy
+    #undef cudaMemcpyToSymbol
+    #undef cudaMemcpyFromSymbol
+    #undef cudaMemcpy2D
+    #undef cudaMemcpyToArray
+    #undef cudaMemcpy2DToArray
+    #undef cudaMemcpyFromArray
+    #undef cudaMemcpy2DFromArray
+    #undef cudaMemcpyArrayToArray
+    #undef cudaMemcpy2DArrayToArray
+    #undef cudaMemcpy3D
+    #undef cudaMemcpy3DPeer
+    #undef cudaMemset
+    #undef cudaMemset2D
+    #undef cudaMemset3D
+    #undef cudaMemcpyAsync
+    #undef cudaMemcpyToSymbolAsync
+    #undef cudaMemcpyFromSymbolAsync
+    #undef cudaMemcpy2DAsync
+    #undef cudaMemcpyToArrayAsync
+    #undef cudaMemcpy2DToArrayAsync
+    #undef cudaMemcpyFromArrayAsync
+    #undef cudaMemcpy2DFromArrayAsync
+    #undef cudaMemcpy3DAsync
+    #undef cudaMemcpy3DPeerAsync
+    #undef cudaMemcpyBatchAsync
+    #undef cudaMemcpy3DBatchAsync
+    #undef cudaMemsetAsync
+    #undef cudaMemset2DAsync
+    #undef cudaMemset3DAsync
+    #undef cudaStreamQuery
+    #undef cudaStreamGetDevice
+    #undef cudaStreamGetFlags
+    #undef cudaStreamGetId
+    #undef cudaStreamGetPriority
+    #undef cudaEventRecord
+    #undef cudaEventRecordWithFlags
+    #undef cudaStreamWaitEvent
+    #undef cudaStreamAddCallback
+    #undef cudaStreamAttachMemAsync
+    #undef cudaStreamSynchronize
+    #undef cudaLaunchKernel
+    #undef cudaLaunchKernelExC
+    #undef cudaLaunchHostFunc
+    #undef cudaMemPrefetchAsync
+    #undef cudaMemPrefetchAsync_v2
+    #undef cudaLaunchCooperativeKernel
+    #undef cudaSignalExternalSemaphoresAsync
+    #undef cudaWaitExternalSemaphoresAsync
+    #undef cudaGraphInstantiateWithParams
+    #undef cudaGraphUpload
+    #undef cudaGraphLaunch
+    #undef cudaStreamBeginCapture
+    #undef cudaStreamBeginCaptureToGraph
+    #undef cudaStreamEndCapture
+    #undef cudaStreamIsCapturing
+    #undef cudaStreamGetCaptureInfo
+    #undef cudaStreamGetCaptureInfo_v2
+    #undef cudaStreamGetCaptureInfo_v3
+    #undef cudaStreamUpdateCaptureDependencies
+    #undef cudaStreamUpdateCaptureDependencies_v2
+    #undef cudaStreamCopyAttributes
+    #undef cudaStreamGetAttribute
+    #undef cudaStreamSetAttribute
+    #undef cudaMallocAsync
+    #undef cudaFreeAsync
+    #undef cudaMallocFromPoolAsync
+    #undef cudaGetDriverEntryPoint
+    #undef cudaGetDriverEntryPointByVersion
+
+    #undef cudaGetDeviceProperties
+
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyToSymbol(const void *symbol, const void *src, size_t count, size_t offset __dv(0), enum cudaMemcpyKind kind __dv(cudaMemcpyHostToDevice));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyFromSymbol(void *dst, const void *symbol, size_t count, size_t offset __dv(0), enum cudaMemcpyKind kind __dv(cudaMemcpyDeviceToHost));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy2D(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t count, enum cudaMemcpyKind kind);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyFromArray(void *dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, enum cudaMemcpyKind kind);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DFromArray(void *dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, enum cudaMemcpyKind kind);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t count, enum cudaMemcpyKind kind __dv(cudaMemcpyDeviceToDevice));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t width, size_t height, enum cudaMemcpyKind kind __dv(cudaMemcpyDeviceToDevice));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy3D(const struct cudaMemcpy3DParms *p);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy3DPeer(const struct cudaMemcpy3DPeerParms *p);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyBatchAsync(void **dsts, void **srcs, size_t *sizes, size_t count, struct cudaMemcpyAttributes *attrs, size_t *attrsIdxs, size_t numAttrs, size_t *failIdx, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy3DBatchAsync(size_t numOps, struct cudaMemcpy3DBatchOp *opList, size_t *failIdx, unsigned long long flags, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaMemset(void *devPtr, int value, size_t count);
+    extern __host__ cudaError_t CUDARTAPI cudaMemset2D(void *devPtr, size_t pitch, int value, size_t width, size_t height);
+    extern __host__ cudaError_t CUDARTAPI cudaMemset3D(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpyAsync(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyToSymbolAsync(const void *symbol, const void *src, size_t count, size_t offset, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyFromSymbolAsync(void *dst, const void *symbol, size_t count, size_t offset, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy2DAsync(void *dst, size_t dpitch, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void *src, size_t spitch, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpyFromArrayAsync(void *dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy2DFromArrayAsync(void *dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, enum cudaMemcpyKind kind, cudaStream_t stream __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemcpy3DAsync(const struct cudaMemcpy3DParms *p, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaMemcpy3DPeerAsync(const struct cudaMemcpy3DPeerParms *p, cudaStream_t stream __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemsetAsync(void *devPtr, int value, size_t count, cudaStream_t stream __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset2DAsync(void *devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaMemset3DAsync(struct cudaPitchedPtr pitchedDevPtr, int value, struct cudaExtent extent, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaStreamQuery(cudaStream_t stream);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetDevice(cudaStream_t hStream, int *device);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetFlags(cudaStream_t hStream, unsigned int *flags);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetId(cudaStream_t hStream, unsigned long long *streamId);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamGetPriority(cudaStream_t hStream, int *priority);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecord(cudaEvent_t event, cudaStream_t stream __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream __dv(0), unsigned int flags __dv(0));
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamAddCallback(cudaStream_t stream, cudaStreamCallback_t callback, void *userData, unsigned int flags);
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaStreamAttachMemAsync(cudaStream_t stream, void *devPtr, size_t length, unsigned int flags);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamSynchronize(cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaLaunchKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaLaunchKernelExC(const cudaLaunchConfig_t *config, const void *func, void **args);
+    extern __host__ cudaError_t CUDARTAPI cudaLaunchCooperativeKernel(const void *func, dim3 gridDim, dim3 blockDim, void **args, size_t sharedMem, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaLaunchHostFunc(cudaStream_t stream, cudaHostFn_t fn, void *userData);
+    extern __host__ cudaError_t CUDARTAPI cudaMemPrefetchAsync(const void *devPtr, size_t count, int dstDevice, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaMemPrefetchAsync_v2(const void *devPtr, size_t count, struct cudaMemLocation location, unsigned int flags, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaSignalExternalSemaphoresAsync(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreSignalParams_v1 *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaSignalExternalSemaphoresAsync_ptsz(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreSignalParams_v1 *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaSignalExternalSemaphoresAsync_v2(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreSignalParams *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaWaitExternalSemaphoresAsync(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreWaitParams_v1 *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaWaitExternalSemaphoresAsync_ptsz(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreWaitParams_v1 *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaWaitExternalSemaphoresAsync_v2(const cudaExternalSemaphore_t *extSemArray, const struct cudaExternalSemaphoreWaitParams *paramsArray, unsigned int numExtSems, cudaStream_t stream __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaGraphInstantiateWithParams(cudaGraphExec_t *pGraphExec, cudaGraph_t graph, cudaGraphInstantiateParams *instantiateParams);
+    extern __host__ cudaError_t CUDARTAPI cudaGraphUpload(cudaGraphExec_t graphExec, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamBeginCapture(cudaStream_t stream, enum cudaStreamCaptureMode mode);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamBeginCaptureToGraph(cudaStream_t stream, cudaGraph_t graph, const cudaGraphNode_t *dependencies, const cudaGraphEdgeData *dependencyData, size_t numDependencies, enum cudaStreamCaptureMode mode);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamEndCapture(cudaStream_t stream, cudaGraph_t *pGraph);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamIsCapturing(cudaStream_t stream, enum cudaStreamCaptureStatus *pCaptureStatus);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamGetCaptureInfo(cudaStream_t stream, enum cudaStreamCaptureStatus *captureStatus_out, unsigned long long *id_out);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamGetCaptureInfo_ptsz(cudaStream_t stream, enum cudaStreamCaptureStatus *captureStatus_out, unsigned long long *id_out);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamGetCaptureInfo_v2(cudaStream_t stream, enum cudaStreamCaptureStatus *captureStatus_out, unsigned long long *id_out __dv(0), cudaGraph_t *graph_out __dv(0), const cudaGraphNode_t **dependencies_out __dv(0), size_t *numDependencies_out __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaStreamGetCaptureInfo_v3(cudaStream_t stream, enum cudaStreamCaptureStatus *captureStatus_out, unsigned long long *id_out __dv(0), cudaGraph_t *graph_out __dv(0), const cudaGraphNode_t **dependencies_out __dv(0), const cudaGraphEdgeData **edgeData_out __dv(0), size_t *numDependencies_out __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaStreamUpdateCaptureDependencies(cudaStream_t stream, cudaGraphNode_t *dependencies, size_t numDependencies, unsigned int flags __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaStreamUpdateCaptureDependencies_v2(cudaStream_t stream, cudaGraphNode_t *dependencies, const cudaGraphEdgeData *dependencyData, size_t numDependencies, unsigned int flags __dv(0));
+    extern __host__ cudaError_t CUDARTAPI cudaStreamCopyAttributes(cudaStream_t dstStream, cudaStream_t srcStream);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamGetAttribute(cudaStream_t stream, cudaStreamAttrID attr, cudaStreamAttrValue *value);
+    extern __host__ cudaError_t CUDARTAPI cudaStreamSetAttribute(cudaStream_t stream, cudaStreamAttrID attr, const cudaStreamAttrValue *param);
+
+    extern __host__ cudaError_t CUDARTAPI cudaMallocAsync(void **devPtr, size_t size, cudaStream_t hStream);
+    extern __host__ cudaError_t CUDARTAPI cudaFreeAsync(void *devPtr, cudaStream_t hStream);
+    extern __host__ cudaError_t CUDARTAPI cudaMallocFromPoolAsync(void **ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream);
+    extern __host__ cudaError_t CUDARTAPI cudaGetDriverEntryPoint(const char *symbol, void **funcPtr, unsigned long long flags, enum cudaDriverEntryPointQueryResult *driverStatus);
+    extern __host__ cudaError_t CUDARTAPI cudaGetDriverEntryPointByVersion(const char *symbol, void **funcPtr, unsigned int cudaVersion, unsigned long long flags, enum cudaDriverEntryPointQueryResult *driverStatus);
+
+    extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaGetDeviceProperties(struct cudaDeviceProp *prop, int device);
+
+#elif defined(__CUDART_API_PER_THREAD_DEFAULT_STREAM)
+    // nvcc stubs reference the 'cudaLaunch'/'cudaLaunchKernel' identifier even if it was defined
+    // to 'cudaLaunch_ptsz'/'cudaLaunchKernel_ptsz'. Redirect through a static inline function.
+    #undef cudaLaunchKernel
+    static __inline__ __host__ cudaError_t cudaLaunchKernel(const void *func, 
+                                                            dim3 gridDim, dim3 blockDim, 
+                                                            void **args, size_t sharedMem, 
+                                                            cudaStream_t stream)
+    {
+        return cudaLaunchKernel_ptsz(func, gridDim, blockDim, args, sharedMem, stream);
+    }
+    #define cudaLaunchKernel __CUDART_API_PTSZ(cudaLaunchKernel)
+    #undef cudaLaunchKernelExC
+    static __inline__ __host__ cudaError_t cudaLaunchKernelExC(const cudaLaunchConfig_t *config,
+                                                               const void *func,
+                                                                  void **args)
+    {
+        return cudaLaunchKernelExC_ptsz(config, func, args);
+    }
+    #define cudaLaunchKernelExC __CUDART_API_PTSZ(cudaLaunchKernelExC)
+#endif
+
+#if defined(__cplusplus)
+}
+
+#endif /* __cplusplus */
+
+#undef EXCLUDE_FROM_RTC
+#endif /* !__CUDACC_RTC__ */
+
+#undef __dv
+#undef __CUDA_DEPRECATED
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_RUNTIME_API_H__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_RUNTIME_API_H__
+#endif
+
+#endif /* !__CUDA_RUNTIME_API_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_surface_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_surface_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..4a35c215668e98006c3eaa286deb70461eb1fa62
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_surface_types.h
@@ -0,0 +1,76 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_SURFACE_TYPES_H__)
+#define __CUDA_SURFACE_TYPES_H__
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined(__CUDACC_RTC__)
+#define EXCLUDE_FROM_RTC
+#include "channel_descriptor.h"
+#undef EXCLUDE_FROM_RTC
+#endif /* !__CUDACC_RTC__ */
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#endif /* !__CUDA_SURFACE_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_texture_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_texture_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f723db5c682a7b4b05491219df8993f0f6ebd59
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_texture_types.h
@@ -0,0 +1,76 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_TEXTURE_TYPES_H__)
+#define __CUDA_TEXTURE_TYPES_H__
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined(__CUDACC_RTC__)
+#define EXCLUDE_FROM_RTC
+#include "channel_descriptor.h"
+#undef EXCLUDE_FROM_RTC
+#endif /* !__CUDACC_RTC__ */
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#endif /* !__CUDA_TEXTURE_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_vdpau_interop.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_vdpau_interop.h
new file mode 100644
index 0000000000000000000000000000000000000000..2cf1ba357eb02ed82afc2f1812627a8a2d88c6f7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cuda_vdpau_interop.h
@@ -0,0 +1,201 @@
+/*
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_VDPAU_INTEROP_H__)
+#define __CUDA_VDPAU_INTEROP_H__
+
+#include "cuda_runtime_api.h"
+
+#include <vdpau/vdpau.h>
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * \addtogroup CUDART_VDPAU VDPAU Interoperability
+ * This section describes the VDPAU interoperability functions of the CUDA
+ * runtime application programming interface.
+ *
+ * @{
+ */
+
+/**
+ * \brief Gets the CUDA device associated with a VdpDevice.
+ *
+ * Returns the CUDA device associated with a VdpDevice, if applicable.
+ *
+ * \param device - Returns the device associated with vdpDevice, or -1 if
+ * the device associated with vdpDevice is not a compute device.
+ * \param vdpDevice - A VdpDevice handle
+ * \param vdpGetProcAddress - VDPAU's VdpGetProcAddress function pointer
+ *
+ * \return
+ * ::cudaSuccess
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaVDPAUSetVDPAUDevice,
+ * ::cuVDPAUGetDevice
+ */
+extern __host__ cudaError_t CUDARTAPI cudaVDPAUGetDevice(int *device, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+
+/**
+ * \brief Sets a CUDA device to use VDPAU interoperability
+ *
+ * Records \p vdpDevice as the VdpDevice for VDPAU interoperability 
+ * with the CUDA device \p device and sets \p device as the current 
+ * device for the calling host thread.
+ *
+ * This function will immediately initialize the primary context on 
+ * \p device if needed.
+ *
+ * If \p device has already been initialized then this call will fail 
+ * with the error ::cudaErrorSetOnActiveProcess.  In this case it is 
+ * necessary to reset \p device using ::cudaDeviceReset() before 
+ * VDPAU interoperability on \p device may be enabled.
+ *
+ * \param device - Device to use for VDPAU interoperability
+ * \param vdpDevice - The VdpDevice to interoperate with
+ * \param vdpGetProcAddress - VDPAU's VdpGetProcAddress function pointer
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorSetOnActiveProcess
+ * \notefnerr
+ *
+ * \sa ::cudaGraphicsVDPAURegisterVideoSurface,
+ * ::cudaGraphicsVDPAURegisterOutputSurface,
+ * ::cudaDeviceReset
+ */
+extern __host__ cudaError_t CUDARTAPI cudaVDPAUSetVDPAUDevice(int device, VdpDevice vdpDevice, VdpGetProcAddress *vdpGetProcAddress);
+
+/**
+ * \brief Register a VdpVideoSurface object
+ *
+ * Registers the VdpVideoSurface specified by \p vdpSurface for access by CUDA.
+ * A handle to the registered object is returned as \p resource.
+ * The surface's intended usage is specified using \p flags, as follows:
+ *
+ * - ::cudaGraphicsMapFlagsNone: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::cudaGraphicsMapFlagsReadOnly: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::cudaGraphicsMapFlagsWriteDiscard: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * \param resource - Pointer to the returned object handle
+ * \param vdpSurface - VDPAU object to be registered
+ * \param flags - Map flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaVDPAUSetVDPAUDevice,
+ * ::cudaGraphicsUnregisterResource,
+ * ::cudaGraphicsSubResourceGetMappedArray,
+ * ::cuGraphicsVDPAURegisterVideoSurface
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsVDPAURegisterVideoSurface(struct cudaGraphicsResource **resource, VdpVideoSurface vdpSurface, unsigned int flags);
+
+/**
+ * \brief Register a VdpOutputSurface object
+ *
+ * Registers the VdpOutputSurface specified by \p vdpSurface for access by CUDA.
+ * A handle to the registered object is returned as \p resource.
+ * The surface's intended usage is specified using \p flags, as follows:
+ *
+ * - ::cudaGraphicsMapFlagsNone: Specifies no hints about how this
+ *   resource will be used. It is therefore assumed that this resource will be
+ *   read from and written to by CUDA. This is the default value.
+ * - ::cudaGraphicsMapFlagsReadOnly: Specifies that CUDA
+ *   will not write to this resource.
+ * - ::cudaGraphicsMapFlagsWriteDiscard: Specifies that
+ *   CUDA will not read from this resource and will write over the
+ *   entire contents of the resource, so none of the data previously
+ *   stored in the resource will be preserved.
+ *
+ * \param resource - Pointer to the returned object handle
+ * \param vdpSurface - VDPAU object to be registered
+ * \param flags - Map flags
+ *
+ * \return
+ * ::cudaSuccess,
+ * ::cudaErrorInvalidDevice,
+ * ::cudaErrorInvalidValue,
+ * ::cudaErrorInvalidResourceHandle,
+ * ::cudaErrorUnknown
+ * \notefnerr
+ *
+ * \sa
+ * ::cudaVDPAUSetVDPAUDevice,
+ * ::cudaGraphicsUnregisterResource,
+ * ::cudaGraphicsSubResourceGetMappedArray,
+ * ::cuGraphicsVDPAURegisterOutputSurface
+ */
+extern __host__ cudaError_t CUDARTAPI cudaGraphicsVDPAURegisterOutputSurface(struct cudaGraphicsResource **resource, VdpOutputSurface vdpSurface, unsigned int flags);
+
+/** @} */ /* END CUDART_VDPAU */
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* __CUDA_VDPAU_INTEROP_H__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudart_platform.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudart_platform.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f022bbe349eba2219a6b74f1ea315c1ce8551b7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/cudart_platform.h
@@ -0,0 +1,57 @@
+/*
+ * Copyright 2016 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef __CUDART_PLATFORM_H__
+#define __CUDART_PLATFORM_H__
+
+#if ((defined(__linux__) || defined(__QNX__)) && (defined(__arm__) || defined(__aarch64__) || defined(__x86_64__)))
+#define isEglSupported 1
+#endif
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_atomic_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_atomic_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..3fa21ad8c1caef27fe00c315759f9379c247302c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_atomic_functions.h
@@ -0,0 +1,193 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__DEVICE_ATOMIC_FUNCTIONS_H__)
+#define __DEVICE_ATOMIC_FUNCTIONS_H__
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if defined(__CUDACC_RTC__)
+#define __DEVICE_ATOMIC_FUNCTIONS_DECL__ __device__
+#elif defined(_NVHPC_CUDA)
+# define __DEVICE_ATOMIC_FUNCTIONS_DECL__ extern __device__ __cudart_builtin__
+#else /* __CUDACC_RTC__ */
+#define __DEVICE_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/* Add !defined(_NVHPC_CUDA) to avoid empty function definition in PGI CUDA
+ * C++ compiler where the macro __CUDA_ARCH__ is not defined. */
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ */
+
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicAdd(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicAdd(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicSub(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicSub(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicExch(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicExch(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ float atomicExch(float *address, float val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicMin(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicMin(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicMax(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicMax(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicInc(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicDec(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicAnd(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicAnd(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicOr(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicOr(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicXor(int *address, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicXor(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicCAS(int *address, int compare, int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicCAS(unsigned int *address, unsigned int compare, unsigned int val) __DEF_IF_HOST
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if defined(_WIN32)
+# define __DEPRECATED__(msg) __declspec(deprecated(msg))
+#elif (defined(__GNUC__) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 5 && !defined(__clang__))))
+# define __DEPRECATED__(msg) __attribute__((deprecated))
+#else
+# define __DEPRECATED__(msg) __attribute__((deprecated(msg)))
+#endif
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is not valid on compute_70 and above, and should be replaced with "#x"_sync()."\
+    "To continue using "#x"(), specify virtual architecture compute_60 when targeting sm_70 and above, for example, using the pair of compiler options: -arch=compute_60 -code=sm_70."
+#elif defined(_NVHPC_CUDA)
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is not valid on cc70 and above, and should be replaced with "#x"_sync()."
+#else
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is deprecated in favor of "#x"_sync() and may be removed in a future release (Use -Wno-deprecated-declarations to suppress this warning)."
+#endif
+
+extern "C"
+{
+extern __device__ __device_builtin__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__any)) int __any(int cond);
+extern __device__ __device_builtin__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__all)) int __all(int cond);
+}
+
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned long long int atomicAdd(unsigned long long int *address, unsigned long long int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned long long int atomicExch(unsigned long long int *address, unsigned long long int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned long long int atomicCAS(unsigned long long int *address, unsigned long long int compare, unsigned long long int val) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__any)) bool any(bool cond) __DEF_IF_HOST
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__all)) bool all(bool cond) __DEF_IF_HOST
+
+#undef __DEPRECATED__
+#undef __WSB_DEPRECATION_MESSAGE
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEF_IF_HOST
+#undef __DEVICE_ATOMIC_FUNCTIONS_DECL__
+
+#if !defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)
+#include "device_atomic_functions.hpp"
+#endif /* !__CUDACC_RTC__ && defined(__CUDA_ARCH__) */
+
+#undef EXCLUDE_FROM_RTC
+
+#endif /* !__DEVICE_ATOMIC_FUNCTIONS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_atomic_functions.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_atomic_functions.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..153ac712aab4288e4c16dd229460b677e7b61152
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_atomic_functions.hpp
@@ -0,0 +1,254 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__DEVICE_ATOMIC_FUNCTIONS_HPP__)
+#define __DEVICE_ATOMIC_FUNCTIONS_HPP__
+
+#if defined(__CUDACC_RTC__)
+#define __DEVICE_ATOMIC_FUNCTIONS_DECL__ __device__
+#else /* __CUDACC_RTC__ */
+#define __DEVICE_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+
+extern "C"
+{
+extern __device__ __device_builtin__ int          __iAtomicAdd(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicAdd(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ int          __iAtomicExch(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicExch(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ float        __fAtomicExch(float *address, float val);
+extern __device__ __device_builtin__ int          __iAtomicMin(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicMin(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ int          __iAtomicMax(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicMax(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicInc(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicDec(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ int          __iAtomicAnd(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicAnd(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ int          __iAtomicOr(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicOr(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ int          __iAtomicXor(int *address, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicXor(unsigned int *address, unsigned int val);
+extern __device__ __device_builtin__ int          __iAtomicCAS(int *address, int compare, int val);
+extern __device__ __device_builtin__ unsigned int __uAtomicCAS(unsigned int *address, unsigned int compare, unsigned int val);
+
+
+extern __device__ __device_builtin__ unsigned long long int __ullAtomicAdd(unsigned long long int *address, unsigned long long int val);
+extern __device__ __device_builtin__ unsigned long long int __ullAtomicExch(unsigned long long int *address, unsigned long long int val);
+extern __device__ __device_builtin__ unsigned long long int __ullAtomicCAS(unsigned long long int *address, unsigned long long int compare, unsigned long long int val);
+}
+
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicAdd(int *address, int val)
+{
+  return __iAtomicAdd(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicAdd(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAdd(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicSub(int *address, int val)
+{
+  return __iAtomicAdd(address, (unsigned int)-(int)val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicSub(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAdd(address, (unsigned int)-(int)val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicExch(int *address, int val)
+{
+  return __iAtomicExch(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicExch(unsigned int *address, unsigned int val)
+{
+  return __uAtomicExch(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ float atomicExch(float *address, float val)
+{
+  return __fAtomicExch(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicMin(int *address, int val)
+{
+  return __iAtomicMin(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicMin(unsigned int *address, unsigned int val)
+{
+  return __uAtomicMin(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicMax(int *address, int val)
+{
+  return __iAtomicMax(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicMax(unsigned int *address, unsigned int val)
+{
+  return __uAtomicMax(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicInc(unsigned int *address, unsigned int val)
+{
+  return __uAtomicInc(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicDec(unsigned int *address, unsigned int val)
+{
+  return __uAtomicDec(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicAnd(int *address, int val)
+{
+  return __iAtomicAnd(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicAnd(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAnd(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicOr(int *address, int val)
+{
+  return __iAtomicOr(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicOr(unsigned int *address, unsigned int val)
+{
+  return __uAtomicOr(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicXor(int *address, int val)
+{
+  return __iAtomicXor(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicXor(unsigned int *address, unsigned int val)
+{
+  return __uAtomicXor(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ int atomicCAS(int *address, int compare, int val)
+{
+  return __iAtomicCAS(address, compare, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned int atomicCAS(unsigned int *address, unsigned int compare, unsigned int val)
+{
+  return __uAtomicCAS(address, compare, val);
+}
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned long long int atomicAdd(unsigned long long int *address, unsigned long long int val)
+{
+  return __ullAtomicAdd(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned long long int atomicExch(unsigned long long int *address, unsigned long long int val)
+{
+  return __ullAtomicExch(address, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ unsigned long long int atomicCAS(unsigned long long int *address, unsigned long long int compare, unsigned long long int val)
+{
+  return __ullAtomicCAS(address, compare, val);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ bool any(bool cond)
+{
+  return (bool)__any((int)cond);
+}
+
+__DEVICE_ATOMIC_FUNCTIONS_DECL__ bool all(bool cond)
+{
+  return (bool)__all((int)cond);
+}
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEVICE_ATOMIC_FUNCTIONS_DECL__
+
+#endif /* !__DEVICE_ATOMIC_FUNCTIONS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_double_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_double_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..82b25e59b40aeaf1e475ff3179e49640a44918b8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_double_functions.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#if defined(_MSC_VER)
+#pragma message("device_double_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.")
+#else
+#warning "device_double_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead."
+#endif
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_DOUBLE_FUNCTIONS_H_WRAPPER__
+#endif
+
+#include "crt/device_double_functions.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_DOUBLE_FUNCTIONS_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_DOUBLE_FUNCTIONS_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..0094cc9a0a57f53f47421a8ecc400fb84c26babe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_functions.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#if defined(_MSC_VER)
+#pragma message("device_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.")
+#else
+#warning "device_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead."
+#endif
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_FUNCTIONS_H_WRAPPER__
+#endif
+
+#include "crt/device_functions.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_FUNCTIONS_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_FUNCTIONS_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_launch_parameters.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_launch_parameters.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f552db8faab7d21e90e06a1ea2184a5563d3bf2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_launch_parameters.h
@@ -0,0 +1,118 @@
+/*
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__DEVICE_LAUNCH_PARAMETERS_H__)
+#define __DEVICE_LAUNCH_PARAMETERS_H__
+
+#include "vector_types.h"
+
+#if !defined(__STORAGE__)
+
+#if defined(__CUDACC_RTC__)
+#define __STORAGE__ \
+        extern const __device__
+#else /* !__CUDACC_RTC__ */
+#define __STORAGE__ \
+        extern const
+#endif /* __CUDACC_RTC__ */
+
+#endif /* __STORAGE__ */
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+uint3 __device_builtin__ __STORAGE__ threadIdx;
+uint3 __device_builtin__ __STORAGE__ blockIdx;
+dim3 __device_builtin__ __STORAGE__ blockDim;
+dim3 __device_builtin__ __STORAGE__ gridDim;
+int __device_builtin__ __STORAGE__ warpSize;
+
+#undef __STORAGE__
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#if !defined(__cudaGet_threadIdx)
+
+#define __cudaGet_threadIdx() \
+        threadIdx
+
+#endif /* __cudaGet_threadIdx */
+
+#if !defined(__cudaGet_blockIdx)
+
+#define __cudaGet_blockIdx() \
+        blockIdx
+
+#endif /* __cudaGet_blockIdx */
+
+#if !defined(__cudaGet_blockDim)
+
+#define __cudaGet_blockDim() \
+        blockDim
+
+#endif /* __cudaGet_blockDim */
+
+#if !defined(__cudaGet_gridDim)
+
+#define __cudaGet_gridDim() \
+        gridDim
+
+#endif /* __cudaGet_gridDim */
+
+#if !defined(__cudaGet_warpSize)
+
+#define __cudaGet_warpSize() \
+        warpSize
+
+#endif /* __cudaGet_warpSize */
+
+#endif /* !__DEVICE_LAUNCH_PARAMETERS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b575a1014c6cdb9bf2f722c2a67e329186079e6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/device_types.h
@@ -0,0 +1,81 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__DEVICE_TYPES_H__)
+#define __DEVICE_TYPES_H__
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_TYPES_H__
+#endif
+
+#ifndef __DOXYGEN_ONLY__
+#include "crt/host_defines.h"
+#endif
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+enum __device_builtin__ cudaRoundMode
+{
+    cudaRoundNearest,
+    cudaRoundZero,
+    cudaRoundPosInf,
+    cudaRoundMinInf
+};
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_TYPES_H__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DEVICE_TYPES_H__
+#endif
+
+#endif /* !__DEVICE_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/driver_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/driver_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..94767974220594550d496cad4d14c45349b27737
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/driver_functions.h
@@ -0,0 +1,145 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__DRIVER_FUNCTIONS_H__)
+#define __DRIVER_FUNCTIONS_H__
+
+#include "builtin_types.h"
+#include "crt/host_defines.h"
+#include "driver_types.h"
+
+/**
+ * \addtogroup CUDART_MEMORY
+ *
+ * @{
+ */
+
+/**
+ * \brief Returns a cudaPitchedPtr based on input parameters
+ *
+ * Returns a ::cudaPitchedPtr based on the specified input parameters \p d,
+ * \p p, \p xsz, and \p ysz.
+ *
+ * \param d   - Pointer to allocated memory
+ * \param p   - Pitch of allocated memory in bytes
+ * \param xsz - Logical width of allocation in elements
+ * \param ysz - Logical height of allocation in elements
+ *
+ * \return
+ * ::cudaPitchedPtr specified by \p d, \p p, \p xsz, and \p ysz
+ *
+ * \sa make_cudaExtent, make_cudaPos
+ */
+static __inline__ __host__ struct cudaPitchedPtr make_cudaPitchedPtr(void *d, size_t p, size_t xsz, size_t ysz) 
+{
+  struct cudaPitchedPtr s;
+
+  s.ptr   = d;
+  s.pitch = p;
+  s.xsize = xsz;
+  s.ysize = ysz;
+
+  return s;
+}
+
+/**
+ * \brief Returns a cudaPos based on input parameters
+ *
+ * Returns a ::cudaPos based on the specified input parameters \p x,
+ * \p y, and \p z.
+ *
+ * \param x - X position
+ * \param y - Y position
+ * \param z - Z position
+ *
+ * \return
+ * ::cudaPos specified by \p x, \p y, and \p z
+ *
+ * \sa make_cudaExtent, make_cudaPitchedPtr
+ */
+static __inline__ __host__ struct cudaPos make_cudaPos(size_t x, size_t y, size_t z) 
+{
+  struct cudaPos p;
+
+  p.x = x;
+  p.y = y;
+  p.z = z;
+
+  return p;
+}
+
+/**
+ * \brief Returns a cudaExtent based on input parameters
+ *
+ * Returns a ::cudaExtent based on the specified input parameters \p w,
+ * \p h, and \p d.
+ *
+ * \param w - Width in elements when referring to array memory, in bytes when referring to linear memory
+ * \param h - Height in elements
+ * \param d - Depth in elements
+ *
+ * \return
+ * ::cudaExtent specified by \p w, \p h, and \p d
+ *
+ * \sa make_cudaPitchedPtr, make_cudaPos
+ */
+static __inline__ __host__ struct cudaExtent make_cudaExtent(size_t w, size_t h, size_t d) 
+{
+  struct cudaExtent e;
+
+  e.width  = w;
+  e.height = h;
+  e.depth  = d;
+
+  return e;
+}
+
+/** @} */ /* END CUDART_MEMORY */
+
+#endif /* !__DRIVER_FUNCTIONS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/driver_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/driver_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..541cbc6eb76dfdf42c407ca7c9537b603714d64c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/driver_types.h
@@ -0,0 +1,4110 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__DRIVER_TYPES_H__)
+#define __DRIVER_TYPES_H__
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DRIVER_TYPES_H__
+#endif
+
+#ifndef __DOXYGEN_ONLY__
+#include "crt/host_defines.h"
+#endif
+#include "vector_types.h"
+
+
+
+#ifndef __CUDACC_RTC_MINIMAL__
+/**
+ * \defgroup CUDART_TYPES Data types used by CUDA Runtime
+ * \ingroup CUDART
+ *
+ * @{
+ */
+
+/*******************************************************************************
+*                                                                              *
+*  TYPE DEFINITIONS USED BY RUNTIME API                                        *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined(__CUDA_INTERNAL_COMPILATION__)
+
+
+#if !defined(__CUDACC_RTC__)
+#include <limits.h>
+#include <stddef.h>
+#endif /* !defined(__CUDACC_RTC__) */
+
+#define cudaHostAllocDefault                0x00  /**< Default page-locked allocation flag */
+#define cudaHostAllocPortable               0x01  /**< Pinned memory accessible by all CUDA contexts */
+#define cudaHostAllocMapped                 0x02  /**< Map allocation into device space */
+#define cudaHostAllocWriteCombined          0x04  /**< Write-combined memory */
+
+#define cudaHostRegisterDefault             0x00  /**< Default host memory registration flag */
+#define cudaHostRegisterPortable            0x01  /**< Pinned memory accessible by all CUDA contexts */
+#define cudaHostRegisterMapped              0x02  /**< Map registered memory into device space */
+#define cudaHostRegisterIoMemory            0x04  /**< Memory-mapped I/O space */
+#define cudaHostRegisterReadOnly            0x08  /**< Memory-mapped read-only */
+
+#define cudaPeerAccessDefault               0x00  /**< Default peer addressing enable flag */
+
+#define cudaStreamDefault                   0x00  /**< Default stream flag */
+#define cudaStreamNonBlocking               0x01  /**< Stream does not synchronize with stream 0 (the NULL stream) */
+
+ /**
+ * Legacy stream handle
+ *
+ * Stream handle that can be passed as a cudaStream_t to use an implicit stream
+ * with legacy synchronization behavior.
+ *
+ * See details of the \link_sync_behavior
+ */
+#define cudaStreamLegacy                    ((cudaStream_t)0x1)
+
+/**
+ * Per-thread stream handle
+ *
+ * Stream handle that can be passed as a cudaStream_t to use an implicit stream
+ * with per-thread synchronization behavior.
+ *
+ * See details of the \link_sync_behavior
+ */
+#define cudaStreamPerThread                 ((cudaStream_t)0x2)
+
+#define cudaEventDefault                    0x00  /**< Default event flag */
+#define cudaEventBlockingSync               0x01  /**< Event uses blocking synchronization */
+#define cudaEventDisableTiming              0x02  /**< Event will not record timing data */
+#define cudaEventInterprocess               0x04  /**< Event is suitable for interprocess use. cudaEventDisableTiming must be set */
+
+#define cudaEventRecordDefault              0x00  /**< Default event record flag */
+#define cudaEventRecordExternal             0x01  /**< Event is captured in the graph as an external event node when performing stream capture */
+
+#define cudaEventWaitDefault                0x00  /**< Default event wait flag */
+#define cudaEventWaitExternal               0x01  /**< Event is captured in the graph as an external event node when performing stream capture */
+
+#define cudaDeviceScheduleAuto              0x00  /**< Device flag - Automatic scheduling */
+#define cudaDeviceScheduleSpin              0x01  /**< Device flag - Spin default scheduling */
+#define cudaDeviceScheduleYield             0x02  /**< Device flag - Yield default scheduling */
+#define cudaDeviceScheduleBlockingSync      0x04  /**< Device flag - Use blocking synchronization */
+#define cudaDeviceBlockingSync              0x04  /**< Device flag - Use blocking synchronization 
+                                                    *  \deprecated This flag was deprecated as of CUDA 4.0 and
+                                                    *  replaced with ::cudaDeviceScheduleBlockingSync. */
+#define cudaDeviceScheduleMask              0x07  /**< Device schedule flags mask */
+#define cudaDeviceMapHost                   0x08  /**< Device flag - Support mapped pinned allocations */
+#define cudaDeviceLmemResizeToMax           0x10  /**< Device flag - Keep local memory allocation after launch */
+#define cudaDeviceSyncMemops                0x80  /**< Device flag - Ensure synchronous memory operations on this context will synchronize */
+#define cudaDeviceMask                      0xff  /**< Device flags mask */
+
+#define cudaArrayDefault                    0x00  /**< Default CUDA array allocation flag */
+#define cudaArrayLayered                    0x01  /**< Must be set in cudaMalloc3DArray to create a layered CUDA array */
+#define cudaArraySurfaceLoadStore           0x02  /**< Must be set in cudaMallocArray or cudaMalloc3DArray in order to bind surfaces to the CUDA array */
+#define cudaArrayCubemap                    0x04  /**< Must be set in cudaMalloc3DArray to create a cubemap CUDA array */
+#define cudaArrayTextureGather              0x08  /**< Must be set in cudaMallocArray or cudaMalloc3DArray in order to perform texture gather operations on the CUDA array */
+#define cudaArrayColorAttachment            0x20  /**< Must be set in cudaExternalMemoryGetMappedMipmappedArray if the mipmapped array is used as a color target in a graphics API */
+#define cudaArraySparse                     0x40  /**< Must be set in cudaMallocArray, cudaMalloc3DArray or cudaMallocMipmappedArray in order to create a sparse CUDA array or CUDA mipmapped array */
+#define cudaArrayDeferredMapping            0x80  /**< Must be set in cudaMallocArray, cudaMalloc3DArray or cudaMallocMipmappedArray in order to create a deferred mapping CUDA array or CUDA mipmapped array */
+
+#define cudaIpcMemLazyEnablePeerAccess      0x01  /**< Automatically enable peer access between remote devices as needed */
+
+#define cudaMemAttachGlobal                 0x01  /**< Memory can be accessed by any stream on any device*/
+#define cudaMemAttachHost                   0x02  /**< Memory cannot be accessed by any stream on any device */
+#define cudaMemAttachSingle                 0x04  /**< Memory can only be accessed by a single stream on the associated device */
+
+#define cudaOccupancyDefault                0x00  /**< Default behavior */
+#define cudaOccupancyDisableCachingOverride 0x01  /**< Assume global caching is enabled and cannot be automatically turned off */
+
+#define cudaCpuDeviceId                     ((int)-1) /**< Device id that represents the CPU */
+#define cudaInvalidDeviceId                 ((int)-2) /**< Device id that represents an invalid device */
+#define cudaInitDeviceFlagsAreValid         0x01  /**< Tell the CUDA runtime that DeviceFlags is being set in cudaInitDevice call */
+/**
+ * If set, each kernel launched as part of ::cudaLaunchCooperativeKernelMultiDevice only
+ * waits for prior work in the stream corresponding to that GPU to complete before the
+ * kernel begins execution.
+ */
+#define cudaCooperativeLaunchMultiDeviceNoPreSync  0x01
+
+/**
+ * If set, any subsequent work pushed in a stream that participated in a call to
+ * ::cudaLaunchCooperativeKernelMultiDevice will only wait for the kernel launched on
+ * the GPU corresponding to that stream to complete before it begins execution.
+ */
+#define cudaCooperativeLaunchMultiDeviceNoPostSync 0x02
+
+#endif /* !__CUDA_INTERNAL_COMPILATION__ */
+
+/** \cond impl_private */
+#if defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED)
+#define __CUDA_DEPRECATED
+#elif defined(_MSC_VER)
+#define __CUDA_DEPRECATED __declspec(deprecated)
+#elif defined(__GNUC__)
+#define __CUDA_DEPRECATED __attribute__((deprecated))
+#else
+#define __CUDA_DEPRECATED
+#endif
+/** \endcond impl_private */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+/**
+ * CUDA error types
+ */
+enum __device_builtin__ cudaError
+{
+    /**
+     * The API call returned with no errors. In the case of query calls, this
+     * also means that the operation being queried is complete (see
+     * ::cudaEventQuery() and ::cudaStreamQuery()).
+     */
+    cudaSuccess                           =      0,
+  
+    /**
+     * This indicates that one or more of the parameters passed to the API call
+     * is not within an acceptable range of values.
+     */
+    cudaErrorInvalidValue                 =     1,
+  
+    /**
+     * The API call failed because it was unable to allocate enough memory or
+     * other resources to perform the requested operation.
+     */
+    cudaErrorMemoryAllocation             =      2,
+  
+    /**
+     * The API call failed because the CUDA driver and runtime could not be
+     * initialized.
+     */
+    cudaErrorInitializationError          =      3,
+  
+    /**
+     * This indicates that a CUDA Runtime API call cannot be executed because
+     * it is being called during process shut down, at a point in time after
+     * CUDA driver has been unloaded.
+     */
+    cudaErrorCudartUnloading              =     4,
+
+    /**
+     * This indicates profiler is not initialized for this run. This can
+     * happen when the application is running with external profiling tools
+     * like visual profiler.
+     */
+    cudaErrorProfilerDisabled             =     5,
+
+    /**
+     * \deprecated
+     * This error return is deprecated as of CUDA 5.0. It is no longer an error
+     * to attempt to enable/disable the profiling via ::cudaProfilerStart or
+     * ::cudaProfilerStop without initialization.
+     */
+    cudaErrorProfilerNotInitialized       =     6,
+
+    /**
+     * \deprecated
+     * This error return is deprecated as of CUDA 5.0. It is no longer an error
+     * to call cudaProfilerStart() when profiling is already enabled.
+     */
+    cudaErrorProfilerAlreadyStarted       =     7,
+
+    /**
+     * \deprecated
+     * This error return is deprecated as of CUDA 5.0. It is no longer an error
+     * to call cudaProfilerStop() when profiling is already disabled.
+     */
+     cudaErrorProfilerAlreadyStopped       =    8,
+    /**
+     * This indicates that a kernel launch is requesting resources that can
+     * never be satisfied by the current device. Requesting more shared memory
+     * per block than the device supports will trigger this error, as will
+     * requesting too many threads or blocks. See ::cudaDeviceProp for more
+     * device limitations.
+     */
+    cudaErrorInvalidConfiguration         =      9,
+  
+    /**
+     * This indicates that one or more of the pitch-related parameters passed
+     * to the API call is not within the acceptable range for pitch.
+     */
+    cudaErrorInvalidPitchValue            =     12,
+  
+    /**
+     * This indicates that the symbol name/identifier passed to the API call
+     * is not a valid name or identifier.
+     */
+    cudaErrorInvalidSymbol                =     13,
+
+    /**
+     * This indicates that at least one host pointer passed to the API call is
+     * not a valid host pointer.
+     * \deprecated
+     * This error return is deprecated as of CUDA 10.1.
+     */
+    cudaErrorInvalidHostPointer           =     16,
+  
+    /**
+     * This indicates that at least one device pointer passed to the API call is
+     * not a valid device pointer.
+     * \deprecated
+     * This error return is deprecated as of CUDA 10.1.
+     */
+    cudaErrorInvalidDevicePointer         =     17,
+    /**
+     * This indicates that the texture passed to the API call is not a valid
+     * texture.
+     */
+    cudaErrorInvalidTexture               =     18,
+  
+    /**
+     * This indicates that the texture binding is not valid. This occurs if you
+     * call ::cudaGetTextureAlignmentOffset() with an unbound texture.
+     */
+    cudaErrorInvalidTextureBinding        =     19,
+  
+    /**
+     * This indicates that the channel descriptor passed to the API call is not
+     * valid. This occurs if the format is not one of the formats specified by
+     * ::cudaChannelFormatKind, or if one of the dimensions is invalid.
+     */
+    cudaErrorInvalidChannelDescriptor     =     20,
+  
+    /**
+     * This indicates that the direction of the memcpy passed to the API call is
+     * not one of the types specified by ::cudaMemcpyKind.
+     */
+    cudaErrorInvalidMemcpyDirection       =     21,
+
+    /**
+     * This indicated that the user has taken the address of a constant variable,
+     * which was forbidden up until the CUDA 3.1 release.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Variables in constant
+     * memory may now have their address taken by the runtime via
+     * ::cudaGetSymbolAddress().
+     */
+    cudaErrorAddressOfConstant            =     22,
+  
+    /**
+     * This indicated that a texture fetch was not able to be performed.
+     * This was previously used for device emulation of texture operations.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
+     * removed with the CUDA 3.1 release.
+     */
+    cudaErrorTextureFetchFailed           =     23,
+  
+    /**
+     * This indicated that a texture was not bound for access.
+     * This was previously used for device emulation of texture operations.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
+     * removed with the CUDA 3.1 release.
+     */
+    cudaErrorTextureNotBound              =     24,
+  
+    /**
+     * This indicated that a synchronization operation had failed.
+     * This was previously used for some device emulation functions.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
+     * removed with the CUDA 3.1 release.
+     */
+    cudaErrorSynchronizationError         =     25,
+    /**
+     * This indicates that a non-float texture was being accessed with linear
+     * filtering. This is not supported by CUDA.
+     */
+    cudaErrorInvalidFilterSetting         =     26,
+  
+    /**
+     * This indicates that an attempt was made to read an unsupported data type as a
+     * normalized float. This is not supported by CUDA.
+     */
+    cudaErrorInvalidNormSetting           =     27,
+
+    /**
+     * Mixing of device and device emulation code was not allowed.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
+     * removed with the CUDA 3.1 release.
+     */
+    cudaErrorMixedDeviceExecution         =     28,
+
+    /**
+     * This indicates that the API call is not yet implemented. Production
+     * releases of CUDA will never return this error.
+     * \deprecated
+     * This error return is deprecated as of CUDA 4.1.
+     */
+    cudaErrorNotYetImplemented            =     31,
+  
+    /**
+     * This indicated that an emulated device pointer exceeded the 32-bit address
+     * range.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
+     * removed with the CUDA 3.1 release.
+     */
+    cudaErrorMemoryValueTooLarge          =     32,
+    /**
+     * This indicates that the CUDA driver that the application has loaded is a
+     * stub library. Applications that run with the stub rather than a real
+     * driver loaded will result in CUDA API returning this error.
+     */
+    cudaErrorStubLibrary                  =     34,
+
+    /**
+     * This indicates that the installed NVIDIA CUDA driver is older than the
+     * CUDA runtime library. This is not a supported configuration. Users should
+     * install an updated NVIDIA display driver to allow the application to run.
+     */
+    cudaErrorInsufficientDriver           =     35,
+
+    /**
+     * This indicates that the API call requires a newer CUDA driver than the one
+     * currently installed. Users should install an updated NVIDIA CUDA driver
+     * to allow the API call to succeed.
+     */
+    cudaErrorCallRequiresNewerDriver      =     36,
+  
+    /**
+     * This indicates that the surface passed to the API call is not a valid
+     * surface.
+     */
+    cudaErrorInvalidSurface               =     37,
+  
+    /**
+     * This indicates that multiple global or constant variables (across separate
+     * CUDA source files in the application) share the same string name.
+     */
+    cudaErrorDuplicateVariableName        =     43,
+  
+    /**
+     * This indicates that multiple textures (across separate CUDA source
+     * files in the application) share the same string name.
+     */
+    cudaErrorDuplicateTextureName         =     44,
+  
+    /**
+     * This indicates that multiple surfaces (across separate CUDA source
+     * files in the application) share the same string name.
+     */
+    cudaErrorDuplicateSurfaceName         =     45,
+  
+    /**
+     * This indicates that all CUDA devices are busy or unavailable at the current
+     * time. Devices are often busy/unavailable due to use of
+     * ::cudaComputeModeProhibited, ::cudaComputeModeExclusiveProcess, or when long
+     * running CUDA kernels have filled up the GPU and are blocking new work
+     * from starting. They can also be unavailable due to memory constraints
+     * on a device that already has active CUDA work being performed.
+     */
+    cudaErrorDevicesUnavailable           =     46,
+  
+    /**
+     * This indicates that the current context is not compatible with this
+     * the CUDA Runtime. This can only occur if you are using CUDA
+     * Runtime/Driver interoperability and have created an existing Driver
+     * context using the driver API. The Driver context may be incompatible
+     * either because the Driver context was created using an older version 
+     * of the API, because the Runtime API call expects a primary driver 
+     * context and the Driver context is not primary, or because the Driver 
+     * context has been destroyed. Please see \ref CUDART_DRIVER "Interactions 
+     * with the CUDA Driver API" for more information.
+     */
+    cudaErrorIncompatibleDriverContext    =     49,
+    
+    /**
+     * The device function being invoked (usually via ::cudaLaunchKernel()) was not
+     * previously configured via the ::cudaConfigureCall() function.
+     */
+    cudaErrorMissingConfiguration         =      52,
+
+    /**
+     * This indicated that a previous kernel launch failed. This was previously
+     * used for device emulation of kernel launches.
+     * \deprecated
+     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
+     * removed with the CUDA 3.1 release.
+     */
+    cudaErrorPriorLaunchFailure           =      53,
+    /**
+     * This error indicates that a device runtime grid launch did not occur 
+     * because the depth of the child grid would exceed the maximum supported
+     * number of nested grid launches. 
+     */
+    cudaErrorLaunchMaxDepthExceeded       =     65,
+
+    /**
+     * This error indicates that a grid launch did not occur because the kernel 
+     * uses file-scoped textures which are unsupported by the device runtime. 
+     * Kernels launched via the device runtime only support textures created with 
+     * the Texture Object API's.
+     */
+    cudaErrorLaunchFileScopedTex          =     66,
+
+    /**
+     * This error indicates that a grid launch did not occur because the kernel 
+     * uses file-scoped surfaces which are unsupported by the device runtime.
+     * Kernels launched via the device runtime only support surfaces created with
+     * the Surface Object API's.
+     */
+    cudaErrorLaunchFileScopedSurf         =     67,
+
+    /**
+     * This error indicates that a call to ::cudaDeviceSynchronize made from
+     * the device runtime failed because the call was made at grid depth greater
+     * than than either the default (2 levels of grids) or user specified device
+     * limit ::cudaLimitDevRuntimeSyncDepth. To be able to synchronize on
+     * launched grids at a greater depth successfully, the maximum nested
+     * depth at which ::cudaDeviceSynchronize will be called must be specified
+     * with the ::cudaLimitDevRuntimeSyncDepth limit to the ::cudaDeviceSetLimit
+     * api before the host-side launch of a kernel using the device runtime.
+     * Keep in mind that additional levels of sync depth require the runtime
+     * to reserve large amounts of device memory that cannot be used for
+     * user allocations. Note that ::cudaDeviceSynchronize made from device
+     * runtime is only supported on devices of compute capability < 9.0.
+     */
+    cudaErrorSyncDepthExceeded            =     68,
+
+    /**
+     * This error indicates that a device runtime grid launch failed because
+     * the launch would exceed the limit ::cudaLimitDevRuntimePendingLaunchCount.
+     * For this launch to proceed successfully, ::cudaDeviceSetLimit must be
+     * called to set the ::cudaLimitDevRuntimePendingLaunchCount to be higher 
+     * than the upper bound of outstanding launches that can be issued to the
+     * device runtime. Keep in mind that raising the limit of pending device
+     * runtime launches will require the runtime to reserve device memory that
+     * cannot be used for user allocations.
+     */
+    cudaErrorLaunchPendingCountExceeded   =     69,
+  
+    /**
+     * The requested device function does not exist or is not compiled for the
+     * proper device architecture.
+     */
+    cudaErrorInvalidDeviceFunction        =      98,
+  
+    /**
+     * This indicates that no CUDA-capable devices were detected by the installed
+     * CUDA driver.
+     */
+    cudaErrorNoDevice                     =     100,
+  
+    /**
+     * This indicates that the device ordinal supplied by the user does not
+     * correspond to a valid CUDA device or that the action requested is
+     * invalid for the specified device.
+     */
+    cudaErrorInvalidDevice                =     101,
+
+    /**
+     * This indicates that the device doesn't have a valid Grid License.
+     */
+    cudaErrorDeviceNotLicensed            =     102,
+
+   /**
+    * By default, the CUDA runtime may perform a minimal set of self-tests,
+    * as well as CUDA driver tests, to establish the validity of both.
+    * Introduced in CUDA 11.2, this error return indicates that at least one
+    * of these tests has failed and the validity of either the runtime
+    * or the driver could not be established.
+    */
+   cudaErrorSoftwareValidityNotEstablished  =     103,
+
+    /**
+     * This indicates an internal startup failure in the CUDA runtime.
+     */
+    cudaErrorStartupFailure               =    127,
+  
+    /**
+     * This indicates that the device kernel image is invalid.
+     */
+    cudaErrorInvalidKernelImage           =     200,
+
+    /**
+     * This most frequently indicates that there is no context bound to the
+     * current thread. This can also be returned if the context passed to an
+     * API call is not a valid handle (such as a context that has had
+     * ::cuCtxDestroy() invoked on it). This can also be returned if a user
+     * mixes different API versions (i.e. 3010 context with 3020 API calls).
+     * See ::cuCtxGetApiVersion() for more details.
+     */
+    cudaErrorDeviceUninitialized          =     201,
+
+    /**
+     * This indicates that the buffer object could not be mapped.
+     */
+    cudaErrorMapBufferObjectFailed        =     205,
+  
+    /**
+     * This indicates that the buffer object could not be unmapped.
+     */
+    cudaErrorUnmapBufferObjectFailed      =     206,
+
+    /**
+     * This indicates that the specified array is currently mapped and thus
+     * cannot be destroyed.
+     */
+    cudaErrorArrayIsMapped                =     207,
+
+    /**
+     * This indicates that the resource is already mapped.
+     */
+    cudaErrorAlreadyMapped                =     208,
+  
+    /**
+     * This indicates that there is no kernel image available that is suitable
+     * for the device. This can occur when a user specifies code generation
+     * options for a particular CUDA source file that do not include the
+     * corresponding device configuration.
+     */
+    cudaErrorNoKernelImageForDevice       =     209,
+
+    /**
+     * This indicates that a resource has already been acquired.
+     */
+    cudaErrorAlreadyAcquired              =     210,
+
+    /**
+     * This indicates that a resource is not mapped.
+     */
+    cudaErrorNotMapped                    =     211,
+
+    /**
+     * This indicates that a mapped resource is not available for access as an
+     * array.
+     */
+    cudaErrorNotMappedAsArray             =     212,
+
+    /**
+     * This indicates that a mapped resource is not available for access as a
+     * pointer.
+     */
+    cudaErrorNotMappedAsPointer           =     213,
+  
+    /**
+     * This indicates that an uncorrectable ECC error was detected during
+     * execution.
+     */
+    cudaErrorECCUncorrectable             =     214,
+  
+    /**
+     * This indicates that the ::cudaLimit passed to the API call is not
+     * supported by the active device.
+     */
+    cudaErrorUnsupportedLimit             =     215,
+    
+    /**
+     * This indicates that a call tried to access an exclusive-thread device that 
+     * is already in use by a different thread.
+     */
+    cudaErrorDeviceAlreadyInUse           =     216,
+
+    /**
+     * This error indicates that P2P access is not supported across the given
+     * devices.
+     */
+    cudaErrorPeerAccessUnsupported        =     217,
+
+    /**
+     * A PTX compilation failed. The runtime may fall back to compiling PTX if
+     * an application does not contain a suitable binary for the current device.
+     */
+    cudaErrorInvalidPtx                   =     218,
+
+    /**
+     * This indicates an error with the OpenGL or DirectX context.
+     */
+    cudaErrorInvalidGraphicsContext       =     219,
+
+    /**
+     * This indicates that an uncorrectable NVLink error was detected during the
+     * execution.
+     */
+    cudaErrorNvlinkUncorrectable          =     220,
+
+    /**
+     * This indicates that the PTX JIT compiler library was not found. The JIT Compiler
+     * library is used for PTX compilation. The runtime may fall back to compiling PTX
+     * if an application does not contain a suitable binary for the current device.
+     */
+    cudaErrorJitCompilerNotFound          =     221,
+
+    /**
+     * This indicates that the provided PTX was compiled with an unsupported toolchain.
+     * The most common reason for this, is the PTX was generated by a compiler newer
+     * than what is supported by the CUDA driver and PTX JIT compiler.
+     */
+    cudaErrorUnsupportedPtxVersion        =     222,
+
+    /**
+     * This indicates that the JIT compilation was disabled. The JIT compilation compiles
+     * PTX. The runtime may fall back to compiling PTX if an application does not contain
+     * a suitable binary for the current device.
+     */
+    cudaErrorJitCompilationDisabled       =     223,
+
+    /**
+     * This indicates that the provided execution affinity is not supported by the device.
+     */
+    cudaErrorUnsupportedExecAffinity      =     224,
+
+    /**
+     * This indicates that the code to be compiled by the PTX JIT contains
+     * unsupported call to cudaDeviceSynchronize.
+     */
+    cudaErrorUnsupportedDevSideSync       =     225,
+
+    /**
+     * This indicates that an exception occurred on the device that is now
+     * contained by the GPU's error containment capability. Common causes are -
+     * a. Certain types of invalid accesses of peer GPU memory over nvlink
+     * b. Certain classes of hardware errors
+     * This leaves the process in an inconsistent state and any further CUDA
+     * work will return the same error. To continue using CUDA, the process must
+     * be terminated and relaunched.
+     */
+    cudaErrorContained                    =     226,
+
+    /**
+     * This indicates that the device kernel source is invalid.
+     */
+    cudaErrorInvalidSource                =     300,
+
+    /**
+     * This indicates that the file specified was not found.
+     */
+    cudaErrorFileNotFound                 =     301,
+  
+    /**
+     * This indicates that a link to a shared object failed to resolve.
+     */
+    cudaErrorSharedObjectSymbolNotFound   =     302,
+  
+    /**
+     * This indicates that initialization of a shared object failed.
+     */
+    cudaErrorSharedObjectInitFailed       =     303,
+
+    /**
+     * This error indicates that an OS call failed.
+     */
+    cudaErrorOperatingSystem              =     304,
+  
+    /**
+     * This indicates that a resource handle passed to the API call was not
+     * valid. Resource handles are opaque types like ::cudaStream_t and
+     * ::cudaEvent_t.
+     */
+    cudaErrorInvalidResourceHandle        =     400,
+
+    /**
+     * This indicates that a resource required by the API call is not in a
+     * valid state to perform the requested operation.
+     */
+    cudaErrorIllegalState                 =     401,
+
+    /**
+     * This indicates an attempt was made to introspect an object in a way that
+     * would discard semantically important information. This is either due to
+     * the object using funtionality newer than the API version used to
+     * introspect it or omission of optional return arguments.
+     */
+    cudaErrorLossyQuery                   =     402,
+
+    /**
+     * This indicates that a named symbol was not found. Examples of symbols
+     * are global/constant variable names, driver function names, texture names,
+     * and surface names.
+     */
+    cudaErrorSymbolNotFound               =     500,
+  
+    /**
+     * This indicates that asynchronous operations issued previously have not
+     * completed yet. This result is not actually an error, but must be indicated
+     * differently than ::cudaSuccess (which indicates completion). Calls that
+     * may return this value include ::cudaEventQuery() and ::cudaStreamQuery().
+     */
+    cudaErrorNotReady                     =     600,
+
+    /**
+     * The device encountered a load or store instruction on an invalid memory address.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorIllegalAddress               =     700,
+  
+    /**
+     * This indicates that a launch did not occur because it did not have
+     * appropriate resources. Although this error is similar to
+     * ::cudaErrorInvalidConfiguration, this error usually indicates that the
+     * user has attempted to pass too many arguments to the device kernel, or the
+     * kernel launch specifies too many threads for the kernel's register count.
+     */
+    cudaErrorLaunchOutOfResources         =      701,
+  
+    /**
+     * This indicates that the device kernel took too long to execute. This can
+     * only occur if timeouts are enabled - see the device property
+     * \ref ::cudaDeviceProp::kernelExecTimeoutEnabled "kernelExecTimeoutEnabled"
+     * for more information.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorLaunchTimeout                =      702,
+
+    /**
+     * This error indicates a kernel launch that uses an incompatible texturing
+     * mode.
+     */
+    cudaErrorLaunchIncompatibleTexturing  =     703,
+      
+    /**
+     * This error indicates that a call to ::cudaDeviceEnablePeerAccess() is
+     * trying to re-enable peer addressing on from a context which has already
+     * had peer addressing enabled.
+     */
+    cudaErrorPeerAccessAlreadyEnabled     =     704,
+    
+    /**
+     * This error indicates that ::cudaDeviceDisablePeerAccess() is trying to 
+     * disable peer addressing which has not been enabled yet via 
+     * ::cudaDeviceEnablePeerAccess().
+     */
+    cudaErrorPeerAccessNotEnabled         =     705,
+  
+    /**
+     * This indicates that the user has called ::cudaSetValidDevices(),
+     * ::cudaSetDeviceFlags(), ::cudaD3D9SetDirect3DDevice(),
+     * ::cudaD3D10SetDirect3DDevice, ::cudaD3D11SetDirect3DDevice(), or
+     * ::cudaVDPAUSetVDPAUDevice() after initializing the CUDA runtime by
+     * calling non-device management operations (allocating memory and
+     * launching kernels are examples of non-device management operations).
+     * This error can also be returned if using runtime/driver
+     * interoperability and there is an existing ::CUcontext active on the
+     * host thread.
+     */
+    cudaErrorSetOnActiveProcess           =     708,
+
+    /**
+     * This error indicates that the context current to the calling thread
+     * has been destroyed using ::cuCtxDestroy, or is a primary context which
+     * has not yet been initialized.
+     */
+    cudaErrorContextIsDestroyed           =     709,
+
+    /**
+     * An assert triggered in device code during kernel execution. The device
+     * cannot be used again. All existing allocations are invalid. To continue
+     * using CUDA, the process must be terminated and relaunched.
+     */
+    cudaErrorAssert                        =    710,
+  
+    /**
+     * This error indicates that the hardware resources required to enable
+     * peer access have been exhausted for one or more of the devices 
+     * passed to ::cudaEnablePeerAccess().
+     */
+    cudaErrorTooManyPeers                 =     711,
+  
+    /**
+     * This error indicates that the memory range passed to ::cudaHostRegister()
+     * has already been registered.
+     */
+    cudaErrorHostMemoryAlreadyRegistered  =     712,
+        
+    /**
+     * This error indicates that the pointer passed to ::cudaHostUnregister()
+     * does not correspond to any currently registered memory region.
+     */
+    cudaErrorHostMemoryNotRegistered      =     713,
+
+    /**
+     * Device encountered an error in the call stack during kernel execution,
+     * possibly due to stack corruption or exceeding the stack size limit.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorHardwareStackError           =     714,
+
+    /**
+     * The device encountered an illegal instruction during kernel execution
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorIllegalInstruction           =     715,
+
+    /**
+     * The device encountered a load or store instruction
+     * on a memory address which is not aligned.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorMisalignedAddress            =     716,
+
+    /**
+     * While executing a kernel, the device encountered an instruction
+     * which can only operate on memory locations in certain address spaces
+     * (global, shared, or local), but was supplied a memory address not
+     * belonging to an allowed address space.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorInvalidAddressSpace          =     717,
+
+    /**
+     * The device encountered an invalid program counter.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorInvalidPc                    =     718,
+  
+    /**
+     * An exception occurred on the device while executing a kernel. Common
+     * causes include dereferencing an invalid device pointer and accessing
+     * out of bounds shared memory. Less common cases can be system specific - more
+     * information about these cases can be found in the system specific user guide.
+     * This leaves the process in an inconsistent state and any further CUDA work
+     * will return the same error. To continue using CUDA, the process must be terminated
+     * and relaunched.
+     */
+    cudaErrorLaunchFailure                =      719,
+
+    /**
+     * This error indicates that the number of blocks launched per grid for a kernel that was
+     * launched via either ::cudaLaunchCooperativeKernel or ::cudaLaunchCooperativeKernelMultiDevice
+     * exceeds the maximum number of blocks as allowed by ::cudaOccupancyMaxActiveBlocksPerMultiprocessor
+     * or ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors
+     * as specified by the device attribute ::cudaDevAttrMultiProcessorCount.
+     */
+    cudaErrorCooperativeLaunchTooLarge    =     720,
+
+    /**
+     * An exception occurred on the device while exiting a kernel using tensor memory: the
+     * tensor memory was not completely deallocated. This leaves the process in an inconsistent
+     * state and any further CUDA work will return the same error. To continue using CUDA, the
+     * process must be terminated and relaunched.
+     */
+    cudaErrorTensorMemoryLeak             =     721,
+    
+    /**
+     * This error indicates the attempted operation is not permitted.
+     */
+    cudaErrorNotPermitted                 =     800,
+
+    /**
+     * This error indicates the attempted operation is not supported
+     * on the current system or device.
+     */
+    cudaErrorNotSupported                 =     801,
+
+    /**
+     * This error indicates that the system is not yet ready to start any CUDA
+     * work.  To continue using CUDA, verify the system configuration is in a
+     * valid state and all required driver daemons are actively running.
+     * More information about this error can be found in the system specific
+     * user guide.
+     */
+    cudaErrorSystemNotReady               =     802,
+
+    /**
+     * This error indicates that there is a mismatch between the versions of
+     * the display driver and the CUDA driver. Refer to the compatibility documentation
+     * for supported versions.
+     */
+    cudaErrorSystemDriverMismatch         =     803,
+
+    /**
+     * This error indicates that the system was upgraded to run with forward compatibility
+     * but the visible hardware detected by CUDA does not support this configuration.
+     * Refer to the compatibility documentation for the supported hardware matrix or ensure
+     * that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES
+     * environment variable.
+     */
+    cudaErrorCompatNotSupportedOnDevice   =     804,
+
+    /**
+     * This error indicates that the MPS client failed to connect to the MPS control daemon or the MPS server.
+     */
+    cudaErrorMpsConnectionFailed          =     805,
+
+    /**
+     * This error indicates that the remote procedural call between the MPS server and the MPS client failed.
+     */
+    cudaErrorMpsRpcFailure                =     806,
+
+    /**
+     * This error indicates that the MPS server is not ready to accept new MPS client requests.
+     * This error can be returned when the MPS server is in the process of recovering from a fatal failure.
+     */
+    cudaErrorMpsServerNotReady            =     807,
+
+    /**
+     * This error indicates that the hardware resources required to create MPS client have been exhausted.
+     */
+    cudaErrorMpsMaxClientsReached         =     808,
+
+    /**
+     * This error indicates the the hardware resources required to device connections have been exhausted.
+     */
+    cudaErrorMpsMaxConnectionsReached     =     809,
+
+    /**
+     * This error indicates that the MPS client has been terminated by the server. To continue using CUDA, the process must be terminated and relaunched.
+     */
+    cudaErrorMpsClientTerminated          =     810,
+
+    /**
+     * This error indicates, that the program is using CUDA Dynamic Parallelism, but the current configuration, like MPS, does not support it.
+     */
+    cudaErrorCdpNotSupported              =     811,
+
+    /**
+     * This error indicates, that the program contains an unsupported interaction between different versions of CUDA Dynamic Parallelism.
+     */
+    cudaErrorCdpVersionMismatch           =     812,
+
+    /**
+     * The operation is not permitted when the stream is capturing.
+     */
+    cudaErrorStreamCaptureUnsupported     =    900,
+
+    /**
+     * The current capture sequence on the stream has been invalidated due to
+     * a previous error.
+     */
+    cudaErrorStreamCaptureInvalidated     =    901,
+
+    /**
+     * The operation would have resulted in a merge of two independent capture
+     * sequences.
+     */
+    cudaErrorStreamCaptureMerge           =    902,
+
+    /**
+     * The capture was not initiated in this stream.
+     */
+    cudaErrorStreamCaptureUnmatched       =    903,
+
+    /**
+     * The capture sequence contains a fork that was not joined to the primary
+     * stream.
+     */
+    cudaErrorStreamCaptureUnjoined        =    904,
+
+    /**
+     * A dependency would have been created which crosses the capture sequence
+     * boundary. Only implicit in-stream ordering dependencies are allowed to
+     * cross the boundary.
+     */
+    cudaErrorStreamCaptureIsolation       =    905,
+
+    /**
+     * The operation would have resulted in a disallowed implicit dependency on
+     * a current capture sequence from cudaStreamLegacy.
+     */
+    cudaErrorStreamCaptureImplicit        =    906,
+
+    /**
+     * The operation is not permitted on an event which was last recorded in a
+     * capturing stream.
+     */
+    cudaErrorCapturedEvent                =    907,
+  
+    /**
+     * A stream capture sequence not initiated with the ::cudaStreamCaptureModeRelaxed
+     * argument to ::cudaStreamBeginCapture was passed to ::cudaStreamEndCapture in a
+     * different thread.
+     */
+    cudaErrorStreamCaptureWrongThread     =    908,
+
+    /**
+     * This indicates that the wait operation has timed out.
+     */
+    cudaErrorTimeout                      =    909,
+
+    /**
+     * This error indicates that the graph update was not performed because it included 
+     * changes which violated constraints specific to instantiated graph update.
+     */
+    cudaErrorGraphExecUpdateFailure       =    910,
+
+    /**
+     * This indicates that an async error has occurred in a device outside of CUDA.
+     * If CUDA was waiting for an external device's signal before consuming shared data,
+     * the external device signaled an error indicating that the data is not valid for
+     * consumption. This leaves the process in an inconsistent state and any further CUDA
+     * work will return the same error. To continue using CUDA, the process must be
+     * terminated and relaunched.
+     */
+    cudaErrorExternalDevice               =    911,
+
+    /**
+     * This indicates that a kernel launch error has occurred due to cluster
+     * misconfiguration.
+     */
+    cudaErrorInvalidClusterSize           =    912,
+
+    /**
+     * Indiciates a function handle is not loaded when calling an API that requires
+     * a loaded function.
+     */
+    cudaErrorFunctionNotLoaded            =    913,
+
+    /**
+     * This error indicates one or more resources passed in are not valid resource
+     * types for the operation.
+     */
+    cudaErrorInvalidResourceType          =    914,
+
+    /**
+     * This error indicates one or more resources are insufficient or non-applicable for
+     * the operation.
+     */
+    cudaErrorInvalidResourceConfiguration =    915,
+
+    /**
+     * This indicates that an unknown internal error has occurred.
+     */
+    cudaErrorUnknown                      =    999
+
+    /**
+     * Any unhandled CUDA driver error is added to this value and returned via
+     * the runtime. Production releases of CUDA should not return such errors.
+     * \deprecated
+     * This error return is deprecated as of CUDA 4.1.
+     */
+    , cudaErrorApiFailureBase               =  10000
+};
+
+/**
+ * Channel format kind
+ */
+enum __device_builtin__ cudaChannelFormatKind
+{
+    cudaChannelFormatKindSigned                         =   0,      /**< Signed channel format */
+    cudaChannelFormatKindUnsigned                       =   1,      /**< Unsigned channel format */
+    cudaChannelFormatKindFloat                          =   2,      /**< Float channel format */
+    cudaChannelFormatKindNone                           =   3,      /**< No channel format */
+    cudaChannelFormatKindNV12                           =   4,      /**< Unsigned 8-bit integers, planar 4:2:0 YUV format */
+    cudaChannelFormatKindUnsignedNormalized8X1          =   5,      /**< 1 channel unsigned 8-bit normalized integer */
+    cudaChannelFormatKindUnsignedNormalized8X2          =   6,      /**< 2 channel unsigned 8-bit normalized integer */
+    cudaChannelFormatKindUnsignedNormalized8X4          =   7,      /**< 4 channel unsigned 8-bit normalized integer */
+    cudaChannelFormatKindUnsignedNormalized16X1         =   8,      /**< 1 channel unsigned 16-bit normalized integer */
+    cudaChannelFormatKindUnsignedNormalized16X2         =   9,      /**< 2 channel unsigned 16-bit normalized integer */
+    cudaChannelFormatKindUnsignedNormalized16X4         =   10,     /**< 4 channel unsigned 16-bit normalized integer */
+    cudaChannelFormatKindSignedNormalized8X1            =   11,     /**< 1 channel signed 8-bit normalized integer */
+    cudaChannelFormatKindSignedNormalized8X2            =   12,     /**< 2 channel signed 8-bit normalized integer */
+    cudaChannelFormatKindSignedNormalized8X4            =   13,     /**< 4 channel signed 8-bit normalized integer */
+    cudaChannelFormatKindSignedNormalized16X1           =   14,     /**< 1 channel signed 16-bit normalized integer */
+    cudaChannelFormatKindSignedNormalized16X2           =   15,     /**< 2 channel signed 16-bit normalized integer */
+    cudaChannelFormatKindSignedNormalized16X4           =   16,     /**< 4 channel signed 16-bit normalized integer */
+    cudaChannelFormatKindUnsignedBlockCompressed1       =   17,     /**< 4 channel unsigned normalized block-compressed (BC1 compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed1SRGB   =   18,     /**< 4 channel unsigned normalized block-compressed (BC1 compression) format with sRGB encoding*/
+    cudaChannelFormatKindUnsignedBlockCompressed2       =   19,     /**< 4 channel unsigned normalized block-compressed (BC2 compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed2SRGB   =   20,     /**< 4 channel unsigned normalized block-compressed (BC2 compression) format with sRGB encoding */
+    cudaChannelFormatKindUnsignedBlockCompressed3       =   21,     /**< 4 channel unsigned normalized block-compressed (BC3 compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed3SRGB   =   22,     /**< 4 channel unsigned normalized block-compressed (BC3 compression) format with sRGB encoding */
+    cudaChannelFormatKindUnsignedBlockCompressed4       =   23,     /**< 1 channel unsigned normalized block-compressed (BC4 compression) format */
+    cudaChannelFormatKindSignedBlockCompressed4         =   24,     /**< 1 channel signed normalized block-compressed (BC4 compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed5       =   25,     /**< 2 channel unsigned normalized block-compressed (BC5 compression) format */
+    cudaChannelFormatKindSignedBlockCompressed5         =   26,     /**< 2 channel signed normalized block-compressed (BC5 compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed6H      =   27,     /**< 3 channel unsigned half-float block-compressed (BC6H compression) format */
+    cudaChannelFormatKindSignedBlockCompressed6H        =   28,     /**< 3 channel signed half-float block-compressed (BC6H compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed7       =   29,     /**< 4 channel unsigned normalized block-compressed (BC7 compression) format */
+    cudaChannelFormatKindUnsignedBlockCompressed7SRGB   =   30,     /**< 4 channel unsigned normalized block-compressed (BC7 compression) format with sRGB encoding */
+    cudaChannelFormatKindUnsignedNormalized1010102      =   31      /**< 4 channel unsigned normalized (10-bit, 10-bit, 10-bit, 2-bit) format */
+
+};
+
+/**
+ * CUDA Channel format descriptor
+ */
+struct __device_builtin__ cudaChannelFormatDesc
+{
+    int                        x; /**< x */
+    int                        y; /**< y */
+    int                        z; /**< z */
+    int                        w; /**< w */
+    enum cudaChannelFormatKind f; /**< Channel format kind */
+};
+
+/**
+ * CUDA array
+ */
+typedef struct cudaArray *cudaArray_t;
+
+/**
+ * CUDA array (as source copy argument)
+ */
+typedef const struct cudaArray *cudaArray_const_t;
+
+struct cudaArray;
+
+/**
+ * CUDA mipmapped array
+ */
+typedef struct cudaMipmappedArray *cudaMipmappedArray_t;
+
+/**
+ * CUDA mipmapped array (as source argument)
+ */
+typedef const struct cudaMipmappedArray *cudaMipmappedArray_const_t;
+
+struct cudaMipmappedArray;
+
+/**
+ * Indicates that the layered sparse CUDA array or CUDA mipmapped array has a single mip tail region for all layers
+ */
+#define cudaArraySparsePropertiesSingleMipTail   0x1
+
+/**
+ * Sparse CUDA array and CUDA mipmapped array properties
+ */
+struct __device_builtin__ cudaArraySparseProperties {
+    struct {
+        unsigned int width;             /**< Tile width in elements */
+        unsigned int height;            /**< Tile height in elements */
+        unsigned int depth;             /**< Tile depth in elements */
+    } tileExtent;
+    unsigned int miptailFirstLevel;     /**< First mip level at which the mip tail begins */   
+    unsigned long long miptailSize;     /**< Total size of the mip tail. */
+    unsigned int flags;                 /**< Flags will either be zero or ::cudaArraySparsePropertiesSingleMipTail */
+    unsigned int reserved[4];
+};
+
+/**
+ * CUDA array and CUDA mipmapped array memory requirements
+ */
+struct __device_builtin__ cudaArrayMemoryRequirements {
+    size_t size;                    /**< Total size of the array. */
+    size_t alignment;               /**< Alignment necessary for mapping the array. */
+    unsigned int reserved[4];
+};
+
+/**
+ * CUDA memory types
+ */
+enum __device_builtin__ cudaMemoryType
+{
+    cudaMemoryTypeUnregistered = 0, /**< Unregistered memory */
+    cudaMemoryTypeHost         = 1, /**< Host memory */
+    cudaMemoryTypeDevice       = 2, /**< Device memory */
+    cudaMemoryTypeManaged      = 3  /**< Managed memory */
+};
+
+/**
+ * CUDA memory copy types
+ */
+enum __device_builtin__ cudaMemcpyKind
+{
+    cudaMemcpyHostToHost          =   0,      /**< Host   -> Host */
+    cudaMemcpyHostToDevice        =   1,      /**< Host   -> Device */
+    cudaMemcpyDeviceToHost        =   2,      /**< Device -> Host */
+    cudaMemcpyDeviceToDevice      =   3,      /**< Device -> Device */
+    cudaMemcpyDefault             =   4       /**< Direction of the transfer is inferred from the pointer values. Requires unified virtual addressing */
+};
+
+/**
+ * CUDA Pitched memory pointer
+ *
+ * \sa ::make_cudaPitchedPtr
+ */
+struct __device_builtin__ cudaPitchedPtr
+{
+    void   *ptr;      /**< Pointer to allocated memory */
+    size_t  pitch;    /**< Pitch of allocated memory in bytes */
+    size_t  xsize;    /**< Logical width of allocation in elements */
+    size_t  ysize;    /**< Logical height of allocation in elements */
+};
+
+/**
+ * CUDA extent
+ *
+ * \sa ::make_cudaExtent
+ */
+struct __device_builtin__ cudaExtent
+{
+    size_t width;     /**< Width in elements when referring to array memory, in bytes when referring to linear memory */
+    size_t height;    /**< Height in elements */
+    size_t depth;     /**< Depth in elements */
+};
+
+/**
+ * CUDA 3D position
+ *
+ * \sa ::make_cudaPos
+ */
+struct __device_builtin__ cudaPos
+{
+    size_t x;     /**< x */
+    size_t y;     /**< y */
+    size_t z;     /**< z */
+};
+
+/**
+ * CUDA 3D memory copying parameters
+ */
+struct __device_builtin__ cudaMemcpy3DParms
+{
+    cudaArray_t            srcArray;  /**< Source memory address */
+    struct cudaPos         srcPos;    /**< Source position offset */
+    struct cudaPitchedPtr  srcPtr;    /**< Pitched source memory address */
+  
+    cudaArray_t            dstArray;  /**< Destination memory address */
+    struct cudaPos         dstPos;    /**< Destination position offset */
+    struct cudaPitchedPtr  dstPtr;    /**< Pitched destination memory address */
+  
+    struct cudaExtent      extent;    /**< Requested memory copy size */
+    enum cudaMemcpyKind    kind;      /**< Type of transfer */
+};
+
+/**
+ * Memcpy node parameters
+ */
+struct __device_builtin__ cudaMemcpyNodeParams {
+    int flags;                            /**< Must be zero */
+    int reserved[3];                      /**< Must be zero */
+    struct cudaMemcpy3DParms copyParams;  /**< Parameters for the memory copy */
+};
+
+/**
+ * CUDA 3D cross-device memory copying parameters
+ */
+struct __device_builtin__ cudaMemcpy3DPeerParms
+{
+    cudaArray_t            srcArray;  /**< Source memory address */
+    struct cudaPos         srcPos;    /**< Source position offset */
+    struct cudaPitchedPtr  srcPtr;    /**< Pitched source memory address */
+    int                    srcDevice; /**< Source device */
+  
+    cudaArray_t            dstArray;  /**< Destination memory address */
+    struct cudaPos         dstPos;    /**< Destination position offset */
+    struct cudaPitchedPtr  dstPtr;    /**< Pitched destination memory address */
+    int                    dstDevice; /**< Destination device */
+  
+    struct cudaExtent      extent;    /**< Requested memory copy size */
+};
+
+/**
+ * CUDA Memset node parameters
+ */
+struct __device_builtin__  cudaMemsetParams {
+    void *dst;                              /**< Destination device pointer */
+    size_t pitch;                           /**< Pitch of destination device pointer. Unused if height is 1 */
+    unsigned int value;                     /**< Value to be set */
+    unsigned int elementSize;               /**< Size of each element in bytes. Must be 1, 2, or 4. */
+    size_t width;                           /**< Width of the row in elements */
+    size_t height;                          /**< Number of rows */
+};
+
+/**
+ * CUDA Memset node parameters
+ */
+struct __device_builtin__  cudaMemsetParamsV2 {
+    void *dst;                              /**< Destination device pointer */
+    size_t pitch;                           /**< Pitch of destination device pointer. Unused if height is 1 */
+    unsigned int value;                     /**< Value to be set */
+    unsigned int elementSize;               /**< Size of each element in bytes. Must be 1, 2, or 4. */
+    size_t width;                           /**< Width of the row in elements */
+    size_t height;                          /**< Number of rows */
+};
+
+/**
+ * Specifies performance hint with ::cudaAccessPolicyWindow for hitProp and missProp members.
+ */
+enum __device_builtin__  cudaAccessProperty {
+    cudaAccessPropertyNormal = 0,       /**< Normal cache persistence. */
+    cudaAccessPropertyStreaming = 1,    /**< Streaming access is less likely to persit from cache. */
+    cudaAccessPropertyPersisting = 2    /**< Persisting access is more likely to persist in cache.*/
+};
+
+/**
+ * Specifies an access policy for a window, a contiguous extent of memory
+ * beginning at base_ptr and ending at base_ptr + num_bytes.
+ * Partition into many segments and assign segments such that.
+ * sum of "hit segments" / window == approx. ratio.
+ * sum of "miss segments" / window == approx 1-ratio.
+ * Segments and ratio specifications are fitted to the capabilities of
+ * the architecture.
+ * Accesses in a hit segment apply the hitProp access policy.
+ * Accesses in a miss segment apply the missProp access policy.
+ */
+struct __device_builtin__ cudaAccessPolicyWindow {
+    void *base_ptr;                     /**< Starting address of the access policy window. CUDA driver may align it. */
+    size_t num_bytes;                   /**< Size in bytes of the window policy. CUDA driver may restrict the maximum size and alignment. */
+    float hitRatio;                     /**< hitRatio specifies percentage of lines assigned hitProp, rest are assigned missProp. */
+    enum cudaAccessProperty hitProp;    /**< ::CUaccessProperty set for hit. */
+    enum cudaAccessProperty missProp;   /**< ::CUaccessProperty set for miss. Must be either NORMAL or STREAMING. */
+};
+
+#ifdef _WIN32
+#define CUDART_CB __stdcall
+#else
+#define CUDART_CB
+#endif
+
+/**
+ * CUDA host function
+ * \param userData Argument value passed to the function
+ */
+typedef void (CUDART_CB *cudaHostFn_t)(void *userData);
+
+/**
+ * CUDA host node parameters
+ */
+struct __device_builtin__ cudaHostNodeParams {
+    cudaHostFn_t fn;    /**< The function to call when the node executes */
+    void* userData; /**< Argument to pass to the function */
+};
+
+/**
+ * CUDA host node parameters
+ */
+struct __device_builtin__ cudaHostNodeParamsV2 {
+    cudaHostFn_t fn;    /**< The function to call when the node executes */
+    void* userData; /**< Argument to pass to the function */
+};
+
+/**
+ * Possible stream capture statuses returned by ::cudaStreamIsCapturing
+ */
+enum __device_builtin__ cudaStreamCaptureStatus {
+    cudaStreamCaptureStatusNone        = 0, /**< Stream is not capturing */
+    cudaStreamCaptureStatusActive      = 1, /**< Stream is actively capturing */
+    cudaStreamCaptureStatusInvalidated = 2  /**< Stream is part of a capture sequence that
+                                                   has been invalidated, but not terminated */
+};
+
+/**
+ * Possible modes for stream capture thread interactions. For more details see
+ * ::cudaStreamBeginCapture and ::cudaThreadExchangeStreamCaptureMode
+ */
+enum __device_builtin__ cudaStreamCaptureMode {
+    cudaStreamCaptureModeGlobal      = 0,
+    cudaStreamCaptureModeThreadLocal = 1,
+    cudaStreamCaptureModeRelaxed     = 2
+};
+
+enum __device_builtin__ cudaSynchronizationPolicy {
+    cudaSyncPolicyAuto = 1,
+    cudaSyncPolicySpin = 2,
+    cudaSyncPolicyYield = 3,
+    cudaSyncPolicyBlockingSync = 4
+};
+
+/**
+ * Cluster scheduling policies. These may be passed to ::cudaFuncSetAttribute
+ */
+enum __device_builtin__ cudaClusterSchedulingPolicy {
+    cudaClusterSchedulingPolicyDefault       = 0, /**< the default policy */
+    cudaClusterSchedulingPolicySpread        = 1, /**< spread the blocks within a cluster to the SMs */
+    cudaClusterSchedulingPolicyLoadBalancing = 2  /**< allow the hardware to load-balance the blocks in a cluster to the SMs */
+};
+
+/**
+ * Flags for ::cudaStreamUpdateCaptureDependencies
+ */
+enum __device_builtin__ cudaStreamUpdateCaptureDependenciesFlags {
+    cudaStreamAddCaptureDependencies = 0x0, /**< Add new nodes to the dependency set */
+    cudaStreamSetCaptureDependencies = 0x1  /**< Replace the dependency set with the new nodes */
+};
+
+/**
+ * Flags for user objects for graphs
+ */
+enum __device_builtin__ cudaUserObjectFlags {
+    cudaUserObjectNoDestructorSync = 0x1  /**< Indicates the destructor execution is not synchronized by any CUDA handle. */
+};
+
+/**
+ * Flags for retaining user object references for graphs
+ */
+enum __device_builtin__ cudaUserObjectRetainFlags {
+    cudaGraphUserObjectMove = 0x1  /**< Transfer references from the caller rather than creating new references. */
+};
+
+/**
+ * CUDA graphics interop resource
+ */
+struct cudaGraphicsResource;
+
+/**
+ * CUDA graphics interop register flags
+ */
+enum __device_builtin__ cudaGraphicsRegisterFlags
+{
+    cudaGraphicsRegisterFlagsNone             = 0,  /**< Default */
+    cudaGraphicsRegisterFlagsReadOnly         = 1,  /**< CUDA will not write to this resource */ 
+    cudaGraphicsRegisterFlagsWriteDiscard     = 2,  /**< CUDA will only write to and will not read from this resource */
+    cudaGraphicsRegisterFlagsSurfaceLoadStore = 4,  /**< CUDA will bind this resource to a surface reference */
+    cudaGraphicsRegisterFlagsTextureGather    = 8   /**< CUDA will perform texture gather operations on this resource */
+};
+
+/**
+ * CUDA graphics interop map flags
+ */
+enum __device_builtin__ cudaGraphicsMapFlags
+{
+    cudaGraphicsMapFlagsNone         = 0,  /**< Default; Assume resource can be read/written */
+    cudaGraphicsMapFlagsReadOnly     = 1,  /**< CUDA will not write to this resource */
+    cudaGraphicsMapFlagsWriteDiscard = 2   /**< CUDA will only write to and will not read from this resource */
+};
+
+/**
+ * CUDA graphics interop array indices for cube maps
+ */
+enum __device_builtin__ cudaGraphicsCubeFace 
+{
+    cudaGraphicsCubeFacePositiveX = 0x00, /**< Positive X face of cubemap */
+    cudaGraphicsCubeFaceNegativeX = 0x01, /**< Negative X face of cubemap */
+    cudaGraphicsCubeFacePositiveY = 0x02, /**< Positive Y face of cubemap */
+    cudaGraphicsCubeFaceNegativeY = 0x03, /**< Negative Y face of cubemap */
+    cudaGraphicsCubeFacePositiveZ = 0x04, /**< Positive Z face of cubemap */
+    cudaGraphicsCubeFaceNegativeZ = 0x05  /**< Negative Z face of cubemap */
+};
+
+/**
+ * CUDA resource types
+ */
+enum __device_builtin__ cudaResourceType
+{
+    cudaResourceTypeArray          = 0x00, /**< Array resource */
+    cudaResourceTypeMipmappedArray = 0x01, /**< Mipmapped array resource */
+    cudaResourceTypeLinear         = 0x02, /**< Linear resource */
+    cudaResourceTypePitch2D        = 0x03  /**< Pitch 2D resource */
+};
+
+/**
+ * CUDA texture resource view formats
+ */
+enum __device_builtin__ cudaResourceViewFormat
+{
+    cudaResViewFormatNone                      = 0x00, /**< No resource view format (use underlying resource format) */
+    cudaResViewFormatUnsignedChar1             = 0x01, /**< 1 channel unsigned 8-bit integers */
+    cudaResViewFormatUnsignedChar2             = 0x02, /**< 2 channel unsigned 8-bit integers */
+    cudaResViewFormatUnsignedChar4             = 0x03, /**< 4 channel unsigned 8-bit integers */
+    cudaResViewFormatSignedChar1               = 0x04, /**< 1 channel signed 8-bit integers */
+    cudaResViewFormatSignedChar2               = 0x05, /**< 2 channel signed 8-bit integers */
+    cudaResViewFormatSignedChar4               = 0x06, /**< 4 channel signed 8-bit integers */
+    cudaResViewFormatUnsignedShort1            = 0x07, /**< 1 channel unsigned 16-bit integers */
+    cudaResViewFormatUnsignedShort2            = 0x08, /**< 2 channel unsigned 16-bit integers */
+    cudaResViewFormatUnsignedShort4            = 0x09, /**< 4 channel unsigned 16-bit integers */
+    cudaResViewFormatSignedShort1              = 0x0a, /**< 1 channel signed 16-bit integers */
+    cudaResViewFormatSignedShort2              = 0x0b, /**< 2 channel signed 16-bit integers */
+    cudaResViewFormatSignedShort4              = 0x0c, /**< 4 channel signed 16-bit integers */
+    cudaResViewFormatUnsignedInt1              = 0x0d, /**< 1 channel unsigned 32-bit integers */
+    cudaResViewFormatUnsignedInt2              = 0x0e, /**< 2 channel unsigned 32-bit integers */
+    cudaResViewFormatUnsignedInt4              = 0x0f, /**< 4 channel unsigned 32-bit integers */
+    cudaResViewFormatSignedInt1                = 0x10, /**< 1 channel signed 32-bit integers */
+    cudaResViewFormatSignedInt2                = 0x11, /**< 2 channel signed 32-bit integers */
+    cudaResViewFormatSignedInt4                = 0x12, /**< 4 channel signed 32-bit integers */
+    cudaResViewFormatHalf1                     = 0x13, /**< 1 channel 16-bit floating point */
+    cudaResViewFormatHalf2                     = 0x14, /**< 2 channel 16-bit floating point */
+    cudaResViewFormatHalf4                     = 0x15, /**< 4 channel 16-bit floating point */
+    cudaResViewFormatFloat1                    = 0x16, /**< 1 channel 32-bit floating point */
+    cudaResViewFormatFloat2                    = 0x17, /**< 2 channel 32-bit floating point */
+    cudaResViewFormatFloat4                    = 0x18, /**< 4 channel 32-bit floating point */
+    cudaResViewFormatUnsignedBlockCompressed1  = 0x19, /**< Block compressed 1 */
+    cudaResViewFormatUnsignedBlockCompressed2  = 0x1a, /**< Block compressed 2 */
+    cudaResViewFormatUnsignedBlockCompressed3  = 0x1b, /**< Block compressed 3 */
+    cudaResViewFormatUnsignedBlockCompressed4  = 0x1c, /**< Block compressed 4 unsigned */
+    cudaResViewFormatSignedBlockCompressed4    = 0x1d, /**< Block compressed 4 signed */
+    cudaResViewFormatUnsignedBlockCompressed5  = 0x1e, /**< Block compressed 5 unsigned */
+    cudaResViewFormatSignedBlockCompressed5    = 0x1f, /**< Block compressed 5 signed */
+    cudaResViewFormatUnsignedBlockCompressed6H = 0x20, /**< Block compressed 6 unsigned half-float */
+    cudaResViewFormatSignedBlockCompressed6H   = 0x21, /**< Block compressed 6 signed half-float */
+    cudaResViewFormatUnsignedBlockCompressed7  = 0x22  /**< Block compressed 7 */
+};
+
+/**
+ * CUDA resource descriptor
+ */
+struct __device_builtin__ cudaResourceDesc {
+    enum cudaResourceType resType;             /**< Resource type */
+    
+    union {
+        struct {
+            cudaArray_t array;                 /**< CUDA array */
+        } array;
+        struct {
+            cudaMipmappedArray_t mipmap;       /**< CUDA mipmapped array */
+        } mipmap;
+        struct {
+            void *devPtr;                      /**< Device pointer */
+            struct cudaChannelFormatDesc desc; /**< Channel descriptor */
+            size_t sizeInBytes;                /**< Size in bytes */
+        } linear;
+        struct {
+            void *devPtr;                      /**< Device pointer */
+            struct cudaChannelFormatDesc desc; /**< Channel descriptor */
+            size_t width;                      /**< Width of the array in elements */
+            size_t height;                     /**< Height of the array in elements */
+            size_t pitchInBytes;               /**< Pitch between two rows in bytes */
+        } pitch2D;
+    } res;
+};
+
+/**
+ * CUDA resource view descriptor
+ */
+struct __device_builtin__ cudaResourceViewDesc
+{
+    enum cudaResourceViewFormat format;           /**< Resource view format */
+    size_t                      width;            /**< Width of the resource view */
+    size_t                      height;           /**< Height of the resource view */
+    size_t                      depth;            /**< Depth of the resource view */
+    unsigned int                firstMipmapLevel; /**< First defined mipmap level */
+    unsigned int                lastMipmapLevel;  /**< Last defined mipmap level */
+    unsigned int                firstLayer;       /**< First layer index */
+    unsigned int                lastLayer;        /**< Last layer index */
+};
+
+/**
+ * CUDA pointer attributes
+ */
+struct __device_builtin__ cudaPointerAttributes
+{
+    /**
+     * The type of memory - ::cudaMemoryTypeUnregistered, ::cudaMemoryTypeHost,
+     * ::cudaMemoryTypeDevice or ::cudaMemoryTypeManaged.
+     */
+    enum cudaMemoryType type;
+
+    /** 
+     * The device against which the memory was allocated or registered.
+     * If the memory type is ::cudaMemoryTypeDevice then this identifies 
+     * the device on which the memory referred physically resides.  If
+     * the memory type is ::cudaMemoryTypeHost or::cudaMemoryTypeManaged then
+     * this identifies the device which was current when the memory was allocated
+     * or registered (and if that device is deinitialized then this allocation
+     * will vanish with that device's state).
+     */
+    int device;
+
+    /**
+     * The address which may be dereferenced on the current device to access 
+     * the memory or NULL if no such address exists.
+     */
+    void *devicePointer;
+
+    /**
+     * The address which may be dereferenced on the host to access the
+     * memory or NULL if no such address exists.
+     *
+     * \note CUDA doesn't check if unregistered memory is allocated so this field
+     * may contain invalid pointer if an invalid pointer has been passed to CUDA.
+     */
+    void *hostPointer;
+};
+
+/**
+ * CUDA function attributes
+ */
+struct __device_builtin__ cudaFuncAttributes
+{
+   /**
+    * The size in bytes of statically-allocated shared memory per block
+    * required by this function. This does not include dynamically-allocated
+    * shared memory requested by the user at runtime.
+    */
+   size_t sharedSizeBytes;
+
+   /**
+    * The size in bytes of user-allocated constant memory required by this
+    * function.
+    */
+   size_t constSizeBytes;
+
+   /**
+    * The size in bytes of local memory used by each thread of this function.
+    */
+   size_t localSizeBytes;
+
+   /**
+    * The maximum number of threads per block, beyond which a launch of the
+    * function would fail. This number depends on both the function and the
+    * device on which the function is currently loaded.
+    */
+   int maxThreadsPerBlock;
+
+   /**
+    * The number of registers used by each thread of this function.
+    */
+   int numRegs;
+
+   /**
+    * The PTX virtual architecture version for which the function was
+    * compiled. This value is the major PTX version * 10 + the minor PTX
+    * version, so a PTX version 1.3 function would return the value 13.
+    */
+   int ptxVersion;
+
+   /**
+    * The binary architecture version for which the function was compiled.
+    * This value is the major binary version * 10 + the minor binary version,
+    * so a binary version 1.3 function would return the value 13.
+    */
+   int binaryVersion;
+
+   /**
+    * The attribute to indicate whether the function has been compiled with 
+    * user specified option "-Xptxas --dlcm=ca" set.
+    */
+   int cacheModeCA;
+
+   /**
+    * The maximum size in bytes of dynamic shared memory per block for 
+    * this function. Any launch must have a dynamic shared memory size
+    * smaller than this value.
+    */
+   int maxDynamicSharedSizeBytes;
+
+   /**
+    * On devices where the L1 cache and shared memory use the same hardware resources, 
+    * this sets the shared memory carveout preference, in percent of the maximum shared memory. 
+    * Refer to ::cudaDevAttrMaxSharedMemoryPerMultiprocessor.
+    * This is only a hint, and the driver can choose a different ratio if required to execute the function.
+    * See ::cudaFuncSetAttribute
+    */
+   int preferredShmemCarveout;
+
+   /**
+    * If this attribute is set, the kernel must launch with a valid cluster dimension
+    * specified.
+    */
+   int clusterDimMustBeSet;
+
+   /**
+    * The required cluster width/height/depth in blocks. The values must either
+    * all be 0 or all be positive. The validity of the cluster dimensions is
+    * otherwise checked at launch time.
+    *
+    * If the value is set during compile time, it cannot be set at runtime.
+    * Setting it at runtime should return cudaErrorNotPermitted.
+    * See ::cudaFuncSetAttribute
+    */
+   int requiredClusterWidth;
+   int requiredClusterHeight;
+   int requiredClusterDepth;
+
+   /**
+    * The block scheduling policy of a function.
+    * See ::cudaFuncSetAttribute
+    */
+   int clusterSchedulingPolicyPreference;
+
+   /**
+    * Whether the function can be launched with non-portable cluster size. 1 is
+    * allowed, 0 is disallowed. A non-portable cluster size may only function
+    * on the specific SKUs the program is tested on. The launch might fail if
+    * the program is run on a different hardware platform.
+    *
+    * CUDA API provides ::cudaOccupancyMaxActiveClusters to assist with checking
+    * whether the desired size can be launched on the current device.
+    *
+    * Portable Cluster Size
+    *
+    * A portable cluster size is guaranteed to be functional on all compute
+    * capabilities higher than the target compute capability. The portable
+    * cluster size for sm_90 is 8 blocks per cluster. This value may increase
+    * for future compute capabilities.
+    *
+    * The specific hardware unit may support higher cluster sizes that’s not
+    * guaranteed to be portable.
+    * See ::cudaFuncSetAttribute
+    */
+   int nonPortableClusterSizeAllowed;
+
+   /**
+    * Reserved for future use.
+    */
+   int reserved[16];
+};
+
+/**
+ * CUDA function attributes that can be set using ::cudaFuncSetAttribute
+ */
+enum __device_builtin__ cudaFuncAttribute
+{
+    cudaFuncAttributeMaxDynamicSharedMemorySize = 8, /**< Maximum dynamic shared memory size */
+    cudaFuncAttributePreferredSharedMemoryCarveout = 9, /**< Preferred shared memory-L1 cache split */
+    cudaFuncAttributeClusterDimMustBeSet = 10, /**< Indicator to enforce valid cluster dimension specification on kernel launch */
+    cudaFuncAttributeRequiredClusterWidth = 11, /**< Required cluster width */
+    cudaFuncAttributeRequiredClusterHeight = 12, /**< Required cluster height */
+    cudaFuncAttributeRequiredClusterDepth = 13, /**< Required cluster depth */
+    cudaFuncAttributeNonPortableClusterSizeAllowed = 14, /**< Whether non-portable cluster scheduling policy is supported */
+    cudaFuncAttributeClusterSchedulingPolicyPreference = 15, /**< Required cluster scheduling policy preference */
+    cudaFuncAttributeMax
+};
+
+/**
+ * CUDA function cache configurations
+ */
+enum __device_builtin__ cudaFuncCache
+{
+    cudaFuncCachePreferNone   = 0,    /**< Default function cache configuration, no preference */
+    cudaFuncCachePreferShared = 1,    /**< Prefer larger shared memory and smaller L1 cache  */
+    cudaFuncCachePreferL1     = 2,    /**< Prefer larger L1 cache and smaller shared memory */
+    cudaFuncCachePreferEqual  = 3     /**< Prefer equal size L1 cache and shared memory */
+};
+
+/**
+ * CUDA shared memory configuration
+ * \deprecated
+ */
+enum __device_builtin__ cudaSharedMemConfig
+{
+    cudaSharedMemBankSizeDefault   = 0,
+    cudaSharedMemBankSizeFourByte  = 1,
+    cudaSharedMemBankSizeEightByte = 2
+};
+
+/** 
+ * Shared memory carveout configurations. These may be passed to cudaFuncSetAttribute
+ */
+enum __device_builtin__ cudaSharedCarveout {
+    cudaSharedmemCarveoutDefault      = -1,  /**< No preference for shared memory or L1 (default) */
+    cudaSharedmemCarveoutMaxShared    = 100, /**< Prefer maximum available shared memory, minimum L1 cache */
+    cudaSharedmemCarveoutMaxL1        = 0    /**< Prefer maximum available L1 cache, minimum shared memory */
+};
+
+/**
+ * CUDA device compute modes
+ */
+enum __device_builtin__ cudaComputeMode
+{
+    cudaComputeModeDefault          = 0,  /**< Default compute mode (Multiple threads can use ::cudaSetDevice() with this device) */
+    cudaComputeModeExclusive        = 1,  /**< Compute-exclusive-thread mode (Only one thread in one process will be able to use ::cudaSetDevice() with this device) */
+    cudaComputeModeProhibited       = 2,  /**< Compute-prohibited mode (No threads can use ::cudaSetDevice() with this device) */
+    cudaComputeModeExclusiveProcess = 3   /**< Compute-exclusive-process mode (Many threads in one process will be able to use ::cudaSetDevice() with this device) */
+};
+
+/**
+ * CUDA Limits
+ */
+enum __device_builtin__ cudaLimit
+{
+    cudaLimitStackSize                    = 0x00, /**< GPU thread stack size */
+    cudaLimitPrintfFifoSize               = 0x01, /**< GPU printf FIFO size */
+    cudaLimitMallocHeapSize               = 0x02, /**< GPU malloc heap size */
+    cudaLimitDevRuntimeSyncDepth          = 0x03, /**< GPU device runtime synchronize depth */
+    cudaLimitDevRuntimePendingLaunchCount = 0x04, /**< GPU device runtime pending launch count */
+    cudaLimitMaxL2FetchGranularity        = 0x05, /**< A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint */
+    cudaLimitPersistingL2CacheSize        = 0x06  /**< A size in bytes for L2 persisting lines cache size */
+};
+
+/**
+ * CUDA Memory Advise values
+ */
+enum __device_builtin__ cudaMemoryAdvise
+{
+    cudaMemAdviseSetReadMostly          = 1, /**< Data will mostly be read and only occassionally be written to */
+    cudaMemAdviseUnsetReadMostly        = 2, /**< Undo the effect of ::cudaMemAdviseSetReadMostly */
+    cudaMemAdviseSetPreferredLocation   = 3, /**< Set the preferred location for the data as the specified device */
+    cudaMemAdviseUnsetPreferredLocation = 4, /**< Clear the preferred location for the data */
+    cudaMemAdviseSetAccessedBy          = 5, /**< Data will be accessed by the specified device, so prevent page faults as much as possible */
+    cudaMemAdviseUnsetAccessedBy        = 6  /**< Let the Unified Memory subsystem decide on the page faulting policy for the specified device */
+};
+
+/**
+ * CUDA range attributes
+ */
+enum __device_builtin__ cudaMemRangeAttribute
+{
+    cudaMemRangeAttributeReadMostly                 = 1, /**< Whether the range will mostly be read and only occassionally be written to */
+    cudaMemRangeAttributePreferredLocation          = 2, /**< The preferred location of the range */
+    cudaMemRangeAttributeAccessedBy                 = 3, /**< Memory range has ::cudaMemAdviseSetAccessedBy set for specified device */
+    cudaMemRangeAttributeLastPrefetchLocation       = 4  /**< The last location to which the range was prefetched */
+    , cudaMemRangeAttributePreferredLocationType    = 5  /**< The preferred location type of the range */
+    , cudaMemRangeAttributePreferredLocationId      = 6  /**< The preferred location id of the range */
+    , cudaMemRangeAttributeLastPrefetchLocationType = 7  /**< The last location type to which the range was prefetched */
+    , cudaMemRangeAttributeLastPrefetchLocationId   = 8  /**< The last location id to which the range was prefetched */
+};
+
+/**
+ * CUDA GPUDirect RDMA flush writes APIs supported on the device
+ */
+enum __device_builtin__ cudaFlushGPUDirectRDMAWritesOptions {
+    cudaFlushGPUDirectRDMAWritesOptionHost   = 1<<0, /**< ::cudaDeviceFlushGPUDirectRDMAWrites() and its CUDA Driver API counterpart are supported on the device. */
+    cudaFlushGPUDirectRDMAWritesOptionMemOps = 1<<1  /**< The ::CU_STREAM_WAIT_VALUE_FLUSH flag and the ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the CUDA device. */
+};
+
+/**
+ * CUDA GPUDirect RDMA flush writes ordering features of the device
+ */
+enum __device_builtin__ cudaGPUDirectRDMAWritesOrdering {
+    cudaGPUDirectRDMAWritesOrderingNone       = 0,   /**< The device does not natively support ordering of GPUDirect RDMA writes. ::cudaFlushGPUDirectRDMAWrites() can be leveraged if supported. */
+    cudaGPUDirectRDMAWritesOrderingOwner      = 100, /**< Natively, the device can consistently consume GPUDirect RDMA writes, although other CUDA devices may not. */
+    cudaGPUDirectRDMAWritesOrderingAllDevices = 200  /**< Any CUDA device in the system can consistently consume GPUDirect RDMA writes to this device. */
+};
+
+/**
+ * CUDA GPUDirect RDMA flush writes scopes
+ */
+enum __device_builtin__ cudaFlushGPUDirectRDMAWritesScope {
+    cudaFlushGPUDirectRDMAWritesToOwner      = 100, /**< Blocks until remote writes are visible to the CUDA device context owning the data. */
+    cudaFlushGPUDirectRDMAWritesToAllDevices = 200  /**< Blocks until remote writes are visible to all CUDA device contexts. */
+};
+
+/**
+ * CUDA GPUDirect RDMA flush writes targets
+ */
+enum __device_builtin__ cudaFlushGPUDirectRDMAWritesTarget {
+    cudaFlushGPUDirectRDMAWritesTargetCurrentDevice /**< Sets the target for ::cudaDeviceFlushGPUDirectRDMAWrites() to the currently active CUDA device context. */
+};
+
+
+/**
+ * CUDA device attributes
+ */
+enum __device_builtin__ cudaDeviceAttr
+{
+    cudaDevAttrMaxThreadsPerBlock             = 1,  /**< Maximum number of threads per block */
+    cudaDevAttrMaxBlockDimX                   = 2,  /**< Maximum block dimension X */
+    cudaDevAttrMaxBlockDimY                   = 3,  /**< Maximum block dimension Y */
+    cudaDevAttrMaxBlockDimZ                   = 4,  /**< Maximum block dimension Z */
+    cudaDevAttrMaxGridDimX                    = 5,  /**< Maximum grid dimension X */
+    cudaDevAttrMaxGridDimY                    = 6,  /**< Maximum grid dimension Y */
+    cudaDevAttrMaxGridDimZ                    = 7,  /**< Maximum grid dimension Z */
+    cudaDevAttrMaxSharedMemoryPerBlock        = 8,  /**< Maximum shared memory available per block in bytes */
+    cudaDevAttrTotalConstantMemory            = 9,  /**< Memory available on device for __constant__ variables in a CUDA C kernel in bytes */
+    cudaDevAttrWarpSize                       = 10, /**< Warp size in threads */
+    cudaDevAttrMaxPitch                       = 11, /**< Maximum pitch in bytes allowed by memory copies */
+    cudaDevAttrMaxRegistersPerBlock           = 12, /**< Maximum number of 32-bit registers available per block */
+    cudaDevAttrClockRate                      = 13, /**< Peak clock frequency in kilohertz */
+    cudaDevAttrTextureAlignment               = 14, /**< Alignment requirement for textures */
+    cudaDevAttrGpuOverlap                     = 15, /**< Device can possibly copy memory and execute a kernel concurrently */
+    cudaDevAttrMultiProcessorCount            = 16, /**< Number of multiprocessors on device */
+    cudaDevAttrKernelExecTimeout              = 17, /**< Specifies whether there is a run time limit on kernels */
+    cudaDevAttrIntegrated                     = 18, /**< Device is integrated with host memory */
+    cudaDevAttrCanMapHostMemory               = 19, /**< Device can map host memory into CUDA address space */
+    cudaDevAttrComputeMode                    = 20, /**< Compute mode (See ::cudaComputeMode for details) */
+    cudaDevAttrMaxTexture1DWidth              = 21, /**< Maximum 1D texture width */
+    cudaDevAttrMaxTexture2DWidth              = 22, /**< Maximum 2D texture width */
+    cudaDevAttrMaxTexture2DHeight             = 23, /**< Maximum 2D texture height */
+    cudaDevAttrMaxTexture3DWidth              = 24, /**< Maximum 3D texture width */
+    cudaDevAttrMaxTexture3DHeight             = 25, /**< Maximum 3D texture height */
+    cudaDevAttrMaxTexture3DDepth              = 26, /**< Maximum 3D texture depth */
+    cudaDevAttrMaxTexture2DLayeredWidth       = 27, /**< Maximum 2D layered texture width */
+    cudaDevAttrMaxTexture2DLayeredHeight      = 28, /**< Maximum 2D layered texture height */
+    cudaDevAttrMaxTexture2DLayeredLayers      = 29, /**< Maximum layers in a 2D layered texture */
+    cudaDevAttrSurfaceAlignment               = 30, /**< Alignment requirement for surfaces */
+    cudaDevAttrConcurrentKernels              = 31, /**< Device can possibly execute multiple kernels concurrently */
+    cudaDevAttrEccEnabled                     = 32, /**< Device has ECC support enabled */
+    cudaDevAttrPciBusId                       = 33, /**< PCI bus ID of the device */
+    cudaDevAttrPciDeviceId                    = 34, /**< PCI device ID of the device */
+    cudaDevAttrTccDriver                      = 35, /**< Device is using TCC driver model */
+    cudaDevAttrMemoryClockRate                = 36, /**< Peak memory clock frequency in kilohertz */
+    cudaDevAttrGlobalMemoryBusWidth           = 37, /**< Global memory bus width in bits */
+    cudaDevAttrL2CacheSize                    = 38, /**< Size of L2 cache in bytes */
+    cudaDevAttrMaxThreadsPerMultiProcessor    = 39, /**< Maximum resident threads per multiprocessor */
+    cudaDevAttrAsyncEngineCount               = 40, /**< Number of asynchronous engines */
+    cudaDevAttrUnifiedAddressing              = 41, /**< Device shares a unified address space with the host */    
+    cudaDevAttrMaxTexture1DLayeredWidth       = 42, /**< Maximum 1D layered texture width */
+    cudaDevAttrMaxTexture1DLayeredLayers      = 43, /**< Maximum layers in a 1D layered texture */
+    cudaDevAttrMaxTexture2DGatherWidth        = 45, /**< Maximum 2D texture width if cudaArrayTextureGather is set */
+    cudaDevAttrMaxTexture2DGatherHeight       = 46, /**< Maximum 2D texture height if cudaArrayTextureGather is set */
+    cudaDevAttrMaxTexture3DWidthAlt           = 47, /**< Alternate maximum 3D texture width */
+    cudaDevAttrMaxTexture3DHeightAlt          = 48, /**< Alternate maximum 3D texture height */
+    cudaDevAttrMaxTexture3DDepthAlt           = 49, /**< Alternate maximum 3D texture depth */
+    cudaDevAttrPciDomainId                    = 50, /**< PCI domain ID of the device */
+    cudaDevAttrTexturePitchAlignment          = 51, /**< Pitch alignment requirement for textures */
+    cudaDevAttrMaxTextureCubemapWidth         = 52, /**< Maximum cubemap texture width/height */
+    cudaDevAttrMaxTextureCubemapLayeredWidth  = 53, /**< Maximum cubemap layered texture width/height */
+    cudaDevAttrMaxTextureCubemapLayeredLayers = 54, /**< Maximum layers in a cubemap layered texture */
+    cudaDevAttrMaxSurface1DWidth              = 55, /**< Maximum 1D surface width */
+    cudaDevAttrMaxSurface2DWidth              = 56, /**< Maximum 2D surface width */
+    cudaDevAttrMaxSurface2DHeight             = 57, /**< Maximum 2D surface height */
+    cudaDevAttrMaxSurface3DWidth              = 58, /**< Maximum 3D surface width */
+    cudaDevAttrMaxSurface3DHeight             = 59, /**< Maximum 3D surface height */
+    cudaDevAttrMaxSurface3DDepth              = 60, /**< Maximum 3D surface depth */
+    cudaDevAttrMaxSurface1DLayeredWidth       = 61, /**< Maximum 1D layered surface width */
+    cudaDevAttrMaxSurface1DLayeredLayers      = 62, /**< Maximum layers in a 1D layered surface */
+    cudaDevAttrMaxSurface2DLayeredWidth       = 63, /**< Maximum 2D layered surface width */
+    cudaDevAttrMaxSurface2DLayeredHeight      = 64, /**< Maximum 2D layered surface height */
+    cudaDevAttrMaxSurface2DLayeredLayers      = 65, /**< Maximum layers in a 2D layered surface */
+    cudaDevAttrMaxSurfaceCubemapWidth         = 66, /**< Maximum cubemap surface width */
+    cudaDevAttrMaxSurfaceCubemapLayeredWidth  = 67, /**< Maximum cubemap layered surface width */
+    cudaDevAttrMaxSurfaceCubemapLayeredLayers = 68, /**< Maximum layers in a cubemap layered surface */
+    cudaDevAttrMaxTexture1DLinearWidth        = 69, /**< Maximum 1D linear texture width */
+    cudaDevAttrMaxTexture2DLinearWidth        = 70, /**< Maximum 2D linear texture width */
+    cudaDevAttrMaxTexture2DLinearHeight       = 71, /**< Maximum 2D linear texture height */
+    cudaDevAttrMaxTexture2DLinearPitch        = 72, /**< Maximum 2D linear texture pitch in bytes */
+    cudaDevAttrMaxTexture2DMipmappedWidth     = 73, /**< Maximum mipmapped 2D texture width */
+    cudaDevAttrMaxTexture2DMipmappedHeight    = 74, /**< Maximum mipmapped 2D texture height */
+    cudaDevAttrComputeCapabilityMajor         = 75, /**< Major compute capability version number */ 
+    cudaDevAttrComputeCapabilityMinor         = 76, /**< Minor compute capability version number */
+    cudaDevAttrMaxTexture1DMipmappedWidth     = 77, /**< Maximum mipmapped 1D texture width */
+    cudaDevAttrStreamPrioritiesSupported      = 78, /**< Device supports stream priorities */
+    cudaDevAttrGlobalL1CacheSupported         = 79, /**< Device supports caching globals in L1 */
+    cudaDevAttrLocalL1CacheSupported          = 80, /**< Device supports caching locals in L1 */
+    cudaDevAttrMaxSharedMemoryPerMultiprocessor = 81, /**< Maximum shared memory available per multiprocessor in bytes */
+    cudaDevAttrMaxRegistersPerMultiprocessor  = 82, /**< Maximum number of 32-bit registers available per multiprocessor */
+    cudaDevAttrManagedMemory                  = 83, /**< Device can allocate managed memory on this system */
+    cudaDevAttrIsMultiGpuBoard                = 84, /**< Device is on a multi-GPU board */
+    cudaDevAttrMultiGpuBoardGroupID           = 85, /**< Unique identifier for a group of devices on the same multi-GPU board */
+    cudaDevAttrHostNativeAtomicSupported      = 86, /**< Link between the device and the host supports native atomic operations */
+    cudaDevAttrSingleToDoublePrecisionPerfRatio = 87, /**< Ratio of single precision performance (in floating-point operations per second) to double precision performance */
+    cudaDevAttrPageableMemoryAccess           = 88, /**< Device supports coherently accessing pageable memory without calling cudaHostRegister on it */
+    cudaDevAttrConcurrentManagedAccess        = 89, /**< Device can coherently access managed memory concurrently with the CPU */
+    cudaDevAttrComputePreemptionSupported     = 90, /**< Device supports Compute Preemption */
+    cudaDevAttrCanUseHostPointerForRegisteredMem = 91, /**< Device can access host registered memory at the same virtual address as the CPU */
+    cudaDevAttrReserved92                     = 92,
+    cudaDevAttrReserved93                     = 93,
+    cudaDevAttrReserved94                     = 94,
+    cudaDevAttrCooperativeLaunch              = 95, /**< Device supports launching cooperative kernels via ::cudaLaunchCooperativeKernel*/
+    cudaDevAttrCooperativeMultiDeviceLaunch   = 96, /**< Deprecated, cudaLaunchCooperativeKernelMultiDevice is deprecated. */
+    cudaDevAttrMaxSharedMemoryPerBlockOptin   = 97, /**< The maximum optin shared memory per block. This value may vary by chip. See ::cudaFuncSetAttribute */
+    cudaDevAttrCanFlushRemoteWrites           = 98, /**< Device supports flushing of outstanding remote writes. */
+    cudaDevAttrHostRegisterSupported          = 99, /**< Device supports host memory registration via ::cudaHostRegister. */
+    cudaDevAttrPageableMemoryAccessUsesHostPageTables = 100, /**< Device accesses pageable memory via the host's page tables. */
+    cudaDevAttrDirectManagedMemAccessFromHost = 101, /**< Host can directly access managed memory on the device without migration. */
+    cudaDevAttrMaxBlocksPerMultiprocessor     = 106, /**< Maximum number of blocks per multiprocessor */
+    cudaDevAttrMaxPersistingL2CacheSize       = 108, /**< Maximum L2 persisting lines capacity setting in bytes. */
+    cudaDevAttrMaxAccessPolicyWindowSize      = 109, /**< Maximum value of cudaAccessPolicyWindow::num_bytes. */
+    cudaDevAttrReservedSharedMemoryPerBlock   = 111, /**< Shared memory reserved by CUDA driver per block in bytes */
+    cudaDevAttrSparseCudaArraySupported       = 112, /**< Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays */
+    cudaDevAttrHostRegisterReadOnlySupported  = 113,  /**< Device supports using the ::cudaHostRegister flag cudaHostRegisterReadOnly to register memory that must be mapped as read-only to the GPU */
+    cudaDevAttrTimelineSemaphoreInteropSupported = 114,  /**< External timeline semaphore interop is supported on the device */
+    cudaDevAttrMaxTimelineSemaphoreInteropSupported = 114,  /**< Deprecated, External timeline semaphore interop is supported on the device */
+    cudaDevAttrMemoryPoolsSupported           = 115, /**< Device supports using the ::cudaMallocAsync and ::cudaMemPool family of APIs */
+    cudaDevAttrGPUDirectRDMASupported         = 116, /**< Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information) */
+    cudaDevAttrGPUDirectRDMAFlushWritesOptions = 117, /**< The returned attribute shall be interpreted as a bitmask, where the individual bits are listed in the ::cudaFlushGPUDirectRDMAWritesOptions enum */
+    cudaDevAttrGPUDirectRDMAWritesOrdering    = 118, /**< GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See ::cudaGPUDirectRDMAWritesOrdering for the numerical values returned here. */
+    cudaDevAttrMemoryPoolSupportedHandleTypes = 119, /**< Handle types supported with mempool based IPC */
+    cudaDevAttrClusterLaunch                  = 120, /**< Indicates device supports cluster launch */
+    cudaDevAttrDeferredMappingCudaArraySupported = 121, /**< Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays */
+    cudaDevAttrReserved122                    = 122,
+    cudaDevAttrReserved123                    = 123,
+    cudaDevAttrReserved124                    = 124,
+    cudaDevAttrIpcEventSupport                = 125, /**< Device supports IPC Events. */ 
+    cudaDevAttrMemSyncDomainCount             = 126, /**< Number of memory synchronization domains the device supports. */
+    cudaDevAttrReserved127                    = 127,
+    cudaDevAttrReserved128                    = 128,
+    cudaDevAttrReserved129                    = 129,
+    cudaDevAttrNumaConfig                     = 130, /**< NUMA configuration of a device: value is of type ::cudaDeviceNumaConfig enum */
+    cudaDevAttrNumaId                         = 131, /**< NUMA node ID of the GPU memory */
+    cudaDevAttrReserved132                    = 132,
+    cudaDevAttrMpsEnabled                     = 133, /**< Contexts created on this device will be shared via MPS */
+    cudaDevAttrHostNumaId                     = 134, /**< NUMA ID of the host node closest to the device or -1 when system does not support NUMA */
+    cudaDevAttrD3D12CigSupported              = 135, /**< Device supports CIG with D3D12. */
+    cudaDevAttrGpuPciDeviceId                 = 139, /**< The combined 16-bit PCI device ID and 16-bit PCI vendor ID. */
+    cudaDevAttrGpuPciSubsystemId              = 140, /**< The combined 16-bit PCI subsystem ID and 16-bit PCI subsystem vendor ID. */
+    cudaDevAttrHostNumaMultinodeIpcSupported  = 143, /**< Device supports HostNuma location IPC between nodes in a multi-node system. */
+    cudaDevAttrMax
+};
+
+/**
+ * CUDA memory pool attributes
+ */
+enum __device_builtin__ cudaMemPoolAttr
+{
+    /**
+     * (value type = int)
+     * Allow cuMemAllocAsync to use memory asynchronously freed
+     * in another streams as long as a stream ordering dependency
+     * of the allocating stream on the free action exists.
+     * Cuda events and null stream interactions can create the required
+     * stream ordered dependencies. (default enabled)
+     */
+    cudaMemPoolReuseFollowEventDependencies   = 0x1,
+
+    /**
+     * (value type = int)
+     * Allow reuse of already completed frees when there is no dependency
+     * between the free and allocation. (default enabled)
+     */
+    cudaMemPoolReuseAllowOpportunistic        = 0x2,
+
+    /**
+     * (value type = int)
+     * Allow cuMemAllocAsync to insert new stream dependencies
+     * in order to establish the stream ordering required to reuse
+     * a piece of memory released by cuFreeAsync (default enabled).
+     */
+    cudaMemPoolReuseAllowInternalDependencies = 0x3,
+
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of reserved memory in bytes to hold onto before trying
+     * to release memory back to the OS. When more than the release
+     * threshold bytes of memory are held by the memory pool, the
+     * allocator will try to release memory back to the OS on the
+     * next call to stream, event or context synchronize. (default 0)
+     */
+    cudaMemPoolAttrReleaseThreshold           = 0x4,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of backing memory currently allocated for the mempool.
+     */
+    cudaMemPoolAttrReservedMemCurrent         = 0x5,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of backing memory allocated for the mempool since the
+     * last time it was reset. High watermark can only be reset to zero.
+     */
+    cudaMemPoolAttrReservedMemHigh            = 0x6,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of memory from the pool that is currently in use by the application.
+     */
+    cudaMemPoolAttrUsedMemCurrent             = 0x7,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of the amount of memory from the pool that was in use by the application since
+     * the last time it was reset. High watermark can only be reset to zero.
+     */
+    cudaMemPoolAttrUsedMemHigh                = 0x8
+};
+
+/**
+ * Specifies the type of location 
+ */
+enum __device_builtin__ cudaMemLocationType {
+    cudaMemLocationTypeInvalid = 0,
+    cudaMemLocationTypeDevice = 1  /**< Location is a device location, thus id is a device ordinal */
+    , cudaMemLocationTypeHost = 2 /**< Location is host, id is ignored */
+    , cudaMemLocationTypeHostNuma = 3 /**< Location is a host NUMA node, thus id is a host NUMA node id */
+    , cudaMemLocationTypeHostNumaCurrent = 4 /**< Location is the host NUMA node closest to the current thread's CPU, id is ignored */
+};
+
+/**
+ * Specifies a memory location.
+ *
+ * To specify a gpu, set type = ::cudaMemLocationTypeDevice and set id = the gpu's device ordinal.
+ * To specify a cpu NUMA node, set type = ::cudaMemLocationTypeHostNuma and set id = host NUMA node id.
+ */
+struct __device_builtin__ cudaMemLocation {
+    enum cudaMemLocationType type;  /**< Specifies the location type, which modifies the meaning of id. */
+    int id;                         /**< identifier for a given this location's ::CUmemLocationType. */
+};
+
+/**
+ * Specifies the memory protection flags for mapping.
+ */
+enum __device_builtin__ cudaMemAccessFlags {
+    cudaMemAccessFlagsProtNone      = 0,  /**< Default, make the address range not accessible */
+    cudaMemAccessFlagsProtRead      = 1,  /**< Make the address range read accessible */
+    cudaMemAccessFlagsProtReadWrite = 3   /**< Make the address range read-write accessible */
+};
+
+/**
+ * Memory access descriptor
+ */
+struct __device_builtin__ cudaMemAccessDesc {
+    struct cudaMemLocation  location; /**< Location on which the request is to change it's accessibility */
+    enum cudaMemAccessFlags flags;    /**< ::CUmemProt accessibility flags to set on the request */
+};
+
+/**
+ * Defines the allocation types available
+ */
+enum __device_builtin__ cudaMemAllocationType {
+    cudaMemAllocationTypeInvalid = 0x0,
+    /** This allocation type is 'pinned', i.e. cannot migrate from its current
+      * location while the application is actively using it
+      */
+    cudaMemAllocationTypePinned  = 0x1,
+    cudaMemAllocationTypeMax     = 0x7FFFFFFF 
+};
+
+/**
+ * Flags for specifying particular handle types
+ */
+enum __device_builtin__ cudaMemAllocationHandleType {
+    cudaMemHandleTypeNone                    = 0x0,  /**< Does not allow any export mechanism. > */
+    cudaMemHandleTypePosixFileDescriptor     = 0x1,  /**< Allows a file descriptor to be used for exporting. Permitted only on POSIX systems. (int) */
+    cudaMemHandleTypeWin32                   = 0x2,  /**< Allows a Win32 NT handle to be used for exporting. (HANDLE) */
+    cudaMemHandleTypeWin32Kmt                = 0x4,   /**< Allows a Win32 KMT handle to be used for exporting. (D3DKMT_HANDLE) */
+    cudaMemHandleTypeFabric                  = 0x8  /**< Allows a fabric handle to be used for exporting. (cudaMemFabricHandle_t) */
+};
+
+/**
+ * This flag, if set, indicates that the memory will be used as a buffer for
+ * hardware accelerated decompression.
+ */
+#define cudaMemPoolCreateUsageHwDecompress 0x2
+
+/**
+ * Specifies the properties of allocations made from the pool.
+ */
+struct __device_builtin__ cudaMemPoolProps {
+    enum cudaMemAllocationType         allocType;   /**< Allocation type. Currently must be specified as cudaMemAllocationTypePinned */
+    enum cudaMemAllocationHandleType   handleTypes; /**< Handle types that will be supported by allocations from the pool. */
+    struct cudaMemLocation             location;    /**< Location allocations should reside. */
+    /**
+     * Windows-specific LPSECURITYATTRIBUTES required when
+     * ::cudaMemHandleTypeWin32 is specified.  This security attribute defines
+     * the scope of which exported allocations may be tranferred to other
+     * processes.  In all other cases, this field is required to be zero.
+     */
+    void                              *win32SecurityAttributes;
+    size_t                             maxSize;     /**< Maximum pool size. When set to 0, defaults to a system dependent value.*/
+    unsigned short                     usage;        /**< Bitmask indicating intended usage for the pool. */
+    unsigned char                      reserved[54]; /**< reserved for future use, must be 0 */
+};
+
+/**
+ * Opaque data for exporting a pool allocation
+ */
+struct __device_builtin__ cudaMemPoolPtrExportData {
+    unsigned char reserved[64];
+};
+
+/**
+ * Memory allocation node parameters
+ */
+struct __device_builtin__ cudaMemAllocNodeParams {
+    /**
+    * in: location where the allocation should reside (specified in ::location).
+    * ::handleTypes must be ::cudaMemHandleTypeNone. IPC is not supported.
+    */
+    struct cudaMemPoolProps         poolProps;       /**< in: array of memory access descriptors. Used to describe peer GPU access */
+    const struct cudaMemAccessDesc *accessDescs;     /**< in: number of memory access descriptors.  Must not exceed the number of GPUs. */
+    size_t                          accessDescCount; /**< in: Number of `accessDescs`s */
+    size_t                          bytesize;        /**< in: size in bytes of the requested allocation */
+    void                           *dptr;            /**< out: address of the allocation returned by CUDA */
+};
+
+/**
+ * Memory allocation node parameters
+ */
+struct __device_builtin__ cudaMemAllocNodeParamsV2 {
+    /**
+    * in: location where the allocation should reside (specified in ::location).
+    * ::handleTypes must be ::cudaMemHandleTypeNone. IPC is not supported.
+    */
+    struct cudaMemPoolProps         poolProps;       /**< in: array of memory access descriptors. Used to describe peer GPU access */
+    const struct cudaMemAccessDesc *accessDescs;     /**< in: number of memory access descriptors.  Must not exceed the number of GPUs. */
+    size_t                          accessDescCount; /**< in: Number of `accessDescs`s */
+    size_t                          bytesize;        /**< in: size in bytes of the requested allocation */
+    void                           *dptr;            /**< out: address of the allocation returned by CUDA */
+};
+
+/**
+ * Memory free node parameters
+ */
+struct __device_builtin__ cudaMemFreeNodeParams {
+    void *dptr; /**< in: the pointer to free */
+};
+
+/**
+ * Graph memory attributes
+ */
+enum __device_builtin__ cudaGraphMemAttributeType {
+    /**
+     * (value type = cuuint64_t)
+     * Amount of memory, in bytes, currently associated with graphs.
+     */
+    cudaGraphMemAttrUsedMemCurrent      = 0x0,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of memory, in bytes, associated with graphs since the
+     * last time it was reset.  High watermark can only be reset to zero.
+     */
+    cudaGraphMemAttrUsedMemHigh         = 0x1,
+
+    /**
+     * (value type = cuuint64_t)
+     * Amount of memory, in bytes, currently allocated for use by
+     * the CUDA graphs asynchronous allocator.
+     */
+    cudaGraphMemAttrReservedMemCurrent  = 0x2,
+
+    /**
+     * (value type = cuuint64_t)
+     * High watermark of memory, in bytes, currently allocated for use by
+     * the CUDA graphs asynchronous allocator.
+     */
+    cudaGraphMemAttrReservedMemHigh     = 0x3
+};
+
+/**
+ * Flags to specify for copies within a batch. For more details see ::cudaMemcpyBatchAsync.
+ */
+enum __device_builtin__ cudaMemcpyFlags {
+    cudaMemcpyFlagDefault                  = 0x0,
+
+    /**
+     * Hint to the driver to try and overlap the copy with compute work on the SMs.
+     */
+    cudaMemcpyFlagPreferOverlapWithCompute = 0x1
+};
+
+enum __device_builtin__ cudaMemcpySrcAccessOrder {
+    /**
+     * Default invalid.
+     */
+    cudaMemcpySrcAccessOrderInvalid       = 0x0,
+
+    /**
+     * Indicates that access to the source pointer must be in stream order.
+     */
+    cudaMemcpySrcAccessOrderStream        = 0x1,
+
+    /**
+     * Indicates that access to the source pointer can be out of stream order and all
+     * accesses must be complete before the API call returns. This flag is suited for
+     * ephemeral sources (ex., stack variables) when it's known that no prior operations
+     * in the stream can be accessing the memory and also that the lifetime of the memory
+     * is limited to the scope that the source variable was declared in. Specifying
+     * this flag allows the driver to optimize the copy and removes the need for the user
+     * to synchronize the stream after the API call.
+     */
+    cudaMemcpySrcAccessOrderDuringApiCall = 0x2,
+
+    /**
+     * Indicates that access to the source pointer can be out of stream order and the accesses
+     * can happen even after the API call returns. This flag is suited for host pointers
+     * allocated outside CUDA (ex., via malloc) when it's known that no prior operations
+     * in the stream can be accessing the memory. Specifying this flag allows the driver
+     * to optimize the copy on certain platforms.
+     */
+    cudaMemcpySrcAccessOrderAny           = 0x3,
+
+    cudaMemcpySrcAccessOrderMax           = 0x7FFFFFFF
+};
+
+/**
+ * Attributes specific to copies within a batch. For more details on usage see ::cudaMemcpyBatchAsync.
+ */
+struct __device_builtin__ cudaMemcpyAttributes {
+    enum cudaMemcpySrcAccessOrder srcAccessOrder;  /**< Source access ordering to be observed for copies with this attribute. */
+    struct cudaMemLocation srcLocHint;             /**< Hint location for the source operand. Ignored when the pointers are not managed memory or memory allocated outside CUDA. */
+    struct cudaMemLocation dstLocHint;             /**< Hint location for the destination operand. Ignored when the pointers are not managed memory or memory allocated outside CUDA. */
+    unsigned int flags;                            /**< Additional flags for copies with this attribute. See ::cudaMemcpyFlags. */
+};
+
+/**
+ * These flags allow applications to convey the operand type for individual copies specified in ::cudaMemcpy3DBatchAsync.
+ */
+enum __device_builtin__ cudaMemcpy3DOperandType {
+    cudaMemcpyOperandTypePointer = 0x1,            /**< Memcpy operand is a valid pointer. */
+    cudaMemcpyOperandTypeArray = 0x2,              /**< Memcpy operand is a CUarray. */
+    cudaMemcpyOperandTypeMax = 0x7FFFFFFF
+};
+
+/**
+ * Struct representing offset into a ::cudaArray_t in elements
+ */
+struct __device_builtin__ cudaOffset3D {
+    size_t x;
+    size_t y;
+    size_t z;
+};
+
+/**
+ * Struct representing an operand for copy with ::cudaMemcpy3DBatchAsync
+ */
+struct __device_builtin__ cudaMemcpy3DOperand {
+    enum cudaMemcpy3DOperandType type;
+    union {
+        /**
+         * Struct representing an operand when ::cudaMemcpy3DOperand::type is ::cudaMemcpyOperandTypePointer
+         */
+        struct {
+            void *ptr;
+            size_t rowLength;                /**< Length of each row in elements. */ 
+            size_t layerHeight;              /**< Height of each layer in elements. */ 
+            struct cudaMemLocation locHint;  /**< Hint location for the operand. Ignored when the pointers are not managed memory or memory allocated outside CUDA. */
+        } ptr;
+
+        /**
+         * Struct representing an operand when ::cudaMemcpy3DOperand::type is ::cudaMemcpyOperandTypeArray
+         */
+        struct {
+            cudaArray_t array;
+            struct cudaOffset3D offset;
+        } array;
+    } op;  
+};
+
+struct __device_builtin__ cudaMemcpy3DBatchOp {
+    struct cudaMemcpy3DOperand src;                /**< Source memcpy operand. */
+    struct cudaMemcpy3DOperand dst;                /**< Destination memcpy operand. */
+    struct cudaExtent extent;                      /**< Extents of the memcpy between src and dst. The width, height and depth components must not be 0.*/
+    enum cudaMemcpySrcAccessOrder srcAccessOrder;  /**< Source access ordering to be observed for copy from src to dst. */
+    unsigned int flags;                            /**< Additional flags for copy from src to dst. See ::cudaMemcpyFlags. */
+};
+
+/**
+ * CUDA device P2P attributes
+ */
+
+enum __device_builtin__ cudaDeviceP2PAttr {
+    cudaDevP2PAttrPerformanceRank              = 1, /**< A relative value indicating the performance of the link between two devices */
+    cudaDevP2PAttrAccessSupported              = 2, /**< Peer access is enabled */
+    cudaDevP2PAttrNativeAtomicSupported        = 3, /**< Native atomic operation over the link supported */
+    cudaDevP2PAttrCudaArrayAccessSupported     = 4  /**< Accessing CUDA arrays over the link supported */
+};
+
+/**
+ * CUDA UUID types
+ */
+#ifndef CU_UUID_HAS_BEEN_DEFINED
+#define CU_UUID_HAS_BEEN_DEFINED
+struct __device_builtin__ CUuuid_st {     /**< CUDA definition of UUID */
+    char bytes[16];
+};
+typedef __device_builtin__ struct CUuuid_st CUuuid;
+#endif
+typedef __device_builtin__ struct CUuuid_st cudaUUID_t;
+
+/**
+ * CUDA device properties
+ */
+struct __device_builtin__ cudaDeviceProp
+{
+    char         name[256];                  /**< ASCII string identifying device */
+    cudaUUID_t   uuid;                       /**< 16-byte unique identifier */
+    char         luid[8];                    /**< 8-byte locally unique identifier. Value is undefined on TCC and non-Windows platforms */
+    unsigned int luidDeviceNodeMask;         /**< LUID device node mask. Value is undefined on TCC and non-Windows platforms */
+    size_t       totalGlobalMem;             /**< Global memory available on device in bytes */
+    size_t       sharedMemPerBlock;          /**< Shared memory available per block in bytes */
+    int          regsPerBlock;               /**< 32-bit registers available per block */
+    int          warpSize;                   /**< Warp size in threads */
+    size_t       memPitch;                   /**< Maximum pitch in bytes allowed by memory copies */
+    int          maxThreadsPerBlock;         /**< Maximum number of threads per block */
+    int          maxThreadsDim[3];           /**< Maximum size of each dimension of a block */
+    int          maxGridSize[3];             /**< Maximum size of each dimension of a grid */
+    int          clockRate;                  /**< Deprecated, Clock frequency in kilohertz */
+    size_t       totalConstMem;              /**< Constant memory available on device in bytes */
+    int          major;                      /**< Major compute capability */
+    int          minor;                      /**< Minor compute capability */
+    size_t       textureAlignment;           /**< Alignment requirement for textures */
+    size_t       texturePitchAlignment;      /**< Pitch alignment requirement for texture references bound to pitched memory */
+    int          deviceOverlap;              /**< Device can concurrently copy memory and execute a kernel. Deprecated. Use instead asyncEngineCount. */
+    int          multiProcessorCount;        /**< Number of multiprocessors on device */
+    int          kernelExecTimeoutEnabled;   /**< Deprecated, Specified whether there is a run time limit on kernels */
+    int          integrated;                 /**< Device is integrated as opposed to discrete */
+    int          canMapHostMemory;           /**< Device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer */
+    int          computeMode;                /**< Deprecated, Compute mode (See ::cudaComputeMode) */
+    int          maxTexture1D;               /**< Maximum 1D texture size */
+    int          maxTexture1DMipmap;         /**< Maximum 1D mipmapped texture size */
+    int          maxTexture1DLinear;         /**< Deprecated, do not use. Use cudaDeviceGetTexture1DLinearMaxWidth() or cuDeviceGetTexture1DLinearMaxWidth() instead. */
+    int          maxTexture2D[2];            /**< Maximum 2D texture dimensions */
+    int          maxTexture2DMipmap[2];      /**< Maximum 2D mipmapped texture dimensions */
+    int          maxTexture2DLinear[3];      /**< Maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory */
+    int          maxTexture2DGather[2];      /**< Maximum 2D texture dimensions if texture gather operations have to be performed */
+    int          maxTexture3D[3];            /**< Maximum 3D texture dimensions */
+    int          maxTexture3DAlt[3];         /**< Maximum alternate 3D texture dimensions */
+    int          maxTextureCubemap;          /**< Maximum Cubemap texture dimensions */
+    int          maxTexture1DLayered[2];     /**< Maximum 1D layered texture dimensions */
+    int          maxTexture2DLayered[3];     /**< Maximum 2D layered texture dimensions */
+    int          maxTextureCubemapLayered[2];/**< Maximum Cubemap layered texture dimensions */
+    int          maxSurface1D;               /**< Maximum 1D surface size */
+    int          maxSurface2D[2];            /**< Maximum 2D surface dimensions */
+    int          maxSurface3D[3];            /**< Maximum 3D surface dimensions */
+    int          maxSurface1DLayered[2];     /**< Maximum 1D layered surface dimensions */
+    int          maxSurface2DLayered[3];     /**< Maximum 2D layered surface dimensions */
+    int          maxSurfaceCubemap;          /**< Maximum Cubemap surface dimensions */
+    int          maxSurfaceCubemapLayered[2];/**< Maximum Cubemap layered surface dimensions */
+    size_t       surfaceAlignment;           /**< Alignment requirements for surfaces */
+    int          concurrentKernels;          /**< Device can possibly execute multiple kernels concurrently */
+    int          ECCEnabled;                 /**< Device has ECC support enabled */
+    int          pciBusID;                   /**< PCI bus ID of the device */
+    int          pciDeviceID;                /**< PCI device ID of the device */
+    int          pciDomainID;                /**< PCI domain ID of the device */
+    int          tccDriver;                  /**< 1 if device is a Tesla device using TCC driver, 0 otherwise */
+    int          asyncEngineCount;           /**< Number of asynchronous engines */
+    int          unifiedAddressing;          /**< Device shares a unified address space with the host */
+    int          memoryClockRate;            /**< Deprecated, Peak memory clock frequency in kilohertz */
+    int          memoryBusWidth;             /**< Global memory bus width in bits */
+    int          l2CacheSize;                /**< Size of L2 cache in bytes */
+    int          persistingL2CacheMaxSize;   /**< Device's maximum l2 persisting lines capacity setting in bytes */
+    int          maxThreadsPerMultiProcessor;/**< Maximum resident threads per multiprocessor */
+    int          streamPrioritiesSupported;  /**< Device supports stream priorities */
+    int          globalL1CacheSupported;     /**< Device supports caching globals in L1 */
+    int          localL1CacheSupported;      /**< Device supports caching locals in L1 */
+    size_t       sharedMemPerMultiprocessor; /**< Shared memory available per multiprocessor in bytes */
+    int          regsPerMultiprocessor;      /**< 32-bit registers available per multiprocessor */
+    int          managedMemory;              /**< Device supports allocating managed memory on this system */
+    int          isMultiGpuBoard;            /**< Device is on a multi-GPU board */
+    int          multiGpuBoardGroupID;       /**< Unique identifier for a group of devices on the same multi-GPU board */
+    int          hostNativeAtomicSupported;  /**< Link between the device and the host supports native atomic operations */
+    int          singleToDoublePrecisionPerfRatio; /**< Deprecated, Ratio of single precision performance (in floating-point operations per second) to double precision performance */
+    int          pageableMemoryAccess;       /**< Device supports coherently accessing pageable memory without calling cudaHostRegister on it */
+    int          concurrentManagedAccess;    /**< Device can coherently access managed memory concurrently with the CPU */
+    int          computePreemptionSupported; /**< Device supports Compute Preemption */
+    int          canUseHostPointerForRegisteredMem; /**< Device can access host registered memory at the same virtual address as the CPU */
+    int          cooperativeLaunch;          /**< Device supports launching cooperative kernels via ::cudaLaunchCooperativeKernel */
+    int          cooperativeMultiDeviceLaunch; /**< Deprecated, cudaLaunchCooperativeKernelMultiDevice is deprecated. */
+    size_t       sharedMemPerBlockOptin;     /**< Per device maximum shared memory per block usable by special opt in */
+    int          pageableMemoryAccessUsesHostPageTables; /**< Device accesses pageable memory via the host's page tables */
+    int          directManagedMemAccessFromHost; /**< Host can directly access managed memory on the device without migration. */
+    int          maxBlocksPerMultiProcessor; /**< Maximum number of resident blocks per multiprocessor */
+    int          accessPolicyMaxWindowSize;  /**< The maximum value of ::cudaAccessPolicyWindow::num_bytes. */
+    size_t       reservedSharedMemPerBlock;  /**< Shared memory reserved by CUDA driver per block in bytes */
+    int          hostRegisterSupported;      /**< Device supports host memory registration via ::cudaHostRegister. */
+    int          sparseCudaArraySupported;   /**< 1 if the device supports sparse CUDA arrays and sparse CUDA mipmapped arrays, 0 otherwise */
+    int          hostRegisterReadOnlySupported; /**< Device supports using the ::cudaHostRegister flag cudaHostRegisterReadOnly to register memory that must be mapped as read-only to the GPU */
+    int          timelineSemaphoreInteropSupported; /**< External timeline semaphore interop is supported on the device */
+    int          memoryPoolsSupported;       /**< 1 if the device supports using the cudaMallocAsync and cudaMemPool family of APIs, 0 otherwise */
+    int          gpuDirectRDMASupported;     /**< 1 if the device supports GPUDirect RDMA APIs, 0 otherwise */
+    unsigned int gpuDirectRDMAFlushWritesOptions; /**< Bitmask to be interpreted according to the ::cudaFlushGPUDirectRDMAWritesOptions enum */
+    int          gpuDirectRDMAWritesOrdering;/**< See the ::cudaGPUDirectRDMAWritesOrdering enum for numerical values */
+    unsigned int memoryPoolSupportedHandleTypes; /**< Bitmask of handle types supported with mempool-based IPC */
+    int          deferredMappingCudaArraySupported; /**< 1 if the device supports deferred mapping CUDA arrays and CUDA mipmapped arrays */
+    int          ipcEventSupported;          /**< Device supports IPC Events. */
+    int          clusterLaunch;              /**< Indicates device supports cluster launch */
+    int          unifiedFunctionPointers;    /**< Indicates device supports unified pointers */
+    int          reserved[63];               /**< Reserved for future use */
+};
+
+/**
+ * CUDA IPC Handle Size
+ */
+#define CUDA_IPC_HANDLE_SIZE 64
+
+/**
+ * CUDA IPC event handle
+ */
+typedef __device_builtin__ struct __device_builtin__ cudaIpcEventHandle_st
+{
+    char reserved[CUDA_IPC_HANDLE_SIZE];
+}cudaIpcEventHandle_t;
+
+/**
+ * CUDA IPC memory handle
+ */
+typedef __device_builtin__ struct __device_builtin__ cudaIpcMemHandle_st 
+{
+    char reserved[CUDA_IPC_HANDLE_SIZE];
+}cudaIpcMemHandle_t;
+
+/*
+ * CUDA Mem Fabric Handle
+ */
+typedef __device_builtin__ struct __device_builtin__ cudaMemFabricHandle_st 
+{
+    char reserved[CUDA_IPC_HANDLE_SIZE];
+}cudaMemFabricHandle_t;
+
+/**
+ * External memory handle types
+ */
+enum __device_builtin__ cudaExternalMemoryHandleType {
+    /**
+     * Handle is an opaque file descriptor
+     */
+    cudaExternalMemoryHandleTypeOpaqueFd         = 1,
+    /**
+     * Handle is an opaque shared NT handle
+     */
+    cudaExternalMemoryHandleTypeOpaqueWin32      = 2,
+    /**
+     * Handle is an opaque, globally shared handle
+     */
+    cudaExternalMemoryHandleTypeOpaqueWin32Kmt   = 3,
+    /**
+     * Handle is a D3D12 heap object
+     */
+    cudaExternalMemoryHandleTypeD3D12Heap        = 4,
+    /**
+     * Handle is a D3D12 committed resource
+     */
+    cudaExternalMemoryHandleTypeD3D12Resource    = 5,
+    /**
+    *  Handle is a shared NT handle to a D3D11 resource
+    */
+    cudaExternalMemoryHandleTypeD3D11Resource    = 6,
+    /**
+    *  Handle is a globally shared handle to a D3D11 resource
+    */
+    cudaExternalMemoryHandleTypeD3D11ResourceKmt = 7,
+    /**
+    *  Handle is an NvSciBuf object
+    */
+    cudaExternalMemoryHandleTypeNvSciBuf         = 8
+};
+
+/**
+ * Indicates that the external memory object is a dedicated resource
+ */
+#define cudaExternalMemoryDedicated   0x1
+
+/** When the /p flags parameter of ::cudaExternalSemaphoreSignalParams
+ * contains this flag, it indicates that signaling an external semaphore object
+ * should skip performing appropriate memory synchronization operations over all
+ * the external memory objects that are imported as ::cudaExternalMemoryHandleTypeNvSciBuf,
+ * which otherwise are performed by default to ensure data coherency with other
+ * importers of the same NvSciBuf memory objects.
+ */
+#define cudaExternalSemaphoreSignalSkipNvSciBufMemSync     0x01
+
+/** When the /p flags parameter of ::cudaExternalSemaphoreWaitParams
+ * contains this flag, it indicates that waiting an external semaphore object
+ * should skip performing appropriate memory synchronization operations over all
+ * the external memory objects that are imported as ::cudaExternalMemoryHandleTypeNvSciBuf,
+ * which otherwise are performed by default to ensure data coherency with other
+ * importers of the same NvSciBuf memory objects.
+ */
+#define cudaExternalSemaphoreWaitSkipNvSciBufMemSync       0x02
+
+/**
+ * When /p flags of ::cudaDeviceGetNvSciSyncAttributes is set to this,
+ * it indicates that application need signaler specific NvSciSyncAttr
+ * to be filled by ::cudaDeviceGetNvSciSyncAttributes.
+ */
+#define cudaNvSciSyncAttrSignal       0x1
+
+/**
+ * When /p flags of ::cudaDeviceGetNvSciSyncAttributes is set to this,
+ * it indicates that application need waiter specific NvSciSyncAttr
+ * to be filled by ::cudaDeviceGetNvSciSyncAttributes.
+ */
+#define cudaNvSciSyncAttrWait         0x2
+
+/**
+ * External memory handle descriptor
+ */
+struct __device_builtin__ cudaExternalMemoryHandleDesc {
+    /**
+     * Type of the handle
+     */
+    enum  cudaExternalMemoryHandleType type;
+    union {
+        /**
+         * File descriptor referencing the memory object. Valid
+         * when type is
+         * ::cudaExternalMemoryHandleTypeOpaqueFd
+         */
+        int fd;
+        /**
+         * Win32 handle referencing the semaphore object. Valid when
+         * type is one of the following:
+         * - ::cudaExternalMemoryHandleTypeOpaqueWin32
+         * - ::cudaExternalMemoryHandleTypeOpaqueWin32Kmt
+         * - ::cudaExternalMemoryHandleTypeD3D12Heap 
+         * - ::cudaExternalMemoryHandleTypeD3D12Resource
+		 * - ::cudaExternalMemoryHandleTypeD3D11Resource
+		 * - ::cudaExternalMemoryHandleTypeD3D11ResourceKmt
+         * Exactly one of 'handle' and 'name' must be non-NULL. If
+         * type is one of the following: 
+         * ::cudaExternalMemoryHandleTypeOpaqueWin32Kmt
+         * ::cudaExternalMemoryHandleTypeD3D11ResourceKmt
+         * then 'name' must be NULL.
+         */
+        struct {
+            /**
+             * Valid NT handle. Must be NULL if 'name' is non-NULL
+             */
+            void *handle;
+            /**
+             * Name of a valid memory object.
+             * Must be NULL if 'handle' is non-NULL.
+             */
+            const void *name;
+        } win32;
+        /**
+         * A handle representing NvSciBuf Object. Valid when type
+         * is ::cudaExternalMemoryHandleTypeNvSciBuf
+         */
+        const void *nvSciBufObject;
+    } handle;
+    /**
+     * Size of the memory allocation
+     */
+    unsigned long long size;
+    /**
+     * Flags must either be zero or ::cudaExternalMemoryDedicated
+     */
+    unsigned int flags;
+};
+
+/**
+ * External memory buffer descriptor
+ */
+struct __device_builtin__ cudaExternalMemoryBufferDesc {
+    /**
+     * Offset into the memory object where the buffer's base is
+     */
+    unsigned long long offset;
+    /**
+     * Size of the buffer
+     */
+    unsigned long long size;
+    /**
+     * Flags reserved for future use. Must be zero.
+     */
+    unsigned int flags;
+};
+ 
+/**
+ * External memory mipmap descriptor
+ */
+struct __device_builtin__ cudaExternalMemoryMipmappedArrayDesc {
+    /**
+     * Offset into the memory object where the base level of the
+     * mipmap chain is.
+     */
+    unsigned long long offset;
+    /**
+     * Format of base level of the mipmap chain
+     */
+    struct cudaChannelFormatDesc formatDesc;
+    /**
+     * Dimensions of base level of the mipmap chain
+     */
+    struct cudaExtent extent;
+    /**
+     * Flags associated with CUDA mipmapped arrays.
+     * See ::cudaMallocMipmappedArray
+     */
+    unsigned int flags;
+    /**
+     * Total number of levels in the mipmap chain
+     */
+    unsigned int numLevels;
+};
+ 
+/**
+ * External semaphore handle types
+ */
+enum __device_builtin__ cudaExternalSemaphoreHandleType {
+    /**
+     * Handle is an opaque file descriptor
+     */
+    cudaExternalSemaphoreHandleTypeOpaqueFd       = 1,
+    /**
+     * Handle is an opaque shared NT handle
+     */
+    cudaExternalSemaphoreHandleTypeOpaqueWin32    = 2,
+    /**
+     * Handle is an opaque, globally shared handle
+     */
+    cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt = 3,
+    /**
+     * Handle is a shared NT handle referencing a D3D12 fence object
+     */
+    cudaExternalSemaphoreHandleTypeD3D12Fence     = 4,
+    /**
+     * Handle is a shared NT handle referencing a D3D11 fence object
+     */
+    cudaExternalSemaphoreHandleTypeD3D11Fence     = 5,
+    /**
+     * Opaque handle to NvSciSync Object
+     */
+     cudaExternalSemaphoreHandleTypeNvSciSync     = 6,
+    /**
+     * Handle is a shared NT handle referencing a D3D11 keyed mutex object
+     */
+    cudaExternalSemaphoreHandleTypeKeyedMutex     = 7,
+    /**
+     * Handle is a shared KMT handle referencing a D3D11 keyed mutex object
+     */
+    cudaExternalSemaphoreHandleTypeKeyedMutexKmt  = 8,
+    /**
+     * Handle is an opaque handle file descriptor referencing a timeline semaphore
+     */
+    cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd  = 9,
+    /**
+     * Handle is an opaque handle file descriptor referencing a timeline semaphore
+     */
+    cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32  = 10
+};
+
+/**
+ * External semaphore handle descriptor
+ */
+struct __device_builtin__ cudaExternalSemaphoreHandleDesc {
+    /**
+     * Type of the handle
+     */
+    enum cudaExternalSemaphoreHandleType type;
+    union {
+        /**
+         * File descriptor referencing the semaphore object. Valid when
+         * type is one of the following:
+         * - ::cudaExternalSemaphoreHandleTypeOpaqueFd
+         * - ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd
+         */
+        int fd;
+        /**
+         * Win32 handle referencing the semaphore object. Valid when
+         * type is one of the following:
+         * - ::cudaExternalSemaphoreHandleTypeOpaqueWin32
+         * - ::cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt
+         * - ::cudaExternalSemaphoreHandleTypeD3D12Fence
+         * - ::cudaExternalSemaphoreHandleTypeD3D11Fence
+         * - ::cudaExternalSemaphoreHandleTypeKeyedMutex
+         * - ::cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32
+         * Exactly one of 'handle' and 'name' must be non-NULL. If
+         * type is one of the following:
+         * ::cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt
+         * ::cudaExternalSemaphoreHandleTypeKeyedMutexKmt
+         * then 'name' must be NULL.
+         */
+        struct {
+            /**
+             * Valid NT handle. Must be NULL if 'name' is non-NULL
+             */
+            void *handle;
+            /**
+             * Name of a valid synchronization primitive.
+             * Must be NULL if 'handle' is non-NULL.
+             */
+            const void *name;
+        } win32;
+        /**
+         * Valid NvSciSyncObj. Must be non NULL
+         */
+        const void* nvSciSyncObj;
+    } handle;
+    /**
+     * Flags reserved for the future. Must be zero.
+     */
+    unsigned int flags;
+};
+
+/**
+ * External semaphore signal parameters(deprecated)
+ */
+struct __device_builtin__ cudaExternalSemaphoreSignalParams_v1 {
+    struct {
+        /**
+         * Parameters for fence objects
+         */
+        struct {
+            /**
+             * Value of fence to be signaled
+             */
+            unsigned long long value;
+        } fence;
+        union {
+            /**
+             * Pointer to NvSciSyncFence. Valid if ::cudaExternalSemaphoreHandleType
+             * is of type ::cudaExternalSemaphoreHandleTypeNvSciSync.
+             */
+            void *fence;
+            unsigned long long reserved;
+        } nvSciSync;
+        /**
+         * Parameters for keyed mutex objects
+         */
+        struct {
+            /*
+             * Value of key to release the mutex with
+             */
+            unsigned long long key;
+        } keyedMutex;
+    } params;
+    /**
+     * Only when ::cudaExternalSemaphoreSignalParams is used to
+     * signal a ::cudaExternalSemaphore_t of type
+     * ::cudaExternalSemaphoreHandleTypeNvSciSync, the valid flag is 
+     * ::cudaExternalSemaphoreSignalSkipNvSciBufMemSync: which indicates
+     * that while signaling the ::cudaExternalSemaphore_t, no memory
+     * synchronization operations should be performed for any external memory
+     * object imported as ::cudaExternalMemoryHandleTypeNvSciBuf.
+     * For all other types of ::cudaExternalSemaphore_t, flags must be zero.
+     */
+    unsigned int flags;
+};
+
+/**
+* External semaphore wait parameters(deprecated)
+*/
+struct __device_builtin__ cudaExternalSemaphoreWaitParams_v1 {
+    struct {
+        /**
+        * Parameters for fence objects
+        */
+        struct {
+            /**
+            * Value of fence to be waited on
+            */
+            unsigned long long value;
+        } fence;
+        union {
+            /**
+             * Pointer to NvSciSyncFence. Valid if ::cudaExternalSemaphoreHandleType
+             * is of type ::cudaExternalSemaphoreHandleTypeNvSciSync.
+             */
+            void *fence;
+            unsigned long long reserved;
+        } nvSciSync;
+        /**
+         * Parameters for keyed mutex objects
+         */
+        struct {
+            /**
+             * Value of key to acquire the mutex with
+             */
+            unsigned long long key;
+            /**
+             * Timeout in milliseconds to wait to acquire the mutex
+             */
+            unsigned int timeoutMs;
+        } keyedMutex;
+    } params;
+    /**
+     * Only when ::cudaExternalSemaphoreSignalParams is used to
+     * signal a ::cudaExternalSemaphore_t of type
+     * ::cudaExternalSemaphoreHandleTypeNvSciSync, the valid flag is 
+     * ::cudaExternalSemaphoreSignalSkipNvSciBufMemSync: which indicates
+     * that while waiting for the ::cudaExternalSemaphore_t, no memory
+     * synchronization operations should be performed for any external memory
+     * object imported as ::cudaExternalMemoryHandleTypeNvSciBuf.
+     * For all other types of ::cudaExternalSemaphore_t, flags must be zero.
+     */
+    unsigned int flags;
+};
+
+/**
+ * External semaphore signal parameters, compatible with driver type
+ */
+struct __device_builtin__ cudaExternalSemaphoreSignalParams{
+    struct {
+        /**
+         * Parameters for fence objects
+         */
+        struct {
+            /**
+             * Value of fence to be signaled
+             */
+            unsigned long long value;
+        } fence;
+        union {
+            /**
+             * Pointer to NvSciSyncFence. Valid if ::cudaExternalSemaphoreHandleType
+             * is of type ::cudaExternalSemaphoreHandleTypeNvSciSync.
+             */
+            void *fence;
+            unsigned long long reserved;
+        } nvSciSync;
+        /**
+         * Parameters for keyed mutex objects
+         */
+        struct {
+            /*
+             * Value of key to release the mutex with
+             */
+            unsigned long long key;
+        } keyedMutex;
+        unsigned int reserved[12];
+    } params;
+    /**
+     * Only when ::cudaExternalSemaphoreSignalParams is used to
+     * signal a ::cudaExternalSemaphore_t of type
+     * ::cudaExternalSemaphoreHandleTypeNvSciSync, the valid flag is 
+     * ::cudaExternalSemaphoreSignalSkipNvSciBufMemSync: which indicates
+     * that while signaling the ::cudaExternalSemaphore_t, no memory
+     * synchronization operations should be performed for any external memory
+     * object imported as ::cudaExternalMemoryHandleTypeNvSciBuf.
+     * For all other types of ::cudaExternalSemaphore_t, flags must be zero.
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+};
+
+/**
+ * External semaphore wait parameters, compatible with driver type
+ */
+struct __device_builtin__ cudaExternalSemaphoreWaitParams {
+    struct {
+        /**
+        * Parameters for fence objects
+        */
+        struct {
+            /**
+            * Value of fence to be waited on
+            */
+            unsigned long long value;
+        } fence;
+        union {
+            /**
+             * Pointer to NvSciSyncFence. Valid if ::cudaExternalSemaphoreHandleType
+             * is of type ::cudaExternalSemaphoreHandleTypeNvSciSync.
+             */
+            void *fence;
+            unsigned long long reserved;
+        } nvSciSync;
+        /**
+         * Parameters for keyed mutex objects
+         */
+        struct {
+            /**
+             * Value of key to acquire the mutex with
+             */
+            unsigned long long key;
+            /**
+             * Timeout in milliseconds to wait to acquire the mutex
+             */
+            unsigned int timeoutMs;
+        } keyedMutex;
+        unsigned int reserved[10];
+    } params;
+    /**
+     * Only when ::cudaExternalSemaphoreSignalParams is used to
+     * signal a ::cudaExternalSemaphore_t of type
+     * ::cudaExternalSemaphoreHandleTypeNvSciSync, the valid flag is 
+     * ::cudaExternalSemaphoreSignalSkipNvSciBufMemSync: which indicates
+     * that while waiting for the ::cudaExternalSemaphore_t, no memory
+     * synchronization operations should be performed for any external memory
+     * object imported as ::cudaExternalMemoryHandleTypeNvSciBuf.
+     * For all other types of ::cudaExternalSemaphore_t, flags must be zero.
+     */
+    unsigned int flags;
+    unsigned int reserved[16];
+};
+
+/*******************************************************************************
+*                                                                              *
+*  SHORTHAND TYPE DEFINITION USED BY RUNTIME API                               *
+*                                                                              *
+*******************************************************************************/
+
+/**
+ * CUDA Error types
+ */
+typedef __device_builtin__ enum cudaError cudaError_t;
+
+/**
+ * CUDA stream
+ */
+typedef __device_builtin__ struct CUstream_st *cudaStream_t;
+
+/**
+ * CUDA event types
+ */
+typedef __device_builtin__ struct CUevent_st *cudaEvent_t;
+
+/**
+ * CUDA graphics resource types
+ */
+typedef __device_builtin__ struct cudaGraphicsResource *cudaGraphicsResource_t;
+
+/**
+ * CUDA external memory
+ */
+typedef __device_builtin__ struct CUexternalMemory_st *cudaExternalMemory_t;
+
+/**
+ * CUDA external semaphore
+ */
+typedef __device_builtin__ struct CUexternalSemaphore_st *cudaExternalSemaphore_t;
+
+/**
+ * CUDA graph
+ */
+typedef __device_builtin__ struct CUgraph_st *cudaGraph_t;
+
+/**
+ * CUDA graph node.
+ */
+typedef __device_builtin__ struct CUgraphNode_st *cudaGraphNode_t;
+
+/**
+ * CUDA user object for graphs
+ */
+typedef __device_builtin__ struct CUuserObject_st *cudaUserObject_t;
+
+/**
+ * CUDA handle for conditional graph nodes
+ */
+typedef __device_builtin__ unsigned long long cudaGraphConditionalHandle;
+
+/**
+ * CUDA function
+ */
+typedef __device_builtin__ struct CUfunc_st *cudaFunction_t;
+
+/**
+ * CUDA kernel
+ */
+typedef __device_builtin__ struct CUkern_st *cudaKernel_t;
+
+/**
+ * Online compiler and linker options
+ */
+enum __device_builtin__ cudaJitOption
+{
+    /**
+     * Max number of registers that a thread may use.\n
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+     */
+    cudaJitMaxRegisters = 0,
+
+    /**
+     * IN: Specifies minimum number of threads per block to target compilation
+     * for\n
+     * OUT: Returns the number of threads the compiler actually targeted.
+     * This restricts the resource utilization of the compiler (e.g. max
+     * registers) such that a block with the given number of threads should be
+     * able to launch based on register limitations. Note, this option does not
+     * currently take into account any other resource limitations, such as
+     * shared memory utilization.\n
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+     */
+    cudaJitThreadsPerBlock = 1,
+
+    /**
+     * Overwrites the option value with the total wall clock time, in
+     * milliseconds, spent in the compiler and linker\n
+     * Option type: float\n
+     * Applies to: compiler and linker
+     */
+    cudaJitWallTime = 2,
+
+    /**
+     * Pointer to a buffer in which to print any log messages
+     * that are informational in nature (the buffer size is specified via
+     * option ::cudaJitInfoLogBufferSizeBytes)\n
+     * Option type: char *\n
+     * Applies to: compiler and linker
+     */
+    cudaJitInfoLogBuffer = 3,
+
+    /**
+     * IN: Log buffer size in bytes.  Log messages will be capped at this size
+     * (including null terminator)\n
+     * OUT: Amount of log buffer filled with messages\n
+     * Option type: unsigned int\n
+     * Applies to: compiler and linker
+     */
+    cudaJitInfoLogBufferSizeBytes = 4,
+
+    /**
+     * Pointer to a buffer in which to print any log messages that
+     * reflect errors (the buffer size is specified via option
+     * ::cudaJitErrorLogBufferSizeBytes)\n
+     * Option type: char *\n
+     * Applies to: compiler and linker
+     */
+    cudaJitErrorLogBuffer = 5,
+
+    /**
+     * IN: Log buffer size in bytes.  Log messages will be capped at this size
+     * (including null terminator)\n
+     * OUT: Amount of log buffer filled with messages\n
+     * Option type: unsigned int\n
+     * Applies to: compiler and linker
+     */
+    cudaJitErrorLogBufferSizeBytes = 6,
+
+    /**
+     * Level of optimizations to apply to generated code (0 - 4), with 4
+     * being the default and highest level of optimizations.\n
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+     */
+    cudaJitOptimizationLevel = 7,
+
+    /**
+     * Specifies choice of fallback strategy if matching cubin is not found.
+     * Choice is based on supplied ::cudaJit_Fallback.
+     * Option type: unsigned int for enumerated type ::cudaJit_Fallback\n
+     * Applies to: compiler only
+     */
+    cudaJitFallbackStrategy = 10,
+
+    /**
+     * Specifies whether to create debug information in output (-g)
+     * (0: false, default)\n
+     * Option type: int\n
+     * Applies to: compiler and linker
+     */
+    cudaJitGenerateDebugInfo = 11,
+
+    /**
+     * Generate verbose log messages (0: false, default)\n
+     * Option type: int\n
+     * Applies to: compiler and linker
+     */
+    cudaJitLogVerbose = 12,
+
+    /**
+     * Generate line number information (-lineinfo) (0: false, default)\n
+     * Option type: int\n
+     * Applies to: compiler only
+     */
+    cudaJitGenerateLineInfo = 13,
+
+    /**
+     * Specifies whether to enable caching explicitly (-dlcm) \n
+     * Choice is based on supplied ::cudaJit_CacheMode.\n
+     * Option type: unsigned int for enumerated type ::cudaJit_CacheMode\n
+     * Applies to: compiler only
+     */
+    cudaJitCacheMode = 14,
+
+    /**
+     * Generate position independent code (0: false)\n
+     * Option type: int\n
+     * Applies to: compiler only
+     */
+    cudaJitPositionIndependentCode = 30,
+
+    /**
+     * This option hints to the JIT compiler the minimum number of CTAs from the
+     * kernel’s grid to be mapped to a SM. This option is ignored when used together
+     * with ::cudaJitMaxRegisters or ::cudaJitThreadsPerBlock.
+     * Optimizations based on this option need ::cudaJitMaxThreadsPerBlock to
+     * be specified as well. For kernels already using PTX directive .minnctapersm,
+     * this option will be ignored by default. Use ::cudaJitOverrideDirectiveValues
+     * to let this option take precedence over the PTX directive.
+     * Option type: unsigned int\n
+     * Applies to: compiler only
+    */
+    cudaJitMinCtaPerSm = 31,
+
+     /**
+     * Maximum number threads in a thread block, computed as the product of
+     * the maximum extent specifed for each dimension of the block. This limit
+     * is guaranteed not to be exeeded in any invocation of the kernel. Exceeding
+     * the the maximum number of threads results in runtime error or kernel launch
+     * failure. For kernels already using PTX directive .maxntid, this option will
+     * be ignored by default. Use ::cudaJitOverrideDirectiveValues to let this
+     * option take precedence over the PTX directive.
+     * Option type: int\n
+     * Applies to: compiler only
+    */
+    cudaJitMaxThreadsPerBlock = 32,
+
+    /**
+     * This option lets the values specified using ::cudaJitMaxRegisters,
+     * ::cudaJitThreadsPerBlock, ::cudaJitMaxThreadsPerBlock and
+     * ::cudaJitMinCtaPerSm take precedence over any PTX directives.
+     * (0: Disable, default; 1: Enable)
+     * Option type: int\n
+     * Applies to: compiler only
+    */
+    cudaJitOverrideDirectiveValues = 33,
+};
+
+
+/**
+ * Library options to be specified with ::cudaLibraryLoadData() or ::cudaLibraryLoadFromFile()
+ */
+enum __device_builtin__ cudaLibraryOption
+{
+    cudaLibraryHostUniversalFunctionAndDataTable = 0,
+
+    /**
+     * Specifes that the argument \p code passed to ::cudaLibraryLoadData() will be preserved.
+     * Specifying this option will let the driver know that \p code can be accessed at any point
+     * until ::cudaLibraryUnload(). The default behavior is for the driver to allocate and
+     * maintain its own copy of \p code. Note that this is only a memory usage optimization
+     * hint and the driver can choose to ignore it if required.
+     * Specifying this option with ::cudaLibraryLoadFromFile() is invalid and
+     * will return ::cudaErrorInvalidValue.
+     */
+    cudaLibraryBinaryIsPreserved = 1,
+};
+
+struct __device_builtin__ cudalibraryHostUniversalFunctionAndDataTable
+{
+    void *functionTable;
+    size_t functionWindowSize;
+    void *dataTable;
+    size_t dataWindowSize;
+};
+
+/**
+ * Caching modes for dlcm
+ */
+enum __device_builtin__ cudaJit_CacheMode
+{
+    cudaJitCacheOptionNone = 0,   /**< Compile with no -dlcm flag specified */
+    cudaJitCacheOptionCG,         /**< Compile with L1 cache disabled */
+    cudaJitCacheOptionCA          /**< Compile with L1 cache enabled */
+};
+
+/**
+ * Cubin matching fallback strategies
+ */
+enum __device_builtin__ cudaJit_Fallback
+{
+    cudaPreferPtx = 0,  /**< Prefer to compile ptx if exact binary match not found */
+
+    cudaPreferBinary    /**< Prefer to fall back to compatible binary code if exact match not found */
+};
+
+/**
+ * CUDA library
+ */
+typedef __device_builtin__ struct CUlib_st *cudaLibrary_t;
+
+/**
+ * CUDA memory pool
+ */
+typedef __device_builtin__ struct CUmemPoolHandle_st *cudaMemPool_t;
+
+/**
+ * CUDA cooperative group scope
+ */
+enum __device_builtin__ cudaCGScope {
+    cudaCGScopeInvalid   = 0, /**< Invalid cooperative group scope */
+    cudaCGScopeGrid      = 1, /**< Scope represented by a grid_group */
+    cudaCGScopeMultiGrid = 2  /**< Scope represented by a multi_grid_group */
+};
+
+/**
+ * CUDA launch parameters
+ */
+struct __device_builtin__ cudaLaunchParams
+{
+    void *func;          /**< Device function symbol */
+    dim3 gridDim;        /**< Grid dimentions */
+    dim3 blockDim;       /**< Block dimentions */
+    void **args;         /**< Arguments */
+    size_t sharedMem;    /**< Shared memory */
+    cudaStream_t stream; /**< Stream identifier */
+};
+
+/**
+ * CUDA GPU kernel node parameters
+ */
+struct __device_builtin__ cudaKernelNodeParams {
+    void* func;                     /**< Kernel to launch */
+    dim3 gridDim;                   /**< Grid dimensions */
+    dim3 blockDim;                  /**< Block dimensions */
+    unsigned int sharedMemBytes;    /**< Dynamic shared-memory size per thread block in bytes */
+    void **kernelParams;            /**< Array of pointers to individual kernel arguments*/
+    void **extra;                   /**< Pointer to kernel arguments in the "extra" format */
+};
+
+/**
+ * CUDA GPU kernel node parameters
+ */
+struct __device_builtin__ cudaKernelNodeParamsV2 {
+    void* func;                     /**< Kernel to launch */
+    #if !defined(__cplusplus) || __cplusplus >= 201103L
+        dim3 gridDim;                   /**< Grid dimensions */
+        dim3 blockDim;                  /**< Block dimensions */
+    #else
+        /* Union members cannot have nontrivial constructors until C++11. */
+        uint3 gridDim;                  /**< Grid dimensions */
+        uint3 blockDim;                 /**< Block dimensions */
+    #endif
+    unsigned int sharedMemBytes;    /**< Dynamic shared-memory size per thread block in bytes */
+    void **kernelParams;            /**< Array of pointers to individual kernel arguments*/
+    void **extra;                   /**< Pointer to kernel arguments in the "extra" format */
+};
+
+/**
+ * External semaphore signal node parameters
+ */
+struct __device_builtin__ cudaExternalSemaphoreSignalNodeParams {
+    cudaExternalSemaphore_t* extSemArray;                        /**< Array of external semaphore handles. */
+    const struct cudaExternalSemaphoreSignalParams* paramsArray; /**< Array of external semaphore signal parameters. */
+    unsigned int numExtSems;                                     /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+};
+
+/**
+ * External semaphore signal node parameters
+ */
+struct __device_builtin__ cudaExternalSemaphoreSignalNodeParamsV2 {
+    cudaExternalSemaphore_t* extSemArray;                        /**< Array of external semaphore handles. */
+    const struct cudaExternalSemaphoreSignalParams* paramsArray; /**< Array of external semaphore signal parameters. */
+    unsigned int numExtSems;                                     /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+};
+
+/**
+ * External semaphore wait node parameters
+ */
+struct __device_builtin__ cudaExternalSemaphoreWaitNodeParams {
+    cudaExternalSemaphore_t* extSemArray;                      /**< Array of external semaphore handles. */
+    const struct cudaExternalSemaphoreWaitParams* paramsArray; /**< Array of external semaphore wait parameters. */
+    unsigned int numExtSems;                                   /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+};
+
+/**
+ * External semaphore wait node parameters
+ */
+struct __device_builtin__ cudaExternalSemaphoreWaitNodeParamsV2 {
+    cudaExternalSemaphore_t* extSemArray;                      /**< Array of external semaphore handles. */
+    const struct cudaExternalSemaphoreWaitParams* paramsArray; /**< Array of external semaphore wait parameters. */
+    unsigned int numExtSems;                                   /**< Number of handles and parameters supplied in extSemArray and paramsArray. */
+};
+
+enum __device_builtin__ cudaGraphConditionalHandleFlags {
+    cudaGraphCondAssignDefault = 1 /**< Apply default handle value when graph is launched. */
+};
+
+/**
+ * CUDA conditional node types
+ */
+enum __device_builtin__ cudaGraphConditionalNodeType {
+    cudaGraphCondTypeIf  = 0,    /**< Conditional 'if/else' Node. Body[0] executed if condition is non-zero.  If \p size == 2, an optional ELSE graph is created and this is executed if the condition is zero. */
+    cudaGraphCondTypeWhile = 1,  /**< Conditional 'while' Node. Body executed repeatedly while condition value is non-zero. */
+    cudaGraphCondTypeSwitch = 2, /**< Conditional 'switch' Node. Body[n] is executed once, where 'n' is the value of the condition. If the condition does not match a body index, no body is launched. */
+};
+
+/**
+ * CUDA conditional node parameters
+ */
+struct __device_builtin__ cudaConditionalNodeParams {
+    cudaGraphConditionalHandle handle;       /**< Conditional node handle.
+                                                  Handles must be created in advance of creating the node
+                                                  using ::cudaGraphConditionalHandleCreate. */
+    enum cudaGraphConditionalNodeType type;  /**< Type of conditional node. */
+    unsigned int size;                       /**< Size of graph output array.  Allowed values are 1 for cudaGraphCondTypeWhile, 1 or 2
+                                                  for cudaGraphCondTypeWhile, or any value greater than zero for cudaGraphCondTypeSwitch. */
+    cudaGraph_t *phGraph_out;                /**< CUDA-owned array populated with conditional node child graphs during creation of the node.
+                                                  Valid for the lifetime of the conditional node.
+                                                  The contents of the graph(s) are subject to the following constraints:
+                                                  
+                                                  - Allowed node types are kernel nodes, empty nodes, child graphs, memsets,
+                                                    memcopies, and conditionals. This applies recursively to child graphs and conditional bodies.
+                                                  - All kernels, including kernels in nested conditionals or child graphs at any level,
+                                                    must belong to the same CUDA context.
+                                                  
+                                                  These graphs may be populated using graph node creation APIs or ::cudaStreamBeginCaptureToGraph.
+                                                  cudaGraphCondTypeIf:
+                                                  phGraph_out[0] is executed when the condition is non-zero.  If \p size == 2, phGraph_out[1] will
+                                                  be executed when the condition is zero.
+                                                  cudaGraphCondTypeWhile:
+                                                  phGraph_out[0] is executed as long as the condition is non-zero.
+                                                  cudaGraphCondTypeSwitch:
+                                                  phGraph_out[n] is executed when the condition is equal to n.  If the condition >= \p size,
+                                                  no body graph is executed.
+                                         */
+};
+
+/**
+* CUDA Graph node types
+*/
+enum __device_builtin__ cudaGraphNodeType {
+    cudaGraphNodeTypeKernel      = 0x00, /**< GPU kernel node */
+    cudaGraphNodeTypeMemcpy      = 0x01, /**< Memcpy node */
+    cudaGraphNodeTypeMemset      = 0x02, /**< Memset node */
+    cudaGraphNodeTypeHost        = 0x03, /**< Host (executable) node */
+    cudaGraphNodeTypeGraph       = 0x04, /**< Node which executes an embedded graph */
+    cudaGraphNodeTypeEmpty       = 0x05, /**< Empty (no-op) node */
+    cudaGraphNodeTypeWaitEvent   = 0x06, /**< External event wait node */
+    cudaGraphNodeTypeEventRecord = 0x07, /**< External event record node */
+    cudaGraphNodeTypeExtSemaphoreSignal = 0x08, /**< External semaphore signal node */
+    cudaGraphNodeTypeExtSemaphoreWait = 0x09, /**< External semaphore wait node */
+    cudaGraphNodeTypeMemAlloc    = 0x0a, /**< Memory allocation node */
+    cudaGraphNodeTypeMemFree     = 0x0b, /**< Memory free node */
+    cudaGraphNodeTypeConditional = 0x0d, /**< Conditional node
+                                              
+                                              May be used to implement a conditional execution path or loop
+                                              inside of a graph. The graph(s) contained within the body of the conditional node
+                                              can be selectively executed or iterated upon based on the value of a conditional
+                                              variable.
+                                              
+                                              Handles must be created in advance of creating the node
+                                              using ::cudaGraphConditionalHandleCreate.
+                                              
+                                              The following restrictions apply to graphs which contain conditional nodes:
+                                                The graph cannot be used in a child node.
+                                                Only one instantiation of the graph may exist at any point in time.
+                                                The graph cannot be cloned.
+                                              
+                                              To set the control value, supply a default value when creating the handle and/or
+                                              call ::cudaGraphSetConditional from device code.*/
+    cudaGraphNodeTypeCount
+};
+
+/**
+ * Child graph node parameters
+ */
+struct __device_builtin__ cudaChildGraphNodeParams {
+    cudaGraph_t graph; /**< The child graph to clone into the node for node creation, or
+                            a handle to the graph owned by the node for node query */
+};
+
+/**
+ * Event record node parameters
+ */
+struct __device_builtin__ cudaEventRecordNodeParams {
+    cudaEvent_t event; /**< The event to record when the node executes */
+};
+
+/**
+ * Event wait node parameters
+ */
+struct __device_builtin__ cudaEventWaitNodeParams {
+    cudaEvent_t event; /**< The event to wait on from the node */
+};
+
+/**
+ * Graph node parameters.  See ::cudaGraphAddNode.
+ */
+struct __device_builtin__ cudaGraphNodeParams {
+    enum cudaGraphNodeType type; /**< Type of the node */
+    int reserved0[3];            /**< Reserved.  Must be zero. */
+
+    union {
+        long long                                      reserved1[29]; /**< Padding. Unused bytes must be zero. */
+        struct cudaKernelNodeParamsV2                  kernel;        /**< Kernel node parameters. */
+        struct cudaMemcpyNodeParams                    memcpy;        /**< Memcpy node parameters. */
+        struct cudaMemsetParamsV2                      memset;        /**< Memset node parameters. */
+        struct cudaHostNodeParamsV2                    host;          /**< Host node parameters. */
+        struct cudaChildGraphNodeParams                graph;         /**< Child graph node parameters. */
+        struct cudaEventWaitNodeParams                 eventWait;     /**< Event wait node parameters. */
+        struct cudaEventRecordNodeParams               eventRecord;   /**< Event record node parameters. */
+        struct cudaExternalSemaphoreSignalNodeParamsV2 extSemSignal;  /**< External semaphore signal node parameters. */
+        struct cudaExternalSemaphoreWaitNodeParamsV2   extSemWait;    /**< External semaphore wait node parameters. */
+        struct cudaMemAllocNodeParamsV2                alloc;         /**< Memory allocation node parameters. */
+        struct cudaMemFreeNodeParams                   free;          /**< Memory free node parameters. */
+        struct cudaConditionalNodeParams               conditional;   /**< Conditional node parameters. */
+    };
+
+    long long reserved2; /**< Reserved bytes. Must be zero. */
+};
+
+/**
+ * Type annotations that can be applied to graph edges as part of ::cudaGraphEdgeData.
+ */
+typedef __device_builtin__ enum cudaGraphDependencyType_enum {
+    cudaGraphDependencyTypeDefault = 0, /**< This is an ordinary dependency. */
+    cudaGraphDependencyTypeProgrammatic = 1  /**< This dependency type allows the downstream node to
+                                                  use \c cudaGridDependencySynchronize(). It may only be used
+                                                  between kernel nodes, and must be used with either the
+                                                  ::cudaGraphKernelNodePortProgrammatic or
+                                                  ::cudaGraphKernelNodePortLaunchCompletion outgoing port. */
+} cudaGraphDependencyType;
+
+/**
+ * Optional annotation for edges in a CUDA graph. Note, all edges implicitly have annotations and
+ * default to a zero-initialized value if not specified. A zero-initialized struct indicates a
+ * standard full serialization of two nodes with memory visibility.
+ */
+typedef __device_builtin__ struct cudaGraphEdgeData_st {
+    unsigned char from_port; /**< This indicates when the dependency is triggered from the upstream
+                                  node on the edge. The meaning is specfic to the node type. A value
+                                  of 0 in all cases means full completion of the upstream node, with
+                                  memory visibility to the downstream node or portion thereof
+                                  (indicated by \c to_port).
+                                  <br>
+                                  Only kernel nodes define non-zero ports. A kernel node
+                                  can use the following output port types:
+                                  ::cudaGraphKernelNodePortDefault, ::cudaGraphKernelNodePortProgrammatic,
+                                  or ::cudaGraphKernelNodePortLaunchCompletion. */
+    unsigned char to_port; /**< This indicates what portion of the downstream node is dependent on
+                                the upstream node or portion thereof (indicated by \c from_port). The
+                                meaning is specific to the node type. A value of 0 in all cases means
+                                the entirety of the downstream node is dependent on the upstream work.
+                                <br>
+                                Currently no node types define non-zero ports. Accordingly, this field
+                                must be set to zero. */
+    unsigned char type; /**< This should be populated with a value from ::cudaGraphDependencyType. (It
+                             is typed as char due to compiler-specific layout of bitfields.) See
+                             ::cudaGraphDependencyType. */
+    unsigned char reserved[5]; /**< These bytes are unused and must be zeroed. This ensures
+                                    compatibility if additional fields are added in the future. */
+} cudaGraphEdgeData;
+
+/**
+ * This port activates when the kernel has finished executing.
+ */
+#define cudaGraphKernelNodePortDefault 0
+/**
+ * This port activates when all blocks of the kernel have performed cudaTriggerProgrammaticLaunchCompletion()
+ * or have terminated. It must be used with edge type ::cudaGraphDependencyTypeProgrammatic. See also
+ * ::cudaLaunchAttributeProgrammaticEvent.
+ */
+#define cudaGraphKernelNodePortProgrammatic 1
+/**
+ * This port activates when all blocks of the kernel have begun execution. See also
+ * ::cudaLaunchAttributeLaunchCompletionEvent.
+ */
+#define cudaGraphKernelNodePortLaunchCompletion 2
+
+/**
+ * CUDA executable (launchable) graph
+ */
+typedef struct CUgraphExec_st* cudaGraphExec_t;
+
+/**
+* CUDA Graph Update error types
+*/
+enum __device_builtin__ cudaGraphExecUpdateResult {
+    cudaGraphExecUpdateSuccess                = 0x0, /**< The update succeeded */
+    cudaGraphExecUpdateError                  = 0x1, /**< The update failed for an unexpected reason which is described in the return value of the function */
+    cudaGraphExecUpdateErrorTopologyChanged   = 0x2, /**< The update failed because the topology changed */
+    cudaGraphExecUpdateErrorNodeTypeChanged   = 0x3, /**< The update failed because a node type changed */
+    cudaGraphExecUpdateErrorFunctionChanged   = 0x4, /**< The update failed because the function of a kernel node changed (CUDA driver < 11.2) */
+    cudaGraphExecUpdateErrorParametersChanged = 0x5, /**< The update failed because the parameters changed in a way that is not supported */
+    cudaGraphExecUpdateErrorNotSupported      = 0x6, /**< The update failed because something about the node is not supported */
+    cudaGraphExecUpdateErrorUnsupportedFunctionChange = 0x7, /**< The update failed because the function of a kernel node changed in an unsupported way */
+    cudaGraphExecUpdateErrorAttributesChanged = 0x8 /**< The update failed because the node attributes changed in a way that is not supported */
+};
+
+/**
+ * Graph instantiation results
+*/
+typedef __device_builtin__ enum cudaGraphInstantiateResult {
+    cudaGraphInstantiateSuccess = 0,                       /**< Instantiation succeeded */
+    cudaGraphInstantiateError = 1,                         /**< Instantiation failed for an unexpected reason which is described in the return value of the function */
+    cudaGraphInstantiateInvalidStructure = 2,              /**< Instantiation failed due to invalid structure, such as cycles */
+    cudaGraphInstantiateNodeOperationNotSupported = 3,     /**< Instantiation for device launch failed because the graph contained an unsupported operation */
+    cudaGraphInstantiateMultipleDevicesNotSupported = 4,   /**< Instantiation for device launch failed due to the nodes belonging to different contexts */
+    cudaGraphInstantiateConditionalHandleUnused = 5        /**< One or more conditional handles are not associated with conditional nodes */
+} cudaGraphInstantiateResult;
+
+/**
+ * Graph instantiation parameters
+ */
+typedef __device_builtin__ struct cudaGraphInstantiateParams_st
+{
+    unsigned long long flags;              /**< Instantiation flags */
+    cudaStream_t uploadStream;             /**< Upload stream */
+    cudaGraphNode_t errNode_out;           /**< The node which caused instantiation to fail, if any */
+    cudaGraphInstantiateResult result_out; /**< Whether instantiation was successful.  If it failed, the reason why */
+} cudaGraphInstantiateParams;
+
+/**
+ * Result information returned by cudaGraphExecUpdate
+ */
+typedef __device_builtin__ struct cudaGraphExecUpdateResultInfo_st {
+    /**
+     * Gives more specific detail when a cuda graph update fails. 
+     */
+    enum cudaGraphExecUpdateResult result;
+
+    /**
+     * The "to node" of the error edge when the topologies do not match.
+     * The error node when the error is associated with a specific node.
+     * NULL when the error is generic.
+     */
+    cudaGraphNode_t errorNode;
+
+    /**
+     * The from node of error edge when the topologies do not match. Otherwise NULL.
+     */
+    cudaGraphNode_t errorFromNode;
+} cudaGraphExecUpdateResultInfo;
+
+/**
+ * CUDA device node handle for device-side node update
+ */
+typedef struct CUgraphDeviceUpdatableNode_st* cudaGraphDeviceNode_t;
+
+/**
+ * Specifies the field to update when performing multiple node updates from the device
+ */
+enum __device_builtin__ cudaGraphKernelNodeField
+{
+    cudaGraphKernelNodeFieldInvalid = 0, /**< Invalid field */
+    cudaGraphKernelNodeFieldGridDim,     /**< Grid dimension update */
+    cudaGraphKernelNodeFieldParam,       /**< Kernel parameter update */
+    cudaGraphKernelNodeFieldEnabled      /**< Node enable/disable */
+};
+
+/**
+ * Struct to specify a single node update to pass as part of a larger array to ::cudaGraphKernelNodeUpdatesApply
+ */
+struct __device_builtin__ cudaGraphKernelNodeUpdate {
+    cudaGraphDeviceNode_t node;     /**< Node to update */
+    enum cudaGraphKernelNodeField field; /**< Which type of update to apply. Determines how updateData is interpreted */
+    union {
+#if !defined(__cplusplus) || __cplusplus >= 201103L
+        dim3 gridDim;               /**< Grid dimensions */
+#else
+        /* Union members cannot have nontrivial constructors until C++11. */
+        uint3 gridDim;              /**< Grid dimensions */
+#endif
+        struct {
+            const void *pValue;     /**< Kernel parameter data to write in */
+            size_t offset;          /**< Offset into the parameter buffer at which to apply the update */
+            size_t size;            /**< Number of bytes to update */
+        } param;                    /**< Kernel parameter data */
+        unsigned int isEnabled;     /**< Node enable/disable data. Nonzero if the node should be enabled, 0 if it should be disabled */
+    } updateData;                   /**< Update data to apply. Which field is used depends on field's value */
+};
+
+/**
+ * Flags to specify search options to be used with ::cudaGetDriverEntryPoint
+ * For more details see ::cuGetProcAddress
+ */ 
+enum __device_builtin__ cudaGetDriverEntryPointFlags {
+    cudaEnableDefault                = 0x0, /**< Default search mode for driver symbols. */
+    cudaEnableLegacyStream           = 0x1, /**< Search for legacy versions of driver symbols. */
+    cudaEnablePerThreadDefaultStream = 0x2  /**< Search for per-thread versions of driver symbols. */
+};
+
+/**
+ * Enum for status from obtaining driver entry points, used with ::cudaApiGetDriverEntryPoint
+ */
+enum __device_builtin__ cudaDriverEntryPointQueryResult {
+    cudaDriverEntryPointSuccess             = 0,  /**< Search for symbol found a match */
+    cudaDriverEntryPointSymbolNotFound      = 1,  /**< Search for symbol was not found */
+    cudaDriverEntryPointVersionNotSufficent = 2   /**< Search for symbol was found but version wasn't great enough */
+};
+
+/**
+ * CUDA Graph debug write options
+ */
+enum __device_builtin__ cudaGraphDebugDotFlags {
+    cudaGraphDebugDotFlagsVerbose                  = 1<<0,  /**< Output all debug data as if every debug flag is enabled */
+    cudaGraphDebugDotFlagsKernelNodeParams         = 1<<2,  /**< Adds cudaKernelNodeParams to output */
+    cudaGraphDebugDotFlagsMemcpyNodeParams         = 1<<3,  /**< Adds cudaMemcpy3DParms to output */
+    cudaGraphDebugDotFlagsMemsetNodeParams         = 1<<4,  /**< Adds cudaMemsetParams to output */
+    cudaGraphDebugDotFlagsHostNodeParams           = 1<<5,  /**< Adds cudaHostNodeParams to output */
+    cudaGraphDebugDotFlagsEventNodeParams          = 1<<6,  /**< Adds cudaEvent_t handle from record and wait nodes to output */
+    cudaGraphDebugDotFlagsExtSemasSignalNodeParams = 1<<7,  /**< Adds cudaExternalSemaphoreSignalNodeParams values to output */
+    cudaGraphDebugDotFlagsExtSemasWaitNodeParams   = 1<<8,  /**< Adds cudaExternalSemaphoreWaitNodeParams to output */
+    cudaGraphDebugDotFlagsKernelNodeAttributes     = 1<<9,  /**< Adds cudaKernelNodeAttrID values to output */
+    cudaGraphDebugDotFlagsHandles                  = 1<<10, /**< Adds node handles and every kernel function handle to output */
+    cudaGraphDebugDotFlagsConditionalNodeParams    = 1<<15, /**< Adds cudaConditionalNodeParams to output */
+};
+
+/**
+ * Flags for instantiating a graph
+ */
+enum __device_builtin__ cudaGraphInstantiateFlags {
+    cudaGraphInstantiateFlagAutoFreeOnLaunch = 1 /**< Automatically free memory allocated in a graph before relaunching. */
+  , cudaGraphInstantiateFlagUpload           = 2 /**< Automatically upload the graph after instantiation. Only supported by                                                                                                                                                                                                                                                                                                     
+                                                      ::cudaGraphInstantiateWithParams.  The upload will be performed using the                                                                                                                                                                                                                                                                                                   
+                                                      stream provided in \p instantiateParams. */                                                                                                                                                                                                                                                                                                                               
+  , cudaGraphInstantiateFlagDeviceLaunch     = 4 /**< Instantiate the graph to be launchable from the device. This flag can only                                                                                                                                                                                                                                                                                                
+                                                      be used on platforms which support unified addressing. This flag cannot be                                                                                                                                                                                                                                                                                                
+                                                      used in conjunction with cudaGraphInstantiateFlagAutoFreeOnLaunch. */                                                                                                                                                                                                                                                                                              
+  , cudaGraphInstantiateFlagUseNodePriority  = 8 /**< Run the graph using the per-node priority attributes rather than the
+                                                      priority of the stream it is launched into. */
+};
+
+/**
+ * Memory Synchronization Domain
+ *
+ * A kernel can be launched in a specified memory synchronization domain that affects all memory operations issued by
+ * that kernel. A memory barrier issued in one domain will only order memory operations in that domain, thus eliminating
+ * latency increase from memory barriers ordering unrelated traffic.
+ *
+ * By default, kernels are launched in domain 0. Kernel launched with ::cudaLaunchMemSyncDomainRemote will have a
+ * different domain ID. User may also alter the domain ID with ::cudaLaunchMemSyncDomainMap for a specific stream /
+ * graph node / kernel launch. See ::cudaLaunchAttributeMemSyncDomain, ::cudaStreamSetAttribute, ::cudaLaunchKernelEx,
+ * ::cudaGraphKernelNodeSetAttribute.
+ *
+ * Memory operations done in kernels launched in different domains are considered system-scope distanced. In other
+ * words, a GPU scoped memory synchronization is not sufficient for memory order to be observed by kernels in another
+ * memory synchronization domain even if they are on the same GPU.
+ */
+typedef __device_builtin__ enum cudaLaunchMemSyncDomain {
+    cudaLaunchMemSyncDomainDefault = 0,    /**< Launch kernels in the default domain */
+    cudaLaunchMemSyncDomainRemote  = 1     /**< Launch kernels in the remote domain */
+} cudaLaunchMemSyncDomain;
+
+/**
+ * Memory Synchronization Domain map
+ *
+ * See ::cudaLaunchMemSyncDomain.
+ *
+ * By default, kernels are launched in domain 0. Kernel launched with ::cudaLaunchMemSyncDomainRemote will have a
+ * different domain ID. User may also alter the domain ID with ::cudaLaunchMemSyncDomainMap for a specific stream /
+ * graph node / kernel launch. See ::cudaLaunchAttributeMemSyncDomainMap.
+ *
+ * Domain ID range is available through ::cudaDevAttrMemSyncDomainCount.
+ */
+typedef __device_builtin__ struct cudaLaunchMemSyncDomainMap_st {
+    unsigned char default_;                /**< The default domain ID to use for designated kernels */
+    unsigned char remote;                  /**< The remote domain ID to use for designated kernels */
+} cudaLaunchMemSyncDomainMap;
+
+/**
+ * Launch attributes enum; used as id field of ::cudaLaunchAttribute
+ */
+typedef __device_builtin__ enum cudaLaunchAttributeID {
+    cudaLaunchAttributeIgnore                = 0 /**< Ignored entry, for convenient composition */
+  , cudaLaunchAttributeAccessPolicyWindow    = 1 /**< Valid for streams, graph nodes, launches. See
+                                                    ::cudaLaunchAttributeValue::accessPolicyWindow. */
+  , cudaLaunchAttributeCooperative           = 2 /**< Valid for graph nodes, launches. See
+                                                    ::cudaLaunchAttributeValue::cooperative. */
+  , cudaLaunchAttributeSynchronizationPolicy = 3 /**< Valid for streams. See ::cudaLaunchAttributeValue::syncPolicy. */
+  , cudaLaunchAttributeClusterDimension                  = 4 /**< Valid for graph nodes, launches. See
+                                                                ::cudaLaunchAttributeValue::clusterDim. */
+  , cudaLaunchAttributeClusterSchedulingPolicyPreference = 5 /**< Valid for graph nodes, launches. See
+                                                                ::cudaLaunchAttributeValue::clusterSchedulingPolicyPreference. */
+  , cudaLaunchAttributeProgrammaticStreamSerialization   = 6 /**< Valid for launches. Setting
+                                                                  ::cudaLaunchAttributeValue::programmaticStreamSerializationAllowed
+                                                                  to non-0 signals that the kernel will use programmatic
+                                                                  means to resolve its stream dependency, so that the
+                                                                  CUDA runtime should opportunistically allow the grid's
+                                                                  execution to overlap with the previous kernel in the
+                                                                  stream, if that kernel requests the overlap. The
+                                                                  dependent launches can choose to wait on the
+                                                                  dependency using the programmatic sync
+                                                                  (cudaGridDependencySynchronize() or equivalent PTX
+                                                                  instructions). */
+  , cudaLaunchAttributeProgrammaticEvent                 = 7 /**< Valid for launches. Set
+                                                                  ::cudaLaunchAttributeValue::programmaticEvent to
+                                                                  record the event. Event recorded through this launch
+                                                                  attribute is guaranteed to only trigger after all
+                                                                  block in the associated kernel trigger the event.  A
+                                                                  block can trigger the event programmatically in a
+                                                                  future CUDA release. A trigger can also be inserted at
+                                                                  the beginning of each block's execution if
+                                                                  triggerAtBlockStart is set to non-0. The dependent
+                                                                  launches can choose to wait on the dependency using
+                                                                  the programmatic sync (cudaGridDependencySynchronize()
+                                                                  or equivalent PTX instructions). Note that dependents
+                                                                  (including the CPU thread calling
+                                                                  cudaEventSynchronize()) are not guaranteed to observe
+                                                                  the release precisely when it is released. For
+                                                                  example, cudaEventSynchronize() may only observe the
+                                                                  event trigger long after the associated kernel has
+                                                                  completed. This recording type is primarily meant for
+                                                                  establishing programmatic dependency between device
+                                                                  tasks. Note also this type of dependency allows, but
+                                                                  does not guarantee, concurrent execution of tasks.
+                                                                  <br>
+                                                                  The event supplied must not be an interprocess or
+                                                                  interop event. The event must disable timing (i.e.
+                                                                  must be created with the ::cudaEventDisableTiming flag
+                                                                  set). */
+  , cudaLaunchAttributePriority              = 8 /**< Valid for streams, graph nodes, launches. See
+                                                    ::cudaLaunchAttributeValue::priority. */
+  , cudaLaunchAttributeMemSyncDomainMap                  = 9 /**< Valid for streams, graph nodes, launches. See
+                                                                ::cudaLaunchAttributeValue::memSyncDomainMap. */
+  , cudaLaunchAttributeMemSyncDomain                    = 10 /**< Valid for streams, graph nodes, launches. See
+                                                                ::cudaLaunchAttributeValue::memSyncDomain. */
+  , cudaLaunchAttributePreferredClusterDimension = 11 /**< Valid for graph nodes and launches. Set
+                                                           ::cudaLaunchAttributeValue::preferredClusterDim
+                                                           to allow the kernel launch to specify a preferred substitute
+                                                           cluster dimension. Blocks may be grouped according to either
+                                                           the dimensions specified with this attribute (grouped into a
+                                                           "preferred substitute cluster"), or the one specified with
+                                                           ::cudaLaunchAttributeClusterDimension attribute (grouped
+                                                           into a "regular cluster"). The cluster dimensions of a
+                                                           "preferred substitute cluster" shall be an integer multiple
+                                                           greater than zero of the regular cluster dimensions. The
+                                                           device will attempt - on a best-effort basis - to group
+                                                           thread blocks into preferred clusters over grouping them
+                                                           into regular clusters. When it deems necessary (primarily
+                                                           when the device temporarily runs out of physical resources
+                                                           to launch the larger preferred clusters), the device may
+                                                           switch to launch the regular clusters instead to attempt to
+                                                           utilize as much of the physical device resources as possible.
+                                                           <br>
+                                                           Each type of cluster will have its enumeration / coordinate
+                                                           setup as if the grid consists solely of its type of cluster.
+                                                           For example, if the preferred substitute cluster dimensions
+                                                           double the regular cluster dimensions, there might be
+                                                           simultaneously a regular cluster indexed at (1,0,0), and a
+                                                           preferred cluster indexed at (1,0,0). In this example, the
+                                                           preferred substitute cluster (1,0,0) replaces regular
+                                                           clusters (2,0,0) and (3,0,0) and groups their blocks.
+                                                           <br>
+                                                           This attribute will only take effect when a regular cluster
+                                                           dimension has been specified. The preferred substitute cluster
+                                                           dimension must be an integer multiple greater than zero of the
+                                                           regular cluster dimension and must divide the grid. It must
+                                                           also be no more than `maxBlocksPerCluster`, if it is set in
+                                                           the kernel's `__launch_bounds__`. Otherwise it must be less
+                                                           than the maximum value the driver can support. Otherwise,
+                                                           setting this attribute to a value physically unable to fit on
+                                                           any particular device is permitted. */
+  , cudaLaunchAttributeLaunchCompletionEvent = 12 /**< Valid for launches. Set
+                                                       ::cudaLaunchAttributeValue::launchCompletionEvent to record the
+                                                       event.
+                                                       <br>
+                                                       Nominally, the event is triggered once all blocks of the kernel
+                                                       have begun execution. Currently this is a best effort. If a kernel
+                                                       B has a launch completion dependency on a kernel A, B may wait
+                                                       until A is complete. Alternatively, blocks of B may begin before
+                                                       all blocks of A have begun, for example if B can claim execution
+                                                       resources unavailable to A (e.g. they run on different GPUs) or
+                                                       if B is a higher priority than A.
+                                                       Exercise caution if such an ordering inversion could lead
+                                                       to deadlock.
+                                                       <br>
+                                                       A launch completion event is nominally similar to a programmatic
+                                                       event with \c triggerAtBlockStart set except that it is not
+                                                       visible to \c cudaGridDependencySynchronize() and can be used with
+                                                       compute capability less than 9.0.
+                                                       <br>
+                                                       The event supplied must not be an interprocess or interop event.
+                                                       The event must disable timing (i.e. must be created with the
+                                                       ::cudaEventDisableTiming flag set). */
+  , cudaLaunchAttributeDeviceUpdatableKernelNode = 13 /**< Valid for graph nodes, launches. This attribute is graphs-only,
+                                                           and passing it to a launch in a non-capturing stream will result
+                                                           in an error.
+                                                           <br>
+                                                           :cudaLaunchAttributeValue::deviceUpdatableKernelNode::deviceUpdatable can 
+                                                           only be set to 0 or 1. Setting the field to 1 indicates that the
+                                                           corresponding kernel node should be device-updatable. On success, a handle
+                                                           will be returned via
+                                                           ::cudaLaunchAttributeValue::deviceUpdatableKernelNode::devNode which can be
+                                                           passed to the various device-side update functions to update the node's
+                                                           kernel parameters from within another kernel. For more information on the
+                                                           types of device updates that can be made, as well as the relevant limitations
+                                                           thereof, see ::cudaGraphKernelNodeUpdatesApply.
+                                                           <br>
+                                                           Nodes which are device-updatable have additional restrictions compared to
+                                                           regular kernel nodes. Firstly, device-updatable nodes cannot be removed
+                                                           from their graph via ::cudaGraphDestroyNode. Additionally, once opted-in
+                                                           to this functionality, a node cannot opt out, and any attempt to set the
+                                                           deviceUpdatable attribute to 0 will result in an error. Device-updatable
+                                                           kernel nodes also cannot have their attributes copied to/from another kernel
+                                                           node via ::cudaGraphKernelNodeCopyAttributes. Graphs containing one or more
+                                                           device-updatable nodes also do not allow multiple instantiation, and neither
+                                                           the graph nor its instantiated version can be passed to ::cudaGraphExecUpdate.
+                                                           <br>
+                                                           If a graph contains device-updatable nodes and updates those nodes from the device
+                                                           from within the graph, the graph must be uploaded with ::cuGraphUpload before it
+                                                           is launched. For such a graph, if host-side executable graph updates are made to the
+                                                           device-updatable nodes, the graph must be uploaded before it is launched again. */
+  , cudaLaunchAttributePreferredSharedMemoryCarveout = 14 /**< Valid for launches. On devices where the L1 cache and shared memory use the
+                                                               same hardware resources, setting ::cudaLaunchAttributeValue::sharedMemCarveout 
+                                                               to a percentage between 0-100 signals sets the shared memory carveout 
+                                                               preference in percent of the total shared memory for that kernel launch. 
+                                                               This attribute takes precedence over ::cudaFuncAttributePreferredSharedMemoryCarveout.
+                                                               This is only a hint, and the driver can choose a different configuration if
+                                                               required for the launch.*/  
+} cudaLaunchAttributeID;
+
+/**
+ * Launch attributes union; used as value field of ::cudaLaunchAttribute
+ */
+typedef __device_builtin__ union cudaLaunchAttributeValue {
+    char pad[64]; /* Pad to 64 bytes */
+    struct cudaAccessPolicyWindow accessPolicyWindow; /**< Value of launch attribute ::cudaLaunchAttributeAccessPolicyWindow. */
+    int cooperative; /**< Value of launch attribute ::cudaLaunchAttributeCooperative. Nonzero indicates a cooperative
+                        kernel (see ::cudaLaunchCooperativeKernel). */
+    enum cudaSynchronizationPolicy syncPolicy; /**< Value of launch attribute
+                                                  ::cudaLaunchAttributeSynchronizationPolicy. ::cudaSynchronizationPolicy
+                                                  for work queued up in this stream. */
+    /**
+     * Value of launch attribute ::cudaLaunchAttributeClusterDimension that
+     * represents the desired cluster dimensions for the kernel. Opaque type
+     * with the following fields:
+     *     - \p x - The X dimension of the cluster, in blocks. Must be a divisor
+     *              of the grid X dimension.
+     *     - \p y - The Y dimension of the cluster, in blocks. Must be a divisor
+     *              of the grid Y dimension.
+     *     - \p z - The Z dimension of the cluster, in blocks. Must be a divisor
+     *              of the grid Z dimension.
+     */
+    struct {
+        unsigned int x;
+        unsigned int y;
+        unsigned int z;
+    } clusterDim;
+    enum cudaClusterSchedulingPolicy clusterSchedulingPolicyPreference; /**< Value of launch attribute
+                                                                           ::cudaLaunchAttributeClusterSchedulingPolicyPreference. Cluster
+                                                                           scheduling policy preference for the kernel. */
+    int programmaticStreamSerializationAllowed; /**< Value of launch attribute
+                                                   ::cudaLaunchAttributeProgrammaticStreamSerialization. */
+
+    /**
+     * Value of launch attribute ::cudaLaunchAttributeProgrammaticEvent
+     * with the following fields:
+     *     - \p cudaEvent_t event - Event to fire when all blocks trigger it.
+     *     - \p int flags;        - Event record flags, see ::cudaEventRecordWithFlags. Does not accept
+     *                               ::cudaEventRecordExternal.
+     *     - \p int triggerAtBlockStart - If this is set to non-0, each block launch will automatically trigger the event.
+     */
+    struct {
+        cudaEvent_t event;
+        int flags;
+        int triggerAtBlockStart;
+    } programmaticEvent;
+    int priority; /**< Value of launch attribute ::cudaLaunchAttributePriority. Execution priority of the kernel. */
+    cudaLaunchMemSyncDomainMap memSyncDomainMap; /**< Value of launch attribute
+                                                    ::cudaLaunchAttributeMemSyncDomainMap. See
+                                                    ::cudaLaunchMemSyncDomainMap. */
+    cudaLaunchMemSyncDomain memSyncDomain;       /**< Value of launch attribute ::cudaLaunchAttributeMemSyncDomain. See
+                                                    ::cudaLaunchMemSyncDomain. */
+    /**
+     * Value of launch attribute ::cudaLaunchAttributePreferredClusterDimension
+     * that represents the desired preferred cluster dimensions for the kernel.
+     * Opaque type with the following fields:
+     *     - \p x - The X dimension of the preferred cluster, in blocks. Must be
+     *              a divisor of the grid X dimension, and must be a multiple of
+     *              the \p x field of ::cudaLaunchAttributeValue::clusterDim.
+     *     - \p y - The Y dimension of the preferred cluster, in blocks. Must be
+     *              a divisor of the grid Y dimension, and must be a multiple of
+     *              the \p y field of ::cudaLaunchAttributeValue::clusterDim.
+     *     - \p z - The Z dimension of the preferred cluster, in blocks. Must be
+     *              equal to the \p z field of ::cudaLaunchAttributeValue::clusterDim.
+     */
+    struct {
+        unsigned int x;
+        unsigned int y;
+        unsigned int z;
+    } preferredClusterDim;
+
+    /**
+     * Value of launch attribute ::cudaLaunchAttributeLaunchCompletionEvent
+     * with the following fields:
+     *     - \p cudaEvent_t event - Event to fire when the last block launches.
+     *     - \p int flags - Event record flags, see ::cudaEventRecordWithFlags. Does not accept
+     *                   ::cudaEventRecordExternal.
+     */
+    struct {
+        cudaEvent_t event;
+        int flags;
+    } launchCompletionEvent;
+
+    /**
+     * Value of launch attribute ::cudaLaunchAttributeDeviceUpdatableKernelNode
+     * with the following fields:
+     *    - \p int deviceUpdatable - Whether or not the resulting kernel node should be device-updatable.
+     *    - \p cudaGraphDeviceNode_t devNode - Returns a handle to pass to the various device-side update functions.
+     */
+    struct {
+        int deviceUpdatable;
+        cudaGraphDeviceNode_t devNode;
+    } deviceUpdatableKernelNode;
+    unsigned int sharedMemCarveout; /**< Value of launch attribute ::cudaLaunchAttributePreferredSharedMemoryCarveout. */
+} cudaLaunchAttributeValue;
+
+/**
+ * Launch attribute
+ */
+typedef __device_builtin__ struct cudaLaunchAttribute_st {
+    cudaLaunchAttributeID id; /**< Attribute to set */
+    char pad[8 - sizeof(cudaLaunchAttributeID)];
+    cudaLaunchAttributeValue val; /**< Value of the attribute */
+} cudaLaunchAttribute;
+
+/**
+ * CUDA extensible launch configuration
+ */
+typedef __device_builtin__ struct cudaLaunchConfig_st {
+    dim3 gridDim;               /**< Grid dimensions */
+    dim3 blockDim;              /**< Block dimensions */
+    size_t dynamicSmemBytes;    /**< Dynamic shared-memory size per thread block in bytes */
+    cudaStream_t stream;        /**< Stream identifier */
+    cudaLaunchAttribute *attrs; /**< List of attributes; nullable if ::cudaLaunchConfig_t::numAttrs == 0 */
+    unsigned int numAttrs;      /**< Number of attributes populated in ::cudaLaunchConfig_t::attrs */
+} cudaLaunchConfig_t;
+
+#define cudaStreamAttrID cudaLaunchAttributeID
+#define cudaStreamAttributeAccessPolicyWindow    cudaLaunchAttributeAccessPolicyWindow
+#define cudaStreamAttributeSynchronizationPolicy cudaLaunchAttributeSynchronizationPolicy
+#define cudaStreamAttributeMemSyncDomainMap      cudaLaunchAttributeMemSyncDomainMap
+#define cudaStreamAttributeMemSyncDomain         cudaLaunchAttributeMemSyncDomain
+#define cudaStreamAttributePriority cudaLaunchAttributePriority
+
+#define cudaStreamAttrValue cudaLaunchAttributeValue
+
+#define cudaKernelNodeAttrID cudaLaunchAttributeID
+#define cudaKernelNodeAttributeAccessPolicyWindow cudaLaunchAttributeAccessPolicyWindow
+#define cudaKernelNodeAttributeCooperative        cudaLaunchAttributeCooperative
+#define cudaKernelNodeAttributePriority           cudaLaunchAttributePriority
+#define cudaKernelNodeAttributeClusterDimension                     cudaLaunchAttributeClusterDimension
+#define cudaKernelNodeAttributeClusterSchedulingPolicyPreference    cudaLaunchAttributeClusterSchedulingPolicyPreference
+#define cudaKernelNodeAttributeMemSyncDomainMap   cudaLaunchAttributeMemSyncDomainMap
+#define cudaKernelNodeAttributeMemSyncDomain      cudaLaunchAttributeMemSyncDomain
+#define cudaKernelNodeAttributePreferredSharedMemoryCarveout cudaLaunchAttributePreferredSharedMemoryCarveout
+#define cudaKernelNodeAttributeDeviceUpdatableKernelNode cudaLaunchAttributeDeviceUpdatableKernelNode
+
+#define cudaKernelNodeAttrValue cudaLaunchAttributeValue
+
+/**
+ * CUDA device NUMA config
+ */
+enum __device_builtin__  cudaDeviceNumaConfig {
+    cudaDeviceNumaConfigNone  = 0, /**< The GPU is not a NUMA node */
+    cudaDeviceNumaConfigNumaNode, /**< The GPU is a NUMA node, cudaDevAttrNumaId contains its NUMA ID */
+};
+
+/**
+ * CUDA async callback handle
+ */
+typedef struct cudaAsyncCallbackEntry* cudaAsyncCallbackHandle_t;
+
+struct cudaAsyncCallbackEntry;
+
+/**
+* Types of async notification that can occur
+*/
+typedef __device_builtin__ enum cudaAsyncNotificationType_enum {
+    cudaAsyncNotificationTypeOverBudget = 0x1
+} cudaAsyncNotificationType;
+
+/**
+* Information describing an async notification event
+*/
+typedef __device_builtin__ struct cudaAsyncNotificationInfo
+{
+    cudaAsyncNotificationType type;
+    union {
+        struct {
+            unsigned long long bytesOverBudget;
+        } overBudget;
+    } info;
+} cudaAsyncNotificationInfo_t;
+
+typedef void (*cudaAsyncCallback)(cudaAsyncNotificationInfo_t*, void*, cudaAsyncCallbackHandle_t);
+
+
+/** @} */
+/** @} */ /* END CUDART_TYPES */
+
+#endif  /* !__CUDACC_RTC_MINIMAL__ */
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DRIVER_TYPES_H__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_DRIVER_TYPES_H__
+#endif
+
+#undef __CUDA_DEPRECATED
+
+
+
+#endif /* !__DRIVER_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/host_config.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/host_config.h
new file mode 100644
index 0000000000000000000000000000000000000000..785bec4e5c0652f9605ccf9341b7f761a85471ab
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/host_config.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#if defined(_MSC_VER)
+#pragma message("host_config.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.")
+#else
+#warning "host_config.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead."
+#endif
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_HOST_CONFIG_H_WRAPPER__
+#endif
+
+#include "crt/host_config.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_HOST_CONFIG_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_HOST_CONFIG_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/host_defines.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/host_defines.h
new file mode 100644
index 0000000000000000000000000000000000000000..98a9c98a957e8f60e872b94fde762516c5523367
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/host_defines.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#if defined(_MSC_VER)
+#pragma message("host_defines.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.")
+#else
+#warning "host_defines.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead."
+#endif
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_HOST_DEFINES_H_WRAPPER__
+#endif
+
+#include "crt/host_defines.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_HOST_DEFINES_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_HOST_DEFINES_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/library_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/library_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..35872bc5583ebcaea31251b2a0c704f3d11a7191
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/library_types.h
@@ -0,0 +1,111 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__LIBRARY_TYPES_H__)
+#define __LIBRARY_TYPES_H__
+
+
+
+#ifndef __CUDACC_RTC_MINIMAL__
+
+typedef enum cudaDataType_t
+{
+    CUDA_R_16F  =  2, /* real as a half */
+    CUDA_C_16F  =  6, /* complex as a pair of half numbers */
+    CUDA_R_16BF = 14, /* real as a nv_bfloat16 */
+    CUDA_C_16BF = 15, /* complex as a pair of nv_bfloat16 numbers */
+    CUDA_R_32F  =  0, /* real as a float */
+    CUDA_C_32F  =  4, /* complex as a pair of float numbers */
+    CUDA_R_64F  =  1, /* real as a double */
+    CUDA_C_64F  =  5, /* complex as a pair of double numbers */
+    CUDA_R_4I   = 16, /* real as a signed 4-bit int */
+    CUDA_C_4I   = 17, /* complex as a pair of signed 4-bit int numbers */
+    CUDA_R_4U   = 18, /* real as a unsigned 4-bit int */
+    CUDA_C_4U   = 19, /* complex as a pair of unsigned 4-bit int numbers */
+    CUDA_R_8I   =  3, /* real as a signed 8-bit int */
+    CUDA_C_8I   =  7, /* complex as a pair of signed 8-bit int numbers */
+    CUDA_R_8U   =  8, /* real as a unsigned 8-bit int */
+    CUDA_C_8U   =  9, /* complex as a pair of unsigned 8-bit int numbers */
+    CUDA_R_16I  = 20, /* real as a signed 16-bit int */
+    CUDA_C_16I  = 21, /* complex as a pair of signed 16-bit int numbers */
+    CUDA_R_16U  = 22, /* real as a unsigned 16-bit int */
+    CUDA_C_16U  = 23, /* complex as a pair of unsigned 16-bit int numbers */
+    CUDA_R_32I  = 10, /* real as a signed 32-bit int */
+    CUDA_C_32I  = 11, /* complex as a pair of signed 32-bit int numbers */
+    CUDA_R_32U  = 12, /* real as a unsigned 32-bit int */
+    CUDA_C_32U  = 13, /* complex as a pair of unsigned 32-bit int numbers */
+    CUDA_R_64I  = 24, /* real as a signed 64-bit int */
+    CUDA_C_64I  = 25, /* complex as a pair of signed 64-bit int numbers */
+    CUDA_R_64U  = 26, /* real as a unsigned 64-bit int */
+    CUDA_C_64U  = 27, /* complex as a pair of unsigned 64-bit int numbers */
+    CUDA_R_8F_E4M3 = 28, /* real as a nv_fp8_e4m3 */
+    CUDA_R_8F_UE4M3 = CUDA_R_8F_E4M3, /* real as an unsigned nv_fp8_e4m3 */
+    CUDA_R_8F_E5M2 = 29, /* real as a nv_fp8_e5m2 */
+    CUDA_R_8F_UE8M0 = 30,  /* real as an exponent-only unsigned nv_fp8_e8m0 */
+    CUDA_R_6F_E2M3  = 31,  /* real as a nv_fp6_e2m3 */
+    CUDA_R_6F_E3M2  = 32,  /* real as a nv_fp6_e3m2 */
+    CUDA_R_4F_E2M1  = 33,  /* real as a nv_fp4_e2m1 */
+} cudaDataType;
+
+
+typedef enum libraryPropertyType_t
+{
+    MAJOR_VERSION,
+    MINOR_VERSION,
+    PATCH_LEVEL
+} libraryPropertyType;
+
+
+#ifndef __cplusplus
+typedef enum cudaDataType_t cudaDataType_t;
+typedef enum libraryPropertyType_t libraryPropertyType_t;
+#endif
+
+#endif  /* !__CUDACC_RTC_MINIMAL__ */
+#endif /* !__LIBRARY_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/math_constants.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/math_constants.h
new file mode 100644
index 0000000000000000000000000000000000000000..39937e980f88a614d847154f9e4364bd9ba95cbd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/math_constants.h
@@ -0,0 +1,152 @@
+/*
+ * Copyright 1993-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__MATH_CONSTANTS_H__)
+#define __MATH_CONSTANTS_H__
+
+/* single precision constants */
+#define CUDART_INF_F            __int_as_float(0x7f800000U)
+#define CUDART_NAN_F            __int_as_float(0x7fffffffU)
+#define CUDART_MIN_DENORM_F     __int_as_float(0x00000001U)
+#define CUDART_MAX_NORMAL_F     __int_as_float(0x7f7fffffU)
+#define CUDART_NEG_ZERO_F       __int_as_float(0x80000000U)
+#define CUDART_ZERO_F           0.0F
+#define CUDART_ONE_F            1.0F
+#define CUDART_SQRT_HALF_F      0.707106781F
+#define CUDART_SQRT_HALF_HI_F   0.707106781F
+#define CUDART_SQRT_HALF_LO_F   1.210161749e-08F
+#define CUDART_SQRT_TWO_F       1.414213562F
+#define CUDART_THIRD_F          0.333333333F
+#define CUDART_PIO4_F           0.785398163F
+#define CUDART_PIO2_F           1.570796327F
+#define CUDART_3PIO4_F          2.356194490F
+#define CUDART_2_OVER_PI_F      0.636619772F
+#define CUDART_SQRT_2_OVER_PI_F 0.797884561F
+#define CUDART_PI_F             3.141592654F
+#define CUDART_L2E_F            1.442695041F
+#define CUDART_L2T_F            3.321928094F
+#define CUDART_LG2_F            0.301029996F
+#define CUDART_LGE_F            0.434294482F
+#define CUDART_LN2_F            0.693147181F
+#define CUDART_LNT_F            2.302585093F
+#define CUDART_LNPI_F           1.144729886F
+#define CUDART_TWO_TO_M126_F    1.175494351e-38F
+#define CUDART_TWO_TO_126_F     8.507059173e37F
+#define CUDART_NORM_HUGE_F      3.402823466e38F
+#define CUDART_TWO_TO_23_F      8388608.0F
+#define CUDART_TWO_TO_24_F      16777216.0F
+#define CUDART_TWO_TO_31_F      2147483648.0F
+#define CUDART_TWO_TO_32_F      4294967296.0F
+#define CUDART_REMQUO_BITS_F    3U
+#define CUDART_REMQUO_MASK_F    (~((~0U)<<CUDART_REMQUO_BITS_F))
+#define CUDART_TRIG_PLOSS_F     105615.0F
+
+/* double precision constants */
+#define CUDART_INF              __longlong_as_double(0x7ff0000000000000ULL)
+#define CUDART_NAN              __longlong_as_double(0xfff8000000000000ULL)
+#define CUDART_NEG_ZERO         __longlong_as_double(0x8000000000000000ULL)
+#define CUDART_MIN_DENORM       __longlong_as_double(0x0000000000000001ULL)
+#define CUDART_ZERO             0.0
+#define CUDART_ONE              1.0
+#define CUDART_SQRT_TWO         1.4142135623730951e+0
+#define CUDART_SQRT_HALF        7.0710678118654757e-1
+#define CUDART_SQRT_HALF_HI     7.0710678118654757e-1
+#define CUDART_SQRT_HALF_LO   (-4.8336466567264567e-17)
+#define CUDART_THIRD            3.3333333333333333e-1
+#define CUDART_TWOTHIRD         6.6666666666666667e-1
+#define CUDART_PIO4             7.8539816339744828e-1
+#define CUDART_PIO4_HI          7.8539816339744828e-1
+#define CUDART_PIO4_LO          3.0616169978683830e-17
+#define CUDART_PIO2             1.5707963267948966e+0
+#define CUDART_PIO2_HI          1.5707963267948966e+0
+#define CUDART_PIO2_LO          6.1232339957367660e-17
+#define CUDART_3PIO4            2.3561944901923448e+0
+#define CUDART_2_OVER_PI        6.3661977236758138e-1
+#define CUDART_PI               3.1415926535897931e+0
+#define CUDART_PI_HI            3.1415926535897931e+0
+#define CUDART_PI_LO            1.2246467991473532e-16
+#define CUDART_SQRT_2PI         2.5066282746310007e+0
+#define CUDART_SQRT_2PI_HI      2.5066282746310007e+0
+#define CUDART_SQRT_2PI_LO    (-1.8328579980459167e-16)
+#define CUDART_SQRT_PIO2        1.2533141373155003e+0
+#define CUDART_SQRT_PIO2_HI     1.2533141373155003e+0
+#define CUDART_SQRT_PIO2_LO   (-9.1642899902295834e-17)
+#define CUDART_SQRT_2OPI        7.9788456080286536e-1
+#define CUDART_L2E              1.4426950408889634e+0
+#define CUDART_L2E_HI           1.4426950408889634e+0
+#define CUDART_L2E_LO           2.0355273740931033e-17
+#define CUDART_L2T              3.3219280948873622e+0
+#define CUDART_LG2              3.0102999566398120e-1
+#define CUDART_LG2_HI           3.0102999566398120e-1
+#define CUDART_LG2_LO         (-2.8037281277851704e-18)
+#define CUDART_LGE              4.3429448190325182e-1
+#define CUDART_LGE_HI           4.3429448190325182e-1
+#define CUDART_LGE_LO           1.09831965021676510e-17
+#define CUDART_LN2              6.9314718055994529e-1
+#define CUDART_LN2_HI           6.9314718055994529e-1
+#define CUDART_LN2_LO           2.3190468138462996e-17
+#define CUDART_LNT              2.3025850929940459e+0
+#define CUDART_LNT_HI           2.3025850929940459e+0
+#define CUDART_LNT_LO         (-2.1707562233822494e-16)
+#define CUDART_LNPI             1.1447298858494002e+0
+#define CUDART_LN2_X_1024       7.0978271289338397e+2
+#define CUDART_LN2_X_1025       7.1047586007394398e+2
+#define CUDART_LN2_X_1075       7.4513321910194122e+2
+#define CUDART_LG2_X_1024       3.0825471555991675e+2
+#define CUDART_LG2_X_1075       3.2360724533877976e+2
+#define CUDART_TWO_TO_23        8388608.0
+#define CUDART_TWO_TO_52        4503599627370496.0
+#define CUDART_TWO_TO_53        9007199254740992.0
+#define CUDART_TWO_TO_54        18014398509481984.0
+#define CUDART_TWO_TO_M54       5.5511151231257827e-17
+#define CUDART_TWO_TO_M1022     2.22507385850720140e-308
+#define CUDART_TRIG_PLOSS       2147483648.0
+#define CUDART_DBL2INT_CVT      6755399441055744.0
+
+#endif /* !__MATH_CONSTANTS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/math_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/math_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..bc806976784e494edc905d8b8bd9ad138054bbea
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/math_functions.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#if defined(_MSC_VER)
+#pragma message("math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.")
+#else
+#warning "math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead."
+#endif
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_MATH_FUNCTIONS_H_WRAPPER__
+#endif
+
+#include "crt/math_functions.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_MATH_FUNCTIONS_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_MATH_FUNCTIONS_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/mma.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/mma.h
new file mode 100644
index 0000000000000000000000000000000000000000..9f36f671c0b3a4e95cbb7bddbe41e75ac783b722
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/mma.h
@@ -0,0 +1,60 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_MMA_H_WRAPPER__
+#endif
+
+#include "crt/mma.h"
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_MMA_H_WRAPPER__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_CUDA_MMA_H_WRAPPER__
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_atomic_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_atomic_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..7fa9d6f2f96f48ec46d2da816256be238cf70343
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_atomic_functions.h
@@ -0,0 +1,101 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if !defined(__SM_20_ATOMIC_FUNCTIONS_H__)
+#define __SM_20_ATOMIC_FUNCTIONS_H__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_20_ATOMIC_FUNCTIONS_DECL__ __device__
+#elif defined(_NVHPC_CUDA)
+#define __SM_20_ATOMIC_FUNCTIONS_DECL__ extern __device__ __cudart_builtin__
+#else /* __CUDACC_RTC__ */
+#define __SM_20_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ */
+
+
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_20_ATOMIC_FUNCTIONS_DECL__ float atomicAdd(float *address, float val) __DEF_IF_HOST
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEF_IF_HOST
+#undef __SM_20_ATOMIC_FUNCTIONS_DECL__
+
+#if !defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)
+#include "sm_20_atomic_functions.hpp"
+#endif /* !__CUDACC_RTC__ && defined(__CUDA_ARCH__) */
+
+#endif /* !__SM_20_ATOMIC_FUNCTIONS_H__ */
+
+#undef EXCLUDE_FROM_RTC
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_atomic_functions.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_atomic_functions.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..d13b04ae4fb58970eb17ffefc08ae112e3cd24f4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_atomic_functions.hpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_20_ATOMIC_FUNCTIONS_HPP__)
+#define __SM_20_ATOMIC_FUNCTIONS_HPP__
+
+#if defined(__CUDA_ARCH__) || defined(_NVHPC_CUDA)
+extern "C"
+{
+extern __device__ __device_builtin__ float __fAtomicAdd(float *address, float val);
+}
+#endif /* __CUDA_ARCH__ */
+
+#if defined(__CUDACC_RTC__)
+#define __SM_20_ATOMIC_FUNCTIONS_DECL__ __device__
+#else /* __CUDACC_RTC__ */
+#define __SM_20_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_20_ATOMIC_FUNCTIONS_DECL__ float atomicAdd(float *address, float val)
+{
+  return __fAtomicAdd(address, val);
+}
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_20_ATOMIC_FUNCTIONS_DECL__
+
+#endif /* !__SM_20_ATOMIC_FUNCTIONS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_intrinsics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_intrinsics.h
new file mode 100644
index 0000000000000000000000000000000000000000..f0405fd837a83ad6b5a65ddcba6d5be707fa5a3d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_intrinsics.h
@@ -0,0 +1,1156 @@
+/*
+ * Copyright 1993-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if !defined(__SM_20_INTRINSICS_H__)
+#define __SM_20_INTRINSICS_H__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_20_INTRINSICS_DECL__ __device__
+#define __COMMON_INTRINSICS_DECL__ __device__
+#else /* __CUDACC_RTC__ */
+#define __SM_20_INTRINSICS_DECL__ static __inline__ __device__
+#define __COMMON_INTRINSICS_DECL__ static __inline__ __host__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ && !_NVHPC_CUDA */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ || _NVHPC_CUDA */
+
+#if defined(_WIN32)
+# define __DEPRECATED__(msg) __declspec(deprecated(msg))
+#elif (defined(__GNUC__) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 5 && !defined(__clang__))))
+# define __DEPRECATED__(msg) __attribute__((deprecated))
+#else
+# define __DEPRECATED__(msg) __attribute__((deprecated(msg)))
+#endif
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is not valid on compute_70 and above, and should be replaced with "#x"_sync()."\
+    "To continue using "#x"(), specify virtual architecture compute_60 when targeting sm_70 and above, for example, using the pair of compiler options: -arch=compute_60 -code=sm_70."
+#elif defined(_NVHPC_CUDA)
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is not valid on cc70 and above, and should be replaced with "#x"_sync()."
+#else
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is deprecated in favor of "#x"_sync() and may be removed in a future release (Use -Wno-deprecated-declarations to suppress this warning)."
+#endif
+
+extern "C"
+{
+extern __device__ __device_builtin__ void                   __threadfence_system(void);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Divide two floating-point values in round-to-nearest-even mode.
+ *
+ * Divides two floating-point values \p x by \p y in round-to-nearest-even mode.
+ *
+ * \return Returns \p x / \p y.
+ * - sign of the quotient \p x / \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __ddiv_rn(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns NaN.
+ * - __ddiv_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __ddiv_rn(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p x.
+ * - __ddiv_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - __ddiv_rn(\p x, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __ddiv_rn(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for \p y \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __ddiv_rn(double x, double y);
+/**      
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Divide two floating-point values in round-towards-zero mode.
+ *
+ * Divides two floating-point values \p x by \p y in round-towards-zero mode.
+ *
+ * \return Returns \p x / \p y.
+ * - sign of the quotient \p x / \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __ddiv_rz(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns NaN.
+ * - __ddiv_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __ddiv_rz(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p x.
+ * - __ddiv_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - __ddiv_rz(\p x, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __ddiv_rz(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for \p y \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __ddiv_rz(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Divide two floating-point values in round-up mode.
+ * 
+ * Divides two floating-point values \p x by \p y in round-up (to positive infinity) mode.
+ *    
+ * \return Returns \p x / \p y.
+ * - sign of the quotient \p x / \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __ddiv_ru(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns NaN.
+ * - __ddiv_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __ddiv_ru(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p x.
+ * - __ddiv_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - __ddiv_ru(\p x, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __ddiv_ru(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for \p y \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __ddiv_ru(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Divide two floating-point values in round-down mode.
+ *
+ * Divides two floating-point values \p x by \p y in round-down (to negative infinity) mode.
+ *
+ * \return Returns \p x / \p y.
+ * - sign of the quotient \p x / \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __ddiv_rd(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns NaN.
+ * - __ddiv_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __ddiv_rd(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p x.
+ * - __ddiv_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - __ddiv_rd(\p x, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __ddiv_rd(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for \p y \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __ddiv_rd(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula
+ *  in round-to-nearest-even mode.
+ * 
+ * Compute the reciprocal of \p x in round-to-nearest-even mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __drcp_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula
+ *  in round-towards-zero mode.
+ *
+ * Compute the reciprocal of \p x in round-towards-zero mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __drcp_rz(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula
+ *  in round-up mode.
+ * 
+ * Compute the reciprocal of \p x in round-up (to positive infinity) mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __drcp_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula
+ *  in round-down mode.
+ * 
+ * Compute the reciprocal of \p x in round-down (to negative infinity) mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \frac{1}{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __drcp_rd(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula
+ *  in round-to-nearest-even mode.
+ * 
+ * Compute the square root of \p x in round-to-nearest-even mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __dsqrt_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula
+ *  in round-towards-zero mode.
+ * 
+ * Compute the square root of \p x in round-towards-zero mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __dsqrt_rz(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula
+ *  in round-up mode.
+ * 
+ * Compute the square root of \p x in round-up (to positive infinity) mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __dsqrt_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula
+ *  in round-down mode.
+ * 
+ * Compute the square root of \p x in round-down (to negative infinity) mode.
+ *
+ * \return Returns 
+ * \cuda_math_formula \sqrt{x} \end_cuda_math_formula.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_requires_fermi
+ */
+extern __device__ __device_builtin__ double                __dsqrt_rd(double x);
+extern __device__ __device_builtin__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__ballot)) unsigned int __ballot(int);
+extern __device__ __device_builtin__ int                   __syncthreads_count(int);
+extern __device__ __device_builtin__ int                   __syncthreads_and(int);
+extern __device__ __device_builtin__ int                   __syncthreads_or(int);
+extern __device__ __device_builtin__ long long int         clock64(void);
+
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_SINGLE
+ * \brief Compute fused multiply-add operation in round-to-nearest-even mode, ignore \p -ftz=true compiler flag
+ *
+ * Behavior is the same as ::__fmaf_rn(\p x, \p y, \p z), the difference is in
+ * handling denormalized inputs and outputs: \p -ftz compiler flag has no effect.
+ */
+extern __device__ __device_builtin__ float                  __fmaf_ieee_rn(float x, float y, float z);
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_SINGLE
+ * \brief Compute fused multiply-add operation in round-down mode, ignore \p -ftz=true compiler flag
+ *
+ * Behavior is the same as ::__fmaf_rd(\p x, \p y, \p z), the difference is in
+ * handling denormalized inputs and outputs: \p -ftz compiler flag has no effect.
+ */
+extern __device__ __device_builtin__ float                  __fmaf_ieee_rd(float x, float y, float z);
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_SINGLE
+ * \brief Compute fused multiply-add operation in round-up mode, ignore \p -ftz=true compiler flag
+ *
+ * Behavior is the same as ::__fmaf_ru(\p x, \p y, \p z), the difference is in
+ * handling denormalized inputs and outputs: \p -ftz compiler flag has no effect.
+ */
+extern __device__ __device_builtin__ float                  __fmaf_ieee_ru(float x, float y, float z);
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_SINGLE
+ * \brief Compute fused multiply-add operation in round-towards-zero mode, ignore \p -ftz=true compiler flag
+ *
+ * Behavior is the same as ::__fmaf_rz(\p x, \p y, \p z), the difference is in
+ * handling denormalized inputs and outputs: \p -ftz compiler flag has no effect.
+ */
+extern __device__ __device_builtin__ float                  __fmaf_ieee_rz(float x, float y, float z);
+
+
+// SM_13 intrinsics
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Reinterpret bits in a double as a 64-bit signed integer.
+ *
+ * Reinterpret the bits in the double-precision floating-point value \p x
+ * as a signed 64-bit integer.
+ * \return Returns reinterpreted value.
+ */
+extern __device__ __device_builtin__ long long int         __double_as_longlong(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Reinterpret bits in a 64-bit signed integer as a double.
+ *
+ * Reinterpret the bits in the 64-bit signed integer value \p x as
+ * a double-precision floating-point value.
+ * \return Returns reinterpreted value.
+ */
+extern __device__ __device_builtin__ double                __longlong_as_double(long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation in round-to-nearest-even mode.
+ *
+ * Computes the value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single ternary operation, rounding the
+ * result once in round-to-nearest-even mode.
+ *
+ * \return Returns the rounded value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation.
+ * - __fma_rn(
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_rn(
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_rn(\p x, \p y, 
+ * \cuda_math_formula -\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula +\infty \end_cuda_math_formula.
+ * - __fma_rn(\p x, \p y, 
+ * \cuda_math_formula +\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula -\infty \end_cuda_math_formula.
+ * - __fma_rn(\p x, \p y, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_rn(\p x, \p y, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula +0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_rn(\p x, \p y, \p z) returns \cuda_math_formula +0 \end_cuda_math_formula if \cuda_math_formula x \times y + z \end_cuda_math_formula is exactly zero and \cuda_math_formula z \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ */
+extern __device__ __device_builtin__ double                __fma_rn(double x, double y, double z);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation in round-towards-zero mode.
+ *
+ * Computes the value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single ternary operation, rounding the
+ * result once in round-towards-zero mode.
+ *
+ * \return Returns the rounded value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation.
+ * - __fma_rz(
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_rz(
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_rz(\p x, \p y, 
+ * \cuda_math_formula -\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula +\infty \end_cuda_math_formula.
+ * - __fma_rz(\p x, \p y, 
+ * \cuda_math_formula +\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula -\infty \end_cuda_math_formula.
+ * - __fma_rz(\p x, \p y, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_rz(\p x, \p y, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula +0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_rz(\p x, \p y, \p z) returns \cuda_math_formula +0 \end_cuda_math_formula if \cuda_math_formula x \times y + z \end_cuda_math_formula is exactly zero and \cuda_math_formula z \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ */
+extern __device__ __device_builtin__ double                __fma_rz(double x, double y, double z);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation in round-up mode.
+ *
+ * Computes the value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single ternary operation, rounding the
+ * result once in round-up (to positive infinity) mode.
+ *
+ * \return Returns the rounded value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation.
+ * - __fma_ru(
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_ru(
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_ru(\p x, \p y, 
+ * \cuda_math_formula -\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula +\infty \end_cuda_math_formula.
+ * - __fma_ru(\p x, \p y, 
+ * \cuda_math_formula +\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula -\infty \end_cuda_math_formula.
+ * - __fma_ru(\p x, \p y, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_ru(\p x, \p y, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula +0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_ru(\p x, \p y, \p z) returns \cuda_math_formula +0 \end_cuda_math_formula if \cuda_math_formula x \times y + z \end_cuda_math_formula is exactly zero and \cuda_math_formula z \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ */
+extern __device__ __device_builtin__ double                __fma_ru(double x, double y, double z);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Compute 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation in round-down mode.
+ *
+ * Computes the value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single ternary operation, rounding the
+ * result once in round-down (to negative infinity) mode.
+ *
+ * \return Returns the rounded value of 
+ * \cuda_math_formula x \times y + z \end_cuda_math_formula
+ *  as a single operation.
+ * - __fma_rd(
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_rd(
+ * \cuda_math_formula \pm 0 \end_cuda_math_formula
+ * , 
+ * \cuda_math_formula \pm \infty \end_cuda_math_formula
+ * , \p z) returns NaN.
+ * - __fma_rd(\p x, \p y, 
+ * \cuda_math_formula -\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula +\infty \end_cuda_math_formula.
+ * - __fma_rd(\p x, \p y, 
+ * \cuda_math_formula +\infty \end_cuda_math_formula
+ * ) returns NaN if
+ * \cuda_math_formula x \times y \end_cuda_math_formula
+ *  is an exact 
+ * \cuda_math_formula -\infty \end_cuda_math_formula.
+ * - __fma_rd(\p x, \p y, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_rd(\p x, \p y, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula -0 \end_cuda_math_formula if \cuda_math_formula x \times y \end_cuda_math_formula is exact \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __fma_rd(\p x, \p y, \p z) returns \cuda_math_formula -0 \end_cuda_math_formula if \cuda_math_formula x \times y + z \end_cuda_math_formula is exactly zero and \cuda_math_formula z \neq 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ */
+extern __device__ __device_builtin__ double                __fma_rd(double x, double y, double z);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Add two floating-point values in round-to-nearest-even mode.
+ *
+ * Adds two floating-point values \p x and \p y in round-to-nearest-even mode.
+ *
+ * \return Returns \p x + \p y.
+ * - __dadd_rn(\p x, \p y) is equivalent to __dadd_rn(\p y, \p x).
+ * - __dadd_rn(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p x.
+ * - __dadd_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dadd_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns NaN.
+ * - __dadd_rn(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dadd_rn(\p x, \p -x) returns \cuda_math_formula +0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dadd_rn(double x, double y);
+/**      
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Add two floating-point values in round-towards-zero mode.
+ *
+ * Adds two floating-point values \p x and \p y in round-towards-zero mode.
+ *
+ * \return Returns \p x + \p y.
+ * - __dadd_rz(\p x, \p y) is equivalent to __dadd_rz(\p y, \p x).
+ * - __dadd_rz(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p x.
+ * - __dadd_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dadd_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns NaN.
+ * - __dadd_rz(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dadd_rz(\p x, \p -x) returns \cuda_math_formula +0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dadd_rz(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Add two floating-point values in round-up mode.
+ * 
+ * Adds two floating-point values \p x and \p y in round-up (to positive infinity) mode.
+ *    
+ * \return Returns \p x + \p y.
+ * - __dadd_ru(\p x, \p y) is equivalent to __dadd_ru(\p y, \p x).
+ * - __dadd_ru(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p x.
+ * - __dadd_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dadd_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns NaN.
+ * - __dadd_ru(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dadd_ru(\p x, \p -x) returns \cuda_math_formula +0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */ 
+extern __device__ __device_builtin__ double                __dadd_ru(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Add two floating-point values in round-down mode.
+ *
+ * Adds two floating-point values \p x and \p y in round-down (to negative infinity) mode.
+ *
+ * \return Returns \p x + \p y.
+ * - __dadd_rd(\p x, \p y) is equivalent to __dadd_rd(\p y, \p x).
+ * - __dadd_rd(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p x.
+ * - __dadd_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dadd_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns NaN.
+ * - __dadd_rd(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dadd_rd(\p x, \p -x) returns \cuda_math_formula -0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dadd_rd(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Subtract two floating-point values in round-to-nearest-even mode.
+ *
+ * Subtracts two floating-point values \p x and \p y in round-to-nearest-even mode.
+ *
+ * \return Returns \p x - \p y.
+ * - __dsub_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p y.
+ * - __dsub_rn(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \mp\infty \end_cuda_math_formula for finite \p x.
+ * - __dsub_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dsub_rn(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dsub_rn(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dsub_rn(\p x, \p x) returns \cuda_math_formula +0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dsub_rn(double x, double y);
+/**      
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Subtract two floating-point values in round-towards-zero mode.
+ *
+ * Subtracts two floating-point values \p x and \p y in round-towards-zero mode.
+ *
+ * \return Returns \p x - \p y.
+ * - __dsub_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p y.
+ * - __dsub_rz(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \mp\infty \end_cuda_math_formula for finite \p x.
+ * - __dsub_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dsub_rz(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dsub_rz(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dsub_rz(\p x, \p x) returns \cuda_math_formula +0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dsub_rz(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Subtract two floating-point values in round-up mode.
+ * 
+ * Subtracts two floating-point values \p x and \p y in round-up (to positive infinity) mode.
+ *    
+ * \return Returns \p x - \p y.
+ * - __dsub_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p y.
+ * - __dsub_ru(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \mp\infty \end_cuda_math_formula for finite \p x.
+ * - __dsub_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dsub_ru(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dsub_ru(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dsub_ru(\p x, \p x) returns \cuda_math_formula +0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */ 
+extern __device__ __device_builtin__ double                __dsub_ru(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Subtract two floating-point values in round-down mode.
+ *
+ * Subtracts two floating-point values \p x and \p y in round-down (to negative infinity) mode.
+ *
+ * \return Returns \p x - \p y.
+ * - __dsub_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \p y) returns \cuda_math_formula \pm\infty \end_cuda_math_formula for finite \p y.
+ * - __dsub_rd(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \mp\infty \end_cuda_math_formula for finite \p x.
+ * - __dsub_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dsub_rd(\cuda_math_formula \pm\infty \end_cuda_math_formula, \cuda_math_formula \mp\infty \end_cuda_math_formula) returns \cuda_math_formula \pm\infty \end_cuda_math_formula.
+ * - __dsub_rd(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \mp 0 \end_cuda_math_formula) returns \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - __dsub_rd(\p x, \p x) returns \cuda_math_formula -0 \end_cuda_math_formula for finite \p x, including \cuda_math_formula \pm 0 \end_cuda_math_formula.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dsub_rd(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Multiply two floating-point values in round-to-nearest-even mode.
+ *
+ * Multiplies two floating-point values \p x and \p y in round-to-nearest-even mode.
+ *
+ * \return Returns \p x * \p y.
+ * - sign of the product \p x * \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __dmul_rn(\p x, \p y) is equivalent to __dmul_rn(\p y, \p x).
+ * - __dmul_rn(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __dmul_rn(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dmul_rn(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dmul_rn(double x, double y);
+/**      
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Multiply two floating-point values in round-towards-zero mode.
+ *
+ * Multiplies two floating-point values \p x and \p y in round-towards-zero mode.
+ *
+ * \return Returns \p x * \p y.
+ * - sign of the product \p x * \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __dmul_rz(\p x, \p y) is equivalent to __dmul_rz(\p y, \p x).
+ * - __dmul_rz(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __dmul_rz(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dmul_rz(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dmul_rz(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Multiply two floating-point values in round-up mode.
+ * 
+ * Multiplies two floating-point values \p x and \p y in round-up (to positive infinity) mode.
+ *    
+ * \return Returns \p x * \p y.
+ * - sign of the product \p x * \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __dmul_ru(\p x, \p y) is equivalent to __dmul_ru(\p y, \p x).
+ * - __dmul_ru(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __dmul_ru(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dmul_ru(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dmul_ru(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_DOUBLE
+ * \brief Multiply two floating-point values in round-down mode.
+ *
+ * Multiplies two floating-point values \p x and \p y in round-down (to negative infinity) mode.
+ *
+ * \return Returns \p x * \p y.
+ * - sign of the product \p x * \p y is XOR of the signs of \p x and \p y when neither inputs nor result are NaN.
+ * - __dmul_rd(\p x, \p y) is equivalent to __dmul_rd(\p y, \p x).
+ * - __dmul_rd(\p x, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns \cuda_math_formula \infty \end_cuda_math_formula of appropriate sign for \p x \cuda_math_formula \neq 0 \end_cuda_math_formula.
+ * - __dmul_rd(\cuda_math_formula \pm 0 \end_cuda_math_formula, \cuda_math_formula \pm\infty \end_cuda_math_formula) returns NaN.
+ * - __dmul_rd(\cuda_math_formula \pm 0 \end_cuda_math_formula, \p y) returns \cuda_math_formula 0 \end_cuda_math_formula of appropriate sign for finite \p y.
+ * - If either argument is NaN, NaN is returned.
+ *
+ * \note_accuracy_double_intrinsic
+ * \note_nofma
+ */
+extern __device__ __device_builtin__ double                __dmul_rd(double x, double y);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a float in round-to-nearest-even mode.
+ *
+ * Convert the double-precision floating-point value \p x to a single-precision
+ * floating-point value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ float                 __double2float_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a float in round-towards-zero mode.
+ *
+ * Convert the double-precision floating-point value \p x to a single-precision
+ * floating-point value in round-towards-zero mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ float                 __double2float_rz(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a float in round-up mode.
+ *
+ * Convert the double-precision floating-point value \p x to a single-precision
+ * floating-point value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ float                 __double2float_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a float in round-down mode.
+ *
+ * Convert the double-precision floating-point value \p x to a single-precision
+ * floating-point value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ float                 __double2float_rd(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a signed int in round-to-nearest-even mode.
+ *
+ * Convert the double-precision floating-point value \p x to a
+ * signed integer value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ int                   __double2int_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a signed int in round-up mode.
+ *
+ * Convert the double-precision floating-point value \p x to a
+ * signed integer value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ int                   __double2int_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a signed int in round-down mode.
+ *
+ * Convert the double-precision floating-point value \p x to a
+ * signed integer value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ int                   __double2int_rd(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to an unsigned int in round-to-nearest-even mode.
+ *
+ * Convert the double-precision floating-point value \p x to an
+ * unsigned integer value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ unsigned int          __double2uint_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to an unsigned int in round-up mode.
+ *
+ * Convert the double-precision floating-point value \p x to an
+ * unsigned integer value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ unsigned int          __double2uint_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to an unsigned int in round-down mode.
+ *
+ * Convert the double-precision floating-point value \p x to an
+ * unsigned integer value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ unsigned int          __double2uint_rd(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a signed 64-bit int in round-to-nearest-even mode.
+ *
+ * Convert the double-precision floating-point value \p x to a
+ * signed 64-bit integer value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ long long int          __double2ll_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a signed 64-bit int in round-up mode.
+ *
+ * Convert the double-precision floating-point value \p x to a
+ * signed 64-bit integer value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ long long int          __double2ll_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to a signed 64-bit int in round-down mode.
+ *
+ * Convert the double-precision floating-point value \p x to a
+ * signed 64-bit integer value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ long long int          __double2ll_rd(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to an unsigned 64-bit int in round-to-nearest-even mode.
+ *
+ * Convert the double-precision floating-point value \p x to an
+ * unsigned 64-bit integer value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ unsigned long long int __double2ull_rn(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to an unsigned 64-bit int in round-up mode.
+ *
+ * Convert the double-precision floating-point value \p x to an
+ * unsigned 64-bit integer value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ unsigned long long int __double2ull_ru(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a double to an unsigned 64-bit int in round-down mode.
+ *
+ * Convert the double-precision floating-point value \p x to an
+ * unsigned 64-bit integer value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ * \note_fp_to_int_out_of_range_undefined
+ */
+extern __device__ __device_builtin__ unsigned long long int __double2ull_rd(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a signed int to a double.
+ *
+ * Convert the signed integer value \p x to a double-precision floating-point value.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __int2double_rn(int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert an unsigned int to a double.
+ *
+ * Convert the unsigned integer value \p x to a double-precision floating-point value.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __uint2double_rn(unsigned int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a signed 64-bit int to a double in round-to-nearest-even mode.
+ *
+ * Convert the signed 64-bit integer value \p x to a double-precision floating-point
+ * value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ll2double_rn(long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a signed 64-bit int to a double in round-towards-zero mode.
+ *
+ * Convert the signed 64-bit integer value \p x to a double-precision floating-point
+ * value in round-towards-zero mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ll2double_rz(long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a signed 64-bit int to a double in round-up mode.
+ *
+ * Convert the signed 64-bit integer value \p x to a double-precision floating-point
+ * value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ll2double_ru(long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert a signed 64-bit int to a double in round-down mode.
+ *
+ * Convert the signed 64-bit integer value \p x to a double-precision floating-point
+ * value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ll2double_rd(long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert an unsigned 64-bit int to a double in round-to-nearest-even mode.
+ *
+ * Convert the unsigned 64-bit integer value \p x to a double-precision floating-point
+ * value in round-to-nearest-even mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ull2double_rn(unsigned long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert an unsigned 64-bit int to a double in round-towards-zero mode.
+ *
+ * Convert the unsigned 64-bit integer value \p x to a double-precision floating-point
+ * value in round-towards-zero mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ull2double_rz(unsigned long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert an unsigned 64-bit int to a double in round-up mode.
+ *
+ * Convert the unsigned 64-bit integer value \p x to a double-precision floating-point
+ * value in round-up (to positive infinity) mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ull2double_ru(unsigned long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Convert an unsigned 64-bit int to a double in round-down mode.
+ *
+ * Convert the unsigned 64-bit integer value \p x to a double-precision floating-point
+ * value in round-down (to negative infinity) mode.
+ * \return Returns converted value.
+ */
+extern __device__ __device_builtin__ double                 __ull2double_rd(unsigned long long int x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Reinterpret high 32 bits in a double as a signed integer.
+ *
+ * Reinterpret the high 32 bits in the double-precision floating-point value \p x
+ * as a signed integer.
+ * \return Returns reinterpreted value.
+ */
+extern __device__ __device_builtin__ int                    __double2hiint(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Reinterpret low 32 bits in a double as a signed integer.
+ *
+ * Reinterpret the low 32 bits in the double-precision floating-point value \p x
+ * as a signed integer.
+ * \return Returns reinterpreted value.
+ */
+extern __device__ __device_builtin__ int                    __double2loint(double x);
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_CAST
+ * \brief Reinterpret high and low 32-bit integer values as a double.
+ *
+ * Reinterpret the integer value of \p hi as the high 32 bits of a 
+ * double-precision floating-point value and the integer value of \p lo
+ * as the low 32 bits of the same double-precision floating-point value.
+ * \return Returns reinterpreted value.
+ */
+extern __device__ __device_builtin__ double                 __hiloint2double(int hi, int lo);
+
+
+}
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+__SM_20_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__ballot)) unsigned int ballot(bool pred) __DEF_IF_HOST
+
+__SM_20_INTRINSICS_DECL__ int syncthreads_count(bool pred) __DEF_IF_HOST
+
+__SM_20_INTRINSICS_DECL__ bool syncthreads_and(bool pred) __DEF_IF_HOST
+
+__SM_20_INTRINSICS_DECL__ bool syncthreads_or(bool pred) __DEF_IF_HOST
+
+#undef __DEPRECATED__
+#undef __WSB_DEPRECATION_MESSAGE
+
+__SM_20_INTRINSICS_DECL__ unsigned int __isGlobal(const void *ptr) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ unsigned int __isShared(const void *ptr) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ unsigned int __isConstant(const void *ptr) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ unsigned int __isLocal(const void *ptr) __DEF_IF_HOST
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700)
+__SM_20_INTRINSICS_DECL__ unsigned int __isGridConstant(const void *ptr) __DEF_IF_HOST
+#endif  /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) */
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_global(const void *ptr) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_shared(const void *ptr) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_constant(const void *ptr) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_local(const void *ptr) __DEF_IF_HOST
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700)
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_grid_constant(const void *ptr) __DEF_IF_HOST
+#endif  /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) */
+
+__SM_20_INTRINSICS_DECL__ void * __cvta_global_to_generic(size_t rawbits) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ void * __cvta_shared_to_generic(size_t rawbits) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ void * __cvta_constant_to_generic(size_t rawbits) __DEF_IF_HOST
+__SM_20_INTRINSICS_DECL__ void * __cvta_local_to_generic(size_t rawbits) __DEF_IF_HOST
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700)
+__SM_20_INTRINSICS_DECL__ void * __cvta_grid_constant_to_generic(size_t rawbits) __DEF_IF_HOST
+#endif  /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) */
+
+// notice: update documentation for __nv_bswap*() when more host compilers are supported
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Reverse the order of bytes of the 16-bit unsigned integer.
+ *
+ * Reverse the order of bytes of \p x . Only supported in MSVC and other host 
+ * compilers which define the `__GNUC__` macro, such as GCC and CLANG.
+ *
+ * \return Returns \p x with the order of bytes reversed.
+ */
+__COMMON_INTRINSICS_DECL__ unsigned short __nv_bswap16(unsigned short x) {
+#if defined(__GNUC__)
+    return __builtin_bswap16(x);
+#elif defined(_WIN32)
+    return _byteswap_ushort(x);
+#else
+#error "unsupported platform"
+#endif /* defined(__GNUC__) */
+}
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Reverse the order of bytes of the 32-bit unsigned integer.
+ *
+ * Reverse the order of bytes of \p x . Only supported in MSVC and other host 
+ * compilers which define the `__GNUC__` macro, such as GCC and CLANG.
+ *
+ * \return Returns \p x with the order of bytes reversed.
+ */
+__COMMON_INTRINSICS_DECL__ unsigned int __nv_bswap32(unsigned int x) {
+#if defined(__GNUC__)
+    return __builtin_bswap32(x);
+#elif defined(_WIN32)
+    unsigned long ret = _byteswap_ulong(static_cast<unsigned long>(x));
+    return static_cast<unsigned int>(ret);
+#else
+#error "unsupported platform"
+#endif /* defined(__GNUC__) */
+}
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Reverse the order of bytes of the 64-bit unsigned integer.
+ *
+ * Reverse the order of bytes of \p x . Only supported in MSVC and other host 
+ * compilers which define the `__GNUC__` macro, such as GCC and CLANG.
+ *
+ * \return Returns \p x with the order of bytes reversed.
+ */
+__COMMON_INTRINSICS_DECL__ unsigned long long __nv_bswap64(unsigned long long x) {
+#if defined(__GNUC__)
+    return __builtin_bswap64(x);
+#elif defined(_WIN32)
+    unsigned __int64 ret = _byteswap_uint64(static_cast<unsigned __int64>(x));
+    return static_cast<unsigned long long>(ret);
+#else
+#error "unsupported platform"
+#endif /* defined(__GNUC__) */
+}
+#else
+__COMMON_INTRINSICS_DECL__ unsigned short __nv_bswap16(unsigned short in);
+__COMMON_INTRINSICS_DECL__ unsigned int __nv_bswap32(unsigned int in);
+__COMMON_INTRINSICS_DECL__ unsigned long long __nv_bswap64(unsigned long long in);
+#endif /* !defined(__CUDA_ARCH__) */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEF_IF_HOST
+#undef __SM_20_INTRINSICS_DECL__
+#undef __COMMON_INTRINSICS_DECL__
+
+#if (!defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)) || defined(_NVHPC_CUDA)
+#include "sm_20_intrinsics.hpp"
+#endif /* (!__CUDACC_RTC__ && __CUDA_ARCH__) || _NVHPC_CUDA */
+#endif /* !__SM_20_INTRINSICS_H__ */
+
+#undef EXCLUDE_FROM_RTC
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_intrinsics.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_intrinsics.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..53cac60018360d7ab814092118ba9db4c03fcba9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_20_intrinsics.hpp
@@ -0,0 +1,241 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_20_INTRINSICS_HPP__)
+#define __SM_20_INTRINSICS_HPP__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_20_INTRINSICS_DECL__ __device__
+#define __COMMON_INTRINSICS_DECL__ __device__
+#else /* __CUDACC_RTC__ */
+#define __SM_20_INTRINSICS_DECL__ static __inline__ __device__
+#define __COMMON_INTRINSICS_DECL__ static __inline__ __host__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_20_INTRINSICS_DECL__ unsigned int ballot(bool pred)
+{
+  return __ballot((int)pred);
+}
+
+__SM_20_INTRINSICS_DECL__ int syncthreads_count(bool pred)
+{
+  return __syncthreads_count((int)pred);
+}
+
+__SM_20_INTRINSICS_DECL__ bool syncthreads_and(bool pred)
+{
+  return (bool)__syncthreads_and((int)pred);
+}
+
+__SM_20_INTRINSICS_DECL__ bool syncthreads_or(bool pred)
+{
+  return (bool)__syncthreads_or((int)pred);
+}
+
+
+extern "C" {
+  __device__ unsigned __nv_isGlobal_impl(const void *);
+  __device__ unsigned __nv_isShared_impl(const void *);
+  __device__ unsigned __nv_isConstant_impl(const void *);
+  __device__ unsigned __nv_isLocal_impl(const void *);
+  __device__ unsigned __nv_isGridConstant_impl(const void *);
+}
+
+__SM_20_INTRINSICS_DECL__ unsigned int __isGlobal(const void *ptr)
+{
+  return __nv_isGlobal_impl(ptr); 
+}
+
+__SM_20_INTRINSICS_DECL__ unsigned int __isShared(const void *ptr)
+{
+  return __nv_isShared_impl(ptr); 
+}
+
+__SM_20_INTRINSICS_DECL__ unsigned int __isConstant(const void *ptr)
+{
+  return __nv_isConstant_impl(ptr); 
+}
+
+__SM_20_INTRINSICS_DECL__ unsigned int __isLocal(const void *ptr)
+{
+  return __nv_isLocal_impl(ptr); 
+}
+
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700)
+__SM_20_INTRINSICS_DECL__ unsigned int __isGridConstant(const void *ptr)
+{
+  return __nv_isGridConstant_impl(ptr); 
+}
+#endif  /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) */
+
+extern "C" {
+  __device__ size_t __nv_cvta_generic_to_global_impl(const void *);
+  __device__ size_t __nv_cvta_generic_to_shared_impl(const void *);
+  __device__ size_t __nv_cvta_generic_to_constant_impl(const void *);
+  __device__ size_t __nv_cvta_generic_to_local_impl(const void *);
+  __device__ void * __nv_cvta_global_to_generic_impl(size_t);
+  __device__ void * __nv_cvta_shared_to_generic_impl(size_t);
+  __device__ void * __nv_cvta_constant_to_generic_impl(size_t);
+  __device__ void * __nv_cvta_local_to_generic_impl(size_t);
+}
+
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_global(const void *p)
+{
+  return __nv_cvta_generic_to_global_impl(p);
+}
+
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_shared(const void *p)
+{
+  return __nv_cvta_generic_to_shared_impl(p);
+}
+
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_constant(const void *p)
+{
+  return __nv_cvta_generic_to_constant_impl(p);
+}
+
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_local(const void *p)
+{
+  return __nv_cvta_generic_to_local_impl(p);
+}
+
+__SM_20_INTRINSICS_DECL__ void * __cvta_global_to_generic(size_t rawbits)
+{
+  return __nv_cvta_global_to_generic_impl(rawbits);
+}
+
+__SM_20_INTRINSICS_DECL__ void * __cvta_shared_to_generic(size_t rawbits)
+{
+  return __nv_cvta_shared_to_generic_impl(rawbits);
+}
+
+__SM_20_INTRINSICS_DECL__ void * __cvta_constant_to_generic(size_t rawbits)
+{
+  return __nv_cvta_constant_to_generic_impl(rawbits);
+}
+
+__SM_20_INTRINSICS_DECL__ void * __cvta_local_to_generic(size_t rawbits)
+{
+  return __nv_cvta_local_to_generic_impl(rawbits);
+}
+
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700)
+#if (defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)
+#define __CVTA_PTR_64 1
+#endif
+
+__SM_20_INTRINSICS_DECL__ size_t __cvta_generic_to_grid_constant(const void *ptr)
+{
+#if __CVTA_PTR_64  
+  unsigned long long ret;
+  asm("cvta.to.param.u64 %0, %1;"  : "=l"(ret) : "l"(ptr));
+#else  /* !__CVTA_PTR_64 */
+  unsigned ret;
+  asm("cvta.to.param.u32 %0, %1;"  : "=r"(ret) : "r"(ptr));
+#endif  /* __CVTA_PTR_64 */  
+  return (size_t)ret;
+  
+}
+
+__SM_20_INTRINSICS_DECL__ void * __cvta_grid_constant_to_generic(size_t rawbits)
+{
+  void *ret;
+#if __CVTA_PTR_64  
+  unsigned long long in = rawbits;
+  asm("cvta.param.u64 %0, %1;" : "=l"(ret) : "l"(in));
+#else  /* !__CVTA_PTR_64 */
+  unsigned in = rawbits;
+  asm("cvta.param.u32 %0, %1;" : "=r"(ret) : "r"(in));
+#endif  /* __CVTA_PTR_64 */
+  return ret;
+}
+#undef __CVTA_PTR_64
+#endif  /* !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 700) */
+
+extern "C" {
+  __device__ unsigned short __nv_bswap16_impl(unsigned short);
+  __device__ unsigned int __nv_bswap32_impl(unsigned int);
+  __device__ unsigned long long __nv_bswap64_impl(unsigned long long);
+}
+
+__COMMON_INTRINSICS_DECL__ unsigned short __nv_bswap16(unsigned short in) {
+  return __nv_bswap16_impl(in);
+}
+
+__COMMON_INTRINSICS_DECL__ unsigned int __nv_bswap32(unsigned int in) {
+  return __nv_bswap32_impl(in);
+}
+
+__COMMON_INTRINSICS_DECL__ unsigned long long __nv_bswap64(unsigned long long in) {
+  return __nv_bswap64_impl(in);
+}
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_20_INTRINSICS_DECL__
+#undef __COMMON_INTRINSICS_DECL__
+
+#endif /* !__SM_20_INTRINSICS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_30_intrinsics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_30_intrinsics.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad3bfc500fefaaefdc801e2fe42c27def1ddaa58
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_30_intrinsics.h
@@ -0,0 +1,236 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_30_INTRINSICS_H__)
+#define __SM_30_INTRINSICS_H__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_30_INTRINSICS_DECL__ __device__
+#elif defined(_NVHPC_CUDA)
+#define __SM_30_INTRINSICS_DECL__ extern __device__ __cudart_builtin__
+#else /* !__CUDACC_RTC__ */
+#define __SM_30_INTRINSICS_DECL__ static __device__ __inline__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 300
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/* Add !defined(_NVHPC_CUDA) to avoid empty function definition in CUDA
+ * C++ compiler where the macro __CUDA_ARCH__ is not defined. */
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ */
+
+
+/*******************************************************************************
+*                                                                              *
+*  Below are declarations of SM-3.0 intrinsics which are included as           *
+*  source (instead of being built in to the compiler)                          *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined warpSize && !defined __local_warpSize
+#define warpSize    32
+#define __local_warpSize
+#endif
+
+#if defined(_WIN32)
+# define __DEPRECATED__(msg) __declspec(deprecated(msg))
+#elif (defined(__GNUC__) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 5 && !defined(__clang__))))
+# define __DEPRECATED__(msg) __attribute__((deprecated))
+#else
+# define __DEPRECATED__(msg) __attribute__((deprecated(msg)))
+#endif
+
+#if defined(_NVHPC_CUDA)
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is not valid on cc70 and above, and should be replaced with "#x"_sync()."
+#elif !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700
+#define __WSB_DEPRECATION_MESSAGE(x) #x"() is deprecated in favor of "#x"_sync() and may be removed in a future release (Use -Wno-deprecated-declarations to suppress this warning)."
+#endif
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Find the position of the n-th set to 1 bit in a 32-bit integer.
+ *
+ * Given a 32-bit value \p mask and an integer value \p base (between 0 and 31),
+ * find the n-th (given by \p offset) set bit in \p mask from the \p base bit.
+ * If not found, return 0xFFFFFFFF.
+ *
+ * See also https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#integer-arithmetic-instructions-fns
+ * for more information.
+ *
+ * \return Returns a value between 0 and 32 inclusive representing the position
+ * of the n-th set bit.
+ * - parameter \p base must be <=31, otherwise behavior is undefined.
+ */
+__SM_30_INTRINSICS_DECL__ unsigned  __fns(unsigned mask, unsigned base, int offset) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ void  __barrier_sync(unsigned id) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ void  __barrier_sync_count(unsigned id, unsigned cnt) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ void  __syncwarp(unsigned mask=0xFFFFFFFF) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ int __all_sync(unsigned mask, int pred) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ int __any_sync(unsigned mask, int pred) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ int __uni_sync(unsigned mask, int pred) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned __ballot_sync(unsigned mask, int pred) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned __activemask() __DEF_IF_HOST
+
+// Warp register exchange (shuffle) intrinsics.
+// Notes:
+// a) Warp size is hardcoded to 32 here, because the compiler does not know
+//    the "warpSize" constant at this time
+// b) we cannot map the float __shfl to the int __shfl because it'll mess with
+//    the register number (especially if you're doing two shfls to move a double).
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) int __shfl(int var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) unsigned int __shfl(unsigned int var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) int __shfl_up(int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) unsigned int __shfl_up(unsigned int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) int __shfl_down(int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) unsigned int __shfl_down(unsigned int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) int __shfl_xor(int var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) unsigned int __shfl_xor(unsigned int var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) float __shfl(float var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) float __shfl_up(float var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) float __shfl_down(float var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) float __shfl_xor(float var, int laneMask, int width=warpSize) __DEF_IF_HOST
+#endif
+
+__SM_30_INTRINSICS_DECL__ int __shfl_sync(unsigned mask, int var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_sync(unsigned mask, unsigned int var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ int __shfl_up_sync(unsigned mask, int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_up_sync(unsigned mask, unsigned int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ int __shfl_down_sync(unsigned mask, int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_down_sync(unsigned mask, unsigned int var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ int __shfl_xor_sync(unsigned mask, int var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_xor_sync(unsigned mask, unsigned int var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ float __shfl_sync(unsigned mask, float var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ float __shfl_up_sync(unsigned mask, float var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ float __shfl_down_sync(unsigned mask, float var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ float __shfl_xor_sync(unsigned mask, float var, int laneMask, int width=warpSize) __DEF_IF_HOST
+
+// 64-bits SHFL
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) unsigned long long __shfl(unsigned long long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) long long __shfl(long long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) long long __shfl_up(long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) unsigned long long __shfl_up(unsigned long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) long long __shfl_down(long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) unsigned long long __shfl_down(unsigned long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) long long __shfl_xor(long long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) unsigned long long __shfl_xor(unsigned long long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) double __shfl(double var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) double __shfl_up(double var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) double __shfl_down(double var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) double __shfl_xor(double var, int laneMask, int width=warpSize) __DEF_IF_HOST
+#endif
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_sync(unsigned mask, long long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_sync(unsigned mask, unsigned long long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ long long __shfl_up_sync(unsigned mask, long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_up_sync(unsigned mask, unsigned long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ long long __shfl_down_sync(unsigned mask, long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_down_sync(unsigned mask, unsigned long long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ long long __shfl_xor_sync(unsigned mask, long long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_xor_sync(unsigned mask, unsigned long long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ double __shfl_sync(unsigned mask, double var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ double __shfl_up_sync(unsigned mask, double var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ double __shfl_down_sync(unsigned mask, double var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ double __shfl_xor_sync(unsigned mask, double var, int laneMask, int width=warpSize) __DEF_IF_HOST
+
+// long needs some help to choose between 32-bits and 64-bits
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) long __shfl(long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl)) unsigned long __shfl(unsigned long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) long __shfl_up(long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_up)) unsigned long __shfl_up(unsigned long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) long __shfl_down(long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_down)) unsigned long __shfl_down(unsigned long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) long __shfl_xor(long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ __DEPRECATED__(__WSB_DEPRECATION_MESSAGE(__shfl_xor)) unsigned long __shfl_xor(unsigned long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+#endif
+
+__SM_30_INTRINSICS_DECL__ long __shfl_sync(unsigned mask, long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_sync(unsigned mask, unsigned long var, int srcLane, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ long __shfl_up_sync(unsigned mask, long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_up_sync(unsigned mask, unsigned long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ long __shfl_down_sync(unsigned mask, long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_down_sync(unsigned mask, unsigned long var, unsigned int delta, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ long __shfl_xor_sync(unsigned mask, long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_xor_sync(unsigned mask, unsigned long var, int laneMask, int width=warpSize) __DEF_IF_HOST
+
+#undef __DEPRECATED__
+#undef __WSB_DEPRECATION_MESSAGE
+
+#if defined(__local_warpSize)
+#undef warpSize
+#undef __local_warpSize
+#endif
+
+#endif /* !__CUDA_ARCH__ || __CUDA_ARCH__ >= 300 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEF_IF_HOST
+#undef __SM_30_INTRINSICS_DECL__
+
+#if !defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)
+#include "sm_30_intrinsics.hpp"
+#endif /* !__CUDACC_RTC__ && __CUDA_ARCH__ */
+
+#endif /* !__SM_30_INTRINSICS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_30_intrinsics.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_30_intrinsics.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..a5bcac5ee68c0cf547e4de7c08badf37106639dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_30_intrinsics.hpp
@@ -0,0 +1,604 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_30_INTRINSICS_HPP__)
+#define __SM_30_INTRINSICS_HPP__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_30_INTRINSICS_DECL__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __SM_30_INTRINSICS_DECL__ static __device__ __inline__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 300
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+// In here are intrinsics which are built in to the compiler. These may be
+// referenced by intrinsic implementations from this file.
+extern "C"
+{
+}
+
+/*******************************************************************************
+*                                                                              *
+*  Below are implementations of SM-3.0 intrinsics which are included as        *
+*  source (instead of being built in to the compiler)                          *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined warpSize && !defined __local_warpSize
+#define warpSize    32
+#define __local_warpSize
+#endif
+
+__SM_30_INTRINSICS_DECL__
+unsigned __fns(unsigned mask, unsigned base, int offset) {
+  extern __device__ __device_builtin__ unsigned int __nvvm_fns(unsigned int mask, unsigned int base, int offset);
+  return __nvvm_fns(mask, base, offset);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+void  __barrier_sync(unsigned id) {
+  extern __device__ __device_builtin__ void __nvvm_barrier_sync(unsigned id);
+  return __nvvm_barrier_sync(id);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+void  __barrier_sync_count(unsigned id, unsigned cnt) {
+  extern __device__ __device_builtin__ void __nvvm_barrier_sync_cnt(unsigned id, unsigned cnt);
+  return __nvvm_barrier_sync_cnt(id, cnt);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+void  __syncwarp(unsigned mask) {
+  extern __device__ __device_builtin__ void __nvvm_bar_warp_sync(unsigned mask);
+  return __nvvm_bar_warp_sync(mask);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+int __all_sync(unsigned mask, int pred) {
+  extern __device__ __device_builtin__ int __nvvm_vote_all_sync(unsigned int mask, int pred); 
+  return __nvvm_vote_all_sync(mask, pred);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+int __any_sync(unsigned mask, int pred) {
+  extern __device__ __device_builtin__ int __nvvm_vote_any_sync(unsigned int mask, int pred); 
+  return __nvvm_vote_any_sync(mask, pred);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+int __uni_sync(unsigned mask, int pred) {
+  extern __device__ __device_builtin__ int __nvvm_vote_uni_sync(unsigned int mask, int pred); 
+  return __nvvm_vote_uni_sync(mask, pred);
+}
+
+__SM_30_INTRINSICS_DECL__ 
+unsigned __ballot_sync(unsigned mask, int pred) {
+  extern __device__ __device_builtin__ unsigned int __nvvm_vote_ballot_sync(unsigned int mask, int pred); 
+  return __nvvm_vote_ballot_sync(mask, pred);
+}
+
+__SM_30_INTRINSICS_DECL__
+unsigned __activemask() {
+    unsigned ret;
+    asm volatile ("activemask.b32 %0;" : "=r"(ret));
+    return ret;
+}
+
+// These are removed starting with compute_70 and onwards
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700
+
+__SM_30_INTRINSICS_DECL__ int __shfl(int var, int srcLane, int width) {
+	int ret;
+	int c = ((warpSize-width) << 8) | 0x1f;
+	asm volatile ("shfl.idx.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(var), "r"(srcLane), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl(unsigned int var, int srcLane, int width) {
+	return (unsigned int) __shfl((int)var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ int __shfl_up(int var, unsigned int delta, int width) {
+	int ret;
+	int c = (warpSize-width) << 8;
+	asm volatile ("shfl.up.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(var), "r"(delta), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_up(unsigned int var, unsigned int delta, int width) {
+	return (unsigned int) __shfl_up((int)var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ int __shfl_down(int var, unsigned int delta, int width) {
+	int ret;
+	int c = ((warpSize-width) << 8) | 0x1f;
+	asm volatile ("shfl.down.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(var), "r"(delta), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_down(unsigned int var, unsigned int delta, int width) {
+	return (unsigned int) __shfl_down((int)var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ int __shfl_xor(int var, int laneMask, int width) {
+	int ret;
+	int c = ((warpSize-width) << 8) | 0x1f;
+	asm volatile ("shfl.bfly.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(var), "r"(laneMask), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_xor(unsigned int var, int laneMask, int width) {
+	return (unsigned int) __shfl_xor((int)var, laneMask, width);
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl(float var, int srcLane, int width) {
+	float ret;
+        int c;
+	c = ((warpSize-width) << 8) | 0x1f;
+	asm volatile ("shfl.idx.b32 %0, %1, %2, %3;" : "=f"(ret) : "f"(var), "r"(srcLane), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_up(float var, unsigned int delta, int width) {
+	float ret;
+        int c;
+	c = (warpSize-width) << 8;
+	asm volatile ("shfl.up.b32 %0, %1, %2, %3;" : "=f"(ret) : "f"(var), "r"(delta), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_down(float var, unsigned int delta, int width) {
+	float ret;
+        int c;
+	c = ((warpSize-width) << 8) | 0x1f;
+	asm volatile ("shfl.down.b32 %0, %1, %2, %3;" : "=f"(ret) : "f"(var), "r"(delta), "r"(c));
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_xor(float var, int laneMask, int width) {
+	float ret;
+        int c;
+	c = ((warpSize-width) << 8) | 0x1f;
+	asm volatile ("shfl.bfly.b32 %0, %1, %2, %3;" : "=f"(ret) : "f"(var), "r"(laneMask), "r"(c));
+	return ret;
+}
+
+// 64-bits SHFL
+
+__SM_30_INTRINSICS_DECL__ long long __shfl(long long var, int srcLane, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl(hi, srcLane, width);
+	lo = __shfl(lo, srcLane, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl(unsigned long long var, int srcLane, int width) {
+	return (unsigned long long) __shfl((long long) var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_up(long long var, unsigned int delta, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_up(hi, delta, width);
+	lo = __shfl_up(lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_up(unsigned long long var, unsigned int delta, int width) {
+	return (unsigned long long) __shfl_up((long long) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_down(long long var, unsigned int delta, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_down(hi, delta, width);
+	lo = __shfl_down(lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_down(unsigned long long var, unsigned int delta, int width) {
+	return (unsigned long long) __shfl_down((long long) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_xor(long long var, int laneMask, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_xor(hi, laneMask, width);
+	lo = __shfl_xor(lo, laneMask, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_xor(unsigned long long var, int laneMask, int width) {
+	return (unsigned long long) __shfl_xor((long long) var, laneMask, width);
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl(double var, int srcLane, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl(hi, srcLane, width);
+	lo = __shfl(lo, srcLane, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_up(double var, unsigned int delta, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_up(hi, delta, width);
+	lo = __shfl_up(lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_down(double var, unsigned int delta, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_down(hi, delta, width);
+	lo = __shfl_down(lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_xor(double var, int laneMask, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_xor(hi, laneMask, width);
+	lo = __shfl_xor(lo, laneMask, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl(long var, int srcLane, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl((long long) var, srcLane, width) :
+		__shfl((int) var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl(unsigned long var, int srcLane, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl((unsigned long long) var, srcLane, width) :
+		__shfl((unsigned int) var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl_up(long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_up((long long) var, delta, width) :
+		__shfl_up((int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_up(unsigned long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_up((unsigned long long) var, delta, width) :
+		__shfl_up((unsigned int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl_down(long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_down((long long) var, delta, width) :
+		__shfl_down((int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_down(unsigned long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_down((unsigned long long) var, delta, width) :
+		__shfl_down((unsigned int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl_xor(long var, int laneMask, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_xor((long long) var, laneMask, width) :
+		__shfl_xor((int) var, laneMask, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_xor(unsigned long var, int laneMask, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_xor((unsigned long long) var, laneMask, width) :
+		__shfl_xor((unsigned int) var, laneMask, width);
+}
+
+#endif /* defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 700 */
+
+// Warp register exchange (shuffle) intrinsics.
+// Notes:
+// a) Warp size is hardcoded to 32 here, because the compiler does not know
+//    the "warpSize" constant at this time
+// b) we cannot map the float __shfl to the int __shfl because it'll mess with
+//    the register number (especially if you're doing two shfls to move a double).
+__SM_30_INTRINSICS_DECL__ int __shfl_sync(unsigned mask, int var, int srcLane, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_idx_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+	int c = ((warpSize-width) << 8) | 0x1f;
+        ret = __nvvm_shfl_idx_sync(mask, var, srcLane, c);
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_sync(unsigned mask, unsigned int var, int srcLane, int width) {
+        return (unsigned int) __shfl_sync(mask, (int)var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ int __shfl_up_sync(unsigned mask, int var, unsigned int delta, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_up_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+	int c = (warpSize-width) << 8;
+        ret = __nvvm_shfl_up_sync(mask, var, delta, c);
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_up_sync(unsigned mask, unsigned int var, unsigned int delta, int width) {
+        return (unsigned int) __shfl_up_sync(mask, (int)var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ int __shfl_down_sync(unsigned mask, int var, unsigned int delta, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_down_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+	int c = ((warpSize-width) << 8) | 0x1f;
+        ret = __nvvm_shfl_down_sync(mask, var, delta, c);
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_down_sync(unsigned mask, unsigned int var, unsigned int delta, int width) {
+        return (unsigned int) __shfl_down_sync(mask, (int)var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ int __shfl_xor_sync(unsigned mask, int var, int laneMask, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_bfly_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+	int c = ((warpSize-width) << 8) | 0x1f;
+        ret = __nvvm_shfl_bfly_sync(mask, var, laneMask, c);
+	return ret;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned int __shfl_xor_sync(unsigned mask, unsigned int var, int laneMask, int width) {
+	return (unsigned int) __shfl_xor_sync(mask, (int)var, laneMask, width);
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_sync(unsigned mask, float var, int srcLane, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_idx_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+        int ret;
+        int c;
+	c = ((warpSize-width) << 8) | 0x1f;
+        ret = __nvvm_shfl_idx_sync(mask, __float_as_int(var), srcLane, c);
+	return __int_as_float(ret);
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_up_sync(unsigned mask, float var, unsigned int delta, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_up_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+        int c;
+	c = (warpSize-width) << 8;
+        ret = __nvvm_shfl_up_sync(mask, __float_as_int(var), delta, c);
+	return __int_as_float(ret);
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_down_sync(unsigned mask, float var, unsigned int delta, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_down_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+        int c;
+	c = ((warpSize-width) << 8) | 0x1f;
+        ret = __nvvm_shfl_down_sync(mask, __float_as_int(var), delta, c);
+	return __int_as_float(ret);
+}
+
+__SM_30_INTRINSICS_DECL__ float __shfl_xor_sync(unsigned mask, float var, int laneMask, int width) {
+        extern __device__ __device_builtin__ unsigned __nvvm_shfl_bfly_sync(unsigned mask, unsigned a, unsigned b, unsigned c);
+	int ret;
+        int c;
+	c = ((warpSize-width) << 8) | 0x1f;
+        ret = __nvvm_shfl_bfly_sync(mask, __float_as_int(var), laneMask, c);
+	return __int_as_float(ret);
+}
+
+// 64-bits SHFL
+__SM_30_INTRINSICS_DECL__ long long __shfl_sync(unsigned mask, long long var, int srcLane, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_sync(mask, hi, srcLane, width);
+	lo = __shfl_sync(mask, lo, srcLane, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_sync(unsigned mask, unsigned long long var, int srcLane, int width) {
+        return (unsigned long long) __shfl_sync(mask, (long long) var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_up_sync(unsigned mask, long long var, unsigned int delta, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_up_sync(mask, hi, delta, width);
+	lo = __shfl_up_sync(mask, lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_up_sync(unsigned mask, unsigned long long var, unsigned int delta, int width) {
+        return (unsigned long long) __shfl_up_sync(mask, (long long) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_down_sync(unsigned mask, long long var, unsigned int delta, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_down_sync(mask, hi, delta, width);
+	lo = __shfl_down_sync(mask, lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_down_sync(unsigned mask, unsigned long long var, unsigned int delta, int width) {
+        return (unsigned long long) __shfl_down_sync(mask, (long long) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long long __shfl_xor_sync(unsigned mask, long long var, int laneMask, int width) {
+	int lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "l"(var));
+	hi = __shfl_xor_sync(mask, hi, laneMask, width);
+	lo = __shfl_xor_sync(mask, lo, laneMask, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=l"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long long __shfl_xor_sync(unsigned mask, unsigned long long var, int laneMask, int width) {
+        return (unsigned long long) __shfl_xor_sync(mask, (long long) var, laneMask, width);
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_sync(unsigned mask, double var, int srcLane, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_sync(mask, hi, srcLane, width);
+	lo = __shfl_sync(mask, lo, srcLane, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_up_sync(unsigned mask, double var, unsigned int delta, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_up_sync(mask, hi, delta, width);
+	lo = __shfl_up_sync(mask, lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_down_sync(unsigned mask, double var, unsigned int delta, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_down_sync(mask, hi, delta, width);
+	lo = __shfl_down_sync(mask, lo, delta, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+__SM_30_INTRINSICS_DECL__ double __shfl_xor_sync(unsigned mask, double var, int laneMask, int width) {
+	unsigned lo, hi;
+	asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(var));
+	hi = __shfl_xor_sync(mask, hi, laneMask, width);
+	lo = __shfl_xor_sync(mask, lo, laneMask, width);
+	asm volatile("mov.b64 %0, {%1,%2};" : "=d"(var) : "r"(lo), "r"(hi));
+	return var;
+}
+
+// long needs some help to choose between 32-bits and 64-bits
+
+__SM_30_INTRINSICS_DECL__ long __shfl_sync(unsigned mask, long var, int srcLane, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+                __shfl_sync(mask, (long long) var, srcLane, width) :
+		__shfl_sync(mask, (int) var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_sync(unsigned mask, unsigned long var, int srcLane, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+                __shfl_sync(mask, (unsigned long long) var, srcLane, width) :
+		__shfl_sync(mask, (unsigned int) var, srcLane, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl_up_sync(unsigned mask, long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_up_sync(mask, (long long) var, delta, width) :
+		__shfl_up_sync(mask, (int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_up_sync(unsigned mask, unsigned long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_up_sync(mask, (unsigned long long) var, delta, width) :
+		__shfl_up_sync(mask, (unsigned int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl_down_sync(unsigned mask, long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_down_sync(mask, (long long) var, delta, width) :
+		__shfl_down_sync(mask, (int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_down_sync(unsigned mask, unsigned long var, unsigned int delta, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_down_sync(mask, (unsigned long long) var, delta, width) :
+		__shfl_down_sync(mask, (unsigned int) var, delta, width);
+}
+
+__SM_30_INTRINSICS_DECL__ long __shfl_xor_sync(unsigned mask, long var, int laneMask, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_xor_sync(mask, (long long) var, laneMask, width) :
+		__shfl_xor_sync(mask, (int) var, laneMask, width);
+}
+
+__SM_30_INTRINSICS_DECL__ unsigned long __shfl_xor_sync(unsigned mask, unsigned long var, int laneMask, int width) {
+	return (sizeof(long) == sizeof(long long)) ?
+		__shfl_xor_sync(mask, (unsigned long long) var, laneMask, width) :
+		__shfl_xor_sync(mask, (unsigned int) var, laneMask, width);
+}
+
+#if defined(__local_warpSize)
+#undef warpSize
+#undef __local_warpSize
+#endif
+
+#endif /* _NVHPC_CUDA || !__CUDA_ARCH__ || __CUDA_ARCH__ >= 300 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_30_INTRINSICS_DECL__
+
+#endif /* !__SM_30_INTRINSICS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_atomic_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_atomic_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..d2070bc8bbfc0c5aa58c45ef1d28623d91f4e938
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_atomic_functions.h
@@ -0,0 +1,122 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 35.235 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.35.235 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if !defined(__SM_32_ATOMIC_FUNCTIONS_H__)
+#define __SM_32_ATOMIC_FUNCTIONS_H__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_32_ATOMIC_FUNCTIONS_DECL__ __device__
+#elif defined(_NVHPC_CUDA)
+#define __SM_32_ATOMIC_FUNCTIONS_DECL__ extern __device__ __cudart_builtin__
+#else /* !__CUDACC_RTC__ */
+#define __SM_32_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 320
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ && !_NVHPC_CUDA */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ || _NVHPC_CUDA */
+
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicMin(long long *address, long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicMax(long long *address, long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicAnd(long long *address, long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicOr(long long *address, long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicXor(long long *address, long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicMin(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicMax(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicAnd(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicOr(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicXor(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+#endif /* !__CUDA_ARCH__ || __CUDA_ARCH__ >= 320 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEF_IF_HOST
+#undef __SM_32_ATOMIC_FUNCTIONS_DECL__
+
+#if !defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)
+#include "sm_32_atomic_functions.hpp"
+#endif /* !__CUDACC_RTC__  && defined(__CUDA_ARCH__) */
+
+#endif /* !__SM_32_ATOMIC_FUNCTIONS_H__ */
+
+#undef EXCLUDE_FROM_RTC
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_atomic_functions.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_atomic_functions.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..7cfea072a044ceac7fb96f3bf3006520a108020e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_atomic_functions.hpp
@@ -0,0 +1,151 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 35.235 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.35.235 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_32_ATOMIC_FUNCTIONS_HPP__)
+#define __SM_32_ATOMIC_FUNCTIONS_HPP__
+
+#ifdef __CUDA_ARCH__
+extern "C"
+{
+extern __device__ __device_builtin__ long long __illAtomicMin(long long *address, long long val);
+extern __device__ __device_builtin__ long long __illAtomicMax(long long *address, long long val);
+extern __device__ __device_builtin__ long long __llAtomicAnd(long long *address, long long val);
+extern __device__ __device_builtin__ long long __llAtomicOr(long long *address, long long val);
+extern __device__ __device_builtin__ long long __llAtomicXor(long long *address, long long val);
+extern __device__ __device_builtin__ unsigned long long __ullAtomicMin(unsigned long long *address, unsigned long long val);
+extern __device__ __device_builtin__ unsigned long long __ullAtomicMax(unsigned long long *address, unsigned long long val);
+extern __device__ __device_builtin__ unsigned long long __ullAtomicAnd(unsigned long long *address, unsigned long long val);
+extern __device__ __device_builtin__ unsigned long long __ullAtomicOr (unsigned long long *address, unsigned long long val);
+extern __device__ __device_builtin__ unsigned long long __ullAtomicXor(unsigned long long *address, unsigned long long val);
+}
+#endif /* __CUDA_ARCH__ */
+
+
+#if defined(__CUDACC_RTC__)
+#define __SM_32_ATOMIC_FUNCTIONS_DECL__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __SM_32_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 320
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicMin(long long *address, long long val)
+{
+    return __illAtomicMin(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicMax(long long *address, long long val)
+{
+    return __illAtomicMax(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicAnd(long long *address, long long val)
+{
+    return __llAtomicAnd(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicOr(long long *address, long long val)
+{
+    return __llAtomicOr(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ long long atomicXor(long long *address, long long val)
+{
+    return __llAtomicXor(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicMin(unsigned long long *address, unsigned long long val)
+{
+    return __ullAtomicMin(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicMax(unsigned long long *address, unsigned long long val)
+{
+    return __ullAtomicMax(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicAnd(unsigned long long *address, unsigned long long val)
+{
+    return __ullAtomicAnd(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicOr(unsigned long long *address, unsigned long long val)
+{
+    return __ullAtomicOr(address, val);
+}
+
+__SM_32_ATOMIC_FUNCTIONS_DECL__ unsigned long long atomicXor(unsigned long long *address, unsigned long long val)
+{
+    return __ullAtomicXor(address, val);
+}
+
+#endif /* _NVHPC_CUDA || !__CUDA_ARCH__ || __CUDA_ARCH__ >= 320 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_32_ATOMIC_FUNCTIONS_DECL__
+
+#endif /* !__SM_32_ATOMIC_FUNCTIONS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_intrinsics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_intrinsics.h
new file mode 100644
index 0000000000000000000000000000000000000000..6f493c1c54c7715703d09c70eb78b70b60d208d9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_intrinsics.h
@@ -0,0 +1,516 @@
+/*
+ * Copyright 1993-2020 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_32_INTRINSICS_H__)
+#define __SM_32_INTRINSICS_H__
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if defined(__CUDACC_RTC__)
+#define __SM_32_INTRINSICS_DECL__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __SM_32_INTRINSICS_DECL__ static __device__ __inline__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 320
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if defined(__CUDA_ARCH__) || defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST ;
+#else  /* defined(__CUDA_ARCH__) || defined(_NVHPC_CUDA) */
+#define __DEF_IF_HOST { }
+#endif /* defined(__CUDA_ARCH__) || defined(_NVHPC_CUDA) */
+
+
+/*******************************************************************************
+*                                                                              *
+*  Below are declarations of SM-3.5 intrinsics which are included as           *
+*  source (instead of being built in to the compiler)                          *
+*                                                                              *
+*******************************************************************************/
+/******************************************************************************
+ *                                   __ldg                                    *
+ ******************************************************************************/
+
+__SM_32_INTRINSICS_DECL__ long __ldg(const long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long __ldg(const unsigned long *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ char __ldg(const char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ signed char __ldg(const signed char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short __ldg(const short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int __ldg(const int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ long long __ldg(const long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char2 __ldg(const char2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char4 __ldg(const char4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short2 __ldg(const short2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short4 __ldg(const short4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int2 __ldg(const int2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int4 __ldg(const int4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ longlong2 __ldg(const longlong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldg(const unsigned char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned short __ldg(const unsigned short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned int __ldg(const unsigned int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldg(const unsigned long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar2 __ldg(const uchar2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar4 __ldg(const uchar4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort2 __ldg(const ushort2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort4 __ldg(const ushort4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint2 __ldg(const uint2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint4 __ldg(const uint4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldg(const ulonglong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ float __ldg(const float *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double __ldg(const double *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float2 __ldg(const float2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float4 __ldg(const float4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double2 __ldg(const double2 *ptr) __DEF_IF_HOST
+
+/******************************************************************************
+ *                                   __ldcg                                   *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ long __ldcg(const long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcg(const unsigned long *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ char __ldcg(const char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ signed char __ldcg(const signed char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short __ldcg(const short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int __ldcg(const int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ long long __ldcg(const long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char2 __ldcg(const char2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char4 __ldcg(const char4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short2 __ldcg(const short2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short4 __ldcg(const short4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int2 __ldcg(const int2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int4 __ldcg(const int4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ longlong2 __ldcg(const longlong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldcg(const unsigned char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned short __ldcg(const unsigned short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned int __ldcg(const unsigned int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldcg(const unsigned long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar2 __ldcg(const uchar2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar4 __ldcg(const uchar4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort2 __ldcg(const ushort2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort4 __ldcg(const ushort4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint2 __ldcg(const uint2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint4 __ldcg(const uint4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldcg(const ulonglong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ float __ldcg(const float *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double __ldcg(const double *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float2 __ldcg(const float2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float4 __ldcg(const float4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double2 __ldcg(const double2 *ptr) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __ldca                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ long __ldca(const long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long __ldca(const unsigned long *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ char __ldca(const char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ signed char __ldca(const signed char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short __ldca(const short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int __ldca(const int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ long long __ldca(const long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char2 __ldca(const char2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char4 __ldca(const char4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short2 __ldca(const short2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short4 __ldca(const short4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int2 __ldca(const int2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int4 __ldca(const int4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ longlong2 __ldca(const longlong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldca(const unsigned char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned short __ldca(const unsigned short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned int __ldca(const unsigned int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldca(const unsigned long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar2 __ldca(const uchar2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar4 __ldca(const uchar4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort2 __ldca(const ushort2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort4 __ldca(const ushort4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint2 __ldca(const uint2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint4 __ldca(const uint4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldca(const ulonglong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ float __ldca(const float *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double __ldca(const double *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float2 __ldca(const float2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float4 __ldca(const float4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double2 __ldca(const double2 *ptr) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __ldcs                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ long __ldcs(const long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcs(const unsigned long *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ char __ldcs(const char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ signed char __ldcs(const signed char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short __ldcs(const short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int __ldcs(const int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ long long __ldcs(const long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char2 __ldcs(const char2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char4 __ldcs(const char4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short2 __ldcs(const short2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short4 __ldcs(const short4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int2 __ldcs(const int2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int4 __ldcs(const int4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ longlong2 __ldcs(const longlong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldcs(const unsigned char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned short __ldcs(const unsigned short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned int __ldcs(const unsigned int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldcs(const unsigned long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar2 __ldcs(const uchar2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar4 __ldcs(const uchar4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort2 __ldcs(const ushort2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort4 __ldcs(const ushort4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint2 __ldcs(const uint2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint4 __ldcs(const uint4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldcs(const ulonglong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ float __ldcs(const float *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double __ldcs(const double *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float2 __ldcs(const float2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float4 __ldcs(const float4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double2 __ldcs(const double2 *ptr) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __ldlu                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ long __ldlu(const long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long __ldlu(const unsigned long *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ char __ldlu(const char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ signed char __ldlu(const signed char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short __ldlu(const short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int __ldlu(const int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ long long __ldlu(const long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char2 __ldlu(const char2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char4 __ldlu(const char4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short2 __ldlu(const short2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short4 __ldlu(const short4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int2 __ldlu(const int2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int4 __ldlu(const int4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ longlong2 __ldlu(const longlong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldlu(const unsigned char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned short __ldlu(const unsigned short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned int __ldlu(const unsigned int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldlu(const unsigned long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar2 __ldlu(const uchar2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar4 __ldlu(const uchar4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort2 __ldlu(const ushort2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort4 __ldlu(const ushort4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint2 __ldlu(const uint2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint4 __ldlu(const uint4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldlu(const ulonglong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ float __ldlu(const float *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double __ldlu(const double *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float2 __ldlu(const float2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float4 __ldlu(const float4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double2 __ldlu(const double2 *ptr) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __ldcv                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ long __ldcv(const long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcv(const unsigned long *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ char __ldcv(const char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ signed char __ldcv(const signed char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short __ldcv(const short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int __ldcv(const int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ long long __ldcv(const long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char2 __ldcv(const char2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ char4 __ldcv(const char4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short2 __ldcv(const short2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ short4 __ldcv(const short4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int2 __ldcv(const int2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ int4 __ldcv(const int4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ longlong2 __ldcv(const longlong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldcv(const unsigned char *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned short __ldcv(const unsigned short *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned int __ldcv(const unsigned int *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldcv(const unsigned long long *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar2 __ldcv(const uchar2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uchar4 __ldcv(const uchar4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort2 __ldcv(const ushort2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ushort4 __ldcv(const ushort4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint2 __ldcv(const uint2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ uint4 __ldcv(const uint4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldcv(const ulonglong2 *ptr) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ float __ldcv(const float *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double __ldcv(const double *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float2 __ldcv(const float2 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ float4 __ldcv(const float4 *ptr) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ double2 __ldcv(const double2 *ptr) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __stwb                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ void __stwb(long *ptr, long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned long *ptr, unsigned long value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stwb(char *ptr, char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(signed char *ptr, signed char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(short *ptr, short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(int *ptr, int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(long long *ptr, long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(char2 *ptr, char2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(char4 *ptr, char4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(short2 *ptr, short2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(short4 *ptr, short4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(int2 *ptr, int2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(int4 *ptr, int4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(longlong2 *ptr, longlong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned char *ptr, unsigned char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned short *ptr, unsigned short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned int *ptr, unsigned int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned long long *ptr, unsigned long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(uchar2 *ptr, uchar2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(uchar4 *ptr, uchar4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(ushort2 *ptr, ushort2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(ushort4 *ptr, ushort4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(uint2 *ptr, uint2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(uint4 *ptr, uint4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(ulonglong2 *ptr, ulonglong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stwb(float *ptr, float value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(double *ptr, double value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(float2 *ptr, float2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(float4 *ptr, float4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwb(double2 *ptr, double2 value) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __stcg                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ void __stcg(long *ptr, long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned long *ptr, unsigned long value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stcg(char *ptr, char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(signed char *ptr, signed char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(short *ptr, short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(int *ptr, int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(long long *ptr, long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(char2 *ptr, char2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(char4 *ptr, char4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(short2 *ptr, short2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(short4 *ptr, short4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(int2 *ptr, int2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(int4 *ptr, int4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(longlong2 *ptr, longlong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned char *ptr, unsigned char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned short *ptr, unsigned short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned int *ptr, unsigned int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned long long *ptr, unsigned long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(uchar2 *ptr, uchar2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(uchar4 *ptr, uchar4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(ushort2 *ptr, ushort2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(ushort4 *ptr, ushort4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(uint2 *ptr, uint2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(uint4 *ptr, uint4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(ulonglong2 *ptr, ulonglong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stcg(float *ptr, float value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(double *ptr, double value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(float2 *ptr, float2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(float4 *ptr, float4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcg(double2 *ptr, double2 value) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __stcs                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ void __stcs(long *ptr, long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned long *ptr, unsigned long value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stcs(char *ptr, char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(signed char *ptr, signed char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(short *ptr, short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(int *ptr, int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(long long *ptr, long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(char2 *ptr, char2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(char4 *ptr, char4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(short2 *ptr, short2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(short4 *ptr, short4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(int2 *ptr, int2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(int4 *ptr, int4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(longlong2 *ptr, longlong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned char *ptr, unsigned char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned short *ptr, unsigned short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned int *ptr, unsigned int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned long long *ptr, unsigned long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(uchar2 *ptr, uchar2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(uchar4 *ptr, uchar4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(ushort2 *ptr, ushort2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(ushort4 *ptr, ushort4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(uint2 *ptr, uint2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(uint4 *ptr, uint4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(ulonglong2 *ptr, ulonglong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stcs(float *ptr, float value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(double *ptr, double value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(float2 *ptr, float2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(float4 *ptr, float4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stcs(double2 *ptr, double2 value) __DEF_IF_HOST
+/******************************************************************************
+ *                                   __stwt                                    *
+ ******************************************************************************/
+__SM_32_INTRINSICS_DECL__ void __stwt(long *ptr, long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned long *ptr, unsigned long value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stwt(char *ptr, char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(signed char *ptr, signed char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(short *ptr, short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(int *ptr, int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(long long *ptr, long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(char2 *ptr, char2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(char4 *ptr, char4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(short2 *ptr, short2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(short4 *ptr, short4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(int2 *ptr, int2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(int4 *ptr, int4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(longlong2 *ptr, longlong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned char *ptr, unsigned char value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned short *ptr, unsigned short value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned int *ptr, unsigned int value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned long long *ptr, unsigned long long value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(uchar2 *ptr, uchar2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(uchar4 *ptr, uchar4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(ushort2 *ptr, ushort2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(ushort4 *ptr, ushort4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(uint2 *ptr, uint2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(uint4 *ptr, uint4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(ulonglong2 *ptr, ulonglong2 value) __DEF_IF_HOST
+
+__SM_32_INTRINSICS_DECL__ void __stwt(float *ptr, float value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(double *ptr, double value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(float2 *ptr, float2 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(float4 *ptr, float4 value) __DEF_IF_HOST
+__SM_32_INTRINSICS_DECL__ void __stwt(double2 *ptr, double2 value) __DEF_IF_HOST
+
+
+// SHF is the "funnel shift" operation - an accelerated left/right shift with carry
+// operating on 64-bit quantities, which are concatenations of two 32-bit registers.
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Concatenate \p hi : \p lo, shift left by \p shift & 31 bits, return the most significant 32 bits.
+ *
+ * Shift the 64-bit value formed by concatenating argument \p lo and \p hi left by the amount specified by the argument \p shift.
+ * Argument \p lo holds bits 31:0 and argument \p hi holds bits 63:32 of the 64-bit source value.
+ * The source is shifted left by the wrapped value of \p shift (\p shift & 31).
+ * The most significant 32-bits of the result are returned.
+ *
+ * \return Returns the most significant 32 bits of the shifted 64-bit value.
+ */
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_l(unsigned int lo, unsigned int hi, unsigned int shift) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Concatenate \p hi : \p lo, shift left by min(\p shift, 32) bits, return the most significant 32 bits.
+ *
+ * Shift the 64-bit value formed by concatenating argument \p lo and \p hi left by the amount specified by the argument \p shift.
+ * Argument \p lo holds bits 31:0 and argument \p hi holds bits 63:32 of the 64-bit source value.
+ * The source is shifted left by the clamped value of \p shift (min(\p shift, 32)).
+ * The most significant 32-bits of the result are returned.
+ *
+ * \return Returns the most significant 32 bits of the shifted 64-bit value.
+ */
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_lc(unsigned int lo, unsigned int hi, unsigned int shift) __DEF_IF_HOST
+
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Concatenate \p hi : \p lo, shift right by \p shift & 31 bits, return the least significant 32 bits.
+ *
+ * Shift the 64-bit value formed by concatenating argument \p lo and \p hi right by the amount specified by the argument \p shift.
+ * Argument \p lo holds bits 31:0 and argument \p hi holds bits 63:32 of the 64-bit source value.
+ * The source is shifted right by the wrapped value of \p shift (\p shift & 31).
+ * The least significant 32-bits of the result are returned.
+ *
+ * \return Returns the least significant 32 bits of the shifted 64-bit value.
+ */
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_r(unsigned int lo, unsigned int hi, unsigned int shift) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Concatenate \p hi : \p lo, shift right by min(\p shift, 32) bits, return the least significant 32 bits.
+ *
+ * Shift the 64-bit value formed by concatenating argument \p lo and \p hi right by the amount specified by the argument \p shift.
+ * Argument \p lo holds bits 31:0 and argument \p hi holds bits 63:32 of the 64-bit source value.
+ * The source is shifted right by the clamped value of \p shift (min(\p shift, 32)).
+ * The least significant 32-bits of the result are returned.
+ *
+ * \return Returns the least significant 32 bits of the shifted 64-bit value.
+ */
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_rc(unsigned int lo, unsigned int hi, unsigned int shift) __DEF_IF_HOST
+
+
+#endif /* _NVHPC_CUDA || !__CUDA_ARCH__ || __CUDA_ARCH__ >= 320 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_32_INTRINSICS_DECL__
+
+#if !defined(__CUDACC_RTC__) && (defined(__CUDA_ARCH__) || defined(_NVHPC_CUDA))
+#include "sm_32_intrinsics.hpp"
+#endif /* !defined(__CUDACC_RTC__) && (defined(__CUDA_ARCH__) || defined(_NVHPC_CUDA))  */
+
+#undef EXCLUDE_FROM_RTC
+#endif /* !__SM_32_INTRINSICS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_intrinsics.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_intrinsics.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..d50f9cea5c4d89bc555855a8ca73d617bcfa461a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_32_intrinsics.hpp
@@ -0,0 +1,588 @@
+/*
+ * Copyright 1993-2020 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_32_INTRINSICS_HPP__)
+#define __SM_32_INTRINSICS_HPP__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_32_INTRINSICS_DECL__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __SM_32_INTRINSICS_DECL__ static __device__ __inline__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 320
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+// In here are intrinsics which are built in to the compiler. These may be
+// referenced by intrinsic implementations from this file.
+extern "C"
+{
+    // There are no intrinsics built in to the compiler for SM-3.5,
+    // all intrinsics are now implemented as inline PTX below.
+}
+
+/*******************************************************************************
+*                                                                              *
+*  Below are implementations of SM-3.5 intrinsics which are included as        *
+*  source (instead of being built in to the compiler)                          *
+*                                                                              *
+*******************************************************************************/
+
+// LDG is a "load from global via texture path" command which can exhibit higher
+// bandwidth on GK110 than a regular LD.
+// Define a different pointer storage size for 64 and 32 bit
+#if (defined(_MSC_VER) && defined(_WIN64)) || defined(__LP64__) || defined(__CUDACC_RTC__)
+#define __LDG_PTR   "l"
+#else
+#define __LDG_PTR   "r"
+#endif
+
+/******************************************************************************
+ *                                   __ldg                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ long __ldg(const long *ptr) { unsigned long ret; asm volatile ("ld.global.nc.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldg(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.nc.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ long __ldg(const long *ptr) { unsigned long ret; asm volatile ("ld.global.nc.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldg(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.nc.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ char __ldg(const char *ptr) { unsigned int ret; asm volatile ("ld.global.nc.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (char)ret; }
+__SM_32_INTRINSICS_DECL__ signed char __ldg(const signed char *ptr) { unsigned int ret; asm volatile ("ld.global.nc.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (signed char)ret; }
+__SM_32_INTRINSICS_DECL__ short __ldg(const short *ptr) { unsigned short ret; asm volatile ("ld.global.nc.s16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return (short)ret; }
+__SM_32_INTRINSICS_DECL__ int __ldg(const int *ptr) { unsigned int ret; asm volatile ("ld.global.nc.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (int)ret; }
+__SM_32_INTRINSICS_DECL__ long long __ldg(const long long *ptr) { unsigned long long ret; asm volatile ("ld.global.nc.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long long)ret; }
+__SM_32_INTRINSICS_DECL__ char2 __ldg(const char2 *ptr) { char2 ret; int2 tmp; asm volatile ("ld.global.nc.v2.s8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ char4 __ldg(const char4 *ptr) { char4 ret; int4 tmp; asm volatile ("ld.global.nc.v4.s8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; ret.z = (char)tmp.z; ret.w = (char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ short2 __ldg(const short2 *ptr) { short2 ret; asm volatile ("ld.global.nc.v2.s16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ short4 __ldg(const short4 *ptr) { short4 ret; asm volatile ("ld.global.nc.v4.s16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int2 __ldg(const int2 *ptr) { int2 ret; asm volatile ("ld.global.nc.v2.s32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int4 __ldg(const int4 *ptr) { int4 ret; asm volatile ("ld.global.nc.v4.s32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ longlong2 __ldg(const longlong2 *ptr) { longlong2 ret; asm volatile ("ld.global.nc.v2.s64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldg(const unsigned char *ptr) { unsigned int ret; asm volatile ("ld.global.nc.u8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr));  return (unsigned char)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned short __ldg(const unsigned short *ptr) { unsigned short ret; asm volatile ("ld.global.nc.u16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned int __ldg(const unsigned int *ptr) { unsigned int ret; asm volatile ("ld.global.nc.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldg(const unsigned long long *ptr) { unsigned long long ret; asm volatile ("ld.global.nc.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uchar2 __ldg(const uchar2 *ptr) { uchar2 ret; uint2 tmp; asm volatile ("ld.global.nc.v2.u8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ uchar4 __ldg(const uchar4 *ptr) { uchar4 ret; uint4 tmp; asm volatile ("ld.global.nc.v4.u8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; ret.z = (unsigned char)tmp.z; ret.w = (unsigned char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ ushort2 __ldg(const ushort2 *ptr) { ushort2 ret; asm volatile ("ld.global.nc.v2.u16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ushort4 __ldg(const ushort4 *ptr) { ushort4 ret; asm volatile ("ld.global.nc.v4.u16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint2 __ldg(const uint2 *ptr) { uint2 ret; asm volatile ("ld.global.nc.v2.u32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint4 __ldg(const uint4 *ptr) { uint4 ret; asm volatile ("ld.global.nc.v4.u32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldg(const ulonglong2 *ptr) { ulonglong2 ret; asm volatile ("ld.global.nc.v2.u64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ float __ldg(const float *ptr) { float ret; asm volatile ("ld.global.nc.f32 %0, [%1];"  : "=f"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double __ldg(const double *ptr) { double ret; asm volatile ("ld.global.nc.f64 %0, [%1];"  : "=d"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float2 __ldg(const float2 *ptr) { float2 ret; asm volatile ("ld.global.nc.v2.f32 {%0,%1}, [%2];"  : "=f"(ret.x), "=f"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float4 __ldg(const float4 *ptr) { float4 ret; asm volatile ("ld.global.nc.v4.f32 {%0,%1,%2,%3}, [%4];"  : "=f"(ret.x), "=f"(ret.y), "=f"(ret.z), "=f"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double2 __ldg(const double2 *ptr) { double2 ret; asm volatile ("ld.global.nc.v2.f64 {%0,%1}, [%2];"  : "=d"(ret.x), "=d"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+
+/******************************************************************************
+ *                                   __ldcg                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ long __ldcg(const long *ptr) { unsigned long ret; asm volatile ("ld.global.cg.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcg(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.cg.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ long __ldcg(const long *ptr) { unsigned long ret; asm volatile ("ld.global.cg.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcg(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.cg.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ char __ldcg(const char *ptr) { unsigned int ret; asm volatile ("ld.global.cg.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (char)ret; }
+__SM_32_INTRINSICS_DECL__ signed char __ldcg(const signed char *ptr) { unsigned int ret; asm volatile ("ld.global.cg.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (signed char)ret; }
+__SM_32_INTRINSICS_DECL__ short __ldcg(const short *ptr) { unsigned short ret; asm volatile ("ld.global.cg.s16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return (short)ret; }
+__SM_32_INTRINSICS_DECL__ int __ldcg(const int *ptr) { unsigned int ret; asm volatile ("ld.global.cg.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (int)ret; }
+__SM_32_INTRINSICS_DECL__ long long __ldcg(const long long *ptr) { unsigned long long ret; asm volatile ("ld.global.cg.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long long)ret; }
+__SM_32_INTRINSICS_DECL__ char2 __ldcg(const char2 *ptr) { char2 ret; int2 tmp; asm volatile ("ld.global.cg.v2.s8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ char4 __ldcg(const char4 *ptr) { char4 ret; int4 tmp; asm volatile ("ld.global.cg.v4.s8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; ret.z = (char)tmp.z; ret.w = (char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ short2 __ldcg(const short2 *ptr) { short2 ret; asm volatile ("ld.global.cg.v2.s16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ short4 __ldcg(const short4 *ptr) { short4 ret; asm volatile ("ld.global.cg.v4.s16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int2 __ldcg(const int2 *ptr) { int2 ret; asm volatile ("ld.global.cg.v2.s32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int4 __ldcg(const int4 *ptr) { int4 ret; asm volatile ("ld.global.cg.v4.s32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ longlong2 __ldcg(const longlong2 *ptr) { longlong2 ret; asm volatile ("ld.global.cg.v2.s64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldcg(const unsigned char *ptr) { unsigned int ret; asm volatile ("ld.global.cg.u8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr));  return (unsigned char)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned short __ldcg(const unsigned short *ptr) { unsigned short ret; asm volatile ("ld.global.cg.u16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned int __ldcg(const unsigned int *ptr) { unsigned int ret; asm volatile ("ld.global.cg.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldcg(const unsigned long long *ptr) { unsigned long long ret; asm volatile ("ld.global.cg.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uchar2 __ldcg(const uchar2 *ptr) { uchar2 ret; uint2 tmp; asm volatile ("ld.global.cg.v2.u8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ uchar4 __ldcg(const uchar4 *ptr) { uchar4 ret; uint4 tmp; asm volatile ("ld.global.cg.v4.u8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; ret.z = (unsigned char)tmp.z; ret.w = (unsigned char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ ushort2 __ldcg(const ushort2 *ptr) { ushort2 ret; asm volatile ("ld.global.cg.v2.u16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ushort4 __ldcg(const ushort4 *ptr) { ushort4 ret; asm volatile ("ld.global.cg.v4.u16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint2 __ldcg(const uint2 *ptr) { uint2 ret; asm volatile ("ld.global.cg.v2.u32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint4 __ldcg(const uint4 *ptr) { uint4 ret; asm volatile ("ld.global.cg.v4.u32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldcg(const ulonglong2 *ptr) { ulonglong2 ret; asm volatile ("ld.global.cg.v2.u64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ float __ldcg(const float *ptr) { float ret; asm volatile ("ld.global.cg.f32 %0, [%1];"  : "=f"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double __ldcg(const double *ptr) { double ret; asm volatile ("ld.global.cg.f64 %0, [%1];"  : "=d"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float2 __ldcg(const float2 *ptr) { float2 ret; asm volatile ("ld.global.cg.v2.f32 {%0,%1}, [%2];"  : "=f"(ret.x), "=f"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float4 __ldcg(const float4 *ptr) { float4 ret; asm volatile ("ld.global.cg.v4.f32 {%0,%1,%2,%3}, [%4];"  : "=f"(ret.x), "=f"(ret.y), "=f"(ret.z), "=f"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double2 __ldcg(const double2 *ptr) { double2 ret; asm volatile ("ld.global.cg.v2.f64 {%0,%1}, [%2];"  : "=d"(ret.x), "=d"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+/******************************************************************************
+ *                                   __ldca                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ long __ldca(const long *ptr) { unsigned long ret; asm volatile ("ld.global.ca.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldca(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.ca.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ long __ldca(const long *ptr) { unsigned long ret; asm volatile ("ld.global.ca.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldca(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.ca.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ char __ldca(const char *ptr) { unsigned int ret; asm volatile ("ld.global.ca.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (char)ret; }
+__SM_32_INTRINSICS_DECL__ signed char __ldca(const signed char *ptr) { unsigned int ret; asm volatile ("ld.global.ca.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (signed char)ret; }
+__SM_32_INTRINSICS_DECL__ short __ldca(const short *ptr) { unsigned short ret; asm volatile ("ld.global.ca.s16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return (short)ret; }
+__SM_32_INTRINSICS_DECL__ int __ldca(const int *ptr) { unsigned int ret; asm volatile ("ld.global.ca.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (int)ret; }
+__SM_32_INTRINSICS_DECL__ long long __ldca(const long long *ptr) { unsigned long long ret; asm volatile ("ld.global.ca.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long long)ret; }
+__SM_32_INTRINSICS_DECL__ char2 __ldca(const char2 *ptr) { char2 ret; int2 tmp; asm volatile ("ld.global.ca.v2.s8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ char4 __ldca(const char4 *ptr) { char4 ret; int4 tmp; asm volatile ("ld.global.ca.v4.s8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; ret.z = (char)tmp.z; ret.w = (char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ short2 __ldca(const short2 *ptr) { short2 ret; asm volatile ("ld.global.ca.v2.s16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ short4 __ldca(const short4 *ptr) { short4 ret; asm volatile ("ld.global.ca.v4.s16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int2 __ldca(const int2 *ptr) { int2 ret; asm volatile ("ld.global.ca.v2.s32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int4 __ldca(const int4 *ptr) { int4 ret; asm volatile ("ld.global.ca.v4.s32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ longlong2 __ldca(const longlong2 *ptr) { longlong2 ret; asm volatile ("ld.global.ca.v2.s64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldca(const unsigned char *ptr) { unsigned int ret; asm volatile ("ld.global.ca.u8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr));  return (unsigned char)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned short __ldca(const unsigned short *ptr) { unsigned short ret; asm volatile ("ld.global.ca.u16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned int __ldca(const unsigned int *ptr) { unsigned int ret; asm volatile ("ld.global.ca.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldca(const unsigned long long *ptr) { unsigned long long ret; asm volatile ("ld.global.ca.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uchar2 __ldca(const uchar2 *ptr) { uchar2 ret; uint2 tmp; asm volatile ("ld.global.ca.v2.u8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ uchar4 __ldca(const uchar4 *ptr) { uchar4 ret; uint4 tmp; asm volatile ("ld.global.ca.v4.u8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; ret.z = (unsigned char)tmp.z; ret.w = (unsigned char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ ushort2 __ldca(const ushort2 *ptr) { ushort2 ret; asm volatile ("ld.global.ca.v2.u16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ushort4 __ldca(const ushort4 *ptr) { ushort4 ret; asm volatile ("ld.global.ca.v4.u16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint2 __ldca(const uint2 *ptr) { uint2 ret; asm volatile ("ld.global.ca.v2.u32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint4 __ldca(const uint4 *ptr) { uint4 ret; asm volatile ("ld.global.ca.v4.u32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldca(const ulonglong2 *ptr) { ulonglong2 ret; asm volatile ("ld.global.ca.v2.u64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ float __ldca(const float *ptr) { float ret; asm volatile ("ld.global.ca.f32 %0, [%1];"  : "=f"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double __ldca(const double *ptr) { double ret; asm volatile ("ld.global.ca.f64 %0, [%1];"  : "=d"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float2 __ldca(const float2 *ptr) { float2 ret; asm volatile ("ld.global.ca.v2.f32 {%0,%1}, [%2];"  : "=f"(ret.x), "=f"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float4 __ldca(const float4 *ptr) { float4 ret; asm volatile ("ld.global.ca.v4.f32 {%0,%1,%2,%3}, [%4];"  : "=f"(ret.x), "=f"(ret.y), "=f"(ret.z), "=f"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double2 __ldca(const double2 *ptr) { double2 ret; asm volatile ("ld.global.ca.v2.f64 {%0,%1}, [%2];"  : "=d"(ret.x), "=d"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+/******************************************************************************
+ *                                   __ldcs                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ long __ldcs(const long *ptr) { unsigned long ret; asm volatile ("ld.global.cs.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcs(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.cs.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ long __ldcs(const long *ptr) { unsigned long ret; asm volatile ("ld.global.cs.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcs(const unsigned long *ptr) { unsigned long ret; asm volatile ("ld.global.cs.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ char __ldcs(const char *ptr) { unsigned int ret; asm volatile ("ld.global.cs.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (char)ret; }
+__SM_32_INTRINSICS_DECL__ signed char __ldcs(const signed char *ptr) { unsigned int ret; asm volatile ("ld.global.cs.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (signed char)ret; }
+__SM_32_INTRINSICS_DECL__ short __ldcs(const short *ptr) { unsigned short ret; asm volatile ("ld.global.cs.s16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return (short)ret; }
+__SM_32_INTRINSICS_DECL__ int __ldcs(const int *ptr) { unsigned int ret; asm volatile ("ld.global.cs.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return (int)ret; }
+__SM_32_INTRINSICS_DECL__ long long __ldcs(const long long *ptr) { unsigned long long ret; asm volatile ("ld.global.cs.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return (long long)ret; }
+__SM_32_INTRINSICS_DECL__ char2 __ldcs(const char2 *ptr) { char2 ret; int2 tmp; asm volatile ("ld.global.cs.v2.s8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ char4 __ldcs(const char4 *ptr) { char4 ret; int4 tmp; asm volatile ("ld.global.cs.v4.s8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (char)tmp.x; ret.y = (char)tmp.y; ret.z = (char)tmp.z; ret.w = (char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ short2 __ldcs(const short2 *ptr) { short2 ret; asm volatile ("ld.global.cs.v2.s16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ short4 __ldcs(const short4 *ptr) { short4 ret; asm volatile ("ld.global.cs.v4.s16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int2 __ldcs(const int2 *ptr) { int2 ret; asm volatile ("ld.global.cs.v2.s32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ int4 __ldcs(const int4 *ptr) { int4 ret; asm volatile ("ld.global.cs.v4.s32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ longlong2 __ldcs(const longlong2 *ptr) { longlong2 ret; asm volatile ("ld.global.cs.v2.s64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldcs(const unsigned char *ptr) { unsigned int ret; asm volatile ("ld.global.cs.u8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr));  return (unsigned char)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned short __ldcs(const unsigned short *ptr) { unsigned short ret; asm volatile ("ld.global.cs.u16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned int __ldcs(const unsigned int *ptr) { unsigned int ret; asm volatile ("ld.global.cs.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldcs(const unsigned long long *ptr) { unsigned long long ret; asm volatile ("ld.global.cs.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uchar2 __ldcs(const uchar2 *ptr) { uchar2 ret; uint2 tmp; asm volatile ("ld.global.cs.v2.u8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ uchar4 __ldcs(const uchar4 *ptr) { uchar4 ret; uint4 tmp; asm volatile ("ld.global.cs.v4.u8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr)); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; ret.z = (unsigned char)tmp.z; ret.w = (unsigned char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ ushort2 __ldcs(const ushort2 *ptr) { ushort2 ret; asm volatile ("ld.global.cs.v2.u16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ushort4 __ldcs(const ushort4 *ptr) { ushort4 ret; asm volatile ("ld.global.cs.v4.u16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint2 __ldcs(const uint2 *ptr) { uint2 ret; asm volatile ("ld.global.cs.v2.u32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ uint4 __ldcs(const uint4 *ptr) { uint4 ret; asm volatile ("ld.global.cs.v4.u32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldcs(const ulonglong2 *ptr) { ulonglong2 ret; asm volatile ("ld.global.cs.v2.u64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+__SM_32_INTRINSICS_DECL__ float __ldcs(const float *ptr) { float ret; asm volatile ("ld.global.cs.f32 %0, [%1];"  : "=f"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double __ldcs(const double *ptr) { double ret; asm volatile ("ld.global.cs.f64 %0, [%1];"  : "=d"(ret) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float2 __ldcs(const float2 *ptr) { float2 ret; asm volatile ("ld.global.cs.v2.f32 {%0,%1}, [%2];"  : "=f"(ret.x), "=f"(ret.y) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ float4 __ldcs(const float4 *ptr) { float4 ret; asm volatile ("ld.global.cs.v4.f32 {%0,%1,%2,%3}, [%4];"  : "=f"(ret.x), "=f"(ret.y), "=f"(ret.z), "=f"(ret.w) : __LDG_PTR (ptr)); return ret; }
+__SM_32_INTRINSICS_DECL__ double2 __ldcs(const double2 *ptr) { double2 ret; asm volatile ("ld.global.cs.v2.f64 {%0,%1}, [%2];"  : "=d"(ret.x), "=d"(ret.y) : __LDG_PTR (ptr)); return ret; }
+
+/******************************************************************************
+ *                                   __ldlu                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ long __ldlu(const long *ptr) { unsigned long ret; asm ("ld.global.lu.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldlu(const unsigned long *ptr) { unsigned long ret; asm ("ld.global.lu.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ long __ldlu(const long *ptr) { unsigned long ret; asm ("ld.global.lu.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldlu(const unsigned long *ptr) { unsigned long ret; asm ("ld.global.lu.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ char __ldlu(const char *ptr) { unsigned int ret; asm ("ld.global.lu.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (char)ret; }
+__SM_32_INTRINSICS_DECL__ signed char __ldlu(const signed char *ptr) { unsigned int ret; asm ("ld.global.lu.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (signed char)ret; }
+__SM_32_INTRINSICS_DECL__ short __ldlu(const short *ptr) { unsigned short ret; asm ("ld.global.lu.s16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr) : "memory"); return (short)ret; }
+__SM_32_INTRINSICS_DECL__ int __ldlu(const int *ptr) { unsigned int ret; asm ("ld.global.lu.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (int)ret; }
+__SM_32_INTRINSICS_DECL__ long long __ldlu(const long long *ptr) { unsigned long long ret; asm ("ld.global.lu.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return (long long)ret; }
+__SM_32_INTRINSICS_DECL__ char2 __ldlu(const char2 *ptr) { char2 ret; int2 tmp; asm ("ld.global.lu.v2.s8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr) : "memory"); ret.x = (char)tmp.x; ret.y = (char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ char4 __ldlu(const char4 *ptr) { char4 ret; int4 tmp; asm ("ld.global.lu.v4.s8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr) : "memory"); ret.x = (char)tmp.x; ret.y = (char)tmp.y; ret.z = (char)tmp.z; ret.w = (char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ short2 __ldlu(const short2 *ptr) { short2 ret; asm ("ld.global.lu.v2.s16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ short4 __ldlu(const short4 *ptr) { short4 ret; asm ("ld.global.lu.v4.s16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ int2 __ldlu(const int2 *ptr) { int2 ret; asm ("ld.global.lu.v2.s32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ int4 __ldlu(const int4 *ptr) { int4 ret; asm ("ld.global.lu.v4.s32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ longlong2 __ldlu(const longlong2 *ptr) { longlong2 ret; asm ("ld.global.lu.v2.s64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldlu(const unsigned char *ptr) { unsigned int ret; asm ("ld.global.lu.u8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory");  return (unsigned char)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned short __ldlu(const unsigned short *ptr) { unsigned short ret; asm ("ld.global.lu.u16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned int __ldlu(const unsigned int *ptr) { unsigned int ret; asm ("ld.global.lu.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldlu(const unsigned long long *ptr) { unsigned long long ret; asm ("ld.global.lu.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ uchar2 __ldlu(const uchar2 *ptr) { uchar2 ret; uint2 tmp; asm ("ld.global.lu.v2.u8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr) : "memory"); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ uchar4 __ldlu(const uchar4 *ptr) { uchar4 ret; uint4 tmp; asm ("ld.global.lu.v4.u8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr) : "memory"); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; ret.z = (unsigned char)tmp.z; ret.w = (unsigned char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ ushort2 __ldlu(const ushort2 *ptr) { ushort2 ret; asm ("ld.global.lu.v2.u16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ ushort4 __ldlu(const ushort4 *ptr) { ushort4 ret; asm ("ld.global.lu.v4.u16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ uint2 __ldlu(const uint2 *ptr) { uint2 ret; asm ("ld.global.lu.v2.u32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ uint4 __ldlu(const uint4 *ptr) { uint4 ret; asm ("ld.global.lu.v4.u32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldlu(const ulonglong2 *ptr) { ulonglong2 ret; asm ("ld.global.lu.v2.u64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+
+__SM_32_INTRINSICS_DECL__ float __ldlu(const float *ptr) { float ret; asm ("ld.global.lu.f32 %0, [%1];"  : "=f"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ double __ldlu(const double *ptr) { double ret; asm ("ld.global.lu.f64 %0, [%1];"  : "=d"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ float2 __ldlu(const float2 *ptr) { float2 ret; asm ("ld.global.lu.v2.f32 {%0,%1}, [%2];"  : "=f"(ret.x), "=f"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ float4 __ldlu(const float4 *ptr) { float4 ret; asm ("ld.global.lu.v4.f32 {%0,%1,%2,%3}, [%4];"  : "=f"(ret.x), "=f"(ret.y), "=f"(ret.z), "=f"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ double2 __ldlu(const double2 *ptr) { double2 ret; asm ("ld.global.lu.v2.f64 {%0,%1}, [%2];"  : "=d"(ret.x), "=d"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+
+/******************************************************************************
+ *                                   __ldcv                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ long __ldcv(const long *ptr) { unsigned long ret; asm ("ld.global.cv.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcv(const unsigned long *ptr) { unsigned long ret; asm ("ld.global.cv.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ long __ldcv(const long *ptr) { unsigned long ret; asm ("ld.global.cv.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (long)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long __ldcv(const unsigned long *ptr) { unsigned long ret; asm ("ld.global.cv.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ char __ldcv(const char *ptr) { unsigned int ret; asm ("ld.global.cv.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (char)ret; }
+__SM_32_INTRINSICS_DECL__ signed char __ldcv(const signed char *ptr) { unsigned int ret; asm ("ld.global.cv.s8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (signed char)ret; }
+__SM_32_INTRINSICS_DECL__ short __ldcv(const short *ptr) { unsigned short ret; asm ("ld.global.cv.s16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr) : "memory"); return (short)ret; }
+__SM_32_INTRINSICS_DECL__ int __ldcv(const int *ptr) { unsigned int ret; asm ("ld.global.cv.s32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return (int)ret; }
+__SM_32_INTRINSICS_DECL__ long long __ldcv(const long long *ptr) { unsigned long long ret; asm ("ld.global.cv.s64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return (long long)ret; }
+__SM_32_INTRINSICS_DECL__ char2 __ldcv(const char2 *ptr) { char2 ret; int2 tmp; asm ("ld.global.cv.v2.s8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr) : "memory"); ret.x = (char)tmp.x; ret.y = (char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ char4 __ldcv(const char4 *ptr) { char4 ret; int4 tmp; asm ("ld.global.cv.v4.s8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr) : "memory"); ret.x = (char)tmp.x; ret.y = (char)tmp.y; ret.z = (char)tmp.z; ret.w = (char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ short2 __ldcv(const short2 *ptr) { short2 ret; asm ("ld.global.cv.v2.s16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ short4 __ldcv(const short4 *ptr) { short4 ret; asm ("ld.global.cv.v4.s16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ int2 __ldcv(const int2 *ptr) { int2 ret; asm ("ld.global.cv.v2.s32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ int4 __ldcv(const int4 *ptr) { int4 ret; asm ("ld.global.cv.v4.s32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ longlong2 __ldcv(const longlong2 *ptr) { longlong2 ret; asm ("ld.global.cv.v2.s64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+
+__SM_32_INTRINSICS_DECL__ unsigned char __ldcv(const unsigned char *ptr) { unsigned int ret; asm ("ld.global.cv.u8 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory");  return (unsigned char)ret; }
+__SM_32_INTRINSICS_DECL__ unsigned short __ldcv(const unsigned short *ptr) { unsigned short ret; asm ("ld.global.cv.u16 %0, [%1];"  : "=h"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned int __ldcv(const unsigned int *ptr) { unsigned int ret; asm ("ld.global.cv.u32 %0, [%1];"  : "=r"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ unsigned long long __ldcv(const unsigned long long *ptr) { unsigned long long ret; asm ("ld.global.cv.u64 %0, [%1];"  : "=l"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ uchar2 __ldcv(const uchar2 *ptr) { uchar2 ret; uint2 tmp; asm ("ld.global.cv.v2.u8 {%0,%1}, [%2];"  : "=r"(tmp.x), "=r"(tmp.y) : __LDG_PTR (ptr) : "memory"); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; return ret; }
+__SM_32_INTRINSICS_DECL__ uchar4 __ldcv(const uchar4 *ptr) { uchar4 ret; uint4 tmp; asm ("ld.global.cv.v4.u8 {%0,%1,%2,%3}, [%4];"  : "=r"(tmp.x), "=r"(tmp.y), "=r"(tmp.z), "=r"(tmp.w) : __LDG_PTR (ptr) : "memory"); ret.x = (unsigned char)tmp.x; ret.y = (unsigned char)tmp.y; ret.z = (unsigned char)tmp.z; ret.w = (unsigned char)tmp.w; return ret; }
+__SM_32_INTRINSICS_DECL__ ushort2 __ldcv(const ushort2 *ptr) { ushort2 ret; asm ("ld.global.cv.v2.u16 {%0,%1}, [%2];"  : "=h"(ret.x), "=h"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ ushort4 __ldcv(const ushort4 *ptr) { ushort4 ret; asm ("ld.global.cv.v4.u16 {%0,%1,%2,%3}, [%4];"  : "=h"(ret.x), "=h"(ret.y), "=h"(ret.z), "=h"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ uint2 __ldcv(const uint2 *ptr) { uint2 ret; asm ("ld.global.cv.v2.u32 {%0,%1}, [%2];"  : "=r"(ret.x), "=r"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ uint4 __ldcv(const uint4 *ptr) { uint4 ret; asm ("ld.global.cv.v4.u32 {%0,%1,%2,%3}, [%4];"  : "=r"(ret.x), "=r"(ret.y), "=r"(ret.z), "=r"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ ulonglong2 __ldcv(const ulonglong2 *ptr) { ulonglong2 ret; asm ("ld.global.cv.v2.u64 {%0,%1}, [%2];"  : "=l"(ret.x), "=l"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+
+__SM_32_INTRINSICS_DECL__ float __ldcv(const float *ptr) { float ret; asm ("ld.global.cv.f32 %0, [%1];"  : "=f"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ double __ldcv(const double *ptr) { double ret; asm ("ld.global.cv.f64 %0, [%1];"  : "=d"(ret) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ float2 __ldcv(const float2 *ptr) { float2 ret; asm ("ld.global.cv.v2.f32 {%0,%1}, [%2];"  : "=f"(ret.x), "=f"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ float4 __ldcv(const float4 *ptr) { float4 ret; asm ("ld.global.cv.v4.f32 {%0,%1,%2,%3}, [%4];"  : "=f"(ret.x), "=f"(ret.y), "=f"(ret.z), "=f"(ret.w) : __LDG_PTR (ptr) : "memory"); return ret; }
+__SM_32_INTRINSICS_DECL__ double2 __ldcv(const double2 *ptr) { double2 ret; asm ("ld.global.cv.v2.f64 {%0,%1}, [%2];"  : "=d"(ret.x), "=d"(ret.y) : __LDG_PTR (ptr) : "memory"); return ret; }
+
+/******************************************************************************
+ *                                   __stwb                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ void __stwb(long *ptr, long value) { asm ("st.global.wb.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned long *ptr, unsigned long value) { asm ("st.global.wb.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ void __stwb(long *ptr, long value) { asm ("st.global.wb.s32 [%0], %1;"  :: __LDG_PTR (ptr),  "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned long *ptr, unsigned long value) { asm ("st.global.wb.u32 [%0], %1;" :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ void __stwb(char *ptr, char value) { asm ("st.global.wb.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(signed char *ptr, signed char value) { asm ("st.global.wb.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(short *ptr, short value) { asm ("st.global.wb.s16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(int *ptr, int value) { asm ("st.global.wb.s32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(long long *ptr, long long value) { asm ("st.global.wb.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(char2 *ptr, char2 value) { const int x = value.x, y = value.y; asm ("st.global.wb.v2.s8 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(char4 *ptr, char4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.wb.v4.s8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(short2 *ptr, short2 value) { asm ("st.global.wb.v2.s16 [%0], {%1,%2};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(short4 *ptr, short4 value) { asm ("st.global.wb.v4.s16 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(int2 *ptr, int2 value) { asm ("st.global.wb.v2.s32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(int4 *ptr, int4 value) { asm ("st.global.wb.v4.s32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(longlong2 *ptr, longlong2 value) { asm ("st.global.wb.v2.s64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned char *ptr, unsigned char value) { asm ("st.global.wb.u8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory");  }
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned short *ptr, unsigned short value) { asm ("st.global.wb.u16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned int *ptr, unsigned int value) { asm ("st.global.wb.u32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(unsigned long long *ptr, unsigned long long value) { asm ("st.global.wb.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(uchar2 *ptr, uchar2 value) { const int x = value.x, y = value.y; asm ("st.global.wb.v2.u8 [%0], {%1,%2};"  :: __LDG_PTR (ptr),  "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(uchar4 *ptr, uchar4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.wb.v4.u8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(ushort2 *ptr, ushort2 value) { asm ("st.global.wb.v2.u16 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(ushort4 *ptr, ushort4 value) { asm ("st.global.wb.v4.u16 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(uint2 *ptr, uint2 value) { asm ("st.global.wb.v2.u32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(uint4 *ptr, uint4 value) { asm ("st.global.wb.v4.u32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(ulonglong2 *ptr, ulonglong2 value) { asm ("st.global.wb.v2.u64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stwb(float *ptr, float value) { asm ("st.global.wb.f32 [%0], %1;"  :: __LDG_PTR (ptr), "f"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(double *ptr, double value) { asm ("st.global.wb.f64 [%0], %1;"  :: __LDG_PTR (ptr), "d"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(float2 *ptr, float2 value) { asm ("st.global.wb.v2.f32 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(float4 *ptr, float4 value) { asm ("st.global.wb.v4.f32 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y), "f"(value.z), "f"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwb(double2 *ptr, double2 value) { asm ("st.global.wb.v2.f64 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "d"(value.x), "d"(value.y) : "memory"); }
+
+/******************************************************************************
+ *                                   __stcg                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ void __stcg(long *ptr, long value) { asm ("st.global.cg.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned long *ptr, unsigned long value) { asm ("st.global.cg.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ void __stcg(long *ptr, long value) { asm ("st.global.cg.s32 [%0], %1;"  :: __LDG_PTR (ptr),  "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned long *ptr, unsigned long value) { asm ("st.global.cg.u32 [%0], %1;" :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ void __stcg(char *ptr, char value) { asm ("st.global.cg.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(signed char *ptr, signed char value) { asm ("st.global.cg.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(short *ptr, short value) { asm ("st.global.cg.s16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(int *ptr, int value) { asm ("st.global.cg.s32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(long long *ptr, long long value) { asm ("st.global.cg.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(char2 *ptr, char2 value) { const int x = value.x, y = value.y; asm ("st.global.cg.v2.s8 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(char4 *ptr, char4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.cg.v4.s8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(short2 *ptr, short2 value) { asm ("st.global.cg.v2.s16 [%0], {%1,%2};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(short4 *ptr, short4 value) { asm ("st.global.cg.v4.s16 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(int2 *ptr, int2 value) { asm ("st.global.cg.v2.s32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(int4 *ptr, int4 value) { asm ("st.global.cg.v4.s32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(longlong2 *ptr, longlong2 value) { asm ("st.global.cg.v2.s64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned char *ptr, unsigned char value) { asm ("st.global.cg.u8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory");  }
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned short *ptr, unsigned short value) { asm ("st.global.cg.u16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned int *ptr, unsigned int value) { asm ("st.global.cg.u32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(unsigned long long *ptr, unsigned long long value) { asm ("st.global.cg.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(uchar2 *ptr, uchar2 value) { const int x = value.x, y = value.y; asm ("st.global.cg.v2.u8 [%0], {%1,%2};"  :: __LDG_PTR (ptr),  "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(uchar4 *ptr, uchar4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.cg.v4.u8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(ushort2 *ptr, ushort2 value) { asm ("st.global.cg.v2.u16 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(ushort4 *ptr, ushort4 value) { asm ("st.global.cg.v4.u16 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(uint2 *ptr, uint2 value) { asm ("st.global.cg.v2.u32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(uint4 *ptr, uint4 value) { asm ("st.global.cg.v4.u32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(ulonglong2 *ptr, ulonglong2 value) { asm ("st.global.cg.v2.u64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stcg(float *ptr, float value) { asm ("st.global.cg.f32 [%0], %1;"  :: __LDG_PTR (ptr), "f"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(double *ptr, double value) { asm ("st.global.cg.f64 [%0], %1;"  :: __LDG_PTR (ptr), "d"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(float2 *ptr, float2 value) { asm ("st.global.cg.v2.f32 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(float4 *ptr, float4 value) { asm ("st.global.cg.v4.f32 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y), "f"(value.z), "f"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcg(double2 *ptr, double2 value) { asm ("st.global.cg.v2.f64 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "d"(value.x), "d"(value.y) : "memory"); }
+
+/******************************************************************************
+ *                                   __stcs                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ void __stcs(long *ptr, long value) { asm ("st.global.cs.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned long *ptr, unsigned long value) { asm ("st.global.cs.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ void __stcs(long *ptr, long value) { asm ("st.global.cs.s32 [%0], %1;"  :: __LDG_PTR (ptr),  "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned long *ptr, unsigned long value) { asm ("st.global.cs.u32 [%0], %1;" :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ void __stcs(char *ptr, char value) { asm ("st.global.cs.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(signed char *ptr, signed char value) { asm ("st.global.cs.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(short *ptr, short value) { asm ("st.global.cs.s16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(int *ptr, int value) { asm ("st.global.cs.s32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(long long *ptr, long long value) { asm ("st.global.cs.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(char2 *ptr, char2 value) { const int x = value.x, y = value.y; asm ("st.global.cs.v2.s8 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(char4 *ptr, char4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.cs.v4.s8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(short2 *ptr, short2 value) { asm ("st.global.cs.v2.s16 [%0], {%1,%2};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(short4 *ptr, short4 value) { asm ("st.global.cs.v4.s16 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(int2 *ptr, int2 value) { asm ("st.global.cs.v2.s32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(int4 *ptr, int4 value) { asm ("st.global.cs.v4.s32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(longlong2 *ptr, longlong2 value) { asm ("st.global.cs.v2.s64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned char *ptr, unsigned char value) { asm ("st.global.cs.u8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory");  }
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned short *ptr, unsigned short value) { asm ("st.global.cs.u16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned int *ptr, unsigned int value) { asm ("st.global.cs.u32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(unsigned long long *ptr, unsigned long long value) { asm ("st.global.cs.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(uchar2 *ptr, uchar2 value) { const int x = value.x, y = value.y; asm ("st.global.cs.v2.u8 [%0], {%1,%2};"  :: __LDG_PTR (ptr),  "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(uchar4 *ptr, uchar4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.cs.v4.u8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(ushort2 *ptr, ushort2 value) { asm ("st.global.cs.v2.u16 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(ushort4 *ptr, ushort4 value) { asm ("st.global.cs.v4.u16 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(uint2 *ptr, uint2 value) { asm ("st.global.cs.v2.u32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(uint4 *ptr, uint4 value) { asm ("st.global.cs.v4.u32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(ulonglong2 *ptr, ulonglong2 value) { asm ("st.global.cs.v2.u64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stcs(float *ptr, float value) { asm ("st.global.cs.f32 [%0], %1;"  :: __LDG_PTR (ptr), "f"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(double *ptr, double value) { asm ("st.global.cs.f64 [%0], %1;"  :: __LDG_PTR (ptr), "d"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(float2 *ptr, float2 value) { asm ("st.global.cs.v2.f32 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(float4 *ptr, float4 value) { asm ("st.global.cs.v4.f32 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y), "f"(value.z), "f"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stcs(double2 *ptr, double2 value) { asm ("st.global.cs.v2.f64 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "d"(value.x), "d"(value.y) : "memory"); }
+
+/******************************************************************************
+ *                                   __stwt                                    *
+ ******************************************************************************/
+
+// Size of long is architecture and OS specific.
+#if defined(__LP64__) // 64 bits
+__SM_32_INTRINSICS_DECL__ void __stwt(long *ptr, long value) { asm ("st.global.wt.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned long *ptr, unsigned long value) { asm ("st.global.wt.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+#else // 32 bits
+__SM_32_INTRINSICS_DECL__ void __stwt(long *ptr, long value) { asm ("st.global.wt.s32 [%0], %1;"  :: __LDG_PTR (ptr),  "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned long *ptr, unsigned long value) { asm ("st.global.wt.u32 [%0], %1;" :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+#endif
+
+
+__SM_32_INTRINSICS_DECL__ void __stwt(char *ptr, char value) { asm ("st.global.wt.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(signed char *ptr, signed char value) { asm ("st.global.wt.s8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(short *ptr, short value) { asm ("st.global.wt.s16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(int *ptr, int value) { asm ("st.global.wt.s32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(long long *ptr, long long value) { asm ("st.global.wt.s64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(char2 *ptr, char2 value) { const int x = value.x, y = value.y; asm ("st.global.wt.v2.s8 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(char4 *ptr, char4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.wt.v4.s8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(short2 *ptr, short2 value) { asm ("st.global.wt.v2.s16 [%0], {%1,%2};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(short4 *ptr, short4 value) { asm ("st.global.wt.v4.s16 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(int2 *ptr, int2 value) { asm ("st.global.wt.v2.s32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(int4 *ptr, int4 value) { asm ("st.global.wt.v4.s32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(longlong2 *ptr, longlong2 value) { asm ("st.global.wt.v2.s64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned char *ptr, unsigned char value) { asm ("st.global.wt.u8 [%0], %1;"  :: __LDG_PTR (ptr), "r"((int)value) : "memory");  }
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned short *ptr, unsigned short value) { asm ("st.global.wt.u16 [%0], %1;"  :: __LDG_PTR (ptr), "h"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned int *ptr, unsigned int value) { asm ("st.global.wt.u32 [%0], %1;"  :: __LDG_PTR (ptr), "r"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(unsigned long long *ptr, unsigned long long value) { asm ("st.global.wt.u64 [%0], %1;"  :: __LDG_PTR (ptr), "l"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(uchar2 *ptr, uchar2 value) { const int x = value.x, y = value.y; asm ("st.global.wt.v2.u8 [%0], {%1,%2};"  :: __LDG_PTR (ptr),  "r"(x), "r"(y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(uchar4 *ptr, uchar4 value) { const int x = value.x, y = value.y, z = value.z, w = value.w; asm ("st.global.wt.v4.u8 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(x), "r"(y), "r"(z), "r"(w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(ushort2 *ptr, ushort2 value) { asm ("st.global.wt.v2.u16 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(ushort4 *ptr, ushort4 value) { asm ("st.global.wt.v4.u16 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "h"(value.x), "h"(value.y), "h"(value.z), "h"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(uint2 *ptr, uint2 value) { asm ("st.global.wt.v2.u32 [%0], {%1,%2};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(uint4 *ptr, uint4 value) { asm ("st.global.wt.v4.u32 [%0], {%1,%2,%3,%4};" :: __LDG_PTR (ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(ulonglong2 *ptr, ulonglong2 value) { asm ("st.global.wt.v2.u64 [%0], {%1,%2};" :: __LDG_PTR (ptr), "l"(value.x), "l"(value.y) : "memory"); }
+
+__SM_32_INTRINSICS_DECL__ void __stwt(float *ptr, float value) { asm ("st.global.wt.f32 [%0], %1;"  :: __LDG_PTR (ptr), "f"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(double *ptr, double value) { asm ("st.global.wt.f64 [%0], %1;"  :: __LDG_PTR (ptr), "d"(value) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(float2 *ptr, float2 value) { asm ("st.global.wt.v2.f32 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(float4 *ptr, float4 value) { asm ("st.global.wt.v4.f32 [%0], {%1,%2,%3,%4};"  :: __LDG_PTR (ptr), "f"(value.x), "f"(value.y), "f"(value.z), "f"(value.w) : "memory"); }
+__SM_32_INTRINSICS_DECL__ void __stwt(double2 *ptr, double2 value) { asm ("st.global.wt.v2.f64 [%0], {%1,%2};"  :: __LDG_PTR (ptr), "d"(value.x), "d"(value.y) : "memory"); }
+
+#undef __LDG_PTR
+
+
+// SHF is the "funnel shift" operation - an accelerated left/right shift with carry
+// operating on 64-bit quantities, which are concatenations of two 32-bit registers.
+
+// This shifts [b:a] left by "shift" bits, returning the most significant bits of the result.
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_l(unsigned int lo, unsigned int hi, unsigned int shift)
+{
+    unsigned int ret;
+    asm volatile ("shf.l.wrap.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(lo), "r"(hi), "r"(shift));
+    return ret;
+}
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_lc(unsigned int lo, unsigned int hi, unsigned int shift)
+{
+    unsigned int ret;
+    asm volatile ("shf.l.clamp.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(lo), "r"(hi), "r"(shift));
+    return ret;
+}
+
+// This shifts [b:a] right by "shift" bits, returning the least significant bits of the result.
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_r(unsigned int lo, unsigned int hi, unsigned int shift)
+{
+    unsigned int ret;
+    asm volatile ("shf.r.wrap.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(lo), "r"(hi), "r"(shift));
+    return ret;
+}
+__SM_32_INTRINSICS_DECL__ unsigned int __funnelshift_rc(unsigned int lo, unsigned int hi, unsigned int shift)
+{
+    unsigned int ret;
+    asm volatile ("shf.r.clamp.b32 %0, %1, %2, %3;" : "=r"(ret) : "r"(lo), "r"(hi), "r"(shift));
+    return ret;
+}
+
+
+#endif /* _NVHPC_CUDA || !__CUDA_ARCH__ || __CUDA_ARCH__ >= 320 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_32_INTRINSICS_DECL__
+
+#endif /* !__SM_32_INTRINSICS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_35_atomic_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_35_atomic_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8961079aeac4c9e73a7c2825cf9ea10b171af09
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_35_atomic_functions.h
@@ -0,0 +1,58 @@
+/*
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 35.235 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.35.235 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_35_ATOMIC_FUNCTIONS_H__)
+#define __SM_35_ATOMIC_FUNCTIONS_H__
+
+/*******************************************************************************
+* All sm_35 atomics are supported by sm_32 so simply include its header file   *
+*******************************************************************************/
+#include "sm_32_atomic_functions.h"
+
+#endif /* !__SM_35_ATOMIC_FUNCTIONS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_35_intrinsics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_35_intrinsics.h
new file mode 100644
index 0000000000000000000000000000000000000000..a13a4fbb0133ea5ed9f2fcc317292ae3fe5397af
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_35_intrinsics.h
@@ -0,0 +1,106 @@
+/*
+
+ * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
+
+ *
+
+ * NOTICE TO LICENSEE:
+
+ *
+
+ * This source code and/or documentation ("Licensed Deliverables") are
+
+ * subject to NVIDIA intellectual property rights under U.S. and
+
+ * international Copyright laws.
+
+ *
+
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+
+ * conditions of a form of NVIDIA software license agreement by and
+
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+
+ * the contrary in the License Agreement, reproduction or disclosure
+
+ * of the Licensed Deliverables to any third party without the express
+
+ * written consent of NVIDIA is prohibited.
+
+ *
+
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+
+ * OF THESE LICENSED DELIVERABLES.
+
+ *
+
+ * U.S. Government End Users.  These Licensed Deliverables are a
+
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+
+ * 1995), consisting of "commercial computer software" and "commercial
+
+ * computer software documentation" as such terms are used in 48
+
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+
+ * U.S. Government End Users acquire the Licensed Deliverables with
+
+ * only those rights set forth herein.
+
+ *
+
+ * Any use of the Licensed Deliverables in individual and commercial
+
+ * software must include, in the user documentation and internal
+
+ * comments to the code, the above Disclaimer and U.S. Government End
+
+ * Users Notice.
+
+ */
+
+
+
+#if !defined(__SM_35_INTRINSICS_H__)
+#define __SM_35_INTRINSICS_H__
+
+
+
+
+#endif /* !__SM_35_INTRINSICS_H__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_60_atomic_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_60_atomic_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..53d607cae6467244b2f99bd891632b8679828b54
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_60_atomic_functions.h
@@ -0,0 +1,330 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if !defined(__SM_60_ATOMIC_FUNCTIONS_H__)
+#define __SM_60_ATOMIC_FUNCTIONS_H__
+
+
+#if defined(__CUDACC_RTC__)
+#define __SM_60_ATOMIC_FUNCTIONS_DECL__ __device__
+#elif defined(_NVHPC_CUDA)
+#define __SM_60_ATOMIC_FUNCTIONS_DECL__ extern __device__ __cudart_builtin__
+#else /* __CUDACC_RTC__ */
+#define __SM_60_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/* Add !defined(_NVHPC_CUDA) to avoid empty function definition in CUDA
+ * C++ compiler where the macro __CUDA_ARCH__ is not defined. */
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ */
+
+
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__ double atomicAdd(double *address, double val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAdd_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAdd_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAdd_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAdd_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAdd_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAdd_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicAdd_block(float *address, float val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicAdd_system(float *address, float val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+double atomicAdd_block(double *address, double val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+double atomicAdd_system(double *address, double val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicSub_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicSub_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicSub_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicSub_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicExch_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicExch_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicExch_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicExch_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicExch_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicExch_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicExch_block(float *address, float val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicExch_system(float *address, float val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMin_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMin_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMin_block(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMin_system(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMin_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMin_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMin_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMin_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMax_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMax_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMax_block(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMax_system(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMax_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMax_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMax_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMax_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicInc_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicInc_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicDec_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicDec_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicCAS_block(int *address, int compare, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicCAS_system(int *address, int compare, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicCAS_block(unsigned int *address, unsigned int compare,
+                             unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicCAS_system(unsigned int *address, unsigned int compare,
+                              unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long int atomicCAS_block(unsigned long long int *address,
+                                       unsigned long long int compare,
+                                       unsigned long long int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long int atomicCAS_system(unsigned long long int *address,
+                                        unsigned long long int compare,
+                                        unsigned long long int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAnd_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAnd_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicAnd_block(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicAnd_system(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAnd_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAnd_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAnd_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAnd_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicOr_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicOr_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicOr_block(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicOr_system(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicOr_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicOr_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicOr_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicOr_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicXor_block(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicXor_system(int *address, int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicXor_block(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicXor_system(long long *address, long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicXor_block(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicXor_system(unsigned int *address, unsigned int val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicXor_block(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicXor_system(unsigned long long *address, unsigned long long val) __DEF_IF_HOST
+
+#endif /* !__CUDA_ARCH__ || __CUDA_ARCH__ >= 600 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_60_ATOMIC_FUNCTIONS_DECL__
+#undef __DEF_IF_HOST
+
+#if !defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)
+#include "sm_60_atomic_functions.hpp"
+#endif /* !__CUDACC_RTC__  && defined(__CUDA_ARCH__)  */
+
+#endif /* !__SM_60_ATOMIC_FUNCTIONS_H__ */
+
+#undef EXCLUDE_FROM_RTC
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_60_atomic_functions.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_60_atomic_functions.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..1d6ac004cd92d3af9281143123289bc2353dd494
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_60_atomic_functions.hpp
@@ -0,0 +1,742 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_60_ATOMIC_FUNCTIONS_HPP__)
+#define __SM_60_ATOMIC_FUNCTIONS_HPP__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_60_ATOMIC_FUNCTIONS_DECL__ __device__
+#else /* __CUDACC_RTC__ */
+#define __SM_60_ATOMIC_FUNCTIONS_DECL__ static __inline__ __device__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+extern "C"
+{
+extern __device__ __device_builtin__ double __dAtomicAdd(double *address, double val);
+
+extern __device__ __device_builtin__
+int __iAtomicAdd_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicAdd_system(int *address, int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicAdd_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicAdd_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicAdd_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicAdd_system(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+float __fAtomicAdd_block(float *address, float val);
+
+extern __device__ __device_builtin__
+float __fAtomicAdd_system(float *address, float val);
+
+extern __device__ __device_builtin__
+double __dAtomicAdd_block(double *address, double val);
+
+extern __device__ __device_builtin__
+double __dAtomicAdd_system(double *address, double val);
+
+extern __device__ __device_builtin__
+int __iAtomicExch_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicExch_system(int *address, int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicExch_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicExch_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicExch_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicExch_system(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+float __fAtomicExch_block(float *address, float val);
+
+extern __device__ __device_builtin__
+float __fAtomicExch_system(float *address, float val);
+
+extern __device__ __device_builtin__
+int __iAtomicMin_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicMin_system(int *address, int val);
+
+extern __device__ __device_builtin__
+long long __illAtomicMin_block(long long *address, long long val);
+
+extern __device__ __device_builtin__
+long long __illAtomicMin_system(long long *address, long long val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicMin_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicMin_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicMin_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicMin_system(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+int __iAtomicMax_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicMax_system(int *address, int val);
+
+extern __device__ __device_builtin__
+long long __illAtomicMax_block(long long *address, long long val);
+
+extern __device__ __device_builtin__
+long long __illAtomicMax_system(long long *address, long long val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicMax_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicMax_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicMax_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicMax_system(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicInc_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicInc_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicDec_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicDec_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+int __iAtomicCAS_block(int *address, int compare, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicCAS_system(int *address, int compare, int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicCAS_block(unsigned int *address, unsigned int compare,
+                                unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicCAS_system(unsigned int *address, unsigned int compare,
+                                 unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicCAS_block(unsigned long long int *address,
+                                        unsigned long long int compare,
+                                        unsigned long long int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicCAS_system(unsigned long long int *address,
+                                         unsigned long long int compare,
+                                         unsigned long long int val);
+
+extern __device__ __device_builtin__
+int __iAtomicAnd_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicAnd_system(int *address, int val);
+
+extern __device__ __device_builtin__
+long long __llAtomicAnd_block(long long *address, long long val);
+
+extern __device__ __device_builtin__
+long long __llAtomicAnd_system(long long *address, long long val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicAnd_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicAnd_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicAnd_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicAnd_system(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+int __iAtomicOr_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicOr_system(int *address, int val);
+
+extern __device__ __device_builtin__
+long long __llAtomicOr_block(long long *address, long long val);
+
+extern __device__ __device_builtin__
+long long __llAtomicOr_system(long long *address, long long val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicOr_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicOr_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicOr_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicOr_system(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+int __iAtomicXor_block(int *address, int val);
+
+extern __device__ __device_builtin__
+int __iAtomicXor_system(int *address, int val);
+
+extern __device__ __device_builtin__
+long long __llAtomicXor_block(long long *address, long long val);
+
+extern __device__ __device_builtin__
+long long __llAtomicXor_system(long long *address, long long val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicXor_block(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned int __uAtomicXor_system(unsigned int *address, unsigned int val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicXor_block(unsigned long long *address, unsigned long long val);
+
+extern __device__ __device_builtin__
+unsigned long long __ullAtomicXor_system(unsigned long long *address, unsigned long long val);
+}
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__ double atomicAdd(double *address, double val)
+{
+  return __dAtomicAdd(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAdd_block(int *address, int val)
+{
+  return __iAtomicAdd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAdd_system(int *address, int val)
+{
+  return __iAtomicAdd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAdd_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAdd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAdd_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAdd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAdd_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicAdd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAdd_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicAdd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicAdd_block(float *address, float val)
+{
+  return __fAtomicAdd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicAdd_system(float *address, float val)
+{
+  return __fAtomicAdd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+double atomicAdd_block(double *address, double val)
+{
+  return __dAtomicAdd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+double atomicAdd_system(double *address, double val)
+{
+  return __dAtomicAdd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicSub_block(int *address, int val)
+{
+  return __iAtomicAdd_block(address, (unsigned int)-(int)val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicSub_system(int *address, int val)
+{
+  return __iAtomicAdd_system(address, (unsigned int)-(int)val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicSub_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAdd_block(address, (unsigned int)-(int)val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicSub_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAdd_system(address, (unsigned int)-(int)val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicExch_block(int *address, int val)
+{
+  return __iAtomicExch_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicExch_system(int *address, int val)
+{
+  return __iAtomicExch_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicExch_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicExch_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicExch_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicExch_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicExch_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicExch_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicExch_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicExch_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicExch_block(float *address, float val)
+{
+  return __fAtomicExch_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+float atomicExch_system(float *address, float val)
+{
+  return __fAtomicExch_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMin_block(int *address, int val)
+{
+  return __iAtomicMin_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMin_system(int *address, int val)
+{
+  return __iAtomicMin_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMin_block(long long *address, long long val)
+{
+  return __illAtomicMin_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMin_system(long long *address, long long val)
+{
+  return __illAtomicMin_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMin_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicMin_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMin_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicMin_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMin_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicMin_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMin_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicMin_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMax_block(int *address, int val)
+{
+  return __iAtomicMax_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicMax_system(int *address, int val)
+{
+  return __iAtomicMax_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMax_block(long long *address, long long val)
+{
+  return __illAtomicMax_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicMax_system(long long *address, long long val)
+{
+  return __illAtomicMax_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMax_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicMax_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicMax_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicMax_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMax_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicMax_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicMax_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicMax_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicInc_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicInc_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicInc_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicInc_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicDec_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicDec_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicDec_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicDec_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicCAS_block(int *address, int compare, int val)
+{
+  return __iAtomicCAS_block(address, compare, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicCAS_system(int *address, int compare, int val)
+{
+  return __iAtomicCAS_system(address, compare, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicCAS_block(unsigned int *address, unsigned int compare,
+                             unsigned int val)
+{
+  return __uAtomicCAS_block(address, compare, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicCAS_system(unsigned int *address, unsigned int compare,
+                              unsigned int val)
+{
+  return __uAtomicCAS_system(address, compare, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long int atomicCAS_block(unsigned long long int *address,
+                                       unsigned long long int compare,
+                                       unsigned long long int val)
+{
+  return __ullAtomicCAS_block(address, compare, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long int atomicCAS_system(unsigned long long int *address,
+                                        unsigned long long int compare,
+                                        unsigned long long int val)
+{
+  return __ullAtomicCAS_system(address, compare, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAnd_block(int *address, int val)
+{
+  return __iAtomicAnd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicAnd_system(int *address, int val)
+{
+  return __iAtomicAnd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicAnd_block(long long *address, long long val)
+{
+  return __llAtomicAnd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicAnd_system(long long *address, long long val)
+{
+  return __llAtomicAnd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAnd_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAnd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicAnd_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicAnd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAnd_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicAnd_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicAnd_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicAnd_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicOr_block(int *address, int val)
+{
+  return __iAtomicOr_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicOr_system(int *address, int val)
+{
+  return __iAtomicOr_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicOr_block(long long *address, long long val)
+{
+  return __llAtomicOr_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicOr_system(long long *address, long long val)
+{
+  return __llAtomicOr_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicOr_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicOr_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicOr_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicOr_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicOr_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicOr_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicOr_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicOr_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicXor_block(int *address, int val)
+{
+  return __iAtomicXor_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+int atomicXor_system(int *address, int val)
+{
+  return __iAtomicXor_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicXor_block(long long *address, long long val)
+{
+  return __llAtomicXor_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+long long atomicXor_system(long long *address, long long val)
+{
+  return __llAtomicXor_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicXor_block(unsigned int *address, unsigned int val)
+{
+  return __uAtomicXor_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned int atomicXor_system(unsigned int *address, unsigned int val)
+{
+  return __uAtomicXor_system(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicXor_block(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicXor_block(address, val);
+}
+
+__SM_60_ATOMIC_FUNCTIONS_DECL__
+unsigned long long atomicXor_system(unsigned long long *address, unsigned long long val)
+{
+  return __ullAtomicXor_system(address, val);
+}
+
+#endif /* !__CUDA_ARCH__ || __CUDA_ARCH__ >= 600 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_60_ATOMIC_FUNCTIONS_DECL__
+
+#endif /* !__SM_60_ATOMIC_FUNCTIONS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_61_intrinsics.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_61_intrinsics.h
new file mode 100644
index 0000000000000000000000000000000000000000..40dbe98ac42483b53f96d27280e621608ca24094
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_61_intrinsics.h
@@ -0,0 +1,239 @@
+/*
+ * Copyright 2016-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+//NOTE: For NVRTC, these declarations have been moved into the compiler (to reduce compile time)
+#define EXCLUDE_FROM_RTC
+
+#if !defined(__SM_61_INTRINSICS_H__)
+#define __SM_61_INTRINSICS_H__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_61_INTRINSICS_DECL__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __SM_61_INTRINSICS_DECL__ static __device__ __inline__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 610
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if !defined(__CUDA_ARCH__) && !defined(_NVHPC_CUDA)
+#define __DEF_IF_HOST { }
+#else  /* !__CUDA_ARCH__ && !_NVHPC_CUDA */
+#define __DEF_IF_HOST ;
+#endif /* __CUDA_ARCH__ || _NVHPC_CUDA */
+
+/*******************************************************************************
+*                                                                              *
+*  Below are declarations of SM-6.1 intrinsics which are included as           *
+*  source (instead of being built in to the compiler)                          *
+*                                                                              *
+*******************************************************************************/
+
+
+/******************************************************************************
+ *                                   __dp2a                                   *
+ ******************************************************************************/
+// Generic [_lo]
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p signed \p int16 by \p int8 dot product with \p int32 accumulate,
+ * taking the lower half of the second input.
+ *
+ * \details Extracts two packed 16-bit integers from \p scrA
+ * and two packed 8-bit integers from the lower 16 bits of \p srcB,
+ * then creates two pairwise 8x16 products and adds them together
+ * to a signed 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ int __dp2a_lo(int srcA, int srcB, int c) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p unsigned \p int16 by \p int8 dot product with
+ * \p unsigned \p int32 accumulate, taking the lower half of the second input.
+ *
+ * \details Extracts two packed 16-bit integers from \p scrA
+ * and two packed 8-bit integers from the lower 16 bits of \p srcB,
+ * then creates two pairwise 8x16 products and adds them together
+ * to an unsigned 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_lo(unsigned int srcA, unsigned int srcB, unsigned int c) __DEF_IF_HOST
+// Vector-style [_lo]
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p signed \p int16 by \p int8 dot product with \p int32 accumulate,
+ * taking the lower half of the second input.
+ *
+ * \details Takes two packed 16-bit integers from \p scrA vector
+ * and two packed 8-bit integers from the lower 16 bits of \p srcB vector,
+ * then creates two pairwise 8x16 products and adds them together
+ * to a signed 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ int __dp2a_lo(short2 srcA, char4 srcB, int c) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p unsigned \p int16 by \p int8 dot product with
+ * \p unsigned \p int32 accumulate, taking the lower half of the second input.
+ *
+ * \details Takes two packed 16-bit integers from \p scrA vector
+ * and two packed 8-bit integers from the lower 16 bits of \p srcB vector,
+ * then creates two pairwise 8x16 products and adds them together
+ * to an unsigned 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_lo(ushort2 srcA, uchar4 srcB, unsigned int c) __DEF_IF_HOST
+// Generic [_hi]
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p signed \p int16 by \p int8 dot product with \p int32 accumulate,
+ * taking the upper half of the second input.
+ *
+ * \details Extracts two packed 16-bit integers from \p scrA
+ * and two packed 8-bit integers from the upper 16 bits of \p srcB,
+ * then creates two pairwise 8x16 products and adds them together
+ * to a signed 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ int __dp2a_hi(int srcA, int srcB, int c) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p unsigned \p int16 by \p int8 dot product with
+ * \p unsigned \p int32 accumulate, taking the upper half of the second input.
+ *
+ * \details Extracts two packed 16-bit integers from \p scrA
+ * and two packed 8-bit integers from the upper 16 bits of \p srcB,
+ * then creates two pairwise 8x16 products and adds them together
+ * to an unsigned 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_hi(unsigned int srcA, unsigned int srcB, unsigned int c) __DEF_IF_HOST
+// Vector-style [_hi]
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p signed \p int16 by \p int8 dot product with \p int32 accumulate,
+ * taking the upper half of the second input.
+ *
+ * \details Takes two packed 16-bit integers from \p scrA vector
+ * and two packed 8-bit integers from the upper 16 bits of \p srcB vector,
+ * then creates two pairwise 8x16 products and adds them together
+ * to a signed 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ int __dp2a_hi(short2 srcA, char4 srcB, int c) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Two-way \p unsigned \p int16 by \p int8 dot product with
+ * \p unsigned \p int32 accumulate, taking the upper half of the second input.
+ *
+ * \details Takes two packed 16-bit integers from \p scrA vector
+ * and two packed 8-bit integers from the upper 16 bits of \p srcB vector,
+ * then creates two pairwise 8x16 products and adds them together
+ * to an unsigned 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_hi(ushort2 srcA, uchar4 srcB, unsigned int c) __DEF_IF_HOST
+
+
+/******************************************************************************
+ *                                   __dp4a                                   *
+ ******************************************************************************/
+// Generic
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Four-way \p signed \p int8 dot product with \p int32 accumulate.
+ *
+ * \details Extracts four pairs of packed byte-sized integers from \p scrA
+ * and \p srcB, then creates four pairwise products and adds them together
+ * to a signed 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ int __dp4a(int srcA, int srcB, int c) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Four-way \p unsigned \p int8 dot product with \p unsigned \p int32 accumulate.
+ *
+ * \details Extracts four pairs of packed byte-sized integers from \p scrA
+ * and \p srcB, then creates four pairwise products and adds them together
+ * to an unsigned 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ unsigned int __dp4a(unsigned int srcA, unsigned int srcB, unsigned int c) __DEF_IF_HOST
+// Vector-style
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Four-way \p signed \p int8 dot product with \p int32 accumulate.
+ *
+ * \details Takes four pairs of packed byte-sized integers from \p scrA
+ * and \p srcB vectors, then creates four pairwise products and adds them
+ * together to a signed 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ int __dp4a(char4 srcA, char4 srcB, int c) __DEF_IF_HOST
+/**
+ * \ingroup CUDA_MATH_INTRINSIC_INT
+ * \brief Four-way \p unsigned \p int8 dot product with \p unsigned \p int32 accumulate.
+ *
+ * \details Takes four pairs of packed byte-sized integers from \p scrA
+ * and \p srcB vectors, then creates four pairwise products and adds them
+ * together to an unsigned 32-bit integer \p c.
+ */
+__SM_61_INTRINSICS_DECL__ unsigned int __dp4a(uchar4 srcA, uchar4 srcB, unsigned int c) __DEF_IF_HOST
+
+#endif /* _NVHPC_CUDA || !__CUDA_ARCH__ || __CUDA_ARCH__ >= 610 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __DEF_IF_HOST
+#undef __SM_61_INTRINSICS_DECL__
+
+#if !defined(__CUDACC_RTC__) && defined(__CUDA_ARCH__)
+#include "sm_61_intrinsics.hpp"
+#endif /* !__CUDACC_RTC__ && __CUDA_ARCH__ */
+
+#endif /* !__SM_61_INTRINSICS_H__ */
+#undef EXCLUDE_FROM_RTC
\ No newline at end of file
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_61_intrinsics.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_61_intrinsics.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..5a561384b08a65445eed86bfc96a0694e5b9190c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/sm_61_intrinsics.hpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright 2016 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SM_61_INTRINSICS_HPP__)
+#define __SM_61_INTRINSICS_HPP__
+
+#if defined(__CUDACC_RTC__)
+#define __SM_61_INTRINSICS_DECL__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __SM_61_INTRINSICS_DECL__ static __device__ __inline__
+#endif /* __CUDACC_RTC__ */
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 610
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+/*******************************************************************************
+*                                                                              *
+*  Below are implementations of SM-6.1 intrinsics which are included as        *
+*  source (instead of being built in to the compiler)                          *
+*                                                                              *
+*******************************************************************************/
+
+// 4a
+__SM_61_INTRINSICS_DECL__ int __dp4a(int srcA, int srcB, int c) {
+    int ret;
+    asm volatile ("dp4a.s32.s32 %0, %1, %2, %3;" : "=r"(ret) : "r"(srcA), "r"(srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ unsigned int __dp4a(unsigned int srcA, unsigned int srcB, unsigned int c) {
+    unsigned int ret;
+    asm volatile ("dp4a.u32.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(srcA), "r"(srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ int __dp4a(char4 srcA, char4 srcB, int c) {
+    int ret;
+    asm volatile ("dp4a.s32.s32 %0, %1, %2, %3;" : "=r"(ret) : "r"(*(int *)&srcA), "r"(*(int *)&srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ unsigned int __dp4a(uchar4 srcA, uchar4 srcB, unsigned int c) {
+    unsigned int ret;
+    asm volatile ("dp4a.u32.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(*(unsigned int *)&srcA), "r"(*(unsigned int *)&srcB), "r"(c));
+    return ret;
+}
+
+// 2a.lo
+__SM_61_INTRINSICS_DECL__ int __dp2a_lo(int srcA, int srcB, int c) {
+    int ret;
+    asm volatile ("dp2a.lo.s32.s32 %0, %1, %2, %3;" : "=r"(ret) : "r"(srcA), "r"(srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_lo(unsigned int srcA, unsigned int srcB, unsigned int c) {
+    unsigned int ret;
+    asm volatile ("dp2a.lo.u32.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(srcA), "r"(srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ int __dp2a_lo(short2 srcA, char4 srcB, int c) {
+    int ret;
+    asm volatile ("dp2a.lo.s32.s32 %0, %1, %2, %3;" : "=r"(ret) : "r"(*(int *)&srcA), "r"(*(int *)&srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_lo(ushort2 srcA, uchar4 srcB, unsigned int c) {
+    unsigned int ret;
+    asm volatile ("dp2a.lo.u32.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(*(unsigned int *)&srcA), "r"(*(unsigned int *)&srcB), "r"(c));
+    return ret;
+}
+
+// 2a.hi
+__SM_61_INTRINSICS_DECL__ int __dp2a_hi(int srcA, int srcB, int c) {
+    int ret;
+    asm volatile ("dp2a.hi.s32.s32 %0, %1, %2, %3;" : "=r"(ret) : "r"(srcA), "r"(srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_hi(unsigned int srcA, unsigned int srcB, unsigned int c) {
+    unsigned int ret;
+    asm volatile ("dp2a.hi.u32.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(srcA), "r"(srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ int __dp2a_hi(short2 srcA, char4 srcB, int c) {
+    int ret;
+    asm volatile ("dp2a.hi.s32.s32 %0, %1, %2, %3;" : "=r"(ret) : "r"(*(int *)&srcA), "r"(*(int *)&srcB), "r"(c));
+    return ret;
+}
+
+__SM_61_INTRINSICS_DECL__ unsigned int __dp2a_hi(ushort2 srcA, uchar4 srcB, unsigned int c) {
+    unsigned int ret;
+    asm volatile ("dp2a.hi.u32.u32 %0, %1, %2, %3;" : "=r"(ret) : "r"(*(unsigned int *)&srcA), "r"(*(unsigned int *)&srcB), "r"(c));
+    return ret;
+}
+
+
+#endif /* _NVHPC_CUDA || !__CUDA_ARCH__ || __CUDA_ARCH__ >= 610 */
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#undef __SM_61_INTRINSICS_DECL__
+
+#endif /* !__SM_61_INTRINSICS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..2fb940c1d2bd5ee7b4a5020e12297bc2927e0386
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_functions.h
@@ -0,0 +1,124 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SURFACE_FUNCTIONS_H__)
+#define __SURFACE_FUNCTIONS_H__
+
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+#include "cuda_surface_types.h"
+
+#if defined(_WIN32)
+# define __DEPRECATED__ __declspec(deprecated)
+#else
+# define __DEPRECATED__  __attribute__((deprecated))
+#endif
+
+template <typename T> struct __nv_surf_trait {  typedef void * cast_type; };
+
+template<> struct __nv_surf_trait<char> {  typedef char * cast_type; };
+template<> struct __nv_surf_trait<signed char> {  typedef signed char * cast_type; };
+template<> struct __nv_surf_trait<unsigned char> {  typedef unsigned char * cast_type; };
+template<> struct __nv_surf_trait<char1> {  typedef char1 * cast_type; };
+template<> struct __nv_surf_trait<uchar1> {  typedef uchar1 * cast_type; };
+template<> struct __nv_surf_trait<char2> {  typedef char2 * cast_type; };
+template<> struct __nv_surf_trait<uchar2> {  typedef uchar2 * cast_type; };
+template<> struct __nv_surf_trait<char4> {  typedef char4 * cast_type; };
+template<> struct __nv_surf_trait<uchar4> {  typedef uchar4 * cast_type; };
+template<> struct __nv_surf_trait<short> {  typedef short * cast_type; };
+template<> struct __nv_surf_trait<unsigned short> {  typedef unsigned short * cast_type; };
+template<> struct __nv_surf_trait<short1> {  typedef short1 * cast_type; };
+template<> struct __nv_surf_trait<ushort1> {  typedef ushort1 * cast_type; };
+template<> struct __nv_surf_trait<short2> {  typedef short2 * cast_type; };
+template<> struct __nv_surf_trait<ushort2> {  typedef ushort2 * cast_type; };
+template<> struct __nv_surf_trait<short4> {  typedef short4 * cast_type; };
+template<> struct __nv_surf_trait<ushort4> {  typedef ushort4 * cast_type; };
+template<> struct __nv_surf_trait<int> {  typedef int * cast_type; };
+template<> struct __nv_surf_trait<unsigned int> {  typedef unsigned int * cast_type; };
+template<> struct __nv_surf_trait<int1> {  typedef int1 * cast_type; };
+template<> struct __nv_surf_trait<uint1> {  typedef uint1 * cast_type; };
+template<> struct __nv_surf_trait<int2> {  typedef int2 * cast_type; };
+template<> struct __nv_surf_trait<uint2> {  typedef uint2 * cast_type; };
+template<> struct __nv_surf_trait<int4> {  typedef int4 * cast_type; };
+template<> struct __nv_surf_trait<uint4> {  typedef uint4 * cast_type; };
+template<> struct __nv_surf_trait<long long> {  typedef long long * cast_type; };
+template<> struct __nv_surf_trait<unsigned long long> {  typedef unsigned long long * cast_type; };
+template<> struct __nv_surf_trait<longlong1> {  typedef longlong1 * cast_type; };
+template<> struct __nv_surf_trait<ulonglong1> {  typedef ulonglong1 * cast_type; };
+template<> struct __nv_surf_trait<longlong2> {  typedef longlong2 * cast_type; };
+template<> struct __nv_surf_trait<ulonglong2> {  typedef ulonglong2 * cast_type; };
+#if !defined(__LP64__)
+template<> struct __nv_surf_trait<long> {  typedef int * cast_type; };
+template<> struct __nv_surf_trait<unsigned long> {  typedef unsigned int * cast_type; };
+template<> struct __nv_surf_trait<long1> {  typedef int1 * cast_type; };
+template<> struct __nv_surf_trait<ulong1> {  typedef uint1 * cast_type; };
+template<> struct __nv_surf_trait<long2> {  typedef int2 * cast_type; };
+template<> struct __nv_surf_trait<ulong2> {  typedef uint2 * cast_type; };
+template<> struct __nv_surf_trait<long4> {  typedef uint4 * cast_type; };
+template<> struct __nv_surf_trait<ulong4> {  typedef int4 * cast_type; };
+#endif
+template<> struct __nv_surf_trait<float> {  typedef float * cast_type; };
+template<> struct __nv_surf_trait<float1> {  typedef float1 * cast_type; };
+template<> struct __nv_surf_trait<float2> {  typedef float2 * cast_type; };
+template<> struct __nv_surf_trait<float4> {  typedef float4 * cast_type; };
+
+
+#undef __DEPRECATED__
+
+
+#endif /* __cplusplus && __CUDACC__ */
+#endif /* !__SURFACE_FUNCTIONS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_indirect_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_indirect_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..a93faf052f98d81f8cb65bd9591d08ec90c994d9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_indirect_functions.h
@@ -0,0 +1,243 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+
+#ifndef __SURFACE_INDIRECT_FUNCTIONS_H__
+#define __SURFACE_INDIRECT_FUNCTIONS_H__
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+#include "cuda_runtime_api.h"
+
+template<typename T> struct __nv_isurf_trait { };
+template<> struct __nv_isurf_trait<char> { typedef void type; };
+template<> struct __nv_isurf_trait<signed char> { typedef void type; };
+template<> struct __nv_isurf_trait<char1> { typedef void type; };
+template<> struct __nv_isurf_trait<unsigned char> { typedef void type; };
+template<> struct __nv_isurf_trait<uchar1> { typedef void type; };
+template<> struct __nv_isurf_trait<short> { typedef void type; };
+template<> struct __nv_isurf_trait<short1> { typedef void type; };
+template<> struct __nv_isurf_trait<unsigned short> { typedef void type; };
+template<> struct __nv_isurf_trait<ushort1> { typedef void type; };
+template<> struct __nv_isurf_trait<int> { typedef void type; };
+template<> struct __nv_isurf_trait<int1> { typedef void type; };
+template<> struct __nv_isurf_trait<unsigned int> { typedef void type; };
+template<> struct __nv_isurf_trait<uint1> { typedef void type; };
+template<> struct __nv_isurf_trait<long long> { typedef void type; };
+template<> struct __nv_isurf_trait<longlong1> { typedef void type; };
+template<> struct __nv_isurf_trait<unsigned long long> { typedef void type; };
+template<> struct __nv_isurf_trait<ulonglong1> { typedef void type; };
+template<> struct __nv_isurf_trait<float> { typedef void type; };
+template<> struct __nv_isurf_trait<float1> { typedef void type; };
+
+template<> struct __nv_isurf_trait<char2> { typedef void type; };
+template<> struct __nv_isurf_trait<uchar2> { typedef void type; };
+template<> struct __nv_isurf_trait<short2> { typedef void type; };
+template<> struct __nv_isurf_trait<ushort2> { typedef void type; };
+template<> struct __nv_isurf_trait<int2> { typedef void type; };
+template<> struct __nv_isurf_trait<uint2> { typedef void type; };
+template<> struct __nv_isurf_trait<longlong2> { typedef void type; };
+template<> struct __nv_isurf_trait<ulonglong2> { typedef void type; };
+template<> struct __nv_isurf_trait<float2> { typedef void type; };
+
+template<> struct __nv_isurf_trait<char4> { typedef void type; };
+template<> struct __nv_isurf_trait<uchar4> { typedef void type; };
+template<> struct __nv_isurf_trait<short4> { typedef void type; };
+template<> struct __nv_isurf_trait<ushort4> { typedef void type; };
+template<> struct __nv_isurf_trait<int4> { typedef void type; };
+template<> struct __nv_isurf_trait<uint4> { typedef void type; };
+template<> struct __nv_isurf_trait<float4> { typedef void type; };
+
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type  surf1Dread(T *ptr, cudaSurfaceObject_t obj, int x, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf1Dread", ptr, obj, x, mode);
+}
+
+template <class T>
+static __device__ T surf1Dread(cudaSurfaceObject_t surfObject, int x, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surf1Dread(&ret, surfObject, x, boundaryMode);
+   return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type  surf2Dread(T *ptr, cudaSurfaceObject_t obj, int x, int y, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf2Dread", ptr, obj, x, y, mode);
+}
+
+template <class T>
+static __device__ T surf2Dread(cudaSurfaceObject_t surfObject, int x, int y, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surf2Dread(&ret, surfObject, x, y, boundaryMode);
+   return ret;
+}
+
+
+template <typename T>
+static __device__ typename  __nv_isurf_trait<T>::type  surf3Dread(T *ptr, cudaSurfaceObject_t obj, int x, int y, int z, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf3Dread", ptr, obj, x, y, z, mode);
+}
+
+template <class T>
+static __device__ T surf3Dread(cudaSurfaceObject_t surfObject, int x, int y, int z, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surf3Dread(&ret, surfObject, x, y, z, boundaryMode);
+   return ret;
+}
+
+template <typename T>
+static __device__ typename  __nv_isurf_trait<T>::type  surf1DLayeredread(T *ptr, cudaSurfaceObject_t obj, int x, int layer, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf1DLayeredread", ptr, obj, x, layer, mode);
+}
+
+template <class T>
+static __device__ T surf1DLayeredread(cudaSurfaceObject_t surfObject, int x, int layer, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surf1DLayeredread(&ret, surfObject, x, layer, boundaryMode);
+   return ret;
+}
+
+template <typename T>
+static __device__  typename __nv_isurf_trait<T>::type  surf2DLayeredread(T *ptr, cudaSurfaceObject_t obj, int x, int y, int layer, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf2DLayeredread", ptr, obj, x, y, layer, mode);
+}
+
+template <class T>
+static __device__ T surf2DLayeredread(cudaSurfaceObject_t surfObject, int x, int y, int layer, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surf2DLayeredread(&ret, surfObject, x, y, layer, boundaryMode);
+   return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type  surfCubemapread(T *ptr, cudaSurfaceObject_t obj, int x, int y, int face, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurfCubemapread", ptr, obj, x, y, face, mode);
+}
+
+template <class T>
+static __device__ T surfCubemapread(cudaSurfaceObject_t surfObject, int x, int y, int face, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surfCubemapread(&ret, surfObject, x, y, face, boundaryMode);
+   return ret;
+}
+
+template <typename T>
+static __device__  typename __nv_isurf_trait<T>::type  surfCubemapLayeredread(T *ptr, cudaSurfaceObject_t obj, int x, int y, int layerface, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurfCubemapLayeredread", ptr, obj, x, y, layerface, mode);
+}
+
+template <class T>
+static __device__ T surfCubemapLayeredread(cudaSurfaceObject_t surfObject, int x, int y, int layerface, cudaSurfaceBoundaryMode boundaryMode = cudaBoundaryModeTrap)
+{
+   T ret;
+   surfCubemapLayeredread(&ret, surfObject, x, y, layerface, boundaryMode);
+   return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surf1Dwrite(T val, cudaSurfaceObject_t obj, int x, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{ 
+  __nv_tex_surf_handler("__isurf1Dwrite_v2", &val, obj, x, mode);
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surf2Dwrite(T val, cudaSurfaceObject_t obj, int x, int y, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf2Dwrite_v2", &val, obj, x, y, mode);
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surf3Dwrite(T val, cudaSurfaceObject_t obj, int x, int y, int z, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf3Dwrite_v2", &val, obj, x, y, z, mode);
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surf1DLayeredwrite(T val, cudaSurfaceObject_t obj, int x, int layer, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf1DLayeredwrite_v2", &val, obj, x, layer, mode);
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surf2DLayeredwrite(T val, cudaSurfaceObject_t obj, int x, int y, int layer, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurf2DLayeredwrite_v2", &val, obj, x, y, layer, mode);
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surfCubemapwrite(T val, cudaSurfaceObject_t obj, int x, int y, int face, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurfCubemapwrite_v2", &val, obj, x, y, face, mode);
+}
+
+template <typename T>
+static __device__ typename __nv_isurf_trait<T>::type surfCubemapLayeredwrite(T val, cudaSurfaceObject_t obj, int x, int y, int layerface, cudaSurfaceBoundaryMode mode = cudaBoundaryModeTrap)
+{
+  __nv_tex_surf_handler("__isurfCubemapLayeredwrite_v2", &val, obj, x, y, layerface, mode);
+}
+
+#endif // __cplusplus && __CUDACC__
+
+#endif // __SURFACE_INDIRECT_FUNCTIONS_H__
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..e1d0eccdee2a80132c5dadfcd80643c1b41eb8ec
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/surface_types.h
@@ -0,0 +1,111 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__SURFACE_TYPES_H__)
+#define __SURFACE_TYPES_H__
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "driver_types.h"
+
+#ifndef __CUDACC_RTC_MINIMAL__
+
+/**
+ * \addtogroup CUDART_TYPES
+ *
+ * @{
+ */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#define cudaSurfaceType1D              0x01
+#define cudaSurfaceType2D              0x02
+#define cudaSurfaceType3D              0x03
+#define cudaSurfaceTypeCubemap         0x0C
+#define cudaSurfaceType1DLayered       0xF1
+#define cudaSurfaceType2DLayered       0xF2
+#define cudaSurfaceTypeCubemapLayered  0xFC
+
+/**
+ * CUDA Surface boundary modes
+ */
+enum __device_builtin__ cudaSurfaceBoundaryMode
+{
+    cudaBoundaryModeZero  = 0,    /**< Zero boundary mode */
+    cudaBoundaryModeClamp = 1,    /**< Clamp boundary mode */
+    cudaBoundaryModeTrap  = 2     /**< Trap boundary mode */
+};
+
+/**
+ * CUDA Surface format modes
+ */
+enum __device_builtin__  cudaSurfaceFormatMode
+{
+    cudaFormatModeForced = 0,     /**< Forced format mode */
+    cudaFormatModeAuto = 1        /**< Auto format mode */
+};
+
+/**
+ * An opaque value that represents a CUDA Surface object
+ */
+typedef __device_builtin__ unsigned long long cudaSurfaceObject_t;
+
+/** @} */
+/** @} */ /* END CUDART_TYPES */
+
+#endif  /* !__CUDACC_RTC_MINIMAL__ */
+#endif /* !__SURFACE_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_fetch_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_fetch_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..704e8518da6b3cf7b77e7b9d34638bc06dd3937f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_fetch_functions.h
@@ -0,0 +1,223 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__TEXTURE_FETCH_FUNCTIONS_H__)
+#define __TEXTURE_FETCH_FUNCTIONS_H__
+
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+#include "cuda_texture_types.h"
+
+#if defined(_WIN32)
+# define __DEPRECATED__ __declspec(deprecated)
+#else
+# define __DEPRECATED__  __attribute__((deprecated))
+#endif
+
+
+template <typename T>
+struct __nv_tex_rmet_ret { };
+
+template<> struct __nv_tex_rmet_ret<char> { typedef char type; };
+template<> struct __nv_tex_rmet_ret<signed char> { typedef signed char type; };
+template<> struct __nv_tex_rmet_ret<unsigned char> { typedef unsigned char type; };
+template<> struct __nv_tex_rmet_ret<char1> { typedef char1 type; };
+template<> struct __nv_tex_rmet_ret<uchar1> { typedef uchar1 type; };
+template<> struct __nv_tex_rmet_ret<char2> { typedef char2 type; };
+template<> struct __nv_tex_rmet_ret<uchar2> { typedef uchar2 type; };
+template<> struct __nv_tex_rmet_ret<char4> { typedef char4 type; };
+template<> struct __nv_tex_rmet_ret<uchar4> { typedef uchar4 type; };
+
+template<> struct __nv_tex_rmet_ret<short> { typedef short type; };
+template<> struct __nv_tex_rmet_ret<unsigned short> { typedef unsigned short type; };
+template<> struct __nv_tex_rmet_ret<short1> { typedef short1 type; };
+template<> struct __nv_tex_rmet_ret<ushort1> { typedef ushort1 type; };
+template<> struct __nv_tex_rmet_ret<short2> { typedef short2 type; };
+template<> struct __nv_tex_rmet_ret<ushort2> { typedef ushort2 type; };
+template<> struct __nv_tex_rmet_ret<short4> { typedef short4 type; };
+template<> struct __nv_tex_rmet_ret<ushort4> { typedef ushort4 type; };
+
+template<> struct __nv_tex_rmet_ret<int> { typedef int type; };
+template<> struct __nv_tex_rmet_ret<unsigned int> { typedef unsigned int type; };
+template<> struct __nv_tex_rmet_ret<int1> { typedef int1 type; };
+template<> struct __nv_tex_rmet_ret<uint1> { typedef uint1 type; };
+template<> struct __nv_tex_rmet_ret<int2> { typedef int2 type; };
+template<> struct __nv_tex_rmet_ret<uint2> { typedef uint2 type; };
+template<> struct __nv_tex_rmet_ret<int4> { typedef int4 type; };
+template<> struct __nv_tex_rmet_ret<uint4> { typedef uint4 type; };
+
+#if !defined(__LP64__)
+template<> struct __nv_tex_rmet_ret<long> { typedef long type; };
+template<> struct __nv_tex_rmet_ret<unsigned long> { typedef unsigned long type; };
+template<> struct __nv_tex_rmet_ret<long1> { typedef long1 type; };
+template<> struct __nv_tex_rmet_ret<ulong1> { typedef ulong1 type; };
+template<> struct __nv_tex_rmet_ret<long2> { typedef long2 type; };
+template<> struct __nv_tex_rmet_ret<ulong2> { typedef ulong2 type; };
+template<> struct __nv_tex_rmet_ret<long4> { typedef long4 type; };
+template<> struct __nv_tex_rmet_ret<ulong4> { typedef ulong4 type; };
+#endif /* !__LP64__ */
+template<> struct __nv_tex_rmet_ret<float> { typedef float type; };
+template<> struct __nv_tex_rmet_ret<float1> { typedef float1 type; };
+template<> struct __nv_tex_rmet_ret<float2> { typedef float2 type; };
+template<> struct __nv_tex_rmet_ret<float4> { typedef float4 type; };
+
+
+template <typename T> struct __nv_tex_rmet_cast { typedef T* type;  };
+#if !defined(__LP64__)
+template<> struct __nv_tex_rmet_cast<long> { typedef int *type; };
+template<> struct __nv_tex_rmet_cast<unsigned long> { typedef unsigned int *type; };
+template<> struct __nv_tex_rmet_cast<long1> { typedef int1 *type; };
+template<> struct __nv_tex_rmet_cast<ulong1> { typedef uint1 *type; };
+template<> struct __nv_tex_rmet_cast<long2> { typedef int2 *type; };
+template<> struct __nv_tex_rmet_cast<ulong2> { typedef uint2 *type; };
+template<> struct __nv_tex_rmet_cast<long4> { typedef int4 *type; };
+template<> struct __nv_tex_rmet_cast<ulong4> { typedef uint4 *type; };
+#endif /* !__LP64__ */
+
+template <typename T>
+struct __nv_tex_rmnf_ret { };
+
+template <> struct __nv_tex_rmnf_ret<char> { typedef float type; };
+template <> struct __nv_tex_rmnf_ret<signed char> { typedef float type; };
+template <> struct __nv_tex_rmnf_ret<unsigned char> { typedef float type; };
+template <> struct __nv_tex_rmnf_ret<short> { typedef float type; };
+template <> struct __nv_tex_rmnf_ret<unsigned short> { typedef float type; };
+template <> struct __nv_tex_rmnf_ret<char1> { typedef float1 type; };
+template <> struct __nv_tex_rmnf_ret<uchar1> { typedef float1 type; };
+template <> struct __nv_tex_rmnf_ret<short1> { typedef float1 type; };
+template <> struct __nv_tex_rmnf_ret<ushort1> { typedef float1 type; };
+template <> struct __nv_tex_rmnf_ret<char2> { typedef float2 type; };
+template <> struct __nv_tex_rmnf_ret<uchar2> { typedef float2 type; };
+template <> struct __nv_tex_rmnf_ret<short2> { typedef float2 type; };
+template <> struct __nv_tex_rmnf_ret<ushort2> { typedef float2 type; };
+template <> struct __nv_tex_rmnf_ret<char4> { typedef float4 type; };
+template <> struct __nv_tex_rmnf_ret<uchar4> { typedef float4 type; };
+template <> struct __nv_tex_rmnf_ret<short4> { typedef float4 type; };
+template <> struct __nv_tex_rmnf_ret<ushort4> { typedef float4 type; };
+
+
+template <typename T>
+struct __nv_tex2dgather_ret { };
+template <> struct __nv_tex2dgather_ret<char> { typedef char4 type; };
+template <> struct __nv_tex2dgather_ret<signed char> { typedef char4 type; };
+template <> struct __nv_tex2dgather_ret<char1> { typedef char4 type; };
+template <> struct __nv_tex2dgather_ret<char2> { typedef char4 type; };
+template <> struct __nv_tex2dgather_ret<char3> { typedef char4 type; };
+template <> struct __nv_tex2dgather_ret<char4> { typedef char4 type; };
+template <> struct __nv_tex2dgather_ret<unsigned char> { typedef uchar4 type; };
+template <> struct __nv_tex2dgather_ret<uchar1> { typedef uchar4 type; };
+template <> struct __nv_tex2dgather_ret<uchar2> { typedef uchar4 type; };
+template <> struct __nv_tex2dgather_ret<uchar3> { typedef uchar4 type; };
+template <> struct __nv_tex2dgather_ret<uchar4> { typedef uchar4 type; };
+
+template <> struct __nv_tex2dgather_ret<short> { typedef short4 type; };
+template <> struct __nv_tex2dgather_ret<short1> { typedef short4 type; };
+template <> struct __nv_tex2dgather_ret<short2> { typedef short4 type; };
+template <> struct __nv_tex2dgather_ret<short3> { typedef short4 type; };
+template <> struct __nv_tex2dgather_ret<short4> { typedef short4 type; };
+template <> struct __nv_tex2dgather_ret<unsigned short> { typedef ushort4 type; };
+template <> struct __nv_tex2dgather_ret<ushort1> { typedef ushort4 type; };
+template <> struct __nv_tex2dgather_ret<ushort2> { typedef ushort4 type; };
+template <> struct __nv_tex2dgather_ret<ushort3> { typedef ushort4 type; };
+template <> struct __nv_tex2dgather_ret<ushort4> { typedef ushort4 type; };
+
+template <> struct __nv_tex2dgather_ret<int> { typedef int4 type; };
+template <> struct __nv_tex2dgather_ret<int1> { typedef int4 type; };
+template <> struct __nv_tex2dgather_ret<int2> { typedef int4 type; };
+template <> struct __nv_tex2dgather_ret<int3> { typedef int4 type; };
+template <> struct __nv_tex2dgather_ret<int4> { typedef int4 type; };
+template <> struct __nv_tex2dgather_ret<unsigned int> { typedef uint4 type; };
+template <> struct __nv_tex2dgather_ret<uint1> { typedef uint4 type; };
+template <> struct __nv_tex2dgather_ret<uint2> { typedef uint4 type; };
+template <> struct __nv_tex2dgather_ret<uint3> { typedef uint4 type; };
+template <> struct __nv_tex2dgather_ret<uint4> { typedef uint4 type; };
+
+template <> struct __nv_tex2dgather_ret<float> { typedef float4 type; };
+template <> struct __nv_tex2dgather_ret<float1> { typedef float4 type; };
+template <> struct __nv_tex2dgather_ret<float2> { typedef float4 type; };
+template <> struct __nv_tex2dgather_ret<float3> { typedef float4 type; };
+template <> struct __nv_tex2dgather_ret<float4> { typedef float4 type; };
+
+
+template<typename T> struct __nv_tex2dgather_rmnf_ret { };
+template<> struct __nv_tex2dgather_rmnf_ret<char> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<signed char> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<unsigned char> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<char1> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<uchar1> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<char2> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<uchar2> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<char3> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<uchar3> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<char4> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<uchar4> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<signed short> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<unsigned short> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<short1> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<ushort1> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<short2> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<ushort2> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<short3> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<ushort3> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<short4> { typedef float4 type; };
+template<> struct __nv_tex2dgather_rmnf_ret<ushort4> { typedef float4 type; };
+
+#undef __DEPRECATED__
+
+#endif /* __cplusplus && __CUDACC__ */
+
+#endif /* !__TEXTURE_FETCH_FUNCTIONS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_indirect_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_indirect_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e5537d87294ee78ecec567893a6aaec333db317
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_indirect_functions.h
@@ -0,0 +1,638 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+
+#ifndef __TEXTURE_INDIRECT_FUNCTIONS_H__
+#define __TEXTURE_INDIRECT_FUNCTIONS_H__
+
+
+#if defined(__cplusplus) && defined(__CUDACC__)
+
+
+#include "cuda_runtime_api.h"
+
+
+#if defined(_NVHPC_CUDA) || !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 600)
+#define __NV_TEX_SPARSE 1
+#endif  /* endif */
+
+template <typename T> struct __nv_itex_trait {   };
+template<> struct __nv_itex_trait<char> { typedef void type; };
+template<> struct __nv_itex_trait<signed char> { typedef void type; };
+template<> struct __nv_itex_trait<char1> { typedef void type; };
+template<> struct __nv_itex_trait<char2> { typedef void type; };
+template<> struct __nv_itex_trait<char4> { typedef void type; };
+template<> struct __nv_itex_trait<unsigned char> { typedef void type; };
+template<> struct __nv_itex_trait<uchar1> { typedef void type; };
+template<> struct __nv_itex_trait<uchar2> { typedef void type; };
+template<> struct __nv_itex_trait<uchar4> { typedef void type; };
+template<> struct __nv_itex_trait<short> { typedef void type; };
+template<> struct __nv_itex_trait<short1> { typedef void type; };
+template<> struct __nv_itex_trait<short2> { typedef void type; };
+template<> struct __nv_itex_trait<short4> { typedef void type; };
+template<> struct __nv_itex_trait<unsigned short> { typedef void type; };
+template<> struct __nv_itex_trait<ushort1> { typedef void type; };
+template<> struct __nv_itex_trait<ushort2> { typedef void type; };
+template<> struct __nv_itex_trait<ushort4> { typedef void type; };
+template<> struct __nv_itex_trait<int> { typedef void type; };
+template<> struct __nv_itex_trait<int1> { typedef void type; };
+template<> struct __nv_itex_trait<int2> { typedef void type; };
+template<> struct __nv_itex_trait<int4> { typedef void type; };
+template<> struct __nv_itex_trait<unsigned int> { typedef void type; };
+template<> struct __nv_itex_trait<uint1> { typedef void type; };
+template<> struct __nv_itex_trait<uint2> { typedef void type; };
+template<> struct __nv_itex_trait<uint4> { typedef void type; };
+#if !defined(__LP64__)
+template<> struct __nv_itex_trait<long> { typedef void type; };
+template<> struct __nv_itex_trait<long1> { typedef void type; };
+template<> struct __nv_itex_trait<long2> { typedef void type; };
+template<> struct __nv_itex_trait<long4> { typedef void type; };
+template<> struct __nv_itex_trait<unsigned long> { typedef void type; };
+template<> struct __nv_itex_trait<ulong1> { typedef void type; };
+template<> struct __nv_itex_trait<ulong2> { typedef void type; };
+template<> struct __nv_itex_trait<ulong4> { typedef void type; };
+#endif /* !__LP64__ */
+template<> struct __nv_itex_trait<float> { typedef void type; };
+template<> struct __nv_itex_trait<float1> { typedef void type; };
+template<> struct __nv_itex_trait<float2> { typedef void type; };
+template<> struct __nv_itex_trait<float4> { typedef void type; };
+
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1Dfetch(T *ptr, cudaTextureObject_t obj, int x)
+{
+   __nv_tex_surf_handler("__itex1Dfetch", ptr, obj, x);
+}
+
+template <class T>
+static __device__ T tex1Dfetch(cudaTextureObject_t texObject, int x)
+{
+  T ret;
+  tex1Dfetch(&ret, texObject, x);
+  return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1D(T *ptr, cudaTextureObject_t obj, float x)
+{
+   __nv_tex_surf_handler("__itex1D", ptr, obj, x);
+}
+
+
+template <class T>
+static __device__  T tex1D(cudaTextureObject_t texObject, float x)
+{
+  T ret;
+  tex1D(&ret, texObject, x);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2D(T *ptr, cudaTextureObject_t obj, float x, float y)
+{
+   __nv_tex_surf_handler("__itex2D", ptr, obj, x, y);
+}
+
+template <class T>
+static __device__  T tex2D(cudaTextureObject_t texObject, float x, float y)
+{
+  T ret;
+  tex2D(&ret, texObject, x, y);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2D(T *ptr, cudaTextureObject_t obj, float x, float y, 
+                                                          bool* isResident)
+{
+  unsigned char res;
+   __nv_tex_surf_handler("__itex2D_sparse", ptr, obj, x, y, &res);
+   *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2D(cudaTextureObject_t texObject, float x, float y, bool* isResident)
+{
+  T ret;
+  tex2D(&ret, texObject, x, y, isResident);
+  return ret;
+}
+
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex3D(T *ptr, cudaTextureObject_t obj, float x, float y, float z)
+{
+   __nv_tex_surf_handler("__itex3D", ptr, obj, x, y, z);
+}
+
+template <class T>
+static __device__  T tex3D(cudaTextureObject_t texObject, float x, float y, float z)
+{
+  T ret;
+  tex3D(&ret, texObject, x, y, z);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex3D(T *ptr, cudaTextureObject_t obj, float x, float y, float z, 
+                                                          bool* isResident)
+{
+  unsigned char res;
+   __nv_tex_surf_handler("__itex3D_sparse", ptr, obj, x, y, z, &res);
+   *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex3D(cudaTextureObject_t texObject, float x, float y, float z, bool* isResident)
+{
+  T ret;
+  tex3D(&ret, texObject, x, y, z, isResident);
+  return ret;
+}
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1DLayered(T *ptr, cudaTextureObject_t obj, float x, int layer)
+{
+   __nv_tex_surf_handler("__itex1DLayered", ptr, obj, x, layer);
+}
+
+template <class T>
+static __device__  T tex1DLayered(cudaTextureObject_t texObject, float x, int layer)
+{
+  T ret;
+  tex1DLayered(&ret, texObject, x, layer);
+  return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLayered(T *ptr, cudaTextureObject_t obj, float x, float y, int layer)
+{
+  __nv_tex_surf_handler("__itex2DLayered", ptr, obj, x, y, layer);
+}
+
+template <class T>
+static __device__  T tex2DLayered(cudaTextureObject_t texObject, float x, float y, int layer)
+{
+  T ret;
+  tex2DLayered(&ret, texObject, x, y, layer);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLayered(T *ptr, cudaTextureObject_t obj, float x, float y, int layer, bool* isResident)
+{
+  unsigned char res;
+  __nv_tex_surf_handler("__itex2DLayered_sparse", ptr, obj, x, y, layer, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2DLayered(cudaTextureObject_t texObject, float x, float y, int layer, bool* isResident)
+{
+  T ret;
+  tex2DLayered(&ret, texObject, x, y, layer, isResident);
+  return ret;
+}
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type texCubemap(T *ptr, cudaTextureObject_t obj, float x, float y, float z)
+{
+  __nv_tex_surf_handler("__itexCubemap", ptr, obj, x, y, z);
+}
+
+
+template <class T>
+static __device__  T texCubemap(cudaTextureObject_t texObject, float x, float y, float z)
+{
+  T ret;
+  texCubemap(&ret, texObject, x, y, z);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type texCubemapLayered(T *ptr, cudaTextureObject_t obj, float x, float y, float z, int layer)
+{ 
+  __nv_tex_surf_handler("__itexCubemapLayered", ptr, obj, x, y, z, layer);
+}
+
+template <class T>
+static __device__  T texCubemapLayered(cudaTextureObject_t texObject, float x, float y, float z, int layer)
+{
+  T ret;
+  texCubemapLayered(&ret, texObject, x, y, z, layer);
+  return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2Dgather(T *ptr, cudaTextureObject_t obj, float x, float y, int comp = 0)
+{
+  __nv_tex_surf_handler("__itex2Dgather", ptr, obj, x, y, comp);
+}
+
+template <class T>
+static __device__  T tex2Dgather(cudaTextureObject_t to, float x, float y, int comp = 0)
+{
+  T ret;
+  tex2Dgather(&ret, to, x, y, comp);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2Dgather(T *ptr, cudaTextureObject_t obj, float x, float y, bool* isResident, int comp = 0)
+{
+  unsigned char res;
+  __nv_tex_surf_handler("__itex2Dgather_sparse", ptr, obj, x, y, comp,  &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2Dgather(cudaTextureObject_t to, float x, float y, bool* isResident, int comp = 0)
+{
+  T ret;
+  tex2Dgather(&ret, to, x, y,  isResident, comp);
+  return ret;
+}
+
+#endif  /* __NV_TEX_SPARSE */
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1DLod(T *ptr, cudaTextureObject_t obj, float x, float level)
+{
+  __nv_tex_surf_handler("__itex1DLod", ptr, obj, x, level);
+}
+
+template <class T>
+static __device__  T tex1DLod(cudaTextureObject_t texObject, float x, float level)
+{
+  T ret;
+  tex1DLod(&ret, texObject, x, level);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLod(T *ptr, cudaTextureObject_t obj, float x, float y, float level)
+{
+  __nv_tex_surf_handler("__itex2DLod", ptr, obj, x, y, level);
+}
+
+template <class T>
+static __device__  T tex2DLod(cudaTextureObject_t texObject, float x, float y, float level)
+{
+  T ret;
+  tex2DLod(&ret, texObject, x, y, level);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLod(T *ptr, cudaTextureObject_t obj, float x, float y, float level, bool* isResident)
+{
+  unsigned char res;
+  __nv_tex_surf_handler("__itex2DLod_sparse", ptr, obj, x, y, level, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2DLod(cudaTextureObject_t texObject, float x, float y, float level, bool* isResident)
+{
+  T ret;
+  tex2DLod(&ret, texObject, x, y, level, isResident);
+  return ret;
+}
+
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex3DLod(T *ptr, cudaTextureObject_t obj, float x, float y, float z, float level)
+{ 
+  __nv_tex_surf_handler("__itex3DLod", ptr, obj, x, y, z, level);
+}
+
+template <class T>
+static __device__  T tex3DLod(cudaTextureObject_t texObject, float x, float y, float z, float level)
+{
+  T ret;
+  tex3DLod(&ret, texObject, x, y, z, level);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex3DLod(T *ptr, cudaTextureObject_t obj, float x, float y, float z, float level, bool* isResident)
+{ 
+  unsigned char res;
+  __nv_tex_surf_handler("__itex3DLod_sparse", ptr, obj, x, y, z, level, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex3DLod(cudaTextureObject_t texObject, float x, float y, float z, float level, bool* isResident)
+{
+  T ret;
+  tex3DLod(&ret, texObject, x, y, z, level, isResident);
+  return ret;
+}
+
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1DLayeredLod(T *ptr, cudaTextureObject_t obj, float x, int layer, float level)
+{ 
+  __nv_tex_surf_handler("__itex1DLayeredLod", ptr, obj, x, layer, level);
+}
+
+template <class T>
+static __device__  T tex1DLayeredLod(cudaTextureObject_t texObject, float x, int layer, float level)
+{
+  T ret;
+  tex1DLayeredLod(&ret, texObject, x, layer, level);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLayeredLod(T *ptr, cudaTextureObject_t obj, float x, float y, int layer, float level)
+{ 
+  __nv_tex_surf_handler("__itex2DLayeredLod", ptr, obj, x, y, layer, level);
+}
+
+template <class T>
+static __device__  T tex2DLayeredLod(cudaTextureObject_t texObject, float x, float y, int layer, float level)
+{
+  T ret;
+  tex2DLayeredLod(&ret, texObject, x, y, layer, level);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLayeredLod(T *ptr, cudaTextureObject_t obj, float x, float y, int layer, float level, bool* isResident)
+{ 
+  unsigned char res;
+  __nv_tex_surf_handler("__itex2DLayeredLod_sparse", ptr, obj, x, y, layer, level, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2DLayeredLod(cudaTextureObject_t texObject, float x, float y, int layer, float level, bool* isResident)
+{
+  T ret;
+  tex2DLayeredLod(&ret, texObject, x, y, layer, level, isResident);
+  return ret;
+}
+#endif  /* __NV_TEX_SPARSE */
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type texCubemapLod(T *ptr, cudaTextureObject_t obj, float x, float y, float z, float level)
+{ 
+  __nv_tex_surf_handler("__itexCubemapLod", ptr, obj, x, y, z, level);
+}
+
+template <class T>
+static __device__  T texCubemapLod(cudaTextureObject_t texObject, float x, float y, float z, float level)
+{
+  T ret;
+  texCubemapLod(&ret, texObject, x, y, z, level);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type texCubemapGrad(T *ptr, cudaTextureObject_t obj, float x, float y, float z, float4 dPdx, float4 dPdy)
+{ 
+  __nv_tex_surf_handler("__itexCubemapGrad_v2", ptr, obj, x, y, z, &dPdx, &dPdy);
+}
+
+template <class T>
+static __device__  T texCubemapGrad(cudaTextureObject_t texObject, float x, float y, float z, float4 dPdx, float4 dPdy)
+{
+  T ret;
+  texCubemapGrad(&ret, texObject, x, y, z, dPdx, dPdy);
+  return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type texCubemapLayeredLod(T *ptr, cudaTextureObject_t obj, float x, float y, float z, int layer, float level)
+{ 
+  __nv_tex_surf_handler("__itexCubemapLayeredLod", ptr, obj, x, y, z, layer, level);
+}
+
+template <class T>
+static __device__  T texCubemapLayeredLod(cudaTextureObject_t texObject, float x, float y, float z, int layer, float level)
+{
+  T ret;
+  texCubemapLayeredLod(&ret, texObject, x, y, z, layer, level);
+  return ret;
+}
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1DGrad(T *ptr, cudaTextureObject_t obj, float x, float dPdx, float dPdy)
+{
+  __nv_tex_surf_handler("__itex1DGrad", ptr, obj, x, dPdx, dPdy);
+}
+
+template <class T>
+static __device__  T tex1DGrad(cudaTextureObject_t texObject, float x, float dPdx, float dPdy)
+{
+  T ret;
+  tex1DGrad(&ret, texObject, x, dPdx, dPdy);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DGrad(T *ptr, cudaTextureObject_t obj, float x, float y, float2 dPdx, float2 dPdy)
+{ 
+  __nv_tex_surf_handler("__itex2DGrad_v2", ptr, obj, x, y, &dPdx, &dPdy);
+}
+
+template <class T>
+static __device__  T tex2DGrad(cudaTextureObject_t texObject, float x, float y, float2 dPdx, float2 dPdy)
+{
+  T ret;
+  tex2DGrad(&ret, texObject, x, y, dPdx, dPdy);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DGrad(T *ptr, cudaTextureObject_t obj, float x, float y, float2 dPdx, float2 dPdy, bool* isResident)
+{ 
+  unsigned char res;
+  __nv_tex_surf_handler("__itex2DGrad_sparse", ptr, obj, x, y, &dPdx, &dPdy, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2DGrad(cudaTextureObject_t texObject, float x, float y, float2 dPdx, float2 dPdy, bool* isResident)
+{
+  T ret;
+  tex2DGrad(&ret, texObject, x, y, dPdx, dPdy, isResident);
+  return ret;
+}
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex3DGrad(T *ptr, cudaTextureObject_t obj, float x, float y, float z, float4 dPdx, float4 dPdy)
+{ 
+  __nv_tex_surf_handler("__itex3DGrad_v2", ptr, obj, x, y, z, &dPdx, &dPdy);
+}
+
+template <class T>
+static __device__  T tex3DGrad(cudaTextureObject_t texObject, float x, float y, float z, float4 dPdx, float4 dPdy)
+{
+  T ret;
+  tex3DGrad(&ret, texObject, x, y, z, dPdx, dPdy);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex3DGrad(T *ptr, cudaTextureObject_t obj, float x, float y, float z, float4 dPdx, float4 dPdy, bool* isResident)
+{ 
+  unsigned char res;
+  __nv_tex_surf_handler("__itex3DGrad_sparse", ptr, obj, x, y, z, &dPdx, &dPdy, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex3DGrad(cudaTextureObject_t texObject, float x, float y, float z, float4 dPdx, float4 dPdy, bool* isResident)
+{
+  T ret;
+  tex3DGrad(&ret, texObject, x, y, z, dPdx, dPdy, isResident);
+  return ret;
+}
+
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex1DLayeredGrad(T *ptr, cudaTextureObject_t obj, float x, int layer, float dPdx, float dPdy)
+{ 
+  __nv_tex_surf_handler("__itex1DLayeredGrad", ptr, obj, x, layer, dPdx, dPdy);
+}
+
+template <class T>
+static __device__  T tex1DLayeredGrad(cudaTextureObject_t texObject, float x, int layer, float dPdx, float dPdy)
+{
+  T ret;
+  tex1DLayeredGrad(&ret, texObject, x, layer, dPdx, dPdy);
+  return ret;
+}
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLayeredGrad(T * ptr, cudaTextureObject_t obj, float x, float y, int layer, float2 dPdx, float2 dPdy)
+{ 
+  __nv_tex_surf_handler("__itex2DLayeredGrad_v2", ptr, obj, x, y, layer, &dPdx, &dPdy);
+}
+
+template <class T>
+static __device__  T tex2DLayeredGrad(cudaTextureObject_t texObject, float x, float y, int layer, float2 dPdx, float2 dPdy)
+{
+  T ret;
+  tex2DLayeredGrad(&ret, texObject, x, y, layer, dPdx, dPdy);
+  return ret;
+}
+
+#if __NV_TEX_SPARSE
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type tex2DLayeredGrad(T * ptr, cudaTextureObject_t obj, float x, float y, int layer, float2 dPdx, float2 dPdy, bool* isResident)
+{ 
+  unsigned char res;
+  __nv_tex_surf_handler("__itex2DLayeredGrad_sparse", ptr, obj, x, y, layer, &dPdx, &dPdy, &res);
+  *isResident = (res != 0);
+}
+
+template <class T>
+static __device__  T tex2DLayeredGrad(cudaTextureObject_t texObject, float x, float y, int layer, float2 dPdx, float2 dPdy, bool* isResident)
+{
+  T ret;
+  tex2DLayeredGrad(&ret, texObject, x, y, layer, dPdx, dPdy, isResident);
+  return ret;
+}
+#endif  /* __NV_TEX_SPARSE */
+
+
+template <typename T>
+static __device__ typename __nv_itex_trait<T>::type texCubemapLayeredGrad(T *ptr, cudaTextureObject_t obj, float x, float y, float z, int layer, float4 dPdx, float4 dPdy)
+{
+  __nv_tex_surf_handler("__itexCubemapLayeredGrad_v2", ptr, obj, x, y, z, layer, &dPdx, &dPdy);
+}
+
+template <class T>
+static __device__  T texCubemapLayeredGrad(cudaTextureObject_t texObject, float x, float y, float z, int layer, float4 dPdx, float4 dPdy)
+{
+  T ret;
+  texCubemapLayeredGrad(&ret, texObject, x, y, z, layer, dPdx, dPdy);
+  return ret;
+}
+
+#undef __NV_TEX_SPARSE
+
+#endif // __cplusplus && __CUDACC__
+#endif // __TEXTURE_INDIRECT_FUNCTIONS_H__
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..a3e3e90ef5c02d7f3e62178792fd10f93ba4d85f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/texture_types.h
@@ -0,0 +1,180 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__TEXTURE_TYPES_H__)
+#define __TEXTURE_TYPES_H__
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "driver_types.h"
+
+#ifndef __CUDACC_RTC_MINIMAL__
+
+/**
+ * \addtogroup CUDART_TYPES
+ *
+ * @{
+ */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#define cudaTextureType1D              0x01
+#define cudaTextureType2D              0x02
+#define cudaTextureType3D              0x03
+#define cudaTextureTypeCubemap         0x0C
+#define cudaTextureType1DLayered       0xF1
+#define cudaTextureType2DLayered       0xF2
+#define cudaTextureTypeCubemapLayered  0xFC
+
+/**
+ * CUDA texture address modes
+ */
+enum __device_builtin__ cudaTextureAddressMode
+{
+    cudaAddressModeWrap   = 0,    /**< Wrapping address mode */
+    cudaAddressModeClamp  = 1,    /**< Clamp to edge address mode */
+    cudaAddressModeMirror = 2,    /**< Mirror address mode */
+    cudaAddressModeBorder = 3     /**< Border address mode */
+};
+
+/**
+ * CUDA texture filter modes
+ */
+enum __device_builtin__ cudaTextureFilterMode
+{
+    cudaFilterModePoint  = 0,     /**< Point filter mode */
+    cudaFilterModeLinear = 1      /**< Linear filter mode */
+};
+
+/**
+ * CUDA texture read modes
+ */
+enum __device_builtin__ cudaTextureReadMode
+{
+    cudaReadModeElementType     = 0,  /**< Read texture as specified element type */
+    cudaReadModeNormalizedFloat = 1   /**< Read texture as normalized float */
+};
+
+/**
+ * CUDA texture descriptor
+ */
+struct __device_builtin__ cudaTextureDesc
+{
+    /**
+     * Texture address mode for up to 3 dimensions
+     */
+    enum cudaTextureAddressMode addressMode[3];
+    /**
+     * Texture filter mode
+     */
+    enum cudaTextureFilterMode  filterMode;
+    /**
+     * Texture read mode
+     */
+    enum cudaTextureReadMode    readMode;
+    /**
+     * Perform sRGB->linear conversion during texture read
+     */
+    int                         sRGB;
+    /**
+     * Texture Border Color
+     */
+    float                       borderColor[4];
+    /**
+     * Indicates whether texture reads are normalized or not
+     */
+    int                         normalizedCoords;
+    /**
+     * Limit to the anisotropy ratio
+     */
+    unsigned int                maxAnisotropy;
+    /**
+     * Mipmap filter mode
+     */
+    enum cudaTextureFilterMode  mipmapFilterMode;
+    /**
+     * Offset applied to the supplied mipmap level
+     */
+    float                       mipmapLevelBias;
+    /**
+     * Lower end of the mipmap level range to clamp access to
+     */
+    float                       minMipmapLevelClamp;
+    /**
+     * Upper end of the mipmap level range to clamp access to
+     */
+    float                       maxMipmapLevelClamp;
+    /**
+     * Disable any trilinear filtering optimizations.
+     */
+    int                         disableTrilinearOptimization;
+    /**
+     * Enable seamless cube map filtering.
+     */
+    int                         seamlessCubemap;
+};
+
+/**
+ * An opaque value that represents a CUDA texture object
+ */
+typedef __device_builtin__ unsigned long long cudaTextureObject_t;
+
+/** @} */
+/** @} */ /* END CUDART_TYPES */
+
+#endif  /* !__CUDACC_RTC_MINIMAL__ */
+#endif /* !__TEXTURE_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_functions.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..38a52e3936ca88f6f5437fe8731b6b1cfdc7ca02
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_functions.h
@@ -0,0 +1,181 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__VECTOR_FUNCTIONS_H__)
+#define __VECTOR_FUNCTIONS_H__
+
+/* NOTE: For NVRTC, these declarations have been moved into the compiler 
+   (to reduce compile time) */
+#define EXCLUDE_FROM_RTC
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if defined(__CUDACC_RTC__)
+#define __VECTOR_FUNCTIONS_DECL__ __host__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __VECTOR_FUNCTIONS_DECL__ static __inline__ __host__ __device__
+#endif /* __CUDACC_RTC__ */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__VECTOR_FUNCTIONS_DECL__ char1 make_char1(signed char x);
+
+__VECTOR_FUNCTIONS_DECL__ uchar1 make_uchar1(unsigned char x);
+
+__VECTOR_FUNCTIONS_DECL__ char2 make_char2(signed char x, signed char y);
+
+__VECTOR_FUNCTIONS_DECL__ uchar2 make_uchar2(unsigned char x, unsigned char y);
+
+__VECTOR_FUNCTIONS_DECL__ char3 make_char3(signed char x, signed char y, signed char z);
+
+__VECTOR_FUNCTIONS_DECL__ uchar3 make_uchar3(unsigned char x, unsigned char y, unsigned char z);
+
+__VECTOR_FUNCTIONS_DECL__ char4 make_char4(signed char x, signed char y, signed char z, signed char w);
+
+__VECTOR_FUNCTIONS_DECL__ uchar4 make_uchar4(unsigned char x, unsigned char y, unsigned char z, unsigned char w);
+
+__VECTOR_FUNCTIONS_DECL__ short1 make_short1(short x);
+
+__VECTOR_FUNCTIONS_DECL__ ushort1 make_ushort1(unsigned short x);
+
+__VECTOR_FUNCTIONS_DECL__ short2 make_short2(short x, short y);
+
+__VECTOR_FUNCTIONS_DECL__ ushort2 make_ushort2(unsigned short x, unsigned short y);
+
+__VECTOR_FUNCTIONS_DECL__ short3 make_short3(short x,short y, short z);
+
+__VECTOR_FUNCTIONS_DECL__ ushort3 make_ushort3(unsigned short x, unsigned short y, unsigned short z);
+
+__VECTOR_FUNCTIONS_DECL__ short4 make_short4(short x, short y, short z, short w);
+
+__VECTOR_FUNCTIONS_DECL__ ushort4 make_ushort4(unsigned short x, unsigned short y, unsigned short z, unsigned short w);
+
+__VECTOR_FUNCTIONS_DECL__ int1 make_int1(int x);
+
+__VECTOR_FUNCTIONS_DECL__ uint1 make_uint1(unsigned int x);
+
+__VECTOR_FUNCTIONS_DECL__ int2 make_int2(int x, int y);
+
+__VECTOR_FUNCTIONS_DECL__ uint2 make_uint2(unsigned int x, unsigned int y);
+
+__VECTOR_FUNCTIONS_DECL__ int3 make_int3(int x, int y, int z);
+
+__VECTOR_FUNCTIONS_DECL__ uint3 make_uint3(unsigned int x, unsigned int y, unsigned int z);
+
+__VECTOR_FUNCTIONS_DECL__ int4 make_int4(int x, int y, int z, int w);
+
+__VECTOR_FUNCTIONS_DECL__ uint4 make_uint4(unsigned int x, unsigned int y, unsigned int z, unsigned int w);
+
+__VECTOR_FUNCTIONS_DECL__ long1 make_long1(long int x);
+
+__VECTOR_FUNCTIONS_DECL__ ulong1 make_ulong1(unsigned long int x);
+
+__VECTOR_FUNCTIONS_DECL__ long2 make_long2(long int x, long int y);
+
+__VECTOR_FUNCTIONS_DECL__ ulong2 make_ulong2(unsigned long int x, unsigned long int y);
+
+__VECTOR_FUNCTIONS_DECL__ long3 make_long3(long int x, long int y, long int z);
+
+__VECTOR_FUNCTIONS_DECL__ ulong3 make_ulong3(unsigned long int x, unsigned long int y, unsigned long int z);
+
+__VECTOR_FUNCTIONS_DECL__ long4 make_long4(long int x, long int y, long int z, long int w);
+
+__VECTOR_FUNCTIONS_DECL__ ulong4 make_ulong4(unsigned long int x, unsigned long int y, unsigned long int z, unsigned long int w);
+
+__VECTOR_FUNCTIONS_DECL__ float1 make_float1(float x);
+
+__VECTOR_FUNCTIONS_DECL__ float2 make_float2(float x, float y);
+
+__VECTOR_FUNCTIONS_DECL__ float3 make_float3(float x, float y, float z);
+
+__VECTOR_FUNCTIONS_DECL__ float4 make_float4(float x, float y, float z, float w);
+
+__VECTOR_FUNCTIONS_DECL__ longlong1 make_longlong1(long long int x);
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong1 make_ulonglong1(unsigned long long int x);
+
+__VECTOR_FUNCTIONS_DECL__ longlong2 make_longlong2(long long int x, long long int y);
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong2 make_ulonglong2(unsigned long long int x, unsigned long long int y);
+
+__VECTOR_FUNCTIONS_DECL__ longlong3 make_longlong3(long long int x, long long int y, long long int z);
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong3 make_ulonglong3(unsigned long long int x, unsigned long long int y, unsigned long long int z);
+
+__VECTOR_FUNCTIONS_DECL__ longlong4 make_longlong4(long long int x, long long int y, long long int z, long long int w);
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong4 make_ulonglong4(unsigned long long int x, unsigned long long int y, unsigned long long int z, unsigned long long int w);
+
+__VECTOR_FUNCTIONS_DECL__ double1 make_double1(double x);
+
+__VECTOR_FUNCTIONS_DECL__ double2 make_double2(double x, double y);
+
+__VECTOR_FUNCTIONS_DECL__ double3 make_double3(double x, double y, double z);
+
+__VECTOR_FUNCTIONS_DECL__ double4 make_double4(double x, double y, double z, double w);
+
+#undef __VECTOR_FUNCTIONS_DECL__
+
+#if !defined(__CUDACC_RTC__)
+#include "vector_functions.hpp"
+#endif /* !__CUDACC_RTC__ */
+
+#undef EXCLUDE_FROM_RTC
+
+#endif /* !__VECTOR_FUNCTIONS_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_functions.hpp b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_functions.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..ab69cf38045a7c44dae67e7149d49ac4c6148747
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_functions.hpp
@@ -0,0 +1,316 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__VECTOR_FUNCTIONS_HPP__)
+#define __VECTOR_FUNCTIONS_HPP__
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#include "cuda_runtime_api.h"
+
+#if defined(__CUDACC_RTC__)
+#define __VECTOR_FUNCTIONS_DECL__ __host__ __device__
+#else /* !__CUDACC_RTC__ */
+#define __VECTOR_FUNCTIONS_DECL__ static __inline__ __host__ __device__
+#endif /* __CUDACC_RTC__ */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+__VECTOR_FUNCTIONS_DECL__ char1 make_char1(signed char x)
+{
+  char1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uchar1 make_uchar1(unsigned char x)
+{
+  uchar1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ char2 make_char2(signed char x, signed char y)
+{
+  char2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uchar2 make_uchar2(unsigned char x, unsigned char y)
+{
+  uchar2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ char3 make_char3(signed char x, signed char y, signed char z)
+{
+  char3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uchar3 make_uchar3(unsigned char x, unsigned char y, unsigned char z)
+{
+  uchar3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ char4 make_char4(signed char x, signed char y, signed char z, signed char w)
+{
+  char4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uchar4 make_uchar4(unsigned char x, unsigned char y, unsigned char z, unsigned char w)
+{
+  uchar4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ short1 make_short1(short x)
+{
+  short1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ushort1 make_ushort1(unsigned short x)
+{
+  ushort1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ short2 make_short2(short x, short y)
+{
+  short2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ushort2 make_ushort2(unsigned short x, unsigned short y)
+{
+  ushort2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ short3 make_short3(short x,short y, short z)
+{ 
+  short3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ushort3 make_ushort3(unsigned short x, unsigned short y, unsigned short z)
+{
+  ushort3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ short4 make_short4(short x, short y, short z, short w)
+{
+  short4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ushort4 make_ushort4(unsigned short x, unsigned short y, unsigned short z, unsigned short w)
+{
+  ushort4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ int1 make_int1(int x)
+{
+  int1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uint1 make_uint1(unsigned int x)
+{
+  uint1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ int2 make_int2(int x, int y)
+{
+  int2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uint2 make_uint2(unsigned int x, unsigned int y)
+{
+  uint2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ int3 make_int3(int x, int y, int z)
+{
+  int3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uint3 make_uint3(unsigned int x, unsigned int y, unsigned int z)
+{
+  uint3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ int4 make_int4(int x, int y, int z, int w)
+{
+  int4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ uint4 make_uint4(unsigned int x, unsigned int y, unsigned int z, unsigned int w)
+{
+  uint4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ long1 make_long1(long int x)
+{
+  long1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulong1 make_ulong1(unsigned long int x)
+{
+  ulong1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ long2 make_long2(long int x, long int y)
+{
+  long2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulong2 make_ulong2(unsigned long int x, unsigned long int y)
+{
+  ulong2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ long3 make_long3(long int x, long int y, long int z)
+{
+  long3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulong3 make_ulong3(unsigned long int x, unsigned long int y, unsigned long int z)
+{
+  ulong3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ long4 make_long4(long int x, long int y, long int z, long int w)
+{
+  long4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulong4 make_ulong4(unsigned long int x, unsigned long int y, unsigned long int z, unsigned long int w)
+{
+  ulong4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ float1 make_float1(float x)
+{
+  float1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ float2 make_float2(float x, float y)
+{
+  float2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ float3 make_float3(float x, float y, float z)
+{
+  float3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ float4 make_float4(float x, float y, float z, float w)
+{
+  float4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ longlong1 make_longlong1(long long int x)
+{
+  longlong1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong1 make_ulonglong1(unsigned long long int x)
+{
+  ulonglong1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ longlong2 make_longlong2(long long int x, long long int y)
+{
+  longlong2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong2 make_ulonglong2(unsigned long long int x, unsigned long long int y)
+{
+  ulonglong2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ longlong3 make_longlong3(long long int x, long long int y, long long int z)
+{
+  longlong3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong3 make_ulonglong3(unsigned long long int x, unsigned long long int y, unsigned long long int z)
+{
+  ulonglong3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ longlong4 make_longlong4(long long int x, long long int y, long long int z, long long int w)
+{
+  longlong4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ ulonglong4 make_ulonglong4(unsigned long long int x, unsigned long long int y, unsigned long long int z, unsigned long long int w)
+{
+  ulonglong4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ double1 make_double1(double x)
+{
+  double1 t; t.x = x; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ double2 make_double2(double x, double y)
+{
+  double2 t; t.x = x; t.y = y; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ double3 make_double3(double x, double y, double z)
+{
+  double3 t; t.x = x; t.y = y; t.z = z; return t;
+}
+
+__VECTOR_FUNCTIONS_DECL__ double4 make_double4(double x, double y, double z, double w)
+{
+  double4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
+}
+
+#undef __VECTOR_FUNCTIONS_DECL__
+
+#endif /* !__VECTOR_FUNCTIONS_HPP__ */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_types.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a755e65f36b56644cd25d08603e10a6efc3fb8b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/include/vector_types.h
@@ -0,0 +1,449 @@
+/*
+ * Copyright 1993-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(__VECTOR_TYPES_H__)
+#define __VECTOR_TYPES_H__
+
+#if !defined(__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__)
+#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#define __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_VECTOR_TYPES_H__
+#endif
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#ifndef __DOXYGEN_ONLY__
+#include "crt/host_defines.h"
+#endif
+
+/* NVRTC compiler defines these instead of in the header (to reduce compile time)
+*/
+#ifndef __CUDACC_RTC_BUILTIN_VECTOR_TYPES__
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+#if !defined(__CUDACC__) && !defined(__CUDACC_RTC__) && \
+    defined(_WIN32) && !defined(_WIN64)
+
+#pragma warning(push)
+#pragma warning(disable: 4201 4408)
+
+#define __cuda_builtin_vector_align8(tag, members) \
+struct __device_builtin__ tag                      \
+{                                                  \
+    union                                          \
+    {                                              \
+        struct { members };                        \
+        struct { long long int :1,:0; };           \
+    };                                             \
+}
+
+#else /* !__CUDACC__ && !__CUDACC_RTC__ && _WIN32 && !_WIN64 */
+
+#define __cuda_builtin_vector_align8(tag, members) \
+struct __device_builtin__ __align__(8) tag         \
+{                                                  \
+    members                                        \
+}
+
+#endif /* !__CUDACC__ && !__CUDACC_RTC__ && _WIN32 && !_WIN64 */
+
+struct __device_builtin__ char1
+{
+    signed char x;
+};
+
+struct __device_builtin__ uchar1
+{
+    unsigned char x;
+};
+
+
+struct __device_builtin__ __align__(2) char2
+{
+    signed char x, y;
+};
+
+struct __device_builtin__ __align__(2) uchar2
+{
+    unsigned char x, y;
+};
+
+struct __device_builtin__ char3
+{
+    signed char x, y, z;
+};
+
+struct __device_builtin__ uchar3
+{
+    unsigned char x, y, z;
+};
+
+struct __device_builtin__ __align__(4) char4
+{
+    signed char x, y, z, w;
+};
+
+struct __device_builtin__ __align__(4) uchar4
+{
+    unsigned char x, y, z, w;
+};
+
+struct __device_builtin__ short1
+{
+    short x;
+};
+
+struct __device_builtin__ ushort1
+{
+    unsigned short x;
+};
+
+struct __device_builtin__ __align__(4) short2
+{
+    short x, y;
+};
+
+struct __device_builtin__ __align__(4) ushort2
+{
+    unsigned short x, y;
+};
+
+struct __device_builtin__ short3
+{
+    short x, y, z;
+};
+
+struct __device_builtin__ ushort3
+{
+    unsigned short x, y, z;
+};
+
+__cuda_builtin_vector_align8(short4, short x; short y; short z; short w;);
+__cuda_builtin_vector_align8(ushort4, unsigned short x; unsigned short y; unsigned short z; unsigned short w;);
+
+struct __device_builtin__ int1
+{
+    int x;
+};
+
+struct __device_builtin__ uint1
+{
+    unsigned int x;
+};
+
+__cuda_builtin_vector_align8(int2, int x; int y;);
+__cuda_builtin_vector_align8(uint2, unsigned int x; unsigned int y;);
+
+struct __device_builtin__ int3
+{
+    int x, y, z;
+};
+
+struct __device_builtin__ uint3
+{
+    unsigned int x, y, z;
+};
+
+struct __device_builtin__ __builtin_align__(16) int4
+{
+    int x, y, z, w;
+};
+
+struct __device_builtin__ __builtin_align__(16) uint4
+{
+    unsigned int x, y, z, w;
+};
+
+struct __device_builtin__ long1
+{
+    long int x;
+};
+
+struct __device_builtin__ ulong1
+{
+    unsigned long x;
+};
+
+#if defined(_WIN32)
+__cuda_builtin_vector_align8(long2, long int x; long int y;);
+__cuda_builtin_vector_align8(ulong2, unsigned long int x; unsigned long int y;);
+#else /* !_WIN32 */
+
+struct __device_builtin__ __align__(2*sizeof(long int)) long2
+{
+    long int x, y;
+};
+
+struct __device_builtin__ __align__(2*sizeof(unsigned long int)) ulong2
+{
+    unsigned long int x, y;
+};
+
+#endif /* _WIN32 */
+
+struct __device_builtin__ long3
+{
+    long int x, y, z;
+};
+
+struct __device_builtin__ ulong3
+{
+    unsigned long int x, y, z;
+};
+
+struct __device_builtin__ __builtin_align__(16) long4
+{
+    long int x, y, z, w;
+};
+
+struct __device_builtin__ __builtin_align__(16) ulong4
+{
+    unsigned long int x, y, z, w;
+};
+
+struct __device_builtin__ float1
+{
+    float x;
+};
+
+#if !defined(__CUDACC__) && defined(__arm__) && \
+    defined(__ARM_PCS_VFP) && __GNUC__ == 4 && __GNUC_MINOR__ == 6
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-pedantic"
+
+struct __device_builtin__ __attribute__((aligned(8))) float2
+{
+    float x; float y; float __cuda_gnu_arm_ice_workaround[0];
+};
+
+#pragma GCC poison __cuda_gnu_arm_ice_workaround
+#pragma GCC diagnostic pop
+
+#else /* !__CUDACC__ && __arm__ && __ARM_PCS_VFP &&
+         __GNUC__ == 4&& __GNUC_MINOR__ == 6 */
+
+__cuda_builtin_vector_align8(float2, float x; float y;);
+
+#endif /* !__CUDACC__ && __arm__ && __ARM_PCS_VFP &&
+          __GNUC__ == 4&& __GNUC_MINOR__ == 6 */
+
+struct __device_builtin__ float3
+{
+    float x, y, z;
+};
+
+struct __device_builtin__ __builtin_align__(16) float4
+{
+    float x, y, z, w;
+};
+
+struct __device_builtin__ longlong1
+{
+    long long int x;
+};
+
+struct __device_builtin__ ulonglong1
+{
+    unsigned long long int x;
+};
+
+struct __device_builtin__ __builtin_align__(16) longlong2
+{
+    long long int x, y;
+};
+
+struct __device_builtin__ __builtin_align__(16) ulonglong2
+{
+    unsigned long long int x, y;
+};
+
+struct __device_builtin__ longlong3
+{
+    long long int x, y, z;
+};
+
+struct __device_builtin__ ulonglong3
+{
+    unsigned long long int x, y, z;
+};
+
+struct __device_builtin__ __builtin_align__(16) longlong4
+{
+    long long int x, y, z ,w;
+};
+
+struct __device_builtin__ __builtin_align__(16) ulonglong4
+{
+    unsigned long long int x, y, z, w;
+};
+
+struct __device_builtin__ double1
+{
+    double x;
+};
+
+struct __device_builtin__ __builtin_align__(16) double2
+{
+    double x, y;
+};
+
+struct __device_builtin__ double3
+{
+    double x, y, z;
+};
+
+struct __device_builtin__ __builtin_align__(16) double4
+{
+    double x, y, z, w;
+};
+
+#if !defined(__CUDACC__) && defined(_WIN32) && !defined(_WIN64)
+
+#pragma warning(pop)
+
+#endif /* !__CUDACC__ && _WIN32 && !_WIN64 */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+typedef __device_builtin__ struct char1 char1;
+typedef __device_builtin__ struct uchar1 uchar1;
+typedef __device_builtin__ struct char2 char2;
+typedef __device_builtin__ struct uchar2 uchar2;
+typedef __device_builtin__ struct char3 char3;
+typedef __device_builtin__ struct uchar3 uchar3;
+typedef __device_builtin__ struct char4 char4;
+typedef __device_builtin__ struct uchar4 uchar4;
+typedef __device_builtin__ struct short1 short1;
+typedef __device_builtin__ struct ushort1 ushort1;
+typedef __device_builtin__ struct short2 short2;
+typedef __device_builtin__ struct ushort2 ushort2;
+typedef __device_builtin__ struct short3 short3;
+typedef __device_builtin__ struct ushort3 ushort3;
+typedef __device_builtin__ struct short4 short4;
+typedef __device_builtin__ struct ushort4 ushort4;
+typedef __device_builtin__ struct int1 int1;
+typedef __device_builtin__ struct uint1 uint1;
+typedef __device_builtin__ struct int2 int2;
+typedef __device_builtin__ struct uint2 uint2;
+typedef __device_builtin__ struct int3 int3;
+typedef __device_builtin__ struct uint3 uint3;
+typedef __device_builtin__ struct int4 int4;
+typedef __device_builtin__ struct uint4 uint4;
+typedef __device_builtin__ struct long1 long1;
+typedef __device_builtin__ struct ulong1 ulong1;
+typedef __device_builtin__ struct long2 long2;
+typedef __device_builtin__ struct ulong2 ulong2;
+typedef __device_builtin__ struct long3 long3;
+typedef __device_builtin__ struct ulong3 ulong3;
+typedef __device_builtin__ struct long4 long4;
+typedef __device_builtin__ struct ulong4 ulong4;
+typedef __device_builtin__ struct float1 float1;
+typedef __device_builtin__ struct float2 float2;
+typedef __device_builtin__ struct float3 float3;
+typedef __device_builtin__ struct float4 float4;
+typedef __device_builtin__ struct longlong1 longlong1;
+typedef __device_builtin__ struct ulonglong1 ulonglong1;
+typedef __device_builtin__ struct longlong2 longlong2;
+typedef __device_builtin__ struct ulonglong2 ulonglong2;
+typedef __device_builtin__ struct longlong3 longlong3;
+typedef __device_builtin__ struct ulonglong3 ulonglong3;
+typedef __device_builtin__ struct longlong4 longlong4;
+typedef __device_builtin__ struct ulonglong4 ulonglong4;
+typedef __device_builtin__ struct double1 double1;
+typedef __device_builtin__ struct double2 double2;
+typedef __device_builtin__ struct double3 double3;
+typedef __device_builtin__ struct double4 double4;
+
+#undef  __cuda_builtin_vector_align8
+
+#endif /* !defined(__CUDACC_RTC_BUILTIN_VECTOR_TYPES__) */
+
+/*******************************************************************************
+*                                                                              *
+*                                                                              *
+*                                                                              *
+*******************************************************************************/
+
+struct __device_builtin__ dim3
+{
+    unsigned int x, y, z;
+#if defined(__cplusplus)
+#if __cplusplus >= 201103L
+    __host__ __device__ constexpr dim3(unsigned int vx = 1, unsigned int vy = 1, unsigned int vz = 1) : x(vx), y(vy), z(vz) {}
+    __host__ __device__ constexpr dim3(uint3 v) : x(v.x), y(v.y), z(v.z) {}
+    __host__ __device__ constexpr operator uint3(void) const { return uint3{x, y, z}; }
+#else
+    __host__ __device__ dim3(unsigned int vx = 1, unsigned int vy = 1, unsigned int vz = 1) : x(vx), y(vy), z(vz) {}
+    __host__ __device__ dim3(uint3 v) : x(v.x), y(v.y), z(v.z) {}
+    __host__ __device__ operator uint3(void) const { uint3 t; t.x = x; t.y = y; t.z = z; return t; }
+#endif
+#endif /* __cplusplus */
+};
+
+typedef __device_builtin__ struct dim3 dim3;
+
+#if defined(__UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_VECTOR_TYPES_H__)
+#undef __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
+#undef __UNDEF_CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS_VECTOR_TYPES_H__
+#endif
+
+#endif /* !__VECTOR_TYPES_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12
new file mode 100644
index 0000000000000000000000000000000000000000..4bff80847b31e48a2ccc85390129a46297cd0539
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3a75b33af334a3486d197dbd1584a2985183ba4688d237a2be5f2f679329920
+size 728800
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e08847c95f1294bc99e96e737a53cc6ebb7a458
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn.h
@@ -0,0 +1,68 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn : Neural Networks Library  */
+
+#if !defined(CUDNN_H_)
+#define CUDNN_H_
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#include <cuda_runtime_api.h>
+#include "cudnn_version.h"
+#include "cudnn_graph.h"
+#include "cudnn_ops.h"
+#include "cudnn_adv.h"
+#include "cudnn_cnn.h"
+
+#if defined(__cplusplus)
+}
+#endif
+#endif /* CUDNN_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv.h
new file mode 100644
index 0000000000000000000000000000000000000000..5d9bef65d5323dd3354299569d869191a07615cf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv.h
@@ -0,0 +1,669 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn_adv : cuDNN's advanced and experimental features.
+
+*/
+
+#if !defined(CUDNN_ADV_H_)
+#define CUDNN_ADV_H_
+
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_ADV_MAJOR 9
+#define CUDNN_ADV_MINOR 10
+#define CUDNN_ADV_PATCH 2
+
+#if (CUDNN_ADV_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_MINOR != CUDNN_MINOR) || (CUDNN_ADV_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN ADV INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* BASIC RNN API */
+
+typedef enum {
+    CUDNN_RNN_ALGO_STANDARD               = 0,
+    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1,
+    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2,
+    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
+    CUDNN_RNN_ALGO_COUNT                  = 4,
+} cudnnRNNAlgo_t;
+
+typedef enum {
+    CUDNN_FWD_MODE_INFERENCE = 0,
+    CUDNN_FWD_MODE_TRAINING  = 1,
+} cudnnForwardMode_t;
+
+typedef enum {
+    CUDNN_RNN_RELU = 0, /* basic RNN cell type with ReLu activation */
+    CUDNN_RNN_TANH = 1, /* basic RNN cell type with tanh activation */
+    CUDNN_LSTM     = 2, /* LSTM with optional recurrent projection and clipping */
+    CUDNN_GRU      = 3, /* Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1); */
+} cudnnRNNMode_t;
+
+typedef enum {
+    CUDNN_RNN_NO_BIAS         = 0, /* rnn cell formulas do not use biases */
+    CUDNN_RNN_SINGLE_INP_BIAS = 1, /* rnn cell formulas use one input bias in input GEMM */
+    CUDNN_RNN_DOUBLE_BIAS     = 2, /* default, rnn cell formulas use two bias vectors */
+    CUDNN_RNN_SINGLE_REC_BIAS = 3  /* rnn cell formulas use one recurrent bias in recurrent GEMM */
+} cudnnRNNBiasMode_t;
+
+typedef enum {
+    CUDNN_UNIDIRECTIONAL = 0, /* single direction network */
+    CUDNN_BIDIRECTIONAL  = 1, /* output concatination at each layer */
+} cudnnDirectionMode_t;
+
+typedef enum {
+    CUDNN_LINEAR_INPUT = 0, /* adjustable weight matrix in first layer input GEMM */
+    CUDNN_SKIP_INPUT   = 1, /* fixed identity matrix in the first layer input GEMM */
+} cudnnRNNInputMode_t;
+
+typedef enum {
+    CUDNN_RNN_CLIP_NONE   = 0, /* disables LSTM cell clipping */
+    CUDNN_RNN_CLIP_MINMAX = 1, /* enables LSTM cell clipping */
+} cudnnRNNClipMode_t;
+
+typedef enum {
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED   = 0, /* padded, outer stride from one time-step to the next */
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_PACKED     = 1, /* sequence length sorted and packed as in basic RNN api */
+    CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED = 2, /* padded, outer stride from one batch to the next */
+} cudnnRNNDataLayout_t;
+
+/* For auxFlags in cudnnSetRNNDescriptor_v8() */
+#define CUDNN_RNN_PADDED_IO_DISABLED 0
+#define CUDNN_RNN_PADDED_IO_ENABLED (1U << 0)
+
+struct cudnnRNNStruct;
+typedef struct cudnnRNNStruct *cudnnRNNDescriptor_t;
+
+struct cudnnRNNDataStruct;
+typedef struct cudnnRNNDataStruct *cudnnRNNDataDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDescriptor(cudnnRNNDescriptor_t *rnnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDescriptor(cudnnRNNDescriptor_t rnnDesc);
+
+/*
+ * mathPrec in cudnnSetRNNDescriptor_v8() specifies compute precision.
+ * Compute precision is further modified by mathType that sets the
+ * preferred option for using NVIDIA Tensor Cores.  dataType specify
+ * input/output data type and weight/bias type.
+ */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t algo,
+                         cudnnRNNMode_t cellMode,
+                         cudnnRNNBiasMode_t biasMode,
+                         cudnnDirectionMode_t dirMode,
+                         cudnnRNNInputMode_t inputMode,
+                         cudnnDataType_t dataType,
+                         cudnnDataType_t mathPrec,
+                         cudnnMathType_t mathType,
+                         int32_t inputSize,
+                         int32_t hiddenSize,
+                         int32_t projSize,
+                         int32_t numLayers,
+                         cudnnDropoutDescriptor_t dropoutDesc,
+                         uint32_t auxFlags);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t *algo,
+                         cudnnRNNMode_t *cellMode,
+                         cudnnRNNBiasMode_t *biasMode,
+                         cudnnDirectionMode_t *dirMode,
+                         cudnnRNNInputMode_t *inputMode,
+                         cudnnDataType_t *dataType,
+                         cudnnDataType_t *mathPrec,
+                         cudnnMathType_t *mathType,
+                         int32_t *inputSize,
+                         int32_t *hiddenSize,
+                         int32_t *projSize,
+                         int32_t *numLayers,
+                         cudnnDropoutDescriptor_t *dropoutDesc,
+                         uint32_t *auxFlags);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t clipMode,
+                   cudnnNanPropagation_t clipNanOpt,
+                   double lclip,
+                   double rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip_v9(cudnnRNNDescriptor_t rnnDesc, cudnnRNNClipMode_t clipMode, double lclip, double rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t *clipMode,
+                   cudnnNanPropagation_t *clipNanOpt,
+                   double *lclip,
+                   double *rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip_v9(cudnnRNNDescriptor_t rnnDesc, cudnnRNNClipMode_t *clipMode, double *lclip, double *rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBuildRNNDynamic(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, int miniBatch);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNTempSpaceSizes(cudnnHandle_t handle,
+                          cudnnRNNDescriptor_t rnnDesc,
+                          cudnnForwardMode_t fwdMode,
+                          cudnnRNNDataDescriptor_t xDesc,
+                          size_t *workSpaceSize,
+                          size_t *reserveSpaceSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightSpaceSize(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, size_t *weightSpaceSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightParams(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        int32_t pseudoLayer,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        int32_t linLayerID,
+                        cudnnTensorDescriptor_t mDesc,
+                        void **mAddr,
+                        cudnnTensorDescriptor_t bDesc,
+                        void **bAddr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDataDescriptor(cudnnRNNDataDescriptor_t *rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t dataType,
+                          cudnnRNNDataLayout_t layout,
+                          int maxSeqLength,
+                          int batchSize,
+                          int vectorSize,
+                          const int seqLengthArray[], /* length of each sequence in the batch */
+                          void *paddingFill);         /* symbol for filling padding position in output */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t *dataType,
+                          cudnnRNNDataLayout_t *layout,
+                          int *maxSeqLength,
+                          int *batchSize,
+                          int *vectorSize,
+                          int arrayLengthRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNForward(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnForwardMode_t fwdMode,
+                const int32_t devSeqLengths[],
+                cudnnRNNDataDescriptor_t xDesc,
+                const void *x,
+                cudnnRNNDataDescriptor_t yDesc,
+                void *y,
+                cudnnTensorDescriptor_t hDesc,
+                const void *hx,
+                void *hy,
+                cudnnTensorDescriptor_t cDesc,
+                const void *cx,
+                void *cy,
+                size_t weightSpaceSize,
+                const void *weightSpace,
+                size_t workSpaceSize,
+                void *workSpace,
+                size_t reserveSpaceSize,
+                void *reserveSpace);
+
+/* Sequence data descriptor */
+
+typedef enum {
+    CUDNN_SEQDATA_TIME_DIM  = 0, /* index in time */
+    CUDNN_SEQDATA_BATCH_DIM = 1, /* index in batch */
+    CUDNN_SEQDATA_BEAM_DIM  = 2, /* index in beam */
+    CUDNN_SEQDATA_VECT_DIM  = 3  /* index in vector */
+} cudnnSeqDataAxis_t;
+
+struct cudnnSeqDataStruct;
+typedef struct cudnnSeqDataStruct *cudnnSeqDataDescriptor_t CUDNN_DEPRECATED;
+
+#define CUDNN_SEQDATA_DIM_COUNT 4 /* dimension count */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateSeqDataDescriptor(cudnnSeqDataDescriptor_t *seqDataDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroySeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetSeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t dataType,
+                          int nbDims,
+                          const int dimA[],
+                          const cudnnSeqDataAxis_t axes[],
+                          size_t seqLengthArraySize,
+                          const int seqLengthArray[],
+                          void *paddingFill);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetSeqDataDescriptor(const cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t *dataType,
+                          int *nbDims,
+                          int nbDimsRequested,
+                          int dimA[],
+                          cudnnSeqDataAxis_t axes[],
+                          size_t *seqLengthArraySize,
+                          size_t seqLengthSizeRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+/* Multihead Attention */
+
+/*
+ * Multi-head attention options passed via 'attnMode' in cudnnSetAttnDescriptor().
+ * Use the bitwise OR operator to combine several settings listed below.  Additional
+ * minor options can be added here w/o changing or introducing new API functions.
+ */
+#define CUDNN_ATTN_QUERYMAP_ALL_TO_ONE 0         /* multiple Q-s map to a single (K,V) set when beam size > 1 */
+#define CUDNN_ATTN_QUERYMAP_ONE_TO_ONE (1U << 0) /* multiple Q-s map to multiple (K,V) sets when beam size > 1 */
+#define CUDNN_ATTN_DISABLE_PROJ_BIASES 0         /* no biases in attention input and output projections */
+#define CUDNN_ATTN_ENABLE_PROJ_BIASES (1U << 1)  /* use biases in attention input and output projections */
+
+struct cudnnAttnStruct;
+typedef struct cudnnAttnStruct *cudnnAttnDescriptor_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAttnDescriptor(cudnnAttnDescriptor_t *attnDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAttnDescriptor(cudnnAttnDescriptor_t attnDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned attnMode,
+                       int nHeads,
+                       double smScaler,
+                       cudnnDataType_t dataType,
+                       cudnnDataType_t computePrec,
+                       cudnnMathType_t mathType,
+                       cudnnDropoutDescriptor_t attnDropoutDesc,
+                       cudnnDropoutDescriptor_t postDropoutDesc,
+                       int qSize,
+                       int kSize,
+                       int vSize,
+                       int qProjSize,
+                       int kProjSize,
+                       int vProjSize,
+                       int oProjSize,
+                       int qoMaxSeqLength,
+                       int kvMaxSeqLength,
+                       int maxBatchSize,
+                       int maxBeamSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned *attnMode,
+                       int *nHeads,
+                       double *smScaler,
+                       cudnnDataType_t *dataType,
+                       cudnnDataType_t *computePrec,
+                       cudnnMathType_t *mathType,
+                       cudnnDropoutDescriptor_t *attnDropoutDesc,
+                       cudnnDropoutDescriptor_t *postDropoutDesc,
+                       int *qSize,
+                       int *kSize,
+                       int *vSize,
+                       int *qProjSize,
+                       int *kProjSize,
+                       int *vProjSize,
+                       int *oProjSize,
+                       int *qoMaxSeqLength,
+                       int *kvMaxSeqLength,
+                       int *maxBatchSize,
+                       int *maxBeamSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnBuffers(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             size_t *weightSizeInBytes,
+                             size_t *workSpaceSizeInBytes,
+                             size_t *reserveSpaceSizeInBytes);
+
+typedef enum {
+    CUDNN_MH_ATTN_Q_WEIGHTS = 0, /* input projection weights for 'queries' */
+    CUDNN_MH_ATTN_K_WEIGHTS = 1, /* input projection weights for 'keys' */
+    CUDNN_MH_ATTN_V_WEIGHTS = 2, /* input projection weights for 'values' */
+    CUDNN_MH_ATTN_O_WEIGHTS = 3, /* output projection weights */
+    CUDNN_MH_ATTN_Q_BIASES  = 4, /* input projection bias tensor for 'queries' */
+    CUDNN_MH_ATTN_K_BIASES  = 5, /* input projection bias for 'keys' */
+    CUDNN_MH_ATTN_V_BIASES  = 6, /* input projection bias for 'values' */
+    CUDNN_MH_ATTN_O_BIASES  = 7, /* output projection biases */
+} cudnnMultiHeadAttnWeightKind_t;
+
+#define CUDNN_ATTN_WKIND_COUNT 8 /* Number of attention weight/bias tensors */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnWeights(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             cudnnMultiHeadAttnWeightKind_t wKind,
+                             size_t weightSizeInBytes,
+                             const void *weights,
+                             cudnnTensorDescriptor_t wDesc,
+                             void **wAddr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnForward(cudnnHandle_t handle,
+                          const cudnnAttnDescriptor_t attnDesc,
+                          int currIdx,
+                          const int loWinIdx[],
+                          const int hiWinIdx[],
+                          const int devSeqLengthsQO[],
+                          const int devSeqLengthsKV[],
+                          const cudnnSeqDataDescriptor_t qDesc,
+                          const void *queries,
+                          const void *residuals,
+                          const cudnnSeqDataDescriptor_t kDesc,
+                          const void *keys,
+                          const cudnnSeqDataDescriptor_t vDesc,
+                          const void *values,
+                          const cudnnSeqDataDescriptor_t oDesc,
+                          void *out,
+                          size_t weightSizeInBytes,
+                          const void *weights,
+                          size_t workSpaceSizeInBytes,
+                          void *workSpace,
+                          size_t reserveSpaceSizeInBytes,
+                          void *reserveSpace);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnAdvVersionCheck(void);
+
+typedef enum {
+    CUDNN_WGRAD_MODE_ADD = 0, /* add partial gradients to wgrad output buffers */
+    CUDNN_WGRAD_MODE_SET = 1, /* write partial gradients to wgrad output buffers */
+} cudnnWgradMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardData_v8(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        const int32_t devSeqLengths[],
+                        cudnnRNNDataDescriptor_t yDesc,
+                        const void *y,
+                        const void *dy,
+                        cudnnRNNDataDescriptor_t xDesc,
+                        void *dx,
+                        cudnnTensorDescriptor_t hDesc,
+                        const void *hx,
+                        const void *dhy,
+                        void *dhx,
+                        cudnnTensorDescriptor_t cDesc,
+                        const void *cx,
+                        const void *dcy,
+                        void *dcx,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        size_t workSpaceSize,
+                        void *workSpace,
+                        size_t reserveSpaceSize,
+                        void *reserveSpace);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeights_v8(cudnnHandle_t handle,
+                           cudnnRNNDescriptor_t rnnDesc,
+                           cudnnWgradMode_t addGrad,
+                           const int32_t devSeqLengths[],
+                           cudnnRNNDataDescriptor_t xDesc,
+                           const void *x,
+                           cudnnTensorDescriptor_t hDesc,
+                           const void *hx,
+                           cudnnRNNDataDescriptor_t yDesc,
+                           const void *y,
+                           size_t weightSpaceSize,
+                           void *dweightSpace,
+                           size_t workSpaceSize,
+                           void *workSpace,
+                           size_t reserveSpaceSize,
+                           void *reserveSpace);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardData(cudnnHandle_t handle,
+                               const cudnnAttnDescriptor_t attnDesc,
+                               const int loWinIdx[],
+                               const int hiWinIdx[],
+                               const int devSeqLengthsDQDO[],
+                               const int devSeqLengthsDKDV[],
+                               const cudnnSeqDataDescriptor_t doDesc,
+                               const void *dout,
+                               const cudnnSeqDataDescriptor_t dqDesc,
+                               void *dqueries,
+                               const void *queries,
+                               const cudnnSeqDataDescriptor_t dkDesc,
+                               void *dkeys,
+                               const void *keys,
+                               const cudnnSeqDataDescriptor_t dvDesc,
+                               void *dvalues,
+                               const void *values,
+                               size_t weightSizeInBytes,
+                               const void *weights,
+                               size_t workSpaceSizeInBytes,
+                               void *workSpace,
+                               size_t reserveSpaceSizeInBytes,
+                               void *reserveSpace);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardWeights(cudnnHandle_t handle,
+                                  const cudnnAttnDescriptor_t attnDesc,
+                                  cudnnWgradMode_t addGrad,
+                                  const cudnnSeqDataDescriptor_t qDesc,
+                                  const void *queries,
+                                  const cudnnSeqDataDescriptor_t kDesc,
+                                  const void *keys,
+                                  const cudnnSeqDataDescriptor_t vDesc,
+                                  const void *values,
+                                  const cudnnSeqDataDescriptor_t doDesc,
+                                  const void *dout,
+                                  size_t weightSizeInBytes,
+                                  const void *weights,
+                                  void *dweights,
+                                  size_t workSpaceSizeInBytes,
+                                  void *workSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  void *reserveSpace);
+
+/*
+ * CTC (Connectionist Temporal Classification) loss descriptor create/destory/set/get functions
+ */
+/* Input normalization mode for loss function */
+typedef enum {
+    CUDNN_LOSS_NORMALIZATION_NONE    = 0,
+    CUDNN_LOSS_NORMALIZATION_SOFTMAX = 1,
+} cudnnLossNormalizationMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateCTCLossDescriptor(cudnnCTCLossDescriptor_t *ctcLossDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t compType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t compType,
+                            cudnnLossNormalizationMode_t normMode,
+                            cudnnNanPropagation_t gradMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t compType,
+                             cudnnLossNormalizationMode_t normMode,
+                             cudnnNanPropagation_t gradMode,
+                             int maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor_v9(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t compType,
+                             cudnnLossNormalizationMode_t normMode,
+                             cudnnCTCGradMode_t ctcGradMode,
+                             int maxLabelLength);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t *compType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t *compType,
+                            cudnnLossNormalizationMode_t *normMode,
+                            cudnnNanPropagation_t *gradMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t *compType,
+                             cudnnLossNormalizationMode_t *normMode,
+                             cudnnNanPropagation_t *gradMode,
+                             int *maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor_v9(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t *compType,
+                             cudnnLossNormalizationMode_t *normMode,
+                             cudnnCTCGradMode_t *ctcGradMode,
+                             int *maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc);
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size)  */
+    const void *probs,                       /* probabilities after softmax, in GPU memory */
+    const int hostLabels[],                  /* labels, in CPU memory */
+    const int hostLabelLengths[],            /* the length of each label, in CPU memory */
+    const int hostInputLengths[],            /* the lengths of timing steps in each batch, in CPU memory */
+    void *costs,                             /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,         /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    void *workspace,              /* pointer to the workspace, in GPU memory */
+    size_t workSpaceSizeInBytes); /* size of the workspace */
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size)  */
+    const void *probs,                       /* probabilities after softmax, in GPU memory */
+    const int labels[],                      /* labels, in GPU memory */
+    const int labelLengths[],                /* the length of each label, in GPU memory */
+    const int inputLengths[],                /* the lengths of timing steps in each batch, in GPU memory */
+    void *costs,                             /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,             /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    size_t workSpaceSizeInBytes, /* size of the workspace */
+    void *workspace);            /* pointer to the workspace, in GPU memory */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    const int *labels,                           /* labels, in CPU memory */
+    const int *labelLengths,                     /* the length of each label, in CPU memory */
+    const int *inputLengths,                     /* the lengths of timing steps in each batch, in CPU memory */
+    cudnnCTCLossAlgo_t algo,                     /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    size_t *sizeInBytes); /* pointer to the returned workspace size */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    size_t *sizeInBytes);                        /* pointer to the returned workspace size */
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_ADV_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..5d9bef65d5323dd3354299569d869191a07615cf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_v9.h
@@ -0,0 +1,669 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn_adv : cuDNN's advanced and experimental features.
+
+*/
+
+#if !defined(CUDNN_ADV_H_)
+#define CUDNN_ADV_H_
+
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_ADV_MAJOR 9
+#define CUDNN_ADV_MINOR 10
+#define CUDNN_ADV_PATCH 2
+
+#if (CUDNN_ADV_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_MINOR != CUDNN_MINOR) || (CUDNN_ADV_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN ADV INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* BASIC RNN API */
+
+typedef enum {
+    CUDNN_RNN_ALGO_STANDARD               = 0,
+    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1,
+    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2,
+    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
+    CUDNN_RNN_ALGO_COUNT                  = 4,
+} cudnnRNNAlgo_t;
+
+typedef enum {
+    CUDNN_FWD_MODE_INFERENCE = 0,
+    CUDNN_FWD_MODE_TRAINING  = 1,
+} cudnnForwardMode_t;
+
+typedef enum {
+    CUDNN_RNN_RELU = 0, /* basic RNN cell type with ReLu activation */
+    CUDNN_RNN_TANH = 1, /* basic RNN cell type with tanh activation */
+    CUDNN_LSTM     = 2, /* LSTM with optional recurrent projection and clipping */
+    CUDNN_GRU      = 3, /* Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1); */
+} cudnnRNNMode_t;
+
+typedef enum {
+    CUDNN_RNN_NO_BIAS         = 0, /* rnn cell formulas do not use biases */
+    CUDNN_RNN_SINGLE_INP_BIAS = 1, /* rnn cell formulas use one input bias in input GEMM */
+    CUDNN_RNN_DOUBLE_BIAS     = 2, /* default, rnn cell formulas use two bias vectors */
+    CUDNN_RNN_SINGLE_REC_BIAS = 3  /* rnn cell formulas use one recurrent bias in recurrent GEMM */
+} cudnnRNNBiasMode_t;
+
+typedef enum {
+    CUDNN_UNIDIRECTIONAL = 0, /* single direction network */
+    CUDNN_BIDIRECTIONAL  = 1, /* output concatination at each layer */
+} cudnnDirectionMode_t;
+
+typedef enum {
+    CUDNN_LINEAR_INPUT = 0, /* adjustable weight matrix in first layer input GEMM */
+    CUDNN_SKIP_INPUT   = 1, /* fixed identity matrix in the first layer input GEMM */
+} cudnnRNNInputMode_t;
+
+typedef enum {
+    CUDNN_RNN_CLIP_NONE   = 0, /* disables LSTM cell clipping */
+    CUDNN_RNN_CLIP_MINMAX = 1, /* enables LSTM cell clipping */
+} cudnnRNNClipMode_t;
+
+typedef enum {
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED   = 0, /* padded, outer stride from one time-step to the next */
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_PACKED     = 1, /* sequence length sorted and packed as in basic RNN api */
+    CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED = 2, /* padded, outer stride from one batch to the next */
+} cudnnRNNDataLayout_t;
+
+/* For auxFlags in cudnnSetRNNDescriptor_v8() */
+#define CUDNN_RNN_PADDED_IO_DISABLED 0
+#define CUDNN_RNN_PADDED_IO_ENABLED (1U << 0)
+
+struct cudnnRNNStruct;
+typedef struct cudnnRNNStruct *cudnnRNNDescriptor_t;
+
+struct cudnnRNNDataStruct;
+typedef struct cudnnRNNDataStruct *cudnnRNNDataDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDescriptor(cudnnRNNDescriptor_t *rnnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDescriptor(cudnnRNNDescriptor_t rnnDesc);
+
+/*
+ * mathPrec in cudnnSetRNNDescriptor_v8() specifies compute precision.
+ * Compute precision is further modified by mathType that sets the
+ * preferred option for using NVIDIA Tensor Cores.  dataType specify
+ * input/output data type and weight/bias type.
+ */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t algo,
+                         cudnnRNNMode_t cellMode,
+                         cudnnRNNBiasMode_t biasMode,
+                         cudnnDirectionMode_t dirMode,
+                         cudnnRNNInputMode_t inputMode,
+                         cudnnDataType_t dataType,
+                         cudnnDataType_t mathPrec,
+                         cudnnMathType_t mathType,
+                         int32_t inputSize,
+                         int32_t hiddenSize,
+                         int32_t projSize,
+                         int32_t numLayers,
+                         cudnnDropoutDescriptor_t dropoutDesc,
+                         uint32_t auxFlags);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t *algo,
+                         cudnnRNNMode_t *cellMode,
+                         cudnnRNNBiasMode_t *biasMode,
+                         cudnnDirectionMode_t *dirMode,
+                         cudnnRNNInputMode_t *inputMode,
+                         cudnnDataType_t *dataType,
+                         cudnnDataType_t *mathPrec,
+                         cudnnMathType_t *mathType,
+                         int32_t *inputSize,
+                         int32_t *hiddenSize,
+                         int32_t *projSize,
+                         int32_t *numLayers,
+                         cudnnDropoutDescriptor_t *dropoutDesc,
+                         uint32_t *auxFlags);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t clipMode,
+                   cudnnNanPropagation_t clipNanOpt,
+                   double lclip,
+                   double rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip_v9(cudnnRNNDescriptor_t rnnDesc, cudnnRNNClipMode_t clipMode, double lclip, double rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t *clipMode,
+                   cudnnNanPropagation_t *clipNanOpt,
+                   double *lclip,
+                   double *rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip_v9(cudnnRNNDescriptor_t rnnDesc, cudnnRNNClipMode_t *clipMode, double *lclip, double *rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBuildRNNDynamic(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, int miniBatch);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNTempSpaceSizes(cudnnHandle_t handle,
+                          cudnnRNNDescriptor_t rnnDesc,
+                          cudnnForwardMode_t fwdMode,
+                          cudnnRNNDataDescriptor_t xDesc,
+                          size_t *workSpaceSize,
+                          size_t *reserveSpaceSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightSpaceSize(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, size_t *weightSpaceSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightParams(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        int32_t pseudoLayer,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        int32_t linLayerID,
+                        cudnnTensorDescriptor_t mDesc,
+                        void **mAddr,
+                        cudnnTensorDescriptor_t bDesc,
+                        void **bAddr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDataDescriptor(cudnnRNNDataDescriptor_t *rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t dataType,
+                          cudnnRNNDataLayout_t layout,
+                          int maxSeqLength,
+                          int batchSize,
+                          int vectorSize,
+                          const int seqLengthArray[], /* length of each sequence in the batch */
+                          void *paddingFill);         /* symbol for filling padding position in output */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t *dataType,
+                          cudnnRNNDataLayout_t *layout,
+                          int *maxSeqLength,
+                          int *batchSize,
+                          int *vectorSize,
+                          int arrayLengthRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNForward(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnForwardMode_t fwdMode,
+                const int32_t devSeqLengths[],
+                cudnnRNNDataDescriptor_t xDesc,
+                const void *x,
+                cudnnRNNDataDescriptor_t yDesc,
+                void *y,
+                cudnnTensorDescriptor_t hDesc,
+                const void *hx,
+                void *hy,
+                cudnnTensorDescriptor_t cDesc,
+                const void *cx,
+                void *cy,
+                size_t weightSpaceSize,
+                const void *weightSpace,
+                size_t workSpaceSize,
+                void *workSpace,
+                size_t reserveSpaceSize,
+                void *reserveSpace);
+
+/* Sequence data descriptor */
+
+typedef enum {
+    CUDNN_SEQDATA_TIME_DIM  = 0, /* index in time */
+    CUDNN_SEQDATA_BATCH_DIM = 1, /* index in batch */
+    CUDNN_SEQDATA_BEAM_DIM  = 2, /* index in beam */
+    CUDNN_SEQDATA_VECT_DIM  = 3  /* index in vector */
+} cudnnSeqDataAxis_t;
+
+struct cudnnSeqDataStruct;
+typedef struct cudnnSeqDataStruct *cudnnSeqDataDescriptor_t CUDNN_DEPRECATED;
+
+#define CUDNN_SEQDATA_DIM_COUNT 4 /* dimension count */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateSeqDataDescriptor(cudnnSeqDataDescriptor_t *seqDataDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroySeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetSeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t dataType,
+                          int nbDims,
+                          const int dimA[],
+                          const cudnnSeqDataAxis_t axes[],
+                          size_t seqLengthArraySize,
+                          const int seqLengthArray[],
+                          void *paddingFill);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetSeqDataDescriptor(const cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t *dataType,
+                          int *nbDims,
+                          int nbDimsRequested,
+                          int dimA[],
+                          cudnnSeqDataAxis_t axes[],
+                          size_t *seqLengthArraySize,
+                          size_t seqLengthSizeRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+/* Multihead Attention */
+
+/*
+ * Multi-head attention options passed via 'attnMode' in cudnnSetAttnDescriptor().
+ * Use the bitwise OR operator to combine several settings listed below.  Additional
+ * minor options can be added here w/o changing or introducing new API functions.
+ */
+#define CUDNN_ATTN_QUERYMAP_ALL_TO_ONE 0         /* multiple Q-s map to a single (K,V) set when beam size > 1 */
+#define CUDNN_ATTN_QUERYMAP_ONE_TO_ONE (1U << 0) /* multiple Q-s map to multiple (K,V) sets when beam size > 1 */
+#define CUDNN_ATTN_DISABLE_PROJ_BIASES 0         /* no biases in attention input and output projections */
+#define CUDNN_ATTN_ENABLE_PROJ_BIASES (1U << 1)  /* use biases in attention input and output projections */
+
+struct cudnnAttnStruct;
+typedef struct cudnnAttnStruct *cudnnAttnDescriptor_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAttnDescriptor(cudnnAttnDescriptor_t *attnDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAttnDescriptor(cudnnAttnDescriptor_t attnDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned attnMode,
+                       int nHeads,
+                       double smScaler,
+                       cudnnDataType_t dataType,
+                       cudnnDataType_t computePrec,
+                       cudnnMathType_t mathType,
+                       cudnnDropoutDescriptor_t attnDropoutDesc,
+                       cudnnDropoutDescriptor_t postDropoutDesc,
+                       int qSize,
+                       int kSize,
+                       int vSize,
+                       int qProjSize,
+                       int kProjSize,
+                       int vProjSize,
+                       int oProjSize,
+                       int qoMaxSeqLength,
+                       int kvMaxSeqLength,
+                       int maxBatchSize,
+                       int maxBeamSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned *attnMode,
+                       int *nHeads,
+                       double *smScaler,
+                       cudnnDataType_t *dataType,
+                       cudnnDataType_t *computePrec,
+                       cudnnMathType_t *mathType,
+                       cudnnDropoutDescriptor_t *attnDropoutDesc,
+                       cudnnDropoutDescriptor_t *postDropoutDesc,
+                       int *qSize,
+                       int *kSize,
+                       int *vSize,
+                       int *qProjSize,
+                       int *kProjSize,
+                       int *vProjSize,
+                       int *oProjSize,
+                       int *qoMaxSeqLength,
+                       int *kvMaxSeqLength,
+                       int *maxBatchSize,
+                       int *maxBeamSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnBuffers(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             size_t *weightSizeInBytes,
+                             size_t *workSpaceSizeInBytes,
+                             size_t *reserveSpaceSizeInBytes);
+
+typedef enum {
+    CUDNN_MH_ATTN_Q_WEIGHTS = 0, /* input projection weights for 'queries' */
+    CUDNN_MH_ATTN_K_WEIGHTS = 1, /* input projection weights for 'keys' */
+    CUDNN_MH_ATTN_V_WEIGHTS = 2, /* input projection weights for 'values' */
+    CUDNN_MH_ATTN_O_WEIGHTS = 3, /* output projection weights */
+    CUDNN_MH_ATTN_Q_BIASES  = 4, /* input projection bias tensor for 'queries' */
+    CUDNN_MH_ATTN_K_BIASES  = 5, /* input projection bias for 'keys' */
+    CUDNN_MH_ATTN_V_BIASES  = 6, /* input projection bias for 'values' */
+    CUDNN_MH_ATTN_O_BIASES  = 7, /* output projection biases */
+} cudnnMultiHeadAttnWeightKind_t;
+
+#define CUDNN_ATTN_WKIND_COUNT 8 /* Number of attention weight/bias tensors */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnWeights(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             cudnnMultiHeadAttnWeightKind_t wKind,
+                             size_t weightSizeInBytes,
+                             const void *weights,
+                             cudnnTensorDescriptor_t wDesc,
+                             void **wAddr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnForward(cudnnHandle_t handle,
+                          const cudnnAttnDescriptor_t attnDesc,
+                          int currIdx,
+                          const int loWinIdx[],
+                          const int hiWinIdx[],
+                          const int devSeqLengthsQO[],
+                          const int devSeqLengthsKV[],
+                          const cudnnSeqDataDescriptor_t qDesc,
+                          const void *queries,
+                          const void *residuals,
+                          const cudnnSeqDataDescriptor_t kDesc,
+                          const void *keys,
+                          const cudnnSeqDataDescriptor_t vDesc,
+                          const void *values,
+                          const cudnnSeqDataDescriptor_t oDesc,
+                          void *out,
+                          size_t weightSizeInBytes,
+                          const void *weights,
+                          size_t workSpaceSizeInBytes,
+                          void *workSpace,
+                          size_t reserveSpaceSizeInBytes,
+                          void *reserveSpace);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnAdvVersionCheck(void);
+
+typedef enum {
+    CUDNN_WGRAD_MODE_ADD = 0, /* add partial gradients to wgrad output buffers */
+    CUDNN_WGRAD_MODE_SET = 1, /* write partial gradients to wgrad output buffers */
+} cudnnWgradMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardData_v8(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        const int32_t devSeqLengths[],
+                        cudnnRNNDataDescriptor_t yDesc,
+                        const void *y,
+                        const void *dy,
+                        cudnnRNNDataDescriptor_t xDesc,
+                        void *dx,
+                        cudnnTensorDescriptor_t hDesc,
+                        const void *hx,
+                        const void *dhy,
+                        void *dhx,
+                        cudnnTensorDescriptor_t cDesc,
+                        const void *cx,
+                        const void *dcy,
+                        void *dcx,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        size_t workSpaceSize,
+                        void *workSpace,
+                        size_t reserveSpaceSize,
+                        void *reserveSpace);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeights_v8(cudnnHandle_t handle,
+                           cudnnRNNDescriptor_t rnnDesc,
+                           cudnnWgradMode_t addGrad,
+                           const int32_t devSeqLengths[],
+                           cudnnRNNDataDescriptor_t xDesc,
+                           const void *x,
+                           cudnnTensorDescriptor_t hDesc,
+                           const void *hx,
+                           cudnnRNNDataDescriptor_t yDesc,
+                           const void *y,
+                           size_t weightSpaceSize,
+                           void *dweightSpace,
+                           size_t workSpaceSize,
+                           void *workSpace,
+                           size_t reserveSpaceSize,
+                           void *reserveSpace);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardData(cudnnHandle_t handle,
+                               const cudnnAttnDescriptor_t attnDesc,
+                               const int loWinIdx[],
+                               const int hiWinIdx[],
+                               const int devSeqLengthsDQDO[],
+                               const int devSeqLengthsDKDV[],
+                               const cudnnSeqDataDescriptor_t doDesc,
+                               const void *dout,
+                               const cudnnSeqDataDescriptor_t dqDesc,
+                               void *dqueries,
+                               const void *queries,
+                               const cudnnSeqDataDescriptor_t dkDesc,
+                               void *dkeys,
+                               const void *keys,
+                               const cudnnSeqDataDescriptor_t dvDesc,
+                               void *dvalues,
+                               const void *values,
+                               size_t weightSizeInBytes,
+                               const void *weights,
+                               size_t workSpaceSizeInBytes,
+                               void *workSpace,
+                               size_t reserveSpaceSizeInBytes,
+                               void *reserveSpace);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardWeights(cudnnHandle_t handle,
+                                  const cudnnAttnDescriptor_t attnDesc,
+                                  cudnnWgradMode_t addGrad,
+                                  const cudnnSeqDataDescriptor_t qDesc,
+                                  const void *queries,
+                                  const cudnnSeqDataDescriptor_t kDesc,
+                                  const void *keys,
+                                  const cudnnSeqDataDescriptor_t vDesc,
+                                  const void *values,
+                                  const cudnnSeqDataDescriptor_t doDesc,
+                                  const void *dout,
+                                  size_t weightSizeInBytes,
+                                  const void *weights,
+                                  void *dweights,
+                                  size_t workSpaceSizeInBytes,
+                                  void *workSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  void *reserveSpace);
+
+/*
+ * CTC (Connectionist Temporal Classification) loss descriptor create/destory/set/get functions
+ */
+/* Input normalization mode for loss function */
+typedef enum {
+    CUDNN_LOSS_NORMALIZATION_NONE    = 0,
+    CUDNN_LOSS_NORMALIZATION_SOFTMAX = 1,
+} cudnnLossNormalizationMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateCTCLossDescriptor(cudnnCTCLossDescriptor_t *ctcLossDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t compType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t compType,
+                            cudnnLossNormalizationMode_t normMode,
+                            cudnnNanPropagation_t gradMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t compType,
+                             cudnnLossNormalizationMode_t normMode,
+                             cudnnNanPropagation_t gradMode,
+                             int maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor_v9(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t compType,
+                             cudnnLossNormalizationMode_t normMode,
+                             cudnnCTCGradMode_t ctcGradMode,
+                             int maxLabelLength);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t *compType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t *compType,
+                            cudnnLossNormalizationMode_t *normMode,
+                            cudnnNanPropagation_t *gradMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t *compType,
+                             cudnnLossNormalizationMode_t *normMode,
+                             cudnnNanPropagation_t *gradMode,
+                             int *maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor_v9(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t *compType,
+                             cudnnLossNormalizationMode_t *normMode,
+                             cudnnCTCGradMode_t *ctcGradMode,
+                             int *maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc);
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size)  */
+    const void *probs,                       /* probabilities after softmax, in GPU memory */
+    const int hostLabels[],                  /* labels, in CPU memory */
+    const int hostLabelLengths[],            /* the length of each label, in CPU memory */
+    const int hostInputLengths[],            /* the lengths of timing steps in each batch, in CPU memory */
+    void *costs,                             /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,         /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    void *workspace,              /* pointer to the workspace, in GPU memory */
+    size_t workSpaceSizeInBytes); /* size of the workspace */
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size)  */
+    const void *probs,                       /* probabilities after softmax, in GPU memory */
+    const int labels[],                      /* labels, in GPU memory */
+    const int labelLengths[],                /* the length of each label, in GPU memory */
+    const int inputLengths[],                /* the lengths of timing steps in each batch, in GPU memory */
+    void *costs,                             /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,             /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    size_t workSpaceSizeInBytes, /* size of the workspace */
+    void *workspace);            /* pointer to the workspace, in GPU memory */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    const int *labels,                           /* labels, in CPU memory */
+    const int *labelLengths,                     /* the length of each label, in CPU memory */
+    const int *inputLengths,                     /* the lengths of timing steps in each batch, in CPU memory */
+    cudnnCTCLossAlgo_t algo,                     /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    size_t *sizeInBytes); /* pointer to the returned workspace size */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    size_t *sizeInBytes);                        /* pointer to the returned workspace size */
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_ADV_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a378e2087f7a45c423f65d213d98c4fa20f3a52
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend.h
@@ -0,0 +1,60 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDNN_BACKEND_H_
+#define _CUDNN_BACKEND_H_
+
+/*
+ * The content of this header has been moved into cudnn_graph.h.
+ * This header is kept for the backward compatibility purpose.
+ */
+
+#include "cudnn_graph.h"
+
+#endif /* _CUDNN_BACKEND_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a378e2087f7a45c423f65d213d98c4fa20f3a52
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend_v9.h
@@ -0,0 +1,60 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDNN_BACKEND_H_
+#define _CUDNN_BACKEND_H_
+
+/*
+ * The content of this header has been moved into cudnn_graph.h.
+ * This header is kept for the backward compatibility purpose.
+ */
+
+#include "cudnn_graph.h"
+
+#endif /* _CUDNN_BACKEND_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn.h
new file mode 100644
index 0000000000000000000000000000000000000000..e988a8a033df31e35a37aeba12b9a7cdc1d7ed60
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn.h
@@ -0,0 +1,693 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_cnn : cuDNN's basic definitions and CNN functions.
+ */
+
+#if !defined(CUDNN_CNN_H_)
+#define CUDNN_CNN_H_
+
+#pragma once
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_CNN_MAJOR 9
+#define CUDNN_CNN_MINOR 10
+#define CUDNN_CNN_PATCH 2
+
+#if (CUDNN_CNN_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_MINOR != CUDNN_MINOR) || (CUDNN_CNN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN CNN INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef struct cudnnConvolutionStruct *cudnnConvolutionDescriptor_t CUDNN_DEPRECATED;
+
+typedef struct cudnnConvolutionFwdAlgoPerfStruct {
+    cudnnConvolutionFwdAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionFwdAlgoPerf_t CUDNN_DEPRECATED;
+
+/* Create an instance of convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateConvolutionDescriptor(cudnnConvolutionDescriptor_t *convDesc);
+
+/* Destroy an instance of convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyConvolutionDescriptor(cudnnConvolutionDescriptor_t convDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t mathType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t *mathType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int groupCount);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int *groupCount);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t reorderType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t *reorderType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolution2dDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int pad_h,      /* zero-padding height */
+                                int pad_w,      /* zero-padding width */
+                                int u,          /* vertical filter stride */
+                                int v,          /* horizontal filter stride */
+                                int dilation_h, /* filter dilation in the vertical dimension */
+                                int dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int *pad_h,      /* zero-padding height */
+                                int *pad_w,      /* zero-padding width */
+                                int *u,          /* vertical filter stride */
+                                int *v,          /* horizontal filter stride */
+                                int *dilation_h, /* filter dilation in the vertical dimension */
+                                int *dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionNdDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLength, /* nbDims-2 size */
+                                const int padA[],
+                                const int filterStrideA[],
+                                const int dilationA[],
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType); /* convolution data type */
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLengthRequested,
+                                int *arrayLength,
+                                int padA[],
+                                int strideA[],
+                                int dilationA[],
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType); /* convolution data type */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int *n,
+                                      int *c,
+                                      int *h,
+                                      int *w);
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int nbDims,
+                                      int tensorOuputDimA[]);
+
+/* helper function to provide the convolution forward algo that fit best the requirement */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithm_v7(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t srcDesc,
+                                       const cudnnFilterDescriptor_t filterDesc,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t destDesc,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithm(cudnnHandle_t handle,
+                                     const cudnnTensorDescriptor_t xDesc,
+                                     const cudnnFilterDescriptor_t wDesc,
+                                     const cudnnConvolutionDescriptor_t convDesc,
+                                     const cudnnTensorDescriptor_t yDesc,
+                                     const int requestedAlgoCount,
+                                     int *returnedAlgoCount,
+                                     cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t xDesc,
+                                       const void *x,
+                                       const cudnnFilterDescriptor_t wDesc,
+                                       const void *w,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t yDesc,
+                                       void *y,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults,
+                                       void *workSpace,
+                                       size_t workSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnIm2Col(cudnnHandle_t handle,
+            const cudnnTensorDescriptor_t xDesc,
+            const void *x,
+            const cudnnFilterDescriptor_t wDesc,
+            const cudnnConvolutionDescriptor_t convDesc,
+            void *colBuffer);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnReorderFilterAndBias(cudnnHandle_t handle,
+                          const cudnnFilterDescriptor_t filterDesc,
+                          cudnnReorderType_t reorderType,
+                          const void *filterData,
+                          void *reorderedFilterData,
+                          int reorderBias,
+                          const void *biasData,
+                          void *reorderedBiasData);
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle_t handle,
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const cudnnFilterDescriptor_t wDesc,
+                                        const cudnnConvolutionDescriptor_t convDesc,
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        cudnnConvolutionFwdAlgo_t algo,
+                                        size_t *sizeInBytes);
+
+/* Convolution functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform the forward pass for batch convolution */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionForward(cudnnHandle_t handle,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const cudnnFilterDescriptor_t wDesc,
+                        const void *w,
+                        const cudnnConvolutionDescriptor_t convDesc,
+                        cudnnConvolutionFwdAlgo_t algo,
+                        void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t yDesc,
+                        void *y);
+
+/* Fused conv/bias/activation operation : y = Act( alpha1 * conv(x) + alpha2 * z + bias ) */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBiasActivationForward(cudnnHandle_t handle,
+                                      const void *alpha1,
+                                      const cudnnTensorDescriptor_t xDesc,
+                                      const void *x,
+                                      const cudnnFilterDescriptor_t wDesc,
+                                      const void *w,
+                                      const cudnnConvolutionDescriptor_t convDesc,
+                                      cudnnConvolutionFwdAlgo_t algo,
+                                      void *workSpace,
+                                      size_t workSpaceSizeInBytes,
+                                      const void *alpha2,
+                                      const cudnnTensorDescriptor_t zDesc,
+                                      const void *z,
+                                      const cudnnTensorDescriptor_t biasDesc,
+                                      const void *bias,
+                                      const cudnnActivationDescriptor_t activationDesc,
+                                      const cudnnTensorDescriptor_t yDesc,
+                                      void *y);
+
+/* helper function to provide the convolution backward data algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdDataAlgoPerfStruct {
+    cudnnConvolutionBwdDataAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdDataAlgoPerf_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithm(cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t wDesc,
+                                          const cudnnTensorDescriptor_t dyDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t dxDesc,
+                                          const int requestedAlgoCount,
+                                          int *returnedAlgoCount,
+                                          cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithmEx(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t wDesc,
+                                            const void *w,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const void *dy,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t dxDesc,
+                                            void *dx,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults,
+                                            void *workSpace,
+                                            size_t workSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithm_v7(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t filterDesc,
+                                            const cudnnTensorDescriptor_t diffDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t gradDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataWorkspaceSize(cudnnHandle_t handle,
+                                             const cudnnFilterDescriptor_t wDesc,
+                                             const cudnnTensorDescriptor_t dyDesc,
+                                             const cudnnConvolutionDescriptor_t convDesc,
+                                             const cudnnTensorDescriptor_t dxDesc,
+                                             cudnnConvolutionBwdDataAlgo_t algo,
+                                             size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardData(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnFilterDescriptor_t wDesc,
+                             const void *w,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnConvolutionDescriptor_t convDesc,
+                             cudnnConvolutionBwdDataAlgo_t algo,
+                             void *workSpace,
+                             size_t workSpaceSizeInBytes,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+/* Helper function to calculate folding descriptors for dgrad */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFoldedConvBackwardDataDescriptors(const cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t filterDesc,
+                                          const cudnnTensorDescriptor_t diffDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t gradDesc,
+                                          const cudnnTensorFormat_t transformFormat,
+                                          cudnnFilterDescriptor_t foldedFilterDesc,
+                                          cudnnTensorDescriptor_t paddedDiffDesc,
+                                          cudnnConvolutionDescriptor_t foldedConvDesc,
+                                          cudnnTensorDescriptor_t foldedGradDesc,
+                                          cudnnTensorTransformDescriptor_t filterFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t diffPadTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradUnfoldTransDesc);
+
+/* cudnnFusedOps... */
+struct cudnnFusedOpsConstParamStruct;
+typedef struct cudnnFusedOpsConstParamStruct *cudnnFusedOpsConstParamPack_t CUDNN_DEPRECATED;
+
+struct cudnnFusedOpsVariantParamStruct;
+typedef struct cudnnFusedOpsVariantParamStruct *cudnnFusedOpsVariantParamPack_t CUDNN_DEPRECATED;
+
+struct cudnnFusedOpsPlanStruct;
+typedef struct cudnnFusedOpsPlanStruct *cudnnFusedOpsPlan_t CUDNN_DEPRECATED;
+
+typedef enum {
+    /* each op in [ ] can be disabled by passing NULL ptr */
+    /* [per channel scale], [per channel bias], [activation], convolution, [generate BN stats] */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_CONV_BNSTATS = 0,
+    /* [per channel scale], [per channel bias], [activation], convolutionBackwardWeights */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_WGRAD = 1,
+    /* utility for BN training in BN-conv fusion */
+    /* computes the equivalent scale and bias from ySum ySqSum and learned scale, bias */
+    /* optionally update running stats and generate saved stats */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_TRAINING = 2,
+    /* utility for BN inference in BN-conv fusion */
+    /* computes the equivalent scale and bias from learned running stats and learned scale, bias */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_INFERENCE = 3,
+    /* reserved for future use: convolution, [per channel scale], [per channel bias], [residual add], [activation] */
+    CUDNN_FUSED_CONV_SCALE_BIAS_ADD_ACTIVATION = 4,
+    /* reserved for future use: [per channel scale], [per channel bias], [residual add],  activation, bitmask */
+    CUDNN_FUSED_SCALE_BIAS_ADD_ACTIVATION_GEN_BITMASK = 5,
+    /* reserved for future use */
+    CUDNN_FUSED_DACTIVATION_FORK_DBATCHNORM = 6,
+} cudnnFusedOps_t CUDNN_DEPRECATED;
+
+typedef enum {
+    /* set XDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get XDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_XDESC = 0,
+    /* set/get XDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_XDATA_PLACEHOLDER = 1,
+    /* set/get BN_MODE: pass cudnnBatchNormMode_t* */
+    CUDNN_PARAM_BN_MODE = 2,
+    /* set CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_EQSCALEBIAS_DESC = 3,
+    /* set/get BN_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQSCALE_PLACEHOLDER = 4,
+    /* set/get BN_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQBIAS_PLACEHOLDER = 5,
+    /* set ACTIVATION_DESC: pass previously initialized cudnnActivationDescriptor_t */
+    /* get ACTIVATION_DESC: pass previously created cudnnActivationDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_DESC = 6,
+    /* set CONV_DESC: pass previously initialized cudnnConvolutionDescriptor_t */
+    /* get CONV_DESC: pass previously created cudnnConvolutionDescriptor_t */
+    CUDNN_PARAM_CONV_DESC = 7,
+    /* set WDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get WDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_WDESC = 8,
+    /* set/get WDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_WDATA_PLACEHOLDER = 9,
+    /* set DWDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get DWDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_DWDESC = 10,
+    /* set/get DWDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DWDATA_PLACEHOLDER = 11,
+    /* set YDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YDESC = 12,
+    /* set/get YDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YDATA_PLACEHOLDER = 13,
+    /* set DYDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DYDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DYDESC = 14,
+    /* set/get DYDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DYDATA_PLACEHOLDER = 15,
+    /* set YSTATS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YSTATS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YSTATS_DESC = 16,
+    /* set/get YSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSUM_PLACEHOLDER = 17,
+    /* set/get YSQSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSQSUM_PLACEHOLDER = 18,
+    /* set CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC = 19,
+    /* set/get CUDNN_PARAM_BN_SCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SCALE_PLACEHOLDER = 20,
+    /* set/get CUDNN_PARAM_BN_BIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_BIAS_PLACEHOLDER = 21,
+    /* set/get CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER = 22,
+    /* set/get CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER = 23,
+    /* set/get CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER = 24,
+    /* set/get CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER = 25,
+
+    /* set ZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ZDESC = 26,
+    /* set/get ZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ZDATA_PLACEHOLDER = 27,
+    /* set BN_Z_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get BN_Z_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_Z_EQSCALEBIAS_DESC = 28,
+    /* set/get BN_Z_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQSCALE_PLACEHOLDER = 29,
+    /* set/get BN_Z_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQBIAS_PLACEHOLDER = 30,
+
+    /* set ACTIVATION_BITMASK_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ACTIVATION_BITMASK_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_BITMASK_DESC = 31,
+    /* set/get ACTIVATION_BITMASK_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ACTIVATION_BITMASK_PLACEHOLDER = 32,
+
+    /* set DXDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DXDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DXDESC = 33,
+    /* set/get DXDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DXDATA_PLACEHOLDER = 34,
+    /* set DZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DZDESC = 35,
+    /* set/get DZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DZDATA_PLACEHOLDER = 36,
+    /* set/get CUDNN_PARAM_BN_DSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DSCALE_PLACEHOLDER = 37,
+    /* set/get CUDNN_PARAM_BN_DBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DBIAS_PLACEHOLDER = 38,
+} cudnnFusedOpsConstParamLabel_t CUDNN_DEPRECATED;
+
+typedef enum {
+    CUDNN_PTR_NULL         = 0,
+    CUDNN_PTR_ELEM_ALIGNED = 1,
+    CUDNN_PTR_16B_ALIGNED  = 2,
+} cudnnFusedOpsPointerPlaceHolder_t CUDNN_DEPRECATED;
+
+typedef enum {
+    /* set: pass void* pointing to dev memory */
+    /* get: pass void** pointing to host memory */
+    CUDNN_PTR_XDATA              = 0,
+    CUDNN_PTR_BN_EQSCALE         = 1,
+    CUDNN_PTR_BN_EQBIAS          = 2,
+    CUDNN_PTR_WDATA              = 3,
+    CUDNN_PTR_DWDATA             = 4,
+    CUDNN_PTR_YDATA              = 5,
+    CUDNN_PTR_DYDATA             = 6,
+    CUDNN_PTR_YSUM               = 7,
+    CUDNN_PTR_YSQSUM             = 8,
+    CUDNN_PTR_WORKSPACE          = 9,
+    CUDNN_PTR_BN_SCALE           = 10,
+    CUDNN_PTR_BN_BIAS            = 11,
+    CUDNN_PTR_BN_SAVED_MEAN      = 12,
+    CUDNN_PTR_BN_SAVED_INVSTD    = 13,
+    CUDNN_PTR_BN_RUNNING_MEAN    = 14,
+    CUDNN_PTR_BN_RUNNING_VAR     = 15,
+    CUDNN_PTR_ZDATA              = 16,
+    CUDNN_PTR_BN_Z_EQSCALE       = 17,
+    CUDNN_PTR_BN_Z_EQBIAS        = 18,
+    CUDNN_PTR_ACTIVATION_BITMASK = 19,
+    CUDNN_PTR_DXDATA             = 20,
+    CUDNN_PTR_DZDATA             = 21,
+    CUDNN_PTR_BN_DSCALE          = 22,
+    CUDNN_PTR_BN_DBIAS           = 23,
+
+    /* set/get: pass size_t* pointing to host memory */
+    CUDNN_SCALAR_SIZE_T_WORKSPACE_SIZE_IN_BYTES = 100,
+    /* set/get: pass int64_t* pointing to host memory */
+    CUDNN_SCALAR_INT64_T_BN_ACCUMULATION_COUNT = 101,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EXP_AVG_FACTOR = 102,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EPSILON = 103,
+} cudnnFusedOpsVariantParamLabel_t CUDNN_DEPRECATED;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCnnVersionCheck(void);
+
+/* helper function to provide the convolution backward filter algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdFilterAlgoPerfStruct {
+    cudnnConvolutionBwdFilterAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdFilterAlgoPerf_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithm(cudnnHandle_t handle,
+                                            const cudnnTensorDescriptor_t xDesc,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnFilterDescriptor_t dwDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithmEx(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              const void *x,
+                                              const cudnnTensorDescriptor_t dyDesc,
+                                              const void *y,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t dwDesc,
+                                              void *dw,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults,
+                                              void *workSpace,
+                                              size_t workSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithm_v7(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t srcDesc,
+                                              const cudnnTensorDescriptor_t diffDesc,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t gradDesc,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnnHandle_t handle,
+                                               const cudnnTensorDescriptor_t xDesc,
+                                               const cudnnTensorDescriptor_t dyDesc,
+                                               const cudnnConvolutionDescriptor_t convDesc,
+                                               const cudnnFilterDescriptor_t gradDesc,
+                                               cudnnConvolutionBwdFilterAlgo_t algo,
+                                               size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardFilter(cudnnHandle_t handle,
+                               const void *alpha,
+                               const cudnnTensorDescriptor_t xDesc,
+                               const void *x,
+                               const cudnnTensorDescriptor_t dyDesc,
+                               const void *dy,
+                               const cudnnConvolutionDescriptor_t convDesc,
+                               cudnnConvolutionBwdFilterAlgo_t algo,
+                               void *workSpace,
+                               size_t workSpaceSizeInBytes,
+                               const void *beta,
+                               const cudnnFilterDescriptor_t dwDesc,
+                               void *dw);
+
+/* Function to compute the bias gradient for batch convolution */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardBias(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dbDesc,
+                             void *db);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t *constPack, cudnnFusedOps_t ops);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t constPack);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsConstParamPackAttribute(cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        const void *param);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsConstParamPackAttribute(const cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        void *param,
+                                        int *isNULL);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t *varPack, cudnnFusedOps_t ops);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t varPack);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsVariantParamPackAttribute(cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsVariantParamPackAttribute(const cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsPlan(cudnnFusedOpsPlan_t *plan, cudnnFusedOps_t ops);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsPlan(cudnnFusedOpsPlan_t plan);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMakeFusedOpsPlan(cudnnHandle_t handle,
+                      cudnnFusedOpsPlan_t plan,
+                      const cudnnFusedOpsConstParamPack_t constPack,
+                      size_t *workspaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFusedOpsExecute(cudnnHandle_t handle, const cudnnFusedOpsPlan_t plan, cudnnFusedOpsVariantParamPack_t varPack);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_CNN_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..e988a8a033df31e35a37aeba12b9a7cdc1d7ed60
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_v9.h
@@ -0,0 +1,693 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_cnn : cuDNN's basic definitions and CNN functions.
+ */
+
+#if !defined(CUDNN_CNN_H_)
+#define CUDNN_CNN_H_
+
+#pragma once
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_CNN_MAJOR 9
+#define CUDNN_CNN_MINOR 10
+#define CUDNN_CNN_PATCH 2
+
+#if (CUDNN_CNN_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_MINOR != CUDNN_MINOR) || (CUDNN_CNN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN CNN INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef struct cudnnConvolutionStruct *cudnnConvolutionDescriptor_t CUDNN_DEPRECATED;
+
+typedef struct cudnnConvolutionFwdAlgoPerfStruct {
+    cudnnConvolutionFwdAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionFwdAlgoPerf_t CUDNN_DEPRECATED;
+
+/* Create an instance of convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateConvolutionDescriptor(cudnnConvolutionDescriptor_t *convDesc);
+
+/* Destroy an instance of convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyConvolutionDescriptor(cudnnConvolutionDescriptor_t convDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t mathType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t *mathType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int groupCount);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int *groupCount);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t reorderType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t *reorderType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolution2dDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int pad_h,      /* zero-padding height */
+                                int pad_w,      /* zero-padding width */
+                                int u,          /* vertical filter stride */
+                                int v,          /* horizontal filter stride */
+                                int dilation_h, /* filter dilation in the vertical dimension */
+                                int dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int *pad_h,      /* zero-padding height */
+                                int *pad_w,      /* zero-padding width */
+                                int *u,          /* vertical filter stride */
+                                int *v,          /* horizontal filter stride */
+                                int *dilation_h, /* filter dilation in the vertical dimension */
+                                int *dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionNdDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLength, /* nbDims-2 size */
+                                const int padA[],
+                                const int filterStrideA[],
+                                const int dilationA[],
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType); /* convolution data type */
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLengthRequested,
+                                int *arrayLength,
+                                int padA[],
+                                int strideA[],
+                                int dilationA[],
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType); /* convolution data type */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int *n,
+                                      int *c,
+                                      int *h,
+                                      int *w);
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int nbDims,
+                                      int tensorOuputDimA[]);
+
+/* helper function to provide the convolution forward algo that fit best the requirement */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithm_v7(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t srcDesc,
+                                       const cudnnFilterDescriptor_t filterDesc,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t destDesc,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithm(cudnnHandle_t handle,
+                                     const cudnnTensorDescriptor_t xDesc,
+                                     const cudnnFilterDescriptor_t wDesc,
+                                     const cudnnConvolutionDescriptor_t convDesc,
+                                     const cudnnTensorDescriptor_t yDesc,
+                                     const int requestedAlgoCount,
+                                     int *returnedAlgoCount,
+                                     cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t xDesc,
+                                       const void *x,
+                                       const cudnnFilterDescriptor_t wDesc,
+                                       const void *w,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t yDesc,
+                                       void *y,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults,
+                                       void *workSpace,
+                                       size_t workSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnIm2Col(cudnnHandle_t handle,
+            const cudnnTensorDescriptor_t xDesc,
+            const void *x,
+            const cudnnFilterDescriptor_t wDesc,
+            const cudnnConvolutionDescriptor_t convDesc,
+            void *colBuffer);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnReorderFilterAndBias(cudnnHandle_t handle,
+                          const cudnnFilterDescriptor_t filterDesc,
+                          cudnnReorderType_t reorderType,
+                          const void *filterData,
+                          void *reorderedFilterData,
+                          int reorderBias,
+                          const void *biasData,
+                          void *reorderedBiasData);
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle_t handle,
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const cudnnFilterDescriptor_t wDesc,
+                                        const cudnnConvolutionDescriptor_t convDesc,
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        cudnnConvolutionFwdAlgo_t algo,
+                                        size_t *sizeInBytes);
+
+/* Convolution functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform the forward pass for batch convolution */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionForward(cudnnHandle_t handle,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const cudnnFilterDescriptor_t wDesc,
+                        const void *w,
+                        const cudnnConvolutionDescriptor_t convDesc,
+                        cudnnConvolutionFwdAlgo_t algo,
+                        void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t yDesc,
+                        void *y);
+
+/* Fused conv/bias/activation operation : y = Act( alpha1 * conv(x) + alpha2 * z + bias ) */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBiasActivationForward(cudnnHandle_t handle,
+                                      const void *alpha1,
+                                      const cudnnTensorDescriptor_t xDesc,
+                                      const void *x,
+                                      const cudnnFilterDescriptor_t wDesc,
+                                      const void *w,
+                                      const cudnnConvolutionDescriptor_t convDesc,
+                                      cudnnConvolutionFwdAlgo_t algo,
+                                      void *workSpace,
+                                      size_t workSpaceSizeInBytes,
+                                      const void *alpha2,
+                                      const cudnnTensorDescriptor_t zDesc,
+                                      const void *z,
+                                      const cudnnTensorDescriptor_t biasDesc,
+                                      const void *bias,
+                                      const cudnnActivationDescriptor_t activationDesc,
+                                      const cudnnTensorDescriptor_t yDesc,
+                                      void *y);
+
+/* helper function to provide the convolution backward data algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdDataAlgoPerfStruct {
+    cudnnConvolutionBwdDataAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdDataAlgoPerf_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithm(cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t wDesc,
+                                          const cudnnTensorDescriptor_t dyDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t dxDesc,
+                                          const int requestedAlgoCount,
+                                          int *returnedAlgoCount,
+                                          cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithmEx(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t wDesc,
+                                            const void *w,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const void *dy,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t dxDesc,
+                                            void *dx,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults,
+                                            void *workSpace,
+                                            size_t workSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithm_v7(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t filterDesc,
+                                            const cudnnTensorDescriptor_t diffDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t gradDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataWorkspaceSize(cudnnHandle_t handle,
+                                             const cudnnFilterDescriptor_t wDesc,
+                                             const cudnnTensorDescriptor_t dyDesc,
+                                             const cudnnConvolutionDescriptor_t convDesc,
+                                             const cudnnTensorDescriptor_t dxDesc,
+                                             cudnnConvolutionBwdDataAlgo_t algo,
+                                             size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardData(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnFilterDescriptor_t wDesc,
+                             const void *w,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnConvolutionDescriptor_t convDesc,
+                             cudnnConvolutionBwdDataAlgo_t algo,
+                             void *workSpace,
+                             size_t workSpaceSizeInBytes,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+/* Helper function to calculate folding descriptors for dgrad */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFoldedConvBackwardDataDescriptors(const cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t filterDesc,
+                                          const cudnnTensorDescriptor_t diffDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t gradDesc,
+                                          const cudnnTensorFormat_t transformFormat,
+                                          cudnnFilterDescriptor_t foldedFilterDesc,
+                                          cudnnTensorDescriptor_t paddedDiffDesc,
+                                          cudnnConvolutionDescriptor_t foldedConvDesc,
+                                          cudnnTensorDescriptor_t foldedGradDesc,
+                                          cudnnTensorTransformDescriptor_t filterFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t diffPadTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradUnfoldTransDesc);
+
+/* cudnnFusedOps... */
+struct cudnnFusedOpsConstParamStruct;
+typedef struct cudnnFusedOpsConstParamStruct *cudnnFusedOpsConstParamPack_t CUDNN_DEPRECATED;
+
+struct cudnnFusedOpsVariantParamStruct;
+typedef struct cudnnFusedOpsVariantParamStruct *cudnnFusedOpsVariantParamPack_t CUDNN_DEPRECATED;
+
+struct cudnnFusedOpsPlanStruct;
+typedef struct cudnnFusedOpsPlanStruct *cudnnFusedOpsPlan_t CUDNN_DEPRECATED;
+
+typedef enum {
+    /* each op in [ ] can be disabled by passing NULL ptr */
+    /* [per channel scale], [per channel bias], [activation], convolution, [generate BN stats] */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_CONV_BNSTATS = 0,
+    /* [per channel scale], [per channel bias], [activation], convolutionBackwardWeights */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_WGRAD = 1,
+    /* utility for BN training in BN-conv fusion */
+    /* computes the equivalent scale and bias from ySum ySqSum and learned scale, bias */
+    /* optionally update running stats and generate saved stats */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_TRAINING = 2,
+    /* utility for BN inference in BN-conv fusion */
+    /* computes the equivalent scale and bias from learned running stats and learned scale, bias */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_INFERENCE = 3,
+    /* reserved for future use: convolution, [per channel scale], [per channel bias], [residual add], [activation] */
+    CUDNN_FUSED_CONV_SCALE_BIAS_ADD_ACTIVATION = 4,
+    /* reserved for future use: [per channel scale], [per channel bias], [residual add],  activation, bitmask */
+    CUDNN_FUSED_SCALE_BIAS_ADD_ACTIVATION_GEN_BITMASK = 5,
+    /* reserved for future use */
+    CUDNN_FUSED_DACTIVATION_FORK_DBATCHNORM = 6,
+} cudnnFusedOps_t CUDNN_DEPRECATED;
+
+typedef enum {
+    /* set XDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get XDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_XDESC = 0,
+    /* set/get XDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_XDATA_PLACEHOLDER = 1,
+    /* set/get BN_MODE: pass cudnnBatchNormMode_t* */
+    CUDNN_PARAM_BN_MODE = 2,
+    /* set CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_EQSCALEBIAS_DESC = 3,
+    /* set/get BN_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQSCALE_PLACEHOLDER = 4,
+    /* set/get BN_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQBIAS_PLACEHOLDER = 5,
+    /* set ACTIVATION_DESC: pass previously initialized cudnnActivationDescriptor_t */
+    /* get ACTIVATION_DESC: pass previously created cudnnActivationDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_DESC = 6,
+    /* set CONV_DESC: pass previously initialized cudnnConvolutionDescriptor_t */
+    /* get CONV_DESC: pass previously created cudnnConvolutionDescriptor_t */
+    CUDNN_PARAM_CONV_DESC = 7,
+    /* set WDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get WDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_WDESC = 8,
+    /* set/get WDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_WDATA_PLACEHOLDER = 9,
+    /* set DWDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get DWDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_DWDESC = 10,
+    /* set/get DWDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DWDATA_PLACEHOLDER = 11,
+    /* set YDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YDESC = 12,
+    /* set/get YDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YDATA_PLACEHOLDER = 13,
+    /* set DYDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DYDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DYDESC = 14,
+    /* set/get DYDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DYDATA_PLACEHOLDER = 15,
+    /* set YSTATS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YSTATS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YSTATS_DESC = 16,
+    /* set/get YSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSUM_PLACEHOLDER = 17,
+    /* set/get YSQSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSQSUM_PLACEHOLDER = 18,
+    /* set CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC = 19,
+    /* set/get CUDNN_PARAM_BN_SCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SCALE_PLACEHOLDER = 20,
+    /* set/get CUDNN_PARAM_BN_BIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_BIAS_PLACEHOLDER = 21,
+    /* set/get CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER = 22,
+    /* set/get CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER = 23,
+    /* set/get CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER = 24,
+    /* set/get CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER = 25,
+
+    /* set ZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ZDESC = 26,
+    /* set/get ZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ZDATA_PLACEHOLDER = 27,
+    /* set BN_Z_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get BN_Z_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_Z_EQSCALEBIAS_DESC = 28,
+    /* set/get BN_Z_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQSCALE_PLACEHOLDER = 29,
+    /* set/get BN_Z_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQBIAS_PLACEHOLDER = 30,
+
+    /* set ACTIVATION_BITMASK_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ACTIVATION_BITMASK_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_BITMASK_DESC = 31,
+    /* set/get ACTIVATION_BITMASK_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ACTIVATION_BITMASK_PLACEHOLDER = 32,
+
+    /* set DXDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DXDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DXDESC = 33,
+    /* set/get DXDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DXDATA_PLACEHOLDER = 34,
+    /* set DZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DZDESC = 35,
+    /* set/get DZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DZDATA_PLACEHOLDER = 36,
+    /* set/get CUDNN_PARAM_BN_DSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DSCALE_PLACEHOLDER = 37,
+    /* set/get CUDNN_PARAM_BN_DBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DBIAS_PLACEHOLDER = 38,
+} cudnnFusedOpsConstParamLabel_t CUDNN_DEPRECATED;
+
+typedef enum {
+    CUDNN_PTR_NULL         = 0,
+    CUDNN_PTR_ELEM_ALIGNED = 1,
+    CUDNN_PTR_16B_ALIGNED  = 2,
+} cudnnFusedOpsPointerPlaceHolder_t CUDNN_DEPRECATED;
+
+typedef enum {
+    /* set: pass void* pointing to dev memory */
+    /* get: pass void** pointing to host memory */
+    CUDNN_PTR_XDATA              = 0,
+    CUDNN_PTR_BN_EQSCALE         = 1,
+    CUDNN_PTR_BN_EQBIAS          = 2,
+    CUDNN_PTR_WDATA              = 3,
+    CUDNN_PTR_DWDATA             = 4,
+    CUDNN_PTR_YDATA              = 5,
+    CUDNN_PTR_DYDATA             = 6,
+    CUDNN_PTR_YSUM               = 7,
+    CUDNN_PTR_YSQSUM             = 8,
+    CUDNN_PTR_WORKSPACE          = 9,
+    CUDNN_PTR_BN_SCALE           = 10,
+    CUDNN_PTR_BN_BIAS            = 11,
+    CUDNN_PTR_BN_SAVED_MEAN      = 12,
+    CUDNN_PTR_BN_SAVED_INVSTD    = 13,
+    CUDNN_PTR_BN_RUNNING_MEAN    = 14,
+    CUDNN_PTR_BN_RUNNING_VAR     = 15,
+    CUDNN_PTR_ZDATA              = 16,
+    CUDNN_PTR_BN_Z_EQSCALE       = 17,
+    CUDNN_PTR_BN_Z_EQBIAS        = 18,
+    CUDNN_PTR_ACTIVATION_BITMASK = 19,
+    CUDNN_PTR_DXDATA             = 20,
+    CUDNN_PTR_DZDATA             = 21,
+    CUDNN_PTR_BN_DSCALE          = 22,
+    CUDNN_PTR_BN_DBIAS           = 23,
+
+    /* set/get: pass size_t* pointing to host memory */
+    CUDNN_SCALAR_SIZE_T_WORKSPACE_SIZE_IN_BYTES = 100,
+    /* set/get: pass int64_t* pointing to host memory */
+    CUDNN_SCALAR_INT64_T_BN_ACCUMULATION_COUNT = 101,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EXP_AVG_FACTOR = 102,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EPSILON = 103,
+} cudnnFusedOpsVariantParamLabel_t CUDNN_DEPRECATED;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCnnVersionCheck(void);
+
+/* helper function to provide the convolution backward filter algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdFilterAlgoPerfStruct {
+    cudnnConvolutionBwdFilterAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdFilterAlgoPerf_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithm(cudnnHandle_t handle,
+                                            const cudnnTensorDescriptor_t xDesc,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnFilterDescriptor_t dwDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithmEx(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              const void *x,
+                                              const cudnnTensorDescriptor_t dyDesc,
+                                              const void *y,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t dwDesc,
+                                              void *dw,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults,
+                                              void *workSpace,
+                                              size_t workSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithm_v7(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t srcDesc,
+                                              const cudnnTensorDescriptor_t diffDesc,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t gradDesc,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnnHandle_t handle,
+                                               const cudnnTensorDescriptor_t xDesc,
+                                               const cudnnTensorDescriptor_t dyDesc,
+                                               const cudnnConvolutionDescriptor_t convDesc,
+                                               const cudnnFilterDescriptor_t gradDesc,
+                                               cudnnConvolutionBwdFilterAlgo_t algo,
+                                               size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardFilter(cudnnHandle_t handle,
+                               const void *alpha,
+                               const cudnnTensorDescriptor_t xDesc,
+                               const void *x,
+                               const cudnnTensorDescriptor_t dyDesc,
+                               const void *dy,
+                               const cudnnConvolutionDescriptor_t convDesc,
+                               cudnnConvolutionBwdFilterAlgo_t algo,
+                               void *workSpace,
+                               size_t workSpaceSizeInBytes,
+                               const void *beta,
+                               const cudnnFilterDescriptor_t dwDesc,
+                               void *dw);
+
+/* Function to compute the bias gradient for batch convolution */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardBias(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dbDesc,
+                             void *db);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t *constPack, cudnnFusedOps_t ops);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t constPack);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsConstParamPackAttribute(cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        const void *param);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsConstParamPackAttribute(const cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        void *param,
+                                        int *isNULL);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t *varPack, cudnnFusedOps_t ops);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t varPack);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsVariantParamPackAttribute(cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsVariantParamPackAttribute(const cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsPlan(cudnnFusedOpsPlan_t *plan, cudnnFusedOps_t ops);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsPlan(cudnnFusedOpsPlan_t plan);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnMakeFusedOpsPlan(cudnnHandle_t handle,
+                      cudnnFusedOpsPlan_t plan,
+                      const cudnnFusedOpsConstParamPack_t constPack,
+                      size_t *workspaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFusedOpsExecute(cudnnHandle_t handle, const cudnnFusedOpsPlan_t plan, cudnnFusedOpsVariantParamPack_t varPack);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_CNN_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_graph.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_graph.h
new file mode 100644
index 0000000000000000000000000000000000000000..389fba220c579e08519072255f2aea9a5da2d3e5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_graph.h
@@ -0,0 +1,992 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_graph : cuDNN's basic definitions operations.
+ */
+
+#if !defined(CUDNN_GRAPH_H_)
+#define CUDNN_GRAPH_H_
+
+#include <cuda_runtime_api.h>
+#include <library_types.h>
+
+#include <stdint.h>
+
+#include "cudnn_version.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_GRAPH_MAJOR 9
+#define CUDNN_GRAPH_MINOR 10
+#define CUDNN_GRAPH_PATCH 2
+
+#if (CUDNN_GRAPH_MAJOR != CUDNN_MAJOR) || (CUDNN_GRAPH_MINOR != CUDNN_MINOR) || (CUDNN_GRAPH_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN GRAPH!!!
+#endif
+
+#ifndef CUDNNWINAPI
+#ifdef _WIN32
+#define CUDNNWINAPI __stdcall
+#else
+#define CUDNNWINAPI
+#endif
+#endif
+
+/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
+#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
+/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
+#define CUDNN_DEPRECATED __attribute__((deprecated))
+#define CUDNN_DEPRECATED_ENUM __attribute__((deprecated))
+#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
+/* Microsoft Visual C++ */
+#define CUDNN_DEPRECATED __declspec(deprecated)
+#define CUDNN_DEPRECATED_ENUM __declspec(deprecated)
+#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
+/* C++14 compilers */
+#define CUDNN_DEPRECATED [[deprecated]]
+#define CUDNN_DEPRECATED_ENUM [[deprecated]]
+#else
+/* No support for the deprecated attribute */
+#define CUDNN_DEPRECATED
+#define CUDNN_DEPRECATED_ENUM
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudnnContext;
+typedef struct cudnnContext *cudnnHandle_t;
+
+size_t CUDNNWINAPI
+cudnnGetVersion(void);
+
+size_t CUDNNWINAPI
+cudnnGetMaxDeviceVersion(void);
+
+/* Returns CUDA Runtime version statically linked against cudnn */
+size_t CUDNNWINAPI
+cudnnGetCudartVersion(void);
+
+/*
+ * CUDNN return codes
+ */
+typedef enum {
+    CUDNN_STATUS_SUCCESS = 0,
+
+    /* Uncategorized errors */
+    CUDNN_STATUS_NOT_INITIALIZED                = 1001,
+    CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH    = 1002,
+    CUDNN_STATUS_SERIALIZATION_VERSION_MISMATCH = 1003,
+    CUDNN_STATUS_DEPRECATED                     = 1004,
+    CUDNN_STATUS_LICENSE_ERROR                  = 1005,
+    CUDNN_STATUS_RUNTIME_IN_PROGRESS            = 1006,
+    CUDNN_STATUS_RUNTIME_FP_OVERFLOW            = 1007,
+    CUDNN_STATUS_SUBLIBRARY_LOADING_FAILED      = 1008,
+
+    CUDNN_STATUS_BAD_PARAM                     = 2000,
+    CUDNN_STATUS_BAD_PARAM_NULL_POINTER        = 2002,
+    CUDNN_STATUS_BAD_PARAM_MISALIGNED_POINTER  = 2003,
+    CUDNN_STATUS_BAD_PARAM_NOT_FINALIZED       = 2004,
+    CUDNN_STATUS_BAD_PARAM_OUT_OF_BOUND        = 2005,
+    CUDNN_STATUS_BAD_PARAM_SIZE_INSUFFICIENT   = 2006,
+    CUDNN_STATUS_BAD_PARAM_STREAM_MISMATCH     = 2007,
+    CUDNN_STATUS_BAD_PARAM_SHAPE_MISMATCH      = 2008,
+    CUDNN_STATUS_BAD_PARAM_DUPLICATED_ENTRIES  = 2009,
+    CUDNN_STATUS_BAD_PARAM_ATTRIBUTE_TYPE      = 2010,
+    CUDNN_STATUS_BAD_PARAM_CUDA_GRAPH_MISMATCH = 2011,
+    CUDNN_STATUS_BAD_PARAM_DESCRIPTOR_TYPE     = 2012,
+
+    CUDNN_STATUS_NOT_SUPPORTED                              = 3000,
+    CUDNN_STATUS_NOT_SUPPORTED_GRAPH_PATTERN                = 3001,
+    CUDNN_STATUS_NOT_SUPPORTED_SHAPE                        = 3002,
+    CUDNN_STATUS_NOT_SUPPORTED_DATA_TYPE                    = 3003,
+    CUDNN_STATUS_NOT_SUPPORTED_LAYOUT                       = 3004,
+    CUDNN_STATUS_NOT_SUPPORTED_INCOMPATIBLE_CUDA_DRIVER     = 3005,
+    CUDNN_STATUS_NOT_SUPPORTED_INCOMPATIBLE_CUDART          = 3006,
+    CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH                = 3007,
+    CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING = 3008,
+    CUDNN_STATUS_NOT_SUPPORTED_SUBLIBRARY_UNAVAILABLE       = 3009,
+    CUDNN_STATUS_NOT_SUPPORTED_SHARED_MEMORY_INSUFFICIENT   = 3010,
+    CUDNN_STATUS_NOT_SUPPORTED_PADDING                      = 3011,
+    CUDNN_STATUS_NOT_SUPPORTED_BAD_LAUNCH_PARAM             = 3012,
+    CUDNN_STATUS_NOT_SUPPORTED_CUDA_GRAPH_NATIVE_API        = 3013,
+
+    CUDNN_STATUS_INTERNAL_ERROR                          = 4000,
+    CUDNN_STATUS_INTERNAL_ERROR_COMPILATION_FAILED       = 4001,
+    CUDNN_STATUS_INTERNAL_ERROR_UNEXPECTED_VALUE         = 4002,
+    CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED   = 4003,
+    CUDNN_STATUS_INTERNAL_ERROR_DEVICE_ALLOCATION_FAILED = 4004,
+    CUDNN_STATUS_INTERNAL_ERROR_BAD_LAUNCH_PARAM         = 4005,
+    CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED  = 4006,
+
+    CUDNN_STATUS_EXECUTION_FAILED             = 5000,
+    CUDNN_STATUS_EXECUTION_FAILED_CUDA_DRIVER = 5001,
+    CUDNN_STATUS_EXECUTION_FAILED_CUBLAS      = 5002,
+    CUDNN_STATUS_EXECUTION_FAILED_CUDART      = 5003,
+    CUDNN_STATUS_EXECUTION_FAILED_CURAND      = 5004,
+
+    CUDNN_STATUS_ALLOC_FAILED CUDNN_DEPRECATED_ENUM  = CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED,
+    CUDNN_STATUS_INVALID_VALUE CUDNN_DEPRECATED_ENUM = 2001 /* please transition to CUDNN_STATUS_BAD_PARAM instead */,
+    CUDNN_STATUS_ARCH_MISMATCH CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH,
+    CUDNN_STATUS_MAPPING_ERROR CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED,
+    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING CUDNN_DEPRECATED_ENUM =
+        CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING,
+    CUDNN_STATUS_VERSION_MISMATCH CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH,
+} cudnnStatus_t;
+
+#define CUDNN_STATUS_FULL_ERROR_CODE(category, specific_err) ((cudnnStatus_t)(0 + (category) + (specific_err)))
+#define CUDNN_STATUS_CATEGORY(full_error_code) ((full_error_code) / 1000 * 1000)
+#define CUDNN_STATUS_SPECIFIC_ERROR(full_error_code) ((full_error_code) % 1000)
+
+/* human-readable error messages */
+const char *CUDNNWINAPI
+cudnnGetErrorString(cudnnStatus_t status);
+
+void CUDNNWINAPI
+cudnnGetLastErrorString(char *message, size_t max_size);
+
+/* Forward definition in this version only */
+typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t CUDNN_DEPRECATED;
+
+typedef enum {
+    CUDNN_ERRQUERY_RAWCODE     = 0,
+    CUDNN_ERRQUERY_NONBLOCKING = 1,
+    CUDNN_ERRQUERY_BLOCKING    = 2,
+} cudnnErrQueryMode_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetProperty(libraryPropertyType type, int *value);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreate(cudnnHandle_t *handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroy(cudnnHandle_t handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
+/*
+ * CUDNN data type
+ */
+typedef enum {
+    CUDNN_DATA_FLOAT                         = 0,
+    CUDNN_DATA_DOUBLE                        = 1,
+    CUDNN_DATA_HALF                          = 2,
+    CUDNN_DATA_INT8                          = 3,
+    CUDNN_DATA_INT32                         = 4,
+    CUDNN_DATA_INT8x4 CUDNN_DEPRECATED_ENUM  = 5,
+    CUDNN_DATA_UINT8                         = 6,
+    CUDNN_DATA_UINT8x4 CUDNN_DEPRECATED_ENUM = 7,
+    CUDNN_DATA_INT8x32 CUDNN_DEPRECATED_ENUM = 8,
+    CUDNN_DATA_BFLOAT16                      = 9,
+    CUDNN_DATA_INT64                         = 10,
+    CUDNN_DATA_BOOLEAN                       = 11,
+    CUDNN_DATA_FP8_E4M3                      = 12,
+    CUDNN_DATA_FP8_E5M2                      = 13,
+    CUDNN_DATA_FAST_FLOAT_FOR_FP8            = 14,
+    CUDNN_DATA_FP8_E8M0                      = 15,
+    CUDNN_DATA_FP4_E2M1                      = 16,
+} cudnnDataType_t;
+
+/*
+ * CUDNN math type
+ */
+typedef enum {
+    CUDNN_DEFAULT_MATH                    = 0,
+    CUDNN_TENSOR_OP_MATH                  = 1,
+    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
+    CUDNN_FMA_MATH                        = 3,
+} cudnnMathType_t;
+
+/*
+ * CUDNN propagate Nan
+ */
+typedef enum {
+    CUDNN_NOT_PROPAGATE_NAN CUDNN_DEPRECATED_ENUM = 0,
+    CUDNN_PROPAGATE_NAN CUDNN_DEPRECATED_ENUM     = 1,
+} cudnnNanPropagation_t;
+
+/*
+ * Behavior for OOB samples. OOB samples are samples where L+R > T is encountered during the gradient calculation. If
+ * gradMode is set to CUDNN_CTC_SKIP_OOB_GRADIENTS, then the CTC loss function does not write to the gradient buffer for
+ * that sample. Instead, the current values, even not finite, are retained. If gradMode is set to
+ * CUDNN_CTC_ZERO_OOB_GRADIENTS, then the gradient for that sample is set to zero. This guarantees a finite gradient.
+ */
+typedef enum {
+    CUDNN_CTC_ZERO_OOB_GRADIENTS = 0,
+    CUDNN_CTC_SKIP_OOB_GRADIENTS = 1,
+} cudnnCTCGradMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_NCHW        = 0, /* row major (wStride = 1, hStride = w) */
+    CUDNN_TENSOR_NHWC        = 1, /* feature maps interleaved ( cStride = 1 )*/
+    CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
+} cudnnTensorFormat_t;
+
+/*
+ * CUDNN ReduceTensor op type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_ADD          = 0,
+    CUDNN_REDUCE_TENSOR_MUL          = 1,
+    CUDNN_REDUCE_TENSOR_MIN          = 2,
+    CUDNN_REDUCE_TENSOR_MAX          = 3,
+    CUDNN_REDUCE_TENSOR_AMAX         = 4,
+    CUDNN_REDUCE_TENSOR_AVG          = 5,
+    CUDNN_REDUCE_TENSOR_NORM1        = 6,
+    CUDNN_REDUCE_TENSOR_NORM2        = 7,
+    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
+} cudnnReduceTensorOp_t;
+
+/*
+ * activation mode
+ */
+typedef enum {
+    CUDNN_ACTIVATION_SIGMOID      = 0,
+    CUDNN_ACTIVATION_RELU         = 1,
+    CUDNN_ACTIVATION_TANH         = 2,
+    CUDNN_ACTIVATION_CLIPPED_RELU = 3,
+    CUDNN_ACTIVATION_ELU          = 4,
+    CUDNN_ACTIVATION_IDENTITY     = 5,
+    CUDNN_ACTIVATION_SWISH        = 6
+} cudnnActivationMode_t CUDNN_DEPRECATED;
+
+typedef enum {
+    CUDNN_SEV_FATAL   = 0,
+    CUDNN_SEV_ERROR   = 1,
+    CUDNN_SEV_WARNING = 2,
+    CUDNN_SEV_INFO    = 3,
+} cudnnSeverity_t;
+
+/* Message masks to be used with cudnnSetCallback() */
+#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
+#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
+#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
+
+/* struct containing useful informaiton for each API call */
+typedef struct cudnnDebugStruct {
+    unsigned cudnn_version;
+    cudnnStatus_t cudnnStatus;
+    unsigned time_sec;      /* epoch time in seconds */
+    unsigned time_usec;     /* microseconds part of epoch time */
+    unsigned time_delta;    /* time since start in seconds */
+    cudnnHandle_t handle;   /* cudnn handle */
+    cudaStream_t stream;    /* cuda stream ID */
+    unsigned long long pid; /* process ID */
+    unsigned long long tid; /* thread ID */
+    int cudaDeviceId;       /* CUDA device ID */
+    int reserved[15];       /* reserved for future use */
+} cudnnDebug_t;
+
+typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGraphVersionCheck(void);
+
+/* Maximum supported number of tensor dimensions */
+#define CUDNN_DIM_MAX 8
+
+/*
+ *  convolution mode
+ */
+typedef enum { CUDNN_CONVOLUTION = 0, CUDNN_CROSS_CORRELATION = 1 } cudnnConvolutionMode_t;
+
+/*
+ * CUDNN Reorder
+ */
+typedef enum {
+    CUDNN_DEFAULT_REORDER = 0,
+    CUDNN_NO_REORDER      = 1,
+} cudnnReorderType_t CUDNN_DEPRECATED;
+
+typedef void *cudnnBackendDescriptor_t;
+
+typedef struct cudnnFractionStruct {
+    int64_t numerator;
+    int64_t denominator;
+} cudnnFraction_t;
+
+typedef enum {
+    CUDNN_POINTWISE_ADD        = 0,
+    CUDNN_POINTWISE_ADD_SQUARE = 5,
+    CUDNN_POINTWISE_DIV        = 6,
+    CUDNN_POINTWISE_MAX        = 3,
+    CUDNN_POINTWISE_MIN        = 2,
+    CUDNN_POINTWISE_MOD        = 7,
+    CUDNN_POINTWISE_MUL        = 1,
+    CUDNN_POINTWISE_POW        = 8,
+    CUDNN_POINTWISE_SUB        = 9,
+
+    CUDNN_POINTWISE_ABS        = 10,
+    CUDNN_POINTWISE_CEIL       = 11,
+    CUDNN_POINTWISE_COS        = 12,
+    CUDNN_POINTWISE_EXP        = 13,
+    CUDNN_POINTWISE_FLOOR      = 14,
+    CUDNN_POINTWISE_LOG        = 15,
+    CUDNN_POINTWISE_NEG        = 16,
+    CUDNN_POINTWISE_RSQRT      = 17,
+    CUDNN_POINTWISE_SIN        = 18,
+    CUDNN_POINTWISE_SQRT       = 4,
+    CUDNN_POINTWISE_TAN        = 19,
+    CUDNN_POINTWISE_ERF        = 20,
+    CUDNN_POINTWISE_IDENTITY   = 21,
+    CUDNN_POINTWISE_RECIPROCAL = 22,
+    CUDNN_POINTWISE_ATAN2      = 23,
+
+    CUDNN_POINTWISE_RELU_FWD             = 100,
+    CUDNN_POINTWISE_TANH_FWD             = 101,
+    CUDNN_POINTWISE_SIGMOID_FWD          = 102,
+    CUDNN_POINTWISE_ELU_FWD              = 103,
+    CUDNN_POINTWISE_GELU_FWD             = 104,
+    CUDNN_POINTWISE_SOFTPLUS_FWD         = 105,
+    CUDNN_POINTWISE_SWISH_FWD            = 106,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_FWD = 107,
+
+    CUDNN_POINTWISE_RELU_BWD             = 200,
+    CUDNN_POINTWISE_TANH_BWD             = 201,
+    CUDNN_POINTWISE_SIGMOID_BWD          = 202,
+    CUDNN_POINTWISE_ELU_BWD              = 203,
+    CUDNN_POINTWISE_GELU_BWD             = 204,
+    CUDNN_POINTWISE_SOFTPLUS_BWD         = 205,
+    CUDNN_POINTWISE_SWISH_BWD            = 206,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_BWD = 207,
+
+    CUDNN_POINTWISE_CMP_EQ  = 300,
+    CUDNN_POINTWISE_CMP_NEQ = 301,
+    CUDNN_POINTWISE_CMP_GT  = 302,
+    CUDNN_POINTWISE_CMP_GE  = 303,
+    CUDNN_POINTWISE_CMP_LT  = 304,
+    CUDNN_POINTWISE_CMP_LE  = 305,
+
+    CUDNN_POINTWISE_LOGICAL_AND = 400,
+    CUDNN_POINTWISE_LOGICAL_OR  = 401,
+    CUDNN_POINTWISE_LOGICAL_NOT = 402,
+
+    CUDNN_POINTWISE_GEN_INDEX = 501,
+
+    CUDNN_POINTWISE_BINARY_SELECT = 601,
+} cudnnPointwiseMode_t;
+
+typedef enum {
+    CUDNN_RESAMPLE_NEAREST                 = 0,
+    CUDNN_RESAMPLE_BILINEAR                = 1,
+    CUDNN_RESAMPLE_AVGPOOL                 = 2,
+    CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING = 2,
+    CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING = 4,
+    CUDNN_RESAMPLE_MAXPOOL                 = 3,
+} cudnnResampleMode_t;
+
+typedef enum {
+    CUDNN_SIGNAL_SET  = 0,
+    CUDNN_SIGNAL_WAIT = 1,
+} cudnnSignalMode_t;
+
+typedef enum {
+    CUDNN_GENSTATS_SUM_SQSUM = 0,
+} cudnnGenStatsMode_t;
+
+typedef enum {
+    CUDNN_BN_FINALIZE_STATISTICS_TRAINING  = 0,
+    CUDNN_BN_FINALIZE_STATISTICS_INFERENCE = 1,
+} cudnnBnFinalizeStatsMode_t;
+
+typedef enum {
+    CUDNN_RNG_DISTRIBUTION_BERNOULLI = 0,
+    CUDNN_RNG_DISTRIBUTION_UNIFORM   = 1,
+    CUDNN_RNG_DISTRIBUTION_NORMAL    = 2,
+} cudnnRngDistribution_t;
+
+typedef enum {
+    CUDNN_ATTR_POINTWISE_MODE                                  = 0,
+    CUDNN_ATTR_POINTWISE_MATH_PREC                             = 1,
+    CUDNN_ATTR_POINTWISE_NAN_PROPAGATION CUDNN_DEPRECATED_ENUM = 2,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP                       = 3,
+    CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP                       = 4,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE                 = 5,
+    CUDNN_ATTR_POINTWISE_ELU_ALPHA                             = 6,
+    CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA                         = 7,
+    CUDNN_ATTR_POINTWISE_SWISH_BETA                            = 8,
+    CUDNN_ATTR_POINTWISE_AXIS                                  = 9,
+
+    CUDNN_ATTR_CONVOLUTION_COMP_TYPE      = 100,
+    CUDNN_ATTR_CONVOLUTION_CONV_MODE      = 101,
+    CUDNN_ATTR_CONVOLUTION_DILATIONS      = 102,
+    CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES = 103,
+    CUDNN_ATTR_CONVOLUTION_POST_PADDINGS  = 104,
+    CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS   = 105,
+    CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS   = 106,
+
+    CUDNN_ATTR_ENGINEHEUR_MODE            = 200,
+    CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH = 201,
+    CUDNN_ATTR_ENGINEHEUR_RESULTS         = 202,
+    CUDNN_ATTR_ENGINEHEUR_SM_COUNT_TARGET = 203,
+    CUDNN_ATTR_ENGINEHEUR_DEVICEPROP      = 204,
+
+    CUDNN_ATTR_ENGINECFG_ENGINE             = 300,
+    CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO  = 301,
+    CUDNN_ATTR_ENGINECFG_KNOB_CHOICES       = 302,
+    CUDNN_ATTR_ENGINECFG_WORKSPACE_SIZE     = 303,
+    CUDNN_ATTR_ENGINECFG_SHARED_MEMORY_USED = 304,
+
+    CUDNN_ATTR_EXECUTION_PLAN_HANDLE CUDNN_DEPRECATED_ENUM = 400,
+    CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG                = 401,
+    CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE               = 402,
+    CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS   = 403,
+    CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS   = 404,
+    CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION          = 405,
+    CUDNN_ATTR_EXECUTION_PLAN_KERNEL_CACHE                 = 406,
+    CUDNN_ATTR_EXECUTION_PLAN_DEVICEPROP                   = 407,
+
+    CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID            = 500,
+    CUDNN_ATTR_INTERMEDIATE_INFO_SIZE                 = 501,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS  = 502,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES = 503,
+
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE  = 600,
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE = 601,
+
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA        = 700,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA         = 701,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC    = 702,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W            = 703,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X            = 704,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y            = 705,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA       = 706,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA        = 707,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC   = 708,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W           = 709,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX          = 710,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY          = 711,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA     = 712,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA      = 713,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC = 714,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW        = 715,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X         = 716,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY        = 717,
+
+    CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR = 750,
+    CUDNN_ATTR_OPERATION_POINTWISE_XDESC         = 751,
+    CUDNN_ATTR_OPERATION_POINTWISE_BDESC         = 752,
+    CUDNN_ATTR_OPERATION_POINTWISE_YDESC         = 753,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1        = 754,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2        = 755,
+    CUDNN_ATTR_OPERATION_POINTWISE_DXDESC        = 756,
+    CUDNN_ATTR_OPERATION_POINTWISE_DYDESC        = 757,
+    CUDNN_ATTR_OPERATION_POINTWISE_TDESC         = 758,
+
+    CUDNN_ATTR_OPERATION_GENSTATS_MODE      = 770,
+    CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC = 771,
+    CUDNN_ATTR_OPERATION_GENSTATS_XDESC     = 772,
+    CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC   = 773,
+    CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC = 774,
+
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE                = 780,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC                 = 781,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC                = 782,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC             = 783,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC                = 784,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC                 = 785,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC    = 786,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC     = 787,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC = 788,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC  = 789,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC           = 790,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC        = 791,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC             = 792,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC              = 793,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC          = 794,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC              = 795,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERATE_FACTOR_DESC   = 796,
+
+    CUDNN_ATTR_OPERATIONGRAPH_HANDLE CUDNN_DEPRECATED_ENUM = 800,
+    CUDNN_ATTR_OPERATIONGRAPH_OPS                          = 801,
+    CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT          = 802,
+    CUDNN_ATTR_OPERATIONGRAPH_IS_DYNAMIC_SHAPE_ENABLED     = 803,
+    CUDNN_ATTR_OPERATIONGRAPH_IS_SAME_TOPOLOGY             = 804,
+
+    CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT       = 900,
+    CUDNN_ATTR_TENSOR_DATA_TYPE            = 901,
+    CUDNN_ATTR_TENSOR_DIMENSIONS           = 902,
+    CUDNN_ATTR_TENSOR_STRIDES              = 903,
+    CUDNN_ATTR_TENSOR_VECTOR_COUNT         = 904,
+    CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION = 905,
+    CUDNN_ATTR_TENSOR_UNIQUE_ID            = 906,
+    CUDNN_ATTR_TENSOR_IS_VIRTUAL           = 907,
+    CUDNN_ATTR_TENSOR_IS_BY_VALUE          = 908,
+    CUDNN_ATTR_TENSOR_REORDERING_MODE      = 909,
+    CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC   = 913,
+
+    CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS    = 1000,
+    CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS = 1001,
+    CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES = 1002,
+    CUDNN_ATTR_VARIANT_PACK_WORKSPACE     = 1003,
+
+    CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID = 1100,
+    CUDNN_ATTR_LAYOUT_INFO_TYPES      = 1101,
+
+    CUDNN_ATTR_KNOB_INFO_TYPE          = 1200,
+    CUDNN_ATTR_KNOB_INFO_MAXIMUM_VALUE = 1201,
+    CUDNN_ATTR_KNOB_INFO_MINIMUM_VALUE = 1202,
+    CUDNN_ATTR_KNOB_INFO_STRIDE        = 1203,
+
+    CUDNN_ATTR_ENGINE_OPERATION_GRAPH = 1300,
+    CUDNN_ATTR_ENGINE_GLOBAL_INDEX    = 1301,
+    CUDNN_ATTR_ENGINE_KNOB_INFO       = 1302,
+    CUDNN_ATTR_ENGINE_NUMERICAL_NOTE  = 1303,
+    CUDNN_ATTR_ENGINE_LAYOUT_INFO     = 1304,
+    CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE   = 1305,
+    CUDNN_ATTR_ENGINE_SM_COUNT_TARGET = 1306,
+    CUDNN_ATTR_ENGINE_DEVICEPROP      = 1307,
+
+    CUDNN_ATTR_MATMUL_COMP_TYPE     = 1500,
+    CUDNN_ATTR_MATMUL_PADDING_VALUE = 1503,
+
+    CUDNN_ATTR_OPERATION_MATMUL_ADESC                                                 = 1520,
+    CUDNN_ATTR_OPERATION_MATMUL_BDESC                                                 = 1521,
+    CUDNN_ATTR_OPERATION_MATMUL_CDESC                                                 = 1522,
+    CUDNN_ATTR_OPERATION_MATMUL_DESC                                                  = 1523,
+    CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT CUDNN_DEPRECATED_ENUM = 1524,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC                                  = 1525,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC                                  = 1526,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC                                  = 1527,
+
+    CUDNN_ATTR_REDUCTION_OPERATOR  = 1600,
+    CUDNN_ATTR_REDUCTION_COMP_TYPE = 1601,
+
+    CUDNN_ATTR_OPERATION_REDUCTION_XDESC = 1610,
+    CUDNN_ATTR_OPERATION_REDUCTION_YDESC = 1611,
+    CUDNN_ATTR_OPERATION_REDUCTION_DESC  = 1612,
+
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        = 1620,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        = 1621,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      = 1622,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    = 1623,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           = 1624,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          = 1625,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   = 1626,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    = 1627,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC = 1628,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  = 1629,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          = 1630,
+
+    CUDNN_ATTR_RESAMPLE_MODE            = 1700,
+    CUDNN_ATTR_RESAMPLE_COMP_TYPE       = 1701,
+    CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS    = 1702,
+    CUDNN_ATTR_RESAMPLE_POST_PADDINGS   = 1703,
+    CUDNN_ATTR_RESAMPLE_PRE_PADDINGS    = 1704,
+    CUDNN_ATTR_RESAMPLE_STRIDES         = 1705,
+    CUDNN_ATTR_RESAMPLE_WINDOW_DIMS     = 1706,
+    CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION = 1707,
+    CUDNN_ATTR_RESAMPLE_PADDING_MODE    = 1708,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC                       = 1710,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC                       = 1711,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC                     = 1712,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA CUDNN_DEPRECATED_ENUM = 1713,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA CUDNN_DEPRECATED_ENUM  = 1714,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC                        = 1716,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC                      = 1720,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC                      = 1721,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC                     = 1722,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA CUDNN_DEPRECATED_ENUM = 1723,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA CUDNN_DEPRECATED_ENUM  = 1724,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC                        = 1725,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_XDESC                       = 1726,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_YDESC                       = 1727,
+
+    CUDNN_ATTR_OPERATION_CONCAT_AXIS          = 1800,
+    CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS   = 1801,
+    CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX = 1802,
+    CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC   = 1803,
+
+    CUDNN_ATTR_OPERATION_SIGNAL_MODE     = 1900,
+    CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC = 1901,
+    CUDNN_ATTR_OPERATION_SIGNAL_VALUE    = 1902,
+    CUDNN_ATTR_OPERATION_SIGNAL_XDESC    = 1903,
+    CUDNN_ATTR_OPERATION_SIGNAL_YDESC    = 1904,
+
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_CONTAINER_DESC  = 1950,
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_YDESC           = 1951,
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_SEQUENCE_DESC   = 1952,
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_PAGE_TABLE_DESC = 1953,
+
+    CUDNN_ATTR_OPERATION_NORM_FWD_MODE                     = 2000,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PHASE                    = 2001,
+    CUDNN_ATTR_OPERATION_NORM_FWD_XDESC                    = 2002,
+    CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC                = 2003,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC        = 2004,
+    CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC               = 2005,
+    CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC                = 2006,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC             = 2007,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC      = 2008,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC  = 2009,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC   = 2010,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC = 2011,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC  = 2012,
+    CUDNN_ATTR_OPERATION_NORM_FWD_YDESC                    = 2013,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS          = 2014,
+
+    CUDNN_ATTR_OPERATION_NORM_BWD_MODE              = 2100,
+    CUDNN_ATTR_OPERATION_NORM_BWD_XDESC             = 2101,
+    CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC         = 2102,
+    CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC = 2103,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC            = 2104,
+    CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC        = 2105,
+    CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC      = 2106,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC       = 2107,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC        = 2108,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC            = 2109,
+    CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS   = 2110,
+
+    CUDNN_ATTR_OPERATION_RESHAPE_XDESC = 2200,
+    CUDNN_ATTR_OPERATION_RESHAPE_YDESC = 2201,
+
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_XDESC                = 2250,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_YDESC                = 2251,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_LOWER_BANDWIDTH      = 2252,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_UPPER_BANDWIDTH      = 2253,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_AXIS                 = 2254,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_PAD_VALUE            = 2255,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_KV_TOKEN_OFFSET_DESC = 2256,
+
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_XDESC           = 2270,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_YDESC           = 2271,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_LOWER_BANDWIDTH = 2272,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_UPPER_BANDWIDTH = 2273,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_AXIS            = 2274,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_PAD_VALUE       = 2275,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MAX_TOKEN_VALUE        = 2276,
+
+    CUDNN_ATTR_RNG_DISTRIBUTION                   = 2300,
+    CUDNN_ATTR_RNG_NORMAL_DIST_MEAN               = 2301,
+    CUDNN_ATTR_RNG_NORMAL_DIST_STANDARD_DEVIATION = 2302,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MAXIMUM           = 2303,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MINIMUM           = 2304,
+    CUDNN_ATTR_RNG_BERNOULLI_DIST_PROBABILITY     = 2305,
+
+    CUDNN_ATTR_OPERATION_RNG_YDESC       = 2310,
+    CUDNN_ATTR_OPERATION_RNG_SEED        = 2311,
+    CUDNN_ATTR_OPERATION_RNG_DESC        = 2312,
+    CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC = 2313,
+
+    CUDNN_ATTR_KERNEL_CACHE_OPERATION_GRAPH            = 2400,
+    CUDNN_ATTR_KERNEL_CACHE_IS_ENGINECFG_KERNEL_CACHED = 2401,
+    CUDNN_ATTR_KERNEL_CACHE_JSON_REPRESENTATION        = 2402,
+
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_XDESC      = 2500,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_YDESC      = 2501,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_SCALE_DESC = 2502,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_MATH_PREC  = 2503,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_BLOCK_SIZE = 2504,
+
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_XDESC      = 2600,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_SCALE_DESC = 2601,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_YDESC      = 2602,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_MATH_PREC  = 2603,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_BLOCK_SIZE = 2604,
+
+    CUDNN_ATTR_DEVICEPROP_DEVICE_ID           = 2700,
+    CUDNN_ATTR_DEVICEPROP_HANDLE              = 2701,
+    CUDNN_ATTR_DEVICEPROP_JSON_REPRESENTATION = 2702,
+} cudnnBackendAttributeName_t;
+
+typedef enum {
+    CUDNN_TYPE_HANDLE                                = 0,
+    CUDNN_TYPE_DATA_TYPE                             = 1,
+    CUDNN_TYPE_BOOLEAN                               = 2,
+    CUDNN_TYPE_INT64                                 = 3,
+    CUDNN_TYPE_FLOAT                                 = 4,
+    CUDNN_TYPE_DOUBLE                                = 5,
+    CUDNN_TYPE_VOID_PTR                              = 6,
+    CUDNN_TYPE_CONVOLUTION_MODE                      = 7,
+    CUDNN_TYPE_HEUR_MODE                             = 8,
+    CUDNN_TYPE_KNOB_TYPE                             = 9,
+    CUDNN_TYPE_NAN_PROPOGATION CUDNN_DEPRECATED_ENUM = 10,
+    CUDNN_TYPE_NUMERICAL_NOTE                        = 11,
+    CUDNN_TYPE_LAYOUT_TYPE                           = 12,
+    CUDNN_TYPE_ATTRIB_NAME                           = 13,
+    CUDNN_TYPE_POINTWISE_MODE                        = 14,
+    CUDNN_TYPE_BACKEND_DESCRIPTOR                    = 15,
+    CUDNN_TYPE_GENSTATS_MODE                         = 16,
+    CUDNN_TYPE_BN_FINALIZE_STATS_MODE                = 17,
+    CUDNN_TYPE_REDUCTION_OPERATOR_TYPE               = 18,
+    CUDNN_TYPE_BEHAVIOR_NOTE                         = 19,
+    CUDNN_TYPE_TENSOR_REORDERING_MODE                = 20,
+    CUDNN_TYPE_RESAMPLE_MODE                         = 21,
+    CUDNN_TYPE_PADDING_MODE                          = 22,
+    CUDNN_TYPE_INT32                                 = 23,
+    CUDNN_TYPE_CHAR                                  = 24,
+    CUDNN_TYPE_SIGNAL_MODE                           = 25,
+    CUDNN_TYPE_FRACTION                              = 26,
+    CUDNN_TYPE_NORM_MODE                             = 27,
+    CUDNN_TYPE_NORM_FWD_PHASE                        = 28,
+    CUDNN_TYPE_RNG_DISTRIBUTION                      = 29,
+} cudnnBackendAttributeType_t;
+
+typedef enum {
+    CUDNN_BACKEND_POINTWISE_DESCRIPTOR                             = 0,
+    CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR                           = 1,
+    CUDNN_BACKEND_ENGINE_DESCRIPTOR                                = 2,
+    CUDNN_BACKEND_ENGINECFG_DESCRIPTOR                             = 3,
+    CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR                            = 4,
+    CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR                        = 5,
+    CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR                     = 6,
+    CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR                           = 7,
+    CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR                             = 8,
+    CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR                           = 9,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR         = 10,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR = 11,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR   = 12,
+    CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR                   = 13,
+    CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR                   = 14,
+    CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR                        = 15,
+    CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR                          = 16,
+    CUDNN_BACKEND_TENSOR_DESCRIPTOR                                = 17,
+    CUDNN_BACKEND_MATMUL_DESCRIPTOR                                = 18,
+    CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR                      = 19,
+    CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR      = 20,
+    CUDNN_BACKEND_REDUCTION_DESCRIPTOR                             = 21,
+    CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR                   = 22,
+    CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR              = 23,
+    CUDNN_BACKEND_RESAMPLE_DESCRIPTOR                              = 24,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR                = 25,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR                = 26,
+    CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR                      = 27,
+    CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR                      = 28,
+    CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR                = 29,
+    CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR               = 30,
+    CUDNN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR                     = 31,
+    CUDNN_BACKEND_RNG_DESCRIPTOR                                   = 32,
+    CUDNN_BACKEND_OPERATION_RNG_DESCRIPTOR                         = 33,
+    CUDNN_BACKEND_KERNEL_CACHE_DESCRIPTOR                          = 34,
+    CUDNN_BACKEND_OPERATION_PAGED_CACHE_LOAD_DESCRIPTOR            = 35,
+    CUDNN_BACKEND_OPERATION_BLOCK_SCALE_QUANTIZE_DESCRIPTOR        = 36,
+    CUDNN_BACKEND_OPERATION_BLOCK_SCALE_DEQUANTIZE_DESCRIPTOR      = 37,
+    CUDNN_BACKEND_DEVICEPROP_DESCRIPTOR                            = 38,
+    CUDNN_BACKEND_OPERATION_EXPAND_BAND_MATRIX_DESCRIPTOR          = 39,
+    CUDNN_BACKEND_OPERATION_CONTRACT_BAND_MATRIX_DESCRIPTOR        = 40,
+} cudnnBackendDescriptorType_t;
+
+typedef enum {
+    CUDNN_NUMERICAL_NOTE_TENSOR_CORE                 = 0,
+    CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS         = 1,
+    CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION = 2,
+    CUDNN_NUMERICAL_NOTE_FFT                         = 3,
+    CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC            = 4,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD                    = 5,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_4x4           = 6,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_6x6           = 7,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_13x13         = 8,
+    CUDNN_NUMERICAL_NOTE_STRICT_NAN_PROP             = 9,
+    CUDNN_NUMERICAL_NOTE_TYPE_COUNT                  = 10,
+} cudnnBackendNumericalNote_t;
+
+typedef enum {
+    CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION             = 0,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_FILTER_INT8x32_REORDER = 1,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_BIAS_INT8x32_REORDER   = 2,
+    CUDNN_BEHAVIOR_NOTE_SUPPORTS_CUDA_GRAPH_NATIVE_API  = 3,
+    CUDNN_BEHAVIOR_NOTE_TYPE_COUNT                      = 4,
+} cudnnBackendBehaviorNote_t;
+
+typedef enum {
+    CUDNN_KNOB_TYPE_SPLIT_K CUDNN_DEPRECATED_ENUM          = 0,
+    CUDNN_KNOB_TYPE_SWIZZLE                                = 1,
+    CUDNN_KNOB_TYPE_TILE_SIZE                              = 2,
+    CUDNN_KNOB_TYPE_USE_TEX CUDNN_DEPRECATED_ENUM          = 3,
+    CUDNN_KNOB_TYPE_EDGE                                   = 4,
+    CUDNN_KNOB_TYPE_KBLOCK CUDNN_DEPRECATED_ENUM           = 5,
+    CUDNN_KNOB_TYPE_LDGA CUDNN_DEPRECATED_ENUM             = 6,
+    CUDNN_KNOB_TYPE_LDGB CUDNN_DEPRECATED_ENUM             = 7,
+    CUDNN_KNOB_TYPE_CHUNK_K CUDNN_DEPRECATED_ENUM          = 8,
+    CUDNN_KNOB_TYPE_SPLIT_H CUDNN_DEPRECATED_ENUM          = 9,
+    CUDNN_KNOB_TYPE_WINO_TILE CUDNN_DEPRECATED_ENUM        = 10,
+    CUDNN_KNOB_TYPE_MULTIPLY                               = 11,
+    CUDNN_KNOB_TYPE_SPLIT_K_BUF                            = 12,
+    CUDNN_KNOB_TYPE_TILEK                                  = 13,
+    CUDNN_KNOB_TYPE_STAGES                                 = 14,
+    CUDNN_KNOB_TYPE_REDUCTION_MODE                         = 15,
+    CUDNN_KNOB_TYPE_CTA_SPLIT_K_MODE CUDNN_DEPRECATED_ENUM = 16,
+    CUDNN_KNOB_TYPE_SPLIT_K_SLC                            = 17,
+    CUDNN_KNOB_TYPE_IDX_MODE                               = 18,
+    CUDNN_KNOB_TYPE_SLICED CUDNN_DEPRECATED_ENUM           = 19,
+    CUDNN_KNOB_TYPE_SPLIT_RS CUDNN_DEPRECATED_ENUM         = 20,
+    CUDNN_KNOB_TYPE_SINGLEBUFFER CUDNN_DEPRECATED_ENUM     = 21,
+    CUDNN_KNOB_TYPE_LDGC CUDNN_DEPRECATED_ENUM             = 22,
+    CUDNN_KNOB_TYPE_SPECFILT                               = 23,
+    CUDNN_KNOB_TYPE_KERNEL_CFG                             = 24,
+    CUDNN_KNOB_TYPE_WORKSPACE                              = 25,
+    CUDNN_KNOB_TYPE_TILE_CGA CUDNN_DEPRECATED_ENUM         = 26,
+    CUDNN_KNOB_TYPE_TILE_CGA_M                             = 27,
+    CUDNN_KNOB_TYPE_TILE_CGA_N                             = 28,
+    CUDNN_KNOB_TYPE_BLOCK_SIZE                             = 29,
+    CUDNN_KNOB_TYPE_OCCUPANCY                              = 30,
+    CUDNN_KNOB_TYPE_ARRAY_SIZE_PER_THREAD                  = 31,
+    CUDNN_KNOB_TYPE_NUM_C_PER_BLOCK CUDNN_DEPRECATED_ENUM  = 32,
+    CUDNN_KNOB_TYPE_SPLIT_COLS                             = 33,
+    CUDNN_KNOB_TYPE_TILE_ROWS                              = 34,
+    CUDNN_KNOB_TYPE_TILE_COLS                              = 35,
+    CUDNN_KNOB_TYPE_LOAD_SIZE                              = 36,
+    CUDNN_KNOB_TYPE_CTA_COUNT                              = 37,
+    CUDNN_KNOB_TYPE_STREAM_K                               = 38,
+    CUDNN_KNOB_TYPE_SPLIT_P_SLC                            = 39,
+    CUDNN_KNOB_TYPE_TILE_M                                 = 40,
+    CUDNN_KNOB_TYPE_TILE_N                                 = 41,
+    CUDNN_KNOB_TYPE_WARP_SPEC_CFG                          = 42,
+    CUDNN_KNOB_TYPE_COUNTS                                 = 43,
+} cudnnBackendKnobType_t;
+
+typedef enum {
+    CUDNN_LAYOUT_TYPE_PREFERRED_NCHW   = 0,
+    CUDNN_LAYOUT_TYPE_PREFERRED_NHWC   = 1,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD4CK = 2,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD8CK = 3,
+    CUDNN_LAYOUT_TYPE_COUNT            = 4,
+} cudnnBackendLayoutType_t;
+
+typedef enum {
+    CUDNN_HEUR_MODE_INSTANT  = 0,
+    CUDNN_HEUR_MODE_B        = 1,
+    CUDNN_HEUR_MODE_FALLBACK = 2,
+    CUDNN_HEUR_MODE_A        = 3,
+    CUDNN_HEUR_MODES_COUNT   = 4,
+} cudnnBackendHeurMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_REORDERING_NONE     = 0,
+    CUDNN_TENSOR_REORDERING_INT8x32  = 1,
+    CUDNN_TENSOR_REORDERING_F16x16   = 2,
+    CUDNN_TENSOR_REORDERING_F8_128x4 = 3,
+} cudnnBackendTensorReordering_t;
+
+typedef enum {
+    CUDNN_ZERO_PAD     = 0,
+    CUDNN_NEG_INF_PAD  = 1,
+    CUDNN_EDGE_VAL_PAD = 2,
+} cudnnPaddingMode_t;
+
+typedef enum {
+    CUDNN_LAYER_NORM     = 0,
+    CUDNN_INSTANCE_NORM  = 1,
+    CUDNN_BATCH_NORM     = 2,
+    CUDNN_GROUP_NORM     = 3,
+    CUDNN_RMS_NORM       = 4,
+    CUDNN_ADA_LAYER_NORM = 5,
+} cudnnBackendNormMode_t;
+
+typedef enum {
+    CUDNN_NORM_FWD_INFERENCE = 0,
+    CUDNN_NORM_FWD_TRAINING  = 1,
+} cudnnBackendNormFwdPhase_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendCreateDescriptor(cudnnBackendDescriptorType_t descriptorType, cudnnBackendDescriptor_t *descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendDestroyDescriptor(cudnnBackendDescriptor_t descriptor);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBackendInitialize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendFinalize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendSetAttribute(cudnnBackendDescriptor_t descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t elementCount,
+                         const void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendGetAttribute(cudnnBackendDescriptor_t const descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t requestedElementCount,
+                         int64_t *elementCount,
+                         void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendExecute(cudnnHandle_t handle, cudnnBackendDescriptor_t executionPlan, cudnnBackendDescriptor_t variantPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendPopulateCudaGraph(cudnnHandle_t handle,
+                              cudnnBackendDescriptor_t executionPlan,
+                              cudnnBackendDescriptor_t variantPack,
+                              cudaGraph_t graph);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendUpdateCudaGraph(cudnnHandle_t handle,
+                            cudnnBackendDescriptor_t executionPlan,
+                            cudnnBackendDescriptor_t variantPack,
+                            cudaGraph_t graph);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_GRAPH_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_graph_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_graph_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..389fba220c579e08519072255f2aea9a5da2d3e5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_graph_v9.h
@@ -0,0 +1,992 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_graph : cuDNN's basic definitions operations.
+ */
+
+#if !defined(CUDNN_GRAPH_H_)
+#define CUDNN_GRAPH_H_
+
+#include <cuda_runtime_api.h>
+#include <library_types.h>
+
+#include <stdint.h>
+
+#include "cudnn_version.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_GRAPH_MAJOR 9
+#define CUDNN_GRAPH_MINOR 10
+#define CUDNN_GRAPH_PATCH 2
+
+#if (CUDNN_GRAPH_MAJOR != CUDNN_MAJOR) || (CUDNN_GRAPH_MINOR != CUDNN_MINOR) || (CUDNN_GRAPH_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN GRAPH!!!
+#endif
+
+#ifndef CUDNNWINAPI
+#ifdef _WIN32
+#define CUDNNWINAPI __stdcall
+#else
+#define CUDNNWINAPI
+#endif
+#endif
+
+/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
+#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
+/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
+#define CUDNN_DEPRECATED __attribute__((deprecated))
+#define CUDNN_DEPRECATED_ENUM __attribute__((deprecated))
+#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
+/* Microsoft Visual C++ */
+#define CUDNN_DEPRECATED __declspec(deprecated)
+#define CUDNN_DEPRECATED_ENUM __declspec(deprecated)
+#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
+/* C++14 compilers */
+#define CUDNN_DEPRECATED [[deprecated]]
+#define CUDNN_DEPRECATED_ENUM [[deprecated]]
+#else
+/* No support for the deprecated attribute */
+#define CUDNN_DEPRECATED
+#define CUDNN_DEPRECATED_ENUM
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudnnContext;
+typedef struct cudnnContext *cudnnHandle_t;
+
+size_t CUDNNWINAPI
+cudnnGetVersion(void);
+
+size_t CUDNNWINAPI
+cudnnGetMaxDeviceVersion(void);
+
+/* Returns CUDA Runtime version statically linked against cudnn */
+size_t CUDNNWINAPI
+cudnnGetCudartVersion(void);
+
+/*
+ * CUDNN return codes
+ */
+typedef enum {
+    CUDNN_STATUS_SUCCESS = 0,
+
+    /* Uncategorized errors */
+    CUDNN_STATUS_NOT_INITIALIZED                = 1001,
+    CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH    = 1002,
+    CUDNN_STATUS_SERIALIZATION_VERSION_MISMATCH = 1003,
+    CUDNN_STATUS_DEPRECATED                     = 1004,
+    CUDNN_STATUS_LICENSE_ERROR                  = 1005,
+    CUDNN_STATUS_RUNTIME_IN_PROGRESS            = 1006,
+    CUDNN_STATUS_RUNTIME_FP_OVERFLOW            = 1007,
+    CUDNN_STATUS_SUBLIBRARY_LOADING_FAILED      = 1008,
+
+    CUDNN_STATUS_BAD_PARAM                     = 2000,
+    CUDNN_STATUS_BAD_PARAM_NULL_POINTER        = 2002,
+    CUDNN_STATUS_BAD_PARAM_MISALIGNED_POINTER  = 2003,
+    CUDNN_STATUS_BAD_PARAM_NOT_FINALIZED       = 2004,
+    CUDNN_STATUS_BAD_PARAM_OUT_OF_BOUND        = 2005,
+    CUDNN_STATUS_BAD_PARAM_SIZE_INSUFFICIENT   = 2006,
+    CUDNN_STATUS_BAD_PARAM_STREAM_MISMATCH     = 2007,
+    CUDNN_STATUS_BAD_PARAM_SHAPE_MISMATCH      = 2008,
+    CUDNN_STATUS_BAD_PARAM_DUPLICATED_ENTRIES  = 2009,
+    CUDNN_STATUS_BAD_PARAM_ATTRIBUTE_TYPE      = 2010,
+    CUDNN_STATUS_BAD_PARAM_CUDA_GRAPH_MISMATCH = 2011,
+    CUDNN_STATUS_BAD_PARAM_DESCRIPTOR_TYPE     = 2012,
+
+    CUDNN_STATUS_NOT_SUPPORTED                              = 3000,
+    CUDNN_STATUS_NOT_SUPPORTED_GRAPH_PATTERN                = 3001,
+    CUDNN_STATUS_NOT_SUPPORTED_SHAPE                        = 3002,
+    CUDNN_STATUS_NOT_SUPPORTED_DATA_TYPE                    = 3003,
+    CUDNN_STATUS_NOT_SUPPORTED_LAYOUT                       = 3004,
+    CUDNN_STATUS_NOT_SUPPORTED_INCOMPATIBLE_CUDA_DRIVER     = 3005,
+    CUDNN_STATUS_NOT_SUPPORTED_INCOMPATIBLE_CUDART          = 3006,
+    CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH                = 3007,
+    CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING = 3008,
+    CUDNN_STATUS_NOT_SUPPORTED_SUBLIBRARY_UNAVAILABLE       = 3009,
+    CUDNN_STATUS_NOT_SUPPORTED_SHARED_MEMORY_INSUFFICIENT   = 3010,
+    CUDNN_STATUS_NOT_SUPPORTED_PADDING                      = 3011,
+    CUDNN_STATUS_NOT_SUPPORTED_BAD_LAUNCH_PARAM             = 3012,
+    CUDNN_STATUS_NOT_SUPPORTED_CUDA_GRAPH_NATIVE_API        = 3013,
+
+    CUDNN_STATUS_INTERNAL_ERROR                          = 4000,
+    CUDNN_STATUS_INTERNAL_ERROR_COMPILATION_FAILED       = 4001,
+    CUDNN_STATUS_INTERNAL_ERROR_UNEXPECTED_VALUE         = 4002,
+    CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED   = 4003,
+    CUDNN_STATUS_INTERNAL_ERROR_DEVICE_ALLOCATION_FAILED = 4004,
+    CUDNN_STATUS_INTERNAL_ERROR_BAD_LAUNCH_PARAM         = 4005,
+    CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED  = 4006,
+
+    CUDNN_STATUS_EXECUTION_FAILED             = 5000,
+    CUDNN_STATUS_EXECUTION_FAILED_CUDA_DRIVER = 5001,
+    CUDNN_STATUS_EXECUTION_FAILED_CUBLAS      = 5002,
+    CUDNN_STATUS_EXECUTION_FAILED_CUDART      = 5003,
+    CUDNN_STATUS_EXECUTION_FAILED_CURAND      = 5004,
+
+    CUDNN_STATUS_ALLOC_FAILED CUDNN_DEPRECATED_ENUM  = CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED,
+    CUDNN_STATUS_INVALID_VALUE CUDNN_DEPRECATED_ENUM = 2001 /* please transition to CUDNN_STATUS_BAD_PARAM instead */,
+    CUDNN_STATUS_ARCH_MISMATCH CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH,
+    CUDNN_STATUS_MAPPING_ERROR CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED,
+    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING CUDNN_DEPRECATED_ENUM =
+        CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING,
+    CUDNN_STATUS_VERSION_MISMATCH CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH,
+} cudnnStatus_t;
+
+#define CUDNN_STATUS_FULL_ERROR_CODE(category, specific_err) ((cudnnStatus_t)(0 + (category) + (specific_err)))
+#define CUDNN_STATUS_CATEGORY(full_error_code) ((full_error_code) / 1000 * 1000)
+#define CUDNN_STATUS_SPECIFIC_ERROR(full_error_code) ((full_error_code) % 1000)
+
+/* human-readable error messages */
+const char *CUDNNWINAPI
+cudnnGetErrorString(cudnnStatus_t status);
+
+void CUDNNWINAPI
+cudnnGetLastErrorString(char *message, size_t max_size);
+
+/* Forward definition in this version only */
+typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t CUDNN_DEPRECATED;
+
+typedef enum {
+    CUDNN_ERRQUERY_RAWCODE     = 0,
+    CUDNN_ERRQUERY_NONBLOCKING = 1,
+    CUDNN_ERRQUERY_BLOCKING    = 2,
+} cudnnErrQueryMode_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetProperty(libraryPropertyType type, int *value);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreate(cudnnHandle_t *handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroy(cudnnHandle_t handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
+/*
+ * CUDNN data type
+ */
+typedef enum {
+    CUDNN_DATA_FLOAT                         = 0,
+    CUDNN_DATA_DOUBLE                        = 1,
+    CUDNN_DATA_HALF                          = 2,
+    CUDNN_DATA_INT8                          = 3,
+    CUDNN_DATA_INT32                         = 4,
+    CUDNN_DATA_INT8x4 CUDNN_DEPRECATED_ENUM  = 5,
+    CUDNN_DATA_UINT8                         = 6,
+    CUDNN_DATA_UINT8x4 CUDNN_DEPRECATED_ENUM = 7,
+    CUDNN_DATA_INT8x32 CUDNN_DEPRECATED_ENUM = 8,
+    CUDNN_DATA_BFLOAT16                      = 9,
+    CUDNN_DATA_INT64                         = 10,
+    CUDNN_DATA_BOOLEAN                       = 11,
+    CUDNN_DATA_FP8_E4M3                      = 12,
+    CUDNN_DATA_FP8_E5M2                      = 13,
+    CUDNN_DATA_FAST_FLOAT_FOR_FP8            = 14,
+    CUDNN_DATA_FP8_E8M0                      = 15,
+    CUDNN_DATA_FP4_E2M1                      = 16,
+} cudnnDataType_t;
+
+/*
+ * CUDNN math type
+ */
+typedef enum {
+    CUDNN_DEFAULT_MATH                    = 0,
+    CUDNN_TENSOR_OP_MATH                  = 1,
+    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
+    CUDNN_FMA_MATH                        = 3,
+} cudnnMathType_t;
+
+/*
+ * CUDNN propagate Nan
+ */
+typedef enum {
+    CUDNN_NOT_PROPAGATE_NAN CUDNN_DEPRECATED_ENUM = 0,
+    CUDNN_PROPAGATE_NAN CUDNN_DEPRECATED_ENUM     = 1,
+} cudnnNanPropagation_t;
+
+/*
+ * Behavior for OOB samples. OOB samples are samples where L+R > T is encountered during the gradient calculation. If
+ * gradMode is set to CUDNN_CTC_SKIP_OOB_GRADIENTS, then the CTC loss function does not write to the gradient buffer for
+ * that sample. Instead, the current values, even not finite, are retained. If gradMode is set to
+ * CUDNN_CTC_ZERO_OOB_GRADIENTS, then the gradient for that sample is set to zero. This guarantees a finite gradient.
+ */
+typedef enum {
+    CUDNN_CTC_ZERO_OOB_GRADIENTS = 0,
+    CUDNN_CTC_SKIP_OOB_GRADIENTS = 1,
+} cudnnCTCGradMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_NCHW        = 0, /* row major (wStride = 1, hStride = w) */
+    CUDNN_TENSOR_NHWC        = 1, /* feature maps interleaved ( cStride = 1 )*/
+    CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
+} cudnnTensorFormat_t;
+
+/*
+ * CUDNN ReduceTensor op type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_ADD          = 0,
+    CUDNN_REDUCE_TENSOR_MUL          = 1,
+    CUDNN_REDUCE_TENSOR_MIN          = 2,
+    CUDNN_REDUCE_TENSOR_MAX          = 3,
+    CUDNN_REDUCE_TENSOR_AMAX         = 4,
+    CUDNN_REDUCE_TENSOR_AVG          = 5,
+    CUDNN_REDUCE_TENSOR_NORM1        = 6,
+    CUDNN_REDUCE_TENSOR_NORM2        = 7,
+    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
+} cudnnReduceTensorOp_t;
+
+/*
+ * activation mode
+ */
+typedef enum {
+    CUDNN_ACTIVATION_SIGMOID      = 0,
+    CUDNN_ACTIVATION_RELU         = 1,
+    CUDNN_ACTIVATION_TANH         = 2,
+    CUDNN_ACTIVATION_CLIPPED_RELU = 3,
+    CUDNN_ACTIVATION_ELU          = 4,
+    CUDNN_ACTIVATION_IDENTITY     = 5,
+    CUDNN_ACTIVATION_SWISH        = 6
+} cudnnActivationMode_t CUDNN_DEPRECATED;
+
+typedef enum {
+    CUDNN_SEV_FATAL   = 0,
+    CUDNN_SEV_ERROR   = 1,
+    CUDNN_SEV_WARNING = 2,
+    CUDNN_SEV_INFO    = 3,
+} cudnnSeverity_t;
+
+/* Message masks to be used with cudnnSetCallback() */
+#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
+#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
+#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
+
+/* struct containing useful informaiton for each API call */
+typedef struct cudnnDebugStruct {
+    unsigned cudnn_version;
+    cudnnStatus_t cudnnStatus;
+    unsigned time_sec;      /* epoch time in seconds */
+    unsigned time_usec;     /* microseconds part of epoch time */
+    unsigned time_delta;    /* time since start in seconds */
+    cudnnHandle_t handle;   /* cudnn handle */
+    cudaStream_t stream;    /* cuda stream ID */
+    unsigned long long pid; /* process ID */
+    unsigned long long tid; /* thread ID */
+    int cudaDeviceId;       /* CUDA device ID */
+    int reserved[15];       /* reserved for future use */
+} cudnnDebug_t;
+
+typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGraphVersionCheck(void);
+
+/* Maximum supported number of tensor dimensions */
+#define CUDNN_DIM_MAX 8
+
+/*
+ *  convolution mode
+ */
+typedef enum { CUDNN_CONVOLUTION = 0, CUDNN_CROSS_CORRELATION = 1 } cudnnConvolutionMode_t;
+
+/*
+ * CUDNN Reorder
+ */
+typedef enum {
+    CUDNN_DEFAULT_REORDER = 0,
+    CUDNN_NO_REORDER      = 1,
+} cudnnReorderType_t CUDNN_DEPRECATED;
+
+typedef void *cudnnBackendDescriptor_t;
+
+typedef struct cudnnFractionStruct {
+    int64_t numerator;
+    int64_t denominator;
+} cudnnFraction_t;
+
+typedef enum {
+    CUDNN_POINTWISE_ADD        = 0,
+    CUDNN_POINTWISE_ADD_SQUARE = 5,
+    CUDNN_POINTWISE_DIV        = 6,
+    CUDNN_POINTWISE_MAX        = 3,
+    CUDNN_POINTWISE_MIN        = 2,
+    CUDNN_POINTWISE_MOD        = 7,
+    CUDNN_POINTWISE_MUL        = 1,
+    CUDNN_POINTWISE_POW        = 8,
+    CUDNN_POINTWISE_SUB        = 9,
+
+    CUDNN_POINTWISE_ABS        = 10,
+    CUDNN_POINTWISE_CEIL       = 11,
+    CUDNN_POINTWISE_COS        = 12,
+    CUDNN_POINTWISE_EXP        = 13,
+    CUDNN_POINTWISE_FLOOR      = 14,
+    CUDNN_POINTWISE_LOG        = 15,
+    CUDNN_POINTWISE_NEG        = 16,
+    CUDNN_POINTWISE_RSQRT      = 17,
+    CUDNN_POINTWISE_SIN        = 18,
+    CUDNN_POINTWISE_SQRT       = 4,
+    CUDNN_POINTWISE_TAN        = 19,
+    CUDNN_POINTWISE_ERF        = 20,
+    CUDNN_POINTWISE_IDENTITY   = 21,
+    CUDNN_POINTWISE_RECIPROCAL = 22,
+    CUDNN_POINTWISE_ATAN2      = 23,
+
+    CUDNN_POINTWISE_RELU_FWD             = 100,
+    CUDNN_POINTWISE_TANH_FWD             = 101,
+    CUDNN_POINTWISE_SIGMOID_FWD          = 102,
+    CUDNN_POINTWISE_ELU_FWD              = 103,
+    CUDNN_POINTWISE_GELU_FWD             = 104,
+    CUDNN_POINTWISE_SOFTPLUS_FWD         = 105,
+    CUDNN_POINTWISE_SWISH_FWD            = 106,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_FWD = 107,
+
+    CUDNN_POINTWISE_RELU_BWD             = 200,
+    CUDNN_POINTWISE_TANH_BWD             = 201,
+    CUDNN_POINTWISE_SIGMOID_BWD          = 202,
+    CUDNN_POINTWISE_ELU_BWD              = 203,
+    CUDNN_POINTWISE_GELU_BWD             = 204,
+    CUDNN_POINTWISE_SOFTPLUS_BWD         = 205,
+    CUDNN_POINTWISE_SWISH_BWD            = 206,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_BWD = 207,
+
+    CUDNN_POINTWISE_CMP_EQ  = 300,
+    CUDNN_POINTWISE_CMP_NEQ = 301,
+    CUDNN_POINTWISE_CMP_GT  = 302,
+    CUDNN_POINTWISE_CMP_GE  = 303,
+    CUDNN_POINTWISE_CMP_LT  = 304,
+    CUDNN_POINTWISE_CMP_LE  = 305,
+
+    CUDNN_POINTWISE_LOGICAL_AND = 400,
+    CUDNN_POINTWISE_LOGICAL_OR  = 401,
+    CUDNN_POINTWISE_LOGICAL_NOT = 402,
+
+    CUDNN_POINTWISE_GEN_INDEX = 501,
+
+    CUDNN_POINTWISE_BINARY_SELECT = 601,
+} cudnnPointwiseMode_t;
+
+typedef enum {
+    CUDNN_RESAMPLE_NEAREST                 = 0,
+    CUDNN_RESAMPLE_BILINEAR                = 1,
+    CUDNN_RESAMPLE_AVGPOOL                 = 2,
+    CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING = 2,
+    CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING = 4,
+    CUDNN_RESAMPLE_MAXPOOL                 = 3,
+} cudnnResampleMode_t;
+
+typedef enum {
+    CUDNN_SIGNAL_SET  = 0,
+    CUDNN_SIGNAL_WAIT = 1,
+} cudnnSignalMode_t;
+
+typedef enum {
+    CUDNN_GENSTATS_SUM_SQSUM = 0,
+} cudnnGenStatsMode_t;
+
+typedef enum {
+    CUDNN_BN_FINALIZE_STATISTICS_TRAINING  = 0,
+    CUDNN_BN_FINALIZE_STATISTICS_INFERENCE = 1,
+} cudnnBnFinalizeStatsMode_t;
+
+typedef enum {
+    CUDNN_RNG_DISTRIBUTION_BERNOULLI = 0,
+    CUDNN_RNG_DISTRIBUTION_UNIFORM   = 1,
+    CUDNN_RNG_DISTRIBUTION_NORMAL    = 2,
+} cudnnRngDistribution_t;
+
+typedef enum {
+    CUDNN_ATTR_POINTWISE_MODE                                  = 0,
+    CUDNN_ATTR_POINTWISE_MATH_PREC                             = 1,
+    CUDNN_ATTR_POINTWISE_NAN_PROPAGATION CUDNN_DEPRECATED_ENUM = 2,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP                       = 3,
+    CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP                       = 4,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE                 = 5,
+    CUDNN_ATTR_POINTWISE_ELU_ALPHA                             = 6,
+    CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA                         = 7,
+    CUDNN_ATTR_POINTWISE_SWISH_BETA                            = 8,
+    CUDNN_ATTR_POINTWISE_AXIS                                  = 9,
+
+    CUDNN_ATTR_CONVOLUTION_COMP_TYPE      = 100,
+    CUDNN_ATTR_CONVOLUTION_CONV_MODE      = 101,
+    CUDNN_ATTR_CONVOLUTION_DILATIONS      = 102,
+    CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES = 103,
+    CUDNN_ATTR_CONVOLUTION_POST_PADDINGS  = 104,
+    CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS   = 105,
+    CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS   = 106,
+
+    CUDNN_ATTR_ENGINEHEUR_MODE            = 200,
+    CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH = 201,
+    CUDNN_ATTR_ENGINEHEUR_RESULTS         = 202,
+    CUDNN_ATTR_ENGINEHEUR_SM_COUNT_TARGET = 203,
+    CUDNN_ATTR_ENGINEHEUR_DEVICEPROP      = 204,
+
+    CUDNN_ATTR_ENGINECFG_ENGINE             = 300,
+    CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO  = 301,
+    CUDNN_ATTR_ENGINECFG_KNOB_CHOICES       = 302,
+    CUDNN_ATTR_ENGINECFG_WORKSPACE_SIZE     = 303,
+    CUDNN_ATTR_ENGINECFG_SHARED_MEMORY_USED = 304,
+
+    CUDNN_ATTR_EXECUTION_PLAN_HANDLE CUDNN_DEPRECATED_ENUM = 400,
+    CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG                = 401,
+    CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE               = 402,
+    CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS   = 403,
+    CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS   = 404,
+    CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION          = 405,
+    CUDNN_ATTR_EXECUTION_PLAN_KERNEL_CACHE                 = 406,
+    CUDNN_ATTR_EXECUTION_PLAN_DEVICEPROP                   = 407,
+
+    CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID            = 500,
+    CUDNN_ATTR_INTERMEDIATE_INFO_SIZE                 = 501,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS  = 502,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES = 503,
+
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE  = 600,
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE = 601,
+
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA        = 700,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA         = 701,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC    = 702,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W            = 703,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X            = 704,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y            = 705,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA       = 706,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA        = 707,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC   = 708,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W           = 709,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX          = 710,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY          = 711,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA     = 712,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA      = 713,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC = 714,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW        = 715,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X         = 716,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY        = 717,
+
+    CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR = 750,
+    CUDNN_ATTR_OPERATION_POINTWISE_XDESC         = 751,
+    CUDNN_ATTR_OPERATION_POINTWISE_BDESC         = 752,
+    CUDNN_ATTR_OPERATION_POINTWISE_YDESC         = 753,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1        = 754,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2        = 755,
+    CUDNN_ATTR_OPERATION_POINTWISE_DXDESC        = 756,
+    CUDNN_ATTR_OPERATION_POINTWISE_DYDESC        = 757,
+    CUDNN_ATTR_OPERATION_POINTWISE_TDESC         = 758,
+
+    CUDNN_ATTR_OPERATION_GENSTATS_MODE      = 770,
+    CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC = 771,
+    CUDNN_ATTR_OPERATION_GENSTATS_XDESC     = 772,
+    CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC   = 773,
+    CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC = 774,
+
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE                = 780,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC                 = 781,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC                = 782,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC             = 783,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC                = 784,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC                 = 785,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC    = 786,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC     = 787,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC = 788,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC  = 789,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC           = 790,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC        = 791,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC             = 792,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC              = 793,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC          = 794,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC              = 795,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERATE_FACTOR_DESC   = 796,
+
+    CUDNN_ATTR_OPERATIONGRAPH_HANDLE CUDNN_DEPRECATED_ENUM = 800,
+    CUDNN_ATTR_OPERATIONGRAPH_OPS                          = 801,
+    CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT          = 802,
+    CUDNN_ATTR_OPERATIONGRAPH_IS_DYNAMIC_SHAPE_ENABLED     = 803,
+    CUDNN_ATTR_OPERATIONGRAPH_IS_SAME_TOPOLOGY             = 804,
+
+    CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT       = 900,
+    CUDNN_ATTR_TENSOR_DATA_TYPE            = 901,
+    CUDNN_ATTR_TENSOR_DIMENSIONS           = 902,
+    CUDNN_ATTR_TENSOR_STRIDES              = 903,
+    CUDNN_ATTR_TENSOR_VECTOR_COUNT         = 904,
+    CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION = 905,
+    CUDNN_ATTR_TENSOR_UNIQUE_ID            = 906,
+    CUDNN_ATTR_TENSOR_IS_VIRTUAL           = 907,
+    CUDNN_ATTR_TENSOR_IS_BY_VALUE          = 908,
+    CUDNN_ATTR_TENSOR_REORDERING_MODE      = 909,
+    CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC   = 913,
+
+    CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS    = 1000,
+    CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS = 1001,
+    CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES = 1002,
+    CUDNN_ATTR_VARIANT_PACK_WORKSPACE     = 1003,
+
+    CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID = 1100,
+    CUDNN_ATTR_LAYOUT_INFO_TYPES      = 1101,
+
+    CUDNN_ATTR_KNOB_INFO_TYPE          = 1200,
+    CUDNN_ATTR_KNOB_INFO_MAXIMUM_VALUE = 1201,
+    CUDNN_ATTR_KNOB_INFO_MINIMUM_VALUE = 1202,
+    CUDNN_ATTR_KNOB_INFO_STRIDE        = 1203,
+
+    CUDNN_ATTR_ENGINE_OPERATION_GRAPH = 1300,
+    CUDNN_ATTR_ENGINE_GLOBAL_INDEX    = 1301,
+    CUDNN_ATTR_ENGINE_KNOB_INFO       = 1302,
+    CUDNN_ATTR_ENGINE_NUMERICAL_NOTE  = 1303,
+    CUDNN_ATTR_ENGINE_LAYOUT_INFO     = 1304,
+    CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE   = 1305,
+    CUDNN_ATTR_ENGINE_SM_COUNT_TARGET = 1306,
+    CUDNN_ATTR_ENGINE_DEVICEPROP      = 1307,
+
+    CUDNN_ATTR_MATMUL_COMP_TYPE     = 1500,
+    CUDNN_ATTR_MATMUL_PADDING_VALUE = 1503,
+
+    CUDNN_ATTR_OPERATION_MATMUL_ADESC                                                 = 1520,
+    CUDNN_ATTR_OPERATION_MATMUL_BDESC                                                 = 1521,
+    CUDNN_ATTR_OPERATION_MATMUL_CDESC                                                 = 1522,
+    CUDNN_ATTR_OPERATION_MATMUL_DESC                                                  = 1523,
+    CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT CUDNN_DEPRECATED_ENUM = 1524,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC                                  = 1525,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC                                  = 1526,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC                                  = 1527,
+
+    CUDNN_ATTR_REDUCTION_OPERATOR  = 1600,
+    CUDNN_ATTR_REDUCTION_COMP_TYPE = 1601,
+
+    CUDNN_ATTR_OPERATION_REDUCTION_XDESC = 1610,
+    CUDNN_ATTR_OPERATION_REDUCTION_YDESC = 1611,
+    CUDNN_ATTR_OPERATION_REDUCTION_DESC  = 1612,
+
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        = 1620,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        = 1621,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      = 1622,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    = 1623,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           = 1624,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          = 1625,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   = 1626,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    = 1627,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC = 1628,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  = 1629,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          = 1630,
+
+    CUDNN_ATTR_RESAMPLE_MODE            = 1700,
+    CUDNN_ATTR_RESAMPLE_COMP_TYPE       = 1701,
+    CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS    = 1702,
+    CUDNN_ATTR_RESAMPLE_POST_PADDINGS   = 1703,
+    CUDNN_ATTR_RESAMPLE_PRE_PADDINGS    = 1704,
+    CUDNN_ATTR_RESAMPLE_STRIDES         = 1705,
+    CUDNN_ATTR_RESAMPLE_WINDOW_DIMS     = 1706,
+    CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION = 1707,
+    CUDNN_ATTR_RESAMPLE_PADDING_MODE    = 1708,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC                       = 1710,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC                       = 1711,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC                     = 1712,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA CUDNN_DEPRECATED_ENUM = 1713,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA CUDNN_DEPRECATED_ENUM  = 1714,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC                        = 1716,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC                      = 1720,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC                      = 1721,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC                     = 1722,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA CUDNN_DEPRECATED_ENUM = 1723,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA CUDNN_DEPRECATED_ENUM  = 1724,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC                        = 1725,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_XDESC                       = 1726,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_YDESC                       = 1727,
+
+    CUDNN_ATTR_OPERATION_CONCAT_AXIS          = 1800,
+    CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS   = 1801,
+    CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX = 1802,
+    CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC   = 1803,
+
+    CUDNN_ATTR_OPERATION_SIGNAL_MODE     = 1900,
+    CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC = 1901,
+    CUDNN_ATTR_OPERATION_SIGNAL_VALUE    = 1902,
+    CUDNN_ATTR_OPERATION_SIGNAL_XDESC    = 1903,
+    CUDNN_ATTR_OPERATION_SIGNAL_YDESC    = 1904,
+
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_CONTAINER_DESC  = 1950,
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_YDESC           = 1951,
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_SEQUENCE_DESC   = 1952,
+    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_PAGE_TABLE_DESC = 1953,
+
+    CUDNN_ATTR_OPERATION_NORM_FWD_MODE                     = 2000,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PHASE                    = 2001,
+    CUDNN_ATTR_OPERATION_NORM_FWD_XDESC                    = 2002,
+    CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC                = 2003,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC        = 2004,
+    CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC               = 2005,
+    CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC                = 2006,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC             = 2007,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC      = 2008,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC  = 2009,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC   = 2010,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC = 2011,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC  = 2012,
+    CUDNN_ATTR_OPERATION_NORM_FWD_YDESC                    = 2013,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS          = 2014,
+
+    CUDNN_ATTR_OPERATION_NORM_BWD_MODE              = 2100,
+    CUDNN_ATTR_OPERATION_NORM_BWD_XDESC             = 2101,
+    CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC         = 2102,
+    CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC = 2103,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC            = 2104,
+    CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC        = 2105,
+    CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC      = 2106,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC       = 2107,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC        = 2108,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC            = 2109,
+    CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS   = 2110,
+
+    CUDNN_ATTR_OPERATION_RESHAPE_XDESC = 2200,
+    CUDNN_ATTR_OPERATION_RESHAPE_YDESC = 2201,
+
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_XDESC                = 2250,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_YDESC                = 2251,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_LOWER_BANDWIDTH      = 2252,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_UPPER_BANDWIDTH      = 2253,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_AXIS                 = 2254,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_PAD_VALUE            = 2255,
+    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_KV_TOKEN_OFFSET_DESC = 2256,
+
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_XDESC           = 2270,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_YDESC           = 2271,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_LOWER_BANDWIDTH = 2272,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_UPPER_BANDWIDTH = 2273,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_AXIS            = 2274,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_PAD_VALUE       = 2275,
+    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MAX_TOKEN_VALUE        = 2276,
+
+    CUDNN_ATTR_RNG_DISTRIBUTION                   = 2300,
+    CUDNN_ATTR_RNG_NORMAL_DIST_MEAN               = 2301,
+    CUDNN_ATTR_RNG_NORMAL_DIST_STANDARD_DEVIATION = 2302,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MAXIMUM           = 2303,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MINIMUM           = 2304,
+    CUDNN_ATTR_RNG_BERNOULLI_DIST_PROBABILITY     = 2305,
+
+    CUDNN_ATTR_OPERATION_RNG_YDESC       = 2310,
+    CUDNN_ATTR_OPERATION_RNG_SEED        = 2311,
+    CUDNN_ATTR_OPERATION_RNG_DESC        = 2312,
+    CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC = 2313,
+
+    CUDNN_ATTR_KERNEL_CACHE_OPERATION_GRAPH            = 2400,
+    CUDNN_ATTR_KERNEL_CACHE_IS_ENGINECFG_KERNEL_CACHED = 2401,
+    CUDNN_ATTR_KERNEL_CACHE_JSON_REPRESENTATION        = 2402,
+
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_XDESC      = 2500,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_YDESC      = 2501,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_SCALE_DESC = 2502,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_MATH_PREC  = 2503,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_BLOCK_SIZE = 2504,
+
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_XDESC      = 2600,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_SCALE_DESC = 2601,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_YDESC      = 2602,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_MATH_PREC  = 2603,
+    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_BLOCK_SIZE = 2604,
+
+    CUDNN_ATTR_DEVICEPROP_DEVICE_ID           = 2700,
+    CUDNN_ATTR_DEVICEPROP_HANDLE              = 2701,
+    CUDNN_ATTR_DEVICEPROP_JSON_REPRESENTATION = 2702,
+} cudnnBackendAttributeName_t;
+
+typedef enum {
+    CUDNN_TYPE_HANDLE                                = 0,
+    CUDNN_TYPE_DATA_TYPE                             = 1,
+    CUDNN_TYPE_BOOLEAN                               = 2,
+    CUDNN_TYPE_INT64                                 = 3,
+    CUDNN_TYPE_FLOAT                                 = 4,
+    CUDNN_TYPE_DOUBLE                                = 5,
+    CUDNN_TYPE_VOID_PTR                              = 6,
+    CUDNN_TYPE_CONVOLUTION_MODE                      = 7,
+    CUDNN_TYPE_HEUR_MODE                             = 8,
+    CUDNN_TYPE_KNOB_TYPE                             = 9,
+    CUDNN_TYPE_NAN_PROPOGATION CUDNN_DEPRECATED_ENUM = 10,
+    CUDNN_TYPE_NUMERICAL_NOTE                        = 11,
+    CUDNN_TYPE_LAYOUT_TYPE                           = 12,
+    CUDNN_TYPE_ATTRIB_NAME                           = 13,
+    CUDNN_TYPE_POINTWISE_MODE                        = 14,
+    CUDNN_TYPE_BACKEND_DESCRIPTOR                    = 15,
+    CUDNN_TYPE_GENSTATS_MODE                         = 16,
+    CUDNN_TYPE_BN_FINALIZE_STATS_MODE                = 17,
+    CUDNN_TYPE_REDUCTION_OPERATOR_TYPE               = 18,
+    CUDNN_TYPE_BEHAVIOR_NOTE                         = 19,
+    CUDNN_TYPE_TENSOR_REORDERING_MODE                = 20,
+    CUDNN_TYPE_RESAMPLE_MODE                         = 21,
+    CUDNN_TYPE_PADDING_MODE                          = 22,
+    CUDNN_TYPE_INT32                                 = 23,
+    CUDNN_TYPE_CHAR                                  = 24,
+    CUDNN_TYPE_SIGNAL_MODE                           = 25,
+    CUDNN_TYPE_FRACTION                              = 26,
+    CUDNN_TYPE_NORM_MODE                             = 27,
+    CUDNN_TYPE_NORM_FWD_PHASE                        = 28,
+    CUDNN_TYPE_RNG_DISTRIBUTION                      = 29,
+} cudnnBackendAttributeType_t;
+
+typedef enum {
+    CUDNN_BACKEND_POINTWISE_DESCRIPTOR                             = 0,
+    CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR                           = 1,
+    CUDNN_BACKEND_ENGINE_DESCRIPTOR                                = 2,
+    CUDNN_BACKEND_ENGINECFG_DESCRIPTOR                             = 3,
+    CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR                            = 4,
+    CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR                        = 5,
+    CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR                     = 6,
+    CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR                           = 7,
+    CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR                             = 8,
+    CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR                           = 9,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR         = 10,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR = 11,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR   = 12,
+    CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR                   = 13,
+    CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR                   = 14,
+    CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR                        = 15,
+    CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR                          = 16,
+    CUDNN_BACKEND_TENSOR_DESCRIPTOR                                = 17,
+    CUDNN_BACKEND_MATMUL_DESCRIPTOR                                = 18,
+    CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR                      = 19,
+    CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR      = 20,
+    CUDNN_BACKEND_REDUCTION_DESCRIPTOR                             = 21,
+    CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR                   = 22,
+    CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR              = 23,
+    CUDNN_BACKEND_RESAMPLE_DESCRIPTOR                              = 24,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR                = 25,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR                = 26,
+    CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR                      = 27,
+    CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR                      = 28,
+    CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR                = 29,
+    CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR               = 30,
+    CUDNN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR                     = 31,
+    CUDNN_BACKEND_RNG_DESCRIPTOR                                   = 32,
+    CUDNN_BACKEND_OPERATION_RNG_DESCRIPTOR                         = 33,
+    CUDNN_BACKEND_KERNEL_CACHE_DESCRIPTOR                          = 34,
+    CUDNN_BACKEND_OPERATION_PAGED_CACHE_LOAD_DESCRIPTOR            = 35,
+    CUDNN_BACKEND_OPERATION_BLOCK_SCALE_QUANTIZE_DESCRIPTOR        = 36,
+    CUDNN_BACKEND_OPERATION_BLOCK_SCALE_DEQUANTIZE_DESCRIPTOR      = 37,
+    CUDNN_BACKEND_DEVICEPROP_DESCRIPTOR                            = 38,
+    CUDNN_BACKEND_OPERATION_EXPAND_BAND_MATRIX_DESCRIPTOR          = 39,
+    CUDNN_BACKEND_OPERATION_CONTRACT_BAND_MATRIX_DESCRIPTOR        = 40,
+} cudnnBackendDescriptorType_t;
+
+typedef enum {
+    CUDNN_NUMERICAL_NOTE_TENSOR_CORE                 = 0,
+    CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS         = 1,
+    CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION = 2,
+    CUDNN_NUMERICAL_NOTE_FFT                         = 3,
+    CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC            = 4,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD                    = 5,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_4x4           = 6,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_6x6           = 7,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_13x13         = 8,
+    CUDNN_NUMERICAL_NOTE_STRICT_NAN_PROP             = 9,
+    CUDNN_NUMERICAL_NOTE_TYPE_COUNT                  = 10,
+} cudnnBackendNumericalNote_t;
+
+typedef enum {
+    CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION             = 0,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_FILTER_INT8x32_REORDER = 1,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_BIAS_INT8x32_REORDER   = 2,
+    CUDNN_BEHAVIOR_NOTE_SUPPORTS_CUDA_GRAPH_NATIVE_API  = 3,
+    CUDNN_BEHAVIOR_NOTE_TYPE_COUNT                      = 4,
+} cudnnBackendBehaviorNote_t;
+
+typedef enum {
+    CUDNN_KNOB_TYPE_SPLIT_K CUDNN_DEPRECATED_ENUM          = 0,
+    CUDNN_KNOB_TYPE_SWIZZLE                                = 1,
+    CUDNN_KNOB_TYPE_TILE_SIZE                              = 2,
+    CUDNN_KNOB_TYPE_USE_TEX CUDNN_DEPRECATED_ENUM          = 3,
+    CUDNN_KNOB_TYPE_EDGE                                   = 4,
+    CUDNN_KNOB_TYPE_KBLOCK CUDNN_DEPRECATED_ENUM           = 5,
+    CUDNN_KNOB_TYPE_LDGA CUDNN_DEPRECATED_ENUM             = 6,
+    CUDNN_KNOB_TYPE_LDGB CUDNN_DEPRECATED_ENUM             = 7,
+    CUDNN_KNOB_TYPE_CHUNK_K CUDNN_DEPRECATED_ENUM          = 8,
+    CUDNN_KNOB_TYPE_SPLIT_H CUDNN_DEPRECATED_ENUM          = 9,
+    CUDNN_KNOB_TYPE_WINO_TILE CUDNN_DEPRECATED_ENUM        = 10,
+    CUDNN_KNOB_TYPE_MULTIPLY                               = 11,
+    CUDNN_KNOB_TYPE_SPLIT_K_BUF                            = 12,
+    CUDNN_KNOB_TYPE_TILEK                                  = 13,
+    CUDNN_KNOB_TYPE_STAGES                                 = 14,
+    CUDNN_KNOB_TYPE_REDUCTION_MODE                         = 15,
+    CUDNN_KNOB_TYPE_CTA_SPLIT_K_MODE CUDNN_DEPRECATED_ENUM = 16,
+    CUDNN_KNOB_TYPE_SPLIT_K_SLC                            = 17,
+    CUDNN_KNOB_TYPE_IDX_MODE                               = 18,
+    CUDNN_KNOB_TYPE_SLICED CUDNN_DEPRECATED_ENUM           = 19,
+    CUDNN_KNOB_TYPE_SPLIT_RS CUDNN_DEPRECATED_ENUM         = 20,
+    CUDNN_KNOB_TYPE_SINGLEBUFFER CUDNN_DEPRECATED_ENUM     = 21,
+    CUDNN_KNOB_TYPE_LDGC CUDNN_DEPRECATED_ENUM             = 22,
+    CUDNN_KNOB_TYPE_SPECFILT                               = 23,
+    CUDNN_KNOB_TYPE_KERNEL_CFG                             = 24,
+    CUDNN_KNOB_TYPE_WORKSPACE                              = 25,
+    CUDNN_KNOB_TYPE_TILE_CGA CUDNN_DEPRECATED_ENUM         = 26,
+    CUDNN_KNOB_TYPE_TILE_CGA_M                             = 27,
+    CUDNN_KNOB_TYPE_TILE_CGA_N                             = 28,
+    CUDNN_KNOB_TYPE_BLOCK_SIZE                             = 29,
+    CUDNN_KNOB_TYPE_OCCUPANCY                              = 30,
+    CUDNN_KNOB_TYPE_ARRAY_SIZE_PER_THREAD                  = 31,
+    CUDNN_KNOB_TYPE_NUM_C_PER_BLOCK CUDNN_DEPRECATED_ENUM  = 32,
+    CUDNN_KNOB_TYPE_SPLIT_COLS                             = 33,
+    CUDNN_KNOB_TYPE_TILE_ROWS                              = 34,
+    CUDNN_KNOB_TYPE_TILE_COLS                              = 35,
+    CUDNN_KNOB_TYPE_LOAD_SIZE                              = 36,
+    CUDNN_KNOB_TYPE_CTA_COUNT                              = 37,
+    CUDNN_KNOB_TYPE_STREAM_K                               = 38,
+    CUDNN_KNOB_TYPE_SPLIT_P_SLC                            = 39,
+    CUDNN_KNOB_TYPE_TILE_M                                 = 40,
+    CUDNN_KNOB_TYPE_TILE_N                                 = 41,
+    CUDNN_KNOB_TYPE_WARP_SPEC_CFG                          = 42,
+    CUDNN_KNOB_TYPE_COUNTS                                 = 43,
+} cudnnBackendKnobType_t;
+
+typedef enum {
+    CUDNN_LAYOUT_TYPE_PREFERRED_NCHW   = 0,
+    CUDNN_LAYOUT_TYPE_PREFERRED_NHWC   = 1,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD4CK = 2,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD8CK = 3,
+    CUDNN_LAYOUT_TYPE_COUNT            = 4,
+} cudnnBackendLayoutType_t;
+
+typedef enum {
+    CUDNN_HEUR_MODE_INSTANT  = 0,
+    CUDNN_HEUR_MODE_B        = 1,
+    CUDNN_HEUR_MODE_FALLBACK = 2,
+    CUDNN_HEUR_MODE_A        = 3,
+    CUDNN_HEUR_MODES_COUNT   = 4,
+} cudnnBackendHeurMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_REORDERING_NONE     = 0,
+    CUDNN_TENSOR_REORDERING_INT8x32  = 1,
+    CUDNN_TENSOR_REORDERING_F16x16   = 2,
+    CUDNN_TENSOR_REORDERING_F8_128x4 = 3,
+} cudnnBackendTensorReordering_t;
+
+typedef enum {
+    CUDNN_ZERO_PAD     = 0,
+    CUDNN_NEG_INF_PAD  = 1,
+    CUDNN_EDGE_VAL_PAD = 2,
+} cudnnPaddingMode_t;
+
+typedef enum {
+    CUDNN_LAYER_NORM     = 0,
+    CUDNN_INSTANCE_NORM  = 1,
+    CUDNN_BATCH_NORM     = 2,
+    CUDNN_GROUP_NORM     = 3,
+    CUDNN_RMS_NORM       = 4,
+    CUDNN_ADA_LAYER_NORM = 5,
+} cudnnBackendNormMode_t;
+
+typedef enum {
+    CUDNN_NORM_FWD_INFERENCE = 0,
+    CUDNN_NORM_FWD_TRAINING  = 1,
+} cudnnBackendNormFwdPhase_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendCreateDescriptor(cudnnBackendDescriptorType_t descriptorType, cudnnBackendDescriptor_t *descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendDestroyDescriptor(cudnnBackendDescriptor_t descriptor);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBackendInitialize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendFinalize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendSetAttribute(cudnnBackendDescriptor_t descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t elementCount,
+                         const void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendGetAttribute(cudnnBackendDescriptor_t const descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t requestedElementCount,
+                         int64_t *elementCount,
+                         void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendExecute(cudnnHandle_t handle, cudnnBackendDescriptor_t executionPlan, cudnnBackendDescriptor_t variantPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendPopulateCudaGraph(cudnnHandle_t handle,
+                              cudnnBackendDescriptor_t executionPlan,
+                              cudnnBackendDescriptor_t variantPack,
+                              cudaGraph_t graph);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendUpdateCudaGraph(cudnnHandle_t handle,
+                            cudnnBackendDescriptor_t executionPlan,
+                            cudnnBackendDescriptor_t variantPack,
+                            cudaGraph_t graph);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_GRAPH_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..471a0e59d67228ab8a74159517418f217ab86324
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops.h
@@ -0,0 +1,1316 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops : cuDNN's basic definitions and basic operations.
+ */
+
+#if !defined(CUDNN_OPS_H_)
+#define CUDNN_OPS_H_
+
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_graph.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_MAJOR 9
+#define CUDNN_OPS_MINOR 10
+#define CUDNN_OPS_PATCH 2
+
+#if (CUDNN_OPS_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_MINOR != CUDNN_MINOR) || (CUDNN_OPS_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* Data structures to represent Image/Filter and the Neural Network Layer */
+typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
+typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
+typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
+typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
+typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t CUDNN_DEPRECATED;
+/*
+ * CUDNN Determinism
+ */
+typedef enum {
+    CUDNN_NON_DETERMINISTIC = 0,
+    CUDNN_DETERMINISTIC     = 1,
+} cudnnDeterminism_t;
+
+/* Create an instance of a generic Tensor descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnTensorFormat_t format,
+                           cudnnDataType_t dataType, /* image data type */
+                           int n,                    /* number of inputs (batch size) */
+                           int c,                    /* number of input feature maps */
+                           int h,                    /* height of input section */
+                           int w);                   /* width of input section */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnDataType_t dataType, /* image data type */
+                             int n,                    /* number of inputs (batch size) */
+                             int c,                    /* number of input feature maps */
+                             int h,                    /* height of input section */
+                             int w,                    /* width of input section */
+                             int nStride,
+                             int cStride,
+                             int hStride,
+                             int wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           int *n,                    /* number of inputs (batch size) */
+                           int *c,                    /* number of input feature maps  */
+                           int *h,                    /* height of input section */
+                           int *w,                    /* width of input section */
+                           int *nStride,
+                           int *cStride,
+                           int *hStride,
+                           int *wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t dataType,
+                           int nbDims,
+                           const int dimA[],
+                           const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnTensorFormat_t format,
+                             cudnnDataType_t dataType,
+                             int nbDims,
+                             const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType,
+                           int *nbDims,
+                           int dimA[],
+                           int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
+
+/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
+
+   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
+   input_stride :  c x h x h_stride
+   feature_stride : h x h_stride
+   h_stride  :  >= w  ( h_stride = w if no padding)
+   w_stride  : 1
+
+
+   2)Example of all images in row major with features maps interleaved
+   input_stride :  c x h x h_stride
+   feature_stride : 1
+   h_stride  :  w x c
+   w_stride  : c
+
+   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
+   input_stride :  c x w x w_stride
+   feature_stride : w x w_stride
+   h_stride  :  1
+   w_stride  :  >= h
+
+*/
+
+/* Destroy an instance of Tensor4d descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
+
+/* Fold/unfold transforms */
+typedef enum {
+    CUDNN_TRANSFORM_FOLD   = 0U,
+    CUDNN_TRANSFORM_UNFOLD = 1U,
+} cudnnFoldingDirection_t;
+
+/** Create a destination descriptor for cudnnTransformTensor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       cudnnTensorDescriptor_t destDesc,
+                       size_t *destSizeInBytes);
+
+/** Create an empty tensor transform descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
+
+/** Initialize a previously created tensor transform descriptor. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  const uint32_t nbDims,
+                                  const cudnnTensorFormat_t destFormat,
+                                  const int32_t padBeforeA[],
+                                  const int32_t padAfterA[],
+                                  const uint32_t foldA[],
+                                  const cudnnFoldingDirection_t direction);
+
+/**
+ * Retrieves the values stored in a previously initialized tensor transform
+ * descriptor.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  uint32_t nbDimsRequested,
+                                  cudnnTensorFormat_t *destFormat,
+                                  int32_t padBeforeA[],
+                                  int32_t padAfterA[],
+                                  uint32_t foldA[],
+                                  cudnnFoldingDirection_t *direction);
+
+/**
+ * Destroys a previously created tensor transform descriptor.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
+
+/* Tensor layout conversion helper (y = alpha * x + beta * y) */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensor(cudnnHandle_t handle,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t yDesc,
+                     void *y);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensorEx(cudnnHandle_t handle,
+                       const cudnnTensorTransformDescriptor_t transDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       const void *srcData,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t destDesc,
+                       void *destData);
+
+/* Tensor Bias addition : C = alpha * A + beta * C  */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnAddTensor(cudnnHandle_t handle,
+               const void *alpha,
+               const cudnnTensorDescriptor_t aDesc,
+               const void *A,
+               const void *beta,
+               const cudnnTensorDescriptor_t cDesc,
+               void *C);
+
+/*
+ * CUDNN OpTensor op type
+ */
+typedef enum {
+    CUDNN_OP_TENSOR_ADD  = 0,
+    CUDNN_OP_TENSOR_MUL  = 1,
+    CUDNN_OP_TENSOR_MIN  = 2,
+    CUDNN_OP_TENSOR_MAX  = 3,
+    CUDNN_OP_TENSOR_SQRT = 4,
+    CUDNN_OP_TENSOR_NOT  = 5,
+} cudnnOpTensorOp_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t opTensorOp,
+                           cudnnDataType_t opTensorCompType,
+                           cudnnNanPropagation_t opTensorNanOpt);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t *opTensorOp,
+                           cudnnDataType_t *opTensorCompType,
+                           cudnnNanPropagation_t *opTensorNanOpt);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
+
+/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
+/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnOpTensor(cudnnHandle_t handle,
+              const cudnnOpTensorDescriptor_t opTensorDesc,
+              const void *alpha1,
+              const cudnnTensorDescriptor_t aDesc,
+              const void *A,
+              const void *alpha2,
+              const cudnnTensorDescriptor_t bDesc,
+              const void *B,
+              const void *beta,
+              const cudnnTensorDescriptor_t cDesc,
+              void *C);
+
+/*
+ * CUDNN ReduceTensor indices type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0,
+    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
+} cudnnReduceTensorIndices_t CUDNN_DEPRECATED;
+
+/*
+ * CUDNN tensor indices type size (all unsigned)
+ * Currently not supported, default is 32 bit unsigned.
+ */
+typedef enum {
+    CUDNN_32BIT_INDICES = 0,
+    CUDNN_64BIT_INDICES = 1,
+    CUDNN_16BIT_INDICES = 2,
+    CUDNN_8BIT_INDICES  = 3,
+} cudnnIndicesType_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t reduceTensorOp,
+                               cudnnDataType_t reduceTensorCompType,
+                               cudnnNanPropagation_t reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t reduceTensorIndices,
+                               cudnnIndicesType_t reduceTensorIndicesType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t *reduceTensorOp,
+                               cudnnDataType_t *reduceTensorCompType,
+                               cudnnNanPropagation_t *reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t *reduceTensorIndices,
+                               cudnnIndicesType_t *reduceTensorIndicesType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
+
+/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
+ * output tensors */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionIndicesSize(cudnnHandle_t handle,
+                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                             const cudnnTensorDescriptor_t aDesc,
+                             const cudnnTensorDescriptor_t cDesc,
+                             size_t *sizeInBytes);
+
+/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
+ * tensors */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
+                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               const cudnnTensorDescriptor_t aDesc,
+                               const cudnnTensorDescriptor_t cDesc,
+                               size_t *sizeInBytes);
+
+/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
+/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
+/* The indices space is ignored for reduce ops other than min or max. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnReduceTensor(cudnnHandle_t handle,
+                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                  void *indices,
+                  size_t indicesSizeInBytes,
+                  void *workspace,
+                  size_t workspaceSizeInBytes,
+                  const void *alpha,
+                  const cudnnTensorDescriptor_t aDesc,
+                  const void *A,
+                  const void *beta,
+                  const cudnnTensorDescriptor_t cDesc,
+                  void *C);
+
+/* Set all values of a tensor to a given value : y[i] = value[0] */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
+
+/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
+
+/* Create an instance of FilterStruct */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int k,  /* number of output feature maps */
+                           int c,  /* number of input feature maps */
+                           int h,  /* height of each input filter */
+                           int w); /* width of  each input filter */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *k,  /* number of output feature maps */
+                           int *c,  /* number of input feature maps */
+                           int *h,  /* height of each input filter */
+                           int *w); /* width of  each input filter */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int nbDims,
+                           const int filterDimA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *nbDims,
+                           int filterDimA[]);
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnTransformFilter(cudnnHandle_t handle,
+                     const cudnnTensorTransformDescriptor_t transDesc,
+                     const void *alpha,
+                     const cudnnFilterDescriptor_t srcDesc,
+                     const void *srcData,
+                     const void *beta,
+                     const cudnnFilterDescriptor_t destDesc,
+                     void *destData);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
+
+/*
+ *  softmax algorithm
+ */
+typedef enum {
+    CUDNN_SOFTMAX_FAST     = 0, /* straightforward implementation */
+    CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
+    CUDNN_SOFTMAX_LOG      = 2
+} cudnnSoftmaxAlgorithm_t;
+
+typedef enum {
+    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
+    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /* compute the softmax over all C for each H, W, N */
+} cudnnSoftmaxMode_t;
+
+/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxForward(cudnnHandle_t handle,
+                    cudnnSoftmaxAlgorithm_t algo,
+                    cudnnSoftmaxMode_t mode,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ *  pooling mode
+ */
+typedef enum {
+    CUDNN_POOLING_MAX                           = 0,
+    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
+    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
+    CUDNN_POOLING_MAX_DETERMINISTIC             = 3
+} cudnnPoolingMode_t CUDNN_DEPRECATED;
+
+/* Create an instance of pooling descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t mode,
+                            cudnnNanPropagation_t maxpoolingNanOpt,
+                            int windowHeight,
+                            int windowWidth,
+                            int verticalPadding,
+                            int horizontalPadding,
+                            int verticalStride,
+                            int horizontalStride);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *windowHeight,
+                            int *windowWidth,
+                            int *verticalPadding,
+                            int *horizontalPadding,
+                            int *verticalStride,
+                            int *horizontalStride);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            const cudnnPoolingMode_t mode,
+                            const cudnnNanPropagation_t maxpoolingNanOpt,
+                            int nbDims,
+                            const int windowDimA[],
+                            const int paddingA[],
+                            const int strideA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            int nbDimsRequested,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *nbDims,
+                            int windowDimA[],
+                            int paddingA[],
+                            int strideA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int nbDims,
+                                  int outputTensorDimA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int *n,
+                                  int *c,
+                                  int *h,
+                                  int *w);
+
+/* Destroy an instance of pooling descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
+
+/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward pooling */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnPoolingForward(cudnnHandle_t handle,
+                    const cudnnPoolingDescriptor_t poolingDesc,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t mode,
+                             cudnnNanPropagation_t reluNanOpt,
+                             double coef); /* ceiling for clipped RELU, alpha for ELU */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t *mode,
+                             cudnnNanPropagation_t *reluNanOpt,
+                             double *coef); /* ceiling for clipped RELU, alpha for ELU */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
+
+/* Function to perform forward activation  */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnActivationForward(cudnnHandle_t handle,
+                       cudnnActivationDescriptor_t activationDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t xDesc,
+                       const void *x,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t yDesc,
+                       void *y);
+
+/*
+ * Create an instance of LRN (Local Response Normalization) descriptor
+ * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
+
+#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
+#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
+#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
+#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
+
+/* LRN layer mode */
+typedef enum {
+    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
+} cudnnLRNMode_t;
+
+/*
+ * Uses a window [center-lookBehind, center+lookAhead], where
+ * lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
+ * Values of double parameters cast to tensor data type.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
+/*
+ * Retrieve the settings currently stored in an LRN layer descriptor
+ * Any of the provided pointers can be NULL (no corresponding value will be returned)
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
+
+/* Destroy an instance of LRN descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
+
+/* LRN functions: output = alpha * normalize(x) + beta * old_y */
+
+/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelForward(cudnnHandle_t handle,
+                            cudnnLRNDescriptor_t normDesc,
+                            cudnnLRNMode_t lrnMode,
+                            const void *alpha,
+                            const cudnnTensorDescriptor_t xDesc,
+                            const void *x,
+                            const void *beta,
+                            const cudnnTensorDescriptor_t yDesc,
+                            void *y);
+
+typedef enum {
+    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
+} cudnnDivNormMode_t;
+
+/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
+                                  cudnnLRNDescriptor_t normDesc,
+                                  cudnnDivNormMode_t mode,
+                                  const void *alpha,
+                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
+                                  const void *x,
+                                  const void *means, /* if NULL, means are assumed to be zero */
+                                  void *temp,
+                                  void *temp2,
+                                  const void *beta,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *y);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_BATCHNORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_BATCHNORM_SPATIAL = 1,
+
+    /*
+     * bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
+     * May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
+     */
+    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
+} cudnnBatchNormMode_t CUDNN_DEPRECATED;
+
+#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for BatchNormalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
+                              const cudnnTensorDescriptor_t xDesc,
+                              cudnnBatchNormMode_t mode);
+
+typedef enum {
+    CUDNN_BATCHNORM_OPS_BN                = 0, /* do batch normalization only */
+    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /* do batchNorm, then activation */
+    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
+} cudnnBatchNormOps_t CUDNN_DEPRECATED;
+
+/*
+ * Performs Batch Normalization during Inference:
+ * y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
+ * with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
+ * according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
+                                        cudnnBatchNormMode_t mode,
+                                        const void *alpha, /* alpha[0] = result blend factor */
+                                        const void *beta,  /* beta[0] = dest layer blend factor */
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const void *x, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        void *y, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                        const void *bnScale,
+                                        const void *bnBias,
+                                        const void *estimatedMean,
+                                        const void *estimatedVariance,
+                                        double epsilon);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_NORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_NORM_PER_CHANNEL = 1,
+} cudnnNormMode_t CUDNN_DEPRECATED;
+
+typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t CUDNN_DEPRECATED;
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for Normalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
+                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
+                                const cudnnTensorDescriptor_t xDesc,
+                                cudnnNormMode_t mode,
+                                int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+typedef enum {
+    CUDNN_NORM_OPS_NORM                = 0, /* do normalization only */
+    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /* do Norm, then activation */
+    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
+} cudnnNormOps_t CUDNN_DEPRECATED;
+
+/*
+ * Performs Normalization during Inference:
+ * y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
+ * with normScale, normBias, runningMean, runningInvVariance tensors indexed
+ * according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardInference(cudnnHandle_t handle,
+                                   cudnnNormMode_t mode,
+                                   cudnnNormOps_t normOps,
+                                   cudnnNormAlgo_t algo,
+                                   const void *alpha, /* alpha[0] = result blend factor */
+                                   const void *beta,  /* beta[0] = dest layer blend factor */
+                                   const cudnnTensorDescriptor_t xDesc,
+                                   const void *x, /* NxCxHxW */
+                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                   const void *normScale,
+                                   const void *normBias,
+                                   const cudnnTensorDescriptor_t normMeanVarDesc,
+                                   const void *estimatedMean,
+                                   const void *estimatedVariance,
+                                   const cudnnTensorDescriptor_t zDesc,
+                                   const void *z,
+                                   cudnnActivationDescriptor_t activationDesc,
+                                   const cudnnTensorDescriptor_t yDesc,
+                                   void *y, /* NxCxHxW */
+                                   double epsilon,
+                                   int groupCnt); /* Place hold for future work*/
+
+/* APIs for spatial transformer network*/
+typedef enum {
+    CUDNN_SAMPLER_BILINEAR = 0,
+} cudnnSamplerType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
+                                       cudnnSamplerType_t samplerType,
+                                       cudnnDataType_t dataType,
+                                       const int nbDims,
+                                       const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
+                                   const cudnnSpatialTransformerDescriptor_t stDesc,
+                                   const void *theta,
+                                   void *grid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
+                             cudnnSpatialTransformerDescriptor_t stDesc,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *grid,
+                             const void *beta,
+                             cudnnTensorDescriptor_t yDesc,
+                             void *y);
+
+typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
+
+/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
+
+/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float dropout,
+                          void *states,
+                          size_t stateSizeInBytes,
+                          unsigned long long seed);
+
+/* Restores the dropout descriptor to a previously saved-off state */
+cudnnStatus_t CUDNNWINAPI
+cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                              cudnnHandle_t handle,
+                              float dropout,
+                              void *states,
+                              size_t stateSizeInBytes,
+                              unsigned long long seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float *dropout,
+                          void **states,
+                          unsigned long long *seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutForward(cudnnHandle_t handle,
+                    const cudnnDropoutDescriptor_t dropoutDesc,
+                    const cudnnTensorDescriptor_t xdesc,
+                    const void *x,
+                    const cudnnTensorDescriptor_t ydesc,
+                    void *y,
+                    void *reserveSpace,
+                    size_t reserveSpaceSizeInBytes);
+
+/* TODO: move these enums out to the appropriate submodule */
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0,
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
+    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2,
+    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7,
+    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8
+} cudnnConvolutionFwdAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /* not implemented */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7
+} cudnnConvolutionBwdFilterAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6
+} cudnnConvolutionBwdDataAlgo_t;
+
+typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsVersionCheck(void);
+
+/* Function to perform backward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxBackward(cudnnHandle_t handle,
+                     cudnnSoftmaxAlgorithm_t algo,
+                     cudnnSoftmaxMode_t mode,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward pooling */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnPoolingBackward(cudnnHandle_t handle,
+                     const cudnnPoolingDescriptor_t poolingDesc,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward activation  */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnActivationBackward(cudnnHandle_t handle,
+                        cudnnActivationDescriptor_t activationDesc,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t yDesc,
+                        const void *y,
+                        const cudnnTensorDescriptor_t dyDesc,
+                        const void *dy,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t dxDesc,
+                        void *dx);
+
+/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
+                             cudnnLRNDescriptor_t normDesc,
+                             cudnnLRNMode_t lrnMode,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t yDesc,
+                             const void *y,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
+                                   cudnnLRNDescriptor_t normDesc,
+                                   cudnnDivNormMode_t mode,
+                                   const void *alpha,
+                                   const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
+                                   const void *x,
+                                   const void *means, /* if NULL, means are assumed to be zero */
+                                   const void *dy,
+                                   void *temp,
+                                   void *temp2,
+                                   const void *beta,
+                                   const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
+                                   void *dx,                                   /* output x differential */
+                                   void *dMeans); /* output means differential, can be NULL */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
+                                                         cudnnBatchNormMode_t mode,
+                                                         cudnnBatchNormOps_t bnOps,
+                                                         const cudnnTensorDescriptor_t xDesc,
+                                                         const cudnnTensorDescriptor_t zDesc,
+                                                         const cudnnTensorDescriptor_t yDesc,
+                                                         const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                                         const cudnnActivationDescriptor_t activationDesc,
+                                                         size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnBatchNormMode_t mode,
+                                                  cudnnBatchNormOps_t bnOps,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t dyDesc,
+                                                  const cudnnTensorDescriptor_t dzDesc,
+                                                  const cudnnTensorDescriptor_t dxDesc,
+                                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
+                                                     cudnnBatchNormMode_t mode,
+                                                     cudnnBatchNormOps_t bnOps,
+                                                     const cudnnActivationDescriptor_t activationDesc,
+                                                     const cudnnTensorDescriptor_t xDesc,
+                                                     size_t *sizeInBytes);
+
+/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTraining(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *x, /* NxCxHxW */
+    const cudnnTensorDescriptor_t yDesc,
+    void *y, /* NxCxHxW */
+
+    /* Shared desc for the next 6 tensors in the argument list.
+       Data type to be set as follows:
+       type = (typeOf(x) == double) ? double : float
+       Dimensions for this descriptor depend on normalization mode
+       - Spatial Normalization : tensors are expected to have dims 1xCx1x1
+        (normalization is performed across NxHxW)
+       - Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
+        (normalization is performed across N) */
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+
+    /* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
+    const void *bnScale,
+    const void *bnBias,
+
+    /* MUST use factor=1 in the very first call of a complete training cycle.
+       Use a factor=1/(1+n) at N-th call to the function to get
+       Cumulative Moving Average (CMA) behavior
+       CMA[n] = (x[1]+...+x[n])/n
+       Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
+       ((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
+       CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
+    double exponentialAverageFactor,
+
+    /* Used in Training phase only.
+       runningMean = newMean*factor + runningMean*(1-factor) */
+    void *resultRunningMean,
+    /* Output in training mode, input in inference. Is the moving average
+       of  variance[x] (factor is applied in the same way as for runningMean) */
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance);
+
+/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTrainingEx(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+    cudnnBatchNormOps_t bnOps,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *xData,
+    const cudnnTensorDescriptor_t zDesc,
+    const void *zData,
+    const cudnnTensorDescriptor_t yDesc,
+    void *yData,
+
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+    const void *bnScale,
+    const void *bnBias,
+
+    double exponentialAverageFactor,
+    void *resultRunningMean,
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance,
+
+    cudnnActivationDescriptor_t activationDesc,
+    void *workspace,
+    size_t workSpaceSizeInBytes,
+    void *reserveSpace,
+    size_t reserveSpaceSizeInBytes);
+
+/* Performs backward pass of Batch Normalization layer. Returns x gradient,
+ * bnScale gradient and bnBias gradient */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackward(cudnnHandle_t handle,
+                                cudnnBatchNormMode_t mode,
+                                const void *alphaDataDiff,
+                                const void *betaDataDiff,
+                                const void *alphaParamDiff,
+                                const void *betaParamDiff,
+                                const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
+                                const void *x,
+                                const cudnnTensorDescriptor_t dyDesc,
+                                const void *dy,
+                                const cudnnTensorDescriptor_t dxDesc,
+                                void *dx,
+                                /* Shared tensor desc for the 4 tensors below */
+                                const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                const void *bnScale, /* bnBias doesn't affect backpropagation */
+                                /* scale and bias diff are not backpropagated below this layer */
+                                void *dBnScaleResult,
+                                void *dBnBiasResult,
+                                /* Same epsilon as forward pass */
+                                double epsilon,
+
+                                /* Optionally cached intermediate results from
+                                   forward pass */
+                                const void *savedMean,
+                                const void *savedInvVariance);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
+                                  cudnnBatchNormMode_t mode,
+                                  cudnnBatchNormOps_t bnOps,
+
+                                  const void *alphaDataDiff,
+                                  const void *betaDataDiff,
+                                  const void *alphaParamDiff,
+                                  const void *betaParamDiff,
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  const void *yData,
+                                  const cudnnTensorDescriptor_t dyDesc,
+                                  const void *dyData,
+                                  const cudnnTensorDescriptor_t dzDesc,
+                                  void *dzData,
+                                  const cudnnTensorDescriptor_t dxDesc,
+                                  void *dxData,
+
+                                  /* Shared tensor desc for the 4 tensors below */
+                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                  const void *bnScaleData,
+                                  const void *bnBiasData, /* needed if there is activation */
+                                  void *dBnScaleData,
+                                  void *dBnBiasData,
+                                  double epsilon, /* Same epsilon as forward pass */
+
+                                  /* Optionally cached intermediate results from
+                                     forward pass */
+                                  const void *savedMean,
+                                  const void *savedInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  void *workSpace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnNormMode_t mode,
+                                                  cudnnNormOps_t normOps,
+                                                  cudnnNormAlgo_t algo,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t zDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                                  size_t *sizeInBytes,
+                                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
+                                           cudnnNormMode_t mode,
+                                           cudnnNormOps_t normOps,
+                                           cudnnNormAlgo_t algo,
+                                           const cudnnTensorDescriptor_t xDesc,
+                                           const cudnnTensorDescriptor_t yDesc,
+                                           const cudnnTensorDescriptor_t dyDesc,
+                                           const cudnnTensorDescriptor_t dzDesc,
+                                           const cudnnTensorDescriptor_t dxDesc,
+                                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                                           const cudnnActivationDescriptor_t activationDesc,
+                                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                                           size_t *sizeInBytes,
+                                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
+                                              cudnnNormMode_t mode,
+                                              cudnnNormOps_t normOps,
+                                              cudnnNormAlgo_t algo,
+                                              const cudnnActivationDescriptor_t activationDesc,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              size_t *sizeInBytes,
+                                              int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+/* Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean and inverse variances */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardTraining(cudnnHandle_t handle,
+                                  cudnnNormMode_t mode,
+                                  cudnnNormOps_t normOps,
+                                  cudnnNormAlgo_t algo,
+                                  const void *alpha, /* alpha[0] = result blend factor */
+                                  const void *beta,  /* beta[0] = dest layer blend factor */
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                  const void *normScale,
+                                  const void *normBias,
+                                  double exponentialAverageFactor,
+                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                  void *resultRunningMean,
+                                  void *resultRunningVariance,
+                                  /* Has to be >= 0. Should be the same in forward and backward functions. */
+                                  double epsilon,
+                                  /* Optionally save intermediate results from the forward pass here
+                                     - can be reused to speed up backward pass. NULL if unused */
+                                  void *resultSaveMean,
+                                  void *resultSaveInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  const cudnnTensorDescriptor_t zDesc,
+                                  const void *zData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *yData,
+                                  void *workspace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationBackward(cudnnHandle_t handle,
+                           cudnnNormMode_t mode,
+                           cudnnNormOps_t normOps,
+                           cudnnNormAlgo_t algo,
+                           const void *alphaDataDiff,
+                           const void *betaDataDiff,
+                           const void *alphaParamDiff,
+                           const void *betaParamDiff,
+                           const cudnnTensorDescriptor_t xDesc,
+                           const void *xData,
+                           const cudnnTensorDescriptor_t yDesc,
+                           const void *yData,
+                           const cudnnTensorDescriptor_t dyDesc,
+                           const void *dyData,
+                           const cudnnTensorDescriptor_t dzDesc,
+                           void *dzData,
+                           const cudnnTensorDescriptor_t dxDesc,
+                           void *dxData,
+                           /* Shared tensor desc for the 4 tensors below */
+                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                           const void *normScaleData,
+                           const void *normBiasData, /* needed if there is activation */
+                           void *dNormScaleData,
+                           void *dNormBiasData,
+                           double epsilon, /* Same epsilon as forward pass */
+                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                           /* Optionally cached intermediate results from
+                              forward pass */
+                           const void *savedMean,
+                           const void *savedInvVariance,
+                           cudnnActivationDescriptor_t activationDesc,
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes,
+                           void *reserveSpace,
+                           size_t reserveSpaceSizeInBytes,
+                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
+                                    const cudnnSpatialTransformerDescriptor_t stDesc,
+                                    const void *dgrid,
+                                    void *dtheta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
+                              cudnnSpatialTransformerDescriptor_t stDesc,
+                              const void *alpha,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const void *x,
+                              const void *beta,
+                              const cudnnTensorDescriptor_t dxDesc,
+                              void *dx,
+                              const void *alphaDgrid,
+                              const cudnnTensorDescriptor_t dyDesc,
+                              const void *dy,
+                              const void *grid,
+                              const void *betaDgrid,
+                              void *dgrid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutBackward(cudnnHandle_t handle,
+                     const cudnnDropoutDescriptor_t dropoutDesc,
+                     const cudnnTensorDescriptor_t dydesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dxdesc,
+                     void *dx,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..471a0e59d67228ab8a74159517418f217ab86324
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_v9.h
@@ -0,0 +1,1316 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops : cuDNN's basic definitions and basic operations.
+ */
+
+#if !defined(CUDNN_OPS_H_)
+#define CUDNN_OPS_H_
+
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_graph.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_MAJOR 9
+#define CUDNN_OPS_MINOR 10
+#define CUDNN_OPS_PATCH 2
+
+#if (CUDNN_OPS_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_MINOR != CUDNN_MINOR) || (CUDNN_OPS_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* Data structures to represent Image/Filter and the Neural Network Layer */
+typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
+typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
+typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
+typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t CUDNN_DEPRECATED;
+typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
+typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t CUDNN_DEPRECATED;
+/*
+ * CUDNN Determinism
+ */
+typedef enum {
+    CUDNN_NON_DETERMINISTIC = 0,
+    CUDNN_DETERMINISTIC     = 1,
+} cudnnDeterminism_t;
+
+/* Create an instance of a generic Tensor descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnTensorFormat_t format,
+                           cudnnDataType_t dataType, /* image data type */
+                           int n,                    /* number of inputs (batch size) */
+                           int c,                    /* number of input feature maps */
+                           int h,                    /* height of input section */
+                           int w);                   /* width of input section */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnDataType_t dataType, /* image data type */
+                             int n,                    /* number of inputs (batch size) */
+                             int c,                    /* number of input feature maps */
+                             int h,                    /* height of input section */
+                             int w,                    /* width of input section */
+                             int nStride,
+                             int cStride,
+                             int hStride,
+                             int wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           int *n,                    /* number of inputs (batch size) */
+                           int *c,                    /* number of input feature maps  */
+                           int *h,                    /* height of input section */
+                           int *w,                    /* width of input section */
+                           int *nStride,
+                           int *cStride,
+                           int *hStride,
+                           int *wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t dataType,
+                           int nbDims,
+                           const int dimA[],
+                           const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnTensorFormat_t format,
+                             cudnnDataType_t dataType,
+                             int nbDims,
+                             const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType,
+                           int *nbDims,
+                           int dimA[],
+                           int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
+
+/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
+
+   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
+   input_stride :  c x h x h_stride
+   feature_stride : h x h_stride
+   h_stride  :  >= w  ( h_stride = w if no padding)
+   w_stride  : 1
+
+
+   2)Example of all images in row major with features maps interleaved
+   input_stride :  c x h x h_stride
+   feature_stride : 1
+   h_stride  :  w x c
+   w_stride  : c
+
+   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
+   input_stride :  c x w x w_stride
+   feature_stride : w x w_stride
+   h_stride  :  1
+   w_stride  :  >= h
+
+*/
+
+/* Destroy an instance of Tensor4d descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
+
+/* Fold/unfold transforms */
+typedef enum {
+    CUDNN_TRANSFORM_FOLD   = 0U,
+    CUDNN_TRANSFORM_UNFOLD = 1U,
+} cudnnFoldingDirection_t;
+
+/** Create a destination descriptor for cudnnTransformTensor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       cudnnTensorDescriptor_t destDesc,
+                       size_t *destSizeInBytes);
+
+/** Create an empty tensor transform descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
+
+/** Initialize a previously created tensor transform descriptor. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  const uint32_t nbDims,
+                                  const cudnnTensorFormat_t destFormat,
+                                  const int32_t padBeforeA[],
+                                  const int32_t padAfterA[],
+                                  const uint32_t foldA[],
+                                  const cudnnFoldingDirection_t direction);
+
+/**
+ * Retrieves the values stored in a previously initialized tensor transform
+ * descriptor.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  uint32_t nbDimsRequested,
+                                  cudnnTensorFormat_t *destFormat,
+                                  int32_t padBeforeA[],
+                                  int32_t padAfterA[],
+                                  uint32_t foldA[],
+                                  cudnnFoldingDirection_t *direction);
+
+/**
+ * Destroys a previously created tensor transform descriptor.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
+
+/* Tensor layout conversion helper (y = alpha * x + beta * y) */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensor(cudnnHandle_t handle,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t yDesc,
+                     void *y);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensorEx(cudnnHandle_t handle,
+                       const cudnnTensorTransformDescriptor_t transDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       const void *srcData,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t destDesc,
+                       void *destData);
+
+/* Tensor Bias addition : C = alpha * A + beta * C  */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnAddTensor(cudnnHandle_t handle,
+               const void *alpha,
+               const cudnnTensorDescriptor_t aDesc,
+               const void *A,
+               const void *beta,
+               const cudnnTensorDescriptor_t cDesc,
+               void *C);
+
+/*
+ * CUDNN OpTensor op type
+ */
+typedef enum {
+    CUDNN_OP_TENSOR_ADD  = 0,
+    CUDNN_OP_TENSOR_MUL  = 1,
+    CUDNN_OP_TENSOR_MIN  = 2,
+    CUDNN_OP_TENSOR_MAX  = 3,
+    CUDNN_OP_TENSOR_SQRT = 4,
+    CUDNN_OP_TENSOR_NOT  = 5,
+} cudnnOpTensorOp_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t opTensorOp,
+                           cudnnDataType_t opTensorCompType,
+                           cudnnNanPropagation_t opTensorNanOpt);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t *opTensorOp,
+                           cudnnDataType_t *opTensorCompType,
+                           cudnnNanPropagation_t *opTensorNanOpt);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
+
+/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
+/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnOpTensor(cudnnHandle_t handle,
+              const cudnnOpTensorDescriptor_t opTensorDesc,
+              const void *alpha1,
+              const cudnnTensorDescriptor_t aDesc,
+              const void *A,
+              const void *alpha2,
+              const cudnnTensorDescriptor_t bDesc,
+              const void *B,
+              const void *beta,
+              const cudnnTensorDescriptor_t cDesc,
+              void *C);
+
+/*
+ * CUDNN ReduceTensor indices type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0,
+    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
+} cudnnReduceTensorIndices_t CUDNN_DEPRECATED;
+
+/*
+ * CUDNN tensor indices type size (all unsigned)
+ * Currently not supported, default is 32 bit unsigned.
+ */
+typedef enum {
+    CUDNN_32BIT_INDICES = 0,
+    CUDNN_64BIT_INDICES = 1,
+    CUDNN_16BIT_INDICES = 2,
+    CUDNN_8BIT_INDICES  = 3,
+} cudnnIndicesType_t CUDNN_DEPRECATED;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t reduceTensorOp,
+                               cudnnDataType_t reduceTensorCompType,
+                               cudnnNanPropagation_t reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t reduceTensorIndices,
+                               cudnnIndicesType_t reduceTensorIndicesType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t *reduceTensorOp,
+                               cudnnDataType_t *reduceTensorCompType,
+                               cudnnNanPropagation_t *reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t *reduceTensorIndices,
+                               cudnnIndicesType_t *reduceTensorIndicesType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
+
+/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
+ * output tensors */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionIndicesSize(cudnnHandle_t handle,
+                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                             const cudnnTensorDescriptor_t aDesc,
+                             const cudnnTensorDescriptor_t cDesc,
+                             size_t *sizeInBytes);
+
+/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
+ * tensors */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
+                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               const cudnnTensorDescriptor_t aDesc,
+                               const cudnnTensorDescriptor_t cDesc,
+                               size_t *sizeInBytes);
+
+/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
+/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
+/* The indices space is ignored for reduce ops other than min or max. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnReduceTensor(cudnnHandle_t handle,
+                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                  void *indices,
+                  size_t indicesSizeInBytes,
+                  void *workspace,
+                  size_t workspaceSizeInBytes,
+                  const void *alpha,
+                  const cudnnTensorDescriptor_t aDesc,
+                  const void *A,
+                  const void *beta,
+                  const cudnnTensorDescriptor_t cDesc,
+                  void *C);
+
+/* Set all values of a tensor to a given value : y[i] = value[0] */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
+
+/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
+
+/* Create an instance of FilterStruct */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int k,  /* number of output feature maps */
+                           int c,  /* number of input feature maps */
+                           int h,  /* height of each input filter */
+                           int w); /* width of  each input filter */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *k,  /* number of output feature maps */
+                           int *c,  /* number of input feature maps */
+                           int *h,  /* height of each input filter */
+                           int *w); /* width of  each input filter */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int nbDims,
+                           const int filterDimA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *nbDims,
+                           int filterDimA[]);
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnTransformFilter(cudnnHandle_t handle,
+                     const cudnnTensorTransformDescriptor_t transDesc,
+                     const void *alpha,
+                     const cudnnFilterDescriptor_t srcDesc,
+                     const void *srcData,
+                     const void *beta,
+                     const cudnnFilterDescriptor_t destDesc,
+                     void *destData);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
+
+/*
+ *  softmax algorithm
+ */
+typedef enum {
+    CUDNN_SOFTMAX_FAST     = 0, /* straightforward implementation */
+    CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
+    CUDNN_SOFTMAX_LOG      = 2
+} cudnnSoftmaxAlgorithm_t;
+
+typedef enum {
+    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
+    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /* compute the softmax over all C for each H, W, N */
+} cudnnSoftmaxMode_t;
+
+/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxForward(cudnnHandle_t handle,
+                    cudnnSoftmaxAlgorithm_t algo,
+                    cudnnSoftmaxMode_t mode,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ *  pooling mode
+ */
+typedef enum {
+    CUDNN_POOLING_MAX                           = 0,
+    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
+    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
+    CUDNN_POOLING_MAX_DETERMINISTIC             = 3
+} cudnnPoolingMode_t CUDNN_DEPRECATED;
+
+/* Create an instance of pooling descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t mode,
+                            cudnnNanPropagation_t maxpoolingNanOpt,
+                            int windowHeight,
+                            int windowWidth,
+                            int verticalPadding,
+                            int horizontalPadding,
+                            int verticalStride,
+                            int horizontalStride);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *windowHeight,
+                            int *windowWidth,
+                            int *verticalPadding,
+                            int *horizontalPadding,
+                            int *verticalStride,
+                            int *horizontalStride);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            const cudnnPoolingMode_t mode,
+                            const cudnnNanPropagation_t maxpoolingNanOpt,
+                            int nbDims,
+                            const int windowDimA[],
+                            const int paddingA[],
+                            const int strideA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            int nbDimsRequested,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *nbDims,
+                            int windowDimA[],
+                            int paddingA[],
+                            int strideA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int nbDims,
+                                  int outputTensorDimA[]);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int *n,
+                                  int *c,
+                                  int *h,
+                                  int *w);
+
+/* Destroy an instance of pooling descriptor */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
+
+/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward pooling */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnPoolingForward(cudnnHandle_t handle,
+                    const cudnnPoolingDescriptor_t poolingDesc,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t mode,
+                             cudnnNanPropagation_t reluNanOpt,
+                             double coef); /* ceiling for clipped RELU, alpha for ELU */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t *mode,
+                             cudnnNanPropagation_t *reluNanOpt,
+                             double *coef); /* ceiling for clipped RELU, alpha for ELU */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
+
+/* Function to perform forward activation  */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnActivationForward(cudnnHandle_t handle,
+                       cudnnActivationDescriptor_t activationDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t xDesc,
+                       const void *x,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t yDesc,
+                       void *y);
+
+/*
+ * Create an instance of LRN (Local Response Normalization) descriptor
+ * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
+
+#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
+#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
+#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
+#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
+
+/* LRN layer mode */
+typedef enum {
+    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
+} cudnnLRNMode_t;
+
+/*
+ * Uses a window [center-lookBehind, center+lookAhead], where
+ * lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
+ * Values of double parameters cast to tensor data type.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
+/*
+ * Retrieve the settings currently stored in an LRN layer descriptor
+ * Any of the provided pointers can be NULL (no corresponding value will be returned)
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
+
+/* Destroy an instance of LRN descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
+
+/* LRN functions: output = alpha * normalize(x) + beta * old_y */
+
+/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelForward(cudnnHandle_t handle,
+                            cudnnLRNDescriptor_t normDesc,
+                            cudnnLRNMode_t lrnMode,
+                            const void *alpha,
+                            const cudnnTensorDescriptor_t xDesc,
+                            const void *x,
+                            const void *beta,
+                            const cudnnTensorDescriptor_t yDesc,
+                            void *y);
+
+typedef enum {
+    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
+} cudnnDivNormMode_t;
+
+/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
+                                  cudnnLRNDescriptor_t normDesc,
+                                  cudnnDivNormMode_t mode,
+                                  const void *alpha,
+                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
+                                  const void *x,
+                                  const void *means, /* if NULL, means are assumed to be zero */
+                                  void *temp,
+                                  void *temp2,
+                                  const void *beta,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *y);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_BATCHNORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_BATCHNORM_SPATIAL = 1,
+
+    /*
+     * bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
+     * May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
+     */
+    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
+} cudnnBatchNormMode_t CUDNN_DEPRECATED;
+
+#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for BatchNormalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
+                              const cudnnTensorDescriptor_t xDesc,
+                              cudnnBatchNormMode_t mode);
+
+typedef enum {
+    CUDNN_BATCHNORM_OPS_BN                = 0, /* do batch normalization only */
+    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /* do batchNorm, then activation */
+    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
+} cudnnBatchNormOps_t CUDNN_DEPRECATED;
+
+/*
+ * Performs Batch Normalization during Inference:
+ * y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
+ * with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
+ * according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
+                                        cudnnBatchNormMode_t mode,
+                                        const void *alpha, /* alpha[0] = result blend factor */
+                                        const void *beta,  /* beta[0] = dest layer blend factor */
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const void *x, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        void *y, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                        const void *bnScale,
+                                        const void *bnBias,
+                                        const void *estimatedMean,
+                                        const void *estimatedVariance,
+                                        double epsilon);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_NORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_NORM_PER_CHANNEL = 1,
+} cudnnNormMode_t CUDNN_DEPRECATED;
+
+typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t CUDNN_DEPRECATED;
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for Normalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
+                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
+                                const cudnnTensorDescriptor_t xDesc,
+                                cudnnNormMode_t mode,
+                                int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+typedef enum {
+    CUDNN_NORM_OPS_NORM                = 0, /* do normalization only */
+    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /* do Norm, then activation */
+    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
+} cudnnNormOps_t CUDNN_DEPRECATED;
+
+/*
+ * Performs Normalization during Inference:
+ * y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
+ * with normScale, normBias, runningMean, runningInvVariance tensors indexed
+ * according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardInference(cudnnHandle_t handle,
+                                   cudnnNormMode_t mode,
+                                   cudnnNormOps_t normOps,
+                                   cudnnNormAlgo_t algo,
+                                   const void *alpha, /* alpha[0] = result blend factor */
+                                   const void *beta,  /* beta[0] = dest layer blend factor */
+                                   const cudnnTensorDescriptor_t xDesc,
+                                   const void *x, /* NxCxHxW */
+                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                   const void *normScale,
+                                   const void *normBias,
+                                   const cudnnTensorDescriptor_t normMeanVarDesc,
+                                   const void *estimatedMean,
+                                   const void *estimatedVariance,
+                                   const cudnnTensorDescriptor_t zDesc,
+                                   const void *z,
+                                   cudnnActivationDescriptor_t activationDesc,
+                                   const cudnnTensorDescriptor_t yDesc,
+                                   void *y, /* NxCxHxW */
+                                   double epsilon,
+                                   int groupCnt); /* Place hold for future work*/
+
+/* APIs for spatial transformer network*/
+typedef enum {
+    CUDNN_SAMPLER_BILINEAR = 0,
+} cudnnSamplerType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
+                                       cudnnSamplerType_t samplerType,
+                                       cudnnDataType_t dataType,
+                                       const int nbDims,
+                                       const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
+                                   const cudnnSpatialTransformerDescriptor_t stDesc,
+                                   const void *theta,
+                                   void *grid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
+                             cudnnSpatialTransformerDescriptor_t stDesc,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *grid,
+                             const void *beta,
+                             cudnnTensorDescriptor_t yDesc,
+                             void *y);
+
+typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
+
+/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
+
+/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float dropout,
+                          void *states,
+                          size_t stateSizeInBytes,
+                          unsigned long long seed);
+
+/* Restores the dropout descriptor to a previously saved-off state */
+cudnnStatus_t CUDNNWINAPI
+cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                              cudnnHandle_t handle,
+                              float dropout,
+                              void *states,
+                              size_t stateSizeInBytes,
+                              unsigned long long seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float *dropout,
+                          void **states,
+                          unsigned long long *seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutForward(cudnnHandle_t handle,
+                    const cudnnDropoutDescriptor_t dropoutDesc,
+                    const cudnnTensorDescriptor_t xdesc,
+                    const void *x,
+                    const cudnnTensorDescriptor_t ydesc,
+                    void *y,
+                    void *reserveSpace,
+                    size_t reserveSpaceSizeInBytes);
+
+/* TODO: move these enums out to the appropriate submodule */
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0,
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
+    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2,
+    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7,
+    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8
+} cudnnConvolutionFwdAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /* not implemented */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7
+} cudnnConvolutionBwdFilterAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6
+} cudnnConvolutionBwdDataAlgo_t;
+
+typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsVersionCheck(void);
+
+/* Function to perform backward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxBackward(cudnnHandle_t handle,
+                     cudnnSoftmaxAlgorithm_t algo,
+                     cudnnSoftmaxMode_t mode,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward pooling */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnPoolingBackward(cudnnHandle_t handle,
+                     const cudnnPoolingDescriptor_t poolingDesc,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward activation  */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnActivationBackward(cudnnHandle_t handle,
+                        cudnnActivationDescriptor_t activationDesc,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t yDesc,
+                        const void *y,
+                        const cudnnTensorDescriptor_t dyDesc,
+                        const void *dy,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t dxDesc,
+                        void *dx);
+
+/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
+                             cudnnLRNDescriptor_t normDesc,
+                             cudnnLRNMode_t lrnMode,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t yDesc,
+                             const void *y,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
+                                   cudnnLRNDescriptor_t normDesc,
+                                   cudnnDivNormMode_t mode,
+                                   const void *alpha,
+                                   const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
+                                   const void *x,
+                                   const void *means, /* if NULL, means are assumed to be zero */
+                                   const void *dy,
+                                   void *temp,
+                                   void *temp2,
+                                   const void *beta,
+                                   const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
+                                   void *dx,                                   /* output x differential */
+                                   void *dMeans); /* output means differential, can be NULL */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
+                                                         cudnnBatchNormMode_t mode,
+                                                         cudnnBatchNormOps_t bnOps,
+                                                         const cudnnTensorDescriptor_t xDesc,
+                                                         const cudnnTensorDescriptor_t zDesc,
+                                                         const cudnnTensorDescriptor_t yDesc,
+                                                         const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                                         const cudnnActivationDescriptor_t activationDesc,
+                                                         size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnBatchNormMode_t mode,
+                                                  cudnnBatchNormOps_t bnOps,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t dyDesc,
+                                                  const cudnnTensorDescriptor_t dzDesc,
+                                                  const cudnnTensorDescriptor_t dxDesc,
+                                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
+                                                     cudnnBatchNormMode_t mode,
+                                                     cudnnBatchNormOps_t bnOps,
+                                                     const cudnnActivationDescriptor_t activationDesc,
+                                                     const cudnnTensorDescriptor_t xDesc,
+                                                     size_t *sizeInBytes);
+
+/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTraining(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *x, /* NxCxHxW */
+    const cudnnTensorDescriptor_t yDesc,
+    void *y, /* NxCxHxW */
+
+    /* Shared desc for the next 6 tensors in the argument list.
+       Data type to be set as follows:
+       type = (typeOf(x) == double) ? double : float
+       Dimensions for this descriptor depend on normalization mode
+       - Spatial Normalization : tensors are expected to have dims 1xCx1x1
+        (normalization is performed across NxHxW)
+       - Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
+        (normalization is performed across N) */
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+
+    /* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
+    const void *bnScale,
+    const void *bnBias,
+
+    /* MUST use factor=1 in the very first call of a complete training cycle.
+       Use a factor=1/(1+n) at N-th call to the function to get
+       Cumulative Moving Average (CMA) behavior
+       CMA[n] = (x[1]+...+x[n])/n
+       Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
+       ((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
+       CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
+    double exponentialAverageFactor,
+
+    /* Used in Training phase only.
+       runningMean = newMean*factor + runningMean*(1-factor) */
+    void *resultRunningMean,
+    /* Output in training mode, input in inference. Is the moving average
+       of  variance[x] (factor is applied in the same way as for runningMean) */
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance);
+
+/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTrainingEx(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+    cudnnBatchNormOps_t bnOps,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *xData,
+    const cudnnTensorDescriptor_t zDesc,
+    const void *zData,
+    const cudnnTensorDescriptor_t yDesc,
+    void *yData,
+
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+    const void *bnScale,
+    const void *bnBias,
+
+    double exponentialAverageFactor,
+    void *resultRunningMean,
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance,
+
+    cudnnActivationDescriptor_t activationDesc,
+    void *workspace,
+    size_t workSpaceSizeInBytes,
+    void *reserveSpace,
+    size_t reserveSpaceSizeInBytes);
+
+/* Performs backward pass of Batch Normalization layer. Returns x gradient,
+ * bnScale gradient and bnBias gradient */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackward(cudnnHandle_t handle,
+                                cudnnBatchNormMode_t mode,
+                                const void *alphaDataDiff,
+                                const void *betaDataDiff,
+                                const void *alphaParamDiff,
+                                const void *betaParamDiff,
+                                const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
+                                const void *x,
+                                const cudnnTensorDescriptor_t dyDesc,
+                                const void *dy,
+                                const cudnnTensorDescriptor_t dxDesc,
+                                void *dx,
+                                /* Shared tensor desc for the 4 tensors below */
+                                const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                const void *bnScale, /* bnBias doesn't affect backpropagation */
+                                /* scale and bias diff are not backpropagated below this layer */
+                                void *dBnScaleResult,
+                                void *dBnBiasResult,
+                                /* Same epsilon as forward pass */
+                                double epsilon,
+
+                                /* Optionally cached intermediate results from
+                                   forward pass */
+                                const void *savedMean,
+                                const void *savedInvVariance);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
+                                  cudnnBatchNormMode_t mode,
+                                  cudnnBatchNormOps_t bnOps,
+
+                                  const void *alphaDataDiff,
+                                  const void *betaDataDiff,
+                                  const void *alphaParamDiff,
+                                  const void *betaParamDiff,
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  const void *yData,
+                                  const cudnnTensorDescriptor_t dyDesc,
+                                  const void *dyData,
+                                  const cudnnTensorDescriptor_t dzDesc,
+                                  void *dzData,
+                                  const cudnnTensorDescriptor_t dxDesc,
+                                  void *dxData,
+
+                                  /* Shared tensor desc for the 4 tensors below */
+                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                  const void *bnScaleData,
+                                  const void *bnBiasData, /* needed if there is activation */
+                                  void *dBnScaleData,
+                                  void *dBnBiasData,
+                                  double epsilon, /* Same epsilon as forward pass */
+
+                                  /* Optionally cached intermediate results from
+                                     forward pass */
+                                  const void *savedMean,
+                                  const void *savedInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  void *workSpace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnNormMode_t mode,
+                                                  cudnnNormOps_t normOps,
+                                                  cudnnNormAlgo_t algo,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t zDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                                  size_t *sizeInBytes,
+                                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
+                                           cudnnNormMode_t mode,
+                                           cudnnNormOps_t normOps,
+                                           cudnnNormAlgo_t algo,
+                                           const cudnnTensorDescriptor_t xDesc,
+                                           const cudnnTensorDescriptor_t yDesc,
+                                           const cudnnTensorDescriptor_t dyDesc,
+                                           const cudnnTensorDescriptor_t dzDesc,
+                                           const cudnnTensorDescriptor_t dxDesc,
+                                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                                           const cudnnActivationDescriptor_t activationDesc,
+                                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                                           size_t *sizeInBytes,
+                                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
+                                              cudnnNormMode_t mode,
+                                              cudnnNormOps_t normOps,
+                                              cudnnNormAlgo_t algo,
+                                              const cudnnActivationDescriptor_t activationDesc,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              size_t *sizeInBytes,
+                                              int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+/* Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean and inverse variances */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardTraining(cudnnHandle_t handle,
+                                  cudnnNormMode_t mode,
+                                  cudnnNormOps_t normOps,
+                                  cudnnNormAlgo_t algo,
+                                  const void *alpha, /* alpha[0] = result blend factor */
+                                  const void *beta,  /* beta[0] = dest layer blend factor */
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                  const void *normScale,
+                                  const void *normBias,
+                                  double exponentialAverageFactor,
+                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                  void *resultRunningMean,
+                                  void *resultRunningVariance,
+                                  /* Has to be >= 0. Should be the same in forward and backward functions. */
+                                  double epsilon,
+                                  /* Optionally save intermediate results from the forward pass here
+                                     - can be reused to speed up backward pass. NULL if unused */
+                                  void *resultSaveMean,
+                                  void *resultSaveInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  const cudnnTensorDescriptor_t zDesc,
+                                  const void *zData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *yData,
+                                  void *workspace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationBackward(cudnnHandle_t handle,
+                           cudnnNormMode_t mode,
+                           cudnnNormOps_t normOps,
+                           cudnnNormAlgo_t algo,
+                           const void *alphaDataDiff,
+                           const void *betaDataDiff,
+                           const void *alphaParamDiff,
+                           const void *betaParamDiff,
+                           const cudnnTensorDescriptor_t xDesc,
+                           const void *xData,
+                           const cudnnTensorDescriptor_t yDesc,
+                           const void *yData,
+                           const cudnnTensorDescriptor_t dyDesc,
+                           const void *dyData,
+                           const cudnnTensorDescriptor_t dzDesc,
+                           void *dzData,
+                           const cudnnTensorDescriptor_t dxDesc,
+                           void *dxData,
+                           /* Shared tensor desc for the 4 tensors below */
+                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                           const void *normScaleData,
+                           const void *normBiasData, /* needed if there is activation */
+                           void *dNormScaleData,
+                           void *dNormBiasData,
+                           double epsilon, /* Same epsilon as forward pass */
+                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                           /* Optionally cached intermediate results from
+                              forward pass */
+                           const void *savedMean,
+                           const void *savedInvVariance,
+                           cudnnActivationDescriptor_t activationDesc,
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes,
+                           void *reserveSpace,
+                           size_t reserveSpaceSizeInBytes,
+                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
+                                    const cudnnSpatialTransformerDescriptor_t stDesc,
+                                    const void *dgrid,
+                                    void *dtheta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
+                              cudnnSpatialTransformerDescriptor_t stDesc,
+                              const void *alpha,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const void *x,
+                              const void *beta,
+                              const cudnnTensorDescriptor_t dxDesc,
+                              void *dx,
+                              const void *alphaDgrid,
+                              const cudnnTensorDescriptor_t dyDesc,
+                              const void *dy,
+                              const void *grid,
+                              const void *betaDgrid,
+                              void *dgrid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutBackward(cudnnHandle_t handle,
+                     const cudnnDropoutDescriptor_t dropoutDesc,
+                     const cudnnTensorDescriptor_t dydesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dxdesc,
+                     void *dx,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e08847c95f1294bc99e96e737a53cc6ebb7a458
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_v9.h
@@ -0,0 +1,68 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn : Neural Networks Library  */
+
+#if !defined(CUDNN_H_)
+#define CUDNN_H_
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#include <cuda_runtime_api.h>
+#include "cudnn_version.h"
+#include "cudnn_graph.h"
+#include "cudnn_ops.h"
+#include "cudnn_adv.h"
+#include "cudnn_cnn.h"
+
+#if defined(__cplusplus)
+}
+#endif
+#endif /* CUDNN_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_version.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_version.h
new file mode 100644
index 0000000000000000000000000000000000000000..1af101fac7672614e3af52cbc32c57bc2104f498
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_version.h
@@ -0,0 +1,70 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/**
+ * \file: The master cuDNN version file.
+ */
+
+#ifndef CUDNN_VERSION_H_
+#define CUDNN_VERSION_H_
+
+#define CUDNN_MAJOR 9
+#define CUDNN_MINOR 10
+#define CUDNN_PATCHLEVEL 2
+
+#define CUDNN_VERSION (CUDNN_MAJOR * 10000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
+
+/* cannot use constexpr here since this is a C-only file */
+/* Below is the max SM version this cuDNN library is aware of and supports natively */
+
+#define CUDNN_MAX_SM_MAJOR_NUMBER 12
+#define CUDNN_MAX_SM_MINOR_NUMBER 0
+#define CUDNN_MAX_DEVICE_VERSION (CUDNN_MAX_SM_MAJOR_NUMBER * 100 + CUDNN_MAX_SM_MINOR_NUMBER * 10)
+
+#endif /* CUDNN_VERSION_H */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_version_v9.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_version_v9.h
new file mode 100644
index 0000000000000000000000000000000000000000..1af101fac7672614e3af52cbc32c57bc2104f498
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/include/cudnn_version_v9.h
@@ -0,0 +1,70 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/**
+ * \file: The master cuDNN version file.
+ */
+
+#ifndef CUDNN_VERSION_H_
+#define CUDNN_VERSION_H_
+
+#define CUDNN_MAJOR 9
+#define CUDNN_MINOR 10
+#define CUDNN_PATCHLEVEL 2
+
+#define CUDNN_VERSION (CUDNN_MAJOR * 10000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
+
+/* cannot use constexpr here since this is a C-only file */
+/* Below is the max SM version this cuDNN library is aware of and supports natively */
+
+#define CUDNN_MAX_SM_MAJOR_NUMBER 12
+#define CUDNN_MAX_SM_MINOR_NUMBER 0
+#define CUDNN_MAX_DEVICE_VERSION (CUDNN_MAX_SM_MAJOR_NUMBER * 100 + CUDNN_MAX_SM_MINOR_NUMBER * 10)
+
+#endif /* CUDNN_VERSION_H */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/lib/libcudnn.so.9 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/lib/libcudnn.so.9
new file mode 100644
index 0000000000000000000000000000000000000000..bf5d797eaff7321e2206c1c301736509b099604c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/lib/libcudnn.so.9
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b68ea689be647bce63cb3c2d9edb589add415405a7e9f296d69ca029cae0b8b
+size 125136
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_graph.so.9 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_graph.so.9
new file mode 100644
index 0000000000000000000000000000000000000000..bc1dc399f5d7d92c18690922887c5a70898f9344
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_graph.so.9
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:787c955dce49091ead850e4536666594095ea9f92a8a08879d8ddad466674657
+size 4439912
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cudalibxt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cudalibxt.h
new file mode 100644
index 0000000000000000000000000000000000000000..94fcf4745fafa04f57678ba5ee64103f8ebd6444
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cudalibxt.h
@@ -0,0 +1,97 @@
+ /* Copyright 2013,2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+/*!
+* \file cudalibxt.h  
+* \brief Public header file for the NVIDIA library multi-GPU support structures  
+*/ 
+
+#ifndef _CUDA_LIB_XT_H_
+#define _CUDA_LIB_XT_H_
+#include <cuda_runtime.h>
+
+#define CUDA_XT_DESCRIPTOR_VERSION 0x01000000 // This is added to CUDART_VERSION
+
+enum cudaXtCopyType_t {
+    LIB_XT_COPY_HOST_TO_DEVICE,
+    LIB_XT_COPY_DEVICE_TO_HOST,
+    LIB_XT_COPY_DEVICE_TO_DEVICE
+} ;
+typedef enum cudaXtCopyType_t cudaLibXtCopyType;
+
+enum libFormat_t {
+    LIB_FORMAT_CUFFT        = 0x0,
+    LIB_FORMAT_UNDEFINED    = 0x1
+};
+
+typedef enum libFormat_t libFormat;
+
+#define MAX_CUDA_DESCRIPTOR_GPUS 64
+
+struct cudaXtDesc_t{
+    int version;                             //descriptor version
+    int nGPUs;                               //number of GPUs 
+    int GPUs[MAX_CUDA_DESCRIPTOR_GPUS];      //array of device IDs
+    void *data[MAX_CUDA_DESCRIPTOR_GPUS];    //array of pointers to data, one per GPU
+    size_t size[MAX_CUDA_DESCRIPTOR_GPUS];   //array of data sizes, one per GPU
+    void *cudaXtState;                       //opaque CUDA utility structure
+};
+typedef struct cudaXtDesc_t cudaXtDesc;
+
+struct cudaLibXtDesc_t{
+    int version;                //descriptor version
+    cudaXtDesc *descriptor;     //multi-GPU memory descriptor
+    libFormat library;          //which library recognizes the format
+    int subFormat;              //library specific enumerator of sub formats
+    void *libDescriptor;        //library specific descriptor e.g. FFT transform plan object
+};
+typedef struct cudaLibXtDesc_t cudaLibXtDesc;
+
+
+#endif
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufft.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufft.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b6a1137c3677fd80c25b46ec9153b9024c00337
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufft.h
@@ -0,0 +1,335 @@
+ /* Copyright 2005-2021 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+/*!
+* \file cufft.h
+* \brief Public header file for the NVIDIA CUDA FFT library (CUFFT)
+*/
+
+#ifndef _CUFFT_H_
+#define _CUFFT_H_
+
+
+#include "cuComplex.h"
+#include "driver_types.h"
+#include "library_types.h"
+
+#ifndef CUFFTAPI
+#ifdef _WIN32
+#define CUFFTAPI __stdcall
+#elif __GNUC__ >= 4
+#define CUFFTAPI __attribute__ ((visibility ("default")))
+#else
+#define CUFFTAPI
+#endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#define CUFFT_VER_MAJOR 11
+#define CUFFT_VER_MINOR 3
+#define CUFFT_VER_PATCH 3
+#define CUFFT_VER_BUILD 83
+
+#define CUFFT_VERSION 11303
+
+// CUFFT API function return values
+typedef enum cufftResult_t {
+  CUFFT_SUCCESS        = 0x0,
+  CUFFT_INVALID_PLAN   = 0x1,
+  CUFFT_ALLOC_FAILED   = 0x2,
+  CUFFT_INVALID_TYPE   = 0x3,
+  CUFFT_INVALID_VALUE  = 0x4,
+  CUFFT_INTERNAL_ERROR = 0x5,
+  CUFFT_EXEC_FAILED    = 0x6,
+  CUFFT_SETUP_FAILED   = 0x7,
+  CUFFT_INVALID_SIZE   = 0x8,
+  CUFFT_UNALIGNED_DATA = 0x9,
+  CUFFT_INCOMPLETE_PARAMETER_LIST = 0xA,
+  CUFFT_INVALID_DEVICE = 0xB,
+  CUFFT_PARSE_ERROR = 0xC,
+  CUFFT_NO_WORKSPACE = 0xD,
+  CUFFT_NOT_IMPLEMENTED = 0xE,
+  CUFFT_LICENSE_ERROR = 0x0F,
+  CUFFT_NOT_SUPPORTED = 0x10
+
+} cufftResult;
+
+#define MAX_CUFFT_ERROR 0x11
+
+
+// CUFFT defines and supports the following data types
+
+
+// cufftReal is a single-precision, floating-point real data type.
+// cufftDoubleReal is a double-precision, real data type.
+typedef float cufftReal;
+typedef double cufftDoubleReal;
+
+// cufftComplex is a single-precision, floating-point complex data type that
+// consists of interleaved real and imaginary components.
+// cufftDoubleComplex is the double-precision equivalent.
+typedef cuComplex cufftComplex;
+typedef cuDoubleComplex cufftDoubleComplex;
+
+// CUFFT transform directions
+#define CUFFT_FORWARD -1 // Forward FFT
+#define CUFFT_INVERSE  1 // Inverse FFT
+
+// CUFFT supports the following transform types
+typedef enum cufftType_t {
+  CUFFT_R2C = 0x2a,     // Real to Complex (interleaved)
+  CUFFT_C2R = 0x2c,     // Complex (interleaved) to Real
+  CUFFT_C2C = 0x29,     // Complex to Complex, interleaved
+  CUFFT_D2Z = 0x6a,     // Double to Double-Complex
+  CUFFT_Z2D = 0x6c,     // Double-Complex to Double
+  CUFFT_Z2Z = 0x69      // Double-Complex to Double-Complex
+} cufftType;
+
+// CUFFT supports the following data layouts
+typedef enum cufftCompatibility_t {
+    CUFFT_COMPATIBILITY_FFTW_PADDING    = 0x01    // The default value
+} cufftCompatibility;
+
+#define CUFFT_COMPATIBILITY_DEFAULT   CUFFT_COMPATIBILITY_FFTW_PADDING
+
+//
+// structure definition used by the shim between old and new APIs
+//
+#define MAX_SHIM_RANK 3
+
+// cufftHandle is a handle type used to store and access CUFFT plans.
+typedef int cufftHandle;
+
+
+cufftResult CUFFTAPI cufftPlan1d(cufftHandle *plan,
+                                 int nx,
+                                 cufftType type,
+                                 int batch);
+
+cufftResult CUFFTAPI cufftPlan2d(cufftHandle *plan,
+                                 int nx, int ny,
+                                 cufftType type);
+
+cufftResult CUFFTAPI cufftPlan3d(cufftHandle *plan,
+                                 int nx, int ny, int nz,
+                                 cufftType type);
+
+cufftResult CUFFTAPI cufftPlanMany(cufftHandle *plan,
+                                   int rank,
+                                   int *n,
+                                   int *inembed, int istride, int idist,
+                                   int *onembed, int ostride, int odist,
+                                   cufftType type,
+                                   int batch);
+
+cufftResult CUFFTAPI cufftMakePlan1d(cufftHandle plan,
+                                     int nx,
+                                     cufftType type,
+                                     int batch,
+                                     size_t *workSize);
+
+cufftResult CUFFTAPI cufftMakePlan2d(cufftHandle plan,
+                                     int nx, int ny,
+                                     cufftType type,
+                                     size_t *workSize);
+
+cufftResult CUFFTAPI cufftMakePlan3d(cufftHandle plan,
+                                     int nx, int ny, int nz,
+                                     cufftType type,
+                                     size_t *workSize);
+
+cufftResult CUFFTAPI cufftMakePlanMany(cufftHandle plan,
+                                       int rank,
+                                       int *n,
+                                       int *inembed, int istride, int idist,
+                                       int *onembed, int ostride, int odist,
+                                       cufftType type,
+                                       int batch,
+                                       size_t *workSize);
+
+cufftResult CUFFTAPI cufftMakePlanMany64(cufftHandle plan,
+                                         int rank,
+                                         long long int *n,
+                                         long long int *inembed,
+                                         long long int istride,
+                                         long long int idist,
+                                         long long int *onembed,
+                                         long long int ostride, long long int odist,
+                                         cufftType type,
+                                         long long int batch,
+                                         size_t * workSize);
+
+cufftResult CUFFTAPI cufftGetSizeMany64(cufftHandle plan,
+                                        int rank,
+                                        long long int *n,
+                                        long long int *inembed,
+                                        long long int istride, long long int idist,
+                                        long long int *onembed,
+                                        long long int ostride, long long int odist,
+                                        cufftType type,
+                                        long long int batch,
+                                        size_t *workSize);
+
+
+
+
+cufftResult CUFFTAPI cufftEstimate1d(int nx,
+                                     cufftType type,
+                                     int batch,
+                                     size_t *workSize);
+
+cufftResult CUFFTAPI cufftEstimate2d(int nx, int ny,
+                                     cufftType type,
+                                     size_t *workSize);
+
+cufftResult CUFFTAPI cufftEstimate3d(int nx, int ny, int nz,
+                                     cufftType type,
+                                     size_t *workSize);
+
+cufftResult CUFFTAPI cufftEstimateMany(int rank,
+                                       int *n,
+                                       int *inembed, int istride, int idist,
+                                       int *onembed, int ostride, int odist,
+                                       cufftType type,
+                                       int batch,
+                                       size_t *workSize);
+
+cufftResult CUFFTAPI cufftCreate(cufftHandle * handle);
+
+cufftResult CUFFTAPI cufftGetSize1d(cufftHandle handle,
+                                    int nx,
+                                    cufftType type,
+                                    int batch,
+                                    size_t *workSize );
+
+cufftResult CUFFTAPI cufftGetSize2d(cufftHandle handle,
+                                    int nx, int ny,
+                                    cufftType type,
+                                    size_t *workSize);
+
+cufftResult CUFFTAPI cufftGetSize3d(cufftHandle handle,
+                                    int nx, int ny, int nz,
+                                    cufftType type,
+                                    size_t *workSize);
+
+cufftResult CUFFTAPI cufftGetSizeMany(cufftHandle handle,
+                                      int rank, int *n,
+                                      int *inembed, int istride, int idist,
+                                      int *onembed, int ostride, int odist,
+                                      cufftType type, int batch, size_t *workArea);
+
+cufftResult CUFFTAPI cufftGetSize(cufftHandle handle, size_t *workSize);
+
+cufftResult CUFFTAPI cufftSetWorkArea(cufftHandle plan, void *workArea);
+
+cufftResult CUFFTAPI cufftSetAutoAllocation(cufftHandle plan, int autoAllocate);
+
+cufftResult CUFFTAPI cufftExecC2C(cufftHandle plan,
+                                  cufftComplex *idata,
+                                  cufftComplex *odata,
+                                  int direction);
+
+cufftResult CUFFTAPI cufftExecR2C(cufftHandle plan,
+                                  cufftReal *idata,
+                                  cufftComplex *odata);
+
+cufftResult CUFFTAPI cufftExecC2R(cufftHandle plan,
+                                  cufftComplex *idata,
+                                  cufftReal *odata);
+
+cufftResult CUFFTAPI cufftExecZ2Z(cufftHandle plan,
+                                  cufftDoubleComplex *idata,
+                                  cufftDoubleComplex *odata,
+                                  int direction);
+
+cufftResult CUFFTAPI cufftExecD2Z(cufftHandle plan,
+                                  cufftDoubleReal *idata,
+                                  cufftDoubleComplex *odata);
+
+cufftResult CUFFTAPI cufftExecZ2D(cufftHandle plan,
+                                  cufftDoubleComplex *idata,
+                                  cufftDoubleReal *odata);
+
+
+// utility functions
+cufftResult CUFFTAPI cufftSetStream(cufftHandle plan,
+                                    cudaStream_t stream);
+
+cufftResult CUFFTAPI cufftDestroy(cufftHandle plan);
+
+cufftResult CUFFTAPI cufftGetVersion(int *version);
+
+cufftResult CUFFTAPI cufftGetProperty(libraryPropertyType type,
+                                      int *value);
+
+//
+// Set/Get PlanProperty APIs configures per-plan behavior 
+//
+typedef enum cufftProperty_t {
+    NVFFT_PLAN_PROPERTY_INT64_PATIENT_JIT = 0x1,
+    NVFFT_PLAN_PROPERTY_INT64_MAX_NUM_HOST_THREADS = 0x2,
+} cufftProperty;
+
+cufftResult CUFFTAPI cufftSetPlanPropertyInt64(cufftHandle plan, 
+                                               cufftProperty property, 
+                                               const long long int inputValueInt);
+
+cufftResult CUFFTAPI cufftGetPlanPropertyInt64(cufftHandle plan, 
+                                               cufftProperty property, 
+                                               long long int* returnPtrValue);
+
+cufftResult CUFFTAPI cufftResetPlanProperty(cufftHandle plan, cufftProperty property);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _CUFFT_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufftXt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufftXt.h
new file mode 100644
index 0000000000000000000000000000000000000000..cb587e061cd85e5cfc8d6cb7015f575778e45ec7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufftXt.h
@@ -0,0 +1,278 @@
+
+ /* Copyright 2005-2021 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+/*!
+* \file cufftXt.h
+* \brief Public header file for the NVIDIA CUDA FFT library (CUFFT)
+*/
+
+#ifndef _CUFFTXT_H_
+#define _CUFFTXT_H_
+#include "cudalibxt.h"
+#include "cufft.h"
+
+
+#ifndef CUFFTAPI
+#ifdef _WIN32
+#define CUFFTAPI __stdcall
+#else
+#define CUFFTAPI
+#endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+//
+// cufftXtSubFormat identifies the data layout of
+// a memory descriptor owned by cufft.
+// note that multi GPU cufft does not yet support out-of-place transforms
+//
+
+typedef enum cufftXtSubFormat_t {
+    CUFFT_XT_FORMAT_INPUT = 0x00,              //by default input is in linear order across GPUs
+    CUFFT_XT_FORMAT_OUTPUT = 0x01,             //by default output is in scrambled order depending on transform
+    CUFFT_XT_FORMAT_INPLACE = 0x02,            //by default inplace is input order, which is linear across GPUs
+    CUFFT_XT_FORMAT_INPLACE_SHUFFLED = 0x03,   //shuffled output order after execution of the transform
+    CUFFT_XT_FORMAT_1D_INPUT_SHUFFLED = 0x04,  //shuffled input order prior to execution of 1D transforms
+    CUFFT_XT_FORMAT_DISTRIBUTED_INPUT = 0x05,
+    CUFFT_XT_FORMAT_DISTRIBUTED_OUTPUT = 0x06,
+    CUFFT_FORMAT_UNDEFINED = 0x07
+} cufftXtSubFormat;
+
+//
+// cufftXtCopyType specifies the type of copy for cufftXtMemcpy
+//
+typedef enum cufftXtCopyType_t {
+    CUFFT_COPY_HOST_TO_DEVICE = 0x00,
+    CUFFT_COPY_DEVICE_TO_HOST = 0x01,
+    CUFFT_COPY_DEVICE_TO_DEVICE = 0x02,
+    CUFFT_COPY_UNDEFINED = 0x03
+} cufftXtCopyType;
+
+//
+// cufftXtQueryType specifies the type of query for cufftXtQueryPlan
+//
+typedef enum cufftXtQueryType_t {
+    CUFFT_QUERY_1D_FACTORS = 0x00,
+    CUFFT_QUERY_UNDEFINED = 0x01
+} cufftXtQueryType;
+
+typedef struct cufftXt1dFactors_t {
+    long long int size;
+    long long int stringCount;
+    long long int stringLength;
+    long long int substringLength;
+    long long int factor1;
+    long long int factor2;
+    long long int stringMask;
+    long long int substringMask;
+    long long int factor1Mask;
+    long long int factor2Mask;
+    int stringShift;
+    int substringShift;
+    int factor1Shift;
+    int factor2Shift;
+} cufftXt1dFactors;
+
+//
+// cufftXtWorkAreaPolicy specifies policy for cufftXtSetWorkAreaPolicy
+//
+typedef enum cufftXtWorkAreaPolicy_t {
+    CUFFT_WORKAREA_MINIMAL = 0, /* maximum reduction */
+    CUFFT_WORKAREA_USER = 1, /* use workSize parameter as limit */
+    CUFFT_WORKAREA_PERFORMANCE = 2, /* default - 1x overhead or more, maximum performance */
+} cufftXtWorkAreaPolicy;
+
+// multi-GPU routines
+cufftResult CUFFTAPI cufftXtSetGPUs(cufftHandle handle, int nGPUs, int *whichGPUs);
+
+cufftResult CUFFTAPI cufftXtMalloc(cufftHandle plan,
+                                   cudaLibXtDesc ** descriptor,
+                                   cufftXtSubFormat format);
+
+cufftResult CUFFTAPI cufftXtMemcpy(cufftHandle plan,
+                                   void *dstPointer,
+                                   void *srcPointer,
+                                   cufftXtCopyType type);
+
+cufftResult CUFFTAPI cufftXtFree(cudaLibXtDesc *descriptor);
+
+cufftResult CUFFTAPI cufftXtSetWorkArea(cufftHandle plan, void **workArea);
+
+cufftResult CUFFTAPI cufftXtExecDescriptorC2C(cufftHandle plan,
+                                              cudaLibXtDesc *input,
+                                              cudaLibXtDesc *output,
+                                              int direction);
+
+cufftResult CUFFTAPI cufftXtExecDescriptorR2C(cufftHandle plan,
+                                              cudaLibXtDesc *input,
+                                              cudaLibXtDesc *output);
+
+cufftResult CUFFTAPI cufftXtExecDescriptorC2R(cufftHandle plan,
+                                              cudaLibXtDesc *input,
+                                              cudaLibXtDesc *output);
+
+cufftResult CUFFTAPI cufftXtExecDescriptorZ2Z(cufftHandle plan,
+                                              cudaLibXtDesc *input,
+                                              cudaLibXtDesc *output,
+                                              int direction);
+
+cufftResult CUFFTAPI cufftXtExecDescriptorD2Z(cufftHandle plan,
+                                              cudaLibXtDesc *input,
+                                              cudaLibXtDesc *output);
+
+cufftResult CUFFTAPI cufftXtExecDescriptorZ2D(cufftHandle plan,
+                                              cudaLibXtDesc *input,
+                                              cudaLibXtDesc *output);
+
+// Utility functions
+
+cufftResult CUFFTAPI cufftXtQueryPlan(cufftHandle plan, void *queryStruct, cufftXtQueryType queryType);
+
+
+// callbacks
+
+
+typedef enum cufftXtCallbackType_t {
+    CUFFT_CB_LD_COMPLEX = 0x0,
+    CUFFT_CB_LD_COMPLEX_DOUBLE = 0x1,
+    CUFFT_CB_LD_REAL = 0x2,
+    CUFFT_CB_LD_REAL_DOUBLE = 0x3,
+    CUFFT_CB_ST_COMPLEX = 0x4,
+    CUFFT_CB_ST_COMPLEX_DOUBLE = 0x5,
+    CUFFT_CB_ST_REAL = 0x6,
+    CUFFT_CB_ST_REAL_DOUBLE = 0x7,
+    CUFFT_CB_UNDEFINED = 0x8
+
+} cufftXtCallbackType;
+
+// Legacy callbacks
+typedef cufftComplex (*cufftCallbackLoadC)(void *dataIn, size_t offset, void *callerInfo, void *sharedPointer);
+typedef cufftDoubleComplex (*cufftCallbackLoadZ)(void *dataIn, size_t offset, void *callerInfo, void *sharedPointer);
+typedef cufftReal (*cufftCallbackLoadR)(void *dataIn, size_t offset, void *callerInfo, void *sharedPointer);
+typedef cufftDoubleReal(*cufftCallbackLoadD)(void *dataIn, size_t offset, void *callerInfo, void *sharedPointer);
+
+typedef void (*cufftCallbackStoreC)(void *dataOut, size_t offset, cufftComplex element, void *callerInfo, void *sharedPointer);
+typedef void (*cufftCallbackStoreZ)(void *dataOut, size_t offset, cufftDoubleComplex element, void *callerInfo, void *sharedPointer);
+typedef void (*cufftCallbackStoreR)(void *dataOut, size_t offset, cufftReal element, void *callerInfo, void *sharedPointer);
+typedef void (*cufftCallbackStoreD)(void *dataOut, size_t offset, cufftDoubleReal element, void *callerInfo, void *sharedPointer);
+
+// LTO callbacks (notice the different offset type vs. legacy callbacks)
+typedef cufftComplex (*cufftJITCallbackLoadC)(void *dataIn, unsigned long long offset, void *callerInfo, void *sharedPointer);
+typedef cufftDoubleComplex (*cufftJITCallbackLoadZ)(void *dataIn, unsigned long long offset, void *callerInfo, void *sharedPointer);
+typedef cufftReal (*cufftJITCallbackLoadR)(void *dataIn, unsigned long long offset, void *callerInfo, void *sharedPointer);
+typedef cufftDoubleReal(*cufftJITCallbackLoadD)(void *dataIn, unsigned long long offset, void *callerInfo, void *sharedPointer);
+
+typedef void (*cufftJITCallbackStoreC)(void *dataOut, unsigned long long offset, cufftComplex element, void *callerInfo, void *sharedPointer);
+typedef void (*cufftJITCallbackStoreZ)(void *dataOut, unsigned long long offset, cufftDoubleComplex element, void *callerInfo, void *sharedPointer);
+typedef void (*cufftJITCallbackStoreR)(void *dataOut, unsigned long long offset, cufftReal element, void *callerInfo, void *sharedPointer);
+typedef void (*cufftJITCallbackStoreD)(void *dataOut, unsigned long long offset, cufftDoubleReal element, void *callerInfo, void *sharedPointer);
+
+cufftResult CUFFTAPI cufftXtSetCallback(cufftHandle plan, void **callback_routine, cufftXtCallbackType cbType, void **caller_info);
+cufftResult CUFFTAPI cufftXtClearCallback(cufftHandle plan, cufftXtCallbackType cbType);
+cufftResult CUFFTAPI cufftXtSetCallbackSharedSize(cufftHandle plan, cufftXtCallbackType cbType, size_t sharedSize);
+
+cufftResult CUFFTAPI __cufftXtSetJITCallback_12_7(cufftHandle plan, const char* lto_callback_symbol_name, const void *lto_callback_fatbin, size_t lto_callback_fatbin_size, cufftXtCallbackType type, void **caller_info );
+
+#ifndef CUFFT_NO_INLINE
+static inline cufftResult cufftXtSetJITCallback(cufftHandle plan, const char* lto_callback_symbol_name, const void *lto_callback_fatbin, size_t lto_callback_fatbin_size, cufftXtCallbackType type, void **caller_info) {
+   return __cufftXtSetJITCallback_12_7(plan, lto_callback_symbol_name, lto_callback_fatbin, lto_callback_fatbin_size, type, caller_info);
+};
+#endif
+
+cufftResult CUFFTAPI cufftXtMakePlanMany(cufftHandle plan,
+                                         int rank,
+                                         long long int *n,
+                                         long long int *inembed,
+                                         long long int istride,
+                                         long long int idist,
+                                         cudaDataType inputtype,
+                                         long long int *onembed,
+                                         long long int ostride,
+                                         long long int odist,
+                                         cudaDataType outputtype,
+                                         long long int batch,
+                                         size_t *workSize,
+                                       	 cudaDataType executiontype);
+
+cufftResult CUFFTAPI cufftXtGetSizeMany(cufftHandle plan,
+                                        int rank,
+                                        long long int *n,
+                                        long long int *inembed,
+                                        long long int istride,
+                                        long long int idist,
+                                        cudaDataType inputtype,
+                                        long long int *onembed,
+                                        long long int ostride,
+                                        long long int odist,
+                                        cudaDataType outputtype,
+                                        long long int batch,
+                                        size_t *workSize,
+                                        cudaDataType executiontype);
+
+
+cufftResult CUFFTAPI cufftXtExec(cufftHandle plan,
+                                 void *input,
+                                 void *output,
+                                 int direction);
+
+cufftResult CUFFTAPI cufftXtExecDescriptor(cufftHandle plan,
+                                           cudaLibXtDesc *input,
+                                           cudaLibXtDesc *output,
+                                           int direction);
+
+cufftResult CUFFTAPI cufftXtSetWorkAreaPolicy(cufftHandle plan, cufftXtWorkAreaPolicy policy, size_t *workSize);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufftw.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufftw.h
new file mode 100644
index 0000000000000000000000000000000000000000..dcf72370f777bc0aaa76dddae9a34efd667a5922
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/include/cufftw.h
@@ -0,0 +1,465 @@
+
+ /* Copyright 2005-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+/*!
+* \file cufftw.h
+* \brief Public header file for the NVIDIA CUDA FFTW library (CUFFTW)
+*/
+
+#ifndef _CUFFTW_H_
+#define _CUFFTW_H_
+
+
+#include <stdio.h>
+#include "cufft.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Transform direction
+#define FFTW_FORWARD -1
+#define FFTW_INVERSE  1
+#define FFTW_BACKWARD 1
+
+// Planner flags
+#define FFTW_ESTIMATE           0x01
+#define FFTW_MEASURE            0x02
+#define FFTW_PATIENT            0x03
+#define FFTW_EXHAUSTIVE         0x04
+#define FFTW_WISDOM_ONLY        0x05
+
+// Algorithm restriction flags
+#define FFTW_DESTROY_INPUT      0x08
+#define FFTW_PRESERVE_INPUT     0x0C
+#define FFTW_UNALIGNED          0x10
+    
+// CUFFTW defines and supports the following data types
+
+// note if complex.h has been included we use the C99 complex types
+#if !defined(FFTW_NO_Complex) && defined(_Complex_I) && defined (complex)
+  typedef double _Complex fftw_complex;
+  typedef float _Complex fftwf_complex;
+#else
+  typedef double fftw_complex[2];
+  typedef float fftwf_complex[2];
+#endif
+
+typedef void *fftw_plan;
+
+typedef void *fftwf_plan;
+
+typedef struct {
+  int n;
+  int is;
+  int os;
+} fftw_iodim;
+
+typedef fftw_iodim fftwf_iodim;
+
+typedef struct {
+  ptrdiff_t n;
+  ptrdiff_t is;
+  ptrdiff_t os;
+} fftw_iodim64;
+
+typedef fftw_iodim64 fftwf_iodim64;
+
+// CUFFTW defines and supports the following double precision APIs
+
+fftw_plan CUFFTAPI fftw_plan_dft_1d(int n, 
+                                    fftw_complex *in,
+                                    fftw_complex *out, 
+                                    int sign, 
+                                    unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_2d(int n0,
+                                    int n1, 
+                                    fftw_complex *in,
+                                    fftw_complex *out, 
+                                    int sign, 
+                                    unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_3d(int n0,
+                                    int n1,
+                                    int n2, 
+                                    fftw_complex *in,
+                                    fftw_complex *out, 
+                                    int sign, 
+                                    unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft(int rank,
+                                 const int *n,
+                                 fftw_complex *in,
+                                 fftw_complex *out, 
+                                 int sign, 
+                                 unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_r2c_1d(int n, 
+                                        double *in,
+                                        fftw_complex *out, 
+                                        unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_r2c_2d(int n0,
+                                        int n1, 
+                                        double *in,
+                                        fftw_complex *out, 
+                                        unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_r2c_3d(int n0,
+                                        int n1,
+                                        int n2, 
+                                        double *in,
+                                        fftw_complex *out, 
+                                        unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_r2c(int rank,
+                                     const int *n,
+                                     double *in,
+                                     fftw_complex *out, 
+                                     unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_c2r_1d(int n, 
+                                        fftw_complex *in,
+                                        double *out, 
+                                        unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_c2r_2d(int n0,
+                                        int n1, 
+                                        fftw_complex *in,
+                                        double *out, 
+                                        unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_c2r_3d(int n0,
+                                        int n1,
+                                        int n2, 
+                                        fftw_complex *in,
+                                        double *out, 
+                                        unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_dft_c2r(int rank,
+                                     const int *n,
+                                     fftw_complex *in,
+                                     double *out, 
+                                     unsigned flags);
+
+
+fftw_plan CUFFTAPI fftw_plan_many_dft(int rank,
+                                      const int *n,
+                                      int batch,
+                                      fftw_complex *in,
+                                      const int *inembed, int istride, int idist,
+                                      fftw_complex *out,
+                                      const int *onembed, int ostride, int odist,
+                                      int sign, unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_many_dft_r2c(int rank,
+                                          const int *n,
+                                          int batch,
+                                          double *in,
+                                          const int *inembed, int istride, int idist,
+                                          fftw_complex *out,
+                                          const int *onembed, int ostride, int odist,
+                                          unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_many_dft_c2r(int rank,
+                                          const int *n,
+                                          int batch,
+                                          fftw_complex *in,
+                                          const int *inembed, int istride, int idist,
+                                          double *out,
+                                          const int *onembed, int ostride, int odist,
+                                          unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_guru_dft(int rank, const fftw_iodim *dims,
+                                      int batch_rank, const fftw_iodim *batch_dims,
+                                      fftw_complex *in, fftw_complex *out,
+                                      int sign, unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_guru_dft_r2c(int rank, const fftw_iodim *dims,
+                                          int batch_rank, const fftw_iodim *batch_dims,
+                                          double *in, fftw_complex *out, 
+                                          unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_guru_dft_c2r(int rank, const fftw_iodim *dims,
+                                          int batch_rank, const fftw_iodim *batch_dims,
+                                          fftw_complex *in, double *out, 
+                                          unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_guru64_dft(int rank, const fftw_iodim64* dims,
+                                        int batch_rank, const fftw_iodim64* batch_dims,
+                                        fftw_complex* in, fftw_complex* out,
+                                        int sign, unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_guru64_dft_r2c(int rank, const fftw_iodim64* dims,
+                                            int batch_rank, const fftw_iodim64* batch_dims,
+                                            double* in, fftw_complex* out,
+                                            unsigned flags);
+
+fftw_plan CUFFTAPI fftw_plan_guru64_dft_c2r(int rank, const fftw_iodim64* dims,
+                                            int batch_rank, const fftw_iodim64* batch_dims,
+                                            fftw_complex* in, double* out,
+                                            unsigned flags);
+
+void CUFFTAPI fftw_execute(const fftw_plan plan);
+
+void CUFFTAPI fftw_execute_dft(const fftw_plan plan, 
+                               fftw_complex *idata,
+                               fftw_complex *odata);
+
+void CUFFTAPI fftw_execute_dft_r2c(const fftw_plan plan, 
+                                   double *idata,
+                                   fftw_complex *odata);
+
+void CUFFTAPI fftw_execute_dft_c2r(const fftw_plan plan, 
+                                   fftw_complex *idata,
+                                   double *odata);
+
+// CUFFTW defines and supports the following single precision APIs
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_1d(int n, 
+                                      fftwf_complex *in,
+                                      fftwf_complex *out, 
+                                      int sign, 
+                                      unsigned flags);
+                                   
+fftwf_plan CUFFTAPI fftwf_plan_dft_2d(int n0,
+                                      int n1, 
+                                      fftwf_complex *in,
+                                      fftwf_complex *out, 
+                                      int sign, 
+                                      unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_3d(int n0,
+                                      int n1,
+                                      int n2, 
+                                      fftwf_complex *in,
+                                      fftwf_complex *out, 
+                                      int sign, 
+                                      unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft(int rank,
+                                   const int *n,
+                                   fftwf_complex *in,
+                                   fftwf_complex *out, 
+                                   int sign, 
+                                   unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_r2c_1d(int n, 
+                                          float *in,
+                                          fftwf_complex *out, 
+                                          unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_r2c_2d(int n0,
+                                          int n1, 
+                                          float *in,
+                                          fftwf_complex *out, 
+                                          unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_r2c_3d(int n0,
+                                          int n1,
+                                          int n2, 
+                                          float *in,
+                                          fftwf_complex *out, 
+                                          unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_r2c(int rank,
+                                       const int *n,
+                                       float *in,
+                                       fftwf_complex *out, 
+                                       unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_c2r_1d(int n, 
+                                          fftwf_complex *in,
+                                          float *out, 
+                                          unsigned flags);
+                                      
+fftwf_plan CUFFTAPI fftwf_plan_dft_c2r_2d(int n0,
+                                          int n1, 
+                                          fftwf_complex *in,
+                                          float *out, 
+                                          unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_c2r_3d(int n0,
+                                        int n1,
+                                        int n2, 
+                                        fftwf_complex *in,
+                                        float *out, 
+                                        unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_dft_c2r(int rank,
+                                       const int *n,
+                                       fftwf_complex *in,
+                                       float *out, 
+                                       unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_many_dft(int rank,
+                                        const int *n,
+                                        int batch,
+                                        fftwf_complex *in,
+                                        const int *inembed, int istride, int idist,
+                                        fftwf_complex *out,
+                                        const int *onembed, int ostride, int odist,
+                                        int sign, unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_many_dft_r2c(int rank,
+                                            const int *n,
+                                            int batch,
+                                            float *in,
+                                            const int *inembed, int istride, int idist,
+                                            fftwf_complex *out,
+                                            const int *onembed, int ostride, int odist,
+                                            unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_many_dft_c2r(int rank,
+                                            const int *n,
+                                            int batch,
+                                            fftwf_complex *in,
+                                            const int *inembed, int istride, int idist,
+                                            float *out,
+                                            const int *onembed, int ostride, int odist,
+                                            unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_guru_dft(int rank, const fftwf_iodim *dims,
+                                        int batch_rank, const fftwf_iodim *batch_dims,
+                                        fftwf_complex *in, fftwf_complex *out,
+                                        int sign, unsigned flags);
+                                        
+fftwf_plan CUFFTAPI fftwf_plan_guru_dft_r2c(int rank, const fftwf_iodim *dims,
+                                            int batch_rank, const fftwf_iodim *batch_dims,
+                                            float *in, fftwf_complex *out, 
+                                            unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_guru_dft_c2r(int rank, const fftwf_iodim *dims,
+                                            int batch_rank, const fftwf_iodim *batch_dims,
+                                            fftwf_complex *in, float *out, 
+                                            unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_guru64_dft(int rank, const fftwf_iodim64* dims,
+                                          int batch_rank, const fftwf_iodim64* batch_dims,
+                                          fftwf_complex* in, fftwf_complex* out,
+                                          int sign, unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_guru64_dft_r2c(int rank, const fftwf_iodim64* dims,
+                                              int batch_rank, const fftwf_iodim64* batch_dims,
+                                              float* in, fftwf_complex* out,
+                                              unsigned flags);
+
+fftwf_plan CUFFTAPI fftwf_plan_guru64_dft_c2r(int rank, const fftwf_iodim64* dims,
+                                              int batch_rank, const fftwf_iodim64* batch_dims,
+                                              fftwf_complex* in, float* out,
+                                              unsigned flags);
+
+void CUFFTAPI fftwf_execute(const fftw_plan plan);
+
+void CUFFTAPI fftwf_execute_dft(const fftwf_plan plan, 
+                                fftwf_complex *idata,
+                                fftwf_complex *odata);
+
+void CUFFTAPI fftwf_execute_dft_r2c(const fftwf_plan plan, 
+                                    float *idata,
+                                    fftwf_complex *odata);
+
+void CUFFTAPI fftwf_execute_dft_c2r(const fftwf_plan plan, 
+                                    fftwf_complex *idata,
+                                    float *odata);
+
+#ifdef _WIN32
+#define _CUFFTAPI(T) T CUFFTAPI
+#else
+#define _CUFFTAPI(T) CUFFTAPI T
+#endif
+
+// CUFFTW defines and supports the following support APIs
+
+_CUFFTAPI(void *) fftw_malloc(size_t n);
+
+_CUFFTAPI(void *) fftwf_malloc(size_t n);
+
+void CUFFTAPI fftw_free(void *pointer);
+
+void CUFFTAPI fftwf_free(void *pointer);
+
+void CUFFTAPI fftw_export_wisdom_to_file(FILE * output_file);
+
+void CUFFTAPI fftwf_export_wisdom_to_file(FILE * output_file);
+
+int CUFFTAPI fftw_import_wisdom_from_file(FILE * input_file);
+
+int CUFFTAPI fftwf_import_wisdom_from_file(FILE * input_file);
+
+void CUFFTAPI fftw_print_plan(const fftw_plan plan);                                
+
+void CUFFTAPI fftwf_print_plan(const fftwf_plan plan);
+
+void CUFFTAPI fftw_set_timelimit(double seconds);
+
+void CUFFTAPI fftwf_set_timelimit(double seconds);
+
+double CUFFTAPI fftw_cost(const fftw_plan plan);
+                               
+double CUFFTAPI fftwf_cost(const fftw_plan plan);
+
+void CUFFTAPI fftw_flops(const fftw_plan plan, double *add, double *mul, double *fma);
+
+void CUFFTAPI fftwf_flops(const fftw_plan plan, double *add, double *mul, double *fma);
+
+void CUFFTAPI fftw_destroy_plan(fftw_plan plan);
+
+void CUFFTAPI fftwf_destroy_plan(fftwf_plan plan);
+
+void CUFFTAPI fftw_cleanup(void);
+
+void CUFFTAPI fftwf_cleanup(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _CUFFTW_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufft/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/include/cufile.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/include/cufile.h
new file mode 100644
index 0000000000000000000000000000000000000000..ffc3e05b7b786e36fdd700623f53319641c0e908
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cufile/include/cufile.h
@@ -0,0 +1,740 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/**
+ * @file cufile.h
+ * @brief  cuFile C APIs
+ *
+ * This file contains all the C APIs to perform GPUDirect Storage supported IO operations
+ */
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/// @cond DOXYGEN_SKIP_MACRO
+#ifndef __CUFILE_H_
+#define __CUFILE_H_
+
+#include <stdlib.h>
+#include <stdbool.h>
+
+#include <cuda.h>
+#include <arpa/inet.h>
+#include <sys/socket.h>
+
+#define CUFILEOP_BASE_ERR 5000
+
+//Note :Data path errors are captured via standard error codes
+#define CUFILEOP_STATUS_ENTRIES \
+	CUFILE_OP(0,                      CU_FILE_SUCCESS, cufile success) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 1,  CU_FILE_DRIVER_NOT_INITIALIZED, nvidia-fs driver is not loaded. Set allow_compat_mode to true in cufile.json file to enable compatible mode) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 2,  CU_FILE_DRIVER_INVALID_PROPS, invalid property) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 3,  CU_FILE_DRIVER_UNSUPPORTED_LIMIT, property range error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 4,  CU_FILE_DRIVER_VERSION_MISMATCH, nvidia-fs driver version mismatch) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 5,  CU_FILE_DRIVER_VERSION_READ_ERROR, nvidia-fs driver version read error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 6,  CU_FILE_DRIVER_CLOSING, driver shutdown in progress) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 7,  CU_FILE_PLATFORM_NOT_SUPPORTED, GPUDirect Storage not supported on current platform) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 8,  CU_FILE_IO_NOT_SUPPORTED, GPUDirect Storage not supported on current file) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 9,  CU_FILE_DEVICE_NOT_SUPPORTED, GPUDirect Storage not supported on current GPU) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 10, CU_FILE_NVFS_DRIVER_ERROR, nvidia-fs driver ioctl error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 11, CU_FILE_CUDA_DRIVER_ERROR, CUDA Driver API error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 12, CU_FILE_CUDA_POINTER_INVALID, invalid device pointer) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 13, CU_FILE_CUDA_MEMORY_TYPE_INVALID, invalid pointer memory type) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 14, CU_FILE_CUDA_POINTER_RANGE_ERROR, pointer range exceeds allocated address range) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 15, CU_FILE_CUDA_CONTEXT_MISMATCH, cuda context mismatch) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 16, CU_FILE_INVALID_MAPPING_SIZE, access beyond maximum pinned size) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 17, CU_FILE_INVALID_MAPPING_RANGE, access beyond mapped size) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 18, CU_FILE_INVALID_FILE_TYPE, unsupported file type) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 19, CU_FILE_INVALID_FILE_OPEN_FLAG, unsupported file open flags) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 20, CU_FILE_DIO_NOT_SET, fd direct IO not set) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 22, CU_FILE_INVALID_VALUE, invalid arguments) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 23, CU_FILE_MEMORY_ALREADY_REGISTERED, device pointer already registered) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 24, CU_FILE_MEMORY_NOT_REGISTERED, device pointer lookup failure) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 25, CU_FILE_PERMISSION_DENIED, driver or file access error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 26, CU_FILE_DRIVER_ALREADY_OPEN, driver is already open) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 27, CU_FILE_HANDLE_NOT_REGISTERED, file descriptor is not registered) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 28, CU_FILE_HANDLE_ALREADY_REGISTERED, file descriptor is already registered) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 29, CU_FILE_DEVICE_NOT_FOUND, GPU device not found) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 30, CU_FILE_INTERNAL_ERROR, internal error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 31, CU_FILE_GETNEWFD_FAILED, failed to obtain new file descriptor) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 33, CU_FILE_NVFS_SETUP_ERROR, NVFS driver initialization error) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 34, CU_FILE_IO_DISABLED, GPUDirect Storage disabled by config on current file)\
+	CUFILE_OP(CUFILEOP_BASE_ERR + 35, CU_FILE_BATCH_SUBMIT_FAILED, failed to submit batch operation)\
+	CUFILE_OP(CUFILEOP_BASE_ERR + 36, CU_FILE_GPU_MEMORY_PINNING_FAILED, failed to allocate pinned GPU Memory) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 37, CU_FILE_BATCH_FULL, queue full for batch operation) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 38, CU_FILE_ASYNC_NOT_SUPPORTED, cuFile stream operation not supported) \
+	CUFILE_OP(CUFILEOP_BASE_ERR + 39, CU_FILE_IO_MAX_ERROR, GPUDirect Storage Max Error)
+
+
+/**
+ * @brief cufileop status enum
+ *
+ * @note on success the error code is set to  @ref CU_FILE_SUCCESS.
+ * @note The error code can be inspected using @ref IS_CUFILE_ERR and @ref CUFILE_ERRSTR.
+ * @note The error code if set to @ref CU_FILE_CUDA_DRIVER_ERROR, then cuda error can be inspected using @ref IS_CUDA_ERR and @ref CU_FILE_CUDA_ERR.
+ * @note Data path errors are captured via standard error codes
+ */
+typedef enum CUfileOpError {
+        /// @cond DOXYGEN_SKIP_MACRO
+	#define CUFILE_OP(code, name, string) name = code,
+	CUFILEOP_STATUS_ENTRIES
+	#undef CUFILE_OP
+        ///@endcond
+} CUfileOpError;
+
+/// @endcond
+
+/**
+ * @brief cufileop status string
+ */
+static inline const char *cufileop_status_error(CUfileOpError status)
+{
+	switch (status) {
+	/// @cond DOXYGEN_SKIP_MACRO
+	#define CUFILE_OP(code, name, string) \
+	case name: return #string;
+	CUFILEOP_STATUS_ENTRIES
+	#undef CUFILE_OP
+	///@endcond
+	default:return "unknown cufile error";
+	}
+}
+
+/**
+ * @brief cufileop status string
+ */
+typedef struct CUfileError {
+
+	CUfileOpError err; // cufile error
+
+	CUresult cu_err; // cuda driver error
+
+}CUfileError_t;
+
+/**
+ * @brief  error macros to inspect error status of type @ref CUfileOpError
+ */
+
+#define IS_CUFILE_ERR(err) \
+	(abs((err)) > CUFILEOP_BASE_ERR)
+
+#define CUFILE_ERRSTR(err) \
+	cufileop_status_error((CUfileOpError)abs((err)))
+
+#define IS_CUDA_ERR(status) \
+	((status).err == CU_FILE_CUDA_DRIVER_ERROR)
+
+#define CU_FILE_CUDA_ERR(status) ((status).cu_err)
+
+/* driver properties */
+typedef enum CUfileDriverStatusFlags {
+        CU_FILE_LUSTRE_SUPPORTED = 0, /*!< Support for DDN LUSTRE */
+
+        CU_FILE_WEKAFS_SUPPORTED = 1, /*!< Support for WEKAFS */
+
+        CU_FILE_NFS_SUPPORTED = 2, /*!< Support for NFS */
+
+        CU_FILE_GPFS_SUPPORTED = 3, /*! < Support for GPFS */
+
+        CU_FILE_NVME_SUPPORTED = 4, /*!< Support for NVMe */
+
+        CU_FILE_NVMEOF_SUPPORTED = 5, /*!< Support for NVMeOF */
+
+        CU_FILE_SCSI_SUPPORTED = 6, /*!< Support for SCSI */
+
+	CU_FILE_SCALEFLUX_CSD_SUPPORTED = 7, /*!< Support for Scaleflux CSD*/
+
+	CU_FILE_NVMESH_SUPPORTED = 8, /*!< Support for NVMesh Block Dev*/
+	CU_FILE_BEEGFS_SUPPORTED = 9, /*!< Support for BeeGFS */
+    //10 is reserved for YRCloudFile
+        CU_FILE_NVME_P2P_SUPPORTED = 11,   /*!< Support for NVMe using PCI P2PDMA */
+
+}CUfileDriverStatusFlags_t;
+
+typedef enum CUfileDriverControlFlags {
+        CU_FILE_USE_POLL_MODE = 0 , /*!< use POLL mode. properties.use_poll_mode*/
+
+        CU_FILE_ALLOW_COMPAT_MODE = 1/*!< allow COMPATIBILITY mode. properties.allow_compat_mode*/
+
+}CUfileDriverControlFlags_t;
+
+typedef enum CUfileFeatureFlags {
+        CU_FILE_DYN_ROUTING_SUPPORTED = 0, /*!< Support for Dynamic routing to handle devices across the PCIe bridges */
+
+        CU_FILE_BATCH_IO_SUPPORTED = 1, /*!<  Unsupported */
+
+        CU_FILE_STREAMS_SUPPORTED = 2, /*!<  Unsupported */
+
+        CU_FILE_PARALLEL_IO_SUPPORTED = 3 /*!<  Unsupported */
+}CUfileFeatureFlags_t;
+
+typedef struct CUfileDrvProps {
+        struct {
+                unsigned int major_version;
+
+                unsigned int minor_version;
+
+                size_t poll_thresh_size;
+
+                size_t max_direct_io_size;
+
+                unsigned int dstatusflags;
+
+                unsigned int dcontrolflags;
+
+        } nvfs;
+
+        unsigned int fflags;
+
+        unsigned int max_device_cache_size;
+
+	unsigned int per_buffer_cache_size;
+
+        unsigned int max_device_pinned_mem_size;
+
+        unsigned int max_batch_io_size;
+        unsigned int max_batch_io_timeout_msecs;
+}CUfileDrvProps_t;
+
+typedef struct sockaddr sockaddr_t;
+
+typedef struct cufileRDMAInfo
+{
+        int version;
+        int desc_len;
+        const char *desc_str;
+}cufileRDMAInfo_t;
+
+#define CU_FILE_RDMA_REGISTER 1
+#define CU_FILE_RDMA_RELAXED_ORDERING (1<<1)
+
+
+
+typedef struct CUfileFSOps {
+      /* NULL means discover using fstat */
+      const char* (*fs_type) (void *handle);
+
+      /* list of host addresses to use,  NULL means no restriction */
+      int (*getRDMADeviceList)(void *handle, sockaddr_t **hostaddrs);
+
+      /* -1 no pref */
+      int (*getRDMADevicePriority)(void *handle, char*, size_t,
+                                loff_t, sockaddr_t* hostaddr);
+
+      /* NULL means try VFS */
+      ssize_t (*read) (void *handle, char*, size_t, loff_t, cufileRDMAInfo_t*);
+      ssize_t (*write) (void *handle, const char *, size_t, loff_t , cufileRDMAInfo_t*);
+}CUfileFSOps_t;
+
+/* File Handle */
+enum CUfileFileHandleType {
+	CU_FILE_HANDLE_TYPE_OPAQUE_FD = 1,   /*!< Linux based fd */
+
+	CU_FILE_HANDLE_TYPE_OPAQUE_WIN32 = 2, /*!< Windows based handle (unsupported) */
+
+        CU_FILE_HANDLE_TYPE_USERSPACE_FS = 3,  /* Userspace based FS */
+};
+
+typedef struct CUfileDescr_t {
+	enum CUfileFileHandleType type; /* type of file being registered */
+	union {
+		int fd; 		/* Linux   */
+		void *handle; 		/* Windows */
+	} handle;
+        const CUfileFSOps_t *fs_ops;     /* file system operation table */
+}CUfileDescr_t;
+
+/**
+ * @brief File handle type
+ *
+ */
+typedef void* CUfileHandle_t;
+
+
+#pragma GCC visibility push(default)
+
+/**
+ * @brief cuFileHandleRegister is required, and performs extra checking that is memoized to provide increased performance on later cuFile operations.
+ *
+ * @param fh @ref CUfileHandle_t opaque file handle for IO operations
+ * @param descr @ref CUfileDescr_t  file descriptor (OS agnostic)
+ *
+ * @return      CU_FILE_SUCCESS on successful completion. fh will be updated for use in @ref cuFileRead, @ref cuFileWrite, @ref cuFileHandleDeregister
+ * @return      CU_FILE_DRIVER_NOT_INITIALIZED on failure to load driver
+ * @return      CU_FILE_IO_NOT_SUPPORTED -  if filesystem is not supported
+ * @return      CU_FILE_INVALID_VALUE if null or bad api arguments
+ * @return      CU_FILE_INVALID_FILE_OPEN_FLAG if file is opened with unsupported modes like no O_DIRECT
+ * @return      CU_FILE_INVALID_FILE_TYPE if filepath is not valid or is not a regular file
+ * @return      CU_FILE_HANDLE_ALREADY_REGISTERED if file handle/descriptor is already registered
+ *
+ * <b>Description</b>
+ * cuFileHandleRegister registers the open file descriptor for use with cuFile IO operations.
+ *
+ * This API will ensure that the file’s descriptor is checked for GPUDirect Storage support and returns a valid file handle on CU_FILE_SUCCESS.
+ *
+ * @note the file needs to be opened in O_DIRECT mode to support GPUDirect Storage.
+ *
+ * @see cuFileRead
+ * @see cuFileWrite
+ * @see cuFileHandleDeregister
+ *
+ */
+CUfileError_t cuFileHandleRegister(CUfileHandle_t *fh, CUfileDescr_t *descr);
+
+/**
+ * @brief releases a registered filehandle from cuFile
+ *
+ * @param fh @ref CUfileHandle_t file handle
+ *
+ * @return void
+ *
+ * @see cuFileHandleRegister
+ */
+void cuFileHandleDeregister(CUfileHandle_t fh);
+
+/**
+ * @brief register an existing cudaMalloced memory with cuFile to pin for GPUDirect Storage access or 
+ * register host allocated memory with cuFile.
+ *
+ * @param bufPtr_base buffer pointer allocated
+ * @param length  size of memory region from the above specified bufPtr
+ * @param flags   CU_FILE_RDMA_REGISTER
+ *
+ * @return  CU_FILE_SUCCESS on success
+ * @return  CU_FILE_NVFS_DRIVER_ERROR
+ * @return  CU_FILE_INVALID_VALUE
+ * @return  CU_FILE_CUDA_ERROR for unsuported memory type
+ * @return  CU_FILE_MEMORY_ALREADY_REGISTERED on error
+ * @return  CU_FILE_GPU_MEMORY_PINNING_FAILED if not enough pinned memory is available
+ * @note This memory will be use to perform GPU direct DMA from the supported storage.
+ * @warning This API is intended for usecases where the memory is used as streaming buffer that is reused across multiple cuFile IO operations before calling @ref cuFileBufDeregister
+ *
+ * @see cuFileBufDeregister
+ * @see cuFileRead
+ * @see cuFileWrite
+ */
+CUfileError_t cuFileBufRegister(const void *bufPtr_base, size_t length, int flags);
+
+/**
+ * @brief  deregister an already registered device or host memory from cuFile
+ *
+ * @param bufPtr_base  buffer pointer to deregister
+ *
+ * @return  CU_FILE_SUCCESS on success
+ * @return  CU_FILE_INVALID_VALUE on invalid memory pointer or unregistered memory pointer
+ *
+ * @see cuFileBufRegister
+ * @see cuFileRead
+ * @see cuFileWrite
+ */
+
+CUfileError_t cuFileBufDeregister(const void *bufPtr_base);
+
+/**
+ * @brief read data from a registered file handle to a specified device or host memory
+ *
+ * @param fh @ref CUfileHandle_t opaque file handle
+ * @param bufPtr_base  base address of buffer in device or host memory
+ * @param size    size bytes to read
+ * @param file_offset  file-offset from begining of the file
+ * @param bufPtr_offset  offset relative to the bufPtr_base pointer to read into.
+ *
+ * @return  size of bytes successfully read
+ * @return  -1 on error, in which case errno is set to indicate filesystem errors.
+ * @return  all other errors will return a negative integer value of @ref CUfileOpError enum value.
+ *
+ * @note  If the bufPtr is not registered with @ref cuFileBufRegister, the data will be buffered through preallocated pinned buffers if needed.
+ * @note  This is useful for applications that need to perform IO to unaligned file offsets and/or size. This is also recommended
+ *        for cases where the BAR1 memory size is smaller than the size of the allocated memory.
+ *
+ * @see cuFileBufRegister
+ * @see cuFileHandleRegister
+ * @see cuFileWrite
+ */
+
+ssize_t cuFileRead(CUfileHandle_t fh, void *bufPtr_base, size_t size, off_t file_offset, off_t bufPtr_offset);
+
+/**
+ * @brief  write data from a specified device or host memory to a registered file handle
+ *
+ * @param fh @ref CUfileHandle_t opaque file handle
+ * @param bufPtr_base  base address of buffer in device or host memory
+ * @param size    size bytes to write
+ * @param file_offset  file-offset from begining of the file
+ * @param bufPtr_offset  offset relative to the bufPtr_base pointer to write from.
+ *
+ * @return  size of bytes successfully written
+ * @return  -1 on error, in which case errno is set to indicate filesystem errors.
+ * @return  all other errors will return a negative integer value of @ref CUfileOpError enum value.
+ *
+ * @note  If the bufPtr is not registered with @ref cuFileBufRegister, the data will be buffered through preallocated pinned buffers if needed.
+ * @note  This is useful for applications that need to perform IO to unaligned file offsets and/or size. This is also recommended
+ *        for cases where the BAR1 memory size is smaller than the size of the allocated memory.
+ *
+ * @see cuFileBufRegister
+ * @see cuFileHandleRegister
+ * @see cuFileRead
+ */
+
+ssize_t cuFileWrite(CUfileHandle_t fh, const void *bufPtr_base, size_t size, off_t file_offset, off_t bufPtr_offset);
+
+// CUFile Driver APIs
+
+/**
+ * @brief
+ * Initialize the cuFile library and open the nvidia-fs driver
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED
+ * @return CU_FILE_DRIVER_VERSION_MISMATCH on driver version mismatch error
+ *
+ * @see cuFileDriverClose
+ */
+CUfileError_t cuFileDriverOpen(void);
+
+CUfileError_t cuFileDriverClose(void);
+#define cuFileDriverClose cuFileDriverClose_v2
+/**
+ * @brief
+ * reset the cuFile library and release the nvidia-fs driver
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_CLOSING if there are any active IO operations using @ref cuFileRead or @ref cuFileWrite
+ *
+ * @see cuFileDriverOpen
+ */
+CUfileError_t cuFileDriverClose(void);
+
+/**
+ * @brief
+ * returns use count of cufile drivers at that moment by the process.
+ */
+long cuFileUseCount(void);
+
+/**
+ * @brief
+ * Gets the Driver session properties
+ *
+ * @return CU_FILE_SUCCESS on success
+ *
+ * @see cuFileDriverSetPollMode
+ * @see cuFileDriverSetMaxDirectIOSize
+ * @see cuFileDriverSetMaxCacheSize
+ * @see cuFileDriverSetMaxPinnedMemSize
+ */
+CUfileError_t cuFileDriverGetProperties(CUfileDrvProps_t *props);
+
+/**
+ * @brief
+ * Sets whether the Read/Write APIs use polling to do IO operations
+ *
+ * @param  poll boolean to indicate whether to use poll mode or not
+ * @param  poll_threshold_size max IO size to use for POLLING mode in KB
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED if the driver is not initialized
+ * @return CU_FILE_DRIVER_VERSION_MISMATCH, CU_FILE_DRIVER_UNSUPPORTED_LIMIT on error
+ *
+ * @warning This is an advanced command and should be tuned based on available system memory
+ *
+ * @see cuFileDriverGetProperties
+ */
+CUfileError_t cuFileDriverSetPollMode(bool poll, size_t poll_threshold_size);
+
+/**
+ * @brief
+ * Control parameter to set max IO size(KB) used by the library to talk to nvidia-fs driver
+ *
+ * @param  max_direct_io_size maximum allowed direct io size in KB
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED if the driver is not initialized
+ * @return CU_FILE_DRIVER_VERSION_MISMATCH, CU_FILE_DRIVER_UNSUPPORTED_LIMIT on error
+ *
+ * @warning This is an advanced command and should be tuned based on available system memory
+ *
+ * @see cuFileDriverGetProperties
+ *
+ */
+CUfileError_t cuFileDriverSetMaxDirectIOSize(size_t max_direct_io_size);
+
+/**
+ * @brief
+ * Control parameter to set maximum GPU memory reserved per device by the library for internal buffering
+ *
+ * @param  max_cache_size The maximum GPU buffer space per device used for internal use in KB
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED if the driver is not initialized
+ * @return CU_FILE_DRIVER_VERSION_MISMATCH, CU_FILE_DRIVER_UNSUPPORTED_LIMIT on error
+ *
+ * @warning This is an advanced command and should be tuned based on supported GPU memory
+ *
+ * @see cuFileDriverGetProperties
+ */
+CUfileError_t cuFileDriverSetMaxCacheSize(size_t max_cache_size);
+
+/**
+ * @brief
+ * Sets maximum buffer space that is pinned in KB for use by @ref cuFileBufRegister
+ *
+ * @param max_pinned_size maximum buffer space that is pinned in KB
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED if the driver is not initialized
+ * @return CU_FILE_DRIVER_VERSION_MISMATCH, CU_FILE_DRIVER_UNSUPPORTED_LIMIT on error
+ *
+ * @warning This is an advanced command and should be tuned based on supported GPU memory
+ *
+ * @see cuFileDriverGetProperties
+ *
+ */
+CUfileError_t cuFileDriverSetMaxPinnedMemSize(size_t max_pinned_size);
+
+//Experimental Batch API's
+
+
+typedef enum CUfileOpcode {
+		CUFILE_READ = 0,
+		CUFILE_WRITE
+}CUfileOpcode_t;
+
+typedef enum CUFILEStatus_enum {
+	CUFILE_WAITING = 0x000001,  /* required value prior to submission */
+	CUFILE_PENDING = 0x000002,  /* once enqueued */
+	CUFILE_INVALID = 0x000004,  /* request was ill-formed or could not be enqueued */
+	CUFILE_CANCELED = 0x000008, /* request successfully canceled */
+	CUFILE_COMPLETE = 0x0000010, /* request successfully completed */
+	CUFILE_TIMEOUT = 0x0000020,  /* request timed out */
+	CUFILE_FAILED  = 0x0000040  /* unable to complete */
+}CUfileStatus_t;
+typedef enum cufileBatchMode {
+	CUFILE_BATCH = 1,
+} CUfileBatchMode_t;
+typedef struct CUfileIOParams {
+	CUfileBatchMode_t mode; // Must be the very first field.
+	union {
+		struct  {
+			void *devPtr_base; //This can be a device memory or a host memory pointer.
+			off_t file_offset;
+			off_t devPtr_offset; 
+			size_t size;
+		}batch;
+	}u;
+	CUfileHandle_t fh;
+	CUfileOpcode_t opcode;
+	void *cookie;
+}CUfileIOParams_t;
+typedef struct CUfileIOEvents {
+	void *cookie;
+	CUfileStatus_t   status;      /* status of the operation */
+	size_t ret; /* -ve error or amount of I/O done. */
+}CUfileIOEvents_t;
+
+typedef void* CUfileBatchHandle_t;
+
+CUfileError_t cuFileBatchIOSetUp(CUfileBatchHandle_t *batch_idp, unsigned nr);
+CUfileError_t cuFileBatchIOSubmit(CUfileBatchHandle_t batch_idp, unsigned nr, CUfileIOParams_t *iocbp, unsigned int flags);
+CUfileError_t cuFileBatchIOGetStatus(CUfileBatchHandle_t batch_idp, unsigned min_nr, unsigned* nr,
+				CUfileIOEvents_t *iocbp, struct timespec* timeout);
+CUfileError_t cuFileBatchIOCancel(CUfileBatchHandle_t batch_idp);
+void cuFileBatchIODestroy(CUfileBatchHandle_t batch_idp);
+
+//Async API's with cuda streams
+
+// cuFile stream API registration flags
+// buffer pointer offset is set at submission time
+#define CU_FILE_STREAM_FIXED_BUF_OFFSET         1
+// file offset is set at submission time
+#define CU_FILE_STREAM_FIXED_FILE_OFFSET        2
+// file size is set at submission time
+#define CU_FILE_STREAM_FIXED_FILE_SIZE          4
+// size, offset and buffer offset are 4k aligned
+#define CU_FILE_STREAM_PAGE_ALIGNED_INPUTS      8
+
+/**
+ *@brief
+
+ * @param fh The cuFile handle for the file.
+ * @param bufPtr_base  base address of buffer in device or host memory
+ * @param size_p  pointer to size bytes to read
+ * @note  *size_p if the size is not known at the time of submission, then must provide the max possible size for I/O request.
+ * @param file_offset_p  pointer to file-offset from begining of the file
+ * @param bufPtr_offset_p  pointer to offset relative to the bufPtr_base pointer to read into.
+ * @param bytes_read_p  pointer to the number of bytes that were successfully read.
+ * @param CUstream stream cuda stream for the operation.
+ *
+ * @return  size of bytes successfully read in *bytes_read_p
+ * @return  -1 on error, in which case errno is set to indicate filesystem errors.
+ * @return  all other errors will return a negative integer value of @ref CUfileOpError enum value.
+ *
+ * @note  If the bufPtr_base is not registered with @ref cuFileBufRegister, the data will be buffered through preallocated pinned buffers.
+ * @note  This is useful for applications that need to perform IO to unaligned file offsets and/or size. This is also recommended
+ *        for cases where the BAR1 memory size is smaller than the size of the allocated memory.
+ * @note  If the stream is registered with cuFileStreamRegister, the IO setup and teardown overhead will be reduced.
+ * @note  on cuda stream errors, the user must call cuFileStreamDeregister to release any outstanding cuFile resources for the stream.
+ *
+ *
+ * @see cuFileBufRegister
+ * @see cuFileHandleRegister
+ * @see cuFileRead
+ * @see cuFileStreamRegister
+ * @see cuFileStreamDeregister
+ */
+
+CUfileError_t cuFileReadAsync(CUfileHandle_t fh, void *bufPtr_base,
+                        size_t *size_p, off_t *file_offset_p, off_t *bufPtr_offset_p, ssize_t *bytes_read_p, CUstream stream);
+
+/**
+ *@brief
+
+* @param fh The cuFile handle for the file.
+ * @param bufPtr_base  base address of buffer in device or host memory
+ * @param size_p    pointer to size bytes to write.
+ * @note  *size_p if the size is not known at the time of submission, then must provide the max possible size for I/O request.
+ * @param file_offset_p  pointer to file-offset from begining of the file
+ * @param bufPtr_offset_p  pointer to offset relative to the bufPtr_base pointer to write from.
+ * @param bytes_written_p pointer to the number of bytes that were successfully written.
+ * @param CUstream cuda stream for the operation.
+ *
+ * @return  size of bytes successfully written in *bytes_written_p
+ * @return  -1 on error, in which case errno is set to indicate filesystem errors.
+ * @return  all other errors will return a negative integer value of @ref CUfileOpError enum value.
+ *
+ * @note  If the bufPtr_base is not registered with @ref cuFileBufRegister, the data will be buffered through preallocated pinned buffers.
+ * @note  This is useful for applications that need to perform IO to unaligned file offsets and/or size. This is also recommended
+ *        for cases where the BAR1 memory size is smaller than the size of the allocated memory.
+ * @note  If the stream is registered with cuFileStreamRegister prior to this call, the IO setup and teardown overhead will be reduced.
+ * @note  on cuda stream errors, the user must call cuFileStreamDeregister to release any outstanding cuFile resources for the stream.
+ *
+ * @see cuFileBufRegister
+ * @see cuFileHandleRegister
+ * @see cuFileWrite
+ * @see cuFileStreamRegister
+ * @see cuFileStreamDeregister
+ */
+
+CUfileError_t cuFileWriteAsync(CUfileHandle_t fh, void *bufPtr_base,
+                        size_t *size_p, off_t *file_offset_p, off_t *bufPtr_offset_p, ssize_t *bytes_written_p, CUstream stream);
+
+/**
+ *@brief
+
+ * @param CUstream cuda stream for the operation.
+ * @param flags for the stream to improve the stream execution of IO based on input parameters.
+ * @note  supported FLAGS are
+ * @note CU_FILE_STREAM_FIXED_BUF_OFFSET - buffer pointer offset is set at submission time
+ * @note CU_FILE_STREAM_FIXED_FILE_OFFSET - file offset is set at submission time
+ * @note CU_FILE_STREAM_FIXED_FILE_SIZE  - file size is set at submission time
+ * @note CU_FILE_STREAM_PAGE_ALIGNED_INPUTS - size, offset and buffer offset are 4k aligned
+ *
+ * @note  allocates resources needed to support cuFile operations asynchronously for the cuda stream
+ * @note  This is useful for applications that need to perform IO to unaligned file offsets and/or size. This is also recommended
+ *        for cases where the BAR1 memory size is smaller than the size of the allocated memory.
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED if the driver is not initialized
+ * @return CU_FILE_INVALID_VALUE if the stream is invalid
+ *
+ * @see cuFileReadAsync
+ * @see cuFileWriteAsync
+ * @see cuFileStreamDeregister
+ */
+
+CUfileError_t cuFileStreamRegister(CUstream stream, unsigned flags);
+
+/**
+ *@brief
+
+ * @param CUstream cuda stream for the operation.
+ *
+ * @note  deallocates resources used by previous cuFile asynchronous operations for the cuda stream
+ * @note  highly recommend to call after cuda stream errors to release any outstanding cuFile resources for this stream
+ * @note  must be called before cuStreamDestroy call for the specified stream.
+ * @note  This is useful for applications that need to perform IO to unaligned file offsets and/or size. This is also recommended
+ *        for cases where the BAR1 memory size is smaller than the size of the allocated memory.
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_DRIVER_NOT_INITIALIZED if the driver is not initialized
+ * @return CU_FILE_INVALID_VALUE if the stream is invalid
+ *
+ * @see cuFileReadAsync
+ * @see cuFileWriteAsync
+ * @see cuFileStreamRegister
+ */
+
+CUfileError_t cuFileStreamDeregister(CUstream stream);
+
+/**
+ *@brief
+
+ * @returns cufile library version.
+ * 
+ * @The version is returned as (1000 major + 10 minor).
+ * @For example, CUFILE 1.7.0 would be represented by 1070.
+ * @note  This is useful for applications that need to inquire the library.
+ *
+ * @return CU_FILE_SUCCESS on success
+ * @return CU_FILE_INVALID_VALUE if the input parameter is null.
+ * @return CU_FILE_DRIVER_VERSION_READ_ERROR if the version is not available.
+ *
+ */
+
+CUfileError_t cuFileGetVersion(int *version);
+
+#pragma GCC visibility pop
+
+/// @cond DOXYGEN_SKIP_MACRO
+#endif // CUFILE_H
+/// @endcond
+#ifdef __cplusplus
+}
+#endif
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@@ -0,0 +1,1080 @@
+
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#if !defined(CURAND_H_)
+#define CURAND_H_
+
+/**
+ * \defgroup HOST Host API
+ *
+ * @{
+ */
+#ifndef __CUDACC_RTC__
+#include <cuda_runtime.h>
+#endif
+
+#ifndef CURANDAPI
+#ifdef _WIN32
+#define CURANDAPI __stdcall
+#else
+#define CURANDAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+#define CURAND_VER_MAJOR 10
+#define CURAND_VER_MINOR 3
+#define CURAND_VER_PATCH 9
+#define CURAND_VER_BUILD 90
+#define CURAND_VERSION (CURAND_VER_MAJOR * 1000 + \
+                        CURAND_VER_MINOR *  100 + \
+                        CURAND_VER_PATCH)
+/* CURAND Host API datatypes */
+
+/**
+ * @{
+ */
+
+/**
+ * CURAND function call status types
+ */
+enum curandStatus {
+    CURAND_STATUS_SUCCESS = 0, ///< No errors
+    CURAND_STATUS_VERSION_MISMATCH = 100, ///< Header file and linked library version do not match
+    CURAND_STATUS_NOT_INITIALIZED = 101, ///< Generator not initialized
+    CURAND_STATUS_ALLOCATION_FAILED = 102, ///< Memory allocation failed
+    CURAND_STATUS_TYPE_ERROR = 103, ///< Generator is wrong type
+    CURAND_STATUS_OUT_OF_RANGE = 104, ///< Argument out of range
+    CURAND_STATUS_LENGTH_NOT_MULTIPLE = 105, ///< Length requested is not a multple of dimension
+    CURAND_STATUS_DOUBLE_PRECISION_REQUIRED = 106, ///< GPU does not have double precision required by MRG32k3a
+    CURAND_STATUS_LAUNCH_FAILURE = 201, ///< Kernel launch failure
+    CURAND_STATUS_PREEXISTING_FAILURE = 202, ///< Preexisting failure on library entry
+    CURAND_STATUS_INITIALIZATION_FAILED = 203, ///< Initialization of CUDA failed
+    CURAND_STATUS_ARCH_MISMATCH = 204, ///< Architecture mismatch, GPU does not support requested feature
+    CURAND_STATUS_INTERNAL_ERROR = 999 ///< Internal library error
+};
+
+/*
+ * CURAND function call status types
+*/
+/** \cond UNHIDE_TYPEDEFS */
+typedef enum curandStatus curandStatus_t;
+/** \endcond */
+
+/**
+ * CURAND generator types
+ */
+enum curandRngType {
+    CURAND_RNG_TEST = 0,
+    CURAND_RNG_PSEUDO_DEFAULT = 100, ///< Default pseudorandom generator
+    CURAND_RNG_PSEUDO_XORWOW = 101, ///< XORWOW pseudorandom generator
+    CURAND_RNG_PSEUDO_MRG32K3A = 121, ///< MRG32k3a pseudorandom generator
+    CURAND_RNG_PSEUDO_MTGP32 = 141, ///< Mersenne Twister MTGP32 pseudorandom generator
+    CURAND_RNG_PSEUDO_MT19937 = 142, ///< Mersenne Twister MT19937 pseudorandom generator
+    CURAND_RNG_PSEUDO_PHILOX4_32_10 = 161, ///< PHILOX-4x32-10 pseudorandom generator
+    CURAND_RNG_QUASI_DEFAULT = 200, ///< Default quasirandom generator
+    CURAND_RNG_QUASI_SOBOL32 = 201, ///< Sobol32 quasirandom generator
+    CURAND_RNG_QUASI_SCRAMBLED_SOBOL32 = 202,  ///< Scrambled Sobol32 quasirandom generator
+    CURAND_RNG_QUASI_SOBOL64 = 203, ///< Sobol64 quasirandom generator
+    CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 = 204  ///< Scrambled Sobol64 quasirandom generator
+};
+
+/*
+ * CURAND generator types
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef enum curandRngType curandRngType_t;
+/** \endcond */
+
+/**
+ * CURAND ordering of results in memory
+ */
+enum curandOrdering {
+    CURAND_ORDERING_PSEUDO_BEST = 100, ///< Best ordering for pseudorandom results
+    CURAND_ORDERING_PSEUDO_DEFAULT = 101, ///< Specific default thread sequence for pseudorandom results, same as CURAND_ORDERING_PSEUDO_BEST
+    CURAND_ORDERING_PSEUDO_SEEDED = 102, ///< Specific seeding pattern for fast lower quality pseudorandom results
+    CURAND_ORDERING_PSEUDO_LEGACY = 103, ///< Specific legacy sequence for pseudorandom results, guaranteed to remain the same for all cuRAND release
+    CURAND_ORDERING_PSEUDO_DYNAMIC = 104, ///< Specific ordering adjusted to the device it is being executed on, provides the best performance
+    CURAND_ORDERING_QUASI_DEFAULT = 201 ///< Specific n-dimensional ordering for quasirandom results
+};
+
+/*
+ * CURAND ordering of results in memory
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef enum curandOrdering curandOrdering_t;
+/** \endcond */
+
+/**
+ * CURAND choice of direction vector set
+ */
+enum curandDirectionVectorSet {
+    CURAND_DIRECTION_VECTORS_32_JOEKUO6 = 101, ///< Specific set of 32-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions
+    CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6 = 102, ///< Specific set of 32-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions, and scrambled
+    CURAND_DIRECTION_VECTORS_64_JOEKUO6 = 103, ///< Specific set of 64-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions
+    CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6 = 104 ///< Specific set of 64-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions, and scrambled
+};
+
+/*
+ * CURAND choice of direction vector set
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef enum curandDirectionVectorSet curandDirectionVectorSet_t;
+/** \endcond */
+
+/**
+ * CURAND array of 32-bit direction vectors
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef unsigned int curandDirectionVectors32_t[32];
+/** \endcond */
+
+ /**
+ * CURAND array of 64-bit direction vectors
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef unsigned long long curandDirectionVectors64_t[64];
+/** \endcond **/
+
+/**
+ * CURAND generator (opaque)
+ */
+struct curandGenerator_st;
+
+/**
+ * CURAND generator
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandGenerator_st *curandGenerator_t;
+/** \endcond */
+
+/**
+ * CURAND distribution
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef double curandDistribution_st;
+typedef curandDistribution_st *curandDistribution_t;
+typedef struct curandDistributionShift_st *curandDistributionShift_t;
+/** \endcond */
+/**
+ * CURAND distribution M2
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandDistributionM2Shift_st *curandDistributionM2Shift_t;
+typedef struct curandHistogramM2_st *curandHistogramM2_t;
+typedef unsigned int curandHistogramM2K_st;
+typedef curandHistogramM2K_st *curandHistogramM2K_t;
+typedef curandDistribution_st curandHistogramM2V_st;
+typedef curandHistogramM2V_st *curandHistogramM2V_t;
+
+typedef struct curandDiscreteDistribution_st *curandDiscreteDistribution_t;
+/** \endcond */
+
+/*
+ * CURAND METHOD
+ */
+/** \cond UNHIDE_ENUMS */
+enum curandMethod {
+    CURAND_CHOOSE_BEST = 0, // choose best depends on args
+    CURAND_ITR = 1,
+    CURAND_KNUTH = 2,
+    CURAND_HITR = 3,
+    CURAND_M1 = 4,
+    CURAND_M2 = 5,
+    CURAND_BINARY_SEARCH = 6,
+    CURAND_DISCRETE_GAUSS = 7,
+    CURAND_REJECTION = 8,
+    CURAND_DEVICE_API = 9,
+    CURAND_FAST_REJECTION = 10,
+    CURAND_3RD = 11,
+    CURAND_DEFINITION = 12,
+    CURAND_POISSON = 13
+};
+
+typedef enum curandMethod curandMethod_t;
+/** \endcond */
+
+
+#ifndef __CUDACC_RTC__
+
+/**
+ * @}
+ */
+
+/**
+ * \brief Create new random number generator.
+ *
+ * Creates a new random number generator of type \p rng_type
+ * and returns it in \p *generator.
+ *
+ * Legal values for \p rng_type are:
+ * - CURAND_RNG_PSEUDO_DEFAULT
+ * - CURAND_RNG_PSEUDO_XORWOW
+ * - CURAND_RNG_PSEUDO_MRG32K3A
+ * - CURAND_RNG_PSEUDO_MTGP32
+ * - CURAND_RNG_PSEUDO_MT19937
+ * - CURAND_RNG_PSEUDO_PHILOX4_32_10
+ * - CURAND_RNG_QUASI_DEFAULT
+ * - CURAND_RNG_QUASI_SOBOL32
+ * - CURAND_RNG_QUASI_SCRAMBLED_SOBOL32
+ * - CURAND_RNG_QUASI_SOBOL64
+ * - CURAND_RNG_QUASI_SCRAMBLED_SOBOL64
+ *
+ * When \p rng_type is CURAND_RNG_PSEUDO_DEFAULT, the type chosen
+ * is CURAND_RNG_PSEUDO_XORWOW.  \n
+ * When \p rng_type is CURAND_RNG_QUASI_DEFAULT,
+ * the type chosen is CURAND_RNG_QUASI_SOBOL32.
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_XORWOW are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_MRG32K3A are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_MTGP32 are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_MT19937 are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * * The default values for \p rng_type = CURAND_RNG_PSEUDO_PHILOX4_32_10 are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL32 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL64 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBBLED_SOBOL32 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * \param generator - Pointer to generator
+ * \param rng_type - Type of generator to create
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED, if memory could not be allocated \n
+ * - CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU \n
+ * - CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the
+ *   dynamically linked library version \n
+ * - CURAND_STATUS_TYPE_ERROR if the value for \p rng_type is invalid \n
+ * - CURAND_STATUS_SUCCESS if generator was created successfully \n
+ *
+ */
+curandStatus_t CURANDAPI
+curandCreateGenerator(curandGenerator_t *generator, curandRngType_t rng_type);
+
+/**
+ * \brief Create new host CPU random number generator.
+ *
+ * Creates a new host CPU random number generator of type \p rng_type
+ * and returns it in \p *generator.
+ *
+ * Legal values for \p rng_type are:
+ * - CURAND_RNG_PSEUDO_DEFAULT
+ * - CURAND_RNG_PSEUDO_XORWOW
+ * - CURAND_RNG_PSEUDO_MRG32K3A
+ * - CURAND_RNG_PSEUDO_MTGP32
+ * - CURAND_RNG_PSEUDO_MT19937
+ * - CURAND_RNG_PSEUDO_PHILOX4_32_10
+ * - CURAND_RNG_QUASI_DEFAULT
+ * - CURAND_RNG_QUASI_SOBOL32
+ * - CURAND_RNG_QUASI_SCRAMBLED_SOBOL32
+ * - CURAND_RNG_QUASI_SOBOL64
+ * - CURAND_RNG_QUASI_SCRAMBLED_SOBOL64
+ *
+ * When \p rng_type is CURAND_RNG_PSEUDO_DEFAULT, the type chosen
+ * is CURAND_RNG_PSEUDO_XORWOW.  \n
+ * When \p rng_type is CURAND_RNG_QUASI_DEFAULT,
+ * the type chosen is CURAND_RNG_QUASI_SOBOL32.
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_XORWOW are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_MRG32K3A are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_MTGP32 are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_PSEUDO_MT19937 are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * * The default values for \p rng_type = CURAND_RNG_PSEUDO_PHILOX4_32_10 are:
+ * - \p seed = 0
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_PSEUDO_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL32 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SOBOL64 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL32 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * The default values for \p rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 are:
+ * - \p dimensions = 1
+ * - \p offset = 0
+ * - \p ordering = CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * \param generator - Pointer to generator
+ * \param rng_type - Type of generator to create
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU \n
+ * - CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the
+ *   dynamically linked library version \n
+ * - CURAND_STATUS_TYPE_ERROR if the value for \p rng_type is invalid \n
+ * - CURAND_STATUS_SUCCESS if generator was created successfully \n
+ */
+curandStatus_t CURANDAPI
+curandCreateGeneratorHost(curandGenerator_t *generator, curandRngType_t rng_type);
+
+/**
+ * \brief Destroy an existing generator.
+ *
+ * Destroy an existing generator and free all memory associated with its state.
+ *
+ * \param generator - Generator to destroy
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_SUCCESS if generator was destroyed successfully \n
+ */
+curandStatus_t CURANDAPI
+curandDestroyGenerator(curandGenerator_t generator);
+
+/**
+ * \brief Return the version number of the library.
+ *
+ * Return in \p *version the version number of the dynamically linked CURAND
+ * library.  The format is the same as CUDART_VERSION from the CUDA Runtime.
+ * The only supported configuration is CURAND version equal to CUDA Runtime
+ * version.
+ *
+ * \param version - CURAND library version
+ *
+ * \return
+ * - CURAND_STATUS_SUCCESS if the version number was successfully returned \n
+ */
+curandStatus_t CURANDAPI
+curandGetVersion(int *version);
+
+/**
+* \brief Return the value of the curand property.
+*
+* Return in \p *value the number for the property described by \p type of the
+* dynamically linked CURAND library.
+*
+* \param type - CUDA library property
+* \param value - integer value for the requested property
+*
+* \return
+* - CURAND_STATUS_SUCCESS if the property value was successfully returned \n
+* - CURAND_STATUS_OUT_OF_RANGE if the property type is not recognized \n
+*/
+curandStatus_t CURANDAPI
+curandGetProperty(libraryPropertyType type, int *value);
+
+
+/**
+ * \brief Set the current stream for CURAND kernel launches.
+ *
+ * Set the current stream for CURAND kernel launches.  All library functions
+ * will use this stream until set again.
+ *
+ * \param generator - Generator to modify
+ * \param stream - Stream to use or ::NULL for null stream
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_SUCCESS if stream was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandSetStream(curandGenerator_t generator, cudaStream_t stream);
+
+/**
+ * \brief Set the seed value of the pseudo-random number generator.
+ *
+ * Set the seed value of the pseudorandom number generator.
+ * All values of seed are valid.  Different seeds will produce different sequences.
+ * Different seeds will often not be statistically correlated with each other,
+ * but some pairs of seed values may generate sequences which are statistically correlated.
+ *
+ * \param generator - Generator to modify
+ * \param seed - Seed value
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_TYPE_ERROR if the generator is not a pseudorandom number generator \n
+ * - CURAND_STATUS_SUCCESS if generator seed was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandSetPseudoRandomGeneratorSeed(curandGenerator_t generator, unsigned long long seed);
+
+/**
+ * \brief Set the absolute offset of the pseudo or quasirandom number generator.
+ *
+ * Set the absolute offset of the pseudo or quasirandom number generator.
+ *
+ * All values of offset are valid.  The offset position is absolute, not
+ * relative to the current position in the sequence.
+ *
+ * \param generator - Generator to modify
+ * \param offset - Absolute offset position
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_SUCCESS if generator offset was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandSetGeneratorOffset(curandGenerator_t generator, unsigned long long offset);
+
+/**
+ * \brief Set the ordering of results of the pseudo or quasirandom number generator.
+ *
+ * Set the ordering of results of the pseudo or quasirandom number generator.
+ *
+ * Legal values of \p order for pseudorandom generators are:
+ * - CURAND_ORDERING_PSEUDO_DEFAULT
+ * - CURAND_ORDERING_PSEUDO_BEST
+ * - CURAND_ORDERING_PSEUDO_SEEDED
+ * - CURAND_ORDERING_PSEUDO_LEGACY
+ *
+ * Legal values of \p order for quasirandom generators are:
+ * - CURAND_ORDERING_QUASI_DEFAULT
+ *
+ * \param generator - Generator to modify
+ * \param order - Ordering of results
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_OUT_OF_RANGE if the ordering is not valid \n
+ * - CURAND_STATUS_SUCCESS if generator ordering was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandSetGeneratorOrdering(curandGenerator_t generator, curandOrdering_t order);
+
+/**
+ * \brief Set the number of dimensions.
+ *
+ * Set the number of dimensions to be generated by the quasirandom number
+ * generator.
+ *
+ * Legal values for \p num_dimensions are 1 to 20000.
+ *
+ * \param generator - Generator to modify
+ * \param num_dimensions - Number of dimensions
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_OUT_OF_RANGE if num_dimensions is not valid \n
+ * - CURAND_STATUS_TYPE_ERROR if the generator is not a quasirandom number generator \n
+ * - CURAND_STATUS_SUCCESS if generator ordering was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandSetQuasiRandomGeneratorDimensions(curandGenerator_t generator, unsigned int num_dimensions);
+
+/**
+ * \brief Generate 32-bit pseudo or quasirandom numbers.
+ *
+ * Use \p generator to generate \p num 32-bit results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 32-bit values with every bit random.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param num - Number of random 32-bit values to generate
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *     a previous kernel launch \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_TYPE_ERROR if the generator is a 64 bit quasirandom generator.
+ * (use ::curandGenerateLongLong() with 64 bit quasirandom generators)
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerate(curandGenerator_t generator, unsigned int *outputPtr, size_t num);
+
+/**
+ * \brief Generate 64-bit quasirandom numbers.
+ *
+ * Use \p generator to generate \p num 64-bit results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 64-bit values with every bit random.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param num - Number of random 64-bit values to generate
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *     a previous kernel launch \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_TYPE_ERROR if the generator is not a 64 bit quasirandom generator\n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateLongLong(curandGenerator_t generator, unsigned long long *outputPtr, size_t num);
+
+/**
+ * \brief Generate uniformly distributed floats.
+ *
+ * Use \p generator to generate \p num float results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 32-bit floating point values between \p 0.0f and \p 1.0f,
+ * excluding \p 0.0f and including \p 1.0f.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param num - Number of floats to generate
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateUniform(curandGenerator_t generator, float *outputPtr, size_t num);
+
+/**
+ * \brief Generate uniformly distributed doubles.
+ *
+ * Use \p generator to generate \p num double results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 64-bit double precision floating point values between
+ * \p 0.0 and \p 1.0, excluding \p 0.0 and including \p 1.0.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param num - Number of doubles to generate
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension \n
+ * - CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateUniformDouble(curandGenerator_t generator, double *outputPtr, size_t num);
+
+/**
+ * \brief Generate normally distributed doubles.
+ *
+ * Use \p generator to generate \p n float results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 32-bit floating point values with mean \p mean and standard
+ * deviation \p stddev.
+ *
+ * Normally distributed results are generated from pseudorandom generators
+ * with a Box-Muller transform, and so require \p n to be even.
+ * Quasirandom generators use an inverse cumulative distribution
+ * function to preserve dimensionality.
+ *
+ * There may be slight numerical differences between results generated
+ * on the GPU with generators created with ::curandCreateGenerator()
+ * and results calculated on the CPU with generators created with
+ * ::curandCreateGeneratorHost().  These differences arise because of
+ * differences in results for transcendental functions.  In addition,
+ * future versions of CURAND may use newer versions of the CUDA math
+ * library, so different versions of CURAND may give slightly different
+ * numerical values.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param n - Number of floats to generate
+ * \param mean - Mean of normal distribution
+ * \param stddev - Standard deviation of normal distribution
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension, or is not a multiple
+ *    of two for pseudorandom generators \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateNormal(curandGenerator_t generator, float *outputPtr,
+                     size_t n, float mean, float stddev);
+
+/**
+ * \brief Generate normally distributed doubles.
+ *
+ * Use \p generator to generate \p n double results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 64-bit floating point values with mean \p mean and standard
+ * deviation \p stddev.
+ *
+ * Normally distributed results are generated from pseudorandom generators
+ * with a Box-Muller transform, and so require \p n to be even.
+ * Quasirandom generators use an inverse cumulative distribution
+ * function to preserve dimensionality.
+ *
+ * There may be slight numerical differences between results generated
+ * on the GPU with generators created with ::curandCreateGenerator()
+ * and results calculated on the CPU with generators created with
+ * ::curandCreateGeneratorHost().  These differences arise because of
+ * differences in results for transcendental functions.  In addition,
+ * future versions of CURAND may use newer versions of the CUDA math
+ * library, so different versions of CURAND may give slightly different
+ * numerical values.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param n - Number of doubles to generate
+ * \param mean - Mean of normal distribution
+ * \param stddev - Standard deviation of normal distribution
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension, or is not a multiple
+ *    of two for pseudorandom generators \n
+ * - CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateNormalDouble(curandGenerator_t generator, double *outputPtr,
+                     size_t n, double mean, double stddev);
+
+/**
+ * \brief Generate log-normally distributed floats.
+ *
+ * Use \p generator to generate \p n float results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 32-bit floating point values with log-normal distribution based on
+ * an associated normal distribution with mean \p mean and standard deviation \p stddev.
+ *
+ * Normally distributed results are generated from pseudorandom generators
+ * with a Box-Muller transform, and so require \p n to be even.
+ * Quasirandom generators use an inverse cumulative distribution
+ * function to preserve dimensionality.
+ * The normally distributed results are transformed into log-normal distribution.
+ *
+ * There may be slight numerical differences between results generated
+ * on the GPU with generators created with ::curandCreateGenerator()
+ * and results calculated on the CPU with generators created with
+ * ::curandCreateGeneratorHost().  These differences arise because of
+ * differences in results for transcendental functions.  In addition,
+ * future versions of CURAND may use newer versions of the CUDA math
+ * library, so different versions of CURAND may give slightly different
+ * numerical values.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param n - Number of floats to generate
+ * \param mean - Mean of associated normal distribution
+ * \param stddev - Standard deviation of associated normal distribution
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension, or is not a multiple
+ *    of two for pseudorandom generators \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateLogNormal(curandGenerator_t generator, float *outputPtr,
+                     size_t n, float mean, float stddev);
+
+/**
+ * \brief Generate log-normally distributed doubles.
+ *
+ * Use \p generator to generate \p n double results into the device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 64-bit floating point values with log-normal distribution based on
+ * an associated normal distribution with mean \p mean and standard deviation \p stddev.
+ *
+ * Normally distributed results are generated from pseudorandom generators
+ * with a Box-Muller transform, and so require \p n to be even.
+ * Quasirandom generators use an inverse cumulative distribution
+ * function to preserve dimensionality.
+ * The normally distributed results are transformed into log-normal distribution.
+ *
+ * There may be slight numerical differences between results generated
+ * on the GPU with generators created with ::curandCreateGenerator()
+ * and results calculated on the CPU with generators created with
+ * ::curandCreateGeneratorHost().  These differences arise because of
+ * differences in results for transcendental functions.  In addition,
+ * future versions of CURAND may use newer versions of the CUDA math
+ * library, so different versions of CURAND may give slightly different
+ * numerical values.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param n - Number of doubles to generate
+ * \param mean - Mean of normal distribution
+ * \param stddev - Standard deviation of normal distribution
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension, or is not a multiple
+ *    of two for pseudorandom generators \n
+ * - CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateLogNormalDouble(curandGenerator_t generator, double *outputPtr,
+                     size_t n, double mean, double stddev);
+
+/**
+ * \brief Construct the histogram array for a Poisson distribution.
+ *
+ * Construct the histogram array for the Poisson distribution with lambda \p lambda.
+ * For lambda greater than 2000, an approximation with a normal distribution is used.
+ *
+ * \param lambda - lambda for the Poisson distribution
+ *
+ *
+ * \param discrete_distribution - pointer to the histogram in device memory
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision \n
+ * - CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the distribution pointer was null \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_OUT_OF_RANGE if lambda is non-positive or greater than 400,000 \n
+ * - CURAND_STATUS_SUCCESS if the histogram was generated successfully \n
+ */
+
+curandStatus_t CURANDAPI
+curandCreatePoissonDistribution(double lambda, curandDiscreteDistribution_t *discrete_distribution);
+
+
+
+/**
+ * \brief Destroy the histogram array for a discrete distribution (e.g. Poisson).
+ *
+ * Destroy the histogram array for a discrete distribution created by curandCreatePoissonDistribution.
+ *
+ * \param discrete_distribution - pointer to device memory where the histogram is stored
+ *
+ * \return
+ * - CURAND_STATUS_NOT_INITIALIZED if the histogram was never created \n
+ * - CURAND_STATUS_SUCCESS if the histogram was destroyed successfully \n
+ */
+curandStatus_t CURANDAPI
+curandDestroyDistribution(curandDiscreteDistribution_t discrete_distribution);
+
+
+/**
+ * \brief Generate Poisson-distributed unsigned ints.
+ *
+ * Use \p generator to generate \p n unsigned int results into device memory at
+ * \p outputPtr.  The device memory must have been previously allocated and must be
+ * large enough to hold all the results.  Launches are done with the stream
+ * set using ::curandSetStream(), or the null stream if no stream has been set.
+ *
+ * Results are 32-bit unsigned int point values with Poisson distribution, with lambda \p lambda.
+ *
+ * \param generator - Generator to use
+ * \param outputPtr - Pointer to device memory to store CUDA-generated results, or
+ *                 Pointer to host memory to store CPU-generated results
+ * \param n - Number of unsigned ints to generate
+ * \param lambda - lambda for the Poisson distribution
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *    a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is
+ *    not a multiple of the quasirandom dimension\n
+ * - CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU or sm does not support double precision \n
+ * - CURAND_STATUS_OUT_OF_RANGE if lambda is non-positive or greater than 400,000 \n
+ * - CURAND_STATUS_SUCCESS if the results were generated successfully \n
+ */
+
+curandStatus_t CURANDAPI
+curandGeneratePoisson(curandGenerator_t generator, unsigned int *outputPtr,
+                     size_t n, double lambda);
+// just for internal usage
+curandStatus_t CURANDAPI
+curandGeneratePoissonMethod(curandGenerator_t generator, unsigned int *outputPtr,
+                     size_t n, double lambda, curandMethod_t method);
+
+
+curandStatus_t CURANDAPI
+curandGenerateBinomial(curandGenerator_t generator, unsigned int *outputPtr,
+                       size_t num, unsigned int n, double p);
+// just for internal usage
+curandStatus_t CURANDAPI
+curandGenerateBinomialMethod(curandGenerator_t generator,
+                             unsigned int *outputPtr,
+                             size_t num, unsigned int n, double p,
+                             curandMethod_t method);
+
+
+/**
+ * \brief Setup starting states.
+ *
+ * Generate the starting state of the generator.  This function is
+ * automatically called by generation functions such as
+ * ::curandGenerate() and ::curandGenerateUniform().
+ * It can be called manually for performance testing reasons to separate
+ * timings for starting state generation and random number generation.
+ *
+ * \param generator - Generator to update
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated \n
+ * - CURAND_STATUS_NOT_INITIALIZED if the generator was never created \n
+ * - CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from
+ *     a previous kernel launch \n
+ * - CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason \n
+ * - CURAND_STATUS_SUCCESS if the seeds were generated successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGenerateSeeds(curandGenerator_t generator);
+
+/**
+ * \brief Get direction vectors for 32-bit quasirandom number generation.
+ *
+ * Get a pointer to an array of direction vectors that can be used
+ * for quasirandom number generation.  The resulting pointer will
+ * reference an array of direction vectors in host memory.
+ *
+ * The array contains vectors for many dimensions.  Each dimension
+ * has 32 vectors.  Each individual vector is an unsigned int.
+ *
+ * Legal values for \p set are:
+ * - CURAND_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)
+ * - CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)
+ *
+ * \param vectors - Address of pointer in which to return direction vectors
+ * \param set - Which set of direction vectors to use
+ *
+ * \return
+ * - CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid \n
+ * - CURAND_STATUS_SUCCESS if the pointer was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGetDirectionVectors32(curandDirectionVectors32_t *vectors[], curandDirectionVectorSet_t set);
+
+/**
+ * \brief Get scramble constants for 32-bit scrambled Sobol' .
+ *
+ * Get a pointer to an array of scramble constants that can be used
+ * for quasirandom number generation.  The resulting pointer will
+ * reference an array of unsinged ints in host memory.
+ *
+ * The array contains constants for many dimensions.  Each dimension
+ * has a single unsigned int constant.
+ *
+ * \param constants - Address of pointer in which to return scramble constants
+ *
+ * \return
+ * - CURAND_STATUS_SUCCESS if the pointer was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGetScrambleConstants32(unsigned int * * constants);
+
+/**
+ * \brief Get direction vectors for 64-bit quasirandom number generation.
+ *
+ * Get a pointer to an array of direction vectors that can be used
+ * for quasirandom number generation.  The resulting pointer will
+ * reference an array of direction vectors in host memory.
+ *
+ * The array contains vectors for many dimensions.  Each dimension
+ * has 64 vectors.  Each individual vector is an unsigned long long.
+ *
+ * Legal values for \p set are:
+ * - CURAND_DIRECTION_VECTORS_64_JOEKUO6 (20,000 dimensions)
+ * - CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6 (20,000 dimensions)
+ *
+ * \param vectors - Address of pointer in which to return direction vectors
+ * \param set - Which set of direction vectors to use
+ *
+ * \return
+ * - CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid \n
+ * - CURAND_STATUS_SUCCESS if the pointer was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGetDirectionVectors64(curandDirectionVectors64_t *vectors[], curandDirectionVectorSet_t set);
+
+/**
+ * \brief Get scramble constants for 64-bit scrambled Sobol' .
+ *
+ * Get a pointer to an array of scramble constants that can be used
+ * for quasirandom number generation.  The resulting pointer will
+ * reference an array of unsinged long longs in host memory.
+ *
+ * The array contains constants for many dimensions.  Each dimension
+ * has a single unsigned long long constant.
+ *
+ * \param constants - Address of pointer in which to return scramble constants
+ *
+ * \return
+ * - CURAND_STATUS_SUCCESS if the pointer was set successfully \n
+ */
+curandStatus_t CURANDAPI
+curandGetScrambleConstants64(unsigned long long * * constants);
+
+/** @} */
+
+#endif // __CUDACC_RTC__
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+
+#endif /* !defined(CURAND_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_discrete.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_discrete.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e194487a0e2ec02abcb1dd8634c42141a148d84
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_discrete.h
@@ -0,0 +1,87 @@
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+#if !defined(CURANDDISCRETE_H_)
+#define CURANDDISCRETE_H_
+
+struct curandDistributionShift_st {
+    curandDistribution_t probability;
+    curandDistribution_t host_probability;
+    unsigned int shift;
+    unsigned int length;
+    unsigned int host_gen;
+};
+
+struct curandHistogramM2_st {
+    curandHistogramM2V_t V; 
+    curandHistogramM2V_t host_V; 
+    curandHistogramM2K_t K; 
+    curandHistogramM2K_t host_K; 
+    unsigned int host_gen;
+};
+
+
+struct curandDistributionM2Shift_st {
+    curandHistogramM2_t histogram;
+    curandHistogramM2_t host_histogram;
+    unsigned int shift;
+    unsigned int length;
+    unsigned int host_gen;
+};
+
+struct curandDiscreteDistribution_st {
+    curandDiscreteDistribution_t self_host_ptr;
+    curandDistributionM2Shift_t M2;
+    curandDistributionM2Shift_t host_M2;
+    double stddev;
+    double mean;
+    curandMethod_t method;
+    unsigned int host_gen; 
+};
+
+#endif // !defined(CURANDDISCRETE_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_discrete2.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_discrete2.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac827749840488f66d71f492c14dbec80b57a3d1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_discrete2.h
@@ -0,0 +1,253 @@
+
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#if !defined(CURAND_DISCRETE_H_)
+#define CURAND_DISCRETE_H_
+
+/**
+ * \defgroup DEVICE Device API
+ *
+ * @{
+ */
+
+#ifndef __CUDACC_RTC__
+#include <math.h>
+#endif // __CUDACC_RTC__
+
+#include "curand_mrg32k3a.h"
+#include "curand_mtgp32_kernel.h"
+#include "curand_philox4x32_x.h"
+
+
+template <typename T>
+QUALIFIERS unsigned int _curand_discrete(T x, curandDiscreteDistribution_t discrete_distribution){
+    if (discrete_distribution->method == CURAND_M2){
+        return _curand_M2_double(x, discrete_distribution->M2);
+    }
+    return (unsigned int)((discrete_distribution->stddev * _curand_normal_icdf_double(x)) + discrete_distribution->mean + 0.5);
+}
+
+
+template <typename STATE>
+QUALIFIERS unsigned int curand__discrete(STATE state, curandDiscreteDistribution_t discrete_distribution){
+    if (discrete_distribution->method == CURAND_M2){
+        return curand_M2_double(state, discrete_distribution->M2);
+    }
+    return (unsigned int)((discrete_distribution->stddev * curand_normal_double(state)) + discrete_distribution->mean + 0.5); //Round to nearest
+}
+
+template <typename STATE>
+QUALIFIERS uint4 curand__discrete4(STATE state, curandDiscreteDistribution_t discrete_distribution){
+    if (discrete_distribution->method == CURAND_M2){
+        return curand_M2_double4(state, discrete_distribution->M2);
+    }
+    double4 _res;
+    uint4 result;
+    _res = curand_normal4_double(state);
+    result.x = (unsigned int)((discrete_distribution->stddev * _res.x) + discrete_distribution->mean + 0.5); //Round to nearest
+    result.y = (unsigned int)((discrete_distribution->stddev * _res.y) + discrete_distribution->mean + 0.5); //Round to nearest
+    result.z = (unsigned int)((discrete_distribution->stddev * _res.z) + discrete_distribution->mean + 0.5); //Round to nearest
+    result.w = (unsigned int)((discrete_distribution->stddev * _res.w) + discrete_distribution->mean + 0.5); //Round to nearest
+    return result;
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a XORWOW generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the XORWOW generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateXORWOW_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a Philox4_32_10 generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the Philox4_32_10 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStatePhilox4_32_10_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return four discrete distributed unsigned ints from a Philox4_32_10 generator.
+ *
+ * Return four single discrete distributed unsigned ints derived from a
+ * distribution defined by \p discrete_distribution from the Philox4_32_10 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS uint4 curand_discrete4(curandStatePhilox4_32_10_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete4(state, discrete_distribution);
+}
+/*
+ * \brief Return a discrete distributed unsigned int from a MRG32k3a generator.
+ *
+ * Re turn a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the MRG32k3a generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateMRG32k3a_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a MTGP32 generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the MTGP32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateMtgp32_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a Sobol32 generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateSobol32_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a scrambled Sobol32 generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateScrambledSobol32_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a Sobol64 generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateSobol64_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+/*
+ * \brief Return a discrete distributed unsigned int from a scrambled Sobol64 generator.
+ *
+ * Return a single discrete distributed unsigned int derived from a
+ * distribution defined by \p discrete_distribution from the scrambled Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param discrete_distribution - ancillary structure for discrete distribution
+ *
+ * \return unsigned int distributed by distribution defined by \p discrete_distribution.
+ */
+QUALIFIERS unsigned int curand_discrete(curandStateScrambledSobol64_t *state, curandDiscreteDistribution_t discrete_distribution)
+{
+    return curand__discrete(state, discrete_distribution);
+}
+
+#endif // !defined(CURAND_DISCRETE_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_globals.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_globals.h
new file mode 100644
index 0000000000000000000000000000000000000000..278d12b69727c4016fdb2452b8ee567f5c969fa0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_globals.h
@@ -0,0 +1,93 @@
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+#ifndef CURAND_GLOBALS_H
+#define CURAND_GLOBALS_H
+
+#define MAX_XOR_N (5)
+#define SKIPAHEAD_BLOCKSIZE (4)
+#define SKIPAHEAD_MASK ((1<<SKIPAHEAD_BLOCKSIZE)-1)
+#define CURAND_2POW32 (4294967296.f)
+#define CURAND_2POW32_DOUBLE (4294967296.)
+#define CURAND_2POW32_INV (2.3283064e-10f)
+#define CURAND_2POW32_INV_DOUBLE (2.3283064365386963e-10) 
+#define CURAND_2POW53_INV_DOUBLE (1.1102230246251565e-16)
+#define CURAND_2POW32_INV_2PI (2.3283064e-10f * 6.2831855f)
+#define CURAND_2PI (6.2831855f)
+#define CURAND_2POW53_INV_2PI_DOUBLE (1.1102230246251565e-16 * 6.2831853071795860)
+#define CURAND_PI_DOUBLE  (3.1415926535897932)
+#define CURAND_2PI_DOUBLE (6.2831853071795860)
+#define CURAND_SQRT2 (-1.4142135f)
+#define CURAND_SQRT2_DOUBLE (-1.4142135623730951)
+
+#define SOBOL64_ITR_BINARY_DIVIDE 2
+#define SOBOL_M2_BINARY_DIVIDE 10
+#define MTGP32_M2_BINARY_DIVIDE 32
+#define MAX_LAMBDA 400000
+#define MIN_GAUSS_LAMBDA 2000
+
+struct normal_args_st {
+    float mean;
+    float stddev;
+};
+
+typedef struct normal_args_st normal_args_t;
+
+struct normal_args_double_st {
+    double mean;
+    double stddev;
+};
+
+typedef struct normal_args_double_st normal_args_double_t;
+
+
+
+
+
+
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_kernel.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_kernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..c9d27c5f271cae931091c85ecfe37b5e7d427bf3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_kernel.h
@@ -0,0 +1,1677 @@
+
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#if !defined(CURAND_KERNEL_H_)
+#define CURAND_KERNEL_H_
+
+/**
+ * \defgroup DEVICE Device API
+ *
+ * @{
+ */
+
+#if !defined(QUALIFIERS)
+#define QUALIFIERS static __forceinline__ __device__
+#endif
+
+/* To prevent unused parameter warnings */
+#if !defined(GCC_UNUSED_PARAMETER)
+#if defined(__GNUC__)
+#define GCC_UNUSED_PARAMETER __attribute__((unused))
+#else
+#define GCC_UNUSED_PARAMETER
+#endif /* defined(__GNUC__) */
+#endif /* !defined(GCC_UNUSED_PARAMETER) */
+
+#include <nv/target>
+
+#ifdef __CUDACC_RTC__
+#define CURAND_DETAIL_USE_CUDA_STL
+#endif
+
+#if __cplusplus >= 201103L
+#   ifdef CURAND_DETAIL_USE_CUDA_STL
+#       define CURAND_STD cuda::std
+#       include <cuda/std/type_traits>
+#   else
+#       define CURAND_STD std
+#       include <type_traits>
+#   endif // CURAND_DETAIL_USE_CUDA_STL
+#else
+// To support C++03 compilation
+#   define CURAND_STD curand_detail
+namespace curand_detail {
+    template<bool B, class T = void>
+    struct enable_if {};
+
+    template<class T>
+    struct enable_if<true, T> { typedef T type; };
+
+    template<class T, class U>
+    struct is_same { static const bool value = false; };
+
+    template<class T>
+    struct is_same<T, T> { static const bool value = true; };
+} // namespace curand_detail
+#endif // __cplusplus >= 201103L
+
+#ifndef __CUDACC_RTC__
+#include <math.h>
+#endif // __CUDACC_RTC__
+
+#include "curand.h"
+#include "curand_discrete.h"
+#include "curand_precalc.h"
+#include "curand_mrg32k3a.h"
+#include "curand_mtgp32_kernel.h"
+#include "curand_philox4x32_x.h"
+#include "curand_globals.h"
+
+/* Test RNG */
+/* This generator uses the formula:
+   x_n = x_(n-1) + 1 mod 2^32
+   x_0 = (unsigned int)seed * 3
+   Subsequences are spaced 31337 steps apart.
+*/
+struct curandStateTest {
+    unsigned int v;
+};
+
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateTest curandStateTest_t;
+/** \endcond */
+
+/* XORSHIFT FAMILY RNGs */
+/* These generators are a family proposed by Marsaglia.  They keep state
+   in 32 bit chunks, then use repeated shift and xor operations to scramble
+   the bits.  The following generators are a combination of a simple Weyl
+   generator with an N variable XORSHIFT generator.
+*/
+
+/* XORSHIFT RNG */
+/* This generator uses the xorwow formula of
+www.jstatsoft.org/v08/i14/paper page 5
+Has period 2^192 - 2^32.
+*/
+/**
+ * CURAND XORWOW state
+ */
+struct curandStateXORWOW;
+
+/*
+ * Implementation details not in reference documentation */
+struct curandStateXORWOW {
+    unsigned int d, v[5];
+    int boxmuller_flag;
+    int boxmuller_flag_double;
+    float boxmuller_extra;
+    double boxmuller_extra_double;
+};
+
+/*
+ * CURAND XORWOW state
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateXORWOW curandStateXORWOW_t;
+
+#define EXTRA_FLAG_NORMAL         0x00000001
+#define EXTRA_FLAG_LOG_NORMAL     0x00000002
+/** \endcond */
+
+/* Combined Multiple Recursive Generators */
+/* These generators are a family proposed by L'Ecuyer.  They keep state
+   in sets of doubles, then use repeated modular arithmetic multiply operations
+   to scramble the bits in each set, and combine the result.
+*/
+
+/* MRG32k3a RNG */
+/* This generator uses the MRG32k3A formula of
+http://www.iro.umontreal.ca/~lecuyer/myftp/streams00/c++/streams4.pdf
+Has period 2^191.
+*/
+
+/* moduli for the recursions */
+/** \cond UNHIDE_DEFINES */
+#define MRG32K3A_MOD1 4294967087.
+#define MRG32K3A_MOD2 4294944443.
+
+/* Constants used in generation */
+
+#define MRG32K3A_A12  1403580.
+#define MRG32K3A_A13N 810728.
+#define MRG32K3A_A21  527612.
+#define MRG32K3A_A23N 1370589.
+#define MRG32K3A_NORM (2.3283065498378288e-10)
+//
+// #define MRG32K3A_BITS_NORM ((double)((POW32_DOUBLE-1.0)/MOD1))
+//  above constant, used verbatim, rounds differently on some host systems.
+#define MRG32K3A_BITS_NORM 1.000000048662
+
+/** \endcond */
+
+
+
+
+/**
+ * CURAND MRG32K3A state
+ */
+struct curandStateMRG32k3a;
+
+/* Implementation details not in reference documentation */
+struct curandStateMRG32k3a {
+    unsigned int s1[3];
+    unsigned int s2[3];
+    int boxmuller_flag;
+    int boxmuller_flag_double;
+    float boxmuller_extra;
+    double boxmuller_extra_double;
+};
+
+/*
+ * CURAND MRG32K3A state
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateMRG32k3a curandStateMRG32k3a_t;
+/** \endcond */
+
+/* SOBOL QRNG */
+/**
+ * CURAND Sobol32 state
+ */
+struct curandStateSobol32;
+
+/* Implementation details not in reference documentation */
+struct curandStateSobol32 {
+    unsigned int i, x, c;
+    unsigned int direction_vectors[32];
+};
+
+/*
+ * CURAND Sobol32 state
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateSobol32 curandStateSobol32_t;
+/** \endcond */
+
+/**
+ * CURAND Scrambled Sobol32 state
+ */
+struct curandStateScrambledSobol32;
+
+/* Implementation details not in reference documentation */
+struct curandStateScrambledSobol32 {
+    unsigned int i, x, c;
+    unsigned int direction_vectors[32];
+};
+
+/*
+ * CURAND Scrambled Sobol32 state
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateScrambledSobol32 curandStateScrambledSobol32_t;
+/** \endcond */
+
+/**
+ * CURAND Sobol64 state
+ */
+struct curandStateSobol64;
+
+/* Implementation details not in reference documentation */
+struct curandStateSobol64 {
+    unsigned long long i, x, c;
+    unsigned long long direction_vectors[64];
+};
+
+/*
+ * CURAND Sobol64 state
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateSobol64 curandStateSobol64_t;
+/** \endcond */
+
+/**
+ * CURAND Scrambled Sobol64 state
+ */
+struct curandStateScrambledSobol64;
+
+/* Implementation details not in reference documentation */
+struct curandStateScrambledSobol64 {
+    unsigned long long i, x, c;
+    unsigned long long direction_vectors[64];
+};
+
+/*
+ * CURAND Scrambled Sobol64 state
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateScrambledSobol64 curandStateScrambledSobol64_t;
+/** \endcond */
+
+/*
+ * Default RNG
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateXORWOW curandState_t;
+typedef struct curandStateXORWOW curandState;
+/** \endcond */
+
+/****************************************************************************/
+/* Utility functions needed by RNGs */
+/****************************************************************************/
+/** \cond UNHIDE_UTILITIES */
+/*
+   multiply vector by matrix, store in result
+   matrix is n x n, measured in 32 bit units
+   matrix is stored in row major order
+   vector and result cannot be same pointer
+*/
+template<int N>
+QUALIFIERS void __curand_matvec_inplace(unsigned int *vector, unsigned int *matrix)
+{
+    unsigned int result[N] = { 0 };
+    for(int i = 0; i < N; i++) {
+        #ifdef __CUDA_ARCH__
+        #pragma unroll 16
+        #endif
+        for(int j = 0; j < 32; j++) {
+            if(vector[i] & (1 << j)) {
+                for(int k = 0; k < N; k++) {
+                    result[k] ^= matrix[N * (i * 32 + j) + k];
+                }
+            }
+        }
+    }
+    for(int i = 0; i < N; i++) {
+        vector[i] = result[i];
+    }
+}
+
+QUALIFIERS void __curand_matvec(unsigned int *vector, unsigned int *matrix,
+                                unsigned int *result, int n)
+{
+    for(int i = 0; i < n; i++) {
+        result[i] = 0;
+    }
+    for(int i = 0; i < n; i++) {
+        for(int j = 0; j < 32; j++) {
+            if(vector[i] & (1 << j)) {
+                for(int k = 0; k < n; k++) {
+                    result[k] ^= matrix[n * (i * 32 + j) + k];
+                }
+            }
+        }
+    }
+}
+
+/* generate identity matrix */
+QUALIFIERS void __curand_matidentity(unsigned int *matrix, int n)
+{
+    int r;
+    for(int i = 0; i < n * 32; i++) {
+        for(int j = 0; j < n; j++) {
+            r = i & 31;
+            if(i / 32 == j) {
+                matrix[i * n + j] = (1 << r);
+            } else {
+                matrix[i * n + j] = 0;
+            }
+        }
+    }
+}
+
+/* multiply matrixA by matrixB, store back in matrixA
+   matrixA and matrixB must not be same matrix */
+QUALIFIERS void __curand_matmat(unsigned int *matrixA, unsigned int *matrixB, int n)
+{
+    unsigned int result[MAX_XOR_N];
+    for(int i = 0; i < n * 32; i++) {
+        __curand_matvec(matrixA + i * n, matrixB, result, n);
+        for(int j = 0; j < n; j++) {
+            matrixA[i * n + j] = result[j];
+        }
+    }
+}
+
+/* copy vectorA to vector */
+QUALIFIERS void __curand_veccopy(unsigned int *vector, unsigned int *vectorA, int n)
+{
+    for(int i = 0; i < n; i++) {
+        vector[i] = vectorA[i];
+    }
+}
+
+/* copy matrixA to matrix */
+QUALIFIERS void __curand_matcopy(unsigned int *matrix, unsigned int *matrixA, int n)
+{
+    for(int i = 0; i < n * n * 32; i++) {
+        matrix[i] = matrixA[i];
+    }
+}
+
+/* compute matrixA to power p, store result in matrix */
+QUALIFIERS void __curand_matpow(unsigned int *matrix, unsigned int *matrixA,
+                                unsigned long long p, int n)
+{
+    unsigned int matrixR[MAX_XOR_N * MAX_XOR_N * 32];
+    unsigned int matrixS[MAX_XOR_N * MAX_XOR_N * 32];
+    __curand_matidentity(matrix, n);
+    __curand_matcopy(matrixR, matrixA, n);
+    while(p) {
+        if(p & 1) {
+            __curand_matmat(matrix, matrixR, n);
+        }
+        __curand_matcopy(matrixS, matrixR, n);
+        __curand_matmat(matrixR, matrixS, n);
+        p >>= 1;
+    }
+}
+
+/****************************************************************************/
+/* Utility functions needed by MRG32k3a RNG                                 */
+/* Matrix operations modulo some integer less than 2**32, done in           */
+/* double precision floating point, with care not to overflow 53 bits       */
+/****************************************************************************/
+
+/* return i mod m.                                                          */
+/* assumes i and m are integers represented accurately in doubles           */
+
+QUALIFIERS double curand_MRGmod(double i, double m)
+{
+    double quo;
+    double rem;
+    quo = floor(i/m);
+    rem = i - (quo*m);
+    if (rem < 0.0) rem += m;
+    return rem;
+}
+
+/* Multiplication modulo m. Inputs i and j less than 2**32                  */
+/* Ensure intermediate results do not exceed 2**53                          */
+
+QUALIFIERS double curand_MRGmodMul(double i, double j, double m)
+{
+    double tempHi;
+    double tempLo;
+
+    tempHi = floor(i/131072.0);
+    tempLo = i - (tempHi*131072.0);
+    tempLo = curand_MRGmod( curand_MRGmod( (tempHi * j), m) * 131072.0 + curand_MRGmod(tempLo * j, m),m);
+
+    if (tempLo < 0.0) tempLo += m;
+    return tempLo;
+}
+
+/* multiply 3 by 3 matrices of doubles, modulo m                            */
+
+QUALIFIERS void curand_MRGmatMul3x3(unsigned int i1[][3],unsigned int i2[][3],unsigned int o[][3],double m)
+{
+    int i,j;
+    double temp[3][3];
+    for (i=0; i<3; i++){
+        for (j=0; j<3; j++){
+            temp[i][j] = ( curand_MRGmodMul(i1[i][0], i2[0][j], m) +
+                           curand_MRGmodMul(i1[i][1], i2[1][j], m) +
+                           curand_MRGmodMul(i1[i][2], i2[2][j], m));
+            temp[i][j] = curand_MRGmod( temp[i][j], m );
+        }
+    }
+    for (i=0; i<3; i++){
+        for (j=0; j<3; j++){
+            o[i][j] = (unsigned int)temp[i][j];
+        }
+    }
+}
+
+/* multiply 3 by 3 matrix times 3 by 1 vector of doubles, modulo m          */
+
+QUALIFIERS void curand_MRGmatVecMul3x3( unsigned int i[][3], unsigned int v[], double m)
+{
+    int k;
+    double t[3];
+    for (k = 0; k < 3; k++) {
+        t[k] = ( curand_MRGmodMul(i[k][0], v[0], m) +
+                 curand_MRGmodMul(i[k][1], v[1], m) +
+                 curand_MRGmodMul(i[k][2], v[2], m) );
+        t[k] = curand_MRGmod( t[k], m );
+    }
+    for (k = 0; k < 3; k++) {
+        v[k] = (unsigned int)t[k];
+    }
+
+}
+
+/* raise a 3 by 3 matrix of doubles to a 64 bit integer power pow, modulo m */
+/* input is index zero of an array of 3 by 3 matrices m,                    */
+/* each m = m[0]**(2**index)                                                */
+
+QUALIFIERS void curand_MRGmatPow3x3( unsigned int in[][3][3], unsigned int o[][3], double m, unsigned long long pow )
+{
+    int i,j;
+    for ( i = 0; i < 3; i++ ) {
+        for ( j = 0; j < 3; j++ ) {
+            o[i][j] = 0;
+            if ( i == j ) o[i][j] = 1;
+        }
+    }
+    i = 0;
+    curand_MRGmatVecMul3x3(o,o[0],m);
+    while (pow) {
+        if ( pow & 1ll ) {
+             curand_MRGmatMul3x3(in[i], o, o, m);
+        }
+        i++;
+        pow >>= 1;
+    }
+}
+
+/* raise a 3 by 3 matrix of doubles to the power                            */
+/* 2 to the power (pow modulo 191), modulo m                                */
+
+QUALIFIERS void curnand_MRGmatPow2Pow3x3( double in[][3], double o[][3], double m, unsigned long pow )
+{
+    unsigned int temp[3][3];
+    int i,j;
+    pow = pow % 191;
+    for ( i = 0; i < 3; i++ ) {
+        for ( j = 0; j < 3; j++ ) {
+            temp[i][j] = (unsigned int)in[i][j];
+        }
+    }
+    while (pow) {
+        curand_MRGmatMul3x3(temp, temp, temp, m);
+        pow--;
+    }
+    for ( i = 0; i < 3; i++ ) {
+        for ( j = 0; j < 3; j++ ) {
+            o[i][j] = temp[i][j];
+        }
+    }
+}
+
+/** \endcond */
+
+/****************************************************************************/
+/* Kernel implementations of RNGs                                           */
+/****************************************************************************/
+
+/* Test RNG */
+
+QUALIFIERS void curand_init(unsigned long long seed,
+                                            unsigned long long subsequence,
+                                            unsigned long long offset,
+                                            curandStateTest_t *state)
+{
+    state->v = (unsigned int)(seed * 3) + (unsigned int)(subsequence * 31337) + \
+                     (unsigned int)offset;
+}
+
+
+QUALIFIERS unsigned int curand(curandStateTest_t *state)
+{
+    unsigned int r = state->v++;
+    return r;
+}
+
+QUALIFIERS void skipahead(unsigned long long n, curandStateTest_t *state)
+{
+    state->v += (unsigned int)n;
+}
+
+/* XORWOW RNG */
+
+template <typename T, int n>
+QUALIFIERS void __curand_generate_skipahead_matrix_xor(unsigned int matrix[])
+{
+    T state;
+    // Generate matrix that advances one step
+    // matrix has n * n * 32 32-bit elements
+    // solve for matrix by stepping single bit states
+    for(int i = 0; i < 32 * n; i++) {
+        state.d = 0;
+        for(int j = 0; j < n; j++) {
+            state.v[j] = 0;
+        }
+        state.v[i / 32] = (1 << (i & 31));
+        curand(&state);
+        for(int j = 0; j < n; j++) {
+            matrix[i * n + j] = state.v[j];
+        }
+    }
+}
+
+template <typename T, int n>
+QUALIFIERS void _skipahead_scratch(unsigned long long x, T *state, unsigned int *scratch)
+{
+    // unsigned int matrix[n * n * 32];
+    unsigned int *matrix = scratch;
+    // unsigned int matrixA[n * n * 32];
+    unsigned int *matrixA = scratch + (n * n * 32);
+    // unsigned int vector[n];
+    unsigned int *vector = scratch + (n * n * 32) + (n * n * 32);
+    // unsigned int result[n];
+    unsigned int *result = scratch + (n * n * 32) + (n * n * 32) + n;
+    unsigned long long p = x;
+    for(int i = 0; i < n; i++) {
+        vector[i] = state->v[i];
+    }
+    int matrix_num = 0;
+    while(p && (matrix_num < PRECALC_NUM_MATRICES - 1)) {
+        for(unsigned int t = 0; t < (p & PRECALC_BLOCK_MASK); t++) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+            __curand_matvec(vector, precalc_xorwow_offset_matrix[matrix_num], result, n);
+,
+            __curand_matvec(vector, precalc_xorwow_offset_matrix_host[matrix_num], result, n);
+)
+            __curand_veccopy(vector, result, n);
+        }
+        p >>= PRECALC_BLOCK_SIZE;
+        matrix_num++;
+    }
+    if(p) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+        __curand_matcopy(matrix, precalc_xorwow_offset_matrix[PRECALC_NUM_MATRICES - 1], n);
+        __curand_matcopy(matrixA, precalc_xorwow_offset_matrix[PRECALC_NUM_MATRICES - 1], n);
+,
+        __curand_matcopy(matrix, precalc_xorwow_offset_matrix_host[PRECALC_NUM_MATRICES - 1], n);
+        __curand_matcopy(matrixA, precalc_xorwow_offset_matrix_host[PRECALC_NUM_MATRICES - 1], n);
+)
+    }
+    while(p) {
+        for(unsigned int t = 0; t < (p & SKIPAHEAD_MASK); t++) {
+            __curand_matvec(vector, matrixA, result, n);
+            __curand_veccopy(vector, result, n);
+        }
+        p >>= SKIPAHEAD_BLOCKSIZE;
+        if(p) {
+            for(int i = 0; i < SKIPAHEAD_BLOCKSIZE; i++) {
+                __curand_matmat(matrix, matrixA, n);
+                __curand_matcopy(matrixA, matrix, n);
+            }
+        }
+    }
+    for(int i = 0; i < n; i++) {
+        state->v[i] = vector[i];
+    }
+    state->d += 362437 * (unsigned int)x;
+}
+
+template <typename T, int n>
+QUALIFIERS void _skipahead_sequence_scratch(unsigned long long x, T *state, unsigned int *scratch)
+{
+    // unsigned int matrix[n * n * 32];
+    unsigned int *matrix = scratch;
+    // unsigned int matrixA[n * n * 32];
+    unsigned int *matrixA = scratch + (n * n * 32);
+    // unsigned int vector[n];
+    unsigned int *vector = scratch + (n * n * 32) + (n * n * 32);
+    // unsigned int result[n];
+    unsigned int *result = scratch + (n * n * 32) + (n * n * 32) + n;
+    unsigned long long p = x;
+    for(int i = 0; i < n; i++) {
+        vector[i] = state->v[i];
+    }
+    int matrix_num = 0;
+    while(p && matrix_num < PRECALC_NUM_MATRICES - 1) {
+        for(unsigned int t = 0; t < (p & PRECALC_BLOCK_MASK); t++) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+            __curand_matvec(vector, precalc_xorwow_matrix[matrix_num], result, n);
+,
+            __curand_matvec(vector, precalc_xorwow_matrix_host[matrix_num], result, n);
+)
+            __curand_veccopy(vector, result, n);
+        }
+        p >>= PRECALC_BLOCK_SIZE;
+        matrix_num++;
+    }
+    if(p) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+        __curand_matcopy(matrix, precalc_xorwow_matrix[PRECALC_NUM_MATRICES - 1], n);
+        __curand_matcopy(matrixA, precalc_xorwow_matrix[PRECALC_NUM_MATRICES - 1], n);
+,
+        __curand_matcopy(matrix, precalc_xorwow_matrix_host[PRECALC_NUM_MATRICES - 1], n);
+        __curand_matcopy(matrixA, precalc_xorwow_matrix_host[PRECALC_NUM_MATRICES - 1], n);
+)
+    }
+    while(p) {
+        for(unsigned int t = 0; t < (p & SKIPAHEAD_MASK); t++) {
+            __curand_matvec(vector, matrixA, result, n);
+            __curand_veccopy(vector, result, n);
+        }
+        p >>= SKIPAHEAD_BLOCKSIZE;
+        if(p) {
+            for(int i = 0; i < SKIPAHEAD_BLOCKSIZE; i++) {
+                __curand_matmat(matrix, matrixA, n);
+                __curand_matcopy(matrixA, matrix, n);
+            }
+        }
+    }
+    for(int i = 0; i < n; i++) {
+        state->v[i] = vector[i];
+    }
+    /* No update of state->d needed, guaranteed to be a multiple of 2^32 */
+}
+
+template <typename T, int N>
+QUALIFIERS void _skipahead_inplace(const unsigned long long x, T *state)
+{
+    unsigned long long p = x;
+    int matrix_num = 0;
+    while(p) {
+        for(unsigned int t = 0; t < (p & PRECALC_BLOCK_MASK); t++) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+            __curand_matvec_inplace<N>(state->v, precalc_xorwow_offset_matrix[matrix_num]);
+,
+            __curand_matvec_inplace<N>(state->v, precalc_xorwow_offset_matrix_host[matrix_num]);
+)
+        }
+        p >>= PRECALC_BLOCK_SIZE;
+        matrix_num++;
+    }
+    state->d += 362437 * (unsigned int)x;
+}
+
+template <typename T, int N>
+QUALIFIERS void _skipahead_sequence_inplace(unsigned long long x, T *state)
+{
+    int matrix_num = 0;
+    while(x) {
+        for(unsigned int t = 0; t < (x & PRECALC_BLOCK_MASK); t++) {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+            __curand_matvec_inplace<N>(state->v, precalc_xorwow_matrix[matrix_num]);
+,
+            __curand_matvec_inplace<N>(state->v, precalc_xorwow_matrix_host[matrix_num]);
+)
+        }
+        x >>= PRECALC_BLOCK_SIZE;
+        matrix_num++;
+    }
+    /* No update of state->d needed, guaranteed to be a multiple of 2^32 */
+}
+
+/**
+ * \brief Update XORWOW state to skip \p n elements.
+ *
+ * Update the XORWOW state in \p state to skip ahead \p n elements.
+ *
+ * All values of \p n are valid.  Large values require more computation and so
+ * will take more time to complete.
+ *
+ * \param n - Number of elements to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead(unsigned long long n, curandStateXORWOW_t *state)
+{
+    _skipahead_inplace<curandStateXORWOW_t, 5>(n, state);
+}
+
+/**
+ * \brief Update XORWOW state to skip ahead \p n subsequences.
+ *
+ * Update the XORWOW state in \p state to skip ahead \p n subsequences.  Each
+ * subsequence is \xmlonly<ph outputclass="xmlonly">2<sup>67</sup></ph>\endxmlonly elements long, so this means the function will skip ahead
+ * \xmlonly<ph outputclass="xmlonly">2<sup>67</sup></ph>\endxmlonly  * n elements.
+ *
+ * All values of \p n are valid.  Large values require more computation and so
+ * will take more time to complete.
+ *
+ * \param n - Number of subsequences to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead_sequence(unsigned long long n, curandStateXORWOW_t *state)
+{
+    _skipahead_sequence_inplace<curandStateXORWOW_t, 5>(n, state);
+}
+
+QUALIFIERS void _curand_init_scratch(unsigned long long seed,
+                                     unsigned long long subsequence,
+                                     unsigned long long offset,
+                                     curandStateXORWOW_t *state,
+                                     unsigned int *scratch)
+{
+    // Break up seed, apply salt
+    // Constants are arbitrary nonzero values
+    unsigned int s0 = ((unsigned int)seed) ^ 0xaad26b49UL;
+    unsigned int s1 = (unsigned int)(seed >> 32) ^ 0xf7dcefddUL;
+    // Simple multiplication to mix up bits
+    // Constants are arbitrary odd values
+    unsigned int t0 = 1099087573UL * s0;
+    unsigned int t1 = 2591861531UL * s1;
+    state->d = 6615241 + t1 + t0;
+    state->v[0] = 123456789UL + t0;
+    state->v[1] = 362436069UL ^ t0;
+    state->v[2] = 521288629UL + t1;
+    state->v[3] = 88675123UL ^ t1;
+    state->v[4] = 5783321UL + t0;
+    _skipahead_sequence_scratch<curandStateXORWOW_t, 5>(subsequence, state, scratch);
+    _skipahead_scratch<curandStateXORWOW_t, 5>(offset, state, scratch);
+    state->boxmuller_flag = 0;
+    state->boxmuller_flag_double = 0;
+    state->boxmuller_extra = 0.f;
+    state->boxmuller_extra_double = 0.;
+}
+
+QUALIFIERS void _curand_init_inplace(unsigned long long seed,
+                                     unsigned long long subsequence,
+                                     unsigned long long offset,
+                                     curandStateXORWOW_t *state)
+{
+    // Break up seed, apply salt
+    // Constants are arbitrary nonzero values
+    unsigned int s0 = ((unsigned int)seed) ^ 0xaad26b49UL;
+    unsigned int s1 = (unsigned int)(seed >> 32) ^ 0xf7dcefddUL;
+    // Simple multiplication to mix up bits
+    // Constants are arbitrary odd values
+    unsigned int t0 = 1099087573UL * s0;
+    unsigned int t1 = 2591861531UL * s1;
+    state->d = 6615241 + t1 + t0;
+    state->v[0] = 123456789UL + t0;
+    state->v[1] = 362436069UL ^ t0;
+    state->v[2] = 521288629UL + t1;
+    state->v[3] = 88675123UL ^ t1;
+    state->v[4] = 5783321UL + t0;
+    _skipahead_sequence_inplace<curandStateXORWOW_t, 5>(subsequence, state);
+    _skipahead_inplace<curandStateXORWOW_t, 5>(offset, state);
+    state->boxmuller_flag = 0;
+    state->boxmuller_flag_double = 0;
+    state->boxmuller_extra = 0.f;
+    state->boxmuller_extra_double = 0.;
+}
+
+/**
+ * \brief Initialize XORWOW state.
+ *
+ * Initialize XORWOW state in \p state with the given \p seed, \p subsequence,
+ * and \p offset.
+ *
+ * All input values of \p seed, \p subsequence, and \p offset are legal.  Large
+ * values for \p subsequence and \p offset require more computation and so will
+ * take more time to complete.
+ *
+ * A value of 0 for \p seed sets the state to the values of the original
+ * published version of the \p xorwow algorithm.
+ *
+ * \param seed - Arbitrary bits to use as a seed
+ * \param subsequence - Subsequence to start at
+ * \param offset - Absolute offset into sequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(unsigned long long seed,
+                            unsigned long long subsequence,
+                            unsigned long long offset,
+                            curandStateXORWOW_t *state)
+{
+    _curand_init_inplace(seed, subsequence, offset, state);
+}
+
+/**
+ * \brief Return 32-bits of pseudorandomness from an XORWOW generator.
+ *
+ * Return 32-bits of pseudorandomness from the XORWOW generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 32-bits of pseudorandomness as an unsigned int, all bits valid to use.
+ */
+QUALIFIERS unsigned int curand(curandStateXORWOW_t *state)
+{
+    unsigned int t;
+    t = (state->v[0] ^ (state->v[0] >> 2));
+    state->v[0] = state->v[1];
+    state->v[1] = state->v[2];
+    state->v[2] = state->v[3];
+    state->v[3] = state->v[4];
+    state->v[4] = (state->v[4] ^ (state->v[4] <<4)) ^ (t ^ (t << 1));
+    state->d += 362437;
+    return state->v[4] + state->d;
+}
+
+
+/**
+ * \brief Return 32-bits of pseudorandomness from an Philox4_32_10 generator.
+ *
+ * Return 32-bits of pseudorandomness from the Philox4_32_10 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 32-bits of pseudorandomness as an unsigned int, all bits valid to use.
+ */
+
+QUALIFIERS unsigned int curand(curandStatePhilox4_32_10_t *state)
+{
+    // Maintain the invariant: output[STATE] is always "good" and
+    //  is the next value to be returned by curand.
+    unsigned int ret;
+    switch(state->STATE++){
+    default:
+        ret = state->output.x;
+        break;
+    case 1:
+        ret = state->output.y;
+        break;
+    case 2:
+        ret = state->output.z;
+        break;
+    case 3:
+        ret = state->output.w;
+        break;
+    }
+    if(state->STATE == 4){
+        Philox_State_Incr(state);
+        state->output = curand_Philox4x32_10(state->ctr,state->key);
+        state->STATE = 0;
+    }
+    return ret;
+}
+
+/**
+ * \brief Return tuple of 4 32-bit pseudorandoms from a Philox4_32_10 generator.
+ *
+ * Return 128 bits of pseudorandomness from the Philox4_32_10 generator in \p state,
+ * increment position of generator by four.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 128-bits of pseudorandomness as a uint4, all bits valid to use.
+ */
+
+QUALIFIERS uint4 curand4(curandStatePhilox4_32_10_t *state)
+{
+    uint4 r;
+
+    uint4 tmp = state->output;
+    Philox_State_Incr(state);
+    state->output= curand_Philox4x32_10(state->ctr,state->key);
+    switch(state->STATE){
+    case 0:
+        return tmp;
+    case 1:
+        r.x = tmp.y;
+        r.y = tmp.z;
+        r.z = tmp.w;
+        r.w = state->output.x;
+        break;
+    case 2:
+        r.x = tmp.z;
+        r.y = tmp.w;
+        r.z = state->output.x;
+        r.w = state->output.y;
+        break;
+    case 3:
+        r.x = tmp.w;
+        r.y = state->output.x;
+        r.z = state->output.y;
+        r.w = state->output.z;
+        break;
+    default:
+        // NOT possible but needed to avoid compiler warnings
+        return tmp;
+    }
+    return r;
+}
+
+/**
+ * \brief Update Philox4_32_10 state to skip \p n elements.
+ *
+ * Update the Philox4_32_10 state in \p state to skip ahead \p n elements.
+ *
+ * All values of \p n are valid.
+ *
+ * \param n - Number of elements to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead(unsigned long long n, curandStatePhilox4_32_10_t *state)
+{
+    state->STATE += (n & 3);
+    n /= 4;
+    if( state->STATE > 3 ){
+        n += 1;
+        state->STATE -= 4;
+    }
+    Philox_State_Incr(state, n);
+    state->output = curand_Philox4x32_10(state->ctr,state->key);
+}
+
+/**
+ * \brief Update Philox4_32_10 state to skip ahead \p n subsequences.
+ *
+ * Update the Philox4_32_10 state in \p state to skip ahead \p n subsequences.  Each
+ * subsequence is \xmlonly<ph outputclass="xmlonly">2<sup>66</sup></ph>\endxmlonly elements long, so this means the function will skip ahead
+ * \xmlonly<ph outputclass="xmlonly">2<sup>66</sup></ph>\endxmlonly * n elements.
+ *
+ * All values of \p n are valid.
+ *
+ * \param n - Number of subsequences to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead_sequence(unsigned long long n, curandStatePhilox4_32_10_t *state)
+{
+    Philox_State_Incr_hi(state, n);
+    state->output = curand_Philox4x32_10(state->ctr,state->key);
+}
+
+/**
+ * \brief Initialize Philox4_32_10 state.
+ *
+ * Initialize Philox4_32_10 state in \p state with the given \p seed, p\ subsequence,
+ * and \p offset.
+ *
+ * All input values for \p seed, \p subseqence and \p offset are legal.  Each of the
+ * \xmlonly<ph outputclass="xmlonly">2<sup>64</sup></ph>\endxmlonly possible
+ * values of seed selects an independent sequence of length
+ * \xmlonly<ph outputclass="xmlonly">2<sup>130</sup></ph>\endxmlonly.
+ * The first
+ * \xmlonly<ph outputclass="xmlonly">2<sup>66</sup> * subsequence + offset</ph>\endxmlonly.
+ * values of the sequence are skipped.
+ * I.e., subsequences are of length
+ * \xmlonly<ph outputclass="xmlonly">2<sup>66</sup></ph>\endxmlonly.
+ *
+ * \param seed - Arbitrary bits to use as a seed
+ * \param subsequence - Subsequence to start at
+ * \param offset - Absolute offset into subsequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(unsigned long long seed,
+                                 unsigned long long subsequence,
+                                 unsigned long long offset,
+                                 curandStatePhilox4_32_10_t *state)
+{
+    state->ctr = make_uint4(0, 0, 0, 0);
+    state->key.x = (unsigned int)seed;
+    state->key.y = (unsigned int)(seed>>32);
+    state->STATE = 0;
+    state->boxmuller_flag = 0;
+    state->boxmuller_flag_double = 0;
+    state->boxmuller_extra = 0.f;
+    state->boxmuller_extra_double = 0.;
+    skipahead_sequence(subsequence, state);
+    skipahead(offset, state);
+}
+
+
+/* MRG32k3a RNG */
+
+/* Base generator for MRG32k3a                                              */
+QUALIFIERS unsigned long long __curand_umad(GCC_UNUSED_PARAMETER unsigned int a, GCC_UNUSED_PARAMETER unsigned int b, GCC_UNUSED_PARAMETER unsigned long long c)
+{
+    unsigned long long r = 0;
+NV_IF_TARGET(NV_PROVIDES_SM_61,
+    asm("mad.wide.u32 %0, %1, %2, %3;"
+        : "=l"(r) : "r"(a), "r"(b), "l"(c));
+)
+    return r;
+}
+QUALIFIERS unsigned long long __curand_umul(GCC_UNUSED_PARAMETER unsigned int a, GCC_UNUSED_PARAMETER unsigned int b)
+{
+    unsigned long long r = 0;
+NV_IF_TARGET(NV_PROVIDES_SM_61,
+    asm("mul.wide.u32 %0, %1, %2;"
+        : "=l"(r) : "r"(a), "r"(b));
+)
+    return r;
+}
+QUALIFIERS double curand_MRG32k3a (curandStateMRG32k3a_t *state)
+{
+NV_IF_TARGET(NV_PROVIDES_SM_61,
+    const unsigned int m1  = 4294967087u;
+    const unsigned int m2  = 4294944443u;
+    const unsigned int m1c = 209u;
+    const unsigned int m2c = 22853u;
+    const unsigned int a12  = 1403580u;
+    const unsigned int a13n = 810728u;
+    const unsigned int a21  = 527612u;
+    const unsigned int a23n = 1370589u;
+
+    unsigned long long p1;
+    unsigned long long p2;
+    const unsigned long long p3 = __curand_umul(a13n, m1 - state->s1[0]);
+    p1 = __curand_umad(a12, state->s1[1], p3);
+
+    // Putting addition inside and changing umul to umad
+    // slowed this function down on GV100
+    p1 =  __curand_umul(p1 >> 32, m1c) + (p1 & 0xffffffff);
+    if (p1 >= m1) p1 -= m1;
+
+    state->s1[0] = state->s1[1]; state->s1[1] = state->s1[2]; state->s1[2] = p1;
+    const unsigned long long p4 = __curand_umul(a23n, m2 - state->s2[0]);
+    p2 = __curand_umad(a21, state->s2[2], p4);
+
+    // Putting addition inside and changing umul to umad
+    // slowed this function down on GV100
+    p2 =  __curand_umul(p2 >> 32, m2c) + (p2 & 0xffffffff);
+    p2 =  __curand_umul(p2 >> 32, m2c) + (p2 & 0xffffffff);
+    if (p2 >= m2) p2 -= m2;
+
+    state->s2[0] = state->s2[1]; state->s2[1] = state->s2[2]; state->s2[2] = p2;
+
+    const unsigned int p5 = (unsigned int)p1 - (unsigned int)p2;
+    if(p1 <= p2) return p5 + m1;
+    return p5;
+)
+NV_IF_TARGET(NV_IS_DEVICE,
+/*  nj's implementation */
+    const double m1 = 4294967087.;
+    const double m2 = 4294944443.;
+    const double a12  = 1403580.;
+    const double a13n = 810728.;
+    const double a21  = 527612.;
+    const double a23n = 1370589.;
+
+    const double rh1 =  2.3283065498378290e-010;  /* (1.0 / m1)__hi */
+    const double rl1 = -1.7354913086174288e-026;  /* (1.0 / m1)__lo */
+    const double rh2 =  2.3283188252407387e-010;  /* (1.0 / m2)__hi */
+    const double rl2 =  2.4081018096503646e-026;  /* (1.0 / m2)__lo */
+
+    double q;
+    double p1;
+    double p2;
+    p1 = a12 * state->s1[1] - a13n * state->s1[0];
+    q = trunc (fma (p1, rh1, p1 * rl1));
+    p1 -= q * m1;
+    if (p1 < 0.0) p1 += m1;
+    state->s1[0] = state->s1[1];   state->s1[1] = state->s1[2];   state->s1[2] = (unsigned int)p1;
+    p2 = a21 * state->s2[2] - a23n * state->s2[0];
+    q = trunc (fma (p2, rh2, p2 * rl2));
+    p2 -= q * m2;
+    if (p2 < 0.0) p2 += m2;
+    state->s2[0] = state->s2[1];   state->s2[1] = state->s2[2];   state->s2[2] = (unsigned int)p2;
+    if (p1 <= p2) return (p1 - p2 + m1);
+    else return (p1 - p2);
+)
+/* end nj's implementation */
+    double p1;
+    double p2;
+    double r;
+    p1 = (MRG32K3A_A12 * state->s1[1]) - (MRG32K3A_A13N * state->s1[0]);
+    p1 = curand_MRGmod(p1, MRG32K3A_MOD1);
+    if (p1 < 0.0) p1 += MRG32K3A_MOD1;
+    state->s1[0] = state->s1[1];
+    state->s1[1] = state->s1[2];
+    state->s1[2] = (unsigned int)p1;
+    p2 = (MRG32K3A_A21 * state->s2[2]) - (MRG32K3A_A23N * state->s2[0]);
+    p2 = curand_MRGmod(p2, MRG32K3A_MOD2);
+    if (p2 < 0) p2 += MRG32K3A_MOD2;
+    state->s2[0] = state->s2[1];
+    state->s2[1] = state->s2[2];
+    state->s2[2] = (unsigned int)p2;
+    r = p1 - p2;
+    if (r <= 0) r += MRG32K3A_MOD1;
+    return r;
+}
+
+
+/**
+ * \brief Return 32-bits of pseudorandomness from an MRG32k3a generator.
+ *
+ * Return 32-bits of pseudorandomness from the MRG32k3a generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 32-bits of pseudorandomness as an unsigned int, all bits valid to use.
+ */
+QUALIFIERS unsigned int curand(curandStateMRG32k3a_t *state)
+{
+    double dRet;
+    dRet = (double)curand_MRG32k3a(state)*(double)MRG32K3A_BITS_NORM;
+    return (unsigned int)dRet;
+}
+
+
+
+/**
+ * \brief Update MRG32k3a state to skip \p n elements.
+ *
+ * Update the MRG32k3a state in \p state to skip ahead \p n elements.
+ *
+ * All values of \p n are valid.  Large values require more computation and so
+ * will take more time to complete.
+ *
+ * \param n - Number of elements to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead(unsigned long long n, curandStateMRG32k3a_t *state)
+{
+    unsigned int t[3][3];
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    curand_MRGmatPow3x3( mrg32k3aM1, t, MRG32K3A_MOD1, n);
+    curand_MRGmatVecMul3x3( t, state->s1, MRG32K3A_MOD1);
+    curand_MRGmatPow3x3(mrg32k3aM2, t, MRG32K3A_MOD2, n);
+    curand_MRGmatVecMul3x3( t, state->s2, MRG32K3A_MOD2);
+,
+    curand_MRGmatPow3x3( mrg32k3aM1Host, t, MRG32K3A_MOD1, n);
+    curand_MRGmatVecMul3x3( t, state->s1, MRG32K3A_MOD1);
+    curand_MRGmatPow3x3(mrg32k3aM2Host, t, MRG32K3A_MOD2, n);
+    curand_MRGmatVecMul3x3( t, state->s2, MRG32K3A_MOD2);
+)
+}
+
+/**
+ * \brief Update MRG32k3a state to skip ahead \p n subsequences.
+ *
+ * Update the MRG32k3a state in \p state to skip ahead \p n subsequences.  Each
+ * subsequence is \xmlonly<ph outputclass="xmlonly">2<sup>127</sup></ph>\endxmlonly
+ *
+ * \xmlonly<ph outputclass="xmlonly">2<sup>76</sup></ph>\endxmlonly elements long, so this means the function will skip ahead
+ * \xmlonly<ph outputclass="xmlonly">2<sup>67</sup></ph>\endxmlonly * n elements.
+ *
+ * Valid values of \p n are 0 to \xmlonly<ph outputclass="xmlonly">2<sup>51</sup></ph>\endxmlonly.  Note \p n will be masked to 51 bits
+ *
+ * \param n - Number of subsequences to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead_subsequence(unsigned long long n, curandStateMRG32k3a_t *state)
+{
+    unsigned int t[3][3];
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    curand_MRGmatPow3x3( mrg32k3aM1SubSeq, t, MRG32K3A_MOD1, n);
+    curand_MRGmatVecMul3x3( t, state->s1, MRG32K3A_MOD1);
+    curand_MRGmatPow3x3( mrg32k3aM2SubSeq, t, MRG32K3A_MOD2, n);
+    curand_MRGmatVecMul3x3( t, state->s2, MRG32K3A_MOD2);
+,
+    curand_MRGmatPow3x3( mrg32k3aM1SubSeqHost, t, MRG32K3A_MOD1, n);
+    curand_MRGmatVecMul3x3( t, state->s1, MRG32K3A_MOD1);
+    curand_MRGmatPow3x3( mrg32k3aM2SubSeqHost, t, MRG32K3A_MOD2, n);
+    curand_MRGmatVecMul3x3( t, state->s2, MRG32K3A_MOD2);
+)
+}
+
+/**
+ * \brief Update MRG32k3a state to skip ahead \p n sequences.
+ *
+ * Update the MRG32k3a state in \p state to skip ahead \p n sequences.  Each
+ * sequence is \xmlonly<ph outputclass="xmlonly">2<sup>127</sup></ph>\endxmlonly elements long, so this means the function will skip ahead
+ * \xmlonly<ph outputclass="xmlonly">2<sup>127</sup></ph>\endxmlonly * n elements.
+ *
+ * All values of \p n are valid.  Large values require more computation and so
+ * will take more time to complete.
+ *
+ * \param n - Number of sequences to skip
+ * \param state - Pointer to state to update
+ */
+QUALIFIERS void skipahead_sequence(unsigned long long n, curandStateMRG32k3a_t *state)
+{
+    unsigned int t[3][3];
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    curand_MRGmatPow3x3( mrg32k3aM1Seq, t, MRG32K3A_MOD1, n);
+    curand_MRGmatVecMul3x3( t, state->s1, MRG32K3A_MOD1);
+    curand_MRGmatPow3x3(  mrg32k3aM2Seq, t, MRG32K3A_MOD2, n);
+    curand_MRGmatVecMul3x3( t, state->s2, MRG32K3A_MOD2);
+,
+    curand_MRGmatPow3x3( mrg32k3aM1SeqHost, t, MRG32K3A_MOD1, n);
+    curand_MRGmatVecMul3x3( t, state->s1, MRG32K3A_MOD1);
+    curand_MRGmatPow3x3(  mrg32k3aM2SeqHost, t, MRG32K3A_MOD2, n);
+    curand_MRGmatVecMul3x3( t, state->s2, MRG32K3A_MOD2);
+)
+}
+
+
+/**
+ * \brief Initialize MRG32k3a state.
+ *
+ * Initialize MRG32k3a state in \p state with the given \p seed, \p subsequence,
+ * and \p offset.
+ *
+ * All input values of \p seed, \p subsequence, and \p offset are legal.
+ * \p subsequence will be truncated to 51 bits to avoid running into the next sequence
+ *
+ * A value of 0 for \p seed sets the state to the values of the original
+ * published version of the \p MRG32k3a algorithm.
+ *
+ * \param seed - Arbitrary bits to use as a seed
+ * \param subsequence - Subsequence to start at
+ * \param offset - Absolute offset into sequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(unsigned long long seed,
+                            unsigned long long subsequence,
+                            unsigned long long offset,
+                            curandStateMRG32k3a_t *state)
+{
+    int i;
+    for ( i=0; i<3; i++ ) {
+        state->s1[i] = 12345u;
+        state->s2[i] = 12345u;
+    }
+    if (seed != 0ull) {
+        unsigned int x1 = ((unsigned int)seed) ^ 0x55555555UL;
+        unsigned int x2 = (unsigned int)((seed >> 32) ^ 0xAAAAAAAAUL);
+        state->s1[0] = (unsigned int)curand_MRGmodMul(x1, state->s1[0], MRG32K3A_MOD1);
+        state->s1[1] = (unsigned int)curand_MRGmodMul(x2, state->s1[1], MRG32K3A_MOD1);
+        state->s1[2] = (unsigned int)curand_MRGmodMul(x1, state->s1[2], MRG32K3A_MOD1);
+        state->s2[0] = (unsigned int)curand_MRGmodMul(x2, state->s2[0], MRG32K3A_MOD2);
+        state->s2[1] = (unsigned int)curand_MRGmodMul(x1, state->s2[1], MRG32K3A_MOD2);
+        state->s2[2] = (unsigned int)curand_MRGmodMul(x2, state->s2[2], MRG32K3A_MOD2);
+    }
+    skipahead_subsequence( subsequence, state );
+    skipahead( offset, state );
+    state->boxmuller_flag = 0;
+    state->boxmuller_flag_double = 0;
+    state->boxmuller_extra = 0.f;
+    state->boxmuller_extra_double = 0.;
+}
+
+/**
+ * \brief Update Sobol32 state to skip \p n elements.
+ *
+ * Update the Sobol32 state in \p state to skip ahead \p n elements.
+ *
+ * All values of \p n are valid.
+ *
+ * \param n - Number of elements to skip
+ * \param state - Pointer to state to update
+ */
+template <typename T>
+QUALIFIERS
+typename CURAND_STD::enable_if<CURAND_STD::is_same<curandStateSobol32_t*, T>::value || CURAND_STD::is_same<curandStateScrambledSobol32_t*, T>::value>::type
+skipahead(unsigned int n, T state)
+{
+    unsigned int i_gray;
+    state->x = state->c;
+    state->i += n;
+    /* Convert state->i to gray code */
+    i_gray = state->i ^ (state->i >> 1);
+    for(unsigned int k = 0; k < 32; k++) {
+        if(i_gray & (1 << k)) {
+            state->x ^= state->direction_vectors[k];
+        }
+    }
+    return;
+}
+
+/**
+ * \brief Update Sobol64 state to skip \p n elements.
+ *
+ * Update the Sobol64 state in \p state to skip ahead \p n elements.
+ *
+ * All values of \p n are valid.
+ *
+ * \param n - Number of elements to skip
+ * \param state - Pointer to state to update
+ */
+template <typename T>
+QUALIFIERS
+typename CURAND_STD::enable_if<CURAND_STD::is_same<curandStateSobol64_t*, T>::value || CURAND_STD::is_same<curandStateScrambledSobol64_t*, T>::value>::type
+skipahead(unsigned long long n, T state)
+{
+    unsigned long long i_gray;
+    state->x = state->c;
+    state->i += n;
+    /* Convert state->i to gray code */
+    i_gray = state->i ^ (state->i >> 1);
+    for(unsigned k = 0; k < 64; k++) {
+        if(i_gray & (1ULL << k)) {
+            state->x ^= state->direction_vectors[k];
+        }
+    }
+    return;
+}
+
+/**
+ * \brief Initialize Sobol32 state.
+ *
+ * Initialize Sobol32 state in \p state with the given \p direction \p vectors and
+ * \p offset.
+ *
+ * The direction vector is a device pointer to an array of 32 unsigned ints.
+ * All input values of \p offset are legal.
+ *
+ * \param direction_vectors - Pointer to array of 32 unsigned ints representing the
+ * direction vectors for the desired dimension
+ * \param offset - Absolute offset into sequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(curandDirectionVectors32_t direction_vectors,
+                                            unsigned int offset,
+                                            curandStateSobol32_t *state)
+{
+    state->i = 0;
+    state->c = 0;
+    for(int i = 0; i < 32; i++) {
+        state->direction_vectors[i] = direction_vectors[i];
+    }
+    state->x = 0;
+    skipahead<curandStateSobol32_t *>(offset, state);
+}
+/**
+ * \brief Initialize Scrambled Sobol32 state.
+ *
+ * Initialize Sobol32 state in \p state with the given \p direction \p vectors and
+ * \p offset.
+ *
+ * The direction vector is a device pointer to an array of 32 unsigned ints.
+ * All input values of \p offset are legal.
+ *
+ * \param direction_vectors - Pointer to array of 32 unsigned ints representing the
+ direction vectors for the desired dimension
+ * \param scramble_c Scramble constant
+ * \param offset - Absolute offset into sequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(curandDirectionVectors32_t direction_vectors,
+                                            unsigned int scramble_c,
+                                            unsigned int offset,
+                                            curandStateScrambledSobol32_t *state)
+{
+    state->i = 0;
+    state->c = scramble_c;
+    for(int i = 0; i < 32; i++) {
+        state->direction_vectors[i] = direction_vectors[i];
+    }
+    state->x = state->c;
+    skipahead<curandStateScrambledSobol32_t *>(offset, state);
+}
+
+QUALIFIERS int __curand_find_trailing_zero(unsigned int x)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    int y = __ffs(~x);
+    if(y)
+        return y - 1;
+    return 31;
+,
+    int i = 1;
+    while(x & 1) {
+        i++;
+        x >>= 1;
+    }
+    i = i - 1;
+    return i == 32 ? 31 : i;
+)
+}
+
+QUALIFIERS int __curand_find_trailing_zero(unsigned long long x)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    int y = __ffsll(~x);
+    if(y)
+        return y - 1;
+    return 63;
+,
+    int i = 1;
+    while(x & 1) {
+        i++;
+        x >>= 1;
+    }
+    i = i - 1;
+    return i == 64 ? 63 : i;
+)
+}
+
+/**
+ * \brief Initialize Sobol64 state.
+ *
+ * Initialize Sobol64 state in \p state with the given \p direction \p vectors and
+ * \p offset.
+ *
+ * The direction vector is a device pointer to an array of 64 unsigned long longs.
+ * All input values of \p offset are legal.
+ *
+ * \param direction_vectors - Pointer to array of 64 unsigned long longs representing the
+ direction vectors for the desired dimension
+ * \param offset - Absolute offset into sequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(curandDirectionVectors64_t direction_vectors,
+                                            unsigned long long offset,
+                                            curandStateSobol64_t *state)
+{
+    state->i = 0;
+    state->c = 0;
+    for(int i = 0; i < 64; i++) {
+        state->direction_vectors[i] = direction_vectors[i];
+    }
+    state->x = 0;
+    skipahead<curandStateSobol64_t *>(offset, state);
+}
+
+/**
+ * \brief Initialize Scrambled Sobol64 state.
+ *
+ * Initialize Sobol64 state in \p state with the given \p direction \p vectors and
+ * \p offset.
+ *
+ * The direction vector is a device pointer to an array of 64 unsigned long longs.
+ * All input values of \p offset are legal.
+ *
+ * \param direction_vectors - Pointer to array of 64 unsigned long longs representing the
+ direction vectors for the desired dimension
+ * \param scramble_c Scramble constant
+ * \param offset - Absolute offset into sequence
+ * \param state - Pointer to state to initialize
+ */
+QUALIFIERS void curand_init(curandDirectionVectors64_t direction_vectors,
+                                            unsigned long long scramble_c,
+                                            unsigned long long offset,
+                                            curandStateScrambledSobol64_t *state)
+{
+    state->i = 0;
+    state->c = scramble_c;
+    for(int i = 0; i < 64; i++) {
+        state->direction_vectors[i] = direction_vectors[i];
+    }
+    state->x = state->c;
+    skipahead<curandStateScrambledSobol64_t *>(offset, state);
+}
+
+/**
+ * \brief Return 32-bits of quasirandomness from a Sobol32 generator.
+ *
+ * Return 32-bits of quasirandomness from the Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 32-bits of quasirandomness as an unsigned int, all bits valid to use.
+ */
+
+QUALIFIERS unsigned int curand(curandStateSobol32_t * state)
+{
+    /* Moving from i to i+1 element in gray code is flipping one bit,
+       the trailing zero bit of i
+    */
+    unsigned int res = state->x;
+    state->x ^= state->direction_vectors[__curand_find_trailing_zero(state->i)];
+    state->i ++;
+    return res;
+}
+
+/**
+ * \brief Return 32-bits of quasirandomness from a scrambled Sobol32 generator.
+ *
+ * Return 32-bits of quasirandomness from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 32-bits of quasirandomness as an unsigned int, all bits valid to use.
+ */
+
+QUALIFIERS unsigned int curand(curandStateScrambledSobol32_t * state)
+{
+    /* Moving from i to i+1 element in gray code is flipping one bit,
+       the trailing zero bit of i
+    */
+    unsigned int res = state->x;
+    state->x ^= state->direction_vectors[__curand_find_trailing_zero(state->i)];
+    state->i ++;
+    return res;
+}
+
+/**
+ * \brief Return 64-bits of quasirandomness from a Sobol64 generator.
+ *
+ * Return 64-bits of quasirandomness from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 64-bits of quasirandomness as an unsigned long long, all bits valid to use.
+ */
+
+QUALIFIERS unsigned long long curand(curandStateSobol64_t * state)
+{
+    /* Moving from i to i+1 element in gray code is flipping one bit,
+       the trailing zero bit of i
+    */
+    unsigned long long res = state->x;
+    state->x ^= state->direction_vectors[__curand_find_trailing_zero(state->i)];
+    state->i ++;
+    return res;
+}
+
+/**
+ * \brief Return 64-bits of quasirandomness from a scrambled Sobol64 generator.
+ *
+ * Return 64-bits of quasirandomness from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 64-bits of quasirandomness as an unsigned long long, all bits valid to use.
+ */
+
+QUALIFIERS unsigned long long curand(curandStateScrambledSobol64_t * state)
+{
+    /* Moving from i to i+1 element in gray code is flipping one bit,
+       the trailing zero bit of i
+    */
+    unsigned long long res = state->x;
+    state->x ^= state->direction_vectors[__curand_find_trailing_zero(state->i)];
+    state->i ++;
+    return res;
+}
+
+#include "curand_uniform.h"
+#include "curand_normal.h"
+#include "curand_lognormal.h"
+#include "curand_poisson.h"
+#include "curand_discrete2.h"
+
+__device__ static inline unsigned int *__get_precalculated_matrix(int n)
+{
+    if(n == 0) {
+        return precalc_xorwow_matrix[n];
+    }
+    if(n == 2) {
+        return precalc_xorwow_offset_matrix[n];
+    }
+    return precalc_xorwow_matrix[n];
+}
+
+#ifndef __CUDACC_RTC__
+__host__ static inline unsigned int *__get_precalculated_matrix_host(int n)
+{
+    if(n == 1) {
+        return precalc_xorwow_matrix_host[n];
+    }
+    if(n == 3) {
+        return precalc_xorwow_offset_matrix_host[n];
+    }
+    return precalc_xorwow_matrix_host[n];
+}
+#endif // #ifndef __CUDACC_RTC__
+
+__device__ static inline unsigned int *__get_mrg32k3a_matrix(int n)
+{
+    if(n == 0) {
+        return mrg32k3aM1[n][0];
+    }
+    if(n == 2) {
+        return mrg32k3aM2[n][0];
+    }
+    if(n == 4) {
+        return mrg32k3aM1SubSeq[n][0];
+    }
+    if(n == 6) {
+        return mrg32k3aM2SubSeq[n][0];
+    }
+    if(n == 8) {
+        return mrg32k3aM1Seq[n][0];
+    }
+    if(n == 10) {
+        return mrg32k3aM2Seq[n][0];
+    }
+    return mrg32k3aM1[n][0];
+}
+
+#ifndef __CUDACC_RTC__
+__host__ static inline unsigned int *__get_mrg32k3a_matrix_host(int n)
+{
+    if(n == 1) {
+        return mrg32k3aM1Host[n][0];
+    }
+    if(n == 3) {
+        return mrg32k3aM2Host[n][0];
+    }
+    if(n == 5) {
+        return mrg32k3aM1SubSeqHost[n][0];
+    }
+    if(n == 7) {
+        return mrg32k3aM2SubSeqHost[n][0];
+    }
+    if(n == 9) {
+        return mrg32k3aM1SeqHost[n][0];
+    }
+    if(n == 11) {
+        return mrg32k3aM2SeqHost[n][0];
+    }
+    return mrg32k3aM1Host[n][0];
+}
+
+__host__ static inline double *__get__cr_lgamma_table_host(void) {
+    return __cr_lgamma_table;
+}
+#endif // #ifndef __CUDACC_RTC__
+
+/** @} */
+
+#endif // !defined(CURAND_KERNEL_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_lognormal.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_lognormal.h
new file mode 100644
index 0000000000000000000000000000000000000000..2ebb27599e54cef8325e0d43457f09283d13cb88
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_lognormal.h
@@ -0,0 +1,697 @@
+
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#if !defined(CURAND_LOGNORMAL_H_)
+#define CURAND_LOGNORMAL_H_
+
+/**
+ * \defgroup DEVICE Device API
+ *
+ * @{
+ */
+
+#ifndef __CUDACC_RTC__
+#include <math.h>
+#endif // __CUDACC_RTC__
+
+#include "curand_mrg32k3a.h"
+#include "curand_mtgp32_kernel.h"
+#include "curand_philox4x32_x.h"
+
+/**
+ * \brief Return a log-normally distributed float from an XORWOW generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the XORWOW generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, transforms them to log-normal distribution,
+ * then returns them one at a time.
+ * See ::curand_log_normal2() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state  - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateXORWOW_t *state, float mean, float stddev)
+{
+    if(state->boxmuller_flag != EXTRA_FLAG_LOG_NORMAL) {
+        unsigned int x, y;
+        x = curand(state);
+        y = curand(state);
+        float2 v = _curand_box_muller(x, y);
+        state->boxmuller_extra = expf(mean + (stddev * v.y));
+        state->boxmuller_flag = EXTRA_FLAG_LOG_NORMAL;
+        return expf(mean + (stddev * v.x));
+    }
+    state->boxmuller_flag = 0;
+    return state->boxmuller_extra;
+}
+
+/**
+ * \brief Return a log-normally distributed float from an Philox4_32_10 generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Philox4_32_10 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, transforms them to log-normal distribution,
+ * then returns them one at a time.
+ * See ::curand_log_normal2() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state  - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+
+QUALIFIERS float curand_log_normal(curandStatePhilox4_32_10_t *state, float mean, float stddev)
+{
+    if(state->boxmuller_flag != EXTRA_FLAG_LOG_NORMAL) {
+        unsigned int x, y;
+        x = curand(state);
+        y = curand(state);
+        float2 v = _curand_box_muller(x, y);
+        state->boxmuller_extra = expf(mean + (stddev * v.y));
+        state->boxmuller_flag = EXTRA_FLAG_LOG_NORMAL;
+        return expf(mean + (stddev * v.x));
+    }
+    state->boxmuller_flag = 0;
+    return state->boxmuller_extra;
+}
+
+/**
+ * \brief Return two normally distributed floats from an XORWOW generator.
+ *
+ * Return two log-normally distributed floats derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the XORWOW generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then transforms them to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float2 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float2 curand_log_normal2(curandStateXORWOW_t *state, float mean, float stddev)
+{
+    float2 v = curand_box_muller(state);
+    v.x = expf(mean + (stddev * v.x));
+    v.y = expf(mean + (stddev * v.y));
+    return v;
+}
+
+/**
+ * \brief Return two normally distributed floats from an Philox4_32_10 generator.
+ *
+ * Return two log-normally distributed floats derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Philox4_32_10 generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then transforms them to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float2 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float2 curand_log_normal2(curandStatePhilox4_32_10_t *state, float mean, float stddev)
+{
+    float2 v = curand_box_muller(state);
+    v.x = expf(mean + (stddev * v.x));
+    v.y = expf(mean + (stddev * v.y));
+    return v;
+}
+/**
+ * \brief Return four normally distributed floats from an Philox4_32_10 generator.
+ *
+ * Return four log-normally distributed floats derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Philox4_32_10 generator in \p state,
+ * increment position of generator by four.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then transforms them to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float4 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float4 curand_log_normal4(curandStatePhilox4_32_10_t *state, float mean, float stddev)
+{
+    float4 v = curand_box_muller4(state);
+    v.x = expf(mean + (stddev * v.x));
+    v.y = expf(mean + (stddev * v.y));
+    v.z = expf(mean + (stddev * v.z));
+    v.w = expf(mean + (stddev * v.w));
+    return v;
+}
+
+/**
+ * \brief Return a log-normally distributed float from an MRG32k3a generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the MRG32k3a generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, transforms them to log-normal distribution,
+ * then returns them one at a time.
+ * See ::curand_log_normal2() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state  - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateMRG32k3a_t *state, float mean, float stddev)
+{
+    if(state->boxmuller_flag != EXTRA_FLAG_LOG_NORMAL) {
+        float2 v = curand_box_muller_mrg(state);
+        state->boxmuller_extra = expf(mean + (stddev * v.y));
+        state->boxmuller_flag = EXTRA_FLAG_LOG_NORMAL;
+        return expf(mean + (stddev * v.x));
+    }
+    state->boxmuller_flag = 0;
+    return state->boxmuller_extra;
+}
+
+/**
+ * \brief Return two normally distributed floats from an MRG32k3a generator.
+ *
+ * Return two log-normally distributed floats derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the MRG32k3a generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then transforms them to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float2 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float2 curand_log_normal2(curandStateMRG32k3a_t *state, float mean, float stddev)
+{
+    float2 v = curand_box_muller_mrg(state);
+    v.x = expf(mean + (stddev * v.x));
+    v.y = expf(mean + (stddev * v.y));
+    return v;
+}
+
+/**
+ * \brief Return a log-normally distributed float from an MTGP32 generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the MTGP32 generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate a normally distributed result, then transforms the result
+ * to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateMtgp32_t *state, float mean, float stddev)
+{
+    return expf(mean + (stddev * _curand_normal_icdf(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed float from a Sobol32 generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate a normally distributed result, then transforms the result
+ * to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateSobol32_t *state, float mean, float stddev)
+{
+    return expf(mean + (stddev * _curand_normal_icdf(curand(state))));
+}
+/**
+ * \brief Return a log-normally distributed float from a scrambled Sobol32 generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate a normally distributed result, then transforms the result
+ * to log-normal.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateScrambledSobol32_t *state, float mean, float stddev)
+{
+    return expf(mean + (stddev * _curand_normal_icdf(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed float from a Sobol64 generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results, then converts to log-normal
+ * distribution.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateSobol64_t *state, float mean, float stddev)
+{
+    return expf(mean + (stddev * _curand_normal_icdf(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed float from a scrambled Sobol64 generator.
+ *
+ * Return a single log-normally distributed float derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the scrambled Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results, then converts to log-normal
+ * distribution.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed float with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS float curand_log_normal(curandStateScrambledSobol64_t *state, float mean, float stddev)
+{
+    return expf(mean + (stddev * _curand_normal_icdf(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed double from an XORWOW generator.
+ *
+ * Return a single normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the XORWOW generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, transforms them to log-normal distribution,
+ * then returns them one at a time.
+ * See ::curand_log_normal2_double() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+
+QUALIFIERS double curand_log_normal_double(curandStateXORWOW_t *state, double mean, double stddev)
+{
+    if(state->boxmuller_flag_double != EXTRA_FLAG_LOG_NORMAL) {
+        unsigned int x0, x1, y0, y1;
+        x0 = curand(state);
+        x1 = curand(state);
+        y0 = curand(state);
+        y1 = curand(state);
+        double2 v = _curand_box_muller_double(x0, x1, y0, y1);
+        state->boxmuller_extra_double = exp(mean + (stddev * v.y));
+        state->boxmuller_flag_double = EXTRA_FLAG_LOG_NORMAL;
+        return exp(mean + (stddev * v.x));
+    }
+    state->boxmuller_flag_double = 0;
+    return state->boxmuller_extra_double;
+}
+
+/**
+ * \brief Return a log-normally distributed double from an Philox4_32_10 generator.
+ *
+ * Return a single normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Philox4_32_10 generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, transforms them to log-normal distribution,
+ * then returns them one at a time.
+ * See ::curand_log_normal2_double() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+
+QUALIFIERS double curand_log_normal_double(curandStatePhilox4_32_10_t *state, double mean, double stddev)
+{
+    if(state->boxmuller_flag_double != EXTRA_FLAG_LOG_NORMAL) {
+        uint4 _x;
+        _x = curand4(state);
+        double2 v = _curand_box_muller_double(_x.x, _x.y, _x.z, _x.w);
+        state->boxmuller_extra_double = exp(mean + (stddev * v.y));
+        state->boxmuller_flag_double = EXTRA_FLAG_LOG_NORMAL;
+        return exp(mean + (stddev * v.x));
+    }
+    state->boxmuller_flag_double = 0;
+    return state->boxmuller_extra_double;
+}
+
+
+/**
+ * \brief Return two log-normally distributed doubles from an XORWOW generator.
+ *
+ * Return two log-normally distributed doubles derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the XORWOW generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, and transforms them to log-normal distribution,.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double2 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double2 curand_log_normal2_double(curandStateXORWOW_t *state, double mean, double stddev)
+{
+    double2 v = curand_box_muller_double(state);
+    v.x = exp(mean + (stddev * v.x));
+    v.y = exp(mean + (stddev * v.y));
+    return v;
+}
+
+/**
+ * \brief Return two log-normally distributed doubles from an Philox4_32_10 generator.
+ *
+ * Return two log-normally distributed doubles derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Philox4_32_10 generator in \p state,
+ * increment position of generator by four.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, and transforms them to log-normal distribution,.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double4 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double2 curand_log_normal2_double(curandStatePhilox4_32_10_t *state, double mean, double stddev)
+{
+    double2 v = curand_box_muller2_double(state);
+    v.x = exp(mean + (stddev * v.x));
+    v.y = exp(mean + (stddev * v.y));
+    return v;
+}
+// nor part of API
+QUALIFIERS double4 curand_log_normal4_double(curandStatePhilox4_32_10_t *state, double mean, double stddev)
+{
+    double4 v = curand_box_muller4_double(state);
+    v.x = exp(mean + (stddev * v.x));
+    v.y = exp(mean + (stddev * v.y));
+    v.z = exp(mean + (stddev * v.z));
+    v.w = exp(mean + (stddev * v.w));
+    return v;
+}
+
+/**
+ * \brief Return a log-normally distributed double from an MRG32k3a generator.
+ *
+ * Return a single normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the MRG32k3a generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, transforms them to log-normal distribution,
+ * then returns them one at a time.
+ * See ::curand_log_normal2_double() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double curand_log_normal_double(curandStateMRG32k3a_t *state, double mean, double stddev)
+{
+    if(state->boxmuller_flag_double != EXTRA_FLAG_LOG_NORMAL) {
+        double2 v = curand_box_muller_mrg_double(state);
+        state->boxmuller_extra_double = exp(mean + (stddev * v.y));
+        state->boxmuller_flag_double = EXTRA_FLAG_LOG_NORMAL;
+        return exp(mean + (stddev * v.x));
+    }
+    state->boxmuller_flag_double = 0;
+    return state->boxmuller_extra_double;
+}
+
+/**
+ * \brief Return two log-normally distributed doubles from an MRG32k3a generator.
+ *
+ * Return two log-normally distributed doubles derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the MRG32k3a generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, and transforms them to log-normal distribution,.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double2 where each element is from a
+ * distribution with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double2 curand_log_normal2_double(curandStateMRG32k3a_t *state, double mean, double stddev)
+{
+    double2 v = curand_box_muller_mrg_double(state);
+    v.x = exp(mean + (stddev * v.x));
+    v.y = exp(mean + (stddev * v.y));
+    return v;
+}
+
+/**
+ * \brief Return a log-normally distributed double from an MTGP32 generator.
+ *
+ * Return a single log-normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the MTGP32 generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results, and transforms them into
+ * log-normal distribution.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double curand_log_normal_double(curandStateMtgp32_t *state, double mean, double stddev)
+{
+    return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed double from a Sobol32 generator.
+ *
+ * Return a single log-normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results, and transforms them into
+ * log-normal distribution.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double curand_log_normal_double(curandStateSobol32_t *state, double mean, double stddev)
+{
+    return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed double from a scrambled Sobol32 generator.
+ *
+ * Return a single log-normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results, and transforms them into
+ * log-normal distribution.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double curand_log_normal_double(curandStateScrambledSobol32_t *state, double mean, double stddev)
+{
+    return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed double from a Sobol64 generator.
+ *
+ * Return a single normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double curand_log_normal_double(curandStateSobol64_t *state, double mean, double stddev)
+{
+    return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))));
+}
+
+/**
+ * \brief Return a log-normally distributed double from a scrambled Sobol64 generator.
+ *
+ * Return a single normally distributed double derived from a normal
+ * distribution with mean \p mean and standard deviation \p stddev
+ * from the scrambled Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ * \param mean   - Mean of the related normal distribution
+ * \param stddev - Standard deviation of the related normal distribution
+ *
+ * \return Log-normally distributed double with mean \p mean and standard deviation \p stddev
+ */
+QUALIFIERS double curand_log_normal_double(curandStateScrambledSobol64_t *state, double mean, double stddev)
+{
+    return exp(mean + (stddev * _curand_normal_icdf_double(curand(state))));
+}
+
+#endif // !defined(CURAND_LOGNORMAL_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mrg32k3a.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mrg32k3a.h
new file mode 100644
index 0000000000000000000000000000000000000000..a052a9c3b3f45dcf1d7d0369d0d62d8c4719e3ee
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mrg32k3a.h
@@ -0,0 +1,3721 @@
+/*
+ * Copyright 2010-2018 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+#if !defined(CURAND_MRG32K3A_MATRICES_H_)
+#define CURAND_MRG32K3A_MATRICES_H_
+
+#if defined(__CUDACC_RDC__) && (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+#define CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS inline __device__
+#else
+#define CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS static __device__
+#endif
+
+#if (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+#define CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS inline
+#else
+#define CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS static
+#endif
+
+    /* base matrices */
+
+    /* these are not actually used in the runtime code. They are   */
+    /* used in computing the skipahead matrices, and are included  */
+    /* for reference                                               */
+    /*
+    double M1[3][3] = {        0.,       1.,       0.,
+                               0.,       0.,       1.,
+                         -810728., 1403580.,       0. };
+
+    double M2[3][3] = {        0.,       1.,       0.,
+                               0.,       0.,       1.,
+                        -1370589.,       0.,  527612. };
+
+    */
+
+    /* Base matrices to power 2 to the power n, n the first array index, from 0..63 */
+CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS unsigned int mrg32k3aM1[64][3][3] = {
+        {
+            {               0u,               1u,               0u },
+            {               0u,               0u,               1u },
+            {      4294156359u,         1403580u,               0u }
+        },
+        {
+            {               0u,               0u,               1u },
+            {      4294156359u,         1403580u,               0u },
+            {               0u,      4294156359u,         1403580u }
+        },
+        {
+            {               0u,      4294156359u,         1403580u },
+            {       244671815u,      2941890554u,      4294156359u },
+            {       149925673u,       489343630u,      2941890554u }
+        },
+        {
+            {      1527363550u,      2758233149u,      1831234280u },
+            {      4072640363u,       939574583u,      2758233149u },
+            {      2064391165u,      3228066636u,       939574583u }
+        },
+        {
+            {       736416029u,      2961816100u,       342112271u },
+            {       387300998u,      1062452522u,      2961816100u },
+            {      2955879160u,       340793741u,      1062452522u }
+        },
+        {
+            {      1243502014u,      2218748291u,      1709215645u },
+            {      2019641772u,      3847560959u,      2218748291u },
+            {      3866010231u,      2305448679u,      3847560959u }
+        },
+        {
+            {      3241775219u,      3453352062u,      3721871040u },
+            {      4062454730u,         3015754u,      3453352062u },
+            {       919711945u,       613405362u,         3015754u }
+        },
+        {
+            {      1955221006u,      1414472808u,      1746037714u },
+            {      3653507277u,      1644962013u,      1414472808u },
+            {      3501544776u,      2336229602u,      1644962013u }
+        },
+        {
+            {      1170096663u,        49135452u,      3441537107u },
+            {      1857945175u,      1649398389u,        49135452u },
+            {       333002869u,      3109147376u,      1649398389u }
+        },
+        {
+            {      2299034194u,      2297111910u,       862649200u },
+            {      1399961132u,       996706937u,      2297111910u },
+            {      3439056503u,      1481993076u,       996706937u }
+        },
+        {
+            {      4146310528u,       458782589u,      1007330283u },
+            {      4241015765u,      3979619964u,       458782589u },
+            {       553886495u,      2186897562u,      3979619964u }
+        },
+        {
+            {      3630027893u,      2130448350u,       292773857u },
+            {      1392525159u,      1299285967u,      2130448350u },
+            {      2589171163u,      1217405758u,      1299285967u }
+        },
+        {
+            {       892409263u,      1999175811u,      2979225418u },
+            {      1996163538u,      2148702503u,      1999175811u },
+            {      3922720782u,       103819730u,      2148702503u }
+        },
+        {
+            {      1586003016u,      2114210471u,      3240775579u },
+            {      2777288607u,      1400478398u,      2114210471u },
+            {      3018215420u,       535326008u,      1400478398u }
+        },
+        {
+            {      2188531273u,      1783231160u,      3576659343u },
+            {      1908318389u,       379210133u,      1783231160u },
+            {       554369329u,       250053591u,       379210133u }
+        },
+        {
+            {      4022841636u,      3951951872u,      2143424240u },
+            {      1046219306u,      1591992468u,      3951951872u },
+            {      1510277444u,       381333958u,      1591992468u }
+        },
+        {
+            {      2256493727u,      3715182130u,       642697923u },
+            {      3615342722u,      3975008370u,      3715182130u },
+            {      2405650329u,       754337639u,      3975008370u }
+        },
+        {
+            {      1286664224u,       627406673u,       963516608u },
+            {      1541344588u,       460768826u,       627406673u },
+            {      1089892553u,      2717717970u,       460768826u }
+        },
+        {
+            {      2956342842u,      3471097641u,      2353092905u },
+            {      2996150472u,       420480221u,      3471097641u },
+            {      2221681883u,       372736411u,       420480221u }
+        },
+        {
+            {       420492906u,       153526651u,      3499730988u },
+            {      2662640502u,      3278195133u,       153526651u },
+            {      4086436419u,      2510762118u,      3278195133u }
+        },
+        {
+            {      3310184147u,      2228376089u,       823220763u },
+            {      3992771814u,      1693168425u,      2228376089u },
+            {      2295790366u,      1401872772u,      1693168425u }
+        },
+        {
+            {      2529428830u,      1497104068u,      4253248635u },
+            {      3746310018u,       630867741u,      1497104068u },
+            {       627043435u,       721725795u,       630867741u }
+        },
+        {
+            {      2571072593u,      3039669025u,      1591031831u },
+            {       526054481u,       661344445u,      3039669025u },
+            {      4246010312u,       735391270u,       661344445u }
+        },
+        {
+            {      1847312821u,      4042890210u,      4241772463u },
+            {       606605705u,      2644799309u,      4042890210u },
+            {      2658402822u,      1342278931u,      2644799309u }
+        },
+        {
+            {      2409846784u,      1096138313u,      1416249993u },
+            {      1501878241u,       138013862u,      1096138313u },
+            {      1617749306u,      1975136163u,       138013862u }
+        },
+        {
+            {       599453422u,        73950522u,      2965395603u },
+            {        55354701u,      3855242202u,        73950522u },
+            {      3981734504u,      3354399019u,      3855242202u }
+        },
+        {
+            {      4271076381u,       813410089u,      3461955319u },
+            {      1044920137u,      3029005516u,       813410089u },
+            {      3501837362u,      3321539504u,      3029005516u }
+        },
+        {
+            {      3058183515u,       941408572u,      1783998098u },
+            {      1546486080u,      4116985007u,       941408572u },
+            {      2247500745u,      1460625377u,      4116985007u }
+        },
+        {
+            {      4216782514u,      3352801941u,      2315095646u },
+            {       639029973u,        94451952u,      3352801941u },
+            {      1242898773u,      3964593332u,        94451952u }
+        },
+        {
+            {      2264905138u,      1926285644u,      1108147171u },
+            {      2390706911u,       385258225u,      1926285644u },
+            {      3569882325u,      3728744670u,       385258225u }
+        },
+        {
+            {       270679073u,      1065683096u,      2992662885u },
+            {      4196917281u,      2886425156u,      1065683096u },
+            {       749134119u,      1849148167u,      2886425156u }
+        },
+        {
+            {        35689930u,      1378151623u,       951629713u },
+            {       673810920u,       948843427u,      1378151623u },
+            {      3808868984u,       927013635u,       948843427u }
+        },
+        {
+            {      1891490872u,      1130489594u,      3734864133u },
+            {      1457450350u,      3362920032u,      1130489594u },
+            {       638998846u,      1401175590u,      3362920032u }
+        },
+        {
+            {      2254459023u,      2384691454u,      1730098031u },
+            {      2844861718u,      1807491073u,      2384691454u },
+            {       351423668u,      1570264155u,      1807491073u }
+        },
+        {
+            {      3047429268u,      4245359555u,      2449575498u },
+            {      1797081212u,      1237196477u,      4245359555u },
+            {       143400628u,      3663731096u,      1237196477u }
+        },
+        {
+            {      3313321106u,      4263819658u,      1047529624u },
+            {      3719941673u,      3155049403u,      4263819658u },
+            {      1981313839u,      4281524426u,      3155049403u }
+        },
+        {
+            {      2005252417u,      3263186729u,      1535805957u },
+            {      2951515865u,      1729281525u,      3263186729u },
+            {      1141249417u,      2268963059u,      1729281525u }
+        },
+        {
+            {      2367065164u,        83908466u,      4294308508u },
+            {      1352516724u,      1416676049u,        83908466u },
+            {      1040867745u,      1304732377u,      1416676049u }
+        },
+        {
+            {      3214147257u,      1434230503u,      2944821434u },
+            {      2753040912u,      4041536918u,      1434230503u },
+            {      1317260239u,       338830578u,      4041536918u }
+        },
+        {
+            {       300628476u,      2054743463u,      1499597869u },
+            {      1762244284u,      1422043015u,      2054743463u },
+            {      3581125669u,      1207561803u,      1422043015u }
+        },
+        {
+            {      4171745404u,      4064983592u,      1934508265u },
+            {      3049723261u,      1744636487u,      4064983592u },
+            {       947753516u,      3952135907u,      1744636487u }
+        },
+        {
+            {      1625369148u,      3577024659u,      2778677259u },
+            {      1729967818u,      1049600974u,      3577024659u },
+            {      2089137344u,      1569794605u,      1049600974u }
+        },
+        {
+            {      1373068765u,      3958611830u,       569117280u },
+            {       410042396u,      3551255470u,      3958611830u },
+            {       869476379u,      1680625376u,      3551255470u }
+        },
+        {
+            {      2108618602u,      2543645250u,       913717833u },
+            {      2111984988u,      1012482542u,      2543645250u },
+            {      2545745615u,      3141042890u,      1012482542u }
+        },
+        {
+            {      1157293598u,       584852249u,      2272893205u },
+            {      1631801979u,      3013855247u,       584852249u },
+            {      3977310441u,        82049263u,      3013855247u }
+        },
+        {
+            {      3580234334u,      3137526662u,      2403875621u },
+            {      3580869206u,      3670086228u,      3137526662u },
+            {       656744553u,      1764904195u,      3670086228u }
+        },
+        {
+            {      2792496861u,      3634185196u,      3887031679u },
+            {      3601823850u,      3464838365u,      3634185196u },
+            {      3136165138u,      2842987937u,      3464838365u }
+        },
+        {
+            {      1362557480u,      3230022138u,      4278720212u },
+            {      3427386258u,      3848976950u,      3230022138u },
+            {      2109817045u,      2441486578u,      3848976950u }
+        },
+        {
+            {      1198519135u,      2007945401u,         3868481u },
+            {      3335076429u,      2082683147u,      2007945401u },
+            {      2341088247u,       888193479u,      2082683147u }
+        },
+        {
+            {      3473925387u,      3193380570u,       565138859u },
+            {       307060547u,       782210925u,      3193380570u },
+            {       167617770u,      2180014252u,       782210925u }
+        },
+        {
+            {      3811588895u,      3303532086u,      2766583698u },
+            {       908630605u,      2665400165u,      3303532086u },
+            {      2499994113u,      3316180851u,      2665400165u }
+        },
+        {
+            {      4288926968u,      3033075037u,      1505732852u },
+            {      1531633406u,       645804125u,      3033075037u },
+            {      2942690261u,      2205365640u,       645804125u }
+        },
+        {
+            {      3976196483u,      3651411522u,      1652430357u },
+            {      1690405883u,      1294990760u,      3651411522u },
+            {       209339647u,      3088484327u,      1294990760u }
+        },
+        {
+            {      3171589548u,      2291131070u,      2093793287u },
+            {      2997812074u,      4093879780u,      2291131070u },
+            {      3255666800u,       858124816u,      4093879780u }
+        },
+        {
+            {      4113016361u,      2999667479u,      3995043314u },
+            {      1333973326u,      4007774239u,      2999667479u },
+            {      3322921863u,      4278103786u,      4007774239u }
+        },
+        {
+            {       925786347u,      2109676036u,      1879981040u },
+            {      1701566570u,      1489702270u,      2109676036u },
+            {      2719807628u,       158549605u,      1489702270u }
+        },
+        {
+            {      2255405265u,      3460246357u,       218033453u },
+            {      2135115875u,       359516994u,      3460246357u },
+            {      3568862459u,      3114762683u,       359516994u }
+        },
+        {
+            {       773148471u,      4117539411u,      3073622315u },
+            {      3807175775u,       186466108u,      4117539411u },
+            {      2842197411u,       651334129u,       186466108u }
+        },
+        {
+            {       615242951u,      1475251263u,      3586439101u },
+            {      1693917167u,      3058812486u,      1475251263u },
+            {       568701600u,      1164226398u,      3058812486u }
+        },
+        {
+            {      1632636204u,        15370275u,      2061555515u },
+            {      4187505695u,      1741164221u,        15370275u },
+            {      2882176274u,      3978412194u,      1741164221u }
+        },
+        {
+            {      3446066703u,       344820524u,        74213775u },
+            {      1008543583u,      2579620192u,       344820524u },
+            {      3753911358u,      1538453821u,      2579620192u }
+        },
+        {
+            {      3600859892u,      1269921024u,      4069458760u },
+            {      2050939727u,      2222725697u,      1269921024u },
+            {      3208347646u,       690898125u,      2222725697u }
+        },
+        {
+            {       599407451u,      2806239788u,      1742216102u },
+            {       975123999u,       764869161u,      2806239788u },
+            {      2729710367u,      1845257036u,       764869161u }
+        },
+        {
+            {       967330218u,      3464884028u,      3444447102u },
+            {       580449578u,      1343714307u,      3464884028u },
+            {      1775329096u,      4027221761u,      1343714307u }
+        }
+    };
+
+#ifndef __CUDACC_RTC__
+CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS unsigned int mrg32k3aM1Host[64][3][3] = {
+        {
+            {               0u,               1u,               0u },
+            {               0u,               0u,               1u },
+            {      4294156359u,         1403580u,               0u }
+        },
+        {
+            {               0u,               0u,               1u },
+            {      4294156359u,         1403580u,               0u },
+            {               0u,      4294156359u,         1403580u }
+        },
+        {
+            {               0u,      4294156359u,         1403580u },
+            {       244671815u,      2941890554u,      4294156359u },
+            {       149925673u,       489343630u,      2941890554u }
+        },
+        {
+            {      1527363550u,      2758233149u,      1831234280u },
+            {      4072640363u,       939574583u,      2758233149u },
+            {      2064391165u,      3228066636u,       939574583u }
+        },
+        {
+            {       736416029u,      2961816100u,       342112271u },
+            {       387300998u,      1062452522u,      2961816100u },
+            {      2955879160u,       340793741u,      1062452522u }
+        },
+        {
+            {      1243502014u,      2218748291u,      1709215645u },
+            {      2019641772u,      3847560959u,      2218748291u },
+            {      3866010231u,      2305448679u,      3847560959u }
+        },
+        {
+            {      3241775219u,      3453352062u,      3721871040u },
+            {      4062454730u,         3015754u,      3453352062u },
+            {       919711945u,       613405362u,         3015754u }
+        },
+        {
+            {      1955221006u,      1414472808u,      1746037714u },
+            {      3653507277u,      1644962013u,      1414472808u },
+            {      3501544776u,      2336229602u,      1644962013u }
+        },
+        {
+            {      1170096663u,        49135452u,      3441537107u },
+            {      1857945175u,      1649398389u,        49135452u },
+            {       333002869u,      3109147376u,      1649398389u }
+        },
+        {
+            {      2299034194u,      2297111910u,       862649200u },
+            {      1399961132u,       996706937u,      2297111910u },
+            {      3439056503u,      1481993076u,       996706937u }
+        },
+        {
+            {      4146310528u,       458782589u,      1007330283u },
+            {      4241015765u,      3979619964u,       458782589u },
+            {       553886495u,      2186897562u,      3979619964u }
+        },
+        {
+            {      3630027893u,      2130448350u,       292773857u },
+            {      1392525159u,      1299285967u,      2130448350u },
+            {      2589171163u,      1217405758u,      1299285967u }
+        },
+        {
+            {       892409263u,      1999175811u,      2979225418u },
+            {      1996163538u,      2148702503u,      1999175811u },
+            {      3922720782u,       103819730u,      2148702503u }
+        },
+        {
+            {      1586003016u,      2114210471u,      3240775579u },
+            {      2777288607u,      1400478398u,      2114210471u },
+            {      3018215420u,       535326008u,      1400478398u }
+        },
+        {
+            {      2188531273u,      1783231160u,      3576659343u },
+            {      1908318389u,       379210133u,      1783231160u },
+            {       554369329u,       250053591u,       379210133u }
+        },
+        {
+            {      4022841636u,      3951951872u,      2143424240u },
+            {      1046219306u,      1591992468u,      3951951872u },
+            {      1510277444u,       381333958u,      1591992468u }
+        },
+        {
+            {      2256493727u,      3715182130u,       642697923u },
+            {      3615342722u,      3975008370u,      3715182130u },
+            {      2405650329u,       754337639u,      3975008370u }
+        },
+        {
+            {      1286664224u,       627406673u,       963516608u },
+            {      1541344588u,       460768826u,       627406673u },
+            {      1089892553u,      2717717970u,       460768826u }
+        },
+        {
+            {      2956342842u,      3471097641u,      2353092905u },
+            {      2996150472u,       420480221u,      3471097641u },
+            {      2221681883u,       372736411u,       420480221u }
+        },
+        {
+            {       420492906u,       153526651u,      3499730988u },
+            {      2662640502u,      3278195133u,       153526651u },
+            {      4086436419u,      2510762118u,      3278195133u }
+        },
+        {
+            {      3310184147u,      2228376089u,       823220763u },
+            {      3992771814u,      1693168425u,      2228376089u },
+            {      2295790366u,      1401872772u,      1693168425u }
+        },
+        {
+            {      2529428830u,      1497104068u,      4253248635u },
+            {      3746310018u,       630867741u,      1497104068u },
+            {       627043435u,       721725795u,       630867741u }
+        },
+        {
+            {      2571072593u,      3039669025u,      1591031831u },
+            {       526054481u,       661344445u,      3039669025u },
+            {      4246010312u,       735391270u,       661344445u }
+        },
+        {
+            {      1847312821u,      4042890210u,      4241772463u },
+            {       606605705u,      2644799309u,      4042890210u },
+            {      2658402822u,      1342278931u,      2644799309u }
+        },
+        {
+            {      2409846784u,      1096138313u,      1416249993u },
+            {      1501878241u,       138013862u,      1096138313u },
+            {      1617749306u,      1975136163u,       138013862u }
+        },
+        {
+            {       599453422u,        73950522u,      2965395603u },
+            {        55354701u,      3855242202u,        73950522u },
+            {      3981734504u,      3354399019u,      3855242202u }
+        },
+        {
+            {      4271076381u,       813410089u,      3461955319u },
+            {      1044920137u,      3029005516u,       813410089u },
+            {      3501837362u,      3321539504u,      3029005516u }
+        },
+        {
+            {      3058183515u,       941408572u,      1783998098u },
+            {      1546486080u,      4116985007u,       941408572u },
+            {      2247500745u,      1460625377u,      4116985007u }
+        },
+        {
+            {      4216782514u,      3352801941u,      2315095646u },
+            {       639029973u,        94451952u,      3352801941u },
+            {      1242898773u,      3964593332u,        94451952u }
+        },
+        {
+            {      2264905138u,      1926285644u,      1108147171u },
+            {      2390706911u,       385258225u,      1926285644u },
+            {      3569882325u,      3728744670u,       385258225u }
+        },
+        {
+            {       270679073u,      1065683096u,      2992662885u },
+            {      4196917281u,      2886425156u,      1065683096u },
+            {       749134119u,      1849148167u,      2886425156u }
+        },
+        {
+            {        35689930u,      1378151623u,       951629713u },
+            {       673810920u,       948843427u,      1378151623u },
+            {      3808868984u,       927013635u,       948843427u }
+        },
+        {
+            {      1891490872u,      1130489594u,      3734864133u },
+            {      1457450350u,      3362920032u,      1130489594u },
+            {       638998846u,      1401175590u,      3362920032u }
+        },
+        {
+            {      2254459023u,      2384691454u,      1730098031u },
+            {      2844861718u,      1807491073u,      2384691454u },
+            {       351423668u,      1570264155u,      1807491073u }
+        },
+        {
+            {      3047429268u,      4245359555u,      2449575498u },
+            {      1797081212u,      1237196477u,      4245359555u },
+            {       143400628u,      3663731096u,      1237196477u }
+        },
+        {
+            {      3313321106u,      4263819658u,      1047529624u },
+            {      3719941673u,      3155049403u,      4263819658u },
+            {      1981313839u,      4281524426u,      3155049403u }
+        },
+        {
+            {      2005252417u,      3263186729u,      1535805957u },
+            {      2951515865u,      1729281525u,      3263186729u },
+            {      1141249417u,      2268963059u,      1729281525u }
+        },
+        {
+            {      2367065164u,        83908466u,      4294308508u },
+            {      1352516724u,      1416676049u,        83908466u },
+            {      1040867745u,      1304732377u,      1416676049u }
+        },
+        {
+            {      3214147257u,      1434230503u,      2944821434u },
+            {      2753040912u,      4041536918u,      1434230503u },
+            {      1317260239u,       338830578u,      4041536918u }
+        },
+        {
+            {       300628476u,      2054743463u,      1499597869u },
+            {      1762244284u,      1422043015u,      2054743463u },
+            {      3581125669u,      1207561803u,      1422043015u }
+        },
+        {
+            {      4171745404u,      4064983592u,      1934508265u },
+            {      3049723261u,      1744636487u,      4064983592u },
+            {       947753516u,      3952135907u,      1744636487u }
+        },
+        {
+            {      1625369148u,      3577024659u,      2778677259u },
+            {      1729967818u,      1049600974u,      3577024659u },
+            {      2089137344u,      1569794605u,      1049600974u }
+        },
+        {
+            {      1373068765u,      3958611830u,       569117280u },
+            {       410042396u,      3551255470u,      3958611830u },
+            {       869476379u,      1680625376u,      3551255470u }
+        },
+        {
+            {      2108618602u,      2543645250u,       913717833u },
+            {      2111984988u,      1012482542u,      2543645250u },
+            {      2545745615u,      3141042890u,      1012482542u }
+        },
+        {
+            {      1157293598u,       584852249u,      2272893205u },
+            {      1631801979u,      3013855247u,       584852249u },
+            {      3977310441u,        82049263u,      3013855247u }
+        },
+        {
+            {      3580234334u,      3137526662u,      2403875621u },
+            {      3580869206u,      3670086228u,      3137526662u },
+            {       656744553u,      1764904195u,      3670086228u }
+        },
+        {
+            {      2792496861u,      3634185196u,      3887031679u },
+            {      3601823850u,      3464838365u,      3634185196u },
+            {      3136165138u,      2842987937u,      3464838365u }
+        },
+        {
+            {      1362557480u,      3230022138u,      4278720212u },
+            {      3427386258u,      3848976950u,      3230022138u },
+            {      2109817045u,      2441486578u,      3848976950u }
+        },
+        {
+            {      1198519135u,      2007945401u,         3868481u },
+            {      3335076429u,      2082683147u,      2007945401u },
+            {      2341088247u,       888193479u,      2082683147u }
+        },
+        {
+            {      3473925387u,      3193380570u,       565138859u },
+            {       307060547u,       782210925u,      3193380570u },
+            {       167617770u,      2180014252u,       782210925u }
+        },
+        {
+            {      3811588895u,      3303532086u,      2766583698u },
+            {       908630605u,      2665400165u,      3303532086u },
+            {      2499994113u,      3316180851u,      2665400165u }
+        },
+        {
+            {      4288926968u,      3033075037u,      1505732852u },
+            {      1531633406u,       645804125u,      3033075037u },
+            {      2942690261u,      2205365640u,       645804125u }
+        },
+        {
+            {      3976196483u,      3651411522u,      1652430357u },
+            {      1690405883u,      1294990760u,      3651411522u },
+            {       209339647u,      3088484327u,      1294990760u }
+        },
+        {
+            {      3171589548u,      2291131070u,      2093793287u },
+            {      2997812074u,      4093879780u,      2291131070u },
+            {      3255666800u,       858124816u,      4093879780u }
+        },
+        {
+            {      4113016361u,      2999667479u,      3995043314u },
+            {      1333973326u,      4007774239u,      2999667479u },
+            {      3322921863u,      4278103786u,      4007774239u }
+        },
+        {
+            {       925786347u,      2109676036u,      1879981040u },
+            {      1701566570u,      1489702270u,      2109676036u },
+            {      2719807628u,       158549605u,      1489702270u }
+        },
+        {
+            {      2255405265u,      3460246357u,       218033453u },
+            {      2135115875u,       359516994u,      3460246357u },
+            {      3568862459u,      3114762683u,       359516994u }
+        },
+        {
+            {       773148471u,      4117539411u,      3073622315u },
+            {      3807175775u,       186466108u,      4117539411u },
+            {      2842197411u,       651334129u,       186466108u }
+        },
+        {
+            {       615242951u,      1475251263u,      3586439101u },
+            {      1693917167u,      3058812486u,      1475251263u },
+            {       568701600u,      1164226398u,      3058812486u }
+        },
+        {
+            {      1632636204u,        15370275u,      2061555515u },
+            {      4187505695u,      1741164221u,        15370275u },
+            {      2882176274u,      3978412194u,      1741164221u }
+        },
+        {
+            {      3446066703u,       344820524u,        74213775u },
+            {      1008543583u,      2579620192u,       344820524u },
+            {      3753911358u,      1538453821u,      2579620192u }
+        },
+        {
+            {      3600859892u,      1269921024u,      4069458760u },
+            {      2050939727u,      2222725697u,      1269921024u },
+            {      3208347646u,       690898125u,      2222725697u }
+        },
+        {
+            {       599407451u,      2806239788u,      1742216102u },
+            {       975123999u,       764869161u,      2806239788u },
+            {      2729710367u,      1845257036u,       764869161u }
+        },
+        {
+            {       967330218u,      3464884028u,      3444447102u },
+            {       580449578u,      1343714307u,      3464884028u },
+            {      1775329096u,      4027221761u,      1343714307u }
+        }
+    };
+#endif // ifndef __CUDACC_RTC__
+
+CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS unsigned int mrg32k3aM2[64][3][3] = {
+        {
+            {               0u,               1u,               0u },
+            {               0u,               0u,               1u },
+            {      4293573854u,               0u,          527612u }
+        },
+        {
+            {               0u,               0u,               1u },
+            {      4293573854u,               0u,          527612u },
+            {      2706407399u,      4293573854u,      3497978192u }
+        },
+        {
+            {      2706407399u,      4293573854u,      3497978192u },
+            {      1431525864u,      2706407399u,      3281754271u },
+            {        97673890u,      1431525864u,      1673476130u }
+        },
+        {
+            {      3405842137u,      2680076935u,       893509979u },
+            {      4035147174u,      3405842137u,      3280220074u },
+            {      2623373296u,      4035147174u,       361718588u }
+        },
+        {
+            {       818368950u,      3790774567u,      3542344109u },
+            {      1817134745u,       818368950u,      3321940838u },
+            {      3493477402u,      1817134745u,      2854655037u }
+        },
+        {
+            {       498682467u,      2928649385u,       811441367u },
+            {      1777037472u,       498682467u,       479207863u },
+            {      3058260025u,      1777037472u,      1528225099u }
+        },
+        {
+            {      3893311647u,      3140922085u,        64039185u },
+            {        82107183u,      3893311647u,      2655465224u },
+            {      1674879036u,        82107183u,      1089381262u }
+        },
+        {
+            {        28639152u,      3496041927u,      2231910770u },
+            {      3174683233u,        28639152u,      2828785870u },
+            {      3681140872u,      3174683233u,      3910194649u }
+        },
+        {
+            {      1463826069u,       300842059u,      3313769518u },
+            {      1799677538u,      1463826069u,      3174861078u },
+            {      1882279394u,      1799677538u,      3509975160u }
+        },
+        {
+            {      2092194020u,       184076987u,      2202401252u },
+            {      3103629604u,      2092194020u,      3409560232u },
+            {      4257445059u,      3103629604u,      2390202783u }
+        },
+        {
+            {       812917091u,      2574011276u,      4168802395u },
+            {       209817750u,       812917091u,      2974870628u },
+            {      3238802184u,       209817750u,      3692836406u }
+        },
+        {
+            {       477309738u,      3314523413u,      3442242150u },
+            {      2755731404u,       477309738u,      2782713347u },
+            {      1606221490u,      2755731404u,      1033463096u }
+        },
+        {
+            {      2155469603u,      3326516116u,      3843369786u },
+            {       288604458u,      2155469603u,       571673571u },
+            {      1501677614u,       288604458u,      2928213494u }
+        },
+        {
+            {      2082469029u,       749754403u,      3963963316u },
+            {      2764859700u,      2082469029u,      3576428059u },
+            {      2840894706u,      2764859700u,      1782279859u }
+        },
+        {
+            {      3760163766u,      1041986082u,      1799196192u },
+            {      1022129134u,      3760163766u,      1332558840u },
+            {       276873446u,      1022129134u,      3979423632u }
+        },
+        {
+            {      1021313167u,      1312544548u,      1716381787u },
+            {      3037868518u,      1021313167u,       199085085u },
+            {      2582787611u,      3037868518u,      3539882179u }
+        },
+        {
+            {      2569413030u,      1631336015u,      2594942403u },
+            {      1030618503u,      2569413030u,      3467650326u },
+            {      1998739584u,      1030618503u,      3174552073u }
+        },
+        {
+            {      2334639309u,      3114094203u,       601680947u },
+            {      2110199318u,      2334639309u,       678342865u },
+            {      1649523168u,      2110199318u,      2154948056u }
+        },
+        {
+            {       563657176u,       191330473u,      1641595774u },
+            {       780563537u,       563657176u,      3029522338u },
+            {      2037330914u,       780563537u,      2084602709u }
+        },
+        {
+            {      3414769923u,      1968799026u,      2238126504u },
+            {       832866376u,      3414769923u,      3754780168u },
+            {      2165145850u,       832866376u,      1594768331u }
+        },
+        {
+            {      1646861218u,      2317984620u,      2301581548u },
+            {      2672536210u,      1646861218u,       359763062u },
+            {      2391283983u,      2672536210u,      1885870777u }
+        },
+        {
+            {       841254072u,      3765813448u,      1635365181u },
+            {      2013240130u,       841254072u,       605925849u },
+            {      3743932305u,      2013240130u,       400681955u }
+        },
+        {
+            {      1930213004u,      2072952279u,      3077694794u },
+            {      3579956569u,      1930213004u,      2478539210u },
+            {      1960229502u,      3579956569u,      1455652656u }
+        },
+        {
+            {      1097613522u,      1784540933u,      1194440107u },
+            {       321747515u,      1097613522u,      1225209584u },
+            {        74521379u,       321747515u,      4288531000u }
+        },
+        {
+            {       143812745u,      3254530816u,      3514348856u },
+            {       769295000u,       143812745u,      2468210728u },
+            {      1927161272u,       769295000u,       522705580u }
+        },
+        {
+            {      2692035063u,      2596905012u,      1643240704u },
+            {      1103432342u,      2692035063u,      1446182108u },
+            {      4161111774u,      1103432342u,      3076435551u }
+        },
+        {
+            {      2375319030u,      1391532370u,      3742334018u },
+            {      1202100604u,      2375319030u,      4098434768u },
+            {      2327872488u,      1202100604u,      1471526950u }
+        },
+        {
+            {      4269164791u,      2795313144u,      2507855960u },
+            {      4245372460u,      4269164791u,      4094914553u },
+            {      3873219634u,      4245372460u,      1473695507u }
+        },
+        {
+            {       513890845u,      1208902926u,      2870530442u },
+            {      1984873167u,       513890845u,      1257532340u },
+            {      1212627640u,      1984873167u,      2354363842u }
+        },
+        {
+            {      1848364568u,      1552116673u,      3496528455u },
+            {      4160778291u,      1848364568u,       141769900u },
+            {      3611019106u,      4160778291u,       596424080u }
+        },
+        {
+            {       364070020u,      3520039729u,       837362349u },
+            {      2544671570u,       364070020u,      2188646679u },
+            {       163978331u,      2544671570u,       672947816u }
+        },
+        {
+            {      1192700714u,      3968150021u,       298357363u },
+            {       635565666u,      1192700714u,      2589432341u },
+            {      2548654227u,       635565666u,      3531570992u }
+        },
+        {
+            {      2709640529u,       676525399u,       875361870u },
+            {      1315499519u,      2709640529u,      3842690720u },
+            {      3300994644u,      1315499519u,      2446760804u }
+        },
+        {
+            {      2742149264u,      1410604392u,      3032350755u },
+            {      3774935330u,      2742149264u,       597633965u },
+            {      4085935803u,      3774935330u,      3952463556u }
+        },
+        {
+            {      3878579563u,       845297523u,      1721916511u },
+            {      2077922420u,      3878579563u,      3651360351u },
+            {      2177255734u,      2077922420u,      3791239282u }
+        },
+        {
+            {      1570315355u,      4252790045u,      3522351060u },
+            {      2324624266u,      1570315355u,      3594939336u },
+            {      1725087354u,      2324624266u,      1338343327u }
+        },
+        {
+            {      2305761589u,       381933244u,      3663579047u },
+            {      1355307047u,      2305761589u,       313617972u },
+            {       992174375u,      1355307047u,      3881593435u }
+        },
+        {
+            {      1667857811u,      1564715297u,      2263851601u },
+            {      3791771273u,      1667857811u,      4196134923u },
+            {      3347975047u,      3791771273u,       615040705u }
+        },
+        {
+            {      4093947334u,      3454015638u,      2815567716u },
+            {      4261953004u,      4093947334u,      3973733876u },
+            {      2979573134u,      4261953004u,      3757047667u }
+        },
+        {
+            {       250120061u,       570149551u,      1513430926u },
+            {      3178644752u,       250120061u,      1701869032u },
+            {      4172515680u,      3178644752u,      4213855850u }
+        },
+        {
+            {      4158106802u,      3062358456u,      1815738463u },
+            {      1379176112u,      4158106802u,      3926509890u },
+            {      2842564878u,      1379176112u,      2852219546u }
+        },
+        {
+            {       931848746u,       256263523u,      2633569246u },
+            {      3284646837u,       931848746u,      2567084715u },
+            {       415258465u,      3284646837u,      2017565947u }
+        },
+        {
+            {      1648005210u,      1032291296u,      3987397422u },
+            {      1831496020u,      1648005210u,      2829448427u },
+            {      1821082272u,      1831496020u,      2917140265u }
+        },
+        {
+            {      4161327077u,       489964129u,      3870847744u },
+            {      1669447863u,      4161327077u,      4292947198u },
+            {      1522417114u,      1669447863u,      2652286672u }
+        },
+        {
+            {      1270934555u,      3136631324u,       505612043u },
+            {      2981474723u,      1270934555u,      2528619024u },
+            {       625182639u,      2981474723u,      1008985039u }
+        },
+        {
+            {       280996820u,       143706137u,      3013099060u },
+            {      1797675893u,       280996820u,      3743985508u },
+            {      1123794455u,      1797675893u,      2460119169u }
+        },
+        {
+            {       919218027u,      4154920441u,      1125672685u },
+            {      3933041881u,       919218027u,       474242849u },
+            {       564891116u,      3933041881u,      2263904321u }
+        },
+        {
+            {      2920112852u,      1965329198u,      1177141043u },
+            {      2135250851u,      2920112852u,       969184056u },
+            {       296035385u,      2135250851u,      4267827987u }
+        },
+        {
+            {      1481142942u,      4120754772u,      1088557292u },
+            {       265491023u,      1481142942u,      2860005744u },
+            {       301796252u,       265491023u,      1935975979u }
+        },
+        {
+            {      2111859033u,      2813610100u,      1001476468u },
+            {        73849832u,      2111859033u,      3980799998u },
+            {      3330206241u,        73849832u,      1933943506u }
+        },
+        {
+            {      1781286360u,      3661231931u,      3509383709u },
+            {      2753158871u,      1781286360u,      3119883109u },
+            {      3576525143u,      2753158871u,       551079002u }
+        },
+        {
+            {      1185024844u,       587779104u,      1004942725u },
+            {      3763632860u,      1185024844u,       947424568u },
+            {      3811666068u,      3763632860u,      2352253462u }
+        },
+        {
+            {      1310227170u,       218138208u,      3172947233u },
+            {       766129426u,      1310227170u,      1808643264u },
+            {      2226659371u,       766129426u,      3853798112u }
+        },
+        {
+            {      2230902378u,      4243560874u,      2491962392u },
+            {      3836629116u,      2230902378u,      3637515403u },
+            {      2846140932u,      3836629116u,      3083355464u }
+        },
+        {
+            {       999448569u,      1464488480u,      3344426626u },
+            {       946166795u,       999448569u,       340856814u },
+            {      3686999436u,       946166795u,      3231079441u }
+        },
+        {
+            {      1226155368u,      3477563770u,       550006884u },
+            {      2378667355u,      1226155368u,      1493409040u },
+            {       260364836u,      2378667355u,      4133888397u }
+        },
+        {
+            {      1277901832u,       310796286u,      2818511068u },
+            {      3088910653u,      1277901832u,      3303406025u },
+            {      2507911914u,      3088910653u,      3712928074u }
+        },
+        {
+            {       481918378u,       339570348u,      1728801469u },
+            {      1623163429u,       481918378u,      2209094694u },
+            {      3146982514u,      1623163429u,       508445538u }
+        },
+        {
+            {      3138921230u,      2381863183u,      1992357430u },
+            {      1024510915u,      3138921230u,      2122851650u },
+            {      1453455184u,      1024510915u,       941946604u }
+        },
+        {
+            {      2465372719u,      1391015357u,      3328905025u },
+            {      1821933605u,      2465372719u,      1343489680u },
+            {      3648970313u,      1821933605u,      1816599716u }
+        },
+        {
+            {       118634664u,      3358712512u,      2492792220u },
+            {       348833376u,       118634664u,      2495544591u },
+            {      3235582254u,       348833376u,      4043157504u }
+        },
+        {
+            {      2303067090u,      3371139074u,      1967771133u },
+            {       598630070u,      2303067090u,      1819012637u },
+            {      2049250561u,       598630070u,      4093044926u }
+        },
+        {
+            {      3035321857u,      3971176093u,       226779704u },
+            {      3361614254u,      3035321857u,      2807125404u },
+            {       326640887u,      3361614254u,      3147308542u }
+        },
+        {
+            {      1774298149u,      4179629947u,      3145006948u },
+            {      1688753503u,      1774298149u,        94869516u },
+            {      2327946901u,      1688753503u,      2786835219u }
+        }
+    };
+
+#ifndef __CUDACC_RTC__
+CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS unsigned int mrg32k3aM2Host[64][3][3] = {
+        {
+            {               0u,               1u,               0u },
+            {               0u,               0u,               1u },
+            {      4293573854u,               0u,          527612u }
+        },
+        {
+            {               0u,               0u,               1u },
+            {      4293573854u,               0u,          527612u },
+            {      2706407399u,      4293573854u,      3497978192u }
+        },
+        {
+            {      2706407399u,      4293573854u,      3497978192u },
+            {      1431525864u,      2706407399u,      3281754271u },
+            {        97673890u,      1431525864u,      1673476130u }
+        },
+        {
+            {      3405842137u,      2680076935u,       893509979u },
+            {      4035147174u,      3405842137u,      3280220074u },
+            {      2623373296u,      4035147174u,       361718588u }
+        },
+        {
+            {       818368950u,      3790774567u,      3542344109u },
+            {      1817134745u,       818368950u,      3321940838u },
+            {      3493477402u,      1817134745u,      2854655037u }
+        },
+        {
+            {       498682467u,      2928649385u,       811441367u },
+            {      1777037472u,       498682467u,       479207863u },
+            {      3058260025u,      1777037472u,      1528225099u }
+        },
+        {
+            {      3893311647u,      3140922085u,        64039185u },
+            {        82107183u,      3893311647u,      2655465224u },
+            {      1674879036u,        82107183u,      1089381262u }
+        },
+        {
+            {        28639152u,      3496041927u,      2231910770u },
+            {      3174683233u,        28639152u,      2828785870u },
+            {      3681140872u,      3174683233u,      3910194649u }
+        },
+        {
+            {      1463826069u,       300842059u,      3313769518u },
+            {      1799677538u,      1463826069u,      3174861078u },
+            {      1882279394u,      1799677538u,      3509975160u }
+        },
+        {
+            {      2092194020u,       184076987u,      2202401252u },
+            {      3103629604u,      2092194020u,      3409560232u },
+            {      4257445059u,      3103629604u,      2390202783u }
+        },
+        {
+            {       812917091u,      2574011276u,      4168802395u },
+            {       209817750u,       812917091u,      2974870628u },
+            {      3238802184u,       209817750u,      3692836406u }
+        },
+        {
+            {       477309738u,      3314523413u,      3442242150u },
+            {      2755731404u,       477309738u,      2782713347u },
+            {      1606221490u,      2755731404u,      1033463096u }
+        },
+        {
+            {      2155469603u,      3326516116u,      3843369786u },
+            {       288604458u,      2155469603u,       571673571u },
+            {      1501677614u,       288604458u,      2928213494u }
+        },
+        {
+            {      2082469029u,       749754403u,      3963963316u },
+            {      2764859700u,      2082469029u,      3576428059u },
+            {      2840894706u,      2764859700u,      1782279859u }
+        },
+        {
+            {      3760163766u,      1041986082u,      1799196192u },
+            {      1022129134u,      3760163766u,      1332558840u },
+            {       276873446u,      1022129134u,      3979423632u }
+        },
+        {
+            {      1021313167u,      1312544548u,      1716381787u },
+            {      3037868518u,      1021313167u,       199085085u },
+            {      2582787611u,      3037868518u,      3539882179u }
+        },
+        {
+            {      2569413030u,      1631336015u,      2594942403u },
+            {      1030618503u,      2569413030u,      3467650326u },
+            {      1998739584u,      1030618503u,      3174552073u }
+        },
+        {
+            {      2334639309u,      3114094203u,       601680947u },
+            {      2110199318u,      2334639309u,       678342865u },
+            {      1649523168u,      2110199318u,      2154948056u }
+        },
+        {
+            {       563657176u,       191330473u,      1641595774u },
+            {       780563537u,       563657176u,      3029522338u },
+            {      2037330914u,       780563537u,      2084602709u }
+        },
+        {
+            {      3414769923u,      1968799026u,      2238126504u },
+            {       832866376u,      3414769923u,      3754780168u },
+            {      2165145850u,       832866376u,      1594768331u }
+        },
+        {
+            {      1646861218u,      2317984620u,      2301581548u },
+            {      2672536210u,      1646861218u,       359763062u },
+            {      2391283983u,      2672536210u,      1885870777u }
+        },
+        {
+            {       841254072u,      3765813448u,      1635365181u },
+            {      2013240130u,       841254072u,       605925849u },
+            {      3743932305u,      2013240130u,       400681955u }
+        },
+        {
+            {      1930213004u,      2072952279u,      3077694794u },
+            {      3579956569u,      1930213004u,      2478539210u },
+            {      1960229502u,      3579956569u,      1455652656u }
+        },
+        {
+            {      1097613522u,      1784540933u,      1194440107u },
+            {       321747515u,      1097613522u,      1225209584u },
+            {        74521379u,       321747515u,      4288531000u }
+        },
+        {
+            {       143812745u,      3254530816u,      3514348856u },
+            {       769295000u,       143812745u,      2468210728u },
+            {      1927161272u,       769295000u,       522705580u }
+        },
+        {
+            {      2692035063u,      2596905012u,      1643240704u },
+            {      1103432342u,      2692035063u,      1446182108u },
+            {      4161111774u,      1103432342u,      3076435551u }
+        },
+        {
+            {      2375319030u,      1391532370u,      3742334018u },
+            {      1202100604u,      2375319030u,      4098434768u },
+            {      2327872488u,      1202100604u,      1471526950u }
+        },
+        {
+            {      4269164791u,      2795313144u,      2507855960u },
+            {      4245372460u,      4269164791u,      4094914553u },
+            {      3873219634u,      4245372460u,      1473695507u }
+        },
+        {
+            {       513890845u,      1208902926u,      2870530442u },
+            {      1984873167u,       513890845u,      1257532340u },
+            {      1212627640u,      1984873167u,      2354363842u }
+        },
+        {
+            {      1848364568u,      1552116673u,      3496528455u },
+            {      4160778291u,      1848364568u,       141769900u },
+            {      3611019106u,      4160778291u,       596424080u }
+        },
+        {
+            {       364070020u,      3520039729u,       837362349u },
+            {      2544671570u,       364070020u,      2188646679u },
+            {       163978331u,      2544671570u,       672947816u }
+        },
+        {
+            {      1192700714u,      3968150021u,       298357363u },
+            {       635565666u,      1192700714u,      2589432341u },
+            {      2548654227u,       635565666u,      3531570992u }
+        },
+        {
+            {      2709640529u,       676525399u,       875361870u },
+            {      1315499519u,      2709640529u,      3842690720u },
+            {      3300994644u,      1315499519u,      2446760804u }
+        },
+        {
+            {      2742149264u,      1410604392u,      3032350755u },
+            {      3774935330u,      2742149264u,       597633965u },
+            {      4085935803u,      3774935330u,      3952463556u }
+        },
+        {
+            {      3878579563u,       845297523u,      1721916511u },
+            {      2077922420u,      3878579563u,      3651360351u },
+            {      2177255734u,      2077922420u,      3791239282u }
+        },
+        {
+            {      1570315355u,      4252790045u,      3522351060u },
+            {      2324624266u,      1570315355u,      3594939336u },
+            {      1725087354u,      2324624266u,      1338343327u }
+        },
+        {
+            {      2305761589u,       381933244u,      3663579047u },
+            {      1355307047u,      2305761589u,       313617972u },
+            {       992174375u,      1355307047u,      3881593435u }
+        },
+        {
+            {      1667857811u,      1564715297u,      2263851601u },
+            {      3791771273u,      1667857811u,      4196134923u },
+            {      3347975047u,      3791771273u,       615040705u }
+        },
+        {
+            {      4093947334u,      3454015638u,      2815567716u },
+            {      4261953004u,      4093947334u,      3973733876u },
+            {      2979573134u,      4261953004u,      3757047667u }
+        },
+        {
+            {       250120061u,       570149551u,      1513430926u },
+            {      3178644752u,       250120061u,      1701869032u },
+            {      4172515680u,      3178644752u,      4213855850u }
+        },
+        {
+            {      4158106802u,      3062358456u,      1815738463u },
+            {      1379176112u,      4158106802u,      3926509890u },
+            {      2842564878u,      1379176112u,      2852219546u }
+        },
+        {
+            {       931848746u,       256263523u,      2633569246u },
+            {      3284646837u,       931848746u,      2567084715u },
+            {       415258465u,      3284646837u,      2017565947u }
+        },
+        {
+            {      1648005210u,      1032291296u,      3987397422u },
+            {      1831496020u,      1648005210u,      2829448427u },
+            {      1821082272u,      1831496020u,      2917140265u }
+        },
+        {
+            {      4161327077u,       489964129u,      3870847744u },
+            {      1669447863u,      4161327077u,      4292947198u },
+            {      1522417114u,      1669447863u,      2652286672u }
+        },
+        {
+            {      1270934555u,      3136631324u,       505612043u },
+            {      2981474723u,      1270934555u,      2528619024u },
+            {       625182639u,      2981474723u,      1008985039u }
+        },
+        {
+            {       280996820u,       143706137u,      3013099060u },
+            {      1797675893u,       280996820u,      3743985508u },
+            {      1123794455u,      1797675893u,      2460119169u }
+        },
+        {
+            {       919218027u,      4154920441u,      1125672685u },
+            {      3933041881u,       919218027u,       474242849u },
+            {       564891116u,      3933041881u,      2263904321u }
+        },
+        {
+            {      2920112852u,      1965329198u,      1177141043u },
+            {      2135250851u,      2920112852u,       969184056u },
+            {       296035385u,      2135250851u,      4267827987u }
+        },
+        {
+            {      1481142942u,      4120754772u,      1088557292u },
+            {       265491023u,      1481142942u,      2860005744u },
+            {       301796252u,       265491023u,      1935975979u }
+        },
+        {
+            {      2111859033u,      2813610100u,      1001476468u },
+            {        73849832u,      2111859033u,      3980799998u },
+            {      3330206241u,        73849832u,      1933943506u }
+        },
+        {
+            {      1781286360u,      3661231931u,      3509383709u },
+            {      2753158871u,      1781286360u,      3119883109u },
+            {      3576525143u,      2753158871u,       551079002u }
+        },
+        {
+            {      1185024844u,       587779104u,      1004942725u },
+            {      3763632860u,      1185024844u,       947424568u },
+            {      3811666068u,      3763632860u,      2352253462u }
+        },
+        {
+            {      1310227170u,       218138208u,      3172947233u },
+            {       766129426u,      1310227170u,      1808643264u },
+            {      2226659371u,       766129426u,      3853798112u }
+        },
+        {
+            {      2230902378u,      4243560874u,      2491962392u },
+            {      3836629116u,      2230902378u,      3637515403u },
+            {      2846140932u,      3836629116u,      3083355464u }
+        },
+        {
+            {       999448569u,      1464488480u,      3344426626u },
+            {       946166795u,       999448569u,       340856814u },
+            {      3686999436u,       946166795u,      3231079441u }
+        },
+        {
+            {      1226155368u,      3477563770u,       550006884u },
+            {      2378667355u,      1226155368u,      1493409040u },
+            {       260364836u,      2378667355u,      4133888397u }
+        },
+        {
+            {      1277901832u,       310796286u,      2818511068u },
+            {      3088910653u,      1277901832u,      3303406025u },
+            {      2507911914u,      3088910653u,      3712928074u }
+        },
+        {
+            {       481918378u,       339570348u,      1728801469u },
+            {      1623163429u,       481918378u,      2209094694u },
+            {      3146982514u,      1623163429u,       508445538u }
+        },
+        {
+            {      3138921230u,      2381863183u,      1992357430u },
+            {      1024510915u,      3138921230u,      2122851650u },
+            {      1453455184u,      1024510915u,       941946604u }
+        },
+        {
+            {      2465372719u,      1391015357u,      3328905025u },
+            {      1821933605u,      2465372719u,      1343489680u },
+            {      3648970313u,      1821933605u,      1816599716u }
+        },
+        {
+            {       118634664u,      3358712512u,      2492792220u },
+            {       348833376u,       118634664u,      2495544591u },
+            {      3235582254u,       348833376u,      4043157504u }
+        },
+        {
+            {      2303067090u,      3371139074u,      1967771133u },
+            {       598630070u,      2303067090u,      1819012637u },
+            {      2049250561u,       598630070u,      4093044926u }
+        },
+        {
+            {      3035321857u,      3971176093u,       226779704u },
+            {      3361614254u,      3035321857u,      2807125404u },
+            {       326640887u,      3361614254u,      3147308542u }
+        },
+        {
+            {      1774298149u,      4179629947u,      3145006948u },
+            {      1688753503u,      1774298149u,        94869516u },
+            {      2327946901u,      1688753503u,      2786835219u }
+        }
+    };
+
+#endif // ifndef __CUDACC_RTC__
+
+/*Base matrices to power (2 to the power 76) to power 2 to power n + 1u, n the first array index, from 0..63*/
+
+CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS unsigned int mrg32k3aM1SubSeq[56][3][3] = {
+        {
+            {        82758667u,      1871391091u,      4127413238u },
+            {      3672831523u,        69195019u,      1871391091u },
+            {      3672091415u,      3528743235u,        69195019u }
+        },
+        {
+            {      3361372532u,      2329303404u,        99651939u },
+            {      2008671965u,      2931758910u,      2329303404u },
+            {      1113529483u,      2374097189u,      2931758910u }
+        },
+        {
+            {      1831590873u,      1588259595u,      1314332382u },
+            {      2385989343u,      2508077280u,      1588259595u },
+            {      1787615788u,       661437137u,      2508077280u }
+        },
+        {
+            {      2326052247u,      4183591379u,      4049009082u },
+            {      2604529491u,      1453913233u,      4183591379u },
+            {      2311925423u,      1805360390u,      1453913233u }
+        },
+        {
+            {      3956367490u,       604461629u,      1257432102u },
+            {       794711716u,      1155867175u,       604461629u },
+            {      1777070788u,       429445904u,      1155867175u }
+        },
+        {
+            {      1686241617u,      1257046062u,      1427609439u },
+            {       490376081u,       387798431u,      1257046062u },
+            {       235551485u,      1312672615u,       387798431u }
+        },
+        {
+            {      2362447880u,      3445363024u,      3160262066u },
+            {      2426867845u,      4194339866u,      3445363024u },
+            {      1046144413u,      4177893681u,      4194339866u }
+        },
+        {
+            {      4251175413u,      3559576374u,      3107663662u },
+            {       697539134u,      1909472435u,      3559576374u },
+            {       280754246u,       375835695u,      1909472435u }
+        },
+        {
+            {      1099512970u,       712404985u,      1571467521u },
+            {       546519870u,      1135109300u,       712404985u },
+            {      3325312332u,      2352874613u,      1135109300u }
+        },
+        {
+            {      1945425936u,      1653045514u,       381988982u },
+            {      3733376326u,       414410025u,      1653045514u },
+            {      1181583679u,      1185848176u,       414410025u }
+        },
+        {
+            {      2526336124u,      3019211015u,      4215964965u },
+            {      2683163472u,      4188191530u,      3019211015u },
+            {      2964651598u,       293801056u,      4188191530u }
+        },
+        {
+            {      1444052678u,      2253324417u,        39719589u },
+            {      1880267534u,      2391992038u,      2253324417u },
+            {       987740265u,      3691889508u,      2391992038u }
+        },
+        {
+            {       166599066u,      2335494420u,      1232261118u },
+            {      2227597731u,      2570600780u,      2335494420u },
+            {      2700034538u,      3460843234u,      2570600780u }
+        },
+        {
+            {      2511338360u,      1188954576u,      1251401239u },
+            {      2511664974u,       292276982u,      1188954576u },
+            {       697844082u,      3093661552u,       292276982u }
+        },
+        {
+            {      3624650744u,        51993077u,      3540268009u },
+            {      3252828938u,      3710319575u,        51993077u },
+            {      2858628849u,      3910069381u,      3710319575u }
+        },
+        {
+            {       655966702u,       754002362u,      1646581402u },
+            {      1958331075u,       475572423u,       754002362u },
+            {      3248619000u,      3228514800u,       475572423u }
+        },
+        {
+            {      2760311307u,      4166372813u,       741596417u },
+            {      2282679206u,      3090782630u,      4166372813u },
+            {      3242468721u,      1628442374u,      3090782630u }
+        },
+        {
+            {      4265279407u,      3532111852u,      1754687396u },
+            {       500404765u,      2603727025u,      3532111852u },
+            {      1428367254u,      3149485478u,      2603727025u }
+        },
+        {
+            {      2873769531u,      2081104178u,       596284397u },
+            {      4153800443u,      1261269623u,      2081104178u },
+            {      3967600061u,      1830023157u,      1261269623u }
+        },
+        {
+            {       278611533u,      2229285304u,      3443204327u },
+            {      3110641420u,        77498444u,      2229285304u },
+            {      3904070810u,      1070507239u,        77498444u }
+        },
+        {
+            {       544639534u,       568528663u,      2177189807u },
+            {      2475829068u,       121482268u,       568528663u },
+            {       876978915u,      3116647617u,       121482268u }
+        },
+        {
+            {      1547862823u,      2404658587u,      4191448009u },
+            {      2158188804u,      2976916793u,      2404658587u },
+            {       168571747u,      1691884706u,      2976916793u }
+        },
+        {
+            {      3208213311u,      4212638780u,      3235157352u },
+            {       671148556u,      2951207765u,      4212638780u },
+            {      2075145516u,      2395485231u,      2951207765u }
+        },
+        {
+            {      4080517315u,      2133433101u,      4043998180u },
+            {      2044221845u,       867670560u,      2133433101u },
+            {       834432416u,      3613001199u,       867670560u }
+        },
+        {
+            {      4102885735u,      1319434267u,      2678775073u },
+            {       740092580u,       607380970u,      1319434267u },
+            {      2198271844u,      2610193258u,       607380970u }
+        },
+        {
+            {      1165218048u,      1317690360u,      1189150958u },
+            {       399240205u,      2507168618u,      1317690360u },
+            {      2988334517u,      2687593413u,      2507168618u }
+        },
+        {
+            {      1028861702u,      4082006648u,       338232527u },
+            {      1888486946u,      1842080991u,      4082006648u },
+            {      3903826366u,      3109935091u,      1842080991u }
+        },
+        {
+            {       614134826u,      2261996505u,      2888080641u },
+            {       710199359u,      2773979788u,      2261996505u },
+            {      1144301620u,      2554371815u,      2773979788u }
+        },
+        {
+            {      4056173823u,      1285620078u,       357420018u },
+            {      2423072612u,      2309408315u,      1285620078u },
+            {      1533175115u,      2760088020u,      2309408315u }
+        },
+        {
+            {      4264130267u,       815015434u,      3142242173u },
+            {       180649975u,      2500813569u,       815015434u },
+            {      3378723563u,       829683767u,      2500813569u }
+        },
+        {
+            {      4174387531u,      1030729435u,      2812778314u },
+            {      1752988797u,      4044178729u,      1030729435u },
+            {       467969301u,       554748104u,      4044178729u }
+        },
+        {
+            {      1348429235u,      2928743274u,      3776082629u },
+            {      3607529209u,      3069812185u,      2928743274u },
+            {      2542432347u,      3208181168u,      3069812185u }
+        },
+        {
+            {      4064845753u,       668285756u,      3816217625u },
+            {      3713143233u,      1380634204u,       668285756u },
+            {      3533700508u,      1192551435u,      1380634204u }
+        },
+        {
+            {      1515684518u,      1706771705u,       728123349u },
+            {      3174850469u,      2057456462u,      1706771705u },
+            {      3410402985u,      2897339640u,      2057456462u }
+        },
+        {
+            {      3082272717u,       531091457u,      1390161328u },
+            {      3895139973u,      2171402857u,       531091457u },
+            {      4030688141u,      3049703400u,      2171402857u }
+        },
+        {
+            {      1241147206u,      3193892819u,      1244284192u },
+            {        65180262u,      4065669017u,      3193892819u },
+            {      1484817937u,      3661081858u,      4065669017u }
+        },
+        {
+            {      1438760812u,      3491341751u,      3414470157u },
+            {      2805337292u,       272266053u,      3491341751u },
+            {       824109230u,      3202556526u,       272266053u }
+        },
+        {
+            {       135412706u,      3627115412u,      2345042216u },
+            {      1565169824u,      2166856449u,      3627115412u },
+            {      1026946745u,      3467845248u,      2166856449u }
+        },
+        {
+            {      1889419951u,      3256876154u,      1240505488u },
+            {      1254783743u,       989966800u,      3256876154u },
+            {      1995297400u,      3692472918u,       989966800u }
+        },
+        {
+            {      3206226875u,       285700890u,       496017472u },
+            {      2515316194u,      2129675196u,       285700890u },
+            {      1863853990u,      2673457552u,      2129675196u }
+        },
+        {
+            {      4163770641u,       255160418u,       772100749u },
+            {      1987092456u,      3237660221u,       255160418u },
+            {      1394381051u,      4216039401u,      3237660221u }
+        },
+        {
+            {      2133915627u,      2713747584u,       627765421u },
+            {      2300605925u,        35690583u,      2713747584u },
+            {      2918902946u,      2638220304u,        35690583u }
+        },
+        {
+            {      2587549655u,       998684270u,      4292130625u },
+            {      1791772791u,      2820705344u,       998684270u },
+            {       124590158u,      3831143549u,      2820705344u }
+        },
+        {
+            {       978482299u,      3200877282u,       497605289u },
+            {      3717741518u,      3737164414u,      3200877282u },
+            {      4046686626u,       861393946u,      3737164414u }
+        },
+        {
+            {      2665561897u,       300934584u,      3179822945u },
+            {       893043137u,      2031413512u,       300934584u },
+            {      3806926970u,      2413249929u,      2031413512u }
+        },
+        {
+            {      1417581911u,      3071835354u,      2575196237u },
+            {      4101127251u,      1375339216u,      3071835354u },
+            {       847617977u,      3632503316u,      1375339216u }
+        },
+        {
+            {      2747488994u,      3296604805u,       898095468u },
+            {      1742777145u,       219265369u,      3296604805u },
+            {       823714885u,       667779292u,       219265369u }
+        },
+        {
+            {      2640209692u,      3040506537u,      3626115220u },
+            {       161827078u,       852668118u,      3040506537u },
+            {      3856381322u,      3360242076u,       852668118u }
+        },
+        {
+            {      3734246393u,      4151553160u,      4177051283u },
+            {       266522866u,      1731798531u,      4151553160u },
+            {       632196679u,      3864297722u,      1731798531u }
+        },
+        {
+            {      1694175127u,      1087914338u,      2384195794u },
+            {      2764925057u,       505782858u,      1087914338u },
+            {      3235634082u,       807915248u,       505782858u }
+        },
+        {
+            {      2402749950u,      2353776151u,        75909174u },
+            {       890570951u,      1752665661u,      2353776151u },
+            {      3120241607u,      3862435696u,      1752665661u }
+        }
+    };
+
+#ifndef __CUDACC_RTC__
+CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS unsigned int mrg32k3aM1SubSeqHost[56][3][3] = {
+        {
+            {        82758667u,      1871391091u,      4127413238u },
+            {      3672831523u,        69195019u,      1871391091u },
+            {      3672091415u,      3528743235u,        69195019u }
+        },
+        {
+            {      3361372532u,      2329303404u,        99651939u },
+            {      2008671965u,      2931758910u,      2329303404u },
+            {      1113529483u,      2374097189u,      2931758910u }
+        },
+        {
+            {      1831590873u,      1588259595u,      1314332382u },
+            {      2385989343u,      2508077280u,      1588259595u },
+            {      1787615788u,       661437137u,      2508077280u }
+        },
+        {
+            {      2326052247u,      4183591379u,      4049009082u },
+            {      2604529491u,      1453913233u,      4183591379u },
+            {      2311925423u,      1805360390u,      1453913233u }
+        },
+        {
+            {      3956367490u,       604461629u,      1257432102u },
+            {       794711716u,      1155867175u,       604461629u },
+            {      1777070788u,       429445904u,      1155867175u }
+        },
+        {
+            {      1686241617u,      1257046062u,      1427609439u },
+            {       490376081u,       387798431u,      1257046062u },
+            {       235551485u,      1312672615u,       387798431u }
+        },
+        {
+            {      2362447880u,      3445363024u,      3160262066u },
+            {      2426867845u,      4194339866u,      3445363024u },
+            {      1046144413u,      4177893681u,      4194339866u }
+        },
+        {
+            {      4251175413u,      3559576374u,      3107663662u },
+            {       697539134u,      1909472435u,      3559576374u },
+            {       280754246u,       375835695u,      1909472435u }
+        },
+        {
+            {      1099512970u,       712404985u,      1571467521u },
+            {       546519870u,      1135109300u,       712404985u },
+            {      3325312332u,      2352874613u,      1135109300u }
+        },
+        {
+            {      1945425936u,      1653045514u,       381988982u },
+            {      3733376326u,       414410025u,      1653045514u },
+            {      1181583679u,      1185848176u,       414410025u }
+        },
+        {
+            {      2526336124u,      3019211015u,      4215964965u },
+            {      2683163472u,      4188191530u,      3019211015u },
+            {      2964651598u,       293801056u,      4188191530u }
+        },
+        {
+            {      1444052678u,      2253324417u,        39719589u },
+            {      1880267534u,      2391992038u,      2253324417u },
+            {       987740265u,      3691889508u,      2391992038u }
+        },
+        {
+            {       166599066u,      2335494420u,      1232261118u },
+            {      2227597731u,      2570600780u,      2335494420u },
+            {      2700034538u,      3460843234u,      2570600780u }
+        },
+        {
+            {      2511338360u,      1188954576u,      1251401239u },
+            {      2511664974u,       292276982u,      1188954576u },
+            {       697844082u,      3093661552u,       292276982u }
+        },
+        {
+            {      3624650744u,        51993077u,      3540268009u },
+            {      3252828938u,      3710319575u,        51993077u },
+            {      2858628849u,      3910069381u,      3710319575u }
+        },
+        {
+            {       655966702u,       754002362u,      1646581402u },
+            {      1958331075u,       475572423u,       754002362u },
+            {      3248619000u,      3228514800u,       475572423u }
+        },
+        {
+            {      2760311307u,      4166372813u,       741596417u },
+            {      2282679206u,      3090782630u,      4166372813u },
+            {      3242468721u,      1628442374u,      3090782630u }
+        },
+        {
+            {      4265279407u,      3532111852u,      1754687396u },
+            {       500404765u,      2603727025u,      3532111852u },
+            {      1428367254u,      3149485478u,      2603727025u }
+        },
+        {
+            {      2873769531u,      2081104178u,       596284397u },
+            {      4153800443u,      1261269623u,      2081104178u },
+            {      3967600061u,      1830023157u,      1261269623u }
+        },
+        {
+            {       278611533u,      2229285304u,      3443204327u },
+            {      3110641420u,        77498444u,      2229285304u },
+            {      3904070810u,      1070507239u,        77498444u }
+        },
+        {
+            {       544639534u,       568528663u,      2177189807u },
+            {      2475829068u,       121482268u,       568528663u },
+            {       876978915u,      3116647617u,       121482268u }
+        },
+        {
+            {      1547862823u,      2404658587u,      4191448009u },
+            {      2158188804u,      2976916793u,      2404658587u },
+            {       168571747u,      1691884706u,      2976916793u }
+        },
+        {
+            {      3208213311u,      4212638780u,      3235157352u },
+            {       671148556u,      2951207765u,      4212638780u },
+            {      2075145516u,      2395485231u,      2951207765u }
+        },
+        {
+            {      4080517315u,      2133433101u,      4043998180u },
+            {      2044221845u,       867670560u,      2133433101u },
+            {       834432416u,      3613001199u,       867670560u }
+        },
+        {
+            {      4102885735u,      1319434267u,      2678775073u },
+            {       740092580u,       607380970u,      1319434267u },
+            {      2198271844u,      2610193258u,       607380970u }
+        },
+        {
+            {      1165218048u,      1317690360u,      1189150958u },
+            {       399240205u,      2507168618u,      1317690360u },
+            {      2988334517u,      2687593413u,      2507168618u }
+        },
+        {
+            {      1028861702u,      4082006648u,       338232527u },
+            {      1888486946u,      1842080991u,      4082006648u },
+            {      3903826366u,      3109935091u,      1842080991u }
+        },
+        {
+            {       614134826u,      2261996505u,      2888080641u },
+            {       710199359u,      2773979788u,      2261996505u },
+            {      1144301620u,      2554371815u,      2773979788u }
+        },
+        {
+            {      4056173823u,      1285620078u,       357420018u },
+            {      2423072612u,      2309408315u,      1285620078u },
+            {      1533175115u,      2760088020u,      2309408315u }
+        },
+        {
+            {      4264130267u,       815015434u,      3142242173u },
+            {       180649975u,      2500813569u,       815015434u },
+            {      3378723563u,       829683767u,      2500813569u }
+        },
+        {
+            {      4174387531u,      1030729435u,      2812778314u },
+            {      1752988797u,      4044178729u,      1030729435u },
+            {       467969301u,       554748104u,      4044178729u }
+        },
+        {
+            {      1348429235u,      2928743274u,      3776082629u },
+            {      3607529209u,      3069812185u,      2928743274u },
+            {      2542432347u,      3208181168u,      3069812185u }
+        },
+        {
+            {      4064845753u,       668285756u,      3816217625u },
+            {      3713143233u,      1380634204u,       668285756u },
+            {      3533700508u,      1192551435u,      1380634204u }
+        },
+        {
+            {      1515684518u,      1706771705u,       728123349u },
+            {      3174850469u,      2057456462u,      1706771705u },
+            {      3410402985u,      2897339640u,      2057456462u }
+        },
+        {
+            {      3082272717u,       531091457u,      1390161328u },
+            {      3895139973u,      2171402857u,       531091457u },
+            {      4030688141u,      3049703400u,      2171402857u }
+        },
+        {
+            {      1241147206u,      3193892819u,      1244284192u },
+            {        65180262u,      4065669017u,      3193892819u },
+            {      1484817937u,      3661081858u,      4065669017u }
+        },
+        {
+            {      1438760812u,      3491341751u,      3414470157u },
+            {      2805337292u,       272266053u,      3491341751u },
+            {       824109230u,      3202556526u,       272266053u }
+        },
+        {
+            {       135412706u,      3627115412u,      2345042216u },
+            {      1565169824u,      2166856449u,      3627115412u },
+            {      1026946745u,      3467845248u,      2166856449u }
+        },
+        {
+            {      1889419951u,      3256876154u,      1240505488u },
+            {      1254783743u,       989966800u,      3256876154u },
+            {      1995297400u,      3692472918u,       989966800u }
+        },
+        {
+            {      3206226875u,       285700890u,       496017472u },
+            {      2515316194u,      2129675196u,       285700890u },
+            {      1863853990u,      2673457552u,      2129675196u }
+        },
+        {
+            {      4163770641u,       255160418u,       772100749u },
+            {      1987092456u,      3237660221u,       255160418u },
+            {      1394381051u,      4216039401u,      3237660221u }
+        },
+        {
+            {      2133915627u,      2713747584u,       627765421u },
+            {      2300605925u,        35690583u,      2713747584u },
+            {      2918902946u,      2638220304u,        35690583u }
+        },
+        {
+            {      2587549655u,       998684270u,      4292130625u },
+            {      1791772791u,      2820705344u,       998684270u },
+            {       124590158u,      3831143549u,      2820705344u }
+        },
+        {
+            {       978482299u,      3200877282u,       497605289u },
+            {      3717741518u,      3737164414u,      3200877282u },
+            {      4046686626u,       861393946u,      3737164414u }
+        },
+        {
+            {      2665561897u,       300934584u,      3179822945u },
+            {       893043137u,      2031413512u,       300934584u },
+            {      3806926970u,      2413249929u,      2031413512u }
+        },
+        {
+            {      1417581911u,      3071835354u,      2575196237u },
+            {      4101127251u,      1375339216u,      3071835354u },
+            {       847617977u,      3632503316u,      1375339216u }
+        },
+        {
+            {      2747488994u,      3296604805u,       898095468u },
+            {      1742777145u,       219265369u,      3296604805u },
+            {       823714885u,       667779292u,       219265369u }
+        },
+        {
+            {      2640209692u,      3040506537u,      3626115220u },
+            {       161827078u,       852668118u,      3040506537u },
+            {      3856381322u,      3360242076u,       852668118u }
+        },
+        {
+            {      3734246393u,      4151553160u,      4177051283u },
+            {       266522866u,      1731798531u,      4151553160u },
+            {       632196679u,      3864297722u,      1731798531u }
+        },
+        {
+            {      1694175127u,      1087914338u,      2384195794u },
+            {      2764925057u,       505782858u,      1087914338u },
+            {      3235634082u,       807915248u,       505782858u }
+        },
+        {
+            {      2402749950u,      2353776151u,        75909174u },
+            {       890570951u,      1752665661u,      2353776151u },
+            {      3120241607u,      3862435696u,      1752665661u }
+        }
+    };
+#endif // #ifndef __CUDACC_RTC__
+
+CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS unsigned int mrg32k3aM2SubSeq[56][3][3] = {
+        {
+            {      1511326704u,      3759209742u,      1610795712u },
+            {      4292754251u,      1511326704u,      3889917532u },
+            {      3859662829u,      4292754251u,      3708466080u }
+        },
+        {
+            {       972103006u,       964807713u,       878035866u },
+            {      4248550197u,       972103006u,      1926628839u },
+            {      1448629089u,      4248550197u,      3196114006u }
+        },
+        {
+            {      3497384788u,      3174249442u,      3182508868u },
+            {      3864816447u,      3497384788u,      3038399593u },
+            {      2546884738u,      3864816447u,      2980208068u }
+        },
+        {
+            {      1776335558u,      1189944887u,      4095757548u },
+            {      3813600746u,      1776335558u,       789475914u },
+            {      4119698302u,      3813600746u,      2145357457u }
+        },
+        {
+            {      4022832294u,      4130146837u,      1942923647u },
+            {      1675130777u,      4022832294u,       916677004u },
+            {      4089786548u,      1675130777u,       116540512u }
+        },
+        {
+            {       165639584u,      1205513289u,      2037453462u },
+            {      1444587280u,       165639584u,       161923120u },
+            {      2617085459u,      1444587280u,      2006913311u }
+        },
+        {
+            {      3458099202u,      3062421748u,      4052486999u },
+            {      1064270720u,      3458099202u,       230768332u },
+            {      4056228301u,      1064270720u,      2219267779u }
+        },
+        {
+            {       296275263u,      3452455838u,      2081462173u },
+            {      1789143993u,       296275263u,      3463234943u },
+            {      2097389984u,      1789143993u,      3447191459u }
+        },
+        {
+            {      2828288883u,      3866690251u,       410553827u },
+            {      1587005542u,      2828288883u,      1469478670u },
+            {      2766486018u,      1587005542u,      2627363449u }
+        },
+        {
+            {      3288027530u,       412403981u,      2458742268u },
+            {      4267121909u,      3288027530u,       138566505u },
+            {       420803572u,      4267121909u,      4094554844u }
+        },
+        {
+            {      3844599430u,      2430152838u,      3283485436u },
+            {      2486244684u,      3844599430u,      4252427633u },
+            {      3560842909u,      2486244684u,      3960267499u }
+        },
+        {
+            {        67933059u,      1294996291u,      2657888382u },
+            {       513233413u,        67933059u,      1379805031u },
+            {        44564058u,       513233413u,        86971645u }
+        },
+        {
+            {      2732588524u,      1866530072u,       818237694u },
+            {      2540507736u,      2732588524u,      3257104212u },
+            {      1164400003u,      2540507736u,      1124501551u }
+        },
+        {
+            {      4199239222u,      3155848463u,      2121388468u },
+            {      1135554501u,      4199239222u,      2056492193u },
+            {      3251740389u,      1135554501u,      2343537248u }
+        },
+        {
+            {       550710036u,       500329021u,      1075236085u },
+            {       356444753u,       550710036u,      1634965500u },
+            {        58733535u,       356444753u,      1261552815u }
+        },
+        {
+            {       708689546u,       419139045u,      2012018174u },
+            {       706488081u,       708689546u,      1113760995u },
+            {       585555005u,       706488081u,        76092226u }
+        },
+        {
+            {      1293182265u,      3168473803u,       366230236u },
+            {      3319068849u,      1293182265u,      1085259665u },
+            {      1675229290u,      3319068849u,      3912300371u }
+        },
+        {
+            {      3186089068u,      4188864734u,      1211781402u },
+            {       756122322u,      3186089068u,       578262892u },
+            {      2518961174u,       756122322u,      1658665581u }
+        },
+        {
+            {      1347291439u,      2050427676u,       736113023u },
+            {      4102191254u,      1347291439u,       878627148u },
+            {      1293500383u,      4102191254u,       745646810u }
+        },
+        {
+            {      4196897331u,      3436564969u,      1900167098u },
+            {      3108887846u,      4196897331u,      2697923227u },
+            {      1405263476u,      3108887846u,       314631094u }
+        },
+        {
+            {       958383622u,      3694638688u,      1150087061u },
+            {      3770009830u,       958383622u,       793326651u },
+            {       533700213u,      3770009830u,      1513734026u }
+        },
+        {
+            {      4119603367u,      3479396923u,      3534176399u },
+            {      3765397477u,      4119603367u,      1458031003u },
+            {      3380901602u,      3765397477u,      2684083587u }
+        },
+        {
+            {       980937351u,      2094378936u,       448446028u },
+            {      1421333909u,       980937351u,      3405683645u },
+            {       323724368u,      1421333909u,       338680738u }
+        },
+        {
+            {      2942968846u,      4293637338u,      3549906544u },
+            {       527851489u,      2942968846u,      3852871282u },
+            {      4209198933u,       527851489u,      1091268872u }
+        },
+        {
+            {      1975983015u,      2092556693u,       611187071u },
+            {      3982652344u,      1975983015u,      3001736262u },
+            {      2055073597u,      3982652344u,      1875181995u }
+        },
+        {
+            {      2970221269u,       880904779u,      2447465272u },
+            {      2888742196u,      2970221269u,      3521651749u },
+            {      3019977656u,      2888742196u,      2712717326u }
+        },
+        {
+            {       419134859u,      2976059897u,       747864206u },
+            {      4101695717u,       419134859u,      4264593116u },
+            {      2657991148u,      4101695717u,      2542621682u }
+        },
+        {
+            {      4043135299u,      1612983166u,      1149778656u },
+            {      1267010518u,      4043135299u,      3496325546u },
+            {      3094232897u,      1267010518u,      2949176293u }
+        },
+        {
+            {      3949395794u,      1774568686u,      2123036003u },
+            {      2182983404u,      3949395794u,      2355671350u },
+            {      2820933455u,      2182983404u,       513963325u }
+        },
+        {
+            {      3046911698u,      2576744453u,      2492729814u },
+            {      4277866093u,      3046911698u,      3146977604u },
+            {      2249371766u,      4277866093u,      3622293976u }
+        },
+        {
+            {      1391529818u,       423458502u,      2587125255u },
+            {      3536237833u,      1391529818u,       985347517u },
+            {       157623850u,      3536237833u,      1015566287u }
+        },
+        {
+            {        48329260u,      2599277669u,       821961664u },
+            {       902187690u,        48329260u,      1716556555u },
+            {      4019658974u,       902187690u,       950730510u }
+        },
+        {
+            {      1318489562u,      1530977112u,      3713577419u },
+            {      4270158447u,      1318489562u,      1654940598u },
+            {      2679964938u,      4270158447u,      1337075195u }
+        },
+        {
+            {       770600793u,      3249576224u,      3578552768u },
+            {      2710443459u,       770600793u,      2990852339u },
+            {      3098163705u,      2710443459u,       522138188u }
+        },
+        {
+            {      2803285489u,      1922250286u,      3164022812u },
+            {       477609731u,      2803285489u,      2140252218u },
+            {      2252852611u,       477609731u,      3058519788u }
+        },
+        {
+            {       208329741u,      3633562083u,      3548346666u },
+            {      3892091460u,       208329741u,       516833304u },
+            {      3440632377u,      3892091460u,      1638833719u }
+        },
+        {
+            {      1816075033u,      3570111203u,       959489356u },
+            {      3482051486u,      1816075033u,       861657108u },
+            {      3119495098u,      3482051486u,      2576849579u }
+        },
+        {
+            {      4240216888u,      2891584407u,      2102314945u },
+            {      4064489450u,      4240216888u,      1427441010u },
+            {      2441164913u,      4064489450u,      3558527186u }
+        },
+        {
+            {      2918371295u,        65155283u,      3469357011u },
+            {      3579773554u,      2918371295u,      3494391959u },
+            {      3266584309u,      3579773554u,      3837485479u }
+        },
+        {
+            {      2959420453u,      1365016881u,      4082486022u },
+            {       236489012u,      2959420453u,      3802558529u },
+            {      2687043642u,       236489012u,      2547086826u }
+        },
+        {
+            {      4185325422u,      2762854843u,      3200044912u },
+            {      3664909559u,      4185325422u,      3543921700u },
+            {      4240262918u,      3664909559u,      2853212443u }
+        },
+        {
+            {      2618500928u,      4237264351u,      1470046497u },
+            {      1893990098u,      2618500928u,      2982567031u },
+            {      3017062825u,      1893990098u,      3195556801u }
+        },
+        {
+            {      1868464655u,      3407681142u,      1652841784u },
+            {      1678569574u,      1868464655u,      4162480901u },
+            {      1477016185u,      1678569574u,      4145063890u }
+        },
+        {
+            {       792188465u,      4251338402u,      2219407026u },
+            {      3840340879u,       792188465u,      3493367465u },
+            {      2979958414u,      3840340879u,      2338974139u }
+        },
+        {
+            {       478845700u,      2378167062u,       882114621u },
+            {      1674533845u,       478845700u,      3572905305u },
+            {      3571222880u,      1674533845u,      1242316901u }
+        },
+        {
+            {      2636090868u,      1972761498u,        71690719u },
+            {      1228103463u,      2636090868u,      1280685025u },
+            {      3741735502u,      1228103463u,       994061750u }
+        },
+        {
+            {      1156725261u,      1100755307u,       221922891u },
+            {      2892200461u,      1156725261u,      1505716533u },
+            {      2287613563u,      2892200461u,      3689457190u }
+        },
+        {
+            {      1387244644u,      3135090808u,      1243609165u },
+            {      1724967466u,      1387244644u,      3296353235u },
+            {      1064364031u,      1724967466u,      2107521044u }
+        },
+        {
+            {      2822471992u,      2034317853u,      2071407475u },
+            {       170903528u,      2822471992u,      1322162887u },
+            {      2524982332u,       170903528u,      2656231333u }
+        },
+        {
+            {      3653936868u,      3893194049u,      2484299328u },
+            {      1313746234u,      3653936868u,      1705346273u },
+            {      1397638018u,      1313746234u,      4015529545u }
+        },
+        {
+            {      4129760842u,      1671665759u,      1677834656u },
+            {      3200005334u,      4129760842u,      3486207172u },
+            {      2850728736u,      3200005334u,      3076201597u }
+        }
+    };
+
+#ifndef __CUDACC_RTC__
+CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS unsigned int mrg32k3aM2SubSeqHost[56][3][3] = {
+        {
+            {      1511326704u,      3759209742u,      1610795712u },
+            {      4292754251u,      1511326704u,      3889917532u },
+            {      3859662829u,      4292754251u,      3708466080u }
+        },
+        {
+            {       972103006u,       964807713u,       878035866u },
+            {      4248550197u,       972103006u,      1926628839u },
+            {      1448629089u,      4248550197u,      3196114006u }
+        },
+        {
+            {      3497384788u,      3174249442u,      3182508868u },
+            {      3864816447u,      3497384788u,      3038399593u },
+            {      2546884738u,      3864816447u,      2980208068u }
+        },
+        {
+            {      1776335558u,      1189944887u,      4095757548u },
+            {      3813600746u,      1776335558u,       789475914u },
+            {      4119698302u,      3813600746u,      2145357457u }
+        },
+        {
+            {      4022832294u,      4130146837u,      1942923647u },
+            {      1675130777u,      4022832294u,       916677004u },
+            {      4089786548u,      1675130777u,       116540512u }
+        },
+        {
+            {       165639584u,      1205513289u,      2037453462u },
+            {      1444587280u,       165639584u,       161923120u },
+            {      2617085459u,      1444587280u,      2006913311u }
+        },
+        {
+            {      3458099202u,      3062421748u,      4052486999u },
+            {      1064270720u,      3458099202u,       230768332u },
+            {      4056228301u,      1064270720u,      2219267779u }
+        },
+        {
+            {       296275263u,      3452455838u,      2081462173u },
+            {      1789143993u,       296275263u,      3463234943u },
+            {      2097389984u,      1789143993u,      3447191459u }
+        },
+        {
+            {      2828288883u,      3866690251u,       410553827u },
+            {      1587005542u,      2828288883u,      1469478670u },
+            {      2766486018u,      1587005542u,      2627363449u }
+        },
+        {
+            {      3288027530u,       412403981u,      2458742268u },
+            {      4267121909u,      3288027530u,       138566505u },
+            {       420803572u,      4267121909u,      4094554844u }
+        },
+        {
+            {      3844599430u,      2430152838u,      3283485436u },
+            {      2486244684u,      3844599430u,      4252427633u },
+            {      3560842909u,      2486244684u,      3960267499u }
+        },
+        {
+            {        67933059u,      1294996291u,      2657888382u },
+            {       513233413u,        67933059u,      1379805031u },
+            {        44564058u,       513233413u,        86971645u }
+        },
+        {
+            {      2732588524u,      1866530072u,       818237694u },
+            {      2540507736u,      2732588524u,      3257104212u },
+            {      1164400003u,      2540507736u,      1124501551u }
+        },
+        {
+            {      4199239222u,      3155848463u,      2121388468u },
+            {      1135554501u,      4199239222u,      2056492193u },
+            {      3251740389u,      1135554501u,      2343537248u }
+        },
+        {
+            {       550710036u,       500329021u,      1075236085u },
+            {       356444753u,       550710036u,      1634965500u },
+            {        58733535u,       356444753u,      1261552815u }
+        },
+        {
+            {       708689546u,       419139045u,      2012018174u },
+            {       706488081u,       708689546u,      1113760995u },
+            {       585555005u,       706488081u,        76092226u }
+        },
+        {
+            {      1293182265u,      3168473803u,       366230236u },
+            {      3319068849u,      1293182265u,      1085259665u },
+            {      1675229290u,      3319068849u,      3912300371u }
+        },
+        {
+            {      3186089068u,      4188864734u,      1211781402u },
+            {       756122322u,      3186089068u,       578262892u },
+            {      2518961174u,       756122322u,      1658665581u }
+        },
+        {
+            {      1347291439u,      2050427676u,       736113023u },
+            {      4102191254u,      1347291439u,       878627148u },
+            {      1293500383u,      4102191254u,       745646810u }
+        },
+        {
+            {      4196897331u,      3436564969u,      1900167098u },
+            {      3108887846u,      4196897331u,      2697923227u },
+            {      1405263476u,      3108887846u,       314631094u }
+        },
+        {
+            {       958383622u,      3694638688u,      1150087061u },
+            {      3770009830u,       958383622u,       793326651u },
+            {       533700213u,      3770009830u,      1513734026u }
+        },
+        {
+            {      4119603367u,      3479396923u,      3534176399u },
+            {      3765397477u,      4119603367u,      1458031003u },
+            {      3380901602u,      3765397477u,      2684083587u }
+        },
+        {
+            {       980937351u,      2094378936u,       448446028u },
+            {      1421333909u,       980937351u,      3405683645u },
+            {       323724368u,      1421333909u,       338680738u }
+        },
+        {
+            {      2942968846u,      4293637338u,      3549906544u },
+            {       527851489u,      2942968846u,      3852871282u },
+            {      4209198933u,       527851489u,      1091268872u }
+        },
+        {
+            {      1975983015u,      2092556693u,       611187071u },
+            {      3982652344u,      1975983015u,      3001736262u },
+            {      2055073597u,      3982652344u,      1875181995u }
+        },
+        {
+            {      2970221269u,       880904779u,      2447465272u },
+            {      2888742196u,      2970221269u,      3521651749u },
+            {      3019977656u,      2888742196u,      2712717326u }
+        },
+        {
+            {       419134859u,      2976059897u,       747864206u },
+            {      4101695717u,       419134859u,      4264593116u },
+            {      2657991148u,      4101695717u,      2542621682u }
+        },
+        {
+            {      4043135299u,      1612983166u,      1149778656u },
+            {      1267010518u,      4043135299u,      3496325546u },
+            {      3094232897u,      1267010518u,      2949176293u }
+        },
+        {
+            {      3949395794u,      1774568686u,      2123036003u },
+            {      2182983404u,      3949395794u,      2355671350u },
+            {      2820933455u,      2182983404u,       513963325u }
+        },
+        {
+            {      3046911698u,      2576744453u,      2492729814u },
+            {      4277866093u,      3046911698u,      3146977604u },
+            {      2249371766u,      4277866093u,      3622293976u }
+        },
+        {
+            {      1391529818u,       423458502u,      2587125255u },
+            {      3536237833u,      1391529818u,       985347517u },
+            {       157623850u,      3536237833u,      1015566287u }
+        },
+        {
+            {        48329260u,      2599277669u,       821961664u },
+            {       902187690u,        48329260u,      1716556555u },
+            {      4019658974u,       902187690u,       950730510u }
+        },
+        {
+            {      1318489562u,      1530977112u,      3713577419u },
+            {      4270158447u,      1318489562u,      1654940598u },
+            {      2679964938u,      4270158447u,      1337075195u }
+        },
+        {
+            {       770600793u,      3249576224u,      3578552768u },
+            {      2710443459u,       770600793u,      2990852339u },
+            {      3098163705u,      2710443459u,       522138188u }
+        },
+        {
+            {      2803285489u,      1922250286u,      3164022812u },
+            {       477609731u,      2803285489u,      2140252218u },
+            {      2252852611u,       477609731u,      3058519788u }
+        },
+        {
+            {       208329741u,      3633562083u,      3548346666u },
+            {      3892091460u,       208329741u,       516833304u },
+            {      3440632377u,      3892091460u,      1638833719u }
+        },
+        {
+            {      1816075033u,      3570111203u,       959489356u },
+            {      3482051486u,      1816075033u,       861657108u },
+            {      3119495098u,      3482051486u,      2576849579u }
+        },
+        {
+            {      4240216888u,      2891584407u,      2102314945u },
+            {      4064489450u,      4240216888u,      1427441010u },
+            {      2441164913u,      4064489450u,      3558527186u }
+        },
+        {
+            {      2918371295u,        65155283u,      3469357011u },
+            {      3579773554u,      2918371295u,      3494391959u },
+            {      3266584309u,      3579773554u,      3837485479u }
+        },
+        {
+            {      2959420453u,      1365016881u,      4082486022u },
+            {       236489012u,      2959420453u,      3802558529u },
+            {      2687043642u,       236489012u,      2547086826u }
+        },
+        {
+            {      4185325422u,      2762854843u,      3200044912u },
+            {      3664909559u,      4185325422u,      3543921700u },
+            {      4240262918u,      3664909559u,      2853212443u }
+        },
+        {
+            {      2618500928u,      4237264351u,      1470046497u },
+            {      1893990098u,      2618500928u,      2982567031u },
+            {      3017062825u,      1893990098u,      3195556801u }
+        },
+        {
+            {      1868464655u,      3407681142u,      1652841784u },
+            {      1678569574u,      1868464655u,      4162480901u },
+            {      1477016185u,      1678569574u,      4145063890u }
+        },
+        {
+            {       792188465u,      4251338402u,      2219407026u },
+            {      3840340879u,       792188465u,      3493367465u },
+            {      2979958414u,      3840340879u,      2338974139u }
+        },
+        {
+            {       478845700u,      2378167062u,       882114621u },
+            {      1674533845u,       478845700u,      3572905305u },
+            {      3571222880u,      1674533845u,      1242316901u }
+        },
+        {
+            {      2636090868u,      1972761498u,        71690719u },
+            {      1228103463u,      2636090868u,      1280685025u },
+            {      3741735502u,      1228103463u,       994061750u }
+        },
+        {
+            {      1156725261u,      1100755307u,       221922891u },
+            {      2892200461u,      1156725261u,      1505716533u },
+            {      2287613563u,      2892200461u,      3689457190u }
+        },
+        {
+            {      1387244644u,      3135090808u,      1243609165u },
+            {      1724967466u,      1387244644u,      3296353235u },
+            {      1064364031u,      1724967466u,      2107521044u }
+        },
+        {
+            {      2822471992u,      2034317853u,      2071407475u },
+            {       170903528u,      2822471992u,      1322162887u },
+            {      2524982332u,       170903528u,      2656231333u }
+        },
+        {
+            {      3653936868u,      3893194049u,      2484299328u },
+            {      1313746234u,      3653936868u,      1705346273u },
+            {      1397638018u,      1313746234u,      4015529545u }
+        },
+        {
+            {      4129760842u,      1671665759u,      1677834656u },
+            {      3200005334u,      4129760842u,      3486207172u },
+            {      2850728736u,      3200005334u,      3076201597u }
+        }
+    };
+#endif // #ifndef __CUDACC_RTC__
+    /*Base matrices to power (2 to the power 127) to power 2 to power n+1u, n the first array index, from 0..63*/
+
+CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS unsigned int mrg32k3aM1Seq[64][3][3] = {
+        {
+            {      2427906178u,      3580155704u,       949770784u },
+            {       226153695u,      1230515664u,      3580155704u },
+            {      1988835001u,       986791581u,      1230515664u }
+        },
+        {
+            {      1774047142u,      3199155377u,      3106427820u },
+            {      1901920839u,      4290900039u,      3199155377u },
+            {      4178980191u,       280623348u,      4290900039u }
+        },
+        {
+            {      3567524348u,      1934119675u,      3188270128u },
+            {      2997767678u,       826363896u,      1934119675u },
+            {       262952343u,       614326610u,       826363896u }
+        },
+        {
+            {      1625613062u,      4288164505u,      2481284279u },
+            {      4273461426u,      1177260757u,      4288164505u },
+            {       305959988u,      4017252267u,      1177260757u }
+        },
+        {
+            {       337929267u,       333342539u,       418300166u },
+            {      2944208672u,       379097734u,       333342539u },
+            {      2084056909u,      3625475947u,       379097734u }
+        },
+        {
+            {      1189899255u,      1307754719u,      1214919992u },
+            {      3736721708u,      3514751918u,      1307754719u },
+            {       732435953u,      2021244538u,      3514751918u }
+        },
+        {
+            {      4089172695u,      1533534334u,       525643282u },
+            {      1497577018u,      1335684482u,      1533534334u },
+            {      2079007086u,      3977541427u,      1335684482u }
+        },
+        {
+            {      3075256652u,      2762754934u,      3846844247u },
+            {      3057872364u,      3274545167u,      2762754934u },
+            {      4028573983u,       938934351u,      3274545167u }
+        },
+        {
+            {      2597859300u,      2880151048u,      2523330453u },
+            {      1121709186u,       175667448u,      2880151048u },
+            {      4182510911u,      1723133625u,       175667448u }
+        },
+        {
+            {       484148868u,      1404283933u,      2982534313u },
+            {      3736767353u,      3179865161u,      1404283933u },
+            {       391120388u,      3758716888u,      3179865161u }
+        },
+        {
+            {      2138867468u,      1128973399u,      2133702321u },
+            {      1613561693u,      3622350766u,      1128973399u },
+            {      1500151924u,      3759983985u,      3622350766u }
+        },
+        {
+            {      3027706760u,      3786576552u,      2698781808u },
+            {      2810527099u,        90498489u,      3786576552u },
+            {      4220122612u,      1855245979u,        90498489u }
+        },
+        {
+            {      3739389517u,      1110440720u,       917457922u },
+            {      2163873618u,      3707591763u,      1110440720u },
+            {      2667061910u,      2533383962u,      3707591763u }
+        },
+        {
+            {      1545226000u,      1812182123u,      3693349190u },
+            {      3422065122u,      3291428549u,      1812182123u },
+            {      1193168720u,      2072837757u,      3291428549u }
+        },
+        {
+            {      3230096243u,      2131723358u,      3262178024u },
+            {      2882890127u,      4088518247u,      2131723358u },
+            {      3991553306u,      1282224087u,      4088518247u }
+        },
+        {
+            {       301207261u,      1722796810u,      3697719854u },
+            {      3350228505u,      3410986694u,      1722796810u },
+            {      3684514720u,      2846958957u,      3410986694u }
+        },
+        {
+            {      1532963114u,      4236235786u,      3871128158u },
+            {      3540401964u,      1285250577u,      4236235786u },
+            {      1105070646u,      2764245175u,      1285250577u }
+        },
+        {
+            {       210906218u,      3068599594u,      3034582784u },
+            {       340633153u,      4004365908u,      3068599594u },
+            {      4238928187u,      2299166464u,      4004365908u }
+        },
+        {
+            {      2274701639u,      3955606166u,      3081246407u },
+            {      3199954992u,      3948054919u,      3955606166u },
+            {      2399101442u,      3438340286u,      3948054919u }
+        },
+        {
+            {       504137100u,      1182303684u,       201533985u },
+            {      4188299661u,      3042453580u,      1182303684u },
+            {      2578519273u,      2674782930u,      3042453580u }
+        },
+        {
+            {      1382964588u,      2578452047u,      3140440866u },
+            {       261861891u,      1076783073u,      2578452047u },
+            {      1634588989u,       164438428u,      1076783073u }
+        },
+        {
+            {      2529186343u,       526867394u,      3102803247u },
+            {      2687252475u,      2908898908u,       526867394u },
+            {      1213100579u,        86050422u,      2908898908u }
+        },
+        {
+            {      2690118316u,       538108523u,       790337895u },
+            {      4193870709u,      1053552056u,       538108523u },
+            {      1635227281u,      4002399925u,      1053552056u }
+        },
+        {
+            {      2123712957u,      4205383007u,      1812304090u },
+            {      1095349745u,       166243972u,      4205383007u },
+            {       428569070u,      2128782357u,       166243972u }
+        },
+        {
+            {      1330151766u,      3569679412u,      4107175982u },
+            {      3808641551u,      3621125056u,      3569679412u },
+            {      4262164578u,      1927692878u,      3621125056u }
+        },
+        {
+            {      3606295184u,      2442739556u,      3894922338u },
+            {      1629626641u,      2729678535u,      2442739556u },
+            {      3379124758u,      4279360935u,      2729678535u }
+        },
+        {
+            {      1052092278u,      4249024666u,       919210106u },
+            {      3253349463u,      3629539480u,      4249024666u },
+            {       852514024u,      4025926501u,      3629539480u }
+        },
+        {
+            {        12394571u,      1252747620u,      2133571953u },
+            {      4227339509u,      3197545170u,      1252747620u },
+            {      1884529704u,      1976203831u,      3197545170u }
+        },
+        {
+            {      2986331025u,      2671019282u,      2847338542u },
+            {      3173738401u,      3542657885u,      2671019282u },
+            {       745203060u,      1546667401u,      3542657885u }
+        },
+        {
+            {      2613012997u,      2311336951u,      2911336433u },
+            {      1493974713u,        92565032u,      2311336951u },
+            {      2786645250u,       257065974u,        92565032u }
+        },
+        {
+            {      3424925004u,      2776053372u,      2204068573u },
+            {      3770626858u,      2509257810u,      2776053372u },
+            {      2979919489u,      1146336783u,      2509257810u }
+        },
+        {
+            {      1474384834u,       827894421u,       515339473u },
+            {      1373055755u,      1949809417u,       827894421u },
+            {      3088339524u,      1194193824u,      1949809417u }
+        },
+        {
+            {      1825805135u,      1289872272u,      3700877161u },
+            {      3433422861u,      4062509844u,      1289872272u },
+            {      3019008744u,      2060641859u,      4062509844u }
+        },
+        {
+            {      3842597153u,      4253338264u,      3424495942u },
+            {       698444416u,        60268595u,      4253338264u },
+            {      4096010585u,        47309624u,        60268595u }
+        },
+        {
+            {      2662288323u,      2043518992u,      1593435980u },
+            {      1330201507u,      3618850300u,      2043518992u },
+            {      2538793204u,       271787962u,      3618850300u }
+        },
+        {
+            {       741020448u,       997594656u,      2398808739u },
+            {      1160477043u,      1522130854u,       997594656u },
+            {      3036916315u,      2847712653u,      1522130854u }
+        },
+        {
+            {      2654964886u,      1889728930u,        53329096u },
+            {      2042322941u,      1621136330u,      1889728930u },
+            {      1553642730u,       784545882u,      1621136330u }
+        },
+        {
+            {      1715219514u,      2831829177u,       929124824u },
+            {       997274536u,       404228189u,      2831829177u },
+            {      1386575385u,      4107238699u,       404228189u }
+        },
+        {
+            {      3928131551u,      2912523524u,      1840499723u },
+            {      4216003022u,      2970489088u,      2912523524u },
+            {      1158689953u,      1425511081u,      2970489088u }
+        },
+        {
+            {      2807004452u,      2510299562u,       271603006u },
+            {      2505735035u,      2370490899u,      2510299562u },
+            {        10873814u,      2450376936u,      2370490899u }
+        },
+        {
+            {      2000734342u,      1113679064u,      2502160539u },
+            {      1475266926u,      2787925323u,      1113679064u },
+            {      1475797635u,      3044470744u,      2787925323u }
+        },
+        {
+            {      1457157056u,      1252556678u,      3073232607u },
+            {      1926798761u,      3639907189u,      1252556678u },
+            {      2067740348u,      2256217204u,      3639907189u }
+        },
+        {
+            {      3740999688u,      1035400458u,      3162437311u },
+            {      4126312242u,       686702830u,      1035400458u },
+            {      1699805291u,       667792040u,       686702830u }
+        },
+        {
+            {      2422495016u,      3203768688u,      1858240466u },
+            {       848719394u,      4092709154u,      3203768688u },
+            {       659945473u,      1863075174u,      4092709154u }
+        },
+        {
+            {       246817944u,       871751352u,      2834051003u },
+            {      3976202597u,      3721214025u,       871751352u },
+            {       783929942u,       745295675u,      3721214025u }
+        },
+        {
+            {      3811740424u,      3603608092u,      2365398362u },
+            {      3826150877u,      2906557036u,      3603608092u },
+            {      2300510686u,       966815948u,      2906557036u }
+        },
+        {
+            {      2816329160u,        18201123u,      3367710570u },
+            {       437309679u,      2220769388u,        18201123u },
+            {      1346863388u,       705296543u,      2220769388u }
+        },
+        {
+            {      3310028953u,      1662315499u,       132645114u },
+            {      2572908401u,      3105849797u,      1662315499u },
+            {      1937586849u,      1735620028u,      3105849797u }
+        },
+        {
+            {       461386353u,      1359675853u,      3599822966u },
+            {       106675209u,      2044154050u,      1359675853u },
+            {      1787730088u,      1149892630u,      2044154050u }
+        },
+        {
+            {      3303902397u,       345146034u,      1417149696u },
+            {      2231869247u,      1116882637u,       345146034u },
+            {      1846832385u,        79626976u,      1116882637u }
+        },
+        {
+            {      2765049417u,      3117782790u,      1805260159u },
+            {      3796182890u,      1101141726u,      3117782790u },
+            {       224270120u,      1004001443u,      1101141726u }
+        },
+        {
+            {        89118668u,      2494198515u,      1356989069u },
+            {      2490435731u,       997151755u,      2494198515u },
+            {      1175528637u,      3444341166u,       997151755u }
+        },
+        {
+            {      2340639019u,       510225634u,       286119182u },
+            {      2045217287u,      1194574818u,       510225634u },
+            {      2662281592u,      1728500627u,      1194574818u }
+        },
+        {
+            {       210787847u,      1189120688u,      2848040407u },
+            {      1087786165u,      2343328484u,      1189120688u },
+            {      3465141330u,      2893041005u,      2343328484u }
+        },
+        {
+            {      3438170226u,      3236285682u,       962036916u },
+            {      2873263091u,       215280489u,      3236285682u },
+            {       730413847u,      1474823842u,       215280489u }
+        },
+        {
+            {      1566461658u,       133010024u,      2886695328u },
+            {      2835827516u,       653809404u,       133010024u },
+            {      3082882924u,      3710942807u,       653809404u }
+        },
+        {
+            {      4201558916u,      1263786956u,       326001602u },
+            {       762846463u,       621546357u,      1263786956u },
+            {      2697142404u,      1156650856u,       621546357u }
+        },
+        {
+            {      2655768102u,      2339029465u,      2430211448u },
+            {      2669906627u,       403962847u,      2339029465u },
+            {      1483118807u,       639660658u,       403962847u }
+        },
+        {
+            {      3508595200u,      4228486662u,       754946994u },
+            {      1913148390u,      3500531602u,      4228486662u },
+            {           24637u,      3773159052u,      3500531602u }
+        },
+        {
+            {      4024866227u,      1143874914u,      3205058469u },
+            {      2970344133u,      2873927273u,      1143874914u },
+            {      2167114735u,      4095476435u,      2873927273u }
+        },
+        {
+            {      1479401095u,      2958366486u,      3027708794u },
+            {      2704486034u,      3574053987u,      2958366486u },
+            {      3630964515u,      1276667706u,      3574053987u }
+        },
+        {
+            {      2035927380u,      1363628533u,       818363998u },
+            {      3023327955u,      3968427114u,      1363628533u },
+            {      1284825950u,      2871663372u,      3968427114u }
+        },
+        {
+            {      3827747418u,      3897287251u,      4106993377u },
+            {      1527779946u,      3221052941u,      3897287251u },
+            {      4178727866u,      4281160673u,      3221052941u }
+        },
+        {
+            {      1174358892u,      2835476193u,       959978619u },
+            {       850076464u,      3774782533u,      2835476193u },
+            {      3880910680u,      3237990203u,      3774782533u }
+        }
+    };
+
+#ifndef __CUDACC_RTC__
+CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS unsigned int mrg32k3aM1SeqHost[64][3][3] = {
+        {
+            {      2427906178u,      3580155704u,       949770784u },
+            {       226153695u,      1230515664u,      3580155704u },
+            {      1988835001u,       986791581u,      1230515664u }
+        },
+        {
+            {      1774047142u,      3199155377u,      3106427820u },
+            {      1901920839u,      4290900039u,      3199155377u },
+            {      4178980191u,       280623348u,      4290900039u }
+        },
+        {
+            {      3567524348u,      1934119675u,      3188270128u },
+            {      2997767678u,       826363896u,      1934119675u },
+            {       262952343u,       614326610u,       826363896u }
+        },
+        {
+            {      1625613062u,      4288164505u,      2481284279u },
+            {      4273461426u,      1177260757u,      4288164505u },
+            {       305959988u,      4017252267u,      1177260757u }
+        },
+        {
+            {       337929267u,       333342539u,       418300166u },
+            {      2944208672u,       379097734u,       333342539u },
+            {      2084056909u,      3625475947u,       379097734u }
+        },
+        {
+            {      1189899255u,      1307754719u,      1214919992u },
+            {      3736721708u,      3514751918u,      1307754719u },
+            {       732435953u,      2021244538u,      3514751918u }
+        },
+        {
+            {      4089172695u,      1533534334u,       525643282u },
+            {      1497577018u,      1335684482u,      1533534334u },
+            {      2079007086u,      3977541427u,      1335684482u }
+        },
+        {
+            {      3075256652u,      2762754934u,      3846844247u },
+            {      3057872364u,      3274545167u,      2762754934u },
+            {      4028573983u,       938934351u,      3274545167u }
+        },
+        {
+            {      2597859300u,      2880151048u,      2523330453u },
+            {      1121709186u,       175667448u,      2880151048u },
+            {      4182510911u,      1723133625u,       175667448u }
+        },
+        {
+            {       484148868u,      1404283933u,      2982534313u },
+            {      3736767353u,      3179865161u,      1404283933u },
+            {       391120388u,      3758716888u,      3179865161u }
+        },
+        {
+            {      2138867468u,      1128973399u,      2133702321u },
+            {      1613561693u,      3622350766u,      1128973399u },
+            {      1500151924u,      3759983985u,      3622350766u }
+        },
+        {
+            {      3027706760u,      3786576552u,      2698781808u },
+            {      2810527099u,        90498489u,      3786576552u },
+            {      4220122612u,      1855245979u,        90498489u }
+        },
+        {
+            {      3739389517u,      1110440720u,       917457922u },
+            {      2163873618u,      3707591763u,      1110440720u },
+            {      2667061910u,      2533383962u,      3707591763u }
+        },
+        {
+            {      1545226000u,      1812182123u,      3693349190u },
+            {      3422065122u,      3291428549u,      1812182123u },
+            {      1193168720u,      2072837757u,      3291428549u }
+        },
+        {
+            {      3230096243u,      2131723358u,      3262178024u },
+            {      2882890127u,      4088518247u,      2131723358u },
+            {      3991553306u,      1282224087u,      4088518247u }
+        },
+        {
+            {       301207261u,      1722796810u,      3697719854u },
+            {      3350228505u,      3410986694u,      1722796810u },
+            {      3684514720u,      2846958957u,      3410986694u }
+        },
+        {
+            {      1532963114u,      4236235786u,      3871128158u },
+            {      3540401964u,      1285250577u,      4236235786u },
+            {      1105070646u,      2764245175u,      1285250577u }
+        },
+        {
+            {       210906218u,      3068599594u,      3034582784u },
+            {       340633153u,      4004365908u,      3068599594u },
+            {      4238928187u,      2299166464u,      4004365908u }
+        },
+        {
+            {      2274701639u,      3955606166u,      3081246407u },
+            {      3199954992u,      3948054919u,      3955606166u },
+            {      2399101442u,      3438340286u,      3948054919u }
+        },
+        {
+            {       504137100u,      1182303684u,       201533985u },
+            {      4188299661u,      3042453580u,      1182303684u },
+            {      2578519273u,      2674782930u,      3042453580u }
+        },
+        {
+            {      1382964588u,      2578452047u,      3140440866u },
+            {       261861891u,      1076783073u,      2578452047u },
+            {      1634588989u,       164438428u,      1076783073u }
+        },
+        {
+            {      2529186343u,       526867394u,      3102803247u },
+            {      2687252475u,      2908898908u,       526867394u },
+            {      1213100579u,        86050422u,      2908898908u }
+        },
+        {
+            {      2690118316u,       538108523u,       790337895u },
+            {      4193870709u,      1053552056u,       538108523u },
+            {      1635227281u,      4002399925u,      1053552056u }
+        },
+        {
+            {      2123712957u,      4205383007u,      1812304090u },
+            {      1095349745u,       166243972u,      4205383007u },
+            {       428569070u,      2128782357u,       166243972u }
+        },
+        {
+            {      1330151766u,      3569679412u,      4107175982u },
+            {      3808641551u,      3621125056u,      3569679412u },
+            {      4262164578u,      1927692878u,      3621125056u }
+        },
+        {
+            {      3606295184u,      2442739556u,      3894922338u },
+            {      1629626641u,      2729678535u,      2442739556u },
+            {      3379124758u,      4279360935u,      2729678535u }
+        },
+        {
+            {      1052092278u,      4249024666u,       919210106u },
+            {      3253349463u,      3629539480u,      4249024666u },
+            {       852514024u,      4025926501u,      3629539480u }
+        },
+        {
+            {        12394571u,      1252747620u,      2133571953u },
+            {      4227339509u,      3197545170u,      1252747620u },
+            {      1884529704u,      1976203831u,      3197545170u }
+        },
+        {
+            {      2986331025u,      2671019282u,      2847338542u },
+            {      3173738401u,      3542657885u,      2671019282u },
+            {       745203060u,      1546667401u,      3542657885u }
+        },
+        {
+            {      2613012997u,      2311336951u,      2911336433u },
+            {      1493974713u,        92565032u,      2311336951u },
+            {      2786645250u,       257065974u,        92565032u }
+        },
+        {
+            {      3424925004u,      2776053372u,      2204068573u },
+            {      3770626858u,      2509257810u,      2776053372u },
+            {      2979919489u,      1146336783u,      2509257810u }
+        },
+        {
+            {      1474384834u,       827894421u,       515339473u },
+            {      1373055755u,      1949809417u,       827894421u },
+            {      3088339524u,      1194193824u,      1949809417u }
+        },
+        {
+            {      1825805135u,      1289872272u,      3700877161u },
+            {      3433422861u,      4062509844u,      1289872272u },
+            {      3019008744u,      2060641859u,      4062509844u }
+        },
+        {
+            {      3842597153u,      4253338264u,      3424495942u },
+            {       698444416u,        60268595u,      4253338264u },
+            {      4096010585u,        47309624u,        60268595u }
+        },
+        {
+            {      2662288323u,      2043518992u,      1593435980u },
+            {      1330201507u,      3618850300u,      2043518992u },
+            {      2538793204u,       271787962u,      3618850300u }
+        },
+        {
+            {       741020448u,       997594656u,      2398808739u },
+            {      1160477043u,      1522130854u,       997594656u },
+            {      3036916315u,      2847712653u,      1522130854u }
+        },
+        {
+            {      2654964886u,      1889728930u,        53329096u },
+            {      2042322941u,      1621136330u,      1889728930u },
+            {      1553642730u,       784545882u,      1621136330u }
+        },
+        {
+            {      1715219514u,      2831829177u,       929124824u },
+            {       997274536u,       404228189u,      2831829177u },
+            {      1386575385u,      4107238699u,       404228189u }
+        },
+        {
+            {      3928131551u,      2912523524u,      1840499723u },
+            {      4216003022u,      2970489088u,      2912523524u },
+            {      1158689953u,      1425511081u,      2970489088u }
+        },
+        {
+            {      2807004452u,      2510299562u,       271603006u },
+            {      2505735035u,      2370490899u,      2510299562u },
+            {        10873814u,      2450376936u,      2370490899u }
+        },
+        {
+            {      2000734342u,      1113679064u,      2502160539u },
+            {      1475266926u,      2787925323u,      1113679064u },
+            {      1475797635u,      3044470744u,      2787925323u }
+        },
+        {
+            {      1457157056u,      1252556678u,      3073232607u },
+            {      1926798761u,      3639907189u,      1252556678u },
+            {      2067740348u,      2256217204u,      3639907189u }
+        },
+        {
+            {      3740999688u,      1035400458u,      3162437311u },
+            {      4126312242u,       686702830u,      1035400458u },
+            {      1699805291u,       667792040u,       686702830u }
+        },
+        {
+            {      2422495016u,      3203768688u,      1858240466u },
+            {       848719394u,      4092709154u,      3203768688u },
+            {       659945473u,      1863075174u,      4092709154u }
+        },
+        {
+            {       246817944u,       871751352u,      2834051003u },
+            {      3976202597u,      3721214025u,       871751352u },
+            {       783929942u,       745295675u,      3721214025u }
+        },
+        {
+            {      3811740424u,      3603608092u,      2365398362u },
+            {      3826150877u,      2906557036u,      3603608092u },
+            {      2300510686u,       966815948u,      2906557036u }
+        },
+        {
+            {      2816329160u,        18201123u,      3367710570u },
+            {       437309679u,      2220769388u,        18201123u },
+            {      1346863388u,       705296543u,      2220769388u }
+        },
+        {
+            {      3310028953u,      1662315499u,       132645114u },
+            {      2572908401u,      3105849797u,      1662315499u },
+            {      1937586849u,      1735620028u,      3105849797u }
+        },
+        {
+            {       461386353u,      1359675853u,      3599822966u },
+            {       106675209u,      2044154050u,      1359675853u },
+            {      1787730088u,      1149892630u,      2044154050u }
+        },
+        {
+            {      3303902397u,       345146034u,      1417149696u },
+            {      2231869247u,      1116882637u,       345146034u },
+            {      1846832385u,        79626976u,      1116882637u }
+        },
+        {
+            {      2765049417u,      3117782790u,      1805260159u },
+            {      3796182890u,      1101141726u,      3117782790u },
+            {       224270120u,      1004001443u,      1101141726u }
+        },
+        {
+            {        89118668u,      2494198515u,      1356989069u },
+            {      2490435731u,       997151755u,      2494198515u },
+            {      1175528637u,      3444341166u,       997151755u }
+        },
+        {
+            {      2340639019u,       510225634u,       286119182u },
+            {      2045217287u,      1194574818u,       510225634u },
+            {      2662281592u,      1728500627u,      1194574818u }
+        },
+        {
+            {       210787847u,      1189120688u,      2848040407u },
+            {      1087786165u,      2343328484u,      1189120688u },
+            {      3465141330u,      2893041005u,      2343328484u }
+        },
+        {
+            {      3438170226u,      3236285682u,       962036916u },
+            {      2873263091u,       215280489u,      3236285682u },
+            {       730413847u,      1474823842u,       215280489u }
+        },
+        {
+            {      1566461658u,       133010024u,      2886695328u },
+            {      2835827516u,       653809404u,       133010024u },
+            {      3082882924u,      3710942807u,       653809404u }
+        },
+        {
+            {      4201558916u,      1263786956u,       326001602u },
+            {       762846463u,       621546357u,      1263786956u },
+            {      2697142404u,      1156650856u,       621546357u }
+        },
+        {
+            {      2655768102u,      2339029465u,      2430211448u },
+            {      2669906627u,       403962847u,      2339029465u },
+            {      1483118807u,       639660658u,       403962847u }
+        },
+        {
+            {      3508595200u,      4228486662u,       754946994u },
+            {      1913148390u,      3500531602u,      4228486662u },
+            {           24637u,      3773159052u,      3500531602u }
+        },
+        {
+            {      4024866227u,      1143874914u,      3205058469u },
+            {      2970344133u,      2873927273u,      1143874914u },
+            {      2167114735u,      4095476435u,      2873927273u }
+        },
+        {
+            {      1479401095u,      2958366486u,      3027708794u },
+            {      2704486034u,      3574053987u,      2958366486u },
+            {      3630964515u,      1276667706u,      3574053987u }
+        },
+        {
+            {      2035927380u,      1363628533u,       818363998u },
+            {      3023327955u,      3968427114u,      1363628533u },
+            {      1284825950u,      2871663372u,      3968427114u }
+        },
+        {
+            {      3827747418u,      3897287251u,      4106993377u },
+            {      1527779946u,      3221052941u,      3897287251u },
+            {      4178727866u,      4281160673u,      3221052941u }
+        },
+        {
+            {      1174358892u,      2835476193u,       959978619u },
+            {       850076464u,      3774782533u,      2835476193u },
+            {      3880910680u,      3237990203u,      3774782533u }
+        }
+    };
+#endif // #ifndef __CUDACC_RTC__
+
+CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS unsigned int mrg32k3aM2Seq[64][3][3] = {
+        {
+            {      1464411153u,       277697599u,      1610723613u },
+            {        32183930u,      1464411153u,      1022607788u },
+            {      2824425944u,        32183930u,      2093834863u }
+        },
+        {
+            {      3492361727u,      1027004383u,      3167429889u },
+            {      3674905362u,      3492361727u,      3572939265u },
+            {      4270409313u,      3674905362u,       698814233u }
+        },
+        {
+            {       880482061u,       205175925u,      4070445105u },
+            {      2208329119u,       880482061u,      1933248566u },
+            {      3741227945u,      2208329119u,      3962062826u }
+        },
+        {
+            {      4184605179u,      1189429800u,       567967482u },
+            {       107217966u,      4184605179u,       784865788u },
+            {       549462420u,       107217966u,      3134382704u }
+        },
+        {
+            {      2732536445u,      1231107067u,      3374588386u },
+            {       409954030u,      2732536445u,      1044831206u },
+            {      3398162498u,       409954030u,      3505648581u }
+        },
+        {
+            {      2169560691u,      1076348534u,       637306236u },
+            {      3704346564u,      2169560691u,       293694496u },
+            {       632453145u,      3704346564u,      1609425246u }
+        },
+        {
+            {       372115891u,      3928812480u,      2830541169u },
+            {      3056527841u,       372115891u,      1924239834u },
+            {      3044937468u,      3056527841u,       547142630u }
+        },
+        {
+            {      1660852083u,      3635660815u,      1389092450u },
+            {      1025573319u,      1660852083u,      3276803366u },
+            {      4036331438u,      1025573319u,      4092197741u }
+        },
+        {
+            {      1360732901u,      2887812973u,      4101068693u },
+            {        52572783u,      1360732901u,       112458461u },
+            {      2636566855u,        52572783u,      1136777988u }
+        },
+        {
+            {      3455696508u,       536919193u,      3978804036u },
+            {      3094157668u,      3455696508u,      3821833900u },
+            {      2278849016u,      3094157668u,      2531965909u }
+        },
+        {
+            {      2125991744u,       890897326u,      3790557569u },
+            {      1433592392u,      2125991744u,      3671109604u },
+            {       808215503u,      1433592392u,      2446306581u }
+        },
+        {
+            {      3524411799u,       932865240u,      1838275365u },
+            {      1789634890u,      3524411799u,      4130736474u },
+            {      2252266098u,      1789634890u,      3048775967u }
+        },
+        {
+            {      1773339925u,       948403862u,      1999624391u },
+            {       983864203u,      1773339925u,      3734776305u },
+            {       314407045u,       983864203u,      2648614071u }
+        },
+        {
+            {       321802921u,      1099164995u,      2112167358u },
+            {      3760936985u,       321802921u,      1003573324u },
+            {      3758858458u,      3760936985u,      4014658840u }
+        },
+        {
+            {      2196438580u,       805386227u,      4266375092u },
+            {      4124675351u,      2196438580u,      2527961345u },
+            {        94452540u,      4124675351u,      2825656399u }
+        },
+        {
+            {        66735368u,      2228005807u,      4186703168u },
+            {      2624855312u,        66735368u,      2708679078u },
+            {      4098470056u,      2624855312u,      1773862183u }
+        },
+        {
+            {      3072642883u,      2746897053u,      2690305546u },
+            {      1105106652u,      3072642883u,      4047666135u },
+            {      2862886282u,      1105106652u,      3597347398u }
+        },
+        {
+            {       232906611u,      3873338256u,      4051554873u },
+            {      3027413363u,       232906611u,      3159432673u },
+            {      3872967050u,      3027413363u,       987156327u }
+        },
+        {
+            {      1160686753u,      3676603152u,      1635979789u },
+            {      1447386846u,      1160686753u,      2670438424u },
+            {       816212890u,      1447386846u,      4288868534u }
+        },
+        {
+            {      3825238244u,      1445162354u,      2362389441u },
+            {      3440193648u,      3825238244u,      3520937545u },
+            {      2652790808u,      3440193648u,       405299994u }
+        },
+        {
+            {      1984094858u,       532165989u,      2027397575u },
+            {      1455977136u,      1984094858u,      2433255524u },
+            {      1039994763u,      1455977136u,      2069333087u }
+        },
+        {
+            {      3680843319u,      2332949611u,      3516795313u },
+            {      2033851810u,      3680843319u,      3843367307u },
+            {      3686294589u,      2033851810u,      3912995069u }
+        },
+        {
+            {       967423689u,      1724183394u,       635932799u },
+            {       641380480u,       967423689u,      2145297779u },
+            {      1723000412u,       641380480u,       455633660u }
+        },
+        {
+            {      2130938335u,      1534972306u,      2511584766u },
+            {       273828453u,      2130938335u,      3112810093u },
+            {      4084843716u,       273828453u,      1399334152u }
+        },
+        {
+            {       168278549u,       541167592u,       190177712u },
+            {       403188859u,       168278549u,      2092073970u },
+            {        58789558u,       403188859u,      2777887189u }
+        },
+        {
+            {       634843389u,      4082275720u,      2092828966u },
+            {       351187677u,       634843389u,      1312056270u },
+            {      3347241070u,       351187677u,      2417192332u }
+        },
+        {
+            {       443276110u,      1113643788u,       271102234u },
+            {      3083745876u,       443276110u,      3370743767u },
+            {      4200577503u,      3083745876u,      3298601960u }
+        },
+        {
+            {      3533393557u,       764977733u,      3400275098u },
+            {       144639933u,      3533393557u,      2646475951u },
+            {        77963866u,       144639933u,      3794766611u }
+        },
+        {
+            {      4064854722u,      1198665008u,      2872196602u },
+            {      3274748603u,      4064854722u,      4164637970u },
+            {      4238693771u,      3274748603u,      1981721347u }
+        },
+        {
+            {      2279220396u,      2355957139u,      1417574285u },
+            {       885864931u,      2279220396u,      1344421653u },
+            {      1895527787u,       885864931u,      3726919367u }
+        },
+        {
+            {      2898100178u,      2427331008u,       348923199u },
+            {      3175444953u,      2898100178u,      4290541487u },
+            {       246118669u,      3175444953u,      3410622769u }
+        },
+        {
+            {       284442065u,      4064194676u,      2295560707u },
+            {      4182706556u,       284442065u,      3696899246u },
+            {      1201342255u,      4182706556u,      1145356382u }
+        },
+        {
+            {       656615546u,       442908965u,      3724738272u },
+            {      1624967553u,       656615546u,       798014134u },
+            {      1157949454u,      1624967553u,       496247378u }
+        },
+        {
+            {       265689579u,       675056541u,      3009083380u },
+            {      3820679930u,       265689579u,      2961990151u },
+            {       562287964u,      3820679930u,      1853486796u }
+        },
+        {
+            {      1675739167u,      2319843005u,       760605578u },
+            {      4161492847u,      1675739167u,       226142150u },
+            {      1017447188u,      4161492847u,      3431158427u }
+        },
+        {
+            {      1759873736u,      2334568602u,      2154570180u },
+            {      1812793060u,      1759873736u,      2111094408u },
+            {      1168460586u,      1812793060u,      2495653141u }
+        },
+        {
+            {       317621194u,       868104288u,       664971082u },
+            {      2340275074u,       317621194u,      2168960688u },
+            {       725706104u,      2340275074u,      3532023115u }
+        },
+        {
+            {      3926931954u,      2907684453u,       615601328u },
+            {      1132340715u,      3926931954u,       676995757u },
+            {      1154819290u,      1132340715u,      1662727700u }
+        },
+        {
+            {      3921782078u,      3376494857u,      2969567377u },
+            {       475345024u,      3921782078u,      4206379953u },
+            {      1795936544u,       475345024u,       934679595u }
+        },
+        {
+            {      3119292228u,       741613041u,      2083352304u },
+            {      1047885963u,      3119292228u,      1581078542u },
+            {      1065969969u,      1047885963u,       661718928u }
+        },
+        {
+            {      3643472111u,      2870554228u,      3995474529u },
+            {      3804264051u,      3643472111u,      1366457944u },
+            {      1246805564u,      3804264051u,       993186530u }
+        },
+        {
+            {       796711791u,      3878204845u,      3160293932u },
+            {       255632881u,       796711791u,      3778927111u },
+            {      3472564181u,       255632881u,       388382377u }
+        },
+        {
+            {      1776984101u,      1742284034u,      3449763933u },
+            {      1349354417u,      1776984101u,      1264780832u },
+            {       715722511u,      1349354417u,      1213319489u }
+        },
+        {
+            {      4261866865u,      1914382786u,       201872335u },
+            {       614207188u,      4261866865u,      1853554849u },
+            {      2046042882u,       614207188u,      3193186353u }
+        },
+        {
+            {      2210205512u,      2847073169u,      3324925707u },
+            {      1251969297u,      2210205512u,      3491451503u },
+            {       470400916u,      1251969297u,      2184392547u }
+        },
+        {
+            {      1523590942u,      2391111113u,        68341529u },
+            {       295466806u,      1523590942u,      4143310876u },
+            {      3527253079u,       295466806u,      4059123142u }
+        },
+        {
+            {      1406902110u,      3735012720u,      1774518130u },
+            {      1814959027u,      1406902110u,      1560544267u },
+            {       346472965u,      1814959027u,       964257199u }
+        },
+        {
+            {       855309653u,      4208503105u,      1518467541u },
+            {      2025248418u,       855309653u,      4148125749u },
+            {      1349947330u,      2025248418u,      1168504873u }
+        },
+        {
+            {      2375338156u,      3629519168u,       409696181u },
+            {       252401654u,      2375338156u,      3992097193u },
+            {      2793725401u,       252401654u,      1350184085u }
+        },
+        {
+            {       873141039u,      3885583138u,       361604799u },
+            {      3554143374u,       873141039u,       894746180u },
+            {      1919765327u,      3554143374u,       876210854u }
+        },
+        {
+            {       246368794u,      1703793169u,      2317362874u },
+            {      2300930144u,       246368794u,      2560214589u },
+            {      2016163623u,      2300930144u,      1504276775u }
+        },
+        {
+            {      1574610921u,      2147546631u,      4103450226u },
+            {       107416526u,      1574610921u,      1773803959u },
+            {      1402542742u,       107416526u,       550063800u }
+        },
+        {
+            {       363388665u,       592194244u,      1746615522u },
+            {      2637234667u,       363388665u,      4031408742u },
+            {      2895130475u,      2637234667u,       296510335u }
+        },
+        {
+            {      3997368560u,      3047771871u,      3178383826u },
+            {      1160174754u,      3997368560u,      4027094919u },
+            {      1234984211u,      1160174754u,      4226264344u }
+        },
+        {
+            {      3303179301u,      4243968063u,      3235964171u },
+            {      1776841674u,      3303179301u,      2867287469u },
+            {      1500495759u,      1776841674u,      1708226553u }
+        },
+        {
+            {      1482944153u,      3192311574u,       354466071u },
+            {      3932773012u,      1482944153u,       389193591u },
+            {      3350181058u,      3932773012u,      3398059015u }
+        },
+        {
+            {       640968550u,      3226860971u,       922372912u },
+            {      1254989667u,       640968550u,      2383815228u },
+            {      2027371896u,      1254989667u,      2925300409u }
+        },
+        {
+            {      2313146046u,      3910187183u,      1377591475u },
+            {      1689291784u,      2313146046u,      4255405993u },
+            {      1650609719u,      1689291784u,      1897624297u }
+        },
+        {
+            {      3656310954u,       882924050u,      2702189958u },
+            {      3185020283u,      3656310954u,      1923190496u },
+            {      2449669145u,      3185020283u,      4235849984u }
+        },
+        {
+            {       377232416u,      1498446142u,      4229103619u },
+            {      3926377906u,       377232416u,       600268838u },
+            {       511317726u,      3926377906u,       216160452u }
+        },
+        {
+            {      1969399344u,      3273966859u,      4220943579u },
+            {      3952111894u,      1969399344u,       575096961u },
+            {      3815277103u,      3952111894u,       792177412u }
+        },
+        {
+            {      2957238169u,      1410010554u,      1523740068u },
+            {      3949237584u,      2957238169u,        74149658u },
+            {      2564746147u,      3949237584u,      2557663578u }
+        },
+        {
+            {      3377318569u,      1927835240u,      2556102508u },
+            {      3022040116u,      3377318569u,      2549406364u },
+            {      2387074241u,      3022040116u,      1477293711u }
+        },
+        {
+            {       257306870u,      1748489735u,       547809226u },
+            {      3708493374u,       257306870u,      4183546362u },
+            {         4435502u,      3708493374u,      1607696753u }
+        }
+    };
+
+#ifndef __CUDACC_RTC__
+CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS unsigned int mrg32k3aM2SeqHost[64][3][3] = {
+        {
+            {      1464411153u,       277697599u,      1610723613u },
+            {        32183930u,      1464411153u,      1022607788u },
+            {      2824425944u,        32183930u,      2093834863u }
+        },
+        {
+            {      3492361727u,      1027004383u,      3167429889u },
+            {      3674905362u,      3492361727u,      3572939265u },
+            {      4270409313u,      3674905362u,       698814233u }
+        },
+        {
+            {       880482061u,       205175925u,      4070445105u },
+            {      2208329119u,       880482061u,      1933248566u },
+            {      3741227945u,      2208329119u,      3962062826u }
+        },
+        {
+            {      4184605179u,      1189429800u,       567967482u },
+            {       107217966u,      4184605179u,       784865788u },
+            {       549462420u,       107217966u,      3134382704u }
+        },
+        {
+            {      2732536445u,      1231107067u,      3374588386u },
+            {       409954030u,      2732536445u,      1044831206u },
+            {      3398162498u,       409954030u,      3505648581u }
+        },
+        {
+            {      2169560691u,      1076348534u,       637306236u },
+            {      3704346564u,      2169560691u,       293694496u },
+            {       632453145u,      3704346564u,      1609425246u }
+        },
+        {
+            {       372115891u,      3928812480u,      2830541169u },
+            {      3056527841u,       372115891u,      1924239834u },
+            {      3044937468u,      3056527841u,       547142630u }
+        },
+        {
+            {      1660852083u,      3635660815u,      1389092450u },
+            {      1025573319u,      1660852083u,      3276803366u },
+            {      4036331438u,      1025573319u,      4092197741u }
+        },
+        {
+            {      1360732901u,      2887812973u,      4101068693u },
+            {        52572783u,      1360732901u,       112458461u },
+            {      2636566855u,        52572783u,      1136777988u }
+        },
+        {
+            {      3455696508u,       536919193u,      3978804036u },
+            {      3094157668u,      3455696508u,      3821833900u },
+            {      2278849016u,      3094157668u,      2531965909u }
+        },
+        {
+            {      2125991744u,       890897326u,      3790557569u },
+            {      1433592392u,      2125991744u,      3671109604u },
+            {       808215503u,      1433592392u,      2446306581u }
+        },
+        {
+            {      3524411799u,       932865240u,      1838275365u },
+            {      1789634890u,      3524411799u,      4130736474u },
+            {      2252266098u,      1789634890u,      3048775967u }
+        },
+        {
+            {      1773339925u,       948403862u,      1999624391u },
+            {       983864203u,      1773339925u,      3734776305u },
+            {       314407045u,       983864203u,      2648614071u }
+        },
+        {
+            {       321802921u,      1099164995u,      2112167358u },
+            {      3760936985u,       321802921u,      1003573324u },
+            {      3758858458u,      3760936985u,      4014658840u }
+        },
+        {
+            {      2196438580u,       805386227u,      4266375092u },
+            {      4124675351u,      2196438580u,      2527961345u },
+            {        94452540u,      4124675351u,      2825656399u }
+        },
+        {
+            {        66735368u,      2228005807u,      4186703168u },
+            {      2624855312u,        66735368u,      2708679078u },
+            {      4098470056u,      2624855312u,      1773862183u }
+        },
+        {
+            {      3072642883u,      2746897053u,      2690305546u },
+            {      1105106652u,      3072642883u,      4047666135u },
+            {      2862886282u,      1105106652u,      3597347398u }
+        },
+        {
+            {       232906611u,      3873338256u,      4051554873u },
+            {      3027413363u,       232906611u,      3159432673u },
+            {      3872967050u,      3027413363u,       987156327u }
+        },
+        {
+            {      1160686753u,      3676603152u,      1635979789u },
+            {      1447386846u,      1160686753u,      2670438424u },
+            {       816212890u,      1447386846u,      4288868534u }
+        },
+        {
+            {      3825238244u,      1445162354u,      2362389441u },
+            {      3440193648u,      3825238244u,      3520937545u },
+            {      2652790808u,      3440193648u,       405299994u }
+        },
+        {
+            {      1984094858u,       532165989u,      2027397575u },
+            {      1455977136u,      1984094858u,      2433255524u },
+            {      1039994763u,      1455977136u,      2069333087u }
+        },
+        {
+            {      3680843319u,      2332949611u,      3516795313u },
+            {      2033851810u,      3680843319u,      3843367307u },
+            {      3686294589u,      2033851810u,      3912995069u }
+        },
+        {
+            {       967423689u,      1724183394u,       635932799u },
+            {       641380480u,       967423689u,      2145297779u },
+            {      1723000412u,       641380480u,       455633660u }
+        },
+        {
+            {      2130938335u,      1534972306u,      2511584766u },
+            {       273828453u,      2130938335u,      3112810093u },
+            {      4084843716u,       273828453u,      1399334152u }
+        },
+        {
+            {       168278549u,       541167592u,       190177712u },
+            {       403188859u,       168278549u,      2092073970u },
+            {        58789558u,       403188859u,      2777887189u }
+        },
+        {
+            {       634843389u,      4082275720u,      2092828966u },
+            {       351187677u,       634843389u,      1312056270u },
+            {      3347241070u,       351187677u,      2417192332u }
+        },
+        {
+            {       443276110u,      1113643788u,       271102234u },
+            {      3083745876u,       443276110u,      3370743767u },
+            {      4200577503u,      3083745876u,      3298601960u }
+        },
+        {
+            {      3533393557u,       764977733u,      3400275098u },
+            {       144639933u,      3533393557u,      2646475951u },
+            {        77963866u,       144639933u,      3794766611u }
+        },
+        {
+            {      4064854722u,      1198665008u,      2872196602u },
+            {      3274748603u,      4064854722u,      4164637970u },
+            {      4238693771u,      3274748603u,      1981721347u }
+        },
+        {
+            {      2279220396u,      2355957139u,      1417574285u },
+            {       885864931u,      2279220396u,      1344421653u },
+            {      1895527787u,       885864931u,      3726919367u }
+        },
+        {
+            {      2898100178u,      2427331008u,       348923199u },
+            {      3175444953u,      2898100178u,      4290541487u },
+            {       246118669u,      3175444953u,      3410622769u }
+        },
+        {
+            {       284442065u,      4064194676u,      2295560707u },
+            {      4182706556u,       284442065u,      3696899246u },
+            {      1201342255u,      4182706556u,      1145356382u }
+        },
+        {
+            {       656615546u,       442908965u,      3724738272u },
+            {      1624967553u,       656615546u,       798014134u },
+            {      1157949454u,      1624967553u,       496247378u }
+        },
+        {
+            {       265689579u,       675056541u,      3009083380u },
+            {      3820679930u,       265689579u,      2961990151u },
+            {       562287964u,      3820679930u,      1853486796u }
+        },
+        {
+            {      1675739167u,      2319843005u,       760605578u },
+            {      4161492847u,      1675739167u,       226142150u },
+            {      1017447188u,      4161492847u,      3431158427u }
+        },
+        {
+            {      1759873736u,      2334568602u,      2154570180u },
+            {      1812793060u,      1759873736u,      2111094408u },
+            {      1168460586u,      1812793060u,      2495653141u }
+        },
+        {
+            {       317621194u,       868104288u,       664971082u },
+            {      2340275074u,       317621194u,      2168960688u },
+            {       725706104u,      2340275074u,      3532023115u }
+        },
+        {
+            {      3926931954u,      2907684453u,       615601328u },
+            {      1132340715u,      3926931954u,       676995757u },
+            {      1154819290u,      1132340715u,      1662727700u }
+        },
+        {
+            {      3921782078u,      3376494857u,      2969567377u },
+            {       475345024u,      3921782078u,      4206379953u },
+            {      1795936544u,       475345024u,       934679595u }
+        },
+        {
+            {      3119292228u,       741613041u,      2083352304u },
+            {      1047885963u,      3119292228u,      1581078542u },
+            {      1065969969u,      1047885963u,       661718928u }
+        },
+        {
+            {      3643472111u,      2870554228u,      3995474529u },
+            {      3804264051u,      3643472111u,      1366457944u },
+            {      1246805564u,      3804264051u,       993186530u }
+        },
+        {
+            {       796711791u,      3878204845u,      3160293932u },
+            {       255632881u,       796711791u,      3778927111u },
+            {      3472564181u,       255632881u,       388382377u }
+        },
+        {
+            {      1776984101u,      1742284034u,      3449763933u },
+            {      1349354417u,      1776984101u,      1264780832u },
+            {       715722511u,      1349354417u,      1213319489u }
+        },
+        {
+            {      4261866865u,      1914382786u,       201872335u },
+            {       614207188u,      4261866865u,      1853554849u },
+            {      2046042882u,       614207188u,      3193186353u }
+        },
+        {
+            {      2210205512u,      2847073169u,      3324925707u },
+            {      1251969297u,      2210205512u,      3491451503u },
+            {       470400916u,      1251969297u,      2184392547u }
+        },
+        {
+            {      1523590942u,      2391111113u,        68341529u },
+            {       295466806u,      1523590942u,      4143310876u },
+            {      3527253079u,       295466806u,      4059123142u }
+        },
+        {
+            {      1406902110u,      3735012720u,      1774518130u },
+            {      1814959027u,      1406902110u,      1560544267u },
+            {       346472965u,      1814959027u,       964257199u }
+        },
+        {
+            {       855309653u,      4208503105u,      1518467541u },
+            {      2025248418u,       855309653u,      4148125749u },
+            {      1349947330u,      2025248418u,      1168504873u }
+        },
+        {
+            {      2375338156u,      3629519168u,       409696181u },
+            {       252401654u,      2375338156u,      3992097193u },
+            {      2793725401u,       252401654u,      1350184085u }
+        },
+        {
+            {       873141039u,      3885583138u,       361604799u },
+            {      3554143374u,       873141039u,       894746180u },
+            {      1919765327u,      3554143374u,       876210854u }
+        },
+        {
+            {       246368794u,      1703793169u,      2317362874u },
+            {      2300930144u,       246368794u,      2560214589u },
+            {      2016163623u,      2300930144u,      1504276775u }
+        },
+        {
+            {      1574610921u,      2147546631u,      4103450226u },
+            {       107416526u,      1574610921u,      1773803959u },
+            {      1402542742u,       107416526u,       550063800u }
+        },
+        {
+            {       363388665u,       592194244u,      1746615522u },
+            {      2637234667u,       363388665u,      4031408742u },
+            {      2895130475u,      2637234667u,       296510335u }
+        },
+        {
+            {      3997368560u,      3047771871u,      3178383826u },
+            {      1160174754u,      3997368560u,      4027094919u },
+            {      1234984211u,      1160174754u,      4226264344u }
+        },
+        {
+            {      3303179301u,      4243968063u,      3235964171u },
+            {      1776841674u,      3303179301u,      2867287469u },
+            {      1500495759u,      1776841674u,      1708226553u }
+        },
+        {
+            {      1482944153u,      3192311574u,       354466071u },
+            {      3932773012u,      1482944153u,       389193591u },
+            {      3350181058u,      3932773012u,      3398059015u }
+        },
+        {
+            {       640968550u,      3226860971u,       922372912u },
+            {      1254989667u,       640968550u,      2383815228u },
+            {      2027371896u,      1254989667u,      2925300409u }
+        },
+        {
+            {      2313146046u,      3910187183u,      1377591475u },
+            {      1689291784u,      2313146046u,      4255405993u },
+            {      1650609719u,      1689291784u,      1897624297u }
+        },
+        {
+            {      3656310954u,       882924050u,      2702189958u },
+            {      3185020283u,      3656310954u,      1923190496u },
+            {      2449669145u,      3185020283u,      4235849984u }
+        },
+        {
+            {       377232416u,      1498446142u,      4229103619u },
+            {      3926377906u,       377232416u,       600268838u },
+            {       511317726u,      3926377906u,       216160452u }
+        },
+        {
+            {      1969399344u,      3273966859u,      4220943579u },
+            {      3952111894u,      1969399344u,       575096961u },
+            {      3815277103u,      3952111894u,       792177412u }
+        },
+        {
+            {      2957238169u,      1410010554u,      1523740068u },
+            {      3949237584u,      2957238169u,        74149658u },
+            {      2564746147u,      3949237584u,      2557663578u }
+        },
+        {
+            {      3377318569u,      1927835240u,      2556102508u },
+            {      3022040116u,      3377318569u,      2549406364u },
+            {      2387074241u,      3022040116u,      1477293711u }
+        },
+        {
+            {       257306870u,      1748489735u,       547809226u },
+            {      3708493374u,       257306870u,      4183546362u },
+            {         4435502u,      3708493374u,      1607696753u }
+        }
+    };
+#endif // ifndef __CUDACC_RTC__
+
+#ifdef CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS
+#undef CURAND_MRG32K3A_MATRICES_DEVICE_QUALIFIERS
+#endif
+
+#ifdef CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS
+#undef CURAND_MRG32K3A_MATRICES_HOST_QUALIFIERS
+#endif
+
+#endif /* !defined(CURAND_MRG32K3A_MATRICES_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32.h
new file mode 100644
index 0000000000000000000000000000000000000000..ab8a5a5b78629ba86dbb996a7561b415e90dfb53
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32.h
@@ -0,0 +1,210 @@
+/*
+ * Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef CURAND_MTGP32_H
+#define CURAND_MTGP32_H
+/*
+ * @file curand_mtgp32.h
+ *
+ * @brief Mersenne Twister for Graphic Processors (mtgp32), which
+ * generates 32-bit unsigned integers and single precision floating
+ * point numbers based on IEEE 754 format.
+ *
+ * @author Mutsuo Saito (Hiroshima University)
+ * @author Makoto Matsumoto (Hiroshima University)
+ *
+ */
+/*
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.  All rights reserved.
+ * Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.  All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ * 
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University nor the names of
+ *       its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written
+ *       permission.
+ * 
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+
+#define MTGPDC_MEXP 11213
+#define MTGPDC_N 351
+#define MTGPDC_FLOOR_2P 256
+#define MTGPDC_CEIL_2P 512
+#define MTGPDC_PARAM_TABLE mtgp32dc_params_fast_11213
+#define MTGP32_STATE_SIZE 1024
+#define MTGP32_STATE_MASK 1023
+#define CURAND_NUM_MTGP32_PARAMS 200
+#define MEXP 11213
+#define THREAD_NUM MTGPDC_FLOOR_2P
+#define LARGE_SIZE (THREAD_NUM * 3)
+#define TBL_SIZE 16
+
+/**
+ * \addtogroup DEVICE Device API
+ *
+ * @{
+ */
+
+/*
+ * \struct MTGP32_PARAMS_FAST_T
+ * MTGP32 parameters.
+ * Some element is redundant to keep structure simple.
+ *
+ * \b pos is a pick up position which is selected to have good
+ * performance on graphic processors.  3 < \b pos < Q, where Q is a
+ * maximum number such that the size of status array - Q is a power of
+ * 2.  For example, when \b mexp is 44497, size of 32-bit status array
+ * is 696, and Q is 184, then \b pos is between 4 and 183. This means
+ * 512 parallel calculations is allowed when \b mexp is 44497.
+ *
+ * \b poly_sha1 is SHA1 digest of the characteristic polynomial of
+ * state transition function. SHA1 is calculated based on printing
+ * form of the polynomial. This is important when we use parameters
+ * generated by the dynamic creator which
+ *
+ * \b mask This is a mask to make the dimension of state space have
+ * just Mersenne Prime. This is redundant.
+ */
+
+struct mtgp32_params_fast;
+
+struct mtgp32_params_fast {
+    int mexp;			/*< Mersenne exponent. This is redundant. */
+    int pos;			/*< pick up position. */
+    int sh1;			/*< shift value 1. 0 < sh1 < 32. */
+    int sh2;			/*< shift value 2. 0 < sh2 < 32. */
+    unsigned int tbl[16];		/*< a small matrix. */
+    unsigned int tmp_tbl[16];	/*< a small matrix for tempering. */
+    unsigned int flt_tmp_tbl[16];	/*< a small matrix for tempering and
+                 converting to float. */
+    unsigned int mask;		/*< This is a mask for state space */
+    unsigned char poly_sha1[21]; /*< SHA1 digest */
+};
+
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct mtgp32_params_fast mtgp32_params_fast_t;
+/** \endcond */
+
+/*
+ * Generator Parameters.
+ */
+struct mtgp32_kernel_params;
+struct mtgp32_kernel_params {
+    unsigned int pos_tbl[CURAND_NUM_MTGP32_PARAMS];
+    unsigned int param_tbl[CURAND_NUM_MTGP32_PARAMS][TBL_SIZE];
+    unsigned int temper_tbl[CURAND_NUM_MTGP32_PARAMS][TBL_SIZE];
+    unsigned int single_temper_tbl[CURAND_NUM_MTGP32_PARAMS][TBL_SIZE];
+    unsigned int sh1_tbl[CURAND_NUM_MTGP32_PARAMS];
+    unsigned int sh2_tbl[CURAND_NUM_MTGP32_PARAMS];
+    unsigned int mask[1];
+};
+
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct mtgp32_kernel_params mtgp32_kernel_params_t;
+/** \endcond */
+
+
+
+/*
+ * kernel I/O
+ * This structure must be initialized before first use.
+ */
+
+/* MTGP (Mersenne Twister) RNG */
+/* This generator uses the Mersenne Twister algorithm of
+ * http://arxiv.org/abs/1005.4973v2
+ * Has period 2^11213.
+*/
+
+/**
+ * CURAND MTGP32 state 
+ */
+struct curandStateMtgp32;
+
+struct curandStateMtgp32 {
+    unsigned int s[MTGP32_STATE_SIZE];
+    int offset;
+    int pIdx;
+    mtgp32_kernel_params_t * k;
+};
+
+/*
+ * CURAND MTGP32 state 
+ */
+/** \cond UNHIDE_TYPEDEFS */
+typedef struct curandStateMtgp32 curandStateMtgp32_t;
+/** \endcond */
+
+/** @} */
+
+#endif
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32_host.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32_host.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b9a2d31b314194e6999aeb7046db67b6c9e19ac
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32_host.h
@@ -0,0 +1,516 @@
+/*
+ * Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * curand_mtgp32_host.h
+ *
+ *
+ * MTGP32-11213
+ *
+ * Mersenne Twister RNG for the GPU
+ *
+ * The period of generated integers is 2<sup>11213</sup>-1.
+ *
+ * This code generates 32-bit unsigned integers, and
+ * single precision floating point numbers uniformly distributed
+ * in the range [1, 2). (float r; 1.0 <= r < 2.0)
+ */
+
+/*
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.  All rights reserved.
+ * Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.  All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University nor the names of
+ *       its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written
+ *       permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+#if !defined CURAND_MTGP32_HOST_H
+#define CURAND_MTGP32_HOST_H
+
+#if !defined(QUALIFIERS)
+#define QUALIFIERS static inline __device__
+#endif
+
+#include <cuda_runtime.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <string.h>
+#include "curand.h"
+#include "curand_mtgp32.h"
+#include "curand_mtgp32dc_p_11213.h"
+
+
+/**
+ * \addtogroup DEVICE Device API
+ *
+ * @{
+ */
+
+static const unsigned int non_zero = 0x4d544750;
+
+/*
+ * This function represents a function used in the initialization
+ * by mtgp32_init_by_array() and mtgp32_init_by_str().
+ * @param[in] x 32-bit integer
+ * @return 32-bit integer
+ */
+static __forceinline__ unsigned int ini_func1(unsigned int x) {
+    return (x ^ (x >> 27)) * (1664525);
+}
+
+/*
+ * This function represents a function used in the initialization
+ * by mtgp32_init_by_array() and mtgp32_init_by_str().
+ * @param[in] x 32-bit integer
+ * @return 32-bit integer
+ */
+static __forceinline__ unsigned int ini_func2(unsigned int x) {
+    return (x ^ (x >> 27)) * (1566083941);
+}
+
+/*
+ * This function initializes the internal state array with a 32-bit
+ * integer seed. The allocated memory should be freed by calling
+ * mtgp32_free(). \b para should be one of the elements in the
+ * parameter table (mtgp32-param-ref.c).
+ *
+ * This function is call by cuda program, because cuda program uses
+ * another structure and another allocation method.
+ *
+ * @param[out] array MTGP internal status vector.
+ * @param[in] para parameter structure
+ * @param[in] seed a 32-bit integer used as the seed.
+ */
+static __forceinline__ __host__
+void mtgp32_init_state(unsigned int state[],
+                       const mtgp32_params_fast_t *para, unsigned int seed) {
+    int i;
+    int size = para->mexp / 32 + 1;
+    unsigned int hidden_seed;
+    unsigned int tmp;
+    hidden_seed = para->tbl[4] ^ (para->tbl[8] << 16);
+    tmp = hidden_seed;
+    tmp += tmp >> 16;
+    tmp += tmp >> 8;
+    memset(state, tmp & 0xff, sizeof(unsigned int) * size);
+    state[0] = seed;
+    state[1] = hidden_seed;
+    for (i = 1; i < size; i++) {
+        state[i] ^= (1812433253) * (state[i - 1] ^ (state[i - 1] >> 30)) + i;
+    }
+}
+
+/*
+ * This function initializes the internal state array
+ * with a 32-bit integer array. \b para should be one of the elements in
+ * the parameter table (mtgp32-param-ref.c).
+ *
+ * @param[out] mtgp32 MTGP structure.
+ * @param[in] para parameter structure
+ * @param[in] array a 32-bit integer array used as a seed.
+ * @param[in] length length of the array.
+ * @return CURAND_STATUS_SUCCESS
+ */
+static __forceinline__ __host__
+int mtgp32_init_by_array(unsigned int state[],
+                         const mtgp32_params_fast_t *para,
+                         unsigned int *array, int length) {
+    int i, j, count;
+    unsigned int r;
+    int lag;
+    int mid;
+    int size = para->mexp / 32 + 1;
+    unsigned int hidden_seed;
+    unsigned int tmp;
+
+    if (size >= 623) {
+    lag = 11;
+    } else if (size >= 68) {
+    lag = 7;
+    } else if (size >= 39) {
+    lag = 5;
+    } else {
+    lag = 3;
+    }
+    mid = (size - lag) / 2;
+
+    hidden_seed = para->tbl[4] ^ (para->tbl[8] << 16);
+    tmp = hidden_seed;
+    tmp += tmp >> 16;
+    tmp += tmp >> 8;
+    memset(state, tmp & 0xff, sizeof(unsigned int) * size);
+    state[0] = hidden_seed;
+
+    if (length + 1 > size) {
+    count = length + 1;
+    } else {
+    count = size;
+    }
+    r = ini_func1(state[0] ^ state[mid] ^ state[size - 1]);
+    state[mid] += r;
+    r += length;
+    state[(mid + lag) % size] += r;
+    state[0] = r;
+    i = 1;
+    count--;
+    for (i = 1, j = 0; (j < count) && (j < length); j++) {
+    r = ini_func1(state[i] ^ state[(i + mid) % size]
+              ^ state[(i + size - 1) % size]);
+    state[(i + mid) % size] += r;
+    r += array[j] + i;
+    state[(i + mid + lag) % size] += r;
+    state[i] = r;
+    i = (i + 1) % size;
+    }
+    for (; j < count; j++) {
+    r = ini_func1(state[i] ^ state[(i + mid) % size]
+              ^ state[(i + size - 1) % size]);
+    state[(i + mid) % size] += r;
+    r += i;
+    state[(i + mid + lag) % size] += r;
+    state[i] = r;
+    i = (i + 1) % size;
+    }
+    for (j = 0; j < size; j++) {
+    r = ini_func2(state[i] + state[(i + mid) % size]
+              + state[(i + size - 1) % size]);
+    state[(i + mid) % size] ^= r;
+    r -= i;
+    state[(i + mid + lag) % size] ^= r;
+    state[i] = r;
+    i = (i + 1) % size;
+    }
+    if (state[size - 1] == 0) {
+    state[size - 1] = non_zero;
+    }
+    return 0;
+}
+
+/*
+ * This function initializes the internal state array
+ * with a character array. \b para should be one of the elements in
+ * the parameter table (mtgp32-param-ref.c).
+ * This is the same algorithm with mtgp32_init_by_array(), but hope to
+ * be more useful.
+ *
+ * @param[out] mtgp32 MTGP structure.
+ * @param[in] para parameter structure
+ * @param[in] array a character array used as a seed. (terminated by zero.)
+ * @return memory allocation result. if 0 then O.K.
+ */
+static __forceinline__ __host__
+int mtgp32_init_by_str(unsigned int state[],
+                       const mtgp32_params_fast_t *para, unsigned char *array) {
+    int i, j, count;
+    unsigned int r;
+    int lag;
+    int mid;
+    int size = para->mexp / 32 + 1;
+    int length = (unsigned int)strlen((char *)array);
+    unsigned int hidden_seed;
+    unsigned int tmp;
+
+    if (size >= 623) {
+    lag = 11;
+    } else if (size >= 68) {
+    lag = 7;
+    } else if (size >= 39) {
+    lag = 5;
+    } else {
+    lag = 3;
+    }
+    mid = (size - lag) / 2;
+
+    hidden_seed = para->tbl[4] ^ (para->tbl[8] << 16);
+    tmp = hidden_seed;
+    tmp += tmp >> 16;
+    tmp += tmp >> 8;
+    memset(state, tmp & 0xff, sizeof(unsigned int) * size);
+    state[0] = hidden_seed;
+
+    if (length + 1 > size) {
+    count = length + 1;
+    } else {
+    count = size;
+    }
+    r = ini_func1(state[0] ^ state[mid] ^ state[size - 1]);
+    state[mid] += r;
+    r += length;
+    state[(mid + lag) % size] += r;
+    state[0] = r;
+    i = 1;
+    count--;
+    for (i = 1, j = 0; (j < count) && (j < length); j++) {
+    r = ini_func1(state[i] ^ state[(i + mid) % size]
+              ^ state[(i + size - 1) % size]);
+    state[(i + mid) % size] += r;
+    r += array[j] + i;
+    state[(i + mid + lag) % size] += r;
+    state[i] = r;
+    i = (i + 1) % size;
+    }
+    for (; j < count; j++) {
+    r = ini_func1(state[i] ^ state[(i + mid) % size]
+              ^ state[(i + size - 1) % size]);
+    state[(i + mid) % size] += r;
+    r += i;
+    state[(i + mid + lag) % size] += r;
+    state[i] = r;
+    i = (i + 1) % size;
+    }
+    for (j = 0; j < size; j++) {
+    r = ini_func2(state[i] + state[(i + mid) % size]
+              + state[(i + size - 1) % size]);
+    state[(i + mid) % size] ^= r;
+    r -= i;
+    state[(i + mid + lag) % size] ^= r;
+    state[i] = r;
+    i = (i + 1) % size;
+    }
+    if (state[size - 1] == 0) {
+    state[size - 1] = non_zero;
+    }
+    return 0;
+}
+
+template<typename ParamsType>
+static __forceinline__ __host__
+curandStatus_t curandMakeMTGP32ConstantsImpl(const mtgp32_params_fast_t params[], ParamsType * p, const int block_num)
+{
+    const int size1 = sizeof(unsigned int) * block_num;
+    const int size2 = sizeof(unsigned int) * block_num * TBL_SIZE;
+    unsigned int *h_pos_tbl;
+    unsigned int *h_sh1_tbl;
+    unsigned int *h_sh2_tbl;
+    unsigned int *h_param_tbl;
+    unsigned int *h_temper_tbl;
+    unsigned int *h_single_temper_tbl;
+    unsigned int *h_mask;
+    curandStatus_t status = CURAND_STATUS_SUCCESS;
+
+    h_pos_tbl = (unsigned int *)malloc(size1);
+    h_sh1_tbl = (unsigned int *)malloc(size1);
+    h_sh2_tbl = (unsigned int *)malloc(size1);
+    h_param_tbl = (unsigned int *)malloc(size2);
+    h_temper_tbl = (unsigned int *)malloc(size2);
+    h_single_temper_tbl = (unsigned int *)malloc(size2);
+    h_mask = (unsigned int *)malloc(sizeof(unsigned int));
+    if (h_pos_tbl == NULL
+	    || h_sh1_tbl == NULL
+	    || h_sh2_tbl == NULL
+	    || h_param_tbl == NULL
+	    || h_temper_tbl == NULL
+	    || h_single_temper_tbl == NULL
+	    || h_mask == NULL) {
+        if (h_pos_tbl != NULL) free(h_pos_tbl);
+        if (h_sh1_tbl != NULL) free(h_sh1_tbl);
+        if (h_sh2_tbl != NULL) free(h_sh2_tbl);
+        if (h_param_tbl != NULL) free(h_param_tbl);
+        if (h_temper_tbl != NULL) free(h_temper_tbl);
+        if (h_single_temper_tbl != NULL) free(h_single_temper_tbl);
+        if (h_mask != NULL) free(h_mask);
+        status = CURAND_STATUS_ALLOCATION_FAILED;
+    } else {
+
+        h_mask[0] = params[0].mask;
+        for (int i = 0; i < block_num; i++) {
+	        h_pos_tbl[i] = params[i].pos;
+	        h_sh1_tbl[i] = params[i].sh1;
+	        h_sh2_tbl[i] = params[i].sh2;
+	        for (int j = 0; j < TBL_SIZE; j++) {
+	            h_param_tbl[i * TBL_SIZE + j] = params[i].tbl[j];
+	            h_temper_tbl[i * TBL_SIZE + j] = params[i].tmp_tbl[j];
+	            h_single_temper_tbl[i * TBL_SIZE + j] = params[i].flt_tmp_tbl[j];
+	        }
+        }
+        if (cudaMemcpy( p->pos_tbl,
+                        h_pos_tbl, size1, cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        } else
+        if (cudaMemcpy( p->sh1_tbl,
+                        h_sh1_tbl, size1, cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        } else
+        if (cudaMemcpy( p->sh2_tbl,
+                        h_sh2_tbl, size1, cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        } else
+        if (cudaMemcpy( p->param_tbl,
+                        h_param_tbl, size2, cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        } else
+        if (cudaMemcpy( p->temper_tbl,
+                        h_temper_tbl, size2, cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        } else
+        if (cudaMemcpy( p->single_temper_tbl,
+                        h_single_temper_tbl, size2, cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        } else
+        if (cudaMemcpy( p->mask,
+                        h_mask, sizeof(unsigned int), cudaMemcpyHostToDevice) != cudaSuccess)
+        {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        }
+    }
+    if (h_pos_tbl != NULL) free(h_pos_tbl);
+    if (h_sh1_tbl != NULL) free(h_sh1_tbl);
+    if (h_sh2_tbl != NULL) free(h_sh2_tbl);
+    if (h_param_tbl != NULL) free(h_param_tbl);
+    if (h_temper_tbl != NULL) free(h_temper_tbl);
+    if (h_single_temper_tbl != NULL)free(h_single_temper_tbl);
+    if (h_mask != NULL) free(h_mask);
+    return status;
+}
+
+/**
+ * \brief Set up constant parameters for the mtgp32 generator
+ *
+ * This host-side helper function re-organizes CURAND_NUM_MTGP32_PARAMS sets of
+ * generator parameters for use by kernel functions and copies the
+ * result to the specified location in device memory.
+ *
+ * \param params - Pointer to an array of type mtgp32_params_fast_t in host memory
+ * \param p - pointer to a structure of type mtgp32_kernel_params_t in device memory.
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if host memory could not be allocated
+ * - CURAND_STATUS_INITIALIZATION_FAILED if the copy to device memory failed
+ * - CURAND_STATUS_SUCCESS otherwise
+ */
+static __forceinline__ __host__
+curandStatus_t curandMakeMTGP32Constants(const mtgp32_params_fast_t params[], mtgp32_kernel_params_t * p)
+{
+    return curandMakeMTGP32ConstantsImpl(params, p, CURAND_NUM_MTGP32_PARAMS);
+}
+
+/**
+ * \brief Set up initial states for the mtgp32 generator
+ *
+ * This host-side helper function initializes a number of states (one parameter set per state) for
+ * an mtgp32 generator. To accomplish this it allocates a state array in host memory,
+ * initializes that array, and copies the result to device memory.
+ *
+ * \param s - pointer to an array of states in device memory
+ * \param params - Pointer to an array of type mtgp32_params_fast_t in host memory
+ * \param k - pointer to a structure of type mtgp32_kernel_params_t in device memory
+ * \param n - number of parameter sets/states to initialize
+ * \param seed - seed value
+ *
+ * \return
+ * - CURAND_STATUS_ALLOCATION_FAILED if host memory state could not be allocated
+ * - CURAND_STATUS_INITIALIZATION_FAILED if the copy to device memory failed
+ * - CURAND_STATUS_SUCCESS otherwise
+ */
+static __forceinline__ __host__
+curandStatus_t CURANDAPI curandMakeMTGP32KernelState(curandStateMtgp32_t *s,
+                                                     mtgp32_params_fast_t params[],
+                                                     mtgp32_kernel_params_t *k,
+                                                     int n,
+                                                     unsigned long long seed)
+{
+    int i;
+    curandStatus_t status = CURAND_STATUS_SUCCESS;
+    curandStateMtgp32_t *h_status =(curandStateMtgp32_t *) malloc(sizeof(curandStateMtgp32_t) * n);
+    if (h_status == NULL) {
+        status = CURAND_STATUS_ALLOCATION_FAILED;
+    } else {
+        seed = seed ^ (seed >> 32);
+        for (i = 0; i < n; i++) {
+            mtgp32_init_state(&(h_status[i].s[0]), &params[i],(unsigned int)seed + i + 1);
+            h_status[i].offset = 0;
+            h_status[i].pIdx = i;
+            h_status[i].k = k;
+        }
+        if (cudaMemcpy(s, h_status,
+                       sizeof(curandStateMtgp32_t) * n,
+                       cudaMemcpyHostToDevice) != cudaSuccess) {
+            status = CURAND_STATUS_INITIALIZATION_FAILED;
+        }
+     }
+    free(h_status);
+    return status;
+}
+
+/** @} */
+
+#endif
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32_kernel.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32_kernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..087cdd0796d1e6d6731acd74a07718dfd1312275
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32_kernel.h
@@ -0,0 +1,386 @@
+/*
+ * Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * curand_mtgp32_kernel.h
+ *
+ *
+ * MTGP32-11213
+ *
+ * Mersenne Twister RNG for the GPU
+ *
+ * The period of generated integers is 2<sup>11213</sup>-1.
+ *
+ * This code generates 32-bit unsigned integers, and
+ * single precision floating point numbers uniformly distributed
+ * in the range [1, 2). (float r; 1.0 <= r < 2.0)
+ */
+
+/*
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.  All rights reserved.
+ * Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.  All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University nor the names of
+ *       its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written
+ *       permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+#if !defined CURAND_MTGP32_KERNEL_H
+#define CURAND_MTGP32_KERNEL_H
+
+#if !defined(QUALIFIERS)
+#define QUALIFIERS static __forceinline__ __device__
+#endif
+
+#ifndef __CUDACC_RTC__
+#include <cuda_runtime.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <string.h>
+#endif // ifndef __CUDACC_RTC__
+#include <nv/target>
+#include "curand.h"
+#include "curand_mtgp32.h"
+
+/**
+ * \addtogroup DEVICE Device API
+ *
+ * @{
+ */
+
+#ifndef __CUDA_ARCH__
+// define blockDim and threadIdx for host compatibility call
+extern const dim3 blockDim;
+extern const uint3 threadIdx;
+#endif
+
+
+/*
+ * The function of the recursion formula calculation.
+ *
+ * @param[in] X1 the farthest part of state array.
+ * @param[in] X2 the second farthest part of state array.
+ * @param[in] Y a part of state array.
+ * @param[in] bid block id.
+ * @return output
+ */
+QUALIFIERS unsigned int para_rec(mtgp32_kernel_params_t * k,unsigned int X1, unsigned int X2, unsigned int Y, int bid) {
+    unsigned int X = (X1 & k->mask[0]) ^ X2;
+    unsigned int MAT;
+
+    X ^= X << k->sh1_tbl[bid];
+    Y = X ^ (Y >> k->sh2_tbl[bid]);
+    MAT = k->param_tbl[bid][Y & 0x0f];
+    return Y ^ MAT;
+}
+
+/*
+ * The tempering function.
+ *
+ * @param[in] V the output value should be tempered.
+ * @param[in] T the tempering helper value.
+ * @param[in] bid block id.
+ * @return the tempered value.
+ */
+QUALIFIERS unsigned int temper(mtgp32_kernel_params_t * k,unsigned int V, unsigned int T, int bid) {
+    unsigned int MAT;
+
+    T ^= T >> 16;
+    T ^= T >> 8;
+    MAT = k->temper_tbl[bid][T & 0x0f];
+    return V ^ MAT;
+}
+
+/*
+ * The tempering and converting function.
+ * By using the preset table, converting to IEEE format
+ * and tempering are done simultaneously.
+ *
+ * @param[in] V the output value should be tempered.
+ * @param[in] T the tempering helper value.
+ * @param[in] bid block id.
+ * @return the tempered and converted value.
+ */
+QUALIFIERS unsigned int temper_single(mtgp32_kernel_params_t * k,unsigned int V, unsigned int T, int bid) {
+    unsigned int MAT;
+    unsigned int r;
+
+    T ^= T >> 16;
+    T ^= T >> 8;
+    MAT = k->single_temper_tbl[bid][T & 0x0f];
+    r = (V >> 9) ^ MAT;
+    return r;
+}
+
+/**
+ * \brief Return 32-bits of pseudorandomness from a mtgp32 generator.
+ *
+ * Return 32-bits of pseudorandomness from the mtgp32 generator in \p state,
+ * increment position of generator by the number of threads in the block.
+ * Note the number of threads in the block can not exceed 256.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 32-bits of pseudorandomness as an unsigned int, all bits valid to use.
+ */
+QUALIFIERS unsigned int curand(curandStateMtgp32_t *state)
+{
+    unsigned int t;
+    unsigned int d;
+    int pos = state->k->pos_tbl[state->pIdx];
+    unsigned int r;
+    unsigned int o;
+
+    d = blockDim.z * blockDim.y * blockDim.x;
+    //assert( d <= 256 );
+    t = (blockDim.z * blockDim.y * threadIdx.z) + (blockDim.x * threadIdx.y) + threadIdx.x;
+    r = para_rec(state->k, state->s[(t + state->offset) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + 1) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + pos) & MTGP32_STATE_MASK],
+             state->pIdx);
+
+    state->s[(t + state->offset + MTGPDC_N) & MTGP32_STATE_MASK] = r;
+    o = temper(state->k, r,
+           state->s[(t + state->offset + pos -1) & MTGP32_STATE_MASK],
+           state->pIdx);
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    if (t == 0)
+    {
+        state->offset = (state->offset + d) & MTGP32_STATE_MASK;
+    }
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    return o;
+
+}
+/**
+ * \brief Return 32-bits of pseudorandomness from a specific position in a mtgp32 generator.
+ *
+ * Return 32-bits of pseudorandomness from position \p index of the mtgp32 generator in \p state,
+ * increment position of generator by \p n positions, which must be the total number of positions
+ * upddated in the state by the thread block, for this invocation.
+ *
+ * Note :
+ * Thread indices must range from 0...\ n - 1.
+ * The number of positions updated may not exceed 256.
+ * A thread block may update more than one state, but a given state may not be updated by more than one thread block.
+ *
+ * \param state - Pointer to state to update
+ * \param index - Index (0..255) of the position within the state to draw from and update
+ * \param n - The total number of postions in this state that are being updated by this invocation
+ *
+ * \return 32-bits of pseudorandomness as an unsigned int, all bits valid to use.
+ */
+QUALIFIERS unsigned int curand_mtgp32_specific(curandStateMtgp32_t *state, unsigned char index, unsigned char n)
+{
+    unsigned int t;
+    int pos = state->k->pos_tbl[state->pIdx];
+    unsigned int r;
+    unsigned int o;
+
+    t = index;
+    r = para_rec(state->k, state->s[(t + state->offset) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + 1) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + pos) & MTGP32_STATE_MASK],
+             state->pIdx);
+
+    state->s[(t + state->offset + MTGPDC_N) & MTGP32_STATE_MASK] = r;
+    o = temper(state->k, r,
+           state->s[(t + state->offset + pos -1) & MTGP32_STATE_MASK],
+           state->pIdx);
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    if (index == 0)
+    {
+        state->offset = (state->offset + n) & MTGP32_STATE_MASK;
+    }
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    return o;
+}
+/**
+ * \brief Return a uniformly distributed float from a mtgp32 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the mtgp32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * Note: This alternate derivation of a uniform float is provided for completeness
+ * with the original source
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_mtgp32_single(curandStateMtgp32_t *state)
+{
+    unsigned int t;
+    unsigned int d;
+    int pos = state->k->pos_tbl[state->pIdx];
+    unsigned int r;
+    unsigned int o_u;
+    float o_f;
+
+
+    t = blockDim.z * blockDim.y;
+    d = t * blockDim.x;
+    //assert( d <= 256 );
+    t += threadIdx.x;
+    r = para_rec(state->k, state->s[(t + state->offset) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + 1) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + pos) & MTGP32_STATE_MASK],
+             state->pIdx);
+
+    state->s[t] = r;
+    o_u = temper_single(state->k, r,
+                        state->s[(t + state->offset + pos -1) & MTGP32_STATE_MASK],
+                        state->pIdx);
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    if (threadIdx.x == 0)
+    {
+        state->offset = (state->offset + d) & MTGP32_STATE_MASK;
+    }
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    memcpy(&o_f, &o_u, sizeof(o_u));
+    return o_f;
+}
+
+/**
+ * \brief Return a uniformly distributed float from a specific position in a mtgp32 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from position \p index of the mtgp32 generator in \p state, and
+ * increment position of generator by \p n positions, which must be the total number of positions
+ * upddated in the state by the thread block, for this invocation.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * Note 1:
+ * Thread indices must range from 0...\p n - 1.
+ * The number of positions updated may not exceed 256.
+ * A thread block may update more than one state, but a given state may not be updated by more than one thread block.
+ *
+ * Note 2: This alternate derivation of a uniform float is provided for completeness
+ * with the original source
+ *
+ * \param state - Pointer to state to update
+ * \param index - Index (0..255) of the position within the state to draw from and update
+ * \param n - The total number of postions in this state that are being updated by this invocation
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_mtgp32_single_specific(curandStateMtgp32_t *state, unsigned char index, unsigned char n)
+{
+    unsigned int t;
+    int pos = state->k->pos_tbl[state->pIdx];
+    unsigned int r;
+    unsigned int o_u;
+    float o_f;
+
+    t = index;
+    r = para_rec(state->k, state->s[(t + state->offset) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + 1) & MTGP32_STATE_MASK],
+             state->s[(t + state->offset + pos) & MTGP32_STATE_MASK],
+             state->pIdx);
+
+    state->s[t] = r;
+    o_u = temper_single(state->k, r,
+                        state->s[(t + state->offset + pos -1) & MTGP32_STATE_MASK],
+                        state->pIdx);
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    if (threadIdx.x == 0)
+    {
+        state->offset = (state->offset + n) & MTGP32_STATE_MASK;
+    }
+NV_IF_TARGET(NV_IS_DEVICE,
+    __syncthreads();
+)
+    memcpy(&o_f, &o_u, sizeof(o_u));
+    return o_f;
+}
+
+/** @} */
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32dc_p_11213.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32dc_p_11213.h
new file mode 100644
index 0000000000000000000000000000000000000000..56a8994c7f2016e4cd2bed77cf6b6516a42f26be
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_mtgp32dc_p_11213.h
@@ -0,0 +1,11710 @@
+/*
+ * Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * Multiple sets of generator parameters for Mersenne Twister
+ * with period 2**11213 -1
+ */
+/*
+ * Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+ * University.  All rights reserved.
+ * Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+ * University and University of Tokyo.  All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *     * Redistributions of source code must retain the above copyright
+ *       notice, this list of conditions and the following disclaimer.
+ *     * Redistributions in binary form must reproduce the above
+ *       copyright notice, this list of conditions and the following
+ *       disclaimer in the documentation and/or other materials provided
+ *       with the distribution.
+ *     * Neither the name of the Hiroshima University nor the names of
+ *       its contributors may be used to endorse or promote products
+ *       derived from this software without specific prior written
+ *       permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#if !defined CURAND_MTGP32DC_P_11213_H
+#define CURAND_MTGP32DC_P_11213_H
+
+#include "curand_mtgp32.h"
+
+
+#if (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+inline const int mtgpdc_params_11213_num = 200;
+#else
+static const int mtgpdc_params_11213_num = 200;
+#endif
+
+#if (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+inline mtgp32_params_fast_t mtgp32dc_params_fast_11213[]
+#else
+static mtgp32_params_fast_t mtgp32dc_params_fast_11213[]
+#endif
+ = {
+    {
+        /* No.0 delta:1599 weight:665 */
+        11213,
+        88,
+        19,
+        5,
+        {(0x00000000),
+         (0xaba4d62c),
+         (0xbb076f87),
+         (0x10a3b9ab),
+         (0x22000000),
+         (0x89a4d62c),
+         (0x99076f87),
+         (0x32a3b9ab),
+         (0x000095ba),
+         (0xaba44396),
+         (0xbb07fa3d),
+         (0x10a32c11),
+         (0x220095ba),
+         (0x89a44396),
+         (0x9907fa3d),
+         (0x32a32c11)},
+        {(0x00000000),
+         (0x06100000),
+         (0x25d80000),
+         (0x23c80000),
+         (0x282c0000),
+         (0x2e3c0000),
+         (0x0df40000),
+         (0x0be40000),
+         (0x3302de00),
+         (0x3512de00),
+         (0x16dade00),
+         (0x10cade00),
+         (0x1b2ede00),
+         (0x1d3ede00),
+         (0x3ef6de00),
+         (0x38e6de00)},
+        {(0x3f800000),
+         (0x3f830800),
+         (0x3f92ec00),
+         (0x3f91e400),
+         (0x3f941600),
+         (0x3f971e00),
+         (0x3f86fa00),
+         (0x3f85f200),
+         (0x3f99816f),
+         (0x3f9a896f),
+         (0x3f8b6d6f),
+         (0x3f88656f),
+         (0x3f8d976f),
+         (0x3f8e9f6f),
+         (0x3f9f7b6f),
+         (0x3f9c736f)},
+        (0xfff80000),
+        {0xcb,0xb0,0x3f,0xaa,0x65,0x0d,0xbd,0x1c,0x8c,0xc2,
+         0x91,0x00,0x87,0x25,0x7f,0xf8,0x6f,0x23,0xf2,0x18,0x00}
+    },
+    {
+        /* No.1 delta:1185 weight:1129 */
+        11213,
+        84,
+        15,
+        12,
+        {(0x00000000),
+         (0x9a975707),
+         (0x75c21b11),
+         (0xef554c16),
+         (0x15500019),
+         (0x8fc7571e),
+         (0x60921b08),
+         (0xfa054c0f),
+         (0x0000d269),
+         (0x9a97856e),
+         (0x75c2c978),
+         (0xef559e7f),
+         (0x1550d270),
+         (0x8fc78577),
+         (0x6092c961),
+         (0xfa059e66)},
+        {(0x00000000),
+         (0x00646c00),
+         (0x0810f000),
+         (0x08749c00),
+         (0x37028000),
+         (0x3766ec00),
+         (0x3f127000),
+         (0x3f761c00),
+         (0x00505e00),
+         (0x00343200),
+         (0x0840ae00),
+         (0x0824c200),
+         (0x3752de00),
+         (0x3736b200),
+         (0x3f422e00),
+         (0x3f264200)},
+        {(0x3f800000),
+         (0x3f803236),
+         (0x3f840878),
+         (0x3f843a4e),
+         (0x3f9b8140),
+         (0x3f9bb376),
+         (0x3f9f8938),
+         (0x3f9fbb0e),
+         (0x3f80282f),
+         (0x3f801a19),
+         (0x3f842057),
+         (0x3f841261),
+         (0x3f9ba96f),
+         (0x3f9b9b59),
+         (0x3f9fa117),
+         (0x3f9f9321)},
+        (0xfff80000),
+        {0x53,0xe0,0x89,0xe0,0x47,0x42,0x00,0xbd,0xe9,0x04,
+         0xc9,0x11,0x5c,0xe3,0x97,0x94,0x94,0xd8,0xbb,0xfc,0x00}
+    },
+    {
+        /* No.2 delta:2848 weight:755 */
+        11213,
+        25,
+        4,
+        18,
+        {(0x00000000),
+         (0x507cbad4),
+         (0xe742103b),
+         (0xb73eaaef),
+         (0x71c00027),
+         (0x21bcbaf3),
+         (0x9682101c),
+         (0xc6feaac8),
+         (0x0000dd6c),
+         (0x507c67b8),
+         (0xe742cd57),
+         (0xb73e7783),
+         (0x71c0dd4b),
+         (0x21bc679f),
+         (0x9682cd70),
+         (0xc6fe77a4)},
+        {(0x00000000),
+         (0x04210200),
+         (0x08040000),
+         (0x0c250200),
+         (0x11400000),
+         (0x15610200),
+         (0x19440000),
+         (0x1d650200),
+         (0x206f1e00),
+         (0x244e1c00),
+         (0x286b1e00),
+         (0x2c4a1c00),
+         (0x312f1e00),
+         (0x350e1c00),
+         (0x392b1e00),
+         (0x3d0a1c00)},
+        {(0x3f800000),
+         (0x3f821081),
+         (0x3f840200),
+         (0x3f861281),
+         (0x3f88a000),
+         (0x3f8ab081),
+         (0x3f8ca200),
+         (0x3f8eb281),
+         (0x3f90378f),
+         (0x3f92270e),
+         (0x3f94358f),
+         (0x3f96250e),
+         (0x3f98978f),
+         (0x3f9a870e),
+         (0x3f9c958f),
+         (0x3f9e850e)},
+        (0xfff80000),
+        {0x9e,0x64,0xa3,0x28,0x10,0x66,0x35,0x72,0xe3,0x07,
+         0xf7,0xcf,0x5a,0xe3,0x57,0x6f,0x90,0x0c,0x28,0x79,0x00}
+    },
+    {
+        /* No.3 delta:1100 weight:1261 */
+        11213,
+        42,
+        20,
+        9,
+        {(0x00000000),
+         (0x3ef0ef20),
+         (0x9fddd9ea),
+         (0xa12d36ca),
+         (0x10300036),
+         (0x2ec0ef16),
+         (0x8fedd9dc),
+         (0xb11d36fc),
+         (0x00005ced),
+         (0x3ef0b3cd),
+         (0x9fdd8507),
+         (0xa12d6a27),
+         (0x10305cdb),
+         (0x2ec0b3fb),
+         (0x8fed8531),
+         (0xb11d6a11)},
+        {(0x00000000),
+         (0x24470c00),
+         (0x00268200),
+         (0x24618e00),
+         (0x00530000),
+         (0x24140c00),
+         (0x00758200),
+         (0x24328e00),
+         (0x105c5e00),
+         (0x341b5200),
+         (0x107adc00),
+         (0x343dd000),
+         (0x100f5e00),
+         (0x34485200),
+         (0x1029dc00),
+         (0x346ed000)},
+        {(0x3f800000),
+         (0x3f922386),
+         (0x3f801341),
+         (0x3f9230c7),
+         (0x3f802980),
+         (0x3f920a06),
+         (0x3f803ac1),
+         (0x3f921947),
+         (0x3f882e2f),
+         (0x3f9a0da9),
+         (0x3f883d6e),
+         (0x3f9a1ee8),
+         (0x3f8807af),
+         (0x3f9a2429),
+         (0x3f8814ee),
+         (0x3f9a3768)},
+        (0xfff80000),
+        {0x9b,0x1d,0x8b,0x59,0x56,0x8e,0x7b,0x75,0x24,0x4a,
+         0xaf,0x49,0x6e,0xcf,0xcb,0x5d,0x67,0x00,0x31,0xac,0x00}
+    },
+    {
+        /* No.4 delta:5389 weight:1871 */
+        11213,
+        22,
+        1,
+        5,
+        {(0x00000000),
+         (0xb004feb9),
+         (0x03b7c894),
+         (0xb3b3362d),
+         (0x0750004d),
+         (0xb754fef4),
+         (0x04e7c8d9),
+         (0xb4e33660),
+         (0x000049e4),
+         (0xb004b75d),
+         (0x03b78170),
+         (0xb3b37fc9),
+         (0x075049a9),
+         (0xb754b710),
+         (0x04e7813d),
+         (0xb4e37f84)},
+        {(0x00000000),
+         (0x48698800),
+         (0x04010000),
+         (0x4c688800),
+         (0x60340000),
+         (0x285d8800),
+         (0x64350000),
+         (0x2c5c8800),
+         (0x20065e00),
+         (0x686fd600),
+         (0x24075e00),
+         (0x6c6ed600),
+         (0x40325e00),
+         (0x085bd600),
+         (0x44335e00),
+         (0x0c5ad600)},
+        {(0x3f800000),
+         (0x3fa434c4),
+         (0x3f820080),
+         (0x3fa63444),
+         (0x3fb01a00),
+         (0x3f942ec4),
+         (0x3fb21a80),
+         (0x3f962e44),
+         (0x3f90032f),
+         (0x3fb437eb),
+         (0x3f9203af),
+         (0x3fb6376b),
+         (0x3fa0192f),
+         (0x3f842deb),
+         (0x3fa219af),
+         (0x3f862d6b)},
+        (0xfff80000),
+        {0xbb,0x80,0x47,0x21,0x2c,0xbd,0xd4,0x7b,0xf3,0xb3,
+         0xbd,0x4d,0x05,0x36,0xf5,0x4d,0xe7,0x89,0xdc,0x11,0x00}
+    },
+    {
+        /* No.5 delta:2280 weight:1243 */
+        11213,
+        11,
+        16,
+        1,
+        {(0x00000000),
+         (0x646a361c),
+         (0xe36e4823),
+         (0x87047e3f),
+         (0x2b20005f),
+         (0x4f4a3643),
+         (0xc84e487c),
+         (0xac247e60),
+         (0x0000caac),
+         (0x646afcb0),
+         (0xe36e828f),
+         (0x8704b493),
+         (0x2b20caf3),
+         (0x4f4afcef),
+         (0xc84e82d0),
+         (0xac24b4cc)},
+        {(0x00000000),
+         (0xd49c4200),
+         (0x0b98f000),
+         (0xdf04b200),
+         (0x24007200),
+         (0xf09c3000),
+         (0x2f988200),
+         (0xfb04c000),
+         (0x5d311e00),
+         (0x89ad5c00),
+         (0x56a9ee00),
+         (0x8235ac00),
+         (0x79316c00),
+         (0xadad2e00),
+         (0x72a99c00),
+         (0xa635de00)},
+        {(0x3f800000),
+         (0x3fea4e21),
+         (0x3f85cc78),
+         (0x3fef8259),
+         (0x3f920039),
+         (0x3ff84e18),
+         (0x3f97cc41),
+         (0x3ffd8260),
+         (0x3fae988f),
+         (0x3fc4d6ae),
+         (0x3fab54f7),
+         (0x3fc11ad6),
+         (0x3fbc98b6),
+         (0x3fd6d697),
+         (0x3fb954ce),
+         (0x3fd31aef)},
+        (0xfff80000),
+        {0x24,0x94,0x81,0x62,0xb4,0x6f,0x10,0x4e,0x66,0xd1,
+         0xb4,0xdd,0xa0,0x92,0x2e,0xdf,0x8d,0x6b,0x03,0xa3,0x00}
+    },
+    {
+        /* No.6 delta:722 weight:1357 */
+        11213,
+        76,
+        16,
+        6,
+        {(0x00000000),
+         (0x1bb96b3c),
+         (0x7c6323a4),
+         (0x67da4898),
+         (0x22400061),
+         (0x39f96b5d),
+         (0x5e2323c5),
+         (0x459a48f9),
+         (0x00004c61),
+         (0x1bb9275d),
+         (0x7c636fc5),
+         (0x67da04f9),
+         (0x22404c00),
+         (0x39f9273c),
+         (0x5e236fa4),
+         (0x459a0498)},
+        {(0x00000000),
+         (0x08258800),
+         (0x1042c000),
+         (0x18674800),
+         (0x00203000),
+         (0x0805b800),
+         (0x1062f000),
+         (0x18477800),
+         (0x70c01e00),
+         (0x78e59600),
+         (0x6082de00),
+         (0x68a75600),
+         (0x70e02e00),
+         (0x78c5a600),
+         (0x60a2ee00),
+         (0x68876600)},
+        {(0x3f800000),
+         (0x3f8412c4),
+         (0x3f882160),
+         (0x3f8c33a4),
+         (0x3f801018),
+         (0x3f8402dc),
+         (0x3f883178),
+         (0x3f8c23bc),
+         (0x3fb8600f),
+         (0x3fbc72cb),
+         (0x3fb0416f),
+         (0x3fb453ab),
+         (0x3fb87017),
+         (0x3fbc62d3),
+         (0x3fb05177),
+         (0x3fb443b3)},
+        (0xfff80000),
+        {0x06,0x1a,0x0a,0x3e,0x05,0xb0,0xd2,0xab,0x76,0xef,
+         0xe5,0xf6,0xe4,0x09,0x02,0x41,0x4b,0x21,0xef,0x76,0x00}
+    },
+    {
+        /* No.7 delta:3952 weight:825 */
+        11213,
+        11,
+        15,
+        16,
+        {(0x00000000),
+         (0xa49b7ddb),
+         (0xbc75e45f),
+         (0x18ee9984),
+         (0x75600076),
+         (0xd1fb7dad),
+         (0xc915e429),
+         (0x6d8e99f2),
+         (0x0000c4f9),
+         (0xa49bb922),
+         (0xbc7520a6),
+         (0x18ee5d7d),
+         (0x7560c48f),
+         (0xd1fbb954),
+         (0xc91520d0),
+         (0x6d8e5d0b)},
+        {(0x00000000),
+         (0xf0015000),
+         (0x0a4c2c00),
+         (0xfa4d7c00),
+         (0x0482b000),
+         (0xf483e000),
+         (0x0ece9c00),
+         (0xfecfcc00),
+         (0x45105e00),
+         (0xb5110e00),
+         (0x4f5c7200),
+         (0xbf5d2200),
+         (0x4192ee00),
+         (0xb193be00),
+         (0x4bdec200),
+         (0xbbdf9200)},
+        {(0x3f800000),
+         (0x3ff800a8),
+         (0x3f852616),
+         (0x3ffd26be),
+         (0x3f824158),
+         (0x3ffa41f0),
+         (0x3f87674e),
+         (0x3fff67e6),
+         (0x3fa2882f),
+         (0x3fda8887),
+         (0x3fa7ae39),
+         (0x3fdfae91),
+         (0x3fa0c977),
+         (0x3fd8c9df),
+         (0x3fa5ef61),
+         (0x3fddefc9)},
+        (0xfff80000),
+        {0xa9,0x6f,0x34,0xf9,0xdd,0xff,0x0a,0x48,0x3c,0x3f,
+         0x3e,0x6f,0x74,0x70,0x37,0x33,0x19,0xe4,0x24,0x9b,0x00}
+    },
+    {
+        /* No.8 delta:4274 weight:1347 */
+        11213,
+        42,
+        6,
+        11,
+        {(0x00000000),
+         (0x311b3c94),
+         (0xa1e0c93f),
+         (0x90fbf5ab),
+         (0x3c600082),
+         (0x0d7b3c16),
+         (0x9d80c9bd),
+         (0xac9bf529),
+         (0x0000b622),
+         (0x311b8ab6),
+         (0xa1e07f1d),
+         (0x90fb4389),
+         (0x3c60b6a0),
+         (0x0d7b8a34),
+         (0x9d807f9f),
+         (0xac9b430b)},
+        {(0x00000000),
+         (0x80000000),
+         (0x00000000),
+         (0x80000000),
+         (0xc0000000),
+         (0x40000000),
+         (0xc0000000),
+         (0x40000000),
+         (0x20001e00),
+         (0xa0001e00),
+         (0x20001e00),
+         (0xa0001e00),
+         (0xe0001e00),
+         (0x60001e00),
+         (0xe0001e00),
+         (0x60001e00)},
+        {(0x3f800000),
+         (0x3fc00000),
+         (0x3f800000),
+         (0x3fc00000),
+         (0x3fe00000),
+         (0x3fa00000),
+         (0x3fe00000),
+         (0x3fa00000),
+         (0x3f90000f),
+         (0x3fd0000f),
+         (0x3f90000f),
+         (0x3fd0000f),
+         (0x3ff0000f),
+         (0x3fb0000f),
+         (0x3ff0000f),
+         (0x3fb0000f)},
+        (0xfff80000),
+        {0x3c,0x5a,0x73,0x0e,0xab,0x68,0x32,0x57,0x6f,0xe6,
+         0x1d,0x93,0x6e,0xbd,0xef,0x1a,0x6f,0x25,0x1f,0x70,0x00}
+    },
+    {
+        /* No.9 delta:1902 weight:1001 */
+        11213,
+        60,
+        6,
+        11,
+        {(0x00000000),
+         (0xbc8ed6c0),
+         (0x4d9fbb3d),
+         (0xf1116dfd),
+         (0x8e70009d),
+         (0x32fed65d),
+         (0xc3efbba0),
+         (0x7f616d60),
+         (0x0000a00a),
+         (0xbc8e76ca),
+         (0x4d9f1b37),
+         (0xf111cdf7),
+         (0x8e70a097),
+         (0x32fe7657),
+         (0xc3ef1baa),
+         (0x7f61cd6a)},
+        {(0x00000000),
+         (0xc0400400),
+         (0x00220000),
+         (0xc0620400),
+         (0x303e0000),
+         (0xf07e0400),
+         (0x301c0000),
+         (0xf05c0400),
+         (0x40021e00),
+         (0x80421a00),
+         (0x40201e00),
+         (0x80601a00),
+         (0x703c1e00),
+         (0xb07c1a00),
+         (0x701e1e00),
+         (0xb05e1a00)},
+        {(0x3f800000),
+         (0x3fe02002),
+         (0x3f801100),
+         (0x3fe03102),
+         (0x3f981f00),
+         (0x3ff83f02),
+         (0x3f980e00),
+         (0x3ff82e02),
+         (0x3fa0010f),
+         (0x3fc0210d),
+         (0x3fa0100f),
+         (0x3fc0300d),
+         (0x3fb81e0f),
+         (0x3fd83e0d),
+         (0x3fb80f0f),
+         (0x3fd82f0d)},
+        (0xfff80000),
+        {0x83,0x62,0x6c,0x11,0xe4,0x19,0x2b,0x9b,0x87,0x4d,
+         0x8e,0x68,0xde,0x69,0x56,0xc5,0x44,0x33,0x4f,0x20,0x00}
+    },
+    {
+        /* No.10 delta:1148 weight:927 */
+        11213,
+        45,
+        12,
+        13,
+        {(0x00000000),
+         (0x06bc3338),
+         (0xdde8b9f9),
+         (0xdb548ac1),
+         (0xfb2000a5),
+         (0xfd9c339d),
+         (0x26c8b95c),
+         (0x20748a64),
+         (0x0000a4ec),
+         (0x06bc97d4),
+         (0xdde81d15),
+         (0xdb542e2d),
+         (0xfb20a449),
+         (0xfd9c9771),
+         (0x26c81db0),
+         (0x20742e88)},
+        {(0x00000000),
+         (0x0c494c00),
+         (0x42040c00),
+         (0x4e4d4000),
+         (0x20090800),
+         (0x2c404400),
+         (0x620d0400),
+         (0x6e444800),
+         (0x10409e00),
+         (0x1c09d200),
+         (0x52449200),
+         (0x5e0dde00),
+         (0x30499600),
+         (0x3c00da00),
+         (0x724d9a00),
+         (0x7e04d600)},
+        {(0x3f800000),
+         (0x3f8624a6),
+         (0x3fa10206),
+         (0x3fa726a0),
+         (0x3f900484),
+         (0x3f962022),
+         (0x3fb10682),
+         (0x3fb72224),
+         (0x3f88204f),
+         (0x3f8e04e9),
+         (0x3fa92249),
+         (0x3faf06ef),
+         (0x3f9824cb),
+         (0x3f9e006d),
+         (0x3fb926cd),
+         (0x3fbf026b)},
+        (0xfff80000),
+        {0xf8,0x39,0x8a,0x36,0x0f,0x90,0x4f,0x3c,0x04,0x0c,
+         0x0a,0x02,0xd4,0x04,0x1b,0xc9,0xc4,0x8e,0x15,0x55,0x00}
+    },
+    {
+        /* No.11 delta:2163 weight:1669 */
+        11213,
+        80,
+        6,
+        9,
+        {(0x00000000),
+         (0x247f444d),
+         (0xb70a5ab0),
+         (0x93751efd),
+         (0xc44000b5),
+         (0xe03f44f8),
+         (0x734a5a05),
+         (0x57351e48),
+         (0x0000554f),
+         (0x247f1102),
+         (0xb70a0fff),
+         (0x93754bb2),
+         (0xc44055fa),
+         (0xe03f11b7),
+         (0x734a0f4a),
+         (0x57354b07)},
+        {(0x00000000),
+         (0x30200a00),
+         (0x106c0000),
+         (0x204c0a00),
+         (0x00180400),
+         (0x30380e00),
+         (0x10740400),
+         (0x20540e00),
+         (0x100a1e00),
+         (0x202a1400),
+         (0x00661e00),
+         (0x30461400),
+         (0x10121a00),
+         (0x20321000),
+         (0x007e1a00),
+         (0x305e1000)},
+        {(0x3f800000),
+         (0x3f981005),
+         (0x3f883600),
+         (0x3f902605),
+         (0x3f800c02),
+         (0x3f981c07),
+         (0x3f883a02),
+         (0x3f902a07),
+         (0x3f88050f),
+         (0x3f90150a),
+         (0x3f80330f),
+         (0x3f98230a),
+         (0x3f88090d),
+         (0x3f901908),
+         (0x3f803f0d),
+         (0x3f982f08)},
+        (0xfff80000),
+        {0x98,0xb0,0x30,0x32,0xcf,0x75,0x63,0xeb,0x3a,0xb6,
+         0x4f,0x70,0x47,0x4e,0x6a,0xd1,0x7b,0x40,0xbd,0x90,0x00}
+    },
+    {
+        /* No.12 delta:4985 weight:689 */
+        11213,
+        81,
+        8,
+        18,
+        {(0x00000000),
+         (0xcdcab880),
+         (0xdedf1226),
+         (0x1315aaa6),
+         (0xd17000c6),
+         (0x1cbab846),
+         (0x0faf12e0),
+         (0xc265aa60),
+         (0x0000b647),
+         (0xcdca0ec7),
+         (0xdedfa461),
+         (0x13151ce1),
+         (0xd170b681),
+         (0x1cba0e01),
+         (0x0fafa4a7),
+         (0xc2651c27)},
+        {(0x00000000),
+         (0x601c0000),
+         (0x08780000),
+         (0x68640000),
+         (0x00740000),
+         (0x60680000),
+         (0x080c0000),
+         (0x68100000),
+         (0x00a01e00),
+         (0x60bc1e00),
+         (0x08d81e00),
+         (0x68c41e00),
+         (0x00d41e00),
+         (0x60c81e00),
+         (0x08ac1e00),
+         (0x68b01e00)},
+        {(0x3f800000),
+         (0x3fb00e00),
+         (0x3f843c00),
+         (0x3fb43200),
+         (0x3f803a00),
+         (0x3fb03400),
+         (0x3f840600),
+         (0x3fb40800),
+         (0x3f80500f),
+         (0x3fb05e0f),
+         (0x3f846c0f),
+         (0x3fb4620f),
+         (0x3f806a0f),
+         (0x3fb0640f),
+         (0x3f84560f),
+         (0x3fb4580f)},
+        (0xfff80000),
+        {0x90,0xf2,0x51,0xc2,0x2d,0xc4,0x52,0x73,0x15,0xfa,
+         0xeb,0xc3,0x64,0x51,0x48,0xf7,0xb1,0x26,0x38,0xb4,0x00}
+    },
+    {
+        /* No.13 delta:6473 weight:513 */
+        11213,
+        16,
+        1,
+        19,
+        {(0x00000000),
+         (0x2310a4f2),
+         (0x91c8cf8c),
+         (0xb2d86b7e),
+         (0x89c000d1),
+         (0xaad0a423),
+         (0x1808cf5d),
+         (0x3b186baf),
+         (0x0000f299),
+         (0x2310566b),
+         (0x91c83d15),
+         (0xb2d899e7),
+         (0x89c0f248),
+         (0xaad056ba),
+         (0x18083dc4),
+         (0x3b189936)},
+        {(0x00000000),
+         (0x4c020000),
+         (0xa6800000),
+         (0xea820000),
+         (0x15000000),
+         (0x59020000),
+         (0xb3800000),
+         (0xff820000),
+         (0x29201e00),
+         (0x65221e00),
+         (0x8fa01e00),
+         (0xc3a21e00),
+         (0x3c201e00),
+         (0x70221e00),
+         (0x9aa01e00),
+         (0xd6a21e00)},
+        {(0x3f800000),
+         (0x3fa60100),
+         (0x3fd34000),
+         (0x3ff54100),
+         (0x3f8a8000),
+         (0x3fac8100),
+         (0x3fd9c000),
+         (0x3fffc100),
+         (0x3f94900f),
+         (0x3fb2910f),
+         (0x3fc7d00f),
+         (0x3fe1d10f),
+         (0x3f9e100f),
+         (0x3fb8110f),
+         (0x3fcd500f),
+         (0x3feb510f)},
+        (0xfff80000),
+        {0x89,0x7c,0x3c,0xc6,0x3d,0x0a,0xad,0xf2,0xa0,0xf5,
+         0x0a,0x4c,0x4e,0x83,0xc5,0x45,0xdb,0xcb,0xb5,0x98,0x00}
+    },
+    {
+        /* No.14 delta:1389 weight:725 */
+        11213,
+        63,
+        14,
+        18,
+        {(0x00000000),
+         (0x81c7909c),
+         (0xecb46b0c),
+         (0x6d73fb90),
+         (0xf33000ef),
+         (0x72f79073),
+         (0x1f846be3),
+         (0x9e43fb7f),
+         (0x0000e902),
+         (0x81c7799e),
+         (0xecb4820e),
+         (0x6d731292),
+         (0xf330e9ed),
+         (0x72f77971),
+         (0x1f8482e1),
+         (0x9e43127d)},
+        {(0x00000000),
+         (0x09001000),
+         (0x02010000),
+         (0x0b011000),
+         (0x00201800),
+         (0x09200800),
+         (0x02211800),
+         (0x0b210800),
+         (0x30039e00),
+         (0x39038e00),
+         (0x32029e00),
+         (0x3b028e00),
+         (0x30238600),
+         (0x39239600),
+         (0x32228600),
+         (0x3b229600)},
+        {(0x3f800000),
+         (0x3f848008),
+         (0x3f810080),
+         (0x3f858088),
+         (0x3f80100c),
+         (0x3f849004),
+         (0x3f81108c),
+         (0x3f859084),
+         (0x3f9801cf),
+         (0x3f9c81c7),
+         (0x3f99014f),
+         (0x3f9d8147),
+         (0x3f9811c3),
+         (0x3f9c91cb),
+         (0x3f991143),
+         (0x3f9d914b)},
+        (0xfff80000),
+        {0xb7,0x0b,0xee,0xcb,0xc6,0x29,0x7c,0xe3,0xc5,0x58,
+         0x71,0xe7,0x4b,0xe5,0xc9,0x63,0xbd,0xa4,0x75,0xc7,0x00}
+    },
+    {
+        /* No.15 delta:8365 weight:1135 */
+        11213,
+        38,
+        28,
+        1,
+        {(0x00000000),
+         (0xe56c7b3b),
+         (0x759d951b),
+         (0x90f1ee20),
+         (0x1a6000fa),
+         (0xff0c7bc1),
+         (0x6ffd95e1),
+         (0x8a91eeda),
+         (0x00004474),
+         (0xe56c3f4f),
+         (0x759dd16f),
+         (0x90f1aa54),
+         (0x1a60448e),
+         (0xff0c3fb5),
+         (0x6ffdd195),
+         (0x8a91aaae)},
+        {(0x00000000),
+         (0xc0410a00),
+         (0xe70c0400),
+         (0x274d0e00),
+         (0xfa3e8000),
+         (0x3a7f8a00),
+         (0x1d328400),
+         (0xdd738e00),
+         (0x20801e00),
+         (0xe0c11400),
+         (0xc78c1a00),
+         (0x07cd1000),
+         (0xdabe9e00),
+         (0x1aff9400),
+         (0x3db29a00),
+         (0xfdf39000)},
+        {(0x3f800000),
+         (0x3fe02085),
+         (0x3ff38602),
+         (0x3f93a687),
+         (0x3ffd1f40),
+         (0x3f9d3fc5),
+         (0x3f8e9942),
+         (0x3feeb9c7),
+         (0x3f90400f),
+         (0x3ff0608a),
+         (0x3fe3c60d),
+         (0x3f83e688),
+         (0x3fed5f4f),
+         (0x3f8d7fca),
+         (0x3f9ed94d),
+         (0x3ffef9c8)},
+        (0xfff80000),
+        {0xd2,0xa4,0x8f,0xf8,0x54,0x0f,0xe3,0x14,0x17,0xeb,
+         0x17,0x78,0x41,0x78,0x69,0xbb,0x4b,0x01,0x85,0x8e,0x00}
+    },
+    {
+        /* No.16 delta:6253 weight:641 */
+        11213,
+        3,
+        30,
+        2,
+        {(0x00000000),
+         (0x83a2a3cb),
+         (0x52e0a64c),
+         (0xd1420587),
+         (0x9850010b),
+         (0x1bf2a2c0),
+         (0xcab0a747),
+         (0x4912048c),
+         (0x0000aaa5),
+         (0x83a2096e),
+         (0x52e00ce9),
+         (0xd142af22),
+         (0x9850abae),
+         (0x1bf20865),
+         (0xcab00de2),
+         (0x4912ae29)},
+        {(0x00000000),
+         (0xe9100000),
+         (0x94d00000),
+         (0x7dc00000),
+         (0xdbcc0000),
+         (0x32dc0000),
+         (0x4f1c0000),
+         (0xa60c0000),
+         (0x09d01e00),
+         (0xe0c01e00),
+         (0x9d001e00),
+         (0x74101e00),
+         (0xd21c1e00),
+         (0x3b0c1e00),
+         (0x46cc1e00),
+         (0xafdc1e00)},
+        {(0x3f800000),
+         (0x3ff48800),
+         (0x3fca6800),
+         (0x3fbee000),
+         (0x3fede600),
+         (0x3f996e00),
+         (0x3fa78e00),
+         (0x3fd30600),
+         (0x3f84e80f),
+         (0x3ff0600f),
+         (0x3fce800f),
+         (0x3fba080f),
+         (0x3fe90e0f),
+         (0x3f9d860f),
+         (0x3fa3660f),
+         (0x3fd7ee0f)},
+        (0xfff80000),
+        {0xa6,0xc6,0x12,0x64,0xaf,0x79,0x40,0x50,0x7b,0xb9,
+         0xa9,0xfa,0x9a,0xc0,0x7a,0xaf,0x63,0x83,0x39,0x6c,0x00}
+    },
+    {
+        /* No.17 delta:5329 weight:467 */
+        11213,
+        55,
+        1,
+        16,
+        {(0x00000000),
+         (0x2ce39cc8),
+         (0x6312ea87),
+         (0x4ff1764f),
+         (0x9ef00113),
+         (0xb2139ddb),
+         (0xfde2eb94),
+         (0xd101775c),
+         (0x00007ddc),
+         (0x2ce3e114),
+         (0x6312975b),
+         (0x4ff10b93),
+         (0x9ef07ccf),
+         (0xb213e007),
+         (0xfde29648),
+         (0xd1010a80)},
+        {(0x00000000),
+         (0x0c021000),
+         (0xe3400000),
+         (0xef421000),
+         (0x02000000),
+         (0x0e021000),
+         (0xe1400000),
+         (0xed421000),
+         (0x50a01e00),
+         (0x5ca20e00),
+         (0xb3e01e00),
+         (0xbfe20e00),
+         (0x52a01e00),
+         (0x5ea20e00),
+         (0xb1e01e00),
+         (0xbde20e00)},
+        {(0x3f800000),
+         (0x3f860108),
+         (0x3ff1a000),
+         (0x3ff7a108),
+         (0x3f810000),
+         (0x3f870108),
+         (0x3ff0a000),
+         (0x3ff6a108),
+         (0x3fa8500f),
+         (0x3fae5107),
+         (0x3fd9f00f),
+         (0x3fdff107),
+         (0x3fa9500f),
+         (0x3faf5107),
+         (0x3fd8f00f),
+         (0x3fdef107)},
+        (0xfff80000),
+        {0xe4,0xb8,0x1d,0x0f,0x15,0xe2,0xd6,0xd2,0x30,0x67,
+         0x18,0x06,0x38,0xd0,0x63,0x09,0x4d,0xc1,0xaa,0x48,0x00}
+    },
+    {
+        /* No.18 delta:3046 weight:699 */
+        11213,
+        9,
+        9,
+        15,
+        {(0x00000000),
+         (0x228294e5),
+         (0xc1861e7b),
+         (0xe3048a9e),
+         (0x6250012a),
+         (0x40d295cf),
+         (0xa3d61f51),
+         (0x81548bb4),
+         (0x00005800),
+         (0x2282cce5),
+         (0xc186467b),
+         (0xe304d29e),
+         (0x6250592a),
+         (0x40d2cdcf),
+         (0xa3d64751),
+         (0x8154d3b4)},
+        {(0x00000000),
+         (0x57910c00),
+         (0xd07cc400),
+         (0x87edc800),
+         (0xa8036800),
+         (0xff926400),
+         (0x787fac00),
+         (0x2feea000),
+         (0x00521e00),
+         (0x57c31200),
+         (0xd02eda00),
+         (0x87bfd600),
+         (0xa8517600),
+         (0xffc07a00),
+         (0x782db200),
+         (0x2fbcbe00)},
+        {(0x3f800000),
+         (0x3fabc886),
+         (0x3fe83e62),
+         (0x3fc3f6e4),
+         (0x3fd401b4),
+         (0x3fffc932),
+         (0x3fbc3fd6),
+         (0x3f97f750),
+         (0x3f80290f),
+         (0x3fabe189),
+         (0x3fe8176d),
+         (0x3fc3dfeb),
+         (0x3fd428bb),
+         (0x3fffe03d),
+         (0x3fbc16d9),
+         (0x3f97de5f)},
+        (0xfff80000),
+        {0x3d,0xb7,0x41,0x11,0xf8,0x6e,0xe6,0x89,0x71,0x95,
+         0x3b,0x7c,0x3a,0x38,0x60,0x12,0x4c,0xec,0xaa,0x83,0x00}
+    },
+    {
+        /* No.19 delta:786 weight:1431 */
+        11213,
+        75,
+        17,
+        6,
+        {(0x00000000),
+         (0x819ee5dc),
+         (0x4ffaa079),
+         (0xce6445a5),
+         (0xe6300137),
+         (0x67aee4eb),
+         (0xa9caa14e),
+         (0x28544492),
+         (0x00008a74),
+         (0x819e6fa8),
+         (0x4ffa2a0d),
+         (0xce64cfd1),
+         (0xe6308b43),
+         (0x67ae6e9f),
+         (0xa9ca2b3a),
+         (0x2854cee6)},
+        {(0x00000000),
+         (0x98018000),
+         (0x20039800),
+         (0xb8021800),
+         (0x40104000),
+         (0xd811c000),
+         (0x6013d800),
+         (0xf8125800),
+         (0x00033e00),
+         (0x9802be00),
+         (0x2000a600),
+         (0xb8012600),
+         (0x40137e00),
+         (0xd812fe00),
+         (0x6010e600),
+         (0xf8116600)},
+        {(0x3f800000),
+         (0x3fcc00c0),
+         (0x3f9001cc),
+         (0x3fdc010c),
+         (0x3fa00820),
+         (0x3fec08e0),
+         (0x3fb009ec),
+         (0x3ffc092c),
+         (0x3f80019f),
+         (0x3fcc015f),
+         (0x3f900053),
+         (0x3fdc0093),
+         (0x3fa009bf),
+         (0x3fec097f),
+         (0x3fb00873),
+         (0x3ffc08b3)},
+        (0xfff80000),
+        {0x90,0xc1,0xc9,0xe7,0xea,0xaf,0xd9,0x5b,0x1f,0x93,
+         0x8d,0xcd,0x9b,0xf4,0xbb,0x91,0x58,0xb2,0x03,0x70,0x00}
+    },
+    {
+        /* No.20 delta:846 weight:909 */
+        11213,
+        70,
+        15,
+        6,
+        {(0x00000000),
+         (0xc56d80c5),
+         (0x4bb862e9),
+         (0x8ed5e22c),
+         (0x92e00143),
+         (0x578d8186),
+         (0xd95863aa),
+         (0x1c35e36f),
+         (0x000095b5),
+         (0xc56d1570),
+         (0x4bb8f75c),
+         (0x8ed57799),
+         (0x92e094f6),
+         (0x578d1433),
+         (0xd958f61f),
+         (0x1c3576da)},
+        {(0x00000000),
+         (0x20642000),
+         (0x2530d800),
+         (0x0554f800),
+         (0x01a05200),
+         (0x21c47200),
+         (0x24908a00),
+         (0x04f4aa00),
+         (0x00521e00),
+         (0x20363e00),
+         (0x2562c600),
+         (0x0506e600),
+         (0x01f24c00),
+         (0x21966c00),
+         (0x24c29400),
+         (0x04a6b400)},
+        {(0x3f800000),
+         (0x3f903210),
+         (0x3f92986c),
+         (0x3f82aa7c),
+         (0x3f80d029),
+         (0x3f90e239),
+         (0x3f924845),
+         (0x3f827a55),
+         (0x3f80290f),
+         (0x3f901b1f),
+         (0x3f92b163),
+         (0x3f828373),
+         (0x3f80f926),
+         (0x3f90cb36),
+         (0x3f92614a),
+         (0x3f82535a)},
+        (0xfff80000),
+        {0xd6,0xb1,0xe1,0x27,0x40,0x54,0x48,0x67,0x71,0xe5,
+         0x2a,0x38,0xbf,0xa7,0x93,0x82,0x17,0xcd,0x71,0x33,0x00}
+    },
+    {
+        /* No.21 delta:1909 weight:729 */
+        11213,
+        63,
+        15,
+        17,
+        {(0x00000000),
+         (0x3c495454),
+         (0xf3a1ddf0),
+         (0xcfe889a4),
+         (0x1b200159),
+         (0x2769550d),
+         (0xe881dca9),
+         (0xd4c888fd),
+         (0x0000cd3b),
+         (0x3c49996f),
+         (0xf3a110cb),
+         (0xcfe8449f),
+         (0x1b20cc62),
+         (0x27699836),
+         (0xe8811192),
+         (0xd4c845c6)},
+        {(0x00000000),
+         (0x08027000),
+         (0x9651d400),
+         (0x9e53a400),
+         (0x0040a400),
+         (0x0842d400),
+         (0x96117000),
+         (0x9e130000),
+         (0x006e9e00),
+         (0x086cee00),
+         (0x963f4a00),
+         (0x9e3d3a00),
+         (0x002e3a00),
+         (0x082c4a00),
+         (0x967fee00),
+         (0x9e7d9e00)},
+        {(0x3f800000),
+         (0x3f840138),
+         (0x3fcb28ea),
+         (0x3fcf29d2),
+         (0x3f802052),
+         (0x3f84216a),
+         (0x3fcb08b8),
+         (0x3fcf0980),
+         (0x3f80374f),
+         (0x3f843677),
+         (0x3fcb1fa5),
+         (0x3fcf1e9d),
+         (0x3f80171d),
+         (0x3f841625),
+         (0x3fcb3ff7),
+         (0x3fcf3ecf)},
+        (0xfff80000),
+        {0xc8,0xdc,0x70,0x0a,0x23,0xa5,0x20,0x35,0xa2,0xd7,
+         0xd1,0x53,0xfc,0xe0,0xc1,0xde,0xc0,0x2b,0x65,0x4d,0x00}
+    },
+    {
+        /* No.22 delta:2420 weight:985 */
+        11213,
+        32,
+        7,
+        15,
+        {(0x00000000),
+         (0xd25d9ecf),
+         (0xb1288d77),
+         (0x637513b8),
+         (0xc3700160),
+         (0x112d9faf),
+         (0x72588c17),
+         (0xa00512d8),
+         (0x000093d5),
+         (0xd25d0d1a),
+         (0xb1281ea2),
+         (0x6375806d),
+         (0xc37092b5),
+         (0x112d0c7a),
+         (0x72581fc2),
+         (0xa005810d)},
+        {(0x00000000),
+         (0x10134200),
+         (0x028b0000),
+         (0x12984200),
+         (0x50480000),
+         (0x405b4200),
+         (0x52c30000),
+         (0x42d04200),
+         (0x10ee5e00),
+         (0x00fd1c00),
+         (0x12655e00),
+         (0x02761c00),
+         (0x40a65e00),
+         (0x50b51c00),
+         (0x422d5e00),
+         (0x523e1c00)},
+        {(0x3f800000),
+         (0x3f8809a1),
+         (0x3f814580),
+         (0x3f894c21),
+         (0x3fa82400),
+         (0x3fa02da1),
+         (0x3fa96180),
+         (0x3fa16821),
+         (0x3f88772f),
+         (0x3f807e8e),
+         (0x3f8932af),
+         (0x3f813b0e),
+         (0x3fa0532f),
+         (0x3fa85a8e),
+         (0x3fa116af),
+         (0x3fa91f0e)},
+        (0xfff80000),
+        {0xd0,0xc2,0xd1,0x3e,0x09,0x41,0xc7,0x9c,0x09,0x33,
+         0x25,0x5f,0x84,0x21,0xc4,0xcc,0xc5,0x43,0x2a,0x14,0x00}
+    },
+    {
+        /* No.23 delta:985 weight:863 */
+        11213,
+        70,
+        12,
+        10,
+        {(0x00000000),
+         (0x6e6163b2),
+         (0xa3cecdea),
+         (0xcdafae58),
+         (0xb670017d),
+         (0xd81162cf),
+         (0x15becc97),
+         (0x7bdfaf25),
+         (0x0000d554),
+         (0x6e61b6e6),
+         (0xa3ce18be),
+         (0xcdaf7b0c),
+         (0xb670d429),
+         (0xd811b79b),
+         (0x15be19c3),
+         (0x7bdf7a71)},
+        {(0x00000000),
+         (0x0338d800),
+         (0x00545800),
+         (0x036c8000),
+         (0x01521000),
+         (0x026ac800),
+         (0x01064800),
+         (0x023e9000),
+         (0x0020de00),
+         (0x03180600),
+         (0x00748600),
+         (0x034c5e00),
+         (0x0172ce00),
+         (0x024a1600),
+         (0x01269600),
+         (0x021e4e00)},
+        {(0x3f800000),
+         (0x3f819c6c),
+         (0x3f802a2c),
+         (0x3f81b640),
+         (0x3f80a908),
+         (0x3f813564),
+         (0x3f808324),
+         (0x3f811f48),
+         (0x3f80106f),
+         (0x3f818c03),
+         (0x3f803a43),
+         (0x3f81a62f),
+         (0x3f80b967),
+         (0x3f81250b),
+         (0x3f80934b),
+         (0x3f810f27)},
+        (0xfff80000),
+        {0x57,0xda,0x1e,0x1e,0x0f,0xc1,0x5c,0xac,0x32,0x8f,
+         0xe4,0x94,0x24,0xf0,0x86,0x24,0x1b,0xde,0x44,0x16,0x00}
+    },
+    {
+        /* No.24 delta:1573 weight:1043 */
+        11213,
+        58,
+        21,
+        2,
+        {(0x00000000),
+         (0xa6c80c3d),
+         (0x3502d818),
+         (0x93cad425),
+         (0xda700189),
+         (0x7cb80db4),
+         (0xef72d991),
+         (0x49bad5ac),
+         (0x0000cb8c),
+         (0xa6c8c7b1),
+         (0x35021394),
+         (0x93ca1fa9),
+         (0xda70ca05),
+         (0x7cb8c638),
+         (0xef72121d),
+         (0x49ba1e20)},
+        {(0x00000000),
+         (0xd66f0400),
+         (0x014ca600),
+         (0xd723a200),
+         (0x00442a00),
+         (0xd62b2e00),
+         (0x01088c00),
+         (0xd7678800),
+         (0x0c835e00),
+         (0xdaec5a00),
+         (0x0dcff800),
+         (0xdba0fc00),
+         (0x0cc77400),
+         (0xdaa87000),
+         (0x0d8bd200),
+         (0xdbe4d600)},
+        {(0x3f800000),
+         (0x3feb3782),
+         (0x3f80a653),
+         (0x3feb91d1),
+         (0x3f802215),
+         (0x3feb1597),
+         (0x3f808446),
+         (0x3febb3c4),
+         (0x3f8641af),
+         (0x3fed762d),
+         (0x3f86e7fc),
+         (0x3fedd07e),
+         (0x3f8663ba),
+         (0x3fed5438),
+         (0x3f86c5e9),
+         (0x3fedf26b)},
+        (0xfff80000),
+        {0xd3,0x5b,0x43,0x3f,0x00,0x55,0xf8,0x40,0xfe,0x8f,
+         0xd0,0xe0,0xac,0x48,0x50,0xf2,0x50,0x2a,0xb7,0xcf,0x00}
+    },
+    {
+        /* No.25 delta:1860 weight:1169 */
+        11213,
+        33,
+        7,
+        10,
+        {(0x00000000),
+         (0x3f9ccf17),
+         (0xcdd7a2a1),
+         (0xf24b6db6),
+         (0xf9c00195),
+         (0xc65cce82),
+         (0x3417a334),
+         (0x0b8b6c23),
+         (0x00006c32),
+         (0x3f9ca325),
+         (0xcdd7ce93),
+         (0xf24b0184),
+         (0xf9c06da7),
+         (0xc65ca2b0),
+         (0x3417cf06),
+         (0x0b8b0011)},
+        {(0x00000000),
+         (0x043a0400),
+         (0x00714000),
+         (0x044b4400),
+         (0x10408400),
+         (0x147a8000),
+         (0x1031c400),
+         (0x140bc000),
+         (0x70049e00),
+         (0x743e9a00),
+         (0x7075de00),
+         (0x744fda00),
+         (0x60441a00),
+         (0x647e1e00),
+         (0x60355a00),
+         (0x640f5e00)},
+        {(0x3f800000),
+         (0x3f821d02),
+         (0x3f8038a0),
+         (0x3f8225a2),
+         (0x3f882042),
+         (0x3f8a3d40),
+         (0x3f8818e2),
+         (0x3f8a05e0),
+         (0x3fb8024f),
+         (0x3fba1f4d),
+         (0x3fb83aef),
+         (0x3fba27ed),
+         (0x3fb0220d),
+         (0x3fb23f0f),
+         (0x3fb01aad),
+         (0x3fb207af)},
+        (0xfff80000),
+        {0x6f,0x6a,0x15,0x14,0x9e,0x4e,0xca,0xcd,0xe1,0xfb,
+         0x26,0x23,0xa0,0xa9,0x21,0x22,0xca,0x8c,0x68,0x8f,0x00}
+    },
+    {
+        /* No.26 delta:2274 weight:1019 */
+        11213,
+        18,
+        7,
+        13,
+        {(0x00000000),
+         (0x83f4f835),
+         (0x34e8f3fa),
+         (0xb71c0bcf),
+         (0xf21001a3),
+         (0x71e4f996),
+         (0xc6f8f259),
+         (0x450c0a6c),
+         (0x00009d49),
+         (0x83f4657c),
+         (0x34e86eb3),
+         (0xb71c9686),
+         (0xf2109cea),
+         (0x71e464df),
+         (0xc6f86f10),
+         (0x450c9725)},
+        {(0x00000000),
+         (0x69050000),
+         (0x0b0a1800),
+         (0x620f1800),
+         (0x41a06000),
+         (0x28a56000),
+         (0x4aaa7800),
+         (0x23af7800),
+         (0x046b1e00),
+         (0x6d6e1e00),
+         (0x0f610600),
+         (0x66640600),
+         (0x45cb7e00),
+         (0x2cce7e00),
+         (0x4ec16600),
+         (0x27c46600)},
+        {(0x3f800000),
+         (0x3fb48280),
+         (0x3f85850c),
+         (0x3fb1078c),
+         (0x3fa0d030),
+         (0x3f9452b0),
+         (0x3fa5553c),
+         (0x3f91d7bc),
+         (0x3f82358f),
+         (0x3fb6b70f),
+         (0x3f87b083),
+         (0x3fb33203),
+         (0x3fa2e5bf),
+         (0x3f96673f),
+         (0x3fa760b3),
+         (0x3f93e233)},
+        (0xfff80000),
+        {0x6c,0xda,0xe3,0x3d,0xac,0x72,0x40,0x41,0xca,0xa3,
+         0xf7,0xdf,0xff,0x2d,0x57,0xea,0x1f,0x8d,0x6c,0x04,0x00}
+    },
+    {
+        /* No.27 delta:2503 weight:711 */
+        11213,
+        9,
+        12,
+        13,
+        {(0x00000000),
+         (0xc54f7f87),
+         (0x6904a950),
+         (0xac4bd6d7),
+         (0x85c001bc),
+         (0x408f7e3b),
+         (0xecc4a8ec),
+         (0x298bd76b),
+         (0x00007b77),
+         (0xc54f04f0),
+         (0x6904d227),
+         (0xac4bada0),
+         (0x85c07acb),
+         (0x408f054c),
+         (0xecc4d39b),
+         (0x298bac1c)},
+        {(0x00000000),
+         (0xc7002c00),
+         (0x5a61c800),
+         (0x9d61e400),
+         (0x12013800),
+         (0xd5011400),
+         (0x4860f000),
+         (0x8f60dc00),
+         (0xc8c01e00),
+         (0x0fc03200),
+         (0x92a1d600),
+         (0x55a1fa00),
+         (0xdac12600),
+         (0x1dc10a00),
+         (0x80a0ee00),
+         (0x47a0c200)},
+        {(0x3f800000),
+         (0x3fe38016),
+         (0x3fad30e4),
+         (0x3fceb0f2),
+         (0x3f89009c),
+         (0x3fea808a),
+         (0x3fa43078),
+         (0x3fc7b06e),
+         (0x3fe4600f),
+         (0x3f87e019),
+         (0x3fc950eb),
+         (0x3faad0fd),
+         (0x3fed6093),
+         (0x3f8ee085),
+         (0x3fc05077),
+         (0x3fa3d061)},
+        (0xfff80000),
+        {0xd6,0xd3,0xee,0x8e,0x9a,0x94,0x64,0x4c,0x3a,0x7b,
+         0x1d,0x2f,0x64,0xef,0x67,0x33,0x0d,0x49,0x2e,0x20,0x00}
+    },
+    {
+        /* No.28 delta:1642 weight:1597 */
+        11213,
+        14,
+        16,
+        3,
+        {(0x00000000),
+         (0x8147d21a),
+         (0x627aba0f),
+         (0xe33d6815),
+         (0x279001c1),
+         (0xa6d7d3db),
+         (0x45eabbce),
+         (0xc4ad69d4),
+         (0x0000f4d1),
+         (0x814726cb),
+         (0x627a4ede),
+         (0xe33d9cc4),
+         (0x2790f510),
+         (0xa6d7270a),
+         (0x45ea4f1f),
+         (0xc4ad9d05)},
+        {(0x00000000),
+         (0x210c6a00),
+         (0x0071b800),
+         (0x217dd200),
+         (0xcc021a00),
+         (0xed0e7000),
+         (0xcc73a200),
+         (0xed7fc800),
+         (0x41b2de00),
+         (0x60beb400),
+         (0x41c36600),
+         (0x60cf0c00),
+         (0x8db0c400),
+         (0xacbcae00),
+         (0x8dc17c00),
+         (0xaccd1600)},
+        {(0x3f800000),
+         (0x3f908635),
+         (0x3f8038dc),
+         (0x3f90bee9),
+         (0x3fe6010d),
+         (0x3ff68738),
+         (0x3fe639d1),
+         (0x3ff6bfe4),
+         (0x3fa0d96f),
+         (0x3fb05f5a),
+         (0x3fa0e1b3),
+         (0x3fb06786),
+         (0x3fc6d862),
+         (0x3fd65e57),
+         (0x3fc6e0be),
+         (0x3fd6668b)},
+        (0xfff80000),
+        {0x58,0xce,0x35,0x39,0xc0,0x04,0xad,0xd6,0xdd,0xb7,
+         0x5d,0xf1,0xac,0xf9,0x85,0xea,0x3f,0x74,0x52,0x6d,0x00}
+    },
+    {
+        /* No.29 delta:1888 weight:1763 */
+        11213,
+        91,
+        4,
+        2,
+        {(0x00000000),
+         (0xa620066e),
+         (0x498ca8fb),
+         (0xefacae95),
+         (0xc3f001d3),
+         (0x65d007bd),
+         (0x8a7ca928),
+         (0x2c5caf46),
+         (0x0000927c),
+         (0xa6209412),
+         (0x498c3a87),
+         (0xefac3ce9),
+         (0xc3f093af),
+         (0x65d095c1),
+         (0x8a7c3b54),
+         (0x2c5c3d3a)},
+        {(0x00000000),
+         (0x29909200),
+         (0x0074c600),
+         (0x29e45400),
+         (0x400e7000),
+         (0x699ee200),
+         (0x407ab600),
+         (0x69ea2400),
+         (0x44029e00),
+         (0x6d920c00),
+         (0x44765800),
+         (0x6de6ca00),
+         (0x040cee00),
+         (0x2d9c7c00),
+         (0x04782800),
+         (0x2de8ba00)},
+        {(0x3f800000),
+         (0x3f94c849),
+         (0x3f803a63),
+         (0x3f94f22a),
+         (0x3fa00738),
+         (0x3fb4cf71),
+         (0x3fa03d5b),
+         (0x3fb4f512),
+         (0x3fa2014f),
+         (0x3fb6c906),
+         (0x3fa23b2c),
+         (0x3fb6f365),
+         (0x3f820677),
+         (0x3f96ce3e),
+         (0x3f823c14),
+         (0x3f96f45d)},
+        (0xfff80000),
+        {0xbf,0x14,0xa8,0x90,0x29,0x75,0x15,0xde,0x68,0x12,
+         0x39,0xb3,0x6e,0x2f,0xcb,0x19,0x1f,0xe6,0x2c,0x9b,0x00}
+    },
+    {
+        /* No.30 delta:1194 weight:873 */
+        11213,
+        90,
+        10,
+        14,
+        {(0x00000000),
+         (0xdd216696),
+         (0x738a5c01),
+         (0xaeab3a97),
+         (0x402001ec),
+         (0x9d01677a),
+         (0x33aa5ded),
+         (0xee8b3b7b),
+         (0x0000c5c9),
+         (0xdd21a35f),
+         (0x738a99c8),
+         (0xaeabff5e),
+         (0x4020c425),
+         (0x9d01a2b3),
+         (0x33aa9824),
+         (0xee8bfeb2)},
+        {(0x00000000),
+         (0x193a0000),
+         (0x07a49000),
+         (0x1e9e9000),
+         (0x00658800),
+         (0x195f8800),
+         (0x07c11800),
+         (0x1efb1800),
+         (0x4456fe00),
+         (0x5d6cfe00),
+         (0x43f26e00),
+         (0x5ac86e00),
+         (0x44337600),
+         (0x5d097600),
+         (0x4397e600),
+         (0x5aade600)},
+        {(0x3f800000),
+         (0x3f8c9d00),
+         (0x3f83d248),
+         (0x3f8f4f48),
+         (0x3f8032c4),
+         (0x3f8cafc4),
+         (0x3f83e08c),
+         (0x3f8f7d8c),
+         (0x3fa22b7f),
+         (0x3faeb67f),
+         (0x3fa1f937),
+         (0x3fad6437),
+         (0x3fa219bb),
+         (0x3fae84bb),
+         (0x3fa1cbf3),
+         (0x3fad56f3)},
+        (0xfff80000),
+        {0x45,0xbe,0xc0,0x65,0xca,0x28,0x44,0x0e,0x06,0x4e,
+         0xa0,0xbf,0x48,0xa8,0x8e,0x39,0x64,0x68,0xe1,0x30,0x00}
+    },
+    {
+        /* No.31 delta:1509 weight:1313 */
+        11213,
+        86,
+        6,
+        7,
+        {(0x00000000),
+         (0x41d73f34),
+         (0x0930d7f4),
+         (0x48e7e8c0),
+         (0x65b001f9),
+         (0x24673ecd),
+         (0x6c80d60d),
+         (0x2d57e939),
+         (0x00008ad2),
+         (0x41d7b5e6),
+         (0x09305d26),
+         (0x48e76212),
+         (0x65b08b2b),
+         (0x2467b41f),
+         (0x6c805cdf),
+         (0x2d5763eb)},
+        {(0x00000000),
+         (0x02010000),
+         (0x10332000),
+         (0x12322000),
+         (0x10030000),
+         (0x12020000),
+         (0x00302000),
+         (0x02312000),
+         (0x403a9e00),
+         (0x423b9e00),
+         (0x5009be00),
+         (0x5208be00),
+         (0x50399e00),
+         (0x52389e00),
+         (0x400abe00),
+         (0x420bbe00)},
+        {(0x3f800000),
+         (0x3f810080),
+         (0x3f881990),
+         (0x3f891910),
+         (0x3f880180),
+         (0x3f890100),
+         (0x3f801810),
+         (0x3f811890),
+         (0x3fa01d4f),
+         (0x3fa11dcf),
+         (0x3fa804df),
+         (0x3fa9045f),
+         (0x3fa81ccf),
+         (0x3fa91c4f),
+         (0x3fa0055f),
+         (0x3fa105df)},
+        (0xfff80000),
+        {0x9f,0xb2,0x3a,0xbb,0xab,0xbd,0xc1,0x43,0x82,0x94,
+         0x1f,0x8e,0xdf,0x89,0xbb,0xf6,0xee,0x6a,0x10,0x54,0x00}
+    },
+    {
+        /* No.32 delta:3454 weight:869 */
+        11213,
+        36,
+        3,
+        15,
+        {(0x00000000),
+         (0x1709ea04),
+         (0x8e9b8c43),
+         (0x99926647),
+         (0xdd100203),
+         (0xca19e807),
+         (0x538b8e40),
+         (0x44826444),
+         (0x0000f3b3),
+         (0x170919b7),
+         (0x8e9b7ff0),
+         (0x999295f4),
+         (0xdd10f1b0),
+         (0xca191bb4),
+         (0x538b7df3),
+         (0x448297f7)},
+        {(0x00000000),
+         (0x28228000),
+         (0x1fc10000),
+         (0x37e38000),
+         (0x00400000),
+         (0x28628000),
+         (0x1f810000),
+         (0x37a38000),
+         (0x12b01e00),
+         (0x3a929e00),
+         (0x0d711e00),
+         (0x25539e00),
+         (0x12f01e00),
+         (0x3ad29e00),
+         (0x0d311e00),
+         (0x25139e00)},
+        {(0x3f800000),
+         (0x3f941140),
+         (0x3f8fe080),
+         (0x3f9bf1c0),
+         (0x3f802000),
+         (0x3f943140),
+         (0x3f8fc080),
+         (0x3f9bd1c0),
+         (0x3f89580f),
+         (0x3f9d494f),
+         (0x3f86b88f),
+         (0x3f92a9cf),
+         (0x3f89780f),
+         (0x3f9d694f),
+         (0x3f86988f),
+         (0x3f9289cf)},
+        (0xfff80000),
+        {0xbd,0x88,0xcf,0x70,0x9b,0x4f,0x1b,0xe7,0xe6,0xab,
+         0x62,0x27,0x66,0xaf,0x36,0xbb,0x10,0x8b,0x27,0xd7,0x00}
+    },
+    {
+        /* No.33 delta:1105 weight:1263 */
+        11213,
+        40,
+        9,
+        3,
+        {(0x00000000),
+         (0x4406f6da),
+         (0xf592d5cb),
+         (0xb1942311),
+         (0xe0900211),
+         (0xa496f4cb),
+         (0x1502d7da),
+         (0x51042100),
+         (0x0000075b),
+         (0x4406f181),
+         (0xf592d290),
+         (0xb194244a),
+         (0xe090054a),
+         (0xa496f390),
+         (0x1502d081),
+         (0x5104265b)},
+        {(0x00000000),
+         (0x10151000),
+         (0x10640000),
+         (0x00711000),
+         (0x20081800),
+         (0x301d0800),
+         (0x306c1800),
+         (0x20790800),
+         (0x00025e00),
+         (0x10174e00),
+         (0x10665e00),
+         (0x00734e00),
+         (0x200a4600),
+         (0x301f5600),
+         (0x306e4600),
+         (0x207b5600)},
+        {(0x3f800000),
+         (0x3f880a88),
+         (0x3f883200),
+         (0x3f803888),
+         (0x3f90040c),
+         (0x3f980e84),
+         (0x3f98360c),
+         (0x3f903c84),
+         (0x3f80012f),
+         (0x3f880ba7),
+         (0x3f88332f),
+         (0x3f8039a7),
+         (0x3f900523),
+         (0x3f980fab),
+         (0x3f983723),
+         (0x3f903dab)},
+        (0xfff80000),
+        {0x13,0x48,0x32,0x45,0xca,0x30,0xd6,0xa0,0xae,0xd3,
+         0x68,0x38,0x03,0x61,0x73,0x81,0x24,0xd8,0x43,0xf3,0x00}
+    },
+    {
+        /* No.34 delta:7663 weight:713 */
+        11213,
+        62,
+        2,
+        15,
+        {(0x00000000),
+         (0x9fe7bf95),
+         (0x7e1ef3d4),
+         (0xe1f94c41),
+         (0x32e00221),
+         (0xad07bdb4),
+         (0x4cfef1f5),
+         (0xd3194e60),
+         (0x00005fc6),
+         (0x9fe7e053),
+         (0x7e1eac12),
+         (0xe1f91387),
+         (0x32e05de7),
+         (0xad07e272),
+         (0x4cfeae33),
+         (0xd31911a6)},
+        {(0x00000000),
+         (0x00000000),
+         (0x00000000),
+         (0x00000000),
+         (0x20000000),
+         (0x20000000),
+         (0x20000000),
+         (0x20000000),
+         (0x50001e00),
+         (0x50001e00),
+         (0x50001e00),
+         (0x50001e00),
+         (0x70001e00),
+         (0x70001e00),
+         (0x70001e00),
+         (0x70001e00)},
+        {(0x3f800000),
+         (0x3f800000),
+         (0x3f800000),
+         (0x3f800000),
+         (0x3f900000),
+         (0x3f900000),
+         (0x3f900000),
+         (0x3f900000),
+         (0x3fa8000f),
+         (0x3fa8000f),
+         (0x3fa8000f),
+         (0x3fa8000f),
+         (0x3fb8000f),
+         (0x3fb8000f),
+         (0x3fb8000f),
+         (0x3fb8000f)},
+        (0xfff80000),
+        {0x05,0x3c,0xe6,0xed,0x20,0xd0,0x5e,0x82,0xc3,0x47,
+         0x89,0x62,0x9a,0x00,0x4f,0xdf,0x8a,0xe2,0x89,0xe4,0x00}
+    },
+    {
+        /* No.35 delta:1045 weight:1387 */
+        11213,
+        66,
+        18,
+        8,
+        {(0x00000000),
+         (0xcc054daa),
+         (0x64ce00f1),
+         (0xa8cb4d5b),
+         (0xc6200238),
+         (0x0a254f92),
+         (0xa2ee02c9),
+         (0x6eeb4f63),
+         (0x00006fce),
+         (0xcc052264),
+         (0x64ce6f3f),
+         (0xa8cb2295),
+         (0xc6206df6),
+         (0x0a25205c),
+         (0xa2ee6d07),
+         (0x6eeb20ad)},
+        {(0x00000000),
+         (0xd07d1a00),
+         (0x08927800),
+         (0xd8ef6200),
+         (0x00a4c000),
+         (0xd0d9da00),
+         (0x0836b800),
+         (0xd84ba200),
+         (0x00025e00),
+         (0xd07f4400),
+         (0x08902600),
+         (0xd8ed3c00),
+         (0x00a69e00),
+         (0xd0db8400),
+         (0x0834e600),
+         (0xd849fc00)},
+        {(0x3f800000),
+         (0x3fe83e8d),
+         (0x3f84493c),
+         (0x3fec77b1),
+         (0x3f805260),
+         (0x3fe86ced),
+         (0x3f841b5c),
+         (0x3fec25d1),
+         (0x3f80012f),
+         (0x3fe83fa2),
+         (0x3f844813),
+         (0x3fec769e),
+         (0x3f80534f),
+         (0x3fe86dc2),
+         (0x3f841a73),
+         (0x3fec24fe)},
+        (0xfff80000),
+        {0x53,0xdb,0x48,0x5e,0x80,0xb8,0xf7,0x7d,0xd4,0x43,
+         0xc8,0x11,0x1f,0x88,0x87,0x4f,0x05,0x24,0x64,0x44,0x00}
+    },
+    {
+        /* No.36 delta:2076 weight:1703 */
+        11213,
+        75,
+        9,
+        2,
+        {(0x00000000),
+         (0xc42d5e09),
+         (0x3ecbb703),
+         (0xfae6e90a),
+         (0xe9b00243),
+         (0x2d9d5c4a),
+         (0xd77bb540),
+         (0x1356eb49),
+         (0x00003832),
+         (0xc42d663b),
+         (0x3ecb8f31),
+         (0xfae6d138),
+         (0xe9b03a71),
+         (0x2d9d6478),
+         (0xd77b8d72),
+         (0x1356d37b)},
+        {(0x00000000),
+         (0x003c8000),
+         (0x00700000),
+         (0x004c8000),
+         (0x00080000),
+         (0x00348000),
+         (0x00780000),
+         (0x00448000),
+         (0x00021e00),
+         (0x003e9e00),
+         (0x00721e00),
+         (0x004e9e00),
+         (0x000a1e00),
+         (0x00369e00),
+         (0x007a1e00),
+         (0x00469e00)},
+        {(0x3f800000),
+         (0x3f801e40),
+         (0x3f803800),
+         (0x3f802640),
+         (0x3f800400),
+         (0x3f801a40),
+         (0x3f803c00),
+         (0x3f802240),
+         (0x3f80010f),
+         (0x3f801f4f),
+         (0x3f80390f),
+         (0x3f80274f),
+         (0x3f80050f),
+         (0x3f801b4f),
+         (0x3f803d0f),
+         (0x3f80234f)},
+        (0xfff80000),
+        {0x83,0xeb,0x26,0xe2,0x25,0xfd,0x8b,0x40,0xfc,0xd8,
+         0x09,0x63,0xa1,0xb0,0x60,0x56,0xec,0x05,0xc2,0x27,0x00}
+    },
+    {
+        /* No.37 delta:2017 weight:1375 */
+        11213,
+        20,
+        23,
+        4,
+        {(0x00000000),
+         (0xf3dee608),
+         (0x43968c18),
+         (0xb0486a10),
+         (0x82100252),
+         (0x71cee45a),
+         (0xc1868e4a),
+         (0x32586842),
+         (0x0000b8e9),
+         (0xf3de5ee1),
+         (0x439634f1),
+         (0xb048d2f9),
+         (0x8210babb),
+         (0x71ce5cb3),
+         (0xc18636a3),
+         (0x3258d0ab)},
+        {(0x00000000),
+         (0x0f350000),
+         (0x08b40000),
+         (0x07810000),
+         (0x20c1a000),
+         (0x2ff4a000),
+         (0x2875a000),
+         (0x2740a000),
+         (0x201c1e00),
+         (0x2f291e00),
+         (0x28a81e00),
+         (0x279d1e00),
+         (0x00ddbe00),
+         (0x0fe8be00),
+         (0x0869be00),
+         (0x075cbe00)},
+        {(0x3f800000),
+         (0x3f879a80),
+         (0x3f845a00),
+         (0x3f83c080),
+         (0x3f9060d0),
+         (0x3f97fa50),
+         (0x3f943ad0),
+         (0x3f93a050),
+         (0x3f900e0f),
+         (0x3f97948f),
+         (0x3f94540f),
+         (0x3f93ce8f),
+         (0x3f806edf),
+         (0x3f87f45f),
+         (0x3f8434df),
+         (0x3f83ae5f)},
+        (0xfff80000),
+        {0x64,0x1e,0x04,0xb6,0x86,0x5e,0xd5,0x07,0x5c,0xb3,
+         0xbe,0x3a,0x1b,0xc8,0xe4,0x45,0x9e,0xc4,0xcf,0x11,0x00}
+    },
+    {
+        /* No.38 delta:2221 weight:1197 */
+        11213,
+        37,
+        24,
+        7,
+        {(0x00000000),
+         (0x4fb7bfce),
+         (0x968c3f7b),
+         (0xd93b80b5),
+         (0x11300267),
+         (0x5e87bda9),
+         (0x87bc3d1c),
+         (0xc80b82d2),
+         (0x000002b3),
+         (0x4fb7bd7d),
+         (0x968c3dc8),
+         (0xd93b8206),
+         (0x113000d4),
+         (0x5e87bf1a),
+         (0x87bc3faf),
+         (0xc80b8061)},
+        {(0x00000000),
+         (0x207f2400),
+         (0x0138c000),
+         (0x2147e400),
+         (0x00160000),
+         (0x20692400),
+         (0x012ec000),
+         (0x2151e400),
+         (0x30127e00),
+         (0x106d5a00),
+         (0x312abe00),
+         (0x11559a00),
+         (0x30047e00),
+         (0x107b5a00),
+         (0x313cbe00),
+         (0x11439a00)},
+        {(0x3f800000),
+         (0x3f903f92),
+         (0x3f809c60),
+         (0x3f90a3f2),
+         (0x3f800b00),
+         (0x3f903492),
+         (0x3f809760),
+         (0x3f90a8f2),
+         (0x3f98093f),
+         (0x3f8836ad),
+         (0x3f98955f),
+         (0x3f88aacd),
+         (0x3f98023f),
+         (0x3f883dad),
+         (0x3f989e5f),
+         (0x3f88a1cd)},
+        (0xfff80000),
+        {0x56,0x6e,0x0e,0xb6,0x39,0x79,0xe8,0x2f,0xdb,0x3a,
+         0xb8,0xa6,0xb7,0x15,0xb4,0x26,0x23,0x62,0x6c,0x7b,0x00}
+    },
+    {
+        /* No.39 delta:3255 weight:871 */
+        11213,
+        59,
+        27,
+        3,
+        {(0x00000000),
+         (0xd9be1e48),
+         (0x75016ef8),
+         (0xacbf70b0),
+         (0x0e00027a),
+         (0xd7be1c32),
+         (0x7b016c82),
+         (0xa2bf72ca),
+         (0x00004209),
+         (0xd9be5c41),
+         (0x75012cf1),
+         (0xacbf32b9),
+         (0x0e004073),
+         (0xd7be5e3b),
+         (0x7b012e8b),
+         (0xa2bf30c3)},
+        {(0x00000000),
+         (0x14ad8000),
+         (0x05013000),
+         (0x11acb000),
+         (0x092e2000),
+         (0x1d83a000),
+         (0x0c2f1000),
+         (0x18829000),
+         (0x07501e00),
+         (0x13fd9e00),
+         (0x02512e00),
+         (0x16fcae00),
+         (0x0e7e3e00),
+         (0x1ad3be00),
+         (0x0b7f0e00),
+         (0x1fd28e00)},
+        {(0x3f800000),
+         (0x3f8a56c0),
+         (0x3f828098),
+         (0x3f88d658),
+         (0x3f849710),
+         (0x3f8ec1d0),
+         (0x3f861788),
+         (0x3f8c4148),
+         (0x3f83a80f),
+         (0x3f89fecf),
+         (0x3f812897),
+         (0x3f8b7e57),
+         (0x3f873f1f),
+         (0x3f8d69df),
+         (0x3f85bf87),
+         (0x3f8fe947)},
+        (0xfff80000),
+        {0xde,0x46,0xf8,0xe9,0xab,0x16,0xb9,0x71,0xfd,0x07,
+         0x49,0x31,0xbb,0x29,0xb9,0xce,0xd3,0x1e,0x43,0x2e,0x00}
+    },
+    {
+        /* No.40 delta:3532 weight:1325 */
+        11213,
+        8,
+        11,
+        4,
+        {(0x00000000),
+         (0xe314ffbd),
+         (0x98f9b31f),
+         (0x7bed4ca2),
+         (0x71b0028c),
+         (0x92a4fd31),
+         (0xe949b193),
+         (0x0a5d4e2e),
+         (0x0000e823),
+         (0xe314179e),
+         (0x98f95b3c),
+         (0x7beda481),
+         (0x71b0eaaf),
+         (0x92a41512),
+         (0xe94959b0),
+         (0x0a5da60d)},
+        {(0x00000000),
+         (0x0c000000),
+         (0x0a800000),
+         (0x06800000),
+         (0x16100000),
+         (0x1a100000),
+         (0x1c900000),
+         (0x10900000),
+         (0x95801e00),
+         (0x99801e00),
+         (0x9f001e00),
+         (0x93001e00),
+         (0x83901e00),
+         (0x8f901e00),
+         (0x89101e00),
+         (0x85101e00)},
+        {(0x3f800000),
+         (0x3f860000),
+         (0x3f854000),
+         (0x3f834000),
+         (0x3f8b0800),
+         (0x3f8d0800),
+         (0x3f8e4800),
+         (0x3f884800),
+         (0x3fcac00f),
+         (0x3fccc00f),
+         (0x3fcf800f),
+         (0x3fc9800f),
+         (0x3fc1c80f),
+         (0x3fc7c80f),
+         (0x3fc4880f),
+         (0x3fc2880f)},
+        (0xfff80000),
+        {0xc3,0xa5,0xf2,0x8c,0xb3,0x3e,0xd2,0x53,0x92,0x0c,
+         0x01,0x4a,0x5c,0x0a,0x47,0xe9,0x85,0xa2,0xf5,0x7f,0x00}
+    },
+    {
+        /* No.41 delta:5222 weight:1281 */
+        11213,
+        90,
+        27,
+        2,
+        {(0x00000000),
+         (0xf1807db4),
+         (0x9f84df41),
+         (0x6e04a2f5),
+         (0x04200296),
+         (0xf5a07f22),
+         (0x9ba4ddd7),
+         (0x6a24a063),
+         (0x00005700),
+         (0xf1802ab4),
+         (0x9f848841),
+         (0x6e04f5f5),
+         (0x04205596),
+         (0xf5a02822),
+         (0x9ba48ad7),
+         (0x6a24f763)},
+        {(0x00000000),
+         (0xe9519000),
+         (0x1a06a800),
+         (0xf3573800),
+         (0x0d032000),
+         (0xe452b000),
+         (0x17058800),
+         (0xfe541800),
+         (0x036ade00),
+         (0xea3b4e00),
+         (0x196c7600),
+         (0xf03de600),
+         (0x0e69fe00),
+         (0xe7386e00),
+         (0x146f5600),
+         (0xfd3ec600)},
+        {(0x3f800000),
+         (0x3ff4a8c8),
+         (0x3f8d0354),
+         (0x3ff9ab9c),
+         (0x3f868190),
+         (0x3ff22958),
+         (0x3f8b82c4),
+         (0x3fff2a0c),
+         (0x3f81b56f),
+         (0x3ff51da7),
+         (0x3f8cb63b),
+         (0x3ff81ef3),
+         (0x3f8734ff),
+         (0x3ff39c37),
+         (0x3f8a37ab),
+         (0x3ffe9f63)},
+        (0xfff80000),
+        {0x08,0x0d,0xba,0x61,0x69,0x6b,0x70,0x44,0xbf,0x27,
+         0x4a,0xe9,0xcb,0x98,0x26,0xb9,0xb3,0x5d,0x82,0x3d,0x00}
+    },
+    {
+        /* No.42 delta:1107 weight:1153 */
+        11213,
+        48,
+        16,
+        10,
+        {(0x00000000),
+         (0x6adad7b8),
+         (0x649ee9f9),
+         (0x0e443e41),
+         (0x1b2002a0),
+         (0x71fad518),
+         (0x7fbeeb59),
+         (0x15643ce1),
+         (0x0000f400),
+         (0x6ada23b8),
+         (0x649e1df9),
+         (0x0e44ca41),
+         (0x1b20f6a0),
+         (0x71fa2118),
+         (0x7fbe1f59),
+         (0x1564c8e1)},
+        {(0x00000000),
+         (0x09409c00),
+         (0x0060c000),
+         (0x09205c00),
+         (0x04104a00),
+         (0x0d50d600),
+         (0x04708a00),
+         (0x0d301600),
+         (0x00087e00),
+         (0x0948e200),
+         (0x0068be00),
+         (0x09282200),
+         (0x04183400),
+         (0x0d58a800),
+         (0x0478f400),
+         (0x0d386800)},
+        {(0x3f800000),
+         (0x3f84a04e),
+         (0x3f803060),
+         (0x3f84902e),
+         (0x3f820825),
+         (0x3f86a86b),
+         (0x3f823845),
+         (0x3f86980b),
+         (0x3f80043f),
+         (0x3f84a471),
+         (0x3f80345f),
+         (0x3f849411),
+         (0x3f820c1a),
+         (0x3f86ac54),
+         (0x3f823c7a),
+         (0x3f869c34)},
+        (0xfff80000),
+        {0xb4,0xff,0xa8,0x22,0xa3,0x34,0x7b,0xfd,0xd4,0xca,
+         0xef,0x82,0x97,0x4e,0x9d,0x89,0xeb,0xb2,0x55,0x2c,0x00}
+    },
+    {
+        /* No.43 delta:3289 weight:739 */
+        11213,
+        90,
+        3,
+        15,
+        {(0x00000000),
+         (0x12510afd),
+         (0xc2094a89),
+         (0xd0584074),
+         (0x5ce002b7),
+         (0x4eb1084a),
+         (0x9ee9483e),
+         (0x8cb842c3),
+         (0x00009dee),
+         (0x12519713),
+         (0xc209d767),
+         (0xd058dd9a),
+         (0x5ce09f59),
+         (0x4eb195a4),
+         (0x9ee9d5d0),
+         (0x8cb8df2d)},
+        {(0x00000000),
+         (0x0c230400),
+         (0x033c0000),
+         (0x0f1f0400),
+         (0x03b08000),
+         (0x0f938400),
+         (0x008c8000),
+         (0x0caf8400),
+         (0x08705e00),
+         (0x04535a00),
+         (0x0b4c5e00),
+         (0x076f5a00),
+         (0x0bc0de00),
+         (0x07e3da00),
+         (0x08fcde00),
+         (0x04dfda00)},
+        {(0x3f800000),
+         (0x3f861182),
+         (0x3f819e00),
+         (0x3f878f82),
+         (0x3f81d840),
+         (0x3f87c9c2),
+         (0x3f804640),
+         (0x3f8657c2),
+         (0x3f84382f),
+         (0x3f8229ad),
+         (0x3f85a62f),
+         (0x3f83b7ad),
+         (0x3f85e06f),
+         (0x3f83f1ed),
+         (0x3f847e6f),
+         (0x3f826fed)},
+        (0xfff80000),
+        {0x47,0xd8,0xbd,0x66,0xb2,0xe8,0x3c,0x2c,0x45,0x90,
+         0x88,0x27,0x9c,0x8f,0x27,0x40,0xc8,0xd9,0x0e,0xd6,0x00}
+    },
+    {
+        /* No.44 delta:1477 weight:1191 */
+        11213,
+        33,
+        20,
+        2,
+        {(0x00000000),
+         (0x3f4d49c2),
+         (0xdca457bf),
+         (0xe3e91e7d),
+         (0x5d5002cf),
+         (0x621d4b0d),
+         (0x81f45570),
+         (0xbeb91cb2),
+         (0x0000c277),
+         (0x3f4d8bb5),
+         (0xdca495c8),
+         (0xe3e9dc0a),
+         (0x5d50c0b8),
+         (0x621d897a),
+         (0x81f49707),
+         (0xbeb9dec5)},
+        {(0x00000000),
+         (0x0275b800),
+         (0x7149c400),
+         (0x733c7c00),
+         (0x1058c000),
+         (0x122d7800),
+         (0x61110400),
+         (0x6364bc00),
+         (0x006f3e00),
+         (0x021a8600),
+         (0x7126fa00),
+         (0x73534200),
+         (0x1037fe00),
+         (0x12424600),
+         (0x617e3a00),
+         (0x630b8200)},
+        {(0x3f800000),
+         (0x3f813adc),
+         (0x3fb8a4e2),
+         (0x3fb99e3e),
+         (0x3f882c60),
+         (0x3f8916bc),
+         (0x3fb08882),
+         (0x3fb1b25e),
+         (0x3f80379f),
+         (0x3f810d43),
+         (0x3fb8937d),
+         (0x3fb9a9a1),
+         (0x3f881bff),
+         (0x3f892123),
+         (0x3fb0bf1d),
+         (0x3fb185c1)},
+        (0xfff80000),
+        {0x43,0xbd,0xc4,0xf1,0xb1,0xd7,0x24,0x47,0xc5,0xd0,
+         0xa4,0xbc,0xc9,0x66,0xb3,0x4b,0x26,0xd1,0x26,0x85,0x00}
+    },
+    {
+        /* No.45 delta:1261 weight:1353 */
+        11213,
+        24,
+        18,
+        5,
+        {(0x00000000),
+         (0xabac4677),
+         (0x903dfbe7),
+         (0x3b91bd90),
+         (0xf83002d0),
+         (0x539c44a7),
+         (0x680df937),
+         (0xc3a1bf40),
+         (0x00004840),
+         (0xabac0e37),
+         (0x903db3a7),
+         (0x3b91f5d0),
+         (0xf8304a90),
+         (0x539c0ce7),
+         (0x680db177),
+         (0xc3a1f700)},
+        {(0x00000000),
+         (0x00762800),
+         (0x200acc00),
+         (0x207ce400),
+         (0x6ea81000),
+         (0x6ede3800),
+         (0x4ea2dc00),
+         (0x4ed4f400),
+         (0x004e1e00),
+         (0x00383600),
+         (0x2044d200),
+         (0x2032fa00),
+         (0x6ee60e00),
+         (0x6e902600),
+         (0x4eecc200),
+         (0x4e9aea00)},
+        {(0x3f800000),
+         (0x3f803b14),
+         (0x3f900566),
+         (0x3f903e72),
+         (0x3fb75408),
+         (0x3fb76f1c),
+         (0x3fa7516e),
+         (0x3fa76a7a),
+         (0x3f80270f),
+         (0x3f801c1b),
+         (0x3f902269),
+         (0x3f90197d),
+         (0x3fb77307),
+         (0x3fb74813),
+         (0x3fa77661),
+         (0x3fa74d75)},
+        (0xfff80000),
+        {0x1c,0x79,0xc4,0xaf,0x4a,0xca,0x6f,0x97,0x73,0x87,
+         0xe2,0xfa,0x1b,0x02,0x4e,0xfc,0xc2,0xc5,0x85,0xa8,0x00}
+    },
+    {
+        /* No.46 delta:2871 weight:1351 */
+        11213,
+        24,
+        4,
+        11,
+        {(0x00000000),
+         (0x5f7bd2a5),
+         (0x9b479801),
+         (0xc43c4aa4),
+         (0x793002ea),
+         (0x264bd04f),
+         (0xe2779aeb),
+         (0xbd0c484e),
+         (0x0000737e),
+         (0x5f7ba1db),
+         (0x9b47eb7f),
+         (0xc43c39da),
+         (0x79307194),
+         (0x264ba331),
+         (0xe277e995),
+         (0xbd0c3b30)},
+        {(0x00000000),
+         (0x0a8c0000),
+         (0x10810000),
+         (0x1a0d0000),
+         (0x00600800),
+         (0x0aec0800),
+         (0x10e10800),
+         (0x1a6d0800),
+         (0x40101e00),
+         (0x4a9c1e00),
+         (0x50911e00),
+         (0x5a1d1e00),
+         (0x40701600),
+         (0x4afc1600),
+         (0x50f11600),
+         (0x5a7d1600)},
+        {(0x3f800000),
+         (0x3f854600),
+         (0x3f884080),
+         (0x3f8d0680),
+         (0x3f803004),
+         (0x3f857604),
+         (0x3f887084),
+         (0x3f8d3684),
+         (0x3fa0080f),
+         (0x3fa54e0f),
+         (0x3fa8488f),
+         (0x3fad0e8f),
+         (0x3fa0380b),
+         (0x3fa57e0b),
+         (0x3fa8788b),
+         (0x3fad3e8b)},
+        (0xfff80000),
+        {0x23,0xc7,0xec,0x47,0xae,0x9d,0xf4,0x29,0xa3,0xce,
+         0x13,0x6c,0x06,0x67,0x5b,0x3a,0xb2,0xac,0x33,0xdb,0x00}
+    },
+    {
+        /* No.47 delta:4370 weight:1245 */
+        11213,
+        26,
+        30,
+        4,
+        {(0x00000000),
+         (0x1af999ae),
+         (0x1bfe6053),
+         (0x0107f9fd),
+         (0xb1f002f1),
+         (0xab099b5f),
+         (0xaa0e62a2),
+         (0xb0f7fb0c),
+         (0x0000281b),
+         (0x1af9b1b5),
+         (0x1bfe4848),
+         (0x0107d1e6),
+         (0xb1f02aea),
+         (0xab09b344),
+         (0xaa0e4ab9),
+         (0xb0f7d317)},
+        {(0x00000000),
+         (0x0c201000),
+         (0x03140000),
+         (0x0f341000),
+         (0xe4200800),
+         (0xe8001800),
+         (0xe7340800),
+         (0xeb141800),
+         (0x08001e00),
+         (0x04200e00),
+         (0x0b141e00),
+         (0x07340e00),
+         (0xec201600),
+         (0xe0000600),
+         (0xef341600),
+         (0xe3140600)},
+        {(0x3f800000),
+         (0x3f861008),
+         (0x3f818a00),
+         (0x3f879a08),
+         (0x3ff21004),
+         (0x3ff4000c),
+         (0x3ff39a04),
+         (0x3ff58a0c),
+         (0x3f84000f),
+         (0x3f821007),
+         (0x3f858a0f),
+         (0x3f839a07),
+         (0x3ff6100b),
+         (0x3ff00003),
+         (0x3ff79a0b),
+         (0x3ff18a03)},
+        (0xfff80000),
+        {0x98,0x58,0x06,0x9e,0x81,0x14,0x32,0xd7,0x93,0x32,
+         0x65,0xc8,0x13,0x43,0x02,0x53,0x1e,0xd3,0xaf,0x94,0x00}
+    },
+    {
+        /* No.48 delta:1177 weight:859 */
+        11213,
+        75,
+        13,
+        16,
+        {(0x00000000),
+         (0x8e9ecf83),
+         (0xb438b3ee),
+         (0x3aa67c6d),
+         (0xbd800305),
+         (0x331ecc86),
+         (0x09b8b0eb),
+         (0x87267f68),
+         (0x0000d641),
+         (0x8e9e19c2),
+         (0xb43865af),
+         (0x3aa6aa2c),
+         (0xbd80d544),
+         (0x331e1ac7),
+         (0x09b866aa),
+         (0x8726a929)},
+        {(0x00000000),
+         (0x0f7d1c00),
+         (0x76262000),
+         (0x795b3c00),
+         (0x41532000),
+         (0x4e2e3c00),
+         (0x37750000),
+         (0x38081c00),
+         (0x00429e00),
+         (0x0f3f8200),
+         (0x7664be00),
+         (0x7919a200),
+         (0x4111be00),
+         (0x4e6ca200),
+         (0x37379e00),
+         (0x384a8200)},
+        {(0x3f800000),
+         (0x3f87be8e),
+         (0x3fbb1310),
+         (0x3fbcad9e),
+         (0x3fa0a990),
+         (0x3fa7171e),
+         (0x3f9bba80),
+         (0x3f9c040e),
+         (0x3f80214f),
+         (0x3f879fc1),
+         (0x3fbb325f),
+         (0x3fbc8cd1),
+         (0x3fa088df),
+         (0x3fa73651),
+         (0x3f9b9bcf),
+         (0x3f9c2541)},
+        (0xfff80000),
+        {0xc9,0xe0,0x77,0x04,0x4f,0x92,0x96,0xd4,0x05,0xdc,
+         0x3e,0x3c,0xa6,0xf3,0x8e,0x2d,0xdb,0xf2,0x8c,0xe4,0x00}
+    },
+    {
+        /* No.49 delta:3224 weight:947 */
+        11213,
+        65,
+        7,
+        13,
+        {(0x00000000),
+         (0x9221eadf),
+         (0x5003b0d0),
+         (0xc2225a0f),
+         (0x0d800317),
+         (0x9fa1e9c8),
+         (0x5d83b3c7),
+         (0xcfa25918),
+         (0x00005031),
+         (0x9221baee),
+         (0x5003e0e1),
+         (0xc2220a3e),
+         (0x0d805326),
+         (0x9fa1b9f9),
+         (0x5d83e3f6),
+         (0xcfa20929)},
+        {(0x00000000),
+         (0x00400800),
+         (0x60600000),
+         (0x60200800),
+         (0x00000000),
+         (0x00400800),
+         (0x60600000),
+         (0x60200800),
+         (0x30101e00),
+         (0x30501600),
+         (0x50701e00),
+         (0x50301600),
+         (0x30101e00),
+         (0x30501600),
+         (0x50701e00),
+         (0x50301600)},
+        {(0x3f800000),
+         (0x3f802004),
+         (0x3fb03000),
+         (0x3fb01004),
+         (0x3f800000),
+         (0x3f802004),
+         (0x3fb03000),
+         (0x3fb01004),
+         (0x3f98080f),
+         (0x3f98280b),
+         (0x3fa8380f),
+         (0x3fa8180b),
+         (0x3f98080f),
+         (0x3f98280b),
+         (0x3fa8380f),
+         (0x3fa8180b)},
+        (0xfff80000),
+        {0xc1,0x70,0x72,0x7f,0xb1,0x1e,0x6e,0xc5,0xec,0xcc,
+         0x69,0xd8,0x0b,0xba,0x4e,0x2c,0x73,0x46,0x81,0x52,0x00}
+    },
+    {
+        /* No.50 delta:6665 weight:1221 */
+        11213,
+        84,
+        1,
+        9,
+        {(0x00000000),
+         (0xbdb9b41e),
+         (0x9876c465),
+         (0x25cf707b),
+         (0x25c0032f),
+         (0x9879b731),
+         (0xbdb6c74a),
+         (0x000f7354),
+         (0x00003009),
+         (0xbdb98417),
+         (0x9876f46c),
+         (0x25cf4072),
+         (0x25c03326),
+         (0x98798738),
+         (0xbdb6f743),
+         (0x000f435d)},
+        {(0x00000000),
+         (0x51000000),
+         (0x20000000),
+         (0x71000000),
+         (0x04200000),
+         (0x55200000),
+         (0x24200000),
+         (0x75200000),
+         (0x17201e00),
+         (0x46201e00),
+         (0x37201e00),
+         (0x66201e00),
+         (0x13001e00),
+         (0x42001e00),
+         (0x33001e00),
+         (0x62001e00)},
+        {(0x3f800000),
+         (0x3fa88000),
+         (0x3f900000),
+         (0x3fb88000),
+         (0x3f821000),
+         (0x3faa9000),
+         (0x3f921000),
+         (0x3fba9000),
+         (0x3f8b900f),
+         (0x3fa3100f),
+         (0x3f9b900f),
+         (0x3fb3100f),
+         (0x3f89800f),
+         (0x3fa1000f),
+         (0x3f99800f),
+         (0x3fb1000f)},
+        (0xfff80000),
+        {0xdf,0xd6,0xd2,0xb4,0x0a,0xa4,0x35,0xbe,0x0f,0xa6,
+         0x48,0x42,0xfe,0x0c,0x0f,0xa3,0xa9,0x64,0x1b,0x93,0x00}
+    },
+    {
+        /* No.51 delta:6604 weight:1075 */
+        11213,
+        61,
+        3,
+        13,
+        {(0x00000000),
+         (0xe7ec9698),
+         (0xdc943f6e),
+         (0x3b78a9f6),
+         (0xdf600330),
+         (0x388c95a8),
+         (0x03f43c5e),
+         (0xe418aac6),
+         (0x000058c3),
+         (0xe7ecce5b),
+         (0xdc9467ad),
+         (0x3b78f135),
+         (0xdf605bf3),
+         (0x388ccd6b),
+         (0x03f4649d),
+         (0xe418f205)},
+        {(0x00000000),
+         (0x80000000),
+         (0x50000000),
+         (0xd0000000),
+         (0x20000000),
+         (0xa0000000),
+         (0x70000000),
+         (0xf0000000),
+         (0x00001e00),
+         (0x80001e00),
+         (0x50001e00),
+         (0xd0001e00),
+         (0x20001e00),
+         (0xa0001e00),
+         (0x70001e00),
+         (0xf0001e00)},
+        {(0x3f800000),
+         (0x3fc00000),
+         (0x3fa80000),
+         (0x3fe80000),
+         (0x3f900000),
+         (0x3fd00000),
+         (0x3fb80000),
+         (0x3ff80000),
+         (0x3f80000f),
+         (0x3fc0000f),
+         (0x3fa8000f),
+         (0x3fe8000f),
+         (0x3f90000f),
+         (0x3fd0000f),
+         (0x3fb8000f),
+         (0x3ff8000f)},
+        (0xfff80000),
+        {0xf2,0x95,0xcc,0x96,0x13,0xa0,0x75,0xc2,0xea,0x1a,
+         0x5c,0x2d,0xcc,0x41,0x22,0xf8,0x87,0x8d,0x52,0xc0,0x00}
+    },
+    {
+        /* No.52 delta:980 weight:1115 */
+        11213,
+        75,
+        17,
+        12,
+        {(0x00000000),
+         (0x619af787),
+         (0x463e8326),
+         (0x27a474a1),
+         (0xc5c00348),
+         (0xa45af4cf),
+         (0x83fe806e),
+         (0xe26477e9),
+         (0x00008ae3),
+         (0x619a7d64),
+         (0x463e09c5),
+         (0x27a4fe42),
+         (0xc5c089ab),
+         (0xa45a7e2c),
+         (0x83fe0a8d),
+         (0xe264fd0a)},
+        {(0x00000000),
+         (0x107c1400),
+         (0x81204400),
+         (0x915c5000),
+         (0x00113400),
+         (0x106d2000),
+         (0x81317000),
+         (0x914d6400),
+         (0x000ede00),
+         (0x1072ca00),
+         (0x812e9a00),
+         (0x91528e00),
+         (0x001fea00),
+         (0x1063fe00),
+         (0x813fae00),
+         (0x9143ba00)},
+        {(0x3f800000),
+         (0x3f883e0a),
+         (0x3fc09022),
+         (0x3fc8ae28),
+         (0x3f80089a),
+         (0x3f883690),
+         (0x3fc098b8),
+         (0x3fc8a6b2),
+         (0x3f80076f),
+         (0x3f883965),
+         (0x3fc0974d),
+         (0x3fc8a947),
+         (0x3f800ff5),
+         (0x3f8831ff),
+         (0x3fc09fd7),
+         (0x3fc8a1dd)},
+        (0xfff80000),
+        {0x31,0x3b,0x59,0xba,0x4b,0xfa,0x15,0x6d,0x68,0x38,
+         0xd0,0x59,0x4b,0xc5,0xee,0xd6,0xd1,0x72,0x77,0x56,0x00}
+    },
+    {
+        /* No.53 delta:2128 weight:1031 */
+        11213,
+        90,
+        18,
+        10,
+        {(0x00000000),
+         (0x54ddf61b),
+         (0x3289e26b),
+         (0x66541470),
+         (0x8c500359),
+         (0xd88df542),
+         (0xbed9e132),
+         (0xea041729),
+         (0x0000fb30),
+         (0x54dd0d2b),
+         (0x3289195b),
+         (0x6654ef40),
+         (0x8c50f869),
+         (0xd88d0e72),
+         (0xbed91a02),
+         (0xea04ec19)},
+        {(0x00000000),
+         (0x80710000),
+         (0x704e0000),
+         (0xf03f0000),
+         (0x40450000),
+         (0xc0340000),
+         (0x300b0000),
+         (0xb07a0000),
+         (0x000c1e00),
+         (0x807d1e00),
+         (0x70421e00),
+         (0xf0331e00),
+         (0x40491e00),
+         (0xc0381e00),
+         (0x30071e00),
+         (0xb0761e00)},
+        {(0x3f800000),
+         (0x3fc03880),
+         (0x3fb82700),
+         (0x3ff81f80),
+         (0x3fa02280),
+         (0x3fe01a00),
+         (0x3f980580),
+         (0x3fd83d00),
+         (0x3f80060f),
+         (0x3fc03e8f),
+         (0x3fb8210f),
+         (0x3ff8198f),
+         (0x3fa0248f),
+         (0x3fe01c0f),
+         (0x3f98038f),
+         (0x3fd83b0f)},
+        (0xfff80000),
+        {0x2c,0x15,0xc7,0x91,0x8f,0xe7,0xba,0x43,0x56,0x9b,
+         0xf8,0xd4,0x59,0xf2,0x0c,0x4e,0xf2,0x42,0x9a,0xfe,0x00}
+    },
+    {
+        /* No.54 delta:2047 weight:935 */
+        11213,
+        73,
+        6,
+        15,
+        {(0x00000000),
+         (0x1b3e4d84),
+         (0x40583cec),
+         (0x5b667168),
+         (0x82600363),
+         (0x995e4ee7),
+         (0xc2383f8f),
+         (0xd906720b),
+         (0x000025e9),
+         (0x1b3e686d),
+         (0x40581905),
+         (0x5b665481),
+         (0x8260268a),
+         (0x995e6b0e),
+         (0xc2381a66),
+         (0xd90657e2)},
+        {(0x00000000),
+         (0x00524000),
+         (0x52804000),
+         (0x52d20000),
+         (0x00232000),
+         (0x00716000),
+         (0x52a36000),
+         (0x52f12000),
+         (0x21c51e00),
+         (0x21975e00),
+         (0x73455e00),
+         (0x73171e00),
+         (0x21e63e00),
+         (0x21b47e00),
+         (0x73667e00),
+         (0x73343e00)},
+        {(0x3f800000),
+         (0x3f802920),
+         (0x3fa94020),
+         (0x3fa96900),
+         (0x3f801190),
+         (0x3f8038b0),
+         (0x3fa951b0),
+         (0x3fa97890),
+         (0x3f90e28f),
+         (0x3f90cbaf),
+         (0x3fb9a2af),
+         (0x3fb98b8f),
+         (0x3f90f31f),
+         (0x3f90da3f),
+         (0x3fb9b33f),
+         (0x3fb99a1f)},
+        (0xfff80000),
+        {0x67,0xd5,0xbe,0x19,0x7f,0x4e,0x37,0xca,0xeb,0x0b,
+         0x09,0x52,0xc2,0x91,0x23,0x02,0xb9,0xa6,0xdd,0x1e,0x00}
+    },
+    {
+        /* No.55 delta:1367 weight:1089 */
+        11213,
+        30,
+        14,
+        10,
+        {(0x00000000),
+         (0x3d4e7360),
+         (0xd47d559c),
+         (0xe93326fc),
+         (0x10e00371),
+         (0x2dae7011),
+         (0xc49d56ed),
+         (0xf9d3258d),
+         (0x0000f71b),
+         (0x3d4e847b),
+         (0xd47da287),
+         (0xe933d1e7),
+         (0x10e0f46a),
+         (0x2dae870a),
+         (0xc49da1f6),
+         (0xf9d3d296)},
+        {(0x00000000),
+         (0x6b729000),
+         (0x206f1800),
+         (0x4b1d8800),
+         (0x00501a00),
+         (0x6b228a00),
+         (0x203f0200),
+         (0x4b4d9200),
+         (0x20103e00),
+         (0x4b62ae00),
+         (0x007f2600),
+         (0x6b0db600),
+         (0x20402400),
+         (0x4b32b400),
+         (0x002f3c00),
+         (0x6b5dac00)},
+        {(0x3f800000),
+         (0x3fb5b948),
+         (0x3f90378c),
+         (0x3fa58ec4),
+         (0x3f80280d),
+         (0x3fb59145),
+         (0x3f901f81),
+         (0x3fa5a6c9),
+         (0x3f90081f),
+         (0x3fa5b157),
+         (0x3f803f93),
+         (0x3fb586db),
+         (0x3f902012),
+         (0x3fa5995a),
+         (0x3f80179e),
+         (0x3fb5aed6)},
+        (0xfff80000),
+        {0x74,0x06,0xc4,0x94,0x50,0xc8,0x89,0xd9,0x6c,0x24,
+         0x1b,0x4e,0x7f,0x96,0xbe,0x0a,0xb0,0xbb,0xa9,0x75,0x00}
+    },
+    {
+        /* No.56 delta:1232 weight:1251 */
+        11213,
+        56,
+        20,
+        8,
+        {(0x00000000),
+         (0x303bfd65),
+         (0x96ea74b0),
+         (0xa6d189d5),
+         (0x1d200385),
+         (0x2d1bfee0),
+         (0x8bca7735),
+         (0xbbf18a50),
+         (0x0000e0a3),
+         (0x303b1dc6),
+         (0x96ea9413),
+         (0xa6d16976),
+         (0x1d20e326),
+         (0x2d1b1e43),
+         (0x8bca9796),
+         (0xbbf16af3)},
+        {(0x00000000),
+         (0x0e9c1400),
+         (0x00661e00),
+         (0x0efa0a00),
+         (0x20125000),
+         (0x2e8e4400),
+         (0x20744e00),
+         (0x2ee85a00),
+         (0x001abe00),
+         (0x0e86aa00),
+         (0x007ca000),
+         (0x0ee0b400),
+         (0x2008ee00),
+         (0x2e94fa00),
+         (0x206ef000),
+         (0x2ef2e400)},
+        {(0x3f800000),
+         (0x3f874e0a),
+         (0x3f80330f),
+         (0x3f877d05),
+         (0x3f900928),
+         (0x3f974722),
+         (0x3f903a27),
+         (0x3f97742d),
+         (0x3f800d5f),
+         (0x3f874355),
+         (0x3f803e50),
+         (0x3f87705a),
+         (0x3f900477),
+         (0x3f974a7d),
+         (0x3f903778),
+         (0x3f977972)},
+        (0xfff80000),
+        {0x9f,0xb8,0x5c,0x52,0x53,0x38,0x40,0xed,0x9b,0x43,
+         0xe8,0x89,0x8c,0xd0,0x44,0x25,0x4b,0xbf,0xca,0xd0,0x00}
+    },
+    {
+        /* No.57 delta:1379 weight:873 */
+        11213,
+        89,
+        13,
+        1,
+        {(0x00000000),
+         (0xfacfdd0b),
+         (0x22ecafbf),
+         (0xd82372b4),
+         (0x85500390),
+         (0x7f9fde9b),
+         (0xa7bcac2f),
+         (0x5d737124),
+         (0x0000d423),
+         (0xfacf0928),
+         (0x22ec7b9c),
+         (0xd823a697),
+         (0x8550d7b3),
+         (0x7f9f0ab8),
+         (0xa7bc780c),
+         (0x5d73a507)},
+        {(0x00000000),
+         (0x2c081000),
+         (0x05201800),
+         (0x29280800),
+         (0x20e00400),
+         (0x0ce81400),
+         (0x25c01c00),
+         (0x09c80c00),
+         (0x11481e00),
+         (0x3d400e00),
+         (0x14680600),
+         (0x38601600),
+         (0x31a81a00),
+         (0x1da00a00),
+         (0x34880200),
+         (0x18801200)},
+        {(0x3f800000),
+         (0x3f960408),
+         (0x3f82900c),
+         (0x3f949404),
+         (0x3f907002),
+         (0x3f86740a),
+         (0x3f92e00e),
+         (0x3f84e406),
+         (0x3f88a40f),
+         (0x3f9ea007),
+         (0x3f8a3403),
+         (0x3f9c300b),
+         (0x3f98d40d),
+         (0x3f8ed005),
+         (0x3f9a4401),
+         (0x3f8c4009)},
+        (0xfff80000),
+        {0x08,0x0f,0x24,0x9d,0x3a,0xba,0x6d,0xb4,0xb0,0x1a,
+         0x1c,0x4b,0x44,0xe7,0x44,0xf7,0xc9,0x4a,0x5f,0x97,0x00}
+    },
+    {
+        /* No.58 delta:904 weight:1097 */
+        11213,
+        60,
+        16,
+        2,
+        {(0x00000000),
+         (0xda86476d),
+         (0x6c05798d),
+         (0xb6833ee0),
+         (0xb72003ae),
+         (0x6da644c3),
+         (0xdb257a23),
+         (0x01a33d4e),
+         (0x00001489),
+         (0xda8653e4),
+         (0x6c056d04),
+         (0xb6832a69),
+         (0xb7201727),
+         (0x6da6504a),
+         (0xdb256eaa),
+         (0x01a329c7)},
+        {(0x00000000),
+         (0x0c74f000),
+         (0x0059a800),
+         (0x0c2d5800),
+         (0x02445000),
+         (0x0e30a000),
+         (0x021df800),
+         (0x0e690800),
+         (0x0902de00),
+         (0x05762e00),
+         (0x095b7600),
+         (0x052f8600),
+         (0x0b468e00),
+         (0x07327e00),
+         (0x0b1f2600),
+         (0x076bd600)},
+        {(0x3f800000),
+         (0x3f863a78),
+         (0x3f802cd4),
+         (0x3f8616ac),
+         (0x3f812228),
+         (0x3f871850),
+         (0x3f810efc),
+         (0x3f873484),
+         (0x3f84816f),
+         (0x3f82bb17),
+         (0x3f84adbb),
+         (0x3f8297c3),
+         (0x3f85a347),
+         (0x3f83993f),
+         (0x3f858f93),
+         (0x3f83b5eb)},
+        (0xfff80000),
+        {0xb8,0xe0,0xa0,0x3b,0x2e,0x11,0x17,0x83,0x7b,0x75,
+         0x85,0x89,0xfa,0x1e,0x49,0xb4,0x5c,0xe6,0xb8,0xc1,0x00}
+    },
+    {
+        /* No.59 delta:7724 weight:1539 */
+        11213,
+        42,
+        28,
+        4,
+        {(0x00000000),
+         (0xdfefef22),
+         (0x804185a4),
+         (0x5fae6a86),
+         (0x54b003b3),
+         (0x8b5fec91),
+         (0xd4f18617),
+         (0x0b1e6935),
+         (0x00009211),
+         (0xdfef7d33),
+         (0x804117b5),
+         (0x5faef897),
+         (0x54b091a2),
+         (0x8b5f7e80),
+         (0xd4f11406),
+         (0x0b1efb24)},
+        {(0x00000000),
+         (0x38020000),
+         (0x08014000),
+         (0x30034000),
+         (0x04000800),
+         (0x3c020800),
+         (0x0c014800),
+         (0x34034800),
+         (0x08001e00),
+         (0x30021e00),
+         (0x00015e00),
+         (0x38035e00),
+         (0x0c001600),
+         (0x34021600),
+         (0x04015600),
+         (0x3c035600)},
+        {(0x3f800000),
+         (0x3f9c0100),
+         (0x3f8400a0),
+         (0x3f9801a0),
+         (0x3f820004),
+         (0x3f9e0104),
+         (0x3f8600a4),
+         (0x3f9a01a4),
+         (0x3f84000f),
+         (0x3f98010f),
+         (0x3f8000af),
+         (0x3f9c01af),
+         (0x3f86000b),
+         (0x3f9a010b),
+         (0x3f8200ab),
+         (0x3f9e01ab)},
+        (0xfff80000),
+        {0x91,0xba,0x8c,0xac,0x44,0xa7,0x81,0xa7,0xed,0x37,
+         0x7e,0x8d,0xaf,0xe5,0x08,0xe6,0xc7,0x1d,0xfd,0xe5,0x00}
+    },
+    {
+        /* No.60 delta:2411 weight:1317 */
+        11213,
+        13,
+        24,
+        5,
+        {(0x00000000),
+         (0x9dd3ce8f),
+         (0x90da0878),
+         (0x0d09c6f7),
+         (0xad8003cf),
+         (0x3053cd40),
+         (0x3d5a0bb7),
+         (0xa089c538),
+         (0x0000bcb0),
+         (0x9dd3723f),
+         (0x90dab4c8),
+         (0x0d097a47),
+         (0xad80bf7f),
+         (0x305371f0),
+         (0x3d5ab707),
+         (0xa0897988)},
+        {(0x00000000),
+         (0x4c0b9800),
+         (0x0fa54000),
+         (0x43aed800),
+         (0x0067c000),
+         (0x4c6c5800),
+         (0x0fc28000),
+         (0x43c91800),
+         (0x0a105e00),
+         (0x461bc600),
+         (0x05b51e00),
+         (0x49be8600),
+         (0x0a779e00),
+         (0x467c0600),
+         (0x05d2de00),
+         (0x49d94600)},
+        {(0x3f800000),
+         (0x3fa605cc),
+         (0x3f87d2a0),
+         (0x3fa1d76c),
+         (0x3f8033e0),
+         (0x3fa6362c),
+         (0x3f87e140),
+         (0x3fa1e48c),
+         (0x3f85082f),
+         (0x3fa30de3),
+         (0x3f82da8f),
+         (0x3fa4df43),
+         (0x3f853bcf),
+         (0x3fa33e03),
+         (0x3f82e96f),
+         (0x3fa4eca3)},
+        (0xfff80000),
+        {0xac,0x0b,0xb8,0x4d,0x6f,0xbd,0x47,0x8b,0x3f,0x9c,
+         0x92,0xd0,0x32,0xf9,0x89,0x38,0xda,0x67,0xea,0xbe,0x00}
+    },
+    {
+        /* No.61 delta:1265 weight:827 */
+        11213,
+        52,
+        17,
+        15,
+        {(0x00000000),
+         (0xa4b47608),
+         (0x36bc0fca),
+         (0x920879c2),
+         (0xdeb003d6),
+         (0x7a0475de),
+         (0xe80c0c1c),
+         (0x4cb87a14),
+         (0x0000fbcf),
+         (0xa4b48dc7),
+         (0x36bcf405),
+         (0x9208820d),
+         (0xdeb0f819),
+         (0x7a048e11),
+         (0xe80cf7d3),
+         (0x4cb881db)},
+        {(0x00000000),
+         (0x0813ae00),
+         (0x20151000),
+         (0x2806be00),
+         (0x4029c000),
+         (0x483a6e00),
+         (0x603cd000),
+         (0x682f7e00),
+         (0x52035e00),
+         (0x5a10f000),
+         (0x72164e00),
+         (0x7a05e000),
+         (0x122a9e00),
+         (0x1a393000),
+         (0x323f8e00),
+         (0x3a2c2000)},
+        {(0x3f800000),
+         (0x3f8409d7),
+         (0x3f900a88),
+         (0x3f94035f),
+         (0x3fa014e0),
+         (0x3fa41d37),
+         (0x3fb01e68),
+         (0x3fb417bf),
+         (0x3fa901af),
+         (0x3fad0878),
+         (0x3fb90b27),
+         (0x3fbd02f0),
+         (0x3f89154f),
+         (0x3f8d1c98),
+         (0x3f991fc7),
+         (0x3f9d1610)},
+        (0xfff80000),
+        {0xb2,0x25,0x08,0xf6,0xcd,0xda,0xfa,0xdb,0x2f,0xe7,
+         0x81,0x83,0x7f,0xb8,0x75,0x05,0xff,0x2c,0x1a,0x80,0x00}
+    },
+    {
+        /* No.62 delta:3397 weight:1223 */
+        11213,
+        44,
+        3,
+        7,
+        {(0x00000000),
+         (0x89ffcb3c),
+         (0xafe67560),
+         (0x2619be5c),
+         (0xf79003e8),
+         (0x7e6fc8d4),
+         (0x58767688),
+         (0xd189bdb4),
+         (0x0000dcdc),
+         (0x89ff17e0),
+         (0xafe6a9bc),
+         (0x26196280),
+         (0xf790df34),
+         (0x7e6f1408),
+         (0x5876aa54),
+         (0xd1896168)},
+        {(0x00000000),
+         (0x00710000),
+         (0xb00e0800),
+         (0xb07f0800),
+         (0x90120000),
+         (0x90630000),
+         (0x201c0800),
+         (0x206d0800),
+         (0x40091e00),
+         (0x40781e00),
+         (0xf0071600),
+         (0xf0761600),
+         (0xd01b1e00),
+         (0xd06a1e00),
+         (0x60151600),
+         (0x60641600)},
+        {(0x3f800000),
+         (0x3f803880),
+         (0x3fd80704),
+         (0x3fd83f84),
+         (0x3fc80900),
+         (0x3fc83180),
+         (0x3f900e04),
+         (0x3f903684),
+         (0x3fa0048f),
+         (0x3fa03c0f),
+         (0x3ff8038b),
+         (0x3ff83b0b),
+         (0x3fe80d8f),
+         (0x3fe8350f),
+         (0x3fb00a8b),
+         (0x3fb0320b)},
+        (0xfff80000),
+        {0x65,0x92,0xc9,0x30,0xd4,0x39,0xe2,0xf8,0x3a,0x06,
+         0x72,0x5a,0xf0,0xe9,0x6f,0x1d,0x41,0xb8,0x38,0xcb,0x00}
+    },
+    {
+        /* No.63 delta:1253 weight:1063 */
+        11213,
+        40,
+        15,
+        8,
+        {(0x00000000),
+         (0xf0f1fa2b),
+         (0xc095cbf9),
+         (0x306431d2),
+         (0x84c003f6),
+         (0x7431f9dd),
+         (0x4455c80f),
+         (0xb4a43224),
+         (0x000041b7),
+         (0xf0f1bb9c),
+         (0xc0958a4e),
+         (0x30647065),
+         (0x84c04241),
+         (0x7431b86a),
+         (0x445589b8),
+         (0xb4a47393)},
+        {(0x00000000),
+         (0x000f4800),
+         (0x00c47000),
+         (0x00cb3800),
+         (0x001ec000),
+         (0x00118800),
+         (0x00dab000),
+         (0x00d5f800),
+         (0xc01d9e00),
+         (0xc012d600),
+         (0xc0d9ee00),
+         (0xc0d6a600),
+         (0xc0035e00),
+         (0xc00c1600),
+         (0xc0c72e00),
+         (0xc0c86600)},
+        {(0x3f800000),
+         (0x3f8007a4),
+         (0x3f806238),
+         (0x3f80659c),
+         (0x3f800f60),
+         (0x3f8008c4),
+         (0x3f806d58),
+         (0x3f806afc),
+         (0x3fe00ecf),
+         (0x3fe0096b),
+         (0x3fe06cf7),
+         (0x3fe06b53),
+         (0x3fe001af),
+         (0x3fe0060b),
+         (0x3fe06397),
+         (0x3fe06433)},
+        (0xfff80000),
+        {0x6a,0x42,0x24,0x72,0x49,0x2e,0x6c,0xc6,0x39,0xde,
+         0x29,0x45,0x5e,0xba,0x3b,0xda,0xc6,0xb7,0x79,0xd2,0x00}
+    },
+    {
+        /* No.64 delta:5028 weight:833 */
+        11213,
+        40,
+        3,
+        16,
+        {(0x00000000),
+         (0x0fda9709),
+         (0xdb5d90d9),
+         (0xd48707d0),
+         (0x78500401),
+         (0x778a9308),
+         (0xa30d94d8),
+         (0xacd703d1),
+         (0x00000aa4),
+         (0x0fda9dad),
+         (0xdb5d9a7d),
+         (0xd4870d74),
+         (0x78500ea5),
+         (0x778a99ac),
+         (0xa30d9e7c),
+         (0xacd70975)},
+        {(0x00000000),
+         (0x19200000),
+         (0x83a00000),
+         (0x9a800000),
+         (0x00800000),
+         (0x19a00000),
+         (0x83200000),
+         (0x9a000000),
+         (0x41001e00),
+         (0x58201e00),
+         (0xc2a01e00),
+         (0xdb801e00),
+         (0x41801e00),
+         (0x58a01e00),
+         (0xc2201e00),
+         (0xdb001e00)},
+        {(0x3f800000),
+         (0x3f8c9000),
+         (0x3fc1d000),
+         (0x3fcd4000),
+         (0x3f804000),
+         (0x3f8cd000),
+         (0x3fc19000),
+         (0x3fcd0000),
+         (0x3fa0800f),
+         (0x3fac100f),
+         (0x3fe1500f),
+         (0x3fedc00f),
+         (0x3fa0c00f),
+         (0x3fac500f),
+         (0x3fe1100f),
+         (0x3fed800f)},
+        (0xfff80000),
+        {0x7f,0xa7,0x79,0xc1,0x02,0x5a,0xb9,0x48,0xd7,0xc2,
+         0x1b,0xde,0x64,0x28,0xca,0x0c,0xa1,0xb8,0xbd,0x71,0x00}
+    },
+    {
+        /* No.65 delta:1199 weight:1181 */
+        11213,
+        35,
+        20,
+        9,
+        {(0x00000000),
+         (0xf97f5fb7),
+         (0x820665df),
+         (0x7b793a68),
+         (0xeda00417),
+         (0x14df5ba0),
+         (0x6fa661c8),
+         (0x96d93e7f),
+         (0x0000be8a),
+         (0xf97fe13d),
+         (0x8206db55),
+         (0x7b7984e2),
+         (0xeda0ba9d),
+         (0x14dfe52a),
+         (0x6fa6df42),
+         (0x96d980f5)},
+        {(0x00000000),
+         (0x0c163000),
+         (0x000c1000),
+         (0x0c1a2000),
+         (0xc0028000),
+         (0xcc14b000),
+         (0xc00e9000),
+         (0xcc18a000),
+         (0x7101de00),
+         (0x7d17ee00),
+         (0x710dce00),
+         (0x7d1bfe00),
+         (0xb1035e00),
+         (0xbd156e00),
+         (0xb10f4e00),
+         (0xbd197e00)},
+        {(0x3f800000),
+         (0x3f860b18),
+         (0x3f800608),
+         (0x3f860d10),
+         (0x3fe00140),
+         (0x3fe60a58),
+         (0x3fe00748),
+         (0x3fe60c50),
+         (0x3fb880ef),
+         (0x3fbe8bf7),
+         (0x3fb886e7),
+         (0x3fbe8dff),
+         (0x3fd881af),
+         (0x3fde8ab7),
+         (0x3fd887a7),
+         (0x3fde8cbf)},
+        (0xfff80000),
+        {0x44,0x09,0x7e,0x16,0x6a,0x0b,0xbf,0xd6,0xf6,0x83,
+         0xc1,0x33,0x24,0x8d,0x24,0x40,0xc5,0xee,0xb0,0xd2,0x00}
+    },
+    {
+        /* No.66 delta:1553 weight:539 */
+        11213,
+        26,
+        13,
+        19,
+        {(0x00000000),
+         (0xc75eb6dc),
+         (0xfa4bc661),
+         (0x3d1570bd),
+         (0x6f600428),
+         (0xa83eb2f4),
+         (0x952bc249),
+         (0x52757495),
+         (0x0000df90),
+         (0xc75e694c),
+         (0xfa4b19f1),
+         (0x3d15af2d),
+         (0x6f60dbb8),
+         (0xa83e6d64),
+         (0x952b1dd9),
+         (0x5275ab05)},
+        {(0x00000000),
+         (0x00521800),
+         (0x20647400),
+         (0x20366c00),
+         (0x60dc1800),
+         (0x608e0000),
+         (0x40b86c00),
+         (0x40ea7400),
+         (0x10341e00),
+         (0x10660600),
+         (0x30506a00),
+         (0x30027200),
+         (0x70e80600),
+         (0x70ba1e00),
+         (0x508c7200),
+         (0x50de6a00)},
+        {(0x3f800000),
+         (0x3f80290c),
+         (0x3f90323a),
+         (0x3f901b36),
+         (0x3fb06e0c),
+         (0x3fb04700),
+         (0x3fa05c36),
+         (0x3fa0753a),
+         (0x3f881a0f),
+         (0x3f883303),
+         (0x3f982835),
+         (0x3f980139),
+         (0x3fb87403),
+         (0x3fb85d0f),
+         (0x3fa84639),
+         (0x3fa86f35)},
+        (0xfff80000),
+        {0xe3,0x81,0xfe,0x0e,0xae,0xa6,0x17,0x99,0xb5,0xdc,
+         0x36,0xa3,0xa9,0x84,0x5e,0xac,0xaf,0x0b,0xa6,0x5e,0x00}
+    },
+    {
+        /* No.67 delta:1370 weight:1349 */
+        11213,
+        50,
+        8,
+        4,
+        {(0x00000000),
+         (0xf2eee92e),
+         (0x184a48a6),
+         (0xeaa4a188),
+         (0x3cb00439),
+         (0xce5eed17),
+         (0x24fa4c9f),
+         (0xd614a5b1),
+         (0x0000c236),
+         (0xf2ee2b18),
+         (0x184a8a90),
+         (0xeaa463be),
+         (0x3cb0c60f),
+         (0xce5e2f21),
+         (0x24fa8ea9),
+         (0xd6146787)},
+        {(0x00000000),
+         (0x40028000),
+         (0x70014000),
+         (0x3003c000),
+         (0x20002000),
+         (0x6002a000),
+         (0x50016000),
+         (0x1003e000),
+         (0x10001e00),
+         (0x50029e00),
+         (0x60015e00),
+         (0x2003de00),
+         (0x30003e00),
+         (0x7002be00),
+         (0x40017e00),
+         (0x0003fe00)},
+        {(0x3f800000),
+         (0x3fa00140),
+         (0x3fb800a0),
+         (0x3f9801e0),
+         (0x3f900010),
+         (0x3fb00150),
+         (0x3fa800b0),
+         (0x3f8801f0),
+         (0x3f88000f),
+         (0x3fa8014f),
+         (0x3fb000af),
+         (0x3f9001ef),
+         (0x3f98001f),
+         (0x3fb8015f),
+         (0x3fa000bf),
+         (0x3f8001ff)},
+        (0xfff80000),
+        {0xce,0xab,0xcf,0x53,0x4e,0xc1,0xc1,0xd0,0x64,0x84,
+         0xd7,0xc3,0xad,0x13,0xad,0xf4,0xf7,0x14,0xe1,0xd6,0x00}
+    },
+    {
+        /* No.68 delta:1769 weight:1053 */
+        11213,
+        44,
+        23,
+        7,
+        {(0x00000000),
+         (0x1c10f784),
+         (0xb81f8711),
+         (0xa40f7095),
+         (0xd0d00449),
+         (0xccc0f3cd),
+         (0x68cf8358),
+         (0x74df74dc),
+         (0x00006582),
+         (0x1c109206),
+         (0xb81fe293),
+         (0xa40f1517),
+         (0xd0d061cb),
+         (0xccc0964f),
+         (0x68cfe6da),
+         (0x74df115e)},
+        {(0x00000000),
+         (0x009da000),
+         (0x40746000),
+         (0x40e9c000),
+         (0x00028400),
+         (0x009f2400),
+         (0x4076e400),
+         (0x40eb4400),
+         (0x700bbe00),
+         (0x70961e00),
+         (0x307fde00),
+         (0x30e27e00),
+         (0x70093a00),
+         (0x70949a00),
+         (0x307d5a00),
+         (0x30e0fa00)},
+        {(0x3f800000),
+         (0x3f804ed0),
+         (0x3fa03a30),
+         (0x3fa074e0),
+         (0x3f800142),
+         (0x3f804f92),
+         (0x3fa03b72),
+         (0x3fa075a2),
+         (0x3fb805df),
+         (0x3fb84b0f),
+         (0x3f983fef),
+         (0x3f98713f),
+         (0x3fb8049d),
+         (0x3fb84a4d),
+         (0x3f983ead),
+         (0x3f98707d)},
+        (0xfff80000),
+        {0x25,0xc5,0x2b,0x7e,0x94,0x70,0x50,0xab,0x68,0x79,
+         0xe0,0x34,0x49,0x1e,0xd3,0x2a,0xe2,0x9f,0xef,0x26,0x00}
+    },
+    {
+        /* No.69 delta:1887 weight:1253 */
+        11213,
+        64,
+        9,
+        8,
+        {(0x00000000),
+         (0x4cbf4fb8),
+         (0xcc209ffc),
+         (0x809fd044),
+         (0x7e700455),
+         (0x32cf4bed),
+         (0xb2509ba9),
+         (0xfeefd411),
+         (0x0000612d),
+         (0x4cbf2e95),
+         (0xcc20fed1),
+         (0x809fb169),
+         (0x7e706578),
+         (0x32cf2ac0),
+         (0xb250fa84),
+         (0xfeefb53c)},
+        {(0x00000000),
+         (0x50090000),
+         (0x00035000),
+         (0x500a5000),
+         (0x04019000),
+         (0x54089000),
+         (0x0402c000),
+         (0x540bc000),
+         (0x05027e00),
+         (0x550b7e00),
+         (0x05012e00),
+         (0x55082e00),
+         (0x0103ee00),
+         (0x510aee00),
+         (0x0100be00),
+         (0x5109be00)},
+        {(0x3f800000),
+         (0x3fa80480),
+         (0x3f8001a8),
+         (0x3fa80528),
+         (0x3f8200c8),
+         (0x3faa0448),
+         (0x3f820160),
+         (0x3faa05e0),
+         (0x3f82813f),
+         (0x3faa85bf),
+         (0x3f828097),
+         (0x3faa8417),
+         (0x3f8081f7),
+         (0x3fa88577),
+         (0x3f80805f),
+         (0x3fa884df)},
+        (0xfff80000),
+        {0x1f,0x11,0x3a,0x53,0x30,0xf5,0x3a,0x92,0x69,0x8f,
+         0x5e,0x40,0x86,0x58,0x16,0xa4,0x7f,0x33,0x04,0xc6,0x00}
+    },
+    {
+        /* No.70 delta:1651 weight:1401 */
+        11213,
+        35,
+        9,
+        8,
+        {(0x00000000),
+         (0xcaa73bcd),
+         (0x48ffa480),
+         (0x82589f4d),
+         (0x0b600460),
+         (0xc1c73fad),
+         (0x439fa0e0),
+         (0x89389b2d),
+         (0x0000d8fb),
+         (0xcaa7e336),
+         (0x48ff7c7b),
+         (0x825847b6),
+         (0x0b60dc9b),
+         (0xc1c7e756),
+         (0x439f781b),
+         (0x893843d6)},
+        {(0x00000000),
+         (0x90604200),
+         (0x20222000),
+         (0xb0426200),
+         (0x5023a800),
+         (0xc043ea00),
+         (0x70018800),
+         (0xe061ca00),
+         (0x16031e00),
+         (0x86635c00),
+         (0x36213e00),
+         (0xa6417c00),
+         (0x4620b600),
+         (0xd640f400),
+         (0x66029600),
+         (0xf662d400)},
+        {(0x3f800000),
+         (0x3fc83021),
+         (0x3f901110),
+         (0x3fd82131),
+         (0x3fa811d4),
+         (0x3fe021f5),
+         (0x3fb800c4),
+         (0x3ff030e5),
+         (0x3f8b018f),
+         (0x3fc331ae),
+         (0x3f9b109f),
+         (0x3fd320be),
+         (0x3fa3105b),
+         (0x3feb207a),
+         (0x3fb3014b),
+         (0x3ffb316a)},
+        (0xfff80000),
+        {0xf7,0xac,0x3b,0x82,0x60,0x2e,0xa2,0x38,0xd5,0xbd,
+         0x9e,0x07,0x19,0x05,0x37,0x68,0xa8,0x2b,0xcc,0x5b,0x00}
+    },
+    {
+        /* No.71 delta:805 weight:1559 */
+        11213,
+        85,
+        17,
+        2,
+        {(0x00000000),
+         (0x4ea39f4b),
+         (0xeedcca5e),
+         (0xa07f5515),
+         (0x1a80047d),
+         (0x54239b36),
+         (0xf45cce23),
+         (0xbaff5168),
+         (0x000051a3),
+         (0x4ea3cee8),
+         (0xeedc9bfd),
+         (0xa07f04b6),
+         (0x1a8055de),
+         (0x5423ca95),
+         (0xf45c9f80),
+         (0xbaff00cb)},
+        {(0x00000000),
+         (0x0879e800),
+         (0x10141400),
+         (0x186dfc00),
+         (0x006c0800),
+         (0x0815e000),
+         (0x10781c00),
+         (0x1801f400),
+         (0x40237e00),
+         (0x485a9600),
+         (0x50376a00),
+         (0x584e8200),
+         (0x404f7600),
+         (0x48369e00),
+         (0x505b6200),
+         (0x58228a00)},
+        {(0x3f800000),
+         (0x3f843cf4),
+         (0x3f880a0a),
+         (0x3f8c36fe),
+         (0x3f803604),
+         (0x3f840af0),
+         (0x3f883c0e),
+         (0x3f8c00fa),
+         (0x3fa011bf),
+         (0x3fa42d4b),
+         (0x3fa81bb5),
+         (0x3fac2741),
+         (0x3fa027bb),
+         (0x3fa41b4f),
+         (0x3fa82db1),
+         (0x3fac1145)},
+        (0xfff80000),
+        {0x81,0x81,0xe0,0x37,0x51,0x1b,0x87,0x01,0xa3,0x40,
+         0x70,0xd0,0xb9,0x63,0xbb,0xc0,0xe9,0x49,0x0b,0x62,0x00}
+    },
+    {
+        /* No.72 delta:1929 weight:1383 */
+        11213,
+        77,
+        8,
+        10,
+        {(0x00000000),
+         (0x9625646a),
+         (0xa06390e5),
+         (0x3646f48f),
+         (0x6c400487),
+         (0xfa6560ed),
+         (0xcc239462),
+         (0x5a06f008),
+         (0x00006232),
+         (0x96250658),
+         (0xa063f2d7),
+         (0x364696bd),
+         (0x6c4066b5),
+         (0xfa6502df),
+         (0xcc23f650),
+         (0x5a06923a)},
+        {(0x00000000),
+         (0x60350000),
+         (0x08a08000),
+         (0x68958000),
+         (0x00120000),
+         (0x60270000),
+         (0x08b28000),
+         (0x68878000),
+         (0x60021e00),
+         (0x00371e00),
+         (0x68a29e00),
+         (0x08979e00),
+         (0x60101e00),
+         (0x00251e00),
+         (0x68b09e00),
+         (0x08859e00)},
+        {(0x3f800000),
+         (0x3fb01a80),
+         (0x3f845040),
+         (0x3fb44ac0),
+         (0x3f800900),
+         (0x3fb01380),
+         (0x3f845940),
+         (0x3fb443c0),
+         (0x3fb0010f),
+         (0x3f801b8f),
+         (0x3fb4514f),
+         (0x3f844bcf),
+         (0x3fb0080f),
+         (0x3f80128f),
+         (0x3fb4584f),
+         (0x3f8442cf)},
+        (0xfff80000),
+        {0xec,0xe8,0xd6,0x3f,0xf0,0x4f,0x6e,0xc9,0x92,0xb9,
+         0x0b,0xf3,0x46,0x01,0x83,0x06,0xba,0xbb,0xfc,0x3c,0x00}
+    },
+    {
+        /* No.73 delta:1312 weight:1037 */
+        11213,
+        79,
+        14,
+        13,
+        {(0x00000000),
+         (0x3c3b4194),
+         (0x3b4a522a),
+         (0x077113be),
+         (0x70e00495),
+         (0x4cdb4501),
+         (0x4baa56bf),
+         (0x7791172b),
+         (0x00001842),
+         (0x3c3b59d6),
+         (0x3b4a4a68),
+         (0x07710bfc),
+         (0x70e01cd7),
+         (0x4cdb5d43),
+         (0x4baa4efd),
+         (0x77910f69)},
+        {(0x00000000),
+         (0x5e1d3000),
+         (0x08002800),
+         (0x561d1800),
+         (0x00404000),
+         (0x5e5d7000),
+         (0x08406800),
+         (0x565d5800),
+         (0xa7841e00),
+         (0xf9992e00),
+         (0xaf843600),
+         (0xf1990600),
+         (0xa7c45e00),
+         (0xf9d96e00),
+         (0xafc47600),
+         (0xf1d94600)},
+        {(0x3f800000),
+         (0x3faf0e98),
+         (0x3f840014),
+         (0x3fab0e8c),
+         (0x3f802020),
+         (0x3faf2eb8),
+         (0x3f842034),
+         (0x3fab2eac),
+         (0x3fd3c20f),
+         (0x3ffccc97),
+         (0x3fd7c21b),
+         (0x3ff8cc83),
+         (0x3fd3e22f),
+         (0x3ffcecb7),
+         (0x3fd7e23b),
+         (0x3ff8eca3)},
+        (0xfff80000),
+        {0xe5,0x84,0x28,0x6a,0xc7,0xb8,0xff,0x35,0x7e,0x4a,
+         0xd0,0x29,0x99,0x65,0xc9,0xf9,0x00,0xb5,0xa5,0x73,0x00}
+    },
+    {
+        /* No.74 delta:1274 weight:753 */
+        11213,
+        83,
+        10,
+        18,
+        {(0x00000000),
+         (0xdb6fd096),
+         (0x7bead936),
+         (0xa08509a0),
+         (0x059004a2),
+         (0xdeffd434),
+         (0x7e7add94),
+         (0xa5150d02),
+         (0x00003dd4),
+         (0xdb6fed42),
+         (0x7beae4e2),
+         (0xa0853474),
+         (0x05903976),
+         (0xdeffe9e0),
+         (0x7e7ae040),
+         (0xa51530d6)},
+        {(0x00000000),
+         (0x0862a000),
+         (0x20176000),
+         (0x2875c000),
+         (0x21182000),
+         (0x297a8000),
+         (0x010f4000),
+         (0x096de000),
+         (0x100c1e00),
+         (0x186ebe00),
+         (0x301b7e00),
+         (0x3879de00),
+         (0x31143e00),
+         (0x39769e00),
+         (0x11035e00),
+         (0x1961fe00)},
+        {(0x3f800000),
+         (0x3f843150),
+         (0x3f900bb0),
+         (0x3f943ae0),
+         (0x3f908c10),
+         (0x3f94bd40),
+         (0x3f8087a0),
+         (0x3f84b6f0),
+         (0x3f88060f),
+         (0x3f8c375f),
+         (0x3f980dbf),
+         (0x3f9c3cef),
+         (0x3f988a1f),
+         (0x3f9cbb4f),
+         (0x3f8881af),
+         (0x3f8cb0ff)},
+        (0xfff80000),
+        {0x94,0x6b,0xe1,0x97,0xcf,0xd7,0x1d,0x69,0x3d,0xf1,
+         0xd3,0x31,0x9b,0x3f,0x74,0x40,0xe4,0x78,0x37,0x93,0x00}
+    },
+    {
+        /* No.75 delta:3240 weight:999 */
+        11213,
+        25,
+        3,
+        14,
+        {(0x00000000),
+         (0x5231ede5),
+         (0x75f41157),
+         (0x27c5fcb2),
+         (0x231004bc),
+         (0x7121e959),
+         (0x56e415eb),
+         (0x04d5f80e),
+         (0x000014a2),
+         (0x5231f947),
+         (0x75f405f5),
+         (0x27c5e810),
+         (0x2310101e),
+         (0x7121fdfb),
+         (0x56e40149),
+         (0x04d5ecac)},
+        {(0x00000000),
+         (0x09c10000),
+         (0x50f28000),
+         (0x59338000),
+         (0x12424000),
+         (0x1b834000),
+         (0x42b0c000),
+         (0x4b71c000),
+         (0x02361e00),
+         (0x0bf71e00),
+         (0x52c49e00),
+         (0x5b059e00),
+         (0x10745e00),
+         (0x19b55e00),
+         (0x4086de00),
+         (0x4947de00)},
+        {(0x3f800000),
+         (0x3f84e080),
+         (0x3fa87940),
+         (0x3fac99c0),
+         (0x3f892120),
+         (0x3f8dc1a0),
+         (0x3fa15860),
+         (0x3fa5b8e0),
+         (0x3f811b0f),
+         (0x3f85fb8f),
+         (0x3fa9624f),
+         (0x3fad82cf),
+         (0x3f883a2f),
+         (0x3f8cdaaf),
+         (0x3fa0436f),
+         (0x3fa4a3ef)},
+        (0xfff80000),
+        {0x22,0xa1,0x8f,0x2e,0x7e,0xb0,0x8f,0x95,0xab,0x7b,
+         0xe4,0x15,0x6f,0x5a,0x78,0x2a,0xeb,0x47,0xfe,0xa6,0x00}
+    },
+    {
+        /* No.76 delta:1884 weight:1457 */
+        11213,
+        11,
+        13,
+        3,
+        {(0x00000000),
+         (0xb165bd1d),
+         (0x1f5b5cd1),
+         (0xae3ee1cc),
+         (0x07b004ca),
+         (0xb6d5b9d7),
+         (0x18eb581b),
+         (0xa98ee506),
+         (0x00000ebc),
+         (0xb165b3a1),
+         (0x1f5b526d),
+         (0xae3eef70),
+         (0x07b00a76),
+         (0xb6d5b76b),
+         (0x18eb56a7),
+         (0xa98eebba)},
+        {(0x00000000),
+         (0x2a1ef400),
+         (0x43091000),
+         (0x6917e400),
+         (0x05182000),
+         (0x2f06d400),
+         (0x46113000),
+         (0x6c0fc400),
+         (0x20c41e00),
+         (0x0adaea00),
+         (0x63cd0e00),
+         (0x49d3fa00),
+         (0x25dc3e00),
+         (0x0fc2ca00),
+         (0x66d52e00),
+         (0x4ccbda00)},
+        {(0x3f800000),
+         (0x3f950f7a),
+         (0x3fa18488),
+         (0x3fb48bf2),
+         (0x3f828c10),
+         (0x3f97836a),
+         (0x3fa30898),
+         (0x3fb607e2),
+         (0x3f90620f),
+         (0x3f856d75),
+         (0x3fb1e687),
+         (0x3fa4e9fd),
+         (0x3f92ee1f),
+         (0x3f87e165),
+         (0x3fb36a97),
+         (0x3fa665ed)},
+        (0xfff80000),
+        {0x5f,0x66,0x78,0x0e,0x66,0x3f,0x37,0x9d,0x75,0xa2,
+         0x49,0x78,0xb7,0x8c,0xf1,0x7c,0x84,0xf3,0xab,0x84,0x00}
+    },
+    {
+        /* No.77 delta:1037 weight:1439 */
+        11213,
+        43,
+        15,
+        7,
+        {(0x00000000),
+         (0x3b7738f7),
+         (0x2ee7f42b),
+         (0x1590ccdc),
+         (0x8c3004de),
+         (0xb7473c29),
+         (0xa2d7f0f5),
+         (0x99a0c802),
+         (0x0000b3f9),
+         (0x3b778b0e),
+         (0x2ee747d2),
+         (0x15907f25),
+         (0x8c30b727),
+         (0xb7478fd0),
+         (0xa2d7430c),
+         (0x99a07bfb)},
+        {(0x00000000),
+         (0x20009400),
+         (0x70030800),
+         (0x50039c00),
+         (0x90011600),
+         (0xb0018200),
+         (0xe0021e00),
+         (0xc0028a00),
+         (0x40005e00),
+         (0x6000ca00),
+         (0x30035600),
+         (0x1003c200),
+         (0xd0014800),
+         (0xf001dc00),
+         (0xa0024000),
+         (0x8002d400)},
+        {(0x3f800000),
+         (0x3f90004a),
+         (0x3fb80184),
+         (0x3fa801ce),
+         (0x3fc8008b),
+         (0x3fd800c1),
+         (0x3ff0010f),
+         (0x3fe00145),
+         (0x3fa0002f),
+         (0x3fb00065),
+         (0x3f9801ab),
+         (0x3f8801e1),
+         (0x3fe800a4),
+         (0x3ff800ee),
+         (0x3fd00120),
+         (0x3fc0016a)},
+        (0xfff80000),
+        {0x4d,0xd9,0x1c,0xbc,0x61,0x94,0x41,0xb3,0x64,0x5a,
+         0xe0,0x27,0x21,0x33,0x58,0x8a,0x3a,0x27,0xe7,0x61,0x00}
+    },
+    {
+        /* No.78 delta:3771 weight:1149 */
+        11213,
+        67,
+        6,
+        13,
+        {(0x00000000),
+         (0xeb935e7e),
+         (0x12dd9004),
+         (0xf94ece7a),
+         (0x518004ec),
+         (0xba135a92),
+         (0x435d94e8),
+         (0xa8ceca96),
+         (0x00002915),
+         (0xeb93776b),
+         (0x12ddb911),
+         (0xf94ee76f),
+         (0x51802df9),
+         (0xba137387),
+         (0x435dbdfd),
+         (0xa8cee383)},
+        {(0x00000000),
+         (0x00600000),
+         (0x005c0000),
+         (0x003c0000),
+         (0xa7000000),
+         (0xa7600000),
+         (0xa75c0000),
+         (0xa73c0000),
+         (0x00381e00),
+         (0x00581e00),
+         (0x00641e00),
+         (0x00041e00),
+         (0xa7381e00),
+         (0xa7581e00),
+         (0xa7641e00),
+         (0xa7041e00)},
+        {(0x3f800000),
+         (0x3f803000),
+         (0x3f802e00),
+         (0x3f801e00),
+         (0x3fd38000),
+         (0x3fd3b000),
+         (0x3fd3ae00),
+         (0x3fd39e00),
+         (0x3f801c0f),
+         (0x3f802c0f),
+         (0x3f80320f),
+         (0x3f80020f),
+         (0x3fd39c0f),
+         (0x3fd3ac0f),
+         (0x3fd3b20f),
+         (0x3fd3820f)},
+        (0xfff80000),
+        {0x61,0xb9,0x79,0x4a,0xf6,0x0b,0x90,0x19,0x41,0xed,
+         0xc7,0x02,0x30,0x6e,0x66,0x3a,0xa0,0x21,0x38,0x4d,0x00}
+    },
+    {
+        /* No.79 delta:8905 weight:995 */
+        11213,
+        37,
+        2,
+        14,
+        {(0x00000000),
+         (0xc386d224),
+         (0xa19cefca),
+         (0x621a3dee),
+         (0x73c004f0),
+         (0xb046d6d4),
+         (0xd25ceb3a),
+         (0x11da391e),
+         (0x00006023),
+         (0xc386b207),
+         (0xa19c8fe9),
+         (0x621a5dcd),
+         (0x73c064d3),
+         (0xb046b6f7),
+         (0xd25c8b19),
+         (0x11da593d)},
+        {(0x00000000),
+         (0x50000000),
+         (0x60000000),
+         (0x30000000),
+         (0x00000000),
+         (0x50000000),
+         (0x60000000),
+         (0x30000000),
+         (0x10001e00),
+         (0x40001e00),
+         (0x70001e00),
+         (0x20001e00),
+         (0x10001e00),
+         (0x40001e00),
+         (0x70001e00),
+         (0x20001e00)},
+        {(0x3f800000),
+         (0x3fa80000),
+         (0x3fb00000),
+         (0x3f980000),
+         (0x3f800000),
+         (0x3fa80000),
+         (0x3fb00000),
+         (0x3f980000),
+         (0x3f88000f),
+         (0x3fa0000f),
+         (0x3fb8000f),
+         (0x3f90000f),
+         (0x3f88000f),
+         (0x3fa0000f),
+         (0x3fb8000f),
+         (0x3f90000f)},
+        (0xfff80000),
+        {0xcd,0x77,0xa6,0x4e,0x16,0x97,0x82,0x51,0x40,0x8c,
+         0x23,0x3d,0x69,0x41,0x55,0xf4,0x6d,0xb5,0x97,0xc4,0x00}
+    },
+    {
+        /* No.80 delta:1084 weight:1327 */
+        11213,
+        41,
+        20,
+        7,
+        {(0x00000000),
+         (0x03b0124c),
+         (0x2709d148),
+         (0x24b9c304),
+         (0xd150050f),
+         (0xd2e01743),
+         (0xf659d447),
+         (0xf5e9c60b),
+         (0x0000e5a0),
+         (0x03b0f7ec),
+         (0x270934e8),
+         (0x24b926a4),
+         (0xd150e0af),
+         (0xd2e0f2e3),
+         (0xf65931e7),
+         (0xf5e923ab)},
+        {(0x00000000),
+         (0x58fc3200),
+         (0x70059800),
+         (0x28f9aa00),
+         (0x00443a00),
+         (0x58b80800),
+         (0x7041a200),
+         (0x28bd9000),
+         (0x60225e00),
+         (0x38de6c00),
+         (0x1027c600),
+         (0x48dbf400),
+         (0x60666400),
+         (0x389a5600),
+         (0x1063fc00),
+         (0x489fce00)},
+        {(0x3f800000),
+         (0x3fac7e19),
+         (0x3fb802cc),
+         (0x3f947cd5),
+         (0x3f80221d),
+         (0x3fac5c04),
+         (0x3fb820d1),
+         (0x3f945ec8),
+         (0x3fb0112f),
+         (0x3f9c6f36),
+         (0x3f8813e3),
+         (0x3fa46dfa),
+         (0x3fb03332),
+         (0x3f9c4d2b),
+         (0x3f8831fe),
+         (0x3fa44fe7)},
+        (0xfff80000),
+        {0x17,0x06,0xb5,0x56,0x08,0x24,0x02,0x45,0x7d,0xec,
+         0xaa,0x6e,0xee,0x42,0x3d,0x61,0xec,0x7b,0x95,0xda,0x00}
+    },
+    {
+        /* No.81 delta:2107 weight:929 */
+        11213,
+        68,
+        7,
+        13,
+        {(0x00000000),
+         (0xa2680084),
+         (0x72c07011),
+         (0xd0a87095),
+         (0xe9c00518),
+         (0x4ba8059c),
+         (0x9b007509),
+         (0x3968758d),
+         (0x000019d6),
+         (0xa2681952),
+         (0x72c069c7),
+         (0xd0a86943),
+         (0xe9c01cce),
+         (0x4ba81c4a),
+         (0x9b006cdf),
+         (0x39686c5b)},
+        {(0x00000000),
+         (0x06214000),
+         (0x50182000),
+         (0x56396000),
+         (0x004c0000),
+         (0x066d4000),
+         (0x50542000),
+         (0x56756000),
+         (0x003abe00),
+         (0x061bfe00),
+         (0x50229e00),
+         (0x5603de00),
+         (0x0076be00),
+         (0x0657fe00),
+         (0x506e9e00),
+         (0x564fde00)},
+        {(0x3f800000),
+         (0x3f8310a0),
+         (0x3fa80c10),
+         (0x3fab1cb0),
+         (0x3f802600),
+         (0x3f8336a0),
+         (0x3fa82a10),
+         (0x3fab3ab0),
+         (0x3f801d5f),
+         (0x3f830dff),
+         (0x3fa8114f),
+         (0x3fab01ef),
+         (0x3f803b5f),
+         (0x3f832bff),
+         (0x3fa8374f),
+         (0x3fab27ef)},
+        (0xfff80000),
+        {0x06,0xc1,0x16,0x3a,0x4b,0xf5,0xcc,0xe4,0x06,0xc5,
+         0x47,0x64,0xd8,0x26,0x6e,0xa3,0xd2,0xe3,0xfc,0x67,0x00}
+    },
+    {
+        /* No.82 delta:1653 weight:993 */
+        11213,
+        67,
+        15,
+        1,
+        {(0x00000000),
+         (0x205bddc2),
+         (0x51a5fb05),
+         (0x71fe26c7),
+         (0xbc800525),
+         (0x9cdbd8e7),
+         (0xed25fe20),
+         (0xcd7e23e2),
+         (0x0000ae60),
+         (0x205b73a2),
+         (0x51a55565),
+         (0x71fe88a7),
+         (0xbc80ab45),
+         (0x9cdb7687),
+         (0xed255040),
+         (0xcd7e8d82)},
+        {(0x00000000),
+         (0x200d8e00),
+         (0x01004000),
+         (0x210dce00),
+         (0x1710c000),
+         (0x371d4e00),
+         (0x16108000),
+         (0x361d0e00),
+         (0x00409e00),
+         (0x204d1000),
+         (0x0140de00),
+         (0x214d5000),
+         (0x17505e00),
+         (0x375dd000),
+         (0x16501e00),
+         (0x365d9000)},
+        {(0x3f800000),
+         (0x3f9006c7),
+         (0x3f808020),
+         (0x3f9086e7),
+         (0x3f8b8860),
+         (0x3f9b8ea7),
+         (0x3f8b0840),
+         (0x3f9b0e87),
+         (0x3f80204f),
+         (0x3f902688),
+         (0x3f80a06f),
+         (0x3f90a6a8),
+         (0x3f8ba82f),
+         (0x3f9baee8),
+         (0x3f8b280f),
+         (0x3f9b2ec8)},
+        (0xfff80000),
+        {0x31,0x5a,0x14,0x56,0x22,0x69,0x71,0xb4,0xed,0xe9,
+         0x57,0x3c,0xb3,0xa3,0xc2,0xd8,0x5c,0x3b,0xf9,0x7a,0x00}
+    },
+    {
+        /* No.83 delta:2591 weight:1355 */
+        11213,
+        78,
+        3,
+        3,
+        {(0x00000000),
+         (0x3fa6f3ae),
+         (0x11561526),
+         (0x2ef0e688),
+         (0x3f40053d),
+         (0x00e6f693),
+         (0x2e16101b),
+         (0x11b0e3b5),
+         (0x000038dd),
+         (0x3fa6cb73),
+         (0x11562dfb),
+         (0x2ef0de55),
+         (0x3f403de0),
+         (0x00e6ce4e),
+         (0x2e1628c6),
+         (0x11b0db68)},
+        {(0x00000000),
+         (0x88264000),
+         (0x60420000),
+         (0xe8644000),
+         (0x20102000),
+         (0xa8366000),
+         (0x40522000),
+         (0xc8746000),
+         (0x00791e00),
+         (0x885f5e00),
+         (0x603b1e00),
+         (0xe81d5e00),
+         (0x20693e00),
+         (0xa84f7e00),
+         (0x402b3e00),
+         (0xc80d7e00)},
+        {(0x3f800000),
+         (0x3fc41320),
+         (0x3fb02100),
+         (0x3ff43220),
+         (0x3f900810),
+         (0x3fd41b30),
+         (0x3fa02910),
+         (0x3fe43a30),
+         (0x3f803c8f),
+         (0x3fc42faf),
+         (0x3fb01d8f),
+         (0x3ff40eaf),
+         (0x3f90349f),
+         (0x3fd427bf),
+         (0x3fa0159f),
+         (0x3fe406bf)},
+        (0xfff80000),
+        {0x95,0xde,0xea,0xd2,0x91,0x96,0x3a,0x31,0x18,0xb4,
+         0x29,0x3c,0x05,0x83,0x13,0x20,0xa6,0xbb,0xfa,0x90,0x00}
+    },
+    {
+        /* No.84 delta:1724 weight:1387 */
+        11213,
+        31,
+        24,
+        7,
+        {(0x00000000),
+         (0x6f77a064),
+         (0x9d4e3ea4),
+         (0xf2399ec0),
+         (0xdab00543),
+         (0xb5c7a527),
+         (0x47fe3be7),
+         (0x28899b83),
+         (0x0000ba57),
+         (0x6f771a33),
+         (0x9d4e84f3),
+         (0xf2392497),
+         (0xdab0bf14),
+         (0xb5c71f70),
+         (0x47fe81b0),
+         (0x288921d4)},
+        {(0x00000000),
+         (0x4052c800),
+         (0x403d7000),
+         (0x006fb800),
+         (0x01748400),
+         (0x41264c00),
+         (0x4149f400),
+         (0x011b3c00),
+         (0x30183e00),
+         (0x704af600),
+         (0x70254e00),
+         (0x30778600),
+         (0x316cba00),
+         (0x713e7200),
+         (0x7151ca00),
+         (0x31030200)},
+        {(0x3f800000),
+         (0x3fa02964),
+         (0x3fa01eb8),
+         (0x3f8037dc),
+         (0x3f80ba42),
+         (0x3fa09326),
+         (0x3fa0a4fa),
+         (0x3f808d9e),
+         (0x3f980c1f),
+         (0x3fb8257b),
+         (0x3fb812a7),
+         (0x3f983bc3),
+         (0x3f98b65d),
+         (0x3fb89f39),
+         (0x3fb8a8e5),
+         (0x3f988181)},
+        (0xfff80000),
+        {0x24,0x42,0xb5,0x8a,0x26,0x25,0x0b,0x43,0xb5,0xa1,
+         0xfd,0xf3,0xef,0xe4,0x58,0x1d,0x2f,0x82,0x54,0xe1,0x00}
+    },
+    {
+        /* No.85 delta:1315 weight:1167 */
+        11213,
+        28,
+        18,
+        8,
+        {(0x00000000),
+         (0x7039a7b2),
+         (0x655a69d6),
+         (0x1563ce64),
+         (0x55800552),
+         (0x25b9a2e0),
+         (0x30da6c84),
+         (0x40e3cb36),
+         (0x0000db22),
+         (0x70397c90),
+         (0x655ab2f4),
+         (0x15631546),
+         (0x5580de70),
+         (0x25b979c2),
+         (0x30dab7a6),
+         (0x40e31014)},
+        {(0x00000000),
+         (0x091ae000),
+         (0xc01fd200),
+         (0xc9053200),
+         (0x60072600),
+         (0x691dc600),
+         (0xa018f400),
+         (0xa9021400),
+         (0x40083e00),
+         (0x4912de00),
+         (0x8017ec00),
+         (0x890d0c00),
+         (0x200f1800),
+         (0x2915f800),
+         (0xe010ca00),
+         (0xe90a2a00)},
+        {(0x3f800000),
+         (0x3f848d70),
+         (0x3fe00fe9),
+         (0x3fe48299),
+         (0x3fb00393),
+         (0x3fb48ee3),
+         (0x3fd00c7a),
+         (0x3fd4810a),
+         (0x3fa0041f),
+         (0x3fa4896f),
+         (0x3fc00bf6),
+         (0x3fc48686),
+         (0x3f90078c),
+         (0x3f948afc),
+         (0x3ff00865),
+         (0x3ff48515)},
+        (0xfff80000),
+        {0x54,0x00,0xe5,0x09,0x83,0x8a,0xa3,0xac,0xe2,0x03,
+         0xe4,0x5f,0x87,0xaa,0x93,0x3c,0x49,0xef,0xf1,0x12,0x00}
+    },
+    {
+        /* No.86 delta:1805 weight:1023 */
+        11213,
+        40,
+        17,
+        1,
+        {(0x00000000),
+         (0xd727963d),
+         (0x760f18b0),
+         (0xa1288e8d),
+         (0xe2d00563),
+         (0x35f7935e),
+         (0x94df1dd3),
+         (0x43f88bee),
+         (0x0000c942),
+         (0xd7275f7f),
+         (0x760fd1f2),
+         (0xa12847cf),
+         (0xe2d0cc21),
+         (0x35f75a1c),
+         (0x94dfd491),
+         (0x43f842ac)},
+        {(0x00000000),
+         (0x207c0400),
+         (0x56934000),
+         (0x76ef4400),
+         (0x00009e00),
+         (0x207c9a00),
+         (0x5693de00),
+         (0x76efda00),
+         (0x20009e00),
+         (0x007c9a00),
+         (0x7693de00),
+         (0x56efda00),
+         (0x20000000),
+         (0x007c0400),
+         (0x76934000),
+         (0x56ef4400)},
+        {(0x3f800000),
+         (0x3f903e02),
+         (0x3fab49a0),
+         (0x3fbb77a2),
+         (0x3f80004f),
+         (0x3f903e4d),
+         (0x3fab49ef),
+         (0x3fbb77ed),
+         (0x3f90004f),
+         (0x3f803e4d),
+         (0x3fbb49ef),
+         (0x3fab77ed),
+         (0x3f900000),
+         (0x3f803e02),
+         (0x3fbb49a0),
+         (0x3fab77a2)},
+        (0xfff80000),
+        {0xc8,0x53,0x9a,0xab,0x96,0xd8,0xe7,0x02,0xe6,0xf1,
+         0x74,0x36,0x00,0x32,0x32,0x6e,0x36,0xe4,0x78,0x88,0x00}
+    },
+    {
+        /* No.87 delta:2165 weight:1423 */
+        11213,
+        29,
+        6,
+        7,
+        {(0x00000000),
+         (0xb0a4f75d),
+         (0xd99a0020),
+         (0x693ef77d),
+         (0x0010057f),
+         (0xb0b4f222),
+         (0xd98a055f),
+         (0x692ef202),
+         (0x00002860),
+         (0xb0a4df3d),
+         (0xd99a2840),
+         (0x693edf1d),
+         (0x00102d1f),
+         (0xb0b4da42),
+         (0xd98a2d3f),
+         (0x692eda62)},
+        {(0x00000000),
+         (0x00c02000),
+         (0x10102000),
+         (0x10d00000),
+         (0x700c0000),
+         (0x70cc2000),
+         (0x601c2000),
+         (0x60dc0000),
+         (0x00701e00),
+         (0x00b03e00),
+         (0x10603e00),
+         (0x10a01e00),
+         (0x707c1e00),
+         (0x70bc3e00),
+         (0x606c3e00),
+         (0x60ac1e00)},
+        {(0x3f800000),
+         (0x3f806010),
+         (0x3f880810),
+         (0x3f886800),
+         (0x3fb80600),
+         (0x3fb86610),
+         (0x3fb00e10),
+         (0x3fb06e00),
+         (0x3f80380f),
+         (0x3f80581f),
+         (0x3f88301f),
+         (0x3f88500f),
+         (0x3fb83e0f),
+         (0x3fb85e1f),
+         (0x3fb0361f),
+         (0x3fb0560f)},
+        (0xfff80000),
+        {0xc8,0x26,0xe1,0xb8,0xbd,0xd2,0x06,0x99,0x21,0x8b,
+         0xa2,0x82,0x21,0x08,0x66,0x43,0x39,0x7d,0x22,0x3f,0x00}
+    },
+    {
+        /* No.88 delta:2728 weight:929 */
+        11213,
+        9,
+        5,
+        12,
+        {(0x00000000),
+         (0xba91b8b7),
+         (0x1f94657f),
+         (0xa505ddc8),
+         (0xc8900581),
+         (0x7201bd36),
+         (0xd70460fe),
+         (0x6d95d849),
+         (0x00009507),
+         (0xba912db0),
+         (0x1f94f078),
+         (0xa50548cf),
+         (0xc8909086),
+         (0x72012831),
+         (0xd704f5f9),
+         (0x6d954d4e)},
+        {(0x00000000),
+         (0x74d60000),
+         (0x48810000),
+         (0x3c570000),
+         (0x07840000),
+         (0x73520000),
+         (0x4f050000),
+         (0x3bd30000),
+         (0xa0019e00),
+         (0xd4d79e00),
+         (0xe8809e00),
+         (0x9c569e00),
+         (0xa7859e00),
+         (0xd3539e00),
+         (0xef049e00),
+         (0x9bd29e00)},
+        {(0x3f800000),
+         (0x3fba6b00),
+         (0x3fa44080),
+         (0x3f9e2b80),
+         (0x3f83c200),
+         (0x3fb9a900),
+         (0x3fa78280),
+         (0x3f9de980),
+         (0x3fd000cf),
+         (0x3fea6bcf),
+         (0x3ff4404f),
+         (0x3fce2b4f),
+         (0x3fd3c2cf),
+         (0x3fe9a9cf),
+         (0x3ff7824f),
+         (0x3fcde94f)},
+        (0xfff80000),
+        {0xef,0x82,0x25,0x92,0x4d,0x84,0x32,0xcc,0x51,0x79,
+         0xa6,0xf1,0xa8,0xb8,0xd3,0xc8,0xf4,0x1f,0x61,0x14,0x00}
+    },
+    {
+        /* No.89 delta:1302 weight:829 */
+        11213,
+        52,
+        17,
+        15,
+        {(0x00000000),
+         (0xff9c667b),
+         (0x076e42cb),
+         (0xf8f224b0),
+         (0x5a200593),
+         (0xa5bc63e8),
+         (0x5d4e4758),
+         (0xa2d22123),
+         (0x0000fbe9),
+         (0xff9c9d92),
+         (0x076eb922),
+         (0xf8f2df59),
+         (0x5a20fe7a),
+         (0xa5bc9801),
+         (0x5d4ebcb1),
+         (0xa2d2daca)},
+        {(0x00000000),
+         (0x168c2000),
+         (0x08819000),
+         (0x1e0db000),
+         (0x8400d800),
+         (0x928cf800),
+         (0x8c814800),
+         (0x9a0d6800),
+         (0x0a023e00),
+         (0x1c8e1e00),
+         (0x0283ae00),
+         (0x140f8e00),
+         (0x8e02e600),
+         (0x988ec600),
+         (0x86837600),
+         (0x900f5600)},
+        {(0x3f800000),
+         (0x3f8b4610),
+         (0x3f8440c8),
+         (0x3f8f06d8),
+         (0x3fc2006c),
+         (0x3fc9467c),
+         (0x3fc640a4),
+         (0x3fcd06b4),
+         (0x3f85011f),
+         (0x3f8e470f),
+         (0x3f8141d7),
+         (0x3f8a07c7),
+         (0x3fc70173),
+         (0x3fcc4763),
+         (0x3fc341bb),
+         (0x3fc807ab)},
+        (0xfff80000),
+        {0x44,0x22,0x55,0x0c,0x46,0x02,0x05,0x6b,0x49,0xa7,
+         0x79,0xcc,0xa0,0x5b,0x5c,0xeb,0x82,0xb3,0x48,0x1e,0x00}
+    },
+    {
+        /* No.90 delta:1794 weight:683 */
+        11213,
+        71,
+        7,
+        3,
+        {(0x00000000),
+         (0x28fcb37c),
+         (0x3de27e63),
+         (0x151ecd1f),
+         (0x99d005af),
+         (0xb12cb6d3),
+         (0xa4327bcc),
+         (0x8ccec8b0),
+         (0x0000473d),
+         (0x28fcf441),
+         (0x3de2395e),
+         (0x151e8a22),
+         (0x99d04292),
+         (0xb12cf1ee),
+         (0xa4323cf1),
+         (0x8cce8f8d)},
+        {(0x00000000),
+         (0x00170000),
+         (0x00059200),
+         (0x00129200),
+         (0x61088000),
+         (0x611f8000),
+         (0x610d1200),
+         (0x611a1200),
+         (0x22825e00),
+         (0x22955e00),
+         (0x2287cc00),
+         (0x2290cc00),
+         (0x438ade00),
+         (0x439dde00),
+         (0x438f4c00),
+         (0x43984c00)},
+        {(0x3f800000),
+         (0x3f800b80),
+         (0x3f8002c9),
+         (0x3f800949),
+         (0x3fb08440),
+         (0x3fb08fc0),
+         (0x3fb08689),
+         (0x3fb08d09),
+         (0x3f91412f),
+         (0x3f914aaf),
+         (0x3f9143e6),
+         (0x3f914866),
+         (0x3fa1c56f),
+         (0x3fa1ceef),
+         (0x3fa1c7a6),
+         (0x3fa1cc26)},
+        (0xfff80000),
+        {0x63,0x96,0x53,0x66,0x89,0x8f,0x5a,0x0f,0x8c,0x80,
+         0x0a,0x9a,0x17,0xd9,0x66,0x91,0xf8,0x92,0x97,0x62,0x00}
+    },
+    {
+        /* No.91 delta:10527 weight:393 */
+        11213,
+        5,
+        2,
+        19,
+        {(0x00000000),
+         (0x9ea45304),
+         (0x56ae02a1),
+         (0xc80a51a5),
+         (0x9ad005b9),
+         (0x047456bd),
+         (0xcc7e0718),
+         (0x52da541c),
+         (0x0000c554),
+         (0x9ea49650),
+         (0x56aec7f5),
+         (0xc80a94f1),
+         (0x9ad0c0ed),
+         (0x047493e9),
+         (0xcc7ec24c),
+         (0x52da9148)},
+        {(0x00000000),
+         (0x64000000),
+         (0x0f800000),
+         (0x6b800000),
+         (0x00000000),
+         (0x64000000),
+         (0x0f800000),
+         (0x6b800000),
+         (0x00001e00),
+         (0x64001e00),
+         (0x0f801e00),
+         (0x6b801e00),
+         (0x00001e00),
+         (0x64001e00),
+         (0x0f801e00),
+         (0x6b801e00)},
+        {(0x3f800000),
+         (0x3fb20000),
+         (0x3f87c000),
+         (0x3fb5c000),
+         (0x3f800000),
+         (0x3fb20000),
+         (0x3f87c000),
+         (0x3fb5c000),
+         (0x3f80000f),
+         (0x3fb2000f),
+         (0x3f87c00f),
+         (0x3fb5c00f),
+         (0x3f80000f),
+         (0x3fb2000f),
+         (0x3f87c00f),
+         (0x3fb5c00f)},
+        (0xfff80000),
+        {0xd2,0x73,0xa3,0x8b,0x1a,0x90,0xef,0x94,0xe3,0x4a,
+         0x16,0x2f,0x38,0x48,0xd9,0x68,0xf2,0xdf,0xe5,0xab,0x00}
+    },
+    {
+        /* No.92 delta:2494 weight:1233 */
+        11213,
+        7,
+        21,
+        4,
+        {(0x00000000),
+         (0x67c6fdcf),
+         (0x39165a15),
+         (0x5ed0a7da),
+         (0xa11005c0),
+         (0xc6d6f80f),
+         (0x98065fd5),
+         (0xffc0a21a),
+         (0x0000cdee),
+         (0x67c63021),
+         (0x391697fb),
+         (0x5ed06a34),
+         (0xa110c82e),
+         (0xc6d635e1),
+         (0x9806923b),
+         (0xffc06ff4)},
+        {(0x00000000),
+         (0xc64d0800),
+         (0x67f58000),
+         (0xa1b88800),
+         (0x11ca8200),
+         (0xd7878a00),
+         (0x763f0200),
+         (0xb0720a00),
+         (0x4eb51e00),
+         (0x88f81600),
+         (0x29409e00),
+         (0xef0d9600),
+         (0x5f7f9c00),
+         (0x99329400),
+         (0x388a1c00),
+         (0xfec71400)},
+        {(0x3f800000),
+         (0x3fe32684),
+         (0x3fb3fac0),
+         (0x3fd0dc44),
+         (0x3f88e541),
+         (0x3febc3c5),
+         (0x3fbb1f81),
+         (0x3fd83905),
+         (0x3fa75a8f),
+         (0x3fc47c0b),
+         (0x3f94a04f),
+         (0x3ff786cb),
+         (0x3fafbfce),
+         (0x3fcc994a),
+         (0x3f9c450e),
+         (0x3fff638a)},
+        (0xfff80000),
+        {0x9e,0xa5,0x58,0x0a,0xe1,0x2c,0x68,0x94,0x2b,0x3f,
+         0x4b,0xcd,0xe6,0xee,0x21,0x60,0xf3,0xf9,0x28,0xef,0x00}
+    },
+    {
+        /* No.93 delta:2858 weight:713 */
+        11213,
+        6,
+        11,
+        15,
+        {(0x00000000),
+         (0xce341fc2),
+         (0x74808815),
+         (0xbab497d7),
+         (0x396005de),
+         (0xf7541a1c),
+         (0x4de08dcb),
+         (0x83d49209),
+         (0x0000dee4),
+         (0xce34c126),
+         (0x748056f1),
+         (0xbab44933),
+         (0x3960db3a),
+         (0xf754c4f8),
+         (0x4de0532f),
+         (0x83d44ced)},
+        {(0x00000000),
+         (0x0af25000),
+         (0x0c419c00),
+         (0x06b3cc00),
+         (0x82144400),
+         (0x88e61400),
+         (0x8e55d800),
+         (0x84a78800),
+         (0xe0053e00),
+         (0xeaf76e00),
+         (0xec44a200),
+         (0xe6b6f200),
+         (0x62117a00),
+         (0x68e32a00),
+         (0x6e50e600),
+         (0x64a2b600)},
+        {(0x3f800000),
+         (0x3f857928),
+         (0x3f8620ce),
+         (0x3f8359e6),
+         (0x3fc10a22),
+         (0x3fc4730a),
+         (0x3fc72aec),
+         (0x3fc253c4),
+         (0x3ff0029f),
+         (0x3ff57bb7),
+         (0x3ff62251),
+         (0x3ff35b79),
+         (0x3fb108bd),
+         (0x3fb47195),
+         (0x3fb72873),
+         (0x3fb2515b)},
+        (0xfff80000),
+        {0x67,0xfb,0x7b,0xdf,0x91,0xd7,0xa6,0xcb,0xfa,0x0c,
+         0x9c,0xcf,0x32,0x59,0xd4,0x14,0x88,0x02,0x20,0xbd,0x00}
+    },
+    {
+        /* No.94 delta:1428 weight:871 */
+        11213,
+        51,
+        24,
+        3,
+        {(0x00000000),
+         (0x4055c0f5),
+         (0x3f9446d5),
+         (0x7fc18620),
+         (0xbd5005e5),
+         (0xfd05c510),
+         (0x82c44330),
+         (0xc29183c5),
+         (0x0000a8c2),
+         (0x40556837),
+         (0x3f94ee17),
+         (0x7fc12ee2),
+         (0xbd50ad27),
+         (0xfd056dd2),
+         (0x82c4ebf2),
+         (0xc2912b07)},
+        {(0x00000000),
+         (0x6064a000),
+         (0x4ef89000),
+         (0x2e9c3000),
+         (0x08a1d000),
+         (0x68c57000),
+         (0x46594000),
+         (0x263de000),
+         (0x0c127e00),
+         (0x6c76de00),
+         (0x42eaee00),
+         (0x228e4e00),
+         (0x04b3ae00),
+         (0x64d70e00),
+         (0x4a4b3e00),
+         (0x2a2f9e00)},
+        {(0x3f800000),
+         (0x3fb03250),
+         (0x3fa77c48),
+         (0x3f974e18),
+         (0x3f8450e8),
+         (0x3fb462b8),
+         (0x3fa32ca0),
+         (0x3f931ef0),
+         (0x3f86093f),
+         (0x3fb63b6f),
+         (0x3fa17577),
+         (0x3f914727),
+         (0x3f8259d7),
+         (0x3fb26b87),
+         (0x3fa5259f),
+         (0x3f9517cf)},
+        (0xfff80000),
+        {0xfc,0xe1,0x89,0x6f,0x64,0xe4,0x4b,0x42,0xe3,0x27,
+         0xa3,0x38,0x46,0x33,0x4f,0xf6,0x63,0x31,0xbb,0x80,0x00}
+    },
+    {
+        /* No.95 delta:1914 weight:999 */
+        11213,
+        43,
+        8,
+        13,
+        {(0x00000000),
+         (0x9c0723c5),
+         (0xc882db89),
+         (0x5485f84c),
+         (0x994005f3),
+         (0x05472636),
+         (0x51c2de7a),
+         (0xcdc5fdbf),
+         (0x000019bd),
+         (0x9c073a78),
+         (0xc882c234),
+         (0x5485e1f1),
+         (0x99401c4e),
+         (0x05473f8b),
+         (0x51c2c7c7),
+         (0xcdc5e402)},
+        {(0x00000000),
+         (0x5061f000),
+         (0x105bc000),
+         (0x403a3000),
+         (0x00740400),
+         (0x5015f400),
+         (0x102fc400),
+         (0x404e3400),
+         (0x041c1e00),
+         (0x547dee00),
+         (0x1447de00),
+         (0x44262e00),
+         (0x04681a00),
+         (0x5409ea00),
+         (0x1433da00),
+         (0x44522a00)},
+        {(0x3f800000),
+         (0x3fa830f8),
+         (0x3f882de0),
+         (0x3fa01d18),
+         (0x3f803a02),
+         (0x3fa80afa),
+         (0x3f8817e2),
+         (0x3fa0271a),
+         (0x3f820e0f),
+         (0x3faa3ef7),
+         (0x3f8a23ef),
+         (0x3fa21317),
+         (0x3f82340d),
+         (0x3faa04f5),
+         (0x3f8a19ed),
+         (0x3fa22915)},
+        (0xfff80000),
+        {0xbf,0x5e,0xb4,0x48,0x47,0x8e,0xcb,0x36,0x4b,0x6a,
+         0xc7,0x2c,0x1c,0xc9,0xdc,0x0d,0x4b,0x46,0x15,0xdf,0x00}
+    },
+    {
+        /* No.96 delta:1078 weight:1383 */
+        11213,
+        93,
+        22,
+        5,
+        {(0x00000000),
+         (0x4598c5d2),
+         (0x2a3c70f7),
+         (0x6fa4b525),
+         (0xf4e0060f),
+         (0xb178c3dd),
+         (0xdedc76f8),
+         (0x9b44b32a),
+         (0x0000ee71),
+         (0x45982ba3),
+         (0x2a3c9e86),
+         (0x6fa45b54),
+         (0xf4e0e87e),
+         (0xb1782dac),
+         (0xdedc9889),
+         (0x9b445d5b)},
+        {(0x00000000),
+         (0x40220000),
+         (0x20738000),
+         (0x60518000),
+         (0x007e2000),
+         (0x405c2000),
+         (0x200da000),
+         (0x602fa000),
+         (0x80035e00),
+         (0xc0215e00),
+         (0xa070de00),
+         (0xe052de00),
+         (0x807d7e00),
+         (0xc05f7e00),
+         (0xa00efe00),
+         (0xe02cfe00)},
+        {(0x3f800000),
+         (0x3fa01100),
+         (0x3f9039c0),
+         (0x3fb028c0),
+         (0x3f803f10),
+         (0x3fa02e10),
+         (0x3f9006d0),
+         (0x3fb017d0),
+         (0x3fc001af),
+         (0x3fe010af),
+         (0x3fd0386f),
+         (0x3ff0296f),
+         (0x3fc03ebf),
+         (0x3fe02fbf),
+         (0x3fd0077f),
+         (0x3ff0167f)},
+        (0xfff80000),
+        {0xf7,0x01,0xdc,0x94,0xdb,0xfe,0x75,0xe3,0x41,0x0d,
+         0xa7,0xcf,0xdb,0xc4,0xf8,0x63,0x39,0xf1,0x73,0x83,0x00}
+    },
+    {
+        /* No.97 delta:1680 weight:855 */
+        11213,
+        56,
+        11,
+        18,
+        {(0x00000000),
+         (0xed377fcb),
+         (0x935b7bcf),
+         (0x7e6c0404),
+         (0xb1b0061d),
+         (0x5c8779d6),
+         (0x22eb7dd2),
+         (0xcfdc0219),
+         (0x0000dc19),
+         (0xed37a3d2),
+         (0x935ba7d6),
+         (0x7e6cd81d),
+         (0xb1b0da04),
+         (0x5c87a5cf),
+         (0x22eba1cb),
+         (0xcfdcde00)},
+        {(0x00000000),
+         (0x10100000),
+         (0x201c0000),
+         (0x300c0000),
+         (0x30000000),
+         (0x20100000),
+         (0x101c0000),
+         (0x000c0000),
+         (0x40051e00),
+         (0x50151e00),
+         (0x60191e00),
+         (0x70091e00),
+         (0x70051e00),
+         (0x60151e00),
+         (0x50191e00),
+         (0x40091e00)},
+        {(0x3f800000),
+         (0x3f880800),
+         (0x3f900e00),
+         (0x3f980600),
+         (0x3f980000),
+         (0x3f900800),
+         (0x3f880e00),
+         (0x3f800600),
+         (0x3fa0028f),
+         (0x3fa80a8f),
+         (0x3fb00c8f),
+         (0x3fb8048f),
+         (0x3fb8028f),
+         (0x3fb00a8f),
+         (0x3fa80c8f),
+         (0x3fa0048f)},
+        (0xfff80000),
+        {0xa7,0x90,0x32,0xf0,0xe5,0x54,0x81,0x86,0xda,0xa2,
+         0x04,0x5e,0xda,0xe5,0xcd,0x25,0x97,0x20,0x5f,0xd8,0x00}
+    },
+    {
+        /* No.98 delta:4818 weight:1887 */
+        11213,
+        73,
+        1,
+        4,
+        {(0x00000000),
+         (0xf3234424),
+         (0xe4236cf9),
+         (0x170028dd),
+         (0x62d00623),
+         (0x91f34207),
+         (0x86f36ada),
+         (0x75d02efe),
+         (0x00003707),
+         (0xf3237323),
+         (0xe4235bfe),
+         (0x17001fda),
+         (0x62d03124),
+         (0x91f37500),
+         (0x86f35ddd),
+         (0x75d019f9)},
+        {(0x00000000),
+         (0x40550000),
+         (0x40300000),
+         (0x00650000),
+         (0x7a81c000),
+         (0x3ad4c000),
+         (0x3ab1c000),
+         (0x7ae4c000),
+         (0x54351e00),
+         (0x14601e00),
+         (0x14051e00),
+         (0x54501e00),
+         (0x2eb4de00),
+         (0x6ee1de00),
+         (0x6e84de00),
+         (0x2ed1de00)},
+        {(0x3f800000),
+         (0x3fa02a80),
+         (0x3fa01800),
+         (0x3f803280),
+         (0x3fbd40e0),
+         (0x3f9d6a60),
+         (0x3f9d58e0),
+         (0x3fbd7260),
+         (0x3faa1a8f),
+         (0x3f8a300f),
+         (0x3f8a028f),
+         (0x3faa280f),
+         (0x3f975a6f),
+         (0x3fb770ef),
+         (0x3fb7426f),
+         (0x3f9768ef)},
+        (0xfff80000),
+        {0x78,0x4c,0x48,0x62,0xcb,0x43,0x6e,0xae,0x79,0x3f,
+         0xa2,0x90,0xc7,0xd5,0xf6,0x2c,0x3a,0x94,0xf6,0x88,0x00}
+    },
+    {
+        /* No.99 delta:4941 weight:807 */
+        11213,
+        35,
+        6,
+        17,
+        {(0x00000000),
+         (0xceb13d37),
+         (0x6cab25b0),
+         (0xa21a1887),
+         (0x2d100639),
+         (0xe3a13b0e),
+         (0x41bb2389),
+         (0x8f0a1ebe),
+         (0x00002bd0),
+         (0xceb116e7),
+         (0x6cab0e60),
+         (0xa21a3357),
+         (0x2d102de9),
+         (0xe3a110de),
+         (0x41bb0859),
+         (0x8f0a356e)},
+        {(0x00000000),
+         (0x07201000),
+         (0x42300200),
+         (0x45101200),
+         (0x40000000),
+         (0x47201000),
+         (0x02300200),
+         (0x05101200),
+         (0x70001e00),
+         (0x77200e00),
+         (0x32301c00),
+         (0x35100c00),
+         (0x30001e00),
+         (0x37200e00),
+         (0x72301c00),
+         (0x75100c00)},
+        {(0x3f800000),
+         (0x3f839008),
+         (0x3fa11801),
+         (0x3fa28809),
+         (0x3fa00000),
+         (0x3fa39008),
+         (0x3f811801),
+         (0x3f828809),
+         (0x3fb8000f),
+         (0x3fbb9007),
+         (0x3f99180e),
+         (0x3f9a8806),
+         (0x3f98000f),
+         (0x3f9b9007),
+         (0x3fb9180e),
+         (0x3fba8806)},
+        (0xfff80000),
+        {0x47,0x35,0x44,0x50,0x63,0x4a,0x6a,0xcd,0x53,0x03,
+         0xdd,0x48,0x13,0x12,0x80,0xdf,0x0e,0x77,0xfa,0xf3,0x00}
+    },
+    {
+        /* No.100 delta:1308 weight:863 */
+        11213,
+        86,
+        13,
+        14,
+        {(0x00000000),
+         (0xc5ba19cb),
+         (0x67d04c0f),
+         (0xa26a55c4),
+         (0x36300642),
+         (0xf38a1f89),
+         (0x51e04a4d),
+         (0x945a5386),
+         (0x0000f011),
+         (0xc5bae9da),
+         (0x67d0bc1e),
+         (0xa26aa5d5),
+         (0x3630f653),
+         (0xf38aef98),
+         (0x51e0ba5c),
+         (0x945aa397)},
+        {(0x00000000),
+         (0x09233a00),
+         (0x40645e00),
+         (0x49476400),
+         (0x1030a000),
+         (0x19139a00),
+         (0x5054fe00),
+         (0x5977c400),
+         (0x30c05e00),
+         (0x39e36400),
+         (0x70a40000),
+         (0x79873a00),
+         (0x20f0fe00),
+         (0x29d3c400),
+         (0x6094a000),
+         (0x69b79a00)},
+        {(0x3f800000),
+         (0x3f84919d),
+         (0x3fa0322f),
+         (0x3fa4a3b2),
+         (0x3f881850),
+         (0x3f8c89cd),
+         (0x3fa82a7f),
+         (0x3facbbe2),
+         (0x3f98602f),
+         (0x3f9cf1b2),
+         (0x3fb85200),
+         (0x3fbcc39d),
+         (0x3f90787f),
+         (0x3f94e9e2),
+         (0x3fb04a50),
+         (0x3fb4dbcd)},
+        (0xfff80000),
+        {0xbe,0xe1,0xe6,0x46,0x8d,0x04,0x11,0x44,0x1a,0x37,
+         0x82,0xd9,0x6f,0xd6,0x9e,0xd5,0x25,0x58,0x1e,0xae,0x00}
+    },
+    {
+        /* No.101 delta:3747 weight:693 */
+        11213,
+        93,
+        7,
+        18,
+        {(0x00000000),
+         (0x34b395ad),
+         (0x437cf2d0),
+         (0x77cf677d),
+         (0xa770065c),
+         (0x93c393f1),
+         (0xe40cf48c),
+         (0xd0bf6121),
+         (0x0000f47f),
+         (0x34b361d2),
+         (0x437c06af),
+         (0x77cf9302),
+         (0xa770f223),
+         (0x93c3678e),
+         (0xe40c00f3),
+         (0xd0bf955e)},
+        {(0x00000000),
+         (0x40700000),
+         (0x00580000),
+         (0x40280000),
+         (0x40680000),
+         (0x00180000),
+         (0x40300000),
+         (0x00400000),
+         (0x21601e00),
+         (0x61101e00),
+         (0x21381e00),
+         (0x61481e00),
+         (0x61081e00),
+         (0x21781e00),
+         (0x61501e00),
+         (0x21201e00)},
+        {(0x3f800000),
+         (0x3fa03800),
+         (0x3f802c00),
+         (0x3fa01400),
+         (0x3fa03400),
+         (0x3f800c00),
+         (0x3fa01800),
+         (0x3f802000),
+         (0x3f90b00f),
+         (0x3fb0880f),
+         (0x3f909c0f),
+         (0x3fb0a40f),
+         (0x3fb0840f),
+         (0x3f90bc0f),
+         (0x3fb0a80f),
+         (0x3f90900f)},
+        (0xfff80000),
+        {0xe2,0xf1,0x5a,0xc6,0xba,0x8b,0xae,0x08,0x43,0xcc,
+         0x54,0xa6,0xe9,0x8f,0x81,0x0a,0x85,0xe3,0x5e,0xef,0x00}
+    },
+    {
+        /* No.102 delta:3462 weight:1273 */
+        11213,
+        55,
+        23,
+        8,
+        {(0x00000000),
+         (0xc3cc98c8),
+         (0xca3b10a5),
+         (0x09f7886d),
+         (0x18300660),
+         (0xdbfc9ea8),
+         (0xd20b16c5),
+         (0x11c78e0d),
+         (0x000030c4),
+         (0xc3cca80c),
+         (0xca3b2061),
+         (0x09f7b8a9),
+         (0x183036a4),
+         (0xdbfcae6c),
+         (0xd20b2601),
+         (0x11c7bec9)},
+        {(0x00000000),
+         (0x08000000),
+         (0x00800000),
+         (0x08800000),
+         (0x88200000),
+         (0x80200000),
+         (0x88a00000),
+         (0x80a00000),
+         (0x03201e00),
+         (0x0b201e00),
+         (0x03a01e00),
+         (0x0ba01e00),
+         (0x8b001e00),
+         (0x83001e00),
+         (0x8b801e00),
+         (0x83801e00)},
+        {(0x3f800000),
+         (0x3f840000),
+         (0x3f804000),
+         (0x3f844000),
+         (0x3fc41000),
+         (0x3fc01000),
+         (0x3fc45000),
+         (0x3fc05000),
+         (0x3f81900f),
+         (0x3f85900f),
+         (0x3f81d00f),
+         (0x3f85d00f),
+         (0x3fc5800f),
+         (0x3fc1800f),
+         (0x3fc5c00f),
+         (0x3fc1c00f)},
+        (0xfff80000),
+        {0x43,0x1f,0x6f,0x01,0x80,0xf4,0x2e,0x31,0xcd,0xc1,
+         0xb9,0x2f,0x6b,0x5c,0x0e,0x0b,0x35,0xa5,0x70,0x7e,0x00}
+    },
+    {
+        /* No.103 delta:5054 weight:1635 */
+        11213,
+        64,
+        1,
+        2,
+        {(0x00000000),
+         (0x4f30581d),
+         (0x5542c11f),
+         (0x1a729902),
+         (0x9da00670),
+         (0xd2905e6d),
+         (0xc8e2c76f),
+         (0x87d29f72),
+         (0x00007e47),
+         (0x4f30265a),
+         (0x5542bf58),
+         (0x1a72e745),
+         (0x9da07837),
+         (0xd290202a),
+         (0xc8e2b928),
+         (0x87d2e135)},
+        {(0x00000000),
+         (0x0be21000),
+         (0x55018000),
+         (0x5ee39000),
+         (0x83944800),
+         (0x88765800),
+         (0xd695c800),
+         (0xdd77d800),
+         (0x00669e00),
+         (0x0b848e00),
+         (0x55671e00),
+         (0x5e850e00),
+         (0x83f2d600),
+         (0x8810c600),
+         (0xd6f35600),
+         (0xdd114600)},
+        {(0x3f800000),
+         (0x3f85f108),
+         (0x3faa80c0),
+         (0x3faf71c8),
+         (0x3fc1ca24),
+         (0x3fc43b2c),
+         (0x3feb4ae4),
+         (0x3feebbec),
+         (0x3f80334f),
+         (0x3f85c247),
+         (0x3faab38f),
+         (0x3faf4287),
+         (0x3fc1f96b),
+         (0x3fc40863),
+         (0x3feb79ab),
+         (0x3fee88a3)},
+        (0xfff80000),
+        {0x44,0x10,0x9f,0x8c,0xd9,0xbc,0x8e,0xb6,0xd5,0x14,
+         0x72,0x32,0xdd,0xf2,0x3c,0xea,0xb3,0x38,0xb9,0x31,0x00}
+    },
+    {
+        /* No.104 delta:2496 weight:907 */
+        11213,
+        79,
+        15,
+        14,
+        {(0x00000000),
+         (0x5c0202d2),
+         (0x1c3f4858),
+         (0x403d4a8a),
+         (0xd2f0068f),
+         (0x8ef2045d),
+         (0xcecf4ed7),
+         (0x92cd4c05),
+         (0x0000d76d),
+         (0x5c02d5bf),
+         (0x1c3f9f35),
+         (0x403d9de7),
+         (0xd2f0d1e2),
+         (0x8ef2d330),
+         (0xcecf99ba),
+         (0x92cd9b68)},
+        {(0x00000000),
+         (0x6b8d0400),
+         (0x1053a000),
+         (0x7bdea400),
+         (0x19807000),
+         (0x720d7400),
+         (0x09d3d000),
+         (0x625ed400),
+         (0x08441e00),
+         (0x63c91a00),
+         (0x1817be00),
+         (0x739aba00),
+         (0x11c46e00),
+         (0x7a496a00),
+         (0x0197ce00),
+         (0x6a1aca00)},
+        {(0x3f800000),
+         (0x3fb5c682),
+         (0x3f8829d0),
+         (0x3fbdef52),
+         (0x3f8cc038),
+         (0x3fb906ba),
+         (0x3f84e9e8),
+         (0x3fb12f6a),
+         (0x3f84220f),
+         (0x3fb1e48d),
+         (0x3f8c0bdf),
+         (0x3fb9cd5d),
+         (0x3f88e237),
+         (0x3fbd24b5),
+         (0x3f80cbe7),
+         (0x3fb50d65)},
+        (0xfff80000),
+        {0xb7,0xe4,0x3c,0x74,0x28,0xb1,0x2f,0xa4,0x5c,0x37,
+         0xb1,0x69,0xd9,0x95,0x83,0xd9,0xa6,0x36,0x5b,0x12,0x00}
+    },
+    {
+        /* No.105 delta:1174 weight:1019 */
+        11213,
+        91,
+        17,
+        14,
+        {(0x00000000),
+         (0xa83f2177),
+         (0xa0b43c75),
+         (0x088b1d02),
+         (0x43300696),
+         (0xeb0f27e1),
+         (0xe3843ae3),
+         (0x4bbb1b94),
+         (0x00007f34),
+         (0xa83f5e43),
+         (0xa0b44341),
+         (0x088b6236),
+         (0x433079a2),
+         (0xeb0f58d5),
+         (0xe38445d7),
+         (0x4bbb64a0)},
+        {(0x00000000),
+         (0x0115c000),
+         (0x90090000),
+         (0x911cc000),
+         (0x10422000),
+         (0x1157e000),
+         (0x804b2000),
+         (0x815ee000),
+         (0x10393e00),
+         (0x112cfe00),
+         (0x80303e00),
+         (0x8125fe00),
+         (0x007b1e00),
+         (0x016ede00),
+         (0x90721e00),
+         (0x9167de00)},
+        {(0x3f800000),
+         (0x3f808ae0),
+         (0x3fc80480),
+         (0x3fc88e60),
+         (0x3f882110),
+         (0x3f88abf0),
+         (0x3fc02590),
+         (0x3fc0af70),
+         (0x3f881c9f),
+         (0x3f88967f),
+         (0x3fc0181f),
+         (0x3fc092ff),
+         (0x3f803d8f),
+         (0x3f80b76f),
+         (0x3fc8390f),
+         (0x3fc8b3ef)},
+        (0xfff80000),
+        {0xe2,0x4f,0x2f,0xcb,0x05,0x76,0xce,0x0b,0xc7,0x7b,
+         0xcc,0x65,0x06,0xce,0x2c,0x0c,0x3c,0xb6,0x2c,0x79,0x00}
+    },
+    {
+        /* No.106 delta:1564 weight:925 */
+        11213,
+        40,
+        9,
+        1,
+        {(0x00000000),
+         (0xd288f412),
+         (0x0b33c7cb),
+         (0xd9bb33d9),
+         (0xe0d006a8),
+         (0x3258f2ba),
+         (0xebe3c163),
+         (0x396b3571),
+         (0x00004470),
+         (0xd288b062),
+         (0x0b3383bb),
+         (0xd9bb77a9),
+         (0xe0d042d8),
+         (0x3258b6ca),
+         (0xebe38513),
+         (0x396b7101)},
+        {(0x00000000),
+         (0x46794000),
+         (0x004d0000),
+         (0x46344000),
+         (0x007f0200),
+         (0x46064200),
+         (0x00320200),
+         (0x464b4200),
+         (0x00235e00),
+         (0x465a1e00),
+         (0x006e5e00),
+         (0x46171e00),
+         (0x005c5c00),
+         (0x46251c00),
+         (0x00115c00),
+         (0x46681c00)},
+        {(0x3f800000),
+         (0x3fa33ca0),
+         (0x3f802680),
+         (0x3fa31a20),
+         (0x3f803f81),
+         (0x3fa30321),
+         (0x3f801901),
+         (0x3fa325a1),
+         (0x3f8011af),
+         (0x3fa32d0f),
+         (0x3f80372f),
+         (0x3fa30b8f),
+         (0x3f802e2e),
+         (0x3fa3128e),
+         (0x3f8008ae),
+         (0x3fa3340e)},
+        (0xfff80000),
+        {0xc1,0x6e,0x8a,0x33,0x35,0x9f,0x07,0x03,0x76,0x11,
+         0xe5,0x21,0xc3,0xdb,0x18,0x40,0x05,0xa0,0xd6,0x4c,0x00}
+    },
+    {
+        /* No.107 delta:3386 weight:1851 */
+        11213,
+        48,
+        2,
+        6,
+        {(0x00000000),
+         (0x5199ee9c),
+         (0x0606c085),
+         (0x579f2e19),
+         (0xe08006b0),
+         (0xb119e82c),
+         (0xe686c635),
+         (0xb71f28a9),
+         (0x000086be),
+         (0x51996822),
+         (0x0606463b),
+         (0x579fa8a7),
+         (0xe080800e),
+         (0xb1196e92),
+         (0xe686408b),
+         (0xb71fae17)},
+        {(0x00000000),
+         (0x08250000),
+         (0x0a400800),
+         (0x02650800),
+         (0x10040000),
+         (0x18210000),
+         (0x1a440800),
+         (0x12610800),
+         (0x01081e00),
+         (0x092d1e00),
+         (0x0b481600),
+         (0x036d1600),
+         (0x110c1e00),
+         (0x19291e00),
+         (0x1b4c1600),
+         (0x13691600)},
+        {(0x3f800000),
+         (0x3f841280),
+         (0x3f852004),
+         (0x3f813284),
+         (0x3f880200),
+         (0x3f8c1080),
+         (0x3f8d2204),
+         (0x3f893084),
+         (0x3f80840f),
+         (0x3f84968f),
+         (0x3f85a40b),
+         (0x3f81b68b),
+         (0x3f88860f),
+         (0x3f8c948f),
+         (0x3f8da60b),
+         (0x3f89b48b)},
+        (0xfff80000),
+        {0x29,0x35,0xcb,0x9b,0x48,0x71,0x31,0x12,0x0b,0x2e,
+         0xd3,0x98,0x06,0x50,0x05,0x3b,0xac,0x89,0x1a,0xaf,0x00}
+    },
+    {
+        /* No.108 delta:2469 weight:1251 */
+        11213,
+        31,
+        7,
+        1,
+        {(0x00000000),
+         (0x4efca9a0),
+         (0x3a920750),
+         (0x746eaef0),
+         (0x958006ce),
+         (0xdb7caf6e),
+         (0xaf12019e),
+         (0xe1eea83e),
+         (0x0000ee3c),
+         (0x4efc479c),
+         (0x3a92e96c),
+         (0x746e40cc),
+         (0x9580e8f2),
+         (0xdb7c4152),
+         (0xaf12efa2),
+         (0xe1ee4602)},
+        {(0x00000000),
+         (0x405c0000),
+         (0x004ba200),
+         (0x4017a200),
+         (0x00600000),
+         (0x403c0000),
+         (0x002ba200),
+         (0x4077a200),
+         (0x80481e00),
+         (0xc0141e00),
+         (0x8003bc00),
+         (0xc05fbc00),
+         (0x80281e00),
+         (0xc0741e00),
+         (0x8063bc00),
+         (0xc03fbc00)},
+        {(0x3f800000),
+         (0x3fa02e00),
+         (0x3f8025d1),
+         (0x3fa00bd1),
+         (0x3f803000),
+         (0x3fa01e00),
+         (0x3f8015d1),
+         (0x3fa03bd1),
+         (0x3fc0240f),
+         (0x3fe00a0f),
+         (0x3fc001de),
+         (0x3fe02fde),
+         (0x3fc0140f),
+         (0x3fe03a0f),
+         (0x3fc031de),
+         (0x3fe01fde)},
+        (0xfff80000),
+        {0x05,0x56,0x79,0x8d,0xa1,0x79,0x94,0x9b,0xfd,0xfa,
+         0xd1,0xd9,0x42,0x04,0x4f,0x21,0x8e,0xdb,0xe7,0xa3,0x00}
+    },
+    {
+        /* No.109 delta:997 weight:681 */
+        11213,
+        71,
+        19,
+        5,
+        {(0x00000000),
+         (0x21e405bb),
+         (0x4f97981d),
+         (0x6e739da6),
+         (0x01d006d7),
+         (0x2034036c),
+         (0x4e479eca),
+         (0x6fa39b71),
+         (0x0000e6b0),
+         (0x21e4e30b),
+         (0x4f977ead),
+         (0x6e737b16),
+         (0x01d0e067),
+         (0x2034e5dc),
+         (0x4e47787a),
+         (0x6fa37dc1)},
+        {(0x00000000),
+         (0x40267000),
+         (0x0682d000),
+         (0x46a4a000),
+         (0x11832000),
+         (0x51a55000),
+         (0x1701f000),
+         (0x57278000),
+         (0x40581e00),
+         (0x007e6e00),
+         (0x46dace00),
+         (0x06fcbe00),
+         (0x51db3e00),
+         (0x11fd4e00),
+         (0x5759ee00),
+         (0x177f9e00)},
+        {(0x3f800000),
+         (0x3fa01338),
+         (0x3f834168),
+         (0x3fa35250),
+         (0x3f88c190),
+         (0x3fa8d2a8),
+         (0x3f8b80f8),
+         (0x3fab93c0),
+         (0x3fa02c0f),
+         (0x3f803f37),
+         (0x3fa36d67),
+         (0x3f837e5f),
+         (0x3fa8ed9f),
+         (0x3f88fea7),
+         (0x3fabacf7),
+         (0x3f8bbfcf)},
+        (0xfff80000),
+        {0x33,0x76,0x14,0x81,0x56,0x54,0xf0,0x4f,0x12,0x90,
+         0xf4,0x03,0x95,0x9c,0x1a,0x38,0x51,0x80,0x32,0xaa,0x00}
+    },
+    {
+        /* No.110 delta:2599 weight:829 */
+        11213,
+        8,
+        11,
+        14,
+        {(0x00000000),
+         (0x4654c25e),
+         (0xa8eaa1d6),
+         (0xeebe6388),
+         (0x52a006e9),
+         (0x14f4c4b7),
+         (0xfa4aa73f),
+         (0xbc1e6561),
+         (0x000019ff),
+         (0x4654dba1),
+         (0xa8eab829),
+         (0xeebe7a77),
+         (0x52a01f16),
+         (0x14f4dd48),
+         (0xfa4abec0),
+         (0xbc1e7c9e)},
+        {(0x00000000),
+         (0xfe272000),
+         (0x8207f000),
+         (0x7c20d000),
+         (0x4e344800),
+         (0xb0136800),
+         (0xcc33b800),
+         (0x32149800),
+         (0x01611e00),
+         (0xff463e00),
+         (0x8366ee00),
+         (0x7d41ce00),
+         (0x4f555600),
+         (0xb1727600),
+         (0xcd52a600),
+         (0x33758600)},
+        {(0x3f800000),
+         (0x3fff1390),
+         (0x3fc103f8),
+         (0x3fbe1068),
+         (0x3fa71a24),
+         (0x3fd809b4),
+         (0x3fe619dc),
+         (0x3f990a4c),
+         (0x3f80b08f),
+         (0x3fffa31f),
+         (0x3fc1b377),
+         (0x3fbea0e7),
+         (0x3fa7aaab),
+         (0x3fd8b93b),
+         (0x3fe6a953),
+         (0x3f99bac3)},
+        (0xfff80000),
+        {0xe0,0x6d,0x93,0xcf,0x6d,0x0f,0xa4,0xf7,0x93,0x3e,
+         0x67,0xb4,0x20,0xb8,0x51,0x3a,0xe3,0xcd,0xf5,0xf9,0x00}
+    },
+    {
+        /* No.111 delta:2048 weight:1649 */
+        11213,
+        12,
+        8,
+        4,
+        {(0x00000000),
+         (0xbf40afc5),
+         (0xdef9c9b1),
+         (0x61b96674),
+         (0xfee006f8),
+         (0x41a0a93d),
+         (0x2019cf49),
+         (0x9f59608c),
+         (0x000056c5),
+         (0xbf40f900),
+         (0xdef99f74),
+         (0x61b930b1),
+         (0xfee0503d),
+         (0x41a0fff8),
+         (0x2019998c),
+         (0x9f593649)},
+        {(0x00000000),
+         (0x2fa10c00),
+         (0x020b0000),
+         (0x2daa0c00),
+         (0x020ae000),
+         (0x2dabec00),
+         (0x0001e000),
+         (0x2fa0ec00),
+         (0x28021e00),
+         (0x07a31200),
+         (0x2a091e00),
+         (0x05a81200),
+         (0x2a08fe00),
+         (0x05a9f200),
+         (0x2803fe00),
+         (0x07a2f200)},
+        {(0x3f800000),
+         (0x3f97d086),
+         (0x3f810580),
+         (0x3f96d506),
+         (0x3f810570),
+         (0x3f96d5f6),
+         (0x3f8000f0),
+         (0x3f97d076),
+         (0x3f94010f),
+         (0x3f83d189),
+         (0x3f95048f),
+         (0x3f82d409),
+         (0x3f95047f),
+         (0x3f82d4f9),
+         (0x3f9401ff),
+         (0x3f83d179)},
+        (0xfff80000),
+        {0x4a,0x15,0xe8,0xf1,0x59,0x83,0x8e,0x52,0x8a,0x24,
+         0xa4,0xb5,0x5d,0xfb,0x87,0xe0,0x22,0xe3,0x18,0x37,0x00}
+    },
+    {
+        /* No.112 delta:2152 weight:1369 */
+        11213,
+        81,
+        6,
+        10,
+        {(0x00000000),
+         (0xfbea0aeb),
+         (0xba6ad14d),
+         (0x4180dba6),
+         (0x21e00705),
+         (0xda0a0dee),
+         (0x9b8ad648),
+         (0x6060dca3),
+         (0x00003510),
+         (0xfbea3ffb),
+         (0xba6ae45d),
+         (0x4180eeb6),
+         (0x21e03215),
+         (0xda0a38fe),
+         (0x9b8ae358),
+         (0x6060e9b3)},
+        {(0x00000000),
+         (0x01428000),
+         (0x04604000),
+         (0x0522c000),
+         (0x00081000),
+         (0x014a9000),
+         (0x04685000),
+         (0x052ad000),
+         (0x20261e00),
+         (0x21649e00),
+         (0x24465e00),
+         (0x2504de00),
+         (0x202e0e00),
+         (0x216c8e00),
+         (0x244e4e00),
+         (0x250cce00)},
+        {(0x3f800000),
+         (0x3f80a140),
+         (0x3f823020),
+         (0x3f829160),
+         (0x3f800408),
+         (0x3f80a548),
+         (0x3f823428),
+         (0x3f829568),
+         (0x3f90130f),
+         (0x3f90b24f),
+         (0x3f92232f),
+         (0x3f92826f),
+         (0x3f901707),
+         (0x3f90b647),
+         (0x3f922727),
+         (0x3f928667)},
+        (0xfff80000),
+        {0xfd,0x72,0xaa,0xc7,0x20,0x44,0x28,0xe5,0x03,0x3e,
+         0xf9,0x6d,0xad,0x9f,0x51,0x0c,0x87,0xf6,0x43,0x0f,0x00}
+    },
+    {
+        /* No.113 delta:1871 weight:1215 */
+        11213,
+        61,
+        14,
+        10,
+        {(0x00000000),
+         (0x11856d8d),
+         (0xaac84415),
+         (0xbb4d2998),
+         (0xae00071a),
+         (0xbf856a97),
+         (0x04c8430f),
+         (0x154d2e82),
+         (0x0000cc65),
+         (0x1185a1e8),
+         (0xaac88870),
+         (0xbb4de5fd),
+         (0xae00cb7f),
+         (0xbf85a6f2),
+         (0x04c88f6a),
+         (0x154de2e7)},
+        {(0x00000000),
+         (0x006d0000),
+         (0x00b10000),
+         (0x00dc0000),
+         (0x04490000),
+         (0x04240000),
+         (0x04f80000),
+         (0x04950000),
+         (0x00421e00),
+         (0x002f1e00),
+         (0x00f31e00),
+         (0x009e1e00),
+         (0x040b1e00),
+         (0x04661e00),
+         (0x04ba1e00),
+         (0x04d71e00)},
+        {(0x3f800000),
+         (0x3f803680),
+         (0x3f805880),
+         (0x3f806e00),
+         (0x3f822480),
+         (0x3f821200),
+         (0x3f827c00),
+         (0x3f824a80),
+         (0x3f80210f),
+         (0x3f80178f),
+         (0x3f80798f),
+         (0x3f804f0f),
+         (0x3f82058f),
+         (0x3f82330f),
+         (0x3f825d0f),
+         (0x3f826b8f)},
+        (0xfff80000),
+        {0x98,0xdb,0x7c,0xec,0x90,0x27,0x75,0x69,0x6c,0x3d,
+         0x4c,0xec,0xa2,0x8e,0xab,0xef,0xd1,0x0c,0xbd,0x8c,0x00}
+    },
+    {
+        /* No.114 delta:1950 weight:1011 */
+        11213,
+        20,
+        16,
+        13,
+        {(0x00000000),
+         (0xbdc99be3),
+         (0xaafee229),
+         (0x173779ca),
+         (0x7300072e),
+         (0xcec99ccd),
+         (0xd9fee507),
+         (0x64377ee4),
+         (0x0000b504),
+         (0xbdc92ee7),
+         (0xaafe572d),
+         (0x1737ccce),
+         (0x7300b22a),
+         (0xcec929c9),
+         (0xd9fe5003),
+         (0x6437cbe0)},
+        {(0x00000000),
+         (0x016ec000),
+         (0x8ce40000),
+         (0x8d8ac000),
+         (0x1c548000),
+         (0x1d3a4000),
+         (0x90b08000),
+         (0x91de4000),
+         (0x00869e00),
+         (0x01e85e00),
+         (0x8c629e00),
+         (0x8d0c5e00),
+         (0x1cd21e00),
+         (0x1dbcde00),
+         (0x90361e00),
+         (0x9158de00)},
+        {(0x3f800000),
+         (0x3f80b760),
+         (0x3fc67200),
+         (0x3fc6c560),
+         (0x3f8e2a40),
+         (0x3f8e9d20),
+         (0x3fc85840),
+         (0x3fc8ef20),
+         (0x3f80434f),
+         (0x3f80f42f),
+         (0x3fc6314f),
+         (0x3fc6862f),
+         (0x3f8e690f),
+         (0x3f8ede6f),
+         (0x3fc81b0f),
+         (0x3fc8ac6f)},
+        (0xfff80000),
+        {0xa7,0x22,0x09,0x2a,0x46,0xe2,0xa3,0x13,0x26,0x8f,
+         0xa5,0xc6,0x28,0x12,0x57,0xb4,0x27,0x86,0xf1,0x1d,0x00}
+    },
+    {
+        /* No.115 delta:1732 weight:1445 */
+        11213,
+        29,
+        17,
+        7,
+        {(0x00000000),
+         (0xdb25076c),
+         (0x9bf7e9d5),
+         (0x40d2eeb9),
+         (0x95f00737),
+         (0x4ed5005b),
+         (0x0e07eee2),
+         (0xd522e98e),
+         (0x0000f5f0),
+         (0xdb25f29c),
+         (0x9bf71c25),
+         (0x40d21b49),
+         (0x95f0f2c7),
+         (0x4ed5f5ab),
+         (0x0e071b12),
+         (0xd5221c7e)},
+        {(0x00000000),
+         (0x481e0000),
+         (0x80040000),
+         (0xc81a0000),
+         (0x007d0000),
+         (0x48630000),
+         (0x80790000),
+         (0xc8670000),
+         (0x40121e00),
+         (0x080c1e00),
+         (0xc0161e00),
+         (0x88081e00),
+         (0x406f1e00),
+         (0x08711e00),
+         (0xc06b1e00),
+         (0x88751e00)},
+        {(0x3f800000),
+         (0x3fa40f00),
+         (0x3fc00200),
+         (0x3fe40d00),
+         (0x3f803e80),
+         (0x3fa43180),
+         (0x3fc03c80),
+         (0x3fe43380),
+         (0x3fa0090f),
+         (0x3f84060f),
+         (0x3fe00b0f),
+         (0x3fc4040f),
+         (0x3fa0378f),
+         (0x3f84388f),
+         (0x3fe0358f),
+         (0x3fc43a8f)},
+        (0xfff80000),
+        {0xc2,0xbe,0xe6,0x5f,0xf5,0x28,0x34,0x1c,0x5e,0x2d,
+         0xdb,0x99,0x36,0x5d,0x04,0x52,0x9e,0x76,0x9e,0xd5,0x00}
+    },
+    {
+        /* No.116 delta:1707 weight:1245 */
+        11213,
+        30,
+        22,
+        3,
+        {(0x00000000),
+         (0x0a5f3de8),
+         (0xcb7a16d6),
+         (0xc1252b3e),
+         (0xfcd0074b),
+         (0xf68f3aa3),
+         (0x37aa119d),
+         (0x3df52c75),
+         (0x00003bff),
+         (0x0a5f0617),
+         (0xcb7a2d29),
+         (0xc12510c1),
+         (0xfcd03cb4),
+         (0xf68f015c),
+         (0x37aa2a62),
+         (0x3df5178a)},
+        {(0x00000000),
+         (0x402e8e00),
+         (0x0073d000),
+         (0x405d5e00),
+         (0x206d4000),
+         (0x6043ce00),
+         (0x201e9000),
+         (0x60301e00),
+         (0xd21dfe00),
+         (0x92337000),
+         (0xd26e2e00),
+         (0x9240a000),
+         (0xf270be00),
+         (0xb25e3000),
+         (0xf2036e00),
+         (0xb22de000)},
+        {(0x3f800000),
+         (0x3fa01747),
+         (0x3f8039e8),
+         (0x3fa02eaf),
+         (0x3f9036a0),
+         (0x3fb021e7),
+         (0x3f900f48),
+         (0x3fb0180f),
+         (0x3fe90eff),
+         (0x3fc919b8),
+         (0x3fe93717),
+         (0x3fc92050),
+         (0x3ff9385f),
+         (0x3fd92f18),
+         (0x3ff901b7),
+         (0x3fd916f0)},
+        (0xfff80000),
+        {0x30,0x04,0x85,0xba,0x89,0x2d,0x19,0x39,0x57,0x1c,
+         0x54,0xa8,0xb5,0x0a,0x05,0x27,0x14,0x6a,0xed,0xe0,0x00}
+    },
+    {
+        /* No.117 delta:2917 weight:1743 */
+        11213,
+        37,
+        3,
+        6,
+        {(0x00000000),
+         (0x501b23d2),
+         (0x2770f82a),
+         (0x776bdbf8),
+         (0x67300751),
+         (0x372b2483),
+         (0x4040ff7b),
+         (0x105bdca9),
+         (0x000038d9),
+         (0x501b1b0b),
+         (0x2770c0f3),
+         (0x776be321),
+         (0x67303f88),
+         (0x372b1c5a),
+         (0x4040c7a2),
+         (0x105be470)},
+        {(0x00000000),
+         (0x20542200),
+         (0x40387800),
+         (0x606c5a00),
+         (0x14402000),
+         (0x34140200),
+         (0x54785800),
+         (0x742c7a00),
+         (0x1840de00),
+         (0x3814fc00),
+         (0x5878a600),
+         (0x782c8400),
+         (0x0c00fe00),
+         (0x2c54dc00),
+         (0x4c388600),
+         (0x6c6ca400)},
+        {(0x3f800000),
+         (0x3f902a11),
+         (0x3fa01c3c),
+         (0x3fb0362d),
+         (0x3f8a2010),
+         (0x3f9a0a01),
+         (0x3faa3c2c),
+         (0x3fba163d),
+         (0x3f8c206f),
+         (0x3f9c0a7e),
+         (0x3fac3c53),
+         (0x3fbc1642),
+         (0x3f86007f),
+         (0x3f962a6e),
+         (0x3fa61c43),
+         (0x3fb63652)},
+        (0xfff80000),
+        {0x86,0x60,0xf6,0x24,0x0e,0xff,0xd7,0xe9,0x32,0x8f,
+         0xa0,0x96,0x77,0x67,0x0e,0xec,0x2e,0x00,0x3b,0x13,0x00}
+    },
+    {
+        /* No.118 delta:983 weight:1587 */
+        11213,
+        62,
+        19,
+        4,
+        {(0x00000000),
+         (0x08eb784a),
+         (0x736759ff),
+         (0x7b8c21b5),
+         (0x0ca0076b),
+         (0x044b7f21),
+         (0x7fc75e94),
+         (0x772c26de),
+         (0x0000d939),
+         (0x08eba173),
+         (0x736780c6),
+         (0x7b8cf88c),
+         (0x0ca0de52),
+         (0x044ba618),
+         (0x7fc787ad),
+         (0x772cffe7)},
+        {(0x00000000),
+         (0x041eba00),
+         (0x20637000),
+         (0x247dca00),
+         (0x20110000),
+         (0x240fba00),
+         (0x00727000),
+         (0x046cca00),
+         (0x10001e00),
+         (0x141ea400),
+         (0x30636e00),
+         (0x347dd400),
+         (0x30111e00),
+         (0x340fa400),
+         (0x10726e00),
+         (0x146cd400)},
+        {(0x3f800000),
+         (0x3f820f5d),
+         (0x3f9031b8),
+         (0x3f923ee5),
+         (0x3f900880),
+         (0x3f9207dd),
+         (0x3f803938),
+         (0x3f823665),
+         (0x3f88000f),
+         (0x3f8a0f52),
+         (0x3f9831b7),
+         (0x3f9a3eea),
+         (0x3f98088f),
+         (0x3f9a07d2),
+         (0x3f883937),
+         (0x3f8a366a)},
+        (0xfff80000),
+        {0x70,0x7f,0x78,0xb3,0x65,0x02,0xe0,0xb9,0x55,0x49,
+         0xd9,0x60,0xa8,0xa5,0xd3,0x10,0x3b,0x08,0x55,0xfd,0x00}
+    },
+    {
+        /* No.119 delta:1397 weight:1065 */
+        11213,
+        57,
+        14,
+        11,
+        {(0x00000000),
+         (0x5e421ddc),
+         (0x638787c4),
+         (0x3dc59a18),
+         (0x8e00077d),
+         (0xd0421aa1),
+         (0xed8780b9),
+         (0xb3c59d65),
+         (0x0000840f),
+         (0x5e4299d3),
+         (0x638703cb),
+         (0x3dc51e17),
+         (0x8e008372),
+         (0xd0429eae),
+         (0xed8704b6),
+         (0xb3c5196a)},
+        {(0x00000000),
+         (0x08e5ec00),
+         (0x245ea400),
+         (0x2cbb4800),
+         (0x00688000),
+         (0x088d6c00),
+         (0x24362400),
+         (0x2cd3c800),
+         (0x00111e00),
+         (0x08f4f200),
+         (0x244fba00),
+         (0x2caa5600),
+         (0x00799e00),
+         (0x089c7200),
+         (0x24273a00),
+         (0x2cc2d600)},
+        {(0x3f800000),
+         (0x3f8472f6),
+         (0x3f922f52),
+         (0x3f965da4),
+         (0x3f803440),
+         (0x3f8446b6),
+         (0x3f921b12),
+         (0x3f9669e4),
+         (0x3f80088f),
+         (0x3f847a79),
+         (0x3f9227dd),
+         (0x3f96552b),
+         (0x3f803ccf),
+         (0x3f844e39),
+         (0x3f92139d),
+         (0x3f96616b)},
+        (0xfff80000),
+        {0x72,0xeb,0x0e,0x84,0xe7,0xef,0x0c,0xb3,0x72,0xaf,
+         0x80,0x06,0xbc,0xf9,0x33,0xf7,0x21,0x72,0x27,0x28,0x00}
+    },
+    {
+        /* No.120 delta:1392 weight:827 */
+        11213,
+        46,
+        15,
+        17,
+        {(0x00000000),
+         (0xa557bf77),
+         (0x097f4c89),
+         (0xac28f3fe),
+         (0xffa0078b),
+         (0x5af7b8fc),
+         (0xf6df4b02),
+         (0x5388f475),
+         (0x00006249),
+         (0xa557dd3e),
+         (0x097f2ec0),
+         (0xac2891b7),
+         (0xffa065c2),
+         (0x5af7dab5),
+         (0xf6df294b),
+         (0x5388963c)},
+        {(0x00000000),
+         (0x68748400),
+         (0x4200ae00),
+         (0x2a742a00),
+         (0x1c086000),
+         (0x747ce400),
+         (0x5e08ce00),
+         (0x367c4a00),
+         (0x0046fe00),
+         (0x68327a00),
+         (0x42465000),
+         (0x2a32d400),
+         (0x1c4e9e00),
+         (0x743a1a00),
+         (0x5e4e3000),
+         (0x363ab400)},
+        {(0x3f800000),
+         (0x3fb43a42),
+         (0x3fa10057),
+         (0x3f953a15),
+         (0x3f8e0430),
+         (0x3fba3e72),
+         (0x3faf0467),
+         (0x3f9b3e25),
+         (0x3f80237f),
+         (0x3fb4193d),
+         (0x3fa12328),
+         (0x3f95196a),
+         (0x3f8e274f),
+         (0x3fba1d0d),
+         (0x3faf2718),
+         (0x3f9b1d5a)},
+        (0xfff80000),
+        {0x7c,0xb2,0x6a,0xff,0x78,0xfa,0xd6,0x78,0xf9,0xba,
+         0x77,0x1d,0x84,0xb3,0xbb,0xcb,0xac,0xdf,0x51,0x54,0x00}
+    },
+    {
+        /* No.121 delta:1663 weight:775 */
+        11213,
+        82,
+        9,
+        17,
+        {(0x00000000),
+         (0x1fe0e7a6),
+         (0x67406a7f),
+         (0x78a08dd9),
+         (0xda30079d),
+         (0xc5d0e03b),
+         (0xbd706de2),
+         (0xa2908a44),
+         (0x000076e4),
+         (0x1fe09142),
+         (0x67401c9b),
+         (0x78a0fb3d),
+         (0xda307179),
+         (0xc5d096df),
+         (0xbd701b06),
+         (0xa290fca0)},
+        {(0x00000000),
+         (0x100e1000),
+         (0x20734000),
+         (0x307d5000),
+         (0x5008e200),
+         (0x4006f200),
+         (0x707ba200),
+         (0x6075b200),
+         (0x3242de00),
+         (0x224cce00),
+         (0x12319e00),
+         (0x023f8e00),
+         (0x624a3c00),
+         (0x72442c00),
+         (0x42397c00),
+         (0x52376c00)},
+        {(0x3f800000),
+         (0x3f880708),
+         (0x3f9039a0),
+         (0x3f983ea8),
+         (0x3fa80471),
+         (0x3fa00379),
+         (0x3fb83dd1),
+         (0x3fb03ad9),
+         (0x3f99216f),
+         (0x3f912667),
+         (0x3f8918cf),
+         (0x3f811fc7),
+         (0x3fb1251e),
+         (0x3fb92216),
+         (0x3fa11cbe),
+         (0x3fa91bb6)},
+        (0xfff80000),
+        {0xa3,0xd8,0x55,0xcd,0x98,0xdc,0x68,0x95,0x68,0xdf,
+         0xcf,0x56,0x03,0x2a,0x94,0x75,0x10,0xec,0x0a,0x47,0x00}
+    },
+    {
+        /* No.122 delta:1328 weight:751 */
+        11213,
+        67,
+        14,
+        18,
+        {(0x00000000),
+         (0x76bc3aa8),
+         (0xc37bdc20),
+         (0xb5c7e688),
+         (0x38b007ab),
+         (0x4e0c3d03),
+         (0xfbcbdb8b),
+         (0x8d77e123),
+         (0x0000bd02),
+         (0x76bc87aa),
+         (0xc37b6122),
+         (0xb5c75b8a),
+         (0x38b0baa9),
+         (0x4e0c8001),
+         (0xfbcb6689),
+         (0x8d775c21)},
+        {(0x00000000),
+         (0x1112c200),
+         (0x18038000),
+         (0x09114200),
+         (0x00065000),
+         (0x11149200),
+         (0x1805d000),
+         (0x09171200),
+         (0x6001de00),
+         (0x71131c00),
+         (0x78025e00),
+         (0x69109c00),
+         (0x60078e00),
+         (0x71154c00),
+         (0x78040e00),
+         (0x6916cc00)},
+        {(0x3f800000),
+         (0x3f888961),
+         (0x3f8c01c0),
+         (0x3f8488a1),
+         (0x3f800328),
+         (0x3f888a49),
+         (0x3f8c02e8),
+         (0x3f848b89),
+         (0x3fb000ef),
+         (0x3fb8898e),
+         (0x3fbc012f),
+         (0x3fb4884e),
+         (0x3fb003c7),
+         (0x3fb88aa6),
+         (0x3fbc0207),
+         (0x3fb48b66)},
+        (0xfff80000),
+        {0x18,0x30,0x32,0x35,0xe2,0xb4,0xcd,0x94,0x5e,0x6b,
+         0xe6,0x78,0x0b,0x92,0x24,0xbf,0x7c,0xc5,0xbd,0xe2,0x00}
+    },
+    {
+        /* No.123 delta:1657 weight:573 */
+        11213,
+        70,
+        8,
+        19,
+        {(0x00000000),
+         (0xfd59bd60),
+         (0xe0041fb5),
+         (0x1d5da2d5),
+         (0x836007b6),
+         (0x7e39bad6),
+         (0x63641803),
+         (0x9e3da563),
+         (0x0000b1f6),
+         (0xfd590c96),
+         (0xe004ae43),
+         (0x1d5d1323),
+         (0x8360b640),
+         (0x7e390b20),
+         (0x6364a9f5),
+         (0x9e3d1495)},
+        {(0x00000000),
+         (0x57b14800),
+         (0x5868a400),
+         (0x0fd9ec00),
+         (0x084c7000),
+         (0x5ffd3800),
+         (0x5024d400),
+         (0x07959c00),
+         (0x10121e00),
+         (0x47a35600),
+         (0x487aba00),
+         (0x1fcbf200),
+         (0x185e6e00),
+         (0x4fef2600),
+         (0x4036ca00),
+         (0x17878200)},
+        {(0x3f800000),
+         (0x3fabd8a4),
+         (0x3fac3452),
+         (0x3f87ecf6),
+         (0x3f842638),
+         (0x3faffe9c),
+         (0x3fa8126a),
+         (0x3f83cace),
+         (0x3f88090f),
+         (0x3fa3d1ab),
+         (0x3fa43d5d),
+         (0x3f8fe5f9),
+         (0x3f8c2f37),
+         (0x3fa7f793),
+         (0x3fa01b65),
+         (0x3f8bc3c1)},
+        (0xfff80000),
+        {0x0b,0xcb,0xeb,0x33,0x75,0x01,0x5e,0xeb,0x92,0xfb,
+         0x37,0xae,0xe0,0x31,0x80,0xa9,0x06,0x42,0x2b,0xd5,0x00}
+    },
+    {
+        /* No.124 delta:1016 weight:1635 */
+        11213,
+        80,
+        18,
+        2,
+        {(0x00000000),
+         (0x65b814ab),
+         (0x0b7c8e4e),
+         (0x6ec49ae5),
+         (0xe3e007c1),
+         (0x8658136a),
+         (0xe89c898f),
+         (0x8d249d24),
+         (0x00008bf0),
+         (0x65b89f5b),
+         (0x0b7c05be),
+         (0x6ec41115),
+         (0xe3e08c31),
+         (0x8658989a),
+         (0xe89c027f),
+         (0x8d2416d4)},
+        {(0x00000000),
+         (0x110f9000),
+         (0x08401000),
+         (0x194f8000),
+         (0x20040000),
+         (0x310b9000),
+         (0x28441000),
+         (0x394b8000),
+         (0x00025e00),
+         (0x110dce00),
+         (0x08424e00),
+         (0x194dde00),
+         (0x20065e00),
+         (0x3109ce00),
+         (0x28464e00),
+         (0x3949de00)},
+        {(0x3f800000),
+         (0x3f8887c8),
+         (0x3f842008),
+         (0x3f8ca7c0),
+         (0x3f900200),
+         (0x3f9885c8),
+         (0x3f942208),
+         (0x3f9ca5c0),
+         (0x3f80012f),
+         (0x3f8886e7),
+         (0x3f842127),
+         (0x3f8ca6ef),
+         (0x3f90032f),
+         (0x3f9884e7),
+         (0x3f942327),
+         (0x3f9ca4ef)},
+        (0xfff80000),
+        {0xe8,0x1c,0x9d,0xcc,0x4a,0x97,0xc0,0xae,0x9d,0xce,
+         0x1a,0xa4,0x45,0x9f,0x41,0xc4,0x83,0xd9,0x62,0x72,0x00}
+    },
+    {
+        /* No.125 delta:1214 weight:1175 */
+        11213,
+        28,
+        16,
+        7,
+        {(0x00000000),
+         (0x7b11b46d),
+         (0x3e529f7c),
+         (0x45432b11),
+         (0x05a007d2),
+         (0x7eb1b3bf),
+         (0x3bf298ae),
+         (0x40e32cc3),
+         (0x00001cdb),
+         (0x7b11a8b6),
+         (0x3e5283a7),
+         (0x454337ca),
+         (0x05a01b09),
+         (0x7eb1af64),
+         (0x3bf28475),
+         (0x40e33018)},
+        {(0x00000000),
+         (0x4460cc00),
+         (0x50de6c00),
+         (0x14bea000),
+         (0x43011e00),
+         (0x0761d200),
+         (0x13df7200),
+         (0x57bfbe00),
+         (0x41807e00),
+         (0x05e0b200),
+         (0x115e1200),
+         (0x553ede00),
+         (0x02816000),
+         (0x46e1ac00),
+         (0x525f0c00),
+         (0x163fc000)},
+        {(0x3f800000),
+         (0x3fa23066),
+         (0x3fa86f36),
+         (0x3f8a5f50),
+         (0x3fa1808f),
+         (0x3f83b0e9),
+         (0x3f89efb9),
+         (0x3fabdfdf),
+         (0x3fa0c03f),
+         (0x3f82f059),
+         (0x3f88af09),
+         (0x3faa9f6f),
+         (0x3f8140b0),
+         (0x3fa370d6),
+         (0x3fa92f86),
+         (0x3f8b1fe0)},
+        (0xfff80000),
+        {0x5c,0x4c,0x7f,0xad,0xc2,0x0a,0xe3,0x36,0x3a,0x3f,
+         0x3e,0x28,0x97,0x26,0xce,0xab,0xdb,0x12,0x07,0x20,0x00}
+    },
+    {
+        /* No.126 delta:962 weight:1245 */
+        11213,
+        78,
+        18,
+        5,
+        {(0x00000000),
+         (0xee0a807e),
+         (0xe937683b),
+         (0x073de845),
+         (0x14f007e9),
+         (0xfafa8797),
+         (0xfdc76fd2),
+         (0x13cdefac),
+         (0x0000f676),
+         (0xee0a7608),
+         (0xe9379e4d),
+         (0x073d1e33),
+         (0x14f0f19f),
+         (0xfafa71e1),
+         (0xfdc799a4),
+         (0x13cd19da)},
+        {(0x00000000),
+         (0xb028bc00),
+         (0x08652400),
+         (0xb84d9800),
+         (0x14028600),
+         (0xa42a3a00),
+         (0x1c67a200),
+         (0xac4f1e00),
+         (0x00023e00),
+         (0xb02a8200),
+         (0x08671a00),
+         (0xb84fa600),
+         (0x1400b800),
+         (0xa4280400),
+         (0x1c659c00),
+         (0xac4d2000)},
+        {(0x3f800000),
+         (0x3fd8145e),
+         (0x3f843292),
+         (0x3fdc26cc),
+         (0x3f8a0143),
+         (0x3fd2151d),
+         (0x3f8e33d1),
+         (0x3fd6278f),
+         (0x3f80011f),
+         (0x3fd81541),
+         (0x3f84338d),
+         (0x3fdc27d3),
+         (0x3f8a005c),
+         (0x3fd21402),
+         (0x3f8e32ce),
+         (0x3fd62690)},
+        (0xfff80000),
+        {0x1f,0xae,0x0b,0x7d,0x23,0x0c,0x6e,0xd6,0x26,0xa9,
+         0x92,0xbc,0x8f,0x01,0x36,0x36,0x07,0xe9,0x2c,0x7c,0x00}
+    },
+    {
+        /* No.127 delta:4656 weight:1185 */
+        11213,
+        57,
+        4,
+        13,
+        {(0x00000000),
+         (0x7d46d3c2),
+         (0x107426fc),
+         (0x6d32f53e),
+         (0x832007fe),
+         (0xfe66d43c),
+         (0x93542102),
+         (0xee12f2c0),
+         (0x00009ac7),
+         (0x7d464905),
+         (0x1074bc3b),
+         (0x6d326ff9),
+         (0x83209d39),
+         (0xfe664efb),
+         (0x9354bbc5),
+         (0xee126807)},
+        {(0x00000000),
+         (0x18401000),
+         (0x14000000),
+         (0x0c401000),
+         (0x00000000),
+         (0x18401000),
+         (0x14000000),
+         (0x0c401000),
+         (0x10001e00),
+         (0x08400e00),
+         (0x04001e00),
+         (0x1c400e00),
+         (0x10001e00),
+         (0x08400e00),
+         (0x04001e00),
+         (0x1c400e00)},
+        {(0x3f800000),
+         (0x3f8c2008),
+         (0x3f8a0000),
+         (0x3f862008),
+         (0x3f800000),
+         (0x3f8c2008),
+         (0x3f8a0000),
+         (0x3f862008),
+         (0x3f88000f),
+         (0x3f842007),
+         (0x3f82000f),
+         (0x3f8e2007),
+         (0x3f88000f),
+         (0x3f842007),
+         (0x3f82000f),
+         (0x3f8e2007)},
+        (0xfff80000),
+        {0x2b,0xf5,0xba,0x2c,0x06,0x65,0xd9,0xae,0x19,0x02,
+         0xa4,0xb4,0xe6,0x92,0x03,0x77,0xb7,0xb8,0x8c,0x23,0x00}
+    },
+    {
+        /* No.128 delta:2214 weight:1371 */
+        11213,
+        43,
+        23,
+        2,
+        {(0x00000000),
+         (0xe454d1ab),
+         (0x80c8a6d9),
+         (0x649c7772),
+         (0xb0f00808),
+         (0x54a4d9a3),
+         (0x3038aed1),
+         (0xd46c7f7a),
+         (0x00004f07),
+         (0xe4549eac),
+         (0x80c8e9de),
+         (0x649c3875),
+         (0xb0f0470f),
+         (0x54a496a4),
+         (0x3038e1d6),
+         (0xd46c307d)},
+        {(0x00000000),
+         (0xca494000),
+         (0x00538000),
+         (0xca1ac000),
+         (0x10bd2000),
+         (0xdaf46000),
+         (0x10eea000),
+         (0xdaa7e000),
+         (0x18905e00),
+         (0xd2d91e00),
+         (0x18c3de00),
+         (0xd28a9e00),
+         (0x082d7e00),
+         (0xc2643e00),
+         (0x087efe00),
+         (0xc237be00)},
+        {(0x3f800000),
+         (0x3fe524a0),
+         (0x3f8029c0),
+         (0x3fe50d60),
+         (0x3f885e90),
+         (0x3fed7a30),
+         (0x3f887750),
+         (0x3fed53f0),
+         (0x3f8c482f),
+         (0x3fe96c8f),
+         (0x3f8c61ef),
+         (0x3fe9454f),
+         (0x3f8416bf),
+         (0x3fe1321f),
+         (0x3f843f7f),
+         (0x3fe11bdf)},
+        (0xfff80000),
+        {0xf5,0xb9,0x08,0x2c,0x6d,0x40,0xb0,0x38,0xb5,0x70,
+         0x83,0x86,0xbb,0x54,0xe7,0x5e,0x64,0xf3,0x40,0x61,0x00}
+    },
+    {
+        /* No.129 delta:886 weight:915 */
+        11213,
+        70,
+        12,
+        5,
+        {(0x00000000),
+         (0x242c5090),
+         (0x10199a60),
+         (0x3435caf0),
+         (0xe9d00810),
+         (0xcdfc5880),
+         (0xf9c99270),
+         (0xdde5c2e0),
+         (0x00003567),
+         (0x242c65f7),
+         (0x1019af07),
+         (0x3435ff97),
+         (0xe9d03d77),
+         (0xcdfc6de7),
+         (0xf9c9a717),
+         (0xdde5f787)},
+        {(0x00000000),
+         (0x004e4000),
+         (0x1009c000),
+         (0x10478000),
+         (0x0e025000),
+         (0x0e4c1000),
+         (0x1e0b9000),
+         (0x1e45d000),
+         (0x10345e00),
+         (0x107a1e00),
+         (0x003d9e00),
+         (0x0073de00),
+         (0x1e360e00),
+         (0x1e784e00),
+         (0x0e3fce00),
+         (0x0e718e00)},
+        {(0x3f800000),
+         (0x3f802720),
+         (0x3f8804e0),
+         (0x3f8823c0),
+         (0x3f870128),
+         (0x3f872608),
+         (0x3f8f05c8),
+         (0x3f8f22e8),
+         (0x3f881a2f),
+         (0x3f883d0f),
+         (0x3f801ecf),
+         (0x3f8039ef),
+         (0x3f8f1b07),
+         (0x3f8f3c27),
+         (0x3f871fe7),
+         (0x3f8738c7)},
+        (0xfff80000),
+        {0xae,0x65,0xce,0x51,0xe8,0x63,0xb5,0x45,0x7a,0xb5,
+         0x06,0x3b,0x06,0x88,0x6c,0x62,0x83,0x37,0xc8,0xb9,0x00}
+    },
+    {
+        /* No.130 delta:2602 weight:1519 */
+        11213,
+        18,
+        4,
+        6,
+        {(0x00000000),
+         (0x4fa484d0),
+         (0x546a6e84),
+         (0x1bceea54),
+         (0x4a400828),
+         (0x05e48cf8),
+         (0x1e2a66ac),
+         (0x518ee27c),
+         (0x0000bda0),
+         (0x4fa43970),
+         (0x546ad324),
+         (0x1bce57f4),
+         (0x4a40b588),
+         (0x05e43158),
+         (0x1e2adb0c),
+         (0x518e5fdc)},
+        {(0x00000000),
+         (0x13081400),
+         (0x928e3000),
+         (0x81862400),
+         (0x010e0000),
+         (0x12061400),
+         (0x93803000),
+         (0x80882400),
+         (0x10581e00),
+         (0x03500a00),
+         (0x82d62e00),
+         (0x91de3a00),
+         (0x11561e00),
+         (0x025e0a00),
+         (0x83d82e00),
+         (0x90d03a00)},
+        {(0x3f800000),
+         (0x3f89840a),
+         (0x3fc94718),
+         (0x3fc0c312),
+         (0x3f808700),
+         (0x3f89030a),
+         (0x3fc9c018),
+         (0x3fc04412),
+         (0x3f882c0f),
+         (0x3f81a805),
+         (0x3fc16b17),
+         (0x3fc8ef1d),
+         (0x3f88ab0f),
+         (0x3f812f05),
+         (0x3fc1ec17),
+         (0x3fc8681d)},
+        (0xfff80000),
+        {0x13,0xcb,0x05,0x11,0x83,0xac,0xeb,0x2f,0xe5,0x15,
+         0x1a,0x0e,0x87,0xa3,0x4d,0xac,0x2b,0xc0,0xb1,0xf3,0x00}
+    },
+    {
+        /* No.131 delta:1006 weight:1303 */
+        11213,
+        69,
+        16,
+        7,
+        {(0x00000000),
+         (0xeb61a85a),
+         (0x9e89b6f4),
+         (0x75e81eae),
+         (0x4cc00837),
+         (0xa7a1a06d),
+         (0xd249bec3),
+         (0x39281699),
+         (0x00006d8a),
+         (0xeb61c5d0),
+         (0x9e89db7e),
+         (0x75e87324),
+         (0x4cc065bd),
+         (0xa7a1cde7),
+         (0xd249d349),
+         (0x39287b13)},
+        {(0x00000000),
+         (0x20bcb000),
+         (0x51807000),
+         (0x713cc000),
+         (0x02a2a000),
+         (0x221e1000),
+         (0x5322d000),
+         (0x739e6000),
+         (0x00067e00),
+         (0x20bace00),
+         (0x51860e00),
+         (0x713abe00),
+         (0x02a4de00),
+         (0x22186e00),
+         (0x5324ae00),
+         (0x73981e00)},
+        {(0x3f800000),
+         (0x3f905e58),
+         (0x3fa8c038),
+         (0x3fb89e60),
+         (0x3f815150),
+         (0x3f910f08),
+         (0x3fa99168),
+         (0x3fb9cf30),
+         (0x3f80033f),
+         (0x3f905d67),
+         (0x3fa8c307),
+         (0x3fb89d5f),
+         (0x3f81526f),
+         (0x3f910c37),
+         (0x3fa99257),
+         (0x3fb9cc0f)},
+        (0xfff80000),
+        {0xbc,0xf3,0x6a,0x80,0xa6,0xee,0x61,0x78,0xa8,0x3d,
+         0x8c,0xee,0x29,0x1b,0x5e,0xfd,0x0b,0xf8,0x0f,0x41,0x00}
+    },
+    {
+        /* No.132 delta:2899 weight:1651 */
+        11213,
+        57,
+        3,
+        7,
+        {(0x00000000),
+         (0x7a913b6d),
+         (0xeed573ba),
+         (0x944448d7),
+         (0xfbd00846),
+         (0x8141332b),
+         (0x15057bfc),
+         (0x6f944091),
+         (0x00003675),
+         (0x7a910d18),
+         (0xeed545cf),
+         (0x94447ea2),
+         (0xfbd03e33),
+         (0x8141055e),
+         (0x15054d89),
+         (0x6f9476e4)},
+        {(0x00000000),
+         (0x28410000),
+         (0x92350000),
+         (0xba740000),
+         (0x00601000),
+         (0x28211000),
+         (0x92551000),
+         (0xba141000),
+         (0x20161e00),
+         (0x08571e00),
+         (0xb2231e00),
+         (0x9a621e00),
+         (0x20760e00),
+         (0x08370e00),
+         (0xb2430e00),
+         (0x9a020e00)},
+        {(0x3f800000),
+         (0x3f942080),
+         (0x3fc91a80),
+         (0x3fdd3a00),
+         (0x3f803008),
+         (0x3f941088),
+         (0x3fc92a88),
+         (0x3fdd0a08),
+         (0x3f900b0f),
+         (0x3f842b8f),
+         (0x3fd9118f),
+         (0x3fcd310f),
+         (0x3f903b07),
+         (0x3f841b87),
+         (0x3fd92187),
+         (0x3fcd0107)},
+        (0xfff80000),
+        {0xa3,0x9f,0x64,0xc5,0x74,0x6d,0x2d,0x17,0xe7,0x4d,
+         0x9a,0xaf,0xeb,0xb2,0x24,0x54,0x22,0x7a,0x93,0x8c,0x00}
+    },
+    {
+        /* No.133 delta:1681 weight:1015 */
+        11213,
+        83,
+        12,
+        13,
+        {(0x00000000),
+         (0x9193fd4c),
+         (0x375d8e7c),
+         (0xa6ce7330),
+         (0xbf90085d),
+         (0x2e03f511),
+         (0x88cd8621),
+         (0x195e7b6d),
+         (0x0000af07),
+         (0x9193524b),
+         (0x375d217b),
+         (0xa6cedc37),
+         (0xbf90a75a),
+         (0x2e035a16),
+         (0x88cd2926),
+         (0x195ed46a)},
+        {(0x00000000),
+         (0x10740800),
+         (0x200dc000),
+         (0x3079c800),
+         (0x2056f800),
+         (0x3022f000),
+         (0x005b3800),
+         (0x102f3000),
+         (0x20031e00),
+         (0x30771600),
+         (0x000ede00),
+         (0x107ad600),
+         (0x0055e600),
+         (0x1021ee00),
+         (0x20582600),
+         (0x302c2e00)},
+        {(0x3f800000),
+         (0x3f883a04),
+         (0x3f9006e0),
+         (0x3f983ce4),
+         (0x3f902b7c),
+         (0x3f981178),
+         (0x3f802d9c),
+         (0x3f881798),
+         (0x3f90018f),
+         (0x3f983b8b),
+         (0x3f80076f),
+         (0x3f883d6b),
+         (0x3f802af3),
+         (0x3f8810f7),
+         (0x3f902c13),
+         (0x3f981617)},
+        (0xfff80000),
+        {0x5d,0xf8,0x9e,0x34,0xb8,0x35,0x07,0x7f,0xd2,0xe0,
+         0x4f,0x6e,0x48,0xe9,0xdf,0x81,0x9d,0xf1,0x95,0x7a,0x00}
+    },
+    {
+        /* No.134 delta:2863 weight:755 */
+        11213,
+        84,
+        6,
+        17,
+        {(0x00000000),
+         (0x61047d07),
+         (0x957fb46d),
+         (0xf47bc96a),
+         (0x58f00860),
+         (0x39f47567),
+         (0xcd8fbc0d),
+         (0xac8bc10a),
+         (0x0000e9c0),
+         (0x610494c7),
+         (0x957f5dad),
+         (0xf47b20aa),
+         (0x58f0e1a0),
+         (0x39f49ca7),
+         (0xcd8f55cd),
+         (0xac8b28ca)},
+        {(0x00000000),
+         (0x007c0000),
+         (0x13020000),
+         (0x137e0000),
+         (0x00020000),
+         (0x007e0000),
+         (0x13000000),
+         (0x137c0000),
+         (0x20401e00),
+         (0x203c1e00),
+         (0x33421e00),
+         (0x333e1e00),
+         (0x20421e00),
+         (0x203e1e00),
+         (0x33401e00),
+         (0x333c1e00)},
+        {(0x3f800000),
+         (0x3f803e00),
+         (0x3f898100),
+         (0x3f89bf00),
+         (0x3f800100),
+         (0x3f803f00),
+         (0x3f898000),
+         (0x3f89be00),
+         (0x3f90200f),
+         (0x3f901e0f),
+         (0x3f99a10f),
+         (0x3f999f0f),
+         (0x3f90210f),
+         (0x3f901f0f),
+         (0x3f99a00f),
+         (0x3f999e0f)},
+        (0xfff80000),
+        {0xcf,0x5c,0xa5,0xf4,0x7e,0x51,0x25,0xe8,0xf1,0x2b,
+         0x6a,0x13,0xd4,0x31,0xca,0x76,0x97,0x3d,0x44,0x1a,0x00}
+    },
+    {
+        /* No.135 delta:1967 weight:665 */
+        11213,
+        19,
+        12,
+        19,
+        {(0x00000000),
+         (0xabbc4cf0),
+         (0xdf51c626),
+         (0x74ed8ad6),
+         (0x74a00878),
+         (0xdf1c4488),
+         (0xabf1ce5e),
+         (0x004d82ae),
+         (0x0000858b),
+         (0xabbcc97b),
+         (0xdf5143ad),
+         (0x74ed0f5d),
+         (0x74a08df3),
+         (0xdf1cc103),
+         (0xabf14bd5),
+         (0x004d0725)},
+        {(0x00000000),
+         (0x41001800),
+         (0x004bc000),
+         (0x414bd800),
+         (0x8b86e200),
+         (0xca86fa00),
+         (0x8bcd2200),
+         (0xcacd3a00),
+         (0x70061e00),
+         (0x31060600),
+         (0x704dde00),
+         (0x314dc600),
+         (0xfb80fc00),
+         (0xba80e400),
+         (0xfbcb3c00),
+         (0xbacb2400)},
+        {(0x3f800000),
+         (0x3fa0800c),
+         (0x3f8025e0),
+         (0x3fa0a5ec),
+         (0x3fc5c371),
+         (0x3fe5437d),
+         (0x3fc5e691),
+         (0x3fe5669d),
+         (0x3fb8030f),
+         (0x3f988303),
+         (0x3fb826ef),
+         (0x3f98a6e3),
+         (0x3ffdc07e),
+         (0x3fdd4072),
+         (0x3ffde59e),
+         (0x3fdd6592)},
+        (0xfff80000),
+        {0xa2,0xe2,0xa5,0xe0,0x11,0x34,0xcb,0xf8,0xfc,0xa2,
+         0x71,0xc6,0xbf,0xe2,0xc1,0x23,0x58,0x95,0xf4,0x86,0x00}
+    },
+    {
+        /* No.136 delta:918 weight:1551 */
+        11213,
+        47,
+        18,
+        4,
+        {(0x00000000),
+         (0x09adadc4),
+         (0xf017020b),
+         (0xf9baafcf),
+         (0x03300880),
+         (0x0a9da544),
+         (0xf3270a8b),
+         (0xfa8aa74f),
+         (0x0000cfe7),
+         (0x09ad6223),
+         (0xf017cdec),
+         (0xf9ba6028),
+         (0x0330c767),
+         (0x0a9d6aa3),
+         (0xf327c56c),
+         (0xfa8a68a8)},
+        {(0x00000000),
+         (0x18747400),
+         (0x204c5200),
+         (0x38382600),
+         (0x10026c00),
+         (0x08761800),
+         (0x304e3e00),
+         (0x283a4a00),
+         (0x10011e00),
+         (0x08756a00),
+         (0x304d4c00),
+         (0x28393800),
+         (0x00037200),
+         (0x18770600),
+         (0x204f2000),
+         (0x383b5400)},
+        {(0x3f800000),
+         (0x3f8c3a3a),
+         (0x3f902629),
+         (0x3f9c1c13),
+         (0x3f880136),
+         (0x3f843b0c),
+         (0x3f98271f),
+         (0x3f941d25),
+         (0x3f88008f),
+         (0x3f843ab5),
+         (0x3f9826a6),
+         (0x3f941c9c),
+         (0x3f8001b9),
+         (0x3f8c3b83),
+         (0x3f902790),
+         (0x3f9c1daa)},
+        (0xfff80000),
+        {0xfd,0x25,0x81,0xf4,0x6f,0xd2,0x73,0x0c,0x1d,0xf8,
+         0x3c,0x0b,0xd2,0x33,0xf7,0x54,0x12,0xcd,0x88,0xa9,0x00}
+    },
+    {
+        /* No.137 delta:4265 weight:777 */
+        11213,
+        70,
+        4,
+        16,
+        {(0x00000000),
+         (0xab4eac1a),
+         (0x41078fcd),
+         (0xea4923d7),
+         (0x9d20089e),
+         (0x366ea484),
+         (0xdc278753),
+         (0x77692b49),
+         (0x0000022a),
+         (0xab4eae30),
+         (0x41078de7),
+         (0xea4921fd),
+         (0x9d200ab4),
+         (0x366ea6ae),
+         (0xdc278579),
+         (0x77692963)},
+        {(0x00000000),
+         (0x39400800),
+         (0x00500000),
+         (0x39100800),
+         (0x002c0000),
+         (0x396c0800),
+         (0x007c0000),
+         (0x393c0800),
+         (0x5e881e00),
+         (0x67c81600),
+         (0x5ed81e00),
+         (0x67981600),
+         (0x5ea41e00),
+         (0x67e41600),
+         (0x5ef41e00),
+         (0x67b41600)},
+        {(0x3f800000),
+         (0x3f9ca004),
+         (0x3f802800),
+         (0x3f9c8804),
+         (0x3f801600),
+         (0x3f9cb604),
+         (0x3f803e00),
+         (0x3f9c9e04),
+         (0x3faf440f),
+         (0x3fb3e40b),
+         (0x3faf6c0f),
+         (0x3fb3cc0b),
+         (0x3faf520f),
+         (0x3fb3f20b),
+         (0x3faf7a0f),
+         (0x3fb3da0b)},
+        (0xfff80000),
+        {0x67,0x39,0x69,0x46,0xb6,0xab,0xda,0x03,0x1a,0xb9,
+         0x33,0x5c,0x43,0x1d,0xc4,0x88,0x65,0xf4,0x6e,0x7c,0x00}
+    },
+    {
+        /* No.138 delta:2916 weight:1245 */
+        11213,
+        12,
+        4,
+        11,
+        {(0x00000000),
+         (0x8bd9bb70),
+         (0x6536d8cf),
+         (0xeeef63bf),
+         (0x83c008a3),
+         (0x0819b3d3),
+         (0xe6f6d06c),
+         (0x6d2f6b1c),
+         (0x00006357),
+         (0x8bd9d827),
+         (0x6536bb98),
+         (0xeeef00e8),
+         (0x83c06bf4),
+         (0x0819d084),
+         (0xe6f6b33b),
+         (0x6d2f084b)},
+        {(0x00000000),
+         (0x10b20000),
+         (0x0c210800),
+         (0x1c930800),
+         (0x29540000),
+         (0x39e60000),
+         (0x25750800),
+         (0x35c70800),
+         (0x02001e00),
+         (0x12b21e00),
+         (0x0e211600),
+         (0x1e931600),
+         (0x2b541e00),
+         (0x3be61e00),
+         (0x27751600),
+         (0x37c71600)},
+        {(0x3f800000),
+         (0x3f885900),
+         (0x3f861084),
+         (0x3f8e4984),
+         (0x3f94aa00),
+         (0x3f9cf300),
+         (0x3f92ba84),
+         (0x3f9ae384),
+         (0x3f81000f),
+         (0x3f89590f),
+         (0x3f87108b),
+         (0x3f8f498b),
+         (0x3f95aa0f),
+         (0x3f9df30f),
+         (0x3f93ba8b),
+         (0x3f9be38b)},
+        (0xfff80000),
+        {0xf3,0xfa,0x1e,0x93,0x8b,0xfb,0x73,0xc0,0xf1,0x10,
+         0x33,0xac,0x5f,0x6b,0x3c,0x07,0xa2,0x98,0x75,0xed,0x00}
+    },
+    {
+        /* No.139 delta:2344 weight:1631 */
+        11213,
+        19,
+        8,
+        7,
+        {(0x00000000),
+         (0xfee0d62f),
+         (0x304aad76),
+         (0xceaa7b59),
+         (0x871008b7),
+         (0x79f0de98),
+         (0xb75aa5c1),
+         (0x49ba73ee),
+         (0x00003b80),
+         (0xfee0edaf),
+         (0x304a96f6),
+         (0xceaa40d9),
+         (0x87103337),
+         (0x79f0e518),
+         (0xb75a9e41),
+         (0x49ba486e)},
+        {(0x00000000),
+         (0x05204400),
+         (0x00640000),
+         (0x05444400),
+         (0x00510400),
+         (0x05714000),
+         (0x00350400),
+         (0x05154000),
+         (0x32861e00),
+         (0x37a65a00),
+         (0x32e21e00),
+         (0x37c25a00),
+         (0x32d71a00),
+         (0x37f75e00),
+         (0x32b31a00),
+         (0x37935e00)},
+        {(0x3f800000),
+         (0x3f829022),
+         (0x3f803200),
+         (0x3f82a222),
+         (0x3f802882),
+         (0x3f82b8a0),
+         (0x3f801a82),
+         (0x3f828aa0),
+         (0x3f99430f),
+         (0x3f9bd32d),
+         (0x3f99710f),
+         (0x3f9be12d),
+         (0x3f996b8d),
+         (0x3f9bfbaf),
+         (0x3f99598d),
+         (0x3f9bc9af)},
+        (0xfff80000),
+        {0xaf,0x4b,0x20,0xda,0xcd,0xb1,0xdf,0x23,0x89,0x9b,
+         0x9d,0xaf,0x0b,0x97,0x1e,0x9f,0x61,0xe7,0xfc,0x34,0x00}
+    },
+    {
+        /* No.140 delta:8901 weight:1275 */
+        11213,
+        67,
+        29,
+        1,
+        {(0x00000000),
+         (0x10b888fe),
+         (0xd0c6c2fc),
+         (0xc07e4a02),
+         (0x93a008cd),
+         (0x83188033),
+         (0x4366ca31),
+         (0x53de42cf),
+         (0x00009412),
+         (0x10b81cec),
+         (0xd0c656ee),
+         (0xc07ede10),
+         (0x93a09cdf),
+         (0x83181421),
+         (0x43665e23),
+         (0x53ded6dd)},
+        {(0x00000000),
+         (0xd4620000),
+         (0x7a000000),
+         (0xae620000),
+         (0x01000000),
+         (0xd5620000),
+         (0x7b000000),
+         (0xaf620000),
+         (0x20a01e00),
+         (0xf4c21e00),
+         (0x5aa01e00),
+         (0x8ec21e00),
+         (0x21a01e00),
+         (0xf5c21e00),
+         (0x5ba01e00),
+         (0x8fc21e00)},
+        {(0x3f800000),
+         (0x3fea3100),
+         (0x3fbd0000),
+         (0x3fd73100),
+         (0x3f808000),
+         (0x3feab100),
+         (0x3fbd8000),
+         (0x3fd7b100),
+         (0x3f90500f),
+         (0x3ffa610f),
+         (0x3fad500f),
+         (0x3fc7610f),
+         (0x3f90d00f),
+         (0x3ffae10f),
+         (0x3fadd00f),
+         (0x3fc7e10f)},
+        (0xfff80000),
+        {0x57,0x96,0xba,0x7d,0x3b,0x31,0xd7,0xac,0xb2,0xe9,
+         0x75,0xcd,0xdb,0x72,0x76,0x05,0x30,0x7f,0xd3,0xa0,0x00}
+    },
+    {
+        /* No.141 delta:3341 weight:741 */
+        11213,
+        28,
+        3,
+        17,
+        {(0x00000000),
+         (0xac86815e),
+         (0xed762180),
+         (0x41f0a0de),
+         (0x81a008df),
+         (0x2d268981),
+         (0x6cd6295f),
+         (0xc050a801),
+         (0x00004e54),
+         (0xac86cf0a),
+         (0xed766fd4),
+         (0x41f0ee8a),
+         (0x81a0468b),
+         (0x2d26c7d5),
+         (0x6cd6670b),
+         (0xc050e655)},
+        {(0x00000000),
+         (0x9a110000),
+         (0x0f440000),
+         (0x95550000),
+         (0x70020000),
+         (0xea130000),
+         (0x7f460000),
+         (0xe5570000),
+         (0x21561e00),
+         (0xbb471e00),
+         (0x2e121e00),
+         (0xb4031e00),
+         (0x51541e00),
+         (0xcb451e00),
+         (0x5e101e00),
+         (0xc4011e00)},
+        {(0x3f800000),
+         (0x3fcd0880),
+         (0x3f87a200),
+         (0x3fcaaa80),
+         (0x3fb80100),
+         (0x3ff50980),
+         (0x3fbfa300),
+         (0x3ff2ab80),
+         (0x3f90ab0f),
+         (0x3fdda38f),
+         (0x3f97090f),
+         (0x3fda018f),
+         (0x3fa8aa0f),
+         (0x3fe5a28f),
+         (0x3faf080f),
+         (0x3fe2008f)},
+        (0xfff80000),
+        {0xcc,0x1d,0x62,0xab,0x61,0x1e,0xfc,0xe5,0x74,0x60,
+         0xa2,0x5f,0xd0,0xfe,0xff,0x0e,0xfb,0x75,0x83,0x88,0x00}
+    },
+    {
+        /* No.142 delta:5114 weight:1233 */
+        11213,
+        35,
+        30,
+        3,
+        {(0x00000000),
+         (0xb9cd2997),
+         (0xab1ac509),
+         (0x12d7ec9e),
+         (0x5b2008ea),
+         (0xe2ed217d),
+         (0xf03acde3),
+         (0x49f7e474),
+         (0x0000ef9a),
+         (0xb9cdc60d),
+         (0xab1a2a93),
+         (0x12d70304),
+         (0x5b20e770),
+         (0xe2edcee7),
+         (0xf03a2279),
+         (0x49f70bee)},
+        {(0x00000000),
+         (0xaef05000),
+         (0x2d801000),
+         (0x83704000),
+         (0x2390a000),
+         (0x8d60f000),
+         (0x0e10b000),
+         (0xa0e0e000),
+         (0x01001e00),
+         (0xaff04e00),
+         (0x2c800e00),
+         (0x82705e00),
+         (0x2290be00),
+         (0x8c60ee00),
+         (0x0f10ae00),
+         (0xa1e0fe00)},
+        {(0x3f800000),
+         (0x3fd77828),
+         (0x3f96c008),
+         (0x3fc1b820),
+         (0x3f91c850),
+         (0x3fc6b078),
+         (0x3f870858),
+         (0x3fd07070),
+         (0x3f80800f),
+         (0x3fd7f827),
+         (0x3f964007),
+         (0x3fc1382f),
+         (0x3f91485f),
+         (0x3fc63077),
+         (0x3f878857),
+         (0x3fd0f07f)},
+        (0xfff80000),
+        {0xce,0x51,0x4c,0x65,0x7a,0xae,0xbf,0x6f,0xd6,0x0b,
+         0x0c,0x85,0x1c,0x15,0xe3,0xbf,0x9f,0x2b,0xa1,0x5d,0x00}
+    },
+    {
+        /* No.143 delta:2115 weight:1465 */
+        11213,
+        11,
+        20,
+        4,
+        {(0x00000000),
+         (0x34795048),
+         (0x0b9d244e),
+         (0x3fe47406),
+         (0xc90008f2),
+         (0xfd7958ba),
+         (0xc29d2cbc),
+         (0xf6e47cf4),
+         (0x00008680),
+         (0x3479d6c8),
+         (0x0b9da2ce),
+         (0x3fe4f286),
+         (0xc9008e72),
+         (0xfd79de3a),
+         (0xc29daa3c),
+         (0xf6e4fa74)},
+        {(0x00000000),
+         (0xc1592000),
+         (0x16984000),
+         (0xd7c16000),
+         (0x21226800),
+         (0xe07b4800),
+         (0x37ba2800),
+         (0xf6e30800),
+         (0x08345e00),
+         (0xc96d7e00),
+         (0x1eac1e00),
+         (0xdff53e00),
+         (0x29163600),
+         (0xe84f1600),
+         (0x3f8e7600),
+         (0xfed75600)},
+        {(0x3f800000),
+         (0x3fe0ac90),
+         (0x3f8b4c20),
+         (0x3febe0b0),
+         (0x3f909134),
+         (0x3ff03da4),
+         (0x3f9bdd14),
+         (0x3ffb7184),
+         (0x3f841a2f),
+         (0x3fe4b6bf),
+         (0x3f8f560f),
+         (0x3feffa9f),
+         (0x3f948b1b),
+         (0x3ff4278b),
+         (0x3f9fc73b),
+         (0x3fff6bab)},
+        (0xfff80000),
+        {0x5f,0xfb,0xd7,0xc9,0x75,0x94,0x0e,0x84,0xfd,0x2b,
+         0xc3,0xaf,0xdd,0x54,0xe4,0x81,0x96,0x14,0x65,0x90,0x00}
+    },
+    {
+        /* No.144 delta:1074 weight:843 */
+        11213,
+        50,
+        20,
+        11,
+        {(0x00000000),
+         (0xa8afcf6f),
+         (0x497c3b6e),
+         (0xe1d3f401),
+         (0xa0800909),
+         (0x082fc666),
+         (0xe9fc3267),
+         (0x4153fd08),
+         (0x0000ec80),
+         (0xa8af23ef),
+         (0x497cd7ee),
+         (0xe1d31881),
+         (0xa080e589),
+         (0x082f2ae6),
+         (0xe9fcdee7),
+         (0x41531188)},
+        {(0x00000000),
+         (0x00081200),
+         (0xd0708400),
+         (0xd0789600),
+         (0x70302a00),
+         (0x70383800),
+         (0xa040ae00),
+         (0xa048bc00),
+         (0x1040fe00),
+         (0x1048ec00),
+         (0xc0307a00),
+         (0xc0386800),
+         (0x6070d400),
+         (0x6078c600),
+         (0xb0005000),
+         (0xb0084200)},
+        {(0x3f800000),
+         (0x3f800409),
+         (0x3fe83842),
+         (0x3fe83c4b),
+         (0x3fb81815),
+         (0x3fb81c1c),
+         (0x3fd02057),
+         (0x3fd0245e),
+         (0x3f88207f),
+         (0x3f882476),
+         (0x3fe0183d),
+         (0x3fe01c34),
+         (0x3fb0386a),
+         (0x3fb03c63),
+         (0x3fd80028),
+         (0x3fd80421)},
+        (0xfff80000),
+        {0x95,0xfe,0x49,0x06,0x5e,0xae,0xca,0x7d,0x0a,0xce,
+         0xf9,0x80,0x70,0xc2,0x03,0xa9,0x1c,0xc3,0x16,0x85,0x00}
+    },
+    {
+        /* No.145 delta:2918 weight:721 */
+        11213,
+        86,
+        4,
+        18,
+        {(0x00000000),
+         (0x3bebda5d),
+         (0x18b70390),
+         (0x235cd9cd),
+         (0x2c60091c),
+         (0x178bd341),
+         (0x34d70a8c),
+         (0x0f3cd0d1),
+         (0x00004f75),
+         (0x3beb9528),
+         (0x18b74ce5),
+         (0x235c96b8),
+         (0x2c604669),
+         (0x178b9c34),
+         (0x34d745f9),
+         (0x0f3c9fa4)},
+        {(0x00000000),
+         (0x1e710200),
+         (0x09800000),
+         (0x17f10200),
+         (0x00c00000),
+         (0x1eb10200),
+         (0x09400000),
+         (0x17310200),
+         (0x30431e00),
+         (0x2e321c00),
+         (0x39c31e00),
+         (0x27b21c00),
+         (0x30831e00),
+         (0x2ef21c00),
+         (0x39031e00),
+         (0x27721c00)},
+        {(0x3f800000),
+         (0x3f8f3881),
+         (0x3f84c000),
+         (0x3f8bf881),
+         (0x3f806000),
+         (0x3f8f5881),
+         (0x3f84a000),
+         (0x3f8b9881),
+         (0x3f98218f),
+         (0x3f97190e),
+         (0x3f9ce18f),
+         (0x3f93d90e),
+         (0x3f98418f),
+         (0x3f97790e),
+         (0x3f9c818f),
+         (0x3f93b90e)},
+        (0xfff80000),
+        {0x3c,0x75,0x1b,0x34,0x56,0x7d,0x5c,0xdd,0x5a,0x41,
+         0xb9,0x82,0x6d,0x1d,0xc3,0x11,0x62,0x1e,0xea,0x73,0x00}
+    },
+    {
+        /* No.146 delta:2588 weight:993 */
+        11213,
+        77,
+        8,
+        11,
+        {(0x00000000),
+         (0x29b76eba),
+         (0xb5519d7b),
+         (0x9ce6f3c1),
+         (0xac000920),
+         (0x85b7679a),
+         (0x1951945b),
+         (0x30e6fae1),
+         (0x00002499),
+         (0x29b74a23),
+         (0xb551b9e2),
+         (0x9ce6d758),
+         (0xac002db9),
+         (0x85b74303),
+         (0x1951b0c2),
+         (0x30e6de78)},
+        {(0x00000000),
+         (0x00350000),
+         (0x086a0000),
+         (0x085f0000),
+         (0x085c0000),
+         (0x08690000),
+         (0x00360000),
+         (0x00030000),
+         (0x00519e00),
+         (0x00649e00),
+         (0x083b9e00),
+         (0x080e9e00),
+         (0x080d9e00),
+         (0x08389e00),
+         (0x00679e00),
+         (0x00529e00)},
+        {(0x3f800000),
+         (0x3f801a80),
+         (0x3f843500),
+         (0x3f842f80),
+         (0x3f842e00),
+         (0x3f843480),
+         (0x3f801b00),
+         (0x3f800180),
+         (0x3f8028cf),
+         (0x3f80324f),
+         (0x3f841dcf),
+         (0x3f84074f),
+         (0x3f8406cf),
+         (0x3f841c4f),
+         (0x3f8033cf),
+         (0x3f80294f)},
+        (0xfff80000),
+        {0x78,0x89,0x90,0x85,0x2f,0x08,0xc7,0x62,0xed,0x99,
+         0x04,0xfb,0xe5,0x26,0x8d,0x20,0x08,0xbc,0xe0,0x01,0x00}
+    },
+    {
+        /* No.147 delta:1196 weight:1067 */
+        11213,
+        65,
+        15,
+        11,
+        {(0x00000000),
+         (0xe3c8118c),
+         (0x346df077),
+         (0xd7a5e1fb),
+         (0xff500936),
+         (0x1c9818ba),
+         (0xcb3df941),
+         (0x28f5e8cd),
+         (0x000057eb),
+         (0xe3c84667),
+         (0x346da79c),
+         (0xd7a5b610),
+         (0xff505edd),
+         (0x1c984f51),
+         (0xcb3daeaa),
+         (0x28f5bf26)},
+        {(0x00000000),
+         (0x00411800),
+         (0x0c060000),
+         (0x0c471800),
+         (0x260a4800),
+         (0x264b5000),
+         (0x2a0c4800),
+         (0x2a4d5000),
+         (0x0003de00),
+         (0x0042c600),
+         (0x0c05de00),
+         (0x0c44c600),
+         (0x26099600),
+         (0x26488e00),
+         (0x2a0f9600),
+         (0x2a4e8e00)},
+        {(0x3f800000),
+         (0x3f80208c),
+         (0x3f860300),
+         (0x3f86238c),
+         (0x3f930524),
+         (0x3f9325a8),
+         (0x3f950624),
+         (0x3f9526a8),
+         (0x3f8001ef),
+         (0x3f802163),
+         (0x3f8602ef),
+         (0x3f862263),
+         (0x3f9304cb),
+         (0x3f932447),
+         (0x3f9507cb),
+         (0x3f952747)},
+        (0xfff80000),
+        {0x1f,0x15,0xcd,0x92,0x4b,0xcd,0x46,0xa5,0x75,0xd5,
+         0x6e,0xf4,0x6d,0x40,0x10,0x3c,0xa0,0xd0,0x56,0x85,0x00}
+    },
+    {
+        /* No.148 delta:995 weight:1397 */
+        11213,
+        48,
+        20,
+        6,
+        {(0x00000000),
+         (0x81afe9a5),
+         (0xc7e54dd3),
+         (0x464aa476),
+         (0x42100942),
+         (0xc3bfe0e7),
+         (0x85f54491),
+         (0x045aad34),
+         (0x0000d573),
+         (0x81af3cd6),
+         (0xc7e598a0),
+         (0x464a7105),
+         (0x4210dc31),
+         (0xc3bf3594),
+         (0x85f591e2),
+         (0x045a7847)},
+        {(0x00000000),
+         (0x0c8d6800),
+         (0x00660a00),
+         (0x0ceb6200),
+         (0x0190c600),
+         (0x0d1dae00),
+         (0x01f6cc00),
+         (0x0d7ba400),
+         (0x00423e00),
+         (0x0ccf5600),
+         (0x00243400),
+         (0x0ca95c00),
+         (0x01d2f800),
+         (0x0d5f9000),
+         (0x01b4f200),
+         (0x0d399a00)},
+        {(0x3f800000),
+         (0x3f8646b4),
+         (0x3f803305),
+         (0x3f8675b1),
+         (0x3f80c863),
+         (0x3f868ed7),
+         (0x3f80fb66),
+         (0x3f86bdd2),
+         (0x3f80211f),
+         (0x3f8667ab),
+         (0x3f80121a),
+         (0x3f8654ae),
+         (0x3f80e97c),
+         (0x3f86afc8),
+         (0x3f80da79),
+         (0x3f869ccd)},
+        (0xfff80000),
+        {0x89,0x5d,0x4d,0xfa,0x78,0x98,0x8a,0x59,0x89,0x1d,
+         0x43,0x6d,0x08,0x21,0x0f,0x15,0xef,0xea,0xed,0x4f,0x00}
+    },
+    {
+        /* No.149 delta:1807 weight:1017 */
+        11213,
+        25,
+        17,
+        13,
+        {(0x00000000),
+         (0x95fafe23),
+         (0x258e5d0f),
+         (0xb074a32c),
+         (0x5b000950),
+         (0xcefaf773),
+         (0x7e8e545f),
+         (0xeb74aa7c),
+         (0x00007b8f),
+         (0x95fa85ac),
+         (0x258e2680),
+         (0xb074d8a3),
+         (0x5b0072df),
+         (0xcefa8cfc),
+         (0x7e8e2fd0),
+         (0xeb74d1f3)},
+        {(0x00000000),
+         (0x40690000),
+         (0x107c4000),
+         (0x50154000),
+         (0x0061b000),
+         (0x4008b000),
+         (0x101df000),
+         (0x5074f000),
+         (0x10221e00),
+         (0x504b1e00),
+         (0x005e5e00),
+         (0x40375e00),
+         (0x1043ae00),
+         (0x502aae00),
+         (0x003fee00),
+         (0x4056ee00)},
+        {(0x3f800000),
+         (0x3fa03480),
+         (0x3f883e20),
+         (0x3fa80aa0),
+         (0x3f8030d8),
+         (0x3fa00458),
+         (0x3f880ef8),
+         (0x3fa83a78),
+         (0x3f88110f),
+         (0x3fa8258f),
+         (0x3f802f2f),
+         (0x3fa01baf),
+         (0x3f8821d7),
+         (0x3fa81557),
+         (0x3f801ff7),
+         (0x3fa02b77)},
+        (0xfff80000),
+        {0x29,0x89,0x68,0x24,0x4a,0xc0,0x9c,0xf5,0x79,0x14,
+         0xc1,0x26,0xaa,0x3d,0xbf,0x1f,0xc1,0xbc,0x78,0x97,0x00}
+    },
+    {
+        /* No.150 delta:2642 weight:975 */
+        11213,
+        44,
+        4,
+        15,
+        {(0x00000000),
+         (0x3cf7745b),
+         (0x8a251fd4),
+         (0xb6d26b8f),
+         (0xc580096c),
+         (0xf9777d37),
+         (0x4fa516b8),
+         (0x735262e3),
+         (0x00007ddf),
+         (0x3cf70984),
+         (0x8a25620b),
+         (0xb6d21650),
+         (0xc58074b3),
+         (0xf97700e8),
+         (0x4fa56b67),
+         (0x73521f3c)},
+        {(0x00000000),
+         (0x52780000),
+         (0x13600000),
+         (0x41180000),
+         (0x08cc0000),
+         (0x5ab40000),
+         (0x1bac0000),
+         (0x49d40000),
+         (0x02401e00),
+         (0x50381e00),
+         (0x11201e00),
+         (0x43581e00),
+         (0x0a8c1e00),
+         (0x58f41e00),
+         (0x19ec1e00),
+         (0x4b941e00)},
+        {(0x3f800000),
+         (0x3fa93c00),
+         (0x3f89b000),
+         (0x3fa08c00),
+         (0x3f846600),
+         (0x3fad5a00),
+         (0x3f8dd600),
+         (0x3fa4ea00),
+         (0x3f81200f),
+         (0x3fa81c0f),
+         (0x3f88900f),
+         (0x3fa1ac0f),
+         (0x3f85460f),
+         (0x3fac7a0f),
+         (0x3f8cf60f),
+         (0x3fa5ca0f)},
+        (0xfff80000),
+        {0x1c,0x8b,0x81,0x2c,0x97,0xf0,0xd6,0x0f,0xeb,0x28,
+         0x7e,0xbd,0x56,0xe1,0x95,0x03,0x72,0xd1,0x0a,0x96,0x00}
+    },
+    {
+        /* No.151 delta:962 weight:1023 */
+        11213,
+        78,
+        12,
+        10,
+        {(0x00000000),
+         (0x171b50e0),
+         (0x997dbf57),
+         (0x8e66efb7),
+         (0x22d00977),
+         (0x35cb5997),
+         (0xbbadb620),
+         (0xacb6e6c0),
+         (0x00001c87),
+         (0x171b4c67),
+         (0x997da3d0),
+         (0x8e66f330),
+         (0x22d015f0),
+         (0x35cb4510),
+         (0xbbadaaa7),
+         (0xacb6fa47)},
+        {(0x00000000),
+         (0x21821c00),
+         (0x10102200),
+         (0x31923e00),
+         (0x60023000),
+         (0x41802c00),
+         (0x70121200),
+         (0x51900e00),
+         (0xc100de00),
+         (0xe082c200),
+         (0xd110fc00),
+         (0xf092e000),
+         (0xa102ee00),
+         (0x8080f200),
+         (0xb112cc00),
+         (0x9090d000)},
+        {(0x3f800000),
+         (0x3f90c10e),
+         (0x3f880811),
+         (0x3f98c91f),
+         (0x3fb00118),
+         (0x3fa0c016),
+         (0x3fb80909),
+         (0x3fa8c807),
+         (0x3fe0806f),
+         (0x3ff04161),
+         (0x3fe8887e),
+         (0x3ff84970),
+         (0x3fd08177),
+         (0x3fc04079),
+         (0x3fd88966),
+         (0x3fc84868)},
+        (0xfff80000),
+        {0xb1,0xe6,0xdf,0x01,0xdd,0xa2,0x07,0x31,0x50,0x37,
+         0xbf,0x34,0x2a,0x6d,0xfe,0xe7,0x69,0x32,0x01,0x60,0x00}
+    },
+    {
+        /* No.152 delta:1302 weight:1041 */
+        11213,
+        55,
+        10,
+        13,
+        {(0x00000000),
+         (0xd0556a8d),
+         (0x5e4d31d5),
+         (0x8e185b58),
+         (0xe6600989),
+         (0x36356304),
+         (0xb82d385c),
+         (0x687852d1),
+         (0x000019bb),
+         (0xd0557336),
+         (0x5e4d286e),
+         (0x8e1842e3),
+         (0xe6601032),
+         (0x36357abf),
+         (0xb82d21e7),
+         (0x68784b6a)},
+        {(0x00000000),
+         (0x627c4000),
+         (0x3034a000),
+         (0x5248e000),
+         (0x00038000),
+         (0x627fc000),
+         (0x30372000),
+         (0x524b6000),
+         (0x80519e00),
+         (0xe22dde00),
+         (0xb0653e00),
+         (0xd2197e00),
+         (0x80521e00),
+         (0xe22e5e00),
+         (0xb066be00),
+         (0xd21afe00)},
+        {(0x3f800000),
+         (0x3fb13e20),
+         (0x3f981a50),
+         (0x3fa92470),
+         (0x3f8001c0),
+         (0x3fb13fe0),
+         (0x3f981b90),
+         (0x3fa925b0),
+         (0x3fc028cf),
+         (0x3ff116ef),
+         (0x3fd8329f),
+         (0x3fe90cbf),
+         (0x3fc0290f),
+         (0x3ff1172f),
+         (0x3fd8335f),
+         (0x3fe90d7f)},
+        (0xfff80000),
+        {0x9e,0x26,0x73,0xd6,0xbe,0x9b,0x5f,0x7d,0x6b,0x71,
+         0x07,0x55,0x14,0xdf,0x70,0x0c,0x4a,0x9a,0xdd,0x23,0x00}
+    },
+    {
+        /* No.153 delta:1084 weight:1123 */
+        11213,
+        86,
+        22,
+        10,
+        {(0x00000000),
+         (0xa2a5f5b9),
+         (0x2b421269),
+         (0x89e7e7d0),
+         (0x54300991),
+         (0xf695fc28),
+         (0x7f721bf8),
+         (0xddd7ee41),
+         (0x0000dc1c),
+         (0xa2a529a5),
+         (0x2b42ce75),
+         (0x89e73bcc),
+         (0x5430d58d),
+         (0xf6952034),
+         (0x7f72c7e4),
+         (0xddd7325d)},
+        {(0x00000000),
+         (0x40cc0000),
+         (0x50668000),
+         (0x10aa8000),
+         (0x0069f000),
+         (0x40a5f000),
+         (0x500f7000),
+         (0x10c37000),
+         (0x00131e00),
+         (0x40df1e00),
+         (0x50759e00),
+         (0x10b99e00),
+         (0x007aee00),
+         (0x40b6ee00),
+         (0x501c6e00),
+         (0x10d06e00)},
+        {(0x3f800000),
+         (0x3fa06600),
+         (0x3fa83340),
+         (0x3f885540),
+         (0x3f8034f8),
+         (0x3fa052f8),
+         (0x3fa807b8),
+         (0x3f8861b8),
+         (0x3f80098f),
+         (0x3fa06f8f),
+         (0x3fa83acf),
+         (0x3f885ccf),
+         (0x3f803d77),
+         (0x3fa05b77),
+         (0x3fa80e37),
+         (0x3f886837)},
+        (0xfff80000),
+        {0x27,0x0a,0x3f,0xdb,0x36,0x65,0x8b,0x9c,0x6c,0x3d,
+         0xb3,0x81,0x6b,0x82,0xdc,0xaf,0x77,0xa0,0x5a,0x41,0x00}
+    },
+    {
+        /* No.154 delta:1218 weight:1125 */
+        11213,
+        33,
+        17,
+        9,
+        {(0x00000000),
+         (0xbc510269),
+         (0x187fd997),
+         (0xa42edbfe),
+         (0xb2d009a5),
+         (0x0e810bcc),
+         (0xaaafd032),
+         (0x16fed25b),
+         (0x00008c92),
+         (0xbc518efb),
+         (0x187f5505),
+         (0xa42e576c),
+         (0xb2d08537),
+         (0x0e81875e),
+         (0xaaaf5ca0),
+         (0x16fe5ec9)},
+        {(0x00000000),
+         (0x4674aa00),
+         (0x20885800),
+         (0x66fcf200),
+         (0x010e8000),
+         (0x477a2a00),
+         (0x2186d800),
+         (0x67f27200),
+         (0x02c2be00),
+         (0x44b61400),
+         (0x224ae600),
+         (0x643e4c00),
+         (0x03cc3e00),
+         (0x45b89400),
+         (0x23446600),
+         (0x6530cc00)},
+        {(0x3f800000),
+         (0x3fa33a55),
+         (0x3f90442c),
+         (0x3fb37e79),
+         (0x3f808740),
+         (0x3fa3bd15),
+         (0x3f90c36c),
+         (0x3fb3f939),
+         (0x3f81615f),
+         (0x3fa25b0a),
+         (0x3f912573),
+         (0x3fb21f26),
+         (0x3f81e61f),
+         (0x3fa2dc4a),
+         (0x3f91a233),
+         (0x3fb29866)},
+        (0xfff80000),
+        {0xcd,0xbe,0xfa,0xd2,0x8e,0x1c,0x9f,0x67,0x49,0xe4,
+         0xe4,0x6c,0xb2,0xe3,0x01,0xb2,0x56,0x77,0x18,0x40,0x00}
+    },
+    {
+        /* No.155 delta:2783 weight:955 */
+        11213,
+        59,
+        3,
+        7,
+        {(0x00000000),
+         (0xac2382ba),
+         (0x6ff1673d),
+         (0xc3d2e587),
+         (0x37a009b6),
+         (0x9b838b0c),
+         (0x58516e8b),
+         (0xf472ec31),
+         (0x0000a5f6),
+         (0xac23274c),
+         (0x6ff1c2cb),
+         (0xc3d24071),
+         (0x37a0ac40),
+         (0x9b832efa),
+         (0x5851cb7d),
+         (0xf47249c7)},
+        {(0x00000000),
+         (0x14228400),
+         (0x00948000),
+         (0x14b60400),
+         (0x206a8400),
+         (0x34480000),
+         (0x20fe0400),
+         (0x34dc8000),
+         (0x40481e00),
+         (0x546a9a00),
+         (0x40dc9e00),
+         (0x54fe1a00),
+         (0x60229a00),
+         (0x74001e00),
+         (0x60b61a00),
+         (0x74949e00)},
+        {(0x3f800000),
+         (0x3f8a1142),
+         (0x3f804a40),
+         (0x3f8a5b02),
+         (0x3f903542),
+         (0x3f9a2400),
+         (0x3f907f02),
+         (0x3f9a6e40),
+         (0x3fa0240f),
+         (0x3faa354d),
+         (0x3fa06e4f),
+         (0x3faa7f0d),
+         (0x3fb0114d),
+         (0x3fba000f),
+         (0x3fb05b0d),
+         (0x3fba4a4f)},
+        (0xfff80000),
+        {0x12,0x6b,0xe6,0x1a,0xef,0x03,0x6a,0x5a,0xeb,0x44,
+         0xd4,0xe8,0x31,0x17,0x3d,0x9b,0xd7,0xd8,0xc8,0xd2,0x00}
+    },
+    {
+        /* No.156 delta:2681 weight:1401 */
+        11213,
+        11,
+        22,
+        2,
+        {(0x00000000),
+         (0xfcaff0c3),
+         (0xee10c3a9),
+         (0x12bf336a),
+         (0x1ea009c1),
+         (0xe20ff902),
+         (0xf0b0ca68),
+         (0x0c1f3aab),
+         (0x0000a3d9),
+         (0xfcaf531a),
+         (0xee106070),
+         (0x12bf90b3),
+         (0x1ea0aa18),
+         (0xe20f5adb),
+         (0xf0b069b1),
+         (0x0c1f9972)},
+        {(0x00000000),
+         (0x8f318000),
+         (0x0c949000),
+         (0x83a51000),
+         (0x61378000),
+         (0xee060000),
+         (0x6da31000),
+         (0xe2929000),
+         (0x4044de00),
+         (0xcf755e00),
+         (0x4cd04e00),
+         (0xc3e1ce00),
+         (0x21735e00),
+         (0xae42de00),
+         (0x2de7ce00),
+         (0xa2d64e00)},
+        {(0x3f800000),
+         (0x3fc798c0),
+         (0x3f864a48),
+         (0x3fc1d288),
+         (0x3fb09bc0),
+         (0x3ff70300),
+         (0x3fb6d188),
+         (0x3ff14948),
+         (0x3fa0226f),
+         (0x3fe7baaf),
+         (0x3fa66827),
+         (0x3fe1f0e7),
+         (0x3f90b9af),
+         (0x3fd7216f),
+         (0x3f96f3e7),
+         (0x3fd16b27)},
+        (0xfff80000),
+        {0x83,0x73,0xde,0x94,0x03,0x6f,0x08,0x85,0xc8,0x3c,
+         0xf2,0x9a,0xb2,0x9d,0x8f,0xfe,0x34,0x78,0xe6,0x25,0x00}
+    },
+    {
+        /* No.157 delta:4633 weight:903 */
+        11213,
+        70,
+        1,
+        8,
+        {(0x00000000),
+         (0x4732d8c0),
+         (0x2145def8),
+         (0x66770638),
+         (0x8ec009da),
+         (0xc9f2d11a),
+         (0xaf85d722),
+         (0xe8b70fe2),
+         (0x000031b0),
+         (0x4732e970),
+         (0x2145ef48),
+         (0x66773788),
+         (0x8ec0386a),
+         (0xc9f2e0aa),
+         (0xaf85e692),
+         (0xe8b73e52)},
+        {(0x00000000),
+         (0x10400000),
+         (0x24640000),
+         (0x34240000),
+         (0x81100000),
+         (0x91500000),
+         (0xa5740000),
+         (0xb5340000),
+         (0x08431e00),
+         (0x18031e00),
+         (0x2c271e00),
+         (0x3c671e00),
+         (0x89531e00),
+         (0x99131e00),
+         (0xad371e00),
+         (0xbd771e00)},
+        {(0x3f800000),
+         (0x3f882000),
+         (0x3f923200),
+         (0x3f9a1200),
+         (0x3fc08800),
+         (0x3fc8a800),
+         (0x3fd2ba00),
+         (0x3fda9a00),
+         (0x3f84218f),
+         (0x3f8c018f),
+         (0x3f96138f),
+         (0x3f9e338f),
+         (0x3fc4a98f),
+         (0x3fcc898f),
+         (0x3fd69b8f),
+         (0x3fdebb8f)},
+        (0xfff80000),
+        {0xdd,0x87,0x76,0x32,0x84,0x88,0x7a,0x5f,0x52,0x09,
+         0x1f,0x36,0xae,0xfa,0x7b,0x5d,0xb4,0x87,0x91,0x59,0x00}
+    },
+    {
+        /* No.158 delta:1376 weight:811 */
+        11213,
+        58,
+        16,
+        14,
+        {(0x00000000),
+         (0xdc5d3a0a),
+         (0x023cad01),
+         (0xde61970b),
+         (0x286009e2),
+         (0xf43d33e8),
+         (0x2a5ca4e3),
+         (0xf6019ee9),
+         (0x00009c42),
+         (0xdc5da648),
+         (0x023c3143),
+         (0xde610b49),
+         (0x286095a0),
+         (0xf43dafaa),
+         (0x2a5c38a1),
+         (0xf60102ab)},
+        {(0x00000000),
+         (0x50081000),
+         (0x305e0000),
+         (0x60561000),
+         (0x10324000),
+         (0x403a5000),
+         (0x206c4000),
+         (0x70645000),
+         (0x20011e00),
+         (0x70090e00),
+         (0x105f1e00),
+         (0x40570e00),
+         (0x30335e00),
+         (0x603b4e00),
+         (0x006d5e00),
+         (0x50654e00)},
+        {(0x3f800000),
+         (0x3fa80408),
+         (0x3f982f00),
+         (0x3fb02b08),
+         (0x3f881920),
+         (0x3fa01d28),
+         (0x3f903620),
+         (0x3fb83228),
+         (0x3f90008f),
+         (0x3fb80487),
+         (0x3f882f8f),
+         (0x3fa02b87),
+         (0x3f9819af),
+         (0x3fb01da7),
+         (0x3f8036af),
+         (0x3fa832a7)},
+        (0xfff80000),
+        {0x96,0xe0,0xc6,0xef,0x7f,0x51,0x3c,0x13,0xd3,0x63,
+         0x80,0x54,0xc8,0x09,0xcc,0x33,0x8e,0x9a,0x15,0x91,0x00}
+    },
+    {
+        /* No.159 delta:1647 weight:1099 */
+        11213,
+        91,
+        19,
+        1,
+        {(0x00000000),
+         (0xeffea95b),
+         (0x587a7fdf),
+         (0xb784d684),
+         (0xe5b009ff),
+         (0x0a4ea0a4),
+         (0xbdca7620),
+         (0x5234df7b),
+         (0x00008677),
+         (0xeffe2f2c),
+         (0x587af9a8),
+         (0xb78450f3),
+         (0xe5b08f88),
+         (0x0a4e26d3),
+         (0xbdcaf057),
+         (0x5234590c)},
+        {(0x00000000),
+         (0xc3463c00),
+         (0x20814400),
+         (0xe3c77800),
+         (0x04036400),
+         (0xc7455800),
+         (0x24822000),
+         (0xe7c41c00),
+         (0x66201e00),
+         (0xa5662200),
+         (0x46a15a00),
+         (0x85e76600),
+         (0x62237a00),
+         (0xa1654600),
+         (0x42a23e00),
+         (0x81e40200)},
+        {(0x3f800000),
+         (0x3fe1a31e),
+         (0x3f9040a2),
+         (0x3ff1e3bc),
+         (0x3f8201b2),
+         (0x3fe3a2ac),
+         (0x3f924110),
+         (0x3ff3e20e),
+         (0x3fb3100f),
+         (0x3fd2b311),
+         (0x3fa350ad),
+         (0x3fc2f3b3),
+         (0x3fb111bd),
+         (0x3fd0b2a3),
+         (0x3fa1511f),
+         (0x3fc0f201)},
+        (0xfff80000),
+        {0x6f,0x70,0x62,0xec,0x14,0x82,0xd2,0xb9,0xc0,0x8d,
+         0xdc,0xae,0x07,0x77,0xb8,0x94,0x3f,0xc9,0xfa,0xe1,0x00}
+    },
+    {
+        /* No.160 delta:2967 weight:901 */
+        11213,
+        53,
+        18,
+        1,
+        {(0x00000000),
+         (0xaa302b40),
+         (0x85ec337a),
+         (0x2fdc183a),
+         (0x3dd00a0a),
+         (0x97e0214a),
+         (0xb83c3970),
+         (0x120c1230),
+         (0x00008c9b),
+         (0xaa30a7db),
+         (0x85ecbfe1),
+         (0x2fdc94a1),
+         (0x3dd08691),
+         (0x97e0add1),
+         (0xb83cb5eb),
+         (0x120c9eab)},
+        {(0x00000000),
+         (0x0e4c0c00),
+         (0x81a28000),
+         (0x8fee8c00),
+         (0x01009400),
+         (0x0f4c9800),
+         (0x80a21400),
+         (0x8eee1800),
+         (0x09031e00),
+         (0x074f1200),
+         (0x88a19e00),
+         (0x86ed9200),
+         (0x08038a00),
+         (0x064f8600),
+         (0x89a10a00),
+         (0x87ed0600)},
+        {(0x3f800000),
+         (0x3f872606),
+         (0x3fc0d140),
+         (0x3fc7f746),
+         (0x3f80804a),
+         (0x3f87a64c),
+         (0x3fc0510a),
+         (0x3fc7770c),
+         (0x3f84818f),
+         (0x3f83a789),
+         (0x3fc450cf),
+         (0x3fc376c9),
+         (0x3f8401c5),
+         (0x3f8327c3),
+         (0x3fc4d085),
+         (0x3fc3f683)},
+        (0xfff80000),
+        {0xa5,0x57,0xd6,0x41,0xd4,0x56,0x49,0x1c,0xe3,0x86,
+         0x35,0xfc,0x5c,0xaf,0xd9,0xba,0x3e,0xfe,0x47,0xfa,0x00}
+    },
+    {
+        /* No.161 delta:1285 weight:1009 */
+        11213,
+        44,
+        22,
+        9,
+        {(0x00000000),
+         (0xbd887100),
+         (0x6d55a627),
+         (0xd0ddd727),
+         (0x18000a1d),
+         (0xa5887b1d),
+         (0x7555ac3a),
+         (0xc8dddd3a),
+         (0x0000acfa),
+         (0xbd88ddfa),
+         (0x6d550add),
+         (0xd0dd7bdd),
+         (0x1800a6e7),
+         (0xa588d7e7),
+         (0x755500c0),
+         (0xc8dd71c0)},
+        {(0x00000000),
+         (0x0fe36000),
+         (0x0045f000),
+         (0x0fa69000),
+         (0x121a2000),
+         (0x1df94000),
+         (0x125fd000),
+         (0x1dbcb000),
+         (0x00371e00),
+         (0x0fd47e00),
+         (0x0072ee00),
+         (0x0f918e00),
+         (0x122d3e00),
+         (0x1dce5e00),
+         (0x1268ce00),
+         (0x1d8bae00)},
+        {(0x3f800000),
+         (0x3f87f1b0),
+         (0x3f8022f8),
+         (0x3f87d348),
+         (0x3f890d10),
+         (0x3f8efca0),
+         (0x3f892fe8),
+         (0x3f8ede58),
+         (0x3f801b8f),
+         (0x3f87ea3f),
+         (0x3f803977),
+         (0x3f87c8c7),
+         (0x3f89169f),
+         (0x3f8ee72f),
+         (0x3f893467),
+         (0x3f8ec5d7)},
+        (0xfff80000),
+        {0xb3,0x0a,0x52,0x2b,0xc3,0xf3,0x8a,0x0b,0x60,0xcf,
+         0x50,0x81,0xc0,0x03,0x13,0x44,0x56,0xfd,0xe5,0x6c,0x00}
+    },
+    {
+        /* No.162 delta:3653 weight:1321 */
+        11213,
+        77,
+        4,
+        1,
+        {(0x00000000),
+         (0x09eb7b50),
+         (0x4a578b30),
+         (0x43bcf060),
+         (0xc4b00a20),
+         (0xcd5b7170),
+         (0x8ee78110),
+         (0x870cfa40),
+         (0x000030d1),
+         (0x09eb4b81),
+         (0x4a57bbe1),
+         (0x43bcc0b1),
+         (0xc4b03af1),
+         (0xcd5b41a1),
+         (0x8ee7b1c1),
+         (0x870cca91)},
+        {(0x00000000),
+         (0x20630000),
+         (0x00492000),
+         (0x202a2000),
+         (0x400a0000),
+         (0x60690000),
+         (0x40432000),
+         (0x60202000),
+         (0xc0027e00),
+         (0xe0617e00),
+         (0xc04b5e00),
+         (0xe0285e00),
+         (0x80087e00),
+         (0xa06b7e00),
+         (0x80415e00),
+         (0xa0225e00)},
+        {(0x3f800000),
+         (0x3f903180),
+         (0x3f802490),
+         (0x3f901510),
+         (0x3fa00500),
+         (0x3fb03480),
+         (0x3fa02190),
+         (0x3fb01010),
+         (0x3fe0013f),
+         (0x3ff030bf),
+         (0x3fe025af),
+         (0x3ff0142f),
+         (0x3fc0043f),
+         (0x3fd035bf),
+         (0x3fc020af),
+         (0x3fd0112f)},
+        (0xfff80000),
+        {0xed,0x0f,0x9e,0x5d,0x4b,0x7d,0x2e,0x4f,0xf8,0xa4,
+         0xba,0x23,0xdc,0x1a,0x0e,0x40,0xa9,0xff,0xbf,0xfa,0x00}
+    },
+    {
+        /* No.163 delta:1515 weight:829 */
+        11213,
+        59,
+        16,
+        13,
+        {(0x00000000),
+         (0xd192fecc),
+         (0x1c86d46e),
+         (0xcd142aa2),
+         (0xeb900a31),
+         (0x3a02f4fd),
+         (0xf716de5f),
+         (0x26842093),
+         (0x00009c7b),
+         (0xd19262b7),
+         (0x1c864815),
+         (0xcd14b6d9),
+         (0xeb90964a),
+         (0x3a026886),
+         (0xf7164224),
+         (0x2684bce8)},
+        {(0x00000000),
+         (0xc9688000),
+         (0x08400200),
+         (0xc1288200),
+         (0x082d4800),
+         (0xc145c800),
+         (0x006d4a00),
+         (0xc905ca00),
+         (0x312c1e00),
+         (0xf8449e00),
+         (0x396c1c00),
+         (0xf0049c00),
+         (0x39015600),
+         (0xf069d600),
+         (0x31415400),
+         (0xf829d400)},
+        {(0x3f800000),
+         (0x3fe4b440),
+         (0x3f842001),
+         (0x3fe09441),
+         (0x3f8416a4),
+         (0x3fe0a2e4),
+         (0x3f8036a5),
+         (0x3fe482e5),
+         (0x3f98960f),
+         (0x3ffc224f),
+         (0x3f9cb60e),
+         (0x3ff8024e),
+         (0x3f9c80ab),
+         (0x3ff834eb),
+         (0x3f98a0aa),
+         (0x3ffc14ea)},
+        (0xfff80000),
+        {0xe7,0xaa,0x57,0x85,0x45,0x30,0x30,0xea,0x81,0x55,
+         0x14,0xf3,0xe1,0x42,0x5a,0xfc,0x05,0x1e,0xd3,0xf0,0x00}
+    },
+    {
+        /* No.164 delta:5942 weight:1403 */
+        11213,
+        49,
+        28,
+        3,
+        {(0x00000000),
+         (0x96bbff16),
+         (0x0e2dd758),
+         (0x9896284e),
+         (0xb5700a46),
+         (0x23cbf550),
+         (0xbb5ddd1e),
+         (0x2de62208),
+         (0x0000f8d9),
+         (0x96bb07cf),
+         (0x0e2d2f81),
+         (0x9896d097),
+         (0xb570f29f),
+         (0x23cb0d89),
+         (0xbb5d25c7),
+         (0x2de6dad1)},
+        {(0x00000000),
+         (0x44940000),
+         (0x03b80000),
+         (0x472c0000),
+         (0x48600000),
+         (0x0cf40000),
+         (0x4bd80000),
+         (0x0f4c0000),
+         (0x00801e00),
+         (0x44141e00),
+         (0x03381e00),
+         (0x47ac1e00),
+         (0x48e01e00),
+         (0x0c741e00),
+         (0x4b581e00),
+         (0x0fcc1e00)},
+        {(0x3f800000),
+         (0x3fa24a00),
+         (0x3f81dc00),
+         (0x3fa39600),
+         (0x3fa43000),
+         (0x3f867a00),
+         (0x3fa5ec00),
+         (0x3f87a600),
+         (0x3f80400f),
+         (0x3fa20a0f),
+         (0x3f819c0f),
+         (0x3fa3d60f),
+         (0x3fa4700f),
+         (0x3f863a0f),
+         (0x3fa5ac0f),
+         (0x3f87e60f)},
+        (0xfff80000),
+        {0x76,0xb3,0x89,0x96,0x29,0x62,0x6a,0x7b,0xe7,0x6f,
+         0x4a,0x6c,0x1f,0xbf,0x96,0x66,0x8d,0x45,0x5d,0x5c,0x00}
+    },
+    {
+        /* No.165 delta:1776 weight:1045 */
+        11213,
+        74,
+        10,
+        13,
+        {(0x00000000),
+         (0x24c994e7),
+         (0x7e990c2c),
+         (0x5a5098cb),
+         (0xb9000a51),
+         (0x9dc99eb6),
+         (0xc799067d),
+         (0xe350929a),
+         (0x0000dbdf),
+         (0x24c94f38),
+         (0x7e99d7f3),
+         (0x5a504314),
+         (0xb900d18e),
+         (0x9dc94569),
+         (0xc799dda2),
+         (0xe3504945)},
+        {(0x00000000),
+         (0x00764000),
+         (0x000d4000),
+         (0x007b0000),
+         (0x102e6000),
+         (0x10582000),
+         (0x10232000),
+         (0x10556000),
+         (0x10065e00),
+         (0x10701e00),
+         (0x100b1e00),
+         (0x107d5e00),
+         (0x00283e00),
+         (0x005e7e00),
+         (0x00257e00),
+         (0x00533e00)},
+        {(0x3f800000),
+         (0x3f803b20),
+         (0x3f8006a0),
+         (0x3f803d80),
+         (0x3f881730),
+         (0x3f882c10),
+         (0x3f881190),
+         (0x3f882ab0),
+         (0x3f88032f),
+         (0x3f88380f),
+         (0x3f88058f),
+         (0x3f883eaf),
+         (0x3f80141f),
+         (0x3f802f3f),
+         (0x3f8012bf),
+         (0x3f80299f)},
+        (0xfff80000),
+        {0xfb,0xa8,0xac,0x3b,0xfe,0x8d,0xcc,0xf9,0x64,0x79,
+         0xa4,0xdb,0x6d,0x8f,0x8a,0x6e,0xb9,0x91,0x4e,0xb0,0x00}
+    },
+    {
+        /* No.166 delta:1278 weight:1311 */
+        11213,
+        38,
+        18,
+        10,
+        {(0x00000000),
+         (0x1aa51933),
+         (0x7654762b),
+         (0x6cf16f18),
+         (0x06100a69),
+         (0x1cb5135a),
+         (0x70447c42),
+         (0x6ae16571),
+         (0x0000bdcd),
+         (0x1aa5a4fe),
+         (0x7654cbe6),
+         (0x6cf1d2d5),
+         (0x0610b7a4),
+         (0x1cb5ae97),
+         (0x7044c18f),
+         (0x6ae1d8bc)},
+        {(0x00000000),
+         (0x1c020c00),
+         (0x10065600),
+         (0x0c045a00),
+         (0xa4030000),
+         (0xb8010c00),
+         (0xb4055600),
+         (0xa8075a00),
+         (0x51205e00),
+         (0x4d225200),
+         (0x41260800),
+         (0x5d240400),
+         (0xf5235e00),
+         (0xe9215200),
+         (0xe5250800),
+         (0xf9270400)},
+        {(0x3f800000),
+         (0x3f8e0106),
+         (0x3f88032b),
+         (0x3f86022d),
+         (0x3fd20180),
+         (0x3fdc0086),
+         (0x3fda02ab),
+         (0x3fd403ad),
+         (0x3fa8902f),
+         (0x3fa69129),
+         (0x3fa09304),
+         (0x3fae9202),
+         (0x3ffa91af),
+         (0x3ff490a9),
+         (0x3ff29284),
+         (0x3ffc9382)},
+        (0xfff80000),
+        {0xe2,0x4b,0xe1,0x7f,0x94,0x30,0x00,0x2c,0x65,0x3e,
+         0x59,0x45,0xfa,0x87,0x44,0x25,0xe1,0x2d,0xb7,0xdb,0x00}
+    },
+    {
+        /* No.167 delta:1582 weight:743 */
+        11213,
+        80,
+        9,
+        17,
+        {(0x00000000),
+         (0xf8f257fd),
+         (0x26c7fb3f),
+         (0xde35acc2),
+         (0xba900a7d),
+         (0x42625d80),
+         (0x9c57f142),
+         (0x64a5a6bf),
+         (0x00008157),
+         (0xf8f2d6aa),
+         (0x26c77a68),
+         (0xde352d95),
+         (0xba908b2a),
+         (0x4262dcd7),
+         (0x9c577015),
+         (0x64a527e8)},
+        {(0x00000000),
+         (0x884f0000),
+         (0x11035000),
+         (0x994c5000),
+         (0x2a02a800),
+         (0xa24da800),
+         (0x3b01f800),
+         (0xb34ef800),
+         (0x10041e00),
+         (0x984b1e00),
+         (0x01074e00),
+         (0x89484e00),
+         (0x3a06b600),
+         (0xb249b600),
+         (0x2b05e600),
+         (0xa34ae600)},
+        {(0x3f800000),
+         (0x3fc42780),
+         (0x3f8881a8),
+         (0x3fcca628),
+         (0x3f950154),
+         (0x3fd126d4),
+         (0x3f9d80fc),
+         (0x3fd9a77c),
+         (0x3f88020f),
+         (0x3fcc258f),
+         (0x3f8083a7),
+         (0x3fc4a427),
+         (0x3f9d035b),
+         (0x3fd924db),
+         (0x3f9582f3),
+         (0x3fd1a573)},
+        (0xfff80000),
+        {0xad,0x7a,0xb9,0x26,0x28,0x43,0xfc,0x1a,0x71,0x9d,
+         0xfe,0xb1,0x4c,0x75,0x45,0xcd,0x8d,0xf2,0x4d,0x01,0x00}
+    },
+    {
+        /* No.168 delta:7619 weight:787 */
+        11213,
+        59,
+        26,
+        1,
+        {(0x00000000),
+         (0x98e8b6d9),
+         (0x521f6a74),
+         (0xcaf7dcad),
+         (0x53d00a8d),
+         (0xcb38bc54),
+         (0x01cf60f9),
+         (0x9927d620),
+         (0x0000f72a),
+         (0x98e841f3),
+         (0x521f9d5e),
+         (0xcaf72b87),
+         (0x53d0fda7),
+         (0xcb384b7e),
+         (0x01cf97d3),
+         (0x9927210a)},
+        {(0x00000000),
+         (0x4e200000),
+         (0x70542000),
+         (0x3e742000),
+         (0x13942000),
+         (0x5db42000),
+         (0x63c00000),
+         (0x2de00000),
+         (0x08001e00),
+         (0x46201e00),
+         (0x78543e00),
+         (0x36743e00),
+         (0x1b943e00),
+         (0x55b43e00),
+         (0x6bc01e00),
+         (0x25e01e00)},
+        {(0x3f800000),
+         (0x3fa71000),
+         (0x3fb82a10),
+         (0x3f9f3a10),
+         (0x3f89ca10),
+         (0x3faeda10),
+         (0x3fb1e000),
+         (0x3f96f000),
+         (0x3f84000f),
+         (0x3fa3100f),
+         (0x3fbc2a1f),
+         (0x3f9b3a1f),
+         (0x3f8dca1f),
+         (0x3faada1f),
+         (0x3fb5e00f),
+         (0x3f92f00f)},
+        (0xfff80000),
+        {0xc1,0xeb,0x15,0xa4,0x17,0x5a,0x94,0xa3,0xdd,0x5b,
+         0x7e,0x69,0x12,0x88,0xd9,0x94,0x36,0x81,0x7c,0x98,0x00}
+    },
+    {
+        /* No.169 delta:1710 weight:1097 */
+        11213,
+        57,
+        14,
+        11,
+        {(0x00000000),
+         (0x5e421ddc),
+         (0x638787c4),
+         (0x3dc59a18),
+         (0x8e000a9d),
+         (0xd0421741),
+         (0xed878d59),
+         (0xb3c59085),
+         (0x0000840f),
+         (0x5e4299d3),
+         (0x638703cb),
+         (0x3dc51e17),
+         (0x8e008e92),
+         (0xd042934e),
+         (0xed870956),
+         (0xb3c5148a)},
+        {(0x00000000),
+         (0x4a752000),
+         (0xd0784000),
+         (0x9a0d6000),
+         (0x004c4000),
+         (0x4a396000),
+         (0xd0340000),
+         (0x9a412000),
+         (0x00005e00),
+         (0x4a757e00),
+         (0xd0781e00),
+         (0x9a0d3e00),
+         (0x004c1e00),
+         (0x4a393e00),
+         (0xd0345e00),
+         (0x9a417e00)},
+        {(0x3f800000),
+         (0x3fa53a90),
+         (0x3fe83c20),
+         (0x3fcd06b0),
+         (0x3f802620),
+         (0x3fa51cb0),
+         (0x3fe81a00),
+         (0x3fcd2090),
+         (0x3f80002f),
+         (0x3fa53abf),
+         (0x3fe83c0f),
+         (0x3fcd069f),
+         (0x3f80260f),
+         (0x3fa51c9f),
+         (0x3fe81a2f),
+         (0x3fcd20bf)},
+        (0xfff80000),
+        {0x63,0xf7,0x0a,0xee,0x1d,0x41,0x31,0xa9,0xfd,0x48,
+         0x35,0xb8,0x86,0xd8,0xf7,0x25,0x29,0x14,0xb0,0x12,0x00}
+    },
+    {
+        /* No.170 delta:4372 weight:995 */
+        11213,
+        28,
+        7,
+        15,
+        {(0x00000000),
+         (0x655fb5a5),
+         (0x87997101),
+         (0xe2c6c4a4),
+         (0x59f00aaa),
+         (0x3cafbf0f),
+         (0xde697bab),
+         (0xbb36ce0e),
+         (0x0000365d),
+         (0x655f83f8),
+         (0x8799475c),
+         (0xe2c6f2f9),
+         (0x59f03cf7),
+         (0x3caf8952),
+         (0xde694df6),
+         (0xbb36f853)},
+        {(0x00000000),
+         (0x81898800),
+         (0xd202e000),
+         (0x538b6800),
+         (0x08d64000),
+         (0x895fc800),
+         (0xdad4a000),
+         (0x5b5d2800),
+         (0x004a5e00),
+         (0x81c3d600),
+         (0xd248be00),
+         (0x53c13600),
+         (0x089c1e00),
+         (0x89159600),
+         (0xda9efe00),
+         (0x5b177600)},
+        {(0x3f800000),
+         (0x3fc0c4c4),
+         (0x3fe90170),
+         (0x3fa9c5b4),
+         (0x3f846b20),
+         (0x3fc4afe4),
+         (0x3fed6a50),
+         (0x3fadae94),
+         (0x3f80252f),
+         (0x3fc0e1eb),
+         (0x3fe9245f),
+         (0x3fa9e09b),
+         (0x3f844e0f),
+         (0x3fc48acb),
+         (0x3fed4f7f),
+         (0x3fad8bbb)},
+        (0xfff80000),
+        {0x89,0xf5,0x02,0x8f,0x88,0xdf,0x79,0x2c,0xc5,0x67,
+         0x1f,0x9e,0xd5,0x1b,0xd9,0x5a,0x79,0x7f,0xca,0x71,0x00}
+    },
+    {
+        /* No.171 delta:3023 weight:1549 */
+        11213,
+        32,
+        3,
+        5,
+        {(0x00000000),
+         (0x3b134609),
+         (0x5b97bf87),
+         (0x6084f98e),
+         (0xdbb00aba),
+         (0xe0a34cb3),
+         (0x8027b53d),
+         (0xbb34f334),
+         (0x00000ea9),
+         (0x3b1348a0),
+         (0x5b97b12e),
+         (0x6084f727),
+         (0xdbb00413),
+         (0xe0a3421a),
+         (0x8027bb94),
+         (0xbb34fd9d)},
+        {(0x00000000),
+         (0x04030000),
+         (0x00c10000),
+         (0x04c20000),
+         (0x304a8000),
+         (0x34498000),
+         (0x308b8000),
+         (0x34888000),
+         (0x00679e00),
+         (0x04649e00),
+         (0x00a69e00),
+         (0x04a59e00),
+         (0x302d1e00),
+         (0x342e1e00),
+         (0x30ec1e00),
+         (0x34ef1e00)},
+        {(0x3f800000),
+         (0x3f820180),
+         (0x3f806080),
+         (0x3f826100),
+         (0x3f982540),
+         (0x3f9a24c0),
+         (0x3f9845c0),
+         (0x3f9a4440),
+         (0x3f8033cf),
+         (0x3f82324f),
+         (0x3f80534f),
+         (0x3f8252cf),
+         (0x3f98168f),
+         (0x3f9a170f),
+         (0x3f98760f),
+         (0x3f9a778f)},
+        (0xfff80000),
+        {0x6a,0x8d,0xed,0xda,0x5f,0x16,0x85,0xea,0xd9,0x4c,
+         0x66,0x31,0xe2,0x9c,0xc6,0x66,0x6d,0x10,0xa0,0xf9,0x00}
+    },
+    {
+        /* No.172 delta:892 weight:1461 */
+        11213,
+        41,
+        18,
+        4,
+        {(0x00000000),
+         (0x51c43b00),
+         (0x7d71cf7d),
+         (0x2cb5f47d),
+         (0x03f00ac3),
+         (0x523431c3),
+         (0x7e81c5be),
+         (0x2f45febe),
+         (0x0000fa3d),
+         (0x51c4c13d),
+         (0x7d713540),
+         (0x2cb50e40),
+         (0x03f0f0fe),
+         (0x5234cbfe),
+         (0x7e813f83),
+         (0x2f450483)},
+        {(0x00000000),
+         (0x40672000),
+         (0x00508c00),
+         (0x4037ac00),
+         (0x00402000),
+         (0x40270000),
+         (0x0010ac00),
+         (0x40778c00),
+         (0x0e881e00),
+         (0x4eef3e00),
+         (0x0ed89200),
+         (0x4ebfb200),
+         (0x0ec83e00),
+         (0x4eaf1e00),
+         (0x0e98b200),
+         (0x4eff9200)},
+        {(0x3f800000),
+         (0x3fa03390),
+         (0x3f802846),
+         (0x3fa01bd6),
+         (0x3f802010),
+         (0x3fa01380),
+         (0x3f800856),
+         (0x3fa03bc6),
+         (0x3f87440f),
+         (0x3fa7779f),
+         (0x3f876c49),
+         (0x3fa75fd9),
+         (0x3f87641f),
+         (0x3fa7578f),
+         (0x3f874c59),
+         (0x3fa77fc9)},
+        (0xfff80000),
+        {0xc5,0x69,0xaf,0xb6,0xea,0x60,0x78,0x16,0x94,0x3a,
+         0x36,0x01,0x5a,0xe7,0x99,0xcd,0x09,0x6a,0xda,0xea,0x00}
+    },
+    {
+        /* No.173 delta:1013 weight:1115 */
+        11213,
+        91,
+        19,
+        8,
+        {(0x00000000),
+         (0xd6aede60),
+         (0xdfd2815a),
+         (0x097c5f3a),
+         (0xbc400ad0),
+         (0x6aeed4b0),
+         (0x63928b8a),
+         (0xb53c55ea),
+         (0x000003b3),
+         (0xd6aeddd3),
+         (0xdfd282e9),
+         (0x097c5c89),
+         (0xbc400963),
+         (0x6aeed703),
+         (0x63928839),
+         (0xb53c5659)},
+        {(0x00000000),
+         (0x24590000),
+         (0x08c14000),
+         (0x2c984000),
+         (0x80083800),
+         (0xa4513800),
+         (0x88c97800),
+         (0xac907800),
+         (0x50c09e00),
+         (0x74999e00),
+         (0x5801de00),
+         (0x7c58de00),
+         (0xd0c8a600),
+         (0xf491a600),
+         (0xd809e600),
+         (0xfc50e600)},
+        {(0x3f800000),
+         (0x3f922c80),
+         (0x3f8460a0),
+         (0x3f964c20),
+         (0x3fc0041c),
+         (0x3fd2289c),
+         (0x3fc464bc),
+         (0x3fd6483c),
+         (0x3fa8604f),
+         (0x3fba4ccf),
+         (0x3fac00ef),
+         (0x3fbe2c6f),
+         (0x3fe86453),
+         (0x3ffa48d3),
+         (0x3fec04f3),
+         (0x3ffe2873)},
+        (0xfff80000),
+        {0x55,0x6c,0x5c,0x20,0xdd,0xbb,0xa3,0xad,0x56,0x67,
+         0x8f,0xcf,0xc9,0xb4,0xc5,0x0f,0x66,0xf7,0xb5,0xcc,0x00}
+    },
+    {
+        /* No.174 delta:2582 weight:887 */
+        11213,
+        40,
+        4,
+        1,
+        {(0x00000000),
+         (0x70c0d1b0),
+         (0x83972550),
+         (0xf357f4e0),
+         (0xbbc00ae0),
+         (0xcb00db50),
+         (0x38572fb0),
+         (0x4897fe00),
+         (0x0000df80),
+         (0x70c00e30),
+         (0x8397fad0),
+         (0xf3572b60),
+         (0xbbc0d560),
+         (0xcb0004d0),
+         (0x3857f030),
+         (0x48972180)},
+        {(0x00000000),
+         (0x00b20200),
+         (0x00430000),
+         (0x00f10200),
+         (0x40024000),
+         (0x40b04200),
+         (0x40414000),
+         (0x40f34200),
+         (0x6401de00),
+         (0x64b3dc00),
+         (0x6442de00),
+         (0x64f0dc00),
+         (0x24039e00),
+         (0x24b19c00),
+         (0x24409e00),
+         (0x24f29c00)},
+        {(0x3f800000),
+         (0x3f805901),
+         (0x3f802180),
+         (0x3f807881),
+         (0x3fa00120),
+         (0x3fa05821),
+         (0x3fa020a0),
+         (0x3fa079a1),
+         (0x3fb200ef),
+         (0x3fb259ee),
+         (0x3fb2216f),
+         (0x3fb2786e),
+         (0x3f9201cf),
+         (0x3f9258ce),
+         (0x3f92204f),
+         (0x3f92794e)},
+        (0xfff80000),
+        {0x4c,0x34,0x96,0xf1,0x70,0xe5,0x52,0xb1,0x4d,0x38,
+         0xa6,0x34,0x26,0x09,0x5c,0x6c,0x92,0xe6,0x3e,0xcb,0x00}
+    },
+    {
+        /* No.175 delta:1962 weight:1531 */
+        11213,
+        11,
+        19,
+        4,
+        {(0x00000000),
+         (0x91291aaa),
+         (0xdc02bffd),
+         (0x4d2ba557),
+         (0x1fc00af5),
+         (0x8ee9105f),
+         (0xc3c2b508),
+         (0x52ebafa2),
+         (0x0000add4),
+         (0x9129b77e),
+         (0xdc021229),
+         (0x4d2b0883),
+         (0x1fc0a721),
+         (0x8ee9bd8b),
+         (0xc3c218dc),
+         (0x52eb0276)},
+        {(0x00000000),
+         (0x09ff0c00),
+         (0x09141600),
+         (0x00eb1a00),
+         (0x7510e000),
+         (0x7cefec00),
+         (0x7c04f600),
+         (0x75fbfa00),
+         (0x05509e00),
+         (0x0caf9200),
+         (0x0c448800),
+         (0x05bb8400),
+         (0x70407e00),
+         (0x79bf7200),
+         (0x79546800),
+         (0x70ab6400)},
+        {(0x3f800000),
+         (0x3f84ff86),
+         (0x3f848a0b),
+         (0x3f80758d),
+         (0x3fba8870),
+         (0x3fbe77f6),
+         (0x3fbe027b),
+         (0x3fbafdfd),
+         (0x3f82a84f),
+         (0x3f8657c9),
+         (0x3f862244),
+         (0x3f82ddc2),
+         (0x3fb8203f),
+         (0x3fbcdfb9),
+         (0x3fbcaa34),
+         (0x3fb855b2)},
+        (0xfff80000),
+        {0x6f,0x40,0xbd,0xae,0x48,0xc1,0x7d,0x5b,0x99,0x1a,
+         0x4e,0x57,0x61,0x39,0x1e,0x68,0xb1,0xf7,0xc0,0xaa,0x00}
+    },
+    {
+        /* No.176 delta:2277 weight:543 */
+        11213,
+        33,
+        7,
+        19,
+        {(0x00000000),
+         (0x8044010b),
+         (0x62414b11),
+         (0xe2054a1a),
+         (0xbb700b06),
+         (0x3b340a0d),
+         (0xd9314017),
+         (0x5975411c),
+         (0x000099a5),
+         (0x804498ae),
+         (0x6241d2b4),
+         (0xe205d3bf),
+         (0xbb7092a3),
+         (0x3b3493a8),
+         (0xd931d9b2),
+         (0x5975d8b9)},
+        {(0x00000000),
+         (0x0c3e0800),
+         (0x80be4000),
+         (0x8c804800),
+         (0x00483000),
+         (0x0c763800),
+         (0x80f67000),
+         (0x8cc87800),
+         (0x00791e00),
+         (0x0c471600),
+         (0x80c75e00),
+         (0x8cf95600),
+         (0x00312e00),
+         (0x0c0f2600),
+         (0x808f6e00),
+         (0x8cb16600)},
+        {(0x3f800000),
+         (0x3f861f04),
+         (0x3fc05f20),
+         (0x3fc64024),
+         (0x3f802418),
+         (0x3f863b1c),
+         (0x3fc07b38),
+         (0x3fc6643c),
+         (0x3f803c8f),
+         (0x3f86238b),
+         (0x3fc063af),
+         (0x3fc67cab),
+         (0x3f801897),
+         (0x3f860793),
+         (0x3fc047b7),
+         (0x3fc658b3)},
+        (0xfff80000),
+        {0xfc,0x12,0x23,0x4e,0x03,0xa3,0xf6,0xfd,0x28,0xb5,
+         0xf1,0x0e,0x0a,0x5d,0xed,0x4a,0xd6,0x0c,0xfa,0x9a,0x00}
+    },
+    {
+        /* No.177 delta:1331 weight:1179 */
+        11213,
+        60,
+        21,
+        4,
+        {(0x00000000),
+         (0x8e46be68),
+         (0x9cb3a5d7),
+         (0x12f51bbf),
+         (0x3e600b12),
+         (0xb026b57a),
+         (0xa2d3aec5),
+         (0x2c9510ad),
+         (0x0000942c),
+         (0x8e462a44),
+         (0x9cb331fb),
+         (0x12f58f93),
+         (0x3e609f3e),
+         (0xb0262156),
+         (0xa2d33ae9),
+         (0x2c958481)},
+        {(0x00000000),
+         (0x0940a800),
+         (0x388c9000),
+         (0x31cc3800),
+         (0x00608000),
+         (0x09202800),
+         (0x38ec1000),
+         (0x31acb800),
+         (0x005c7e00),
+         (0x091cd600),
+         (0x38d0ee00),
+         (0x31904600),
+         (0x003cfe00),
+         (0x097c5600),
+         (0x38b06e00),
+         (0x31f0c600)},
+        {(0x3f800000),
+         (0x3f84a054),
+         (0x3f9c4648),
+         (0x3f98e61c),
+         (0x3f803040),
+         (0x3f849014),
+         (0x3f9c7608),
+         (0x3f98d65c),
+         (0x3f802e3f),
+         (0x3f848e6b),
+         (0x3f9c6877),
+         (0x3f98c823),
+         (0x3f801e7f),
+         (0x3f84be2b),
+         (0x3f9c5837),
+         (0x3f98f863)},
+        (0xfff80000),
+        {0x71,0x3a,0xb0,0x71,0xe1,0x95,0x3c,0xba,0x23,0x00,
+         0x7a,0xc6,0x93,0x4a,0xb7,0xb8,0xeb,0x00,0x24,0x8e,0x00}
+    },
+    {
+        /* No.178 delta:3213 weight:657 */
+        11213,
+        46,
+        22,
+        1,
+        {(0x00000000),
+         (0x53fdf1b0),
+         (0x32502578),
+         (0x61add4c8),
+         (0x46c00b28),
+         (0x153dfa98),
+         (0x74902e50),
+         (0x276ddfe0),
+         (0x00006d70),
+         (0x53fd9cc0),
+         (0x32504808),
+         (0x61adb9b8),
+         (0x46c06658),
+         (0x153d97e8),
+         (0x74904320),
+         (0x276db290)},
+        {(0x00000000),
+         (0x44633a00),
+         (0x44038600),
+         (0x0060bc00),
+         (0x1819c200),
+         (0x5c7af800),
+         (0x5c1a4400),
+         (0x18797e00),
+         (0x21eade00),
+         (0x6589e400),
+         (0x65e95800),
+         (0x218a6200),
+         (0x39f31c00),
+         (0x7d902600),
+         (0x7df09a00),
+         (0x3993a000)},
+        {(0x3f800000),
+         (0x3fa2319d),
+         (0x3fa201c3),
+         (0x3f80305e),
+         (0x3f8c0ce1),
+         (0x3fae3d7c),
+         (0x3fae0d22),
+         (0x3f8c3cbf),
+         (0x3f90f56f),
+         (0x3fb2c4f2),
+         (0x3fb2f4ac),
+         (0x3f90c531),
+         (0x3f9cf98e),
+         (0x3fbec813),
+         (0x3fbef84d),
+         (0x3f9cc9d0)},
+        (0xfff80000),
+        {0x92,0xb2,0x66,0x1e,0x08,0xb8,0x0b,0x02,0x41,0xdc,
+         0x45,0x04,0x59,0x65,0x0b,0x55,0x82,0x05,0xeb,0x6d,0x00}
+    },
+    {
+        /* No.179 delta:4972 weight:1077 */
+        11213,
+        16,
+        7,
+        11,
+        {(0x00000000),
+         (0xcf8962a9),
+         (0xb6d797a1),
+         (0x795ef508),
+         (0xfbb00b36),
+         (0x3439699f),
+         (0x4d679c97),
+         (0x82eefe3e),
+         (0x0000d59e),
+         (0xcf89b737),
+         (0xb6d7423f),
+         (0x795e2096),
+         (0xfbb0dea8),
+         (0x3439bc01),
+         (0x4d674909),
+         (0x82ee2ba0)},
+        {(0x00000000),
+         (0x01000000),
+         (0x08000000),
+         (0x09000000),
+         (0x41800000),
+         (0x40800000),
+         (0x49800000),
+         (0x48800000),
+         (0x06801e00),
+         (0x07801e00),
+         (0x0e801e00),
+         (0x0f801e00),
+         (0x47001e00),
+         (0x46001e00),
+         (0x4f001e00),
+         (0x4e001e00)},
+        {(0x3f800000),
+         (0x3f808000),
+         (0x3f840000),
+         (0x3f848000),
+         (0x3fa0c000),
+         (0x3fa04000),
+         (0x3fa4c000),
+         (0x3fa44000),
+         (0x3f83400f),
+         (0x3f83c00f),
+         (0x3f87400f),
+         (0x3f87c00f),
+         (0x3fa3800f),
+         (0x3fa3000f),
+         (0x3fa7800f),
+         (0x3fa7000f)},
+        (0xfff80000),
+        {0x55,0xe1,0xc9,0x26,0x74,0x2c,0xed,0x5c,0x7b,0x89,
+         0xe7,0x1d,0x6f,0x5d,0x02,0xe5,0x4a,0x19,0x8c,0x04,0x00}
+    },
+    {
+        /* No.180 delta:2285 weight:1529 */
+        11213,
+        16,
+        5,
+        9,
+        {(0x00000000),
+         (0x5766ef3b),
+         (0xaca2499c),
+         (0xfbc4a6a7),
+         (0x63800b4f),
+         (0x34e6e474),
+         (0xcf2242d3),
+         (0x9844ade8),
+         (0x0000a19a),
+         (0x57664ea1),
+         (0xaca2e806),
+         (0xfbc4073d),
+         (0x6380aad5),
+         (0x34e645ee),
+         (0xcf22e349),
+         (0x98440c72)},
+        {(0x00000000),
+         (0x1901c200),
+         (0x88118000),
+         (0x91104200),
+         (0x02ef8000),
+         (0x1bee4200),
+         (0x8afe0000),
+         (0x93ffc200),
+         (0x3087be00),
+         (0x29867c00),
+         (0xb8963e00),
+         (0xa197fc00),
+         (0x32683e00),
+         (0x2b69fc00),
+         (0xba79be00),
+         (0xa3787c00)},
+        {(0x3f800000),
+         (0x3f8c80e1),
+         (0x3fc408c0),
+         (0x3fc88821),
+         (0x3f8177c0),
+         (0x3f8df721),
+         (0x3fc57f00),
+         (0x3fc9ffe1),
+         (0x3f9843df),
+         (0x3f94c33e),
+         (0x3fdc4b1f),
+         (0x3fd0cbfe),
+         (0x3f99341f),
+         (0x3f95b4fe),
+         (0x3fdd3cdf),
+         (0x3fd1bc3e)},
+        (0xfff80000),
+        {0x4b,0xb0,0xb5,0xae,0x6b,0x5e,0xea,0x44,0x3b,0xd2,
+         0xcf,0xf3,0x8a,0x8d,0xff,0x98,0xeb,0xce,0xa2,0x22,0x00}
+    },
+    {
+        /* No.181 delta:1320 weight:1461 */
+        11213,
+        52,
+        21,
+        2,
+        {(0x00000000),
+         (0x2f3716e3),
+         (0x0af6f7a8),
+         (0x25c1e14b),
+         (0x67900b5a),
+         (0x48a71db9),
+         (0x6d66fcf2),
+         (0x4251ea11),
+         (0x0000adb5),
+         (0x2f37bb56),
+         (0x0af65a1d),
+         (0x25c14cfe),
+         (0x6790a6ef),
+         (0x48a7b00c),
+         (0x6d665147),
+         (0x425147a4)},
+        {(0x00000000),
+         (0xc0295e00),
+         (0x1040c000),
+         (0xd0699e00),
+         (0x20fc7200),
+         (0xe0d52c00),
+         (0x30bcb200),
+         (0xf095ec00),
+         (0x10a2fe00),
+         (0xd08ba000),
+         (0x00e23e00),
+         (0xc0cb6000),
+         (0x305e8c00),
+         (0xf077d200),
+         (0x201e4c00),
+         (0xe0371200)},
+        {(0x3f800000),
+         (0x3fe014af),
+         (0x3f882060),
+         (0x3fe834cf),
+         (0x3f907e39),
+         (0x3ff06a96),
+         (0x3f985e59),
+         (0x3ff84af6),
+         (0x3f88517f),
+         (0x3fe845d0),
+         (0x3f80711f),
+         (0x3fe065b0),
+         (0x3f982f46),
+         (0x3ff83be9),
+         (0x3f900f26),
+         (0x3ff01b89)},
+        (0xfff80000),
+        {0xf7,0x30,0xe4,0xa1,0xb6,0x2c,0xff,0x05,0x23,0x13,
+         0xd1,0x56,0xd7,0x7a,0x22,0x24,0xae,0xf7,0x34,0x52,0x00}
+    },
+    {
+        /* No.182 delta:789 weight:1509 */
+        11213,
+        74,
+        16,
+        6,
+        {(0x00000000),
+         (0xe416060b),
+         (0xd6bfa624),
+         (0x32a9a02f),
+         (0x56500b61),
+         (0xb2460d6a),
+         (0x80efad45),
+         (0x64f9ab4e),
+         (0x0000ce80),
+         (0xe416c88b),
+         (0xd6bf68a4),
+         (0x32a96eaf),
+         (0x5650c5e1),
+         (0xb246c3ea),
+         (0x80ef63c5),
+         (0x64f965ce)},
+        {(0x00000000),
+         (0x804c0800),
+         (0x107ad000),
+         (0x9036d800),
+         (0x0055c400),
+         (0x8019cc00),
+         (0x102f1400),
+         (0x90631c00),
+         (0x00033e00),
+         (0x804f3600),
+         (0x1079ee00),
+         (0x9035e600),
+         (0x0056fa00),
+         (0x801af200),
+         (0x102c2a00),
+         (0x90602200)},
+        {(0x3f800000),
+         (0x3fc02604),
+         (0x3f883d68),
+         (0x3fc81b6c),
+         (0x3f802ae2),
+         (0x3fc00ce6),
+         (0x3f88178a),
+         (0x3fc8318e),
+         (0x3f80019f),
+         (0x3fc0279b),
+         (0x3f883cf7),
+         (0x3fc81af3),
+         (0x3f802b7d),
+         (0x3fc00d79),
+         (0x3f881615),
+         (0x3fc83011)},
+        (0xfff80000),
+        {0xa2,0xea,0x48,0x1e,0xb3,0x8f,0x99,0x75,0xba,0x12,
+         0x9c,0xbf,0xf3,0xa0,0xb8,0x5d,0xc8,0x38,0xc4,0x74,0x00}
+    },
+    {
+        /* No.183 delta:1268 weight:1109 */
+        11213,
+        42,
+        13,
+        11,
+        {(0x00000000),
+         (0xf7ba01fd),
+         (0xaa8773ac),
+         (0x5d3d7251),
+         (0xa5b00b7d),
+         (0x520a0a80),
+         (0x0f3778d1),
+         (0xf88d792c),
+         (0x0000aaa0),
+         (0xf7baab5d),
+         (0xaa87d90c),
+         (0x5d3dd8f1),
+         (0xa5b0a1dd),
+         (0x520aa020),
+         (0x0f37d271),
+         (0xf88dd38c)},
+        {(0x00000000),
+         (0x92abc000),
+         (0x00443800),
+         (0x92eff800),
+         (0x00c12000),
+         (0x926ae000),
+         (0x00851800),
+         (0x922ed800),
+         (0x02a3be00),
+         (0x90087e00),
+         (0x02e78600),
+         (0x904c4600),
+         (0x02629e00),
+         (0x90c95e00),
+         (0x0226a600),
+         (0x908d6600)},
+        {(0x3f800000),
+         (0x3fc955e0),
+         (0x3f80221c),
+         (0x3fc977fc),
+         (0x3f806090),
+         (0x3fc93570),
+         (0x3f80428c),
+         (0x3fc9176c),
+         (0x3f8151df),
+         (0x3fc8043f),
+         (0x3f8173c3),
+         (0x3fc82623),
+         (0x3f81314f),
+         (0x3fc864af),
+         (0x3f811353),
+         (0x3fc846b3)},
+        (0xfff80000),
+        {0xb0,0x87,0xce,0x22,0xcb,0xa4,0x10,0xdc,0xdb,0x3e,
+         0x3c,0x67,0xae,0x3f,0xc0,0x11,0xf3,0xec,0xe2,0x06,0x00}
+    },
+    {
+        /* No.184 delta:1092 weight:1295 */
+        11213,
+        91,
+        17,
+        8,
+        {(0x00000000),
+         (0x2fa2608e),
+         (0x8f680761),
+         (0xa0ca67ef),
+         (0x13a00b8a),
+         (0x3c026b04),
+         (0x9cc80ceb),
+         (0xb36a6c65),
+         (0x00007f70),
+         (0x2fa21ffe),
+         (0x8f687811),
+         (0xa0ca189f),
+         (0x13a074fa),
+         (0x3c021474),
+         (0x9cc8739b),
+         (0xb36a1315)},
+        {(0x00000000),
+         (0x089d6000),
+         (0x1a00e200),
+         (0x129d8200),
+         (0x40df4000),
+         (0x48422000),
+         (0x5adfa200),
+         (0x5242c200),
+         (0x01a6de00),
+         (0x093bbe00),
+         (0x1ba63c00),
+         (0x133b5c00),
+         (0x41799e00),
+         (0x49e4fe00),
+         (0x5b797c00),
+         (0x53e41c00)},
+        {(0x3f800000),
+         (0x3f844eb0),
+         (0x3f8d0071),
+         (0x3f894ec1),
+         (0x3fa06fa0),
+         (0x3fa42110),
+         (0x3fad6fd1),
+         (0x3fa92161),
+         (0x3f80d36f),
+         (0x3f849ddf),
+         (0x3f8dd31e),
+         (0x3f899dae),
+         (0x3fa0bccf),
+         (0x3fa4f27f),
+         (0x3fadbcbe),
+         (0x3fa9f20e)},
+        (0xfff80000),
+        {0xbd,0xb1,0x02,0x3f,0x46,0x65,0xe7,0x62,0xbf,0x9e,
+         0x12,0xaa,0x33,0xbe,0x0c,0x5d,0x89,0x02,0x2c,0x11,0x00}
+    },
+    {
+        /* No.185 delta:8146 weight:1071 */
+        11213,
+        66,
+        28,
+        1,
+        {(0x00000000),
+         (0xc3e3a60c),
+         (0x80c000cd),
+         (0x4323a6c1),
+         (0xda800b97),
+         (0x1963ad9b),
+         (0x5a400b5a),
+         (0x99a3ad56),
+         (0x000058ad),
+         (0xc3e3fea1),
+         (0x80c05860),
+         (0x4323fe6c),
+         (0xda80533a),
+         (0x1963f536),
+         (0x5a4053f7),
+         (0x99a3f5fb)},
+        {(0x00000000),
+         (0x89d10a00),
+         (0x403c0200),
+         (0xc9ed0800),
+         (0x53520000),
+         (0xda830a00),
+         (0x136e0200),
+         (0x9abf0800),
+         (0x61609e00),
+         (0xe8b19400),
+         (0x215c9c00),
+         (0xa88d9600),
+         (0x32329e00),
+         (0xbbe39400),
+         (0x720e9c00),
+         (0xfbdf9600)},
+        {(0x3f800000),
+         (0x3fc4e885),
+         (0x3fa01e01),
+         (0x3fe4f684),
+         (0x3fa9a900),
+         (0x3fed4185),
+         (0x3f89b701),
+         (0x3fcd5f84),
+         (0x3fb0b04f),
+         (0x3ff458ca),
+         (0x3f90ae4e),
+         (0x3fd446cb),
+         (0x3f99194f),
+         (0x3fddf1ca),
+         (0x3fb9074e),
+         (0x3ffdefcb)},
+        (0xfff80000),
+        {0xf7,0x3c,0x9c,0x40,0x28,0xb5,0xbe,0xbf,0x2e,0x97,
+         0xa1,0x61,0x3b,0x82,0x2a,0x82,0x3c,0xa0,0xf4,0x90,0x00}
+    },
+    {
+        /* No.186 delta:2697 weight:1957 */
+        11213,
+        43,
+        3,
+        3,
+        {(0x00000000),
+         (0x7a0a48fc),
+         (0x29a53edd),
+         (0x53af7621),
+         (0x08f00ba0),
+         (0x72fa435c),
+         (0x2155357d),
+         (0x5b5f7d81),
+         (0x0000132f),
+         (0x7a0a5bd3),
+         (0x29a52df2),
+         (0x53af650e),
+         (0x08f0188f),
+         (0x72fa5073),
+         (0x21552652),
+         (0x5b5f6eae)},
+        {(0x00000000),
+         (0xa2021000),
+         (0x1c21a000),
+         (0xbe23b000),
+         (0x00408000),
+         (0xa2429000),
+         (0x1c612000),
+         (0xbe633000),
+         (0x40201e00),
+         (0xe2220e00),
+         (0x5c01be00),
+         (0xfe03ae00),
+         (0x40609e00),
+         (0xe2628e00),
+         (0x5c413e00),
+         (0xfe432e00)},
+        {(0x3f800000),
+         (0x3fd10108),
+         (0x3f8e10d0),
+         (0x3fdf11d8),
+         (0x3f802040),
+         (0x3fd12148),
+         (0x3f8e3090),
+         (0x3fdf3198),
+         (0x3fa0100f),
+         (0x3ff11107),
+         (0x3fae00df),
+         (0x3fff01d7),
+         (0x3fa0304f),
+         (0x3ff13147),
+         (0x3fae209f),
+         (0x3fff2197)},
+        (0xfff80000),
+        {0x1a,0x79,0x8f,0xb2,0xec,0xc2,0xee,0xfc,0x66,0xa6,
+         0x37,0x12,0x4d,0x78,0x6e,0xa1,0xb0,0x60,0x86,0x37,0x00}
+    },
+    {
+        /* No.187 delta:2420 weight:1355 */
+        11213,
+        28,
+        24,
+        3,
+        {(0x00000000),
+         (0x9580742c),
+         (0xce45a063),
+         (0x5bc5d44f),
+         (0x33f00bbf),
+         (0xa6707f93),
+         (0xfdb5abdc),
+         (0x6835dff0),
+         (0x000009ba),
+         (0x95807d96),
+         (0xce45a9d9),
+         (0x5bc5ddf5),
+         (0x33f00205),
+         (0xa6707629),
+         (0xfdb5a266),
+         (0x6835d64a)},
+        {(0x00000000),
+         (0x40bf0200),
+         (0x00440200),
+         (0x40fb0000),
+         (0x3411d000),
+         (0x74aed200),
+         (0x3455d200),
+         (0x74ead000),
+         (0x9082de00),
+         (0xd03ddc00),
+         (0x90c6dc00),
+         (0xd079de00),
+         (0xa4930e00),
+         (0xe42c0c00),
+         (0xa4d70c00),
+         (0xe4680e00)},
+        {(0x3f800000),
+         (0x3fa05f81),
+         (0x3f802201),
+         (0x3fa07d80),
+         (0x3f9a08e8),
+         (0x3fba5769),
+         (0x3f9a2ae9),
+         (0x3fba7568),
+         (0x3fc8416f),
+         (0x3fe81eee),
+         (0x3fc8636e),
+         (0x3fe83cef),
+         (0x3fd24987),
+         (0x3ff21606),
+         (0x3fd26b86),
+         (0x3ff23407)},
+        (0xfff80000),
+        {0x20,0xe4,0x7d,0x6c,0xc4,0xf9,0x2b,0x4a,0x56,0x18,
+         0x64,0xa0,0x86,0x2c,0x45,0x4a,0x42,0x3a,0x22,0x49,0x00}
+    },
+    {
+        /* No.188 delta:1841 weight:735 */
+        11213,
+        62,
+        10,
+        18,
+        {(0x00000000),
+         (0x179a1ef2),
+         (0x535f148e),
+         (0x44c50a7c),
+         (0x33700bc3),
+         (0x24ea1531),
+         (0x602f1f4d),
+         (0x77b501bf),
+         (0x0000df3e),
+         (0x179ac1cc),
+         (0x535fcbb0),
+         (0x44c5d542),
+         (0x3370d4fd),
+         (0x24eaca0f),
+         (0x602fc073),
+         (0x77b5de81)},
+        {(0x00000000),
+         (0x30b90000),
+         (0x083c0000),
+         (0x38850000),
+         (0x40070000),
+         (0x70be0000),
+         (0x483b0000),
+         (0x78820000),
+         (0x10651e00),
+         (0x20dc1e00),
+         (0x18591e00),
+         (0x28e01e00),
+         (0x50621e00),
+         (0x60db1e00),
+         (0x585e1e00),
+         (0x68e71e00)},
+        {(0x3f800000),
+         (0x3f985c80),
+         (0x3f841e00),
+         (0x3f9c4280),
+         (0x3fa00380),
+         (0x3fb85f00),
+         (0x3fa41d80),
+         (0x3fbc4100),
+         (0x3f88328f),
+         (0x3f906e0f),
+         (0x3f8c2c8f),
+         (0x3f94700f),
+         (0x3fa8310f),
+         (0x3fb06d8f),
+         (0x3fac2f0f),
+         (0x3fb4738f)},
+        (0xfff80000),
+        {0x09,0x19,0x78,0xe9,0x81,0xea,0xdf,0x95,0x46,0x6b,
+         0x4f,0xa1,0xca,0x2b,0xbc,0x8a,0x0b,0xaa,0x44,0xb8,0x00}
+    },
+    {
+        /* No.189 delta:3318 weight:1469 */
+        11213,
+        81,
+        3,
+        7,
+        {(0x00000000),
+         (0xfe728280),
+         (0xcc048331),
+         (0x327601b1),
+         (0x42200bd8),
+         (0xbc528958),
+         (0x8e2488e9),
+         (0x70560a69),
+         (0x0000d1a0),
+         (0xfe725320),
+         (0xcc045291),
+         (0x3276d011),
+         (0x4220da78),
+         (0xbc5258f8),
+         (0x8e245949),
+         (0x7056dbc9)},
+        {(0x00000000),
+         (0x12010000),
+         (0x53002000),
+         (0x41012000),
+         (0x20414000),
+         (0x32404000),
+         (0x73416000),
+         (0x61406000),
+         (0x20321e00),
+         (0x32331e00),
+         (0x73323e00),
+         (0x61333e00),
+         (0x00735e00),
+         (0x12725e00),
+         (0x53737e00),
+         (0x41727e00)},
+        {(0x3f800000),
+         (0x3f890080),
+         (0x3fa98010),
+         (0x3fa08090),
+         (0x3f9020a0),
+         (0x3f992020),
+         (0x3fb9a0b0),
+         (0x3fb0a030),
+         (0x3f90190f),
+         (0x3f99198f),
+         (0x3fb9991f),
+         (0x3fb0999f),
+         (0x3f8039af),
+         (0x3f89392f),
+         (0x3fa9b9bf),
+         (0x3fa0b93f)},
+        (0xfff80000),
+        {0x0f,0xe8,0x93,0xf4,0x0e,0x7e,0xaf,0xe0,0x5d,0x20,
+         0x58,0x92,0x06,0x7d,0x5b,0x05,0x6b,0x8a,0x33,0xbb,0x00}
+    },
+    {
+        /* No.190 delta:1194 weight:821 */
+        11213,
+        72,
+        11,
+        16,
+        {(0x00000000),
+         (0x44a385c2),
+         (0xca99870f),
+         (0x8e3a02cd),
+         (0x06c00be8),
+         (0x42638e2a),
+         (0xcc598ce7),
+         (0x88fa0925),
+         (0x00007bbf),
+         (0x44a3fe7d),
+         (0xca99fcb0),
+         (0x8e3a7972),
+         (0x06c07057),
+         (0x4263f595),
+         (0xcc59f758),
+         (0x88fa729a)},
+        {(0x00000000),
+         (0x00527800),
+         (0x302f0400),
+         (0x307d7c00),
+         (0x20017000),
+         (0x20530800),
+         (0x102e7400),
+         (0x107c0c00),
+         (0x10429e00),
+         (0x1010e600),
+         (0x206d9a00),
+         (0x203fe200),
+         (0x3043ee00),
+         (0x30119600),
+         (0x006cea00),
+         (0x003e9200)},
+        {(0x3f800000),
+         (0x3f80293c),
+         (0x3f981782),
+         (0x3f983ebe),
+         (0x3f9000b8),
+         (0x3f902984),
+         (0x3f88173a),
+         (0x3f883e06),
+         (0x3f88214f),
+         (0x3f880873),
+         (0x3f9036cd),
+         (0x3f901ff1),
+         (0x3f9821f7),
+         (0x3f9808cb),
+         (0x3f803675),
+         (0x3f801f49)},
+        (0xfff80000),
+        {0xba,0xb7,0xd5,0xc8,0xec,0x6d,0xc1,0x0c,0x19,0x38,
+         0x0f,0x27,0x93,0x3b,0xd8,0x74,0xa1,0x4e,0x45,0x89,0x00}
+    },
+    {
+        /* No.191 delta:1166 weight:1045 */
+        11213,
+        40,
+        11,
+        9,
+        {(0x00000000),
+         (0x212f4af4),
+         (0xf0432caf),
+         (0xd16c665b),
+         (0x11d00bf5),
+         (0x30ff4101),
+         (0xe193275a),
+         (0xc0bc6dae),
+         (0x0000755f),
+         (0x212f3fab),
+         (0xf04359f0),
+         (0xd16c1304),
+         (0x11d07eaa),
+         (0x30ff345e),
+         (0xe1935205),
+         (0xc0bc18f1)},
+        {(0x00000000),
+         (0x08528000),
+         (0x70084000),
+         (0x785ac000),
+         (0x00196000),
+         (0x084be000),
+         (0x70112000),
+         (0x7843a000),
+         (0x0024fe00),
+         (0x08767e00),
+         (0x702cbe00),
+         (0x787e3e00),
+         (0x003d9e00),
+         (0x086f1e00),
+         (0x7035de00),
+         (0x78675e00)},
+        {(0x3f800000),
+         (0x3f842940),
+         (0x3fb80420),
+         (0x3fbc2d60),
+         (0x3f800cb0),
+         (0x3f8425f0),
+         (0x3fb80890),
+         (0x3fbc21d0),
+         (0x3f80127f),
+         (0x3f843b3f),
+         (0x3fb8165f),
+         (0x3fbc3f1f),
+         (0x3f801ecf),
+         (0x3f84378f),
+         (0x3fb81aef),
+         (0x3fbc33af)},
+        (0xfff80000),
+        {0xbf,0xbb,0x3f,0x17,0x6e,0x1a,0x3b,0x45,0x9b,0x2a,
+         0x22,0x6a,0xf9,0xf5,0xba,0x97,0x3b,0x8a,0xec,0xd6,0x00}
+    },
+    {
+        /* No.192 delta:4742 weight:1471 */
+        11213,
+        27,
+        4,
+        6,
+        {(0x00000000),
+         (0x7fe173b6),
+         (0x51169f27),
+         (0x2ef7ec91),
+         (0x0a300c00),
+         (0x75d17fb6),
+         (0x5b269327),
+         (0x24c7e091),
+         (0x00001d00),
+         (0x7fe16eb6),
+         (0x51168227),
+         (0x2ef7f191),
+         (0x0a301100),
+         (0x75d162b6),
+         (0x5b268e27),
+         (0x24c7fd91)},
+        {(0x00000000),
+         (0x41420000),
+         (0x00010000),
+         (0x41430000),
+         (0x29040000),
+         (0x68460000),
+         (0x29050000),
+         (0x68470000),
+         (0x00009e00),
+         (0x41429e00),
+         (0x00019e00),
+         (0x41439e00),
+         (0x29049e00),
+         (0x68469e00),
+         (0x29059e00),
+         (0x68479e00)},
+        {(0x3f800000),
+         (0x3fa0a100),
+         (0x3f800080),
+         (0x3fa0a180),
+         (0x3f948200),
+         (0x3fb42300),
+         (0x3f948280),
+         (0x3fb42380),
+         (0x3f80004f),
+         (0x3fa0a14f),
+         (0x3f8000cf),
+         (0x3fa0a1cf),
+         (0x3f94824f),
+         (0x3fb4234f),
+         (0x3f9482cf),
+         (0x3fb423cf)},
+        (0xfff80000),
+        {0xb4,0x7f,0x09,0x23,0xa8,0x3f,0x5e,0x9d,0x3e,0x97,
+         0x19,0x94,0x35,0x0d,0xdb,0x3e,0xb7,0x43,0x15,0xd0,0x00}
+    },
+    {
+        /* No.193 delta:3962 weight:1171 */
+        11213,
+        92,
+        2,
+        13,
+        {(0x00000000),
+         (0x76b1ced9),
+         (0x928504d8),
+         (0xe434ca01),
+         (0x7b300c1a),
+         (0x0d81c2c3),
+         (0xe9b508c2),
+         (0x9f04c61b),
+         (0x0000b2d9),
+         (0x76b17c00),
+         (0x9285b601),
+         (0xe43478d8),
+         (0x7b30bec3),
+         (0x0d81701a),
+         (0xe9b5ba1b),
+         (0x9f0474c2)},
+        {(0x00000000),
+         (0x08400000),
+         (0x06400000),
+         (0x0e000000),
+         (0x01200000),
+         (0x09600000),
+         (0x07600000),
+         (0x0f200000),
+         (0x04c01e00),
+         (0x0c801e00),
+         (0x02801e00),
+         (0x0ac01e00),
+         (0x05e01e00),
+         (0x0da01e00),
+         (0x03a01e00),
+         (0x0be01e00)},
+        {(0x3f800000),
+         (0x3f842000),
+         (0x3f832000),
+         (0x3f870000),
+         (0x3f809000),
+         (0x3f84b000),
+         (0x3f83b000),
+         (0x3f879000),
+         (0x3f82600f),
+         (0x3f86400f),
+         (0x3f81400f),
+         (0x3f85600f),
+         (0x3f82f00f),
+         (0x3f86d00f),
+         (0x3f81d00f),
+         (0x3f85f00f)},
+        (0xfff80000),
+        {0xdf,0x8e,0xab,0x00,0xdb,0xdc,0xe0,0x7a,0xb0,0x4c,
+         0x05,0x23,0x25,0x58,0x15,0xac,0x74,0x97,0x22,0x21,0x00}
+    },
+    {
+        /* No.194 delta:2215 weight:1499 */
+        11213,
+        63,
+        26,
+        4,
+        {(0x00000000),
+         (0xad5d413b),
+         (0x166ef61d),
+         (0xbb33b726),
+         (0x9e000c20),
+         (0x335d4d1b),
+         (0x886efa3d),
+         (0x2533bb06),
+         (0x000077be),
+         (0xad5d3685),
+         (0x166e81a3),
+         (0xbb33c098),
+         (0x9e007b9e),
+         (0x335d3aa5),
+         (0x886e8d83),
+         (0x2533ccb8)},
+        {(0x00000000),
+         (0x10720800),
+         (0x60810000),
+         (0x70f30800),
+         (0x300c0000),
+         (0x207e0800),
+         (0x508d0000),
+         (0x40ff0800),
+         (0x20023e00),
+         (0x30703600),
+         (0x40833e00),
+         (0x50f13600),
+         (0x100e3e00),
+         (0x007c3600),
+         (0x708f3e00),
+         (0x60fd3600)},
+        {(0x3f800000),
+         (0x3f883904),
+         (0x3fb04080),
+         (0x3fb87984),
+         (0x3f980600),
+         (0x3f903f04),
+         (0x3fa84680),
+         (0x3fa07f84),
+         (0x3f90011f),
+         (0x3f98381b),
+         (0x3fa0419f),
+         (0x3fa8789b),
+         (0x3f88071f),
+         (0x3f803e1b),
+         (0x3fb8479f),
+         (0x3fb07e9b)},
+        (0xfff80000),
+        {0x31,0xb8,0x64,0x9a,0xae,0xad,0xc8,0xf8,0x53,0x9b,
+         0x64,0x2d,0x8c,0xb0,0xd2,0xa7,0x8e,0x2e,0xb2,0xc8,0x00}
+    },
+    {
+        /* No.195 delta:1215 weight:463 */
+        11213,
+        60,
+        13,
+        19,
+        {(0x00000000),
+         (0x60520e47),
+         (0xae8c1b90),
+         (0xcede15d7),
+         (0xc1c00c35),
+         (0xa1920272),
+         (0x6f4c17a5),
+         (0x0f1e19e2),
+         (0x0000fd60),
+         (0x6052f327),
+         (0xae8ce6f0),
+         (0xcedee8b7),
+         (0xc1c0f155),
+         (0xa192ff12),
+         (0x6f4ceac5),
+         (0x0f1ee482)},
+        {(0x00000000),
+         (0x02e00000),
+         (0x10cd1200),
+         (0x122d1200),
+         (0x14101800),
+         (0x16f01800),
+         (0x04dd0a00),
+         (0x063d0a00),
+         (0x0057be00),
+         (0x02b7be00),
+         (0x109aac00),
+         (0x127aac00),
+         (0x1447a600),
+         (0x16a7a600),
+         (0x048ab400),
+         (0x066ab400)},
+        {(0x3f800000),
+         (0x3f817000),
+         (0x3f886689),
+         (0x3f891689),
+         (0x3f8a080c),
+         (0x3f8b780c),
+         (0x3f826e85),
+         (0x3f831e85),
+         (0x3f802bdf),
+         (0x3f815bdf),
+         (0x3f884d56),
+         (0x3f893d56),
+         (0x3f8a23d3),
+         (0x3f8b53d3),
+         (0x3f82455a),
+         (0x3f83355a)},
+        (0xfff80000),
+        {0x67,0xca,0xf4,0x9a,0x13,0x78,0x64,0xbc,0x60,0x6d,
+         0x5b,0x5f,0x48,0x61,0x94,0x6f,0xdf,0x81,0x2c,0xe1,0x00}
+    },
+    {
+        /* No.196 delta:939 weight:857 */
+        11213,
+        51,
+        11,
+        2,
+        {(0x00000000),
+         (0xa61e8990),
+         (0x67d50999),
+         (0xc1cb8009),
+         (0xa3f00c49),
+         (0x05ee85d9),
+         (0xc42505d0),
+         (0x623b8c40),
+         (0x00000381),
+         (0xa61e8a11),
+         (0x67d50a18),
+         (0xc1cb8388),
+         (0xa3f00fc8),
+         (0x05ee8658),
+         (0xc4250651),
+         (0x623b8fc1)},
+        {(0x00000000),
+         (0x08105000),
+         (0x0002b000),
+         (0x0812e000),
+         (0x90084c00),
+         (0x98181c00),
+         (0x900afc00),
+         (0x981aac00),
+         (0x01007e00),
+         (0x09102e00),
+         (0x0102ce00),
+         (0x09129e00),
+         (0x91083200),
+         (0x99186200),
+         (0x910a8200),
+         (0x991ad200)},
+        {(0x3f800000),
+         (0x3f840828),
+         (0x3f800158),
+         (0x3f840970),
+         (0x3fc80426),
+         (0x3fcc0c0e),
+         (0x3fc8057e),
+         (0x3fcc0d56),
+         (0x3f80803f),
+         (0x3f848817),
+         (0x3f808167),
+         (0x3f84894f),
+         (0x3fc88419),
+         (0x3fcc8c31),
+         (0x3fc88541),
+         (0x3fcc8d69)},
+        (0xfff80000),
+        {0x2c,0x79,0x20,0x40,0xe3,0x10,0xb7,0x11,0xcb,0xf4,
+         0xba,0x4e,0xee,0x8b,0x5f,0x86,0xa9,0xdb,0x06,0x27,0x00}
+    },
+    {
+        /* No.197 delta:1583 weight:1233 */
+        11213,
+        26,
+        20,
+        8,
+        {(0x00000000),
+         (0x33fe7f24),
+         (0x88809fc9),
+         (0xbb7ee0ed),
+         (0x68600c55),
+         (0x5b9e7371),
+         (0xe0e0939c),
+         (0xd31eecb8),
+         (0x0000a12e),
+         (0x33fede0a),
+         (0x88803ee7),
+         (0xbb7e41c3),
+         (0x6860ad7b),
+         (0x5b9ed25f),
+         (0xe0e032b2),
+         (0xd31e4d96)},
+        {(0x00000000),
+         (0x08750800),
+         (0x82099800),
+         (0x8a7c9000),
+         (0x40e2b000),
+         (0x4897b800),
+         (0xc2eb2800),
+         (0xca9e2000),
+         (0x004a5e00),
+         (0x083f5600),
+         (0x8243c600),
+         (0x8a36ce00),
+         (0x40a8ee00),
+         (0x48dde600),
+         (0xc2a17600),
+         (0xcad47e00)},
+        {(0x3f800000),
+         (0x3f843a84),
+         (0x3fc104cc),
+         (0x3fc53e48),
+         (0x3fa07158),
+         (0x3fa44bdc),
+         (0x3fe17594),
+         (0x3fe54f10),
+         (0x3f80252f),
+         (0x3f841fab),
+         (0x3fc121e3),
+         (0x3fc51b67),
+         (0x3fa05477),
+         (0x3fa46ef3),
+         (0x3fe150bb),
+         (0x3fe56a3f)},
+        (0xfff80000),
+        {0xb0,0x71,0x91,0x64,0xda,0x74,0xb8,0xc6,0x16,0x8c,
+         0x5a,0x7e,0x26,0xea,0x75,0x3a,0x0b,0xa4,0xd9,0x97,0x00}
+    },
+    {
+        /* No.198 delta:1565 weight:813 */
+        11213,
+        89,
+        7,
+        10,
+        {(0x00000000),
+         (0x98a83f1e),
+         (0xcb779281),
+         (0x53dfad9f),
+         (0x20400c6a),
+         (0xb8e83374),
+         (0xeb379eeb),
+         (0x739fa1f5),
+         (0x000092fc),
+         (0x98a8ade2),
+         (0xcb77007d),
+         (0x53df3f63),
+         (0x20409e96),
+         (0xb8e8a188),
+         (0xeb370c17),
+         (0x739f3309)},
+        {(0x00000000),
+         (0x467a0c00),
+         (0x04059800),
+         (0x427f9400),
+         (0x01093800),
+         (0x47733400),
+         (0x050ca000),
+         (0x4376ac00),
+         (0xc0e0fe00),
+         (0x869af200),
+         (0xc4e56600),
+         (0x829f6a00),
+         (0xc1e9c600),
+         (0x8793ca00),
+         (0xc5ec5e00),
+         (0x83965200)},
+        {(0x3f800000),
+         (0x3fa33d06),
+         (0x3f8202cc),
+         (0x3fa13fca),
+         (0x3f80849c),
+         (0x3fa3b99a),
+         (0x3f828650),
+         (0x3fa1bb56),
+         (0x3fe0707f),
+         (0x3fc34d79),
+         (0x3fe272b3),
+         (0x3fc14fb5),
+         (0x3fe0f4e3),
+         (0x3fc3c9e5),
+         (0x3fe2f62f),
+         (0x3fc1cb29)},
+        (0xfff80000),
+        {0x32,0x19,0xd6,0x71,0xe2,0x9d,0xf7,0x99,0x34,0xa0,
+         0x5f,0x9a,0xcd,0x9f,0x42,0x5a,0x43,0x47,0xee,0xb2,0x00}
+    },
+    {
+        /* No.199 delta:3921 weight:921 */
+        11213,
+        49,
+        3,
+        15,
+        {(0x00000000),
+         (0xbb75ffa8),
+         (0xa0b33e2a),
+         (0x1bc6c182),
+         (0x9a200c78),
+         (0x2155f3d0),
+         (0x3a933252),
+         (0x81e6cdfa),
+         (0x000015a3),
+         (0xbb75ea0b),
+         (0xa0b32b89),
+         (0x1bc6d421),
+         (0x9a2019db),
+         (0x2155e673),
+         (0x3a9327f1),
+         (0x81e6d859)},
+        {(0x00000000),
+         (0x45540800),
+         (0x07840400),
+         (0x42d00c00),
+         (0x08200000),
+         (0x4d740800),
+         (0x0fa40400),
+         (0x4af00c00),
+         (0x02201e00),
+         (0x47741600),
+         (0x05a41a00),
+         (0x40f01200),
+         (0x0a001e00),
+         (0x4f541600),
+         (0x0d841a00),
+         (0x48d01200)},
+        {(0x3f800000),
+         (0x3fa2aa04),
+         (0x3f83c202),
+         (0x3fa16806),
+         (0x3f841000),
+         (0x3fa6ba04),
+         (0x3f87d202),
+         (0x3fa57806),
+         (0x3f81100f),
+         (0x3fa3ba0b),
+         (0x3f82d20d),
+         (0x3fa07809),
+         (0x3f85000f),
+         (0x3fa7aa0b),
+         (0x3f86c20d),
+         (0x3fa46809)},
+        (0xfff80000),
+        {0x86,0x1e,0x5b,0xa8,0x8e,0x65,0x6f,0xc0,0xbc,0x92,
+         0x81,0x1c,0x6d,0xbd,0x0f,0xd4,0x7e,0x57,0x5f,0x75,0x00}
+    }
+};
+
+#endif
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_normal.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_normal.h
new file mode 100644
index 0000000000000000000000000000000000000000..e3c91001032acf253959df8b8a5464d038ee252b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_normal.h
@@ -0,0 +1,840 @@
+
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#if !defined(CURAND_NORMAL_H_)
+#define CURAND_NORMAL_H_
+
+/**
+ * \defgroup DEVICE Device API
+ *
+ * @{
+ */
+
+#ifndef __CUDACC_RTC__
+#include <math.h>
+#endif // __CUDACC_RTC__
+#include <nv/target>
+
+#include "curand_mrg32k3a.h"
+#include "curand_mtgp32_kernel.h"
+#include "curand_philox4x32_x.h"
+#include "curand_normal_static.h"
+
+QUALIFIERS float2 _curand_box_muller(unsigned int x, unsigned int y)
+{
+    float2 result;
+    float u = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2);
+    float v = y * CURAND_2POW32_INV_2PI + (CURAND_2POW32_INV_2PI/2);
+    float s;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    s = sqrtf(-2.0f * logf(u));
+    __sincosf(v, &result.x, &result.y);
+,
+    s = sqrtf(-2.0f * logf(u));
+    result.x = sinf(v);
+    result.y = cosf(v);
+)
+    result.x *= s;
+    result.y *= s;
+    return result;
+}
+
+QUALIFIERS float2 curand_box_muller_mrg(curandStateMRG32k3a_t * state)
+{
+    float x, y;
+    x = curand_uniform(state);
+    y = curand_uniform(state) * CURAND_2PI;
+    float2 result;
+    float s;
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    s = sqrtf(-2.0f * logf(x));
+    __sincosf(y, &result.x, &result.y);
+,
+    s = sqrtf(-2.0f * logf(x));
+    result.x = sinf(y);
+    result.y = cosf(y);
+)
+    result.x *= s;
+    result.y *= s;
+    return result;
+}
+
+QUALIFIERS double2
+_curand_box_muller_double(unsigned int x0, unsigned int x1,
+                          unsigned int y0, unsigned int y1)
+{
+    double2 result;
+    unsigned long long zx = (unsigned long long)x0 ^
+        ((unsigned long long)x1 << (53 - 32));
+    double u = zx * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
+    unsigned long long zy = (unsigned long long)y0 ^
+        ((unsigned long long)y1 << (53 - 32));
+    double v = zy * (CURAND_2POW53_INV_DOUBLE*2.0) + CURAND_2POW53_INV_DOUBLE;
+    double s = sqrt(-2.0 * log(u));
+
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    sincospi(v, &result.x, &result.y);
+,
+    result.x = sin(v*CURAND_PI_DOUBLE);
+    result.y = cos(v*CURAND_PI_DOUBLE);
+)
+    result.x *= s;
+    result.y *= s;
+
+    return result;
+}
+
+QUALIFIERS double2
+curand_box_muller_mrg_double(curandStateMRG32k3a_t * state)
+{
+    double x, y;
+    double2 result;
+    x = curand_uniform_double(state);
+    y = curand_uniform_double(state) * 2.0;
+
+    double s = sqrt(-2.0 * log(x));
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    sincospi(y, &result.x, &result.y);
+,
+    result.x = sin(y*CURAND_PI_DOUBLE);
+    result.y = cos(y*CURAND_PI_DOUBLE);
+)
+    result.x *= s;
+    result.y *= s;
+    return result;
+}
+
+template <typename R>
+QUALIFIERS float2 curand_box_muller(R *state)
+{
+    float2 result;
+    unsigned int x = curand(state);
+    unsigned int y = curand(state);
+    result = _curand_box_muller(x, y);
+    return result;
+}
+
+template <typename R>
+QUALIFIERS float4 curand_box_muller4(R *state)
+{
+    float4 result;
+    float2 _result;
+    uint4 x = curand4(state);
+    //unsigned int y = curand(state);
+    _result = _curand_box_muller(x.x, x.y);
+    result.x = _result.x;
+    result.y = _result.y;
+    _result = _curand_box_muller(x.z, x.w);
+    result.z = _result.x;
+    result.w = _result.y;
+    return result;
+}
+
+template <typename R>
+QUALIFIERS double2 curand_box_muller_double(R *state)
+{
+    double2 result;
+    unsigned int x0 = curand(state);
+    unsigned int x1 = curand(state);
+    unsigned int y0 = curand(state);
+    unsigned int y1 = curand(state);
+    result = _curand_box_muller_double(x0, x1, y0, y1);
+    return result;
+}
+
+template <typename R>
+QUALIFIERS double2 curand_box_muller2_double(R *state)
+{
+    double2 result;
+    uint4 _x;
+    _x = curand4(state);
+    result = _curand_box_muller_double(_x.x, _x.y, _x.z, _x.w);
+    return result;
+}
+
+
+template <typename R>
+QUALIFIERS double4 curand_box_muller4_double(R *state)
+{
+    double4 result;
+    double2 _res1;
+    double2 _res2;
+    uint4 _x;
+    uint4 _y;
+    _x = curand4(state);
+    _y = curand4(state);
+    _res1 = _curand_box_muller_double(_x.x, _x.y, _x.z, _x.w);
+    _res2 = _curand_box_muller_double(_y.x, _y.y, _y.z, _y.w);
+    result.x = _res1.x;
+    result.y = _res1.y;
+    result.z = _res2.x;
+    result.w = _res2.y;
+    return result;
+}
+
+//QUALIFIERS float _curand_normal_icdf(unsigned int x)
+//{
+//#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
+//    float s = CURAND_SQRT2;
+//    // Mirror to avoid loss of precision
+//    if(x > 0x80000000UL) {
+//        x = 0xffffffffUL - x;
+//        s = -s;
+//    }
+//    float p = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+//    // p is in (0, 0.5], 2p is in (0, 1]
+//    return s * erfcinvf(2.0f * p);
+//#else
+//    x++;    //suppress warnings
+//    return 0.0f;
+//#endif
+//}
+//
+//QUALIFIERS float _curand_normal_icdf(unsigned long long x)
+//{
+//#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
+//    unsigned int t = (unsigned int)(x >> 32);
+//    float s = CURAND_SQRT2;
+//    // Mirror to avoid loss of precision
+//    if(t > 0x80000000UL) {
+//        t = 0xffffffffUL - t;
+//        s = -s;
+//    }
+//    float p = t * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+//    // p is in (0, 0.5], 2p is in (0, 1]
+//    return s * erfcinvf(2.0f * p);
+//#else
+//    x++;
+//    return 0.0f;
+//#endif
+//}
+//
+//QUALIFIERS double _curand_normal_icdf_double(unsigned int x)
+//{
+//#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
+//    double s = CURAND_SQRT2_DOUBLE;
+//    // Mirror to avoid loss of precision
+//    if(x > 0x80000000UL) {
+//        x = 0xffffffffUL - x;
+//        s = -s;
+//    }
+//    double p = x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0);
+//    // p is in (0, 0.5], 2p is in (0, 1]
+//    return s * erfcinv(2.0 * p);
+//#else
+//    x++;
+//    return 0.0;
+//#endif
+//}
+//
+//QUALIFIERS double _curand_normal_icdf_double(unsigned long long x)
+//{
+//#if __CUDA_ARCH__ > 0 || defined(HOST_HAVE_ERFCINVF)
+//    double s = CURAND_SQRT2_DOUBLE;
+//    x >>= 11;
+//    // Mirror to avoid loss of precision
+//    if(x > 0x10000000000000UL) {
+//        x = 0x1fffffffffffffUL - x;
+//        s = -s;
+//    }
+//    double p = x * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
+//    // p is in (0, 0.5], 2p is in (0, 1]
+//    return s * erfcinv(2.0 * p);
+//#else
+//    x++;
+//    return 0.0;
+//#endif
+//}
+//
+
+/**
+ * \brief Return a normally distributed float from an XORWOW generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the XORWOW generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then returns them one at a time.
+ * See ::curand_normal2() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateXORWOW_t *state)
+{
+    if(state->boxmuller_flag != EXTRA_FLAG_NORMAL) {
+        unsigned int x, y;
+        x = curand(state);
+        y = curand(state);
+        float2 v = _curand_box_muller(x, y);
+        state->boxmuller_extra = v.y;
+        state->boxmuller_flag = EXTRA_FLAG_NORMAL;
+        return v.x;
+    }
+    state->boxmuller_flag = 0;
+    return state->boxmuller_extra;
+}
+
+/**
+ * \brief Return a normally distributed float from an Philox4_32_10 generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the Philox4_32_10 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then returns them one at a time.
+ * See ::curand_normal2() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+
+QUALIFIERS float curand_normal(curandStatePhilox4_32_10_t *state)
+{
+    if(state->boxmuller_flag != EXTRA_FLAG_NORMAL) {
+        unsigned int x, y;
+        x = curand(state);
+        y = curand(state);
+        float2 v = _curand_box_muller(x, y);
+        state->boxmuller_extra = v.y;
+        state->boxmuller_flag = EXTRA_FLAG_NORMAL;
+        return v.x;
+    }
+    state->boxmuller_flag = 0;
+    return state->boxmuller_extra;
+}
+
+
+
+/**
+ * \brief Return a normally distributed float from an MRG32k3a generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the MRG32k3a generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then returns them one at a time.
+ * See ::curand_normal2() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateMRG32k3a_t *state)
+{
+    if(state->boxmuller_flag != EXTRA_FLAG_NORMAL) {
+        float2 v = curand_box_muller_mrg(state);
+        state->boxmuller_extra = v.y;
+        state->boxmuller_flag = EXTRA_FLAG_NORMAL;
+        return v.x;
+    }
+    state->boxmuller_flag = 0;
+    return state->boxmuller_extra;
+}
+
+/**
+ * \brief Return two normally distributed floats from an XORWOW generator.
+ *
+ * Return two normally distributed floats with mean \p 0.0f and
+ * standard deviation \p 1.0f from the XORWOW generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float2 where each element is from a
+ * distribution with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float2 curand_normal2(curandStateXORWOW_t *state)
+{
+    return curand_box_muller(state);
+}
+/**
+ * \brief Return two normally distributed floats from an Philox4_32_10 generator.
+ *
+ * Return two normally distributed floats with mean \p 0.0f and
+ * standard deviation \p 1.0f from the Philox4_32_10 generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float2 where each element is from a
+ * distribution with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float2 curand_normal2(curandStatePhilox4_32_10_t *state)
+{
+    return curand_box_muller(state);
+}
+
+/**
+ * \brief Return four normally distributed floats from an Philox4_32_10 generator.
+ *
+ * Return four normally distributed floats with mean \p 0.0f and
+ * standard deviation \p 1.0f from the Philox4_32_10 generator in \p state,
+ * increment position of generator by four.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float2 where each element is from a
+ * distribution with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float4 curand_normal4(curandStatePhilox4_32_10_t *state)
+{
+    return curand_box_muller4(state);
+}
+
+
+
+/**
+ * \brief Return two normally distributed floats from an MRG32k3a generator.
+ *
+ * Return two normally distributed floats with mean \p 0.0f and
+ * standard deviation \p 1.0f from the MRG32k3a generator in \p state,
+ * increment position of generator by two.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float2 where each element is from a
+ * distribution with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float2 curand_normal2(curandStateMRG32k3a_t *state)
+{
+    return curand_box_muller_mrg(state);
+}
+
+/**
+ * \brief Return a normally distributed float from a MTGP32 generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the MTGP32 generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateMtgp32_t *state)
+{
+    return _curand_normal_icdf(curand(state));
+}
+/**
+ * \brief Return a normally distributed float from a Sobol32 generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateSobol32_t *state)
+{
+    return _curand_normal_icdf(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed float from a scrambled Sobol32 generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateScrambledSobol32_t *state)
+{
+    return _curand_normal_icdf(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed float from a Sobol64 generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateSobol64_t *state)
+{
+    return _curand_normal_icdf(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed float from a scrambled Sobol64 generator.
+ *
+ * Return a single normally distributed float with mean \p 0.0f and
+ * standard deviation \p 1.0f from the scrambled Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed float with mean \p 0.0f and standard deviation \p 1.0f
+ */
+QUALIFIERS float curand_normal(curandStateScrambledSobol64_t *state)
+{
+    return _curand_normal_icdf(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed double from an XORWOW generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the XORWOW generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then returns them one at a time.
+ * See ::curand_normal2_double() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateXORWOW_t *state)
+{
+    if(state->boxmuller_flag_double != EXTRA_FLAG_NORMAL) {
+        unsigned int x0, x1, y0, y1;
+        x0 = curand(state);
+        x1 = curand(state);
+        y0 = curand(state);
+        y1 = curand(state);
+        double2 v = _curand_box_muller_double(x0, x1, y0, y1);
+        state->boxmuller_extra_double = v.y;
+        state->boxmuller_flag_double = EXTRA_FLAG_NORMAL;
+        return v.x;
+    }
+    state->boxmuller_flag_double = 0;
+    return state->boxmuller_extra_double;
+}
+
+/**
+ * \brief Return a normally distributed double from an Philox4_32_10 generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the Philox4_32_10 generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then returns them one at a time.
+ * See ::curand_normal2_double() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+
+QUALIFIERS double curand_normal_double(curandStatePhilox4_32_10_t *state)
+{
+    if(state->boxmuller_flag_double != EXTRA_FLAG_NORMAL) {
+        uint4 _x;
+        _x = curand4(state);
+        double2 v = _curand_box_muller_double(_x.x, _x.y, _x.z, _x.w);
+        state->boxmuller_extra_double = v.y;
+        state->boxmuller_flag_double = EXTRA_FLAG_NORMAL;
+        return v.x;
+    }
+    state->boxmuller_flag_double = 0;
+    return state->boxmuller_extra_double;
+}
+
+
+/**
+ * \brief Return a normally distributed double from an MRG32k3a generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the XORWOW generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results, then returns them one at a time.
+ * See ::curand_normal2_double() for a more efficient version that returns
+ * both results at once.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateMRG32k3a_t *state)
+{
+    if(state->boxmuller_flag_double != EXTRA_FLAG_NORMAL) {
+        double2 v = curand_box_muller_mrg_double(state);
+        state->boxmuller_extra_double = v.y;
+        state->boxmuller_flag_double = EXTRA_FLAG_NORMAL;
+        return v.x;
+    }
+    state->boxmuller_flag_double = 0;
+    return state->boxmuller_extra_double;
+}
+
+/**
+ * \brief Return two normally distributed doubles from an XORWOW generator.
+ *
+ * Return two normally distributed doubles with mean \p 0.0 and
+ * standard deviation \p 1.0 from the XORWOW generator in \p state,
+ * increment position of generator by 2.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double2 where each element is from a
+ * distribution with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double2 curand_normal2_double(curandStateXORWOW_t *state)
+{
+    return curand_box_muller_double(state);
+}
+
+/**
+ * \brief Return two normally distributed doubles from an Philox4_32_10 generator.
+ *
+ * Return two normally distributed doubles with mean \p 0.0 and
+ * standard deviation \p 1.0 from the Philox4_32_10 generator in \p state,
+ * increment position of generator by 2.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double2 where each element is from a
+ * distribution with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double2 curand_normal2_double(curandStatePhilox4_32_10_t *state)
+{
+    uint4 _x;
+    double2 result;
+
+    _x = curand4(state);
+    double2 v1 = _curand_box_muller_double(_x.x, _x.y, _x.z, _x.w);
+    result.x = v1.x;
+    result.y = v1.y;
+
+    return result;
+}
+
+ // not a part of API
+QUALIFIERS double4 curand_normal4_double(curandStatePhilox4_32_10_t *state)
+{
+    uint4 _x;
+    uint4 _y;
+    double4 result;
+
+    _x = curand4(state);
+    _y = curand4(state);
+    double2 v1 = _curand_box_muller_double(_x.x, _x.y, _x.z, _x.w);
+    double2 v2 = _curand_box_muller_double(_y.x, _y.y, _y.z, _y.w);
+    result.x = v1.x;
+    result.y = v1.y;
+    result.z = v2.x;
+    result.w = v2.y;
+
+    return result;
+}
+
+
+/**
+ * \brief Return two normally distributed doubles from an MRG32k3a generator.
+ *
+ * Return two normally distributed doubles with mean \p 0.0 and
+ * standard deviation \p 1.0 from the MRG32k3a generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses a Box-Muller transform to generate two
+ * normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double2 where each element is from a
+ * distribution with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double2 curand_normal2_double(curandStateMRG32k3a_t *state)
+{
+    return curand_box_muller_mrg_double(state);
+}
+
+/**
+ * \brief Return a normally distributed double from an MTGP32 generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the MTGP32 generator in \p state,
+ * increment position of generator.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateMtgp32_t *state)
+{
+    return _curand_normal_icdf_double(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed double from an Sobol32 generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateSobol32_t *state)
+{
+    return _curand_normal_icdf_double(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed double from a scrambled Sobol32 generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the scrambled Sobol32 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateScrambledSobol32_t *state)
+{
+    return _curand_normal_icdf_double(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed double from a Sobol64 generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateSobol64_t *state)
+{
+    return _curand_normal_icdf_double(curand(state));
+}
+
+/**
+ * \brief Return a normally distributed double from a scrambled Sobol64 generator.
+ *
+ * Return a single normally distributed double with mean \p 0.0 and
+ * standard deviation \p 1.0 from the scrambled Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * The implementation uses the inverse cumulative distribution function
+ * to generate normally distributed results.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return Normally distributed double with mean \p 0.0 and standard deviation \p 1.0
+ */
+QUALIFIERS double curand_normal_double(curandStateScrambledSobol64_t *state)
+{
+    return _curand_normal_icdf_double(curand(state));
+}
+#endif // !defined(CURAND_NORMAL_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_normal_static.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_normal_static.h
new file mode 100644
index 0000000000000000000000000000000000000000..f731101c22504b9cb68c1c5694d6087bc66df18a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_normal_static.h
@@ -0,0 +1,134 @@
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+#ifndef CURAND_NORMAL_STATIC_H
+#define CURAND_NORMAL_STATIC_H
+
+#define QUALIFIERS_STATIC __host__ __device__ __forceinline__
+
+#include <nv/target>
+#if defined(HOST_HAVE_ERFCINVF)
+  #define IF_DEVICE_OR_HAVE_ERFCINVF(t, f) _NV_BLOCK_EXPAND(t)
+#else
+  #define IF_DEVICE_OR_HAVE_ERFCINVF(t, f) NV_IF_ELSE_TARGET(NV_IS_DEVICE, t, f)
+#endif
+
+QUALIFIERS_STATIC float _curand_normal_icdf(unsigned int x)
+{
+IF_DEVICE_OR_HAVE_ERFCINVF(
+    float s = CURAND_SQRT2;
+    // Mirror to avoid loss of precision
+    if(x > 0x80000000UL) {
+        x = 0xffffffffUL - x;
+        s = -s;
+    }
+    float p = x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+    // p is in (0, 0.5], 2p is in (0, 1]
+    return s * erfcinvf(2.0f * p);
+,
+    x++;    //suppress warnings
+    return 0.0f;
+)
+}
+
+QUALIFIERS_STATIC float _curand_normal_icdf(unsigned long long x)
+{
+IF_DEVICE_OR_HAVE_ERFCINVF(
+    unsigned int t = (unsigned int)(x >> 32);
+    float s = CURAND_SQRT2;
+    // Mirror to avoid loss of precision
+    if(t > 0x80000000UL) {
+        t = 0xffffffffUL - t;
+        s = -s;
+    }
+    float p = t * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+    // p is in (0 - 0.5] 2p is in (0 - 1]
+    return s * erfcinvf(2.0f * p);
+,
+    x++;
+    return 0.0f;
+)
+}
+
+QUALIFIERS_STATIC double _curand_normal_icdf_double(unsigned int x)
+{
+IF_DEVICE_OR_HAVE_ERFCINVF(
+    double s = CURAND_SQRT2_DOUBLE;
+    // Mirror to avoid loss of precision
+    if(x > 0x80000000UL) {
+        x = 0xffffffffUL - x;
+        s = -s;
+    }
+    double p = x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0);
+    // p is in (0 - 0.5] 2p is in (0 - 1]
+    return s * erfcinv(2.0 * p);
+,
+    x++;
+    return 0.0;
+)
+}
+
+QUALIFIERS_STATIC double _curand_normal_icdf_double(unsigned long long x)
+{
+IF_DEVICE_OR_HAVE_ERFCINVF(
+    double s = CURAND_SQRT2_DOUBLE;
+    x >>= 11;
+    // Mirror to avoid loss of precision
+    if(x > 0x10000000000000UL) {
+        x = 0x1fffffffffffffUL - x;
+        s = -s;
+    }
+    double p = x * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
+    // p is in (0 - 0.5] 2p is in (0 - 1]
+    return s * erfcinv(2.0 * p);
+,
+    x++;
+    return 0.0;
+)
+}
+#undef QUALIFIERS_STATIC
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_philox4x32_x.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_philox4x32_x.h
new file mode 100644
index 0000000000000000000000000000000000000000..3dbda4a0ce9ef3e11460320728886f7feb10cf2d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_philox4x32_x.h
@@ -0,0 +1,195 @@
+/* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+ *                   
+ * NOTICE TO LICENSEE:
+ *
+ * The source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * The Licensed Deliverables contained herein are PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+/*
+   Copyright 2010-2011, D. E. Shaw Research.
+   All rights reserved.
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions are
+met:
+
+ * Redistributions of source code must retain the above copyright
+ notice, this list of conditions, and the following disclaimer.
+
+ * Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions, and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+ * Neither the name of D. E. Shaw Research nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#ifndef CURAND_PHILOX4X32_X__H_
+#define CURAND_PHILOX4X32_X__H_
+#include <nv/target>
+
+#if !defined(QUALIFIERS)
+#define QUALIFIERS static __forceinline__ __device__
+#endif
+
+#define PHILOX_W32_0   (0x9E3779B9)
+#define PHILOX_W32_1   (0xBB67AE85)
+#define PHILOX_M4x32_0 (0xD2511F53)
+#define PHILOX_M4x32_1 (0xCD9E8D57)
+
+struct curandStatePhilox4_32_10 {
+   uint4 ctr;
+   uint4 output;
+   uint2 key;
+   unsigned int STATE;
+   int boxmuller_flag;
+   int boxmuller_flag_double;
+   float boxmuller_extra;
+   double boxmuller_extra_double;
+};
+
+typedef struct curandStatePhilox4_32_10 curandStatePhilox4_32_10_t;
+
+
+QUALIFIERS void Philox_State_Incr(curandStatePhilox4_32_10_t* s, unsigned long long n)
+{
+   unsigned int nlo = (unsigned int)(n);
+   unsigned int nhi = (unsigned int)(n>>32);
+
+   s->ctr.x += nlo;
+   if( s->ctr.x < nlo )
+      nhi++;
+
+   s->ctr.y += nhi;
+   if(nhi <= s->ctr.y)
+      return;
+   if(++s->ctr.z) return;
+   ++s->ctr.w;
+}
+
+QUALIFIERS void Philox_State_Incr_hi(curandStatePhilox4_32_10_t* s, unsigned long long n)
+{
+   unsigned int nlo = (unsigned int)(n);
+   unsigned int nhi = (unsigned int)(n>>32);
+
+   s->ctr.z += nlo;
+   if( s->ctr.z < nlo )
+      nhi++;
+
+   s->ctr.w += nhi;
+}
+
+
+
+QUALIFIERS void Philox_State_Incr(curandStatePhilox4_32_10_t* s)
+{
+   if(++s->ctr.x) return;
+   if(++s->ctr.y) return;
+   if(++s->ctr.z) return;
+   ++s->ctr.w;
+}
+
+
+QUALIFIERS unsigned int mulhilo32(unsigned int a, unsigned int b, unsigned int* hip)
+{
+NV_IF_ELSE_TARGET(NV_IS_HOST,
+   // host code
+   unsigned long long product = ((unsigned long long)a) * ((unsigned long long)b);
+   *hip = product >> 32;
+   return (unsigned int)product;
+,
+   // device code
+   *hip = __umulhi(a,b);
+   return a*b;
+)
+}
+
+QUALIFIERS uint4 _philox4x32round(uint4 ctr, uint2 key)
+{
+   unsigned int hi0;
+   unsigned int hi1;
+   unsigned int lo0 = mulhilo32(PHILOX_M4x32_0, ctr.x, &hi0);
+   unsigned int lo1 = mulhilo32(PHILOX_M4x32_1, ctr.z, &hi1);
+
+   uint4 ret  = {hi1^ctr.y^key.x, lo1, hi0^ctr.w^key.y, lo0};
+   return ret;
+}
+
+QUALIFIERS uint4 curand_Philox4x32_10( uint4 c, uint2 k)
+{
+   c = _philox4x32round(c, k);                           // 1 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 2
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 3 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 4 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 5 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 6 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 7 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 8 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   c = _philox4x32round(c, k);                           // 9 
+   k.x += PHILOX_W32_0; k.y += PHILOX_W32_1;
+   return _philox4x32round(c, k);                        // 10
+}
+
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_poisson.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_poisson.h
new file mode 100644
index 0000000000000000000000000000000000000000..7881194cb868be14a197b581d7c82bf9dd16d617
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_poisson.h
@@ -0,0 +1,763 @@
+
+ /* Copyright 2010-2014 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#if !defined(CURAND_POISSON_H_)
+#define CURAND_POISSON_H_
+
+/**
+ * \defgroup DEVICE Device API
+ *
+ * @{
+ */
+
+#ifndef __CUDACC_RTC__
+#include <math.h>
+#endif // __CUDACC_RTC__
+
+#include <nv/target>
+
+#include "curand_mrg32k3a.h"
+#include "curand_mtgp32_kernel.h"
+#include "curand_philox4x32_x.h"
+
+#define CR_CUDART_PI               3.1415926535897931e+0
+#define CR_CUDART_TWO_TO_52        4503599627370496.0
+
+
+QUALIFIERS float __cr_rsqrt(float a)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm ("rsqrt.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
+,
+    a = 1.0f / sqrtf (a);
+)
+    return a;
+}
+
+QUALIFIERS float __cr_exp (float a)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    a = a * 1.4426950408889634074;
+    asm ("ex2.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
+,
+    a = expf (a);
+)
+    return a;
+}
+
+QUALIFIERS float __cr_log (float a)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm ("lg2.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
+    a = a * 0.69314718055994530942;
+,
+    a = logf (a);
+)
+    return a;
+}
+
+QUALIFIERS float __cr_rcp (float a)
+{
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+    asm ("rcp.approx.f32.ftz %0, %1;" : "=f"(a) : "f"(a));
+,
+    a = 1.0f / a;
+)
+    return a;
+}
+
+/* Computes regularized gamma function:  gammainc(a,x)/gamma(a) */
+QUALIFIERS float __cr_pgammainc (float a, float x)
+{
+    float t, alpha, beta;
+
+    /* First level parametrization constants */
+    float ma1 = 1.43248035075540910f,
+          ma2 = 0.12400979329415655f,
+          ma3 = 0.00025361074907033f,
+          mb1 = 0.21096734870196546f,
+          mb2 = 1.97381164089999420f,
+          mb3 = 0.94201734077887530f;
+
+    /* Second level parametrization constants (depends only on a) */
+
+    alpha = __cr_rsqrt (a - ma2);
+    alpha = ma1 * alpha + ma3;
+    beta = __cr_rsqrt (a - mb2);
+    beta = mb1 * beta + mb3;
+
+    /* Final approximation (depends on a and x) */
+
+    t = a - x;
+    t = alpha * t - beta;
+    t = 1.0f + __cr_exp (t);
+    t = t * t;
+    t = __cr_rcp (t);
+
+    /* Negative a,x or a,x=NAN requires special handling */
+    //t = !(x > 0 && a >= 0) ? 0.0 : t;
+
+    return t;
+}
+
+/* Computes inverse of pgammainc */
+QUALIFIERS float __cr_pgammaincinv (float a, float y)
+{
+    float t, alpha, beta;
+
+    /* First level parametrization constants */
+
+    float ma1 = 1.43248035075540910f,
+          ma2 = 0.12400979329415655f,
+          ma3 = 0.00025361074907033f,
+          mb1 = 0.21096734870196546f,
+          mb2 = 1.97381164089999420f,
+          mb3 = 0.94201734077887530f;
+
+    /* Second level parametrization constants (depends only on a) */
+
+    alpha = __cr_rsqrt (a - ma2);
+    alpha = ma1 * alpha + ma3;
+    beta = __cr_rsqrt (a - mb2);
+    beta = mb1 * beta + mb3;
+
+    /* Final approximation (depends on a and y) */
+
+    t = __cr_rsqrt (y) - 1.0f;
+    t = __cr_log (t);
+    t = beta + t;
+    t = - t * __cr_rcp (alpha) + a;
+    /* Negative a,x or a,x=NAN requires special handling */
+    //t = !(y > 0 && a >= 0) ? 0.0 : t;
+    return t;
+}
+
+#if defined(__CUDACC_RDC__) && (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+inline __constant__ double __cr_lgamma_table [] = {
+#else
+static __constant__ double __cr_lgamma_table [] = {
+#endif
+    0.000000000000000000e-1,
+    0.000000000000000000e-1,
+    6.931471805599453094e-1,
+    1.791759469228055001e0,
+    3.178053830347945620e0,
+    4.787491742782045994e0,
+    6.579251212010100995e0,
+    8.525161361065414300e0,
+    1.060460290274525023e1
+};
+
+
+QUALIFIERS double __cr_lgamma_integer(int a)
+{
+    double s;
+    double t;
+    double fa = fabs((float)a);
+    double sum;
+
+    if (a > 8) {
+        /* Stirling approximation; coefficients from Hart et al, "Computer
+         * Approximations", Wiley 1968. Approximation 5404.
+         */
+        s = 1.0 / fa;
+        t = s * s;
+        sum =          -0.1633436431e-2;
+        sum = sum * t + 0.83645878922e-3;
+        sum = sum * t - 0.5951896861197e-3;
+        sum = sum * t + 0.793650576493454e-3;
+        sum = sum * t - 0.277777777735865004e-2;
+        sum = sum * t + 0.833333333333331018375e-1;
+        sum = sum * s + 0.918938533204672;
+        s = 0.5 * log (fa);
+        t = fa - 0.5;
+        s = s * t;
+        t = s - fa;
+        s = s + sum;
+        t = t + s;
+        return t;
+    } else {
+NV_IF_ELSE_TARGET(NV_IS_DEVICE,
+        return __cr_lgamma_table [(int) fa-1];
+,
+        switch(a) {
+            case 1: return 0.000000000000000000e-1;
+            case 2: return 0.000000000000000000e-1;
+            case 3: return 6.931471805599453094e-1;
+            case 4: return 1.791759469228055001e0;
+            case 5: return 3.178053830347945620e0;
+            case 6: return 4.787491742782045994e0;
+            case 7: return 6.579251212010100995e0;
+            case 8: return 8.525161361065414300e0;
+            default: return 1.060460290274525023e1;
+        }
+)
+    }
+}
+
+#define KNUTH_FLOAT_CONST 60.0
+template <typename T>
+// Donald E. Knuth Seminumerical Algorithms. The Art of Computer Programming, Volume 2
+QUALIFIERS unsigned int curand_poisson_knuth(T *state, float lambda)
+{
+  unsigned int k = 0;
+  float p = expf(lambda);
+  do{
+      k++;
+      p *= curand_uniform(state);
+  }while (p > 1.0);
+  return k-1;
+}
+
+template <typename T>
+// Donald E. Knuth Seminumerical Algorithms. The Art of Computer Programming, Volume 2
+QUALIFIERS uint4 curand_poisson_knuth4(T *state, float lambda)
+{
+  uint4 k = {0,0,0,0};
+  float exp_lambda = expf(lambda);
+  float4 p={ exp_lambda,exp_lambda,exp_lambda,exp_lambda };
+  do{
+      k.x++;
+      p.x *= curand_uniform(state);
+  }while (p.x > 1.0);
+  do{
+      k.y++;
+      p.y *= curand_uniform(state);
+  }while (p.y > 1.0);
+  do{
+      k.z++;
+      p.z *= curand_uniform(state);
+  }while (p.z > 1.0);
+  do{
+      k.w++;
+      p.w *= curand_uniform(state);
+  }while (p.w > 1.0);
+
+  k.x--;
+  k.y--;
+  k.z--;
+  k.w--;
+  return k;
+}
+
+template <typename T>
+// Marsaglia, Tsang, Wang Journal of Statistical Software, square histogram.
+QUALIFIERS unsigned int _curand_M2_double(T x, curandDistributionM2Shift_t distributionM2)
+{
+    double u = _curand_uniform_double(x);
+    int j = (int) floor(distributionM2->length*u);
+
+    double histogramVj;
+    unsigned int histogramKj;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_35,
+    histogramVj = __ldg( &(distributionM2->histogram->V[j]));
+    histogramKj = __ldg( &(distributionM2->histogram->K[j]));
+,
+    histogramVj = distributionM2->histogram->V[j];
+    histogramKj = distributionM2->histogram->K[j];
+)
+    //if (u < distributionM2->histogram->V[j]) return distributionM2->shift + j;
+    //return distributionM2->shift + distributionM2->histogram->K[j];
+    if (u < histogramVj) return distributionM2->shift + j;
+    return distributionM2->shift + histogramKj;
+}
+
+template <typename T>
+// Marsaglia, Tsang, Wang Journal of Statistical Software, square histogram.
+QUALIFIERS uint4 _curand_M2_double4(T x, curandDistributionM2Shift_t distributionM2)
+{
+    double4 u;
+    uint4 result = {0,0,0,0};
+    int4 flag = {1,1,1,1};
+
+    u.x = _curand_uniform_double(x.x);
+    u.y = _curand_uniform_double(x.y);
+    u.z = _curand_uniform_double(x.z);
+    u.w = _curand_uniform_double(x.w);
+
+    int4 j;
+    j.x = (int) floor(distributionM2->length*u.x);
+    j.y = (int) floor(distributionM2->length*u.y);
+    j.z = (int) floor(distributionM2->length*u.z);
+    j.w = (int) floor(distributionM2->length*u.w);
+//    int result;
+
+    double histogramVjx;
+    double histogramVjy;
+    double histogramVjz;
+    double histogramVjw;
+    unsigned int histogramKjx;
+    unsigned int histogramKjy;
+    unsigned int histogramKjz;
+    unsigned int histogramKjw;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_35,
+    histogramVjx =  __ldg( &(distributionM2->histogram->V[j.x]));
+    histogramVjy =  __ldg( &(distributionM2->histogram->V[j.y]));
+    histogramVjz =  __ldg( &(distributionM2->histogram->V[j.z]));
+    histogramVjw =  __ldg( &(distributionM2->histogram->V[j.w]));
+
+    histogramKjx = __ldg( &(distributionM2->histogram->K[j.x]));
+    histogramKjy = __ldg( &(distributionM2->histogram->K[j.y]));
+    histogramKjz = __ldg( &(distributionM2->histogram->K[j.z]));
+    histogramKjw = __ldg( &(distributionM2->histogram->K[j.w]));
+,
+    histogramVjx =  distributionM2->histogram->V[j.x];
+    histogramVjy =  distributionM2->histogram->V[j.y];
+    histogramVjz =  distributionM2->histogram->V[j.z];
+    histogramVjw =  distributionM2->histogram->V[j.w];
+
+    histogramKjx = distributionM2->histogram->K[j.x];
+    histogramKjy = distributionM2->histogram->K[j.y];
+    histogramKjz = distributionM2->histogram->K[j.z];
+    histogramKjw = distributionM2->histogram->K[j.w];
+)
+
+    if (u.x < histogramVjx){ result.x = distributionM2->shift + j.x; flag.x = 0; }
+    if (u.y < histogramVjy){ result.y = distributionM2->shift + j.y; flag.y = 0; }
+    if (u.z < histogramVjz){ result.z = distributionM2->shift + j.z; flag.z = 0; }
+    if (u.w < histogramVjw){ result.w = distributionM2->shift + j.w; flag.w = 0; }
+    //return distributionM2->shift + distributionM2->histogram->K[j];
+
+    if(flag.x) result.x = distributionM2->shift + histogramKjx;
+    if(flag.y) result.y = distributionM2->shift + histogramKjy;
+    if(flag.z) result.z = distributionM2->shift + histogramKjz;
+    if(flag.w) result.w = distributionM2->shift + histogramKjw;
+
+    return result;
+}
+
+template <typename STATE>
+QUALIFIERS unsigned int curand_M2_double(STATE *state, curandDistributionM2Shift_t distributionM2)
+{
+    return _curand_M2_double(curand(state), distributionM2);
+}
+
+template <typename STATE>
+QUALIFIERS uint4 curand_M2_double4(STATE *state, curandDistributionM2Shift_t distributionM2)
+{
+    return _curand_M2_double4(curand4(state), distributionM2);
+}
+
+
+template <typename T>
+QUALIFIERS unsigned int _curand_binary_search_double(T x, curandDistributionShift_t distribution)
+{
+    double u = _curand_uniform_double(x);
+    int min = 0;
+    int max = distribution->length-1;
+    do{
+        int mid = (max + min)/2;
+        double probability_mid;
+NV_IF_ELSE_TARGET(NV_PROVIDES_SM_35,
+        probability_mid = __ldg( &(distribution->probability[mid]));
+,
+        probability_mid = distribution->probability[mid];
+)
+        if (u <= probability_mid){
+            max = mid;
+        }else{
+            min = mid+1;
+        }
+    }while (min < max);
+    return distribution->shift + min;
+}
+
+template <typename STATE>
+QUALIFIERS unsigned int curand_binary_search_double(STATE *state, curandDistributionShift_t distribution)
+{
+    return _curand_binary_search_double(curand(state), distribution);
+}
+
+// Generates uniformly distributed double values in range (0.0; 1.0) from uniformly distributed
+// unsigned int. We can't use standard _curand_uniform_double since it can generate 1.0.
+// This is required only for _curand_poisson_ITR_double.
+QUALIFIERS double _curand_uniform_double_excluding_one(unsigned int x)
+{
+    return x * CURAND_2POW32_INV_DOUBLE + (CURAND_2POW32_INV_DOUBLE/2.0);
+}
+
+// Overload for unsigned long long.
+// This is required only for _curand_poisson_ITR_double.
+QUALIFIERS double _curand_uniform_double_excluding_one(unsigned long long x)
+{
+    return (x >> 11) * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/4.0);
+}
+
+#define MAGIC_DOUBLE_CONST 500.0
+template <typename T>
+//George S. Fishman Discrete-event simulation: modeling, programming, and analysis
+QUALIFIERS unsigned int _curand_poisson_ITR_double(T x, double lambda)
+{
+  double L,p = 1.0;
+  double q = 1.0;
+  unsigned int k = 0;
+  int pow=0;
+  // This algorithm requires u to be in (0;1) range, however, _curand_uniform_double
+  // returns a number in range (0;1]. If u is 1.0 the inner loop never ends. The
+  // following operation transforms the range from (0;1] to (0;1).
+  double u = _curand_uniform_double_excluding_one(x);
+  do{
+      if (lambda > (double)(pow+MAGIC_DOUBLE_CONST)){
+          L = exp(-MAGIC_DOUBLE_CONST);
+      }else{
+          L = exp((double)(pow - lambda));
+      }
+      p *= L;
+      q *= L;
+      pow += (int) MAGIC_DOUBLE_CONST;
+      while (u > q){
+        k++;
+        p *= ((double)lambda / (double) k);
+        q += p;
+      }
+  }while((double)pow < lambda);
+  return k;
+}
+
+template <typename T>
+/* Rejection Method for Poisson distribution based on gammainc approximation */
+QUALIFIERS unsigned int curand_poisson_gammainc(T state, float lambda){
+    float y, x, t, z,v;
+    float logl = __cr_log (lambda);
+    while (true) {
+        y = curand_uniform (state);
+        x = __cr_pgammaincinv (lambda, y);
+        x = floorf (x);
+        z = curand_uniform (state);
+        v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
+        z = z*v;
+        t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
+        if ((z < t) && (v>=1e-20))
+            break;
+    }
+    return (unsigned int)x;
+}
+
+template <typename T>
+/* Rejection Method for Poisson distribution based on gammainc approximation */
+QUALIFIERS uint4 curand_poisson_gammainc4(T state, float lambda){
+    uint4 result;
+    float y, x, t, z,v;
+    float logl = __cr_log (lambda);
+    while (true) {
+        y = curand_uniform(state);
+        x = __cr_pgammaincinv (lambda, y);
+        x = floorf (x);
+        z = curand_uniform (state);
+        v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
+        z = z*v;
+        t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
+        if ((z < t) && (v>=1e-20))
+            break;
+    }
+    result.x = (unsigned int)x;
+
+    while (true) {
+        y = curand_uniform(state);
+        x = __cr_pgammaincinv (lambda, y);
+        x = floorf (x);
+        z = curand_uniform (state);
+        v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
+        z = z*v;
+        t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
+        if ((z < t) && (v>=1e-20))
+            break;
+    }
+    result.y = (unsigned int)x;
+
+    while (true) {
+        y = curand_uniform(state);
+        x = __cr_pgammaincinv (lambda, y);
+        x = floorf (x);
+        z = curand_uniform (state);
+        v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
+        z = z*v;
+        t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
+        if ((z < t) && (v>=1e-20))
+            break;
+    }
+    result.z = (unsigned int)x;
+
+    while (true) {
+        y = curand_uniform(state);
+        x = __cr_pgammaincinv (lambda, y);
+        x = floorf (x);
+        z = curand_uniform (state);
+        v = (__cr_pgammainc (lambda, x + 1.0f) - __cr_pgammainc (lambda, x)) * 1.3f;
+        z = z*v;
+        t = (float)__cr_exp (-lambda + x * logl - (float)__cr_lgamma_integer ((int)(1.0f + x)));
+        if ((z < t) && (v>=1e-20))
+            break;
+    }
+    result.w = (unsigned int)x;
+
+    return result;
+}
+// Note below that the round to nearest integer, where needed,is done in line with code that
+// assumes the range of values is < 2**32
+
+template <typename T>
+QUALIFIERS unsigned int _curand_poisson(T x, double lambda)
+{
+    if (lambda < 1000)
+        return _curand_poisson_ITR_double(x, lambda);
+    return (unsigned int)((sqrt(lambda) * _curand_normal_icdf_double(x)) + lambda + 0.5); //Round to nearest
+}
+
+template <typename T>
+QUALIFIERS unsigned int _curand_poisson_from_normal(T x, double lambda)
+{
+    return (unsigned int)((sqrt(lambda) * _curand_normal_icdf(x)) + lambda + 0.5); //Round to nearest
+}
+
+template <typename STATE>
+QUALIFIERS unsigned int curand_poisson_from_normal(STATE state, double lambda)
+{
+    return (unsigned int)((sqrt(lambda) * curand_normal(state)) + lambda + 0.5); //Round to nearest
+}
+
+template <typename STATE>
+QUALIFIERS uint4 curand_poisson_from_normal4(STATE state, double lambda)
+{
+   uint4 result;
+   float4 _res;
+
+   _res = curand_normal4(state);
+
+   result.x = (unsigned int)((sqrt(lambda) * _res.x) + lambda + 0.5); //Round to nearest
+   result.y = (unsigned int)((sqrt(lambda) * _res.y) + lambda + 0.5); //Round to nearest
+   result.z = (unsigned int)((sqrt(lambda) * _res.z) + lambda + 0.5); //Round to nearest
+   result.w = (unsigned int)((sqrt(lambda) * _res.w) + lambda + 0.5); //Round to nearest
+   return result; //Round to nearest
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a XORWOW generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the XORWOW generator in \p state,
+ * increment the  position of the generator by a variable amount, depending
+ * on the algorithm used.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStateXORWOW_t *state, double lambda)
+{
+    if (lambda < 64)
+        return curand_poisson_knuth(state, (float)lambda);
+    if (lambda > 4000)
+        return (unsigned int)((sqrt(lambda) * curand_normal_double(state)) + lambda + 0.5); //Round to nearest
+    return curand_poisson_gammainc(state, (float)lambda);
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a Philox4_32_10 generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the Philox4_32_10 generator in \p state,
+ * increment the  position of the generator by a variable amount, depending
+ * on the algorithm used.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStatePhilox4_32_10_t *state, double lambda)
+{
+    if (lambda < 64)
+        return curand_poisson_knuth(state, (float)lambda);
+    if (lambda > 4000)
+        return (unsigned int)((sqrt(lambda) * curand_normal_double(state)) + lambda + 0.5); //Round to nearest
+    return curand_poisson_gammainc(state, (float)lambda);
+}
+/**
+ * \brief Return four Poisson-distributed unsigned ints from a Philox4_32_10 generator.
+ *
+ * Return a four unsigned ints from a Poisson
+ * distribution with lambda \p lambda from the Philox4_32_10 generator in \p state,
+ * increment the  position of the generator by a variable amount, depending
+ * on the algorithm used.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS uint4 curand_poisson4(curandStatePhilox4_32_10_t *state, double lambda)
+{
+    uint4 result;
+    double4 _res;
+    if (lambda < 64)
+        return curand_poisson_knuth4(state, (float)lambda);
+    if (lambda > 4000) {
+        _res = curand_normal4_double(state);
+        result.x = (unsigned int)((sqrt(lambda) * _res.x) + lambda + 0.5); //Round to nearest
+        result.y = (unsigned int)((sqrt(lambda) * _res.y) + lambda + 0.5); //Round to nearest
+        result.z = (unsigned int)((sqrt(lambda) * _res.z) + lambda + 0.5); //Round to nearest
+        result.w = (unsigned int)((sqrt(lambda) * _res.w) + lambda + 0.5); //Round to nearest
+    	return result;
+    }
+    return curand_poisson_gammainc4(state, (float)lambda);
+}
+
+
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a MRG32k3A generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the MRG32k3a generator in \p state,
+ * increment the position of the generator by a variable amount, depending
+ * on the algorithm used.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStateMRG32k3a_t *state, double lambda)
+{
+    if (lambda < 64)
+        return curand_poisson_knuth(state, (float)lambda);
+    if (lambda > 4000)
+        return (unsigned int)((sqrt(lambda) * curand_normal_double(state)) + lambda + 0.5); //Round to nearest
+    return curand_poisson_gammainc(state, (float)lambda);
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a MTGP32 generator.
+ *
+ * Return a single int from a Poisson
+ * distribution with lambda \p lambda from the MTGP32 generator in \p state,
+ * increment the position of the generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStateMtgp32_t *state, double lambda)
+{
+    return _curand_poisson(curand(state), lambda);
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a Sobol32 generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the Sobol32 generator in \p state,
+ * increment the position of the generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+
+QUALIFIERS unsigned int curand_poisson(curandStateSobol32_t *state, double lambda)
+{
+    return _curand_poisson(curand(state), lambda);
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a scrambled Sobol32 generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the scrambled Sobol32 generator in \p state,
+ * increment the position of the generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStateScrambledSobol32_t *state, double lambda)
+{
+    return _curand_poisson(curand(state), lambda);
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a Sobol64 generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStateSobol64_t *state, double lambda)
+{
+    return _curand_poisson(curand(state), lambda);
+}
+
+/**
+ * \brief Return a Poisson-distributed unsigned int from a scrambled Sobol64 generator.
+ *
+ * Return a single unsigned int from a Poisson
+ * distribution with lambda \p lambda from the scrambled Sobol64 generator in \p state,
+ * increment position of generator by one.
+ *
+ * \param state - Pointer to state to update
+ * \param lambda - Lambda of the Poisson distribution
+ *
+ * \return Poisson-distributed unsigned int with lambda \p lambda
+ */
+QUALIFIERS unsigned int curand_poisson(curandStateScrambledSobol64_t *state, double lambda)
+{
+    return _curand_poisson(curand(state), lambda);
+}
+#endif // !defined(CURAND_POISSON_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_precalc.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_precalc.h
new file mode 100644
index 0000000000000000000000000000000000000000..01818fb642856eb09f7e3fa0b8202d34c37c8566
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_precalc.h
@@ -0,0 +1,3548 @@
+/* Copyright 2010-2012 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+#if !defined(CURAND_XORWOW_PRECALCULATED_H_)
+#define CURAND_XORWOW_PRECALCULATED_H_
+
+#define PRECALC_NUM_MATRICES (32)
+#define PRECALC_BLOCK_SIZE (2)
+#define PRECALC_BLOCK_MASK ((1<<PRECALC_BLOCK_SIZE)-1)
+#define XORWOW_SEQUENCE_SPACING (67)
+
+#if defined(__CUDACC_RDC__) && (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+#define CURAND_XORWOW_PRECALCULATED_DEVICE_QUALIFIERS inline __device__
+#else
+#define CURAND_XORWOW_PRECALCULATED_DEVICE_QUALIFIERS static __device__
+#endif
+
+#if (__cplusplus >= 201703L) && defined(__cpp_inline_variables)
+#define CURAND_XORWOW_PRECALCULATED_HOST_QUALIFIERS inline
+#else
+#define CURAND_XORWOW_PRECALCULATED_HOST_QUALIFIERS static
+#endif
+CURAND_XORWOW_PRECALCULATED_DEVICE_QUALIFIERS unsigned int precalc_xorwow_matrix[32][800] = {
+{
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+{
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+},
+{
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+625648993UL, 1111327015UL, 3975736867UL, 2402230281UL, 4092718962UL, 1349560774UL, 3521430425UL, 2827154828UL, 1067483357UL, 3495429909UL, 2808148912UL, 2034644068UL, 725679489UL, 3705541400UL, 1308182381UL, 212242504UL, 3001720496UL, 2982141597UL, 1090931027UL, 755020243UL, 2510536004UL, 307638580UL, 452574019UL, 2384876926UL, 2147764179UL, 3227931749UL, 1701580099UL, 3034185952UL, 2765119653UL, 3279755577UL, 1054678914UL, 2659043235UL, 
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+3765223460UL, 866296319UL, 1169380319UL, 2919436659UL, 3370646420UL, 1866719277UL, 3226685069UL, 4252262342UL, 1835269960UL, 1170376930UL, 1357078768UL, 269175192UL, 3826888026UL, 3430363541UL, 1920758494UL, 51532769UL, 2919489927UL, 1568325914UL, 3184357856UL, 43519013UL, 2108988015UL, 1398495041UL, 2844640139UL, 2317092036UL, 1774750014UL, 2690907136UL, 1834465421UL, 1106469655UL, 2149810726UL, 4265420439UL, 2048218411UL, 1399986034UL, 
+1361619115UL, 2504769226UL, 913700780UL, 2382994726UL, 4292849877UL, 1381838410UL, 250258264UL, 1828569640UL, 1732718872UL, 1869949326UL, 835188347UL, 4180489913UL, 3049522050UL, 535168392UL, 3972173823UL, 2763844722UL, 3401884753UL, 3750694101UL, 851518496UL, 1015521371UL, 1511969218UL, 1597622074UL, 3810841601UL, 3326003776UL, 3141062630UL, 552856274UL, 4059179808UL, 175647012UL, 3893497501UL, 1805118717UL, 1064213711UL, 2310866839UL, 
+1397146463UL, 1798096676UL, 279868399UL, 1926726615UL, 2773068510UL, 347721208UL, 4099183723UL, 509136218UL, 2833615756UL, 3960499694UL, 4236258712UL, 1765641675UL, 535748563UL, 354515646UL, 3307314159UL, 3160079941UL, 3252681800UL, 2568363625UL, 3818514182UL, 3738662353UL, 899056999UL, 2531772068UL, 647726503UL, 2895823632UL, 393777910UL, 1759531813UL, 2363148604UL, 2931477989UL, 3381169914UL, 3877595131UL, 2375539210UL, 557544627UL, 
+273611522UL, 2717517554UL, 1935966767UL, 1738732887UL, 29153600UL, 20993454UL, 3758163226UL, 1692844400UL, 2176938194UL, 378940221UL, 2888599759UL, 1173120554UL, 2732575460UL, 3912766812UL, 522606644UL, 1925230852UL, 3887440328UL, 2111843275UL, 3549473366UL, 922916775UL, 2889744544UL, 2970467682UL, 3039277863UL, 990580154UL, 55435595UL, 1665634070UL, 3043418336UL, 2792050230UL, 2762503138UL, 1402344059UL, 2099263558UL, 3945248675UL, 
+3925566467UL, 2413979948UL, 463637252UL, 3768636616UL, 3374572388UL, 2217956879UL, 791988933UL, 382210765UL, 1715859444UL, 3462446413UL, 971427992UL, 3255404695UL, 2001750035UL, 2214129237UL, 320812374UL, 3688098101UL, 920365480UL, 2819401059UL, 2932570681UL, 3749857130UL, 523943786UL, 1271514748UL, 4078439472UL, 3501181265UL, 2475869985UL, 1797996951UL, 2300820710UL, 3994893924UL, 1739992082UL, 2475950326UL, 3780826558UL, 1018851411UL, 
+},
+
+};
+#ifndef __CUDACC_RTC__
+CURAND_XORWOW_PRECALCULATED_HOST_QUALIFIERS unsigned int precalc_xorwow_matrix_host[32][800] = {
+{
+850664906UL, 2293210629UL, 1517805917UL, 1215500405UL, 1612415445UL, 645388200UL, 824349799UL, 3517232886UL, 4075591755UL, 3089899292UL, 4249786064UL, 3811424903UL, 1100783479UL, 53649761UL, 2817264826UL, 3159462529UL, 1654848550UL, 950025444UL, 3095510002UL, 4080567211UL, 4111078399UL, 3241719305UL, 2788212779UL, 4256963770UL, 2426893717UL, 4190211142UL, 1420776905UL, 3780537969UL, 1102912875UL, 1657948873UL, 3354905256UL, 2519610308UL, 
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+208707366UL, 2741570297UL, 440313302UL, 864288468UL, 1564945290UL, 1050929272UL, 3037450392UL, 1101323242UL, 1200278943UL, 3005564105UL, 3847988630UL, 3251750599UL, 2608433412UL, 3106720723UL, 1522694503UL, 3857782840UL, 4282681349UL, 2229263718UL, 4106780914UL, 125648941UL, 1933617693UL, 2971178569UL, 3537872030UL, 448962137UL, 652123777UL, 2393871920UL, 3938047691UL, 244410098UL, 3110791961UL, 3122318189UL, 877378106UL, 3683644255UL, 
+4279094311UL, 3638987055UL, 667681197UL, 1679868535UL, 1938378101UL, 1331340184UL, 734163051UL, 3409564713UL, 955108672UL, 3969637663UL, 156515523UL, 1871394552UL, 590275639UL, 3237133664UL, 898438533UL, 2291347006UL, 644781653UL, 3575493549UL, 1206698159UL, 2484805619UL, 2931447110UL, 2411269190UL, 3866437145UL, 161562563UL, 3077166456UL, 792874130UL, 3193406610UL, 2500233218UL, 596837225UL, 3667458052UL, 3239960816UL, 2271901243UL, 
+},
+{
+3975736867UL, 2402230281UL, 4092718962UL, 3100052505UL, 3277909563UL, 2827154828UL, 1067483357UL, 3495429909UL, 426635932UL, 2702495453UL, 725679489UL, 3705541400UL, 1308182381UL, 27549785UL, 3000675918UL, 2982141597UL, 1090931027UL, 755020243UL, 3986354189UL, 2529541113UL, 452574019UL, 2384876926UL, 2147764179UL, 1360907484UL, 2072364695UL, 3034185952UL, 2765119653UL, 3279755577UL, 3828140333UL, 582568392UL, 4228353628UL, 701214306UL, 
+2460043371UL, 3943376509UL, 2443090800UL, 2481277520UL, 859309333UL, 2928621220UL, 1933644685UL, 3803162893UL, 3310629548UL, 2361261213UL, 790233558UL, 2517540072UL, 2823327610UL, 2952921690UL, 3295251862UL, 1089451775UL, 2637751681UL, 1648031370UL, 1343061717UL, 2355026672UL, 67684812UL, 4019593497UL, 2636283634UL, 1051433451UL, 51111285UL, 15338687UL, 3779021741UL, 3987886044UL, 70037785UL, 2009147353UL, 4236701871UL, 928261128UL, 
+2185183571UL, 2793993680UL, 2975111058UL, 3730415022UL, 3316612678UL, 823585671UL, 4153354125UL, 509071385UL, 2056228251UL, 4034784810UL, 96820040UL, 169863045UL, 932848332UL, 2282651407UL, 747279843UL, 1387211022UL, 2410099142UL, 3394315084UL, 3191572807UL, 4073182500UL, 3768455462UL, 3712420663UL, 3000991259UL, 249137656UL, 2477445202UL, 3952155443UL, 392730170UL, 4208559971UL, 24751401UL, 661761054UL, 1574175475UL, 2715927647UL, 
+985309803UL, 2570053358UL, 619269634UL, 3890591314UL, 1129119636UL, 3133886450UL, 328788870UL, 3449809720UL, 1380118080UL, 2719792059UL, 691527418UL, 3487733607UL, 3819095050UL, 3367871088UL, 709089170UL, 1057897966UL, 1938975941UL, 4082466714UL, 251564920UL, 3083496965UL, 1040123365UL, 295024253UL, 2788334176UL, 3430095934UL, 3641758945UL, 2029993123UL, 3231254260UL, 150555625UL, 2270671577UL, 2032382533UL, 2088497043UL, 1392075576UL, 
+644811077UL, 2122632989UL, 3224165725UL, 1571908345UL, 2558692460UL, 1493305706UL, 4064652450UL, 448105905UL, 699188129UL, 2017324335UL, 4286307548UL, 2415725473UL, 3976741021UL, 3526784185UL, 2882973520UL, 3420335125UL, 2034028744UL, 1425242390UL, 982315917UL, 2614735561UL, 2439972944UL, 2518992720UL, 3792239985UL, 3260669732UL, 2586472751UL, 3432756715UL, 1318634102UL, 3722487277UL, 3037304631UL, 433233786UL, 3750002877UL, 2504731459UL, 
+1111327015UL, 3975736867UL, 2402230281UL, 4092718962UL, 3100052505UL, 3521430425UL, 2827154828UL, 1067483357UL, 3495429909UL, 426635932UL, 2034644068UL, 725679489UL, 3705541400UL, 1308182381UL, 27549785UL, 3001720496UL, 2982141597UL, 1090931027UL, 755020243UL, 3986354189UL, 307638580UL, 452574019UL, 2384876926UL, 2147764179UL, 1360907484UL, 1701580099UL, 3034185952UL, 2765119653UL, 3279755577UL, 3828140333UL, 2659043235UL, 4228353628UL, 
+701214306UL, 2460043371UL, 3943376509UL, 2084857792UL, 2481277520UL, 859309333UL, 2928621220UL, 1933644685UL, 4152646669UL, 3310629548UL, 2361261213UL, 790233558UL, 2517540072UL, 481283060UL, 2952921690UL, 3295251862UL, 1089451775UL, 2637751681UL, 2915212660UL, 1343061717UL, 2355026672UL, 67684812UL, 4019593497UL, 3290479436UL, 1051433451UL, 51111285UL, 15338687UL, 3779021741UL, 1430944862UL, 70037785UL, 2009147353UL, 4236701871UL, 
+928261128UL, 2063919641UL, 2793993680UL, 2975111058UL, 3730415022UL, 3316612678UL, 2373806232UL, 4153354125UL, 509071385UL, 2056228251UL, 4034784810UL, 1912268707UL, 169863045UL, 932848332UL, 2282651407UL, 747279843UL, 3712980941UL, 2410099142UL, 3394315084UL, 3191572807UL, 4073182500UL, 4262344652UL, 3712420663UL, 3000991259UL, 249137656UL, 2477445202UL, 3374467273UL, 392730170UL, 4208559971UL, 24751401UL, 661761054UL, 1670592959UL, 
+2715927647UL, 985309803UL, 2570053358UL, 619269634UL, 830547082UL, 1129119636UL, 3133886450UL, 328788870UL, 3449809720UL, 202644333UL, 2719792059UL, 691527418UL, 3487733607UL, 3819095050UL, 1400269159UL, 709089170UL, 1057897966UL, 1938975941UL, 4082466714UL, 3393893128UL, 3083496965UL, 1040123365UL, 295024253UL, 2788334176UL, 1219456UL, 3641758945UL, 2029993123UL, 3231254260UL, 150555625UL, 3713963210UL, 2032382533UL, 2088497043UL, 
+1392075576UL, 644811077UL, 3733090890UL, 3224165725UL, 1571908345UL, 2558692460UL, 1493305706UL, 1678929187UL, 448105905UL, 699188129UL, 2017324335UL, 4286307548UL, 3368868963UL, 3976741021UL, 3526784185UL, 2882973520UL, 3420335125UL, 3233347584UL, 1425242390UL, 982315917UL, 2614735561UL, 2439972944UL, 4172908214UL, 3792239985UL, 3260669732UL, 2586472751UL, 3432756715UL, 1926157640UL, 3722487277UL, 3037304631UL, 433233786UL, 3750002877UL, 
+625648993UL, 1111327015UL, 3975736867UL, 2402230281UL, 4092718962UL, 1349560774UL, 3521430425UL, 2827154828UL, 1067483357UL, 3495429909UL, 2808148912UL, 2034644068UL, 725679489UL, 3705541400UL, 1308182381UL, 212242504UL, 3001720496UL, 2982141597UL, 1090931027UL, 755020243UL, 2510536004UL, 307638580UL, 452574019UL, 2384876926UL, 2147764179UL, 3227931749UL, 1701580099UL, 3034185952UL, 2765119653UL, 3279755577UL, 1054678914UL, 2659043235UL, 
+4228353628UL, 701214306UL, 2460043371UL, 381309305UL, 2084857792UL, 2481277520UL, 859309333UL, 2928621220UL, 891630344UL, 4152646669UL, 3310629548UL, 2361261213UL, 790233558UL, 1490030690UL, 481283060UL, 2952921690UL, 3295251862UL, 1089451775UL, 2025962691UL, 2915212660UL, 1343061717UL, 2355026672UL, 67684812UL, 2217081575UL, 3290479436UL, 1051433451UL, 51111285UL, 15338687UL, 3455020635UL, 1430944862UL, 70037785UL, 2009147353UL, 
+4236701871UL, 1155691935UL, 2063919641UL, 2793993680UL, 2975111058UL, 3730415022UL, 403147571UL, 2373806232UL, 4153354125UL, 509071385UL, 2056228251UL, 444685935UL, 1912268707UL, 169863045UL, 932848332UL, 2282651407UL, 2077207745UL, 3712980941UL, 2410099142UL, 3394315084UL, 3191572807UL, 640536184UL, 4262344652UL, 3712420663UL, 3000991259UL, 249137656UL, 368243227UL, 3374467273UL, 392730170UL, 4208559971UL, 24751401UL, 495648080UL, 
+1670592959UL, 2715927647UL, 985309803UL, 2570053358UL, 2181488546UL, 830547082UL, 1129119636UL, 3133886450UL, 328788870UL, 2497762979UL, 202644333UL, 2719792059UL, 691527418UL, 3487733607UL, 1976943620UL, 1400269159UL, 709089170UL, 1057897966UL, 1938975941UL, 2071351862UL, 3393893128UL, 3083496965UL, 1040123365UL, 295024253UL, 1440317859UL, 1219456UL, 3641758945UL, 2029993123UL, 3231254260UL, 952956380UL, 3713963210UL, 2032382533UL, 
+2088497043UL, 1392075576UL, 4180475645UL, 3733090890UL, 3224165725UL, 1571908345UL, 2558692460UL, 3482549931UL, 1678929187UL, 448105905UL, 699188129UL, 2017324335UL, 2431113987UL, 3368868963UL, 3976741021UL, 3526784185UL, 2882973520UL, 1900625235UL, 3233347584UL, 1425242390UL, 982315917UL, 2614735561UL, 1128074864UL, 4172908214UL, 3792239985UL, 3260669732UL, 2586472751UL, 4095880420UL, 1926157640UL, 3722487277UL, 3037304631UL, 433233786UL, 
+2927295412UL, 625648993UL, 1111327015UL, 3975736867UL, 2402230281UL, 259216032UL, 1349560774UL, 3521430425UL, 2827154828UL, 1067483357UL, 989690947UL, 2808148912UL, 2034644068UL, 725679489UL, 3705541400UL, 588787520UL, 212242504UL, 3001720496UL, 2982141597UL, 1090931027UL, 1235811382UL, 2510536004UL, 307638580UL, 452574019UL, 2384876926UL, 3536994565UL, 3227931749UL, 1701580099UL, 3034185952UL, 2765119653UL, 463890041UL, 1054678914UL, 
+2659043235UL, 4228353628UL, 701214306UL, 3085494195UL, 381309305UL, 2084857792UL, 2481277520UL, 859309333UL, 3760199179UL, 891630344UL, 4152646669UL, 3310629548UL, 2361261213UL, 2550680915UL, 1490030690UL, 481283060UL, 2952921690UL, 3295251862UL, 4195487760UL, 2025962691UL, 2915212660UL, 1343061717UL, 2355026672UL, 339445869UL, 2217081575UL, 3290479436UL, 1051433451UL, 51111285UL, 1113202216UL, 3455020635UL, 1430944862UL, 70037785UL, 
+2009147353UL, 3982848623UL, 1155691935UL, 2063919641UL, 2793993680UL, 2975111058UL, 1725337613UL, 403147571UL, 2373806232UL, 4153354125UL, 509071385UL, 1474832043UL, 444685935UL, 1912268707UL, 169863045UL, 932848332UL, 1500855137UL, 2077207745UL, 3712980941UL, 2410099142UL, 3394315084UL, 2800379966UL, 640536184UL, 4262344652UL, 3712420663UL, 3000991259UL, 1028021485UL, 368243227UL, 3374467273UL, 392730170UL, 4208559971UL, 108468246UL, 
+495648080UL, 1670592959UL, 2715927647UL, 985309803UL, 61959589UL, 2181488546UL, 830547082UL, 1129119636UL, 3133886450UL, 3912020361UL, 2497762979UL, 202644333UL, 2719792059UL, 691527418UL, 1984193076UL, 1976943620UL, 1400269159UL, 709089170UL, 1057897966UL, 2381612490UL, 2071351862UL, 3393893128UL, 3083496965UL, 1040123365UL, 391784014UL, 1440317859UL, 1219456UL, 3641758945UL, 2029993123UL, 2260373342UL, 952956380UL, 3713963210UL, 
+2032382533UL, 2088497043UL, 135943164UL, 4180475645UL, 3733090890UL, 3224165725UL, 1571908345UL, 2660287325UL, 3482549931UL, 1678929187UL, 448105905UL, 699188129UL, 4104693318UL, 2431113987UL, 3368868963UL, 3976741021UL, 3526784185UL, 113762138UL, 1900625235UL, 3233347584UL, 1425242390UL, 982315917UL, 599246177UL, 1128074864UL, 4172908214UL, 3792239985UL, 3260669732UL, 2309689974UL, 4095880420UL, 1926157640UL, 3722487277UL, 3037304631UL, 
+3765223460UL, 866296319UL, 1169380319UL, 2919436659UL, 3370646420UL, 1866719277UL, 3226685069UL, 4252262342UL, 1835269960UL, 1170376930UL, 1357078768UL, 269175192UL, 3826888026UL, 3430363541UL, 1920758494UL, 51532769UL, 2919489927UL, 1568325914UL, 3184357856UL, 43519013UL, 2108988015UL, 1398495041UL, 2844640139UL, 2317092036UL, 1774750014UL, 2690907136UL, 1834465421UL, 1106469655UL, 2149810726UL, 4265420439UL, 2048218411UL, 1399986034UL, 
+1361619115UL, 2504769226UL, 913700780UL, 2382994726UL, 4292849877UL, 1381838410UL, 250258264UL, 1828569640UL, 1732718872UL, 1869949326UL, 835188347UL, 4180489913UL, 3049522050UL, 535168392UL, 3972173823UL, 2763844722UL, 3401884753UL, 3750694101UL, 851518496UL, 1015521371UL, 1511969218UL, 1597622074UL, 3810841601UL, 3326003776UL, 3141062630UL, 552856274UL, 4059179808UL, 175647012UL, 3893497501UL, 1805118717UL, 1064213711UL, 2310866839UL, 
+1397146463UL, 1798096676UL, 279868399UL, 1926726615UL, 2773068510UL, 347721208UL, 4099183723UL, 509136218UL, 2833615756UL, 3960499694UL, 4236258712UL, 1765641675UL, 535748563UL, 354515646UL, 3307314159UL, 3160079941UL, 3252681800UL, 2568363625UL, 3818514182UL, 3738662353UL, 899056999UL, 2531772068UL, 647726503UL, 2895823632UL, 393777910UL, 1759531813UL, 2363148604UL, 2931477989UL, 3381169914UL, 3877595131UL, 2375539210UL, 557544627UL, 
+273611522UL, 2717517554UL, 1935966767UL, 1738732887UL, 29153600UL, 20993454UL, 3758163226UL, 1692844400UL, 2176938194UL, 378940221UL, 2888599759UL, 1173120554UL, 2732575460UL, 3912766812UL, 522606644UL, 1925230852UL, 3887440328UL, 2111843275UL, 3549473366UL, 922916775UL, 2889744544UL, 2970467682UL, 3039277863UL, 990580154UL, 55435595UL, 1665634070UL, 3043418336UL, 2792050230UL, 2762503138UL, 1402344059UL, 2099263558UL, 3945248675UL, 
+3925566467UL, 2413979948UL, 463637252UL, 3768636616UL, 3374572388UL, 2217956879UL, 791988933UL, 382210765UL, 1715859444UL, 3462446413UL, 971427992UL, 3255404695UL, 2001750035UL, 2214129237UL, 320812374UL, 3688098101UL, 920365480UL, 2819401059UL, 2932570681UL, 3749857130UL, 523943786UL, 1271514748UL, 4078439472UL, 3501181265UL, 2475869985UL, 1797996951UL, 2300820710UL, 3994893924UL, 1739992082UL, 2475950326UL, 3780826558UL, 1018851411UL, 
+},
+
+};
+#endif
+CURAND_XORWOW_PRECALCULATED_DEVICE_QUALIFIERS unsigned int precalc_xorwow_offset_matrix[32][800] = {
+{
+0UL, 0UL, 0UL, 0UL, 3UL, 0UL, 0UL, 0UL, 0UL, 6UL, 0UL, 0UL, 0UL, 0UL, 15UL, 0UL, 0UL, 0UL, 0UL, 30UL, 0UL, 0UL, 0UL, 0UL, 60UL, 0UL, 0UL, 0UL, 0UL, 120UL, 0UL, 0UL, 
+0UL, 0UL, 240UL, 0UL, 0UL, 0UL, 0UL, 480UL, 0UL, 0UL, 0UL, 0UL, 960UL, 0UL, 0UL, 0UL, 0UL, 1920UL, 0UL, 0UL, 0UL, 0UL, 3840UL, 0UL, 0UL, 0UL, 0UL, 7680UL, 0UL, 0UL, 0UL, 0UL, 
+15360UL, 0UL, 0UL, 0UL, 0UL, 30720UL, 0UL, 0UL, 0UL, 0UL, 61440UL, 0UL, 0UL, 0UL, 0UL, 122880UL, 0UL, 0UL, 0UL, 0UL, 245760UL, 0UL, 0UL, 0UL, 0UL, 491520UL, 0UL, 0UL, 0UL, 0UL, 983040UL, 0UL, 
+0UL, 0UL, 0UL, 1966080UL, 0UL, 0UL, 0UL, 0UL, 3932160UL, 0UL, 0UL, 0UL, 0UL, 7864320UL, 0UL, 0UL, 0UL, 0UL, 15728640UL, 0UL, 0UL, 0UL, 0UL, 31457280UL, 0UL, 0UL, 0UL, 0UL, 62914560UL, 0UL, 0UL, 0UL, 
+0UL, 125829120UL, 0UL, 0UL, 0UL, 0UL, 251658240UL, 0UL, 0UL, 0UL, 0UL, 503316480UL, 0UL, 0UL, 0UL, 0UL, 1006632960UL, 0UL, 0UL, 0UL, 0UL, 2013265920UL, 0UL, 0UL, 0UL, 0UL, 4026531840UL, 0UL, 0UL, 0UL, 0UL, 3758096384UL, 
+1UL, 0UL, 0UL, 0UL, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 0UL, 
+0UL, 0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL, 0UL, 0UL, 0UL, 
+0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 524288UL, 
+0UL, 0UL, 0UL, 0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL, 0UL, 33554432UL, 0UL, 0UL, 
+0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL, 134217728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL, 0UL, 0UL, 
+0UL, 1UL, 0UL, 0UL, 0UL, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 
+0UL, 0UL, 0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL, 0UL, 0UL, 
+0UL, 0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 
+524288UL, 0UL, 0UL, 0UL, 0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL, 0UL, 33554432UL, 0UL, 
+0UL, 0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL, 134217728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL, 0UL, 
+0UL, 0UL, 1UL, 0UL, 0UL, 0UL, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 
+64UL, 0UL, 0UL, 0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL, 0UL, 
+0UL, 0UL, 0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 
+0UL, 524288UL, 0UL, 0UL, 0UL, 0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL, 0UL, 33554432UL, 
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+1859724785UL, 1693717788UL, 2071056894UL, 1263790655UL, 228419012UL, 2908736862UL, 2564478937UL, 3031631424UL, 3912597408UL, 3862306448UL, 966714666UL, 3546175061UL, 2376551740UL, 1137628506UL, 1114919961UL, 1561255376UL, 450760279UL, 2472341690UL, 685997424UL, 2456661198UL, 3122124160UL, 1873063065UL, 1058649390UL, 1581230869UL, 2996925693UL, 3791666219UL, 813496775UL, 1095426642UL, 2940190462UL, 1642720015UL, 2802023399UL, 3857233976UL, 
+98163536UL, 4157153745UL, 1578965959UL, 1640659450UL, 3115605568UL, 2987293678UL, 1119786914UL, 1748408698UL, 4278091706UL, 996447434UL, 3991775183UL, 3161418733UL, 4123935663UL, 3398421232UL, 1089784804UL, 3659038742UL, 1387393667UL, 770706529UL, 1694361696UL, 2711412312UL, 832142534UL, 3911092159UL, 335435644UL, 166035542UL, 3563501139UL, 1002098855UL, 337936437UL, 2961857543UL, 1306446339UL, 1156926454UL, 3255026725UL, 4072134049UL, 
+1717290230UL, 1323146393UL, 2156340433UL, 2065716367UL, 2597996276UL, 3402032152UL, 779574284UL, 2369501052UL, 2316224856UL, 2720986136UL, 3016786025UL, 2916554213UL, 3476215746UL, 1132150235UL, 2619889920UL, 1279664685UL, 679206534UL, 4014394509UL, 3624968312UL, 1480455625UL, 725015758UL, 707677352UL, 3764409715UL, 1938306480UL, 2171474419UL, 3379664161UL, 684262379UL, 2142433069UL, 43407198UL, 1398850259UL, 2059135843UL, 240266749UL, 
+3788738212UL, 118513026UL, 820245055UL, 1152812311UL, 1398373423UL, 3188977726UL, 872620936UL, 2084649448UL, 807979538UL, 819501992UL, 615447916UL, 3393148006UL, 1765623964UL, 2514767257UL, 3711360450UL, 2941886951UL, 3739102698UL, 4022385962UL, 2306039667UL, 3321267290UL, 2179238310UL, 3192652502UL, 2118792870UL, 2571142127UL, 761776508UL, 873010906UL, 1609627751UL, 4260021041UL, 1747852747UL, 960771906UL, 2647903291UL, 77475681UL, 
+1282566533UL, 4022186916UL, 2681128032UL, 1554542462UL, 3181701944UL, 1168469070UL, 74236514UL, 2806532232UL, 3981048887UL, 1888842784UL, 2888607878UL, 1763028723UL, 701886756UL, 4124077776UL, 3738147505UL, 4066663138UL, 3816449863UL, 921061872UL, 2956972182UL, 3159072916UL, 3337110888UL, 3552795700UL, 2281281091UL, 671098116UL, 1282750020UL, 1008618197UL, 2363767765UL, 1812013295UL, 1854965999UL, 131027176UL, 666394000UL, 2062217824UL, 
+1763334218UL, 551118598UL, 1277961175UL, 3523893635UL, 1855881150UL, 2067903393UL, 2590963277UL, 3214508854UL, 1604911832UL, 1906690475UL, 389417851UL, 2711591984UL, 427723436UL, 1039703630UL, 639602991UL, 444779318UL, 2722002973UL, 3927985419UL, 1297446054UL, 298277450UL, 656022205UL, 134304205UL, 3847728042UL, 3339100423UL, 407022043UL, 1282443442UL, 3173884578UL, 1417906094UL, 2364502739UL, 2158353472UL, 2402775649UL, 1807696073UL, 
+2837535198UL, 705887737UL, 2129202688UL, 3853676283UL, 1388329793UL, 875153687UL, 2367465660UL, 2763058233UL, 2500632304UL, 2196920062UL, 491306883UL, 277753357UL, 3868415380UL, 324867643UL, 3654474955UL, 2569410351UL, 1128175417UL, 1853572398UL, 1133201743UL, 662085935UL, 2263514999UL, 3077768113UL, 3309730620UL, 3602394176UL, 3747458070UL, 188422725UL, 813812450UL, 1502276531UL, 3909138356UL, 2766044599UL, 3760928321UL, 573108836UL, 
+},
+{
+1240264181UL, 1624064648UL, 3039823158UL, 2013985253UL, 1473300299UL, 2762062141UL, 3273470484UL, 1889745445UL, 2516996174UL, 3190376531UL, 996186898UL, 3893981177UL, 1268272590UL, 3226095713UL, 153038465UL, 2184871198UL, 3224094011UL, 2526518401UL, 1738960059UL, 1187560605UL, 4194384320UL, 2837011297UL, 3638232350UL, 367907454UL, 574009898UL, 1948901330UL, 60430044UL, 1569835584UL, 3160561697UL, 321792583UL, 3179087993UL, 1936928378UL, 
+412346905UL, 4020812489UL, 2603392174UL, 3499496781UL, 1499441233UL, 1062415256UL, 1347130973UL, 1823246794UL, 3411391800UL, 4253618056UL, 1507733072UL, 1605629518UL, 1503312494UL, 8035741UL, 4038904206UL, 2408545792UL, 969543501UL, 954847087UL, 956553276UL, 3096241999UL, 2566194741UL, 84678421UL, 3882676079UL, 2483934330UL, 3673546814UL, 2461422466UL, 620385599UL, 898325340UL, 2145883445UL, 3653728520UL, 3744850294UL, 2441124935UL, 
+904854507UL, 3216304963UL, 2373268568UL, 2354362010UL, 1245572787UL, 2894748714UL, 2889136188UL, 3716879184UL, 1766013949UL, 1305712667UL, 1227530310UL, 4051221847UL, 925440190UL, 1508686692UL, 1104647879UL, 1496666754UL, 3300504219UL, 127787091UL, 1528394637UL, 1739640835UL, 2475711496UL, 3792639955UL, 1450796299UL, 1634217367UL, 3289785095UL, 2149949989UL, 811612039UL, 1750779366UL, 1157474938UL, 514004414UL, 2264909096UL, 3730411668UL, 
+3308882513UL, 1834571716UL, 378288317UL, 3800023701UL, 763396788UL, 1597708317UL, 983953861UL, 94566098UL, 1548157668UL, 3755427117UL, 1646496505UL, 3748241449UL, 3439805936UL, 2321644449UL, 3805706235UL, 4220083901UL, 1069923823UL, 2984004391UL, 3824885361UL, 1967477766UL, 218978249UL, 348955028UL, 3188651823UL, 1008338679UL, 2331688720UL, 1562995454UL, 1837179689UL, 3033872688UL, 3007293665UL, 1759522678UL, 319754369UL, 2763991927UL, 
+1983149629UL, 1353197132UL, 1489552694UL, 2990539062UL, 3244609108UL, 669775440UL, 886127995UL, 1636688014UL, 1251222487UL, 2351883247UL, 3261502906UL, 3139614137UL, 3203790139UL, 2777648095UL, 3693390579UL, 3540514982UL, 3200191735UL, 750726325UL, 1014534145UL, 2091792357UL, 3931704474UL, 1383925867UL, 2038878506UL, 2247134268UL, 2840132188UL, 61137652UL, 1162051299UL, 399657268UL, 1682018695UL, 2640231287UL, 1733438115UL, 3611823506UL, 
+2077891037UL, 1240264181UL, 1624064648UL, 3039823158UL, 2013985253UL, 4188888201UL, 2762062141UL, 3273470484UL, 1889745445UL, 2516996174UL, 2621448256UL, 996186898UL, 3893981177UL, 1268272590UL, 3226095713UL, 952803645UL, 2184871198UL, 3224094011UL, 2526518401UL, 1738960059UL, 738368399UL, 4194384320UL, 2837011297UL, 3638232350UL, 367907454UL, 3772812520UL, 1948901330UL, 60430044UL, 1569835584UL, 3160561697UL, 1655622513UL, 3179087993UL, 
+1936928378UL, 412346905UL, 4020812489UL, 3754224996UL, 3499496781UL, 1499441233UL, 1062415256UL, 1347130973UL, 1167581269UL, 3411391800UL, 4253618056UL, 1507733072UL, 1605629518UL, 1867781671UL, 8035741UL, 4038904206UL, 2408545792UL, 969543501UL, 3189323143UL, 956553276UL, 3096241999UL, 2566194741UL, 84678421UL, 996778900UL, 2483934330UL, 3673546814UL, 2461422466UL, 620385599UL, 3129088144UL, 2145883445UL, 3653728520UL, 3744850294UL, 
+2441124935UL, 4230756652UL, 3216304963UL, 2373268568UL, 2354362010UL, 1245572787UL, 1600525238UL, 2889136188UL, 3716879184UL, 1766013949UL, 1305712667UL, 59908073UL, 4051221847UL, 925440190UL, 1508686692UL, 1104647879UL, 2931214731UL, 3300504219UL, 127787091UL, 1528394637UL, 1739640835UL, 62963469UL, 3792639955UL, 1450796299UL, 1634217367UL, 3289785095UL, 667987389UL, 811612039UL, 1750779366UL, 1157474938UL, 514004414UL, 2737193098UL, 
+3730411668UL, 3308882513UL, 1834571716UL, 378288317UL, 3452657469UL, 763396788UL, 1597708317UL, 983953861UL, 94566098UL, 2752347916UL, 3755427117UL, 1646496505UL, 3748241449UL, 3439805936UL, 4222757079UL, 3805706235UL, 4220083901UL, 1069923823UL, 2984004391UL, 3887639520UL, 1967477766UL, 218978249UL, 348955028UL, 3188651823UL, 4168456281UL, 2331688720UL, 1562995454UL, 1837179689UL, 3033872688UL, 814903833UL, 1759522678UL, 319754369UL, 
+2763991927UL, 1983149629UL, 3818528075UL, 1489552694UL, 2990539062UL, 3244609108UL, 669775440UL, 1004789460UL, 1636688014UL, 1251222487UL, 2351883247UL, 3261502906UL, 4143823654UL, 3203790139UL, 2777648095UL, 3693390579UL, 3540514982UL, 153421222UL, 750726325UL, 1014534145UL, 2091792357UL, 3931704474UL, 4018591985UL, 2038878506UL, 2247134268UL, 2840132188UL, 61137652UL, 1455028838UL, 399657268UL, 1682018695UL, 2640231287UL, 1733438115UL, 
+1853142849UL, 2077891037UL, 1240264181UL, 1624064648UL, 3039823158UL, 2235369076UL, 4188888201UL, 2762062141UL, 3273470484UL, 1889745445UL, 3627876603UL, 2621448256UL, 996186898UL, 3893981177UL, 1268272590UL, 2687846008UL, 952803645UL, 2184871198UL, 3224094011UL, 2526518401UL, 861379413UL, 738368399UL, 4194384320UL, 2837011297UL, 3638232350UL, 3753321702UL, 3772812520UL, 1948901330UL, 60430044UL, 1569835584UL, 581506474UL, 1655622513UL, 
+3179087993UL, 1936928378UL, 412346905UL, 2710043900UL, 3754224996UL, 3499496781UL, 1499441233UL, 1062415256UL, 2704745463UL, 1167581269UL, 3411391800UL, 4253618056UL, 1507733072UL, 4215403465UL, 1867781671UL, 8035741UL, 4038904206UL, 2408545792UL, 3252742933UL, 3189323143UL, 956553276UL, 3096241999UL, 2566194741UL, 1865159158UL, 996778900UL, 2483934330UL, 3673546814UL, 2461422466UL, 3123557619UL, 3129088144UL, 2145883445UL, 3653728520UL, 
+3744850294UL, 21840044UL, 4230756652UL, 3216304963UL, 2373268568UL, 2354362010UL, 1934462999UL, 1600525238UL, 2889136188UL, 3716879184UL, 1766013949UL, 2822794708UL, 59908073UL, 4051221847UL, 925440190UL, 1508686692UL, 2938291976UL, 2931214731UL, 3300504219UL, 127787091UL, 1528394637UL, 1914923136UL, 62963469UL, 3792639955UL, 1450796299UL, 1634217367UL, 257322213UL, 667987389UL, 811612039UL, 1750779366UL, 1157474938UL, 3083649350UL, 
+2737193098UL, 3730411668UL, 3308882513UL, 1834571716UL, 2778729422UL, 3452657469UL, 763396788UL, 1597708317UL, 983953861UL, 1337754195UL, 2752347916UL, 3755427117UL, 1646496505UL, 3748241449UL, 3942745717UL, 4222757079UL, 3805706235UL, 4220083901UL, 1069923823UL, 1314928500UL, 3887639520UL, 1967477766UL, 218978249UL, 348955028UL, 3425797638UL, 4168456281UL, 2331688720UL, 1562995454UL, 1837179689UL, 1814071277UL, 814903833UL, 1759522678UL, 
+319754369UL, 2763991927UL, 1079270448UL, 3818528075UL, 1489552694UL, 2990539062UL, 3244609108UL, 2944573315UL, 1004789460UL, 1636688014UL, 1251222487UL, 2351883247UL, 1356892540UL, 4143823654UL, 3203790139UL, 2777648095UL, 3693390579UL, 983917956UL, 153421222UL, 750726325UL, 1014534145UL, 2091792357UL, 296882400UL, 4018591985UL, 2038878506UL, 2247134268UL, 2840132188UL, 3508266160UL, 1455028838UL, 399657268UL, 1682018695UL, 2640231287UL, 
+2480988791UL, 1853142849UL, 2077891037UL, 1240264181UL, 1624064648UL, 1741738969UL, 2235369076UL, 4188888201UL, 2762062141UL, 3273470484UL, 3569498651UL, 3627876603UL, 2621448256UL, 996186898UL, 3893981177UL, 4026533880UL, 2687846008UL, 952803645UL, 2184871198UL, 3224094011UL, 1290870737UL, 861379413UL, 738368399UL, 4194384320UL, 2837011297UL, 3833099205UL, 3753321702UL, 3772812520UL, 1948901330UL, 60430044UL, 4131290878UL, 581506474UL, 
+1655622513UL, 3179087993UL, 1936928378UL, 2379952582UL, 2710043900UL, 3754224996UL, 3499496781UL, 1499441233UL, 593780490UL, 2704745463UL, 1167581269UL, 3411391800UL, 4253618056UL, 621889762UL, 4215403465UL, 1867781671UL, 8035741UL, 4038904206UL, 2045289976UL, 3252742933UL, 3189323143UL, 956553276UL, 3096241999UL, 2188329018UL, 1865159158UL, 996778900UL, 2483934330UL, 3673546814UL, 2717648418UL, 3123557619UL, 3129088144UL, 2145883445UL, 
+3653728520UL, 1528077261UL, 21840044UL, 4230756652UL, 3216304963UL, 2373268568UL, 803158556UL, 1934462999UL, 1600525238UL, 2889136188UL, 3716879184UL, 161827512UL, 2822794708UL, 59908073UL, 4051221847UL, 925440190UL, 3599942370UL, 2938291976UL, 2931214731UL, 3300504219UL, 127787091UL, 4082579845UL, 1914923136UL, 62963469UL, 3792639955UL, 1450796299UL, 2035446714UL, 257322213UL, 667987389UL, 811612039UL, 1750779366UL, 2344204796UL, 
+3083649350UL, 2737193098UL, 3730411668UL, 3308882513UL, 2765191583UL, 2778729422UL, 3452657469UL, 763396788UL, 1597708317UL, 1854746879UL, 1337754195UL, 2752347916UL, 3755427117UL, 1646496505UL, 4020292301UL, 3942745717UL, 4222757079UL, 3805706235UL, 4220083901UL, 1408262601UL, 1314928500UL, 3887639520UL, 1967477766UL, 218978249UL, 2173193841UL, 3425797638UL, 4168456281UL, 2331688720UL, 1562995454UL, 2835294077UL, 1814071277UL, 814903833UL, 
+1759522678UL, 319754369UL, 4048528178UL, 1079270448UL, 3818528075UL, 1489552694UL, 2990539062UL, 787253600UL, 2944573315UL, 1004789460UL, 1636688014UL, 1251222487UL, 3584515216UL, 1356892540UL, 4143823654UL, 3203790139UL, 2777648095UL, 1681621541UL, 983917956UL, 153421222UL, 750726325UL, 1014534145UL, 3951869055UL, 296882400UL, 4018591985UL, 2038878506UL, 2247134268UL, 1990726826UL, 3508266160UL, 1455028838UL, 399657268UL, 1682018695UL, 
+3360119279UL, 3151120565UL, 3011208718UL, 3694535943UL, 104562665UL, 2827623271UL, 249712003UL, 3413221355UL, 2347164236UL, 3227498378UL, 1805068659UL, 2118219686UL, 1568133029UL, 902801951UL, 175637375UL, 3812819970UL, 2162769758UL, 3845613089UL, 1795179477UL, 171494391UL, 3765826349UL, 1725798906UL, 345463508UL, 2481043227UL, 226569380UL, 3250095421UL, 1085199388UL, 3107594542UL, 4011388155UL, 1092611190UL, 3239339214UL, 4211849464UL, 
+4109911546UL, 81212018UL, 3691937144UL, 2477407396UL, 3320520455UL, 3070067913UL, 3808621884UL, 252917069UL, 3394860294UL, 1092442235UL, 2876536384UL, 1684120191UL, 431096075UL, 1701716708UL, 639881684UL, 3066183997UL, 3660504927UL, 2047274UL, 3424756424UL, 760932520UL, 2457976057UL, 1705265011UL, 2691137533UL, 3684307557UL, 3532744498UL, 2319162513UL, 1015534908UL, 1907173398UL, 2820698743UL, 1264455116UL, 2323788906UL, 3062240844UL, 
+1878550513UL, 1717353426UL, 1805673248UL, 62425157UL, 3662381032UL, 1964107209UL, 2559831960UL, 2117844804UL, 1228721677UL, 4240498866UL, 3212920337UL, 2338600301UL, 931588693UL, 2379606585UL, 3643222352UL, 4154645082UL, 1115847065UL, 2079427925UL, 2256943798UL, 2795103368UL, 2688136486UL, 1458062143UL, 1767222217UL, 635424385UL, 284062050UL, 1547163554UL, 3380046528UL, 1145758046UL, 3935976713UL, 4017430175UL, 3863367362UL, 3041367424UL, 
+303263160UL, 1465965696UL, 3757919837UL, 3083072836UL, 4024514094UL, 1381331179UL, 2393446325UL, 3256476469UL, 4066482738UL, 3437941107UL, 1051266504UL, 921764078UL, 2933305619UL, 1358097211UL, 4100978724UL, 2709958834UL, 574590507UL, 961767386UL, 21100886UL, 753746372UL, 4072632446UL, 733729367UL, 3060214669UL, 289165105UL, 426065754UL, 2036100240UL, 2172365757UL, 502856627UL, 84490194UL, 2630806596UL, 1206161269UL, 1009438449UL, 
+569581317UL, 1836947000UL, 3125379675UL, 1756936428UL, 3772694822UL, 3670337911UL, 3020603818UL, 2376224883UL, 2539951453UL, 2053395002UL, 3525193914UL, 1991480838UL, 3786481083UL, 873873707UL, 1693894743UL, 2450223985UL, 754878026UL, 1943356492UL, 401524329UL, 759931885UL, 611231307UL, 147950334UL, 599693701UL, 3358729722UL, 3649058074UL, 906423787UL, 1333804225UL, 875187278UL, 1115838692UL, 2476325972UL, 3307226674UL, 3539078918UL, 
+},
+
+};
+#ifndef __CUDACC_RTC__
+CURAND_XORWOW_PRECALCULATED_HOST_QUALIFIERS unsigned int precalc_xorwow_offset_matrix_host[32][800] = {
+{
+0UL, 0UL, 0UL, 0UL, 3UL, 0UL, 0UL, 0UL, 0UL, 6UL, 0UL, 0UL, 0UL, 0UL, 15UL, 0UL, 0UL, 0UL, 0UL, 30UL, 0UL, 0UL, 0UL, 0UL, 60UL, 0UL, 0UL, 0UL, 0UL, 120UL, 0UL, 0UL, 
+0UL, 0UL, 240UL, 0UL, 0UL, 0UL, 0UL, 480UL, 0UL, 0UL, 0UL, 0UL, 960UL, 0UL, 0UL, 0UL, 0UL, 1920UL, 0UL, 0UL, 0UL, 0UL, 3840UL, 0UL, 0UL, 0UL, 0UL, 7680UL, 0UL, 0UL, 0UL, 0UL, 
+15360UL, 0UL, 0UL, 0UL, 0UL, 30720UL, 0UL, 0UL, 0UL, 0UL, 61440UL, 0UL, 0UL, 0UL, 0UL, 122880UL, 0UL, 0UL, 0UL, 0UL, 245760UL, 0UL, 0UL, 0UL, 0UL, 491520UL, 0UL, 0UL, 0UL, 0UL, 983040UL, 0UL, 
+0UL, 0UL, 0UL, 1966080UL, 0UL, 0UL, 0UL, 0UL, 3932160UL, 0UL, 0UL, 0UL, 0UL, 7864320UL, 0UL, 0UL, 0UL, 0UL, 15728640UL, 0UL, 0UL, 0UL, 0UL, 31457280UL, 0UL, 0UL, 0UL, 0UL, 62914560UL, 0UL, 0UL, 0UL, 
+0UL, 125829120UL, 0UL, 0UL, 0UL, 0UL, 251658240UL, 0UL, 0UL, 0UL, 0UL, 503316480UL, 0UL, 0UL, 0UL, 0UL, 1006632960UL, 0UL, 0UL, 0UL, 0UL, 2013265920UL, 0UL, 0UL, 0UL, 0UL, 4026531840UL, 0UL, 0UL, 0UL, 0UL, 3758096384UL, 
+1UL, 0UL, 0UL, 0UL, 0UL, 2UL, 0UL, 0UL, 0UL, 0UL, 4UL, 0UL, 0UL, 0UL, 0UL, 8UL, 0UL, 0UL, 0UL, 0UL, 16UL, 0UL, 0UL, 0UL, 0UL, 32UL, 0UL, 0UL, 0UL, 0UL, 64UL, 0UL, 
+0UL, 0UL, 0UL, 128UL, 0UL, 0UL, 0UL, 0UL, 256UL, 0UL, 0UL, 0UL, 0UL, 512UL, 0UL, 0UL, 0UL, 0UL, 1024UL, 0UL, 0UL, 0UL, 0UL, 2048UL, 0UL, 0UL, 0UL, 0UL, 4096UL, 0UL, 0UL, 0UL, 
+0UL, 8192UL, 0UL, 0UL, 0UL, 0UL, 16384UL, 0UL, 0UL, 0UL, 0UL, 32768UL, 0UL, 0UL, 0UL, 0UL, 65536UL, 0UL, 0UL, 0UL, 0UL, 131072UL, 0UL, 0UL, 0UL, 0UL, 262144UL, 0UL, 0UL, 0UL, 0UL, 524288UL, 
+0UL, 0UL, 0UL, 0UL, 1048576UL, 0UL, 0UL, 0UL, 0UL, 2097152UL, 0UL, 0UL, 0UL, 0UL, 4194304UL, 0UL, 0UL, 0UL, 0UL, 8388608UL, 0UL, 0UL, 0UL, 0UL, 16777216UL, 0UL, 0UL, 0UL, 0UL, 33554432UL, 0UL, 0UL, 
+0UL, 0UL, 67108864UL, 0UL, 0UL, 0UL, 0UL, 134217728UL, 0UL, 0UL, 0UL, 0UL, 268435456UL, 0UL, 0UL, 0UL, 0UL, 536870912UL, 0UL, 0UL, 0UL, 0UL, 1073741824UL, 0UL, 0UL, 0UL, 0UL, 2147483648UL, 0UL, 0UL, 0UL, 0UL, 
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+4109911546UL, 81212018UL, 3691937144UL, 2477407396UL, 3320520455UL, 3070067913UL, 3808621884UL, 252917069UL, 3394860294UL, 1092442235UL, 2876536384UL, 1684120191UL, 431096075UL, 1701716708UL, 639881684UL, 3066183997UL, 3660504927UL, 2047274UL, 3424756424UL, 760932520UL, 2457976057UL, 1705265011UL, 2691137533UL, 3684307557UL, 3532744498UL, 2319162513UL, 1015534908UL, 1907173398UL, 2820698743UL, 1264455116UL, 2323788906UL, 3062240844UL, 
+1878550513UL, 1717353426UL, 1805673248UL, 62425157UL, 3662381032UL, 1964107209UL, 2559831960UL, 2117844804UL, 1228721677UL, 4240498866UL, 3212920337UL, 2338600301UL, 931588693UL, 2379606585UL, 3643222352UL, 4154645082UL, 1115847065UL, 2079427925UL, 2256943798UL, 2795103368UL, 2688136486UL, 1458062143UL, 1767222217UL, 635424385UL, 284062050UL, 1547163554UL, 3380046528UL, 1145758046UL, 3935976713UL, 4017430175UL, 3863367362UL, 3041367424UL, 
+303263160UL, 1465965696UL, 3757919837UL, 3083072836UL, 4024514094UL, 1381331179UL, 2393446325UL, 3256476469UL, 4066482738UL, 3437941107UL, 1051266504UL, 921764078UL, 2933305619UL, 1358097211UL, 4100978724UL, 2709958834UL, 574590507UL, 961767386UL, 21100886UL, 753746372UL, 4072632446UL, 733729367UL, 3060214669UL, 289165105UL, 426065754UL, 2036100240UL, 2172365757UL, 502856627UL, 84490194UL, 2630806596UL, 1206161269UL, 1009438449UL, 
+569581317UL, 1836947000UL, 3125379675UL, 1756936428UL, 3772694822UL, 3670337911UL, 3020603818UL, 2376224883UL, 2539951453UL, 2053395002UL, 3525193914UL, 1991480838UL, 3786481083UL, 873873707UL, 1693894743UL, 2450223985UL, 754878026UL, 1943356492UL, 401524329UL, 759931885UL, 611231307UL, 147950334UL, 599693701UL, 3358729722UL, 3649058074UL, 906423787UL, 1333804225UL, 875187278UL, 1115838692UL, 2476325972UL, 3307226674UL, 3539078918UL, 
+},
+
+};
+#endif
+
+#ifdef CURAND_XORWOW_PRECALCULATED_DEVICE_QUALIFIERS
+#undef CURAND_XORWOW_PRECALCULATED_DEVICE_QUALIFIERS
+#endif
+
+#ifdef CURAND_XORWOW_PRECALCULATED_HOST_QUALIFIERS
+#undef CURAND_XORWOW_PRECALCULATED_HOST_QUALIFIERS
+#endif
+
+#endif // CURAND_XORWOW_PRECALCULATED_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_uniform.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_uniform.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a4af8afa328c186d9ea33a8c8226e19aba4793e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/include/curand_uniform.h
@@ -0,0 +1,498 @@
+
+ /* Copyright 2010-2018 NVIDIA Corporation.  All rights reserved.
+  *
+  * NOTICE TO LICENSEE:
+  *
+  * The source code and/or documentation ("Licensed Deliverables") are
+  * subject to NVIDIA intellectual property rights under U.S. and
+  * international Copyright laws.
+  *
+  * The Licensed Deliverables contained herein are PROPRIETARY and
+  * CONFIDENTIAL to NVIDIA and are being provided under the terms and
+  * conditions of a form of NVIDIA software license agreement by and
+  * between NVIDIA and Licensee ("License Agreement") or electronically
+  * accepted by Licensee.  Notwithstanding any terms or conditions to
+  * the contrary in the License Agreement, reproduction or disclosure
+  * of the Licensed Deliverables to any third party without the express
+  * written consent of NVIDIA is prohibited.
+  *
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+  * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  THEY ARE
+  * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+  * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+  * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+  * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+  * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+  * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+  * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+  * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+  * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+  * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+  * OF THESE LICENSED DELIVERABLES.
+  *
+  * U.S. Government End Users.  These Licensed Deliverables are a
+  * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+  * 1995), consisting of "commercial computer software" and "commercial
+  * computer software documentation" as such terms are used in 48
+  * C.F.R. 12.212 (SEPT 1995) and are provided to the U.S. Government
+  * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+  * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+  * U.S. Government End Users acquire the Licensed Deliverables with
+  * only those rights set forth herein.
+  *
+  * Any use of the Licensed Deliverables in individual and commercial
+  * software must include, in the user documentation and internal
+  * comments to the code, the above Disclaimer and U.S. Government End
+  * Users Notice.
+  */
+
+
+#if !defined(CURAND_UNIFORM_H_)
+#define CURAND_UNIFORM_H_
+
+/**
+ * \defgroup DEVICE Device API
+ *
+ * @{
+ */
+
+#ifndef __CUDACC_RTC__
+#include <math.h>
+#endif // __CUDACC_RTC__
+
+#include "curand_mrg32k3a.h"
+#include "curand_mtgp32_kernel.h"
+#include "curand_philox4x32_x.h"
+
+
+QUALIFIERS float _curand_uniform(unsigned int x)
+{
+    return x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+}
+
+QUALIFIERS float4 _curand_uniform4(uint4 x)
+{
+    float4 y;
+    y.x = x.x * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+    y.y = x.y * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+    y.z = x.z * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+    y.w = x.w * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+    return y;
+}
+
+QUALIFIERS float _curand_uniform(unsigned long long x)
+{
+    unsigned int t;
+    t = (unsigned int)(x >> 32);
+    return t * CURAND_2POW32_INV + (CURAND_2POW32_INV/2.0f);
+}
+
+QUALIFIERS double _curand_uniform_double(unsigned int x)
+{
+    return x * CURAND_2POW32_INV_DOUBLE + CURAND_2POW32_INV_DOUBLE;
+}
+
+QUALIFIERS double _curand_uniform_double(unsigned long long x)
+{
+    return (x >> 11) * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
+}
+
+QUALIFIERS double _curand_uniform_double_hq(unsigned int x, unsigned int y)
+{
+    unsigned long long z = (unsigned long long)x ^
+        ((unsigned long long)y << (53 - 32));
+    return z * CURAND_2POW53_INV_DOUBLE + (CURAND_2POW53_INV_DOUBLE/2.0);
+}
+
+QUALIFIERS float curand_uniform(curandStateTest_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+
+QUALIFIERS double curand_uniform_double(curandStateTest_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed float from an XORWOW generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the XORWOW generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation may use any number of calls to \p curand() to
+ * get enough random bits to create the return value.  The current
+ * implementation uses one call.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateXORWOW_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed double from an XORWOW generator.
+ *
+ * Return a uniformly distributed double between \p 0.0 and \p 1.0
+ * from the XORWOW generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation may use any number of calls to \p curand() to
+ * get enough random bits to create the return value.  The current
+ * implementation uses exactly two calls.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0 and \p 1.0
+ */
+QUALIFIERS double curand_uniform_double(curandStateXORWOW_t *state)
+{
+    unsigned int x, y;
+    x = curand(state);
+    y = curand(state);
+    return _curand_uniform_double_hq(x, y);
+}
+/**
+ * \brief Return a uniformly distributed float from an MRG32k3a generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the MRG32k3a generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation returns up to 23 bits of mantissa, with the minimum
+ * return value \f$ 2^{-32} \f$
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateMRG32k3a_t *state)
+{
+    return ((float)(curand_MRG32k3a(state)*MRG32K3A_NORM));
+}
+
+/**
+ * \brief Return a uniformly distributed double from an MRG32k3a generator.
+ *
+ * Return a uniformly distributed double between \p 0.0 and \p 1.0
+ * from the MRG32k3a generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * Note the implementation returns at most 32 random bits of mantissa as
+ * outlined in the seminal paper by L'Ecuyer.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0 and \p 1.0
+ */
+QUALIFIERS double curand_uniform_double(curandStateMRG32k3a_t *state)
+{
+    return curand_MRG32k3a(state)*MRG32K3A_NORM;
+}
+
+
+
+/**
+ * \brief Return a uniformly distributed tuple of 2 doubles from an Philox4_32_10 generator.
+ *
+ * Return a uniformly distributed 2 doubles (double4) between \p 0.0 and \p 1.0
+ * from the Philox4_32_10 generator in \p state, increment position of generator by 4.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return 2 uniformly distributed doubles between \p 0.0 and \p 1.0
+ */
+
+QUALIFIERS double2 curand_uniform2_double(curandStatePhilox4_32_10_t *state)
+{
+    uint4 _x;
+    double2 result;
+    _x = curand4(state);
+    result.x = _curand_uniform_double_hq(_x.x,_x.y);
+    result.y = _curand_uniform_double_hq(_x.z,_x.w);
+    return result;
+}
+
+
+// not a part of API
+QUALIFIERS double4 curand_uniform4_double(curandStatePhilox4_32_10_t *state)
+{
+    uint4 _x, _y;
+    double4 result;
+    _x = curand4(state);
+    _y = curand4(state);
+    result.x = _curand_uniform_double_hq(_x.x,_x.y);
+    result.y = _curand_uniform_double_hq(_x.z,_x.w);
+    result.z = _curand_uniform_double_hq(_y.x,_y.y);
+    result.w = _curand_uniform_double_hq(_y.z,_y.w);
+    return result;
+}
+
+/**
+ * \brief Return a uniformly distributed float from a Philox4_32_10 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the Philox4_32_10 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0 and \p 1.0
+ *
+ */
+QUALIFIERS float curand_uniform(curandStatePhilox4_32_10_t *state)
+{
+   return _curand_uniform(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed tuple of 4 floats from a Philox4_32_10 generator.
+ *
+ * Return a uniformly distributed 4 floats between \p 0.0f and \p 1.0f
+ * from the Philox4_32_10 generator in \p state, increment position of generator by 4.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0 and \p 1.0
+ *
+ */
+QUALIFIERS float4 curand_uniform4(curandStatePhilox4_32_10_t *state)
+{
+   return _curand_uniform4(curand4(state));
+}
+
+/**
+ * \brief Return a uniformly distributed float from a MTGP32 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the MTGP32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateMtgp32_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+/**
+ * \brief Return a uniformly distributed double from a MTGP32 generator.
+ *
+ * Return a uniformly distributed double between \p 0.0f and \p 1.0f
+ * from the MTGP32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * Note that the implementation uses only 32 random bits to generate a single double
+ * precision value.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS double curand_uniform_double(curandStateMtgp32_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed double from a Philox4_32_10 generator.
+ *
+ * Return a uniformly distributed double between \p 0.0f and \p 1.0f
+ * from the Philox4_32_10 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * Note that the implementation uses only 32 random bits to generate a single double
+ * precision value.
+ *
+ * \p curand_uniform2_double() is recommended for higher quality uniformly distributed
+ * double precision values.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0f and \p 1.0f
+ */
+
+QUALIFIERS double curand_uniform_double(curandStatePhilox4_32_10_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+
+
+/**
+ * \brief Return a uniformly distributed float from a Sobol32 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the Sobol32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand().
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateSobol32_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed double from a Sobol32 generator.
+ *
+ * Return a uniformly distributed double between \p 0.0 and \p 1.0
+ * from the Sobol32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand()
+ * to preserve the quasirandom properties of the sequence.
+ *
+ * Note that the implementation uses only 32 random bits to generate a single double
+ * precision value.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0 and \p 1.0
+ */
+QUALIFIERS double curand_uniform_double(curandStateSobol32_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+/**
+ * \brief Return a uniformly distributed float from a scrambled Sobol32 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the scrambled Sobol32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand().
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateScrambledSobol32_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed double from a scrambled Sobol32 generator.
+ *
+ * Return a uniformly distributed double between \p 0.0 and \p 1.0
+ * from the scrambled Sobol32 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand()
+ * to preserve the quasirandom properties of the sequence.
+ *
+ * Note that the implementation uses only 32 random bits to generate a single double
+ * precision value.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0 and \p 1.0
+ */
+QUALIFIERS double curand_uniform_double(curandStateScrambledSobol32_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+/**
+ * \brief Return a uniformly distributed float from a Sobol64 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the Sobol64 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand().
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateSobol64_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed double from a Sobol64 generator.
+ *
+ * Return a uniformly distributed double between \p 0.0 and \p 1.0
+ * from the Sobol64 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand()
+ * to preserve the quasirandom properties of the sequence.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0 and \p 1.0
+ */
+QUALIFIERS double curand_uniform_double(curandStateSobol64_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+/**
+ * \brief Return a uniformly distributed float from a scrambled Sobol64 generator.
+ *
+ * Return a uniformly distributed float between \p 0.0f and \p 1.0f
+ * from the scrambled Sobol64 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0f but includes \p 1.0f.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand().
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed float between \p 0.0f and \p 1.0f
+ */
+QUALIFIERS float curand_uniform(curandStateScrambledSobol64_t *state)
+{
+    return _curand_uniform(curand(state));
+}
+
+/**
+ * \brief Return a uniformly distributed double from a scrambled Sobol64 generator.
+ *
+ * Return a uniformly distributed double between \p 0.0 and \p 1.0
+ * from the scrambled Sobol64 generator in \p state, increment position of generator.
+ * Output range excludes \p 0.0 but includes \p 1.0.  Denormalized floating
+ * point outputs are never returned.
+ *
+ * The implementation is guaranteed to use a single call to \p curand()
+ * to preserve the quasirandom properties of the sequence.
+ *
+ * \param state - Pointer to state to update
+ *
+ * \return uniformly distributed double between \p 0.0 and \p 1.0
+ */
+QUALIFIERS double curand_uniform_double(curandStateScrambledSobol64_t *state)
+{
+    return _curand_uniform_double(curand(state));
+}
+
+#endif // !defined(CURAND_UNIFORM_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/curand/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverDn.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverDn.h
new file mode 100644
index 0000000000000000000000000000000000000000..e2c011d2f1d8c1fd4a25729ee851c09599c0c4a3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverDn.h
@@ -0,0 +1,5009 @@
+/*
+ * Copyright 2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cuSolverDN : Dense Linear Algebra Library
+
+*/
+
+#if !defined(CUSOLVERDN_H_)
+  #define CUSOLVERDN_H_
+
+struct cusolverDnContext;
+typedef struct cusolverDnContext *cusolverDnHandle_t;
+
+struct syevjInfo;
+typedef struct syevjInfo *syevjInfo_t;
+
+struct gesvdjInfo;
+typedef struct gesvdjInfo *gesvdjInfo_t;
+
+//------------------------------------------------------
+// opaque cusolverDnIRS structure for IRS solver
+struct cusolverDnIRSParams;
+typedef struct cusolverDnIRSParams *cusolverDnIRSParams_t;
+
+struct cusolverDnIRSInfos;
+typedef struct cusolverDnIRSInfos *cusolverDnIRSInfos_t;
+//------------------------------------------------------
+
+struct cusolverDnParams;
+typedef struct cusolverDnParams *cusolverDnParams_t;
+
+typedef enum {
+  CUSOLVERDN_GETRF = 0,
+  CUSOLVERDN_POTRF = 1
+} cusolverDnFunction_t;
+
+typedef enum {
+  CUSOLVER_DETERMINISTIC_RESULTS = 1,
+  CUSOLVER_ALLOW_NON_DETERMINISTIC_RESULTS = 2
+} cusolverDeterministicMode_t;
+
+  #include <stdio.h>
+
+  #include "cuComplex.h" /* import complex data type */
+  #include "cublas_v2.h"
+  #include "cusolver_common.h"
+
+  /*******************************************************************************/
+  #ifdef __cplusplus
+extern "C" {
+  #endif
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCreate(cusolverDnHandle_t *handle);
+  cusolverStatus_t CUSOLVERAPI cusolverDnDestroy(cusolverDnHandle_t handle);
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnSetStream(cusolverDnHandle_t handle, cudaStream_t streamId);
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnGetStream(cusolverDnHandle_t handle, cudaStream_t *streamId);
+
+  //============================================================
+  // Deterministic Mode
+  //============================================================
+  cusolverStatus_t CUSOLVERAPI cusolverDnSetDeterministicMode(cusolverDnHandle_t
+    handle, cusolverDeterministicMode_t mode);
+  cusolverStatus_t CUSOLVERAPI cusolverDnGetDeterministicMode(cusolverDnHandle_t
+    handle, cusolverDeterministicMode_t* mode);
+
+  //============================================================
+  // IRS headers
+  //============================================================
+
+  // =============================================================================
+  // IRS helper function API
+  // =============================================================================
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSParamsCreate(cusolverDnIRSParams_t *params_ptr);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSParamsDestroy(cusolverDnIRSParams_t params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsSetRefinementSolver(
+    cusolverDnIRSParams_t   params,
+    cusolverIRSRefinement_t refinement_solver);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsSetSolverMainPrecision(
+    cusolverDnIRSParams_t params,
+    cusolverPrecType_t    solver_main_precision);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsSetSolverLowestPrecision(
+    cusolverDnIRSParams_t params,
+    cusolverPrecType_t    solver_lowest_precision);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsSetSolverPrecisions(
+    cusolverDnIRSParams_t params,
+    cusolverPrecType_t    solver_main_precision,
+    cusolverPrecType_t    solver_lowest_precision);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSParamsSetTol(cusolverDnIRSParams_t params, double val);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSParamsSetTolInner(cusolverDnIRSParams_t params, double val);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsSetMaxIters(
+    cusolverDnIRSParams_t params,
+    cusolver_int_t        maxiters);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsSetMaxItersInner(
+    cusolverDnIRSParams_t params,
+    cusolver_int_t        maxiters_inner);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSParamsGetMaxIters(
+    cusolverDnIRSParams_t params,
+    cusolver_int_t *      maxiters);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSParamsEnableFallback(cusolverDnIRSParams_t params);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSParamsDisableFallback(cusolverDnIRSParams_t params);
+
+  // =============================================================================
+  // cusolverDnIRSInfos prototypes
+  // =============================================================================
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSInfosDestroy(cusolverDnIRSInfos_t infos);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSInfosCreate(cusolverDnIRSInfos_t *infos_ptr);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSInfosGetNiters(
+    cusolverDnIRSInfos_t infos,
+    cusolver_int_t *     niters);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSInfosGetOuterNiters(
+    cusolverDnIRSInfos_t infos,
+    cusolver_int_t *     outer_niters);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnIRSInfosRequestResidual(cusolverDnIRSInfos_t infos);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSInfosGetResidualHistory(
+    cusolverDnIRSInfos_t infos,
+    void **              residual_history);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSInfosGetMaxIters(
+    cusolverDnIRSInfos_t infos,
+    cusolver_int_t *     maxiters);
+
+  //============================================================
+  //  IRS functions API
+  //============================================================
+
+  /*******************************************************************************/ /*
+                                                                                     * [ZZ, ZC, ZK, ZE, ZY, CC, CK, CE, CY, DD, DS, DH, DB, DX, SS, SH, SB, SX]gesv
+                                                                                     * users API Prototypes */
+  /*******************************************************************************/
+  cusolverStatus_t CUSOLVERAPI cusolverDnZZgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZCgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZKgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZEgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZYgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCCgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCEgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCKgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCYgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDDgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDSgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDHgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDBgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDXgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSSgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSHgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSBgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSXgesv(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  /*******************************************************************************/
+
+  /*******************************************************************************/ /*
+                                                                                     * [ZZ, ZC, ZK, ZE, ZY, CC, CK, CE, CY, DD, DS, DH, DB, DX, SS, SH, SB, SX]gesv_bufferSize
+                                                                                     * users API Prototypes */
+  /*******************************************************************************/
+  cusolverStatus_t CUSOLVERAPI cusolverDnZZgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZCgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZKgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZEgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZYgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCCgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCKgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCEgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCYgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDDgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDSgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDHgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDBgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDXgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSSgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSHgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSBgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSXgesv_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    cusolver_int_t *   dipiv,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+  /*******************************************************************************/
+
+  /*******************************************************************************/ /*
+                                                                                     * [ZZ, ZC, ZK, ZE, ZY, CC, CK, CE, CY, DD, DS, DH, DB, DX, SS, SH, SB, SX]gels
+                                                                                     * users API Prototypes */
+  /*******************************************************************************/
+  cusolverStatus_t CUSOLVERAPI cusolverDnZZgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZCgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZKgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZEgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZYgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCCgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCKgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCEgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCYgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDDgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDSgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDHgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDBgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDXgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSSgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSHgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSBgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSXgels(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t             lwork_bytes,
+    cusolver_int_t *   iter,
+    cusolver_int_t *   d_info);
+  /*******************************************************************************/
+
+  /*******************************************************************************/ /*
+                                                                                     * [ZZ, ZC, ZK, ZE, ZY, CC, CK, CE, CY, DD, DS, DH, DB, DX, SS, SH, SB, SX]gels_bufferSize
+                                                                                     * API prototypes */
+  /*******************************************************************************/
+  cusolverStatus_t CUSOLVERAPI cusolverDnZZgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZCgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZKgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZEgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZYgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuDoubleComplex *  dA,
+    cusolver_int_t     ldda,
+    cuDoubleComplex *  dB,
+    cusolver_int_t     lddb,
+    cuDoubleComplex *  dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCCgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCKgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCEgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCYgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    cuComplex *        dA,
+    cusolver_int_t     ldda,
+    cuComplex *        dB,
+    cusolver_int_t     lddb,
+    cuComplex *        dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDDgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDSgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDHgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDBgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDXgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    double *           dA,
+    cusolver_int_t     ldda,
+    double *           dB,
+    cusolver_int_t     lddb,
+    double *           dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSSgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSHgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSBgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSXgels_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolver_int_t     m,
+    cusolver_int_t     n,
+    cusolver_int_t     nrhs,
+    float *            dA,
+    cusolver_int_t     ldda,
+    float *            dB,
+    cusolver_int_t     lddb,
+    float *            dX,
+    cusolver_int_t     lddx,
+    void *             dWorkspace,
+    size_t *           lwork_bytes);
+  /*******************************************************************************/
+
+  /*******************************************************************************/ /*
+                                                                                     * expert users API for IRS Prototypes
+                                                                                     * */
+  /*******************************************************************************/
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSXgesv(
+    cusolverDnHandle_t    handle,
+    cusolverDnIRSParams_t gesv_irs_params,
+    cusolverDnIRSInfos_t  gesv_irs_infos,
+    cusolver_int_t        n,
+    cusolver_int_t        nrhs,
+    void *                dA,
+    cusolver_int_t        ldda,
+    void *                dB,
+    cusolver_int_t        lddb,
+    void *                dX,
+    cusolver_int_t        lddx,
+    void *                dWorkspace,
+    size_t                lwork_bytes,
+    cusolver_int_t *      niters,
+    cusolver_int_t *      d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSXgesv_bufferSize(
+    cusolverDnHandle_t    handle,
+    cusolverDnIRSParams_t params,
+    cusolver_int_t        n,
+    cusolver_int_t        nrhs,
+    size_t *              lwork_bytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSXgels(
+    cusolverDnHandle_t    handle,
+    cusolverDnIRSParams_t gels_irs_params,
+    cusolverDnIRSInfos_t  gels_irs_infos,
+    cusolver_int_t        m,
+    cusolver_int_t        n,
+    cusolver_int_t        nrhs,
+    void *                dA,
+    cusolver_int_t        ldda,
+    void *                dB,
+    cusolver_int_t        lddb,
+    void *                dX,
+    cusolver_int_t        lddx,
+    void *                dWorkspace,
+    size_t                lwork_bytes,
+    cusolver_int_t *      niters,
+    cusolver_int_t *      d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnIRSXgels_bufferSize(
+    cusolverDnHandle_t    handle,
+    cusolverDnIRSParams_t params,
+    cusolver_int_t        m,
+    cusolver_int_t        n,
+    cusolver_int_t        nrhs,
+    size_t *              lwork_bytes);
+  /*******************************************************************************/
+
+  /* Cholesky factorization and its solver */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotrs(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs,
+    const float *      A,
+    int                lda,
+    float *            B,
+    int                ldb,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotrs(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs,
+    const double *     A,
+    int                lda,
+    double *           B,
+    int                ldb,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotrs(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs,
+    const cuComplex *  A,
+    int                lda,
+    cuComplex *        B,
+    int                ldb,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotrs(
+    cusolverDnHandle_t     handle,
+    cublasFillMode_t       uplo,
+    int                    n,
+    int                    nrhs,
+    const cuDoubleComplex *A,
+    int                    lda,
+    cuDoubleComplex *      B,
+    int                    ldb,
+    int *                  devInfo);
+
+  /* batched Cholesky factorization and its solver */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotrfBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            Aarray[],
+    int                lda,
+    int *              infoArray,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotrfBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           Aarray[],
+    int                lda,
+    int *              infoArray,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotrfBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        Aarray[],
+    int                lda,
+    int *              infoArray,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotrfBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  Aarray[],
+    int                lda,
+    int *              infoArray,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotrsBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs, /* only support rhs = 1*/
+    float *            A[],
+    int                lda,
+    float *            B[],
+    int                ldb,
+    int *              d_info,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotrsBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs, /* only support rhs = 1*/
+    double *           A[],
+    int                lda,
+    double *           B[],
+    int                ldb,
+    int *              d_info,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotrsBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs, /* only support rhs = 1*/
+    cuComplex *        A[],
+    int                lda,
+    cuComplex *        B[],
+    int                ldb,
+    int *              d_info,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotrsBatched(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    int                nrhs, /* only support rhs = 1*/
+    cuDoubleComplex *  A[],
+    int                lda,
+    cuDoubleComplex *  B[],
+    int                ldb,
+    int *              d_info,
+    int                batchSize);
+
+  /* s.p.d. matrix inversion (POTRI) and auxiliary routines (TRTRI and LAUUM) */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSpotri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDpotri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCpotri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZpotri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXtrtri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    cublasDiagType_t   diag,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXtrtri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    cublasDiagType_t   diag,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              devInfo);
+
+  /* lauum, auxiliar routine for s.p.d matrix inversion */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSlauum_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDlauum_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnClauum_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZlauum_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSlauum(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDlauum(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnClauum(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZlauum(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              devInfo);
+
+  /* LU Factorization */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgetrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgetrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgetrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgetrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgetrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            Workspace,
+    int *              devIpiv,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgetrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           Workspace,
+    int *              devIpiv,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgetrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        Workspace,
+    int *              devIpiv,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgetrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  Workspace,
+    int *              devIpiv,
+    int *              devInfo);
+
+  /* Row pivoting */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSlaswp(
+    cusolverDnHandle_t handle,
+    int                n,
+    float *            A,
+    int                lda,
+    int                k1,
+    int                k2,
+    const int *        devIpiv,
+    int                incx);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDlaswp(
+    cusolverDnHandle_t handle,
+    int                n,
+    double *           A,
+    int                lda,
+    int                k1,
+    int                k2,
+    const int *        devIpiv,
+    int                incx);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnClaswp(
+    cusolverDnHandle_t handle,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int                k1,
+    int                k2,
+    const int *        devIpiv,
+    int                incx);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZlaswp(
+    cusolverDnHandle_t handle,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int                k1,
+    int                k2,
+    const int *        devIpiv,
+    int                incx);
+
+  /* LU solve */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgetrs(
+    cusolverDnHandle_t handle,
+    cublasOperation_t  trans,
+    int                n,
+    int                nrhs,
+    const float *      A,
+    int                lda,
+    const int *        devIpiv,
+    float *            B,
+    int                ldb,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgetrs(
+    cusolverDnHandle_t handle,
+    cublasOperation_t  trans,
+    int                n,
+    int                nrhs,
+    const double *     A,
+    int                lda,
+    const int *        devIpiv,
+    double *           B,
+    int                ldb,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgetrs(
+    cusolverDnHandle_t handle,
+    cublasOperation_t  trans,
+    int                n,
+    int                nrhs,
+    const cuComplex *  A,
+    int                lda,
+    const int *        devIpiv,
+    cuComplex *        B,
+    int                ldb,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgetrs(
+    cusolverDnHandle_t     handle,
+    cublasOperation_t      trans,
+    int                    n,
+    int                    nrhs,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const int *            devIpiv,
+    cuDoubleComplex *      B,
+    int                    ldb,
+    int *                  devInfo);
+
+  /* QR factorization */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgeqrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgeqrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgeqrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgeqrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgeqrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            TAU,
+    float *            Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgeqrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           TAU,
+    double *           Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgeqrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        TAU,
+    cuComplex *        Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgeqrf(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  TAU,
+    cuDoubleComplex *  Workspace,
+    int                Lwork,
+    int *              devInfo);
+
+  /* generate unitary matrix Q from QR factorization */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSorgqr_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int                k,
+    const float *      A,
+    int                lda,
+    const float *      tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDorgqr_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int                k,
+    const double *     A,
+    int                lda,
+    const double *     tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCungqr_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int                k,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZungqr_bufferSize(
+    cusolverDnHandle_t     handle,
+    int                    m,
+    int                    n,
+    int                    k,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSorgqr(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int                k,
+    float *            A,
+    int                lda,
+    const float *      tau,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDorgqr(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int                k,
+    double *           A,
+    int                lda,
+    const double *     tau,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCungqr(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int                k,
+    cuComplex *        A,
+    int                lda,
+    const cuComplex *  tau,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZungqr(
+    cusolverDnHandle_t     handle,
+    int                    m,
+    int                    n,
+    int                    k,
+    cuDoubleComplex *      A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    cuDoubleComplex *      work,
+    int                    lwork,
+    int *                  info);
+
+  /* compute Q**T*b in solve min||A*x = b|| */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSormqr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    int                k,
+    const float *      A,
+    int                lda,
+    const float *      tau,
+    const float *      C,
+    int                ldc,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDormqr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    int                k,
+    const double *     A,
+    int                lda,
+    const double *     tau,
+    const double *     C,
+    int                ldc,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCunmqr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    int                k,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  tau,
+    const cuComplex *  C,
+    int                ldc,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZunmqr_bufferSize(
+    cusolverDnHandle_t     handle,
+    cublasSideMode_t       side,
+    cublasOperation_t      trans,
+    int                    m,
+    int                    n,
+    int                    k,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    const cuDoubleComplex *C,
+    int                    ldc,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSormqr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    int                k,
+    const float *      A,
+    int                lda,
+    const float *      tau,
+    float *            C,
+    int                ldc,
+    float *            work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDormqr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    int                k,
+    const double *     A,
+    int                lda,
+    const double *     tau,
+    double *           C,
+    int                ldc,
+    double *           work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCunmqr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    int                k,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  tau,
+    cuComplex *        C,
+    int                ldc,
+    cuComplex *        work,
+    int                lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZunmqr(
+    cusolverDnHandle_t     handle,
+    cublasSideMode_t       side,
+    cublasOperation_t      trans,
+    int                    m,
+    int                    n,
+    int                    k,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    cuDoubleComplex *      C,
+    int                    ldc,
+    cuDoubleComplex *      work,
+    int                    lwork,
+    int *                  devInfo);
+
+  /* L*D*L**T,U*D*U**T factorization */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsytrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsytrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCsytrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZsytrf_bufferSize(
+    cusolverDnHandle_t handle,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsytrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    int *              ipiv,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsytrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    int *              ipiv,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCsytrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    int *              ipiv,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZsytrf(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    int *              ipiv,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* Symmetric indefinite solve (SYTRS) */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsytrs_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    int64_t            nrhs,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    const int64_t *    ipiv,
+    cudaDataType       dataTypeB,
+    void *             B,
+    int64_t            ldb,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsytrs(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    int64_t            nrhs,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    const int64_t *    ipiv,
+    cudaDataType       dataTypeB,
+    void *             B,
+    int64_t            ldb,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* Symmetric indefinite inversion (sytri) */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsytri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    const int *        ipiv,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsytri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    const int *        ipiv,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCsytri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    const int *        ipiv,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZsytri_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    const int *        ipiv,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsytri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    const int *        ipiv,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsytri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    const int *        ipiv,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCsytri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    const int *        ipiv,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZsytri(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    const int *        ipiv,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* bidiagonal factorization */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgebrd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgebrd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgebrd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgebrd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              Lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgebrd(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            D,
+    float *            E,
+    float *            TAUQ,
+    float *            TAUP,
+    float *            Work,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgebrd(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           D,
+    double *           E,
+    double *           TAUQ,
+    double *           TAUP,
+    double *           Work,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgebrd(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            D,
+    float *            E,
+    cuComplex *        TAUQ,
+    cuComplex *        TAUP,
+    cuComplex *        Work,
+    int                Lwork,
+    int *              devInfo);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgebrd(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           D,
+    double *           E,
+    cuDoubleComplex *  TAUQ,
+    cuDoubleComplex *  TAUP,
+    cuDoubleComplex *  Work,
+    int                Lwork,
+    int *              devInfo);
+
+  /* generates one of the unitary matrices Q or P**T determined by GEBRD*/
+  cusolverStatus_t CUSOLVERAPI cusolverDnSorgbr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    int                m,
+    int                n,
+    int                k,
+    const float *      A,
+    int                lda,
+    const float *      tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDorgbr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    int                m,
+    int                n,
+    int                k,
+    const double *     A,
+    int                lda,
+    const double *     tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCungbr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    int                m,
+    int                n,
+    int                k,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZungbr_bufferSize(
+    cusolverDnHandle_t     handle,
+    cublasSideMode_t       side,
+    int                    m,
+    int                    n,
+    int                    k,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSorgbr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    int                m,
+    int                n,
+    int                k,
+    float *            A,
+    int                lda,
+    const float *      tau,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDorgbr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    int                m,
+    int                n,
+    int                k,
+    double *           A,
+    int                lda,
+    const double *     tau,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCungbr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    int                m,
+    int                n,
+    int                k,
+    cuComplex *        A,
+    int                lda,
+    const cuComplex *  tau,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZungbr(
+    cusolverDnHandle_t     handle,
+    cublasSideMode_t       side,
+    int                    m,
+    int                    n,
+    int                    k,
+    cuDoubleComplex *      A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    cuDoubleComplex *      work,
+    int                    lwork,
+    int *                  info);
+
+  /* tridiagonal factorization */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsytrd_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      d,
+    const float *      e,
+    const float *      tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsytrd_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     d,
+    const double *     e,
+    const double *     tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChetrd_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const float *      d,
+    const float *      e,
+    const cuComplex *  tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhetrd_bufferSize(
+    cusolverDnHandle_t     handle,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const double *         d,
+    const double *         e,
+    const cuDoubleComplex *tau,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsytrd(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            d,
+    float *            e,
+    float *            tau,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsytrd(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           d,
+    double *           e,
+    double *           tau,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChetrd(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            d,
+    float *            e,
+    cuComplex *        tau,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhetrd(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           d,
+    double *           e,
+    cuDoubleComplex *  tau,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* generate unitary Q comes from sytrd */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSorgtr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDorgtr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCungtr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  tau,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZungtr_bufferSize(
+    cusolverDnHandle_t     handle,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSorgtr(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    const float *      tau,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDorgtr(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    const double *     tau,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCungtr(
+    cusolverDnHandle_t handle,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    const cuComplex *  tau,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZungtr(
+    cusolverDnHandle_t     handle,
+    cublasFillMode_t       uplo,
+    int                    n,
+    cuDoubleComplex *      A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    cuDoubleComplex *      work,
+    int                    lwork,
+    int *                  info);
+
+  /* compute op(Q)*C or C*op(Q) where Q comes from sytrd */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSormtr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      tau,
+    const float *      C,
+    int                ldc,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDormtr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     tau,
+    const double *     C,
+    int                ldc,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCunmtr_bufferSize(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  tau,
+    const cuComplex *  C,
+    int                ldc,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZunmtr_bufferSize(
+    cusolverDnHandle_t     handle,
+    cublasSideMode_t       side,
+    cublasFillMode_t       uplo,
+    cublasOperation_t      trans,
+    int                    m,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *tau,
+    const cuDoubleComplex *C,
+    int                    ldc,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSormtr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            tau,
+    float *            C,
+    int                ldc,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDormtr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           tau,
+    double *           C,
+    int                ldc,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCunmtr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        tau,
+    cuComplex *        C,
+    int                ldc,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZunmtr(
+    cusolverDnHandle_t handle,
+    cublasSideMode_t   side,
+    cublasFillMode_t   uplo,
+    cublasOperation_t  trans,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  tau,
+    cuDoubleComplex *  C,
+    int                ldc,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* singular value decomposition, A = U * Sigma * V^H */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvd_bufferSize(
+    cusolverDnHandle_t handle,
+    int                m,
+    int                n,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvd(
+    cusolverDnHandle_t handle,
+    signed char        jobu,
+    signed char        jobvt,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            S,
+    float *            U,
+    int                ldu,
+    float *            VT,
+    int                ldvt,
+    float *            work,
+    int                lwork,
+    float *            rwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvd(
+    cusolverDnHandle_t handle,
+    signed char        jobu,
+    signed char        jobvt,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           S,
+    double *           U,
+    int                ldu,
+    double *           VT,
+    int                ldvt,
+    double *           work,
+    int                lwork,
+    double *           rwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvd(
+    cusolverDnHandle_t handle,
+    signed char        jobu,
+    signed char        jobvt,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            S,
+    cuComplex *        U,
+    int                ldu,
+    cuComplex *        VT,
+    int                ldvt,
+    cuComplex *        work,
+    int                lwork,
+    float *            rwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvd(
+    cusolverDnHandle_t handle,
+    signed char        jobu,
+    signed char        jobvt,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           S,
+    cuDoubleComplex *  U,
+    int                ldu,
+    cuDoubleComplex *  VT,
+    int                ldvt,
+    cuDoubleComplex *  work,
+    int                lwork,
+    double *           rwork,
+    int *              info);
+
+  /* standard symmetric eigenvalue solver, A*x = lambda*x, by divide-and-conquer
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevd_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const double *         W,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevd(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevd(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevd(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevd(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* standard selective symmetric eigenvalue solver, A*x = lambda*x, by
+   * divide-and-conquer  */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    double             vl,
+    double             vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    const double *     W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevdx_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    cusolverEigRange_t     range,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    double                 vl,
+    double                 vu,
+    int                    il,
+    int                    iu,
+    int *                  meig,
+    const double *         W,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevdx(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevdx(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double             vl,
+    double             vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevdx(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevdx(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double             vl,
+    double             vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* selective generalized symmetric eigenvalue solver, A*x = lambda*B*x, by
+   * divide-and-conquer  */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsygvdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      B,
+    int                ldb,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsygvdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     B,
+    int                ldb,
+    double             vl,
+    double             vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    const double *     W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChegvdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  B,
+    int                ldb,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhegvdx_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigType_t      itype,
+    cusolverEigMode_t      jobz,
+    cusolverEigRange_t     range,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *B,
+    int                    ldb,
+    double                 vl,
+    double                 vu,
+    int                    il,
+    int                    iu,
+    int *                  meig,
+    const double *         W,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsygvdx(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            B,
+    int                ldb,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsygvdx(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           B,
+    int                ldb,
+    double             vl,
+    double             vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChegvdx(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        B,
+    int                ldb,
+    float              vl,
+    float              vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhegvdx(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  B,
+    int                ldb,
+    double             vl,
+    double             vu,
+    int                il,
+    int                iu,
+    int *              meig,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  /* generalized symmetric eigenvalue solver, A*x = lambda*B*x, by
+   * divide-and-conquer  */
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsygvd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      B,
+    int                ldb,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsygvd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     B,
+    int                ldb,
+    const double *     W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChegvd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  B,
+    int                ldb,
+    const float *      W,
+    int *              lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhegvd_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigType_t      itype,
+    cusolverEigMode_t      jobz,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *B,
+    int                    ldb,
+    const double *         W,
+    int *                  lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsygvd(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            B,
+    int                ldb,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsygvd(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           B,
+    int                ldb,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChegvd(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        B,
+    int                ldb,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhegvd(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  B,
+    int                ldb,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCreateSyevjInfo(syevjInfo_t *info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDestroySyevjInfo(syevjInfo_t info);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnXsyevjSetTolerance(syevjInfo_t info, double tolerance);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnXsyevjSetMaxSweeps(syevjInfo_t info, int max_sweeps);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnXsyevjSetSortEig(syevjInfo_t info, int sort_eig);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevjGetResidual(
+    cusolverDnHandle_t handle,
+    syevjInfo_t        info,
+    double *           residual);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevjGetSweeps(
+    cusolverDnHandle_t handle,
+    syevjInfo_t        info,
+    int *              executed_sweeps);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevjBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      W,
+    int *              lwork,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevjBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     W,
+    int *              lwork,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevjBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const float *      W,
+    int *              lwork,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevjBatched_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const double *         W,
+    int *                  lwork,
+    syevjInfo_t            params,
+    int                    batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      W,
+    int *              lwork,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     W,
+    int *              lwork,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const float *      W,
+    int *              lwork,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevj_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const double *         W,
+    int *                  lwork,
+    syevjInfo_t            params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsyevj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsyevj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCheevj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZheevj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsygvj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      B,
+    int                ldb,
+    const float *      W,
+    int *              lwork,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsygvj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     B,
+    int                ldb,
+    const double *     W,
+    int *              lwork,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChegvj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const cuComplex *  B,
+    int                ldb,
+    const float *      W,
+    int *              lwork,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhegvj_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigType_t      itype,
+    cusolverEigMode_t      jobz,
+    cublasFillMode_t       uplo,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const cuDoubleComplex *B,
+    int                    ldb,
+    const double *         W,
+    int *                  lwork,
+    syevjInfo_t            params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSsygvj(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            B,
+    int                ldb,
+    float *            W,
+    float *            work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDsygvj(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           B,
+    int                ldb,
+    double *           W,
+    double *           work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnChegvj(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    cuComplex *        B,
+    int                ldb,
+    float *            W,
+    cuComplex *        work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZhegvj(
+    cusolverDnHandle_t handle,
+    cusolverEigType_t  itype,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    cuDoubleComplex *  B,
+    int                ldb,
+    double *           W,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info,
+    syevjInfo_t        params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCreateGesvdjInfo(gesvdjInfo_t *info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDestroyGesvdjInfo(gesvdjInfo_t info);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnXgesvdjSetTolerance(gesvdjInfo_t info, double tolerance);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnXgesvdjSetMaxSweeps(gesvdjInfo_t info, int max_sweeps);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnXgesvdjSetSortEig(gesvdjInfo_t info, int sort_svd);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvdjGetResidual(
+    cusolverDnHandle_t handle,
+    gesvdjInfo_t       info,
+    double *           residual);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvdjGetSweeps(
+    cusolverDnHandle_t handle,
+    gesvdjInfo_t       info,
+    int *              executed_sweeps);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvdjBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      S,
+    const float *      U,
+    int                ldu,
+    const float *      V,
+    int                ldv,
+    int *              lwork,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvdjBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     S,
+    const double *     U,
+    int                ldu,
+    const double *     V,
+    int                ldv,
+    int *              lwork,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvdjBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const float *      S,
+    const cuComplex *  U,
+    int                ldu,
+    const cuComplex *  V,
+    int                ldv,
+    int *              lwork,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvdjBatched_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    int                    m,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const double *         S,
+    const cuDoubleComplex *U,
+    int                    ldu,
+    const cuDoubleComplex *V,
+    int                    ldv,
+    int *                  lwork,
+    gesvdjInfo_t           params,
+    int                    batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvdjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            S,
+    float *            U,
+    int                ldu,
+    float *            V,
+    int                ldv,
+    float *            work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvdjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           S,
+    double *           U,
+    int                ldu,
+    double *           V,
+    int                ldv,
+    double *           work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvdjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            S,
+    cuComplex *        U,
+    int                ldu,
+    cuComplex *        V,
+    int                ldv,
+    cuComplex *        work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvdjBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           S,
+    cuDoubleComplex *  U,
+    int                ldu,
+    cuDoubleComplex *  V,
+    int                ldv,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvdj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    const float *      A,
+    int                lda,
+    const float *      S,
+    const float *      U,
+    int                ldu,
+    const float *      V,
+    int                ldv,
+    int *              lwork,
+    gesvdjInfo_t       params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvdj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    const double *     A,
+    int                lda,
+    const double *     S,
+    const double *     U,
+    int                ldu,
+    const double *     V,
+    int                ldv,
+    int *              lwork,
+    gesvdjInfo_t       params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvdj_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    const cuComplex *  A,
+    int                lda,
+    const float *      S,
+    const cuComplex *  U,
+    int                ldu,
+    const cuComplex *  V,
+    int                ldv,
+    int *              lwork,
+    gesvdjInfo_t       params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvdj_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    int                    econ,
+    int                    m,
+    int                    n,
+    const cuDoubleComplex *A,
+    int                    lda,
+    const double *         S,
+    const cuDoubleComplex *U,
+    int                    ldu,
+    const cuDoubleComplex *V,
+    int                    ldv,
+    int *                  lwork,
+    gesvdjInfo_t           params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvdj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    float *            A,
+    int                lda,
+    float *            S,
+    float *            U,
+    int                ldu,
+    float *            V,
+    int                ldv,
+    float *            work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvdj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    double *           A,
+    int                lda,
+    double *           S,
+    double *           U,
+    int                ldu,
+    double *           V,
+    int                ldv,
+    double *           work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvdj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    cuComplex *        A,
+    int                lda,
+    float *            S,
+    cuComplex *        U,
+    int                ldu,
+    cuComplex *        V,
+    int                ldv,
+    cuComplex *        work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvdj(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int                m,
+    int                n,
+    cuDoubleComplex *  A,
+    int                lda,
+    double *           S,
+    cuDoubleComplex *  U,
+    int                ldu,
+    cuDoubleComplex *  V,
+    int                ldv,
+    cuDoubleComplex *  work,
+    int                lwork,
+    int *              info,
+    gesvdjInfo_t       params);
+
+  /* batched approximate SVD */
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvdaStridedBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                rank,
+    int                m,
+    int                n,
+    const float *      d_A,
+    int                lda,
+    long long int      strideA,
+    const float *      d_S,
+    long long int      strideS,
+    const float *      d_U,
+    int                ldu,
+    long long int      strideU,
+    const float *      d_V,
+    int                ldv,
+    long long int      strideV,
+    int *              lwork,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvdaStridedBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                rank,
+    int                m,
+    int                n,
+    const double *     d_A,
+    int                lda,
+    long long int      strideA,
+    const double *     d_S,
+    long long int      strideS,
+    const double *     d_U,
+    int                ldu,
+    long long int      strideU,
+    const double *     d_V,
+    int                ldv,
+    long long int      strideV,
+    int *              lwork,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvdaStridedBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                rank,
+    int                m,
+    int                n,
+    const cuComplex *  d_A,
+    int                lda,
+    long long int      strideA,
+    const float *      d_S,
+    long long int      strideS,
+    const cuComplex *  d_U,
+    int                ldu,
+    long long int      strideU,
+    const cuComplex *  d_V,
+    int                ldv,
+    long long int      strideV,
+    int *              lwork,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvdaStridedBatched_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    int                    rank,
+    int                    m,
+    int                    n,
+    const cuDoubleComplex *d_A,
+    int                    lda,
+    long long int          strideA,
+    const double *         d_S,
+    long long int          strideS,
+    const cuDoubleComplex *d_U,
+    int                    ldu,
+    long long int          strideU,
+    const cuDoubleComplex *d_V,
+    int                    ldv,
+    long long int          strideV,
+    int *                  lwork,
+    int                    batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSgesvdaStridedBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                rank,
+    int                m,
+    int                n,
+    const float *      d_A,
+    int                lda,
+    long long int      strideA,
+    float *            d_S,
+    long long int      strideS,
+    float *            d_U,
+    int                ldu,
+    long long int      strideU,
+    float *            d_V,
+    int                ldv,
+    long long int      strideV,
+    float *            d_work,
+    int                lwork,
+    int *              d_info,
+    double *           h_R_nrmF,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnDgesvdaStridedBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                rank,
+    int                m,
+    int                n,
+    const double *     d_A,
+    int                lda,
+    long long int      strideA,
+    double *           d_S,
+    long long int      strideS,
+    double *           d_U,
+    int                ldu,
+    long long int      strideU,
+    double *           d_V,
+    int                ldv,
+    long long int      strideV,
+    double *           d_work,
+    int                lwork,
+    int *              d_info,
+    double *           h_R_nrmF,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnCgesvdaStridedBatched(
+    cusolverDnHandle_t handle,
+    cusolverEigMode_t  jobz,
+    int                rank,
+    int                m,
+    int                n,
+    const cuComplex *  d_A,
+    int                lda,
+    long long int      strideA,
+    float *            d_S,
+    long long int      strideS,
+    cuComplex *        d_U,
+    int                ldu,
+    long long int      strideU,
+    cuComplex *        d_V,
+    int                ldv,
+    long long int      strideV,
+    cuComplex *        d_work,
+    int                lwork,
+    int *              d_info,
+    double *           h_R_nrmF,
+    int                batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnZgesvdaStridedBatched(
+    cusolverDnHandle_t     handle,
+    cusolverEigMode_t      jobz,
+    int                    rank,
+    int                    m,
+    int                    n,
+    const cuDoubleComplex *d_A,
+    int                    lda,
+    long long int          strideA,
+    double *               d_S,
+    long long int          strideS,
+    cuDoubleComplex *      d_U,
+    int                    ldu,
+    long long int          strideU,
+    cuDoubleComplex *      d_V,
+    int                    ldv,
+    long long int          strideV,
+    cuDoubleComplex *      d_work,
+    int                    lwork,
+    int *                  d_info,
+    double *               h_R_nrmF,
+    int                    batchSize);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnCreateParams(cusolverDnParams_t *params);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnDestroyParams(cusolverDnParams_t params);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnSetAdvOptions(
+    cusolverDnParams_t   params,
+    cusolverDnFunction_t function,
+    cusolverAlgMode_t    algo);
+
+  /* 64-bit API for POTRF */
+  CUSOLVER_DEPRECATED(cusolverDnXpotrf_bufferSize)
+  cusolverStatus_t CUSOLVERAPI cusolverDnPotrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cusolverDnXpotrf)
+  cusolverStatus_t CUSOLVERAPI cusolverDnPotrf(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       computeType,
+    void *             pBuffer,
+    size_t             workspaceInBytes,
+    int *              info);
+
+  /* 64-bit API for POTRS */
+  CUSOLVER_DEPRECATED(cusolverDnXpotrs)
+  cusolverStatus_t CUSOLVERAPI cusolverDnPotrs(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    int64_t            nrhs,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeB,
+    void *             B,
+    int64_t            ldb,
+    int *              info);
+
+  /* 64-bit API for GEQRF */
+  CUSOLVER_DEPRECATED(cusolverDnXgeqrf_bufferSize)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGeqrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeTau,
+    const void *       tau,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cusolverDnXgeqrf)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGeqrf(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeTau,
+    void *             tau,
+    cudaDataType       computeType,
+    void *             pBuffer,
+    size_t             workspaceInBytes,
+    int *              info);
+
+  /* 64-bit API for GETRF */
+  CUSOLVER_DEPRECATED(cusolverDnXgetrf_bufferSize)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGetrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cusolverDnXgetrf)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGetrf(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    int64_t *          ipiv,
+    cudaDataType       computeType,
+    void *             pBuffer,
+    size_t             workspaceInBytes,
+    int *              info);
+
+  /* 64-bit API for GETRS */
+  CUSOLVER_DEPRECATED(cusolverDnXgetrs)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGetrs(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasOperation_t  trans,
+    int64_t            n,
+    int64_t            nrhs,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    const int64_t *    ipiv,
+    cudaDataType       dataTypeB,
+    void *             B,
+    int64_t            ldb,
+    int *              info);
+
+  /* 64-bit API for SYEVD */
+  CUSOLVER_DEPRECATED(cusolverDnXsyevd_bufferSize)
+  cusolverStatus_t CUSOLVERAPI cusolverDnSyevd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    const void *       W,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cusolverDnXsyevd)
+  cusolverStatus_t CUSOLVERAPI cusolverDnSyevd(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    void *             W,
+    cudaDataType       computeType,
+    void *             pBuffer,
+    size_t             workspaceInBytes,
+    int *              info);
+
+  /* 64-bit API for SYEVDX */
+  CUSOLVER_DEPRECATED(cusolverDnXsyevdx_bufferSize)
+  cusolverStatus_t CUSOLVERAPI cusolverDnSyevdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    void *             vl,
+    void *             vu,
+    int64_t            il,
+    int64_t            iu,
+    int64_t *          h_meig,
+    cudaDataType       dataTypeW,
+    const void *       W,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cusolverDnXsyevdx)
+  cusolverStatus_t CUSOLVERAPI cusolverDnSyevdx(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    void *             vl,
+    void *             vu,
+    int64_t            il,
+    int64_t            iu,
+    int64_t *          meig64,
+    cudaDataType       dataTypeW,
+    void *             W,
+    cudaDataType       computeType,
+    void *             pBuffer,
+    size_t             workspaceInBytes,
+    int *              info);
+
+  /* 64-bit API for GESVD */
+  CUSOLVER_DEPRECATED(cusolverDnXgesvd_bufferSize)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGesvd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    signed char        jobu,
+    signed char        jobvt,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeS,
+    const void *       S,
+    cudaDataType       dataTypeU,
+    const void *       U,
+    int64_t            ldu,
+    cudaDataType       dataTypeVT,
+    const void *       VT,
+    int64_t            ldvt,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cusolverDnXgesvd)
+  cusolverStatus_t CUSOLVERAPI cusolverDnGesvd(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    signed char        jobu,
+    signed char        jobvt,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeS,
+    void *             S,
+    cudaDataType       dataTypeU,
+    void *             U,
+    int64_t            ldu,
+    cudaDataType       dataTypeVT,
+    void *             VT,
+    int64_t            ldvt,
+    cudaDataType       computeType,
+    void *             pBuffer,
+    size_t             workspaceInBytes,
+    int *              info);
+
+  /*
+   * new 64-bit API
+   */
+  /* 64-bit API for POTRF */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXpotrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXpotrf(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* 64-bit API for POTRS */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXpotrs(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    int64_t            nrhs,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeB,
+    void *             B,
+    int64_t            ldb,
+    int *              info);
+
+  /* 64-bit API for GEQRF */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgeqrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeTau,
+    const void *       tau,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgeqrf(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeTau,
+    void *             tau,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* 64-bit API for GETRF */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgetrf_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgetrf(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    int64_t *          ipiv,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* 64-bit API for GETRS */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgetrs(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cublasOperation_t  trans,
+    int64_t            n,
+    int64_t            nrhs,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    const int64_t *    ipiv,
+    cudaDataType       dataTypeB,
+    void *             B,
+    int64_t            ldb,
+    int *              info);
+
+  /* 64-bit API for SYEVD */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    const void *       W,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevd(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    void *             W,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* 64-bit API for batched SYEV */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevBatched_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    const void *       W,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost,
+    int64_t            batchSize);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevBatched(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    void *             W,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info,
+    int64_t            batchSize);
+
+  /* 64-bit API for SYEVDX */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevdx_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    void *             vl,
+    void *             vu,
+    int64_t            il,
+    int64_t            iu,
+    int64_t *          h_meig,
+    cudaDataType       dataTypeW,
+    const void *       W,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXsyevdx(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    cusolverEigRange_t range,
+    cublasFillMode_t   uplo,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    void *             vl,
+    void *             vu,
+    int64_t            il,
+    int64_t            iu,
+    int64_t *          meig64,
+    cudaDataType       dataTypeW,
+    void *             W,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* 64-bit API for GEEV */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgeev_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobvl,
+    cusolverEigMode_t  jobvr,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void*        A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    const void*        W,
+    cudaDataType       dataTypeVL,
+    const void*        VL,
+    int64_t            ldvl,
+    cudaDataType       dataTypeVR,
+    const void*        VR,
+    int64_t            ldvr,
+    cudaDataType       computeType,
+    size_t*            workspaceInBytesOnDevice,
+    size_t*            workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgeev(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobvl,
+    cusolverEigMode_t  jobvr,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void*              A,
+    int64_t            lda,
+    cudaDataType       dataTypeW,
+    void*              W,
+    cudaDataType       dataTypeVL,
+    void*              VL,
+    int64_t            ldvl,
+    cudaDataType       dataTypeVR,
+    void*              VR,
+    int64_t            ldvr,
+    cudaDataType       computeType,
+    void*              bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void*              bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int*               info);
+
+  /* 64-bit API for GESVD */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvd_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    signed char        jobu,
+    signed char        jobvt,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeS,
+    const void *       S,
+    cudaDataType       dataTypeU,
+    const void *       U,
+    int64_t            ldu,
+    cudaDataType       dataTypeVT,
+    const void *       VT,
+    int64_t            ldvt,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvd(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    signed char        jobu,
+    signed char        jobvt,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeS,
+    void *             S,
+    cudaDataType       dataTypeU,
+    void *             U,
+    int64_t            ldu,
+    cudaDataType       dataTypeVT,
+    void *             VT,
+    int64_t            ldvt,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              info);
+
+  /* 64-bit API for GESVDP */
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvdp_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeS,
+    const void *       S,
+    cudaDataType       dataTypeU,
+    const void *       U,
+    int64_t            ldu,
+    cudaDataType       dataTypeV,
+    const void *       V,
+    int64_t            ldv,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvdp(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    cusolverEigMode_t  jobz,
+    int                econ,
+    int64_t            m,
+    int64_t            n,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeS,
+    void *             S,
+    cudaDataType       dataTypeU,
+    void *             U,
+    int64_t            ldu,
+    cudaDataType       dataTypeV,
+    void *             V,
+    int64_t            ldv,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              d_info,
+    double *           h_err_sigma);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvdr_bufferSize(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    signed char        jobu,
+    signed char        jobv,
+    int64_t            m,
+    int64_t            n,
+    int64_t            k,
+    int64_t            p,
+    int64_t            niters,
+    cudaDataType       dataTypeA,
+    const void *       A,
+    int64_t            lda,
+    cudaDataType       dataTypeSrand,
+    const void *       Srand,
+    cudaDataType       dataTypeUrand,
+    const void *       Urand,
+    int64_t            ldUrand,
+    cudaDataType       dataTypeVrand,
+    const void *       Vrand,
+    int64_t            ldVrand,
+    cudaDataType       computeType,
+    size_t *           workspaceInBytesOnDevice,
+    size_t *           workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXgesvdr(
+    cusolverDnHandle_t handle,
+    cusolverDnParams_t params,
+    signed char        jobu,
+    signed char        jobv,
+    int64_t            m,
+    int64_t            n,
+    int64_t            k,
+    int64_t            p,
+    int64_t            niters,
+    cudaDataType       dataTypeA,
+    void *             A,
+    int64_t            lda,
+    cudaDataType       dataTypeSrand,
+    void *             Srand,
+    cudaDataType       dataTypeUrand,
+    void *             Urand,
+    int64_t            ldUrand,
+    cudaDataType       dataTypeVrand,
+    void *             Vrand,
+    int64_t            ldVrand,
+    cudaDataType       computeType,
+    void *             bufferOnDevice,
+    size_t             workspaceInBytesOnDevice,
+    void *             bufferOnHost,
+    size_t             workspaceInBytesOnHost,
+    int *              d_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXlarft_bufferSize(
+    cusolverDnHandle_t     handle,
+    cusolverDnParams_t     params,
+    cusolverDirectMode_t   direct,
+    cusolverStorevMode_t   storev,
+    int64_t                n,
+    int64_t                k,
+    cudaDataType           dataTypeV,
+    const void            *V,
+    int64_t                ldv,
+    cudaDataType           dataTypeTau,
+    const void            *tau,
+    cudaDataType           dataTypeT,
+    void                  *T,
+    int64_t                ldt,
+    cudaDataType           computeType,
+    size_t                *workspaceInBytesOnDevice,
+    size_t                *workspaceInBytesOnHost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnXlarft(
+    cusolverDnHandle_t     handle,
+    cusolverDnParams_t     params,
+    cusolverDirectMode_t   direct,
+    cusolverStorevMode_t   storev,
+    int64_t                n,
+    int64_t                k,
+    cudaDataType           dataTypeV,
+    const void            *V,
+    int64_t                ldv,
+    cudaDataType           dataTypeTau,
+    const void            *tau,
+    cudaDataType           dataTypeT,
+    void                  *T,
+    int64_t                ldt,
+    cudaDataType           computeType,
+    void                  *bufferOnDevice,
+    size_t                 workspaceInBytesOnDevice,
+    void                  *bufferOnHost,
+    size_t                 workspaceInBytesOnHost);
+
+  typedef void (*cusolverDnLoggerCallback_t)(
+    int         logLevel,
+    const char *functionName,
+    const char *message);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverDnLoggerSetCallback(cusolverDnLoggerCallback_t callback);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnLoggerSetFile(FILE *file);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnLoggerOpenFile(const char *logFile);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnLoggerSetLevel(int level);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnLoggerSetMask(int mask);
+
+  cusolverStatus_t CUSOLVERAPI cusolverDnLoggerForceDisable();
+
+  #if defined(__cplusplus)
+}
+  #endif /* __cplusplus */
+
+#endif /* !defined(CUDENSE_H_) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverMg.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverMg.h
new file mode 100644
index 0000000000000000000000000000000000000000..7702191f7253d66cf998016f6ae9f14149fbbb0b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverMg.h
@@ -0,0 +1,318 @@
+/*
+ * Copyright 2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(CUSOLVERMG_H_)
+  #define CUSOLVERMG_H_
+
+  #include <stdint.h>
+  #include "cusolverDn.h"
+
+  #if defined(__cplusplus)
+extern "C" {
+  #endif /* __cplusplus */
+
+  struct cusolverMgContext;
+  typedef struct cusolverMgContext *cusolverMgHandle_t;
+
+  /**
+   * \beief This enum decides how 1D device Ids (or process ranks) get mapped to
+   * a 2D grid.
+   */
+  typedef enum {
+
+    CUDALIBMG_GRID_MAPPING_ROW_MAJOR = 1,
+    CUDALIBMG_GRID_MAPPING_COL_MAJOR = 0
+
+  } cusolverMgGridMapping_t;
+
+  /** \brief Opaque structure of the distributed grid */
+  typedef void *cudaLibMgGrid_t;
+  /** \brief Opaque structure of the distributed matrix descriptor */
+  typedef void *cudaLibMgMatrixDesc_t;
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgCreate(cusolverMgHandle_t *handle);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgDestroy(cusolverMgHandle_t handle);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgDeviceSelect(
+    cusolverMgHandle_t handle,
+    int                nbDevices,
+    int                deviceId[]);
+
+  /**
+   * \brief Allocates resources related to the shared memory device grid.
+   * \param[out] grid the opaque data strcuture that holds the grid
+   * \param[in] numRowDevices number of devices in the row
+   * \param[in] numColDevices number of devices in the column
+   * \param[in] deviceId This array of size height * width stores the
+   *            device-ids of the 2D grid; each entry must correspond to a valid
+   * gpu or to -1 (denoting CPU). \param[in] mapping whether the 2D grid is in
+   * row/column major \returns the status code
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverMgCreateDeviceGrid(
+    cudaLibMgGrid_t *       grid,
+    int32_t                 numRowDevices,
+    int32_t                 numColDevices,
+    const int32_t           deviceId[],
+    cusolverMgGridMapping_t mapping);
+
+  /**
+   * \brief Releases the allocated resources related to the distributed grid.
+   * \param[in] grid the opaque data strcuture that holds the distributed grid
+   * \returns the status code
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverMgDestroyGrid(cudaLibMgGrid_t grid);
+
+  /**
+   * \brief Allocates resources related to the distributed matrix descriptor.
+   * \param[out] desc the opaque data strcuture that holds the descriptor
+   * \param[in] numRows number of total rows
+   * \param[in] numCols number of total columns
+   * \param[in] rowBlockSize row block size
+   * \param[in] colBlockSize column block size
+   * \param[in] dataType the data type of each element in cudaDataType
+   * \param[in] grid the opaque data structure of the distributed grid
+   * \returns the status code
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverMgCreateMatrixDesc(
+    cudaLibMgMatrixDesc_t *desc,
+    int64_t                numRows,
+    int64_t                numCols,
+    int64_t                rowBlockSize,
+    int64_t                colBlockSize,
+    cudaDataType           dataType,
+    const cudaLibMgGrid_t  grid);
+
+  /**
+   * \brief Releases the allocated resources related to the distributed matrix
+   * descriptor. \param[in] desc the opaque data strcuture that holds the
+   * descriptor \returns the status code
+   */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverMgDestroyMatrixDesc(cudaLibMgMatrixDesc_t desc);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgSyevd_bufferSize(
+    cusolverMgHandle_t    handle,
+    cusolverEigMode_t     jobz,
+    cublasFillMode_t      uplo,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    void *                W,
+    cudaDataType          dataTypeW,
+    cudaDataType          computeType,
+    int64_t *             lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgSyevd(
+    cusolverMgHandle_t    handle,
+    cusolverEigMode_t     jobz,
+    cublasFillMode_t      uplo,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    void *                W,
+    cudaDataType          dataTypeW,
+    cudaDataType          computeType,
+    void *                array_d_work[],
+    int64_t               lwork,
+    int *                 info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgGetrf_bufferSize(
+    cusolverMgHandle_t    handle,
+    int                   M,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    int *                 array_d_IPIV[],
+    cudaDataType          computeType,
+    int64_t *             lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgGetrf(
+    cusolverMgHandle_t    handle,
+    int                   M,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    int *                 array_d_IPIV[],
+    cudaDataType          computeType,
+    void *                array_d_work[],
+    int64_t               lwork,
+    int *                 info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgGetrs_bufferSize(
+    cusolverMgHandle_t    handle,
+    cublasOperation_t     TRANS,
+    int                   N,
+    int                   NRHS,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    int *                 array_d_IPIV[],
+    void *                array_d_B[],
+    int                   IB,
+    int                   JB,
+    cudaLibMgMatrixDesc_t descrB,
+    cudaDataType          computeType,
+    int64_t *             lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgGetrs(
+    cusolverMgHandle_t    handle,
+    cublasOperation_t     TRANS,
+    int                   N,
+    int                   NRHS,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    int *                 array_d_IPIV[],
+    void *                array_d_B[],
+    int                   IB,
+    int                   JB,
+    cudaLibMgMatrixDesc_t descrB,
+    cudaDataType          computeType,
+    void *                array_d_work[],
+    int64_t               lwork,
+    int *                 info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgPotrf_bufferSize(
+    cusolverMgHandle_t    handle,
+    cublasFillMode_t      uplo,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    cudaDataType          computeType,
+    int64_t *             lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgPotrf(
+    cusolverMgHandle_t    handle,
+    cublasFillMode_t      uplo,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    cudaDataType          computeType,
+    void *                array_d_work[],
+    int64_t               lwork,
+    int *                 h_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgPotrs_bufferSize(
+    cusolverMgHandle_t    handle,
+    cublasFillMode_t      uplo,
+    int                   n,
+    int                   nrhs,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    void *                array_d_B[],
+    int                   IB,
+    int                   JB,
+    cudaLibMgMatrixDesc_t descrB,
+    cudaDataType          computeType,
+    int64_t *             lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgPotrs(
+    cusolverMgHandle_t    handle,
+    cublasFillMode_t      uplo,
+    int                   n,
+    int                   nrhs,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    void *                array_d_B[],
+    int                   IB,
+    int                   JB,
+    cudaLibMgMatrixDesc_t descrB,
+    cudaDataType          computeType,
+    void *                array_d_work[],
+    int64_t               lwork,
+    int *                 h_info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgPotri_bufferSize(
+    cusolverMgHandle_t    handle,
+    cublasFillMode_t      uplo,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    cudaDataType          computeType,
+    int64_t *             lwork);
+
+  cusolverStatus_t CUSOLVERAPI cusolverMgPotri(
+    cusolverMgHandle_t    handle,
+    cublasFillMode_t      uplo,
+    int                   N,
+    void *                array_d_A[],
+    int                   IA,
+    int                   JA,
+    cudaLibMgMatrixDesc_t descrA,
+    cudaDataType          computeType,
+    void *                array_d_work[],
+    int64_t               lwork,
+    int *                 h_info);
+
+  #if defined(__cplusplus)
+}
+  #endif /* __cplusplus */
+
+#endif // CUSOLVERMG_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverRf.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverRf.h
new file mode 100644
index 0000000000000000000000000000000000000000..8c29fe1a59693ec3fa4e848fba3bf1577cab57e3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverRf.h
@@ -0,0 +1,339 @@
+/*
+ * Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(CUSOLVERRF_H_)
+  #define CUSOLVERRF_H_
+
+  #include "driver_types.h"
+  #include "cuComplex.h"
+  #include "cusolver_common.h"
+
+  #if defined(__cplusplus)
+extern "C" {
+  #endif /* __cplusplus */
+
+  /* CUSOLVERRF mode */
+  typedef enum {
+    CUSOLVERRF_RESET_VALUES_FAST_MODE_OFF = 0, // default
+    CUSOLVERRF_RESET_VALUES_FAST_MODE_ON = 1
+  } cusolverRfResetValuesFastMode_t;
+
+  /* CUSOLVERRF matrix format */
+  typedef enum {
+    CUSOLVERRF_MATRIX_FORMAT_CSR = 0, // default
+    CUSOLVERRF_MATRIX_FORMAT_CSC = 1
+  } cusolverRfMatrixFormat_t;
+
+  /* CUSOLVERRF unit diagonal */
+  typedef enum {
+    CUSOLVERRF_UNIT_DIAGONAL_STORED_L = 0, // default
+    CUSOLVERRF_UNIT_DIAGONAL_STORED_U = 1,
+    CUSOLVERRF_UNIT_DIAGONAL_ASSUMED_L = 2,
+    CUSOLVERRF_UNIT_DIAGONAL_ASSUMED_U = 3
+  } cusolverRfUnitDiagonal_t;
+
+  /* CUSOLVERRF factorization algorithm */
+  typedef enum {
+    CUSOLVERRF_FACTORIZATION_ALG0 = 0, // default
+    CUSOLVERRF_FACTORIZATION_ALG1 = 1,
+    CUSOLVERRF_FACTORIZATION_ALG2 = 2,
+  } cusolverRfFactorization_t;
+
+  /* CUSOLVERRF triangular solve algorithm */
+  typedef enum {
+    CUSOLVERRF_TRIANGULAR_SOLVE_ALG1 = 1, // default
+    CUSOLVERRF_TRIANGULAR_SOLVE_ALG2 = 2,
+    CUSOLVERRF_TRIANGULAR_SOLVE_ALG3 = 3
+  } cusolverRfTriangularSolve_t;
+
+  /* CUSOLVERRF numeric boost report */
+  typedef enum {
+    CUSOLVERRF_NUMERIC_BOOST_NOT_USED = 0, // default
+    CUSOLVERRF_NUMERIC_BOOST_USED = 1
+  } cusolverRfNumericBoostReport_t;
+
+  /* Opaque structure holding CUSOLVERRF library common */
+  struct cusolverRfCommon;
+  typedef struct cusolverRfCommon* cusolverRfHandle_t;
+
+  /* CUSOLVERRF create (allocate memory) and destroy (free memory) in the handle
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverRfCreate(cusolverRfHandle_t* handle);
+  cusolverStatus_t CUSOLVERAPI cusolverRfDestroy(cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF set and get input format */
+  cusolverStatus_t CUSOLVERAPI cusolverRfGetMatrixFormat(
+    cusolverRfHandle_t        handle,
+    cusolverRfMatrixFormat_t* format,
+    cusolverRfUnitDiagonal_t* diag);
+
+  cusolverStatus_t CUSOLVERAPI cusolverRfSetMatrixFormat(
+    cusolverRfHandle_t       handle,
+    cusolverRfMatrixFormat_t format,
+    cusolverRfUnitDiagonal_t diag);
+
+  /* CUSOLVERRF set and get numeric properties */
+  cusolverStatus_t CUSOLVERAPI cusolverRfSetNumericProperties(
+    cusolverRfHandle_t handle,
+    double             zero,
+    double             boost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverRfGetNumericProperties(
+    cusolverRfHandle_t handle,
+    double*            zero,
+    double*            boost);
+
+  cusolverStatus_t CUSOLVERAPI cusolverRfGetNumericBoostReport(
+    cusolverRfHandle_t              handle,
+    cusolverRfNumericBoostReport_t* report);
+
+  /* CUSOLVERRF choose the triangular solve algorithm */
+  cusolverStatus_t CUSOLVERAPI cusolverRfSetAlgs(
+    cusolverRfHandle_t          handle,
+    cusolverRfFactorization_t   factAlg,
+    cusolverRfTriangularSolve_t solveAlg);
+
+  cusolverStatus_t CUSOLVERAPI cusolverRfGetAlgs(
+    cusolverRfHandle_t           handle,
+    cusolverRfFactorization_t*   factAlg,
+    cusolverRfTriangularSolve_t* solveAlg);
+
+  /* CUSOLVERRF set and get fast mode */
+  cusolverStatus_t CUSOLVERAPI cusolverRfGetResetValuesFastMode(
+    cusolverRfHandle_t               handle,
+    cusolverRfResetValuesFastMode_t* fastMode);
+
+  cusolverStatus_t CUSOLVERAPI cusolverRfSetResetValuesFastMode(
+    cusolverRfHandle_t              handle,
+    cusolverRfResetValuesFastMode_t fastMode);
+
+  /*** Non-Batched Routines ***/
+  /* CUSOLVERRF setup of internal structures from host or device memory */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfSetupHost(/* Input (in the host memory) */
+                        int     n,
+                        int     nnzA,
+                        int*    h_csrRowPtrA,
+                        int*    h_csrColIndA,
+                        double* h_csrValA,
+                        int     nnzL,
+                        int*    h_csrRowPtrL,
+                        int*    h_csrColIndL,
+                        double* h_csrValL,
+                        int     nnzU,
+                        int*    h_csrRowPtrU,
+                        int*    h_csrColIndU,
+                        double* h_csrValU,
+                        int*    h_P,
+                        int*    h_Q,
+                        /* Output */
+                        cusolverRfHandle_t handle);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfSetupDevice(/* Input (in the device memory) */
+                          int     n,
+                          int     nnzA,
+                          int*    csrRowPtrA,
+                          int*    csrColIndA,
+                          double* csrValA,
+                          int     nnzL,
+                          int*    csrRowPtrL,
+                          int*    csrColIndL,
+                          double* csrValL,
+                          int     nnzU,
+                          int*    csrRowPtrU,
+                          int*    csrColIndU,
+                          double* csrValU,
+                          int*    P,
+                          int*    Q,
+                          /* Output */
+                          cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF update the matrix values (assuming the reordering, pivoting
+     and consequently the sparsity pattern of L and U did not change),
+     and zero out the remaining values. */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfResetValues(/* Input (in the device memory) */
+                          int     n,
+                          int     nnzA,
+                          int*    csrRowPtrA,
+                          int*    csrColIndA,
+                          double* csrValA,
+                          int*    P,
+                          int*    Q,
+                          /* Output */
+                          cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF analysis (for parallelism) */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverRfAnalyze(cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF re-factorization (for parallelism) */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverRfRefactor(cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF extraction: Get L & U packed into a single matrix M */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfAccessBundledFactorsDevice(/* Input */
+                                         cusolverRfHandle_t handle,
+                                         /* Output (in the host memory) */
+                                         int* nnzM,
+                                         /* Output (in the device memory) */
+                                         int**    Mp,
+                                         int**    Mi,
+                                         double** Mx);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfExtractBundledFactorsHost(/* Input */
+                                        cusolverRfHandle_t handle,
+                                        /* Output (in the host memory) */
+                                        int*     h_nnzM,
+                                        int**    h_Mp,
+                                        int**    h_Mi,
+                                        double** h_Mx);
+
+  /* CUSOLVERRF extraction: Get L & U individually */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfExtractSplitFactorsHost(/* Input */
+                                      cusolverRfHandle_t handle,
+                                      /* Output (in the host memory) */
+                                      int*     h_nnzL,
+                                      int**    h_csrRowPtrL,
+                                      int**    h_csrColIndL,
+                                      double** h_csrValL,
+                                      int*     h_nnzU,
+                                      int**    h_csrRowPtrU,
+                                      int**    h_csrColIndU,
+                                      double** h_csrValU);
+
+  /* CUSOLVERRF (forward and backward triangular) solves */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI
+    cusolverRfSolve(/* Input (in the device memory) */
+                    cusolverRfHandle_t handle,
+                    int*               P,
+                    int*               Q,
+                    int                nrhs, // only nrhs=1 is supported
+                    double*            Temp, // of size ldt*nrhs (ldt>=n)
+                    int                ldt,
+                    /* Input/Output (in the device memory) */
+                    double* XF,
+                    /* Input */
+                    int ldxf);
+
+  /*** Batched Routines ***/
+  /* CUSOLVERRF-batch setup of internal structures from host */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverRfBatchSetupHost(/* Input (in the host memory)*/
+                             int     batchSize,
+                             int     n,
+                             int     nnzA,
+                             int*    h_csrRowPtrA,
+                             int*    h_csrColIndA,
+                             double* h_csrValA_array[],
+                             int     nnzL,
+                             int*    h_csrRowPtrL,
+                             int*    h_csrColIndL,
+                             double* h_csrValL,
+                             int     nnzU,
+                             int*    h_csrRowPtrU,
+                             int*    h_csrColIndU,
+                             double* h_csrValU,
+                             int*    h_P,
+                             int*    h_Q,
+                             /* Output (in the device memory) */
+                             cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF-batch update the matrix values (assuming the reordering,
+     pivoting and consequently the sparsity pattern of L and U did not change),
+     and zero out the remaining values. */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverRfBatchResetValues(/* Input (in the device memory) */
+                               int     batchSize,
+                               int     n,
+                               int     nnzA,
+                               int*    csrRowPtrA,
+                               int*    csrColIndA,
+                               double* csrValA_array[],
+                               int*    P,
+                               int*    Q,
+                               /* Output */
+                               cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF-batch analysis (for parallelism) */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverRfBatchAnalyze(cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF-batch re-factorization (for parallelism) */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverRfBatchRefactor(cusolverRfHandle_t handle);
+
+  /* CUSOLVERRF-batch (forward and backward triangular) solves */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverRfBatchSolve(/* Input (in the device memory) */
+                         cusolverRfHandle_t handle,
+                         int*               P,
+                         int*               Q,
+                         int                nrhs, // only nrhs=1 is supported
+                         double* Temp, // of size 2*batchSize*(n*nrhs)
+                         int     ldt,  // only ldt=n is supported
+                         /* Input/Output (in the device memory) */
+                         double* XF_array[],
+                         /* Input */
+                         int ldxf);
+
+  /* CUSOLVERRF-batch obtain the position of zero pivot */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverRfBatchZeroPivot(/* Input */
+                             cusolverRfHandle_t handle,
+                             /* Output (in the host memory) */
+                             int* position);
+
+  #if defined(__cplusplus)
+}
+  #endif /* __cplusplus */
+
+#endif /* CUSOLVERRF_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverSp.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverSp.h
new file mode 100644
index 0000000000000000000000000000000000000000..4222108cf93e78e1b055c06385fcf0c55b0ec057
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverSp.h
@@ -0,0 +1,923 @@
+/*
+ * Copyright 2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(CUSOLVERSP_H_)
+  #define CUSOLVERSP_H_
+
+  #include "cusparse.h"
+  #include "cublas_v2.h"
+  #include "cusolver_common.h"
+
+  #if defined(__cplusplus)
+extern "C" {
+  #endif /* __cplusplus */
+
+  struct cusolverSpContext;
+  typedef struct cusolverSpContext *cusolverSpHandle_t;
+
+  struct csrqrInfo;
+  typedef struct csrqrInfo *csrqrInfo_t;
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCreate(cusolverSpHandle_t *handle);
+  cusolverStatus_t CUSOLVERAPI cusolverSpDestroy(cusolverSpHandle_t handle);
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpSetStream(cusolverSpHandle_t handle, cudaStream_t streamId);
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpGetStream(cusolverSpHandle_t handle, cudaStream_t *streamId);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrissymHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrEndPtrA,
+    const int *              csrColIndA,
+    int *                    issym);
+
+  /* -------- GPU linear solver by LU factorization
+   *       solve A*x = b, A can be singular
+   * [ls] stands for linear solve
+   * [v] stands for vector
+   * [lu] stands for LU factorization
+   */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrlsvluHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const float *            b,
+    float                    tol,
+    int                      reorder,
+    float *                  x,
+    int *                    singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrlsvluHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const double *           b,
+    double                   tol,
+    int                      reorder,
+    double *                 x,
+    int *                    singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrlsvluHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuComplex *        b,
+    float                    tol,
+    int                      reorder,
+    cuComplex *              x,
+    int *                    singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrlsvluHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuDoubleComplex *  b,
+    double                   tol,
+    int                      reorder,
+    cuDoubleComplex *        x,
+    int *                    singularity);
+
+  /* -------- GPU linear solver by QR factorization
+   *       solve A*x = b, A can be singular
+   * [ls] stands for linear solve
+   * [v] stands for vector
+   * [qr] stands for QR factorization
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrlsvqr(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const float *            b,
+    float                    tol,
+    int                      reorder,
+    float *                  x,
+    int *                    singularity);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrlsvqr(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const double *           b,
+    double                   tol,
+    int                      reorder,
+    double *                 x,
+    int *                    singularity);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrlsvqr(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const cuComplex *        b,
+    float                    tol,
+    int                      reorder,
+    cuComplex *              x,
+    int *                    singularity);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrlsvqr(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const cuDoubleComplex *  b,
+    double                   tol,
+    int                      reorder,
+    cuDoubleComplex *        x,
+    int *                    singularity);
+
+  /* -------- CPU linear solver by QR factorization
+   *       solve A*x = b, A can be singular
+   * [ls] stands for linear solve
+   * [v] stands for vector
+   * [qr] stands for QR factorization
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrlsvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const float *            b,
+    float                    tol,
+    int                      reorder,
+    float *                  x,
+    int *                    singularity);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrlsvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const double *           b,
+    double                   tol,
+    int                      reorder,
+    double *                 x,
+    int *                    singularity);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrlsvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuComplex *        b,
+    float                    tol,
+    int                      reorder,
+    cuComplex *              x,
+    int *                    singularity);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrlsvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuDoubleComplex *  b,
+    double                   tol,
+    int                      reorder,
+    cuDoubleComplex *        x,
+    int *                    singularity);
+
+  /* -------- CPU linear solver by Cholesky factorization
+   *       solve A*x = b, A can be singular
+   * [ls] stands for linear solve
+   * [v] stands for vector
+   * [chol] stands for Cholesky factorization
+   *
+   * Only works for symmetric positive definite matrix.
+   * The upper part of A is ignored.
+   */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrlsvcholHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const float *            b,
+    float                    tol,
+    int                      reorder,
+    float *                  x,
+    int *                    singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrlsvcholHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const double *           b,
+    double                   tol,
+    int                      reorder,
+    double *                 x,
+    int *                    singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrlsvcholHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const cuComplex *        b,
+    float                    tol,
+    int                      reorder,
+    cuComplex *              x,
+    int *                    singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrlsvcholHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const cuDoubleComplex *  b,
+    double                   tol,
+    int                      reorder,
+    cuDoubleComplex *        x,
+    int *                    singularity);
+
+  /* -------- GPU linear solver by Cholesky factorization
+   *       solve A*x = b, A can be singular
+   * [ls] stands for linear solve
+   * [v] stands for vector
+   * [chol] stands for Cholesky factorization
+   *
+   * Only works for symmetric positive definite matrix.
+   * The upper part of A is ignored.
+   */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrlsvchol(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const float *            b,
+    float                    tol,
+    int                      reorder,
+    // output
+    float *x,
+    int *  singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrlsvchol(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const double *           b,
+    double                   tol,
+    int                      reorder,
+    // output
+    double *x,
+    int *   singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrlsvchol(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const cuComplex *        b,
+    float                    tol,
+    int                      reorder,
+    // output
+    cuComplex *x,
+    int *      singularity);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrlsvchol(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    const cuDoubleComplex *  b,
+    double                   tol,
+    int                      reorder,
+    // output
+    cuDoubleComplex *x,
+    int *            singularity);
+
+  /* ----------- CPU least square solver by QR factorization
+   *       solve min|b - A*x|
+   * [lsq] stands for least square
+   * [v] stands for vector
+   * [qr] stands for QR factorization
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrlsqvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const float *            b,
+    float                    tol,
+    int *                    rankA,
+    float *                  x,
+    int *                    p,
+    float *                  min_norm);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrlsqvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const double *           b,
+    double                   tol,
+    int *                    rankA,
+    double *                 x,
+    int *                    p,
+    double *                 min_norm);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrlsqvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuComplex *        b,
+    float                    tol,
+    int *                    rankA,
+    cuComplex *              x,
+    int *                    p,
+    float *                  min_norm);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrlsqvqrHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuDoubleComplex *  b,
+    double                   tol,
+    int *                    rankA,
+    cuDoubleComplex *        x,
+    int *                    p,
+    double *                 min_norm);
+
+  /* --------- CPU eigenvalue solver by shift inverse
+   *      solve A*x = lambda * x
+   *   where lambda is the eigenvalue nearest mu0.
+   * [eig] stands for eigenvalue solver
+   * [si] stands for shift-inverse
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsreigvsiHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    float                    mu0,
+    const float *            x0,
+    int                      maxite,
+    float                    tol,
+    float *                  mu,
+    float *                  x);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsreigvsiHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    double                   mu0,
+    const double *           x0,
+    int                      maxite,
+    double                   tol,
+    double *                 mu,
+    double *                 x);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsreigvsiHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuComplex                mu0,
+    const cuComplex *        x0,
+    int                      maxite,
+    float                    tol,
+    cuComplex *              mu,
+    cuComplex *              x);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsreigvsiHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuDoubleComplex          mu0,
+    const cuDoubleComplex *  x0,
+    int                      maxite,
+    double                   tol,
+    cuDoubleComplex *        mu,
+    cuDoubleComplex *        x);
+
+  /* --------- GPU eigenvalue solver by shift inverse
+   *      solve A*x = lambda * x
+   *   where lambda is the eigenvalue nearest mu0.
+   * [eig] stands for eigenvalue solver
+   * [si] stands for shift-inverse
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsreigvsi(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    float                    mu0,
+    const float *            x0,
+    int                      maxite,
+    float                    eps,
+    float *                  mu,
+    float *                  x);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsreigvsi(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    double                   mu0,
+    const double *           x0,
+    int                      maxite,
+    double                   eps,
+    double *                 mu,
+    double *                 x);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsreigvsi(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuComplex                mu0,
+    const cuComplex *        x0,
+    int                      maxite,
+    float                    eps,
+    cuComplex *              mu,
+    cuComplex *              x);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsreigvsi(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuDoubleComplex          mu0,
+    const cuDoubleComplex *  x0,
+    int                      maxite,
+    double                   eps,
+    cuDoubleComplex *        mu,
+    cuDoubleComplex *        x);
+
+  // ----------- enclosed eigenvalues
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsreigsHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuComplex                left_bottom_corner,
+    cuComplex                right_upper_corner,
+    int *                    num_eigs);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsreigsHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuDoubleComplex          left_bottom_corner,
+    cuDoubleComplex          right_upper_corner,
+    int *                    num_eigs);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsreigsHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuComplex                left_bottom_corner,
+    cuComplex                right_upper_corner,
+    int *                    num_eigs);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsreigsHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuDoubleComplex          left_bottom_corner,
+    cuDoubleComplex          right_upper_corner,
+    int *                    num_eigs);
+
+  /* --------- CPU symrcm
+   *   Symmetric reverse Cuthill McKee permutation
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrsymrcmHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    p);
+
+  /* --------- CPU symmdq
+   *   Symmetric minimum degree algorithm by quotient graph
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrsymmdqHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    p);
+
+  /* --------- CPU symmdq
+   *   Symmetric Approximate minimum degree algorithm by quotient graph
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrsymamdHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    p);
+
+  /* --------- CPU metis
+   *   symmetric reordering
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrmetisndHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const int64_t *          options,
+    int *                    p);
+
+  /* --------- CPU zfd
+   *  Zero free diagonal reordering
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrzfdHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    P,
+    int *                    numnz);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrzfdHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    P,
+    int *                    numnz);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrzfdHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    P,
+    int *                    numnz);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrzfdHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    int *                    P,
+    int *                    numnz);
+
+  /* --------- CPU permuation
+   *   P*A*Q^T
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrperm_bufferSizeHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const int *              p,
+    const int *              q,
+    size_t *                 bufferSizeInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrpermHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    int *                    csrRowPtrA,
+    int *                    csrColIndA,
+    const int *              p,
+    const int *              q,
+    int *                    map,
+    void *                   pBuffer);
+
+  /*
+   *  Low-level API: Batched QR
+   *
+   */
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCreateCsrqrInfo(csrqrInfo_t *info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDestroyCsrqrInfo(csrqrInfo_t info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrqrAnalysisBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfo_t              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrBufferInfoBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrBufferInfoBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrBufferInfoBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrBufferInfoBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrVal,
+    const int *              csrRowPtr,
+    const int *              csrColInd,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrsvBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const float *            b,
+    float *                  x,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    void *                   pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrsvBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const double *           b,
+    double *                 x,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    void *                   pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrsvBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuComplex *        b,
+    cuComplex *              x,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    void *                   pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrsvBatched(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnz,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    const cuDoubleComplex *  b,
+    cuDoubleComplex *        x,
+    int                      batchSize,
+    csrqrInfo_t              info,
+    void *                   pBuffer);
+
+  #if defined(__cplusplus)
+}
+  #endif /* __cplusplus */
+
+#endif // define CUSOLVERSP_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverSp_LOWLEVEL_PREVIEW.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverSp_LOWLEVEL_PREVIEW.h
new file mode 100644
index 0000000000000000000000000000000000000000..d9a52792abf1cc7594201bd5f4ddaefb600f6128
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolverSp_LOWLEVEL_PREVIEW.h
@@ -0,0 +1,1107 @@
+/*
+ * Copyright 2015 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(CUSOLVERSP_LOWLEVEL_PREVIEW_H_)
+  #define CUSOLVERSP_LOWLEVEL_PREVIEW_H_
+
+  #include "cusolverSp.h"
+
+  #if defined(__cplusplus)
+extern "C" {
+  #endif /* __cplusplus */
+
+  struct csrluInfoHost;
+  typedef struct csrluInfoHost *csrluInfoHost_t;
+
+  struct csrqrInfoHost;
+  typedef struct csrqrInfoHost *csrqrInfoHost_t;
+
+  struct csrcholInfoHost;
+  typedef struct csrcholInfoHost *csrcholInfoHost_t;
+
+  struct csrcholInfo;
+  typedef struct csrcholInfo *csrcholInfo_t;
+
+  /*
+   * Low level API for CPU LU
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpCreateCsrluInfoHost(csrluInfoHost_t *info);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpDestroyCsrluInfoHost(csrluInfoHost_t info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpXcsrluAnalysisHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrluBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrluBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrluBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrluBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrluFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    float                    pivot_threshold,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrluFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    double                   pivot_threshold,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrluFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    float                    pivot_threshold,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrluFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrluInfoHost_t          info,
+    double                   pivot_threshold,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrluZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrluInfoHost_t    info,
+    float              tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrluZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrluInfoHost_t    info,
+    double             tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrluZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrluInfoHost_t    info,
+    float              tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrluZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrluInfoHost_t    info,
+    double             tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrluSolveHost(
+    cusolverSpHandle_t handle,
+    int                n,
+    const float *      b,
+    float *            x,
+    csrluInfoHost_t    info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrluSolveHost(
+    cusolverSpHandle_t handle,
+    int                n,
+    const double *     b,
+    double *           x,
+    csrluInfoHost_t    info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrluSolveHost(
+    cusolverSpHandle_t handle,
+    int                n,
+    const cuComplex *  b,
+    cuComplex *        x,
+    csrluInfoHost_t    info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrluSolveHost(
+    cusolverSpHandle_t     handle,
+    int                    n,
+    const cuDoubleComplex *b,
+    cuDoubleComplex *      x,
+    csrluInfoHost_t        info,
+    void *                 pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpXcsrluNnzHost(
+    cusolverSpHandle_t handle,
+    int *              nnzLRef,
+    int *              nnzURef,
+    csrluInfoHost_t    info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrluExtractHost(
+    cusolverSpHandle_t       handle,
+    int *                    P,
+    int *                    Q,
+    const cusparseMatDescr_t descrL,
+    float *                  csrValL,
+    int *                    csrRowPtrL,
+    int *                    csrColIndL,
+    const cusparseMatDescr_t descrU,
+    float *                  csrValU,
+    int *                    csrRowPtrU,
+    int *                    csrColIndU,
+    csrluInfoHost_t          info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrluExtractHost(
+    cusolverSpHandle_t       handle,
+    int *                    P,
+    int *                    Q,
+    const cusparseMatDescr_t descrL,
+    double *                 csrValL,
+    int *                    csrRowPtrL,
+    int *                    csrColIndL,
+    const cusparseMatDescr_t descrU,
+    double *                 csrValU,
+    int *                    csrRowPtrU,
+    int *                    csrColIndU,
+    csrluInfoHost_t          info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrluExtractHost(
+    cusolverSpHandle_t       handle,
+    int *                    P,
+    int *                    Q,
+    const cusparseMatDescr_t descrL,
+    cuComplex *              csrValL,
+    int *                    csrRowPtrL,
+    int *                    csrColIndL,
+    const cusparseMatDescr_t descrU,
+    cuComplex *              csrValU,
+    int *                    csrRowPtrU,
+    int *                    csrColIndU,
+    csrluInfoHost_t          info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrluExtractHost(
+    cusolverSpHandle_t       handle,
+    int *                    P,
+    int *                    Q,
+    const cusparseMatDescr_t descrL,
+    cuDoubleComplex *        csrValL,
+    int *                    csrRowPtrL,
+    int *                    csrColIndL,
+    const cusparseMatDescr_t descrU,
+    cuDoubleComplex *        csrValU,
+    int *                    csrRowPtrU,
+    int *                    csrColIndU,
+    csrluInfoHost_t          info,
+    void *                   pBuffer);
+
+  /*
+   * Low level API for CPU QR
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpCreateCsrqrInfoHost(csrqrInfoHost_t *info);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpDestroyCsrqrInfoHost(csrqrInfoHost_t info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrqrAnalysisHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfoHost_t          info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfoHost_t          info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrSetupHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    float                    mu,
+    csrqrInfoHost_t          info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrSetupHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    double                   mu,
+    csrqrInfoHost_t          info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrSetupHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuComplex                mu,
+    csrqrInfoHost_t          info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrSetupHost(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuDoubleComplex          mu,
+    csrqrInfoHost_t          info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrFactorHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    float *            b,
+    float *            x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrFactorHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    double *           b,
+    double *           x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrFactorHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    cuComplex *        b,
+    cuComplex *        x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrFactorHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    cuDoubleComplex *  b,
+    cuDoubleComplex *  x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrqrInfoHost_t    info,
+    float              tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrqrInfoHost_t    info,
+    double             tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrqrInfoHost_t    info,
+    float              tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrqrInfoHost_t    info,
+    double             tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrSolveHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    float *            b,
+    float *            x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrSolveHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    double *           b,
+    double *           x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrSolveHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        b,
+    cuComplex *        x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrSolveHost(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  b,
+    cuDoubleComplex *  x,
+    csrqrInfoHost_t    info,
+    void *             pBuffer);
+
+  /*
+   * Low level API for GPU QR
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI cusolverSpXcsrqrAnalysis(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfo_t              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrqrInfo_t              info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrSetup(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    float                    mu,
+    csrqrInfo_t              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrSetup(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    double                   mu,
+    csrqrInfo_t              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrSetup(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuComplex                mu,
+    csrqrInfo_t              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrSetup(
+    cusolverSpHandle_t       handle,
+    int                      m,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    cuDoubleComplex          mu,
+    csrqrInfo_t              info);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrFactor(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    float *            b,
+    float *            x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrFactor(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    double *           b,
+    double *           x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrFactor(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    cuComplex *        b,
+    cuComplex *        x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrFactor(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    int                nnzA,
+    cuDoubleComplex *  b,
+    cuDoubleComplex *  x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrZeroPivot(
+    cusolverSpHandle_t handle,
+    csrqrInfo_t        info,
+    float              tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrZeroPivot(
+    cusolverSpHandle_t handle,
+    csrqrInfo_t        info,
+    double             tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrZeroPivot(
+    cusolverSpHandle_t handle,
+    csrqrInfo_t        info,
+    float              tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrZeroPivot(
+    cusolverSpHandle_t handle,
+    csrqrInfo_t        info,
+    double             tol,
+    int *              position);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpScsrqrSolve(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    float *            b,
+    float *            x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpDcsrqrSolve(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    double *           b,
+    double *           x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpCcsrqrSolve(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    cuComplex *        b,
+    cuComplex *        x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  cusolverStatus_t CUSOLVERAPI cusolverSpZcsrqrSolve(
+    cusolverSpHandle_t handle,
+    int                m,
+    int                n,
+    cuDoubleComplex *  b,
+    cuDoubleComplex *  x,
+    csrqrInfo_t        info,
+    void *             pBuffer);
+
+  /*
+   * Low level API for CPU Cholesky
+   *
+   */
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpCreateCsrcholInfoHost(csrcholInfoHost_t *info);
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverSpDestroyCsrcholInfoHost(csrcholInfoHost_t info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpXcsrcholAnalysisHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholBufferInfoHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholFactorHost(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfoHost_t        info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrcholInfoHost_t  info,
+    float              tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrcholInfoHost_t  info,
+    double             tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrcholInfoHost_t  info,
+    float              tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholZeroPivotHost(
+    cusolverSpHandle_t handle,
+    csrcholInfoHost_t  info,
+    double             tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholSolveHost(
+    cusolverSpHandle_t handle,
+    int                n,
+    const float *      b,
+    float *            x,
+    csrcholInfoHost_t  info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholSolveHost(
+    cusolverSpHandle_t handle,
+    int                n,
+    const double *     b,
+    double *           x,
+    csrcholInfoHost_t  info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholSolveHost(
+    cusolverSpHandle_t handle,
+    int                n,
+    const cuComplex *  b,
+    cuComplex *        x,
+    csrcholInfoHost_t  info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholSolveHost(
+    cusolverSpHandle_t     handle,
+    int                    n,
+    const cuDoubleComplex *b,
+    cuDoubleComplex *      x,
+    csrcholInfoHost_t      info,
+    void *                 pBuffer);
+
+  /*
+   * Low level API for GPU Cholesky
+   *
+   */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCreateCsrcholInfo(csrcholInfo_t *info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDestroyCsrcholInfo(csrcholInfo_t info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpXcsrcholAnalysis(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholBufferInfo(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    size_t *                 internalDataInBytes,
+    size_t *                 workspaceInBytes);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholFactor(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const float *            csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholFactor(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const double *           csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholFactor(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuComplex *        csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholFactor(
+    cusolverSpHandle_t       handle,
+    int                      n,
+    int                      nnzA,
+    const cusparseMatDescr_t descrA,
+    const cuDoubleComplex *  csrValA,
+    const int *              csrRowPtrA,
+    const int *              csrColIndA,
+    csrcholInfo_t            info,
+    void *                   pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholZeroPivot(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    float              tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholZeroPivot(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    double             tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholZeroPivot(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    float              tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholZeroPivot(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    double             tol,
+    int *              position);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholSolve(
+    cusolverSpHandle_t handle,
+    int                n,
+    const float *      b,
+    float *            x,
+    csrcholInfo_t      info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholSolve(
+    cusolverSpHandle_t handle,
+    int                n,
+    const double *     b,
+    double *           x,
+    csrcholInfo_t      info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholSolve(
+    cusolverSpHandle_t handle,
+    int                n,
+    const cuComplex *  b,
+    cuComplex *        x,
+    csrcholInfo_t      info,
+    void *             pBuffer);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholSolve(
+    cusolverSpHandle_t     handle,
+    int                    n,
+    const cuDoubleComplex *b,
+    cuDoubleComplex *      x,
+    csrcholInfo_t          info,
+    void *                 pBuffer);
+
+  /*
+   * "diag" is a device array of size N.
+   * cusolverSp<t>csrcholDiag returns diag(L) to "diag" where A(P,P) = L*L**T
+   * "diag" can estimate det(A) because det(A(P,P)) = det(A) = det(L)^2 if A =
+   * L*L**T.
+   *
+   * cusolverSp<t>csrcholDiag must be called after cusolverSp<t>csrcholFactor.
+   * otherwise "diag" is wrong.
+   */
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpScsrcholDiag(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    float *            diag);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpDcsrcholDiag(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    double *           diag);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpCcsrcholDiag(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    float *            diag);
+
+  CUSOLVER_DEPRECATED(cuDSS) cusolverStatus_t CUSOLVERAPI cusolverSpZcsrcholDiag(
+    cusolverSpHandle_t handle,
+    csrcholInfo_t      info,
+    double *           diag);
+
+  #if defined(__cplusplus)
+}
+  #endif /* __cplusplus */
+
+#endif // CUSOLVERSP_LOWLEVEL_PREVIEW_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolver_common.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolver_common.h
new file mode 100644
index 0000000000000000000000000000000000000000..82f8ad5294795dc6c851d76861f809c93988f631
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/include/cusolver_common.h
@@ -0,0 +1,261 @@
+/*
+ * Copyright 2014 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(CUSOLVER_COMMON_H_)
+  #define CUSOLVER_COMMON_H_
+
+  #include "library_types.h"
+
+  #ifndef CUSOLVERAPI
+    #ifdef _WIN32
+      #define CUSOLVERAPI __stdcall
+    #else
+      #define CUSOLVERAPI
+    #endif
+  #endif
+
+  #if defined(_MSC_VER)
+typedef __int64 int64_t;
+  #else
+    #include <inttypes.h>
+  #endif
+
+typedef int cusolver_int_t;
+
+  #define CUSOLVER_VER_MAJOR 11
+  #define CUSOLVER_VER_MINOR 7
+  #define CUSOLVER_VER_PATCH 3
+  #define CUSOLVER_VER_BUILD 90
+  #define CUSOLVER_VERSION                                                     \
+    (CUSOLVER_VER_MAJOR * 1000 + CUSOLVER_VER_MINOR * 100 + CUSOLVER_VER_PATCH)
+
+//------------------------------------------------------------------------------
+
+  #if !defined(_MSC_VER)
+    #define CUSOLVER_CPP_VERSION __cplusplus
+  #elif _MSC_FULL_VER >= 190024210 // Visual Studio 2015 Update 3
+    #define CUSOLVER_CPP_VERSION _MSVC_LANG
+  #else
+    #define CUSOLVER_CPP_VERSION 0
+  #endif
+
+//------------------------------------------------------------------------------
+
+  #if !defined(DISABLE_CUSOLVER_DEPRECATED)
+
+    #if CUSOLVER_CPP_VERSION >= 201402L
+
+      #define CUSOLVER_DEPRECATED(new_func)                                    \
+        [[deprecated("please use " #new_func " instead")]]
+
+    #elif defined(_MSC_VER)
+
+      #define CUSOLVER_DEPRECATED(new_func)                                    \
+        __declspec(deprecated("please use " #new_func " instead"))
+
+    #elif defined(__INTEL_COMPILER) || defined(__clang__) ||                   \
+      (defined(__GNUC__) &&                                                    \
+       (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 5)))
+
+      #define CUSOLVER_DEPRECATED(new_func)                                    \
+        __attribute__((deprecated("please use " #new_func " instead")))
+
+    #elif defined(__GNUC__) || defined(__xlc__)
+
+      #define CUSOLVER_DEPRECATED(new_func) __attribute__((deprecated))
+
+    #else
+
+      #define CUSOLVER_DEPRECATED(new_func)
+
+    #endif // defined(__cplusplus) && __cplusplus >= 201402L
+  //------------------------------------------------------------------------------
+
+    #if CUSOLVER_CPP_VERSION >= 201703L
+
+      #define CUSOLVER_DEPRECATED_ENUM(new_enum)                               \
+        [[deprecated("please use " #new_enum " instead")]]
+
+    #elif defined(__clang__) ||                                                \
+      (defined(__GNUC__) && __GNUC__ >= 6 && !defined(__PGI))
+
+      #define CUSOLVER_DEPRECATED_ENUM(new_enum)                               \
+        __attribute__((deprecated("please use " #new_enum " instead")))
+
+    #else
+
+      #define CUSOLVER_DEPRECATED_ENUM(new_enum)
+
+    #endif // defined(__cplusplus) && __cplusplus >= 201402L
+
+  #else // defined(DISABLE_CUSOLVER_DEPRECATED)
+
+    #define CUSOLVER_DEPRECATED(new_func)
+    #define CUSOLVER_DEPRECATED_ENUM(new_enum)
+
+  #endif // !defined(DISABLE_CUSOLVER_DEPRECATED)
+
+  #undef CUSOLVER_CPP_VERSION
+
+  #if defined(__cplusplus)
+extern "C" {
+  #endif /* __cplusplus */
+
+  typedef enum {
+    CUSOLVER_STATUS_SUCCESS = 0,
+    CUSOLVER_STATUS_NOT_INITIALIZED = 1,
+    CUSOLVER_STATUS_ALLOC_FAILED = 2,
+    CUSOLVER_STATUS_INVALID_VALUE = 3,
+    CUSOLVER_STATUS_ARCH_MISMATCH = 4,
+    CUSOLVER_STATUS_MAPPING_ERROR = 5,
+    CUSOLVER_STATUS_EXECUTION_FAILED = 6,
+    CUSOLVER_STATUS_INTERNAL_ERROR = 7,
+    CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED = 8,
+    CUSOLVER_STATUS_NOT_SUPPORTED = 9,
+    CUSOLVER_STATUS_ZERO_PIVOT = 10,
+    CUSOLVER_STATUS_INVALID_LICENSE = 11,
+    CUSOLVER_STATUS_IRS_PARAMS_NOT_INITIALIZED = 12,
+    CUSOLVER_STATUS_IRS_PARAMS_INVALID = 13,
+    CUSOLVER_STATUS_IRS_PARAMS_INVALID_PREC = 14,
+    CUSOLVER_STATUS_IRS_PARAMS_INVALID_REFINE = 15,
+    CUSOLVER_STATUS_IRS_PARAMS_INVALID_MAXITER = 16,
+    CUSOLVER_STATUS_IRS_INTERNAL_ERROR = 20,
+    CUSOLVER_STATUS_IRS_NOT_SUPPORTED = 21,
+    CUSOLVER_STATUS_IRS_OUT_OF_RANGE = 22,
+    CUSOLVER_STATUS_IRS_NRHS_NOT_SUPPORTED_FOR_REFINE_GMRES = 23,
+    CUSOLVER_STATUS_IRS_INFOS_NOT_INITIALIZED = 25,
+    CUSOLVER_STATUS_IRS_INFOS_NOT_DESTROYED = 26,
+    CUSOLVER_STATUS_IRS_MATRIX_SINGULAR = 30,
+    CUSOLVER_STATUS_INVALID_WORKSPACE = 31
+  } cusolverStatus_t;
+
+  typedef enum {
+    CUSOLVER_EIG_TYPE_1 = 1,
+    CUSOLVER_EIG_TYPE_2 = 2,
+    CUSOLVER_EIG_TYPE_3 = 3
+  } cusolverEigType_t;
+
+  typedef enum {
+    CUSOLVER_EIG_MODE_NOVECTOR = 0,
+    CUSOLVER_EIG_MODE_VECTOR = 1
+  } cusolverEigMode_t;
+
+  typedef enum {
+    CUSOLVER_EIG_RANGE_ALL = 1001,
+    CUSOLVER_EIG_RANGE_I = 1002,
+    CUSOLVER_EIG_RANGE_V = 1003,
+  } cusolverEigRange_t;
+
+  typedef enum {
+    CUSOLVER_INF_NORM = 104,
+    CUSOLVER_MAX_NORM = 105,
+    CUSOLVER_ONE_NORM = 106,
+    CUSOLVER_FRO_NORM = 107,
+  } cusolverNorm_t;
+
+  typedef enum {
+    CUSOLVER_IRS_REFINE_NOT_SET = 1100,
+    CUSOLVER_IRS_REFINE_NONE = 1101,
+    CUSOLVER_IRS_REFINE_CLASSICAL = 1102,
+    CUSOLVER_IRS_REFINE_CLASSICAL_GMRES = 1103,
+    CUSOLVER_IRS_REFINE_GMRES = 1104,
+    CUSOLVER_IRS_REFINE_GMRES_GMRES = 1105,
+    CUSOLVER_IRS_REFINE_GMRES_NOPCOND = 1106,
+
+    CUSOLVER_PREC_DD = 1150,
+    CUSOLVER_PREC_SS = 1151,
+    CUSOLVER_PREC_SHT = 1152,
+
+  } cusolverIRSRefinement_t;
+
+  typedef enum {
+    CUSOLVER_R_8I = 1201,
+    CUSOLVER_R_8U = 1202,
+    CUSOLVER_R_64F = 1203,
+    CUSOLVER_R_32F = 1204,
+    CUSOLVER_R_16F = 1205,
+    CUSOLVER_R_16BF = 1206,
+    CUSOLVER_R_TF32 = 1207,
+    CUSOLVER_R_AP = 1208,
+    CUSOLVER_C_8I = 1211,
+    CUSOLVER_C_8U = 1212,
+    CUSOLVER_C_64F = 1213,
+    CUSOLVER_C_32F = 1214,
+    CUSOLVER_C_16F = 1215,
+    CUSOLVER_C_16BF = 1216,
+    CUSOLVER_C_TF32 = 1217,
+    CUSOLVER_C_AP = 1218,
+  } cusolverPrecType_t;
+
+  typedef enum {
+    CUSOLVER_ALG_0 = 0, /* default algorithm */
+    CUSOLVER_ALG_1 = 1,
+    CUSOLVER_ALG_2 = 2
+  } cusolverAlgMode_t;
+
+  typedef enum {
+    CUBLAS_STOREV_COLUMNWISE = 0,
+    CUBLAS_STOREV_ROWWISE = 1
+  } cusolverStorevMode_t;
+
+  typedef enum {
+    CUBLAS_DIRECT_FORWARD = 0,
+    CUBLAS_DIRECT_BACKWARD = 1
+  } cusolverDirectMode_t;
+
+  cusolverStatus_t CUSOLVERAPI
+    cusolverGetProperty(libraryPropertyType type, int *value);
+
+  cusolverStatus_t CUSOLVERAPI cusolverGetVersion(int *version);
+
+  #if defined(__cplusplus)
+}
+  #endif /* __cplusplus */
+
+#endif // CUSOLVER_COMMON_H_
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/lib/libcusolverMg.so.11 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/lib/libcusolverMg.so.11
new file mode 100644
index 0000000000000000000000000000000000000000..52ddebdef58cb26fbb7a71ef1e3e06770738d908
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusolver/lib/libcusolverMg.so.11
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d947654631c536e9099abcc9fe551ece97d0ffeb8fbab142189ea2c85bc55e8e
+size 161493576
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/cusparse.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/cusparse.h
new file mode 100644
index 0000000000000000000000000000000000000000..1575d7417c99ceab22c09ab107babe84f11adaed
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/cusparse.h
@@ -0,0 +1,6139 @@
+/*
+ * Copyright 1993-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+#if !defined(CUSPARSE_H_)
+#define CUSPARSE_H_
+
+#include <cuComplex.h>        // cuComplex
+#include <cuda_runtime_api.h> // cudaStream_t
+#include <library_types.h>    // CUDA_R_32F
+#include <stdint.h>           // int64_t
+#include <stdio.h>            // FILE*
+
+#if defined(__cplusplus)
+#   include <cuda_fp16.h>     // __half
+#endif // defined(__cplusplus)
+
+//##############################################################################
+//# CUSPARSE VERSION INFORMATION
+//##############################################################################
+
+#define CUSPARSE_VER_MAJOR 12
+#define CUSPARSE_VER_MINOR 5
+#define CUSPARSE_VER_PATCH 8
+#define CUSPARSE_VER_BUILD 93
+#define CUSPARSE_VERSION (CUSPARSE_VER_MAJOR * 1000 + \
+                          CUSPARSE_VER_MINOR *  100 + \
+                          CUSPARSE_VER_PATCH)
+
+// #############################################################################
+// # BASIC MACROS
+// #############################################################################
+
+#if !defined(CUSPARSEAPI)
+#    if defined(_WIN32)
+#        define CUSPARSEAPI __stdcall
+#    else
+#        define CUSPARSEAPI
+#    endif
+#endif
+
+//------------------------------------------------------------------------------
+
+#if !defined(_MSC_VER)
+#   define CUSPARSE_CPP_VERSION __cplusplus
+#elif _MSC_FULL_VER >= 190024210 // Visual Studio 2015 Update 3
+#   define CUSPARSE_CPP_VERSION _MSVC_LANG
+#else
+#   define CUSPARSE_CPP_VERSION 0
+#endif
+
+// #############################################################################
+// # CUSPARSE_DEPRECATED MACRO
+// #############################################################################
+
+#if !defined(DISABLE_CUSPARSE_DEPRECATED)
+
+#   if CUSPARSE_CPP_VERSION >= 201402L
+
+#       define CUSPARSE_DEPRECATED_REPLACE_WITH(new_func)                      \
+            [[deprecated("please use " #new_func " instead")]]
+
+#       define CUSPARSE_DEPRECATED                                             \
+         [[deprecated("The routine will be removed in the next major release")]]
+
+#       define CUSPARSE_DEPRECATED_TYPE                                        \
+         [[deprecated("The type will be removed in the next major release")]]
+
+#       define CUSPARSE_DEPRECATED_TYPE_MSVC
+
+#   elif defined(_MSC_VER)
+
+#       define CUSPARSE_DEPRECATED_REPLACE_WITH(new_func)                      \
+            __declspec(deprecated("please use " #new_func " instead"))
+
+#       define CUSPARSE_DEPRECATED                                             \
+            __declspec(deprecated(                                             \
+                "The routine will be removed in the next major release"))
+
+#       define CUSPARSE_DEPRECATED_TYPE
+
+#       define CUSPARSE_DEPRECATED_TYPE_MSVC
+            __declspec(deprecated(                                             \
+                "The type will be removed in the next major release"))
+
+#   elif defined(__INTEL_COMPILER) || defined(__clang__) ||                    \
+         (defined(__GNUC__) &&                                                 \
+          (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 5)))
+
+#       define CUSPARSE_DEPRECATED_REPLACE_WITH(new_func)                      \
+            __attribute__((deprecated("please use " #new_func " instead")))
+
+#       define CUSPARSE_DEPRECATED                                             \
+            __attribute__((deprecated(                                         \
+                "The routine will be removed in the next major release")))
+
+#       define CUSPARSE_DEPRECATED_TYPE                                        \
+            __attribute__((deprecated(                                         \
+                "The type will be removed in the next major release")))
+
+#       define CUSPARSE_DEPRECATED_TYPE_MSVC
+
+#   elif defined(__GNUC__)
+
+#       define CUSPARSE_DEPRECATED_REPLACE_WITH(new_func)                      \
+            __attribute__((deprecated))
+
+#       define CUSPARSE_DEPRECATED      __attribute__((deprecated))
+#       define CUSPARSE_DEPRECATED_TYPE __attribute__((deprecated))
+#       define CUSPARSE_DEPRECATED_TYPE_MSVC
+
+#   else
+
+#       define CUSPARSE_DEPRECATED_REPLACE_WITH(new_func)
+#       define CUSPARSE_DEPRECATED
+#       define CUSPARSE_DEPRECATED_TYPE
+#       define CUSPARSE_DEPRECATED_TYPE_MSVC
+
+#   endif // defined(__cplusplus) && __cplusplus >= 201402L
+//------------------------------------------------------------------------------
+
+#   if CUSPARSE_CPP_VERSION >= 201703L
+
+#       define CUSPARSE_DEPRECATED_ENUM_REPLACE_WITH(new_enum)                 \
+            [[deprecated("please use " #new_enum " instead")]]
+
+#       define CUSPARSE_DEPRECATED_ENUM                                        \
+            [[deprecated("The enum will be removed in the next major release")]]
+
+#   elif defined(__clang__) ||                                                 \
+         (defined(__GNUC__) && __GNUC__ >= 6 && !defined(__PGI))
+
+#       define CUSPARSE_DEPRECATED_ENUM_REPLACE_WITH(new_enum)                 \
+            __attribute__((deprecated("please use " #new_enum " instead")))
+
+#       define CUSPARSE_DEPRECATED_ENUM                                        \
+            __attribute__((deprecated(                                         \
+                "The enum will be removed in the next major release")))
+
+#   else
+
+#       define CUSPARSE_DEPRECATED_ENUM_REPLACE_WITH(new_enum)
+#       define CUSPARSE_DEPRECATED_ENUM
+
+#   endif // defined(__cplusplus) && __cplusplus >= 201402L
+
+#else // defined(DISABLE_CUSPARSE_DEPRECATED)
+
+#   define CUSPARSE_DEPRECATED_REPLACE_WITH(new_func)
+#   define CUSPARSE_DEPRECATED
+#   define CUSPARSE_DEPRECATED_TYPE
+#   define CUSPARSE_DEPRECATED_TYPE_MSVC
+#   define CUSPARSE_DEPRECATED_ENUM_REPLACE_WITH(new_enum)
+#   define CUSPARSE_DEPRECATED_ENUM
+
+#endif // !defined(DISABLE_CUSPARSE_DEPRECATED)
+
+#undef CUSPARSE_CPP_VERSION
+
+//------------------------------------------------------------------------------
+
+#if defined(__cplusplus)
+extern "C" {
+#endif // defined(__cplusplus)
+
+//##############################################################################
+//# OPAQUE DATA STRUCTURES
+//##############################################################################
+
+struct cusparseContext;
+typedef struct cusparseContext* cusparseHandle_t;
+
+struct cusparseMatDescr;
+typedef struct cusparseMatDescr* cusparseMatDescr_t;
+
+struct bsrsv2Info;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct bsrsv2Info* bsrsv2Info_t CUSPARSE_DEPRECATED_TYPE;
+
+struct bsrsm2Info;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct bsrsm2Info* bsrsm2Info_t CUSPARSE_DEPRECATED_TYPE;
+
+struct csric02Info;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct csric02Info* csric02Info_t CUSPARSE_DEPRECATED_TYPE;
+
+struct bsric02Info;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct bsric02Info* bsric02Info_t CUSPARSE_DEPRECATED_TYPE;
+
+struct csrilu02Info;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct csrilu02Info* csrilu02Info_t CUSPARSE_DEPRECATED_TYPE;
+
+struct bsrilu02Info;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct bsrilu02Info* bsrilu02Info_t CUSPARSE_DEPRECATED_TYPE;
+
+struct csru2csrInfo;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct csru2csrInfo* csru2csrInfo_t CUSPARSE_DEPRECATED_TYPE;
+
+struct cusparseColorInfo;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct cusparseColorInfo* cusparseColorInfo_t CUSPARSE_DEPRECATED_TYPE;
+
+struct pruneInfo;
+typedef CUSPARSE_DEPRECATED_TYPE_MSVC
+struct pruneInfo* pruneInfo_t CUSPARSE_DEPRECATED_TYPE;
+
+//##############################################################################
+//# ENUMERATORS
+//##############################################################################
+
+typedef enum {
+    CUSPARSE_STATUS_SUCCESS                   = 0,
+    CUSPARSE_STATUS_NOT_INITIALIZED           = 1,
+    CUSPARSE_STATUS_ALLOC_FAILED              = 2,
+    CUSPARSE_STATUS_INVALID_VALUE             = 3,
+    CUSPARSE_STATUS_ARCH_MISMATCH             = 4,
+    CUSPARSE_STATUS_MAPPING_ERROR             = 5,
+    CUSPARSE_STATUS_EXECUTION_FAILED          = 6,
+    CUSPARSE_STATUS_INTERNAL_ERROR            = 7,
+    CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED = 8,
+    CUSPARSE_STATUS_ZERO_PIVOT                = 9,
+    CUSPARSE_STATUS_NOT_SUPPORTED             = 10,
+    CUSPARSE_STATUS_INSUFFICIENT_RESOURCES    = 11
+} cusparseStatus_t;
+
+typedef enum {
+    CUSPARSE_POINTER_MODE_HOST   = 0,
+    CUSPARSE_POINTER_MODE_DEVICE = 1
+} cusparsePointerMode_t;
+
+typedef enum {
+    CUSPARSE_ACTION_SYMBOLIC = 0,
+    CUSPARSE_ACTION_NUMERIC  = 1
+} cusparseAction_t;
+
+typedef enum {
+    CUSPARSE_MATRIX_TYPE_GENERAL    = 0,
+    CUSPARSE_MATRIX_TYPE_SYMMETRIC  = 1,
+    CUSPARSE_MATRIX_TYPE_HERMITIAN  = 2,
+    CUSPARSE_MATRIX_TYPE_TRIANGULAR = 3
+} cusparseMatrixType_t;
+
+typedef enum {
+    CUSPARSE_FILL_MODE_LOWER = 0,
+    CUSPARSE_FILL_MODE_UPPER = 1
+} cusparseFillMode_t;
+
+typedef enum {
+    CUSPARSE_DIAG_TYPE_NON_UNIT = 0,
+    CUSPARSE_DIAG_TYPE_UNIT     = 1
+} cusparseDiagType_t;
+
+typedef enum {
+    CUSPARSE_INDEX_BASE_ZERO = 0,
+    CUSPARSE_INDEX_BASE_ONE  = 1
+} cusparseIndexBase_t;
+
+typedef enum {
+    CUSPARSE_OPERATION_NON_TRANSPOSE       = 0,
+    CUSPARSE_OPERATION_TRANSPOSE           = 1,
+    CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE = 2
+} cusparseOperation_t;
+
+typedef enum {
+    CUSPARSE_DIRECTION_ROW    = 0,
+    CUSPARSE_DIRECTION_COLUMN = 1
+} cusparseDirection_t;
+
+typedef enum {
+    CUSPARSE_SOLVE_POLICY_NO_LEVEL = 0,
+    CUSPARSE_SOLVE_POLICY_USE_LEVEL = 1
+} cusparseSolvePolicy_t CUSPARSE_DEPRECATED_TYPE;
+
+typedef enum {
+    CUSPARSE_COLOR_ALG0 = 0, // default
+    CUSPARSE_COLOR_ALG1 = 1
+} cusparseColorAlg_t CUSPARSE_DEPRECATED_TYPE;
+
+//##############################################################################
+//# INITIALIZATION AND MANAGEMENT ROUTINES
+//##############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreate(cusparseHandle_t* handle);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroy(cusparseHandle_t handle);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseGetVersion(cusparseHandle_t handle,
+                   int*             version);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseGetProperty(libraryPropertyType type,
+                    int*                value);
+
+const char* CUSPARSEAPI
+cusparseGetErrorName(cusparseStatus_t status);
+
+const char* CUSPARSEAPI
+cusparseGetErrorString(cusparseStatus_t status);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSetStream(cusparseHandle_t handle,
+                  cudaStream_t     streamId);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseGetStream(cusparseHandle_t handle,
+                  cudaStream_t*    streamId);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseGetPointerMode(cusparseHandle_t       handle,
+                       cusparsePointerMode_t* mode);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSetPointerMode(cusparseHandle_t      handle,
+                       cusparsePointerMode_t mode);
+
+//##############################################################################
+//# LOGGING APIs
+//##############################################################################
+
+typedef void (*cusparseLoggerCallback_t)(int         logLevel,
+                                         const char* functionName,
+                                         const char* message);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseLoggerSetCallback(cusparseLoggerCallback_t callback);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseLoggerSetFile(FILE* file);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseLoggerOpenFile(const char* logFile);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseLoggerSetLevel(int level);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseLoggerSetMask(int mask);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseLoggerForceDisable(void);
+
+//##############################################################################
+//# HELPER ROUTINES
+//##############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateMatDescr(cusparseMatDescr_t* descrA);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyMatDescr(cusparseMatDescr_t descrA);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSetMatType(cusparseMatDescr_t   descrA,
+                   cusparseMatrixType_t type);
+
+cusparseMatrixType_t CUSPARSEAPI
+cusparseGetMatType(const cusparseMatDescr_t descrA);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSetMatFillMode(cusparseMatDescr_t descrA,
+                       cusparseFillMode_t fillMode);
+
+cusparseFillMode_t CUSPARSEAPI
+cusparseGetMatFillMode(const cusparseMatDescr_t descrA);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSetMatDiagType(cusparseMatDescr_t descrA,
+                       cusparseDiagType_t diagType);
+
+cusparseDiagType_t CUSPARSEAPI
+cusparseGetMatDiagType(const cusparseMatDescr_t descrA);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSetMatIndexBase(cusparseMatDescr_t  descrA,
+                        cusparseIndexBase_t base);
+
+cusparseIndexBase_t CUSPARSEAPI
+cusparseGetMatIndexBase(const cusparseMatDescr_t descrA);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateCsric02Info(csric02Info_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyCsric02Info(csric02Info_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateBsric02Info(bsric02Info_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyBsric02Info(bsric02Info_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateCsrilu02Info(csrilu02Info_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyCsrilu02Info(csrilu02Info_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateBsrilu02Info(bsrilu02Info_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyBsrilu02Info(bsrilu02Info_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateBsrsv2Info(bsrsv2Info_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyBsrsv2Info(bsrsv2Info_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateBsrsm2Info(bsrsm2Info_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyBsrsm2Info(bsrsm2Info_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateCsru2csrInfo(csru2csrInfo_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyCsru2csrInfo(csru2csrInfo_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateColorInfo(cusparseColorInfo_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyColorInfo(cusparseColorInfo_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreatePruneInfo(pruneInfo_t* info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyPruneInfo(pruneInfo_t info);
+
+//##############################################################################
+//# SPARSE LEVEL 2 ROUTINES
+//##############################################################################
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSgemvi(cusparseHandle_t    handle,
+               cusparseOperation_t transA,
+               int                 m,
+               int                 n,
+               const float*        alpha,
+               const float*        A,
+               int                 lda,
+               int                 nnz,
+               const float*        xVal,
+               const int*          xInd,
+               const float*        beta,
+               float*              y,
+               cusparseIndexBase_t idxBase,
+               void*               pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSgemvi_bufferSize(cusparseHandle_t    handle,
+                          cusparseOperation_t transA,
+                          int                 m,
+                          int                 n,
+                          int                 nnz,
+                          int*                pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDgemvi(cusparseHandle_t    handle,
+               cusparseOperation_t transA,
+               int                 m,
+               int                 n,
+               const double*       alpha,
+               const double*       A,
+               int                 lda,
+               int                 nnz,
+               const double*       xVal,
+               const int*          xInd,
+               const double*       beta,
+               double*             y,
+               cusparseIndexBase_t idxBase,
+               void*               pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDgemvi_bufferSize(cusparseHandle_t    handle,
+                          cusparseOperation_t transA,
+                          int                 m,
+                          int                 n,
+                          int                 nnz,
+                          int*                pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCgemvi(cusparseHandle_t    handle,
+               cusparseOperation_t transA,
+               int                 m,
+               int                 n,
+               const cuComplex*    alpha,
+               const cuComplex*    A,
+               int                 lda,
+               int                 nnz,
+               const cuComplex*    xVal,
+               const int*          xInd,
+               const cuComplex*    beta,
+               cuComplex*          y,
+               cusparseIndexBase_t idxBase,
+               void*               pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCgemvi_bufferSize(cusparseHandle_t    handle,
+                          cusparseOperation_t transA,
+                          int                 m,
+                          int                 n,
+                          int                 nnz,
+                          int*                pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZgemvi(cusparseHandle_t       handle,
+               cusparseOperation_t    transA,
+               int                    m,
+               int                    n,
+               const cuDoubleComplex* alpha,
+               const cuDoubleComplex* A,
+               int                    lda,
+               int                    nnz,
+               const cuDoubleComplex* xVal,
+               const int*             xInd,
+               const cuDoubleComplex* beta,
+               cuDoubleComplex*       y,
+               cusparseIndexBase_t    idxBase,
+               void*                  pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZgemvi_bufferSize(cusparseHandle_t    handle,
+                          cusparseOperation_t transA,
+                          int                 m,
+                          int                 n,
+                          int                 nnz,
+                          int*                pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrmv(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               int                      mb,
+               int                      nb,
+               int                      nnzb,
+               const float*             alpha,
+               const cusparseMatDescr_t descrA,
+               const float*             bsrSortedValA,
+               const int*               bsrSortedRowPtrA,
+               const int*               bsrSortedColIndA,
+               int                      blockDim,
+               const float*             x,
+               const float*             beta,
+               float*                   y);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrmv(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               int                      mb,
+               int                      nb,
+               int                      nnzb,
+               const double*            alpha,
+               const cusparseMatDescr_t descrA,
+               const double*            bsrSortedValA,
+               const int*               bsrSortedRowPtrA,
+               const int*               bsrSortedColIndA,
+               int                      blockDim,
+               const double*            x,
+               const double*            beta,
+               double*                  y);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrmv(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               int                      mb,
+               int                      nb,
+               int                      nnzb,
+               const cuComplex*         alpha,
+               const cusparseMatDescr_t descrA,
+               const cuComplex*         bsrSortedValA,
+               const int*               bsrSortedRowPtrA,
+               const int*               bsrSortedColIndA,
+               int                      blockDim,
+               const cuComplex*         x,
+               const cuComplex*         beta,
+               cuComplex*               y);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrmv(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               int                      mb,
+               int                      nb,
+               int                      nnzb,
+               const cuDoubleComplex*   alpha,
+               const cusparseMatDescr_t descrA,
+               const cuDoubleComplex*   bsrSortedValA,
+               const int*               bsrSortedRowPtrA,
+               const int*               bsrSortedColIndA,
+               int                      blockDim,
+               const cuDoubleComplex*   x,
+               const cuDoubleComplex*   beta,
+               cuDoubleComplex*         y);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrxmv(cusparseHandle_t         handle,
+                cusparseDirection_t      dirA,
+                cusparseOperation_t      transA,
+                int                      sizeOfMask,
+                int                      mb,
+                int                      nb,
+                int                      nnzb,
+                const float*             alpha,
+                const cusparseMatDescr_t descrA,
+                const float*             bsrSortedValA,
+                const int*               bsrSortedMaskPtrA,
+                const int*               bsrSortedRowPtrA,
+                const int*               bsrSortedEndPtrA,
+                const int*               bsrSortedColIndA,
+                int                      blockDim,
+                const float*             x,
+                const float*             beta,
+                float*                   y);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrxmv(cusparseHandle_t         handle,
+                cusparseDirection_t      dirA,
+                cusparseOperation_t      transA,
+                int                      sizeOfMask,
+                int                      mb,
+                int                      nb,
+                int                      nnzb,
+                const double*            alpha,
+                const cusparseMatDescr_t descrA,
+                const double*            bsrSortedValA,
+                const int*               bsrSortedMaskPtrA,
+                const int*               bsrSortedRowPtrA,
+                const int*               bsrSortedEndPtrA,
+                const int*               bsrSortedColIndA,
+                int                      blockDim,
+                const double*            x,
+                const double*            beta,
+                double*                  y);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrxmv(cusparseHandle_t         handle,
+                cusparseDirection_t      dirA,
+                cusparseOperation_t      transA,
+                int                      sizeOfMask,
+                int                      mb,
+                int                      nb,
+                int                      nnzb,
+                const cuComplex*         alpha,
+                const cusparseMatDescr_t descrA,
+                const cuComplex*         bsrSortedValA,
+                const int*               bsrSortedMaskPtrA,
+                const int*               bsrSortedRowPtrA,
+                const int*               bsrSortedEndPtrA,
+                const int*               bsrSortedColIndA,
+                int                      blockDim,
+                const cuComplex*         x,
+                const cuComplex*         beta,
+                cuComplex*               y);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrxmv(cusparseHandle_t         handle,
+                cusparseDirection_t      dirA,
+                cusparseOperation_t      transA,
+                int                      sizeOfMask,
+                int                      mb,
+                int                      nb,
+                int                      nnzb,
+                const cuDoubleComplex*   alpha,
+                const cusparseMatDescr_t descrA,
+                const cuDoubleComplex*   bsrSortedValA,
+                const int*               bsrSortedMaskPtrA,
+                const int*               bsrSortedRowPtrA,
+                const int*               bsrSortedEndPtrA,
+                const int*               bsrSortedColIndA,
+                int                      blockDim,
+                const cuDoubleComplex*   x,
+                const cuDoubleComplex*   beta,
+                cuDoubleComplex*         y);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXbsrsv2_zeroPivot(cusparseHandle_t handle,
+                          bsrsv2Info_t     info,
+                          int*             position);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsv2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           float*                   bsrSortedValA,
+                           const int*               bsrSortedRowPtrA,
+                           const int*               bsrSortedColIndA,
+                           int                      blockDim,
+                           bsrsv2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsv2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           double*                  bsrSortedValA,
+                           const int*               bsrSortedRowPtrA,
+                           const int*               bsrSortedColIndA,
+                           int                      blockDim,
+                           bsrsv2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsv2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           cuComplex*               bsrSortedValA,
+                           const int*               bsrSortedRowPtrA,
+                           const int*               bsrSortedColIndA,
+                           int                      blockDim,
+                           bsrsv2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsv2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           cuDoubleComplex*         bsrSortedValA,
+                           const int*               bsrSortedRowPtrA,
+                           const int*               bsrSortedColIndA,
+                           int                      blockDim,
+                           bsrsv2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsv2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              int                      mb,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              float*                   bsrSortedValA,
+                              const int*               bsrSortedRowPtrA,
+                              const int*               bsrSortedColIndA,
+                              int                      blockSize,
+                              bsrsv2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsv2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              int                      mb,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              double*                  bsrSortedValA,
+                              const int*               bsrSortedRowPtrA,
+                              const int*               bsrSortedColIndA,
+                              int                      blockSize,
+                              bsrsv2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsv2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              int                      mb,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              cuComplex*               bsrSortedValA,
+                              const int*               bsrSortedRowPtrA,
+                              const int*               bsrSortedColIndA,
+                              int                      blockSize,
+                              bsrsv2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsv2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              int                      mb,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              cuDoubleComplex*         bsrSortedValA,
+                              const int*               bsrSortedRowPtrA,
+                              const int*               bsrSortedColIndA,
+                              int                      blockSize,
+                              bsrsv2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsv2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         int                      mb,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const float*             bsrSortedValA,
+                         const int*               bsrSortedRowPtrA,
+                         const int*               bsrSortedColIndA,
+                         int                      blockDim,
+                         bsrsv2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsv2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         int                      mb,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const double*            bsrSortedValA,
+                         const int*               bsrSortedRowPtrA,
+                         const int*               bsrSortedColIndA,
+                         int                      blockDim,
+                         bsrsv2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsv2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         int                      mb,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const cuComplex*         bsrSortedValA,
+                         const int*               bsrSortedRowPtrA,
+                         const int*               bsrSortedColIndA,
+                         int                      blockDim,
+                         bsrsv2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsv2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         int                      mb,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const cuDoubleComplex*   bsrSortedValA,
+                         const int*               bsrSortedRowPtrA,
+                         const int*               bsrSortedColIndA,
+                         int                      blockDim,
+                         bsrsv2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsv2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      int                      mb,
+                      int                      nnzb,
+                      const float*             alpha,
+                      const cusparseMatDescr_t descrA,
+                      const float*             bsrSortedValA,
+                      const int*               bsrSortedRowPtrA,
+                      const int*               bsrSortedColIndA,
+                      int                      blockDim,
+                      bsrsv2Info_t             info,
+                      const float*             f,
+                      float*                   x,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsv2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      int                      mb,
+                      int                      nnzb,
+                      const double*            alpha,
+                      const cusparseMatDescr_t descrA,
+                      const double*            bsrSortedValA,
+                      const int*               bsrSortedRowPtrA,
+                      const int*               bsrSortedColIndA,
+                      int                      blockDim,
+                      bsrsv2Info_t             info,
+                      const double*            f,
+                      double*                  x,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsv2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      int                      mb,
+                      int                      nnzb,
+                      const cuComplex*         alpha,
+                      const cusparseMatDescr_t descrA,
+                      const cuComplex*         bsrSortedValA,
+                      const int*               bsrSortedRowPtrA,
+                      const int*               bsrSortedColIndA,
+                      int                      blockDim,
+                      bsrsv2Info_t             info,
+                      const cuComplex*         f,
+                      cuComplex*               x,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsv2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      int                      mb,
+                      int                      nnzb,
+                      const cuDoubleComplex*   alpha,
+                      const cusparseMatDescr_t descrA,
+                      const cuDoubleComplex*   bsrSortedValA,
+                      const int*               bsrSortedRowPtrA,
+                      const int*               bsrSortedColIndA,
+                      int                      blockDim,
+                      bsrsv2Info_t             info,
+                      const cuDoubleComplex*   f,
+                      cuDoubleComplex*         x,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+//##############################################################################
+//# SPARSE LEVEL 3 ROUTINES
+//##############################################################################
+
+CUSPARSE_DEPRECATED_REPLACE_WITH(cusparseSpMM) // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrmm(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               cusparseOperation_t      transB,
+               int                      mb,
+               int                      n,
+               int                      kb,
+               int                      nnzb,
+               const float*             alpha,
+               const cusparseMatDescr_t descrA,
+               const float* bsrSortedValA,
+               const int*   bsrSortedRowPtrA,
+               const int*   bsrSortedColIndA,
+               const int    blockSize,
+               const float* B,
+               const int    ldb,
+               const float* beta,
+               float*       C,
+               int          ldc);
+
+CUSPARSE_DEPRECATED_REPLACE_WITH(cusparseSpMM) // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrmm(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               cusparseOperation_t      transB,
+               int                      mb,
+               int                      n,
+               int                      kb,
+               int                      nnzb,
+               const double*            alpha,
+               const cusparseMatDescr_t descrA,
+               const double* bsrSortedValA,
+               const int*    bsrSortedRowPtrA,
+               const int*    bsrSortedColIndA,
+               const int     blockSize,
+               const double* B,
+               const int     ldb,
+               const double* beta,
+               double*       C,
+               int           ldc);
+
+CUSPARSE_DEPRECATED_REPLACE_WITH(cusparseSpMM) // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrmm(cusparseHandle_t         handle,
+               cusparseDirection_t      dirA,
+               cusparseOperation_t      transA,
+               cusparseOperation_t      transB,
+               int                      mb,
+               int                      n,
+               int                      kb,
+               int                      nnzb,
+               const cuComplex*         alpha,
+               const cusparseMatDescr_t descrA,
+               const cuComplex* bsrSortedValA,
+               const int*       bsrSortedRowPtrA,
+               const int*       bsrSortedColIndA,
+               const int        blockSize,
+               const cuComplex* B,
+               const int        ldb,
+               const cuComplex* beta,
+               cuComplex*       C,
+               int              ldc);
+
+CUSPARSE_DEPRECATED_REPLACE_WITH(cusparseSpMM) // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrmm(cusparseHandle_t         handle,
+                cusparseDirection_t      dirA,
+                cusparseOperation_t      transA,
+                cusparseOperation_t      transB,
+                int                      mb,
+                int                      n,
+                int                      kb,
+                int                      nnzb,
+                const cuDoubleComplex*   alpha,
+                const cusparseMatDescr_t descrA,
+                const cuDoubleComplex*   bsrSortedValA,
+                const int*               bsrSortedRowPtrA,
+                const int*               bsrSortedColIndA,
+                const int                blockSize,
+                const cuDoubleComplex*   B,
+                const int                ldb,
+                const cuDoubleComplex*   beta,
+                cuDoubleComplex*         C,
+                int                      ldc);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXbsrsm2_zeroPivot(cusparseHandle_t handle,
+                          bsrsm2Info_t     info,
+                          int*             position);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsm2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           cusparseOperation_t      transXY,
+                           int                      mb,
+                           int                      n,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           float*                   bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockSize,
+                           bsrsm2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsm2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           cusparseOperation_t      transXY,
+                           int                      mb,
+                           int                      n,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           double*                  bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockSize,
+                           bsrsm2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsm2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           cusparseOperation_t      transXY,
+                           int                      mb,
+                           int                      n,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           cuComplex*               bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockSize,
+                           bsrsm2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsm2_bufferSize(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           cusparseOperation_t      transA,
+                           cusparseOperation_t      transXY,
+                           int                      mb,
+                           int                      n,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           cuDoubleComplex*         bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockSize,
+                           bsrsm2Info_t             info,
+                           int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsm2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              cusparseOperation_t      transB,
+                              int                      mb,
+                              int                      n,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              float*                   bsrSortedVal,
+                              const int*               bsrSortedRowPtr,
+                              const int*               bsrSortedColInd,
+                              int                      blockSize,
+                              bsrsm2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsm2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              cusparseOperation_t      transB,
+                              int                      mb,
+                              int                      n,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              double*                  bsrSortedVal,
+                              const int*               bsrSortedRowPtr,
+                              const int*               bsrSortedColInd,
+                              int                      blockSize,
+                              bsrsm2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsm2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              cusparseOperation_t      transB,
+                              int                      mb,
+                              int                      n,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              cuComplex*               bsrSortedVal,
+                              const int*               bsrSortedRowPtr,
+                              const int*               bsrSortedColInd,
+                              int                      blockSize,
+                              bsrsm2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsm2_bufferSizeExt(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              cusparseOperation_t      transA,
+                              cusparseOperation_t      transB,
+                              int                      mb,
+                              int                      n,
+                              int                      nnzb,
+                              const cusparseMatDescr_t descrA,
+                              cuDoubleComplex*         bsrSortedVal,
+                              const int*               bsrSortedRowPtr,
+                              const int*               bsrSortedColInd,
+                              int                      blockSize,
+                              bsrsm2Info_t             info,
+                              size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsm2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         cusparseOperation_t      transXY,
+                         int                      mb,
+                         int                      n,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const float*             bsrSortedVal,
+                         const int*               bsrSortedRowPtr,
+                         const int*               bsrSortedColInd,
+                         int                      blockSize,
+                         bsrsm2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsm2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         cusparseOperation_t      transXY,
+                         int                      mb,
+                         int                      n,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const double*            bsrSortedVal,
+                         const int*               bsrSortedRowPtr,
+                         const int*               bsrSortedColInd,
+                         int                      blockSize,
+                         bsrsm2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsm2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         cusparseOperation_t      transXY,
+                         int                      mb,
+                         int                      n,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const cuComplex*         bsrSortedVal,
+                         const int*               bsrSortedRowPtr,
+                         const int*               bsrSortedColInd,
+                         int                      blockSize,
+                         bsrsm2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsm2_analysis(cusparseHandle_t         handle,
+                         cusparseDirection_t      dirA,
+                         cusparseOperation_t      transA,
+                         cusparseOperation_t      transXY,
+                         int                      mb,
+                         int                      n,
+                         int                      nnzb,
+                         const cusparseMatDescr_t descrA,
+                         const cuDoubleComplex*   bsrSortedVal,
+                         const int*               bsrSortedRowPtr,
+                         const int*               bsrSortedColInd,
+                         int                      blockSize,
+                         bsrsm2Info_t             info,
+                         cusparseSolvePolicy_t    policy,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrsm2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      cusparseOperation_t      transXY,
+                      int                      mb,
+                      int                      n,
+                      int                      nnzb,
+                      const float*             alpha,
+                      const cusparseMatDescr_t descrA,
+                      const float*             bsrSortedVal,
+                      const int*               bsrSortedRowPtr,
+                      const int*               bsrSortedColInd,
+                      int                      blockSize,
+                      bsrsm2Info_t             info,
+                      const float*             B,
+                      int                      ldb,
+                      float*                   X,
+                      int                      ldx,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrsm2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      cusparseOperation_t      transXY,
+                      int                      mb,
+                      int                      n,
+                      int                      nnzb,
+                      const double*            alpha,
+                      const cusparseMatDescr_t descrA,
+                      const double*            bsrSortedVal,
+                      const int*               bsrSortedRowPtr,
+                      const int*               bsrSortedColInd,
+                      int                      blockSize,
+                      bsrsm2Info_t             info,
+                      const double*            B,
+                      int                      ldb,
+                      double*                  X,
+                      int                      ldx,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrsm2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      cusparseOperation_t      transXY,
+                      int                      mb,
+                      int                      n,
+                      int                      nnzb,
+                      const cuComplex*         alpha,
+                      const cusparseMatDescr_t descrA,
+                      const cuComplex*         bsrSortedVal,
+                      const int*               bsrSortedRowPtr,
+                      const int*               bsrSortedColInd,
+                      int                      blockSize,
+                      bsrsm2Info_t             info,
+                      const cuComplex*         B,
+                      int                      ldb,
+                      cuComplex*               X,
+                      int                      ldx,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrsm2_solve(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      cusparseOperation_t      transA,
+                      cusparseOperation_t      transXY,
+                      int                      mb,
+                      int                      n,
+                      int                      nnzb,
+                      const cuDoubleComplex*   alpha,
+                      const cusparseMatDescr_t descrA,
+                      const cuDoubleComplex*   bsrSortedVal,
+                      const int*               bsrSortedRowPtr,
+                      const int*               bsrSortedColInd,
+                      int                      blockSize,
+                      bsrsm2Info_t             info,
+                      const cuDoubleComplex*   B,
+                      int                      ldb,
+                      cuDoubleComplex*         X,
+                      int                      ldx,
+                      cusparseSolvePolicy_t    policy,
+                      void*                    pBuffer);
+
+//##############################################################################
+//# PRECONDITIONERS
+//##############################################################################
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrilu02_numericBoost(cusparseHandle_t handle,
+                               csrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               float*           boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrilu02_numericBoost(cusparseHandle_t handle,
+                               csrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               double*          boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrilu02_numericBoost(cusparseHandle_t handle,
+                               csrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               cuComplex*       boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrilu02_numericBoost(cusparseHandle_t handle,
+                               csrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               cuDoubleComplex* boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsrilu02_zeroPivot(cusparseHandle_t handle,
+                            csrilu02Info_t   info,
+                            int*             position);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrilu02_bufferSize(cusparseHandle_t         handle,
+                             int                      m,
+                             int                      nnz,
+                             const cusparseMatDescr_t descrA,
+                             float*                   csrSortedValA,
+                             const int*               csrSortedRowPtrA,
+                             const int*               csrSortedColIndA,
+                             csrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrilu02_bufferSize(cusparseHandle_t         handle,
+                             int                      m,
+                             int                      nnz,
+                             const cusparseMatDescr_t descrA,
+                             double*                  csrSortedValA,
+                             const int*               csrSortedRowPtrA,
+                             const int*               csrSortedColIndA,
+                             csrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrilu02_bufferSize(cusparseHandle_t         handle,
+                             int                      m,
+                             int                      nnz,
+                             const cusparseMatDescr_t descrA,
+                             cuComplex*               csrSortedValA,
+                             const int*               csrSortedRowPtrA,
+                             const int*               csrSortedColIndA,
+                             csrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrilu02_bufferSize(cusparseHandle_t         handle,
+                             int                      m,
+                             int                      nnz,
+                             const cusparseMatDescr_t descrA,
+                             cuDoubleComplex*         csrSortedValA,
+                             const int*               csrSortedRowPtrA,
+                             const int*               csrSortedColIndA,
+                             csrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      nnz,
+                                const cusparseMatDescr_t descrA,
+                                float*                   csrSortedVal,
+                                const int*               csrSortedRowPtr,
+                                const int*               csrSortedColInd,
+                                csrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      nnz,
+                                const cusparseMatDescr_t descrA,
+                                double*                  csrSortedVal,
+                                const int*               csrSortedRowPtr,
+                                const int*               csrSortedColInd,
+                                csrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      nnz,
+                                const cusparseMatDescr_t descrA,
+                                cuComplex*               csrSortedVal,
+                                const int*               csrSortedRowPtr,
+                                const int*               csrSortedColInd,
+                                csrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      nnz,
+                                const cusparseMatDescr_t descrA,
+                                cuDoubleComplex*         csrSortedVal,
+                                const int*               csrSortedRowPtr,
+                                const int*               csrSortedColInd,
+                                csrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrilu02_analysis(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      nnz,
+                           const cusparseMatDescr_t descrA,
+                           const float*             csrSortedValA,
+                           const int*               csrSortedRowPtrA,
+                           const int*               csrSortedColIndA,
+                           csrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrilu02_analysis(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      nnz,
+                           const cusparseMatDescr_t descrA,
+                           const double*            csrSortedValA,
+                           const int*               csrSortedRowPtrA,
+                           const int*               csrSortedColIndA,
+                           csrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrilu02_analysis(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      nnz,
+                           const cusparseMatDescr_t descrA,
+                           const cuComplex*         csrSortedValA,
+                           const int*               csrSortedRowPtrA,
+                           const int*               csrSortedColIndA,
+                           csrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrilu02_analysis(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      nnz,
+                           const cusparseMatDescr_t descrA,
+                           const cuDoubleComplex*   csrSortedValA,
+                           const int*               csrSortedRowPtrA,
+                           const int*               csrSortedColIndA,
+                           csrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrilu02(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  float*                   csrSortedValA_valM,
+                  const int*            csrSortedRowPtrA,
+                  const int*            csrSortedColIndA,
+                  csrilu02Info_t        info,
+                  cusparseSolvePolicy_t policy,
+                  void*                 pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrilu02(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  double*                  csrSortedValA_valM,
+                  const int*            csrSortedRowPtrA,
+                  const int*            csrSortedColIndA,
+                  csrilu02Info_t        info,
+                  cusparseSolvePolicy_t policy,
+                  void*                 pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrilu02(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  cuComplex*               csrSortedValA_valM,
+                  const int*            csrSortedRowPtrA,
+                  const int*            csrSortedColIndA,
+                  csrilu02Info_t        info,
+                  cusparseSolvePolicy_t policy,
+                  void*                 pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrilu02(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  cuDoubleComplex*         csrSortedValA_valM,
+                  const int*            csrSortedRowPtrA,
+                  const int*            csrSortedColIndA,
+                  csrilu02Info_t        info,
+                  cusparseSolvePolicy_t policy,
+                  void*                 pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrilu02_numericBoost(cusparseHandle_t handle,
+                               bsrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               float*           boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrilu02_numericBoost(cusparseHandle_t handle,
+                               bsrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               double*          boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrilu02_numericBoost(cusparseHandle_t handle,
+                               bsrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               cuComplex*       boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrilu02_numericBoost(cusparseHandle_t handle,
+                               bsrilu02Info_t   info,
+                               int              enable_boost,
+                               double*          tol,
+                               cuDoubleComplex* boost_val);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXbsrilu02_zeroPivot(cusparseHandle_t handle,
+                            bsrilu02Info_t   info,
+                            int*             position);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrilu02_bufferSize(cusparseHandle_t         handle,
+                             cusparseDirection_t      dirA,
+                             int                      mb,
+                             int                      nnzb,
+                             const cusparseMatDescr_t descrA,
+                             float*                   bsrSortedVal,
+                             const int*               bsrSortedRowPtr,
+                             const int*               bsrSortedColInd,
+                             int                      blockDim,
+                             bsrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrilu02_bufferSize(cusparseHandle_t         handle,
+                             cusparseDirection_t      dirA,
+                             int                      mb,
+                             int                      nnzb,
+                             const cusparseMatDescr_t descrA,
+                             double*                  bsrSortedVal,
+                             const int*               bsrSortedRowPtr,
+                             const int*               bsrSortedColInd,
+                             int                      blockDim,
+                             bsrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrilu02_bufferSize(cusparseHandle_t         handle,
+                             cusparseDirection_t      dirA,
+                             int                      mb,
+                             int                      nnzb,
+                             const cusparseMatDescr_t descrA,
+                             cuComplex*               bsrSortedVal,
+                             const int*               bsrSortedRowPtr,
+                             const int*               bsrSortedColInd,
+                             int                      blockDim,
+                             bsrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrilu02_bufferSize(cusparseHandle_t         handle,
+                             cusparseDirection_t      dirA,
+                             int                      mb,
+                             int                      nnzb,
+                             const cusparseMatDescr_t descrA,
+                             cuDoubleComplex*         bsrSortedVal,
+                             const int*               bsrSortedRowPtr,
+                             const int*               bsrSortedColInd,
+                             int                      blockDim,
+                             bsrilu02Info_t           info,
+                             int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                float*                   bsrSortedVal,
+                                const int*               bsrSortedRowPtr,
+                                const int*               bsrSortedColInd,
+                                int                      blockSize,
+                                bsrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                double*                  bsrSortedVal,
+                                const int*               bsrSortedRowPtr,
+                                const int*               bsrSortedColInd,
+                                int                      blockSize,
+                                bsrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                cuComplex*               bsrSortedVal,
+                                const int*               bsrSortedRowPtr,
+                                const int*               bsrSortedColInd,
+                                int                      blockSize,
+                                bsrilu02Info_t           info,
+                                size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrilu02_bufferSizeExt(cusparseHandle_t         handle,
+                               cusparseDirection_t      dirA,
+                               int                      mb,
+                               int                      nnzb,
+                               const cusparseMatDescr_t descrA,
+                               cuDoubleComplex*         bsrSortedVal,
+                               const int*               bsrSortedRowPtr,
+                               const int*               bsrSortedColInd,
+                               int                      blockSize,
+                               bsrilu02Info_t           info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrilu02_analysis(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           float*                   bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockDim,
+                           bsrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrilu02_analysis(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           double*                  bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockDim,
+                           bsrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrilu02_analysis(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           cuComplex*               bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockDim,
+                           bsrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrilu02_analysis(cusparseHandle_t         handle,
+                           cusparseDirection_t      dirA,
+                           int                      mb,
+                           int                      nnzb,
+                           const cusparseMatDescr_t descrA,
+                           cuDoubleComplex*         bsrSortedVal,
+                           const int*               bsrSortedRowPtr,
+                           const int*               bsrSortedColInd,
+                           int                      blockDim,
+                           bsrilu02Info_t           info,
+                           cusparseSolvePolicy_t    policy,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsrilu02(cusparseHandle_t         handle,
+                  cusparseDirection_t      dirA,
+                  int                      mb,
+                  int                      nnzb,
+                  const cusparseMatDescr_t descrA,
+                  float*                   bsrSortedVal,
+                  const int*               bsrSortedRowPtr,
+                  const int*               bsrSortedColInd,
+                  int                      blockDim,
+                  bsrilu02Info_t           info,
+                  cusparseSolvePolicy_t    policy,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsrilu02(cusparseHandle_t         handle,
+                  cusparseDirection_t      dirA,
+                  int                      mb,
+                  int                      nnzb,
+                  const cusparseMatDescr_t descrA,
+                  double*                  bsrSortedVal,
+                  const int*               bsrSortedRowPtr,
+                  const int*               bsrSortedColInd,
+                  int                      blockDim,
+                  bsrilu02Info_t           info,
+                  cusparseSolvePolicy_t    policy,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsrilu02(cusparseHandle_t         handle,
+                  cusparseDirection_t      dirA,
+                  int                      mb,
+                  int                      nnzb,
+                  const cusparseMatDescr_t descrA,
+                  cuComplex*               bsrSortedVal,
+                  const int*               bsrSortedRowPtr,
+                  const int*               bsrSortedColInd,
+                  int                      blockDim,
+                  bsrilu02Info_t           info,
+                  cusparseSolvePolicy_t    policy,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsrilu02(cusparseHandle_t         handle,
+                  cusparseDirection_t      dirA,
+                  int                      mb,
+                  int                      nnzb,
+                  const cusparseMatDescr_t descrA,
+                  cuDoubleComplex*         bsrSortedVal,
+                  const int*               bsrSortedRowPtr,
+                  const int*               bsrSortedColInd,
+                  int                      blockDim,
+                  bsrilu02Info_t           info,
+                  cusparseSolvePolicy_t    policy,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsric02_zeroPivot(cusparseHandle_t handle,
+                           csric02Info_t    info,
+                           int*             position);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsric02_bufferSize(cusparseHandle_t         handle,
+                            int                      m,
+                            int                      nnz,
+                            const cusparseMatDescr_t descrA,
+                            float*                   csrSortedValA,
+                            const int*               csrSortedRowPtrA,
+                            const int*               csrSortedColIndA,
+                            csric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsric02_bufferSize(cusparseHandle_t         handle,
+                            int                      m,
+                            int                      nnz,
+                            const cusparseMatDescr_t descrA,
+                            double*                  csrSortedValA,
+                            const int*               csrSortedRowPtrA,
+                            const int*               csrSortedColIndA,
+                            csric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsric02_bufferSize(cusparseHandle_t         handle,
+                            int                      m,
+                            int                      nnz,
+                            const cusparseMatDescr_t descrA,
+                            cuComplex*               csrSortedValA,
+                            const int*               csrSortedRowPtrA,
+                            const int*               csrSortedColIndA,
+                            csric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsric02_bufferSize(cusparseHandle_t         handle,
+                            int                      m,
+                            int                      nnz,
+                            const cusparseMatDescr_t descrA,
+                            cuDoubleComplex*         csrSortedValA,
+                            const int*               csrSortedRowPtrA,
+                            const int*               csrSortedColIndA,
+                            csric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               int                      m,
+                               int                      nnz,
+                               const cusparseMatDescr_t descrA,
+                               float*                   csrSortedVal,
+                               const int*               csrSortedRowPtr,
+                               const int*               csrSortedColInd,
+                               csric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               int                      m,
+                               int                      nnz,
+                               const cusparseMatDescr_t descrA,
+                               double*                  csrSortedVal,
+                               const int*               csrSortedRowPtr,
+                               const int*               csrSortedColInd,
+                               csric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               int                      m,
+                               int                      nnz,
+                               const cusparseMatDescr_t descrA,
+                               cuComplex*               csrSortedVal,
+                               const int*               csrSortedRowPtr,
+                               const int*               csrSortedColInd,
+                               csric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               int                      m,
+                               int                      nnz,
+                               const cusparseMatDescr_t descrA,
+                               cuDoubleComplex*         csrSortedVal,
+                               const int*               csrSortedRowPtr,
+                               const int*               csrSortedColInd,
+                               csric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsric02_analysis(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      nnz,
+                          const cusparseMatDescr_t descrA,
+                          const float*             csrSortedValA,
+                          const int*               csrSortedRowPtrA,
+                          const int*               csrSortedColIndA,
+                          csric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsric02_analysis(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      nnz,
+                          const cusparseMatDescr_t descrA,
+                          const double*            csrSortedValA,
+                          const int*               csrSortedRowPtrA,
+                          const int*               csrSortedColIndA,
+                          csric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsric02_analysis(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      nnz,
+                          const cusparseMatDescr_t descrA,
+                          const cuComplex*         csrSortedValA,
+                          const int*               csrSortedRowPtrA,
+                          const int*               csrSortedColIndA,
+                          csric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsric02_analysis(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      nnz,
+                          const cusparseMatDescr_t descrA,
+                          const cuDoubleComplex*   csrSortedValA,
+                          const int*               csrSortedRowPtrA,
+                          const int*               csrSortedColIndA,
+                          csric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsric02(cusparseHandle_t         handle,
+                 int                      m,
+                 int                      nnz,
+                 const cusparseMatDescr_t descrA,
+                 float*                   csrSortedValA_valM,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 csric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsric02(cusparseHandle_t         handle,
+                 int                      m,
+                 int                      nnz,
+                 const cusparseMatDescr_t descrA,
+                 double*                  csrSortedValA_valM,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 csric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsric02(cusparseHandle_t         handle,
+                 int                      m,
+                 int                      nnz,
+                 const cusparseMatDescr_t descrA,
+                 cuComplex*               csrSortedValA_valM,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 csric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsric02(cusparseHandle_t         handle,
+                 int                      m,
+                 int                      nnz,
+                 const cusparseMatDescr_t descrA,
+                 cuDoubleComplex*         csrSortedValA_valM,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 csric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXbsric02_zeroPivot(cusparseHandle_t handle,
+                           bsric02Info_t    info,
+                           int*             position);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsric02_bufferSize(cusparseHandle_t         handle,
+                            cusparseDirection_t      dirA,
+                            int                      mb,
+                            int                      nnzb,
+                            const cusparseMatDescr_t descrA,
+                            float*                   bsrSortedVal,
+                            const int*               bsrSortedRowPtr,
+                            const int*               bsrSortedColInd,
+                            int                      blockDim,
+                            bsric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsric02_bufferSize(cusparseHandle_t         handle,
+                            cusparseDirection_t      dirA,
+                            int                      mb,
+                            int                      nnzb,
+                            const cusparseMatDescr_t descrA,
+                            double*                  bsrSortedVal,
+                            const int*               bsrSortedRowPtr,
+                            const int*               bsrSortedColInd,
+                            int                      blockDim,
+                            bsric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsric02_bufferSize(cusparseHandle_t         handle,
+                            cusparseDirection_t      dirA,
+                            int                      mb,
+                            int                      nnzb,
+                            const cusparseMatDescr_t descrA,
+                            cuComplex*               bsrSortedVal,
+                            const int*               bsrSortedRowPtr,
+                            const int*               bsrSortedColInd,
+                            int                      blockDim,
+                            bsric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsric02_bufferSize(cusparseHandle_t         handle,
+                            cusparseDirection_t      dirA,
+                            int                      mb,
+                            int                      nnzb,
+                            const cusparseMatDescr_t descrA,
+                            cuDoubleComplex*         bsrSortedVal,
+                            const int*               bsrSortedRowPtr,
+                            const int*               bsrSortedColInd,
+                            int                      blockDim,
+                            bsric02Info_t            info,
+                            int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               cusparseDirection_t      dirA,
+                               int                      mb,
+                               int                      nnzb,
+                               const cusparseMatDescr_t descrA,
+                               float*                   bsrSortedVal,
+                               const int*               bsrSortedRowPtr,
+                               const int*               bsrSortedColInd,
+                               int                      blockSize,
+                               bsric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               cusparseDirection_t      dirA,
+                               int                      mb,
+                               int                      nnzb,
+                               const cusparseMatDescr_t descrA,
+                               double*                  bsrSortedVal,
+                               const int*               bsrSortedRowPtr,
+                               const int*               bsrSortedColInd,
+                               int                      blockSize,
+                               bsric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               cusparseDirection_t      dirA,
+                               int                      mb,
+                               int                      nnzb,
+                               const cusparseMatDescr_t descrA,
+                               cuComplex*               bsrSortedVal,
+                               const int*               bsrSortedRowPtr,
+                               const int*               bsrSortedColInd,
+                               int                      blockSize,
+                               bsric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsric02_bufferSizeExt(cusparseHandle_t         handle,
+                               cusparseDirection_t      dirA,
+                               int                      mb,
+                               int                      nnzb,
+                               const cusparseMatDescr_t descrA,
+                               cuDoubleComplex*         bsrSortedVal,
+                               const int*               bsrSortedRowPtr,
+                               const int*               bsrSortedColInd,
+                               int                      blockSize,
+                               bsric02Info_t            info,
+                               size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsric02_analysis(cusparseHandle_t         handle,
+                          cusparseDirection_t      dirA,
+                          int                      mb,
+                          int                      nnzb,
+                          const cusparseMatDescr_t descrA,
+                          const float*             bsrSortedVal,
+                          const int*               bsrSortedRowPtr,
+                          const int*               bsrSortedColInd,
+                          int                      blockDim,
+                          bsric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pInputBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsric02_analysis(cusparseHandle_t         handle,
+                          cusparseDirection_t      dirA,
+                          int                      mb,
+                          int                      nnzb,
+                          const cusparseMatDescr_t descrA,
+                          const double*            bsrSortedVal,
+                          const int*               bsrSortedRowPtr,
+                          const int*               bsrSortedColInd,
+                          int                      blockDim,
+                          bsric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pInputBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsric02_analysis(cusparseHandle_t         handle,
+                          cusparseDirection_t      dirA,
+                          int                      mb,
+                          int                      nnzb,
+                          const cusparseMatDescr_t descrA,
+                          const cuComplex*         bsrSortedVal,
+                          const int*               bsrSortedRowPtr,
+                          const int*               bsrSortedColInd,
+                          int                      blockDim,
+                          bsric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pInputBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsric02_analysis(cusparseHandle_t         handle,
+                          cusparseDirection_t      dirA,
+                          int                      mb,
+                          int                      nnzb,
+                          const cusparseMatDescr_t descrA,
+                          const cuDoubleComplex*   bsrSortedVal,
+                          const int*               bsrSortedRowPtr,
+                          const int*               bsrSortedColInd,
+                          int                      blockDim,
+                          bsric02Info_t            info,
+                          cusparseSolvePolicy_t    policy,
+                          void*                    pInputBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsric02(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nnzb,
+                 const cusparseMatDescr_t descrA,
+                 float*                   bsrSortedVal,
+                 const int*               bsrSortedRowPtr,
+                 const int*               bsrSortedColInd,
+                 int                      blockDim,
+                 bsric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsric02(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nnzb,
+                 const cusparseMatDescr_t descrA,
+                 double*                  bsrSortedVal,
+                 const int*               bsrSortedRowPtr,
+                 const int*               bsrSortedColInd,
+                 int                      blockDim,
+                 bsric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsric02(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nnzb,
+                 const cusparseMatDescr_t descrA,
+                 cuComplex*               bsrSortedVal,
+                 const int*               bsrSortedRowPtr,
+                 const int*
+                      bsrSortedColInd,
+                 int                      blockDim,
+                 bsric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsric02(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nnzb,
+                 const cusparseMatDescr_t descrA,
+                 cuDoubleComplex*         bsrSortedVal,
+                 const int*               bsrSortedRowPtr,
+                 const int*               bsrSortedColInd,
+                 int                      blockDim,
+                 bsric02Info_t            info,
+                 cusparseSolvePolicy_t    policy,
+                 void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsv2_bufferSizeExt(cusparseHandle_t handle,
+                             int              m,
+                             int              n,
+                             const float*     dl,
+                             const float*     d,
+                             const float*     du,
+                             const float*     B,
+                             int              ldb,
+                             size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsv2_bufferSizeExt(cusparseHandle_t handle,
+                             int              m,
+                             int              n,
+                             const double*    dl,
+                             const double*    d,
+                             const double*    du,
+                             const double*    B,
+                             int              ldb,
+                             size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsv2_bufferSizeExt(cusparseHandle_t handle,
+                             int              m,
+                             int              n,
+                             const cuComplex* dl,
+                             const cuComplex* d,
+                             const cuComplex* du,
+                             const cuComplex* B,
+                             int              ldb,
+                             size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsv2_bufferSizeExt(cusparseHandle_t       handle,
+                             int                    m,
+                             int                    n,
+                             const cuDoubleComplex* dl,
+                             const cuDoubleComplex* d,
+                             const cuDoubleComplex* du,
+                             const cuDoubleComplex* B,
+                             int                    ldb,
+                             size_t*                bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsv2(cusparseHandle_t handle,
+               int              m,
+               int              n,
+               const float*     dl,
+               const float*     d,
+               const float*     du,
+               float*           B,
+               int              ldb,
+               void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsv2(cusparseHandle_t handle,
+               int              m,
+               int              n,
+               const double*    dl,
+               const double*    d,
+               const double*    du,
+               double*          B,
+               int              ldb,
+               void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsv2(cusparseHandle_t handle,
+               int              m,
+               int              n,
+               const cuComplex* dl,
+               const cuComplex* d,
+               const cuComplex* du,
+               cuComplex*       B,
+               int              ldb,
+               void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsv2(cusparseHandle_t       handle,
+               int                    m,
+               int                    n,
+               const cuDoubleComplex* dl,
+               const cuDoubleComplex* d,
+               const cuDoubleComplex* du,
+               cuDoubleComplex*       B,
+               int                    ldb,
+               void*                  pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsv2_nopivot_bufferSizeExt(cusparseHandle_t handle,
+                                     int              m,
+                                     int              n,
+                                     const float*     dl,
+                                     const float*     d,
+                                     const float*     du,
+                                     const float*     B,
+                                     int              ldb,
+                                     size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsv2_nopivot_bufferSizeExt(cusparseHandle_t handle,
+                                     int              m,
+                                     int              n,
+                                     const double*    dl,
+                                     const double*    d,
+                                     const double*    du,
+                                     const double*    B,
+                                     int              ldb,
+                                     size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsv2_nopivot_bufferSizeExt(cusparseHandle_t handle,
+                                     int              m,
+                                     int              n,
+                                     const cuComplex* dl,
+                                     const cuComplex* d,
+                                     const cuComplex* du,
+                                     const cuComplex* B,
+                                     int              ldb,
+                                     size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsv2_nopivot_bufferSizeExt(cusparseHandle_t       handle,
+                                     int                    m,
+                                     int                    n,
+                                     const cuDoubleComplex* dl,
+                                     const cuDoubleComplex* d,
+                                     const cuDoubleComplex* du,
+                                     const cuDoubleComplex* B,
+                                     int                    ldb,
+                                     size_t*                bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsv2_nopivot(cusparseHandle_t handle,
+                       int              m,
+                       int              n,
+                       const float*     dl,
+                       const float*     d,
+                       const float*     du,
+                       float*           B,
+                       int              ldb,
+                       void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsv2_nopivot(cusparseHandle_t handle,
+                       int              m,
+                       int              n,
+                       const double*    dl,
+                       const double*    d,
+                       const double*    du,
+                       double*          B,
+                       int              ldb,
+                       void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsv2_nopivot(cusparseHandle_t handle,
+                       int              m,
+                       int              n,
+                       const cuComplex* dl,
+                       const cuComplex* d,
+                       const cuComplex* du,
+                       cuComplex*       B,
+                       int              ldb,
+                       void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsv2_nopivot(cusparseHandle_t       handle,
+                       int                    m,
+                       int                    n,
+                       const cuDoubleComplex* dl,
+                       const cuDoubleComplex* d,
+                       const cuDoubleComplex* du,
+                       cuDoubleComplex*       B,
+                       int                    ldb,
+                       void*                  pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsv2StridedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                         int              m,
+                                         const float*     dl,
+                                         const float*     d,
+                                         const float*     du,
+                                         const float*     x,
+                                         int              batchCount,
+                                         int              batchStride,
+                                         size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsv2StridedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                         int              m,
+                                         const double*    dl,
+                                         const double*    d,
+                                         const double*    du,
+                                         const double*    x,
+                                         int              batchCount,
+                                         int              batchStride,
+                                         size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsv2StridedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                         int              m,
+                                         const cuComplex* dl,
+                                         const cuComplex* d,
+                                         const cuComplex* du,
+                                         const cuComplex* x,
+                                         int              batchCount,
+                                         int              batchStride,
+                                         size_t*          bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsv2StridedBatch_bufferSizeExt(cusparseHandle_t       handle,
+                                         int                    m,
+                                         const cuDoubleComplex* dl,
+                                         const cuDoubleComplex* d,
+                                         const cuDoubleComplex* du,
+                                         const cuDoubleComplex* x,
+                                         int                    batchCount,
+                                         int                    batchStride,
+                                         size_t* bufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsv2StridedBatch(cusparseHandle_t handle,
+                           int              m,
+                           const float*     dl,
+                           const float*     d,
+                           const float*     du,
+                           float*           x,
+                           int              batchCount,
+                           int              batchStride,
+                           void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsv2StridedBatch(cusparseHandle_t handle,
+                           int              m,
+                           const double*    dl,
+                           const double*    d,
+                           const double*    du,
+                           double*          x,
+                           int              batchCount,
+                           int              batchStride,
+                           void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsv2StridedBatch(cusparseHandle_t handle,
+                           int              m,
+                           const cuComplex* dl,
+                           const cuComplex* d,
+                           const cuComplex* du,
+                           cuComplex*       x,
+                           int              batchCount,
+                           int              batchStride,
+                           void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsv2StridedBatch(cusparseHandle_t       handle,
+                           int                    m,
+                           const cuDoubleComplex* dl,
+                           const cuDoubleComplex* d,
+                           const cuDoubleComplex* du,
+                           cuDoubleComplex*       x,
+                           int                    batchCount,
+                           int                    batchStride,
+                           void*                  pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsvInterleavedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                            int              algo,
+                                            int              m,
+                                            const float*     dl,
+                                            const float*     d,
+                                            const float*     du,
+                                            const float*     x,
+                                            int              batchCount,
+                                            size_t*         pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsvInterleavedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                         int              algo,
+                                         int              m,
+                                         const double*    dl,
+                                         const double*    d,
+                                         const double*    du,
+                                         const double*    x,
+                                         int              batchCount,
+                                         size_t*          pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsvInterleavedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                            int              algo,
+                                            int              m,
+                                            const cuComplex* dl,
+                                            const cuComplex* d,
+                                            const cuComplex* du,
+                                            const cuComplex* x,
+                                            int              batchCount,
+                                            size_t*         pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsvInterleavedBatch_bufferSizeExt(cusparseHandle_t       handle,
+                                            int                    algo,
+                                            int                    m,
+                                            const cuDoubleComplex* dl,
+                                            const cuDoubleComplex* d,
+                                            const cuDoubleComplex* du,
+                                            const cuDoubleComplex* x,
+                                            int                    batchCount,
+                                            size_t*        pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgtsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              float*           dl,
+                              float*           d,
+                              float*           du,
+                              float*           x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgtsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              double*          dl,
+                              double*          d,
+                              double*          du,
+                              double*          x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgtsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              cuComplex*       dl,
+                              cuComplex*       d,
+                              cuComplex*       du,
+                              cuComplex*       x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgtsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              cuDoubleComplex* dl,
+                              cuDoubleComplex* d,
+                              cuDoubleComplex* du,
+                              cuDoubleComplex* x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgpsvInterleavedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                            int              algo,
+                                            int              m,
+                                            const float*     ds,
+                                            const float*     dl,
+                                            const float*     d,
+                                            const float*     du,
+                                            const float*     dw,
+                                            const float*     x,
+                                            int              batchCount,
+                                            size_t*         pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgpsvInterleavedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                            int              algo,
+                                            int              m,
+                                            const double*    ds,
+                                            const double*    dl,
+                                            const double*    d,
+                                            const double*    du,
+                                            const double*    dw,
+                                            const double*    x,
+                                            int              batchCount,
+                                            size_t*         pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgpsvInterleavedBatch_bufferSizeExt(cusparseHandle_t handle,
+                                            int              algo,
+                                            int              m,
+                                            const cuComplex* ds,
+                                            const cuComplex* dl,
+                                            const cuComplex* d,
+                                            const cuComplex* du,
+                                            const cuComplex* dw,
+                                            const cuComplex* x,
+                                            int              batchCount,
+                                            size_t*         pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgpsvInterleavedBatch_bufferSizeExt(cusparseHandle_t       handle,
+                                            int                    algo,
+                                            int                    m,
+                                            const cuDoubleComplex* ds,
+                                            const cuDoubleComplex* dl,
+                                            const cuDoubleComplex* d,
+                                            const cuDoubleComplex* du,
+                                            const cuDoubleComplex* dw,
+                                            const cuDoubleComplex* x,
+                                            int                    batchCount,
+                                            size_t*         pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgpsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              float*           ds,
+                              float*           dl,
+                              float*           d,
+                              float*           du,
+                              float*           dw,
+                              float*           x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgpsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              double*          ds,
+                              double*          dl,
+                              double*          d,
+                              double*          du,
+                              double*          dw,
+                              double*          x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgpsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              cuComplex*       ds,
+                              cuComplex*       dl,
+                              cuComplex*       d,
+                              cuComplex*       du,
+                              cuComplex*       dw,
+                              cuComplex*       x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgpsvInterleavedBatch(cusparseHandle_t handle,
+                              int              algo,
+                              int              m,
+                              cuDoubleComplex* ds,
+                              cuDoubleComplex* dl,
+                              cuDoubleComplex* d,
+                              cuDoubleComplex* du,
+                              cuDoubleComplex* dw,
+                              cuDoubleComplex* x,
+                              int              batchCount,
+                              void*            pBuffer);
+
+//##############################################################################
+//# EXTRA ROUTINES
+//##############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrgeam2_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      n,
+                                const float*             alpha,
+                                const cusparseMatDescr_t descrA,
+                                int                      nnzA,
+                                const float*             csrSortedValA,
+                                const int*               csrSortedRowPtrA,
+                                const int*               csrSortedColIndA,
+                                const float*             beta,
+                                const cusparseMatDescr_t descrB,
+                                int                      nnzB,
+                                const float*             csrSortedValB,
+                                const int*               csrSortedRowPtrB,
+                                const int*               csrSortedColIndB,
+                                const cusparseMatDescr_t descrC,
+                                const float*             csrSortedValC,
+                                const int*               csrSortedRowPtrC,
+                                const int*               csrSortedColIndC,
+                                size_t*                  pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrgeam2_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      n,
+                                const double*            alpha,
+                                const cusparseMatDescr_t descrA,
+                                int                      nnzA,
+                                const double*            csrSortedValA,
+                                const int*               csrSortedRowPtrA,
+                                const int*               csrSortedColIndA,
+                                const double*            beta,
+                                const cusparseMatDescr_t descrB,
+                                int                      nnzB,
+                                const double*            csrSortedValB,
+                                const int*               csrSortedRowPtrB,
+                                const int*               csrSortedColIndB,
+                                const cusparseMatDescr_t descrC,
+                                const double*            csrSortedValC,
+                                const int*               csrSortedRowPtrC,
+                                const int*               csrSortedColIndC,
+                                size_t*                  pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrgeam2_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      n,
+                                const cuComplex*         alpha,
+                                const cusparseMatDescr_t descrA,
+                                int                      nnzA,
+                                const cuComplex*         csrSortedValA,
+                                const int*               csrSortedRowPtrA,
+                                const int*               csrSortedColIndA,
+                                const cuComplex*         beta,
+                                const cusparseMatDescr_t descrB,
+                                int                      nnzB,
+                                const cuComplex*         csrSortedValB,
+                                const int*               csrSortedRowPtrB,
+                                const int*               csrSortedColIndB,
+                                const cusparseMatDescr_t descrC,
+                                const cuComplex*         csrSortedValC,
+                                const int*               csrSortedRowPtrC,
+                                const int*               csrSortedColIndC,
+                                size_t*                  pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrgeam2_bufferSizeExt(cusparseHandle_t         handle,
+                                int                      m,
+                                int                      n,
+                                const cuDoubleComplex*   alpha,
+                                const cusparseMatDescr_t descrA,
+                                int                      nnzA,
+                                const cuDoubleComplex*   csrSortedValA,
+                                const int*               csrSortedRowPtrA,
+                                const int*               csrSortedColIndA,
+                                const cuDoubleComplex*   beta,
+                                const cusparseMatDescr_t descrB,
+                                int                      nnzB,
+                                const cuDoubleComplex*   csrSortedValB,
+                                const int*               csrSortedRowPtrB,
+                                const int*               csrSortedColIndB,
+                                const cusparseMatDescr_t descrC,
+                                const cuDoubleComplex*   csrSortedValC,
+                                const int*               csrSortedRowPtrC,
+                                const int*               csrSortedColIndC,
+                                size_t*                  pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsrgeam2Nnz(cusparseHandle_t         handle,
+                     int                      m,
+                     int                      n,
+                     const cusparseMatDescr_t descrA,
+                     int                      nnzA,
+                     const int*               csrSortedRowPtrA,
+                     const int*               csrSortedColIndA,
+                     const cusparseMatDescr_t descrB,
+                     int                      nnzB,
+                     const int*               csrSortedRowPtrB,
+                     const int*               csrSortedColIndB,
+                     const cusparseMatDescr_t descrC,
+                     int*                     csrSortedRowPtrC,
+                     int*                     nnzTotalDevHostPtr,
+                     void*                    workspace);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrgeam2(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  const float*             alpha,
+                  const cusparseMatDescr_t descrA,
+                  int                      nnzA,
+                  const float*             csrSortedValA,
+                  const int*               csrSortedRowPtrA,
+                  const int*               csrSortedColIndA,
+                  const float*             beta,
+                  const cusparseMatDescr_t descrB,
+                  int                      nnzB,
+                  const float*             csrSortedValB,
+                  const int*               csrSortedRowPtrB,
+                  const int*               csrSortedColIndB,
+                  const cusparseMatDescr_t descrC,
+                  float*                   csrSortedValC,
+                  int*                     csrSortedRowPtrC,
+                  int*                     csrSortedColIndC,
+                  void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrgeam2(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  const double*            alpha,
+                  const cusparseMatDescr_t descrA,
+                  int                      nnzA,
+                  const double*            csrSortedValA,
+                  const int*               csrSortedRowPtrA,
+                  const int*               csrSortedColIndA,
+                  const double*            beta,
+                  const cusparseMatDescr_t descrB,
+                  int                      nnzB,
+                  const double*            csrSortedValB,
+                  const int*               csrSortedRowPtrB,
+                  const int*               csrSortedColIndB,
+                  const cusparseMatDescr_t descrC,
+                  double*                  csrSortedValC,
+                  int*                     csrSortedRowPtrC,
+                  int*                     csrSortedColIndC,
+                  void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrgeam2(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  const cuComplex*         alpha,
+                  const cusparseMatDescr_t descrA,
+                  int                      nnzA,
+                  const cuComplex*         csrSortedValA,
+                  const int*               csrSortedRowPtrA,
+                  const int*               csrSortedColIndA,
+                  const cuComplex*         beta,
+                  const cusparseMatDescr_t descrB,
+                  int                      nnzB,
+                  const cuComplex*         csrSortedValB,
+                  const int*               csrSortedRowPtrB,
+                  const int*               csrSortedColIndB,
+                  const cusparseMatDescr_t descrC,
+                  cuComplex*               csrSortedValC,
+                  int*                     csrSortedRowPtrC,
+                  int*                     csrSortedColIndC,
+                  void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrgeam2(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  const cuDoubleComplex*   alpha,
+                  const cusparseMatDescr_t descrA,
+                  int                      nnzA,
+                  const cuDoubleComplex*   csrSortedValA,
+                  const int*               csrSortedRowPtrA,
+                  const int*               csrSortedColIndA,
+                  const cuDoubleComplex*   beta,
+                  const cusparseMatDescr_t descrB,
+                  int                      nnzB,
+                  const cuDoubleComplex*   csrSortedValB,
+                  const int*               csrSortedRowPtrB,
+                  const int*               csrSortedColIndB,
+                  const cusparseMatDescr_t descrC,
+                  cuDoubleComplex*         csrSortedValC,
+                  int*                     csrSortedRowPtrC,
+                  int*                     csrSortedColIndC,
+                  void*                    pBuffer);
+
+//##############################################################################
+//# SPARSE MATRIX REORDERING
+//##############################################################################
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsrcolor(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  const float*              csrSortedValA,
+                  const int*                csrSortedRowPtrA,
+                  const int*                csrSortedColIndA,
+                  const float*              fractionToColor,
+                  int*                      ncolors,
+                  int*                      coloring,
+                  int*                      reordering,
+                  const cusparseColorInfo_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsrcolor(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  const double*            csrSortedValA,
+                  const int*               csrSortedRowPtrA,
+                  const int*               csrSortedColIndA,
+                  const double*            fractionToColor,
+                  int*                     ncolors,
+                  int*                     coloring,
+                  int*                     reordering,
+                  const cusparseColorInfo_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsrcolor(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  const cuComplex*          csrSortedValA,
+                  const int*                csrSortedRowPtrA,
+                  const int*                csrSortedColIndA,
+                  const float*              fractionToColor,
+                  int*                      ncolors,
+                  int*                      coloring,
+                  int*                      reordering,
+                  const cusparseColorInfo_t info);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsrcolor(cusparseHandle_t          handle,
+                  int                       m,
+                  int                       nnz,
+                  const cusparseMatDescr_t  descrA,
+                  const cuDoubleComplex*    csrSortedValA,
+                  const int*                csrSortedRowPtrA,
+                  const int*                csrSortedColIndA,
+                  const double*             fractionToColor,
+                  int*                      ncolors,
+                  int*                      coloring,
+                  int*                      reordering,
+                  const cusparseColorInfo_t info);
+
+//##############################################################################
+//# SPARSE FORMAT CONVERSION
+//##############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSnnz(cusparseHandle_t         handle,
+             cusparseDirection_t      dirA,
+             int                      m,
+             int                      n,
+             const cusparseMatDescr_t descrA,
+             const float*             A,
+             int                      lda,
+             int*                     nnzPerRowCol,
+             int*                     nnzTotalDevHostPtr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnnz(cusparseHandle_t         handle,
+             cusparseDirection_t      dirA,
+             int                      m,
+             int                      n,
+             const cusparseMatDescr_t descrA,
+             const double*            A,
+             int                      lda,
+             int*                     nnzPerRowCol,
+             int*                     nnzTotalDevHostPtr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCnnz(cusparseHandle_t         handle,
+             cusparseDirection_t      dirA,
+             int                      m,
+             int                      n,
+             const cusparseMatDescr_t descrA,
+             const cuComplex*         A,
+             int                      lda,
+             int*                     nnzPerRowCol,
+             int*                     nnzTotalDevHostPtr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZnnz(cusparseHandle_t         handle,
+             cusparseDirection_t      dirA,
+             int                      m,
+             int                      n,
+             const cusparseMatDescr_t descrA,
+             const cuDoubleComplex*   A,
+             int                      lda,
+             int*                     nnzPerRowCol,
+             int*                     nnzTotalDevHostPtr);
+
+//##############################################################################
+//# SPARSE FORMAT CONVERSION
+//##############################################################################
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSnnz_compress(cusparseHandle_t         handle,
+                      int                      m,
+                      const cusparseMatDescr_t descr,
+                      const float*             csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      int*                     nnzPerRow,
+                      int*                     nnzC,
+                      float                    tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDnnz_compress(cusparseHandle_t         handle,
+                      int                      m,
+                      const cusparseMatDescr_t descr,
+                      const double*            csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      int*                     nnzPerRow,
+                      int*                     nnzC,
+                      double                   tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCnnz_compress(cusparseHandle_t         handle,
+                      int                      m,
+                      const cusparseMatDescr_t descr,
+                      const cuComplex*         csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      int*                     nnzPerRow,
+                      int*                     nnzC,
+                      cuComplex                tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZnnz_compress(cusparseHandle_t         handle,
+                      int                      m,
+                      const cusparseMatDescr_t descr,
+                      const cuDoubleComplex*   csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      int*                     nnzPerRow,
+                      int*                     nnzC,
+                      cuDoubleComplex          tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsr2csr_compress(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      n,
+                          const cusparseMatDescr_t descrA,
+                          const float*             csrSortedValA,
+                          const int*               csrSortedColIndA,
+                          const int*               csrSortedRowPtrA,
+                          int                      nnzA,
+                          const int*               nnzPerRow,
+                          float*                   csrSortedValC,
+                          int*                     csrSortedColIndC,
+                          int*                     csrSortedRowPtrC,
+                          float                    tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsr2csr_compress(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      n,
+                          const cusparseMatDescr_t descrA,
+                          const double*            csrSortedValA,
+                          const int*               csrSortedColIndA,
+                          const int*               csrSortedRowPtrA,
+                          int                      nnzA,
+                          const int*               nnzPerRow,
+                          double*                  csrSortedValC,
+                          int*                     csrSortedColIndC,
+                          int*                     csrSortedRowPtrC,
+                          double                   tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsr2csr_compress(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      n,
+                          const cusparseMatDescr_t descrA,
+                          const cuComplex*         csrSortedValA,
+                          const int*               csrSortedColIndA,
+                          const int*               csrSortedRowPtrA,
+                          int                      nnzA,
+                          const int*               nnzPerRow,
+                          cuComplex*               csrSortedValC,
+                          int*                     csrSortedColIndC,
+                          int*                     csrSortedRowPtrC,
+                          cuComplex                tol);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsr2csr_compress(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      n,
+                          const cusparseMatDescr_t descrA,
+                          const cuDoubleComplex*   csrSortedValA,
+                          const int*               csrSortedColIndA,
+                          const int*               csrSortedRowPtrA,
+                          int                      nnzA,
+                          const int*               nnzPerRow,
+                          cuDoubleComplex*         csrSortedValC,
+                          int*                     csrSortedColIndC,
+                          int*                     csrSortedRowPtrC,
+                          cuDoubleComplex          tol);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcoo2csr(cusparseHandle_t    handle,
+                 const int*          cooRowInd,
+                 int                 nnz,
+                 int                 m,
+                 int*                csrSortedRowPtr,
+                 cusparseIndexBase_t idxBase);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsr2coo(cusparseHandle_t    handle,
+                 const int*          csrSortedRowPtr,
+                 int                 nnz,
+                 int                 m,
+                 int*                cooRowInd,
+                 cusparseIndexBase_t idxBase);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsr2bsrNnz(cusparseHandle_t         handle,
+                    cusparseDirection_t      dirA,
+                    int                      m,
+                    int                      n,
+                    const cusparseMatDescr_t descrA,
+                    const int*               csrSortedRowPtrA,
+                    const int*               csrSortedColIndA,
+                    int                      blockDim,
+                    const cusparseMatDescr_t descrC,
+                    int*                     bsrSortedRowPtrC,
+                    int*                     nnzTotalDevHostPtr);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsr2bsr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      m,
+                 int                      n,
+                 const cusparseMatDescr_t descrA,
+                 const float*             csrSortedValA,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 float*                   bsrSortedValC,
+                 int*                     bsrSortedRowPtrC,
+                 int*                     bsrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsr2bsr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      m,
+                 int                      n,
+                 const cusparseMatDescr_t descrA,
+                 const double*            csrSortedValA,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 double*                  bsrSortedValC,
+                 int*                     bsrSortedRowPtrC,
+                 int*                     bsrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsr2bsr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      m,
+                 int                      n,
+                 const cusparseMatDescr_t descrA,
+                 const cuComplex*         csrSortedValA,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 cuComplex*               bsrSortedValC,
+                 int*                     bsrSortedRowPtrC,
+                 int*                     bsrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsr2bsr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      m,
+                 int                      n,
+                 const cusparseMatDescr_t descrA,
+                 const cuDoubleComplex*   csrSortedValA,
+                 const int*               csrSortedRowPtrA,
+                 const int*               csrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 cuDoubleComplex*         bsrSortedValC,
+                 int*                     bsrSortedRowPtrC,
+                 int*                     bsrSortedColIndC);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSbsr2csr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nb,
+                 const cusparseMatDescr_t descrA,
+                 const float*             bsrSortedValA,
+                 const int*               bsrSortedRowPtrA,
+                 const int*               bsrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 float*                   csrSortedValC,
+                 int*                     csrSortedRowPtrC,
+                 int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDbsr2csr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nb,
+                 const cusparseMatDescr_t descrA,
+                 const double*            bsrSortedValA,
+                 const int*               bsrSortedRowPtrA,
+                 const int*               bsrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 double*                  csrSortedValC,
+                 int*                     csrSortedRowPtrC,
+                 int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCbsr2csr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nb,
+                 const cusparseMatDescr_t descrA,
+                 const cuComplex*         bsrSortedValA,
+                 const int*               bsrSortedRowPtrA,
+                 const int*               bsrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 cuComplex*               csrSortedValC,
+                 int*                     csrSortedRowPtrC,
+                 int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZbsr2csr(cusparseHandle_t         handle,
+                 cusparseDirection_t      dirA,
+                 int                      mb,
+                 int                      nb,
+                 const cusparseMatDescr_t descrA,
+                 const cuDoubleComplex*   bsrSortedValA,
+                 const int*               bsrSortedRowPtrA,
+                 const int*               bsrSortedColIndA,
+                 int                      blockDim,
+                 const cusparseMatDescr_t descrC,
+                 cuDoubleComplex*         csrSortedValC,
+                 int*                     csrSortedRowPtrC,
+                 int*                     csrSortedColIndC);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2gebsc_bufferSize(cusparseHandle_t handle,
+                                int              mb,
+                                int              nb,
+                                int              nnzb,
+                                const float*     bsrSortedVal,
+                                const int*       bsrSortedRowPtr,
+                                const int*       bsrSortedColInd,
+                                int              rowBlockDim,
+                                int              colBlockDim,
+                                int*             pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2gebsc_bufferSize(cusparseHandle_t handle,
+                                int              mb,
+                                int              nb,
+                                int              nnzb,
+                                const double*    bsrSortedVal,
+                                const int*       bsrSortedRowPtr,
+                                const int*       bsrSortedColInd,
+                                int              rowBlockDim,
+                                int              colBlockDim,
+                                int*             pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2gebsc_bufferSize(cusparseHandle_t handle,
+                                int              mb,
+                                int              nb,
+                                int              nnzb,
+                                const cuComplex* bsrSortedVal,
+                                const int*       bsrSortedRowPtr,
+                                const int*       bsrSortedColInd,
+                                int              rowBlockDim,
+                                int              colBlockDim,
+                                int*             pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2gebsc_bufferSize(cusparseHandle_t       handle,
+                                int                    mb,
+                                int                    nb,
+                                int                    nnzb,
+                                const cuDoubleComplex* bsrSortedVal,
+                                const int*             bsrSortedRowPtr,
+                                const int*             bsrSortedColInd,
+                                int                    rowBlockDim,
+                                int                    colBlockDim,
+                                int*                   pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2gebsc_bufferSizeExt(cusparseHandle_t handle,
+                                   int              mb,
+                                   int              nb,
+                                   int              nnzb,
+                                   const float*     bsrSortedVal,
+                                   const int*       bsrSortedRowPtr,
+                                   const int*       bsrSortedColInd,
+                                   int              rowBlockDim,
+                                   int              colBlockDim,
+                                   size_t*          pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2gebsc_bufferSizeExt(cusparseHandle_t handle,
+                                   int              mb,
+                                   int              nb,
+                                   int              nnzb,
+                                   const double*    bsrSortedVal,
+                                   const int*       bsrSortedRowPtr,
+                                   const int*       bsrSortedColInd,
+                                   int              rowBlockDim,
+                                   int              colBlockDim,
+                                   size_t*          pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2gebsc_bufferSizeExt(cusparseHandle_t handle,
+                                   int              mb,
+                                   int              nb,
+                                   int              nnzb,
+                                   const cuComplex* bsrSortedVal,
+                                   const int*       bsrSortedRowPtr,
+                                   const int*       bsrSortedColInd,
+                                   int              rowBlockDim,
+                                   int              colBlockDim,
+                                   size_t*          pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2gebsc_bufferSizeExt(cusparseHandle_t       handle,
+                                   int                    mb,
+                                   int                    nb,
+                                   int                    nnzb,
+                                   const cuDoubleComplex* bsrSortedVal,
+                                   const int*             bsrSortedRowPtr,
+                                   const int*             bsrSortedColInd,
+                                   int                    rowBlockDim,
+                                   int                    colBlockDim,
+                                   size_t*                pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2gebsc(cusparseHandle_t handle,
+                     int              mb,
+                     int              nb,
+                     int              nnzb,
+                     const float*     bsrSortedVal,
+                     const int* bsrSortedRowPtr,
+                     const int* bsrSortedColInd,
+                     int        rowBlockDim,
+                     int        colBlockDim,
+                     float*     bscVal,
+                     int*       bscRowInd,
+                     int*       bscColPtr,
+                     cusparseAction_t copyValues,
+                     cusparseIndexBase_t idxBase,
+                     void*               pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2gebsc(cusparseHandle_t    handle,
+                     int                 mb,
+                     int                 nb,
+                     int                 nnzb,
+                     const double*       bsrSortedVal,
+                     const int*          bsrSortedRowPtr,
+                     const int*          bsrSortedColInd,
+                     int                 rowBlockDim,
+                     int                 colBlockDim,
+                     double*             bscVal,
+                     int*                bscRowInd,
+                     int*                bscColPtr,
+                     cusparseAction_t    copyValues,
+                     cusparseIndexBase_t idxBase,
+                     void*               pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2gebsc(cusparseHandle_t    handle,
+                     int                 mb,
+                     int                 nb,
+                     int                 nnzb,
+                     const cuComplex*    bsrSortedVal,
+                     const int*          bsrSortedRowPtr,
+                     const int*          bsrSortedColInd,
+                     int                 rowBlockDim,
+                     int                 colBlockDim,
+                     cuComplex*          bscVal,
+                     int*                bscRowInd,
+                     int*                bscColPtr,
+                     cusparseAction_t    copyValues,
+                     cusparseIndexBase_t idxBase,
+                     void*               pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2gebsc(cusparseHandle_t       handle,
+                     int                    mb,
+                     int                    nb,
+                     int                    nnzb,
+                     const cuDoubleComplex* bsrSortedVal,
+                     const int*             bsrSortedRowPtr,
+                     const int*             bsrSortedColInd,
+                     int                    rowBlockDim,
+                     int                    colBlockDim,
+                     cuDoubleComplex*       bscVal,
+                     int*                   bscRowInd,
+                     int*                   bscColPtr,
+                     cusparseAction_t       copyValues,
+                     cusparseIndexBase_t    idxBase,
+                     void*                  pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseXgebsr2csr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      mb,
+                   int                      nb,
+                   const cusparseMatDescr_t descrA,
+                   const int*               bsrSortedRowPtrA,
+                   const int*               bsrSortedColIndA,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   const cusparseMatDescr_t descrC,
+                   int*                     csrSortedRowPtrC,
+                   int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2csr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      mb,
+                   int                      nb,
+                   const cusparseMatDescr_t descrA,
+                   const float*             bsrSortedValA,
+                   const int*               bsrSortedRowPtrA,
+                   const int*               bsrSortedColIndA,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   const cusparseMatDescr_t descrC,
+                   float*                   csrSortedValC,
+                   int*                     csrSortedRowPtrC,
+                   int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2csr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      mb,
+                   int                      nb,
+                   const cusparseMatDescr_t descrA,
+                   const double*            bsrSortedValA,
+                   const int*               bsrSortedRowPtrA,
+                   const int*               bsrSortedColIndA,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   const cusparseMatDescr_t descrC,
+                   double*                  csrSortedValC,
+                   int*                     csrSortedRowPtrC,
+                   int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2csr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      mb,
+                   int                      nb,
+                   const cusparseMatDescr_t descrA,
+                   const cuComplex*         bsrSortedValA,
+                   const int*               bsrSortedRowPtrA,
+                   const int*               bsrSortedColIndA,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   const cusparseMatDescr_t descrC,
+                   cuComplex*               csrSortedValC,
+                   int*                     csrSortedRowPtrC,
+                   int*                     csrSortedColIndC);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2csr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      mb,
+                   int                      nb,
+                   const cusparseMatDescr_t descrA,
+                   const cuDoubleComplex*   bsrSortedValA,
+                   const int*               bsrSortedRowPtrA,
+                   const int*               bsrSortedColIndA,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   const cusparseMatDescr_t descrC,
+                   cuDoubleComplex*         csrSortedValC,
+                   int*                     csrSortedRowPtrC,
+                   int*                     csrSortedColIndC);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseScsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              int                      m,
+                              int                      n,
+                              const cusparseMatDescr_t descrA,
+                              const float*             csrSortedValA,
+                              const int*               csrSortedRowPtrA,
+                              const int*               csrSortedColIndA,
+                              int                      rowBlockDim,
+                              int                      colBlockDim,
+                              int*                     pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              int                      m,
+                              int                      n,
+                              const cusparseMatDescr_t descrA,
+                              const double*            csrSortedValA,
+                              const int*               csrSortedRowPtrA,
+                              const int*               csrSortedColIndA,
+                              int                      rowBlockDim,
+                              int                      colBlockDim,
+                              int*                     pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              int                      m,
+                              int                      n,
+                              const cusparseMatDescr_t descrA,
+                              const cuComplex*         csrSortedValA,
+                              const int*               csrSortedRowPtrA,
+                              const int*               csrSortedColIndA,
+                              int                      rowBlockDim,
+                              int                      colBlockDim,
+                              int*                     pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                              cusparseDirection_t      dirA,
+                              int                      m,
+                              int                      n,
+                              const cusparseMatDescr_t descrA,
+                              const cuDoubleComplex*   csrSortedValA,
+                              const int*               csrSortedRowPtrA,
+                              const int*               csrSortedColIndA,
+                              int                      rowBlockDim,
+                              int                      colBlockDim,
+                              int*                     pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseScsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                 cusparseDirection_t      dirA,
+                                 int                      m,
+                                 int                      n,
+                                 const cusparseMatDescr_t descrA,
+                                 const float*             csrSortedValA,
+                                 const int*               csrSortedRowPtrA,
+                                 const int*               csrSortedColIndA,
+                                 int                      rowBlockDim,
+                                 int                      colBlockDim,
+                                 size_t*                  pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                 cusparseDirection_t      dirA,
+                                 int                      m,
+                                 int                      n,
+                                 const cusparseMatDescr_t descrA,
+                                 const double*            csrSortedValA,
+                                 const int*               csrSortedRowPtrA,
+                                 const int*               csrSortedColIndA,
+                                 int                      rowBlockDim,
+                                 int                      colBlockDim,
+                                 size_t*                  pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                 cusparseDirection_t      dirA,
+                                 int                      m,
+                                 int                      n,
+                                 const cusparseMatDescr_t descrA,
+                                 const cuComplex*         csrSortedValA,
+                                 const int*               csrSortedRowPtrA,
+                                 const int*               csrSortedColIndA,
+                                 int                      rowBlockDim,
+                                 int                      colBlockDim,
+                                 size_t*                  pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                 cusparseDirection_t      dirA,
+                                 int                      m,
+                                 int                      n,
+                                 const cusparseMatDescr_t descrA,
+                                 const cuDoubleComplex*   csrSortedValA,
+                                 const int*               csrSortedRowPtrA,
+                                 const int*               csrSortedColIndA,
+                                 int                      rowBlockDim,
+                                 int                      colBlockDim,
+                                 size_t*                  pBufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsr2gebsrNnz(cusparseHandle_t         handle,
+                      cusparseDirection_t      dirA,
+                      int                      m,
+                      int                      n,
+                      const cusparseMatDescr_t descrA,
+                      const int*               csrSortedRowPtrA,
+                      const int*               csrSortedColIndA,
+                      const cusparseMatDescr_t descrC,
+                      int*                     bsrSortedRowPtrC,
+                      int                      rowBlockDim,
+                      int                      colBlockDim,
+                      int*                     nnzTotalDevHostPtr,
+                      void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseScsr2gebsr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      m,
+                   int                      n,
+                   const cusparseMatDescr_t descrA,
+                   const float*             csrSortedValA,
+                   const int*               csrSortedRowPtrA,
+                   const int*               csrSortedColIndA,
+                   const cusparseMatDescr_t descrC,
+                   float*                   bsrSortedValC,
+                   int*                     bsrSortedRowPtrC,
+                   int*                     bsrSortedColIndC,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsr2gebsr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      m,
+                   int                      n,
+                   const cusparseMatDescr_t descrA,
+                   const double*            csrSortedValA,
+                   const int*               csrSortedRowPtrA,
+                   const int*               csrSortedColIndA,
+                   const cusparseMatDescr_t descrC,
+                   double*                  bsrSortedValC,
+                   int*                     bsrSortedRowPtrC,
+                   int*                     bsrSortedColIndC,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsr2gebsr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      m,
+                   int                      n,
+                   const cusparseMatDescr_t descrA,
+                   const cuComplex*         csrSortedValA,
+                   const int*               csrSortedRowPtrA,
+                   const int*               csrSortedColIndA,
+                   const cusparseMatDescr_t descrC,
+                   cuComplex*               bsrSortedValC,
+                   int*                     bsrSortedRowPtrC,
+                   int*                     bsrSortedColIndC,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsr2gebsr(cusparseHandle_t         handle,
+                   cusparseDirection_t      dirA,
+                   int                      m,
+                   int                      n,
+                   const cusparseMatDescr_t descrA,
+                   const cuDoubleComplex*   csrSortedValA,
+                   const int*               csrSortedRowPtrA,
+                   const int*               csrSortedColIndA,
+                   const cusparseMatDescr_t descrC,
+                   cuDoubleComplex*         bsrSortedValC,
+                   int*                     bsrSortedRowPtrC,
+                   int*                     bsrSortedColIndC,
+                   int                      rowBlockDim,
+                   int                      colBlockDim,
+                   void*                    pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                const float*             bsrSortedValA,
+                                const int*               bsrSortedRowPtrA,
+                                const int*               bsrSortedColIndA,
+                                int                      rowBlockDimA,
+                                int                      colBlockDimA,
+                                int                      rowBlockDimC,
+                                int                      colBlockDimC,
+                                int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                const double*            bsrSortedValA,
+                                const int*               bsrSortedRowPtrA,
+                                const int*               bsrSortedColIndA,
+                                int                      rowBlockDimA,
+                                int                      colBlockDimA,
+                                int                      rowBlockDimC,
+                                int                      colBlockDimC,
+                                int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                const cuComplex*         bsrSortedValA,
+                                const int*               bsrSortedRowPtrA,
+                                const int*               bsrSortedColIndA,
+                                int                      rowBlockDimA,
+                                int                      colBlockDimA,
+                                int                      rowBlockDimC,
+                                int                      colBlockDimC,
+                                int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2gebsr_bufferSize(cusparseHandle_t         handle,
+                                cusparseDirection_t      dirA,
+                                int                      mb,
+                                int                      nb,
+                                int                      nnzb,
+                                const cusparseMatDescr_t descrA,
+                                const cuDoubleComplex*   bsrSortedValA,
+                                const int*               bsrSortedRowPtrA,
+                                const int*               bsrSortedColIndA,
+                                int                      rowBlockDimA,
+                                int                      colBlockDimA,
+                                int                      rowBlockDimC,
+                                int                      colBlockDimC,
+                                int*                     pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                   cusparseDirection_t      dirA,
+                                   int                      mb,
+                                   int                      nb,
+                                   int                      nnzb,
+                                   const cusparseMatDescr_t descrA,
+                                   const float*             bsrSortedValA,
+                                   const int*               bsrSortedRowPtrA,
+                                   const int*               bsrSortedColIndA,
+                                   int                      rowBlockDimA,
+                                   int                      colBlockDimA,
+                                   int                      rowBlockDimC,
+                                   int                      colBlockDimC,
+                                   size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                   cusparseDirection_t      dirA,
+                                   int                      mb,
+                                   int                      nb,
+                                   int                      nnzb,
+                                   const cusparseMatDescr_t descrA,
+                                   const double*            bsrSortedValA,
+                                   const int*               bsrSortedRowPtrA,
+                                   const int*               bsrSortedColIndA,
+                                   int                      rowBlockDimA,
+                                   int                      colBlockDimA,
+                                   int                      rowBlockDimC,
+                                   int                      colBlockDimC,
+                                   size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                   cusparseDirection_t      dirA,
+                                   int                      mb,
+                                   int                      nb,
+                                   int                      nnzb,
+                                   const cusparseMatDescr_t descrA,
+                                   const cuComplex*         bsrSortedValA,
+                                   const int*               bsrSortedRowPtrA,
+                                   const int*               bsrSortedColIndA,
+                                   int                      rowBlockDimA,
+                                   int                      colBlockDimA,
+                                   int                      rowBlockDimC,
+                                   int                      colBlockDimC,
+                                   size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2gebsr_bufferSizeExt(cusparseHandle_t         handle,
+                                   cusparseDirection_t      dirA,
+                                   int                      mb,
+                                   int                      nb,
+                                   int                      nnzb,
+                                   const cusparseMatDescr_t descrA,
+                                   const cuDoubleComplex*   bsrSortedValA,
+                                   const int*               bsrSortedRowPtrA,
+                                   const int*               bsrSortedColIndA,
+                                   int                      rowBlockDimA,
+                                   int                      colBlockDimA,
+                                   int                      rowBlockDimC,
+                                   int                      colBlockDimC,
+                                   size_t*                  pBufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseXgebsr2gebsrNnz(cusparseHandle_t         handle,
+                        cusparseDirection_t      dirA,
+                        int                      mb,
+                        int                      nb,
+                        int                      nnzb,
+                        const cusparseMatDescr_t descrA,
+                        const int*               bsrSortedRowPtrA,
+                        const int*               bsrSortedColIndA,
+                        int                      rowBlockDimA,
+                        int                      colBlockDimA,
+                        const cusparseMatDescr_t descrC,
+                        int*                     bsrSortedRowPtrC,
+                        int                      rowBlockDimC,
+                        int                      colBlockDimC,
+                        int*                     nnzTotalDevHostPtr,
+                        void*                    pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSgebsr2gebsr(cusparseHandle_t         handle,
+                     cusparseDirection_t      dirA,
+                     int                      mb,
+                     int                      nb,
+                     int                      nnzb,
+                     const cusparseMatDescr_t descrA,
+                     const float*             bsrSortedValA,
+                     const int*               bsrSortedRowPtrA,
+                     const int*               bsrSortedColIndA,
+                     int                      rowBlockDimA,
+                     int                      colBlockDimA,
+                     const cusparseMatDescr_t descrC,
+                     float*                   bsrSortedValC,
+                     int*                     bsrSortedRowPtrC,
+                     int*                     bsrSortedColIndC,
+                     int                      rowBlockDimC,
+                     int                      colBlockDimC,
+                     void*                    pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseDgebsr2gebsr(cusparseHandle_t         handle,
+                     cusparseDirection_t      dirA,
+                     int                      mb,
+                     int                      nb,
+                     int                      nnzb,
+                     const cusparseMatDescr_t descrA,
+                     const double*            bsrSortedValA,
+                     const int*               bsrSortedRowPtrA,
+                     const int*               bsrSortedColIndA,
+                     int                      rowBlockDimA,
+                     int                      colBlockDimA,
+                     const cusparseMatDescr_t descrC,
+                     double*                  bsrSortedValC,
+                     int*                     bsrSortedRowPtrC,
+                     int*                     bsrSortedColIndC,
+                     int                      rowBlockDimC,
+                     int                      colBlockDimC,
+                     void*                    pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseCgebsr2gebsr(cusparseHandle_t         handle,
+                     cusparseDirection_t      dirA,
+                     int                      mb,
+                     int                      nb,
+                     int                      nnzb,
+                     const cusparseMatDescr_t descrA,
+                     const cuComplex*         bsrSortedValA,
+                     const int*               bsrSortedRowPtrA,
+                     const int*               bsrSortedColIndA,
+                     int                      rowBlockDimA,
+                     int                      colBlockDimA,
+                     const cusparseMatDescr_t descrC,
+                     cuComplex*               bsrSortedValC,
+                     int*                     bsrSortedRowPtrC,
+                     int*                     bsrSortedColIndC,
+                     int                      rowBlockDimC,
+                     int                      colBlockDimC,
+                     void*                    pBuffer);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseZgebsr2gebsr(cusparseHandle_t         handle,
+                     cusparseDirection_t      dirA,
+                     int                      mb,
+                     int                      nb,
+                     int                      nnzb,
+                     const cusparseMatDescr_t descrA,
+                     const cuDoubleComplex*   bsrSortedValA,
+                     const int*               bsrSortedRowPtrA,
+                     const int*               bsrSortedColIndA,
+                     int                      rowBlockDimA,
+                     int                      colBlockDimA,
+                     const cusparseMatDescr_t descrC,
+                     cuDoubleComplex*         bsrSortedValC,
+                     int*                     bsrSortedRowPtrC,
+                     int*                     bsrSortedColIndC,
+                     int                      rowBlockDimC,
+                     int                      colBlockDimC,
+                     void*                    pBuffer);
+
+//##############################################################################
+//# SPARSE MATRIX SORTING
+//##############################################################################
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateIdentityPermutation(cusparseHandle_t handle,
+                                  int              n,
+                                  int*             p);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcoosort_bufferSizeExt(cusparseHandle_t handle,
+                               int              m,
+                               int              n,
+                               int              nnz,
+                               const int*       cooRowsA,
+                               const int*       cooColsA,
+                               size_t*          pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcoosortByRow(cusparseHandle_t handle,
+                      int              m,
+                      int              n,
+                      int              nnz,
+                      int*             cooRowsA,
+                      int*             cooColsA,
+                      int*             P,
+                      void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcoosortByColumn(cusparseHandle_t handle,
+                         int              m,
+                         int              n,
+                         int              nnz,
+                         int*             cooRowsA,
+                         int*             cooColsA,
+                         int*             P,
+                         void*            pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsrsort_bufferSizeExt(cusparseHandle_t handle,
+                               int              m,
+                               int              n,
+                               int              nnz,
+                               const int*       csrRowPtrA,
+                               const int*       csrColIndA,
+                               size_t*          pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcsrsort(cusparseHandle_t         handle,
+                 int                      m,
+                 int                      n,
+                 int                      nnz,
+                 const cusparseMatDescr_t descrA,
+                 const int*               csrRowPtrA,
+                 int*                     csrColIndA,
+                 int*                     P,
+                 void*                    pBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcscsort_bufferSizeExt(cusparseHandle_t handle,
+                               int              m,
+                               int              n,
+                               int              nnz,
+                               const int*       cscColPtrA,
+                               const int*       cscRowIndA,
+                               size_t*          pBufferSizeInBytes);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseXcscsort(cusparseHandle_t         handle,
+                 int                      m,
+                 int                      n,
+                 int                      nnz,
+                 const cusparseMatDescr_t descrA,
+                 const int*               cscColPtrA,
+                 int*                     cscRowIndA,
+                 int*                     P,
+                 void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsru2csr_bufferSizeExt(cusparseHandle_t handle,
+                                int              m,
+                                int              n,
+                                int              nnz,
+                                float*           csrVal,
+                                const int*       csrRowPtr,
+                                int*             csrColInd,
+                                csru2csrInfo_t   info,
+                                size_t*          pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsru2csr_bufferSizeExt(cusparseHandle_t handle,
+                                int              m,
+                                int              n,
+                                int              nnz,
+                                double*          csrVal,
+                                const int*       csrRowPtr,
+                                int*             csrColInd,
+                                csru2csrInfo_t   info,
+                                size_t*          pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsru2csr_bufferSizeExt(cusparseHandle_t handle,
+                                int              m,
+                                int              n,
+                                int              nnz,
+                                cuComplex*       csrVal,
+                                const int*       csrRowPtr,
+                                int*             csrColInd,
+                                csru2csrInfo_t   info,
+                                size_t*          pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsru2csr_bufferSizeExt(cusparseHandle_t handle,
+                                int              m,
+                                int              n,
+                                int              nnz,
+                                cuDoubleComplex* csrVal,
+                                const int*       csrRowPtr,
+                                int*             csrColInd,
+                                csru2csrInfo_t   info,
+                                size_t*          pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsru2csr(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  float*                   csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsru2csr(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  double*                  csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsru2csr(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  cuComplex*               csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsru2csr(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  cuDoubleComplex*         csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseScsr2csru(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  float*                   csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDcsr2csru(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  double*                  csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseCcsr2csru(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  cuComplex*               csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseZcsr2csru(cusparseHandle_t         handle,
+                  int                      m,
+                  int                      n,
+                  int                      nnz,
+                  const cusparseMatDescr_t descrA,
+                  cuDoubleComplex*         csrVal,
+                  const int*               csrRowPtr,
+                  int*                     csrColInd,
+                  csru2csrInfo_t           info,
+                  void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneDense2csr_bufferSizeExt(cusparseHandle_t         handle,
+                                      int                      m,
+                                      int                      n,
+                                      const __half*            A,
+                                      int                      lda,
+                                      const __half*            threshold,
+                                      const cusparseMatDescr_t descrC,
+                                      const __half*            csrSortedValC,
+                                      const int*               csrSortedRowPtrC,
+                                      const int*               csrSortedColIndC,
+                                      size_t* pBufferSizeInBytes);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneDense2csr_bufferSizeExt(cusparseHandle_t         handle,
+                                      int                      m,
+                                      int                      n,
+                                      const float*             A,
+                                      int                      lda,
+                                      const float*             threshold,
+                                      const cusparseMatDescr_t descrC,
+                                      const float*             csrSortedValC,
+                                      const int*               csrSortedRowPtrC,
+                                      const int*               csrSortedColIndC,
+                                      size_t* pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneDense2csr_bufferSizeExt(cusparseHandle_t         handle,
+                                      int                      m,
+                                      int                      n,
+                                      const double*            A,
+                                      int                      lda,
+                                      const double*            threshold,
+                                      const cusparseMatDescr_t descrC,
+                                      const double*            csrSortedValC,
+                                      const int*               csrSortedRowPtrC,
+                                      const int*               csrSortedColIndC,
+                                      size_t*               pBufferSizeInBytes);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneDense2csrNnz(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      n,
+                           const __half*            A,
+                           int                      lda,
+                           const __half*            threshold,
+                           const cusparseMatDescr_t descrC,
+                           int*                     csrRowPtrC,
+                           int*                     nnzTotalDevHostPtr,
+                           void*                    pBuffer);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneDense2csrNnz(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      n,
+                           const float*             A,
+                           int                      lda,
+                           const float*             threshold,
+                           const cusparseMatDescr_t descrC,
+                           int*                     csrRowPtrC,
+                           int*                     nnzTotalDevHostPtr,
+                           void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneDense2csrNnz(cusparseHandle_t         handle,
+                           int                      m,
+                           int                      n,
+                           const double*            A,
+                           int                      lda,
+                           const double*            threshold,
+                           const cusparseMatDescr_t descrC,
+                           int*                     csrSortedRowPtrC,
+                           int*                     nnzTotalDevHostPtr,
+                           void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneDense2csr(cusparseHandle_t         handle,
+                        int                      m,
+                        int                      n,
+                        const __half*            A,
+                        int                      lda,
+                        const __half*            threshold,
+                        const cusparseMatDescr_t descrC,
+                        __half*                  csrSortedValC,
+                        const int*               csrSortedRowPtrC,
+                        int*                     csrSortedColIndC,
+                        void*                    pBuffer);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneDense2csr(cusparseHandle_t         handle,
+                        int                      m,
+                        int                      n,
+                        const float*             A,
+                        int                      lda,
+                        const float*             threshold,
+                        const cusparseMatDescr_t descrC,
+                        float*                   csrSortedValC,
+                        const int*               csrSortedRowPtrC,
+                        int*                     csrSortedColIndC,
+                        void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneDense2csr(cusparseHandle_t         handle,
+                        int                      m,
+                        int                      n,
+                        const double*            A,
+                        int                      lda,
+                        const double*            threshold,
+                        const cusparseMatDescr_t descrC,
+                        double*                  csrSortedValC,
+                        const int*               csrSortedRowPtrC,
+                        int*                     csrSortedColIndC,
+                        void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneCsr2csr_bufferSizeExt(cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    int                      nnzA,
+                                    const cusparseMatDescr_t descrA,
+                                    const __half*            csrSortedValA,
+                                    const int*               csrSortedRowPtrA,
+                                    const int*               csrSortedColIndA,
+                                    const __half*            threshold,
+                                    const cusparseMatDescr_t descrC,
+                                    const __half*            csrSortedValC,
+                                    const int*               csrSortedRowPtrC,
+                                    const int*               csrSortedColIndC,
+                                    size_t* pBufferSizeInBytes);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneCsr2csr_bufferSizeExt(cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    int                      nnzA,
+                                    const cusparseMatDescr_t descrA,
+                                    const float*             csrSortedValA,
+                                    const int*               csrSortedRowPtrA,
+                                    const int*               csrSortedColIndA,
+                                    const float*             threshold,
+                                    const cusparseMatDescr_t descrC,
+                                    const float*             csrSortedValC,
+                                    const int*               csrSortedRowPtrC,
+                                    const int*               csrSortedColIndC,
+                                    size_t*                 pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneCsr2csr_bufferSizeExt(cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    int                      nnzA,
+                                    const cusparseMatDescr_t descrA,
+                                    const double*            csrSortedValA,
+                                    const int*               csrSortedRowPtrA,
+                                    const int*               csrSortedColIndA,
+                                    const double*            threshold,
+                                    const cusparseMatDescr_t descrC,
+                                    const double*            csrSortedValC,
+                                    const int*               csrSortedRowPtrC,
+                                    const int*               csrSortedColIndC,
+                                    size_t*                 pBufferSizeInBytes);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneCsr2csrNnz(cusparseHandle_t         handle,
+                         int                      m,
+                         int                      n,
+                         int                      nnzA,
+                         const cusparseMatDescr_t descrA,
+                         const __half*            csrSortedValA,
+                         const int*               csrSortedRowPtrA,
+                         const int*               csrSortedColIndA,
+                         const __half*            threshold,
+                         const cusparseMatDescr_t descrC,
+                         int*                     csrSortedRowPtrC,
+                         int*                     nnzTotalDevHostPtr,
+                         void*                    pBuffer);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneCsr2csrNnz(cusparseHandle_t         handle,
+                         int                      m,
+                         int                      n,
+                         int                      nnzA,
+                         const cusparseMatDescr_t descrA,
+                         const float*             csrSortedValA,
+                         const int*               csrSortedRowPtrA,
+                         const int*               csrSortedColIndA,
+                         const float*             threshold,
+                         const cusparseMatDescr_t descrC,
+                         int*                     csrSortedRowPtrC,
+                         int*                     nnzTotalDevHostPtr,
+                         void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneCsr2csrNnz(cusparseHandle_t         handle,
+                          int                      m,
+                          int                      n,
+                          int                      nnzA,
+                          const cusparseMatDescr_t descrA,
+                          const double*            csrSortedValA,
+                          const int*               csrSortedRowPtrA,
+                          const int*               csrSortedColIndA,
+                          const double*            threshold,
+                          const cusparseMatDescr_t descrC,
+                          int*                     csrSortedRowPtrC,
+                          int*                     nnzTotalDevHostPtr,
+                          void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneCsr2csr(cusparseHandle_t         handle,
+                      int                      m,
+                      int                      n,
+                      int                      nnzA,
+                      const cusparseMatDescr_t descrA,
+                      const __half*            csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      const int*               csrSortedColIndA,
+                      const __half*            threshold,
+                      const cusparseMatDescr_t descrC,
+                      __half*                  csrSortedValC,
+                      const int*               csrSortedRowPtrC,
+                      int*                     csrSortedColIndC,
+                      void*                    pBuffer);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneCsr2csr(cusparseHandle_t         handle,
+                      int                      m,
+                      int                      n,
+                      int                      nnzA,
+                      const cusparseMatDescr_t descrA,
+                      const float*             csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      const int*               csrSortedColIndA,
+                      const float*             threshold,
+                      const cusparseMatDescr_t descrC,
+                      float*                   csrSortedValC,
+                      const int*               csrSortedRowPtrC,
+                      int*                     csrSortedColIndC,
+                      void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneCsr2csr(cusparseHandle_t         handle,
+                      int                      m,
+                      int                      n,
+                      int                      nnzA,
+                      const cusparseMatDescr_t descrA,
+                      const double*            csrSortedValA,
+                      const int*               csrSortedRowPtrA,
+                      const int*               csrSortedColIndA,
+                      const double*            threshold,
+                      const cusparseMatDescr_t descrC,
+                      double*                  csrSortedValC,
+                      const int*               csrSortedRowPtrC,
+                      int*                     csrSortedColIndC,
+                      void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneDense2csrByPercentage_bufferSizeExt(
+                                   cusparseHandle_t         handle,
+                                   int                      m,
+                                   int                      n,
+                                   const __half*            A,
+                                   int                      lda,
+                                   float                    percentage,
+                                   const cusparseMatDescr_t descrC,
+                                   const __half*            csrSortedValC,
+                                   const int*               csrSortedRowPtrC,
+                                   const int*               csrSortedColIndC,
+                                   pruneInfo_t              info,
+                                   size_t*                  pBufferSizeInBytes);
+
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneDense2csrByPercentage_bufferSizeExt(
+                                   cusparseHandle_t         handle,
+                                   int                      m,
+                                   int                      n,
+                                   const float*             A,
+                                   int                      lda,
+                                   float                    percentage,
+                                   const cusparseMatDescr_t descrC,
+                                   const float*             csrSortedValC,
+                                   const int*               csrSortedRowPtrC,
+                                   const int*               csrSortedColIndC,
+                                   pruneInfo_t              info,
+                                   size_t*                  pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneDense2csrByPercentage_bufferSizeExt(
+                                   cusparseHandle_t         handle,
+                                   int                      m,
+                                   int                      n,
+                                   const double*            A,
+                                   int                      lda,
+                                   float                    percentage,
+                                   const cusparseMatDescr_t descrC,
+                                   const double*            csrSortedValC,
+                                   const int*               csrSortedRowPtrC,
+                                   const int*               csrSortedColIndC,
+                                   pruneInfo_t              info,
+                                   size_t*                  pBufferSizeInBytes);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneDense2csrNnzByPercentage(
+                                    cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    const __half*            A,
+                                    int                      lda,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    int*                     csrRowPtrC,
+                                    int*                     nnzTotalDevHostPtr,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneDense2csrNnzByPercentage(
+                                    cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    const float*             A,
+                                    int                      lda,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    int*                     csrRowPtrC,
+                                    int*                     nnzTotalDevHostPtr,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneDense2csrNnzByPercentage(
+                                    cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    const double*            A,
+                                    int                      lda,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    int*                     csrRowPtrC,
+                                    int*                     nnzTotalDevHostPtr,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneDense2csrByPercentage(cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    const __half*            A,
+                                    int                      lda,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    __half*                  csrSortedValC,
+                                    const int*               csrSortedRowPtrC,
+                                    int*                     csrSortedColIndC,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneDense2csrByPercentage(cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    const float*             A,
+                                    int                      lda,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    float*                   csrSortedValC,
+                                    const int*               csrSortedRowPtrC,
+                                    int*                     csrSortedColIndC,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneDense2csrByPercentage(cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    const double*            A,
+                                    int                      lda,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    double*                  csrSortedValC,
+                                    const int*               csrSortedRowPtrC,
+                                    int*                     csrSortedColIndC,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneCsr2csrByPercentage_bufferSizeExt(
+                                   cusparseHandle_t         handle,
+                                   int                      m,
+                                   int                      n,
+                                   int                      nnzA,
+                                   const cusparseMatDescr_t descrA,
+                                   const __half*            csrSortedValA,
+                                   const int*               csrSortedRowPtrA,
+                                   const int*               csrSortedColIndA,
+                                   float                    percentage,
+                                   const cusparseMatDescr_t descrC,
+                                   const __half*            csrSortedValC,
+                                   const int*               csrSortedRowPtrC,
+                                   const int*               csrSortedColIndC,
+                                   pruneInfo_t              info,
+                                   size_t*                  pBufferSizeInBytes);
+
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneCsr2csrByPercentage_bufferSizeExt(
+                                   cusparseHandle_t         handle,
+                                   int                      m,
+                                   int                      n,
+                                   int                      nnzA,
+                                   const cusparseMatDescr_t descrA,
+                                   const float*             csrSortedValA,
+                                   const int*               csrSortedRowPtrA,
+                                   const int*               csrSortedColIndA,
+                                   float                    percentage,
+                                   const cusparseMatDescr_t descrC,
+                                   const float*             csrSortedValC,
+                                   const int*               csrSortedRowPtrC,
+                                   const int*               csrSortedColIndC,
+                                   pruneInfo_t              info,
+                                   size_t*                  pBufferSizeInBytes);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneCsr2csrByPercentage_bufferSizeExt(
+                                   cusparseHandle_t         handle,
+                                   int                      m,
+                                   int                      n,
+                                   int                      nnzA,
+                                   const cusparseMatDescr_t descrA,
+                                   const double*            csrSortedValA,
+                                   const int*               csrSortedRowPtrA,
+                                   const int*               csrSortedColIndA,
+                                   float                    percentage,
+                                   const cusparseMatDescr_t descrC,
+                                   const double*            csrSortedValC,
+                                   const int*               csrSortedRowPtrC,
+                                   const int*               csrSortedColIndC,
+                                   pruneInfo_t              info,
+                                   size_t*                  pBufferSizeInBytes);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneCsr2csrNnzByPercentage(
+                                    cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    int                      nnzA,
+                                    const cusparseMatDescr_t descrA,
+                                    const __half*            csrSortedValA,
+                                    const int*               csrSortedRowPtrA,
+                                    const int*               csrSortedColIndA,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    int*                     csrSortedRowPtrC,
+                                    int*                     nnzTotalDevHostPtr,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneCsr2csrNnzByPercentage(
+                                    cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    int                      nnzA,
+                                    const cusparseMatDescr_t descrA,
+                                    const float*             csrSortedValA,
+                                    const int*               csrSortedRowPtrA,
+                                    const int*               csrSortedColIndA,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    int*                     csrSortedRowPtrC,
+                                    int*                     nnzTotalDevHostPtr,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneCsr2csrNnzByPercentage(
+                                    cusparseHandle_t         handle,
+                                    int                      m,
+                                    int                      n,
+                                    int                      nnzA,
+                                    const cusparseMatDescr_t descrA,
+                                    const double*            csrSortedValA,
+                                    const int*               csrSortedRowPtrA,
+                                    const int*               csrSortedColIndA,
+                                    float                    percentage,
+                                    const cusparseMatDescr_t descrC,
+                                    int*                     csrSortedRowPtrC,
+                                    int*                     nnzTotalDevHostPtr,
+                                    pruneInfo_t              info,
+                                    void*                    pBuffer);
+
+#if defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseHpruneCsr2csrByPercentage(cusparseHandle_t         handle,
+                                  int                      m,
+                                  int                      n,
+                                  int                      nnzA,
+                                  const cusparseMatDescr_t descrA,
+                                  const __half*            csrSortedValA,
+                                  const int*               csrSortedRowPtrA,
+                                  const int*               csrSortedColIndA,
+                                  float percentage, /* between 0 to 100 */
+                                  const cusparseMatDescr_t descrC,
+                                  __half*                  csrSortedValC,
+                                  const int*               csrSortedRowPtrC,
+                                  int*                     csrSortedColIndC,
+                                  pruneInfo_t              info,
+                                  void*                    pBuffer);
+
+#endif // defined(__cplusplus)
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseSpruneCsr2csrByPercentage(cusparseHandle_t         handle,
+                                  int                      m,
+                                  int                      n,
+                                  int                      nnzA,
+                                  const cusparseMatDescr_t descrA,
+                                  const float*             csrSortedValA,
+                                  const int*               csrSortedRowPtrA,
+                                  const int*               csrSortedColIndA,
+                                  float                    percentage,
+                                  const cusparseMatDescr_t descrC,
+                                  float*                   csrSortedValC,
+                                  const int*               csrSortedRowPtrC,
+                                  int*                     csrSortedColIndC,
+                                  pruneInfo_t              info,
+                                  void*                    pBuffer);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseDpruneCsr2csrByPercentage(cusparseHandle_t         handle,
+                                  int                      m,
+                                  int                      n,
+                                  int                      nnzA,
+                                  const cusparseMatDescr_t descrA,
+                                  const double*            csrSortedValA,
+                                  const int*               csrSortedRowPtrA,
+                                  const int*               csrSortedColIndA,
+                                  float                    percentage,
+                                  const cusparseMatDescr_t descrC,
+                                  double*                  csrSortedValC,
+                                  const int*               csrSortedRowPtrC,
+                                  int*                     csrSortedColIndC,
+                                  pruneInfo_t              info,
+                                  void*                    pBuffer);
+
+//##############################################################################
+//# CSR2CSC
+//##############################################################################
+
+typedef enum {
+    CUSPARSE_CSR2CSC_ALG_DEFAULT = 1,
+    CUSPARSE_CSR2CSC_ALG1 = 1
+} cusparseCsr2CscAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCsr2cscEx2(cusparseHandle_t     handle,
+                   int                  m,
+                   int                  n,
+                   int                  nnz,
+                   const void*          csrVal,
+                   const int*           csrRowPtr,
+                   const int*           csrColInd,
+                   void*                cscVal,
+                   int*                 cscColPtr,
+                   int*                 cscRowInd,
+                   cudaDataType         valType,
+                   cusparseAction_t     copyValues,
+                   cusparseIndexBase_t  idxBase,
+                   cusparseCsr2CscAlg_t alg,
+                   void*                buffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCsr2cscEx2_bufferSize(cusparseHandle_t     handle,
+                              int                  m,
+                              int                  n,
+                              int                  nnz,
+                              const void*          csrVal,
+                              const int*           csrRowPtr,
+                              const int*           csrColInd,
+                              void*                cscVal,
+                              int*                 cscColPtr,
+                              int*                 cscRowInd,
+                              cudaDataType         valType,
+                              cusparseAction_t     copyValues,
+                              cusparseIndexBase_t  idxBase,
+                              cusparseCsr2CscAlg_t alg,
+                              size_t*              bufferSize);
+
+// #############################################################################
+// # GENERIC APIs - Enumerators and Opaque Data Structures
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_FORMAT_CSR            = 1, ///< Compressed Sparse Row (CSR)
+    CUSPARSE_FORMAT_CSC            = 2, ///< Compressed Sparse Column (CSC)
+    CUSPARSE_FORMAT_COO            = 3, ///< Coordinate (COO) - Structure of Arrays
+    CUSPARSE_FORMAT_BLOCKED_ELL    = 5, ///< Blocked ELL
+    CUSPARSE_FORMAT_BSR            = 6, ///< Blocked Compressed Sparse Row (BSR)
+    CUSPARSE_FORMAT_SLICED_ELLPACK = 7 ///< Sliced ELL
+} cusparseFormat_t;
+
+typedef enum {
+    CUSPARSE_ORDER_COL = 1, ///< Column-Major Order - Matrix memory layout
+    CUSPARSE_ORDER_ROW = 2  ///< Row-Major Order - Matrix memory layout
+} cusparseOrder_t;
+
+typedef enum {
+    CUSPARSE_INDEX_16U = 1, ///< 16-bit unsigned integer for matrix/vector
+                            ///< indices
+    CUSPARSE_INDEX_32I = 2, ///< 32-bit signed integer for matrix/vector indices
+    CUSPARSE_INDEX_64I = 3  ///< 64-bit signed integer for matrix/vector indices
+} cusparseIndexType_t;
+
+//------------------------------------------------------------------------------
+
+struct cusparseSpVecDescr;
+struct cusparseDnVecDescr;
+struct cusparseSpMatDescr;
+struct cusparseDnMatDescr;
+
+typedef struct cusparseSpVecDescr* cusparseSpVecDescr_t;
+typedef struct cusparseDnVecDescr* cusparseDnVecDescr_t;
+typedef struct cusparseSpMatDescr* cusparseSpMatDescr_t;
+typedef struct cusparseDnMatDescr* cusparseDnMatDescr_t;
+
+typedef struct cusparseSpVecDescr const* cusparseConstSpVecDescr_t;
+typedef struct cusparseDnVecDescr const* cusparseConstDnVecDescr_t;
+typedef struct cusparseSpMatDescr const* cusparseConstSpMatDescr_t;
+typedef struct cusparseDnMatDescr const* cusparseConstDnMatDescr_t;
+
+// #############################################################################
+// # SPARSE VECTOR DESCRIPTOR
+// #############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateSpVec(cusparseSpVecDescr_t* spVecDescr,
+                    int64_t               size,
+                    int64_t               nnz,
+                    void*                 indices,
+                    void*                 values,
+                    cusparseIndexType_t   idxType,
+                    cusparseIndexBase_t   idxBase,
+                    cudaDataType          valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstSpVec(cusparseConstSpVecDescr_t* spVecDescr,
+                         int64_t                    size,
+                         int64_t                    nnz,
+                         const void*                indices,
+                         const void*                values,
+                         cusparseIndexType_t        idxType,
+                         cusparseIndexBase_t        idxBase,
+                         cudaDataType               valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroySpVec(cusparseConstSpVecDescr_t spVecDescr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpVecGet(cusparseSpVecDescr_t spVecDescr,
+                 int64_t*             size,
+                 int64_t*             nnz,
+                 void**               indices,
+                 void**               values,
+                 cusparseIndexType_t* idxType,
+                 cusparseIndexBase_t* idxBase,
+                 cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstSpVecGet(cusparseConstSpVecDescr_t spVecDescr,
+                      int64_t*             size,
+                      int64_t*             nnz,
+                      const void**         indices,
+                      const void**         values,
+                      cusparseIndexType_t* idxType,
+                      cusparseIndexBase_t* idxBase,
+                      cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpVecGetIndexBase(cusparseConstSpVecDescr_t spVecDescr,
+                          cusparseIndexBase_t*      idxBase);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpVecGetValues(cusparseSpVecDescr_t spVecDescr,
+                       void**               values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstSpVecGetValues(cusparseConstSpVecDescr_t spVecDescr,
+                            const void**              values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpVecSetValues(cusparseSpVecDescr_t spVecDescr,
+                       void*                values);
+
+// #############################################################################
+// # DENSE VECTOR DESCRIPTOR
+// #############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateDnVec(cusparseDnVecDescr_t* dnVecDescr,
+                    int64_t               size,
+                    void*                 values,
+                    cudaDataType          valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstDnVec(cusparseConstDnVecDescr_t* dnVecDescr,
+                         int64_t                    size,
+                         const void*                values,
+                         cudaDataType               valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyDnVec(cusparseConstDnVecDescr_t dnVecDescr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnVecGet(cusparseDnVecDescr_t dnVecDescr,
+                 int64_t*             size,
+                 void**               values,
+                 cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstDnVecGet(cusparseConstDnVecDescr_t dnVecDescr,
+                      int64_t*                  size,
+                      const void**              values,
+                      cudaDataType*             valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnVecGetValues(cusparseDnVecDescr_t dnVecDescr,
+                       void**               values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstDnVecGetValues(cusparseConstDnVecDescr_t dnVecDescr,
+                            const void**              values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnVecSetValues(cusparseDnVecDescr_t dnVecDescr,
+                       void*                values);
+
+// #############################################################################
+// # SPARSE MATRIX DESCRIPTOR
+// #############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroySpMat(cusparseConstSpMatDescr_t spMatDescr);
+
+ cusparseStatus_t CUSPARSEAPI
+cusparseSpMatGetFormat(cusparseConstSpMatDescr_t spMatDescr,
+                       cusparseFormat_t*         format);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatGetIndexBase(cusparseConstSpMatDescr_t spMatDescr,
+                          cusparseIndexBase_t*      idxBase);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatGetValues(cusparseSpMatDescr_t spMatDescr,
+                       void**               values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstSpMatGetValues(cusparseConstSpMatDescr_t spMatDescr,
+                            const void**               values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatSetValues(cusparseSpMatDescr_t spMatDescr,
+                       void*                values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatGetSize(cusparseConstSpMatDescr_t spMatDescr,
+                     int64_t*                  rows,
+                     int64_t*                  cols,
+                     int64_t*                  nnz);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatGetStridedBatch(cusparseConstSpMatDescr_t spMatDescr,
+                             int*                      batchCount);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCooSetStridedBatch(cusparseSpMatDescr_t spMatDescr,
+                           int                  batchCount,
+                           int64_t              batchStride);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCsrSetStridedBatch(cusparseSpMatDescr_t spMatDescr,
+                           int                  batchCount,
+                           int64_t              offsetsBatchStride,
+                           int64_t              columnsValuesBatchStride);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseBsrSetStridedBatch(cusparseSpMatDescr_t spMatDescr,
+                           int                  batchCount,
+                           int64_t              offsetsBatchStride,
+                           int64_t              columnsBatchStride,
+                           int64_t              ValuesBatchStride);
+
+typedef enum {
+    CUSPARSE_SPMAT_FILL_MODE,
+    CUSPARSE_SPMAT_DIAG_TYPE
+} cusparseSpMatAttribute_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatGetAttribute(cusparseConstSpMatDescr_t spMatDescr,
+                          cusparseSpMatAttribute_t  attribute,
+                          void*                     data,
+                          size_t                    dataSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMatSetAttribute(cusparseSpMatDescr_t     spMatDescr,
+                          cusparseSpMatAttribute_t attribute,
+                          void*                    data,
+                          size_t                   dataSize);
+
+//------------------------------------------------------------------------------
+// ### CSR ###
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateCsr(cusparseSpMatDescr_t* spMatDescr,
+                  int64_t               rows,
+                  int64_t               cols,
+                  int64_t               nnz,
+                  void*                 csrRowOffsets,
+                  void*                 csrColInd,
+                  void*                 csrValues,
+                  cusparseIndexType_t   csrRowOffsetsType,
+                  cusparseIndexType_t   csrColIndType,
+                  cusparseIndexBase_t   idxBase,
+                  cudaDataType          valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstCsr(cusparseConstSpMatDescr_t* spMatDescr,
+                       int64_t                    rows,
+                       int64_t                    cols,
+                       int64_t                    nnz,
+                       const void*                csrRowOffsets,
+                       const void*                csrColInd,
+                       const void*                csrValues,
+                       cusparseIndexType_t        csrRowOffsetsType,
+                       cusparseIndexType_t        csrColIndType,
+                       cusparseIndexBase_t        idxBase,
+                       cudaDataType               valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateCsc(cusparseSpMatDescr_t* spMatDescr,
+                  int64_t               rows,
+                  int64_t               cols,
+                  int64_t               nnz,
+                  void*                 cscColOffsets,
+                  void*                 cscRowInd,
+                  void*                 cscValues,
+                  cusparseIndexType_t   cscColOffsetsType,
+                  cusparseIndexType_t   cscRowIndType,
+                  cusparseIndexBase_t   idxBase,
+                  cudaDataType          valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstCsc(cusparseConstSpMatDescr_t* spMatDescr,
+                       int64_t                    rows,
+                       int64_t                    cols,
+                       int64_t                    nnz,
+                       const void*                cscColOffsets,
+                       const void*                cscRowInd,
+                       const void*                cscValues,
+                       cusparseIndexType_t        cscColOffsetsType,
+                       cusparseIndexType_t        cscRowIndType,
+                       cusparseIndexBase_t        idxBase,
+                       cudaDataType               valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCsrGet(cusparseSpMatDescr_t spMatDescr,
+               int64_t*             rows,
+               int64_t*             cols,
+               int64_t*             nnz,
+               void**               csrRowOffsets,
+               void**               csrColInd,
+               void**               csrValues,
+               cusparseIndexType_t* csrRowOffsetsType,
+               cusparseIndexType_t* csrColIndType,
+               cusparseIndexBase_t* idxBase,
+               cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstCsrGet(cusparseConstSpMatDescr_t spMatDescr,
+                    int64_t*                  rows,
+                    int64_t*                  cols,
+                    int64_t*                  nnz,
+                    const void**              csrRowOffsets,
+                    const void**              csrColInd,
+                    const void**              csrValues,
+                    cusparseIndexType_t*      csrRowOffsetsType,
+                    cusparseIndexType_t*      csrColIndType,
+                    cusparseIndexBase_t*      idxBase,
+                    cudaDataType*             valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCscGet(cusparseSpMatDescr_t spMatDescr,
+               int64_t*             rows,
+               int64_t*             cols,
+               int64_t*             nnz,
+               void**               cscColOffsets,
+               void**               cscRowInd,
+               void**               cscValues,
+               cusparseIndexType_t* cscColOffsetsType,
+               cusparseIndexType_t* cscRowIndType,
+               cusparseIndexBase_t* idxBase,
+               cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstCscGet(cusparseConstSpMatDescr_t spMatDescr,
+                    int64_t*                  rows,
+                    int64_t*                  cols,
+                    int64_t*                  nnz,
+                    const void**              cscColOffsets,
+                    const void**              cscRowInd,
+                    const void**              cscValues,
+                    cusparseIndexType_t*      cscColOffsetsType,
+                    cusparseIndexType_t*      cscRowIndType,
+                    cusparseIndexBase_t*      idxBase,
+                    cudaDataType*             valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCsrSetPointers(cusparseSpMatDescr_t spMatDescr,
+                       void*                csrRowOffsets,
+                       void*                csrColInd,
+                       void*                csrValues);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCscSetPointers(cusparseSpMatDescr_t spMatDescr,
+                       void*                cscColOffsets,
+                       void*                cscRowInd,
+                       void*                cscValues);
+
+//------------------------------------------------------------------------------
+// ### BSR ###
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateBsr(cusparseSpMatDescr_t* spMatDescr,
+                  int64_t               brows,
+                  int64_t               bcols,
+                  int64_t               bnnz,
+                  int64_t               rowBlockSize,
+                  int64_t               colBlockSize,
+                  void*                 bsrRowOffsets,
+                  void*                 bsrColInd,
+                  void*                 bsrValues,
+                  cusparseIndexType_t   bsrRowOffsetsType,
+                  cusparseIndexType_t   bsrColIndType,
+                  cusparseIndexBase_t   idxBase,
+                  cudaDataType          valueType,
+                  cusparseOrder_t       order);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstBsr(cusparseConstSpMatDescr_t* spMatDescr,
+                       int64_t                    brows,
+                       int64_t                    bcols,
+                       int64_t                    bnnz,
+                       int64_t                    rowBlockDim,
+                       int64_t                    colBlockDim,
+                       const void*                bsrRowOffsets,
+                       const void*                bsrColInd,
+                       const void*                bsrValues,
+                       cusparseIndexType_t        bsrRowOffsetsType,
+                       cusparseIndexType_t        bsrColIndType,
+                       cusparseIndexBase_t        idxBase,
+                       cudaDataType               valueType,
+                       cusparseOrder_t            order);
+
+//------------------------------------------------------------------------------
+// ### COO ###
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateCoo(cusparseSpMatDescr_t* spMatDescr,
+                  int64_t               rows,
+                  int64_t               cols,
+                  int64_t               nnz,
+                  void*                 cooRowInd,
+                  void*                 cooColInd,
+                  void*                 cooValues,
+                  cusparseIndexType_t   cooIdxType,
+                  cusparseIndexBase_t   idxBase,
+                  cudaDataType          valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstCoo(cusparseConstSpMatDescr_t* spMatDescr,
+                       int64_t                    rows,
+                       int64_t                    cols,
+                       int64_t                    nnz,
+                       const void*                cooRowInd,
+                       const void*                cooColInd,
+                       const void*                cooValues,
+                       cusparseIndexType_t        cooIdxType,
+                       cusparseIndexBase_t        idxBase,
+                       cudaDataType               valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCooGet(cusparseSpMatDescr_t spMatDescr,
+               int64_t*             rows,
+               int64_t*             cols,
+               int64_t*             nnz,
+               void**               cooRowInd,  // COO row indices
+               void**               cooColInd,  // COO column indices
+               void**               cooValues,  // COO values
+               cusparseIndexType_t* idxType,
+               cusparseIndexBase_t* idxBase,
+               cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstCooGet(cusparseConstSpMatDescr_t spMatDescr,
+                    int64_t*                  rows,
+                    int64_t*                  cols,
+                    int64_t*                  nnz,
+                    const void**              cooRowInd,  // COO row indices
+                    const void**              cooColInd,  // COO column indices
+                    const void**              cooValues,  // COO values
+                    cusparseIndexType_t*      idxType,
+                    cusparseIndexBase_t*      idxBase,
+                    cudaDataType*             valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCooSetPointers(cusparseSpMatDescr_t spMatDescr,
+                       void*                cooRows,
+                       void*                cooColumns,
+                       void*                cooValues);
+
+//------------------------------------------------------------------------------
+// ### BLOCKED ELL ###
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateBlockedEll(cusparseSpMatDescr_t* spMatDescr,
+                         int64_t               rows,
+                         int64_t               cols,
+                         int64_t               ellBlockSize,
+                         int64_t               ellCols,
+                         void*                 ellColInd,
+                         void*                 ellValue,
+                         cusparseIndexType_t   ellIdxType,
+                         cusparseIndexBase_t   idxBase,
+                         cudaDataType          valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstBlockedEll(cusparseConstSpMatDescr_t* spMatDescr,
+                              int64_t                    rows,
+                              int64_t                    cols,
+                              int64_t                    ellBlockSize,
+                              int64_t                    ellCols,
+                              const void*                ellColInd,
+                              const void*                ellValue,
+                              cusparseIndexType_t        ellIdxType,
+                              cusparseIndexBase_t        idxBase,
+                              cudaDataType               valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseBlockedEllGet(cusparseSpMatDescr_t spMatDescr,
+                      int64_t*             rows,
+                      int64_t*             cols,
+                      int64_t*             ellBlockSize,
+                      int64_t*             ellCols,
+                      void**               ellColInd,
+                      void**               ellValue,
+                      cusparseIndexType_t* ellIdxType,
+                      cusparseIndexBase_t* idxBase,
+                      cudaDataType*        valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstBlockedEllGet(cusparseConstSpMatDescr_t spMatDescr,
+                           int64_t*                  rows,
+                           int64_t*                  cols,
+                           int64_t*                  ellBlockSize,
+                           int64_t*                  ellCols,
+                           const void**              ellColInd,
+                           const void**              ellValue,
+                           cusparseIndexType_t*      ellIdxType,
+                           cusparseIndexBase_t*      idxBase,
+                           cudaDataType*             valueType);
+
+//------------------------------------------------------------------------------
+// ### Sliced ELLPACK ###
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateSlicedEll(cusparseSpMatDescr_t*   spMatDescr,
+                        int64_t                 rows,
+                        int64_t                 cols,
+                        int64_t                 nnz,
+                        int64_t                 sellValuesSize,
+                        int64_t                 sliceSize,
+	                void*                   sellSliceOffsets,
+                        void*                   sellColInd,
+                        void*                   sellValues,
+			cusparseIndexType_t     sellSliceOffsetsType,
+                        cusparseIndexType_t     sellColIndType,
+                        cusparseIndexBase_t     idxBase,
+                        cudaDataType            valueType);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstSlicedEll(cusparseConstSpMatDescr_t* spMatDescr,
+                             int64_t                    rows,
+                             int64_t                    cols,
+                             int64_t                    nnz,
+                             int64_t                    sellValuesSize,
+                             int64_t                    sliceSize,
+                             const void*                sellSliceOffsets,
+                             const void*                sellColInd,
+                             const void*                sellValues,
+                             cusparseIndexType_t        sellSliceOffsetsType,
+                             cusparseIndexType_t        sellColIndType,
+                             cusparseIndexBase_t        idxBase,
+                             cudaDataType               valueType);
+
+// #############################################################################
+// # DENSE MATRIX DESCRIPTOR
+// #############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateDnMat(cusparseDnMatDescr_t* dnMatDescr,
+                    int64_t               rows,
+                    int64_t               cols,
+                    int64_t               ld,
+                    void*                 values,
+                    cudaDataType          valueType,
+                    cusparseOrder_t       order);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseCreateConstDnMat(cusparseConstDnMatDescr_t* dnMatDescr,
+                         int64_t                    rows,
+                         int64_t                    cols,
+                         int64_t                    ld,
+                         const void*                values,
+                         cudaDataType               valueType,
+                         cusparseOrder_t            order);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDestroyDnMat(cusparseConstDnMatDescr_t dnMatDescr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnMatGet(cusparseDnMatDescr_t dnMatDescr,
+                 int64_t*             rows,
+                 int64_t*             cols,
+                 int64_t*             ld,
+                 void**               values,
+                 cudaDataType*        type,
+                 cusparseOrder_t*     order);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstDnMatGet(cusparseConstDnMatDescr_t dnMatDescr,
+                      int64_t*                  rows,
+                      int64_t*                  cols,
+                      int64_t*                  ld,
+                      const void**              values,
+                      cudaDataType*             type,
+                      cusparseOrder_t*          order);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnMatGetValues(cusparseDnMatDescr_t dnMatDescr,
+                       void**               values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseConstDnMatGetValues(cusparseConstDnMatDescr_t dnMatDescr,
+                            const void**              values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnMatSetValues(cusparseDnMatDescr_t dnMatDescr,
+                       void*                values);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnMatSetStridedBatch(cusparseDnMatDescr_t dnMatDescr,
+                             int                  batchCount,
+                             int64_t              batchStride);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDnMatGetStridedBatch(cusparseConstDnMatDescr_t dnMatDescr,
+                             int*                      batchCount,
+                             int64_t*                  batchStride);
+
+// #############################################################################
+// # VECTOR-VECTOR OPERATIONS
+// #############################################################################
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseAxpby(cusparseHandle_t          handle,
+              const void*               alpha,
+              cusparseConstSpVecDescr_t vecX,
+              const void*               beta,
+              cusparseDnVecDescr_t      vecY);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseGather(cusparseHandle_t          handle,
+               cusparseConstDnVecDescr_t vecY,
+               cusparseSpVecDescr_t      vecX);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseScatter(cusparseHandle_t          handle,
+                cusparseConstSpVecDescr_t vecX,
+                cusparseDnVecDescr_t      vecY);
+
+CUSPARSE_DEPRECATED
+cusparseStatus_t CUSPARSEAPI
+cusparseRot(cusparseHandle_t     handle,
+            const void*          c_coeff,
+            const void*          s_coeff,
+            cusparseSpVecDescr_t vecX,
+            cusparseDnVecDescr_t vecY);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSpVV_bufferSize(cusparseHandle_t          handle,
+                        cusparseOperation_t       opX,
+                        cusparseConstSpVecDescr_t vecX,
+                        cusparseConstDnVecDescr_t vecY,
+                        const void*               result,
+                        cudaDataType              computeType,
+                        size_t*                   bufferSize);
+
+CUSPARSE_DEPRECATED // deprecated in CTK 12.8
+cusparseStatus_t CUSPARSEAPI
+cusparseSpVV(cusparseHandle_t          handle,
+             cusparseOperation_t       opX,
+             cusparseConstSpVecDescr_t vecX,
+             cusparseConstDnVecDescr_t vecY,
+             void*                     result,
+             cudaDataType              computeType,
+             void*                     externalBuffer);
+
+// #############################################################################
+// # SPARSE TO DENSE
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SPARSETODENSE_ALG_DEFAULT = 0
+} cusparseSparseToDenseAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSparseToDense_bufferSize(cusparseHandle_t           handle,
+                                 cusparseConstSpMatDescr_t  matA,
+                                 cusparseDnMatDescr_t       matB,
+                                 cusparseSparseToDenseAlg_t alg,
+                                 size_t*                    bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSparseToDense(cusparseHandle_t           handle,
+                      cusparseConstSpMatDescr_t  matA,
+                      cusparseDnMatDescr_t       matB,
+                      cusparseSparseToDenseAlg_t alg,
+                      void*                      externalBuffer);
+
+// #############################################################################
+// # DENSE TO SPARSE
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_DENSETOSPARSE_ALG_DEFAULT = 0
+} cusparseDenseToSparseAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDenseToSparse_bufferSize(cusparseHandle_t           handle,
+                                 cusparseConstDnMatDescr_t  matA,
+                                 cusparseSpMatDescr_t       matB,
+                                 cusparseDenseToSparseAlg_t alg,
+                                 size_t*                    bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDenseToSparse_analysis(cusparseHandle_t           handle,
+                               cusparseConstDnMatDescr_t  matA,
+                               cusparseSpMatDescr_t       matB,
+                               cusparseDenseToSparseAlg_t alg,
+                               void*                      externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseDenseToSparse_convert(cusparseHandle_t           handle,
+                              cusparseConstDnMatDescr_t  matA,
+                              cusparseSpMatDescr_t       matB,
+                              cusparseDenseToSparseAlg_t alg,
+                              void*                      externalBuffer);
+
+// #############################################################################
+// # SPARSE MATRIX-VECTOR MULTIPLICATION
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SPMV_ALG_DEFAULT = 0,
+    CUSPARSE_SPMV_CSR_ALG1    = 2,
+    CUSPARSE_SPMV_CSR_ALG2    = 3,
+    CUSPARSE_SPMV_COO_ALG1    = 1,
+    CUSPARSE_SPMV_COO_ALG2    = 4,
+    CUSPARSE_SPMV_SELL_ALG1   = 5
+} cusparseSpMVAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMV(cusparseHandle_t          handle,
+             cusparseOperation_t       opA,
+             const void*               alpha,
+             cusparseConstSpMatDescr_t matA,
+             cusparseConstDnVecDescr_t vecX,
+             const void*               beta,
+             cusparseDnVecDescr_t      vecY,
+             cudaDataType              computeType,
+             cusparseSpMVAlg_t         alg,
+             void*                     externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMV_bufferSize(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        const void*               alpha,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstDnVecDescr_t vecX,
+                        const void*               beta,
+                        cusparseDnVecDescr_t      vecY,
+                        cudaDataType              computeType,
+                        cusparseSpMVAlg_t         alg,
+                        size_t*                   bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMV_preprocess(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        const void*               alpha,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstDnVecDescr_t vecX,
+                        const void*               beta,
+                        cusparseDnVecDescr_t      vecY,
+                        cudaDataType              computeType,
+                        cusparseSpMVAlg_t         alg,
+                        void*                     externalBuffer);
+// #############################################################################
+// # SPARSE TRIANGULAR VECTOR SOLVE
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SPSV_ALG_DEFAULT = 0,
+} cusparseSpSVAlg_t;
+
+typedef enum {
+    CUSPARSE_SPSV_UPDATE_GENERAL  = 0,
+    CUSPARSE_SPSV_UPDATE_DIAGONAL = 1
+} cusparseSpSVUpdate_t;
+
+struct cusparseSpSVDescr;
+typedef struct cusparseSpSVDescr* cusparseSpSVDescr_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSV_createDescr(cusparseSpSVDescr_t* descr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSV_destroyDescr(cusparseSpSVDescr_t descr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSV_bufferSize(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        const void*               alpha,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstDnVecDescr_t vecX,
+                        cusparseDnVecDescr_t      vecY,
+                        cudaDataType              computeType,
+                        cusparseSpSVAlg_t         alg,
+                        cusparseSpSVDescr_t       spsvDescr,
+                        size_t*                   bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSV_analysis(cusparseHandle_t          handle,
+                      cusparseOperation_t       opA,
+                      const void*               alpha,
+                      cusparseConstSpMatDescr_t matA,
+                      cusparseConstDnVecDescr_t vecX,
+                      cusparseDnVecDescr_t      vecY,
+                      cudaDataType              computeType,
+                      cusparseSpSVAlg_t         alg,
+                      cusparseSpSVDescr_t       spsvDescr,
+                      void*                     externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSV_solve(cusparseHandle_t          handle,
+                   cusparseOperation_t       opA,
+                   const void*               alpha,
+                   cusparseConstSpMatDescr_t matA,
+                   cusparseConstDnVecDescr_t vecX,
+                   cusparseDnVecDescr_t      vecY,
+                   cudaDataType              computeType,
+                   cusparseSpSVAlg_t         alg,
+                   cusparseSpSVDescr_t       spsvDescr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSV_updateMatrix(cusparseHandle_t      handle,
+				          cusparseSpSVDescr_t   spsvDescr,
+                          void*                 newValues,
+                          cusparseSpSVUpdate_t  updatePart);
+
+
+
+// #############################################################################
+// # SPARSE TRIANGULAR MATRIX SOLVE
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SPSM_ALG_DEFAULT = 0,
+} cusparseSpSMAlg_t;
+
+typedef enum {
+    CUSPARSE_SPSM_UPDATE_GENERAL  = 0,
+    CUSPARSE_SPSM_UPDATE_DIAGONAL = 1
+} cusparseSpSMUpdate_t;
+
+struct cusparseSpSMDescr;
+typedef struct cusparseSpSMDescr* cusparseSpSMDescr_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSM_createDescr(cusparseSpSMDescr_t* descr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSM_destroyDescr(cusparseSpSMDescr_t descr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSM_bufferSize(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        cusparseOperation_t       opB,
+                        const void*               alpha,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstDnMatDescr_t matB,
+                        cusparseDnMatDescr_t      matC,
+                        cudaDataType              computeType,
+                        cusparseSpSMAlg_t         alg,
+                        cusparseSpSMDescr_t       spsmDescr,
+                        size_t*                   bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSM_analysis(cusparseHandle_t          handle,
+                      cusparseOperation_t       opA,
+                      cusparseOperation_t       opB,
+                      const void*               alpha,
+                      cusparseConstSpMatDescr_t matA,
+                      cusparseConstDnMatDescr_t matB,
+                      cusparseDnMatDescr_t      matC,
+                      cudaDataType              computeType,
+                      cusparseSpSMAlg_t         alg,
+                      cusparseSpSMDescr_t       spsmDescr,
+                      void*                     externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSM_solve(cusparseHandle_t          handle,
+                   cusparseOperation_t       opA,
+                   cusparseOperation_t       opB,
+                   const void*               alpha,
+                   cusparseConstSpMatDescr_t matA,
+                   cusparseConstDnMatDescr_t matB,
+                   cusparseDnMatDescr_t      matC,
+                   cudaDataType              computeType,
+                   cusparseSpSMAlg_t         alg,
+                   cusparseSpSMDescr_t       spsmDescr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpSM_updateMatrix(cusparseHandle_t      handle,
+				          cusparseSpSMDescr_t   spsmDescr,
+                          void*                 newValues,
+                          cusparseSpSMUpdate_t  updatePart);
+
+// #############################################################################
+// # SPARSE MATRIX-MATRIX MULTIPLICATION
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SPMM_ALG_DEFAULT      = 0,
+    CUSPARSE_SPMM_COO_ALG1         = 1,
+    CUSPARSE_SPMM_COO_ALG2         = 2,
+    CUSPARSE_SPMM_COO_ALG3         = 3,
+    CUSPARSE_SPMM_COO_ALG4         = 5,
+    CUSPARSE_SPMM_CSR_ALG1         = 4,
+    CUSPARSE_SPMM_CSR_ALG2         = 6,
+    CUSPARSE_SPMM_CSR_ALG3         = 12,
+    CUSPARSE_SPMM_BLOCKED_ELL_ALG1 = 13,
+    CUSPARSE_SPMM_BSR_ALG1         = 14
+} cusparseSpMMAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMM_bufferSize(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        cusparseOperation_t       opB,
+                        const void*               alpha,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstDnMatDescr_t matB,
+                        const void*               beta,
+                        cusparseDnMatDescr_t      matC,
+                        cudaDataType              computeType,
+                        cusparseSpMMAlg_t         alg,
+                        size_t*                   bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMM_preprocess(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        cusparseOperation_t       opB,
+                        const void*               alpha,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstDnMatDescr_t matB,
+                        const void*               beta,
+                        cusparseDnMatDescr_t      matC,
+                        cudaDataType              computeType,
+                        cusparseSpMMAlg_t         alg,
+                        void*                     externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMM(cusparseHandle_t          handle,
+             cusparseOperation_t       opA,
+             cusparseOperation_t       opB,
+             const void*               alpha,
+             cusparseConstSpMatDescr_t matA,
+             cusparseConstDnMatDescr_t matB,
+             const void*               beta,
+             cusparseDnMatDescr_t      matC,
+             cudaDataType              computeType,
+             cusparseSpMMAlg_t         alg,
+             void*                     externalBuffer);
+
+// #############################################################################
+// # SPARSE MATRIX - SPARSE MATRIX MULTIPLICATION (SpGEMM)
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SPGEMM_DEFAULT                 = 0,
+    CUSPARSE_SPGEMM_CSR_ALG_DETERMINITIC    = 1,
+    CUSPARSE_SPGEMM_CSR_ALG_NONDETERMINITIC = 2,
+    CUSPARSE_SPGEMM_ALG1                    = 3,
+    CUSPARSE_SPGEMM_ALG2                    = 4,
+    CUSPARSE_SPGEMM_ALG3                    = 5
+} cusparseSpGEMMAlg_t;
+
+struct cusparseSpGEMMDescr;
+typedef struct cusparseSpGEMMDescr* cusparseSpGEMMDescr_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_createDescr(cusparseSpGEMMDescr_t* descr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_destroyDescr(cusparseSpGEMMDescr_t descr);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_workEstimation(cusparseHandle_t          handle,
+                              cusparseOperation_t       opA,
+                              cusparseOperation_t       opB,
+                              const void*               alpha,
+                              cusparseConstSpMatDescr_t matA,
+                              cusparseConstSpMatDescr_t matB,
+                              const void*               beta,
+                              cusparseSpMatDescr_t      matC,
+                              cudaDataType              computeType,
+                              cusparseSpGEMMAlg_t       alg,
+                              cusparseSpGEMMDescr_t     spgemmDescr,
+                              size_t*                   bufferSize1,
+                              void*                     externalBuffer1);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_getNumProducts(cusparseSpGEMMDescr_t spgemmDescr,
+                              int64_t*              num_prods);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_estimateMemory(cusparseHandle_t          handle,
+                              cusparseOperation_t       opA,
+                              cusparseOperation_t       opB,
+                              const void*               alpha,
+                              cusparseConstSpMatDescr_t matA,
+                              cusparseConstSpMatDescr_t matB,
+                              const void*               beta,
+                              cusparseSpMatDescr_t      matC,
+                              cudaDataType              computeType,
+                              cusparseSpGEMMAlg_t       alg,
+                              cusparseSpGEMMDescr_t     spgemmDescr,
+                              float                     chunk_fraction,
+                              size_t*                   bufferSize3,
+                              void*                     externalBuffer3,
+                              size_t*                   bufferSize2);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_compute(cusparseHandle_t          handle,
+                       cusparseOperation_t       opA,
+                       cusparseOperation_t       opB,
+                       const void*               alpha,
+                       cusparseConstSpMatDescr_t matA,
+                       cusparseConstSpMatDescr_t matB,
+                       const void*               beta,
+                       cusparseSpMatDescr_t      matC,
+                       cudaDataType              computeType,
+                       cusparseSpGEMMAlg_t       alg,
+                       cusparseSpGEMMDescr_t     spgemmDescr,
+                       size_t*                   bufferSize2,
+                       void*                     externalBuffer2);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMM_copy(cusparseHandle_t          handle,
+                    cusparseOperation_t       opA,
+                    cusparseOperation_t       opB,
+                    const void*               alpha,
+                    cusparseConstSpMatDescr_t matA,
+                    cusparseConstSpMatDescr_t matB,
+                    const void*               beta,
+                    cusparseSpMatDescr_t      matC,
+                    cudaDataType              computeType,
+                    cusparseSpGEMMAlg_t       alg,
+                    cusparseSpGEMMDescr_t     spgemmDescr);
+
+// #############################################################################
+// # SPARSE MATRIX - SPARSE MATRIX MULTIPLICATION (SpGEMM) STRUCTURE REUSE
+// #############################################################################
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMMreuse_workEstimation(cusparseHandle_t          handle,
+                                   cusparseOperation_t       opA,
+                                   cusparseOperation_t       opB,
+                                   cusparseConstSpMatDescr_t matA,
+                                   cusparseConstSpMatDescr_t matB,
+                                   cusparseSpMatDescr_t      matC,
+                                   cusparseSpGEMMAlg_t       alg,
+                                   cusparseSpGEMMDescr_t     spgemmDescr,
+                                   size_t*                   bufferSize1,
+                                   void*                     externalBuffer1);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMMreuse_nnz(cusparseHandle_t          handle,
+                        cusparseOperation_t       opA,
+                        cusparseOperation_t       opB,
+                        cusparseConstSpMatDescr_t matA,
+                        cusparseConstSpMatDescr_t matB,
+                        cusparseSpMatDescr_t      matC,
+                        cusparseSpGEMMAlg_t       alg,
+                        cusparseSpGEMMDescr_t     spgemmDescr,
+                        size_t*                   bufferSize2,
+                        void*                     externalBuffer2,
+                        size_t*                   bufferSize3,
+                        void*                     externalBuffer3,
+                        size_t*                   bufferSize4,
+                        void*                     externalBuffer4);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMMreuse_copy(cusparseHandle_t          handle,
+                         cusparseOperation_t       opA,
+                         cusparseOperation_t       opB,
+                         cusparseConstSpMatDescr_t matA,
+                         cusparseConstSpMatDescr_t matB,
+                         cusparseSpMatDescr_t      matC,
+                         cusparseSpGEMMAlg_t       alg,
+                         cusparseSpGEMMDescr_t     spgemmDescr,
+                         size_t*                   bufferSize5,
+                         void*                     externalBuffer5);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpGEMMreuse_compute(cusparseHandle_t          handle,
+                            cusparseOperation_t       opA,
+                            cusparseOperation_t       opB,
+                            const void*               alpha,
+                            cusparseConstSpMatDescr_t matA,
+                            cusparseConstSpMatDescr_t matB,
+                            const void*               beta,
+                            cusparseSpMatDescr_t      matC,
+                            cudaDataType              computeType,
+                            cusparseSpGEMMAlg_t       alg,
+                            cusparseSpGEMMDescr_t     spgemmDescr);
+
+// #############################################################################
+// # SAMPLED DENSE-DENSE MATRIX MULTIPLICATION
+// #############################################################################
+
+typedef enum {
+    CUSPARSE_SDDMM_ALG_DEFAULT = 0
+} cusparseSDDMMAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSDDMM_bufferSize(cusparseHandle_t          handle,
+                         cusparseOperation_t       opA,
+                         cusparseOperation_t       opB,
+                         const void*               alpha,
+                         cusparseConstDnMatDescr_t matA,
+                         cusparseConstDnMatDescr_t matB,
+                         const void*               beta,
+                         cusparseSpMatDescr_t      matC,
+                         cudaDataType              computeType,
+                         cusparseSDDMMAlg_t        alg,
+                         size_t*                   bufferSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSDDMM_preprocess(cusparseHandle_t          handle,
+                         cusparseOperation_t       opA,
+                         cusparseOperation_t       opB,
+                         const void*               alpha,
+                         cusparseConstDnMatDescr_t matA,
+                         cusparseConstDnMatDescr_t matB,
+                         const void*               beta,
+                         cusparseSpMatDescr_t      matC,
+                         cudaDataType              computeType,
+                         cusparseSDDMMAlg_t        alg,
+                         void*                     externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSDDMM(cusparseHandle_t          handle,
+              cusparseOperation_t       opA,
+              cusparseOperation_t       opB,
+              const void*               alpha,
+              cusparseConstDnMatDescr_t matA,
+              cusparseConstDnMatDescr_t matB,
+              const void*               beta,
+              cusparseSpMatDescr_t      matC,
+              cudaDataType              computeType,
+              cusparseSDDMMAlg_t        alg,
+              void*                     externalBuffer);
+
+// #############################################################################
+// # GENERIC APIs WITH CUSTOM OPERATORS (PREVIEW)
+// #############################################################################
+
+struct cusparseSpMMOpPlan;
+typedef struct cusparseSpMMOpPlan*       cusparseSpMMOpPlan_t;
+
+typedef enum {
+    CUSPARSE_SPMM_OP_ALG_DEFAULT
+} cusparseSpMMOpAlg_t;
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMMOp_createPlan(cusparseHandle_t          handle,
+                          cusparseSpMMOpPlan_t*     plan,
+                          cusparseOperation_t       opA,
+                          cusparseOperation_t       opB,
+                          cusparseConstSpMatDescr_t matA,
+                          cusparseConstDnMatDescr_t matB,
+                          cusparseDnMatDescr_t      matC,
+                          cudaDataType              computeType,
+                          cusparseSpMMOpAlg_t       alg,
+                          const void*               addOperationNvvmBuffer,
+                          size_t                    addOperationBufferSize,
+                          const void*               mulOperationNvvmBuffer,
+                          size_t                    mulOperationBufferSize,
+                          const void*               epilogueNvvmBuffer,
+                          size_t                    epilogueBufferSize,
+                          size_t*                   SpMMWorkspaceSize);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMMOp(cusparseSpMMOpPlan_t plan,
+               void*                externalBuffer);
+
+cusparseStatus_t CUSPARSEAPI
+cusparseSpMMOp_destroyPlan(cusparseSpMMOpPlan_t plan);
+
+//------------------------------------------------------------------------------
+
+#if defined(__cplusplus)
+} // extern "C"
+#endif // defined(__cplusplus)
+
+#undef CUSPARSE_DEPRECATED_REPLACE_WITH
+#undef CUSPARSE_DEPRECATED
+#undef CUSPARSE_DEPRECATED_TYPE
+#undef CUSPARSE_DEPRECATED_TYPE_MSVC
+#undef CUSPARSE_DEPRECATED_ENUM_REPLACE_WITH
+#undef CUSPARSE_DEPRECATED_ENUM
+
+#endif // !defined(CUSPARSE_H_)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/cusparse_v2.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/cusparse_v2.h
new file mode 100644
index 0000000000000000000000000000000000000000..f889e1f569d46d1116fe6e302429b3855de43c21
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/include/cusparse_v2.h
@@ -0,0 +1,54 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+#if !defined(CUSPARSE_V2_H_)
+#define CUSPARSE_V2_H_
+
+#include "cusparse.h"
+
+#endif
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparse/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/LICENSE.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..890839b670533e4bbd2d96c3ab36525be2aff773
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/LICENSE.txt
@@ -0,0 +1,165 @@
+LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
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+
+(v. October 12, 2020)
+
+
+
+
+
+
+
+
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+
+
+
+
+cuSPARSELt SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+The terms in this supplement govern your use of the NVIDIA cuSPARSELt SDK under the terms of your license agreement (“Agreement”) as modified by this supplement. Capitalized terms used but not defined below have the meaning assigned to them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is incorporated as an integral part of the Agreement. In the event of conflict between the terms in this supplement and the terms in the Agreement, the terms in this supplement govern.
+
+1.	License Scope. The SDK is licensed for you to develop applications only for use in systems with NVIDIA GPUs.
+
+2. Distribution. The following portions of the SDK are distributable under the Agreement: the runtimes files ending with .so and .h as part of your application.
+
+3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com
+
+(v. October 12, 2020)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/include/cusparseLt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/include/cusparseLt.h
new file mode 100644
index 0000000000000000000000000000000000000000..d68602e1d3411253d6a1a29e55bc5431ef5ccad4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/include/cusparseLt.h
@@ -0,0 +1,401 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * NVIDIA CORPORATION and its licensors retain all intellectual property
+ * and proprietary rights in and to this software, related documentation
+ * and any modifications thereto.  Any use, reproduction, disclosure or
+ * distribution of this software and related documentation without an express
+ * license agreement from NVIDIA CORPORATION is strictly prohibited.
+ */
+#if !defined(CUSPARSELT_HEADER_)
+#define CUSPARSELT_HEADER_
+
+#include "cusparse.h"      // cusparseStatus_t
+
+#include <cstddef>         // size_t
+#include <driver_types.h>  // cudaStream_t
+#include <library_types.h> // cudaDataType
+#include <stdint.h>        // uint8_t
+
+//##############################################################################
+//# CUSPARSELT VERSION INFORMATION
+//##############################################################################
+
+#define CUSPARSELT_VER_MAJOR 0
+#define CUSPARSELT_VER_MINOR 7
+#define CUSPARSELT_VER_PATCH 1
+#define CUSPARSELT_VER_BUILD 0
+#define CUSPARSELT_VERSION (CUSPARSELT_VER_MAJOR * 1000 + \
+                            CUSPARSELT_VER_MINOR *  100 + \
+                            CUSPARSELT_VER_PATCH)
+
+// #############################################################################
+// # MACRO
+// #############################################################################
+
+#if !defined(CUSPARSELT_API)
+#    if defined(_WIN32)
+#        define CUSPARSELT_API __stdcall
+#    else
+#        define CUSPARSELT_API
+#    endif
+#endif
+
+//------------------------------------------------------------------------------
+
+#if defined(__cplusplus)
+extern "C" {
+#endif // defined(__cplusplus)
+
+//##############################################################################
+//# OPAQUE DATA STRUCTURES
+//##############################################################################
+
+typedef struct { uint8_t data[1024]; } cusparseLtHandle_t;
+
+typedef struct { uint8_t data[1024]; } cusparseLtMatDescriptor_t;
+
+typedef struct { uint8_t data[1024]; } cusparseLtMatmulDescriptor_t;
+
+typedef struct { uint8_t data[1024]; } cusparseLtMatmulAlgSelection_t;
+
+typedef struct { uint8_t data[1024]; } cusparseLtMatmulPlan_t;
+
+const char* CUSPARSELT_API
+cusparseLtGetErrorName(cusparseStatus_t status);
+
+const char* CUSPARSELT_API
+cusparseLtGetErrorString(cusparseStatus_t status);
+
+//##############################################################################
+//# INITIALIZATION, DESTROY
+//##############################################################################
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtInit(cusparseLtHandle_t* handle);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtDestroy(const cusparseLtHandle_t* handle);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtGetVersion(const cusparseLtHandle_t* handle,
+                     int*                      version);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtGetProperty(libraryPropertyType propertyType,
+                      int*                value);
+
+//##############################################################################
+//# MATRIX DESCRIPTOR
+//##############################################################################
+// Dense Matrix
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtDenseDescriptorInit(const cusparseLtHandle_t*  handle,
+                              cusparseLtMatDescriptor_t* matDescr,
+                              int64_t                    rows,
+                              int64_t                    cols,
+                              int64_t                    ld,
+                              uint32_t                   alignment,
+                              cudaDataType               valueType,
+                              cusparseOrder_t            order);
+
+//------------------------------------------------------------------------------
+// Structured Matrix
+
+typedef enum {
+    CUSPARSELT_SPARSITY_50_PERCENT
+} cusparseLtSparsity_t;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtStructuredDescriptorInit(const cusparseLtHandle_t*  handle,
+                                   cusparseLtMatDescriptor_t* matDescr,
+                                   int64_t                    rows,
+                                   int64_t                    cols,
+                                   int64_t                    ld,
+                                   uint32_t                   alignment,
+                                   cudaDataType               valueType,
+                                   cusparseOrder_t            order,
+                                   cusparseLtSparsity_t       sparsity);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatDescriptorDestroy(const cusparseLtMatDescriptor_t* matDescr);
+
+//------------------------------------------------------------------------------
+
+typedef enum {
+    CUSPARSELT_MAT_NUM_BATCHES,  // READ/WRITE
+    CUSPARSELT_MAT_BATCH_STRIDE  // READ/WRITE
+} cusparseLtMatDescAttribute_t;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatDescSetAttribute(const cusparseLtHandle_t*    handle,
+                              cusparseLtMatDescriptor_t*   matmulDescr,
+                              cusparseLtMatDescAttribute_t matAttribute,
+                              const void*                  data,
+                              size_t                       dataSize);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatDescGetAttribute(const cusparseLtHandle_t*        handle,
+                              const cusparseLtMatDescriptor_t* matmulDescr,
+                              cusparseLtMatDescAttribute_t     matAttribute,
+                              void*                            data,
+                              size_t                           dataSize);
+
+//##############################################################################
+//# MATMUL DESCRIPTOR
+//##############################################################################
+
+typedef enum {
+    CUSPARSE_COMPUTE_32I,
+    CUSPARSE_COMPUTE_16F,
+    CUSPARSE_COMPUTE_32F
+} cusparseComputeType;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulDescriptorInit(const cusparseLtHandle_t*        handle,
+                               cusparseLtMatmulDescriptor_t*    matmulDescr,
+                               cusparseOperation_t              opA,
+                               cusparseOperation_t              opB,
+                               const cusparseLtMatDescriptor_t* matA,
+                               const cusparseLtMatDescriptor_t* matB,
+                               const cusparseLtMatDescriptor_t* matC,
+                               const cusparseLtMatDescriptor_t* matD,
+                               cusparseComputeType              computeType);
+
+//------------------------------------------------------------------------------
+
+typedef enum {
+    CUSPARSELT_MATMUL_ACTIVATION_RELU,            // READ/WRITE
+    CUSPARSELT_MATMUL_ACTIVATION_RELU_UPPERBOUND, // READ/WRITE
+    CUSPARSELT_MATMUL_ACTIVATION_RELU_THRESHOLD,  // READ/WRITE
+    CUSPARSELT_MATMUL_ACTIVATION_GELU,            // READ/WRITE
+    CUSPARSELT_MATMUL_ACTIVATION_GELU_SCALING,    // READ/WRITE
+    CUSPARSELT_MATMUL_ALPHA_VECTOR_SCALING,       // READ/WRITE
+    CUSPARSELT_MATMUL_BETA_VECTOR_SCALING,        // READ/WRITE
+    CUSPARSELT_MATMUL_BIAS_STRIDE,                // READ/WRITE
+    CUSPARSELT_MATMUL_BIAS_POINTER,               // READ/WRITE
+    CUSPARSELT_MATMUL_SPARSE_MAT_POINTER,         // READ/WRITE
+                                                  //
+    CUSPARSELT_MATMUL_A_SCALE_MODE,               // READ/WRITE
+    CUSPARSELT_MATMUL_B_SCALE_MODE,               // READ/WRITE
+    CUSPARSELT_MATMUL_C_SCALE_MODE,               // READ/WRITE
+    CUSPARSELT_MATMUL_D_SCALE_MODE,               // READ/WRITE
+    CUSPARSELT_MATMUL_D_OUT_SCALE_MODE,           // READ/WRITE
+
+    CUSPARSELT_MATMUL_A_SCALE_POINTER,
+    CUSPARSELT_MATMUL_B_SCALE_POINTER,
+    CUSPARSELT_MATMUL_C_SCALE_POINTER,
+    CUSPARSELT_MATMUL_D_SCALE_POINTER,
+    CUSPARSELT_MATMUL_D_OUT_SCALE_POINTER,
+} cusparseLtMatmulDescAttribute_t;
+
+typedef enum {
+    CUSPARSELT_MATMUL_SCALE_NONE,
+    CUSPARSELT_MATMUL_MATRIX_SCALE_SCALAR_32F,
+    CUSPARSELT_MATMUL_MATRIX_SCALE_VEC32_UE4M3,
+    CUSPARSELT_MATMUL_MATRIX_SCALE_VEC64_UE8M0,
+} cusparseLtMatmulMatrixScale_t;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulDescSetAttribute(const cusparseLtHandle_t*      handle,
+                                cusparseLtMatmulDescriptor_t*   matmulDescr,
+                                cusparseLtMatmulDescAttribute_t matmulAttribute,
+                                const void*                     data,
+                                size_t                          dataSize);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulDescGetAttribute(
+                            const cusparseLtHandle_t*           handle,
+                            const cusparseLtMatmulDescriptor_t* matmulDescr,
+                            cusparseLtMatmulDescAttribute_t     matmulAttribute,
+                            void*                               data,
+                            size_t                              dataSize);
+
+//##############################################################################
+//# ALGORITHM SELECTION
+//##############################################################################
+
+typedef enum {
+    CUSPARSELT_MATMUL_ALG_DEFAULT
+} cusparseLtMatmulAlg_t;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulAlgSelectionInit(
+                            const cusparseLtHandle_t*           handle,
+                            cusparseLtMatmulAlgSelection_t*     algSelection,
+                            const cusparseLtMatmulDescriptor_t* matmulDescr,
+                            cusparseLtMatmulAlg_t               alg);
+
+typedef enum {
+    CUSPARSELT_MATMUL_ALG_CONFIG_ID,     // READ/WRITE
+    CUSPARSELT_MATMUL_ALG_CONFIG_MAX_ID, // READ-ONLY
+    CUSPARSELT_MATMUL_SEARCH_ITERATIONS, // READ/WRITE
+    CUSPARSELT_MATMUL_SPLIT_K,           // READ/WRITE
+    CUSPARSELT_MATMUL_SPLIT_K_MODE,      // READ/WRITE
+    CUSPARSELT_MATMUL_SPLIT_K_BUFFERS    // READ/WRITE
+} cusparseLtMatmulAlgAttribute_t;
+
+typedef enum {
+    CUSPARSELT_INVALID_MODE             = 0,
+    CUSPARSELT_SPLIT_K_MODE_ONE_KERNEL  = 1,
+    CUSPARSELT_SPLIT_K_MODE_TWO_KERNELS = 2,
+    CUSPARSELT_HEURISTIC,
+    CUSPARSELT_DATAPARALLEL,
+    CUSPARSELT_SPLITK,
+    CUSPARSELT_STREAMK
+} cusparseLtSplitKMode_t;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulAlgSetAttribute(const cusparseLtHandle_t*       handle,
+                                cusparseLtMatmulAlgSelection_t* algSelection,
+                                cusparseLtMatmulAlgAttribute_t  attribute,
+                                const void*                     data,
+                                size_t                          dataSize);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulAlgGetAttribute(
+                            const cusparseLtHandle_t*             handle,
+                            const cusparseLtMatmulAlgSelection_t* algSelection,
+                            cusparseLtMatmulAlgAttribute_t        attribute,
+                            void*                                 data,
+                            size_t                                dataSize);
+
+//##############################################################################
+//# MATMUL PLAN
+//##############################################################################
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulGetWorkspace(
+                        const cusparseLtHandle_t*     handle,
+                        const cusparseLtMatmulPlan_t* plan,
+                        size_t*                       workspaceSize);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulPlanInit(const cusparseLtHandle_t*             handle,
+                         cusparseLtMatmulPlan_t*               plan,
+                         const cusparseLtMatmulDescriptor_t*   matmulDescr,
+                         const cusparseLtMatmulAlgSelection_t* algSelection);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulPlanDestroy(const cusparseLtMatmulPlan_t* plan);
+
+//##############################################################################
+//# MATMUL EXECUTION
+//##############################################################################
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmul(const cusparseLtHandle_t*     handle,
+                 const cusparseLtMatmulPlan_t* plan,
+                 const void*                   alpha,
+                 const void*                   d_A,
+                 const void*                   d_B,
+                 const void*                   beta,
+                 const void*                   d_C,
+                 void*                         d_D,
+                 void*                         workspace,
+                 cudaStream_t*                 streams,
+                 int32_t                       numStreams);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtMatmulSearch(const cusparseLtHandle_t* handle,
+                       cusparseLtMatmulPlan_t*   plan,
+                       const void*               alpha,
+                       const void*               d_A,
+                       const void*               d_B,
+                       const void*               beta,
+                       const void*               d_C,
+                       void*                     d_D,
+                       void*                     workspace,
+                       // void*                     device_buf,
+                       cudaStream_t*             streams,
+                       int32_t                   numStreams);
+
+//##############################################################################
+//# HELPER ROUTINES
+//##############################################################################
+// PRUNING
+
+typedef enum {
+    CUSPARSELT_PRUNE_SPMMA_TILE  = 0,
+    CUSPARSELT_PRUNE_SPMMA_STRIP = 1
+} cusparseLtPruneAlg_t;
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMAPrune(const cusparseLtHandle_t*           handle,
+                     const cusparseLtMatmulDescriptor_t* matmulDescr,
+                     const void*                         d_in,
+                     void*                               d_out,
+                     cusparseLtPruneAlg_t                pruneAlg,
+                     cudaStream_t                        stream);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMAPruneCheck(const cusparseLtHandle_t*           handle,
+                          const cusparseLtMatmulDescriptor_t* matmulDescr,
+                          const void*                         d_in,
+                          int*                                valid,
+                          cudaStream_t                        stream);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMAPrune2(const cusparseLtHandle_t*        handle,
+                      const cusparseLtMatDescriptor_t* sparseMatDescr,
+                      int                              isSparseA,
+                      cusparseOperation_t              op,
+                      const void*                      d_in,
+                      void*                            d_out,
+                      cusparseLtPruneAlg_t             pruneAlg,
+                      cudaStream_t                     stream);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMAPruneCheck2(const cusparseLtHandle_t*        handle,
+                           const cusparseLtMatDescriptor_t* sparseMatDescr,
+                           int                              isSparseA,
+                           cusparseOperation_t              op,
+                           const void*                      d_in,
+                           int*                             d_valid,
+                           cudaStream_t                     stream);
+
+//------------------------------------------------------------------------------
+// COMPRESSION
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMACompressedSize(
+                        const cusparseLtHandle_t*     handle,
+                        const cusparseLtMatmulPlan_t* plan,
+                        size_t*                       compressedSize,
+                        size_t*                       compressedBufferSize);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMACompress(const cusparseLtHandle_t*     handle,
+                        const cusparseLtMatmulPlan_t* plan,
+                        const void*                   d_dense,
+                        void*                         d_compressed,
+                        void*                         d_compressed_buffer,
+                        cudaStream_t                  stream);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMACompressedSize2(
+                        const cusparseLtHandle_t*        handle,
+                        const cusparseLtMatDescriptor_t* sparseMatDescr,
+                        size_t*                          compressedSize,
+                        size_t*                          compressedBufferSize);
+
+cusparseStatus_t CUSPARSELT_API
+cusparseLtSpMMACompress2(const cusparseLtHandle_t*        handle,
+                         const cusparseLtMatDescriptor_t* sparseMatDescr,
+                         int                              isSparseA,
+                         cusparseOperation_t              op,
+                         const void*                      d_dense,
+                         void*                            d_compressed,
+                         void*                            d_compressed_buffer,
+                         cudaStream_t                     stream);
+
+//==============================================================================
+//==============================================================================
+
+#if defined(__cplusplus)
+}
+#endif // defined(__cplusplus)
+
+#endif // !defined(CUSPARSELT_HEADER_)
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/lib/libcusparseLt.so.0 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/lib/libcusparseLt.so.0
new file mode 100644
index 0000000000000000000000000000000000000000..563396da27035452e1e5077fccfb349ba438d4df
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/cusparselt/lib/libcusparseLt.so.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3d85a0311eadfd3cec86b0291c9a4b5769cba2f248a9e7e8167fbf3ffa99863
+size 451980513
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nccl/include/nccl.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nccl/include/nccl.h
new file mode 100644
index 0000000000000000000000000000000000000000..5530667066940752a60c34c799fa777f5fb8e35f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nccl/include/nccl.h
@@ -0,0 +1,518 @@
+/*************************************************************************
+ * Copyright (c) 2015-2021, NVIDIA CORPORATION. All rights reserved.
+ *
+ * See LICENSE.txt for license information
+ ************************************************************************/
+
+#ifndef NCCL_H_
+#define NCCL_H_
+
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+#if CUDART_VERSION >= 11000
+#include <cuda_bf16.h>
+#endif
+#if CUDART_VERSION >= 11080
+#include <cuda_fp8.h>
+#endif
+
+#define NCCL_MAJOR 2
+#define NCCL_MINOR 27
+#define NCCL_PATCH 3
+#define NCCL_SUFFIX ""
+
+#define NCCL_VERSION_CODE 22703
+#define NCCL_VERSION(X,Y,Z) (((X) <= 2 && (Y) <= 8) ? (X) * 1000 + (Y) * 100 + (Z) : (X) * 10000 + (Y) * 100 + (Z))
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <limits.h>
+/* Opaque handle to communicator */
+typedef struct ncclComm* ncclComm_t;
+typedef struct ncclWindow* ncclWindow_t;
+#define NCCL_COMM_NULL NULL
+
+#define NCCL_UNIQUE_ID_BYTES 128
+typedef struct { char internal[NCCL_UNIQUE_ID_BYTES]; } ncclUniqueId;
+
+/* Error type */
+typedef enum { ncclSuccess                 =  0,
+               ncclUnhandledCudaError      =  1,
+               ncclSystemError             =  2,
+               ncclInternalError           =  3,
+               ncclInvalidArgument         =  4,
+               ncclInvalidUsage            =  5,
+               ncclRemoteError             =  6,
+               ncclInProgress              =  7,
+               ncclNumResults              =  8 } ncclResult_t;
+
+#define NCCL_CONFIG_UNDEF_INT INT_MIN
+#define NCCL_CONFIG_UNDEF_PTR NULL
+#define NCCL_SPLIT_NOCOLOR -1
+#define NCCL_UNDEF_FLOAT -1.0f
+
+/* Window Registration flags */
+#define NCCL_WIN_DEFAULT 0x00
+#define NCCL_WIN_COLL_SYMMETRIC 0x01
+
+/* NCCL performance policy */
+#define NCCL_CTA_POLICY_DEFAULT 0x00
+#define NCCL_CTA_POLICY_EFFICIENCY 0x01
+
+/* ncclCommShrink flags*/
+#define NCCL_SHRINK_DEFAULT 0x00 /* shrink the parent communicator */
+#define NCCL_SHRINK_ABORT 0x01   /* First, terminate ongoing parent operations, and then shrink the parent communicator */
+
+/* Communicator configuration. Users can assign value to attributes to specify the
+ * behavior of a communicator. */
+typedef struct ncclConfig_v22700 {
+  /* attributes that users should never touch. */
+  size_t size;
+  unsigned int magic;
+  unsigned int version;
+  /* attributes that users are able to customize. */
+  int blocking;
+  int cgaClusterSize;
+  int minCTAs;
+  int maxCTAs;
+  const char *netName;
+  int splitShare;
+  int trafficClass;
+  const char *commName;
+  int collnetEnable;
+  int CTAPolicy;
+  int shrinkShare;
+  int nvlsCTAs;
+} ncclConfig_t;
+
+/* Config initializer must be assigned to initialize config structure when it is created.
+ * Not initialized config will result in NCCL error. */
+#define NCCL_CONFIG_INITIALIZER {                                       \
+  sizeof(ncclConfig_t), /* size */                                      \
+  0xcafebeef,           /* magic */                                     \
+  NCCL_VERSION(NCCL_MAJOR, NCCL_MINOR, NCCL_PATCH), /* version */       \
+  NCCL_CONFIG_UNDEF_INT,                    /* blocking */              \
+  NCCL_CONFIG_UNDEF_INT,                    /* cgaClusterSize */        \
+  NCCL_CONFIG_UNDEF_INT,                    /* minCTAs */               \
+  NCCL_CONFIG_UNDEF_INT,                    /* maxCTAs */               \
+  NCCL_CONFIG_UNDEF_PTR,                    /* netName */               \
+  NCCL_CONFIG_UNDEF_INT,                    /* splitShare */            \
+  NCCL_CONFIG_UNDEF_INT,                    /* trafficClass */          \
+  NCCL_CONFIG_UNDEF_PTR,                    /* commName */              \
+  NCCL_CONFIG_UNDEF_INT,                    /* collnetEnable */         \
+  NCCL_CONFIG_UNDEF_INT,                    /* CTAPolicy */             \
+  NCCL_CONFIG_UNDEF_INT,                    /* shrinkShare */           \
+  NCCL_CONFIG_UNDEF_INT,                    /* nvlsCTAs */              \
+}
+
+/* This struct will be used by ncclGroupSimulateEnd() API to query information about simulation. */
+typedef struct ncclSimInfo_v22200 {
+    size_t size;
+    unsigned int magic;
+    unsigned int version;
+    float estimatedTime;
+} ncclSimInfo_t;
+
+/* NCCL_SIM_INFO_INITIALIZER must be assigned to initialize simInfo structure when it is created.
+ * Not initialized simInfo will result in NCCL error. */
+#define NCCL_SIM_INFO_INITIALIZER {                                         \
+  sizeof(ncclSimInfo_t),                            /* size */              \
+  0x74685283,                                       /* magic */             \
+  NCCL_VERSION(NCCL_MAJOR, NCCL_MINOR, NCCL_PATCH), /* version */           \
+  NCCL_UNDEF_FLOAT                                  /* estimated time */    \
+}
+
+/* NCCL malloc and free function for all types of NCCL optimizations
+ * (e.g. user buffer registration). The actual allocated size might
+ * be larger than requested due to granularity requirement. */
+ncclResult_t  ncclMemAlloc(void** ptr, size_t size);
+ncclResult_t pncclMemAlloc(void** ptr, size_t size);
+
+ncclResult_t  ncclMemFree(void *ptr);
+ncclResult_t pncclMemFree(void *ptr);
+
+/* Return the NCCL_VERSION_CODE of the NCCL library in the supplied integer.
+ * This integer is coded with the MAJOR, MINOR and PATCH level of the
+ * NCCL library
+ */
+ncclResult_t  ncclGetVersion(int *version);
+ncclResult_t pncclGetVersion(int *version);
+
+/* Generates an Id to be used in ncclCommInitRank. ncclGetUniqueId should be
+ * called once and the Id should be distributed to all ranks in the
+ * communicator before calling ncclCommInitRank. */
+ncclResult_t  ncclGetUniqueId(ncclUniqueId* uniqueId);
+ncclResult_t pncclGetUniqueId(ncclUniqueId* uniqueId);
+
+/* Create a new communicator (multi thread/process version) with a configuration
+ * set by users. */
+ncclResult_t  ncclCommInitRankConfig(ncclComm_t* comm, int nranks, ncclUniqueId commId, int rank, ncclConfig_t* config);
+ncclResult_t pncclCommInitRankConfig(ncclComm_t* comm, int nranks, ncclUniqueId commId, int rank, ncclConfig_t* config);
+
+/* Creates a new communicator (multi thread/process version).
+ * rank must be between 0 and nranks-1 and unique within a communicator clique.
+ * Each rank is associated to a CUDA device, which has to be set before calling
+ * ncclCommInitRank.
+ * ncclCommInitRank implicitly syncronizes with other ranks, so it must be
+ * called by different threads/processes or use ncclGroupStart/ncclGroupEnd. */
+ncclResult_t  ncclCommInitRank(ncclComm_t* comm, int nranks, ncclUniqueId commId, int rank);
+ncclResult_t pncclCommInitRank(ncclComm_t* comm, int nranks, ncclUniqueId commId, int rank);
+
+/* Creates a clique of communicators (single process version).
+ * This is a convenience function to create a single-process communicator clique.
+ * Returns an array of ndev newly initialized communicators in comm.
+ * comm should be pre-allocated with size at least ndev*sizeof(ncclComm_t).
+ * If devlist is NULL, the first ndev CUDA devices are used.
+ * Order of devlist defines user-order of processors within the communicator. */
+ncclResult_t  ncclCommInitAll(ncclComm_t* comm, int ndev, const int* devlist);
+ncclResult_t pncclCommInitAll(ncclComm_t* comm, int ndev, const int* devlist);
+
+/* Finalize a communicator. ncclCommFinalize flushes all issued communications,
+ * and marks communicator state as ncclInProgress. The state will change to ncclSuccess
+ * when the communicator is globally quiescent and related resources are freed; then,
+ * calling ncclCommDestroy can locally free the rest of the resources (e.g. communicator
+ * itself) without blocking. */
+ncclResult_t  ncclCommFinalize(ncclComm_t comm);
+ncclResult_t pncclCommFinalize(ncclComm_t comm);
+
+/* Frees local resources associated with communicator object. */
+ncclResult_t  ncclCommDestroy(ncclComm_t comm);
+ncclResult_t pncclCommDestroy(ncclComm_t comm);
+
+/* Frees resources associated with communicator object and aborts any operations
+ * that might still be running on the device. */
+ncclResult_t  ncclCommAbort(ncclComm_t comm);
+ncclResult_t pncclCommAbort(ncclComm_t comm);
+
+/* Creates one or more communicators from an existing one.
+ * Ranks with the same color will end up in the same communicator.
+ * Within the new communicator, key will be used to order ranks.
+ * NCCL_SPLIT_NOCOLOR as color will indicate the rank will not be part of any group
+ * and will therefore return a NULL communicator.
+ * If config is NULL, the new communicator will inherit the original communicator's
+ * configuration*/
+ncclResult_t  ncclCommSplit(ncclComm_t comm, int color, int key, ncclComm_t *newcomm, ncclConfig_t* config);
+ncclResult_t pncclCommSplit(ncclComm_t comm, int color, int key, ncclComm_t *newcomm, ncclConfig_t* config);
+
+/* Shrink existing communicator.
+ * Ranks in excludeRanksList will be removed form the existing communicator.
+ * Within the new communicator, ranks will be re-ordered to fill the gap of removed ones.
+ * If config is NULL, the new communicator will inherit the original communicator's configuration
+ * The flag enables NCCL to adapt to various states of the parent communicator, see NCCL_SHRINK flags.*/
+ncclResult_t  ncclCommShrink(ncclComm_t comm, int* excludeRanksList, int excludeRanksCount, ncclComm_t* newcomm, ncclConfig_t* config, int shrinkFlags);
+ncclResult_t pncclCommShrink(ncclComm_t comm, int* excludeRanksList, int excludeRanksCount, ncclComm_t* newcomm, ncclConfig_t* config, int shrinkFlags);
+
+/* Creates a new communicator (multi thread/process version), similar to ncclCommInitRankConfig.
+ * Allows to use more than one ncclUniqueId (up to one per rank), indicated by nId, to accelerate the init operation.
+ * The number of ncclUniqueIds and their order must be the same for every rank.
+ */
+ncclResult_t ncclCommInitRankScalable(ncclComm_t* newcomm, int nranks, int myrank, int nId, ncclUniqueId* commIds, ncclConfig_t* config);
+ncclResult_t pncclCommInitRankScalable(ncclComm_t* newcomm, int nranks, int myrank, int nId, ncclUniqueId* commIds, ncclConfig_t* config);
+
+/* Returns a string for each error code. */
+const char*  ncclGetErrorString(ncclResult_t result);
+const char* pncclGetErrorString(ncclResult_t result);
+
+/* Returns a human-readable message of the last error that occurred. */
+const char*  ncclGetLastError(ncclComm_t comm);
+const char* pncclGetLastError(ncclComm_t comm);
+
+/* Reload environment variables that determine logging. */
+void  ncclResetDebugInit();
+void pncclResetDebugInit();
+
+/* Checks whether the comm has encountered any asynchronous errors */
+ncclResult_t  ncclCommGetAsyncError(ncclComm_t comm, ncclResult_t *asyncError);
+ncclResult_t pncclCommGetAsyncError(ncclComm_t comm, ncclResult_t *asyncError);
+
+/* Gets the number of ranks in the communicator clique. */
+ncclResult_t  ncclCommCount(const ncclComm_t comm, int* count);
+ncclResult_t pncclCommCount(const ncclComm_t comm, int* count);
+
+/* Returns the cuda device number associated with the communicator. */
+ncclResult_t  ncclCommCuDevice(const ncclComm_t comm, int* device);
+ncclResult_t pncclCommCuDevice(const ncclComm_t comm, int* device);
+
+/* Returns the user-ordered "rank" associated with the communicator. */
+ncclResult_t  ncclCommUserRank(const ncclComm_t comm, int* rank);
+ncclResult_t pncclCommUserRank(const ncclComm_t comm, int* rank);
+
+/* Register CUDA buffer for zero-copy operation */
+ncclResult_t  ncclCommRegister(const ncclComm_t comm, void* buff, size_t size, void** handle);
+ncclResult_t pncclCommRegister(const ncclComm_t comm, void* buff, size_t size, void** handle);
+
+/* Deregister CUDA buffer */
+ncclResult_t  ncclCommDeregister(const ncclComm_t comm, void* handle);
+ncclResult_t pncclCommDeregister(const ncclComm_t comm, void* handle);
+
+/* Register memory window  */
+ncclResult_t  ncclCommWindowRegister(ncclComm_t comm, void* buff, size_t size, ncclWindow_t* win, int winFlags);
+ncclResult_t pncclCommWindowRegister(ncclComm_t comm, void* buff, size_t size, ncclWindow_t* win, int winFlags);
+
+/* Deregister symmetric memory */
+ncclResult_t  ncclCommWindowDeregister(ncclComm_t comm, ncclWindow_t win);
+ncclResult_t pncclCommWindowDeregister(ncclComm_t comm, ncclWindow_t win);
+
+/* Reduction operation selector */
+typedef enum { ncclNumOps_dummy = 5 } ncclRedOp_dummy_t;
+typedef enum { ncclSum        = 0,
+               ncclProd       = 1,
+               ncclMax        = 2,
+               ncclMin        = 3,
+               ncclAvg        = 4,
+               /* ncclNumOps: The number of built-in ncclRedOp_t values. Also
+                * serves as the least possible value for dynamic ncclRedOp_t's
+                * as constructed by ncclRedOpCreate*** functions. */
+               ncclNumOps     = 5,
+               /* ncclMaxRedOp: The largest valid value for ncclRedOp_t.
+                * It is defined to be the largest signed value (since compilers
+                * are permitted to use signed enums) that won't grow
+                * sizeof(ncclRedOp_t) when compared to previous NCCL versions to
+                * maintain ABI compatibility. */
+               ncclMaxRedOp   = 0x7fffffff>>(32-8*sizeof(ncclRedOp_dummy_t))
+             } ncclRedOp_t;
+
+/* Data types */
+typedef enum { ncclInt8       = 0, ncclChar       = 0,
+               ncclUint8      = 1,
+               ncclInt32      = 2, ncclInt        = 2,
+               ncclUint32     = 3,
+               ncclInt64      = 4,
+               ncclUint64     = 5,
+               ncclFloat16    = 6, ncclHalf       = 6,
+               ncclFloat32    = 7, ncclFloat      = 7,
+               ncclFloat64    = 8, ncclDouble     = 8,
+               ncclBfloat16   = 9,
+               ncclFloat8e4m3 = 10,
+               ncclFloat8e5m2 = 11,
+               ncclNumTypes   = 12
+} ncclDataType_t;
+
+/* ncclScalarResidence_t: Location and dereferencing logic for scalar arguments. */
+typedef enum {
+  /* ncclScalarDevice: The scalar is in device-visible memory and will be
+   * dereferenced while the collective is running. */
+  ncclScalarDevice = 0,
+
+  /* ncclScalarHostImmediate: The scalar is in host-visible memory and will be
+   * dereferenced before the ncclRedOpCreate***() function returns. */
+  ncclScalarHostImmediate = 1
+} ncclScalarResidence_t;
+
+/*
+ * ncclRedOpCreatePreMulSum
+ *
+ * Creates a new reduction operator which pre-multiplies input values by a given
+ * scalar locally before reducing them with peer values via summation. For use
+ * only with collectives launched against *comm* and *datatype*. The
+ * *residence* argument indicates how/when the memory pointed to by *scalar*
+ * will be dereferenced. Upon return, the newly created operator's handle
+ * is stored in *op*.
+ */
+ncclResult_t  ncclRedOpCreatePreMulSum(ncclRedOp_t *op, void *scalar, ncclDataType_t datatype, ncclScalarResidence_t residence, ncclComm_t comm);
+ncclResult_t pncclRedOpCreatePreMulSum(ncclRedOp_t *op, void *scalar, ncclDataType_t datatype, ncclScalarResidence_t residence, ncclComm_t comm);
+
+/*
+ * ncclRedOpDestroy
+ *
+ * Destroys the reduction operator *op*. The operator must have been created by
+ * ncclRedOpCreatePreMul with the matching communicator *comm*. An operator may be
+ * destroyed as soon as the last NCCL function which is given that operator returns.
+ */
+ncclResult_t ncclRedOpDestroy(ncclRedOp_t op, ncclComm_t comm);
+ncclResult_t pncclRedOpDestroy(ncclRedOp_t op, ncclComm_t comm);
+
+/*
+ * Collective communication operations
+ *
+ * Collective communication operations must be called separately for each
+ * communicator in a communicator clique.
+ *
+ * They return when operations have been enqueued on the CUDA stream.
+ *
+ * Since they may perform inter-CPU synchronization, each call has to be done
+ * from a different thread or process, or need to use Group Semantics (see
+ * below).
+ */
+
+/*
+ * Reduce
+ *
+ * Reduces data arrays of length count in sendbuff into recvbuff using op
+ * operation.
+ * recvbuff may be NULL on all calls except for root device.
+ * root is the rank (not the CUDA device) where data will reside after the
+ * operation is complete.
+ *
+ * In-place operation will happen if sendbuff == recvbuff.
+ */
+ncclResult_t  ncclReduce(const void* sendbuff, void* recvbuff, size_t count, ncclDataType_t datatype,
+    ncclRedOp_t op, int root, ncclComm_t comm, cudaStream_t stream);
+ncclResult_t pncclReduce(const void* sendbuff, void* recvbuff, size_t count, ncclDataType_t datatype,
+    ncclRedOp_t op, int root, ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * (deprecated) Broadcast (in-place)
+ *
+ * Copies count values from root to all other devices.
+ * root is the rank (not the CUDA device) where data resides before the
+ * operation is started.
+ *
+ * This operation is implicitely in place.
+ */
+ncclResult_t  ncclBcast(void* buff, size_t count, ncclDataType_t datatype, int root,
+    ncclComm_t comm, cudaStream_t stream);
+ncclResult_t pncclBcast(void* buff, size_t count, ncclDataType_t datatype, int root,
+    ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * Broadcast
+ *
+ * Copies count values from root to all other devices.
+ * root is the rank (not the CUDA device) where data resides before the
+ * operation is started.
+ *
+ * In-place operation will happen if sendbuff == recvbuff.
+ */
+ncclResult_t  ncclBroadcast(const void* sendbuff, void* recvbuff, size_t count, ncclDataType_t datatype, int root,
+    ncclComm_t comm, cudaStream_t stream);
+ncclResult_t pncclBroadcast(const void* sendbuff, void* recvbuff, size_t count, ncclDataType_t datatype, int root,
+    ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * All-Reduce
+ *
+ * Reduces data arrays of length count in sendbuff using op operation, and
+ * leaves identical copies of result on each recvbuff.
+ *
+ * In-place operation will happen if sendbuff == recvbuff.
+ */
+ncclResult_t  ncclAllReduce(const void* sendbuff, void* recvbuff, size_t count,
+    ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream);
+ncclResult_t pncclAllReduce(const void* sendbuff, void* recvbuff, size_t count,
+    ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * Reduce-Scatter
+ *
+ * Reduces data in sendbuff using op operation and leaves reduced result
+ * scattered over the devices so that recvbuff on rank i will contain the i-th
+ * block of the result.
+ * Assumes sendcount is equal to nranks*recvcount, which means that sendbuff
+ * should have a size of at least nranks*recvcount elements.
+ *
+ * In-place operations will happen if recvbuff == sendbuff + rank * recvcount.
+ */
+ncclResult_t  ncclReduceScatter(const void* sendbuff, void* recvbuff,
+    size_t recvcount, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm,
+    cudaStream_t stream);
+ncclResult_t pncclReduceScatter(const void* sendbuff, void* recvbuff,
+    size_t recvcount, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm,
+    cudaStream_t stream);
+
+/*
+ * All-Gather
+ *
+ * Each device gathers sendcount values from other GPUs into recvbuff,
+ * receiving data from rank i at offset i*sendcount.
+ * Assumes recvcount is equal to nranks*sendcount, which means that recvbuff
+ * should have a size of at least nranks*sendcount elements.
+ *
+ * In-place operations will happen if sendbuff == recvbuff + rank * sendcount.
+ */
+ncclResult_t  ncclAllGather(const void* sendbuff, void* recvbuff, size_t sendcount,
+    ncclDataType_t datatype, ncclComm_t comm, cudaStream_t stream);
+ncclResult_t pncclAllGather(const void* sendbuff, void* recvbuff, size_t sendcount,
+    ncclDataType_t datatype, ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * Send
+ *
+ * Send data from sendbuff to rank peer.
+ *
+ * Rank peer needs to call ncclRecv with the same datatype and the same count from this
+ * rank.
+ *
+ * This operation is blocking for the GPU. If multiple ncclSend and ncclRecv operations
+ * need to progress concurrently to complete, they must be fused within a ncclGroupStart/
+ * ncclGroupEnd section.
+ */
+ncclResult_t  ncclSend(const void* sendbuff, size_t count, ncclDataType_t datatype, int peer,
+    ncclComm_t comm, cudaStream_t stream);
+ncclResult_t pncclSend(const void* sendbuff, size_t count, ncclDataType_t datatype, int peer,
+    ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * Receive
+ *
+ * Receive data from rank peer into recvbuff.
+ *
+ * Rank peer needs to call ncclSend with the same datatype and the same count to this
+ * rank.
+ *
+ * This operation is blocking for the GPU. If multiple ncclSend and ncclRecv operations
+ * need to progress concurrently to complete, they must be fused within a ncclGroupStart/
+ * ncclGroupEnd section.
+ */
+ncclResult_t pncclRecv(void* recvbuff, size_t count, ncclDataType_t datatype, int peer,
+    ncclComm_t comm, cudaStream_t stream);
+ncclResult_t  ncclRecv(void* recvbuff, size_t count, ncclDataType_t datatype, int peer,
+    ncclComm_t comm, cudaStream_t stream);
+
+/*
+ * Group semantics
+ *
+ * When managing multiple GPUs from a single thread, and since NCCL collective
+ * calls may perform inter-CPU synchronization, we need to "group" calls for
+ * different ranks/devices into a single call.
+ *
+ * Grouping NCCL calls as being part of the same collective operation is done
+ * using ncclGroupStart and ncclGroupEnd. ncclGroupStart will enqueue all
+ * collective calls until the ncclGroupEnd call, which will wait for all calls
+ * to be complete. Note that for collective communication, ncclGroupEnd only
+ * guarantees that the operations are enqueued on the streams, not that
+ * the operation is effectively done.
+ *
+ * Both collective communication and ncclCommInitRank can be used in conjunction
+ * of ncclGroupStart/ncclGroupEnd, but not together.
+ *
+ * Group semantics also allow to fuse multiple operations on the same device
+ * to improve performance (for aggregated collective calls), or to permit
+ * concurrent progress of multiple send/receive operations.
+ */
+
+/*
+ * Group Start
+ *
+ * Start a group call. All calls to NCCL until ncclGroupEnd will be fused into
+ * a single NCCL operation. Nothing will be started on the CUDA stream until
+ * ncclGroupEnd.
+ */
+ncclResult_t  ncclGroupStart();
+ncclResult_t pncclGroupStart();
+
+/*
+ * Group End
+ *
+ * End a group call. Start a fused NCCL operation consisting of all calls since
+ * ncclGroupStart. Operations on the CUDA stream depending on the NCCL operations
+ * need to be called after ncclGroupEnd.
+ */
+ncclResult_t  ncclGroupEnd();
+ncclResult_t pncclGroupEnd();
+
+/*
+ * Group Simulate End
+ *
+ * Simulate a ncclGroupEnd() call and return NCCL's simulation info in a struct.
+ */
+ncclResult_t  ncclGroupSimulateEnd(ncclSimInfo_t* simInfo);
+ncclResult_t pncclGroupSimulateEnd(ncclSimInfo_t* simInfo);
+
+#ifdef __cplusplus
+} // end extern "C"
+#endif
+
+#endif // end include guard
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2
new file mode 100644
index 0000000000000000000000000000000000000000..a4cdc302ba817b19af48b43a30f25d35cf7002e5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1fc36eebc84f4b10923eee21538ed8fa25725647d06f4840bc0fe399a6ed9bc4
+size 429634192
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/include/nvJitLink.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/include/nvJitLink.h
new file mode 100644
index 0000000000000000000000000000000000000000..225a9819f33f7bfb0647ccfa3433101e9cf7d86a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/include/nvJitLink.h
@@ -0,0 +1,531 @@
+/*
+ * NVIDIA_COPYRIGHT_BEGIN
+ *
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * NVIDIA CORPORATION and its licensors retain all intellectual property
+ * and proprietary rights in and to this software, related documentation
+ * and any modifications thereto.  Any use, reproduction, disclosure or
+ * distribution of this software and related documentation without an express
+ * license agreement from NVIDIA CORPORATION is strictly prohibited.
+ *
+ * NVIDIA_COPYRIGHT_END
+ */
+
+#ifndef nvJitLink_INCLUDED
+#define nvJitLink_INCLUDED
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <stdint.h>
+#include <stdlib.h>
+
+/**
+ *
+ * \defgroup error Error codes
+ *
+ */
+
+/** \ingroup error
+ *
+ * \brief    The enumerated type nvJitLinkResult defines API call result codes.
+ *           nvJitLink APIs return nvJitLinkResult codes to indicate the result.
+ */
+
+typedef enum {
+  NVJITLINK_SUCCESS = 0,
+  NVJITLINK_ERROR_UNRECOGNIZED_OPTION,
+  NVJITLINK_ERROR_MISSING_ARCH, // -arch=sm_NN option not specified
+  NVJITLINK_ERROR_INVALID_INPUT,
+  NVJITLINK_ERROR_PTX_COMPILE,
+  NVJITLINK_ERROR_NVVM_COMPILE,
+  NVJITLINK_ERROR_INTERNAL,
+  NVJITLINK_ERROR_THREADPOOL,
+  NVJITLINK_ERROR_UNRECOGNIZED_INPUT,
+  NVJITLINK_ERROR_FINALIZE,
+#ifdef NEW_ERROR_CODES // These error codes will appear in a future CUDA release.
+  NVJITLINK_ERROR_NULL_INPUT,
+  NVJITLINK_ERROR_INCOMPATIBLE_OPTIONS,
+  NVJITLINK_ERROR_INCORRECT_INPUT_TYPE,
+  NVJITLINK_ERROR_ARCH_MISMATCH,
+  NVJITLINK_ERROR_OUTDATED_LIBRARY,
+  NVJITLINK_ERROR_MISSING_FATBIN
+#endif
+} nvJitLinkResult;
+
+#ifndef NEW_ERROR_CODES // To avoid breaking compatibility, we map them to existing error codes for now.
+#define NVJITLINK_ERROR_NULL_INPUT NVJITLINK_ERROR_INVALID_INPUT
+#define NVJITLINK_ERROR_INCOMPATIBLE_OPTIONS NVJITLINK_ERROR_INVALID_INPUT
+#define NVJITLINK_ERROR_INCORRECT_INPUT_TYPE NVJITLINK_ERROR_INVALID_INPUT
+#define NVJITLINK_ERROR_ARCH_MISMATCH NVJITLINK_ERROR_INTERNAL
+#define NVJITLINK_ERROR_OUTDATED_LIBRARY NVJITLINK_ERROR_INTERNAL
+#define NVJITLINK_ERROR_MISSING_FATBIN NVJITLINK_ERROR_INVALID_INPUT
+#endif  
+
+/**
+ *
+ * \defgroup linking Linking
+ *
+ */
+
+/** \ingroup linking
+ *
+ * \brief    The enumerated type nvJitLinkInputType defines the kind of inputs
+ *           that can be passed to nvJitLinkAdd* APIs.
+ */
+
+typedef enum {
+  NVJITLINK_INPUT_NONE = 0, // error
+  NVJITLINK_INPUT_CUBIN = 1,
+  NVJITLINK_INPUT_PTX,
+  NVJITLINK_INPUT_LTOIR,
+  NVJITLINK_INPUT_FATBIN,
+  NVJITLINK_INPUT_OBJECT,
+  NVJITLINK_INPUT_LIBRARY,
+  NVJITLINK_INPUT_INDEX,
+  NVJITLINK_INPUT_ANY = 10 // will dynamically determine one of above types
+} nvJitLinkInputType;
+
+/**
+ * \defgroup options Supported Link Options
+ *
+ * nvJitLink supports the link options below.
+ * Option names are prefixed with a single dash (\c -).
+ * Options that take a value have an assignment operator (\c =)
+ * followed by the option value, with no spaces, e.g. \c "-arch=sm_90".
+ *
+ * The supported options are:
+ * - \c -arch=sm_<N\> \n
+ *   Pass SM architecture value.  See nvcc for valid values of <N\>.
+ *   Can use compute_<N\> value instead if only generating PTX.
+ *   This is a required option.  
+ * - \c -maxrregcount=<N\> \n
+ *   Maximum register count.
+ * - \c -time \n
+ *   Print timing information to InfoLog.
+ * - \c -verbose \n
+ *   Print verbose messages to InfoLog.
+ * - \c -lto \n
+ *   Do link time optimization.
+ * - \c -ptx \n
+ *   Emit ptx after linking instead of cubin; only supported with \c -lto
+ * - \c -O<N\> \n
+ *   Optimization level. Only 0 and 3 are accepted.
+ * - \c -g \n
+ *   Generate debug information.
+ * - \c -lineinfo \n
+ *   Generate line information.
+ * - \c -ftz=<n\> \n
+ *   Flush to zero.
+ * - \c -prec-div=<n\> \n
+ *   Precise divide.
+ * - \c -prec-sqrt=<n\> \n
+ *   Precise square root.
+ * - \c -fma=<n\> \n
+ *   Fast multiply add.
+ * - \c -kernels-used=<name\> \n
+ *   Pass list of kernels that are used; any not in the list can be removed.
+ *   This option can be specified multiple times.
+ * - \c -variables-used=<name\> \n
+ *   Pass list of variables that are used; any not in the list can be removed.
+ *   This option can be specified multiple times.
+ * - \c -optimize-unused-variables \n
+ *   Normally device code optimization is limited by not knowing what the
+ *   host code references.  With this option it can assume that if a variable
+ *   is not referenced in device code then it can be removed.
+ * - \c -Xptxas=<opt\> \n
+ *   Pass <opt\> to ptxas.  This option can be called multiple times.
+ * - \c -split-compile=<N\> \n
+ *   Split compilation maximum thread count. Use 0 to use all available processors.
+ *   Value of 1 disables split compilation (default).
+ * - \c -split-compile-extended=<N\> \n
+ *   A more aggressive form of split compilation available in LTO mode only.
+ *   Accepts a maximum thread count value. Use 0 to use all available processors.
+ *   Value of 1 disables extended split compilation (default).
+ *   Note: This option can potentially impact performance of the compiled binary.
+ * - \c -jump-table-density=<N\> \n
+ *   When doing LTO, specify the case density percentage in switch statements,
+ *   and use it as a minimal threshold to determine whether jump table(brx.idx 
+ *   instruction) will be used to implement a switch statement. Default
+ *   value is 101. The percentage ranges from 0 to 101 inclusively.
+ * - \c -no-cache \n
+ *   Don't cache the intermediate steps of nvJitLink.
+ * - \c -device-stack-protector \n
+ *   Enable stack canaries in device code.
+ *   Stack canaries make it more difficult to exploit certain types of memory safety bugs involving stack-local variables.
+ *   The compiler uses heuristics to assess the risk of such a bug in each function.  Only those functions which are deemed high-risk make use of a stack canary.
+ */
+
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkHandle is the unit of linking, and an opaque handle for
+ *          a program.
+ *
+ * To link inputs, an instance of nvJitLinkHandle must be created first with
+ * nvJitLinkCreate().
+ */
+
+typedef struct nvJitLink* nvJitLinkHandle; // opaque handle
+
+// For versioning we will have separate API version for each library version
+
+extern nvJitLinkResult __nvJitLinkCreate_12_8(
+  nvJitLinkHandle *handle,
+  uint32_t numOptions,
+  const char **options);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkCreate creates an instance of nvJitLinkHandle with the
+ *          given input options, and sets the output parameter \p handle.
+ *
+ * \param   [out] handle       Address of nvJitLink handle.
+ * \param   [in]  numOptions   Number of options passed.
+ * \param   [in]  options      Array of size \p numOptions of option strings.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_UNRECOGNIZED_OPTION\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_MISSING_ARCH\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * It supports options listed in \ref options.
+ *
+ * \see nvJitLinkDestroy
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkCreate(
+  nvJitLinkHandle *handle,
+  uint32_t numOptions,
+  const char **options)
+{
+  return __nvJitLinkCreate_12_8 (handle, numOptions, options);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkDestroy_12_8 (nvJitLinkHandle *handle);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkDestroy frees the memory associated with the given handle
+ *          and sets it to NULL.
+ *
+ * \param    [in] handle      Address of nvJitLink handle.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkCreate
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkDestroy (nvJitLinkHandle *handle)
+{
+  return __nvJitLinkDestroy_12_8 (handle);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkAddData_12_8(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const void *data,
+  size_t size,
+  const char *name); // name can be null
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkAddData adds data image to the link. 
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [in] inputType   kind of input.
+ * \param    [in] data        pointer to data image in memory.
+ * \param    [in] size        size of the data.
+ * \param    [in] name        name of input object.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkAddData(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const void *data,
+  size_t size,
+  const char *name) // name can be null
+{
+  return __nvJitLinkAddData_12_8 (handle, inputType, data, size, name);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkAddFile_12_8(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const char *fileName); // includes path to file
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkAddFile reads data from file and links it in. 
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [in] inputType   kind of input.
+ * \param    [in] fileName    name of file.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkAddFile(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const char *fileName) // includes path to file
+{
+  return __nvJitLinkAddFile_12_8 (handle, inputType, fileName);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkComplete_12_8 (nvJitLinkHandle handle);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkComplete does the actual link. 
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkComplete (nvJitLinkHandle handle)
+{
+  return __nvJitLinkComplete_12_8 (handle);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkGetLinkedCubinSize_12_8(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedCubinSize gets the size of the linked cubin.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the linked cubin.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkGetLinkedCubin
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedCubinSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetLinkedCubinSize_12_8 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetLinkedCubin_12_8(
+  nvJitLinkHandle handle,
+  void *cubin);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedCubin gets the linked cubin.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] cubin      The linked cubin.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * User is responsible for allocating enough space to hold the \p cubin.
+ * \see nvJitLinkGetLinkedCubinSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedCubin(
+  nvJitLinkHandle handle,
+  void *cubin)
+{
+  return __nvJitLinkGetLinkedCubin_12_8 (handle, cubin);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkGetLinkedPtxSize_12_8(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedPtxSize gets the size of the linked ptx.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the linked PTX.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * Linked PTX is only available when using the \c -lto option.
+ * \see nvJitLinkGetLinkedPtx
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedPtxSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetLinkedPtxSize_12_8 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetLinkedPtx_12_8(
+  nvJitLinkHandle handle,
+  char *ptx);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedPtx gets the linked ptx.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] ptx        The linked PTX.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * Linked PTX is only available when using the \c -lto option.
+ * User is responsible for allocating enough space to hold the \p ptx.
+ * \see nvJitLinkGetLinkedPtxSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedPtx(
+  nvJitLinkHandle handle,
+  char *ptx)
+{
+  return __nvJitLinkGetLinkedPtx_12_8 (handle, ptx);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkGetErrorLogSize_12_8(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetErrorLogSize gets the size of the error log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the error log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkGetErrorLog
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetErrorLogSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetErrorLogSize_12_8 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetErrorLog_12_8(
+  nvJitLinkHandle handle,
+  char *log);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetErrorLog puts any error messages in the log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] log        The error log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * User is responsible for allocating enough space to hold the \p log.
+ * \see nvJitLinkGetErrorLogSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetErrorLog(
+  nvJitLinkHandle handle,
+  char *log)
+{
+  return __nvJitLinkGetErrorLog_12_8 (handle, log);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetInfoLogSize_12_8(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetInfoLogSize gets the size of the info log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the info log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkGetInfoLog
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetInfoLogSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetInfoLogSize_12_8 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetInfoLog_12_8(
+  nvJitLinkHandle handle,
+  char *log);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetInfoLog puts any info messages in the log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] log        The info log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * User is responsible for allocating enough space to hold the \p log.
+ * \see nvJitLinkGetInfoLogSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetInfoLog(
+  nvJitLinkHandle handle,
+  char *log)
+{
+  return __nvJitLinkGetInfoLog_12_8 (handle, log);
+}
+#endif
+
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkVersion returns the current version of nvJitLink.
+ *
+ * \param    [out] major        The major version.
+ * \param    [out] minor        The minor version.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ */
+extern nvJitLinkResult nvJitLinkVersion(
+  unsigned int *major,
+  unsigned int *minor);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // nvJitLink_INCLUDED
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvjitlink/lib/__init__.py
new file mode 100644
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h
new file mode 100644
index 0000000000000000000000000000000000000000..2bd2a0ada91cb3552c52a3f4890ecd445925df5c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h
@@ -0,0 +1,1578 @@
+/*
+* Copyright 2009-2024  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+/** \file nvToolsExt.h
+ */
+
+/*
+* Deprecation and upcoming removal warning for NVTX version 2
+* This warning can be suppressed by defining NVTX_SUPPRESS_V2_DEPRECATION_WARNING
+*/
+#ifndef NVTX_SUPPRESS_V2_DEPRECATION_WARNING
+#ifndef NVTX_V2_DEPRECATION_WARNING_MESSAGE
+#define NVTX_V2_DEPRECATION_WARNING_MESSAGE "DEPRECATION NOTICE:  NVTX 2 is deprecated and will be removed from toolkits that distribute it in their next major version (CUDA 12.9).  Please use NVTX 3 instead.  All NVTX 2 API functions are supported in version 3.  The implementation is now header-only, so you no longer need to link with and distribute a dynamic library to use NVTX.  Ensure you are explicitly using NVTX 3 by writing #include <nvtx3/nvToolsExt.h> instead of #include <nvToolsExt.h>.  Remove nvToolsExt from your link command and linker search paths, and stop deploying libnvToolsExt.so or nvToolsExt64_1.dll -- these binary libraries will not be shipped with NVTX 3 for C/C++ usage.  A new library called 'libnvtx3interop.so' or 'nvtx3interop.dll' will be provided for interop with other languages.  This message can be suppressed by defining NVTX_SUPPRESS_V2_DEPRECATION_WARNING before including the NVTX 2 headers."
+#ifdef _MSC_VER
+#define NVTX_V2_WARNING_HELPER2(x) #x
+#define NVTX_V2_WARNING_HELPER(x) NVTX_V2_WARNING_HELPER2(x)
+#pragma message(__FILE__ ":" NVTX_V2_WARNING_HELPER(__LINE__) ": note: \"" NVTX_V2_DEPRECATION_WARNING_MESSAGE "\"\n")
+#else
+#pragma message NVTX_V2_DEPRECATION_WARNING_MESSAGE
+#endif
+#endif /* NVTX_V2_DEPRECATION_WARNING_MESSAGE */
+#endif /* NVTX_SUPPRESS_V2_DEPRECATION_WARNING */
+
+/* ========================================================================= */
+/** \mainpage
+ * \tableofcontents
+ * \section INTRODUCTION Introduction
+ *
+ * The NVIDIA Tools Extension library is a set of functions that a
+ * developer can use to provide additional information to tools.
+ * The additional information is used by the tool to improve
+ * analysis and visualization of data.
+ *
+ * The library introduces close to zero overhead if no tool is
+ * attached to the application.  The overhead when a tool is
+ * attached is specific to the tool.
+ *
+ * \section INITIALIZATION_SECTION Initialization
+ *
+ * Typically the tool's library that plugs into NVTX is indirectly 
+ * loaded via enviromental properties that are platform specific. 
+ * For some platform or special cases, the user may be required 
+ * to instead explicity initialize instead though.   This can also
+ * be helpful to control when the API loads a tool's library instead
+ * of what would typically be the first function call to emit info.
+ * For these rare case, see \ref INITIALIZATION for additional information.
+ *
+ * \section MARKERS_AND_RANGES Markers and Ranges
+ *
+ * Markers and ranges are used to describe events at a specific time (markers)
+ * or over a time span (ranges) during the execution of the application
+ * respectively. 
+ *
+ * \subsection MARKERS Markers
+ * 
+ * Markers denote specific moments in time.
+ * 
+ * 
+ * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on
+ * how to specify the domain.
+ * 
+ * \subsection THREAD_RANGES Thread Ranges
+ *
+ * Thread ranges denote nested time ranges. Nesting is maintained per thread
+ * per domain and does not require any additional correlation mechanism. The
+ * duration of a thread range is defined by the corresponding pair of
+ * nvtxRangePush* to nvtxRangePop API calls.
+ *
+ * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on
+ * how to specify the domain.
+ *
+ * \subsection PROCESS_RANGES Process Ranges
+ *
+ * Process ranges denote a time span that can expose arbitrary concurrency, as 
+ * opposed to thread ranges that only support nesting. In addition the range
+ * start event can happen on a different thread than the end marker. For the 
+ * correlation of a start/end pair an unique correlation ID is used that is
+ * returned from the start API call and needs to be passed into the end API
+ * call.
+ *
+ * \subsection EVENT_ATTRIBUTES Event Attributes
+ *
+ * \ref MARKERS_AND_RANGES can be annotated with various attributes to provide
+ * additional information for an event or to guide the tool's visualization of
+ * the data. Each of the attributes is optional and if left unused the
+ * attributes fall back to a default value. The attributes include:
+ * - color
+ * - category
+ *
+ * To specify any attribute other than the text message, the \ref
+ * EVENT_ATTRIBUTE_STRUCTURE "Event Attribute Structure" must be used.
+ *
+ * \section DOMAINS Domains
+ *
+ * Domains enable developers to scope annotations. By default all events and
+ * annotations are in the default domain. Additional domains can be registered.
+ * This allows developers to scope markers, ranges, and resources names to
+ * avoid conflicts.
+ *
+ * The function ::nvtxDomainCreateA or ::nvtxDomainCreateW is used to create
+ * a named domain.
+ * 
+ * Each domain maintains its own
+ * - categories
+ * - thread range stacks
+ * - registered strings
+ *
+ * The function ::nvtxDomainDestroy marks the end of the domain. Destroying 
+ * a domain unregisters and destroys all objects associated with it such as 
+ * registered strings, resource objects, named categories, and started ranges. 
+ *
+ * \section RESOURCE_NAMING Resource Naming
+ *
+ * This section covers calls that allow to annotate objects with user-provided
+ * names in order to allow for a better analysis of complex trace data. All of
+ * the functions take the handle or the ID of the object to name and the name.
+ * The functions can be called multiple times during the execution of an
+ * application, however, in that case it is implementation dependent which
+ * name will be reported by the tool.
+ * 
+ * \subsection CATEGORY_NAMING Category Naming
+ *
+ * Some function in this library support associating an integer category 
+ * to enable filtering and sorting.  The category naming functions allow 
+ * the application to associate a user friendly name with the integer 
+ * category.  Support for domains have been added in NVTX_VERSION_2 to 
+ * avoid collisions when domains are developed independantly. 
+ *
+ * \subsection RESOURCE_OBJECTS Resource Objects
+ *
+ * Resource objects are a generic mechanism for attaching data to an application 
+ * resource.  The identifier field makes the association to a pointer or handle, 
+ * while the type field helps provide deeper understanding of the identifier as 
+ * well as enabling differentiation in cases where handles generated by different
+ * APIs may collide.  The resource object may also have an associated message to
+ * associate with the application resource, enabling further annotation of this 
+ * object and how it is used.
+ * 
+ * The resource object was introduced in NVTX_VERSION_2 to supersede existing naming
+ * functions and allow the application resource identified by those functions to be
+ * associated to a domain.  The other naming functions are still supported for backward
+ * compatibility but will be associated only to the default domain.
+ *
+ * \subsection RESOURCE_NAMING_OS Resource Naming
+ * 
+ * Some operating system resources creation APIs do not support providing a user friendly 
+ * name, such as some OS thread creation APIs.  This API support resource naming though 
+ * both through resource objects and functions following the pattern 
+ * nvtxName[RESOURCE_TYPE][A|W](identifier, name).  Resource objects introduced in NVTX_VERSION 2 
+ * supersede the other functions with a a more general method of assigning names to OS resources,
+ * along with associating them to domains too.  The older nvtxName* functions are only associated 
+ * with the default domain.
+ * \section EXTENSIONS Optional Extensions
+ * Optional extensions will either appear within the existing sections the extend or appear 
+ * in the "Related Pages" when they introduce new concepts.
+ */
+
+#ifndef NVTOOLSEXT_H_
+#define NVTOOLSEXT_H_
+
+#if defined(_MSC_VER)
+    #ifdef NVTX_EXPORTS
+        #define NVTX_DECLSPEC
+    #else
+        #define NVTX_DECLSPEC __declspec(dllimport)
+    #endif /* NVTX_EXPORTS */
+    #define NVTX_API __stdcall
+    #define NVTX_INLINE_STATIC __inline static
+#else /*defined(__GNUC__)*/
+    #define NVTX_DECLSPEC
+    #define NVTX_API
+    #define NVTX_INLINE_STATIC inline static
+#endif /* Platform */
+
+/**
+ * The nvToolsExt library depends on stdint.h.  If the build tool chain in use
+ * does not include stdint.h then define NVTX_STDINT_TYPES_ALREADY_DEFINED
+ * and define the following types:
+ * <ul>
+ *   <li>uint8_t
+ *   <li>int8_t
+ *   <li>uint16_t
+ *   <li>int16_t
+ *   <li>uint32_t
+ *   <li>int32_t
+ *   <li>uint64_t
+ *   <li>int64_t
+ *   <li>uintptr_t
+ *   <li>intptr_t
+ * </ul>
+ #define NVTX_STDINT_TYPES_ALREADY_DEFINED if you are using your own header file.
+ */
+#ifndef NVTX_STDINT_TYPES_ALREADY_DEFINED
+#include <stdint.h>
+#endif
+
+#include <stddef.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * Tools Extension API version
+ */
+#define NVTX_VERSION 2
+
+/**
+ * Size of the nvtxEventAttributes_t structure.
+ */
+#define NVTX_EVENT_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxEventAttributes_t) ) )
+
+/**
+ * Size of the nvtxInitializationAttributes_t structure.
+ */
+#define NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxInitializationAttributes_t) ) )
+
+#define NVTX_NO_PUSH_POP_TRACKING ((int)-2)
+
+typedef uint64_t nvtxRangeId_t;
+
+
+/* \brief String Handle Structure.
+* \anchor STRING_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a string.  The tools will return a pointer through the API for the application
+* to hold on it's behalf to reference the string in the future.
+*
+*/
+typedef struct nvtxStringHandle* nvtxStringHandle_t;
+
+/* \brief Domain Handle Structure.
+* \anchor DOMAIN_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a domain.  The tools will return a pointer through the API for the application
+* to hold on its behalf to reference the domain in the future.
+*
+*/
+typedef struct nvtxDomainHandle* nvtxDomainHandle_t;
+
+
+
+
+
+
+/* ========================================================================= */
+/** \defgroup GENERAL General
+ * @{
+ */
+
+/** ---------------------------------------------------------------------------
+ * Color Types
+ * ------------------------------------------------------------------------- */
+typedef enum nvtxColorType_t
+{
+    NVTX_COLOR_UNKNOWN  = 0,                 /**< Color attribute is unused. */
+    NVTX_COLOR_ARGB     = 1                  /**< An ARGB color is provided. */
+} nvtxColorType_t;
+
+/** ---------------------------------------------------------------------------
+ * Message Types
+ * ------------------------------------------------------------------------- */
+typedef enum nvtxMessageType_t
+{
+    NVTX_MESSAGE_UNKNOWN          = 0,    /**< Message payload is unused. */
+    NVTX_MESSAGE_TYPE_ASCII       = 1,    /**< A character sequence is used as payload. */
+    NVTX_MESSAGE_TYPE_UNICODE     = 2,     /**< A wide character sequence is used as payload. */
+    /* NVTX_VERSION_2 */
+    NVTX_MESSAGE_TYPE_REGISTERED  = 3     /**< A unique string handle that was registered
+                                                with \ref nvtxDomainRegisterStringA() or 
+                                                \ref nvtxDomainRegisterStringW(). */
+} nvtxMessageType_t;
+
+typedef union nvtxMessageValue_t
+{
+    const char* ascii;
+    const wchar_t* unicode;
+    /* NVTX_VERSION_2 */
+    nvtxStringHandle_t registered;
+} nvtxMessageValue_t;
+
+
+/** @} */ /*END defgroup*/
+
+/* ========================================================================= */
+/** \defgroup INITIALIZATION Initialization
+* @{
+* Typically the tool's library that plugs into NVTX is indirectly
+* loaded via enviromental properties that are platform specific.
+* For some platform or special cases, the user may be required
+* to instead explicity initialize instead though.  This can also
+* be helpful to control when the API loads a tool's library instead
+* of what would typically be the first function call to emit info.
+*/
+
+/** ---------------------------------------------------------------------------
+* Initialization Modes
+* ------------------------------------------------------------------------- */
+typedef enum nvtxInitializationMode_t
+{
+    NVTX_INITIALIZATION_MODE_UNKNOWN = 0,   /**< A platform that supports indirect initialization will attempt this style, otherwise expect failure. */
+    NVTX_INITIALIZATION_MODE_CALLBACK_V1 = 1,   /**< A function pointer conforming to NVTX_VERSION=1 will be used. */
+    NVTX_INITIALIZATION_MODE_CALLBACK_V2 = 2,   /**< A function pointer conforming to NVTX_VERSION=2 will be used. */
+    NVTX_INITIALIZATION_MODE_SIZE
+} nvtxInitializationMode_t;
+
+
+/** \brief Initialization Attribute Structure.
+* \anchor INITIALIZATION_ATTRIBUTE_STRUCTURE
+*
+* This structure is used to describe the attributes used for initialization
+* of the NVTX API.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the event attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+* NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE may be used for the size.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxInitializationAttributes_t for future compatibility
+* \code
+* nvtxInitializationAttributes_t initAttribs = {0};
+* initAttribs.version = NVTX_VERSION;
+* initAttribs.size = NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxInitializationAttributes_t for a specific version
+* \code
+* nvtxInitializationAttributes_t initAttribs = {0};
+* initAttribs.version =2;
+* initAttribs.size = (uint16_t)(sizeof(nvtxInitializationAttributes_v2));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* // Initialize
+* nvtxInitializationAttributes_t initAttribs = {0};
+* initAttribs.version = NVTX_VERSION;
+* initAttribs.size = NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* initAttribs.mode = NVTX_INITIALIZATION_MODE_CALLBACK_V2;
+* initAttribs.fnptr = InitializeInjectionNvtx2;
+* \endcode
+
+* \sa
+* ::nvtxInitializationMode_t
+* ::nvtxInitialize
+*/
+typedef struct nvtxInitializationAttributes_v2
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /**
+    * \brief Mode of initialization.
+    *
+    * The mode of initialization dictates the overall behavior and which
+    * attributes in this struct will be used.
+    *
+    * Default Value is NVTX_INITIALIZATION_MODE_UNKNOWN = 0
+    * \sa
+    * ::nvtxInitializationMode_t
+    */
+    uint32_t mode;
+
+    /**
+    * \brief Function pointer used for initialization if the mode requires
+    *
+    * The user has retrieved this function pointer from the tool library
+    * and would like to use it to initialize.  The mode must be set to a
+    * NVTX_INITIALIZATION_MODE_CALLBACK_V# for this to be used.  The mode
+    * will dictate the expectations for this member.  The function signature
+    * will be cast from void(*)() to the appropriate signature for the mode.
+    * the expected behavior of the function will also depend on the mode
+    * beyond the simple function signature.
+    *
+    * Default Value is NVTX_INITIALIZATION_MODE_UNKNOWN which will either
+    * initialize based on external properties or fail if not supported on
+    * the given platform.
+
+    * \sa
+    * ::nvtxInitializationMode_t
+    */
+    void(*fnptr)(void);
+
+} nvtxInitializationAttributes_v2;
+
+typedef struct nvtxInitializationAttributes_v2 nvtxInitializationAttributes_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Force initialization (optional on most platforms)
+*
+* Force NVTX library to initialize.  On some platform NVTX will implicit initialize
+* upon the first function call into an NVTX API.
+*
+* \return Result codes are simplest to assume NVTX_SUCCESS or !NVTX_SUCCESS
+*
+* \param initAttrib - The initialization attribute structure
+*
+* \sa
+* ::nvtxInitializationAttributes_t
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxInitialize(const nvtxInitializationAttributes_t* initAttrib);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+
+/* ========================================================================= */
+/** \defgroup EVENT_ATTRIBUTES Event Attributes
+* @{
+*/
+
+/** ---------------------------------------------------------------------------
+* Payload Types
+* ------------------------------------------------------------------------- */
+typedef enum nvtxPayloadType_t
+{
+    NVTX_PAYLOAD_UNKNOWN = 0,   /**< Color payload is unused. */
+    NVTX_PAYLOAD_TYPE_UNSIGNED_INT64 = 1,   /**< A 64 bit unsigned integer value is used as payload. */
+    NVTX_PAYLOAD_TYPE_INT64 = 2,   /**< A 64 bit signed integer value is used as payload. */
+    NVTX_PAYLOAD_TYPE_DOUBLE = 3,   /**< A 64 bit floating point value is used as payload. */
+    /* NVTX_VERSION_2 */
+    NVTX_PAYLOAD_TYPE_UNSIGNED_INT32 = 4,   /**< A 32 bit floating point value is used as payload. */
+    NVTX_PAYLOAD_TYPE_INT32 = 5,   /**< A 32 bit floating point value is used as payload. */
+    NVTX_PAYLOAD_TYPE_FLOAT = 6    /**< A 32 bit floating point value is used as payload. */
+} nvtxPayloadType_t;
+
+/** \brief Event Attribute Structure.
+ * \anchor EVENT_ATTRIBUTE_STRUCTURE
+ *
+ * This structure is used to describe the attributes of an event. The layout of
+ * the structure is defined by a specific version of the tools extension
+ * library and can change between different versions of the Tools Extension
+ * library.
+ *
+ * \par Initializing the Attributes
+ *
+ * The caller should always perform the following three tasks when using
+ * attributes:
+ * <ul>
+ *    <li>Zero the structure
+ *    <li>Set the version field
+ *    <li>Set the size field
+ * </ul>
+ *
+ * Zeroing the structure sets all the event attributes types and values
+ * to the default value.
+ *
+ * The version and size field are used by the Tools Extension
+ * implementation to handle multiple versions of the attributes structure.
+ *
+ * It is recommended that the caller use one of the following to methods
+ * to initialize the event attributes structure:
+ *
+ * \par Method 1: Initializing nvtxEventAttributes for future compatibility
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * \endcode
+ *
+ * \par Method 2: Initializing nvtxEventAttributes for a specific version
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = 1;
+ * eventAttrib.size = (uint16_t)(sizeof(nvtxEventAttributes_v1));
+ * \endcode
+ *
+ * If the caller uses Method 1 it is critical that the entire binary
+ * layout of the structure be configured to 0 so that all fields
+ * are initialized to the default value.
+ *
+ * The caller should either use both NVTX_VERSION and
+ * NVTX_EVENT_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+ * and a versioned type (Method 2).  Using a mix of the two methods
+ * will likely cause either source level incompatibility or binary
+ * incompatibility in the future.
+ *
+ * \par Settings Attribute Types and Values
+ *
+ *
+ * \par Example:
+ * \code
+ * // Initialize
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ *
+ * // Configure the Attributes
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF880000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example";
+ * \endcode
+ *
+ * In the example the caller does not have to set the value of
+ * \ref ::nvtxEventAttributes_v2::category or
+ * \ref ::nvtxEventAttributes_v2::payload as these fields were set to
+ * the default value by {0}.
+ * \sa
+ * ::nvtxDomainMarkEx
+ * ::nvtxDomainRangeStartEx
+ * ::nvtxDomainRangePushEx
+ */
+typedef struct nvtxEventAttributes_v2
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /**
+     * \brief ID of the category the event is assigned to.
+     *
+     * A category is a user-controlled ID that can be used to group
+     * events.  The tool may use category IDs to improve filtering or
+     * enable grouping of events in the same category. The functions
+     * \ref ::nvtxNameCategoryA or \ref ::nvtxNameCategoryW can be used
+     * to name a category.
+     *
+     * Default Value is 0
+     */
+    uint32_t category;
+
+    /** \brief Color type specified in this attribute structure.
+     *
+     * Defines the color format of the attribute structure's \ref COLOR_FIELD
+     * "color" field.
+     *
+     * Default Value is NVTX_COLOR_UNKNOWN
+     */
+    int32_t colorType;              /* nvtxColorType_t */
+
+    /** \brief Color assigned to this event. \anchor COLOR_FIELD
+     *
+     * The color that the tool should use to visualize the event.
+     */
+    uint32_t color;
+
+    /**
+     * \brief Payload type specified in this attribute structure.
+     *
+     * Defines the payload format of the attribute structure's \ref PAYLOAD_FIELD
+     * "payload" field.
+     *
+     * Default Value is NVTX_PAYLOAD_UNKNOWN
+     */
+    int32_t payloadType;            /* nvtxPayloadType_t */
+
+    int32_t reserved0;
+
+    /**
+     * \brief Payload assigned to this event. \anchor PAYLOAD_FIELD
+     *
+     * A numerical value that can be used to annotate an event. The tool could
+     * use the payload data to reconstruct graphs and diagrams.
+     */
+    union payload_t
+    {
+        uint64_t ullValue;
+        int64_t llValue;
+        double dValue;
+        /* NVTX_VERSION_2 */
+        uint32_t uiValue;
+        int32_t iValue;
+        float fValue;
+    } payload;
+
+    /** \brief Message type specified in this attribute structure.
+     *
+     * Defines the message format of the attribute structure's \ref MESSAGE_FIELD
+     * "message" field.
+     *
+     * Default Value is NVTX_MESSAGE_UNKNOWN
+     */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure. \anchor MESSAGE_FIELD
+     *
+     * The text message that is attached to an event.
+     */
+    nvtxMessageValue_t message;
+
+} nvtxEventAttributes_v2;
+
+typedef struct nvtxEventAttributes_v2 nvtxEventAttributes_t;
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup MARKERS_AND_RANGES Markers and Ranges
+ *
+ * See \ref MARKERS_AND_RANGES for more details
+ *
+ * @{
+ */
+
+/** \name Marker */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+*
+* A marker can contain a text message or specify additional information
+* using the event attributes structure.  These attributes include a text
+* message, color, category, and a payload. Each of the attributes is optional
+* and can only be sent out using the \ref nvtxDomainMarkEx function.
+*
+* nvtxDomainMarkEx(NULL, event) is equivalent to calling
+* nvtxMarkEx(event).
+*
+* \param domain    - The domain of scoping the category.
+* \param eventAttrib - The event attribute structure defining the marker's
+* attribute types and attribute values.
+*
+* \sa
+* ::nvtxMarkEx
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+ *
+ * A marker can contain a text message or specify additional information
+ * using the event attributes structure.  These attributes include a text
+ * message, color, category, and a payload. Each of the attributes is optional
+ * and can only be sent out using the \ref nvtxMarkEx function.
+ * If \ref nvtxMarkA or \ref nvtxMarkW are used to specify the marker
+ * or if an attribute is unspecified then a default value will be used.
+ *
+ * \param eventAttrib - The event attribute structure defining the marker's
+ * attribute types and attribute values.
+ *
+ * \par Example:
+ * \code
+ * // zero the structure
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * // set the version and the size information
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * // configure the attributes.  0 is the default for all attributes.
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF880000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example nvtxMarkEx";
+ * nvtxMarkEx(&eventAttrib);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainMarkEx
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+ *
+ * A marker created using \ref nvtxMarkA or \ref nvtxMarkW contains only a
+ * text message.
+ *
+ * \param message     - The message associated to this marker event.
+ *
+ * \par Example:
+ * \code
+ * nvtxMarkA("Example nvtxMarkA");
+ * nvtxMarkW(L"Example nvtxMarkW");
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainMarkEx
+ * ::nvtxMarkEx
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message);
+NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message);
+/** @} */
+
+
+/** \name Process Ranges */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range in a domain.
+*
+* \param domain    - The domain of scoping the category.
+* \param eventAttrib - The event attribute structure defining the range's
+* attribute types and attribute values.
+*
+* \return The unique ID used to correlate a pair of Start and End events.
+*
+* \remarks Ranges defined by Start/End can overlap.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "my range";
+* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib);
+* // ...
+* nvtxDomainRangeEnd(rangeId);
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangeEnd
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range.
+ *
+ * \param eventAttrib - The event attribute structure defining the range's
+ * attribute types and attribute values.
+ *
+ * \return The unique ID used to correlate a pair of Start and End events.
+ *
+ * \remarks Ranges defined by Start/End can overlap.
+ *
+ * \par Example:
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * eventAttrib.category = 3;
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF0088FF;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example Range";
+ * nvtxRangeId_t rangeId = nvtxRangeStartEx(&eventAttrib);
+ * // ...
+ * nvtxRangeEnd(rangeId);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangeEnd
+ * ::nvtxDomainRangeStartEx
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range.
+ *
+ * \param message     - The event message associated to this range event.
+ *
+ * \return The unique ID used to correlate a pair of Start and End events.
+ *
+ * \remarks Ranges defined by Start/End can overlap.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangeId_t r1 = nvtxRangeStartA("Range 1");
+ * nvtxRangeId_t r2 = nvtxRangeStartW(L"Range 2");
+ * nvtxRangeEnd(r1);
+ * nvtxRangeEnd(r2);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangeEnd
+ * ::nvtxRangeStartEx
+ * ::nvtxDomainRangeStartEx
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message);
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a process range.
+*
+* \param domain - The domain 
+* \param id - The correlation ID returned from a nvtxRangeStart call.
+*
+* \remarks This function is offered completeness but is an alias for ::nvtxRangeEnd. 
+* It does not need a domain param since that is associated iwth the range ID at ::nvtxDomainRangeStartEx
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "my range";
+* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib);
+* // ...
+* nvtxDomainRangeEnd(rangeId);
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangeStartEx
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a process range.
+ *
+ * \param id - The correlation ID returned from an nvtxRangeStart call.
+ *
+ * \sa
+ * ::nvtxDomainRangeStartEx
+ * ::nvtxRangeStartEx
+ * ::nvtxRangeStartA
+ * ::nvtxRangeStartW
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id);
+/** @} */
+
+/** \name Thread Ranges */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+*
+* \param domain    - The domain of scoping.
+* \param eventAttrib - The event attribute structure defining the range's
+* attribute types and attribute values.
+*
+* \return The 0 based level of range being started. This value is scoped to the domain.
+* If an error occurs, a negative value is returned.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.colorType = NVTX_COLOR_ARGB;
+* eventAttrib.color = 0xFFFF0000;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "Level 0";
+* nvtxDomainRangePushEx(domain, &eventAttrib);
+*
+* // Re-use eventAttrib
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE;
+* eventAttrib.message.unicode = L"Level 1";
+* nvtxDomainRangePushEx(domain, &eventAttrib);
+*
+* nvtxDomainRangePop(domain); //level 1
+* nvtxDomainRangePop(domain); //level 0
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangePop
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+ *
+ * \param eventAttrib - The event attribute structure defining the range's
+ * attribute types and attribute values.
+ *
+ * \return The 0 based level of range being started. This level is per domain.
+ * If an error occurs a negative value is returned.
+ *
+ * \par Example:
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFFFF0000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Level 0";
+ * nvtxRangePushEx(&eventAttrib);
+ *
+ * // Re-use eventAttrib
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE;
+ * eventAttrib.message.unicode = L"Level 1";
+ * nvtxRangePushEx(&eventAttrib);
+ *
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainRangePushEx
+ * ::nvtxRangePop
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+ *
+ * \param message     - The event message associated to this range event.
+ *
+ * \return The 0 based level of range being started.  If an error occurs a
+ * negative value is returned.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangePushA("Level 0");
+ * nvtxRangePushW(L"Level 1");
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainRangePushEx
+ * ::nvtxRangePop
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message);
+NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message);
+/** @} */
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a nested thread range.
+*
+* \return The level of the range being ended. If an error occurs a negative
+* value is returned on the current thread.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreate("example library");
+* nvtxDomainRangePushA(domain, "Level 0");
+* nvtxDomainRangePushW(domain, L"Level 1");
+* nvtxDomainRangePop(domain);
+* nvtxDomainRangePop(domain);
+* \endcode
+*
+* \sa
+* ::nvtxRangePushEx
+* ::nvtxRangePushA
+* ::nvtxRangePushW
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a nested thread range.
+ *
+ * \return The level of the range being ended. If an error occurs a negative
+ * value is returned on the current thread.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangePushA("Level 0");
+ * nvtxRangePushW(L"Level 1");
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangePushEx
+ * ::nvtxRangePushA
+ * ::nvtxRangePushW
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePop(void);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup RESOURCE_NAMING Resource Naming
+ *
+ * See \ref RESOURCE_NAMING for more details
+ *
+ * @{
+ */
+
+
+/*  ------------------------------------------------------------------------- */
+/** \name Functions for Generic Resource Naming*/
+/*  ------------------------------------------------------------------------- */
+
+/*  ------------------------------------------------------------------------- */
+/** \cond SHOW_HIDDEN
+* \brief Resource typing helpers.  
+*
+* Classes are used to make it easy to create a series of resource types 
+* per API without collisions 
+*/
+#define NVTX_RESOURCE_MAKE_TYPE(CLASS, INDEX) ((((uint32_t)(NVTX_RESOURCE_CLASS_ ## CLASS))<<16)|((uint32_t)(INDEX)))
+#define NVTX_RESOURCE_CLASS_GENERIC 1
+/** \endcond */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Generic resource type for when a resource class is not available.
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*
+* \version \NVTX_VERSION_2
+*/
+typedef enum nvtxResourceGenericType_t
+{
+    NVTX_RESOURCE_TYPE_UNKNOWN = 0,
+    NVTX_RESOURCE_TYPE_GENERIC_POINTER = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 1), /**< Generic pointer assumed to have no collisions with other pointers. */
+    NVTX_RESOURCE_TYPE_GENERIC_HANDLE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 2), /**< Generic handle assumed to have no collisions with other handles. */
+    NVTX_RESOURCE_TYPE_GENERIC_THREAD_NATIVE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 3), /**< OS native thread identifier. */
+    NVTX_RESOURCE_TYPE_GENERIC_THREAD_POSIX = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 4) /**< POSIX pthread identifier. */
+} nvtxResourceGenericType_t;
+
+
+
+/** \brief Resource Attribute Structure.
+* \anchor RESOURCE_ATTRIBUTE_STRUCTURE
+*
+* This structure is used to describe the attributes of a resource. The layout of
+* the structure is defined by a specific version of the tools extension
+* library and can change between different versions of the Tools Extension
+* library.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the resource attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxEventAttributes for future compatibility
+* \code
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxEventAttributes for a specific version
+* \code
+* nvtxResourceAttributes_v0 attribs = {0};
+* attribs.version = 2;
+* attribs.size = (uint16_t)(sizeof(nvtxResourceAttributes_v0));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_RESOURCE_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+*
+* // Initialize
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+*
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* \endcode
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*/
+typedef struct nvtxResourceAttributes_v0
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of this attribute
+    * structure.
+    */
+    uint16_t size;
+
+    /**
+    * \brief Identifier type specifies how to interpret the identifier field
+    *
+    * Defines the identifier format of the attribute structure's \ref RESOURCE_IDENTIFIER_FIELD
+    * "identifier" field.
+    *
+    * Default Value is NVTX_RESOURCE_TYPE_UNKNOWN
+    */
+    int32_t identifierType;            /* values from enums following the pattern nvtxResource[name]Type_t */
+
+    /**
+    * \brief Identifier for the resource. 
+    * \anchor RESOURCE_IDENTIFIER_FIELD
+    *
+    * An identifier may be a pointer or a handle to an OS or middleware API object.
+    * The resource type will assist in avoiding collisions where handles values may collide.
+    */
+    union identifier_t
+    {
+        const void* pValue;
+        uint64_t ullValue;
+    } identifier;
+
+    /** \brief Message type specified in this attribute structure.
+    *
+    * Defines the message format of the attribute structure's \ref RESOURCE_MESSAGE_FIELD
+    * "message" field.
+    *
+    * Default Value is NVTX_MESSAGE_UNKNOWN
+    */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure. \anchor RESOURCE_MESSAGE_FIELD
+    *
+    * The text message that is attached to a resource.
+    */
+    nvtxMessageValue_t message;
+
+} nvtxResourceAttributes_v0;
+
+typedef struct nvtxResourceAttributes_v0 nvtxResourceAttributes_t;
+
+/* \cond SHOW_HIDDEN 
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxResourceAttributes_v0) ) )
+typedef struct nvtxResourceHandle* nvtxResourceHandle_t;
+/** \endcond */
+
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Create a resource object to track and associate data with OS and middleware objects
+*
+* Allows users to associate an API handle or pointer with a user-provided name.
+* 
+*
+* \param domain - Domain to own the resource object
+* \param attribs - Attributes to be associated with the resource
+*
+* \return A handle that represents the newly created resource object.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* \endcode
+*
+* \sa
+* ::nvtxResourceAttributes_t
+* ::nvtxDomainResourceDestroy
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Destroy a resource object to track and associate data with OS and middleware objects
+*
+* Allows users to associate an API handle or pointer with a user-provided name.
+*
+* \param resource - Handle to the resource in which to operate.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* nvtxDomainResourceDestroy(handle);
+* \endcode
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource);
+/** @} */
+
+
+/** \name Functions for NVTX Category Naming*/
+
+/* ------------------------------------------------------------------------- */
+/**
+* \brief Annotate an NVTX category used within a domain.
+*
+* Categories are used to group sets of events. Each category is identified
+* through a unique ID and that ID is passed into any of the marker/range
+* events to assign that event to a specific category. The nvtxDomainNameCategory
+* function calls allow the user to assign a name to a category ID that is
+* specific to the domain.
+*
+* nvtxDomainNameCategory(NULL, category, name) is equivalent to calling
+* nvtxNameCategory(category, name).
+*
+* \param domain    - The domain of scoping the category.
+* \param category  - The category ID to name.
+* \param name      - The name of the category.
+*
+* \remarks The category names are tracked per domain.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example");
+* nvtxDomainNameCategoryA(domain, 1, "Memory Allocation");
+* nvtxDomainNameCategoryW(domain, 2, L"Memory Transfer");
+* \endcode
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name);
+/** @} */
+
+/** \brief Annotate an NVTX category.
+ *
+ * Categories are used to group sets of events. Each category is identified
+ * through a unique ID and that ID is passed into any of the marker/range
+ * events to assign that event to a specific category. The nvtxNameCategory
+ * function calls allow the user to assign a name to a category ID.
+ *
+ * \param category - The category ID to name.
+ * \param name     - The name of the category.
+ *
+ * \remarks The category names are tracked per process.
+ *
+ * \par Example:
+ * \code
+ * nvtxNameCategory(1, "Memory Allocation");
+ * nvtxNameCategory(2, "Memory Transfer");
+ * nvtxNameCategory(3, "Memory Object Lifetime");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name);
+/** @} */
+
+/** \name Functions for OS Threads Naming*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotate an OS thread.
+ *
+ * Allows the user to name an active thread of the current process. If an
+ * invalid thread ID is provided or a thread ID from a different process is
+ * used the behavior of the tool is implementation dependent.
+ *
+ * The thread name is associated to the default domain.  To support domains 
+ * use resource objects via ::nvtxDomainResourceCreate.
+ *
+ * \param threadId - The ID of the thread to name.
+ * \param name     - The name of the thread.
+ *
+ * \par Example:
+ * \code
+ * nvtxNameOsThread(GetCurrentThreadId(), "MAIN_THREAD");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup STRING_REGISTRATION String Registration
+*
+* Registered strings are intended to increase performance by lowering instrumentation
+* overhead.  String may be registered once and the handle may be passed in place of
+* a string where an the APIs may allow.
+*
+* See \ref STRING_REGISTRATION for more details
+*
+* @{
+*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Register a string.
+
+* Registers an immutable string with NVTX. Once registered the pointer used
+* to register the domain name can be used in nvtxEventAttributes_t
+* \ref MESSAGE_FIELD. This allows NVTX implementation to skip copying the
+* contents of the message on each event invocation.
+*
+* String registration is an optimization. It is recommended to use string
+* registration if the string will be passed to an event many times.
+*
+* String are not unregistered, except that by unregistering the entire domain
+*
+* \param domain  - Domain handle. If NULL then the global domain is used.
+* \param string    - A unique pointer to a sequence of characters.
+*
+* \return A handle representing the registered string.
+*
+* \par Example:
+* \code
+* nvtxDomainCreateA("com.nvidia.nvtx.example");
+* nvtxStringHandle_t message = nvtxDomainRegisterStringA(domain, "registered string");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_REGISTERED;
+* eventAttrib.message.registered = message;
+* \endcode
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string);
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string);
+/** @} */
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup DOMAINS Domains
+*
+* Domains are used to group events to a developer defined scope. Middleware
+* vendors may also scope their own events to avoid collisions with the
+* the application developer's events, so that the application developer may
+* inspect both parts and easily differentiate or filter them.  By default
+* all events are scoped to a global domain where NULL is provided or when
+* using APIs provided b versions of NVTX below v2
+*
+* Domains are intended to be typically long lived objects with the intention
+* of logically separating events of large modules from each other such as
+* middleware libraries from each other and the main application.
+*
+* See \ref DOMAINS for more details
+*
+* @{
+*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Register a NVTX domain.
+*
+* Domains are used to scope annotations. All NVTX_VERSION_0 and NVTX_VERSION_1
+* annotations are scoped to the global domain. The function nvtxDomainCreate
+* creates a new named domain.
+*
+* Each domain maintains its own nvtxRangePush and nvtxRangePop stack.
+*
+* \param name - A unique string representing the domain.
+*
+* \return A handle representing the domain.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example");
+*
+* nvtxMarkA("nvtxMarkA to global domain");
+*
+* nvtxEventAttributes_t eventAttrib1 = {0};
+* eventAttrib1.version = NVTX_VERSION;
+* eventAttrib1.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib1.message.ascii = "nvtxDomainMarkEx to global domain";
+* nvtxDomainMarkEx(NULL, &eventAttrib1);
+*
+* nvtxEventAttributes_t eventAttrib2 = {0};
+* eventAttrib2.version = NVTX_VERSION;
+* eventAttrib2.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib2.message.ascii = "nvtxDomainMarkEx to com.nvidia.nvtx.example";
+* nvtxDomainMarkEx(domain, &eventAttrib2);
+* nvtxDomainDestroy(domain);
+* \endcode
+*
+* \sa
+* ::nvtxDomainDestroy
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* name);
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Unregister a NVTX domain.
+*
+* Unregisters the domain handle and frees all domain specific resources.
+*
+* \param domain    - the domain handle
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example");
+* nvtxDomainDestroy(domain);
+* \endcode
+*
+* \sa
+* ::nvtxDomainCreateA
+* ::nvtxDomainCreateW
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \cond SHOW_HIDDEN */
+
+#ifdef UNICODE
+    #define nvtxMark            nvtxMarkW
+    #define nvtxRangeStart      nvtxRangeStartW
+    #define nvtxRangePush       nvtxRangePushW
+    #define nvtxNameCategory    nvtxNameCategoryW
+    #define nvtxNameOsThread    nvtxNameOsThreadW
+    /* NVTX_VERSION_2 */
+    #define nvtxDomainCreate         nvtxDomainCreateW
+    #define nvtxDomainRegisterString nvtxDomainRegisterStringW
+    #define nvtxDomainNameCategory   nvtxDomainNameCategoryW
+#else
+    #define nvtxMark            nvtxMarkA
+    #define nvtxRangeStart      nvtxRangeStartA
+    #define nvtxRangePush       nvtxRangePushA
+    #define nvtxNameCategory    nvtxNameCategoryA
+    #define nvtxNameOsThread    nvtxNameOsThreadA
+    /* NVTX_VERSION_2 */
+    #define nvtxDomainCreate         nvtxDomainCreateA
+    #define nvtxDomainRegisterString nvtxDomainRegisterStringA
+    #define nvtxDomainNameCategory   nvtxDomainNameCategoryA    
+#endif
+
+/** \endcond */
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..d2017d54458b7798da17029212c735d69f4b71a8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h
@@ -0,0 +1,164 @@
+/*
+* Copyright 2009-2017  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTOOLSEXT_CUDA_H_
+#define NVTOOLSEXT_CUDA_H_
+
+#include "cuda.h"
+
+#include "nvToolsExt.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for CUDA Resource Naming
+*/
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_CUDA CUDA Resource Naming
+ *
+ * This section covers the API functions that allow to annotate CUDA resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_CUDA  4
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for CUDA
+*/
+typedef enum nvtxResourceCUDAType_t
+{
+    NVTX_RESOURCE_TYPE_CUDA_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDA, 1), /* CUdevice */
+    NVTX_RESOURCE_TYPE_CUDA_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 2), /* CUcontext */
+    NVTX_RESOURCE_TYPE_CUDA_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDA, 3), /* CUstream */
+    NVTX_RESOURCE_TYPE_CUDA_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 4)  /* CUevent */
+} nvtxResourceCUDAType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA device.
+ *
+ * Allows the user to associate a CUDA device with a user-provided name.
+ *
+ * \param device - The handle of the CUDA device to name.
+ * \param name   - The name of the CUDA device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA context.
+ *
+ * Allows the user to associate a CUDA context with a user-provided name.
+ *
+ * \param context - The handle of the CUDA context to name.
+ * \param name    - The name of the CUDA context.
+ *
+ * \par Example:
+ * \code
+ * CUresult status = cuCtxCreate( &cuContext, 0, cuDevice );
+ * if ( CUDA_SUCCESS != status )
+ *     goto Error;
+ * nvtxNameCuContext(cuContext, "CTX_NAME");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA stream.
+ *
+ * Allows the user to associate a CUDA stream with a user-provided name.
+ *
+ * \param stream - The handle of the CUDA stream to name.
+ * \param name   - The name of the CUDA stream.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA event.
+ *
+ * Allows the user to associate a CUDA event with a user-provided name.
+ *
+ * \param event - The handle of the CUDA event to name.
+ * \param name  - The name of the CUDA event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameCuDevice   nvtxNameCuDeviceW
+  #define nvtxNameCuContext  nvtxNameCuContextW
+  #define nvtxNameCuStream   nvtxNameCuStreamW
+  #define nvtxNameCuEvent    nvtxNameCuEventW
+#else
+  #define nvtxNameCuDevice   nvtxNameCuDeviceA
+  #define nvtxNameCuContext  nvtxNameCuContextA
+  #define nvtxNameCuStream   nvtxNameCuStreamA
+  #define nvtxNameCuEvent    nvtxNameCuEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_CUDA_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCudaRt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCudaRt.h
new file mode 100644
index 0000000000000000000000000000000000000000..5af4cebe4e6e19968204bd82fb76bc03840506ae
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCudaRt.h
@@ -0,0 +1,140 @@
+/*
+* Copyright 2009-2017  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTOOLSEXT_CUDART_H_
+#define NVTOOLSEXT_CUDART_H_
+
+#include "cuda.h"
+#include "driver_types.h"
+
+#include "nvToolsExt.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for CUDA Resource Naming
+*/
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_CUDART CUDA Runtime Resource Naming
+ *
+ * This section covers the API functions that allow to annotate CUDA resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_CUDART 5
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for CUDART
+*/
+typedef enum nvtxResourceCUDARTType_t
+{
+    NVTX_RESOURCE_TYPE_CUDART_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDART, 0), /* int device */
+    NVTX_RESOURCE_TYPE_CUDART_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDART, 1), /* cudaStream_t */
+    NVTX_RESOURCE_TYPE_CUDART_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDART, 2)  /* cudaEvent_t */
+} nvtxResourceCUDARTType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA device.
+ *
+ * Allows the user to associate a CUDA device with a user-provided name.
+ *
+ * \param device - The id of the CUDA device to name.
+ * \param name   - The name of the CUDA device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceA(int device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceW(int device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA stream.
+ *
+ * Allows the user to associate a CUDA stream with a user-provided name.
+ *
+ * \param stream - The handle of the CUDA stream to name.
+ * \param name   - The name of the CUDA stream.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamA(cudaStream_t stream, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamW(cudaStream_t stream, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA event.
+ *
+ * Allows the user to associate a CUDA event with a user-provided name.
+ *
+ * \param event - The handle of the CUDA event to name.
+ * \param name  - The name of the CUDA event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventA(cudaEvent_t event, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventW(cudaEvent_t event, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameCudaDevice nvtxNameCudaDeviceW
+  #define nvtxNameCudaStream nvtxNameCudaStreamW
+  #define nvtxNameCudaEvent  nvtxNameCudaEventW
+#else
+  #define nvtxNameCudaDevice nvtxNameCudaDeviceA
+  #define nvtxNameCudaStream nvtxNameCudaStreamA
+  #define nvtxNameCudaEvent  nvtxNameCudaEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_CUDART_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtOpenCL.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtOpenCL.h
new file mode 100644
index 0000000000000000000000000000000000000000..e826610a405a2c32eda5a851f7930b7b3feeb323
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtOpenCL.h
@@ -0,0 +1,214 @@
+/*
+* Copyright 2009-2017 NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTOOLSEXT_OPENCL_H_
+#define NVTOOLSEXT_OPENCL_H_
+
+#include <CL/cl.h>
+
+#include "nvToolsExt.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for OpenCL Resource Naming
+ */
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_OPENCL OpenCL Resource Naming
+ *
+ * This section covers the API functions that allow to annotate OpenCL resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_OPENCL 6 
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for OpenCL
+*/
+typedef enum nvtxResourceOpenCLType_t
+{
+    NVTX_RESOURCE_TYPE_OPENCL_DEVICE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 1),
+    NVTX_RESOURCE_TYPE_OPENCL_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 2),
+    NVTX_RESOURCE_TYPE_OPENCL_COMMANDQUEUE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 3),
+    NVTX_RESOURCE_TYPE_OPENCL_MEMOBJECT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 4),
+    NVTX_RESOURCE_TYPE_OPENCL_SAMPLER = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 5),
+    NVTX_RESOURCE_TYPE_OPENCL_PROGRAM = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 6),
+    NVTX_RESOURCE_TYPE_OPENCL_EVENT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 7)
+} nvtxResourceOpenCLType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL device.
+ *
+ * Allows to associate an OpenCL device with a user-provided name.
+ *
+ * \param device - The handle of the OpenCL device to name.
+ * \param name   - The name of the OpenCL device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceA(cl_device_id device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceW(cl_device_id device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL context.
+ *
+ * Allows to associate an OpenCL context with a user-provided name.
+ *
+ * \param context - The handle of the OpenCL context to name.
+ * \param name    - The name of the OpenCL context.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClContextA(cl_context context, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClContextW(cl_context context, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL command queue.
+ *
+ * Allows to associate an OpenCL command queue with a user-provided name.
+ *
+ * \param command_queue - The handle of the OpenCL command queue to name.
+ * \param name          - The name of the OpenCL command queue.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueA(cl_command_queue command_queue, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueW(cl_command_queue command_queue, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL memory object.
+ *
+ * Allows to associate an OpenCL memory object with a user-provided name.
+ *
+ * \param memobj - The handle of the OpenCL memory object to name.
+ * \param name   - The name of the OpenCL memory object.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectA(cl_mem memobj, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectW(cl_mem memobj, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL sampler.
+ *
+ * Allows to associate an OpenCL sampler with a user-provided name.
+ *
+ * \param sampler - The handle of the OpenCL sampler to name.
+ * \param name    - The name of the OpenCL sampler.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerA(cl_sampler sampler, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerW(cl_sampler sampler, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL program.
+ *
+ * Allows to associate an OpenCL program with a user-provided name.
+ *
+ * \param program - The handle of the OpenCL program to name.
+ * \param name    - The name of the OpenCL program.
+ *
+ * \code
+ * cpProgram = clCreateProgramWithSource(cxGPUContext, 1,
+ *     (const char **) &cSourceCL, &program_length, &ciErrNum);
+ * shrCheckErrorEX(ciErrNum, CL_SUCCESS, pCleanup);
+ * nvtxNameClProgram(cpProgram, L"PROGRAM_NAME");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClProgramA(cl_program program, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClProgramW(cl_program program, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL event.
+ *
+ * Allows to associate an OpenCL event with a user-provided name.
+ *
+ * \param evnt - The handle of the OpenCL event to name.
+ * \param name - The name of the OpenCL event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClEventA(cl_event evnt, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClEventW(cl_event evnt, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameClDevice        nvtxNameClDeviceW
+  #define nvtxNameClContext       nvtxNameClContextW
+  #define nvtxNameClCommandQueue  nvtxNameClCommandQueueW
+  #define nvtxNameClMemObject     nvtxNameClMemObjectW
+  #define nvtxNameClSampler       nvtxNameClSamplerW
+  #define nvtxNameClProgram       nvtxNameClProgramW
+  #define nvtxNameClEvent         nvtxNameClEventW
+#else
+  #define nvtxNameClDevice        nvtxNameClDeviceA
+  #define nvtxNameClContext       nvtxNameClContextA
+  #define nvtxNameClCommandQueue  nvtxNameClCommandQueueA
+  #define nvtxNameClMemObject     nvtxNameClMemObjectA
+  #define nvtxNameClSampler       nvtxNameClSamplerA
+  #define nvtxNameClProgram       nvtxNameClProgramA
+  #define nvtxNameClEvent         nvtxNameClEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_OPENCL_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtSync.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtSync.h
new file mode 100644
index 0000000000000000000000000000000000000000..933e60b6943cbdb08975b67d39764cc97658a5fd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtSync.h
@@ -0,0 +1,406 @@
+/*
+* Copyright 2009-2017  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTOOLSEXT_SYNC_H_
+#define NVTOOLSEXT_SYNC_H_
+
+#include "nvToolsExt.h"
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* \cond SHOW_HIDDEN 
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxSyncUserAttributes_v0) ) )
+/** \endcond */
+
+
+/** 
+* \page PAGE_SYNCHRONIZATION Synchronization
+*
+* This section covers a subset of the API that allow users to track additional
+* synchronization details of their application.   Naming OS synchronization primitives 
+* may allow users to better understand the data collected by traced synchronization 
+* APIs.  Additionally, a user defined synchronization object can allow the users to
+* to tell the tools when the user is building their own synchronization system
+* that do not rely on the OS to provide behaviors and instead use techniques like
+* atomic operations and spinlocks.  
+*
+* See module \ref SYNCHRONIZATION for details.
+*
+* \par Example:
+* \code
+* class MyMutex
+* {
+*     volatile long bLocked;
+*     nvtxSyncUser_t hSync;
+* public:
+*     MyMutex(const char* name, nvtxDomainHandle_t d){
+*          bLocked = 0;
+*
+*          nvtxSyncUserAttributes_t attribs = { 0 };
+*          attribs.version = NVTX_VERSION;
+*          attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE;
+*          attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+*          attribs.message.ascii = name;
+*          hSync = nvtxDomainSyncUserCreate(d, &attribs);
+*     }
+*
+*     ~MyMutex() {
+*          nvtxDomainSyncUserDestroy(hSync);
+*     }
+*
+*     bool Lock() {
+*          nvtxDomainSyncUserAcquireStart(hSync);
+*          bool acquired = __sync_bool_compare_and_swap(&bLocked, 0, 1);//atomic compiler intrinsic 
+
+*          if (acquired) {
+*              nvtxDomainSyncUserAcquireSuccess(hSync);
+*          }
+*          else {
+*              nvtxDomainSyncUserAcquireFailed(hSync);
+*          }
+*          return acquired;
+*     }
+
+*     void Unlock() {
+*          nvtxDomainSyncUserReleasing(hSync);
+*          bLocked = false;
+*     }
+* };
+* \endcode
+* 
+* \version \NVTX_VERSION_2
+*/
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_SYNC_OS 2 /**< Synchronization objects that are OS specific. */
+#define NVTX_RESOURCE_CLASS_SYNC_PTHREAD 3 /**< Synchronization objects that are from the POSIX Threads API (pthread)*/
+/** \endcond */
+
+
+/*  ------------------------------------------------------------------------- */
+/** \defgroup SYNCHRONIZATION Synchronization
+* See page \ref PAGE_SYNCHRONIZATION.
+* @{
+*/
+
+/** \brief Resource type values for OSs with POSIX Thread API support
+ */
+typedef enum nvtxResourceSyncPosixThreadType_t
+{
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 1), /* pthread_mutex_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_CONDITION = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 2), /* pthread_cond_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_RWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 3), /* pthread_rwlock_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_BARRIER = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 4), /* pthread_barrier_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 5), /* pthread_spinlock_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_ONCE = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 6) /* pthread_once_t  */
+} nvtxResourceSyncPosixThreadType_t;
+
+/** \brief Resource type values for Windows OSs
+*/
+typedef enum nvtxResourceSyncWindowsType_t
+{
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_EVENT = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_CRITICAL_SECTION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SRWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5)
+} nvtxResourceSyncWindowsType_t;
+
+/** \brief Resource type values for Linux and Linux derived OSs such as Android
+* \sa
+* ::nvtxResourceSyncPosixThreadType_t
+*/
+typedef enum nvtxResourceSyncLinuxType_t
+{
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_FUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_COMPLETION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_SEQLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 6),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_RCU = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 7)
+} nvtxResourceSyncLinuxType_t;
+
+/** \brief Resource type values for Android come from Linux.
+* \sa
+* ::nvtxResourceSyncLinuxType_t
+* ::nvtxResourceSyncPosixThreadType_t
+*/
+typedef enum nvtxResourceSyncLinuxType_t nvtxResourceSyncAndroidType_t;
+
+/** \brief User Defined Synchronization Object Handle .
+* \anchor SYNCUSER_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a user defined syncrhonization object.  The tools will return a pointer through the API for the application
+* to hold on it's behalf to reference the string in the future.
+*
+*/
+typedef struct nvtxSyncUser* nvtxSyncUser_t;
+
+/** \brief User Defined Synchronization Object Attributes Structure.
+* \anchor USERDEF_SYNC_ATTRIBUTES_STRUCTURE
+*
+* This structure is used to describe the attributes of a user defined synchronization 
+* object.  The layout of the structure is defined by a specific version of the tools 
+* extension library and can change between different versions of the Tools Extension
+* library.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the event attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxEventAttributes for future compatibility
+* \code
+* nvtxSyncUserAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxSyncUserAttributes_t for a specific version
+* \code
+* nvtxSyncUserAttributes_t attribs = {0};
+* attribs.version = 1;
+* attribs.size = (uint16_t)(sizeof(nvtxSyncUserAttributes_t));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* // Initialize
+* nvtxSyncUserAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Example";
+* \endcode
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+*/
+typedef struct nvtxSyncUserAttributes_v0
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /** \brief Message type specified in this attribute structure.
+    *
+    * Defines the message format of the attribute structure's \ref nvtxSyncUserAttributes_v0::message
+    * "message" field.
+    *
+    * Default Value is NVTX_MESSAGE_UNKNOWN
+    */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure.
+    *
+    * The text message that is attached to an event.
+    */
+    nvtxMessageValue_t message;
+
+} nvtxSyncUserAttributes_v0;
+
+typedef struct nvtxSyncUserAttributes_v0 nvtxSyncUserAttributes_t;
+
+/* ------------------------------------------------------------------------- */
+/** \brief Create a user defined synchronization object 
+* This is used to track non-OS synchronization working with spinlocks and atomics
+*
+* \param domain - Domain to own the resource
+* \param attribs - A structure to assign multiple attributes to the object.
+*
+* \return A handle that represents the newly created user defined synchronization object.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC nvtxSyncUser_t NVTX_API nvtxDomainSyncUserCreate(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Destroy a user defined synchronization object
+* This is used to track non-OS synchronization working with spinlocks and atomics
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserDestroy(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools that an attempt to acquire a user defined synchronization object
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireStart(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools of failure in acquiring a user defined synchronization object
+* This should be called after \ref nvtxDomainSyncUserAcquireStart
+* 
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireFailed(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools of success in acquiring a user defined synchronization object
+* This should be called after \ref nvtxDomainSyncUserAcquireStart.
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireSuccess(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools of releasing a reservation on user defined synchronization object
+* This should be called after \ref nvtxDomainSyncUserAcquireSuccess.
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserReleasing(nvtxSyncUser_t handle);
+
+
+/** @} */ /*END defgroup*/
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_SYNC_H_ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExt.h
new file mode 100644
index 0000000000000000000000000000000000000000..9fab7d874340fad50d63d80b6b68f678ec243191
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExt.h
@@ -0,0 +1,1499 @@
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+/** \file nvToolsExt.h
+ */
+
+/* ========================================================================= */
+/** \mainpage
+ * \tableofcontents
+ * \section INTRODUCTION Introduction
+ *
+ * The NVIDIA Tools Extension library is a set of functions that a
+ * developer can use to provide additional information to tools.
+ * The additional information is used by the tool to improve
+ * analysis and visualization of data.
+ *
+ * The library introduces close to zero overhead if no tool is
+ * attached to the application.  The overhead when a tool is
+ * attached is specific to the tool.
+ *
+ * \section INITIALIZATION_SECTION Initialization
+ *
+ * Typically the tool's library that plugs into NVTX is indirectly 
+ * loaded via enviromental properties that are platform specific. 
+ * For some platform or special cases, the user may be required 
+ * to instead explicity initialize instead though.   This can also
+ * be helpful to control when the API loads a tool's library instead
+ * of what would typically be the first function call to emit info.
+ * For these rare case, see \ref INITIALIZATION for additional information.
+ *
+ * \section MARKERS_AND_RANGES Markers and Ranges
+ *
+ * Markers and ranges are used to describe events at a specific time (markers)
+ * or over a time span (ranges) during the execution of the application
+ * respectively. 
+ *
+ * \subsection MARKERS Markers
+ * 
+ * Markers denote specific moments in time.
+ * 
+ * 
+ * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on
+ * how to specify the domain.
+ * 
+ * \subsection THREAD_RANGES Thread Ranges
+ *
+ * Thread ranges denote nested time ranges. Nesting is maintained per thread
+ * per domain and does not require any additional correlation mechanism. The
+ * duration of a thread range is defined by the corresponding pair of
+ * nvtxRangePush* to nvtxRangePop API calls.
+ *
+ * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on
+ * how to specify the domain.
+ *
+ * \subsection PROCESS_RANGES Process Ranges
+ *
+ * Process ranges denote a time span that can expose arbitrary concurrency, as 
+ * opposed to thread ranges that only support nesting. In addition the range
+ * start event can happen on a different thread than the end marker. For the 
+ * correlation of a start/end pair an unique correlation ID is used that is
+ * returned from the start API call and needs to be passed into the end API
+ * call.
+ *
+ * \subsection EVENT_ATTRIBUTES Event Attributes
+ *
+ * \ref MARKERS_AND_RANGES can be annotated with various attributes to provide
+ * additional information for an event or to guide the tool's visualization of
+ * the data. Each of the attributes is optional and if left unused the
+ * attributes fall back to a default value. The attributes include:
+ * - color
+ * - category
+ *
+ * To specify any attribute other than the text message, the \ref
+ * EVENT_ATTRIBUTE_STRUCTURE "Event Attribute Structure" must be used.
+ *
+ * \section DOMAINS Domains
+ *
+ * Domains enable developers to scope annotations. By default all events and
+ * annotations are in the default domain. Additional domains can be registered.
+ * This allows developers to scope markers, ranges, and resources names to
+ * avoid conflicts.
+ *
+ * The function ::nvtxDomainCreateA or ::nvtxDomainCreateW is used to create
+ * a named domain.
+ * 
+ * Each domain maintains its own
+ * - categories
+ * - thread range stacks
+ * - registered strings
+ *
+ * The function ::nvtxDomainDestroy marks the end of the domain. Destroying 
+ * a domain unregisters and destroys all objects associated with it such as 
+ * registered strings, resource objects, named categories, and started ranges. 
+ *
+ * \section RESOURCE_NAMING Resource Naming
+ *
+ * This section covers calls that allow to annotate objects with user-provided
+ * names in order to allow for a better analysis of complex trace data. All of
+ * the functions take the handle or the ID of the object to name and the name.
+ * The functions can be called multiple times during the execution of an
+ * application, however, in that case it is implementation dependent which
+ * name will be reported by the tool.
+ * 
+ * \subsection CATEGORY_NAMING Category Naming
+ *
+ * Some function in this library support associating an integer category 
+ * to enable filtering and sorting.  The category naming functions allow 
+ * the application to associate a user friendly name with the integer 
+ * category.  Support for domains have been added in NVTX_VERSION_2 to 
+ * avoid collisions when domains are developed independantly. 
+ *
+ * \subsection RESOURCE_OBJECTS Resource Objects
+ *
+ * Resource objects are a generic mechanism for attaching data to an application 
+ * resource.  The identifier field makes the association to a pointer or handle, 
+ * while the type field helps provide deeper understanding of the identifier as 
+ * well as enabling differentiation in cases where handles generated by different
+ * APIs may collide.  The resource object may also have an associated message to
+ * associate with the application resource, enabling further annotation of this 
+ * object and how it is used.
+ * 
+ * The resource object was introduced in NVTX_VERSION_2 to supersede existing naming
+ * functions and allow the application resource identified by those functions to be
+ * associated to a domain.  The other naming functions are still supported for backward
+ * compatibility but will be associated only to the default domain.
+ *
+ * \subsection RESOURCE_NAMING_OS Resource Naming
+ * 
+ * Some operating system resources creation APIs do not support providing a user friendly 
+ * name, such as some OS thread creation APIs.  This API support resource naming though 
+ * both through resource objects and functions following the pattern 
+ * nvtxName[RESOURCE_TYPE][A|W](identifier, name).  Resource objects introduced in NVTX_VERSION 2 
+ * supersede the other functions with a a more general method of assigning names to OS resources,
+ * along with associating them to domains too.  The older nvtxName* functions are only associated 
+ * with the default domain.
+ * \section EXTENSIONS Optional Extensions
+ * Optional extensions will either appear within the existing sections the extend or appear 
+ * in the "Related Pages" when they introduce new concepts.
+ */
+
+ /**
+ * Tools Extension API version
+ */
+#if defined(NVTX_VERSION) && NVTX_VERSION < 3
+#error "Trying to #include NVTX version 3 in a source file where an older NVTX version has already been included.  If you are not directly using NVTX (the NVIDIA Tools Extension library), you are getting this error because libraries you are using have included different versions of NVTX.  Suggested solutions are: (1) reorder #includes so the newest NVTX version is included first, (2) avoid using the conflicting libraries in the same .c/.cpp file, or (3) update the library using the older NVTX version to use the newer version instead."
+#endif
+
+/* Header guard */
+#if !defined(NVTX_VERSION)
+#define NVTX_VERSION 3
+
+#if defined(_MSC_VER)
+#define NVTX_API __stdcall
+#define NVTX_INLINE_STATIC __inline static
+#else /*defined(__GNUC__)*/
+#define NVTX_API
+#define NVTX_INLINE_STATIC inline static
+#endif /* Platform */
+
+#if defined(NVTX_NO_IMPL)
+/* When omitting implementation, avoid declaring functions inline */
+/* without definitions, since this causes compiler warnings. */
+#define NVTX_DECLSPEC
+#elif defined(NVTX_EXPORT_API)
+/* Allow overriding definition of NVTX_DECLSPEC when exporting API. */
+/* Default is empty, meaning non-inline with external linkage. */
+#if !defined(NVTX_DECLSPEC)
+#define NVTX_DECLSPEC
+#endif
+#else
+/* Normal NVTX usage defines the NVTX API inline with static */
+/* (internal) linkage. */
+#define NVTX_DECLSPEC NVTX_INLINE_STATIC
+#endif
+
+#include "nvtxDetail/nvtxLinkOnce.h"
+
+#define NVTX_VERSIONED_IDENTIFIER_L3(NAME, VERSION) NAME##_v##VERSION
+#define NVTX_VERSIONED_IDENTIFIER_L2(NAME, VERSION) NVTX_VERSIONED_IDENTIFIER_L3(NAME, VERSION)
+#define NVTX_VERSIONED_IDENTIFIER(NAME) NVTX_VERSIONED_IDENTIFIER_L2(NAME, NVTX_VERSION)
+
+/**
+ * The nvToolsExt library depends on stdint.h.  If the build tool chain in use
+ * does not include stdint.h then define NVTX_STDINT_TYPES_ALREADY_DEFINED
+ * and define the following types:
+ * <ul>
+ *   <li>uint8_t
+ *   <li>int8_t
+ *   <li>uint16_t
+ *   <li>int16_t
+ *   <li>uint32_t
+ *   <li>int32_t
+ *   <li>uint64_t
+ *   <li>int64_t
+ *   <li>uintptr_t
+ *   <li>intptr_t
+ * </ul>
+ * #define NVTX_STDINT_TYPES_ALREADY_DEFINED if you are using your own header file.
+ */
+#ifndef NVTX_STDINT_TYPES_ALREADY_DEFINED
+#include <stdint.h>
+#endif
+
+#include <stddef.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/** 
+* Result Codes
+*/
+
+#define NVTX_SUCCESS 0
+#define NVTX_FAIL 1
+#define NVTX_ERR_INIT_LOAD_PROPERTY 2
+#define NVTX_ERR_INIT_ACCESS_LIBRARY 3
+#define NVTX_ERR_INIT_LOAD_LIBRARY 4
+#define NVTX_ERR_INIT_MISSING_LIBRARY_ENTRY_POINT 5
+#define NVTX_ERR_INIT_FAILED_LIBRARY_ENTRY_POINT 6
+#define NVTX_ERR_NO_INJECTION_LIBRARY_AVAILABLE 7
+
+/**
+ * Size of the nvtxEventAttributes_t structure.
+ */
+#define NVTX_EVENT_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxEventAttributes_t) ) )
+
+#define NVTX_NO_PUSH_POP_TRACKING ((int)-2)
+
+typedef uint64_t nvtxRangeId_t;
+
+/* Forward declaration of opaque domain registration structure */
+struct nvtxDomainRegistration_st;
+typedef struct nvtxDomainRegistration_st nvtxDomainRegistration;
+
+/* \brief Domain Handle Structure.
+* \anchor DOMAIN_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a domain.  This type is returned from tools when using the NVTX API to
+* create a domain.
+*
+*/
+typedef nvtxDomainRegistration* nvtxDomainHandle_t;
+
+/* Forward declaration of opaque string registration structure */
+struct nvtxStringRegistration_st;
+typedef struct nvtxStringRegistration_st nvtxStringRegistration;
+
+/* \brief Registered String Handle Structure.
+* \anchor REGISTERED_STRING_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a registered string.  This type is returned from tools when using the NVTX
+* API to create a registered string.
+*
+*/
+typedef nvtxStringRegistration* nvtxStringHandle_t;
+
+/* ========================================================================= */
+/** \defgroup GENERAL General
+ * @{
+ */
+
+/** ---------------------------------------------------------------------------
+ * Color Types
+ * ------------------------------------------------------------------------- */
+typedef enum nvtxColorType_t
+{
+    NVTX_COLOR_UNKNOWN  = 0,                 /**< Color attribute is unused. */
+    NVTX_COLOR_ARGB     = 1                  /**< An ARGB color is provided. */
+} nvtxColorType_t;
+
+/** ---------------------------------------------------------------------------
+ * Message Types
+ * ------------------------------------------------------------------------- */
+typedef enum nvtxMessageType_t
+{
+    NVTX_MESSAGE_UNKNOWN          = 0,    /**< Message payload is unused. */
+    NVTX_MESSAGE_TYPE_ASCII       = 1,    /**< A character sequence is used as payload. */
+    NVTX_MESSAGE_TYPE_UNICODE     = 2,     /**< A wide character sequence is used as payload. */
+    /* NVTX_VERSION_2 */
+    NVTX_MESSAGE_TYPE_REGISTERED  = 3,    /**< A unique string handle that was registered
+                                                with \ref nvtxDomainRegisterStringA() or 
+                                                \ref nvtxDomainRegisterStringW(). */
+} nvtxMessageType_t;
+
+typedef union nvtxMessageValue_t
+{
+    const char* ascii;
+    const wchar_t* unicode;
+    /* NVTX_VERSION_2 */
+    nvtxStringHandle_t registered;
+} nvtxMessageValue_t;
+
+
+/** @} */ /*END defgroup*/
+/* ------------------------------------------------------------------------- */
+/** \brief Force initialization (optional)
+*
+* Force NVTX library to initialize.  The first call to any NVTX API function
+* will automatically initialize the entire API.  This can make the first call
+* much slower than subsequent calls.  In applications where the first call to
+* NVTX may be in a performance-critical section, calling nvtxInitialize before
+* any performance-critical sections will ensure NVTX initialization occurs at
+* an acceptable time.  Since nvtxInitialize takes no parameters and has no
+* expected behavior besides initialization, it is convenient to add a call to
+* nvtxInitialize in NVTX-instrumented applications that need to force earlier
+* initialization without changing any other code.  For example, if an app's
+* first NVTX call is nvtxDomainCreate, and it is difficult to move that call
+* earlier because the domain handle must be stored in an object only created
+* at that point, adding a call to nvtxInitialize at the top of main() will
+* ensure the later call to nvtxDomainCreate is as fast as possible.
+*
+* \version \NVTX_VERSION_3
+*
+* \param reserved - must be zero or NULL.
+*
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxInitialize(const void* reserved);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+
+/* ========================================================================= */
+/** \defgroup EVENT_ATTRIBUTES Event Attributes
+* @{
+*/
+
+/** ---------------------------------------------------------------------------
+* Payload Types
+* ------------------------------------------------------------------------- */
+typedef enum nvtxPayloadType_t
+{
+    NVTX_PAYLOAD_UNKNOWN = 0,   /**< Color payload is unused. */
+    NVTX_PAYLOAD_TYPE_UNSIGNED_INT64 = 1,   /**< A 64 bit unsigned integer value is used as payload. */
+    NVTX_PAYLOAD_TYPE_INT64 = 2,   /**< A 64 bit signed integer value is used as payload. */
+    NVTX_PAYLOAD_TYPE_DOUBLE = 3,   /**< A 64 bit floating point value is used as payload. */
+    /* NVTX_VERSION_2 */
+    NVTX_PAYLOAD_TYPE_UNSIGNED_INT32 = 4,   /**< A 32 bit floating point value is used as payload. */
+    NVTX_PAYLOAD_TYPE_INT32 = 5,   /**< A 32 bit floating point value is used as payload. */
+    NVTX_PAYLOAD_TYPE_FLOAT = 6    /**< A 32 bit floating point value is used as payload. */
+} nvtxPayloadType_t;
+
+/** \brief Event Attribute Structure.
+ * \anchor EVENT_ATTRIBUTE_STRUCTURE
+ *
+ * This structure is used to describe the attributes of an event. The layout of
+ * the structure is defined by a specific version of the tools extension
+ * library and can change between different versions of the Tools Extension
+ * library.
+ *
+ * \par Initializing the Attributes
+ *
+ * The caller should always perform the following three tasks when using
+ * attributes:
+ * <ul>
+ *    <li>Zero the structure
+ *    <li>Set the version field
+ *    <li>Set the size field
+ * </ul>
+ *
+ * Zeroing the structure sets all the event attributes types and values
+ * to the default value.
+ *
+ * The version and size field are used by the Tools Extension
+ * implementation to handle multiple versions of the attributes structure.
+ *
+ * It is recommended that the caller use one of the following to methods
+ * to initialize the event attributes structure:
+ *
+ * \par Method 1: Initializing nvtxEventAttributes for future compatibility
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * \endcode
+ *
+ * \par Method 2: Initializing nvtxEventAttributes for a specific version
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = 1;
+ * eventAttrib.size = (uint16_t)(sizeof(nvtxEventAttributes_v1));
+ * \endcode
+ *
+ * If the caller uses Method 1 it is critical that the entire binary
+ * layout of the structure be configured to 0 so that all fields
+ * are initialized to the default value.
+ *
+ * The caller should either use both NVTX_VERSION and
+ * NVTX_EVENT_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+ * and a versioned type (Method 2).  Using a mix of the two methods
+ * will likely cause either source level incompatibility or binary
+ * incompatibility in the future.
+ *
+ * \par Settings Attribute Types and Values
+ *
+ *
+ * \par Example:
+ * \code
+ * // Initialize
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ *
+ * // Configure the Attributes
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF880000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example";
+ * \endcode
+ *
+ * In the example the caller does not have to set the value of
+ * \ref ::nvtxEventAttributes_v2::category or
+ * \ref ::nvtxEventAttributes_v2::payload as these fields were set to
+ * the default value by {0}.
+ * \sa
+ * ::nvtxDomainMarkEx
+ * ::nvtxDomainRangeStartEx
+ * ::nvtxDomainRangePushEx
+ */
+typedef struct nvtxEventAttributes_v2
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /**
+     * \brief ID of the category the event is assigned to.
+     *
+     * A category is a user-controlled ID that can be used to group
+     * events.  The tool may use category IDs to improve filtering or
+     * enable grouping of events in the same category. The functions
+     * \ref ::nvtxNameCategoryA or \ref ::nvtxNameCategoryW can be used
+     * to name a category.
+     *
+     * Default Value is 0
+     */
+    uint32_t category;
+
+    /** \brief Color type specified in this attribute structure.
+     *
+     * Defines the color format of the attribute structure's \ref COLOR_FIELD
+     * "color" field.
+     *
+     * Default Value is NVTX_COLOR_UNKNOWN
+     */
+    int32_t colorType;              /* nvtxColorType_t */
+
+    /** \brief Color assigned to this event. \anchor COLOR_FIELD
+     *
+     * The color that the tool should use to visualize the event.
+     */
+    uint32_t color;
+
+    /**
+     * \brief Payload type specified in this attribute structure.
+     *
+     * Defines the payload format of the attribute structure's \ref PAYLOAD_FIELD
+     * "payload" field.
+     *
+     * Default Value is NVTX_PAYLOAD_UNKNOWN
+     */
+    int32_t payloadType;            /* nvtxPayloadType_t */
+
+    int32_t reserved0;
+
+    /**
+     * \brief Payload assigned to this event. \anchor PAYLOAD_FIELD
+     *
+     * A numerical value that can be used to annotate an event. The tool could
+     * use the payload data to reconstruct graphs and diagrams.
+     */
+    union payload_t
+    {
+        uint64_t ullValue;
+        int64_t llValue;
+        double dValue;
+        /* NVTX_VERSION_2 */
+        uint32_t uiValue;
+        int32_t iValue;
+        float fValue;
+    } payload;
+
+    /** \brief Message type specified in this attribute structure.
+     *
+     * Defines the message format of the attribute structure's \ref MESSAGE_FIELD
+     * "message" field.
+     *
+     * Default Value is NVTX_MESSAGE_UNKNOWN
+     */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure. \anchor MESSAGE_FIELD
+     *
+     * The text message that is attached to an event.
+     */
+    nvtxMessageValue_t message;
+
+} nvtxEventAttributes_v2;
+
+typedef struct nvtxEventAttributes_v2 nvtxEventAttributes_t;
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup MARKERS_AND_RANGES Markers and Ranges
+ *
+ * See \ref MARKERS_AND_RANGES for more details
+ *
+ * @{
+ */
+
+/** \name Marker */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+*
+* A marker can contain a text message or specify additional information
+* using the event attributes structure.  These attributes include a text
+* message, color, category, and a payload. Each of the attributes is optional
+* and can only be sent out using the \ref nvtxDomainMarkEx function.
+*
+* nvtxDomainMarkEx(NULL, event) is equivalent to calling
+* nvtxMarkEx(event).
+*
+* \param domain    - The domain of scoping the category.
+* \param eventAttrib - The event attribute structure defining the marker's
+* attribute types and attribute values.
+*
+* \sa
+* ::nvtxMarkEx
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+ *
+ * A marker can contain a text message or specify additional information
+ * using the event attributes structure.  These attributes include a text
+ * message, color, category, and a payload. Each of the attributes is optional
+ * and can only be sent out using the \ref nvtxMarkEx function.
+ * If \ref nvtxMarkA or \ref nvtxMarkW are used to specify the marker
+ * or if an attribute is unspecified then a default value will be used.
+ *
+ * \param eventAttrib - The event attribute structure defining the marker's
+ * attribute types and attribute values.
+ *
+ * \par Example:
+ * \code
+ * // zero the structure
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * // set the version and the size information
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * // configure the attributes.  0 is the default for all attributes.
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF880000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example nvtxMarkEx";
+ * nvtxMarkEx(&eventAttrib);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainMarkEx
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+ *
+ * A marker created using \ref nvtxMarkA or \ref nvtxMarkW contains only a
+ * text message.
+ *
+ * \param message     - The message associated to this marker event.
+ *
+ * \par Example:
+ * \code
+ * nvtxMarkA("Example nvtxMarkA");
+ * nvtxMarkW(L"Example nvtxMarkW");
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainMarkEx
+ * ::nvtxMarkEx
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message);
+NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message);
+/** @} */
+
+
+/** \name Process Ranges */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range in a domain.
+*
+* \param domain    - The domain of scoping the category.
+* \param eventAttrib - The event attribute structure defining the range's
+* attribute types and attribute values.
+*
+* \return The unique ID used to correlate a pair of Start and End events.
+*
+* \remarks Ranges defined by Start/End can overlap.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "my range";
+* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib);
+* // ...
+* nvtxDomainRangeEnd(rangeId);
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangeEnd
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range.
+ *
+ * \param eventAttrib - The event attribute structure defining the range's
+ * attribute types and attribute values.
+ *
+ * \return The unique ID used to correlate a pair of Start and End events.
+ *
+ * \remarks Ranges defined by Start/End can overlap.
+ *
+ * \par Example:
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * eventAttrib.category = 3;
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF0088FF;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example Range";
+ * nvtxRangeId_t rangeId = nvtxRangeStartEx(&eventAttrib);
+ * // ...
+ * nvtxRangeEnd(rangeId);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangeEnd
+ * ::nvtxDomainRangeStartEx
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range.
+ *
+ * \param message     - The event message associated to this range event.
+ *
+ * \return The unique ID used to correlate a pair of Start and End events.
+ *
+ * \remarks Ranges defined by Start/End can overlap.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangeId_t r1 = nvtxRangeStartA("Range 1");
+ * nvtxRangeId_t r2 = nvtxRangeStartW(L"Range 2");
+ * nvtxRangeEnd(r1);
+ * nvtxRangeEnd(r2);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangeEnd
+ * ::nvtxRangeStartEx
+ * ::nvtxDomainRangeStartEx
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message);
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a process range.
+*
+* \param domain - The domain 
+* \param id - The correlation ID returned from a nvtxRangeStart call.
+*
+* \remarks This function is offered completeness but is an alias for ::nvtxRangeEnd. 
+* It does not need a domain param since that is associated iwth the range ID at ::nvtxDomainRangeStartEx
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "my range";
+* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib);
+* // ...
+* nvtxDomainRangeEnd(rangeId);
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangeStartEx
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a process range.
+ *
+ * \param id - The correlation ID returned from an nvtxRangeStart call.
+ *
+ * \sa
+ * ::nvtxDomainRangeStartEx
+ * ::nvtxRangeStartEx
+ * ::nvtxRangeStartA
+ * ::nvtxRangeStartW
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id);
+/** @} */
+
+/** \name Thread Ranges */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+*
+* \param domain    - The domain of scoping.
+* \param eventAttrib - The event attribute structure defining the range's
+* attribute types and attribute values.
+*
+* \return The 0 based level of range being started. This value is scoped to the domain.
+* If an error occurs, a negative value is returned.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.colorType = NVTX_COLOR_ARGB;
+* eventAttrib.color = 0xFFFF0000;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "Level 0";
+* nvtxDomainRangePushEx(domain, &eventAttrib);
+*
+* // Re-use eventAttrib
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE;
+* eventAttrib.message.unicode = L"Level 1";
+* nvtxDomainRangePushEx(domain, &eventAttrib);
+*
+* nvtxDomainRangePop(domain); //level 1
+* nvtxDomainRangePop(domain); //level 0
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangePop
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+ *
+ * \param eventAttrib - The event attribute structure defining the range's
+ * attribute types and attribute values.
+ *
+ * \return The 0 based level of range being started. This level is per domain.
+ * If an error occurs a negative value is returned.
+ *
+ * \par Example:
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFFFF0000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Level 0";
+ * nvtxRangePushEx(&eventAttrib);
+ *
+ * // Re-use eventAttrib
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE;
+ * eventAttrib.message.unicode = L"Level 1";
+ * nvtxRangePushEx(&eventAttrib);
+ *
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainRangePushEx
+ * ::nvtxRangePop
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+ *
+ * \param message     - The event message associated to this range event.
+ *
+ * \return The 0 based level of range being started.  If an error occurs a
+ * negative value is returned.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangePushA("Level 0");
+ * nvtxRangePushW(L"Level 1");
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainRangePushEx
+ * ::nvtxRangePop
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message);
+NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message);
+/** @} */
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a nested thread range.
+*
+* \return The level of the range being ended. If an error occurs a negative
+* value is returned on the current thread.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreate("example library");
+* nvtxDomainRangePushA(domain, "Level 0");
+* nvtxDomainRangePushW(domain, L"Level 1");
+* nvtxDomainRangePop(domain);
+* nvtxDomainRangePop(domain);
+* \endcode
+*
+* \sa
+* ::nvtxRangePushEx
+* ::nvtxRangePushA
+* ::nvtxRangePushW
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a nested thread range.
+ *
+ * \return The level of the range being ended. If an error occurs a negative
+ * value is returned on the current thread.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangePushA("Level 0");
+ * nvtxRangePushW(L"Level 1");
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangePushEx
+ * ::nvtxRangePushA
+ * ::nvtxRangePushW
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePop(void);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup RESOURCE_NAMING Resource Naming
+ *
+ * See \ref RESOURCE_NAMING for more details
+ *
+ * @{
+ */
+
+
+/*  ------------------------------------------------------------------------- */
+/** \name Functions for Generic Resource Naming*/
+/*  ------------------------------------------------------------------------- */
+
+/*  ------------------------------------------------------------------------- */
+/** \cond SHOW_HIDDEN
+* \brief Resource typing helpers.  
+*
+* Classes are used to make it easy to create a series of resource types 
+* per API without collisions 
+*/
+#define NVTX_RESOURCE_MAKE_TYPE(CLASS, INDEX) ((((uint32_t)(NVTX_RESOURCE_CLASS_ ## CLASS))<<16)|((uint32_t)(INDEX)))
+#define NVTX_RESOURCE_CLASS_GENERIC 1
+/** \endcond */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Generic resource type for when a resource class is not available.
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*
+* \version \NVTX_VERSION_2
+*/
+typedef enum nvtxResourceGenericType_t
+{
+    NVTX_RESOURCE_TYPE_UNKNOWN = 0,
+    NVTX_RESOURCE_TYPE_GENERIC_POINTER = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 1), /**< Generic pointer assumed to have no collisions with other pointers. */
+    NVTX_RESOURCE_TYPE_GENERIC_HANDLE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 2), /**< Generic handle assumed to have no collisions with other handles. */
+    NVTX_RESOURCE_TYPE_GENERIC_THREAD_NATIVE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 3), /**< OS native thread identifier. */
+    NVTX_RESOURCE_TYPE_GENERIC_THREAD_POSIX = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 4) /**< POSIX pthread identifier. */
+} nvtxResourceGenericType_t;
+
+
+
+/** \brief Resource Attribute Structure.
+* \anchor RESOURCE_ATTRIBUTE_STRUCTURE
+*
+* This structure is used to describe the attributes of a resource. The layout of
+* the structure is defined by a specific version of the tools extension
+* library and can change between different versions of the Tools Extension
+* library.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the resource attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxEventAttributes for future compatibility
+* \code
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxEventAttributes for a specific version
+* \code
+* nvtxResourceAttributes_v0 attribs = {0};
+* attribs.version = 2;
+* attribs.size = (uint16_t)(sizeof(nvtxResourceAttributes_v0));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_RESOURCE_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+*
+* // Initialize
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+*
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* \endcode
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*/
+typedef struct nvtxResourceAttributes_v0
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of this attribute
+    * structure.
+    */
+    uint16_t size;
+
+    /**
+    * \brief Identifier type specifies how to interpret the identifier field
+    *
+    * Defines the identifier format of the attribute structure's \ref RESOURCE_IDENTIFIER_FIELD
+    * "identifier" field.
+    *
+    * Default Value is NVTX_RESOURCE_TYPE_UNKNOWN
+    */
+    int32_t identifierType;            /* values from enums following the pattern nvtxResource[name]Type_t */
+
+    /**
+    * \brief Identifier for the resource. 
+    * \anchor RESOURCE_IDENTIFIER_FIELD
+    *
+    * An identifier may be a pointer or a handle to an OS or middleware API object.
+    * The resource type will assist in avoiding collisions where handles values may collide.
+    */
+    union identifier_t
+    {
+        const void* pValue;
+        uint64_t ullValue;
+    } identifier;
+
+    /** \brief Message type specified in this attribute structure.
+    *
+    * Defines the message format of the attribute structure's \ref RESOURCE_MESSAGE_FIELD
+    * "message" field.
+    *
+    * Default Value is NVTX_MESSAGE_UNKNOWN
+    */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure. \anchor RESOURCE_MESSAGE_FIELD
+    *
+    * The text message that is attached to a resource.
+    */
+    nvtxMessageValue_t message;
+
+} nvtxResourceAttributes_v0;
+
+typedef struct nvtxResourceAttributes_v0 nvtxResourceAttributes_t;
+
+/* \cond SHOW_HIDDEN 
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxResourceAttributes_v0) ) )
+typedef struct nvtxResourceHandle* nvtxResourceHandle_t;
+/** \endcond */
+
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Create a resource object to track and associate data with OS and middleware objects
+*
+* Allows users to associate an API handle or pointer with a user-provided name.
+* 
+*
+* \param domain - Domain to own the resource object
+* \param attribs - Attributes to be associated with the resource
+*
+* \return A handle that represents the newly created resource object.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* \endcode
+*
+* \sa
+* ::nvtxResourceAttributes_t
+* ::nvtxDomainResourceDestroy
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Destroy a resource object to track and associate data with OS and middleware objects
+*
+* Allows users to associate an API handle or pointer with a user-provided name.
+*
+* \param resource - Handle to the resource in which to operate.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* nvtxDomainResourceDestroy(handle);
+* \endcode
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource);
+/** @} */
+
+
+/** \name Functions for NVTX Category Naming*/
+
+/* ------------------------------------------------------------------------- */
+/**
+* \brief Annotate an NVTX category used within a domain.
+*
+* Categories are used to group sets of events. Each category is identified
+* through a unique ID and that ID is passed into any of the marker/range
+* events to assign that event to a specific category. The nvtxDomainNameCategory
+* function calls allow the user to assign a name to a category ID that is
+* specific to the domain.
+*
+* nvtxDomainNameCategory(NULL, category, name) is equivalent to calling
+* nvtxNameCategory(category, name).
+*
+* \param domain    - The domain of scoping the category.
+* \param category  - The category ID to name.
+* \param name      - The name of the category.
+*
+* \remarks The category names are tracked per domain.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example");
+* nvtxDomainNameCategoryA(domain, 1, "Memory Allocation");
+* nvtxDomainNameCategoryW(domain, 2, L"Memory Transfer");
+* \endcode
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name);
+/** @} */
+
+/** \brief Annotate an NVTX category.
+ *
+ * Categories are used to group sets of events. Each category is identified
+ * through a unique ID and that ID is passed into any of the marker/range
+ * events to assign that event to a specific category. The nvtxNameCategory
+ * function calls allow the user to assign a name to a category ID.
+ *
+ * \param category - The category ID to name.
+ * \param name     - The name of the category.
+ *
+ * \remarks The category names are tracked per process.
+ *
+ * \par Example:
+ * \code
+ * nvtxNameCategory(1, "Memory Allocation");
+ * nvtxNameCategory(2, "Memory Transfer");
+ * nvtxNameCategory(3, "Memory Object Lifetime");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name);
+/** @} */
+
+/** \name Functions for OS Threads Naming*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotate an OS thread.
+ *
+ * Allows the user to name an active thread of the current process. If an
+ * invalid thread ID is provided or a thread ID from a different process is
+ * used the behavior of the tool is implementation dependent.
+ *
+ * Tools expect thread ID to be a number that uniquely identifies the thread
+ * at the time of the call. Note that a thread's ID can be reused after
+ * it is destroyed. Tools may choose how to handle aliasing of thread IDs.
+ *
+ * POSIX pthread_t type returned by pthread_self() may not comply with these
+ * expectations. Please use OS-specific thread ID instead of pthread_t.
+ *
+ * The thread name is associated to the default domain.  To support domains 
+ * use resource objects via ::nvtxDomainResourceCreate.
+ *
+ * \param threadId - The ID of the thread to name.
+ * \param name     - The name of the thread.
+ *
+ * \par Examples:
+ * MS Windows:
+ * \code
+ * #include <windows.h>
+ * nvtxNameOsThread(GetCurrentThreadId(), "Current thread");
+ * nvtxNameOsThread(GetThreadId(SomeThreadHandle), "Other thread");
+ * \endcode
+ *
+ * Android:
+ * \code
+ * #include <unistd.h>
+ * nvtxNameOsThreadA(gettid(), "Current thread");
+ * nvtxNameOsThreadA(getpid(), "Main thread");
+ * \endcode
+ *
+ * Linux:
+ * \code
+ * #include <sys/syscall.h>
+ * nvtxNameOsThreadA(syscall(SYS_gettid), "Current thread");
+ * \endcode
+ * \code
+ * #include <unistd.h>
+ * nvtxNameOsThreadA(getpid(), "Main thread");
+ * \endcode
+ *
+ * OS X:
+ * \code
+ * #include <sys/syscall.h>
+ * nvtxNameOsThreadA(syscall(SYS_thread_selfid), "Current thread");
+ * \endcode
+ * \code
+ * #include <pthread.h>
+ * __uint64_t id;
+ * pthread_threadid_np(pthread_self(), &id);
+ * nvtxNameOsThreadA(id, "Current thread");
+ * pthread_threadid_np(somePThreadId, &id);
+ * nvtxNameOsThreadA(id, "Other thread");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup STRING_REGISTRATION String Registration
+*
+* Registered strings are intended to increase performance by lowering instrumentation
+* overhead.  String may be registered once and the handle may be passed in place of
+* a string where an the APIs may allow.
+*
+* See \ref STRING_REGISTRATION for more details
+*
+* @{
+*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Register a string.
+
+* Registers an immutable string with NVTX. Once registered the pointer used
+* to register the domain name can be used in nvtxEventAttributes_t
+* \ref MESSAGE_FIELD. This allows NVTX implementation to skip copying the
+* contents of the message on each event invocation.
+*
+* String registration is an optimization. It is recommended to use string
+* registration if the string will be passed to an event many times.
+*
+* String are not unregistered, except that by unregistering the entire domain
+*
+* \param domain  - Domain handle. If NULL then the global domain is used.
+* \param string    - A unique pointer to a sequence of characters.
+*
+* \return A handle representing the registered string.
+*
+* \par Example:
+* \code
+* nvtxDomainCreateA("com.nvidia.nvtx.example");
+* nvtxStringHandle_t message = nvtxDomainRegisterStringA(domain, "registered string");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_REGISTERED;
+* eventAttrib.message.registered = message;
+* \endcode
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string);
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string);
+/** @} */
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup DOMAINS Domains
+*
+* Domains are used to group events to a developer defined scope. Middleware
+* vendors may also scope their own events to avoid collisions with the
+* the application developer's events, so that the application developer may
+* inspect both parts and easily differentiate or filter them.  By default
+* all events are scoped to a global domain where NULL is provided or when
+* using APIs provided b versions of NVTX below v2
+*
+* Domains are intended to be typically long lived objects with the intention
+* of logically separating events of large modules from each other such as
+* middleware libraries from each other and the main application.
+*
+* See \ref DOMAINS for more details
+*
+* @{
+*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Register a NVTX domain.
+*
+* Domains are used to scope annotations. All NVTX_VERSION_0 and NVTX_VERSION_1
+* annotations are scoped to the global domain. The function nvtxDomainCreate
+* creates a new named domain.
+*
+* Each domain maintains its own nvtxRangePush and nvtxRangePop stack.
+*
+* \param name - A unique string representing the domain.
+*
+* \return A handle representing the domain.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example");
+*
+* nvtxMarkA("nvtxMarkA to global domain");
+*
+* nvtxEventAttributes_t eventAttrib1 = {0};
+* eventAttrib1.version = NVTX_VERSION;
+* eventAttrib1.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib1.message.ascii = "nvtxDomainMarkEx to global domain";
+* nvtxDomainMarkEx(NULL, &eventAttrib1);
+*
+* nvtxEventAttributes_t eventAttrib2 = {0};
+* eventAttrib2.version = NVTX_VERSION;
+* eventAttrib2.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib2.message.ascii = "nvtxDomainMarkEx to com.nvidia.nvtx.example";
+* nvtxDomainMarkEx(domain, &eventAttrib2);
+* nvtxDomainDestroy(domain);
+* \endcode
+*
+* \sa
+* ::nvtxDomainDestroy
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* name);
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Unregister a NVTX domain.
+*
+* Unregisters the domain handle and frees all domain specific resources.
+*
+* \param domain    - the domain handle
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example");
+* nvtxDomainDestroy(domain);
+* \endcode
+*
+* \sa
+* ::nvtxDomainCreateA
+* ::nvtxDomainCreateW
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \cond SHOW_HIDDEN */
+
+#ifdef UNICODE
+    #define nvtxMark            nvtxMarkW
+    #define nvtxRangeStart      nvtxRangeStartW
+    #define nvtxRangePush       nvtxRangePushW
+    #define nvtxNameCategory    nvtxNameCategoryW
+    #define nvtxNameOsThread    nvtxNameOsThreadW
+    /* NVTX_VERSION_2 */
+    #define nvtxDomainCreate         nvtxDomainCreateW
+    #define nvtxDomainRegisterString nvtxDomainRegisterStringW
+    #define nvtxDomainNameCategory   nvtxDomainNameCategoryW
+#else
+    #define nvtxMark            nvtxMarkA
+    #define nvtxRangeStart      nvtxRangeStartA
+    #define nvtxRangePush       nvtxRangePushA
+    #define nvtxNameCategory    nvtxNameCategoryA
+    #define nvtxNameOsThread    nvtxNameOsThreadA
+    /* NVTX_VERSION_2 */
+    #define nvtxDomainCreate         nvtxDomainCreateA
+    #define nvtxDomainRegisterString nvtxDomainRegisterStringA
+    #define nvtxDomainNameCategory   nvtxDomainNameCategoryA
+#endif
+
+/** \endcond */
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif /* __cplusplus */
+
+#define NVTX_IMPL_GUARD /* Ensure other headers cannot included directly */
+
+#include "nvtxDetail/nvtxTypes.h"
+
+#ifndef NVTX_NO_IMPL
+#include "nvtxDetail/nvtxImpl.h"
+#endif /*NVTX_NO_IMPL*/
+
+#undef NVTX_IMPL_GUARD
+
+#endif /* !defined(NVTX_VERSION) */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b1e71b7779089430b39e0fe3483ad536bb4bb7d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h
@@ -0,0 +1,170 @@
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#include "nvToolsExt.h"
+
+#include "cuda.h"
+
+#ifndef NVTOOLSEXT_CUDA_V3
+#define NVTOOLSEXT_CUDA_V3
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for CUDA Resource Naming
+*/
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_CUDA CUDA Resource Naming
+ *
+ * This section covers the API functions that allow to annotate CUDA resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_CUDA  4
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for CUDA
+*/
+typedef enum nvtxResourceCUDAType_t
+{
+    NVTX_RESOURCE_TYPE_CUDA_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDA, 1), /* CUdevice */
+    NVTX_RESOURCE_TYPE_CUDA_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 2), /* CUcontext */
+    NVTX_RESOURCE_TYPE_CUDA_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDA, 3), /* CUstream */
+    NVTX_RESOURCE_TYPE_CUDA_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 4), /* CUevent */
+} nvtxResourceCUDAType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA device.
+ *
+ * Allows the user to associate a CUDA device with a user-provided name.
+ *
+ * \param device - The handle of the CUDA device to name.
+ * \param name   - The name of the CUDA device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA context.
+ *
+ * Allows the user to associate a CUDA context with a user-provided name.
+ *
+ * \param context - The handle of the CUDA context to name.
+ * \param name    - The name of the CUDA context.
+ *
+ * \par Example:
+ * \code
+ * CUresult status = cuCtxCreate( &cuContext, 0, cuDevice );
+ * if ( CUDA_SUCCESS != status )
+ *     goto Error;
+ * nvtxNameCuContext(cuContext, "CTX_NAME");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA stream.
+ *
+ * Allows the user to associate a CUDA stream with a user-provided name.
+ *
+ * \param stream - The handle of the CUDA stream to name.
+ * \param name   - The name of the CUDA stream.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA event.
+ *
+ * Allows the user to associate a CUDA event with a user-provided name.
+ *
+ * \param event - The handle of the CUDA event to name.
+ * \param name  - The name of the CUDA event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameCuDevice   nvtxNameCuDeviceW
+  #define nvtxNameCuContext  nvtxNameCuContextW
+  #define nvtxNameCuStream   nvtxNameCuStreamW
+  #define nvtxNameCuEvent    nvtxNameCuEventW
+#else
+  #define nvtxNameCuDevice   nvtxNameCuDeviceA
+  #define nvtxNameCuContext  nvtxNameCuContextA
+  #define nvtxNameCuStream   nvtxNameCuStreamA
+  #define nvtxNameCuEvent    nvtxNameCuEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#ifndef NVTX_NO_IMPL
+#define NVTX_IMPL_GUARD_CUDA /* Ensure other headers cannot included directly */
+#include "nvtxDetail/nvtxImplCuda_v3.h"
+#undef NVTX_IMPL_GUARD_CUDA
+#endif /*NVTX_NO_IMPL*/
+
+#endif /* NVTOOLSEXT_CUDA_V3 */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h
new file mode 100644
index 0000000000000000000000000000000000000000..044d3dab5c32e7ec6646f3a1f3e9d89e52d40ba2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h
@@ -0,0 +1,146 @@
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#include "nvToolsExt.h"
+
+#include "cuda.h"
+#include "driver_types.h"
+
+#ifndef NVTOOLSEXT_CUDART_V3
+#define NVTOOLSEXT_CUDART_V3
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for CUDA Resource Naming
+*/
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_CUDART CUDA Runtime Resource Naming
+ *
+ * This section covers the API functions that allow to annotate CUDA resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_CUDART 5
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for CUDART
+*/
+typedef enum nvtxResourceCUDARTType_t
+{
+    NVTX_RESOURCE_TYPE_CUDART_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDART, 0), /* int device */
+    NVTX_RESOURCE_TYPE_CUDART_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDART, 1), /* cudaStream_t */
+    NVTX_RESOURCE_TYPE_CUDART_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDART, 2), /* cudaEvent_t */
+} nvtxResourceCUDARTType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA device.
+ *
+ * Allows the user to associate a CUDA device with a user-provided name.
+ *
+ * \param device - The id of the CUDA device to name.
+ * \param name   - The name of the CUDA device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceA(int device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceW(int device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA stream.
+ *
+ * Allows the user to associate a CUDA stream with a user-provided name.
+ *
+ * \param stream - The handle of the CUDA stream to name.
+ * \param name   - The name of the CUDA stream.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamA(cudaStream_t stream, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamW(cudaStream_t stream, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA event.
+ *
+ * Allows the user to associate a CUDA event with a user-provided name.
+ *
+ * \param event - The handle of the CUDA event to name.
+ * \param name  - The name of the CUDA event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventA(cudaEvent_t event, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventW(cudaEvent_t event, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameCudaDevice nvtxNameCudaDeviceW
+  #define nvtxNameCudaStream nvtxNameCudaStreamW
+  #define nvtxNameCudaEvent  nvtxNameCudaEventW
+#else
+  #define nvtxNameCudaDevice nvtxNameCudaDeviceA
+  #define nvtxNameCudaStream nvtxNameCudaStreamA
+  #define nvtxNameCudaEvent  nvtxNameCudaEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#ifndef NVTX_NO_IMPL
+#define NVTX_IMPL_GUARD_CUDART /* Ensure other headers cannot included directly */
+#include "nvtxDetail/nvtxImplCudaRt_v3.h"
+#undef NVTX_IMPL_GUARD_CUDART
+#endif /*NVTX_NO_IMPL*/
+
+#endif /* NVTOOLSEXT_CUDART_V3 */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h
new file mode 100644
index 0000000000000000000000000000000000000000..38a9290b438d30f8450b6ea2f4e5de68d37d4a97
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h
@@ -0,0 +1,220 @@
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#include "nvToolsExt.h"
+
+#include <CL/cl.h>
+
+#ifndef NVTOOLSEXT_OPENCL_V3
+#define NVTOOLSEXT_OPENCL_V3
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for OpenCL Resource Naming
+ */
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_OPENCL OpenCL Resource Naming
+ *
+ * This section covers the API functions that allow to annotate OpenCL resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_OPENCL 6 
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for OpenCL
+*/
+typedef enum nvtxResourceOpenCLType_t
+{
+    NVTX_RESOURCE_TYPE_OPENCL_DEVICE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 1),
+    NVTX_RESOURCE_TYPE_OPENCL_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 2),
+    NVTX_RESOURCE_TYPE_OPENCL_COMMANDQUEUE = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 3),
+    NVTX_RESOURCE_TYPE_OPENCL_MEMOBJECT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 4),
+    NVTX_RESOURCE_TYPE_OPENCL_SAMPLER = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 5),
+    NVTX_RESOURCE_TYPE_OPENCL_PROGRAM = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 6),
+    NVTX_RESOURCE_TYPE_OPENCL_EVENT = NVTX_RESOURCE_MAKE_TYPE(OPENCL, 7),
+} nvtxResourceOpenCLType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL device.
+ *
+ * Allows to associate an OpenCL device with a user-provided name.
+ *
+ * \param device - The handle of the OpenCL device to name.
+ * \param name   - The name of the OpenCL device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceA(cl_device_id device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceW(cl_device_id device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL context.
+ *
+ * Allows to associate an OpenCL context with a user-provided name.
+ *
+ * \param context - The handle of the OpenCL context to name.
+ * \param name    - The name of the OpenCL context.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClContextA(cl_context context, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClContextW(cl_context context, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL command queue.
+ *
+ * Allows to associate an OpenCL command queue with a user-provided name.
+ *
+ * \param command_queue - The handle of the OpenCL command queue to name.
+ * \param name          - The name of the OpenCL command queue.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueA(cl_command_queue command_queue, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueW(cl_command_queue command_queue, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL memory object.
+ *
+ * Allows to associate an OpenCL memory object with a user-provided name.
+ *
+ * \param memobj - The handle of the OpenCL memory object to name.
+ * \param name   - The name of the OpenCL memory object.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectA(cl_mem memobj, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectW(cl_mem memobj, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL sampler.
+ *
+ * Allows to associate an OpenCL sampler with a user-provided name.
+ *
+ * \param sampler - The handle of the OpenCL sampler to name.
+ * \param name    - The name of the OpenCL sampler.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerA(cl_sampler sampler, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerW(cl_sampler sampler, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL program.
+ *
+ * Allows to associate an OpenCL program with a user-provided name.
+ *
+ * \param program - The handle of the OpenCL program to name.
+ * \param name    - The name of the OpenCL program.
+ *
+ * \code
+ * cpProgram = clCreateProgramWithSource(cxGPUContext, 1,
+ *     (const char **) &cSourceCL, &program_length, &ciErrNum);
+ * shrCheckErrorEX(ciErrNum, CL_SUCCESS, pCleanup);
+ * nvtxNameClProgram(cpProgram, L"PROGRAM_NAME");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClProgramA(cl_program program, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClProgramW(cl_program program, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates an OpenCL event.
+ *
+ * Allows to associate an OpenCL event with a user-provided name.
+ *
+ * \param evnt - The handle of the OpenCL event to name.
+ * \param name - The name of the OpenCL event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameClEventA(cl_event evnt, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameClEventW(cl_event evnt, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameClDevice        nvtxNameClDeviceW
+  #define nvtxNameClContext       nvtxNameClContextW
+  #define nvtxNameClCommandQueue  nvtxNameClCommandQueueW
+  #define nvtxNameClMemObject     nvtxNameClMemObjectW
+  #define nvtxNameClSampler       nvtxNameClSamplerW
+  #define nvtxNameClProgram       nvtxNameClProgramW
+  #define nvtxNameClEvent         nvtxNameClEventW
+#else
+  #define nvtxNameClDevice        nvtxNameClDeviceA
+  #define nvtxNameClContext       nvtxNameClContextA
+  #define nvtxNameClCommandQueue  nvtxNameClCommandQueueA
+  #define nvtxNameClMemObject     nvtxNameClMemObjectA
+  #define nvtxNameClSampler       nvtxNameClSamplerA
+  #define nvtxNameClProgram       nvtxNameClProgramA
+  #define nvtxNameClEvent         nvtxNameClEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#ifndef NVTX_NO_IMPL
+#define NVTX_IMPL_GUARD_OPENCL /* Ensure other headers cannot included directly */
+#include "nvtxDetail/nvtxImplOpenCL_v3.h"
+#undef NVTX_IMPL_GUARD_OPENCL
+#endif /*NVTX_NO_IMPL*/
+
+#endif /* NVTOOLSEXT_OPENCL_V3 */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtSync.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtSync.h
new file mode 100644
index 0000000000000000000000000000000000000000..afc3db98fe1b05d8dfb309221243ae3b4c14dd9d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvToolsExtSync.h
@@ -0,0 +1,411 @@
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#include "nvToolsExt.h"
+
+#ifndef NVTOOLSEXT_SYNC_V3
+#define NVTOOLSEXT_SYNC_V3
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* \cond SHOW_HIDDEN 
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxSyncUserAttributes_v0) ) )
+/** \endcond */
+
+
+/** 
+* \page PAGE_SYNCHRONIZATION Synchronization
+*
+* This section covers a subset of the API that allow users to track additional
+* synchronization details of their application.   Naming OS synchronization primitives 
+* may allow users to better understand the data collected by traced synchronization 
+* APIs.  Additionally, a user defined synchronization object can allow the users to
+* to tell the tools when the user is building their own synchronization system
+* that do not rely on the OS to provide behaviors and instead use techniques like
+* atomic operations and spinlocks.  
+*
+* See module \ref SYNCHRONIZATION for details.
+*
+* \par Example:
+* \code
+* class MyMutex
+* {
+*     volatile long bLocked;
+*     nvtxSyncUser_t hSync;
+* public:
+*     MyMutex(const char* name, nvtxDomainHandle_t d){
+*          bLocked = 0;
+*
+*          nvtxSyncUserAttributes_t attribs = { 0 };
+*          attribs.version = NVTX_VERSION;
+*          attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE;
+*          attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+*          attribs.message.ascii = name;
+*          hSync = nvtxDomainSyncUserCreate(d, &attribs);
+*     }
+*
+*     ~MyMutex() {
+*          nvtxDomainSyncUserDestroy(hSync);
+*     }
+*
+*     bool Lock() {
+*          nvtxDomainSyncUserAcquireStart(hSync);
+*          bool acquired = __sync_bool_compare_and_swap(&bLocked, 0, 1);//atomic compiler intrinsic 
+
+*          if (acquired) {
+*              nvtxDomainSyncUserAcquireSuccess(hSync);
+*          }
+*          else {
+*              nvtxDomainSyncUserAcquireFailed(hSync);
+*          }
+*          return acquired;
+*     }
+
+*     void Unlock() {
+*          nvtxDomainSyncUserReleasing(hSync);
+*          bLocked = false;
+*     }
+* };
+* \endcode
+* 
+* \version \NVTX_VERSION_2
+*/
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_SYNC_OS 2 /**< Synchronization objects that are OS specific. */
+#define NVTX_RESOURCE_CLASS_SYNC_PTHREAD 3 /**< Synchronization objects that are from the POSIX Threads API (pthread)*/
+/** \endcond */
+
+
+/*  ------------------------------------------------------------------------- */
+/** \defgroup SYNCHRONIZATION Synchronization
+* See page \ref PAGE_SYNCHRONIZATION.
+* @{
+*/
+
+/** \brief Resource type values for OSs with POSIX Thread API support
+ */
+typedef enum nvtxResourceSyncPosixThreadType_t
+{
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 1), /* pthread_mutex_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_CONDITION = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 2), /* pthread_cond_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_RWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 3), /* pthread_rwlock_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_BARRIER = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 4), /* pthread_barrier_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 5), /* pthread_spinlock_t  */
+    NVTX_RESOURCE_TYPE_SYNC_PTHREAD_ONCE = NVTX_RESOURCE_MAKE_TYPE(SYNC_PTHREAD, 6) /* pthread_once_t  */
+} nvtxResourceSyncPosixThreadType_t;
+
+/** \brief Resource type values for Windows OSs
+*/
+typedef enum nvtxResourceSyncWindowsType_t
+{
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_EVENT = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_CRITICAL_SECTION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4),
+    NVTX_RESOURCE_TYPE_SYNC_WINDOWS_SRWLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5)
+} nvtxResourceSyncWindowsType_t;
+
+/** \brief Resource type values for Linux and Linux derived OSs such as Android
+* \sa
+* ::nvtxResourceSyncPosixThreadType_t
+*/
+typedef enum nvtxResourceSyncLinuxType_t
+{
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_MUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 1),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_FUTEX = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 2),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_SEMAPHORE = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 3),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_COMPLETION = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 4),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_SPINLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 5),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_SEQLOCK = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 6),
+    NVTX_RESOURCE_TYPE_SYNC_LINUX_RCU = NVTX_RESOURCE_MAKE_TYPE(SYNC_OS, 7)
+} nvtxResourceSyncLinuxType_t;
+
+/** \brief Resource type values for Android come from Linux.
+* \sa
+* ::nvtxResourceSyncLinuxType_t
+* ::nvtxResourceSyncPosixThreadType_t
+*/
+typedef enum nvtxResourceSyncLinuxType_t nvtxResourceSyncAndroidType_t;
+
+/** \brief User Defined Synchronization Object Handle .
+* \anchor SYNCUSER_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a user defined syncrhonization object.  The tools will return a pointer through the API for the application
+* to hold on it's behalf to reference the string in the future.
+*
+*/
+typedef struct nvtxSyncUser* nvtxSyncUser_t;
+
+/** \brief User Defined Synchronization Object Attributes Structure.
+* \anchor USERDEF_SYNC_ATTRIBUTES_STRUCTURE
+*
+* This structure is used to describe the attributes of a user defined synchronization 
+* object.  The layout of the structure is defined by a specific version of the tools 
+* extension library and can change between different versions of the Tools Extension
+* library.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the event attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxEventAttributes for future compatibility
+* \code
+* nvtxSyncUserAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxSyncUserAttributes_t for a specific version
+* \code
+* nvtxSyncUserAttributes_t attribs = {0};
+* attribs.version = 1;
+* attribs.size = (uint16_t)(sizeof(nvtxSyncUserAttributes_t));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* // Initialize
+* nvtxSyncUserAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_SYNCUSER_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Example";
+* \endcode
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+*/
+typedef struct nvtxSyncUserAttributes_v0
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /** \brief Message type specified in this attribute structure.
+    *
+    * Defines the message format of the attribute structure's \ref nvtxSyncUserAttributes_v0::message
+    * "message" field.
+    *
+    * Default Value is NVTX_MESSAGE_UNKNOWN
+    */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure.
+    *
+    * The text message that is attached to an event.
+    */
+    nvtxMessageValue_t message;
+
+} nvtxSyncUserAttributes_v0;
+
+typedef struct nvtxSyncUserAttributes_v0 nvtxSyncUserAttributes_t;
+
+/* ------------------------------------------------------------------------- */
+/** \brief Create a user defined synchronization object 
+* This is used to track non-OS synchronization working with spinlocks and atomics
+*
+* \param domain - Domain to own the resource
+* \param attribs - A structure to assign multiple attributes to the object.
+*
+* \return A handle that represents the newly created user defined synchronization object.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC nvtxSyncUser_t NVTX_API nvtxDomainSyncUserCreate(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Destroy a user defined synchronization object
+* This is used to track non-OS synchronization working with spinlocks and atomics
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserDestroy(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools that an attempt to acquire a user defined synchronization object
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireStart(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools of failure in acquiring a user defined synchronization object
+* This should be called after \ref nvtxDomainSyncUserAcquireStart
+* 
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireFailed(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools of success in acquiring a user defined synchronization object
+* This should be called after \ref nvtxDomainSyncUserAcquireStart.
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireSuccess(nvtxSyncUser_t handle);
+
+/* ------------------------------------------------------------------------- */
+/** \brief Signal to tools of releasing a reservation on user defined synchronization object
+* This should be called after \ref nvtxDomainSyncUserAcquireSuccess.
+*
+* \param handle - A handle to the object to operate on.
+*
+* \sa
+* ::nvtxDomainSyncUserCreate
+* ::nvtxDomainSyncUserDestroy
+* ::nvtxDomainSyncUserAcquireStart
+* ::nvtxDomainSyncUserAcquireFailed
+* ::nvtxDomainSyncUserAcquireSuccess
+* ::nvtxDomainSyncUserReleasing
+*
+* \version \NVTX_VERSION_2
+*/
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserReleasing(nvtxSyncUser_t handle);
+
+
+/** @} */ /*END defgroup*/
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#ifndef NVTX_NO_IMPL
+#define NVTX_IMPL_GUARD_SYNC /* Ensure other headers cannot included directly */
+#include "nvtxDetail/nvtxImplSync_v3.h"
+#undef NVTX_IMPL_GUARD_SYNC
+#endif /*NVTX_NO_IMPL*/
+
+#endif /* NVTOOLSEXT_SYNC_V3 */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..bdc6bd4c72d74ef0f1fe5c2c8b4c49b196927fa7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h
@@ -0,0 +1,469 @@
+/* This file was procedurally generated!  Do not modify this file by hand.  */
+
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTX_IMPL_GUARD
+#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+/* ---- Include required platform headers ---- */
+
+#if defined(_WIN32) 
+
+#include <Windows.h>
+
+#else
+#include <unistd.h>
+
+#if defined(__ANDROID__)
+#include <android/api-level.h> 
+#endif
+
+#if defined(__linux__) || defined(__CYGWIN__)
+#include <sched.h>
+#endif
+
+#include <limits.h>
+#include <dlfcn.h>
+#include <fcntl.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include <sys/types.h>
+#include <unistd.h>
+#include <errno.h>
+
+#include <string.h>
+#include <sys/types.h>
+#include <pthread.h>
+#include <stdlib.h>
+#include <wchar.h>
+
+#endif
+
+/* ---- Define macros used in this file ---- */
+
+#define NVTX_INIT_STATE_FRESH 0
+#define NVTX_INIT_STATE_STARTED 1
+#define NVTX_INIT_STATE_COMPLETE 2
+
+#ifdef NVTX_DEBUG_PRINT
+#ifdef __ANDROID__
+#include <android/log.h>
+#define NVTX_ERR(...) __android_log_print(ANDROID_LOG_ERROR, "NVTOOLSEXT", __VA_ARGS__);
+#define NVTX_INFO(...) __android_log_print(ANDROID_LOG_INFO, "NVTOOLSEXT", __VA_ARGS__);
+#else
+#include <stdio.h>
+#define NVTX_ERR(...) fprintf(stderr, "NVTX_ERROR: " __VA_ARGS__)
+#define NVTX_INFO(...) fprintf(stderr, "NVTX_INFO: " __VA_ARGS__)
+#endif
+#else /* !defined(NVTX_DEBUG_PRINT) */
+#define NVTX_ERR(...)
+#define NVTX_INFO(...)
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+#ifdef __GNUC__
+#pragma GCC visibility push(hidden)
+#endif
+
+/* ---- Forward declare all functions referenced in globals ---- */
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(void);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiGetModuleFunctionTable)(
+    NvtxCallbackModule module,
+    NvtxFunctionTable* out_table,
+    unsigned int* out_size);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiSetInjectionNvtxVersion)(
+    uint32_t version);
+NVTX_LINKONCE_FWDDECL_FUNCTION const void* NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxGetExportTable)(
+    uint32_t exportTableId);
+
+#include "nvtxInitDecls.h"
+
+/* ---- Define all globals ---- */
+
+typedef struct nvtxGlobals_t
+{
+    volatile unsigned int initState;
+    NvtxExportTableCallbacks etblCallbacks;
+    NvtxExportTableVersionInfo etblVersionInfo;
+
+    /* Implementation function pointers */
+    nvtxMarkEx_impl_fntype nvtxMarkEx_impl_fnptr;
+    nvtxMarkA_impl_fntype nvtxMarkA_impl_fnptr;
+    nvtxMarkW_impl_fntype nvtxMarkW_impl_fnptr;
+    nvtxRangeStartEx_impl_fntype nvtxRangeStartEx_impl_fnptr;
+    nvtxRangeStartA_impl_fntype nvtxRangeStartA_impl_fnptr;
+    nvtxRangeStartW_impl_fntype nvtxRangeStartW_impl_fnptr;
+    nvtxRangeEnd_impl_fntype nvtxRangeEnd_impl_fnptr;
+    nvtxRangePushEx_impl_fntype nvtxRangePushEx_impl_fnptr;
+    nvtxRangePushA_impl_fntype nvtxRangePushA_impl_fnptr;
+    nvtxRangePushW_impl_fntype nvtxRangePushW_impl_fnptr;
+    nvtxRangePop_impl_fntype nvtxRangePop_impl_fnptr;
+    nvtxNameCategoryA_impl_fntype nvtxNameCategoryA_impl_fnptr;
+    nvtxNameCategoryW_impl_fntype nvtxNameCategoryW_impl_fnptr;
+    nvtxNameOsThreadA_impl_fntype nvtxNameOsThreadA_impl_fnptr;
+    nvtxNameOsThreadW_impl_fntype nvtxNameOsThreadW_impl_fnptr;
+
+    nvtxNameCuDeviceA_fakeimpl_fntype nvtxNameCuDeviceA_impl_fnptr;
+    nvtxNameCuDeviceW_fakeimpl_fntype nvtxNameCuDeviceW_impl_fnptr;
+    nvtxNameCuContextA_fakeimpl_fntype nvtxNameCuContextA_impl_fnptr;
+    nvtxNameCuContextW_fakeimpl_fntype nvtxNameCuContextW_impl_fnptr;
+    nvtxNameCuStreamA_fakeimpl_fntype nvtxNameCuStreamA_impl_fnptr;
+    nvtxNameCuStreamW_fakeimpl_fntype nvtxNameCuStreamW_impl_fnptr;
+    nvtxNameCuEventA_fakeimpl_fntype nvtxNameCuEventA_impl_fnptr;
+    nvtxNameCuEventW_fakeimpl_fntype nvtxNameCuEventW_impl_fnptr;
+
+    nvtxNameClDeviceA_fakeimpl_fntype nvtxNameClDeviceA_impl_fnptr;
+    nvtxNameClDeviceW_fakeimpl_fntype nvtxNameClDeviceW_impl_fnptr;
+    nvtxNameClContextA_fakeimpl_fntype nvtxNameClContextA_impl_fnptr;
+    nvtxNameClContextW_fakeimpl_fntype nvtxNameClContextW_impl_fnptr;
+    nvtxNameClCommandQueueA_fakeimpl_fntype nvtxNameClCommandQueueA_impl_fnptr;
+    nvtxNameClCommandQueueW_fakeimpl_fntype nvtxNameClCommandQueueW_impl_fnptr;
+    nvtxNameClMemObjectA_fakeimpl_fntype nvtxNameClMemObjectA_impl_fnptr;
+    nvtxNameClMemObjectW_fakeimpl_fntype nvtxNameClMemObjectW_impl_fnptr;
+    nvtxNameClSamplerA_fakeimpl_fntype nvtxNameClSamplerA_impl_fnptr;
+    nvtxNameClSamplerW_fakeimpl_fntype nvtxNameClSamplerW_impl_fnptr;
+    nvtxNameClProgramA_fakeimpl_fntype nvtxNameClProgramA_impl_fnptr;
+    nvtxNameClProgramW_fakeimpl_fntype nvtxNameClProgramW_impl_fnptr;
+    nvtxNameClEventA_fakeimpl_fntype nvtxNameClEventA_impl_fnptr;
+    nvtxNameClEventW_fakeimpl_fntype nvtxNameClEventW_impl_fnptr;
+
+    nvtxNameCudaDeviceA_impl_fntype nvtxNameCudaDeviceA_impl_fnptr;
+    nvtxNameCudaDeviceW_impl_fntype nvtxNameCudaDeviceW_impl_fnptr;
+    nvtxNameCudaStreamA_fakeimpl_fntype nvtxNameCudaStreamA_impl_fnptr;
+    nvtxNameCudaStreamW_fakeimpl_fntype nvtxNameCudaStreamW_impl_fnptr;
+    nvtxNameCudaEventA_fakeimpl_fntype nvtxNameCudaEventA_impl_fnptr;
+    nvtxNameCudaEventW_fakeimpl_fntype nvtxNameCudaEventW_impl_fnptr;
+
+    nvtxDomainMarkEx_impl_fntype nvtxDomainMarkEx_impl_fnptr;
+    nvtxDomainRangeStartEx_impl_fntype nvtxDomainRangeStartEx_impl_fnptr;
+    nvtxDomainRangeEnd_impl_fntype nvtxDomainRangeEnd_impl_fnptr;
+    nvtxDomainRangePushEx_impl_fntype nvtxDomainRangePushEx_impl_fnptr;
+    nvtxDomainRangePop_impl_fntype nvtxDomainRangePop_impl_fnptr;
+    nvtxDomainResourceCreate_impl_fntype nvtxDomainResourceCreate_impl_fnptr;
+    nvtxDomainResourceDestroy_impl_fntype nvtxDomainResourceDestroy_impl_fnptr;
+    nvtxDomainNameCategoryA_impl_fntype nvtxDomainNameCategoryA_impl_fnptr;
+    nvtxDomainNameCategoryW_impl_fntype nvtxDomainNameCategoryW_impl_fnptr;
+    nvtxDomainRegisterStringA_impl_fntype nvtxDomainRegisterStringA_impl_fnptr;
+    nvtxDomainRegisterStringW_impl_fntype nvtxDomainRegisterStringW_impl_fnptr;
+    nvtxDomainCreateA_impl_fntype nvtxDomainCreateA_impl_fnptr;
+    nvtxDomainCreateW_impl_fntype nvtxDomainCreateW_impl_fnptr;
+    nvtxDomainDestroy_impl_fntype nvtxDomainDestroy_impl_fnptr;
+    nvtxInitialize_impl_fntype nvtxInitialize_impl_fnptr;
+
+    nvtxDomainSyncUserCreate_impl_fntype nvtxDomainSyncUserCreate_impl_fnptr;
+    nvtxDomainSyncUserDestroy_impl_fntype nvtxDomainSyncUserDestroy_impl_fnptr;
+    nvtxDomainSyncUserAcquireStart_impl_fntype nvtxDomainSyncUserAcquireStart_impl_fnptr;
+    nvtxDomainSyncUserAcquireFailed_impl_fntype nvtxDomainSyncUserAcquireFailed_impl_fnptr;
+    nvtxDomainSyncUserAcquireSuccess_impl_fntype nvtxDomainSyncUserAcquireSuccess_impl_fnptr;
+    nvtxDomainSyncUserReleasing_impl_fntype nvtxDomainSyncUserReleasing_impl_fnptr;
+
+    /* Tables of function pointers -- Extra null added to the end to ensure
+    *  a crash instead of silent corruption if a tool reads off the end. */
+    NvtxFunctionPointer* functionTable_CORE  [NVTX_CBID_CORE_SIZE   + 1];
+    NvtxFunctionPointer* functionTable_CUDA  [NVTX_CBID_CUDA_SIZE   + 1];
+    NvtxFunctionPointer* functionTable_OPENCL[NVTX_CBID_OPENCL_SIZE + 1];
+    NvtxFunctionPointer* functionTable_CUDART[NVTX_CBID_CUDART_SIZE + 1];
+    NvtxFunctionPointer* functionTable_CORE2 [NVTX_CBID_CORE2_SIZE  + 1];
+    NvtxFunctionPointer* functionTable_SYNC  [NVTX_CBID_SYNC_SIZE   + 1];
+} nvtxGlobals_t;
+
+NVTX_LINKONCE_DEFINE_GLOBAL nvtxGlobals_t NVTX_VERSIONED_IDENTIFIER(nvtxGlobals) =
+{
+    NVTX_INIT_STATE_FRESH,
+
+    {
+        sizeof(NvtxExportTableCallbacks),
+        NVTX_VERSIONED_IDENTIFIER(nvtxEtiGetModuleFunctionTable)
+    },
+    {
+        sizeof(NvtxExportTableVersionInfo),
+        NVTX_VERSION,
+        0,
+        NVTX_VERSIONED_IDENTIFIER(nvtxEtiSetInjectionNvtxVersion)
+    },
+
+    /* Implementation function pointers */
+    NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init),
+
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init),
+
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init),
+
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init),
+
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init),
+
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init),
+    NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init),
+
+    /* Tables of function pointers */
+    {
+        0,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr,
+        0
+    },
+    {
+        0,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr,
+        0
+    },
+    {
+        0,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr,
+        0
+    },
+    {
+        0,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr,
+        0
+    },
+    {
+        0,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr,
+        0
+    },
+    {
+        0,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr,
+        (NvtxFunctionPointer*)&NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr,
+        0
+    }
+};
+
+/* ---- Define static inline implementations of core API functions ---- */
+
+#include "nvtxImplCore.h"
+
+/* ---- Define implementations of export table functions ---- */
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiGetModuleFunctionTable)(
+    NvtxCallbackModule module,
+    NvtxFunctionTable* out_table,
+    unsigned int* out_size)
+{
+    unsigned int bytes = 0;
+    NvtxFunctionTable table = (NvtxFunctionTable)0;
+
+    switch (module)
+    {
+    case NVTX_CB_MODULE_CORE:
+        table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE;
+        bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE);
+        break;
+    case NVTX_CB_MODULE_CUDA:
+        table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDA;
+        bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDA);
+        break;
+    case NVTX_CB_MODULE_OPENCL:
+        table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_OPENCL;
+        bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_OPENCL);
+        break;
+    case NVTX_CB_MODULE_CUDART:
+        table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDART;
+        bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CUDART);
+        break;
+    case NVTX_CB_MODULE_CORE2:
+        table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE2;
+        bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_CORE2);
+        break;
+    case NVTX_CB_MODULE_SYNC:
+        table = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_SYNC;
+        bytes = (unsigned int)sizeof(NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).functionTable_SYNC);
+        break;
+    default: return 0;
+    }
+
+    if (out_size)
+        *out_size = (bytes / (unsigned int)sizeof(NvtxFunctionPointer*)) - 1;
+
+    if (out_table)
+        *out_table = table;
+
+    return 1;
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION const void* NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxGetExportTable)(uint32_t exportTableId)
+{
+    switch (exportTableId)
+    {
+    case NVTX_ETID_CALLBACKS:       return &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).etblCallbacks;
+    case NVTX_ETID_VERSIONINFO:     return &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).etblVersionInfo;
+    default:                        return 0;
+    }
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxEtiSetInjectionNvtxVersion)(uint32_t version)
+{
+    /* Reserved for custom implementations to resolve problems with tools */
+    (void)version;
+}
+
+/* ---- Define implementations of init versions of all API functions ---- */
+
+#include "nvtxInitDefs.h"
+
+/* ---- Define implementations of initialization functions ---- */
+
+#include "nvtxInit.h"
+
+#ifdef __GNUC__
+#pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif /* __cplusplus */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h
new file mode 100644
index 0000000000000000000000000000000000000000..aee1014ecd53f8b980442109e51fdbc7672ff6d0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h
@@ -0,0 +1,299 @@
+NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib)
+{
+#ifndef NVTX_DISABLE
+    nvtxMarkEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr;
+    if(local!=0)
+        (*local)(eventAttrib);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxMarkA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr;
+    if(local!=0)
+        (*local)(message);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxMarkW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr;
+    if(local!=0)
+        (*local)(message);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangeStartEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr;
+    if(local!=0)
+        return (*local)(eventAttrib);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxRangeId_t)0;
+}
+
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangeStartA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr;
+    if(local!=0)
+        return (*local)(message);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxRangeId_t)0;
+}
+
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangeStartW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr;
+    if(local!=0)
+        return (*local)(message);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxRangeId_t)0;
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangeEnd_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr;
+    if(local!=0)
+        (*local)(id);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangePushEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr;
+    if(local!=0)
+        return (*local)(eventAttrib);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (int)NVTX_NO_PUSH_POP_TRACKING;
+}
+
+NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangePushA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr;
+    if(local!=0)
+        return (*local)(message);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (int)NVTX_NO_PUSH_POP_TRACKING;
+}
+
+NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangePushW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr;
+    if(local!=0)
+        return (*local)(message);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (int)NVTX_NO_PUSH_POP_TRACKING;
+}
+
+NVTX_DECLSPEC int NVTX_API nvtxRangePop(void)
+{
+#ifndef NVTX_DISABLE
+    nvtxRangePop_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr;
+    if(local!=0)
+        return (*local)();
+    else
+#endif  /*NVTX_DISABLE*/
+        return (int)NVTX_NO_PUSH_POP_TRACKING;
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCategoryA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr;
+    if(local!=0)
+        (*local)(category, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCategoryW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr;
+    if(local!=0)
+        (*local)(category, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameOsThreadA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr;
+    if(local!=0)
+        (*local)(threadId, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameOsThreadW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr;
+    if(local!=0)
+        (*local)(threadId, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainMarkEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr;
+    if(local!=0)
+        (*local)(domain, eventAttrib);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainRangeStartEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain, eventAttrib);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxRangeId_t)0;
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainRangeEnd_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr;
+    if(local!=0)
+        (*local)(domain, id);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainRangePushEx_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain, eventAttrib);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (int)NVTX_NO_PUSH_POP_TRACKING;
+}
+
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainRangePop_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (int)NVTX_NO_PUSH_POP_TRACKING;
+}
+
+NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainResourceCreate_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain, attribs);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxResourceHandle_t)0;
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainResourceDestroy_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr;
+    if(local!=0)
+        (*local)(resource);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainNameCategoryA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr;
+    if(local!=0)
+        (*local)(domain, category, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainNameCategoryW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr;
+    if(local!=0)
+        (*local)(domain, category, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainRegisterStringA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain, string);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxStringHandle_t)0;
+}
+
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainRegisterStringW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain, string);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxStringHandle_t)0;
+}
+
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainCreateA_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr;
+    if(local!=0)
+        return (*local)(message);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxDomainHandle_t)0;
+}
+
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* message)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainCreateW_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr;
+    if(local!=0)
+        return (*local)(message);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxDomainHandle_t)0;
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainDestroy_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr;
+    if(local!=0)
+        (*local)(domain);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxInitialize(const void* reserved)
+{
+#ifndef NVTX_DISABLE
+    nvtxInitialize_impl_fntype local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr;
+    if(local!=0)
+        (*local)(reserved);
+#endif /*NVTX_DISABLE*/
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h
new file mode 100644
index 0000000000000000000000000000000000000000..24be8bd4722506d4ca318b07519884fc407847cd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h
@@ -0,0 +1,112 @@
+/* This file was procedurally generated!  Do not modify this file by hand.  */
+
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTX_IMPL_GUARD_CUDART
+#error Never include this file directly -- it is automatically included by nvToolsExtCudaRt.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+//typedef void (NVTX_API * nvtxNameCudaDeviceA_impl_fntype)(int device, const char* name);
+//typedef void (NVTX_API * nvtxNameCudaDeviceW_impl_fntype)(int device, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCudaStreamA_impl_fntype)(cudaStream_t stream, const char* name);
+typedef void (NVTX_API * nvtxNameCudaStreamW_impl_fntype)(cudaStream_t stream, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCudaEventA_impl_fntype)(cudaEvent_t event, const char* name);
+typedef void (NVTX_API * nvtxNameCudaEventW_impl_fntype)(cudaEvent_t event, const wchar_t* name);
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceA(int device, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCudaDeviceA_impl_fntype local = (nvtxNameCudaDeviceA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr;
+    if(local!=0)
+        (*local)(device, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaDeviceW(int device, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCudaDeviceW_impl_fntype local = (nvtxNameCudaDeviceW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr;
+    if(local!=0)
+        (*local)(device, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamA(cudaStream_t stream, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCudaStreamA_impl_fntype local = (nvtxNameCudaStreamA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr;
+    if(local!=0)
+        (*local)(stream, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaStreamW(cudaStream_t stream, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCudaStreamW_impl_fntype local = (nvtxNameCudaStreamW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr;
+    if(local!=0)
+        (*local)(stream, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventA(cudaEvent_t event, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCudaEventA_impl_fntype local = (nvtxNameCudaEventA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr;
+    if(local!=0)
+        (*local)(event, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCudaEventW(cudaEvent_t event, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCudaEventW_impl_fntype local = (nvtxNameCudaEventW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr;
+    if(local!=0)
+        (*local)(event, name);
+#endif /*NVTX_DISABLE*/
+}
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif /* __cplusplus */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee30111bd123b2c1706f23e73e3803b8c1a02927
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h
@@ -0,0 +1,133 @@
+/* This file was procedurally generated!  Do not modify this file by hand.  */
+
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTX_IMPL_GUARD_CUDA
+#error Never include this file directly -- it is automatically included by nvToolsExtCuda.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+typedef void (NVTX_API * nvtxNameCuDeviceA_impl_fntype)(CUdevice device, const char* name);
+typedef void (NVTX_API * nvtxNameCuDeviceW_impl_fntype)(CUdevice device, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCuContextA_impl_fntype)(CUcontext context, const char* name);
+typedef void (NVTX_API * nvtxNameCuContextW_impl_fntype)(CUcontext context, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCuStreamA_impl_fntype)(CUstream stream, const char* name);
+typedef void (NVTX_API * nvtxNameCuStreamW_impl_fntype)(CUstream stream, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCuEventA_impl_fntype)(CUevent event, const char* name);
+typedef void (NVTX_API * nvtxNameCuEventW_impl_fntype)(CUevent event, const wchar_t* name);
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuDeviceA_impl_fntype local = (nvtxNameCuDeviceA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr;
+    if(local!=0)
+        (*local)(device, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuDeviceW_impl_fntype local = (nvtxNameCuDeviceW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr;
+    if(local!=0)
+        (*local)(device, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuContextA_impl_fntype local = (nvtxNameCuContextA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr;
+    if(local!=0)
+        (*local)(context, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuContextW_impl_fntype local = (nvtxNameCuContextW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr;
+    if(local!=0)
+        (*local)(context, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuStreamA_impl_fntype local = (nvtxNameCuStreamA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr;
+    if(local!=0)
+        (*local)(stream, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuStreamW_impl_fntype local = (nvtxNameCuStreamW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr;
+    if(local!=0)
+        (*local)(stream, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuEventA_impl_fntype local = (nvtxNameCuEventA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr;
+    if(local!=0)
+        (*local)(event, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameCuEventW_impl_fntype local = (nvtxNameCuEventW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr;
+    if(local!=0)
+        (*local)(event, name);
+#endif /*NVTX_DISABLE*/
+}
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif /* __cplusplus */
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h
new file mode 100644
index 0000000000000000000000000000000000000000..0e73224cce26b49c8f7585be3a41dd0f428fe07e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h
@@ -0,0 +1,192 @@
+/* This file was procedurally generated!  Do not modify this file by hand.  */
+
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTX_IMPL_GUARD_OPENCL
+#error Never include this file directly -- it is automatically included by nvToolsExtCuda.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+typedef void (NVTX_API * nvtxNameClDeviceA_impl_fntype)(cl_device_id device, const char* name);
+typedef void (NVTX_API * nvtxNameClDeviceW_impl_fntype)(cl_device_id device, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClContextA_impl_fntype)(cl_context context, const char* name);
+typedef void (NVTX_API * nvtxNameClContextW_impl_fntype)(cl_context context, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClCommandQueueA_impl_fntype)(cl_command_queue command_queue, const char* name);
+typedef void (NVTX_API * nvtxNameClCommandQueueW_impl_fntype)(cl_command_queue command_queue, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClMemObjectA_impl_fntype)(cl_mem memobj, const char* name);
+typedef void (NVTX_API * nvtxNameClMemObjectW_impl_fntype)(cl_mem memobj, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClSamplerA_impl_fntype)(cl_sampler sampler, const char* name);
+typedef void (NVTX_API * nvtxNameClSamplerW_impl_fntype)(cl_sampler sampler, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClProgramA_impl_fntype)(cl_program program, const char* name);
+typedef void (NVTX_API * nvtxNameClProgramW_impl_fntype)(cl_program program, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClEventA_impl_fntype)(cl_event evnt, const char* name);
+typedef void (NVTX_API * nvtxNameClEventW_impl_fntype)(cl_event evnt, const wchar_t* name);
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceA(cl_device_id device, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClDeviceA_impl_fntype local = (nvtxNameClDeviceA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr;
+    if(local!=0)
+        (*local)(device, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClDeviceW(cl_device_id device, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClDeviceW_impl_fntype local = (nvtxNameClDeviceW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr;
+    if(local!=0)
+        (*local)(device, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClContextA(cl_context context, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClContextA_impl_fntype local = (nvtxNameClContextA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr;
+    if(local!=0)
+        (*local)(context, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClContextW(cl_context context, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClContextW_impl_fntype local = (nvtxNameClContextW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr;
+    if(local!=0)
+        (*local)(context, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueA(cl_command_queue command_queue, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClCommandQueueA_impl_fntype local = (nvtxNameClCommandQueueA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr;
+    if(local!=0)
+        (*local)(command_queue, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClCommandQueueW(cl_command_queue command_queue, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClCommandQueueW_impl_fntype local = (nvtxNameClCommandQueueW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr;
+    if(local!=0)
+        (*local)(command_queue, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectA(cl_mem memobj, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClMemObjectA_impl_fntype local = (nvtxNameClMemObjectA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr;
+    if(local!=0)
+        (*local)(memobj, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClMemObjectW(cl_mem memobj, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClMemObjectW_impl_fntype local = (nvtxNameClMemObjectW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr;
+    if(local!=0)
+        (*local)(memobj, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerA(cl_sampler sampler, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClSamplerA_impl_fntype local = (nvtxNameClSamplerA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr;
+    if(local!=0)
+        (*local)(sampler, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClSamplerW(cl_sampler sampler, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClSamplerW_impl_fntype local = (nvtxNameClSamplerW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr;
+    if(local!=0)
+        (*local)(sampler, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClProgramA(cl_program program, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClProgramA_impl_fntype local = (nvtxNameClProgramA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr;
+    if(local!=0)
+        (*local)(program, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClProgramW(cl_program program, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClProgramW_impl_fntype local = (nvtxNameClProgramW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr;
+    if(local!=0)
+        (*local)(program, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClEventA(cl_event evnt, const char* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClEventA_impl_fntype local = (nvtxNameClEventA_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr;
+    if(local!=0)
+        (*local)(evnt, name);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxNameClEventW(cl_event evnt, const wchar_t* name)
+{
+#ifndef NVTX_DISABLE
+    nvtxNameClEventW_impl_fntype local = (nvtxNameClEventW_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr;
+    if(local!=0)
+        (*local)(evnt, name);
+#endif /*NVTX_DISABLE*/
+}
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif /* __cplusplus */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h
new file mode 100644
index 0000000000000000000000000000000000000000..accc621a3d5071438f6ed7b9e2192ea8ad56977c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h
@@ -0,0 +1,114 @@
+/* This file was procedurally generated!  Do not modify this file by hand.  */
+
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTX_IMPL_GUARD_SYNC
+#error Never include this file directly -- it is automatically included by nvToolsExtCuda.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+typedef nvtxSyncUser_t (NVTX_API * nvtxDomainSyncUserCreate_impl_fntype)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs);
+typedef void (NVTX_API * nvtxDomainSyncUserDestroy_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserAcquireStart_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserAcquireFailed_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserAcquireSuccess_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserReleasing_impl_fntype)(nvtxSyncUser_t handle);
+
+NVTX_DECLSPEC nvtxSyncUser_t NVTX_API nvtxDomainSyncUserCreate(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainSyncUserCreate_impl_fntype local = (nvtxDomainSyncUserCreate_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr;
+    if(local!=0)
+        return (*local)(domain, attribs);
+    else
+#endif  /*NVTX_DISABLE*/
+        return (nvtxSyncUser_t)0;
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserDestroy(nvtxSyncUser_t handle)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainSyncUserDestroy_impl_fntype local = (nvtxDomainSyncUserDestroy_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr;
+    if(local!=0)
+        (*local)(handle);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireStart(nvtxSyncUser_t handle)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainSyncUserAcquireStart_impl_fntype local = (nvtxDomainSyncUserAcquireStart_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr;
+    if(local!=0)
+        (*local)(handle);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireFailed(nvtxSyncUser_t handle)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainSyncUserAcquireFailed_impl_fntype local = (nvtxDomainSyncUserAcquireFailed_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr;
+    if(local!=0)
+        (*local)(handle);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserAcquireSuccess(nvtxSyncUser_t handle)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainSyncUserAcquireSuccess_impl_fntype local = (nvtxDomainSyncUserAcquireSuccess_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr;
+    if(local!=0)
+        (*local)(handle);
+#endif /*NVTX_DISABLE*/
+}
+
+NVTX_DECLSPEC void NVTX_API nvtxDomainSyncUserReleasing(nvtxSyncUser_t handle)
+{
+#ifndef NVTX_DISABLE
+    nvtxDomainSyncUserReleasing_impl_fntype local = (nvtxDomainSyncUserReleasing_impl_fntype)NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr;
+    if(local!=0)
+        (*local)(handle);
+#endif /*NVTX_DISABLE*/
+}
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif /* __cplusplus */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2bbd3a120da351f1ad1d81dde6bfb46244559cc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h
@@ -0,0 +1,343 @@
+/* This file was procedurally generated!  Do not modify this file by hand.  */
+
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTX_IMPL_GUARD
+#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+/* ---- Platform-independent helper definitions and functions ---- */
+
+/* Prefer macros over inline functions to reduce symbol resolution at link time */
+
+#if defined(_WIN32) 
+#define NVTX_PATHCHAR   wchar_t
+#define NVTX_STR(x)     L##x
+#define NVTX_GETENV     _wgetenv
+#define NVTX_BUFSIZE    MAX_PATH
+#define NVTX_DLLHANDLE  HMODULE
+#define NVTX_DLLOPEN(x) LoadLibraryW(x)
+#define NVTX_DLLFUNC    GetProcAddress
+#define NVTX_DLLCLOSE   FreeLibrary
+#define NVTX_YIELD()    SwitchToThread()
+#define NVTX_MEMBAR()   MemoryBarrier()
+#define NVTX_ATOMIC_WRITE_32(address, value)                        InterlockedExchange((volatile LONG*)address, value)
+#define NVTX_ATOMIC_CAS_32(old, address, exchange, comparand) old = InterlockedCompareExchange((volatile LONG*)address, exchange, comparand)
+#elif defined(__GNUC__)
+#define NVTX_PATHCHAR   char
+#define NVTX_STR(x)     x
+#define NVTX_GETENV     getenv
+#define NVTX_BUFSIZE    PATH_MAX
+#define NVTX_DLLHANDLE  void*
+#define NVTX_DLLOPEN(x) dlopen(x, RTLD_LAZY)
+#define NVTX_DLLFUNC    dlsym
+#define NVTX_DLLCLOSE   dlclose
+#define NVTX_YIELD()    sched_yield()
+#define NVTX_MEMBAR()   __sync_synchronize()
+/* Ensure full memory barrier for atomics, to match Windows functions */
+#define NVTX_ATOMIC_WRITE_32(address, value)                  __sync_synchronize();       __sync_lock_test_and_set(address, value)
+#define NVTX_ATOMIC_CAS_32(old, address, exchange, comparand) __sync_synchronize(); old = __sync_val_compare_and_swap(address, exchange, comparand)
+#else
+#error The library does not support your configuration!
+#endif
+
+/* Define this to 1 for platforms that where pre-injected libraries can be discovered. */
+#if defined(_WIN32)
+/* TODO */
+#define NVTX_SUPPORT_ALREADY_INJECTED_LIBRARY 0
+#else
+#define NVTX_SUPPORT_ALREADY_INJECTED_LIBRARY 0
+#endif
+
+/* Define this to 1 for platforms that support environment variables */
+/* TODO: Detect UWP, a.k.a. Windows Store app, and set this to 0. */
+/* Try:  #if defined(WINAPI_FAMILY_PARTITION) && WINAPI_FAMILY_PARTITION(WINAPI_PARTITION_APP) */
+#define NVTX_SUPPORT_ENV_VARS 1
+
+/* Define this to 1 for platforms that support dynamic/shared libraries */
+#define NVTX_SUPPORT_DYNAMIC_INJECTION_LIBRARY 1
+
+/* Injection libraries implementing InitializeInjectionNvtx2 may be statically linked,
+*  and this will override any dynamic injection.  Useful for platforms where dynamic
+*  injection is not available.  Since weak symbols not explicitly marked extern are
+*  guaranteed to be initialized to zero if no definitions are found by the linker, the
+*  dynamic injection process proceeds normally if pfnInitializeInjectionNvtx2 is 0. */
+#if defined(__GNUC__) && !defined(_WIN32) && !defined(__CYGWIN__)
+#define NVTX_SUPPORT_STATIC_INJECTION_LIBRARY 1
+/* To statically inject an NVTX library, define InitializeInjectionNvtx2_fnptr as a normal
+*  symbol (not weak) pointing to the implementation of InitializeInjectionNvtx2 (which
+*  does not need to be named "InitializeInjectionNvtx2" as is necessary in a dynamic
+*  injection library. */
+__attribute__((weak)) NvtxInitializeInjectionNvtxFunc_t InitializeInjectionNvtx2_fnptr;
+#else
+#define NVTX_SUPPORT_STATIC_INJECTION_LIBRARY 0
+#endif
+
+/* This function tries to find or load an NVTX injection library and get the
+*  address of its InitializeInjection2 function.  If such a function pointer
+*  is found, it is called, and passed the address of this NVTX instance's
+*  nvtxGetExportTable function, so the injection can attach to this instance.
+*  If the initialization fails for any reason, any dynamic library loaded will
+*  be freed, and all NVTX implementation functions will be set to no-ops.  If
+*  initialization succeeds, NVTX functions not attached to the tool will be set
+*  to no-ops.  This is implemented as one function instead of several small
+*  functions to minimize the number of weak symbols the linker must resolve.
+*  Order of search is:
+*  - Pre-injected library exporting InitializeInjectionNvtx2
+*  - Loadable library exporting InitializeInjectionNvtx2
+*      - Path specified by env var NVTX_INJECTION??_PATH (?? is 32 or 64)
+*      - On Android, libNvtxInjection??.so within the package (?? is 32 or 64)
+*  - Statically-linked injection library defining InitializeInjectionNvtx2_fnptr
+*/
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_VERSIONED_IDENTIFIER(nvtxInitializeInjectionLibrary)(void);
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_VERSIONED_IDENTIFIER(nvtxInitializeInjectionLibrary)(void)
+{
+    const char* const initFuncName = "InitializeInjectionNvtx2";
+    NvtxInitializeInjectionNvtxFunc_t init_fnptr = (NvtxInitializeInjectionNvtxFunc_t)0;
+    NVTX_DLLHANDLE injectionLibraryHandle = (NVTX_DLLHANDLE)0;
+    int entryPointStatus = 0;
+
+#if NVTX_SUPPORT_ALREADY_INJECTED_LIBRARY
+    /* Use POSIX global symbol chain to query for init function from any module */
+    init_fnptr = (NvtxInitializeInjectionNvtxFunc_t)NVTX_DLLFUNC(0, initFuncName);
+#endif
+
+#if NVTX_SUPPORT_DYNAMIC_INJECTION_LIBRARY
+    /* Try discovering dynamic injection library to load */
+    if (!init_fnptr)
+    {
+#if NVTX_SUPPORT_ENV_VARS
+        /* If env var NVTX_INJECTION64_PATH is set, it should contain the path
+        *  to a 64-bit dynamic NVTX injection library (and similar for 32-bit). */
+        const NVTX_PATHCHAR* const nvtxEnvVarName = (sizeof(void*) == 4)
+            ? NVTX_STR("NVTX_INJECTION32_PATH")
+            : NVTX_STR("NVTX_INJECTION64_PATH");
+#endif /* NVTX_SUPPORT_ENV_VARS */
+        NVTX_PATHCHAR injectionLibraryPathBuf[NVTX_BUFSIZE];
+        const NVTX_PATHCHAR* injectionLibraryPath = (const NVTX_PATHCHAR*)0;
+
+        /* Refer to this variable explicitly in case all references to it are #if'ed out */
+        (void)injectionLibraryPathBuf;
+
+#if NVTX_SUPPORT_ENV_VARS
+        /* Disable the warning for getenv & _wgetenv -- this usage is safe because
+        *  these functions are not called again before using the returned value. */
+#if defined(_MSC_VER)
+#pragma warning( push )
+#pragma warning( disable : 4996 )
+#endif
+        injectionLibraryPath = NVTX_GETENV(nvtxEnvVarName);
+#if defined(_MSC_VER)
+#pragma warning( pop )
+#endif
+#endif
+
+#if defined(__ANDROID__)
+        if (!injectionLibraryPath)
+        {
+            const char *bits = (sizeof(void*) == 4) ? "32" : "64";
+            char cmdlineBuf[32];
+            char pkgName[PATH_MAX];
+            int count;
+            int pid;
+            FILE *fp;
+            size_t bytesRead;
+            size_t pos;
+
+            pid = (int)getpid();
+            count = snprintf(cmdlineBuf, sizeof(cmdlineBuf), "/proc/%d/cmdline", pid);
+            if (count <= 0 || count >= (int)sizeof(cmdlineBuf))
+            {
+                NVTX_ERR("Path buffer too small for: /proc/%d/cmdline\n", pid);
+                return NVTX_ERR_INIT_ACCESS_LIBRARY;
+            }
+
+            fp = fopen(cmdlineBuf, "r");
+            if (!fp)
+            {
+                NVTX_ERR("File couldn't be opened: %s\n", cmdlineBuf);
+                return NVTX_ERR_INIT_ACCESS_LIBRARY;
+            }
+
+            bytesRead = fread(pkgName, 1, sizeof(pkgName) - 1, fp);
+            fclose(fp);
+            if (bytesRead == 0)
+            {
+                NVTX_ERR("Package name couldn't be read from file: %s\n", cmdlineBuf);
+                return NVTX_ERR_INIT_ACCESS_LIBRARY;
+            }
+
+            pkgName[bytesRead] = 0;
+
+            /* String can contain colon as a process separator. In this case the package name is before the colon. */
+            pos = 0;
+            while (pos < bytesRead && pkgName[pos] != ':' && pkgName[pos] != '\0')
+            {
+                ++pos;
+            }
+            pkgName[pos] = 0;
+
+            count = snprintf(injectionLibraryPathBuf, NVTX_BUFSIZE, "/data/data/%s/files/libNvtxInjection%s.so", pkgName, bits);
+            if (count <= 0 || count >= NVTX_BUFSIZE)
+            {
+                NVTX_ERR("Path buffer too small for: /data/data/%s/files/libNvtxInjection%s.so\n", pkgName, bits);
+                return NVTX_ERR_INIT_ACCESS_LIBRARY;
+            }
+
+            /* On Android, verify path is accessible due to aggressive file access restrictions. */
+            /* For dlopen, if the filename contains a leading slash, then it is interpreted as a */
+            /* relative or absolute pathname; otherwise it will follow the rules in ld.so. */
+            if (injectionLibraryPathBuf[0] == '/')
+            {
+#if (__ANDROID_API__ < 21)
+                int access_err = access(injectionLibraryPathBuf, F_OK | R_OK);
+#else
+                int access_err = faccessat(AT_FDCWD, injectionLibraryPathBuf, F_OK | R_OK, 0);
+#endif
+                if (access_err != 0)
+                {
+                    NVTX_ERR("Injection library path wasn't accessible [code=%s] [path=%s]\n", strerror(errno), injectionLibraryPathBuf);
+                    return NVTX_ERR_INIT_ACCESS_LIBRARY;
+                }
+            }
+            injectionLibraryPath = injectionLibraryPathBuf;
+        }
+#endif
+
+        /* At this point, injectionLibraryPath is specified if a dynamic
+        *  injection library was specified by a tool. */
+        if (injectionLibraryPath)
+        {
+            /* Load the injection library */
+            injectionLibraryHandle = NVTX_DLLOPEN(injectionLibraryPath);
+            if (!injectionLibraryHandle)
+            {
+                NVTX_ERR("Failed to load injection library\n");
+                return NVTX_ERR_INIT_LOAD_LIBRARY;
+            }
+            else
+            {
+                /* Attempt to get the injection library's entry-point */
+                init_fnptr = (NvtxInitializeInjectionNvtxFunc_t)NVTX_DLLFUNC(injectionLibraryHandle, initFuncName);
+                if (!init_fnptr)
+                {
+                    NVTX_DLLCLOSE(injectionLibraryHandle);
+                    NVTX_ERR("Failed to get address of function InitializeInjectionNvtx2 from injection library\n");
+                    return NVTX_ERR_INIT_MISSING_LIBRARY_ENTRY_POINT;
+                }
+            }
+        }
+    }
+#endif
+
+#if NVTX_SUPPORT_STATIC_INJECTION_LIBRARY
+    if (!init_fnptr)
+    {
+        /* Check weakly-defined function pointer.  A statically-linked injection can define this as
+        *  a normal symbol and it will take precedence over a dynamic injection. */
+        if (InitializeInjectionNvtx2_fnptr)
+        {
+            init_fnptr = InitializeInjectionNvtx2_fnptr;
+        }
+    }
+#endif
+
+    /* At this point, if init_fnptr is not set, then no tool has specified
+    *  an NVTX injection library -- return non-success result so all NVTX
+    *  API functions will be set to no-ops. */
+    if (!init_fnptr)
+    {
+        return NVTX_ERR_NO_INJECTION_LIBRARY_AVAILABLE;
+    }
+
+    /* Invoke injection library's initialization function.  If it returns
+    *  0 (failure) and a dynamic injection was loaded, unload it. */
+    entryPointStatus = init_fnptr(NVTX_VERSIONED_IDENTIFIER(nvtxGetExportTable));
+    if (entryPointStatus == 0)
+    {
+        NVTX_ERR("Failed to initialize injection library -- initialization function returned 0\n");
+        if (injectionLibraryHandle)
+        {
+            NVTX_DLLCLOSE(injectionLibraryHandle);
+        }
+        return NVTX_ERR_INIT_FAILED_LIBRARY_ENTRY_POINT;
+    }
+
+    return NVTX_SUCCESS;
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)(void)
+{
+    unsigned int old;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState == NVTX_INIT_STATE_COMPLETE)
+    {
+        return;
+    }
+
+    NVTX_ATOMIC_CAS_32(
+        old,
+        &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState,
+        NVTX_INIT_STATE_STARTED,
+        NVTX_INIT_STATE_FRESH);
+    if (old == NVTX_INIT_STATE_FRESH)
+    {
+        int result;
+        int forceAllToNoops;
+
+        /* Load & initialize injection library -- it will assign the function pointers */
+        result = NVTX_VERSIONED_IDENTIFIER(nvtxInitializeInjectionLibrary)();
+
+        /* Set all pointers not assigned by the injection to null */
+        forceAllToNoops = result != NVTX_SUCCESS; /* Set all to null if injection init failed */
+        NVTX_VERSIONED_IDENTIFIER(nvtxSetInitFunctionsToNoops)(forceAllToNoops);
+
+        /* Signal that initialization has finished, so now the assigned function pointers will be used */
+        NVTX_ATOMIC_WRITE_32(
+            &NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState,
+            NVTX_INIT_STATE_COMPLETE);
+    }
+    else /* Spin-wait until initialization has finished */
+    {
+        NVTX_MEMBAR();
+        while (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).initState != NVTX_INIT_STATE_COMPLETE)
+        {
+            NVTX_YIELD();
+            NVTX_MEMBAR();
+        }
+    }
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h
new file mode 100644
index 0000000000000000000000000000000000000000..757a7296093750e75721fb93855d0aa37da64103
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h
@@ -0,0 +1,73 @@
+#ifndef NVTX_IMPL_GUARD
+#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init)(const nvtxEventAttributes_t* eventAttrib);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init)(const char* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init)(const wchar_t* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init)(const nvtxEventAttributes_t* eventAttrib);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init)(const char* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init)(const wchar_t* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init)(nvtxRangeId_t id);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init)(const nvtxEventAttributes_t* eventAttrib);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init)(const char* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init)(const wchar_t* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init)(void);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init)(uint32_t category, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init)(uint32_t category, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init)(uint32_t threadId, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init)(uint32_t threadId, const wchar_t* name);
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init)(nvtx_CUdevice device, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init)(nvtx_CUdevice device, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init)(nvtx_CUcontext context, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init)(nvtx_CUcontext context, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init)(nvtx_CUstream stream, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init)(nvtx_CUstream stream, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init)(nvtx_CUevent event, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init)(nvtx_CUevent event, const wchar_t* name);
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init)(nvtx_cl_device_id device, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init)(nvtx_cl_device_id device, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init)(nvtx_cl_context context, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init)(nvtx_cl_context context, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init)(nvtx_cl_command_queue command_queue, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init)(nvtx_cl_command_queue command_queue, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init)(nvtx_cl_mem memobj, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init)(nvtx_cl_mem memobj, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init)(nvtx_cl_sampler sampler, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init)(nvtx_cl_sampler sampler, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init)(nvtx_cl_program program, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init)(nvtx_cl_program program, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init)(nvtx_cl_event evnt, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init)(nvtx_cl_event evnt, const wchar_t* name);
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init)(int device, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init)(int device, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init)(nvtx_cudaStream_t stream, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init)(nvtx_cudaStream_t stream, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init)(nvtx_cudaEvent_t event, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init)(nvtx_cudaEvent_t event, const wchar_t* name);
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init)(nvtxDomainHandle_t domain, nvtxRangeId_t id);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+NVTX_LINKONCE_FWDDECL_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init)(nvtxDomainHandle_t domain);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxResourceHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init)(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init)(nvtxResourceHandle_t resource);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const char* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init)(nvtxDomainHandle_t domain, const char* string);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init)(nvtxDomainHandle_t domain, const wchar_t* string);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init)(const char* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init)(const wchar_t* message);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init)(nvtxDomainHandle_t domain);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init)(const void* reserved);
+
+NVTX_LINKONCE_FWDDECL_FUNCTION nvtxSyncUser_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init)(nvtxSyncUser_t handle);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init)(nvtxSyncUser_t handle);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init)(nvtxSyncUser_t handle);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init)(nvtxSyncUser_t handle);
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init)(nvtxSyncUser_t handle);
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h
new file mode 100644
index 0000000000000000000000000000000000000000..93d5f0e975059b00f8fb32d95ab06483cc53d80e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h
@@ -0,0 +1,565 @@
+#ifndef NVTX_IMPL_GUARD
+#error Never include this file directly -- it is automatically included by nvToolsExt.h (except when NVTX_NO_IMPL is defined).
+#endif
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init)(const nvtxEventAttributes_t* eventAttrib){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxMarkEx(eventAttrib);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init)(const char* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxMarkA(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init)(const wchar_t* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxMarkW(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init)(const nvtxEventAttributes_t* eventAttrib){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangeStartEx(eventAttrib);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init)(const char* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangeStartA(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init)(const wchar_t* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangeStartW(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init)(nvtxRangeId_t id){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxRangeEnd(id);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init)(const nvtxEventAttributes_t* eventAttrib){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangePushEx(eventAttrib);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init)(const char* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangePushA(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init)(const wchar_t* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangePushW(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init)(void){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxRangePop();
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init)(uint32_t category, const char* name){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxNameCategoryA(category, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init)(uint32_t category, const wchar_t* name){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxNameCategoryW(category, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init)(uint32_t threadId, const char* name){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxNameOsThreadA(threadId, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init)(uint32_t threadId, const wchar_t* name){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxNameOsThreadW(threadId, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxDomainMarkEx(domain, eventAttrib);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxRangeId_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainRangeStartEx(domain, eventAttrib);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init)(nvtxDomainHandle_t domain, nvtxRangeId_t id){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxDomainRangeEnd(domain, id);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainRangePushEx(domain, eventAttrib);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION int NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init)(nvtxDomainHandle_t domain){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainRangePop(domain);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxResourceHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init)(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainResourceCreate(domain, attribs);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init)(nvtxResourceHandle_t resource){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxDomainResourceDestroy(resource);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const char* name){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxDomainNameCategoryA(domain, category, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init)(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxDomainNameCategoryW(domain, category, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init)(nvtxDomainHandle_t domain, const char* string){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainRegisterStringA(domain, string);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxStringHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init)(nvtxDomainHandle_t domain, const wchar_t* string){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainRegisterStringW(domain, string);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init)(const char* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainCreateA(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxDomainHandle_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init)(const wchar_t* message){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    return nvtxDomainCreateW(message);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init)(nvtxDomainHandle_t domain){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxDomainDestroy(domain);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init)(const void* reserved){
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    nvtxInitialize(reserved);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init)(nvtx_CUdevice device, const char* name){
+    nvtxNameCuDeviceA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr;
+    if (local)
+        local(device, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init)(nvtx_CUdevice device, const wchar_t* name){
+    nvtxNameCuDeviceW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr;
+    if (local)
+        local(device, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init)(nvtx_CUcontext context, const char* name){
+    nvtxNameCuContextA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr;
+    if (local)
+        local(context, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init)(nvtx_CUcontext context, const wchar_t* name){
+    nvtxNameCuContextW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr;
+    if (local)
+        local(context, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init)(nvtx_CUstream stream, const char* name){
+    nvtxNameCuStreamA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr;
+    if (local)
+        local(stream, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init)(nvtx_CUstream stream, const wchar_t* name){
+    nvtxNameCuStreamW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr;
+    if (local)
+        local(stream, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init)(nvtx_CUevent event, const char* name){
+    nvtxNameCuEventA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr;
+    if (local)
+        local(event, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init)(nvtx_CUevent event, const wchar_t* name){
+    nvtxNameCuEventW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr;
+    if (local)
+        local(event, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init)(int device, const char* name){
+    nvtxNameCudaDeviceA_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr;
+    if (local)
+        local(device, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init)(int device, const wchar_t* name){
+    nvtxNameCudaDeviceW_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr;
+    if (local)
+        local(device, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init)(nvtx_cudaStream_t stream, const char* name){
+    nvtxNameCudaStreamA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr;
+    if (local)
+        local(stream, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init)(nvtx_cudaStream_t stream, const wchar_t* name){
+    nvtxNameCudaStreamW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr;
+    if (local)
+        local(stream, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init)(nvtx_cudaEvent_t event, const char* name){
+    nvtxNameCudaEventA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr;
+    if (local)
+        local(event, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init)(nvtx_cudaEvent_t event, const wchar_t* name){
+    nvtxNameCudaEventW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr;
+    if (local)
+        local(event, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init)(nvtx_cl_device_id device, const char* name){
+    nvtxNameClDeviceA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr;
+    if (local)
+        local(device, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init)(nvtx_cl_device_id device, const wchar_t* name){
+    nvtxNameClDeviceW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr;
+    if (local)
+        local(device, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init)(nvtx_cl_context context, const char* name){
+    nvtxNameClContextA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr;
+    if (local)
+        local(context, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init)(nvtx_cl_context context, const wchar_t* name){
+    nvtxNameClContextW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr;
+    if (local)
+        local(context, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init)(nvtx_cl_command_queue command_queue, const char* name){
+    nvtxNameClCommandQueueA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr;
+    if (local)
+        local(command_queue, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init)(nvtx_cl_command_queue command_queue, const wchar_t* name){
+    nvtxNameClCommandQueueW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr;
+    if (local)
+        local(command_queue, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init)(nvtx_cl_mem memobj, const char* name){
+    nvtxNameClMemObjectA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr;
+    if (local)
+        local(memobj, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init)(nvtx_cl_mem memobj, const wchar_t* name){
+    nvtxNameClMemObjectW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr;
+    if (local)
+        local(memobj, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init)(nvtx_cl_sampler sampler, const char* name){
+    nvtxNameClSamplerA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr;
+    if (local)
+        local(sampler, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init)(nvtx_cl_sampler sampler, const wchar_t* name){
+    nvtxNameClSamplerW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr;
+    if (local)
+        local(sampler, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init)(nvtx_cl_program program, const char* name){
+    nvtxNameClProgramA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr;
+    if (local)
+        local(program, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init)(nvtx_cl_program program, const wchar_t* name){
+    nvtxNameClProgramW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr;
+    if (local)
+        local(program, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init)(nvtx_cl_event evnt, const char* name){
+    nvtxNameClEventA_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr;
+    if (local)
+        local(evnt, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init)(nvtx_cl_event evnt, const wchar_t* name){
+    nvtxNameClEventW_fakeimpl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr;
+    if (local)
+        local(evnt, name);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION nvtxSyncUser_t NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs){
+    nvtxDomainSyncUserCreate_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr;
+    if (local) {
+        return local(domain, attribs);
+    }
+    return (nvtxSyncUser_t)0;
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init)(nvtxSyncUser_t handle){
+    nvtxDomainSyncUserDestroy_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr;
+    if (local)
+        local(handle);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init)(nvtxSyncUser_t handle){
+    nvtxDomainSyncUserAcquireStart_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr;
+    if (local)
+        local(handle);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init)(nvtxSyncUser_t handle){
+    nvtxDomainSyncUserAcquireFailed_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr;
+    if (local)
+        local(handle);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init)(nvtxSyncUser_t handle){
+    nvtxDomainSyncUserAcquireSuccess_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr;
+    if (local)
+        local(handle);
+}
+
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_API NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init)(nvtxSyncUser_t handle){
+    nvtxDomainSyncUserReleasing_impl_fntype local;
+    NVTX_VERSIONED_IDENTIFIER(nvtxInitOnce)();
+    local = NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr;
+    if (local)
+        local(handle);
+}
+
+NVTX_LINKONCE_FWDDECL_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxSetInitFunctionsToNoops)(int forceAllToNoops);
+NVTX_LINKONCE_DEFINE_FUNCTION void NVTX_VERSIONED_IDENTIFIER(nvtxSetInitFunctionsToNoops)(int forceAllToNoops)
+{
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxMarkEx_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkEx_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxMarkA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxMarkW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxMarkW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartEx_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartEx_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeStartW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeStartW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangeEnd_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangeEnd_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePushEx_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushEx_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePushA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePushW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePushW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxRangePop_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxRangePop_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCategoryW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCategoryW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameOsThreadW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameOsThreadW_impl_fnptr = NULL;
+
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuDeviceW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuDeviceW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuContextW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuContextW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuStreamW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuStreamW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCuEventW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCuEventW_impl_fnptr = NULL;
+
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClDeviceW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClDeviceW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClContextW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClContextW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClCommandQueueW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClCommandQueueW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClMemObjectW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClMemObjectW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClSamplerW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClSamplerW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClProgramW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClProgramW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameClEventW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameClEventW_impl_fnptr = NULL;
+
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaDeviceW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaDeviceW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaStreamW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaStreamW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxNameCudaEventW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxNameCudaEventW_impl_fnptr = NULL;
+
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainMarkEx_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainMarkEx_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeStartEx_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeStartEx_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangeEnd_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangeEnd_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePushEx_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePushEx_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRangePop_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRangePop_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceCreate_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceCreate_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainResourceDestroy_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainResourceDestroy_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainNameCategoryW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainNameCategoryW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainRegisterStringW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainRegisterStringW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateA_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateA_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainCreateW_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainCreateW_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainDestroy_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainDestroy_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxInitialize_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxInitialize_impl_fnptr = NULL;
+
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserCreate_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserCreate_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserDestroy_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserDestroy_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireStart_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireStart_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireFailed_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireFailed_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserAcquireSuccess_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserAcquireSuccess_impl_fnptr = NULL;
+    if (NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr == NVTX_VERSIONED_IDENTIFIER(nvtxDomainSyncUserReleasing_impl_init) || forceAllToNoops)
+        NVTX_VERSIONED_IDENTIFIER(nvtxGlobals).nvtxDomainSyncUserReleasing_impl_fnptr = NULL;
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h
new file mode 100644
index 0000000000000000000000000000000000000000..6c6eaa732464519fc7d93f98b646729dab0db8b9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h
@@ -0,0 +1,75 @@
+#ifndef __NVTX_LINKONCE_H__
+#define __NVTX_LINKONCE_H__
+
+/* This header defines macros to permit making definitions of global variables
+ * and functions in C/C++ header files which may be included multiple times in
+ * a translation unit or linkage unit.  It allows authoring header-only libraries
+ * which can be used by multiple other header-only libraries (either as the same
+ * copy or multiple copies), and does not require any build changes, such as
+ * adding another .c file, linking a static library, or deploying a dynamic
+ * library.  Globals defined with these macros have the property that they have
+ * the same address, pointing to a single instance, for the entire linkage unit.
+ * It is expected but not guaranteed that each linkage unit will have a separate
+ * instance.
+ *
+ * In some situations it is desirable to declare a variable without initializing
+ * it, refer to it in code or other variables' initializers, and then initialize
+ * it later.  Similarly, functions can be prototyped, have their address taken,
+ * and then have their body defined later.  In such cases, use the FWDDECL macros 
+ * when forward-declaring LINKONCE global variables without initializers and
+ * function prototypes, and then use the DEFINE macros when later defining them.
+ * Although in many cases the FWDDECL macro is equivalent to the DEFINE macro,
+ * following this pattern makes code maximally portable.
+ */
+
+#if defined(__MINGW32__) /* MinGW */
+    #define NVTX_LINKONCE_WEAK __attribute__((section(".gnu.linkonce.0.")))
+    #if defined(__cplusplus)
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   __declspec(selectany)
+        #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" inline NVTX_LINKONCE_WEAK
+    #else
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   __declspec(selectany)
+        #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_WEAK
+    #endif
+#elif defined(_MSC_VER) /* MSVC */
+    #if defined(__cplusplus)
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   extern "C" __declspec(selectany)
+        #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" inline
+    #else
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   __declspec(selectany)
+        #define NVTX_LINKONCE_DEFINE_FUNCTION __inline
+    #endif
+#elif defined(__CYGWIN__) && defined(__clang__) /* Clang on Cygwin */
+    #define NVTX_LINKONCE_WEAK __attribute__((section(".gnu.linkonce.0.")))
+    #if defined(__cplusplus)
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   NVTX_LINKONCE_WEAK
+        #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" NVTX_LINKONCE_WEAK
+    #else
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   NVTX_LINKONCE_WEAK
+        #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_WEAK
+    #endif
+#elif defined(__CYGWIN__) /* Assume GCC or compatible */
+    #define NVTX_LINKONCE_WEAK __attribute__((weak))
+    #if defined(__cplusplus)
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   __declspec(selectany)
+        #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" inline
+    #else
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   NVTX_LINKONCE_WEAK
+        #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_WEAK
+    #endif
+#else /* All others: Assume GCC, clang, or compatible */
+    #define NVTX_LINKONCE_WEAK   __attribute__((weak))
+    #define NVTX_LINKONCE_HIDDEN __attribute__((visibility("hidden")))
+    #if defined(__cplusplus)
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   NVTX_LINKONCE_HIDDEN NVTX_LINKONCE_WEAK
+        #define NVTX_LINKONCE_DEFINE_FUNCTION extern "C" NVTX_LINKONCE_HIDDEN inline
+    #else
+        #define NVTX_LINKONCE_DEFINE_GLOBAL   NVTX_LINKONCE_HIDDEN NVTX_LINKONCE_WEAK
+        #define NVTX_LINKONCE_DEFINE_FUNCTION NVTX_LINKONCE_HIDDEN NVTX_LINKONCE_WEAK
+    #endif
+#endif
+
+#define NVTX_LINKONCE_FWDDECL_GLOBAL   NVTX_LINKONCE_DEFINE_GLOBAL   extern
+#define NVTX_LINKONCE_FWDDECL_FUNCTION NVTX_LINKONCE_DEFINE_FUNCTION
+
+#endif /* __NVTX_LINKONCE_H__ */
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ddb1a9b3baff99c78b1c9acc06ac0eb64de8d6d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h
@@ -0,0 +1,333 @@
+/*
+* Copyright 2009-2016  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+* OF USE, DATA OR PROFITS,  WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
+* OR OTHER TORTIOUS ACTION,  ARISING OUT OF OR IN CONNECTION WITH THE USE
+* OR PERFORMANCE OF THIS SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+/* This header defines types which are used by the internal implementation
+*  of NVTX and callback subscribers.  API clients do not use these types,
+*  so they are defined here instead of in nvToolsExt.h to clarify they are
+*  not part of the NVTX client API. */
+
+#ifndef NVTX_IMPL_GUARD
+#error Never include this file directly -- it is automatically included by nvToolsExt.h.
+#endif
+
+/* ------ Dependency-free types binary-compatible with real types ------- */
+
+/* In order to avoid having the NVTX core API headers depend on non-NVTX
+*  headers like cuda.h, NVTX defines binary-compatible types to use for
+*  safely making the initialization versions of all NVTX functions without
+*  needing to have definitions for the real types. */
+
+typedef int   nvtx_CUdevice;
+typedef void* nvtx_CUcontext;
+typedef void* nvtx_CUstream;
+typedef void* nvtx_CUevent;
+
+typedef void* nvtx_cudaStream_t;
+typedef void* nvtx_cudaEvent_t;
+
+typedef void* nvtx_cl_platform_id;
+typedef void* nvtx_cl_device_id;
+typedef void* nvtx_cl_context;
+typedef void* nvtx_cl_command_queue;
+typedef void* nvtx_cl_mem;
+typedef void* nvtx_cl_program;
+typedef void* nvtx_cl_kernel;
+typedef void* nvtx_cl_event;
+typedef void* nvtx_cl_sampler;
+
+typedef struct nvtxSyncUser* nvtxSyncUser_t;
+struct nvtxSyncUserAttributes_v0;
+typedef struct nvtxSyncUserAttributes_v0 nvtxSyncUserAttributes_t;
+
+/* --------- Types for function pointers (with fake API types) ---------- */
+
+typedef void (NVTX_API * nvtxMarkEx_impl_fntype)(const nvtxEventAttributes_t* eventAttrib);
+typedef void (NVTX_API * nvtxMarkA_impl_fntype)(const char* message);
+typedef void (NVTX_API * nvtxMarkW_impl_fntype)(const wchar_t* message);
+typedef nvtxRangeId_t (NVTX_API * nvtxRangeStartEx_impl_fntype)(const nvtxEventAttributes_t* eventAttrib);
+typedef nvtxRangeId_t (NVTX_API * nvtxRangeStartA_impl_fntype)(const char* message);
+typedef nvtxRangeId_t (NVTX_API * nvtxRangeStartW_impl_fntype)(const wchar_t* message);
+typedef void (NVTX_API * nvtxRangeEnd_impl_fntype)(nvtxRangeId_t id);
+typedef int (NVTX_API * nvtxRangePushEx_impl_fntype)(const nvtxEventAttributes_t* eventAttrib);
+typedef int (NVTX_API * nvtxRangePushA_impl_fntype)(const char* message);
+typedef int (NVTX_API * nvtxRangePushW_impl_fntype)(const wchar_t* message);
+typedef int (NVTX_API * nvtxRangePop_impl_fntype)(void);
+typedef void (NVTX_API * nvtxNameCategoryA_impl_fntype)(uint32_t category, const char* name);
+typedef void (NVTX_API * nvtxNameCategoryW_impl_fntype)(uint32_t category, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameOsThreadA_impl_fntype)(uint32_t threadId, const char* name);
+typedef void (NVTX_API * nvtxNameOsThreadW_impl_fntype)(uint32_t threadId, const wchar_t* name);
+
+/* Real impl types are defined in nvtxImplCuda_v3.h, where CUDA headers are included */
+typedef void (NVTX_API * nvtxNameCuDeviceA_fakeimpl_fntype)(nvtx_CUdevice device, const char* name);
+typedef void (NVTX_API * nvtxNameCuDeviceW_fakeimpl_fntype)(nvtx_CUdevice device, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCuContextA_fakeimpl_fntype)(nvtx_CUcontext context, const char* name);
+typedef void (NVTX_API * nvtxNameCuContextW_fakeimpl_fntype)(nvtx_CUcontext context, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCuStreamA_fakeimpl_fntype)(nvtx_CUstream stream, const char* name);
+typedef void (NVTX_API * nvtxNameCuStreamW_fakeimpl_fntype)(nvtx_CUstream stream, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCuEventA_fakeimpl_fntype)(nvtx_CUevent event, const char* name);
+typedef void (NVTX_API * nvtxNameCuEventW_fakeimpl_fntype)(nvtx_CUevent event, const wchar_t* name);
+
+/* Real impl types are defined in nvtxImplOpenCL_v3.h, where OPENCL headers are included */
+typedef void (NVTX_API * nvtxNameClDeviceA_fakeimpl_fntype)(nvtx_cl_device_id device, const char* name);
+typedef void (NVTX_API * nvtxNameClDeviceW_fakeimpl_fntype)(nvtx_cl_device_id device, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClContextA_fakeimpl_fntype)(nvtx_cl_context context, const char* name);
+typedef void (NVTX_API * nvtxNameClContextW_fakeimpl_fntype)(nvtx_cl_context context, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClCommandQueueA_fakeimpl_fntype)(nvtx_cl_command_queue command_queue, const char* name);
+typedef void (NVTX_API * nvtxNameClCommandQueueW_fakeimpl_fntype)(nvtx_cl_command_queue command_queue, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClMemObjectA_fakeimpl_fntype)(nvtx_cl_mem memobj, const char* name);
+typedef void (NVTX_API * nvtxNameClMemObjectW_fakeimpl_fntype)(nvtx_cl_mem memobj, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClSamplerA_fakeimpl_fntype)(nvtx_cl_sampler sampler, const char* name);
+typedef void (NVTX_API * nvtxNameClSamplerW_fakeimpl_fntype)(nvtx_cl_sampler sampler, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClProgramA_fakeimpl_fntype)(nvtx_cl_program program, const char* name);
+typedef void (NVTX_API * nvtxNameClProgramW_fakeimpl_fntype)(nvtx_cl_program program, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameClEventA_fakeimpl_fntype)(nvtx_cl_event evnt, const char* name);
+typedef void (NVTX_API * nvtxNameClEventW_fakeimpl_fntype)(nvtx_cl_event evnt, const wchar_t* name);
+
+/* Real impl types are defined in nvtxImplCudaRt_v3.h, where CUDART headers are included */
+typedef void (NVTX_API * nvtxNameCudaDeviceA_impl_fntype)(int device, const char* name);
+typedef void (NVTX_API * nvtxNameCudaDeviceW_impl_fntype)(int device, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCudaStreamA_fakeimpl_fntype)(nvtx_cudaStream_t stream, const char* name);
+typedef void (NVTX_API * nvtxNameCudaStreamW_fakeimpl_fntype)(nvtx_cudaStream_t stream, const wchar_t* name);
+typedef void (NVTX_API * nvtxNameCudaEventA_fakeimpl_fntype)(nvtx_cudaEvent_t event, const char* name);
+typedef void (NVTX_API * nvtxNameCudaEventW_fakeimpl_fntype)(nvtx_cudaEvent_t event, const wchar_t* name);
+
+typedef void (NVTX_API * nvtxDomainMarkEx_impl_fntype)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+typedef nvtxRangeId_t (NVTX_API * nvtxDomainRangeStartEx_impl_fntype)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+typedef void (NVTX_API * nvtxDomainRangeEnd_impl_fntype)(nvtxDomainHandle_t domain, nvtxRangeId_t id);
+typedef int (NVTX_API * nvtxDomainRangePushEx_impl_fntype)(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+typedef int (NVTX_API * nvtxDomainRangePop_impl_fntype)(nvtxDomainHandle_t domain);
+typedef nvtxResourceHandle_t (NVTX_API * nvtxDomainResourceCreate_impl_fntype)(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs);
+typedef void (NVTX_API * nvtxDomainResourceDestroy_impl_fntype)(nvtxResourceHandle_t resource);
+typedef void (NVTX_API * nvtxDomainNameCategoryA_impl_fntype)(nvtxDomainHandle_t domain, uint32_t category, const char* name);
+typedef void (NVTX_API * nvtxDomainNameCategoryW_impl_fntype)(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name);
+typedef nvtxStringHandle_t (NVTX_API * nvtxDomainRegisterStringA_impl_fntype)(nvtxDomainHandle_t domain, const char* string);
+typedef nvtxStringHandle_t (NVTX_API * nvtxDomainRegisterStringW_impl_fntype)(nvtxDomainHandle_t domain, const wchar_t* string);
+typedef nvtxDomainHandle_t (NVTX_API * nvtxDomainCreateA_impl_fntype)(const char* message);
+typedef nvtxDomainHandle_t (NVTX_API * nvtxDomainCreateW_impl_fntype)(const wchar_t* message);
+typedef void (NVTX_API * nvtxDomainDestroy_impl_fntype)(nvtxDomainHandle_t domain);
+typedef void (NVTX_API * nvtxInitialize_impl_fntype)(const void* reserved);
+
+typedef nvtxSyncUser_t (NVTX_API * nvtxDomainSyncUserCreate_impl_fntype)(nvtxDomainHandle_t domain, const nvtxSyncUserAttributes_t* attribs);
+typedef void (NVTX_API * nvtxDomainSyncUserDestroy_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserAcquireStart_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserAcquireFailed_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserAcquireSuccess_impl_fntype)(nvtxSyncUser_t handle);
+typedef void (NVTX_API * nvtxDomainSyncUserReleasing_impl_fntype)(nvtxSyncUser_t handle);
+
+/* ---------------- Types for callback subscription --------------------- */
+
+typedef const void *(NVTX_API * NvtxGetExportTableFunc_t)(uint32_t exportTableId);
+typedef int (NVTX_API * NvtxInitializeInjectionNvtxFunc_t)(NvtxGetExportTableFunc_t exportTable);
+
+typedef enum NvtxCallbackModule
+{
+    NVTX_CB_MODULE_INVALID                 = 0,
+    NVTX_CB_MODULE_CORE                    = 1,
+    NVTX_CB_MODULE_CUDA                    = 2,
+    NVTX_CB_MODULE_OPENCL                  = 3,
+    NVTX_CB_MODULE_CUDART                  = 4,
+    NVTX_CB_MODULE_CORE2                   = 5,
+    NVTX_CB_MODULE_SYNC                    = 6,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CB_MODULE_SIZE,
+    NVTX_CB_MODULE_FORCE_INT               = 0x7fffffff
+} NvtxCallbackModule;
+
+typedef enum NvtxCallbackIdCore
+{
+    NVTX_CBID_CORE_INVALID                 =  0,
+    NVTX_CBID_CORE_MarkEx                  =  1,
+    NVTX_CBID_CORE_MarkA                   =  2,
+    NVTX_CBID_CORE_MarkW                   =  3,
+    NVTX_CBID_CORE_RangeStartEx            =  4,
+    NVTX_CBID_CORE_RangeStartA             =  5,
+    NVTX_CBID_CORE_RangeStartW             =  6,
+    NVTX_CBID_CORE_RangeEnd                =  7,
+    NVTX_CBID_CORE_RangePushEx             =  8,
+    NVTX_CBID_CORE_RangePushA              =  9,
+    NVTX_CBID_CORE_RangePushW              = 10,
+    NVTX_CBID_CORE_RangePop                = 11,
+    NVTX_CBID_CORE_NameCategoryA           = 12,
+    NVTX_CBID_CORE_NameCategoryW           = 13,
+    NVTX_CBID_CORE_NameOsThreadA           = 14,
+    NVTX_CBID_CORE_NameOsThreadW           = 15,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CBID_CORE_SIZE,
+    NVTX_CBID_CORE_FORCE_INT = 0x7fffffff
+} NvtxCallbackIdCore;
+
+typedef enum NvtxCallbackIdCore2
+{
+    NVTX_CBID_CORE2_INVALID                 = 0,
+    NVTX_CBID_CORE2_DomainMarkEx            = 1,
+    NVTX_CBID_CORE2_DomainRangeStartEx      = 2,
+    NVTX_CBID_CORE2_DomainRangeEnd          = 3,
+    NVTX_CBID_CORE2_DomainRangePushEx       = 4,
+    NVTX_CBID_CORE2_DomainRangePop          = 5,
+    NVTX_CBID_CORE2_DomainResourceCreate    = 6,
+    NVTX_CBID_CORE2_DomainResourceDestroy   = 7,
+    NVTX_CBID_CORE2_DomainNameCategoryA     = 8,
+    NVTX_CBID_CORE2_DomainNameCategoryW     = 9,
+    NVTX_CBID_CORE2_DomainRegisterStringA   = 10,
+    NVTX_CBID_CORE2_DomainRegisterStringW   = 11,
+    NVTX_CBID_CORE2_DomainCreateA           = 12,
+    NVTX_CBID_CORE2_DomainCreateW           = 13,
+    NVTX_CBID_CORE2_DomainDestroy           = 14,
+    NVTX_CBID_CORE2_Initialize              = 15,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CBID_CORE2_SIZE,
+    NVTX_CBID_CORE2_FORCE_INT               = 0x7fffffff
+} NvtxCallbackIdCore2;
+
+typedef enum NvtxCallbackIdCuda
+{
+    NVTX_CBID_CUDA_INVALID                 =  0,
+    NVTX_CBID_CUDA_NameCuDeviceA           =  1,
+    NVTX_CBID_CUDA_NameCuDeviceW           =  2,
+    NVTX_CBID_CUDA_NameCuContextA          =  3,
+    NVTX_CBID_CUDA_NameCuContextW          =  4,
+    NVTX_CBID_CUDA_NameCuStreamA           =  5,
+    NVTX_CBID_CUDA_NameCuStreamW           =  6,
+    NVTX_CBID_CUDA_NameCuEventA            =  7,
+    NVTX_CBID_CUDA_NameCuEventW            =  8,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CBID_CUDA_SIZE,
+    NVTX_CBID_CUDA_FORCE_INT               = 0x7fffffff
+} NvtxCallbackIdCuda;
+
+typedef enum NvtxCallbackIdCudaRt
+{
+    NVTX_CBID_CUDART_INVALID               =  0,
+    NVTX_CBID_CUDART_NameCudaDeviceA       =  1,
+    NVTX_CBID_CUDART_NameCudaDeviceW       =  2,
+    NVTX_CBID_CUDART_NameCudaStreamA       =  3,
+    NVTX_CBID_CUDART_NameCudaStreamW       =  4,
+    NVTX_CBID_CUDART_NameCudaEventA        =  5,
+    NVTX_CBID_CUDART_NameCudaEventW        =  6,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CBID_CUDART_SIZE,
+    NVTX_CBID_CUDART_FORCE_INT             = 0x7fffffff
+} NvtxCallbackIdCudaRt;
+
+typedef enum NvtxCallbackIdOpenCL
+{
+    NVTX_CBID_OPENCL_INVALID               =  0,
+    NVTX_CBID_OPENCL_NameClDeviceA         =  1,
+    NVTX_CBID_OPENCL_NameClDeviceW         =  2,
+    NVTX_CBID_OPENCL_NameClContextA        =  3,
+    NVTX_CBID_OPENCL_NameClContextW        =  4,
+    NVTX_CBID_OPENCL_NameClCommandQueueA   =  5,
+    NVTX_CBID_OPENCL_NameClCommandQueueW   =  6,
+    NVTX_CBID_OPENCL_NameClMemObjectA      =  7,
+    NVTX_CBID_OPENCL_NameClMemObjectW      =  8,
+    NVTX_CBID_OPENCL_NameClSamplerA        =  9,
+    NVTX_CBID_OPENCL_NameClSamplerW        = 10,
+    NVTX_CBID_OPENCL_NameClProgramA        = 11,
+    NVTX_CBID_OPENCL_NameClProgramW        = 12,
+    NVTX_CBID_OPENCL_NameClEventA          = 13,
+    NVTX_CBID_OPENCL_NameClEventW          = 14,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CBID_OPENCL_SIZE,
+    NVTX_CBID_OPENCL_FORCE_INT             = 0x7fffffff
+} NvtxCallbackIdOpenCL;
+
+typedef enum NvtxCallbackIdSync
+{
+    NVTX_CBID_SYNC_INVALID                      = 0,
+    NVTX_CBID_SYNC_DomainSyncUserCreate         = 1,
+    NVTX_CBID_SYNC_DomainSyncUserDestroy        = 2,
+    NVTX_CBID_SYNC_DomainSyncUserAcquireStart   = 3,
+    NVTX_CBID_SYNC_DomainSyncUserAcquireFailed  = 4,
+    NVTX_CBID_SYNC_DomainSyncUserAcquireSuccess = 5,
+    NVTX_CBID_SYNC_DomainSyncUserReleasing      = 6,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_CBID_SYNC_SIZE,
+    NVTX_CBID_SYNC_FORCE_INT                    = 0x7fffffff
+} NvtxCallbackIdSync;
+
+/* IDs for NVTX Export Tables */
+typedef enum NvtxExportTableID
+{
+    NVTX_ETID_INVALID                      = 0,
+    NVTX_ETID_CALLBACKS                    = 1,
+    NVTX_ETID_RESERVED0                    = 2,
+    NVTX_ETID_VERSIONINFO                  = 3,
+    /* --- New constants must only be added directly above this line --- */
+    NVTX_ETID_SIZE,
+    NVTX_ETID_FORCE_INT                    = 0x7fffffff
+} NvtxExportTableID;
+
+typedef void (* NvtxFunctionPointer)(void); /* generic uncallable function pointer, must be casted to appropriate function type */
+typedef NvtxFunctionPointer** NvtxFunctionTable; /* double pointer because array(1) of pointers(2) to function pointers */
+
+typedef struct NvtxExportTableCallbacks
+{
+    size_t struct_size;
+
+    /* returns an array of pointer to function pointers*/
+    int (NVTX_API *GetModuleFunctionTable)(
+        NvtxCallbackModule module,
+        NvtxFunctionTable* out_table,
+        unsigned int* out_size);
+} NvtxExportTableCallbacks;
+
+typedef struct NvtxExportTableVersionInfo
+{
+    /* sizeof(NvtxExportTableVersionInfo) */
+    size_t struct_size;
+
+    /* The API version comes from the NVTX library linked to the app.  The
+    * injection library is can use this info to make some assumptions */
+    uint32_t version;
+
+    /* Reserved for alignment, do not use */
+    uint32_t reserved0;
+
+    /* This must be set by tools when attaching to provide applications
+    *  the ability to, in emergency situations, detect problematic tools
+    *  versions and modify the NVTX source to prevent attaching anything
+    *  that causes trouble in the app.  Currently, this value is ignored. */
+    void (NVTX_API *SetInjectionNvtxVersion)(
+        uint32_t version);
+} NvtxExportTableVersionInfo;
+
+
+
+
+
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/lib/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1 b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1
new file mode 100644
index 0000000000000000000000000000000000000000..0759d4db37a1fdc5d11de80ac521666120cc927e
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1 differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
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+
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+This package contains the operating system driver and
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+
+/usr/local/cuda-#.#
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+/Developer/NVIDIA/CUDA-#.#
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+NVIDIA CUDA Samples
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+Windows platform:
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+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
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+
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+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
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+Mac platform:
+
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+NVIDIA Nsight Visual Studio Edition (Windows only)
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+Description
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+NVIDIA Nsight Development Platform, Visual Studio Edition is a
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+Default Install Location of Nsight Visual Studio Edition
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+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
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+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
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+
+Important NoticeRead before downloading, installing,
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+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
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+2.1. License Scope
+
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+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
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+
+You acknowledge and agree that it is your sole responsibility
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+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
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+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..cb533db38cb400ef0b0e71cea45e7ce506422bfd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-cublas-cu12
+Version: 12.8.4.1
+Summary: CUBLAS native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+CUBLAS native runtime libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..b64e13a7e04f8a097a01e3259e82fd6ef0740a4a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/RECORD
@@ -0,0 +1,23 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cublas/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cublas/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cublas/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cublas/include/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cublas/include/cublas.h,sha256=a0lLqy-k47NuwyDjuueC3W0Mpc908MTU7o5sMJqE-1w,41246
+nvidia/cublas/include/cublasLt.h,sha256=oH9TR01H5CWfGPIjJk6-Ljg8eQOu0gho7TNt83gCmwg,102451
+nvidia/cublas/include/cublasXt.h,sha256=CW9dyXYGSUW1wEXrVVyhU6OxBK1PUvMoYdVGlQT7L9A,37380
+nvidia/cublas/include/cublas_api.h,sha256=A4Jvv9elvoJIDw8ReUP7qBnSxEvdslc-ghW_ycq_qAU,374363
+nvidia/cublas/include/cublas_v2.h,sha256=qxMdB5jb97luEfw61LEAB-Wlr8A9DLBvO4rRypDCNKw,15460
+nvidia/cublas/include/nvblas.h,sha256=dXCLR-2oUiJFzLsDtIAK09m42ct4G0HWdYzBUuDPXpc,23341
+nvidia/cublas/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cublas/lib/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cublas/lib/libcublas.so.12,sha256=Axzmwsv7uUaPBAUnyrXFmQac5WCec-KPh1A4gQY-rCE,116388640
+nvidia/cublas/lib/libcublasLt.so.12,sha256=ELXmYxz4EVxmHriV7RUzgmMItY95VkZvU9I2pAybYiw,751771728
+nvidia/cublas/lib/libnvblas.so.12,sha256=nSRIINMpZvCvR6_mye5vNLIw-v6H6gFB3HEkGl0rNAw,753824
+nvidia_cublas_cu12-12.8.4.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cublas_cu12-12.8.4.1.dist-info/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cublas_cu12-12.8.4.1.dist-info/METADATA,sha256=MQVW8cLDHsUX1LBJ6vEjdAgdHFLI702Ozqsqrx7f8t8,1683
+nvidia_cublas_cu12-12.8.4.1.dist-info/RECORD,,
+nvidia_cublas_cu12-12.8.4.1.dist-info/WHEEL,sha256=wwQXGCXJQEUF59fKjBnVVzOkzi78E6rE_-QuUR4ZT4w,109
+nvidia_cublas_cu12-12.8.4.1.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..6a0d9dc1b21a540a280fda3e78767d22a3b7ed6e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.1)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux_2_27_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cublas_cu12-12.8.4.1.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/METADATA
new file mode 100644
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+Metadata-Version: 2.2
+Name: nvidia-cuda-cupti-cu12
+Version: 12.8.90
+Summary: CUDA profiling tools runtime libs.
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
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+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+Provides libraries to enable third party tools using GPU profiling APIs.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/RECORD
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/WHEEL
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+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_cupti_cu12-12.8.90.dist-info/top_level.txt
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@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/INSTALLER
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@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/License.txt
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@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
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+-------------------------------------------------------
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+This license agreement, including exhibits attached
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+Corporation ("NVIDIA") and governs your use of a NVIDIA
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+This Agreement can be accepted only by an adult of legal age
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+
+If you are entering into this Agreement on behalf of a company
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+by (a) this Agreement, and (b) any applicable law, regulation
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+1.1.7. Reservation of Rights
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+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..f8e857fa2ae30c6714fae1abaa2f8196efda7ab9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-cuda-nvrtc-cu12
+Version: 12.8.93
+Summary: NVRTC native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+NVRTC native runtime libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..612b3195066138b190faed06e4d36b66397f9b6a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/RECORD
@@ -0,0 +1,19 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..1ce22213ca080d1066f9438436aabd77121bada0
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@@ -0,0 +1,6 @@
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+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2010_x86_64
+Tag: py3-none-manylinux_2_12_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.8.93.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
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+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
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+This license agreement, including exhibits attached
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+
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+1.4. No Warranties
+
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+1.6. Termination
+
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+
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+
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+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
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+The SDK is subject to United States export laws and
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+This Agreement and any exhibits incorporated into this
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+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
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+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..14b471170862b26f1dc2b83800d568d5c38533ec
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-cuda-runtime-cu12
+Version: 12.8.90
+Summary: CUDA Runtime native Libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+CUDA Runtime native Libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..5b426a7901c41bbaccb15a7390697e3971091fae
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cuda_runtime_cu12-12.8.90.dist-info/RECORD
@@ -0,0 +1,110 @@
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+Author: Nvidia CUDA Installer Team
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+LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+This license agreement, including exhibits attached ("Agreement”) is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs your use of a NVIDIA software development kit (“SDK”). 
+
+Each SDK has its own set of software and materials, but here is a description of the types of items that may be included in a SDK: source code, header files, APIs, data sets and assets (examples include images, textures, models, scenes, videos, native API input/output files), binary software, sample code, libraries, utility programs, programming code and documentation. 
+
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the SDK is used. 
+
+If you are entering into this Agreement on behalf of a company or other legal entity, you represent that you have the legal authority to bind the entity to this Agreement, in which case “you” will mean the entity you represent. 
+
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not download, install or use the SDK.  
+
+You agree to use the SDK only for purposes that are permitted by (a) this Agreement, and (b) any applicable law, regulation or generally accepted practices or guidelines in the relevant jurisdictions.
+
+1. License. 
+
+1.1 Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants you a non-exclusive, non-transferable license, without the right to sublicense (except as expressly provided in this Agreement) to: 
+
+(i) Install and use the SDK,
+
+(ii) Modify and create derivative works of sample source code delivered in the SDK, and
+ 
+(iii) Distribute those portions of the SDK that are identified in this Agreement as distributable, as incorporated in object code format into a software application that meets the distribution requirements indicated in this Agreement.
+
+1.2 Distribution Requirements
+
+These are the distribution requirements for you to exercise the distribution grant:
+       
+(i) Your application must have material additional functionality, beyond the included portions of the SDK.
+
+(ii) The distributable portions of the SDK shall only be accessed by your application.  
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+(iii)  The following notice shall be included in modifications and derivative works of sample source code distributed: “This software contains source code provided by NVIDIA Corporation.”
+
+(iv)  Unless a developer tool is identified in this Agreement as distributable, it is delivered for your internal use only.
+
+(v) The terms under which you distribute your application must be consistent with the terms of this Agreement, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA’s intellectual property rights. Additionally, you agree that you will protect the privacy, security and legal rights of your application users. 
+
+(vi) You agree to notify NVIDIA in writing of any known or suspected distribution or use of the SDK not in compliance with the requirements of this Agreement, and to enforce the terms of your agreements with respect to distributed SDK.
+
+1.3 Authorized Users
+
+You may allow employees and contractors of your entity or of your subsidiary(ies) to access and use the SDK from your secure network to perform work on your behalf. 
+
+If you are an academic institution you may allow users enrolled or employed by the academic institution to access and use the SDK from your secure network. 
+
+You are responsible for the compliance with the terms of this Agreement by your authorized users. If you become aware that your authorized users didn’t follow the terms of this Agreement, you agree to take reasonable steps to resolve the non-compliance and prevent new occurrences. 
+
+1.4 Pre-Release SDK 
+The SDK versions identified as alpha, beta, preview or otherwise as pre-release, may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, accessibility, availability, and reliability standards relative to commercial versions of NVIDIA software and materials. Use of a pre-release SDK may result in unexpected results, loss of data, project delays or other unpredictable damage or loss. 
+You may use a pre-release SDK at your own risk, understanding that pre-release SDKs are not intended for use in production or business-critical systems. 
+NVIDIA may choose not to make available a commercial version of any pre-release SDK. NVIDIA may also choose to abandon development and terminate the availability of a pre-release SDK at any time without liability. 
+1.5	 Updates
+
+NVIDIA may, at its option, make available patches, workarounds or other updates to this SDK. Unless the updates are provided with their separate governing terms, they are deemed part of the SDK licensed to you as provided in this Agreement.
+
+You agree that the form and content of the SDK that NVIDIA provides may change without prior notice to you. While NVIDIA generally maintains compatibility between versions, NVIDIA may in some cases make changes that introduce incompatibilities in future versions of the SDK.
+
+1.6	 Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be distributed with, third party software licensed by a NVIDIA supplier and/or open source software provided under an open source license. Use of third party software is subject to the third-party license terms, or in the absence of third party terms, the terms of this Agreement. Copyright to third party software is held by the copyright holders indicated in the third-party software or license.
+
+1.7 Reservation of Rights
+
+NVIDIA reserves all rights, title and interest in and to the SDK not expressly granted to you under this Agreement.
+
+2. Limitations. 
+
+The following license limitations apply to your use of the SDK:
+
+2.1 You may not reverse engineer, decompile or disassemble, or remove copyright or other proprietary notices from any portion of the SDK or copies of the SDK. 
+
+2.2 Except as expressly provided in this Agreement, you may not copy, sell, rent, sublicense, transfer, distribute, modify, or create derivative works of any portion of the SDK. 
+
+2.3 Unless you have an agreement with NVIDIA for this purpose, you may not indicate that an application created with the SDK is sponsored or endorsed by NVIDIA. 
+
+2.4 You may not bypass, disable, or circumvent any encryption, security, digital rights management or authentication mechanism in the SDK. 
+
+2.5 You may not use the SDK in any manner that would cause it to become subject to an open source software license. As examples, licenses that require as a condition of use, modification, and/or distribution that the SDK be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge.
+
+2.6  Unless you have an agreement with NVIDIA for this purpose, you may not use the SDK with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SDK for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses. 
+
+2.7 You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney’s fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the SDK outside of the scope of this Agreement, or not in compliance with its terms.
+
+3. Ownership. 
+
+3.1 NVIDIA or its licensors hold all rights, title and interest in and to the SDK and its modifications and derivative works, including their respective intellectual property rights, subject to your rights under Section 3.2. This SDK may include software and materials from NVIDIA’s licensors, and these licensors are intended third party beneficiaries that may enforce this Agreement with respect to their intellectual property rights. 
+
+3.2 You hold all rights, title and interest in and to your applications and your derivative works of the sample source code delivered in the SDK, including their respective intellectual property rights, subject to NVIDIA’s rights under section 3.1.
+
+3.3 You may, but don’t have to, provide to NVIDIA suggestions, feature requests or other feedback regarding the SDK, including possible enhancements or modifications to the SDK. For any feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) it without the payment of any royalties or fees to you. NVIDIA will use feedback at its choice. NVIDIA is constantly looking for ways to improve its products, so you may send feedback to NVIDIA through the developer portal at https://developer.nvidia.com.
+
+4.  No Warranties. 
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF DEALING OR COURSE OF TRADE. 
+
+5.	Limitations of Liability. 
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT. 
+
+These exclusions and limitations of liability shall apply regardless if NVIDIA or its affiliates have been advised of the possibility of such damages, and regardless of whether a remedy fails its essential purpose. These exclusions and limitations of liability form an essential basis of the bargain between the parties, and, absent any of these exclusions or limitations of liability, the provisions of this Agreement, including, without limitation, the economic terms, would be substantially different. 
+
+6.   Termination. 
+
+6.1 This Agreement will continue to apply until terminated by either you or NVIDIA as described below. 
+
+6.2 If you want to terminate this Agreement, you may do so by stopping to use the SDK. 
+
+6.3 NVIDIA may, at any time, terminate this Agreement if: (i) you fail to comply with any term of this Agreement and the non-compliance is not fixed within thirty (30) days following notice from NVIDIA (or immediately if you violate NVIDIA’s intellectual property rights); (ii) you commence or participate in any legal proceeding against NVIDIA with respect to the SDK; or (iii) NVIDIA decides to no longer provide the SDK in a country or, in NVIDIA’s sole discretion, the continued use of it is no longer commercially viable. 
+
+6.4 Upon any termination of this Agreement, you agree to promptly discontinue use of the SDK and destroy all copies in your possession or control. Your prior distributions in accordance with this Agreement are not affected by the termination of this Agreement. Upon written request, you will certify in writing that you have complied with your commitments under this section. Upon any termination of this Agreement all provisions survive except for the licenses granted to you. 
+
+7.  General.  
+ 
+If you wish to assign this Agreement or your rights and obligations, including by merger, consolidation, dissolution or operation of law, contact NVIDIA to ask for permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect. NVIDIA may assign, delegate or transfer this Agreement and its rights and obligations, and if to a non-affiliate you will be notified. 
+
+You agree to cooperate with NVIDIA and provide reasonably requested information to verify your compliance with this Agreement.
+
+This Agreement will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language.
+
+The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this Agreement. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction. 
+
+If any court of competent jurisdiction determines that any provision of this Agreement is illegal, invalid or unenforceable, such provision will be construed as limited to the extent necessary to be consistent with and fully enforceable under the law and the remaining provisions will remain in full force and effect. Unless otherwise specified, remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an independent contractor in the performance of this Agreement. 
+
+The SDK has been developed entirely at private expense and is “commercial items” consisting of “commercial computer software” and “commercial computer software documentation” provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this Agreement pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (b)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SDK into any country, or use the SDK in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this Agreement, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement will be delivered via mail, email or fax. You agree that any notices that NVIDIA sends you electronically will satisfy any legal communication requirements. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this Agreement constitute the entire agreement of the parties with respect to the subject matter of this Agreement and supersede all prior negotiations or documentation exchanged between the parties relating to this SDK license. Any additional and/or conflicting terms on documents issued by you are null, void, and invalid. Any amendment or waiver under this Agreement shall be in writing and signed by representatives of both parties.
+
+(v. January 28, 2020)
+
+
+cuDNN SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+The terms in this supplement govern your use of the NVIDIA cuDNN SDK under the terms of your license agreement (“Agreement”) as modified by this supplement. Capitalized terms used but not defined below have the meaning assigned to them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is incorporated as an integral part of the Agreement. In the event of conflict between the terms in this supplement and the terms in the Agreement, the terms in this supplement govern.  
+
+4.1 License Scope. The SDK is licensed for you to develop applications only for use in systems with NVIDIA GPUs.
+
+2. Distribution. The following portions of the SDK are distributable under the Agreement: the runtime files .so and .h, cudnn64_7.dll, and cudnn.lib. 
+
+In addition to the rights above, for parties that are developing software intended solely for use on Jetson development kits or Jetson modules and running Linux for Tegra software the following shall apply: the SDK may be distributed in its entirety, as provided by NVIDIA and without separation of its components, for you and/or your licensees to create software development kits for use only on the Jetson platform and running Linux for Tegra software.
+
+3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com.
+ (v. January 28, 2020)
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cudnn_cu12-9.10.2.21.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cudnn_cu12-9.10.2.21.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cudnn_cu12-9.10.2.21.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..82f85353688d5a5d26114a8ee58082974d0a0a6c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/METADATA
@@ -0,0 +1,46 @@
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+Name: nvidia-cufft-cu12
+Version: 11.3.3.83
+Summary: CUFFT native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-nvjitlink-cu12
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUFFT native runtime libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/RECORD
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/WHEEL
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+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufft_cu12-11.3.3.83.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
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+NVIDIA Driver
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+
+Description
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+This package contains the operating system driver and
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+
+NVIDIA CUDA Toolkit
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+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
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+
+NVIDIA CUDA Samples
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+Description
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+This package includes over 100+ CUDA examples that demonstrate
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+Default Install Location of CUDA Samples
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+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
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+Linux platform:
+
+/usr/local/cuda-#.#/samples
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+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
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+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
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+NVIDIA Nsight Visual Studio Edition (Windows only)
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+NVIDIA Nsight Development Platform, Visual Studio Edition is a
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+Default Install Location of Nsight Visual Studio Edition
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+Windows platform:
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+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
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+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
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+
+Release Date: July 26, 2018
+---------------------------
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+
+Important NoticeRead before downloading, installing,
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+
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+
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+
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+        thirty (30) days following notice from NVIDIA (or
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+        property rights);
+
+      b. (ii) you commence or participate in any legal
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+
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+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
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+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
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+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
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+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
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+notices or other correspondence to NVIDIA Corporation, 2788
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+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..4d22d2b0a860e67abb882c6ec18c0a5dc468a39f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-cufile-cu12
+Version: 1.13.1.3
+Summary: cuFile GPUDirect libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+cuFile GPUDirect libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..db7b35b42aa60cfaaf791a1e1084a4a2d775cd23
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/RECORD
@@ -0,0 +1,17 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cufile/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cufile/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cufile/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cufile/include/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cufile/include/cufile.h,sha256=5bNDkZZNOF_qHbDBkN04OfwF09BEcrAxxPW61HJSzX0,29530
+nvidia/cufile/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cufile/lib/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cufile/lib/libcufile.so.0,sha256=ovSFQXf2ZsGoxxm0huTLERnr_dhY66pjSctjxVSdJ-g,3209496
+nvidia/cufile/lib/libcufile_rdma.so.1,sha256=mpEoGEkKqSa07RW1ACaa4GTXKHgDFBjOYLjIu773fM8,46528
+nvidia_cufile_cu12-1.13.1.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cufile_cu12-1.13.1.3.dist-info/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cufile_cu12-1.13.1.3.dist-info/METADATA,sha256=33E6JjiagFLRf-48FSFWPgYLJKA3mDs4if_1T5Bu8og,1673
+nvidia_cufile_cu12-1.13.1.3.dist-info/RECORD,,
+nvidia_cufile_cu12-1.13.1.3.dist-info/WHEEL,sha256=ygM8qpYgOvrn5C-8vbfzPi-0iFPECh71lLWqkqrTjYw,144
+nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..287a9d7e1a3d4435e9542cc8216b8c5eaf2c0ed2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cufile_cu12-1.13.1.3.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..7617880958e7c1f9e79db788e5ab17cad840dca2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-curand-cu12
+Version: 10.3.9.90
+Summary: CURAND native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+CURAND native runtime libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..07b5a9a030c9d62778de74655f1b3609a08e5175
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/RECORD
@@ -0,0 +1,32 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-310.pyc,,
+nvidia/curand/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/curand/__pycache__/__init__.cpython-310.pyc,,
+nvidia/curand/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/curand/include/__pycache__/__init__.cpython-310.pyc,,
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/WHEEL
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_curand_cu12-10.3.9.90.dist-info/top_level.txt
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@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/INSTALLER
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@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/License.txt
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@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
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+the use of NVIDIA software. By accepting this agreement, you
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+NVIDIA Driver
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+
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+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
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+/usr/local/cuda-#.#
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+/Developer/NVIDIA/CUDA-#.#
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+NVIDIA CUDA Samples
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+Windows platform:
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+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
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+/usr/local/cuda-#.#/samples
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+$HOME/NVIDIA_CUDA-#.#_Samples
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+/Developer/NVIDIA/CUDA-#.#/samples
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+NVIDIA Nsight Visual Studio Edition (Windows only)
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+Description
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+NVIDIA Nsight Development Platform, Visual Studio Edition is a
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+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
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+Release Date: July 26, 2018
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+
+Important NoticeRead before downloading, installing,
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+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..6d092eb90b55dc0d052f1b28fac1dfda2f61901a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA
@@ -0,0 +1,48 @@
+Metadata-Version: 2.2
+Name: nvidia-cusolver-cu12
+Version: 11.7.3.90
+Summary: CUDA solver native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-cublas-cu12
+Requires-Dist: nvidia-nvjitlink-cu12
+Requires-Dist: nvidia-cusparse-cu12
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUDA solver native runtime libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..a7410554e245feabb3dfdccb56a9c00ad04cc553
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/RECORD
@@ -0,0 +1,22 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
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+nvidia/cusolver/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusolver/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cusolver/include/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusolver/include/cusolverDn.h,sha256=BqiXh-sSll7KxwhjqSuVJvSDxeByqj2xfAK0KyoG3Ks,152029
+nvidia/cusolver/include/cusolverMg.h,sha256=N8989nnS2BleeMyuftbQgBDJ4sMAkLPSnmy_S_7fxng,11549
+nvidia/cusolver/include/cusolverRf.h,sha256=4FN6o6FYml09pS4p-YT5JKbwgZqKLEtf9tQZr50yZo4,14535
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+nvidia/cusolver/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cusolver/lib/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusolver/lib/libcusolver.so.11,sha256=tkuDi_Pi8nNzpZMSr_c6X7fkUjAMQEjbNTp_MNAJodc,243303872
+nvidia/cusolver/lib/libcusolverMg.so.11,sha256=2UdlRjHFNukJmrzJ_lUezpfQ_-uPurFCGJ6iyFvFXo4,161493576
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+nvidia_cusolver_cu12-11.7.3.90.dist-info/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cusolver_cu12-11.7.3.90.dist-info/METADATA,sha256=HwalXcjyrvnrIPAYBEv6dzESRpi6e0NdEcJQxi6vTcM,1826
+nvidia_cusolver_cu12-11.7.3.90.dist-info/RECORD,,
+nvidia_cusolver_cu12-11.7.3.90.dist-info/WHEEL,sha256=VtFLEVB-VX8niQT4kQ5pcQOOqiKvUvqfZe5V14HmU88,109
+nvidia_cusolver_cu12-11.7.3.90.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..476a64f798fcad2101388098d22bc98258e64990
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux_2_27_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusolver_cu12-11.7.3.90.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..6f49c69886f619189dab562063548d38dab861af
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA
@@ -0,0 +1,46 @@
+Metadata-Version: 2.2
+Name: nvidia-cusparse-cu12
+Version: 12.5.8.93
+Summary: CUSPARSE native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-nvjitlink-cu12
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUSPARSE native runtime libraries
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..716c6cc25f79dd0e43b5b439a01fbf5aa4c3e634
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD
@@ -0,0 +1,17 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusparse/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cusparse/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusparse/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cusparse/include/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusparse/include/cusparse.h,sha256=8MDDKz-6d4rFquyjD-g-yWZbEJCD97Qq4_Cv5gh7fuk,296787
+nvidia/cusparse/include/cusparse_v2.h,sha256=jkH2A9hYc-TEF0vuQ_SurbhPNEHkYGUIRuxKXhFAqnw,2587
+nvidia/cusparse/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cusparse/lib/__pycache__/__init__.cpython-310.pyc,,
+nvidia/cusparse/lib/libcusparse.so.12,sha256=Q3ZR0exdDlsbTQe4xJLxU8tIJSIv4tdjIypAf2Z9m7I,387990224
+nvidia_cusparse_cu12-12.5.8.93.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cusparse_cu12-12.5.8.93.dist-info/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cusparse_cu12-12.5.8.93.dist-info/METADATA,sha256=6Ds7-B5qLB3E1N3Nc7KDqI5vf50gOF0oDsAw2_U4TyI,1750
+nvidia_cusparse_cu12-12.5.8.93.dist-info/RECORD,,
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+nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..711dd0f79fe712cec60be7868740cd7d73ccb637
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.1)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparse_cu12-12.5.8.93.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..0d8d05f17dde9634f260d0bdb1dfa5e4e09ac40f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/METADATA
@@ -0,0 +1,138 @@
+Metadata-Version: 2.2
+Name: nvidia-cusparselt-cu12
+Version: 0.7.1
+Summary: NVIDIA cuSPARSELt
+Home-page: https://developer.nvidia.com/cusparselt
+Author: NVIDIA Corporation
+Author-email: cuda_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,machine learning,high-performance computing
+Classifier: Topic :: Scientific/Engineering
+Classifier: Environment :: GPU :: NVIDIA CUDA
+Classifier: Environment :: GPU :: NVIDIA CUDA :: 12
+Description-Content-Type: text/x-rst
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: summary
+
+###################################################################################
+cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication
+###################################################################################
+
+**NVIDIA cuSPARSELt** is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a sparse matrix:
+
+.. math::
+
+   D = Activation(\alpha op(A) \cdot op(B) + \beta op(C) + bias) \cdot scale
+
+where :math:`op(A)/op(B)` refers to in-place operations such as transpose/non-transpose, and :math:`alpha, beta, scale` are scalars.
+
+The *cuSPARSELt APIs* allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types.
+
+**Download:** `developer.nvidia.com/cusparselt/downloads <https://developer.nvidia.com/cusparselt/downloads>`_
+
+**Provide Feedback:** `Math-Libs-Feedback@nvidia.com <mailto:Math-Libs-Feedback@nvidia.com?subject=cuSPARSELt-Feedback>`_
+
+**Examples**:
+`cuSPARSELt Example 1 <https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSELt/matmul>`_,
+`cuSPARSELt Example 2 <https://github.com/NVIDIA/CUDALibrarySamples/tree/master/cuSPARSELt/matmul_advanced>`_
+
+**Blog post**:
+
+- `Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt <https://developer.nvidia.com/blog/exploiting-ampere-structured-sparsity-with-cusparselt/>`_
+- `Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines <https://developer.nvidia.com/blog/structured-sparsity-in-the-nvidia-ampere-architecture-and-applications-in-search-engines/>`__
+- `Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture <https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31552/>`__
+
+================================================================================
+Key Features
+================================================================================
+
+* *NVIDIA Sparse MMA tensor core* support
+* Mixed-precision computation support:
+
+    +--------------+----------------+-----------------+-------------+
+    | Input A/B    | Input C        | Output D        | Compute     |
+    +==============+================+=================+=============+
+    | `FP32`       | `FP32`         | `FP32`          | `FP32`      |
+    +--------------+----------------+-----------------+-------------+
+    | `FP16`       | `FP16`         | `FP16`          | `FP32`      |
+    +              +                +                 +-------------+
+    |              |                |                 | `FP16`      |
+    +--------------+----------------+-----------------+-------------+
+    | `BF16`       | `BF16`         | `BF16`          | `FP32`      |
+    +--------------+----------------+-----------------+-------------+
+    | `INT8`       | `INT8`         | `INT8`          | `INT32`     |
+    +              +----------------+-----------------+             +
+    |              | `INT32`        | `INT32`         |             |
+    +              +----------------+-----------------+             +
+    |              | `FP16`         | `FP16`          |             |
+    +              +----------------+-----------------+             +
+    |              | `BF16`         | `BF16`          |             |
+    +--------------+----------------+-----------------+-------------+
+    | `E4M3`       | `FP16`         | `E4M3`          | `FP32`      |
+    +              +----------------+-----------------+             +
+    |              | `BF16`         | `E4M3`          |             |
+    +              +----------------+-----------------+             +
+    |              | `FP16`         | `FP16`          |             |
+    +              +----------------+-----------------+             +
+    |              | `BF16`         | `BF16`          |             |
+    +              +----------------+-----------------+             +
+    |              | `FP32`         | `FP32`          |             |
+    +--------------+----------------+-----------------+-------------+
+    | `E5M2`       | `FP16`         | `E5M2`          | `FP32`      |
+    +              +----------------+-----------------+             +
+    |              | `BF16`         | `E5M2`          |             |
+    +              +----------------+-----------------+             +
+    |              | `FP16`         | `FP16`          |             |
+    +              +----------------+-----------------+             +
+    |              | `BF16`         | `BF16`          |             |
+    +              +----------------+-----------------+             +
+    |              | `FP32`         | `FP32`          |             |
+    +--------------+----------------+-----------------+-------------+
+
+* Matrix pruning and compression functionalities
+* Activation functions, bias vector, and output scaling
+* Batched computation (multiple matrices in a single run)
+* GEMM Split-K mode
+* Auto-tuning functionality (see `cusparseLtMatmulSearch()`)
+* NVTX ranging and Logging functionalities
+
+================================================================================
+Support
+================================================================================
+
+* *Supported SM Architectures*: `SM 8.0`, `SM 8.6`, `SM 8.9`, `SM 9.0`, `SM 10.0`, `SM 12.0`
+* *Supported CPU architectures and operating systems*:
+
++------------+--------------------+
+| OS         | CPU archs          |
++============+====================+
+| `Windows`  | `x86_64`           |
++------------+--------------------+
+| `Linux`    | `x86_64`, `Arm64`  |
++------------+--------------------+
+
+
+================================================================================
+Documentation
+================================================================================
+
+Please refer to https://docs.nvidia.com/cuda/cusparselt/index.html for the cuSPARSELt documentation.
+
+================================================================================
+Installation
+================================================================================
+
+The cuSPARSELt wheel can be installed as follows:
+
+.. code-block:: bash
+
+   pip install nvidia-cusparselt-cuXX
+
+where XX is the CUDA major version (currently CUDA 12 only is supported).
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/RECORD
new file mode 100644
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@@ -0,0 +1,8 @@
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..b37526f046b54acc36a14784ff0ee77425c6ffa3
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@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_cusparselt_cu12-0.7.1.dist-info/top_level.txt
new file mode 100644
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@@ -0,0 +1 @@
+nvidia/cusparselt
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/INSTALLER
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@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/METADATA
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@@ -0,0 +1,45 @@
+Metadata-Version: 2.4
+Name: nvidia-nccl-cu12
+Version: 2.27.3
+Summary: NVIDIA Collective Communication Library (NCCL) Runtime
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: BSD-3-Clause
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
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+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
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+Dynamic: requires-python
+Dynamic: summary
+
+NCCL (pronounced "Nickel") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, and reduce-scatter. It has been optimized to achieve high bandwidth on any platform using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..b40de80dba1ef3dd1975fd1b70e1537075ea480c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/RECORD
@@ -0,0 +1,8 @@
+nvidia/nccl/include/nccl.h,sha256=a-E7PcjbKHPqQfuDKjT5Mt9fDxwv32LNIpOtJyxBv94,22566
+nvidia/nccl/lib/libnccl.so.2,sha256=H8Nu68hPSxCSPu4hU47Y-iVyVkfQb0hAvA_jmabtm8Q,429634192
+nvidia_nccl_cu12-2.27.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_nccl_cu12-2.27.3.dist-info/METADATA,sha256=h-6ZnFWjGBdh9Ld1ijhVOXdrDLo82k_Q-L_02ibG41U,2019
+nvidia_nccl_cu12-2.27.3.dist-info/RECORD,,
+nvidia_nccl_cu12-2.27.3.dist-info/WHEEL,sha256=gy6FWQgpujK_dnYc155G2NL32NQjpi5ebTEXjh8SGZQ,144
+nvidia_nccl_cu12-2.27.3.dist-info/licenses/License.txt,sha256=DwF0prTgszrCY3W_cpUzB1sy9MUaW2gCo9dC19zcmnY,1895
+nvidia_nccl_cu12-2.27.3.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..29738220b339f398b2cbbc9ac8ccb38a39bdd7b2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (80.9.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/licenses/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/licenses/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bcd1867a02a6a8c1e592b92e2e50f34e531f2d87
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/licenses/License.txt
@@ -0,0 +1,39 @@
+
+ Copyright (c) 2015-2020, NVIDIA CORPORATION. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without
+ modification, are permitted provided that the following conditions
+ are met:
+  * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+  * Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+  * Neither the name of NVIDIA CORPORATION, Lawrence Berkeley National
+    Laboratory, the U.S. Department of Energy, nor the names of their
+    contributors may be used to endorse or promote products derived
+    from this software without specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+ EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+ OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ The U.S. Department of Energy funded the development of this software
+ under subcontract 7078610 with Lawrence Berkeley National Laboratory.
+
+
+This code also includes files from the NVIDIA Tools Extension SDK project.
+
+See:
+
+   https://github.com/NVIDIA/NVTX
+
+for more information and license details.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nccl_cu12-2.27.3.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
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+
+Preface
+-------
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+NVIDIA Driver
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+
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+
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+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..132da4d3c2e9e8c3a1ef56f482a283a26849c2cb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-nvjitlink-cu12
+Version: 12.8.93
+Summary: Nvidia JIT LTO Library
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+NVIDIA compiler library for JIT LTO functionality.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..5f1396b0a3f6d19714eb27ca76669cac73c62493
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD
@@ -0,0 +1,16 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvjitlink/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/nvjitlink/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvjitlink/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/nvjitlink/include/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvjitlink/include/nvJitLink.h,sha256=Vv2Dhlpuwssu2ebjRIU37clsSggc96F7X-A77UTjd_c,17081
+nvidia/nvjitlink/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/nvjitlink/lib/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvjitlink/lib/libnvJitLink.so.12,sha256=A2nmhn1EuABDfeThRtcsZa_Gx1rfZ3oVwuzY5qesE18,94101392
+nvidia_nvjitlink_cu12-12.8.93.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_nvjitlink_cu12-12.8.93.dist-info/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_nvjitlink_cu12-12.8.93.dist-info/METADATA,sha256=d2_qsBLB8-_OiRiXXcCKoBeTqjODLYezd8qgRIytRDU,1695
+nvidia_nvjitlink_cu12-12.8.93.dist-info/RECORD,,
+nvidia_nvjitlink_cu12-12.8.93.dist-info/WHEEL,sha256=6VN0zCSXVxqaCfAW-ySp_g8N0rln0iqt25pKNCbVzrk,144
+nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..1ce22213ca080d1066f9438436aabd77121bada0
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+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2010_x86_64
+Tag: py3-none-manylinux_2_12_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvjitlink_cu12-12.8.93.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/License.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd8b243dfa02d4e7080150180520f742d2861d15
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/License.txt
@@ -0,0 +1,218 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+    TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+    1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
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+
+      "You" (or "Your") shall mean an individual or Legal Entity
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..b31bb610bfc7d774be17ed73f760af6e477d819b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/METADATA
@@ -0,0 +1,44 @@
+Metadata-Version: 2.2
+Name: nvidia-nvtx-cu12
+Version: 12.8.90
+Summary: NVIDIA Tools Extension
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: Apache 2.0
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: requires-python
+Dynamic: summary
+
+A C-based API for annotating events, code ranges, and resources in your applications. Applications which integrate NVTX can use the Visual Profiler to capture and visualize these events and ranges.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..bdb4c5ee2f6ec0dcb9551c2a23e0bf7c8ef5cd25
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/RECORD
@@ -0,0 +1,36 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvtx/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/nvtx/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvtx/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/nvtx/include/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvtx/include/nvToolsExt.h,sha256=TFV6WIqHrKh2wbll0lk3SsSL3KMoDav5cnwgEGuCbXI,55254
+nvidia/nvtx/include/nvToolsExtCuda.h,sha256=UDA1pbmvoRFmlJ11Et9tIMEztOtOVw-10mO27Q6K8jg,6009
+nvidia/nvtx/include/nvToolsExtCudaRt.h,sha256=6IbgdRGObly53jzRqvsZ4FQoTrXJOJwSyCOLuXr9ncA,5192
+nvidia/nvtx/include/nvToolsExtOpenCL.h,sha256=gETZH9ch_o6MYE_BYQ2pj9SSuxyAo1H4ptmRK-DMWSo,8360
+nvidia/nvtx/include/nvToolsExtSync.h,sha256=wqONIiycUPaUUCzQBmCippilgKt8sOL9tpzG773u0nY,14562
+nvidia/nvtx/include/nvtx3/nvToolsExt.h,sha256=TFEF3fx1043EwMdbS7FqvvavwK0koZeGrIOAsCrB12s,52247
+nvidia/nvtx/include/nvtx3/nvToolsExtCuda.h,sha256=4ZbZHUMcmHRf4SdKB7nH0E3uHd_9ZhZBuwuWPItK-Vs,6204
+nvidia/nvtx/include/nvtx3/nvToolsExtCudaRt.h,sha256=boW0zdYobNFFE9wwxCyzBGBLcSGtdbQ5osKjQGNC2E8,5393
+nvidia/nvtx/include/nvtx3/nvToolsExtOpenCL.h,sha256=RPfsZl3lHAPIOCzTipmz07-vaiIO4cxelcx12EjB2L0,8563
+nvidia/nvtx/include/nvtx3/nvToolsExtSync.h,sha256=C-HIVBaupxYom3BqMggQ_ePq1bxFhw8kXsOfYJKBWrI,14756
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImpl.h,sha256=jEnYF3MyLsD72euw2It3Bz0X0GK4Xv_htEd8BeIrPjY,23333
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCore.h,sha256=sYpWqZfYrjsMddxtezPX3qSTIbAOn4dlEoLiYQ9M2nM,9756
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCudaRt_v3.h,sha256=SoaiprvsI80yLmEAnlFX0iFufv6RtKjjMMrVwQZjjQI,4775
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplCuda_v3.h,sha256=IEor-ISqComCRGVDdIzKBLU3eWCuDI0Igqz-eRKKcvg,5550
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplOpenCL_v3.h,sha256=iPR2x74bJE3plFQBT9FWGBaTm4sC-Pll6WAjpKRnz7g,8275
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxImplSync_v3.h,sha256=TqwQfEUVbwc58bpHioE13NMweFhOuHXNql65BnLzhvc,5022
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInit.h,sha256=foajOFacvLGx3BN5ntw5v8o4J3OY4hqkVZE5ZC0x3e4,14716
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDecls.h,sha256=-Qyxcy9CDXOBhEtYZ8L7iYd6daJ9aCeyQM48X0BafMM,9361
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxInitDefs.h,sha256=dLhOV4knhNrmT2DnUNzXreOt_Qc6GAa3yIlmqJFCeVI,35432
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxLinkOnce.h,sha256=Jp-z6LTz_p8fKRulcFfdcskIxzcZ6ybbHkGB9mpJa2M,3863
+nvidia/nvtx/include/nvtx3/nvtxDetail/nvtxTypes.h,sha256=jkbCwyvIP1G-Ef8SwYp4kDi69hjZbzaxKSk7ScgrNI8,17352
+nvidia/nvtx/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/nvtx/lib/__pycache__/__init__.cpython-310.pyc,,
+nvidia/nvtx/lib/libnvToolsExt.so.1,sha256=xJj8urAgKIbCegrerESr8jOt4D0waA_6LSq-k6uI2RM,36024
+nvidia_nvtx_cu12-12.8.90.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_nvtx_cu12-12.8.90.dist-info/License.txt,sha256=nkXoVr7czun2clQILKEYUdlU3i_tdEjEvtGa2aq5mpE,12262
+nvidia_nvtx_cu12-12.8.90.dist-info/METADATA,sha256=xjwj4E6M8EFDch_3GMuoeirlYwpQRRt3-DV6DSYTPPE,1820
+nvidia_nvtx_cu12-12.8.90.dist-info/RECORD,,
+nvidia_nvtx_cu12-12.8.90.dist-info/WHEEL,sha256=ygM8qpYgOvrn5C-8vbfzPi-0iFPECh71lLWqkqrTjYw,144
+nvidia_nvtx_cu12-12.8.90.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..287a9d7e1a3d4435e9542cc8216b8c5eaf2c0ed2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/nvidia_nvtx_cu12-12.8.90.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/LICENSE b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..eb208dada03bbd9975633d6f42eb036c09e33125
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/LICENSE
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2018, Omry Yadan
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..6d3ab67e16a7dfc446c5e7f878569d4fa7e79d6e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/METADATA
@@ -0,0 +1,64 @@
+Metadata-Version: 2.1
+Name: omegaconf
+Version: 2.3.0
+Summary: A flexible configuration library
+Home-page: https://github.com/omry/omegaconf
+Author: Omry Yadan
+Author-email: omry@yadan.net
+Keywords: yaml configuration config
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: antlr4-python3-runtime (==4.9.*)
+Requires-Dist: PyYAML (>=5.1.0)
+Requires-Dist: dataclasses ; python_version == "3.6"
+
+# OmegaConf
+|  | Description |
+| --- | --- |
+| Project | [![PyPI version](https://badge.fury.io/py/omegaconf.svg)](https://badge.fury.io/py/omegaconf)[![Downloads](https://pepy.tech/badge/omegaconf/month)](https://pepy.tech/project/omegaconf?versions=1.4.*&versions=2.0.*&versions=2.1.*)![PyPI - Python Version](https://img.shields.io/pypi/pyversions/omegaconf.svg) |
+| Code quality| [![CircleCI](https://img.shields.io/circleci/build/github/omry/omegaconf?logo=s&token=5de2f8dc2a0dd78438520575431aa533150806e3)](https://circleci.com/gh/omry/omegaconf)[![Coverage Status](https://coveralls.io/repos/github/omry/omegaconf/badge.svg)](https://coveralls.io/github/omry/omegaconf)[![Total alerts](https://img.shields.io/lgtm/alerts/g/omry/omegaconf.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/omry/omegaconf/alerts/)[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/omry/omegaconf.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/omry/omegaconf/context:python)|
+| Docs and support |[![Documentation Status](https://readthedocs.org/projects/omegaconf/badge/?version=2.0_branch)](https://omegaconf.readthedocs.io/en/2.1_branch/)[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/omry/omegaconf/master?filepath=docs%2Fnotebook%2FTutorial.ipynb)|
+
+
+OmegaConf is a hierarchical configuration system, with support for merging configurations from multiple sources (YAML config files, dataclasses/objects and CLI arguments)
+providing a consistent API regardless of how the configuration was created.
+
+## Releases
+
+### Stable (2.2)
+OmegaConf 2.2 is the current stable version.
+* [What's new](https://github.com/omry/omegaconf/releases/tag/v2.2.1)
+* [Documentation](https://omegaconf.readthedocs.io/en/2.2_branch/)
+* [Source code](https://github.com/omry/omegaconf/tree/2.2_branch)
+
+Install with `pip install --upgrade omegaconf`
+
+### Previous release (2.1)
+OmegaConf 2.1 is the current stable version.
+* [What's new](https://github.com/omry/omegaconf/releases/tag/v2.1.1)
+* [Documentation](https://omegaconf.readthedocs.io/en/2.1_branch/)
+* [Slides](https://docs.google.com/presentation/d/e/2PACX-1vT_UIV7hCnquIbLUm4NnkUpXvPEh33IKiUEvPRF850WKA8opOlZOszjKdZ3tPmf8u7hGNP6HpqS-NT5/pub?start=false&loop=false&delayms=3000)
+* [Source code](https://github.com/omry/omegaconf/tree/2.1_branch)
+
+Install with `pip install omegaconf==2.1`
+
+### Previous release (2.0)
+
+* [What's new](https://github.com/omry/omegaconf/releases/tag/v2.0.0)
+* [Documentation](https://omegaconf.readthedocs.io/en/2.0_branch/)
+* [Slides](https://docs.google.com/presentation/d/e/2PACX-1vT_UIV7hCnquIbLUm4NnkUpXvPEh33IKiUEvPRF850WKA8opOlZOszjKdZ3tPmf8u7hGNP6HpqS-NT5/pub?start=false&loop=false&delayms=3000)
+* [Source code](https://github.com/omry/omegaconf/tree/2.0_branch)
+
+Install with `pip install omegaconf==2.0.6`
+
+## Live tutorial
+Run the live tutorial: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/omry/omegaconf/master?filepath=docs%2Fnotebook%2FTutorial.ipynb)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..2e7580b310656dbf4c36dcfeb9b3f2828795013f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/RECORD
@@ -0,0 +1,58 @@
+omegaconf-2.3.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+omegaconf-2.3.0.dist-info/LICENSE,sha256=sxN17V1cvn7C0MTBTfivp6jlUBu-JfcLbLxGFoX0uRo,1518
+omegaconf-2.3.0.dist-info/METADATA,sha256=qy3L8OPnDMddyh2JT4lT596LZR3C3CjwjzUL-leHuts,3865
+omegaconf-2.3.0.dist-info/RECORD,,
+omegaconf-2.3.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+omegaconf-2.3.0.dist-info/WHEEL,sha256=2wepM1nk4DS4eFpYrW1TTqPcoGNfHhhO_i5m4cOimbo,92
+omegaconf-2.3.0.dist-info/top_level.txt,sha256=gKo5sjlMnML5r9-l1eLYBTS1xZ-xAr67mlBSgL9yvSA,25
+omegaconf/__init__.py,sha256=lynwiDHEFBIHMqrBN3HRh0KJevzIap6MyULFNkMmlBc,1170
+omegaconf/__pycache__/__init__.cpython-310.pyc,,
+omegaconf/__pycache__/_impl.cpython-310.pyc,,
+omegaconf/__pycache__/_utils.cpython-310.pyc,,
+omegaconf/__pycache__/base.cpython-310.pyc,,
+omegaconf/__pycache__/basecontainer.cpython-310.pyc,,
+omegaconf/__pycache__/dictconfig.cpython-310.pyc,,
+omegaconf/__pycache__/errors.cpython-310.pyc,,
+omegaconf/__pycache__/grammar_parser.cpython-310.pyc,,
+omegaconf/__pycache__/grammar_visitor.cpython-310.pyc,,
+omegaconf/__pycache__/listconfig.cpython-310.pyc,,
+omegaconf/__pycache__/nodes.cpython-310.pyc,,
+omegaconf/__pycache__/omegaconf.cpython-310.pyc,,
+omegaconf/__pycache__/version.cpython-310.pyc,,
+omegaconf/_impl.py,sha256=CJfcRt8K20BiMMW5u3q8wuQ9hAeAmuYlFxctzPgTIgQ,2877
+omegaconf/_utils.py,sha256=bw8Jpra0B8YkHFrwODrBcKPvgQTyU0pEpskLl7CDBRg,32564
+omegaconf/base.py,sha256=rHWuLw8Iiew_SNJIUkLZy4EfyRb60hyS9X9Hl4OuBvs,32462
+omegaconf/basecontainer.py,sha256=FWL2PZWRb7wP-AGpeN651l_uVnNhZvzzLjaUdDlJiB8,33318
+omegaconf/dictconfig.py,sha256=IPtUPHoak025Q-gvDVqMdS497Xb6tjcsawrHXv4iQ2Y,27855
+omegaconf/errors.py,sha256=ectbX8Kd-5FCJMTBIh6jshqvCnCiXMFGfFQJYGIereQ,3595
+omegaconf/grammar/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+omegaconf/grammar/__pycache__/__init__.cpython-310.pyc,,
+omegaconf/grammar/gen/OmegaConfGrammarLexer.py,sha256=eoog1aofOltw-u61QFeCWZ3A9oa6vK2tjTaX4wmJY14,21689
+omegaconf/grammar/gen/OmegaConfGrammarParser.py,sha256=X0uJc0OieVB_f9yKRZw4OfwRBPOGoiyDMjhz8OKPvZ4,63429
+omegaconf/grammar/gen/OmegaConfGrammarParserListener.py,sha256=ncibh-Nhg68pdJPdMaqeFTP876wvo0lpJqPXhaOmjZU,5843
+omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py,sha256=Amvi244RtYFebNSJCbOwsqdvQW-QgdDKTku0rXnkD5Y,3617
+omegaconf/grammar/gen/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+omegaconf/grammar/gen/__pycache__/OmegaConfGrammarLexer.cpython-310.pyc,,
+omegaconf/grammar/gen/__pycache__/OmegaConfGrammarParser.cpython-310.pyc,,
+omegaconf/grammar/gen/__pycache__/OmegaConfGrammarParserListener.cpython-310.pyc,,
+omegaconf/grammar/gen/__pycache__/OmegaConfGrammarParserVisitor.cpython-310.pyc,,
+omegaconf/grammar/gen/__pycache__/__init__.cpython-310.pyc,,
+omegaconf/grammar_parser.py,sha256=7gvIGsqtsgl5e0XOWhI3Ur58FoIk3iBaX_M8JdyzpYM,5511
+omegaconf/grammar_visitor.py,sha256=L6LOomG24GcPA1-aYzNOmKLup5kYirHTuyp857V7T7A,16055
+omegaconf/listconfig.py,sha256=LyNRrn84BqNiz-TpwrDILat8HhlVwwM6zFmYCTUAdIo,24671
+omegaconf/nodes.py,sha256=1XRIC8sfEoaV4PT_Vm7Z75JMsIc30kKTuJHGyZ2aM0w,17479
+omegaconf/omegaconf.py,sha256=hFeVL0k6PNRqGE7ZUpI2p6sSGPkc9IEvsqr3MNdgU_s,39048
+omegaconf/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+omegaconf/resolvers/__init__.py,sha256=zE-8WJwipGC2cXPq12CGAz49m82F1D1cKMwuV3EoT7k,60
+omegaconf/resolvers/__pycache__/__init__.cpython-310.pyc,,
+omegaconf/resolvers/oc/__init__.py,sha256=gLniKaouCmkm1ZTQOzS1F5jFlCMw7eL-ae6PhaMxNjg,3548
+omegaconf/resolvers/oc/__pycache__/__init__.cpython-310.pyc,,
+omegaconf/resolvers/oc/__pycache__/dict.cpython-310.pyc,,
+omegaconf/resolvers/oc/dict.py,sha256=OL7iZ_aBIt7kYXxhMiFxSyskiX-xXBvGI_DnBbdxqVs,2324
+omegaconf/version.py,sha256=9OuwPWmsrNRBQYmeipe5XnywKY4jw-ZQBrjbo5C5Md4,442
+pydevd_plugins/__init__.py,sha256=byyA3PmxQsWCnqpQzFy44heJq59wOqHr5bSt6knm5vY,180
+pydevd_plugins/__pycache__/__init__.cpython-310.pyc,,
+pydevd_plugins/extensions/__init__.py,sha256=byyA3PmxQsWCnqpQzFy44heJq59wOqHr5bSt6knm5vY,180
+pydevd_plugins/extensions/__pycache__/__init__.cpython-310.pyc,,
+pydevd_plugins/extensions/__pycache__/pydevd_plugin_omegaconf.cpython-310.pyc,,
+pydevd_plugins/extensions/pydevd_plugin_omegaconf.py,sha256=Ac1SCniO-22CsRsMAXhFyyoVSN2R-MUWsOujm-601Ak,4251
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/REQUESTED b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.38.4)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/top_level.txt b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fcd2b36b800878631de7b82d0218569415c6a323
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf-2.3.0.dist-info/top_level.txt
@@ -0,0 +1,2 @@
+omegaconf
+pydevd_plugins
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1670cf08fa977560c2ad1ef2d98910824c1bdc9b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/__init__.py
@@ -0,0 +1,65 @@
+from .base import Container, DictKeyType, Node, SCMode, UnionNode
+from .dictconfig import DictConfig
+from .errors import (
+    KeyValidationError,
+    MissingMandatoryValue,
+    ReadonlyConfigError,
+    UnsupportedValueType,
+    ValidationError,
+)
+from .listconfig import ListConfig
+from .nodes import (
+    AnyNode,
+    BooleanNode,
+    BytesNode,
+    EnumNode,
+    FloatNode,
+    IntegerNode,
+    PathNode,
+    StringNode,
+    ValueNode,
+)
+from .omegaconf import (
+    II,
+    MISSING,
+    SI,
+    OmegaConf,
+    Resolver,
+    flag_override,
+    open_dict,
+    read_write,
+)
+from .version import __version__
+
+__all__ = [
+    "__version__",
+    "MissingMandatoryValue",
+    "ValidationError",
+    "ReadonlyConfigError",
+    "UnsupportedValueType",
+    "KeyValidationError",
+    "Container",
+    "UnionNode",
+    "ListConfig",
+    "DictConfig",
+    "DictKeyType",
+    "OmegaConf",
+    "Resolver",
+    "SCMode",
+    "flag_override",
+    "read_write",
+    "open_dict",
+    "Node",
+    "ValueNode",
+    "AnyNode",
+    "IntegerNode",
+    "StringNode",
+    "BytesNode",
+    "PathNode",
+    "BooleanNode",
+    "EnumNode",
+    "FloatNode",
+    "MISSING",
+    "SI",
+    "II",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/_impl.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c30ef6a239e65302c484b361773a00f85c70909
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/_impl.py
@@ -0,0 +1,101 @@
+from typing import Any
+
+from omegaconf import MISSING, Container, DictConfig, ListConfig, Node, ValueNode
+from omegaconf.errors import ConfigTypeError, InterpolationToMissingValueError
+
+from ._utils import _DEFAULT_MARKER_, _get_value
+
+
+def _resolve_container_value(cfg: Container, key: Any) -> None:
+    node = cfg._get_child(key)
+    assert isinstance(node, Node)
+    if node._is_interpolation():
+        try:
+            resolved = node._dereference_node()
+        except InterpolationToMissingValueError:
+            node._set_value(MISSING)
+        else:
+            if isinstance(resolved, Container):
+                _resolve(resolved)
+            if isinstance(resolved, Container) and isinstance(node, ValueNode):
+                cfg[key] = resolved
+            else:
+                node._set_value(_get_value(resolved))
+    else:
+        _resolve(node)
+
+
+def _resolve(cfg: Node) -> Node:
+    assert isinstance(cfg, Node)
+    if cfg._is_interpolation():
+        try:
+            resolved = cfg._dereference_node()
+        except InterpolationToMissingValueError:
+            cfg._set_value(MISSING)
+        else:
+            cfg._set_value(resolved._value())
+
+    if isinstance(cfg, DictConfig):
+        for k in cfg.keys():
+            _resolve_container_value(cfg, k)
+
+    elif isinstance(cfg, ListConfig):
+        for i in range(len(cfg)):
+            _resolve_container_value(cfg, i)
+
+    return cfg
+
+
+def select_value(
+    cfg: Container,
+    key: str,
+    *,
+    default: Any = _DEFAULT_MARKER_,
+    throw_on_resolution_failure: bool = True,
+    throw_on_missing: bool = False,
+    absolute_key: bool = False,
+) -> Any:
+    node = select_node(
+        cfg=cfg,
+        key=key,
+        throw_on_resolution_failure=throw_on_resolution_failure,
+        throw_on_missing=throw_on_missing,
+        absolute_key=absolute_key,
+    )
+
+    node_not_found = node is None
+    if node_not_found or node._is_missing():
+        if default is not _DEFAULT_MARKER_:
+            return default
+        else:
+            return None
+
+    return _get_value(node)
+
+
+def select_node(
+    cfg: Container,
+    key: str,
+    *,
+    throw_on_resolution_failure: bool = True,
+    throw_on_missing: bool = False,
+    absolute_key: bool = False,
+) -> Any:
+    try:
+        # for non relative keys, the interpretation can be:
+        # 1. relative to cfg
+        # 2. relative to the config root
+        # This is controlled by the absolute_key flag. By default, such keys are relative to cfg.
+        if not absolute_key and not key.startswith("."):
+            key = f".{key}"
+
+        cfg, key = cfg._resolve_key_and_root(key)
+        _root, _last_key, node = cfg._select_impl(
+            key,
+            throw_on_missing=throw_on_missing,
+            throw_on_resolution_failure=throw_on_resolution_failure,
+        )
+    except ConfigTypeError:
+        return None
+
+    return node
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/_utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffec246a38d278831bce26c6f7c233b72f34c1fe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/_utils.py
@@ -0,0 +1,1039 @@
+import copy
+import os
+import pathlib
+import re
+import string
+import sys
+import types
+import warnings
+from contextlib import contextmanager
+from enum import Enum
+from textwrap import dedent
+from typing import (
+    Any,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+    get_type_hints,
+)
+
+import yaml
+
+from .errors import (
+    ConfigIndexError,
+    ConfigTypeError,
+    ConfigValueError,
+    GrammarParseError,
+    OmegaConfBaseException,
+    ValidationError,
+)
+from .grammar_parser import SIMPLE_INTERPOLATION_PATTERN, parse
+
+try:
+    import dataclasses
+
+except ImportError:  # pragma: no cover
+    dataclasses = None  # type: ignore # pragma: no cover
+
+try:
+    import attr
+
+except ImportError:  # pragma: no cover
+    attr = None  # type: ignore # pragma: no cover
+
+NoneType: Type[None] = type(None)
+
+BUILTIN_VALUE_TYPES: Tuple[Type[Any], ...] = (
+    int,
+    float,
+    bool,
+    str,
+    bytes,
+    NoneType,
+)
+
+# Regexprs to match key paths like: a.b, a[b], ..a[c].d, etc.
+# We begin by matching the head (in these examples: a, a, ..a).
+# This can be read as "dots followed by any character but `.` or `[`"
+# Note that a key starting with brackets, like [a], is purposedly *not*
+# matched here and will instead be handled in the next regex below (this
+# is to keep this regex simple).
+KEY_PATH_HEAD = re.compile(r"(\.)*[^.[]*")
+# Then we match other keys. The following expression matches one key and can
+# be read as a choice between two syntaxes:
+#   - `.` followed by anything except `.` or `[` (ex: .b, .d)
+#   - `[` followed by anything then `]` (ex: [b], [c])
+KEY_PATH_OTHER = re.compile(r"\.([^.[]*)|\[(.*?)\]")
+
+
+# source: https://yaml.org/type/bool.html
+YAML_BOOL_TYPES = [
+    "y",
+    "Y",
+    "yes",
+    "Yes",
+    "YES",
+    "n",
+    "N",
+    "no",
+    "No",
+    "NO",
+    "true",
+    "True",
+    "TRUE",
+    "false",
+    "False",
+    "FALSE",
+    "on",
+    "On",
+    "ON",
+    "off",
+    "Off",
+    "OFF",
+]
+
+
+class Marker:
+    def __init__(self, desc: str):
+        self.desc = desc
+
+    def __repr__(self) -> str:
+        return self.desc
+
+
+# To be used as default value when `None` is not an option.
+_DEFAULT_MARKER_: Any = Marker("_DEFAULT_MARKER_")
+
+
+class OmegaConfDumper(yaml.Dumper):  # type: ignore
+    str_representer_added = False
+
+    @staticmethod
+    def str_representer(dumper: yaml.Dumper, data: str) -> yaml.ScalarNode:
+        with_quotes = yaml_is_bool(data) or is_int(data) or is_float(data)
+        return dumper.represent_scalar(
+            yaml.resolver.BaseResolver.DEFAULT_SCALAR_TAG,
+            data,
+            style=("'" if with_quotes else None),
+        )
+
+
+def get_omega_conf_dumper() -> Type[OmegaConfDumper]:
+    if not OmegaConfDumper.str_representer_added:
+        OmegaConfDumper.add_representer(str, OmegaConfDumper.str_representer)
+        OmegaConfDumper.str_representer_added = True
+    return OmegaConfDumper
+
+
+def yaml_is_bool(b: str) -> bool:
+    return b in YAML_BOOL_TYPES
+
+
+def get_yaml_loader() -> Any:
+    class OmegaConfLoader(yaml.SafeLoader):  # type: ignore
+        def construct_mapping(self, node: yaml.Node, deep: bool = False) -> Any:
+            keys = set()
+            for key_node, value_node in node.value:
+                if key_node.tag != yaml.resolver.BaseResolver.DEFAULT_SCALAR_TAG:
+                    continue
+                if key_node.value in keys:
+                    raise yaml.constructor.ConstructorError(
+                        "while constructing a mapping",
+                        node.start_mark,
+                        f"found duplicate key {key_node.value}",
+                        key_node.start_mark,
+                    )
+                keys.add(key_node.value)
+            return super().construct_mapping(node, deep=deep)
+
+    loader = OmegaConfLoader
+    loader.add_implicit_resolver(
+        "tag:yaml.org,2002:float",
+        re.compile(
+            """^(?:
+         [-+]?[0-9]+(?:_[0-9]+)*\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
+        |[-+]?[0-9]+(?:_[0-9]+)*(?:[eE][-+]?[0-9]+)
+        |\\.[0-9]+(?:_[0-9]+)*(?:[eE][-+][0-9]+)?
+        |[-+]?[0-9]+(?:_[0-9]+)*(?::[0-5]?[0-9])+\\.[0-9_]*
+        |[-+]?\\.(?:inf|Inf|INF)
+        |\\.(?:nan|NaN|NAN))$""",
+            re.X,
+        ),
+        list("-+0123456789."),
+    )
+    loader.yaml_implicit_resolvers = {
+        key: [
+            (tag, regexp)
+            for tag, regexp in resolvers
+            if tag != "tag:yaml.org,2002:timestamp"
+        ]
+        for key, resolvers in loader.yaml_implicit_resolvers.items()
+    }
+
+    loader.add_constructor(
+        "tag:yaml.org,2002:python/object/apply:pathlib.Path",
+        lambda loader, node: pathlib.Path(*loader.construct_sequence(node)),
+    )
+    loader.add_constructor(
+        "tag:yaml.org,2002:python/object/apply:pathlib.PosixPath",
+        lambda loader, node: pathlib.PosixPath(*loader.construct_sequence(node)),
+    )
+    loader.add_constructor(
+        "tag:yaml.org,2002:python/object/apply:pathlib.WindowsPath",
+        lambda loader, node: pathlib.WindowsPath(*loader.construct_sequence(node)),
+    )
+
+    return loader
+
+
+def _get_class(path: str) -> type:
+    from importlib import import_module
+
+    module_path, _, class_name = path.rpartition(".")
+    mod = import_module(module_path)
+    try:
+        klass: type = getattr(mod, class_name)
+    except AttributeError:
+        raise ImportError(f"Class {class_name} is not in module {module_path}")
+    return klass
+
+
+def is_union_annotation(type_: Any) -> bool:
+    if sys.version_info >= (3, 10):  # pragma: no cover
+        if isinstance(type_, types.UnionType):
+            return True
+    return getattr(type_, "__origin__", None) is Union
+
+
+def _resolve_optional(type_: Any) -> Tuple[bool, Any]:
+    """Check whether `type_` is equivalent to `typing.Optional[T]` for some T."""
+    if is_union_annotation(type_):
+        args = type_.__args__
+        if NoneType in args:
+            optional = True
+            args = tuple(a for a in args if a is not NoneType)
+        else:
+            optional = False
+        if len(args) == 1:
+            return optional, args[0]
+        elif len(args) >= 2:
+            return optional, Union[args]
+        else:
+            assert False
+
+    if type_ is Any:
+        return True, Any
+
+    if type_ in (None, NoneType):
+        return True, NoneType
+
+    return False, type_
+
+
+def _is_optional(obj: Any, key: Optional[Union[int, str]] = None) -> bool:
+    """Check `obj` metadata to see if the given node is optional."""
+    from .base import Container, Node
+
+    if key is not None:
+        assert isinstance(obj, Container)
+        obj = obj._get_node(key)
+    assert isinstance(obj, Node)
+    return obj._is_optional()
+
+
+def _resolve_forward(type_: Type[Any], module: str) -> Type[Any]:
+    import typing  # lgtm [py/import-and-import-from]
+
+    forward = typing.ForwardRef if hasattr(typing, "ForwardRef") else typing._ForwardRef  # type: ignore
+    if type(type_) is forward:
+        return _get_class(f"{module}.{type_.__forward_arg__}")
+    else:
+        if is_dict_annotation(type_):
+            kt, vt = get_dict_key_value_types(type_)
+            if kt is not None:
+                kt = _resolve_forward(kt, module=module)
+            if vt is not None:
+                vt = _resolve_forward(vt, module=module)
+            return Dict[kt, vt]  # type: ignore
+        if is_list_annotation(type_):
+            et = get_list_element_type(type_)
+            if et is not None:
+                et = _resolve_forward(et, module=module)
+            return List[et]  # type: ignore
+        if is_tuple_annotation(type_):
+            its = get_tuple_item_types(type_)
+            its = tuple(_resolve_forward(it, module=module) for it in its)
+            return Tuple[its]  # type: ignore
+
+        return type_
+
+
+def extract_dict_subclass_data(obj: Any, parent: Any) -> Optional[Dict[str, Any]]:
+    """Check if obj is an instance of a subclass of Dict. If so, extract the Dict keys/values."""
+    from omegaconf.omegaconf import _maybe_wrap
+
+    is_type = isinstance(obj, type)
+    obj_type = obj if is_type else type(obj)
+    subclasses_dict = is_dict_subclass(obj_type)
+
+    if subclasses_dict:
+        warnings.warn(
+            f"Class `{obj_type.__name__}` subclasses `Dict`."
+            + " Subclassing `Dict` in Structured Config classes is deprecated,"
+            + " see github.com/omry/omegaconf/issues/663",
+            UserWarning,
+            stacklevel=9,
+        )
+
+    if is_type:
+        return None
+    elif subclasses_dict:
+        dict_subclass_data = {}
+        key_type, element_type = get_dict_key_value_types(obj_type)
+        for name, value in obj.items():
+            is_optional, type_ = _resolve_optional(element_type)
+            type_ = _resolve_forward(type_, obj.__module__)
+            try:
+                dict_subclass_data[name] = _maybe_wrap(
+                    ref_type=type_,
+                    is_optional=is_optional,
+                    key=name,
+                    value=value,
+                    parent=parent,
+                )
+            except ValidationError as ex:
+                format_and_raise(
+                    node=None, key=name, value=value, cause=ex, msg=str(ex)
+                )
+        return dict_subclass_data
+    else:
+        return None
+
+
+def get_attr_class_fields(obj: Any) -> List["attr.Attribute[Any]"]:
+    is_type = isinstance(obj, type)
+    obj_type = obj if is_type else type(obj)
+    fields = attr.fields_dict(obj_type).values()
+    return [f for f in fields if f.metadata.get("omegaconf_ignore") is not True]
+
+
+def get_attr_data(obj: Any, allow_objects: Optional[bool] = None) -> Dict[str, Any]:
+    from omegaconf.omegaconf import OmegaConf, _maybe_wrap
+
+    flags = {"allow_objects": allow_objects} if allow_objects is not None else {}
+
+    from omegaconf import MISSING
+
+    d = {}
+    is_type = isinstance(obj, type)
+    obj_type = obj if is_type else type(obj)
+    dummy_parent = OmegaConf.create({}, flags=flags)
+    dummy_parent._metadata.object_type = obj_type
+    resolved_hints = get_type_hints(obj_type)
+
+    for attrib in get_attr_class_fields(obj):
+        name = attrib.name
+        is_optional, type_ = _resolve_optional(resolved_hints[name])
+        type_ = _resolve_forward(type_, obj.__module__)
+        if not is_type:
+            value = getattr(obj, name)
+        else:
+            value = attrib.default
+            if value == attr.NOTHING:
+                value = MISSING
+        if is_union_annotation(type_) and not is_supported_union_annotation(type_):
+            e = ConfigValueError(
+                f"Unions of containers are not supported:\n{name}: {type_str(type_)}"
+            )
+            format_and_raise(node=None, key=None, value=value, cause=e, msg=str(e))
+
+        try:
+            d[name] = _maybe_wrap(
+                ref_type=type_,
+                is_optional=is_optional,
+                key=name,
+                value=value,
+                parent=dummy_parent,
+            )
+        except (ValidationError, GrammarParseError) as ex:
+            format_and_raise(
+                node=dummy_parent, key=name, value=value, cause=ex, msg=str(ex)
+            )
+        d[name]._set_parent(None)
+    dict_subclass_data = extract_dict_subclass_data(obj=obj, parent=dummy_parent)
+    if dict_subclass_data is not None:
+        d.update(dict_subclass_data)
+    return d
+
+
+def get_dataclass_fields(obj: Any) -> List["dataclasses.Field[Any]"]:
+    fields = dataclasses.fields(obj)
+    return [f for f in fields if f.metadata.get("omegaconf_ignore") is not True]
+
+
+def get_dataclass_data(
+    obj: Any, allow_objects: Optional[bool] = None
+) -> Dict[str, Any]:
+    from omegaconf.omegaconf import MISSING, OmegaConf, _maybe_wrap
+
+    flags = {"allow_objects": allow_objects} if allow_objects is not None else {}
+    d = {}
+    is_type = isinstance(obj, type)
+    obj_type = get_type_of(obj)
+    dummy_parent = OmegaConf.create({}, flags=flags)
+    dummy_parent._metadata.object_type = obj_type
+    resolved_hints = get_type_hints(obj_type)
+    for field in get_dataclass_fields(obj):
+        name = field.name
+        is_optional, type_ = _resolve_optional(resolved_hints[field.name])
+        type_ = _resolve_forward(type_, obj.__module__)
+        has_default = field.default != dataclasses.MISSING
+        has_default_factory = field.default_factory != dataclasses.MISSING
+
+        if not is_type:
+            value = getattr(obj, name)
+        else:
+            if has_default:
+                value = field.default
+            elif has_default_factory:
+                value = field.default_factory()  # type: ignore
+            else:
+                value = MISSING
+
+        if is_union_annotation(type_) and not is_supported_union_annotation(type_):
+            e = ConfigValueError(
+                f"Unions of containers are not supported:\n{name}: {type_str(type_)}"
+            )
+            format_and_raise(node=None, key=None, value=value, cause=e, msg=str(e))
+        try:
+            d[name] = _maybe_wrap(
+                ref_type=type_,
+                is_optional=is_optional,
+                key=name,
+                value=value,
+                parent=dummy_parent,
+            )
+        except (ValidationError, GrammarParseError) as ex:
+            format_and_raise(
+                node=dummy_parent, key=name, value=value, cause=ex, msg=str(ex)
+            )
+        d[name]._set_parent(None)
+    dict_subclass_data = extract_dict_subclass_data(obj=obj, parent=dummy_parent)
+    if dict_subclass_data is not None:
+        d.update(dict_subclass_data)
+    return d
+
+
+def is_dataclass(obj: Any) -> bool:
+    from omegaconf.base import Node
+
+    if dataclasses is None or isinstance(obj, Node):
+        return False
+    return dataclasses.is_dataclass(obj)
+
+
+def is_attr_class(obj: Any) -> bool:
+    from omegaconf.base import Node
+
+    if attr is None or isinstance(obj, Node):
+        return False
+    return attr.has(obj)
+
+
+def is_structured_config(obj: Any) -> bool:
+    return is_attr_class(obj) or is_dataclass(obj)
+
+
+def is_dataclass_frozen(type_: Any) -> bool:
+    return type_.__dataclass_params__.frozen  # type: ignore
+
+
+def is_attr_frozen(type_: type) -> bool:
+    # This is very hacky and probably fragile as well.
+    # Unfortunately currently there isn't an official API in attr that can detect that.
+    # noinspection PyProtectedMember
+    return type_.__setattr__ == attr._make._frozen_setattrs  # type: ignore
+
+
+def get_type_of(class_or_object: Any) -> Type[Any]:
+    type_ = class_or_object
+    if not isinstance(type_, type):
+        type_ = type(class_or_object)
+    assert isinstance(type_, type)
+    return type_
+
+
+def is_structured_config_frozen(obj: Any) -> bool:
+    type_ = get_type_of(obj)
+
+    if is_dataclass(type_):
+        return is_dataclass_frozen(type_)
+    if is_attr_class(type_):
+        return is_attr_frozen(type_)
+    return False
+
+
+def get_structured_config_init_field_names(obj: Any) -> List[str]:
+    fields: Union[List["dataclasses.Field[Any]"], List["attr.Attribute[Any]"]]
+    if is_dataclass(obj):
+        fields = get_dataclass_fields(obj)
+    elif is_attr_class(obj):
+        fields = get_attr_class_fields(obj)
+    else:
+        raise ValueError(f"Unsupported type: {type(obj).__name__}")
+    return [f.name for f in fields if f.init]
+
+
+def get_structured_config_data(
+    obj: Any, allow_objects: Optional[bool] = None
+) -> Dict[str, Any]:
+    if is_dataclass(obj):
+        return get_dataclass_data(obj, allow_objects=allow_objects)
+    elif is_attr_class(obj):
+        return get_attr_data(obj, allow_objects=allow_objects)
+    else:
+        raise ValueError(f"Unsupported type: {type(obj).__name__}")
+
+
+class ValueKind(Enum):
+    VALUE = 0
+    MANDATORY_MISSING = 1
+    INTERPOLATION = 2
+
+
+def _is_missing_value(value: Any) -> bool:
+    from omegaconf import Node
+
+    if isinstance(value, Node):
+        value = value._value()
+    return _is_missing_literal(value)
+
+
+def _is_missing_literal(value: Any) -> bool:
+    # Uses literal '???' instead of the MISSING const for performance reasons.
+    return isinstance(value, str) and value == "???"
+
+
+def _is_none(
+    value: Any, resolve: bool = False, throw_on_resolution_failure: bool = True
+) -> bool:
+    from omegaconf import Node
+
+    if not isinstance(value, Node):
+        return value is None
+
+    if resolve:
+        value = value._maybe_dereference_node(
+            throw_on_resolution_failure=throw_on_resolution_failure
+        )
+        if not throw_on_resolution_failure and value is None:
+            # Resolution failure: consider that it is *not* None.
+            return False
+        assert isinstance(value, Node)
+
+    return value._is_none()
+
+
+def get_value_kind(
+    value: Any, strict_interpolation_validation: bool = False
+) -> ValueKind:
+    """
+    Determine the kind of a value
+    Examples:
+    VALUE: "10", "20", True
+    MANDATORY_MISSING: "???"
+    INTERPOLATION: "${foo.bar}", "${foo.${bar}}", "${foo:bar}", "[${foo}, ${bar}]",
+                   "ftp://${host}/path", "${foo:${bar}, [true], {'baz': ${baz}}}"
+
+    :param value: Input to classify.
+    :param strict_interpolation_validation: If `True`, then when `value` is a string
+        containing "${", it is parsed to validate the interpolation syntax. If `False`,
+        this parsing step is skipped: this is more efficient, but will not detect errors.
+    """
+
+    if _is_missing_value(value):
+        return ValueKind.MANDATORY_MISSING
+
+    if _is_interpolation(value, strict_interpolation_validation):
+        return ValueKind.INTERPOLATION
+
+    return ValueKind.VALUE
+
+
+def _is_interpolation(v: Any, strict_interpolation_validation: bool = False) -> bool:
+    from omegaconf import Node
+
+    if isinstance(v, Node):
+        v = v._value()
+
+    if isinstance(v, str) and _is_interpolation_string(
+        v, strict_interpolation_validation
+    ):
+        return True
+    return False
+
+
+def _is_interpolation_string(value: str, strict_interpolation_validation: bool) -> bool:
+    # We identify potential interpolations by the presence of "${" in the string.
+    # Note that escaped interpolations (ex: "esc: \${bar}") are identified as
+    # interpolations: this is intended, since they must be processed as interpolations
+    # for the string to be properly un-escaped.
+    # Keep in mind that invalid interpolations will only be detected when
+    # `strict_interpolation_validation` is True.
+    if "${" in value:
+        if strict_interpolation_validation:
+            # First try the cheap regex matching that detects common interpolations.
+            if SIMPLE_INTERPOLATION_PATTERN.match(value) is None:
+                # If no match, do the more expensive grammar parsing to detect errors.
+                parse(value)
+        return True
+    return False
+
+
+def _is_special(value: Any) -> bool:
+    """Special values are None, MISSING, and interpolation."""
+    return _is_none(value) or get_value_kind(value) in (
+        ValueKind.MANDATORY_MISSING,
+        ValueKind.INTERPOLATION,
+    )
+
+
+def is_float(st: str) -> bool:
+    try:
+        float(st)
+        return True
+    except ValueError:
+        return False
+
+
+def is_int(st: str) -> bool:
+    try:
+        int(st)
+        return True
+    except ValueError:
+        return False
+
+
+def is_primitive_list(obj: Any) -> bool:
+    return isinstance(obj, (list, tuple))
+
+
+def is_primitive_dict(obj: Any) -> bool:
+    t = get_type_of(obj)
+    return t is dict
+
+
+def is_dict_annotation(type_: Any) -> bool:
+    if type_ in (dict, Dict):
+        return True
+    origin = getattr(type_, "__origin__", None)
+    # type_dict is a bit hard to detect.
+    # this support is tentative, if it eventually causes issues in other areas it may be dropped.
+    if sys.version_info < (3, 7, 0):  # pragma: no cover
+        typed_dict = hasattr(type_, "__base__") and type_.__base__ == Dict
+        return origin is Dict or type_ is Dict or typed_dict
+    else:  # pragma: no cover
+        typed_dict = hasattr(type_, "__base__") and type_.__base__ == dict
+        return origin is dict or typed_dict
+
+
+def is_list_annotation(type_: Any) -> bool:
+    if type_ in (list, List):
+        return True
+    origin = getattr(type_, "__origin__", None)
+    if sys.version_info < (3, 7, 0):
+        return origin is List or type_ is List  # pragma: no cover
+    else:
+        return origin is list  # pragma: no cover
+
+
+def is_tuple_annotation(type_: Any) -> bool:
+    if type_ in (tuple, Tuple):
+        return True
+    origin = getattr(type_, "__origin__", None)
+    if sys.version_info < (3, 7, 0):
+        return origin is Tuple or type_ is Tuple  # pragma: no cover
+    else:
+        return origin is tuple  # pragma: no cover
+
+
+def is_supported_union_annotation(obj: Any) -> bool:
+    """Currently only primitive types are supported in Unions, e.g. Union[int, str]"""
+    if not is_union_annotation(obj):
+        return False
+    args = obj.__args__
+    return all(is_primitive_type_annotation(arg) for arg in args)
+
+
+def is_dict_subclass(type_: Any) -> bool:
+    return type_ is not None and isinstance(type_, type) and issubclass(type_, Dict)
+
+
+def is_dict(obj: Any) -> bool:
+    return is_primitive_dict(obj) or is_dict_annotation(obj) or is_dict_subclass(obj)
+
+
+def is_primitive_container(obj: Any) -> bool:
+    return is_primitive_list(obj) or is_primitive_dict(obj)
+
+
+def get_list_element_type(ref_type: Optional[Type[Any]]) -> Any:
+    args = getattr(ref_type, "__args__", None)
+    if ref_type is not List and args is not None and args[0]:
+        element_type = args[0]
+    else:
+        element_type = Any
+    return element_type
+
+
+def get_tuple_item_types(ref_type: Type[Any]) -> Tuple[Any, ...]:
+    args = getattr(ref_type, "__args__", None)
+    if args in (None, ()):
+        args = (Any, ...)
+    assert isinstance(args, tuple)
+    return args
+
+
+def get_dict_key_value_types(ref_type: Any) -> Tuple[Any, Any]:
+    args = getattr(ref_type, "__args__", None)
+    if args is None:
+        bases = getattr(ref_type, "__orig_bases__", None)
+        if bases is not None and len(bases) > 0:
+            args = getattr(bases[0], "__args__", None)
+
+    key_type: Any
+    element_type: Any
+    if ref_type is None or ref_type == Dict:
+        key_type = Any
+        element_type = Any
+    else:
+        if args is not None:
+            key_type = args[0]
+            element_type = args[1]
+        else:
+            key_type = Any
+            element_type = Any
+
+    return key_type, element_type
+
+
+def is_valid_value_annotation(type_: Any) -> bool:
+    _, type_ = _resolve_optional(type_)
+    return (
+        type_ is Any
+        or is_primitive_type_annotation(type_)
+        or is_structured_config(type_)
+        or is_container_annotation(type_)
+        or is_supported_union_annotation(type_)
+    )
+
+
+def _valid_dict_key_annotation_type(type_: Any) -> bool:
+    from omegaconf import DictKeyType
+
+    return type_ is None or type_ is Any or issubclass(type_, DictKeyType.__args__)  # type: ignore
+
+
+def is_primitive_type_annotation(type_: Any) -> bool:
+    type_ = get_type_of(type_)
+    return issubclass(type_, (Enum, pathlib.Path)) or type_ in BUILTIN_VALUE_TYPES
+
+
+def _get_value(value: Any) -> Any:
+    from .base import Container, UnionNode
+    from .nodes import ValueNode
+
+    if isinstance(value, ValueNode):
+        return value._value()
+    elif isinstance(value, Container):
+        boxed = value._value()
+        if boxed is None or _is_missing_literal(boxed) or _is_interpolation(boxed):
+            return boxed
+    elif isinstance(value, UnionNode):
+        boxed = value._value()
+        if boxed is None or _is_missing_literal(boxed) or _is_interpolation(boxed):
+            return boxed
+        else:
+            return _get_value(boxed)  # pass through value of boxed node
+
+    # return primitives and regular OmegaConf Containers as is
+    return value
+
+
+def get_type_hint(obj: Any, key: Any = None) -> Optional[Type[Any]]:
+    from omegaconf import Container, Node
+
+    if isinstance(obj, Container):
+        if key is not None:
+            obj = obj._get_node(key)
+    else:
+        if key is not None:
+            raise ValueError("Key must only be provided when obj is a container")
+
+    if isinstance(obj, Node):
+        ref_type = obj._metadata.ref_type
+        if obj._is_optional() and ref_type is not Any:
+            return Optional[ref_type]  # type: ignore
+        else:
+            return ref_type
+    else:
+        return Any  # type: ignore
+
+
+def _raise(ex: Exception, cause: Exception) -> None:
+    # Set the environment variable OC_CAUSE=1 to get a stacktrace that includes the
+    # causing exception.
+    env_var = os.environ["OC_CAUSE"] if "OC_CAUSE" in os.environ else None
+    debugging = sys.gettrace() is not None
+    full_backtrace = (debugging and not env_var == "0") or (env_var == "1")
+    if full_backtrace:
+        ex.__cause__ = cause
+    else:
+        ex.__cause__ = None
+    raise ex.with_traceback(sys.exc_info()[2])  # set env var OC_CAUSE=1 for full trace
+
+
+def format_and_raise(
+    node: Any,
+    key: Any,
+    value: Any,
+    msg: str,
+    cause: Exception,
+    type_override: Any = None,
+) -> None:
+    from omegaconf import OmegaConf
+    from omegaconf.base import Node
+
+    if isinstance(cause, AssertionError):
+        raise
+
+    if isinstance(cause, OmegaConfBaseException) and cause._initialized:
+        ex = cause
+        if type_override is not None:
+            ex = type_override(str(cause))
+            ex.__dict__ = copy.deepcopy(cause.__dict__)
+        _raise(ex, cause)
+
+    object_type: Optional[Type[Any]]
+    object_type_str: Optional[str] = None
+    ref_type: Optional[Type[Any]]
+    ref_type_str: Optional[str]
+
+    child_node: Optional[Node] = None
+    if node is None:
+        full_key = key if key is not None else ""
+        object_type = None
+        ref_type = None
+        ref_type_str = None
+    else:
+        if key is not None and not node._is_none():
+            child_node = node._get_node(key, validate_access=False)
+
+        try:
+            full_key = node._get_full_key(key=key)
+        except Exception as exc:
+            # Since we are handling an exception, raising a different one here would
+            # be misleading. Instead, we display it in the key.
+            full_key = f"<unresolvable due to {type(exc).__name__}: {exc}>"
+
+        object_type = OmegaConf.get_type(node)
+        object_type_str = type_str(object_type)
+
+        ref_type = get_type_hint(node)
+        ref_type_str = type_str(ref_type)
+
+    msg = string.Template(msg).safe_substitute(
+        REF_TYPE=ref_type_str,
+        OBJECT_TYPE=object_type_str,
+        KEY=key,
+        FULL_KEY=full_key,
+        VALUE=value,
+        VALUE_TYPE=type_str(type(value), include_module_name=True),
+        KEY_TYPE=f"{type(key).__name__}",
+    )
+
+    if ref_type not in (None, Any):
+        template = dedent(
+            """\
+            $MSG
+                full_key: $FULL_KEY
+                reference_type=$REF_TYPE
+                object_type=$OBJECT_TYPE"""
+        )
+    else:
+        template = dedent(
+            """\
+            $MSG
+                full_key: $FULL_KEY
+                object_type=$OBJECT_TYPE"""
+        )
+    s = string.Template(template=template)
+
+    message = s.substitute(
+        REF_TYPE=ref_type_str, OBJECT_TYPE=object_type_str, MSG=msg, FULL_KEY=full_key
+    )
+    exception_type = type(cause) if type_override is None else type_override
+    if exception_type == TypeError:
+        exception_type = ConfigTypeError
+    elif exception_type == IndexError:
+        exception_type = ConfigIndexError
+
+    ex = exception_type(f"{message}")
+    if issubclass(exception_type, OmegaConfBaseException):
+        ex._initialized = True
+        ex.msg = message
+        ex.parent_node = node
+        ex.child_node = child_node
+        ex.key = key
+        ex.full_key = full_key
+        ex.value = value
+        ex.object_type = object_type
+        ex.object_type_str = object_type_str
+        ex.ref_type = ref_type
+        ex.ref_type_str = ref_type_str
+
+    _raise(ex, cause)
+
+
+def type_str(t: Any, include_module_name: bool = False) -> str:
+    is_optional, t = _resolve_optional(t)
+    if t is NoneType:
+        return str(t.__name__)
+    if t is Any:
+        return "Any"
+    if t is ...:
+        return "..."
+
+    if hasattr(t, "__name__"):
+        name = str(t.__name__)
+    elif getattr(t, "_name", None) is not None:  # pragma: no cover
+        name = str(t._name)
+    elif getattr(t, "__origin__", None) is not None:  # pragma: no cover
+        name = type_str(t.__origin__)
+    else:
+        name = str(t)
+        if name.startswith("typing."):  # pragma: no cover
+            name = name[len("typing.") :]
+
+    args = getattr(t, "__args__", None)
+    if args is not None:
+        args = ", ".join(
+            [type_str(t, include_module_name=include_module_name) for t in t.__args__]
+        )
+        ret = f"{name}[{args}]"
+    else:
+        ret = name
+    if include_module_name:
+        if (
+            hasattr(t, "__module__")
+            and t.__module__ != "builtins"
+            and t.__module__ != "typing"
+            and not t.__module__.startswith("omegaconf.")
+        ):
+            module_prefix = str(t.__module__) + "."
+        else:
+            module_prefix = ""
+        ret = module_prefix + ret
+    if is_optional:
+        return f"Optional[{ret}]"
+    else:
+        return ret
+
+
+def _ensure_container(target: Any, flags: Optional[Dict[str, bool]] = None) -> Any:
+    from omegaconf import OmegaConf
+
+    if is_primitive_container(target):
+        assert isinstance(target, (list, dict))
+        target = OmegaConf.create(target, flags=flags)
+    elif is_structured_config(target):
+        target = OmegaConf.structured(target, flags=flags)
+    elif not OmegaConf.is_config(target):
+        raise ValueError(
+            "Invalid input. Supports one of "
+            + "[dict,list,DictConfig,ListConfig,dataclass,dataclass instance,attr class,attr class instance]"
+        )
+
+    return target
+
+
+def is_generic_list(type_: Any) -> bool:
+    """
+    Checks if a type is a generic list, for example:
+    list returns False
+    typing.List returns False
+    typing.List[T] returns True
+
+    :param type_: variable type
+    :return: bool
+    """
+    return is_list_annotation(type_) and get_list_element_type(type_) is not None
+
+
+def is_generic_dict(type_: Any) -> bool:
+    """
+    Checks if a type is a generic dict, for example:
+    list returns False
+    typing.List returns False
+    typing.List[T] returns True
+
+    :param type_: variable type
+    :return: bool
+    """
+    return is_dict_annotation(type_) and len(get_dict_key_value_types(type_)) > 0
+
+
+def is_container_annotation(type_: Any) -> bool:
+    return is_list_annotation(type_) or is_dict_annotation(type_)
+
+
+def split_key(key: str) -> List[str]:
+    """
+    Split a full key path into its individual components.
+
+    This is similar to `key.split(".")` but also works with the getitem syntax:
+        "a.b"       -> ["a", "b"]
+        "a[b]"      -> ["a", "b"]
+        ".a.b[c].d" -> ["", "a", "b", "c", "d"]
+        "[a].b"     -> ["a", "b"]
+    """
+    # Obtain the first part of the key (in docstring examples: a, a, .a, '')
+    first = KEY_PATH_HEAD.match(key)
+    assert first is not None
+    first_stop = first.span()[1]
+
+    # `tokens` will contain all elements composing the key.
+    tokens = key[0:first_stop].split(".")
+
+    # Optimization in case `key` has no other component: we are done.
+    if first_stop == len(key):
+        return tokens
+
+    if key[first_stop] == "[" and not tokens[-1]:
+        # This is a special case where the first key starts with brackets, e.g.
+        # [a] or ..[a]. In that case there is an extra "" in `tokens` that we
+        # need to get rid of:
+        #   [a]   -> tokens = [""] but we would like []
+        #   ..[a] -> tokens = ["", "", ""] but we would like ["", ""]
+        tokens.pop()
+
+    # Identify other key elements (in docstring examples: b, b, b/c/d, b)
+    others = KEY_PATH_OTHER.findall(key[first_stop:])
+
+    # There are two groups in the `KEY_PATH_OTHER` regex: one for keys starting
+    # with a dot (.b, .d) and one for keys starting with a bracket ([b], [c]).
+    # Only one group can be non-empty.
+    tokens += [dot_key if dot_key else bracket_key for dot_key, bracket_key in others]
+
+    return tokens
+
+
+# Similar to Python 3.7+'s `contextlib.nullcontext` (which should be used instead,
+# once support for Python 3.6 is dropped).
+@contextmanager
+def nullcontext(enter_result: Any = None) -> Iterator[Any]:
+    yield enter_result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b013d1d9fac789cc28e8ea0a23e586610a32f43
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/base.py
@@ -0,0 +1,962 @@
+import copy
+import sys
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, Type, Union
+
+from antlr4 import ParserRuleContext
+
+from ._utils import (
+    _DEFAULT_MARKER_,
+    NoneType,
+    ValueKind,
+    _get_value,
+    _is_interpolation,
+    _is_missing_value,
+    _is_special,
+    format_and_raise,
+    get_value_kind,
+    is_union_annotation,
+    is_valid_value_annotation,
+    split_key,
+    type_str,
+)
+from .errors import (
+    ConfigKeyError,
+    ConfigTypeError,
+    InterpolationKeyError,
+    InterpolationResolutionError,
+    InterpolationToMissingValueError,
+    InterpolationValidationError,
+    MissingMandatoryValue,
+    UnsupportedInterpolationType,
+    ValidationError,
+)
+from .grammar.gen.OmegaConfGrammarParser import OmegaConfGrammarParser
+from .grammar_parser import parse
+from .grammar_visitor import GrammarVisitor
+
+DictKeyType = Union[str, bytes, int, Enum, float, bool]
+
+
+@dataclass
+class Metadata:
+
+    ref_type: Union[Type[Any], Any]
+
+    object_type: Union[Type[Any], Any]
+
+    optional: bool
+
+    key: Any
+
+    # Flags have 3 modes:
+    #   unset : inherit from parent (None if no parent specifies)
+    #   set to true: flag is true
+    #   set to false: flag is false
+    flags: Optional[Dict[str, bool]] = None
+
+    # If True, when checking the value of a flag, if the flag is not set None is returned
+    # otherwise, the parent node is queried.
+    flags_root: bool = False
+
+    resolver_cache: Dict[str, Any] = field(default_factory=lambda: defaultdict(dict))
+
+    def __post_init__(self) -> None:
+        if self.flags is None:
+            self.flags = {}
+
+    @property
+    def type_hint(self) -> Union[Type[Any], Any]:
+        """Compute `type_hint` from `self.optional` and `self.ref_type`"""
+        # For compatibility with pickled OmegaConf objects created using older
+        # versions of OmegaConf, we store `ref_type` and `object_type`
+        # separately (rather than storing `type_hint` directly).
+        if self.optional:
+            return Optional[self.ref_type]
+        else:
+            return self.ref_type
+
+
+@dataclass
+class ContainerMetadata(Metadata):
+    key_type: Any = None
+    element_type: Any = None
+
+    def __post_init__(self) -> None:
+        if self.ref_type is None:
+            self.ref_type = Any
+        assert self.key_type is Any or isinstance(self.key_type, type)
+        if self.element_type is not None:
+            if not is_valid_value_annotation(self.element_type):
+                raise ValidationError(
+                    f"Unsupported value type: '{type_str(self.element_type, include_module_name=True)}'"
+                )
+
+        if self.flags is None:
+            self.flags = {}
+
+
+class Node(ABC):
+    _metadata: Metadata
+
+    _parent: Optional["Box"]
+    _flags_cache: Optional[Dict[str, Optional[bool]]]
+
+    def __init__(self, parent: Optional["Box"], metadata: Metadata):
+        self.__dict__["_metadata"] = metadata
+        self.__dict__["_parent"] = parent
+        self.__dict__["_flags_cache"] = None
+
+    def __getstate__(self) -> Dict[str, Any]:
+        # Overridden to ensure that the flags cache is cleared on serialization.
+        state_dict = copy.copy(self.__dict__)
+        del state_dict["_flags_cache"]
+        return state_dict
+
+    def __setstate__(self, state_dict: Dict[str, Any]) -> None:
+        self.__dict__.update(state_dict)
+        self.__dict__["_flags_cache"] = None
+
+    def _set_parent(self, parent: Optional["Box"]) -> None:
+        assert parent is None or isinstance(parent, Box)
+        self.__dict__["_parent"] = parent
+        self._invalidate_flags_cache()
+
+    def _invalidate_flags_cache(self) -> None:
+        self.__dict__["_flags_cache"] = None
+
+    def _get_parent(self) -> Optional["Box"]:
+        parent = self.__dict__["_parent"]
+        assert parent is None or isinstance(parent, Box)
+        return parent
+
+    def _get_parent_container(self) -> Optional["Container"]:
+        """
+        Like _get_parent, but returns the grandparent
+        in the case where `self` is wrapped by a UnionNode.
+        """
+        parent = self.__dict__["_parent"]
+        assert parent is None or isinstance(parent, Box)
+
+        if isinstance(parent, UnionNode):
+            grandparent = parent.__dict__["_parent"]
+            assert grandparent is None or isinstance(grandparent, Container)
+            return grandparent
+        else:
+            assert parent is None or isinstance(parent, Container)
+            return parent
+
+    def _set_flag(
+        self,
+        flags: Union[List[str], str],
+        values: Union[List[Optional[bool]], Optional[bool]],
+    ) -> "Node":
+        if isinstance(flags, str):
+            flags = [flags]
+
+        if values is None or isinstance(values, bool):
+            values = [values]
+
+        if len(values) == 1:
+            values = len(flags) * values
+
+        if len(flags) != len(values):
+            raise ValueError("Inconsistent lengths of input flag names and values")
+
+        for idx, flag in enumerate(flags):
+            value = values[idx]
+            if value is None:
+                assert self._metadata.flags is not None
+                if flag in self._metadata.flags:
+                    del self._metadata.flags[flag]
+            else:
+                assert self._metadata.flags is not None
+                self._metadata.flags[flag] = value
+        self._invalidate_flags_cache()
+        return self
+
+    def _get_node_flag(self, flag: str) -> Optional[bool]:
+        """
+        :param flag: flag to inspect
+        :return: the state of the flag on this node.
+        """
+        assert self._metadata.flags is not None
+        return self._metadata.flags.get(flag)
+
+    def _get_flag(self, flag: str) -> Optional[bool]:
+        cache = self.__dict__["_flags_cache"]
+        if cache is None:
+            cache = self.__dict__["_flags_cache"] = {}
+
+        ret = cache.get(flag, _DEFAULT_MARKER_)
+        if ret is _DEFAULT_MARKER_:
+            ret = self._get_flag_no_cache(flag)
+            cache[flag] = ret
+        assert ret is None or isinstance(ret, bool)
+        return ret
+
+    def _get_flag_no_cache(self, flag: str) -> Optional[bool]:
+        """
+        Returns True if this config node flag is set
+        A flag is set if node.set_flag(True) was called
+        or one if it's parents is flag is set
+        :return:
+        """
+        flags = self._metadata.flags
+        assert flags is not None
+        if flag in flags and flags[flag] is not None:
+            return flags[flag]
+
+        if self._is_flags_root():
+            return None
+
+        parent = self._get_parent()
+        if parent is None:
+            return None
+        else:
+            # noinspection PyProtectedMember
+            return parent._get_flag(flag)
+
+    def _format_and_raise(
+        self,
+        key: Any,
+        value: Any,
+        cause: Exception,
+        msg: Optional[str] = None,
+        type_override: Any = None,
+    ) -> None:
+        format_and_raise(
+            node=self,
+            key=key,
+            value=value,
+            msg=str(cause) if msg is None else msg,
+            cause=cause,
+            type_override=type_override,
+        )
+        assert False
+
+    @abstractmethod
+    def _get_full_key(self, key: Optional[Union[DictKeyType, int]]) -> str:
+        ...
+
+    def _dereference_node(self) -> "Node":
+        node = self._dereference_node_impl(throw_on_resolution_failure=True)
+        assert node is not None
+        return node
+
+    def _maybe_dereference_node(
+        self,
+        throw_on_resolution_failure: bool = False,
+        memo: Optional[Set[int]] = None,
+    ) -> Optional["Node"]:
+        return self._dereference_node_impl(
+            throw_on_resolution_failure=throw_on_resolution_failure,
+            memo=memo,
+        )
+
+    def _dereference_node_impl(
+        self,
+        throw_on_resolution_failure: bool,
+        memo: Optional[Set[int]] = None,
+    ) -> Optional["Node"]:
+        if not self._is_interpolation():
+            return self
+
+        parent = self._get_parent_container()
+        if parent is None:
+            if throw_on_resolution_failure:
+                raise InterpolationResolutionError(
+                    "Cannot resolve interpolation for a node without a parent"
+                )
+            return None
+        assert parent is not None
+        key = self._key()
+        return parent._resolve_interpolation_from_parse_tree(
+            parent=parent,
+            key=key,
+            value=self,
+            parse_tree=parse(_get_value(self)),
+            throw_on_resolution_failure=throw_on_resolution_failure,
+            memo=memo,
+        )
+
+    def _get_root(self) -> "Container":
+        root: Optional[Box] = self._get_parent()
+        if root is None:
+            assert isinstance(self, Container)
+            return self
+        assert root is not None and isinstance(root, Box)
+        while root._get_parent() is not None:
+            root = root._get_parent()
+            assert root is not None and isinstance(root, Box)
+        assert root is not None and isinstance(root, Container)
+        return root
+
+    def _is_missing(self) -> bool:
+        """
+        Check if the node's value is `???` (does *not* resolve interpolations).
+        """
+        return _is_missing_value(self)
+
+    def _is_none(self) -> bool:
+        """
+        Check if the node's value is `None` (does *not* resolve interpolations).
+        """
+        return self._value() is None
+
+    @abstractmethod
+    def __eq__(self, other: Any) -> bool:
+        ...
+
+    @abstractmethod
+    def __ne__(self, other: Any) -> bool:
+        ...
+
+    @abstractmethod
+    def __hash__(self) -> int:
+        ...
+
+    @abstractmethod
+    def _value(self) -> Any:
+        ...
+
+    @abstractmethod
+    def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None:
+        ...
+
+    @abstractmethod
+    def _is_optional(self) -> bool:
+        ...
+
+    @abstractmethod
+    def _is_interpolation(self) -> bool:
+        ...
+
+    def _key(self) -> Any:
+        return self._metadata.key
+
+    def _set_key(self, key: Any) -> None:
+        self._metadata.key = key
+
+    def _is_flags_root(self) -> bool:
+        return self._metadata.flags_root
+
+    def _set_flags_root(self, flags_root: bool) -> None:
+        if self._metadata.flags_root != flags_root:
+            self._metadata.flags_root = flags_root
+            self._invalidate_flags_cache()
+
+    def _has_ref_type(self) -> bool:
+        return self._metadata.ref_type is not Any
+
+
+class Box(Node):
+    """
+    Base class for nodes that can contain other nodes.
+    Concrete subclasses include DictConfig, ListConfig, and UnionNode.
+    """
+
+    _content: Any
+
+    def __init__(self, parent: Optional["Box"], metadata: Metadata):
+        super().__init__(parent=parent, metadata=metadata)
+        self.__dict__["_content"] = None
+
+    def __copy__(self) -> Any:
+        # real shallow copy is impossible because of the reference to the parent.
+        return copy.deepcopy(self)
+
+    def _re_parent(self) -> None:
+        from .dictconfig import DictConfig
+        from .listconfig import ListConfig
+
+        # update parents of first level Config nodes to self
+
+        if isinstance(self, DictConfig):
+            content = self.__dict__["_content"]
+            if isinstance(content, dict):
+                for _key, value in self.__dict__["_content"].items():
+                    if value is not None:
+                        value._set_parent(self)
+                    if isinstance(value, Box):
+                        value._re_parent()
+        elif isinstance(self, ListConfig):
+            content = self.__dict__["_content"]
+            if isinstance(content, list):
+                for item in self.__dict__["_content"]:
+                    if item is not None:
+                        item._set_parent(self)
+                    if isinstance(item, Box):
+                        item._re_parent()
+        elif isinstance(self, UnionNode):
+            content = self.__dict__["_content"]
+            if isinstance(content, Node):
+                content._set_parent(self)
+                if isinstance(content, Box):  # pragma: no cover
+                    # No coverage here as support for containers inside
+                    # UnionNode is not yet implemented
+                    content._re_parent()
+
+
+class Container(Box):
+    """
+    Container tagging interface
+    """
+
+    _metadata: ContainerMetadata
+
+    @abstractmethod
+    def _get_child(
+        self,
+        key: Any,
+        validate_access: bool = True,
+        validate_key: bool = True,
+        throw_on_missing_value: bool = False,
+        throw_on_missing_key: bool = False,
+    ) -> Union[Optional[Node], List[Optional[Node]]]:
+        ...
+
+    @abstractmethod
+    def _get_node(
+        self,
+        key: Any,
+        validate_access: bool = True,
+        validate_key: bool = True,
+        throw_on_missing_value: bool = False,
+        throw_on_missing_key: bool = False,
+    ) -> Union[Optional[Node], List[Optional[Node]]]:
+        ...
+
+    @abstractmethod
+    def __delitem__(self, key: Any) -> None:
+        ...
+
+    @abstractmethod
+    def __setitem__(self, key: Any, value: Any) -> None:
+        ...
+
+    @abstractmethod
+    def __iter__(self) -> Iterator[Any]:
+        ...
+
+    @abstractmethod
+    def __getitem__(self, key_or_index: Any) -> Any:
+        ...
+
+    def _resolve_key_and_root(self, key: str) -> Tuple["Container", str]:
+        orig = key
+        if not key.startswith("."):
+            return self._get_root(), key
+        else:
+            root: Optional[Container] = self
+            assert key.startswith(".")
+            while True:
+                assert root is not None
+                key = key[1:]
+                if not key.startswith("."):
+                    break
+                root = root._get_parent_container()
+                if root is None:
+                    raise ConfigKeyError(f"Error resolving key '{orig}'")
+
+            return root, key
+
+    def _select_impl(
+        self,
+        key: str,
+        throw_on_missing: bool,
+        throw_on_resolution_failure: bool,
+        memo: Optional[Set[int]] = None,
+    ) -> Tuple[Optional["Container"], Optional[str], Optional[Node]]:
+        """
+        Select a value using dot separated key sequence
+        """
+        from .omegaconf import _select_one
+
+        if key == "":
+            return self, "", self
+
+        split = split_key(key)
+        root: Optional[Container] = self
+        for i in range(len(split) - 1):
+            if root is None:
+                break
+
+            k = split[i]
+            ret, _ = _select_one(
+                c=root,
+                key=k,
+                throw_on_missing=throw_on_missing,
+                throw_on_type_error=throw_on_resolution_failure,
+            )
+            if isinstance(ret, Node):
+                ret = ret._maybe_dereference_node(
+                    throw_on_resolution_failure=throw_on_resolution_failure,
+                    memo=memo,
+                )
+
+            if ret is not None and not isinstance(ret, Container):
+                parent_key = ".".join(split[0 : i + 1])
+                child_key = split[i + 1]
+                raise ConfigTypeError(
+                    f"Error trying to access {key}: node `{parent_key}` "
+                    f"is not a container and thus cannot contain `{child_key}`"
+                )
+            root = ret
+
+        if root is None:
+            return None, None, None
+
+        last_key = split[-1]
+        value, _ = _select_one(
+            c=root,
+            key=last_key,
+            throw_on_missing=throw_on_missing,
+            throw_on_type_error=throw_on_resolution_failure,
+        )
+        if value is None:
+            return root, last_key, None
+
+        if memo is not None:
+            vid = id(value)
+            if vid in memo:
+                raise InterpolationResolutionError("Recursive interpolation detected")
+            # push to memo "stack"
+            memo.add(vid)
+
+        try:
+            value = root._maybe_resolve_interpolation(
+                parent=root,
+                key=last_key,
+                value=value,
+                throw_on_resolution_failure=throw_on_resolution_failure,
+                memo=memo,
+            )
+        finally:
+            if memo is not None:
+                # pop from memo "stack"
+                memo.remove(vid)
+
+        return root, last_key, value
+
+    def _resolve_interpolation_from_parse_tree(
+        self,
+        parent: Optional["Container"],
+        value: "Node",
+        key: Any,
+        parse_tree: OmegaConfGrammarParser.ConfigValueContext,
+        throw_on_resolution_failure: bool,
+        memo: Optional[Set[int]],
+    ) -> Optional["Node"]:
+        """
+        Resolve an interpolation.
+
+        This happens in two steps:
+            1. The parse tree is visited, which outputs either a `Node` (e.g.,
+               for node interpolations "${foo}"), a string (e.g., for string
+               interpolations "hello ${name}", or any other arbitrary value
+               (e.g., or custom interpolations "${foo:bar}").
+            2. This output is potentially validated and converted when the node
+               being resolved (`value`) is typed.
+
+        If an error occurs in one of the above steps, an `InterpolationResolutionError`
+        (or a subclass of it) is raised, *unless* `throw_on_resolution_failure` is set
+        to `False` (in which case the return value is `None`).
+
+        :param parent: Parent of the node being resolved.
+        :param value: Node being resolved.
+        :param key: The associated key in the parent.
+        :param parse_tree: The parse tree as obtained from `grammar_parser.parse()`.
+        :param throw_on_resolution_failure: If `False`, then exceptions raised during
+            the resolution of the interpolation are silenced, and instead `None` is
+            returned.
+
+        :return: A `Node` that contains the interpolation result. This may be an existing
+            node in the config (in the case of a node interpolation "${foo}"), or a new
+            node that is created to wrap the interpolated value. It is `None` if and only if
+            `throw_on_resolution_failure` is `False` and an error occurs during resolution.
+        """
+
+        try:
+            resolved = self.resolve_parse_tree(
+                parse_tree=parse_tree, node=value, key=key, memo=memo
+            )
+        except InterpolationResolutionError:
+            if throw_on_resolution_failure:
+                raise
+            return None
+
+        return self._validate_and_convert_interpolation_result(
+            parent=parent,
+            value=value,
+            key=key,
+            resolved=resolved,
+            throw_on_resolution_failure=throw_on_resolution_failure,
+        )
+
+    def _validate_and_convert_interpolation_result(
+        self,
+        parent: Optional["Container"],
+        value: "Node",
+        key: Any,
+        resolved: Any,
+        throw_on_resolution_failure: bool,
+    ) -> Optional["Node"]:
+        from .nodes import AnyNode, InterpolationResultNode, ValueNode
+
+        # If the output is not a Node already (e.g., because it is the output of a
+        # custom resolver), then we will need to wrap it within a Node.
+        must_wrap = not isinstance(resolved, Node)
+
+        # If the node is typed, validate (and possibly convert) the result.
+        if isinstance(value, ValueNode) and not isinstance(value, AnyNode):
+            res_value = _get_value(resolved)
+            try:
+                conv_value = value.validate_and_convert(res_value)
+            except ValidationError as e:
+                if throw_on_resolution_failure:
+                    self._format_and_raise(
+                        key=key,
+                        value=res_value,
+                        cause=e,
+                        msg=f"While dereferencing interpolation '{value}': {e}",
+                        type_override=InterpolationValidationError,
+                    )
+                return None
+
+            # If the converted value is of the same type, it means that no conversion
+            # was actually needed. As a result, we can keep the original `resolved`
+            # (and otherwise, the converted value must be wrapped into a new node).
+            if type(conv_value) != type(res_value):
+                must_wrap = True
+                resolved = conv_value
+
+        if must_wrap:
+            return InterpolationResultNode(value=resolved, key=key, parent=parent)
+        else:
+            assert isinstance(resolved, Node)
+            return resolved
+
+    def _validate_not_dereferencing_to_parent(self, node: Node, target: Node) -> None:
+        parent: Optional[Node] = node
+        while parent is not None:
+            if parent is target:
+                raise InterpolationResolutionError(
+                    "Interpolation to parent node detected"
+                )
+            parent = parent._get_parent()
+
+    def _resolve_node_interpolation(
+        self, inter_key: str, memo: Optional[Set[int]]
+    ) -> "Node":
+        """A node interpolation is of the form `${foo.bar}`"""
+        try:
+            root_node, inter_key = self._resolve_key_and_root(inter_key)
+        except ConfigKeyError as exc:
+            raise InterpolationKeyError(
+                f"ConfigKeyError while resolving interpolation: {exc}"
+            ).with_traceback(sys.exc_info()[2])
+
+        try:
+            parent, last_key, value = root_node._select_impl(
+                inter_key,
+                throw_on_missing=True,
+                throw_on_resolution_failure=True,
+                memo=memo,
+            )
+        except MissingMandatoryValue as exc:
+            raise InterpolationToMissingValueError(
+                f"MissingMandatoryValue while resolving interpolation: {exc}"
+            ).with_traceback(sys.exc_info()[2])
+
+        if parent is None or value is None:
+            raise InterpolationKeyError(f"Interpolation key '{inter_key}' not found")
+        else:
+            self._validate_not_dereferencing_to_parent(node=self, target=value)
+            return value
+
+    def _evaluate_custom_resolver(
+        self,
+        key: Any,
+        node: Node,
+        inter_type: str,
+        inter_args: Tuple[Any, ...],
+        inter_args_str: Tuple[str, ...],
+    ) -> Any:
+        from omegaconf import OmegaConf
+
+        resolver = OmegaConf._get_resolver(inter_type)
+        if resolver is not None:
+            root_node = self._get_root()
+            return resolver(
+                root_node,
+                self,
+                node,
+                inter_args,
+                inter_args_str,
+            )
+        else:
+            raise UnsupportedInterpolationType(
+                f"Unsupported interpolation type {inter_type}"
+            )
+
+    def _maybe_resolve_interpolation(
+        self,
+        parent: Optional["Container"],
+        key: Any,
+        value: Node,
+        throw_on_resolution_failure: bool,
+        memo: Optional[Set[int]] = None,
+    ) -> Optional[Node]:
+        value_kind = get_value_kind(value)
+        if value_kind != ValueKind.INTERPOLATION:
+            return value
+
+        parse_tree = parse(_get_value(value))
+        return self._resolve_interpolation_from_parse_tree(
+            parent=parent,
+            value=value,
+            key=key,
+            parse_tree=parse_tree,
+            throw_on_resolution_failure=throw_on_resolution_failure,
+            memo=memo if memo is not None else set(),
+        )
+
+    def resolve_parse_tree(
+        self,
+        parse_tree: ParserRuleContext,
+        node: Node,
+        memo: Optional[Set[int]] = None,
+        key: Optional[Any] = None,
+    ) -> Any:
+        """
+        Resolve a given parse tree into its value.
+
+        We make no assumption here on the type of the tree's root, so that the
+        return value may be of any type.
+        """
+
+        def node_interpolation_callback(
+            inter_key: str, memo: Optional[Set[int]]
+        ) -> Optional["Node"]:
+            return self._resolve_node_interpolation(inter_key=inter_key, memo=memo)
+
+        def resolver_interpolation_callback(
+            name: str, args: Tuple[Any, ...], args_str: Tuple[str, ...]
+        ) -> Any:
+            return self._evaluate_custom_resolver(
+                key=key,
+                node=node,
+                inter_type=name,
+                inter_args=args,
+                inter_args_str=args_str,
+            )
+
+        visitor = GrammarVisitor(
+            node_interpolation_callback=node_interpolation_callback,
+            resolver_interpolation_callback=resolver_interpolation_callback,
+            memo=memo,
+        )
+        try:
+            return visitor.visit(parse_tree)
+        except InterpolationResolutionError:
+            raise
+        except Exception as exc:
+            # Other kinds of exceptions are wrapped in an `InterpolationResolutionError`.
+            raise InterpolationResolutionError(
+                f"{type(exc).__name__} raised while resolving interpolation: {exc}"
+            ).with_traceback(sys.exc_info()[2])
+
+    def _invalidate_flags_cache(self) -> None:
+        from .dictconfig import DictConfig
+        from .listconfig import ListConfig
+
+        # invalidate subtree cache only if the cache is initialized in this node.
+
+        if self.__dict__["_flags_cache"] is not None:
+            self.__dict__["_flags_cache"] = None
+            if isinstance(self, DictConfig):
+                content = self.__dict__["_content"]
+                if isinstance(content, dict):
+                    for value in self.__dict__["_content"].values():
+                        value._invalidate_flags_cache()
+            elif isinstance(self, ListConfig):
+                content = self.__dict__["_content"]
+                if isinstance(content, list):
+                    for item in self.__dict__["_content"]:
+                        item._invalidate_flags_cache()
+
+
+class SCMode(Enum):
+    DICT = 1  # Convert to plain dict
+    DICT_CONFIG = 2  # Keep as OmegaConf DictConfig
+    INSTANTIATE = 3  # Create a dataclass or attrs class instance
+
+
+class UnionNode(Box):
+    """
+    This class handles Union type hints. The `_content` attribute is either a
+    child node that is compatible with the given Union ref_type, or it is a
+    special value (None or MISSING or interpolation).
+
+    Much of the logic for e.g. value assignment and type validation is
+    delegated to the child node. As such, UnionNode functions as a
+    "pass-through" node. User apps and downstream libraries should not need to
+    know about UnionNode (assuming they only use OmegaConf's public API).
+    """
+
+    _parent: Optional[Container]
+    _content: Union[Node, None, str]
+
+    def __init__(
+        self,
+        content: Any,
+        ref_type: Any,
+        is_optional: bool = True,
+        key: Any = None,
+        parent: Optional[Box] = None,
+    ) -> None:
+        try:
+            if not is_union_annotation(ref_type):  # pragma: no cover
+                msg = (
+                    f"UnionNode got unexpected ref_type {ref_type}. Please file a bug"
+                    + " report at https://github.com/omry/omegaconf/issues"
+                )
+                raise AssertionError(msg)
+            if not isinstance(parent, (Container, NoneType)):
+                raise ConfigTypeError("Parent type is not omegaconf.Container")
+            super().__init__(
+                parent=parent,
+                metadata=Metadata(
+                    ref_type=ref_type,
+                    object_type=None,
+                    optional=is_optional,
+                    key=key,
+                    flags={"convert": False},
+                ),
+            )
+            self._set_value(content)
+        except Exception as ex:
+            format_and_raise(node=None, key=key, value=content, msg=str(ex), cause=ex)
+
+    def _get_full_key(self, key: Optional[Union[DictKeyType, int]]) -> str:
+        parent = self._get_parent()
+        if parent is None:
+            if self._metadata.key is None:
+                return ""
+            else:
+                return str(self._metadata.key)
+        else:
+            return parent._get_full_key(self._metadata.key)
+
+    def __eq__(self, other: Any) -> bool:
+        content = self.__dict__["_content"]
+        if isinstance(content, Node):
+            ret = content.__eq__(other)
+        elif isinstance(other, Node):
+            ret = other.__eq__(content)
+        else:
+            ret = content.__eq__(other)
+        assert isinstance(ret, (bool, type(NotImplemented)))
+        return ret
+
+    def __ne__(self, other: Any) -> bool:
+        x = self.__eq__(other)
+        if x is NotImplemented:
+            return NotImplemented
+        return not x
+
+    def __hash__(self) -> int:
+        return hash(self.__dict__["_content"])
+
+    def _value(self) -> Union[Node, None, str]:
+        content = self.__dict__["_content"]
+        assert isinstance(content, (Node, NoneType, str))
+        return content
+
+    def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None:
+        previous_content = self.__dict__["_content"]
+        previous_metadata = self.__dict__["_metadata"]
+        try:
+            self._set_value_impl(value, flags)
+        except Exception as e:
+            self.__dict__["_content"] = previous_content
+            self.__dict__["_metadata"] = previous_metadata
+            raise e
+
+    def _set_value_impl(
+        self, value: Any, flags: Optional[Dict[str, bool]] = None
+    ) -> None:
+        from omegaconf.omegaconf import _node_wrap
+
+        ref_type = self._metadata.ref_type
+        type_hint = self._metadata.type_hint
+
+        value = _get_value(value)
+        if _is_special(value):
+            assert isinstance(value, (str, NoneType))
+            if value is None:
+                if not self._is_optional():
+                    raise ValidationError(
+                        f"Value '$VALUE' is incompatible with type hint '{type_str(type_hint)}'"
+                    )
+            self.__dict__["_content"] = value
+        elif isinstance(value, Container):
+            raise ValidationError(
+                f"Cannot assign container '$VALUE' of type '$VALUE_TYPE' to {type_str(type_hint)}"
+            )
+        else:
+            for candidate_ref_type in ref_type.__args__:
+                try:
+                    self.__dict__["_content"] = _node_wrap(
+                        value=value,
+                        ref_type=candidate_ref_type,
+                        is_optional=False,
+                        key=None,
+                        parent=self,
+                    )
+                    break
+                except ValidationError:
+                    continue
+            else:
+                raise ValidationError(
+                    f"Value '$VALUE' of type '$VALUE_TYPE' is incompatible with type hint '{type_str(type_hint)}'"
+                )
+
+    def _is_optional(self) -> bool:
+        return self.__dict__["_metadata"].optional is True
+
+    def _is_interpolation(self) -> bool:
+        return _is_interpolation(self.__dict__["_content"])
+
+    def __str__(self) -> str:
+        return str(self.__dict__["_content"])
+
+    def __repr__(self) -> str:
+        return repr(self.__dict__["_content"])
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "UnionNode":
+        res = object.__new__(type(self))
+        for key, value in self.__dict__.items():
+            if key not in ("_content", "_parent"):
+                res.__dict__[key] = copy.deepcopy(value, memo=memo)
+
+        src_content = self.__dict__["_content"]
+        if isinstance(src_content, Node):
+            old_parent = src_content.__dict__["_parent"]
+            try:
+                src_content.__dict__["_parent"] = None
+                content_copy = copy.deepcopy(src_content, memo=memo)
+                content_copy.__dict__["_parent"] = res
+            finally:
+                src_content.__dict__["_parent"] = old_parent
+        else:
+            # None and strings can be assigned as is
+            content_copy = src_content
+
+        res.__dict__["_content"] = content_copy
+        res.__dict__["_parent"] = self.__dict__["_parent"]
+        return res
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/basecontainer.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/basecontainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..575df3c72105f9563d29bca8077d7e3fb79742b2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/basecontainer.py
@@ -0,0 +1,916 @@
+import copy
+import sys
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Tuple, Union
+
+import yaml
+
+from ._utils import (
+    _DEFAULT_MARKER_,
+    ValueKind,
+    _ensure_container,
+    _get_value,
+    _is_interpolation,
+    _is_missing_value,
+    _is_none,
+    _is_special,
+    _resolve_optional,
+    get_structured_config_data,
+    get_type_hint,
+    get_value_kind,
+    get_yaml_loader,
+    is_container_annotation,
+    is_dict_annotation,
+    is_list_annotation,
+    is_primitive_dict,
+    is_primitive_type_annotation,
+    is_structured_config,
+    is_tuple_annotation,
+    is_union_annotation,
+)
+from .base import (
+    Box,
+    Container,
+    ContainerMetadata,
+    DictKeyType,
+    Node,
+    SCMode,
+    UnionNode,
+)
+from .errors import (
+    ConfigCycleDetectedException,
+    ConfigTypeError,
+    InterpolationResolutionError,
+    KeyValidationError,
+    MissingMandatoryValue,
+    OmegaConfBaseException,
+    ReadonlyConfigError,
+    ValidationError,
+)
+
+if TYPE_CHECKING:
+    from .dictconfig import DictConfig  # pragma: no cover
+
+
+class BaseContainer(Container, ABC):
+    _resolvers: ClassVar[Dict[str, Any]] = {}
+
+    def __init__(self, parent: Optional[Box], metadata: ContainerMetadata):
+        if not (parent is None or isinstance(parent, Box)):
+            raise ConfigTypeError("Parent type is not omegaconf.Box")
+        super().__init__(parent=parent, metadata=metadata)
+
+    def _get_child(
+        self,
+        key: Any,
+        validate_access: bool = True,
+        validate_key: bool = True,
+        throw_on_missing_value: bool = False,
+        throw_on_missing_key: bool = False,
+    ) -> Union[Optional[Node], List[Optional[Node]]]:
+        """Like _get_node, passing through to the nearest concrete Node."""
+        child = self._get_node(
+            key=key,
+            validate_access=validate_access,
+            validate_key=validate_key,
+            throw_on_missing_value=throw_on_missing_value,
+            throw_on_missing_key=throw_on_missing_key,
+        )
+        if isinstance(child, UnionNode) and not _is_special(child):
+            value = child._value()
+            assert isinstance(value, Node) and not isinstance(value, UnionNode)
+            child = value
+        return child
+
+    def _resolve_with_default(
+        self,
+        key: Union[DictKeyType, int],
+        value: Node,
+        default_value: Any = _DEFAULT_MARKER_,
+    ) -> Any:
+        """returns the value with the specified key, like obj.key and obj['key']"""
+        if _is_missing_value(value):
+            if default_value is not _DEFAULT_MARKER_:
+                return default_value
+            raise MissingMandatoryValue("Missing mandatory value: $FULL_KEY")
+
+        resolved_node = self._maybe_resolve_interpolation(
+            parent=self,
+            key=key,
+            value=value,
+            throw_on_resolution_failure=True,
+        )
+
+        return _get_value(resolved_node)
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+    def __repr__(self) -> str:
+        if self.__dict__["_content"] is None:
+            return "None"
+        elif self._is_interpolation() or self._is_missing():
+            v = self.__dict__["_content"]
+            return f"'{v}'"
+        else:
+            return self.__dict__["_content"].__repr__()  # type: ignore
+
+    # Support pickle
+    def __getstate__(self) -> Dict[str, Any]:
+        dict_copy = copy.copy(self.__dict__)
+
+        # no need to serialize the flags cache, it can be re-constructed later
+        dict_copy.pop("_flags_cache", None)
+
+        dict_copy["_metadata"] = copy.copy(dict_copy["_metadata"])
+        ref_type = self._metadata.ref_type
+        if is_container_annotation(ref_type):
+            if is_dict_annotation(ref_type):
+                dict_copy["_metadata"].ref_type = Dict
+            elif is_list_annotation(ref_type):
+                dict_copy["_metadata"].ref_type = List
+            else:
+                assert False
+        if sys.version_info < (3, 7):  # pragma: no cover
+            element_type = self._metadata.element_type
+            if is_union_annotation(element_type):
+                raise OmegaConfBaseException(
+                    "Serializing structured configs with `Union` element type requires python >= 3.7"
+                )
+        return dict_copy
+
+    # Support pickle
+    def __setstate__(self, d: Dict[str, Any]) -> None:
+        from omegaconf import DictConfig
+        from omegaconf._utils import is_generic_dict, is_generic_list
+
+        if isinstance(self, DictConfig):
+            key_type = d["_metadata"].key_type
+
+            # backward compatibility to load OmegaConf 2.0 configs
+            if key_type is None:
+                key_type = Any
+                d["_metadata"].key_type = key_type
+
+        element_type = d["_metadata"].element_type
+
+        # backward compatibility to load OmegaConf 2.0 configs
+        if element_type is None:
+            element_type = Any
+            d["_metadata"].element_type = element_type
+
+        ref_type = d["_metadata"].ref_type
+        if is_container_annotation(ref_type):
+            if is_generic_dict(ref_type):
+                d["_metadata"].ref_type = Dict[key_type, element_type]  # type: ignore
+            elif is_generic_list(ref_type):
+                d["_metadata"].ref_type = List[element_type]  # type: ignore
+            else:
+                assert False
+
+        d["_flags_cache"] = None
+        self.__dict__.update(d)
+
+    @abstractmethod
+    def __delitem__(self, key: Any) -> None:
+        ...
+
+    def __len__(self) -> int:
+        if self._is_none() or self._is_missing() or self._is_interpolation():
+            return 0
+        content = self.__dict__["_content"]
+        return len(content)
+
+    def merge_with_cli(self) -> None:
+        args_list = sys.argv[1:]
+        self.merge_with_dotlist(args_list)
+
+    def merge_with_dotlist(self, dotlist: List[str]) -> None:
+        from omegaconf import OmegaConf
+
+        def fail() -> None:
+            raise ValueError("Input list must be a list or a tuple of strings")
+
+        if not isinstance(dotlist, (list, tuple)):
+            fail()
+
+        for arg in dotlist:
+            if not isinstance(arg, str):
+                fail()
+
+            idx = arg.find("=")
+            if idx == -1:
+                key = arg
+                value = None
+            else:
+                key = arg[0:idx]
+                value = arg[idx + 1 :]
+                value = yaml.load(value, Loader=get_yaml_loader())
+
+            OmegaConf.update(self, key, value)
+
+    def is_empty(self) -> bool:
+        """return true if config is empty"""
+        return len(self.__dict__["_content"]) == 0
+
+    @staticmethod
+    def _to_content(
+        conf: Container,
+        resolve: bool,
+        throw_on_missing: bool,
+        enum_to_str: bool = False,
+        structured_config_mode: SCMode = SCMode.DICT,
+    ) -> Union[None, Any, str, Dict[DictKeyType, Any], List[Any]]:
+        from omegaconf import MISSING, DictConfig, ListConfig
+
+        def convert(val: Node) -> Any:
+            value = val._value()
+            if enum_to_str and isinstance(value, Enum):
+                value = f"{value.name}"
+
+            return value
+
+        def get_node_value(key: Union[DictKeyType, int]) -> Any:
+            try:
+                node = conf._get_child(key, throw_on_missing_value=throw_on_missing)
+            except MissingMandatoryValue as e:
+                conf._format_and_raise(key=key, value=None, cause=e)
+            assert isinstance(node, Node)
+            if resolve:
+                try:
+                    node = node._dereference_node()
+                except InterpolationResolutionError as e:
+                    conf._format_and_raise(key=key, value=None, cause=e)
+
+            if isinstance(node, Container):
+                value = BaseContainer._to_content(
+                    node,
+                    resolve=resolve,
+                    throw_on_missing=throw_on_missing,
+                    enum_to_str=enum_to_str,
+                    structured_config_mode=structured_config_mode,
+                )
+            else:
+                value = convert(node)
+            return value
+
+        if conf._is_none():
+            return None
+        elif conf._is_missing():
+            if throw_on_missing:
+                conf._format_and_raise(
+                    key=None,
+                    value=None,
+                    cause=MissingMandatoryValue("Missing mandatory value"),
+                )
+            else:
+                return MISSING
+        elif not resolve and conf._is_interpolation():
+            inter = conf._value()
+            assert isinstance(inter, str)
+            return inter
+
+        if resolve:
+            _conf = conf._dereference_node()
+            assert isinstance(_conf, Container)
+            conf = _conf
+
+        if isinstance(conf, DictConfig):
+            if (
+                conf._metadata.object_type not in (dict, None)
+                and structured_config_mode == SCMode.DICT_CONFIG
+            ):
+                return conf
+            if structured_config_mode == SCMode.INSTANTIATE and is_structured_config(
+                conf._metadata.object_type
+            ):
+                return conf._to_object()
+
+            retdict: Dict[DictKeyType, Any] = {}
+            for key in conf.keys():
+                value = get_node_value(key)
+                if enum_to_str and isinstance(key, Enum):
+                    key = f"{key.name}"
+                retdict[key] = value
+            return retdict
+        elif isinstance(conf, ListConfig):
+            retlist: List[Any] = []
+            for index in range(len(conf)):
+                item = get_node_value(index)
+                retlist.append(item)
+
+            return retlist
+        assert False
+
+    @staticmethod
+    def _map_merge(dest: "BaseContainer", src: "BaseContainer") -> None:
+        """merge src into dest and return a new copy, does not modified input"""
+        from omegaconf import AnyNode, DictConfig, ValueNode
+
+        assert isinstance(dest, DictConfig)
+        assert isinstance(src, DictConfig)
+        src_type = src._metadata.object_type
+        src_ref_type = get_type_hint(src)
+        assert src_ref_type is not None
+
+        # If source DictConfig is:
+        #  - None => set the destination DictConfig to None
+        #  - an interpolation => set the destination DictConfig to be the same interpolation
+        if src._is_none() or src._is_interpolation():
+            dest._set_value(src._value())
+            _update_types(node=dest, ref_type=src_ref_type, object_type=src_type)
+            return
+
+        dest._validate_merge(value=src)
+
+        def expand(node: Container) -> None:
+            rt = node._metadata.ref_type
+            val: Any
+            if rt is not Any:
+                if is_dict_annotation(rt):
+                    val = {}
+                elif is_list_annotation(rt) or is_tuple_annotation(rt):
+                    val = []
+                else:
+                    val = rt
+            elif isinstance(node, DictConfig):
+                val = {}
+            else:
+                assert False
+
+            node._set_value(val)
+
+        if (
+            src._is_missing()
+            and not dest._is_missing()
+            and is_structured_config(src_ref_type)
+        ):
+            # Replace `src` with a prototype of its corresponding structured config
+            # whose fields are all missing (to avoid overwriting fields in `dest`).
+            assert src_type is None  # src missing, so src's object_type should be None
+            src_type = src_ref_type
+            src = _create_structured_with_missing_fields(
+                ref_type=src_ref_type, object_type=src_type
+            )
+
+        if (dest._is_interpolation() or dest._is_missing()) and not src._is_missing():
+            expand(dest)
+
+        src_items = list(src) if not src._is_missing() else []
+        for key in src_items:
+            src_node = src._get_node(key, validate_access=False)
+            dest_node = dest._get_node(key, validate_access=False)
+            assert isinstance(src_node, Node)
+            assert dest_node is None or isinstance(dest_node, Node)
+            src_value = _get_value(src_node)
+
+            src_vk = get_value_kind(src_node)
+            src_node_missing = src_vk is ValueKind.MANDATORY_MISSING
+
+            if isinstance(dest_node, DictConfig):
+                dest_node._validate_merge(value=src_node)
+
+            if (
+                isinstance(dest_node, Container)
+                and dest_node._is_none()
+                and not src_node_missing
+                and not _is_none(src_node, resolve=True)
+            ):
+                expand(dest_node)
+
+            if dest_node is not None and dest_node._is_interpolation():
+                target_node = dest_node._maybe_dereference_node()
+                if isinstance(target_node, Container):
+                    dest[key] = target_node
+                    dest_node = dest._get_node(key)
+
+            is_optional, et = _resolve_optional(dest._metadata.element_type)
+            if dest_node is None and is_structured_config(et) and not src_node_missing:
+                # merging into a new node. Use element_type as a base
+                dest[key] = DictConfig(
+                    et, parent=dest, ref_type=et, is_optional=is_optional
+                )
+                dest_node = dest._get_node(key)
+
+            if dest_node is not None:
+                if isinstance(dest_node, BaseContainer):
+                    if isinstance(src_node, BaseContainer):
+                        dest_node._merge_with(src_node)
+                    elif not src_node_missing:
+                        dest.__setitem__(key, src_node)
+                else:
+                    if isinstance(src_node, BaseContainer):
+                        dest.__setitem__(key, src_node)
+                    else:
+                        assert isinstance(dest_node, (ValueNode, UnionNode))
+                        assert isinstance(src_node, (ValueNode, UnionNode))
+                        try:
+                            if isinstance(dest_node, AnyNode):
+                                if src_node_missing:
+                                    node = copy.copy(src_node)
+                                    # if src node is missing, use the value from the dest_node,
+                                    # but validate it against the type of the src node before assigment
+                                    node._set_value(dest_node._value())
+                                else:
+                                    node = src_node
+                                dest.__setitem__(key, node)
+                            else:
+                                if not src_node_missing:
+                                    dest_node._set_value(src_value)
+
+                        except (ValidationError, ReadonlyConfigError) as e:
+                            dest._format_and_raise(key=key, value=src_value, cause=e)
+            else:
+                from omegaconf import open_dict
+
+                if is_structured_config(src_type):
+                    # verified to be compatible above in _validate_merge
+                    with open_dict(dest):
+                        dest[key] = src._get_node(key)
+                else:
+                    dest[key] = src._get_node(key)
+
+        _update_types(node=dest, ref_type=src_ref_type, object_type=src_type)
+
+        # explicit flags on the source config are replacing the flag values in the destination
+        flags = src._metadata.flags
+        assert flags is not None
+        for flag, value in flags.items():
+            if value is not None:
+                dest._set_flag(flag, value)
+
+    @staticmethod
+    def _list_merge(dest: Any, src: Any) -> None:
+        from omegaconf import DictConfig, ListConfig, OmegaConf
+
+        assert isinstance(dest, ListConfig)
+        assert isinstance(src, ListConfig)
+
+        if src._is_none():
+            dest._set_value(None)
+        elif src._is_missing():
+            # do not change dest if src is MISSING.
+            if dest._metadata.element_type is Any:
+                dest._metadata.element_type = src._metadata.element_type
+        elif src._is_interpolation():
+            dest._set_value(src._value())
+        else:
+            temp_target = ListConfig(content=[], parent=dest._get_parent())
+            temp_target.__dict__["_metadata"] = copy.deepcopy(
+                dest.__dict__["_metadata"]
+            )
+            is_optional, et = _resolve_optional(dest._metadata.element_type)
+            if is_structured_config(et):
+                prototype = DictConfig(et, ref_type=et, is_optional=is_optional)
+                for item in src._iter_ex(resolve=False):
+                    if isinstance(item, DictConfig):
+                        item = OmegaConf.merge(prototype, item)
+                    temp_target.append(item)
+            else:
+                for item in src._iter_ex(resolve=False):
+                    temp_target.append(item)
+
+            dest.__dict__["_content"] = temp_target.__dict__["_content"]
+
+        # explicit flags on the source config are replacing the flag values in the destination
+        flags = src._metadata.flags
+        assert flags is not None
+        for flag, value in flags.items():
+            if value is not None:
+                dest._set_flag(flag, value)
+
+    def merge_with(
+        self,
+        *others: Union[
+            "BaseContainer", Dict[str, Any], List[Any], Tuple[Any, ...], Any
+        ],
+    ) -> None:
+        try:
+            self._merge_with(*others)
+        except Exception as e:
+            self._format_and_raise(key=None, value=None, cause=e)
+
+    def _merge_with(
+        self,
+        *others: Union[
+            "BaseContainer", Dict[str, Any], List[Any], Tuple[Any, ...], Any
+        ],
+    ) -> None:
+        from .dictconfig import DictConfig
+        from .listconfig import ListConfig
+
+        """merge a list of other Config objects into this one, overriding as needed"""
+        for other in others:
+            if other is None:
+                raise ValueError("Cannot merge with a None config")
+
+            my_flags = {}
+            if self._get_flag("allow_objects") is True:
+                my_flags = {"allow_objects": True}
+            other = _ensure_container(other, flags=my_flags)
+
+            if isinstance(self, DictConfig) and isinstance(other, DictConfig):
+                BaseContainer._map_merge(self, other)
+            elif isinstance(self, ListConfig) and isinstance(other, ListConfig):
+                BaseContainer._list_merge(self, other)
+            else:
+                raise TypeError("Cannot merge DictConfig with ListConfig")
+
+        # recursively correct the parent hierarchy after the merge
+        self._re_parent()
+
+    # noinspection PyProtectedMember
+    def _set_item_impl(self, key: Any, value: Any) -> None:
+        """
+        Changes the value of the node key with the desired value. If the node key doesn't
+        exist it creates a new one.
+        """
+        from .nodes import AnyNode, ValueNode
+
+        if isinstance(value, Node):
+            do_deepcopy = not self._get_flag("no_deepcopy_set_nodes")
+            if not do_deepcopy and isinstance(value, Box):
+                # if value is from the same config, perform a deepcopy no matter what.
+                if self._get_root() is value._get_root():
+                    do_deepcopy = True
+
+            if do_deepcopy:
+                value = copy.deepcopy(value)
+            value._set_parent(None)
+
+            try:
+                old = value._key()
+                value._set_key(key)
+                self._validate_set(key, value)
+            finally:
+                value._set_key(old)
+        else:
+            self._validate_set(key, value)
+
+        if self._get_flag("readonly"):
+            raise ReadonlyConfigError("Cannot change read-only config container")
+
+        input_is_node = isinstance(value, Node)
+        target_node_ref = self._get_node(key)
+        assert target_node_ref is None or isinstance(target_node_ref, Node)
+
+        input_is_typed_vnode = isinstance(value, ValueNode) and not isinstance(
+            value, AnyNode
+        )
+
+        def get_target_type_hint(val: Any) -> Any:
+            if not is_structured_config(val):
+                type_hint = self._metadata.element_type
+            else:
+                target = self._get_node(key)
+                if target is None:
+                    type_hint = self._metadata.element_type
+                else:
+                    assert isinstance(target, Node)
+                    type_hint = target._metadata.type_hint
+            return type_hint
+
+        target_type_hint = get_target_type_hint(value)
+        _, target_ref_type = _resolve_optional(target_type_hint)
+
+        def assign(value_key: Any, val: Node) -> None:
+            assert val._get_parent() is None
+            v = val
+            v._set_parent(self)
+            v._set_key(value_key)
+            _deep_update_type_hint(node=v, type_hint=self._metadata.element_type)
+            self.__dict__["_content"][value_key] = v
+
+        if input_is_typed_vnode and not is_union_annotation(target_ref_type):
+            assign(key, value)
+        else:
+            # input is not a ValueNode, can be primitive or box
+
+            special_value = _is_special(value)
+            # We use the `Node._set_value` method if the target node exists and:
+            # 1. the target has an explicit ref_type, or
+            # 2. the target is an AnyNode and the input is a primitive type.
+            should_set_value = target_node_ref is not None and (
+                target_node_ref._has_ref_type()
+                or (
+                    isinstance(target_node_ref, AnyNode)
+                    and is_primitive_type_annotation(value)
+                )
+            )
+            if should_set_value:
+                if special_value and isinstance(value, Node):
+                    value = value._value()
+                self.__dict__["_content"][key]._set_value(value)
+            elif input_is_node:
+                if (
+                    special_value
+                    and (
+                        is_container_annotation(target_ref_type)
+                        or is_structured_config(target_ref_type)
+                    )
+                    or is_primitive_type_annotation(target_ref_type)
+                    or is_union_annotation(target_ref_type)
+                ):
+                    value = _get_value(value)
+                    self._wrap_value_and_set(key, value, target_type_hint)
+                else:
+                    assign(key, value)
+            else:
+                self._wrap_value_and_set(key, value, target_type_hint)
+
+    def _wrap_value_and_set(self, key: Any, val: Any, type_hint: Any) -> None:
+        from omegaconf.omegaconf import _maybe_wrap
+
+        is_optional, ref_type = _resolve_optional(type_hint)
+
+        try:
+            wrapped = _maybe_wrap(
+                ref_type=ref_type,
+                key=key,
+                value=val,
+                is_optional=is_optional,
+                parent=self,
+            )
+        except ValidationError as e:
+            self._format_and_raise(key=key, value=val, cause=e)
+        self.__dict__["_content"][key] = wrapped
+
+    @staticmethod
+    def _item_eq(
+        c1: Container,
+        k1: Union[DictKeyType, int],
+        c2: Container,
+        k2: Union[DictKeyType, int],
+    ) -> bool:
+        v1 = c1._get_child(k1)
+        v2 = c2._get_child(k2)
+        assert v1 is not None and v2 is not None
+
+        assert isinstance(v1, Node)
+        assert isinstance(v2, Node)
+
+        if v1._is_none() and v2._is_none():
+            return True
+
+        if v1._is_missing() and v2._is_missing():
+            return True
+
+        v1_inter = v1._is_interpolation()
+        v2_inter = v2._is_interpolation()
+        dv1: Optional[Node] = v1
+        dv2: Optional[Node] = v2
+
+        if v1_inter:
+            dv1 = v1._maybe_dereference_node()
+        if v2_inter:
+            dv2 = v2._maybe_dereference_node()
+
+        if v1_inter and v2_inter:
+            if dv1 is None or dv2 is None:
+                return v1 == v2
+            else:
+                # both are not none, if both are containers compare as container
+                if isinstance(dv1, Container) and isinstance(dv2, Container):
+                    if dv1 != dv2:
+                        return False
+                dv1 = _get_value(dv1)
+                dv2 = _get_value(dv2)
+                return dv1 == dv2
+        elif not v1_inter and not v2_inter:
+            v1 = _get_value(v1)
+            v2 = _get_value(v2)
+            ret = v1 == v2
+            assert isinstance(ret, bool)
+            return ret
+        else:
+            dv1 = _get_value(dv1)
+            dv2 = _get_value(dv2)
+            ret = dv1 == dv2
+            assert isinstance(ret, bool)
+            return ret
+
+    def _is_optional(self) -> bool:
+        return self.__dict__["_metadata"].optional is True
+
+    def _is_interpolation(self) -> bool:
+        return _is_interpolation(self.__dict__["_content"])
+
+    @abstractmethod
+    def _validate_get(self, key: Any, value: Any = None) -> None:
+        ...
+
+    @abstractmethod
+    def _validate_set(self, key: Any, value: Any) -> None:
+        ...
+
+    def _value(self) -> Any:
+        return self.__dict__["_content"]
+
+    def _get_full_key(self, key: Union[DictKeyType, int, slice, None]) -> str:
+        from .listconfig import ListConfig
+        from .omegaconf import _select_one
+
+        if not isinstance(key, (int, str, Enum, float, bool, slice, bytes, type(None))):
+            return ""
+
+        def _slice_to_str(x: slice) -> str:
+            if x.step is not None:
+                return f"{x.start}:{x.stop}:{x.step}"
+            else:
+                return f"{x.start}:{x.stop}"
+
+        def prepand(
+            full_key: str,
+            parent_type: Any,
+            cur_type: Any,
+            key: Optional[Union[DictKeyType, int, slice]],
+        ) -> str:
+            if key is None:
+                return full_key
+
+            if isinstance(key, slice):
+                key = _slice_to_str(key)
+            elif isinstance(key, Enum):
+                key = key.name
+            else:
+                key = str(key)
+
+            assert isinstance(key, str)
+
+            if issubclass(parent_type, ListConfig):
+                if full_key != "":
+                    if issubclass(cur_type, ListConfig):
+                        full_key = f"[{key}]{full_key}"
+                    else:
+                        full_key = f"[{key}].{full_key}"
+                else:
+                    full_key = f"[{key}]"
+            else:
+                if full_key == "":
+                    full_key = key
+                else:
+                    if issubclass(cur_type, ListConfig):
+                        full_key = f"{key}{full_key}"
+                    else:
+                        full_key = f"{key}.{full_key}"
+            return full_key
+
+        if key is not None and key != "":
+            assert isinstance(self, Container)
+            cur, _ = _select_one(
+                c=self, key=str(key), throw_on_missing=False, throw_on_type_error=False
+            )
+            if cur is None:
+                cur = self
+                full_key = prepand("", type(cur), None, key)
+                if cur._key() is not None:
+                    full_key = prepand(
+                        full_key, type(cur._get_parent()), type(cur), cur._key()
+                    )
+            else:
+                full_key = prepand("", type(cur._get_parent()), type(cur), cur._key())
+        else:
+            cur = self
+            if cur._key() is None:
+                return ""
+            full_key = self._key()
+
+        assert cur is not None
+        memo = {id(cur)}  # remember already visited nodes so as to detect cycles
+        while cur._get_parent() is not None:
+            cur = cur._get_parent()
+            if id(cur) in memo:
+                raise ConfigCycleDetectedException(
+                    f"Cycle when iterating over parents of key `{key!s}`"
+                )
+            memo.add(id(cur))
+            assert cur is not None
+            if cur._key() is not None:
+                full_key = prepand(
+                    full_key, type(cur._get_parent()), type(cur), cur._key()
+                )
+
+        return full_key
+
+
+def _create_structured_with_missing_fields(
+    ref_type: type, object_type: Optional[type] = None
+) -> "DictConfig":
+    from . import MISSING, DictConfig
+
+    cfg_data = get_structured_config_data(ref_type)
+    for v in cfg_data.values():
+        v._set_value(MISSING)
+
+    cfg = DictConfig(cfg_data)
+    cfg._metadata.optional, cfg._metadata.ref_type = _resolve_optional(ref_type)
+    cfg._metadata.object_type = object_type
+
+    return cfg
+
+
+def _update_types(node: Node, ref_type: Any, object_type: Optional[type]) -> None:
+    if object_type is not None and not is_primitive_dict(object_type):
+        node._metadata.object_type = object_type
+
+    if node._metadata.ref_type is Any:
+        _deep_update_type_hint(node, ref_type)
+
+
+def _deep_update_type_hint(node: Node, type_hint: Any) -> None:
+    """Ensure node is compatible with type_hint, mutating if necessary."""
+    from omegaconf import DictConfig, ListConfig
+
+    from ._utils import get_dict_key_value_types, get_list_element_type
+
+    if type_hint is Any:
+        return
+
+    _shallow_validate_type_hint(node, type_hint)
+
+    new_is_optional, new_ref_type = _resolve_optional(type_hint)
+    node._metadata.ref_type = new_ref_type
+    node._metadata.optional = new_is_optional
+
+    if is_list_annotation(new_ref_type) and isinstance(node, ListConfig):
+        new_element_type = get_list_element_type(new_ref_type)
+        node._metadata.element_type = new_element_type
+        if not _is_special(node):
+            for i in range(len(node)):
+                _deep_update_subnode(node, i, new_element_type)
+
+    if is_dict_annotation(new_ref_type) and isinstance(node, DictConfig):
+        new_key_type, new_element_type = get_dict_key_value_types(new_ref_type)
+        node._metadata.key_type = new_key_type
+        node._metadata.element_type = new_element_type
+        if not _is_special(node):
+            for key in node:
+                if new_key_type is not Any and not isinstance(key, new_key_type):
+                    raise KeyValidationError(
+                        f"Key {key!r} ({type(key).__name__}) is incompatible"
+                        + f" with key type hint '{new_key_type.__name__}'"
+                    )
+                _deep_update_subnode(node, key, new_element_type)
+
+
+def _deep_update_subnode(node: BaseContainer, key: Any, value_type_hint: Any) -> None:
+    """Get node[key] and ensure it is compatible with value_type_hint, mutating if necessary."""
+    subnode = node._get_node(key)
+    assert isinstance(subnode, Node)
+    if _is_special(subnode):
+        # Ensure special values are wrapped in a Node subclass that
+        # is compatible with the type hint.
+        node._wrap_value_and_set(key, subnode._value(), value_type_hint)
+        subnode = node._get_node(key)
+        assert isinstance(subnode, Node)
+    _deep_update_type_hint(subnode, value_type_hint)
+
+
+def _shallow_validate_type_hint(node: Node, type_hint: Any) -> None:
+    """Error if node's type, content and metadata are not compatible with type_hint."""
+    from omegaconf import DictConfig, ListConfig, ValueNode
+
+    is_optional, ref_type = _resolve_optional(type_hint)
+
+    vk = get_value_kind(node)
+
+    if node._is_none():
+        if not is_optional:
+            value = _get_value(node)
+            raise ValidationError(
+                f"Value {value!r} ({type(value).__name__})"
+                + f" is incompatible with type hint '{ref_type.__name__}'"
+            )
+        return
+    elif vk in (ValueKind.MANDATORY_MISSING, ValueKind.INTERPOLATION):
+        return
+    elif vk == ValueKind.VALUE:
+        if is_primitive_type_annotation(ref_type) and isinstance(node, ValueNode):
+            value = node._value()
+            if not isinstance(value, ref_type):
+                raise ValidationError(
+                    f"Value {value!r} ({type(value).__name__})"
+                    + f" is incompatible with type hint '{ref_type.__name__}'"
+                )
+        elif is_structured_config(ref_type) and isinstance(node, DictConfig):
+            return
+        elif is_dict_annotation(ref_type) and isinstance(node, DictConfig):
+            return
+        elif is_list_annotation(ref_type) and isinstance(node, ListConfig):
+            return
+        else:
+            if isinstance(node, ValueNode):
+                value = node._value()
+                raise ValidationError(
+                    f"Value {value!r} ({type(value).__name__})"
+                    + f" is incompatible with type hint '{ref_type}'"
+                )
+            else:
+                raise ValidationError(
+                    f"'{type(node).__name__}' is incompatible"
+                    + f" with type hint '{ref_type}'"
+                )
+
+    else:
+        assert False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/dictconfig.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/dictconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..da7c8957f1c2e30b4c1889997c85c2a33cf72f01
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/dictconfig.py
@@ -0,0 +1,776 @@
+import copy
+from enum import Enum
+from typing import (
+    Any,
+    Dict,
+    ItemsView,
+    Iterable,
+    Iterator,
+    KeysView,
+    List,
+    MutableMapping,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+
+from ._utils import (
+    _DEFAULT_MARKER_,
+    ValueKind,
+    _get_value,
+    _is_interpolation,
+    _is_missing_literal,
+    _is_missing_value,
+    _is_none,
+    _resolve_optional,
+    _valid_dict_key_annotation_type,
+    format_and_raise,
+    get_structured_config_data,
+    get_structured_config_init_field_names,
+    get_type_of,
+    get_value_kind,
+    is_container_annotation,
+    is_dict,
+    is_primitive_dict,
+    is_structured_config,
+    is_structured_config_frozen,
+    type_str,
+)
+from .base import Box, Container, ContainerMetadata, DictKeyType, Node
+from .basecontainer import BaseContainer
+from .errors import (
+    ConfigAttributeError,
+    ConfigKeyError,
+    ConfigTypeError,
+    InterpolationResolutionError,
+    KeyValidationError,
+    MissingMandatoryValue,
+    OmegaConfBaseException,
+    ReadonlyConfigError,
+    ValidationError,
+)
+from .nodes import EnumNode, ValueNode
+
+
+class DictConfig(BaseContainer, MutableMapping[Any, Any]):
+
+    _metadata: ContainerMetadata
+    _content: Union[Dict[DictKeyType, Node], None, str]
+
+    def __init__(
+        self,
+        content: Union[Dict[DictKeyType, Any], "DictConfig", Any],
+        key: Any = None,
+        parent: Optional[Box] = None,
+        ref_type: Union[Any, Type[Any]] = Any,
+        key_type: Union[Any, Type[Any]] = Any,
+        element_type: Union[Any, Type[Any]] = Any,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> None:
+        try:
+            if isinstance(content, DictConfig):
+                if flags is None:
+                    flags = content._metadata.flags
+            super().__init__(
+                parent=parent,
+                metadata=ContainerMetadata(
+                    key=key,
+                    optional=is_optional,
+                    ref_type=ref_type,
+                    object_type=dict,
+                    key_type=key_type,
+                    element_type=element_type,
+                    flags=flags,
+                ),
+            )
+
+            if not _valid_dict_key_annotation_type(key_type):
+                raise KeyValidationError(f"Unsupported key type {key_type}")
+
+            if is_structured_config(content) or is_structured_config(ref_type):
+                self._set_value(content, flags=flags)
+                if is_structured_config_frozen(content) or is_structured_config_frozen(
+                    ref_type
+                ):
+                    self._set_flag("readonly", True)
+
+            else:
+                if isinstance(content, DictConfig):
+                    metadata = copy.deepcopy(content._metadata)
+                    metadata.key = key
+                    metadata.ref_type = ref_type
+                    metadata.optional = is_optional
+                    metadata.element_type = element_type
+                    metadata.key_type = key_type
+                    self.__dict__["_metadata"] = metadata
+                self._set_value(content, flags=flags)
+        except Exception as ex:
+            format_and_raise(node=None, key=key, value=None, cause=ex, msg=str(ex))
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "DictConfig":
+        res = DictConfig(None)
+        res.__dict__["_metadata"] = copy.deepcopy(self.__dict__["_metadata"], memo=memo)
+        res.__dict__["_flags_cache"] = copy.deepcopy(
+            self.__dict__["_flags_cache"], memo=memo
+        )
+
+        src_content = self.__dict__["_content"]
+        if isinstance(src_content, dict):
+            content_copy = {}
+            for k, v in src_content.items():
+                old_parent = v.__dict__["_parent"]
+                try:
+                    v.__dict__["_parent"] = None
+                    vc = copy.deepcopy(v, memo=memo)
+                    vc.__dict__["_parent"] = res
+                    content_copy[k] = vc
+                finally:
+                    v.__dict__["_parent"] = old_parent
+        else:
+            # None and strings can be assigned as is
+            content_copy = src_content
+
+        res.__dict__["_content"] = content_copy
+        # parent is retained, but not copied
+        res.__dict__["_parent"] = self.__dict__["_parent"]
+        return res
+
+    def copy(self) -> "DictConfig":
+        return copy.copy(self)
+
+    def _is_typed(self) -> bool:
+        return self._metadata.object_type not in (Any, None) and not is_dict(
+            self._metadata.object_type
+        )
+
+    def _validate_get(self, key: Any, value: Any = None) -> None:
+        is_typed = self._is_typed()
+
+        is_struct = self._get_flag("struct") is True
+        if key not in self.__dict__["_content"]:
+            if is_typed:
+                # do not raise an exception if struct is explicitly set to False
+                if self._get_node_flag("struct") is False:
+                    return
+            if is_typed or is_struct:
+                if is_typed:
+                    assert self._metadata.object_type not in (dict, None)
+                    msg = f"Key '{key}' not in '{self._metadata.object_type.__name__}'"
+                else:
+                    msg = f"Key '{key}' is not in struct"
+                self._format_and_raise(
+                    key=key, value=value, cause=ConfigAttributeError(msg)
+                )
+
+    def _validate_set(self, key: Any, value: Any) -> None:
+        from omegaconf import OmegaConf
+
+        vk = get_value_kind(value)
+        if vk == ValueKind.INTERPOLATION:
+            return
+        if _is_none(value):
+            self._validate_non_optional(key, value)
+            return
+        if vk == ValueKind.MANDATORY_MISSING or value is None:
+            return
+
+        target = self._get_node(key) if key is not None else self
+
+        target_has_ref_type = isinstance(
+            target, DictConfig
+        ) and target._metadata.ref_type not in (Any, dict)
+        is_valid_target = target is None or not target_has_ref_type
+
+        if is_valid_target:
+            return
+
+        assert isinstance(target, Node)
+
+        target_type = target._metadata.ref_type
+        value_type = OmegaConf.get_type(value)
+
+        if is_dict(value_type) and is_dict(target_type):
+            return
+        if is_container_annotation(target_type) and not is_container_annotation(
+            value_type
+        ):
+            raise ValidationError(
+                f"Cannot assign {type_str(value_type)} to {type_str(target_type)}"
+            )
+
+        if target_type is not None and value_type is not None:
+            origin = getattr(target_type, "__origin__", target_type)
+            if not issubclass(value_type, origin):
+                self._raise_invalid_value(value, value_type, target_type)
+
+    def _validate_merge(self, value: Any) -> None:
+        from omegaconf import OmegaConf
+
+        dest = self
+        src = value
+
+        self._validate_non_optional(None, src)
+
+        dest_obj_type = OmegaConf.get_type(dest)
+        src_obj_type = OmegaConf.get_type(src)
+
+        if dest._is_missing() and src._metadata.object_type not in (dict, None):
+            self._validate_set(key=None, value=_get_value(src))
+
+        if src._is_missing():
+            return
+
+        validation_error = (
+            dest_obj_type is not None
+            and src_obj_type is not None
+            and is_structured_config(dest_obj_type)
+            and not src._is_none()
+            and not is_dict(src_obj_type)
+            and not issubclass(src_obj_type, dest_obj_type)
+        )
+        if validation_error:
+            msg = (
+                f"Merge error: {type_str(src_obj_type)} is not a "
+                f"subclass of {type_str(dest_obj_type)}. value: {src}"
+            )
+            raise ValidationError(msg)
+
+    def _validate_non_optional(self, key: Optional[DictKeyType], value: Any) -> None:
+        if _is_none(value, resolve=True, throw_on_resolution_failure=False):
+
+            if key is not None:
+                child = self._get_node(key)
+                if child is not None:
+                    assert isinstance(child, Node)
+                    field_is_optional = child._is_optional()
+                else:
+                    field_is_optional, _ = _resolve_optional(
+                        self._metadata.element_type
+                    )
+            else:
+                field_is_optional = self._is_optional()
+
+            if not field_is_optional:
+                self._format_and_raise(
+                    key=key,
+                    value=value,
+                    cause=ValidationError("field '$FULL_KEY' is not Optional"),
+                )
+
+    def _raise_invalid_value(
+        self, value: Any, value_type: Any, target_type: Any
+    ) -> None:
+        assert value_type is not None
+        assert target_type is not None
+        msg = (
+            f"Invalid type assigned: {type_str(value_type)} is not a "
+            f"subclass of {type_str(target_type)}. value: {value}"
+        )
+        raise ValidationError(msg)
+
+    def _validate_and_normalize_key(self, key: Any) -> DictKeyType:
+        return self._s_validate_and_normalize_key(self._metadata.key_type, key)
+
+    def _s_validate_and_normalize_key(self, key_type: Any, key: Any) -> DictKeyType:
+        if key_type is Any:
+            for t in DictKeyType.__args__:  # type: ignore
+                if isinstance(key, t):
+                    return key  # type: ignore
+            raise KeyValidationError("Incompatible key type '$KEY_TYPE'")
+        elif key_type is bool and key in [0, 1]:
+            # Python treats True as 1 and False as 0 when used as dict keys
+            #   assert hash(0) == hash(False)
+            #   assert hash(1) == hash(True)
+            return bool(key)
+        elif key_type in (str, bytes, int, float, bool):  # primitive type
+            if not isinstance(key, key_type):
+                raise KeyValidationError(
+                    f"Key $KEY ($KEY_TYPE) is incompatible with ({key_type.__name__})"
+                )
+
+            return key  # type: ignore
+        elif issubclass(key_type, Enum):
+            try:
+                return EnumNode.validate_and_convert_to_enum(key_type, key)
+            except ValidationError:
+                valid = ", ".join([x for x in key_type.__members__.keys()])
+                raise KeyValidationError(
+                    f"Key '$KEY' is incompatible with the enum type '{key_type.__name__}', valid: [{valid}]"
+                )
+        else:
+            assert False, f"Unsupported key type {key_type}"
+
+    def __setitem__(self, key: DictKeyType, value: Any) -> None:
+        try:
+            self.__set_impl(key=key, value=value)
+        except AttributeError as e:
+            self._format_and_raise(
+                key=key, value=value, type_override=ConfigKeyError, cause=e
+            )
+        except Exception as e:
+            self._format_and_raise(key=key, value=value, cause=e)
+
+    def __set_impl(self, key: DictKeyType, value: Any) -> None:
+        key = self._validate_and_normalize_key(key)
+        self._set_item_impl(key, value)
+
+    # hide content while inspecting in debugger
+    def __dir__(self) -> Iterable[str]:
+        if self._is_missing() or self._is_none():
+            return []
+        return self.__dict__["_content"].keys()  # type: ignore
+
+    def __setattr__(self, key: str, value: Any) -> None:
+        """
+        Allow assigning attributes to DictConfig
+        :param key:
+        :param value:
+        :return:
+        """
+        try:
+            self.__set_impl(key, value)
+        except Exception as e:
+            if isinstance(e, OmegaConfBaseException) and e._initialized:
+                raise e
+            self._format_and_raise(key=key, value=value, cause=e)
+            assert False
+
+    def __getattr__(self, key: str) -> Any:
+        """
+        Allow accessing dictionary values as attributes
+        :param key:
+        :return:
+        """
+        if key == "__name__":
+            raise AttributeError()
+
+        try:
+            return self._get_impl(
+                key=key, default_value=_DEFAULT_MARKER_, validate_key=False
+            )
+        except ConfigKeyError as e:
+            self._format_and_raise(
+                key=key, value=None, cause=e, type_override=ConfigAttributeError
+            )
+        except Exception as e:
+            self._format_and_raise(key=key, value=None, cause=e)
+
+    def __getitem__(self, key: DictKeyType) -> Any:
+        """
+        Allow map style access
+        :param key:
+        :return:
+        """
+
+        try:
+            return self._get_impl(key=key, default_value=_DEFAULT_MARKER_)
+        except AttributeError as e:
+            self._format_and_raise(
+                key=key, value=None, cause=e, type_override=ConfigKeyError
+            )
+        except Exception as e:
+            self._format_and_raise(key=key, value=None, cause=e)
+
+    def __delattr__(self, key: str) -> None:
+        """
+        Allow deleting dictionary values as attributes
+        :param key:
+        :return:
+        """
+        if self._get_flag("readonly"):
+            self._format_and_raise(
+                key=key,
+                value=None,
+                cause=ReadonlyConfigError(
+                    "DictConfig in read-only mode does not support deletion"
+                ),
+            )
+        try:
+            del self.__dict__["_content"][key]
+        except KeyError:
+            msg = "Attribute not found: '$KEY'"
+            self._format_and_raise(key=key, value=None, cause=ConfigAttributeError(msg))
+
+    def __delitem__(self, key: DictKeyType) -> None:
+        key = self._validate_and_normalize_key(key)
+        if self._get_flag("readonly"):
+            self._format_and_raise(
+                key=key,
+                value=None,
+                cause=ReadonlyConfigError(
+                    "DictConfig in read-only mode does not support deletion"
+                ),
+            )
+        if self._get_flag("struct"):
+            self._format_and_raise(
+                key=key,
+                value=None,
+                cause=ConfigTypeError(
+                    "DictConfig in struct mode does not support deletion"
+                ),
+            )
+        if self._is_typed() and self._get_node_flag("struct") is not False:
+            self._format_and_raise(
+                key=key,
+                value=None,
+                cause=ConfigTypeError(
+                    f"{type_str(self._metadata.object_type)} (DictConfig) does not support deletion"
+                ),
+            )
+
+        try:
+            del self.__dict__["_content"][key]
+        except KeyError:
+            msg = "Key not found: '$KEY'"
+            self._format_and_raise(key=key, value=None, cause=ConfigKeyError(msg))
+
+    def get(self, key: DictKeyType, default_value: Any = None) -> Any:
+        """Return the value for `key` if `key` is in the dictionary, else
+        `default_value` (defaulting to `None`)."""
+        try:
+            return self._get_impl(key=key, default_value=default_value)
+        except KeyValidationError as e:
+            self._format_and_raise(key=key, value=None, cause=e)
+
+    def _get_impl(
+        self, key: DictKeyType, default_value: Any, validate_key: bool = True
+    ) -> Any:
+        try:
+            node = self._get_child(
+                key=key, throw_on_missing_key=True, validate_key=validate_key
+            )
+        except (ConfigAttributeError, ConfigKeyError):
+            if default_value is not _DEFAULT_MARKER_:
+                return default_value
+            else:
+                raise
+        assert isinstance(node, Node)
+        return self._resolve_with_default(
+            key=key, value=node, default_value=default_value
+        )
+
+    def _get_node(
+        self,
+        key: DictKeyType,
+        validate_access: bool = True,
+        validate_key: bool = True,
+        throw_on_missing_value: bool = False,
+        throw_on_missing_key: bool = False,
+    ) -> Optional[Node]:
+        try:
+            key = self._validate_and_normalize_key(key)
+        except KeyValidationError:
+            if validate_access and validate_key:
+                raise
+            else:
+                if throw_on_missing_key:
+                    raise ConfigAttributeError
+                else:
+                    return None
+
+        if validate_access:
+            self._validate_get(key)
+
+        value: Optional[Node] = self.__dict__["_content"].get(key)
+        if value is None:
+            if throw_on_missing_key:
+                raise ConfigKeyError(f"Missing key {key!s}")
+        elif throw_on_missing_value and value._is_missing():
+            raise MissingMandatoryValue("Missing mandatory value: $KEY")
+        return value
+
+    def pop(self, key: DictKeyType, default: Any = _DEFAULT_MARKER_) -> Any:
+        try:
+            if self._get_flag("readonly"):
+                raise ReadonlyConfigError("Cannot pop from read-only node")
+            if self._get_flag("struct"):
+                raise ConfigTypeError("DictConfig in struct mode does not support pop")
+            if self._is_typed() and self._get_node_flag("struct") is not False:
+                raise ConfigTypeError(
+                    f"{type_str(self._metadata.object_type)} (DictConfig) does not support pop"
+                )
+            key = self._validate_and_normalize_key(key)
+            node = self._get_child(key=key, validate_access=False)
+            if node is not None:
+                assert isinstance(node, Node)
+                value = self._resolve_with_default(
+                    key=key, value=node, default_value=default
+                )
+
+                del self[key]
+                return value
+            else:
+                if default is not _DEFAULT_MARKER_:
+                    return default
+                else:
+                    full = self._get_full_key(key=key)
+                    if full != key:
+                        raise ConfigKeyError(
+                            f"Key not found: '{key!s}' (path: '{full}')"
+                        )
+                    else:
+                        raise ConfigKeyError(f"Key not found: '{key!s}'")
+        except Exception as e:
+            self._format_and_raise(key=key, value=None, cause=e)
+
+    def keys(self) -> KeysView[DictKeyType]:
+        if self._is_missing() or self._is_interpolation() or self._is_none():
+            return {}.keys()
+        ret = self.__dict__["_content"].keys()
+        assert isinstance(ret, KeysView)
+        return ret
+
+    def __contains__(self, key: object) -> bool:
+        """
+        A key is contained in a DictConfig if there is an associated value and
+        it is not a mandatory missing value ('???').
+        :param key:
+        :return:
+        """
+
+        try:
+            key = self._validate_and_normalize_key(key)
+        except KeyValidationError:
+            return False
+
+        try:
+            node = self._get_child(key)
+            assert node is None or isinstance(node, Node)
+        except (KeyError, AttributeError):
+            node = None
+
+        if node is None:
+            return False
+        else:
+            try:
+                self._resolve_with_default(key=key, value=node)
+                return True
+            except InterpolationResolutionError:
+                # Interpolations that fail count as existing.
+                return True
+            except MissingMandatoryValue:
+                # Missing values count as *not* existing.
+                return False
+
+    def __iter__(self) -> Iterator[DictKeyType]:
+        return iter(self.keys())
+
+    def items(self) -> ItemsView[DictKeyType, Any]:
+        return dict(self.items_ex(resolve=True, keys=None)).items()
+
+    def setdefault(self, key: DictKeyType, default: Any = None) -> Any:
+        if key in self:
+            ret = self.__getitem__(key)
+        else:
+            ret = default
+            self.__setitem__(key, default)
+        return ret
+
+    def items_ex(
+        self, resolve: bool = True, keys: Optional[Sequence[DictKeyType]] = None
+    ) -> List[Tuple[DictKeyType, Any]]:
+        items: List[Tuple[DictKeyType, Any]] = []
+
+        if self._is_none():
+            self._format_and_raise(
+                key=None,
+                value=None,
+                cause=TypeError("Cannot iterate a DictConfig object representing None"),
+            )
+        if self._is_missing():
+            raise MissingMandatoryValue("Cannot iterate a missing DictConfig")
+
+        for key in self.keys():
+            if resolve:
+                value = self[key]
+            else:
+                value = self.__dict__["_content"][key]
+                if isinstance(value, ValueNode):
+                    value = value._value()
+            if keys is None or key in keys:
+                items.append((key, value))
+
+        return items
+
+    def __eq__(self, other: Any) -> bool:
+        if other is None:
+            return self.__dict__["_content"] is None
+        if is_primitive_dict(other) or is_structured_config(other):
+            other = DictConfig(other, flags={"allow_objects": True})
+            return DictConfig._dict_conf_eq(self, other)
+        if isinstance(other, DictConfig):
+            return DictConfig._dict_conf_eq(self, other)
+        if self._is_missing():
+            return _is_missing_literal(other)
+        return NotImplemented
+
+    def __ne__(self, other: Any) -> bool:
+        x = self.__eq__(other)
+        if x is not NotImplemented:
+            return not x
+        return NotImplemented
+
+    def __hash__(self) -> int:
+        return hash(str(self))
+
+    def _promote(self, type_or_prototype: Optional[Type[Any]]) -> None:
+        """
+        Retypes a node.
+        This should only be used in rare circumstances, where you want to dynamically change
+        the runtime structured-type of a DictConfig.
+        It will change the type and add the additional fields based on the input class or object
+        """
+        if type_or_prototype is None:
+            return
+        if not is_structured_config(type_or_prototype):
+            raise ValueError(f"Expected structured config class: {type_or_prototype}")
+
+        from omegaconf import OmegaConf
+
+        proto: DictConfig = OmegaConf.structured(type_or_prototype)
+        object_type = proto._metadata.object_type
+        # remove the type to prevent assignment validation from rejecting the promotion.
+        proto._metadata.object_type = None
+        self.merge_with(proto)
+        # restore the type.
+        self._metadata.object_type = object_type
+
+    def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None:
+        try:
+            previous_content = self.__dict__["_content"]
+            self._set_value_impl(value, flags)
+        except Exception as e:
+            self.__dict__["_content"] = previous_content
+            raise e
+
+    def _set_value_impl(
+        self, value: Any, flags: Optional[Dict[str, bool]] = None
+    ) -> None:
+        from omegaconf import MISSING, flag_override
+
+        if flags is None:
+            flags = {}
+
+        assert not isinstance(value, ValueNode)
+        self._validate_set(key=None, value=value)
+
+        if _is_none(value, resolve=True):
+            self.__dict__["_content"] = None
+            self._metadata.object_type = None
+        elif _is_interpolation(value, strict_interpolation_validation=True):
+            self.__dict__["_content"] = value
+            self._metadata.object_type = None
+        elif _is_missing_value(value):
+            self.__dict__["_content"] = MISSING
+            self._metadata.object_type = None
+        else:
+            self.__dict__["_content"] = {}
+            if is_structured_config(value):
+                self._metadata.object_type = None
+                ao = self._get_flag("allow_objects")
+                data = get_structured_config_data(value, allow_objects=ao)
+                with flag_override(self, ["struct", "readonly"], False):
+                    for k, v in data.items():
+                        self.__setitem__(k, v)
+                self._metadata.object_type = get_type_of(value)
+
+            elif isinstance(value, DictConfig):
+                self._metadata.flags = copy.deepcopy(flags)
+                with flag_override(self, ["struct", "readonly"], False):
+                    for k, v in value.__dict__["_content"].items():
+                        self.__setitem__(k, v)
+                self._metadata.object_type = value._metadata.object_type
+
+            elif isinstance(value, dict):
+                with flag_override(self, ["struct", "readonly"], False):
+                    for k, v in value.items():
+                        self.__setitem__(k, v)
+                self._metadata.object_type = dict
+
+            else:  # pragma: no cover
+                msg = f"Unsupported value type: {value}"
+                raise ValidationError(msg)
+
+    @staticmethod
+    def _dict_conf_eq(d1: "DictConfig", d2: "DictConfig") -> bool:
+
+        d1_none = d1.__dict__["_content"] is None
+        d2_none = d2.__dict__["_content"] is None
+        if d1_none and d2_none:
+            return True
+        if d1_none != d2_none:
+            return False
+
+        assert isinstance(d1, DictConfig)
+        assert isinstance(d2, DictConfig)
+        if len(d1) != len(d2):
+            return False
+        if d1._is_missing() or d2._is_missing():
+            return d1._is_missing() is d2._is_missing()
+
+        for k, v in d1.items_ex(resolve=False):
+            if k not in d2.__dict__["_content"]:
+                return False
+            if not BaseContainer._item_eq(d1, k, d2, k):
+                return False
+
+        return True
+
+    def _to_object(self) -> Any:
+        """
+        Instantiate an instance of `self._metadata.object_type`.
+        This requires `self` to be a structured config.
+        Nested subconfigs are converted by calling `OmegaConf.to_object`.
+        """
+        from omegaconf import OmegaConf
+
+        object_type = self._metadata.object_type
+        assert is_structured_config(object_type)
+        init_field_names = set(get_structured_config_init_field_names(object_type))
+
+        init_field_items: Dict[str, Any] = {}
+        non_init_field_items: Dict[str, Any] = {}
+        for k in self.keys():
+            assert isinstance(k, str)
+            node = self._get_child(k)
+            assert isinstance(node, Node)
+            try:
+                node = node._dereference_node()
+            except InterpolationResolutionError as e:
+                self._format_and_raise(key=k, value=None, cause=e)
+            if node._is_missing():
+                if k not in init_field_names:
+                    continue  # MISSING is ignored for init=False fields
+                self._format_and_raise(
+                    key=k,
+                    value=None,
+                    cause=MissingMandatoryValue(
+                        "Structured config of type `$OBJECT_TYPE` has missing mandatory value: $KEY"
+                    ),
+                )
+            if isinstance(node, Container):
+                v = OmegaConf.to_object(node)
+            else:
+                v = node._value()
+
+            if k in init_field_names:
+                init_field_items[k] = v
+            else:
+                non_init_field_items[k] = v
+
+        try:
+            result = object_type(**init_field_items)
+        except TypeError as exc:
+            self._format_and_raise(
+                key=None,
+                value=None,
+                cause=exc,
+                msg="Could not create instance of `$OBJECT_TYPE`: " + str(exc),
+            )
+
+        for k, v in non_init_field_items.items():
+            setattr(result, k, v)
+        return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/errors.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..60525ef3c62fb89093c0b5aa1147ae6f80b61062
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/errors.py
@@ -0,0 +1,141 @@
+from typing import Any, Optional, Type
+
+
+class OmegaConfBaseException(Exception):
+    # would ideally be typed Optional[Node]
+    parent_node: Any
+    child_node: Any
+    key: Any
+    full_key: Optional[str]
+    value: Any
+    msg: Optional[str]
+    cause: Optional[Exception]
+    object_type: Optional[Type[Any]]
+    object_type_str: Optional[str]
+    ref_type: Optional[Type[Any]]
+    ref_type_str: Optional[str]
+
+    _initialized: bool = False
+
+    def __init__(self, *_args: Any, **_kwargs: Any) -> None:
+        self.parent_node = None
+        self.child_node = None
+        self.key = None
+        self.full_key = None
+        self.value = None
+        self.msg = None
+        self.object_type = None
+        self.ref_type = None
+
+
+class MissingMandatoryValue(OmegaConfBaseException):
+    """Thrown when a variable flagged with '???' value is accessed to
+    indicate that the value was not set"""
+
+
+class KeyValidationError(OmegaConfBaseException, ValueError):
+    """
+    Thrown when an a key of invalid type is used
+    """
+
+
+class ValidationError(OmegaConfBaseException, ValueError):
+    """
+    Thrown when a value fails validation
+    """
+
+
+class UnsupportedValueType(ValidationError, ValueError):
+    """
+    Thrown when an input value is not of supported type
+    """
+
+
+class ReadonlyConfigError(OmegaConfBaseException):
+    """
+    Thrown when someone tries to modify a frozen config
+    """
+
+
+class InterpolationResolutionError(OmegaConfBaseException, ValueError):
+    """
+    Base class for exceptions raised when resolving an interpolation.
+    """
+
+
+class UnsupportedInterpolationType(InterpolationResolutionError):
+    """
+    Thrown when an attempt to use an unregistered interpolation is made
+    """
+
+
+class InterpolationKeyError(InterpolationResolutionError):
+    """
+    Thrown when a node does not exist when resolving an interpolation.
+    """
+
+
+class InterpolationToMissingValueError(InterpolationResolutionError):
+    """
+    Thrown when a node interpolation points to a node that is set to ???.
+    """
+
+
+class InterpolationValidationError(InterpolationResolutionError, ValidationError):
+    """
+    Thrown when the result of an interpolation fails the validation step.
+    """
+
+
+class ConfigKeyError(OmegaConfBaseException, KeyError):
+    """
+    Thrown from DictConfig when a regular dict access would have caused a KeyError.
+    """
+
+    msg: str
+
+    def __init__(self, msg: str) -> None:
+        super().__init__(msg)
+        self.msg = msg
+
+    def __str__(self) -> str:
+        """
+        Workaround to nasty KeyError quirk: https://bugs.python.org/issue2651
+        """
+        return self.msg
+
+
+class ConfigAttributeError(OmegaConfBaseException, AttributeError):
+    """
+    Thrown from a config object when a regular access would have caused an AttributeError.
+    """
+
+
+class ConfigTypeError(OmegaConfBaseException, TypeError):
+    """
+    Thrown from a config object when a regular access would have caused a TypeError.
+    """
+
+
+class ConfigIndexError(OmegaConfBaseException, IndexError):
+    """
+    Thrown from a config object when a regular access would have caused an IndexError.
+    """
+
+
+class ConfigValueError(OmegaConfBaseException, ValueError):
+    """
+    Thrown from a config object when a regular access would have caused a ValueError.
+    """
+
+
+class ConfigCycleDetectedException(OmegaConfBaseException):
+    """
+    Thrown when a cycle is detected in the graph made by config nodes.
+    """
+
+
+class GrammarParseError(OmegaConfBaseException):
+    """
+    Thrown when failing to parse an expression according to the ANTLR grammar.
+    """
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarLexer.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarLexer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bf9416fab12aaf7eb45c73b620037d4800e3496
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarLexer.py
@@ -0,0 +1,337 @@
+# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarLexer.g4 by ANTLR 4.9.3
+from antlr4 import *
+from io import StringIO
+import sys
+if sys.version_info[1] > 5:
+    from typing import TextIO
+else:
+    from typing.io import TextIO
+
+
+
+def serializedATN():
+    with StringIO() as buf:
+        buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\36")
+        buf.write("\u01e7\b\1\b\1\b\1\b\1\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5")
+        buf.write("\t\5\4\6\t\6\4\7\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13")
+        buf.write("\4\f\t\f\4\r\t\r\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t")
+        buf.write("\21\4\22\t\22\4\23\t\23\4\24\t\24\4\25\t\25\4\26\t\26")
+        buf.write("\4\27\t\27\4\30\t\30\4\31\t\31\4\32\t\32\4\33\t\33\4\34")
+        buf.write("\t\34\4\35\t\35\4\36\t\36\4\37\t\37\4 \t \4!\t!\4\"\t")
+        buf.write("\"\4#\t#\4$\t$\4%\t%\4&\t&\4\'\t\'\4(\t(\4)\t)\4*\t*\4")
+        buf.write("+\t+\4,\t,\4-\t-\4.\t.\4/\t/\4\60\t\60\4\61\t\61\4\62")
+        buf.write("\t\62\4\63\t\63\4\64\t\64\4\65\t\65\4\66\t\66\3\2\3\2")
+        buf.write("\3\3\3\3\3\4\3\4\3\4\5\4y\n\4\3\4\7\4|\n\4\f\4\16\4\177")
+        buf.write("\13\4\5\4\u0081\n\4\3\5\3\5\3\5\3\6\3\6\3\6\3\6\3\6\3")
+        buf.write("\7\7\7\u008c\n\7\f\7\16\7\u008f\13\7\3\7\3\7\3\b\7\b\u0094")
+        buf.write("\n\b\f\b\16\b\u0097\13\b\3\b\3\b\3\b\3\b\3\t\6\t\u009e")
+        buf.write("\n\t\r\t\16\t\u009f\3\n\6\n\u00a3\n\n\r\n\16\n\u00a4\3")
+        buf.write("\n\3\n\3\13\3\13\3\13\3\13\3\f\3\f\3\f\3\f\5\f\u00b1\n")
+        buf.write("\f\3\f\3\f\3\r\3\r\5\r\u00b7\n\r\3\r\3\r\3\16\5\16\u00bc")
+        buf.write("\n\16\3\16\3\16\3\16\3\16\3\17\3\17\3\17\3\17\3\20\3\20")
+        buf.write("\3\20\3\20\3\21\5\21\u00cb\n\21\3\21\3\21\5\21\u00cf\n")
+        buf.write("\21\3\22\3\22\5\22\u00d3\n\22\3\23\5\23\u00d6\n\23\3\23")
+        buf.write("\3\23\3\24\5\24\u00db\n\24\3\24\3\24\5\24\u00df\n\24\3")
+        buf.write("\25\3\25\3\25\3\25\5\25\u00e5\n\25\3\25\3\25\3\25\5\25")
+        buf.write("\u00ea\n\25\3\25\7\25\u00ed\n\25\f\25\16\25\u00f0\13\25")
+        buf.write("\5\25\u00f2\n\25\3\26\3\26\5\26\u00f6\n\26\3\26\3\26\5")
+        buf.write("\26\u00fa\n\26\3\26\3\26\5\26\u00fe\n\26\3\26\7\26\u0101")
+        buf.write("\n\26\f\26\16\26\u0104\13\26\3\27\5\27\u0107\n\27\3\27")
+        buf.write("\3\27\3\27\3\27\3\27\3\27\3\27\3\27\5\27\u0111\n\27\3")
+        buf.write("\30\5\30\u0114\n\30\3\30\3\30\3\31\3\31\3\31\3\31\3\31")
+        buf.write("\3\31\3\31\3\31\3\31\5\31\u0121\n\31\3\32\3\32\3\32\3")
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+        buf.write("\3\35\3\35\3\35\3\35\3\35\3\35\3\35\3\35\3\35\3\35\6\35")
+        buf.write("\u014d\n\35\r\35\16\35\u014e\3\36\6\36\u0152\n\36\r\36")
+        buf.write("\16\36\u0153\3\37\3\37\5\37\u0158\n\37\3\37\3\37\3\37")
+        buf.write("\3 \5 \u015e\n \3 \3 \5 \u0162\n \3 \3 \3 \3!\5!\u0168")
+        buf.write("\n!\3!\3!\3!\3!\3\"\3\"\3#\3#\3#\3#\3$\3$\3$\3$\3%\3%")
+        buf.write("\3%\3%\3&\6&\u017d\n&\r&\16&\u017e\3\'\3\'\3\'\3\'\3\'")
+        buf.write("\3(\3(\3(\3(\3)\7)\u018b\n)\f)\16)\u018e\13)\3)\3)\3)")
+        buf.write("\3)\3*\3*\3*\3*\3+\7+\u0199\n+\f+\16+\u019c\13+\3+\3+")
+        buf.write("\3+\3+\3+\3,\6,\u01a4\n,\r,\16,\u01a5\3-\6-\u01a9\n-\r")
+        buf.write("-\16-\u01aa\3-\3-\3.\3.\3.\3.\3/\3/\3/\3/\3/\3\60\3\60")
+        buf.write("\3\60\3\60\3\60\3\61\7\61\u01be\n\61\f\61\16\61\u01c1")
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+        buf.write("\2&\',-/\61AB^^~~\4\2//aa\4\2\13\13\"\"\13\2\13\13\"\"")
+        buf.write("$$)+\60\60<<]_}}\177\177\4\2&&))\5\2&&))^^\4\2$$&&\5\2")
+        buf.write("$$&&^^\2\u0218\2\17\3\2\2\2\2\21\3\2\2\2\2\23\3\2\2\2")
+        buf.write("\2\25\3\2\2\2\2\27\3\2\2\2\2\31\3\2\2\2\3\33\3\2\2\2\3")
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+        buf.write("\2\2\3\'\3\2\2\2\3)\3\2\2\2\3+\3\2\2\2\3\61\3\2\2\2\3")
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+        buf.write("\2\2\2\3=\3\2\2\2\3?\3\2\2\2\4A\3\2\2\2\4C\3\2\2\2\4E")
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+        buf.write("\u01cd\u01cb\3\2\2\2\u01cd\u01ce\3\2\2\2\u01ce\u01d0\3")
+        buf.write("\2\2\2\u01cf\u01cd\3\2\2\2\u01d0\u01d1\7^\2\2\u01d1\u01d2")
+        buf.write("\7$\2\2\u01d2\u01d3\3\2\2\2\u01d3\u01d4\b\63\17\2\u01d4")
+        buf.write("j\3\2\2\2\u01d5\u01d7\5\r\5\2\u01d6\u01d5\3\2\2\2\u01d7")
+        buf.write("\u01d8\3\2\2\2\u01d8\u01d6\3\2\2\2\u01d8\u01d9\3\2\2\2")
+        buf.write("\u01d9\u01da\3\2\2\2\u01da\u01db\b\64\21\2\u01dbl\3\2")
+        buf.write("\2\2\u01dc\u01de\7^\2\2\u01dd\u01dc\3\2\2\2\u01de\u01df")
+        buf.write("\3\2\2\2\u01df\u01dd\3\2\2\2\u01df\u01e0\3\2\2\2\u01e0")
+        buf.write("\u01e1\3\2\2\2\u01e1\u01e2\b\65\4\2\u01e2n\3\2\2\2\u01e3")
+        buf.write("\u01e4\7&\2\2\u01e4\u01e5\3\2\2\2\u01e5\u01e6\b\66\4\2")
+        buf.write("\u01e6p\3\2\2\2\66\2\3\4\5\6x}\u0080\u008d\u0095\u009f")
+        buf.write("\u00a4\u00b0\u00b6\u00bb\u00ca\u00ce\u00d2\u00d5\u00da")
+        buf.write("\u00de\u00e4\u00e9\u00ee\u00f1\u00f5\u00f9\u00fd\u0102")
+        buf.write("\u0106\u0110\u0113\u0120\u012b\u0130\u0132\u014c\u014e")
+        buf.write("\u0153\u0157\u015d\u0161\u0167\u017e\u018c\u019a\u01a5")
+        buf.write("\u01aa\u01bf\u01cd\u01d8\u01df\22\t\6\2\7\4\2\t\3\2\7")
+        buf.write("\3\2\6\2\2\7\5\2\7\6\2\t\16\2\4\3\2\t\f\2\t\r\2\t\24\2")
+        buf.write("\t\4\2\t\25\2\t\32\2\t\33\2")
+        return buf.getvalue()
+
+
+class OmegaConfGrammarLexer(Lexer):
+
+    atn = ATNDeserializer().deserialize(serializedATN())
+
+    decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ]
+
+    VALUE_MODE = 1
+    INTERPOLATION_MODE = 2
+    QUOTED_SINGLE_MODE = 3
+    QUOTED_DOUBLE_MODE = 4
+
+    ANY_STR = 1
+    ESC_INTER = 2
+    TOP_ESC = 3
+    INTER_OPEN = 4
+    BRACE_OPEN = 5
+    BRACE_CLOSE = 6
+    QUOTE_OPEN_SINGLE = 7
+    QUOTE_OPEN_DOUBLE = 8
+    COMMA = 9
+    BRACKET_OPEN = 10
+    BRACKET_CLOSE = 11
+    COLON = 12
+    FLOAT = 13
+    INT = 14
+    BOOL = 15
+    NULL = 16
+    UNQUOTED_CHAR = 17
+    ID = 18
+    ESC = 19
+    WS = 20
+    INTER_CLOSE = 21
+    DOT = 22
+    INTER_KEY = 23
+    MATCHING_QUOTE_CLOSE = 24
+    QUOTED_ESC = 25
+    DOLLAR = 26
+    INTER_BRACKET_OPEN = 27
+    INTER_BRACKET_CLOSE = 28
+
+    channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ]
+
+    modeNames = [ "DEFAULT_MODE", "VALUE_MODE", "INTERPOLATION_MODE", "QUOTED_SINGLE_MODE", 
+                  "QUOTED_DOUBLE_MODE" ]
+
+    literalNames = [ "<INVALID>",
+            "'.'", "'['", "']'" ]
+
+    symbolicNames = [ "<INVALID>",
+            "ANY_STR", "ESC_INTER", "TOP_ESC", "INTER_OPEN", "BRACE_OPEN", 
+            "BRACE_CLOSE", "QUOTE_OPEN_SINGLE", "QUOTE_OPEN_DOUBLE", "COMMA", 
+            "BRACKET_OPEN", "BRACKET_CLOSE", "COLON", "FLOAT", "INT", "BOOL", 
+            "NULL", "UNQUOTED_CHAR", "ID", "ESC", "WS", "INTER_CLOSE", "DOT", 
+            "INTER_KEY", "MATCHING_QUOTE_CLOSE", "QUOTED_ESC", "DOLLAR", 
+            "INTER_BRACKET_OPEN", "INTER_BRACKET_CLOSE" ]
+
+    ruleNames = [ "CHAR", "DIGIT", "INT_UNSIGNED", "ESC_BACKSLASH", "TOP_INTER_OPEN", 
+                  "ANY_STR", "ESC_INTER", "TOP_ESC", "BACKSLASHES", "DOLLAR", 
+                  "INTER_OPEN", "BRACE_OPEN", "BRACE_CLOSE", "QUOTE_OPEN_SINGLE", 
+                  "QUOTE_OPEN_DOUBLE", "COMMA", "BRACKET_OPEN", "BRACKET_CLOSE", 
+                  "COLON", "POINT_FLOAT", "EXPONENT_FLOAT", "FLOAT", "INT", 
+                  "BOOL", "NULL", "UNQUOTED_CHAR", "ID", "ESC", "WS", "NESTED_INTER_OPEN", 
+                  "INTER_COLON", "INTER_CLOSE", "DOT", "INTER_BRACKET_OPEN", 
+                  "INTER_BRACKET_CLOSE", "INTER_ID", "INTER_KEY", "QSINGLE_INTER_OPEN", 
+                  "MATCHING_QUOTE_CLOSE", "QSINGLE_STR", "QSINGLE_ESC_INTER", 
+                  "QSINGLE_ESC_QUOTE", "QUOTED_ESC", "QSINGLE_BACKSLASHES", 
+                  "QSINGLE_DOLLAR", "QDOUBLE_INTER_OPEN", "QDOUBLE_CLOSE", 
+                  "QDOUBLE_STR", "QDOUBLE_ESC_INTER", "QDOUBLE_ESC_QUOTE", 
+                  "QDOUBLE_ESC", "QDOUBLE_BACKSLASHES", "QDOUBLE_DOLLAR" ]
+
+    grammarFileName = "OmegaConfGrammarLexer.g4"
+
+    def __init__(self, input=None, output:TextIO = sys.stdout):
+        super().__init__(input, output)
+        self.checkVersion("4.9.3")
+        self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache())
+        self._actions = None
+        self._predicates = None
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py
new file mode 100644
index 0000000000000000000000000000000000000000..024cbab87fddd6ed3b4abb63105589f673b0e864
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py
@@ -0,0 +1,1595 @@
+# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarParser.g4 by ANTLR 4.9.3
+# encoding: utf-8
+from antlr4 import *
+from io import StringIO
+import sys
+if sys.version_info[1] > 5:
+	from typing import TextIO
+else:
+	from typing.io import TextIO
+
+
+def serializedATN():
+    with StringIO() as buf:
+        buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\3\36")
+        buf.write("\u00b7\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7\t\7")
+        buf.write("\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r\4\16")
+        buf.write("\t\16\4\17\t\17\4\20\t\20\4\21\t\21\3\2\3\2\3\2\3\3\3")
+        buf.write("\3\3\3\3\4\3\4\3\4\3\4\3\4\3\4\6\4/\n\4\r\4\16\4\60\3")
+        buf.write("\5\3\5\3\5\3\5\5\5\67\n\5\3\6\3\6\5\6;\n\6\3\6\3\6\3\7")
+        buf.write("\3\7\3\7\3\7\7\7C\n\7\f\7\16\7F\13\7\5\7H\n\7\3\7\3\7")
+        buf.write("\3\b\3\b\3\b\3\b\3\t\3\t\3\t\5\tS\n\t\7\tU\n\t\f\t\16")
+        buf.write("\tX\13\t\3\t\3\t\5\t\\\n\t\6\t^\n\t\r\t\16\t_\5\tb\n\t")
+        buf.write("\3\n\3\n\5\nf\n\n\3\13\3\13\7\13j\n\13\f\13\16\13m\13")
+        buf.write("\13\3\13\3\13\3\13\3\13\3\13\5\13t\n\13\3\13\3\13\3\13")
+        buf.write("\3\13\3\13\3\13\7\13|\n\13\f\13\16\13\177\13\13\3\13\3")
+        buf.write("\13\3\f\3\f\3\f\3\f\5\f\u0087\n\f\3\f\3\f\3\r\3\r\3\r")
+        buf.write("\5\r\u008e\n\r\3\16\3\16\5\16\u0092\n\16\3\16\3\16\3\16")
+        buf.write("\5\16\u0097\n\16\7\16\u0099\n\16\f\16\16\16\u009c\13\16")
+        buf.write("\3\17\3\17\5\17\u00a0\n\17\3\17\3\17\3\20\3\20\3\20\3")
+        buf.write("\20\3\20\3\20\3\20\3\20\3\20\3\20\6\20\u00ae\n\20\r\20")
+        buf.write("\16\20\u00af\3\21\6\21\u00b3\n\21\r\21\16\21\u00b4\3\21")
+        buf.write("\2\2\22\2\4\6\b\n\f\16\20\22\24\26\30\32\34\36 \2\4\3")
+        buf.write("\2\t\n\3\2\17\26\2\u00ce\2\"\3\2\2\2\4%\3\2\2\2\6.\3\2")
+        buf.write("\2\2\b\66\3\2\2\2\n8\3\2\2\2\f>\3\2\2\2\16K\3\2\2\2\20")
+        buf.write("a\3\2\2\2\22e\3\2\2\2\24g\3\2\2\2\26\u0082\3\2\2\2\30")
+        buf.write("\u008d\3\2\2\2\32\u0091\3\2\2\2\34\u009d\3\2\2\2\36\u00ad")
+        buf.write("\3\2\2\2 \u00b2\3\2\2\2\"#\5\6\4\2#$\7\2\2\3$\3\3\2\2")
+        buf.write("\2%&\5\b\5\2&\'\7\2\2\3\'\5\3\2\2\2(/\5\22\n\2)/\7\3\2")
+        buf.write("\2*/\7\25\2\2+/\7\4\2\2,/\7\5\2\2-/\7\33\2\2.(\3\2\2\2")
+        buf.write(".)\3\2\2\2.*\3\2\2\2.+\3\2\2\2.,\3\2\2\2.-\3\2\2\2/\60")
+        buf.write("\3\2\2\2\60.\3\2\2\2\60\61\3\2\2\2\61\7\3\2\2\2\62\67")
+        buf.write("\5\36\20\2\63\67\5\34\17\2\64\67\5\n\6\2\65\67\5\f\7\2")
+        buf.write("\66\62\3\2\2\2\66\63\3\2\2\2\66\64\3\2\2\2\66\65\3\2\2")
+        buf.write("\2\67\t\3\2\2\28:\7\f\2\29;\5\20\t\2:9\3\2\2\2:;\3\2\2")
+        buf.write("\2;<\3\2\2\2<=\7\r\2\2=\13\3\2\2\2>G\7\7\2\2?D\5\16\b")
+        buf.write("\2@A\7\13\2\2AC\5\16\b\2B@\3\2\2\2CF\3\2\2\2DB\3\2\2\2")
+        buf.write("DE\3\2\2\2EH\3\2\2\2FD\3\2\2\2G?\3\2\2\2GH\3\2\2\2HI\3")
+        buf.write("\2\2\2IJ\7\b\2\2J\r\3\2\2\2KL\5 \21\2LM\7\16\2\2MN\5\b")
+        buf.write("\5\2N\17\3\2\2\2OV\5\b\5\2PR\7\13\2\2QS\5\b\5\2RQ\3\2")
+        buf.write("\2\2RS\3\2\2\2SU\3\2\2\2TP\3\2\2\2UX\3\2\2\2VT\3\2\2\2")
+        buf.write("VW\3\2\2\2Wb\3\2\2\2XV\3\2\2\2Y[\7\13\2\2Z\\\5\b\5\2[")
+        buf.write("Z\3\2\2\2[\\\3\2\2\2\\^\3\2\2\2]Y\3\2\2\2^_\3\2\2\2_]")
+        buf.write("\3\2\2\2_`\3\2\2\2`b\3\2\2\2aO\3\2\2\2a]\3\2\2\2b\21\3")
+        buf.write("\2\2\2cf\5\24\13\2df\5\26\f\2ec\3\2\2\2ed\3\2\2\2f\23")
+        buf.write("\3\2\2\2gk\7\6\2\2hj\7\30\2\2ih\3\2\2\2jm\3\2\2\2ki\3")
+        buf.write("\2\2\2kl\3\2\2\2ls\3\2\2\2mk\3\2\2\2nt\5\30\r\2op\7\f")
+        buf.write("\2\2pq\5\30\r\2qr\7\r\2\2rt\3\2\2\2sn\3\2\2\2so\3\2\2")
+        buf.write("\2t}\3\2\2\2uv\7\30\2\2v|\5\30\r\2wx\7\f\2\2xy\5\30\r")
+        buf.write("\2yz\7\r\2\2z|\3\2\2\2{u\3\2\2\2{w\3\2\2\2|\177\3\2\2")
+        buf.write("\2}{\3\2\2\2}~\3\2\2\2~\u0080\3\2\2\2\177}\3\2\2\2\u0080")
+        buf.write("\u0081\7\27\2\2\u0081\25\3\2\2\2\u0082\u0083\7\6\2\2\u0083")
+        buf.write("\u0084\5\32\16\2\u0084\u0086\7\16\2\2\u0085\u0087\5\20")
+        buf.write("\t\2\u0086\u0085\3\2\2\2\u0086\u0087\3\2\2\2\u0087\u0088")
+        buf.write("\3\2\2\2\u0088\u0089\7\b\2\2\u0089\27\3\2\2\2\u008a\u008e")
+        buf.write("\5\22\n\2\u008b\u008e\7\24\2\2\u008c\u008e\7\31\2\2\u008d")
+        buf.write("\u008a\3\2\2\2\u008d\u008b\3\2\2\2\u008d\u008c\3\2\2\2")
+        buf.write("\u008e\31\3\2\2\2\u008f\u0092\5\22\n\2\u0090\u0092\7\24")
+        buf.write("\2\2\u0091\u008f\3\2\2\2\u0091\u0090\3\2\2\2\u0092\u009a")
+        buf.write("\3\2\2\2\u0093\u0096\7\30\2\2\u0094\u0097\5\22\n\2\u0095")
+        buf.write("\u0097\7\24\2\2\u0096\u0094\3\2\2\2\u0096\u0095\3\2\2")
+        buf.write("\2\u0097\u0099\3\2\2\2\u0098\u0093\3\2\2\2\u0099\u009c")
+        buf.write("\3\2\2\2\u009a\u0098\3\2\2\2\u009a\u009b\3\2\2\2\u009b")
+        buf.write("\33\3\2\2\2\u009c\u009a\3\2\2\2\u009d\u009f\t\2\2\2\u009e")
+        buf.write("\u00a0\5\6\4\2\u009f\u009e\3\2\2\2\u009f\u00a0\3\2\2\2")
+        buf.write("\u00a0\u00a1\3\2\2\2\u00a1\u00a2\7\32\2\2\u00a2\35\3\2")
+        buf.write("\2\2\u00a3\u00ae\7\24\2\2\u00a4\u00ae\7\22\2\2\u00a5\u00ae")
+        buf.write("\7\20\2\2\u00a6\u00ae\7\17\2\2\u00a7\u00ae\7\21\2\2\u00a8")
+        buf.write("\u00ae\7\23\2\2\u00a9\u00ae\7\16\2\2\u00aa\u00ae\7\25")
+        buf.write("\2\2\u00ab\u00ae\7\26\2\2\u00ac\u00ae\5\22\n\2\u00ad\u00a3")
+        buf.write("\3\2\2\2\u00ad\u00a4\3\2\2\2\u00ad\u00a5\3\2\2\2\u00ad")
+        buf.write("\u00a6\3\2\2\2\u00ad\u00a7\3\2\2\2\u00ad\u00a8\3\2\2\2")
+        buf.write("\u00ad\u00a9\3\2\2\2\u00ad\u00aa\3\2\2\2\u00ad\u00ab\3")
+        buf.write("\2\2\2\u00ad\u00ac\3\2\2\2\u00ae\u00af\3\2\2\2\u00af\u00ad")
+        buf.write("\3\2\2\2\u00af\u00b0\3\2\2\2\u00b0\37\3\2\2\2\u00b1\u00b3")
+        buf.write("\t\3\2\2\u00b2\u00b1\3\2\2\2\u00b3\u00b4\3\2\2\2\u00b4")
+        buf.write("\u00b2\3\2\2\2\u00b4\u00b5\3\2\2\2\u00b5!\3\2\2\2\33.")
+        buf.write("\60\66:DGRV[_aeks{}\u0086\u008d\u0091\u0096\u009a\u009f")
+        buf.write("\u00ad\u00af\u00b4")
+        return buf.getvalue()
+
+
+class OmegaConfGrammarParser ( Parser ):
+
+    grammarFileName = "OmegaConfGrammarParser.g4"
+
+    atn = ATNDeserializer().deserialize(serializedATN())
+
+    decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ]
+
+    sharedContextCache = PredictionContextCache()
+
+    literalNames = [ "<INVALID>", "<INVALID>", "<INVALID>", "<INVALID>", 
+                     "<INVALID>", "<INVALID>", "<INVALID>", "<INVALID>", 
+                     "<INVALID>", "<INVALID>", "<INVALID>", "<INVALID>", 
+                     "<INVALID>", "<INVALID>", "<INVALID>", "<INVALID>", 
+                     "<INVALID>", "<INVALID>", "<INVALID>", "<INVALID>", 
+                     "<INVALID>", "<INVALID>", "'.'", "<INVALID>", "<INVALID>", 
+                     "<INVALID>", "<INVALID>", "'['", "']'" ]
+
+    symbolicNames = [ "<INVALID>", "ANY_STR", "ESC_INTER", "TOP_ESC", "INTER_OPEN", 
+                      "BRACE_OPEN", "BRACE_CLOSE", "QUOTE_OPEN_SINGLE", 
+                      "QUOTE_OPEN_DOUBLE", "COMMA", "BRACKET_OPEN", "BRACKET_CLOSE", 
+                      "COLON", "FLOAT", "INT", "BOOL", "NULL", "UNQUOTED_CHAR", 
+                      "ID", "ESC", "WS", "INTER_CLOSE", "DOT", "INTER_KEY", 
+                      "MATCHING_QUOTE_CLOSE", "QUOTED_ESC", "DOLLAR", "INTER_BRACKET_OPEN", 
+                      "INTER_BRACKET_CLOSE" ]
+
+    RULE_configValue = 0
+    RULE_singleElement = 1
+    RULE_text = 2
+    RULE_element = 3
+    RULE_listContainer = 4
+    RULE_dictContainer = 5
+    RULE_dictKeyValuePair = 6
+    RULE_sequence = 7
+    RULE_interpolation = 8
+    RULE_interpolationNode = 9
+    RULE_interpolationResolver = 10
+    RULE_configKey = 11
+    RULE_resolverName = 12
+    RULE_quotedValue = 13
+    RULE_primitive = 14
+    RULE_dictKey = 15
+
+    ruleNames =  [ "configValue", "singleElement", "text", "element", "listContainer", 
+                   "dictContainer", "dictKeyValuePair", "sequence", "interpolation", 
+                   "interpolationNode", "interpolationResolver", "configKey", 
+                   "resolverName", "quotedValue", "primitive", "dictKey" ]
+
+    EOF = Token.EOF
+    ANY_STR=1
+    ESC_INTER=2
+    TOP_ESC=3
+    INTER_OPEN=4
+    BRACE_OPEN=5
+    BRACE_CLOSE=6
+    QUOTE_OPEN_SINGLE=7
+    QUOTE_OPEN_DOUBLE=8
+    COMMA=9
+    BRACKET_OPEN=10
+    BRACKET_CLOSE=11
+    COLON=12
+    FLOAT=13
+    INT=14
+    BOOL=15
+    NULL=16
+    UNQUOTED_CHAR=17
+    ID=18
+    ESC=19
+    WS=20
+    INTER_CLOSE=21
+    DOT=22
+    INTER_KEY=23
+    MATCHING_QUOTE_CLOSE=24
+    QUOTED_ESC=25
+    DOLLAR=26
+    INTER_BRACKET_OPEN=27
+    INTER_BRACKET_CLOSE=28
+
+    def __init__(self, input:TokenStream, output:TextIO = sys.stdout):
+        super().__init__(input, output)
+        self.checkVersion("4.9.3")
+        self._interp = ParserATNSimulator(self, self.atn, self.decisionsToDFA, self.sharedContextCache)
+        self._predicates = None
+
+
+
+
+    class ConfigValueContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def text(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.TextContext,0)
+
+
+        def EOF(self):
+            return self.getToken(OmegaConfGrammarParser.EOF, 0)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_configValue
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterConfigValue" ):
+                listener.enterConfigValue(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitConfigValue" ):
+                listener.exitConfigValue(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitConfigValue" ):
+                return visitor.visitConfigValue(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def configValue(self):
+
+        localctx = OmegaConfGrammarParser.ConfigValueContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 0, self.RULE_configValue)
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 32
+            self.text()
+            self.state = 33
+            self.match(OmegaConfGrammarParser.EOF)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class SingleElementContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def element(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.ElementContext,0)
+
+
+        def EOF(self):
+            return self.getToken(OmegaConfGrammarParser.EOF, 0)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_singleElement
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterSingleElement" ):
+                listener.enterSingleElement(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitSingleElement" ):
+                listener.exitSingleElement(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitSingleElement" ):
+                return visitor.visitSingleElement(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def singleElement(self):
+
+        localctx = OmegaConfGrammarParser.SingleElementContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 2, self.RULE_singleElement)
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 35
+            self.element()
+            self.state = 36
+            self.match(OmegaConfGrammarParser.EOF)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class TextContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def interpolation(self, i:int=None):
+            if i is None:
+                return self.getTypedRuleContexts(OmegaConfGrammarParser.InterpolationContext)
+            else:
+                return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,i)
+
+
+        def ANY_STR(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ANY_STR)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ANY_STR, i)
+
+        def ESC(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ESC)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ESC, i)
+
+        def ESC_INTER(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ESC_INTER)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ESC_INTER, i)
+
+        def TOP_ESC(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.TOP_ESC)
+            else:
+                return self.getToken(OmegaConfGrammarParser.TOP_ESC, i)
+
+        def QUOTED_ESC(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.QUOTED_ESC)
+            else:
+                return self.getToken(OmegaConfGrammarParser.QUOTED_ESC, i)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_text
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterText" ):
+                listener.enterText(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitText" ):
+                listener.exitText(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitText" ):
+                return visitor.visitText(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def text(self):
+
+        localctx = OmegaConfGrammarParser.TextContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 4, self.RULE_text)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 44 
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            while True:
+                self.state = 44
+                self._errHandler.sync(self)
+                token = self._input.LA(1)
+                if token in [OmegaConfGrammarParser.INTER_OPEN]:
+                    self.state = 38
+                    self.interpolation()
+                    pass
+                elif token in [OmegaConfGrammarParser.ANY_STR]:
+                    self.state = 39
+                    self.match(OmegaConfGrammarParser.ANY_STR)
+                    pass
+                elif token in [OmegaConfGrammarParser.ESC]:
+                    self.state = 40
+                    self.match(OmegaConfGrammarParser.ESC)
+                    pass
+                elif token in [OmegaConfGrammarParser.ESC_INTER]:
+                    self.state = 41
+                    self.match(OmegaConfGrammarParser.ESC_INTER)
+                    pass
+                elif token in [OmegaConfGrammarParser.TOP_ESC]:
+                    self.state = 42
+                    self.match(OmegaConfGrammarParser.TOP_ESC)
+                    pass
+                elif token in [OmegaConfGrammarParser.QUOTED_ESC]:
+                    self.state = 43
+                    self.match(OmegaConfGrammarParser.QUOTED_ESC)
+                    pass
+                else:
+                    raise NoViableAltException(self)
+
+                self.state = 46 
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+                if not ((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.ANY_STR) | (1 << OmegaConfGrammarParser.ESC_INTER) | (1 << OmegaConfGrammarParser.TOP_ESC) | (1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.QUOTED_ESC))) != 0)):
+                    break
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class ElementContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def primitive(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.PrimitiveContext,0)
+
+
+        def quotedValue(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.QuotedValueContext,0)
+
+
+        def listContainer(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.ListContainerContext,0)
+
+
+        def dictContainer(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.DictContainerContext,0)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_element
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterElement" ):
+                listener.enterElement(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitElement" ):
+                listener.exitElement(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitElement" ):
+                return visitor.visitElement(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def element(self):
+
+        localctx = OmegaConfGrammarParser.ElementContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 6, self.RULE_element)
+        try:
+            self.state = 52
+            self._errHandler.sync(self)
+            token = self._input.LA(1)
+            if token in [OmegaConfGrammarParser.INTER_OPEN, OmegaConfGrammarParser.COLON, OmegaConfGrammarParser.FLOAT, OmegaConfGrammarParser.INT, OmegaConfGrammarParser.BOOL, OmegaConfGrammarParser.NULL, OmegaConfGrammarParser.UNQUOTED_CHAR, OmegaConfGrammarParser.ID, OmegaConfGrammarParser.ESC, OmegaConfGrammarParser.WS]:
+                self.enterOuterAlt(localctx, 1)
+                self.state = 48
+                self.primitive()
+                pass
+            elif token in [OmegaConfGrammarParser.QUOTE_OPEN_SINGLE, OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE]:
+                self.enterOuterAlt(localctx, 2)
+                self.state = 49
+                self.quotedValue()
+                pass
+            elif token in [OmegaConfGrammarParser.BRACKET_OPEN]:
+                self.enterOuterAlt(localctx, 3)
+                self.state = 50
+                self.listContainer()
+                pass
+            elif token in [OmegaConfGrammarParser.BRACE_OPEN]:
+                self.enterOuterAlt(localctx, 4)
+                self.state = 51
+                self.dictContainer()
+                pass
+            else:
+                raise NoViableAltException(self)
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class ListContainerContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def BRACKET_OPEN(self):
+            return self.getToken(OmegaConfGrammarParser.BRACKET_OPEN, 0)
+
+        def BRACKET_CLOSE(self):
+            return self.getToken(OmegaConfGrammarParser.BRACKET_CLOSE, 0)
+
+        def sequence(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.SequenceContext,0)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_listContainer
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterListContainer" ):
+                listener.enterListContainer(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitListContainer" ):
+                listener.exitListContainer(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitListContainer" ):
+                return visitor.visitListContainer(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def listContainer(self):
+
+        localctx = OmegaConfGrammarParser.ListContainerContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 8, self.RULE_listContainer)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 54
+            self.match(OmegaConfGrammarParser.BRACKET_OPEN)
+            self.state = 56
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.COMMA) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0):
+                self.state = 55
+                self.sequence()
+
+
+            self.state = 58
+            self.match(OmegaConfGrammarParser.BRACKET_CLOSE)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class DictContainerContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def BRACE_OPEN(self):
+            return self.getToken(OmegaConfGrammarParser.BRACE_OPEN, 0)
+
+        def BRACE_CLOSE(self):
+            return self.getToken(OmegaConfGrammarParser.BRACE_CLOSE, 0)
+
+        def dictKeyValuePair(self, i:int=None):
+            if i is None:
+                return self.getTypedRuleContexts(OmegaConfGrammarParser.DictKeyValuePairContext)
+            else:
+                return self.getTypedRuleContext(OmegaConfGrammarParser.DictKeyValuePairContext,i)
+
+
+        def COMMA(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.COMMA)
+            else:
+                return self.getToken(OmegaConfGrammarParser.COMMA, i)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_dictContainer
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterDictContainer" ):
+                listener.enterDictContainer(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitDictContainer" ):
+                listener.exitDictContainer(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitDictContainer" ):
+                return visitor.visitDictContainer(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def dictContainer(self):
+
+        localctx = OmegaConfGrammarParser.DictContainerContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 10, self.RULE_dictContainer)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 60
+            self.match(OmegaConfGrammarParser.BRACE_OPEN)
+            self.state = 69
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0):
+                self.state = 61
+                self.dictKeyValuePair()
+                self.state = 66
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+                while _la==OmegaConfGrammarParser.COMMA:
+                    self.state = 62
+                    self.match(OmegaConfGrammarParser.COMMA)
+                    self.state = 63
+                    self.dictKeyValuePair()
+                    self.state = 68
+                    self._errHandler.sync(self)
+                    _la = self._input.LA(1)
+
+
+
+            self.state = 71
+            self.match(OmegaConfGrammarParser.BRACE_CLOSE)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class DictKeyValuePairContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def dictKey(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.DictKeyContext,0)
+
+
+        def COLON(self):
+            return self.getToken(OmegaConfGrammarParser.COLON, 0)
+
+        def element(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.ElementContext,0)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_dictKeyValuePair
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterDictKeyValuePair" ):
+                listener.enterDictKeyValuePair(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitDictKeyValuePair" ):
+                listener.exitDictKeyValuePair(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitDictKeyValuePair" ):
+                return visitor.visitDictKeyValuePair(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def dictKeyValuePair(self):
+
+        localctx = OmegaConfGrammarParser.DictKeyValuePairContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 12, self.RULE_dictKeyValuePair)
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 73
+            self.dictKey()
+            self.state = 74
+            self.match(OmegaConfGrammarParser.COLON)
+            self.state = 75
+            self.element()
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class SequenceContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def element(self, i:int=None):
+            if i is None:
+                return self.getTypedRuleContexts(OmegaConfGrammarParser.ElementContext)
+            else:
+                return self.getTypedRuleContext(OmegaConfGrammarParser.ElementContext,i)
+
+
+        def COMMA(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.COMMA)
+            else:
+                return self.getToken(OmegaConfGrammarParser.COMMA, i)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_sequence
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterSequence" ):
+                listener.enterSequence(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitSequence" ):
+                listener.exitSequence(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitSequence" ):
+                return visitor.visitSequence(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def sequence(self):
+
+        localctx = OmegaConfGrammarParser.SequenceContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 14, self.RULE_sequence)
+        self._la = 0 # Token type
+        try:
+            self.state = 95
+            self._errHandler.sync(self)
+            token = self._input.LA(1)
+            if token in [OmegaConfGrammarParser.INTER_OPEN, OmegaConfGrammarParser.BRACE_OPEN, OmegaConfGrammarParser.QUOTE_OPEN_SINGLE, OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE, OmegaConfGrammarParser.BRACKET_OPEN, OmegaConfGrammarParser.COLON, OmegaConfGrammarParser.FLOAT, OmegaConfGrammarParser.INT, OmegaConfGrammarParser.BOOL, OmegaConfGrammarParser.NULL, OmegaConfGrammarParser.UNQUOTED_CHAR, OmegaConfGrammarParser.ID, OmegaConfGrammarParser.ESC, OmegaConfGrammarParser.WS]:
+                self.enterOuterAlt(localctx, 1)
+                self.state = 77
+                self.element()
+                self.state = 84
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+                while _la==OmegaConfGrammarParser.COMMA:
+                    self.state = 78
+                    self.match(OmegaConfGrammarParser.COMMA)
+                    self.state = 80
+                    self._errHandler.sync(self)
+                    _la = self._input.LA(1)
+                    if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0):
+                        self.state = 79
+                        self.element()
+
+
+                    self.state = 86
+                    self._errHandler.sync(self)
+                    _la = self._input.LA(1)
+
+                pass
+            elif token in [OmegaConfGrammarParser.COMMA]:
+                self.enterOuterAlt(localctx, 2)
+                self.state = 91 
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+                while True:
+                    self.state = 87
+                    self.match(OmegaConfGrammarParser.COMMA)
+                    self.state = 89
+                    self._errHandler.sync(self)
+                    _la = self._input.LA(1)
+                    if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0):
+                        self.state = 88
+                        self.element()
+
+
+                    self.state = 93 
+                    self._errHandler.sync(self)
+                    _la = self._input.LA(1)
+                    if not (_la==OmegaConfGrammarParser.COMMA):
+                        break
+
+                pass
+            else:
+                raise NoViableAltException(self)
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class InterpolationContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def interpolationNode(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationNodeContext,0)
+
+
+        def interpolationResolver(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationResolverContext,0)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_interpolation
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterInterpolation" ):
+                listener.enterInterpolation(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitInterpolation" ):
+                listener.exitInterpolation(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitInterpolation" ):
+                return visitor.visitInterpolation(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def interpolation(self):
+
+        localctx = OmegaConfGrammarParser.InterpolationContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 16, self.RULE_interpolation)
+        try:
+            self.state = 99
+            self._errHandler.sync(self)
+            la_ = self._interp.adaptivePredict(self._input,11,self._ctx)
+            if la_ == 1:
+                self.enterOuterAlt(localctx, 1)
+                self.state = 97
+                self.interpolationNode()
+                pass
+
+            elif la_ == 2:
+                self.enterOuterAlt(localctx, 2)
+                self.state = 98
+                self.interpolationResolver()
+                pass
+
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class InterpolationNodeContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def INTER_OPEN(self):
+            return self.getToken(OmegaConfGrammarParser.INTER_OPEN, 0)
+
+        def INTER_CLOSE(self):
+            return self.getToken(OmegaConfGrammarParser.INTER_CLOSE, 0)
+
+        def configKey(self, i:int=None):
+            if i is None:
+                return self.getTypedRuleContexts(OmegaConfGrammarParser.ConfigKeyContext)
+            else:
+                return self.getTypedRuleContext(OmegaConfGrammarParser.ConfigKeyContext,i)
+
+
+        def BRACKET_OPEN(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.BRACKET_OPEN)
+            else:
+                return self.getToken(OmegaConfGrammarParser.BRACKET_OPEN, i)
+
+        def BRACKET_CLOSE(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.BRACKET_CLOSE)
+            else:
+                return self.getToken(OmegaConfGrammarParser.BRACKET_CLOSE, i)
+
+        def DOT(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.DOT)
+            else:
+                return self.getToken(OmegaConfGrammarParser.DOT, i)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_interpolationNode
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterInterpolationNode" ):
+                listener.enterInterpolationNode(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitInterpolationNode" ):
+                listener.exitInterpolationNode(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitInterpolationNode" ):
+                return visitor.visitInterpolationNode(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def interpolationNode(self):
+
+        localctx = OmegaConfGrammarParser.InterpolationNodeContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 18, self.RULE_interpolationNode)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 101
+            self.match(OmegaConfGrammarParser.INTER_OPEN)
+            self.state = 105
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            while _la==OmegaConfGrammarParser.DOT:
+                self.state = 102
+                self.match(OmegaConfGrammarParser.DOT)
+                self.state = 107
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+
+            self.state = 113
+            self._errHandler.sync(self)
+            token = self._input.LA(1)
+            if token in [OmegaConfGrammarParser.INTER_OPEN, OmegaConfGrammarParser.ID, OmegaConfGrammarParser.INTER_KEY]:
+                self.state = 108
+                self.configKey()
+                pass
+            elif token in [OmegaConfGrammarParser.BRACKET_OPEN]:
+                self.state = 109
+                self.match(OmegaConfGrammarParser.BRACKET_OPEN)
+                self.state = 110
+                self.configKey()
+                self.state = 111
+                self.match(OmegaConfGrammarParser.BRACKET_CLOSE)
+                pass
+            else:
+                raise NoViableAltException(self)
+
+            self.state = 123
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            while _la==OmegaConfGrammarParser.BRACKET_OPEN or _la==OmegaConfGrammarParser.DOT:
+                self.state = 121
+                self._errHandler.sync(self)
+                token = self._input.LA(1)
+                if token in [OmegaConfGrammarParser.DOT]:
+                    self.state = 115
+                    self.match(OmegaConfGrammarParser.DOT)
+                    self.state = 116
+                    self.configKey()
+                    pass
+                elif token in [OmegaConfGrammarParser.BRACKET_OPEN]:
+                    self.state = 117
+                    self.match(OmegaConfGrammarParser.BRACKET_OPEN)
+                    self.state = 118
+                    self.configKey()
+                    self.state = 119
+                    self.match(OmegaConfGrammarParser.BRACKET_CLOSE)
+                    pass
+                else:
+                    raise NoViableAltException(self)
+
+                self.state = 125
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+
+            self.state = 126
+            self.match(OmegaConfGrammarParser.INTER_CLOSE)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class InterpolationResolverContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def INTER_OPEN(self):
+            return self.getToken(OmegaConfGrammarParser.INTER_OPEN, 0)
+
+        def resolverName(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.ResolverNameContext,0)
+
+
+        def COLON(self):
+            return self.getToken(OmegaConfGrammarParser.COLON, 0)
+
+        def BRACE_CLOSE(self):
+            return self.getToken(OmegaConfGrammarParser.BRACE_CLOSE, 0)
+
+        def sequence(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.SequenceContext,0)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_interpolationResolver
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterInterpolationResolver" ):
+                listener.enterInterpolationResolver(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitInterpolationResolver" ):
+                listener.exitInterpolationResolver(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitInterpolationResolver" ):
+                return visitor.visitInterpolationResolver(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def interpolationResolver(self):
+
+        localctx = OmegaConfGrammarParser.InterpolationResolverContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 20, self.RULE_interpolationResolver)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 128
+            self.match(OmegaConfGrammarParser.INTER_OPEN)
+            self.state = 129
+            self.resolverName()
+            self.state = 130
+            self.match(OmegaConfGrammarParser.COLON)
+            self.state = 132
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.BRACE_OPEN) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_SINGLE) | (1 << OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE) | (1 << OmegaConfGrammarParser.COMMA) | (1 << OmegaConfGrammarParser.BRACKET_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0):
+                self.state = 131
+                self.sequence()
+
+
+            self.state = 134
+            self.match(OmegaConfGrammarParser.BRACE_CLOSE)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class ConfigKeyContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def interpolation(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,0)
+
+
+        def ID(self):
+            return self.getToken(OmegaConfGrammarParser.ID, 0)
+
+        def INTER_KEY(self):
+            return self.getToken(OmegaConfGrammarParser.INTER_KEY, 0)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_configKey
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterConfigKey" ):
+                listener.enterConfigKey(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitConfigKey" ):
+                listener.exitConfigKey(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitConfigKey" ):
+                return visitor.visitConfigKey(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def configKey(self):
+
+        localctx = OmegaConfGrammarParser.ConfigKeyContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 22, self.RULE_configKey)
+        try:
+            self.state = 139
+            self._errHandler.sync(self)
+            token = self._input.LA(1)
+            if token in [OmegaConfGrammarParser.INTER_OPEN]:
+                self.enterOuterAlt(localctx, 1)
+                self.state = 136
+                self.interpolation()
+                pass
+            elif token in [OmegaConfGrammarParser.ID]:
+                self.enterOuterAlt(localctx, 2)
+                self.state = 137
+                self.match(OmegaConfGrammarParser.ID)
+                pass
+            elif token in [OmegaConfGrammarParser.INTER_KEY]:
+                self.enterOuterAlt(localctx, 3)
+                self.state = 138
+                self.match(OmegaConfGrammarParser.INTER_KEY)
+                pass
+            else:
+                raise NoViableAltException(self)
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class ResolverNameContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def interpolation(self, i:int=None):
+            if i is None:
+                return self.getTypedRuleContexts(OmegaConfGrammarParser.InterpolationContext)
+            else:
+                return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,i)
+
+
+        def ID(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ID)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ID, i)
+
+        def DOT(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.DOT)
+            else:
+                return self.getToken(OmegaConfGrammarParser.DOT, i)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_resolverName
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterResolverName" ):
+                listener.enterResolverName(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitResolverName" ):
+                listener.exitResolverName(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitResolverName" ):
+                return visitor.visitResolverName(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def resolverName(self):
+
+        localctx = OmegaConfGrammarParser.ResolverNameContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 24, self.RULE_resolverName)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 143
+            self._errHandler.sync(self)
+            token = self._input.LA(1)
+            if token in [OmegaConfGrammarParser.INTER_OPEN]:
+                self.state = 141
+                self.interpolation()
+                pass
+            elif token in [OmegaConfGrammarParser.ID]:
+                self.state = 142
+                self.match(OmegaConfGrammarParser.ID)
+                pass
+            else:
+                raise NoViableAltException(self)
+
+            self.state = 152
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            while _la==OmegaConfGrammarParser.DOT:
+                self.state = 145
+                self.match(OmegaConfGrammarParser.DOT)
+                self.state = 148
+                self._errHandler.sync(self)
+                token = self._input.LA(1)
+                if token in [OmegaConfGrammarParser.INTER_OPEN]:
+                    self.state = 146
+                    self.interpolation()
+                    pass
+                elif token in [OmegaConfGrammarParser.ID]:
+                    self.state = 147
+                    self.match(OmegaConfGrammarParser.ID)
+                    pass
+                else:
+                    raise NoViableAltException(self)
+
+                self.state = 154
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class QuotedValueContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def MATCHING_QUOTE_CLOSE(self):
+            return self.getToken(OmegaConfGrammarParser.MATCHING_QUOTE_CLOSE, 0)
+
+        def QUOTE_OPEN_SINGLE(self):
+            return self.getToken(OmegaConfGrammarParser.QUOTE_OPEN_SINGLE, 0)
+
+        def QUOTE_OPEN_DOUBLE(self):
+            return self.getToken(OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE, 0)
+
+        def text(self):
+            return self.getTypedRuleContext(OmegaConfGrammarParser.TextContext,0)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_quotedValue
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterQuotedValue" ):
+                listener.enterQuotedValue(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitQuotedValue" ):
+                listener.exitQuotedValue(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitQuotedValue" ):
+                return visitor.visitQuotedValue(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def quotedValue(self):
+
+        localctx = OmegaConfGrammarParser.QuotedValueContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 26, self.RULE_quotedValue)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 155
+            _la = self._input.LA(1)
+            if not(_la==OmegaConfGrammarParser.QUOTE_OPEN_SINGLE or _la==OmegaConfGrammarParser.QUOTE_OPEN_DOUBLE):
+                self._errHandler.recoverInline(self)
+            else:
+                self._errHandler.reportMatch(self)
+                self.consume()
+            self.state = 157
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            if (((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.ANY_STR) | (1 << OmegaConfGrammarParser.ESC_INTER) | (1 << OmegaConfGrammarParser.TOP_ESC) | (1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.QUOTED_ESC))) != 0):
+                self.state = 156
+                self.text()
+
+
+            self.state = 159
+            self.match(OmegaConfGrammarParser.MATCHING_QUOTE_CLOSE)
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class PrimitiveContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def ID(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ID)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ID, i)
+
+        def NULL(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.NULL)
+            else:
+                return self.getToken(OmegaConfGrammarParser.NULL, i)
+
+        def INT(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.INT)
+            else:
+                return self.getToken(OmegaConfGrammarParser.INT, i)
+
+        def FLOAT(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.FLOAT)
+            else:
+                return self.getToken(OmegaConfGrammarParser.FLOAT, i)
+
+        def BOOL(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.BOOL)
+            else:
+                return self.getToken(OmegaConfGrammarParser.BOOL, i)
+
+        def UNQUOTED_CHAR(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.UNQUOTED_CHAR)
+            else:
+                return self.getToken(OmegaConfGrammarParser.UNQUOTED_CHAR, i)
+
+        def COLON(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.COLON)
+            else:
+                return self.getToken(OmegaConfGrammarParser.COLON, i)
+
+        def ESC(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ESC)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ESC, i)
+
+        def WS(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.WS)
+            else:
+                return self.getToken(OmegaConfGrammarParser.WS, i)
+
+        def interpolation(self, i:int=None):
+            if i is None:
+                return self.getTypedRuleContexts(OmegaConfGrammarParser.InterpolationContext)
+            else:
+                return self.getTypedRuleContext(OmegaConfGrammarParser.InterpolationContext,i)
+
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_primitive
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterPrimitive" ):
+                listener.enterPrimitive(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitPrimitive" ):
+                listener.exitPrimitive(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitPrimitive" ):
+                return visitor.visitPrimitive(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def primitive(self):
+
+        localctx = OmegaConfGrammarParser.PrimitiveContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 28, self.RULE_primitive)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 171 
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            while True:
+                self.state = 171
+                self._errHandler.sync(self)
+                token = self._input.LA(1)
+                if token in [OmegaConfGrammarParser.ID]:
+                    self.state = 161
+                    self.match(OmegaConfGrammarParser.ID)
+                    pass
+                elif token in [OmegaConfGrammarParser.NULL]:
+                    self.state = 162
+                    self.match(OmegaConfGrammarParser.NULL)
+                    pass
+                elif token in [OmegaConfGrammarParser.INT]:
+                    self.state = 163
+                    self.match(OmegaConfGrammarParser.INT)
+                    pass
+                elif token in [OmegaConfGrammarParser.FLOAT]:
+                    self.state = 164
+                    self.match(OmegaConfGrammarParser.FLOAT)
+                    pass
+                elif token in [OmegaConfGrammarParser.BOOL]:
+                    self.state = 165
+                    self.match(OmegaConfGrammarParser.BOOL)
+                    pass
+                elif token in [OmegaConfGrammarParser.UNQUOTED_CHAR]:
+                    self.state = 166
+                    self.match(OmegaConfGrammarParser.UNQUOTED_CHAR)
+                    pass
+                elif token in [OmegaConfGrammarParser.COLON]:
+                    self.state = 167
+                    self.match(OmegaConfGrammarParser.COLON)
+                    pass
+                elif token in [OmegaConfGrammarParser.ESC]:
+                    self.state = 168
+                    self.match(OmegaConfGrammarParser.ESC)
+                    pass
+                elif token in [OmegaConfGrammarParser.WS]:
+                    self.state = 169
+                    self.match(OmegaConfGrammarParser.WS)
+                    pass
+                elif token in [OmegaConfGrammarParser.INTER_OPEN]:
+                    self.state = 170
+                    self.interpolation()
+                    pass
+                else:
+                    raise NoViableAltException(self)
+
+                self.state = 173 
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+                if not ((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.INTER_OPEN) | (1 << OmegaConfGrammarParser.COLON) | (1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0)):
+                    break
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+    class DictKeyContext(ParserRuleContext):
+        __slots__ = 'parser'
+
+        def __init__(self, parser, parent:ParserRuleContext=None, invokingState:int=-1):
+            super().__init__(parent, invokingState)
+            self.parser = parser
+
+        def ID(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ID)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ID, i)
+
+        def NULL(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.NULL)
+            else:
+                return self.getToken(OmegaConfGrammarParser.NULL, i)
+
+        def INT(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.INT)
+            else:
+                return self.getToken(OmegaConfGrammarParser.INT, i)
+
+        def FLOAT(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.FLOAT)
+            else:
+                return self.getToken(OmegaConfGrammarParser.FLOAT, i)
+
+        def BOOL(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.BOOL)
+            else:
+                return self.getToken(OmegaConfGrammarParser.BOOL, i)
+
+        def UNQUOTED_CHAR(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.UNQUOTED_CHAR)
+            else:
+                return self.getToken(OmegaConfGrammarParser.UNQUOTED_CHAR, i)
+
+        def ESC(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.ESC)
+            else:
+                return self.getToken(OmegaConfGrammarParser.ESC, i)
+
+        def WS(self, i:int=None):
+            if i is None:
+                return self.getTokens(OmegaConfGrammarParser.WS)
+            else:
+                return self.getToken(OmegaConfGrammarParser.WS, i)
+
+        def getRuleIndex(self):
+            return OmegaConfGrammarParser.RULE_dictKey
+
+        def enterRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "enterDictKey" ):
+                listener.enterDictKey(self)
+
+        def exitRule(self, listener:ParseTreeListener):
+            if hasattr( listener, "exitDictKey" ):
+                listener.exitDictKey(self)
+
+        def accept(self, visitor:ParseTreeVisitor):
+            if hasattr( visitor, "visitDictKey" ):
+                return visitor.visitDictKey(self)
+            else:
+                return visitor.visitChildren(self)
+
+
+
+
+    def dictKey(self):
+
+        localctx = OmegaConfGrammarParser.DictKeyContext(self, self._ctx, self.state)
+        self.enterRule(localctx, 30, self.RULE_dictKey)
+        self._la = 0 # Token type
+        try:
+            self.enterOuterAlt(localctx, 1)
+            self.state = 176 
+            self._errHandler.sync(self)
+            _la = self._input.LA(1)
+            while True:
+                self.state = 175
+                _la = self._input.LA(1)
+                if not((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0)):
+                    self._errHandler.recoverInline(self)
+                else:
+                    self._errHandler.reportMatch(self)
+                    self.consume()
+                self.state = 178 
+                self._errHandler.sync(self)
+                _la = self._input.LA(1)
+                if not ((((_la) & ~0x3f) == 0 and ((1 << _la) & ((1 << OmegaConfGrammarParser.FLOAT) | (1 << OmegaConfGrammarParser.INT) | (1 << OmegaConfGrammarParser.BOOL) | (1 << OmegaConfGrammarParser.NULL) | (1 << OmegaConfGrammarParser.UNQUOTED_CHAR) | (1 << OmegaConfGrammarParser.ID) | (1 << OmegaConfGrammarParser.ESC) | (1 << OmegaConfGrammarParser.WS))) != 0)):
+                    break
+
+        except RecognitionException as re:
+            localctx.exception = re
+            self._errHandler.reportError(self, re)
+            self._errHandler.recover(self, re)
+        finally:
+            self.exitRule()
+        return localctx
+
+
+
+
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserListener.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserListener.py
new file mode 100644
index 0000000000000000000000000000000000000000..d149348181e910757b6a3d6388073b951fac4678
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserListener.py
@@ -0,0 +1,156 @@
+# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarParser.g4 by ANTLR 4.9.3
+from antlr4 import *
+if __name__ is not None and "." in __name__:
+    from .OmegaConfGrammarParser import OmegaConfGrammarParser
+else:
+    from OmegaConfGrammarParser import OmegaConfGrammarParser
+
+# This class defines a complete listener for a parse tree produced by OmegaConfGrammarParser.
+class OmegaConfGrammarParserListener(ParseTreeListener):
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#configValue.
+    def enterConfigValue(self, ctx:OmegaConfGrammarParser.ConfigValueContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#configValue.
+    def exitConfigValue(self, ctx:OmegaConfGrammarParser.ConfigValueContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#singleElement.
+    def enterSingleElement(self, ctx:OmegaConfGrammarParser.SingleElementContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#singleElement.
+    def exitSingleElement(self, ctx:OmegaConfGrammarParser.SingleElementContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#text.
+    def enterText(self, ctx:OmegaConfGrammarParser.TextContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#text.
+    def exitText(self, ctx:OmegaConfGrammarParser.TextContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#element.
+    def enterElement(self, ctx:OmegaConfGrammarParser.ElementContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#element.
+    def exitElement(self, ctx:OmegaConfGrammarParser.ElementContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#listContainer.
+    def enterListContainer(self, ctx:OmegaConfGrammarParser.ListContainerContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#listContainer.
+    def exitListContainer(self, ctx:OmegaConfGrammarParser.ListContainerContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#dictContainer.
+    def enterDictContainer(self, ctx:OmegaConfGrammarParser.DictContainerContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#dictContainer.
+    def exitDictContainer(self, ctx:OmegaConfGrammarParser.DictContainerContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#dictKeyValuePair.
+    def enterDictKeyValuePair(self, ctx:OmegaConfGrammarParser.DictKeyValuePairContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#dictKeyValuePair.
+    def exitDictKeyValuePair(self, ctx:OmegaConfGrammarParser.DictKeyValuePairContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#sequence.
+    def enterSequence(self, ctx:OmegaConfGrammarParser.SequenceContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#sequence.
+    def exitSequence(self, ctx:OmegaConfGrammarParser.SequenceContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#interpolation.
+    def enterInterpolation(self, ctx:OmegaConfGrammarParser.InterpolationContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#interpolation.
+    def exitInterpolation(self, ctx:OmegaConfGrammarParser.InterpolationContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#interpolationNode.
+    def enterInterpolationNode(self, ctx:OmegaConfGrammarParser.InterpolationNodeContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#interpolationNode.
+    def exitInterpolationNode(self, ctx:OmegaConfGrammarParser.InterpolationNodeContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#interpolationResolver.
+    def enterInterpolationResolver(self, ctx:OmegaConfGrammarParser.InterpolationResolverContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#interpolationResolver.
+    def exitInterpolationResolver(self, ctx:OmegaConfGrammarParser.InterpolationResolverContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#configKey.
+    def enterConfigKey(self, ctx:OmegaConfGrammarParser.ConfigKeyContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#configKey.
+    def exitConfigKey(self, ctx:OmegaConfGrammarParser.ConfigKeyContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#resolverName.
+    def enterResolverName(self, ctx:OmegaConfGrammarParser.ResolverNameContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#resolverName.
+    def exitResolverName(self, ctx:OmegaConfGrammarParser.ResolverNameContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#quotedValue.
+    def enterQuotedValue(self, ctx:OmegaConfGrammarParser.QuotedValueContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#quotedValue.
+    def exitQuotedValue(self, ctx:OmegaConfGrammarParser.QuotedValueContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#primitive.
+    def enterPrimitive(self, ctx:OmegaConfGrammarParser.PrimitiveContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#primitive.
+    def exitPrimitive(self, ctx:OmegaConfGrammarParser.PrimitiveContext):
+        pass
+
+
+    # Enter a parse tree produced by OmegaConfGrammarParser#dictKey.
+    def enterDictKey(self, ctx:OmegaConfGrammarParser.DictKeyContext):
+        pass
+
+    # Exit a parse tree produced by OmegaConfGrammarParser#dictKey.
+    def exitDictKey(self, ctx:OmegaConfGrammarParser.DictKeyContext):
+        pass
+
+
+
+del OmegaConfGrammarParser
\ No newline at end of file
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed247dd92f6654d2c862e848204a4679cc23e3e3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParserVisitor.py
@@ -0,0 +1,93 @@
+# Generated from /tmp/build-via-sdist-fm63w174/omegaconf-2.3.0/omegaconf/grammar/OmegaConfGrammarParser.g4 by ANTLR 4.9.3
+from antlr4 import *
+if __name__ is not None and "." in __name__:
+    from .OmegaConfGrammarParser import OmegaConfGrammarParser
+else:
+    from OmegaConfGrammarParser import OmegaConfGrammarParser
+
+# This class defines a complete generic visitor for a parse tree produced by OmegaConfGrammarParser.
+
+class OmegaConfGrammarParserVisitor(ParseTreeVisitor):
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#configValue.
+    def visitConfigValue(self, ctx:OmegaConfGrammarParser.ConfigValueContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#singleElement.
+    def visitSingleElement(self, ctx:OmegaConfGrammarParser.SingleElementContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#text.
+    def visitText(self, ctx:OmegaConfGrammarParser.TextContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#element.
+    def visitElement(self, ctx:OmegaConfGrammarParser.ElementContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#listContainer.
+    def visitListContainer(self, ctx:OmegaConfGrammarParser.ListContainerContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#dictContainer.
+    def visitDictContainer(self, ctx:OmegaConfGrammarParser.DictContainerContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#dictKeyValuePair.
+    def visitDictKeyValuePair(self, ctx:OmegaConfGrammarParser.DictKeyValuePairContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#sequence.
+    def visitSequence(self, ctx:OmegaConfGrammarParser.SequenceContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#interpolation.
+    def visitInterpolation(self, ctx:OmegaConfGrammarParser.InterpolationContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#interpolationNode.
+    def visitInterpolationNode(self, ctx:OmegaConfGrammarParser.InterpolationNodeContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#interpolationResolver.
+    def visitInterpolationResolver(self, ctx:OmegaConfGrammarParser.InterpolationResolverContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#configKey.
+    def visitConfigKey(self, ctx:OmegaConfGrammarParser.ConfigKeyContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#resolverName.
+    def visitResolverName(self, ctx:OmegaConfGrammarParser.ResolverNameContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#quotedValue.
+    def visitQuotedValue(self, ctx:OmegaConfGrammarParser.QuotedValueContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#primitive.
+    def visitPrimitive(self, ctx:OmegaConfGrammarParser.PrimitiveContext):
+        return self.visitChildren(ctx)
+
+
+    # Visit a parse tree produced by OmegaConfGrammarParser#dictKey.
+    def visitDictKey(self, ctx:OmegaConfGrammarParser.DictKeyContext):
+        return self.visitChildren(ctx)
+
+
+
+del OmegaConfGrammarParser
\ No newline at end of file
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar/gen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar_parser.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c883c2cfd49b442a90bda0c7777cccca706e82d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar_parser.py
@@ -0,0 +1,144 @@
+import re
+import threading
+from typing import Any
+
+from antlr4 import CommonTokenStream, InputStream, ParserRuleContext
+from antlr4.error.ErrorListener import ErrorListener
+
+from .errors import GrammarParseError
+
+# Import from visitor in order to check the presence of generated grammar files
+# files in a single place.
+from .grammar_visitor import (  # type: ignore
+    OmegaConfGrammarLexer,
+    OmegaConfGrammarParser,
+)
+
+# Used to cache grammar objects to avoid re-creating them on each call to `parse()`.
+# We use a per-thread cache to make it thread-safe.
+_grammar_cache = threading.local()
+
+# Build regex pattern to efficiently identify typical interpolations.
+# See test `test_match_simple_interpolation_pattern` for examples.
+_config_key = r"[$\w]+"  # foo, $0, $bar, $foo_$bar123$
+_key_maybe_brackets = f"{_config_key}|\\[{_config_key}\\]"  # foo, [foo], [$bar]
+_node_access = f"\\.{_key_maybe_brackets}"  # .foo, [foo], [$bar]
+_node_path = f"(\\.)*({_key_maybe_brackets})({_node_access})*"  # [foo].bar, .foo[bar]
+_node_inter = f"\\${{\\s*{_node_path}\\s*}}"  # node interpolation ${foo.bar}
+_id = "[a-zA-Z_][\\w\\-]*"  # foo, foo_bar, foo-bar, abc123
+_resolver_name = f"({_id}(\\.{_id})*)?"  # foo, ns.bar3, ns_1.ns_2.b0z
+_arg = r"[a-zA-Z_0-9/\-\+.$%*@?|]+"  # string representing a resolver argument
+_args = f"{_arg}(\\s*,\\s*{_arg})*"  # list of resolver arguments
+_resolver_inter = f"\\${{\\s*{_resolver_name}\\s*:\\s*{_args}?\\s*}}"  # ${foo:bar}
+_inter = f"({_node_inter}|{_resolver_inter})"  # any kind of interpolation
+_outer = "([^$]|\\$(?!{))+"  # any character except $ (unless not followed by {)
+SIMPLE_INTERPOLATION_PATTERN = re.compile(
+    f"({_outer})?({_inter}({_outer})?)+$", flags=re.ASCII
+)
+# NOTE: SIMPLE_INTERPOLATION_PATTERN must not generate false positive matches:
+# it must not accept anything that isn't a valid interpolation (per the
+# interpolation grammar defined in `omegaconf/grammar/*.g4`).
+
+
+class OmegaConfErrorListener(ErrorListener):  # type: ignore
+    def syntaxError(
+        self,
+        recognizer: Any,
+        offending_symbol: Any,
+        line: Any,
+        column: Any,
+        msg: Any,
+        e: Any,
+    ) -> None:
+        raise GrammarParseError(str(e) if msg is None else msg) from e
+
+    def reportAmbiguity(
+        self,
+        recognizer: Any,
+        dfa: Any,
+        startIndex: Any,
+        stopIndex: Any,
+        exact: Any,
+        ambigAlts: Any,
+        configs: Any,
+    ) -> None:
+        raise GrammarParseError("ANTLR error: Ambiguity")  # pragma: no cover
+
+    def reportAttemptingFullContext(
+        self,
+        recognizer: Any,
+        dfa: Any,
+        startIndex: Any,
+        stopIndex: Any,
+        conflictingAlts: Any,
+        configs: Any,
+    ) -> None:
+        # Note: for now we raise an error to be safe. However this is mostly a
+        # performance warning, so in the future this may be relaxed if we need
+        # to change the grammar in such a way that this warning cannot be
+        # avoided (another option would be to switch to SLL parsing mode).
+        raise GrammarParseError(
+            "ANTLR error: Attempting Full Context"
+        )  # pragma: no cover
+
+    def reportContextSensitivity(
+        self,
+        recognizer: Any,
+        dfa: Any,
+        startIndex: Any,
+        stopIndex: Any,
+        prediction: Any,
+        configs: Any,
+    ) -> None:
+        raise GrammarParseError("ANTLR error: ContextSensitivity")  # pragma: no cover
+
+
+def parse(
+    value: str, parser_rule: str = "configValue", lexer_mode: str = "DEFAULT_MODE"
+) -> ParserRuleContext:
+    """
+    Parse interpolated string `value` (and return the parse tree).
+    """
+    l_mode = getattr(OmegaConfGrammarLexer, lexer_mode)
+    istream = InputStream(value)
+
+    cached = getattr(_grammar_cache, "data", None)
+    if cached is None:
+        error_listener = OmegaConfErrorListener()
+        lexer = OmegaConfGrammarLexer(istream)
+        lexer.removeErrorListeners()
+        lexer.addErrorListener(error_listener)
+        lexer.mode(l_mode)
+        token_stream = CommonTokenStream(lexer)
+        parser = OmegaConfGrammarParser(token_stream)
+        parser.removeErrorListeners()
+        parser.addErrorListener(error_listener)
+
+        # The two lines below could be enabled in the future if we decide to switch
+        # to SLL prediction mode. Warning though, it has not been fully tested yet!
+        # from antlr4 import PredictionMode
+        # parser._interp.predictionMode = PredictionMode.SLL
+
+        # Note that although the input stream `istream` is implicitly cached within
+        # the lexer, it will be replaced by a new input next time the lexer is re-used.
+        _grammar_cache.data = lexer, token_stream, parser
+
+    else:
+        lexer, token_stream, parser = cached
+        # Replace the old input stream with the new one.
+        lexer.inputStream = istream
+        # Initialize the lexer / token stream / parser to process the new input.
+        lexer.mode(l_mode)
+        token_stream.setTokenSource(lexer)
+        parser.reset()
+
+    try:
+        return getattr(parser, parser_rule)()
+    except Exception as exc:
+        if type(exc) is Exception and str(exc) == "Empty Stack":
+            # This exception is raised by antlr when trying to pop a mode while
+            # no mode has been pushed. We convert it into an `GrammarParseError`
+            # to facilitate exception handling from the caller.
+            raise GrammarParseError("Empty Stack")
+        else:
+            raise
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar_visitor.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar_visitor.py
new file mode 100644
index 0000000000000000000000000000000000000000..1771516dcf78bbc692512d58d4dea55850b957b7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/grammar_visitor.py
@@ -0,0 +1,392 @@
+import sys
+import warnings
+from itertools import zip_longest
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+)
+
+from antlr4 import TerminalNode
+
+from .errors import InterpolationResolutionError
+
+if TYPE_CHECKING:
+    from .base import Node  # noqa F401
+
+try:
+    from omegaconf.grammar.gen.OmegaConfGrammarLexer import OmegaConfGrammarLexer
+    from omegaconf.grammar.gen.OmegaConfGrammarParser import OmegaConfGrammarParser
+    from omegaconf.grammar.gen.OmegaConfGrammarParserVisitor import (
+        OmegaConfGrammarParserVisitor,
+    )
+
+except ModuleNotFoundError:  # pragma: no cover
+    print(
+        "Error importing OmegaConf's generated parsers, run `python setup.py antlr` to regenerate.",
+        file=sys.stderr,
+    )
+    sys.exit(1)
+
+
+class GrammarVisitor(OmegaConfGrammarParserVisitor):
+    def __init__(
+        self,
+        node_interpolation_callback: Callable[
+            [str, Optional[Set[int]]],
+            Optional["Node"],
+        ],
+        resolver_interpolation_callback: Callable[..., Any],
+        memo: Optional[Set[int]],
+        **kw: Dict[Any, Any],
+    ):
+        """
+        Constructor.
+
+        :param node_interpolation_callback: Callback function that is called when
+            needing to resolve a node interpolation. This function should take a single
+            string input which is the key's dot path (ex: `"foo.bar"`).
+
+        :param resolver_interpolation_callback: Callback function that is called when
+            needing to resolve a resolver interpolation. This function should accept
+            three keyword arguments: `name` (str, the name of the resolver),
+            `args` (tuple, the inputs to the resolver), and `args_str` (tuple,
+            the string representation of the inputs to the resolver).
+
+        :param kw: Additional keyword arguments to be forwarded to parent class.
+        """
+        super().__init__(**kw)
+        self.node_interpolation_callback = node_interpolation_callback
+        self.resolver_interpolation_callback = resolver_interpolation_callback
+        self.memo = memo
+
+    def aggregateResult(self, aggregate: List[Any], nextResult: Any) -> List[Any]:
+        raise NotImplementedError
+
+    def defaultResult(self) -> List[Any]:
+        # Raising an exception because not currently used (like `aggregateResult()`).
+        raise NotImplementedError
+
+    def visitConfigKey(self, ctx: OmegaConfGrammarParser.ConfigKeyContext) -> str:
+        from ._utils import _get_value
+
+        # interpolation | ID | INTER_KEY
+        assert ctx.getChildCount() == 1
+        child = ctx.getChild(0)
+        if isinstance(child, OmegaConfGrammarParser.InterpolationContext):
+            res = _get_value(self.visitInterpolation(child))
+            if not isinstance(res, str):
+                raise InterpolationResolutionError(
+                    f"The following interpolation is used to denote a config key and "
+                    f"thus should return a string, but instead returned `{res}` of "
+                    f"type `{type(res)}`: {ctx.getChild(0).getText()}"
+                )
+            return res
+        else:
+            assert isinstance(child, TerminalNode) and isinstance(
+                child.symbol.text, str
+            )
+            return child.symbol.text
+
+    def visitConfigValue(self, ctx: OmegaConfGrammarParser.ConfigValueContext) -> Any:
+        # text EOF
+        assert ctx.getChildCount() == 2
+        return self.visit(ctx.getChild(0))
+
+    def visitDictKey(self, ctx: OmegaConfGrammarParser.DictKeyContext) -> Any:
+        return self._createPrimitive(ctx)
+
+    def visitDictContainer(
+        self, ctx: OmegaConfGrammarParser.DictContainerContext
+    ) -> Dict[Any, Any]:
+        # BRACE_OPEN (dictKeyValuePair (COMMA dictKeyValuePair)*)? BRACE_CLOSE
+        assert ctx.getChildCount() >= 2
+        return dict(
+            self.visitDictKeyValuePair(ctx.getChild(i))
+            for i in range(1, ctx.getChildCount() - 1, 2)
+        )
+
+    def visitElement(self, ctx: OmegaConfGrammarParser.ElementContext) -> Any:
+        # primitive | quotedValue | listContainer | dictContainer
+        assert ctx.getChildCount() == 1
+        return self.visit(ctx.getChild(0))
+
+    def visitInterpolation(
+        self, ctx: OmegaConfGrammarParser.InterpolationContext
+    ) -> Any:
+        assert ctx.getChildCount() == 1  # interpolationNode | interpolationResolver
+        return self.visit(ctx.getChild(0))
+
+    def visitInterpolationNode(
+        self, ctx: OmegaConfGrammarParser.InterpolationNodeContext
+    ) -> Optional["Node"]:
+        # INTER_OPEN
+        # DOT*                                                     // relative interpolation?
+        # (configKey | BRACKET_OPEN configKey BRACKET_CLOSE)       // foo, [foo]
+        # (DOT configKey | BRACKET_OPEN configKey BRACKET_CLOSE)*  // .foo, [foo], .foo[bar], [foo].bar[baz]
+        # INTER_CLOSE;
+
+        assert ctx.getChildCount() >= 3
+
+        inter_key_tokens = []  # parsed elements of the dot path
+        for child in ctx.getChildren():
+            if isinstance(child, TerminalNode):
+                s = child.symbol
+                if s.type in [
+                    OmegaConfGrammarLexer.DOT,
+                    OmegaConfGrammarLexer.BRACKET_OPEN,
+                    OmegaConfGrammarLexer.BRACKET_CLOSE,
+                ]:
+                    inter_key_tokens.append(s.text)
+                else:
+                    assert s.type in (
+                        OmegaConfGrammarLexer.INTER_OPEN,
+                        OmegaConfGrammarLexer.INTER_CLOSE,
+                    )
+            else:
+                assert isinstance(child, OmegaConfGrammarParser.ConfigKeyContext)
+                inter_key_tokens.append(self.visitConfigKey(child))
+
+        inter_key = "".join(inter_key_tokens)
+        return self.node_interpolation_callback(inter_key, self.memo)
+
+    def visitInterpolationResolver(
+        self, ctx: OmegaConfGrammarParser.InterpolationResolverContext
+    ) -> Any:
+
+        # INTER_OPEN resolverName COLON sequence? BRACE_CLOSE
+        assert 4 <= ctx.getChildCount() <= 5
+
+        resolver_name = self.visit(ctx.getChild(1))
+        maybe_seq = ctx.getChild(3)
+        args = []
+        args_str = []
+        if isinstance(maybe_seq, TerminalNode):  # means there are no args
+            assert maybe_seq.symbol.type == OmegaConfGrammarLexer.BRACE_CLOSE
+        else:
+            assert isinstance(maybe_seq, OmegaConfGrammarParser.SequenceContext)
+            for val, txt in self.visitSequence(maybe_seq):
+                args.append(val)
+                args_str.append(txt)
+
+        return self.resolver_interpolation_callback(
+            name=resolver_name,
+            args=tuple(args),
+            args_str=tuple(args_str),
+        )
+
+    def visitDictKeyValuePair(
+        self, ctx: OmegaConfGrammarParser.DictKeyValuePairContext
+    ) -> Tuple[Any, Any]:
+        from ._utils import _get_value
+
+        assert ctx.getChildCount() == 3  # dictKey COLON element
+        key = self.visit(ctx.getChild(0))
+        colon = ctx.getChild(1)
+        assert (
+            isinstance(colon, TerminalNode)
+            and colon.symbol.type == OmegaConfGrammarLexer.COLON
+        )
+        value = _get_value(self.visitElement(ctx.getChild(2)))
+        return key, value
+
+    def visitListContainer(
+        self, ctx: OmegaConfGrammarParser.ListContainerContext
+    ) -> List[Any]:
+        # BRACKET_OPEN sequence? BRACKET_CLOSE;
+        assert ctx.getChildCount() in (2, 3)
+        if ctx.getChildCount() == 2:
+            return []
+        sequence = ctx.getChild(1)
+        assert isinstance(sequence, OmegaConfGrammarParser.SequenceContext)
+        return list(val for val, _ in self.visitSequence(sequence))  # ignore raw text
+
+    def visitPrimitive(self, ctx: OmegaConfGrammarParser.PrimitiveContext) -> Any:
+        return self._createPrimitive(ctx)
+
+    def visitQuotedValue(self, ctx: OmegaConfGrammarParser.QuotedValueContext) -> str:
+        # (QUOTE_OPEN_SINGLE | QUOTE_OPEN_DOUBLE) text? MATCHING_QUOTE_CLOSE
+        n = ctx.getChildCount()
+        assert n in [2, 3]
+        return str(self.visit(ctx.getChild(1))) if n == 3 else ""
+
+    def visitResolverName(self, ctx: OmegaConfGrammarParser.ResolverNameContext) -> str:
+        from ._utils import _get_value
+
+        # (interpolation | ID) (DOT (interpolation | ID))*
+        assert ctx.getChildCount() >= 1
+        items = []
+        for child in list(ctx.getChildren())[::2]:
+            if isinstance(child, TerminalNode):
+                assert child.symbol.type == OmegaConfGrammarLexer.ID
+                items.append(child.symbol.text)
+            else:
+                assert isinstance(child, OmegaConfGrammarParser.InterpolationContext)
+                item = _get_value(self.visitInterpolation(child))
+                if not isinstance(item, str):
+                    raise InterpolationResolutionError(
+                        f"The name of a resolver must be a string, but the interpolation "
+                        f"{child.getText()} resolved to `{item}` which is of type "
+                        f"{type(item)}"
+                    )
+                items.append(item)
+        return ".".join(items)
+
+    def visitSequence(
+        self, ctx: OmegaConfGrammarParser.SequenceContext
+    ) -> Generator[Any, None, None]:
+        from ._utils import _get_value
+
+        # (element (COMMA element?)*) | (COMMA element?)+
+        assert ctx.getChildCount() >= 1
+
+        # DEPRECATED: remove in 2.2 (revert #571)
+        def empty_str_warning() -> None:
+            txt = ctx.getText()
+            warnings.warn(
+                f"In the sequence `{txt}` some elements are missing: please replace "
+                f"them with empty quoted strings. "
+                f"See https://github.com/omry/omegaconf/issues/572 for details.",
+                category=UserWarning,
+            )
+
+        is_previous_comma = True  # whether previous child was a comma (init to True)
+        for child in ctx.getChildren():
+            if isinstance(child, OmegaConfGrammarParser.ElementContext):
+                # Also preserve the original text representation of `child` so
+                # as to allow backward compatibility with old resolvers (registered
+                # with `legacy_register_resolver()`). Note that we cannot just cast
+                # the value to string later as for instance `null` would become "None".
+                yield _get_value(self.visitElement(child)), child.getText()
+                is_previous_comma = False
+            else:
+                assert (
+                    isinstance(child, TerminalNode)
+                    and child.symbol.type == OmegaConfGrammarLexer.COMMA
+                )
+                if is_previous_comma:
+                    empty_str_warning()
+                    yield "", ""
+                else:
+                    is_previous_comma = True
+        if is_previous_comma:
+            # Trailing comma.
+            empty_str_warning()
+            yield "", ""
+
+    def visitSingleElement(
+        self, ctx: OmegaConfGrammarParser.SingleElementContext
+    ) -> Any:
+        # element EOF
+        assert ctx.getChildCount() == 2
+        return self.visit(ctx.getChild(0))
+
+    def visitText(self, ctx: OmegaConfGrammarParser.TextContext) -> Any:
+        # (interpolation | ANY_STR | ESC | ESC_INTER | TOP_ESC | QUOTED_ESC)+
+
+        # Single interpolation? If yes, return its resolved value "as is".
+        if ctx.getChildCount() == 1:
+            c = ctx.getChild(0)
+            if isinstance(c, OmegaConfGrammarParser.InterpolationContext):
+                return self.visitInterpolation(c)
+
+        # Otherwise, concatenate string representations together.
+        return self._unescape(list(ctx.getChildren()))
+
+    def _createPrimitive(
+        self,
+        ctx: Union[
+            OmegaConfGrammarParser.PrimitiveContext,
+            OmegaConfGrammarParser.DictKeyContext,
+        ],
+    ) -> Any:
+        # (ID | NULL | INT | FLOAT | BOOL | UNQUOTED_CHAR | COLON | ESC | WS | interpolation)+
+        if ctx.getChildCount() == 1:
+            child = ctx.getChild(0)
+            if isinstance(child, OmegaConfGrammarParser.InterpolationContext):
+                return self.visitInterpolation(child)
+            assert isinstance(child, TerminalNode)
+            symbol = child.symbol
+            # Parse primitive types.
+            if symbol.type in (
+                OmegaConfGrammarLexer.ID,
+                OmegaConfGrammarLexer.UNQUOTED_CHAR,
+                OmegaConfGrammarLexer.COLON,
+            ):
+                return symbol.text
+            elif symbol.type == OmegaConfGrammarLexer.NULL:
+                return None
+            elif symbol.type == OmegaConfGrammarLexer.INT:
+                return int(symbol.text)
+            elif symbol.type == OmegaConfGrammarLexer.FLOAT:
+                return float(symbol.text)
+            elif symbol.type == OmegaConfGrammarLexer.BOOL:
+                return symbol.text.lower() == "true"
+            elif symbol.type == OmegaConfGrammarLexer.ESC:
+                return self._unescape([child])
+            elif symbol.type == OmegaConfGrammarLexer.WS:  # pragma: no cover
+                # A single WS should have been "consumed" by another token.
+                raise AssertionError("WS should never be reached")
+            assert False, symbol.type
+        # Concatenation of multiple items ==> un-escape the concatenation.
+        return self._unescape(list(ctx.getChildren()))
+
+    def _unescape(
+        self,
+        seq: List[Union[TerminalNode, OmegaConfGrammarParser.InterpolationContext]],
+    ) -> str:
+        """
+        Concatenate all symbols / interpolations in `seq`, unescaping symbols as needed.
+
+        Interpolations are resolved and cast to string *WITHOUT* escaping their result
+        (it is assumed that whatever escaping is required was already handled during the
+        resolving of the interpolation).
+        """
+        chrs = []
+        for node, next_node in zip_longest(seq, seq[1:]):
+            if isinstance(node, TerminalNode):
+                s = node.symbol
+                if s.type == OmegaConfGrammarLexer.ESC_INTER:
+                    # `ESC_INTER` is of the form `\\...\${`: the formula below computes
+                    # the number of characters to keep at the end of the string to remove
+                    # the correct number of backslashes.
+                    text = s.text[-(len(s.text) // 2 + 1) :]
+                elif (
+                    # Character sequence identified as requiring un-escaping.
+                    s.type == OmegaConfGrammarLexer.ESC
+                    or (
+                        # At top level, we need to un-escape backslashes that precede
+                        # an interpolation.
+                        s.type == OmegaConfGrammarLexer.TOP_ESC
+                        and isinstance(
+                            next_node, OmegaConfGrammarParser.InterpolationContext
+                        )
+                    )
+                    or (
+                        # In a quoted sring, we need to un-escape backslashes that
+                        # either end the string, or are followed by an interpolation.
+                        s.type == OmegaConfGrammarLexer.QUOTED_ESC
+                        and (
+                            next_node is None
+                            or isinstance(
+                                next_node, OmegaConfGrammarParser.InterpolationContext
+                            )
+                        )
+                    )
+                ):
+                    text = s.text[1::2]  # un-escape the sequence
+                else:
+                    text = s.text  # keep the original text
+            else:
+                assert isinstance(node, OmegaConfGrammarParser.InterpolationContext)
+                text = str(self.visitInterpolation(node))
+            chrs.append(text)
+
+        return "".join(chrs)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/listconfig.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/listconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3b0f18a4e1cc69f033fec788fe674a2c81dc700
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/listconfig.py
@@ -0,0 +1,679 @@
+import copy
+import itertools
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    Iterator,
+    List,
+    MutableSequence,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+)
+
+from ._utils import (
+    ValueKind,
+    _is_missing_literal,
+    _is_none,
+    _resolve_optional,
+    format_and_raise,
+    get_value_kind,
+    is_int,
+    is_primitive_list,
+    is_structured_config,
+    type_str,
+)
+from .base import Box, ContainerMetadata, Node
+from .basecontainer import BaseContainer
+from .errors import (
+    ConfigAttributeError,
+    ConfigTypeError,
+    ConfigValueError,
+    KeyValidationError,
+    MissingMandatoryValue,
+    ReadonlyConfigError,
+    ValidationError,
+)
+
+
+class ListConfig(BaseContainer, MutableSequence[Any]):
+
+    _content: Union[List[Node], None, str]
+
+    def __init__(
+        self,
+        content: Union[List[Any], Tuple[Any, ...], "ListConfig", str, None],
+        key: Any = None,
+        parent: Optional[Box] = None,
+        element_type: Union[Type[Any], Any] = Any,
+        is_optional: bool = True,
+        ref_type: Union[Type[Any], Any] = Any,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> None:
+        try:
+            if isinstance(content, ListConfig):
+                if flags is None:
+                    flags = content._metadata.flags
+            super().__init__(
+                parent=parent,
+                metadata=ContainerMetadata(
+                    ref_type=ref_type,
+                    object_type=list,
+                    key=key,
+                    optional=is_optional,
+                    element_type=element_type,
+                    key_type=int,
+                    flags=flags,
+                ),
+            )
+
+            if isinstance(content, ListConfig):
+                metadata = copy.deepcopy(content._metadata)
+                metadata.key = key
+                metadata.ref_type = ref_type
+                metadata.optional = is_optional
+                metadata.element_type = element_type
+                self.__dict__["_metadata"] = metadata
+            self._set_value(value=content, flags=flags)
+        except Exception as ex:
+            format_and_raise(node=None, key=key, value=None, cause=ex, msg=str(ex))
+
+    def _validate_get(self, key: Any, value: Any = None) -> None:
+        if not isinstance(key, (int, slice)):
+            raise KeyValidationError(
+                "ListConfig indices must be integers or slices, not $KEY_TYPE"
+            )
+
+    def _validate_set(self, key: Any, value: Any) -> None:
+        from omegaconf import OmegaConf
+
+        self._validate_get(key, value)
+
+        if self._get_flag("readonly"):
+            raise ReadonlyConfigError("ListConfig is read-only")
+
+        if 0 <= key < self.__len__():
+            target = self._get_node(key)
+            if target is not None:
+                assert isinstance(target, Node)
+                if value is None and not target._is_optional():
+                    raise ValidationError(
+                        "$FULL_KEY is not optional and cannot be assigned None"
+                    )
+
+        vk = get_value_kind(value)
+        if vk == ValueKind.MANDATORY_MISSING:
+            return
+        else:
+            is_optional, target_type = _resolve_optional(self._metadata.element_type)
+            value_type = OmegaConf.get_type(value)
+
+            if (value_type is None and not is_optional) or (
+                is_structured_config(target_type)
+                and value_type is not None
+                and not issubclass(value_type, target_type)
+            ):
+                msg = (
+                    f"Invalid type assigned: {type_str(value_type)} is not a "
+                    f"subclass of {type_str(target_type)}. value: {value}"
+                )
+                raise ValidationError(msg)
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "ListConfig":
+        res = ListConfig(None)
+        res.__dict__["_metadata"] = copy.deepcopy(self.__dict__["_metadata"], memo=memo)
+        res.__dict__["_flags_cache"] = copy.deepcopy(
+            self.__dict__["_flags_cache"], memo=memo
+        )
+
+        src_content = self.__dict__["_content"]
+        if isinstance(src_content, list):
+            content_copy: List[Optional[Node]] = []
+            for v in src_content:
+                old_parent = v.__dict__["_parent"]
+                try:
+                    v.__dict__["_parent"] = None
+                    vc = copy.deepcopy(v, memo=memo)
+                    vc.__dict__["_parent"] = res
+                    content_copy.append(vc)
+                finally:
+                    v.__dict__["_parent"] = old_parent
+        else:
+            # None and strings can be assigned as is
+            content_copy = src_content
+
+        res.__dict__["_content"] = content_copy
+        res.__dict__["_parent"] = self.__dict__["_parent"]
+
+        return res
+
+    def copy(self) -> "ListConfig":
+        return copy.copy(self)
+
+    # hide content while inspecting in debugger
+    def __dir__(self) -> Iterable[str]:
+        if self._is_missing() or self._is_none():
+            return []
+        return [str(x) for x in range(0, len(self))]
+
+    def __setattr__(self, key: str, value: Any) -> None:
+        self._format_and_raise(
+            key=key,
+            value=value,
+            cause=ConfigAttributeError("ListConfig does not support attribute access"),
+        )
+        assert False
+
+    def __getattr__(self, key: str) -> Any:
+        # PyCharm is sometimes inspecting __members__, be sure to tell it we don't have that.
+        if key == "__members__":
+            raise AttributeError()
+
+        if key == "__name__":
+            raise AttributeError()
+
+        if is_int(key):
+            return self.__getitem__(int(key))
+        else:
+            self._format_and_raise(
+                key=key,
+                value=None,
+                cause=ConfigAttributeError(
+                    "ListConfig does not support attribute access"
+                ),
+            )
+
+    def __getitem__(self, index: Union[int, slice]) -> Any:
+        try:
+            if self._is_missing():
+                raise MissingMandatoryValue("ListConfig is missing")
+            self._validate_get(index, None)
+            if self._is_none():
+                raise TypeError(
+                    "ListConfig object representing None is not subscriptable"
+                )
+
+            assert isinstance(self.__dict__["_content"], list)
+            if isinstance(index, slice):
+                result = []
+                start, stop, step = self._correct_index_params(index)
+                for slice_idx in itertools.islice(
+                    range(0, len(self)), start, stop, step
+                ):
+                    val = self._resolve_with_default(
+                        key=slice_idx, value=self.__dict__["_content"][slice_idx]
+                    )
+                    result.append(val)
+                if index.step and index.step < 0:
+                    result.reverse()
+                return result
+            else:
+                return self._resolve_with_default(
+                    key=index, value=self.__dict__["_content"][index]
+                )
+        except Exception as e:
+            self._format_and_raise(key=index, value=None, cause=e)
+
+    def _correct_index_params(self, index: slice) -> Tuple[int, int, int]:
+        start = index.start
+        stop = index.stop
+        step = index.step
+        if index.start and index.start < 0:
+            start = self.__len__() + index.start
+        if index.stop and index.stop < 0:
+            stop = self.__len__() + index.stop
+        if index.step and index.step < 0:
+            step = abs(step)
+            if start and stop:
+                if start > stop:
+                    start, stop = stop + 1, start + 1
+                else:
+                    start = stop = 0
+            elif not start and stop:
+                start = list(range(self.__len__() - 1, stop, -step))[0]
+                stop = None
+            elif start and not stop:
+                stop = start + 1
+                start = (stop - 1) % step
+            else:
+                start = (self.__len__() - 1) % step
+        return start, stop, step
+
+    def _set_at_index(self, index: Union[int, slice], value: Any) -> None:
+        self._set_item_impl(index, value)
+
+    def __setitem__(self, index: Union[int, slice], value: Any) -> None:
+        try:
+            if isinstance(index, slice):
+                _ = iter(value)  # check iterable
+                self_indices = index.indices(len(self))
+                indexes = range(*self_indices)
+
+                # Ensure lengths match for extended slice assignment
+                if index.step not in (None, 1):
+                    if len(indexes) != len(value):
+                        raise ValueError(
+                            f"attempt to assign sequence of size {len(value)}"
+                            f" to extended slice of size {len(indexes)}"
+                        )
+
+                # Initialize insertion offsets for empty slices
+                if len(indexes) == 0:
+                    curr_index = self_indices[0] - 1
+                    val_i = -1
+
+                work_copy = self.copy()  # For atomicity manipulate a copy
+
+                # Delete and optionally replace non empty slices
+                only_removed = 0
+                for val_i, i in enumerate(indexes):
+                    curr_index = i - only_removed
+                    del work_copy[curr_index]
+                    if val_i < len(value):
+                        work_copy.insert(curr_index, value[val_i])
+                    else:
+                        only_removed += 1
+
+                # Insert any remaining input items
+                for val_i in range(val_i + 1, len(value)):
+                    curr_index += 1
+                    work_copy.insert(curr_index, value[val_i])
+
+                # Reinitialize self with work_copy
+                self.clear()
+                self.extend(work_copy)
+            else:
+                self._set_at_index(index, value)
+        except Exception as e:
+            self._format_and_raise(key=index, value=value, cause=e)
+
+    def append(self, item: Any) -> None:
+        content = self.__dict__["_content"]
+        index = len(content)
+        content.append(None)
+        try:
+            self._set_item_impl(index, item)
+        except Exception as e:
+            del content[index]
+            self._format_and_raise(key=index, value=item, cause=e)
+            assert False
+
+    def _update_keys(self) -> None:
+        for i in range(len(self)):
+            node = self._get_node(i)
+            if node is not None:
+                assert isinstance(node, Node)
+                node._metadata.key = i
+
+    def insert(self, index: int, item: Any) -> None:
+        from omegaconf.omegaconf import _maybe_wrap
+
+        try:
+            if self._get_flag("readonly"):
+                raise ReadonlyConfigError("Cannot insert into a read-only ListConfig")
+            if self._is_none():
+                raise TypeError(
+                    "Cannot insert into ListConfig object representing None"
+                )
+            if self._is_missing():
+                raise MissingMandatoryValue("Cannot insert into missing ListConfig")
+
+            try:
+                assert isinstance(self.__dict__["_content"], list)
+                # insert place holder
+                self.__dict__["_content"].insert(index, None)
+                is_optional, ref_type = _resolve_optional(self._metadata.element_type)
+                node = _maybe_wrap(
+                    ref_type=ref_type,
+                    key=index,
+                    value=item,
+                    is_optional=is_optional,
+                    parent=self,
+                )
+                self._validate_set(key=index, value=node)
+                self._set_at_index(index, node)
+                self._update_keys()
+            except Exception:
+                del self.__dict__["_content"][index]
+                self._update_keys()
+                raise
+        except Exception as e:
+            self._format_and_raise(key=index, value=item, cause=e)
+            assert False
+
+    def extend(self, lst: Iterable[Any]) -> None:
+        assert isinstance(lst, (tuple, list, ListConfig))
+        for x in lst:
+            self.append(x)
+
+    def remove(self, x: Any) -> None:
+        del self[self.index(x)]
+
+    def __delitem__(self, key: Union[int, slice]) -> None:
+        if self._get_flag("readonly"):
+            self._format_and_raise(
+                key=key,
+                value=None,
+                cause=ReadonlyConfigError(
+                    "Cannot delete item from read-only ListConfig"
+                ),
+            )
+        del self.__dict__["_content"][key]
+        self._update_keys()
+
+    def clear(self) -> None:
+        del self[:]
+
+    def index(
+        self, x: Any, start: Optional[int] = None, end: Optional[int] = None
+    ) -> int:
+        if start is None:
+            start = 0
+        if end is None:
+            end = len(self)
+        assert start >= 0
+        assert end <= len(self)
+        found_idx = -1
+        for idx in range(start, end):
+            item = self[idx]
+            if x == item:
+                found_idx = idx
+                break
+        if found_idx != -1:
+            return found_idx
+        else:
+            self._format_and_raise(
+                key=None,
+                value=None,
+                cause=ConfigValueError("Item not found in ListConfig"),
+            )
+            assert False
+
+    def count(self, x: Any) -> int:
+        c = 0
+        for item in self:
+            if item == x:
+                c = c + 1
+        return c
+
+    def _get_node(
+        self,
+        key: Union[int, slice],
+        validate_access: bool = True,
+        validate_key: bool = True,
+        throw_on_missing_value: bool = False,
+        throw_on_missing_key: bool = False,
+    ) -> Union[Optional[Node], List[Optional[Node]]]:
+        try:
+            if self._is_none():
+                raise TypeError(
+                    "Cannot get_node from a ListConfig object representing None"
+                )
+            if self._is_missing():
+                raise MissingMandatoryValue("Cannot get_node from a missing ListConfig")
+            assert isinstance(self.__dict__["_content"], list)
+            if validate_access:
+                self._validate_get(key)
+
+            value = self.__dict__["_content"][key]
+            if value is not None:
+                if isinstance(key, slice):
+                    assert isinstance(value, list)
+                    for v in value:
+                        if throw_on_missing_value and v._is_missing():
+                            raise MissingMandatoryValue("Missing mandatory value")
+                else:
+                    assert isinstance(value, Node)
+                    if throw_on_missing_value and value._is_missing():
+                        raise MissingMandatoryValue("Missing mandatory value: $KEY")
+            return value
+        except (IndexError, TypeError, MissingMandatoryValue, KeyValidationError) as e:
+            if isinstance(e, MissingMandatoryValue) and throw_on_missing_value:
+                raise
+            if validate_access:
+                self._format_and_raise(key=key, value=None, cause=e)
+                assert False
+            else:
+                return None
+
+    def get(self, index: int, default_value: Any = None) -> Any:
+        try:
+            if self._is_none():
+                raise TypeError("Cannot get from a ListConfig object representing None")
+            if self._is_missing():
+                raise MissingMandatoryValue("Cannot get from a missing ListConfig")
+            self._validate_get(index, None)
+            assert isinstance(self.__dict__["_content"], list)
+            return self._resolve_with_default(
+                key=index,
+                value=self.__dict__["_content"][index],
+                default_value=default_value,
+            )
+        except Exception as e:
+            self._format_and_raise(key=index, value=None, cause=e)
+            assert False
+
+    def pop(self, index: int = -1) -> Any:
+        try:
+            if self._get_flag("readonly"):
+                raise ReadonlyConfigError("Cannot pop from read-only ListConfig")
+            if self._is_none():
+                raise TypeError("Cannot pop from a ListConfig object representing None")
+            if self._is_missing():
+                raise MissingMandatoryValue("Cannot pop from a missing ListConfig")
+
+            assert isinstance(self.__dict__["_content"], list)
+            node = self._get_child(index)
+            assert isinstance(node, Node)
+            ret = self._resolve_with_default(key=index, value=node, default_value=None)
+            del self.__dict__["_content"][index]
+            self._update_keys()
+            return ret
+        except KeyValidationError as e:
+            self._format_and_raise(
+                key=index, value=None, cause=e, type_override=ConfigTypeError
+            )
+            assert False
+        except Exception as e:
+            self._format_and_raise(key=index, value=None, cause=e)
+            assert False
+
+    def sort(
+        self, key: Optional[Callable[[Any], Any]] = None, reverse: bool = False
+    ) -> None:
+        try:
+            if self._get_flag("readonly"):
+                raise ReadonlyConfigError("Cannot sort a read-only ListConfig")
+            if self._is_none():
+                raise TypeError("Cannot sort a ListConfig object representing None")
+            if self._is_missing():
+                raise MissingMandatoryValue("Cannot sort a missing ListConfig")
+
+            if key is None:
+
+                def key1(x: Any) -> Any:
+                    return x._value()
+
+            else:
+
+                def key1(x: Any) -> Any:
+                    return key(x._value())  # type: ignore
+
+            assert isinstance(self.__dict__["_content"], list)
+            self.__dict__["_content"].sort(key=key1, reverse=reverse)
+
+        except Exception as e:
+            self._format_and_raise(key=None, value=None, cause=e)
+            assert False
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, (list, tuple)) or other is None:
+            other = ListConfig(other, flags={"allow_objects": True})
+            return ListConfig._list_eq(self, other)
+        if other is None or isinstance(other, ListConfig):
+            return ListConfig._list_eq(self, other)
+        if self._is_missing():
+            return _is_missing_literal(other)
+        return NotImplemented
+
+    def __ne__(self, other: Any) -> bool:
+        x = self.__eq__(other)
+        if x is not NotImplemented:
+            return not x
+        return NotImplemented
+
+    def __hash__(self) -> int:
+        return hash(str(self))
+
+    def __iter__(self) -> Iterator[Any]:
+        return self._iter_ex(resolve=True)
+
+    class ListIterator(Iterator[Any]):
+        def __init__(self, lst: Any, resolve: bool) -> None:
+            self.resolve = resolve
+            self.iterator = iter(lst.__dict__["_content"])
+            self.index = 0
+            from .nodes import ValueNode
+
+            self.ValueNode = ValueNode
+
+        def __next__(self) -> Any:
+
+            x = next(self.iterator)
+            if self.resolve:
+                x = x._dereference_node()
+                if x._is_missing():
+                    raise MissingMandatoryValue(f"Missing value at index {self.index}")
+
+            self.index = self.index + 1
+            if isinstance(x, self.ValueNode):
+                return x._value()
+            else:
+                # Must be omegaconf.Container. not checking for perf reasons.
+                if x._is_none():
+                    return None
+                return x
+
+        def __repr__(self) -> str:  # pragma: no cover
+            return f"ListConfig.ListIterator(resolve={self.resolve})"
+
+    def _iter_ex(self, resolve: bool) -> Iterator[Any]:
+        try:
+            if self._is_none():
+                raise TypeError("Cannot iterate a ListConfig object representing None")
+            if self._is_missing():
+                raise MissingMandatoryValue("Cannot iterate a missing ListConfig")
+
+            return ListConfig.ListIterator(self, resolve)
+        except (TypeError, MissingMandatoryValue) as e:
+            self._format_and_raise(key=None, value=None, cause=e)
+            assert False
+
+    def __add__(self, other: Union[List[Any], "ListConfig"]) -> "ListConfig":
+        # res is sharing this list's parent to allow interpolation to work as expected
+        res = ListConfig(parent=self._get_parent(), content=[])
+        res.extend(self)
+        res.extend(other)
+        return res
+
+    def __radd__(self, other: Union[List[Any], "ListConfig"]) -> "ListConfig":
+        # res is sharing this list's parent to allow interpolation to work as expected
+        res = ListConfig(parent=self._get_parent(), content=[])
+        res.extend(other)
+        res.extend(self)
+        return res
+
+    def __iadd__(self, other: Iterable[Any]) -> "ListConfig":
+        self.extend(other)
+        return self
+
+    def __contains__(self, item: Any) -> bool:
+        if self._is_none():
+            raise TypeError(
+                "Cannot check if an item is in a ListConfig object representing None"
+            )
+        if self._is_missing():
+            raise MissingMandatoryValue(
+                "Cannot check if an item is in missing ListConfig"
+            )
+
+        lst = self.__dict__["_content"]
+        for x in lst:
+            x = x._dereference_node()
+            if x == item:
+                return True
+        return False
+
+    def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None:
+        try:
+            previous_content = self.__dict__["_content"]
+            previous_metadata = self.__dict__["_metadata"]
+            self._set_value_impl(value, flags)
+        except Exception as e:
+            self.__dict__["_content"] = previous_content
+            self.__dict__["_metadata"] = previous_metadata
+            raise e
+
+    def _set_value_impl(
+        self, value: Any, flags: Optional[Dict[str, bool]] = None
+    ) -> None:
+        from omegaconf import MISSING, flag_override
+
+        if flags is None:
+            flags = {}
+
+        vk = get_value_kind(value, strict_interpolation_validation=True)
+        if _is_none(value):
+            if not self._is_optional():
+                raise ValidationError(
+                    "Non optional ListConfig cannot be constructed from None"
+                )
+            self.__dict__["_content"] = None
+            self._metadata.object_type = None
+        elif vk is ValueKind.MANDATORY_MISSING:
+            self.__dict__["_content"] = MISSING
+            self._metadata.object_type = None
+        elif vk == ValueKind.INTERPOLATION:
+            self.__dict__["_content"] = value
+            self._metadata.object_type = None
+        else:
+            if not (is_primitive_list(value) or isinstance(value, ListConfig)):
+                type_ = type(value)
+                msg = f"Invalid value assigned: {type_.__name__} is not a ListConfig, list or tuple."
+                raise ValidationError(msg)
+
+            self.__dict__["_content"] = []
+            if isinstance(value, ListConfig):
+                self._metadata.flags = copy.deepcopy(flags)
+                # disable struct and readonly for the construction phase
+                # retaining other flags like allow_objects. The real flags are restored at the end of this function
+                with flag_override(self, ["struct", "readonly"], False):
+                    for item in value._iter_ex(resolve=False):
+                        self.append(item)
+            elif is_primitive_list(value):
+                with flag_override(self, ["struct", "readonly"], False):
+                    for item in value:
+                        self.append(item)
+            self._metadata.object_type = list
+
+    @staticmethod
+    def _list_eq(l1: Optional["ListConfig"], l2: Optional["ListConfig"]) -> bool:
+        l1_none = l1.__dict__["_content"] is None
+        l2_none = l2.__dict__["_content"] is None
+        if l1_none and l2_none:
+            return True
+        if l1_none != l2_none:
+            return False
+
+        assert isinstance(l1, ListConfig)
+        assert isinstance(l2, ListConfig)
+        if len(l1) != len(l2):
+            return False
+        for i in range(len(l1)):
+            if not BaseContainer._item_eq(l1, i, l2, i):
+                return False
+
+        return True
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/nodes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f02cde65801fc9b7443595fc80bd8017dd8273b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/nodes.py
@@ -0,0 +1,545 @@
+import copy
+import math
+import sys
+from abc import abstractmethod
+from enum import Enum
+from pathlib import Path
+from typing import Any, Dict, Optional, Type, Union
+
+from omegaconf._utils import (
+    ValueKind,
+    _is_interpolation,
+    get_type_of,
+    get_value_kind,
+    is_primitive_container,
+    type_str,
+)
+from omegaconf.base import Box, DictKeyType, Metadata, Node
+from omegaconf.errors import ReadonlyConfigError, UnsupportedValueType, ValidationError
+
+
+class ValueNode(Node):
+    _val: Any
+
+    def __init__(self, parent: Optional[Box], value: Any, metadata: Metadata):
+        from omegaconf import read_write
+
+        super().__init__(parent=parent, metadata=metadata)
+        with read_write(self):
+            self._set_value(value)  # lgtm [py/init-calls-subclass]
+
+    def _value(self) -> Any:
+        return self._val
+
+    def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None:
+        if self._get_flag("readonly"):
+            raise ReadonlyConfigError("Cannot set value of read-only config node")
+
+        if isinstance(value, str) and get_value_kind(
+            value, strict_interpolation_validation=True
+        ) in (
+            ValueKind.INTERPOLATION,
+            ValueKind.MANDATORY_MISSING,
+        ):
+            self._val = value
+        else:
+            self._val = self.validate_and_convert(value)
+
+    def _strict_validate_type(self, value: Any) -> None:
+        ref_type = self._metadata.ref_type
+        if isinstance(ref_type, type) and type(value) is not ref_type:
+            type_hint = type_str(self._metadata.type_hint)
+            raise ValidationError(
+                f"Value '$VALUE' of type '$VALUE_TYPE' is incompatible with type hint '{type_hint}'"
+            )
+
+    def validate_and_convert(self, value: Any) -> Any:
+        """
+        Validates input and converts to canonical form
+        :param value: input value
+        :return: converted value ("100" may be converted to 100 for example)
+        """
+        if value is None:
+            if self._is_optional():
+                return None
+            ref_type_str = type_str(self._metadata.ref_type)
+            raise ValidationError(
+                f"Incompatible value '{value}' for field of type '{ref_type_str}'"
+            )
+
+        # Subclasses can assume that `value` is not None in
+        # `_validate_and_convert_impl()` and in `_strict_validate_type()`.
+        if self._get_flag("convert") is False:
+            self._strict_validate_type(value)
+            return value
+        else:
+            return self._validate_and_convert_impl(value)
+
+    @abstractmethod
+    def _validate_and_convert_impl(self, value: Any) -> Any:
+        ...
+
+    def __str__(self) -> str:
+        return str(self._val)
+
+    def __repr__(self) -> str:
+        return repr(self._val) if hasattr(self, "_val") else "__INVALID__"
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, AnyNode):
+            return self._val == other._val  # type: ignore
+        else:
+            return self._val == other  # type: ignore
+
+    def __ne__(self, other: Any) -> bool:
+        x = self.__eq__(other)
+        assert x is not NotImplemented
+        return not x
+
+    def __hash__(self) -> int:
+        return hash(self._val)
+
+    def _deepcopy_impl(self, res: Any, memo: Dict[int, Any]) -> None:
+        res.__dict__["_metadata"] = copy.deepcopy(self._metadata, memo=memo)
+        # shallow copy for value to support non-copyable value
+        res.__dict__["_val"] = self._val
+
+        # parent is retained, but not copied
+        res.__dict__["_parent"] = self._parent
+
+    def _is_optional(self) -> bool:
+        return self._metadata.optional
+
+    def _is_interpolation(self) -> bool:
+        return _is_interpolation(self._value())
+
+    def _get_full_key(self, key: Optional[Union[DictKeyType, int]]) -> str:
+        parent = self._get_parent()
+        if parent is None:
+            if self._metadata.key is None:
+                return ""
+            else:
+                return str(self._metadata.key)
+        else:
+            return parent._get_full_key(self._metadata.key)
+
+
+class AnyNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                ref_type=Any, object_type=None, key=key, optional=True, flags=flags
+            ),
+        )
+
+    def _validate_and_convert_impl(self, value: Any) -> Any:
+        from ._utils import is_primitive_type_annotation
+
+        # allow_objects is internal and not an official API. use at your own risk.
+        # Please be aware that this support is subject to change without notice.
+        # If this is deemed useful and supportable it may become an official API.
+
+        if self._get_flag(
+            "allow_objects"
+        ) is not True and not is_primitive_type_annotation(value):
+            t = get_type_of(value)
+            raise UnsupportedValueType(
+                f"Value '{t.__name__}' is not a supported primitive type"
+            )
+        return value
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "AnyNode":
+        res = AnyNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class StringNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=str,
+                object_type=str,
+                flags=flags,
+            ),
+        )
+
+    def _validate_and_convert_impl(self, value: Any) -> str:
+        from omegaconf import OmegaConf
+
+        if (
+            OmegaConf.is_config(value)
+            or is_primitive_container(value)
+            or isinstance(value, bytes)
+        ):
+            raise ValidationError("Cannot convert '$VALUE_TYPE' to string: '$VALUE'")
+        return str(value)
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "StringNode":
+        res = StringNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class PathNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=Path,
+                object_type=Path,
+                flags=flags,
+            ),
+        )
+
+    def _strict_validate_type(self, value: Any) -> None:
+        if not isinstance(value, Path):
+            raise ValidationError(
+                "Value '$VALUE' of type '$VALUE_TYPE' is not an instance of 'pathlib.Path'"
+            )
+
+    def _validate_and_convert_impl(self, value: Any) -> Path:
+        if not isinstance(value, (str, Path)):
+            raise ValidationError(
+                "Value '$VALUE' of type '$VALUE_TYPE' could not be converted to Path"
+            )
+
+        return Path(value)
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "PathNode":
+        res = PathNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class IntegerNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=int,
+                object_type=int,
+                flags=flags,
+            ),
+        )
+
+    def _validate_and_convert_impl(self, value: Any) -> int:
+        try:
+            if type(value) in (str, int):
+                val = int(value)
+            else:
+                raise ValueError()
+        except ValueError:
+            raise ValidationError(
+                "Value '$VALUE' of type '$VALUE_TYPE' could not be converted to Integer"
+            )
+        return val
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "IntegerNode":
+        res = IntegerNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class BytesNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=bytes,
+                object_type=bytes,
+                flags=flags,
+            ),
+        )
+
+    def _validate_and_convert_impl(self, value: Any) -> bytes:
+        if not isinstance(value, bytes):
+            raise ValidationError(
+                "Value '$VALUE' of type '$VALUE_TYPE' is not of type 'bytes'"
+            )
+        return value
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "BytesNode":
+        res = BytesNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class FloatNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=float,
+                object_type=float,
+                flags=flags,
+            ),
+        )
+
+    def _validate_and_convert_impl(self, value: Any) -> float:
+        try:
+            if type(value) in (float, str, int):
+                return float(value)
+            else:
+                raise ValueError()
+        except ValueError:
+            raise ValidationError(
+                "Value '$VALUE' of type '$VALUE_TYPE' could not be converted to Float"
+            )
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, ValueNode):
+            other_val = other._val
+        else:
+            other_val = other
+        if self._val is None and other is None:
+            return True
+        if self._val is None and other is not None:
+            return False
+        if self._val is not None and other is None:
+            return False
+        nan1 = math.isnan(self._val) if isinstance(self._val, float) else False
+        nan2 = math.isnan(other_val) if isinstance(other_val, float) else False
+        return self._val == other_val or (nan1 and nan2)
+
+    def __hash__(self) -> int:
+        return hash(self._val)
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "FloatNode":
+        res = FloatNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class BooleanNode(ValueNode):
+    def __init__(
+        self,
+        value: Any = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=bool,
+                object_type=bool,
+                flags=flags,
+            ),
+        )
+
+    def _validate_and_convert_impl(self, value: Any) -> bool:
+        if isinstance(value, bool):
+            return value
+        if isinstance(value, int):
+            return value != 0
+        elif isinstance(value, str):
+            try:
+                return self._validate_and_convert_impl(int(value))
+            except ValueError as e:
+                if value.lower() in ("yes", "y", "on", "true"):
+                    return True
+                elif value.lower() in ("no", "n", "off", "false"):
+                    return False
+                else:
+                    raise ValidationError(
+                        "Value '$VALUE' is not a valid bool (type $VALUE_TYPE)"
+                    ).with_traceback(sys.exc_info()[2]) from e
+        else:
+            raise ValidationError(
+                "Value '$VALUE' is not a valid bool (type $VALUE_TYPE)"
+            )
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "BooleanNode":
+        res = BooleanNode()
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class EnumNode(ValueNode):  # lgtm [py/missing-equals] : Intentional.
+    """
+    NOTE: EnumNode is serialized to yaml as a string ("Color.BLUE"), not as a fully qualified yaml type.
+    this means serialization to YAML of a typed config (with EnumNode) will not retain the type of the Enum
+    when loaded.
+    This is intentional, Please open an issue against OmegaConf if you wish to discuss this decision.
+    """
+
+    def __init__(
+        self,
+        enum_type: Type[Enum],
+        value: Optional[Union[Enum, str]] = None,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        is_optional: bool = True,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        if not isinstance(enum_type, type) or not issubclass(enum_type, Enum):
+            raise ValidationError(
+                f"EnumNode can only operate on Enum subclasses ({enum_type})"
+            )
+        self.fields: Dict[str, str] = {}
+        self.enum_type: Type[Enum] = enum_type
+        for name, constant in enum_type.__members__.items():
+            self.fields[name] = constant.value
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                key=key,
+                optional=is_optional,
+                ref_type=enum_type,
+                object_type=enum_type,
+                flags=flags,
+            ),
+        )
+
+    def _strict_validate_type(self, value: Any) -> None:
+        ref_type = self._metadata.ref_type
+        if not isinstance(value, ref_type):
+            type_hint = type_str(self._metadata.type_hint)
+            raise ValidationError(
+                f"Value '$VALUE' of type '$VALUE_TYPE' is incompatible with type hint '{type_hint}'"
+            )
+
+    def _validate_and_convert_impl(self, value: Any) -> Enum:
+        return self.validate_and_convert_to_enum(enum_type=self.enum_type, value=value)
+
+    @staticmethod
+    def validate_and_convert_to_enum(enum_type: Type[Enum], value: Any) -> Enum:
+        if not isinstance(value, (str, int)) and not isinstance(value, enum_type):
+            raise ValidationError(
+                f"Value $VALUE ($VALUE_TYPE) is not a valid input for {enum_type}"
+            )
+
+        if isinstance(value, enum_type):
+            return value
+
+        try:
+            if isinstance(value, (float, bool)):
+                raise ValueError
+
+            if isinstance(value, int):
+                return enum_type(value)
+
+            if isinstance(value, str):
+                prefix = f"{enum_type.__name__}."
+                if value.startswith(prefix):
+                    value = value[len(prefix) :]
+                return enum_type[value]
+
+            assert False
+
+        except (ValueError, KeyError) as e:
+            valid = ", ".join([x for x in enum_type.__members__.keys()])
+            raise ValidationError(
+                f"Invalid value '$VALUE', expected one of [{valid}]"
+            ).with_traceback(sys.exc_info()[2]) from e
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "EnumNode":
+        res = EnumNode(enum_type=self.enum_type)
+        self._deepcopy_impl(res, memo)
+        return res
+
+
+class InterpolationResultNode(ValueNode):
+    """
+    Special node type, used to wrap interpolation results.
+    """
+
+    def __init__(
+        self,
+        value: Any,
+        key: Any = None,
+        parent: Optional[Box] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ):
+        super().__init__(
+            parent=parent,
+            value=value,
+            metadata=Metadata(
+                ref_type=Any, object_type=None, key=key, optional=True, flags=flags
+            ),
+        )
+        # In general we should not try to write into interpolation results.
+        if flags is None or "readonly" not in flags:
+            self._set_flag("readonly", True)
+
+    def _set_value(self, value: Any, flags: Optional[Dict[str, bool]] = None) -> None:
+        if self._get_flag("readonly"):
+            raise ReadonlyConfigError("Cannot set value of read-only config node")
+        self._val = self.validate_and_convert(value)
+
+    def _validate_and_convert_impl(self, value: Any) -> Any:
+        # Interpolation results may be anything.
+        return value
+
+    def __deepcopy__(self, memo: Dict[int, Any]) -> "InterpolationResultNode":
+        # Currently there should be no need to deep-copy such nodes.
+        raise NotImplementedError
+
+    def _is_interpolation(self) -> bool:
+        # The result of an interpolation cannot be itself an interpolation.
+        return False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/omegaconf.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/omegaconf.py
new file mode 100644
index 0000000000000000000000000000000000000000..efde14a4e266ede954c479c8809cdb8ab80cb2d5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/omegaconf.py
@@ -0,0 +1,1157 @@
+"""OmegaConf module"""
+import copy
+import inspect
+import io
+import os
+import pathlib
+import sys
+import warnings
+from collections import defaultdict
+from contextlib import contextmanager
+from enum import Enum
+from textwrap import dedent
+from typing import (
+    IO,
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    Union,
+    overload,
+)
+
+import yaml
+
+from . import DictConfig, DictKeyType, ListConfig
+from ._utils import (
+    _DEFAULT_MARKER_,
+    _ensure_container,
+    _get_value,
+    format_and_raise,
+    get_dict_key_value_types,
+    get_list_element_type,
+    get_omega_conf_dumper,
+    get_type_of,
+    is_attr_class,
+    is_dataclass,
+    is_dict_annotation,
+    is_int,
+    is_list_annotation,
+    is_primitive_container,
+    is_primitive_dict,
+    is_primitive_list,
+    is_structured_config,
+    is_tuple_annotation,
+    is_union_annotation,
+    nullcontext,
+    split_key,
+    type_str,
+)
+from .base import Box, Container, Node, SCMode, UnionNode
+from .basecontainer import BaseContainer
+from .errors import (
+    MissingMandatoryValue,
+    OmegaConfBaseException,
+    UnsupportedInterpolationType,
+    ValidationError,
+)
+from .nodes import (
+    AnyNode,
+    BooleanNode,
+    BytesNode,
+    EnumNode,
+    FloatNode,
+    IntegerNode,
+    PathNode,
+    StringNode,
+    ValueNode,
+)
+
+MISSING: Any = "???"
+
+Resolver = Callable[..., Any]
+
+
+def II(interpolation: str) -> Any:
+    """
+    Equivalent to ``${interpolation}``
+
+    :param interpolation:
+    :return: input ``${node}`` with type Any
+    """
+    return "${" + interpolation + "}"
+
+
+def SI(interpolation: str) -> Any:
+    """
+    Use this for String interpolation, for example ``"http://${host}:${port}"``
+
+    :param interpolation: interpolation string
+    :return: input interpolation with type ``Any``
+    """
+    return interpolation
+
+
+def register_default_resolvers() -> None:
+    from omegaconf.resolvers import oc
+
+    OmegaConf.register_new_resolver("oc.create", oc.create)
+    OmegaConf.register_new_resolver("oc.decode", oc.decode)
+    OmegaConf.register_new_resolver("oc.deprecated", oc.deprecated)
+    OmegaConf.register_new_resolver("oc.env", oc.env)
+    OmegaConf.register_new_resolver("oc.select", oc.select)
+    OmegaConf.register_new_resolver("oc.dict.keys", oc.dict.keys)
+    OmegaConf.register_new_resolver("oc.dict.values", oc.dict.values)
+
+
+class OmegaConf:
+    """OmegaConf primary class"""
+
+    def __init__(self) -> None:
+        raise NotImplementedError("Use one of the static construction functions")
+
+    @staticmethod
+    def structured(
+        obj: Any,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> Any:
+        return OmegaConf.create(obj, parent, flags)
+
+    @staticmethod
+    @overload
+    def create(
+        obj: str,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> Union[DictConfig, ListConfig]:
+        ...
+
+    @staticmethod
+    @overload
+    def create(
+        obj: Union[List[Any], Tuple[Any, ...]],
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> ListConfig:
+        ...
+
+    @staticmethod
+    @overload
+    def create(
+        obj: DictConfig,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> DictConfig:
+        ...
+
+    @staticmethod
+    @overload
+    def create(
+        obj: ListConfig,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> ListConfig:
+        ...
+
+    @staticmethod
+    @overload
+    def create(
+        obj: Optional[Dict[Any, Any]] = None,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> DictConfig:
+        ...
+
+    @staticmethod
+    def create(  # noqa F811
+        obj: Any = _DEFAULT_MARKER_,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> Union[DictConfig, ListConfig]:
+        return OmegaConf._create_impl(
+            obj=obj,
+            parent=parent,
+            flags=flags,
+        )
+
+    @staticmethod
+    def load(file_: Union[str, pathlib.Path, IO[Any]]) -> Union[DictConfig, ListConfig]:
+        from ._utils import get_yaml_loader
+
+        if isinstance(file_, (str, pathlib.Path)):
+            with io.open(os.path.abspath(file_), "r", encoding="utf-8") as f:
+                obj = yaml.load(f, Loader=get_yaml_loader())
+        elif getattr(file_, "read", None):
+            obj = yaml.load(file_, Loader=get_yaml_loader())
+        else:
+            raise TypeError("Unexpected file type")
+
+        if obj is not None and not isinstance(obj, (list, dict, str)):
+            raise IOError(  # pragma: no cover
+                f"Invalid loaded object type: {type(obj).__name__}"
+            )
+
+        ret: Union[DictConfig, ListConfig]
+        if obj is None:
+            ret = OmegaConf.create()
+        else:
+            ret = OmegaConf.create(obj)
+        return ret
+
+    @staticmethod
+    def save(
+        config: Any, f: Union[str, pathlib.Path, IO[Any]], resolve: bool = False
+    ) -> None:
+        """
+        Save as configuration object to a file
+
+        :param config: omegaconf.Config object (DictConfig or ListConfig).
+        :param f: filename or file object
+        :param resolve: True to save a resolved config (defaults to False)
+        """
+        if is_dataclass(config) or is_attr_class(config):
+            config = OmegaConf.create(config)
+        data = OmegaConf.to_yaml(config, resolve=resolve)
+        if isinstance(f, (str, pathlib.Path)):
+            with io.open(os.path.abspath(f), "w", encoding="utf-8") as file:
+                file.write(data)
+        elif hasattr(f, "write"):
+            f.write(data)
+            f.flush()
+        else:
+            raise TypeError("Unexpected file type")
+
+    @staticmethod
+    def from_cli(args_list: Optional[List[str]] = None) -> DictConfig:
+        if args_list is None:
+            # Skip program name
+            args_list = sys.argv[1:]
+        return OmegaConf.from_dotlist(args_list)
+
+    @staticmethod
+    def from_dotlist(dotlist: List[str]) -> DictConfig:
+        """
+        Creates config from the content sys.argv or from the specified args list of not None
+
+        :param dotlist: A list of dotlist-style strings, e.g. ``["foo.bar=1", "baz=qux"]``.
+        :return: A ``DictConfig`` object created from the dotlist.
+        """
+        conf = OmegaConf.create()
+        conf.merge_with_dotlist(dotlist)
+        return conf
+
+    @staticmethod
+    def merge(
+        *configs: Union[
+            DictConfig,
+            ListConfig,
+            Dict[DictKeyType, Any],
+            List[Any],
+            Tuple[Any, ...],
+            Any,
+        ],
+    ) -> Union[ListConfig, DictConfig]:
+        """
+        Merge a list of previously created configs into a single one
+
+        :param configs: Input configs
+        :return: the merged config object.
+        """
+        assert len(configs) > 0
+        target = copy.deepcopy(configs[0])
+        target = _ensure_container(target)
+        assert isinstance(target, (DictConfig, ListConfig))
+
+        with flag_override(target, "readonly", False):
+            target.merge_with(*configs[1:])
+            turned_readonly = target._get_flag("readonly") is True
+
+        if turned_readonly:
+            OmegaConf.set_readonly(target, True)
+
+        return target
+
+    @staticmethod
+    def unsafe_merge(
+        *configs: Union[
+            DictConfig,
+            ListConfig,
+            Dict[DictKeyType, Any],
+            List[Any],
+            Tuple[Any, ...],
+            Any,
+        ],
+    ) -> Union[ListConfig, DictConfig]:
+        """
+        Merge a list of previously created configs into a single one
+        This is much faster than OmegaConf.merge() as the input configs are not copied.
+        However, the input configs must not be used after this operation as will become inconsistent.
+
+        :param configs: Input configs
+        :return: the merged config object.
+        """
+        assert len(configs) > 0
+        target = configs[0]
+        target = _ensure_container(target)
+        assert isinstance(target, (DictConfig, ListConfig))
+
+        with flag_override(
+            target, ["readonly", "no_deepcopy_set_nodes"], [False, True]
+        ):
+            target.merge_with(*configs[1:])
+            turned_readonly = target._get_flag("readonly") is True
+
+        if turned_readonly:
+            OmegaConf.set_readonly(target, True)
+
+        return target
+
+    @staticmethod
+    def register_resolver(name: str, resolver: Resolver) -> None:
+        warnings.warn(
+            dedent(
+                """\
+            register_resolver() is deprecated.
+            See https://github.com/omry/omegaconf/issues/426 for migration instructions.
+            """
+            ),
+            stacklevel=2,
+        )
+        return OmegaConf.legacy_register_resolver(name, resolver)
+
+    # This function will eventually be deprecated and removed.
+    @staticmethod
+    def legacy_register_resolver(name: str, resolver: Resolver) -> None:
+        assert callable(resolver), "resolver must be callable"
+        # noinspection PyProtectedMember
+        assert (
+            name not in BaseContainer._resolvers
+        ), f"resolver '{name}' is already registered"
+
+        def resolver_wrapper(
+            config: BaseContainer,
+            parent: BaseContainer,
+            node: Node,
+            args: Tuple[Any, ...],
+            args_str: Tuple[str, ...],
+        ) -> Any:
+            cache = OmegaConf.get_cache(config)[name]
+            # "Un-escape " spaces and commas.
+            args_unesc = [x.replace(r"\ ", " ").replace(r"\,", ",") for x in args_str]
+
+            # Nested interpolations behave in a potentially surprising way with
+            # legacy resolvers (they remain as strings, e.g., "${foo}"). If any
+            # input looks like an interpolation we thus raise an exception.
+            try:
+                bad_arg = next(i for i in args_unesc if "${" in i)
+            except StopIteration:
+                pass
+            else:
+                raise ValueError(
+                    f"Resolver '{name}' was called with argument '{bad_arg}' that appears "
+                    f"to be an interpolation. Nested interpolations are not supported for "
+                    f"resolvers registered with `[legacy_]register_resolver()`, please use "
+                    f"`register_new_resolver()` instead (see "
+                    f"https://github.com/omry/omegaconf/issues/426 for migration instructions)."
+                )
+            key = args_str
+            val = cache[key] if key in cache else resolver(*args_unesc)
+            cache[key] = val
+            return val
+
+        # noinspection PyProtectedMember
+        BaseContainer._resolvers[name] = resolver_wrapper
+
+    @staticmethod
+    def register_new_resolver(
+        name: str,
+        resolver: Resolver,
+        *,
+        replace: bool = False,
+        use_cache: bool = False,
+    ) -> None:
+        """
+        Register a resolver.
+
+        :param name: Name of the resolver.
+        :param resolver: Callable whose arguments are provided in the interpolation,
+            e.g., with ${foo:x,0,${y.z}} these arguments are respectively "x" (str),
+            0 (int) and the value of ``y.z``.
+        :param replace: If set to ``False`` (default), then a ``ValueError`` is raised if
+            an existing resolver has already been registered with the same name.
+            If set to ``True``, then the new resolver replaces the previous one.
+            NOTE: The cache on existing config objects is not affected, use
+            ``OmegaConf.clear_cache(cfg)`` to clear it.
+        :param use_cache: Whether the resolver's outputs should be cached. The cache is
+            based only on the string literals representing the resolver arguments, e.g.,
+            ${foo:${bar}} will always return the same value regardless of the value of
+            ``bar`` if the cache is enabled for ``foo``.
+        """
+        if not callable(resolver):
+            raise TypeError("resolver must be callable")
+        if not name:
+            raise ValueError("cannot use an empty resolver name")
+
+        if not replace and OmegaConf.has_resolver(name):
+            raise ValueError(f"resolver '{name}' is already registered")
+
+        try:
+            sig: Optional[inspect.Signature] = inspect.signature(resolver)
+        except ValueError:
+            sig = None
+
+        def _should_pass(special: str) -> bool:
+            ret = sig is not None and special in sig.parameters
+            if ret and use_cache:
+                raise ValueError(
+                    f"use_cache=True is incompatible with functions that receive the {special}"
+                )
+            return ret
+
+        pass_parent = _should_pass("_parent_")
+        pass_node = _should_pass("_node_")
+        pass_root = _should_pass("_root_")
+
+        def resolver_wrapper(
+            config: BaseContainer,
+            parent: Container,
+            node: Node,
+            args: Tuple[Any, ...],
+            args_str: Tuple[str, ...],
+        ) -> Any:
+            if use_cache:
+                cache = OmegaConf.get_cache(config)[name]
+                try:
+                    return cache[args_str]
+                except KeyError:
+                    pass
+
+            # Call resolver.
+            kwargs: Dict[str, Node] = {}
+            if pass_parent:
+                kwargs["_parent_"] = parent
+            if pass_node:
+                kwargs["_node_"] = node
+            if pass_root:
+                kwargs["_root_"] = config
+
+            ret = resolver(*args, **kwargs)
+
+            if use_cache:
+                cache[args_str] = ret
+            return ret
+
+        # noinspection PyProtectedMember
+        BaseContainer._resolvers[name] = resolver_wrapper
+
+    @classmethod
+    def has_resolver(cls, name: str) -> bool:
+        return cls._get_resolver(name) is not None
+
+    # noinspection PyProtectedMember
+    @staticmethod
+    def clear_resolvers() -> None:
+        """
+        Clear(remove) all OmegaConf resolvers, then re-register OmegaConf's default resolvers.
+        """
+        BaseContainer._resolvers = {}
+        register_default_resolvers()
+
+    @classmethod
+    def clear_resolver(cls, name: str) -> bool:
+        """
+        Clear(remove) any resolver only if it exists.
+
+        Returns a bool: True if resolver is removed and False if not removed.
+
+        .. warning:
+            This method can remove deafult resolvers as well.
+
+        :param name: Name of the resolver.
+        :return: A bool (``True`` if resolver is removed, ``False`` if not found before removing).
+        """
+        if cls.has_resolver(name):
+            BaseContainer._resolvers.pop(name)
+            return True
+        else:
+            # return False if resolver does not exist
+            return False
+
+    @staticmethod
+    def get_cache(conf: BaseContainer) -> Dict[str, Any]:
+        return conf._metadata.resolver_cache
+
+    @staticmethod
+    def set_cache(conf: BaseContainer, cache: Dict[str, Any]) -> None:
+        conf._metadata.resolver_cache = copy.deepcopy(cache)
+
+    @staticmethod
+    def clear_cache(conf: BaseContainer) -> None:
+        OmegaConf.set_cache(conf, defaultdict(dict, {}))
+
+    @staticmethod
+    def copy_cache(from_config: BaseContainer, to_config: BaseContainer) -> None:
+        OmegaConf.set_cache(to_config, OmegaConf.get_cache(from_config))
+
+    @staticmethod
+    def set_readonly(conf: Node, value: Optional[bool]) -> None:
+        # noinspection PyProtectedMember
+        conf._set_flag("readonly", value)
+
+    @staticmethod
+    def is_readonly(conf: Node) -> Optional[bool]:
+        # noinspection PyProtectedMember
+        return conf._get_flag("readonly")
+
+    @staticmethod
+    def set_struct(conf: Container, value: Optional[bool]) -> None:
+        # noinspection PyProtectedMember
+        conf._set_flag("struct", value)
+
+    @staticmethod
+    def is_struct(conf: Container) -> Optional[bool]:
+        # noinspection PyProtectedMember
+        return conf._get_flag("struct")
+
+    @staticmethod
+    def masked_copy(conf: DictConfig, keys: Union[str, List[str]]) -> DictConfig:
+        """
+        Create a masked copy of of this config that contains a subset of the keys
+
+        :param conf: DictConfig object
+        :param keys: keys to preserve in the copy
+        :return: The masked ``DictConfig`` object.
+        """
+        from .dictconfig import DictConfig
+
+        if not isinstance(conf, DictConfig):
+            raise ValueError("masked_copy is only supported for DictConfig")
+
+        if isinstance(keys, str):
+            keys = [keys]
+        content = {key: value for key, value in conf.items_ex(resolve=False, keys=keys)}
+        return DictConfig(content=content)
+
+    @staticmethod
+    def to_container(
+        cfg: Any,
+        *,
+        resolve: bool = False,
+        throw_on_missing: bool = False,
+        enum_to_str: bool = False,
+        structured_config_mode: SCMode = SCMode.DICT,
+    ) -> Union[Dict[DictKeyType, Any], List[Any], None, str, Any]:
+        """
+        Resursively converts an OmegaConf config to a primitive container (dict or list).
+
+        :param cfg: the config to convert
+        :param resolve: True to resolve all values
+        :param throw_on_missing: When True, raise MissingMandatoryValue if any missing values are present.
+            When False (the default), replace missing values with the string "???" in the output container.
+        :param enum_to_str: True to convert Enum keys and values to strings
+        :param structured_config_mode: Specify how Structured Configs (DictConfigs backed by a dataclass) are handled.
+            - By default (``structured_config_mode=SCMode.DICT``) structured configs are converted to plain dicts.
+            - If ``structured_config_mode=SCMode.DICT_CONFIG``, structured config nodes will remain as DictConfig.
+            - If ``structured_config_mode=SCMode.INSTANTIATE``, this function will instantiate structured configs
+              (DictConfigs backed by a dataclass), by creating an instance of the underlying dataclass.
+
+          See also OmegaConf.to_object.
+        :return: A dict or a list representing this config as a primitive container.
+        """
+        if not OmegaConf.is_config(cfg):
+            raise ValueError(
+                f"Input cfg is not an OmegaConf config object ({type_str(type(cfg))})"
+            )
+
+        return BaseContainer._to_content(
+            cfg,
+            resolve=resolve,
+            throw_on_missing=throw_on_missing,
+            enum_to_str=enum_to_str,
+            structured_config_mode=structured_config_mode,
+        )
+
+    @staticmethod
+    def to_object(cfg: Any) -> Union[Dict[DictKeyType, Any], List[Any], None, str, Any]:
+        """
+        Resursively converts an OmegaConf config to a primitive container (dict or list).
+        Any DictConfig objects backed by dataclasses or attrs classes are instantiated
+        as instances of those backing classes.
+
+        This is an alias for OmegaConf.to_container(..., resolve=True, throw_on_missing=True,
+                                                    structured_config_mode=SCMode.INSTANTIATE)
+
+        :param cfg: the config to convert
+        :return: A dict or a list or dataclass representing this config.
+        """
+        return OmegaConf.to_container(
+            cfg=cfg,
+            resolve=True,
+            throw_on_missing=True,
+            enum_to_str=False,
+            structured_config_mode=SCMode.INSTANTIATE,
+        )
+
+    @staticmethod
+    def is_missing(cfg: Any, key: DictKeyType) -> bool:
+        assert isinstance(cfg, Container)
+        try:
+            node = cfg._get_child(key)
+            if node is None:
+                return False
+            assert isinstance(node, Node)
+            return node._is_missing()
+        except (UnsupportedInterpolationType, KeyError, AttributeError):
+            return False
+
+    @staticmethod
+    def is_interpolation(node: Any, key: Optional[Union[int, str]] = None) -> bool:
+        if key is not None:
+            assert isinstance(node, Container)
+            target = node._get_child(key)
+        else:
+            target = node
+        if target is not None:
+            assert isinstance(target, Node)
+            return target._is_interpolation()
+        return False
+
+    @staticmethod
+    def is_list(obj: Any) -> bool:
+        from . import ListConfig
+
+        return isinstance(obj, ListConfig)
+
+    @staticmethod
+    def is_dict(obj: Any) -> bool:
+        from . import DictConfig
+
+        return isinstance(obj, DictConfig)
+
+    @staticmethod
+    def is_config(obj: Any) -> bool:
+        from . import Container
+
+        return isinstance(obj, Container)
+
+    @staticmethod
+    def get_type(obj: Any, key: Optional[str] = None) -> Optional[Type[Any]]:
+        if key is not None:
+            c = obj._get_child(key)
+        else:
+            c = obj
+        return OmegaConf._get_obj_type(c)
+
+    @staticmethod
+    def select(
+        cfg: Container,
+        key: str,
+        *,
+        default: Any = _DEFAULT_MARKER_,
+        throw_on_resolution_failure: bool = True,
+        throw_on_missing: bool = False,
+    ) -> Any:
+        """
+        :param cfg: Config node to select from
+        :param key: Key to select
+        :param default: Default value to return if key is not found
+        :param throw_on_resolution_failure: Raise an exception if an interpolation
+               resolution error occurs, otherwise return None
+        :param throw_on_missing: Raise an exception if an attempt to select a missing key (with the value '???')
+               is made, otherwise return None
+        :return: selected value or None if not found.
+        """
+        from ._impl import select_value
+
+        try:
+            return select_value(
+                cfg=cfg,
+                key=key,
+                default=default,
+                throw_on_resolution_failure=throw_on_resolution_failure,
+                throw_on_missing=throw_on_missing,
+            )
+        except Exception as e:
+            format_and_raise(node=cfg, key=key, value=None, cause=e, msg=str(e))
+
+    @staticmethod
+    def update(
+        cfg: Container,
+        key: str,
+        value: Any = None,
+        *,
+        merge: bool = True,
+        force_add: bool = False,
+    ) -> None:
+        """
+        Updates a dot separated key sequence to a value
+
+        :param cfg: input config to update
+        :param key: key to update (can be a dot separated path)
+        :param value: value to set, if value if a list or a dict it will be merged or set
+            depending on merge_config_values
+        :param merge: If value is a dict or a list, True (default) to merge
+                      into the destination, False to replace the destination.
+        :param force_add: insert the entire path regardless of Struct flag or Structured Config nodes.
+        """
+
+        split = split_key(key)
+        root = cfg
+        for i in range(len(split) - 1):
+            k = split[i]
+            # if next_root is a primitive (string, int etc) replace it with an empty map
+            next_root, key_ = _select_one(root, k, throw_on_missing=False)
+            if not isinstance(next_root, Container):
+                if force_add:
+                    with flag_override(root, "struct", False):
+                        root[key_] = {}
+                else:
+                    root[key_] = {}
+            root = root[key_]
+
+        last = split[-1]
+
+        assert isinstance(
+            root, Container
+        ), f"Unexpected type for root: {type(root).__name__}"
+
+        last_key: Union[str, int] = last
+        if isinstance(root, ListConfig):
+            last_key = int(last)
+
+        ctx = flag_override(root, "struct", False) if force_add else nullcontext()
+        with ctx:
+            if merge and (OmegaConf.is_config(value) or is_primitive_container(value)):
+                assert isinstance(root, BaseContainer)
+                node = root._get_child(last_key)
+                if OmegaConf.is_config(node):
+                    assert isinstance(node, BaseContainer)
+                    node.merge_with(value)
+                    return
+
+            if OmegaConf.is_dict(root):
+                assert isinstance(last_key, str)
+                root.__setattr__(last_key, value)
+            elif OmegaConf.is_list(root):
+                assert isinstance(last_key, int)
+                root.__setitem__(last_key, value)
+            else:
+                assert False
+
+    @staticmethod
+    def to_yaml(cfg: Any, *, resolve: bool = False, sort_keys: bool = False) -> str:
+        """
+        returns a yaml dump of this config object.
+
+        :param cfg: Config object, Structured Config type or instance
+        :param resolve: if True, will return a string with the interpolations resolved, otherwise
+            interpolations are preserved
+        :param sort_keys: If True, will print dict keys in sorted order. default False.
+        :return: A string containing the yaml representation.
+        """
+        cfg = _ensure_container(cfg)
+        container = OmegaConf.to_container(cfg, resolve=resolve, enum_to_str=True)
+        return yaml.dump(  # type: ignore
+            container,
+            default_flow_style=False,
+            allow_unicode=True,
+            sort_keys=sort_keys,
+            Dumper=get_omega_conf_dumper(),
+        )
+
+    @staticmethod
+    def resolve(cfg: Container) -> None:
+        """
+        Resolves all interpolations in the given config object in-place.
+
+        :param cfg: An OmegaConf container (DictConfig, ListConfig)
+                    Raises a ValueError if the input object is not an OmegaConf container.
+        """
+        import omegaconf._impl
+
+        if not OmegaConf.is_config(cfg):
+            # Since this function is mutating the input object in-place, it doesn't make sense to
+            # auto-convert the input object to an OmegaConf container
+            raise ValueError(
+                f"Invalid config type ({type(cfg).__name__}), expected an OmegaConf Container"
+            )
+        omegaconf._impl._resolve(cfg)
+
+    @staticmethod
+    def missing_keys(cfg: Any) -> Set[str]:
+        """
+        Returns a set of missing keys in a dotlist style.
+
+        :param cfg: An ``OmegaConf.Container``,
+                    or a convertible object via ``OmegaConf.create`` (dict, list, ...).
+        :return: set of strings of the missing keys.
+        :raises ValueError: On input not representing a config.
+        """
+        cfg = _ensure_container(cfg)
+        missings: Set[str] = set()
+
+        def gather(_cfg: Container) -> None:
+            itr: Iterable[Any]
+            if isinstance(_cfg, ListConfig):
+                itr = range(len(_cfg))
+            else:
+                itr = _cfg
+
+            for key in itr:
+                if OmegaConf.is_missing(_cfg, key):
+                    missings.add(_cfg._get_full_key(key))
+                elif OmegaConf.is_config(_cfg[key]):
+                    gather(_cfg[key])
+
+        gather(cfg)
+        return missings
+
+    # === private === #
+
+    @staticmethod
+    def _create_impl(  # noqa F811
+        obj: Any = _DEFAULT_MARKER_,
+        parent: Optional[BaseContainer] = None,
+        flags: Optional[Dict[str, bool]] = None,
+    ) -> Union[DictConfig, ListConfig]:
+        try:
+            from ._utils import get_yaml_loader
+            from .dictconfig import DictConfig
+            from .listconfig import ListConfig
+
+            if obj is _DEFAULT_MARKER_:
+                obj = {}
+            if isinstance(obj, str):
+                obj = yaml.load(obj, Loader=get_yaml_loader())
+                if obj is None:
+                    return OmegaConf.create({}, parent=parent, flags=flags)
+                elif isinstance(obj, str):
+                    return OmegaConf.create({obj: None}, parent=parent, flags=flags)
+                else:
+                    assert isinstance(obj, (list, dict))
+                    return OmegaConf.create(obj, parent=parent, flags=flags)
+
+            else:
+                if (
+                    is_primitive_dict(obj)
+                    or OmegaConf.is_dict(obj)
+                    or is_structured_config(obj)
+                    or obj is None
+                ):
+                    if isinstance(obj, DictConfig):
+                        return DictConfig(
+                            content=obj,
+                            parent=parent,
+                            ref_type=obj._metadata.ref_type,
+                            is_optional=obj._metadata.optional,
+                            key_type=obj._metadata.key_type,
+                            element_type=obj._metadata.element_type,
+                            flags=flags,
+                        )
+                    else:
+                        obj_type = OmegaConf.get_type(obj)
+                        key_type, element_type = get_dict_key_value_types(obj_type)
+                        return DictConfig(
+                            content=obj,
+                            parent=parent,
+                            key_type=key_type,
+                            element_type=element_type,
+                            flags=flags,
+                        )
+                elif is_primitive_list(obj) or OmegaConf.is_list(obj):
+                    if isinstance(obj, ListConfig):
+                        return ListConfig(
+                            content=obj,
+                            parent=parent,
+                            element_type=obj._metadata.element_type,
+                            ref_type=obj._metadata.ref_type,
+                            is_optional=obj._metadata.optional,
+                            flags=flags,
+                        )
+                    else:
+                        obj_type = OmegaConf.get_type(obj)
+                        element_type = get_list_element_type(obj_type)
+                        return ListConfig(
+                            content=obj,
+                            parent=parent,
+                            element_type=element_type,
+                            ref_type=Any,
+                            is_optional=True,
+                            flags=flags,
+                        )
+                else:
+                    if isinstance(obj, type):
+                        raise ValidationError(
+                            f"Input class '{obj.__name__}' is not a structured config. "
+                            "did you forget to decorate it as a dataclass?"
+                        )
+                    else:
+                        raise ValidationError(
+                            f"Object of unsupported type: '{type(obj).__name__}'"
+                        )
+        except OmegaConfBaseException as e:
+            format_and_raise(node=None, key=None, value=None, msg=str(e), cause=e)
+            assert False
+
+    @staticmethod
+    def _get_obj_type(c: Any) -> Optional[Type[Any]]:
+        if is_structured_config(c):
+            return get_type_of(c)
+        elif c is None:
+            return None
+        elif isinstance(c, DictConfig):
+            if c._is_none():
+                return None
+            elif c._is_missing():
+                return None
+            else:
+                if is_structured_config(c._metadata.object_type):
+                    return c._metadata.object_type
+                else:
+                    return dict
+        elif isinstance(c, ListConfig):
+            return list
+        elif isinstance(c, ValueNode):
+            return type(c._value())
+        elif isinstance(c, UnionNode):
+            return type(_get_value(c))
+        elif isinstance(c, dict):
+            return dict
+        elif isinstance(c, (list, tuple)):
+            return list
+        else:
+            return get_type_of(c)
+
+    @staticmethod
+    def _get_resolver(
+        name: str,
+    ) -> Optional[
+        Callable[
+            [Container, Container, Node, Tuple[Any, ...], Tuple[str, ...]],
+            Any,
+        ]
+    ]:
+        # noinspection PyProtectedMember
+        return (
+            BaseContainer._resolvers[name] if name in BaseContainer._resolvers else None
+        )
+
+
+# register all default resolvers
+register_default_resolvers()
+
+
+@contextmanager
+def flag_override(
+    config: Node,
+    names: Union[List[str], str],
+    values: Union[List[Optional[bool]], Optional[bool]],
+) -> Generator[Node, None, None]:
+    if isinstance(names, str):
+        names = [names]
+    if values is None or isinstance(values, bool):
+        values = [values]
+
+    prev_states = [config._get_node_flag(name) for name in names]
+
+    try:
+        config._set_flag(names, values)
+        yield config
+    finally:
+        config._set_flag(names, prev_states)
+
+
+@contextmanager
+def read_write(config: Node) -> Generator[Node, None, None]:
+    prev_state = config._get_node_flag("readonly")
+    try:
+        OmegaConf.set_readonly(config, False)
+        yield config
+    finally:
+        OmegaConf.set_readonly(config, prev_state)
+
+
+@contextmanager
+def open_dict(config: Container) -> Generator[Container, None, None]:
+    prev_state = config._get_node_flag("struct")
+    try:
+        OmegaConf.set_struct(config, False)
+        yield config
+    finally:
+        OmegaConf.set_struct(config, prev_state)
+
+
+# === private === #
+
+
+def _node_wrap(
+    parent: Optional[Box],
+    is_optional: bool,
+    value: Any,
+    key: Any,
+    ref_type: Any = Any,
+) -> Node:
+    node: Node
+    if is_dict_annotation(ref_type) or (is_primitive_dict(value) and ref_type is Any):
+        key_type, element_type = get_dict_key_value_types(ref_type)
+        node = DictConfig(
+            content=value,
+            key=key,
+            parent=parent,
+            ref_type=ref_type,
+            is_optional=is_optional,
+            key_type=key_type,
+            element_type=element_type,
+        )
+    elif (is_list_annotation(ref_type) or is_tuple_annotation(ref_type)) or (
+        type(value) in (list, tuple) and ref_type is Any
+    ):
+        element_type = get_list_element_type(ref_type)
+        node = ListConfig(
+            content=value,
+            key=key,
+            parent=parent,
+            is_optional=is_optional,
+            element_type=element_type,
+            ref_type=ref_type,
+        )
+    elif is_structured_config(ref_type) or is_structured_config(value):
+        key_type, element_type = get_dict_key_value_types(value)
+        node = DictConfig(
+            ref_type=ref_type,
+            is_optional=is_optional,
+            content=value,
+            key=key,
+            parent=parent,
+            key_type=key_type,
+            element_type=element_type,
+        )
+    elif is_union_annotation(ref_type):
+        node = UnionNode(
+            content=value,
+            ref_type=ref_type,
+            is_optional=is_optional,
+            key=key,
+            parent=parent,
+        )
+    elif ref_type == Any or ref_type is None:
+        node = AnyNode(value=value, key=key, parent=parent)
+    elif isinstance(ref_type, type) and issubclass(ref_type, Enum):
+        node = EnumNode(
+            enum_type=ref_type,
+            value=value,
+            key=key,
+            parent=parent,
+            is_optional=is_optional,
+        )
+    elif ref_type == int:
+        node = IntegerNode(value=value, key=key, parent=parent, is_optional=is_optional)
+    elif ref_type == float:
+        node = FloatNode(value=value, key=key, parent=parent, is_optional=is_optional)
+    elif ref_type == bool:
+        node = BooleanNode(value=value, key=key, parent=parent, is_optional=is_optional)
+    elif ref_type == str:
+        node = StringNode(value=value, key=key, parent=parent, is_optional=is_optional)
+    elif ref_type == bytes:
+        node = BytesNode(value=value, key=key, parent=parent, is_optional=is_optional)
+    elif ref_type == pathlib.Path:
+        node = PathNode(value=value, key=key, parent=parent, is_optional=is_optional)
+    else:
+        if parent is not None and parent._get_flag("allow_objects") is True:
+            if type(value) in (list, tuple):
+                node = ListConfig(
+                    content=value,
+                    key=key,
+                    parent=parent,
+                    ref_type=ref_type,
+                    is_optional=is_optional,
+                )
+            elif is_primitive_dict(value):
+                node = DictConfig(
+                    content=value,
+                    key=key,
+                    parent=parent,
+                    ref_type=ref_type,
+                    is_optional=is_optional,
+                )
+            else:
+                node = AnyNode(value=value, key=key, parent=parent)
+        else:
+            raise ValidationError(f"Unexpected type annotation: {type_str(ref_type)}")
+    return node
+
+
+def _maybe_wrap(
+    ref_type: Any,
+    key: Any,
+    value: Any,
+    is_optional: bool,
+    parent: Optional[BaseContainer],
+) -> Node:
+    # if already a node, update key and parent and return as is.
+    # NOTE: that this mutate the input node!
+    if isinstance(value, Node):
+        value._set_key(key)
+        value._set_parent(parent)
+        return value
+    else:
+        return _node_wrap(
+            ref_type=ref_type,
+            parent=parent,
+            is_optional=is_optional,
+            value=value,
+            key=key,
+        )
+
+
+def _select_one(
+    c: Container, key: str, throw_on_missing: bool, throw_on_type_error: bool = True
+) -> Tuple[Optional[Node], Union[str, int]]:
+    from .dictconfig import DictConfig
+    from .listconfig import ListConfig
+
+    ret_key: Union[str, int] = key
+    assert isinstance(c, Container), f"Unexpected type: {c}"
+    if c._is_none():
+        return None, ret_key
+
+    if isinstance(c, DictConfig):
+        assert isinstance(ret_key, str)
+        val = c._get_child(ret_key, validate_access=False)
+    elif isinstance(c, ListConfig):
+        assert isinstance(ret_key, str)
+        if not is_int(ret_key):
+            if throw_on_type_error:
+                raise TypeError(
+                    f"Index '{ret_key}' ({type(ret_key).__name__}) is not an int"
+                )
+            else:
+                val = None
+        else:
+            ret_key = int(ret_key)
+            if ret_key < 0 or ret_key + 1 > len(c):
+                val = None
+            else:
+                val = c._get_child(ret_key)
+    else:
+        assert False
+
+    if val is not None:
+        assert isinstance(val, Node)
+        if val._is_missing():
+            if throw_on_missing:
+                raise MissingMandatoryValue(
+                    f"Missing mandatory value: {c._get_full_key(ret_key)}"
+                )
+            else:
+                return val, ret_key
+
+    assert val is None or isinstance(val, Node)
+    return val, ret_key
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/py.typed b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b0bb71168722281af5ddecb46f79e36f63c71e4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/__init__.py
@@ -0,0 +1,5 @@
+from omegaconf.resolvers import oc
+
+__all__ = [
+    "oc",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/oc/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/oc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c177ab4f1b35dfa5502a9b6359439bfca180ba1d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/oc/__init__.py
@@ -0,0 +1,113 @@
+import os
+import string
+import warnings
+from typing import Any, Optional
+
+from omegaconf import Container, Node
+from omegaconf._utils import _DEFAULT_MARKER_, _get_value
+from omegaconf.basecontainer import BaseContainer
+from omegaconf.errors import ConfigKeyError
+from omegaconf.grammar_parser import parse
+from omegaconf.resolvers.oc import dict
+
+
+def create(obj: Any, _parent_: Container) -> Any:
+    """Create a config object from `obj`, similar to `OmegaConf.create`"""
+    from omegaconf import OmegaConf
+
+    assert isinstance(_parent_, BaseContainer)
+    return OmegaConf.create(obj, parent=_parent_)
+
+
+def env(key: str, default: Any = _DEFAULT_MARKER_) -> Optional[str]:
+    """
+    :param key: Environment variable key
+    :param default: Optional default value to use in case the key environment variable is not set.
+                    If default is not a string, it is converted with str(default).
+                    None default is returned as is.
+    :return: The environment variable 'key'. If the environment variable is not set and a default is
+            provided, the default is used. If used, the default is converted to a string with str(default).
+            If the default is None, None is returned (without a string conversion).
+    """
+    try:
+        return os.environ[key]
+    except KeyError:
+        if default is not _DEFAULT_MARKER_:
+            return str(default) if default is not None else None
+        else:
+            raise KeyError(f"Environment variable '{key}' not found")
+
+
+def decode(expr: Optional[str], _parent_: Container, _node_: Node) -> Any:
+    """
+    Parse and evaluate `expr` according to the `singleElement` rule of the grammar.
+
+    If `expr` is `None`, then return `None`.
+    """
+    if expr is None:
+        return None
+
+    if not isinstance(expr, str):
+        raise TypeError(
+            f"`oc.decode` can only take strings or None as input, "
+            f"but `{expr}` is of type {type(expr).__name__}"
+        )
+
+    parse_tree = parse(expr, parser_rule="singleElement", lexer_mode="VALUE_MODE")
+    val = _parent_.resolve_parse_tree(parse_tree, node=_node_)
+    return _get_value(val)
+
+
+def deprecated(
+    key: str,
+    message: str = "'$OLD_KEY' is deprecated. Change your code and config to use '$NEW_KEY'",
+    *,
+    _parent_: Container,
+    _node_: Node,
+) -> Any:
+    from omegaconf._impl import select_node
+
+    if not isinstance(key, str):
+        raise TypeError(
+            f"oc.deprecated: interpolation key type is not a string ({type(key).__name__})"
+        )
+
+    if not isinstance(message, str):
+        raise TypeError(
+            f"oc.deprecated: interpolation message type is not a string ({type(message).__name__})"
+        )
+
+    full_key = _node_._get_full_key(key=None)
+    target_node = select_node(_parent_, key, absolute_key=True)
+    if target_node is None:
+        raise ConfigKeyError(
+            f"In oc.deprecated resolver at '{full_key}': Key not found: '{key}'"
+        )
+    new_key = target_node._get_full_key(key=None)
+    msg = string.Template(message).safe_substitute(
+        OLD_KEY=full_key,
+        NEW_KEY=new_key,
+    )
+    warnings.warn(category=UserWarning, message=msg)
+    return target_node
+
+
+def select(
+    key: str,
+    default: Any = _DEFAULT_MARKER_,
+    *,
+    _parent_: Container,
+) -> Any:
+    from omegaconf._impl import select_value
+
+    return select_value(cfg=_parent_, key=key, absolute_key=True, default=default)
+
+
+__all__ = [
+    "create",
+    "decode",
+    "deprecated",
+    "dict",
+    "env",
+    "select",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/oc/dict.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/oc/dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..adbfd16ac47f2dced46f509c8dfdc8516ba97f4a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/resolvers/oc/dict.py
@@ -0,0 +1,83 @@
+from typing import Any, List
+
+from omegaconf import AnyNode, Container, DictConfig, ListConfig
+from omegaconf._utils import Marker
+from omegaconf.basecontainer import BaseContainer
+from omegaconf.errors import ConfigKeyError
+
+_DEFAULT_SELECT_MARKER_: Any = Marker("_DEFAULT_SELECT_MARKER_")
+
+
+def keys(
+    key: str,
+    _parent_: Container,
+) -> ListConfig:
+    from omegaconf import OmegaConf
+
+    assert isinstance(_parent_, BaseContainer)
+
+    in_dict = _get_and_validate_dict_input(
+        key, parent=_parent_, resolver_name="oc.dict.keys"
+    )
+
+    ret = OmegaConf.create(list(in_dict.keys()), parent=_parent_)
+    assert isinstance(ret, ListConfig)
+    return ret
+
+
+def values(key: str, _root_: BaseContainer, _parent_: Container) -> ListConfig:
+    assert isinstance(_parent_, BaseContainer)
+    in_dict = _get_and_validate_dict_input(
+        key, parent=_parent_, resolver_name="oc.dict.values"
+    )
+
+    content = in_dict._content
+    assert isinstance(content, dict)
+
+    ret = ListConfig([])
+    if key.startswith("."):
+        key = f".{key}"  # extra dot to compensate for extra level of nesting within ret ListConfig
+    for k in content:
+        ref_node = AnyNode(f"${{{key}.{k!s}}}")
+        ret.append(ref_node)
+
+    # Finalize result by setting proper type and parent.
+    element_type: Any = in_dict._metadata.element_type
+    ret._metadata.element_type = element_type
+    ret._metadata.ref_type = List[element_type]
+    ret._set_parent(_parent_)
+
+    return ret
+
+
+def _get_and_validate_dict_input(
+    key: str,
+    parent: BaseContainer,
+    resolver_name: str,
+) -> DictConfig:
+    from omegaconf._impl import select_value
+
+    if not isinstance(key, str):
+        raise TypeError(
+            f"`{resolver_name}` requires a string as input, but obtained `{key}` "
+            f"of type: {type(key).__name__}"
+        )
+
+    in_dict = select_value(
+        parent,
+        key,
+        throw_on_missing=True,
+        absolute_key=True,
+        default=_DEFAULT_SELECT_MARKER_,
+    )
+
+    if in_dict is _DEFAULT_SELECT_MARKER_:
+        raise ConfigKeyError(f"Key not found: '{key}'")
+
+    if not isinstance(in_dict, DictConfig):
+        raise TypeError(
+            f"`{resolver_name}` cannot be applied to objects of type: "
+            f"{type(in_dict).__name__}"
+        )
+
+    return in_dict
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/version.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4989b2ef4670f866a92767403c3f63204a06f81
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/omegaconf/version.py
@@ -0,0 +1,13 @@
+import sys  # pragma: no cover
+
+__version__ = "2.3.0"
+
+msg = """OmegaConf 2.0 and above is compatible with Python 3.6 and newer.
+You have the following options:
+1. Upgrade to Python 3.6 or newer.
+   This is highly recommended. new features will not be added to OmegaConf 1.4.
+2. Continue using OmegaConf 1.4:
+    You can pip install 'OmegaConf<1.5' to do that.
+"""
+if sys.version_info < (3, 6):
+    raise ImportError(msg)  # pragma: no cover
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/INSTALLER b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/METADATA b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..3200e601f970271fdde3fcc74f9af4423655a79d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/METADATA
@@ -0,0 +1,107 @@
+Metadata-Version: 2.4
+Name: packaging
+Version: 26.0
+Summary: Core utilities for Python packages
+Author-email: Donald Stufft <donald@stufft.io>
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-Expression: Apache-2.0 OR BSD-2-Clause
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Typing :: Typed
+License-File: LICENSE
+License-File: LICENSE.APACHE
+License-File: LICENSE.BSD
+Project-URL: Documentation, https://packaging.pypa.io/
+Project-URL: Source, https://github.com/pypa/packaging
+
+packaging
+=========
+
+.. start-intro
+
+Reusable core utilities for various Python Packaging
+`interoperability specifications <https://packaging.python.org/specifications/>`_.
+
+This library provides utilities that implement the interoperability
+specifications which have clearly one correct behaviour (eg: :pep:`440`)
+or benefit greatly from having a single shared implementation (eg: :pep:`425`).
+
+.. end-intro
+
+The ``packaging`` project includes the following: version handling, specifiers,
+markers, requirements, tags, metadata, lockfiles, utilities.
+
+Documentation
+-------------
+
+The `documentation`_ provides information and the API for the following:
+
+- Version Handling
+- Specifiers
+- Markers
+- Requirements
+- Tags
+- Metadata
+- Lockfiles
+- Utilities
+
+Installation
+------------
+
+Use ``pip`` to install these utilities::
+
+    pip install packaging
+
+The ``packaging`` library uses calendar-based versioning (``YY.N``).
+
+Discussion
+----------
+
+If you run into bugs, you can file them in our `issue tracker`_.
+
+You can also join ``#pypa`` on Freenode to ask questions or get involved.
+
+
+.. _`documentation`: https://packaging.pypa.io/
+.. _`issue tracker`: https://github.com/pypa/packaging/issues
+
+
+Code of Conduct
+---------------
+
+Everyone interacting in the packaging project's codebases, issue trackers, chat
+rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_.
+
+.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md
+
+Contributing
+------------
+
+The ``CONTRIBUTING.rst`` file outlines how to contribute to this project as
+well as how to report a potential security issue. The documentation for this
+project also covers information about `project development`_ and `security`_.
+
+.. _`project development`: https://packaging.pypa.io/en/latest/development/
+.. _`security`: https://packaging.pypa.io/en/latest/security/
+
+Project History
+---------------
+
+Please review the ``CHANGELOG.rst`` file or the `Changelog documentation`_ for
+recent changes and project history.
+
+.. _`Changelog documentation`: https://packaging.pypa.io/en/latest/changelog/
+
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/RECORD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..48cf6a5c232aa03a1fb903e44cee6e2c26e7800c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/RECORD
@@ -0,0 +1,42 @@
+packaging-26.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+packaging-26.0.dist-info/METADATA,sha256=M2K7fWom2iliuo2qpHhc0LrKwhq6kIoRlcyPWVgKJlo,3309
+packaging-26.0.dist-info/RECORD,,
+packaging-26.0.dist-info/WHEEL,sha256=G2gURzTEtmeR8nrdXUJfNiB3VYVxigPQ-bEQujpNiNs,82
+packaging-26.0.dist-info/licenses/LICENSE,sha256=ytHvW9NA1z4HS6YU0m996spceUDD2MNIUuZcSQlobEg,197
+packaging-26.0.dist-info/licenses/LICENSE.APACHE,sha256=DVQuDIgE45qn836wDaWnYhSdxoLXgpRRKH4RuTjpRZQ,10174
+packaging-26.0.dist-info/licenses/LICENSE.BSD,sha256=tw5-m3QvHMb5SLNMFqo5_-zpQZY2S8iP8NIYDwAo-sU,1344
+packaging/__init__.py,sha256=y4lVbpeBzCGk-IPDw5BGBZ_b0P3ukEEJZAbGYc6Ey8c,494
+packaging/__pycache__/__init__.cpython-310.pyc,,
+packaging/__pycache__/_elffile.cpython-310.pyc,,
+packaging/__pycache__/_manylinux.cpython-310.pyc,,
+packaging/__pycache__/_musllinux.cpython-310.pyc,,
+packaging/__pycache__/_parser.cpython-310.pyc,,
+packaging/__pycache__/_structures.cpython-310.pyc,,
+packaging/__pycache__/_tokenizer.cpython-310.pyc,,
+packaging/__pycache__/markers.cpython-310.pyc,,
+packaging/__pycache__/metadata.cpython-310.pyc,,
+packaging/__pycache__/pylock.cpython-310.pyc,,
+packaging/__pycache__/requirements.cpython-310.pyc,,
+packaging/__pycache__/specifiers.cpython-310.pyc,,
+packaging/__pycache__/tags.cpython-310.pyc,,
+packaging/__pycache__/utils.cpython-310.pyc,,
+packaging/__pycache__/version.cpython-310.pyc,,
+packaging/_elffile.py,sha256=-sKkptYqzYw2-x3QByJa5mB4rfPWu1pxkZHRx1WAFCY,3211
+packaging/_manylinux.py,sha256=Hf6nB0cOrayEs96-p3oIXAgGnFquv20DO5l-o2_Xnv0,9559
+packaging/_musllinux.py,sha256=Z6swjH3MA7XS3qXnmMN7QPhqP3fnoYI0eQ18e9-HgAE,2707
+packaging/_parser.py,sha256=U_DajsEx2VoC_F46fSVV3hDKNCWoQYkPkasO3dld0ig,10518
+packaging/_structures.py,sha256=Hn49Ta8zV9Wo8GiCL8Nl2ARZY983Un3pruZGVNldPwE,1514
+packaging/_tokenizer.py,sha256=M8EwNIdXeL9NMFuFrQtiOKwjka_xFx8KjRQnfE8O_z8,5421
+packaging/licenses/__init__.py,sha256=TwXLHZCXwSgdFwRLPxW602T6mSieunSFHM6fp8pgW78,5819
+packaging/licenses/__pycache__/__init__.cpython-310.pyc,,
+packaging/licenses/__pycache__/_spdx.cpython-310.pyc,,
+packaging/licenses/_spdx.py,sha256=WW7DXiyg68up_YND_wpRYlr1SHhiV4FfJLQffghhMxQ,51122
+packaging/markers.py,sha256=ZX-cLvW1S3cZcEc0fHI4z7zSx5U2T19yMpDP_mE-CYw,12771
+packaging/metadata.py,sha256=CWVZpN_HfoYMSSDuCP7igOvGgqA9AOmpW8f3qTisfnc,39360
+packaging/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+packaging/pylock.py,sha256=-R1uNfJ4PaLto7Mg62YsGOHgvskuiIEqPwxOywl42Jk,22537
+packaging/requirements.py,sha256=PMCAWD8aNMnVD-6uZMedhBuAVX2573eZ4yPBLXmz04I,2870
+packaging/specifiers.py,sha256=EPNPimY_zFivthv1vdjZYz5IqkKGsnKR2yKh-EVyvZw,40797
+packaging/tags.py,sha256=cXLV1pJD3UtJlDg7Wz3zrfdQhRZqr8jumSAKKAAd2xE,22856
+packaging/utils.py,sha256=N4c6oZzFJy6klTZ3AnkNz7sSkJesuFWPp68LA3B5dAo,5040
+packaging/version.py,sha256=7XWlL2IDYLwDYC0ht6cFEhapLwLWbmyo4rb7sEFj0x8,23272
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/WHEEL b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..d8b9936dad9ab2513fa6979f411560d3b6b57e37
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.12.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..6f62d44e4ef733c0e713afcd2371fed7f2b3de67
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE
@@ -0,0 +1,3 @@
+This software is made available under the terms of *either* of the licenses
+found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made
+under the terms of *both* these licenses.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE.APACHE b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE.APACHE
new file mode 100644
index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE.APACHE
@@ -0,0 +1,177 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
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+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
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+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
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+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
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+
+   2. Grant of Copyright License. Subject to the terms and conditions of
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+   3. Grant of Patent License. Subject to the terms and conditions of
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+
+   4. Redistribution. You may reproduce and distribute copies of the
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
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+
+      (d) If the Work includes a "NOTICE" text file as part of its
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+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
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+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
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+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE.BSD b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE.BSD
new file mode 100644
index 0000000000000000000000000000000000000000..42ce7b75c92fb01a3f6ed17eea363f756b7da582
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging-26.0.dist-info/licenses/LICENSE.BSD
@@ -0,0 +1,23 @@
+Copyright (c) Donald Stufft and individual contributors.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    1. Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..21695a74b5107c96ba4bb2cbca6b7f259dacd330
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/__init__.py
@@ -0,0 +1,15 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+__title__ = "packaging"
+__summary__ = "Core utilities for Python packages"
+__uri__ = "https://github.com/pypa/packaging"
+
+__version__ = "26.0"
+
+__author__ = "Donald Stufft and individual contributors"
+__email__ = "donald@stufft.io"
+
+__license__ = "BSD-2-Clause or Apache-2.0"
+__copyright__ = f"2014 {__author__}"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_elffile.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_elffile.py
new file mode 100644
index 0000000000000000000000000000000000000000..497b0645217512ae2ba8ff61341fd2bbfa3648cd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_elffile.py
@@ -0,0 +1,108 @@
+"""
+ELF file parser.
+
+This provides a class ``ELFFile`` that parses an ELF executable in a similar
+interface to ``ZipFile``. Only the read interface is implemented.
+
+ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html
+"""
+
+from __future__ import annotations
+
+import enum
+import os
+import struct
+from typing import IO
+
+
+class ELFInvalid(ValueError):
+    pass
+
+
+class EIClass(enum.IntEnum):
+    C32 = 1
+    C64 = 2
+
+
+class EIData(enum.IntEnum):
+    Lsb = 1
+    Msb = 2
+
+
+class EMachine(enum.IntEnum):
+    I386 = 3
+    S390 = 22
+    Arm = 40
+    X8664 = 62
+    AArc64 = 183
+
+
+class ELFFile:
+    """
+    Representation of an ELF executable.
+    """
+
+    def __init__(self, f: IO[bytes]) -> None:
+        self._f = f
+
+        try:
+            ident = self._read("16B")
+        except struct.error as e:
+            raise ELFInvalid("unable to parse identification") from e
+        magic = bytes(ident[:4])
+        if magic != b"\x7fELF":
+            raise ELFInvalid(f"invalid magic: {magic!r}")
+
+        self.capacity = ident[4]  # Format for program header (bitness).
+        self.encoding = ident[5]  # Data structure encoding (endianness).
+
+        try:
+            # e_fmt: Format for program header.
+            # p_fmt: Format for section header.
+            # p_idx: Indexes to find p_type, p_offset, and p_filesz.
+            e_fmt, self._p_fmt, self._p_idx = {
+                (1, 1): ("<HHIIIIIHHH", "<IIIIIIII", (0, 1, 4)),  # 32-bit LSB.
+                (1, 2): (">HHIIIIIHHH", ">IIIIIIII", (0, 1, 4)),  # 32-bit MSB.
+                (2, 1): ("<HHIQQQIHHH", "<IIQQQQQQ", (0, 2, 5)),  # 64-bit LSB.
+                (2, 2): (">HHIQQQIHHH", ">IIQQQQQQ", (0, 2, 5)),  # 64-bit MSB.
+            }[(self.capacity, self.encoding)]
+        except KeyError as e:
+            raise ELFInvalid(
+                f"unrecognized capacity ({self.capacity}) or encoding ({self.encoding})"
+            ) from e
+
+        try:
+            (
+                _,
+                self.machine,  # Architecture type.
+                _,
+                _,
+                self._e_phoff,  # Offset of program header.
+                _,
+                self.flags,  # Processor-specific flags.
+                _,
+                self._e_phentsize,  # Size of section.
+                self._e_phnum,  # Number of sections.
+            ) = self._read(e_fmt)
+        except struct.error as e:
+            raise ELFInvalid("unable to parse machine and section information") from e
+
+    def _read(self, fmt: str) -> tuple[int, ...]:
+        return struct.unpack(fmt, self._f.read(struct.calcsize(fmt)))
+
+    @property
+    def interpreter(self) -> str | None:
+        """
+        The path recorded in the ``PT_INTERP`` section header.
+        """
+        for index in range(self._e_phnum):
+            self._f.seek(self._e_phoff + self._e_phentsize * index)
+            try:
+                data = self._read(self._p_fmt)
+            except struct.error:
+                continue
+            if data[self._p_idx[0]] != 3:  # Not PT_INTERP.
+                continue
+            self._f.seek(data[self._p_idx[1]])
+            return os.fsdecode(self._f.read(data[self._p_idx[2]])).strip("\0")
+        return None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_manylinux.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_manylinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e79e8a882be74fe76c80ccf49a9cd68fb636fd4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_manylinux.py
@@ -0,0 +1,262 @@
+from __future__ import annotations
+
+import collections
+import contextlib
+import functools
+import os
+import re
+import sys
+import warnings
+from typing import Generator, Iterator, NamedTuple, Sequence
+
+from ._elffile import EIClass, EIData, ELFFile, EMachine
+
+EF_ARM_ABIMASK = 0xFF000000
+EF_ARM_ABI_VER5 = 0x05000000
+EF_ARM_ABI_FLOAT_HARD = 0x00000400
+
+_ALLOWED_ARCHS = {
+    "x86_64",
+    "aarch64",
+    "ppc64",
+    "ppc64le",
+    "s390x",
+    "loongarch64",
+    "riscv64",
+}
+
+
+# `os.PathLike` not a generic type until Python 3.9, so sticking with `str`
+# as the type for `path` until then.
+@contextlib.contextmanager
+def _parse_elf(path: str) -> Generator[ELFFile | None, None, None]:
+    try:
+        with open(path, "rb") as f:
+            yield ELFFile(f)
+    except (OSError, TypeError, ValueError):
+        yield None
+
+
+def _is_linux_armhf(executable: str) -> bool:
+    # hard-float ABI can be detected from the ELF header of the running
+    # process
+    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
+    with _parse_elf(executable) as f:
+        return (
+            f is not None
+            and f.capacity == EIClass.C32
+            and f.encoding == EIData.Lsb
+            and f.machine == EMachine.Arm
+            and f.flags & EF_ARM_ABIMASK == EF_ARM_ABI_VER5
+            and f.flags & EF_ARM_ABI_FLOAT_HARD == EF_ARM_ABI_FLOAT_HARD
+        )
+
+
+def _is_linux_i686(executable: str) -> bool:
+    with _parse_elf(executable) as f:
+        return (
+            f is not None
+            and f.capacity == EIClass.C32
+            and f.encoding == EIData.Lsb
+            and f.machine == EMachine.I386
+        )
+
+
+def _have_compatible_abi(executable: str, archs: Sequence[str]) -> bool:
+    if "armv7l" in archs:
+        return _is_linux_armhf(executable)
+    if "i686" in archs:
+        return _is_linux_i686(executable)
+    return any(arch in _ALLOWED_ARCHS for arch in archs)
+
+
+# If glibc ever changes its major version, we need to know what the last
+# minor version was, so we can build the complete list of all versions.
+# For now, guess what the highest minor version might be, assume it will
+# be 50 for testing. Once this actually happens, update the dictionary
+# with the actual value.
+_LAST_GLIBC_MINOR: dict[int, int] = collections.defaultdict(lambda: 50)
+
+
+class _GLibCVersion(NamedTuple):
+    major: int
+    minor: int
+
+
+def _glibc_version_string_confstr() -> str | None:
+    """
+    Primary implementation of glibc_version_string using os.confstr.
+    """
+    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
+    # to be broken or missing. This strategy is used in the standard library
+    # platform module.
+    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
+    try:
+        # Should be a string like "glibc 2.17".
+        version_string: str | None = os.confstr("CS_GNU_LIBC_VERSION")
+        assert version_string is not None
+        _, version = version_string.rsplit()
+    except (AssertionError, AttributeError, OSError, ValueError):
+        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
+        return None
+    return version
+
+
+def _glibc_version_string_ctypes() -> str | None:
+    """
+    Fallback implementation of glibc_version_string using ctypes.
+    """
+    try:
+        import ctypes  # noqa: PLC0415
+    except ImportError:
+        return None
+
+    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
+    # manpage says, "If filename is NULL, then the returned handle is for the
+    # main program". This way we can let the linker do the work to figure out
+    # which libc our process is actually using.
+    #
+    # We must also handle the special case where the executable is not a
+    # dynamically linked executable. This can occur when using musl libc,
+    # for example. In this situation, dlopen() will error, leading to an
+    # OSError. Interestingly, at least in the case of musl, there is no
+    # errno set on the OSError. The single string argument used to construct
+    # OSError comes from libc itself and is therefore not portable to
+    # hard code here. In any case, failure to call dlopen() means we
+    # can proceed, so we bail on our attempt.
+    try:
+        process_namespace = ctypes.CDLL(None)
+    except OSError:
+        return None
+
+    try:
+        gnu_get_libc_version = process_namespace.gnu_get_libc_version
+    except AttributeError:
+        # Symbol doesn't exist -> therefore, we are not linked to
+        # glibc.
+        return None
+
+    # Call gnu_get_libc_version, which returns a string like "2.5"
+    gnu_get_libc_version.restype = ctypes.c_char_p
+    version_str: str = gnu_get_libc_version()
+    # py2 / py3 compatibility:
+    if not isinstance(version_str, str):
+        version_str = version_str.decode("ascii")
+
+    return version_str
+
+
+def _glibc_version_string() -> str | None:
+    """Returns glibc version string, or None if not using glibc."""
+    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
+
+
+def _parse_glibc_version(version_str: str) -> _GLibCVersion:
+    """Parse glibc version.
+
+    We use a regexp instead of str.split because we want to discard any
+    random junk that might come after the minor version -- this might happen
+    in patched/forked versions of glibc (e.g. Linaro's version of glibc
+    uses version strings like "2.20-2014.11"). See gh-3588.
+    """
+    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
+    if not m:
+        warnings.warn(
+            f"Expected glibc version with 2 components major.minor, got: {version_str}",
+            RuntimeWarning,
+            stacklevel=2,
+        )
+        return _GLibCVersion(-1, -1)
+    return _GLibCVersion(int(m.group("major")), int(m.group("minor")))
+
+
+@functools.lru_cache
+def _get_glibc_version() -> _GLibCVersion:
+    version_str = _glibc_version_string()
+    if version_str is None:
+        return _GLibCVersion(-1, -1)
+    return _parse_glibc_version(version_str)
+
+
+# From PEP 513, PEP 600
+def _is_compatible(arch: str, version: _GLibCVersion) -> bool:
+    sys_glibc = _get_glibc_version()
+    if sys_glibc < version:
+        return False
+    # Check for presence of _manylinux module.
+    try:
+        import _manylinux  # noqa: PLC0415
+    except ImportError:
+        return True
+    if hasattr(_manylinux, "manylinux_compatible"):
+        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
+        if result is not None:
+            return bool(result)
+        return True
+    if version == _GLibCVersion(2, 5) and hasattr(_manylinux, "manylinux1_compatible"):
+        return bool(_manylinux.manylinux1_compatible)
+    if version == _GLibCVersion(2, 12) and hasattr(
+        _manylinux, "manylinux2010_compatible"
+    ):
+        return bool(_manylinux.manylinux2010_compatible)
+    if version == _GLibCVersion(2, 17) and hasattr(
+        _manylinux, "manylinux2014_compatible"
+    ):
+        return bool(_manylinux.manylinux2014_compatible)
+    return True
+
+
+_LEGACY_MANYLINUX_MAP: dict[_GLibCVersion, str] = {
+    # CentOS 7 w/ glibc 2.17 (PEP 599)
+    _GLibCVersion(2, 17): "manylinux2014",
+    # CentOS 6 w/ glibc 2.12 (PEP 571)
+    _GLibCVersion(2, 12): "manylinux2010",
+    # CentOS 5 w/ glibc 2.5 (PEP 513)
+    _GLibCVersion(2, 5): "manylinux1",
+}
+
+
+def platform_tags(archs: Sequence[str]) -> Iterator[str]:
+    """Generate manylinux tags compatible to the current platform.
+
+    :param archs: Sequence of compatible architectures.
+        The first one shall be the closest to the actual architecture and be the part of
+        platform tag after the ``linux_`` prefix, e.g. ``x86_64``.
+        The ``linux_`` prefix is assumed as a prerequisite for the current platform to
+        be manylinux-compatible.
+
+    :returns: An iterator of compatible manylinux tags.
+    """
+    if not _have_compatible_abi(sys.executable, archs):
+        return
+    # Oldest glibc to be supported regardless of architecture is (2, 17).
+    too_old_glibc2 = _GLibCVersion(2, 16)
+    if set(archs) & {"x86_64", "i686"}:
+        # On x86/i686 also oldest glibc to be supported is (2, 5).
+        too_old_glibc2 = _GLibCVersion(2, 4)
+    current_glibc = _GLibCVersion(*_get_glibc_version())
+    glibc_max_list = [current_glibc]
+    # We can assume compatibility across glibc major versions.
+    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
+    #
+    # Build a list of maximum glibc versions so that we can
+    # output the canonical list of all glibc from current_glibc
+    # down to too_old_glibc2, including all intermediary versions.
+    for glibc_major in range(current_glibc.major - 1, 1, -1):
+        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
+        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
+    for arch in archs:
+        for glibc_max in glibc_max_list:
+            if glibc_max.major == too_old_glibc2.major:
+                min_minor = too_old_glibc2.minor
+            else:
+                # For other glibc major versions oldest supported is (x, 0).
+                min_minor = -1
+            for glibc_minor in range(glibc_max.minor, min_minor, -1):
+                glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
+                if _is_compatible(arch, glibc_version):
+                    yield "manylinux_{}_{}_{}".format(*glibc_version, arch)
+
+                    # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
+                    if legacy_tag := _LEGACY_MANYLINUX_MAP.get(glibc_version):
+                        yield f"{legacy_tag}_{arch}"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_musllinux.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_musllinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8116a79ca80d60657542a23b4bbcbc3c518eaf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_musllinux.py
@@ -0,0 +1,85 @@
+"""PEP 656 support.
+
+This module implements logic to detect if the currently running Python is
+linked against musl, and what musl version is used.
+"""
+
+from __future__ import annotations
+
+import functools
+import re
+import subprocess
+import sys
+from typing import Iterator, NamedTuple, Sequence
+
+from ._elffile import ELFFile
+
+
+class _MuslVersion(NamedTuple):
+    major: int
+    minor: int
+
+
+def _parse_musl_version(output: str) -> _MuslVersion | None:
+    lines = [n for n in (n.strip() for n in output.splitlines()) if n]
+    if len(lines) < 2 or lines[0][:4] != "musl":
+        return None
+    m = re.match(r"Version (\d+)\.(\d+)", lines[1])
+    if not m:
+        return None
+    return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2)))
+
+
+@functools.lru_cache
+def _get_musl_version(executable: str) -> _MuslVersion | None:
+    """Detect currently-running musl runtime version.
+
+    This is done by checking the specified executable's dynamic linking
+    information, and invoking the loader to parse its output for a version
+    string. If the loader is musl, the output would be something like::
+
+        musl libc (x86_64)
+        Version 1.2.2
+        Dynamic Program Loader
+    """
+    try:
+        with open(executable, "rb") as f:
+            ld = ELFFile(f).interpreter
+    except (OSError, TypeError, ValueError):
+        return None
+    if ld is None or "musl" not in ld:
+        return None
+    proc = subprocess.run([ld], check=False, stderr=subprocess.PIPE, text=True)
+    return _parse_musl_version(proc.stderr)
+
+
+def platform_tags(archs: Sequence[str]) -> Iterator[str]:
+    """Generate musllinux tags compatible to the current platform.
+
+    :param archs: Sequence of compatible architectures.
+        The first one shall be the closest to the actual architecture and be the part of
+        platform tag after the ``linux_`` prefix, e.g. ``x86_64``.
+        The ``linux_`` prefix is assumed as a prerequisite for the current platform to
+        be musllinux-compatible.
+
+    :returns: An iterator of compatible musllinux tags.
+    """
+    sys_musl = _get_musl_version(sys.executable)
+    if sys_musl is None:  # Python not dynamically linked against musl.
+        return
+    for arch in archs:
+        for minor in range(sys_musl.minor, -1, -1):
+            yield f"musllinux_{sys_musl.major}_{minor}_{arch}"
+
+
+if __name__ == "__main__":  # pragma: no cover
+    import sysconfig
+
+    plat = sysconfig.get_platform()
+    assert plat.startswith("linux-"), "not linux"
+
+    print("plat:", plat)
+    print("musl:", _get_musl_version(sys.executable))
+    print("tags:", end=" ")
+    for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])):
+        print(t, end="\n      ")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_parser.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6c1f5cd226b926f96a3bb1e9fb0f18d1bd021c9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_parser.py
@@ -0,0 +1,365 @@
+"""Handwritten parser of dependency specifiers.
+
+The docstring for each __parse_* function contains EBNF-inspired grammar representing
+the implementation.
+"""
+
+from __future__ import annotations
+
+import ast
+from typing import List, Literal, NamedTuple, Sequence, Tuple, Union
+
+from ._tokenizer import DEFAULT_RULES, Tokenizer
+
+
+class Node:
+    __slots__ = ("value",)
+
+    def __init__(self, value: str) -> None:
+        self.value = value
+
+    def __str__(self) -> str:
+        return self.value
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}({self.value!r})>"
+
+    def serialize(self) -> str:
+        raise NotImplementedError
+
+
+class Variable(Node):
+    __slots__ = ()
+
+    def serialize(self) -> str:
+        return str(self)
+
+
+class Value(Node):
+    __slots__ = ()
+
+    def serialize(self) -> str:
+        return f'"{self}"'
+
+
+class Op(Node):
+    __slots__ = ()
+
+    def serialize(self) -> str:
+        return str(self)
+
+
+MarkerLogical = Literal["and", "or"]
+MarkerVar = Union[Variable, Value]
+MarkerItem = Tuple[MarkerVar, Op, MarkerVar]
+MarkerAtom = Union[MarkerItem, Sequence["MarkerAtom"]]
+MarkerList = List[Union["MarkerList", MarkerAtom, MarkerLogical]]
+
+
+class ParsedRequirement(NamedTuple):
+    name: str
+    url: str
+    extras: list[str]
+    specifier: str
+    marker: MarkerList | None
+
+
+# --------------------------------------------------------------------------------------
+# Recursive descent parser for dependency specifier
+# --------------------------------------------------------------------------------------
+def parse_requirement(source: str) -> ParsedRequirement:
+    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
+
+
+def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement:
+    """
+    requirement = WS? IDENTIFIER WS? extras WS? requirement_details
+    """
+    tokenizer.consume("WS")
+
+    name_token = tokenizer.expect(
+        "IDENTIFIER", expected="package name at the start of dependency specifier"
+    )
+    name = name_token.text
+    tokenizer.consume("WS")
+
+    extras = _parse_extras(tokenizer)
+    tokenizer.consume("WS")
+
+    url, specifier, marker = _parse_requirement_details(tokenizer)
+    tokenizer.expect("END", expected="end of dependency specifier")
+
+    return ParsedRequirement(name, url, extras, specifier, marker)
+
+
+def _parse_requirement_details(
+    tokenizer: Tokenizer,
+) -> tuple[str, str, MarkerList | None]:
+    """
+    requirement_details = AT URL (WS requirement_marker?)?
+                        | specifier WS? (requirement_marker)?
+    """
+
+    specifier = ""
+    url = ""
+    marker = None
+
+    if tokenizer.check("AT"):
+        tokenizer.read()
+        tokenizer.consume("WS")
+
+        url_start = tokenizer.position
+        url = tokenizer.expect("URL", expected="URL after @").text
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        tokenizer.expect("WS", expected="whitespace after URL")
+
+        # The input might end after whitespace.
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        marker = _parse_requirement_marker(
+            tokenizer,
+            span_start=url_start,
+            expected="semicolon (after URL and whitespace)",
+        )
+    else:
+        specifier_start = tokenizer.position
+        specifier = _parse_specifier(tokenizer)
+        tokenizer.consume("WS")
+
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        marker = _parse_requirement_marker(
+            tokenizer,
+            span_start=specifier_start,
+            expected=(
+                "comma (within version specifier), semicolon (after version specifier)"
+                if specifier
+                else "semicolon (after name with no version specifier)"
+            ),
+        )
+
+    return (url, specifier, marker)
+
+
+def _parse_requirement_marker(
+    tokenizer: Tokenizer, *, span_start: int, expected: str
+) -> MarkerList:
+    """
+    requirement_marker = SEMICOLON marker WS?
+    """
+
+    if not tokenizer.check("SEMICOLON"):
+        tokenizer.raise_syntax_error(
+            f"Expected {expected} or end",
+            span_start=span_start,
+            span_end=None,
+        )
+    tokenizer.read()
+
+    marker = _parse_marker(tokenizer)
+    tokenizer.consume("WS")
+
+    return marker
+
+
+def _parse_extras(tokenizer: Tokenizer) -> list[str]:
+    """
+    extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)?
+    """
+    if not tokenizer.check("LEFT_BRACKET", peek=True):
+        return []
+
+    with tokenizer.enclosing_tokens(
+        "LEFT_BRACKET",
+        "RIGHT_BRACKET",
+        around="extras",
+    ):
+        tokenizer.consume("WS")
+        extras = _parse_extras_list(tokenizer)
+        tokenizer.consume("WS")
+
+    return extras
+
+
+def _parse_extras_list(tokenizer: Tokenizer) -> list[str]:
+    """
+    extras_list = identifier (wsp* ',' wsp* identifier)*
+    """
+    extras: list[str] = []
+
+    if not tokenizer.check("IDENTIFIER"):
+        return extras
+
+    extras.append(tokenizer.read().text)
+
+    while True:
+        tokenizer.consume("WS")
+        if tokenizer.check("IDENTIFIER", peek=True):
+            tokenizer.raise_syntax_error("Expected comma between extra names")
+        elif not tokenizer.check("COMMA"):
+            break
+
+        tokenizer.read()
+        tokenizer.consume("WS")
+
+        extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma")
+        extras.append(extra_token.text)
+
+    return extras
+
+
+def _parse_specifier(tokenizer: Tokenizer) -> str:
+    """
+    specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS
+              | WS? version_many WS?
+    """
+    with tokenizer.enclosing_tokens(
+        "LEFT_PARENTHESIS",
+        "RIGHT_PARENTHESIS",
+        around="version specifier",
+    ):
+        tokenizer.consume("WS")
+        parsed_specifiers = _parse_version_many(tokenizer)
+        tokenizer.consume("WS")
+
+    return parsed_specifiers
+
+
+def _parse_version_many(tokenizer: Tokenizer) -> str:
+    """
+    version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)?
+    """
+    parsed_specifiers = ""
+    while tokenizer.check("SPECIFIER"):
+        span_start = tokenizer.position
+        parsed_specifiers += tokenizer.read().text
+        if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True):
+            tokenizer.raise_syntax_error(
+                ".* suffix can only be used with `==` or `!=` operators",
+                span_start=span_start,
+                span_end=tokenizer.position + 1,
+            )
+        if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True):
+            tokenizer.raise_syntax_error(
+                "Local version label can only be used with `==` or `!=` operators",
+                span_start=span_start,
+                span_end=tokenizer.position,
+            )
+        tokenizer.consume("WS")
+        if not tokenizer.check("COMMA"):
+            break
+        parsed_specifiers += tokenizer.read().text
+        tokenizer.consume("WS")
+
+    return parsed_specifiers
+
+
+# --------------------------------------------------------------------------------------
+# Recursive descent parser for marker expression
+# --------------------------------------------------------------------------------------
+def parse_marker(source: str) -> MarkerList:
+    return _parse_full_marker(Tokenizer(source, rules=DEFAULT_RULES))
+
+
+def _parse_full_marker(tokenizer: Tokenizer) -> MarkerList:
+    retval = _parse_marker(tokenizer)
+    tokenizer.expect("END", expected="end of marker expression")
+    return retval
+
+
+def _parse_marker(tokenizer: Tokenizer) -> MarkerList:
+    """
+    marker = marker_atom (BOOLOP marker_atom)+
+    """
+    expression = [_parse_marker_atom(tokenizer)]
+    while tokenizer.check("BOOLOP"):
+        token = tokenizer.read()
+        expr_right = _parse_marker_atom(tokenizer)
+        expression.extend((token.text, expr_right))
+    return expression
+
+
+def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom:
+    """
+    marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS?
+                | WS? marker_item WS?
+    """
+
+    tokenizer.consume("WS")
+    if tokenizer.check("LEFT_PARENTHESIS", peek=True):
+        with tokenizer.enclosing_tokens(
+            "LEFT_PARENTHESIS",
+            "RIGHT_PARENTHESIS",
+            around="marker expression",
+        ):
+            tokenizer.consume("WS")
+            marker: MarkerAtom = _parse_marker(tokenizer)
+            tokenizer.consume("WS")
+    else:
+        marker = _parse_marker_item(tokenizer)
+    tokenizer.consume("WS")
+    return marker
+
+
+def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem:
+    """
+    marker_item = WS? marker_var WS? marker_op WS? marker_var WS?
+    """
+    tokenizer.consume("WS")
+    marker_var_left = _parse_marker_var(tokenizer)
+    tokenizer.consume("WS")
+    marker_op = _parse_marker_op(tokenizer)
+    tokenizer.consume("WS")
+    marker_var_right = _parse_marker_var(tokenizer)
+    tokenizer.consume("WS")
+    return (marker_var_left, marker_op, marker_var_right)
+
+
+def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar:  # noqa: RET503
+    """
+    marker_var = VARIABLE | QUOTED_STRING
+    """
+    if tokenizer.check("VARIABLE"):
+        return process_env_var(tokenizer.read().text.replace(".", "_"))
+    elif tokenizer.check("QUOTED_STRING"):
+        return process_python_str(tokenizer.read().text)
+    else:
+        tokenizer.raise_syntax_error(
+            message="Expected a marker variable or quoted string"
+        )
+
+
+def process_env_var(env_var: str) -> Variable:
+    if env_var in ("platform_python_implementation", "python_implementation"):
+        return Variable("platform_python_implementation")
+    else:
+        return Variable(env_var)
+
+
+def process_python_str(python_str: str) -> Value:
+    value = ast.literal_eval(python_str)
+    return Value(str(value))
+
+
+def _parse_marker_op(tokenizer: Tokenizer) -> Op:
+    """
+    marker_op = IN | NOT IN | OP
+    """
+    if tokenizer.check("IN"):
+        tokenizer.read()
+        return Op("in")
+    elif tokenizer.check("NOT"):
+        tokenizer.read()
+        tokenizer.expect("WS", expected="whitespace after 'not'")
+        tokenizer.expect("IN", expected="'in' after 'not'")
+        return Op("not in")
+    elif tokenizer.check("OP"):
+        return Op(tokenizer.read().text)
+    else:
+        return tokenizer.raise_syntax_error(
+            "Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, ===, in, not in"
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_structures.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_structures.py
new file mode 100644
index 0000000000000000000000000000000000000000..225e2eee01238571c50595eb104e0b70d5f503c4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_structures.py
@@ -0,0 +1,69 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+import typing
+
+
+@typing.final
+class InfinityType:
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return False
+
+    def __le__(self, other: object) -> bool:
+        return False
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return True
+
+    def __ge__(self, other: object) -> bool:
+        return True
+
+    def __neg__(self: object) -> "NegativeInfinityType":
+        return NegativeInfinity
+
+
+Infinity = InfinityType()
+
+
+@typing.final
+class NegativeInfinityType:
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "-Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return True
+
+    def __le__(self, other: object) -> bool:
+        return True
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return False
+
+    def __ge__(self, other: object) -> bool:
+        return False
+
+    def __neg__(self: object) -> InfinityType:
+        return Infinity
+
+
+NegativeInfinity = NegativeInfinityType()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_tokenizer.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6d20dd3f56f880a92db7409a3e1335cb282a8f2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/_tokenizer.py
@@ -0,0 +1,193 @@
+from __future__ import annotations
+
+import contextlib
+import re
+from dataclasses import dataclass
+from typing import Generator, Mapping, NoReturn
+
+from .specifiers import Specifier
+
+
+@dataclass
+class Token:
+    name: str
+    text: str
+    position: int
+
+
+class ParserSyntaxError(Exception):
+    """The provided source text could not be parsed correctly."""
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        source: str,
+        span: tuple[int, int],
+    ) -> None:
+        self.span = span
+        self.message = message
+        self.source = source
+
+        super().__init__()
+
+    def __str__(self) -> str:
+        marker = " " * self.span[0] + "~" * (self.span[1] - self.span[0]) + "^"
+        return f"{self.message}\n    {self.source}\n    {marker}"
+
+
+DEFAULT_RULES: dict[str, re.Pattern[str]] = {
+    "LEFT_PARENTHESIS": re.compile(r"\("),
+    "RIGHT_PARENTHESIS": re.compile(r"\)"),
+    "LEFT_BRACKET": re.compile(r"\["),
+    "RIGHT_BRACKET": re.compile(r"\]"),
+    "SEMICOLON": re.compile(r";"),
+    "COMMA": re.compile(r","),
+    "QUOTED_STRING": re.compile(
+        r"""
+            (
+                ('[^']*')
+                |
+                ("[^"]*")
+            )
+        """,
+        re.VERBOSE,
+    ),
+    "OP": re.compile(r"(===|==|~=|!=|<=|>=|<|>)"),
+    "BOOLOP": re.compile(r"\b(or|and)\b"),
+    "IN": re.compile(r"\bin\b"),
+    "NOT": re.compile(r"\bnot\b"),
+    "VARIABLE": re.compile(
+        r"""
+            \b(
+                python_version
+                |python_full_version
+                |os[._]name
+                |sys[._]platform
+                |platform_(release|system)
+                |platform[._](version|machine|python_implementation)
+                |python_implementation
+                |implementation_(name|version)
+                |extras?
+                |dependency_groups
+            )\b
+        """,
+        re.VERBOSE,
+    ),
+    "SPECIFIER": re.compile(
+        Specifier._operator_regex_str + Specifier._version_regex_str,
+        re.VERBOSE | re.IGNORECASE,
+    ),
+    "AT": re.compile(r"\@"),
+    "URL": re.compile(r"[^ \t]+"),
+    "IDENTIFIER": re.compile(r"\b[a-zA-Z0-9][a-zA-Z0-9._-]*\b"),
+    "VERSION_PREFIX_TRAIL": re.compile(r"\.\*"),
+    "VERSION_LOCAL_LABEL_TRAIL": re.compile(r"\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*"),
+    "WS": re.compile(r"[ \t]+"),
+    "END": re.compile(r"$"),
+}
+
+
+class Tokenizer:
+    """Context-sensitive token parsing.
+
+    Provides methods to examine the input stream to check whether the next token
+    matches.
+    """
+
+    def __init__(
+        self,
+        source: str,
+        *,
+        rules: Mapping[str, re.Pattern[str]],
+    ) -> None:
+        self.source = source
+        self.rules = rules
+        self.next_token: Token | None = None
+        self.position = 0
+
+    def consume(self, name: str) -> None:
+        """Move beyond provided token name, if at current position."""
+        if self.check(name):
+            self.read()
+
+    def check(self, name: str, *, peek: bool = False) -> bool:
+        """Check whether the next token has the provided name.
+
+        By default, if the check succeeds, the token *must* be read before
+        another check. If `peek` is set to `True`, the token is not loaded and
+        would need to be checked again.
+        """
+        assert self.next_token is None, (
+            f"Cannot check for {name!r}, already have {self.next_token!r}"
+        )
+        assert name in self.rules, f"Unknown token name: {name!r}"
+
+        expression = self.rules[name]
+
+        match = expression.match(self.source, self.position)
+        if match is None:
+            return False
+        if not peek:
+            self.next_token = Token(name, match[0], self.position)
+        return True
+
+    def expect(self, name: str, *, expected: str) -> Token:
+        """Expect a certain token name next, failing with a syntax error otherwise.
+
+        The token is *not* read.
+        """
+        if not self.check(name):
+            raise self.raise_syntax_error(f"Expected {expected}")
+        return self.read()
+
+    def read(self) -> Token:
+        """Consume the next token and return it."""
+        token = self.next_token
+        assert token is not None
+
+        self.position += len(token.text)
+        self.next_token = None
+
+        return token
+
+    def raise_syntax_error(
+        self,
+        message: str,
+        *,
+        span_start: int | None = None,
+        span_end: int | None = None,
+    ) -> NoReturn:
+        """Raise ParserSyntaxError at the given position."""
+        span = (
+            self.position if span_start is None else span_start,
+            self.position if span_end is None else span_end,
+        )
+        raise ParserSyntaxError(
+            message,
+            source=self.source,
+            span=span,
+        )
+
+    @contextlib.contextmanager
+    def enclosing_tokens(
+        self, open_token: str, close_token: str, *, around: str
+    ) -> Generator[None, None, None]:
+        if self.check(open_token):
+            open_position = self.position
+            self.read()
+        else:
+            open_position = None
+
+        yield
+
+        if open_position is None:
+            return
+
+        if not self.check(close_token):
+            self.raise_syntax_error(
+                f"Expected matching {close_token} for {open_token}, after {around}",
+                span_start=open_position,
+            )
+
+        self.read()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/licenses/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/licenses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..335b275fa7575b0a7c525a713fbe0252ad2d956f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/licenses/__init__.py
@@ -0,0 +1,147 @@
+#######################################################################################
+#
+# Adapted from:
+#  https://github.com/pypa/hatch/blob/5352e44/backend/src/hatchling/licenses/parse.py
+#
+# MIT License
+#
+# Copyright (c) 2017-present Ofek Lev <oss@ofek.dev>
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this
+# software and associated documentation files (the "Software"), to deal in the Software
+# without restriction, including without limitation the rights to use, copy, modify,
+# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to the following
+# conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies
+# or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+# OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#
+# With additional allowance of arbitrary `LicenseRef-` identifiers, not just
+# `LicenseRef-Public-Domain` and `LicenseRef-Proprietary`.
+#
+#######################################################################################
+from __future__ import annotations
+
+import re
+from typing import NewType, cast
+
+from ._spdx import EXCEPTIONS, LICENSES
+
+__all__ = [
+    "InvalidLicenseExpression",
+    "NormalizedLicenseExpression",
+    "canonicalize_license_expression",
+]
+
+license_ref_allowed = re.compile("^[A-Za-z0-9.-]*$")
+
+NormalizedLicenseExpression = NewType("NormalizedLicenseExpression", str)
+
+
+class InvalidLicenseExpression(ValueError):
+    """Raised when a license-expression string is invalid
+
+    >>> canonicalize_license_expression("invalid")
+    Traceback (most recent call last):
+        ...
+    packaging.licenses.InvalidLicenseExpression: Invalid license expression: 'invalid'
+    """
+
+
+def canonicalize_license_expression(
+    raw_license_expression: str,
+) -> NormalizedLicenseExpression:
+    if not raw_license_expression:
+        message = f"Invalid license expression: {raw_license_expression!r}"
+        raise InvalidLicenseExpression(message)
+
+    # Pad any parentheses so tokenization can be achieved by merely splitting on
+    # whitespace.
+    license_expression = raw_license_expression.replace("(", " ( ").replace(")", " ) ")
+    licenseref_prefix = "LicenseRef-"
+    license_refs = {
+        ref.lower(): "LicenseRef-" + ref[len(licenseref_prefix) :]
+        for ref in license_expression.split()
+        if ref.lower().startswith(licenseref_prefix.lower())
+    }
+
+    # Normalize to lower case so we can look up licenses/exceptions
+    # and so boolean operators are Python-compatible.
+    license_expression = license_expression.lower()
+
+    tokens = license_expression.split()
+
+    # Rather than implementing a parenthesis/boolean logic parser, create an
+    # expression that Python can parse. Everything that is not involved with the
+    # grammar itself is replaced with the placeholder `False` and the resultant
+    # expression should become a valid Python expression.
+    python_tokens = []
+    for token in tokens:
+        if token not in {"or", "and", "with", "(", ")"}:
+            python_tokens.append("False")
+        elif token == "with":
+            python_tokens.append("or")
+        elif (
+            token == "("
+            and python_tokens
+            and python_tokens[-1] not in {"or", "and", "("}
+        ) or (token == ")" and python_tokens and python_tokens[-1] == "("):
+            message = f"Invalid license expression: {raw_license_expression!r}"
+            raise InvalidLicenseExpression(message)
+        else:
+            python_tokens.append(token)
+
+    python_expression = " ".join(python_tokens)
+    try:
+        compile(python_expression, "", "eval")
+    except SyntaxError:
+        message = f"Invalid license expression: {raw_license_expression!r}"
+        raise InvalidLicenseExpression(message) from None
+
+    # Take a final pass to check for unknown licenses/exceptions.
+    normalized_tokens = []
+    for token in tokens:
+        if token in {"or", "and", "with", "(", ")"}:
+            normalized_tokens.append(token.upper())
+            continue
+
+        if normalized_tokens and normalized_tokens[-1] == "WITH":
+            if token not in EXCEPTIONS:
+                message = f"Unknown license exception: {token!r}"
+                raise InvalidLicenseExpression(message)
+
+            normalized_tokens.append(EXCEPTIONS[token]["id"])
+        else:
+            if token.endswith("+"):
+                final_token = token[:-1]
+                suffix = "+"
+            else:
+                final_token = token
+                suffix = ""
+
+            if final_token.startswith("licenseref-"):
+                if not license_ref_allowed.match(final_token):
+                    message = f"Invalid licenseref: {final_token!r}"
+                    raise InvalidLicenseExpression(message)
+                normalized_tokens.append(license_refs[final_token] + suffix)
+            else:
+                if final_token not in LICENSES:
+                    message = f"Unknown license: {final_token!r}"
+                    raise InvalidLicenseExpression(message)
+                normalized_tokens.append(LICENSES[final_token]["id"] + suffix)
+
+    normalized_expression = " ".join(normalized_tokens)
+
+    return cast(
+        "NormalizedLicenseExpression",
+        normalized_expression.replace("( ", "(").replace(" )", ")"),
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/licenses/_spdx.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/licenses/_spdx.py
new file mode 100644
index 0000000000000000000000000000000000000000..a277af28220b6dbe4599471104d1c7a2bd1e1288
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/licenses/_spdx.py
@@ -0,0 +1,799 @@
+
+from __future__ import annotations
+
+from typing import TypedDict
+
+class SPDXLicense(TypedDict):
+    id: str
+    deprecated: bool
+
+class SPDXException(TypedDict):
+    id: str
+    deprecated: bool
+
+
+VERSION = '3.27.0'
+
+LICENSES: dict[str, SPDXLicense] = {
+    '0bsd': {'id': '0BSD', 'deprecated': False},
+    '3d-slicer-1.0': {'id': '3D-Slicer-1.0', 'deprecated': False},
+    'aal': {'id': 'AAL', 'deprecated': False},
+    'abstyles': {'id': 'Abstyles', 'deprecated': False},
+    'adacore-doc': {'id': 'AdaCore-doc', 'deprecated': False},
+    'adobe-2006': {'id': 'Adobe-2006', 'deprecated': False},
+    'adobe-display-postscript': {'id': 'Adobe-Display-PostScript', 'deprecated': False},
+    'adobe-glyph': {'id': 'Adobe-Glyph', 'deprecated': False},
+    'adobe-utopia': {'id': 'Adobe-Utopia', 'deprecated': False},
+    'adsl': {'id': 'ADSL', 'deprecated': False},
+    'afl-1.1': {'id': 'AFL-1.1', 'deprecated': False},
+    'afl-1.2': {'id': 'AFL-1.2', 'deprecated': False},
+    'afl-2.0': {'id': 'AFL-2.0', 'deprecated': False},
+    'afl-2.1': {'id': 'AFL-2.1', 'deprecated': False},
+    'afl-3.0': {'id': 'AFL-3.0', 'deprecated': False},
+    'afmparse': {'id': 'Afmparse', 'deprecated': False},
+    'agpl-1.0': {'id': 'AGPL-1.0', 'deprecated': True},
+    'agpl-1.0-only': {'id': 'AGPL-1.0-only', 'deprecated': False},
+    'agpl-1.0-or-later': {'id': 'AGPL-1.0-or-later', 'deprecated': False},
+    'agpl-3.0': {'id': 'AGPL-3.0', 'deprecated': True},
+    'agpl-3.0-only': {'id': 'AGPL-3.0-only', 'deprecated': False},
+    'agpl-3.0-or-later': {'id': 'AGPL-3.0-or-later', 'deprecated': False},
+    'aladdin': {'id': 'Aladdin', 'deprecated': False},
+    'amd-newlib': {'id': 'AMD-newlib', 'deprecated': False},
+    'amdplpa': {'id': 'AMDPLPA', 'deprecated': False},
+    'aml': {'id': 'AML', 'deprecated': False},
+    'aml-glslang': {'id': 'AML-glslang', 'deprecated': False},
+    'ampas': {'id': 'AMPAS', 'deprecated': False},
+    'antlr-pd': {'id': 'ANTLR-PD', 'deprecated': False},
+    'antlr-pd-fallback': {'id': 'ANTLR-PD-fallback', 'deprecated': False},
+    'any-osi': {'id': 'any-OSI', 'deprecated': False},
+    'any-osi-perl-modules': {'id': 'any-OSI-perl-modules', 'deprecated': False},
+    'apache-1.0': {'id': 'Apache-1.0', 'deprecated': False},
+    'apache-1.1': {'id': 'Apache-1.1', 'deprecated': False},
+    'apache-2.0': {'id': 'Apache-2.0', 'deprecated': False},
+    'apafml': {'id': 'APAFML', 'deprecated': False},
+    'apl-1.0': {'id': 'APL-1.0', 'deprecated': False},
+    'app-s2p': {'id': 'App-s2p', 'deprecated': False},
+    'apsl-1.0': {'id': 'APSL-1.0', 'deprecated': False},
+    'apsl-1.1': {'id': 'APSL-1.1', 'deprecated': False},
+    'apsl-1.2': {'id': 'APSL-1.2', 'deprecated': False},
+    'apsl-2.0': {'id': 'APSL-2.0', 'deprecated': False},
+    'arphic-1999': {'id': 'Arphic-1999', 'deprecated': False},
+    'artistic-1.0': {'id': 'Artistic-1.0', 'deprecated': False},
+    'artistic-1.0-cl8': {'id': 'Artistic-1.0-cl8', 'deprecated': False},
+    'artistic-1.0-perl': {'id': 'Artistic-1.0-Perl', 'deprecated': False},
+    'artistic-2.0': {'id': 'Artistic-2.0', 'deprecated': False},
+    'artistic-dist': {'id': 'Artistic-dist', 'deprecated': False},
+    'aspell-ru': {'id': 'Aspell-RU', 'deprecated': False},
+    'aswf-digital-assets-1.0': {'id': 'ASWF-Digital-Assets-1.0', 'deprecated': False},
+    'aswf-digital-assets-1.1': {'id': 'ASWF-Digital-Assets-1.1', 'deprecated': False},
+    'baekmuk': {'id': 'Baekmuk', 'deprecated': False},
+    'bahyph': {'id': 'Bahyph', 'deprecated': False},
+    'barr': {'id': 'Barr', 'deprecated': False},
+    'bcrypt-solar-designer': {'id': 'bcrypt-Solar-Designer', 'deprecated': False},
+    'beerware': {'id': 'Beerware', 'deprecated': False},
+    'bitstream-charter': {'id': 'Bitstream-Charter', 'deprecated': False},
+    'bitstream-vera': {'id': 'Bitstream-Vera', 'deprecated': False},
+    'bittorrent-1.0': {'id': 'BitTorrent-1.0', 'deprecated': False},
+    'bittorrent-1.1': {'id': 'BitTorrent-1.1', 'deprecated': False},
+    'blessing': {'id': 'blessing', 'deprecated': False},
+    'blueoak-1.0.0': {'id': 'BlueOak-1.0.0', 'deprecated': False},
+    'boehm-gc': {'id': 'Boehm-GC', 'deprecated': False},
+    'boehm-gc-without-fee': {'id': 'Boehm-GC-without-fee', 'deprecated': False},
+    'borceux': {'id': 'Borceux', 'deprecated': False},
+    'brian-gladman-2-clause': {'id': 'Brian-Gladman-2-Clause', 'deprecated': False},
+    'brian-gladman-3-clause': {'id': 'Brian-Gladman-3-Clause', 'deprecated': False},
+    'bsd-1-clause': {'id': 'BSD-1-Clause', 'deprecated': False},
+    'bsd-2-clause': {'id': 'BSD-2-Clause', 'deprecated': False},
+    'bsd-2-clause-darwin': {'id': 'BSD-2-Clause-Darwin', 'deprecated': False},
+    'bsd-2-clause-first-lines': {'id': 'BSD-2-Clause-first-lines', 'deprecated': False},
+    'bsd-2-clause-freebsd': {'id': 'BSD-2-Clause-FreeBSD', 'deprecated': True},
+    'bsd-2-clause-netbsd': {'id': 'BSD-2-Clause-NetBSD', 'deprecated': True},
+    'bsd-2-clause-patent': {'id': 'BSD-2-Clause-Patent', 'deprecated': False},
+    'bsd-2-clause-pkgconf-disclaimer': {'id': 'BSD-2-Clause-pkgconf-disclaimer', 'deprecated': False},
+    'bsd-2-clause-views': {'id': 'BSD-2-Clause-Views', 'deprecated': False},
+    'bsd-3-clause': {'id': 'BSD-3-Clause', 'deprecated': False},
+    'bsd-3-clause-acpica': {'id': 'BSD-3-Clause-acpica', 'deprecated': False},
+    'bsd-3-clause-attribution': {'id': 'BSD-3-Clause-Attribution', 'deprecated': False},
+    'bsd-3-clause-clear': {'id': 'BSD-3-Clause-Clear', 'deprecated': False},
+    'bsd-3-clause-flex': {'id': 'BSD-3-Clause-flex', 'deprecated': False},
+    'bsd-3-clause-hp': {'id': 'BSD-3-Clause-HP', 'deprecated': False},
+    'bsd-3-clause-lbnl': {'id': 'BSD-3-Clause-LBNL', 'deprecated': False},
+    'bsd-3-clause-modification': {'id': 'BSD-3-Clause-Modification', 'deprecated': False},
+    'bsd-3-clause-no-military-license': {'id': 'BSD-3-Clause-No-Military-License', 'deprecated': False},
+    'bsd-3-clause-no-nuclear-license': {'id': 'BSD-3-Clause-No-Nuclear-License', 'deprecated': False},
+    'bsd-3-clause-no-nuclear-license-2014': {'id': 'BSD-3-Clause-No-Nuclear-License-2014', 'deprecated': False},
+    'bsd-3-clause-no-nuclear-warranty': {'id': 'BSD-3-Clause-No-Nuclear-Warranty', 'deprecated': False},
+    'bsd-3-clause-open-mpi': {'id': 'BSD-3-Clause-Open-MPI', 'deprecated': False},
+    'bsd-3-clause-sun': {'id': 'BSD-3-Clause-Sun', 'deprecated': False},
+    'bsd-4-clause': {'id': 'BSD-4-Clause', 'deprecated': False},
+    'bsd-4-clause-shortened': {'id': 'BSD-4-Clause-Shortened', 'deprecated': False},
+    'bsd-4-clause-uc': {'id': 'BSD-4-Clause-UC', 'deprecated': False},
+    'bsd-4.3reno': {'id': 'BSD-4.3RENO', 'deprecated': False},
+    'bsd-4.3tahoe': {'id': 'BSD-4.3TAHOE', 'deprecated': False},
+    'bsd-advertising-acknowledgement': {'id': 'BSD-Advertising-Acknowledgement', 'deprecated': False},
+    'bsd-attribution-hpnd-disclaimer': {'id': 'BSD-Attribution-HPND-disclaimer', 'deprecated': False},
+    'bsd-inferno-nettverk': {'id': 'BSD-Inferno-Nettverk', 'deprecated': False},
+    'bsd-protection': {'id': 'BSD-Protection', 'deprecated': False},
+    'bsd-source-beginning-file': {'id': 'BSD-Source-beginning-file', 'deprecated': False},
+    'bsd-source-code': {'id': 'BSD-Source-Code', 'deprecated': False},
+    'bsd-systemics': {'id': 'BSD-Systemics', 'deprecated': False},
+    'bsd-systemics-w3works': {'id': 'BSD-Systemics-W3Works', 'deprecated': False},
+    'bsl-1.0': {'id': 'BSL-1.0', 'deprecated': False},
+    'busl-1.1': {'id': 'BUSL-1.1', 'deprecated': False},
+    'bzip2-1.0.5': {'id': 'bzip2-1.0.5', 'deprecated': True},
+    'bzip2-1.0.6': {'id': 'bzip2-1.0.6', 'deprecated': False},
+    'c-uda-1.0': {'id': 'C-UDA-1.0', 'deprecated': False},
+    'cal-1.0': {'id': 'CAL-1.0', 'deprecated': False},
+    'cal-1.0-combined-work-exception': {'id': 'CAL-1.0-Combined-Work-Exception', 'deprecated': False},
+    'caldera': {'id': 'Caldera', 'deprecated': False},
+    'caldera-no-preamble': {'id': 'Caldera-no-preamble', 'deprecated': False},
+    'catharon': {'id': 'Catharon', 'deprecated': False},
+    'catosl-1.1': {'id': 'CATOSL-1.1', 'deprecated': False},
+    'cc-by-1.0': {'id': 'CC-BY-1.0', 'deprecated': False},
+    'cc-by-2.0': {'id': 'CC-BY-2.0', 'deprecated': False},
+    'cc-by-2.5': {'id': 'CC-BY-2.5', 'deprecated': False},
+    'cc-by-2.5-au': {'id': 'CC-BY-2.5-AU', 'deprecated': False},
+    'cc-by-3.0': {'id': 'CC-BY-3.0', 'deprecated': False},
+    'cc-by-3.0-at': {'id': 'CC-BY-3.0-AT', 'deprecated': False},
+    'cc-by-3.0-au': {'id': 'CC-BY-3.0-AU', 'deprecated': False},
+    'cc-by-3.0-de': {'id': 'CC-BY-3.0-DE', 'deprecated': False},
+    'cc-by-3.0-igo': {'id': 'CC-BY-3.0-IGO', 'deprecated': False},
+    'cc-by-3.0-nl': {'id': 'CC-BY-3.0-NL', 'deprecated': False},
+    'cc-by-3.0-us': {'id': 'CC-BY-3.0-US', 'deprecated': False},
+    'cc-by-4.0': {'id': 'CC-BY-4.0', 'deprecated': False},
+    'cc-by-nc-1.0': {'id': 'CC-BY-NC-1.0', 'deprecated': False},
+    'cc-by-nc-2.0': {'id': 'CC-BY-NC-2.0', 'deprecated': False},
+    'cc-by-nc-2.5': {'id': 'CC-BY-NC-2.5', 'deprecated': False},
+    'cc-by-nc-3.0': {'id': 'CC-BY-NC-3.0', 'deprecated': False},
+    'cc-by-nc-3.0-de': {'id': 'CC-BY-NC-3.0-DE', 'deprecated': False},
+    'cc-by-nc-4.0': {'id': 'CC-BY-NC-4.0', 'deprecated': False},
+    'cc-by-nc-nd-1.0': {'id': 'CC-BY-NC-ND-1.0', 'deprecated': False},
+    'cc-by-nc-nd-2.0': {'id': 'CC-BY-NC-ND-2.0', 'deprecated': False},
+    'cc-by-nc-nd-2.5': {'id': 'CC-BY-NC-ND-2.5', 'deprecated': False},
+    'cc-by-nc-nd-3.0': {'id': 'CC-BY-NC-ND-3.0', 'deprecated': False},
+    'cc-by-nc-nd-3.0-de': {'id': 'CC-BY-NC-ND-3.0-DE', 'deprecated': False},
+    'cc-by-nc-nd-3.0-igo': {'id': 'CC-BY-NC-ND-3.0-IGO', 'deprecated': False},
+    'cc-by-nc-nd-4.0': {'id': 'CC-BY-NC-ND-4.0', 'deprecated': False},
+    'cc-by-nc-sa-1.0': {'id': 'CC-BY-NC-SA-1.0', 'deprecated': False},
+    'cc-by-nc-sa-2.0': {'id': 'CC-BY-NC-SA-2.0', 'deprecated': False},
+    'cc-by-nc-sa-2.0-de': {'id': 'CC-BY-NC-SA-2.0-DE', 'deprecated': False},
+    'cc-by-nc-sa-2.0-fr': {'id': 'CC-BY-NC-SA-2.0-FR', 'deprecated': False},
+    'cc-by-nc-sa-2.0-uk': {'id': 'CC-BY-NC-SA-2.0-UK', 'deprecated': False},
+    'cc-by-nc-sa-2.5': {'id': 'CC-BY-NC-SA-2.5', 'deprecated': False},
+    'cc-by-nc-sa-3.0': {'id': 'CC-BY-NC-SA-3.0', 'deprecated': False},
+    'cc-by-nc-sa-3.0-de': {'id': 'CC-BY-NC-SA-3.0-DE', 'deprecated': False},
+    'cc-by-nc-sa-3.0-igo': {'id': 'CC-BY-NC-SA-3.0-IGO', 'deprecated': False},
+    'cc-by-nc-sa-4.0': {'id': 'CC-BY-NC-SA-4.0', 'deprecated': False},
+    'cc-by-nd-1.0': {'id': 'CC-BY-ND-1.0', 'deprecated': False},
+    'cc-by-nd-2.0': {'id': 'CC-BY-ND-2.0', 'deprecated': False},
+    'cc-by-nd-2.5': {'id': 'CC-BY-ND-2.5', 'deprecated': False},
+    'cc-by-nd-3.0': {'id': 'CC-BY-ND-3.0', 'deprecated': False},
+    'cc-by-nd-3.0-de': {'id': 'CC-BY-ND-3.0-DE', 'deprecated': False},
+    'cc-by-nd-4.0': {'id': 'CC-BY-ND-4.0', 'deprecated': False},
+    'cc-by-sa-1.0': {'id': 'CC-BY-SA-1.0', 'deprecated': False},
+    'cc-by-sa-2.0': {'id': 'CC-BY-SA-2.0', 'deprecated': False},
+    'cc-by-sa-2.0-uk': {'id': 'CC-BY-SA-2.0-UK', 'deprecated': False},
+    'cc-by-sa-2.1-jp': {'id': 'CC-BY-SA-2.1-JP', 'deprecated': False},
+    'cc-by-sa-2.5': {'id': 'CC-BY-SA-2.5', 'deprecated': False},
+    'cc-by-sa-3.0': {'id': 'CC-BY-SA-3.0', 'deprecated': False},
+    'cc-by-sa-3.0-at': {'id': 'CC-BY-SA-3.0-AT', 'deprecated': False},
+    'cc-by-sa-3.0-de': {'id': 'CC-BY-SA-3.0-DE', 'deprecated': False},
+    'cc-by-sa-3.0-igo': {'id': 'CC-BY-SA-3.0-IGO', 'deprecated': False},
+    'cc-by-sa-4.0': {'id': 'CC-BY-SA-4.0', 'deprecated': False},
+    'cc-pddc': {'id': 'CC-PDDC', 'deprecated': False},
+    'cc-pdm-1.0': {'id': 'CC-PDM-1.0', 'deprecated': False},
+    'cc-sa-1.0': {'id': 'CC-SA-1.0', 'deprecated': False},
+    'cc0-1.0': {'id': 'CC0-1.0', 'deprecated': False},
+    'cddl-1.0': {'id': 'CDDL-1.0', 'deprecated': False},
+    'cddl-1.1': {'id': 'CDDL-1.1', 'deprecated': False},
+    'cdl-1.0': {'id': 'CDL-1.0', 'deprecated': False},
+    'cdla-permissive-1.0': {'id': 'CDLA-Permissive-1.0', 'deprecated': False},
+    'cdla-permissive-2.0': {'id': 'CDLA-Permissive-2.0', 'deprecated': False},
+    'cdla-sharing-1.0': {'id': 'CDLA-Sharing-1.0', 'deprecated': False},
+    'cecill-1.0': {'id': 'CECILL-1.0', 'deprecated': False},
+    'cecill-1.1': {'id': 'CECILL-1.1', 'deprecated': False},
+    'cecill-2.0': {'id': 'CECILL-2.0', 'deprecated': False},
+    'cecill-2.1': {'id': 'CECILL-2.1', 'deprecated': False},
+    'cecill-b': {'id': 'CECILL-B', 'deprecated': False},
+    'cecill-c': {'id': 'CECILL-C', 'deprecated': False},
+    'cern-ohl-1.1': {'id': 'CERN-OHL-1.1', 'deprecated': False},
+    'cern-ohl-1.2': {'id': 'CERN-OHL-1.2', 'deprecated': False},
+    'cern-ohl-p-2.0': {'id': 'CERN-OHL-P-2.0', 'deprecated': False},
+    'cern-ohl-s-2.0': {'id': 'CERN-OHL-S-2.0', 'deprecated': False},
+    'cern-ohl-w-2.0': {'id': 'CERN-OHL-W-2.0', 'deprecated': False},
+    'cfitsio': {'id': 'CFITSIO', 'deprecated': False},
+    'check-cvs': {'id': 'check-cvs', 'deprecated': False},
+    'checkmk': {'id': 'checkmk', 'deprecated': False},
+    'clartistic': {'id': 'ClArtistic', 'deprecated': False},
+    'clips': {'id': 'Clips', 'deprecated': False},
+    'cmu-mach': {'id': 'CMU-Mach', 'deprecated': False},
+    'cmu-mach-nodoc': {'id': 'CMU-Mach-nodoc', 'deprecated': False},
+    'cnri-jython': {'id': 'CNRI-Jython', 'deprecated': False},
+    'cnri-python': {'id': 'CNRI-Python', 'deprecated': False},
+    'cnri-python-gpl-compatible': {'id': 'CNRI-Python-GPL-Compatible', 'deprecated': False},
+    'coil-1.0': {'id': 'COIL-1.0', 'deprecated': False},
+    'community-spec-1.0': {'id': 'Community-Spec-1.0', 'deprecated': False},
+    'condor-1.1': {'id': 'Condor-1.1', 'deprecated': False},
+    'copyleft-next-0.3.0': {'id': 'copyleft-next-0.3.0', 'deprecated': False},
+    'copyleft-next-0.3.1': {'id': 'copyleft-next-0.3.1', 'deprecated': False},
+    'cornell-lossless-jpeg': {'id': 'Cornell-Lossless-JPEG', 'deprecated': False},
+    'cpal-1.0': {'id': 'CPAL-1.0', 'deprecated': False},
+    'cpl-1.0': {'id': 'CPL-1.0', 'deprecated': False},
+    'cpol-1.02': {'id': 'CPOL-1.02', 'deprecated': False},
+    'cronyx': {'id': 'Cronyx', 'deprecated': False},
+    'crossword': {'id': 'Crossword', 'deprecated': False},
+    'cryptoswift': {'id': 'CryptoSwift', 'deprecated': False},
+    'crystalstacker': {'id': 'CrystalStacker', 'deprecated': False},
+    'cua-opl-1.0': {'id': 'CUA-OPL-1.0', 'deprecated': False},
+    'cube': {'id': 'Cube', 'deprecated': False},
+    'curl': {'id': 'curl', 'deprecated': False},
+    'cve-tou': {'id': 'cve-tou', 'deprecated': False},
+    'd-fsl-1.0': {'id': 'D-FSL-1.0', 'deprecated': False},
+    'dec-3-clause': {'id': 'DEC-3-Clause', 'deprecated': False},
+    'diffmark': {'id': 'diffmark', 'deprecated': False},
+    'dl-de-by-2.0': {'id': 'DL-DE-BY-2.0', 'deprecated': False},
+    'dl-de-zero-2.0': {'id': 'DL-DE-ZERO-2.0', 'deprecated': False},
+    'doc': {'id': 'DOC', 'deprecated': False},
+    'docbook-dtd': {'id': 'DocBook-DTD', 'deprecated': False},
+    'docbook-schema': {'id': 'DocBook-Schema', 'deprecated': False},
+    'docbook-stylesheet': {'id': 'DocBook-Stylesheet', 'deprecated': False},
+    'docbook-xml': {'id': 'DocBook-XML', 'deprecated': False},
+    'dotseqn': {'id': 'Dotseqn', 'deprecated': False},
+    'drl-1.0': {'id': 'DRL-1.0', 'deprecated': False},
+    'drl-1.1': {'id': 'DRL-1.1', 'deprecated': False},
+    'dsdp': {'id': 'DSDP', 'deprecated': False},
+    'dtoa': {'id': 'dtoa', 'deprecated': False},
+    'dvipdfm': {'id': 'dvipdfm', 'deprecated': False},
+    'ecl-1.0': {'id': 'ECL-1.0', 'deprecated': False},
+    'ecl-2.0': {'id': 'ECL-2.0', 'deprecated': False},
+    'ecos-2.0': {'id': 'eCos-2.0', 'deprecated': True},
+    'efl-1.0': {'id': 'EFL-1.0', 'deprecated': False},
+    'efl-2.0': {'id': 'EFL-2.0', 'deprecated': False},
+    'egenix': {'id': 'eGenix', 'deprecated': False},
+    'elastic-2.0': {'id': 'Elastic-2.0', 'deprecated': False},
+    'entessa': {'id': 'Entessa', 'deprecated': False},
+    'epics': {'id': 'EPICS', 'deprecated': False},
+    'epl-1.0': {'id': 'EPL-1.0', 'deprecated': False},
+    'epl-2.0': {'id': 'EPL-2.0', 'deprecated': False},
+    'erlpl-1.1': {'id': 'ErlPL-1.1', 'deprecated': False},
+    'etalab-2.0': {'id': 'etalab-2.0', 'deprecated': False},
+    'eudatagrid': {'id': 'EUDatagrid', 'deprecated': False},
+    'eupl-1.0': {'id': 'EUPL-1.0', 'deprecated': False},
+    'eupl-1.1': {'id': 'EUPL-1.1', 'deprecated': False},
+    'eupl-1.2': {'id': 'EUPL-1.2', 'deprecated': False},
+    'eurosym': {'id': 'Eurosym', 'deprecated': False},
+    'fair': {'id': 'Fair', 'deprecated': False},
+    'fbm': {'id': 'FBM', 'deprecated': False},
+    'fdk-aac': {'id': 'FDK-AAC', 'deprecated': False},
+    'ferguson-twofish': {'id': 'Ferguson-Twofish', 'deprecated': False},
+    'frameworx-1.0': {'id': 'Frameworx-1.0', 'deprecated': False},
+    'freebsd-doc': {'id': 'FreeBSD-DOC', 'deprecated': False},
+    'freeimage': {'id': 'FreeImage', 'deprecated': False},
+    'fsfap': {'id': 'FSFAP', 'deprecated': False},
+    'fsfap-no-warranty-disclaimer': {'id': 'FSFAP-no-warranty-disclaimer', 'deprecated': False},
+    'fsful': {'id': 'FSFUL', 'deprecated': False},
+    'fsfullr': {'id': 'FSFULLR', 'deprecated': False},
+    'fsfullrsd': {'id': 'FSFULLRSD', 'deprecated': False},
+    'fsfullrwd': {'id': 'FSFULLRWD', 'deprecated': False},
+    'fsl-1.1-alv2': {'id': 'FSL-1.1-ALv2', 'deprecated': False},
+    'fsl-1.1-mit': {'id': 'FSL-1.1-MIT', 'deprecated': False},
+    'ftl': {'id': 'FTL', 'deprecated': False},
+    'furuseth': {'id': 'Furuseth', 'deprecated': False},
+    'fwlw': {'id': 'fwlw', 'deprecated': False},
+    'game-programming-gems': {'id': 'Game-Programming-Gems', 'deprecated': False},
+    'gcr-docs': {'id': 'GCR-docs', 'deprecated': False},
+    'gd': {'id': 'GD', 'deprecated': False},
+    'generic-xts': {'id': 'generic-xts', 'deprecated': False},
+    'gfdl-1.1': {'id': 'GFDL-1.1', 'deprecated': True},
+    'gfdl-1.1-invariants-only': {'id': 'GFDL-1.1-invariants-only', 'deprecated': False},
+    'gfdl-1.1-invariants-or-later': {'id': 'GFDL-1.1-invariants-or-later', 'deprecated': False},
+    'gfdl-1.1-no-invariants-only': {'id': 'GFDL-1.1-no-invariants-only', 'deprecated': False},
+    'gfdl-1.1-no-invariants-or-later': {'id': 'GFDL-1.1-no-invariants-or-later', 'deprecated': False},
+    'gfdl-1.1-only': {'id': 'GFDL-1.1-only', 'deprecated': False},
+    'gfdl-1.1-or-later': {'id': 'GFDL-1.1-or-later', 'deprecated': False},
+    'gfdl-1.2': {'id': 'GFDL-1.2', 'deprecated': True},
+    'gfdl-1.2-invariants-only': {'id': 'GFDL-1.2-invariants-only', 'deprecated': False},
+    'gfdl-1.2-invariants-or-later': {'id': 'GFDL-1.2-invariants-or-later', 'deprecated': False},
+    'gfdl-1.2-no-invariants-only': {'id': 'GFDL-1.2-no-invariants-only', 'deprecated': False},
+    'gfdl-1.2-no-invariants-or-later': {'id': 'GFDL-1.2-no-invariants-or-later', 'deprecated': False},
+    'gfdl-1.2-only': {'id': 'GFDL-1.2-only', 'deprecated': False},
+    'gfdl-1.2-or-later': {'id': 'GFDL-1.2-or-later', 'deprecated': False},
+    'gfdl-1.3': {'id': 'GFDL-1.3', 'deprecated': True},
+    'gfdl-1.3-invariants-only': {'id': 'GFDL-1.3-invariants-only', 'deprecated': False},
+    'gfdl-1.3-invariants-or-later': {'id': 'GFDL-1.3-invariants-or-later', 'deprecated': False},
+    'gfdl-1.3-no-invariants-only': {'id': 'GFDL-1.3-no-invariants-only', 'deprecated': False},
+    'gfdl-1.3-no-invariants-or-later': {'id': 'GFDL-1.3-no-invariants-or-later', 'deprecated': False},
+    'gfdl-1.3-only': {'id': 'GFDL-1.3-only', 'deprecated': False},
+    'gfdl-1.3-or-later': {'id': 'GFDL-1.3-or-later', 'deprecated': False},
+    'giftware': {'id': 'Giftware', 'deprecated': False},
+    'gl2ps': {'id': 'GL2PS', 'deprecated': False},
+    'glide': {'id': 'Glide', 'deprecated': False},
+    'glulxe': {'id': 'Glulxe', 'deprecated': False},
+    'glwtpl': {'id': 'GLWTPL', 'deprecated': False},
+    'gnuplot': {'id': 'gnuplot', 'deprecated': False},
+    'gpl-1.0': {'id': 'GPL-1.0', 'deprecated': True},
+    'gpl-1.0+': {'id': 'GPL-1.0+', 'deprecated': True},
+    'gpl-1.0-only': {'id': 'GPL-1.0-only', 'deprecated': False},
+    'gpl-1.0-or-later': {'id': 'GPL-1.0-or-later', 'deprecated': False},
+    'gpl-2.0': {'id': 'GPL-2.0', 'deprecated': True},
+    'gpl-2.0+': {'id': 'GPL-2.0+', 'deprecated': True},
+    'gpl-2.0-only': {'id': 'GPL-2.0-only', 'deprecated': False},
+    'gpl-2.0-or-later': {'id': 'GPL-2.0-or-later', 'deprecated': False},
+    'gpl-2.0-with-autoconf-exception': {'id': 'GPL-2.0-with-autoconf-exception', 'deprecated': True},
+    'gpl-2.0-with-bison-exception': {'id': 'GPL-2.0-with-bison-exception', 'deprecated': True},
+    'gpl-2.0-with-classpath-exception': {'id': 'GPL-2.0-with-classpath-exception', 'deprecated': True},
+    'gpl-2.0-with-font-exception': {'id': 'GPL-2.0-with-font-exception', 'deprecated': True},
+    'gpl-2.0-with-gcc-exception': {'id': 'GPL-2.0-with-GCC-exception', 'deprecated': True},
+    'gpl-3.0': {'id': 'GPL-3.0', 'deprecated': True},
+    'gpl-3.0+': {'id': 'GPL-3.0+', 'deprecated': True},
+    'gpl-3.0-only': {'id': 'GPL-3.0-only', 'deprecated': False},
+    'gpl-3.0-or-later': {'id': 'GPL-3.0-or-later', 'deprecated': False},
+    'gpl-3.0-with-autoconf-exception': {'id': 'GPL-3.0-with-autoconf-exception', 'deprecated': True},
+    'gpl-3.0-with-gcc-exception': {'id': 'GPL-3.0-with-GCC-exception', 'deprecated': True},
+    'graphics-gems': {'id': 'Graphics-Gems', 'deprecated': False},
+    'gsoap-1.3b': {'id': 'gSOAP-1.3b', 'deprecated': False},
+    'gtkbook': {'id': 'gtkbook', 'deprecated': False},
+    'gutmann': {'id': 'Gutmann', 'deprecated': False},
+    'haskellreport': {'id': 'HaskellReport', 'deprecated': False},
+    'hdf5': {'id': 'HDF5', 'deprecated': False},
+    'hdparm': {'id': 'hdparm', 'deprecated': False},
+    'hidapi': {'id': 'HIDAPI', 'deprecated': False},
+    'hippocratic-2.1': {'id': 'Hippocratic-2.1', 'deprecated': False},
+    'hp-1986': {'id': 'HP-1986', 'deprecated': False},
+    'hp-1989': {'id': 'HP-1989', 'deprecated': False},
+    'hpnd': {'id': 'HPND', 'deprecated': False},
+    'hpnd-dec': {'id': 'HPND-DEC', 'deprecated': False},
+    'hpnd-doc': {'id': 'HPND-doc', 'deprecated': False},
+    'hpnd-doc-sell': {'id': 'HPND-doc-sell', 'deprecated': False},
+    'hpnd-export-us': {'id': 'HPND-export-US', 'deprecated': False},
+    'hpnd-export-us-acknowledgement': {'id': 'HPND-export-US-acknowledgement', 'deprecated': False},
+    'hpnd-export-us-modify': {'id': 'HPND-export-US-modify', 'deprecated': False},
+    'hpnd-export2-us': {'id': 'HPND-export2-US', 'deprecated': False},
+    'hpnd-fenneberg-livingston': {'id': 'HPND-Fenneberg-Livingston', 'deprecated': False},
+    'hpnd-inria-imag': {'id': 'HPND-INRIA-IMAG', 'deprecated': False},
+    'hpnd-intel': {'id': 'HPND-Intel', 'deprecated': False},
+    'hpnd-kevlin-henney': {'id': 'HPND-Kevlin-Henney', 'deprecated': False},
+    'hpnd-markus-kuhn': {'id': 'HPND-Markus-Kuhn', 'deprecated': False},
+    'hpnd-merchantability-variant': {'id': 'HPND-merchantability-variant', 'deprecated': False},
+    'hpnd-mit-disclaimer': {'id': 'HPND-MIT-disclaimer', 'deprecated': False},
+    'hpnd-netrek': {'id': 'HPND-Netrek', 'deprecated': False},
+    'hpnd-pbmplus': {'id': 'HPND-Pbmplus', 'deprecated': False},
+    'hpnd-sell-mit-disclaimer-xserver': {'id': 'HPND-sell-MIT-disclaimer-xserver', 'deprecated': False},
+    'hpnd-sell-regexpr': {'id': 'HPND-sell-regexpr', 'deprecated': False},
+    'hpnd-sell-variant': {'id': 'HPND-sell-variant', 'deprecated': False},
+    'hpnd-sell-variant-mit-disclaimer': {'id': 'HPND-sell-variant-MIT-disclaimer', 'deprecated': False},
+    'hpnd-sell-variant-mit-disclaimer-rev': {'id': 'HPND-sell-variant-MIT-disclaimer-rev', 'deprecated': False},
+    'hpnd-uc': {'id': 'HPND-UC', 'deprecated': False},
+    'hpnd-uc-export-us': {'id': 'HPND-UC-export-US', 'deprecated': False},
+    'htmltidy': {'id': 'HTMLTIDY', 'deprecated': False},
+    'ibm-pibs': {'id': 'IBM-pibs', 'deprecated': False},
+    'icu': {'id': 'ICU', 'deprecated': False},
+    'iec-code-components-eula': {'id': 'IEC-Code-Components-EULA', 'deprecated': False},
+    'ijg': {'id': 'IJG', 'deprecated': False},
+    'ijg-short': {'id': 'IJG-short', 'deprecated': False},
+    'imagemagick': {'id': 'ImageMagick', 'deprecated': False},
+    'imatix': {'id': 'iMatix', 'deprecated': False},
+    'imlib2': {'id': 'Imlib2', 'deprecated': False},
+    'info-zip': {'id': 'Info-ZIP', 'deprecated': False},
+    'inner-net-2.0': {'id': 'Inner-Net-2.0', 'deprecated': False},
+    'innosetup': {'id': 'InnoSetup', 'deprecated': False},
+    'intel': {'id': 'Intel', 'deprecated': False},
+    'intel-acpi': {'id': 'Intel-ACPI', 'deprecated': False},
+    'interbase-1.0': {'id': 'Interbase-1.0', 'deprecated': False},
+    'ipa': {'id': 'IPA', 'deprecated': False},
+    'ipl-1.0': {'id': 'IPL-1.0', 'deprecated': False},
+    'isc': {'id': 'ISC', 'deprecated': False},
+    'isc-veillard': {'id': 'ISC-Veillard', 'deprecated': False},
+    'jam': {'id': 'Jam', 'deprecated': False},
+    'jasper-2.0': {'id': 'JasPer-2.0', 'deprecated': False},
+    'jove': {'id': 'jove', 'deprecated': False},
+    'jpl-image': {'id': 'JPL-image', 'deprecated': False},
+    'jpnic': {'id': 'JPNIC', 'deprecated': False},
+    'json': {'id': 'JSON', 'deprecated': False},
+    'kastrup': {'id': 'Kastrup', 'deprecated': False},
+    'kazlib': {'id': 'Kazlib', 'deprecated': False},
+    'knuth-ctan': {'id': 'Knuth-CTAN', 'deprecated': False},
+    'lal-1.2': {'id': 'LAL-1.2', 'deprecated': False},
+    'lal-1.3': {'id': 'LAL-1.3', 'deprecated': False},
+    'latex2e': {'id': 'Latex2e', 'deprecated': False},
+    'latex2e-translated-notice': {'id': 'Latex2e-translated-notice', 'deprecated': False},
+    'leptonica': {'id': 'Leptonica', 'deprecated': False},
+    'lgpl-2.0': {'id': 'LGPL-2.0', 'deprecated': True},
+    'lgpl-2.0+': {'id': 'LGPL-2.0+', 'deprecated': True},
+    'lgpl-2.0-only': {'id': 'LGPL-2.0-only', 'deprecated': False},
+    'lgpl-2.0-or-later': {'id': 'LGPL-2.0-or-later', 'deprecated': False},
+    'lgpl-2.1': {'id': 'LGPL-2.1', 'deprecated': True},
+    'lgpl-2.1+': {'id': 'LGPL-2.1+', 'deprecated': True},
+    'lgpl-2.1-only': {'id': 'LGPL-2.1-only', 'deprecated': False},
+    'lgpl-2.1-or-later': {'id': 'LGPL-2.1-or-later', 'deprecated': False},
+    'lgpl-3.0': {'id': 'LGPL-3.0', 'deprecated': True},
+    'lgpl-3.0+': {'id': 'LGPL-3.0+', 'deprecated': True},
+    'lgpl-3.0-only': {'id': 'LGPL-3.0-only', 'deprecated': False},
+    'lgpl-3.0-or-later': {'id': 'LGPL-3.0-or-later', 'deprecated': False},
+    'lgpllr': {'id': 'LGPLLR', 'deprecated': False},
+    'libpng': {'id': 'Libpng', 'deprecated': False},
+    'libpng-1.6.35': {'id': 'libpng-1.6.35', 'deprecated': False},
+    'libpng-2.0': {'id': 'libpng-2.0', 'deprecated': False},
+    'libselinux-1.0': {'id': 'libselinux-1.0', 'deprecated': False},
+    'libtiff': {'id': 'libtiff', 'deprecated': False},
+    'libutil-david-nugent': {'id': 'libutil-David-Nugent', 'deprecated': False},
+    'liliq-p-1.1': {'id': 'LiLiQ-P-1.1', 'deprecated': False},
+    'liliq-r-1.1': {'id': 'LiLiQ-R-1.1', 'deprecated': False},
+    'liliq-rplus-1.1': {'id': 'LiLiQ-Rplus-1.1', 'deprecated': False},
+    'linux-man-pages-1-para': {'id': 'Linux-man-pages-1-para', 'deprecated': False},
+    'linux-man-pages-copyleft': {'id': 'Linux-man-pages-copyleft', 'deprecated': False},
+    'linux-man-pages-copyleft-2-para': {'id': 'Linux-man-pages-copyleft-2-para', 'deprecated': False},
+    'linux-man-pages-copyleft-var': {'id': 'Linux-man-pages-copyleft-var', 'deprecated': False},
+    'linux-openib': {'id': 'Linux-OpenIB', 'deprecated': False},
+    'loop': {'id': 'LOOP', 'deprecated': False},
+    'lpd-document': {'id': 'LPD-document', 'deprecated': False},
+    'lpl-1.0': {'id': 'LPL-1.0', 'deprecated': False},
+    'lpl-1.02': {'id': 'LPL-1.02', 'deprecated': False},
+    'lppl-1.0': {'id': 'LPPL-1.0', 'deprecated': False},
+    'lppl-1.1': {'id': 'LPPL-1.1', 'deprecated': False},
+    'lppl-1.2': {'id': 'LPPL-1.2', 'deprecated': False},
+    'lppl-1.3a': {'id': 'LPPL-1.3a', 'deprecated': False},
+    'lppl-1.3c': {'id': 'LPPL-1.3c', 'deprecated': False},
+    'lsof': {'id': 'lsof', 'deprecated': False},
+    'lucida-bitmap-fonts': {'id': 'Lucida-Bitmap-Fonts', 'deprecated': False},
+    'lzma-sdk-9.11-to-9.20': {'id': 'LZMA-SDK-9.11-to-9.20', 'deprecated': False},
+    'lzma-sdk-9.22': {'id': 'LZMA-SDK-9.22', 'deprecated': False},
+    'mackerras-3-clause': {'id': 'Mackerras-3-Clause', 'deprecated': False},
+    'mackerras-3-clause-acknowledgment': {'id': 'Mackerras-3-Clause-acknowledgment', 'deprecated': False},
+    'magaz': {'id': 'magaz', 'deprecated': False},
+    'mailprio': {'id': 'mailprio', 'deprecated': False},
+    'makeindex': {'id': 'MakeIndex', 'deprecated': False},
+    'man2html': {'id': 'man2html', 'deprecated': False},
+    'martin-birgmeier': {'id': 'Martin-Birgmeier', 'deprecated': False},
+    'mcphee-slideshow': {'id': 'McPhee-slideshow', 'deprecated': False},
+    'metamail': {'id': 'metamail', 'deprecated': False},
+    'minpack': {'id': 'Minpack', 'deprecated': False},
+    'mips': {'id': 'MIPS', 'deprecated': False},
+    'miros': {'id': 'MirOS', 'deprecated': False},
+    'mit': {'id': 'MIT', 'deprecated': False},
+    'mit-0': {'id': 'MIT-0', 'deprecated': False},
+    'mit-advertising': {'id': 'MIT-advertising', 'deprecated': False},
+    'mit-click': {'id': 'MIT-Click', 'deprecated': False},
+    'mit-cmu': {'id': 'MIT-CMU', 'deprecated': False},
+    'mit-enna': {'id': 'MIT-enna', 'deprecated': False},
+    'mit-feh': {'id': 'MIT-feh', 'deprecated': False},
+    'mit-festival': {'id': 'MIT-Festival', 'deprecated': False},
+    'mit-khronos-old': {'id': 'MIT-Khronos-old', 'deprecated': False},
+    'mit-modern-variant': {'id': 'MIT-Modern-Variant', 'deprecated': False},
+    'mit-open-group': {'id': 'MIT-open-group', 'deprecated': False},
+    'mit-testregex': {'id': 'MIT-testregex', 'deprecated': False},
+    'mit-wu': {'id': 'MIT-Wu', 'deprecated': False},
+    'mitnfa': {'id': 'MITNFA', 'deprecated': False},
+    'mmixware': {'id': 'MMIXware', 'deprecated': False},
+    'motosoto': {'id': 'Motosoto', 'deprecated': False},
+    'mpeg-ssg': {'id': 'MPEG-SSG', 'deprecated': False},
+    'mpi-permissive': {'id': 'mpi-permissive', 'deprecated': False},
+    'mpich2': {'id': 'mpich2', 'deprecated': False},
+    'mpl-1.0': {'id': 'MPL-1.0', 'deprecated': False},
+    'mpl-1.1': {'id': 'MPL-1.1', 'deprecated': False},
+    'mpl-2.0': {'id': 'MPL-2.0', 'deprecated': False},
+    'mpl-2.0-no-copyleft-exception': {'id': 'MPL-2.0-no-copyleft-exception', 'deprecated': False},
+    'mplus': {'id': 'mplus', 'deprecated': False},
+    'ms-lpl': {'id': 'MS-LPL', 'deprecated': False},
+    'ms-pl': {'id': 'MS-PL', 'deprecated': False},
+    'ms-rl': {'id': 'MS-RL', 'deprecated': False},
+    'mtll': {'id': 'MTLL', 'deprecated': False},
+    'mulanpsl-1.0': {'id': 'MulanPSL-1.0', 'deprecated': False},
+    'mulanpsl-2.0': {'id': 'MulanPSL-2.0', 'deprecated': False},
+    'multics': {'id': 'Multics', 'deprecated': False},
+    'mup': {'id': 'Mup', 'deprecated': False},
+    'naist-2003': {'id': 'NAIST-2003', 'deprecated': False},
+    'nasa-1.3': {'id': 'NASA-1.3', 'deprecated': False},
+    'naumen': {'id': 'Naumen', 'deprecated': False},
+    'nbpl-1.0': {'id': 'NBPL-1.0', 'deprecated': False},
+    'ncbi-pd': {'id': 'NCBI-PD', 'deprecated': False},
+    'ncgl-uk-2.0': {'id': 'NCGL-UK-2.0', 'deprecated': False},
+    'ncl': {'id': 'NCL', 'deprecated': False},
+    'ncsa': {'id': 'NCSA', 'deprecated': False},
+    'net-snmp': {'id': 'Net-SNMP', 'deprecated': True},
+    'netcdf': {'id': 'NetCDF', 'deprecated': False},
+    'newsletr': {'id': 'Newsletr', 'deprecated': False},
+    'ngpl': {'id': 'NGPL', 'deprecated': False},
+    'ngrep': {'id': 'ngrep', 'deprecated': False},
+    'nicta-1.0': {'id': 'NICTA-1.0', 'deprecated': False},
+    'nist-pd': {'id': 'NIST-PD', 'deprecated': False},
+    'nist-pd-fallback': {'id': 'NIST-PD-fallback', 'deprecated': False},
+    'nist-software': {'id': 'NIST-Software', 'deprecated': False},
+    'nlod-1.0': {'id': 'NLOD-1.0', 'deprecated': False},
+    'nlod-2.0': {'id': 'NLOD-2.0', 'deprecated': False},
+    'nlpl': {'id': 'NLPL', 'deprecated': False},
+    'nokia': {'id': 'Nokia', 'deprecated': False},
+    'nosl': {'id': 'NOSL', 'deprecated': False},
+    'noweb': {'id': 'Noweb', 'deprecated': False},
+    'npl-1.0': {'id': 'NPL-1.0', 'deprecated': False},
+    'npl-1.1': {'id': 'NPL-1.1', 'deprecated': False},
+    'nposl-3.0': {'id': 'NPOSL-3.0', 'deprecated': False},
+    'nrl': {'id': 'NRL', 'deprecated': False},
+    'ntia-pd': {'id': 'NTIA-PD', 'deprecated': False},
+    'ntp': {'id': 'NTP', 'deprecated': False},
+    'ntp-0': {'id': 'NTP-0', 'deprecated': False},
+    'nunit': {'id': 'Nunit', 'deprecated': True},
+    'o-uda-1.0': {'id': 'O-UDA-1.0', 'deprecated': False},
+    'oar': {'id': 'OAR', 'deprecated': False},
+    'occt-pl': {'id': 'OCCT-PL', 'deprecated': False},
+    'oclc-2.0': {'id': 'OCLC-2.0', 'deprecated': False},
+    'odbl-1.0': {'id': 'ODbL-1.0', 'deprecated': False},
+    'odc-by-1.0': {'id': 'ODC-By-1.0', 'deprecated': False},
+    'offis': {'id': 'OFFIS', 'deprecated': False},
+    'ofl-1.0': {'id': 'OFL-1.0', 'deprecated': False},
+    'ofl-1.0-no-rfn': {'id': 'OFL-1.0-no-RFN', 'deprecated': False},
+    'ofl-1.0-rfn': {'id': 'OFL-1.0-RFN', 'deprecated': False},
+    'ofl-1.1': {'id': 'OFL-1.1', 'deprecated': False},
+    'ofl-1.1-no-rfn': {'id': 'OFL-1.1-no-RFN', 'deprecated': False},
+    'ofl-1.1-rfn': {'id': 'OFL-1.1-RFN', 'deprecated': False},
+    'ogc-1.0': {'id': 'OGC-1.0', 'deprecated': False},
+    'ogdl-taiwan-1.0': {'id': 'OGDL-Taiwan-1.0', 'deprecated': False},
+    'ogl-canada-2.0': {'id': 'OGL-Canada-2.0', 'deprecated': False},
+    'ogl-uk-1.0': {'id': 'OGL-UK-1.0', 'deprecated': False},
+    'ogl-uk-2.0': {'id': 'OGL-UK-2.0', 'deprecated': False},
+    'ogl-uk-3.0': {'id': 'OGL-UK-3.0', 'deprecated': False},
+    'ogtsl': {'id': 'OGTSL', 'deprecated': False},
+    'oldap-1.1': {'id': 'OLDAP-1.1', 'deprecated': False},
+    'oldap-1.2': {'id': 'OLDAP-1.2', 'deprecated': False},
+    'oldap-1.3': {'id': 'OLDAP-1.3', 'deprecated': False},
+    'oldap-1.4': {'id': 'OLDAP-1.4', 'deprecated': False},
+    'oldap-2.0': {'id': 'OLDAP-2.0', 'deprecated': False},
+    'oldap-2.0.1': {'id': 'OLDAP-2.0.1', 'deprecated': False},
+    'oldap-2.1': {'id': 'OLDAP-2.1', 'deprecated': False},
+    'oldap-2.2': {'id': 'OLDAP-2.2', 'deprecated': False},
+    'oldap-2.2.1': {'id': 'OLDAP-2.2.1', 'deprecated': False},
+    'oldap-2.2.2': {'id': 'OLDAP-2.2.2', 'deprecated': False},
+    'oldap-2.3': {'id': 'OLDAP-2.3', 'deprecated': False},
+    'oldap-2.4': {'id': 'OLDAP-2.4', 'deprecated': False},
+    'oldap-2.5': {'id': 'OLDAP-2.5', 'deprecated': False},
+    'oldap-2.6': {'id': 'OLDAP-2.6', 'deprecated': False},
+    'oldap-2.7': {'id': 'OLDAP-2.7', 'deprecated': False},
+    'oldap-2.8': {'id': 'OLDAP-2.8', 'deprecated': False},
+    'olfl-1.3': {'id': 'OLFL-1.3', 'deprecated': False},
+    'oml': {'id': 'OML', 'deprecated': False},
+    'openpbs-2.3': {'id': 'OpenPBS-2.3', 'deprecated': False},
+    'openssl': {'id': 'OpenSSL', 'deprecated': False},
+    'openssl-standalone': {'id': 'OpenSSL-standalone', 'deprecated': False},
+    'openvision': {'id': 'OpenVision', 'deprecated': False},
+    'opl-1.0': {'id': 'OPL-1.0', 'deprecated': False},
+    'opl-uk-3.0': {'id': 'OPL-UK-3.0', 'deprecated': False},
+    'opubl-1.0': {'id': 'OPUBL-1.0', 'deprecated': False},
+    'oset-pl-2.1': {'id': 'OSET-PL-2.1', 'deprecated': False},
+    'osl-1.0': {'id': 'OSL-1.0', 'deprecated': False},
+    'osl-1.1': {'id': 'OSL-1.1', 'deprecated': False},
+    'osl-2.0': {'id': 'OSL-2.0', 'deprecated': False},
+    'osl-2.1': {'id': 'OSL-2.1', 'deprecated': False},
+    'osl-3.0': {'id': 'OSL-3.0', 'deprecated': False},
+    'padl': {'id': 'PADL', 'deprecated': False},
+    'parity-6.0.0': {'id': 'Parity-6.0.0', 'deprecated': False},
+    'parity-7.0.0': {'id': 'Parity-7.0.0', 'deprecated': False},
+    'pddl-1.0': {'id': 'PDDL-1.0', 'deprecated': False},
+    'php-3.0': {'id': 'PHP-3.0', 'deprecated': False},
+    'php-3.01': {'id': 'PHP-3.01', 'deprecated': False},
+    'pixar': {'id': 'Pixar', 'deprecated': False},
+    'pkgconf': {'id': 'pkgconf', 'deprecated': False},
+    'plexus': {'id': 'Plexus', 'deprecated': False},
+    'pnmstitch': {'id': 'pnmstitch', 'deprecated': False},
+    'polyform-noncommercial-1.0.0': {'id': 'PolyForm-Noncommercial-1.0.0', 'deprecated': False},
+    'polyform-small-business-1.0.0': {'id': 'PolyForm-Small-Business-1.0.0', 'deprecated': False},
+    'postgresql': {'id': 'PostgreSQL', 'deprecated': False},
+    'ppl': {'id': 'PPL', 'deprecated': False},
+    'psf-2.0': {'id': 'PSF-2.0', 'deprecated': False},
+    'psfrag': {'id': 'psfrag', 'deprecated': False},
+    'psutils': {'id': 'psutils', 'deprecated': False},
+    'python-2.0': {'id': 'Python-2.0', 'deprecated': False},
+    'python-2.0.1': {'id': 'Python-2.0.1', 'deprecated': False},
+    'python-ldap': {'id': 'python-ldap', 'deprecated': False},
+    'qhull': {'id': 'Qhull', 'deprecated': False},
+    'qpl-1.0': {'id': 'QPL-1.0', 'deprecated': False},
+    'qpl-1.0-inria-2004': {'id': 'QPL-1.0-INRIA-2004', 'deprecated': False},
+    'radvd': {'id': 'radvd', 'deprecated': False},
+    'rdisc': {'id': 'Rdisc', 'deprecated': False},
+    'rhecos-1.1': {'id': 'RHeCos-1.1', 'deprecated': False},
+    'rpl-1.1': {'id': 'RPL-1.1', 'deprecated': False},
+    'rpl-1.5': {'id': 'RPL-1.5', 'deprecated': False},
+    'rpsl-1.0': {'id': 'RPSL-1.0', 'deprecated': False},
+    'rsa-md': {'id': 'RSA-MD', 'deprecated': False},
+    'rscpl': {'id': 'RSCPL', 'deprecated': False},
+    'ruby': {'id': 'Ruby', 'deprecated': False},
+    'ruby-pty': {'id': 'Ruby-pty', 'deprecated': False},
+    'sax-pd': {'id': 'SAX-PD', 'deprecated': False},
+    'sax-pd-2.0': {'id': 'SAX-PD-2.0', 'deprecated': False},
+    'saxpath': {'id': 'Saxpath', 'deprecated': False},
+    'scea': {'id': 'SCEA', 'deprecated': False},
+    'schemereport': {'id': 'SchemeReport', 'deprecated': False},
+    'sendmail': {'id': 'Sendmail', 'deprecated': False},
+    'sendmail-8.23': {'id': 'Sendmail-8.23', 'deprecated': False},
+    'sendmail-open-source-1.1': {'id': 'Sendmail-Open-Source-1.1', 'deprecated': False},
+    'sgi-b-1.0': {'id': 'SGI-B-1.0', 'deprecated': False},
+    'sgi-b-1.1': {'id': 'SGI-B-1.1', 'deprecated': False},
+    'sgi-b-2.0': {'id': 'SGI-B-2.0', 'deprecated': False},
+    'sgi-opengl': {'id': 'SGI-OpenGL', 'deprecated': False},
+    'sgp4': {'id': 'SGP4', 'deprecated': False},
+    'shl-0.5': {'id': 'SHL-0.5', 'deprecated': False},
+    'shl-0.51': {'id': 'SHL-0.51', 'deprecated': False},
+    'simpl-2.0': {'id': 'SimPL-2.0', 'deprecated': False},
+    'sissl': {'id': 'SISSL', 'deprecated': False},
+    'sissl-1.2': {'id': 'SISSL-1.2', 'deprecated': False},
+    'sl': {'id': 'SL', 'deprecated': False},
+    'sleepycat': {'id': 'Sleepycat', 'deprecated': False},
+    'smail-gpl': {'id': 'SMAIL-GPL', 'deprecated': False},
+    'smlnj': {'id': 'SMLNJ', 'deprecated': False},
+    'smppl': {'id': 'SMPPL', 'deprecated': False},
+    'snia': {'id': 'SNIA', 'deprecated': False},
+    'snprintf': {'id': 'snprintf', 'deprecated': False},
+    'sofa': {'id': 'SOFA', 'deprecated': False},
+    'softsurfer': {'id': 'softSurfer', 'deprecated': False},
+    'soundex': {'id': 'Soundex', 'deprecated': False},
+    'spencer-86': {'id': 'Spencer-86', 'deprecated': False},
+    'spencer-94': {'id': 'Spencer-94', 'deprecated': False},
+    'spencer-99': {'id': 'Spencer-99', 'deprecated': False},
+    'spl-1.0': {'id': 'SPL-1.0', 'deprecated': False},
+    'ssh-keyscan': {'id': 'ssh-keyscan', 'deprecated': False},
+    'ssh-openssh': {'id': 'SSH-OpenSSH', 'deprecated': False},
+    'ssh-short': {'id': 'SSH-short', 'deprecated': False},
+    'ssleay-standalone': {'id': 'SSLeay-standalone', 'deprecated': False},
+    'sspl-1.0': {'id': 'SSPL-1.0', 'deprecated': False},
+    'standardml-nj': {'id': 'StandardML-NJ', 'deprecated': True},
+    'sugarcrm-1.1.3': {'id': 'SugarCRM-1.1.3', 'deprecated': False},
+    'sul-1.0': {'id': 'SUL-1.0', 'deprecated': False},
+    'sun-ppp': {'id': 'Sun-PPP', 'deprecated': False},
+    'sun-ppp-2000': {'id': 'Sun-PPP-2000', 'deprecated': False},
+    'sunpro': {'id': 'SunPro', 'deprecated': False},
+    'swl': {'id': 'SWL', 'deprecated': False},
+    'swrule': {'id': 'swrule', 'deprecated': False},
+    'symlinks': {'id': 'Symlinks', 'deprecated': False},
+    'tapr-ohl-1.0': {'id': 'TAPR-OHL-1.0', 'deprecated': False},
+    'tcl': {'id': 'TCL', 'deprecated': False},
+    'tcp-wrappers': {'id': 'TCP-wrappers', 'deprecated': False},
+    'termreadkey': {'id': 'TermReadKey', 'deprecated': False},
+    'tgppl-1.0': {'id': 'TGPPL-1.0', 'deprecated': False},
+    'thirdeye': {'id': 'ThirdEye', 'deprecated': False},
+    'threeparttable': {'id': 'threeparttable', 'deprecated': False},
+    'tmate': {'id': 'TMate', 'deprecated': False},
+    'torque-1.1': {'id': 'TORQUE-1.1', 'deprecated': False},
+    'tosl': {'id': 'TOSL', 'deprecated': False},
+    'tpdl': {'id': 'TPDL', 'deprecated': False},
+    'tpl-1.0': {'id': 'TPL-1.0', 'deprecated': False},
+    'trustedqsl': {'id': 'TrustedQSL', 'deprecated': False},
+    'ttwl': {'id': 'TTWL', 'deprecated': False},
+    'ttyp0': {'id': 'TTYP0', 'deprecated': False},
+    'tu-berlin-1.0': {'id': 'TU-Berlin-1.0', 'deprecated': False},
+    'tu-berlin-2.0': {'id': 'TU-Berlin-2.0', 'deprecated': False},
+    'ubuntu-font-1.0': {'id': 'Ubuntu-font-1.0', 'deprecated': False},
+    'ucar': {'id': 'UCAR', 'deprecated': False},
+    'ucl-1.0': {'id': 'UCL-1.0', 'deprecated': False},
+    'ulem': {'id': 'ulem', 'deprecated': False},
+    'umich-merit': {'id': 'UMich-Merit', 'deprecated': False},
+    'unicode-3.0': {'id': 'Unicode-3.0', 'deprecated': False},
+    'unicode-dfs-2015': {'id': 'Unicode-DFS-2015', 'deprecated': False},
+    'unicode-dfs-2016': {'id': 'Unicode-DFS-2016', 'deprecated': False},
+    'unicode-tou': {'id': 'Unicode-TOU', 'deprecated': False},
+    'unixcrypt': {'id': 'UnixCrypt', 'deprecated': False},
+    'unlicense': {'id': 'Unlicense', 'deprecated': False},
+    'unlicense-libtelnet': {'id': 'Unlicense-libtelnet', 'deprecated': False},
+    'unlicense-libwhirlpool': {'id': 'Unlicense-libwhirlpool', 'deprecated': False},
+    'upl-1.0': {'id': 'UPL-1.0', 'deprecated': False},
+    'urt-rle': {'id': 'URT-RLE', 'deprecated': False},
+    'vim': {'id': 'Vim', 'deprecated': False},
+    'vostrom': {'id': 'VOSTROM', 'deprecated': False},
+    'vsl-1.0': {'id': 'VSL-1.0', 'deprecated': False},
+    'w3c': {'id': 'W3C', 'deprecated': False},
+    'w3c-19980720': {'id': 'W3C-19980720', 'deprecated': False},
+    'w3c-20150513': {'id': 'W3C-20150513', 'deprecated': False},
+    'w3m': {'id': 'w3m', 'deprecated': False},
+    'watcom-1.0': {'id': 'Watcom-1.0', 'deprecated': False},
+    'widget-workshop': {'id': 'Widget-Workshop', 'deprecated': False},
+    'wsuipa': {'id': 'Wsuipa', 'deprecated': False},
+    'wtfpl': {'id': 'WTFPL', 'deprecated': False},
+    'wwl': {'id': 'wwl', 'deprecated': False},
+    'wxwindows': {'id': 'wxWindows', 'deprecated': True},
+    'x11': {'id': 'X11', 'deprecated': False},
+    'x11-distribute-modifications-variant': {'id': 'X11-distribute-modifications-variant', 'deprecated': False},
+    'x11-swapped': {'id': 'X11-swapped', 'deprecated': False},
+    'xdebug-1.03': {'id': 'Xdebug-1.03', 'deprecated': False},
+    'xerox': {'id': 'Xerox', 'deprecated': False},
+    'xfig': {'id': 'Xfig', 'deprecated': False},
+    'xfree86-1.1': {'id': 'XFree86-1.1', 'deprecated': False},
+    'xinetd': {'id': 'xinetd', 'deprecated': False},
+    'xkeyboard-config-zinoviev': {'id': 'xkeyboard-config-Zinoviev', 'deprecated': False},
+    'xlock': {'id': 'xlock', 'deprecated': False},
+    'xnet': {'id': 'Xnet', 'deprecated': False},
+    'xpp': {'id': 'xpp', 'deprecated': False},
+    'xskat': {'id': 'XSkat', 'deprecated': False},
+    'xzoom': {'id': 'xzoom', 'deprecated': False},
+    'ypl-1.0': {'id': 'YPL-1.0', 'deprecated': False},
+    'ypl-1.1': {'id': 'YPL-1.1', 'deprecated': False},
+    'zed': {'id': 'Zed', 'deprecated': False},
+    'zeeff': {'id': 'Zeeff', 'deprecated': False},
+    'zend-2.0': {'id': 'Zend-2.0', 'deprecated': False},
+    'zimbra-1.3': {'id': 'Zimbra-1.3', 'deprecated': False},
+    'zimbra-1.4': {'id': 'Zimbra-1.4', 'deprecated': False},
+    'zlib': {'id': 'Zlib', 'deprecated': False},
+    'zlib-acknowledgement': {'id': 'zlib-acknowledgement', 'deprecated': False},
+    'zpl-1.1': {'id': 'ZPL-1.1', 'deprecated': False},
+    'zpl-2.0': {'id': 'ZPL-2.0', 'deprecated': False},
+    'zpl-2.1': {'id': 'ZPL-2.1', 'deprecated': False},
+}
+
+EXCEPTIONS: dict[str, SPDXException] = {
+    '389-exception': {'id': '389-exception', 'deprecated': False},
+    'asterisk-exception': {'id': 'Asterisk-exception', 'deprecated': False},
+    'asterisk-linking-protocols-exception': {'id': 'Asterisk-linking-protocols-exception', 'deprecated': False},
+    'autoconf-exception-2.0': {'id': 'Autoconf-exception-2.0', 'deprecated': False},
+    'autoconf-exception-3.0': {'id': 'Autoconf-exception-3.0', 'deprecated': False},
+    'autoconf-exception-generic': {'id': 'Autoconf-exception-generic', 'deprecated': False},
+    'autoconf-exception-generic-3.0': {'id': 'Autoconf-exception-generic-3.0', 'deprecated': False},
+    'autoconf-exception-macro': {'id': 'Autoconf-exception-macro', 'deprecated': False},
+    'bison-exception-1.24': {'id': 'Bison-exception-1.24', 'deprecated': False},
+    'bison-exception-2.2': {'id': 'Bison-exception-2.2', 'deprecated': False},
+    'bootloader-exception': {'id': 'Bootloader-exception', 'deprecated': False},
+    'cgal-linking-exception': {'id': 'CGAL-linking-exception', 'deprecated': False},
+    'classpath-exception-2.0': {'id': 'Classpath-exception-2.0', 'deprecated': False},
+    'clisp-exception-2.0': {'id': 'CLISP-exception-2.0', 'deprecated': False},
+    'cryptsetup-openssl-exception': {'id': 'cryptsetup-OpenSSL-exception', 'deprecated': False},
+    'digia-qt-lgpl-exception-1.1': {'id': 'Digia-Qt-LGPL-exception-1.1', 'deprecated': False},
+    'digirule-foss-exception': {'id': 'DigiRule-FOSS-exception', 'deprecated': False},
+    'ecos-exception-2.0': {'id': 'eCos-exception-2.0', 'deprecated': False},
+    'erlang-otp-linking-exception': {'id': 'erlang-otp-linking-exception', 'deprecated': False},
+    'fawkes-runtime-exception': {'id': 'Fawkes-Runtime-exception', 'deprecated': False},
+    'fltk-exception': {'id': 'FLTK-exception', 'deprecated': False},
+    'fmt-exception': {'id': 'fmt-exception', 'deprecated': False},
+    'font-exception-2.0': {'id': 'Font-exception-2.0', 'deprecated': False},
+    'freertos-exception-2.0': {'id': 'freertos-exception-2.0', 'deprecated': False},
+    'gcc-exception-2.0': {'id': 'GCC-exception-2.0', 'deprecated': False},
+    'gcc-exception-2.0-note': {'id': 'GCC-exception-2.0-note', 'deprecated': False},
+    'gcc-exception-3.1': {'id': 'GCC-exception-3.1', 'deprecated': False},
+    'gmsh-exception': {'id': 'Gmsh-exception', 'deprecated': False},
+    'gnat-exception': {'id': 'GNAT-exception', 'deprecated': False},
+    'gnome-examples-exception': {'id': 'GNOME-examples-exception', 'deprecated': False},
+    'gnu-compiler-exception': {'id': 'GNU-compiler-exception', 'deprecated': False},
+    'gnu-javamail-exception': {'id': 'gnu-javamail-exception', 'deprecated': False},
+    'gpl-3.0-389-ds-base-exception': {'id': 'GPL-3.0-389-ds-base-exception', 'deprecated': False},
+    'gpl-3.0-interface-exception': {'id': 'GPL-3.0-interface-exception', 'deprecated': False},
+    'gpl-3.0-linking-exception': {'id': 'GPL-3.0-linking-exception', 'deprecated': False},
+    'gpl-3.0-linking-source-exception': {'id': 'GPL-3.0-linking-source-exception', 'deprecated': False},
+    'gpl-cc-1.0': {'id': 'GPL-CC-1.0', 'deprecated': False},
+    'gstreamer-exception-2005': {'id': 'GStreamer-exception-2005', 'deprecated': False},
+    'gstreamer-exception-2008': {'id': 'GStreamer-exception-2008', 'deprecated': False},
+    'harbour-exception': {'id': 'harbour-exception', 'deprecated': False},
+    'i2p-gpl-java-exception': {'id': 'i2p-gpl-java-exception', 'deprecated': False},
+    'independent-modules-exception': {'id': 'Independent-modules-exception', 'deprecated': False},
+    'kicad-libraries-exception': {'id': 'KiCad-libraries-exception', 'deprecated': False},
+    'lgpl-3.0-linking-exception': {'id': 'LGPL-3.0-linking-exception', 'deprecated': False},
+    'libpri-openh323-exception': {'id': 'libpri-OpenH323-exception', 'deprecated': False},
+    'libtool-exception': {'id': 'Libtool-exception', 'deprecated': False},
+    'linux-syscall-note': {'id': 'Linux-syscall-note', 'deprecated': False},
+    'llgpl': {'id': 'LLGPL', 'deprecated': False},
+    'llvm-exception': {'id': 'LLVM-exception', 'deprecated': False},
+    'lzma-exception': {'id': 'LZMA-exception', 'deprecated': False},
+    'mif-exception': {'id': 'mif-exception', 'deprecated': False},
+    'mxml-exception': {'id': 'mxml-exception', 'deprecated': False},
+    'nokia-qt-exception-1.1': {'id': 'Nokia-Qt-exception-1.1', 'deprecated': True},
+    'ocaml-lgpl-linking-exception': {'id': 'OCaml-LGPL-linking-exception', 'deprecated': False},
+    'occt-exception-1.0': {'id': 'OCCT-exception-1.0', 'deprecated': False},
+    'openjdk-assembly-exception-1.0': {'id': 'OpenJDK-assembly-exception-1.0', 'deprecated': False},
+    'openvpn-openssl-exception': {'id': 'openvpn-openssl-exception', 'deprecated': False},
+    'pcre2-exception': {'id': 'PCRE2-exception', 'deprecated': False},
+    'polyparse-exception': {'id': 'polyparse-exception', 'deprecated': False},
+    'ps-or-pdf-font-exception-20170817': {'id': 'PS-or-PDF-font-exception-20170817', 'deprecated': False},
+    'qpl-1.0-inria-2004-exception': {'id': 'QPL-1.0-INRIA-2004-exception', 'deprecated': False},
+    'qt-gpl-exception-1.0': {'id': 'Qt-GPL-exception-1.0', 'deprecated': False},
+    'qt-lgpl-exception-1.1': {'id': 'Qt-LGPL-exception-1.1', 'deprecated': False},
+    'qwt-exception-1.0': {'id': 'Qwt-exception-1.0', 'deprecated': False},
+    'romic-exception': {'id': 'romic-exception', 'deprecated': False},
+    'rrdtool-floss-exception-2.0': {'id': 'RRDtool-FLOSS-exception-2.0', 'deprecated': False},
+    'sane-exception': {'id': 'SANE-exception', 'deprecated': False},
+    'shl-2.0': {'id': 'SHL-2.0', 'deprecated': False},
+    'shl-2.1': {'id': 'SHL-2.1', 'deprecated': False},
+    'stunnel-exception': {'id': 'stunnel-exception', 'deprecated': False},
+    'swi-exception': {'id': 'SWI-exception', 'deprecated': False},
+    'swift-exception': {'id': 'Swift-exception', 'deprecated': False},
+    'texinfo-exception': {'id': 'Texinfo-exception', 'deprecated': False},
+    'u-boot-exception-2.0': {'id': 'u-boot-exception-2.0', 'deprecated': False},
+    'ubdl-exception': {'id': 'UBDL-exception', 'deprecated': False},
+    'universal-foss-exception-1.0': {'id': 'Universal-FOSS-exception-1.0', 'deprecated': False},
+    'vsftpd-openssl-exception': {'id': 'vsftpd-openssl-exception', 'deprecated': False},
+    'wxwindows-exception-3.1': {'id': 'WxWindows-exception-3.1', 'deprecated': False},
+    'x11vnc-openssl-exception': {'id': 'x11vnc-openssl-exception', 'deprecated': False},
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/markers.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/markers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca3706fe492f4cf0762f7734d84c2d269f88bbc5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/markers.py
@@ -0,0 +1,388 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import operator
+import os
+import platform
+import sys
+from typing import AbstractSet, Callable, Literal, Mapping, TypedDict, Union, cast
+
+from ._parser import MarkerAtom, MarkerList, Op, Value, Variable
+from ._parser import parse_marker as _parse_marker
+from ._tokenizer import ParserSyntaxError
+from .specifiers import InvalidSpecifier, Specifier
+from .utils import canonicalize_name
+
+__all__ = [
+    "Environment",
+    "EvaluateContext",
+    "InvalidMarker",
+    "Marker",
+    "UndefinedComparison",
+    "UndefinedEnvironmentName",
+    "default_environment",
+]
+
+Operator = Callable[[str, Union[str, AbstractSet[str]]], bool]
+EvaluateContext = Literal["metadata", "lock_file", "requirement"]
+MARKERS_ALLOWING_SET = {"extras", "dependency_groups"}
+MARKERS_REQUIRING_VERSION = {
+    "implementation_version",
+    "platform_release",
+    "python_full_version",
+    "python_version",
+}
+
+
+class InvalidMarker(ValueError):
+    """
+    An invalid marker was found, users should refer to PEP 508.
+    """
+
+
+class UndefinedComparison(ValueError):
+    """
+    An invalid operation was attempted on a value that doesn't support it.
+    """
+
+
+class UndefinedEnvironmentName(ValueError):
+    """
+    A name was attempted to be used that does not exist inside of the
+    environment.
+    """
+
+
+class Environment(TypedDict):
+    implementation_name: str
+    """The implementation's identifier, e.g. ``'cpython'``."""
+
+    implementation_version: str
+    """
+    The implementation's version, e.g. ``'3.13.0a2'`` for CPython 3.13.0a2, or
+    ``'7.3.13'`` for PyPy3.10 v7.3.13.
+    """
+
+    os_name: str
+    """
+    The value of :py:data:`os.name`. The name of the operating system dependent module
+    imported, e.g. ``'posix'``.
+    """
+
+    platform_machine: str
+    """
+    Returns the machine type, e.g. ``'i386'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    platform_release: str
+    """
+    The system's release, e.g. ``'2.2.0'`` or ``'NT'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    platform_system: str
+    """
+    The system/OS name, e.g. ``'Linux'``, ``'Windows'`` or ``'Java'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    platform_version: str
+    """
+    The system's release version, e.g. ``'#3 on degas'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    python_full_version: str
+    """
+    The Python version as string ``'major.minor.patchlevel'``.
+
+    Note that unlike the Python :py:data:`sys.version`, this value will always include
+    the patchlevel (it defaults to 0).
+    """
+
+    platform_python_implementation: str
+    """
+    A string identifying the Python implementation, e.g. ``'CPython'``.
+    """
+
+    python_version: str
+    """The Python version as string ``'major.minor'``."""
+
+    sys_platform: str
+    """
+    This string contains a platform identifier that can be used to append
+    platform-specific components to :py:data:`sys.path`, for instance.
+
+    For Unix systems, except on Linux and AIX, this is the lowercased OS name as
+    returned by ``uname -s`` with the first part of the version as returned by
+    ``uname -r`` appended, e.g. ``'sunos5'`` or ``'freebsd8'``, at the time when Python
+    was built.
+    """
+
+
+def _normalize_extras(
+    result: MarkerList | MarkerAtom | str,
+) -> MarkerList | MarkerAtom | str:
+    if not isinstance(result, tuple):
+        return result
+
+    lhs, op, rhs = result
+    if isinstance(lhs, Variable) and lhs.value == "extra":
+        normalized_extra = canonicalize_name(rhs.value)
+        rhs = Value(normalized_extra)
+    elif isinstance(rhs, Variable) and rhs.value == "extra":
+        normalized_extra = canonicalize_name(lhs.value)
+        lhs = Value(normalized_extra)
+    return lhs, op, rhs
+
+
+def _normalize_extra_values(results: MarkerList) -> MarkerList:
+    """
+    Normalize extra values.
+    """
+
+    return [_normalize_extras(r) for r in results]
+
+
+def _format_marker(
+    marker: list[str] | MarkerAtom | str, first: bool | None = True
+) -> str:
+    assert isinstance(marker, (list, tuple, str))
+
+    # Sometimes we have a structure like [[...]] which is a single item list
+    # where the single item is itself it's own list. In that case we want skip
+    # the rest of this function so that we don't get extraneous () on the
+    # outside.
+    if (
+        isinstance(marker, list)
+        and len(marker) == 1
+        and isinstance(marker[0], (list, tuple))
+    ):
+        return _format_marker(marker[0])
+
+    if isinstance(marker, list):
+        inner = (_format_marker(m, first=False) for m in marker)
+        if first:
+            return " ".join(inner)
+        else:
+            return "(" + " ".join(inner) + ")"
+    elif isinstance(marker, tuple):
+        return " ".join([m.serialize() for m in marker])
+    else:
+        return marker
+
+
+_operators: dict[str, Operator] = {
+    "in": lambda lhs, rhs: lhs in rhs,
+    "not in": lambda lhs, rhs: lhs not in rhs,
+    "<": lambda _lhs, _rhs: False,
+    "<=": operator.eq,
+    "==": operator.eq,
+    "!=": operator.ne,
+    ">=": operator.eq,
+    ">": lambda _lhs, _rhs: False,
+}
+
+
+def _eval_op(lhs: str, op: Op, rhs: str | AbstractSet[str], *, key: str) -> bool:
+    op_str = op.serialize()
+    if key in MARKERS_REQUIRING_VERSION:
+        try:
+            spec = Specifier(f"{op_str}{rhs}")
+        except InvalidSpecifier:
+            pass
+        else:
+            return spec.contains(lhs, prereleases=True)
+
+    oper: Operator | None = _operators.get(op_str)
+    if oper is None:
+        raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
+
+    return oper(lhs, rhs)
+
+
+def _normalize(
+    lhs: str, rhs: str | AbstractSet[str], key: str
+) -> tuple[str, str | AbstractSet[str]]:
+    # PEP 685 - Comparison of extra names for optional distribution dependencies
+    # https://peps.python.org/pep-0685/
+    # > When comparing extra names, tools MUST normalize the names being
+    # > compared using the semantics outlined in PEP 503 for names
+    if key == "extra":
+        assert isinstance(rhs, str), "extra value must be a string"
+        # Both sides are normalized at this point already
+        return (lhs, rhs)
+    if key in MARKERS_ALLOWING_SET:
+        if isinstance(rhs, str):  # pragma: no cover
+            return (canonicalize_name(lhs), canonicalize_name(rhs))
+        else:
+            return (canonicalize_name(lhs), {canonicalize_name(v) for v in rhs})
+
+    # other environment markers don't have such standards
+    return lhs, rhs
+
+
+def _evaluate_markers(
+    markers: MarkerList, environment: dict[str, str | AbstractSet[str]]
+) -> bool:
+    groups: list[list[bool]] = [[]]
+
+    for marker in markers:
+        if isinstance(marker, list):
+            groups[-1].append(_evaluate_markers(marker, environment))
+        elif isinstance(marker, tuple):
+            lhs, op, rhs = marker
+
+            if isinstance(lhs, Variable):
+                environment_key = lhs.value
+                lhs_value = environment[environment_key]
+                rhs_value = rhs.value
+            else:
+                lhs_value = lhs.value
+                environment_key = rhs.value
+                rhs_value = environment[environment_key]
+
+            assert isinstance(lhs_value, str), "lhs must be a string"
+            lhs_value, rhs_value = _normalize(lhs_value, rhs_value, key=environment_key)
+            groups[-1].append(_eval_op(lhs_value, op, rhs_value, key=environment_key))
+        elif marker == "or":
+            groups.append([])
+        elif marker == "and":
+            pass
+        else:  # pragma: nocover
+            raise TypeError(f"Unexpected marker {marker!r}")
+
+    return any(all(item) for item in groups)
+
+
+def format_full_version(info: sys._version_info) -> str:
+    version = f"{info.major}.{info.minor}.{info.micro}"
+    kind = info.releaselevel
+    if kind != "final":
+        version += kind[0] + str(info.serial)
+    return version
+
+
+def default_environment() -> Environment:
+    iver = format_full_version(sys.implementation.version)
+    implementation_name = sys.implementation.name
+    return {
+        "implementation_name": implementation_name,
+        "implementation_version": iver,
+        "os_name": os.name,
+        "platform_machine": platform.machine(),
+        "platform_release": platform.release(),
+        "platform_system": platform.system(),
+        "platform_version": platform.version(),
+        "python_full_version": platform.python_version(),
+        "platform_python_implementation": platform.python_implementation(),
+        "python_version": ".".join(platform.python_version_tuple()[:2]),
+        "sys_platform": sys.platform,
+    }
+
+
+class Marker:
+    def __init__(self, marker: str) -> None:
+        # Note: We create a Marker object without calling this constructor in
+        #       packaging.requirements.Requirement. If any additional logic is
+        #       added here, make sure to mirror/adapt Requirement.
+
+        # If this fails and throws an error, the repr still expects _markers to
+        # be defined.
+        self._markers: MarkerList = []
+
+        try:
+            self._markers = _normalize_extra_values(_parse_marker(marker))
+            # The attribute `_markers` can be described in terms of a recursive type:
+            # MarkerList = List[Union[Tuple[Node, ...], str, MarkerList]]
+            #
+            # For example, the following expression:
+            # python_version > "3.6" or (python_version == "3.6" and os_name == "unix")
+            #
+            # is parsed into:
+            # [
+            #     (<Variable('python_version')>, <Op('>')>, <Value('3.6')>),
+            #     'and',
+            #     [
+            #         (<Variable('python_version')>, <Op('==')>, <Value('3.6')>),
+            #         'or',
+            #         (<Variable('os_name')>, <Op('==')>, <Value('unix')>)
+            #     ]
+            # ]
+        except ParserSyntaxError as e:
+            raise InvalidMarker(str(e)) from e
+
+    def __str__(self) -> str:
+        return _format_marker(self._markers)
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}('{self}')>"
+
+    def __hash__(self) -> int:
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Marker):
+            return NotImplemented
+
+        return str(self) == str(other)
+
+    def evaluate(
+        self,
+        environment: Mapping[str, str | AbstractSet[str]] | None = None,
+        context: EvaluateContext = "metadata",
+    ) -> bool:
+        """Evaluate a marker.
+
+        Return the boolean from evaluating the given marker against the
+        environment. environment is an optional argument to override all or
+        part of the determined environment. The *context* parameter specifies what
+        context the markers are being evaluated for, which influences what markers
+        are considered valid. Acceptable values are "metadata" (for core metadata;
+        default), "lock_file", and "requirement" (i.e. all other situations).
+
+        The environment is determined from the current Python process.
+        """
+        current_environment = cast(
+            "dict[str, str | AbstractSet[str]]", default_environment()
+        )
+        if context == "lock_file":
+            current_environment.update(
+                extras=frozenset(), dependency_groups=frozenset()
+            )
+        elif context == "metadata":
+            current_environment["extra"] = ""
+
+        if environment is not None:
+            current_environment.update(environment)
+            if "extra" in current_environment:
+                # The API used to allow setting extra to None. We need to handle
+                # this case for backwards compatibility. Also skip running
+                # normalize name if extra is empty.
+                extra = cast("str | None", current_environment["extra"])
+                current_environment["extra"] = canonicalize_name(extra) if extra else ""
+
+        return _evaluate_markers(
+            self._markers, _repair_python_full_version(current_environment)
+        )
+
+
+def _repair_python_full_version(
+    env: dict[str, str | AbstractSet[str]],
+) -> dict[str, str | AbstractSet[str]]:
+    """
+    Work around platform.python_version() returning something that is not PEP 440
+    compliant for non-tagged Python builds.
+    """
+    python_full_version = cast("str", env["python_full_version"])
+    if python_full_version.endswith("+"):
+        env["python_full_version"] = f"{python_full_version}local"
+    return env
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/metadata.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..253f6b1b7ebd711fdc6bbbab3b56897061bab515
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/metadata.py
@@ -0,0 +1,978 @@
+from __future__ import annotations
+
+import email.feedparser
+import email.header
+import email.message
+import email.parser
+import email.policy
+import keyword
+import pathlib
+import sys
+import typing
+from typing import (
+    Any,
+    Callable,
+    Generic,
+    Literal,
+    TypedDict,
+    cast,
+)
+
+from . import licenses, requirements, specifiers, utils
+from . import version as version_module
+
+if typing.TYPE_CHECKING:
+    from .licenses import NormalizedLicenseExpression
+
+T = typing.TypeVar("T")
+
+
+if sys.version_info >= (3, 11):  # pragma: no cover
+    ExceptionGroup = ExceptionGroup  # noqa: F821
+else:  # pragma: no cover
+
+    class ExceptionGroup(Exception):
+        """A minimal implementation of :external:exc:`ExceptionGroup` from Python 3.11.
+
+        If :external:exc:`ExceptionGroup` is already defined by Python itself,
+        that version is used instead.
+        """
+
+        message: str
+        exceptions: list[Exception]
+
+        def __init__(self, message: str, exceptions: list[Exception]) -> None:
+            self.message = message
+            self.exceptions = exceptions
+
+        def __repr__(self) -> str:
+            return f"{self.__class__.__name__}({self.message!r}, {self.exceptions!r})"
+
+
+class InvalidMetadata(ValueError):
+    """A metadata field contains invalid data."""
+
+    field: str
+    """The name of the field that contains invalid data."""
+
+    def __init__(self, field: str, message: str) -> None:
+        self.field = field
+        super().__init__(message)
+
+
+# The RawMetadata class attempts to make as few assumptions about the underlying
+# serialization formats as possible. The idea is that as long as a serialization
+# formats offer some very basic primitives in *some* way then we can support
+# serializing to and from that format.
+class RawMetadata(TypedDict, total=False):
+    """A dictionary of raw core metadata.
+
+    Each field in core metadata maps to a key of this dictionary (when data is
+    provided). The key is lower-case and underscores are used instead of dashes
+    compared to the equivalent core metadata field. Any core metadata field that
+    can be specified multiple times or can hold multiple values in a single
+    field have a key with a plural name. See :class:`Metadata` whose attributes
+    match the keys of this dictionary.
+
+    Core metadata fields that can be specified multiple times are stored as a
+    list or dict depending on which is appropriate for the field. Any fields
+    which hold multiple values in a single field are stored as a list.
+
+    """
+
+    # Metadata 1.0 - PEP 241
+    metadata_version: str
+    name: str
+    version: str
+    platforms: list[str]
+    summary: str
+    description: str
+    keywords: list[str]
+    home_page: str
+    author: str
+    author_email: str
+    license: str
+
+    # Metadata 1.1 - PEP 314
+    supported_platforms: list[str]
+    download_url: str
+    classifiers: list[str]
+    requires: list[str]
+    provides: list[str]
+    obsoletes: list[str]
+
+    # Metadata 1.2 - PEP 345
+    maintainer: str
+    maintainer_email: str
+    requires_dist: list[str]
+    provides_dist: list[str]
+    obsoletes_dist: list[str]
+    requires_python: str
+    requires_external: list[str]
+    project_urls: dict[str, str]
+
+    # Metadata 2.0
+    # PEP 426 attempted to completely revamp the metadata format
+    # but got stuck without ever being able to build consensus on
+    # it and ultimately ended up withdrawn.
+    #
+    # However, a number of tools had started emitting METADATA with
+    # `2.0` Metadata-Version, so for historical reasons, this version
+    # was skipped.
+
+    # Metadata 2.1 - PEP 566
+    description_content_type: str
+    provides_extra: list[str]
+
+    # Metadata 2.2 - PEP 643
+    dynamic: list[str]
+
+    # Metadata 2.3 - PEP 685
+    # No new fields were added in PEP 685, just some edge case were
+    # tightened up to provide better interoperability.
+
+    # Metadata 2.4 - PEP 639
+    license_expression: str
+    license_files: list[str]
+
+    # Metadata 2.5 - PEP 794
+    import_names: list[str]
+    import_namespaces: list[str]
+
+
+# 'keywords' is special as it's a string in the core metadata spec, but we
+# represent it as a list.
+_STRING_FIELDS = {
+    "author",
+    "author_email",
+    "description",
+    "description_content_type",
+    "download_url",
+    "home_page",
+    "license",
+    "license_expression",
+    "maintainer",
+    "maintainer_email",
+    "metadata_version",
+    "name",
+    "requires_python",
+    "summary",
+    "version",
+}
+
+_LIST_FIELDS = {
+    "classifiers",
+    "dynamic",
+    "license_files",
+    "obsoletes",
+    "obsoletes_dist",
+    "platforms",
+    "provides",
+    "provides_dist",
+    "provides_extra",
+    "requires",
+    "requires_dist",
+    "requires_external",
+    "supported_platforms",
+    "import_names",
+    "import_namespaces",
+}
+
+_DICT_FIELDS = {
+    "project_urls",
+}
+
+
+def _parse_keywords(data: str) -> list[str]:
+    """Split a string of comma-separated keywords into a list of keywords."""
+    return [k.strip() for k in data.split(",")]
+
+
+def _parse_project_urls(data: list[str]) -> dict[str, str]:
+    """Parse a list of label/URL string pairings separated by a comma."""
+    urls = {}
+    for pair in data:
+        # Our logic is slightly tricky here as we want to try and do
+        # *something* reasonable with malformed data.
+        #
+        # The main thing that we have to worry about, is data that does
+        # not have a ',' at all to split the label from the Value. There
+        # isn't a singular right answer here, and we will fail validation
+        # later on (if the caller is validating) so it doesn't *really*
+        # matter, but since the missing value has to be an empty str
+        # and our return value is dict[str, str], if we let the key
+        # be the missing value, then they'd have multiple '' values that
+        # overwrite each other in a accumulating dict.
+        #
+        # The other potential issue is that it's possible to have the
+        # same label multiple times in the metadata, with no solid "right"
+        # answer with what to do in that case. As such, we'll do the only
+        # thing we can, which is treat the field as unparsable and add it
+        # to our list of unparsed fields.
+        #
+        # TODO: The spec doesn't say anything about if the keys should be
+        #       considered case sensitive or not... logically they should
+        #       be case-preserving and case-insensitive, but doing that
+        #       would open up more cases where we might have duplicate
+        #       entries.
+        label, _, url = (s.strip() for s in pair.partition(","))
+
+        if label in urls:
+            # The label already exists in our set of urls, so this field
+            # is unparsable, and we can just add the whole thing to our
+            # unparsable data and stop processing it.
+            raise KeyError("duplicate labels in project urls")
+        urls[label] = url
+
+    return urls
+
+
+def _get_payload(msg: email.message.Message, source: bytes | str) -> str:
+    """Get the body of the message."""
+    # If our source is a str, then our caller has managed encodings for us,
+    # and we don't need to deal with it.
+    if isinstance(source, str):
+        payload = msg.get_payload()
+        assert isinstance(payload, str)
+        return payload
+    # If our source is a bytes, then we're managing the encoding and we need
+    # to deal with it.
+    else:
+        bpayload = msg.get_payload(decode=True)
+        assert isinstance(bpayload, bytes)
+        try:
+            return bpayload.decode("utf8", "strict")
+        except UnicodeDecodeError as exc:
+            raise ValueError("payload in an invalid encoding") from exc
+
+
+# The various parse_FORMAT functions here are intended to be as lenient as
+# possible in their parsing, while still returning a correctly typed
+# RawMetadata.
+#
+# To aid in this, we also generally want to do as little touching of the
+# data as possible, except where there are possibly some historic holdovers
+# that make valid data awkward to work with.
+#
+# While this is a lower level, intermediate format than our ``Metadata``
+# class, some light touch ups can make a massive difference in usability.
+
+# Map METADATA fields to RawMetadata.
+_EMAIL_TO_RAW_MAPPING = {
+    "author": "author",
+    "author-email": "author_email",
+    "classifier": "classifiers",
+    "description": "description",
+    "description-content-type": "description_content_type",
+    "download-url": "download_url",
+    "dynamic": "dynamic",
+    "home-page": "home_page",
+    "import-name": "import_names",
+    "import-namespace": "import_namespaces",
+    "keywords": "keywords",
+    "license": "license",
+    "license-expression": "license_expression",
+    "license-file": "license_files",
+    "maintainer": "maintainer",
+    "maintainer-email": "maintainer_email",
+    "metadata-version": "metadata_version",
+    "name": "name",
+    "obsoletes": "obsoletes",
+    "obsoletes-dist": "obsoletes_dist",
+    "platform": "platforms",
+    "project-url": "project_urls",
+    "provides": "provides",
+    "provides-dist": "provides_dist",
+    "provides-extra": "provides_extra",
+    "requires": "requires",
+    "requires-dist": "requires_dist",
+    "requires-external": "requires_external",
+    "requires-python": "requires_python",
+    "summary": "summary",
+    "supported-platform": "supported_platforms",
+    "version": "version",
+}
+_RAW_TO_EMAIL_MAPPING = {raw: email for email, raw in _EMAIL_TO_RAW_MAPPING.items()}
+
+
+# This class is for writing RFC822 messages
+class RFC822Policy(email.policy.EmailPolicy):
+    """
+    This is :class:`email.policy.EmailPolicy`, but with a simple ``header_store_parse``
+    implementation that handles multi-line values, and some nice defaults.
+    """
+
+    utf8 = True
+    mangle_from_ = False
+    max_line_length = 0
+
+    def header_store_parse(self, name: str, value: str) -> tuple[str, str]:
+        size = len(name) + 2
+        value = value.replace("\n", "\n" + " " * size)
+        return (name, value)
+
+
+# This class is for writing RFC822 messages
+class RFC822Message(email.message.EmailMessage):
+    """
+    This is :class:`email.message.EmailMessage` with two small changes: it defaults to
+    our `RFC822Policy`, and it correctly writes unicode when being called
+    with `bytes()`.
+    """
+
+    def __init__(self) -> None:
+        super().__init__(policy=RFC822Policy())
+
+    def as_bytes(
+        self, unixfrom: bool = False, policy: email.policy.Policy | None = None
+    ) -> bytes:
+        """
+        Return the bytes representation of the message.
+
+        This handles unicode encoding.
+        """
+        return self.as_string(unixfrom, policy=policy).encode("utf-8")
+
+
+def parse_email(data: bytes | str) -> tuple[RawMetadata, dict[str, list[str]]]:
+    """Parse a distribution's metadata stored as email headers (e.g. from ``METADATA``).
+
+    This function returns a two-item tuple of dicts. The first dict is of
+    recognized fields from the core metadata specification. Fields that can be
+    parsed and translated into Python's built-in types are converted
+    appropriately. All other fields are left as-is. Fields that are allowed to
+    appear multiple times are stored as lists.
+
+    The second dict contains all other fields from the metadata. This includes
+    any unrecognized fields. It also includes any fields which are expected to
+    be parsed into a built-in type but were not formatted appropriately. Finally,
+    any fields that are expected to appear only once but are repeated are
+    included in this dict.
+
+    """
+    raw: dict[str, str | list[str] | dict[str, str]] = {}
+    unparsed: dict[str, list[str]] = {}
+
+    if isinstance(data, str):
+        parsed = email.parser.Parser(policy=email.policy.compat32).parsestr(data)
+    else:
+        parsed = email.parser.BytesParser(policy=email.policy.compat32).parsebytes(data)
+
+    # We have to wrap parsed.keys() in a set, because in the case of multiple
+    # values for a key (a list), the key will appear multiple times in the
+    # list of keys, but we're avoiding that by using get_all().
+    for name_with_case in frozenset(parsed.keys()):
+        # Header names in RFC are case insensitive, so we'll normalize to all
+        # lower case to make comparisons easier.
+        name = name_with_case.lower()
+
+        # We use get_all() here, even for fields that aren't multiple use,
+        # because otherwise someone could have e.g. two Name fields, and we
+        # would just silently ignore it rather than doing something about it.
+        headers = parsed.get_all(name) or []
+
+        # The way the email module works when parsing bytes is that it
+        # unconditionally decodes the bytes as ascii using the surrogateescape
+        # handler. When you pull that data back out (such as with get_all() ),
+        # it looks to see if the str has any surrogate escapes, and if it does
+        # it wraps it in a Header object instead of returning the string.
+        #
+        # As such, we'll look for those Header objects, and fix up the encoding.
+        value = []
+        # Flag if we have run into any issues processing the headers, thus
+        # signalling that the data belongs in 'unparsed'.
+        valid_encoding = True
+        for h in headers:
+            # It's unclear if this can return more types than just a Header or
+            # a str, so we'll just assert here to make sure.
+            assert isinstance(h, (email.header.Header, str))
+
+            # If it's a header object, we need to do our little dance to get
+            # the real data out of it. In cases where there is invalid data
+            # we're going to end up with mojibake, but there's no obvious, good
+            # way around that without reimplementing parts of the Header object
+            # ourselves.
+            #
+            # That should be fine since, if mojibacked happens, this key is
+            # going into the unparsed dict anyways.
+            if isinstance(h, email.header.Header):
+                # The Header object stores it's data as chunks, and each chunk
+                # can be independently encoded, so we'll need to check each
+                # of them.
+                chunks: list[tuple[bytes, str | None]] = []
+                for binary, _encoding in email.header.decode_header(h):
+                    try:
+                        binary.decode("utf8", "strict")
+                    except UnicodeDecodeError:
+                        # Enable mojibake.
+                        encoding = "latin1"
+                        valid_encoding = False
+                    else:
+                        encoding = "utf8"
+                    chunks.append((binary, encoding))
+
+                # Turn our chunks back into a Header object, then let that
+                # Header object do the right thing to turn them into a
+                # string for us.
+                value.append(str(email.header.make_header(chunks)))
+            # This is already a string, so just add it.
+            else:
+                value.append(h)
+
+        # We've processed all of our values to get them into a list of str,
+        # but we may have mojibake data, in which case this is an unparsed
+        # field.
+        if not valid_encoding:
+            unparsed[name] = value
+            continue
+
+        raw_name = _EMAIL_TO_RAW_MAPPING.get(name)
+        if raw_name is None:
+            # This is a bit of a weird situation, we've encountered a key that
+            # we don't know what it means, so we don't know whether it's meant
+            # to be a list or not.
+            #
+            # Since we can't really tell one way or another, we'll just leave it
+            # as a list, even though it may be a single item list, because that's
+            # what makes the most sense for email headers.
+            unparsed[name] = value
+            continue
+
+        # If this is one of our string fields, then we'll check to see if our
+        # value is a list of a single item. If it is then we'll assume that
+        # it was emitted as a single string, and unwrap the str from inside
+        # the list.
+        #
+        # If it's any other kind of data, then we haven't the faintest clue
+        # what we should parse it as, and we have to just add it to our list
+        # of unparsed stuff.
+        if raw_name in _STRING_FIELDS and len(value) == 1:
+            raw[raw_name] = value[0]
+        # If this is import_names, we need to special case the empty field
+        # case, which converts to an empty list instead of None. We can't let
+        # the empty case slip through, as it will fail validation.
+        elif raw_name == "import_names" and value == [""]:
+            raw[raw_name] = []
+        # If this is one of our list of string fields, then we can just assign
+        # the value, since email *only* has strings, and our get_all() call
+        # above ensures that this is a list.
+        elif raw_name in _LIST_FIELDS:
+            raw[raw_name] = value
+        # Special Case: Keywords
+        # The keywords field is implemented in the metadata spec as a str,
+        # but it conceptually is a list of strings, and is serialized using
+        # ", ".join(keywords), so we'll do some light data massaging to turn
+        # this into what it logically is.
+        elif raw_name == "keywords" and len(value) == 1:
+            raw[raw_name] = _parse_keywords(value[0])
+        # Special Case: Project-URL
+        # The project urls is implemented in the metadata spec as a list of
+        # specially-formatted strings that represent a key and a value, which
+        # is fundamentally a mapping, however the email format doesn't support
+        # mappings in a sane way, so it was crammed into a list of strings
+        # instead.
+        #
+        # We will do a little light data massaging to turn this into a map as
+        # it logically should be.
+        elif raw_name == "project_urls":
+            try:
+                raw[raw_name] = _parse_project_urls(value)
+            except KeyError:
+                unparsed[name] = value
+        # Nothing that we've done has managed to parse this, so it'll just
+        # throw it in our unparsable data and move on.
+        else:
+            unparsed[name] = value
+
+    # We need to support getting the Description from the message payload in
+    # addition to getting it from the the headers. This does mean, though, there
+    # is the possibility of it being set both ways, in which case we put both
+    # in 'unparsed' since we don't know which is right.
+    try:
+        payload = _get_payload(parsed, data)
+    except ValueError:
+        unparsed.setdefault("description", []).append(
+            parsed.get_payload(decode=isinstance(data, bytes))  # type: ignore[call-overload]
+        )
+    else:
+        if payload:
+            # Check to see if we've already got a description, if so then both
+            # it, and this body move to unparsable.
+            if "description" in raw:
+                description_header = cast("str", raw.pop("description"))
+                unparsed.setdefault("description", []).extend(
+                    [description_header, payload]
+                )
+            elif "description" in unparsed:
+                unparsed["description"].append(payload)
+            else:
+                raw["description"] = payload
+
+    # We need to cast our `raw` to a metadata, because a TypedDict only support
+    # literal key names, but we're computing our key names on purpose, but the
+    # way this function is implemented, our `TypedDict` can only have valid key
+    # names.
+    return cast("RawMetadata", raw), unparsed
+
+
+_NOT_FOUND = object()
+
+
+# Keep the two values in sync.
+_VALID_METADATA_VERSIONS = ["1.0", "1.1", "1.2", "2.1", "2.2", "2.3", "2.4", "2.5"]
+_MetadataVersion = Literal["1.0", "1.1", "1.2", "2.1", "2.2", "2.3", "2.4", "2.5"]
+
+_REQUIRED_ATTRS = frozenset(["metadata_version", "name", "version"])
+
+
+class _Validator(Generic[T]):
+    """Validate a metadata field.
+
+    All _process_*() methods correspond to a core metadata field. The method is
+    called with the field's raw value. If the raw value is valid it is returned
+    in its "enriched" form (e.g. ``version.Version`` for the ``Version`` field).
+    If the raw value is invalid, :exc:`InvalidMetadata` is raised (with a cause
+    as appropriate).
+    """
+
+    name: str
+    raw_name: str
+    added: _MetadataVersion
+
+    def __init__(
+        self,
+        *,
+        added: _MetadataVersion = "1.0",
+    ) -> None:
+        self.added = added
+
+    def __set_name__(self, _owner: Metadata, name: str) -> None:
+        self.name = name
+        self.raw_name = _RAW_TO_EMAIL_MAPPING[name]
+
+    def __get__(self, instance: Metadata, _owner: type[Metadata]) -> T:
+        # With Python 3.8, the caching can be replaced with functools.cached_property().
+        # No need to check the cache as attribute lookup will resolve into the
+        # instance's __dict__ before __get__ is called.
+        cache = instance.__dict__
+        value = instance._raw.get(self.name)
+
+        # To make the _process_* methods easier, we'll check if the value is None
+        # and if this field is NOT a required attribute, and if both of those
+        # things are true, we'll skip the the converter. This will mean that the
+        # converters never have to deal with the None union.
+        if self.name in _REQUIRED_ATTRS or value is not None:
+            try:
+                converter: Callable[[Any], T] = getattr(self, f"_process_{self.name}")
+            except AttributeError:
+                pass
+            else:
+                value = converter(value)
+
+        cache[self.name] = value
+        try:
+            del instance._raw[self.name]  # type: ignore[misc]
+        except KeyError:
+            pass
+
+        return cast("T", value)
+
+    def _invalid_metadata(
+        self, msg: str, cause: Exception | None = None
+    ) -> InvalidMetadata:
+        exc = InvalidMetadata(
+            self.raw_name, msg.format_map({"field": repr(self.raw_name)})
+        )
+        exc.__cause__ = cause
+        return exc
+
+    def _process_metadata_version(self, value: str) -> _MetadataVersion:
+        # Implicitly makes Metadata-Version required.
+        if value not in _VALID_METADATA_VERSIONS:
+            raise self._invalid_metadata(f"{value!r} is not a valid metadata version")
+        return cast("_MetadataVersion", value)
+
+    def _process_name(self, value: str) -> str:
+        if not value:
+            raise self._invalid_metadata("{field} is a required field")
+        # Validate the name as a side-effect.
+        try:
+            utils.canonicalize_name(value, validate=True)
+        except utils.InvalidName as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+        else:
+            return value
+
+    def _process_version(self, value: str) -> version_module.Version:
+        if not value:
+            raise self._invalid_metadata("{field} is a required field")
+        try:
+            return version_module.parse(value)
+        except version_module.InvalidVersion as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+
+    def _process_summary(self, value: str) -> str:
+        """Check the field contains no newlines."""
+        if "\n" in value:
+            raise self._invalid_metadata("{field} must be a single line")
+        return value
+
+    def _process_description_content_type(self, value: str) -> str:
+        content_types = {"text/plain", "text/x-rst", "text/markdown"}
+        message = email.message.EmailMessage()
+        message["content-type"] = value
+
+        content_type, parameters = (
+            # Defaults to `text/plain` if parsing failed.
+            message.get_content_type().lower(),
+            message["content-type"].params,
+        )
+        # Check if content-type is valid or defaulted to `text/plain` and thus was
+        # not parseable.
+        if content_type not in content_types or content_type not in value.lower():
+            raise self._invalid_metadata(
+                f"{{field}} must be one of {list(content_types)}, not {value!r}"
+            )
+
+        charset = parameters.get("charset", "UTF-8")
+        if charset != "UTF-8":
+            raise self._invalid_metadata(
+                f"{{field}} can only specify the UTF-8 charset, not {list(charset)}"
+            )
+
+        markdown_variants = {"GFM", "CommonMark"}
+        variant = parameters.get("variant", "GFM")  # Use an acceptable default.
+        if content_type == "text/markdown" and variant not in markdown_variants:
+            raise self._invalid_metadata(
+                f"valid Markdown variants for {{field}} are {list(markdown_variants)}, "
+                f"not {variant!r}",
+            )
+        return value
+
+    def _process_dynamic(self, value: list[str]) -> list[str]:
+        for dynamic_field in map(str.lower, value):
+            if dynamic_field in {"name", "version", "metadata-version"}:
+                raise self._invalid_metadata(
+                    f"{dynamic_field!r} is not allowed as a dynamic field"
+                )
+            elif dynamic_field not in _EMAIL_TO_RAW_MAPPING:
+                raise self._invalid_metadata(
+                    f"{dynamic_field!r} is not a valid dynamic field"
+                )
+        return list(map(str.lower, value))
+
+    def _process_provides_extra(
+        self,
+        value: list[str],
+    ) -> list[utils.NormalizedName]:
+        normalized_names = []
+        try:
+            for name in value:
+                normalized_names.append(utils.canonicalize_name(name, validate=True))
+        except utils.InvalidName as exc:
+            raise self._invalid_metadata(
+                f"{name!r} is invalid for {{field}}", cause=exc
+            ) from exc
+        else:
+            return normalized_names
+
+    def _process_requires_python(self, value: str) -> specifiers.SpecifierSet:
+        try:
+            return specifiers.SpecifierSet(value)
+        except specifiers.InvalidSpecifier as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+
+    def _process_requires_dist(
+        self,
+        value: list[str],
+    ) -> list[requirements.Requirement]:
+        reqs = []
+        try:
+            for req in value:
+                reqs.append(requirements.Requirement(req))
+        except requirements.InvalidRequirement as exc:
+            raise self._invalid_metadata(
+                f"{req!r} is invalid for {{field}}", cause=exc
+            ) from exc
+        else:
+            return reqs
+
+    def _process_license_expression(self, value: str) -> NormalizedLicenseExpression:
+        try:
+            return licenses.canonicalize_license_expression(value)
+        except ValueError as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+
+    def _process_license_files(self, value: list[str]) -> list[str]:
+        paths = []
+        for path in value:
+            if ".." in path:
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, "
+                    "parent directory indicators are not allowed"
+                )
+            if "*" in path:
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, paths must be resolved"
+                )
+            if (
+                pathlib.PurePosixPath(path).is_absolute()
+                or pathlib.PureWindowsPath(path).is_absolute()
+            ):
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, paths must be relative"
+                )
+            if pathlib.PureWindowsPath(path).as_posix() != path:
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, paths must use '/' delimiter"
+                )
+            paths.append(path)
+        return paths
+
+    def _process_import_names(self, value: list[str]) -> list[str]:
+        for import_name in value:
+            name, semicolon, private = import_name.partition(";")
+            name = name.rstrip()
+            for identifier in name.split("."):
+                if not identifier.isidentifier():
+                    raise self._invalid_metadata(
+                        f"{name!r} is invalid for {{field}}; "
+                        f"{identifier!r} is not a valid identifier"
+                    )
+                elif keyword.iskeyword(identifier):
+                    raise self._invalid_metadata(
+                        f"{name!r} is invalid for {{field}}; "
+                        f"{identifier!r} is a keyword"
+                    )
+            if semicolon and private.lstrip() != "private":
+                raise self._invalid_metadata(
+                    f"{import_name!r} is invalid for {{field}}; "
+                    "the only valid option is 'private'"
+                )
+        return value
+
+    _process_import_namespaces = _process_import_names
+
+
+class Metadata:
+    """Representation of distribution metadata.
+
+    Compared to :class:`RawMetadata`, this class provides objects representing
+    metadata fields instead of only using built-in types. Any invalid metadata
+    will cause :exc:`InvalidMetadata` to be raised (with a
+    :py:attr:`~BaseException.__cause__` attribute as appropriate).
+    """
+
+    _raw: RawMetadata
+
+    @classmethod
+    def from_raw(cls, data: RawMetadata, *, validate: bool = True) -> Metadata:
+        """Create an instance from :class:`RawMetadata`.
+
+        If *validate* is true, all metadata will be validated. All exceptions
+        related to validation will be gathered and raised as an :class:`ExceptionGroup`.
+        """
+        ins = cls()
+        ins._raw = data.copy()  # Mutations occur due to caching enriched values.
+
+        if validate:
+            exceptions: list[Exception] = []
+            try:
+                metadata_version = ins.metadata_version
+                metadata_age = _VALID_METADATA_VERSIONS.index(metadata_version)
+            except InvalidMetadata as metadata_version_exc:
+                exceptions.append(metadata_version_exc)
+                metadata_version = None
+
+            # Make sure to check for the fields that are present, the required
+            # fields (so their absence can be reported).
+            fields_to_check = frozenset(ins._raw) | _REQUIRED_ATTRS
+            # Remove fields that have already been checked.
+            fields_to_check -= {"metadata_version"}
+
+            for key in fields_to_check:
+                try:
+                    if metadata_version:
+                        # Can't use getattr() as that triggers descriptor protocol which
+                        # will fail due to no value for the instance argument.
+                        try:
+                            field_metadata_version = cls.__dict__[key].added
+                        except KeyError:
+                            exc = InvalidMetadata(key, f"unrecognized field: {key!r}")
+                            exceptions.append(exc)
+                            continue
+                        field_age = _VALID_METADATA_VERSIONS.index(
+                            field_metadata_version
+                        )
+                        if field_age > metadata_age:
+                            field = _RAW_TO_EMAIL_MAPPING[key]
+                            exc = InvalidMetadata(
+                                field,
+                                f"{field} introduced in metadata version "
+                                f"{field_metadata_version}, not {metadata_version}",
+                            )
+                            exceptions.append(exc)
+                            continue
+                    getattr(ins, key)
+                except InvalidMetadata as exc:
+                    exceptions.append(exc)
+
+            if exceptions:
+                raise ExceptionGroup("invalid metadata", exceptions)
+
+        return ins
+
+    @classmethod
+    def from_email(cls, data: bytes | str, *, validate: bool = True) -> Metadata:
+        """Parse metadata from email headers.
+
+        If *validate* is true, the metadata will be validated. All exceptions
+        related to validation will be gathered and raised as an :class:`ExceptionGroup`.
+        """
+        raw, unparsed = parse_email(data)
+
+        if validate:
+            exceptions: list[Exception] = []
+            for unparsed_key in unparsed:
+                if unparsed_key in _EMAIL_TO_RAW_MAPPING:
+                    message = f"{unparsed_key!r} has invalid data"
+                else:
+                    message = f"unrecognized field: {unparsed_key!r}"
+                exceptions.append(InvalidMetadata(unparsed_key, message))
+
+            if exceptions:
+                raise ExceptionGroup("unparsed", exceptions)
+
+        try:
+            return cls.from_raw(raw, validate=validate)
+        except ExceptionGroup as exc_group:
+            raise ExceptionGroup(
+                "invalid or unparsed metadata", exc_group.exceptions
+            ) from None
+
+    metadata_version: _Validator[_MetadataVersion] = _Validator()
+    """:external:ref:`core-metadata-metadata-version`
+    (required; validated to be a valid metadata version)"""
+    # `name` is not normalized/typed to NormalizedName so as to provide access to
+    # the original/raw name.
+    name: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-name`
+    (required; validated using :func:`~packaging.utils.canonicalize_name` and its
+    *validate* parameter)"""
+    version: _Validator[version_module.Version] = _Validator()
+    """:external:ref:`core-metadata-version` (required)"""
+    dynamic: _Validator[list[str] | None] = _Validator(
+        added="2.2",
+    )
+    """:external:ref:`core-metadata-dynamic`
+    (validated against core metadata field names and lowercased)"""
+    platforms: _Validator[list[str] | None] = _Validator()
+    """:external:ref:`core-metadata-platform`"""
+    supported_platforms: _Validator[list[str] | None] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-supported-platform`"""
+    summary: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-summary` (validated to contain no newlines)"""
+    description: _Validator[str | None] = _Validator()  # TODO 2.1: can be in body
+    """:external:ref:`core-metadata-description`"""
+    description_content_type: _Validator[str | None] = _Validator(added="2.1")
+    """:external:ref:`core-metadata-description-content-type` (validated)"""
+    keywords: _Validator[list[str] | None] = _Validator()
+    """:external:ref:`core-metadata-keywords`"""
+    home_page: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-home-page`"""
+    download_url: _Validator[str | None] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-download-url`"""
+    author: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-author`"""
+    author_email: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-author-email`"""
+    maintainer: _Validator[str | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-maintainer`"""
+    maintainer_email: _Validator[str | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-maintainer-email`"""
+    license: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-license`"""
+    license_expression: _Validator[NormalizedLicenseExpression | None] = _Validator(
+        added="2.4"
+    )
+    """:external:ref:`core-metadata-license-expression`"""
+    license_files: _Validator[list[str] | None] = _Validator(added="2.4")
+    """:external:ref:`core-metadata-license-file`"""
+    classifiers: _Validator[list[str] | None] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-classifier`"""
+    requires_dist: _Validator[list[requirements.Requirement] | None] = _Validator(
+        added="1.2"
+    )
+    """:external:ref:`core-metadata-requires-dist`"""
+    requires_python: _Validator[specifiers.SpecifierSet | None] = _Validator(
+        added="1.2"
+    )
+    """:external:ref:`core-metadata-requires-python`"""
+    # Because `Requires-External` allows for non-PEP 440 version specifiers, we
+    # don't do any processing on the values.
+    requires_external: _Validator[list[str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-requires-external`"""
+    project_urls: _Validator[dict[str, str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-project-url`"""
+    # PEP 685 lets us raise an error if an extra doesn't pass `Name` validation
+    # regardless of metadata version.
+    provides_extra: _Validator[list[utils.NormalizedName] | None] = _Validator(
+        added="2.1",
+    )
+    """:external:ref:`core-metadata-provides-extra`"""
+    provides_dist: _Validator[list[str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-provides-dist`"""
+    obsoletes_dist: _Validator[list[str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-obsoletes-dist`"""
+    import_names: _Validator[list[str] | None] = _Validator(added="2.5")
+    """:external:ref:`core-metadata-import-name`"""
+    import_namespaces: _Validator[list[str] | None] = _Validator(added="2.5")
+    """:external:ref:`core-metadata-import-namespace`"""
+    requires: _Validator[list[str] | None] = _Validator(added="1.1")
+    """``Requires`` (deprecated)"""
+    provides: _Validator[list[str] | None] = _Validator(added="1.1")
+    """``Provides`` (deprecated)"""
+    obsoletes: _Validator[list[str] | None] = _Validator(added="1.1")
+    """``Obsoletes`` (deprecated)"""
+
+    def as_rfc822(self) -> RFC822Message:
+        """
+        Return an RFC822 message with the metadata.
+        """
+        message = RFC822Message()
+        self._write_metadata(message)
+        return message
+
+    def _write_metadata(self, message: RFC822Message) -> None:
+        """
+        Return an RFC822 message with the metadata.
+        """
+        for name, validator in self.__class__.__dict__.items():
+            if isinstance(validator, _Validator) and name != "description":
+                value = getattr(self, name)
+                email_name = _RAW_TO_EMAIL_MAPPING[name]
+                if value is not None:
+                    if email_name == "project-url":
+                        for label, url in value.items():
+                            message[email_name] = f"{label}, {url}"
+                    elif email_name == "keywords":
+                        message[email_name] = ",".join(value)
+                    elif email_name == "import-name" and value == []:
+                        message[email_name] = ""
+                    elif isinstance(value, list):
+                        for item in value:
+                            message[email_name] = str(item)
+                    else:
+                        message[email_name] = str(value)
+
+        # The description is a special case because it is in the body of the message.
+        if self.description is not None:
+            message.set_payload(self.description)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/py.typed b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/pylock.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/pylock.py
new file mode 100644
index 0000000000000000000000000000000000000000..a564f15246ad65038029f8fefb48621fa64a3abd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/pylock.py
@@ -0,0 +1,635 @@
+from __future__ import annotations
+
+import dataclasses
+import logging
+import re
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from datetime import datetime
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Protocol,
+    TypeVar,
+)
+
+from .markers import Marker
+from .specifiers import SpecifierSet
+from .utils import NormalizedName, is_normalized_name
+from .version import Version
+
+if TYPE_CHECKING:  # pragma: no cover
+    from pathlib import Path
+
+    from typing_extensions import Self
+
+_logger = logging.getLogger(__name__)
+
+__all__ = [
+    "Package",
+    "PackageArchive",
+    "PackageDirectory",
+    "PackageSdist",
+    "PackageVcs",
+    "PackageWheel",
+    "Pylock",
+    "PylockUnsupportedVersionError",
+    "PylockValidationError",
+    "is_valid_pylock_path",
+]
+
+_T = TypeVar("_T")
+_T2 = TypeVar("_T2")
+
+
+class _FromMappingProtocol(Protocol):  # pragma: no cover
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self: ...
+
+
+_FromMappingProtocolT = TypeVar("_FromMappingProtocolT", bound=_FromMappingProtocol)
+
+
+_PYLOCK_FILE_NAME_RE = re.compile(r"^pylock\.([^.]+)\.toml$")
+
+
+def is_valid_pylock_path(path: Path) -> bool:
+    """Check if the given path is a valid pylock file path."""
+    return path.name == "pylock.toml" or bool(_PYLOCK_FILE_NAME_RE.match(path.name))
+
+
+def _toml_key(key: str) -> str:
+    return key.replace("_", "-")
+
+
+def _toml_value(key: str, value: Any) -> Any:  # noqa: ANN401
+    if isinstance(value, (Version, Marker, SpecifierSet)):
+        return str(value)
+    if isinstance(value, Sequence) and key == "environments":
+        return [str(v) for v in value]
+    return value
+
+
+def _toml_dict_factory(data: list[tuple[str, Any]]) -> dict[str, Any]:
+    return {
+        _toml_key(key): _toml_value(key, value)
+        for key, value in data
+        if value is not None
+    }
+
+
+def _get(d: Mapping[str, Any], expected_type: type[_T], key: str) -> _T | None:
+    """Get a value from the dictionary and verify it's the expected type."""
+    if (value := d.get(key)) is None:
+        return None
+    if not isinstance(value, expected_type):
+        raise PylockValidationError(
+            f"Unexpected type {type(value).__name__} "
+            f"(expected {expected_type.__name__})",
+            context=key,
+        )
+    return value
+
+
+def _get_required(d: Mapping[str, Any], expected_type: type[_T], key: str) -> _T:
+    """Get a required value from the dictionary and verify it's the expected type."""
+    if (value := _get(d, expected_type, key)) is None:
+        raise _PylockRequiredKeyError(key)
+    return value
+
+
+def _get_sequence(
+    d: Mapping[str, Any], expected_item_type: type[_T], key: str
+) -> Sequence[_T] | None:
+    """Get a list value from the dictionary and verify it's the expected items type."""
+    if (value := _get(d, Sequence, key)) is None:  # type: ignore[type-abstract]
+        return None
+    if isinstance(value, (str, bytes)):
+        # special case: str and bytes are Sequences, but we want to reject it
+        raise PylockValidationError(
+            f"Unexpected type {type(value).__name__} (expected Sequence)",
+            context=key,
+        )
+    for i, item in enumerate(value):
+        if not isinstance(item, expected_item_type):
+            raise PylockValidationError(
+                f"Unexpected type {type(item).__name__} "
+                f"(expected {expected_item_type.__name__})",
+                context=f"{key}[{i}]",
+            )
+    return value
+
+
+def _get_as(
+    d: Mapping[str, Any],
+    expected_type: type[_T],
+    target_type: Callable[[_T], _T2],
+    key: str,
+) -> _T2 | None:
+    """Get a value from the dictionary, verify it's the expected type,
+    and convert to the target type.
+
+    This assumes the target_type constructor accepts the value.
+    """
+    if (value := _get(d, expected_type, key)) is None:
+        return None
+    try:
+        return target_type(value)
+    except Exception as e:
+        raise PylockValidationError(e, context=key) from e
+
+
+def _get_required_as(
+    d: Mapping[str, Any],
+    expected_type: type[_T],
+    target_type: Callable[[_T], _T2],
+    key: str,
+) -> _T2:
+    """Get a required value from the dict, verify it's the expected type,
+    and convert to the target type."""
+    if (value := _get_as(d, expected_type, target_type, key)) is None:
+        raise _PylockRequiredKeyError(key)
+    return value
+
+
+def _get_sequence_as(
+    d: Mapping[str, Any],
+    expected_item_type: type[_T],
+    target_item_type: Callable[[_T], _T2],
+    key: str,
+) -> list[_T2] | None:
+    """Get list value from dictionary and verify expected items type."""
+    if (value := _get_sequence(d, expected_item_type, key)) is None:
+        return None
+    result = []
+    try:
+        for item in value:
+            typed_item = target_item_type(item)
+            result.append(typed_item)
+    except Exception as e:
+        raise PylockValidationError(e, context=f"{key}[{len(result)}]") from e
+    return result
+
+
+def _get_object(
+    d: Mapping[str, Any], target_type: type[_FromMappingProtocolT], key: str
+) -> _FromMappingProtocolT | None:
+    """Get a dictionary value from the dictionary and convert it to a dataclass."""
+    if (value := _get(d, Mapping, key)) is None:  # type: ignore[type-abstract]
+        return None
+    try:
+        return target_type._from_dict(value)
+    except Exception as e:
+        raise PylockValidationError(e, context=key) from e
+
+
+def _get_sequence_of_objects(
+    d: Mapping[str, Any], target_item_type: type[_FromMappingProtocolT], key: str
+) -> list[_FromMappingProtocolT] | None:
+    """Get a list value from the dictionary and convert its items to a dataclass."""
+    if (value := _get_sequence(d, Mapping, key)) is None:  # type: ignore[type-abstract]
+        return None
+    result: list[_FromMappingProtocolT] = []
+    try:
+        for item in value:
+            typed_item = target_item_type._from_dict(item)
+            result.append(typed_item)
+    except Exception as e:
+        raise PylockValidationError(e, context=f"{key}[{len(result)}]") from e
+    return result
+
+
+def _get_required_sequence_of_objects(
+    d: Mapping[str, Any], target_item_type: type[_FromMappingProtocolT], key: str
+) -> Sequence[_FromMappingProtocolT]:
+    """Get a required list value from the dictionary and convert its items to a
+    dataclass."""
+    if (result := _get_sequence_of_objects(d, target_item_type, key)) is None:
+        raise _PylockRequiredKeyError(key)
+    return result
+
+
+def _validate_normalized_name(name: str) -> NormalizedName:
+    """Validate that a string is a NormalizedName."""
+    if not is_normalized_name(name):
+        raise PylockValidationError(f"Name {name!r} is not normalized")
+    return NormalizedName(name)
+
+
+def _validate_path_url(path: str | None, url: str | None) -> None:
+    if not path and not url:
+        raise PylockValidationError("path or url must be provided")
+
+
+def _validate_hashes(hashes: Mapping[str, Any]) -> Mapping[str, Any]:
+    if not hashes:
+        raise PylockValidationError("At least one hash must be provided")
+    if not all(isinstance(hash_val, str) for hash_val in hashes.values()):
+        raise PylockValidationError("Hash values must be strings")
+    return hashes
+
+
+class PylockValidationError(Exception):
+    """Raised when when input data is not spec-compliant."""
+
+    context: str | None = None
+    message: str
+
+    def __init__(
+        self,
+        cause: str | Exception,
+        *,
+        context: str | None = None,
+    ) -> None:
+        if isinstance(cause, PylockValidationError):
+            if cause.context:
+                self.context = (
+                    f"{context}.{cause.context}" if context else cause.context
+                )
+            else:
+                self.context = context
+            self.message = cause.message
+        else:
+            self.context = context
+            self.message = str(cause)
+
+    def __str__(self) -> str:
+        if self.context:
+            return f"{self.message} in {self.context!r}"
+        return self.message
+
+
+class _PylockRequiredKeyError(PylockValidationError):
+    def __init__(self, key: str) -> None:
+        super().__init__("Missing required value", context=key)
+
+
+class PylockUnsupportedVersionError(PylockValidationError):
+    """Raised when encountering an unsupported `lock_version`."""
+
+
+@dataclass(frozen=True, init=False)
+class PackageVcs:
+    type: str
+    url: str | None = None
+    path: str | None = None
+    requested_revision: str | None = None
+    commit_id: str  # type: ignore[misc]
+    subdirectory: str | None = None
+
+    def __init__(
+        self,
+        *,
+        type: str,
+        url: str | None = None,
+        path: str | None = None,
+        requested_revision: str | None = None,
+        commit_id: str,
+        subdirectory: str | None = None,
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "type", type)
+        object.__setattr__(self, "url", url)
+        object.__setattr__(self, "path", path)
+        object.__setattr__(self, "requested_revision", requested_revision)
+        object.__setattr__(self, "commit_id", commit_id)
+        object.__setattr__(self, "subdirectory", subdirectory)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        package_vcs = cls(
+            type=_get_required(d, str, "type"),
+            url=_get(d, str, "url"),
+            path=_get(d, str, "path"),
+            requested_revision=_get(d, str, "requested-revision"),
+            commit_id=_get_required(d, str, "commit-id"),
+            subdirectory=_get(d, str, "subdirectory"),
+        )
+        _validate_path_url(package_vcs.path, package_vcs.url)
+        return package_vcs
+
+
+@dataclass(frozen=True, init=False)
+class PackageDirectory:
+    path: str
+    editable: bool | None = None
+    subdirectory: str | None = None
+
+    def __init__(
+        self,
+        *,
+        path: str,
+        editable: bool | None = None,
+        subdirectory: str | None = None,
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "path", path)
+        object.__setattr__(self, "editable", editable)
+        object.__setattr__(self, "subdirectory", subdirectory)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        return cls(
+            path=_get_required(d, str, "path"),
+            editable=_get(d, bool, "editable"),
+            subdirectory=_get(d, str, "subdirectory"),
+        )
+
+
+@dataclass(frozen=True, init=False)
+class PackageArchive:
+    url: str | None = None
+    path: str | None = None
+    size: int | None = None
+    upload_time: datetime | None = None
+    hashes: Mapping[str, str]  # type: ignore[misc]
+    subdirectory: str | None = None
+
+    def __init__(
+        self,
+        *,
+        url: str | None = None,
+        path: str | None = None,
+        size: int | None = None,
+        upload_time: datetime | None = None,
+        hashes: Mapping[str, str],
+        subdirectory: str | None = None,
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "url", url)
+        object.__setattr__(self, "path", path)
+        object.__setattr__(self, "size", size)
+        object.__setattr__(self, "upload_time", upload_time)
+        object.__setattr__(self, "hashes", hashes)
+        object.__setattr__(self, "subdirectory", subdirectory)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        package_archive = cls(
+            url=_get(d, str, "url"),
+            path=_get(d, str, "path"),
+            size=_get(d, int, "size"),
+            upload_time=_get(d, datetime, "upload-time"),
+            hashes=_get_required_as(d, Mapping, _validate_hashes, "hashes"),  # type: ignore[type-abstract]
+            subdirectory=_get(d, str, "subdirectory"),
+        )
+        _validate_path_url(package_archive.path, package_archive.url)
+        return package_archive
+
+
+@dataclass(frozen=True, init=False)
+class PackageSdist:
+    name: str | None = None
+    upload_time: datetime | None = None
+    url: str | None = None
+    path: str | None = None
+    size: int | None = None
+    hashes: Mapping[str, str]  # type: ignore[misc]
+
+    def __init__(
+        self,
+        *,
+        name: str | None = None,
+        upload_time: datetime | None = None,
+        url: str | None = None,
+        path: str | None = None,
+        size: int | None = None,
+        hashes: Mapping[str, str],
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "name", name)
+        object.__setattr__(self, "upload_time", upload_time)
+        object.__setattr__(self, "url", url)
+        object.__setattr__(self, "path", path)
+        object.__setattr__(self, "size", size)
+        object.__setattr__(self, "hashes", hashes)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        package_sdist = cls(
+            name=_get(d, str, "name"),
+            upload_time=_get(d, datetime, "upload-time"),
+            url=_get(d, str, "url"),
+            path=_get(d, str, "path"),
+            size=_get(d, int, "size"),
+            hashes=_get_required_as(d, Mapping, _validate_hashes, "hashes"),  # type: ignore[type-abstract]
+        )
+        _validate_path_url(package_sdist.path, package_sdist.url)
+        return package_sdist
+
+
+@dataclass(frozen=True, init=False)
+class PackageWheel:
+    name: str | None = None
+    upload_time: datetime | None = None
+    url: str | None = None
+    path: str | None = None
+    size: int | None = None
+    hashes: Mapping[str, str]  # type: ignore[misc]
+
+    def __init__(
+        self,
+        *,
+        name: str | None = None,
+        upload_time: datetime | None = None,
+        url: str | None = None,
+        path: str | None = None,
+        size: int | None = None,
+        hashes: Mapping[str, str],
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "name", name)
+        object.__setattr__(self, "upload_time", upload_time)
+        object.__setattr__(self, "url", url)
+        object.__setattr__(self, "path", path)
+        object.__setattr__(self, "size", size)
+        object.__setattr__(self, "hashes", hashes)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        package_wheel = cls(
+            name=_get(d, str, "name"),
+            upload_time=_get(d, datetime, "upload-time"),
+            url=_get(d, str, "url"),
+            path=_get(d, str, "path"),
+            size=_get(d, int, "size"),
+            hashes=_get_required_as(d, Mapping, _validate_hashes, "hashes"),  # type: ignore[type-abstract]
+        )
+        _validate_path_url(package_wheel.path, package_wheel.url)
+        return package_wheel
+
+
+@dataclass(frozen=True, init=False)
+class Package:
+    name: NormalizedName
+    version: Version | None = None
+    marker: Marker | None = None
+    requires_python: SpecifierSet | None = None
+    dependencies: Sequence[Mapping[str, Any]] | None = None
+    vcs: PackageVcs | None = None
+    directory: PackageDirectory | None = None
+    archive: PackageArchive | None = None
+    index: str | None = None
+    sdist: PackageSdist | None = None
+    wheels: Sequence[PackageWheel] | None = None
+    attestation_identities: Sequence[Mapping[str, Any]] | None = None
+    tool: Mapping[str, Any] | None = None
+
+    def __init__(
+        self,
+        *,
+        name: NormalizedName,
+        version: Version | None = None,
+        marker: Marker | None = None,
+        requires_python: SpecifierSet | None = None,
+        dependencies: Sequence[Mapping[str, Any]] | None = None,
+        vcs: PackageVcs | None = None,
+        directory: PackageDirectory | None = None,
+        archive: PackageArchive | None = None,
+        index: str | None = None,
+        sdist: PackageSdist | None = None,
+        wheels: Sequence[PackageWheel] | None = None,
+        attestation_identities: Sequence[Mapping[str, Any]] | None = None,
+        tool: Mapping[str, Any] | None = None,
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "name", name)
+        object.__setattr__(self, "version", version)
+        object.__setattr__(self, "marker", marker)
+        object.__setattr__(self, "requires_python", requires_python)
+        object.__setattr__(self, "dependencies", dependencies)
+        object.__setattr__(self, "vcs", vcs)
+        object.__setattr__(self, "directory", directory)
+        object.__setattr__(self, "archive", archive)
+        object.__setattr__(self, "index", index)
+        object.__setattr__(self, "sdist", sdist)
+        object.__setattr__(self, "wheels", wheels)
+        object.__setattr__(self, "attestation_identities", attestation_identities)
+        object.__setattr__(self, "tool", tool)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        package = cls(
+            name=_get_required_as(d, str, _validate_normalized_name, "name"),
+            version=_get_as(d, str, Version, "version"),
+            requires_python=_get_as(d, str, SpecifierSet, "requires-python"),
+            dependencies=_get_sequence(d, Mapping, "dependencies"),  # type: ignore[type-abstract]
+            marker=_get_as(d, str, Marker, "marker"),
+            vcs=_get_object(d, PackageVcs, "vcs"),
+            directory=_get_object(d, PackageDirectory, "directory"),
+            archive=_get_object(d, PackageArchive, "archive"),
+            index=_get(d, str, "index"),
+            sdist=_get_object(d, PackageSdist, "sdist"),
+            wheels=_get_sequence_of_objects(d, PackageWheel, "wheels"),
+            attestation_identities=_get_sequence(d, Mapping, "attestation-identities"),  # type: ignore[type-abstract]
+            tool=_get(d, Mapping, "tool"),  # type: ignore[type-abstract]
+        )
+        distributions = bool(package.sdist) + len(package.wheels or [])
+        direct_urls = (
+            bool(package.vcs) + bool(package.directory) + bool(package.archive)
+        )
+        if distributions > 0 and direct_urls > 0:
+            raise PylockValidationError(
+                "None of vcs, directory, archive must be set if sdist or wheels are set"
+            )
+        if distributions == 0 and direct_urls != 1:
+            raise PylockValidationError(
+                "Exactly one of vcs, directory, archive must be set "
+                "if sdist and wheels are not set"
+            )
+        try:
+            for i, attestation_identity in enumerate(  # noqa: B007
+                package.attestation_identities or []
+            ):
+                _get_required(attestation_identity, str, "kind")
+        except Exception as e:
+            raise PylockValidationError(
+                e, context=f"attestation-identities[{i}]"
+            ) from e
+        return package
+
+    @property
+    def is_direct(self) -> bool:
+        return not (self.sdist or self.wheels)
+
+
+@dataclass(frozen=True, init=False)
+class Pylock:
+    """A class representing a pylock file."""
+
+    lock_version: Version
+    environments: Sequence[Marker] | None = None
+    requires_python: SpecifierSet | None = None
+    extras: Sequence[NormalizedName] | None = None
+    dependency_groups: Sequence[str] | None = None
+    default_groups: Sequence[str] | None = None
+    created_by: str  # type: ignore[misc]
+    packages: Sequence[Package]  # type: ignore[misc]
+    tool: Mapping[str, Any] | None = None
+
+    def __init__(
+        self,
+        *,
+        lock_version: Version,
+        environments: Sequence[Marker] | None = None,
+        requires_python: SpecifierSet | None = None,
+        extras: Sequence[NormalizedName] | None = None,
+        dependency_groups: Sequence[str] | None = None,
+        default_groups: Sequence[str] | None = None,
+        created_by: str,
+        packages: Sequence[Package],
+        tool: Mapping[str, Any] | None = None,
+    ) -> None:
+        # In Python 3.10+ make dataclass kw_only=True and remove __init__
+        object.__setattr__(self, "lock_version", lock_version)
+        object.__setattr__(self, "environments", environments)
+        object.__setattr__(self, "requires_python", requires_python)
+        object.__setattr__(self, "extras", extras)
+        object.__setattr__(self, "dependency_groups", dependency_groups)
+        object.__setattr__(self, "default_groups", default_groups)
+        object.__setattr__(self, "created_by", created_by)
+        object.__setattr__(self, "packages", packages)
+        object.__setattr__(self, "tool", tool)
+
+    @classmethod
+    def _from_dict(cls, d: Mapping[str, Any]) -> Self:
+        pylock = cls(
+            lock_version=_get_required_as(d, str, Version, "lock-version"),
+            environments=_get_sequence_as(d, str, Marker, "environments"),
+            extras=_get_sequence_as(d, str, _validate_normalized_name, "extras"),
+            dependency_groups=_get_sequence(d, str, "dependency-groups"),
+            default_groups=_get_sequence(d, str, "default-groups"),
+            created_by=_get_required(d, str, "created-by"),
+            requires_python=_get_as(d, str, SpecifierSet, "requires-python"),
+            packages=_get_required_sequence_of_objects(d, Package, "packages"),
+            tool=_get(d, Mapping, "tool"),  # type: ignore[type-abstract]
+        )
+        if not Version("1") <= pylock.lock_version < Version("2"):
+            raise PylockUnsupportedVersionError(
+                f"pylock version {pylock.lock_version} is not supported"
+            )
+        if pylock.lock_version > Version("1.0"):
+            _logger.warning(
+                "pylock minor version %s is not supported", pylock.lock_version
+            )
+        return pylock
+
+    @classmethod
+    def from_dict(cls, d: Mapping[str, Any], /) -> Self:
+        """Create and validate a Pylock instance from a TOML dictionary.
+
+        Raises :class:`PylockValidationError` if the input data is not
+        spec-compliant.
+        """
+        return cls._from_dict(d)
+
+    def to_dict(self) -> Mapping[str, Any]:
+        """Convert the Pylock instance to a TOML dictionary."""
+        return dataclasses.asdict(self, dict_factory=_toml_dict_factory)
+
+    def validate(self) -> None:
+        """Validate the Pylock instance against the specification.
+
+        Raises :class:`PylockValidationError` otherwise."""
+        self.from_dict(self.to_dict())
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/requirements.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/requirements.py
new file mode 100644
index 0000000000000000000000000000000000000000..3079be69bf880f47e64dbf62993f0e54754b7315
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/requirements.py
@@ -0,0 +1,86 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+from __future__ import annotations
+
+from typing import Iterator
+
+from ._parser import parse_requirement as _parse_requirement
+from ._tokenizer import ParserSyntaxError
+from .markers import Marker, _normalize_extra_values
+from .specifiers import SpecifierSet
+from .utils import canonicalize_name
+
+
+class InvalidRequirement(ValueError):
+    """
+    An invalid requirement was found, users should refer to PEP 508.
+    """
+
+
+class Requirement:
+    """Parse a requirement.
+
+    Parse a given requirement string into its parts, such as name, specifier,
+    URL, and extras. Raises InvalidRequirement on a badly-formed requirement
+    string.
+    """
+
+    # TODO: Can we test whether something is contained within a requirement?
+    #       If so how do we do that? Do we need to test against the _name_ of
+    #       the thing as well as the version? What about the markers?
+    # TODO: Can we normalize the name and extra name?
+
+    def __init__(self, requirement_string: str) -> None:
+        try:
+            parsed = _parse_requirement(requirement_string)
+        except ParserSyntaxError as e:
+            raise InvalidRequirement(str(e)) from e
+
+        self.name: str = parsed.name
+        self.url: str | None = parsed.url or None
+        self.extras: set[str] = set(parsed.extras or [])
+        self.specifier: SpecifierSet = SpecifierSet(parsed.specifier)
+        self.marker: Marker | None = None
+        if parsed.marker is not None:
+            self.marker = Marker.__new__(Marker)
+            self.marker._markers = _normalize_extra_values(parsed.marker)
+
+    def _iter_parts(self, name: str) -> Iterator[str]:
+        yield name
+
+        if self.extras:
+            formatted_extras = ",".join(sorted(self.extras))
+            yield f"[{formatted_extras}]"
+
+        if self.specifier:
+            yield str(self.specifier)
+
+        if self.url:
+            yield f" @ {self.url}"
+            if self.marker:
+                yield " "
+
+        if self.marker:
+            yield f"; {self.marker}"
+
+    def __str__(self) -> str:
+        return "".join(self._iter_parts(self.name))
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}('{self}')>"
+
+    def __hash__(self) -> int:
+        return hash(tuple(self._iter_parts(canonicalize_name(self.name))))
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Requirement):
+            return NotImplemented
+
+        return (
+            canonicalize_name(self.name) == canonicalize_name(other.name)
+            and self.extras == other.extras
+            and self.specifier == other.specifier
+            and self.url == other.url
+            and self.marker == other.marker
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/specifiers.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/specifiers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d26b0d1ae2d21b77e24b692d5a7e1fd01296edc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/specifiers.py
@@ -0,0 +1,1068 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+"""
+.. testsetup::
+
+    from packaging.specifiers import Specifier, SpecifierSet, InvalidSpecifier
+    from packaging.version import Version
+"""
+
+from __future__ import annotations
+
+import abc
+import itertools
+import re
+from typing import Callable, Final, Iterable, Iterator, TypeVar, Union
+
+from .utils import canonicalize_version
+from .version import InvalidVersion, Version
+
+UnparsedVersion = Union[Version, str]
+UnparsedVersionVar = TypeVar("UnparsedVersionVar", bound=UnparsedVersion)
+CallableOperator = Callable[[Version, str], bool]
+
+
+def _coerce_version(version: UnparsedVersion) -> Version | None:
+    if not isinstance(version, Version):
+        try:
+            version = Version(version)
+        except InvalidVersion:
+            return None
+    return version
+
+
+def _public_version(version: Version) -> Version:
+    return version.__replace__(local=None)
+
+
+def _base_version(version: Version) -> Version:
+    return version.__replace__(pre=None, post=None, dev=None, local=None)
+
+
+class InvalidSpecifier(ValueError):
+    """
+    Raised when attempting to create a :class:`Specifier` with a specifier
+    string that is invalid.
+
+    >>> Specifier("lolwat")
+    Traceback (most recent call last):
+        ...
+    packaging.specifiers.InvalidSpecifier: Invalid specifier: 'lolwat'
+    """
+
+
+class BaseSpecifier(metaclass=abc.ABCMeta):
+    __slots__ = ()
+    __match_args__ = ("_str",)
+
+    @property
+    def _str(self) -> str:
+        """Internal property for match_args"""
+        return str(self)
+
+    @abc.abstractmethod
+    def __str__(self) -> str:
+        """
+        Returns the str representation of this Specifier-like object. This
+        should be representative of the Specifier itself.
+        """
+
+    @abc.abstractmethod
+    def __hash__(self) -> int:
+        """
+        Returns a hash value for this Specifier-like object.
+        """
+
+    @abc.abstractmethod
+    def __eq__(self, other: object) -> bool:
+        """
+        Returns a boolean representing whether or not the two Specifier-like
+        objects are equal.
+
+        :param other: The other object to check against.
+        """
+
+    @property
+    @abc.abstractmethod
+    def prereleases(self) -> bool | None:
+        """Whether or not pre-releases as a whole are allowed.
+
+        This can be set to either ``True`` or ``False`` to explicitly enable or disable
+        prereleases or it can be set to ``None`` (the default) to use default semantics.
+        """
+
+    @prereleases.setter  # noqa: B027
+    def prereleases(self, value: bool) -> None:
+        """Setter for :attr:`prereleases`.
+
+        :param value: The value to set.
+        """
+
+    @abc.abstractmethod
+    def contains(self, item: str, prereleases: bool | None = None) -> bool:
+        """
+        Determines if the given item is contained within this specifier.
+        """
+
+    @abc.abstractmethod
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """
+        Takes an iterable of items and filters them so that only items which
+        are contained within this specifier are allowed in it.
+        """
+
+
+class Specifier(BaseSpecifier):
+    """This class abstracts handling of version specifiers.
+
+    .. tip::
+
+        It is generally not required to instantiate this manually. You should instead
+        prefer to work with :class:`SpecifierSet` instead, which can parse
+        comma-separated version specifiers (which is what package metadata contains).
+    """
+
+    __slots__ = ("_prereleases", "_spec", "_spec_version")
+
+    _operator_regex_str = r"""
+        (?P<operator>(~=|==|!=|<=|>=|<|>|===))
+        """
+    _version_regex_str = r"""
+        (?P<version>
+            (?:
+                # The identity operators allow for an escape hatch that will
+                # do an exact string match of the version you wish to install.
+                # This will not be parsed by PEP 440 and we cannot determine
+                # any semantic meaning from it. This operator is discouraged
+                # but included entirely as an escape hatch.
+                (?<====)  # Only match for the identity operator
+                \s*
+                [^\s;)]*  # The arbitrary version can be just about anything,
+                          # we match everything except for whitespace, a
+                          # semi-colon for marker support, and a closing paren
+                          # since versions can be enclosed in them.
+            )
+            |
+            (?:
+                # The (non)equality operators allow for wild card and local
+                # versions to be specified so we have to define these two
+                # operators separately to enable that.
+                (?<===|!=)            # Only match for equals and not equals
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)*   # release
+
+                # You cannot use a wild card and a pre-release, post-release, a dev or
+                # local version together so group them with a | and make them optional.
+                (?:
+                    \.\*  # Wild card syntax of .*
+                    |
+                    (?:                                  # pre release
+                        [-_\.]?
+                        (alpha|beta|preview|pre|a|b|c|rc)
+                        [-_\.]?
+                        [0-9]*
+                    )?
+                    (?:                                  # post release
+                        (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                    )?
+                    (?:[-_\.]?dev[-_\.]?[0-9]*)?         # dev release
+                    (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
+                )?
+            )
+            |
+            (?:
+                # The compatible operator requires at least two digits in the
+                # release segment.
+                (?<=~=)               # Only match for the compatible operator
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)+   # release  (We have a + instead of a *)
+                (?:                   # pre release
+                    [-_\.]?
+                    (alpha|beta|preview|pre|a|b|c|rc)
+                    [-_\.]?
+                    [0-9]*
+                )?
+                (?:                                   # post release
+                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                )?
+                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
+            )
+            |
+            (?:
+                # All other operators only allow a sub set of what the
+                # (non)equality operators do. Specifically they do not allow
+                # local versions to be specified nor do they allow the prefix
+                # matching wild cards.
+                (?<!==|!=|~=)         # We have special cases for these
+                                      # operators so we want to make sure they
+                                      # don't match here.
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)*   # release
+                (?:                   # pre release
+                    [-_\.]?
+                    (alpha|beta|preview|pre|a|b|c|rc)
+                    [-_\.]?
+                    [0-9]*
+                )?
+                (?:                                   # post release
+                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                )?
+                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
+            )
+        )
+        """
+
+    _regex = re.compile(
+        r"\s*" + _operator_regex_str + _version_regex_str + r"\s*",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    _operators: Final = {
+        "~=": "compatible",
+        "==": "equal",
+        "!=": "not_equal",
+        "<=": "less_than_equal",
+        ">=": "greater_than_equal",
+        "<": "less_than",
+        ">": "greater_than",
+        "===": "arbitrary",
+    }
+
+    def __init__(self, spec: str = "", prereleases: bool | None = None) -> None:
+        """Initialize a Specifier instance.
+
+        :param spec:
+            The string representation of a specifier which will be parsed and
+            normalized before use.
+        :param prereleases:
+            This tells the specifier if it should accept prerelease versions if
+            applicable or not. The default of ``None`` will autodetect it from the
+            given specifiers.
+        :raises InvalidSpecifier:
+            If the given specifier is invalid (i.e. bad syntax).
+        """
+        match = self._regex.fullmatch(spec)
+        if not match:
+            raise InvalidSpecifier(f"Invalid specifier: {spec!r}")
+
+        self._spec: tuple[str, str] = (
+            match.group("operator").strip(),
+            match.group("version").strip(),
+        )
+
+        # Store whether or not this Specifier should accept prereleases
+        self._prereleases = prereleases
+
+        # Specifier version cache
+        self._spec_version: tuple[str, Version] | None = None
+
+    def _get_spec_version(self, version: str) -> Version | None:
+        """One element cache, as only one spec Version is needed per Specifier."""
+        if self._spec_version is not None and self._spec_version[0] == version:
+            return self._spec_version[1]
+
+        version_specifier = _coerce_version(version)
+        if version_specifier is None:
+            return None
+
+        self._spec_version = (version, version_specifier)
+        return version_specifier
+
+    def _require_spec_version(self, version: str) -> Version:
+        """Get spec version, asserting it's valid (not for === operator).
+
+        This method should only be called for operators where version
+        strings are guaranteed to be valid PEP 440 versions (not ===).
+        """
+        spec_version = self._get_spec_version(version)
+        assert spec_version is not None
+        return spec_version
+
+    @property
+    def prereleases(self) -> bool | None:
+        # If there is an explicit prereleases set for this, then we'll just
+        # blindly use that.
+        if self._prereleases is not None:
+            return self._prereleases
+
+        # Only the "!=" operator does not imply prereleases when
+        # the version in the specifier is a prerelease.
+        operator, version_str = self._spec
+        if operator != "!=":
+            # The == specifier with trailing .* cannot include prereleases
+            # e.g. "==1.0a1.*" is not valid.
+            if operator == "==" and version_str.endswith(".*"):
+                return False
+
+            # "===" can have arbitrary string versions, so we cannot parse
+            # those, we take prereleases as unknown (None) for those.
+            version = self._get_spec_version(version_str)
+            if version is None:
+                return None
+
+            # For all other operators, use the check if spec Version
+            # object implies pre-releases.
+            if version.is_prerelease:
+                return True
+
+        return False
+
+    @prereleases.setter
+    def prereleases(self, value: bool | None) -> None:
+        self._prereleases = value
+
+    @property
+    def operator(self) -> str:
+        """The operator of this specifier.
+
+        >>> Specifier("==1.2.3").operator
+        '=='
+        """
+        return self._spec[0]
+
+    @property
+    def version(self) -> str:
+        """The version of this specifier.
+
+        >>> Specifier("==1.2.3").version
+        '1.2.3'
+        """
+        return self._spec[1]
+
+    def __repr__(self) -> str:
+        """A representation of the Specifier that shows all internal state.
+
+        >>> Specifier('>=1.0.0')
+        <Specifier('>=1.0.0')>
+        >>> Specifier('>=1.0.0', prereleases=False)
+        <Specifier('>=1.0.0', prereleases=False)>
+        >>> Specifier('>=1.0.0', prereleases=True)
+        <Specifier('>=1.0.0', prereleases=True)>
+        """
+        pre = (
+            f", prereleases={self.prereleases!r}"
+            if self._prereleases is not None
+            else ""
+        )
+
+        return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
+
+    def __str__(self) -> str:
+        """A string representation of the Specifier that can be round-tripped.
+
+        >>> str(Specifier('>=1.0.0'))
+        '>=1.0.0'
+        >>> str(Specifier('>=1.0.0', prereleases=False))
+        '>=1.0.0'
+        """
+        return "{}{}".format(*self._spec)
+
+    @property
+    def _canonical_spec(self) -> tuple[str, str]:
+        operator, version = self._spec
+        if operator == "===" or version.endswith(".*"):
+            return operator, version
+
+        spec_version = self._require_spec_version(version)
+
+        canonical_version = canonicalize_version(
+            spec_version, strip_trailing_zero=(operator != "~=")
+        )
+
+        return operator, canonical_version
+
+    def __hash__(self) -> int:
+        return hash(self._canonical_spec)
+
+    def __eq__(self, other: object) -> bool:
+        """Whether or not the two Specifier-like objects are equal.
+
+        :param other: The other object to check against.
+
+        The value of :attr:`prereleases` is ignored.
+
+        >>> Specifier("==1.2.3") == Specifier("== 1.2.3.0")
+        True
+        >>> (Specifier("==1.2.3", prereleases=False) ==
+        ...  Specifier("==1.2.3", prereleases=True))
+        True
+        >>> Specifier("==1.2.3") == "==1.2.3"
+        True
+        >>> Specifier("==1.2.3") == Specifier("==1.2.4")
+        False
+        >>> Specifier("==1.2.3") == Specifier("~=1.2.3")
+        False
+        """
+        if isinstance(other, str):
+            try:
+                other = self.__class__(str(other))
+            except InvalidSpecifier:
+                return NotImplemented
+        elif not isinstance(other, self.__class__):
+            return NotImplemented
+
+        return self._canonical_spec == other._canonical_spec
+
+    def _get_operator(self, op: str) -> CallableOperator:
+        operator_callable: CallableOperator = getattr(
+            self, f"_compare_{self._operators[op]}"
+        )
+        return operator_callable
+
+    def _compare_compatible(self, prospective: Version, spec: str) -> bool:
+        # Compatible releases have an equivalent combination of >= and ==. That
+        # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
+        # implement this in terms of the other specifiers instead of
+        # implementing it ourselves. The only thing we need to do is construct
+        # the other specifiers.
+
+        # We want everything but the last item in the version, but we want to
+        # ignore suffix segments.
+        prefix = _version_join(
+            list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
+        )
+
+        # Add the prefix notation to the end of our string
+        prefix += ".*"
+
+        return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
+            prospective, prefix
+        )
+
+    def _compare_equal(self, prospective: Version, spec: str) -> bool:
+        # We need special logic to handle prefix matching
+        if spec.endswith(".*"):
+            # In the case of prefix matching we want to ignore local segment.
+            normalized_prospective = canonicalize_version(
+                _public_version(prospective), strip_trailing_zero=False
+            )
+            # Get the normalized version string ignoring the trailing .*
+            normalized_spec = canonicalize_version(spec[:-2], strip_trailing_zero=False)
+            # Split the spec out by bangs and dots, and pretend that there is
+            # an implicit dot in between a release segment and a pre-release segment.
+            split_spec = _version_split(normalized_spec)
+
+            # Split the prospective version out by bangs and dots, and pretend
+            # that there is an implicit dot in between a release segment and
+            # a pre-release segment.
+            split_prospective = _version_split(normalized_prospective)
+
+            # 0-pad the prospective version before shortening it to get the correct
+            # shortened version.
+            padded_prospective, _ = _pad_version(split_prospective, split_spec)
+
+            # Shorten the prospective version to be the same length as the spec
+            # so that we can determine if the specifier is a prefix of the
+            # prospective version or not.
+            shortened_prospective = padded_prospective[: len(split_spec)]
+
+            return shortened_prospective == split_spec
+        else:
+            # Convert our spec string into a Version
+            spec_version = self._require_spec_version(spec)
+
+            # If the specifier does not have a local segment, then we want to
+            # act as if the prospective version also does not have a local
+            # segment.
+            if not spec_version.local:
+                prospective = _public_version(prospective)
+
+            return prospective == spec_version
+
+    def _compare_not_equal(self, prospective: Version, spec: str) -> bool:
+        return not self._compare_equal(prospective, spec)
+
+    def _compare_less_than_equal(self, prospective: Version, spec: str) -> bool:
+        # NB: Local version identifiers are NOT permitted in the version
+        # specifier, so local version labels can be universally removed from
+        # the prospective version.
+        return _public_version(prospective) <= self._require_spec_version(spec)
+
+    def _compare_greater_than_equal(self, prospective: Version, spec: str) -> bool:
+        # NB: Local version identifiers are NOT permitted in the version
+        # specifier, so local version labels can be universally removed from
+        # the prospective version.
+        return _public_version(prospective) >= self._require_spec_version(spec)
+
+    def _compare_less_than(self, prospective: Version, spec_str: str) -> bool:
+        # Convert our spec to a Version instance, since we'll want to work with
+        # it as a version.
+        spec = self._require_spec_version(spec_str)
+
+        # Check to see if the prospective version is less than the spec
+        # version. If it's not we can short circuit and just return False now
+        # instead of doing extra unneeded work.
+        if not prospective < spec:
+            return False
+
+        # This special case is here so that, unless the specifier itself
+        # includes is a pre-release version, that we do not accept pre-release
+        # versions for the version mentioned in the specifier (e.g. <3.1 should
+        # not match 3.1.dev0, but should match 3.0.dev0).
+        if (
+            not spec.is_prerelease
+            and prospective.is_prerelease
+            and _base_version(prospective) == _base_version(spec)
+        ):
+            return False
+
+        # If we've gotten to here, it means that prospective version is both
+        # less than the spec version *and* it's not a pre-release of the same
+        # version in the spec.
+        return True
+
+    def _compare_greater_than(self, prospective: Version, spec_str: str) -> bool:
+        # Convert our spec to a Version instance, since we'll want to work with
+        # it as a version.
+        spec = self._require_spec_version(spec_str)
+
+        # Check to see if the prospective version is greater than the spec
+        # version. If it's not we can short circuit and just return False now
+        # instead of doing extra unneeded work.
+        if not prospective > spec:
+            return False
+
+        # This special case is here so that, unless the specifier itself
+        # includes is a post-release version, that we do not accept
+        # post-release versions for the version mentioned in the specifier
+        # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
+        if (
+            not spec.is_postrelease
+            and prospective.is_postrelease
+            and _base_version(prospective) == _base_version(spec)
+        ):
+            return False
+
+        # Ensure that we do not allow a local version of the version mentioned
+        # in the specifier, which is technically greater than, to match.
+        if prospective.local is not None and _base_version(
+            prospective
+        ) == _base_version(spec):
+            return False
+
+        # If we've gotten to here, it means that prospective version is both
+        # greater than the spec version *and* it's not a pre-release of the
+        # same version in the spec.
+        return True
+
+    def _compare_arbitrary(self, prospective: Version | str, spec: str) -> bool:
+        return str(prospective).lower() == str(spec).lower()
+
+    def __contains__(self, item: str | Version) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item: The item to check for.
+
+        This is used for the ``in`` operator and behaves the same as
+        :meth:`contains` with no ``prereleases`` argument passed.
+
+        >>> "1.2.3" in Specifier(">=1.2.3")
+        True
+        >>> Version("1.2.3") in Specifier(">=1.2.3")
+        True
+        >>> "1.0.0" in Specifier(">=1.2.3")
+        False
+        >>> "1.3.0a1" in Specifier(">=1.2.3")
+        True
+        >>> "1.3.0a1" in Specifier(">=1.2.3", prereleases=True)
+        True
+        """
+        return self.contains(item)
+
+    def contains(self, item: UnparsedVersion, prereleases: bool | None = None) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item:
+            The item to check for, which can be a version string or a
+            :class:`Version` instance.
+        :param prereleases:
+            Whether or not to match prereleases with this Specifier. If set to
+            ``None`` (the default), it will follow the recommendation from
+            :pep:`440` and match prereleases, as there are no other versions.
+
+        >>> Specifier(">=1.2.3").contains("1.2.3")
+        True
+        >>> Specifier(">=1.2.3").contains(Version("1.2.3"))
+        True
+        >>> Specifier(">=1.2.3").contains("1.0.0")
+        False
+        >>> Specifier(">=1.2.3").contains("1.3.0a1")
+        True
+        >>> Specifier(">=1.2.3", prereleases=False).contains("1.3.0a1")
+        False
+        >>> Specifier(">=1.2.3").contains("1.3.0a1")
+        True
+        """
+
+        return bool(list(self.filter([item], prereleases=prereleases)))
+
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """Filter items in the given iterable, that match the specifier.
+
+        :param iterable:
+            An iterable that can contain version strings and :class:`Version` instances.
+            The items in the iterable will be filtered according to the specifier.
+        :param prereleases:
+            Whether or not to allow prereleases in the returned iterator. If set to
+            ``None`` (the default), it will follow the recommendation from :pep:`440`
+            and match prereleases if there are no other versions.
+
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.2.3", "1.3", Version("1.4")]))
+        ['1.2.3', '1.3', <Version('1.4')>]
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.5a1"]))
+        ['1.5a1']
+        >>> list(Specifier(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        >>> list(Specifier(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+        """
+        prereleases_versions = []
+        found_non_prereleases = False
+
+        # Determine if to include prereleases by default
+        include_prereleases = (
+            prereleases if prereleases is not None else self.prereleases
+        )
+
+        # Get the matching operator
+        operator_callable = self._get_operator(self.operator)
+
+        # Filter versions
+        for version in iterable:
+            parsed_version = _coerce_version(version)
+            if parsed_version is None:
+                # === operator can match arbitrary (non-version) strings
+                if self.operator == "===" and self._compare_arbitrary(
+                    version, self.version
+                ):
+                    yield version
+            elif operator_callable(parsed_version, self.version):
+                # If it's not a prerelease or prereleases are allowed, yield it directly
+                if not parsed_version.is_prerelease or include_prereleases:
+                    found_non_prereleases = True
+                    yield version
+                # Otherwise collect prereleases for potential later use
+                elif prereleases is None and self._prereleases is not False:
+                    prereleases_versions.append(version)
+
+        # If no non-prereleases were found and prereleases weren't
+        # explicitly forbidden, yield the collected prereleases
+        if (
+            not found_non_prereleases
+            and prereleases is None
+            and self._prereleases is not False
+        ):
+            yield from prereleases_versions
+
+
+_prefix_regex = re.compile(r"([0-9]+)((?:a|b|c|rc)[0-9]+)")
+
+
+def _version_split(version: str) -> list[str]:
+    """Split version into components.
+
+    The split components are intended for version comparison. The logic does
+    not attempt to retain the original version string, so joining the
+    components back with :func:`_version_join` may not produce the original
+    version string.
+    """
+    result: list[str] = []
+
+    epoch, _, rest = version.rpartition("!")
+    result.append(epoch or "0")
+
+    for item in rest.split("."):
+        match = _prefix_regex.fullmatch(item)
+        if match:
+            result.extend(match.groups())
+        else:
+            result.append(item)
+    return result
+
+
+def _version_join(components: list[str]) -> str:
+    """Join split version components into a version string.
+
+    This function assumes the input came from :func:`_version_split`, where the
+    first component must be the epoch (either empty or numeric), and all other
+    components numeric.
+    """
+    epoch, *rest = components
+    return f"{epoch}!{'.'.join(rest)}"
+
+
+def _is_not_suffix(segment: str) -> bool:
+    return not any(
+        segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
+    )
+
+
+def _pad_version(left: list[str], right: list[str]) -> tuple[list[str], list[str]]:
+    left_split, right_split = [], []
+
+    # Get the release segment of our versions
+    left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
+    right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
+
+    # Get the rest of our versions
+    left_split.append(left[len(left_split[0]) :])
+    right_split.append(right[len(right_split[0]) :])
+
+    # Insert our padding
+    left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
+    right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
+
+    return (
+        list(itertools.chain.from_iterable(left_split)),
+        list(itertools.chain.from_iterable(right_split)),
+    )
+
+
+class SpecifierSet(BaseSpecifier):
+    """This class abstracts handling of a set of version specifiers.
+
+    It can be passed a single specifier (``>=3.0``), a comma-separated list of
+    specifiers (``>=3.0,!=3.1``), or no specifier at all.
+    """
+
+    __slots__ = ("_prereleases", "_specs")
+
+    def __init__(
+        self,
+        specifiers: str | Iterable[Specifier] = "",
+        prereleases: bool | None = None,
+    ) -> None:
+        """Initialize a SpecifierSet instance.
+
+        :param specifiers:
+            The string representation of a specifier or a comma-separated list of
+            specifiers which will be parsed and normalized before use.
+            May also be an iterable of ``Specifier`` instances, which will be used
+            as is.
+        :param prereleases:
+            This tells the SpecifierSet if it should accept prerelease versions if
+            applicable or not. The default of ``None`` will autodetect it from the
+            given specifiers.
+
+        :raises InvalidSpecifier:
+            If the given ``specifiers`` are not parseable than this exception will be
+            raised.
+        """
+
+        if isinstance(specifiers, str):
+            # Split on `,` to break each individual specifier into its own item, and
+            # strip each item to remove leading/trailing whitespace.
+            split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
+
+            # Make each individual specifier a Specifier and save in a frozen set
+            # for later.
+            self._specs = frozenset(map(Specifier, split_specifiers))
+        else:
+            # Save the supplied specifiers in a frozen set.
+            self._specs = frozenset(specifiers)
+
+        # Store our prereleases value so we can use it later to determine if
+        # we accept prereleases or not.
+        self._prereleases = prereleases
+
+    @property
+    def prereleases(self) -> bool | None:
+        # If we have been given an explicit prerelease modifier, then we'll
+        # pass that through here.
+        if self._prereleases is not None:
+            return self._prereleases
+
+        # If we don't have any specifiers, and we don't have a forced value,
+        # then we'll just return None since we don't know if this should have
+        # pre-releases or not.
+        if not self._specs:
+            return None
+
+        # Otherwise we'll see if any of the given specifiers accept
+        # prereleases, if any of them do we'll return True, otherwise False.
+        if any(s.prereleases for s in self._specs):
+            return True
+
+        return None
+
+    @prereleases.setter
+    def prereleases(self, value: bool | None) -> None:
+        self._prereleases = value
+
+    def __repr__(self) -> str:
+        """A representation of the specifier set that shows all internal state.
+
+        Note that the ordering of the individual specifiers within the set may not
+        match the input string.
+
+        >>> SpecifierSet('>=1.0.0,!=2.0.0')
+        <SpecifierSet('!=2.0.0,>=1.0.0')>
+        >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=False)
+        <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=False)>
+        >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=True)
+        <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=True)>
+        """
+        pre = (
+            f", prereleases={self.prereleases!r}"
+            if self._prereleases is not None
+            else ""
+        )
+
+        return f"<SpecifierSet({str(self)!r}{pre})>"
+
+    def __str__(self) -> str:
+        """A string representation of the specifier set that can be round-tripped.
+
+        Note that the ordering of the individual specifiers within the set may not
+        match the input string.
+
+        >>> str(SpecifierSet(">=1.0.0,!=1.0.1"))
+        '!=1.0.1,>=1.0.0'
+        >>> str(SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False))
+        '!=1.0.1,>=1.0.0'
+        """
+        return ",".join(sorted(str(s) for s in self._specs))
+
+    def __hash__(self) -> int:
+        return hash(self._specs)
+
+    def __and__(self, other: SpecifierSet | str) -> SpecifierSet:
+        """Return a SpecifierSet which is a combination of the two sets.
+
+        :param other: The other object to combine with.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") & '<=2.0.0,!=2.0.1'
+        <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") & SpecifierSet('<=2.0.0,!=2.0.1')
+        <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
+        """
+        if isinstance(other, str):
+            other = SpecifierSet(other)
+        elif not isinstance(other, SpecifierSet):
+            return NotImplemented
+
+        specifier = SpecifierSet()
+        specifier._specs = frozenset(self._specs | other._specs)
+
+        if self._prereleases is None and other._prereleases is not None:
+            specifier._prereleases = other._prereleases
+        elif (
+            self._prereleases is not None and other._prereleases is None
+        ) or self._prereleases == other._prereleases:
+            specifier._prereleases = self._prereleases
+        else:
+            raise ValueError(
+                "Cannot combine SpecifierSets with True and False prerelease overrides."
+            )
+
+        return specifier
+
+    def __eq__(self, other: object) -> bool:
+        """Whether or not the two SpecifierSet-like objects are equal.
+
+        :param other: The other object to check against.
+
+        The value of :attr:`prereleases` is ignored.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> (SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False) ==
+        ...  SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True))
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == ">=1.0.0,!=1.0.1"
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.2")
+        False
+        """
+        if isinstance(other, (str, Specifier)):
+            other = SpecifierSet(str(other))
+        elif not isinstance(other, SpecifierSet):
+            return NotImplemented
+
+        return self._specs == other._specs
+
+    def __len__(self) -> int:
+        """Returns the number of specifiers in this specifier set."""
+        return len(self._specs)
+
+    def __iter__(self) -> Iterator[Specifier]:
+        """
+        Returns an iterator over all the underlying :class:`Specifier` instances
+        in this specifier set.
+
+        >>> sorted(SpecifierSet(">=1.0.0,!=1.0.1"), key=str)
+        [<Specifier('!=1.0.1')>, <Specifier('>=1.0.0')>]
+        """
+        return iter(self._specs)
+
+    def __contains__(self, item: UnparsedVersion) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item: The item to check for.
+
+        This is used for the ``in`` operator and behaves the same as
+        :meth:`contains` with no ``prereleases`` argument passed.
+
+        >>> "1.2.3" in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> Version("1.2.3") in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> "1.0.1" in SpecifierSet(">=1.0.0,!=1.0.1")
+        False
+        >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True)
+        True
+        """
+        return self.contains(item)
+
+    def contains(
+        self,
+        item: UnparsedVersion,
+        prereleases: bool | None = None,
+        installed: bool | None = None,
+    ) -> bool:
+        """Return whether or not the item is contained in this SpecifierSet.
+
+        :param item:
+            The item to check for, which can be a version string or a
+            :class:`Version` instance.
+        :param prereleases:
+            Whether or not to match prereleases with this SpecifierSet. If set to
+            ``None`` (the default), it will follow the recommendation from :pep:`440`
+            and match prereleases, as there are no other versions.
+        :param installed:
+            Whether or not the item is installed. If set to ``True``, it will
+            accept prerelease versions even if the specifier does not allow them.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.2.3")
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains(Version("1.2.3"))
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.0.1")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1")
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False).contains("1.3.0a1")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1", prereleases=True)
+        True
+        """
+        version = _coerce_version(item)
+
+        if version is not None and installed and version.is_prerelease:
+            prereleases = True
+
+        check_item = item if version is None else version
+        return bool(list(self.filter([check_item], prereleases=prereleases)))
+
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """Filter items in the given iterable, that match the specifiers in this set.
+
+        :param iterable:
+            An iterable that can contain version strings and :class:`Version` instances.
+            The items in the iterable will be filtered according to the specifier.
+        :param prereleases:
+            Whether or not to allow prereleases in the returned iterator. If set to
+            ``None`` (the default), it will follow the recommendation from :pep:`440`
+            and match prereleases if there are no other versions.
+
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", Version("1.4")]))
+        ['1.3', <Version('1.4')>]
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.5a1"]))
+        ['1.5a1']
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        >>> list(SpecifierSet(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+
+        An "empty" SpecifierSet will filter items based on the presence of prerelease
+        versions in the set.
+
+        >>> list(SpecifierSet("").filter(["1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(SpecifierSet("").filter(["1.5a1"]))
+        ['1.5a1']
+        >>> list(SpecifierSet("", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+        >>> list(SpecifierSet("").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        """
+        # Determine if we're forcing a prerelease or not, if we're not forcing
+        # one for this particular filter call, then we'll use whatever the
+        # SpecifierSet thinks for whether or not we should support prereleases.
+        if prereleases is None and self.prereleases is not None:
+            prereleases = self.prereleases
+
+        # If we have any specifiers, then we want to wrap our iterable in the
+        # filter method for each one, this will act as a logical AND amongst
+        # each specifier.
+        if self._specs:
+            # When prereleases is None, we need to let all versions through
+            # the individual filters, then decide about prereleases at the end
+            # based on whether any non-prereleases matched ALL specs.
+            for spec in self._specs:
+                iterable = spec.filter(
+                    iterable, prereleases=True if prereleases is None else prereleases
+                )
+
+            if prereleases is not None:
+                # If we have a forced prereleases value,
+                # we can immediately return the iterator.
+                return iter(iterable)
+        else:
+            # Handle empty SpecifierSet cases where prereleases is not None.
+            if prereleases is True:
+                return iter(iterable)
+
+            if prereleases is False:
+                return (
+                    item
+                    for item in iterable
+                    if (version := _coerce_version(item)) is None
+                    or not version.is_prerelease
+                )
+
+        # Finally if prereleases is None, apply PEP 440 logic:
+        # exclude prereleases unless there are no final releases that matched.
+        filtered_items: list[UnparsedVersionVar] = []
+        found_prereleases: list[UnparsedVersionVar] = []
+        found_final_release = False
+
+        for item in iterable:
+            parsed_version = _coerce_version(item)
+            # Arbitrary strings are always included as it is not
+            # possible to determine if they are prereleases,
+            # and they have already passed all specifiers.
+            if parsed_version is None:
+                filtered_items.append(item)
+                found_prereleases.append(item)
+            elif parsed_version.is_prerelease:
+                found_prereleases.append(item)
+            else:
+                filtered_items.append(item)
+                found_final_release = True
+
+        return iter(filtered_items if found_final_release else found_prereleases)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/tags.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/tags.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ef27c897a4df35a2a6923b608a5e04a0a38b9ee
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/tags.py
@@ -0,0 +1,651 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import logging
+import platform
+import re
+import struct
+import subprocess
+import sys
+import sysconfig
+from importlib.machinery import EXTENSION_SUFFIXES
+from typing import (
+    Any,
+    Iterable,
+    Iterator,
+    Sequence,
+    Tuple,
+    cast,
+)
+
+from . import _manylinux, _musllinux
+
+logger = logging.getLogger(__name__)
+
+PythonVersion = Sequence[int]
+AppleVersion = Tuple[int, int]
+
+INTERPRETER_SHORT_NAMES: dict[str, str] = {
+    "python": "py",  # Generic.
+    "cpython": "cp",
+    "pypy": "pp",
+    "ironpython": "ip",
+    "jython": "jy",
+}
+
+
+_32_BIT_INTERPRETER = struct.calcsize("P") == 4
+
+
+class Tag:
+    """
+    A representation of the tag triple for a wheel.
+
+    Instances are considered immutable and thus are hashable. Equality checking
+    is also supported.
+    """
+
+    __slots__ = ["_abi", "_hash", "_interpreter", "_platform"]
+
+    def __init__(self, interpreter: str, abi: str, platform: str) -> None:
+        self._interpreter = interpreter.lower()
+        self._abi = abi.lower()
+        self._platform = platform.lower()
+        # The __hash__ of every single element in a Set[Tag] will be evaluated each time
+        # that a set calls its `.disjoint()` method, which may be called hundreds of
+        # times when scanning a page of links for packages with tags matching that
+        # Set[Tag]. Pre-computing the value here produces significant speedups for
+        # downstream consumers.
+        self._hash = hash((self._interpreter, self._abi, self._platform))
+
+    @property
+    def interpreter(self) -> str:
+        return self._interpreter
+
+    @property
+    def abi(self) -> str:
+        return self._abi
+
+    @property
+    def platform(self) -> str:
+        return self._platform
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Tag):
+            return NotImplemented
+
+        return (
+            (self._hash == other._hash)  # Short-circuit ASAP for perf reasons.
+            and (self._platform == other._platform)
+            and (self._abi == other._abi)
+            and (self._interpreter == other._interpreter)
+        )
+
+    def __hash__(self) -> int:
+        return self._hash
+
+    def __str__(self) -> str:
+        return f"{self._interpreter}-{self._abi}-{self._platform}"
+
+    def __repr__(self) -> str:
+        return f"<{self} @ {id(self)}>"
+
+    def __setstate__(self, state: tuple[None, dict[str, Any]]) -> None:
+        # The cached _hash is wrong when unpickling.
+        _, slots = state
+        for k, v in slots.items():
+            setattr(self, k, v)
+        self._hash = hash((self._interpreter, self._abi, self._platform))
+
+
+def parse_tag(tag: str) -> frozenset[Tag]:
+    """
+    Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances.
+
+    Returning a set is required due to the possibility that the tag is a
+    compressed tag set.
+    """
+    tags = set()
+    interpreters, abis, platforms = tag.split("-")
+    for interpreter in interpreters.split("."):
+        for abi in abis.split("."):
+            for platform_ in platforms.split("."):
+                tags.add(Tag(interpreter, abi, platform_))
+    return frozenset(tags)
+
+
+def _get_config_var(name: str, warn: bool = False) -> int | str | None:
+    value: int | str | None = sysconfig.get_config_var(name)
+    if value is None and warn:
+        logger.debug(
+            "Config variable '%s' is unset, Python ABI tag may be incorrect", name
+        )
+    return value
+
+
+def _normalize_string(string: str) -> str:
+    return string.replace(".", "_").replace("-", "_").replace(" ", "_")
+
+
+def _is_threaded_cpython(abis: list[str]) -> bool:
+    """
+    Determine if the ABI corresponds to a threaded (`--disable-gil`) build.
+
+    The threaded builds are indicated by a "t" in the abiflags.
+    """
+    if len(abis) == 0:
+        return False
+    # expect e.g., cp313
+    m = re.match(r"cp\d+(.*)", abis[0])
+    if not m:
+        return False
+    abiflags = m.group(1)
+    return "t" in abiflags
+
+
+def _abi3_applies(python_version: PythonVersion, threading: bool) -> bool:
+    """
+    Determine if the Python version supports abi3.
+
+    PEP 384 was first implemented in Python 3.2. The threaded (`--disable-gil`)
+    builds do not support abi3.
+    """
+    return len(python_version) > 1 and tuple(python_version) >= (3, 2) and not threading
+
+
+def _cpython_abis(py_version: PythonVersion, warn: bool = False) -> list[str]:
+    py_version = tuple(py_version)  # To allow for version comparison.
+    abis = []
+    version = _version_nodot(py_version[:2])
+    threading = debug = pymalloc = ucs4 = ""
+    with_debug = _get_config_var("Py_DEBUG", warn)
+    has_refcount = hasattr(sys, "gettotalrefcount")
+    # Windows doesn't set Py_DEBUG, so checking for support of debug-compiled
+    # extension modules is the best option.
+    # https://github.com/pypa/pip/issues/3383#issuecomment-173267692
+    has_ext = "_d.pyd" in EXTENSION_SUFFIXES
+    if with_debug or (with_debug is None and (has_refcount or has_ext)):
+        debug = "d"
+    if py_version >= (3, 13) and _get_config_var("Py_GIL_DISABLED", warn):
+        threading = "t"
+    if py_version < (3, 8):
+        with_pymalloc = _get_config_var("WITH_PYMALLOC", warn)
+        if with_pymalloc or with_pymalloc is None:
+            pymalloc = "m"
+        if py_version < (3, 3):
+            unicode_size = _get_config_var("Py_UNICODE_SIZE", warn)
+            if unicode_size == 4 or (
+                unicode_size is None and sys.maxunicode == 0x10FFFF
+            ):
+                ucs4 = "u"
+    elif debug:
+        # Debug builds can also load "normal" extension modules.
+        # We can also assume no UCS-4 or pymalloc requirement.
+        abis.append(f"cp{version}{threading}")
+    abis.insert(0, f"cp{version}{threading}{debug}{pymalloc}{ucs4}")
+    return abis
+
+
+def cpython_tags(
+    python_version: PythonVersion | None = None,
+    abis: Iterable[str] | None = None,
+    platforms: Iterable[str] | None = None,
+    *,
+    warn: bool = False,
+) -> Iterator[Tag]:
+    """
+    Yields the tags for a CPython interpreter.
+
+    The tags consist of:
+    - cp<python_version>-<abi>-<platform>
+    - cp<python_version>-abi3-<platform>
+    - cp<python_version>-none-<platform>
+    - cp<less than python_version>-abi3-<platform>  # Older Python versions down to 3.2.
+
+    If python_version only specifies a major version then user-provided ABIs and
+    the 'none' ABItag will be used.
+
+    If 'abi3' or 'none' are specified in 'abis' then they will be yielded at
+    their normal position and not at the beginning.
+    """
+    if not python_version:
+        python_version = sys.version_info[:2]
+
+    interpreter = f"cp{_version_nodot(python_version[:2])}"
+
+    if abis is None:
+        abis = _cpython_abis(python_version, warn) if len(python_version) > 1 else []
+    abis = list(abis)
+    # 'abi3' and 'none' are explicitly handled later.
+    for explicit_abi in ("abi3", "none"):
+        try:
+            abis.remove(explicit_abi)
+        except ValueError:  # noqa: PERF203
+            pass
+
+    platforms = list(platforms or platform_tags())
+    for abi in abis:
+        for platform_ in platforms:
+            yield Tag(interpreter, abi, platform_)
+
+    threading = _is_threaded_cpython(abis)
+    use_abi3 = _abi3_applies(python_version, threading)
+    if use_abi3:
+        yield from (Tag(interpreter, "abi3", platform_) for platform_ in platforms)
+    yield from (Tag(interpreter, "none", platform_) for platform_ in platforms)
+
+    if use_abi3:
+        for minor_version in range(python_version[1] - 1, 1, -1):
+            for platform_ in platforms:
+                version = _version_nodot((python_version[0], minor_version))
+                interpreter = f"cp{version}"
+                yield Tag(interpreter, "abi3", platform_)
+
+
+def _generic_abi() -> list[str]:
+    """
+    Return the ABI tag based on EXT_SUFFIX.
+    """
+    # The following are examples of `EXT_SUFFIX`.
+    # We want to keep the parts which are related to the ABI and remove the
+    # parts which are related to the platform:
+    # - linux:   '.cpython-310-x86_64-linux-gnu.so' => cp310
+    # - mac:     '.cpython-310-darwin.so'           => cp310
+    # - win:     '.cp310-win_amd64.pyd'             => cp310
+    # - win:     '.pyd'                             => cp37 (uses _cpython_abis())
+    # - pypy:    '.pypy38-pp73-x86_64-linux-gnu.so' => pypy38_pp73
+    # - graalpy: '.graalpy-38-native-x86_64-darwin.dylib'
+    #                                               => graalpy_38_native
+
+    ext_suffix = _get_config_var("EXT_SUFFIX", warn=True)
+    if not isinstance(ext_suffix, str) or ext_suffix[0] != ".":
+        raise SystemError("invalid sysconfig.get_config_var('EXT_SUFFIX')")
+    parts = ext_suffix.split(".")
+    if len(parts) < 3:
+        # CPython3.7 and earlier uses ".pyd" on Windows.
+        return _cpython_abis(sys.version_info[:2])
+    soabi = parts[1]
+    if soabi.startswith("cpython"):
+        # non-windows
+        abi = "cp" + soabi.split("-")[1]
+    elif soabi.startswith("cp"):
+        # windows
+        abi = soabi.split("-")[0]
+    elif soabi.startswith("pypy"):
+        abi = "-".join(soabi.split("-")[:2])
+    elif soabi.startswith("graalpy"):
+        abi = "-".join(soabi.split("-")[:3])
+    elif soabi:
+        # pyston, ironpython, others?
+        abi = soabi
+    else:
+        return []
+    return [_normalize_string(abi)]
+
+
+def generic_tags(
+    interpreter: str | None = None,
+    abis: Iterable[str] | None = None,
+    platforms: Iterable[str] | None = None,
+    *,
+    warn: bool = False,
+) -> Iterator[Tag]:
+    """
+    Yields the tags for a generic interpreter.
+
+    The tags consist of:
+    - <interpreter>-<abi>-<platform>
+
+    The "none" ABI will be added if it was not explicitly provided.
+    """
+    if not interpreter:
+        interp_name = interpreter_name()
+        interp_version = interpreter_version(warn=warn)
+        interpreter = f"{interp_name}{interp_version}"
+    abis = _generic_abi() if abis is None else list(abis)
+    platforms = list(platforms or platform_tags())
+    if "none" not in abis:
+        abis.append("none")
+    for abi in abis:
+        for platform_ in platforms:
+            yield Tag(interpreter, abi, platform_)
+
+
+def _py_interpreter_range(py_version: PythonVersion) -> Iterator[str]:
+    """
+    Yields Python versions in descending order.
+
+    After the latest version, the major-only version will be yielded, and then
+    all previous versions of that major version.
+    """
+    if len(py_version) > 1:
+        yield f"py{_version_nodot(py_version[:2])}"
+    yield f"py{py_version[0]}"
+    if len(py_version) > 1:
+        for minor in range(py_version[1] - 1, -1, -1):
+            yield f"py{_version_nodot((py_version[0], minor))}"
+
+
+def compatible_tags(
+    python_version: PythonVersion | None = None,
+    interpreter: str | None = None,
+    platforms: Iterable[str] | None = None,
+) -> Iterator[Tag]:
+    """
+    Yields the sequence of tags that are compatible with a specific version of Python.
+
+    The tags consist of:
+    - py*-none-<platform>
+    - <interpreter>-none-any  # ... if `interpreter` is provided.
+    - py*-none-any
+    """
+    if not python_version:
+        python_version = sys.version_info[:2]
+    platforms = list(platforms or platform_tags())
+    for version in _py_interpreter_range(python_version):
+        for platform_ in platforms:
+            yield Tag(version, "none", platform_)
+    if interpreter:
+        yield Tag(interpreter, "none", "any")
+    for version in _py_interpreter_range(python_version):
+        yield Tag(version, "none", "any")
+
+
+def _mac_arch(arch: str, is_32bit: bool = _32_BIT_INTERPRETER) -> str:
+    if not is_32bit:
+        return arch
+
+    if arch.startswith("ppc"):
+        return "ppc"
+
+    return "i386"
+
+
+def _mac_binary_formats(version: AppleVersion, cpu_arch: str) -> list[str]:
+    formats = [cpu_arch]
+    if cpu_arch == "x86_64":
+        if version < (10, 4):
+            return []
+        formats.extend(["intel", "fat64", "fat32"])
+
+    elif cpu_arch == "i386":
+        if version < (10, 4):
+            return []
+        formats.extend(["intel", "fat32", "fat"])
+
+    elif cpu_arch == "ppc64":
+        # TODO: Need to care about 32-bit PPC for ppc64 through 10.2?
+        if version > (10, 5) or version < (10, 4):
+            return []
+        formats.append("fat64")
+
+    elif cpu_arch == "ppc":
+        if version > (10, 6):
+            return []
+        formats.extend(["fat32", "fat"])
+
+    if cpu_arch in {"arm64", "x86_64"}:
+        formats.append("universal2")
+
+    if cpu_arch in {"x86_64", "i386", "ppc64", "ppc", "intel"}:
+        formats.append("universal")
+
+    return formats
+
+
+def mac_platforms(
+    version: AppleVersion | None = None, arch: str | None = None
+) -> Iterator[str]:
+    """
+    Yields the platform tags for a macOS system.
+
+    The `version` parameter is a two-item tuple specifying the macOS version to
+    generate platform tags for. The `arch` parameter is the CPU architecture to
+    generate platform tags for. Both parameters default to the appropriate value
+    for the current system.
+    """
+    version_str, _, cpu_arch = platform.mac_ver()
+    if version is None:
+        version = cast("AppleVersion", tuple(map(int, version_str.split(".")[:2])))
+        if version == (10, 16):
+            # When built against an older macOS SDK, Python will report macOS 10.16
+            # instead of the real version.
+            version_str = subprocess.run(
+                [
+                    sys.executable,
+                    "-sS",
+                    "-c",
+                    "import platform; print(platform.mac_ver()[0])",
+                ],
+                check=True,
+                env={"SYSTEM_VERSION_COMPAT": "0"},
+                stdout=subprocess.PIPE,
+                text=True,
+            ).stdout
+            version = cast("AppleVersion", tuple(map(int, version_str.split(".")[:2])))
+
+    if arch is None:
+        arch = _mac_arch(cpu_arch)
+
+    if (10, 0) <= version < (11, 0):
+        # Prior to Mac OS 11, each yearly release of Mac OS bumped the
+        # "minor" version number.  The major version was always 10.
+        major_version = 10
+        for minor_version in range(version[1], -1, -1):
+            compat_version = major_version, minor_version
+            binary_formats = _mac_binary_formats(compat_version, arch)
+            for binary_format in binary_formats:
+                yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+
+    if version >= (11, 0):
+        # Starting with Mac OS 11, each yearly release bumps the major version
+        # number.   The minor versions are now the midyear updates.
+        minor_version = 0
+        for major_version in range(version[0], 10, -1):
+            compat_version = major_version, minor_version
+            binary_formats = _mac_binary_formats(compat_version, arch)
+            for binary_format in binary_formats:
+                yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+
+    if version >= (11, 0):
+        # Mac OS 11 on x86_64 is compatible with binaries from previous releases.
+        # Arm64 support was introduced in 11.0, so no Arm binaries from previous
+        # releases exist.
+        #
+        # However, the "universal2" binary format can have a
+        # macOS version earlier than 11.0 when the x86_64 part of the binary supports
+        # that version of macOS.
+        major_version = 10
+        if arch == "x86_64":
+            for minor_version in range(16, 3, -1):
+                compat_version = major_version, minor_version
+                binary_formats = _mac_binary_formats(compat_version, arch)
+                for binary_format in binary_formats:
+                    yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+        else:
+            for minor_version in range(16, 3, -1):
+                compat_version = major_version, minor_version
+                binary_format = "universal2"
+                yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+
+
+def ios_platforms(
+    version: AppleVersion | None = None, multiarch: str | None = None
+) -> Iterator[str]:
+    """
+    Yields the platform tags for an iOS system.
+
+    :param version: A two-item tuple specifying the iOS version to generate
+        platform tags for. Defaults to the current iOS version.
+    :param multiarch: The CPU architecture+ABI to generate platform tags for -
+        (the value used by `sys.implementation._multiarch` e.g.,
+        `arm64_iphoneos` or `x84_64_iphonesimulator`). Defaults to the current
+        multiarch value.
+    """
+    if version is None:
+        # if iOS is the current platform, ios_ver *must* be defined. However,
+        # it won't exist for CPython versions before 3.13, which causes a mypy
+        # error.
+        _, release, _, _ = platform.ios_ver()  # type: ignore[attr-defined, unused-ignore]
+        version = cast("AppleVersion", tuple(map(int, release.split(".")[:2])))
+
+    if multiarch is None:
+        multiarch = sys.implementation._multiarch
+    multiarch = multiarch.replace("-", "_")
+
+    ios_platform_template = "ios_{major}_{minor}_{multiarch}"
+
+    # Consider any iOS major.minor version from the version requested, down to
+    # 12.0. 12.0 is the first iOS version that is known to have enough features
+    # to support CPython. Consider every possible minor release up to X.9. There
+    # highest the minor has ever gone is 8 (14.8 and 15.8) but having some extra
+    # candidates that won't ever match doesn't really hurt, and it saves us from
+    # having to keep an explicit list of known iOS versions in the code. Return
+    # the results descending order of version number.
+
+    # If the requested major version is less than 12, there won't be any matches.
+    if version[0] < 12:
+        return
+
+    # Consider the actual X.Y version that was requested.
+    yield ios_platform_template.format(
+        major=version[0], minor=version[1], multiarch=multiarch
+    )
+
+    # Consider every minor version from X.0 to the minor version prior to the
+    # version requested by the platform.
+    for minor in range(version[1] - 1, -1, -1):
+        yield ios_platform_template.format(
+            major=version[0], minor=minor, multiarch=multiarch
+        )
+
+    for major in range(version[0] - 1, 11, -1):
+        for minor in range(9, -1, -1):
+            yield ios_platform_template.format(
+                major=major, minor=minor, multiarch=multiarch
+            )
+
+
+def android_platforms(
+    api_level: int | None = None, abi: str | None = None
+) -> Iterator[str]:
+    """
+    Yields the :attr:`~Tag.platform` tags for Android. If this function is invoked on
+    non-Android platforms, the ``api_level`` and ``abi`` arguments are required.
+
+    :param int api_level: The maximum `API level
+        <https://developer.android.com/tools/releases/platforms>`__ to return. Defaults
+        to the current system's version, as returned by ``platform.android_ver``.
+    :param str abi: The `Android ABI <https://developer.android.com/ndk/guides/abis>`__,
+        e.g. ``arm64_v8a``. Defaults to the current system's ABI , as returned by
+        ``sysconfig.get_platform``. Hyphens and periods will be replaced with
+        underscores.
+    """
+    if platform.system() != "Android" and (api_level is None or abi is None):
+        raise TypeError(
+            "on non-Android platforms, the api_level and abi arguments are required"
+        )
+
+    if api_level is None:
+        # Python 3.13 was the first version to return platform.system() == "Android",
+        # and also the first version to define platform.android_ver().
+        api_level = platform.android_ver().api_level  # type: ignore[attr-defined]
+
+    if abi is None:
+        abi = sysconfig.get_platform().split("-")[-1]
+    abi = _normalize_string(abi)
+
+    # 16 is the minimum API level known to have enough features to support CPython
+    # without major patching. Yield every API level from the maximum down to the
+    # minimum, inclusive.
+    min_api_level = 16
+    for ver in range(api_level, min_api_level - 1, -1):
+        yield f"android_{ver}_{abi}"
+
+
+def _linux_platforms(is_32bit: bool = _32_BIT_INTERPRETER) -> Iterator[str]:
+    linux = _normalize_string(sysconfig.get_platform())
+    if not linux.startswith("linux_"):
+        # we should never be here, just yield the sysconfig one and return
+        yield linux
+        return
+    if is_32bit:
+        if linux == "linux_x86_64":
+            linux = "linux_i686"
+        elif linux == "linux_aarch64":
+            linux = "linux_armv8l"
+    _, arch = linux.split("_", 1)
+    archs = {"armv8l": ["armv8l", "armv7l"]}.get(arch, [arch])
+    yield from _manylinux.platform_tags(archs)
+    yield from _musllinux.platform_tags(archs)
+    for arch in archs:
+        yield f"linux_{arch}"
+
+
+def _generic_platforms() -> Iterator[str]:
+    yield _normalize_string(sysconfig.get_platform())
+
+
+def platform_tags() -> Iterator[str]:
+    """
+    Provides the platform tags for this installation.
+    """
+    if platform.system() == "Darwin":
+        return mac_platforms()
+    elif platform.system() == "iOS":
+        return ios_platforms()
+    elif platform.system() == "Android":
+        return android_platforms()
+    elif platform.system() == "Linux":
+        return _linux_platforms()
+    else:
+        return _generic_platforms()
+
+
+def interpreter_name() -> str:
+    """
+    Returns the name of the running interpreter.
+
+    Some implementations have a reserved, two-letter abbreviation which will
+    be returned when appropriate.
+    """
+    name = sys.implementation.name
+    return INTERPRETER_SHORT_NAMES.get(name) or name
+
+
+def interpreter_version(*, warn: bool = False) -> str:
+    """
+    Returns the version of the running interpreter.
+    """
+    version = _get_config_var("py_version_nodot", warn=warn)
+    return str(version) if version else _version_nodot(sys.version_info[:2])
+
+
+def _version_nodot(version: PythonVersion) -> str:
+    return "".join(map(str, version))
+
+
+def sys_tags(*, warn: bool = False) -> Iterator[Tag]:
+    """
+    Returns the sequence of tag triples for the running interpreter.
+
+    The order of the sequence corresponds to priority order for the
+    interpreter, from most to least important.
+    """
+
+    interp_name = interpreter_name()
+    if interp_name == "cp":
+        yield from cpython_tags(warn=warn)
+    else:
+        yield from generic_tags()
+
+    if interp_name == "pp":
+        interp = "pp3"
+    elif interp_name == "cp":
+        interp = "cp" + interpreter_version(warn=warn)
+    else:
+        interp = None
+    yield from compatible_tags(interpreter=interp)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c41c8137f2679a0fac21bb845596e231ae88dbd8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/utils.py
@@ -0,0 +1,158 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import re
+from typing import NewType, Tuple, Union, cast
+
+from .tags import Tag, parse_tag
+from .version import InvalidVersion, Version, _TrimmedRelease
+
+BuildTag = Union[Tuple[()], Tuple[int, str]]
+NormalizedName = NewType("NormalizedName", str)
+
+
+class InvalidName(ValueError):
+    """
+    An invalid distribution name; users should refer to the packaging user guide.
+    """
+
+
+class InvalidWheelFilename(ValueError):
+    """
+    An invalid wheel filename was found, users should refer to PEP 427.
+    """
+
+
+class InvalidSdistFilename(ValueError):
+    """
+    An invalid sdist filename was found, users should refer to the packaging user guide.
+    """
+
+
+# Core metadata spec for `Name`
+_validate_regex = re.compile(r"[A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9]", re.IGNORECASE)
+_normalized_regex = re.compile(r"[a-z0-9]|[a-z0-9]([a-z0-9-](?!--))*[a-z0-9]")
+# PEP 427: The build number must start with a digit.
+_build_tag_regex = re.compile(r"(\d+)(.*)")
+
+
+def canonicalize_name(name: str, *, validate: bool = False) -> NormalizedName:
+    if validate and not _validate_regex.fullmatch(name):
+        raise InvalidName(f"name is invalid: {name!r}")
+    # Ensure all ``.`` and ``_`` are ``-``
+    # Emulates ``re.sub(r"[-_.]+", "-", name).lower()`` from PEP 503
+    # Much faster than re, and even faster than str.translate
+    value = name.lower().replace("_", "-").replace(".", "-")
+    # Condense repeats (faster than regex)
+    while "--" in value:
+        value = value.replace("--", "-")
+    return cast("NormalizedName", value)
+
+
+def is_normalized_name(name: str) -> bool:
+    return _normalized_regex.fullmatch(name) is not None
+
+
+def canonicalize_version(
+    version: Version | str, *, strip_trailing_zero: bool = True
+) -> str:
+    """
+    Return a canonical form of a version as a string.
+
+    >>> canonicalize_version('1.0.1')
+    '1.0.1'
+
+    Per PEP 625, versions may have multiple canonical forms, differing
+    only by trailing zeros.
+
+    >>> canonicalize_version('1.0.0')
+    '1'
+    >>> canonicalize_version('1.0.0', strip_trailing_zero=False)
+    '1.0.0'
+
+    Invalid versions are returned unaltered.
+
+    >>> canonicalize_version('foo bar baz')
+    'foo bar baz'
+    """
+    if isinstance(version, str):
+        try:
+            version = Version(version)
+        except InvalidVersion:
+            return str(version)
+    return str(_TrimmedRelease(version) if strip_trailing_zero else version)
+
+
+def parse_wheel_filename(
+    filename: str,
+) -> tuple[NormalizedName, Version, BuildTag, frozenset[Tag]]:
+    if not filename.endswith(".whl"):
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (extension must be '.whl'): {filename!r}"
+        )
+
+    filename = filename[:-4]
+    dashes = filename.count("-")
+    if dashes not in (4, 5):
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (wrong number of parts): {filename!r}"
+        )
+
+    parts = filename.split("-", dashes - 2)
+    name_part = parts[0]
+    # See PEP 427 for the rules on escaping the project name.
+    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
+        raise InvalidWheelFilename(f"Invalid project name: {filename!r}")
+    name = canonicalize_name(name_part)
+
+    try:
+        version = Version(parts[1])
+    except InvalidVersion as e:
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (invalid version): {filename!r}"
+        ) from e
+
+    if dashes == 5:
+        build_part = parts[2]
+        build_match = _build_tag_regex.match(build_part)
+        if build_match is None:
+            raise InvalidWheelFilename(
+                f"Invalid build number: {build_part} in {filename!r}"
+            )
+        build = cast("BuildTag", (int(build_match.group(1)), build_match.group(2)))
+    else:
+        build = ()
+    tags = parse_tag(parts[-1])
+    return (name, version, build, tags)
+
+
+def parse_sdist_filename(filename: str) -> tuple[NormalizedName, Version]:
+    if filename.endswith(".tar.gz"):
+        file_stem = filename[: -len(".tar.gz")]
+    elif filename.endswith(".zip"):
+        file_stem = filename[: -len(".zip")]
+    else:
+        raise InvalidSdistFilename(
+            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
+            f" {filename!r}"
+        )
+
+    # We are requiring a PEP 440 version, which cannot contain dashes,
+    # so we split on the last dash.
+    name_part, sep, version_part = file_stem.rpartition("-")
+    if not sep:
+        raise InvalidSdistFilename(f"Invalid sdist filename: {filename!r}")
+
+    name = canonicalize_name(name_part)
+
+    try:
+        version = Version(version_part)
+    except InvalidVersion as e:
+        raise InvalidSdistFilename(
+            f"Invalid sdist filename (invalid version): {filename!r}"
+        ) from e
+
+    return (name, version)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/version.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..1206c462d4fcaa670a816e201bb88b27dfc9cf88
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/packaging/version.py
@@ -0,0 +1,792 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+"""
+.. testsetup::
+
+    from packaging.version import parse, Version
+"""
+
+from __future__ import annotations
+
+import re
+import sys
+import typing
+from typing import (
+    Any,
+    Callable,
+    Literal,
+    NamedTuple,
+    SupportsInt,
+    Tuple,
+    TypedDict,
+    Union,
+)
+
+from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
+
+if typing.TYPE_CHECKING:
+    from typing_extensions import Self, Unpack
+
+if sys.version_info >= (3, 13):  # pragma: no cover
+    from warnings import deprecated as _deprecated
+elif typing.TYPE_CHECKING:
+    from typing_extensions import deprecated as _deprecated
+else:  # pragma: no cover
+    import functools
+    import warnings
+
+    def _deprecated(message: str) -> object:
+        def decorator(func: object) -> object:
+            @functools.wraps(func)
+            def wrapper(*args: object, **kwargs: object) -> object:
+                warnings.warn(
+                    message,
+                    category=DeprecationWarning,
+                    stacklevel=2,
+                )
+                return func(*args, **kwargs)
+
+            return wrapper
+
+        return decorator
+
+
+_LETTER_NORMALIZATION = {
+    "alpha": "a",
+    "beta": "b",
+    "c": "rc",
+    "pre": "rc",
+    "preview": "rc",
+    "rev": "post",
+    "r": "post",
+}
+
+__all__ = ["VERSION_PATTERN", "InvalidVersion", "Version", "parse"]
+
+LocalType = Tuple[Union[int, str], ...]
+
+CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]]
+CmpLocalType = Union[
+    NegativeInfinityType,
+    Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...],
+]
+CmpKey = Tuple[
+    int,
+    Tuple[int, ...],
+    CmpPrePostDevType,
+    CmpPrePostDevType,
+    CmpPrePostDevType,
+    CmpLocalType,
+]
+VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool]
+
+
+class _VersionReplace(TypedDict, total=False):
+    epoch: int | None
+    release: tuple[int, ...] | None
+    pre: tuple[Literal["a", "b", "rc"], int] | None
+    post: int | None
+    dev: int | None
+    local: str | None
+
+
+def parse(version: str) -> Version:
+    """Parse the given version string.
+
+    >>> parse('1.0.dev1')
+    <Version('1.0.dev1')>
+
+    :param version: The version string to parse.
+    :raises InvalidVersion: When the version string is not a valid version.
+    """
+    return Version(version)
+
+
+class InvalidVersion(ValueError):
+    """Raised when a version string is not a valid version.
+
+    >>> Version("invalid")
+    Traceback (most recent call last):
+        ...
+    packaging.version.InvalidVersion: Invalid version: 'invalid'
+    """
+
+
+class _BaseVersion:
+    __slots__ = ()
+
+    # This can also be a normal member (see the packaging_legacy package);
+    # we are just requiring it to be readable. Actually defining a property
+    # has runtime effect on subclasses, so it's typing only.
+    if typing.TYPE_CHECKING:
+
+        @property
+        def _key(self) -> tuple[Any, ...]: ...
+
+    def __hash__(self) -> int:
+        return hash(self._key)
+
+    # Please keep the duplicated `isinstance` check
+    # in the six comparisons hereunder
+    # unless you find a way to avoid adding overhead function calls.
+    def __lt__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key < other._key
+
+    def __le__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key <= other._key
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key == other._key
+
+    def __ge__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key >= other._key
+
+    def __gt__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key > other._key
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key != other._key
+
+
+# Deliberately not anchored to the start and end of the string, to make it
+# easier for 3rd party code to reuse
+
+# Note that ++ doesn't behave identically on CPython and PyPy, so not using it here
+_VERSION_PATTERN = r"""
+    v?+                                                   # optional leading v
+    (?:
+        (?:(?P<epoch>[0-9]+)!)?+                          # epoch
+        (?P<release>[0-9]+(?:\.[0-9]+)*+)                 # release segment
+        (?P<pre>                                          # pre-release
+            [._-]?+
+            (?P<pre_l>alpha|a|beta|b|preview|pre|c|rc)
+            [._-]?+
+            (?P<pre_n>[0-9]+)?
+        )?+
+        (?P<post>                                         # post release
+            (?:-(?P<post_n1>[0-9]+))
+            |
+            (?:
+                [._-]?
+                (?P<post_l>post|rev|r)
+                [._-]?
+                (?P<post_n2>[0-9]+)?
+            )
+        )?+
+        (?P<dev>                                          # dev release
+            [._-]?+
+            (?P<dev_l>dev)
+            [._-]?+
+            (?P<dev_n>[0-9]+)?
+        )?+
+    )
+    (?:\+
+        (?P<local>                                        # local version
+            [a-z0-9]+
+            (?:[._-][a-z0-9]+)*+
+        )
+    )?+
+"""
+
+_VERSION_PATTERN_OLD = _VERSION_PATTERN.replace("*+", "*").replace("?+", "?")
+
+# Possessive qualifiers were added in Python 3.11.
+# CPython 3.11.0-3.11.4 had a bug: https://github.com/python/cpython/pull/107795
+# Older PyPy also had a bug.
+VERSION_PATTERN = (
+    _VERSION_PATTERN_OLD
+    if (sys.implementation.name == "cpython" and sys.version_info < (3, 11, 5))
+    or (sys.implementation.name == "pypy" and sys.version_info < (3, 11, 13))
+    or sys.version_info < (3, 11)
+    else _VERSION_PATTERN
+)
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+# Validation pattern for local version in replace()
+_LOCAL_PATTERN = re.compile(r"[a-z0-9]+(?:[._-][a-z0-9]+)*", re.IGNORECASE)
+
+
+def _validate_epoch(value: object, /) -> int:
+    epoch = value or 0
+    if isinstance(epoch, int) and epoch >= 0:
+        return epoch
+    msg = f"epoch must be non-negative integer, got {epoch}"
+    raise InvalidVersion(msg)
+
+
+def _validate_release(value: object, /) -> tuple[int, ...]:
+    release = (0,) if value is None else value
+    if (
+        isinstance(release, tuple)
+        and len(release) > 0
+        and all(isinstance(i, int) and i >= 0 for i in release)
+    ):
+        return release
+    msg = f"release must be a non-empty tuple of non-negative integers, got {release}"
+    raise InvalidVersion(msg)
+
+
+def _validate_pre(value: object, /) -> tuple[Literal["a", "b", "rc"], int] | None:
+    if value is None:
+        return value
+    if (
+        isinstance(value, tuple)
+        and len(value) == 2
+        and value[0] in ("a", "b", "rc")
+        and isinstance(value[1], int)
+        and value[1] >= 0
+    ):
+        return value
+    msg = f"pre must be a tuple of ('a'|'b'|'rc', non-negative int), got {value}"
+    raise InvalidVersion(msg)
+
+
+def _validate_post(value: object, /) -> tuple[Literal["post"], int] | None:
+    if value is None:
+        return value
+    if isinstance(value, int) and value >= 0:
+        return ("post", value)
+    msg = f"post must be non-negative integer, got {value}"
+    raise InvalidVersion(msg)
+
+
+def _validate_dev(value: object, /) -> tuple[Literal["dev"], int] | None:
+    if value is None:
+        return value
+    if isinstance(value, int) and value >= 0:
+        return ("dev", value)
+    msg = f"dev must be non-negative integer, got {value}"
+    raise InvalidVersion(msg)
+
+
+def _validate_local(value: object, /) -> LocalType | None:
+    if value is None:
+        return value
+    if isinstance(value, str) and _LOCAL_PATTERN.fullmatch(value):
+        return _parse_local_version(value)
+    msg = f"local must be a valid version string, got {value!r}"
+    raise InvalidVersion(msg)
+
+
+# Backward compatibility for internals before 26.0. Do not use.
+class _Version(NamedTuple):
+    epoch: int
+    release: tuple[int, ...]
+    dev: tuple[str, int] | None
+    pre: tuple[str, int] | None
+    post: tuple[str, int] | None
+    local: LocalType | None
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    <Version('1.0a5')>
+    >>> v2
+    <Version('1.0')>
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    __slots__ = ("_dev", "_epoch", "_key_cache", "_local", "_post", "_pre", "_release")
+    __match_args__ = ("_str",)
+
+    _regex = re.compile(r"\s*" + VERSION_PATTERN + r"\s*", re.VERBOSE | re.IGNORECASE)
+
+    _epoch: int
+    _release: tuple[int, ...]
+    _dev: tuple[str, int] | None
+    _pre: tuple[str, int] | None
+    _post: tuple[str, int] | None
+    _local: LocalType | None
+
+    _key_cache: CmpKey | None
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+        # Validate the version and parse it into pieces
+        match = self._regex.fullmatch(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: {version!r}")
+        self._epoch = int(match.group("epoch")) if match.group("epoch") else 0
+        self._release = tuple(map(int, match.group("release").split(".")))
+        self._pre = _parse_letter_version(match.group("pre_l"), match.group("pre_n"))
+        self._post = _parse_letter_version(
+            match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+        )
+        self._dev = _parse_letter_version(match.group("dev_l"), match.group("dev_n"))
+        self._local = _parse_local_version(match.group("local"))
+
+        # Key which will be used for sorting
+        self._key_cache = None
+
+    def __replace__(self, **kwargs: Unpack[_VersionReplace]) -> Self:
+        epoch = _validate_epoch(kwargs["epoch"]) if "epoch" in kwargs else self._epoch
+        release = (
+            _validate_release(kwargs["release"])
+            if "release" in kwargs
+            else self._release
+        )
+        pre = _validate_pre(kwargs["pre"]) if "pre" in kwargs else self._pre
+        post = _validate_post(kwargs["post"]) if "post" in kwargs else self._post
+        dev = _validate_dev(kwargs["dev"]) if "dev" in kwargs else self._dev
+        local = _validate_local(kwargs["local"]) if "local" in kwargs else self._local
+
+        if (
+            epoch == self._epoch
+            and release == self._release
+            and pre == self._pre
+            and post == self._post
+            and dev == self._dev
+            and local == self._local
+        ):
+            return self
+
+        new_version = self.__class__.__new__(self.__class__)
+        new_version._key_cache = None
+        new_version._epoch = epoch
+        new_version._release = release
+        new_version._pre = pre
+        new_version._post = post
+        new_version._dev = dev
+        new_version._local = local
+
+        return new_version
+
+    @property
+    def _key(self) -> CmpKey:
+        if self._key_cache is None:
+            self._key_cache = _cmpkey(
+                self._epoch,
+                self._release,
+                self._pre,
+                self._post,
+                self._dev,
+                self._local,
+            )
+        return self._key_cache
+
+    @property
+    @_deprecated("Version._version is private and will be removed soon")
+    def _version(self) -> _Version:
+        return _Version(
+            self._epoch, self._release, self._dev, self._pre, self._post, self._local
+        )
+
+    @_version.setter
+    @_deprecated("Version._version is private and will be removed soon")
+    def _version(self, value: _Version) -> None:
+        self._epoch = value.epoch
+        self._release = value.release
+        self._dev = value.dev
+        self._pre = value.pre
+        self._post = value.post
+        self._local = value.local
+        self._key_cache = None
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        <Version('1.0.0')>
+        """
+        return f"<Version('{self}')>"
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be round-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        # This is a hot function, so not calling self.base_version
+        version = ".".join(map(str, self.release))
+
+        # Epoch
+        if self.epoch:
+            version = f"{self.epoch}!{version}"
+
+        # Pre-release
+        if self.pre is not None:
+            version += "".join(map(str, self.pre))
+
+        # Post-release
+        if self.post is not None:
+            version += f".post{self.post}"
+
+        # Development release
+        if self.dev is not None:
+            version += f".dev{self.dev}"
+
+        # Local version segment
+        if self.local is not None:
+            version += f"+{self.local}"
+
+        return version
+
+    @property
+    def _str(self) -> str:
+        """Internal property for match_args"""
+        return str(self)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._epoch
+
+    @property
+    def release(self) -> tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._release
+
+    @property
+    def pre(self) -> tuple[str, int] | None:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._pre
+
+    @property
+    def post(self) -> int | None:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._post[1] if self._post else None
+
+    @property
+    def dev(self) -> int | None:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._dev[1] if self._dev else None
+
+    @property
+    def local(self) -> str | None:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._local:
+            return ".".join(str(x) for x in self._local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1!1.2.3dev1+abc").public
+        '1!1.2.3.dev1'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3dev1+abc").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        release_segment = ".".join(map(str, self.release))
+        return f"{self.epoch}!{release_segment}" if self.epoch else release_segment
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+class _TrimmedRelease(Version):
+    __slots__ = ()
+
+    def __init__(self, version: str | Version) -> None:
+        if isinstance(version, Version):
+            self._epoch = version._epoch
+            self._release = version._release
+            self._dev = version._dev
+            self._pre = version._pre
+            self._post = version._post
+            self._local = version._local
+            self._key_cache = version._key_cache
+            return
+        super().__init__(version)  # pragma: no cover
+
+    @property
+    def release(self) -> tuple[int, ...]:
+        """
+        Release segment without any trailing zeros.
+
+        >>> _TrimmedRelease('1.0.0').release
+        (1,)
+        >>> _TrimmedRelease('0.0').release
+        (0,)
+        """
+        # This leaves one 0.
+        rel = super().release
+        len_release = len(rel)
+        i = len_release
+        while i > 1 and rel[i - 1] == 0:
+            i -= 1
+        return rel if i == len_release else rel[:i]
+
+
+def _parse_letter_version(
+    letter: str | None, number: str | bytes | SupportsInt | None
+) -> tuple[str, int] | None:
+    if letter:
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        letter = _LETTER_NORMALIZATION.get(letter, letter)
+
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        return letter, int(number or 0)
+
+    if number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        return "post", int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str | None) -> LocalType | None:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: tuple[int, ...],
+    pre: tuple[str, int] | None,
+    post: tuple[str, int] | None,
+    dev: tuple[str, int] | None,
+    local: LocalType | None,
+) -> CmpKey:
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. We will use this for our sorting key.
+    len_release = len(release)
+    i = len_release
+    while i and release[i - 1] == 0:
+        i -= 1
+    _release = release if i == len_release else release[:i]
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca2eba20432924304517be99d5113bc9f57614d2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/__init__.py
@@ -0,0 +1,367 @@
+from __future__ import annotations
+
+import os
+import warnings
+
+__docformat__ = "restructuredtext"
+
+# Let users know if they're missing any of our hard dependencies
+_hard_dependencies = ("numpy", "pytz", "dateutil")
+_missing_dependencies = []
+
+for _dependency in _hard_dependencies:
+    try:
+        __import__(_dependency)
+    except ImportError as _e:  # pragma: no cover
+        _missing_dependencies.append(f"{_dependency}: {_e}")
+
+if _missing_dependencies:  # pragma: no cover
+    raise ImportError(
+        "Unable to import required dependencies:\n" + "\n".join(_missing_dependencies)
+    )
+del _hard_dependencies, _dependency, _missing_dependencies
+
+try:
+    # numpy compat
+    from pandas.compat import (
+        is_numpy_dev as _is_numpy_dev,  # pyright: ignore[reportUnusedImport] # noqa: F401
+    )
+except ImportError as _err:  # pragma: no cover
+    _module = _err.name
+    raise ImportError(
+        f"C extension: {_module} not built. If you want to import "
+        "pandas from the source directory, you may need to run "
+        "'python setup.py build_ext' to build the C extensions first."
+    ) from _err
+
+from pandas._config import (
+    get_option,
+    set_option,
+    reset_option,
+    describe_option,
+    option_context,
+    options,
+)
+
+# let init-time option registration happen
+import pandas.core.config_init  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+from pandas.core.api import (
+    # dtype
+    ArrowDtype,
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+    Float32Dtype,
+    Float64Dtype,
+    CategoricalDtype,
+    PeriodDtype,
+    IntervalDtype,
+    DatetimeTZDtype,
+    StringDtype,
+    BooleanDtype,
+    # missing
+    NA,
+    isna,
+    isnull,
+    notna,
+    notnull,
+    # indexes
+    Index,
+    CategoricalIndex,
+    RangeIndex,
+    MultiIndex,
+    IntervalIndex,
+    TimedeltaIndex,
+    DatetimeIndex,
+    PeriodIndex,
+    IndexSlice,
+    # tseries
+    NaT,
+    Period,
+    period_range,
+    Timedelta,
+    timedelta_range,
+    Timestamp,
+    date_range,
+    bdate_range,
+    Interval,
+    interval_range,
+    DateOffset,
+    # conversion
+    to_numeric,
+    to_datetime,
+    to_timedelta,
+    # misc
+    Flags,
+    Grouper,
+    factorize,
+    unique,
+    value_counts,
+    NamedAgg,
+    array,
+    Categorical,
+    set_eng_float_format,
+    Series,
+    DataFrame,
+)
+
+from pandas.core.dtypes.dtypes import SparseDtype
+
+from pandas.tseries.api import infer_freq
+from pandas.tseries import offsets
+
+from pandas.core.computation.api import eval
+
+from pandas.core.reshape.api import (
+    concat,
+    lreshape,
+    melt,
+    wide_to_long,
+    merge,
+    merge_asof,
+    merge_ordered,
+    crosstab,
+    pivot,
+    pivot_table,
+    get_dummies,
+    from_dummies,
+    cut,
+    qcut,
+)
+
+from pandas import api, arrays, errors, io, plotting, tseries
+from pandas import testing
+from pandas.util._print_versions import show_versions
+
+from pandas.io.api import (
+    # excel
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+    # parsers
+    read_csv,
+    read_fwf,
+    read_table,
+    # pickle
+    read_pickle,
+    to_pickle,
+    # pytables
+    HDFStore,
+    read_hdf,
+    # sql
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+    # misc
+    read_clipboard,
+    read_parquet,
+    read_orc,
+    read_feather,
+    read_gbq,
+    read_html,
+    read_xml,
+    read_json,
+    read_stata,
+    read_sas,
+    read_spss,
+)
+
+from pandas.io.json._normalize import json_normalize
+
+from pandas.util._tester import test
+
+# use the closest tagged version if possible
+_built_with_meson = False
+try:
+    from pandas._version_meson import (  # pyright: ignore [reportMissingImports]
+        __version__,
+        __git_version__,
+    )
+
+    _built_with_meson = True
+except ImportError:
+    from pandas._version import get_versions
+
+    v = get_versions()
+    __version__ = v.get("closest-tag", v["version"])
+    __git_version__ = v.get("full-revisionid")
+    del get_versions, v
+
+# GH#55043 - deprecation of the data_manager option
+if "PANDAS_DATA_MANAGER" in os.environ:
+    warnings.warn(
+        "The env variable PANDAS_DATA_MANAGER is set. The data_manager option is "
+        "deprecated and will be removed in a future version. Only the BlockManager "
+        "will be available. Unset this environment variable to silence this warning.",
+        FutureWarning,
+        stacklevel=2,
+    )
+
+del warnings, os
+
+# module level doc-string
+__doc__ = """
+pandas - a powerful data analysis and manipulation library for Python
+=====================================================================
+
+**pandas** is a Python package providing fast, flexible, and expressive data
+structures designed to make working with "relational" or "labeled" data both
+easy and intuitive. It aims to be the fundamental high-level building block for
+doing practical, **real world** data analysis in Python. Additionally, it has
+the broader goal of becoming **the most powerful and flexible open source data
+analysis / manipulation tool available in any language**. It is already well on
+its way toward this goal.
+
+Main Features
+-------------
+Here are just a few of the things that pandas does well:
+
+  - Easy handling of missing data in floating point as well as non-floating
+    point data.
+  - Size mutability: columns can be inserted and deleted from DataFrame and
+    higher dimensional objects
+  - Automatic and explicit data alignment: objects can be explicitly aligned
+    to a set of labels, or the user can simply ignore the labels and let
+    `Series`, `DataFrame`, etc. automatically align the data for you in
+    computations.
+  - Powerful, flexible group by functionality to perform split-apply-combine
+    operations on data sets, for both aggregating and transforming data.
+  - Make it easy to convert ragged, differently-indexed data in other Python
+    and NumPy data structures into DataFrame objects.
+  - Intelligent label-based slicing, fancy indexing, and subsetting of large
+    data sets.
+  - Intuitive merging and joining data sets.
+  - Flexible reshaping and pivoting of data sets.
+  - Hierarchical labeling of axes (possible to have multiple labels per tick).
+  - Robust IO tools for loading data from flat files (CSV and delimited),
+    Excel files, databases, and saving/loading data from the ultrafast HDF5
+    format.
+  - Time series-specific functionality: date range generation and frequency
+    conversion, moving window statistics, date shifting and lagging.
+"""
+
+# Use __all__ to let type checkers know what is part of the public API.
+# Pandas is not (yet) a py.typed library: the public API is determined
+# based on the documentation.
+__all__ = [
+    "ArrowDtype",
+    "BooleanDtype",
+    "Categorical",
+    "CategoricalDtype",
+    "CategoricalIndex",
+    "DataFrame",
+    "DateOffset",
+    "DatetimeIndex",
+    "DatetimeTZDtype",
+    "ExcelFile",
+    "ExcelWriter",
+    "Flags",
+    "Float32Dtype",
+    "Float64Dtype",
+    "Grouper",
+    "HDFStore",
+    "Index",
+    "IndexSlice",
+    "Int16Dtype",
+    "Int32Dtype",
+    "Int64Dtype",
+    "Int8Dtype",
+    "Interval",
+    "IntervalDtype",
+    "IntervalIndex",
+    "MultiIndex",
+    "NA",
+    "NaT",
+    "NamedAgg",
+    "Period",
+    "PeriodDtype",
+    "PeriodIndex",
+    "RangeIndex",
+    "Series",
+    "SparseDtype",
+    "StringDtype",
+    "Timedelta",
+    "TimedeltaIndex",
+    "Timestamp",
+    "UInt16Dtype",
+    "UInt32Dtype",
+    "UInt64Dtype",
+    "UInt8Dtype",
+    "api",
+    "array",
+    "arrays",
+    "bdate_range",
+    "concat",
+    "crosstab",
+    "cut",
+    "date_range",
+    "describe_option",
+    "errors",
+    "eval",
+    "factorize",
+    "get_dummies",
+    "from_dummies",
+    "get_option",
+    "infer_freq",
+    "interval_range",
+    "io",
+    "isna",
+    "isnull",
+    "json_normalize",
+    "lreshape",
+    "melt",
+    "merge",
+    "merge_asof",
+    "merge_ordered",
+    "notna",
+    "notnull",
+    "offsets",
+    "option_context",
+    "options",
+    "period_range",
+    "pivot",
+    "pivot_table",
+    "plotting",
+    "qcut",
+    "read_clipboard",
+    "read_csv",
+    "read_excel",
+    "read_feather",
+    "read_fwf",
+    "read_gbq",
+    "read_hdf",
+    "read_html",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_pickle",
+    "read_sas",
+    "read_spss",
+    "read_sql",
+    "read_sql_query",
+    "read_sql_table",
+    "read_stata",
+    "read_table",
+    "read_xml",
+    "reset_option",
+    "set_eng_float_format",
+    "set_option",
+    "show_versions",
+    "test",
+    "testing",
+    "timedelta_range",
+    "to_datetime",
+    "to_numeric",
+    "to_pickle",
+    "to_timedelta",
+    "tseries",
+    "unique",
+    "value_counts",
+    "wide_to_long",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..838b6affd283633c2269cb91db4bf9eaebda318e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/__init__.py
@@ -0,0 +1,57 @@
+"""
+pandas._config is considered explicitly upstream of everything else in pandas,
+should have no intra-pandas dependencies.
+
+importing `dates` and `display` ensures that keys needed by _libs
+are initialized.
+"""
+__all__ = [
+    "config",
+    "detect_console_encoding",
+    "get_option",
+    "set_option",
+    "reset_option",
+    "describe_option",
+    "option_context",
+    "options",
+    "using_copy_on_write",
+    "warn_copy_on_write",
+]
+from pandas._config import config
+from pandas._config import dates  # pyright: ignore[reportUnusedImport]  # noqa: F401
+from pandas._config.config import (
+    _global_config,
+    describe_option,
+    get_option,
+    option_context,
+    options,
+    reset_option,
+    set_option,
+)
+from pandas._config.display import detect_console_encoding
+
+
+def using_copy_on_write() -> bool:
+    _mode_options = _global_config["mode"]
+    return (
+        _mode_options["copy_on_write"] is True
+        and _mode_options["data_manager"] == "block"
+    )
+
+
+def warn_copy_on_write() -> bool:
+    _mode_options = _global_config["mode"]
+    return (
+        _mode_options["copy_on_write"] == "warn"
+        and _mode_options["data_manager"] == "block"
+    )
+
+
+def using_nullable_dtypes() -> bool:
+    _mode_options = _global_config["mode"]
+    return _mode_options["nullable_dtypes"]
+
+
+def using_string_dtype() -> bool:
+    _mode_options = _global_config["future"]
+    return _mode_options["infer_string"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/config.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..c391939d22491099652e13ad81e83b201f140b60
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/config.py
@@ -0,0 +1,948 @@
+"""
+The config module holds package-wide configurables and provides
+a uniform API for working with them.
+
+Overview
+========
+
+This module supports the following requirements:
+- options are referenced using keys in dot.notation, e.g. "x.y.option - z".
+- keys are case-insensitive.
+- functions should accept partial/regex keys, when unambiguous.
+- options can be registered by modules at import time.
+- options can be registered at init-time (via core.config_init)
+- options have a default value, and (optionally) a description and
+  validation function associated with them.
+- options can be deprecated, in which case referencing them
+  should produce a warning.
+- deprecated options can optionally be rerouted to a replacement
+  so that accessing a deprecated option reroutes to a differently
+  named option.
+- options can be reset to their default value.
+- all option can be reset to their default value at once.
+- all options in a certain sub - namespace can be reset at once.
+- the user can set / get / reset or ask for the description of an option.
+- a developer can register and mark an option as deprecated.
+- you can register a callback to be invoked when the option value
+  is set or reset. Changing the stored value is considered misuse, but
+  is not verboten.
+
+Implementation
+==============
+
+- Data is stored using nested dictionaries, and should be accessed
+  through the provided API.
+
+- "Registered options" and "Deprecated options" have metadata associated
+  with them, which are stored in auxiliary dictionaries keyed on the
+  fully-qualified key, e.g. "x.y.z.option".
+
+- the config_init module is imported by the package's __init__.py file.
+  placing any register_option() calls there will ensure those options
+  are available as soon as pandas is loaded. If you use register_option
+  in a module, it will only be available after that module is imported,
+  which you should be aware of.
+
+- `config_prefix` is a context_manager (for use with the `with` keyword)
+  which can save developers some typing, see the docstring.
+
+"""
+
+from __future__ import annotations
+
+from contextlib import (
+    ContextDecorator,
+    contextmanager,
+)
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    NamedTuple,
+    cast,
+)
+import warnings
+
+from pandas._typing import (
+    F,
+    T,
+)
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Iterable,
+    )
+
+
+class DeprecatedOption(NamedTuple):
+    key: str
+    msg: str | None
+    rkey: str | None
+    removal_ver: str | None
+
+
+class RegisteredOption(NamedTuple):
+    key: str
+    defval: object
+    doc: str
+    validator: Callable[[object], Any] | None
+    cb: Callable[[str], Any] | None
+
+
+# holds deprecated option metadata
+_deprecated_options: dict[str, DeprecatedOption] = {}
+
+# holds registered option metadata
+_registered_options: dict[str, RegisteredOption] = {}
+
+# holds the current values for registered options
+_global_config: dict[str, Any] = {}
+
+# keys which have a special meaning
+_reserved_keys: list[str] = ["all"]
+
+
+class OptionError(AttributeError, KeyError):
+    """
+    Exception raised for pandas.options.
+
+    Backwards compatible with KeyError checks.
+
+    Examples
+    --------
+    >>> pd.options.context
+    Traceback (most recent call last):
+    OptionError: No such option
+    """
+
+
+#
+# User API
+
+
+def _get_single_key(pat: str, silent: bool) -> str:
+    keys = _select_options(pat)
+    if len(keys) == 0:
+        if not silent:
+            _warn_if_deprecated(pat)
+        raise OptionError(f"No such keys(s): {repr(pat)}")
+    if len(keys) > 1:
+        raise OptionError("Pattern matched multiple keys")
+    key = keys[0]
+
+    if not silent:
+        _warn_if_deprecated(key)
+
+    key = _translate_key(key)
+
+    return key
+
+
+def _get_option(pat: str, silent: bool = False) -> Any:
+    key = _get_single_key(pat, silent)
+
+    # walk the nested dict
+    root, k = _get_root(key)
+    return root[k]
+
+
+def _set_option(*args, **kwargs) -> None:
+    # must at least 1 arg deal with constraints later
+    nargs = len(args)
+    if not nargs or nargs % 2 != 0:
+        raise ValueError("Must provide an even number of non-keyword arguments")
+
+    # default to false
+    silent = kwargs.pop("silent", False)
+
+    if kwargs:
+        kwarg = next(iter(kwargs.keys()))
+        raise TypeError(f'_set_option() got an unexpected keyword argument "{kwarg}"')
+
+    for k, v in zip(args[::2], args[1::2]):
+        key = _get_single_key(k, silent)
+
+        o = _get_registered_option(key)
+        if o and o.validator:
+            o.validator(v)
+
+        # walk the nested dict
+        root, k_root = _get_root(key)
+        root[k_root] = v
+
+        if o.cb:
+            if silent:
+                with warnings.catch_warnings(record=True):
+                    o.cb(key)
+            else:
+                o.cb(key)
+
+
+def _describe_option(pat: str = "", _print_desc: bool = True) -> str | None:
+    keys = _select_options(pat)
+    if len(keys) == 0:
+        raise OptionError("No such keys(s)")
+
+    s = "\n".join([_build_option_description(k) for k in keys])
+
+    if _print_desc:
+        print(s)
+        return None
+    return s
+
+
+def _reset_option(pat: str, silent: bool = False) -> None:
+    keys = _select_options(pat)
+
+    if len(keys) == 0:
+        raise OptionError("No such keys(s)")
+
+    if len(keys) > 1 and len(pat) < 4 and pat != "all":
+        raise ValueError(
+            "You must specify at least 4 characters when "
+            "resetting multiple keys, use the special keyword "
+            '"all" to reset all the options to their default value'
+        )
+
+    for k in keys:
+        _set_option(k, _registered_options[k].defval, silent=silent)
+
+
+def get_default_val(pat: str):
+    key = _get_single_key(pat, silent=True)
+    return _get_registered_option(key).defval
+
+
+class DictWrapper:
+    """provide attribute-style access to a nested dict"""
+
+    d: dict[str, Any]
+
+    def __init__(self, d: dict[str, Any], prefix: str = "") -> None:
+        object.__setattr__(self, "d", d)
+        object.__setattr__(self, "prefix", prefix)
+
+    def __setattr__(self, key: str, val: Any) -> None:
+        prefix = object.__getattribute__(self, "prefix")
+        if prefix:
+            prefix += "."
+        prefix += key
+        # you can't set new keys
+        # can you can't overwrite subtrees
+        if key in self.d and not isinstance(self.d[key], dict):
+            _set_option(prefix, val)
+        else:
+            raise OptionError("You can only set the value of existing options")
+
+    def __getattr__(self, key: str):
+        prefix = object.__getattribute__(self, "prefix")
+        if prefix:
+            prefix += "."
+        prefix += key
+        try:
+            v = object.__getattribute__(self, "d")[key]
+        except KeyError as err:
+            raise OptionError("No such option") from err
+        if isinstance(v, dict):
+            return DictWrapper(v, prefix)
+        else:
+            return _get_option(prefix)
+
+    def __dir__(self) -> list[str]:
+        return list(self.d.keys())
+
+
+# For user convenience,  we'd like to have the available options described
+# in the docstring. For dev convenience we'd like to generate the docstrings
+# dynamically instead of maintaining them by hand. To this, we use the
+# class below which wraps functions inside a callable, and converts
+# __doc__ into a property function. The doctsrings below are templates
+# using the py2.6+ advanced formatting syntax to plug in a concise list
+# of options, and option descriptions.
+
+
+class CallableDynamicDoc(Generic[T]):
+    def __init__(self, func: Callable[..., T], doc_tmpl: str) -> None:
+        self.__doc_tmpl__ = doc_tmpl
+        self.__func__ = func
+
+    def __call__(self, *args, **kwds) -> T:
+        return self.__func__(*args, **kwds)
+
+    # error: Signature of "__doc__" incompatible with supertype "object"
+    @property
+    def __doc__(self) -> str:  # type: ignore[override]
+        opts_desc = _describe_option("all", _print_desc=False)
+        opts_list = pp_options_list(list(_registered_options.keys()))
+        return self.__doc_tmpl__.format(opts_desc=opts_desc, opts_list=opts_list)
+
+
+_get_option_tmpl = """
+get_option(pat)
+
+Retrieves the value of the specified option.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str
+    Regexp which should match a single option.
+    Note: partial matches are supported for convenience, but unless you use the
+    full option name (e.g. x.y.z.option_name), your code may break in future
+    versions if new options with similar names are introduced.
+
+Returns
+-------
+result : the value of the option
+
+Raises
+------
+OptionError : if no such option exists
+
+Notes
+-----
+Please reference the :ref:`User Guide <options>` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.get_option('display.max_columns')  # doctest: +SKIP
+4
+"""
+
+_set_option_tmpl = """
+set_option(pat, value)
+
+Sets the value of the specified option.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str
+    Regexp which should match a single option.
+    Note: partial matches are supported for convenience, but unless you use the
+    full option name (e.g. x.y.z.option_name), your code may break in future
+    versions if new options with similar names are introduced.
+value : object
+    New value of option.
+
+Returns
+-------
+None
+
+Raises
+------
+OptionError if no such option exists
+
+Notes
+-----
+Please reference the :ref:`User Guide <options>` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.set_option('display.max_columns', 4)
+>>> df = pd.DataFrame([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
+>>> df
+   0  1  ...  3   4
+0  1  2  ...  4   5
+1  6  7  ...  9  10
+[2 rows x 5 columns]
+>>> pd.reset_option('display.max_columns')
+"""
+
+_describe_option_tmpl = """
+describe_option(pat, _print_desc=False)
+
+Prints the description for one or more registered options.
+
+Call with no arguments to get a listing for all registered options.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str
+    Regexp pattern. All matching keys will have their description displayed.
+_print_desc : bool, default True
+    If True (default) the description(s) will be printed to stdout.
+    Otherwise, the description(s) will be returned as a unicode string
+    (for testing).
+
+Returns
+-------
+None by default, the description(s) as a unicode string if _print_desc
+is False
+
+Notes
+-----
+Please reference the :ref:`User Guide <options>` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.describe_option('display.max_columns')  # doctest: +SKIP
+display.max_columns : int
+    If max_cols is exceeded, switch to truncate view...
+"""
+
+_reset_option_tmpl = """
+reset_option(pat)
+
+Reset one or more options to their default value.
+
+Pass "all" as argument to reset all options.
+
+Available options:
+
+{opts_list}
+
+Parameters
+----------
+pat : str/regex
+    If specified only options matching `prefix*` will be reset.
+    Note: partial matches are supported for convenience, but unless you
+    use the full option name (e.g. x.y.z.option_name), your code may break
+    in future versions if new options with similar names are introduced.
+
+Returns
+-------
+None
+
+Notes
+-----
+Please reference the :ref:`User Guide <options>` for more information.
+
+The available options with its descriptions:
+
+{opts_desc}
+
+Examples
+--------
+>>> pd.reset_option('display.max_columns')  # doctest: +SKIP
+"""
+
+# bind the functions with their docstrings into a Callable
+# and use that as the functions exposed in pd.api
+get_option = CallableDynamicDoc(_get_option, _get_option_tmpl)
+set_option = CallableDynamicDoc(_set_option, _set_option_tmpl)
+reset_option = CallableDynamicDoc(_reset_option, _reset_option_tmpl)
+describe_option = CallableDynamicDoc(_describe_option, _describe_option_tmpl)
+options = DictWrapper(_global_config)
+
+#
+# Functions for use by pandas developers, in addition to User - api
+
+
+class option_context(ContextDecorator):
+    """
+    Context manager to temporarily set options in the `with` statement context.
+
+    You need to invoke as ``option_context(pat, val, [(pat, val), ...])``.
+
+    Examples
+    --------
+    >>> from pandas import option_context
+    >>> with option_context('display.max_rows', 10, 'display.max_columns', 5):
+    ...     pass
+    """
+
+    def __init__(self, *args) -> None:
+        if len(args) % 2 != 0 or len(args) < 2:
+            raise ValueError(
+                "Need to invoke as option_context(pat, val, [(pat, val), ...])."
+            )
+
+        self.ops = list(zip(args[::2], args[1::2]))
+
+    def __enter__(self) -> None:
+        self.undo = [(pat, _get_option(pat)) for pat, val in self.ops]
+
+        for pat, val in self.ops:
+            _set_option(pat, val, silent=True)
+
+    def __exit__(self, *args) -> None:
+        if self.undo:
+            for pat, val in self.undo:
+                _set_option(pat, val, silent=True)
+
+
+def register_option(
+    key: str,
+    defval: object,
+    doc: str = "",
+    validator: Callable[[object], Any] | None = None,
+    cb: Callable[[str], Any] | None = None,
+) -> None:
+    """
+    Register an option in the package-wide pandas config object
+
+    Parameters
+    ----------
+    key : str
+        Fully-qualified key, e.g. "x.y.option - z".
+    defval : object
+        Default value of the option.
+    doc : str
+        Description of the option.
+    validator : Callable, optional
+        Function of a single argument, should raise `ValueError` if
+        called with a value which is not a legal value for the option.
+    cb
+        a function of a single argument "key", which is called
+        immediately after an option value is set/reset. key is
+        the full name of the option.
+
+    Raises
+    ------
+    ValueError if `validator` is specified and `defval` is not a valid value.
+
+    """
+    import keyword
+    import tokenize
+
+    key = key.lower()
+
+    if key in _registered_options:
+        raise OptionError(f"Option '{key}' has already been registered")
+    if key in _reserved_keys:
+        raise OptionError(f"Option '{key}' is a reserved key")
+
+    # the default value should be legal
+    if validator:
+        validator(defval)
+
+    # walk the nested dict, creating dicts as needed along the path
+    path = key.split(".")
+
+    for k in path:
+        if not re.match("^" + tokenize.Name + "$", k):
+            raise ValueError(f"{k} is not a valid identifier")
+        if keyword.iskeyword(k):
+            raise ValueError(f"{k} is a python keyword")
+
+    cursor = _global_config
+    msg = "Path prefix to option '{option}' is already an option"
+
+    for i, p in enumerate(path[:-1]):
+        if not isinstance(cursor, dict):
+            raise OptionError(msg.format(option=".".join(path[:i])))
+        if p not in cursor:
+            cursor[p] = {}
+        cursor = cursor[p]
+
+    if not isinstance(cursor, dict):
+        raise OptionError(msg.format(option=".".join(path[:-1])))
+
+    cursor[path[-1]] = defval  # initialize
+
+    # save the option metadata
+    _registered_options[key] = RegisteredOption(
+        key=key, defval=defval, doc=doc, validator=validator, cb=cb
+    )
+
+
+def deprecate_option(
+    key: str,
+    msg: str | None = None,
+    rkey: str | None = None,
+    removal_ver: str | None = None,
+) -> None:
+    """
+    Mark option `key` as deprecated, if code attempts to access this option,
+    a warning will be produced, using `msg` if given, or a default message
+    if not.
+    if `rkey` is given, any access to the key will be re-routed to `rkey`.
+
+    Neither the existence of `key` nor that if `rkey` is checked. If they
+    do not exist, any subsequence access will fail as usual, after the
+    deprecation warning is given.
+
+    Parameters
+    ----------
+    key : str
+        Name of the option to be deprecated.
+        must be a fully-qualified option name (e.g "x.y.z.rkey").
+    msg : str, optional
+        Warning message to output when the key is referenced.
+        if no message is given a default message will be emitted.
+    rkey : str, optional
+        Name of an option to reroute access to.
+        If specified, any referenced `key` will be
+        re-routed to `rkey` including set/get/reset.
+        rkey must be a fully-qualified option name (e.g "x.y.z.rkey").
+        used by the default message if no `msg` is specified.
+    removal_ver : str, optional
+        Specifies the version in which this option will
+        be removed. used by the default message if no `msg` is specified.
+
+    Raises
+    ------
+    OptionError
+        If the specified key has already been deprecated.
+    """
+    key = key.lower()
+
+    if key in _deprecated_options:
+        raise OptionError(f"Option '{key}' has already been defined as deprecated.")
+
+    _deprecated_options[key] = DeprecatedOption(key, msg, rkey, removal_ver)
+
+
+#
+# functions internal to the module
+
+
+def _select_options(pat: str) -> list[str]:
+    """
+    returns a list of keys matching `pat`
+
+    if pat=="all", returns all registered options
+    """
+    # short-circuit for exact key
+    if pat in _registered_options:
+        return [pat]
+
+    # else look through all of them
+    keys = sorted(_registered_options.keys())
+    if pat == "all":  # reserved key
+        return keys
+
+    return [k for k in keys if re.search(pat, k, re.I)]
+
+
+def _get_root(key: str) -> tuple[dict[str, Any], str]:
+    path = key.split(".")
+    cursor = _global_config
+    for p in path[:-1]:
+        cursor = cursor[p]
+    return cursor, path[-1]
+
+
+def _is_deprecated(key: str) -> bool:
+    """Returns True if the given option has been deprecated"""
+    key = key.lower()
+    return key in _deprecated_options
+
+
+def _get_deprecated_option(key: str):
+    """
+    Retrieves the metadata for a deprecated option, if `key` is deprecated.
+
+    Returns
+    -------
+    DeprecatedOption (namedtuple) if key is deprecated, None otherwise
+    """
+    try:
+        d = _deprecated_options[key]
+    except KeyError:
+        return None
+    else:
+        return d
+
+
+def _get_registered_option(key: str):
+    """
+    Retrieves the option metadata if `key` is a registered option.
+
+    Returns
+    -------
+    RegisteredOption (namedtuple) if key is deprecated, None otherwise
+    """
+    return _registered_options.get(key)
+
+
+def _translate_key(key: str) -> str:
+    """
+    if key id deprecated and a replacement key defined, will return the
+    replacement key, otherwise returns `key` as - is
+    """
+    d = _get_deprecated_option(key)
+    if d:
+        return d.rkey or key
+    else:
+        return key
+
+
+def _warn_if_deprecated(key: str) -> bool:
+    """
+    Checks if `key` is a deprecated option and if so, prints a warning.
+
+    Returns
+    -------
+    bool - True if `key` is deprecated, False otherwise.
+    """
+    d = _get_deprecated_option(key)
+    if d:
+        if d.msg:
+            warnings.warn(
+                d.msg,
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            msg = f"'{key}' is deprecated"
+            if d.removal_ver:
+                msg += f" and will be removed in {d.removal_ver}"
+            if d.rkey:
+                msg += f", please use '{d.rkey}' instead."
+            else:
+                msg += ", please refrain from using it."
+
+            warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+        return True
+    return False
+
+
+def _build_option_description(k: str) -> str:
+    """Builds a formatted description of a registered option and prints it"""
+    o = _get_registered_option(k)
+    d = _get_deprecated_option(k)
+
+    s = f"{k} "
+
+    if o.doc:
+        s += "\n".join(o.doc.strip().split("\n"))
+    else:
+        s += "No description available."
+
+    if o:
+        s += f"\n    [default: {o.defval}] [currently: {_get_option(k, True)}]"
+
+    if d:
+        rkey = d.rkey or ""
+        s += "\n    (Deprecated"
+        s += f", use `{rkey}` instead."
+        s += ")"
+
+    return s
+
+
+def pp_options_list(keys: Iterable[str], width: int = 80, _print: bool = False):
+    """Builds a concise listing of available options, grouped by prefix"""
+    from itertools import groupby
+    from textwrap import wrap
+
+    def pp(name: str, ks: Iterable[str]) -> list[str]:
+        pfx = "- " + name + ".[" if name else ""
+        ls = wrap(
+            ", ".join(ks),
+            width,
+            initial_indent=pfx,
+            subsequent_indent="  ",
+            break_long_words=False,
+        )
+        if ls and ls[-1] and name:
+            ls[-1] = ls[-1] + "]"
+        return ls
+
+    ls: list[str] = []
+    singles = [x for x in sorted(keys) if x.find(".") < 0]
+    if singles:
+        ls += pp("", singles)
+    keys = [x for x in keys if x.find(".") >= 0]
+
+    for k, g in groupby(sorted(keys), lambda x: x[: x.rfind(".")]):
+        ks = [x[len(k) + 1 :] for x in list(g)]
+        ls += pp(k, ks)
+    s = "\n".join(ls)
+    if _print:
+        print(s)
+    else:
+        return s
+
+
+#
+# helpers
+
+
+@contextmanager
+def config_prefix(prefix: str) -> Generator[None, None, None]:
+    """
+    contextmanager for multiple invocations of API with a common prefix
+
+    supported API functions: (register / get / set )__option
+
+    Warning: This is not thread - safe, and won't work properly if you import
+    the API functions into your module using the "from x import y" construct.
+
+    Example
+    -------
+    import pandas._config.config as cf
+    with cf.config_prefix("display.font"):
+        cf.register_option("color", "red")
+        cf.register_option("size", " 5 pt")
+        cf.set_option(size, " 6 pt")
+        cf.get_option(size)
+        ...
+
+        etc'
+
+    will register options "display.font.color", "display.font.size", set the
+    value of "display.font.size"... and so on.
+    """
+    # Note: reset_option relies on set_option, and on key directly
+    # it does not fit in to this monkey-patching scheme
+
+    global register_option, get_option, set_option
+
+    def wrap(func: F) -> F:
+        def inner(key: str, *args, **kwds):
+            pkey = f"{prefix}.{key}"
+            return func(pkey, *args, **kwds)
+
+        return cast(F, inner)
+
+    _register_option = register_option
+    _get_option = get_option
+    _set_option = set_option
+    set_option = wrap(set_option)
+    get_option = wrap(get_option)
+    register_option = wrap(register_option)
+    try:
+        yield
+    finally:
+        set_option = _set_option
+        get_option = _get_option
+        register_option = _register_option
+
+
+# These factories and methods are handy for use as the validator
+# arg in register_option
+
+
+def is_type_factory(_type: type[Any]) -> Callable[[Any], None]:
+    """
+
+    Parameters
+    ----------
+    `_type` - a type to be compared against (e.g. type(x) == `_type`)
+
+    Returns
+    -------
+    validator - a function of a single argument x , which raises
+                ValueError if type(x) is not equal to `_type`
+
+    """
+
+    def inner(x) -> None:
+        if type(x) != _type:
+            raise ValueError(f"Value must have type '{_type}'")
+
+    return inner
+
+
+def is_instance_factory(_type) -> Callable[[Any], None]:
+    """
+
+    Parameters
+    ----------
+    `_type` - the type to be checked against
+
+    Returns
+    -------
+    validator - a function of a single argument x , which raises
+                ValueError if x is not an instance of `_type`
+
+    """
+    if isinstance(_type, (tuple, list)):
+        _type = tuple(_type)
+        type_repr = "|".join(map(str, _type))
+    else:
+        type_repr = f"'{_type}'"
+
+    def inner(x) -> None:
+        if not isinstance(x, _type):
+            raise ValueError(f"Value must be an instance of {type_repr}")
+
+    return inner
+
+
+def is_one_of_factory(legal_values) -> Callable[[Any], None]:
+    callables = [c for c in legal_values if callable(c)]
+    legal_values = [c for c in legal_values if not callable(c)]
+
+    def inner(x) -> None:
+        if x not in legal_values:
+            if not any(c(x) for c in callables):
+                uvals = [str(lval) for lval in legal_values]
+                pp_values = "|".join(uvals)
+                msg = f"Value must be one of {pp_values}"
+                if len(callables):
+                    msg += " or a callable"
+                raise ValueError(msg)
+
+    return inner
+
+
+def is_nonnegative_int(value: object) -> None:
+    """
+    Verify that value is None or a positive int.
+
+    Parameters
+    ----------
+    value : None or int
+            The `value` to be checked.
+
+    Raises
+    ------
+    ValueError
+        When the value is not None or is a negative integer
+    """
+    if value is None:
+        return
+
+    elif isinstance(value, int):
+        if value >= 0:
+            return
+
+    msg = "Value must be a nonnegative integer or None"
+    raise ValueError(msg)
+
+
+# common type validators, for convenience
+# usage: register_option(... , validator = is_int)
+is_int = is_type_factory(int)
+is_bool = is_type_factory(bool)
+is_float = is_type_factory(float)
+is_str = is_type_factory(str)
+is_text = is_instance_factory((str, bytes))
+
+
+def is_callable(obj) -> bool:
+    """
+
+    Parameters
+    ----------
+    `obj` - the object to be checked
+
+    Returns
+    -------
+    validator - returns True if object is callable
+        raises ValueError otherwise.
+
+    """
+    if not callable(obj):
+        raise ValueError("Value must be a callable")
+    return True
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/dates.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/dates.py
new file mode 100644
index 0000000000000000000000000000000000000000..b37831f96eb73bf2f128929a1769db6c141eebad
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/dates.py
@@ -0,0 +1,25 @@
+"""
+config for datetime formatting
+"""
+from __future__ import annotations
+
+from pandas._config import config as cf
+
+pc_date_dayfirst_doc = """
+: boolean
+    When True, prints and parses dates with the day first, eg 20/01/2005
+"""
+
+pc_date_yearfirst_doc = """
+: boolean
+    When True, prints and parses dates with the year first, eg 2005/01/20
+"""
+
+with cf.config_prefix("display"):
+    # Needed upstream of `_libs` because these are used in tslibs.parsing
+    cf.register_option(
+        "date_dayfirst", False, pc_date_dayfirst_doc, validator=cf.is_bool
+    )
+    cf.register_option(
+        "date_yearfirst", False, pc_date_yearfirst_doc, validator=cf.is_bool
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/display.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/display.py
new file mode 100644
index 0000000000000000000000000000000000000000..df2c3ad36c855d77c33d80c78c3d83ab3c09d5f9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/display.py
@@ -0,0 +1,62 @@
+"""
+Unopinionated display configuration.
+"""
+
+from __future__ import annotations
+
+import locale
+import sys
+
+from pandas._config import config as cf
+
+# -----------------------------------------------------------------------------
+# Global formatting options
+_initial_defencoding: str | None = None
+
+
+def detect_console_encoding() -> str:
+    """
+    Try to find the most capable encoding supported by the console.
+    slightly modified from the way IPython handles the same issue.
+    """
+    global _initial_defencoding
+
+    encoding = None
+    try:
+        encoding = sys.stdout.encoding or sys.stdin.encoding
+    except (AttributeError, OSError):
+        pass
+
+    # try again for something better
+    if not encoding or "ascii" in encoding.lower():
+        try:
+            encoding = locale.getpreferredencoding()
+        except locale.Error:
+            # can be raised by locale.setlocale(), which is
+            #  called by getpreferredencoding
+            #  (on some systems, see stdlib locale docs)
+            pass
+
+    # when all else fails. this will usually be "ascii"
+    if not encoding or "ascii" in encoding.lower():
+        encoding = sys.getdefaultencoding()
+
+    # GH#3360, save the reported defencoding at import time
+    # MPL backends may change it. Make available for debugging.
+    if not _initial_defencoding:
+        _initial_defencoding = sys.getdefaultencoding()
+
+    return encoding
+
+
+pc_encoding_doc = """
+: str/unicode
+    Defaults to the detected encoding of the console.
+    Specifies the encoding to be used for strings returned by to_string,
+    these are generally strings meant to be displayed on the console.
+"""
+
+with cf.config_prefix("display"):
+    cf.register_option(
+        "encoding", detect_console_encoding(), pc_encoding_doc, validator=cf.is_text
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/localization.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/localization.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c1a0ff1395334a55baa6c5d77a71635872fe824
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_config/localization.py
@@ -0,0 +1,172 @@
+"""
+Helpers for configuring locale settings.
+
+Name `localization` is chosen to avoid overlap with builtin `locale` module.
+"""
+from __future__ import annotations
+
+from contextlib import contextmanager
+import locale
+import platform
+import re
+import subprocess
+from typing import TYPE_CHECKING
+
+from pandas._config.config import options
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+@contextmanager
+def set_locale(
+    new_locale: str | tuple[str, str], lc_var: int = locale.LC_ALL
+) -> Generator[str | tuple[str, str], None, None]:
+    """
+    Context manager for temporarily setting a locale.
+
+    Parameters
+    ----------
+    new_locale : str or tuple
+        A string of the form <language_country>.<encoding>. For example to set
+        the current locale to US English with a UTF8 encoding, you would pass
+        "en_US.UTF-8".
+    lc_var : int, default `locale.LC_ALL`
+        The category of the locale being set.
+
+    Notes
+    -----
+    This is useful when you want to run a particular block of code under a
+    particular locale, without globally setting the locale. This probably isn't
+    thread-safe.
+    """
+    # getlocale is not always compliant with setlocale, use setlocale. GH#46595
+    current_locale = locale.setlocale(lc_var)
+
+    try:
+        locale.setlocale(lc_var, new_locale)
+        normalized_code, normalized_encoding = locale.getlocale()
+        if normalized_code is not None and normalized_encoding is not None:
+            yield f"{normalized_code}.{normalized_encoding}"
+        else:
+            yield new_locale
+    finally:
+        locale.setlocale(lc_var, current_locale)
+
+
+def can_set_locale(lc: str, lc_var: int = locale.LC_ALL) -> bool:
+    """
+    Check to see if we can set a locale, and subsequently get the locale,
+    without raising an Exception.
+
+    Parameters
+    ----------
+    lc : str
+        The locale to attempt to set.
+    lc_var : int, default `locale.LC_ALL`
+        The category of the locale being set.
+
+    Returns
+    -------
+    bool
+        Whether the passed locale can be set
+    """
+    try:
+        with set_locale(lc, lc_var=lc_var):
+            pass
+    except (ValueError, locale.Error):
+        # horrible name for a Exception subclass
+        return False
+    else:
+        return True
+
+
+def _valid_locales(locales: list[str] | str, normalize: bool) -> list[str]:
+    """
+    Return a list of normalized locales that do not throw an ``Exception``
+    when set.
+
+    Parameters
+    ----------
+    locales : str
+        A string where each locale is separated by a newline.
+    normalize : bool
+        Whether to call ``locale.normalize`` on each locale.
+
+    Returns
+    -------
+    valid_locales : list
+        A list of valid locales.
+    """
+    return [
+        loc
+        for loc in (
+            locale.normalize(loc.strip()) if normalize else loc.strip()
+            for loc in locales
+        )
+        if can_set_locale(loc)
+    ]
+
+
+def get_locales(
+    prefix: str | None = None,
+    normalize: bool = True,
+) -> list[str]:
+    """
+    Get all the locales that are available on the system.
+
+    Parameters
+    ----------
+    prefix : str
+        If not ``None`` then return only those locales with the prefix
+        provided. For example to get all English language locales (those that
+        start with ``"en"``), pass ``prefix="en"``.
+    normalize : bool
+        Call ``locale.normalize`` on the resulting list of available locales.
+        If ``True``, only locales that can be set without throwing an
+        ``Exception`` are returned.
+
+    Returns
+    -------
+    locales : list of strings
+        A list of locale strings that can be set with ``locale.setlocale()``.
+        For example::
+
+            locale.setlocale(locale.LC_ALL, locale_string)
+
+    On error will return an empty list (no locale available, e.g. Windows)
+
+    """
+    if platform.system() in ("Linux", "Darwin"):
+        raw_locales = subprocess.check_output(["locale", "-a"])
+    else:
+        # Other platforms e.g. windows platforms don't define "locale -a"
+        #  Note: is_platform_windows causes circular import here
+        return []
+
+    try:
+        # raw_locales is "\n" separated list of locales
+        # it may contain non-decodable parts, so split
+        # extract what we can and then rejoin.
+        split_raw_locales = raw_locales.split(b"\n")
+        out_locales = []
+        for x in split_raw_locales:
+            try:
+                out_locales.append(str(x, encoding=options.display.encoding))
+            except UnicodeError:
+                # 'locale -a' is used to populated 'raw_locales' and on
+                # Redhat 7 Linux (and maybe others) prints locale names
+                # using windows-1252 encoding.  Bug only triggered by
+                # a few special characters and when there is an
+                # extensive list of installed locales.
+                out_locales.append(str(x, encoding="windows-1252"))
+
+    except TypeError:
+        pass
+
+    if prefix is None:
+        return _valid_locales(out_locales, normalize)
+
+    pattern = re.compile(f"{prefix}.*")
+    found = pattern.findall("\n".join(out_locales))
+    return _valid_locales(found, normalize)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..26a872a90e4934c929d168228125f2e43936c774
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/__init__.py
@@ -0,0 +1,27 @@
+__all__ = [
+    "NaT",
+    "NaTType",
+    "OutOfBoundsDatetime",
+    "Period",
+    "Timedelta",
+    "Timestamp",
+    "iNaT",
+    "Interval",
+]
+
+
+# Below imports needs to happen first to ensure pandas top level
+# module gets monkeypatched with the pandas_datetime_CAPI
+# see pandas_datetime_exec in pd_datetime.c
+import pandas._libs.pandas_parser  # isort: skip # type: ignore[reportUnusedImport]
+import pandas._libs.pandas_datetime  # noqa: F401 # isort: skip # type: ignore[reportUnusedImport]
+from pandas._libs.interval import Interval
+from pandas._libs.tslibs import (
+    NaT,
+    NaTType,
+    OutOfBoundsDatetime,
+    Period,
+    Timedelta,
+    Timestamp,
+    iNaT,
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/algos.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/algos.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..caf5425dfc7b44cbb2fea103e56d4584709eca1e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/algos.pyi
@@ -0,0 +1,416 @@
+from typing import Any
+
+import numpy as np
+
+from pandas._typing import npt
+
+class Infinity:
+    def __eq__(self, other) -> bool: ...
+    def __ne__(self, other) -> bool: ...
+    def __lt__(self, other) -> bool: ...
+    def __le__(self, other) -> bool: ...
+    def __gt__(self, other) -> bool: ...
+    def __ge__(self, other) -> bool: ...
+
+class NegInfinity:
+    def __eq__(self, other) -> bool: ...
+    def __ne__(self, other) -> bool: ...
+    def __lt__(self, other) -> bool: ...
+    def __le__(self, other) -> bool: ...
+    def __gt__(self, other) -> bool: ...
+    def __ge__(self, other) -> bool: ...
+
+def unique_deltas(
+    arr: np.ndarray,  # const int64_t[:]
+) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=1]
+def is_lexsorted(list_of_arrays: list[npt.NDArray[np.int64]]) -> bool: ...
+def groupsort_indexer(
+    index: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+) -> tuple[
+    np.ndarray,  # ndarray[int64_t, ndim=1]
+    np.ndarray,  # ndarray[int64_t, ndim=1]
+]: ...
+def kth_smallest(
+    arr: np.ndarray,  # numeric[:]
+    k: int,
+) -> Any: ...  # numeric
+
+# ----------------------------------------------------------------------
+# Pairwise correlation/covariance
+
+def nancorr(
+    mat: npt.NDArray[np.float64],  # const float64_t[:, :]
+    cov: bool = ...,
+    minp: int | None = ...,
+) -> npt.NDArray[np.float64]: ...  # ndarray[float64_t, ndim=2]
+def nancorr_spearman(
+    mat: npt.NDArray[np.float64],  # ndarray[float64_t, ndim=2]
+    minp: int = ...,
+) -> npt.NDArray[np.float64]: ...  # ndarray[float64_t, ndim=2]
+
+# ----------------------------------------------------------------------
+
+def validate_limit(nobs: int | None, limit=...) -> int: ...
+def get_fill_indexer(
+    mask: npt.NDArray[np.bool_],
+    limit: int | None = None,
+) -> npt.NDArray[np.intp]: ...
+def pad(
+    old: np.ndarray,  # ndarray[numeric_object_t]
+    new: np.ndarray,  # ndarray[numeric_object_t]
+    limit=...,
+) -> npt.NDArray[np.intp]: ...  # np.ndarray[np.intp, ndim=1]
+def pad_inplace(
+    values: np.ndarray,  # numeric_object_t[:]
+    mask: np.ndarray,  # uint8_t[:]
+    limit=...,
+) -> None: ...
+def pad_2d_inplace(
+    values: np.ndarray,  # numeric_object_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:, :]
+    limit=...,
+) -> None: ...
+def backfill(
+    old: np.ndarray,  # ndarray[numeric_object_t]
+    new: np.ndarray,  # ndarray[numeric_object_t]
+    limit=...,
+) -> npt.NDArray[np.intp]: ...  # np.ndarray[np.intp, ndim=1]
+def backfill_inplace(
+    values: np.ndarray,  # numeric_object_t[:]
+    mask: np.ndarray,  # uint8_t[:]
+    limit=...,
+) -> None: ...
+def backfill_2d_inplace(
+    values: np.ndarray,  # numeric_object_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:, :]
+    limit=...,
+) -> None: ...
+def is_monotonic(
+    arr: np.ndarray,  # ndarray[numeric_object_t, ndim=1]
+    timelike: bool,
+) -> tuple[bool, bool, bool]: ...
+
+# ----------------------------------------------------------------------
+# rank_1d, rank_2d
+# ----------------------------------------------------------------------
+
+def rank_1d(
+    values: np.ndarray,  # ndarray[numeric_object_t, ndim=1]
+    labels: np.ndarray | None = ...,  # const int64_t[:]=None
+    is_datetimelike: bool = ...,
+    ties_method=...,
+    ascending: bool = ...,
+    pct: bool = ...,
+    na_option=...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> np.ndarray: ...  # np.ndarray[float64_t, ndim=1]
+def rank_2d(
+    in_arr: np.ndarray,  # ndarray[numeric_object_t, ndim=2]
+    axis: int = ...,
+    is_datetimelike: bool = ...,
+    ties_method=...,
+    ascending: bool = ...,
+    na_option=...,
+    pct: bool = ...,
+) -> np.ndarray: ...  # np.ndarray[float64_t, ndim=1]
+def diff_2d(
+    arr: np.ndarray,  # ndarray[diff_t, ndim=2]
+    out: np.ndarray,  # ndarray[out_t, ndim=2]
+    periods: int,
+    axis: int,
+    datetimelike: bool = ...,
+) -> None: ...
+def ensure_platform_int(arr: object) -> npt.NDArray[np.intp]: ...
+def ensure_object(arr: object) -> npt.NDArray[np.object_]: ...
+def ensure_float64(arr: object) -> npt.NDArray[np.float64]: ...
+def ensure_int8(arr: object) -> npt.NDArray[np.int8]: ...
+def ensure_int16(arr: object) -> npt.NDArray[np.int16]: ...
+def ensure_int32(arr: object) -> npt.NDArray[np.int32]: ...
+def ensure_int64(arr: object) -> npt.NDArray[np.int64]: ...
+def ensure_uint64(arr: object) -> npt.NDArray[np.uint64]: ...
+def take_1d_int8_int8(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int8_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int8_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int8_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_int16(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int16_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int32_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int32_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int64_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_int64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_float32_float32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_float32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_float64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_object_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_bool_bool(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_1d_bool_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_int8(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int8_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_int16(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int16_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int32_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int32_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int64_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_int64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_float32_float32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_float32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_float64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_object_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_bool_bool(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis0_bool_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_int8(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int8_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_int16(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int16_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int32_int32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int32_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int64_int64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_int64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_float32_float32(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_float32_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_float64_float64(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_object_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_bool_bool(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_axis1_bool_object(
+    values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=...
+) -> None: ...
+def take_2d_multi_int8_int8(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int8_int32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int8_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int8_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_int16(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_int32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int16_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int32_int32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int32_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int32_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int64_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_float32_float32(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_float32_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_float64_float64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_object_object(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_bool_bool(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_bool_object(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
+def take_2d_multi_int64_int64(
+    values: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value=...,
+) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/arrays.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/arrays.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..86f69c3cdfc75f3101a8e0afe59f9b102d90ad79
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/arrays.pyi
@@ -0,0 +1,40 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    AxisInt,
+    DtypeObj,
+    Self,
+    Shape,
+)
+
+class NDArrayBacked:
+    _dtype: DtypeObj
+    _ndarray: np.ndarray
+    def __init__(self, values: np.ndarray, dtype: DtypeObj) -> None: ...
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, dtype: DtypeObj): ...
+    def _from_backing_data(self, values: np.ndarray): ...
+    def __setstate__(self, state): ...
+    def __len__(self) -> int: ...
+    @property
+    def shape(self) -> Shape: ...
+    @property
+    def ndim(self) -> int: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def nbytes(self) -> int: ...
+    def copy(self, order=...): ...
+    def delete(self, loc, axis=...): ...
+    def swapaxes(self, axis1, axis2): ...
+    def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...
+    def reshape(self, *args, **kwargs): ...
+    def ravel(self, order=...): ...
+    @property
+    def T(self): ...
+    @classmethod
+    def _concat_same_type(
+        cls, to_concat: Sequence[Self], axis: AxisInt = ...
+    ) -> Self: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..1e2d684397cfc5b34dd293d51bc2412ee74890e6
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/byteswap.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/byteswap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bb0dbfc6a50b1bb7cd509dc5b3dfeed55ad70b09
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/byteswap.pyi
@@ -0,0 +1,5 @@
+def read_float_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ...
+def read_double_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ...
+def read_uint16_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
+def read_uint32_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
+def read_uint64_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/groupby.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/groupby.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eebd9331ab393d832c8c0099084a95a6aed0c460
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/groupby.pyi
@@ -0,0 +1,217 @@
+from typing import Literal
+
+import numpy as np
+
+from pandas._typing import npt
+
+def group_median_float64(
+    out: np.ndarray,  # ndarray[float64_t, ndim=2]
+    counts: npt.NDArray[np.int64],
+    values: np.ndarray,  # ndarray[float64_t, ndim=2]
+    labels: npt.NDArray[np.int64],
+    min_count: int = ...,  # Py_ssize_t
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cumprod(
+    out: np.ndarray,  # float64_t[:, ::1]
+    values: np.ndarray,  # const float64_t[:, :]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cumsum(
+    out: np.ndarray,  # int64float_t[:, ::1]
+    values: np.ndarray,  # ndarray[int64float_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_shift_indexer(
+    out: np.ndarray,  # int64_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    periods: int,
+) -> None: ...
+def group_fillna_indexer(
+    out: np.ndarray,  # ndarray[intp_t]
+    labels: np.ndarray,  # ndarray[int64_t]
+    sorted_labels: npt.NDArray[np.intp],
+    mask: npt.NDArray[np.uint8],
+    limit: int,  # int64_t
+    dropna: bool,
+) -> None: ...
+def group_any_all(
+    out: np.ndarray,  # uint8_t[::1]
+    values: np.ndarray,  # const uint8_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: np.ndarray,  # const uint8_t[::1]
+    val_test: Literal["any", "all"],
+    skipna: bool,
+    result_mask: np.ndarray | None,
+) -> None: ...
+def group_sum(
+    out: np.ndarray,  # complexfloatingintuint_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[complexfloatingintuint_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    mask: np.ndarray | None,
+    result_mask: np.ndarray | None = ...,
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    initial: object = ...,
+) -> None: ...
+def group_prod(
+    out: np.ndarray,  # int64float_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[int64float_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    mask: np.ndarray | None,
+    result_mask: np.ndarray | None = ...,
+    min_count: int = ...,
+) -> None: ...
+def group_var(
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    ddof: int = ...,  # int64_t
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    is_datetimelike: bool = ...,
+    name: str = ...,
+) -> None: ...
+def group_skew(
+    out: np.ndarray,  # float64_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[float64_T, ndim=2]
+    labels: np.ndarray,  # const intp_t[::1]
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_mean(
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    is_datetimelike: bool = ...,  # bint
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_ohlc(
+    out: np.ndarray,  # floatingintuint_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floatingintuint_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_quantile(
+    out: npt.NDArray[np.float64],
+    values: np.ndarray,  # ndarray[numeric, ndim=1]
+    labels: npt.NDArray[np.intp],
+    mask: npt.NDArray[np.uint8],
+    qs: npt.NDArray[np.float64],  # const
+    starts: npt.NDArray[np.int64],
+    ends: npt.NDArray[np.int64],
+    interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+    result_mask: np.ndarray | None,
+    is_datetimelike: bool,
+) -> None: ...
+def group_last(
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: npt.NDArray[np.bool_] | None,
+    result_mask: npt.NDArray[np.bool_] | None = ...,
+    min_count: int = ...,  # Py_ssize_t
+    is_datetimelike: bool = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_nth(
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: npt.NDArray[np.bool_] | None,
+    result_mask: npt.NDArray[np.bool_] | None = ...,
+    min_count: int = ...,  # int64_t
+    rank: int = ...,  # int64_t
+    is_datetimelike: bool = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_rank(
+    out: np.ndarray,  # float64_t[:, ::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    ties_method: Literal["average", "min", "max", "first", "dense"] = ...,
+    ascending: bool = ...,
+    pct: bool = ...,
+    na_option: Literal["keep", "top", "bottom"] = ...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> None: ...
+def group_max(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_min(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_idxmin_idxmax(
+    out: npt.NDArray[np.intp],
+    counts: npt.NDArray[np.int64],
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: npt.NDArray[np.intp],
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    name: str = ...,
+    skipna: bool = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cummin(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_cummax(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/hashing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/hashing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8361026e4a87d462e04c53f7f1f8aee8a7f6ffe0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/hashing.pyi
@@ -0,0 +1,9 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def hash_object_array(
+    arr: npt.NDArray[np.object_],
+    key: str,
+    encoding: str = ...,
+) -> npt.NDArray[np.uint64]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/hashtable.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/hashtable.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..555ec73acd9b2e9ccbb5ea145db77862e2c54dbf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/hashtable.pyi
@@ -0,0 +1,252 @@
+from typing import (
+    Any,
+    Hashable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import npt
+
+def unique_label_indices(
+    labels: np.ndarray,  # const int64_t[:]
+) -> np.ndarray: ...
+
+class Factorizer:
+    count: int
+    uniques: Any
+    def __init__(self, size_hint: int) -> None: ...
+    def get_count(self) -> int: ...
+    def factorize(
+        self,
+        values: np.ndarray,
+        na_sentinel=...,
+        na_value=...,
+        mask=...,
+    ) -> npt.NDArray[np.intp]: ...
+
+class ObjectFactorizer(Factorizer):
+    table: PyObjectHashTable
+    uniques: ObjectVector
+
+class Int64Factorizer(Factorizer):
+    table: Int64HashTable
+    uniques: Int64Vector
+
+class UInt64Factorizer(Factorizer):
+    table: UInt64HashTable
+    uniques: UInt64Vector
+
+class Int32Factorizer(Factorizer):
+    table: Int32HashTable
+    uniques: Int32Vector
+
+class UInt32Factorizer(Factorizer):
+    table: UInt32HashTable
+    uniques: UInt32Vector
+
+class Int16Factorizer(Factorizer):
+    table: Int16HashTable
+    uniques: Int16Vector
+
+class UInt16Factorizer(Factorizer):
+    table: UInt16HashTable
+    uniques: UInt16Vector
+
+class Int8Factorizer(Factorizer):
+    table: Int8HashTable
+    uniques: Int8Vector
+
+class UInt8Factorizer(Factorizer):
+    table: UInt8HashTable
+    uniques: UInt8Vector
+
+class Float64Factorizer(Factorizer):
+    table: Float64HashTable
+    uniques: Float64Vector
+
+class Float32Factorizer(Factorizer):
+    table: Float32HashTable
+    uniques: Float32Vector
+
+class Complex64Factorizer(Factorizer):
+    table: Complex64HashTable
+    uniques: Complex64Vector
+
+class Complex128Factorizer(Factorizer):
+    table: Complex128HashTable
+    uniques: Complex128Vector
+
+class Int64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int64]: ...
+
+class Int32Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int32]: ...
+
+class Int16Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int16]: ...
+
+class Int8Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.int8]: ...
+
+class UInt64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint64]: ...
+
+class UInt32Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint32]: ...
+
+class UInt16Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint16]: ...
+
+class UInt8Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.uint8]: ...
+
+class Float64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.float64]: ...
+
+class Float32Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.float32]: ...
+
+class Complex128Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.complex128]: ...
+
+class Complex64Vector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.complex64]: ...
+
+class StringVector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.object_]: ...
+
+class ObjectVector:
+    def __init__(self, *args) -> None: ...
+    def __len__(self) -> int: ...
+    def to_array(self) -> npt.NDArray[np.object_]: ...
+
+class HashTable:
+    # NB: The base HashTable class does _not_ actually have these methods;
+    #  we are putting them here for the sake of mypy to avoid
+    #  reproducing them in each subclass below.
+    def __init__(self, size_hint: int = ..., uses_mask: bool = ...) -> None: ...
+    def __len__(self) -> int: ...
+    def __contains__(self, key: Hashable) -> bool: ...
+    def sizeof(self, deep: bool = ...) -> int: ...
+    def get_state(self) -> dict[str, int]: ...
+    # TODO: `val/key` type is subclass-specific
+    def get_item(self, val): ...  # TODO: return type?
+    def set_item(self, key, val) -> None: ...
+    def get_na(self): ...  # TODO: return type?
+    def set_na(self, val) -> None: ...
+    def map_locations(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        mask: npt.NDArray[np.bool_] | None = ...,
+    ) -> None: ...
+    def lookup(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        mask: npt.NDArray[np.bool_] | None = ...,
+    ) -> npt.NDArray[np.intp]: ...
+    def get_labels(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        uniques,  # SubclassTypeVector
+        count_prior: int = ...,
+        na_sentinel: int = ...,
+        na_value: object = ...,
+        mask=...,
+    ) -> npt.NDArray[np.intp]: ...
+    def unique(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        return_inverse: bool = ...,
+        mask=...,
+    ) -> (
+        tuple[
+            np.ndarray,  # np.ndarray[subclass-specific]
+            npt.NDArray[np.intp],
+        ]
+        | np.ndarray
+    ): ...  # np.ndarray[subclass-specific]
+    def factorize(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        na_sentinel: int = ...,
+        na_value: object = ...,
+        mask=...,
+        ignore_na: bool = True,
+    ) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ...  # np.ndarray[subclass-specific]
+
+class Complex128HashTable(HashTable): ...
+class Complex64HashTable(HashTable): ...
+class Float64HashTable(HashTable): ...
+class Float32HashTable(HashTable): ...
+
+class Int64HashTable(HashTable):
+    # Only Int64HashTable has get_labels_groupby, map_keys_to_values
+    def get_labels_groupby(
+        self,
+        values: npt.NDArray[np.int64],  # const int64_t[:]
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]: ...
+    def map_keys_to_values(
+        self,
+        keys: npt.NDArray[np.int64],
+        values: npt.NDArray[np.int64],  # const int64_t[:]
+    ) -> None: ...
+
+class Int32HashTable(HashTable): ...
+class Int16HashTable(HashTable): ...
+class Int8HashTable(HashTable): ...
+class UInt64HashTable(HashTable): ...
+class UInt32HashTable(HashTable): ...
+class UInt16HashTable(HashTable): ...
+class UInt8HashTable(HashTable): ...
+class StringHashTable(HashTable): ...
+class PyObjectHashTable(HashTable): ...
+class IntpHashTable(HashTable): ...
+
+def duplicated(
+    values: np.ndarray,
+    keep: Literal["last", "first", False] = ...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> npt.NDArray[np.bool_]: ...
+def mode(
+    values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = ...
+) -> np.ndarray: ...
+def value_count(
+    values: np.ndarray,
+    dropna: bool,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> tuple[np.ndarray, npt.NDArray[np.int64], int]: ...  # np.ndarray[same-as-values]
+
+# arr and values should have same dtype
+def ismember(
+    arr: np.ndarray,
+    values: np.ndarray,
+) -> npt.NDArray[np.bool_]: ...
+def object_hash(obj) -> int: ...
+def objects_are_equal(a, b) -> bool: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/index.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/index.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9c3791a642768ccfb13f681a31ac993c606c2304
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/index.pyi
@@ -0,0 +1,103 @@
+import numpy as np
+
+from pandas._typing import npt
+
+from pandas import MultiIndex
+from pandas.core.arrays import ExtensionArray
+
+multiindex_nulls_shift: int
+
+class IndexEngine:
+    over_size_threshold: bool
+    def __init__(self, values: np.ndarray) -> None: ...
+    def __contains__(self, val: object) -> bool: ...
+
+    # -> int | slice | np.ndarray[bool]
+    def get_loc(self, val: object) -> int | slice | np.ndarray: ...
+    def sizeof(self, deep: bool = ...) -> int: ...
+    def __sizeof__(self) -> int: ...
+    @property
+    def is_unique(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    @property
+    def is_monotonic_decreasing(self) -> bool: ...
+    @property
+    def is_mapping_populated(self) -> bool: ...
+    def clear_mapping(self): ...
+    def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ...
+    def get_indexer_non_unique(
+        self,
+        targets: np.ndarray,
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+
+class MaskedIndexEngine(IndexEngine):
+    def __init__(self, values: object) -> None: ...
+    def get_indexer_non_unique(
+        self, targets: object
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+
+class Float64Engine(IndexEngine): ...
+class Float32Engine(IndexEngine): ...
+class Complex128Engine(IndexEngine): ...
+class Complex64Engine(IndexEngine): ...
+class Int64Engine(IndexEngine): ...
+class Int32Engine(IndexEngine): ...
+class Int16Engine(IndexEngine): ...
+class Int8Engine(IndexEngine): ...
+class UInt64Engine(IndexEngine): ...
+class UInt32Engine(IndexEngine): ...
+class UInt16Engine(IndexEngine): ...
+class UInt8Engine(IndexEngine): ...
+class ObjectEngine(IndexEngine): ...
+class DatetimeEngine(Int64Engine): ...
+class TimedeltaEngine(DatetimeEngine): ...
+class PeriodEngine(Int64Engine): ...
+class BoolEngine(UInt8Engine): ...
+class MaskedFloat64Engine(MaskedIndexEngine): ...
+class MaskedFloat32Engine(MaskedIndexEngine): ...
+class MaskedComplex128Engine(MaskedIndexEngine): ...
+class MaskedComplex64Engine(MaskedIndexEngine): ...
+class MaskedInt64Engine(MaskedIndexEngine): ...
+class MaskedInt32Engine(MaskedIndexEngine): ...
+class MaskedInt16Engine(MaskedIndexEngine): ...
+class MaskedInt8Engine(MaskedIndexEngine): ...
+class MaskedUInt64Engine(MaskedIndexEngine): ...
+class MaskedUInt32Engine(MaskedIndexEngine): ...
+class MaskedUInt16Engine(MaskedIndexEngine): ...
+class MaskedUInt8Engine(MaskedIndexEngine): ...
+class MaskedBoolEngine(MaskedUInt8Engine): ...
+
+class StringObjectEngine(ObjectEngine):
+    def __init__(self, values: object, na_value) -> None: ...
+
+class BaseMultiIndexCodesEngine:
+    levels: list[np.ndarray]
+    offsets: np.ndarray  # ndarray[uint64_t, ndim=1]
+
+    def __init__(
+        self,
+        levels: list[np.ndarray],  # all entries hashable
+        labels: list[np.ndarray],  # all entries integer-dtyped
+        offsets: np.ndarray,  # np.ndarray[np.uint64, ndim=1]
+    ) -> None: ...
+    def get_indexer(self, target: npt.NDArray[np.object_]) -> npt.NDArray[np.intp]: ...
+    def _extract_level_codes(self, target: MultiIndex) -> np.ndarray: ...
+
+class ExtensionEngine:
+    def __init__(self, values: ExtensionArray) -> None: ...
+    def __contains__(self, val: object) -> bool: ...
+    def get_loc(self, val: object) -> int | slice | np.ndarray: ...
+    def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ...
+    def get_indexer_non_unique(
+        self,
+        targets: np.ndarray,
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+    @property
+    def is_unique(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    @property
+    def is_monotonic_decreasing(self) -> bool: ...
+    def sizeof(self, deep: bool = ...) -> int: ...
+    def clear_mapping(self): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..341562b045543017dc4668fc61988e7ab60bb00a
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/indexing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/indexing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3ae5c5044a2f75452fa57ba578af2c7b4c78ec96
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/indexing.pyi
@@ -0,0 +1,17 @@
+from typing import (
+    Generic,
+    TypeVar,
+)
+
+from pandas.core.indexing import IndexingMixin
+
+_IndexingMixinT = TypeVar("_IndexingMixinT", bound=IndexingMixin)
+
+class NDFrameIndexerBase(Generic[_IndexingMixinT]):
+    name: str
+    # in practice obj is either a DataFrame or a Series
+    obj: _IndexingMixinT
+
+    def __init__(self, name: str, obj: _IndexingMixinT) -> None: ...
+    @property
+    def ndim(self) -> int: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/internals.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/internals.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ffe6c7730bcdcfb8af1407bf55b59170c4093699
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/internals.pyi
@@ -0,0 +1,94 @@
+from typing import (
+    Iterator,
+    Sequence,
+    final,
+    overload,
+)
+import weakref
+
+import numpy as np
+
+from pandas._typing import (
+    ArrayLike,
+    Self,
+    npt,
+)
+
+from pandas import Index
+from pandas.core.internals.blocks import Block as B
+
+def slice_len(slc: slice, objlen: int = ...) -> int: ...
+def get_concat_blkno_indexers(
+    blknos_list: list[npt.NDArray[np.intp]],
+) -> list[tuple[npt.NDArray[np.intp], BlockPlacement]]: ...
+def get_blkno_indexers(
+    blknos: np.ndarray,  # int64_t[:]
+    group: bool = ...,
+) -> list[tuple[int, slice | np.ndarray]]: ...
+def get_blkno_placements(
+    blknos: np.ndarray,
+    group: bool = ...,
+) -> Iterator[tuple[int, BlockPlacement]]: ...
+def update_blklocs_and_blknos(
+    blklocs: npt.NDArray[np.intp],
+    blknos: npt.NDArray[np.intp],
+    loc: int,
+    nblocks: int,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+@final
+class BlockPlacement:
+    def __init__(self, val: int | slice | np.ndarray) -> None: ...
+    @property
+    def indexer(self) -> np.ndarray | slice: ...
+    @property
+    def as_array(self) -> np.ndarray: ...
+    @property
+    def as_slice(self) -> slice: ...
+    @property
+    def is_slice_like(self) -> bool: ...
+    @overload
+    def __getitem__(
+        self, loc: slice | Sequence[int] | npt.NDArray[np.intp]
+    ) -> BlockPlacement: ...
+    @overload
+    def __getitem__(self, loc: int) -> int: ...
+    def __iter__(self) -> Iterator[int]: ...
+    def __len__(self) -> int: ...
+    def delete(self, loc) -> BlockPlacement: ...
+    def add(self, other) -> BlockPlacement: ...
+    def append(self, others: list[BlockPlacement]) -> BlockPlacement: ...
+    def tile_for_unstack(self, factor: int) -> npt.NDArray[np.intp]: ...
+
+class Block:
+    _mgr_locs: BlockPlacement
+    ndim: int
+    values: ArrayLike
+    refs: BlockValuesRefs
+    def __init__(
+        self,
+        values: ArrayLike,
+        placement: BlockPlacement,
+        ndim: int,
+        refs: BlockValuesRefs | None = ...,
+    ) -> None: ...
+    def slice_block_rows(self, slicer: slice) -> Self: ...
+
+class BlockManager:
+    blocks: tuple[B, ...]
+    axes: list[Index]
+    _known_consolidated: bool
+    _is_consolidated: bool
+    _blknos: np.ndarray
+    _blklocs: np.ndarray
+    def __init__(
+        self, blocks: tuple[B, ...], axes: list[Index], verify_integrity=...
+    ) -> None: ...
+    def get_slice(self, slobj: slice, axis: int = ...) -> Self: ...
+    def _rebuild_blknos_and_blklocs(self) -> None: ...
+
+class BlockValuesRefs:
+    referenced_blocks: list[weakref.ref]
+    def __init__(self, blk: Block | None = ...) -> None: ...
+    def add_reference(self, blk: Block) -> None: ...
+    def add_index_reference(self, index: Index) -> None: ...
+    def has_reference(self) -> bool: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/interval.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/interval.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..587fdf84f2f85520713352bbcab29804c95621e5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/interval.pyi
@@ -0,0 +1,174 @@
+from typing import (
+    Any,
+    Generic,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+import numpy.typing as npt
+
+from pandas._typing import (
+    IntervalClosedType,
+    Timedelta,
+    Timestamp,
+)
+
+VALID_CLOSED: frozenset[str]
+
+_OrderableScalarT = TypeVar("_OrderableScalarT", int, float)
+_OrderableTimesT = TypeVar("_OrderableTimesT", Timestamp, Timedelta)
+_OrderableT = TypeVar("_OrderableT", int, float, Timestamp, Timedelta)
+
+class _LengthDescriptor:
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableScalarT], owner: Any
+    ) -> _OrderableScalarT: ...
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableTimesT], owner: Any
+    ) -> Timedelta: ...
+
+class _MidDescriptor:
+    @overload
+    def __get__(self, instance: Interval[_OrderableScalarT], owner: Any) -> float: ...
+    @overload
+    def __get__(
+        self, instance: Interval[_OrderableTimesT], owner: Any
+    ) -> _OrderableTimesT: ...
+
+class IntervalMixin:
+    @property
+    def closed_left(self) -> bool: ...
+    @property
+    def closed_right(self) -> bool: ...
+    @property
+    def open_left(self) -> bool: ...
+    @property
+    def open_right(self) -> bool: ...
+    @property
+    def is_empty(self) -> bool: ...
+    def _check_closed_matches(self, other: IntervalMixin, name: str = ...) -> None: ...
+
+class Interval(IntervalMixin, Generic[_OrderableT]):
+    @property
+    def left(self: Interval[_OrderableT]) -> _OrderableT: ...
+    @property
+    def right(self: Interval[_OrderableT]) -> _OrderableT: ...
+    @property
+    def closed(self) -> IntervalClosedType: ...
+    mid: _MidDescriptor
+    length: _LengthDescriptor
+    def __init__(
+        self,
+        left: _OrderableT,
+        right: _OrderableT,
+        closed: IntervalClosedType = ...,
+    ) -> None: ...
+    def __hash__(self) -> int: ...
+    @overload
+    def __contains__(
+        self: Interval[Timedelta], key: Timedelta | Interval[Timedelta]
+    ) -> bool: ...
+    @overload
+    def __contains__(
+        self: Interval[Timestamp], key: Timestamp | Interval[Timestamp]
+    ) -> bool: ...
+    @overload
+    def __contains__(
+        self: Interval[_OrderableScalarT],
+        key: _OrderableScalarT | Interval[_OrderableScalarT],
+    ) -> bool: ...
+    @overload
+    def __add__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __add__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __add__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __radd__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __radd__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __radd__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __sub__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __sub__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __sub__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __rsub__(
+        self: Interval[_OrderableTimesT], y: Timedelta
+    ) -> Interval[_OrderableTimesT]: ...
+    @overload
+    def __rsub__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __rsub__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __mul__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __mul__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __rmul__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __rmul__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __truediv__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __truediv__(self: Interval[float], y: float) -> Interval[float]: ...
+    @overload
+    def __floordiv__(
+        self: Interval[int], y: _OrderableScalarT
+    ) -> Interval[_OrderableScalarT]: ...
+    @overload
+    def __floordiv__(self: Interval[float], y: float) -> Interval[float]: ...
+    def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ...
+
+def intervals_to_interval_bounds(
+    intervals: np.ndarray, validate_closed: bool = ...
+) -> tuple[np.ndarray, np.ndarray, IntervalClosedType]: ...
+
+class IntervalTree(IntervalMixin):
+    def __init__(
+        self,
+        left: np.ndarray,
+        right: np.ndarray,
+        closed: IntervalClosedType = ...,
+        leaf_size: int = ...,
+    ) -> None: ...
+    @property
+    def mid(self) -> np.ndarray: ...
+    @property
+    def length(self) -> np.ndarray: ...
+    def get_indexer(self, target) -> npt.NDArray[np.intp]: ...
+    def get_indexer_non_unique(
+        self, target
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+    _na_count: int
+    @property
+    def is_overlapping(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    def clear_mapping(self) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/join.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/join.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1d4e8c90bc5593eae650319e9ca1b58cbd7eed73
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/join.pyi
@@ -0,0 +1,79 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def inner_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+    sort: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def left_outer_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+    sort: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def full_outer_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def ffill_indexer(
+    indexer: np.ndarray,  # const intp_t[:]
+) -> npt.NDArray[np.intp]: ...
+def left_join_indexer_unique(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> npt.NDArray[np.intp]: ...
+def left_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def inner_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def outer_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def asof_join_backward_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def asof_join_forward_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def asof_join_nearest_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..8e23e2241f455840d8b4d4015d7cbdddd410122d
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/json.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/json.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bc4fe68573b94290cfba2e66950c1b6d45ccf0dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/json.pyi
@@ -0,0 +1,23 @@
+from typing import (
+    Any,
+    Callable,
+)
+
+def ujson_dumps(
+    obj: Any,
+    ensure_ascii: bool = ...,
+    double_precision: int = ...,
+    indent: int = ...,
+    orient: str = ...,
+    date_unit: str = ...,
+    iso_dates: bool = ...,
+    default_handler: None
+    | Callable[[Any], str | float | bool | list | dict | None] = ...,
+) -> str: ...
+def ujson_loads(
+    s: str,
+    precise_float: bool = ...,
+    numpy: bool = ...,
+    dtype: None = ...,
+    labelled: bool = ...,
+) -> Any: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/lib.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/lib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..71a4d3ae2575ffd646568080485d9afada47d599
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/lib.pyi
@@ -0,0 +1,216 @@
+# TODO(npdtypes): Many types specified here can be made more specific/accurate;
+#  the more specific versions are specified in comments
+from decimal import Decimal
+from typing import (
+    Any,
+    Callable,
+    Final,
+    Generator,
+    Hashable,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.interval import Interval
+from pandas._libs.tslibs import Period
+from pandas._typing import (
+    ArrayLike,
+    DtypeObj,
+    TypeGuard,
+    npt,
+)
+
+# placeholder until we can specify np.ndarray[object, ndim=2]
+ndarray_obj_2d = np.ndarray
+
+from enum import Enum
+
+class _NoDefault(Enum):
+    no_default = ...
+
+no_default: Final = _NoDefault.no_default
+NoDefault: TypeAlias = Literal[_NoDefault.no_default]
+
+i8max: int
+u8max: int
+
+def is_np_dtype(dtype: object, kinds: str | None = ...) -> TypeGuard[np.dtype]: ...
+def item_from_zerodim(val: object) -> object: ...
+def infer_dtype(value: object, skipna: bool = ...) -> str: ...
+def is_iterator(obj: object) -> bool: ...
+def is_scalar(val: object) -> bool: ...
+def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
+def is_pyarrow_array(obj: object) -> bool: ...
+def is_period(val: object) -> TypeGuard[Period]: ...
+def is_interval(obj: object) -> TypeGuard[Interval]: ...
+def is_decimal(obj: object) -> TypeGuard[Decimal]: ...
+def is_complex(obj: object) -> TypeGuard[complex]: ...
+def is_bool(obj: object) -> TypeGuard[bool | np.bool_]: ...
+def is_integer(obj: object) -> TypeGuard[int | np.integer]: ...
+def is_int_or_none(obj) -> bool: ...
+def is_float(obj: object) -> TypeGuard[float]: ...
+def is_interval_array(values: np.ndarray) -> bool: ...
+def is_datetime64_array(values: np.ndarray, skipna: bool = True) -> bool: ...
+def is_timedelta_or_timedelta64_array(
+    values: np.ndarray, skipna: bool = True
+) -> bool: ...
+def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ...
+def is_time_array(values: np.ndarray, skipna: bool = ...): ...
+def is_date_array(values: np.ndarray, skipna: bool = ...): ...
+def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...
+def is_string_array(values: np.ndarray, skipna: bool = ...): ...
+def is_float_array(values: np.ndarray): ...
+def is_integer_array(values: np.ndarray, skipna: bool = ...): ...
+def is_bool_array(values: np.ndarray, skipna: bool = ...): ...
+def fast_multiget(
+    mapping: dict,
+    keys: np.ndarray,  # object[:]
+    default=...,
+) -> np.ndarray: ...
+def fast_unique_multiple_list_gen(gen: Generator, sort: bool = ...) -> list: ...
+def fast_unique_multiple_list(lists: list, sort: bool | None = ...) -> list: ...
+def map_infer(
+    arr: np.ndarray,
+    f: Callable[[Any], Any],
+    convert: bool = ...,
+    ignore_na: bool = ...,
+) -> np.ndarray: ...
+@overload
+def maybe_convert_objects(
+    objects: npt.NDArray[np.object_],
+    *,
+    try_float: bool = ...,
+    safe: bool = ...,
+    convert_numeric: bool = ...,
+    convert_non_numeric: Literal[False] = ...,
+    convert_string: Literal[False] = ...,
+    convert_to_nullable_dtype: Literal[False] = ...,
+    dtype_if_all_nat: DtypeObj | None = ...,
+) -> npt.NDArray[np.object_ | np.number]: ...
+@overload
+def maybe_convert_objects(
+    objects: npt.NDArray[np.object_],
+    *,
+    try_float: bool = ...,
+    safe: bool = ...,
+    convert_numeric: bool = ...,
+    convert_non_numeric: bool = ...,
+    convert_string: bool = ...,
+    convert_to_nullable_dtype: Literal[True] = ...,
+    dtype_if_all_nat: DtypeObj | None = ...,
+) -> ArrayLike: ...
+@overload
+def maybe_convert_objects(
+    objects: npt.NDArray[np.object_],
+    *,
+    try_float: bool = ...,
+    safe: bool = ...,
+    convert_numeric: bool = ...,
+    convert_non_numeric: bool = ...,
+    convert_string: bool = ...,
+    convert_to_nullable_dtype: bool = ...,
+    dtype_if_all_nat: DtypeObj | None = ...,
+) -> ArrayLike: ...
+@overload
+def maybe_convert_numeric(
+    values: npt.NDArray[np.object_],
+    na_values: set,
+    convert_empty: bool = ...,
+    coerce_numeric: bool = ...,
+    convert_to_masked_nullable: Literal[False] = ...,
+) -> tuple[np.ndarray, None]: ...
+@overload
+def maybe_convert_numeric(
+    values: npt.NDArray[np.object_],
+    na_values: set,
+    convert_empty: bool = ...,
+    coerce_numeric: bool = ...,
+    *,
+    convert_to_masked_nullable: Literal[True],
+) -> tuple[np.ndarray, np.ndarray]: ...
+
+# TODO: restrict `arr`?
+def ensure_string_array(
+    arr,
+    na_value: object = ...,
+    convert_na_value: bool = ...,
+    copy: bool = ...,
+    skipna: bool = ...,
+) -> npt.NDArray[np.object_]: ...
+def convert_nans_to_NA(
+    arr: npt.NDArray[np.object_],
+) -> npt.NDArray[np.object_]: ...
+def fast_zip(ndarrays: list) -> npt.NDArray[np.object_]: ...
+
+# TODO: can we be more specific about rows?
+def to_object_array_tuples(rows: object) -> ndarray_obj_2d: ...
+def tuples_to_object_array(
+    tuples: npt.NDArray[np.object_],
+) -> ndarray_obj_2d: ...
+
+# TODO: can we be more specific about rows?
+def to_object_array(rows: object, min_width: int = ...) -> ndarray_obj_2d: ...
+def dicts_to_array(dicts: list, columns: list) -> ndarray_obj_2d: ...
+def maybe_booleans_to_slice(
+    mask: npt.NDArray[np.uint8],
+) -> slice | npt.NDArray[np.uint8]: ...
+def maybe_indices_to_slice(
+    indices: npt.NDArray[np.intp],
+    max_len: int,
+) -> slice | npt.NDArray[np.intp]: ...
+def is_all_arraylike(obj: list) -> bool: ...
+
+# -----------------------------------------------------------------
+# Functions which in reality take memoryviews
+
+def memory_usage_of_objects(arr: np.ndarray) -> int: ...  # object[:]  # np.int64
+def map_infer_mask(
+    arr: np.ndarray,
+    f: Callable[[Any], Any],
+    mask: np.ndarray,  # const uint8_t[:]
+    convert: bool = ...,
+    na_value: Any = ...,
+    dtype: np.dtype = ...,
+) -> np.ndarray: ...
+def indices_fast(
+    index: npt.NDArray[np.intp],
+    labels: np.ndarray,  # const int64_t[:]
+    keys: list,
+    sorted_labels: list[npt.NDArray[np.int64]],
+) -> dict[Hashable, npt.NDArray[np.intp]]: ...
+def generate_slices(
+    labels: np.ndarray, ngroups: int  # const intp_t[:]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...
+def count_level_2d(
+    mask: np.ndarray,  # ndarray[uint8_t, ndim=2, cast=True],
+    labels: np.ndarray,  # const intp_t[:]
+    max_bin: int,
+) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=2]
+def get_level_sorter(
+    codes: np.ndarray,  # const int64_t[:]
+    starts: np.ndarray,  # const intp_t[:]
+) -> np.ndarray: ...  # np.ndarray[np.intp, ndim=1]
+def generate_bins_dt64(
+    values: npt.NDArray[np.int64],
+    binner: np.ndarray,  # const int64_t[:]
+    closed: object = ...,
+    hasnans: bool = ...,
+) -> np.ndarray: ...  # np.ndarray[np.int64, ndim=1]
+def array_equivalent_object(
+    left: npt.NDArray[np.object_],
+    right: npt.NDArray[np.object_],
+) -> bool: ...
+def has_infs(arr: np.ndarray) -> bool: ...  # const floating[:]
+def has_only_ints_or_nan(arr: np.ndarray) -> bool: ...  # const floating[:]
+def get_reverse_indexer(
+    indexer: np.ndarray,  # const intp_t[:]
+    length: int,
+) -> npt.NDArray[np.intp]: ...
+def is_bool_list(obj: list) -> bool: ...
+def dtypes_all_equal(types: list[DtypeObj]) -> bool: ...
+def is_range_indexer(
+    left: np.ndarray, n: int  # np.ndarray[np.int64, ndim=1]
+) -> bool: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/missing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/missing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..282dcee3ed6cfdd5cd83e5c78a69c422831b4cac
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/missing.pyi
@@ -0,0 +1,16 @@
+import numpy as np
+from numpy import typing as npt
+
+class NAType:
+    def __new__(cls, *args, **kwargs): ...
+
+NA: NAType
+
+def is_matching_na(
+    left: object, right: object, nan_matches_none: bool = ...
+) -> bool: ...
+def isposinf_scalar(val: object) -> bool: ...
+def isneginf_scalar(val: object) -> bool: ...
+def checknull(val: object, inf_as_na: bool = ...) -> bool: ...
+def isnaobj(arr: np.ndarray, inf_as_na: bool = ...) -> npt.NDArray[np.bool_]: ...
+def is_numeric_na(values: np.ndarray) -> npt.NDArray[np.bool_]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6738a1dff4a9eb272f3e6c88a31b0bd7386aebd2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops.pyi
@@ -0,0 +1,51 @@
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._typing import npt
+
+_BinOp: TypeAlias = Callable[[Any, Any], Any]
+_BoolOp: TypeAlias = Callable[[Any, Any], bool]
+
+def scalar_compare(
+    values: np.ndarray,  # object[:]
+    val: object,
+    op: _BoolOp,  # {operator.eq, operator.ne, ...}
+) -> npt.NDArray[np.bool_]: ...
+def vec_compare(
+    left: npt.NDArray[np.object_],
+    right: npt.NDArray[np.object_],
+    op: _BoolOp,  # {operator.eq, operator.ne, ...}
+) -> npt.NDArray[np.bool_]: ...
+def scalar_binop(
+    values: np.ndarray,  # object[:]
+    val: object,
+    op: _BinOp,  # binary operator
+) -> np.ndarray: ...
+def vec_binop(
+    left: np.ndarray,  # object[:]
+    right: np.ndarray,  # object[:]
+    op: _BinOp,  # binary operator
+) -> np.ndarray: ...
+@overload
+def maybe_convert_bool(
+    arr: npt.NDArray[np.object_],
+    true_values: Iterable | None = None,
+    false_values: Iterable | None = None,
+    convert_to_masked_nullable: Literal[False] = ...,
+) -> tuple[np.ndarray, None]: ...
+@overload
+def maybe_convert_bool(
+    arr: npt.NDArray[np.object_],
+    true_values: Iterable = ...,
+    false_values: Iterable = ...,
+    *,
+    convert_to_masked_nullable: Literal[True],
+) -> tuple[np.ndarray, np.ndarray]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..0c0fba5ce2335a0cb6e3b8ffc5667f6f395f7449
Binary files /dev/null and b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so differ
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..91b5a4dbaaebc177191d3189f12e4e20d56ca0fa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi
@@ -0,0 +1,5 @@
+import numpy as np
+
+def maybe_dispatch_ufunc_to_dunder_op(
+    self, ufunc: np.ufunc, method: str, *inputs, **kwargs
+): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/parsers.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/parsers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..253bb7303cefb81f61692c8d7bd9812a191d9ac5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/parsers.pyi
@@ -0,0 +1,77 @@
+from typing import (
+    Hashable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    ArrayLike,
+    Dtype,
+    npt,
+)
+
+STR_NA_VALUES: set[str]
+DEFAULT_BUFFER_HEURISTIC: int
+
+def sanitize_objects(
+    values: npt.NDArray[np.object_],
+    na_values: set,
+) -> int: ...
+
+class TextReader:
+    unnamed_cols: set[str]
+    table_width: int  # int64_t
+    leading_cols: int  # int64_t
+    header: list[list[int]]  # non-negative integers
+    def __init__(
+        self,
+        source,
+        delimiter: bytes | str = ...,  # single-character only
+        header=...,
+        header_start: int = ...,  # int64_t
+        header_end: int = ...,  # uint64_t
+        index_col=...,
+        names=...,
+        tokenize_chunksize: int = ...,  # int64_t
+        delim_whitespace: bool = ...,
+        converters=...,
+        skipinitialspace: bool = ...,
+        escapechar: bytes | str | None = ...,  # single-character only
+        doublequote: bool = ...,
+        quotechar: str | bytes | None = ...,  # at most 1 character
+        quoting: int = ...,
+        lineterminator: bytes | str | None = ...,  # at most 1 character
+        comment=...,
+        decimal: bytes | str = ...,  # single-character only
+        thousands: bytes | str | None = ...,  # single-character only
+        dtype: Dtype | dict[Hashable, Dtype] = ...,
+        usecols=...,
+        error_bad_lines: bool = ...,
+        warn_bad_lines: bool = ...,
+        na_filter: bool = ...,
+        na_values=...,
+        na_fvalues=...,
+        keep_default_na: bool = ...,
+        true_values=...,
+        false_values=...,
+        allow_leading_cols: bool = ...,
+        skiprows=...,
+        skipfooter: int = ...,  # int64_t
+        verbose: bool = ...,
+        float_precision: Literal["round_trip", "legacy", "high"] | None = ...,
+        skip_blank_lines: bool = ...,
+        encoding_errors: bytes | str = ...,
+    ) -> None: ...
+    def set_noconvert(self, i: int) -> None: ...
+    def remove_noconvert(self, i: int) -> None: ...
+    def close(self) -> None: ...
+    def read(self, rows: int | None = ...) -> dict[int, ArrayLike]: ...
+    def read_low_memory(self, rows: int | None) -> list[dict[int, ArrayLike]]: ...
+
+# _maybe_upcast, na_values are only exposed for testing
+na_values: dict
+
+def _maybe_upcast(
+    arr, use_dtype_backend: bool = ..., dtype_backend: str = ...
+) -> np.ndarray: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..904c8f08799738e066b048afbd1782e41cb9437e
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/properties.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/properties.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..aaa44a0cf47bf8635727ea9f354227de72bbff29
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/properties.pyi
@@ -0,0 +1,27 @@
+from typing import (
+    Sequence,
+    overload,
+)
+
+from pandas._typing import (
+    AnyArrayLike,
+    DataFrame,
+    Index,
+    Series,
+)
+
+# note: this is a lie to make type checkers happy (they special
+# case property). cache_readonly uses attribute names similar to
+# property (fget) but it does not provide fset and fdel.
+cache_readonly = property
+
+class AxisProperty:
+    axis: int
+    def __init__(self, axis: int = ..., doc: str = ...) -> None: ...
+    @overload
+    def __get__(self, obj: DataFrame | Series, type) -> Index: ...
+    @overload
+    def __get__(self, obj: None, type) -> AxisProperty: ...
+    def __set__(
+        self, obj: DataFrame | Series, value: AnyArrayLike | Sequence
+    ) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/reshape.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/reshape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..110687fcd0c313c45e8b025083fa5790fb9913b1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/reshape.pyi
@@ -0,0 +1,16 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def unstack(
+    values: np.ndarray,  # reshape_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:]
+    stride: int,
+    length: int,
+    width: int,
+    new_values: np.ndarray,  # reshape_t[:, :]
+    new_mask: np.ndarray,  # uint8_t[:, :]
+) -> None: ...
+def explode(
+    values: npt.NDArray[np.object_],
+) -> tuple[npt.NDArray[np.object_], npt.NDArray[np.int64]]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/sas.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/sas.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5d65e2b56b5916ed1e76e1409e4f75c652ee8fc9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/sas.pyi
@@ -0,0 +1,7 @@
+from pandas.io.sas.sas7bdat import SAS7BDATReader
+
+class Parser:
+    def __init__(self, parser: SAS7BDATReader) -> None: ...
+    def read(self, nrows: int) -> None: ...
+
+def get_subheader_index(signature: bytes) -> int: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/sparse.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/sparse.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..536265b25425e9e7ecabd37f095115442a7cb209
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/sparse.pyi
@@ -0,0 +1,51 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    Self,
+    npt,
+)
+
+class SparseIndex:
+    length: int
+    npoints: int
+    def __init__(self) -> None: ...
+    @property
+    def ngaps(self) -> int: ...
+    @property
+    def nbytes(self) -> int: ...
+    @property
+    def indices(self) -> npt.NDArray[np.int32]: ...
+    def equals(self, other) -> bool: ...
+    def lookup(self, index: int) -> np.int32: ...
+    def lookup_array(self, indexer: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]: ...
+    def to_int_index(self) -> IntIndex: ...
+    def to_block_index(self) -> BlockIndex: ...
+    def intersect(self, y_: SparseIndex) -> Self: ...
+    def make_union(self, y_: SparseIndex) -> Self: ...
+
+class IntIndex(SparseIndex):
+    indices: npt.NDArray[np.int32]
+    def __init__(
+        self, length: int, indices: Sequence[int], check_integrity: bool = ...
+    ) -> None: ...
+
+class BlockIndex(SparseIndex):
+    nblocks: int
+    blocs: np.ndarray
+    blengths: np.ndarray
+    def __init__(
+        self, length: int, blocs: np.ndarray, blengths: np.ndarray
+    ) -> None: ...
+
+    # Override to have correct parameters
+    def intersect(self, other: SparseIndex) -> Self: ...
+    def make_union(self, y: SparseIndex) -> Self: ...
+
+def make_mask_object_ndarray(
+    arr: npt.NDArray[np.object_], fill_value
+) -> npt.NDArray[np.bool_]: ...
+def get_blocks(
+    indices: npt.NDArray[np.int32],
+) -> tuple[npt.NDArray[np.int32], npt.NDArray[np.int32]]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/testing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..01da496975f512b204defb06fcd19a36e235f97a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/testing.pyi
@@ -0,0 +1,12 @@
+def assert_dict_equal(a, b, compare_keys: bool = ...): ...
+def assert_almost_equal(
+    a,
+    b,
+    rtol: float = ...,
+    atol: float = ...,
+    check_dtype: bool = ...,
+    obj=...,
+    lobj=...,
+    robj=...,
+    index_values=...,
+): ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslib.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5a340c1d88bc439663c2e482c9eb7270f8ebc5c6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslib.pyi
@@ -0,0 +1,37 @@
+from datetime import tzinfo
+
+import numpy as np
+
+from pandas._typing import npt
+
+def format_array_from_datetime(
+    values: npt.NDArray[np.int64],
+    tz: tzinfo | None = ...,
+    format: str | None = ...,
+    na_rep: str | float = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.object_]: ...
+def array_with_unit_to_datetime(
+    values: npt.NDArray[np.object_],
+    unit: str,
+    errors: str = ...,
+) -> tuple[np.ndarray, tzinfo | None]: ...
+def first_non_null(values: np.ndarray) -> int: ...
+def array_to_datetime(
+    values: npt.NDArray[np.object_],
+    errors: str = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    creso: int = ...,
+) -> tuple[np.ndarray, tzinfo | None]: ...
+
+# returned ndarray may be object dtype or datetime64[ns]
+
+def array_to_datetime_with_tz(
+    values: npt.NDArray[np.object_],
+    tz: tzinfo,
+    dayfirst: bool,
+    yearfirst: bool,
+    creso: int,
+) -> npt.NDArray[np.int64]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..88a9a259ac8ec87255b25b3e15375b1c92099999
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/__init__.py
@@ -0,0 +1,87 @@
+__all__ = [
+    "dtypes",
+    "localize_pydatetime",
+    "NaT",
+    "NaTType",
+    "iNaT",
+    "nat_strings",
+    "OutOfBoundsDatetime",
+    "OutOfBoundsTimedelta",
+    "IncompatibleFrequency",
+    "Period",
+    "Resolution",
+    "Timedelta",
+    "normalize_i8_timestamps",
+    "is_date_array_normalized",
+    "dt64arr_to_periodarr",
+    "delta_to_nanoseconds",
+    "ints_to_pydatetime",
+    "ints_to_pytimedelta",
+    "get_resolution",
+    "Timestamp",
+    "tz_convert_from_utc_single",
+    "tz_convert_from_utc",
+    "to_offset",
+    "Tick",
+    "BaseOffset",
+    "tz_compare",
+    "is_unitless",
+    "astype_overflowsafe",
+    "get_unit_from_dtype",
+    "periods_per_day",
+    "periods_per_second",
+    "guess_datetime_format",
+    "add_overflowsafe",
+    "get_supported_dtype",
+    "is_supported_dtype",
+]
+
+from pandas._libs.tslibs import dtypes  # pylint: disable=import-self
+from pandas._libs.tslibs.conversion import localize_pydatetime
+from pandas._libs.tslibs.dtypes import (
+    Resolution,
+    periods_per_day,
+    periods_per_second,
+)
+from pandas._libs.tslibs.nattype import (
+    NaT,
+    NaTType,
+    iNaT,
+    nat_strings,
+)
+from pandas._libs.tslibs.np_datetime import (
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+    add_overflowsafe,
+    astype_overflowsafe,
+    get_supported_dtype,
+    is_supported_dtype,
+    is_unitless,
+    py_get_unit_from_dtype as get_unit_from_dtype,
+)
+from pandas._libs.tslibs.offsets import (
+    BaseOffset,
+    Tick,
+    to_offset,
+)
+from pandas._libs.tslibs.parsing import guess_datetime_format
+from pandas._libs.tslibs.period import (
+    IncompatibleFrequency,
+    Period,
+)
+from pandas._libs.tslibs.timedeltas import (
+    Timedelta,
+    delta_to_nanoseconds,
+    ints_to_pytimedelta,
+)
+from pandas._libs.tslibs.timestamps import Timestamp
+from pandas._libs.tslibs.timezones import tz_compare
+from pandas._libs.tslibs.tzconversion import tz_convert_from_utc_single
+from pandas._libs.tslibs.vectorized import (
+    dt64arr_to_periodarr,
+    get_resolution,
+    ints_to_pydatetime,
+    is_date_array_normalized,
+    normalize_i8_timestamps,
+    tz_convert_from_utc,
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so
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diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..993f18a61d74aaa643e9790df70ede618b917223
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi
@@ -0,0 +1,12 @@
+DAYS: list[str]
+MONTH_ALIASES: dict[int, str]
+MONTH_NUMBERS: dict[str, int]
+MONTHS: list[str]
+int_to_weekday: dict[int, str]
+
+def get_firstbday(year: int, month: int) -> int: ...
+def get_lastbday(year: int, month: int) -> int: ...
+def get_day_of_year(year: int, month: int, day: int) -> int: ...
+def get_iso_calendar(year: int, month: int, day: int) -> tuple[int, int, int]: ...
+def get_week_of_year(year: int, month: int, day: int) -> int: ...
+def get_days_in_month(year: int, month: int) -> int: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..26affae577f4d3f4ecda2ac9c1bc0cb748a35d4d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi
@@ -0,0 +1,14 @@
+from datetime import (
+    datetime,
+    tzinfo,
+)
+
+import numpy as np
+
+DT64NS_DTYPE: np.dtype
+TD64NS_DTYPE: np.dtype
+
+def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ...
+def cast_from_unit_vectorized(
+    values: np.ndarray, unit: str, out_unit: str = ...
+) -> np.ndarray: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7fdeb88d498ac88b6d023e25409e8f756b12c07a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi
@@ -0,0 +1,83 @@
+from enum import Enum
+
+OFFSET_TO_PERIOD_FREQSTR: dict[str, str]
+
+def periods_per_day(reso: int = ...) -> int: ...
+def periods_per_second(reso: int) -> int: ...
+def abbrev_to_npy_unit(abbrev: str | None) -> int: ...
+def freq_to_period_freqstr(freq_n: int, freq_name: str) -> str: ...
+
+class PeriodDtypeBase:
+    _dtype_code: int  # PeriodDtypeCode
+    _n: int
+
+    # actually __cinit__
+    def __new__(cls, code: int, n: int): ...
+    @property
+    def _freq_group_code(self) -> int: ...
+    @property
+    def _resolution_obj(self) -> Resolution: ...
+    def _get_to_timestamp_base(self) -> int: ...
+    @property
+    def _freqstr(self) -> str: ...
+    def __hash__(self) -> int: ...
+    def _is_tick_like(self) -> bool: ...
+    @property
+    def _creso(self) -> int: ...
+    @property
+    def _td64_unit(self) -> str: ...
+
+class FreqGroup(Enum):
+    FR_ANN: int
+    FR_QTR: int
+    FR_MTH: int
+    FR_WK: int
+    FR_BUS: int
+    FR_DAY: int
+    FR_HR: int
+    FR_MIN: int
+    FR_SEC: int
+    FR_MS: int
+    FR_US: int
+    FR_NS: int
+    FR_UND: int
+    @staticmethod
+    def from_period_dtype_code(code: int) -> FreqGroup: ...
+
+class Resolution(Enum):
+    RESO_NS: int
+    RESO_US: int
+    RESO_MS: int
+    RESO_SEC: int
+    RESO_MIN: int
+    RESO_HR: int
+    RESO_DAY: int
+    RESO_MTH: int
+    RESO_QTR: int
+    RESO_YR: int
+    def __lt__(self, other: Resolution) -> bool: ...
+    def __ge__(self, other: Resolution) -> bool: ...
+    @property
+    def attrname(self) -> str: ...
+    @classmethod
+    def from_attrname(cls, attrname: str) -> Resolution: ...
+    @classmethod
+    def get_reso_from_freqstr(cls, freq: str) -> Resolution: ...
+    @property
+    def attr_abbrev(self) -> str: ...
+
+class NpyDatetimeUnit(Enum):
+    NPY_FR_Y: int
+    NPY_FR_M: int
+    NPY_FR_W: int
+    NPY_FR_D: int
+    NPY_FR_h: int
+    NPY_FR_m: int
+    NPY_FR_s: int
+    NPY_FR_ms: int
+    NPY_FR_us: int
+    NPY_FR_ns: int
+    NPY_FR_ps: int
+    NPY_FR_fs: int
+    NPY_FR_as: int
+    NPY_FR_GENERIC: int
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c6cfd44e9f6ab76ee4bc3be7c765569991514d18
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi
@@ -0,0 +1,62 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def build_field_sarray(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int,  # NPY_DATETIMEUNIT
+) -> np.ndarray: ...
+def month_position_check(fields, weekdays) -> str | None: ...
+def get_date_name_field(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    field: str,
+    locale: str | None = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.object_]: ...
+def get_start_end_field(
+    dtindex: npt.NDArray[np.int64],
+    field: str,
+    freqstr: str | None = ...,
+    month_kw: int = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.bool_]: ...
+def get_date_field(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    field: str,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int32]: ...
+def get_timedelta_field(
+    tdindex: npt.NDArray[np.int64],  # const int64_t[:]
+    field: str,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int32]: ...
+def get_timedelta_days(
+    tdindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
+def isleapyear_arr(
+    years: np.ndarray,
+) -> npt.NDArray[np.bool_]: ...
+def build_isocalendar_sarray(
+    dtindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int,  # NPY_DATETIMEUNIT
+) -> np.ndarray: ...
+def _get_locale_names(name_type: str, locale: str | None = ...): ...
+
+class RoundTo:
+    @property
+    def MINUS_INFTY(self) -> int: ...
+    @property
+    def PLUS_INFTY(self) -> int: ...
+    @property
+    def NEAREST_HALF_EVEN(self) -> int: ...
+    @property
+    def NEAREST_HALF_PLUS_INFTY(self) -> int: ...
+    @property
+    def NEAREST_HALF_MINUS_INFTY(self) -> int: ...
+
+def round_nsint64(
+    values: npt.NDArray[np.int64],
+    mode: RoundTo,
+    nanos: int,
+) -> npt.NDArray[np.int64]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bd86a6fdc2174a38bdd0845019e0afd6f2aecbf1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi
@@ -0,0 +1,141 @@
+from datetime import (
+    datetime,
+    timedelta,
+    tzinfo as _tzinfo,
+)
+import typing
+
+import numpy as np
+
+from pandas._libs.tslibs.period import Period
+from pandas._typing import Self
+
+NaT: NaTType
+iNaT: int
+nat_strings: set[str]
+
+_NaTComparisonTypes: typing.TypeAlias = (
+    datetime | timedelta | Period | np.datetime64 | np.timedelta64
+)
+
+class _NatComparison:
+    def __call__(self, other: _NaTComparisonTypes) -> bool: ...
+
+class NaTType:
+    _value: np.int64
+    @property
+    def value(self) -> int: ...
+    @property
+    def asm8(self) -> np.datetime64: ...
+    def to_datetime64(self) -> np.datetime64: ...
+    def to_numpy(
+        self, dtype: np.dtype | str | None = ..., copy: bool = ...
+    ) -> np.datetime64 | np.timedelta64: ...
+    @property
+    def is_leap_year(self) -> bool: ...
+    @property
+    def is_month_start(self) -> bool: ...
+    @property
+    def is_quarter_start(self) -> bool: ...
+    @property
+    def is_year_start(self) -> bool: ...
+    @property
+    def is_month_end(self) -> bool: ...
+    @property
+    def is_quarter_end(self) -> bool: ...
+    @property
+    def is_year_end(self) -> bool: ...
+    @property
+    def day_of_year(self) -> float: ...
+    @property
+    def dayofyear(self) -> float: ...
+    @property
+    def days_in_month(self) -> float: ...
+    @property
+    def daysinmonth(self) -> float: ...
+    @property
+    def day_of_week(self) -> float: ...
+    @property
+    def dayofweek(self) -> float: ...
+    @property
+    def week(self) -> float: ...
+    @property
+    def weekofyear(self) -> float: ...
+    def day_name(self) -> float: ...
+    def month_name(self) -> float: ...
+    def weekday(self) -> float: ...
+    def isoweekday(self) -> float: ...
+    def total_seconds(self) -> float: ...
+    def today(self, *args, **kwargs) -> NaTType: ...
+    def now(self, *args, **kwargs) -> NaTType: ...
+    def to_pydatetime(self) -> NaTType: ...
+    def date(self) -> NaTType: ...
+    def round(self) -> NaTType: ...
+    def floor(self) -> NaTType: ...
+    def ceil(self) -> NaTType: ...
+    @property
+    def tzinfo(self) -> None: ...
+    @property
+    def tz(self) -> None: ...
+    def tz_convert(self, tz: _tzinfo | str | None) -> NaTType: ...
+    def tz_localize(
+        self,
+        tz: _tzinfo | str | None,
+        ambiguous: str = ...,
+        nonexistent: str = ...,
+    ) -> NaTType: ...
+    def replace(
+        self,
+        year: int | None = ...,
+        month: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+        microsecond: int | None = ...,
+        nanosecond: int | None = ...,
+        tzinfo: _tzinfo | None = ...,
+        fold: int | None = ...,
+    ) -> NaTType: ...
+    @property
+    def year(self) -> float: ...
+    @property
+    def quarter(self) -> float: ...
+    @property
+    def month(self) -> float: ...
+    @property
+    def day(self) -> float: ...
+    @property
+    def hour(self) -> float: ...
+    @property
+    def minute(self) -> float: ...
+    @property
+    def second(self) -> float: ...
+    @property
+    def millisecond(self) -> float: ...
+    @property
+    def microsecond(self) -> float: ...
+    @property
+    def nanosecond(self) -> float: ...
+    # inject Timedelta properties
+    @property
+    def days(self) -> float: ...
+    @property
+    def microseconds(self) -> float: ...
+    @property
+    def nanoseconds(self) -> float: ...
+    # inject Period properties
+    @property
+    def qyear(self) -> float: ...
+    def __eq__(self, other: object) -> bool: ...
+    def __ne__(self, other: object) -> bool: ...
+    __lt__: _NatComparison
+    __le__: _NatComparison
+    __gt__: _NatComparison
+    __ge__: _NatComparison
+    def __sub__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __rsub__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __add__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __radd__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __hash__(self) -> int: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> NaTType: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..00ef35c50e53251d5ca6f6c6d5ad28a67a695a21
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi
@@ -0,0 +1,27 @@
+import numpy as np
+
+from pandas._typing import npt
+
+class OutOfBoundsDatetime(ValueError): ...
+class OutOfBoundsTimedelta(ValueError): ...
+
+# only exposed for testing
+def py_get_unit_from_dtype(dtype: np.dtype): ...
+def py_td64_to_tdstruct(td64: int, unit: int) -> dict: ...
+def astype_overflowsafe(
+    values: np.ndarray,
+    dtype: np.dtype,
+    copy: bool = ...,
+    round_ok: bool = ...,
+    is_coerce: bool = ...,
+) -> np.ndarray: ...
+def is_unitless(dtype: np.dtype) -> bool: ...
+def compare_mismatched_resolutions(
+    left: np.ndarray, right: np.ndarray, op
+) -> npt.NDArray[np.bool_]: ...
+def add_overflowsafe(
+    left: npt.NDArray[np.int64],
+    right: npt.NDArray[np.int64],
+) -> npt.NDArray[np.int64]: ...
+def get_supported_dtype(dtype: np.dtype) -> np.dtype: ...
+def is_supported_dtype(dtype: np.dtype) -> bool: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..758f68693ecb3eacb880ae91c63766110d3ed130
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e5fb3e5090f1b8b279994f9cb5a5eee4a5b24fa449c9b742065f494bde6f2fc
+size 1053440
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/offsets.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/offsets.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7eb8dc0813868de9ca96c086f5506619719937f4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/offsets.pyi
@@ -0,0 +1,287 @@
+from datetime import (
+    datetime,
+    time,
+    timedelta,
+)
+from typing import (
+    Any,
+    Collection,
+    Literal,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs.nattype import NaTType
+from pandas._typing import (
+    OffsetCalendar,
+    Self,
+    npt,
+)
+
+from .timedeltas import Timedelta
+
+_BaseOffsetT = TypeVar("_BaseOffsetT", bound=BaseOffset)
+_DatetimeT = TypeVar("_DatetimeT", bound=datetime)
+_TimedeltaT = TypeVar("_TimedeltaT", bound=timedelta)
+
+_relativedelta_kwds: set[str]
+prefix_mapping: dict[str, type]
+
+class ApplyTypeError(TypeError): ...
+
+class BaseOffset:
+    n: int
+    normalize: bool
+    def __init__(self, n: int = ..., normalize: bool = ...) -> None: ...
+    def __eq__(self, other) -> bool: ...
+    def __ne__(self, other) -> bool: ...
+    def __hash__(self) -> int: ...
+    @property
+    def kwds(self) -> dict: ...
+    @property
+    def base(self) -> BaseOffset: ...
+    @overload
+    def __add__(self, other: npt.NDArray[np.object_]) -> npt.NDArray[np.object_]: ...
+    @overload
+    def __add__(self, other: BaseOffset) -> Self: ...
+    @overload
+    def __add__(self, other: _DatetimeT) -> _DatetimeT: ...
+    @overload
+    def __add__(self, other: _TimedeltaT) -> _TimedeltaT: ...
+    @overload
+    def __radd__(self, other: npt.NDArray[np.object_]) -> npt.NDArray[np.object_]: ...
+    @overload
+    def __radd__(self, other: BaseOffset) -> Self: ...
+    @overload
+    def __radd__(self, other: _DatetimeT) -> _DatetimeT: ...
+    @overload
+    def __radd__(self, other: _TimedeltaT) -> _TimedeltaT: ...
+    @overload
+    def __radd__(self, other: NaTType) -> NaTType: ...
+    def __sub__(self, other: BaseOffset) -> Self: ...
+    @overload
+    def __rsub__(self, other: npt.NDArray[np.object_]) -> npt.NDArray[np.object_]: ...
+    @overload
+    def __rsub__(self, other: BaseOffset): ...
+    @overload
+    def __rsub__(self, other: _DatetimeT) -> _DatetimeT: ...
+    @overload
+    def __rsub__(self, other: _TimedeltaT) -> _TimedeltaT: ...
+    @overload
+    def __mul__(self, other: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __mul__(self, other: int): ...
+    @overload
+    def __rmul__(self, other: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __rmul__(self, other: int) -> Self: ...
+    def __neg__(self) -> Self: ...
+    def copy(self) -> Self: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def rule_code(self) -> str: ...
+    @property
+    def freqstr(self) -> str: ...
+    def _apply(self, other): ...
+    def _apply_array(self, dtarr: np.ndarray) -> np.ndarray: ...
+    def rollback(self, dt: datetime) -> datetime: ...
+    def rollforward(self, dt: datetime) -> datetime: ...
+    def is_on_offset(self, dt: datetime) -> bool: ...
+    def __setstate__(self, state) -> None: ...
+    def __getstate__(self): ...
+    @property
+    def nanos(self) -> int: ...
+    def is_anchored(self) -> bool: ...
+
+def _get_offset(name: str) -> BaseOffset: ...
+
+class SingleConstructorOffset(BaseOffset):
+    @classmethod
+    def _from_name(cls, suffix: None = ...): ...
+    def __reduce__(self): ...
+
+@overload
+def to_offset(freq: None, is_period: bool = ...) -> None: ...
+@overload
+def to_offset(freq: _BaseOffsetT, is_period: bool = ...) -> _BaseOffsetT: ...
+@overload
+def to_offset(freq: timedelta | str, is_period: bool = ...) -> BaseOffset: ...
+
+class Tick(SingleConstructorOffset):
+    _creso: int
+    _prefix: str
+    def __init__(self, n: int = ..., normalize: bool = ...) -> None: ...
+    @property
+    def delta(self) -> Timedelta: ...
+    @property
+    def nanos(self) -> int: ...
+
+def delta_to_tick(delta: timedelta) -> Tick: ...
+
+class Day(Tick): ...
+class Hour(Tick): ...
+class Minute(Tick): ...
+class Second(Tick): ...
+class Milli(Tick): ...
+class Micro(Tick): ...
+class Nano(Tick): ...
+
+class RelativeDeltaOffset(BaseOffset):
+    def __init__(self, n: int = ..., normalize: bool = ..., **kwds: Any) -> None: ...
+
+class BusinessMixin(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., offset: timedelta = ...
+    ) -> None: ...
+
+class BusinessDay(BusinessMixin): ...
+
+class BusinessHour(BusinessMixin):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        start: str | time | Collection[str | time] = ...,
+        end: str | time | Collection[str | time] = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class WeekOfMonthMixin(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., weekday: int = ...
+    ) -> None: ...
+
+class YearOffset(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., month: int | None = ...
+    ) -> None: ...
+
+class BYearEnd(YearOffset): ...
+class BYearBegin(YearOffset): ...
+class YearEnd(YearOffset): ...
+class YearBegin(YearOffset): ...
+
+class QuarterOffset(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., startingMonth: int | None = ...
+    ) -> None: ...
+
+class BQuarterEnd(QuarterOffset): ...
+class BQuarterBegin(QuarterOffset): ...
+class QuarterEnd(QuarterOffset): ...
+class QuarterBegin(QuarterOffset): ...
+class MonthOffset(SingleConstructorOffset): ...
+class MonthEnd(MonthOffset): ...
+class MonthBegin(MonthOffset): ...
+class BusinessMonthEnd(MonthOffset): ...
+class BusinessMonthBegin(MonthOffset): ...
+
+class SemiMonthOffset(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., day_of_month: int | None = ...
+    ) -> None: ...
+
+class SemiMonthEnd(SemiMonthOffset): ...
+class SemiMonthBegin(SemiMonthOffset): ...
+
+class Week(SingleConstructorOffset):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., weekday: int | None = ...
+    ) -> None: ...
+
+class WeekOfMonth(WeekOfMonthMixin):
+    def __init__(
+        self, n: int = ..., normalize: bool = ..., week: int = ..., weekday: int = ...
+    ) -> None: ...
+
+class LastWeekOfMonth(WeekOfMonthMixin): ...
+
+class FY5253Mixin(SingleConstructorOffset):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekday: int = ...,
+        startingMonth: int = ...,
+        variation: Literal["nearest", "last"] = ...,
+    ) -> None: ...
+
+class FY5253(FY5253Mixin): ...
+
+class FY5253Quarter(FY5253Mixin):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekday: int = ...,
+        startingMonth: int = ...,
+        qtr_with_extra_week: int = ...,
+        variation: Literal["nearest", "last"] = ...,
+    ) -> None: ...
+
+class Easter(SingleConstructorOffset): ...
+
+class _CustomBusinessMonth(BusinessMixin):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekmask: str = ...,
+        holidays: list | None = ...,
+        calendar: OffsetCalendar | None = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class CustomBusinessDay(BusinessDay):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekmask: str = ...,
+        holidays: list | None = ...,
+        calendar: OffsetCalendar | None = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class CustomBusinessHour(BusinessHour):
+    def __init__(
+        self,
+        n: int = ...,
+        normalize: bool = ...,
+        weekmask: str = ...,
+        holidays: list | None = ...,
+        calendar: OffsetCalendar | None = ...,
+        start: str | time | Collection[str | time] = ...,
+        end: str | time | Collection[str | time] = ...,
+        offset: timedelta = ...,
+    ) -> None: ...
+
+class CustomBusinessMonthEnd(_CustomBusinessMonth): ...
+class CustomBusinessMonthBegin(_CustomBusinessMonth): ...
+class OffsetMeta(type): ...
+class DateOffset(RelativeDeltaOffset, metaclass=OffsetMeta): ...
+
+BDay = BusinessDay
+BMonthEnd = BusinessMonthEnd
+BMonthBegin = BusinessMonthBegin
+CBMonthEnd = CustomBusinessMonthEnd
+CBMonthBegin = CustomBusinessMonthBegin
+CDay = CustomBusinessDay
+
+def roll_qtrday(
+    other: datetime, n: int, month: int, day_opt: str, modby: int
+) -> int: ...
+
+INVALID_FREQ_ERR_MSG: Literal["Invalid frequency: {0}"]
+
+def shift_months(
+    dtindex: npt.NDArray[np.int64],
+    months: int,
+    day_opt: str | None = ...,
+    reso: int = ...,
+) -> npt.NDArray[np.int64]: ...
+
+_offset_map: dict[str, BaseOffset]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/parsing.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/parsing.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..dedd833ed1e4d9fb3fc179917191587be4cfbfd1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/parsing.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f78f2961eda0d1fbe672fd4e14692d4c36f2a6a6a570e634cb347bec89c7e35
+size 440800
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/parsing.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/parsing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..40394f915d4b0f12d3631d997c46381757152bfe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/parsing.pyi
@@ -0,0 +1,33 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas._typing import npt
+
+class DateParseError(ValueError): ...
+
+def py_parse_datetime_string(
+    date_string: str,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+) -> datetime: ...
+def parse_datetime_string_with_reso(
+    date_string: str,
+    freq: str | None = ...,
+    dayfirst: bool | None = ...,
+    yearfirst: bool | None = ...,
+) -> tuple[datetime, str]: ...
+def _does_string_look_like_datetime(py_string: str) -> bool: ...
+def quarter_to_myear(year: int, quarter: int, freq: str) -> tuple[int, int]: ...
+def try_parse_dates(
+    values: npt.NDArray[np.object_],  # object[:]
+    parser,
+) -> npt.NDArray[np.object_]: ...
+def guess_datetime_format(
+    dt_str: str,
+    dayfirst: bool | None = ...,
+) -> str | None: ...
+def concat_date_cols(
+    date_cols: tuple,
+) -> npt.NDArray[np.object_]: ...
+def get_rule_month(source: str) -> str: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/period.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/period.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..304613b4b2796022a30ad30b69af5e197cb70eef
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/period.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8b6fa43da7b6ba1c79b4e8b7a6e74e1a8e6b90a9db7eab1e20341ea7b7ee7d7
+size 511792
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/period.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/period.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22f3bdbe668decaac0c53cf080ae5c3f098d7a48
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/period.pyi
@@ -0,0 +1,135 @@
+from datetime import timedelta
+from typing import Literal
+
+import numpy as np
+
+from pandas._libs.tslibs.dtypes import PeriodDtypeBase
+from pandas._libs.tslibs.nattype import NaTType
+from pandas._libs.tslibs.offsets import BaseOffset
+from pandas._libs.tslibs.timestamps import Timestamp
+from pandas._typing import (
+    Frequency,
+    npt,
+)
+
+INVALID_FREQ_ERR_MSG: str
+DIFFERENT_FREQ: str
+
+class IncompatibleFrequency(ValueError): ...
+
+def periodarr_to_dt64arr(
+    periodarr: npt.NDArray[np.int64],  # const int64_t[:]
+    freq: int,
+) -> npt.NDArray[np.int64]: ...
+def period_asfreq_arr(
+    arr: npt.NDArray[np.int64],
+    freq1: int,
+    freq2: int,
+    end: bool,
+) -> npt.NDArray[np.int64]: ...
+def get_period_field_arr(
+    field: str,
+    arr: npt.NDArray[np.int64],  # const int64_t[:]
+    freq: int,
+) -> npt.NDArray[np.int64]: ...
+def from_ordinals(
+    values: npt.NDArray[np.int64],  # const int64_t[:]
+    freq: timedelta | BaseOffset | str,
+) -> npt.NDArray[np.int64]: ...
+def extract_ordinals(
+    values: npt.NDArray[np.object_],
+    freq: Frequency | int,
+) -> npt.NDArray[np.int64]: ...
+def extract_freq(
+    values: npt.NDArray[np.object_],
+) -> BaseOffset: ...
+def period_array_strftime(
+    values: npt.NDArray[np.int64],
+    dtype_code: int,
+    na_rep,
+    date_format: str | None,
+) -> npt.NDArray[np.object_]: ...
+
+# exposed for tests
+def period_asfreq(ordinal: int, freq1: int, freq2: int, end: bool) -> int: ...
+def period_ordinal(
+    y: int, m: int, d: int, h: int, min: int, s: int, us: int, ps: int, freq: int
+) -> int: ...
+def freq_to_dtype_code(freq: BaseOffset) -> int: ...
+def validate_end_alias(how: str) -> Literal["E", "S"]: ...
+
+class PeriodMixin:
+    @property
+    def end_time(self) -> Timestamp: ...
+    @property
+    def start_time(self) -> Timestamp: ...
+    def _require_matching_freq(self, other: BaseOffset, base: bool = ...) -> None: ...
+
+class Period(PeriodMixin):
+    ordinal: int  # int64_t
+    freq: BaseOffset
+    _dtype: PeriodDtypeBase
+
+    # error: "__new__" must return a class instance (got "Union[Period, NaTType]")
+    def __new__(  # type: ignore[misc]
+        cls,
+        value=...,
+        freq: int | str | BaseOffset | None = ...,
+        ordinal: int | None = ...,
+        year: int | None = ...,
+        month: int | None = ...,
+        quarter: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+    ) -> Period | NaTType: ...
+    @classmethod
+    def _maybe_convert_freq(cls, freq) -> BaseOffset: ...
+    @classmethod
+    def _from_ordinal(cls, ordinal: int, freq: BaseOffset) -> Period: ...
+    @classmethod
+    def now(cls, freq: Frequency) -> Period: ...
+    def strftime(self, fmt: str | None) -> str: ...
+    def to_timestamp(
+        self,
+        freq: str | BaseOffset | None = ...,
+        how: str = ...,
+    ) -> Timestamp: ...
+    def asfreq(self, freq: str | BaseOffset, how: str = ...) -> Period: ...
+    @property
+    def freqstr(self) -> str: ...
+    @property
+    def is_leap_year(self) -> bool: ...
+    @property
+    def daysinmonth(self) -> int: ...
+    @property
+    def days_in_month(self) -> int: ...
+    @property
+    def qyear(self) -> int: ...
+    @property
+    def quarter(self) -> int: ...
+    @property
+    def day_of_year(self) -> int: ...
+    @property
+    def weekday(self) -> int: ...
+    @property
+    def day_of_week(self) -> int: ...
+    @property
+    def week(self) -> int: ...
+    @property
+    def weekofyear(self) -> int: ...
+    @property
+    def second(self) -> int: ...
+    @property
+    def minute(self) -> int: ...
+    @property
+    def hour(self) -> int: ...
+    @property
+    def day(self) -> int: ...
+    @property
+    def month(self) -> int: ...
+    @property
+    def year(self) -> int: ...
+    def __sub__(self, other) -> Period | BaseOffset: ...
+    def __add__(self, other) -> Period: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/strptime.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/strptime.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..6a1749db4f631448f17dc48bbb9232c3b31ca415
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/strptime.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1492b83afa52bb7cd364315628a396e9a5f412f83faf8518767285ccaa3f34f7
+size 386480
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/strptime.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/strptime.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0ec1a1e25a2b3cfe974baebfe32d686435f73e11
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/strptime.pyi
@@ -0,0 +1,14 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def array_strptime(
+    values: npt.NDArray[np.object_],
+    fmt: str | None,
+    exact: bool = ...,
+    errors: str = ...,
+    utc: bool = ...,
+    creso: int = ...,  # NPY_DATETIMEUNIT
+) -> tuple[np.ndarray, np.ndarray]: ...
+
+# first ndarray is M8[ns], second is object ndarray of tzinfo | None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..00188d947ef466faddc7aab196f75944a8f47bd8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3b84b6589c6c3d6d71b538d7df9a545e072feafc13f950d077f932a762884bf
+size 599280
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..24ec6c8891a89a7af4042df066fec6bc9d7b0e04
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.pyi
@@ -0,0 +1,174 @@
+from datetime import timedelta
+from typing import (
+    ClassVar,
+    Literal,
+    TypeAlias,
+    TypeVar,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    NaTType,
+    Tick,
+)
+from pandas._typing import (
+    Frequency,
+    Self,
+    npt,
+)
+
+# This should be kept consistent with the keys in the dict timedelta_abbrevs
+# in pandas/_libs/tslibs/timedeltas.pyx
+UnitChoices: TypeAlias = Literal[
+    "Y",
+    "y",
+    "M",
+    "W",
+    "w",
+    "D",
+    "d",
+    "days",
+    "day",
+    "hours",
+    "hour",
+    "hr",
+    "h",
+    "m",
+    "minute",
+    "min",
+    "minutes",
+    "T",
+    "t",
+    "s",
+    "seconds",
+    "sec",
+    "second",
+    "ms",
+    "milliseconds",
+    "millisecond",
+    "milli",
+    "millis",
+    "L",
+    "l",
+    "us",
+    "microseconds",
+    "microsecond",
+    "µs",
+    "micro",
+    "micros",
+    "u",
+    "ns",
+    "nanoseconds",
+    "nano",
+    "nanos",
+    "nanosecond",
+    "n",
+]
+_S = TypeVar("_S", bound=timedelta)
+
+def get_unit_for_round(freq, creso: int) -> int: ...
+def disallow_ambiguous_unit(unit: str | None) -> None: ...
+def ints_to_pytimedelta(
+    m8values: npt.NDArray[np.timedelta64],
+    box: bool = ...,
+) -> npt.NDArray[np.object_]: ...
+def array_to_timedelta64(
+    values: npt.NDArray[np.object_],
+    unit: str | None = ...,
+    errors: str = ...,
+) -> np.ndarray: ...  # np.ndarray[m8ns]
+def parse_timedelta_unit(unit: str | None) -> UnitChoices: ...
+def delta_to_nanoseconds(
+    delta: np.timedelta64 | timedelta | Tick,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+    round_ok: bool = ...,
+) -> int: ...
+def floordiv_object_array(
+    left: np.ndarray, right: npt.NDArray[np.object_]
+) -> np.ndarray: ...
+def truediv_object_array(
+    left: np.ndarray, right: npt.NDArray[np.object_]
+) -> np.ndarray: ...
+
+class Timedelta(timedelta):
+    _creso: int
+    min: ClassVar[Timedelta]
+    max: ClassVar[Timedelta]
+    resolution: ClassVar[Timedelta]
+    value: int  # np.int64
+    _value: int  # np.int64
+    # error: "__new__" must return a class instance (got "Union[Timestamp, NaTType]")
+    def __new__(  # type: ignore[misc]
+        cls: type[_S],
+        value=...,
+        unit: str | None = ...,
+        **kwargs: float | np.integer | np.floating,
+    ) -> _S | NaTType: ...
+    @classmethod
+    def _from_value_and_reso(cls, value: np.int64, reso: int) -> Timedelta: ...
+    @property
+    def days(self) -> int: ...
+    @property
+    def seconds(self) -> int: ...
+    @property
+    def microseconds(self) -> int: ...
+    def total_seconds(self) -> float: ...
+    def to_pytimedelta(self) -> timedelta: ...
+    def to_timedelta64(self) -> np.timedelta64: ...
+    @property
+    def asm8(self) -> np.timedelta64: ...
+    # TODO: round/floor/ceil could return NaT?
+    def round(self, freq: Frequency) -> Self: ...
+    def floor(self, freq: Frequency) -> Self: ...
+    def ceil(self, freq: Frequency) -> Self: ...
+    @property
+    def resolution_string(self) -> str: ...
+    def __add__(self, other: timedelta) -> Timedelta: ...
+    def __radd__(self, other: timedelta) -> Timedelta: ...
+    def __sub__(self, other: timedelta) -> Timedelta: ...
+    def __rsub__(self, other: timedelta) -> Timedelta: ...
+    def __neg__(self) -> Timedelta: ...
+    def __pos__(self) -> Timedelta: ...
+    def __abs__(self) -> Timedelta: ...
+    def __mul__(self, other: float) -> Timedelta: ...
+    def __rmul__(self, other: float) -> Timedelta: ...
+    # error: Signature of "__floordiv__" incompatible with supertype "timedelta"
+    @overload  # type: ignore[override]
+    def __floordiv__(self, other: timedelta) -> int: ...
+    @overload
+    def __floordiv__(self, other: float) -> Timedelta: ...
+    @overload
+    def __floordiv__(
+        self, other: npt.NDArray[np.timedelta64]
+    ) -> npt.NDArray[np.intp]: ...
+    @overload
+    def __floordiv__(
+        self, other: npt.NDArray[np.number]
+    ) -> npt.NDArray[np.timedelta64] | Timedelta: ...
+    @overload
+    def __rfloordiv__(self, other: timedelta | str) -> int: ...
+    @overload
+    def __rfloordiv__(self, other: None | NaTType) -> NaTType: ...
+    @overload
+    def __rfloordiv__(self, other: np.ndarray) -> npt.NDArray[np.timedelta64]: ...
+    @overload
+    def __truediv__(self, other: timedelta) -> float: ...
+    @overload
+    def __truediv__(self, other: float) -> Timedelta: ...
+    def __mod__(self, other: timedelta) -> Timedelta: ...
+    def __divmod__(self, other: timedelta) -> tuple[int, Timedelta]: ...
+    def __le__(self, other: timedelta) -> bool: ...
+    def __lt__(self, other: timedelta) -> bool: ...
+    def __ge__(self, other: timedelta) -> bool: ...
+    def __gt__(self, other: timedelta) -> bool: ...
+    def __hash__(self) -> int: ...
+    def isoformat(self) -> str: ...
+    def to_numpy(
+        self, dtype: npt.DTypeLike = ..., copy: bool = False
+    ) -> np.timedelta64: ...
+    def view(self, dtype: npt.DTypeLike) -> object: ...
+    @property
+    def unit(self) -> str: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> Timedelta: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timestamps.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timestamps.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..5d3a6f49e1e8c75746bb912e5cf7c565ab19f1c6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timestamps.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1fa5831699d234bd5e99187f7166a11559b7b020bf9603f3926c978604b8f73
+size 624512
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c769b09d1b7a1adc4532dd367bcbc4cf522419cd
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyi
@@ -0,0 +1,241 @@
+from datetime import (
+    date as _date,
+    datetime,
+    time as _time,
+    timedelta,
+    tzinfo as _tzinfo,
+)
+from time import struct_time
+from typing import (
+    ClassVar,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaTType,
+    Period,
+    Tick,
+    Timedelta,
+)
+from pandas._typing import (
+    Self,
+    TimestampNonexistent,
+)
+
+_TimeZones: TypeAlias = str | _tzinfo | None | int
+
+def integer_op_not_supported(obj: object) -> TypeError: ...
+
+class Timestamp(datetime):
+    _creso: int
+    min: ClassVar[Timestamp]
+    max: ClassVar[Timestamp]
+
+    resolution: ClassVar[Timedelta]
+    _value: int  # np.int64
+    # error: "__new__" must return a class instance (got "Union[Timestamp, NaTType]")
+    def __new__(  # type: ignore[misc]
+        cls: type[Self],
+        ts_input: np.integer | float | str | _date | datetime | np.datetime64 = ...,
+        year: int | None = ...,
+        month: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+        microsecond: int | None = ...,
+        tzinfo: _tzinfo | None = ...,
+        *,
+        nanosecond: int | None = ...,
+        tz: _TimeZones = ...,
+        unit: str | int | None = ...,
+        fold: int | None = ...,
+    ) -> Self | NaTType: ...
+    @classmethod
+    def _from_value_and_reso(
+        cls, value: int, reso: int, tz: _TimeZones
+    ) -> Timestamp: ...
+    @property
+    def value(self) -> int: ...  # np.int64
+    @property
+    def year(self) -> int: ...
+    @property
+    def month(self) -> int: ...
+    @property
+    def day(self) -> int: ...
+    @property
+    def hour(self) -> int: ...
+    @property
+    def minute(self) -> int: ...
+    @property
+    def second(self) -> int: ...
+    @property
+    def microsecond(self) -> int: ...
+    @property
+    def nanosecond(self) -> int: ...
+    @property
+    def tzinfo(self) -> _tzinfo | None: ...
+    @property
+    def tz(self) -> _tzinfo | None: ...
+    @property
+    def fold(self) -> int: ...
+    @classmethod
+    def fromtimestamp(cls, ts: float, tz: _TimeZones = ...) -> Self: ...
+    @classmethod
+    def utcfromtimestamp(cls, ts: float) -> Self: ...
+    @classmethod
+    def today(cls, tz: _TimeZones = ...) -> Self: ...
+    @classmethod
+    def fromordinal(
+        cls,
+        ordinal: int,
+        tz: _TimeZones = ...,
+    ) -> Self: ...
+    @classmethod
+    def now(cls, tz: _TimeZones = ...) -> Self: ...
+    @classmethod
+    def utcnow(cls) -> Self: ...
+    # error: Signature of "combine" incompatible with supertype "datetime"
+    @classmethod
+    def combine(  # type: ignore[override]
+        cls, date: _date, time: _time
+    ) -> datetime: ...
+    @classmethod
+    def fromisoformat(cls, date_string: str) -> Self: ...
+    def strftime(self, format: str) -> str: ...
+    def __format__(self, fmt: str) -> str: ...
+    def toordinal(self) -> int: ...
+    def timetuple(self) -> struct_time: ...
+    def timestamp(self) -> float: ...
+    def utctimetuple(self) -> struct_time: ...
+    def date(self) -> _date: ...
+    def time(self) -> _time: ...
+    def timetz(self) -> _time: ...
+    # LSP violation: nanosecond is not present in datetime.datetime.replace
+    # and has positional args following it
+    def replace(  # type: ignore[override]
+        self,
+        year: int | None = ...,
+        month: int | None = ...,
+        day: int | None = ...,
+        hour: int | None = ...,
+        minute: int | None = ...,
+        second: int | None = ...,
+        microsecond: int | None = ...,
+        nanosecond: int | None = ...,
+        tzinfo: _tzinfo | type[object] | None = ...,
+        fold: int | None = ...,
+    ) -> Self: ...
+    # LSP violation: datetime.datetime.astimezone has a default value for tz
+    def astimezone(self, tz: _TimeZones) -> Self: ...  # type: ignore[override]
+    def ctime(self) -> str: ...
+    def isoformat(self, sep: str = ..., timespec: str = ...) -> str: ...
+    @classmethod
+    def strptime(
+        # Note: strptime is actually disabled and raises NotImplementedError
+        cls,
+        date_string: str,
+        format: str,
+    ) -> Self: ...
+    def utcoffset(self) -> timedelta | None: ...
+    def tzname(self) -> str | None: ...
+    def dst(self) -> timedelta | None: ...
+    def __le__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    def __lt__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    def __ge__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    def __gt__(self, other: datetime) -> bool: ...  # type: ignore[override]
+    # error: Signature of "__add__" incompatible with supertype "date"/"datetime"
+    @overload  # type: ignore[override]
+    def __add__(self, other: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __add__(self, other: timedelta | np.timedelta64 | Tick) -> Self: ...
+    def __radd__(self, other: timedelta) -> Self: ...
+    @overload  # type: ignore[override]
+    def __sub__(self, other: datetime) -> Timedelta: ...
+    @overload
+    def __sub__(self, other: timedelta | np.timedelta64 | Tick) -> Self: ...
+    def __hash__(self) -> int: ...
+    def weekday(self) -> int: ...
+    def isoweekday(self) -> int: ...
+    # Return type "Tuple[int, int, int]" of "isocalendar" incompatible with return
+    # type "_IsoCalendarDate" in supertype "date"
+    def isocalendar(self) -> tuple[int, int, int]: ...  # type: ignore[override]
+    @property
+    def is_leap_year(self) -> bool: ...
+    @property
+    def is_month_start(self) -> bool: ...
+    @property
+    def is_quarter_start(self) -> bool: ...
+    @property
+    def is_year_start(self) -> bool: ...
+    @property
+    def is_month_end(self) -> bool: ...
+    @property
+    def is_quarter_end(self) -> bool: ...
+    @property
+    def is_year_end(self) -> bool: ...
+    def to_pydatetime(self, warn: bool = ...) -> datetime: ...
+    def to_datetime64(self) -> np.datetime64: ...
+    def to_period(self, freq: BaseOffset | str | None = None) -> Period: ...
+    def to_julian_date(self) -> np.float64: ...
+    @property
+    def asm8(self) -> np.datetime64: ...
+    def tz_convert(self, tz: _TimeZones) -> Self: ...
+    # TODO: could return NaT?
+    def tz_localize(
+        self,
+        tz: _TimeZones,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def normalize(self) -> Self: ...
+    # TODO: round/floor/ceil could return NaT?
+    def round(
+        self,
+        freq: str,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def floor(
+        self,
+        freq: str,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def ceil(
+        self,
+        freq: str,
+        ambiguous: bool | Literal["raise", "NaT"] = ...,
+        nonexistent: TimestampNonexistent = ...,
+    ) -> Self: ...
+    def day_name(self, locale: str | None = ...) -> str: ...
+    def month_name(self, locale: str | None = ...) -> str: ...
+    @property
+    def day_of_week(self) -> int: ...
+    @property
+    def dayofweek(self) -> int: ...
+    @property
+    def day_of_year(self) -> int: ...
+    @property
+    def dayofyear(self) -> int: ...
+    @property
+    def quarter(self) -> int: ...
+    @property
+    def week(self) -> int: ...
+    def to_numpy(
+        self, dtype: np.dtype | None = ..., copy: bool = ...
+    ) -> np.datetime64: ...
+    @property
+    def _date_repr(self) -> str: ...
+    @property
+    def days_in_month(self) -> int: ...
+    @property
+    def daysinmonth(self) -> int: ...
+    @property
+    def unit(self) -> str: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> Timestamp: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timezones.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timezones.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..85f67ed4baabae7cf2b9b29a04e44b79af225f68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timezones.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07ec62e718c772cda488c50484de7e6b005ffe671567dbe6421685e6874be75d
+size 278896
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timezones.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timezones.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4e9f0c6ae6c33447ebc86d3daf5bf5cedbe5b0cb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/timezones.pyi
@@ -0,0 +1,21 @@
+from datetime import (
+    datetime,
+    tzinfo,
+)
+from typing import Callable
+
+import numpy as np
+
+# imported from dateutil.tz
+dateutil_gettz: Callable[[str], tzinfo]
+
+def tz_standardize(tz: tzinfo) -> tzinfo: ...
+def tz_compare(start: tzinfo | None, end: tzinfo | None) -> bool: ...
+def infer_tzinfo(
+    start: datetime | None,
+    end: datetime | None,
+) -> tzinfo | None: ...
+def maybe_get_tz(tz: str | int | np.int64 | tzinfo | None) -> tzinfo | None: ...
+def get_timezone(tz: tzinfo) -> tzinfo | str: ...
+def is_utc(tz: tzinfo | None) -> bool: ...
+def is_fixed_offset(tz: tzinfo) -> bool: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..d9fa9b90e8c2faf7471ce29b0bc5b5a5e4d6d372
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa055df2d1e30ef1d62e6ffb0af2c6e1e3ae1de42312f71fbb90a4840c6e3be7
+size 333008
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2108fa0f35547191c3db683f8cc1015e5bda4abb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/tzconversion.pyi
@@ -0,0 +1,21 @@
+from datetime import (
+    timedelta,
+    tzinfo,
+)
+from typing import Iterable
+
+import numpy as np
+
+from pandas._typing import npt
+
+# tz_convert_from_utc_single exposed for testing
+def tz_convert_from_utc_single(
+    utc_val: np.int64, tz: tzinfo, creso: int = ...
+) -> np.int64: ...
+def tz_localize_to_utc(
+    vals: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    ambiguous: str | bool | Iterable[bool] | None = ...,
+    nonexistent: str | timedelta | np.timedelta64 | None = ...,
+    creso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/vectorized.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/vectorized.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..dd65968730d600c89144819166b3682d9ac39568
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/vectorized.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53feed8a3a38ffcdb15296c4d7059085cdc62525ffdffc47b592e93038aa82c9
+size 242320
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/vectorized.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/vectorized.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..de19f592da62bbeb0b3ab267039abd902d4bb854
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/tslibs/vectorized.pyi
@@ -0,0 +1,43 @@
+"""
+For cython types that cannot be represented precisely, closest-available
+python equivalents are used, and the precise types kept as adjacent comments.
+"""
+from datetime import tzinfo
+
+import numpy as np
+
+from pandas._libs.tslibs.dtypes import Resolution
+from pandas._typing import npt
+
+def dt64arr_to_periodarr(
+    stamps: npt.NDArray[np.int64],
+    freq: int,
+    tz: tzinfo | None,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
+def is_date_array_normalized(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    reso: int,  # NPY_DATETIMEUNIT
+) -> bool: ...
+def normalize_i8_timestamps(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    reso: int,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
+def get_resolution(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> Resolution: ...
+def ints_to_pydatetime(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None = ...,
+    box: str = ...,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.object_]: ...
+def tz_convert_from_utc(
+    stamps: npt.NDArray[np.int64],
+    tz: tzinfo | None,
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..6231f9ab1cd4ee2e33d2f7d337ded46595fe9805
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79ab3b3a83c989c9d106b98ecf98acb0f6f254535ca75ac54c298f56b55672c1
+size 415112
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a6cfbec9b15b9aa13a15dab3ff35b0d8761f1036
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.pyi
@@ -0,0 +1,127 @@
+from typing import (
+    Any,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    WindowingRankType,
+    npt,
+)
+
+def roll_sum(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_mean(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_var(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+    ddof: int = ...,
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_skew(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_kurt(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_median_c(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_max(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_min(
+    values: np.ndarray,  # np.ndarray[np.float64]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_quantile(
+    values: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+    quantile: float,  # float64_t
+    interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_rank(
+    values: np.ndarray,
+    start: np.ndarray,
+    end: np.ndarray,
+    minp: int,
+    percentile: bool,
+    method: WindowingRankType,
+    ascending: bool,
+) -> np.ndarray: ...  # np.ndarray[float]
+def roll_apply(
+    obj: object,
+    start: np.ndarray,  # np.ndarray[np.int64]
+    end: np.ndarray,  # np.ndarray[np.int64]
+    minp: int,  # int64_t
+    function: Callable[..., Any],
+    raw: bool,
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+) -> npt.NDArray[np.float64]: ...
+def roll_weighted_sum(
+    values: np.ndarray,  # const float64_t[:]
+    weights: np.ndarray,  # const float64_t[:]
+    minp: int,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def roll_weighted_mean(
+    values: np.ndarray,  # const float64_t[:]
+    weights: np.ndarray,  # const float64_t[:]
+    minp: int,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def roll_weighted_var(
+    values: np.ndarray,  # const float64_t[:]
+    weights: np.ndarray,  # const float64_t[:]
+    minp: int,  # int64_t
+    ddof: int,  # unsigned int
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def ewm(
+    vals: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # const int64_t[:]
+    end: np.ndarray,  # const int64_t[:]
+    minp: int,
+    com: float,  # float64_t
+    adjust: bool,
+    ignore_na: bool,
+    deltas: np.ndarray | None = None,  # const float64_t[:]
+    normalize: bool = True,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
+def ewmcov(
+    input_x: np.ndarray,  # const float64_t[:]
+    start: np.ndarray,  # const int64_t[:]
+    end: np.ndarray,  # const int64_t[:]
+    minp: int,
+    input_y: np.ndarray,  # const float64_t[:]
+    com: float,  # float64_t
+    adjust: bool,
+    ignore_na: bool,
+    bias: bool,
+) -> np.ndarray: ...  # np.ndarray[np.float64]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/indexers.cpython-310-x86_64-linux-gnu.so b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/indexers.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..b850daeb9e7533fd719caa04f7192588d869519f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/indexers.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6231647e971a905eb0548e034b0a1d9b7e8a71218021e25114f1e0ae80ab8ed
+size 209040
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/indexers.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/indexers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c9bc64be34ac9a41d14fef33b0fc76bdf66527e9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/indexers.pyi
@@ -0,0 +1,12 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def calculate_variable_window_bounds(
+    num_values: int,  # int64_t
+    window_size: int,  # int64_t
+    min_periods,
+    center: bool,
+    closed: str | None,
+    index: np.ndarray,  # const int64_t[:]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/writers.pyi b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/writers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7b41856525dadf79a2bf4b29c7ddebfedaa880db
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/writers.pyi
@@ -0,0 +1,20 @@
+import numpy as np
+
+from pandas._typing import ArrayLike
+
+def write_csv_rows(
+    data: list[ArrayLike],
+    data_index: np.ndarray,
+    nlevels: int,
+    cols: np.ndarray,
+    writer: object,  # _csv.writer
+) -> None: ...
+def convert_json_to_lines(arr: str) -> str: ...
+def max_len_string_array(
+    arr: np.ndarray,  # pandas_string[:]
+) -> int: ...
+def word_len(val: object) -> int: ...
+def string_array_replace_from_nan_rep(
+    arr: np.ndarray,  # np.ndarray[object, ndim=1]
+    nan_rep: object,
+) -> None: ...
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7197f23ce1e4981fd7dcea5bdc4f8db8810f277
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/__init__.py
@@ -0,0 +1,635 @@
+from __future__ import annotations
+
+from decimal import Decimal
+import operator
+import os
+from sys import byteorder
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    ContextManager,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+from pandas._config.localization import (
+    can_set_locale,
+    get_locales,
+    set_locale,
+)
+
+from pandas.compat import pa_version_under10p1
+
+import pandas as pd
+from pandas import (
+    ArrowDtype,
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+)
+from pandas._testing._io import (
+    round_trip_localpath,
+    round_trip_pathlib,
+    round_trip_pickle,
+    write_to_compressed,
+)
+from pandas._testing._warnings import (
+    assert_produces_warning,
+    maybe_produces_warning,
+)
+from pandas._testing.asserters import (
+    assert_almost_equal,
+    assert_attr_equal,
+    assert_categorical_equal,
+    assert_class_equal,
+    assert_contains_all,
+    assert_copy,
+    assert_datetime_array_equal,
+    assert_dict_equal,
+    assert_equal,
+    assert_extension_array_equal,
+    assert_frame_equal,
+    assert_index_equal,
+    assert_indexing_slices_equivalent,
+    assert_interval_array_equal,
+    assert_is_sorted,
+    assert_is_valid_plot_return_object,
+    assert_metadata_equivalent,
+    assert_numpy_array_equal,
+    assert_period_array_equal,
+    assert_series_equal,
+    assert_sp_array_equal,
+    assert_timedelta_array_equal,
+    raise_assert_detail,
+)
+from pandas._testing.compat import (
+    get_dtype,
+    get_obj,
+)
+from pandas._testing.contexts import (
+    assert_cow_warning,
+    decompress_file,
+    ensure_clean,
+    raises_chained_assignment_error,
+    set_timezone,
+    use_numexpr,
+    with_csv_dialect,
+)
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    NumpyExtensionArray,
+)
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Dtype,
+        NpDtype,
+    )
+
+
+UNSIGNED_INT_NUMPY_DTYPES: list[NpDtype] = ["uint8", "uint16", "uint32", "uint64"]
+UNSIGNED_INT_EA_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
+SIGNED_INT_NUMPY_DTYPES: list[NpDtype] = [int, "int8", "int16", "int32", "int64"]
+SIGNED_INT_EA_DTYPES: list[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
+ALL_INT_NUMPY_DTYPES = UNSIGNED_INT_NUMPY_DTYPES + SIGNED_INT_NUMPY_DTYPES
+ALL_INT_EA_DTYPES = UNSIGNED_INT_EA_DTYPES + SIGNED_INT_EA_DTYPES
+ALL_INT_DTYPES: list[Dtype] = [*ALL_INT_NUMPY_DTYPES, *ALL_INT_EA_DTYPES]
+
+FLOAT_NUMPY_DTYPES: list[NpDtype] = [float, "float32", "float64"]
+FLOAT_EA_DTYPES: list[Dtype] = ["Float32", "Float64"]
+ALL_FLOAT_DTYPES: list[Dtype] = [*FLOAT_NUMPY_DTYPES, *FLOAT_EA_DTYPES]
+
+COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
+if using_string_dtype():
+    STRING_DTYPES: list[Dtype] = ["U"]
+else:
+    STRING_DTYPES: list[Dtype] = [str, "str", "U"]  # type: ignore[no-redef]
+COMPLEX_FLOAT_DTYPES: list[Dtype] = [*COMPLEX_DTYPES, *FLOAT_NUMPY_DTYPES]
+
+DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]
+TIMEDELTA64_DTYPES: list[Dtype] = ["timedelta64[ns]", "m8[ns]"]
+
+BOOL_DTYPES: list[Dtype] = [bool, "bool"]
+BYTES_DTYPES: list[Dtype] = [bytes, "bytes"]
+OBJECT_DTYPES: list[Dtype] = [object, "object"]
+
+ALL_REAL_NUMPY_DTYPES = FLOAT_NUMPY_DTYPES + ALL_INT_NUMPY_DTYPES
+ALL_REAL_EXTENSION_DTYPES = FLOAT_EA_DTYPES + ALL_INT_EA_DTYPES
+ALL_REAL_DTYPES: list[Dtype] = [*ALL_REAL_NUMPY_DTYPES, *ALL_REAL_EXTENSION_DTYPES]
+ALL_NUMERIC_DTYPES: list[Dtype] = [*ALL_REAL_DTYPES, *COMPLEX_DTYPES]
+
+ALL_NUMPY_DTYPES = (
+    ALL_REAL_NUMPY_DTYPES
+    + COMPLEX_DTYPES
+    + STRING_DTYPES
+    + DATETIME64_DTYPES
+    + TIMEDELTA64_DTYPES
+    + BOOL_DTYPES
+    + OBJECT_DTYPES
+    + BYTES_DTYPES
+)
+
+NARROW_NP_DTYPES = [
+    np.float16,
+    np.float32,
+    np.int8,
+    np.int16,
+    np.int32,
+    np.uint8,
+    np.uint16,
+    np.uint32,
+]
+
+PYTHON_DATA_TYPES = [
+    str,
+    int,
+    float,
+    complex,
+    list,
+    tuple,
+    range,
+    dict,
+    set,
+    frozenset,
+    bool,
+    bytes,
+    bytearray,
+    memoryview,
+]
+
+ENDIAN = {"little": "<", "big": ">"}[byteorder]
+
+NULL_OBJECTS = [None, np.nan, pd.NaT, float("nan"), pd.NA, Decimal("NaN")]
+NP_NAT_OBJECTS = [
+    cls("NaT", unit)
+    for cls in [np.datetime64, np.timedelta64]
+    for unit in [
+        "Y",
+        "M",
+        "W",
+        "D",
+        "h",
+        "m",
+        "s",
+        "ms",
+        "us",
+        "ns",
+        "ps",
+        "fs",
+        "as",
+    ]
+]
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+
+    UNSIGNED_INT_PYARROW_DTYPES = [pa.uint8(), pa.uint16(), pa.uint32(), pa.uint64()]
+    SIGNED_INT_PYARROW_DTYPES = [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
+    ALL_INT_PYARROW_DTYPES = UNSIGNED_INT_PYARROW_DTYPES + SIGNED_INT_PYARROW_DTYPES
+    ALL_INT_PYARROW_DTYPES_STR_REPR = [
+        str(ArrowDtype(typ)) for typ in ALL_INT_PYARROW_DTYPES
+    ]
+
+    # pa.float16 doesn't seem supported
+    # https://github.com/apache/arrow/blob/master/python/pyarrow/src/arrow/python/helpers.cc#L86
+    FLOAT_PYARROW_DTYPES = [pa.float32(), pa.float64()]
+    FLOAT_PYARROW_DTYPES_STR_REPR = [
+        str(ArrowDtype(typ)) for typ in FLOAT_PYARROW_DTYPES
+    ]
+    DECIMAL_PYARROW_DTYPES = [pa.decimal128(7, 3)]
+    STRING_PYARROW_DTYPES = [pa.string()]
+    BINARY_PYARROW_DTYPES = [pa.binary()]
+
+    TIME_PYARROW_DTYPES = [
+        pa.time32("s"),
+        pa.time32("ms"),
+        pa.time64("us"),
+        pa.time64("ns"),
+    ]
+    DATE_PYARROW_DTYPES = [pa.date32(), pa.date64()]
+    DATETIME_PYARROW_DTYPES = [
+        pa.timestamp(unit=unit, tz=tz)
+        for unit in ["s", "ms", "us", "ns"]
+        for tz in [None, "UTC", "US/Pacific", "US/Eastern"]
+    ]
+    TIMEDELTA_PYARROW_DTYPES = [pa.duration(unit) for unit in ["s", "ms", "us", "ns"]]
+
+    BOOL_PYARROW_DTYPES = [pa.bool_()]
+
+    # TODO: Add container like pyarrow types:
+    #  https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
+    ALL_PYARROW_DTYPES = (
+        ALL_INT_PYARROW_DTYPES
+        + FLOAT_PYARROW_DTYPES
+        + DECIMAL_PYARROW_DTYPES
+        + STRING_PYARROW_DTYPES
+        + BINARY_PYARROW_DTYPES
+        + TIME_PYARROW_DTYPES
+        + DATE_PYARROW_DTYPES
+        + DATETIME_PYARROW_DTYPES
+        + TIMEDELTA_PYARROW_DTYPES
+        + BOOL_PYARROW_DTYPES
+    )
+    ALL_REAL_PYARROW_DTYPES_STR_REPR = (
+        ALL_INT_PYARROW_DTYPES_STR_REPR + FLOAT_PYARROW_DTYPES_STR_REPR
+    )
+else:
+    FLOAT_PYARROW_DTYPES_STR_REPR = []
+    ALL_INT_PYARROW_DTYPES_STR_REPR = []
+    ALL_PYARROW_DTYPES = []
+    ALL_REAL_PYARROW_DTYPES_STR_REPR = []
+
+ALL_REAL_NULLABLE_DTYPES = (
+    FLOAT_NUMPY_DTYPES + ALL_REAL_EXTENSION_DTYPES + ALL_REAL_PYARROW_DTYPES_STR_REPR
+)
+
+arithmetic_dunder_methods = [
+    "__add__",
+    "__radd__",
+    "__sub__",
+    "__rsub__",
+    "__mul__",
+    "__rmul__",
+    "__floordiv__",
+    "__rfloordiv__",
+    "__truediv__",
+    "__rtruediv__",
+    "__pow__",
+    "__rpow__",
+    "__mod__",
+    "__rmod__",
+]
+
+comparison_dunder_methods = ["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"]
+
+
+# -----------------------------------------------------------------------------
+# Comparators
+
+
+def box_expected(expected, box_cls, transpose: bool = True):
+    """
+    Helper function to wrap the expected output of a test in a given box_class.
+
+    Parameters
+    ----------
+    expected : np.ndarray, Index, Series
+    box_cls : {Index, Series, DataFrame}
+
+    Returns
+    -------
+    subclass of box_cls
+    """
+    if box_cls is pd.array:
+        if isinstance(expected, RangeIndex):
+            # pd.array would return an IntegerArray
+            expected = NumpyExtensionArray(np.asarray(expected._values))
+        else:
+            expected = pd.array(expected, copy=False)
+    elif box_cls is Index:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Index(expected)
+    elif box_cls is Series:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Series(expected)
+    elif box_cls is DataFrame:
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Dtype inference", category=FutureWarning)
+            expected = Series(expected).to_frame()
+        if transpose:
+            # for vector operations, we need a DataFrame to be a single-row,
+            #  not a single-column, in order to operate against non-DataFrame
+            #  vectors of the same length. But convert to two rows to avoid
+            #  single-row special cases in datetime arithmetic
+            expected = expected.T
+            expected = pd.concat([expected] * 2, ignore_index=True)
+    elif box_cls is np.ndarray or box_cls is np.array:
+        expected = np.array(expected)
+    elif box_cls is to_array:
+        expected = to_array(expected)
+    else:
+        raise NotImplementedError(box_cls)
+    return expected
+
+
+def to_array(obj):
+    """
+    Similar to pd.array, but does not cast numpy dtypes to nullable dtypes.
+    """
+    # temporary implementation until we get pd.array in place
+    dtype = getattr(obj, "dtype", None)
+
+    if dtype is None:
+        return np.asarray(obj)
+
+    return extract_array(obj, extract_numpy=True)
+
+
+class SubclassedSeries(Series):
+    _metadata = ["testattr", "name"]
+
+    @property
+    def _constructor(self):
+        # For testing, those properties return a generic callable, and not
+        # the actual class. In this case that is equivalent, but it is to
+        # ensure we don't rely on the property returning a class
+        # See https://github.com/pandas-dev/pandas/pull/46018 and
+        # https://github.com/pandas-dev/pandas/issues/32638 and linked issues
+        return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
+
+    @property
+    def _constructor_expanddim(self):
+        return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
+
+
+class SubclassedDataFrame(DataFrame):
+    _metadata = ["testattr"]
+
+    @property
+    def _constructor(self):
+        return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
+
+    @property
+    def _constructor_sliced(self):
+        return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
+
+
+def convert_rows_list_to_csv_str(rows_list: list[str]) -> str:
+    """
+    Convert list of CSV rows to single CSV-formatted string for current OS.
+
+    This method is used for creating expected value of to_csv() method.
+
+    Parameters
+    ----------
+    rows_list : List[str]
+        Each element represents the row of csv.
+
+    Returns
+    -------
+    str
+        Expected output of to_csv() in current OS.
+    """
+    sep = os.linesep
+    return sep.join(rows_list) + sep
+
+
+def external_error_raised(expected_exception: type[Exception]) -> ContextManager:
+    """
+    Helper function to mark pytest.raises that have an external error message.
+
+    Parameters
+    ----------
+    expected_exception : Exception
+        Expected error to raise.
+
+    Returns
+    -------
+    Callable
+        Regular `pytest.raises` function with `match` equal to `None`.
+    """
+    import pytest
+
+    return pytest.raises(expected_exception, match=None)
+
+
+cython_table = pd.core.common._cython_table.items()
+
+
+def get_cython_table_params(ndframe, func_names_and_expected):
+    """
+    Combine frame, functions from com._cython_table
+    keys and expected result.
+
+    Parameters
+    ----------
+    ndframe : DataFrame or Series
+    func_names_and_expected : Sequence of two items
+        The first item is a name of a NDFrame method ('sum', 'prod') etc.
+        The second item is the expected return value.
+
+    Returns
+    -------
+    list
+        List of three items (DataFrame, function, expected result)
+    """
+    results = []
+    for func_name, expected in func_names_and_expected:
+        results.append((ndframe, func_name, expected))
+        results += [
+            (ndframe, func, expected)
+            for func, name in cython_table
+            if name == func_name
+        ]
+    return results
+
+
+def get_op_from_name(op_name: str) -> Callable:
+    """
+    The operator function for a given op name.
+
+    Parameters
+    ----------
+    op_name : str
+        The op name, in form of "add" or "__add__".
+
+    Returns
+    -------
+    function
+        A function performing the operation.
+    """
+    short_opname = op_name.strip("_")
+    try:
+        op = getattr(operator, short_opname)
+    except AttributeError:
+        # Assume it is the reverse operator
+        rop = getattr(operator, short_opname[1:])
+        op = lambda x, y: rop(y, x)
+
+    return op
+
+
+# -----------------------------------------------------------------------------
+# Indexing test helpers
+
+
+def getitem(x):
+    return x
+
+
+def setitem(x):
+    return x
+
+
+def loc(x):
+    return x.loc
+
+
+def iloc(x):
+    return x.iloc
+
+
+def at(x):
+    return x.at
+
+
+def iat(x):
+    return x.iat
+
+
+# -----------------------------------------------------------------------------
+
+_UNITS = ["s", "ms", "us", "ns"]
+
+
+def get_finest_unit(left: str, right: str):
+    """
+    Find the higher of two datetime64 units.
+    """
+    if _UNITS.index(left) >= _UNITS.index(right):
+        return left
+    return right
+
+
+def shares_memory(left, right) -> bool:
+    """
+    Pandas-compat for np.shares_memory.
+    """
+    if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+        return np.shares_memory(left, right)
+    elif isinstance(left, np.ndarray):
+        # Call with reversed args to get to unpacking logic below.
+        return shares_memory(right, left)
+
+    if isinstance(left, RangeIndex):
+        return False
+    if isinstance(left, MultiIndex):
+        return shares_memory(left._codes, right)
+    if isinstance(left, (Index, Series)):
+        if isinstance(right, (Index, Series)):
+            return shares_memory(left._values, right._values)
+        return shares_memory(left._values, right)
+
+    if isinstance(left, NDArrayBackedExtensionArray):
+        return shares_memory(left._ndarray, right)
+    if isinstance(left, pd.core.arrays.SparseArray):
+        return shares_memory(left.sp_values, right)
+    if isinstance(left, pd.core.arrays.IntervalArray):
+        return shares_memory(left._left, right) or shares_memory(left._right, right)
+
+    if isinstance(left, ArrowExtensionArray):
+        if isinstance(right, ArrowExtensionArray):
+            # https://github.com/pandas-dev/pandas/pull/43930#discussion_r736862669
+            left_pa_data = left._pa_array
+            right_pa_data = right._pa_array
+            left_buf1 = left_pa_data.chunk(0).buffers()[1]
+            right_buf1 = right_pa_data.chunk(0).buffers()[1]
+            return left_buf1.address == right_buf1.address
+        else:
+            # if we have one one ArrowExtensionArray and one other array, assume
+            # they can only share memory if they share the same numpy buffer
+            return np.shares_memory(left, right)
+
+    if isinstance(left, BaseMaskedArray) and isinstance(right, BaseMaskedArray):
+        # By convention, we'll say these share memory if they share *either*
+        #  the _data or the _mask
+        return np.shares_memory(left._data, right._data) or np.shares_memory(
+            left._mask, right._mask
+        )
+
+    if isinstance(left, DataFrame) and len(left._mgr.arrays) == 1:
+        arr = left._mgr.arrays[0]
+        return shares_memory(arr, right)
+
+    raise NotImplementedError(type(left), type(right))
+
+
+__all__ = [
+    "ALL_INT_EA_DTYPES",
+    "ALL_INT_NUMPY_DTYPES",
+    "ALL_NUMPY_DTYPES",
+    "ALL_REAL_NUMPY_DTYPES",
+    "assert_almost_equal",
+    "assert_attr_equal",
+    "assert_categorical_equal",
+    "assert_class_equal",
+    "assert_contains_all",
+    "assert_copy",
+    "assert_datetime_array_equal",
+    "assert_dict_equal",
+    "assert_equal",
+    "assert_extension_array_equal",
+    "assert_frame_equal",
+    "assert_index_equal",
+    "assert_indexing_slices_equivalent",
+    "assert_interval_array_equal",
+    "assert_is_sorted",
+    "assert_is_valid_plot_return_object",
+    "assert_metadata_equivalent",
+    "assert_numpy_array_equal",
+    "assert_period_array_equal",
+    "assert_produces_warning",
+    "assert_series_equal",
+    "assert_sp_array_equal",
+    "assert_timedelta_array_equal",
+    "assert_cow_warning",
+    "at",
+    "BOOL_DTYPES",
+    "box_expected",
+    "BYTES_DTYPES",
+    "can_set_locale",
+    "COMPLEX_DTYPES",
+    "convert_rows_list_to_csv_str",
+    "DATETIME64_DTYPES",
+    "decompress_file",
+    "ENDIAN",
+    "ensure_clean",
+    "external_error_raised",
+    "FLOAT_EA_DTYPES",
+    "FLOAT_NUMPY_DTYPES",
+    "get_cython_table_params",
+    "get_dtype",
+    "getitem",
+    "get_locales",
+    "get_finest_unit",
+    "get_obj",
+    "get_op_from_name",
+    "iat",
+    "iloc",
+    "loc",
+    "maybe_produces_warning",
+    "NARROW_NP_DTYPES",
+    "NP_NAT_OBJECTS",
+    "NULL_OBJECTS",
+    "OBJECT_DTYPES",
+    "raise_assert_detail",
+    "raises_chained_assignment_error",
+    "round_trip_localpath",
+    "round_trip_pathlib",
+    "round_trip_pickle",
+    "setitem",
+    "set_locale",
+    "set_timezone",
+    "shares_memory",
+    "SIGNED_INT_EA_DTYPES",
+    "SIGNED_INT_NUMPY_DTYPES",
+    "STRING_DTYPES",
+    "SubclassedDataFrame",
+    "SubclassedSeries",
+    "TIMEDELTA64_DTYPES",
+    "to_array",
+    "UNSIGNED_INT_EA_DTYPES",
+    "UNSIGNED_INT_NUMPY_DTYPES",
+    "use_numexpr",
+    "with_csv_dialect",
+    "write_to_compressed",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_hypothesis.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_hypothesis.py
new file mode 100644
index 0000000000000000000000000000000000000000..084ca9c306d192a2543108249dbc345d1259be01
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_hypothesis.py
@@ -0,0 +1,93 @@
+"""
+Hypothesis data generator helpers.
+"""
+from datetime import datetime
+
+from hypothesis import strategies as st
+from hypothesis.extra.dateutil import timezones as dateutil_timezones
+from hypothesis.extra.pytz import timezones as pytz_timezones
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+
+from pandas.tseries.offsets import (
+    BMonthBegin,
+    BMonthEnd,
+    BQuarterBegin,
+    BQuarterEnd,
+    BYearBegin,
+    BYearEnd,
+    MonthBegin,
+    MonthEnd,
+    QuarterBegin,
+    QuarterEnd,
+    YearBegin,
+    YearEnd,
+)
+
+OPTIONAL_INTS = st.lists(st.one_of(st.integers(), st.none()), max_size=10, min_size=3)
+
+OPTIONAL_FLOATS = st.lists(st.one_of(st.floats(), st.none()), max_size=10, min_size=3)
+
+OPTIONAL_TEXT = st.lists(st.one_of(st.none(), st.text()), max_size=10, min_size=3)
+
+OPTIONAL_DICTS = st.lists(
+    st.one_of(st.none(), st.dictionaries(st.text(), st.integers())),
+    max_size=10,
+    min_size=3,
+)
+
+OPTIONAL_LISTS = st.lists(
+    st.one_of(st.none(), st.lists(st.text(), max_size=10, min_size=3)),
+    max_size=10,
+    min_size=3,
+)
+
+OPTIONAL_ONE_OF_ALL = st.one_of(
+    OPTIONAL_DICTS, OPTIONAL_FLOATS, OPTIONAL_INTS, OPTIONAL_LISTS, OPTIONAL_TEXT
+)
+
+if is_platform_windows():
+    DATETIME_NO_TZ = st.datetimes(min_value=datetime(1900, 1, 1))
+else:
+    DATETIME_NO_TZ = st.datetimes()
+
+DATETIME_JAN_1_1900_OPTIONAL_TZ = st.datetimes(
+    min_value=pd.Timestamp(
+        1900, 1, 1
+    ).to_pydatetime(),  # pyright: ignore[reportGeneralTypeIssues]
+    max_value=pd.Timestamp(
+        1900, 1, 1
+    ).to_pydatetime(),  # pyright: ignore[reportGeneralTypeIssues]
+    timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()),
+)
+
+DATETIME_IN_PD_TIMESTAMP_RANGE_NO_TZ = st.datetimes(
+    min_value=pd.Timestamp.min.to_pydatetime(warn=False),
+    max_value=pd.Timestamp.max.to_pydatetime(warn=False),
+)
+
+INT_NEG_999_TO_POS_999 = st.integers(-999, 999)
+
+# The strategy for each type is registered in conftest.py, as they don't carry
+# enough runtime information (e.g. type hints) to infer how to build them.
+YQM_OFFSET = st.one_of(
+    *map(
+        st.from_type,
+        [
+            MonthBegin,
+            MonthEnd,
+            BMonthBegin,
+            BMonthEnd,
+            QuarterBegin,
+            QuarterEnd,
+            BQuarterBegin,
+            BQuarterEnd,
+            YearBegin,
+            YearEnd,
+            BYearBegin,
+            BYearEnd,
+        ],
+    )
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_io.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..95977edb600ade42a8f8a1fada2b5085cee1da56
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_io.py
@@ -0,0 +1,170 @@
+from __future__ import annotations
+
+import gzip
+import io
+import pathlib
+import tarfile
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import uuid
+import zipfile
+
+from pandas.compat import (
+    get_bz2_file,
+    get_lzma_file,
+)
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+from pandas._testing.contexts import ensure_clean
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        FilePath,
+        ReadPickleBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ------------------------------------------------------------------
+# File-IO
+
+
+def round_trip_pickle(
+    obj: Any, path: FilePath | ReadPickleBuffer | None = None
+) -> DataFrame | Series:
+    """
+    Pickle an object and then read it again.
+
+    Parameters
+    ----------
+    obj : any object
+        The object to pickle and then re-read.
+    path : str, path object or file-like object, default None
+        The path where the pickled object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was pickled and then re-read.
+    """
+    _path = path
+    if _path is None:
+        _path = f"__{uuid.uuid4()}__.pickle"
+    with ensure_clean(_path) as temp_path:
+        pd.to_pickle(obj, temp_path)
+        return pd.read_pickle(temp_path)
+
+
+def round_trip_pathlib(writer, reader, path: str | None = None):
+    """
+    Write an object to file specified by a pathlib.Path and read it back
+
+    Parameters
+    ----------
+    writer : callable bound to pandas object
+        IO writing function (e.g. DataFrame.to_csv )
+    reader : callable
+        IO reading function (e.g. pd.read_csv )
+    path : str, default None
+        The path where the object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was serialized and then re-read.
+    """
+    Path = pathlib.Path
+    if path is None:
+        path = "___pathlib___"
+    with ensure_clean(path) as path:
+        writer(Path(path))  # type: ignore[arg-type]
+        obj = reader(Path(path))  # type: ignore[arg-type]
+    return obj
+
+
+def round_trip_localpath(writer, reader, path: str | None = None):
+    """
+    Write an object to file specified by a py.path LocalPath and read it back.
+
+    Parameters
+    ----------
+    writer : callable bound to pandas object
+        IO writing function (e.g. DataFrame.to_csv )
+    reader : callable
+        IO reading function (e.g. pd.read_csv )
+    path : str, default None
+        The path where the object is written and then read.
+
+    Returns
+    -------
+    pandas object
+        The original object that was serialized and then re-read.
+    """
+    import pytest
+
+    LocalPath = pytest.importorskip("py.path").local
+    if path is None:
+        path = "___localpath___"
+    with ensure_clean(path) as path:
+        writer(LocalPath(path))
+        obj = reader(LocalPath(path))
+    return obj
+
+
+def write_to_compressed(compression, path, data, dest: str = "test") -> None:
+    """
+    Write data to a compressed file.
+
+    Parameters
+    ----------
+    compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd'}
+        The compression type to use.
+    path : str
+        The file path to write the data.
+    data : str
+        The data to write.
+    dest : str, default "test"
+        The destination file (for ZIP only)
+
+    Raises
+    ------
+    ValueError : An invalid compression value was passed in.
+    """
+    args: tuple[Any, ...] = (data,)
+    mode = "wb"
+    method = "write"
+    compress_method: Callable
+
+    if compression == "zip":
+        compress_method = zipfile.ZipFile
+        mode = "w"
+        args = (dest, data)
+        method = "writestr"
+    elif compression == "tar":
+        compress_method = tarfile.TarFile
+        mode = "w"
+        file = tarfile.TarInfo(name=dest)
+        bytes = io.BytesIO(data)
+        file.size = len(data)
+        args = (file, bytes)
+        method = "addfile"
+    elif compression == "gzip":
+        compress_method = gzip.GzipFile
+    elif compression == "bz2":
+        compress_method = get_bz2_file()
+    elif compression == "zstd":
+        compress_method = import_optional_dependency("zstandard").open
+    elif compression == "xz":
+        compress_method = get_lzma_file()
+    else:
+        raise ValueError(f"Unrecognized compression type: {compression}")
+
+    with compress_method(path, mode=mode) as f:
+        getattr(f, method)(*args)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_warnings.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_warnings.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9a287942f2dac5ddbaf49168db280ec2ba3f2c4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/_warnings.py
@@ -0,0 +1,232 @@
+from __future__ import annotations
+
+from contextlib import (
+    contextmanager,
+    nullcontext,
+)
+import inspect
+import re
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+from pandas.compat import PY311
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Sequence,
+    )
+
+
+@contextmanager
+def assert_produces_warning(
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None = Warning,
+    filter_level: Literal[
+        "error", "ignore", "always", "default", "module", "once"
+    ] = "always",
+    check_stacklevel: bool = True,
+    raise_on_extra_warnings: bool = True,
+    match: str | None = None,
+) -> Generator[list[warnings.WarningMessage], None, None]:
+    """
+    Context manager for running code expected to either raise a specific warning,
+    multiple specific warnings, or not raise any warnings. Verifies that the code
+    raises the expected warning(s), and that it does not raise any other unexpected
+    warnings. It is basically a wrapper around ``warnings.catch_warnings``.
+
+    Parameters
+    ----------
+    expected_warning : {Warning, False, tuple[Warning, ...], None}, default Warning
+        The type of Exception raised. ``exception.Warning`` is the base
+        class for all warnings. To raise multiple types of exceptions,
+        pass them as a tuple. To check that no warning is returned,
+        specify ``False`` or ``None``.
+    filter_level : str or None, default "always"
+        Specifies whether warnings are ignored, displayed, or turned
+        into errors.
+        Valid values are:
+
+        * "error" - turns matching warnings into exceptions
+        * "ignore" - discard the warning
+        * "always" - always emit a warning
+        * "default" - print the warning the first time it is generated
+          from each location
+        * "module" - print the warning the first time it is generated
+          from each module
+        * "once" - print the warning the first time it is generated
+
+    check_stacklevel : bool, default True
+        If True, displays the line that called the function containing
+        the warning to show were the function is called. Otherwise, the
+        line that implements the function is displayed.
+    raise_on_extra_warnings : bool, default True
+        Whether extra warnings not of the type `expected_warning` should
+        cause the test to fail.
+    match : str, optional
+        Match warning message.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> with assert_produces_warning():
+    ...     warnings.warn(UserWarning())
+    ...
+    >>> with assert_produces_warning(False):
+    ...     warnings.warn(RuntimeWarning())
+    ...
+    Traceback (most recent call last):
+        ...
+    AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
+    >>> with assert_produces_warning(UserWarning):
+    ...     warnings.warn(RuntimeWarning())
+    Traceback (most recent call last):
+        ...
+    AssertionError: Did not see expected warning of class 'UserWarning'.
+
+    ..warn:: This is *not* thread-safe.
+    """
+    __tracebackhide__ = True
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter(filter_level)
+        try:
+            yield w
+        finally:
+            if expected_warning:
+                expected_warning = cast(type[Warning], expected_warning)
+                _assert_caught_expected_warning(
+                    caught_warnings=w,
+                    expected_warning=expected_warning,
+                    match=match,
+                    check_stacklevel=check_stacklevel,
+                )
+            if raise_on_extra_warnings:
+                _assert_caught_no_extra_warnings(
+                    caught_warnings=w,
+                    expected_warning=expected_warning,
+                )
+
+
+def maybe_produces_warning(warning: type[Warning], condition: bool, **kwargs):
+    """
+    Return a context manager that possibly checks a warning based on the condition
+    """
+    if condition:
+        return assert_produces_warning(warning, **kwargs)
+    else:
+        return nullcontext()
+
+
+def _assert_caught_expected_warning(
+    *,
+    caught_warnings: Sequence[warnings.WarningMessage],
+    expected_warning: type[Warning],
+    match: str | None,
+    check_stacklevel: bool,
+) -> None:
+    """Assert that there was the expected warning among the caught warnings."""
+    saw_warning = False
+    matched_message = False
+    unmatched_messages = []
+
+    for actual_warning in caught_warnings:
+        if issubclass(actual_warning.category, expected_warning):
+            saw_warning = True
+
+            if check_stacklevel:
+                _assert_raised_with_correct_stacklevel(actual_warning)
+
+            if match is not None:
+                if re.search(match, str(actual_warning.message)):
+                    matched_message = True
+                else:
+                    unmatched_messages.append(actual_warning.message)
+
+    if not saw_warning:
+        raise AssertionError(
+            f"Did not see expected warning of class "
+            f"{repr(expected_warning.__name__)}"
+        )
+
+    if match and not matched_message:
+        raise AssertionError(
+            f"Did not see warning {repr(expected_warning.__name__)} "
+            f"matching '{match}'. The emitted warning messages are "
+            f"{unmatched_messages}"
+        )
+
+
+def _assert_caught_no_extra_warnings(
+    *,
+    caught_warnings: Sequence[warnings.WarningMessage],
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None,
+) -> None:
+    """Assert that no extra warnings apart from the expected ones are caught."""
+    extra_warnings = []
+
+    for actual_warning in caught_warnings:
+        if _is_unexpected_warning(actual_warning, expected_warning):
+            # GH#38630 pytest.filterwarnings does not suppress these.
+            if actual_warning.category == ResourceWarning:
+                # GH 44732: Don't make the CI flaky by filtering SSL-related
+                # ResourceWarning from dependencies
+                if "unclosed <ssl.SSLSocket" in str(actual_warning.message):
+                    continue
+                # GH 44844: Matplotlib leaves font files open during the entire process
+                # upon import. Don't make CI flaky if ResourceWarning raised
+                # due to these open files.
+                if any("matplotlib" in mod for mod in sys.modules):
+                    continue
+            if PY311 and actual_warning.category == EncodingWarning:
+                # EncodingWarnings are checked in the CI
+                # pyproject.toml errors on EncodingWarnings in pandas
+                # Ignore EncodingWarnings from other libraries
+                continue
+            extra_warnings.append(
+                (
+                    actual_warning.category.__name__,
+                    actual_warning.message,
+                    actual_warning.filename,
+                    actual_warning.lineno,
+                )
+            )
+
+    if extra_warnings:
+        raise AssertionError(f"Caused unexpected warning(s): {repr(extra_warnings)}")
+
+
+def _is_unexpected_warning(
+    actual_warning: warnings.WarningMessage,
+    expected_warning: type[Warning] | bool | tuple[type[Warning], ...] | None,
+) -> bool:
+    """Check if the actual warning issued is unexpected."""
+    if actual_warning and not expected_warning:
+        return True
+    expected_warning = cast(type[Warning], expected_warning)
+    return bool(not issubclass(actual_warning.category, expected_warning))
+
+
+def _assert_raised_with_correct_stacklevel(
+    actual_warning: warnings.WarningMessage,
+) -> None:
+    # https://stackoverflow.com/questions/17407119/python-inspect-stack-is-slow
+    frame = inspect.currentframe()
+    for _ in range(4):
+        frame = frame.f_back  # type: ignore[union-attr]
+    try:
+        caller_filename = inspect.getfile(frame)  # type: ignore[arg-type]
+    finally:
+        # See note in
+        # https://docs.python.org/3/library/inspect.html#inspect.Traceback
+        del frame
+    msg = (
+        "Warning not set with correct stacklevel. "
+        f"File where warning is raised: {actual_warning.filename} != "
+        f"{caller_filename}. Warning message: {actual_warning.message}"
+    )
+    assert actual_warning.filename == caller_filename, msg
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/asserters.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/asserters.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1f9844669c8c99848796e102878848d565bbd5c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/asserters.py
@@ -0,0 +1,1459 @@
+from __future__ import annotations
+
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    NoReturn,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.missing import is_matching_na
+from pandas._libs.sparse import SparseIndex
+import pandas._libs.testing as _testing
+from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float_dtype,
+    is_integer_dtype,
+    is_number,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    NumpyEADtype,
+)
+from pandas.core.dtypes.missing import array_equivalent
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalDtype,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+)
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    IntervalArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.indexes.api import safe_sort_index
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+def assert_almost_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = "equiv",
+    rtol: float = 1.0e-5,
+    atol: float = 1.0e-8,
+    **kwargs,
+) -> None:
+    """
+    Check that the left and right objects are approximately equal.
+
+    By approximately equal, we refer to objects that are numbers or that
+    contain numbers which may be equivalent to specific levels of precision.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    check_dtype : bool or {'equiv'}, default 'equiv'
+        Check dtype if both a and b are the same type. If 'equiv' is passed in,
+        then `RangeIndex` and `Index` with int64 dtype are also considered
+        equivalent when doing type checking.
+    rtol : float, default 1e-5
+        Relative tolerance.
+    atol : float, default 1e-8
+        Absolute tolerance.
+    """
+    if isinstance(left, Index):
+        assert_index_equal(
+            left,
+            right,
+            check_exact=False,
+            exact=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    elif isinstance(left, Series):
+        assert_series_equal(
+            left,
+            right,
+            check_exact=False,
+            check_dtype=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    elif isinstance(left, DataFrame):
+        assert_frame_equal(
+            left,
+            right,
+            check_exact=False,
+            check_dtype=check_dtype,
+            rtol=rtol,
+            atol=atol,
+            **kwargs,
+        )
+
+    else:
+        # Other sequences.
+        if check_dtype:
+            if is_number(left) and is_number(right):
+                # Do not compare numeric classes, like np.float64 and float.
+                pass
+            elif is_bool(left) and is_bool(right):
+                # Do not compare bool classes, like np.bool_ and bool.
+                pass
+            else:
+                if isinstance(left, np.ndarray) or isinstance(right, np.ndarray):
+                    obj = "numpy array"
+                else:
+                    obj = "Input"
+                assert_class_equal(left, right, obj=obj)
+
+        # if we have "equiv", this becomes True
+        _testing.assert_almost_equal(
+            left, right, check_dtype=bool(check_dtype), rtol=rtol, atol=atol, **kwargs
+        )
+
+
+def _check_isinstance(left, right, cls) -> None:
+    """
+    Helper method for our assert_* methods that ensures that
+    the two objects being compared have the right type before
+    proceeding with the comparison.
+
+    Parameters
+    ----------
+    left : The first object being compared.
+    right : The second object being compared.
+    cls : The class type to check against.
+
+    Raises
+    ------
+    AssertionError : Either `left` or `right` is not an instance of `cls`.
+    """
+    cls_name = cls.__name__
+
+    if not isinstance(left, cls):
+        raise AssertionError(
+            f"{cls_name} Expected type {cls}, found {type(left)} instead"
+        )
+    if not isinstance(right, cls):
+        raise AssertionError(
+            f"{cls_name} Expected type {cls}, found {type(right)} instead"
+        )
+
+
+def assert_dict_equal(left, right, compare_keys: bool = True) -> None:
+    _check_isinstance(left, right, dict)
+    _testing.assert_dict_equal(left, right, compare_keys=compare_keys)
+
+
+def assert_index_equal(
+    left: Index,
+    right: Index,
+    exact: bool | str = "equiv",
+    check_names: bool = True,
+    check_exact: bool = True,
+    check_categorical: bool = True,
+    check_order: bool = True,
+    rtol: float = 1.0e-5,
+    atol: float = 1.0e-8,
+    obj: str = "Index",
+) -> None:
+    """
+    Check that left and right Index are equal.
+
+    Parameters
+    ----------
+    left : Index
+    right : Index
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted for
+        Index with an int64 dtype as well.
+    check_names : bool, default True
+        Whether to check the names attribute.
+    check_exact : bool, default True
+        Whether to compare number exactly.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_order : bool, default True
+        Whether to compare the order of index entries as well as their values.
+        If True, both indexes must contain the same elements, in the same order.
+        If False, both indexes must contain the same elements, but in any order.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'Index'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Index([1, 2, 3])
+    >>> b = pd.Index([1, 2, 3])
+    >>> tm.assert_index_equal(a, b)
+    """
+    __tracebackhide__ = True
+
+    def _check_types(left, right, obj: str = "Index") -> None:
+        if not exact:
+            return
+
+        assert_class_equal(left, right, exact=exact, obj=obj)
+        assert_attr_equal("inferred_type", left, right, obj=obj)
+
+        # Skip exact dtype checking when `check_categorical` is False
+        if isinstance(left.dtype, CategoricalDtype) and isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            if check_categorical:
+                assert_attr_equal("dtype", left, right, obj=obj)
+                assert_index_equal(left.categories, right.categories, exact=exact)
+            return
+
+        assert_attr_equal("dtype", left, right, obj=obj)
+
+    # instance validation
+    _check_isinstance(left, right, Index)
+
+    # class / dtype comparison
+    _check_types(left, right, obj=obj)
+
+    # level comparison
+    if left.nlevels != right.nlevels:
+        msg1 = f"{obj} levels are different"
+        msg2 = f"{left.nlevels}, {left}"
+        msg3 = f"{right.nlevels}, {right}"
+        raise_assert_detail(obj, msg1, msg2, msg3)
+
+    # length comparison
+    if len(left) != len(right):
+        msg1 = f"{obj} length are different"
+        msg2 = f"{len(left)}, {left}"
+        msg3 = f"{len(right)}, {right}"
+        raise_assert_detail(obj, msg1, msg2, msg3)
+
+    # If order doesn't matter then sort the index entries
+    if not check_order:
+        left = safe_sort_index(left)
+        right = safe_sort_index(right)
+
+    # MultiIndex special comparison for little-friendly error messages
+    if isinstance(left, MultiIndex):
+        right = cast(MultiIndex, right)
+
+        for level in range(left.nlevels):
+            lobj = f"MultiIndex level [{level}]"
+            try:
+                # try comparison on levels/codes to avoid densifying MultiIndex
+                assert_index_equal(
+                    left.levels[level],
+                    right.levels[level],
+                    exact=exact,
+                    check_names=check_names,
+                    check_exact=check_exact,
+                    check_categorical=check_categorical,
+                    rtol=rtol,
+                    atol=atol,
+                    obj=lobj,
+                )
+                assert_numpy_array_equal(left.codes[level], right.codes[level])
+            except AssertionError:
+                llevel = left.get_level_values(level)
+                rlevel = right.get_level_values(level)
+
+                assert_index_equal(
+                    llevel,
+                    rlevel,
+                    exact=exact,
+                    check_names=check_names,
+                    check_exact=check_exact,
+                    check_categorical=check_categorical,
+                    rtol=rtol,
+                    atol=atol,
+                    obj=lobj,
+                )
+            # get_level_values may change dtype
+            _check_types(left.levels[level], right.levels[level], obj=obj)
+
+    # skip exact index checking when `check_categorical` is False
+    elif check_exact and check_categorical:
+        if not left.equals(right):
+            mismatch = left._values != right._values
+
+            if not isinstance(mismatch, np.ndarray):
+                mismatch = cast("ExtensionArray", mismatch).fillna(True)
+
+            diff = np.sum(mismatch.astype(int)) * 100.0 / len(left)
+            msg = f"{obj} values are different ({np.round(diff, 5)} %)"
+            raise_assert_detail(obj, msg, left, right)
+    else:
+        # if we have "equiv", this becomes True
+        exact_bool = bool(exact)
+        _testing.assert_almost_equal(
+            left.values,
+            right.values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=exact_bool,
+            obj=obj,
+            lobj=left,
+            robj=right,
+        )
+
+    # metadata comparison
+    if check_names:
+        assert_attr_equal("names", left, right, obj=obj)
+    if isinstance(left, PeriodIndex) or isinstance(right, PeriodIndex):
+        assert_attr_equal("dtype", left, right, obj=obj)
+    if isinstance(left, IntervalIndex) or isinstance(right, IntervalIndex):
+        assert_interval_array_equal(left._values, right._values)
+
+    if check_categorical:
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
+
+
+def assert_class_equal(
+    left, right, exact: bool | str = True, obj: str = "Input"
+) -> None:
+    """
+    Checks classes are equal.
+    """
+    __tracebackhide__ = True
+
+    def repr_class(x):
+        if isinstance(x, Index):
+            # return Index as it is to include values in the error message
+            return x
+
+        return type(x).__name__
+
+    def is_class_equiv(idx: Index) -> bool:
+        """Classes that are a RangeIndex (sub-)instance or exactly an `Index` .
+
+        This only checks class equivalence. There is a separate check that the
+        dtype is int64.
+        """
+        return type(idx) is Index or isinstance(idx, RangeIndex)
+
+    if type(left) == type(right):
+        return
+
+    if exact == "equiv":
+        if is_class_equiv(left) and is_class_equiv(right):
+            return
+
+    msg = f"{obj} classes are different"
+    raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
+
+
+def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None:
+    """
+    Check attributes are equal. Both objects must have attribute.
+
+    Parameters
+    ----------
+    attr : str
+        Attribute name being compared.
+    left : object
+    right : object
+    obj : str, default 'Attributes'
+        Specify object name being compared, internally used to show appropriate
+        assertion message
+    """
+    __tracebackhide__ = True
+
+    left_attr = getattr(left, attr)
+    right_attr = getattr(right, attr)
+
+    if left_attr is right_attr or is_matching_na(left_attr, right_attr):
+        # e.g. both np.nan, both NaT, both pd.NA, ...
+        return None
+
+    try:
+        result = left_attr == right_attr
+    except TypeError:
+        # datetimetz on rhs may raise TypeError
+        result = False
+    if (left_attr is pd.NA) ^ (right_attr is pd.NA):
+        result = False
+    elif not isinstance(result, bool):
+        result = result.all()
+
+    if not result:
+        msg = f'Attribute "{attr}" are different'
+        raise_assert_detail(obj, msg, left_attr, right_attr)
+    return None
+
+
+def assert_is_valid_plot_return_object(objs) -> None:
+    from matplotlib.artist import Artist
+    from matplotlib.axes import Axes
+
+    if isinstance(objs, (Series, np.ndarray)):
+        if isinstance(objs, Series):
+            objs = objs._values
+        for el in objs.ravel():
+            msg = (
+                "one of 'objs' is not a matplotlib Axes instance, "
+                f"type encountered {repr(type(el).__name__)}"
+            )
+            assert isinstance(el, (Axes, dict)), msg
+    else:
+        msg = (
+            "objs is neither an ndarray of Artist instances nor a single "
+            "ArtistArtist instance, tuple, or dict, 'objs' is a "
+            f"{repr(type(objs).__name__)}"
+        )
+        assert isinstance(objs, (Artist, tuple, dict)), msg
+
+
+def assert_is_sorted(seq) -> None:
+    """Assert that the sequence is sorted."""
+    if isinstance(seq, (Index, Series)):
+        seq = seq.values
+    # sorting does not change precisions
+    if isinstance(seq, np.ndarray):
+        assert_numpy_array_equal(seq, np.sort(np.array(seq)))
+    else:
+        assert_extension_array_equal(seq, seq[seq.argsort()])
+
+
+def assert_categorical_equal(
+    left,
+    right,
+    check_dtype: bool = True,
+    check_category_order: bool = True,
+    obj: str = "Categorical",
+) -> None:
+    """
+    Test that Categoricals are equivalent.
+
+    Parameters
+    ----------
+    left : Categorical
+    right : Categorical
+    check_dtype : bool, default True
+        Check that integer dtype of the codes are the same.
+    check_category_order : bool, default True
+        Whether the order of the categories should be compared, which
+        implies identical integer codes.  If False, only the resulting
+        values are compared.  The ordered attribute is
+        checked regardless.
+    obj : str, default 'Categorical'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    """
+    _check_isinstance(left, right, Categorical)
+
+    exact: bool | str
+    if isinstance(left.categories, RangeIndex) or isinstance(
+        right.categories, RangeIndex
+    ):
+        exact = "equiv"
+    else:
+        # We still want to require exact matches for Index
+        exact = True
+
+    if check_category_order:
+        assert_index_equal(
+            left.categories, right.categories, obj=f"{obj}.categories", exact=exact
+        )
+        assert_numpy_array_equal(
+            left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes"
+        )
+    else:
+        try:
+            lc = left.categories.sort_values()
+            rc = right.categories.sort_values()
+        except TypeError:
+            # e.g. '<' not supported between instances of 'int' and 'str'
+            lc, rc = left.categories, right.categories
+        assert_index_equal(lc, rc, obj=f"{obj}.categories", exact=exact)
+        assert_index_equal(
+            left.categories.take(left.codes),
+            right.categories.take(right.codes),
+            obj=f"{obj}.values",
+            exact=exact,
+        )
+
+    assert_attr_equal("ordered", left, right, obj=obj)
+
+
+def assert_interval_array_equal(
+    left, right, exact: bool | Literal["equiv"] = "equiv", obj: str = "IntervalArray"
+) -> None:
+    """
+    Test that two IntervalArrays are equivalent.
+
+    Parameters
+    ----------
+    left, right : IntervalArray
+        The IntervalArrays to compare.
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted for
+        Index with an int64 dtype as well.
+    obj : str, default 'IntervalArray'
+        Specify object name being compared, internally used to show appropriate
+        assertion message
+    """
+    _check_isinstance(left, right, IntervalArray)
+
+    kwargs = {}
+    if left._left.dtype.kind in "mM":
+        # We have a DatetimeArray or TimedeltaArray
+        kwargs["check_freq"] = False
+
+    assert_equal(left._left, right._left, obj=f"{obj}.left", **kwargs)
+    assert_equal(left._right, right._right, obj=f"{obj}.left", **kwargs)
+
+    assert_attr_equal("closed", left, right, obj=obj)
+
+
+def assert_period_array_equal(left, right, obj: str = "PeriodArray") -> None:
+    _check_isinstance(left, right, PeriodArray)
+
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    assert_attr_equal("dtype", left, right, obj=obj)
+
+
+def assert_datetime_array_equal(
+    left, right, obj: str = "DatetimeArray", check_freq: bool = True
+) -> None:
+    __tracebackhide__ = True
+    _check_isinstance(left, right, DatetimeArray)
+
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    if check_freq:
+        assert_attr_equal("freq", left, right, obj=obj)
+    assert_attr_equal("tz", left, right, obj=obj)
+
+
+def assert_timedelta_array_equal(
+    left, right, obj: str = "TimedeltaArray", check_freq: bool = True
+) -> None:
+    __tracebackhide__ = True
+    _check_isinstance(left, right, TimedeltaArray)
+    assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray")
+    if check_freq:
+        assert_attr_equal("freq", left, right, obj=obj)
+
+
+def raise_assert_detail(
+    obj, message, left, right, diff=None, first_diff=None, index_values=None
+) -> NoReturn:
+    __tracebackhide__ = True
+
+    msg = f"""{obj} are different
+
+{message}"""
+
+    if isinstance(index_values, Index):
+        index_values = np.asarray(index_values)
+
+    if isinstance(index_values, np.ndarray):
+        msg += f"\n[index]: {pprint_thing(index_values)}"
+
+    if isinstance(left, np.ndarray):
+        left = pprint_thing(left)
+    elif isinstance(left, (CategoricalDtype, NumpyEADtype)):
+        left = repr(left)
+    elif isinstance(left, StringDtype):
+        # TODO(infer_string) this special case could be avoided if we have
+        # a more informative repr https://github.com/pandas-dev/pandas/issues/59342
+        left = f"StringDtype(storage={left.storage}, na_value={left.na_value})"
+
+    if isinstance(right, np.ndarray):
+        right = pprint_thing(right)
+    elif isinstance(right, (CategoricalDtype, NumpyEADtype)):
+        right = repr(right)
+    elif isinstance(right, StringDtype):
+        right = f"StringDtype(storage={right.storage}, na_value={right.na_value})"
+
+    msg += f"""
+[left]:  {left}
+[right]: {right}"""
+
+    if diff is not None:
+        msg += f"\n[diff]: {diff}"
+
+    if first_diff is not None:
+        msg += f"\n{first_diff}"
+
+    raise AssertionError(msg)
+
+
+def assert_numpy_array_equal(
+    left,
+    right,
+    strict_nan: bool = False,
+    check_dtype: bool | Literal["equiv"] = True,
+    err_msg=None,
+    check_same=None,
+    obj: str = "numpy array",
+    index_values=None,
+) -> None:
+    """
+    Check that 'np.ndarray' is equivalent.
+
+    Parameters
+    ----------
+    left, right : numpy.ndarray or iterable
+        The two arrays to be compared.
+    strict_nan : bool, default False
+        If True, consider NaN and None to be different.
+    check_dtype : bool, default True
+        Check dtype if both a and b are np.ndarray.
+    err_msg : str, default None
+        If provided, used as assertion message.
+    check_same : None|'copy'|'same', default None
+        Ensure left and right refer/do not refer to the same memory area.
+    obj : str, default 'numpy array'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    index_values : Index | numpy.ndarray, default None
+        optional index (shared by both left and right), used in output.
+    """
+    __tracebackhide__ = True
+
+    # instance validation
+    # Show a detailed error message when classes are different
+    assert_class_equal(left, right, obj=obj)
+    # both classes must be an np.ndarray
+    _check_isinstance(left, right, np.ndarray)
+
+    def _get_base(obj):
+        return obj.base if getattr(obj, "base", None) is not None else obj
+
+    left_base = _get_base(left)
+    right_base = _get_base(right)
+
+    if check_same == "same":
+        if left_base is not right_base:
+            raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}")
+    elif check_same == "copy":
+        if left_base is right_base:
+            raise AssertionError(f"{repr(left_base)} is {repr(right_base)}")
+
+    def _raise(left, right, err_msg) -> NoReturn:
+        if err_msg is None:
+            if left.shape != right.shape:
+                raise_assert_detail(
+                    obj, f"{obj} shapes are different", left.shape, right.shape
+                )
+
+            diff = 0
+            for left_arr, right_arr in zip(left, right):
+                # count up differences
+                if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
+                    diff += 1
+
+            diff = diff * 100.0 / left.size
+            msg = f"{obj} values are different ({np.round(diff, 5)} %)"
+            raise_assert_detail(obj, msg, left, right, index_values=index_values)
+
+        raise AssertionError(err_msg)
+
+    # compare shape and values
+    if not array_equivalent(left, right, strict_nan=strict_nan):
+        _raise(left, right, err_msg)
+
+    if check_dtype:
+        if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+            assert_attr_equal("dtype", left, right, obj=obj)
+
+
+def assert_extension_array_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    index_values=None,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "ExtensionArray",
+) -> None:
+    """
+    Check that left and right ExtensionArrays are equal.
+
+    Parameters
+    ----------
+    left, right : ExtensionArray
+        The two arrays to compare.
+    check_dtype : bool, default True
+        Whether to check if the ExtensionArray dtypes are identical.
+    index_values : Index | numpy.ndarray, default None
+        Optional index (shared by both left and right), used in output.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'ExtensionArray'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+        .. versionadded:: 2.0.0
+
+    Notes
+    -----
+    Missing values are checked separately from valid values.
+    A mask of missing values is computed for each and checked to match.
+    The remaining all-valid values are cast to object dtype and checked.
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Series([1, 2, 3, 4])
+    >>> b, c = a.array, a.array
+    >>> tm.assert_extension_array_equal(b, c)
+    """
+    if (
+        check_exact is lib.no_default
+        and rtol is lib.no_default
+        and atol is lib.no_default
+    ):
+        check_exact = (
+            is_numeric_dtype(left.dtype)
+            and not is_float_dtype(left.dtype)
+            or is_numeric_dtype(right.dtype)
+            and not is_float_dtype(right.dtype)
+        )
+    elif check_exact is lib.no_default:
+        check_exact = False
+
+    rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    atol = atol if atol is not lib.no_default else 1.0e-8
+
+    assert isinstance(left, ExtensionArray), "left is not an ExtensionArray"
+    assert isinstance(right, ExtensionArray), "right is not an ExtensionArray"
+    if check_dtype:
+        assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
+
+    if (
+        isinstance(left, DatetimeLikeArrayMixin)
+        and isinstance(right, DatetimeLikeArrayMixin)
+        and type(right) == type(left)
+    ):
+        # GH 52449
+        if not check_dtype and left.dtype.kind in "mM":
+            if not isinstance(left.dtype, np.dtype):
+                l_unit = cast(DatetimeTZDtype, left.dtype).unit
+            else:
+                l_unit = np.datetime_data(left.dtype)[0]
+            if not isinstance(right.dtype, np.dtype):
+                r_unit = cast(DatetimeTZDtype, right.dtype).unit
+            else:
+                r_unit = np.datetime_data(right.dtype)[0]
+            if (
+                l_unit != r_unit
+                and compare_mismatched_resolutions(
+                    left._ndarray, right._ndarray, operator.eq
+                ).all()
+            ):
+                return
+        # Avoid slow object-dtype comparisons
+        # np.asarray for case where we have a np.MaskedArray
+        assert_numpy_array_equal(
+            np.asarray(left.asi8),
+            np.asarray(right.asi8),
+            index_values=index_values,
+            obj=obj,
+        )
+        return
+
+    left_na = np.asarray(left.isna())
+    right_na = np.asarray(right.isna())
+    assert_numpy_array_equal(
+        left_na, right_na, obj=f"{obj} NA mask", index_values=index_values
+    )
+
+    # Specifically for StringArrayNumpySemantics, validate here we have a valid array
+    if (
+        isinstance(left.dtype, StringDtype)
+        and left.dtype.storage == "python"
+        and left.dtype.na_value is np.nan
+    ):
+        assert np.all(
+            [np.isnan(val) for val in left._ndarray[left_na]]  # type: ignore[attr-defined]
+        ), "wrong missing value sentinels"
+    if (
+        isinstance(right.dtype, StringDtype)
+        and right.dtype.storage == "python"
+        and right.dtype.na_value is np.nan
+    ):
+        assert np.all(
+            [np.isnan(val) for val in right._ndarray[right_na]]  # type: ignore[attr-defined]
+        ), "wrong missing value sentinels"
+
+    left_valid = left[~left_na].to_numpy(dtype=object)
+    right_valid = right[~right_na].to_numpy(dtype=object)
+    if check_exact:
+        assert_numpy_array_equal(
+            left_valid, right_valid, obj=obj, index_values=index_values
+        )
+    else:
+        _testing.assert_almost_equal(
+            left_valid,
+            right_valid,
+            check_dtype=bool(check_dtype),
+            rtol=rtol,
+            atol=atol,
+            obj=obj,
+            index_values=index_values,
+        )
+
+
+# This could be refactored to use the NDFrame.equals method
+def assert_series_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    check_index_type: bool | Literal["equiv"] = "equiv",
+    check_series_type: bool = True,
+    check_names: bool = True,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    check_datetimelike_compat: bool = False,
+    check_categorical: bool = True,
+    check_category_order: bool = True,
+    check_freq: bool = True,
+    check_flags: bool = True,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "Series",
+    *,
+    check_index: bool = True,
+    check_like: bool = False,
+) -> None:
+    """
+    Check that left and right Series are equal.
+
+    Parameters
+    ----------
+    left : Series
+    right : Series
+    check_dtype : bool, default True
+        Whether to check the Series dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_series_type : bool, default True
+         Whether to check the Series class is identical.
+    check_names : bool, default True
+        Whether to check the Series and Index names attribute.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    check_datetimelike_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_category_order : bool, default True
+        Whether to compare category order of internal Categoricals.
+    check_freq : bool, default True
+        Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
+    check_flags : bool, default True
+        Whether to check the `flags` attribute.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'Series'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+    check_index : bool, default True
+        Whether to check index equivalence. If False, then compare only values.
+
+        .. versionadded:: 1.3.0
+    check_like : bool, default False
+        If True, ignore the order of the index. Must be False if check_index is False.
+        Note: same labels must be with the same data.
+
+        .. versionadded:: 1.5.0
+
+    Examples
+    --------
+    >>> from pandas import testing as tm
+    >>> a = pd.Series([1, 2, 3, 4])
+    >>> b = pd.Series([1, 2, 3, 4])
+    >>> tm.assert_series_equal(a, b)
+    """
+    __tracebackhide__ = True
+    check_exact_index = False if check_exact is lib.no_default else check_exact
+    if (
+        check_exact is lib.no_default
+        and rtol is lib.no_default
+        and atol is lib.no_default
+    ):
+        check_exact = (
+            is_numeric_dtype(left.dtype)
+            and not is_float_dtype(left.dtype)
+            or is_numeric_dtype(right.dtype)
+            and not is_float_dtype(right.dtype)
+        )
+    elif check_exact is lib.no_default:
+        check_exact = False
+
+    rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    atol = atol if atol is not lib.no_default else 1.0e-8
+
+    if not check_index and check_like:
+        raise ValueError("check_like must be False if check_index is False")
+
+    # instance validation
+    _check_isinstance(left, right, Series)
+
+    if check_series_type:
+        assert_class_equal(left, right, obj=obj)
+
+    # length comparison
+    if len(left) != len(right):
+        msg1 = f"{len(left)}, {left.index}"
+        msg2 = f"{len(right)}, {right.index}"
+        raise_assert_detail(obj, "Series length are different", msg1, msg2)
+
+    if check_flags:
+        assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
+
+    if check_index:
+        # GH #38183
+        assert_index_equal(
+            left.index,
+            right.index,
+            exact=check_index_type,
+            check_names=check_names,
+            check_exact=check_exact_index,
+            check_categorical=check_categorical,
+            check_order=not check_like,
+            rtol=rtol,
+            atol=atol,
+            obj=f"{obj}.index",
+        )
+
+    if check_like:
+        left = left.reindex_like(right)
+
+    if check_freq and isinstance(left.index, (DatetimeIndex, TimedeltaIndex)):
+        lidx = left.index
+        ridx = right.index
+        assert lidx.freq == ridx.freq, (lidx.freq, ridx.freq)
+
+    if check_dtype:
+        # We want to skip exact dtype checking when `check_categorical`
+        # is False. We'll still raise if only one is a `Categorical`,
+        # regardless of `check_categorical`
+        if (
+            isinstance(left.dtype, CategoricalDtype)
+            and isinstance(right.dtype, CategoricalDtype)
+            and not check_categorical
+        ):
+            pass
+        else:
+            assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
+    if check_exact:
+        left_values = left._values
+        right_values = right._values
+        # Only check exact if dtype is numeric
+        if isinstance(left_values, ExtensionArray) and isinstance(
+            right_values, ExtensionArray
+        ):
+            assert_extension_array_equal(
+                left_values,
+                right_values,
+                check_dtype=check_dtype,
+                index_values=left.index,
+                obj=str(obj),
+            )
+        else:
+            # convert both to NumPy if not, check_dtype would raise earlier
+            lv, rv = left_values, right_values
+            if isinstance(left_values, ExtensionArray):
+                lv = left_values.to_numpy()
+            if isinstance(right_values, ExtensionArray):
+                rv = right_values.to_numpy()
+            assert_numpy_array_equal(
+                lv,
+                rv,
+                check_dtype=check_dtype,
+                obj=str(obj),
+                index_values=left.index,
+            )
+    elif check_datetimelike_compat and (
+        needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)
+    ):
+        # we want to check only if we have compat dtypes
+        # e.g. integer and M|m are NOT compat, but we can simply check
+        # the values in that case
+
+        # datetimelike may have different objects (e.g. datetime.datetime
+        # vs Timestamp) but will compare equal
+        if not Index(left._values).equals(Index(right._values)):
+            msg = (
+                f"[datetimelike_compat=True] {left._values} "
+                f"is not equal to {right._values}."
+            )
+            raise AssertionError(msg)
+    elif isinstance(left.dtype, IntervalDtype) and isinstance(
+        right.dtype, IntervalDtype
+    ):
+        assert_interval_array_equal(left.array, right.array)
+    elif isinstance(left.dtype, CategoricalDtype) or isinstance(
+        right.dtype, CategoricalDtype
+    ):
+        _testing.assert_almost_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=bool(check_dtype),
+            obj=str(obj),
+            index_values=left.index,
+        )
+    elif isinstance(left.dtype, ExtensionDtype) and isinstance(
+        right.dtype, ExtensionDtype
+    ):
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    elif is_extension_array_dtype_and_needs_i8_conversion(
+        left.dtype, right.dtype
+    ) or is_extension_array_dtype_and_needs_i8_conversion(right.dtype, left.dtype):
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    elif needs_i8_conversion(left.dtype) and needs_i8_conversion(right.dtype):
+        # DatetimeArray or TimedeltaArray
+        assert_extension_array_equal(
+            left._values,
+            right._values,
+            check_dtype=check_dtype,
+            index_values=left.index,
+            obj=str(obj),
+        )
+    else:
+        _testing.assert_almost_equal(
+            left._values,
+            right._values,
+            rtol=rtol,
+            atol=atol,
+            check_dtype=bool(check_dtype),
+            obj=str(obj),
+            index_values=left.index,
+        )
+
+    # metadata comparison
+    if check_names:
+        assert_attr_equal("name", left, right, obj=obj)
+
+    if check_categorical:
+        if isinstance(left.dtype, CategoricalDtype) or isinstance(
+            right.dtype, CategoricalDtype
+        ):
+            assert_categorical_equal(
+                left._values,
+                right._values,
+                obj=f"{obj} category",
+                check_category_order=check_category_order,
+            )
+
+
+# This could be refactored to use the NDFrame.equals method
+def assert_frame_equal(
+    left,
+    right,
+    check_dtype: bool | Literal["equiv"] = True,
+    check_index_type: bool | Literal["equiv"] = "equiv",
+    check_column_type: bool | Literal["equiv"] = "equiv",
+    check_frame_type: bool = True,
+    check_names: bool = True,
+    by_blocks: bool = False,
+    check_exact: bool | lib.NoDefault = lib.no_default,
+    check_datetimelike_compat: bool = False,
+    check_categorical: bool = True,
+    check_like: bool = False,
+    check_freq: bool = True,
+    check_flags: bool = True,
+    rtol: float | lib.NoDefault = lib.no_default,
+    atol: float | lib.NoDefault = lib.no_default,
+    obj: str = "DataFrame",
+) -> None:
+    """
+    Check that left and right DataFrame are equal.
+
+    This function is intended to compare two DataFrames and output any
+    differences. It is mostly intended for use in unit tests.
+    Additional parameters allow varying the strictness of the
+    equality checks performed.
+
+    Parameters
+    ----------
+    left : DataFrame
+        First DataFrame to compare.
+    right : DataFrame
+        Second DataFrame to compare.
+    check_dtype : bool, default True
+        Whether to check the DataFrame dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_column_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the columns class, dtype and inferred_type
+        are identical. Is passed as the ``exact`` argument of
+        :func:`assert_index_equal`.
+    check_frame_type : bool, default True
+        Whether to check the DataFrame class is identical.
+    check_names : bool, default True
+        Whether to check that the `names` attribute for both the `index`
+        and `column` attributes of the DataFrame is identical.
+    by_blocks : bool, default False
+        Specify how to compare internal data. If False, compare by columns.
+        If True, compare by blocks.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+
+        .. versionchanged:: 2.2.0
+
+            Defaults to True for integer dtypes if none of
+            ``check_exact``, ``rtol`` and ``atol`` are specified.
+    check_datetimelike_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_like : bool, default False
+        If True, ignore the order of index & columns.
+        Note: index labels must match their respective rows
+        (same as in columns) - same labels must be with the same data.
+    check_freq : bool, default True
+        Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
+    check_flags : bool, default True
+        Whether to check the `flags` attribute.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when check_exact is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when check_exact is False.
+    obj : str, default 'DataFrame'
+        Specify object name being compared, internally used to show appropriate
+        assertion message.
+
+    See Also
+    --------
+    assert_series_equal : Equivalent method for asserting Series equality.
+    DataFrame.equals : Check DataFrame equality.
+
+    Examples
+    --------
+    This example shows comparing two DataFrames that are equal
+    but with columns of differing dtypes.
+
+    >>> from pandas.testing import assert_frame_equal
+    >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
+    >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
+
+    df1 equals itself.
+
+    >>> assert_frame_equal(df1, df1)
+
+    df1 differs from df2 as column 'b' is of a different type.
+
+    >>> assert_frame_equal(df1, df2)
+    Traceback (most recent call last):
+    ...
+    AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different
+
+    Attribute "dtype" are different
+    [left]:  int64
+    [right]: float64
+
+    Ignore differing dtypes in columns with check_dtype.
+
+    >>> assert_frame_equal(df1, df2, check_dtype=False)
+    """
+    __tracebackhide__ = True
+    _rtol = rtol if rtol is not lib.no_default else 1.0e-5
+    _atol = atol if atol is not lib.no_default else 1.0e-8
+    _check_exact = check_exact if check_exact is not lib.no_default else False
+
+    # instance validation
+    _check_isinstance(left, right, DataFrame)
+
+    if check_frame_type:
+        assert isinstance(left, type(right))
+        # assert_class_equal(left, right, obj=obj)
+
+    # shape comparison
+    if left.shape != right.shape:
+        raise_assert_detail(
+            obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}"
+        )
+
+    if check_flags:
+        assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
+
+    # index comparison
+    assert_index_equal(
+        left.index,
+        right.index,
+        exact=check_index_type,
+        check_names=check_names,
+        check_exact=_check_exact,
+        check_categorical=check_categorical,
+        check_order=not check_like,
+        rtol=_rtol,
+        atol=_atol,
+        obj=f"{obj}.index",
+    )
+
+    # column comparison
+    assert_index_equal(
+        left.columns,
+        right.columns,
+        exact=check_column_type,
+        check_names=check_names,
+        check_exact=_check_exact,
+        check_categorical=check_categorical,
+        check_order=not check_like,
+        rtol=_rtol,
+        atol=_atol,
+        obj=f"{obj}.columns",
+    )
+
+    if check_like:
+        left = left.reindex_like(right)
+
+    # compare by blocks
+    if by_blocks:
+        rblocks = right._to_dict_of_blocks()
+        lblocks = left._to_dict_of_blocks()
+        for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
+            assert dtype in lblocks
+            assert dtype in rblocks
+            assert_frame_equal(
+                lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj
+            )
+
+    # compare by columns
+    else:
+        for i, col in enumerate(left.columns):
+            # We have already checked that columns match, so we can do
+            #  fast location-based lookups
+            lcol = left._ixs(i, axis=1)
+            rcol = right._ixs(i, axis=1)
+
+            # GH #38183
+            # use check_index=False, because we do not want to run
+            # assert_index_equal for each column,
+            # as we already checked it for the whole dataframe before.
+            assert_series_equal(
+                lcol,
+                rcol,
+                check_dtype=check_dtype,
+                check_index_type=check_index_type,
+                check_exact=check_exact,
+                check_names=check_names,
+                check_datetimelike_compat=check_datetimelike_compat,
+                check_categorical=check_categorical,
+                check_freq=check_freq,
+                obj=f'{obj}.iloc[:, {i}] (column name="{col}")',
+                rtol=rtol,
+                atol=atol,
+                check_index=False,
+                check_flags=False,
+            )
+
+
+def assert_equal(left, right, **kwargs) -> None:
+    """
+    Wrapper for tm.assert_*_equal to dispatch to the appropriate test function.
+
+    Parameters
+    ----------
+    left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray
+        The two items to be compared.
+    **kwargs
+        All keyword arguments are passed through to the underlying assert method.
+    """
+    __tracebackhide__ = True
+
+    if isinstance(left, Index):
+        assert_index_equal(left, right, **kwargs)
+        if isinstance(left, (DatetimeIndex, TimedeltaIndex)):
+            assert left.freq == right.freq, (left.freq, right.freq)
+    elif isinstance(left, Series):
+        assert_series_equal(left, right, **kwargs)
+    elif isinstance(left, DataFrame):
+        assert_frame_equal(left, right, **kwargs)
+    elif isinstance(left, IntervalArray):
+        assert_interval_array_equal(left, right, **kwargs)
+    elif isinstance(left, PeriodArray):
+        assert_period_array_equal(left, right, **kwargs)
+    elif isinstance(left, DatetimeArray):
+        assert_datetime_array_equal(left, right, **kwargs)
+    elif isinstance(left, TimedeltaArray):
+        assert_timedelta_array_equal(left, right, **kwargs)
+    elif isinstance(left, ExtensionArray):
+        assert_extension_array_equal(left, right, **kwargs)
+    elif isinstance(left, np.ndarray):
+        assert_numpy_array_equal(left, right, **kwargs)
+    elif isinstance(left, str):
+        assert kwargs == {}
+        assert left == right
+    else:
+        assert kwargs == {}
+        assert_almost_equal(left, right)
+
+
+def assert_sp_array_equal(left, right) -> None:
+    """
+    Check that the left and right SparseArray are equal.
+
+    Parameters
+    ----------
+    left : SparseArray
+    right : SparseArray
+    """
+    _check_isinstance(left, right, pd.arrays.SparseArray)
+
+    assert_numpy_array_equal(left.sp_values, right.sp_values)
+
+    # SparseIndex comparison
+    assert isinstance(left.sp_index, SparseIndex)
+    assert isinstance(right.sp_index, SparseIndex)
+
+    left_index = left.sp_index
+    right_index = right.sp_index
+
+    if not left_index.equals(right_index):
+        raise_assert_detail(
+            "SparseArray.index", "index are not equal", left_index, right_index
+        )
+    else:
+        # Just ensure a
+        pass
+
+    assert_attr_equal("fill_value", left, right)
+    assert_attr_equal("dtype", left, right)
+    assert_numpy_array_equal(left.to_dense(), right.to_dense())
+
+
+def assert_contains_all(iterable, dic) -> None:
+    for k in iterable:
+        assert k in dic, f"Did not contain item: {repr(k)}"
+
+
+def assert_copy(iter1, iter2, **eql_kwargs) -> None:
+    """
+    iter1, iter2: iterables that produce elements
+    comparable with assert_almost_equal
+
+    Checks that the elements are equal, but not
+    the same object. (Does not check that items
+    in sequences are also not the same object)
+    """
+    for elem1, elem2 in zip(iter1, iter2):
+        assert_almost_equal(elem1, elem2, **eql_kwargs)
+        msg = (
+            f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be "
+            "different objects, but they were the same object."
+        )
+        assert elem1 is not elem2, msg
+
+
+def is_extension_array_dtype_and_needs_i8_conversion(
+    left_dtype: DtypeObj, right_dtype: DtypeObj
+) -> bool:
+    """
+    Checks that we have the combination of an ExtensionArraydtype and
+    a dtype that should be converted to int64
+
+    Returns
+    -------
+    bool
+
+    Related to issue #37609
+    """
+    return isinstance(left_dtype, ExtensionDtype) and needs_i8_conversion(right_dtype)
+
+
+def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice) -> None:
+    """
+    Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable,
+    ser[l_slc].
+    """
+    expected = ser.iloc[i_slc]
+
+    assert_series_equal(ser.loc[l_slc], expected)
+
+    if not is_integer_dtype(ser.index):
+        # For integer indices, .loc and plain getitem are position-based.
+        assert_series_equal(ser[l_slc], expected)
+
+
+def assert_metadata_equivalent(
+    left: DataFrame | Series, right: DataFrame | Series | None = None
+) -> None:
+    """
+    Check that ._metadata attributes are equivalent.
+    """
+    for attr in left._metadata:
+        val = getattr(left, attr, None)
+        if right is None:
+            assert val is None
+        else:
+            assert val == getattr(right, attr, None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/compat.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc352ba7b8f2f5a5548d4d5749d3b48ac838aced
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/compat.py
@@ -0,0 +1,29 @@
+"""
+Helpers for sharing tests between DataFrame/Series
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas import DataFrame
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+def get_dtype(obj) -> DtypeObj:
+    if isinstance(obj, DataFrame):
+        # Note: we are assuming only one column
+        return obj.dtypes.iat[0]
+    else:
+        return obj.dtype
+
+
+def get_obj(df: DataFrame, klass):
+    """
+    For sharing tests using frame_or_series, either return the DataFrame
+    unchanged or return it's first column as a Series.
+    """
+    if klass is DataFrame:
+        return df
+    return df._ixs(0, axis=1)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/contexts.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/contexts.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa409a90a22f86b03dbcabcce28d4f4663876a4b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_testing/contexts.py
@@ -0,0 +1,261 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+import os
+from pathlib import Path
+import tempfile
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+)
+import uuid
+
+from pandas._config import using_copy_on_write
+
+from pandas.compat import (
+    PYPY,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.errors import ChainedAssignmentError
+
+from pandas import set_option
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+    from pandas._typing import (
+        BaseBuffer,
+        CompressionOptions,
+        FilePath,
+    )
+
+
+@contextmanager
+def decompress_file(
+    path: FilePath | BaseBuffer, compression: CompressionOptions
+) -> Generator[IO[bytes], None, None]:
+    """
+    Open a compressed file and return a file object.
+
+    Parameters
+    ----------
+    path : str
+        The path where the file is read from.
+
+    compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd', None}
+        Name of the decompression to use
+
+    Returns
+    -------
+    file object
+    """
+    with get_handle(path, "rb", compression=compression, is_text=False) as handle:
+        yield handle.handle
+
+
+@contextmanager
+def set_timezone(tz: str) -> Generator[None, None, None]:
+    """
+    Context manager for temporarily setting a timezone.
+
+    Parameters
+    ----------
+    tz : str
+        A string representing a valid timezone.
+
+    Examples
+    --------
+    >>> from datetime import datetime
+    >>> from dateutil.tz import tzlocal
+    >>> tzlocal().tzname(datetime(2021, 1, 1))  # doctest: +SKIP
+    'IST'
+
+    >>> with set_timezone('US/Eastern'):
+    ...     tzlocal().tzname(datetime(2021, 1, 1))
+    ...
+    'EST'
+    """
+    import time
+
+    def setTZ(tz) -> None:
+        if hasattr(time, "tzset"):
+            if tz is None:
+                try:
+                    del os.environ["TZ"]
+                except KeyError:
+                    pass
+            else:
+                os.environ["TZ"] = tz
+                time.tzset()
+
+    orig_tz = os.environ.get("TZ")
+    setTZ(tz)
+    try:
+        yield
+    finally:
+        setTZ(orig_tz)
+
+
+@contextmanager
+def ensure_clean(
+    filename=None, return_filelike: bool = False, **kwargs: Any
+) -> Generator[Any, None, None]:
+    """
+    Gets a temporary path and agrees to remove on close.
+
+    This implementation does not use tempfile.mkstemp to avoid having a file handle.
+    If the code using the returned path wants to delete the file itself, windows
+    requires that no program has a file handle to it.
+
+    Parameters
+    ----------
+    filename : str (optional)
+        suffix of the created file.
+    return_filelike : bool (default False)
+        if True, returns a file-like which is *always* cleaned. Necessary for
+        savefig and other functions which want to append extensions.
+    **kwargs
+        Additional keywords are passed to open().
+
+    """
+    folder = Path(tempfile.gettempdir())
+
+    if filename is None:
+        filename = ""
+    filename = str(uuid.uuid4()) + filename
+    path = folder / filename
+
+    path.touch()
+
+    handle_or_str: str | IO = str(path)
+    encoding = kwargs.pop("encoding", None)
+    if return_filelike:
+        kwargs.setdefault("mode", "w+b")
+        if encoding is None and "b" not in kwargs["mode"]:
+            encoding = "utf-8"
+        handle_or_str = open(path, encoding=encoding, **kwargs)
+
+    try:
+        yield handle_or_str
+    finally:
+        if not isinstance(handle_or_str, str):
+            handle_or_str.close()
+        if path.is_file():
+            path.unlink()
+
+
+@contextmanager
+def with_csv_dialect(name: str, **kwargs) -> Generator[None, None, None]:
+    """
+    Context manager to temporarily register a CSV dialect for parsing CSV.
+
+    Parameters
+    ----------
+    name : str
+        The name of the dialect.
+    kwargs : mapping
+        The parameters for the dialect.
+
+    Raises
+    ------
+    ValueError : the name of the dialect conflicts with a builtin one.
+
+    See Also
+    --------
+    csv : Python's CSV library.
+    """
+    import csv
+
+    _BUILTIN_DIALECTS = {"excel", "excel-tab", "unix"}
+
+    if name in _BUILTIN_DIALECTS:
+        raise ValueError("Cannot override builtin dialect.")
+
+    csv.register_dialect(name, **kwargs)
+    try:
+        yield
+    finally:
+        csv.unregister_dialect(name)
+
+
+@contextmanager
+def use_numexpr(use, min_elements=None) -> Generator[None, None, None]:
+    from pandas.core.computation import expressions as expr
+
+    if min_elements is None:
+        min_elements = expr._MIN_ELEMENTS
+
+    olduse = expr.USE_NUMEXPR
+    oldmin = expr._MIN_ELEMENTS
+    set_option("compute.use_numexpr", use)
+    expr._MIN_ELEMENTS = min_elements
+    try:
+        yield
+    finally:
+        expr._MIN_ELEMENTS = oldmin
+        set_option("compute.use_numexpr", olduse)
+
+
+def raises_chained_assignment_error(warn=True, extra_warnings=(), extra_match=()):
+    from pandas._testing import assert_produces_warning
+
+    if not warn:
+        from contextlib import nullcontext
+
+        return nullcontext()
+
+    if (PYPY or WARNING_CHECK_DISABLED) and not extra_warnings:
+        from contextlib import nullcontext
+
+        return nullcontext()
+    elif (PYPY or WARNING_CHECK_DISABLED) and extra_warnings:
+        return assert_produces_warning(
+            extra_warnings,
+            match="|".join(extra_match),
+        )
+    else:
+        if using_copy_on_write():
+            warning = ChainedAssignmentError
+            match = (
+                "A value is trying to be set on a copy of a DataFrame or Series "
+                "through chained assignment"
+            )
+        else:
+            warning = FutureWarning  # type: ignore[assignment]
+            # TODO update match
+            match = "ChainedAssignmentError"
+        if extra_warnings:
+            warning = (warning, *extra_warnings)  # type: ignore[assignment]
+        return assert_produces_warning(
+            warning,
+            match="|".join((match, *extra_match)),
+        )
+
+
+def assert_cow_warning(warn=True, match=None, **kwargs):
+    """
+    Assert that a warning is raised in the CoW warning mode.
+
+    Parameters
+    ----------
+    warn : bool, default True
+        By default, check that a warning is raised. Can be turned off by passing False.
+    match : str
+        The warning message to match against, if different from the default.
+    kwargs
+        Passed through to assert_produces_warning
+    """
+    from pandas._testing import assert_produces_warning
+
+    if not warn or WARNING_CHECK_DISABLED:
+        from contextlib import nullcontext
+
+        return nullcontext()
+
+    if not match:
+        match = "Setting a value on a view"
+
+    return assert_produces_warning(FutureWarning, match=match, **kwargs)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_typing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..3df9a47a35fca32547947560a8df1cea1d1863c2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_typing.py
@@ -0,0 +1,525 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterator,
+    Mapping,
+    MutableMapping,
+    Sequence,
+)
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+    tzinfo,
+)
+from os import PathLike
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    Optional,
+    Protocol,
+    Type as type_t,
+    TypeVar,
+    Union,
+    overload,
+)
+
+import numpy as np
+
+# To prevent import cycles place any internal imports in the branch below
+# and use a string literal forward reference to it in subsequent types
+# https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+    from pandas._libs import (
+        NaTType,
+        Period,
+        Timedelta,
+        Timestamp,
+    )
+    from pandas._libs.tslibs import BaseOffset
+
+    from pandas.core.dtypes.dtypes import ExtensionDtype
+
+    from pandas import Interval
+    from pandas.arrays import (
+        DatetimeArray,
+        TimedeltaArray,
+    )
+    from pandas.core.arrays.base import ExtensionArray
+    from pandas.core.frame import DataFrame
+    from pandas.core.generic import NDFrame
+    from pandas.core.groupby.generic import (
+        DataFrameGroupBy,
+        GroupBy,
+        SeriesGroupBy,
+    )
+    from pandas.core.indexes.base import Index
+    from pandas.core.internals import (
+        ArrayManager,
+        BlockManager,
+        SingleArrayManager,
+        SingleBlockManager,
+    )
+    from pandas.core.resample import Resampler
+    from pandas.core.series import Series
+    from pandas.core.window.rolling import BaseWindow
+
+    from pandas.io.formats.format import EngFormatter
+    from pandas.tseries.holiday import AbstractHolidayCalendar
+
+    ScalarLike_co = Union[
+        int,
+        float,
+        complex,
+        str,
+        bytes,
+        np.generic,
+    ]
+
+    # numpy compatible types
+    NumpyValueArrayLike = Union[ScalarLike_co, npt.ArrayLike]
+    # Name "npt._ArrayLikeInt_co" is not defined  [name-defined]
+    NumpySorter = Optional[npt._ArrayLikeInt_co]  # type: ignore[name-defined]
+
+    from typing import SupportsIndex
+
+    if sys.version_info >= (3, 10):
+        from typing import TypeGuard  # pyright: ignore[reportUnusedImport]
+    else:
+        from typing_extensions import TypeGuard  # pyright: ignore[reportUnusedImport]
+
+    if sys.version_info >= (3, 11):
+        from typing import Self  # pyright: ignore[reportUnusedImport]
+    else:
+        from typing_extensions import Self  # pyright: ignore[reportUnusedImport]
+else:
+    npt: Any = None
+    Self: Any = None
+    TypeGuard: Any = None
+
+HashableT = TypeVar("HashableT", bound=Hashable)
+MutableMappingT = TypeVar("MutableMappingT", bound=MutableMapping)
+
+# array-like
+
+ArrayLike = Union["ExtensionArray", np.ndarray]
+AnyArrayLike = Union[ArrayLike, "Index", "Series"]
+TimeArrayLike = Union["DatetimeArray", "TimedeltaArray"]
+
+# list-like
+
+# from https://github.com/hauntsaninja/useful_types
+# includes Sequence-like objects but excludes str and bytes
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class SequenceNotStr(Protocol[_T_co]):
+    @overload
+    def __getitem__(self, index: SupportsIndex, /) -> _T_co:
+        ...
+
+    @overload
+    def __getitem__(self, index: slice, /) -> Sequence[_T_co]:
+        ...
+
+    def __contains__(self, value: object, /) -> bool:
+        ...
+
+    def __len__(self) -> int:
+        ...
+
+    def __iter__(self) -> Iterator[_T_co]:
+        ...
+
+    def index(self, value: Any, /, start: int = 0, stop: int = ...) -> int:
+        ...
+
+    def count(self, value: Any, /) -> int:
+        ...
+
+    def __reversed__(self) -> Iterator[_T_co]:
+        ...
+
+
+ListLike = Union[AnyArrayLike, SequenceNotStr, range]
+
+# scalars
+
+PythonScalar = Union[str, float, bool]
+DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"]
+PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
+Scalar = Union[PythonScalar, PandasScalar, np.datetime64, np.timedelta64, date]
+IntStrT = TypeVar("IntStrT", bound=Union[int, str])
+
+
+# timestamp and timedelta convertible types
+
+TimestampConvertibleTypes = Union[
+    "Timestamp", date, np.datetime64, np.int64, float, str
+]
+TimestampNonexistent = Union[
+    Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta
+]
+TimedeltaConvertibleTypes = Union[
+    "Timedelta", timedelta, np.timedelta64, np.int64, float, str
+]
+Timezone = Union[str, tzinfo]
+
+ToTimestampHow = Literal["s", "e", "start", "end"]
+
+# NDFrameT is stricter and ensures that the same subclass of NDFrame always is
+# used. E.g. `def func(a: NDFrameT) -> NDFrameT: ...` means that if a
+# Series is passed into a function, a Series is always returned and if a DataFrame is
+# passed in, a DataFrame is always returned.
+NDFrameT = TypeVar("NDFrameT", bound="NDFrame")
+
+NumpyIndexT = TypeVar("NumpyIndexT", np.ndarray, "Index")
+
+AxisInt = int
+Axis = Union[AxisInt, Literal["index", "columns", "rows"]]
+IndexLabel = Union[Hashable, Sequence[Hashable]]
+Level = Hashable
+Shape = tuple[int, ...]
+Suffixes = tuple[Optional[str], Optional[str]]
+Ordered = Optional[bool]
+JSONSerializable = Optional[Union[PythonScalar, list, dict]]
+Frequency = Union[str, "BaseOffset"]
+Axes = ListLike
+
+RandomState = Union[
+    int,
+    np.ndarray,
+    np.random.Generator,
+    np.random.BitGenerator,
+    np.random.RandomState,
+]
+
+# dtypes
+NpDtype = Union[str, np.dtype, type_t[Union[str, complex, bool, object]]]
+Dtype = Union["ExtensionDtype", NpDtype]
+AstypeArg = Union["ExtensionDtype", "npt.DTypeLike"]
+# DtypeArg specifies all allowable dtypes in a functions its dtype argument
+DtypeArg = Union[Dtype, dict[Hashable, Dtype]]
+DtypeObj = Union[np.dtype, "ExtensionDtype"]
+
+# converters
+ConvertersArg = dict[Hashable, Callable[[Dtype], Dtype]]
+
+# parse_dates
+ParseDatesArg = Union[
+    bool, list[Hashable], list[list[Hashable]], dict[Hashable, list[Hashable]]
+]
+
+# For functions like rename that convert one label to another
+Renamer = Union[Mapping[Any, Hashable], Callable[[Any], Hashable]]
+
+# to maintain type information across generic functions and parametrization
+T = TypeVar("T")
+
+# used in decorators to preserve the signature of the function it decorates
+# see https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
+FuncType = Callable[..., Any]
+F = TypeVar("F", bound=FuncType)
+
+# types of vectorized key functions for DataFrame::sort_values and
+# DataFrame::sort_index, among others
+ValueKeyFunc = Optional[Callable[["Series"], Union["Series", AnyArrayLike]]]
+IndexKeyFunc = Optional[Callable[["Index"], Union["Index", AnyArrayLike]]]
+
+# types of `func` kwarg for DataFrame.aggregate and Series.aggregate
+AggFuncTypeBase = Union[Callable, str]
+AggFuncTypeDict = MutableMapping[
+    Hashable, Union[AggFuncTypeBase, list[AggFuncTypeBase]]
+]
+AggFuncType = Union[
+    AggFuncTypeBase,
+    list[AggFuncTypeBase],
+    AggFuncTypeDict,
+]
+AggObjType = Union[
+    "Series",
+    "DataFrame",
+    "GroupBy",
+    "SeriesGroupBy",
+    "DataFrameGroupBy",
+    "BaseWindow",
+    "Resampler",
+]
+
+PythonFuncType = Callable[[Any], Any]
+
+# filenames and file-like-objects
+AnyStr_co = TypeVar("AnyStr_co", str, bytes, covariant=True)
+AnyStr_contra = TypeVar("AnyStr_contra", str, bytes, contravariant=True)
+
+
+class BaseBuffer(Protocol):
+    @property
+    def mode(self) -> str:
+        # for _get_filepath_or_buffer
+        ...
+
+    def seek(self, __offset: int, __whence: int = ...) -> int:
+        # with one argument: gzip.GzipFile, bz2.BZ2File
+        # with two arguments: zip.ZipFile, read_sas
+        ...
+
+    def seekable(self) -> bool:
+        # for bz2.BZ2File
+        ...
+
+    def tell(self) -> int:
+        # for zip.ZipFile, read_stata, to_stata
+        ...
+
+
+class ReadBuffer(BaseBuffer, Protocol[AnyStr_co]):
+    def read(self, __n: int = ...) -> AnyStr_co:
+        # for BytesIOWrapper, gzip.GzipFile, bz2.BZ2File
+        ...
+
+
+class WriteBuffer(BaseBuffer, Protocol[AnyStr_contra]):
+    def write(self, __b: AnyStr_contra) -> Any:
+        # for gzip.GzipFile, bz2.BZ2File
+        ...
+
+    def flush(self) -> Any:
+        # for gzip.GzipFile, bz2.BZ2File
+        ...
+
+
+class ReadPickleBuffer(ReadBuffer[bytes], Protocol):
+    def readline(self) -> bytes:
+        ...
+
+
+class WriteExcelBuffer(WriteBuffer[bytes], Protocol):
+    def truncate(self, size: int | None = ...) -> int:
+        ...
+
+
+class ReadCsvBuffer(ReadBuffer[AnyStr_co], Protocol):
+    def __iter__(self) -> Iterator[AnyStr_co]:
+        # for engine=python
+        ...
+
+    def fileno(self) -> int:
+        # for _MMapWrapper
+        ...
+
+    def readline(self) -> AnyStr_co:
+        # for engine=python
+        ...
+
+    @property
+    def closed(self) -> bool:
+        # for enine=pyarrow
+        ...
+
+
+FilePath = Union[str, "PathLike[str]"]
+
+# for arbitrary kwargs passed during reading/writing files
+StorageOptions = Optional[dict[str, Any]]
+
+
+# compression keywords and compression
+CompressionDict = dict[str, Any]
+CompressionOptions = Optional[
+    Union[Literal["infer", "gzip", "bz2", "zip", "xz", "zstd", "tar"], CompressionDict]
+]
+
+# types in DataFrameFormatter
+FormattersType = Union[
+    list[Callable], tuple[Callable, ...], Mapping[Union[str, int], Callable]
+]
+ColspaceType = Mapping[Hashable, Union[str, int]]
+FloatFormatType = Union[str, Callable, "EngFormatter"]
+ColspaceArgType = Union[
+    str, int, Sequence[Union[str, int]], Mapping[Hashable, Union[str, int]]
+]
+
+# Arguments for fillna()
+FillnaOptions = Literal["backfill", "bfill", "ffill", "pad"]
+InterpolateOptions = Literal[
+    "linear",
+    "time",
+    "index",
+    "values",
+    "nearest",
+    "zero",
+    "slinear",
+    "quadratic",
+    "cubic",
+    "barycentric",
+    "polynomial",
+    "krogh",
+    "piecewise_polynomial",
+    "spline",
+    "pchip",
+    "akima",
+    "cubicspline",
+    "from_derivatives",
+]
+
+# internals
+Manager = Union[
+    "ArrayManager", "SingleArrayManager", "BlockManager", "SingleBlockManager"
+]
+SingleManager = Union["SingleArrayManager", "SingleBlockManager"]
+Manager2D = Union["ArrayManager", "BlockManager"]
+
+# indexing
+# PositionalIndexer -> valid 1D positional indexer, e.g. can pass
+# to ndarray.__getitem__
+# ScalarIndexer is for a single value as the index
+# SequenceIndexer is for list like or slices (but not tuples)
+# PositionalIndexerTuple is extends the PositionalIndexer for 2D arrays
+# These are used in various __getitem__ overloads
+# TODO(typing#684): add Ellipsis, see
+# https://github.com/python/typing/issues/684#issuecomment-548203158
+# https://bugs.python.org/issue41810
+# Using List[int] here rather than Sequence[int] to disallow tuples.
+ScalarIndexer = Union[int, np.integer]
+SequenceIndexer = Union[slice, list[int], np.ndarray]
+PositionalIndexer = Union[ScalarIndexer, SequenceIndexer]
+PositionalIndexerTuple = tuple[PositionalIndexer, PositionalIndexer]
+PositionalIndexer2D = Union[PositionalIndexer, PositionalIndexerTuple]
+if TYPE_CHECKING:
+    TakeIndexer = Union[Sequence[int], Sequence[np.integer], npt.NDArray[np.integer]]
+else:
+    TakeIndexer = Any
+
+# Shared by functions such as drop and astype
+IgnoreRaise = Literal["ignore", "raise"]
+
+# Windowing rank methods
+WindowingRankType = Literal["average", "min", "max"]
+
+# read_csv engines
+CSVEngine = Literal["c", "python", "pyarrow", "python-fwf"]
+
+# read_json engines
+JSONEngine = Literal["ujson", "pyarrow"]
+
+# read_xml parsers
+XMLParsers = Literal["lxml", "etree"]
+
+# read_html flavors
+HTMLFlavors = Literal["lxml", "html5lib", "bs4"]
+
+# Interval closed type
+IntervalLeftRight = Literal["left", "right"]
+IntervalClosedType = Union[IntervalLeftRight, Literal["both", "neither"]]
+
+# datetime and NaTType
+DatetimeNaTType = Union[datetime, "NaTType"]
+DateTimeErrorChoices = Union[IgnoreRaise, Literal["coerce"]]
+
+# sort_index
+SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
+NaPosition = Literal["first", "last"]
+
+# Arguments for nsmalles and n_largest
+NsmallestNlargestKeep = Literal["first", "last", "all"]
+
+# quantile interpolation
+QuantileInterpolation = Literal["linear", "lower", "higher", "midpoint", "nearest"]
+
+# plotting
+PlottingOrientation = Literal["horizontal", "vertical"]
+
+# dropna
+AnyAll = Literal["any", "all"]
+
+# merge
+MergeHow = Literal["left", "right", "inner", "outer", "cross"]
+MergeValidate = Literal[
+    "one_to_one",
+    "1:1",
+    "one_to_many",
+    "1:m",
+    "many_to_one",
+    "m:1",
+    "many_to_many",
+    "m:m",
+]
+
+# join
+JoinHow = Literal["left", "right", "inner", "outer"]
+JoinValidate = Literal[
+    "one_to_one",
+    "1:1",
+    "one_to_many",
+    "1:m",
+    "many_to_one",
+    "m:1",
+    "many_to_many",
+    "m:m",
+]
+
+# reindex
+ReindexMethod = Union[FillnaOptions, Literal["nearest"]]
+
+MatplotlibColor = Union[str, Sequence[float]]
+TimeGrouperOrigin = Union[
+    "Timestamp", Literal["epoch", "start", "start_day", "end", "end_day"]
+]
+TimeAmbiguous = Union[Literal["infer", "NaT", "raise"], "npt.NDArray[np.bool_]"]
+TimeNonexistent = Union[
+    Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta
+]
+DropKeep = Literal["first", "last", False]
+CorrelationMethod = Union[
+    Literal["pearson", "kendall", "spearman"], Callable[[np.ndarray, np.ndarray], float]
+]
+AlignJoin = Literal["outer", "inner", "left", "right"]
+DtypeBackend = Literal["pyarrow", "numpy_nullable"]
+
+TimeUnit = Literal["s", "ms", "us", "ns"]
+OpenFileErrors = Literal[
+    "strict",
+    "ignore",
+    "replace",
+    "surrogateescape",
+    "xmlcharrefreplace",
+    "backslashreplace",
+    "namereplace",
+]
+
+# update
+UpdateJoin = Literal["left"]
+
+# applymap
+NaAction = Literal["ignore"]
+
+# from_dict
+FromDictOrient = Literal["columns", "index", "tight"]
+
+# to_gbc
+ToGbqIfexist = Literal["fail", "replace", "append"]
+
+# to_stata
+ToStataByteorder = Literal[">", "<", "little", "big"]
+
+# ExcelWriter
+ExcelWriterIfSheetExists = Literal["error", "new", "replace", "overlay"]
+
+# Offsets
+OffsetCalendar = Union[np.busdaycalendar, "AbstractHolidayCalendar"]
+
+# read_csv: usecols
+UsecolsArgType = Union[
+    SequenceNotStr[Hashable],
+    range,
+    AnyArrayLike,
+    Callable[[HashableT], bool],
+    None,
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_version.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d9a4555018d906c11433c95ef0277a7550c862
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_version.py
@@ -0,0 +1,692 @@
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain.
+# Generated by versioneer-0.28
+# https://github.com/python-versioneer/python-versioneer
+
+"""Git implementation of _version.py."""
+
+import errno
+import functools
+import os
+import re
+import subprocess
+import sys
+from typing import Callable
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = " (HEAD, tag: v2.3.3, origin/2.3.x)"
+    git_full = "9c8bc3e55188c8aff37207a74f1dd144980b8874"
+    git_date = "2025-09-30 00:12:09 +0200"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = "v"
+    cfg.parentdir_prefix = "pandas-"
+    cfg.versionfile_source = "pandas/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY: dict[str, str] = {}
+HANDLERS: dict[str, dict[str, Callable]] = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+
+    popen_kwargs = {}
+    if sys.platform == "win32":
+        # This hides the console window if pythonw.exe is used
+        startupinfo = subprocess.STARTUPINFO()
+        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        popen_kwargs["startupinfo"] = startupinfo
+
+    for command in commands:
+        dispcmd = str([command] + args)
+        try:
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen(
+                [command] + args,
+                cwd=cwd,
+                env=env,
+                stdout=subprocess.PIPE,
+                stderr=(subprocess.PIPE if hide_stderr else None),
+                **popen_kwargs,
+            )
+            break
+        except OSError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print(f"unable to run {dispcmd}")
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print(f"unable to find command, tried {commands}")
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print(f"unable to run {dispcmd} (error)")
+            print(f"stdout was {stdout}")
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {
+                "version": dirname[len(parentdir_prefix) :],
+                "full-revisionid": None,
+                "dirty": False,
+                "error": None,
+                "date": None,
+            }
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print(
+            f"Tried directories {str(rootdirs)} \
+            but none started with prefix {parentdir_prefix}"
+        )
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, encoding="utf-8") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except OSError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r"\d", r)}
+        if verbose:
+            print(f"discarding '{','.join(refs - tags)}', no digits")
+    if verbose:
+        print(f"likely tags: {','.join(sorted(tags))}")
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix) :]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r"\d", r):
+                continue
+            if verbose:
+                print(f"picking {r}")
+            return {
+                "version": r,
+                "full-revisionid": keywords["full"].strip(),
+                "dirty": False,
+                "error": None,
+                "date": date,
+            }
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {
+        "version": "0+unknown",
+        "full-revisionid": keywords["full"].strip(),
+        "dirty": False,
+        "error": "no suitable tags",
+        "date": None,
+    }
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    # GIT_DIR can interfere with correct operation of Versioneer.
+    # It may be intended to be passed to the Versioneer-versioned project,
+    # but that should not change where we get our version from.
+    env = os.environ.copy()
+    env.pop("GIT_DIR", None)
+    runner = functools.partial(runner, env=env)
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose)
+    if rc != 0:
+        if verbose:
+            print(f"Directory {root} not under git control")
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(
+        GITS,
+        [
+            "describe",
+            "--tags",
+            "--dirty",
+            "--always",
+            "--long",
+            "--match",
+            f"{tag_prefix}[[:digit:]]*",
+        ],
+        cwd=root,
+    )
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[: git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
+        if not mo:
+            # unparsable. Maybe git-describe is misbehaving?
+            pieces["error"] = f"unable to parse git-describe output: '{describe_out}'"
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces[
+                "error"
+            ] = f"tag '{full_tag}' doesn't start with prefix '{tag_prefix}'"
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix) :]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root)
+        pieces["distance"] = len(out.split())  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces) -> str:
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += f"{pieces['distance']}.g{pieces['short']}"
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = f"0+untagged.{pieces['distance']}.g{pieces['short']}"
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += f"{pieces['distance']}.g{pieces['short']}"
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += f"+untagged.{pieces['distance']}.g{pieces['short']}"
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def pep440_split_post(ver):
+    """Split pep440 version string at the post-release segment.
+
+    Returns the release segments before the post-release and the
+    post-release version number (or -1 if no post-release segment is present).
+    """
+    vc = str.split(ver, ".post")
+    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
+
+
+def render_pep440_pre(pieces):
+    """TAG[.postN.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        if pieces["distance"]:
+            # update the post release segment
+            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
+            rendered = tag_version
+            if post_version is not None:
+                rendered += f".post{post_version + 1}.dev{pieces['distance']}"
+            else:
+                rendered += f".post0.dev{pieces['distance']}"
+        else:
+            # no commits, use the tag as the version
+            rendered = pieces["closest-tag"]
+    else:
+        # exception #1
+        rendered = f"0.post0.dev{pieces['distance']}"
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += f".post{pieces['distance']}"
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += f"g{pieces['short']}"
+    else:
+        # exception #1
+        rendered = f"0.post{pieces['distance']}"
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += f"+g{pieces['short']}"
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += f".post{pieces['distance']}"
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += f"g{pieces['short']}"
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = f"0.post{pieces['distance']}"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += f"+g{pieces['short']}"
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += f"0.post{pieces['distance']}"
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = f"0.post{pieces['distance']}"
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += f"-{pieces['distance']}-g{pieces['short']}"
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += f"-{pieces['distance']}-g{pieces['short']}"
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {
+            "version": "unknown",
+            "full-revisionid": pieces.get("long"),
+            "dirty": None,
+            "error": pieces["error"],
+            "date": None,
+        }
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError(f"unknown style '{style}'")
+
+    return {
+        "version": rendered,
+        "full-revisionid": pieces["long"],
+        "dirty": pieces["dirty"],
+        "error": None,
+        "date": pieces.get("date"),
+    }
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split("/"):
+            root = os.path.dirname(root)
+    except NameError:
+        return {
+            "version": "0+unknown",
+            "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to find root of source tree",
+            "date": None,
+        }
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {
+        "version": "0+unknown",
+        "full-revisionid": None,
+        "dirty": None,
+        "error": "unable to compute version",
+        "date": None,
+    }
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_version_meson.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_version_meson.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6c87a5bfbdbaf252f1b13ec01220ac1c58d9984
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_version_meson.py
@@ -0,0 +1,2 @@
+__version__="2.3.3"
+__git_version__="9c8bc3e55188c8aff37207a74f1dd144980b8874"
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d42b6541fdf8817b996ef9804db8a87a2bcd2c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/__init__.py
@@ -0,0 +1,16 @@
+""" public toolkit API """
+from pandas.api import (
+    extensions,
+    indexers,
+    interchange,
+    types,
+    typing,
+)
+
+__all__ = [
+    "interchange",
+    "extensions",
+    "indexers",
+    "types",
+    "typing",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/extensions/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/extensions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea5f1ba926899f9d11e34e70181ed77cae7ead1d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/extensions/__init__.py
@@ -0,0 +1,33 @@
+"""
+Public API for extending pandas objects.
+"""
+
+from pandas._libs.lib import no_default
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    register_extension_dtype,
+)
+
+from pandas.core.accessor import (
+    register_dataframe_accessor,
+    register_index_accessor,
+    register_series_accessor,
+)
+from pandas.core.algorithms import take
+from pandas.core.arrays import (
+    ExtensionArray,
+    ExtensionScalarOpsMixin,
+)
+
+__all__ = [
+    "no_default",
+    "ExtensionDtype",
+    "register_extension_dtype",
+    "register_dataframe_accessor",
+    "register_index_accessor",
+    "register_series_accessor",
+    "take",
+    "ExtensionArray",
+    "ExtensionScalarOpsMixin",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/indexers/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/indexers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..78357f11dc3b79f13490b91c69ef5457fbfa9768
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/indexers/__init__.py
@@ -0,0 +1,17 @@
+"""
+Public API for Rolling Window Indexers.
+"""
+
+from pandas.core.indexers import check_array_indexer
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    FixedForwardWindowIndexer,
+    VariableOffsetWindowIndexer,
+)
+
+__all__ = [
+    "check_array_indexer",
+    "BaseIndexer",
+    "FixedForwardWindowIndexer",
+    "VariableOffsetWindowIndexer",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/interchange/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/interchange/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f3a73bc46b3109c3c13e1a3468a69aed2ffb2e8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/interchange/__init__.py
@@ -0,0 +1,8 @@
+"""
+Public API for DataFrame interchange protocol.
+"""
+
+from pandas.core.interchange.dataframe_protocol import DataFrame
+from pandas.core.interchange.from_dataframe import from_dataframe
+
+__all__ = ["from_dataframe", "DataFrame"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/types/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c601086bb9f86a633d6a3f2245779fea9428bf95
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/types/__init__.py
@@ -0,0 +1,23 @@
+"""
+Public toolkit API.
+"""
+
+from pandas._libs.lib import infer_dtype
+
+from pandas.core.dtypes.api import *  # noqa: F403
+from pandas.core.dtypes.concat import union_categoricals
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+
+__all__ = [
+    "infer_dtype",
+    "union_categoricals",
+    "CategoricalDtype",
+    "DatetimeTZDtype",
+    "IntervalDtype",
+    "PeriodDtype",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/typing/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/typing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b5d2cb06b523508b15025be804a66daaaaf7a45
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/api/typing/__init__.py
@@ -0,0 +1,55 @@
+"""
+Public API classes that store intermediate results useful for type-hinting.
+"""
+
+from pandas._libs import NaTType
+from pandas._libs.missing import NAType
+
+from pandas.core.groupby import (
+    DataFrameGroupBy,
+    SeriesGroupBy,
+)
+from pandas.core.resample import (
+    DatetimeIndexResamplerGroupby,
+    PeriodIndexResamplerGroupby,
+    Resampler,
+    TimedeltaIndexResamplerGroupby,
+    TimeGrouper,
+)
+from pandas.core.window import (
+    Expanding,
+    ExpandingGroupby,
+    ExponentialMovingWindow,
+    ExponentialMovingWindowGroupby,
+    Rolling,
+    RollingGroupby,
+    Window,
+)
+
+# TODO: Can't import Styler without importing jinja2
+# from pandas.io.formats.style import Styler
+from pandas.io.json._json import JsonReader
+from pandas.io.stata import StataReader
+
+__all__ = [
+    "DataFrameGroupBy",
+    "DatetimeIndexResamplerGroupby",
+    "Expanding",
+    "ExpandingGroupby",
+    "ExponentialMovingWindow",
+    "ExponentialMovingWindowGroupby",
+    "JsonReader",
+    "NaTType",
+    "NAType",
+    "PeriodIndexResamplerGroupby",
+    "Resampler",
+    "Rolling",
+    "RollingGroupby",
+    "SeriesGroupBy",
+    "StataReader",
+    # See TODO above
+    # "Styler",
+    "TimedeltaIndexResamplerGroupby",
+    "TimeGrouper",
+    "Window",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/arrays/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/arrays/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a11755275d00e070bea6ab73a881b98d0b976551
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/arrays/__init__.py
@@ -0,0 +1,53 @@
+"""
+All of pandas' ExtensionArrays.
+
+See :ref:`extending.extension-types` for more.
+"""
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    ArrowStringArray,
+    BooleanArray,
+    Categorical,
+    DatetimeArray,
+    FloatingArray,
+    IntegerArray,
+    IntervalArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    SparseArray,
+    StringArray,
+    TimedeltaArray,
+)
+
+__all__ = [
+    "ArrowExtensionArray",
+    "ArrowStringArray",
+    "BooleanArray",
+    "Categorical",
+    "DatetimeArray",
+    "FloatingArray",
+    "IntegerArray",
+    "IntervalArray",
+    "NumpyExtensionArray",
+    "PeriodArray",
+    "SparseArray",
+    "StringArray",
+    "TimedeltaArray",
+]
+
+
+def __getattr__(name: str) -> type[NumpyExtensionArray]:
+    if name == "PandasArray":
+        # GH#53694
+        import warnings
+
+        from pandas.util._exceptions import find_stack_level
+
+        warnings.warn(
+            "PandasArray has been renamed NumpyExtensionArray. Use that "
+            "instead. This alias will be removed in a future version.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return NumpyExtensionArray
+    raise AttributeError(f"module 'pandas.arrays' has no attribute '{name}'")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8554be0a18b1bb6a7ad6a0b43fe5cfc29f27864
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/__init__.py
@@ -0,0 +1,213 @@
+"""
+compat
+======
+
+Cross-compatible functions for different versions of Python.
+
+Other items:
+* platform checker
+"""
+from __future__ import annotations
+
+import os
+import platform
+import sys
+from typing import TYPE_CHECKING
+
+from pandas.compat._constants import (
+    IS64,
+    ISMUSL,
+    PY310,
+    PY311,
+    PY312,
+    PY314,
+    PYPY,
+    WARNING_CHECK_DISABLED,
+)
+import pandas.compat.compressors
+from pandas.compat.numpy import is_numpy_dev
+from pandas.compat.pyarrow import (
+    HAS_PYARROW,
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under14p0,
+    pa_version_under14p1,
+    pa_version_under16p0,
+    pa_version_under17p0,
+    pa_version_under18p0,
+    pa_version_under19p0,
+    pa_version_under20p0,
+    pa_version_under21p0,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def set_function_name(f: F, name: str, cls: type) -> F:
+    """
+    Bind the name/qualname attributes of the function.
+    """
+    f.__name__ = name
+    f.__qualname__ = f"{cls.__name__}.{name}"
+    f.__module__ = cls.__module__
+    return f
+
+
+def is_platform_little_endian() -> bool:
+    """
+    Checking if the running platform is little endian.
+
+    Returns
+    -------
+    bool
+        True if the running platform is little endian.
+    """
+    return sys.byteorder == "little"
+
+
+def is_platform_windows() -> bool:
+    """
+    Checking if the running platform is windows.
+
+    Returns
+    -------
+    bool
+        True if the running platform is windows.
+    """
+    return sys.platform in ["win32", "cygwin"]
+
+
+def is_platform_linux() -> bool:
+    """
+    Checking if the running platform is linux.
+
+    Returns
+    -------
+    bool
+        True if the running platform is linux.
+    """
+    return sys.platform == "linux"
+
+
+def is_platform_mac() -> bool:
+    """
+    Checking if the running platform is mac.
+
+    Returns
+    -------
+    bool
+        True if the running platform is mac.
+    """
+    return sys.platform == "darwin"
+
+
+def is_platform_arm() -> bool:
+    """
+    Checking if the running platform use ARM architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("arm64", "aarch64") or platform.machine().startswith(
+        "armv"
+    )
+
+
+def is_platform_power() -> bool:
+    """
+    Checking if the running platform use Power architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("ppc64", "ppc64le")
+
+
+def is_ci_environment() -> bool:
+    """
+    Checking if running in a continuous integration environment by checking
+    the PANDAS_CI environment variable.
+
+    Returns
+    -------
+    bool
+        True if the running in a continuous integration environment.
+    """
+    return os.environ.get("PANDAS_CI", "0") == "1"
+
+
+def get_lzma_file() -> type[pandas.compat.compressors.LZMAFile]:
+    """
+    Importing the `LZMAFile` class from the `lzma` module.
+
+    Returns
+    -------
+    class
+        The `LZMAFile` class from the `lzma` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `lzma` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_lzma:
+        raise RuntimeError(
+            "lzma module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.LZMAFile
+
+
+def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
+    """
+    Importing the `BZ2File` class from the `bz2` module.
+
+    Returns
+    -------
+    class
+        The `BZ2File` class from the `bz2` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `bz2` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_bz2:
+        raise RuntimeError(
+            "bz2 module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.BZ2File
+
+
+__all__ = [
+    "is_numpy_dev",
+    "pa_version_under10p1",
+    "pa_version_under11p0",
+    "pa_version_under13p0",
+    "pa_version_under14p0",
+    "pa_version_under14p1",
+    "pa_version_under16p0",
+    "pa_version_under17p0",
+    "pa_version_under18p0",
+    "pa_version_under19p0",
+    "pa_version_under20p0",
+    "pa_version_under21p0",
+    "HAS_PYARROW",
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PY314",
+    "PYPY",
+    "WARNING_CHECK_DISABLED",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/_constants.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb011603712c61ae1dcc745148a6dfb2c05b803f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/_constants.py
@@ -0,0 +1,34 @@
+"""
+_constants
+======
+
+Constants relevant for the Python implementation.
+"""
+
+from __future__ import annotations
+
+import platform
+import sys
+import sysconfig
+
+IS64 = sys.maxsize > 2**32
+
+PY310 = sys.version_info >= (3, 10)
+PY311 = sys.version_info >= (3, 11)
+PY312 = sys.version_info >= (3, 12)
+PY314 = sys.version_info >= (3, 14)
+PYPY = platform.python_implementation() == "PyPy"
+ISMUSL = "musl" in (sysconfig.get_config_var("HOST_GNU_TYPE") or "")
+REF_COUNT = 2 if PY311 else 3
+WARNING_CHECK_DISABLED = PY314
+
+
+__all__ = [
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PY314",
+    "PYPY",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/_optional.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/_optional.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bc6cd46f09a7e4b103658f9c2ec9a69d93d00b9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/_optional.py
@@ -0,0 +1,168 @@
+from __future__ import annotations
+
+import importlib
+import sys
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    import types
+
+# Update install.rst & setup.cfg when updating versions!
+
+VERSIONS = {
+    "adbc-driver-postgresql": "0.8.0",
+    "adbc-driver-sqlite": "0.8.0",
+    "bs4": "4.11.2",
+    "blosc": "1.21.3",
+    "bottleneck": "1.3.6",
+    "dataframe-api-compat": "0.1.7",
+    "fastparquet": "2022.12.0",
+    "fsspec": "2022.11.0",
+    "html5lib": "1.1",
+    "hypothesis": "6.46.1",
+    "gcsfs": "2022.11.0",
+    "jinja2": "3.1.2",
+    "lxml.etree": "4.9.2",
+    "matplotlib": "3.6.3",
+    "numba": "0.56.4",
+    "numexpr": "2.8.4",
+    "odfpy": "1.4.1",
+    "openpyxl": "3.1.0",
+    "pandas_gbq": "0.19.0",
+    "psycopg2": "2.9.6",  # (dt dec pq3 ext lo64)
+    "pymysql": "1.0.2",
+    "pyarrow": "10.0.1",
+    "pyreadstat": "1.2.0",
+    "pytest": "7.3.2",
+    "python-calamine": "0.1.7",
+    "pyxlsb": "1.0.10",
+    "s3fs": "2022.11.0",
+    "scipy": "1.10.0",
+    "sqlalchemy": "2.0.0",
+    "tables": "3.8.0",
+    "tabulate": "0.9.0",
+    "xarray": "2022.12.0",
+    "xlrd": "2.0.1",
+    "xlsxwriter": "3.0.5",
+    "zstandard": "0.19.0",
+    "tzdata": "2022.7",
+    "qtpy": "2.3.0",
+    "pyqt5": "5.15.9",
+}
+
+# A mapping from import name to package name (on PyPI) for packages where
+# these two names are different.
+
+INSTALL_MAPPING = {
+    "bs4": "beautifulsoup4",
+    "bottleneck": "Bottleneck",
+    "jinja2": "Jinja2",
+    "lxml.etree": "lxml",
+    "odf": "odfpy",
+    "pandas_gbq": "pandas-gbq",
+    "python_calamine": "python-calamine",
+    "sqlalchemy": "SQLAlchemy",
+    "tables": "pytables",
+}
+
+
+def get_version(module: types.ModuleType) -> str:
+    version = getattr(module, "__version__", None)
+
+    if version is None:
+        raise ImportError(f"Can't determine version for {module.__name__}")
+    if module.__name__ == "psycopg2":
+        # psycopg2 appends " (dt dec pq3 ext lo64)" to it's version
+        version = version.split()[0]
+    return version
+
+
+def import_optional_dependency(
+    name: str,
+    extra: str = "",
+    errors: str = "raise",
+    min_version: str | None = None,
+):
+    """
+    Import an optional dependency.
+
+    By default, if a dependency is missing an ImportError with a nice
+    message will be raised. If a dependency is present, but too old,
+    we raise.
+
+    Parameters
+    ----------
+    name : str
+        The module name.
+    extra : str
+        Additional text to include in the ImportError message.
+    errors : str {'raise', 'warn', 'ignore'}
+        What to do when a dependency is not found or its version is too old.
+
+        * raise : Raise an ImportError
+        * warn : Only applicable when a module's version is to old.
+          Warns that the version is too old and returns None
+        * ignore: If the module is not installed, return None, otherwise,
+          return the module, even if the version is too old.
+          It's expected that users validate the version locally when
+          using ``errors="ignore"`` (see. ``io/html.py``)
+    min_version : str, default None
+        Specify a minimum version that is different from the global pandas
+        minimum version required.
+    Returns
+    -------
+    maybe_module : Optional[ModuleType]
+        The imported module, when found and the version is correct.
+        None is returned when the package is not found and `errors`
+        is False, or when the package's version is too old and `errors`
+        is ``'warn'`` or ``'ignore'``.
+    """
+    assert errors in {"warn", "raise", "ignore"}
+
+    package_name = INSTALL_MAPPING.get(name)
+    install_name = package_name if package_name is not None else name
+
+    msg = (
+        f"Missing optional dependency '{install_name}'. {extra} "
+        f"Use pip or conda to install {install_name}."
+    )
+    try:
+        module = importlib.import_module(name)
+    except ImportError:
+        if errors == "raise":
+            raise ImportError(msg)
+        return None
+
+    # Handle submodules: if we have submodule, grab parent module from sys.modules
+    parent = name.split(".")[0]
+    if parent != name:
+        install_name = parent
+        module_to_get = sys.modules[install_name]
+    else:
+        module_to_get = module
+    minimum_version = min_version if min_version is not None else VERSIONS.get(parent)
+    if minimum_version:
+        version = get_version(module_to_get)
+        if version and Version(version) < Version(minimum_version):
+            msg = (
+                f"Pandas requires version '{minimum_version}' or newer of '{parent}' "
+                f"(version '{version}' currently installed)."
+            )
+            if errors == "warn":
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return None
+            elif errors == "raise":
+                raise ImportError(msg)
+            else:
+                return None
+
+    return module
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/compressors.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/compressors.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f31e34c092c9672559ca2f5194cb1da7083d03b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/compressors.py
@@ -0,0 +1,77 @@
+"""
+Patched ``BZ2File`` and ``LZMAFile`` to handle pickle protocol 5.
+"""
+
+from __future__ import annotations
+
+from pickle import PickleBuffer
+
+from pandas.compat._constants import PY310
+
+try:
+    import bz2
+
+    has_bz2 = True
+except ImportError:
+    has_bz2 = False
+
+try:
+    import lzma
+
+    has_lzma = True
+except ImportError:
+    has_lzma = False
+
+
+def flatten_buffer(
+    b: bytes | bytearray | memoryview | PickleBuffer,
+) -> bytes | bytearray | memoryview:
+    """
+    Return some 1-D `uint8` typed buffer.
+
+    Coerces anything that does not match that description to one that does
+    without copying if possible (otherwise will copy).
+    """
+
+    if isinstance(b, (bytes, bytearray)):
+        return b
+
+    if not isinstance(b, PickleBuffer):
+        b = PickleBuffer(b)
+
+    try:
+        # coerce to 1-D `uint8` C-contiguous `memoryview` zero-copy
+        return b.raw()
+    except BufferError:
+        # perform in-memory copy if buffer is not contiguous
+        return memoryview(b).tobytes("A")
+
+
+if has_bz2:
+
+    class BZ2File(bz2.BZ2File):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `bz2.BZ2File` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))
+
+
+if has_lzma:
+
+    class LZMAFile(lzma.LZMAFile):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `lzma.LZMAFile` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/numpy/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/numpy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a06761d03887bc2a2dd186fe2d3ac781f7222aeb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/numpy/__init__.py
@@ -0,0 +1,53 @@
+""" support numpy compatibility across versions """
+import warnings
+
+import numpy as np
+
+from pandas.util.version import Version
+
+# numpy versioning
+_np_version = np.__version__
+_nlv = Version(_np_version)
+np_version_lt1p23 = _nlv < Version("1.23")
+np_version_gte1p24 = _nlv >= Version("1.24")
+np_version_gte1p24p3 = _nlv >= Version("1.24.3")
+np_version_gte1p25 = _nlv >= Version("1.25")
+np_version_gt2 = _nlv >= Version("2.0.0")
+is_numpy_dev = _nlv.dev is not None
+_min_numpy_ver = "1.22.4"
+
+
+if _nlv < Version(_min_numpy_ver):
+    raise ImportError(
+        f"this version of pandas is incompatible with numpy < {_min_numpy_ver}\n"
+        f"your numpy version is {_np_version}.\n"
+        f"Please upgrade numpy to >= {_min_numpy_ver} to use this pandas version"
+    )
+
+
+np_long: type
+np_ulong: type
+
+if np_version_gt2:
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                r".*In the future `np\.long` will be defined as.*",
+                FutureWarning,
+            )
+            np_long = np.long  # type: ignore[attr-defined]
+            np_ulong = np.ulong  # type: ignore[attr-defined]
+    except AttributeError:
+        np_long = np.int_
+        np_ulong = np.uint
+else:
+    np_long = np.int_
+    np_ulong = np.uint
+
+
+__all__ = [
+    "np",
+    "_np_version",
+    "is_numpy_dev",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/numpy/function.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/numpy/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..4df30f7f4a8a79984ca6de521ac058bd30fd8faf
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/numpy/function.py
@@ -0,0 +1,418 @@
+"""
+For compatibility with numpy libraries, pandas functions or methods have to
+accept '*args' and '**kwargs' parameters to accommodate numpy arguments that
+are not actually used or respected in the pandas implementation.
+
+To ensure that users do not abuse these parameters, validation is performed in
+'validators.py' to make sure that any extra parameters passed correspond ONLY
+to those in the numpy signature. Part of that validation includes whether or
+not the user attempted to pass in non-default values for these extraneous
+parameters. As we want to discourage users from relying on these parameters
+when calling the pandas implementation, we want them only to pass in the
+default values for these parameters.
+
+This module provides a set of commonly used default arguments for functions and
+methods that are spread throughout the codebase. This module will make it
+easier to adjust to future upstream changes in the analogous numpy signatures.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    overload,
+)
+
+import numpy as np
+from numpy import ndarray
+
+from pandas._libs.lib import (
+    is_bool,
+    is_integer,
+)
+from pandas.errors import UnsupportedFunctionCall
+from pandas.util._validators import (
+    validate_args,
+    validate_args_and_kwargs,
+    validate_kwargs,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+    )
+
+    AxisNoneT = TypeVar("AxisNoneT", Axis, None)
+
+
+class CompatValidator:
+    def __init__(
+        self,
+        defaults,
+        fname=None,
+        method: str | None = None,
+        max_fname_arg_count=None,
+    ) -> None:
+        self.fname = fname
+        self.method = method
+        self.defaults = defaults
+        self.max_fname_arg_count = max_fname_arg_count
+
+    def __call__(
+        self,
+        args,
+        kwargs,
+        fname=None,
+        max_fname_arg_count=None,
+        method: str | None = None,
+    ) -> None:
+        if not args and not kwargs:
+            return None
+
+        fname = self.fname if fname is None else fname
+        max_fname_arg_count = (
+            self.max_fname_arg_count
+            if max_fname_arg_count is None
+            else max_fname_arg_count
+        )
+        method = self.method if method is None else method
+
+        if method == "args":
+            validate_args(fname, args, max_fname_arg_count, self.defaults)
+        elif method == "kwargs":
+            validate_kwargs(fname, kwargs, self.defaults)
+        elif method == "both":
+            validate_args_and_kwargs(
+                fname, args, kwargs, max_fname_arg_count, self.defaults
+            )
+        else:
+            raise ValueError(f"invalid validation method '{method}'")
+
+
+ARGMINMAX_DEFAULTS = {"out": None}
+validate_argmin = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmin", method="both", max_fname_arg_count=1
+)
+validate_argmax = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmax", method="both", max_fname_arg_count=1
+)
+
+
+def process_skipna(skipna: bool | ndarray | None, args) -> tuple[bool, Any]:
+    if isinstance(skipna, ndarray) or skipna is None:
+        args = (skipna,) + args
+        skipna = True
+
+    return skipna, args
+
+
+def validate_argmin_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmin' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmin(args, kwargs)
+    return skipna
+
+
+def validate_argmax_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmax' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmax(args, kwargs)
+    return skipna
+
+
+ARGSORT_DEFAULTS: dict[str, int | str | None] = {}
+ARGSORT_DEFAULTS["axis"] = -1
+ARGSORT_DEFAULTS["kind"] = "quicksort"
+ARGSORT_DEFAULTS["order"] = None
+ARGSORT_DEFAULTS["kind"] = None
+ARGSORT_DEFAULTS["stable"] = None
+
+
+validate_argsort = CompatValidator(
+    ARGSORT_DEFAULTS, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+# two different signatures of argsort, this second validation for when the
+# `kind` param is supported
+ARGSORT_DEFAULTS_KIND: dict[str, int | None] = {}
+ARGSORT_DEFAULTS_KIND["axis"] = -1
+ARGSORT_DEFAULTS_KIND["order"] = None
+ARGSORT_DEFAULTS_KIND["stable"] = None
+validate_argsort_kind = CompatValidator(
+    ARGSORT_DEFAULTS_KIND, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+
+def validate_argsort_with_ascending(ascending: bool | int | None, args, kwargs) -> bool:
+    """
+    If 'Categorical.argsort' is called via the 'numpy' library, the first
+    parameter in its signature is 'axis', which takes either an integer or
+    'None', so check if the 'ascending' parameter has either integer type or is
+    None, since 'ascending' itself should be a boolean
+    """
+    if is_integer(ascending) or ascending is None:
+        args = (ascending,) + args
+        ascending = True
+
+    validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
+    ascending = cast(bool, ascending)
+    return ascending
+
+
+CLIP_DEFAULTS: dict[str, Any] = {"out": None}
+validate_clip = CompatValidator(
+    CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
+)
+
+
+@overload
+def validate_clip_with_axis(axis: ndarray, args, kwargs) -> None:
+    ...
+
+
+@overload
+def validate_clip_with_axis(axis: AxisNoneT, args, kwargs) -> AxisNoneT:
+    ...
+
+
+def validate_clip_with_axis(
+    axis: ndarray | AxisNoneT, args, kwargs
+) -> AxisNoneT | None:
+    """
+    If 'NDFrame.clip' is called via the numpy library, the third parameter in
+    its signature is 'out', which can takes an ndarray, so check if the 'axis'
+    parameter is an instance of ndarray, since 'axis' itself should either be
+    an integer or None
+    """
+    if isinstance(axis, ndarray):
+        args = (axis,) + args
+        # error: Incompatible types in assignment (expression has type "None",
+        # variable has type "Union[ndarray[Any, Any], str, int]")
+        axis = None  # type: ignore[assignment]
+
+    validate_clip(args, kwargs)
+    # error: Incompatible return value type (got "Union[ndarray[Any, Any],
+    # str, int]", expected "Union[str, int, None]")
+    return axis  # type: ignore[return-value]
+
+
+CUM_FUNC_DEFAULTS: dict[str, Any] = {}
+CUM_FUNC_DEFAULTS["dtype"] = None
+CUM_FUNC_DEFAULTS["out"] = None
+validate_cum_func = CompatValidator(
+    CUM_FUNC_DEFAULTS, method="both", max_fname_arg_count=1
+)
+validate_cumsum = CompatValidator(
+    CUM_FUNC_DEFAULTS, fname="cumsum", method="both", max_fname_arg_count=1
+)
+
+
+def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so
+    check if the 'skipna' parameter is a boolean or not
+    """
+    if not is_bool(skipna):
+        args = (skipna,) + args
+        skipna = True
+    elif isinstance(skipna, np.bool_):
+        skipna = bool(skipna)
+
+    validate_cum_func(args, kwargs, fname=name)
+    return skipna
+
+
+ALLANY_DEFAULTS: dict[str, bool | None] = {}
+ALLANY_DEFAULTS["dtype"] = None
+ALLANY_DEFAULTS["out"] = None
+ALLANY_DEFAULTS["keepdims"] = False
+ALLANY_DEFAULTS["axis"] = None
+validate_all = CompatValidator(
+    ALLANY_DEFAULTS, fname="all", method="both", max_fname_arg_count=1
+)
+validate_any = CompatValidator(
+    ALLANY_DEFAULTS, fname="any", method="both", max_fname_arg_count=1
+)
+
+LOGICAL_FUNC_DEFAULTS = {"out": None, "keepdims": False}
+validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
+
+MINMAX_DEFAULTS = {"axis": None, "dtype": None, "out": None, "keepdims": False}
+validate_min = CompatValidator(
+    MINMAX_DEFAULTS, fname="min", method="both", max_fname_arg_count=1
+)
+validate_max = CompatValidator(
+    MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
+)
+
+RESHAPE_DEFAULTS: dict[str, str] = {"order": "C"}
+validate_reshape = CompatValidator(
+    RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
+)
+
+REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
+validate_repeat = CompatValidator(
+    REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
+)
+
+ROUND_DEFAULTS: dict[str, Any] = {"out": None}
+validate_round = CompatValidator(
+    ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
+)
+
+SORT_DEFAULTS: dict[str, int | str | None] = {}
+SORT_DEFAULTS["axis"] = -1
+SORT_DEFAULTS["kind"] = "quicksort"
+SORT_DEFAULTS["order"] = None
+validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
+
+STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
+STAT_FUNC_DEFAULTS["dtype"] = None
+STAT_FUNC_DEFAULTS["out"] = None
+
+SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+SUM_DEFAULTS["axis"] = None
+SUM_DEFAULTS["keepdims"] = False
+SUM_DEFAULTS["initial"] = None
+
+PROD_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEAN_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+MEDIAN_DEFAULTS["overwrite_input"] = False
+MEDIAN_DEFAULTS["keepdims"] = False
+
+STAT_FUNC_DEFAULTS["keepdims"] = False
+
+validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, method="kwargs")
+validate_sum = CompatValidator(
+    SUM_DEFAULTS, fname="sum", method="both", max_fname_arg_count=1
+)
+validate_prod = CompatValidator(
+    PROD_DEFAULTS, fname="prod", method="both", max_fname_arg_count=1
+)
+validate_mean = CompatValidator(
+    MEAN_DEFAULTS, fname="mean", method="both", max_fname_arg_count=1
+)
+validate_median = CompatValidator(
+    MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
+)
+
+STAT_DDOF_FUNC_DEFAULTS: dict[str, bool | None] = {}
+STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
+STAT_DDOF_FUNC_DEFAULTS["out"] = None
+STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
+validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
+
+TAKE_DEFAULTS: dict[str, str | None] = {}
+TAKE_DEFAULTS["out"] = None
+TAKE_DEFAULTS["mode"] = "raise"
+validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
+
+
+def validate_take_with_convert(convert: ndarray | bool | None, args, kwargs) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'axis', which takes either an ndarray or 'None', so check
+    if the 'convert' parameter is either an instance of ndarray or is None
+    """
+    if isinstance(convert, ndarray) or convert is None:
+        args = (convert,) + args
+        convert = True
+
+    validate_take(args, kwargs, max_fname_arg_count=3, method="both")
+    return convert
+
+
+TRANSPOSE_DEFAULTS = {"axes": None}
+validate_transpose = CompatValidator(
+    TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
+)
+
+
+def validate_groupby_func(name: str, args, kwargs, allowed=None) -> None:
+    """
+    'args' and 'kwargs' should be empty, except for allowed kwargs because all
+    of their necessary parameters are explicitly listed in the function
+    signature
+    """
+    if allowed is None:
+        allowed = []
+
+    kwargs = set(kwargs) - set(allowed)
+
+    if len(args) + len(kwargs) > 0:
+        raise UnsupportedFunctionCall(
+            "numpy operations are not valid with groupby. "
+            f"Use .groupby(...).{name}() instead"
+        )
+
+
+RESAMPLER_NUMPY_OPS = ("min", "max", "sum", "prod", "mean", "std", "var")
+
+
+def validate_resampler_func(method: str, args, kwargs) -> None:
+    """
+    'args' and 'kwargs' should be empty because all of their necessary
+    parameters are explicitly listed in the function signature
+    """
+    if len(args) + len(kwargs) > 0:
+        if method in RESAMPLER_NUMPY_OPS:
+            raise UnsupportedFunctionCall(
+                "numpy operations are not valid with resample. "
+                f"Use .resample(...).{method}() instead"
+            )
+        raise TypeError("too many arguments passed in")
+
+
+def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:
+    """
+    Ensure that the axis argument passed to min, max, argmin, or argmax is zero
+    or None, as otherwise it will be incorrectly ignored.
+
+    Parameters
+    ----------
+    axis : int or None
+    ndim : int, default 1
+
+    Raises
+    ------
+    ValueError
+    """
+    if axis is None:
+        return
+    if axis >= ndim or (axis < 0 and ndim + axis < 0):
+        raise ValueError(f"`axis` must be fewer than the number of dimensions ({ndim})")
+
+
+_validation_funcs = {
+    "median": validate_median,
+    "mean": validate_mean,
+    "min": validate_min,
+    "max": validate_max,
+    "sum": validate_sum,
+    "prod": validate_prod,
+}
+
+
+def validate_func(fname, args, kwargs) -> None:
+    if fname not in _validation_funcs:
+        return validate_stat_func(args, kwargs, fname=fname)
+
+    validation_func = _validation_funcs[fname]
+    return validation_func(args, kwargs)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/pickle_compat.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/pickle_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd98087c06c18634304c29d88837017a6952a4fc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/pickle_compat.py
@@ -0,0 +1,262 @@
+"""
+Support pre-0.12 series pickle compatibility.
+"""
+from __future__ import annotations
+
+import contextlib
+import copy
+import io
+import pickle as pkl
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import BaseOffset
+
+from pandas import Index
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.internals import BlockManager
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+def load_reduce(self) -> None:
+    stack = self.stack
+    args = stack.pop()
+    func = stack[-1]
+
+    try:
+        stack[-1] = func(*args)
+        return
+    except TypeError as err:
+        # If we have a deprecated function,
+        # try to replace and try again.
+
+        msg = "_reconstruct: First argument must be a sub-type of ndarray"
+
+        if msg in str(err):
+            try:
+                cls = args[0]
+                stack[-1] = object.__new__(cls)
+                return
+            except TypeError:
+                pass
+        elif args and isinstance(args[0], type) and issubclass(args[0], BaseOffset):
+            # TypeError: object.__new__(Day) is not safe, use Day.__new__()
+            cls = args[0]
+            stack[-1] = cls.__new__(*args)
+            return
+        elif args and issubclass(args[0], PeriodArray):
+            cls = args[0]
+            stack[-1] = NDArrayBacked.__new__(*args)
+            return
+
+        raise
+
+
+# If classes are moved, provide compat here.
+_class_locations_map = {
+    ("pandas.core.sparse.array", "SparseArray"): ("pandas.core.arrays", "SparseArray"),
+    # 15477
+    ("pandas.core.base", "FrozenNDArray"): ("numpy", "ndarray"),
+    # Re-routing unpickle block logic to go through _unpickle_block instead
+    # for pandas <= 1.3.5
+    ("pandas.core.internals.blocks", "new_block"): (
+        "pandas._libs.internals",
+        "_unpickle_block",
+    ),
+    ("pandas.core.indexes.frozen", "FrozenNDArray"): ("numpy", "ndarray"),
+    ("pandas.core.base", "FrozenList"): ("pandas.core.indexes.frozen", "FrozenList"),
+    # 10890
+    ("pandas.core.series", "TimeSeries"): ("pandas.core.series", "Series"),
+    ("pandas.sparse.series", "SparseTimeSeries"): (
+        "pandas.core.sparse.series",
+        "SparseSeries",
+    ),
+    # 12588, extensions moving
+    ("pandas._sparse", "BlockIndex"): ("pandas._libs.sparse", "BlockIndex"),
+    ("pandas.tslib", "Timestamp"): ("pandas._libs.tslib", "Timestamp"),
+    # 18543 moving period
+    ("pandas._period", "Period"): ("pandas._libs.tslibs.period", "Period"),
+    ("pandas._libs.period", "Period"): ("pandas._libs.tslibs.period", "Period"),
+    # 18014 moved __nat_unpickle from _libs.tslib-->_libs.tslibs.nattype
+    ("pandas.tslib", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "__nat_unpickle",
+    ),
+    ("pandas._libs.tslib", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "__nat_unpickle",
+    ),
+    # 15998 top-level dirs moving
+    ("pandas.sparse.array", "SparseArray"): (
+        "pandas.core.arrays.sparse",
+        "SparseArray",
+    ),
+    ("pandas.indexes.base", "_new_Index"): ("pandas.core.indexes.base", "_new_Index"),
+    ("pandas.indexes.base", "Index"): ("pandas.core.indexes.base", "Index"),
+    ("pandas.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
+    ),
+    ("pandas.indexes.range", "RangeIndex"): ("pandas.core.indexes.range", "RangeIndex"),
+    ("pandas.indexes.multi", "MultiIndex"): ("pandas.core.indexes.multi", "MultiIndex"),
+    ("pandas.tseries.index", "_new_DatetimeIndex"): (
+        "pandas.core.indexes.datetimes",
+        "_new_DatetimeIndex",
+    ),
+    ("pandas.tseries.index", "DatetimeIndex"): (
+        "pandas.core.indexes.datetimes",
+        "DatetimeIndex",
+    ),
+    ("pandas.tseries.period", "PeriodIndex"): (
+        "pandas.core.indexes.period",
+        "PeriodIndex",
+    ),
+    # 19269, arrays moving
+    ("pandas.core.categorical", "Categorical"): ("pandas.core.arrays", "Categorical"),
+    # 19939, add timedeltaindex, float64index compat from 15998 move
+    ("pandas.tseries.tdi", "TimedeltaIndex"): (
+        "pandas.core.indexes.timedeltas",
+        "TimedeltaIndex",
+    ),
+    ("pandas.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
+    ),
+    # 50775, remove Int64Index, UInt64Index & Float64Index from codabase
+    ("pandas.core.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "UInt64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.arrays.sparse.dtype", "SparseDtype"): (
+        "pandas.core.dtypes.dtypes",
+        "SparseDtype",
+    ),
+}
+
+
+# our Unpickler sub-class to override methods and some dispatcher
+# functions for compat and uses a non-public class of the pickle module.
+
+
+class Unpickler(pkl._Unpickler):
+    def find_class(self, module, name):
+        # override superclass
+        key = (module, name)
+        module, name = _class_locations_map.get(key, key)
+        return super().find_class(module, name)
+
+
+Unpickler.dispatch = copy.copy(Unpickler.dispatch)
+Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce
+
+
+def load_newobj(self) -> None:
+    args = self.stack.pop()
+    cls = self.stack[-1]
+
+    # compat
+    if issubclass(cls, Index):
+        obj = object.__new__(cls)
+    elif issubclass(cls, DatetimeArray) and not args:
+        arr = np.array([], dtype="M8[ns]")
+        obj = cls.__new__(cls, arr, arr.dtype)
+    elif issubclass(cls, TimedeltaArray) and not args:
+        arr = np.array([], dtype="m8[ns]")
+        obj = cls.__new__(cls, arr, arr.dtype)
+    elif cls is BlockManager and not args:
+        obj = cls.__new__(cls, (), [], False)
+    else:
+        obj = cls.__new__(cls, *args)
+
+    self.stack[-1] = obj
+
+
+Unpickler.dispatch[pkl.NEWOBJ[0]] = load_newobj
+
+
+def load_newobj_ex(self) -> None:
+    kwargs = self.stack.pop()
+    args = self.stack.pop()
+    cls = self.stack.pop()
+
+    # compat
+    if issubclass(cls, Index):
+        obj = object.__new__(cls)
+    else:
+        obj = cls.__new__(cls, *args, **kwargs)
+    self.append(obj)
+
+
+try:
+    Unpickler.dispatch[pkl.NEWOBJ_EX[0]] = load_newobj_ex
+except (AttributeError, KeyError):
+    pass
+
+
+def load(fh, encoding: str | None = None, is_verbose: bool = False):
+    """
+    Load a pickle, with a provided encoding,
+
+    Parameters
+    ----------
+    fh : a filelike object
+    encoding : an optional encoding
+    is_verbose : show exception output
+    """
+    try:
+        fh.seek(0)
+        if encoding is not None:
+            up = Unpickler(fh, encoding=encoding)
+        else:
+            up = Unpickler(fh)
+        # "Unpickler" has no attribute "is_verbose"  [attr-defined]
+        up.is_verbose = is_verbose  # type: ignore[attr-defined]
+
+        return up.load()
+    except (ValueError, TypeError):
+        raise
+
+
+def loads(
+    bytes_object: bytes,
+    *,
+    fix_imports: bool = True,
+    encoding: str = "ASCII",
+    errors: str = "strict",
+):
+    """
+    Analogous to pickle._loads.
+    """
+    fd = io.BytesIO(bytes_object)
+    return Unpickler(
+        fd, fix_imports=fix_imports, encoding=encoding, errors=errors
+    ).load()
+
+
+@contextlib.contextmanager
+def patch_pickle() -> Generator[None, None, None]:
+    """
+    Temporarily patch pickle to use our unpickler.
+    """
+    orig_loads = pkl.loads
+    try:
+        setattr(pkl, "loads", loads)
+        yield
+    finally:
+        setattr(pkl, "loads", orig_loads)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/pyarrow.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/pyarrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..aacf2cd85c8ca3a9c1604d5314a7c247998e00c8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/compat/pyarrow.py
@@ -0,0 +1,39 @@
+""" support pyarrow compatibility across versions """
+
+from __future__ import annotations
+
+from pandas.util.version import Version
+
+try:
+    import pyarrow as pa
+
+    _palv = Version(Version(pa.__version__).base_version)
+    pa_version_under10p1 = _palv < Version("10.0.1")
+    pa_version_under11p0 = _palv < Version("11.0.0")
+    pa_version_under12p0 = _palv < Version("12.0.0")
+    pa_version_under13p0 = _palv < Version("13.0.0")
+    pa_version_under14p0 = _palv < Version("14.0.0")
+    pa_version_under14p1 = _palv < Version("14.0.1")
+    pa_version_under15p0 = _palv < Version("15.0.0")
+    pa_version_under16p0 = _palv < Version("16.0.0")
+    pa_version_under17p0 = _palv < Version("17.0.0")
+    pa_version_under18p0 = _palv < Version("18.0.0")
+    pa_version_under19p0 = _palv < Version("19.0.0")
+    pa_version_under20p0 = _palv < Version("20.0.0")
+    pa_version_under21p0 = _palv < Version("21.0.0")
+    HAS_PYARROW = True
+except ImportError:
+    pa_version_under10p1 = True
+    pa_version_under11p0 = True
+    pa_version_under12p0 = True
+    pa_version_under13p0 = True
+    pa_version_under14p0 = True
+    pa_version_under14p1 = True
+    pa_version_under15p0 = True
+    pa_version_under16p0 = True
+    pa_version_under17p0 = True
+    pa_version_under18p0 = True
+    pa_version_under19p0 = True
+    pa_version_under20p0 = True
+    pa_version_under21p0 = True
+    HAS_PYARROW = False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/conftest.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..35fe5cb475cde0e6ea6c181c6326b06cadc763ea
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/conftest.py
@@ -0,0 +1,2065 @@
+"""
+This file is very long and growing, but it was decided to not split it yet, as
+it's still manageable (2020-03-17, ~1.1k LoC). See gh-31989
+
+Instead of splitting it was decided to define sections here:
+- Configuration / Settings
+- Autouse fixtures
+- Common arguments
+- Missing values & co.
+- Classes
+- Indices
+- Series'
+- DataFrames
+- Operators & Operations
+- Data sets/files
+- Time zones
+- Dtypes
+- Misc
+"""
+from __future__ import annotations
+
+from collections import abc
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+    timezone,
+)
+from decimal import Decimal
+import operator
+import os
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+from dateutil.tz import (
+    tzlocal,
+    tzutc,
+)
+import hypothesis
+from hypothesis import strategies as st
+import numpy as np
+import pytest
+from pytz import (
+    FixedOffset,
+    utc,
+)
+
+from pandas._config.config import _get_option
+
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    IntervalDtype,
+)
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Interval,
+    IntervalIndex,
+    Period,
+    RangeIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+)
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+try:
+    import pyarrow as pa
+except ImportError:
+    has_pyarrow = False
+else:
+    del pa
+    has_pyarrow = True
+
+import zoneinfo
+
+try:
+    zoneinfo.ZoneInfo("UTC")
+except zoneinfo.ZoneInfoNotFoundError:
+    zoneinfo = None  # type: ignore[assignment]
+
+
+# ----------------------------------------------------------------
+# Configuration / Settings
+# ----------------------------------------------------------------
+# pytest
+
+
+def pytest_addoption(parser) -> None:
+    parser.addoption(
+        "--no-strict-data-files",
+        action="store_false",
+        help="Don't fail if a test is skipped for missing data file.",
+    )
+
+
+def ignore_doctest_warning(item: pytest.Item, path: str, message: str) -> None:
+    """Ignore doctest warning.
+
+    Parameters
+    ----------
+    item : pytest.Item
+        pytest test item.
+    path : str
+        Module path to Python object, e.g. "pandas.core.frame.DataFrame.append". A
+        warning will be filtered when item.name ends with in given path. So it is
+        sufficient to specify e.g. "DataFrame.append".
+    message : str
+        Message to be filtered.
+    """
+    if item.name.endswith(path):
+        item.add_marker(pytest.mark.filterwarnings(f"ignore:{message}"))
+
+
+def pytest_collection_modifyitems(items, config) -> None:
+    is_doctest = config.getoption("--doctest-modules") or config.getoption(
+        "--doctest-cython", default=False
+    )
+
+    # Warnings from doctests that can be ignored; place reason in comment above.
+    # Each entry specifies (path, message) - see the ignore_doctest_warning function
+    ignored_doctest_warnings = [
+        ("is_int64_dtype", "is_int64_dtype is deprecated"),
+        ("is_interval_dtype", "is_interval_dtype is deprecated"),
+        ("is_period_dtype", "is_period_dtype is deprecated"),
+        ("is_datetime64tz_dtype", "is_datetime64tz_dtype is deprecated"),
+        ("is_categorical_dtype", "is_categorical_dtype is deprecated"),
+        ("is_sparse", "is_sparse is deprecated"),
+        ("DataFrameGroupBy.fillna", "DataFrameGroupBy.fillna is deprecated"),
+        ("NDFrame.replace", "The 'method' keyword"),
+        ("NDFrame.replace", "Series.replace without 'value'"),
+        ("NDFrame.clip", "Downcasting behavior in Series and DataFrame methods"),
+        ("Series.idxmin", "The behavior of Series.idxmin"),
+        ("Series.idxmax", "The behavior of Series.idxmax"),
+        ("SeriesGroupBy.fillna", "SeriesGroupBy.fillna is deprecated"),
+        ("SeriesGroupBy.idxmin", "The behavior of Series.idxmin"),
+        ("SeriesGroupBy.idxmax", "The behavior of Series.idxmax"),
+        # Docstring divides by zero to show behavior difference
+        ("missing.mask_zero_div_zero", "divide by zero encountered"),
+        (
+            "to_pydatetime",
+            "The behavior of DatetimeProperties.to_pydatetime is deprecated",
+        ),
+        (
+            "pandas.core.generic.NDFrame.bool",
+            "(Series|DataFrame).bool is now deprecated and will be removed "
+            "in future version of pandas",
+        ),
+        (
+            "pandas.core.generic.NDFrame.first",
+            "first is deprecated and will be removed in a future version. "
+            "Please create a mask and filter using `.loc` instead",
+        ),
+        (
+            "Resampler.fillna",
+            "DatetimeIndexResampler.fillna is deprecated",
+        ),
+        (
+            "DataFrameGroupBy.fillna",
+            "DataFrameGroupBy.fillna with 'method' is deprecated",
+        ),
+        (
+            "DataFrameGroupBy.fillna",
+            "DataFrame.fillna with 'method' is deprecated",
+        ),
+        ("read_parquet", "Passing a BlockManager to DataFrame is deprecated"),
+    ]
+
+    if is_doctest:
+        for item in items:
+            for path, message in ignored_doctest_warnings:
+                ignore_doctest_warning(item, path, message)
+
+
+hypothesis_health_checks = [hypothesis.HealthCheck.too_slow]
+if Version(hypothesis.__version__) >= Version("6.83.2"):
+    hypothesis_health_checks.append(hypothesis.HealthCheck.differing_executors)
+
+# Hypothesis
+hypothesis.settings.register_profile(
+    "ci",
+    # Hypothesis timing checks are tuned for scalars by default, so we bump
+    # them from 200ms to 500ms per test case as the global default.  If this
+    # is too short for a specific test, (a) try to make it faster, and (b)
+    # if it really is slow add `@settings(deadline=...)` with a working value,
+    # or `deadline=None` to entirely disable timeouts for that test.
+    # 2022-02-09: Changed deadline from 500 -> None. Deadline leads to
+    # non-actionable, flaky CI failures (# GH 24641, 44969, 45118, 44969)
+    deadline=None,
+    suppress_health_check=tuple(hypothesis_health_checks),
+)
+hypothesis.settings.load_profile("ci")
+
+# Registering these strategies makes them globally available via st.from_type,
+# which is use for offsets in tests/tseries/offsets/test_offsets_properties.py
+for name in "MonthBegin MonthEnd BMonthBegin BMonthEnd".split():
+    cls = getattr(pd.tseries.offsets, name)
+    st.register_type_strategy(
+        cls, st.builds(cls, n=st.integers(-99, 99), normalize=st.booleans())
+    )
+
+for name in "YearBegin YearEnd BYearBegin BYearEnd".split():
+    cls = getattr(pd.tseries.offsets, name)
+    st.register_type_strategy(
+        cls,
+        st.builds(
+            cls,
+            n=st.integers(-5, 5),
+            normalize=st.booleans(),
+            month=st.integers(min_value=1, max_value=12),
+        ),
+    )
+
+for name in "QuarterBegin QuarterEnd BQuarterBegin BQuarterEnd".split():
+    cls = getattr(pd.tseries.offsets, name)
+    st.register_type_strategy(
+        cls,
+        st.builds(
+            cls,
+            n=st.integers(-24, 24),
+            normalize=st.booleans(),
+            startingMonth=st.integers(min_value=1, max_value=12),
+        ),
+    )
+
+
+# ----------------------------------------------------------------
+# Autouse fixtures
+# ----------------------------------------------------------------
+
+
+# https://github.com/pytest-dev/pytest/issues/11873
+# Would like to avoid autouse=True, but cannot as of pytest 8.0.0
+@pytest.fixture(autouse=True)
+def add_doctest_imports(doctest_namespace) -> None:
+    """
+    Make `np` and `pd` names available for doctests.
+    """
+    doctest_namespace["np"] = np
+    doctest_namespace["pd"] = pd
+
+
+@pytest.fixture(autouse=True)
+def configure_tests() -> None:
+    """
+    Configure settings for all tests and test modules.
+    """
+    pd.set_option("chained_assignment", "raise")
+
+
+# ----------------------------------------------------------------
+# Common arguments
+# ----------------------------------------------------------------
+@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: f"axis={repr(x)}")
+def axis(request):
+    """
+    Fixture for returning the axis numbers of a DataFrame.
+    """
+    return request.param
+
+
+axis_frame = axis
+
+
+@pytest.fixture(params=[1, "columns"], ids=lambda x: f"axis={repr(x)}")
+def axis_1(request):
+    """
+    Fixture for returning aliases of axis 1 of a DataFrame.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False, None])
+def observed(request):
+    """
+    Pass in the observed keyword to groupby for [True, False]
+    This indicates whether categoricals should return values for
+    values which are not in the grouper [False / None], or only values which
+    appear in the grouper [True]. [None] is supported for future compatibility
+    if we decide to change the default (and would need to warn if this
+    parameter is not passed).
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False, None])
+def ordered(request):
+    """
+    Boolean 'ordered' parameter for Categorical.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def skipna(request):
+    """
+    Boolean 'skipna' parameter.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["first", "last", False])
+def keep(request):
+    """
+    Valid values for the 'keep' parameter used in
+    .duplicated or .drop_duplicates
+    """
+    return request.param
+
+
+@pytest.fixture(params=["both", "neither", "left", "right"])
+def inclusive_endpoints_fixture(request):
+    """
+    Fixture for trying all interval 'inclusive' parameters.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["left", "right", "both", "neither"])
+def closed(request):
+    """
+    Fixture for trying all interval closed parameters.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["left", "right", "both", "neither"])
+def other_closed(request):
+    """
+    Secondary closed fixture to allow parametrizing over all pairs of closed.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        None,
+        "gzip",
+        "bz2",
+        "zip",
+        "xz",
+        "tar",
+        pytest.param("zstd", marks=td.skip_if_no("zstandard")),
+    ]
+)
+def compression(request):
+    """
+    Fixture for trying common compression types in compression tests.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        "gzip",
+        "bz2",
+        "zip",
+        "xz",
+        "tar",
+        pytest.param("zstd", marks=td.skip_if_no("zstandard")),
+    ]
+)
+def compression_only(request):
+    """
+    Fixture for trying common compression types in compression tests excluding
+    uncompressed case.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def writable(request):
+    """
+    Fixture that an array is writable.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["inner", "outer", "left", "right"])
+def join_type(request):
+    """
+    Fixture for trying all types of join operations.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["nlargest", "nsmallest"])
+def nselect_method(request):
+    """
+    Fixture for trying all nselect methods.
+    """
+    return request.param
+
+
+# ----------------------------------------------------------------
+# Missing values & co.
+# ----------------------------------------------------------------
+@pytest.fixture(params=tm.NULL_OBJECTS, ids=lambda x: type(x).__name__)
+def nulls_fixture(request):
+    """
+    Fixture for each null type in pandas.
+    """
+    return request.param
+
+
+nulls_fixture2 = nulls_fixture  # Generate cartesian product of nulls_fixture
+
+
+@pytest.fixture(params=[None, np.nan, pd.NaT])
+def unique_nulls_fixture(request):
+    """
+    Fixture for each null type in pandas, each null type exactly once.
+    """
+    return request.param
+
+
+# Generate cartesian product of unique_nulls_fixture:
+unique_nulls_fixture2 = unique_nulls_fixture
+
+
+@pytest.fixture(params=tm.NP_NAT_OBJECTS, ids=lambda x: type(x).__name__)
+def np_nat_fixture(request):
+    """
+    Fixture for each NaT type in numpy.
+    """
+    return request.param
+
+
+# Generate cartesian product of np_nat_fixture:
+np_nat_fixture2 = np_nat_fixture
+
+
+# ----------------------------------------------------------------
+# Classes
+# ----------------------------------------------------------------
+
+
+@pytest.fixture(params=[DataFrame, Series])
+def frame_or_series(request):
+    """
+    Fixture to parametrize over DataFrame and Series.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[Index, Series], ids=["index", "series"])
+def index_or_series(request):
+    """
+    Fixture to parametrize over Index and Series, made necessary by a mypy
+    bug, giving an error:
+
+    List item 0 has incompatible type "Type[Series]"; expected "Type[PandasObject]"
+
+    See GH#29725
+    """
+    return request.param
+
+
+# Generate cartesian product of index_or_series fixture:
+index_or_series2 = index_or_series
+
+
+@pytest.fixture(params=[Index, Series, pd.array], ids=["index", "series", "array"])
+def index_or_series_or_array(request):
+    """
+    Fixture to parametrize over Index, Series, and ExtensionArray
+    """
+    return request.param
+
+
+@pytest.fixture(params=[Index, Series, DataFrame, pd.array], ids=lambda x: x.__name__)
+def box_with_array(request):
+    """
+    Fixture to test behavior for Index, Series, DataFrame, and pandas Array
+    classes
+    """
+    return request.param
+
+
+box_with_array2 = box_with_array
+
+
+@pytest.fixture
+def dict_subclass() -> type[dict]:
+    """
+    Fixture for a dictionary subclass.
+    """
+
+    class TestSubDict(dict):
+        def __init__(self, *args, **kwargs) -> None:
+            dict.__init__(self, *args, **kwargs)
+
+    return TestSubDict
+
+
+@pytest.fixture
+def non_dict_mapping_subclass() -> type[abc.Mapping]:
+    """
+    Fixture for a non-mapping dictionary subclass.
+    """
+
+    class TestNonDictMapping(abc.Mapping):
+        def __init__(self, underlying_dict) -> None:
+            self._data = underlying_dict
+
+        def __getitem__(self, key):
+            return self._data.__getitem__(key)
+
+        def __iter__(self) -> Iterator:
+            return self._data.__iter__()
+
+        def __len__(self) -> int:
+            return self._data.__len__()
+
+    return TestNonDictMapping
+
+
+# ----------------------------------------------------------------
+# Indices
+# ----------------------------------------------------------------
+@pytest.fixture
+def multiindex_year_month_day_dataframe_random_data():
+    """
+    DataFrame with 3 level MultiIndex (year, month, day) covering
+    first 100 business days from 2000-01-01 with random data
+    """
+    tdf = DataFrame(
+        np.random.default_rng(2).standard_normal((100, 4)),
+        columns=Index(list("ABCD")),
+        index=date_range("2000-01-01", periods=100, freq="B"),
+    )
+    ymd = tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()
+    # use int64 Index, to make sure things work
+    ymd.index = ymd.index.set_levels([lev.astype("i8") for lev in ymd.index.levels])
+    ymd.index.set_names(["year", "month", "day"], inplace=True)
+    return ymd
+
+
+@pytest.fixture
+def lexsorted_two_level_string_multiindex() -> MultiIndex:
+    """
+    2-level MultiIndex, lexsorted, with string names.
+    """
+    return MultiIndex(
+        levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+
+
+@pytest.fixture
+def multiindex_dataframe_random_data(
+    lexsorted_two_level_string_multiindex,
+) -> DataFrame:
+    """DataFrame with 2 level MultiIndex with random data"""
+    index = lexsorted_two_level_string_multiindex
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((10, 3)),
+        index=index,
+        columns=Index(["A", "B", "C"], name="exp"),
+    )
+
+
+def _create_multiindex():
+    """
+    MultiIndex used to test the general functionality of this object
+    """
+
+    # See Also: tests.multi.conftest.idx
+    major_axis = Index(["foo", "bar", "baz", "qux"])
+    minor_axis = Index(["one", "two"])
+
+    major_codes = np.array([0, 0, 1, 2, 3, 3])
+    minor_codes = np.array([0, 1, 0, 1, 0, 1])
+    index_names = ["first", "second"]
+    return MultiIndex(
+        levels=[major_axis, minor_axis],
+        codes=[major_codes, minor_codes],
+        names=index_names,
+        verify_integrity=False,
+    )
+
+
+def _create_mi_with_dt64tz_level():
+    """
+    MultiIndex with a level that is a tzaware DatetimeIndex.
+    """
+    # GH#8367 round trip with pickle
+    return MultiIndex.from_product(
+        [[1, 2], ["a", "b"], date_range("20130101", periods=3, tz="US/Eastern")],
+        names=["one", "two", "three"],
+    )
+
+
+indices_dict = {
+    "object": Index([f"pandas_{i}" for i in range(100)], dtype=object),
+    "string": Index([f"pandas_{i}" for i in range(100)], dtype="str"),
+    "datetime": date_range("2020-01-01", periods=100),
+    "datetime-tz": date_range("2020-01-01", periods=100, tz="US/Pacific"),
+    "period": period_range("2020-01-01", periods=100, freq="D"),
+    "timedelta": timedelta_range(start="1 day", periods=100, freq="D"),
+    "range": RangeIndex(100),
+    "int8": Index(np.arange(100), dtype="int8"),
+    "int16": Index(np.arange(100), dtype="int16"),
+    "int32": Index(np.arange(100), dtype="int32"),
+    "int64": Index(np.arange(100), dtype="int64"),
+    "uint8": Index(np.arange(100), dtype="uint8"),
+    "uint16": Index(np.arange(100), dtype="uint16"),
+    "uint32": Index(np.arange(100), dtype="uint32"),
+    "uint64": Index(np.arange(100), dtype="uint64"),
+    "float32": Index(np.arange(100), dtype="float32"),
+    "float64": Index(np.arange(100), dtype="float64"),
+    "bool-object": Index([True, False] * 5, dtype=object),
+    "bool-dtype": Index([True, False] * 5, dtype=bool),
+    "complex64": Index(
+        np.arange(100, dtype="complex64") + 1.0j * np.arange(100, dtype="complex64")
+    ),
+    "complex128": Index(
+        np.arange(100, dtype="complex128") + 1.0j * np.arange(100, dtype="complex128")
+    ),
+    "categorical": CategoricalIndex(list("abcd") * 25),
+    "interval": IntervalIndex.from_breaks(np.linspace(0, 100, num=101)),
+    "empty": Index([]),
+    "tuples": MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])),
+    "mi-with-dt64tz-level": _create_mi_with_dt64tz_level(),
+    "multi": _create_multiindex(),
+    "repeats": Index([0, 0, 1, 1, 2, 2]),
+    "nullable_int": Index(np.arange(100), dtype="Int64"),
+    "nullable_uint": Index(np.arange(100), dtype="UInt16"),
+    "nullable_float": Index(np.arange(100), dtype="Float32"),
+    "nullable_bool": Index(np.arange(100).astype(bool), dtype="boolean"),
+    "string-python": Index(
+        pd.array([f"pandas_{i}" for i in range(100)], dtype="string[python]")
+    ),
+}
+if has_pyarrow:
+    idx = Index(pd.array([f"pandas_{i}" for i in range(100)], dtype="string[pyarrow]"))
+    indices_dict["string-pyarrow"] = idx
+
+
+@pytest.fixture(params=indices_dict.keys())
+def index(request):
+    """
+    Fixture for many "simple" kinds of indices.
+
+    These indices are unlikely to cover corner cases, e.g.
+        - no names
+        - no NaTs/NaNs
+        - no values near implementation bounds
+        - ...
+    """
+    # copy to avoid mutation, e.g. setting .name
+    return indices_dict[request.param].copy()
+
+
+# Needed to generate cartesian product of indices
+index_fixture2 = index
+
+
+@pytest.fixture(
+    params=[
+        key for key, value in indices_dict.items() if not isinstance(value, MultiIndex)
+    ]
+)
+def index_flat(request):
+    """
+    index fixture, but excluding MultiIndex cases.
+    """
+    key = request.param
+    return indices_dict[key].copy()
+
+
+# Alias so we can test with cartesian product of index_flat
+index_flat2 = index_flat
+
+
+@pytest.fixture(
+    params=[
+        key
+        for key, value in indices_dict.items()
+        if not (
+            key.startswith(("int", "uint", "float"))
+            or key in ["range", "empty", "repeats", "bool-dtype"]
+        )
+        and not isinstance(value, MultiIndex)
+    ]
+)
+def index_with_missing(request):
+    """
+    Fixture for indices with missing values.
+
+    Integer-dtype and empty cases are excluded because they cannot hold missing
+    values.
+
+    MultiIndex is excluded because isna() is not defined for MultiIndex.
+    """
+
+    # GH 35538. Use deep copy to avoid illusive bug on np-dev
+    # GHA pipeline that writes into indices_dict despite copy
+    ind = indices_dict[request.param].copy(deep=True)
+    vals = ind.values.copy()
+    if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]:
+        # For setting missing values in the top level of MultiIndex
+        vals = ind.tolist()
+        vals[0] = (None,) + vals[0][1:]
+        vals[-1] = (None,) + vals[-1][1:]
+        return MultiIndex.from_tuples(vals)
+    else:
+        vals[0] = None
+        vals[-1] = None
+        return type(ind)(vals)
+
+
+# ----------------------------------------------------------------
+# Series'
+# ----------------------------------------------------------------
+@pytest.fixture
+def string_series() -> Series:
+    """
+    Fixture for Series of floats with Index of unique strings
+    """
+    return Series(
+        np.arange(30, dtype=np.float64) * 1.1,
+        index=Index([f"i_{i}" for i in range(30)]),
+        name="series",
+    )
+
+
+@pytest.fixture
+def object_series() -> Series:
+    """
+    Fixture for Series of dtype object with Index of unique strings
+    """
+    data = [f"foo_{i}" for i in range(30)]
+    index = Index([f"bar_{i}" for i in range(30)])
+    return Series(data, index=index, name="objects", dtype=object)
+
+
+@pytest.fixture
+def datetime_series() -> Series:
+    """
+    Fixture for Series of floats with DatetimeIndex
+    """
+    return Series(
+        np.random.default_rng(2).standard_normal(30),
+        index=date_range("2000-01-01", periods=30, freq="B"),
+        name="ts",
+    )
+
+
+def _create_series(index):
+    """Helper for the _series dict"""
+    size = len(index)
+    data = np.random.default_rng(2).standard_normal(size)
+    return Series(data, index=index, name="a", copy=False)
+
+
+_series = {
+    f"series-with-{index_id}-index": _create_series(index)
+    for index_id, index in indices_dict.items()
+}
+
+
+@pytest.fixture
+def series_with_simple_index(index) -> Series:
+    """
+    Fixture for tests on series with changing types of indices.
+    """
+    return _create_series(index)
+
+
+_narrow_series = {
+    f"{dtype.__name__}-series": Series(
+        range(30), index=[f"i-{i}" for i in range(30)], name="a", dtype=dtype
+    )
+    for dtype in tm.NARROW_NP_DTYPES
+}
+
+
+_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}
+
+
+@pytest.fixture(params=_index_or_series_objs.keys())
+def index_or_series_obj(request):
+    """
+    Fixture for tests on indexes, series and series with a narrow dtype
+    copy to avoid mutation, e.g. setting .name
+    """
+    return _index_or_series_objs[request.param].copy(deep=True)
+
+
+_typ_objects_series = {
+    f"{dtype.__name__}-series": Series(dtype) for dtype in tm.PYTHON_DATA_TYPES
+}
+
+
+_index_or_series_memory_objs = {
+    **indices_dict,
+    **_series,
+    **_narrow_series,
+    **_typ_objects_series,
+}
+
+
+@pytest.fixture(params=_index_or_series_memory_objs.keys())
+def index_or_series_memory_obj(request):
+    """
+    Fixture for tests on indexes, series, series with a narrow dtype and
+    series with empty objects type
+    copy to avoid mutation, e.g. setting .name
+    """
+    return _index_or_series_memory_objs[request.param].copy(deep=True)
+
+
+# ----------------------------------------------------------------
+# DataFrames
+# ----------------------------------------------------------------
+@pytest.fixture
+def int_frame() -> DataFrame:
+    """
+    Fixture for DataFrame of ints with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D']
+    """
+    return DataFrame(
+        np.ones((30, 4), dtype=np.int64),
+        index=Index([f"foo_{i}" for i in range(30)]),
+        columns=Index(list("ABCD")),
+    )
+
+
+@pytest.fixture
+def float_frame() -> DataFrame:
+    """
+    Fixture for DataFrame of floats with index of unique strings
+
+    Columns are ['A', 'B', 'C', 'D'].
+    """
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((30, 4)),
+        index=Index([f"foo_{i}" for i in range(30)]),
+        columns=Index(list("ABCD")),
+    )
+
+
+@pytest.fixture
+def rand_series_with_duplicate_datetimeindex() -> Series:
+    """
+    Fixture for Series with a DatetimeIndex that has duplicates.
+    """
+    dates = [
+        datetime(2000, 1, 2),
+        datetime(2000, 1, 2),
+        datetime(2000, 1, 2),
+        datetime(2000, 1, 3),
+        datetime(2000, 1, 3),
+        datetime(2000, 1, 3),
+        datetime(2000, 1, 4),
+        datetime(2000, 1, 4),
+        datetime(2000, 1, 4),
+        datetime(2000, 1, 5),
+    ]
+
+    return Series(np.random.default_rng(2).standard_normal(len(dates)), index=dates)
+
+
+# ----------------------------------------------------------------
+# Scalars
+# ----------------------------------------------------------------
+@pytest.fixture(
+    params=[
+        (Interval(left=0, right=5), IntervalDtype("int64", "right")),
+        (Interval(left=0.1, right=0.5), IntervalDtype("float64", "right")),
+        (Period("2012-01", freq="M"), "period[M]"),
+        (Period("2012-02-01", freq="D"), "period[D]"),
+        (
+            Timestamp("2011-01-01", tz="US/Eastern"),
+            DatetimeTZDtype(unit="s", tz="US/Eastern"),
+        ),
+        (Timedelta(seconds=500), "timedelta64[ns]"),
+    ]
+)
+def ea_scalar_and_dtype(request):
+    return request.param
+
+
+# ----------------------------------------------------------------
+# Operators & Operations
+# ----------------------------------------------------------------
+
+
+@pytest.fixture(params=tm.arithmetic_dunder_methods)
+def all_arithmetic_operators(request):
+    """
+    Fixture for dunder names for common arithmetic operations.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        operator.add,
+        ops.radd,
+        operator.sub,
+        ops.rsub,
+        operator.mul,
+        ops.rmul,
+        operator.truediv,
+        ops.rtruediv,
+        operator.floordiv,
+        ops.rfloordiv,
+        operator.mod,
+        ops.rmod,
+        operator.pow,
+        ops.rpow,
+        operator.eq,
+        operator.ne,
+        operator.lt,
+        operator.le,
+        operator.gt,
+        operator.ge,
+        operator.and_,
+        ops.rand_,
+        operator.xor,
+        ops.rxor,
+        operator.or_,
+        ops.ror_,
+    ]
+)
+def all_binary_operators(request):
+    """
+    Fixture for operator and roperator arithmetic, comparison, and logical ops.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        operator.add,
+        ops.radd,
+        operator.sub,
+        ops.rsub,
+        operator.mul,
+        ops.rmul,
+        operator.truediv,
+        ops.rtruediv,
+        operator.floordiv,
+        ops.rfloordiv,
+        operator.mod,
+        ops.rmod,
+        operator.pow,
+        ops.rpow,
+    ]
+)
+def all_arithmetic_functions(request):
+    """
+    Fixture for operator and roperator arithmetic functions.
+
+    Notes
+    -----
+    This includes divmod and rdivmod, whereas all_arithmetic_operators
+    does not.
+    """
+    return request.param
+
+
+_all_numeric_reductions = [
+    "count",
+    "sum",
+    "max",
+    "min",
+    "mean",
+    "prod",
+    "std",
+    "var",
+    "median",
+    "kurt",
+    "skew",
+    "sem",
+]
+
+
+@pytest.fixture(params=_all_numeric_reductions)
+def all_numeric_reductions(request):
+    """
+    Fixture for numeric reduction names.
+    """
+    return request.param
+
+
+_all_boolean_reductions = ["all", "any"]
+
+
+@pytest.fixture(params=_all_boolean_reductions)
+def all_boolean_reductions(request):
+    """
+    Fixture for boolean reduction names.
+    """
+    return request.param
+
+
+_all_reductions = _all_numeric_reductions + _all_boolean_reductions
+
+
+@pytest.fixture(params=_all_reductions)
+def all_reductions(request):
+    """
+    Fixture for all (boolean + numeric) reduction names.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        operator.eq,
+        operator.ne,
+        operator.gt,
+        operator.ge,
+        operator.lt,
+        operator.le,
+    ]
+)
+def comparison_op(request):
+    """
+    Fixture for operator module comparison functions.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["__le__", "__lt__", "__ge__", "__gt__"])
+def compare_operators_no_eq_ne(request):
+    """
+    Fixture for dunder names for compare operations except == and !=
+
+    * >=
+    * >
+    * <
+    * <=
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=["__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__"]
+)
+def all_logical_operators(request):
+    """
+    Fixture for dunder names for common logical operations
+
+    * |
+    * &
+    * ^
+    """
+    return request.param
+
+
+_all_numeric_accumulations = ["cumsum", "cumprod", "cummin", "cummax"]
+
+
+@pytest.fixture(params=_all_numeric_accumulations)
+def all_numeric_accumulations(request):
+    """
+    Fixture for numeric accumulation names
+    """
+    return request.param
+
+
+# ----------------------------------------------------------------
+# Data sets/files
+# ----------------------------------------------------------------
+@pytest.fixture
+def strict_data_files(pytestconfig):
+    """
+    Returns the configuration for the test setting `--no-strict-data-files`.
+    """
+    return pytestconfig.getoption("--no-strict-data-files")
+
+
+@pytest.fixture
+def datapath(strict_data_files: str) -> Callable[..., str]:
+    """
+    Get the path to a data file.
+
+    Parameters
+    ----------
+    path : str
+        Path to the file, relative to ``pandas/tests/``
+
+    Returns
+    -------
+    path including ``pandas/tests``.
+
+    Raises
+    ------
+    ValueError
+        If the path doesn't exist and the --no-strict-data-files option is not set.
+    """
+    BASE_PATH = os.path.join(os.path.dirname(__file__), "tests")
+
+    def deco(*args):
+        path = os.path.join(BASE_PATH, *args)
+        if not os.path.exists(path):
+            if strict_data_files:
+                raise ValueError(
+                    f"Could not find file {path} and --no-strict-data-files is not set."
+                )
+            pytest.skip(f"Could not find {path}.")
+        return path
+
+    return deco
+
+
+# ----------------------------------------------------------------
+# Time zones
+# ----------------------------------------------------------------
+TIMEZONES = [
+    None,
+    "UTC",
+    "US/Eastern",
+    "Asia/Tokyo",
+    "dateutil/US/Pacific",
+    "dateutil/Asia/Singapore",
+    "+01:15",
+    "-02:15",
+    "UTC+01:15",
+    "UTC-02:15",
+    tzutc(),
+    tzlocal(),
+    FixedOffset(300),
+    FixedOffset(0),
+    FixedOffset(-300),
+    timezone.utc,
+    timezone(timedelta(hours=1)),
+    timezone(timedelta(hours=-1), name="foo"),
+]
+if zoneinfo is not None:
+    TIMEZONES.extend(
+        [
+            zoneinfo.ZoneInfo("US/Pacific"),  # type: ignore[list-item]
+            zoneinfo.ZoneInfo("UTC"),  # type: ignore[list-item]
+        ]
+    )
+TIMEZONE_IDS = [repr(i) for i in TIMEZONES]
+
+
+@td.parametrize_fixture_doc(str(TIMEZONE_IDS))
+@pytest.fixture(params=TIMEZONES, ids=TIMEZONE_IDS)
+def tz_naive_fixture(request):
+    """
+    Fixture for trying timezones including default (None): {0}
+    """
+    return request.param
+
+
+@td.parametrize_fixture_doc(str(TIMEZONE_IDS[1:]))
+@pytest.fixture(params=TIMEZONES[1:], ids=TIMEZONE_IDS[1:])
+def tz_aware_fixture(request):
+    """
+    Fixture for trying explicit timezones: {0}
+    """
+    return request.param
+
+
+# Generate cartesian product of tz_aware_fixture:
+tz_aware_fixture2 = tz_aware_fixture
+
+
+_UTCS = ["utc", "dateutil/UTC", utc, tzutc(), timezone.utc]
+if zoneinfo is not None:
+    _UTCS.append(zoneinfo.ZoneInfo("UTC"))
+
+
+@pytest.fixture(params=_UTCS)
+def utc_fixture(request):
+    """
+    Fixture to provide variants of UTC timezone strings and tzinfo objects.
+    """
+    return request.param
+
+
+utc_fixture2 = utc_fixture
+
+
+@pytest.fixture(params=["s", "ms", "us", "ns"])
+def unit(request):
+    """
+    datetime64 units we support.
+    """
+    return request.param
+
+
+unit2 = unit
+
+
+# ----------------------------------------------------------------
+# Dtypes
+# ----------------------------------------------------------------
+@pytest.fixture(params=tm.STRING_DTYPES)
+def string_dtype(request):
+    """
+    Parametrized fixture for string dtypes.
+
+    * str
+    * 'str'
+    * 'U'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def string_dtype_no_object(request):
+    """
+    Parametrized fixture for string dtypes.
+    * 'string[python]' (NA variant)
+    * 'string[pyarrow]' (NA variant)
+    * 'str' (NaN variant, with pyarrow)
+    * 'str' (NaN variant, without pyarrow)
+    """
+    # need to instantiate the StringDtype here instead of in the params
+    # to avoid importing pyarrow during test collection
+    storage, na_value = request.param
+    return pd.StringDtype(storage, na_value)
+
+
+@pytest.fixture(
+    params=[
+        "string[python]",
+        pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def nullable_string_dtype(request):
+    """
+    Parametrized fixture for string dtypes.
+
+    * 'string[python]'
+    * 'string[pyarrow]'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def pyarrow_string_dtype(request):
+    """
+    Parametrized fixture for string dtypes backed by Pyarrow.
+
+    * 'str[pyarrow]'
+    * 'string[pyarrow]'
+    """
+    return pd.StringDtype(*request.param)
+
+
+@pytest.fixture(
+    params=[
+        "python",
+        pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def string_storage(request):
+    """
+    Parametrized fixture for pd.options.mode.string_storage.
+
+    * 'python'
+    * 'pyarrow'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def string_dtype_arguments(request):
+    """
+    Parametrized fixture for StringDtype storage and na_value.
+
+    * 'python' + pd.NA
+    * 'pyarrow' + pd.NA
+    * 'pyarrow' + np.nan
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        "numpy_nullable",
+        pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def dtype_backend(request):
+    """
+    Parametrized fixture for pd.options.mode.string_storage.
+
+    * 'python'
+    * 'pyarrow'
+    """
+    return request.param
+
+
+# Alias so we can test with cartesian product of string_storage
+string_storage2 = string_storage
+string_dtype_arguments2 = string_dtype_arguments
+
+
+@pytest.fixture(params=tm.BYTES_DTYPES)
+def bytes_dtype(request):
+    """
+    Parametrized fixture for bytes dtypes.
+
+    * bytes
+    * 'bytes'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.OBJECT_DTYPES)
+def object_dtype(request):
+    """
+    Parametrized fixture for object dtypes.
+
+    * object
+    * 'object'
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        np.dtype("object"),
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=object",
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def any_string_dtype(request):
+    """
+    Parametrized fixture for string dtypes.
+    * 'object'
+    * 'string[python]' (NA variant)
+    * 'string[pyarrow]' (NA variant)
+    * 'str' (NaN variant, with pyarrow)
+    * 'str' (NaN variant, without pyarrow)
+    """
+    if isinstance(request.param, np.dtype):
+        return request.param
+    else:
+        # need to instantiate the StringDtype here instead of in the params
+        # to avoid importing pyarrow during test collection
+        storage, na_value = request.param
+        return pd.StringDtype(storage, na_value)
+
+
+@pytest.fixture(params=tm.DATETIME64_DTYPES)
+def datetime64_dtype(request):
+    """
+    Parametrized fixture for datetime64 dtypes.
+
+    * 'datetime64[ns]'
+    * 'M8[ns]'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.TIMEDELTA64_DTYPES)
+def timedelta64_dtype(request):
+    """
+    Parametrized fixture for timedelta64 dtypes.
+
+    * 'timedelta64[ns]'
+    * 'm8[ns]'
+    """
+    return request.param
+
+
+@pytest.fixture
+def fixed_now_ts() -> Timestamp:
+    """
+    Fixture emits fixed Timestamp.now()
+    """
+    return Timestamp(  # pyright: ignore[reportGeneralTypeIssues]
+        year=2021, month=1, day=1, hour=12, minute=4, second=13, microsecond=22
+    )
+
+
+@pytest.fixture(params=tm.FLOAT_NUMPY_DTYPES)
+def float_numpy_dtype(request):
+    """
+    Parameterized fixture for float dtypes.
+
+    * float
+    * 'float32'
+    * 'float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.FLOAT_EA_DTYPES)
+def float_ea_dtype(request):
+    """
+    Parameterized fixture for float dtypes.
+
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_FLOAT_DTYPES)
+def any_float_dtype(request):
+    """
+    Parameterized fixture for float dtypes.
+
+    * float
+    * 'float32'
+    * 'float64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.COMPLEX_DTYPES)
+def complex_dtype(request):
+    """
+    Parameterized fixture for complex dtypes.
+
+    * complex
+    * 'complex64'
+    * 'complex128'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.COMPLEX_FLOAT_DTYPES)
+def complex_or_float_dtype(request):
+    """
+    Parameterized fixture for complex and numpy float dtypes.
+
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * float
+    * 'float32'
+    * 'float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.SIGNED_INT_NUMPY_DTYPES)
+def any_signed_int_numpy_dtype(request):
+    """
+    Parameterized fixture for signed integer dtypes.
+
+    * int
+    * 'int8'
+    * 'int16'
+    * 'int32'
+    * 'int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.UNSIGNED_INT_NUMPY_DTYPES)
+def any_unsigned_int_numpy_dtype(request):
+    """
+    Parameterized fixture for unsigned integer dtypes.
+
+    * 'uint8'
+    * 'uint16'
+    * 'uint32'
+    * 'uint64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_NUMPY_DTYPES)
+def any_int_numpy_dtype(request):
+    """
+    Parameterized fixture for any integer dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_EA_DTYPES)
+def any_int_ea_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype.
+
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_DTYPES)
+def any_int_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES)
+def any_numeric_ea_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype and
+    any float ea dtypes.
+
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+#  Unsupported operand types for + ("List[Union[str, ExtensionDtype, dtype[Any],
+#  Type[object]]]" and "List[str]")
+@pytest.fixture(
+    params=tm.ALL_INT_EA_DTYPES
+    + tm.FLOAT_EA_DTYPES
+    + tm.ALL_INT_PYARROW_DTYPES_STR_REPR
+    + tm.FLOAT_PYARROW_DTYPES_STR_REPR  # type: ignore[operator]
+)
+def any_numeric_ea_and_arrow_dtype(request):
+    """
+    Parameterized fixture for any nullable integer dtype and
+    any float ea dtypes.
+
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    * 'uint8[pyarrow]'
+    * 'int8[pyarrow]'
+    * 'uint16[pyarrow]'
+    * 'int16[pyarrow]'
+    * 'uint32[pyarrow]'
+    * 'int32[pyarrow]'
+    * 'uint64[pyarrow]'
+    * 'int64[pyarrow]'
+    * 'float32[pyarrow]'
+    * 'float64[pyarrow]'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.SIGNED_INT_EA_DTYPES)
+def any_signed_int_ea_dtype(request):
+    """
+    Parameterized fixture for any signed nullable integer dtype.
+
+    * 'Int8'
+    * 'Int16'
+    * 'Int32'
+    * 'Int64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_REAL_NUMPY_DTYPES)
+def any_real_numpy_dtype(request):
+    """
+    Parameterized fixture for any (purely) real numeric dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_REAL_DTYPES)
+def any_real_numeric_dtype(request):
+    """
+    Parameterized fixture for any (purely) real numeric dtype.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+
+    and associated ea dtypes.
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_NUMPY_DTYPES)
+def any_numpy_dtype(request):
+    """
+    Parameterized fixture for all numpy dtypes.
+
+    * bool
+    * 'bool'
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * str
+    * 'str'
+    * 'U'
+    * bytes
+    * 'bytes'
+    * 'datetime64[ns]'
+    * 'M8[ns]'
+    * 'timedelta64[ns]'
+    * 'm8[ns]'
+    * object
+    * 'object'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_REAL_NULLABLE_DTYPES)
+def any_real_nullable_dtype(request):
+    """
+    Parameterized fixture for all real dtypes that can hold NA.
+
+    * float
+    * 'float32'
+    * 'float64'
+    * 'Float32'
+    * 'Float64'
+    * 'UInt8'
+    * 'UInt16'
+    * 'UInt32'
+    * 'UInt64'
+    * 'Int8'
+    * 'Int16'
+    * 'Int32'
+    * 'Int64'
+    * 'uint8[pyarrow]'
+    * 'uint16[pyarrow]'
+    * 'uint32[pyarrow]'
+    * 'uint64[pyarrow]'
+    * 'int8[pyarrow]'
+    * 'int16[pyarrow]'
+    * 'int32[pyarrow]'
+    * 'int64[pyarrow]'
+    * 'float[pyarrow]'
+    * 'double[pyarrow]'
+    """
+    return request.param
+
+
+@pytest.fixture(params=tm.ALL_NUMERIC_DTYPES)
+def any_numeric_dtype(request):
+    """
+    Parameterized fixture for all numeric dtypes.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
+# categoricals are handled separately
+_any_skipna_inferred_dtype = [
+    ("string", ["a", np.nan, "c"]),
+    ("string", ["a", pd.NA, "c"]),
+    ("mixed", ["a", pd.NaT, "c"]),  # pd.NaT not considered valid by is_string_array
+    ("bytes", [b"a", np.nan, b"c"]),
+    ("empty", [np.nan, np.nan, np.nan]),
+    ("empty", []),
+    ("mixed-integer", ["a", np.nan, 2]),
+    ("mixed", ["a", np.nan, 2.0]),
+    ("floating", [1.0, np.nan, 2.0]),
+    ("integer", [1, np.nan, 2]),
+    ("mixed-integer-float", [1, np.nan, 2.0]),
+    ("decimal", [Decimal(1), np.nan, Decimal(2)]),
+    ("boolean", [True, np.nan, False]),
+    ("boolean", [True, pd.NA, False]),
+    ("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]),
+    ("datetime", [Timestamp("20130101"), np.nan, Timestamp("20180101")]),
+    ("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]),
+    ("complex", [1 + 1j, np.nan, 2 + 2j]),
+    # The following dtype is commented out due to GH 23554
+    # ('timedelta64', [np.timedelta64(1, 'D'),
+    #                  np.nan, np.timedelta64(2, 'D')]),
+    ("timedelta", [timedelta(1), np.nan, timedelta(2)]),
+    ("time", [time(1), np.nan, time(2)]),
+    ("period", [Period(2013), pd.NaT, Period(2018)]),
+    ("interval", [Interval(0, 1), np.nan, Interval(0, 2)]),
+]
+ids, _ = zip(*_any_skipna_inferred_dtype)  # use inferred type as fixture-id
+
+
+@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids)
+def any_skipna_inferred_dtype(request):
+    """
+    Fixture for all inferred dtypes from _libs.lib.infer_dtype
+
+    The covered (inferred) types are:
+    * 'string'
+    * 'empty'
+    * 'bytes'
+    * 'mixed'
+    * 'mixed-integer'
+    * 'mixed-integer-float'
+    * 'floating'
+    * 'integer'
+    * 'decimal'
+    * 'boolean'
+    * 'datetime64'
+    * 'datetime'
+    * 'date'
+    * 'timedelta'
+    * 'time'
+    * 'period'
+    * 'interval'
+
+    Returns
+    -------
+    inferred_dtype : str
+        The string for the inferred dtype from _libs.lib.infer_dtype
+    values : np.ndarray
+        An array of object dtype that will be inferred to have
+        `inferred_dtype`
+
+    Examples
+    --------
+    >>> from pandas._libs import lib
+    >>>
+    >>> def test_something(any_skipna_inferred_dtype):
+    ...     inferred_dtype, values = any_skipna_inferred_dtype
+    ...     # will pass
+    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
+    """
+    inferred_dtype, values = request.param
+    values = np.array(values, dtype=object)  # object dtype to avoid casting
+
+    # correctness of inference tested in tests/dtypes/test_inference.py
+    return inferred_dtype, values
+
+
+# ----------------------------------------------------------------
+# Misc
+# ----------------------------------------------------------------
+@pytest.fixture
+def ip():
+    """
+    Get an instance of IPython.InteractiveShell.
+
+    Will raise a skip if IPython is not installed.
+    """
+    pytest.importorskip("IPython", minversion="6.0.0")
+    from IPython.core.interactiveshell import InteractiveShell
+
+    # GH#35711 make sure sqlite history file handle is not leaked
+    from traitlets.config import Config  # isort:skip
+
+    c = Config()
+    c.HistoryManager.hist_file = ":memory:"
+
+    return InteractiveShell(config=c)
+
+
+@pytest.fixture(params=["bsr", "coo", "csc", "csr", "dia", "dok", "lil"])
+def spmatrix(request):
+    """
+    Yields scipy sparse matrix classes.
+    """
+    sparse = pytest.importorskip("scipy.sparse")
+
+    return getattr(sparse, request.param + "_matrix")
+
+
+@pytest.fixture(
+    params=[
+        getattr(pd.offsets, o)
+        for o in pd.offsets.__all__
+        if issubclass(getattr(pd.offsets, o), pd.offsets.Tick) and o != "Tick"
+    ]
+)
+def tick_classes(request):
+    """
+    Fixture for Tick based datetime offsets available for a time series.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[None, lambda x: x])
+def sort_by_key(request):
+    """
+    Simple fixture for testing keys in sorting methods.
+    Tests None (no key) and the identity key.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("foo", None, None),
+        ("Egon", "Venkman", None),
+        ("NCC1701D", "NCC1701D", "NCC1701D"),
+        # possibly-matching NAs
+        (np.nan, np.nan, np.nan),
+        (np.nan, pd.NaT, None),
+        (np.nan, pd.NA, None),
+        (pd.NA, pd.NA, pd.NA),
+    ]
+)
+def names(request) -> tuple[Hashable, Hashable, Hashable]:
+    """
+    A 3-tuple of names, the first two for operands, the last for a result.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.setitem, tm.loc, tm.iloc])
+def indexer_sli(request):
+    """
+    Parametrize over __setitem__, loc.__setitem__, iloc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.loc, tm.iloc])
+def indexer_li(request):
+    """
+    Parametrize over loc.__getitem__, iloc.__getitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.setitem, tm.iloc])
+def indexer_si(request):
+    """
+    Parametrize over __setitem__, iloc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.setitem, tm.loc])
+def indexer_sl(request):
+    """
+    Parametrize over __setitem__, loc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.at, tm.loc])
+def indexer_al(request):
+    """
+    Parametrize over at.__setitem__, loc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture(params=[tm.iat, tm.iloc])
+def indexer_ial(request):
+    """
+    Parametrize over iat.__setitem__, iloc.__setitem__
+    """
+    return request.param
+
+
+@pytest.fixture
+def using_array_manager() -> bool:
+    """
+    Fixture to check if the array manager is being used.
+    """
+    return _get_option("mode.data_manager", silent=True) == "array"
+
+
+@pytest.fixture
+def using_copy_on_write() -> bool:
+    """
+    Fixture to check if Copy-on-Write is enabled.
+    """
+    return (
+        pd.options.mode.copy_on_write is True
+        and _get_option("mode.data_manager", silent=True) == "block"
+    )
+
+
+@pytest.fixture
+def warn_copy_on_write() -> bool:
+    """
+    Fixture to check if Copy-on-Write is in warning mode.
+    """
+    return (
+        pd.options.mode.copy_on_write == "warn"
+        and _get_option("mode.data_manager", silent=True) == "block"
+    )
+
+
+@pytest.fixture
+def using_infer_string() -> bool:
+    """
+    Fixture to check if infer string option is enabled.
+    """
+    return pd.options.future.infer_string is True
+
+
+warsaws = ["Europe/Warsaw", "dateutil/Europe/Warsaw"]
+if zoneinfo is not None:
+    warsaws.append(zoneinfo.ZoneInfo("Europe/Warsaw"))  # type: ignore[arg-type]
+
+
+@pytest.fixture(params=warsaws)
+def warsaw(request) -> str:
+    """
+    tzinfo for Europe/Warsaw using pytz, dateutil, or zoneinfo.
+    """
+    return request.param
+
+
+@pytest.fixture()
+def arrow_string_storage():
+    return ("pyarrow", "pyarrow_numpy")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/executor.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a26acb7df60a20a253ca3b864568c984bb86e3d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/executor.py
@@ -0,0 +1,239 @@
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import Scalar
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+
+@functools.cache
+def generate_apply_looper(func, nopython=True, nogil=True, parallel=False):
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+    nb_compat_func = numba.extending.register_jitable(func)
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def nb_looper(values, axis):
+        # Operate on the first row/col in order to get
+        # the output shape
+        if axis == 0:
+            first_elem = values[:, 0]
+            dim0 = values.shape[1]
+        else:
+            first_elem = values[0]
+            dim0 = values.shape[0]
+        res0 = nb_compat_func(first_elem)
+        # Use np.asarray to get shape for
+        # https://github.com/numba/numba/issues/4202#issuecomment-1185981507
+        buf_shape = (dim0,) + np.atleast_1d(np.asarray(res0)).shape
+        if axis == 0:
+            buf_shape = buf_shape[::-1]
+        buff = np.empty(buf_shape)
+
+        if axis == 1:
+            buff[0] = res0
+            for i in numba.prange(1, values.shape[0]):
+                buff[i] = nb_compat_func(values[i])
+        else:
+            buff[:, 0] = res0
+            for j in numba.prange(1, values.shape[1]):
+                buff[:, j] = nb_compat_func(values[:, j])
+        return buff
+
+    return nb_looper
+
+
+@functools.cache
+def make_looper(func, result_dtype, is_grouped_kernel, nopython, nogil, parallel):
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    if is_grouped_kernel:
+
+        @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+        def column_looper(
+            values: np.ndarray,
+            labels: np.ndarray,
+            ngroups: int,
+            min_periods: int,
+            *args,
+        ):
+            result = np.empty((values.shape[0], ngroups), dtype=result_dtype)
+            na_positions = {}
+            for i in numba.prange(values.shape[0]):
+                output, na_pos = func(
+                    values[i], result_dtype, labels, ngroups, min_periods, *args
+                )
+                result[i] = output
+                if len(na_pos) > 0:
+                    na_positions[i] = np.array(na_pos)
+            return result, na_positions
+
+    else:
+
+        @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+        def column_looper(
+            values: np.ndarray,
+            start: np.ndarray,
+            end: np.ndarray,
+            min_periods: int,
+            *args,
+        ):
+            result = np.empty((values.shape[0], len(start)), dtype=result_dtype)
+            na_positions = {}
+            for i in numba.prange(values.shape[0]):
+                output, na_pos = func(
+                    values[i], result_dtype, start, end, min_periods, *args
+                )
+                result[i] = output
+                if len(na_pos) > 0:
+                    na_positions[i] = np.array(na_pos)
+            return result, na_positions
+
+    return column_looper
+
+
+default_dtype_mapping: dict[np.dtype, Any] = {
+    np.dtype("int8"): np.int64,
+    np.dtype("int16"): np.int64,
+    np.dtype("int32"): np.int64,
+    np.dtype("int64"): np.int64,
+    np.dtype("uint8"): np.uint64,
+    np.dtype("uint16"): np.uint64,
+    np.dtype("uint32"): np.uint64,
+    np.dtype("uint64"): np.uint64,
+    np.dtype("float32"): np.float64,
+    np.dtype("float64"): np.float64,
+    np.dtype("complex64"): np.complex128,
+    np.dtype("complex128"): np.complex128,
+}
+
+
+# TODO: Preserve complex dtypes
+
+float_dtype_mapping: dict[np.dtype, Any] = {
+    np.dtype("int8"): np.float64,
+    np.dtype("int16"): np.float64,
+    np.dtype("int32"): np.float64,
+    np.dtype("int64"): np.float64,
+    np.dtype("uint8"): np.float64,
+    np.dtype("uint16"): np.float64,
+    np.dtype("uint32"): np.float64,
+    np.dtype("uint64"): np.float64,
+    np.dtype("float32"): np.float64,
+    np.dtype("float64"): np.float64,
+    np.dtype("complex64"): np.float64,
+    np.dtype("complex128"): np.float64,
+}
+
+identity_dtype_mapping: dict[np.dtype, Any] = {
+    np.dtype("int8"): np.int8,
+    np.dtype("int16"): np.int16,
+    np.dtype("int32"): np.int32,
+    np.dtype("int64"): np.int64,
+    np.dtype("uint8"): np.uint8,
+    np.dtype("uint16"): np.uint16,
+    np.dtype("uint32"): np.uint32,
+    np.dtype("uint64"): np.uint64,
+    np.dtype("float32"): np.float32,
+    np.dtype("float64"): np.float64,
+    np.dtype("complex64"): np.complex64,
+    np.dtype("complex128"): np.complex128,
+}
+
+
+def generate_shared_aggregator(
+    func: Callable[..., Scalar],
+    dtype_mapping: dict[np.dtype, np.dtype],
+    is_grouped_kernel: bool,
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a Numba function that loops over the columns 2D object and applies
+    a 1D numba kernel over each column.
+
+    Parameters
+    ----------
+    func : function
+        aggregation function to be applied to each column
+    dtype_mapping: dict or None
+        If not None, maps a dtype to a result dtype.
+        Otherwise, will fall back to default mapping.
+    is_grouped_kernel: bool, default False
+        Whether func operates using the group labels (True)
+        or using starts/ends arrays
+
+        If true, you also need to pass the number of groups to this function
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+
+    # A wrapper around the looper function,
+    # to dispatch based on dtype since numba is unable to do that in nopython mode
+
+    # It also post-processes the values by inserting nans where number of observations
+    # is less than min_periods
+    # Cannot do this in numba nopython mode
+    # (you'll run into type-unification error when you cast int -> float)
+    def looper_wrapper(
+        values,
+        start=None,
+        end=None,
+        labels=None,
+        ngroups=None,
+        min_periods: int = 0,
+        **kwargs,
+    ):
+        result_dtype = dtype_mapping[values.dtype]
+        column_looper = make_looper(
+            func, result_dtype, is_grouped_kernel, nopython, nogil, parallel
+        )
+        # Need to unpack kwargs since numba only supports *args
+        if is_grouped_kernel:
+            result, na_positions = column_looper(
+                values, labels, ngroups, min_periods, *kwargs.values()
+            )
+        else:
+            result, na_positions = column_looper(
+                values, start, end, min_periods, *kwargs.values()
+            )
+        if result.dtype.kind == "i":
+            # Look if na_positions is not empty
+            # If so, convert the whole block
+            # This is OK since int dtype cannot hold nan,
+            # so if min_periods not satisfied for 1 col, it is not satisfied for
+            # all columns at that index
+            for na_pos in na_positions.values():
+                if len(na_pos) > 0:
+                    result = result.astype("float64")
+                    break
+        # TODO: Optimize this
+        for i, na_pos in na_positions.items():
+            if len(na_pos) > 0:
+                result[i, na_pos] = np.nan
+        return result
+
+    return looper_wrapper
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/extensions.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b05f12295a729176aadae19bb1d17f3191f98806
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/extensions.py
@@ -0,0 +1,585 @@
+# Disable type checking for this module since numba's internals
+# are not typed, and we use numba's internals via its extension API
+# mypy: ignore-errors
+"""
+Utility classes/functions to let numba recognize
+pandas Index/Series/DataFrame
+
+Mostly vendored from https://github.com/numba/numba/blob/main/numba/tests/pdlike_usecase.py
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+import operator
+
+import numba
+from numba import types
+from numba.core import cgutils
+from numba.core.datamodel import models
+from numba.core.extending import (
+    NativeValue,
+    box,
+    lower_builtin,
+    make_attribute_wrapper,
+    overload,
+    overload_attribute,
+    overload_method,
+    register_model,
+    type_callable,
+    typeof_impl,
+    unbox,
+)
+from numba.core.imputils import impl_ret_borrowed
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.indexes.base import Index
+from pandas.core.indexing import _iLocIndexer
+from pandas.core.internals import SingleBlockManager
+from pandas.core.series import Series
+
+
+# Helper function to hack around fact that Index casts numpy string dtype to object
+#
+# Idea is to set an attribute on a Index called _numba_data
+# that is the original data, or the object data casted to numpy string dtype,
+# with a context manager that is unset afterwards
+@contextmanager
+def set_numba_data(index: Index):
+    numba_data = index._data
+    if numba_data.dtype in (object, "string"):
+        numba_data = np.asarray(numba_data)
+        if not lib.is_string_array(numba_data):
+            raise ValueError(
+                "The numba engine only supports using string or numeric column names"
+            )
+        numba_data = numba_data.astype("U")
+    try:
+        index._numba_data = numba_data
+        yield index
+    finally:
+        del index._numba_data
+
+
+# TODO: Range index support
+# (this currently lowers OK, but does not round-trip)
+class IndexType(types.Type):
+    """
+    The type class for Index objects.
+    """
+
+    def __init__(self, dtype, layout, pyclass: any) -> None:
+        self.pyclass = pyclass
+        name = f"index({dtype}, {layout})"
+        self.dtype = dtype
+        self.layout = layout
+        super().__init__(name)
+
+    @property
+    def key(self):
+        return self.pyclass, self.dtype, self.layout
+
+    @property
+    def as_array(self):
+        return types.Array(self.dtype, 1, self.layout)
+
+    def copy(self, dtype=None, ndim: int = 1, layout=None):
+        assert ndim == 1
+        if dtype is None:
+            dtype = self.dtype
+        layout = layout or self.layout
+        return type(self)(dtype, layout, self.pyclass)
+
+
+class SeriesType(types.Type):
+    """
+    The type class for Series objects.
+    """
+
+    def __init__(self, dtype, index, namety) -> None:
+        assert isinstance(index, IndexType)
+        self.dtype = dtype
+        self.index = index
+        self.values = types.Array(self.dtype, 1, "C")
+        self.namety = namety
+        name = f"series({dtype}, {index}, {namety})"
+        super().__init__(name)
+
+    @property
+    def key(self):
+        return self.dtype, self.index, self.namety
+
+    @property
+    def as_array(self):
+        return self.values
+
+    def copy(self, dtype=None, ndim: int = 1, layout: str = "C"):
+        assert ndim == 1
+        assert layout == "C"
+        if dtype is None:
+            dtype = self.dtype
+        return type(self)(dtype, self.index, self.namety)
+
+
+@typeof_impl.register(Index)
+def typeof_index(val, c):
+    """
+    This will assume that only strings are in object dtype
+    index.
+    (you should check this before this gets lowered down to numba)
+    """
+    # arrty = typeof_impl(val._data, c)
+    arrty = typeof_impl(val._numba_data, c)
+    assert arrty.ndim == 1
+    return IndexType(arrty.dtype, arrty.layout, type(val))
+
+
+@typeof_impl.register(Series)
+def typeof_series(val, c):
+    index = typeof_impl(val.index, c)
+    arrty = typeof_impl(val.values, c)
+    namety = typeof_impl(val.name, c)
+    assert arrty.ndim == 1
+    assert arrty.layout == "C"
+    return SeriesType(arrty.dtype, index, namety)
+
+
+@type_callable(Series)
+def type_series_constructor(context):
+    def typer(data, index, name=None):
+        if isinstance(index, IndexType) and isinstance(data, types.Array):
+            assert data.ndim == 1
+            if name is None:
+                name = types.intp
+            return SeriesType(data.dtype, index, name)
+
+    return typer
+
+
+@type_callable(Index)
+def type_index_constructor(context):
+    def typer(data, hashmap=None):
+        if isinstance(data, types.Array):
+            assert data.layout == "C"
+            assert data.ndim == 1
+            assert hashmap is None or isinstance(hashmap, types.DictType)
+            return IndexType(data.dtype, layout=data.layout, pyclass=Index)
+
+    return typer
+
+
+# Backend extensions for Index and Series and Frame
+@register_model(IndexType)
+class IndexModel(models.StructModel):
+    def __init__(self, dmm, fe_type) -> None:
+        # We don't want the numpy string scalar type in our hashmap
+        members = [
+            ("data", fe_type.as_array),
+            # This is an attempt to emulate our hashtable code with a numba
+            # typed dict
+            # It maps from values in the index to their integer positions in the array
+            ("hashmap", types.DictType(fe_type.dtype, types.intp)),
+            # Pointer to the Index object this was created from, or that it
+            # boxes to
+            # https://numba.discourse.group/t/qst-how-to-cache-the-boxing-of-an-object/2128/2?u=lithomas1
+            ("parent", types.pyobject),
+        ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+@register_model(SeriesType)
+class SeriesModel(models.StructModel):
+    def __init__(self, dmm, fe_type) -> None:
+        members = [
+            ("index", fe_type.index),
+            ("values", fe_type.as_array),
+            ("name", fe_type.namety),
+        ]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IndexType, "data", "_data")
+make_attribute_wrapper(IndexType, "hashmap", "hashmap")
+
+make_attribute_wrapper(SeriesType, "index", "index")
+make_attribute_wrapper(SeriesType, "values", "values")
+make_attribute_wrapper(SeriesType, "name", "name")
+
+
+@lower_builtin(Series, types.Array, IndexType)
+def pdseries_constructor(context, builder, sig, args):
+    data, index = args
+    series = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    series.index = index
+    series.values = data
+    series.name = context.get_constant(types.intp, 0)
+    return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@lower_builtin(Series, types.Array, IndexType, types.intp)
+@lower_builtin(Series, types.Array, IndexType, types.float64)
+@lower_builtin(Series, types.Array, IndexType, types.unicode_type)
+def pdseries_constructor_with_name(context, builder, sig, args):
+    data, index, name = args
+    series = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    series.index = index
+    series.values = data
+    series.name = name
+    return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@lower_builtin(Index, types.Array, types.DictType, types.pyobject)
+def index_constructor_2arg(context, builder, sig, args):
+    (data, hashmap, parent) = args
+    index = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+    index.data = data
+    index.hashmap = hashmap
+    index.parent = parent
+    return impl_ret_borrowed(context, builder, sig.return_type, index._getvalue())
+
+
+@lower_builtin(Index, types.Array, types.DictType)
+def index_constructor_2arg_parent(context, builder, sig, args):
+    # Basically same as index_constructor_1arg, but also lets you specify the
+    # parent object
+    (data, hashmap) = args
+    index = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+    index.data = data
+    index.hashmap = hashmap
+    return impl_ret_borrowed(context, builder, sig.return_type, index._getvalue())
+
+
+@lower_builtin(Index, types.Array)
+def index_constructor_1arg(context, builder, sig, args):
+    from numba.typed import Dict
+
+    key_type = sig.return_type.dtype
+    value_type = types.intp
+
+    def index_impl(data):
+        return Index(data, Dict.empty(key_type, value_type))
+
+    return context.compile_internal(builder, index_impl, sig, args)
+
+
+# Helper to convert the unicodecharseq (numpy string scalar) into a unicode_type
+# (regular string)
+def maybe_cast_str(x):
+    # Dummy function that numba can overload
+    pass
+
+
+@overload(maybe_cast_str)
+def maybe_cast_str_impl(x):
+    """Converts numba UnicodeCharSeq (numpy string scalar) -> unicode type (string).
+    Is a no-op for other types."""
+    if isinstance(x, types.UnicodeCharSeq):
+        return lambda x: str(x)
+    else:
+        return lambda x: x
+
+
+@unbox(IndexType)
+def unbox_index(typ, obj, c):
+    """
+    Convert a Index object to a native structure.
+
+    Note: Object dtype is not allowed here
+    """
+    data_obj = c.pyapi.object_getattr_string(obj, "_numba_data")
+    index = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+    # If we see an object array, assume its been validated as only containing strings
+    # We still need to do the conversion though
+    index.data = c.unbox(typ.as_array, data_obj).value
+    typed_dict_obj = c.pyapi.unserialize(c.pyapi.serialize_object(numba.typed.Dict))
+    # Create an empty typed dict in numba for the hashmap for indexing
+    # equiv of numba.typed.Dict.empty(typ.dtype, types.intp)
+    arr_type_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.dtype))
+    intp_type_obj = c.pyapi.unserialize(c.pyapi.serialize_object(types.intp))
+    hashmap_obj = c.pyapi.call_method(
+        typed_dict_obj, "empty", (arr_type_obj, intp_type_obj)
+    )
+    index.hashmap = c.unbox(types.DictType(typ.dtype, types.intp), hashmap_obj).value
+    # Set the parent for speedy boxing.
+    index.parent = obj
+
+    # Decrefs
+    c.pyapi.decref(data_obj)
+    c.pyapi.decref(arr_type_obj)
+    c.pyapi.decref(intp_type_obj)
+    c.pyapi.decref(typed_dict_obj)
+
+    return NativeValue(index._getvalue())
+
+
+@unbox(SeriesType)
+def unbox_series(typ, obj, c):
+    """
+    Convert a Series object to a native structure.
+    """
+    index_obj = c.pyapi.object_getattr_string(obj, "index")
+    values_obj = c.pyapi.object_getattr_string(obj, "values")
+    name_obj = c.pyapi.object_getattr_string(obj, "name")
+
+    series = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+    series.index = c.unbox(typ.index, index_obj).value
+    series.values = c.unbox(typ.values, values_obj).value
+    series.name = c.unbox(typ.namety, name_obj).value
+
+    # Decrefs
+    c.pyapi.decref(index_obj)
+    c.pyapi.decref(values_obj)
+    c.pyapi.decref(name_obj)
+
+    return NativeValue(series._getvalue())
+
+
+@box(IndexType)
+def box_index(typ, val, c):
+    """
+    Convert a native index structure to a Index object.
+
+    If our native index is of a numpy string dtype, we'll cast it to
+    object.
+    """
+    # First build a Numpy array object, then wrap it in a Index
+    index = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+
+    res = cgutils.alloca_once_value(c.builder, index.parent)
+
+    # Does parent exist?
+    # (it means already boxed once, or Index same as original df.index or df.columns)
+    # xref https://github.com/numba/numba/blob/596e8a55334cc46854e3192766e643767bd7c934/numba/core/boxing.py#L593C17-L593C17
+    with c.builder.if_else(cgutils.is_not_null(c.builder, index.parent)) as (
+        has_parent,
+        otherwise,
+    ):
+        with has_parent:
+            c.pyapi.incref(index.parent)
+        with otherwise:
+            # TODO: preserve the original class for the index
+            # Also need preserve the name of the Index
+            # class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.pyclass))
+            class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Index))
+            array_obj = c.box(typ.as_array, index.data)
+            if isinstance(typ.dtype, types.UnicodeCharSeq):
+                # We converted to numpy string dtype, convert back
+                # to object since _simple_new won't do that for uss
+                object_str_obj = c.pyapi.unserialize(c.pyapi.serialize_object("object"))
+                array_obj = c.pyapi.call_method(array_obj, "astype", (object_str_obj,))
+                c.pyapi.decref(object_str_obj)
+            # this is basically Index._simple_new(array_obj, name_obj) in python
+            index_obj = c.pyapi.call_method(class_obj, "_simple_new", (array_obj,))
+            index.parent = index_obj
+            c.builder.store(index_obj, res)
+
+            # Decrefs
+            c.pyapi.decref(class_obj)
+            c.pyapi.decref(array_obj)
+    return c.builder.load(res)
+
+
+@box(SeriesType)
+def box_series(typ, val, c):
+    """
+    Convert a native series structure to a Series object.
+    """
+    series = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+    series_const_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Series._from_mgr))
+    mgr_const_obj = c.pyapi.unserialize(
+        c.pyapi.serialize_object(SingleBlockManager.from_array)
+    )
+    index_obj = c.box(typ.index, series.index)
+    array_obj = c.box(typ.as_array, series.values)
+    name_obj = c.box(typ.namety, series.name)
+    # This is basically equivalent of
+    # pd.Series(data=array_obj, index=index_obj)
+    # To improve perf, we will construct the Series from a manager
+    # object to avoid checks.
+    # We'll also set the name attribute manually to avoid validation
+    mgr_obj = c.pyapi.call_function_objargs(
+        mgr_const_obj,
+        (
+            array_obj,
+            index_obj,
+        ),
+    )
+    mgr_axes_obj = c.pyapi.object_getattr_string(mgr_obj, "axes")
+    # Series._constructor_from_mgr(mgr, axes)
+    series_obj = c.pyapi.call_function_objargs(
+        series_const_obj, (mgr_obj, mgr_axes_obj)
+    )
+    c.pyapi.object_setattr_string(series_obj, "_name", name_obj)
+
+    # Decrefs
+    c.pyapi.decref(series_const_obj)
+    c.pyapi.decref(mgr_axes_obj)
+    c.pyapi.decref(mgr_obj)
+    c.pyapi.decref(mgr_const_obj)
+    c.pyapi.decref(index_obj)
+    c.pyapi.decref(array_obj)
+    c.pyapi.decref(name_obj)
+
+    return series_obj
+
+
+# Add common series reductions (e.g. mean, sum),
+# and also add common binops (e.g. add, sub, mul, div)
+def generate_series_reduction(ser_reduction, ser_method):
+    @overload_method(SeriesType, ser_reduction)
+    def series_reduction(series):
+        def series_reduction_impl(series):
+            return ser_method(series.values)
+
+        return series_reduction_impl
+
+    return series_reduction
+
+
+def generate_series_binop(binop):
+    @overload(binop)
+    def series_binop(series1, value):
+        if isinstance(series1, SeriesType):
+            if isinstance(value, SeriesType):
+
+                def series_binop_impl(series1, series2):
+                    # TODO: Check index matching?
+                    return Series(
+                        binop(series1.values, series2.values),
+                        series1.index,
+                        series1.name,
+                    )
+
+                return series_binop_impl
+            else:
+
+                def series_binop_impl(series1, value):
+                    return Series(
+                        binop(series1.values, value), series1.index, series1.name
+                    )
+
+                return series_binop_impl
+
+    return series_binop
+
+
+series_reductions = [
+    ("sum", np.sum),
+    ("mean", np.mean),
+    # Disabled due to discrepancies between numba std. dev
+    # and pandas std. dev (no way to specify dof)
+    # ("std", np.std),
+    # ("var", np.var),
+    ("min", np.min),
+    ("max", np.max),
+]
+for reduction, reduction_method in series_reductions:
+    generate_series_reduction(reduction, reduction_method)
+
+series_binops = [operator.add, operator.sub, operator.mul, operator.truediv]
+
+for ser_binop in series_binops:
+    generate_series_binop(ser_binop)
+
+
+# get_loc on Index
+@overload_method(IndexType, "get_loc")
+def index_get_loc(index, item):
+    def index_get_loc_impl(index, item):
+        # Initialize the hash table if not initialized
+        if len(index.hashmap) == 0:
+            for i, val in enumerate(index._data):
+                index.hashmap[val] = i
+        return index.hashmap[item]
+
+    return index_get_loc_impl
+
+
+# Indexing for Series/Index
+@overload(operator.getitem)
+def series_indexing(series, item):
+    if isinstance(series, SeriesType):
+
+        def series_getitem(series, item):
+            loc = series.index.get_loc(item)
+            return series.iloc[loc]
+
+        return series_getitem
+
+
+@overload(operator.getitem)
+def index_indexing(index, idx):
+    if isinstance(index, IndexType):
+
+        def index_getitem(index, idx):
+            return index._data[idx]
+
+        return index_getitem
+
+
+class IlocType(types.Type):
+    def __init__(self, obj_type) -> None:
+        self.obj_type = obj_type
+        name = f"iLocIndexer({obj_type})"
+        super().__init__(name=name)
+
+    @property
+    def key(self):
+        return self.obj_type
+
+
+@typeof_impl.register(_iLocIndexer)
+def typeof_iloc(val, c):
+    objtype = typeof_impl(val.obj, c)
+    return IlocType(objtype)
+
+
+@type_callable(_iLocIndexer)
+def type_iloc_constructor(context):
+    def typer(obj):
+        if isinstance(obj, SeriesType):
+            return IlocType(obj)
+
+    return typer
+
+
+@lower_builtin(_iLocIndexer, SeriesType)
+def iloc_constructor(context, builder, sig, args):
+    (obj,) = args
+    iloc_indexer = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    iloc_indexer.obj = obj
+    return impl_ret_borrowed(
+        context, builder, sig.return_type, iloc_indexer._getvalue()
+    )
+
+
+@register_model(IlocType)
+class ILocModel(models.StructModel):
+    def __init__(self, dmm, fe_type) -> None:
+        members = [("obj", fe_type.obj_type)]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IlocType, "obj", "obj")
+
+
+@overload_attribute(SeriesType, "iloc")
+def series_iloc(series):
+    def get(series):
+        return _iLocIndexer(series)
+
+    return get
+
+
+@overload(operator.getitem)
+def iloc_getitem(iloc_indexer, i):
+    if isinstance(iloc_indexer, IlocType):
+
+        def getitem_impl(iloc_indexer, i):
+            return iloc_indexer.obj.values[i]
+
+        return getitem_impl
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1116c61c4ca8e48d94a6c9c6222aaf545d989e86
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/__init__.py
@@ -0,0 +1,27 @@
+from pandas.core._numba.kernels.mean_ import (
+    grouped_mean,
+    sliding_mean,
+)
+from pandas.core._numba.kernels.min_max_ import (
+    grouped_min_max,
+    sliding_min_max,
+)
+from pandas.core._numba.kernels.sum_ import (
+    grouped_sum,
+    sliding_sum,
+)
+from pandas.core._numba.kernels.var_ import (
+    grouped_var,
+    sliding_var,
+)
+
+__all__ = [
+    "sliding_mean",
+    "grouped_mean",
+    "sliding_sum",
+    "grouped_sum",
+    "sliding_var",
+    "grouped_var",
+    "sliding_min_max",
+    "grouped_min_max",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/mean_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/mean_.py
new file mode 100644
index 0000000000000000000000000000000000000000..f415804781753372a5715b6ffee6a7ab8cc70b64
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/mean_.py
@@ -0,0 +1,196 @@
+"""
+Numba 1D mean kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+from pandas.core._numba.kernels.shared import is_monotonic_increasing
+from pandas.core._numba.kernels.sum_ import grouped_kahan_sum
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def add_mean(
+    val: float,
+    nobs: int,
+    sum_x: float,
+    neg_ct: int,
+    compensation: float,
+    num_consecutive_same_value: int,
+    prev_value: float,
+) -> tuple[int, float, int, float, int, float]:
+    if not np.isnan(val):
+        nobs += 1
+        y = val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+        if val < 0:
+            neg_ct += 1
+
+        if val == prev_value:
+            num_consecutive_same_value += 1
+        else:
+            num_consecutive_same_value = 1
+        prev_value = val
+
+    return nobs, sum_x, neg_ct, compensation, num_consecutive_same_value, prev_value
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def remove_mean(
+    val: float, nobs: int, sum_x: float, neg_ct: int, compensation: float
+) -> tuple[int, float, int, float]:
+    if not np.isnan(val):
+        nobs -= 1
+        y = -val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+        if val < 0:
+            neg_ct -= 1
+    return nobs, sum_x, neg_ct, compensation
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_mean(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    sum_x = 0.0
+    neg_ct = 0
+    compensation_add = 0.0
+    compensation_remove = 0.0
+
+    is_monotonic_increasing_bounds = is_monotonic_increasing(
+        start
+    ) and is_monotonic_increasing(end)
+
+    output = np.empty(N, dtype=result_dtype)
+
+    for i in range(N):
+        s = start[i]
+        e = end[i]
+        if i == 0 or not is_monotonic_increasing_bounds:
+            prev_value = values[s]
+            num_consecutive_same_value = 0
+
+            for j in range(s, e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_mean(
+                    val,
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
+                )
+        else:
+            for j in range(start[i - 1], s):
+                val = values[j]
+                nobs, sum_x, neg_ct, compensation_remove = remove_mean(
+                    val, nobs, sum_x, neg_ct, compensation_remove
+                )
+
+            for j in range(end[i - 1], e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_mean(
+                    val,
+                    nobs,
+                    sum_x,
+                    neg_ct,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
+                )
+
+        if nobs >= min_periods and nobs > 0:
+            result = sum_x / nobs
+            if num_consecutive_same_value >= nobs:
+                result = prev_value
+            elif neg_ct == 0 and result < 0:
+                result = 0
+            elif neg_ct == nobs and result > 0:
+                result = 0
+        else:
+            result = np.nan
+
+        output[i] = result
+
+        if not is_monotonic_increasing_bounds:
+            nobs = 0
+            sum_x = 0.0
+            neg_ct = 0
+            compensation_remove = 0.0
+
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_mean(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    output, nobs_arr, comp_arr, consecutive_counts, prev_vals = grouped_kahan_sum(
+        values, result_dtype, labels, ngroups
+    )
+
+    # Post-processing, replace sums that don't satisfy min_periods
+    for lab in range(ngroups):
+        nobs = nobs_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+        sum_x = output[lab]
+        if nobs >= min_periods:
+            if num_consecutive_same_value >= nobs:
+                result = prev_value * nobs
+            else:
+                result = sum_x
+        else:
+            result = np.nan
+        result /= nobs
+        output[lab] = result
+
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/min_max_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/min_max_.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9803980e64a6bf2ec5b274acd582850e3a07420
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/min_max_.py
@@ -0,0 +1,125 @@
+"""
+Numba 1D min/max kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_min_max(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+    is_max: bool,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    output = np.empty(N, dtype=result_dtype)
+    na_pos = []
+    # Use deque once numba supports it
+    # https://github.com/numba/numba/issues/7417
+    Q: list = []
+    W: list = []
+    for i in range(N):
+        curr_win_size = end[i] - start[i]
+        if i == 0:
+            st = start[i]
+        else:
+            st = end[i - 1]
+
+        for k in range(st, end[i]):
+            ai = values[k]
+            if not np.isnan(ai):
+                nobs += 1
+            elif is_max:
+                ai = -np.inf
+            else:
+                ai = np.inf
+            # Discard previous entries if we find new min or max
+            if is_max:
+                while Q and ((ai >= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
+                    Q.pop()
+            else:
+                while Q and ((ai <= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
+                    Q.pop()
+            Q.append(k)
+            W.append(k)
+
+        # Discard entries outside and left of current window
+        while Q and Q[0] <= start[i] - 1:
+            Q.pop(0)
+        while W and W[0] <= start[i] - 1:
+            if not np.isnan(values[W[0]]):
+                nobs -= 1
+            W.pop(0)
+
+        # Save output based on index in input value array
+        if Q and curr_win_size > 0 and nobs >= min_periods:
+            output[i] = values[Q[0]]
+        else:
+            if values.dtype.kind != "i":
+                output[i] = np.nan
+            else:
+                na_pos.append(i)
+
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_min_max(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+    is_max: bool,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(labels)
+    nobs = np.zeros(ngroups, dtype=np.int64)
+    na_pos = []
+    output = np.empty(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+        if lab < 0:
+            continue
+
+        if values.dtype.kind == "i" or not np.isnan(val):
+            nobs[lab] += 1
+        else:
+            # NaN value cannot be a min/max value
+            continue
+
+        if nobs[lab] == 1:
+            # First element in group, set output equal to this
+            output[lab] = val
+            continue
+
+        if is_max:
+            if val > output[lab]:
+                output[lab] = val
+        else:
+            if val < output[lab]:
+                output[lab] = val
+
+    # Set labels that don't satisfy min_periods as np.nan
+    for lab, count in enumerate(nobs):
+        if count < min_periods:
+            na_pos.append(lab)
+
+    return output, na_pos
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/shared.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/shared.py
new file mode 100644
index 0000000000000000000000000000000000000000..c52372fe6b08f3ec9ec6e836341e08ac804d50f3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/shared.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+
+if TYPE_CHECKING:
+    import numpy as np
+
+
+@numba.jit(
+    # error: Any? not callable
+    numba.boolean(numba.int64[:]),  # type: ignore[misc]
+    nopython=True,
+    nogil=True,
+    parallel=False,
+)
+def is_monotonic_increasing(bounds: np.ndarray) -> bool:
+    """Check if int64 values are monotonically increasing."""
+    n = len(bounds)
+    if n < 2:
+        return True
+    prev = bounds[0]
+    for i in range(1, n):
+        cur = bounds[i]
+        if cur < prev:
+            return False
+        prev = cur
+    return True
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/sum_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/sum_.py
new file mode 100644
index 0000000000000000000000000000000000000000..94db84267ceecb83234fc2e0b231566a2fdffd66
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/sum_.py
@@ -0,0 +1,244 @@
+"""
+Numba 1D sum kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numba
+from numba.extending import register_jitable
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+from pandas.core._numba.kernels.shared import is_monotonic_increasing
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def add_sum(
+    val: Any,
+    nobs: int,
+    sum_x: Any,
+    compensation: Any,
+    num_consecutive_same_value: int,
+    prev_value: Any,
+) -> tuple[int, Any, Any, int, Any]:
+    if not np.isnan(val):
+        nobs += 1
+        y = val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+
+        if val == prev_value:
+            num_consecutive_same_value += 1
+        else:
+            num_consecutive_same_value = 1
+        prev_value = val
+
+    return nobs, sum_x, compensation, num_consecutive_same_value, prev_value
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def remove_sum(
+    val: Any, nobs: int, sum_x: Any, compensation: Any
+) -> tuple[int, Any, Any]:
+    if not np.isnan(val):
+        nobs -= 1
+        y = -val - compensation
+        t = sum_x + y
+        compensation = t - sum_x - y
+        sum_x = t
+    return nobs, sum_x, compensation
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_sum(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    dtype = values.dtype
+
+    na_val: object = np.nan
+    if dtype.kind == "i":
+        na_val = 0
+
+    N = len(start)
+    nobs = 0
+    sum_x = 0
+    compensation_add = 0
+    compensation_remove = 0
+    na_pos = []
+
+    is_monotonic_increasing_bounds = is_monotonic_increasing(
+        start
+    ) and is_monotonic_increasing(end)
+
+    output = np.empty(N, dtype=result_dtype)
+
+    for i in range(N):
+        s = start[i]
+        e = end[i]
+        if i == 0 or not is_monotonic_increasing_bounds:
+            prev_value = values[s]
+            num_consecutive_same_value = 0
+
+            for j in range(s, e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_sum(
+                    val,
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+        else:
+            for j in range(start[i - 1], s):
+                val = values[j]
+                nobs, sum_x, compensation_remove = remove_sum(
+                    val, nobs, sum_x, compensation_remove
+                )
+
+            for j in range(end[i - 1], e):
+                val = values[j]
+                (
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_sum(
+                    val,
+                    nobs,
+                    sum_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+
+        if nobs == 0 == min_periods:
+            result: object = 0
+        elif nobs >= min_periods:
+            if num_consecutive_same_value >= nobs:
+                result = prev_value * nobs
+            else:
+                result = sum_x
+        else:
+            result = na_val
+            if dtype.kind == "i":
+                na_pos.append(i)
+
+        output[i] = result
+
+        if not is_monotonic_increasing_bounds:
+            nobs = 0
+            sum_x = 0
+            compensation_remove = 0
+
+    return output, na_pos
+
+
+# Mypy/pyright don't like the fact that the decorator is untyped
+@register_jitable  # type: ignore[misc]
+def grouped_kahan_sum(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+) -> tuple[
+    np.ndarray, npt.NDArray[np.int64], np.ndarray, npt.NDArray[np.int64], np.ndarray
+]:
+    N = len(labels)
+
+    nobs_arr = np.zeros(ngroups, dtype=np.int64)
+    comp_arr = np.zeros(ngroups, dtype=values.dtype)
+    consecutive_counts = np.zeros(ngroups, dtype=np.int64)
+    prev_vals = np.zeros(ngroups, dtype=values.dtype)
+    output = np.zeros(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+
+        if lab < 0:
+            continue
+
+        sum_x = output[lab]
+        nobs = nobs_arr[lab]
+        compensation_add = comp_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+
+        (
+            nobs,
+            sum_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        ) = add_sum(
+            val,
+            nobs,
+            sum_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        )
+
+        output[lab] = sum_x
+        consecutive_counts[lab] = num_consecutive_same_value
+        prev_vals[lab] = prev_value
+        comp_arr[lab] = compensation_add
+        nobs_arr[lab] = nobs
+    return output, nobs_arr, comp_arr, consecutive_counts, prev_vals
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_sum(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+) -> tuple[np.ndarray, list[int]]:
+    na_pos = []
+
+    output, nobs_arr, comp_arr, consecutive_counts, prev_vals = grouped_kahan_sum(
+        values, result_dtype, labels, ngroups
+    )
+
+    # Post-processing, replace sums that don't satisfy min_periods
+    for lab in range(ngroups):
+        nobs = nobs_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+        sum_x = output[lab]
+        if nobs >= min_periods:
+            if num_consecutive_same_value >= nobs:
+                result = prev_value * nobs
+            else:
+                result = sum_x
+        else:
+            result = sum_x  # Don't change val, will be replaced by nan later
+            na_pos.append(lab)
+        output[lab] = result
+
+    return output, na_pos
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/var_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/var_.py
new file mode 100644
index 0000000000000000000000000000000000000000..c63d0b90b0fc3f4a4a532b9ce85e57d8bd823fb1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/_numba/kernels/var_.py
@@ -0,0 +1,245 @@
+"""
+Numba 1D var kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+from pandas.core._numba.kernels.shared import is_monotonic_increasing
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def add_var(
+    val: float,
+    nobs: int,
+    mean_x: float,
+    ssqdm_x: float,
+    compensation: float,
+    num_consecutive_same_value: int,
+    prev_value: float,
+) -> tuple[int, float, float, float, int, float]:
+    if not np.isnan(val):
+        if val == prev_value:
+            num_consecutive_same_value += 1
+        else:
+            num_consecutive_same_value = 1
+        prev_value = val
+
+        nobs += 1
+        prev_mean = mean_x - compensation
+        y = val - compensation
+        t = y - mean_x
+        compensation = t + mean_x - y
+        delta = t
+        if nobs:
+            mean_x += delta / nobs
+        else:
+            mean_x = 0
+        ssqdm_x += (val - prev_mean) * (val - mean_x)
+    return nobs, mean_x, ssqdm_x, compensation, num_consecutive_same_value, prev_value
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def remove_var(
+    val: float, nobs: int, mean_x: float, ssqdm_x: float, compensation: float
+) -> tuple[int, float, float, float]:
+    if not np.isnan(val):
+        nobs -= 1
+        if nobs:
+            prev_mean = mean_x - compensation
+            y = val - compensation
+            t = y - mean_x
+            compensation = t + mean_x - y
+            delta = t
+            mean_x -= delta / nobs
+            ssqdm_x -= (val - prev_mean) * (val - mean_x)
+        else:
+            mean_x = 0
+            ssqdm_x = 0
+    return nobs, mean_x, ssqdm_x, compensation
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_var(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+    ddof: int = 1,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    mean_x = 0.0
+    ssqdm_x = 0.0
+    compensation_add = 0.0
+    compensation_remove = 0.0
+
+    min_periods = max(min_periods, 1)
+    is_monotonic_increasing_bounds = is_monotonic_increasing(
+        start
+    ) and is_monotonic_increasing(end)
+
+    output = np.empty(N, dtype=result_dtype)
+
+    for i in range(N):
+        s = start[i]
+        e = end[i]
+        if i == 0 or not is_monotonic_increasing_bounds:
+            prev_value = values[s]
+            num_consecutive_same_value = 0
+
+            for j in range(s, e):
+                val = values[j]
+                (
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_var(
+                    val,
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+        else:
+            for j in range(start[i - 1], s):
+                val = values[j]
+                nobs, mean_x, ssqdm_x, compensation_remove = remove_var(
+                    val, nobs, mean_x, ssqdm_x, compensation_remove
+                )
+
+            for j in range(end[i - 1], e):
+                val = values[j]
+                (
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_var(
+                    val,
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
+
+        if nobs >= min_periods and nobs > ddof:
+            if nobs == 1 or num_consecutive_same_value >= nobs:
+                result = 0.0
+            else:
+                result = ssqdm_x / (nobs - ddof)
+        else:
+            result = np.nan
+
+        output[i] = result
+
+        if not is_monotonic_increasing_bounds:
+            nobs = 0
+            mean_x = 0.0
+            ssqdm_x = 0.0
+            compensation_remove = 0.0
+
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_var(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+    ddof: int = 1,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(labels)
+
+    nobs_arr = np.zeros(ngroups, dtype=np.int64)
+    comp_arr = np.zeros(ngroups, dtype=values.dtype)
+    consecutive_counts = np.zeros(ngroups, dtype=np.int64)
+    prev_vals = np.zeros(ngroups, dtype=values.dtype)
+    output = np.zeros(ngroups, dtype=result_dtype)
+    means = np.zeros(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+
+        if lab < 0:
+            continue
+
+        mean_x = means[lab]
+        ssqdm_x = output[lab]
+        nobs = nobs_arr[lab]
+        compensation_add = comp_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        prev_value = prev_vals[lab]
+
+        (
+            nobs,
+            mean_x,
+            ssqdm_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        ) = add_var(
+            val,
+            nobs,
+            mean_x,
+            ssqdm_x,
+            compensation_add,
+            num_consecutive_same_value,
+            prev_value,
+        )
+
+        output[lab] = ssqdm_x
+        means[lab] = mean_x
+        consecutive_counts[lab] = num_consecutive_same_value
+        prev_vals[lab] = prev_value
+        comp_arr[lab] = compensation_add
+        nobs_arr[lab] = nobs
+
+    # Post-processing, replace vars that don't satisfy min_periods
+    for lab in range(ngroups):
+        nobs = nobs_arr[lab]
+        num_consecutive_same_value = consecutive_counts[lab]
+        ssqdm_x = output[lab]
+        if nobs >= min_periods and nobs > ddof:
+            if nobs == 1 or num_consecutive_same_value >= nobs:
+                result = 0.0
+            else:
+                result = ssqdm_x / (nobs - ddof)
+        else:
+            result = np.nan
+        output[lab] = result
+
+    # Second pass to get the std.dev
+    # na_position is empty list since float64 can already hold nans
+    # Do list comprehension, since numba cannot figure out that na_pos is
+    # empty list of ints on its own
+    na_pos = [0 for i in range(0)]
+    return output, na_pos
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/accessor.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..698abb2ec4989b2f95f6b2c012f03873bba328cb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/accessor.py
@@ -0,0 +1,340 @@
+"""
+
+accessor.py contains base classes for implementing accessor properties
+that can be mixed into or pinned onto other pandas classes.
+
+"""
+from __future__ import annotations
+
+from typing import (
+    Callable,
+    final,
+)
+import warnings
+
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+
+class DirNamesMixin:
+    _accessors: set[str] = set()
+    _hidden_attrs: frozenset[str] = frozenset()
+
+    @final
+    def _dir_deletions(self) -> set[str]:
+        """
+        Delete unwanted __dir__ for this object.
+        """
+        return self._accessors | self._hidden_attrs
+
+    def _dir_additions(self) -> set[str]:
+        """
+        Add additional __dir__ for this object.
+        """
+        return {accessor for accessor in self._accessors if hasattr(self, accessor)}
+
+    def __dir__(self) -> list[str]:
+        """
+        Provide method name lookup and completion.
+
+        Notes
+        -----
+        Only provide 'public' methods.
+        """
+        rv = set(super().__dir__())
+        rv = (rv - self._dir_deletions()) | self._dir_additions()
+        return sorted(rv)
+
+
+class PandasDelegate:
+    """
+    Abstract base class for delegating methods/properties.
+    """
+
+    def _delegate_property_get(self, name: str, *args, **kwargs):
+        raise TypeError(f"You cannot access the property {name}")
+
+    def _delegate_property_set(self, name: str, value, *args, **kwargs):
+        raise TypeError(f"The property {name} cannot be set")
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        raise TypeError(f"You cannot call method {name}")
+
+    @classmethod
+    def _add_delegate_accessors(
+        cls,
+        delegate,
+        accessors: list[str],
+        typ: str,
+        overwrite: bool = False,
+        accessor_mapping: Callable[[str], str] = lambda x: x,
+        raise_on_missing: bool = True,
+    ) -> None:
+        """
+        Add accessors to cls from the delegate class.
+
+        Parameters
+        ----------
+        cls
+            Class to add the methods/properties to.
+        delegate
+            Class to get methods/properties and doc-strings.
+        accessors : list of str
+            List of accessors to add.
+        typ : {'property', 'method'}
+        overwrite : bool, default False
+            Overwrite the method/property in the target class if it exists.
+        accessor_mapping: Callable, default lambda x: x
+            Callable to map the delegate's function to the cls' function.
+        raise_on_missing: bool, default True
+            Raise if an accessor does not exist on delegate.
+            False skips the missing accessor.
+        """
+
+        def _create_delegator_property(name: str):
+            def _getter(self):
+                return self._delegate_property_get(name)
+
+            def _setter(self, new_values):
+                return self._delegate_property_set(name, new_values)
+
+            _getter.__name__ = name
+            _setter.__name__ = name
+
+            return property(
+                fget=_getter,
+                fset=_setter,
+                doc=getattr(delegate, accessor_mapping(name)).__doc__,
+            )
+
+        def _create_delegator_method(name: str):
+            def f(self, *args, **kwargs):
+                return self._delegate_method(name, *args, **kwargs)
+
+            f.__name__ = name
+            f.__doc__ = getattr(delegate, accessor_mapping(name)).__doc__
+
+            return f
+
+        for name in accessors:
+            if (
+                not raise_on_missing
+                and getattr(delegate, accessor_mapping(name), None) is None
+            ):
+                continue
+
+            if typ == "property":
+                f = _create_delegator_property(name)
+            else:
+                f = _create_delegator_method(name)
+
+            # don't overwrite existing methods/properties
+            if overwrite or not hasattr(cls, name):
+                setattr(cls, name, f)
+
+
+def delegate_names(
+    delegate,
+    accessors: list[str],
+    typ: str,
+    overwrite: bool = False,
+    accessor_mapping: Callable[[str], str] = lambda x: x,
+    raise_on_missing: bool = True,
+):
+    """
+    Add delegated names to a class using a class decorator.  This provides
+    an alternative usage to directly calling `_add_delegate_accessors`
+    below a class definition.
+
+    Parameters
+    ----------
+    delegate : object
+        The class to get methods/properties & doc-strings.
+    accessors : Sequence[str]
+        List of accessor to add.
+    typ : {'property', 'method'}
+    overwrite : bool, default False
+       Overwrite the method/property in the target class if it exists.
+    accessor_mapping: Callable, default lambda x: x
+        Callable to map the delegate's function to the cls' function.
+    raise_on_missing: bool, default True
+        Raise if an accessor does not exist on delegate.
+        False skips the missing accessor.
+
+    Returns
+    -------
+    callable
+        A class decorator.
+
+    Examples
+    --------
+    @delegate_names(Categorical, ["categories", "ordered"], "property")
+    class CategoricalAccessor(PandasDelegate):
+        [...]
+    """
+
+    def add_delegate_accessors(cls):
+        cls._add_delegate_accessors(
+            delegate,
+            accessors,
+            typ,
+            overwrite=overwrite,
+            accessor_mapping=accessor_mapping,
+            raise_on_missing=raise_on_missing,
+        )
+        return cls
+
+    return add_delegate_accessors
+
+
+# Ported with modifications from xarray; licence at LICENSES/XARRAY_LICENSE
+# https://github.com/pydata/xarray/blob/master/xarray/core/extensions.py
+# 1. We don't need to catch and re-raise AttributeErrors as RuntimeErrors
+# 2. We use a UserWarning instead of a custom Warning
+
+
+class CachedAccessor:
+    """
+    Custom property-like object.
+
+    A descriptor for caching accessors.
+
+    Parameters
+    ----------
+    name : str
+        Namespace that will be accessed under, e.g. ``df.foo``.
+    accessor : cls
+        Class with the extension methods.
+
+    Notes
+    -----
+    For accessor, The class's __init__ method assumes that one of
+    ``Series``, ``DataFrame`` or ``Index`` as the
+    single argument ``data``.
+    """
+
+    def __init__(self, name: str, accessor) -> None:
+        self._name = name
+        self._accessor = accessor
+
+    def __get__(self, obj, cls):
+        if obj is None:
+            # we're accessing the attribute of the class, i.e., Dataset.geo
+            return self._accessor
+        accessor_obj = self._accessor(obj)
+        # Replace the property with the accessor object. Inspired by:
+        # https://www.pydanny.com/cached-property.html
+        # We need to use object.__setattr__ because we overwrite __setattr__ on
+        # NDFrame
+        object.__setattr__(obj, self._name, accessor_obj)
+        return accessor_obj
+
+
+@doc(klass="", others="")
+def _register_accessor(name: str, cls):
+    """
+    Register a custom accessor on {klass} objects.
+
+    Parameters
+    ----------
+    name : str
+        Name under which the accessor should be registered. A warning is issued
+        if this name conflicts with a preexisting attribute.
+
+    Returns
+    -------
+    callable
+        A class decorator.
+
+    See Also
+    --------
+    register_dataframe_accessor : Register a custom accessor on DataFrame objects.
+    register_series_accessor : Register a custom accessor on Series objects.
+    register_index_accessor : Register a custom accessor on Index objects.
+
+    Notes
+    -----
+    When accessed, your accessor will be initialized with the pandas object
+    the user is interacting with. So the signature must be
+
+    .. code-block:: python
+
+        def __init__(self, pandas_object):  # noqa: E999
+            ...
+
+    For consistency with pandas methods, you should raise an ``AttributeError``
+    if the data passed to your accessor has an incorrect dtype.
+
+    >>> pd.Series(['a', 'b']).dt
+    Traceback (most recent call last):
+    ...
+    AttributeError: Can only use .dt accessor with datetimelike values
+
+    Examples
+    --------
+    In your library code::
+
+        import pandas as pd
+
+        @pd.api.extensions.register_dataframe_accessor("geo")
+        class GeoAccessor:
+            def __init__(self, pandas_obj):
+                self._obj = pandas_obj
+
+            @property
+            def center(self):
+                # return the geographic center point of this DataFrame
+                lat = self._obj.latitude
+                lon = self._obj.longitude
+                return (float(lon.mean()), float(lat.mean()))
+
+            def plot(self):
+                # plot this array's data on a map, e.g., using Cartopy
+                pass
+
+    Back in an interactive IPython session:
+
+        .. code-block:: ipython
+
+            In [1]: ds = pd.DataFrame({{"longitude": np.linspace(0, 10),
+               ...:                    "latitude": np.linspace(0, 20)}})
+            In [2]: ds.geo.center
+            Out[2]: (5.0, 10.0)
+            In [3]: ds.geo.plot()  # plots data on a map
+    """
+
+    def decorator(accessor):
+        if hasattr(cls, name):
+            warnings.warn(
+                f"registration of accessor {repr(accessor)} under name "
+                f"{repr(name)} for type {repr(cls)} is overriding a preexisting "
+                f"attribute with the same name.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+        setattr(cls, name, CachedAccessor(name, accessor))
+        cls._accessors.add(name)
+        return accessor
+
+    return decorator
+
+
+@doc(_register_accessor, klass="DataFrame")
+def register_dataframe_accessor(name: str):
+    from pandas import DataFrame
+
+    return _register_accessor(name, DataFrame)
+
+
+@doc(_register_accessor, klass="Series")
+def register_series_accessor(name: str):
+    from pandas import Series
+
+    return _register_accessor(name, Series)
+
+
+@doc(_register_accessor, klass="Index")
+def register_index_accessor(name: str):
+    from pandas import Index
+
+    return _register_accessor(name, Index)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/algorithms.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/algorithms.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6084880bea5d0b8771bf31be1f1e6f5fecdc094
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/algorithms.py
@@ -0,0 +1,1747 @@
+"""
+Generic data algorithms. This module is experimental at the moment and not
+intended for public consumption
+"""
+from __future__ import annotations
+
+import decimal
+import operator
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    algos,
+    hashtable as htable,
+    iNaT,
+    lib,
+)
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    AxisInt,
+    DtypeObj,
+    TakeIndexer,
+    npt,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    construct_1d_object_array_from_listlike,
+    np_find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_float64,
+    ensure_object,
+    ensure_platform_int,
+    is_array_like,
+    is_bool_dtype,
+    is_complex_dtype,
+    is_dict_like,
+    is_extension_array_dtype,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_signed_integer_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    BaseMaskedDtype,
+    CategoricalDtype,
+    ExtensionDtype,
+    NumpyEADtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDatetimeArray,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+    ABCTimedeltaArray,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas.core.array_algos.take import take_nd
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import validate_indices
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ListLike,
+        NumpySorter,
+        NumpyValueArrayLike,
+    )
+
+    from pandas import (
+        Categorical,
+        Index,
+        Series,
+    )
+    from pandas.core.arrays import (
+        BaseMaskedArray,
+        ExtensionArray,
+    )
+
+
+# --------------- #
+# dtype access    #
+# --------------- #
+def _ensure_data(values: ArrayLike) -> np.ndarray:
+    """
+    routine to ensure that our data is of the correct
+    input dtype for lower-level routines
+
+    This will coerce:
+    - ints -> int64
+    - uint -> uint64
+    - bool -> uint8
+    - datetimelike -> i8
+    - datetime64tz -> i8 (in local tz)
+    - categorical -> codes
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+
+    Returns
+    -------
+    np.ndarray
+    """
+
+    if not isinstance(values, ABCMultiIndex):
+        # extract_array would raise
+        values = extract_array(values, extract_numpy=True)
+
+    if is_object_dtype(values.dtype):
+        return ensure_object(np.asarray(values))
+
+    elif isinstance(values.dtype, BaseMaskedDtype):
+        # i.e. BooleanArray, FloatingArray, IntegerArray
+        values = cast("BaseMaskedArray", values)
+        if not values._hasna:
+            # No pd.NAs -> We can avoid an object-dtype cast (and copy) GH#41816
+            #  recurse to avoid re-implementing logic for eg bool->uint8
+            return _ensure_data(values._data)
+        return np.asarray(values)
+
+    elif isinstance(values.dtype, CategoricalDtype):
+        # NB: cases that go through here should NOT be using _reconstruct_data
+        #  on the back-end.
+        values = cast("Categorical", values)
+        return values.codes
+
+    elif is_bool_dtype(values.dtype):
+        if isinstance(values, np.ndarray):
+            # i.e. actually dtype == np.dtype("bool")
+            return np.asarray(values).view("uint8")
+        else:
+            # e.g. Sparse[bool, False]  # TODO: no test cases get here
+            return np.asarray(values).astype("uint8", copy=False)
+
+    elif is_integer_dtype(values.dtype):
+        return np.asarray(values)
+
+    elif is_float_dtype(values.dtype):
+        # Note: checking `values.dtype == "float128"` raises on Windows and 32bit
+        # error: Item "ExtensionDtype" of "Union[Any, ExtensionDtype, dtype[Any]]"
+        # has no attribute "itemsize"
+        if values.dtype.itemsize in [2, 12, 16]:  # type: ignore[union-attr]
+            # we dont (yet) have float128 hashtable support
+            return ensure_float64(values)
+        return np.asarray(values)
+
+    elif is_complex_dtype(values.dtype):
+        return cast(np.ndarray, values)
+
+    # datetimelike
+    elif needs_i8_conversion(values.dtype):
+        npvalues = values.view("i8")
+        npvalues = cast(np.ndarray, npvalues)
+        return npvalues
+
+    # we have failed, return object
+    values = np.asarray(values, dtype=object)
+    return ensure_object(values)
+
+
+def _reconstruct_data(
+    values: ArrayLike, dtype: DtypeObj, original: AnyArrayLike
+) -> ArrayLike:
+    """
+    reverse of _ensure_data
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    dtype : np.dtype or ExtensionDtype
+    original : AnyArrayLike
+
+    Returns
+    -------
+    ExtensionArray or np.ndarray
+    """
+    if isinstance(values, ABCExtensionArray) and values.dtype == dtype:
+        # Catch DatetimeArray/TimedeltaArray
+        return values
+
+    if not isinstance(dtype, np.dtype):
+        # i.e. ExtensionDtype; note we have ruled out above the possibility
+        #  that values.dtype == dtype
+        cls = dtype.construct_array_type()
+
+        values = cls._from_sequence(values, dtype=dtype)
+
+    else:
+        values = values.astype(dtype, copy=False)
+
+    return values
+
+
+def _ensure_arraylike(values, func_name: str) -> ArrayLike:
+    """
+    ensure that we are arraylike if not already
+    """
+    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
+        # GH#52986
+        if func_name != "isin-targets":
+            # Make an exception for the comps argument in isin.
+            warnings.warn(
+                f"{func_name} with argument that is not not a Series, Index, "
+                "ExtensionArray, or np.ndarray is deprecated and will raise in a "
+                "future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        inferred = lib.infer_dtype(values, skipna=False)
+        if inferred in ["mixed", "string", "mixed-integer"]:
+            # "mixed-integer" to ensure we do not cast ["ss", 42] to str GH#22160
+            if isinstance(values, tuple):
+                values = list(values)
+            values = construct_1d_object_array_from_listlike(values)
+        else:
+            values = np.asarray(values)
+    return values
+
+
+_hashtables = {
+    "complex128": htable.Complex128HashTable,
+    "complex64": htable.Complex64HashTable,
+    "float64": htable.Float64HashTable,
+    "float32": htable.Float32HashTable,
+    "uint64": htable.UInt64HashTable,
+    "uint32": htable.UInt32HashTable,
+    "uint16": htable.UInt16HashTable,
+    "uint8": htable.UInt8HashTable,
+    "int64": htable.Int64HashTable,
+    "int32": htable.Int32HashTable,
+    "int16": htable.Int16HashTable,
+    "int8": htable.Int8HashTable,
+    "string": htable.StringHashTable,
+    "object": htable.PyObjectHashTable,
+}
+
+
+def _get_hashtable_algo(values: np.ndarray):
+    """
+    Parameters
+    ----------
+    values : np.ndarray
+
+    Returns
+    -------
+    htable : HashTable subclass
+    values : ndarray
+    """
+    values = _ensure_data(values)
+
+    ndtype = _check_object_for_strings(values)
+    hashtable = _hashtables[ndtype]
+    return hashtable, values
+
+
+def _check_object_for_strings(values: np.ndarray) -> str:
+    """
+    Check if we can use string hashtable instead of object hashtable.
+
+    Parameters
+    ----------
+    values : ndarray
+
+    Returns
+    -------
+    str
+    """
+    ndtype = values.dtype.name
+    if ndtype == "object":
+        # it's cheaper to use a String Hash Table than Object; we infer
+        # including nulls because that is the only difference between
+        # StringHashTable and ObjectHashtable
+        if lib.is_string_array(values, skipna=False):
+            ndtype = "string"
+    return ndtype
+
+
+# --------------- #
+# top-level algos #
+# --------------- #
+
+
+def unique(values):
+    """
+    Return unique values based on a hash table.
+
+    Uniques are returned in order of appearance. This does NOT sort.
+
+    Significantly faster than numpy.unique for long enough sequences.
+    Includes NA values.
+
+    Parameters
+    ----------
+    values : 1d array-like
+
+    Returns
+    -------
+    numpy.ndarray or ExtensionArray
+
+        The return can be:
+
+        * Index : when the input is an Index
+        * Categorical : when the input is a Categorical dtype
+        * ndarray : when the input is a Series/ndarray
+
+        Return numpy.ndarray or ExtensionArray.
+
+    See Also
+    --------
+    Index.unique : Return unique values from an Index.
+    Series.unique : Return unique values of Series object.
+
+    Examples
+    --------
+    >>> pd.unique(pd.Series([2, 1, 3, 3]))
+    array([2, 1, 3])
+
+    >>> pd.unique(pd.Series([2] + [1] * 5))
+    array([2, 1])
+
+    >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
+    array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
+
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
+    <DatetimeArray>
+    ['2016-01-01 00:00:00-05:00']
+    Length: 1, dtype: datetime64[ns, US/Eastern]
+
+    >>> pd.unique(
+    ...     pd.Index(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
+    DatetimeIndex(['2016-01-01 00:00:00-05:00'],
+            dtype='datetime64[ns, US/Eastern]',
+            freq=None)
+
+    >>> pd.unique(np.array(list("baabc"), dtype="O"))
+    array(['b', 'a', 'c'], dtype=object)
+
+    An unordered Categorical will return categories in the
+    order of appearance.
+
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
+    ['b', 'a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
+    ['b', 'a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    An ordered Categorical preserves the category ordering.
+
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
+    ...     )
+    ... )
+    ['b', 'a', 'c']
+    Categories (3, object): ['a' < 'b' < 'c']
+
+    An array of tuples
+
+    >>> pd.unique(pd.Series([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")]).values)
+    array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
+    """
+    return unique_with_mask(values)
+
+
+def nunique_ints(values: ArrayLike) -> int:
+    """
+    Return the number of unique values for integer array-likes.
+
+    Significantly faster than pandas.unique for long enough sequences.
+    No checks are done to ensure input is integral.
+
+    Parameters
+    ----------
+    values : 1d array-like
+
+    Returns
+    -------
+    int : The number of unique values in ``values``
+    """
+    if len(values) == 0:
+        return 0
+    values = _ensure_data(values)
+    # bincount requires intp
+    result = (np.bincount(values.ravel().astype("intp")) != 0).sum()
+    return result
+
+
+def unique_with_mask(values, mask: npt.NDArray[np.bool_] | None = None):
+    """See algorithms.unique for docs. Takes a mask for masked arrays."""
+    values = _ensure_arraylike(values, func_name="unique")
+
+    if isinstance(values.dtype, ExtensionDtype):
+        # Dispatch to extension dtype's unique.
+        return values.unique()
+
+    original = values
+    hashtable, values = _get_hashtable_algo(values)
+
+    table = hashtable(len(values))
+    if mask is None:
+        uniques = table.unique(values)
+        uniques = _reconstruct_data(uniques, original.dtype, original)
+        return uniques
+
+    else:
+        uniques, mask = table.unique(values, mask=mask)
+        uniques = _reconstruct_data(uniques, original.dtype, original)
+        assert mask is not None  # for mypy
+        return uniques, mask.astype("bool")
+
+
+unique1d = unique
+
+
+_MINIMUM_COMP_ARR_LEN = 1_000_000
+
+
+def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
+    """
+    Compute the isin boolean array.
+
+    Parameters
+    ----------
+    comps : list-like
+    values : list-like
+
+    Returns
+    -------
+    ndarray[bool]
+        Same length as `comps`.
+    """
+    if not is_list_like(comps):
+        raise TypeError(
+            "only list-like objects are allowed to be passed "
+            f"to isin(), you passed a `{type(comps).__name__}`"
+        )
+    if not is_list_like(values):
+        raise TypeError(
+            "only list-like objects are allowed to be passed "
+            f"to isin(), you passed a `{type(values).__name__}`"
+        )
+
+    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
+        orig_values = list(values)
+        values = _ensure_arraylike(orig_values, func_name="isin-targets")
+
+        if (
+            len(values) > 0
+            and values.dtype.kind in "iufcb"
+            and not is_signed_integer_dtype(comps)
+        ):
+            # GH#46485 Use object to avoid upcast to float64 later
+            # TODO: Share with _find_common_type_compat
+            values = construct_1d_object_array_from_listlike(orig_values)
+
+    elif isinstance(values, ABCMultiIndex):
+        # Avoid raising in extract_array
+        values = np.array(values)
+    else:
+        values = extract_array(values, extract_numpy=True, extract_range=True)
+
+    comps_array = _ensure_arraylike(comps, func_name="isin")
+    comps_array = extract_array(comps_array, extract_numpy=True)
+    if not isinstance(comps_array, np.ndarray):
+        # i.e. Extension Array
+        return comps_array.isin(values)
+
+    elif needs_i8_conversion(comps_array.dtype):
+        # Dispatch to DatetimeLikeArrayMixin.isin
+        return pd_array(comps_array).isin(values)
+    elif needs_i8_conversion(values.dtype) and not is_object_dtype(comps_array.dtype):
+        # e.g. comps_array are integers and values are datetime64s
+        return np.zeros(comps_array.shape, dtype=bool)
+        # TODO: not quite right ... Sparse/Categorical
+    elif needs_i8_conversion(values.dtype):
+        return isin(comps_array, values.astype(object))
+
+    elif isinstance(values.dtype, ExtensionDtype):
+        return isin(np.asarray(comps_array), np.asarray(values))
+
+    # GH16012
+    # Ensure np.isin doesn't get object types or it *may* throw an exception
+    # Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
+    # isin is faster for small sizes
+    if (
+        len(comps_array) > _MINIMUM_COMP_ARR_LEN
+        and len(values) <= 26
+        and comps_array.dtype != object
+    ):
+        # If the values include nan we need to check for nan explicitly
+        # since np.nan it not equal to np.nan
+        if isna(values).any():
+
+            def f(c, v):
+                return np.logical_or(np.isin(c, v).ravel(), np.isnan(c))
+
+        else:
+            f = lambda a, b: np.isin(a, b).ravel()
+
+    else:
+        common = np_find_common_type(values.dtype, comps_array.dtype)
+        values = values.astype(common, copy=False)
+        comps_array = comps_array.astype(common, copy=False)
+        f = htable.ismember
+
+    return f(comps_array, values)
+
+
+def factorize_array(
+    values: np.ndarray,
+    use_na_sentinel: bool = True,
+    size_hint: int | None = None,
+    na_value: object = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[npt.NDArray[np.intp], np.ndarray]:
+    """
+    Factorize a numpy array to codes and uniques.
+
+    This doesn't do any coercion of types or unboxing before factorization.
+
+    Parameters
+    ----------
+    values : ndarray
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NaN values. If False,
+        NaN values will be encoded as non-negative integers and will not drop the
+        NaN from the uniques of the values.
+    size_hint : int, optional
+        Passed through to the hashtable's 'get_labels' method
+    na_value : object, optional
+        A value in `values` to consider missing. Note: only use this
+        parameter when you know that you don't have any values pandas would
+        consider missing in the array (NaN for float data, iNaT for
+        datetimes, etc.).
+    mask : ndarray[bool], optional
+        If not None, the mask is used as indicator for missing values
+        (True = missing, False = valid) instead of `na_value` or
+        condition "val != val".
+
+    Returns
+    -------
+    codes : ndarray[np.intp]
+    uniques : ndarray
+    """
+    original = values
+    if values.dtype.kind in "mM":
+        # _get_hashtable_algo will cast dt64/td64 to i8 via _ensure_data, so we
+        #  need to do the same to na_value. We are assuming here that the passed
+        #  na_value is an appropriately-typed NaT.
+        # e.g. test_where_datetimelike_categorical
+        na_value = iNaT
+
+    hash_klass, values = _get_hashtable_algo(values)
+
+    table = hash_klass(size_hint or len(values))
+    uniques, codes = table.factorize(
+        values,
+        na_sentinel=-1,
+        na_value=na_value,
+        mask=mask,
+        ignore_na=use_na_sentinel,
+    )
+
+    # re-cast e.g. i8->dt64/td64, uint8->bool
+    uniques = _reconstruct_data(uniques, original.dtype, original)
+
+    codes = ensure_platform_int(codes)
+    return codes, uniques
+
+
+@doc(
+    values=dedent(
+        """\
+    values : sequence
+        A 1-D sequence. Sequences that aren't pandas objects are
+        coerced to ndarrays before factorization.
+    """
+    ),
+    sort=dedent(
+        """\
+    sort : bool, default False
+        Sort `uniques` and shuffle `codes` to maintain the
+        relationship.
+    """
+    ),
+    size_hint=dedent(
+        """\
+    size_hint : int, optional
+        Hint to the hashtable sizer.
+    """
+    ),
+)
+def factorize(
+    values,
+    sort: bool = False,
+    use_na_sentinel: bool = True,
+    size_hint: int | None = None,
+) -> tuple[np.ndarray, np.ndarray | Index]:
+    """
+    Encode the object as an enumerated type or categorical variable.
+
+    This method is useful for obtaining a numeric representation of an
+    array when all that matters is identifying distinct values. `factorize`
+    is available as both a top-level function :func:`pandas.factorize`,
+    and as a method :meth:`Series.factorize` and :meth:`Index.factorize`.
+
+    Parameters
+    ----------
+    {values}{sort}
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NaN values. If False,
+        NaN values will be encoded as non-negative integers and will not drop the
+        NaN from the uniques of the values.
+
+        .. versionadded:: 1.5.0
+    {size_hint}\
+
+    Returns
+    -------
+    codes : ndarray
+        An integer ndarray that's an indexer into `uniques`.
+        ``uniques.take(codes)`` will have the same values as `values`.
+    uniques : ndarray, Index, or Categorical
+        The unique valid values. When `values` is Categorical, `uniques`
+        is a Categorical. When `values` is some other pandas object, an
+        `Index` is returned. Otherwise, a 1-D ndarray is returned.
+
+        .. note::
+
+           Even if there's a missing value in `values`, `uniques` will
+           *not* contain an entry for it.
+
+    See Also
+    --------
+    cut : Discretize continuous-valued array.
+    unique : Find the unique value in an array.
+
+    Notes
+    -----
+    Reference :ref:`the user guide <reshaping.factorize>` for more examples.
+
+    Examples
+    --------
+    These examples all show factorize as a top-level method like
+    ``pd.factorize(values)``. The results are identical for methods like
+    :meth:`Series.factorize`.
+
+    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"))
+    >>> codes
+    array([0, 0, 1, 2, 0])
+    >>> uniques
+    array(['b', 'a', 'c'], dtype=object)
+
+    With ``sort=True``, the `uniques` will be sorted, and `codes` will be
+    shuffled so that the relationship is the maintained.
+
+    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"),
+    ...                               sort=True)
+    >>> codes
+    array([1, 1, 0, 2, 1])
+    >>> uniques
+    array(['a', 'b', 'c'], dtype=object)
+
+    When ``use_na_sentinel=True`` (the default), missing values are indicated in
+    the `codes` with the sentinel value ``-1`` and missing values are not
+    included in `uniques`.
+
+    >>> codes, uniques = pd.factorize(np.array(['b', None, 'a', 'c', 'b'], dtype="O"))
+    >>> codes
+    array([ 0, -1,  1,  2,  0])
+    >>> uniques
+    array(['b', 'a', 'c'], dtype=object)
+
+    Thus far, we've only factorized lists (which are internally coerced to
+    NumPy arrays). When factorizing pandas objects, the type of `uniques`
+    will differ. For Categoricals, a `Categorical` is returned.
+
+    >>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
+    >>> codes, uniques = pd.factorize(cat)
+    >>> codes
+    array([0, 0, 1])
+    >>> uniques
+    ['a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    Notice that ``'b'`` is in ``uniques.categories``, despite not being
+    present in ``cat.values``.
+
+    For all other pandas objects, an Index of the appropriate type is
+    returned.
+
+    >>> cat = pd.Series(['a', 'a', 'c'])
+    >>> codes, uniques = pd.factorize(cat)
+    >>> codes
+    array([0, 0, 1])
+    >>> uniques
+    Index(['a', 'c'], dtype='object')
+
+    If NaN is in the values, and we want to include NaN in the uniques of the
+    values, it can be achieved by setting ``use_na_sentinel=False``.
+
+    >>> values = np.array([1, 2, 1, np.nan])
+    >>> codes, uniques = pd.factorize(values)  # default: use_na_sentinel=True
+    >>> codes
+    array([ 0,  1,  0, -1])
+    >>> uniques
+    array([1., 2.])
+
+    >>> codes, uniques = pd.factorize(values, use_na_sentinel=False)
+    >>> codes
+    array([0, 1, 0, 2])
+    >>> uniques
+    array([ 1.,  2., nan])
+    """
+    # Implementation notes: This method is responsible for 3 things
+    # 1.) coercing data to array-like (ndarray, Index, extension array)
+    # 2.) factorizing codes and uniques
+    # 3.) Maybe boxing the uniques in an Index
+    #
+    # Step 2 is dispatched to extension types (like Categorical). They are
+    # responsible only for factorization. All data coercion, sorting and boxing
+    # should happen here.
+    if isinstance(values, (ABCIndex, ABCSeries)):
+        return values.factorize(sort=sort, use_na_sentinel=use_na_sentinel)
+
+    values = _ensure_arraylike(values, func_name="factorize")
+    original = values
+
+    if (
+        isinstance(values, (ABCDatetimeArray, ABCTimedeltaArray))
+        and values.freq is not None
+    ):
+        # The presence of 'freq' means we can fast-path sorting and know there
+        #  aren't NAs
+        codes, uniques = values.factorize(sort=sort)
+        return codes, uniques
+
+    elif not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        codes, uniques = values.factorize(use_na_sentinel=use_na_sentinel)
+
+    else:
+        values = np.asarray(values)  # convert DTA/TDA/MultiIndex
+
+        if not use_na_sentinel and values.dtype == object:
+            # factorize can now handle differentiating various types of null values.
+            # These can only occur when the array has object dtype.
+            # However, for backwards compatibility we only use the null for the
+            # provided dtype. This may be revisited in the future, see GH#48476.
+            null_mask = isna(values)
+            if null_mask.any():
+                na_value = na_value_for_dtype(values.dtype, compat=False)
+                # Don't modify (potentially user-provided) array
+                values = np.where(null_mask, na_value, values)
+
+        codes, uniques = factorize_array(
+            values,
+            use_na_sentinel=use_na_sentinel,
+            size_hint=size_hint,
+        )
+
+    if sort and len(uniques) > 0:
+        uniques, codes = safe_sort(
+            uniques,
+            codes,
+            use_na_sentinel=use_na_sentinel,
+            assume_unique=True,
+            verify=False,
+        )
+
+    uniques = _reconstruct_data(uniques, original.dtype, original)
+
+    return codes, uniques
+
+
+def value_counts(
+    values,
+    sort: bool = True,
+    ascending: bool = False,
+    normalize: bool = False,
+    bins=None,
+    dropna: bool = True,
+) -> Series:
+    """
+    Compute a histogram of the counts of non-null values.
+
+    Parameters
+    ----------
+    values : ndarray (1-d)
+    sort : bool, default True
+        Sort by values
+    ascending : bool, default False
+        Sort in ascending order
+    normalize: bool, default False
+        If True then compute a relative histogram
+    bins : integer, optional
+        Rather than count values, group them into half-open bins,
+        convenience for pd.cut, only works with numeric data
+    dropna : bool, default True
+        Don't include counts of NaN
+
+    Returns
+    -------
+    Series
+    """
+    warnings.warn(
+        # GH#53493
+        "pandas.value_counts is deprecated and will be removed in a "
+        "future version. Use pd.Series(obj).value_counts() instead.",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    return value_counts_internal(
+        values,
+        sort=sort,
+        ascending=ascending,
+        normalize=normalize,
+        bins=bins,
+        dropna=dropna,
+    )
+
+
+def value_counts_internal(
+    values,
+    sort: bool = True,
+    ascending: bool = False,
+    normalize: bool = False,
+    bins=None,
+    dropna: bool = True,
+) -> Series:
+    from pandas import (
+        Index,
+        Series,
+    )
+
+    index_name = getattr(values, "name", None)
+    name = "proportion" if normalize else "count"
+
+    if bins is not None:
+        from pandas.core.reshape.tile import cut
+
+        if isinstance(values, Series):
+            values = values._values
+
+        try:
+            ii = cut(values, bins, include_lowest=True)
+        except TypeError as err:
+            raise TypeError("bins argument only works with numeric data.") from err
+
+        # count, remove nulls (from the index), and but the bins
+        result = ii.value_counts(dropna=dropna)
+        result.name = name
+        result = result[result.index.notna()]
+        result.index = result.index.astype("interval")
+        result = result.sort_index()
+
+        # if we are dropna and we have NO values
+        if dropna and (result._values == 0).all():
+            result = result.iloc[0:0]
+
+        # normalizing is by len of all (regardless of dropna)
+        counts = np.array([len(ii)])
+
+    else:
+        if is_extension_array_dtype(values):
+            # handle Categorical and sparse,
+            result = Series(values, copy=False)._values.value_counts(dropna=dropna)
+            result.name = name
+            result.index.name = index_name
+            counts = result._values
+            if not isinstance(counts, np.ndarray):
+                # e.g. ArrowExtensionArray
+                counts = np.asarray(counts)
+
+        elif isinstance(values, ABCMultiIndex):
+            # GH49558
+            levels = list(range(values.nlevels))
+            result = (
+                Series(index=values, name=name)
+                .groupby(level=levels, dropna=dropna)
+                .size()
+            )
+            result.index.names = values.names
+            counts = result._values
+
+        else:
+            values = _ensure_arraylike(values, func_name="value_counts")
+            keys, counts, _ = value_counts_arraylike(values, dropna)
+            if keys.dtype == np.float16:
+                keys = keys.astype(np.float32)
+
+            # For backwards compatibility, we let Index do its normal type
+            #  inference, _except_ for if if infers from object to bool.
+            idx = Index(keys)
+            if idx.dtype in [bool, "string"] and keys.dtype == object:
+                idx = idx.astype(object)
+            elif (
+                idx.dtype != keys.dtype  # noqa: PLR1714  # # pylint: disable=R1714
+                and idx.dtype != "string"
+            ):
+                warnings.warn(
+                    # GH#56161
+                    "The behavior of value_counts with object-dtype is deprecated. "
+                    "In a future version, this will *not* perform dtype inference "
+                    "on the resulting index. To retain the old behavior, use "
+                    "`result.index = result.index.infer_objects()`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            idx.name = index_name
+
+            result = Series(counts, index=idx, name=name, copy=False)
+
+    if sort:
+        result = result.sort_values(ascending=ascending)
+
+    if normalize:
+        result = result / counts.sum()
+
+    return result
+
+
+# Called once from SparseArray, otherwise could be private
+def value_counts_arraylike(
+    values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = None
+) -> tuple[ArrayLike, npt.NDArray[np.int64], int]:
+    """
+    Parameters
+    ----------
+    values : np.ndarray
+    dropna : bool
+    mask : np.ndarray[bool] or None, default None
+
+    Returns
+    -------
+    uniques : np.ndarray
+    counts : np.ndarray[np.int64]
+    """
+    original = values
+    values = _ensure_data(values)
+
+    keys, counts, na_counter = htable.value_count(values, dropna, mask=mask)
+
+    if needs_i8_conversion(original.dtype):
+        # datetime, timedelta, or period
+
+        if dropna:
+            mask = keys != iNaT
+            keys, counts = keys[mask], counts[mask]
+
+    res_keys = _reconstruct_data(keys, original.dtype, original)
+    return res_keys, counts, na_counter
+
+
+def duplicated(
+    values: ArrayLike,
+    keep: Literal["first", "last", False] = "first",
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> npt.NDArray[np.bool_]:
+    """
+    Return boolean ndarray denoting duplicate values.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+        Array over which to check for duplicate values.
+    keep : {'first', 'last', False}, default 'first'
+        - ``first`` : Mark duplicates as ``True`` except for the first
+          occurrence.
+        - ``last`` : Mark duplicates as ``True`` except for the last
+          occurrence.
+        - False : Mark all duplicates as ``True``.
+    mask : ndarray[bool], optional
+        array indicating which elements to exclude from checking
+
+    Returns
+    -------
+    duplicated : ndarray[bool]
+    """
+    values = _ensure_data(values)
+    return htable.duplicated(values, keep=keep, mask=mask)
+
+
+def mode(
+    values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None = None
+) -> ArrayLike:
+    """
+    Returns the mode(s) of an array.
+
+    Parameters
+    ----------
+    values : array-like
+        Array over which to check for duplicate values.
+    dropna : bool, default True
+        Don't consider counts of NaN/NaT.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    values = _ensure_arraylike(values, func_name="mode")
+    original = values
+
+    if needs_i8_conversion(values.dtype):
+        # Got here with ndarray; dispatch to DatetimeArray/TimedeltaArray.
+        values = ensure_wrapped_if_datetimelike(values)
+        values = cast("ExtensionArray", values)
+        return values._mode(dropna=dropna)
+
+    values = _ensure_data(values)
+
+    npresult, res_mask = htable.mode(values, dropna=dropna, mask=mask)
+    if res_mask is not None:
+        return npresult, res_mask  # type: ignore[return-value]
+
+    try:
+        npresult = safe_sort(npresult)
+    except TypeError as err:
+        warnings.warn(
+            f"Unable to sort modes: {err}",
+            stacklevel=find_stack_level(),
+        )
+
+    result = _reconstruct_data(npresult, original.dtype, original)
+    return result
+
+
+def rank(
+    values: ArrayLike,
+    axis: AxisInt = 0,
+    method: str = "average",
+    na_option: str = "keep",
+    ascending: bool = True,
+    pct: bool = False,
+) -> npt.NDArray[np.float64]:
+    """
+    Rank the values along a given axis.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+        Array whose values will be ranked. The number of dimensions in this
+        array must not exceed 2.
+    axis : int, default 0
+        Axis over which to perform rankings.
+    method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+        The method by which tiebreaks are broken during the ranking.
+    na_option : {'keep', 'top'}, default 'keep'
+        The method by which NaNs are placed in the ranking.
+        - ``keep``: rank each NaN value with a NaN ranking
+        - ``top``: replace each NaN with either +/- inf so that they
+                   there are ranked at the top
+    ascending : bool, default True
+        Whether or not the elements should be ranked in ascending order.
+    pct : bool, default False
+        Whether or not to the display the returned rankings in integer form
+        (e.g. 1, 2, 3) or in percentile form (e.g. 0.333..., 0.666..., 1).
+    """
+    is_datetimelike = needs_i8_conversion(values.dtype)
+    values = _ensure_data(values)
+
+    if values.ndim == 1:
+        ranks = algos.rank_1d(
+            values,
+            is_datetimelike=is_datetimelike,
+            ties_method=method,
+            ascending=ascending,
+            na_option=na_option,
+            pct=pct,
+        )
+    elif values.ndim == 2:
+        ranks = algos.rank_2d(
+            values,
+            axis=axis,
+            is_datetimelike=is_datetimelike,
+            ties_method=method,
+            ascending=ascending,
+            na_option=na_option,
+            pct=pct,
+        )
+    else:
+        raise TypeError("Array with ndim > 2 are not supported.")
+
+    return ranks
+
+
+# ---- #
+# take #
+# ---- #
+
+
+def take(
+    arr,
+    indices: TakeIndexer,
+    axis: AxisInt = 0,
+    allow_fill: bool = False,
+    fill_value=None,
+):
+    """
+    Take elements from an array.
+
+    Parameters
+    ----------
+    arr : array-like or scalar value
+        Non array-likes (sequences/scalars without a dtype) are coerced
+        to an ndarray.
+
+        .. deprecated:: 2.1.0
+            Passing an argument other than a numpy.ndarray, ExtensionArray,
+            Index, or Series is deprecated.
+
+    indices : sequence of int or one-dimensional np.ndarray of int
+        Indices to be taken.
+    axis : int, default 0
+        The axis over which to select values.
+    allow_fill : bool, default False
+        How to handle negative values in `indices`.
+
+        * False: negative values in `indices` indicate positional indices
+          from the right (the default). This is similar to :func:`numpy.take`.
+
+        * True: negative values in `indices` indicate
+          missing values. These values are set to `fill_value`. Any other
+          negative values raise a ``ValueError``.
+
+    fill_value : any, optional
+        Fill value to use for NA-indices when `allow_fill` is True.
+        This may be ``None``, in which case the default NA value for
+        the type (``self.dtype.na_value``) is used.
+
+        For multi-dimensional `arr`, each *element* is filled with
+        `fill_value`.
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+        Same type as the input.
+
+    Raises
+    ------
+    IndexError
+        When `indices` is out of bounds for the array.
+    ValueError
+        When the indexer contains negative values other than ``-1``
+        and `allow_fill` is True.
+
+    Notes
+    -----
+    When `allow_fill` is False, `indices` may be whatever dimensionality
+    is accepted by NumPy for `arr`.
+
+    When `allow_fill` is True, `indices` should be 1-D.
+
+    See Also
+    --------
+    numpy.take : Take elements from an array along an axis.
+
+    Examples
+    --------
+    >>> import pandas as pd
+
+    With the default ``allow_fill=False``, negative numbers indicate
+    positional indices from the right.
+
+    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1])
+    array([10, 10, 30])
+
+    Setting ``allow_fill=True`` will place `fill_value` in those positions.
+
+    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True)
+    array([10., 10., nan])
+
+    >>> pd.api.extensions.take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True,
+    ...      fill_value=-10)
+    array([ 10,  10, -10])
+    """
+    if not isinstance(arr, (np.ndarray, ABCExtensionArray, ABCIndex, ABCSeries)):
+        # GH#52981
+        warnings.warn(
+            "pd.api.extensions.take accepting non-standard inputs is deprecated "
+            "and will raise in a future version. Pass either a numpy.ndarray, "
+            "ExtensionArray, Index, or Series instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if not is_array_like(arr):
+        arr = np.asarray(arr)
+
+    indices = ensure_platform_int(indices)
+
+    if allow_fill:
+        # Pandas style, -1 means NA
+        validate_indices(indices, arr.shape[axis])
+        result = take_nd(
+            arr, indices, axis=axis, allow_fill=True, fill_value=fill_value
+        )
+    else:
+        # NumPy style
+        result = arr.take(indices, axis=axis)
+    return result
+
+
+# ------------ #
+# searchsorted #
+# ------------ #
+
+
+def searchsorted(
+    arr: ArrayLike,
+    value: NumpyValueArrayLike | ExtensionArray,
+    side: Literal["left", "right"] = "left",
+    sorter: NumpySorter | None = None,
+) -> npt.NDArray[np.intp] | np.intp:
+    """
+    Find indices where elements should be inserted to maintain order.
+
+    Find the indices into a sorted array `arr` (a) such that, if the
+    corresponding elements in `value` were inserted before the indices,
+    the order of `arr` would be preserved.
+
+    Assuming that `arr` is sorted:
+
+    ======  ================================
+    `side`  returned index `i` satisfies
+    ======  ================================
+    left    ``arr[i-1] < value <= self[i]``
+    right   ``arr[i-1] <= value < self[i]``
+    ======  ================================
+
+    Parameters
+    ----------
+    arr: np.ndarray, ExtensionArray, Series
+        Input array. If `sorter` is None, then it must be sorted in
+        ascending order, otherwise `sorter` must be an array of indices
+        that sort it.
+    value : array-like or scalar
+        Values to insert into `arr`.
+    side : {'left', 'right'}, optional
+        If 'left', the index of the first suitable location found is given.
+        If 'right', return the last such index.  If there is no suitable
+        index, return either 0 or N (where N is the length of `self`).
+    sorter : 1-D array-like, optional
+        Optional array of integer indices that sort array a into ascending
+        order. They are typically the result of argsort.
+
+    Returns
+    -------
+    array of ints or int
+        If value is array-like, array of insertion points.
+        If value is scalar, a single integer.
+
+    See Also
+    --------
+    numpy.searchsorted : Similar method from NumPy.
+    """
+    if sorter is not None:
+        sorter = ensure_platform_int(sorter)
+
+    if (
+        isinstance(arr, np.ndarray)
+        and arr.dtype.kind in "iu"
+        and (is_integer(value) or is_integer_dtype(value))
+    ):
+        # if `arr` and `value` have different dtypes, `arr` would be
+        # recast by numpy, causing a slow search.
+        # Before searching below, we therefore try to give `value` the
+        # same dtype as `arr`, while guarding against integer overflows.
+        iinfo = np.iinfo(arr.dtype.type)
+        value_arr = np.array([value]) if is_integer(value) else np.array(value)
+        if (value_arr >= iinfo.min).all() and (value_arr <= iinfo.max).all():
+            # value within bounds, so no overflow, so can convert value dtype
+            # to dtype of arr
+            dtype = arr.dtype
+        else:
+            dtype = value_arr.dtype
+
+        if is_integer(value):
+            # We know that value is int
+            value = cast(int, dtype.type(value))
+        else:
+            value = pd_array(cast(ArrayLike, value), dtype=dtype)
+    else:
+        # E.g. if `arr` is an array with dtype='datetime64[ns]'
+        # and `value` is a pd.Timestamp, we may need to convert value
+        arr = ensure_wrapped_if_datetimelike(arr)
+
+    # Argument 1 to "searchsorted" of "ndarray" has incompatible type
+    # "Union[NumpyValueArrayLike, ExtensionArray]"; expected "NumpyValueArrayLike"
+    return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]
+
+
+# ---- #
+# diff #
+# ---- #
+
+_diff_special = {"float64", "float32", "int64", "int32", "int16", "int8"}
+
+
+def diff(arr, n: int, axis: AxisInt = 0):
+    """
+    difference of n between self,
+    analogous to s-s.shift(n)
+
+    Parameters
+    ----------
+    arr : ndarray or ExtensionArray
+    n : int
+        number of periods
+    axis : {0, 1}
+        axis to shift on
+    stacklevel : int, default 3
+        The stacklevel for the lost dtype warning.
+
+    Returns
+    -------
+    shifted
+    """
+
+    n = int(n)
+    na = np.nan
+    dtype = arr.dtype
+
+    is_bool = is_bool_dtype(dtype)
+    if is_bool:
+        op = operator.xor
+    else:
+        op = operator.sub
+
+    if isinstance(dtype, NumpyEADtype):
+        # NumpyExtensionArray cannot necessarily hold shifted versions of itself.
+        arr = arr.to_numpy()
+        dtype = arr.dtype
+
+    if not isinstance(arr, np.ndarray):
+        # i.e ExtensionArray
+        if hasattr(arr, f"__{op.__name__}__"):
+            if axis != 0:
+                raise ValueError(f"cannot diff {type(arr).__name__} on axis={axis}")
+            return op(arr, arr.shift(n))
+        else:
+            raise TypeError(
+                f"{type(arr).__name__} has no 'diff' method. "
+                "Convert to a suitable dtype prior to calling 'diff'."
+            )
+
+    is_timedelta = False
+    if arr.dtype.kind in "mM":
+        dtype = np.int64
+        arr = arr.view("i8")
+        na = iNaT
+        is_timedelta = True
+
+    elif is_bool:
+        # We have to cast in order to be able to hold np.nan
+        dtype = np.object_
+
+    elif dtype.kind in "iu":
+        # We have to cast in order to be able to hold np.nan
+
+        # int8, int16 are incompatible with float64,
+        # see https://github.com/cython/cython/issues/2646
+        if arr.dtype.name in ["int8", "int16"]:
+            dtype = np.float32
+        else:
+            dtype = np.float64
+
+    orig_ndim = arr.ndim
+    if orig_ndim == 1:
+        # reshape so we can always use algos.diff_2d
+        arr = arr.reshape(-1, 1)
+        # TODO: require axis == 0
+
+    dtype = np.dtype(dtype)
+    out_arr = np.empty(arr.shape, dtype=dtype)
+
+    na_indexer = [slice(None)] * 2
+    na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
+    out_arr[tuple(na_indexer)] = na
+
+    if arr.dtype.name in _diff_special:
+        # TODO: can diff_2d dtype specialization troubles be fixed by defining
+        #  out_arr inside diff_2d?
+        algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
+    else:
+        # To keep mypy happy, _res_indexer is a list while res_indexer is
+        #  a tuple, ditto for lag_indexer.
+        _res_indexer = [slice(None)] * 2
+        _res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
+        res_indexer = tuple(_res_indexer)
+
+        _lag_indexer = [slice(None)] * 2
+        _lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
+        lag_indexer = tuple(_lag_indexer)
+
+        out_arr[res_indexer] = op(arr[res_indexer], arr[lag_indexer])
+
+    if is_timedelta:
+        out_arr = out_arr.view("timedelta64[ns]")
+
+    if orig_ndim == 1:
+        out_arr = out_arr[:, 0]
+    return out_arr
+
+
+# --------------------------------------------------------------------
+# Helper functions
+
+
+# Note: safe_sort is in algorithms.py instead of sorting.py because it is
+#  low-dependency, is used in this module, and used private methods from
+#  this module.
+def safe_sort(
+    values: Index | ArrayLike,
+    codes: npt.NDArray[np.intp] | None = None,
+    use_na_sentinel: bool = True,
+    assume_unique: bool = False,
+    verify: bool = True,
+) -> AnyArrayLike | tuple[AnyArrayLike, np.ndarray]:
+    """
+    Sort ``values`` and reorder corresponding ``codes``.
+
+    ``values`` should be unique if ``codes`` is not None.
+    Safe for use with mixed types (int, str), orders ints before strs.
+
+    Parameters
+    ----------
+    values : list-like
+        Sequence; must be unique if ``codes`` is not None.
+    codes : np.ndarray[intp] or None, default None
+        Indices to ``values``. All out of bound indices are treated as
+        "not found" and will be masked with ``-1``.
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NaN values. If False,
+        NaN values will be encoded as non-negative integers and will not drop the
+        NaN from the uniques of the values.
+    assume_unique : bool, default False
+        When True, ``values`` are assumed to be unique, which can speed up
+        the calculation. Ignored when ``codes`` is None.
+    verify : bool, default True
+        Check if codes are out of bound for the values and put out of bound
+        codes equal to ``-1``. If ``verify=False``, it is assumed there
+        are no out of bound codes. Ignored when ``codes`` is None.
+
+    Returns
+    -------
+    ordered : AnyArrayLike
+        Sorted ``values``
+    new_codes : ndarray
+        Reordered ``codes``; returned when ``codes`` is not None.
+
+    Raises
+    ------
+    TypeError
+        * If ``values`` is not list-like or if ``codes`` is neither None
+        nor list-like
+        * If ``values`` cannot be sorted
+    ValueError
+        * If ``codes`` is not None and ``values`` contain duplicates.
+    """
+    if not isinstance(values, (np.ndarray, ABCExtensionArray, ABCIndex)):
+        raise TypeError(
+            "Only np.ndarray, ExtensionArray, and Index objects are allowed to "
+            "be passed to safe_sort as values"
+        )
+
+    sorter = None
+    ordered: AnyArrayLike
+
+    if (
+        not isinstance(values.dtype, ExtensionDtype)
+        and lib.infer_dtype(values, skipna=False) == "mixed-integer"
+    ):
+        ordered = _sort_mixed(values)
+    else:
+        try:
+            sorter = values.argsort()
+            ordered = values.take(sorter)
+        except (TypeError, decimal.InvalidOperation):
+            # Previous sorters failed or were not applicable, try `_sort_mixed`
+            # which would work, but which fails for special case of 1d arrays
+            # with tuples.
+            if values.size and isinstance(values[0], tuple):
+                # error: Argument 1 to "_sort_tuples" has incompatible type
+                # "Union[Index, ExtensionArray, ndarray[Any, Any]]"; expected
+                # "ndarray[Any, Any]"
+                ordered = _sort_tuples(values)  # type: ignore[arg-type]
+            else:
+                ordered = _sort_mixed(values)
+
+    # codes:
+
+    if codes is None:
+        return ordered
+
+    if not is_list_like(codes):
+        raise TypeError(
+            "Only list-like objects or None are allowed to "
+            "be passed to safe_sort as codes"
+        )
+    codes = ensure_platform_int(np.asarray(codes))
+
+    if not assume_unique and not len(unique(values)) == len(values):
+        raise ValueError("values should be unique if codes is not None")
+
+    if sorter is None:
+        # mixed types
+        # error: Argument 1 to "_get_hashtable_algo" has incompatible type
+        # "Union[Index, ExtensionArray, ndarray[Any, Any]]"; expected
+        # "ndarray[Any, Any]"
+        hash_klass, values = _get_hashtable_algo(values)  # type: ignore[arg-type]
+        t = hash_klass(len(values))
+        t.map_locations(values)
+        sorter = ensure_platform_int(t.lookup(ordered))
+
+    if use_na_sentinel:
+        # take_nd is faster, but only works for na_sentinels of -1
+        order2 = sorter.argsort()
+        if verify:
+            mask = (codes < -len(values)) | (codes >= len(values))
+            codes[mask] = 0
+        else:
+            mask = None
+        new_codes = take_nd(order2, codes, fill_value=-1)
+    else:
+        reverse_indexer = np.empty(len(sorter), dtype=int)
+        reverse_indexer.put(sorter, np.arange(len(sorter)))
+        # Out of bound indices will be masked with `-1` next, so we
+        # may deal with them here without performance loss using `mode='wrap'`
+        new_codes = reverse_indexer.take(codes, mode="wrap")
+
+        if use_na_sentinel:
+            mask = codes == -1
+            if verify:
+                mask = mask | (codes < -len(values)) | (codes >= len(values))
+
+    if use_na_sentinel and mask is not None:
+        np.putmask(new_codes, mask, -1)
+
+    return ordered, ensure_platform_int(new_codes)
+
+
+def _sort_mixed(values) -> AnyArrayLike:
+    """order ints before strings before nulls in 1d arrays"""
+    str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
+    null_pos = np.array([isna(x) for x in values], dtype=bool)
+    num_pos = ~str_pos & ~null_pos
+    str_argsort = np.argsort(values[str_pos])
+    num_argsort = np.argsort(values[num_pos])
+    # convert boolean arrays to positional indices, then order by underlying values
+    str_locs = str_pos.nonzero()[0].take(str_argsort)
+    num_locs = num_pos.nonzero()[0].take(num_argsort)
+    null_locs = null_pos.nonzero()[0]
+    locs = np.concatenate([num_locs, str_locs, null_locs])
+    return values.take(locs)
+
+
+def _sort_tuples(values: np.ndarray) -> np.ndarray:
+    """
+    Convert array of tuples (1d) to array of arrays (2d).
+    We need to keep the columns separately as they contain different types and
+    nans (can't use `np.sort` as it may fail when str and nan are mixed in a
+    column as types cannot be compared).
+    """
+    from pandas.core.internals.construction import to_arrays
+    from pandas.core.sorting import lexsort_indexer
+
+    arrays, _ = to_arrays(values, None)
+    indexer = lexsort_indexer(arrays, orders=True)
+    return values[indexer]
+
+
+def union_with_duplicates(
+    lvals: ArrayLike | Index, rvals: ArrayLike | Index
+) -> ArrayLike | Index:
+    """
+    Extracts the union from lvals and rvals with respect to duplicates and nans in
+    both arrays.
+
+    Parameters
+    ----------
+    lvals: np.ndarray or ExtensionArray
+        left values which is ordered in front.
+    rvals: np.ndarray or ExtensionArray
+        right values ordered after lvals.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+        Containing the unsorted union of both arrays.
+
+    Notes
+    -----
+    Caller is responsible for ensuring lvals.dtype == rvals.dtype.
+    """
+    from pandas import Series
+
+    with warnings.catch_warnings():
+        # filter warning from object dtype inference; we will end up discarding
+        # the index here, so the deprecation does not affect the end result here.
+        warnings.filterwarnings(
+            "ignore",
+            "The behavior of value_counts with object-dtype is deprecated",
+            category=FutureWarning,
+        )
+        l_count = value_counts_internal(lvals, dropna=False)
+        r_count = value_counts_internal(rvals, dropna=False)
+    l_count, r_count = l_count.align(r_count, fill_value=0)
+    final_count = np.maximum(l_count.values, r_count.values)
+    final_count = Series(final_count, index=l_count.index, dtype="int", copy=False)
+    if isinstance(lvals, ABCMultiIndex) and isinstance(rvals, ABCMultiIndex):
+        unique_vals = lvals.append(rvals).unique()
+    else:
+        if isinstance(lvals, ABCIndex):
+            lvals = lvals._values
+        if isinstance(rvals, ABCIndex):
+            rvals = rvals._values
+        # error: List item 0 has incompatible type "Union[ExtensionArray,
+        # ndarray[Any, Any], Index]"; expected "Union[ExtensionArray,
+        # ndarray[Any, Any]]"
+        combined = concat_compat([lvals, rvals])  # type: ignore[list-item]
+        unique_vals = unique(combined)
+        unique_vals = ensure_wrapped_if_datetimelike(unique_vals)
+    repeats = final_count.reindex(unique_vals).values
+    return np.repeat(unique_vals, repeats)
+
+
+def map_array(
+    arr: ArrayLike,
+    mapper,
+    na_action: Literal["ignore"] | None = None,
+    convert: bool = True,
+) -> np.ndarray | ExtensionArray | Index:
+    """
+    Map values using an input mapping or function.
+
+    Parameters
+    ----------
+    mapper : function, dict, or Series
+        Mapping correspondence.
+    na_action : {None, 'ignore'}, default None
+        If 'ignore', propagate NA values, without passing them to the
+        mapping correspondence.
+    convert : bool, default True
+        Try to find better dtype for elementwise function results. If
+        False, leave as dtype=object.
+
+    Returns
+    -------
+    Union[ndarray, Index, ExtensionArray]
+        The output of the mapping function applied to the array.
+        If the function returns a tuple with more than one element
+        a MultiIndex will be returned.
+    """
+    if na_action not in (None, "ignore"):
+        msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
+        raise ValueError(msg)
+
+    # we can fastpath dict/Series to an efficient map
+    # as we know that we are not going to have to yield
+    # python types
+    if is_dict_like(mapper):
+        if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
+            # If a dictionary subclass defines a default value method,
+            # convert mapper to a lookup function (GH #15999).
+            dict_with_default = mapper
+            mapper = lambda x: dict_with_default[
+                np.nan if isinstance(x, float) and np.isnan(x) else x
+            ]
+        else:
+            # Dictionary does not have a default. Thus it's safe to
+            # convert to an Series for efficiency.
+            # we specify the keys here to handle the
+            # possibility that they are tuples
+
+            # The return value of mapping with an empty mapper is
+            # expected to be pd.Series(np.nan, ...). As np.nan is
+            # of dtype float64 the return value of this method should
+            # be float64 as well
+            from pandas import Series
+
+            if len(mapper) == 0:
+                mapper = Series(mapper, dtype=np.float64)
+            else:
+                mapper = Series(mapper)
+
+    if isinstance(mapper, ABCSeries):
+        if na_action == "ignore":
+            mapper = mapper[mapper.index.notna()]
+
+        # Since values were input this means we came from either
+        # a dict or a series and mapper should be an index
+        indexer = mapper.index.get_indexer(arr)
+        new_values = take_nd(mapper._values, indexer)
+
+        return new_values
+
+    if not len(arr):
+        return arr.copy()
+
+    # we must convert to python types
+    values = arr.astype(object, copy=False)
+    if na_action is None:
+        return lib.map_infer(values, mapper, convert=convert)
+    else:
+        return lib.map_infer_mask(
+            values, mapper, mask=isna(values).view(np.uint8), convert=convert
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cfe5ffc0170d61bca248bca43dda7c2f082496f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/api.py
@@ -0,0 +1,140 @@
+from pandas._libs import (
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+)
+from pandas._libs.missing import NA
+
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    isnull,
+    notna,
+    notnull,
+)
+
+from pandas.core.algorithms import (
+    factorize,
+    unique,
+    value_counts,
+)
+from pandas.core.arrays import Categorical
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import array
+from pandas.core.flags import Flags
+from pandas.core.groupby import (
+    Grouper,
+    NamedAgg,
+)
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    TimedeltaIndex,
+)
+from pandas.core.indexes.datetimes import (
+    bdate_range,
+    date_range,
+)
+from pandas.core.indexes.interval import (
+    Interval,
+    interval_range,
+)
+from pandas.core.indexes.period import period_range
+from pandas.core.indexes.timedeltas import timedelta_range
+from pandas.core.indexing import IndexSlice
+from pandas.core.series import Series
+from pandas.core.tools.datetimes import to_datetime
+from pandas.core.tools.numeric import to_numeric
+from pandas.core.tools.timedeltas import to_timedelta
+
+from pandas.io.formats.format import set_eng_float_format
+from pandas.tseries.offsets import DateOffset
+
+# DataFrame needs to be imported after NamedAgg to avoid a circular import
+from pandas.core.frame import DataFrame  # isort:skip
+
+__all__ = [
+    "array",
+    "ArrowDtype",
+    "bdate_range",
+    "BooleanDtype",
+    "Categorical",
+    "CategoricalDtype",
+    "CategoricalIndex",
+    "DataFrame",
+    "DateOffset",
+    "date_range",
+    "DatetimeIndex",
+    "DatetimeTZDtype",
+    "factorize",
+    "Flags",
+    "Float32Dtype",
+    "Float64Dtype",
+    "Grouper",
+    "Index",
+    "IndexSlice",
+    "Int16Dtype",
+    "Int32Dtype",
+    "Int64Dtype",
+    "Int8Dtype",
+    "Interval",
+    "IntervalDtype",
+    "IntervalIndex",
+    "interval_range",
+    "isna",
+    "isnull",
+    "MultiIndex",
+    "NA",
+    "NamedAgg",
+    "NaT",
+    "notna",
+    "notnull",
+    "Period",
+    "PeriodDtype",
+    "PeriodIndex",
+    "period_range",
+    "RangeIndex",
+    "Series",
+    "set_eng_float_format",
+    "StringDtype",
+    "Timedelta",
+    "TimedeltaIndex",
+    "timedelta_range",
+    "Timestamp",
+    "to_datetime",
+    "to_numeric",
+    "to_timedelta",
+    "UInt16Dtype",
+    "UInt32Dtype",
+    "UInt64Dtype",
+    "UInt8Dtype",
+    "unique",
+    "value_counts",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/apply.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/apply.py
new file mode 100644
index 0000000000000000000000000000000000000000..fafc9ee1b69283f75c9aa4b72768d1fd92f5a70a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/apply.py
@@ -0,0 +1,2057 @@
+from __future__ import annotations
+
+import abc
+from collections import defaultdict
+import functools
+from functools import partial
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import option_context
+
+from pandas._libs import lib
+from pandas._libs.internals import BlockValuesRefs
+from pandas._typing import (
+    AggFuncType,
+    AggFuncTypeBase,
+    AggFuncTypeDict,
+    AggObjType,
+    Axis,
+    AxisInt,
+    NDFrameT,
+    npt,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import SpecificationError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import is_nested_object
+from pandas.core.dtypes.common import (
+    is_dict_like,
+    is_extension_array_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_sequence,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCNDFrame,
+    ABCSeries,
+)
+
+from pandas.core._numba.executor import generate_apply_looper
+import pandas.core.common as com
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Hashable,
+        Iterable,
+        MutableMapping,
+        Sequence,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+    from pandas.core.groupby import GroupBy
+    from pandas.core.resample import Resampler
+    from pandas.core.window.rolling import BaseWindow
+
+
+ResType = dict[int, Any]
+
+
+def frame_apply(
+    obj: DataFrame,
+    func: AggFuncType,
+    axis: Axis = 0,
+    raw: bool = False,
+    result_type: str | None = None,
+    by_row: Literal[False, "compat"] = "compat",
+    engine: str = "python",
+    engine_kwargs: dict[str, bool] | None = None,
+    args=None,
+    kwargs=None,
+) -> FrameApply:
+    """construct and return a row or column based frame apply object"""
+    axis = obj._get_axis_number(axis)
+    klass: type[FrameApply]
+    if axis == 0:
+        klass = FrameRowApply
+    elif axis == 1:
+        klass = FrameColumnApply
+
+    _, func, _, _ = reconstruct_func(func, **kwargs)
+    assert func is not None
+
+    return klass(
+        obj,
+        func,
+        raw=raw,
+        result_type=result_type,
+        by_row=by_row,
+        engine=engine,
+        engine_kwargs=engine_kwargs,
+        args=args,
+        kwargs=kwargs,
+    )
+
+
+class Apply(metaclass=abc.ABCMeta):
+    axis: AxisInt
+
+    def __init__(
+        self,
+        obj: AggObjType,
+        func: AggFuncType,
+        raw: bool,
+        result_type: str | None,
+        *,
+        by_row: Literal[False, "compat", "_compat"] = "compat",
+        engine: str = "python",
+        engine_kwargs: dict[str, bool] | None = None,
+        args,
+        kwargs,
+    ) -> None:
+        self.obj = obj
+        self.raw = raw
+
+        assert by_row is False or by_row in ["compat", "_compat"]
+        self.by_row = by_row
+
+        self.args = args or ()
+        self.kwargs = kwargs or {}
+
+        self.engine = engine
+        self.engine_kwargs = {} if engine_kwargs is None else engine_kwargs
+
+        if result_type not in [None, "reduce", "broadcast", "expand"]:
+            raise ValueError(
+                "invalid value for result_type, must be one "
+                "of {None, 'reduce', 'broadcast', 'expand'}"
+            )
+
+        self.result_type = result_type
+
+        self.func = func
+
+    @abc.abstractmethod
+    def apply(self) -> DataFrame | Series:
+        pass
+
+    @abc.abstractmethod
+    def agg_or_apply_list_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        pass
+
+    @abc.abstractmethod
+    def agg_or_apply_dict_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        pass
+
+    def agg(self) -> DataFrame | Series | None:
+        """
+        Provide an implementation for the aggregators.
+
+        Returns
+        -------
+        Result of aggregation, or None if agg cannot be performed by
+        this method.
+        """
+        obj = self.obj
+        func = self.func
+        args = self.args
+        kwargs = self.kwargs
+
+        if isinstance(func, str):
+            return self.apply_str()
+
+        if is_dict_like(func):
+            return self.agg_dict_like()
+        elif is_list_like(func):
+            # we require a list, but not a 'str'
+            return self.agg_list_like()
+
+        if callable(func):
+            f = com.get_cython_func(func)
+            if f and not args and not kwargs:
+                warn_alias_replacement(obj, func, f)
+                return getattr(obj, f)()
+
+        # caller can react
+        return None
+
+    def transform(self) -> DataFrame | Series:
+        """
+        Transform a DataFrame or Series.
+
+        Returns
+        -------
+        DataFrame or Series
+            Result of applying ``func`` along the given axis of the
+            Series or DataFrame.
+
+        Raises
+        ------
+        ValueError
+            If the transform function fails or does not transform.
+        """
+        obj = self.obj
+        func = self.func
+        axis = self.axis
+        args = self.args
+        kwargs = self.kwargs
+
+        is_series = obj.ndim == 1
+
+        if obj._get_axis_number(axis) == 1:
+            assert not is_series
+            return obj.T.transform(func, 0, *args, **kwargs).T
+
+        if is_list_like(func) and not is_dict_like(func):
+            func = cast(list[AggFuncTypeBase], func)
+            # Convert func equivalent dict
+            if is_series:
+                func = {com.get_callable_name(v) or v: v for v in func}
+            else:
+                func = {col: func for col in obj}
+
+        if is_dict_like(func):
+            func = cast(AggFuncTypeDict, func)
+            return self.transform_dict_like(func)
+
+        # func is either str or callable
+        func = cast(AggFuncTypeBase, func)
+        try:
+            result = self.transform_str_or_callable(func)
+        except TypeError:
+            raise
+        except Exception as err:
+            raise ValueError("Transform function failed") from err
+
+        # Functions that transform may return empty Series/DataFrame
+        # when the dtype is not appropriate
+        if (
+            isinstance(result, (ABCSeries, ABCDataFrame))
+            and result.empty
+            and not obj.empty
+        ):
+            raise ValueError("Transform function failed")
+        # error: Argument 1 to "__get__" of "AxisProperty" has incompatible type
+        # "Union[Series, DataFrame, GroupBy[Any], SeriesGroupBy,
+        # DataFrameGroupBy, BaseWindow, Resampler]"; expected "Union[DataFrame,
+        # Series]"
+        if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals(
+            obj.index  # type: ignore[arg-type]
+        ):
+            raise ValueError("Function did not transform")
+
+        return result
+
+    def transform_dict_like(self, func) -> DataFrame:
+        """
+        Compute transform in the case of a dict-like func
+        """
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+        args = self.args
+        kwargs = self.kwargs
+
+        # transform is currently only for Series/DataFrame
+        assert isinstance(obj, ABCNDFrame)
+
+        if len(func) == 0:
+            raise ValueError("No transform functions were provided")
+
+        func = self.normalize_dictlike_arg("transform", obj, func)
+
+        results: dict[Hashable, DataFrame | Series] = {}
+        for name, how in func.items():
+            colg = obj._gotitem(name, ndim=1)
+            results[name] = colg.transform(how, 0, *args, **kwargs)
+        return concat(results, axis=1)
+
+    def transform_str_or_callable(self, func) -> DataFrame | Series:
+        """
+        Compute transform in the case of a string or callable func
+        """
+        obj = self.obj
+        args = self.args
+        kwargs = self.kwargs
+
+        if isinstance(func, str):
+            return self._apply_str(obj, func, *args, **kwargs)
+
+        if not args and not kwargs:
+            f = com.get_cython_func(func)
+            if f:
+                warn_alias_replacement(obj, func, f)
+                return getattr(obj, f)()
+
+        # Two possible ways to use a UDF - apply or call directly
+        try:
+            return obj.apply(func, args=args, **kwargs)
+        except Exception:
+            return func(obj, *args, **kwargs)
+
+    def agg_list_like(self) -> DataFrame | Series:
+        """
+        Compute aggregation in the case of a list-like argument.
+
+        Returns
+        -------
+        Result of aggregation.
+        """
+        return self.agg_or_apply_list_like(op_name="agg")
+
+    def compute_list_like(
+        self,
+        op_name: Literal["agg", "apply"],
+        selected_obj: Series | DataFrame,
+        kwargs: dict[str, Any],
+    ) -> tuple[list[Hashable] | Index, list[Any]]:
+        """
+        Compute agg/apply results for like-like input.
+
+        Parameters
+        ----------
+        op_name : {"agg", "apply"}
+            Operation being performed.
+        selected_obj : Series or DataFrame
+            Data to perform operation on.
+        kwargs : dict
+            Keyword arguments to pass to the functions.
+
+        Returns
+        -------
+        keys : list[Hashable] or Index
+            Index labels for result.
+        results : list
+            Data for result. When aggregating with a Series, this can contain any
+            Python objects.
+        """
+        func = cast(list[AggFuncTypeBase], self.func)
+        obj = self.obj
+
+        results = []
+        keys = []
+
+        # degenerate case
+        if selected_obj.ndim == 1:
+            for a in func:
+                colg = obj._gotitem(selected_obj.name, ndim=1, subset=selected_obj)
+                args = (
+                    [self.axis, *self.args]
+                    if include_axis(op_name, colg)
+                    else self.args
+                )
+                new_res = getattr(colg, op_name)(a, *args, **kwargs)
+                results.append(new_res)
+
+                # make sure we find a good name
+                name = com.get_callable_name(a) or a
+                keys.append(name)
+
+        else:
+            indices = []
+            for index, col in enumerate(selected_obj):
+                colg = obj._gotitem(col, ndim=1, subset=selected_obj.iloc[:, index])
+                args = (
+                    [self.axis, *self.args]
+                    if include_axis(op_name, colg)
+                    else self.args
+                )
+                new_res = getattr(colg, op_name)(func, *args, **kwargs)
+                results.append(new_res)
+                indices.append(index)
+            # error: Incompatible types in assignment (expression has type "Any |
+            # Index", variable has type "list[Any | Callable[..., Any] | str]")
+            keys = selected_obj.columns.take(indices)  # type: ignore[assignment]
+
+        return keys, results
+
+    def wrap_results_list_like(
+        self, keys: Iterable[Hashable], results: list[Series | DataFrame]
+    ):
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+
+        try:
+            return concat(results, keys=keys, axis=1, sort=False)
+        except TypeError as err:
+            # we are concatting non-NDFrame objects,
+            # e.g. a list of scalars
+            from pandas import Series
+
+            result = Series(results, index=keys, name=obj.name)
+            if is_nested_object(result):
+                raise ValueError(
+                    "cannot combine transform and aggregation operations"
+                ) from err
+            return result
+
+    def agg_dict_like(self) -> DataFrame | Series:
+        """
+        Compute aggregation in the case of a dict-like argument.
+
+        Returns
+        -------
+        Result of aggregation.
+        """
+        return self.agg_or_apply_dict_like(op_name="agg")
+
+    def compute_dict_like(
+        self,
+        op_name: Literal["agg", "apply"],
+        selected_obj: Series | DataFrame,
+        selection: Hashable | Sequence[Hashable],
+        kwargs: dict[str, Any],
+    ) -> tuple[list[Hashable], list[Any]]:
+        """
+        Compute agg/apply results for dict-like input.
+
+        Parameters
+        ----------
+        op_name : {"agg", "apply"}
+            Operation being performed.
+        selected_obj : Series or DataFrame
+            Data to perform operation on.
+        selection : hashable or sequence of hashables
+            Used by GroupBy, Window, and Resample if selection is applied to the object.
+        kwargs : dict
+            Keyword arguments to pass to the functions.
+
+        Returns
+        -------
+        keys : list[hashable]
+            Index labels for result.
+        results : list
+            Data for result. When aggregating with a Series, this can contain any
+            Python object.
+        """
+        from pandas.core.groupby.generic import (
+            DataFrameGroupBy,
+            SeriesGroupBy,
+        )
+
+        obj = self.obj
+        is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
+        func = cast(AggFuncTypeDict, self.func)
+        func = self.normalize_dictlike_arg(op_name, selected_obj, func)
+
+        is_non_unique_col = (
+            selected_obj.ndim == 2
+            and selected_obj.columns.nunique() < len(selected_obj.columns)
+        )
+
+        if selected_obj.ndim == 1:
+            # key only used for output
+            colg = obj._gotitem(selection, ndim=1)
+            results = [getattr(colg, op_name)(how, **kwargs) for _, how in func.items()]
+            keys = list(func.keys())
+        elif not is_groupby and is_non_unique_col:
+            # key used for column selection and output
+            # GH#51099
+            results = []
+            keys = []
+            for key, how in func.items():
+                indices = selected_obj.columns.get_indexer_for([key])
+                labels = selected_obj.columns.take(indices)
+                label_to_indices = defaultdict(list)
+                for index, label in zip(indices, labels):
+                    label_to_indices[label].append(index)
+
+                key_data = [
+                    getattr(selected_obj._ixs(indice, axis=1), op_name)(how, **kwargs)
+                    for label, indices in label_to_indices.items()
+                    for indice in indices
+                ]
+
+                keys += [key] * len(key_data)
+                results += key_data
+        else:
+            # key used for column selection and output
+            results = [
+                getattr(obj._gotitem(key, ndim=1), op_name)(how, **kwargs)
+                for key, how in func.items()
+            ]
+            keys = list(func.keys())
+
+        return keys, results
+
+    def wrap_results_dict_like(
+        self,
+        selected_obj: Series | DataFrame,
+        result_index: list[Hashable],
+        result_data: list,
+    ):
+        from pandas import Index
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+
+        # Avoid making two isinstance calls in all and any below
+        is_ndframe = [isinstance(r, ABCNDFrame) for r in result_data]
+
+        if all(is_ndframe):
+            results = dict(zip(result_index, result_data))
+            keys_to_use: Iterable[Hashable]
+            keys_to_use = [k for k in result_index if not results[k].empty]
+            # Have to check, if at least one DataFrame is not empty.
+            keys_to_use = keys_to_use if keys_to_use != [] else result_index
+            if selected_obj.ndim == 2:
+                # keys are columns, so we can preserve names
+                ktu = Index(keys_to_use)
+                ktu._set_names(selected_obj.columns.names)
+                keys_to_use = ktu
+
+            axis: AxisInt = 0 if isinstance(obj, ABCSeries) else 1
+            result = concat(
+                {k: results[k] for k in keys_to_use},
+                axis=axis,
+                keys=keys_to_use,
+            )
+        elif any(is_ndframe):
+            # There is a mix of NDFrames and scalars
+            raise ValueError(
+                "cannot perform both aggregation "
+                "and transformation operations "
+                "simultaneously"
+            )
+        else:
+            from pandas import Series
+
+            # we have a list of scalars
+            # GH 36212 use name only if obj is a series
+            if obj.ndim == 1:
+                obj = cast("Series", obj)
+                name = obj.name
+            else:
+                name = None
+
+            result = Series(result_data, index=result_index, name=name)
+
+        return result
+
+    def apply_str(self) -> DataFrame | Series:
+        """
+        Compute apply in case of a string.
+
+        Returns
+        -------
+        result: Series or DataFrame
+        """
+        # Caller is responsible for checking isinstance(self.f, str)
+        func = cast(str, self.func)
+
+        obj = self.obj
+
+        from pandas.core.groupby.generic import (
+            DataFrameGroupBy,
+            SeriesGroupBy,
+        )
+
+        # Support for `frame.transform('method')`
+        # Some methods (shift, etc.) require the axis argument, others
+        # don't, so inspect and insert if necessary.
+        method = getattr(obj, func, None)
+        if callable(method):
+            sig = inspect.getfullargspec(method)
+            arg_names = (*sig.args, *sig.kwonlyargs)
+            if self.axis != 0 and (
+                "axis" not in arg_names or func in ("corrwith", "skew")
+            ):
+                raise ValueError(f"Operation {func} does not support axis=1")
+            if "axis" in arg_names:
+                if isinstance(obj, (SeriesGroupBy, DataFrameGroupBy)):
+                    # Try to avoid FutureWarning for deprecated axis keyword;
+                    # If self.axis matches the axis we would get by not passing
+                    #  axis, we safely exclude the keyword.
+
+                    default_axis = 0
+                    if func in ["idxmax", "idxmin"]:
+                        # DataFrameGroupBy.idxmax, idxmin axis defaults to self.axis,
+                        # whereas other axis keywords default to 0
+                        default_axis = self.obj.axis
+
+                    if default_axis != self.axis:
+                        self.kwargs["axis"] = self.axis
+                else:
+                    self.kwargs["axis"] = self.axis
+        return self._apply_str(obj, func, *self.args, **self.kwargs)
+
+    def apply_list_or_dict_like(self) -> DataFrame | Series:
+        """
+        Compute apply in case of a list-like or dict-like.
+
+        Returns
+        -------
+        result: Series, DataFrame, or None
+            Result when self.func is a list-like or dict-like, None otherwise.
+        """
+
+        if self.engine == "numba":
+            raise NotImplementedError(
+                "The 'numba' engine doesn't support list-like/"
+                "dict likes of callables yet."
+            )
+
+        if self.axis == 1 and isinstance(self.obj, ABCDataFrame):
+            return self.obj.T.apply(self.func, 0, args=self.args, **self.kwargs).T
+
+        func = self.func
+        kwargs = self.kwargs
+
+        if is_dict_like(func):
+            result = self.agg_or_apply_dict_like(op_name="apply")
+        else:
+            result = self.agg_or_apply_list_like(op_name="apply")
+
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
+
+        return result
+
+    def normalize_dictlike_arg(
+        self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict
+    ) -> AggFuncTypeDict:
+        """
+        Handler for dict-like argument.
+
+        Ensures that necessary columns exist if obj is a DataFrame, and
+        that a nested renamer is not passed. Also normalizes to all lists
+        when values consists of a mix of list and non-lists.
+        """
+        assert how in ("apply", "agg", "transform")
+
+        # Can't use func.values(); wouldn't work for a Series
+        if (
+            how == "agg"
+            and isinstance(obj, ABCSeries)
+            and any(is_list_like(v) for _, v in func.items())
+        ) or (any(is_dict_like(v) for _, v in func.items())):
+            # GH 15931 - deprecation of renaming keys
+            raise SpecificationError("nested renamer is not supported")
+
+        if obj.ndim != 1:
+            # Check for missing columns on a frame
+            from pandas import Index
+
+            cols = Index(list(func.keys())).difference(obj.columns, sort=True)
+            if len(cols) > 0:
+                raise KeyError(f"Column(s) {list(cols)} do not exist")
+
+        aggregator_types = (list, tuple, dict)
+
+        # if we have a dict of any non-scalars
+        # eg. {'A' : ['mean']}, normalize all to
+        # be list-likes
+        # Cannot use func.values() because arg may be a Series
+        if any(isinstance(x, aggregator_types) for _, x in func.items()):
+            new_func: AggFuncTypeDict = {}
+            for k, v in func.items():
+                if not isinstance(v, aggregator_types):
+                    new_func[k] = [v]
+                else:
+                    new_func[k] = v
+            func = new_func
+        return func
+
+    def _apply_str(self, obj, func: str, *args, **kwargs):
+        """
+        if arg is a string, then try to operate on it:
+        - try to find a function (or attribute) on obj
+        - try to find a numpy function
+        - raise
+        """
+        assert isinstance(func, str)
+
+        if hasattr(obj, func):
+            f = getattr(obj, func)
+            if callable(f):
+                return f(*args, **kwargs)
+
+            # people may aggregate on a non-callable attribute
+            # but don't let them think they can pass args to it
+            assert len(args) == 0
+            assert len([kwarg for kwarg in kwargs if kwarg not in ["axis"]]) == 0
+            return f
+        elif hasattr(np, func) and hasattr(obj, "__array__"):
+            # in particular exclude Window
+            f = getattr(np, func)
+            return f(obj, *args, **kwargs)
+        else:
+            msg = f"'{func}' is not a valid function for '{type(obj).__name__}' object"
+            raise AttributeError(msg)
+
+
+class NDFrameApply(Apply):
+    """
+    Methods shared by FrameApply and SeriesApply but
+    not GroupByApply or ResamplerWindowApply
+    """
+
+    obj: DataFrame | Series
+
+    @property
+    def index(self) -> Index:
+        return self.obj.index
+
+    @property
+    def agg_axis(self) -> Index:
+        return self.obj._get_agg_axis(self.axis)
+
+    def agg_or_apply_list_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        obj = self.obj
+        kwargs = self.kwargs
+
+        if op_name == "apply":
+            if isinstance(self, FrameApply):
+                by_row = self.by_row
+
+            elif isinstance(self, SeriesApply):
+                by_row = "_compat" if self.by_row else False
+            else:
+                by_row = False
+            kwargs = {**kwargs, "by_row": by_row}
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        keys, results = self.compute_list_like(op_name, obj, kwargs)
+        result = self.wrap_results_list_like(keys, results)
+        return result
+
+    def agg_or_apply_dict_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        assert op_name in ["agg", "apply"]
+        obj = self.obj
+
+        kwargs = {}
+        if op_name == "apply":
+            by_row = "_compat" if self.by_row else False
+            kwargs.update({"by_row": by_row})
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        selection = None
+        result_index, result_data = self.compute_dict_like(
+            op_name, obj, selection, kwargs
+        )
+        result = self.wrap_results_dict_like(obj, result_index, result_data)
+        return result
+
+
+class FrameApply(NDFrameApply):
+    obj: DataFrame
+
+    def __init__(
+        self,
+        obj: AggObjType,
+        func: AggFuncType,
+        raw: bool,
+        result_type: str | None,
+        *,
+        by_row: Literal[False, "compat"] = False,
+        engine: str = "python",
+        engine_kwargs: dict[str, bool] | None = None,
+        args,
+        kwargs,
+    ) -> None:
+        if by_row is not False and by_row != "compat":
+            raise ValueError(f"by_row={by_row} not allowed")
+        super().__init__(
+            obj,
+            func,
+            raw,
+            result_type,
+            by_row=by_row,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    # ---------------------------------------------------------------
+    # Abstract Methods
+
+    @property
+    @abc.abstractmethod
+    def result_index(self) -> Index:
+        pass
+
+    @property
+    @abc.abstractmethod
+    def result_columns(self) -> Index:
+        pass
+
+    @property
+    @abc.abstractmethod
+    def series_generator(self) -> Generator[Series, None, None]:
+        pass
+
+    @staticmethod
+    @functools.cache
+    @abc.abstractmethod
+    def generate_numba_apply_func(
+        func, nogil=True, nopython=True, parallel=False
+    ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+        pass
+
+    @abc.abstractmethod
+    def apply_with_numba(self):
+        pass
+
+    def validate_values_for_numba(self):
+        # Validate column dtyps all OK
+        for colname, dtype in self.obj.dtypes.items():
+            if not is_numeric_dtype(dtype):
+                raise ValueError(
+                    f"Column {colname} must have a numeric dtype. "
+                    f"Found '{dtype}' instead"
+                )
+            if is_extension_array_dtype(dtype):
+                raise ValueError(
+                    f"Column {colname} is backed by an extension array, "
+                    f"which is not supported by the numba engine."
+                )
+
+    @abc.abstractmethod
+    def wrap_results_for_axis(
+        self, results: ResType, res_index: Index
+    ) -> DataFrame | Series:
+        pass
+
+    # ---------------------------------------------------------------
+
+    @property
+    def res_columns(self) -> Index:
+        return self.result_columns
+
+    @property
+    def columns(self) -> Index:
+        return self.obj.columns
+
+    @cache_readonly
+    def values(self):
+        return self.obj.values
+
+    def apply(self) -> DataFrame | Series:
+        """compute the results"""
+
+        # dispatch to handle list-like or dict-like
+        if is_list_like(self.func):
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support lists of callables yet"
+                )
+            return self.apply_list_or_dict_like()
+
+        # all empty
+        if len(self.columns) == 0 and len(self.index) == 0:
+            return self.apply_empty_result()
+
+        # string dispatch
+        if isinstance(self.func, str):
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support using "
+                    "a string as the callable function"
+                )
+            return self.apply_str()
+
+        # ufunc
+        elif isinstance(self.func, np.ufunc):
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support "
+                    "using a numpy ufunc as the callable function"
+                )
+            with np.errstate(all="ignore"):
+                results = self.obj._mgr.apply("apply", func=self.func)
+            # _constructor will retain self.index and self.columns
+            return self.obj._constructor_from_mgr(results, axes=results.axes)
+
+        # broadcasting
+        if self.result_type == "broadcast":
+            if self.engine == "numba":
+                raise NotImplementedError(
+                    "the 'numba' engine doesn't support result_type='broadcast'"
+                )
+            return self.apply_broadcast(self.obj)
+
+        # one axis empty
+        elif not all(self.obj.shape):
+            return self.apply_empty_result()
+
+        # raw
+        elif self.raw:
+            return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs)
+
+        return self.apply_standard()
+
+    def agg(self):
+        obj = self.obj
+        axis = self.axis
+
+        # TODO: Avoid having to change state
+        self.obj = self.obj if self.axis == 0 else self.obj.T
+        self.axis = 0
+
+        result = None
+        try:
+            result = super().agg()
+        finally:
+            self.obj = obj
+            self.axis = axis
+
+        if axis == 1:
+            result = result.T if result is not None else result
+
+        if result is None:
+            result = self.obj.apply(self.func, axis, args=self.args, **self.kwargs)
+
+        return result
+
+    def apply_empty_result(self):
+        """
+        we have an empty result; at least 1 axis is 0
+
+        we will try to apply the function to an empty
+        series in order to see if this is a reduction function
+        """
+        assert callable(self.func)
+
+        # we are not asked to reduce or infer reduction
+        # so just return a copy of the existing object
+        if self.result_type not in ["reduce", None]:
+            return self.obj.copy()
+
+        # we may need to infer
+        should_reduce = self.result_type == "reduce"
+
+        from pandas import Series
+
+        if not should_reduce:
+            try:
+                if self.axis == 0:
+                    r = self.func(
+                        Series([], dtype=np.float64), *self.args, **self.kwargs
+                    )
+                else:
+                    r = self.func(
+                        Series(index=self.columns, dtype=np.float64),
+                        *self.args,
+                        **self.kwargs,
+                    )
+            except Exception:
+                pass
+            else:
+                should_reduce = not isinstance(r, Series)
+
+        if should_reduce:
+            if len(self.agg_axis):
+                r = self.func(Series([], dtype=np.float64), *self.args, **self.kwargs)
+            else:
+                r = np.nan
+
+            return self.obj._constructor_sliced(r, index=self.agg_axis)
+        else:
+            return self.obj.copy()
+
+    def apply_raw(self, engine="python", engine_kwargs=None):
+        """apply to the values as a numpy array"""
+
+        def wrap_function(func):
+            """
+            Wrap user supplied function to work around numpy issue.
+
+            see https://github.com/numpy/numpy/issues/8352
+            """
+
+            def wrapper(*args, **kwargs):
+                result = func(*args, **kwargs)
+                if isinstance(result, str):
+                    result = np.array(result, dtype=object)
+                return result
+
+            return wrapper
+
+        if engine == "numba":
+            engine_kwargs = {} if engine_kwargs is None else engine_kwargs
+
+            # error: Argument 1 to "__call__" of "_lru_cache_wrapper" has
+            # incompatible type "Callable[..., Any] | str | list[Callable
+            # [..., Any] | str] | dict[Hashable,Callable[..., Any] | str |
+            # list[Callable[..., Any] | str]]"; expected "Hashable"
+            nb_looper = generate_apply_looper(
+                self.func, **engine_kwargs  # type: ignore[arg-type]
+            )
+            result = nb_looper(self.values, self.axis)
+            # If we made the result 2-D, squeeze it back to 1-D
+            result = np.squeeze(result)
+        else:
+            result = np.apply_along_axis(
+                wrap_function(self.func),
+                self.axis,
+                self.values,
+                *self.args,
+                **self.kwargs,
+            )
+
+        # TODO: mixed type case
+        if result.ndim == 2:
+            return self.obj._constructor(result, index=self.index, columns=self.columns)
+        else:
+            return self.obj._constructor_sliced(result, index=self.agg_axis)
+
+    def apply_broadcast(self, target: DataFrame) -> DataFrame:
+        assert callable(self.func)
+
+        result_values = np.empty_like(target.values)
+
+        # axis which we want to compare compliance
+        result_compare = target.shape[0]
+
+        for i, col in enumerate(target.columns):
+            res = self.func(target[col], *self.args, **self.kwargs)
+            ares = np.asarray(res).ndim
+
+            # must be a scalar or 1d
+            if ares > 1:
+                raise ValueError("too many dims to broadcast")
+            if ares == 1:
+                # must match return dim
+                if result_compare != len(res):
+                    raise ValueError("cannot broadcast result")
+
+            result_values[:, i] = res
+
+        # we *always* preserve the original index / columns
+        result = self.obj._constructor(
+            result_values, index=target.index, columns=target.columns
+        )
+        return result
+
+    def apply_standard(self):
+        if self.engine == "python":
+            results, res_index = self.apply_series_generator()
+        else:
+            results, res_index = self.apply_series_numba()
+
+        # wrap results
+        return self.wrap_results(results, res_index)
+
+    def apply_series_generator(self) -> tuple[ResType, Index]:
+        assert callable(self.func)
+
+        series_gen = self.series_generator
+        res_index = self.result_index
+
+        results = {}
+
+        with option_context("mode.chained_assignment", None):
+            for i, v in enumerate(series_gen):
+                # ignore SettingWithCopy here in case the user mutates
+                results[i] = self.func(v, *self.args, **self.kwargs)
+                if isinstance(results[i], ABCSeries):
+                    # If we have a view on v, we need to make a copy because
+                    #  series_generator will swap out the underlying data
+                    results[i] = results[i].copy(deep=False)
+
+        return results, res_index
+
+    def apply_series_numba(self):
+        if self.engine_kwargs.get("parallel", False):
+            raise NotImplementedError(
+                "Parallel apply is not supported when raw=False and engine='numba'"
+            )
+        if not self.obj.index.is_unique or not self.columns.is_unique:
+            raise NotImplementedError(
+                "The index/columns must be unique when raw=False and engine='numba'"
+            )
+        self.validate_values_for_numba()
+        results = self.apply_with_numba()
+        return results, self.result_index
+
+    def wrap_results(self, results: ResType, res_index: Index) -> DataFrame | Series:
+        from pandas import Series
+
+        # see if we can infer the results
+        if len(results) > 0 and 0 in results and is_sequence(results[0]):
+            return self.wrap_results_for_axis(results, res_index)
+
+        # dict of scalars
+
+        # the default dtype of an empty Series is `object`, but this
+        # code can be hit by df.mean() where the result should have dtype
+        # float64 even if it's an empty Series.
+        constructor_sliced = self.obj._constructor_sliced
+        if len(results) == 0 and constructor_sliced is Series:
+            result = constructor_sliced(results, dtype=np.float64)
+        else:
+            result = constructor_sliced(results)
+        result.index = res_index
+
+        return result
+
+    def apply_str(self) -> DataFrame | Series:
+        # Caller is responsible for checking isinstance(self.func, str)
+        # TODO: GH#39993 - Avoid special-casing by replacing with lambda
+        if self.func == "size":
+            # Special-cased because DataFrame.size returns a single scalar
+            obj = self.obj
+            value = obj.shape[self.axis]
+            return obj._constructor_sliced(value, index=self.agg_axis)
+        return super().apply_str()
+
+
+class FrameRowApply(FrameApply):
+    axis: AxisInt = 0
+
+    @property
+    def series_generator(self) -> Generator[Series, None, None]:
+        return (self.obj._ixs(i, axis=1) for i in range(len(self.columns)))
+
+    @staticmethod
+    @functools.cache
+    def generate_numba_apply_func(
+        func, nogil=True, nopython=True, parallel=False
+    ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+        numba = import_optional_dependency("numba")
+        from pandas import Series
+
+        # Import helper from extensions to cast string object -> np strings
+        # Note: This also has the side effect of loading our numba extensions
+        from pandas.core._numba.extensions import maybe_cast_str
+
+        jitted_udf = numba.extending.register_jitable(func)
+
+        # Currently the parallel argument doesn't get passed through here
+        # (it's disabled) since the dicts in numba aren't thread-safe.
+        @numba.jit(nogil=nogil, nopython=nopython, parallel=parallel)
+        def numba_func(values, col_names, df_index):
+            results = {}
+            for j in range(values.shape[1]):
+                # Create the series
+                ser = Series(
+                    values[:, j], index=df_index, name=maybe_cast_str(col_names[j])
+                )
+                results[j] = jitted_udf(ser)
+            return results
+
+        return numba_func
+
+    def apply_with_numba(self) -> dict[int, Any]:
+        nb_func = self.generate_numba_apply_func(
+            cast(Callable, self.func), **self.engine_kwargs
+        )
+        from pandas.core._numba.extensions import set_numba_data
+
+        index = self.obj.index
+        columns = self.obj.columns
+
+        # Convert from numba dict to regular dict
+        # Our isinstance checks in the df constructor don't pass for numbas typed dict
+        with set_numba_data(index) as index, set_numba_data(columns) as columns:
+            res = dict(nb_func(self.values, columns, index))
+        return res
+
+    @property
+    def result_index(self) -> Index:
+        return self.columns
+
+    @property
+    def result_columns(self) -> Index:
+        return self.index
+
+    def wrap_results_for_axis(
+        self, results: ResType, res_index: Index
+    ) -> DataFrame | Series:
+        """return the results for the rows"""
+
+        if self.result_type == "reduce":
+            # e.g. test_apply_dict GH#8735
+            res = self.obj._constructor_sliced(results)
+            res.index = res_index
+            return res
+
+        elif self.result_type is None and all(
+            isinstance(x, dict) for x in results.values()
+        ):
+            # Our operation was a to_dict op e.g.
+            #  test_apply_dict GH#8735, test_apply_reduce_to_dict GH#25196 #37544
+            res = self.obj._constructor_sliced(results)
+            res.index = res_index
+            return res
+
+        try:
+            result = self.obj._constructor(data=results)
+        except ValueError as err:
+            if "All arrays must be of the same length" in str(err):
+                # e.g. result = [[2, 3], [1.5], ['foo', 'bar']]
+                #  see test_agg_listlike_result GH#29587
+                res = self.obj._constructor_sliced(results)
+                res.index = res_index
+                return res
+            else:
+                raise
+
+        if not isinstance(results[0], ABCSeries):
+            if len(result.index) == len(self.res_columns):
+                result.index = self.res_columns
+
+        if len(result.columns) == len(res_index):
+            result.columns = res_index
+
+        return result
+
+
+class FrameColumnApply(FrameApply):
+    axis: AxisInt = 1
+
+    def apply_broadcast(self, target: DataFrame) -> DataFrame:
+        result = super().apply_broadcast(target.T)
+        return result.T
+
+    @property
+    def series_generator(self) -> Generator[Series, None, None]:
+        values = self.values
+        values = ensure_wrapped_if_datetimelike(values)
+        assert len(values) > 0
+
+        # We create one Series object, and will swap out the data inside
+        #  of it.  Kids: don't do this at home.
+        ser = self.obj._ixs(0, axis=0)
+        mgr = ser._mgr
+
+        is_view = mgr.blocks[0].refs.has_reference()  # type: ignore[union-attr]
+
+        if isinstance(ser.dtype, ExtensionDtype):
+            # values will be incorrect for this block
+            # TODO(EA2D): special case would be unnecessary with 2D EAs
+            obj = self.obj
+            for i in range(len(obj)):
+                yield obj._ixs(i, axis=0)
+
+        else:
+            for arr, name in zip(values, self.index):
+                # GH#35462 re-pin mgr in case setitem changed it
+                ser._mgr = mgr
+                mgr.set_values(arr)
+                object.__setattr__(ser, "_name", name)
+                if not is_view:
+                    # In apply_series_generator we store the a shallow copy of the
+                    # result, which potentially increases the ref count of this reused
+                    # `ser` object (depending on the result of the applied function)
+                    # -> if that happened and `ser` is already a copy, then we reset
+                    # the refs here to avoid triggering a unnecessary CoW inside the
+                    # applied function (https://github.com/pandas-dev/pandas/pull/56212)
+                    mgr.blocks[0].refs = BlockValuesRefs(mgr.blocks[0])  # type: ignore[union-attr]
+                yield ser
+
+    @staticmethod
+    @functools.cache
+    def generate_numba_apply_func(
+        func, nogil=True, nopython=True, parallel=False
+    ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+        numba = import_optional_dependency("numba")
+        from pandas import Series
+        from pandas.core._numba.extensions import maybe_cast_str
+
+        jitted_udf = numba.extending.register_jitable(func)
+
+        @numba.jit(nogil=nogil, nopython=nopython, parallel=parallel)
+        def numba_func(values, col_names_index, index):
+            results = {}
+            # Currently the parallel argument doesn't get passed through here
+            # (it's disabled) since the dicts in numba aren't thread-safe.
+            for i in range(values.shape[0]):
+                # Create the series
+                # TODO: values corrupted without the copy
+                ser = Series(
+                    values[i].copy(),
+                    index=col_names_index,
+                    name=maybe_cast_str(index[i]),
+                )
+                results[i] = jitted_udf(ser)
+
+            return results
+
+        return numba_func
+
+    def apply_with_numba(self) -> dict[int, Any]:
+        nb_func = self.generate_numba_apply_func(
+            cast(Callable, self.func), **self.engine_kwargs
+        )
+
+        from pandas.core._numba.extensions import set_numba_data
+
+        # Convert from numba dict to regular dict
+        # Our isinstance checks in the df constructor don't pass for numbas typed dict
+        with set_numba_data(self.obj.index) as index, set_numba_data(
+            self.columns
+        ) as columns:
+            res = dict(nb_func(self.values, columns, index))
+
+        return res
+
+    @property
+    def result_index(self) -> Index:
+        return self.index
+
+    @property
+    def result_columns(self) -> Index:
+        return self.columns
+
+    def wrap_results_for_axis(
+        self, results: ResType, res_index: Index
+    ) -> DataFrame | Series:
+        """return the results for the columns"""
+        result: DataFrame | Series
+
+        # we have requested to expand
+        if self.result_type == "expand":
+            result = self.infer_to_same_shape(results, res_index)
+
+        # we have a non-series and don't want inference
+        elif not isinstance(results[0], ABCSeries):
+            result = self.obj._constructor_sliced(results)
+            result.index = res_index
+
+        # we may want to infer results
+        else:
+            result = self.infer_to_same_shape(results, res_index)
+
+        return result
+
+    def infer_to_same_shape(self, results: ResType, res_index: Index) -> DataFrame:
+        """infer the results to the same shape as the input object"""
+        result = self.obj._constructor(data=results)
+        result = result.T
+
+        # set the index
+        result.index = res_index
+
+        # infer dtypes
+        result = result.infer_objects(copy=False)
+
+        return result
+
+
+class SeriesApply(NDFrameApply):
+    obj: Series
+    axis: AxisInt = 0
+    by_row: Literal[False, "compat", "_compat"]  # only relevant for apply()
+
+    def __init__(
+        self,
+        obj: Series,
+        func: AggFuncType,
+        *,
+        convert_dtype: bool | lib.NoDefault = lib.no_default,
+        by_row: Literal[False, "compat", "_compat"] = "compat",
+        args,
+        kwargs,
+    ) -> None:
+        if convert_dtype is lib.no_default:
+            convert_dtype = True
+        else:
+            warnings.warn(
+                "the convert_dtype parameter is deprecated and will be removed in a "
+                "future version.  Do ``ser.astype(object).apply()`` "
+                "instead if you want ``convert_dtype=False``.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        self.convert_dtype = convert_dtype
+
+        super().__init__(
+            obj,
+            func,
+            raw=False,
+            result_type=None,
+            by_row=by_row,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def apply(self) -> DataFrame | Series:
+        obj = self.obj
+
+        if len(obj) == 0:
+            return self.apply_empty_result()
+
+        # dispatch to handle list-like or dict-like
+        if is_list_like(self.func):
+            return self.apply_list_or_dict_like()
+
+        if isinstance(self.func, str):
+            # if we are a string, try to dispatch
+            return self.apply_str()
+
+        if self.by_row == "_compat":
+            return self.apply_compat()
+
+        # self.func is Callable
+        return self.apply_standard()
+
+    def agg(self):
+        result = super().agg()
+        if result is None:
+            obj = self.obj
+            func = self.func
+            # string, list-like, and dict-like are entirely handled in super
+            assert callable(func)
+
+            # GH53325: The setup below is just to keep current behavior while emitting a
+            # deprecation message. In the future this will all be replaced with a simple
+            # `result = f(self.obj, *self.args, **self.kwargs)`.
+            try:
+                result = obj.apply(func, args=self.args, **self.kwargs)
+            except (ValueError, AttributeError, TypeError):
+                result = func(obj, *self.args, **self.kwargs)
+            else:
+                msg = (
+                    f"using {func} in {type(obj).__name__}.agg cannot aggregate and "
+                    f"has been deprecated. Use {type(obj).__name__}.transform to "
+                    f"keep behavior unchanged."
+                )
+                warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+
+        return result
+
+    def apply_empty_result(self) -> Series:
+        obj = self.obj
+        return obj._constructor(dtype=obj.dtype, index=obj.index).__finalize__(
+            obj, method="apply"
+        )
+
+    def apply_compat(self):
+        """compat apply method for funcs in listlikes and dictlikes.
+
+         Used for each callable when giving listlikes and dictlikes of callables to
+         apply. Needed for compatibility with Pandas < v2.1.
+
+        .. versionadded:: 2.1.0
+        """
+        obj = self.obj
+        func = self.func
+
+        if callable(func):
+            f = com.get_cython_func(func)
+            if f and not self.args and not self.kwargs:
+                return obj.apply(func, by_row=False)
+
+        try:
+            result = obj.apply(func, by_row="compat")
+        except (ValueError, AttributeError, TypeError):
+            result = obj.apply(func, by_row=False)
+        return result
+
+    def apply_standard(self) -> DataFrame | Series:
+        # caller is responsible for ensuring that f is Callable
+        func = cast(Callable, self.func)
+        obj = self.obj
+
+        if isinstance(func, np.ufunc):
+            with np.errstate(all="ignore"):
+                return func(obj, *self.args, **self.kwargs)
+        elif not self.by_row:
+            return func(obj, *self.args, **self.kwargs)
+
+        if self.args or self.kwargs:
+            # _map_values does not support args/kwargs
+            def curried(x):
+                return func(x, *self.args, **self.kwargs)
+
+        else:
+            curried = func
+
+        # row-wise access
+        # apply doesn't have a `na_action` keyword and for backward compat reasons
+        # we need to give `na_action="ignore"` for categorical data.
+        # TODO: remove the `na_action="ignore"` when that default has been changed in
+        #  Categorical (GH51645).
+        action = "ignore" if isinstance(obj.dtype, CategoricalDtype) else None
+        mapped = obj._map_values(
+            mapper=curried, na_action=action, convert=self.convert_dtype
+        )
+
+        if len(mapped) and isinstance(mapped[0], ABCSeries):
+            # GH#43986 Need to do list(mapped) in order to get treated as nested
+            #  See also GH#25959 regarding EA support
+            return obj._constructor_expanddim(list(mapped), index=obj.index)
+        else:
+            return obj._constructor(mapped, index=obj.index).__finalize__(
+                obj, method="apply"
+            )
+
+
+class GroupByApply(Apply):
+    obj: GroupBy | Resampler | BaseWindow
+
+    def __init__(
+        self,
+        obj: GroupBy[NDFrameT],
+        func: AggFuncType,
+        *,
+        args,
+        kwargs,
+    ) -> None:
+        kwargs = kwargs.copy()
+        self.axis = obj.obj._get_axis_number(kwargs.get("axis", 0))
+        super().__init__(
+            obj,
+            func,
+            raw=False,
+            result_type=None,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def apply(self):
+        raise NotImplementedError
+
+    def transform(self):
+        raise NotImplementedError
+
+    def agg_or_apply_list_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        obj = self.obj
+        kwargs = self.kwargs
+        if op_name == "apply":
+            kwargs = {**kwargs, "by_row": False}
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        if obj._selected_obj.ndim == 1:
+            # For SeriesGroupBy this matches _obj_with_exclusions
+            selected_obj = obj._selected_obj
+        else:
+            selected_obj = obj._obj_with_exclusions
+
+        # Only set as_index=True on groupby objects, not Window or Resample
+        # that inherit from this class.
+        with com.temp_setattr(
+            obj, "as_index", True, condition=hasattr(obj, "as_index")
+        ):
+            keys, results = self.compute_list_like(op_name, selected_obj, kwargs)
+        result = self.wrap_results_list_like(keys, results)
+        return result
+
+    def agg_or_apply_dict_like(
+        self, op_name: Literal["agg", "apply"]
+    ) -> DataFrame | Series:
+        from pandas.core.groupby.generic import (
+            DataFrameGroupBy,
+            SeriesGroupBy,
+        )
+
+        assert op_name in ["agg", "apply"]
+
+        obj = self.obj
+        kwargs = {}
+        if op_name == "apply":
+            by_row = "_compat" if self.by_row else False
+            kwargs.update({"by_row": by_row})
+
+        if getattr(obj, "axis", 0) == 1:
+            raise NotImplementedError("axis other than 0 is not supported")
+
+        selected_obj = obj._selected_obj
+        selection = obj._selection
+
+        is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
+
+        # Numba Groupby engine/engine-kwargs passthrough
+        if is_groupby:
+            engine = self.kwargs.get("engine", None)
+            engine_kwargs = self.kwargs.get("engine_kwargs", None)
+            kwargs.update({"engine": engine, "engine_kwargs": engine_kwargs})
+
+        with com.temp_setattr(
+            obj, "as_index", True, condition=hasattr(obj, "as_index")
+        ):
+            result_index, result_data = self.compute_dict_like(
+                op_name, selected_obj, selection, kwargs
+            )
+        result = self.wrap_results_dict_like(selected_obj, result_index, result_data)
+        return result
+
+
+class ResamplerWindowApply(GroupByApply):
+    axis: AxisInt = 0
+    obj: Resampler | BaseWindow
+
+    def __init__(
+        self,
+        obj: Resampler | BaseWindow,
+        func: AggFuncType,
+        *,
+        args,
+        kwargs,
+    ) -> None:
+        super(GroupByApply, self).__init__(
+            obj,
+            func,
+            raw=False,
+            result_type=None,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    def apply(self):
+        raise NotImplementedError
+
+    def transform(self):
+        raise NotImplementedError
+
+
+def reconstruct_func(
+    func: AggFuncType | None, **kwargs
+) -> tuple[bool, AggFuncType, tuple[str, ...] | None, npt.NDArray[np.intp] | None]:
+    """
+    This is the internal function to reconstruct func given if there is relabeling
+    or not and also normalize the keyword to get new order of columns.
+
+    If named aggregation is applied, `func` will be None, and kwargs contains the
+    column and aggregation function information to be parsed;
+    If named aggregation is not applied, `func` is either string (e.g. 'min') or
+    Callable, or list of them (e.g. ['min', np.max]), or the dictionary of column name
+    and str/Callable/list of them (e.g. {'A': 'min'}, or {'A': [np.min, lambda x: x]})
+
+    If relabeling is True, will return relabeling, reconstructed func, column
+    names, and the reconstructed order of columns.
+    If relabeling is False, the columns and order will be None.
+
+    Parameters
+    ----------
+    func: agg function (e.g. 'min' or Callable) or list of agg functions
+        (e.g. ['min', np.max]) or dictionary (e.g. {'A': ['min', np.max]}).
+    **kwargs: dict, kwargs used in is_multi_agg_with_relabel and
+        normalize_keyword_aggregation function for relabelling
+
+    Returns
+    -------
+    relabelling: bool, if there is relabelling or not
+    func: normalized and mangled func
+    columns: tuple of column names
+    order: array of columns indices
+
+    Examples
+    --------
+    >>> reconstruct_func(None, **{"foo": ("col", "min")})
+    (True, defaultdict(<class 'list'>, {'col': ['min']}), ('foo',), array([0]))
+
+    >>> reconstruct_func("min")
+    (False, 'min', None, None)
+    """
+    relabeling = func is None and is_multi_agg_with_relabel(**kwargs)
+    columns: tuple[str, ...] | None = None
+    order: npt.NDArray[np.intp] | None = None
+
+    if not relabeling:
+        if isinstance(func, list) and len(func) > len(set(func)):
+            # GH 28426 will raise error if duplicated function names are used and
+            # there is no reassigned name
+            raise SpecificationError(
+                "Function names must be unique if there is no new column names "
+                "assigned"
+            )
+        if func is None:
+            # nicer error message
+            raise TypeError("Must provide 'func' or tuples of '(column, aggfunc).")
+
+    if relabeling:
+        # error: Incompatible types in assignment (expression has type
+        # "MutableMapping[Hashable, list[Callable[..., Any] | str]]", variable has type
+        # "Callable[..., Any] | str | list[Callable[..., Any] | str] |
+        # MutableMapping[Hashable, Callable[..., Any] | str | list[Callable[..., Any] |
+        # str]] | None")
+        func, columns, order = normalize_keyword_aggregation(  # type: ignore[assignment]
+            kwargs
+        )
+    assert func is not None
+
+    return relabeling, func, columns, order
+
+
+def is_multi_agg_with_relabel(**kwargs) -> bool:
+    """
+    Check whether kwargs passed to .agg look like multi-agg with relabeling.
+
+    Parameters
+    ----------
+    **kwargs : dict
+
+    Returns
+    -------
+    bool
+
+    Examples
+    --------
+    >>> is_multi_agg_with_relabel(a="max")
+    False
+    >>> is_multi_agg_with_relabel(a_max=("a", "max"), a_min=("a", "min"))
+    True
+    >>> is_multi_agg_with_relabel()
+    False
+    """
+    return all(isinstance(v, tuple) and len(v) == 2 for v in kwargs.values()) and (
+        len(kwargs) > 0
+    )
+
+
+def normalize_keyword_aggregation(
+    kwargs: dict,
+) -> tuple[
+    MutableMapping[Hashable, list[AggFuncTypeBase]],
+    tuple[str, ...],
+    npt.NDArray[np.intp],
+]:
+    """
+    Normalize user-provided "named aggregation" kwargs.
+    Transforms from the new ``Mapping[str, NamedAgg]`` style kwargs
+    to the old Dict[str, List[scalar]]].
+
+    Parameters
+    ----------
+    kwargs : dict
+
+    Returns
+    -------
+    aggspec : dict
+        The transformed kwargs.
+    columns : tuple[str, ...]
+        The user-provided keys.
+    col_idx_order : List[int]
+        List of columns indices.
+
+    Examples
+    --------
+    >>> normalize_keyword_aggregation({"output": ("input", "sum")})
+    (defaultdict(<class 'list'>, {'input': ['sum']}), ('output',), array([0]))
+    """
+    from pandas.core.indexes.base import Index
+
+    # Normalize the aggregation functions as Mapping[column, List[func]],
+    # process normally, then fixup the names.
+    # TODO: aggspec type: typing.Dict[str, List[AggScalar]]
+    aggspec = defaultdict(list)
+    order = []
+    columns, pairs = list(zip(*kwargs.items()))
+
+    for column, aggfunc in pairs:
+        aggspec[column].append(aggfunc)
+        order.append((column, com.get_callable_name(aggfunc) or aggfunc))
+
+    # uniquify aggfunc name if duplicated in order list
+    uniquified_order = _make_unique_kwarg_list(order)
+
+    # GH 25719, due to aggspec will change the order of assigned columns in aggregation
+    # uniquified_aggspec will store uniquified order list and will compare it with order
+    # based on index
+    aggspec_order = [
+        (column, com.get_callable_name(aggfunc) or aggfunc)
+        for column, aggfuncs in aggspec.items()
+        for aggfunc in aggfuncs
+    ]
+    uniquified_aggspec = _make_unique_kwarg_list(aggspec_order)
+
+    # get the new index of columns by comparison
+    col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order)
+    return aggspec, columns, col_idx_order
+
+
+def _make_unique_kwarg_list(
+    seq: Sequence[tuple[Any, Any]]
+) -> Sequence[tuple[Any, Any]]:
+    """
+    Uniquify aggfunc name of the pairs in the order list
+
+    Examples:
+    --------
+    >>> kwarg_list = [('a', '<lambda>'), ('a', '<lambda>'), ('b', '<lambda>')]
+    >>> _make_unique_kwarg_list(kwarg_list)
+    [('a', '<lambda>_0'), ('a', '<lambda>_1'), ('b', '<lambda>')]
+    """
+    return [
+        (pair[0], f"{pair[1]}_{seq[:i].count(pair)}") if seq.count(pair) > 1 else pair
+        for i, pair in enumerate(seq)
+    ]
+
+
+def relabel_result(
+    result: DataFrame | Series,
+    func: dict[str, list[Callable | str]],
+    columns: Iterable[Hashable],
+    order: Iterable[int],
+) -> dict[Hashable, Series]:
+    """
+    Internal function to reorder result if relabelling is True for
+    dataframe.agg, and return the reordered result in dict.
+
+    Parameters:
+    ----------
+    result: Result from aggregation
+    func: Dict of (column name, funcs)
+    columns: New columns name for relabelling
+    order: New order for relabelling
+
+    Examples
+    --------
+    >>> from pandas.core.apply import relabel_result
+    >>> result = pd.DataFrame(
+    ...     {"A": [np.nan, 2, np.nan], "C": [6, np.nan, np.nan], "B": [np.nan, 4, 2.5]},
+    ...     index=["max", "mean", "min"]
+    ... )
+    >>> funcs = {"A": ["max"], "C": ["max"], "B": ["mean", "min"]}
+    >>> columns = ("foo", "aab", "bar", "dat")
+    >>> order = [0, 1, 2, 3]
+    >>> result_in_dict = relabel_result(result, funcs, columns, order)
+    >>> pd.DataFrame(result_in_dict, index=columns)
+           A    C    B
+    foo  2.0  NaN  NaN
+    aab  NaN  6.0  NaN
+    bar  NaN  NaN  4.0
+    dat  NaN  NaN  2.5
+    """
+    from pandas.core.indexes.base import Index
+
+    reordered_indexes = [
+        pair[0] for pair in sorted(zip(columns, order), key=lambda t: t[1])
+    ]
+    reordered_result_in_dict: dict[Hashable, Series] = {}
+    idx = 0
+
+    reorder_mask = not isinstance(result, ABCSeries) and len(result.columns) > 1
+    for col, fun in func.items():
+        s = result[col].dropna()
+
+        # In the `_aggregate`, the callable names are obtained and used in `result`, and
+        # these names are ordered alphabetically. e.g.
+        #           C2   C1
+        # <lambda>   1  NaN
+        # amax     NaN  4.0
+        # max      NaN  4.0
+        # sum     18.0  6.0
+        # Therefore, the order of functions for each column could be shuffled
+        # accordingly so need to get the callable name if it is not parsed names, and
+        # reorder the aggregated result for each column.
+        # e.g. if df.agg(c1=("C2", sum), c2=("C2", lambda x: min(x))), correct order is
+        # [sum, <lambda>], but in `result`, it will be [<lambda>, sum], and we need to
+        # reorder so that aggregated values map to their functions regarding the order.
+
+        # However there is only one column being used for aggregation, not need to
+        # reorder since the index is not sorted, and keep as is in `funcs`, e.g.
+        #         A
+        # min   1.0
+        # mean  1.5
+        # mean  1.5
+        if reorder_mask:
+            fun = [
+                com.get_callable_name(f) if not isinstance(f, str) else f for f in fun
+            ]
+            col_idx_order = Index(s.index).get_indexer(fun)
+            s = s.iloc[col_idx_order]
+
+        # assign the new user-provided "named aggregation" as index names, and reindex
+        # it based on the whole user-provided names.
+        s.index = reordered_indexes[idx : idx + len(fun)]
+        reordered_result_in_dict[col] = s.reindex(columns, copy=False)
+        idx = idx + len(fun)
+    return reordered_result_in_dict
+
+
+def reconstruct_and_relabel_result(result, func, **kwargs) -> DataFrame | Series:
+    from pandas import DataFrame
+
+    relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+
+    if relabeling:
+        # This is to keep the order to columns occurrence unchanged, and also
+        # keep the order of new columns occurrence unchanged
+
+        # For the return values of reconstruct_func, if relabeling is
+        # False, columns and order will be None.
+        assert columns is not None
+        assert order is not None
+
+        result_in_dict = relabel_result(result, func, columns, order)
+        result = DataFrame(result_in_dict, index=columns)
+
+    return result
+
+
+# TODO: Can't use, because mypy doesn't like us setting __name__
+#   error: "partial[Any]" has no attribute "__name__"
+# the type is:
+#   typing.Sequence[Callable[..., ScalarResult]]
+#     -> typing.Sequence[Callable[..., ScalarResult]]:
+
+
+def _managle_lambda_list(aggfuncs: Sequence[Any]) -> Sequence[Any]:
+    """
+    Possibly mangle a list of aggfuncs.
+
+    Parameters
+    ----------
+    aggfuncs : Sequence
+
+    Returns
+    -------
+    mangled: list-like
+        A new AggSpec sequence, where lambdas have been converted
+        to have unique names.
+
+    Notes
+    -----
+    If just one aggfunc is passed, the name will not be mangled.
+    """
+    if len(aggfuncs) <= 1:
+        # don't mangle for .agg([lambda x: .])
+        return aggfuncs
+    i = 0
+    mangled_aggfuncs = []
+    for aggfunc in aggfuncs:
+        if com.get_callable_name(aggfunc) == "<lambda>":
+            aggfunc = partial(aggfunc)
+            aggfunc.__name__ = f"<lambda_{i}>"
+            i += 1
+        mangled_aggfuncs.append(aggfunc)
+
+    return mangled_aggfuncs
+
+
+def maybe_mangle_lambdas(agg_spec: Any) -> Any:
+    """
+    Make new lambdas with unique names.
+
+    Parameters
+    ----------
+    agg_spec : Any
+        An argument to GroupBy.agg.
+        Non-dict-like `agg_spec` are pass through as is.
+        For dict-like `agg_spec` a new spec is returned
+        with name-mangled lambdas.
+
+    Returns
+    -------
+    mangled : Any
+        Same type as the input.
+
+    Examples
+    --------
+    >>> maybe_mangle_lambdas('sum')
+    'sum'
+    >>> maybe_mangle_lambdas([lambda: 1, lambda: 2])  # doctest: +SKIP
+    [<function __main__.<lambda_0>,
+     <function pandas...._make_lambda.<locals>.f(*args, **kwargs)>]
+    """
+    is_dict = is_dict_like(agg_spec)
+    if not (is_dict or is_list_like(agg_spec)):
+        return agg_spec
+    mangled_aggspec = type(agg_spec)()  # dict or OrderedDict
+
+    if is_dict:
+        for key, aggfuncs in agg_spec.items():
+            if is_list_like(aggfuncs) and not is_dict_like(aggfuncs):
+                mangled_aggfuncs = _managle_lambda_list(aggfuncs)
+            else:
+                mangled_aggfuncs = aggfuncs
+
+            mangled_aggspec[key] = mangled_aggfuncs
+    else:
+        mangled_aggspec = _managle_lambda_list(agg_spec)
+
+    return mangled_aggspec
+
+
+def validate_func_kwargs(
+    kwargs: dict,
+) -> tuple[list[str], list[str | Callable[..., Any]]]:
+    """
+    Validates types of user-provided "named aggregation" kwargs.
+    `TypeError` is raised if aggfunc is not `str` or callable.
+
+    Parameters
+    ----------
+    kwargs : dict
+
+    Returns
+    -------
+    columns : List[str]
+        List of user-provided keys.
+    func : List[Union[str, callable[...,Any]]]
+        List of user-provided aggfuncs
+
+    Examples
+    --------
+    >>> validate_func_kwargs({'one': 'min', 'two': 'max'})
+    (['one', 'two'], ['min', 'max'])
+    """
+    tuple_given_message = "func is expected but received {} in **kwargs."
+    columns = list(kwargs)
+    func = []
+    for col_func in kwargs.values():
+        if not (isinstance(col_func, str) or callable(col_func)):
+            raise TypeError(tuple_given_message.format(type(col_func).__name__))
+        func.append(col_func)
+    if not columns:
+        no_arg_message = "Must provide 'func' or named aggregation **kwargs."
+        raise TypeError(no_arg_message)
+    return columns, func
+
+
+def include_axis(op_name: Literal["agg", "apply"], colg: Series | DataFrame) -> bool:
+    return isinstance(colg, ABCDataFrame) or (
+        isinstance(colg, ABCSeries) and op_name == "agg"
+    )
+
+
+def warn_alias_replacement(
+    obj: AggObjType,
+    func: Callable,
+    alias: str,
+) -> None:
+    if alias.startswith("np."):
+        full_alias = alias
+    else:
+        full_alias = f"{type(obj).__name__}.{alias}"
+        alias = f'"{alias}"'
+    warnings.warn(
+        f"The provided callable {func} is currently using "
+        f"{full_alias}. In a future version of pandas, "
+        f"the provided callable will be used directly. To keep current "
+        f"behavior pass the string {alias} instead.",
+        category=FutureWarning,
+        stacklevel=find_stack_level(),
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7655a013c6cf3fca754086fdeb29b806220d5e4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/__init__.py
@@ -0,0 +1,9 @@
+"""
+core.array_algos is for algorithms that operate on ndarray and ExtensionArray.
+These should:
+
+- Assume that any Index, Series, or DataFrame objects have already been unwrapped.
+- Assume that any list arguments have already been cast to ndarray/EA.
+- Not depend on Index, Series, or DataFrame, nor import any of these.
+- May dispatch to ExtensionArray methods, but should not import from core.arrays.
+"""
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py
new file mode 100644
index 0000000000000000000000000000000000000000..825fe60ee6cf88a2186a5f501c8696cecaf2657d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py
@@ -0,0 +1,67 @@
+"""
+datetimelke_accumulations.py is for accumulations of datetimelike extension arrays
+"""
+
+from __future__ import annotations
+
+from typing import Callable
+
+import numpy as np
+
+from pandas._libs import iNaT
+
+from pandas.core.dtypes.missing import isna
+
+
+def _cum_func(
+    func: Callable,
+    values: np.ndarray,
+    *,
+    skipna: bool = True,
+):
+    """
+    Accumulations for 1D datetimelike arrays.
+
+    Parameters
+    ----------
+    func : np.cumsum, np.maximum.accumulate, np.minimum.accumulate
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation). Values is changed is modified inplace.
+    skipna : bool, default True
+        Whether to skip NA.
+    """
+    try:
+        fill_value = {
+            np.maximum.accumulate: np.iinfo(np.int64).min,
+            np.cumsum: 0,
+            np.minimum.accumulate: np.iinfo(np.int64).max,
+        }[func]
+    except KeyError:
+        raise ValueError(f"No accumulation for {func} implemented on BaseMaskedArray")
+
+    mask = isna(values)
+    y = values.view("i8")
+    y[mask] = fill_value
+
+    if not skipna:
+        mask = np.maximum.accumulate(mask)
+
+    result = func(y)
+    result[mask] = iNaT
+
+    if values.dtype.kind in "mM":
+        return result.view(values.dtype.base)
+    return result
+
+
+def cumsum(values: np.ndarray, *, skipna: bool = True) -> np.ndarray:
+    return _cum_func(np.cumsum, values, skipna=skipna)
+
+
+def cummin(values: np.ndarray, *, skipna: bool = True):
+    return _cum_func(np.minimum.accumulate, values, skipna=skipna)
+
+
+def cummax(values: np.ndarray, *, skipna: bool = True):
+    return _cum_func(np.maximum.accumulate, values, skipna=skipna)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad9e96d398a242dc64de2018b749fd2dbca7ed78
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py
@@ -0,0 +1,90 @@
+"""
+masked_accumulations.py is for accumulation algorithms using a mask-based approach
+for missing values.
+"""
+
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+def _cum_func(
+    func: Callable,
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+):
+    """
+    Accumulations for 1D masked array.
+
+    We will modify values in place to replace NAs with the appropriate fill value.
+
+    Parameters
+    ----------
+    func : np.cumsum, np.cumprod, np.maximum.accumulate, np.minimum.accumulate
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation).
+    mask : np.ndarray
+        Boolean numpy array (True values indicate missing values).
+    skipna : bool, default True
+        Whether to skip NA.
+    """
+    dtype_info: np.iinfo | np.finfo
+    if values.dtype.kind == "f":
+        dtype_info = np.finfo(values.dtype.type)
+    elif values.dtype.kind in "iu":
+        dtype_info = np.iinfo(values.dtype.type)
+    elif values.dtype.kind == "b":
+        # Max value of bool is 1, but since we are setting into a boolean
+        # array, 255 is fine as well. Min value has to be 0 when setting
+        # into the boolean array.
+        dtype_info = np.iinfo(np.uint8)
+    else:
+        raise NotImplementedError(
+            f"No masked accumulation defined for dtype {values.dtype.type}"
+        )
+    try:
+        fill_value = {
+            np.cumprod: 1,
+            np.maximum.accumulate: dtype_info.min,
+            np.cumsum: 0,
+            np.minimum.accumulate: dtype_info.max,
+        }[func]
+    except KeyError:
+        raise NotImplementedError(
+            f"No accumulation for {func} implemented on BaseMaskedArray"
+        )
+
+    values[mask] = fill_value
+
+    if not skipna:
+        mask = np.maximum.accumulate(mask)
+
+    values = func(values)
+    return values, mask
+
+
+def cumsum(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.cumsum, values, mask, skipna=skipna)
+
+
+def cumprod(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.cumprod, values, mask, skipna=skipna)
+
+
+def cummin(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.minimum.accumulate, values, mask, skipna=skipna)
+
+
+def cummax(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True):
+    return _cum_func(np.maximum.accumulate, values, mask, skipna=skipna)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bf97729a79b1436fdf466eade06e0049984c1d0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py
@@ -0,0 +1,201 @@
+"""
+masked_reductions.py is for reduction algorithms using a mask-based approach
+for missing values.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import missing as libmissing
+
+from pandas.core.nanops import check_below_min_count
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AxisInt,
+        npt,
+    )
+
+
+def _reductions(
+    func: Callable,
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    min_count: int = 0,
+    axis: AxisInt | None = None,
+    **kwargs,
+):
+    """
+    Sum, mean or product for 1D masked array.
+
+    Parameters
+    ----------
+    func : np.sum or np.prod
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation).
+    mask : np.ndarray[bool]
+        Boolean numpy array (True values indicate missing values).
+    skipna : bool, default True
+        Whether to skip NA.
+    min_count : int, default 0
+        The required number of valid values to perform the operation. If fewer than
+        ``min_count`` non-NA values are present the result will be NA.
+    axis : int, optional, default None
+    """
+    if not skipna:
+        if mask.any() or check_below_min_count(values.shape, None, min_count):
+            return libmissing.NA
+        else:
+            return func(values, axis=axis, **kwargs)
+    else:
+        if check_below_min_count(values.shape, mask, min_count) and (
+            axis is None or values.ndim == 1
+        ):
+            return libmissing.NA
+
+        if values.dtype == np.dtype(object):
+            # object dtype does not support `where` without passing an initial
+            values = values[~mask]
+            return func(values, axis=axis, **kwargs)
+        return func(values, where=~mask, axis=axis, **kwargs)
+
+
+def sum(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    min_count: int = 0,
+    axis: AxisInt | None = None,
+):
+    return _reductions(
+        np.sum, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
+    )
+
+
+def prod(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    min_count: int = 0,
+    axis: AxisInt | None = None,
+):
+    return _reductions(
+        np.prod, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
+    )
+
+
+def _minmax(
+    func: Callable,
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    """
+    Reduction for 1D masked array.
+
+    Parameters
+    ----------
+    func : np.min or np.max
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation).
+    mask : np.ndarray[bool]
+        Boolean numpy array (True values indicate missing values).
+    skipna : bool, default True
+        Whether to skip NA.
+    axis : int, optional, default None
+    """
+    if not skipna:
+        if mask.any() or not values.size:
+            # min/max with empty array raise in numpy, pandas returns NA
+            return libmissing.NA
+        else:
+            return func(values, axis=axis)
+    else:
+        subset = values[~mask]
+        if subset.size:
+            return func(subset, axis=axis)
+        else:
+            # min/max with empty array raise in numpy, pandas returns NA
+            return libmissing.NA
+
+
+def min(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    return _minmax(np.min, values=values, mask=mask, skipna=skipna, axis=axis)
+
+
+def max(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    return _minmax(np.max, values=values, mask=mask, skipna=skipna, axis=axis)
+
+
+def mean(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+):
+    if not values.size or mask.all():
+        return libmissing.NA
+    return _reductions(np.mean, values=values, mask=mask, skipna=skipna, axis=axis)
+
+
+def var(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+    ddof: int = 1,
+):
+    if not values.size or mask.all():
+        return libmissing.NA
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        return _reductions(
+            np.var, values=values, mask=mask, skipna=skipna, axis=axis, ddof=ddof
+        )
+
+
+def std(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    *,
+    skipna: bool = True,
+    axis: AxisInt | None = None,
+    ddof: int = 1,
+):
+    if not values.size or mask.all():
+        return libmissing.NA
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning)
+        return _reductions(
+            np.std, values=values, mask=mask, skipna=skipna, axis=axis, ddof=ddof
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/putmask.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/putmask.py
new file mode 100644
index 0000000000000000000000000000000000000000..f65d2d20e028e36b35a397d8ac973f184ce1412c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/putmask.py
@@ -0,0 +1,149 @@
+"""
+EA-compatible analogue to np.putmask
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.cast import infer_dtype_from
+from pandas.core.dtypes.common import is_list_like
+
+from pandas.core.arrays import ExtensionArray
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+    from pandas import MultiIndex
+
+
+def putmask_inplace(values: ArrayLike, mask: npt.NDArray[np.bool_], value: Any) -> None:
+    """
+    ExtensionArray-compatible implementation of np.putmask.  The main
+    difference is we do not handle repeating or truncating like numpy.
+
+    Parameters
+    ----------
+    values: np.ndarray or ExtensionArray
+    mask : np.ndarray[bool]
+        We assume extract_bool_array has already been called.
+    value : Any
+    """
+
+    if (
+        not isinstance(values, np.ndarray)
+        or (values.dtype == object and not lib.is_scalar(value))
+        # GH#43424: np.putmask raises TypeError if we cannot cast between types with
+        # rule = "safe", a stricter guarantee we may not have here
+        or (
+            isinstance(value, np.ndarray) and not np.can_cast(value.dtype, values.dtype)
+        )
+    ):
+        # GH#19266 using np.putmask gives unexpected results with listlike value
+        #  along with object dtype
+        if is_list_like(value) and len(value) == len(values):
+            values[mask] = value[mask]
+        else:
+            values[mask] = value
+    else:
+        # GH#37833 np.putmask is more performant than __setitem__
+        np.putmask(values, mask, value)
+
+
+def putmask_without_repeat(
+    values: np.ndarray, mask: npt.NDArray[np.bool_], new: Any
+) -> None:
+    """
+    np.putmask will truncate or repeat if `new` is a listlike with
+    len(new) != len(values).  We require an exact match.
+
+    Parameters
+    ----------
+    values : np.ndarray
+    mask : np.ndarray[bool]
+    new : Any
+    """
+    if getattr(new, "ndim", 0) >= 1:
+        new = new.astype(values.dtype, copy=False)
+
+    # TODO: this prob needs some better checking for 2D cases
+    nlocs = mask.sum()
+    if nlocs > 0 and is_list_like(new) and getattr(new, "ndim", 1) == 1:
+        shape = np.shape(new)
+        # np.shape compat for if setitem_datetimelike_compat
+        #  changed arraylike to list e.g. test_where_dt64_2d
+        if nlocs == shape[-1]:
+            # GH#30567
+            # If length of ``new`` is less than the length of ``values``,
+            # `np.putmask` would first repeat the ``new`` array and then
+            # assign the masked values hence produces incorrect result.
+            # `np.place` on the other hand uses the ``new`` values at it is
+            # to place in the masked locations of ``values``
+            np.place(values, mask, new)
+            # i.e. values[mask] = new
+        elif mask.shape[-1] == shape[-1] or shape[-1] == 1:
+            np.putmask(values, mask, new)
+        else:
+            raise ValueError("cannot assign mismatch length to masked array")
+    else:
+        np.putmask(values, mask, new)
+
+
+def validate_putmask(
+    values: ArrayLike | MultiIndex, mask: np.ndarray
+) -> tuple[npt.NDArray[np.bool_], bool]:
+    """
+    Validate mask and check if this putmask operation is a no-op.
+    """
+    mask = extract_bool_array(mask)
+    if mask.shape != values.shape:
+        raise ValueError("putmask: mask and data must be the same size")
+
+    noop = not mask.any()
+    return mask, noop
+
+
+def extract_bool_array(mask: ArrayLike) -> npt.NDArray[np.bool_]:
+    """
+    If we have a SparseArray or BooleanArray, convert it to ndarray[bool].
+    """
+    if isinstance(mask, ExtensionArray):
+        # We could have BooleanArray, Sparse[bool], ...
+        #  Except for BooleanArray, this is equivalent to just
+        #  np.asarray(mask, dtype=bool)
+        mask = mask.to_numpy(dtype=bool, na_value=False)
+
+    mask = np.asarray(mask, dtype=bool)
+    return mask
+
+
+def setitem_datetimelike_compat(values: np.ndarray, num_set: int, other):
+    """
+    Parameters
+    ----------
+    values : np.ndarray
+    num_set : int
+        For putmask, this is mask.sum()
+    other : Any
+    """
+    if values.dtype == object:
+        dtype, _ = infer_dtype_from(other)
+
+        if lib.is_np_dtype(dtype, "mM"):
+            # https://github.com/numpy/numpy/issues/12550
+            #  timedelta64 will incorrectly cast to int
+            if not is_list_like(other):
+                other = [other] * num_set
+            else:
+                other = list(other)
+
+    return other
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/quantile.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/quantile.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c933294fb944f04dd3e9a64e4731ea4349254f9
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/quantile.py
@@ -0,0 +1,226 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Scalar,
+        npt,
+    )
+
+
+def quantile_compat(
+    values: ArrayLike, qs: npt.NDArray[np.float64], interpolation: str
+) -> ArrayLike:
+    """
+    Compute the quantiles of the given values for each quantile in `qs`.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    qs : np.ndarray[float64]
+    interpolation : str
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    if isinstance(values, np.ndarray):
+        fill_value = na_value_for_dtype(values.dtype, compat=False)
+        mask = isna(values)
+        return quantile_with_mask(values, mask, fill_value, qs, interpolation)
+    else:
+        return values._quantile(qs, interpolation)
+
+
+def quantile_with_mask(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    fill_value,
+    qs: npt.NDArray[np.float64],
+    interpolation: str,
+) -> np.ndarray:
+    """
+    Compute the quantiles of the given values for each quantile in `qs`.
+
+    Parameters
+    ----------
+    values : np.ndarray
+        For ExtensionArray, this is _values_for_factorize()[0]
+    mask : np.ndarray[bool]
+        mask = isna(values)
+        For ExtensionArray, this is computed before calling _value_for_factorize
+    fill_value : Scalar
+        The value to interpret fill NA entries with
+        For ExtensionArray, this is _values_for_factorize()[1]
+    qs : np.ndarray[float64]
+    interpolation : str
+        Type of interpolation
+
+    Returns
+    -------
+    np.ndarray
+
+    Notes
+    -----
+    Assumes values is already 2D.  For ExtensionArray this means np.atleast_2d
+    has been called on _values_for_factorize()[0]
+
+    Quantile is computed along axis=1.
+    """
+    assert values.shape == mask.shape
+    if values.ndim == 1:
+        # unsqueeze, operate, re-squeeze
+        values = np.atleast_2d(values)
+        mask = np.atleast_2d(mask)
+        res_values = quantile_with_mask(values, mask, fill_value, qs, interpolation)
+        return res_values[0]
+
+    assert values.ndim == 2
+
+    is_empty = values.shape[1] == 0
+
+    if is_empty:
+        # create the array of na_values
+        # 2d len(values) * len(qs)
+        flat = np.array([fill_value] * len(qs))
+        result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
+    else:
+        result = _nanpercentile(
+            values,
+            qs * 100.0,
+            na_value=fill_value,
+            mask=mask,
+            interpolation=interpolation,
+        )
+
+        result = np.asarray(result)
+        result = result.T
+
+    return result
+
+
+def _nanpercentile_1d(
+    values: np.ndarray,
+    mask: npt.NDArray[np.bool_],
+    qs: npt.NDArray[np.float64],
+    na_value: Scalar,
+    interpolation: str,
+) -> Scalar | np.ndarray:
+    """
+    Wrapper for np.percentile that skips missing values, specialized to
+    1-dimensional case.
+
+    Parameters
+    ----------
+    values : array over which to find quantiles
+    mask : ndarray[bool]
+        locations in values that should be considered missing
+    qs : np.ndarray[float64] of quantile indices to find
+    na_value : scalar
+        value to return for empty or all-null values
+    interpolation : str
+
+    Returns
+    -------
+    quantiles : scalar or array
+    """
+    # mask is Union[ExtensionArray, ndarray]
+    values = values[~mask]
+
+    if len(values) == 0:
+        # Can't pass dtype=values.dtype here bc we might have na_value=np.nan
+        #  with values.dtype=int64 see test_quantile_empty
+        # equiv: 'np.array([na_value] * len(qs))' but much faster
+        return np.full(len(qs), na_value)
+
+    return np.percentile(
+        values,
+        qs,
+        # error: No overload variant of "percentile" matches argument
+        # types "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]"
+        # , "Dict[str, str]"  [call-overload]
+        method=interpolation,  # type: ignore[call-overload]
+    )
+
+
+def _nanpercentile(
+    values: np.ndarray,
+    qs: npt.NDArray[np.float64],
+    *,
+    na_value,
+    mask: npt.NDArray[np.bool_],
+    interpolation: str,
+):
+    """
+    Wrapper for np.percentile that skips missing values.
+
+    Parameters
+    ----------
+    values : np.ndarray[ndim=2]  over which to find quantiles
+    qs : np.ndarray[float64] of quantile indices to find
+    na_value : scalar
+        value to return for empty or all-null values
+    mask : np.ndarray[bool]
+        locations in values that should be considered missing
+    interpolation : str
+
+    Returns
+    -------
+    quantiles : scalar or array
+    """
+
+    if values.dtype.kind in "mM":
+        # need to cast to integer to avoid rounding errors in numpy
+        result = _nanpercentile(
+            values.view("i8"),
+            qs=qs,
+            na_value=na_value.view("i8"),
+            mask=mask,
+            interpolation=interpolation,
+        )
+
+        # Note: we have to do `astype` and not view because in general we
+        #  have float result at this point, not i8
+        return result.astype(values.dtype)
+
+    if mask.any():
+        # Caller is responsible for ensuring mask shape match
+        assert mask.shape == values.shape
+        result = [
+            _nanpercentile_1d(val, m, qs, na_value, interpolation=interpolation)
+            for (val, m) in zip(list(values), list(mask))
+        ]
+        if values.dtype.kind == "f":
+            # preserve itemsize
+            result = np.asarray(result, dtype=values.dtype).T
+        else:
+            result = np.asarray(result).T
+            if (
+                result.dtype != values.dtype
+                and not mask.all()
+                and (result == result.astype(values.dtype, copy=False)).all()
+            ):
+                # mask.all() will never get cast back to int
+                # e.g. values id integer dtype and result is floating dtype,
+                #  only cast back to integer dtype if result values are all-integer.
+                result = result.astype(values.dtype, copy=False)
+        return result
+    else:
+        return np.percentile(
+            values,
+            qs,
+            axis=1,
+            # error: No overload variant of "percentile" matches argument types
+            # "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]",
+            # "int", "Dict[str, str]"  [call-overload]
+            method=interpolation,  # type: ignore[call-overload]
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/replace.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..16239ed8c46970f38159ae12bc9a45da31ffd617
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/replace.py
@@ -0,0 +1,155 @@
+"""
+Methods used by Block.replace and related methods.
+"""
+from __future__ import annotations
+
+import operator
+import re
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_re,
+    is_re_compilable,
+)
+from pandas.core.dtypes.missing import isna
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Scalar,
+        npt,
+    )
+
+
+def should_use_regex(regex: bool, to_replace: Any) -> bool:
+    """
+    Decide whether to treat `to_replace` as a regular expression.
+    """
+    if is_re(to_replace):
+        regex = True
+
+    regex = regex and is_re_compilable(to_replace)
+
+    # Don't use regex if the pattern is empty.
+    regex = regex and re.compile(to_replace).pattern != ""
+    return regex
+
+
+def compare_or_regex_search(
+    a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: npt.NDArray[np.bool_]
+) -> ArrayLike:
+    """
+    Compare two array-like inputs of the same shape or two scalar values
+
+    Calls operator.eq or re.search, depending on regex argument. If regex is
+    True, perform an element-wise regex matching.
+
+    Parameters
+    ----------
+    a : array-like
+    b : scalar or regex pattern
+    regex : bool
+    mask : np.ndarray[bool]
+
+    Returns
+    -------
+    mask : array-like of bool
+    """
+    if isna(b):
+        return ~mask
+
+    def _check_comparison_types(
+        result: ArrayLike | bool, a: ArrayLike, b: Scalar | Pattern
+    ):
+        """
+        Raises an error if the two arrays (a,b) cannot be compared.
+        Otherwise, returns the comparison result as expected.
+        """
+        if is_bool(result) and isinstance(a, np.ndarray):
+            type_names = [type(a).__name__, type(b).__name__]
+
+            type_names[0] = f"ndarray(dtype={a.dtype})"
+
+            raise TypeError(
+                f"Cannot compare types {repr(type_names[0])} and {repr(type_names[1])}"
+            )
+
+    if not regex or not should_use_regex(regex, b):
+        # TODO: should use missing.mask_missing?
+        op = lambda x: operator.eq(x, b)
+    else:
+        op = np.vectorize(
+            lambda x: bool(re.search(b, x))
+            if isinstance(x, str) and isinstance(b, (str, Pattern))
+            else False
+        )
+
+    # GH#32621 use mask to avoid comparing to NAs
+    if isinstance(a, np.ndarray) and mask is not None:
+        a = a[mask]
+        result = op(a)
+
+        if isinstance(result, np.ndarray):
+            # The shape of the mask can differ to that of the result
+            # since we may compare only a subset of a's or b's elements
+            tmp = np.zeros(mask.shape, dtype=np.bool_)
+            np.place(tmp, mask, result)
+            result = tmp
+    else:
+        result = op(a)
+
+    _check_comparison_types(result, a, b)
+    return result
+
+
+def replace_regex(
+    values: ArrayLike, rx: re.Pattern, value, mask: npt.NDArray[np.bool_] | None
+) -> None:
+    """
+    Parameters
+    ----------
+    values : ArrayLike
+        Object dtype.
+    rx : re.Pattern
+    value : Any
+    mask : np.ndarray[bool], optional
+
+    Notes
+    -----
+    Alters values in-place.
+    """
+
+    # deal with replacing values with objects (strings) that match but
+    # whose replacement is not a string (numeric, nan, object)
+    if isna(value) or not isinstance(value, str):
+
+        def re_replacer(s):
+            if is_re(rx) and isinstance(s, str):
+                return value if rx.search(s) is not None else s
+            else:
+                return s
+
+    else:
+        # value is guaranteed to be a string here, s can be either a string
+        # or null if it's null it gets returned
+        def re_replacer(s):
+            if is_re(rx) and isinstance(s, str):
+                return rx.sub(value, s)
+            else:
+                return s
+
+    f = np.vectorize(re_replacer, otypes=[np.object_])
+
+    if mask is None:
+        values[:] = f(values)
+    else:
+        if values.ndim != mask.ndim:
+            mask = np.broadcast_to(mask, values.shape)
+        values[mask] = f(values[mask])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/take.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/take.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac674e31586e72040a8e1313f232a00299b961ee
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/take.py
@@ -0,0 +1,594 @@
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+
+from pandas.core.dtypes.cast import maybe_promote
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_1d_only_ea_dtype,
+)
+from pandas.core.dtypes.missing import na_value_for_dtype
+
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        npt,
+    )
+
+    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+    from pandas.core.arrays.base import ExtensionArray
+
+
+@overload
+def take_nd(
+    arr: np.ndarray,
+    indexer,
+    axis: AxisInt = ...,
+    fill_value=...,
+    allow_fill: bool = ...,
+) -> np.ndarray:
+    ...
+
+
+@overload
+def take_nd(
+    arr: ExtensionArray,
+    indexer,
+    axis: AxisInt = ...,
+    fill_value=...,
+    allow_fill: bool = ...,
+) -> ArrayLike:
+    ...
+
+
+def take_nd(
+    arr: ArrayLike,
+    indexer,
+    axis: AxisInt = 0,
+    fill_value=lib.no_default,
+    allow_fill: bool = True,
+) -> ArrayLike:
+    """
+    Specialized Cython take which sets NaN values in one pass
+
+    This dispatches to ``take`` defined on ExtensionArrays.
+
+    Note: this function assumes that the indexer is a valid(ated) indexer with
+    no out of bound indices.
+
+    Parameters
+    ----------
+    arr : np.ndarray or ExtensionArray
+        Input array.
+    indexer : ndarray
+        1-D array of indices to take, subarrays corresponding to -1 value
+        indices are filed with fill_value
+    axis : int, default 0
+        Axis to take from
+    fill_value : any, default np.nan
+        Fill value to replace -1 values with
+    allow_fill : bool, default True
+        If False, indexer is assumed to contain no -1 values so no filling
+        will be done.  This short-circuits computation of a mask.  Result is
+        undefined if allow_fill == False and -1 is present in indexer.
+
+    Returns
+    -------
+    subarray : np.ndarray or ExtensionArray
+        May be the same type as the input, or cast to an ndarray.
+    """
+    if fill_value is lib.no_default:
+        fill_value = na_value_for_dtype(arr.dtype, compat=False)
+    elif lib.is_np_dtype(arr.dtype, "mM"):
+        dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+        if arr.dtype != dtype:
+            # EA.take is strict about returning a new object of the same type
+            # so for that case cast upfront
+            arr = arr.astype(dtype)
+
+    if not isinstance(arr, np.ndarray):
+        # i.e. ExtensionArray,
+        # includes for EA to catch DatetimeArray, TimedeltaArray
+        if not is_1d_only_ea_dtype(arr.dtype):
+            # i.e. DatetimeArray, TimedeltaArray
+            arr = cast("NDArrayBackedExtensionArray", arr)
+            return arr.take(
+                indexer, fill_value=fill_value, allow_fill=allow_fill, axis=axis
+            )
+
+        return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
+
+    arr = np.asarray(arr)
+    return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)
+
+
+def _take_nd_ndarray(
+    arr: np.ndarray,
+    indexer: npt.NDArray[np.intp] | None,
+    axis: AxisInt,
+    fill_value,
+    allow_fill: bool,
+) -> np.ndarray:
+    if indexer is None:
+        indexer = np.arange(arr.shape[axis], dtype=np.intp)
+        dtype, fill_value = arr.dtype, arr.dtype.type()
+    else:
+        indexer = ensure_platform_int(indexer)
+
+    dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
+        arr, indexer, fill_value, allow_fill
+    )
+
+    flip_order = False
+    if arr.ndim == 2 and arr.flags.f_contiguous:
+        flip_order = True
+
+    if flip_order:
+        arr = arr.T
+        axis = arr.ndim - axis - 1
+
+    # at this point, it's guaranteed that dtype can hold both the arr values
+    # and the fill_value
+    out_shape_ = list(arr.shape)
+    out_shape_[axis] = len(indexer)
+    out_shape = tuple(out_shape_)
+    if arr.flags.f_contiguous and axis == arr.ndim - 1:
+        # minor tweak that can make an order-of-magnitude difference
+        # for dataframes initialized directly from 2-d ndarrays
+        # (s.t. df.values is c-contiguous and df._mgr.blocks[0] is its
+        # f-contiguous transpose)
+        out = np.empty(out_shape, dtype=dtype, order="F")
+    else:
+        out = np.empty(out_shape, dtype=dtype)
+
+    func = _get_take_nd_function(
+        arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info
+    )
+    func(arr, indexer, out, fill_value)
+
+    if flip_order:
+        out = out.T
+    return out
+
+
+def take_1d(
+    arr: ArrayLike,
+    indexer: npt.NDArray[np.intp],
+    fill_value=None,
+    allow_fill: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> ArrayLike:
+    """
+    Specialized version for 1D arrays. Differences compared to `take_nd`:
+
+    - Assumes input array has already been converted to numpy array / EA
+    - Assumes indexer is already guaranteed to be intp dtype ndarray
+    - Only works for 1D arrays
+
+    To ensure the lowest possible overhead.
+
+    Note: similarly to `take_nd`, this function assumes that the indexer is
+    a valid(ated) indexer with no out of bound indices.
+
+    Parameters
+    ----------
+    arr : np.ndarray or ExtensionArray
+        Input array.
+    indexer : ndarray
+        1-D array of indices to take (validated indices, intp dtype).
+    fill_value : any, default np.nan
+        Fill value to replace -1 values with
+    allow_fill : bool, default True
+        If False, indexer is assumed to contain no -1 values so no filling
+        will be done.  This short-circuits computation of a mask. Result is
+        undefined if allow_fill == False and -1 is present in indexer.
+    mask : np.ndarray, optional, default None
+        If `allow_fill` is True, and the mask (where indexer == -1) is already
+        known, it can be passed to avoid recomputation.
+    """
+    if not isinstance(arr, np.ndarray):
+        # ExtensionArray -> dispatch to their method
+        return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
+
+    if not allow_fill:
+        return arr.take(indexer)
+
+    dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
+        arr, indexer, fill_value, True, mask
+    )
+
+    # at this point, it's guaranteed that dtype can hold both the arr values
+    # and the fill_value
+    out = np.empty(indexer.shape, dtype=dtype)
+
+    func = _get_take_nd_function(
+        arr.ndim, arr.dtype, out.dtype, axis=0, mask_info=mask_info
+    )
+    func(arr, indexer, out, fill_value)
+
+    return out
+
+
+def take_2d_multi(
+    arr: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    fill_value=np.nan,
+) -> np.ndarray:
+    """
+    Specialized Cython take which sets NaN values in one pass.
+    """
+    # This is only called from one place in DataFrame._reindex_multi,
+    #  so we know indexer is well-behaved.
+    assert indexer is not None
+    assert indexer[0] is not None
+    assert indexer[1] is not None
+
+    row_idx, col_idx = indexer
+
+    row_idx = ensure_platform_int(row_idx)
+    col_idx = ensure_platform_int(col_idx)
+    indexer = row_idx, col_idx
+    mask_info = None
+
+    # check for promotion based on types only (do this first because
+    # it's faster than computing a mask)
+    dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+    if dtype != arr.dtype:
+        # check if promotion is actually required based on indexer
+        row_mask = row_idx == -1
+        col_mask = col_idx == -1
+        row_needs = row_mask.any()
+        col_needs = col_mask.any()
+        mask_info = (row_mask, col_mask), (row_needs, col_needs)
+
+        if not (row_needs or col_needs):
+            # if not, then depromote, set fill_value to dummy
+            # (it won't be used but we don't want the cython code
+            # to crash when trying to cast it to dtype)
+            dtype, fill_value = arr.dtype, arr.dtype.type()
+
+    # at this point, it's guaranteed that dtype can hold both the arr values
+    # and the fill_value
+    out_shape = len(row_idx), len(col_idx)
+    out = np.empty(out_shape, dtype=dtype)
+
+    func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None)
+    if func is None and arr.dtype != out.dtype:
+        func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None)
+        if func is not None:
+            func = _convert_wrapper(func, out.dtype)
+
+    if func is not None:
+        func(arr, indexer, out=out, fill_value=fill_value)
+    else:
+        # test_reindex_multi
+        _take_2d_multi_object(
+            arr, indexer, out, fill_value=fill_value, mask_info=mask_info
+        )
+
+    return out
+
+
+@functools.lru_cache
+def _get_take_nd_function_cached(
+    ndim: int, arr_dtype: np.dtype, out_dtype: np.dtype, axis: AxisInt
+):
+    """
+    Part of _get_take_nd_function below that doesn't need `mask_info` and thus
+    can be cached (mask_info potentially contains a numpy ndarray which is not
+    hashable and thus cannot be used as argument for cached function).
+    """
+    tup = (arr_dtype.name, out_dtype.name)
+    if ndim == 1:
+        func = _take_1d_dict.get(tup, None)
+    elif ndim == 2:
+        if axis == 0:
+            func = _take_2d_axis0_dict.get(tup, None)
+        else:
+            func = _take_2d_axis1_dict.get(tup, None)
+    if func is not None:
+        return func
+
+    # We get here with string, uint, float16, and complex dtypes that could
+    #  potentially be handled in algos_take_helper.
+    #  Also a couple with (M8[ns], object) and (m8[ns], object)
+    tup = (out_dtype.name, out_dtype.name)
+    if ndim == 1:
+        func = _take_1d_dict.get(tup, None)
+    elif ndim == 2:
+        if axis == 0:
+            func = _take_2d_axis0_dict.get(tup, None)
+        else:
+            func = _take_2d_axis1_dict.get(tup, None)
+    if func is not None:
+        func = _convert_wrapper(func, out_dtype)
+        return func
+
+    return None
+
+
+def _get_take_nd_function(
+    ndim: int,
+    arr_dtype: np.dtype,
+    out_dtype: np.dtype,
+    axis: AxisInt = 0,
+    mask_info=None,
+):
+    """
+    Get the appropriate "take" implementation for the given dimension, axis
+    and dtypes.
+    """
+    func = None
+    if ndim <= 2:
+        # for this part we don't need `mask_info` -> use the cached algo lookup
+        func = _get_take_nd_function_cached(ndim, arr_dtype, out_dtype, axis)
+
+    if func is None:
+
+        def func(arr, indexer, out, fill_value=np.nan) -> None:
+            indexer = ensure_platform_int(indexer)
+            _take_nd_object(
+                arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info
+            )
+
+    return func
+
+
+def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None):
+    def wrapper(
+        arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan
+    ) -> None:
+        if arr_dtype is not None:
+            arr = arr.view(arr_dtype)
+        if out_dtype is not None:
+            out = out.view(out_dtype)
+        if fill_wrap is not None:
+            # FIXME: if we get here with dt64/td64 we need to be sure we have
+            #  matching resos
+            if fill_value.dtype.kind == "m":
+                fill_value = fill_value.astype("m8[ns]")
+            else:
+                fill_value = fill_value.astype("M8[ns]")
+            fill_value = fill_wrap(fill_value)
+
+        f(arr, indexer, out, fill_value=fill_value)
+
+    return wrapper
+
+
+def _convert_wrapper(f, conv_dtype):
+    def wrapper(
+        arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan
+    ) -> None:
+        if conv_dtype == object:
+            # GH#39755 avoid casting dt64/td64 to integers
+            arr = ensure_wrapped_if_datetimelike(arr)
+        arr = arr.astype(conv_dtype)
+        f(arr, indexer, out, fill_value=fill_value)
+
+    return wrapper
+
+
+_take_1d_dict = {
+    ("int8", "int8"): libalgos.take_1d_int8_int8,
+    ("int8", "int32"): libalgos.take_1d_int8_int32,
+    ("int8", "int64"): libalgos.take_1d_int8_int64,
+    ("int8", "float64"): libalgos.take_1d_int8_float64,
+    ("int16", "int16"): libalgos.take_1d_int16_int16,
+    ("int16", "int32"): libalgos.take_1d_int16_int32,
+    ("int16", "int64"): libalgos.take_1d_int16_int64,
+    ("int16", "float64"): libalgos.take_1d_int16_float64,
+    ("int32", "int32"): libalgos.take_1d_int32_int32,
+    ("int32", "int64"): libalgos.take_1d_int32_int64,
+    ("int32", "float64"): libalgos.take_1d_int32_float64,
+    ("int64", "int64"): libalgos.take_1d_int64_int64,
+    ("int64", "float64"): libalgos.take_1d_int64_float64,
+    ("float32", "float32"): libalgos.take_1d_float32_float32,
+    ("float32", "float64"): libalgos.take_1d_float32_float64,
+    ("float64", "float64"): libalgos.take_1d_float64_float64,
+    ("object", "object"): libalgos.take_1d_object_object,
+    ("bool", "bool"): _view_wrapper(libalgos.take_1d_bool_bool, np.uint8, np.uint8),
+    ("bool", "object"): _view_wrapper(libalgos.take_1d_bool_object, np.uint8, None),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64
+    ),
+}
+
+_take_2d_axis0_dict = {
+    ("int8", "int8"): libalgos.take_2d_axis0_int8_int8,
+    ("int8", "int32"): libalgos.take_2d_axis0_int8_int32,
+    ("int8", "int64"): libalgos.take_2d_axis0_int8_int64,
+    ("int8", "float64"): libalgos.take_2d_axis0_int8_float64,
+    ("int16", "int16"): libalgos.take_2d_axis0_int16_int16,
+    ("int16", "int32"): libalgos.take_2d_axis0_int16_int32,
+    ("int16", "int64"): libalgos.take_2d_axis0_int16_int64,
+    ("int16", "float64"): libalgos.take_2d_axis0_int16_float64,
+    ("int32", "int32"): libalgos.take_2d_axis0_int32_int32,
+    ("int32", "int64"): libalgos.take_2d_axis0_int32_int64,
+    ("int32", "float64"): libalgos.take_2d_axis0_int32_float64,
+    ("int64", "int64"): libalgos.take_2d_axis0_int64_int64,
+    ("int64", "float64"): libalgos.take_2d_axis0_int64_float64,
+    ("float32", "float32"): libalgos.take_2d_axis0_float32_float32,
+    ("float32", "float64"): libalgos.take_2d_axis0_float32_float64,
+    ("float64", "float64"): libalgos.take_2d_axis0_float64_float64,
+    ("object", "object"): libalgos.take_2d_axis0_object_object,
+    ("bool", "bool"): _view_wrapper(
+        libalgos.take_2d_axis0_bool_bool, np.uint8, np.uint8
+    ),
+    ("bool", "object"): _view_wrapper(
+        libalgos.take_2d_axis0_bool_object, np.uint8, None
+    ),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+}
+
+_take_2d_axis1_dict = {
+    ("int8", "int8"): libalgos.take_2d_axis1_int8_int8,
+    ("int8", "int32"): libalgos.take_2d_axis1_int8_int32,
+    ("int8", "int64"): libalgos.take_2d_axis1_int8_int64,
+    ("int8", "float64"): libalgos.take_2d_axis1_int8_float64,
+    ("int16", "int16"): libalgos.take_2d_axis1_int16_int16,
+    ("int16", "int32"): libalgos.take_2d_axis1_int16_int32,
+    ("int16", "int64"): libalgos.take_2d_axis1_int16_int64,
+    ("int16", "float64"): libalgos.take_2d_axis1_int16_float64,
+    ("int32", "int32"): libalgos.take_2d_axis1_int32_int32,
+    ("int32", "int64"): libalgos.take_2d_axis1_int32_int64,
+    ("int32", "float64"): libalgos.take_2d_axis1_int32_float64,
+    ("int64", "int64"): libalgos.take_2d_axis1_int64_int64,
+    ("int64", "float64"): libalgos.take_2d_axis1_int64_float64,
+    ("float32", "float32"): libalgos.take_2d_axis1_float32_float32,
+    ("float32", "float64"): libalgos.take_2d_axis1_float32_float64,
+    ("float64", "float64"): libalgos.take_2d_axis1_float64_float64,
+    ("object", "object"): libalgos.take_2d_axis1_object_object,
+    ("bool", "bool"): _view_wrapper(
+        libalgos.take_2d_axis1_bool_bool, np.uint8, np.uint8
+    ),
+    ("bool", "object"): _view_wrapper(
+        libalgos.take_2d_axis1_bool_object, np.uint8, None
+    ),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+}
+
+_take_2d_multi_dict = {
+    ("int8", "int8"): libalgos.take_2d_multi_int8_int8,
+    ("int8", "int32"): libalgos.take_2d_multi_int8_int32,
+    ("int8", "int64"): libalgos.take_2d_multi_int8_int64,
+    ("int8", "float64"): libalgos.take_2d_multi_int8_float64,
+    ("int16", "int16"): libalgos.take_2d_multi_int16_int16,
+    ("int16", "int32"): libalgos.take_2d_multi_int16_int32,
+    ("int16", "int64"): libalgos.take_2d_multi_int16_int64,
+    ("int16", "float64"): libalgos.take_2d_multi_int16_float64,
+    ("int32", "int32"): libalgos.take_2d_multi_int32_int32,
+    ("int32", "int64"): libalgos.take_2d_multi_int32_int64,
+    ("int32", "float64"): libalgos.take_2d_multi_int32_float64,
+    ("int64", "int64"): libalgos.take_2d_multi_int64_int64,
+    ("int64", "float64"): libalgos.take_2d_multi_int64_float64,
+    ("float32", "float32"): libalgos.take_2d_multi_float32_float32,
+    ("float32", "float64"): libalgos.take_2d_multi_float32_float64,
+    ("float64", "float64"): libalgos.take_2d_multi_float64_float64,
+    ("object", "object"): libalgos.take_2d_multi_object_object,
+    ("bool", "bool"): _view_wrapper(
+        libalgos.take_2d_multi_bool_bool, np.uint8, np.uint8
+    ),
+    ("bool", "object"): _view_wrapper(
+        libalgos.take_2d_multi_bool_object, np.uint8, None
+    ),
+    ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
+        libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
+}
+
+
+def _take_nd_object(
+    arr: np.ndarray,
+    indexer: npt.NDArray[np.intp],
+    out: np.ndarray,
+    axis: AxisInt,
+    fill_value,
+    mask_info,
+) -> None:
+    if mask_info is not None:
+        mask, needs_masking = mask_info
+    else:
+        mask = indexer == -1
+        needs_masking = mask.any()
+    if arr.dtype != out.dtype:
+        arr = arr.astype(out.dtype)
+    if arr.shape[axis] > 0:
+        arr.take(indexer, axis=axis, out=out)
+    if needs_masking:
+        outindexer = [slice(None)] * arr.ndim
+        outindexer[axis] = mask
+        out[tuple(outindexer)] = fill_value
+
+
+def _take_2d_multi_object(
+    arr: np.ndarray,
+    indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],
+    out: np.ndarray,
+    fill_value,
+    mask_info,
+) -> None:
+    # this is not ideal, performance-wise, but it's better than raising
+    # an exception (best to optimize in Cython to avoid getting here)
+    row_idx, col_idx = indexer  # both np.intp
+    if mask_info is not None:
+        (row_mask, col_mask), (row_needs, col_needs) = mask_info
+    else:
+        row_mask = row_idx == -1
+        col_mask = col_idx == -1
+        row_needs = row_mask.any()
+        col_needs = col_mask.any()
+    if fill_value is not None:
+        if row_needs:
+            out[row_mask, :] = fill_value
+        if col_needs:
+            out[:, col_mask] = fill_value
+    for i, u_ in enumerate(row_idx):
+        if u_ != -1:
+            for j, v in enumerate(col_idx):
+                if v != -1:
+                    out[i, j] = arr[u_, v]
+
+
+def _take_preprocess_indexer_and_fill_value(
+    arr: np.ndarray,
+    indexer: npt.NDArray[np.intp],
+    fill_value,
+    allow_fill: bool,
+    mask: npt.NDArray[np.bool_] | None = None,
+):
+    mask_info: tuple[np.ndarray | None, bool] | None = None
+
+    if not allow_fill:
+        dtype, fill_value = arr.dtype, arr.dtype.type()
+        mask_info = None, False
+    else:
+        # check for promotion based on types only (do this first because
+        # it's faster than computing a mask)
+        dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+        if dtype != arr.dtype:
+            # check if promotion is actually required based on indexer
+            if mask is not None:
+                needs_masking = True
+            else:
+                mask = indexer == -1
+                needs_masking = bool(mask.any())
+            mask_info = mask, needs_masking
+            if not needs_masking:
+                # if not, then depromote, set fill_value to dummy
+                # (it won't be used but we don't want the cython code
+                # to crash when trying to cast it to dtype)
+                dtype, fill_value = arr.dtype, arr.dtype.type()
+
+    return dtype, fill_value, mask_info
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/transforms.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec67244949e3db92cc811b19cdfcd5d1dd2b4de8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/array_algos/transforms.py
@@ -0,0 +1,50 @@
+"""
+transforms.py is for shape-preserving functions.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AxisInt,
+        Scalar,
+    )
+
+
+def shift(
+    values: np.ndarray, periods: int, axis: AxisInt, fill_value: Scalar
+) -> np.ndarray:
+    new_values = values
+
+    if periods == 0 or values.size == 0:
+        return new_values.copy()
+
+    # make sure array sent to np.roll is c_contiguous
+    f_ordered = values.flags.f_contiguous
+    if f_ordered:
+        new_values = new_values.T
+        axis = new_values.ndim - axis - 1
+
+    if new_values.size:
+        new_values = np.roll(
+            new_values,
+            np.intp(periods),
+            axis=axis,
+        )
+
+    axis_indexer = [slice(None)] * values.ndim
+    if periods > 0:
+        axis_indexer[axis] = slice(None, periods)
+    else:
+        axis_indexer[axis] = slice(periods, None)
+    new_values[tuple(axis_indexer)] = fill_value
+
+    # restore original order
+    if f_ordered:
+        new_values = new_values.T
+
+    return new_values
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arraylike.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arraylike.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd585b3e1946880ca79f97b0b6d4a2990bdd32d1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arraylike.py
@@ -0,0 +1,530 @@
+"""
+Methods that can be shared by many array-like classes or subclasses:
+    Series
+    Index
+    ExtensionArray
+"""
+from __future__ import annotations
+
+import operator
+from typing import Any
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
+
+from pandas.core.dtypes.generic import ABCNDFrame
+
+from pandas.core import roperator
+from pandas.core.construction import extract_array
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+REDUCTION_ALIASES = {
+    "maximum": "max",
+    "minimum": "min",
+    "add": "sum",
+    "multiply": "prod",
+}
+
+
+class OpsMixin:
+    # -------------------------------------------------------------
+    # Comparisons
+
+    def _cmp_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__eq__")
+    def __eq__(self, other):
+        return self._cmp_method(other, operator.eq)
+
+    @unpack_zerodim_and_defer("__ne__")
+    def __ne__(self, other):
+        return self._cmp_method(other, operator.ne)
+
+    @unpack_zerodim_and_defer("__lt__")
+    def __lt__(self, other):
+        return self._cmp_method(other, operator.lt)
+
+    @unpack_zerodim_and_defer("__le__")
+    def __le__(self, other):
+        return self._cmp_method(other, operator.le)
+
+    @unpack_zerodim_and_defer("__gt__")
+    def __gt__(self, other):
+        return self._cmp_method(other, operator.gt)
+
+    @unpack_zerodim_and_defer("__ge__")
+    def __ge__(self, other):
+        return self._cmp_method(other, operator.ge)
+
+    # -------------------------------------------------------------
+    # Logical Methods
+
+    def _logical_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__and__")
+    def __and__(self, other):
+        return self._logical_method(other, operator.and_)
+
+    @unpack_zerodim_and_defer("__rand__")
+    def __rand__(self, other):
+        return self._logical_method(other, roperator.rand_)
+
+    @unpack_zerodim_and_defer("__or__")
+    def __or__(self, other):
+        return self._logical_method(other, operator.or_)
+
+    @unpack_zerodim_and_defer("__ror__")
+    def __ror__(self, other):
+        return self._logical_method(other, roperator.ror_)
+
+    @unpack_zerodim_and_defer("__xor__")
+    def __xor__(self, other):
+        return self._logical_method(other, operator.xor)
+
+    @unpack_zerodim_and_defer("__rxor__")
+    def __rxor__(self, other):
+        return self._logical_method(other, roperator.rxor)
+
+    # -------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _arith_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__add__")
+    def __add__(self, other):
+        """
+        Get Addition of DataFrame and other, column-wise.
+
+        Equivalent to ``DataFrame.add(other)``.
+
+        Parameters
+        ----------
+        other : scalar, sequence, Series, dict or DataFrame
+            Object to be added to the DataFrame.
+
+        Returns
+        -------
+        DataFrame
+            The result of adding ``other`` to DataFrame.
+
+        See Also
+        --------
+        DataFrame.add : Add a DataFrame and another object, with option for index-
+            or column-oriented addition.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
+        ...                   index=['elk', 'moose'])
+        >>> df
+               height  weight
+        elk       1.5     500
+        moose     2.6     800
+
+        Adding a scalar affects all rows and columns.
+
+        >>> df[['height', 'weight']] + 1.5
+               height  weight
+        elk       3.0   501.5
+        moose     4.1   801.5
+
+        Each element of a list is added to a column of the DataFrame, in order.
+
+        >>> df[['height', 'weight']] + [0.5, 1.5]
+               height  weight
+        elk       2.0   501.5
+        moose     3.1   801.5
+
+        Keys of a dictionary are aligned to the DataFrame, based on column names;
+        each value in the dictionary is added to the corresponding column.
+
+        >>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
+               height  weight
+        elk       2.0   501.5
+        moose     3.1   801.5
+
+        When `other` is a :class:`Series`, the index of `other` is aligned with the
+        columns of the DataFrame.
+
+        >>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
+        >>> df[['height', 'weight']] + s1
+               height  weight
+        elk       3.0   500.5
+        moose     4.1   800.5
+
+        Even when the index of `other` is the same as the index of the DataFrame,
+        the :class:`Series` will not be reoriented. If index-wise alignment is desired,
+        :meth:`DataFrame.add` should be used with `axis='index'`.
+
+        >>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
+        >>> df[['height', 'weight']] + s2
+               elk  height  moose  weight
+        elk    NaN     NaN    NaN     NaN
+        moose  NaN     NaN    NaN     NaN
+
+        >>> df[['height', 'weight']].add(s2, axis='index')
+               height  weight
+        elk       2.0   500.5
+        moose     4.1   801.5
+
+        When `other` is a :class:`DataFrame`, both columns names and the
+        index are aligned.
+
+        >>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
+        ...                      index=['elk', 'moose', 'deer'])
+        >>> df[['height', 'weight']] + other
+               height  weight
+        deer      NaN     NaN
+        elk       1.7     NaN
+        moose     3.0     NaN
+        """
+        return self._arith_method(other, operator.add)
+
+    @unpack_zerodim_and_defer("__radd__")
+    def __radd__(self, other):
+        return self._arith_method(other, roperator.radd)
+
+    @unpack_zerodim_and_defer("__sub__")
+    def __sub__(self, other):
+        return self._arith_method(other, operator.sub)
+
+    @unpack_zerodim_and_defer("__rsub__")
+    def __rsub__(self, other):
+        return self._arith_method(other, roperator.rsub)
+
+    @unpack_zerodim_and_defer("__mul__")
+    def __mul__(self, other):
+        return self._arith_method(other, operator.mul)
+
+    @unpack_zerodim_and_defer("__rmul__")
+    def __rmul__(self, other):
+        return self._arith_method(other, roperator.rmul)
+
+    @unpack_zerodim_and_defer("__truediv__")
+    def __truediv__(self, other):
+        return self._arith_method(other, operator.truediv)
+
+    @unpack_zerodim_and_defer("__rtruediv__")
+    def __rtruediv__(self, other):
+        return self._arith_method(other, roperator.rtruediv)
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        return self._arith_method(other, operator.floordiv)
+
+    @unpack_zerodim_and_defer("__rfloordiv")
+    def __rfloordiv__(self, other):
+        return self._arith_method(other, roperator.rfloordiv)
+
+    @unpack_zerodim_and_defer("__mod__")
+    def __mod__(self, other):
+        return self._arith_method(other, operator.mod)
+
+    @unpack_zerodim_and_defer("__rmod__")
+    def __rmod__(self, other):
+        return self._arith_method(other, roperator.rmod)
+
+    @unpack_zerodim_and_defer("__divmod__")
+    def __divmod__(self, other):
+        return self._arith_method(other, divmod)
+
+    @unpack_zerodim_and_defer("__rdivmod__")
+    def __rdivmod__(self, other):
+        return self._arith_method(other, roperator.rdivmod)
+
+    @unpack_zerodim_and_defer("__pow__")
+    def __pow__(self, other):
+        return self._arith_method(other, operator.pow)
+
+    @unpack_zerodim_and_defer("__rpow__")
+    def __rpow__(self, other):
+        return self._arith_method(other, roperator.rpow)
+
+
+# -----------------------------------------------------------------------------
+# Helpers to implement __array_ufunc__
+
+
+def array_ufunc(self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
+    """
+    Compatibility with numpy ufuncs.
+
+    See also
+    --------
+    numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
+    """
+    from pandas.core.frame import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+    from pandas.core.internals import (
+        ArrayManager,
+        BlockManager,
+    )
+
+    cls = type(self)
+
+    kwargs = _standardize_out_kwarg(**kwargs)
+
+    # for binary ops, use our custom dunder methods
+    result = maybe_dispatch_ufunc_to_dunder_op(self, ufunc, method, *inputs, **kwargs)
+    if result is not NotImplemented:
+        return result
+
+    # Determine if we should defer.
+    no_defer = (
+        np.ndarray.__array_ufunc__,
+        cls.__array_ufunc__,
+    )
+
+    for item in inputs:
+        higher_priority = (
+            hasattr(item, "__array_priority__")
+            and item.__array_priority__ > self.__array_priority__
+        )
+        has_array_ufunc = (
+            hasattr(item, "__array_ufunc__")
+            and type(item).__array_ufunc__ not in no_defer
+            and not isinstance(item, self._HANDLED_TYPES)
+        )
+        if higher_priority or has_array_ufunc:
+            return NotImplemented
+
+    # align all the inputs.
+    types = tuple(type(x) for x in inputs)
+    alignable = [x for x, t in zip(inputs, types) if issubclass(t, NDFrame)]
+
+    if len(alignable) > 1:
+        # This triggers alignment.
+        # At the moment, there aren't any ufuncs with more than two inputs
+        # so this ends up just being x1.index | x2.index, but we write
+        # it to handle *args.
+        set_types = set(types)
+        if len(set_types) > 1 and {DataFrame, Series}.issubset(set_types):
+            # We currently don't handle ufunc(DataFrame, Series)
+            # well. Previously this raised an internal ValueError. We might
+            # support it someday, so raise a NotImplementedError.
+            raise NotImplementedError(
+                f"Cannot apply ufunc {ufunc} to mixed DataFrame and Series inputs."
+            )
+        axes = self.axes
+        for obj in alignable[1:]:
+            # this relies on the fact that we aren't handling mixed
+            # series / frame ufuncs.
+            for i, (ax1, ax2) in enumerate(zip(axes, obj.axes)):
+                axes[i] = ax1.union(ax2)
+
+        reconstruct_axes = dict(zip(self._AXIS_ORDERS, axes))
+        inputs = tuple(
+            x.reindex(**reconstruct_axes) if issubclass(t, NDFrame) else x
+            for x, t in zip(inputs, types)
+        )
+    else:
+        reconstruct_axes = dict(zip(self._AXIS_ORDERS, self.axes))
+
+    if self.ndim == 1:
+        names = [getattr(x, "name") for x in inputs if hasattr(x, "name")]
+        name = names[0] if len(set(names)) == 1 else None
+        reconstruct_kwargs = {"name": name}
+    else:
+        reconstruct_kwargs = {}
+
+    def reconstruct(result):
+        if ufunc.nout > 1:
+            # np.modf, np.frexp, np.divmod
+            return tuple(_reconstruct(x) for x in result)
+
+        return _reconstruct(result)
+
+    def _reconstruct(result):
+        if lib.is_scalar(result):
+            return result
+
+        if result.ndim != self.ndim:
+            if method == "outer":
+                raise NotImplementedError
+            return result
+        if isinstance(result, (BlockManager, ArrayManager)):
+            # we went through BlockManager.apply e.g. np.sqrt
+            result = self._constructor_from_mgr(result, axes=result.axes)
+        else:
+            # we converted an array, lost our axes
+            result = self._constructor(
+                result, **reconstruct_axes, **reconstruct_kwargs, copy=False
+            )
+        # TODO: When we support multiple values in __finalize__, this
+        # should pass alignable to `__finalize__` instead of self.
+        # Then `np.add(a, b)` would consider attrs from both a and b
+        # when a and b are NDFrames.
+        if len(alignable) == 1:
+            result = result.__finalize__(self)
+        return result
+
+    if "out" in kwargs:
+        # e.g. test_multiindex_get_loc
+        result = dispatch_ufunc_with_out(self, ufunc, method, *inputs, **kwargs)
+        return reconstruct(result)
+
+    if method == "reduce":
+        # e.g. test.series.test_ufunc.test_reduce
+        result = dispatch_reduction_ufunc(self, ufunc, method, *inputs, **kwargs)
+        if result is not NotImplemented:
+            return result
+
+    # We still get here with kwargs `axis` for e.g. np.maximum.accumulate
+    #  and `dtype` and `keepdims` for np.ptp
+
+    if self.ndim > 1 and (len(inputs) > 1 or ufunc.nout > 1):
+        # Just give up on preserving types in the complex case.
+        # In theory we could preserve them for them.
+        # * nout>1 is doable if BlockManager.apply took nout and
+        #   returned a Tuple[BlockManager].
+        # * len(inputs) > 1 is doable when we know that we have
+        #   aligned blocks / dtypes.
+
+        # e.g. my_ufunc, modf, logaddexp, heaviside, subtract, add
+        inputs = tuple(np.asarray(x) for x in inputs)
+        # Note: we can't use default_array_ufunc here bc reindexing means
+        #  that `self` may not be among `inputs`
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+    elif self.ndim == 1:
+        # ufunc(series, ...)
+        inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+    else:
+        # ufunc(dataframe)
+        if method == "__call__" and not kwargs:
+            # for np.<ufunc>(..) calls
+            # kwargs cannot necessarily be handled block-by-block, so only
+            # take this path if there are no kwargs
+            mgr = inputs[0]._mgr
+            result = mgr.apply(getattr(ufunc, method))
+        else:
+            # otherwise specific ufunc methods (eg np.<ufunc>.accumulate(..))
+            # Those can have an axis keyword and thus can't be called block-by-block
+            result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)
+            # e.g. np.negative (only one reached), with "where" and "out" in kwargs
+
+    result = reconstruct(result)
+    return result
+
+
+def _standardize_out_kwarg(**kwargs) -> dict:
+    """
+    If kwargs contain "out1" and "out2", replace that with a tuple "out"
+
+    np.divmod, np.modf, np.frexp can have either `out=(out1, out2)` or
+    `out1=out1, out2=out2)`
+    """
+    if "out" not in kwargs and "out1" in kwargs and "out2" in kwargs:
+        out1 = kwargs.pop("out1")
+        out2 = kwargs.pop("out2")
+        out = (out1, out2)
+        kwargs["out"] = out
+    return kwargs
+
+
+def dispatch_ufunc_with_out(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    If we have an `out` keyword, then call the ufunc without `out` and then
+    set the result into the given `out`.
+    """
+
+    # Note: we assume _standardize_out_kwarg has already been called.
+    out = kwargs.pop("out")
+    where = kwargs.pop("where", None)
+
+    result = getattr(ufunc, method)(*inputs, **kwargs)
+
+    if result is NotImplemented:
+        return NotImplemented
+
+    if isinstance(result, tuple):
+        # i.e. np.divmod, np.modf, np.frexp
+        if not isinstance(out, tuple) or len(out) != len(result):
+            raise NotImplementedError
+
+        for arr, res in zip(out, result):
+            _assign_where(arr, res, where)
+
+        return out
+
+    if isinstance(out, tuple):
+        if len(out) == 1:
+            out = out[0]
+        else:
+            raise NotImplementedError
+
+    _assign_where(out, result, where)
+    return out
+
+
+def _assign_where(out, result, where) -> None:
+    """
+    Set a ufunc result into 'out', masking with a 'where' argument if necessary.
+    """
+    if where is None:
+        # no 'where' arg passed to ufunc
+        out[:] = result
+    else:
+        np.putmask(out, where, result)
+
+
+def default_array_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    Fallback to the behavior we would get if we did not define __array_ufunc__.
+
+    Notes
+    -----
+    We are assuming that `self` is among `inputs`.
+    """
+    if not any(x is self for x in inputs):
+        raise NotImplementedError
+
+    new_inputs = [x if x is not self else np.asarray(x) for x in inputs]
+
+    return getattr(ufunc, method)(*new_inputs, **kwargs)
+
+
+def dispatch_reduction_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    Dispatch ufunc reductions to self's reduction methods.
+    """
+    assert method == "reduce"
+
+    if len(inputs) != 1 or inputs[0] is not self:
+        return NotImplemented
+
+    if ufunc.__name__ not in REDUCTION_ALIASES:
+        return NotImplemented
+
+    method_name = REDUCTION_ALIASES[ufunc.__name__]
+
+    # NB: we are assuming that min/max represent minimum/maximum methods,
+    #  which would not be accurate for e.g. Timestamp.min
+    if not hasattr(self, method_name):
+        return NotImplemented
+
+    if self.ndim > 1:
+        if isinstance(self, ABCNDFrame):
+            # TODO: test cases where this doesn't hold, i.e. 2D DTA/TDA
+            kwargs["numeric_only"] = False
+
+        if "axis" not in kwargs:
+            # For DataFrame reductions we don't want the default axis=0
+            # Note: np.min is not a ufunc, but uses array_function_dispatch,
+            #  so calls DataFrame.min (without ever getting here) with the np.min
+            #  default of axis=None, which DataFrame.min catches and changes to axis=0.
+            # np.minimum.reduce(df) gets here bc axis is not in kwargs,
+            #  so we set axis=0 to match the behaviorof np.minimum.reduce(df.values)
+            kwargs["axis"] = 0
+
+    # By default, numpy's reductions do not skip NaNs, so we have to
+    #  pass skipna=False
+    return getattr(self, method_name)(skipna=False, **kwargs)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..245a171fea74bc9409a315b64d157a37b3da6eaa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/__init__.py
@@ -0,0 +1,43 @@
+from pandas.core.arrays.arrow import ArrowExtensionArray
+from pandas.core.arrays.base import (
+    ExtensionArray,
+    ExtensionOpsMixin,
+    ExtensionScalarOpsMixin,
+)
+from pandas.core.arrays.boolean import BooleanArray
+from pandas.core.arrays.categorical import Categorical
+from pandas.core.arrays.datetimes import DatetimeArray
+from pandas.core.arrays.floating import FloatingArray
+from pandas.core.arrays.integer import IntegerArray
+from pandas.core.arrays.interval import IntervalArray
+from pandas.core.arrays.masked import BaseMaskedArray
+from pandas.core.arrays.numpy_ import NumpyExtensionArray
+from pandas.core.arrays.period import (
+    PeriodArray,
+    period_array,
+)
+from pandas.core.arrays.sparse import SparseArray
+from pandas.core.arrays.string_ import StringArray
+from pandas.core.arrays.string_arrow import ArrowStringArray
+from pandas.core.arrays.timedeltas import TimedeltaArray
+
+__all__ = [
+    "ArrowExtensionArray",
+    "ExtensionArray",
+    "ExtensionOpsMixin",
+    "ExtensionScalarOpsMixin",
+    "ArrowStringArray",
+    "BaseMaskedArray",
+    "BooleanArray",
+    "Categorical",
+    "DatetimeArray",
+    "FloatingArray",
+    "IntegerArray",
+    "IntervalArray",
+    "NumpyExtensionArray",
+    "PeriodArray",
+    "period_array",
+    "SparseArray",
+    "StringArray",
+    "TimedeltaArray",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_arrow_string_mixins.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_arrow_string_mixins.py
new file mode 100644
index 0000000000000000000000000000000000000000..c99ee7d02a2260fb48b40d2bf5048fc960d47b2e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_arrow_string_mixins.py
@@ -0,0 +1,377 @@
+from __future__ import annotations
+
+from functools import partial
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under17p0,
+    pa_version_under21p0,
+)
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from pandas._typing import (
+        Scalar,
+        Self,
+    )
+
+
+class ArrowStringArrayMixin:
+    _pa_array: pa.ChunkedArray
+
+    def __init__(self, *args, **kwargs) -> None:
+        raise NotImplementedError
+
+    def _convert_bool_result(self, result, na=lib.no_default, method_name=None):
+        # Convert a bool-dtype result to the appropriate result type
+        raise NotImplementedError
+
+    def _convert_int_result(self, result):
+        # Convert an integer-dtype result to the appropriate result type
+        raise NotImplementedError
+
+    def _apply_elementwise(self, func: Callable) -> list[list[Any]]:
+        raise NotImplementedError
+
+    def _str_len(self):
+        result = pc.utf8_length(self._pa_array)
+        return self._convert_int_result(result)
+
+    def _str_lower(self) -> Self:
+        return type(self)(pc.utf8_lower(self._pa_array))
+
+    def _str_upper(self) -> Self:
+        return type(self)(pc.utf8_upper(self._pa_array))
+
+    def _str_strip(self, to_strip=None) -> Self:
+        if to_strip is None:
+            result = pc.utf8_trim_whitespace(self._pa_array)
+        else:
+            result = pc.utf8_trim(self._pa_array, characters=to_strip)
+        return type(self)(result)
+
+    def _str_lstrip(self, to_strip=None) -> Self:
+        if to_strip is None:
+            result = pc.utf8_ltrim_whitespace(self._pa_array)
+        else:
+            result = pc.utf8_ltrim(self._pa_array, characters=to_strip)
+        return type(self)(result)
+
+    def _str_rstrip(self, to_strip=None) -> Self:
+        if to_strip is None:
+            result = pc.utf8_rtrim_whitespace(self._pa_array)
+        else:
+            result = pc.utf8_rtrim(self._pa_array, characters=to_strip)
+        return type(self)(result)
+
+    def _str_pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        if side == "left":
+            pa_pad = pc.utf8_lpad
+        elif side == "right":
+            pa_pad = pc.utf8_rpad
+        elif side == "both":
+            if pa_version_under17p0:
+                # GH#59624 fall back to object dtype
+                from pandas import array as pd_array
+
+                obj_arr = self.astype(object, copy=False)  # type: ignore[attr-defined]
+                obj = pd_array(obj_arr, dtype=object)
+                result = obj._str_pad(width, side, fillchar)  # type: ignore[attr-defined]
+                return type(self)._from_sequence(result, dtype=self.dtype)  # type: ignore[attr-defined]
+            else:
+                # GH#54792
+                # https://github.com/apache/arrow/issues/15053#issuecomment-2317032347
+                lean_left = (width % 2) == 0
+                pa_pad = partial(pc.utf8_center, lean_left_on_odd_padding=lean_left)
+        else:
+            raise ValueError(
+                f"Invalid side: {side}. Side must be one of 'left', 'right', 'both'"
+            )
+        return type(self)(pa_pad(self._pa_array, width=width, padding=fillchar))
+
+    def _str_get(self, i: int):
+        lengths = pc.utf8_length(self._pa_array)
+        if i >= 0:
+            out_of_bounds = pc.greater_equal(i, lengths)
+            start = i
+            stop = i + 1
+            step = 1
+        else:
+            out_of_bounds = pc.greater(-i, lengths)
+            start = i
+            stop = i - 1
+            step = -1
+        not_out_of_bounds = pc.invert(out_of_bounds.fill_null(True))
+        selected = pc.utf8_slice_codeunits(
+            self._pa_array, start=start, stop=stop, step=step
+        )
+        null_value = pa.scalar(None, type=self._pa_array.type)
+        result = pc.if_else(not_out_of_bounds, selected, null_value)
+        return type(self)(result)
+
+    def _str_slice(
+        self, start: int | None = None, stop: int | None = None, step: int | None = None
+    ):
+        if pa_version_under11p0:
+            # GH#59724
+            result = self._apply_elementwise(lambda val: val[start:stop:step])
+            return type(self)(pa.chunked_array(result, type=self._pa_array.type))
+        if start is None:
+            if step is not None and step < 0:
+                # GH#59710
+                start = -1
+            else:
+                start = 0
+        if step is None:
+            step = 1
+        return type(self)(
+            pc.utf8_slice_codeunits(self._pa_array, start=start, stop=stop, step=step)
+        )
+
+    def _str_slice_replace(
+        self, start: int | None = None, stop: int | None = None, repl: str | None = None
+    ):
+        if repl is None:
+            repl = ""
+        if start is None:
+            start = 0
+        if stop is None:
+            stop = np.iinfo(np.int64).max
+        return type(self)(pc.utf8_replace_slice(self._pa_array, start, stop, repl))
+
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ) -> Self:
+        if (
+            isinstance(pat, re.Pattern)
+            or callable(repl)
+            or not case
+            or flags
+            or (
+                isinstance(repl, str)
+                and (r"\g<" in repl or re.search(r"\\\d", repl) is not None)
+            )
+        ):
+            raise NotImplementedError(
+                "replace is not supported with a re.Pattern, callable repl, "
+                "case=False, flags!=0, or when the replacement string contains "
+                "named group references (\\g<...>, \\d+)"
+            )
+
+        func = pc.replace_substring_regex if regex else pc.replace_substring
+        # https://github.com/apache/arrow/issues/39149
+        # GH 56404, unexpected behavior with negative max_replacements with pyarrow.
+        pa_max_replacements = None if n < 0 else n
+        result = func(
+            self._pa_array,
+            pattern=pat,
+            replacement=repl,
+            max_replacements=pa_max_replacements,
+        )
+        return type(self)(result)
+
+    def _str_capitalize(self) -> Self:
+        return type(self)(pc.utf8_capitalize(self._pa_array))
+
+    def _str_title(self):
+        return type(self)(pc.utf8_title(self._pa_array))
+
+    def _str_swapcase(self):
+        return type(self)(pc.utf8_swapcase(self._pa_array))
+
+    def _str_removeprefix(self, prefix: str):
+        if not pa_version_under13p0:
+            starts_with = pc.starts_with(self._pa_array, pattern=prefix)
+            removed = pc.utf8_slice_codeunits(self._pa_array, len(prefix))
+            result = pc.if_else(starts_with, removed, self._pa_array)
+            return type(self)(result)
+        predicate = lambda val: val.removeprefix(prefix)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_removesuffix(self, suffix: str):
+        ends_with = pc.ends_with(self._pa_array, pattern=suffix)
+        removed = pc.utf8_slice_codeunits(self._pa_array, 0, stop=-len(suffix))
+        result = pc.if_else(ends_with, removed, self._pa_array)
+        return type(self)(result)
+
+    def _str_startswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ):
+        if isinstance(pat, str):
+            result = pc.starts_with(self._pa_array, pattern=pat)
+        else:
+            if len(pat) == 0:
+                # For empty tuple we return null for missing values and False
+                #  for valid values.
+                result = pc.if_else(pc.is_null(self._pa_array), None, False)
+            else:
+                result = pc.starts_with(self._pa_array, pattern=pat[0])
+
+                for p in pat[1:]:
+                    result = pc.or_(result, pc.starts_with(self._pa_array, pattern=p))
+        return self._convert_bool_result(result, na=na, method_name="startswith")
+
+    def _str_endswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ):
+        if isinstance(pat, str):
+            result = pc.ends_with(self._pa_array, pattern=pat)
+        else:
+            if len(pat) == 0:
+                # For empty tuple we return null for missing values and False
+                #  for valid values.
+                result = pc.if_else(pc.is_null(self._pa_array), None, False)
+            else:
+                result = pc.ends_with(self._pa_array, pattern=pat[0])
+
+                for p in pat[1:]:
+                    result = pc.or_(result, pc.ends_with(self._pa_array, pattern=p))
+        return self._convert_bool_result(result, na=na, method_name="endswith")
+
+    def _str_isalnum(self):
+        result = pc.utf8_is_alnum(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isalpha(self):
+        result = pc.utf8_is_alpha(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isdecimal(self):
+        result = pc.utf8_is_decimal(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isdigit(self):
+        if pa_version_under21p0:
+            # https://github.com/pandas-dev/pandas/issues/61466
+            res_list = self._apply_elementwise(str.isdigit)
+            return self._convert_bool_result(
+                pa.chunked_array(res_list, type=pa.bool_())
+            )
+        result = pc.utf8_is_digit(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_islower(self):
+        result = pc.utf8_is_lower(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isnumeric(self):
+        result = pc.utf8_is_numeric(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isspace(self):
+        result = pc.utf8_is_space(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_istitle(self):
+        result = pc.utf8_is_title(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_isupper(self):
+        result = pc.utf8_is_upper(self._pa_array)
+        return self._convert_bool_result(result)
+
+    def _str_contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+        regex: bool = True,
+    ):
+        if flags:
+            raise NotImplementedError(f"contains not implemented with {flags=}")
+
+        if regex:
+            pa_contains = pc.match_substring_regex
+        else:
+            pa_contains = pc.match_substring
+        result = pa_contains(self._pa_array, pat, ignore_case=not case)
+        return self._convert_bool_result(result, na=na, method_name="contains")
+
+    def _str_match(
+        self,
+        pat: str,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if not pat.startswith("^"):
+            pat = f"^({pat})"
+        return self._str_contains(pat, case, flags, na, regex=True)
+
+    def _str_fullmatch(
+        self,
+        pat: str,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if (not pat.endswith("$") or pat.endswith("\\$")) and not pat.startswith("^"):
+            pat = f"^({pat})$"
+        elif not pat.endswith("$") or pat.endswith("\\$"):
+            pat = f"^({pat[1:]})$"
+        elif not pat.startswith("^"):
+            pat = f"^({pat[0:-1]})$"
+        return self._str_match(pat, case, flags, na)
+
+    def _str_find(self, sub: str, start: int = 0, end: int | None = None):
+        if (
+            pa_version_under13p0
+            and not (start != 0 and end is not None)
+            and not (start == 0 and end is None)
+        ):
+            # GH#59562
+            res_list = self._apply_elementwise(lambda val: val.find(sub, start, end))
+            return self._convert_int_result(pa.chunked_array(res_list))
+
+        if (start == 0 or start is None) and end is None:
+            result = pc.find_substring(self._pa_array, sub)
+        else:
+            if sub == "":
+                # GH#56792
+                res_list = self._apply_elementwise(
+                    lambda val: val.find(sub, start, end)
+                )
+                return self._convert_int_result(pa.chunked_array(res_list))
+            if start is None:
+                start_offset = 0
+                start = 0
+            elif start < 0:
+                start_offset = pc.add(start, pc.utf8_length(self._pa_array))
+                start_offset = pc.if_else(pc.less(start_offset, 0), 0, start_offset)
+            else:
+                start_offset = start
+            slices = pc.utf8_slice_codeunits(self._pa_array, start, stop=end)
+            result = pc.find_substring(slices, sub)
+            found = pc.not_equal(result, pa.scalar(-1, type=result.type))
+            offset_result = pc.add(result, start_offset)
+            result = pc.if_else(found, offset_result, -1)
+        return self._convert_int_result(result)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_mixins.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_mixins.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb6861a8dd00ff29edb398f0a8cc6ca73205c78d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_mixins.py
@@ -0,0 +1,544 @@
+from __future__ import annotations
+
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import is_supported_dtype
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    Dtype,
+    F,
+    FillnaOptions,
+    PositionalIndexer2D,
+    PositionalIndexerTuple,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    Shape,
+    TakeIndexer,
+    npt,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._validators import (
+    validate_bool_kwarg,
+    validate_fillna_kwargs,
+    validate_insert_loc,
+)
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import array_equivalent
+
+from pandas.core import missing
+from pandas.core.algorithms import (
+    take,
+    unique,
+    value_counts_internal as value_counts,
+)
+from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.array_algos.transforms import shift
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.construction import extract_array
+from pandas.core.indexers import check_array_indexer
+from pandas.core.sorting import nargminmax
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        NumpySorter,
+        NumpyValueArrayLike,
+    )
+
+    from pandas import Series
+
+
+def ravel_compat(meth: F) -> F:
+    """
+    Decorator to ravel a 2D array before passing it to a cython operation,
+    then reshape the result to our own shape.
+    """
+
+    @wraps(meth)
+    def method(self, *args, **kwargs):
+        if self.ndim == 1:
+            return meth(self, *args, **kwargs)
+
+        flags = self._ndarray.flags
+        flat = self.ravel("K")
+        result = meth(flat, *args, **kwargs)
+        order = "F" if flags.f_contiguous else "C"
+        return result.reshape(self.shape, order=order)
+
+    return cast(F, method)
+
+
+class NDArrayBackedExtensionArray(NDArrayBacked, ExtensionArray):
+    """
+    ExtensionArray that is backed by a single NumPy ndarray.
+    """
+
+    _ndarray: np.ndarray
+
+    # scalar used to denote NA value inside our self._ndarray, e.g. -1
+    #  for Categorical, iNaT for Period. Outside of object dtype,
+    #  self.isna() should be exactly locations in self._ndarray with
+    #  _internal_fill_value.
+    _internal_fill_value: Any
+
+    def _box_func(self, x):
+        """
+        Wrap numpy type in our dtype.type if necessary.
+        """
+        return x
+
+    def _validate_scalar(self, value):
+        # used by NDArrayBackedExtensionIndex.insert
+        raise AbstractMethodError(self)
+
+    # ------------------------------------------------------------------------
+
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        # We handle datetime64, datetime64tz, timedelta64, and period
+        #  dtypes here. Everything else we pass through to the underlying
+        #  ndarray.
+        if dtype is None or dtype is self.dtype:
+            return self._from_backing_data(self._ndarray)
+
+        if isinstance(dtype, type):
+            # we sometimes pass non-dtype objects, e.g np.ndarray;
+            #  pass those through to the underlying ndarray
+            return self._ndarray.view(dtype)
+
+        dtype = pandas_dtype(dtype)
+        arr = self._ndarray
+
+        if isinstance(dtype, PeriodDtype):
+            cls = dtype.construct_array_type()
+            return cls(arr.view("i8"), dtype=dtype)
+        elif isinstance(dtype, DatetimeTZDtype):
+            dt_cls = dtype.construct_array_type()
+            dt64_values = arr.view(f"M8[{dtype.unit}]")
+            return dt_cls._simple_new(dt64_values, dtype=dtype)
+        elif lib.is_np_dtype(dtype, "M") and is_supported_dtype(dtype):
+            from pandas.core.arrays import DatetimeArray
+
+            dt64_values = arr.view(dtype)
+            return DatetimeArray._simple_new(dt64_values, dtype=dtype)
+
+        elif lib.is_np_dtype(dtype, "m") and is_supported_dtype(dtype):
+            from pandas.core.arrays import TimedeltaArray
+
+            td64_values = arr.view(dtype)
+            return TimedeltaArray._simple_new(td64_values, dtype=dtype)
+
+        # error: Argument "dtype" to "view" of "_ArrayOrScalarCommon" has incompatible
+        # type "Union[ExtensionDtype, dtype[Any]]"; expected "Union[dtype[Any], None,
+        # type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any, Union[int,
+        # Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
+        return arr.view(dtype=dtype)  # type: ignore[arg-type]
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        *,
+        allow_fill: bool = False,
+        fill_value: Any = None,
+        axis: AxisInt = 0,
+    ) -> Self:
+        if allow_fill:
+            fill_value = self._validate_scalar(fill_value)
+
+        new_data = take(
+            self._ndarray,
+            indices,
+            allow_fill=allow_fill,
+            fill_value=fill_value,
+            axis=axis,
+        )
+        return self._from_backing_data(new_data)
+
+    # ------------------------------------------------------------------------
+
+    def equals(self, other) -> bool:
+        if type(self) is not type(other):
+            return False
+        if self.dtype != other.dtype:
+            return False
+        return bool(array_equivalent(self._ndarray, other._ndarray, dtype_equal=True))
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        assert values.dtype == original._ndarray.dtype
+        return original._from_backing_data(values)
+
+    def _values_for_argsort(self) -> np.ndarray:
+        return self._ndarray
+
+    def _values_for_factorize(self):
+        return self._ndarray, self._internal_fill_value
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        from pandas.core.util.hashing import hash_array
+
+        values = self._ndarray
+        return hash_array(
+            values, encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+
+    # Signature of "argmin" incompatible with supertype "ExtensionArray"
+    def argmin(self, axis: AxisInt = 0, skipna: bool = True):  # type: ignore[override]
+        # override base class by adding axis keyword
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmin", axis=axis)
+
+    # Signature of "argmax" incompatible with supertype "ExtensionArray"
+    def argmax(self, axis: AxisInt = 0, skipna: bool = True):  # type: ignore[override]
+        # override base class by adding axis keyword
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmax", axis=axis)
+
+    def unique(self) -> Self:
+        new_data = unique(self._ndarray)
+        return self._from_backing_data(new_data)
+
+    @classmethod
+    @doc(ExtensionArray._concat_same_type)
+    def _concat_same_type(
+        cls,
+        to_concat: Sequence[Self],
+        axis: AxisInt = 0,
+    ) -> Self:
+        if not lib.dtypes_all_equal([x.dtype for x in to_concat]):
+            dtypes = {str(x.dtype) for x in to_concat}
+            raise ValueError("to_concat must have the same dtype", dtypes)
+
+        return super()._concat_same_type(to_concat, axis=axis)
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        npvalue = self._validate_setitem_value(value)
+        return self._ndarray.searchsorted(npvalue, side=side, sorter=sorter)
+
+    @doc(ExtensionArray.shift)
+    def shift(self, periods: int = 1, fill_value=None):
+        # NB: shift is always along axis=0
+        axis = 0
+        fill_value = self._validate_scalar(fill_value)
+        new_values = shift(self._ndarray, periods, axis, fill_value)
+
+        return self._from_backing_data(new_values)
+
+    def __setitem__(self, key, value) -> None:
+        key = check_array_indexer(self, key)
+        value = self._validate_setitem_value(value)
+        self._ndarray[key] = value
+
+    def _validate_setitem_value(self, value):
+        return value
+
+    @overload
+    def __getitem__(self, key: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(
+        self,
+        key: SequenceIndexer | PositionalIndexerTuple,
+    ) -> Self:
+        ...
+
+    def __getitem__(
+        self,
+        key: PositionalIndexer2D,
+    ) -> Self | Any:
+        if lib.is_integer(key):
+            # fast-path
+            result = self._ndarray[key]
+            if self.ndim == 1:
+                return self._box_func(result)
+            return self._from_backing_data(result)
+
+        # error: Incompatible types in assignment (expression has type "ExtensionArray",
+        # variable has type "Union[int, slice, ndarray]")
+        key = extract_array(key, extract_numpy=True)  # type: ignore[assignment]
+        key = check_array_indexer(self, key)
+        result = self._ndarray[key]
+        if lib.is_scalar(result):
+            return self._box_func(result)
+
+        result = self._from_backing_data(result)
+        return result
+
+    def _fill_mask_inplace(
+        self, method: str, limit: int | None, mask: npt.NDArray[np.bool_]
+    ) -> None:
+        # (for now) when self.ndim == 2, we assume axis=0
+        func = missing.get_fill_func(method, ndim=self.ndim)
+        func(self._ndarray.T, limit=limit, mask=mask.T)
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        mask = self.isna()
+        if mask.any():
+            # (for now) when self.ndim == 2, we assume axis=0
+            func = missing.get_fill_func(method, ndim=self.ndim)
+
+            npvalues = self._ndarray.T
+            if copy:
+                npvalues = npvalues.copy()
+            func(npvalues, limit=limit, limit_area=limit_area, mask=mask.T)
+            npvalues = npvalues.T
+
+            if copy:
+                new_values = self._from_backing_data(npvalues)
+            else:
+                new_values = self
+
+        else:
+            if copy:
+                new_values = self.copy()
+            else:
+                new_values = self
+        return new_values
+
+    @doc(ExtensionArray.fillna)
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        value, method = validate_fillna_kwargs(
+            value, method, validate_scalar_dict_value=False
+        )
+
+        mask = self.isna()
+        # error: Argument 2 to "check_value_size" has incompatible type
+        # "ExtensionArray"; expected "ndarray"
+        value = missing.check_value_size(
+            value, mask, len(self)  # type: ignore[arg-type]
+        )
+
+        if mask.any():
+            if method is not None:
+                # (for now) when self.ndim == 2, we assume axis=0
+                func = missing.get_fill_func(method, ndim=self.ndim)
+                npvalues = self._ndarray.T
+                if copy:
+                    npvalues = npvalues.copy()
+                func(npvalues, limit=limit, mask=mask.T)
+                npvalues = npvalues.T
+
+                # TODO: NumpyExtensionArray didn't used to copy, need tests
+                #  for this
+                new_values = self._from_backing_data(npvalues)
+            else:
+                # fill with value
+                if copy:
+                    new_values = self.copy()
+                else:
+                    new_values = self[:]
+                new_values[mask] = value
+        else:
+            # We validate the fill_value even if there is nothing to fill
+            if value is not None:
+                self._validate_setitem_value(value)
+
+            if not copy:
+                new_values = self[:]
+            else:
+                new_values = self.copy()
+        return new_values
+
+    # ------------------------------------------------------------------------
+    # Reductions
+
+    def _wrap_reduction_result(self, axis: AxisInt | None, result):
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
+
+    # ------------------------------------------------------------------------
+    # __array_function__ methods
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        """
+        Analogue to np.putmask(self, mask, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+
+        Raises
+        ------
+        TypeError
+            If value cannot be cast to self.dtype.
+        """
+        value = self._validate_setitem_value(value)
+
+        np.putmask(self._ndarray, mask, value)
+
+    def _where(self: Self, mask: npt.NDArray[np.bool_], value) -> Self:
+        """
+        Analogue to np.where(mask, self, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+
+        Raises
+        ------
+        TypeError
+            If value cannot be cast to self.dtype.
+        """
+        value = self._validate_setitem_value(value)
+
+        res_values = np.where(mask, self._ndarray, value)
+        if res_values.dtype != self._ndarray.dtype:
+            raise AssertionError(
+                # GH#56410
+                "Something has gone wrong, please report a bug at "
+                "github.com/pandas-dev/pandas/"
+            )
+        return self._from_backing_data(res_values)
+
+    # ------------------------------------------------------------------------
+    # Index compat methods
+
+    def insert(self, loc: int, item) -> Self:
+        """
+        Make new ExtensionArray inserting new item at location. Follows
+        Python list.append semantics for negative values.
+
+        Parameters
+        ----------
+        loc : int
+        item : object
+
+        Returns
+        -------
+        type(self)
+        """
+        loc = validate_insert_loc(loc, len(self))
+
+        code = self._validate_scalar(item)
+
+        new_vals = np.concatenate(
+            (
+                self._ndarray[:loc],
+                np.asarray([code], dtype=self._ndarray.dtype),
+                self._ndarray[loc:],
+            )
+        )
+        return self._from_backing_data(new_vals)
+
+    # ------------------------------------------------------------------------
+    # Additional array methods
+    #  These are not part of the EA API, but we implement them because
+    #  pandas assumes they're there.
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of unique values.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NA values.
+
+        Returns
+        -------
+        Series
+        """
+        if self.ndim != 1:
+            raise NotImplementedError
+
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        if dropna:
+            # error: Unsupported operand type for ~ ("ExtensionArray")
+            values = self[~self.isna()]._ndarray  # type: ignore[operator]
+        else:
+            values = self._ndarray
+
+        result = value_counts(values, sort=False, dropna=dropna)
+
+        index_arr = self._from_backing_data(np.asarray(result.index._data))
+        index = Index(index_arr, name=result.index.name)
+        return Series(result._values, index=index, name=result.name, copy=False)
+
+    def _quantile(
+        self,
+        qs: npt.NDArray[np.float64],
+        interpolation: str,
+    ) -> Self:
+        # TODO: disable for Categorical if not ordered?
+
+        mask = np.asarray(self.isna())
+        arr = self._ndarray
+        fill_value = self._internal_fill_value
+
+        res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
+        if res_values.dtype == self._ndarray.dtype:
+            return self._from_backing_data(res_values)
+        else:
+            # e.g. test_quantile_empty we are empty integer dtype and res_values
+            #  has floating dtype
+            # TODO: technically __init__ isn't defined here.
+            #  Should we raise NotImplementedError and handle this on NumpyEA?
+            return type(self)(res_values)  # type: ignore[call-arg]
+
+    # ------------------------------------------------------------------------
+    # numpy-like methods
+
+    @classmethod
+    def _empty(cls, shape: Shape, dtype: ExtensionDtype) -> Self:
+        """
+        Analogous to np.empty(shape, dtype=dtype)
+
+        Parameters
+        ----------
+        shape : tuple[int]
+        dtype : ExtensionDtype
+        """
+        # The base implementation uses a naive approach to find the dtype
+        #  for the backing ndarray
+        arr = cls._from_sequence([], dtype=dtype)
+        backing = np.empty(shape, dtype=arr._ndarray.dtype)
+        return arr._from_backing_data(backing)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_ranges.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_ranges.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e89391324ad4a90235da230250758662822678f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_ranges.py
@@ -0,0 +1,207 @@
+"""
+Helper functions to generate range-like data for DatetimeArray
+(and possibly TimedeltaArray/PeriodArray)
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.lib import i8max
+from pandas._libs.tslibs import (
+    BaseOffset,
+    OutOfBoundsDatetime,
+    Timedelta,
+    Timestamp,
+    iNaT,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+def generate_regular_range(
+    start: Timestamp | Timedelta | None,
+    end: Timestamp | Timedelta | None,
+    periods: int | None,
+    freq: BaseOffset,
+    unit: str = "ns",
+) -> npt.NDArray[np.intp]:
+    """
+    Generate a range of dates or timestamps with the spans between dates
+    described by the given `freq` DateOffset.
+
+    Parameters
+    ----------
+    start : Timedelta, Timestamp or None
+        First point of produced date range.
+    end : Timedelta, Timestamp or None
+        Last point of produced date range.
+    periods : int or None
+        Number of periods in produced date range.
+    freq : Tick
+        Describes space between dates in produced date range.
+    unit : str, default "ns"
+        The resolution the output is meant to represent.
+
+    Returns
+    -------
+    ndarray[np.int64]
+        Representing the given resolution.
+    """
+    istart = start._value if start is not None else None
+    iend = end._value if end is not None else None
+    freq.nanos  # raises if non-fixed frequency
+    td = Timedelta(freq)
+    b: int
+    e: int
+    try:
+        td = td.as_unit(unit, round_ok=False)
+    except ValueError as err:
+        raise ValueError(
+            f"freq={freq} is incompatible with unit={unit}. "
+            "Use a lower freq or a higher unit instead."
+        ) from err
+    stride = int(td._value)
+
+    if periods is None and istart is not None and iend is not None:
+        b = istart
+        # cannot just use e = Timestamp(end) + 1 because arange breaks when
+        # stride is too large, see GH10887
+        e = b + (iend - b) // stride * stride + stride // 2 + 1
+    elif istart is not None and periods is not None:
+        b = istart
+        e = _generate_range_overflow_safe(b, periods, stride, side="start")
+    elif iend is not None and periods is not None:
+        e = iend + stride
+        b = _generate_range_overflow_safe(e, periods, stride, side="end")
+    else:
+        raise ValueError(
+            "at least 'start' or 'end' should be specified if a 'period' is given."
+        )
+
+    with np.errstate(over="raise"):
+        # If the range is sufficiently large, np.arange may overflow
+        #  and incorrectly return an empty array if not caught.
+        try:
+            values = np.arange(b, e, stride, dtype=np.int64)
+        except FloatingPointError:
+            xdr = [b]
+            while xdr[-1] != e:
+                xdr.append(xdr[-1] + stride)
+            values = np.array(xdr[:-1], dtype=np.int64)
+    return values
+
+
+def _generate_range_overflow_safe(
+    endpoint: int, periods: int, stride: int, side: str = "start"
+) -> int:
+    """
+    Calculate the second endpoint for passing to np.arange, checking
+    to avoid an integer overflow.  Catch OverflowError and re-raise
+    as OutOfBoundsDatetime.
+
+    Parameters
+    ----------
+    endpoint : int
+        nanosecond timestamp of the known endpoint of the desired range
+    periods : int
+        number of periods in the desired range
+    stride : int
+        nanoseconds between periods in the desired range
+    side : {'start', 'end'}
+        which end of the range `endpoint` refers to
+
+    Returns
+    -------
+    other_end : int
+
+    Raises
+    ------
+    OutOfBoundsDatetime
+    """
+    # GH#14187 raise instead of incorrectly wrapping around
+    assert side in ["start", "end"]
+
+    i64max = np.uint64(i8max)
+    msg = f"Cannot generate range with {side}={endpoint} and periods={periods}"
+
+    with np.errstate(over="raise"):
+        # if periods * strides cannot be multiplied within the *uint64* bounds,
+        #  we cannot salvage the operation by recursing, so raise
+        try:
+            addend = np.uint64(periods) * np.uint64(np.abs(stride))
+        except FloatingPointError as err:
+            raise OutOfBoundsDatetime(msg) from err
+
+    if np.abs(addend) <= i64max:
+        # relatively easy case without casting concerns
+        return _generate_range_overflow_safe_signed(endpoint, periods, stride, side)
+
+    elif (endpoint > 0 and side == "start" and stride > 0) or (
+        endpoint < 0 < stride and side == "end"
+    ):
+        # no chance of not-overflowing
+        raise OutOfBoundsDatetime(msg)
+
+    elif side == "end" and endpoint - stride <= i64max < endpoint:
+        # in _generate_regular_range we added `stride` thereby overflowing
+        #  the bounds.  Adjust to fix this.
+        return _generate_range_overflow_safe(
+            endpoint - stride, periods - 1, stride, side
+        )
+
+    # split into smaller pieces
+    mid_periods = periods // 2
+    remaining = periods - mid_periods
+    assert 0 < remaining < periods, (remaining, periods, endpoint, stride)
+
+    midpoint = int(_generate_range_overflow_safe(endpoint, mid_periods, stride, side))
+    return _generate_range_overflow_safe(midpoint, remaining, stride, side)
+
+
+def _generate_range_overflow_safe_signed(
+    endpoint: int, periods: int, stride: int, side: str
+) -> int:
+    """
+    A special case for _generate_range_overflow_safe where `periods * stride`
+    can be calculated without overflowing int64 bounds.
+    """
+    assert side in ["start", "end"]
+    if side == "end":
+        stride *= -1
+
+    with np.errstate(over="raise"):
+        addend = np.int64(periods) * np.int64(stride)
+        try:
+            # easy case with no overflows
+            result = np.int64(endpoint) + addend
+            if result == iNaT:
+                # Putting this into a DatetimeArray/TimedeltaArray
+                #  would incorrectly be interpreted as NaT
+                raise OverflowError
+            return int(result)
+        except (FloatingPointError, OverflowError):
+            # with endpoint negative and addend positive we risk
+            #  FloatingPointError; with reversed signed we risk OverflowError
+            pass
+
+        # if stride and endpoint had opposite signs, then endpoint + addend
+        #  should never overflow.  so they must have the same signs
+        assert (stride > 0 and endpoint >= 0) or (stride < 0 and endpoint <= 0)
+
+        if stride > 0:
+            # watch out for very special case in which we just slightly
+            #  exceed implementation bounds, but when passing the result to
+            #  np.arange will get a result slightly within the bounds
+
+            uresult = np.uint64(endpoint) + np.uint64(addend)
+            i64max = np.uint64(i8max)
+            assert uresult > i64max
+            if uresult <= i64max + np.uint64(stride):
+                return int(uresult)
+
+    raise OutOfBoundsDatetime(
+        f"Cannot generate range with {side}={endpoint} and periods={periods}"
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b46396d5efdfa4301a5362c8a5a71678345479b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/_utils.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.errors import LossySetitemError
+
+from pandas.core.dtypes.cast import np_can_hold_element
+from pandas.core.dtypes.common import is_numeric_dtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+
+def to_numpy_dtype_inference(
+    arr: ArrayLike, dtype: npt.DTypeLike | None, na_value, hasna: bool
+) -> tuple[npt.DTypeLike, Any]:
+    if dtype is None and is_numeric_dtype(arr.dtype):
+        dtype_given = False
+        if hasna:
+            if arr.dtype.kind == "b":
+                dtype = np.dtype(np.object_)
+            else:
+                if arr.dtype.kind in "iu":
+                    dtype = np.dtype(np.float64)
+                else:
+                    dtype = arr.dtype.numpy_dtype  # type: ignore[union-attr]
+                if na_value is lib.no_default:
+                    na_value = np.nan
+        else:
+            dtype = arr.dtype.numpy_dtype  # type: ignore[union-attr]
+    elif dtype is not None:
+        dtype = np.dtype(dtype)
+        dtype_given = True
+    else:
+        dtype_given = True
+
+    if na_value is lib.no_default:
+        if dtype is None or not hasna:
+            na_value = arr.dtype.na_value
+        elif dtype.kind == "f":  # type: ignore[union-attr]
+            na_value = np.nan
+        elif dtype.kind == "M":  # type: ignore[union-attr]
+            na_value = np.datetime64("nat")
+        elif dtype.kind == "m":  # type: ignore[union-attr]
+            na_value = np.timedelta64("nat")
+        else:
+            na_value = arr.dtype.na_value
+
+    if not dtype_given and hasna:
+        try:
+            np_can_hold_element(dtype, na_value)  # type: ignore[arg-type]
+        except LossySetitemError:
+            dtype = np.dtype(np.object_)
+    return dtype, na_value
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fc50f786fc6a6c51f78ef9ebd4ee6ed26a2bab3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/__init__.py
@@ -0,0 +1,7 @@
+from pandas.core.arrays.arrow.accessors import (
+    ListAccessor,
+    StructAccessor,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+
+__all__ = ["ArrowExtensionArray", "StructAccessor", "ListAccessor"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/_arrow_utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/_arrow_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..285c3fd465ffcb9f507ebd1b3a0e3e6f55b76987
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/_arrow_utils.py
@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+import numpy as np
+import pyarrow
+
+
+def pyarrow_array_to_numpy_and_mask(
+    arr, dtype: np.dtype
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Convert a primitive pyarrow.Array to a numpy array and boolean mask based
+    on the buffers of the Array.
+
+    At the moment pyarrow.BooleanArray is not supported.
+
+    Parameters
+    ----------
+    arr : pyarrow.Array
+    dtype : numpy.dtype
+
+    Returns
+    -------
+    (data, mask)
+        Tuple of two numpy arrays with the raw data (with specified dtype) and
+        a boolean mask (validity mask, so False means missing)
+    """
+    dtype = np.dtype(dtype)
+
+    if pyarrow.types.is_null(arr.type):
+        # No initialization of data is needed since everything is null
+        data = np.empty(len(arr), dtype=dtype)
+        mask = np.zeros(len(arr), dtype=bool)
+        return data, mask
+    buflist = arr.buffers()
+    # Since Arrow buffers might contain padding and the data might be offset,
+    # the buffer gets sliced here before handing it to numpy.
+    # See also https://github.com/pandas-dev/pandas/issues/40896
+    offset = arr.offset * dtype.itemsize
+    length = len(arr) * dtype.itemsize
+    data_buf = buflist[1][offset : offset + length]
+    data = np.frombuffer(data_buf, dtype=dtype)
+    bitmask = buflist[0]
+    if bitmask is not None:
+        mask = pyarrow.BooleanArray.from_buffers(
+            pyarrow.bool_(), len(arr), [None, bitmask], offset=arr.offset
+        )
+        mask = np.asarray(mask)
+    else:
+        mask = np.ones(len(arr), dtype=bool)
+    return data, mask
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/accessors.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/accessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f0784eaa3fd45e278cef083c3a606023827da0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/accessors.py
@@ -0,0 +1,473 @@
+"""Accessors for arrow-backed data."""
+
+from __future__ import annotations
+
+from abc import (
+    ABCMeta,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+)
+
+from pandas.core.dtypes.common import is_list_like
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+    from pandas.core.dtypes.dtypes import ArrowDtype
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class ArrowAccessor(metaclass=ABCMeta):
+    @abstractmethod
+    def __init__(self, data, validation_msg: str) -> None:
+        self._data = data
+        self._validation_msg = validation_msg
+        self._validate(data)
+
+    @abstractmethod
+    def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
+        pass
+
+    def _validate(self, data):
+        dtype = data.dtype
+        if pa_version_under10p1 or not isinstance(dtype, ArrowDtype):
+            # Raise AttributeError so that inspect can handle non-struct Series.
+            raise AttributeError(self._validation_msg.format(dtype=dtype))
+
+        if not self._is_valid_pyarrow_dtype(dtype.pyarrow_dtype):
+            # Raise AttributeError so that inspect can handle invalid Series.
+            raise AttributeError(self._validation_msg.format(dtype=dtype))
+
+    @property
+    def _pa_array(self):
+        return self._data.array._pa_array
+
+
+class ListAccessor(ArrowAccessor):
+    """
+    Accessor object for list data properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series
+        Series containing Arrow list data.
+    """
+
+    def __init__(self, data=None) -> None:
+        super().__init__(
+            data,
+            validation_msg="Can only use the '.list' accessor with "
+            "'list[pyarrow]' dtype, not {dtype}.",
+        )
+
+    def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
+        return (
+            pa.types.is_list(pyarrow_dtype)
+            or pa.types.is_fixed_size_list(pyarrow_dtype)
+            or pa.types.is_large_list(pyarrow_dtype)
+        )
+
+    def len(self) -> Series:
+        """
+        Return the length of each list in the Series.
+
+        Returns
+        -------
+        pandas.Series
+            The length of each list.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [3],
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.list_(
+        ...         pa.int64()
+        ...     ))
+        ... )
+        >>> s.list.len()
+        0    3
+        1    1
+        dtype: int32[pyarrow]
+        """
+        from pandas import Series
+
+        value_lengths = pc.list_value_length(self._pa_array)
+        return Series(value_lengths, dtype=ArrowDtype(value_lengths.type))
+
+    def __getitem__(self, key: int | slice) -> Series:
+        """
+        Index or slice lists in the Series.
+
+        Parameters
+        ----------
+        key : int | slice
+            Index or slice of indices to access from each list.
+
+        Returns
+        -------
+        pandas.Series
+            The list at requested index.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [3],
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.list_(
+        ...         pa.int64()
+        ...     ))
+        ... )
+        >>> s.list[0]
+        0    1
+        1    3
+        dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        if isinstance(key, int):
+            # TODO: Support negative key but pyarrow does not allow
+            # element index to be an array.
+            # if key < 0:
+            #     key = pc.add(key, pc.list_value_length(self._pa_array))
+            element = pc.list_element(self._pa_array, key)
+            return Series(element, dtype=ArrowDtype(element.type))
+        elif isinstance(key, slice):
+            if pa_version_under11p0:
+                raise NotImplementedError(
+                    f"List slice not supported by pyarrow {pa.__version__}."
+                )
+
+            # TODO: Support negative start/stop/step, ideally this would be added
+            # upstream in pyarrow.
+            start, stop, step = key.start, key.stop, key.step
+            if start is None:
+                # TODO: When adding negative step support
+                #  this should be setto last element of array
+                # when step is negative.
+                start = 0
+            if step is None:
+                step = 1
+            sliced = pc.list_slice(self._pa_array, start, stop, step)
+            return Series(sliced, dtype=ArrowDtype(sliced.type))
+        else:
+            raise ValueError(f"key must be an int or slice, got {type(key).__name__}")
+
+    def __iter__(self) -> Iterator:
+        raise TypeError(f"'{type(self).__name__}' object is not iterable")
+
+    def flatten(self) -> Series:
+        """
+        Flatten list values.
+
+        Returns
+        -------
+        pandas.Series
+            The data from all lists in the series flattened.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [3],
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.list_(
+        ...         pa.int64()
+        ...     ))
+        ... )
+        >>> s.list.flatten()
+        0    1
+        1    2
+        2    3
+        3    3
+        dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        flattened = pc.list_flatten(self._pa_array)
+        return Series(flattened, dtype=ArrowDtype(flattened.type))
+
+
+class StructAccessor(ArrowAccessor):
+    """
+    Accessor object for structured data properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series
+        Series containing Arrow struct data.
+    """
+
+    def __init__(self, data=None) -> None:
+        super().__init__(
+            data,
+            validation_msg=(
+                "Can only use the '.struct' accessor with 'struct[pyarrow]' "
+                "dtype, not {dtype}."
+            ),
+        )
+
+    def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
+        return pa.types.is_struct(pyarrow_dtype)
+
+    @property
+    def dtypes(self) -> Series:
+        """
+        Return the dtype object of each child field of the struct.
+
+        Returns
+        -------
+        pandas.Series
+            The data type of each child field.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+        >>> s.struct.dtypes
+        version     int64[pyarrow]
+        project    string[pyarrow]
+        dtype: object
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        pa_type = self._data.dtype.pyarrow_dtype
+        types = [ArrowDtype(struct.type) for struct in pa_type]
+        names = [struct.name for struct in pa_type]
+        return Series(types, index=Index(names))
+
+    def field(
+        self,
+        name_or_index: list[str]
+        | list[bytes]
+        | list[int]
+        | pc.Expression
+        | bytes
+        | str
+        | int,
+    ) -> Series:
+        """
+        Extract a child field of a struct as a Series.
+
+        Parameters
+        ----------
+        name_or_index : str | bytes | int | expression | list
+            Name or index of the child field to extract.
+
+            For list-like inputs, this will index into a nested
+            struct.
+
+        Returns
+        -------
+        pandas.Series
+            The data corresponding to the selected child field.
+
+        See Also
+        --------
+        Series.struct.explode : Return all child fields as a DataFrame.
+
+        Notes
+        -----
+        The name of the resulting Series will be set using the following
+        rules:
+
+        - For string, bytes, or integer `name_or_index` (or a list of these, for
+          a nested selection), the Series name is set to the selected
+          field's name.
+        - For a :class:`pyarrow.compute.Expression`, this is set to
+          the string form of the expression.
+        - For list-like `name_or_index`, the name will be set to the
+          name of the final field selected.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+
+        Extract by field name.
+
+        >>> s.struct.field("project")
+        0    pandas
+        1    pandas
+        2     numpy
+        Name: project, dtype: string[pyarrow]
+
+        Extract by field index.
+
+        >>> s.struct.field(0)
+        0    1
+        1    2
+        2    1
+        Name: version, dtype: int64[pyarrow]
+
+        Or an expression
+
+        >>> import pyarrow.compute as pc
+        >>> s.struct.field(pc.field("project"))
+        0    pandas
+        1    pandas
+        2     numpy
+        Name: project, dtype: string[pyarrow]
+
+        For nested struct types, you can pass a list of values to index
+        multiple levels:
+
+        >>> version_type = pa.struct([
+        ...     ("major", pa.int64()),
+        ...     ("minor", pa.int64()),
+        ... ])
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": {"major": 1, "minor": 5}, "project": "pandas"},
+        ...         {"version": {"major": 2, "minor": 1}, "project": "pandas"},
+        ...         {"version": {"major": 1, "minor": 26}, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", version_type), ("project", pa.string())]
+        ...     ))
+        ... )
+        >>> s.struct.field(["version", "minor"])
+        0     5
+        1     1
+        2    26
+        Name: minor, dtype: int64[pyarrow]
+        >>> s.struct.field([0, 0])
+        0    1
+        1    2
+        2    1
+        Name: major, dtype: int64[pyarrow]
+        """
+        from pandas import Series
+
+        def get_name(
+            level_name_or_index: list[str]
+            | list[bytes]
+            | list[int]
+            | pc.Expression
+            | bytes
+            | str
+            | int,
+            data: pa.ChunkedArray,
+        ):
+            if isinstance(level_name_or_index, int):
+                name = data.type.field(level_name_or_index).name
+            elif isinstance(level_name_or_index, (str, bytes)):
+                name = level_name_or_index
+            elif isinstance(level_name_or_index, pc.Expression):
+                name = str(level_name_or_index)
+            elif is_list_like(level_name_or_index):
+                # For nested input like [2, 1, 2]
+                # iteratively get the struct and field name. The last
+                # one is used for the name of the index.
+                level_name_or_index = list(reversed(level_name_or_index))
+                selected = data
+                while level_name_or_index:
+                    # we need the cast, otherwise mypy complains about
+                    # getting ints, bytes, or str here, which isn't possible.
+                    level_name_or_index = cast(list, level_name_or_index)
+                    name_or_index = level_name_or_index.pop()
+                    name = get_name(name_or_index, selected)
+                    selected = selected.type.field(selected.type.get_field_index(name))
+                    name = selected.name
+            else:
+                raise ValueError(
+                    "name_or_index must be an int, str, bytes, "
+                    "pyarrow.compute.Expression, or list of those"
+                )
+            return name
+
+        pa_arr = self._data.array._pa_array
+        name = get_name(name_or_index, pa_arr)
+        field_arr = pc.struct_field(pa_arr, name_or_index)
+
+        return Series(
+            field_arr,
+            dtype=ArrowDtype(field_arr.type),
+            index=self._data.index,
+            name=name,
+        )
+
+    def explode(self) -> DataFrame:
+        """
+        Extract all child fields of a struct as a DataFrame.
+
+        Returns
+        -------
+        pandas.DataFrame
+            The data corresponding to all child fields.
+
+        See Also
+        --------
+        Series.struct.field : Return a single child field as a Series.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> s = pd.Series(
+        ...     [
+        ...         {"version": 1, "project": "pandas"},
+        ...         {"version": 2, "project": "pandas"},
+        ...         {"version": 1, "project": "numpy"},
+        ...     ],
+        ...     dtype=pd.ArrowDtype(pa.struct(
+        ...         [("version", pa.int64()), ("project", pa.string())]
+        ...     ))
+        ... )
+
+        >>> s.struct.explode()
+           version project
+        0        1  pandas
+        1        2  pandas
+        2        1   numpy
+        """
+        from pandas import concat
+
+        pa_type = self._pa_array.type
+        return concat(
+            [self.field(i) for i in range(pa_type.num_fields)], axis="columns"
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/array.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..e16a126ac10ee3ee3a00d8d967ad2a038e37ff5c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/array.py
@@ -0,0 +1,2969 @@
+from __future__ import annotations
+
+import functools
+import operator
+from pathlib import Path
+import re
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+import unicodedata
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+    timezones,
+)
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_fillna_kwargs
+
+from pandas.core.dtypes.cast import (
+    can_hold_element,
+    infer_dtype_from_scalar,
+)
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_float_dtype,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    algorithms as algos,
+    missing,
+    ops,
+    roperator,
+)
+from pandas.core.algorithms import map_array
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._arrow_string_mixins import ArrowStringArrayMixin
+from pandas.core.arrays._utils import to_numpy_dtype_inference
+from pandas.core.arrays.base import (
+    ExtensionArray,
+    ExtensionArraySupportsAnyAll,
+)
+from pandas.core.arrays.masked import BaseMaskedArray
+from pandas.core.arrays.string_ import StringDtype
+import pandas.core.common as com
+from pandas.core.indexers import (
+    check_array_indexer,
+    unpack_tuple_and_ellipses,
+    validate_indices,
+)
+from pandas.core.nanops import check_below_min_count
+from pandas.core.strings.base import BaseStringArrayMethods
+
+from pandas.io._util import _arrow_dtype_mapping
+from pandas.tseries.frequencies import to_offset
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+    from pandas.core.dtypes.dtypes import ArrowDtype
+
+    ARROW_CMP_FUNCS = {
+        "eq": pc.equal,
+        "ne": pc.not_equal,
+        "lt": pc.less,
+        "gt": pc.greater,
+        "le": pc.less_equal,
+        "ge": pc.greater_equal,
+    }
+
+    ARROW_LOGICAL_FUNCS = {
+        "and_": pc.and_kleene,
+        "rand_": lambda x, y: pc.and_kleene(y, x),
+        "or_": pc.or_kleene,
+        "ror_": lambda x, y: pc.or_kleene(y, x),
+        "xor": pc.xor,
+        "rxor": lambda x, y: pc.xor(y, x),
+    }
+
+    ARROW_BIT_WISE_FUNCS = {
+        "and_": pc.bit_wise_and,
+        "rand_": lambda x, y: pc.bit_wise_and(y, x),
+        "or_": pc.bit_wise_or,
+        "ror_": lambda x, y: pc.bit_wise_or(y, x),
+        "xor": pc.bit_wise_xor,
+        "rxor": lambda x, y: pc.bit_wise_xor(y, x),
+    }
+
+    def cast_for_truediv(
+        arrow_array: pa.ChunkedArray, pa_object: pa.Array | pa.Scalar
+    ) -> tuple[pa.ChunkedArray, pa.Array | pa.Scalar]:
+        # Ensure int / int -> float mirroring Python/Numpy behavior
+        # as pc.divide_checked(int, int) -> int
+        if pa.types.is_integer(arrow_array.type) and pa.types.is_integer(
+            pa_object.type
+        ):
+            # GH: 56645.
+            # https://github.com/apache/arrow/issues/35563
+            return pc.cast(arrow_array, pa.float64(), safe=False), pc.cast(
+                pa_object, pa.float64(), safe=False
+            )
+
+        return arrow_array, pa_object
+
+    def floordiv_compat(
+        left: pa.ChunkedArray | pa.Array | pa.Scalar,
+        right: pa.ChunkedArray | pa.Array | pa.Scalar,
+    ) -> pa.ChunkedArray:
+        # TODO: Replace with pyarrow floordiv kernel.
+        # https://github.com/apache/arrow/issues/39386
+        if pa.types.is_integer(left.type) and pa.types.is_integer(right.type):
+            divided = pc.divide_checked(left, right)
+            if pa.types.is_signed_integer(divided.type):
+                # GH 56676
+                has_remainder = pc.not_equal(pc.multiply(divided, right), left)
+                has_one_negative_operand = pc.less(
+                    pc.bit_wise_xor(left, right),
+                    pa.scalar(0, type=divided.type),
+                )
+                result = pc.if_else(
+                    pc.and_(
+                        has_remainder,
+                        has_one_negative_operand,
+                    ),
+                    # GH: 55561
+                    pc.subtract(divided, pa.scalar(1, type=divided.type)),
+                    divided,
+                )
+            else:
+                result = divided
+            result = result.cast(left.type)
+        else:
+            divided = pc.divide(left, right)
+            result = pc.floor(divided)
+        return result
+
+    ARROW_ARITHMETIC_FUNCS = {
+        "add": pc.add_checked,
+        "radd": lambda x, y: pc.add_checked(y, x),
+        "sub": pc.subtract_checked,
+        "rsub": lambda x, y: pc.subtract_checked(y, x),
+        "mul": pc.multiply_checked,
+        "rmul": lambda x, y: pc.multiply_checked(y, x),
+        "truediv": lambda x, y: pc.divide(*cast_for_truediv(x, y)),
+        "rtruediv": lambda x, y: pc.divide(*cast_for_truediv(y, x)),
+        "floordiv": lambda x, y: floordiv_compat(x, y),
+        "rfloordiv": lambda x, y: floordiv_compat(y, x),
+        "mod": NotImplemented,
+        "rmod": NotImplemented,
+        "divmod": NotImplemented,
+        "rdivmod": NotImplemented,
+        "pow": pc.power_checked,
+        "rpow": lambda x, y: pc.power_checked(y, x),
+    }
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        Dtype,
+        FillnaOptions,
+        InterpolateOptions,
+        Iterator,
+        NpDtype,
+        NumpySorter,
+        NumpyValueArrayLike,
+        PositionalIndexer,
+        Scalar,
+        Self,
+        SortKind,
+        TakeIndexer,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
+    )
+
+    from pandas import Series
+    from pandas.core.arrays.datetimes import DatetimeArray
+    from pandas.core.arrays.timedeltas import TimedeltaArray
+
+
+def get_unit_from_pa_dtype(pa_dtype):
+    # https://github.com/pandas-dev/pandas/pull/50998#discussion_r1100344804
+    if pa_version_under11p0:
+        unit = str(pa_dtype).split("[", 1)[-1][:-1]
+        if unit not in ["s", "ms", "us", "ns"]:
+            raise ValueError(pa_dtype)
+        return unit
+    return pa_dtype.unit
+
+
+def to_pyarrow_type(
+    dtype: ArrowDtype | pa.DataType | Dtype | None,
+) -> pa.DataType | None:
+    """
+    Convert dtype to a pyarrow type instance.
+    """
+    if isinstance(dtype, ArrowDtype):
+        return dtype.pyarrow_dtype
+    elif isinstance(dtype, pa.DataType):
+        return dtype
+    elif isinstance(dtype, DatetimeTZDtype):
+        return pa.timestamp(dtype.unit, dtype.tz)
+    elif dtype:
+        try:
+            # Accepts python types too
+            # Doesn't handle all numpy types
+            return pa.from_numpy_dtype(dtype)
+        except pa.ArrowNotImplementedError:
+            pass
+    return None
+
+
+class ArrowExtensionArray(  # type: ignore[misc]
+    OpsMixin,
+    ExtensionArraySupportsAnyAll,
+    ArrowStringArrayMixin,
+    BaseStringArrayMethods,
+):
+    """
+    Pandas ExtensionArray backed by a PyArrow ChunkedArray.
+
+    .. warning::
+
+       ArrowExtensionArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : pyarrow.Array or pyarrow.ChunkedArray
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    ArrowExtensionArray
+
+    Notes
+    -----
+    Most methods are implemented using `pyarrow compute functions. <https://arrow.apache.org/docs/python/api/compute.html>`__
+    Some methods may either raise an exception or raise a ``PerformanceWarning`` if an
+    associated compute function is not available based on the installed version of PyArrow.
+
+    Please install the latest version of PyArrow to enable the best functionality and avoid
+    potential bugs in prior versions of PyArrow.
+
+    Examples
+    --------
+    Create an ArrowExtensionArray with :func:`pandas.array`:
+
+    >>> pd.array([1, 1, None], dtype="int64[pyarrow]")
+    <ArrowExtensionArray>
+    [1, 1, <NA>]
+    Length: 3, dtype: int64[pyarrow]
+    """  # noqa: E501 (http link too long)
+
+    _pa_array: pa.ChunkedArray
+    _dtype: ArrowDtype
+
+    def __init__(self, values: pa.Array | pa.ChunkedArray) -> None:
+        if pa_version_under10p1:
+            msg = "pyarrow>=10.0.1 is required for PyArrow backed ArrowExtensionArray."
+            raise ImportError(msg)
+        if isinstance(values, pa.Array):
+            self._pa_array = pa.chunked_array([values])
+        elif isinstance(values, pa.ChunkedArray):
+            self._pa_array = values
+        else:
+            raise ValueError(
+                f"Unsupported type '{type(values)}' for ArrowExtensionArray"
+            )
+        self._dtype = ArrowDtype(self._pa_array.type)
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        """
+        Construct a new ExtensionArray from a sequence of scalars.
+        """
+        pa_type = to_pyarrow_type(dtype)
+        pa_array = cls._box_pa_array(scalars, pa_type=pa_type, copy=copy)
+        arr = cls(pa_array)
+        return arr
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ):
+        """
+        Construct a new ExtensionArray from a sequence of strings.
+        """
+        pa_type = to_pyarrow_type(dtype)
+        if (
+            pa_type is None
+            or pa.types.is_binary(pa_type)
+            or pa.types.is_string(pa_type)
+            or pa.types.is_large_string(pa_type)
+        ):
+            # pa_type is None: Let pa.array infer
+            # pa_type is string/binary: scalars already correct type
+            scalars = strings
+        elif pa.types.is_timestamp(pa_type):
+            from pandas.core.tools.datetimes import to_datetime
+
+            scalars = to_datetime(strings, errors="raise")
+        elif pa.types.is_date(pa_type):
+            from pandas.core.tools.datetimes import to_datetime
+
+            scalars = to_datetime(strings, errors="raise").date
+        elif pa.types.is_duration(pa_type):
+            from pandas.core.tools.timedeltas import to_timedelta
+
+            scalars = to_timedelta(strings, errors="raise")
+            if pa_type.unit != "ns":
+                # GH51175: test_from_sequence_of_strings_pa_array
+                # attempt to parse as int64 reflecting pyarrow's
+                # duration to string casting behavior
+                mask = isna(scalars)
+                if not isinstance(strings, (pa.Array, pa.ChunkedArray)):
+                    strings = pa.array(strings, type=pa.string(), from_pandas=True)
+                strings = pc.if_else(mask, None, strings)
+                try:
+                    scalars = strings.cast(pa.int64())
+                except pa.ArrowInvalid:
+                    pass
+        elif pa.types.is_time(pa_type):
+            from pandas.core.tools.times import to_time
+
+            # "coerce" to allow "null times" (None) to not raise
+            scalars = to_time(strings, errors="coerce")
+        elif pa.types.is_boolean(pa_type):
+            # pyarrow string->bool casting is case-insensitive:
+            #   "true" or "1" -> True
+            #   "false" or "0" -> False
+            # Note: BooleanArray was previously used to parse these strings
+            #   and allows "1.0" and "0.0". Pyarrow casting does not support
+            #   this, but we allow it here.
+            if isinstance(strings, (pa.Array, pa.ChunkedArray)):
+                scalars = strings
+            else:
+                scalars = pa.array(strings, type=pa.string(), from_pandas=True)
+            scalars = pc.if_else(pc.equal(scalars, "1.0"), "1", scalars)
+            scalars = pc.if_else(pc.equal(scalars, "0.0"), "0", scalars)
+            scalars = scalars.cast(pa.bool_())
+        elif (
+            pa.types.is_integer(pa_type)
+            or pa.types.is_floating(pa_type)
+            or pa.types.is_decimal(pa_type)
+        ):
+            from pandas.core.tools.numeric import to_numeric
+
+            scalars = to_numeric(strings, errors="raise")
+        else:
+            raise NotImplementedError(
+                f"Converting strings to {pa_type} is not implemented."
+            )
+        return cls._from_sequence(scalars, dtype=pa_type, copy=copy)
+
+    @classmethod
+    def _box_pa(
+        cls, value, pa_type: pa.DataType | None = None
+    ) -> pa.Array | pa.ChunkedArray | pa.Scalar:
+        """
+        Box value into a pyarrow Array, ChunkedArray or Scalar.
+
+        Parameters
+        ----------
+        value : any
+        pa_type : pa.DataType | None
+
+        Returns
+        -------
+        pa.Array or pa.ChunkedArray or pa.Scalar
+        """
+        if isinstance(value, pa.Scalar) or not is_list_like(value):
+            return cls._box_pa_scalar(value, pa_type)
+        return cls._box_pa_array(value, pa_type)
+
+    @classmethod
+    def _box_pa_scalar(cls, value, pa_type: pa.DataType | None = None) -> pa.Scalar:
+        """
+        Box value into a pyarrow Scalar.
+
+        Parameters
+        ----------
+        value : any
+        pa_type : pa.DataType | None
+
+        Returns
+        -------
+        pa.Scalar
+        """
+        if isinstance(value, pa.Scalar):
+            pa_scalar = value
+        elif isna(value):
+            pa_scalar = pa.scalar(None, type=pa_type)
+        else:
+            # Workaround https://github.com/apache/arrow/issues/37291
+            if isinstance(value, Timedelta):
+                if pa_type is None:
+                    pa_type = pa.duration(value.unit)
+                elif value.unit != pa_type.unit:
+                    value = value.as_unit(pa_type.unit)
+                value = value._value
+            elif isinstance(value, Timestamp):
+                if pa_type is None:
+                    pa_type = pa.timestamp(value.unit, tz=value.tz)
+                elif value.unit != pa_type.unit:
+                    value = value.as_unit(pa_type.unit)
+                value = value._value
+
+            pa_scalar = pa.scalar(value, type=pa_type, from_pandas=True)
+
+        if pa_type is not None and pa_scalar.type != pa_type:
+            pa_scalar = pa_scalar.cast(pa_type)
+
+        return pa_scalar
+
+    @classmethod
+    def _box_pa_array(
+        cls, value, pa_type: pa.DataType | None = None, copy: bool = False
+    ) -> pa.Array | pa.ChunkedArray:
+        """
+        Box value into a pyarrow Array or ChunkedArray.
+
+        Parameters
+        ----------
+        value : Sequence
+        pa_type : pa.DataType | None
+
+        Returns
+        -------
+        pa.Array or pa.ChunkedArray
+        """
+        if isinstance(value, cls):
+            pa_array = value._pa_array
+        elif isinstance(value, (pa.Array, pa.ChunkedArray)):
+            pa_array = value
+        elif isinstance(value, BaseMaskedArray):
+            # GH 52625
+            if copy:
+                value = value.copy()
+            pa_array = value.__arrow_array__()
+        else:
+            if (
+                isinstance(value, np.ndarray)
+                and pa_type is not None
+                and (
+                    pa.types.is_large_binary(pa_type)
+                    or pa.types.is_large_string(pa_type)
+                )
+            ):
+                # See https://github.com/apache/arrow/issues/35289
+                value = value.tolist()
+            elif copy and is_array_like(value):
+                # pa array should not get updated when numpy array is updated
+                value = value.copy()
+
+            if (
+                pa_type is not None
+                and pa.types.is_duration(pa_type)
+                and (not isinstance(value, np.ndarray) or value.dtype.kind not in "mi")
+            ):
+                # Workaround https://github.com/apache/arrow/issues/37291
+                from pandas.core.tools.timedeltas import to_timedelta
+
+                value = to_timedelta(value, unit=pa_type.unit).as_unit(pa_type.unit)
+                value = value.to_numpy()
+
+            try:
+                pa_array = pa.array(value, type=pa_type, from_pandas=True)
+            except (pa.ArrowInvalid, pa.ArrowTypeError):
+                # GH50430: let pyarrow infer type, then cast
+                pa_array = pa.array(value, from_pandas=True)
+
+            if pa_type is None and pa.types.is_duration(pa_array.type):
+                # Workaround https://github.com/apache/arrow/issues/37291
+                from pandas.core.tools.timedeltas import to_timedelta
+
+                value = to_timedelta(value)
+                value = value.to_numpy()
+                pa_array = pa.array(value, type=pa_type, from_pandas=True)
+
+            if pa.types.is_duration(pa_array.type) and pa_array.null_count > 0:
+                # GH52843: upstream bug for duration types when originally
+                # constructed with data containing numpy NaT.
+                # https://github.com/apache/arrow/issues/35088
+                arr = cls(pa_array)
+                arr = arr.fillna(arr.dtype.na_value)
+                pa_array = arr._pa_array
+
+        if pa_type is not None and pa_array.type != pa_type:
+            if pa.types.is_dictionary(pa_type):
+                pa_array = pa_array.dictionary_encode()
+            else:
+                try:
+                    pa_array = pa_array.cast(pa_type)
+                except (
+                    pa.ArrowInvalid,
+                    pa.ArrowTypeError,
+                    pa.ArrowNotImplementedError,
+                ):
+                    if pa.types.is_string(pa_array.type) or pa.types.is_large_string(
+                        pa_array.type
+                    ):
+                        # TODO: Move logic in _from_sequence_of_strings into
+                        # _box_pa_array
+                        return cls._from_sequence_of_strings(
+                            value, dtype=pa_type
+                        )._pa_array
+                    else:
+                        raise
+
+        return pa_array
+
+    def __getitem__(self, item: PositionalIndexer):
+        """Select a subset of self.
+
+        Parameters
+        ----------
+        item : int, slice, or ndarray
+            * int: The position in 'self' to get.
+            * slice: A slice object, where 'start', 'stop', and 'step' are
+              integers or None
+            * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'
+
+        Returns
+        -------
+        item : scalar or ExtensionArray
+
+        Notes
+        -----
+        For scalar ``item``, return a scalar value suitable for the array's
+        type. This should be an instance of ``self.dtype.type``.
+        For slice ``key``, return an instance of ``ExtensionArray``, even
+        if the slice is length 0 or 1.
+        For a boolean mask, return an instance of ``ExtensionArray``, filtered
+        to the values where ``item`` is True.
+        """
+        item = check_array_indexer(self, item)
+
+        if isinstance(item, np.ndarray):
+            if not len(item):
+                # Removable once we migrate StringDtype[pyarrow] to ArrowDtype[string]
+                if (
+                    isinstance(self._dtype, StringDtype)
+                    and self._dtype.storage == "pyarrow"
+                ):
+                    # TODO(infer_string) should this be large_string?
+                    pa_dtype = pa.string()
+                else:
+                    pa_dtype = self._dtype.pyarrow_dtype
+                return type(self)(pa.chunked_array([], type=pa_dtype))
+            elif item.dtype.kind in "iu":
+                return self.take(item)
+            elif item.dtype.kind == "b":
+                return type(self)(self._pa_array.filter(item))
+            else:
+                raise IndexError(
+                    "Only integers, slices and integer or "
+                    "boolean arrays are valid indices."
+                )
+        elif isinstance(item, tuple):
+            item = unpack_tuple_and_ellipses(item)
+
+        if item is Ellipsis:
+            # TODO: should be handled by pyarrow?
+            item = slice(None)
+
+        if is_scalar(item) and not is_integer(item):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
+        # We are not an array indexer, so maybe e.g. a slice or integer
+        # indexer. We dispatch to pyarrow.
+        if isinstance(item, slice):
+            # Arrow bug https://github.com/apache/arrow/issues/38768
+            if item.start == item.stop:
+                pass
+            elif (
+                item.stop is not None
+                and item.stop < -len(self)
+                and item.step is not None
+                and item.step < 0
+            ):
+                item = slice(item.start, None, item.step)
+
+        value = self._pa_array[item]
+        if isinstance(value, pa.ChunkedArray):
+            return type(self)(value)
+        else:
+            pa_type = self._pa_array.type
+            scalar = value.as_py()
+            if scalar is None:
+                return self._dtype.na_value
+            elif pa.types.is_timestamp(pa_type) and pa_type.unit != "ns":
+                # GH 53326
+                return Timestamp(scalar).as_unit(pa_type.unit)
+            elif pa.types.is_duration(pa_type) and pa_type.unit != "ns":
+                # GH 53326
+                return Timedelta(scalar).as_unit(pa_type.unit)
+            else:
+                return scalar
+
+    def __iter__(self) -> Iterator[Any]:
+        """
+        Iterate over elements of the array.
+        """
+        na_value = self._dtype.na_value
+        # GH 53326
+        pa_type = self._pa_array.type
+        box_timestamp = pa.types.is_timestamp(pa_type) and pa_type.unit != "ns"
+        box_timedelta = pa.types.is_duration(pa_type) and pa_type.unit != "ns"
+        for value in self._pa_array:
+            val = value.as_py()
+            if val is None:
+                yield na_value
+            elif box_timestamp:
+                yield Timestamp(val).as_unit(pa_type.unit)
+            elif box_timedelta:
+                yield Timedelta(val).as_unit(pa_type.unit)
+            else:
+                yield val
+
+    def __arrow_array__(self, type=None):
+        """Convert myself to a pyarrow ChunkedArray."""
+        return self._pa_array
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """Correctly construct numpy arrays when passed to `np.asarray()`."""
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif copy is None:
+            # `to_numpy(copy=False)` has the meaning of NumPy `copy=None`.
+            copy = False
+
+        return self.to_numpy(dtype=dtype, copy=copy)
+
+    def __invert__(self) -> Self:
+        # This is a bit wise op for integer types
+        if pa.types.is_integer(self._pa_array.type):
+            return type(self)(pc.bit_wise_not(self._pa_array))
+        elif pa.types.is_string(self._pa_array.type) or pa.types.is_large_string(
+            self._pa_array.type
+        ):
+            # Raise TypeError instead of pa.ArrowNotImplementedError
+            raise TypeError("__invert__ is not supported for string dtypes")
+        else:
+            return type(self)(pc.invert(self._pa_array))
+
+    def __neg__(self) -> Self:
+        try:
+            return type(self)(pc.negate_checked(self._pa_array))
+        except pa.ArrowNotImplementedError as err:
+            raise TypeError(
+                f"unary '-' not supported for dtype '{self.dtype}'"
+            ) from err
+
+    def __pos__(self) -> Self:
+        return type(self)(self._pa_array)
+
+    def __abs__(self) -> Self:
+        return type(self)(pc.abs_checked(self._pa_array))
+
+    # GH 42600: __getstate__/__setstate__ not necessary once
+    # https://issues.apache.org/jira/browse/ARROW-10739 is addressed
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["_pa_array"] = self._pa_array.combine_chunks()
+        return state
+
+    def __setstate__(self, state) -> None:
+        if "_data" in state:
+            data = state.pop("_data")
+        else:
+            data = state["_pa_array"]
+        state["_pa_array"] = pa.chunked_array(data)
+        self.__dict__.update(state)
+
+    def _cmp_method(self, other, op):
+        pc_func = ARROW_CMP_FUNCS[op.__name__]
+        if isinstance(other, (ExtensionArray, np.ndarray, list)):
+            try:
+                boxed = self._box_pa(other)
+            except pa.lib.ArrowInvalid:
+                # e.g. GH#60228 [1, "b"] we have to operate pointwise
+                res_values = [op(x, y) for x, y in zip(self, other)]
+                result = pa.array(res_values, type=pa.bool_(), from_pandas=True)
+            else:
+                try:
+                    result = pc_func(self._pa_array, boxed)
+                except pa.ArrowNotImplementedError:
+                    result = ops.invalid_comparison(self, other, op)
+                    result = pa.array(result, type=pa.bool_())
+
+        elif is_scalar(other):
+            try:
+                result = pc_func(self._pa_array, self._box_pa(other))
+            except (pa.lib.ArrowNotImplementedError, pa.lib.ArrowInvalid):
+                mask = isna(self) | isna(other)
+                valid = ~mask
+                result = np.zeros(len(self), dtype="bool")
+                np_array = np.array(self)
+                try:
+                    result[valid] = op(np_array[valid], other)
+                except TypeError:
+                    result = ops.invalid_comparison(self, other, op)
+                result = pa.array(result, type=pa.bool_())
+                result = pc.if_else(valid, result, None)
+        else:
+            raise NotImplementedError(
+                f"{op.__name__} not implemented for {type(other)}"
+            )
+        return ArrowExtensionArray(result)
+
+    def _op_method_error_message(self, other, op) -> str:
+        if hasattr(other, "dtype"):
+            other_type = f"dtype '{other.dtype}'"
+        else:
+            other_type = f"object of type {type(other)}"
+        return (
+            f"operation '{op.__name__}' not supported for "
+            f"dtype '{self.dtype}' with {other_type}"
+        )
+
+    def _evaluate_op_method(self, other, op, arrow_funcs) -> Self:
+        pa_type = self._pa_array.type
+        other_original = other
+        other = self._box_pa(other)
+
+        if (
+            pa.types.is_string(pa_type)
+            or pa.types.is_large_string(pa_type)
+            or pa.types.is_binary(pa_type)
+        ):
+            if op in [operator.add, roperator.radd]:
+                sep = pa.scalar("", type=pa_type)
+                try:
+                    if op is operator.add:
+                        result = pc.binary_join_element_wise(self._pa_array, other, sep)
+                    elif op is roperator.radd:
+                        result = pc.binary_join_element_wise(other, self._pa_array, sep)
+                except pa.ArrowNotImplementedError as err:
+                    raise TypeError(
+                        self._op_method_error_message(other_original, op)
+                    ) from err
+                return type(self)(result)
+            elif op in [operator.mul, roperator.rmul]:
+                binary = self._pa_array
+                integral = other
+                if not pa.types.is_integer(integral.type):
+                    raise TypeError("Can only string multiply by an integer.")
+                pa_integral = pc.if_else(pc.less(integral, 0), 0, integral)
+                result = pc.binary_repeat(binary, pa_integral)
+                return type(self)(result)
+        elif (
+            pa.types.is_string(other.type)
+            or pa.types.is_binary(other.type)
+            or pa.types.is_large_string(other.type)
+        ) and op in [operator.mul, roperator.rmul]:
+            binary = other
+            integral = self._pa_array
+            if not pa.types.is_integer(integral.type):
+                raise TypeError("Can only string multiply by an integer.")
+            pa_integral = pc.if_else(pc.less(integral, 0), 0, integral)
+            result = pc.binary_repeat(binary, pa_integral)
+            return type(self)(result)
+        if (
+            isinstance(other, pa.Scalar)
+            and pc.is_null(other).as_py()
+            and op.__name__ in ARROW_LOGICAL_FUNCS
+        ):
+            # pyarrow kleene ops require null to be typed
+            other = other.cast(pa_type)
+
+        pc_func = arrow_funcs[op.__name__]
+        if pc_func is NotImplemented:
+            if pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type):
+                raise TypeError(self._op_method_error_message(other_original, op))
+            raise NotImplementedError(f"{op.__name__} not implemented.")
+
+        try:
+            result = pc_func(self._pa_array, other)
+        except pa.ArrowNotImplementedError as err:
+            raise TypeError(self._op_method_error_message(other_original, op)) from err
+        return type(self)(result)
+
+    def _logical_method(self, other, op):
+        # For integer types `^`, `|`, `&` are bitwise operators and return
+        # integer types. Otherwise these are boolean ops.
+        if pa.types.is_integer(self._pa_array.type):
+            return self._evaluate_op_method(other, op, ARROW_BIT_WISE_FUNCS)
+        elif (
+            (
+                pa.types.is_string(self._pa_array.type)
+                or pa.types.is_large_string(self._pa_array.type)
+            )
+            and op in (roperator.ror_, roperator.rand_, roperator.rxor)
+            and isinstance(other, np.ndarray)
+            and other.dtype == bool
+        ):
+            # GH#60234 backward compatibility for the move to StringDtype in 3.0
+            op_name = op.__name__[1:].strip("_")
+            warnings.warn(
+                f"'{op_name}' operations between boolean dtype and {self.dtype} are "
+                "deprecated and will raise in a future version. Explicitly "
+                "cast the strings to a boolean dtype before operating instead.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+            return op(other, self.astype(bool))
+        else:
+            return self._evaluate_op_method(other, op, ARROW_LOGICAL_FUNCS)
+
+    def _arith_method(self, other, op) -> Self | npt.NDArray[np.object_]:
+        if (
+            op in [operator.truediv, roperator.rtruediv]
+            and isinstance(other, Path)
+            and (
+                pa.types.is_string(self._pa_array.type)
+                or pa.types.is_large_string(self._pa_array.type)
+            )
+        ):
+            # GH#61940
+            return np.array(
+                [
+                    op(x, other) if isinstance(x, str) else self.dtype.na_value
+                    for x in self
+                ],
+                dtype=object,
+            )
+        return self._evaluate_op_method(other, op, ARROW_ARITHMETIC_FUNCS)
+
+    def equals(self, other) -> bool:
+        if not isinstance(other, ArrowExtensionArray):
+            return False
+        # I'm told that pyarrow makes __eq__ behave like pandas' equals;
+        #  TODO: is this documented somewhere?
+        return self._pa_array == other._pa_array
+
+    @property
+    def dtype(self) -> ArrowDtype:
+        """
+        An instance of 'ExtensionDtype'.
+        """
+        return self._dtype
+
+    @property
+    def nbytes(self) -> int:
+        """
+        The number of bytes needed to store this object in memory.
+        """
+        return self._pa_array.nbytes
+
+    def __len__(self) -> int:
+        """
+        Length of this array.
+
+        Returns
+        -------
+        length : int
+        """
+        return len(self._pa_array)
+
+    def __contains__(self, key) -> bool:
+        # https://github.com/pandas-dev/pandas/pull/51307#issuecomment-1426372604
+        if isna(key) and key is not self.dtype.na_value:
+            if self.dtype.kind == "f" and lib.is_float(key):
+                return pc.any(pc.is_nan(self._pa_array)).as_py()
+
+            # e.g. date or timestamp types we do not allow None here to match pd.NA
+            return False
+            # TODO: maybe complex? object?
+
+        return bool(super().__contains__(key))
+
+    @property
+    def _hasna(self) -> bool:
+        return self._pa_array.null_count > 0
+
+    def isna(self) -> npt.NDArray[np.bool_]:
+        """
+        Boolean NumPy array indicating if each value is missing.
+
+        This should return a 1-D array the same length as 'self'.
+        """
+        # GH51630: fast paths
+        null_count = self._pa_array.null_count
+        if null_count == 0:
+            return np.zeros(len(self), dtype=np.bool_)
+        elif null_count == len(self):
+            return np.ones(len(self), dtype=np.bool_)
+
+        return self._pa_array.is_null().to_numpy()
+
+    def any(self, *, skipna: bool = True, **kwargs):
+        """
+        Return whether any element is truthy.
+
+        Returns False unless there is at least one element that is truthy.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
+        is used as for logical operations.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be False, as for an empty array.
+            If `skipna` is False, the result will still be True if there is
+            at least one element that is truthy, otherwise NA will be returned
+            if there are NA's present.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        ArrowExtensionArray.all : Return whether all elements are truthy.
+
+        Examples
+        --------
+        The result indicates whether any element is truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, False, True], dtype="boolean[pyarrow]").any()
+        True
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").any()
+        True
+        >>> pd.array([False, False, pd.NA], dtype="boolean[pyarrow]").any()
+        False
+        >>> pd.array([], dtype="boolean[pyarrow]").any()
+        False
+        >>> pd.array([pd.NA], dtype="boolean[pyarrow]").any()
+        False
+        >>> pd.array([pd.NA], dtype="float64[pyarrow]").any()
+        False
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        True
+        >>> pd.array([1, 0, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        True
+        >>> pd.array([False, False, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        <NA>
+        >>> pd.array([0, 0, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
+        <NA>
+        """
+        return self._reduce("any", skipna=skipna, **kwargs)
+
+    def all(self, *, skipna: bool = True, **kwargs):
+        """
+        Return whether all elements are truthy.
+
+        Returns True unless there is at least one element that is falsey.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
+        is used as for logical operations.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be True, as for an empty array.
+            If `skipna` is False, the result will still be False if there is
+            at least one element that is falsey, otherwise NA will be returned
+            if there are NA's present.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        ArrowExtensionArray.any : Return whether any element is truthy.
+
+        Examples
+        --------
+        The result indicates whether all elements are truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, True, pd.NA], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([1, 1, pd.NA], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").all()
+        False
+        >>> pd.array([], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([pd.NA], dtype="boolean[pyarrow]").all()
+        True
+        >>> pd.array([pd.NA], dtype="float64[pyarrow]").all()
+        True
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, True, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        <NA>
+        >>> pd.array([1, 1, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        <NA>
+        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        False
+        >>> pd.array([1, 0, pd.NA], dtype="boolean[pyarrow]").all(skipna=False)
+        False
+        """
+        return self._reduce("all", skipna=skipna, **kwargs)
+
+    def argsort(
+        self,
+        *,
+        ascending: bool = True,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        **kwargs,
+    ) -> np.ndarray:
+        order = "ascending" if ascending else "descending"
+        null_placement = {"last": "at_end", "first": "at_start"}.get(na_position, None)
+        if null_placement is None:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        result = pc.array_sort_indices(
+            self._pa_array, order=order, null_placement=null_placement
+        )
+        np_result = result.to_numpy()
+        return np_result.astype(np.intp, copy=False)
+
+    def _argmin_max(self, skipna: bool, method: str) -> int:
+        if self._pa_array.length() in (0, self._pa_array.null_count) or (
+            self._hasna and not skipna
+        ):
+            # For empty or all null, pyarrow returns -1 but pandas expects TypeError
+            # For skipna=False and data w/ null, pandas expects NotImplementedError
+            # let ExtensionArray.arg{max|min} raise
+            return getattr(super(), f"arg{method}")(skipna=skipna)
+
+        data = self._pa_array
+        if pa.types.is_duration(data.type):
+            data = data.cast(pa.int64())
+
+        value = getattr(pc, method)(data, skip_nulls=skipna)
+        return pc.index(data, value).as_py()
+
+    def argmin(self, skipna: bool = True) -> int:
+        return self._argmin_max(skipna, "min")
+
+    def argmax(self, skipna: bool = True) -> int:
+        return self._argmin_max(skipna, "max")
+
+    def copy(self) -> Self:
+        """
+        Return a shallow copy of the array.
+
+        Underlying ChunkedArray is immutable, so a deep copy is unnecessary.
+
+        Returns
+        -------
+        type(self)
+        """
+        return type(self)(self._pa_array)
+
+    def dropna(self) -> Self:
+        """
+        Return ArrowExtensionArray without NA values.
+
+        Returns
+        -------
+        ArrowExtensionArray
+        """
+        return type(self)(pc.drop_null(self._pa_array))
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        if not self._hasna:
+            # TODO(CoW): Not necessary anymore when CoW is the default
+            return self.copy()
+
+        if limit is None and limit_area is None:
+            method = missing.clean_fill_method(method)
+            try:
+                if method == "pad":
+                    return type(self)(pc.fill_null_forward(self._pa_array))
+                elif method == "backfill":
+                    return type(self)(pc.fill_null_backward(self._pa_array))
+            except pa.ArrowNotImplementedError:
+                # ArrowNotImplementedError: Function 'coalesce' has no kernel
+                #   matching input types (duration[ns], duration[ns])
+                # TODO: remove try/except wrapper if/when pyarrow implements
+                #   a kernel for duration types.
+                pass
+
+        # TODO(3.0): after EA.fillna 'method' deprecation is enforced, we can remove
+        #  this method entirely.
+        return super()._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+
+    @doc(ExtensionArray.fillna)
+    def fillna(
+        self,
+        value: object | ArrayLike | None = None,
+        method: FillnaOptions | None = None,
+        limit: int | None = None,
+        copy: bool = True,
+    ) -> Self:
+        value, method = validate_fillna_kwargs(value, method)
+
+        if not self._hasna:
+            # TODO(CoW): Not necessary anymore when CoW is the default
+            return self.copy()
+
+        if limit is not None:
+            return super().fillna(value=value, method=method, limit=limit, copy=copy)
+
+        if method is not None:
+            return super().fillna(method=method, limit=limit, copy=copy)
+
+        if isinstance(value, (np.ndarray, ExtensionArray)):
+            # Similar to check_value_size, but we do not mask here since we may
+            #  end up passing it to the super() method.
+            if len(value) != len(self):
+                raise ValueError(
+                    f"Length of 'value' does not match. Got ({len(value)}) "
+                    f" expected {len(self)}"
+                )
+
+        try:
+            fill_value = self._box_pa(value, pa_type=self._pa_array.type)
+        except pa.ArrowTypeError as err:
+            msg = f"Invalid value '{value!s}' for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
+
+        try:
+            return type(self)(pc.fill_null(self._pa_array, fill_value=fill_value))
+        except pa.ArrowNotImplementedError:
+            # ArrowNotImplementedError: Function 'coalesce' has no kernel
+            #   matching input types (duration[ns], duration[ns])
+            # TODO: remove try/except wrapper if/when pyarrow implements
+            #   a kernel for duration types.
+            pass
+
+        return super().fillna(value=value, method=method, limit=limit, copy=copy)
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        # short-circuit to return all False array.
+        if not len(values):
+            return np.zeros(len(self), dtype=bool)
+
+        result = pc.is_in(self._pa_array, value_set=pa.array(values, from_pandas=True))
+        # pyarrow 2.0.0 returned nulls, so we explicitly specify dtype to convert nulls
+        # to False
+        return np.array(result, dtype=np.bool_)
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, Any]:
+        """
+        Return an array and missing value suitable for factorization.
+
+        Returns
+        -------
+        values : ndarray
+        na_value : pd.NA
+
+        Notes
+        -----
+        The values returned by this method are also used in
+        :func:`pandas.util.hash_pandas_object`.
+        """
+        values = self._pa_array.to_numpy()
+        return values, self.dtype.na_value
+
+    @doc(ExtensionArray.factorize)
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, ExtensionArray]:
+        null_encoding = "mask" if use_na_sentinel else "encode"
+
+        data = self._pa_array
+        pa_type = data.type
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
+            data = data.cast(pa.int64())
+
+        if pa.types.is_dictionary(data.type):
+            encoded = data
+        else:
+            encoded = data.dictionary_encode(null_encoding=null_encoding)
+        if encoded.length() == 0:
+            indices = np.array([], dtype=np.intp)
+            uniques = type(self)(pa.chunked_array([], type=encoded.type.value_type))
+        else:
+            # GH 54844
+            combined = encoded.combine_chunks()
+            pa_indices = combined.indices
+            if pa_indices.null_count > 0:
+                pa_indices = pc.fill_null(pa_indices, -1)
+            indices = pa_indices.to_numpy(zero_copy_only=False, writable=True).astype(
+                np.intp, copy=False
+            )
+            uniques = type(self)(combined.dictionary)
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            uniques = cast(ArrowExtensionArray, uniques.astype(self.dtype))
+        return indices, uniques
+
+    def reshape(self, *args, **kwargs):
+        raise NotImplementedError(
+            f"{type(self)} does not support reshape "
+            f"as backed by a 1D pyarrow.ChunkedArray."
+        )
+
+    def round(self, decimals: int = 0, *args, **kwargs) -> Self:
+        """
+        Round each value in the array a to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, default 0
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+        *args, **kwargs
+            Additional arguments and keywords have no effect.
+
+        Returns
+        -------
+        ArrowExtensionArray
+            Rounded values of the ArrowExtensionArray.
+
+        See Also
+        --------
+        DataFrame.round : Round values of a DataFrame.
+        Series.round : Round values of a Series.
+        """
+        return type(self)(pc.round(self._pa_array, ndigits=decimals))
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if self._hasna:
+            raise ValueError(
+                "searchsorted requires array to be sorted, which is impossible "
+                "with NAs present."
+            )
+        if isinstance(value, ExtensionArray):
+            value = value.astype(object)
+        # Base class searchsorted would cast to object, which is *much* slower.
+        dtype = None
+        if isinstance(self.dtype, ArrowDtype):
+            pa_dtype = self.dtype.pyarrow_dtype
+            if (
+                pa.types.is_timestamp(pa_dtype) or pa.types.is_duration(pa_dtype)
+            ) and pa_dtype.unit == "ns":
+                # np.array[datetime/timedelta].searchsorted(datetime/timedelta)
+                # erroneously fails when numpy type resolution is nanoseconds
+                dtype = object
+        return self.to_numpy(dtype=dtype).searchsorted(value, side=side, sorter=sorter)
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        allow_fill: bool = False,
+        fill_value: Any = None,
+    ) -> ArrowExtensionArray:
+        """
+        Take elements from an array.
+
+        Parameters
+        ----------
+        indices : sequence of int or one-dimensional np.ndarray of int
+            Indices to be taken.
+        allow_fill : bool, default False
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
+        fill_value : any, optional
+            Fill value to use for NA-indices when `allow_fill` is True.
+            This may be ``None``, in which case the default NA value for
+            the type, ``self.dtype.na_value``, is used.
+
+            For many ExtensionArrays, there will be two representations of
+            `fill_value`: a user-facing "boxed" scalar, and a low-level
+            physical NA value. `fill_value` should be the user-facing version,
+            and the implementation should handle translating that to the
+            physical version for processing the take if necessary.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Raises
+        ------
+        IndexError
+            When the indices are out of bounds for the array.
+        ValueError
+            When `indices` contains negative values other than ``-1``
+            and `allow_fill` is True.
+
+        See Also
+        --------
+        numpy.take
+        api.extensions.take
+
+        Notes
+        -----
+        ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
+        ``iloc``, when `indices` is a sequence of values. Additionally,
+        it's called by :meth:`Series.reindex`, or any other method
+        that causes realignment, with a `fill_value`.
+        """
+        indices_array = np.asanyarray(indices)
+
+        if len(self._pa_array) == 0 and (indices_array >= 0).any():
+            raise IndexError("cannot do a non-empty take")
+        if indices_array.size > 0 and indices_array.max() >= len(self._pa_array):
+            raise IndexError("out of bounds value in 'indices'.")
+
+        if allow_fill:
+            fill_mask = indices_array < 0
+            if fill_mask.any():
+                validate_indices(indices_array, len(self._pa_array))
+                # TODO(ARROW-9433): Treat negative indices as NULL
+                indices_array = pa.array(indices_array, mask=fill_mask)
+                result = self._pa_array.take(indices_array)
+                if isna(fill_value):
+                    return type(self)(result)
+                # TODO: ArrowNotImplementedError: Function fill_null has no
+                # kernel matching input types (array[string], scalar[string])
+                result = type(self)(result)
+                result[fill_mask] = fill_value
+                return result
+                # return type(self)(pc.fill_null(result, pa.scalar(fill_value)))
+            else:
+                # Nothing to fill
+                return type(self)(self._pa_array.take(indices))
+        else:  # allow_fill=False
+            # TODO(ARROW-9432): Treat negative indices as indices from the right.
+            if (indices_array < 0).any():
+                # Don't modify in-place
+                indices_array = np.copy(indices_array)
+                indices_array[indices_array < 0] += len(self._pa_array)
+            return type(self)(self._pa_array.take(indices_array))
+
+    def _maybe_convert_datelike_array(self):
+        """Maybe convert to a datelike array."""
+        pa_type = self._pa_array.type
+        if pa.types.is_timestamp(pa_type):
+            return self._to_datetimearray()
+        elif pa.types.is_duration(pa_type):
+            return self._to_timedeltaarray()
+        return self
+
+    def _to_datetimearray(self) -> DatetimeArray:
+        """Convert a pyarrow timestamp typed array to a DatetimeArray."""
+        from pandas.core.arrays.datetimes import (
+            DatetimeArray,
+            tz_to_dtype,
+        )
+
+        pa_type = self._pa_array.type
+        assert pa.types.is_timestamp(pa_type)
+        np_dtype = np.dtype(f"M8[{pa_type.unit}]")
+        dtype = tz_to_dtype(pa_type.tz, pa_type.unit)
+        np_array = self._pa_array.to_numpy()
+        np_array = np_array.astype(np_dtype)
+        return DatetimeArray._simple_new(np_array, dtype=dtype)
+
+    def _to_timedeltaarray(self) -> TimedeltaArray:
+        """Convert a pyarrow duration typed array to a TimedeltaArray."""
+        from pandas.core.arrays.timedeltas import TimedeltaArray
+
+        pa_type = self._pa_array.type
+        assert pa.types.is_duration(pa_type)
+        np_dtype = np.dtype(f"m8[{pa_type.unit}]")
+        np_array = self._pa_array.to_numpy()
+        np_array = np_array.astype(np_dtype)
+        return TimedeltaArray._simple_new(np_array, dtype=np_dtype)
+
+    def _values_for_json(self) -> np.ndarray:
+        if is_numeric_dtype(self.dtype):
+            return np.asarray(self, dtype=object)
+        return super()._values_for_json()
+
+    @doc(ExtensionArray.to_numpy)
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        original_na_value = na_value
+        dtype, na_value = to_numpy_dtype_inference(self, dtype, na_value, self._hasna)
+        pa_type = self._pa_array.type
+        if not self._hasna or isna(na_value) or pa.types.is_null(pa_type):
+            data = self
+        else:
+            data = self.fillna(na_value)
+            copy = False
+
+        if pa.types.is_timestamp(pa_type) or pa.types.is_duration(pa_type):
+            # GH 55997
+            if dtype != object and na_value is self.dtype.na_value:
+                na_value = lib.no_default
+            result = data._maybe_convert_datelike_array().to_numpy(
+                dtype=dtype, na_value=na_value
+            )
+        elif pa.types.is_time(pa_type) or pa.types.is_date(pa_type):
+            # convert to list of python datetime.time objects before
+            # wrapping in ndarray
+            result = np.array(list(data), dtype=dtype)
+            if data._hasna:
+                result[data.isna()] = na_value
+        elif pa.types.is_null(pa_type):
+            if dtype is not None and isna(na_value):
+                na_value = None
+            result = np.full(len(data), fill_value=na_value, dtype=dtype)
+        elif not data._hasna or (
+            pa.types.is_floating(pa_type)
+            and (
+                na_value is np.nan
+                or original_na_value is lib.no_default
+                and is_float_dtype(dtype)
+            )
+        ):
+            result = data._pa_array.to_numpy()
+            if dtype is not None:
+                result = result.astype(dtype, copy=False)
+            if copy:
+                result = result.copy()
+        else:
+            if dtype is None:
+                empty = pa.array([], type=pa_type).to_numpy(zero_copy_only=False)
+                if can_hold_element(empty, na_value):
+                    dtype = empty.dtype
+                else:
+                    dtype = np.object_
+            result = np.empty(len(data), dtype=dtype)
+            mask = data.isna()
+            result[mask] = na_value
+            result[~mask] = data[~mask]._pa_array.to_numpy()
+        return result
+
+    def map(self, mapper, na_action=None):
+        if is_numeric_dtype(self.dtype):
+            return map_array(self.to_numpy(), mapper, na_action=na_action)
+        else:
+            return super().map(mapper, na_action)
+
+    @doc(ExtensionArray.duplicated)
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        pa_type = self._pa_array.type
+        if pa.types.is_floating(pa_type) or pa.types.is_integer(pa_type):
+            values = self.to_numpy(na_value=0)
+        elif pa.types.is_boolean(pa_type):
+            values = self.to_numpy(na_value=False)
+        elif pa.types.is_temporal(pa_type):
+            if pa_type.bit_width == 32:
+                pa_type = pa.int32()
+            else:
+                pa_type = pa.int64()
+            arr = self.astype(ArrowDtype(pa_type))
+            values = arr.to_numpy(na_value=0)
+        else:
+            # factorize the values to avoid the performance penalty of
+            # converting to object dtype
+            values = self.factorize()[0]
+
+        mask = self.isna() if self._hasna else None
+        return algos.duplicated(values, keep=keep, mask=mask)
+
+    def unique(self) -> Self:
+        """
+        Compute the ArrowExtensionArray of unique values.
+
+        Returns
+        -------
+        ArrowExtensionArray
+        """
+        pa_type = self._pa_array.type
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
+            data = self._pa_array.cast(pa.int64())
+        else:
+            data = self._pa_array
+
+        pa_result = pc.unique(data)
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            pa_result = pa_result.cast(pa_type)
+
+        return type(self)(pa_result)
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of each unique value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of missing values.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        pa_type = self._pa_array.type
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            # https://github.com/apache/arrow/issues/15226#issuecomment-1376578323
+            data = self._pa_array.cast(pa.int64())
+        else:
+            data = self._pa_array
+
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        vc = data.value_counts()
+
+        values = vc.field(0)
+        counts = vc.field(1)
+        if dropna and data.null_count > 0:
+            mask = values.is_valid()
+            values = values.filter(mask)
+            counts = counts.filter(mask)
+
+        if pa_version_under11p0 and pa.types.is_duration(pa_type):
+            values = values.cast(pa_type)
+
+        counts = ArrowExtensionArray(counts)
+
+        index = Index(type(self)(values))
+
+        return Series(counts, index=index, name="count", copy=False)
+
+    @classmethod
+    def _concat_same_type(cls, to_concat) -> Self:
+        """
+        Concatenate multiple ArrowExtensionArrays.
+
+        Parameters
+        ----------
+        to_concat : sequence of ArrowExtensionArrays
+
+        Returns
+        -------
+        ArrowExtensionArray
+        """
+        chunks = [array for ea in to_concat for array in ea._pa_array.iterchunks()]
+        if to_concat[0].dtype == "string":
+            # StringDtype has no attribute pyarrow_dtype
+            pa_dtype = pa.large_string()
+        else:
+            pa_dtype = to_concat[0].dtype.pyarrow_dtype
+        arr = pa.chunked_array(chunks, type=pa_dtype)
+        return cls(arr)
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> ArrowExtensionArray | ExtensionArray:
+        """
+        Return an ExtensionArray performing an accumulation operation.
+
+        The underlying data type might change.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            - cummin
+            - cummax
+            - cumsum
+            - cumprod
+        skipna : bool, default True
+            If True, skip NA values.
+        **kwargs
+            Additional keyword arguments passed to the accumulation function.
+            Currently, there is no supported kwarg.
+
+        Returns
+        -------
+        array
+
+        Raises
+        ------
+        NotImplementedError : subclass does not define accumulations
+        """
+        if is_string_dtype(self):
+            return self._str_accumulate(name=name, skipna=skipna, **kwargs)
+
+        pyarrow_name = {
+            "cummax": "cumulative_max",
+            "cummin": "cumulative_min",
+            "cumprod": "cumulative_prod_checked",
+            "cumsum": "cumulative_sum_checked",
+        }.get(name, name)
+        pyarrow_meth = getattr(pc, pyarrow_name, None)
+        if pyarrow_meth is None:
+            return super()._accumulate(name, skipna=skipna, **kwargs)
+
+        data_to_accum = self._pa_array
+
+        pa_dtype = data_to_accum.type
+
+        convert_to_int = (
+            pa.types.is_temporal(pa_dtype) and name in ["cummax", "cummin"]
+        ) or (pa.types.is_duration(pa_dtype) and name == "cumsum")
+
+        if convert_to_int:
+            if pa_dtype.bit_width == 32:
+                data_to_accum = data_to_accum.cast(pa.int32())
+            else:
+                data_to_accum = data_to_accum.cast(pa.int64())
+
+        try:
+            result = pyarrow_meth(data_to_accum, skip_nulls=skipna, **kwargs)
+        except pa.ArrowNotImplementedError as err:
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
+
+        if convert_to_int:
+            result = result.cast(pa_dtype)
+
+        return type(self)(result)
+
+    def _str_accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> ArrowExtensionArray | ExtensionArray:
+        """
+        Accumulate implementation for strings, see `_accumulate` docstring for details.
+
+        pyarrow.compute does not implement these methods for strings.
+        """
+        if name == "cumprod":
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg)
+
+        # We may need to strip out trailing NA values
+        tail: pa.array | None = None
+        na_mask: pa.array | None = None
+        pa_array = self._pa_array
+        np_func = {
+            "cumsum": np.cumsum,
+            "cummin": np.minimum.accumulate,
+            "cummax": np.maximum.accumulate,
+        }[name]
+
+        if self._hasna:
+            na_mask = pc.is_null(pa_array)
+            if pc.all(na_mask) == pa.scalar(True):
+                return type(self)(pa_array)
+            if skipna:
+                if name == "cumsum":
+                    pa_array = pc.fill_null(pa_array, "")
+                else:
+                    # We can retain the running min/max by forward/backward filling.
+                    pa_array = pc.fill_null_forward(pa_array)
+                    pa_array = pc.fill_null_backward(pa_array)
+            else:
+                # When not skipping NA values, the result should be null from
+                # the first NA value onward.
+                idx = pc.index(na_mask, True).as_py()
+                tail = pa.nulls(len(pa_array) - idx, type=pa_array.type)
+                pa_array = pa_array[:idx]
+
+        # error: Cannot call function of unknown type
+        pa_result = pa.array(np_func(pa_array), type=pa_array.type)  # type: ignore[operator]
+
+        if tail is not None:
+            pa_result = pa.concat_arrays([pa_result, tail])
+        elif na_mask is not None:
+            pa_result = pc.if_else(na_mask, None, pa_result)
+
+        result = type(self)(pa_result)
+        return result
+
+    def _reduce_pyarrow(self, name: str, *, skipna: bool = True, **kwargs) -> pa.Scalar:
+        """
+        Return a pyarrow scalar result of performing the reduction operation.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            { any, all, min, max, sum, mean, median, prod,
+            std, var, sem, kurt, skew }.
+        skipna : bool, default True
+            If True, skip NaN values.
+        **kwargs
+            Additional keyword arguments passed to the reduction function.
+            Currently, `ddof` is the only supported kwarg.
+
+        Returns
+        -------
+        pyarrow scalar
+
+        Raises
+        ------
+        TypeError : subclass does not define reductions
+        """
+        pa_type = self._pa_array.type
+
+        data_to_reduce = self._pa_array
+
+        cast_kwargs = {} if pa_version_under13p0 else {"safe": False}
+
+        if name in ["any", "all"] and (
+            pa.types.is_integer(pa_type)
+            or pa.types.is_floating(pa_type)
+            or pa.types.is_duration(pa_type)
+            or pa.types.is_decimal(pa_type)
+        ):
+            # pyarrow only supports any/all for boolean dtype, we allow
+            #  for other dtypes, matching our non-pyarrow behavior
+
+            if pa.types.is_duration(pa_type):
+                data_to_cmp = self._pa_array.cast(pa.int64())
+            else:
+                data_to_cmp = self._pa_array
+
+            not_eq = pc.not_equal(data_to_cmp, 0)
+            data_to_reduce = not_eq
+
+        elif name in ["min", "max", "sum"] and pa.types.is_duration(pa_type):
+            data_to_reduce = self._pa_array.cast(pa.int64())
+
+        elif name in ["median", "mean", "std", "sem"] and pa.types.is_temporal(pa_type):
+            nbits = pa_type.bit_width
+            if nbits == 32:
+                data_to_reduce = self._pa_array.cast(pa.int32())
+            else:
+                data_to_reduce = self._pa_array.cast(pa.int64())
+
+        if name == "sem":
+
+            def pyarrow_meth(data, skip_nulls, **kwargs):
+                numerator = pc.stddev(data, skip_nulls=skip_nulls, **kwargs)
+                denominator = pc.sqrt_checked(pc.count(self._pa_array))
+                return pc.divide_checked(numerator, denominator)
+
+        elif name == "sum" and (
+            pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type)
+        ):
+
+            def pyarrow_meth(data, skip_nulls, min_count=0):  # type: ignore[misc]
+                mask = pc.is_null(data) if data.null_count > 0 else None
+                if skip_nulls:
+                    if min_count > 0 and check_below_min_count(
+                        (len(data),),
+                        None if mask is None else mask.to_numpy(),
+                        min_count,
+                    ):
+                        return pa.scalar(None, type=data.type)
+                    if data.null_count > 0:
+                        # binary_join returns null if there is any null ->
+                        # have to filter out any nulls
+                        data = data.filter(pc.invert(mask))
+                else:
+                    if mask is not None or check_below_min_count(
+                        (len(data),), None, min_count
+                    ):
+                        return pa.scalar(None, type=data.type)
+
+                if pa.types.is_large_string(data.type):
+                    # binary_join only supports string, not large_string
+                    data = data.cast(pa.string())
+                data_list = pa.ListArray.from_arrays(
+                    [0, len(data)], data.combine_chunks()
+                )[0]
+                return pc.binary_join(data_list, "")
+
+        else:
+            pyarrow_name = {
+                "median": "quantile",
+                "prod": "product",
+                "std": "stddev",
+                "var": "variance",
+            }.get(name, name)
+            # error: Incompatible types in assignment
+            # (expression has type "Optional[Any]", variable has type
+            # "Callable[[Any, Any, KwArg(Any)], Any]")
+            pyarrow_meth = getattr(pc, pyarrow_name, None)  # type: ignore[assignment]
+            if pyarrow_meth is None:
+                # Let ExtensionArray._reduce raise the TypeError
+                return super()._reduce(name, skipna=skipna, **kwargs)
+
+        # GH51624: pyarrow defaults to min_count=1, pandas behavior is min_count=0
+        if name in ["any", "all"] and "min_count" not in kwargs:
+            kwargs["min_count"] = 0
+        elif name == "median":
+            # GH 52679: Use quantile instead of approximate_median
+            kwargs["q"] = 0.5
+
+        try:
+            result = pyarrow_meth(data_to_reduce, skip_nulls=skipna, **kwargs)
+        except (AttributeError, NotImplementedError, TypeError) as err:
+            msg = (
+                f"'{type(self).__name__}' with dtype {self.dtype} "
+                f"does not support reduction '{name}' with pyarrow "
+                f"version {pa.__version__}. '{name}' may be supported by "
+                f"upgrading pyarrow."
+            )
+            raise TypeError(msg) from err
+        if name == "median":
+            # GH 52679: Use quantile instead of approximate_median; returns array
+            result = result[0]
+        if pc.is_null(result).as_py():
+            return result
+
+        if name in ["min", "max", "sum"] and pa.types.is_duration(pa_type):
+            result = result.cast(pa_type)
+        if name in ["median", "mean"] and pa.types.is_temporal(pa_type):
+            if not pa_version_under13p0:
+                nbits = pa_type.bit_width
+                if nbits == 32:
+                    result = result.cast(pa.int32(), **cast_kwargs)
+                else:
+                    result = result.cast(pa.int64(), **cast_kwargs)
+            result = result.cast(pa_type)
+        if name in ["std", "sem"] and pa.types.is_temporal(pa_type):
+            result = result.cast(pa.int64(), **cast_kwargs)
+            if pa.types.is_duration(pa_type):
+                result = result.cast(pa_type)
+            elif pa.types.is_time(pa_type):
+                unit = get_unit_from_pa_dtype(pa_type)
+                result = result.cast(pa.duration(unit))
+            elif pa.types.is_date(pa_type):
+                # go with closest available unit, i.e. "s"
+                result = result.cast(pa.duration("s"))
+            else:
+                # i.e. timestamp
+                result = result.cast(pa.duration(pa_type.unit))
+
+        return result
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        """
+        Return a scalar result of performing the reduction operation.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            { any, all, min, max, sum, mean, median, prod,
+            std, var, sem, kurt, skew }.
+        skipna : bool, default True
+            If True, skip NaN values.
+        **kwargs
+            Additional keyword arguments passed to the reduction function.
+            Currently, `ddof` is the only supported kwarg.
+
+        Returns
+        -------
+        scalar
+
+        Raises
+        ------
+        TypeError : subclass does not define reductions
+        """
+        result = self._reduce_calc(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        if isinstance(result, pa.Array):
+            return type(self)(result)
+        else:
+            return result
+
+    def _reduce_calc(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        pa_result = self._reduce_pyarrow(name, skipna=skipna, **kwargs)
+
+        if keepdims:
+            if isinstance(pa_result, pa.Scalar):
+                result = pa.array([pa_result.as_py()], type=pa_result.type)
+            else:
+                result = pa.array(
+                    [pa_result],
+                    type=to_pyarrow_type(infer_dtype_from_scalar(pa_result)[0]),
+                )
+            return result
+
+        if pc.is_null(pa_result).as_py():
+            return self.dtype.na_value
+        elif isinstance(pa_result, pa.Scalar):
+            return pa_result.as_py()
+        else:
+            return pa_result
+
+    def _explode(self):
+        """
+        See Series.explode.__doc__.
+        """
+        # child class explode method supports only list types; return
+        # default implementation for non list types.
+        if not hasattr(self.dtype, "pyarrow_dtype") or (
+            not pa.types.is_list(self.dtype.pyarrow_dtype)
+        ):
+            return super()._explode()
+        values = self
+        counts = pa.compute.list_value_length(values._pa_array)
+        counts = counts.fill_null(1).to_numpy()
+        fill_value = pa.scalar([None], type=self._pa_array.type)
+        mask = counts == 0
+        if mask.any():
+            values = values.copy()
+            values[mask] = fill_value
+            counts = counts.copy()
+            counts[mask] = 1
+        values = values.fillna(fill_value)
+        values = type(self)(pa.compute.list_flatten(values._pa_array))
+        return values, counts
+
+    def __setitem__(self, key, value) -> None:
+        """Set one or more values inplace.
+
+        Parameters
+        ----------
+        key : int, ndarray, or slice
+            When called from, e.g. ``Series.__setitem__``, ``key`` will be
+            one of
+
+            * scalar int
+            * ndarray of integers.
+            * boolean ndarray
+            * slice object
+
+        value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
+            value or values to be set of ``key``.
+
+        Returns
+        -------
+        None
+        """
+        # GH50085: unwrap 1D indexers
+        if isinstance(key, tuple) and len(key) == 1:
+            key = key[0]
+
+        key = check_array_indexer(self, key)
+        value = self._maybe_convert_setitem_value(value)
+
+        if com.is_null_slice(key):
+            # fast path (GH50248)
+            data = self._if_else(True, value, self._pa_array)
+
+        elif is_integer(key):
+            # fast path
+            key = cast(int, key)
+            n = len(self)
+            if key < 0:
+                key += n
+            if not 0 <= key < n:
+                raise IndexError(
+                    f"index {key} is out of bounds for axis 0 with size {n}"
+                )
+            if isinstance(value, pa.Scalar):
+                value = value.as_py()
+            elif is_list_like(value):
+                raise ValueError("Length of indexer and values mismatch")
+            chunks = [
+                *self._pa_array[:key].chunks,
+                pa.array([value], type=self._pa_array.type, from_pandas=True),
+                *self._pa_array[key + 1 :].chunks,
+            ]
+            data = pa.chunked_array(chunks).combine_chunks()
+
+        elif is_bool_dtype(key):
+            key = np.asarray(key, dtype=np.bool_)
+            data = self._replace_with_mask(self._pa_array, key, value)
+
+        elif is_scalar(value) or isinstance(value, pa.Scalar):
+            mask = np.zeros(len(self), dtype=np.bool_)
+            mask[key] = True
+            data = self._if_else(mask, value, self._pa_array)
+
+        else:
+            indices = np.arange(len(self))[key]
+            if len(indices) != len(value):
+                raise ValueError("Length of indexer and values mismatch")
+            if len(indices) == 0:
+                return
+            argsort = np.argsort(indices)
+            indices = indices[argsort]
+            value = value.take(argsort)
+            mask = np.zeros(len(self), dtype=np.bool_)
+            mask[indices] = True
+            data = self._replace_with_mask(self._pa_array, mask, value)
+
+        if isinstance(data, pa.Array):
+            data = pa.chunked_array([data])
+        self._pa_array = data
+
+    def _rank_calc(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        if axis != 0:
+            ranked = super()._rank(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+            # keep dtypes consistent with the implementation below
+            if method == "average" or pct:
+                pa_type = pa.float64()
+            else:
+                pa_type = pa.uint64()
+            result = pa.array(ranked, type=pa_type, from_pandas=True)
+            return result
+
+        data = self._pa_array.combine_chunks()
+        sort_keys = "ascending" if ascending else "descending"
+        null_placement = "at_start" if na_option == "top" else "at_end"
+        tiebreaker = "min" if method == "average" else method
+
+        result = pc.rank(
+            data,
+            sort_keys=sort_keys,
+            null_placement=null_placement,
+            tiebreaker=tiebreaker,
+        )
+
+        if na_option == "keep":
+            mask = pc.is_null(self._pa_array)
+            null = pa.scalar(None, type=result.type)
+            result = pc.if_else(mask, null, result)
+
+        if method == "average":
+            result_max = pc.rank(
+                data,
+                sort_keys=sort_keys,
+                null_placement=null_placement,
+                tiebreaker="max",
+            )
+            result_max = result_max.cast(pa.float64())
+            result_min = result.cast(pa.float64())
+            result = pc.divide(pc.add(result_min, result_max), 2)
+
+        if pct:
+            if not pa.types.is_floating(result.type):
+                result = result.cast(pa.float64())
+            if method == "dense":
+                divisor = pc.max(result)
+            else:
+                divisor = pc.count(result)
+            result = pc.divide(result, divisor)
+
+        return result
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        return self._convert_rank_result(
+            self._rank_calc(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+        )
+
+    def _quantile(self, qs: npt.NDArray[np.float64], interpolation: str) -> Self:
+        """
+        Compute the quantiles of self for each quantile in `qs`.
+
+        Parameters
+        ----------
+        qs : np.ndarray[float64]
+        interpolation: str
+
+        Returns
+        -------
+        same type as self
+        """
+        pa_dtype = self._pa_array.type
+
+        data = self._pa_array
+        if pa.types.is_temporal(pa_dtype):
+            # https://github.com/apache/arrow/issues/33769 in these cases
+            #  we can cast to ints and back
+            nbits = pa_dtype.bit_width
+            if nbits == 32:
+                data = data.cast(pa.int32())
+            else:
+                data = data.cast(pa.int64())
+
+        result = pc.quantile(data, q=qs, interpolation=interpolation)
+
+        if pa.types.is_temporal(pa_dtype):
+            if pa.types.is_floating(result.type):
+                result = pc.floor(result)
+            nbits = pa_dtype.bit_width
+            if nbits == 32:
+                result = result.cast(pa.int32())
+            else:
+                result = result.cast(pa.int64())
+            result = result.cast(pa_dtype)
+
+        return type(self)(result)
+
+    def _mode(self, dropna: bool = True) -> Self:
+        """
+        Returns the mode(s) of the ExtensionArray.
+
+        Always returns `ExtensionArray` even if only one value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NA values.
+
+        Returns
+        -------
+        same type as self
+            Sorted, if possible.
+        """
+        pa_type = self._pa_array.type
+        if pa.types.is_temporal(pa_type):
+            nbits = pa_type.bit_width
+            if nbits == 32:
+                data = self._pa_array.cast(pa.int32())
+            elif nbits == 64:
+                data = self._pa_array.cast(pa.int64())
+            else:
+                raise NotImplementedError(pa_type)
+        else:
+            data = self._pa_array
+
+        if dropna:
+            data = data.drop_null()
+
+        res = pc.value_counts(data)
+        most_common = res.field("values").filter(
+            pc.equal(res.field("counts"), pc.max(res.field("counts")))
+        )
+
+        if pa.types.is_temporal(pa_type):
+            most_common = most_common.cast(pa_type)
+
+        most_common = most_common.take(pc.array_sort_indices(most_common))
+        return type(self)(most_common)
+
+    def _maybe_convert_setitem_value(self, value):
+        """Maybe convert value to be pyarrow compatible."""
+        try:
+            value = self._box_pa(value, self._pa_array.type)
+        except pa.ArrowTypeError as err:
+            msg = f"Invalid value '{value!s}' for dtype '{self.dtype}'"
+            raise TypeError(msg) from err
+        return value
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
+        mask = self.isna()
+        if self.dtype.kind == "f":
+            data = self._pa_array.to_numpy()
+        elif self.dtype.kind in "iu":
+            data = self.to_numpy(dtype="f8", na_value=0.0)
+        else:
+            raise NotImplementedError(
+                f"interpolate is not implemented for dtype={self.dtype}"
+            )
+
+        missing.interpolate_2d_inplace(
+            data,
+            method=method,
+            axis=0,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            mask=mask,
+            **kwargs,
+        )
+        return type(self)(self._box_pa_array(pa.array(data, mask=mask)))
+
+    @classmethod
+    def _if_else(
+        cls,
+        cond: npt.NDArray[np.bool_] | bool,
+        left: ArrayLike | Scalar,
+        right: ArrayLike | Scalar,
+    ):
+        """
+        Choose values based on a condition.
+
+        Analogous to pyarrow.compute.if_else, with logic
+        to fallback to numpy for unsupported types.
+
+        Parameters
+        ----------
+        cond : npt.NDArray[np.bool_] or bool
+        left : ArrayLike | Scalar
+        right : ArrayLike | Scalar
+
+        Returns
+        -------
+        pa.Array
+        """
+        try:
+            return pc.if_else(cond, left, right)
+        except pa.ArrowNotImplementedError:
+            pass
+
+        def _to_numpy_and_type(value) -> tuple[np.ndarray, pa.DataType | None]:
+            if isinstance(value, (pa.Array, pa.ChunkedArray)):
+                pa_type = value.type
+            elif isinstance(value, pa.Scalar):
+                pa_type = value.type
+                value = value.as_py()
+            else:
+                pa_type = None
+            return np.array(value, dtype=object), pa_type
+
+        left, left_type = _to_numpy_and_type(left)
+        right, right_type = _to_numpy_and_type(right)
+        pa_type = left_type or right_type
+        result = np.where(cond, left, right)
+        return pa.array(result, type=pa_type, from_pandas=True)
+
+    @classmethod
+    def _replace_with_mask(
+        cls,
+        values: pa.Array | pa.ChunkedArray,
+        mask: npt.NDArray[np.bool_] | bool,
+        replacements: ArrayLike | Scalar,
+    ):
+        """
+        Replace items selected with a mask.
+
+        Analogous to pyarrow.compute.replace_with_mask, with logic
+        to fallback to numpy for unsupported types.
+
+        Parameters
+        ----------
+        values : pa.Array or pa.ChunkedArray
+        mask : npt.NDArray[np.bool_] or bool
+        replacements : ArrayLike or Scalar
+            Replacement value(s)
+
+        Returns
+        -------
+        pa.Array or pa.ChunkedArray
+        """
+        if isinstance(replacements, pa.ChunkedArray):
+            # replacements must be array or scalar, not ChunkedArray
+            replacements = replacements.combine_chunks()
+        if isinstance(values, pa.ChunkedArray) and pa.types.is_boolean(values.type):
+            # GH#52059 replace_with_mask segfaults for chunked array
+            # https://github.com/apache/arrow/issues/34634
+            values = values.combine_chunks()
+        try:
+            return pc.replace_with_mask(values, mask, replacements)
+        except pa.ArrowNotImplementedError:
+            pass
+        if isinstance(replacements, pa.Array):
+            replacements = np.array(replacements, dtype=object)
+        elif isinstance(replacements, pa.Scalar):
+            replacements = replacements.as_py()
+        result = np.array(values, dtype=object)
+        result[mask] = replacements
+        return pa.array(result, type=values.type, from_pandas=True)
+
+    # ------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _to_masked(self):
+        pa_dtype = self._pa_array.type
+
+        if pa.types.is_floating(pa_dtype) or pa.types.is_integer(pa_dtype):
+            na_value = 1
+        elif pa.types.is_boolean(pa_dtype):
+            na_value = True
+        else:
+            raise NotImplementedError
+
+        dtype = _arrow_dtype_mapping()[pa_dtype]
+        mask = self.isna()
+        arr = self.to_numpy(dtype=dtype.numpy_dtype, na_value=na_value)
+        return dtype.construct_array_type()(arr, mask)
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        if isinstance(self.dtype, StringDtype):
+            if how in [
+                "prod",
+                "mean",
+                "median",
+                "cumsum",
+                "cumprod",
+                "std",
+                "sem",
+                "var",
+                "skew",
+            ]:
+                raise TypeError(
+                    f"dtype '{self.dtype}' does not support operation '{how}'"
+                )
+            return super()._groupby_op(
+                how=how,
+                has_dropped_na=has_dropped_na,
+                min_count=min_count,
+                ngroups=ngroups,
+                ids=ids,
+                **kwargs,
+            )
+
+        # maybe convert to a compatible dtype optimized for groupby
+        values: ExtensionArray
+        pa_type = self._pa_array.type
+        if pa.types.is_timestamp(pa_type):
+            values = self._to_datetimearray()
+        elif pa.types.is_duration(pa_type):
+            values = self._to_timedeltaarray()
+        else:
+            values = self._to_masked()
+
+        result = values._groupby_op(
+            how=how,
+            has_dropped_na=has_dropped_na,
+            min_count=min_count,
+            ngroups=ngroups,
+            ids=ids,
+            **kwargs,
+        )
+        if isinstance(result, np.ndarray):
+            return result
+        return type(self)._from_sequence(result, copy=False)
+
+    def _apply_elementwise(self, func: Callable) -> list[list[Any]]:
+        """Apply a callable to each element while maintaining the chunking structure."""
+        return [
+            [
+                None if val is None else func(val)
+                for val in chunk.to_numpy(zero_copy_only=False)
+            ]
+            for chunk in self._pa_array.iterchunks()
+        ]
+
+    def _convert_bool_result(self, result, na=lib.no_default, method_name=None):
+        if na is not lib.no_default and not isna(
+            na
+        ):  # pyright: ignore [reportGeneralTypeIssues]
+            result = result.fill_null(na)
+        return type(self)(result)
+
+    def _convert_int_result(self, result):
+        return type(self)(result)
+
+    def _convert_rank_result(self, result):
+        return type(self)(result)
+
+    def _str_count(self, pat: str, flags: int = 0):
+        if flags:
+            raise NotImplementedError(f"count not implemented with {flags=}")
+        return type(self)(pc.count_substring_regex(self._pa_array, pat))
+
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        if not isinstance(repeats, int):
+            raise NotImplementedError(
+                f"repeat is not implemented when repeats is {type(repeats).__name__}"
+            )
+        else:
+            return type(self)(pc.binary_repeat(self._pa_array, repeats))
+
+    def _str_join(self, sep: str):
+        if pa.types.is_string(self._pa_array.type) or pa.types.is_large_string(
+            self._pa_array.type
+        ):
+            result = self._apply_elementwise(list)
+            result = pa.chunked_array(result, type=pa.list_(pa.string()))
+        else:
+            result = self._pa_array
+        return type(self)(pc.binary_join(result, sep))
+
+    def _str_partition(self, sep: str, expand: bool):
+        predicate = lambda val: val.partition(sep)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_rpartition(self, sep: str, expand: bool):
+        predicate = lambda val: val.rpartition(sep)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_casefold(self):
+        predicate = lambda val: val.casefold()
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_encode(self, encoding: str, errors: str = "strict"):
+        predicate = lambda val: val.encode(encoding, errors)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_extract(self, pat: str, flags: int = 0, expand: bool = True):
+        if flags:
+            raise NotImplementedError("Only flags=0 is implemented.")
+        groups = re.compile(pat).groupindex.keys()
+        if len(groups) == 0:
+            raise ValueError(f"{pat=} must contain a symbolic group name.")
+        result = pc.extract_regex(self._pa_array, pat)
+        if expand:
+            return {
+                col: type(self)(pc.struct_field(result, [i]))
+                for col, i in zip(groups, range(result.type.num_fields))
+            }
+        else:
+            return type(self)(pc.struct_field(result, [0]))
+
+    def _str_findall(self, pat: str, flags: int = 0):
+        regex = re.compile(pat, flags=flags)
+        predicate = lambda val: regex.findall(val)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_get_dummies(self, sep: str = "|"):
+        split = pc.split_pattern(self._pa_array, sep)
+        flattened_values = pc.list_flatten(split)
+        uniques = flattened_values.unique()
+        uniques_sorted = uniques.take(pa.compute.array_sort_indices(uniques))
+        lengths = pc.list_value_length(split).fill_null(0).to_numpy()
+        n_rows = len(self)
+        n_cols = len(uniques)
+        indices = pc.index_in(flattened_values, uniques_sorted).to_numpy()
+        indices = indices + np.arange(n_rows).repeat(lengths) * n_cols
+        dummies = np.zeros(n_rows * n_cols, dtype=np.bool_)
+        dummies[indices] = True
+        dummies = dummies.reshape((n_rows, n_cols))
+        result = type(self)(pa.array(list(dummies)))
+        return result, uniques_sorted.to_pylist()
+
+    def _str_index(self, sub: str, start: int = 0, end: int | None = None):
+        predicate = lambda val: val.index(sub, start, end)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_rindex(self, sub: str, start: int = 0, end: int | None = None):
+        predicate = lambda val: val.rindex(sub, start, end)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_normalize(self, form: str):
+        predicate = lambda val: unicodedata.normalize(form, val)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_rfind(self, sub: str, start: int = 0, end=None):
+        predicate = lambda val: val.rfind(sub, start, end)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_split(
+        self,
+        pat: str | None = None,
+        n: int | None = -1,
+        expand: bool = False,
+        regex: bool | None = None,
+    ):
+        if n in {-1, 0}:
+            n = None
+        if pat is None:
+            split_func = pc.utf8_split_whitespace
+        elif regex:
+            split_func = functools.partial(pc.split_pattern_regex, pattern=pat)
+        else:
+            split_func = functools.partial(pc.split_pattern, pattern=pat)
+        return type(self)(split_func(self._pa_array, max_splits=n))
+
+    def _str_rsplit(self, pat: str | None = None, n: int | None = -1):
+        if n in {-1, 0}:
+            n = None
+        if pat is None:
+            return type(self)(
+                pc.utf8_split_whitespace(self._pa_array, max_splits=n, reverse=True)
+            )
+        else:
+            return type(self)(
+                pc.split_pattern(self._pa_array, pat, max_splits=n, reverse=True)
+            )
+
+    def _str_translate(self, table: dict[int, str]):
+        predicate = lambda val: val.translate(table)
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    def _str_wrap(self, width: int, **kwargs):
+        kwargs["width"] = width
+        tw = textwrap.TextWrapper(**kwargs)
+        predicate = lambda val: "\n".join(tw.wrap(val))
+        result = self._apply_elementwise(predicate)
+        return type(self)(pa.chunked_array(result))
+
+    @property
+    def _dt_days(self):
+        return type(self)(
+            pa.array(self._to_timedeltaarray().days, from_pandas=True, type=pa.int32())
+        )
+
+    @property
+    def _dt_hours(self):
+        return type(self)(
+            pa.array(
+                [
+                    td.components.hours if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_minutes(self):
+        return type(self)(
+            pa.array(
+                [
+                    td.components.minutes if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_seconds(self):
+        return type(self)(
+            pa.array(
+                self._to_timedeltaarray().seconds, from_pandas=True, type=pa.int32()
+            )
+        )
+
+    @property
+    def _dt_milliseconds(self):
+        return type(self)(
+            pa.array(
+                [
+                    td.components.milliseconds if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_microseconds(self):
+        return type(self)(
+            pa.array(
+                self._to_timedeltaarray().microseconds,
+                from_pandas=True,
+                type=pa.int32(),
+            )
+        )
+
+    @property
+    def _dt_nanoseconds(self):
+        return type(self)(
+            pa.array(
+                self._to_timedeltaarray().nanoseconds, from_pandas=True, type=pa.int32()
+            )
+        )
+
+    def _dt_to_pytimedelta(self):
+        data = self._pa_array.to_pylist()
+        if self._dtype.pyarrow_dtype.unit == "ns":
+            data = [None if ts is None else ts.to_pytimedelta() for ts in data]
+        return np.array(data, dtype=object)
+
+    def _dt_total_seconds(self):
+        return type(self)(
+            pa.array(self._to_timedeltaarray().total_seconds(), from_pandas=True)
+        )
+
+    def _dt_as_unit(self, unit: str):
+        if pa.types.is_date(self.dtype.pyarrow_dtype):
+            raise NotImplementedError("as_unit not implemented for date types")
+        pd_array = self._maybe_convert_datelike_array()
+        # Don't just cast _pa_array in order to follow pandas unit conversion rules
+        return type(self)(pa.array(pd_array.as_unit(unit), from_pandas=True))
+
+    @property
+    def _dt_year(self):
+        return type(self)(pc.year(self._pa_array))
+
+    @property
+    def _dt_day(self):
+        return type(self)(pc.day(self._pa_array))
+
+    @property
+    def _dt_day_of_week(self):
+        return type(self)(pc.day_of_week(self._pa_array))
+
+    _dt_dayofweek = _dt_day_of_week
+    _dt_weekday = _dt_day_of_week
+
+    @property
+    def _dt_day_of_year(self):
+        return type(self)(pc.day_of_year(self._pa_array))
+
+    _dt_dayofyear = _dt_day_of_year
+
+    @property
+    def _dt_hour(self):
+        return type(self)(pc.hour(self._pa_array))
+
+    def _dt_isocalendar(self):
+        return type(self)(pc.iso_calendar(self._pa_array))
+
+    @property
+    def _dt_is_leap_year(self):
+        return type(self)(pc.is_leap_year(self._pa_array))
+
+    @property
+    def _dt_is_month_start(self):
+        return type(self)(pc.equal(pc.day(self._pa_array), 1))
+
+    @property
+    def _dt_is_month_end(self):
+        result = pc.equal(
+            pc.days_between(
+                pc.floor_temporal(self._pa_array, unit="day"),
+                pc.ceil_temporal(self._pa_array, unit="month"),
+            ),
+            1,
+        )
+        return type(self)(result)
+
+    @property
+    def _dt_is_year_start(self):
+        return type(self)(
+            pc.and_(
+                pc.equal(pc.month(self._pa_array), 1),
+                pc.equal(pc.day(self._pa_array), 1),
+            )
+        )
+
+    @property
+    def _dt_is_year_end(self):
+        return type(self)(
+            pc.and_(
+                pc.equal(pc.month(self._pa_array), 12),
+                pc.equal(pc.day(self._pa_array), 31),
+            )
+        )
+
+    @property
+    def _dt_is_quarter_start(self):
+        result = pc.equal(
+            pc.floor_temporal(self._pa_array, unit="quarter"),
+            pc.floor_temporal(self._pa_array, unit="day"),
+        )
+        return type(self)(result)
+
+    @property
+    def _dt_is_quarter_end(self):
+        result = pc.equal(
+            pc.days_between(
+                pc.floor_temporal(self._pa_array, unit="day"),
+                pc.ceil_temporal(self._pa_array, unit="quarter"),
+            ),
+            1,
+        )
+        return type(self)(result)
+
+    @property
+    def _dt_days_in_month(self):
+        result = pc.days_between(
+            pc.floor_temporal(self._pa_array, unit="month"),
+            pc.ceil_temporal(self._pa_array, unit="month"),
+        )
+        return type(self)(result)
+
+    _dt_daysinmonth = _dt_days_in_month
+
+    @property
+    def _dt_microsecond(self):
+        return type(self)(pc.microsecond(self._pa_array))
+
+    @property
+    def _dt_minute(self):
+        return type(self)(pc.minute(self._pa_array))
+
+    @property
+    def _dt_month(self):
+        return type(self)(pc.month(self._pa_array))
+
+    @property
+    def _dt_nanosecond(self):
+        return type(self)(pc.nanosecond(self._pa_array))
+
+    @property
+    def _dt_quarter(self):
+        return type(self)(pc.quarter(self._pa_array))
+
+    @property
+    def _dt_second(self):
+        return type(self)(pc.second(self._pa_array))
+
+    @property
+    def _dt_date(self):
+        return type(self)(self._pa_array.cast(pa.date32()))
+
+    @property
+    def _dt_time(self):
+        unit = (
+            self.dtype.pyarrow_dtype.unit
+            if self.dtype.pyarrow_dtype.unit in {"us", "ns"}
+            else "ns"
+        )
+        return type(self)(self._pa_array.cast(pa.time64(unit)))
+
+    @property
+    def _dt_tz(self):
+        return timezones.maybe_get_tz(self.dtype.pyarrow_dtype.tz)
+
+    @property
+    def _dt_unit(self):
+        return self.dtype.pyarrow_dtype.unit
+
+    def _dt_normalize(self):
+        return type(self)(pc.floor_temporal(self._pa_array, 1, "day"))
+
+    def _dt_strftime(self, format: str):
+        return type(self)(pc.strftime(self._pa_array, format=format))
+
+    def _round_temporally(
+        self,
+        method: Literal["ceil", "floor", "round"],
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        if ambiguous != "raise":
+            raise NotImplementedError("ambiguous is not supported.")
+        if nonexistent != "raise":
+            raise NotImplementedError("nonexistent is not supported.")
+        offset = to_offset(freq)
+        if offset is None:
+            raise ValueError(f"Must specify a valid frequency: {freq}")
+        pa_supported_unit = {
+            "Y": "year",
+            "YS": "year",
+            "Q": "quarter",
+            "QS": "quarter",
+            "M": "month",
+            "MS": "month",
+            "W": "week",
+            "D": "day",
+            "h": "hour",
+            "min": "minute",
+            "s": "second",
+            "ms": "millisecond",
+            "us": "microsecond",
+            "ns": "nanosecond",
+        }
+        unit = pa_supported_unit.get(offset._prefix, None)
+        if unit is None:
+            raise ValueError(f"{freq=} is not supported")
+        multiple = offset.n
+        rounding_method = getattr(pc, f"{method}_temporal")
+        return type(self)(rounding_method(self._pa_array, multiple=multiple, unit=unit))
+
+    def _dt_ceil(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        return self._round_temporally("ceil", freq, ambiguous, nonexistent)
+
+    def _dt_floor(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        return self._round_temporally("floor", freq, ambiguous, nonexistent)
+
+    def _dt_round(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        return self._round_temporally("round", freq, ambiguous, nonexistent)
+
+    def _dt_day_name(self, locale: str | None = None):
+        if locale is None:
+            locale = "C"
+        return type(self)(pc.strftime(self._pa_array, format="%A", locale=locale))
+
+    def _dt_month_name(self, locale: str | None = None):
+        if locale is None:
+            locale = "C"
+        return type(self)(pc.strftime(self._pa_array, format="%B", locale=locale))
+
+    def _dt_to_pydatetime(self):
+        if pa.types.is_date(self.dtype.pyarrow_dtype):
+            raise ValueError(
+                f"to_pydatetime cannot be called with {self.dtype.pyarrow_dtype} type. "
+                "Convert to pyarrow timestamp type."
+            )
+        data = self._pa_array.to_pylist()
+        if self._dtype.pyarrow_dtype.unit == "ns":
+            data = [None if ts is None else ts.to_pydatetime(warn=False) for ts in data]
+        return np.array(data, dtype=object)
+
+    def _dt_tz_localize(
+        self,
+        tz,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
+        if ambiguous != "raise":
+            raise NotImplementedError(f"{ambiguous=} is not supported")
+        nonexistent_pa = {
+            "raise": "raise",
+            "shift_backward": "earliest",
+            "shift_forward": "latest",
+        }.get(
+            nonexistent, None  # type: ignore[arg-type]
+        )
+        if nonexistent_pa is None:
+            raise NotImplementedError(f"{nonexistent=} is not supported")
+        if tz is None:
+            result = self._pa_array.cast(pa.timestamp(self.dtype.pyarrow_dtype.unit))
+        else:
+            result = pc.assume_timezone(
+                self._pa_array, str(tz), ambiguous=ambiguous, nonexistent=nonexistent_pa
+            )
+        return type(self)(result)
+
+    def _dt_tz_convert(self, tz):
+        if self.dtype.pyarrow_dtype.tz is None:
+            raise TypeError(
+                "Cannot convert tz-naive timestamps, use tz_localize to localize"
+            )
+        current_unit = self.dtype.pyarrow_dtype.unit
+        result = self._pa_array.cast(pa.timestamp(current_unit, tz))
+        return type(self)(result)
+
+
+def transpose_homogeneous_pyarrow(
+    arrays: Sequence[ArrowExtensionArray],
+) -> list[ArrowExtensionArray]:
+    """Transpose arrow extension arrays in a list, but faster.
+
+    Input should be a list of arrays of equal length and all have the same
+    dtype. The caller is responsible for ensuring validity of input data.
+    """
+    arrays = list(arrays)
+    nrows, ncols = len(arrays[0]), len(arrays)
+    indices = np.arange(nrows * ncols).reshape(ncols, nrows).T.flatten()
+    arr = pa.chunked_array([chunk for arr in arrays for chunk in arr._pa_array.chunks])
+    arr = arr.take(indices)
+    return [ArrowExtensionArray(arr.slice(i * ncols, ncols)) for i in range(nrows)]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/extension_types.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/extension_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..72bfd6f2212f8fae6ea7786599de44beaeb3f902
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/arrow/extension_types.py
@@ -0,0 +1,174 @@
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING
+
+import pyarrow
+
+from pandas.compat import pa_version_under14p1
+
+from pandas.core.dtypes.dtypes import (
+    IntervalDtype,
+    PeriodDtype,
+)
+
+from pandas.core.arrays.interval import VALID_CLOSED
+
+if TYPE_CHECKING:
+    from pandas._typing import IntervalClosedType
+
+
+class ArrowPeriodType(pyarrow.ExtensionType):
+    def __init__(self, freq) -> None:
+        # attributes need to be set first before calling
+        # super init (as that calls serialize)
+        self._freq = freq
+        pyarrow.ExtensionType.__init__(self, pyarrow.int64(), "pandas.period")
+
+    @property
+    def freq(self):
+        return self._freq
+
+    def __arrow_ext_serialize__(self) -> bytes:
+        metadata = {"freq": self.freq}
+        return json.dumps(metadata).encode()
+
+    @classmethod
+    def __arrow_ext_deserialize__(cls, storage_type, serialized) -> ArrowPeriodType:
+        metadata = json.loads(serialized.decode())
+        return ArrowPeriodType(metadata["freq"])
+
+    def __eq__(self, other):
+        if isinstance(other, pyarrow.BaseExtensionType):
+            return type(self) == type(other) and self.freq == other.freq
+        else:
+            return NotImplemented
+
+    def __ne__(self, other) -> bool:
+        return not self == other
+
+    def __hash__(self) -> int:
+        return hash((str(self), self.freq))
+
+    def to_pandas_dtype(self) -> PeriodDtype:
+        return PeriodDtype(freq=self.freq)
+
+
+# register the type with a dummy instance
+_period_type = ArrowPeriodType("D")
+pyarrow.register_extension_type(_period_type)
+
+
+class ArrowIntervalType(pyarrow.ExtensionType):
+    def __init__(self, subtype, closed: IntervalClosedType) -> None:
+        # attributes need to be set first before calling
+        # super init (as that calls serialize)
+        assert closed in VALID_CLOSED
+        self._closed: IntervalClosedType = closed
+        if not isinstance(subtype, pyarrow.DataType):
+            subtype = pyarrow.type_for_alias(str(subtype))
+        self._subtype = subtype
+
+        storage_type = pyarrow.struct([("left", subtype), ("right", subtype)])
+        pyarrow.ExtensionType.__init__(self, storage_type, "pandas.interval")
+
+    @property
+    def subtype(self):
+        return self._subtype
+
+    @property
+    def closed(self) -> IntervalClosedType:
+        return self._closed
+
+    def __arrow_ext_serialize__(self) -> bytes:
+        metadata = {"subtype": str(self.subtype), "closed": self.closed}
+        return json.dumps(metadata).encode()
+
+    @classmethod
+    def __arrow_ext_deserialize__(cls, storage_type, serialized) -> ArrowIntervalType:
+        metadata = json.loads(serialized.decode())
+        subtype = pyarrow.type_for_alias(metadata["subtype"])
+        closed = metadata["closed"]
+        return ArrowIntervalType(subtype, closed)
+
+    def __eq__(self, other):
+        if isinstance(other, pyarrow.BaseExtensionType):
+            return (
+                type(self) == type(other)
+                and self.subtype == other.subtype
+                and self.closed == other.closed
+            )
+        else:
+            return NotImplemented
+
+    def __ne__(self, other) -> bool:
+        return not self == other
+
+    def __hash__(self) -> int:
+        return hash((str(self), str(self.subtype), self.closed))
+
+    def to_pandas_dtype(self) -> IntervalDtype:
+        return IntervalDtype(self.subtype.to_pandas_dtype(), self.closed)
+
+
+# register the type with a dummy instance
+_interval_type = ArrowIntervalType(pyarrow.int64(), "left")
+pyarrow.register_extension_type(_interval_type)
+
+
+_ERROR_MSG = """\
+Disallowed deserialization of 'arrow.py_extension_type':
+storage_type = {storage_type}
+serialized = {serialized}
+pickle disassembly:\n{pickle_disassembly}
+
+Reading of untrusted Parquet or Feather files with a PyExtensionType column
+allows arbitrary code execution.
+If you trust this file, you can enable reading the extension type by one of:
+
+- upgrading to pyarrow >= 14.0.1, and call `pa.PyExtensionType.set_auto_load(True)`
+- install pyarrow-hotfix (`pip install pyarrow-hotfix`) and disable it by running
+  `import pyarrow_hotfix; pyarrow_hotfix.uninstall()`
+
+We strongly recommend updating your Parquet/Feather files to use extension types
+derived from `pyarrow.ExtensionType` instead, and register this type explicitly.
+"""
+
+
+def patch_pyarrow():
+    # starting from pyarrow 14.0.1, it has its own mechanism
+    if not pa_version_under14p1:
+        return
+
+    # if https://github.com/pitrou/pyarrow-hotfix was installed and enabled
+    if getattr(pyarrow, "_hotfix_installed", False):
+        return
+
+    class ForbiddenExtensionType(pyarrow.ExtensionType):
+        def __arrow_ext_serialize__(self):
+            return b""
+
+        @classmethod
+        def __arrow_ext_deserialize__(cls, storage_type, serialized):
+            import io
+            import pickletools
+
+            out = io.StringIO()
+            pickletools.dis(serialized, out)
+            raise RuntimeError(
+                _ERROR_MSG.format(
+                    storage_type=storage_type,
+                    serialized=serialized,
+                    pickle_disassembly=out.getvalue(),
+                )
+            )
+
+    pyarrow.unregister_extension_type("arrow.py_extension_type")
+    pyarrow.register_extension_type(
+        ForbiddenExtensionType(pyarrow.null(), "arrow.py_extension_type")
+    )
+
+    pyarrow._hotfix_installed = True
+
+
+patch_pyarrow()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..62458b89f9c089fb663c2524b4dd585000336712
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/base.py
@@ -0,0 +1,2612 @@
+"""
+An interface for extending pandas with custom arrays.
+
+.. warning::
+
+   This is an experimental API and subject to breaking changes
+   without warning.
+"""
+from __future__ import annotations
+
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+from pandas.compat import set_function_name
+from pandas.compat.numpy import function as nv
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    validate_bool_kwarg,
+    validate_fillna_kwargs,
+    validate_insert_loc,
+)
+
+from pandas.core.dtypes.cast import maybe_cast_pointwise_result
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    arraylike,
+    missing,
+    roperator,
+)
+from pandas.core.algorithms import (
+    duplicated,
+    factorize_array,
+    isin,
+    map_array,
+    mode,
+    rank,
+    unique,
+)
+from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.missing import _fill_limit_area_1d
+from pandas.core.sorting import (
+    nargminmax,
+    nargsort,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        AstypeArg,
+        AxisInt,
+        Dtype,
+        DtypeObj,
+        FillnaOptions,
+        InterpolateOptions,
+        NumpySorter,
+        NumpyValueArrayLike,
+        PositionalIndexer,
+        ScalarIndexer,
+        Self,
+        SequenceIndexer,
+        Shape,
+        SortKind,
+        TakeIndexer,
+        npt,
+    )
+
+    from pandas import Index
+
+_extension_array_shared_docs: dict[str, str] = {}
+
+
+class ExtensionArray:
+    """
+    Abstract base class for custom 1-D array types.
+
+    pandas will recognize instances of this class as proper arrays
+    with a custom type and will not attempt to coerce them to objects. They
+    may be stored directly inside a :class:`DataFrame` or :class:`Series`.
+
+    Attributes
+    ----------
+    dtype
+    nbytes
+    ndim
+    shape
+
+    Methods
+    -------
+    argsort
+    astype
+    copy
+    dropna
+    duplicated
+    factorize
+    fillna
+    equals
+    insert
+    interpolate
+    isin
+    isna
+    ravel
+    repeat
+    searchsorted
+    shift
+    take
+    tolist
+    unique
+    view
+    _accumulate
+    _concat_same_type
+    _explode
+    _formatter
+    _from_factorized
+    _from_sequence
+    _from_sequence_of_strings
+    _hash_pandas_object
+    _pad_or_backfill
+    _reduce
+    _values_for_argsort
+    _values_for_factorize
+
+    Notes
+    -----
+    The interface includes the following abstract methods that must be
+    implemented by subclasses:
+
+    * _from_sequence
+    * _from_factorized
+    * __getitem__
+    * __len__
+    * __eq__
+    * dtype
+    * nbytes
+    * isna
+    * take
+    * copy
+    * _concat_same_type
+    * interpolate
+
+    A default repr displaying the type, (truncated) data, length,
+    and dtype is provided. It can be customized or replaced by
+    by overriding:
+
+    * __repr__ : A default repr for the ExtensionArray.
+    * _formatter : Print scalars inside a Series or DataFrame.
+
+    Some methods require casting the ExtensionArray to an ndarray of Python
+    objects with ``self.astype(object)``, which may be expensive. When
+    performance is a concern, we highly recommend overriding the following
+    methods:
+
+    * fillna
+    * _pad_or_backfill
+    * dropna
+    * unique
+    * factorize / _values_for_factorize
+    * argsort, argmax, argmin / _values_for_argsort
+    * searchsorted
+    * map
+
+    The remaining methods implemented on this class should be performant,
+    as they only compose abstract methods. Still, a more efficient
+    implementation may be available, and these methods can be overridden.
+
+    One can implement methods to handle array accumulations or reductions.
+
+    * _accumulate
+    * _reduce
+
+    One can implement methods to handle parsing from strings that will be used
+    in methods such as ``pandas.io.parsers.read_csv``.
+
+    * _from_sequence_of_strings
+
+    This class does not inherit from 'abc.ABCMeta' for performance reasons.
+    Methods and properties required by the interface raise
+    ``pandas.errors.AbstractMethodError`` and no ``register`` method is
+    provided for registering virtual subclasses.
+
+    ExtensionArrays are limited to 1 dimension.
+
+    They may be backed by none, one, or many NumPy arrays. For example,
+    ``pandas.Categorical`` is an extension array backed by two arrays,
+    one for codes and one for categories. An array of IPv6 address may
+    be backed by a NumPy structured array with two fields, one for the
+    lower 64 bits and one for the upper 64 bits. Or they may be backed
+    by some other storage type, like Python lists. Pandas makes no
+    assumptions on how the data are stored, just that it can be converted
+    to a NumPy array.
+    The ExtensionArray interface does not impose any rules on how this data
+    is stored. However, currently, the backing data cannot be stored in
+    attributes called ``.values`` or ``._values`` to ensure full compatibility
+    with pandas internals. But other names as ``.data``, ``._data``,
+    ``._items``, ... can be freely used.
+
+    If implementing NumPy's ``__array_ufunc__`` interface, pandas expects
+    that
+
+    1. You defer by returning ``NotImplemented`` when any Series are present
+       in `inputs`. Pandas will extract the arrays and call the ufunc again.
+    2. You define a ``_HANDLED_TYPES`` tuple as an attribute on the class.
+       Pandas inspect this to determine whether the ufunc is valid for the
+       types present.
+
+    See :ref:`extending.extension.ufunc` for more.
+
+    By default, ExtensionArrays are not hashable.  Immutable subclasses may
+    override this behavior.
+
+    Examples
+    --------
+    Please see the following:
+
+    https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/list/array.py
+    """
+
+    # '_typ' is for pandas.core.dtypes.generic.ABCExtensionArray.
+    # Don't override this.
+    _typ = "extension"
+
+    # similar to __array_priority__, positions ExtensionArray after Index,
+    #  Series, and DataFrame.  EA subclasses may override to choose which EA
+    #  subclass takes priority. If overriding, the value should always be
+    #  strictly less than 2000 to be below Index.__pandas_priority__.
+    __pandas_priority__ = 1000
+
+    # ------------------------------------------------------------------------
+    # Constructors
+    # ------------------------------------------------------------------------
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        """
+        Construct a new ExtensionArray from a sequence of scalars.
+
+        Parameters
+        ----------
+        scalars : Sequence
+            Each element will be an instance of the scalar type for this
+            array, ``cls.dtype.type`` or be converted into this type in this method.
+        dtype : dtype, optional
+            Construct for this particular dtype. This should be a Dtype
+            compatible with the ExtensionArray.
+        copy : bool, default False
+            If True, copy the underlying data.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> pd.arrays.IntegerArray._from_sequence([4, 5])
+        <IntegerArray>
+        [4, 5]
+        Length: 2, dtype: Int64
+        """
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        """
+        Strict analogue to _from_sequence, allowing only sequences of scalars
+        that should be specifically inferred to the given dtype.
+
+        Parameters
+        ----------
+        scalars : sequence
+        dtype : ExtensionDtype
+
+        Raises
+        ------
+        TypeError or ValueError
+
+        Notes
+        -----
+        This is called in a try/except block when casting the result of a
+        pointwise operation.
+        """
+        try:
+            return cls._from_sequence(scalars, dtype=dtype, copy=False)
+        except (ValueError, TypeError):
+            raise
+        except Exception:
+            warnings.warn(
+                "_from_scalars should only raise ValueError or TypeError. "
+                "Consider overriding _from_scalars where appropriate.",
+                stacklevel=find_stack_level(),
+            )
+            raise
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ):
+        """
+        Construct a new ExtensionArray from a sequence of strings.
+
+        Parameters
+        ----------
+        strings : Sequence
+            Each element will be an instance of the scalar type for this
+            array, ``cls.dtype.type``.
+        dtype : dtype, optional
+            Construct for this particular dtype. This should be a Dtype
+            compatible with the ExtensionArray.
+        copy : bool, default False
+            If True, copy the underlying data.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> pd.arrays.IntegerArray._from_sequence_of_strings(["1", "2", "3"])
+        <IntegerArray>
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        """
+        Reconstruct an ExtensionArray after factorization.
+
+        Parameters
+        ----------
+        values : ndarray
+            An integer ndarray with the factorized values.
+        original : ExtensionArray
+            The original ExtensionArray that factorize was called on.
+
+        See Also
+        --------
+        factorize : Top-level factorize method that dispatches here.
+        ExtensionArray.factorize : Encode the extension array as an enumerated type.
+
+        Examples
+        --------
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1),
+        ...                                      pd.Interval(1, 5), pd.Interval(1, 5)])
+        >>> codes, uniques = pd.factorize(interv_arr)
+        >>> pd.arrays.IntervalArray._from_factorized(uniques, interv_arr)
+        <IntervalArray>
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        """
+        raise AbstractMethodError(cls)
+
+    # ------------------------------------------------------------------------
+    # Must be a Sequence
+    # ------------------------------------------------------------------------
+    @overload
+    def __getitem__(self, item: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(self, item: SequenceIndexer) -> Self:
+        ...
+
+    def __getitem__(self, item: PositionalIndexer) -> Self | Any:
+        """
+        Select a subset of self.
+
+        Parameters
+        ----------
+        item : int, slice, or ndarray
+            * int: The position in 'self' to get.
+
+            * slice: A slice object, where 'start', 'stop', and 'step' are
+              integers or None
+
+            * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'
+
+            * list[int]:  A list of int
+
+        Returns
+        -------
+        item : scalar or ExtensionArray
+
+        Notes
+        -----
+        For scalar ``item``, return a scalar value suitable for the array's
+        type. This should be an instance of ``self.dtype.type``.
+
+        For slice ``key``, return an instance of ``ExtensionArray``, even
+        if the slice is length 0 or 1.
+
+        For a boolean mask, return an instance of ``ExtensionArray``, filtered
+        to the values where ``item`` is True.
+        """
+        raise AbstractMethodError(self)
+
+    def __setitem__(self, key, value) -> None:
+        """
+        Set one or more values inplace.
+
+        This method is not required to satisfy the pandas extension array
+        interface.
+
+        Parameters
+        ----------
+        key : int, ndarray, or slice
+            When called from, e.g. ``Series.__setitem__``, ``key`` will be
+            one of
+
+            * scalar int
+            * ndarray of integers.
+            * boolean ndarray
+            * slice object
+
+        value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
+            value or values to be set of ``key``.
+
+        Returns
+        -------
+        None
+        """
+        # Some notes to the ExtensionArray implementer who may have ended up
+        # here. While this method is not required for the interface, if you
+        # *do* choose to implement __setitem__, then some semantics should be
+        # observed:
+        #
+        # * Setting multiple values : ExtensionArrays should support setting
+        #   multiple values at once, 'key' will be a sequence of integers and
+        #  'value' will be a same-length sequence.
+        #
+        # * Broadcasting : For a sequence 'key' and a scalar 'value',
+        #   each position in 'key' should be set to 'value'.
+        #
+        # * Coercion : Most users will expect basic coercion to work. For
+        #   example, a string like '2018-01-01' is coerced to a datetime
+        #   when setting on a datetime64ns array. In general, if the
+        #   __init__ method coerces that value, then so should __setitem__
+        # Note, also, that Series/DataFrame.where internally use __setitem__
+        # on a copy of the data.
+        raise NotImplementedError(f"{type(self)} does not implement __setitem__.")
+
+    def __len__(self) -> int:
+        """
+        Length of this array
+
+        Returns
+        -------
+        length : int
+        """
+        raise AbstractMethodError(self)
+
+    def __iter__(self) -> Iterator[Any]:
+        """
+        Iterate over elements of the array.
+        """
+        # This needs to be implemented so that pandas recognizes extension
+        # arrays as list-like. The default implementation makes successive
+        # calls to ``__getitem__``, which may be slower than necessary.
+        for i in range(len(self)):
+            yield self[i]
+
+    def __contains__(self, item: object) -> bool | np.bool_:
+        """
+        Return for `item in self`.
+        """
+        # GH37867
+        # comparisons of any item to pd.NA always return pd.NA, so e.g. "a" in [pd.NA]
+        # would raise a TypeError. The implementation below works around that.
+        if is_scalar(item) and isna(item):
+            if not self._can_hold_na:
+                return False
+            elif item is self.dtype.na_value or isinstance(item, self.dtype.type):
+                return self._hasna
+            else:
+                return False
+        else:
+            # error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
+            # attribute "any"
+            return (item == self).any()  # type: ignore[union-attr]
+
+    # error: Signature of "__eq__" incompatible with supertype "object"
+    def __eq__(self, other: object) -> ArrayLike:  # type: ignore[override]
+        """
+        Return for `self == other` (element-wise equality).
+        """
+        # Implementer note: this should return a boolean numpy ndarray or
+        # a boolean ExtensionArray.
+        # When `other` is one of Series, Index, or DataFrame, this method should
+        # return NotImplemented (to ensure that those objects are responsible for
+        # first unpacking the arrays, and then dispatch the operation to the
+        # underlying arrays)
+        raise AbstractMethodError(self)
+
+    # error: Signature of "__ne__" incompatible with supertype "object"
+    def __ne__(self, other: object) -> ArrayLike:  # type: ignore[override]
+        """
+        Return for `self != other` (element-wise in-equality).
+        """
+        # error: Unsupported operand type for ~ ("ExtensionArray")
+        return ~(self == other)  # type: ignore[operator]
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert to a NumPy ndarray.
+
+        This is similar to :meth:`numpy.asarray`, but may provide additional control
+        over how the conversion is done.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to pass to :meth:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is a not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, optional
+            The value to use for missing values. The default value depends
+            on `dtype` and the type of the array.
+
+        Returns
+        -------
+        numpy.ndarray
+        """
+        result = np.asarray(self, dtype=dtype)
+        if copy or na_value is not lib.no_default:
+            result = result.copy()
+        if na_value is not lib.no_default:
+            result[self.isna()] = na_value
+        return result
+
+    # ------------------------------------------------------------------------
+    # Required attributes
+    # ------------------------------------------------------------------------
+
+    @property
+    def dtype(self) -> ExtensionDtype:
+        """
+        An instance of ExtensionDtype.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).dtype
+        Int64Dtype()
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def shape(self) -> Shape:
+        """
+        Return a tuple of the array dimensions.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.shape
+        (3,)
+        """
+        return (len(self),)
+
+    @property
+    def size(self) -> int:
+        """
+        The number of elements in the array.
+        """
+        # error: Incompatible return value type (got "signedinteger[_64Bit]",
+        # expected "int")  [return-value]
+        return np.prod(self.shape)  # type: ignore[return-value]
+
+    @property
+    def ndim(self) -> int:
+        """
+        Extension Arrays are only allowed to be 1-dimensional.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.ndim
+        1
+        """
+        return 1
+
+    @property
+    def nbytes(self) -> int:
+        """
+        The number of bytes needed to store this object in memory.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).nbytes
+        27
+        """
+        # If this is expensive to compute, return an approximate lower bound
+        # on the number of bytes needed.
+        raise AbstractMethodError(self)
+
+    # ------------------------------------------------------------------------
+    # Additional Methods
+    # ------------------------------------------------------------------------
+
+    @overload
+    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
+        ...
+
+    @overload
+    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
+        ...
+
+    @overload
+    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
+        ...
+
+    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
+        """
+        Cast to a NumPy array or ExtensionArray with 'dtype'.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+        copy : bool, default True
+            Whether to copy the data, even if not necessary. If False,
+            a copy is made only if the old dtype does not match the
+            new dtype.
+
+        Returns
+        -------
+        np.ndarray or pandas.api.extensions.ExtensionArray
+            An ``ExtensionArray`` if ``dtype`` is ``ExtensionDtype``,
+            otherwise a Numpy ndarray with ``dtype`` for its dtype.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr
+        <IntegerArray>
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+
+        Casting to another ``ExtensionDtype`` returns an ``ExtensionArray``:
+
+        >>> arr1 = arr.astype('Float64')
+        >>> arr1
+        <FloatingArray>
+        [1.0, 2.0, 3.0]
+        Length: 3, dtype: Float64
+        >>> arr1.dtype
+        Float64Dtype()
+
+        Otherwise, we will get a Numpy ndarray:
+
+        >>> arr2 = arr.astype('float64')
+        >>> arr2
+        array([1., 2., 3.])
+        >>> arr2.dtype
+        dtype('float64')
+        """
+        dtype = pandas_dtype(dtype)
+        if dtype == self.dtype:
+            if not copy:
+                return self
+            else:
+                return self.copy()
+
+        if isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            return cls._from_sequence(self, dtype=dtype, copy=copy)
+
+        elif lib.is_np_dtype(dtype, "M"):
+            from pandas.core.arrays import DatetimeArray
+
+            return DatetimeArray._from_sequence(self, dtype=dtype, copy=copy)
+
+        elif lib.is_np_dtype(dtype, "m"):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._from_sequence(self, dtype=dtype, copy=copy)
+
+        if not copy:
+            return np.asarray(self, dtype=dtype)
+        else:
+            return np.array(self, dtype=dtype, copy=copy)
+
+    def isna(self) -> np.ndarray | ExtensionArraySupportsAnyAll:
+        """
+        A 1-D array indicating if each value is missing.
+
+        Returns
+        -------
+        numpy.ndarray or pandas.api.extensions.ExtensionArray
+            In most cases, this should return a NumPy ndarray. For
+            exceptional cases like ``SparseArray``, where returning
+            an ndarray would be expensive, an ExtensionArray may be
+            returned.
+
+        Notes
+        -----
+        If returning an ExtensionArray, then
+
+        * ``na_values._is_boolean`` should be True
+        * `na_values` should implement :func:`ExtensionArray._reduce`
+        * ``na_values.any`` and ``na_values.all`` should be implemented
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, np.nan, np.nan])
+        >>> arr.isna()
+        array([False, False,  True,  True])
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def _hasna(self) -> bool:
+        # GH#22680
+        """
+        Equivalent to `self.isna().any()`.
+
+        Some ExtensionArray subclasses may be able to optimize this check.
+        """
+        return bool(self.isna().any())
+
+    def _values_for_argsort(self) -> np.ndarray:
+        """
+        Return values for sorting.
+
+        Returns
+        -------
+        ndarray
+            The transformed values should maintain the ordering between values
+            within the array.
+
+        See Also
+        --------
+        ExtensionArray.argsort : Return the indices that would sort this array.
+
+        Notes
+        -----
+        The caller is responsible for *not* modifying these values in-place, so
+        it is safe for implementers to give views on ``self``.
+
+        Functions that use this (e.g. ``ExtensionArray.argsort``) should ignore
+        entries with missing values in the original array (according to
+        ``self.isna()``). This means that the corresponding entries in the returned
+        array don't need to be modified to sort correctly.
+
+        Examples
+        --------
+        In most cases, this is the underlying Numpy array of the ``ExtensionArray``:
+
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr._values_for_argsort()
+        array([1, 2, 3])
+        """
+        # Note: this is used in `ExtensionArray.argsort/argmin/argmax`.
+        return np.array(self)
+
+    def argsort(
+        self,
+        *,
+        ascending: bool = True,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Return the indices that would sort this array.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            Whether the indices should result in an ascending
+            or descending sort.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+            Sorting algorithm.
+        na_position : {'first', 'last'}, default 'last'
+            If ``'first'``, put ``NaN`` values at the beginning.
+            If ``'last'``, put ``NaN`` values at the end.
+        *args, **kwargs:
+            Passed through to :func:`numpy.argsort`.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            Array of indices that sort ``self``. If NaN values are contained,
+            NaN values are placed at the end.
+
+        See Also
+        --------
+        numpy.argsort : Sorting implementation used internally.
+
+        Examples
+        --------
+        >>> arr = pd.array([3, 1, 2, 5, 4])
+        >>> arr.argsort()
+        array([1, 2, 0, 4, 3])
+        """
+        # Implementer note: You have two places to override the behavior of
+        # argsort.
+        # 1. _values_for_argsort : construct the values passed to np.argsort
+        # 2. argsort : total control over sorting. In case of overriding this,
+        #    it is recommended to also override argmax/argmin
+        ascending = nv.validate_argsort_with_ascending(ascending, (), kwargs)
+
+        values = self._values_for_argsort()
+        return nargsort(
+            values,
+            kind=kind,
+            ascending=ascending,
+            na_position=na_position,
+            mask=np.asarray(self.isna()),
+        )
+
+    def argmin(self, skipna: bool = True) -> int:
+        """
+        Return the index of minimum value.
+
+        In case of multiple occurrences of the minimum value, the index
+        corresponding to the first occurrence is returned.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+
+        Returns
+        -------
+        int
+
+        See Also
+        --------
+        ExtensionArray.argmax : Return the index of the maximum value.
+
+        Examples
+        --------
+        >>> arr = pd.array([3, 1, 2, 5, 4])
+        >>> arr.argmin()
+        1
+        """
+        # Implementer note: You have two places to override the behavior of
+        # argmin.
+        # 1. _values_for_argsort : construct the values used in nargminmax
+        # 2. argmin itself : total control over sorting.
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmin")
+
+    def argmax(self, skipna: bool = True) -> int:
+        """
+        Return the index of maximum value.
+
+        In case of multiple occurrences of the maximum value, the index
+        corresponding to the first occurrence is returned.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+
+        Returns
+        -------
+        int
+
+        See Also
+        --------
+        ExtensionArray.argmin : Return the index of the minimum value.
+
+        Examples
+        --------
+        >>> arr = pd.array([3, 1, 2, 5, 4])
+        >>> arr.argmax()
+        3
+        """
+        # Implementer note: You have two places to override the behavior of
+        # argmax.
+        # 1. _values_for_argsort : construct the values used in nargminmax
+        # 2. argmax itself : total control over sorting.
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return nargminmax(self, "argmax")
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index: Index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See DataFrame.interpolate.__doc__.
+
+        Examples
+        --------
+        >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3]))
+        >>> arr.interpolate(method="linear",
+        ...                 limit=3,
+        ...                 limit_direction="forward",
+        ...                 index=pd.Index([1, 2, 3, 4]),
+        ...                 fill_value=1,
+        ...                 copy=False,
+        ...                 axis=0,
+        ...                 limit_area="inside"
+        ...                 )
+        <NumpyExtensionArray>
+        [0.0, 1.0, 2.0, 3.0]
+        Length: 4, dtype: float64
+        """
+        # NB: we return type(self) even if copy=False
+        raise NotImplementedError(
+            f"{type(self).__name__} does not implement interpolate"
+        )
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        Pad or backfill values, used by Series/DataFrame ffill and bfill.
+
+        Parameters
+        ----------
+        method : {'backfill', 'bfill', 'pad', 'ffill'}
+            Method to use for filling holes in reindexed Series:
+
+            * pad / ffill: propagate last valid observation forward to next valid.
+            * backfill / bfill: use NEXT valid observation to fill gap.
+
+        limit : int, default None
+            This is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+
+        copy : bool, default True
+            Whether to make a copy of the data before filling. If False, then
+            the original should be modified and no new memory should be allocated.
+            For ExtensionArray subclasses that cannot do this, it is at the
+            author's discretion whether to ignore "copy=False" or to raise.
+            The base class implementation ignores the keyword if any NAs are
+            present.
+
+        Returns
+        -------
+        Same type as self
+
+        Examples
+        --------
+        >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
+        >>> arr._pad_or_backfill(method="backfill", limit=1)
+        <IntegerArray>
+        [<NA>, 2, 2, 3, <NA>, <NA>]
+        Length: 6, dtype: Int64
+        """
+
+        # If a 3rd-party EA has implemented this functionality in fillna,
+        #  we warn that they need to implement _pad_or_backfill instead.
+        if (
+            type(self).fillna is not ExtensionArray.fillna
+            and type(self)._pad_or_backfill is ExtensionArray._pad_or_backfill
+        ):
+            # Check for _pad_or_backfill here allows us to call
+            #  super()._pad_or_backfill without getting this warning
+            warnings.warn(
+                "ExtensionArray.fillna 'method' keyword is deprecated. "
+                "In a future version. arr._pad_or_backfill will be called "
+                "instead. 3rd-party ExtensionArray authors need to implement "
+                "_pad_or_backfill.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+            if limit_area is not None:
+                raise NotImplementedError(
+                    f"{type(self).__name__} does not implement limit_area "
+                    "(added in pandas 2.2). 3rd-party ExtnsionArray authors "
+                    "need to add this argument to _pad_or_backfill."
+                )
+            return self.fillna(method=method, limit=limit)
+
+        mask = self.isna()
+
+        if mask.any():
+            # NB: the base class does not respect the "copy" keyword
+            meth = missing.clean_fill_method(method)
+
+            npmask = np.asarray(mask)
+            if limit_area is not None and not npmask.all():
+                _fill_limit_area_1d(npmask, limit_area)
+            if meth == "pad":
+                indexer = libalgos.get_fill_indexer(npmask, limit=limit)
+                return self.take(indexer, allow_fill=True)
+            else:
+                # i.e. meth == "backfill"
+                indexer = libalgos.get_fill_indexer(npmask[::-1], limit=limit)[::-1]
+                return self[::-1].take(indexer, allow_fill=True)
+
+        else:
+            if not copy:
+                return self
+            new_values = self.copy()
+        return new_values
+
+    def fillna(
+        self,
+        value: object | ArrayLike | None = None,
+        method: FillnaOptions | None = None,
+        limit: int | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, array-like
+            If a scalar value is passed it is used to fill all missing values.
+            Alternatively, an array-like "value" can be given. It's expected
+            that the array-like have the same length as 'self'.
+        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+            Method to use for filling holes in reindexed Series:
+
+            * pad / ffill: propagate last valid observation forward to next valid.
+            * backfill / bfill: use NEXT valid observation to fill gap.
+
+            .. deprecated:: 2.1.0
+
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+
+            .. deprecated:: 2.1.0
+
+        copy : bool, default True
+            Whether to make a copy of the data before filling. If False, then
+            the original should be modified and no new memory should be allocated.
+            For ExtensionArray subclasses that cannot do this, it is at the
+            author's discretion whether to ignore "copy=False" or to raise.
+            The base class implementation ignores the keyword in pad/backfill
+            cases.
+
+        Returns
+        -------
+        ExtensionArray
+            With NA/NaN filled.
+
+        Examples
+        --------
+        >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
+        >>> arr.fillna(0)
+        <IntegerArray>
+        [0, 0, 2, 3, 0, 0]
+        Length: 6, dtype: Int64
+        """
+        if method is not None:
+            warnings.warn(
+                f"The 'method' keyword in {type(self).__name__}.fillna is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        value, method = validate_fillna_kwargs(value, method)
+
+        mask = self.isna()
+        # error: Argument 2 to "check_value_size" has incompatible type
+        # "ExtensionArray"; expected "ndarray"
+        value = missing.check_value_size(
+            value, mask, len(self)  # type: ignore[arg-type]
+        )
+
+        if mask.any():
+            if method is not None:
+                meth = missing.clean_fill_method(method)
+
+                npmask = np.asarray(mask)
+                if meth == "pad":
+                    indexer = libalgos.get_fill_indexer(npmask, limit=limit)
+                    return self.take(indexer, allow_fill=True)
+                else:
+                    # i.e. meth == "backfill"
+                    indexer = libalgos.get_fill_indexer(npmask[::-1], limit=limit)[::-1]
+                    return self[::-1].take(indexer, allow_fill=True)
+            else:
+                # fill with value
+                if not copy:
+                    new_values = self[:]
+                else:
+                    new_values = self.copy()
+                new_values[mask] = value
+        else:
+            if not copy:
+                new_values = self[:]
+            else:
+                new_values = self.copy()
+        return new_values
+
+    def dropna(self) -> Self:
+        """
+        Return ExtensionArray without NA values.
+
+        Returns
+        -------
+
+        Examples
+        --------
+        >>> pd.array([1, 2, np.nan]).dropna()
+        <IntegerArray>
+        [1, 2]
+        Length: 2, dtype: Int64
+        """
+        # error: Unsupported operand type for ~ ("ExtensionArray")
+        return self[~self.isna()]  # type: ignore[operator]
+
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        """
+        Return boolean ndarray denoting duplicate values.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
+            - False : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        ndarray[bool]
+
+        Examples
+        --------
+        >>> pd.array([1, 1, 2, 3, 3], dtype="Int64").duplicated()
+        array([False,  True, False, False,  True])
+        """
+        mask = self.isna().astype(np.bool_, copy=False)
+        return duplicated(values=self, keep=keep, mask=mask)
+
+    def shift(self, periods: int = 1, fill_value: object = None) -> ExtensionArray:
+        """
+        Shift values by desired number.
+
+        Newly introduced missing values are filled with
+        ``self.dtype.na_value``.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            The number of periods to shift. Negative values are allowed
+            for shifting backwards.
+
+        fill_value : object, optional
+            The scalar value to use for newly introduced missing values.
+            The default is ``self.dtype.na_value``.
+
+        Returns
+        -------
+        ExtensionArray
+            Shifted.
+
+        Notes
+        -----
+        If ``self`` is empty or ``periods`` is 0, a copy of ``self`` is
+        returned.
+
+        If ``periods > len(self)``, then an array of size
+        len(self) is returned, with all values filled with
+        ``self.dtype.na_value``.
+
+        For 2-dimensional ExtensionArrays, we are always shifting along axis=0.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.shift(2)
+        <IntegerArray>
+        [<NA>, <NA>, 1]
+        Length: 3, dtype: Int64
+        """
+        # Note: this implementation assumes that `self.dtype.na_value` can be
+        # stored in an instance of your ExtensionArray with `self.dtype`.
+        if not len(self) or periods == 0:
+            return self.copy()
+
+        if isna(fill_value):
+            fill_value = self.dtype.na_value
+
+        empty = self._from_sequence(
+            [fill_value] * min(abs(periods), len(self)), dtype=self.dtype
+        )
+        if periods > 0:
+            a = empty
+            b = self[:-periods]
+        else:
+            a = self[abs(periods) :]
+            b = empty
+        return self._concat_same_type([a, b])
+
+    def unique(self) -> Self:
+        """
+        Compute the ExtensionArray of unique values.
+
+        Returns
+        -------
+        pandas.api.extensions.ExtensionArray
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3, 1, 2, 3])
+        >>> arr.unique()
+        <IntegerArray>
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        uniques = unique(self.astype(object))
+        return self._from_sequence(uniques, dtype=self.dtype)
+
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        """
+        Find indices where elements should be inserted to maintain order.
+
+        Find the indices into a sorted array `self` (a) such that, if the
+        corresponding elements in `value` were inserted before the indices,
+        the order of `self` would be preserved.
+
+        Assuming that `self` is sorted:
+
+        ======  ================================
+        `side`  returned index `i` satisfies
+        ======  ================================
+        left    ``self[i-1] < value <= self[i]``
+        right   ``self[i-1] <= value < self[i]``
+        ======  ================================
+
+        Parameters
+        ----------
+        value : array-like, list or scalar
+            Value(s) to insert into `self`.
+        side : {'left', 'right'}, optional
+            If 'left', the index of the first suitable location found is given.
+            If 'right', return the last such index.  If there is no suitable
+            index, return either 0 or N (where N is the length of `self`).
+        sorter : 1-D array-like, optional
+            Optional array of integer indices that sort array a into ascending
+            order. They are typically the result of argsort.
+
+        Returns
+        -------
+        array of ints or int
+            If value is array-like, array of insertion points.
+            If value is scalar, a single integer.
+
+        See Also
+        --------
+        numpy.searchsorted : Similar method from NumPy.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3, 5])
+        >>> arr.searchsorted([4])
+        array([3])
+        """
+        # Note: the base tests provided by pandas only test the basics.
+        # We do not test
+        # 1. Values outside the range of the `data_for_sorting` fixture
+        # 2. Values between the values in the `data_for_sorting` fixture
+        # 3. Missing values.
+        arr = self.astype(object)
+        if isinstance(value, ExtensionArray):
+            value = value.astype(object)
+        return arr.searchsorted(value, side=side, sorter=sorter)
+
+    def equals(self, other: object) -> bool:
+        """
+        Return if another array is equivalent to this array.
+
+        Equivalent means that both arrays have the same shape and dtype, and
+        all values compare equal. Missing values in the same location are
+        considered equal (in contrast with normal equality).
+
+        Parameters
+        ----------
+        other : ExtensionArray
+            Array to compare to this Array.
+
+        Returns
+        -------
+        boolean
+            Whether the arrays are equivalent.
+
+        Examples
+        --------
+        >>> arr1 = pd.array([1, 2, np.nan])
+        >>> arr2 = pd.array([1, 2, np.nan])
+        >>> arr1.equals(arr2)
+        True
+        """
+        if type(self) != type(other):
+            return False
+        other = cast(ExtensionArray, other)
+        if self.dtype != other.dtype:
+            return False
+        elif len(self) != len(other):
+            return False
+        else:
+            equal_values = self == other
+            if isinstance(equal_values, ExtensionArray):
+                # boolean array with NA -> fill with False
+                equal_values = equal_values.fillna(False)
+            # error: Unsupported left operand type for & ("ExtensionArray")
+            equal_na = self.isna() & other.isna()  # type: ignore[operator]
+            return bool((equal_values | equal_na).all())
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        """
+        Pointwise comparison for set containment in the given values.
+
+        Roughly equivalent to `np.array([x in values for x in self])`
+
+        Parameters
+        ----------
+        values : np.ndarray or ExtensionArray
+
+        Returns
+        -------
+        np.ndarray[bool]
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.isin([1])
+        <BooleanArray>
+        [True, False, False]
+        Length: 3, dtype: boolean
+        """
+        return isin(np.asarray(self), values)
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, Any]:
+        """
+        Return an array and missing value suitable for factorization.
+
+        Returns
+        -------
+        values : ndarray
+            An array suitable for factorization. This should maintain order
+            and be a supported dtype (Float64, Int64, UInt64, String, Object).
+            By default, the extension array is cast to object dtype.
+        na_value : object
+            The value in `values` to consider missing. This will be treated
+            as NA in the factorization routines, so it will be coded as
+            `-1` and not included in `uniques`. By default,
+            ``np.nan`` is used.
+
+        Notes
+        -----
+        The values returned by this method are also used in
+        :func:`pandas.util.hash_pandas_object`. If needed, this can be
+        overridden in the ``self._hash_pandas_object()`` method.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3])._values_for_factorize()
+        (array([1, 2, 3], dtype=object), nan)
+        """
+        return self.astype(object), np.nan
+
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, ExtensionArray]:
+        """
+        Encode the extension array as an enumerated type.
+
+        Parameters
+        ----------
+        use_na_sentinel : bool, default True
+            If True, the sentinel -1 will be used for NaN values. If False,
+            NaN values will be encoded as non-negative integers and will not drop the
+            NaN from the uniques of the values.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        codes : ndarray
+            An integer NumPy array that's an indexer into the original
+            ExtensionArray.
+        uniques : ExtensionArray
+            An ExtensionArray containing the unique values of `self`.
+
+            .. note::
+
+               uniques will *not* contain an entry for the NA value of
+               the ExtensionArray if there are any missing values present
+               in `self`.
+
+        See Also
+        --------
+        factorize : Top-level factorize method that dispatches here.
+
+        Notes
+        -----
+        :meth:`pandas.factorize` offers a `sort` keyword as well.
+
+        Examples
+        --------
+        >>> idx1 = pd.PeriodIndex(["2014-01", "2014-01", "2014-02", "2014-02",
+        ...                       "2014-03", "2014-03"], freq="M")
+        >>> arr, idx = idx1.factorize()
+        >>> arr
+        array([0, 0, 1, 1, 2, 2])
+        >>> idx
+        PeriodIndex(['2014-01', '2014-02', '2014-03'], dtype='period[M]')
+        """
+        # Implementer note: There are two ways to override the behavior of
+        # pandas.factorize
+        # 1. _values_for_factorize and _from_factorize.
+        #    Specify the values passed to pandas' internal factorization
+        #    routines, and how to convert from those values back to the
+        #    original ExtensionArray.
+        # 2. ExtensionArray.factorize.
+        #    Complete control over factorization.
+        arr, na_value = self._values_for_factorize()
+
+        codes, uniques = factorize_array(
+            arr, use_na_sentinel=use_na_sentinel, na_value=na_value
+        )
+
+        uniques_ea = self._from_factorized(uniques, self)
+        return codes, uniques_ea
+
+    _extension_array_shared_docs[
+        "repeat"
+    ] = """
+        Repeat elements of a %(klass)s.
+
+        Returns a new %(klass)s where each element of the current %(klass)s
+        is repeated consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int or array of ints
+            The number of repetitions for each element. This should be a
+            non-negative integer. Repeating 0 times will return an empty
+            %(klass)s.
+        axis : None
+            Must be ``None``. Has no effect but is accepted for compatibility
+            with numpy.
+
+        Returns
+        -------
+        %(klass)s
+            Newly created %(klass)s with repeated elements.
+
+        See Also
+        --------
+        Series.repeat : Equivalent function for Series.
+        Index.repeat : Equivalent function for Index.
+        numpy.repeat : Similar method for :class:`numpy.ndarray`.
+        ExtensionArray.take : Take arbitrary positions.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b', 'c'])
+        >>> cat
+        ['a', 'b', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> cat.repeat(2)
+        ['a', 'a', 'b', 'b', 'c', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> cat.repeat([1, 2, 3])
+        ['a', 'b', 'b', 'c', 'c', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        """
+
+    @Substitution(klass="ExtensionArray")
+    @Appender(_extension_array_shared_docs["repeat"])
+    def repeat(self, repeats: int | Sequence[int], axis: AxisInt | None = None) -> Self:
+        nv.validate_repeat((), {"axis": axis})
+        ind = np.arange(len(self)).repeat(repeats)
+        return self.take(ind)
+
+    # ------------------------------------------------------------------------
+    # Indexing methods
+    # ------------------------------------------------------------------------
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        *,
+        allow_fill: bool = False,
+        fill_value: Any = None,
+    ) -> Self:
+        """
+        Take elements from an array.
+
+        Parameters
+        ----------
+        indices : sequence of int or one-dimensional np.ndarray of int
+            Indices to be taken.
+        allow_fill : bool, default False
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
+        fill_value : any, optional
+            Fill value to use for NA-indices when `allow_fill` is True.
+            This may be ``None``, in which case the default NA value for
+            the type, ``self.dtype.na_value``, is used.
+
+            For many ExtensionArrays, there will be two representations of
+            `fill_value`: a user-facing "boxed" scalar, and a low-level
+            physical NA value. `fill_value` should be the user-facing version,
+            and the implementation should handle translating that to the
+            physical version for processing the take if necessary.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Raises
+        ------
+        IndexError
+            When the indices are out of bounds for the array.
+        ValueError
+            When `indices` contains negative values other than ``-1``
+            and `allow_fill` is True.
+
+        See Also
+        --------
+        numpy.take : Take elements from an array along an axis.
+        api.extensions.take : Take elements from an array.
+
+        Notes
+        -----
+        ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
+        ``iloc``, when `indices` is a sequence of values. Additionally,
+        it's called by :meth:`Series.reindex`, or any other method
+        that causes realignment, with a `fill_value`.
+
+        Examples
+        --------
+        Here's an example implementation, which relies on casting the
+        extension array to object dtype. This uses the helper method
+        :func:`pandas.api.extensions.take`.
+
+        .. code-block:: python
+
+           def take(self, indices, allow_fill=False, fill_value=None):
+               from pandas.core.algorithms import take
+
+               # If the ExtensionArray is backed by an ndarray, then
+               # just pass that here instead of coercing to object.
+               data = self.astype(object)
+
+               if allow_fill and fill_value is None:
+                   fill_value = self.dtype.na_value
+
+               # fill value should always be translated from the scalar
+               # type for the array, to the physical storage type for
+               # the data, before passing to take.
+
+               result = take(data, indices, fill_value=fill_value,
+                             allow_fill=allow_fill)
+               return self._from_sequence(result, dtype=self.dtype)
+        """
+        # Implementer note: The `fill_value` parameter should be a user-facing
+        # value, an instance of self.dtype.type. When passed `fill_value=None`,
+        # the default of `self.dtype.na_value` should be used.
+        # This may differ from the physical storage type your ExtensionArray
+        # uses. In this case, your implementation is responsible for casting
+        # the user-facing type to the storage type, before using
+        # pandas.api.extensions.take
+        raise AbstractMethodError(self)
+
+    def copy(self) -> Self:
+        """
+        Return a copy of the array.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr2 = arr.copy()
+        >>> arr[0] = 2
+        >>> arr2
+        <IntegerArray>
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        raise AbstractMethodError(self)
+
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        """
+        Return a view on the array.
+
+        Parameters
+        ----------
+        dtype : str, np.dtype, or ExtensionDtype, optional
+            Default None.
+
+        Returns
+        -------
+        ExtensionArray or np.ndarray
+            A view on the :class:`ExtensionArray`'s data.
+
+        Examples
+        --------
+        This gives view on the underlying data of an ``ExtensionArray`` and is not a
+        copy. Modifications on either the view or the original ``ExtensionArray``
+        will be reflectd on the underlying data:
+
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr2 = arr.view()
+        >>> arr[0] = 2
+        >>> arr2
+        <IntegerArray>
+        [2, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        # NB:
+        # - This must return a *new* object referencing the same data, not self.
+        # - The only case that *must* be implemented is with dtype=None,
+        #   giving a view with the same dtype as self.
+        if dtype is not None:
+            raise NotImplementedError(dtype)
+        return self[:]
+
+    # ------------------------------------------------------------------------
+    # Printing
+    # ------------------------------------------------------------------------
+
+    def __repr__(self) -> str:
+        if self.ndim > 1:
+            return self._repr_2d()
+
+        from pandas.io.formats.printing import format_object_summary
+
+        # the short repr has no trailing newline, while the truncated
+        # repr does. So we include a newline in our template, and strip
+        # any trailing newlines from format_object_summary
+        data = format_object_summary(
+            self, self._formatter(), indent_for_name=False
+        ).rstrip(", \n")
+        class_name = f"<{type(self).__name__}>\n"
+        footer = self._get_repr_footer()
+        return f"{class_name}{data}\n{footer}"
+
+    def _get_repr_footer(self) -> str:
+        # GH#24278
+        if self.ndim > 1:
+            return f"Shape: {self.shape}, dtype: {self.dtype}"
+        return f"Length: {len(self)}, dtype: {self.dtype}"
+
+    def _repr_2d(self) -> str:
+        from pandas.io.formats.printing import format_object_summary
+
+        # the short repr has no trailing newline, while the truncated
+        # repr does. So we include a newline in our template, and strip
+        # any trailing newlines from format_object_summary
+        lines = [
+            format_object_summary(x, self._formatter(), indent_for_name=False).rstrip(
+                ", \n"
+            )
+            for x in self
+        ]
+        data = ",\n".join(lines)
+        class_name = f"<{type(self).__name__}>"
+        footer = self._get_repr_footer()
+        return f"{class_name}\n[\n{data}\n]\n{footer}"
+
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
+        """
+        Formatting function for scalar values.
+
+        This is used in the default '__repr__'. The returned formatting
+        function receives instances of your scalar type.
+
+        Parameters
+        ----------
+        boxed : bool, default False
+            An indicated for whether or not your array is being printed
+            within a Series, DataFrame, or Index (True), or just by
+            itself (False). This may be useful if you want scalar values
+            to appear differently within a Series versus on its own (e.g.
+            quoted or not).
+
+        Returns
+        -------
+        Callable[[Any], str]
+            A callable that gets instances of the scalar type and
+            returns a string. By default, :func:`repr` is used
+            when ``boxed=False`` and :func:`str` is used when
+            ``boxed=True``.
+
+        Examples
+        --------
+        >>> class MyExtensionArray(pd.arrays.NumpyExtensionArray):
+        ...     def _formatter(self, boxed=False):
+        ...         return lambda x: '*' + str(x) + '*' if boxed else repr(x) + '*'
+        >>> MyExtensionArray(np.array([1, 2, 3, 4]))
+        <MyExtensionArray>
+        [1*, 2*, 3*, 4*]
+        Length: 4, dtype: int64
+        """
+        if boxed:
+            return str
+        return repr
+
+    # ------------------------------------------------------------------------
+    # Reshaping
+    # ------------------------------------------------------------------------
+
+    def transpose(self, *axes: int) -> ExtensionArray:
+        """
+        Return a transposed view on this array.
+
+        Because ExtensionArrays are always 1D, this is a no-op.  It is included
+        for compatibility with np.ndarray.
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).transpose()
+        <IntegerArray>
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        return self[:]
+
+    @property
+    def T(self) -> ExtensionArray:
+        return self.transpose()
+
+    def ravel(self, order: Literal["C", "F", "A", "K"] | None = "C") -> ExtensionArray:
+        """
+        Return a flattened view on this array.
+
+        Parameters
+        ----------
+        order : {None, 'C', 'F', 'A', 'K'}, default 'C'
+
+        Returns
+        -------
+        ExtensionArray
+
+        Notes
+        -----
+        - Because ExtensionArrays are 1D-only, this is a no-op.
+        - The "order" argument is ignored, is for compatibility with NumPy.
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3]).ravel()
+        <IntegerArray>
+        [1, 2, 3]
+        Length: 3, dtype: Int64
+        """
+        return self
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[Self]) -> Self:
+        """
+        Concatenate multiple array of this dtype.
+
+        Parameters
+        ----------
+        to_concat : sequence of this type
+
+        Returns
+        -------
+        ExtensionArray
+
+        Examples
+        --------
+        >>> arr1 = pd.array([1, 2, 3])
+        >>> arr2 = pd.array([4, 5, 6])
+        >>> pd.arrays.IntegerArray._concat_same_type([arr1, arr2])
+        <IntegerArray>
+        [1, 2, 3, 4, 5, 6]
+        Length: 6, dtype: Int64
+        """
+        # Implementer note: this method will only be called with a sequence of
+        # ExtensionArrays of this class and with the same dtype as self. This
+        # should allow "easy" concatenation (no upcasting needed), and result
+        # in a new ExtensionArray of the same dtype.
+        # Note: this strict behaviour is only guaranteed starting with pandas 1.1
+        raise AbstractMethodError(cls)
+
+    # The _can_hold_na attribute is set to True so that pandas internals
+    # will use the ExtensionDtype.na_value as the NA value in operations
+    # such as take(), reindex(), shift(), etc.  In addition, those results
+    # will then be of the ExtensionArray subclass rather than an array
+    # of objects
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        return self.dtype._can_hold_na
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> ExtensionArray:
+        """
+        Return an ExtensionArray performing an accumulation operation.
+
+        The underlying data type might change.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            - cummin
+            - cummax
+            - cumsum
+            - cumprod
+        skipna : bool, default True
+            If True, skip NA values.
+        **kwargs
+            Additional keyword arguments passed to the accumulation function.
+            Currently, there is no supported kwarg.
+
+        Returns
+        -------
+        array
+
+        Raises
+        ------
+        NotImplementedError : subclass does not define accumulations
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr._accumulate(name='cumsum')
+        <IntegerArray>
+        [1, 3, 6]
+        Length: 3, dtype: Int64
+        """
+        raise NotImplementedError(f"cannot perform {name} with type {self.dtype}")
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        """
+        Return a scalar result of performing the reduction operation.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            { any, all, min, max, sum, mean, median, prod,
+            std, var, sem, kurt, skew }.
+        skipna : bool, default True
+            If True, skip NaN values.
+        keepdims : bool, default False
+            If False, a scalar is returned.
+            If True, the result has dimension with size one along the reduced axis.
+
+            .. versionadded:: 2.1
+
+               This parameter is not required in the _reduce signature to keep backward
+               compatibility, but will become required in the future. If the parameter
+               is not found in the method signature, a FutureWarning will be emitted.
+        **kwargs
+            Additional keyword arguments passed to the reduction function.
+            Currently, `ddof` is the only supported kwarg.
+
+        Returns
+        -------
+        scalar
+
+        Raises
+        ------
+        TypeError : subclass does not define reductions
+
+        Examples
+        --------
+        >>> pd.array([1, 2, 3])._reduce("min")
+        1
+        """
+        meth = getattr(self, name, None)
+        if meth is None:
+            raise TypeError(
+                f"'{type(self).__name__}' with dtype {self.dtype} "
+                f"does not support reduction '{name}'"
+            )
+        result = meth(skipna=skipna, **kwargs)
+        if keepdims:
+            result = np.array([result])
+
+        return result
+
+    # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
+    # Incompatible types in assignment (expression has type "None", base class
+    # "object" defined the type as "Callable[[object], int]")
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+
+    # ------------------------------------------------------------------------
+    # Non-Optimized Default Methods; in the case of the private methods here,
+    #  these are not guaranteed to be stable across pandas versions.
+
+    def _values_for_json(self) -> np.ndarray:
+        """
+        Specify how to render our entries in to_json.
+
+        Notes
+        -----
+        The dtype on the returned ndarray is not restricted, but for non-native
+        types that are not specifically handled in objToJSON.c, to_json is
+        liable to raise. In these cases, it may be safer to return an ndarray
+        of strings.
+        """
+        return np.asarray(self)
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        """
+        Hook for hash_pandas_object.
+
+        Default is to use the values returned by _values_for_factorize.
+
+        Parameters
+        ----------
+        encoding : str
+            Encoding for data & key when strings.
+        hash_key : str
+            Hash_key for string key to encode.
+        categorize : bool
+            Whether to first categorize object arrays before hashing. This is more
+            efficient when the array contains duplicate values.
+
+        Returns
+        -------
+        np.ndarray[uint64]
+
+        Examples
+        --------
+        >>> pd.array([1, 2])._hash_pandas_object(encoding='utf-8',
+        ...                                      hash_key="1000000000000000",
+        ...                                      categorize=False
+        ...                                      )
+        array([ 6238072747940578789, 15839785061582574730], dtype=uint64)
+        """
+        from pandas.core.util.hashing import hash_array
+
+        values, _ = self._values_for_factorize()
+        return hash_array(
+            values, encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+
+    def _explode(self) -> tuple[Self, npt.NDArray[np.uint64]]:
+        """
+        Transform each element of list-like to a row.
+
+        For arrays that do not contain list-like elements the default
+        implementation of this method just returns a copy and an array
+        of ones (unchanged index).
+
+        Returns
+        -------
+        ExtensionArray
+            Array with the exploded values.
+        np.ndarray[uint64]
+            The original lengths of each list-like for determining the
+            resulting index.
+
+        See Also
+        --------
+        Series.explode : The method on the ``Series`` object that this
+            extension array method is meant to support.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> a = pd.array([[1, 2, 3], [4], [5, 6]],
+        ...              dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+        >>> a._explode()
+        (<ArrowExtensionArray>
+        [1, 2, 3, 4, 5, 6]
+        Length: 6, dtype: int64[pyarrow], array([3, 1, 2], dtype=int32))
+        """
+        values = self.copy()
+        counts = np.ones(shape=(len(self),), dtype=np.uint64)
+        return values, counts
+
+    def tolist(self) -> list:
+        """
+        Return a list of the values.
+
+        These are each a scalar type, which is a Python scalar
+        (for str, int, float) or a pandas scalar
+        (for Timestamp/Timedelta/Interval/Period)
+
+        Returns
+        -------
+        list
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.tolist()
+        [1, 2, 3]
+        """
+        if self.ndim > 1:
+            return [x.tolist() for x in self]
+        return list(self)
+
+    def delete(self, loc: PositionalIndexer) -> Self:
+        indexer = np.delete(np.arange(len(self)), loc)
+        return self.take(indexer)
+
+    def insert(self, loc: int, item) -> Self:
+        """
+        Insert an item at the given position.
+
+        Parameters
+        ----------
+        loc : int
+        item : scalar-like
+
+        Returns
+        -------
+        same type as self
+
+        Notes
+        -----
+        This method should be both type and dtype-preserving.  If the item
+        cannot be held in an array of this type/dtype, either ValueError or
+        TypeError should be raised.
+
+        The default implementation relies on _from_sequence to raise on invalid
+        items.
+
+        Examples
+        --------
+        >>> arr = pd.array([1, 2, 3])
+        >>> arr.insert(2, -1)
+        <IntegerArray>
+        [1, 2, -1, 3]
+        Length: 4, dtype: Int64
+        """
+        loc = validate_insert_loc(loc, len(self))
+
+        item_arr = type(self)._from_sequence([item], dtype=self.dtype)
+
+        return type(self)._concat_same_type([self[:loc], item_arr, self[loc:]])
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        """
+        Analogue to np.putmask(self, mask, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+            If listlike, must be arraylike with same length as self.
+
+        Returns
+        -------
+        None
+
+        Notes
+        -----
+        Unlike np.putmask, we do not repeat listlike values with mismatched length.
+        'value' should either be a scalar or an arraylike with the same length
+        as self.
+        """
+        if is_list_like(value):
+            val = value[mask]
+        else:
+            val = value
+
+        self[mask] = val
+
+    def _where(self, mask: npt.NDArray[np.bool_], value) -> Self:
+        """
+        Analogue to np.where(mask, self, value)
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+        value : scalar or listlike
+
+        Returns
+        -------
+        same type as self
+        """
+        result = self.copy()
+
+        if is_list_like(value):
+            val = value[~mask]
+        else:
+            val = value
+
+        result[~mask] = val
+        return result
+
+    # TODO(3.0): this can be removed once GH#33302 deprecation is enforced
+    def _fill_mask_inplace(
+        self, method: str, limit: int | None, mask: npt.NDArray[np.bool_]
+    ) -> None:
+        """
+        Replace values in locations specified by 'mask' using pad or backfill.
+
+        See also
+        --------
+        ExtensionArray.fillna
+        """
+        func = missing.get_fill_func(method)
+        npvalues = self.astype(object)
+        # NB: if we don't copy mask here, it may be altered inplace, which
+        #  would mess up the `self[mask] = ...` below.
+        func(npvalues, limit=limit, mask=mask.copy())
+        new_values = self._from_sequence(npvalues, dtype=self.dtype)
+        self[mask] = new_values[mask]
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if axis != 0:
+            raise NotImplementedError
+
+        return rank(
+            self._values_for_argsort(),
+            axis=axis,
+            method=method,
+            na_option=na_option,
+            ascending=ascending,
+            pct=pct,
+        )
+
+    @classmethod
+    def _empty(cls, shape: Shape, dtype: ExtensionDtype):
+        """
+        Create an ExtensionArray with the given shape and dtype.
+
+        See also
+        --------
+        ExtensionDtype.empty
+            ExtensionDtype.empty is the 'official' public version of this API.
+        """
+        # Implementer note: while ExtensionDtype.empty is the public way to
+        # call this method, it is still required to implement this `_empty`
+        # method as well (it is called internally in pandas)
+        obj = cls._from_sequence([], dtype=dtype)
+
+        taker = np.broadcast_to(np.intp(-1), shape)
+        result = obj.take(taker, allow_fill=True)
+        if not isinstance(result, cls) or dtype != result.dtype:
+            raise NotImplementedError(
+                f"Default 'empty' implementation is invalid for dtype='{dtype}'"
+            )
+        return result
+
+    def _quantile(self, qs: npt.NDArray[np.float64], interpolation: str) -> Self:
+        """
+        Compute the quantiles of self for each quantile in `qs`.
+
+        Parameters
+        ----------
+        qs : np.ndarray[float64]
+        interpolation: str
+
+        Returns
+        -------
+        same type as self
+        """
+        mask = np.asarray(self.isna())
+        arr = np.asarray(self)
+        fill_value = np.nan
+
+        res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
+        return type(self)._from_sequence(res_values)
+
+    def _mode(self, dropna: bool = True) -> Self:
+        """
+        Returns the mode(s) of the ExtensionArray.
+
+        Always returns `ExtensionArray` even if only one value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NA values.
+
+        Returns
+        -------
+        same type as self
+            Sorted, if possible.
+        """
+        # error: Incompatible return value type (got "Union[ExtensionArray,
+        # ndarray[Any, Any]]", expected "Self")
+        return mode(self, dropna=dropna)  # type: ignore[return-value]
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        if any(
+            isinstance(other, (ABCSeries, ABCIndex, ABCDataFrame)) for other in inputs
+        ):
+            return NotImplemented
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        return arraylike.default_array_ufunc(self, ufunc, method, *inputs, **kwargs)
+
+    def map(self, mapper, na_action=None):
+        """
+        Map values using an input mapping or function.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NA values, without passing them to the
+            mapping correspondence. If 'ignore' is not supported, a
+            ``NotImplementedError`` should be raised.
+
+        Returns
+        -------
+        Union[ndarray, Index, ExtensionArray]
+            The output of the mapping function applied to the array.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+        """
+        return map_array(self, mapper, na_action=na_action)
+
+    # ------------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ) -> ArrayLike:
+        """
+        Dispatch GroupBy reduction or transformation operation.
+
+        This is an *experimental* API to allow ExtensionArray authors to implement
+        reductions and transformations. The API is subject to change.
+
+        Parameters
+        ----------
+        how : {'any', 'all', 'sum', 'prod', 'min', 'max', 'mean', 'median',
+               'median', 'var', 'std', 'sem', 'nth', 'last', 'ohlc',
+               'cumprod', 'cumsum', 'cummin', 'cummax', 'rank'}
+        has_dropped_na : bool
+        min_count : int
+        ngroups : int
+        ids : np.ndarray[np.intp]
+            ids[i] gives the integer label for the group that self[i] belongs to.
+        **kwargs : operation-specific
+            'any', 'all' -> ['skipna']
+            'var', 'std', 'sem' -> ['ddof']
+            'cumprod', 'cumsum', 'cummin', 'cummax' -> ['skipna']
+            'rank' -> ['ties_method', 'ascending', 'na_option', 'pct']
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        from pandas.core.arrays.string_ import StringDtype
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        initial: Any = 0
+        # GH#43682
+        if isinstance(self.dtype, StringDtype):
+            # StringArray
+            if op.how in [
+                "prod",
+                "mean",
+                "median",
+                "cumsum",
+                "cumprod",
+                "std",
+                "sem",
+                "var",
+                "skew",
+            ]:
+                raise TypeError(
+                    f"dtype '{self.dtype}' does not support operation '{how}'"
+                )
+            if op.how not in ["any", "all"]:
+                # Fail early to avoid conversion to object
+                op._get_cython_function(op.kind, op.how, np.dtype(object), False)
+
+            arr = self
+            if op.how == "sum":
+                initial = ""
+                # https://github.com/pandas-dev/pandas/issues/60229
+                # All NA should result in the empty string.
+                if min_count == 0:
+                    arr = arr.fillna("")
+            npvalues = arr.to_numpy(object, na_value=np.nan)
+        else:
+            raise NotImplementedError(
+                f"function is not implemented for this dtype: {self.dtype}"
+            )
+
+        res_values = op._cython_op_ndim_compat(
+            npvalues,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=None,
+            initial=initial,
+            **kwargs,
+        )
+
+        if op.how in op.cast_blocklist:
+            # i.e. how in ["rank"], since other cast_blocklist methods don't go
+            #  through cython_operation
+            return res_values
+
+        if isinstance(self.dtype, StringDtype):
+            dtype = self.dtype
+            string_array_cls = dtype.construct_array_type()
+            return string_array_cls._from_sequence(res_values, dtype=dtype)
+
+        else:
+            raise NotImplementedError
+
+
+class ExtensionArraySupportsAnyAll(ExtensionArray):
+    def any(self, *, skipna: bool = True) -> bool:
+        raise AbstractMethodError(self)
+
+    def all(self, *, skipna: bool = True) -> bool:
+        raise AbstractMethodError(self)
+
+
+class ExtensionOpsMixin:
+    """
+    A base class for linking the operators to their dunder names.
+
+    .. note::
+
+       You may want to set ``__array_priority__`` if you want your
+       implementation to be called when involved in binary operations
+       with NumPy arrays.
+    """
+
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _add_arithmetic_ops(cls) -> None:
+        setattr(cls, "__add__", cls._create_arithmetic_method(operator.add))
+        setattr(cls, "__radd__", cls._create_arithmetic_method(roperator.radd))
+        setattr(cls, "__sub__", cls._create_arithmetic_method(operator.sub))
+        setattr(cls, "__rsub__", cls._create_arithmetic_method(roperator.rsub))
+        setattr(cls, "__mul__", cls._create_arithmetic_method(operator.mul))
+        setattr(cls, "__rmul__", cls._create_arithmetic_method(roperator.rmul))
+        setattr(cls, "__pow__", cls._create_arithmetic_method(operator.pow))
+        setattr(cls, "__rpow__", cls._create_arithmetic_method(roperator.rpow))
+        setattr(cls, "__mod__", cls._create_arithmetic_method(operator.mod))
+        setattr(cls, "__rmod__", cls._create_arithmetic_method(roperator.rmod))
+        setattr(cls, "__floordiv__", cls._create_arithmetic_method(operator.floordiv))
+        setattr(
+            cls, "__rfloordiv__", cls._create_arithmetic_method(roperator.rfloordiv)
+        )
+        setattr(cls, "__truediv__", cls._create_arithmetic_method(operator.truediv))
+        setattr(cls, "__rtruediv__", cls._create_arithmetic_method(roperator.rtruediv))
+        setattr(cls, "__divmod__", cls._create_arithmetic_method(divmod))
+        setattr(cls, "__rdivmod__", cls._create_arithmetic_method(roperator.rdivmod))
+
+    @classmethod
+    def _create_comparison_method(cls, op):
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _add_comparison_ops(cls) -> None:
+        setattr(cls, "__eq__", cls._create_comparison_method(operator.eq))
+        setattr(cls, "__ne__", cls._create_comparison_method(operator.ne))
+        setattr(cls, "__lt__", cls._create_comparison_method(operator.lt))
+        setattr(cls, "__gt__", cls._create_comparison_method(operator.gt))
+        setattr(cls, "__le__", cls._create_comparison_method(operator.le))
+        setattr(cls, "__ge__", cls._create_comparison_method(operator.ge))
+
+    @classmethod
+    def _create_logical_method(cls, op):
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _add_logical_ops(cls) -> None:
+        setattr(cls, "__and__", cls._create_logical_method(operator.and_))
+        setattr(cls, "__rand__", cls._create_logical_method(roperator.rand_))
+        setattr(cls, "__or__", cls._create_logical_method(operator.or_))
+        setattr(cls, "__ror__", cls._create_logical_method(roperator.ror_))
+        setattr(cls, "__xor__", cls._create_logical_method(operator.xor))
+        setattr(cls, "__rxor__", cls._create_logical_method(roperator.rxor))
+
+
+class ExtensionScalarOpsMixin(ExtensionOpsMixin):
+    """
+    A mixin for defining ops on an ExtensionArray.
+
+    It is assumed that the underlying scalar objects have the operators
+    already defined.
+
+    Notes
+    -----
+    If you have defined a subclass MyExtensionArray(ExtensionArray), then
+    use MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin) to
+    get the arithmetic operators.  After the definition of MyExtensionArray,
+    insert the lines
+
+    MyExtensionArray._add_arithmetic_ops()
+    MyExtensionArray._add_comparison_ops()
+
+    to link the operators to your class.
+
+    .. note::
+
+       You may want to set ``__array_priority__`` if you want your
+       implementation to be called when involved in binary operations
+       with NumPy arrays.
+    """
+
+    @classmethod
+    def _create_method(cls, op, coerce_to_dtype: bool = True, result_dtype=None):
+        """
+        A class method that returns a method that will correspond to an
+        operator for an ExtensionArray subclass, by dispatching to the
+        relevant operator defined on the individual elements of the
+        ExtensionArray.
+
+        Parameters
+        ----------
+        op : function
+            An operator that takes arguments op(a, b)
+        coerce_to_dtype : bool, default True
+            boolean indicating whether to attempt to convert
+            the result to the underlying ExtensionArray dtype.
+            If it's not possible to create a new ExtensionArray with the
+            values, an ndarray is returned instead.
+
+        Returns
+        -------
+        Callable[[Any, Any], Union[ndarray, ExtensionArray]]
+            A method that can be bound to a class. When used, the method
+            receives the two arguments, one of which is the instance of
+            this class, and should return an ExtensionArray or an ndarray.
+
+            Returning an ndarray may be necessary when the result of the
+            `op` cannot be stored in the ExtensionArray. The dtype of the
+            ndarray uses NumPy's normal inference rules.
+
+        Examples
+        --------
+        Given an ExtensionArray subclass called MyExtensionArray, use
+
+            __add__ = cls._create_method(operator.add)
+
+        in the class definition of MyExtensionArray to create the operator
+        for addition, that will be based on the operator implementation
+        of the underlying elements of the ExtensionArray
+        """
+
+        def _binop(self, other):
+            def convert_values(param):
+                if isinstance(param, ExtensionArray) or is_list_like(param):
+                    ovalues = param
+                else:  # Assume its an object
+                    ovalues = [param] * len(self)
+                return ovalues
+
+            if isinstance(other, (ABCSeries, ABCIndex, ABCDataFrame)):
+                # rely on pandas to unbox and dispatch to us
+                return NotImplemented
+
+            lvalues = self
+            rvalues = convert_values(other)
+
+            # If the operator is not defined for the underlying objects,
+            # a TypeError should be raised
+            res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
+
+            def _maybe_convert(arr):
+                if coerce_to_dtype:
+                    # https://github.com/pandas-dev/pandas/issues/22850
+                    # We catch all regular exceptions here, and fall back
+                    # to an ndarray.
+                    res = maybe_cast_pointwise_result(arr, self.dtype, same_dtype=False)
+                    if not isinstance(res, type(self)):
+                        # exception raised in _from_sequence; ensure we have ndarray
+                        res = np.asarray(arr)
+                else:
+                    res = np.asarray(arr, dtype=result_dtype)
+                return res
+
+            if op.__name__ in {"divmod", "rdivmod"}:
+                a, b = zip(*res)
+                return _maybe_convert(a), _maybe_convert(b)
+
+            return _maybe_convert(res)
+
+        op_name = f"__{op.__name__}__"
+        return set_function_name(_binop, op_name, cls)
+
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        return cls._create_method(op)
+
+    @classmethod
+    def _create_comparison_method(cls, op):
+        return cls._create_method(op, coerce_to_dtype=False, result_dtype=bool)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/boolean.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/boolean.py
new file mode 100644
index 0000000000000000000000000000000000000000..04e6f0a0bcdde9a11550fcec8274e09fe8429430
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/boolean.py
@@ -0,0 +1,407 @@
+from __future__ import annotations
+
+import numbers
+from typing import (
+    TYPE_CHECKING,
+    ClassVar,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import register_extension_dtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import ops
+from pandas.core.array_algos import masked_accumulations
+from pandas.core.arrays.masked import (
+    BaseMaskedArray,
+    BaseMaskedDtype,
+)
+
+if TYPE_CHECKING:
+    import pyarrow
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+        type_t,
+    )
+
+
+@register_extension_dtype
+class BooleanDtype(BaseMaskedDtype):
+    """
+    Extension dtype for boolean data.
+
+    .. warning::
+
+       BooleanDtype is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.BooleanDtype()
+    BooleanDtype
+    """
+
+    name: ClassVar[str] = "boolean"
+
+    # https://github.com/python/mypy/issues/4125
+    # error: Signature of "type" incompatible with supertype "BaseMaskedDtype"
+    @property
+    def type(self) -> type:  # type: ignore[override]
+        return np.bool_
+
+    @property
+    def kind(self) -> str:
+        return "b"
+
+    @property
+    def numpy_dtype(self) -> np.dtype:
+        return np.dtype("bool")
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[BooleanArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return BooleanArray
+
+    def __repr__(self) -> str:
+        return "BooleanDtype"
+
+    @property
+    def _is_boolean(self) -> bool:
+        return True
+
+    @property
+    def _is_numeric(self) -> bool:
+        return True
+
+    def __from_arrow__(
+        self, array: pyarrow.Array | pyarrow.ChunkedArray
+    ) -> BooleanArray:
+        """
+        Construct BooleanArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        if array.type != pyarrow.bool_() and not pyarrow.types.is_null(array.type):
+            raise TypeError(f"Expected array of boolean type, got {array.type} instead")
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+            length = len(array)
+        else:
+            # pyarrow.ChunkedArray
+            chunks = array.chunks
+            length = array.length()
+
+        if pyarrow.types.is_null(array.type):
+            mask = np.ones(length, dtype=bool)
+            # No need to init data, since all null
+            data = np.empty(length, dtype=bool)
+            return BooleanArray(data, mask)
+
+        results = []
+        for arr in chunks:
+            buflist = arr.buffers()
+            data = pyarrow.BooleanArray.from_buffers(
+                arr.type, len(arr), [None, buflist[1]], offset=arr.offset
+            ).to_numpy(zero_copy_only=False)
+            if arr.null_count != 0:
+                mask = pyarrow.BooleanArray.from_buffers(
+                    arr.type, len(arr), [None, buflist[0]], offset=arr.offset
+                ).to_numpy(zero_copy_only=False)
+                mask = ~mask
+            else:
+                mask = np.zeros(len(arr), dtype=bool)
+
+            bool_arr = BooleanArray(data, mask)
+            results.append(bool_arr)
+
+        if not results:
+            return BooleanArray(
+                np.array([], dtype=np.bool_), np.array([], dtype=np.bool_)
+            )
+        else:
+            return BooleanArray._concat_same_type(results)
+
+
+def coerce_to_array(
+    values, mask=None, copy: bool = False
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Coerce the input values array to numpy arrays with a mask.
+
+    Parameters
+    ----------
+    values : 1D list-like
+    mask : bool 1D array, optional
+    copy : bool, default False
+        if True, copy the input
+
+    Returns
+    -------
+    tuple of (values, mask)
+    """
+    if isinstance(values, BooleanArray):
+        if mask is not None:
+            raise ValueError("cannot pass mask for BooleanArray input")
+        values, mask = values._data, values._mask
+        if copy:
+            values = values.copy()
+            mask = mask.copy()
+        return values, mask
+
+    mask_values = None
+    if isinstance(values, np.ndarray) and values.dtype == np.bool_:
+        if copy:
+            values = values.copy()
+    elif isinstance(values, np.ndarray) and values.dtype.kind in "iufcb":
+        mask_values = isna(values)
+
+        values_bool = np.zeros(len(values), dtype=bool)
+        values_bool[~mask_values] = values[~mask_values].astype(bool)
+
+        if not np.all(
+            values_bool[~mask_values].astype(values.dtype) == values[~mask_values]
+        ):
+            raise TypeError("Need to pass bool-like values")
+
+        values = values_bool
+    else:
+        values_object = np.asarray(values, dtype=object)
+
+        inferred_dtype = lib.infer_dtype(values_object, skipna=True)
+        integer_like = ("floating", "integer", "mixed-integer-float")
+        if inferred_dtype not in ("boolean", "empty") + integer_like:
+            raise TypeError("Need to pass bool-like values")
+
+        # mypy does not narrow the type of mask_values to npt.NDArray[np.bool_]
+        # within this branch, it assumes it can also be None
+        mask_values = cast("npt.NDArray[np.bool_]", isna(values_object))
+        values = np.zeros(len(values), dtype=bool)
+        values[~mask_values] = values_object[~mask_values].astype(bool)
+
+        # if the values were integer-like, validate it were actually 0/1's
+        if (inferred_dtype in integer_like) and not (
+            np.all(
+                values[~mask_values].astype(float)
+                == values_object[~mask_values].astype(float)
+            )
+        ):
+            raise TypeError("Need to pass bool-like values")
+
+    if mask is None and mask_values is None:
+        mask = np.zeros(values.shape, dtype=bool)
+    elif mask is None:
+        mask = mask_values
+    else:
+        if isinstance(mask, np.ndarray) and mask.dtype == np.bool_:
+            if mask_values is not None:
+                mask = mask | mask_values
+            else:
+                if copy:
+                    mask = mask.copy()
+        else:
+            mask = np.array(mask, dtype=bool)
+            if mask_values is not None:
+                mask = mask | mask_values
+
+    if values.shape != mask.shape:
+        raise ValueError("values.shape and mask.shape must match")
+
+    return values, mask
+
+
+class BooleanArray(BaseMaskedArray):
+    """
+    Array of boolean (True/False) data with missing values.
+
+    This is a pandas Extension array for boolean data, under the hood
+    represented by 2 numpy arrays: a boolean array with the data and
+    a boolean array with the mask (True indicating missing).
+
+    BooleanArray implements Kleene logic (sometimes called three-value
+    logic) for logical operations. See :ref:`boolean.kleene` for more.
+
+    To construct an BooleanArray from generic array-like input, use
+    :func:`pandas.array` specifying ``dtype="boolean"`` (see examples
+    below).
+
+    .. warning::
+
+       BooleanArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : numpy.ndarray
+        A 1-d boolean-dtype array with the data.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values (True
+        indicates missing).
+    copy : bool, default False
+        Whether to copy the `values` and `mask` arrays.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    BooleanArray
+
+    Examples
+    --------
+    Create an BooleanArray with :func:`pandas.array`:
+
+    >>> pd.array([True, False, None], dtype="boolean")
+    <BooleanArray>
+    [True, False, <NA>]
+    Length: 3, dtype: boolean
+    """
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = False
+    # Fill values used for any/all
+    # Incompatible types in assignment (expression has type "bool", base class
+    # "BaseMaskedArray" defined the type as "<typing special form>")
+    _truthy_value = True  # type: ignore[assignment]
+    _falsey_value = False  # type: ignore[assignment]
+    _TRUE_VALUES = {"True", "TRUE", "true", "1", "1.0"}
+    _FALSE_VALUES = {"False", "FALSE", "false", "0", "0.0"}
+
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, mask: npt.NDArray[np.bool_]) -> Self:
+        result = super()._simple_new(values, mask)
+        result._dtype = BooleanDtype()
+        return result
+
+    def __init__(
+        self, values: np.ndarray, mask: np.ndarray, copy: bool = False
+    ) -> None:
+        if not (isinstance(values, np.ndarray) and values.dtype == np.bool_):
+            raise TypeError(
+                "values should be boolean numpy array. Use "
+                "the 'pd.array' function instead"
+            )
+        self._dtype = BooleanDtype()
+        super().__init__(values, mask, copy=copy)
+
+    @property
+    def dtype(self) -> BooleanDtype:
+        return self._dtype
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls,
+        strings: list[str],
+        *,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        true_values: list[str] | None = None,
+        false_values: list[str] | None = None,
+    ) -> BooleanArray:
+        true_values_union = cls._TRUE_VALUES.union(true_values or [])
+        false_values_union = cls._FALSE_VALUES.union(false_values or [])
+
+        def map_string(s) -> bool:
+            if s in true_values_union:
+                return True
+            elif s in false_values_union:
+                return False
+            else:
+                raise ValueError(f"{s} cannot be cast to bool")
+
+        scalars = np.array(strings, dtype=object)
+        mask = isna(scalars)
+        scalars[~mask] = list(map(map_string, scalars[~mask]))
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
+
+    _HANDLED_TYPES = (np.ndarray, numbers.Number, bool, np.bool_)
+
+    @classmethod
+    def _coerce_to_array(
+        cls, value, *, dtype: DtypeObj, copy: bool = False
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if dtype:
+            assert dtype == "boolean"
+        return coerce_to_array(value, copy=copy)
+
+    def _logical_method(self, other, op):
+        assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}
+        other_is_scalar = lib.is_scalar(other)
+        mask = None
+
+        if isinstance(other, BooleanArray):
+            other, mask = other._data, other._mask
+        elif is_list_like(other):
+            other = np.asarray(other, dtype="bool")
+            if other.ndim > 1:
+                raise NotImplementedError("can only perform ops with 1-d structures")
+            other, mask = coerce_to_array(other, copy=False)
+        elif isinstance(other, np.bool_):
+            other = other.item()
+
+        if other_is_scalar and other is not libmissing.NA and not lib.is_bool(other):
+            raise TypeError(
+                "'other' should be pandas.NA or a bool. "
+                f"Got {type(other).__name__} instead."
+            )
+
+        if not other_is_scalar and len(self) != len(other):
+            raise ValueError("Lengths must match")
+
+        if op.__name__ in {"or_", "ror_"}:
+            result, mask = ops.kleene_or(self._data, other, self._mask, mask)
+        elif op.__name__ in {"and_", "rand_"}:
+            result, mask = ops.kleene_and(self._data, other, self._mask, mask)
+        else:
+            # i.e. xor, rxor
+            result, mask = ops.kleene_xor(self._data, other, self._mask, mask)
+
+        # i.e. BooleanArray
+        return self._maybe_mask_result(result, mask)
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> BaseMaskedArray:
+        data = self._data
+        mask = self._mask
+        if name in ("cummin", "cummax"):
+            op = getattr(masked_accumulations, name)
+            data, mask = op(data, mask, skipna=skipna, **kwargs)
+            return self._simple_new(data, mask)
+        else:
+            from pandas.core.arrays import IntegerArray
+
+            return IntegerArray(data.astype(int), mask)._accumulate(
+                name, skipna=skipna, **kwargs
+            )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/categorical.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bee4740b39510bf049aadc348d9e911915fe239
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/categorical.py
@@ -0,0 +1,3111 @@
+from __future__ import annotations
+
+from csv import QUOTE_NONNUMERIC
+from functools import partial
+import operator
+from shutil import get_terminal_size
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import (
+    NaT,
+    algos as libalgos,
+    lib,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas.compat.numpy import function as nv
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.cast import (
+    coerce_indexer_dtype,
+    find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
+    is_any_real_numeric_dtype,
+    is_bool_dtype,
+    is_dict_like,
+    is_hashable,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    CategoricalDtypeType,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core import (
+    algorithms,
+    arraylike,
+    ops,
+)
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.algorithms import (
+    factorize,
+    take_nd,
+)
+from pandas.core.arrays._mixins import (
+    NDArrayBackedExtensionArray,
+    ravel_compat,
+)
+from pandas.core.base import (
+    ExtensionArray,
+    NoNewAttributesMixin,
+    PandasObject,
+)
+import pandas.core.common as com
+from pandas.core.construction import (
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.ops.common import unpack_zerodim_and_defer
+from pandas.core.sorting import nargsort
+from pandas.core.strings.object_array import ObjectStringArrayMixin
+
+from pandas.io.formats import console
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        AstypeArg,
+        AxisInt,
+        Dtype,
+        DtypeObj,
+        NpDtype,
+        Ordered,
+        Self,
+        Shape,
+        SortKind,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+def _cat_compare_op(op):
+    opname = f"__{op.__name__}__"
+    fill_value = op is operator.ne
+
+    @unpack_zerodim_and_defer(opname)
+    def func(self, other):
+        hashable = is_hashable(other)
+        if is_list_like(other) and len(other) != len(self) and not hashable:
+            # in hashable case we may have a tuple that is itself a category
+            raise ValueError("Lengths must match.")
+
+        if not self.ordered:
+            if opname in ["__lt__", "__gt__", "__le__", "__ge__"]:
+                raise TypeError(
+                    "Unordered Categoricals can only compare equality or not"
+                )
+        if isinstance(other, Categorical):
+            # Two Categoricals can only be compared if the categories are
+            # the same (maybe up to ordering, depending on ordered)
+
+            msg = "Categoricals can only be compared if 'categories' are the same."
+            if not self._categories_match_up_to_permutation(other):
+                raise TypeError(msg)
+
+            if not self.ordered and not self.categories.equals(other.categories):
+                # both unordered and different order
+                other_codes = recode_for_categories(
+                    other.codes, other.categories, self.categories, copy=False
+                )
+            else:
+                other_codes = other._codes
+
+            ret = op(self._codes, other_codes)
+            mask = (self._codes == -1) | (other_codes == -1)
+            if mask.any():
+                ret[mask] = fill_value
+            return ret
+
+        if hashable:
+            if other in self.categories:
+                i = self._unbox_scalar(other)
+                ret = op(self._codes, i)
+
+                if opname not in {"__eq__", "__ge__", "__gt__"}:
+                    # GH#29820 performance trick; get_loc will always give i>=0,
+                    #  so in the cases (__ne__, __le__, __lt__) the setting
+                    #  here is a no-op, so can be skipped.
+                    mask = self._codes == -1
+                    ret[mask] = fill_value
+                return ret
+            else:
+                return ops.invalid_comparison(self, other, op)
+        else:
+            # allow categorical vs object dtype array comparisons for equality
+            # these are only positional comparisons
+            if opname not in ["__eq__", "__ne__"]:
+                raise TypeError(
+                    f"Cannot compare a Categorical for op {opname} with "
+                    f"type {type(other)}.\nIf you want to compare values, "
+                    "use 'np.asarray(cat) <op> other'."
+                )
+
+            if isinstance(other, ExtensionArray) and needs_i8_conversion(other.dtype):
+                # We would return NotImplemented here, but that messes up
+                #  ExtensionIndex's wrapped methods
+                return op(other, self)
+            return getattr(np.array(self), opname)(np.array(other))
+
+    func.__name__ = opname
+
+    return func
+
+
+def contains(cat, key, container) -> bool:
+    """
+    Helper for membership check for ``key`` in ``cat``.
+
+    This is a helper method for :method:`__contains__`
+    and :class:`CategoricalIndex.__contains__`.
+
+    Returns True if ``key`` is in ``cat.categories`` and the
+    location of ``key`` in ``categories`` is in ``container``.
+
+    Parameters
+    ----------
+    cat : :class:`Categorical`or :class:`categoricalIndex`
+    key : a hashable object
+        The key to check membership for.
+    container : Container (e.g. list-like or mapping)
+        The container to check for membership in.
+
+    Returns
+    -------
+    is_in : bool
+        True if ``key`` is in ``self.categories`` and location of
+        ``key`` in ``categories`` is in ``container``, else False.
+
+    Notes
+    -----
+    This method does not check for NaN values. Do that separately
+    before calling this method.
+    """
+    hash(key)
+
+    # get location of key in categories.
+    # If a KeyError, the key isn't in categories, so logically
+    #  can't be in container either.
+    try:
+        loc = cat.categories.get_loc(key)
+    except (KeyError, TypeError):
+        return False
+
+    # loc is the location of key in categories, but also the *value*
+    # for key in container. So, `key` may be in categories,
+    # but still not in `container`. Example ('b' in categories,
+    # but not in values):
+    # 'b' in Categorical(['a'], categories=['a', 'b'])  # False
+    if is_scalar(loc):
+        return loc in container
+    else:
+        # if categories is an IntervalIndex, loc is an array.
+        return any(loc_ in container for loc_ in loc)
+
+
+class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMixin):
+    """
+    Represent a categorical variable in classic R / S-plus fashion.
+
+    `Categoricals` can only take on a limited, and usually fixed, number
+    of possible values (`categories`). In contrast to statistical categorical
+    variables, a `Categorical` might have an order, but numerical operations
+    (additions, divisions, ...) are not possible.
+
+    All values of the `Categorical` are either in `categories` or `np.nan`.
+    Assigning values outside of `categories` will raise a `ValueError`. Order
+    is defined by the order of the `categories`, not lexical order of the
+    values.
+
+    Parameters
+    ----------
+    values : list-like
+        The values of the categorical. If categories are given, values not in
+        categories will be replaced with NaN.
+    categories : Index-like (unique), optional
+        The unique categories for this categorical. If not given, the
+        categories are assumed to be the unique values of `values` (sorted, if
+        possible, otherwise in the order in which they appear).
+    ordered : bool, default False
+        Whether or not this categorical is treated as a ordered categorical.
+        If True, the resulting categorical will be ordered.
+        An ordered categorical respects, when sorted, the order of its
+        `categories` attribute (which in turn is the `categories` argument, if
+        provided).
+    dtype : CategoricalDtype
+        An instance of ``CategoricalDtype`` to use for this categorical.
+
+    Attributes
+    ----------
+    categories : Index
+        The categories of this categorical.
+    codes : ndarray
+        The codes (integer positions, which point to the categories) of this
+        categorical, read only.
+    ordered : bool
+        Whether or not this Categorical is ordered.
+    dtype : CategoricalDtype
+        The instance of ``CategoricalDtype`` storing the ``categories``
+        and ``ordered``.
+
+    Methods
+    -------
+    from_codes
+    __array__
+
+    Raises
+    ------
+    ValueError
+        If the categories do not validate.
+    TypeError
+        If an explicit ``ordered=True`` is given but no `categories` and the
+        `values` are not sortable.
+
+    See Also
+    --------
+    CategoricalDtype : Type for categorical data.
+    CategoricalIndex : An Index with an underlying ``Categorical``.
+
+    Notes
+    -----
+    See the `user guide
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`__
+    for more.
+
+    Examples
+    --------
+    >>> pd.Categorical([1, 2, 3, 1, 2, 3])
+    [1, 2, 3, 1, 2, 3]
+    Categories (3, int64): [1, 2, 3]
+
+    >>> pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
+    ['a', 'b', 'c', 'a', 'b', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    Missing values are not included as a category.
+
+    >>> c = pd.Categorical([1, 2, 3, 1, 2, 3, np.nan])
+    >>> c
+    [1, 2, 3, 1, 2, 3, NaN]
+    Categories (3, int64): [1, 2, 3]
+
+    However, their presence is indicated in the `codes` attribute
+    by code `-1`.
+
+    >>> c.codes
+    array([ 0,  1,  2,  0,  1,  2, -1], dtype=int8)
+
+    Ordered `Categoricals` can be sorted according to the custom order
+    of the categories and can have a min and max value.
+
+    >>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,
+    ...                    categories=['c', 'b', 'a'])
+    >>> c
+    ['a', 'b', 'c', 'a', 'b', 'c']
+    Categories (3, object): ['c' < 'b' < 'a']
+    >>> c.min()
+    'c'
+    """
+
+    # For comparisons, so that numpy uses our implementation if the compare
+    # ops, which raise
+    __array_priority__ = 1000
+    # tolist is not actually deprecated, just suppressed in the __dir__
+    _hidden_attrs = PandasObject._hidden_attrs | frozenset(["tolist"])
+    _typ = "categorical"
+
+    _dtype: CategoricalDtype
+
+    @classmethod
+    # error: Argument 2 of "_simple_new" is incompatible with supertype
+    # "NDArrayBacked"; supertype defines the argument type as
+    # "Union[dtype[Any], ExtensionDtype]"
+    def _simple_new(  # type: ignore[override]
+        cls, codes: np.ndarray, dtype: CategoricalDtype
+    ) -> Self:
+        # NB: This is not _quite_ as simple as the "usual" _simple_new
+        codes = coerce_indexer_dtype(codes, dtype.categories)
+        dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
+        return super()._simple_new(codes, dtype)
+
+    def __init__(
+        self,
+        values,
+        categories=None,
+        ordered=None,
+        dtype: Dtype | None = None,
+        fastpath: bool | lib.NoDefault = lib.no_default,
+        copy: bool = True,
+    ) -> None:
+        if fastpath is not lib.no_default:
+            # GH#20110
+            warnings.warn(
+                "The 'fastpath' keyword in Categorical is deprecated and will "
+                "be removed in a future version. Use Categorical.from_codes instead",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            fastpath = False
+
+        dtype = CategoricalDtype._from_values_or_dtype(
+            values, categories, ordered, dtype
+        )
+        # At this point, dtype is always a CategoricalDtype, but
+        # we may have dtype.categories be None, and we need to
+        # infer categories in a factorization step further below
+
+        if fastpath:
+            codes = coerce_indexer_dtype(values, dtype.categories)
+            dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
+            super().__init__(codes, dtype)
+            return
+
+        if not is_list_like(values):
+            # GH#38433
+            raise TypeError("Categorical input must be list-like")
+
+        # null_mask indicates missing values we want to exclude from inference.
+        # This means: only missing values in list-likes (not arrays/ndframes).
+        null_mask = np.array(False)
+
+        # sanitize input
+        vdtype = getattr(values, "dtype", None)
+        if isinstance(vdtype, CategoricalDtype):
+            if dtype.categories is None:
+                dtype = CategoricalDtype(values.categories, dtype.ordered)
+        elif not isinstance(values, (ABCIndex, ABCSeries, ExtensionArray)):
+            values = com.convert_to_list_like(values)
+            if isinstance(values, list) and len(values) == 0:
+                # By convention, empty lists result in object dtype:
+                values = np.array([], dtype=object)
+            elif isinstance(values, np.ndarray):
+                if values.ndim > 1:
+                    # preempt sanitize_array from raising ValueError
+                    raise NotImplementedError(
+                        "> 1 ndim Categorical are not supported at this time"
+                    )
+                values = sanitize_array(values, None)
+            else:
+                # i.e. must be a list
+                arr = sanitize_array(values, None)
+                null_mask = isna(arr)
+                if null_mask.any():
+                    # We remove null values here, then below will re-insert
+                    #  them, grep "full_codes"
+                    arr_list = [values[idx] for idx in np.where(~null_mask)[0]]
+
+                    # GH#44900 Do not cast to float if we have only missing values
+                    if arr_list or arr.dtype == "object":
+                        sanitize_dtype = None
+                    else:
+                        sanitize_dtype = arr.dtype
+
+                    arr = sanitize_array(arr_list, None, dtype=sanitize_dtype)
+                values = arr
+
+        if dtype.categories is None:
+            if isinstance(values.dtype, ArrowDtype) and issubclass(
+                values.dtype.type, CategoricalDtypeType
+            ):
+                arr = values._pa_array.combine_chunks()
+                categories = arr.dictionary.to_pandas(types_mapper=ArrowDtype)
+                codes = arr.indices.to_numpy()
+                dtype = CategoricalDtype(categories, values.dtype.pyarrow_dtype.ordered)
+            else:
+                if not isinstance(values, ABCIndex):
+                    # in particular RangeIndex xref test_index_equal_range_categories
+                    values = sanitize_array(values, None)
+                try:
+                    codes, categories = factorize(values, sort=True)
+                except TypeError as err:
+                    codes, categories = factorize(values, sort=False)
+                    if dtype.ordered:
+                        # raise, as we don't have a sortable data structure and so
+                        # the user should give us one by specifying categories
+                        raise TypeError(
+                            "'values' is not ordered, please "
+                            "explicitly specify the categories order "
+                            "by passing in a categories argument."
+                        ) from err
+
+                # we're inferring from values
+                dtype = CategoricalDtype(categories, dtype.ordered)
+
+        elif isinstance(values.dtype, CategoricalDtype):
+            old_codes = extract_array(values)._codes
+            codes = recode_for_categories(
+                old_codes, values.dtype.categories, dtype.categories, copy=copy
+            )
+
+        else:
+            codes = _get_codes_for_values(values, dtype.categories)
+
+        if null_mask.any():
+            # Reinsert -1 placeholders for previously removed missing values
+            full_codes = -np.ones(null_mask.shape, dtype=codes.dtype)
+            full_codes[~null_mask] = codes
+            codes = full_codes
+
+        dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
+        arr = coerce_indexer_dtype(codes, dtype.categories)
+        super().__init__(arr, dtype)
+
+    @property
+    def dtype(self) -> CategoricalDtype:
+        """
+        The :class:`~pandas.api.types.CategoricalDtype` for this instance.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat
+        ['a', 'b']
+        Categories (2, object): ['a' < 'b']
+        >>> cat.dtype
+        CategoricalDtype(categories=['a', 'b'], ordered=True, categories_dtype=object)
+        """
+        return self._dtype
+
+    @property
+    def _internal_fill_value(self) -> int:
+        # using the specific numpy integer instead of python int to get
+        #  the correct dtype back from _quantile in the all-NA case
+        dtype = self._ndarray.dtype
+        return dtype.type(-1)
+
+    @classmethod
+    def _from_sequence(
+        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        return cls(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        if dtype is None:
+            # The _from_scalars strictness doesn't make much sense in this case.
+            raise NotImplementedError
+
+        res = cls._from_sequence(scalars, dtype=dtype)
+
+        # if there are any non-category elements in scalars, these will be
+        #  converted to NAs in res.
+        mask = isna(scalars)
+        if not (mask == res.isna()).all():
+            # Some non-category element in scalars got converted to NA in res.
+            raise ValueError
+        return res
+
+    @overload
+    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
+        ...
+
+    @overload
+    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
+        ...
+
+    @overload
+    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
+        ...
+
+    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
+        """
+        Coerce this type to another dtype
+
+        Parameters
+        ----------
+        dtype : numpy dtype or pandas type
+        copy : bool, default True
+            By default, astype always returns a newly allocated object.
+            If copy is set to False and dtype is categorical, the original
+            object is returned.
+        """
+        dtype = pandas_dtype(dtype)
+        if self.dtype is dtype:
+            result = self.copy() if copy else self
+
+        elif isinstance(dtype, CategoricalDtype):
+            # GH 10696/18593/18630
+            dtype = self.dtype.update_dtype(dtype)
+            self = self.copy() if copy else self
+            result = self._set_dtype(dtype)
+
+        elif isinstance(dtype, ExtensionDtype):
+            return super().astype(dtype, copy=copy)
+
+        elif dtype.kind in "iu" and self.isna().any():
+            raise ValueError("Cannot convert float NaN to integer")
+
+        elif len(self.codes) == 0 or len(self.categories) == 0:
+            # For NumPy 1.x compatibility we cannot use copy=None.  And
+            # `copy=False` has the meaning of `copy=None` here:
+            if not copy:
+                result = np.asarray(self, dtype=dtype)
+            else:
+                result = np.array(self, dtype=dtype)
+
+        else:
+            # GH8628 (PERF): astype category codes instead of astyping array
+            new_cats = self.categories._values
+
+            try:
+                new_cats = new_cats.astype(dtype=dtype, copy=copy)
+                fill_value = self.categories._na_value
+                if not is_valid_na_for_dtype(fill_value, dtype):
+                    fill_value = lib.item_from_zerodim(
+                        np.array(self.categories._na_value).astype(dtype)
+                    )
+            except (
+                TypeError,  # downstream error msg for CategoricalIndex is misleading
+                ValueError,
+            ):
+                msg = f"Cannot cast {self.categories.dtype} dtype to {dtype}"
+                raise ValueError(msg)
+
+            result = take_nd(
+                new_cats, ensure_platform_int(self._codes), fill_value=fill_value
+            )
+
+        return result
+
+    def to_list(self):
+        """
+        Alias for tolist.
+        """
+        # GH#51254
+        warnings.warn(
+            "Categorical.to_list is deprecated and will be removed in a future "
+            "version. Use obj.tolist() instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.tolist()
+
+    @classmethod
+    def _from_inferred_categories(
+        cls, inferred_categories, inferred_codes, dtype, true_values=None
+    ) -> Self:
+        """
+        Construct a Categorical from inferred values.
+
+        For inferred categories (`dtype` is None) the categories are sorted.
+        For explicit `dtype`, the `inferred_categories` are cast to the
+        appropriate type.
+
+        Parameters
+        ----------
+        inferred_categories : Index
+        inferred_codes : Index
+        dtype : CategoricalDtype or 'category'
+        true_values : list, optional
+            If none are provided, the default ones are
+            "True", "TRUE", and "true."
+
+        Returns
+        -------
+        Categorical
+        """
+        from pandas import (
+            Index,
+            to_datetime,
+            to_numeric,
+            to_timedelta,
+        )
+
+        cats = Index(inferred_categories)
+        known_categories = (
+            isinstance(dtype, CategoricalDtype) and dtype.categories is not None
+        )
+
+        if known_categories:
+            # Convert to a specialized type with `dtype` if specified.
+            if is_any_real_numeric_dtype(dtype.categories.dtype):
+                cats = to_numeric(inferred_categories, errors="coerce")
+            elif lib.is_np_dtype(dtype.categories.dtype, "M"):
+                cats = to_datetime(inferred_categories, errors="coerce")
+            elif lib.is_np_dtype(dtype.categories.dtype, "m"):
+                cats = to_timedelta(inferred_categories, errors="coerce")
+            elif is_bool_dtype(dtype.categories.dtype):
+                if true_values is None:
+                    true_values = ["True", "TRUE", "true"]
+
+                # error: Incompatible types in assignment (expression has type
+                # "ndarray", variable has type "Index")
+                cats = cats.isin(true_values)  # type: ignore[assignment]
+
+        if known_categories:
+            # Recode from observation order to dtype.categories order.
+            categories = dtype.categories
+            codes = recode_for_categories(inferred_codes, cats, categories)
+        elif not cats.is_monotonic_increasing:
+            # Sort categories and recode for unknown categories.
+            unsorted = cats.copy()
+            categories = cats.sort_values()
+
+            codes = recode_for_categories(inferred_codes, unsorted, categories)
+            dtype = CategoricalDtype(categories, ordered=False)
+        else:
+            dtype = CategoricalDtype(cats, ordered=False)
+            codes = inferred_codes
+
+        return cls._simple_new(codes, dtype=dtype)
+
+    @classmethod
+    def from_codes(
+        cls,
+        codes,
+        categories=None,
+        ordered=None,
+        dtype: Dtype | None = None,
+        validate: bool = True,
+    ) -> Self:
+        """
+        Make a Categorical type from codes and categories or dtype.
+
+        This constructor is useful if you already have codes and
+        categories/dtype and so do not need the (computation intensive)
+        factorization step, which is usually done on the constructor.
+
+        If your data does not follow this convention, please use the normal
+        constructor.
+
+        Parameters
+        ----------
+        codes : array-like of int
+            An integer array, where each integer points to a category in
+            categories or dtype.categories, or else is -1 for NaN.
+        categories : index-like, optional
+            The categories for the categorical. Items need to be unique.
+            If the categories are not given here, then they must be provided
+            in `dtype`.
+        ordered : bool, optional
+            Whether or not this categorical is treated as an ordered
+            categorical. If not given here or in `dtype`, the resulting
+            categorical will be unordered.
+        dtype : CategoricalDtype or "category", optional
+            If :class:`CategoricalDtype`, cannot be used together with
+            `categories` or `ordered`.
+        validate : bool, default True
+            If True, validate that the codes are valid for the dtype.
+            If False, don't validate that the codes are valid. Be careful about skipping
+            validation, as invalid codes can lead to severe problems, such as segfaults.
+
+            .. versionadded:: 2.1.0
+
+        Returns
+        -------
+        Categorical
+
+        Examples
+        --------
+        >>> dtype = pd.CategoricalDtype(['a', 'b'], ordered=True)
+        >>> pd.Categorical.from_codes(codes=[0, 1, 0, 1], dtype=dtype)
+        ['a', 'b', 'a', 'b']
+        Categories (2, object): ['a' < 'b']
+        """
+        dtype = CategoricalDtype._from_values_or_dtype(
+            categories=categories, ordered=ordered, dtype=dtype
+        )
+        if dtype.categories is None:
+            msg = (
+                "The categories must be provided in 'categories' or "
+                "'dtype'. Both were None."
+            )
+            raise ValueError(msg)
+
+        if validate:
+            # beware: non-valid codes may segfault
+            codes = cls._validate_codes_for_dtype(codes, dtype=dtype)
+
+        return cls._simple_new(codes, dtype=dtype)
+
+    # ------------------------------------------------------------------
+    # Categories/Codes/Ordered
+
+    @property
+    def categories(self) -> Index:
+        """
+        The categories of this categorical.
+
+        Setting assigns new values to each category (effectively a rename of
+        each individual category).
+
+        The assigned value has to be a list-like object. All items must be
+        unique and the number of items in the new categories must be the same
+        as the number of items in the old categories.
+
+        Raises
+        ------
+        ValueError
+            If the new categories do not validate as categories or if the
+            number of new categories is unequal the number of old categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser.cat.categories
+        Index(['a', 'b', 'c'], dtype='object')
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], categories=['b', 'c', 'd'])
+        >>> ser = pd.Series(raw_cat)
+        >>> ser.cat.categories
+        Index(['b', 'c', 'd'], dtype='object')
+
+        For :class:`pandas.Categorical`:
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat.categories
+        Index(['a', 'b'], dtype='object')
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'c', 'b', 'a', 'c', 'b'])
+        >>> ci.categories
+        Index(['a', 'b', 'c'], dtype='object')
+
+        >>> ci = pd.CategoricalIndex(['a', 'c'], categories=['c', 'b', 'a'])
+        >>> ci.categories
+        Index(['c', 'b', 'a'], dtype='object')
+        """
+        return self.dtype.categories
+
+    @property
+    def ordered(self) -> Ordered:
+        """
+        Whether the categories have an ordered relationship.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser.cat.ordered
+        False
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], ordered=True)
+        >>> ser = pd.Series(raw_cat)
+        >>> ser.cat.ordered
+        True
+
+        For :class:`pandas.Categorical`:
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat.ordered
+        True
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=False)
+        >>> cat.ordered
+        False
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b'], ordered=True)
+        >>> ci.ordered
+        True
+
+        >>> ci = pd.CategoricalIndex(['a', 'b'], ordered=False)
+        >>> ci.ordered
+        False
+        """
+        return self.dtype.ordered
+
+    @property
+    def codes(self) -> np.ndarray:
+        """
+        The category codes of this categorical index.
+
+        Codes are an array of integers which are the positions of the actual
+        values in the categories array.
+
+        There is no setter, use the other categorical methods and the normal item
+        setter to change values in the categorical.
+
+        Returns
+        -------
+        ndarray[int]
+            A non-writable view of the ``codes`` array.
+
+        Examples
+        --------
+        For :class:`pandas.Categorical`:
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+        >>> cat.codes
+        array([0, 1], dtype=int8)
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'])
+        >>> ci.codes
+        array([0, 1, 2, 0, 1, 2], dtype=int8)
+
+        >>> ci = pd.CategoricalIndex(['a', 'c'], categories=['c', 'b', 'a'])
+        >>> ci.codes
+        array([2, 0], dtype=int8)
+        """
+        v = self._codes.view()
+        v.flags.writeable = False
+        return v
+
+    def _set_categories(self, categories, fastpath: bool = False) -> None:
+        """
+        Sets new categories inplace
+
+        Parameters
+        ----------
+        fastpath : bool, default False
+           Don't perform validation of the categories for uniqueness or nulls
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'b'])
+        >>> c
+        ['a', 'b']
+        Categories (2, object): ['a', 'b']
+
+        >>> c._set_categories(pd.Index(['a', 'c']))
+        >>> c
+        ['a', 'c']
+        Categories (2, object): ['a', 'c']
+        """
+        if fastpath:
+            new_dtype = CategoricalDtype._from_fastpath(categories, self.ordered)
+        else:
+            new_dtype = CategoricalDtype(categories, ordered=self.ordered)
+        if (
+            not fastpath
+            and self.dtype.categories is not None
+            and len(new_dtype.categories) != len(self.dtype.categories)
+        ):
+            raise ValueError(
+                "new categories need to have the same number of "
+                "items as the old categories!"
+            )
+
+        super().__init__(self._ndarray, new_dtype)
+
+    def _set_dtype(self, dtype: CategoricalDtype) -> Self:
+        """
+        Internal method for directly updating the CategoricalDtype
+
+        Parameters
+        ----------
+        dtype : CategoricalDtype
+
+        Notes
+        -----
+        We don't do any validation here. It's assumed that the dtype is
+        a (valid) instance of `CategoricalDtype`.
+        """
+        codes = recode_for_categories(self.codes, self.categories, dtype.categories)
+        return type(self)._simple_new(codes, dtype=dtype)
+
+    def set_ordered(self, value: bool) -> Self:
+        """
+        Set the ordered attribute to the boolean value.
+
+        Parameters
+        ----------
+        value : bool
+           Set whether this categorical is ordered (True) or not (False).
+        """
+        new_dtype = CategoricalDtype(self.categories, ordered=value)
+        cat = self.copy()
+        NDArrayBacked.__init__(cat, cat._ndarray, new_dtype)
+        return cat
+
+    def as_ordered(self) -> Self:
+        """
+        Set the Categorical to be ordered.
+
+        Returns
+        -------
+        Categorical
+            Ordered Categorical.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser.cat.ordered
+        False
+        >>> ser = ser.cat.as_ordered()
+        >>> ser.cat.ordered
+        True
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'])
+        >>> ci.ordered
+        False
+        >>> ci = ci.as_ordered()
+        >>> ci.ordered
+        True
+        """
+        return self.set_ordered(True)
+
+    def as_unordered(self) -> Self:
+        """
+        Set the Categorical to be unordered.
+
+        Returns
+        -------
+        Categorical
+            Unordered Categorical.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], ordered=True)
+        >>> ser = pd.Series(raw_cat)
+        >>> ser.cat.ordered
+        True
+        >>> ser = ser.cat.as_unordered()
+        >>> ser.cat.ordered
+        False
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'], ordered=True)
+        >>> ci.ordered
+        True
+        >>> ci = ci.as_unordered()
+        >>> ci.ordered
+        False
+        """
+        return self.set_ordered(False)
+
+    def set_categories(self, new_categories, ordered=None, rename: bool = False):
+        """
+        Set the categories to the specified new categories.
+
+        ``new_categories`` can include new categories (which will result in
+        unused categories) or remove old categories (which results in values
+        set to ``NaN``). If ``rename=True``, the categories will simply be renamed
+        (less or more items than in old categories will result in values set to
+        ``NaN`` or in unused categories respectively).
+
+        This method can be used to perform more than one action of adding,
+        removing, and reordering simultaneously and is therefore faster than
+        performing the individual steps via the more specialised methods.
+
+        On the other hand this methods does not do checks (e.g., whether the
+        old categories are included in the new categories on a reorder), which
+        can result in surprising changes, for example when using special string
+        dtypes, which does not considers a S1 string equal to a single char
+        python string.
+
+        Parameters
+        ----------
+        new_categories : Index-like
+           The categories in new order.
+        ordered : bool, default False
+           Whether or not the categorical is treated as a ordered categorical.
+           If not given, do not change the ordered information.
+        rename : bool, default False
+           Whether or not the new_categories should be considered as a rename
+           of the old categories or as reordered categories.
+
+        Returns
+        -------
+        Categorical with reordered categories.
+
+        Raises
+        ------
+        ValueError
+            If new_categories does not validate as categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> raw_cat = pd.Categorical(['a', 'b', 'c', 'A'],
+        ...                           categories=['a', 'b', 'c'], ordered=True)
+        >>> ser = pd.Series(raw_cat)
+        >>> ser
+        0   a
+        1   b
+        2   c
+        3   NaN
+        dtype: category
+        Categories (3, object): ['a' < 'b' < 'c']
+
+        >>> ser.cat.set_categories(['A', 'B', 'C'], rename=True)
+        0   A
+        1   B
+        2   C
+        3   NaN
+        dtype: category
+        Categories (3, object): ['A' < 'B' < 'C']
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'A'],
+        ...                          categories=['a', 'b', 'c'], ordered=True)
+        >>> ci
+        CategoricalIndex(['a', 'b', 'c', nan], categories=['a', 'b', 'c'],
+                         ordered=True, dtype='category')
+
+        >>> ci.set_categories(['A', 'b', 'c'])
+        CategoricalIndex([nan, 'b', 'c', nan], categories=['A', 'b', 'c'],
+                         ordered=True, dtype='category')
+        >>> ci.set_categories(['A', 'b', 'c'], rename=True)
+        CategoricalIndex(['A', 'b', 'c', nan], categories=['A', 'b', 'c'],
+                         ordered=True, dtype='category')
+        """
+
+        if ordered is None:
+            ordered = self.dtype.ordered
+        new_dtype = CategoricalDtype(new_categories, ordered=ordered)
+
+        cat = self.copy()
+        if rename:
+            if cat.dtype.categories is not None and len(new_dtype.categories) < len(
+                cat.dtype.categories
+            ):
+                # remove all _codes which are larger and set to -1/NaN
+                cat._codes[cat._codes >= len(new_dtype.categories)] = -1
+            codes = cat._codes
+        else:
+            codes = recode_for_categories(
+                cat.codes, cat.categories, new_dtype.categories
+            )
+        NDArrayBacked.__init__(cat, codes, new_dtype)
+        return cat
+
+    def rename_categories(self, new_categories) -> Self:
+        """
+        Rename categories.
+
+        Parameters
+        ----------
+        new_categories : list-like, dict-like or callable
+
+            New categories which will replace old categories.
+
+            * list-like: all items must be unique and the number of items in
+              the new categories must match the existing number of categories.
+
+            * dict-like: specifies a mapping from
+              old categories to new. Categories not contained in the mapping
+              are passed through and extra categories in the mapping are
+              ignored.
+
+            * callable : a callable that is called on all items in the old
+              categories and whose return values comprise the new categories.
+
+        Returns
+        -------
+        Categorical
+            Categorical with renamed categories.
+
+        Raises
+        ------
+        ValueError
+            If new categories are list-like and do not have the same number of
+            items than the current categories or do not validate as categories
+
+        See Also
+        --------
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'a', 'b'])
+        >>> c.rename_categories([0, 1])
+        [0, 0, 1]
+        Categories (2, int64): [0, 1]
+
+        For dict-like ``new_categories``, extra keys are ignored and
+        categories not in the dictionary are passed through
+
+        >>> c.rename_categories({'a': 'A', 'c': 'C'})
+        ['A', 'A', 'b']
+        Categories (2, object): ['A', 'b']
+
+        You may also provide a callable to create the new categories
+
+        >>> c.rename_categories(lambda x: x.upper())
+        ['A', 'A', 'B']
+        Categories (2, object): ['A', 'B']
+        """
+
+        if is_dict_like(new_categories):
+            new_categories = [
+                new_categories.get(item, item) for item in self.categories
+            ]
+        elif callable(new_categories):
+            new_categories = [new_categories(item) for item in self.categories]
+
+        cat = self.copy()
+        cat._set_categories(new_categories)
+        return cat
+
+    def reorder_categories(self, new_categories, ordered=None) -> Self:
+        """
+        Reorder categories as specified in new_categories.
+
+        ``new_categories`` need to include all old categories and no new category
+        items.
+
+        Parameters
+        ----------
+        new_categories : Index-like
+           The categories in new order.
+        ordered : bool, optional
+           Whether or not the categorical is treated as a ordered categorical.
+           If not given, do not change the ordered information.
+
+        Returns
+        -------
+        Categorical
+            Categorical with reordered categories.
+
+        Raises
+        ------
+        ValueError
+            If the new categories do not contain all old category items or any
+            new ones
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        For :class:`pandas.Series`:
+
+        >>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
+        >>> ser = ser.cat.reorder_categories(['c', 'b', 'a'], ordered=True)
+        >>> ser
+        0   a
+        1   b
+        2   c
+        3   a
+        dtype: category
+        Categories (3, object): ['c' < 'b' < 'a']
+
+        >>> ser.sort_values()
+        2   c
+        1   b
+        0   a
+        3   a
+        dtype: category
+        Categories (3, object): ['c' < 'b' < 'a']
+
+        For :class:`pandas.CategoricalIndex`:
+
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a'])
+        >>> ci
+        CategoricalIndex(['a', 'b', 'c', 'a'], categories=['a', 'b', 'c'],
+                         ordered=False, dtype='category')
+        >>> ci.reorder_categories(['c', 'b', 'a'], ordered=True)
+        CategoricalIndex(['a', 'b', 'c', 'a'], categories=['c', 'b', 'a'],
+                         ordered=True, dtype='category')
+        """
+        if (
+            len(self.categories) != len(new_categories)
+            or not self.categories.difference(new_categories).empty
+        ):
+            raise ValueError(
+                "items in new_categories are not the same as in old categories"
+            )
+        return self.set_categories(new_categories, ordered=ordered)
+
+    def add_categories(self, new_categories) -> Self:
+        """
+        Add new categories.
+
+        `new_categories` will be included at the last/highest place in the
+        categories and will be unused directly after this call.
+
+        Parameters
+        ----------
+        new_categories : category or list-like of category
+           The new categories to be included.
+
+        Returns
+        -------
+        Categorical
+            Categorical with new categories added.
+
+        Raises
+        ------
+        ValueError
+            If the new categories include old categories or do not validate as
+            categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        remove_categories : Remove the specified categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['c', 'b', 'c'])
+        >>> c
+        ['c', 'b', 'c']
+        Categories (2, object): ['b', 'c']
+
+        >>> c.add_categories(['d', 'a'])
+        ['c', 'b', 'c']
+        Categories (4, object): ['b', 'c', 'd', 'a']
+        """
+
+        if not is_list_like(new_categories):
+            new_categories = [new_categories]
+        already_included = set(new_categories) & set(self.dtype.categories)
+        if len(already_included) != 0:
+            raise ValueError(
+                f"new categories must not include old categories: {already_included}"
+            )
+
+        if hasattr(new_categories, "dtype"):
+            from pandas import Series
+
+            dtype = find_common_type(
+                [self.dtype.categories.dtype, new_categories.dtype]
+            )
+            new_categories = Series(
+                list(self.dtype.categories) + list(new_categories), dtype=dtype
+            )
+        else:
+            new_categories = list(self.dtype.categories) + list(new_categories)
+
+        new_dtype = CategoricalDtype(new_categories, self.ordered)
+        cat = self.copy()
+        codes = coerce_indexer_dtype(cat._ndarray, new_dtype.categories)
+        NDArrayBacked.__init__(cat, codes, new_dtype)
+        return cat
+
+    def remove_categories(self, removals) -> Self:
+        """
+        Remove the specified categories.
+
+        `removals` must be included in the old categories. Values which were in
+        the removed categories will be set to NaN
+
+        Parameters
+        ----------
+        removals : category or list of categories
+           The categories which should be removed.
+
+        Returns
+        -------
+        Categorical
+            Categorical with removed categories.
+
+        Raises
+        ------
+        ValueError
+            If the removals are not contained in the categories
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_unused_categories : Remove categories which are not used.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'c', 'b', 'c', 'd'])
+        >>> c
+        ['a', 'c', 'b', 'c', 'd']
+        Categories (4, object): ['a', 'b', 'c', 'd']
+
+        >>> c.remove_categories(['d', 'a'])
+        [NaN, 'c', 'b', 'c', NaN]
+        Categories (2, object): ['b', 'c']
+        """
+        from pandas import Index
+
+        if not is_list_like(removals):
+            removals = [removals]
+
+        removals = Index(removals).unique().dropna()
+        new_categories = (
+            self.dtype.categories.difference(removals, sort=False)
+            if self.dtype.ordered is True
+            else self.dtype.categories.difference(removals)
+        )
+        not_included = removals.difference(self.dtype.categories)
+
+        if len(not_included) != 0:
+            not_included = set(not_included)
+            raise ValueError(f"removals must all be in old categories: {not_included}")
+
+        return self.set_categories(new_categories, ordered=self.ordered, rename=False)
+
+    def remove_unused_categories(self) -> Self:
+        """
+        Remove categories which are not used.
+
+        Returns
+        -------
+        Categorical
+            Categorical with unused categories dropped.
+
+        See Also
+        --------
+        rename_categories : Rename categories.
+        reorder_categories : Reorder categories.
+        add_categories : Add new categories.
+        remove_categories : Remove the specified categories.
+        set_categories : Set the categories to the specified ones.
+
+        Examples
+        --------
+        >>> c = pd.Categorical(['a', 'c', 'b', 'c', 'd'])
+        >>> c
+        ['a', 'c', 'b', 'c', 'd']
+        Categories (4, object): ['a', 'b', 'c', 'd']
+
+        >>> c[2] = 'a'
+        >>> c[4] = 'c'
+        >>> c
+        ['a', 'c', 'a', 'c', 'c']
+        Categories (4, object): ['a', 'b', 'c', 'd']
+
+        >>> c.remove_unused_categories()
+        ['a', 'c', 'a', 'c', 'c']
+        Categories (2, object): ['a', 'c']
+        """
+        idx, inv = np.unique(self._codes, return_inverse=True)
+
+        if idx.size != 0 and idx[0] == -1:  # na sentinel
+            idx, inv = idx[1:], inv - 1
+
+        new_categories = self.dtype.categories.take(idx)
+        new_dtype = CategoricalDtype._from_fastpath(
+            new_categories, ordered=self.ordered
+        )
+        new_codes = coerce_indexer_dtype(inv, new_dtype.categories)
+
+        cat = self.copy()
+        NDArrayBacked.__init__(cat, new_codes, new_dtype)
+        return cat
+
+    # ------------------------------------------------------------------
+
+    def map(
+        self,
+        mapper,
+        na_action: Literal["ignore"] | None | lib.NoDefault = lib.no_default,
+    ):
+        """
+        Map categories using an input mapping or function.
+
+        Maps the categories to new categories. If the mapping correspondence is
+        one-to-one the result is a :class:`~pandas.Categorical` which has the
+        same order property as the original, otherwise a :class:`~pandas.Index`
+        is returned. NaN values are unaffected.
+
+        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
+        mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
+        will be returned.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default 'ignore'
+            If 'ignore', propagate NaN values, without passing them to the
+            mapping correspondence.
+
+            .. deprecated:: 2.1.0
+
+               The default value of 'ignore' has been deprecated and will be changed to
+               None in the future.
+
+        Returns
+        -------
+        pandas.Categorical or pandas.Index
+            Mapped categorical.
+
+        See Also
+        --------
+        CategoricalIndex.map : Apply a mapping correspondence on a
+            :class:`~pandas.CategoricalIndex`.
+        Index.map : Apply a mapping correspondence on an
+            :class:`~pandas.Index`.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+        Series.apply : Apply more complex functions on a
+            :class:`~pandas.Series`.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b', 'c'])
+        >>> cat
+        ['a', 'b', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> cat.map(lambda x: x.upper(), na_action=None)
+        ['A', 'B', 'C']
+        Categories (3, object): ['A', 'B', 'C']
+        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'third'}, na_action=None)
+        ['first', 'second', 'third']
+        Categories (3, object): ['first', 'second', 'third']
+
+        If the mapping is one-to-one the ordering of the categories is
+        preserved:
+
+        >>> cat = pd.Categorical(['a', 'b', 'c'], ordered=True)
+        >>> cat
+        ['a', 'b', 'c']
+        Categories (3, object): ['a' < 'b' < 'c']
+        >>> cat.map({'a': 3, 'b': 2, 'c': 1}, na_action=None)
+        [3, 2, 1]
+        Categories (3, int64): [3 < 2 < 1]
+
+        If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
+
+        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'first'}, na_action=None)
+        Index(['first', 'second', 'first'], dtype='object')
+
+        If a `dict` is used, all unmapped categories are mapped to `NaN` and
+        the result is an :class:`~pandas.Index`:
+
+        >>> cat.map({'a': 'first', 'b': 'second'}, na_action=None)
+        Index(['first', 'second', nan], dtype='object')
+        """
+        if na_action is lib.no_default:
+            warnings.warn(
+                "The default value of 'ignore' for the `na_action` parameter in "
+                "pandas.Categorical.map is deprecated and will be "
+                "changed to 'None' in a future version. Please set na_action to the "
+                "desired value to avoid seeing this warning",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            na_action = "ignore"
+
+        assert callable(mapper) or is_dict_like(mapper)
+
+        new_categories = self.categories.map(mapper)
+
+        has_nans = np.any(self._codes == -1)
+
+        na_val = np.nan
+        if na_action is None and has_nans:
+            na_val = mapper(np.nan) if callable(mapper) else mapper.get(np.nan, np.nan)
+
+        if new_categories.is_unique and not new_categories.hasnans and na_val is np.nan:
+            new_dtype = CategoricalDtype(new_categories, ordered=self.ordered)
+            return self.from_codes(self._codes.copy(), dtype=new_dtype, validate=False)
+
+        if has_nans:
+            new_categories = new_categories.insert(len(new_categories), na_val)
+
+        return np.take(new_categories, self._codes)
+
+    __eq__ = _cat_compare_op(operator.eq)
+    __ne__ = _cat_compare_op(operator.ne)
+    __lt__ = _cat_compare_op(operator.lt)
+    __gt__ = _cat_compare_op(operator.gt)
+    __le__ = _cat_compare_op(operator.le)
+    __ge__ = _cat_compare_op(operator.ge)
+
+    # -------------------------------------------------------------
+    # Validators; ideally these can be de-duplicated
+
+    def _validate_setitem_value(self, value):
+        if not is_hashable(value):
+            # wrap scalars and hashable-listlikes in list
+            return self._validate_listlike(value)
+        else:
+            return self._validate_scalar(value)
+
+    def _validate_scalar(self, fill_value):
+        """
+        Convert a user-facing fill_value to a representation to use with our
+        underlying ndarray, raising TypeError if this is not possible.
+
+        Parameters
+        ----------
+        fill_value : object
+
+        Returns
+        -------
+        fill_value : int
+
+        Raises
+        ------
+        TypeError
+        """
+
+        if is_valid_na_for_dtype(fill_value, self.categories.dtype):
+            fill_value = -1
+        elif fill_value in self.categories:
+            fill_value = self._unbox_scalar(fill_value)
+        else:
+            raise TypeError(
+                "Cannot setitem on a Categorical with a new "
+                f"category ({fill_value}), set the categories first"
+            ) from None
+        return fill_value
+
+    @classmethod
+    def _validate_codes_for_dtype(cls, codes, *, dtype: CategoricalDtype) -> np.ndarray:
+        if isinstance(codes, ExtensionArray) and is_integer_dtype(codes.dtype):
+            # Avoid the implicit conversion of Int to object
+            if isna(codes).any():
+                raise ValueError("codes cannot contain NA values")
+            codes = codes.to_numpy(dtype=np.int64)
+        else:
+            codes = np.asarray(codes)
+        if len(codes) and codes.dtype.kind not in "iu":
+            raise ValueError("codes need to be array-like integers")
+
+        if len(codes) and (codes.max() >= len(dtype.categories) or codes.min() < -1):
+            raise ValueError("codes need to be between -1 and len(categories)-1")
+        return codes
+
+    # -------------------------------------------------------------
+
+    @ravel_compat
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        The numpy array interface.
+
+        Users should not call this directly. Rather, it is invoked by
+        :func:`numpy.array` and :func:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : np.dtype or None
+            Specifies the the dtype for the array.
+
+        copy : bool or None, optional
+            See :func:`numpy.asarray`.
+
+        Returns
+        -------
+        numpy.array
+            A numpy array of either the specified dtype or,
+            if dtype==None (default), the same dtype as
+            categorical.categories.dtype.
+
+        Examples
+        --------
+
+        >>> cat = pd.Categorical(['a', 'b'], ordered=True)
+
+        The following calls ``cat.__array__``
+
+        >>> np.asarray(cat)
+        array(['a', 'b'], dtype=object)
+        """
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        ret = take_nd(self.categories._values, self._codes)
+        # When we're a Categorical[ExtensionArray], like Interval,
+        # we need to ensure __array__ gets all the way to an
+        # ndarray.
+
+        # `take_nd` should already make a copy, so don't force again.
+        return np.asarray(ret, dtype=dtype)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        # for binary ops, use our custom dunder methods
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_numpy_ufuncs_out
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            # e.g. TestCategoricalAnalytics::test_min_max_ordered
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        # for all other cases, raise for now (similarly as what happens in
+        # Series.__array_prepare__)
+        raise TypeError(
+            f"Object with dtype {self.dtype} cannot perform "
+            f"the numpy op {ufunc.__name__}"
+        )
+
+    def __setstate__(self, state) -> None:
+        """Necessary for making this object picklable"""
+        if not isinstance(state, dict):
+            return super().__setstate__(state)
+
+        if "_dtype" not in state:
+            state["_dtype"] = CategoricalDtype(state["_categories"], state["_ordered"])
+
+        if "_codes" in state and "_ndarray" not in state:
+            # backward compat, changed what is property vs attribute
+            state["_ndarray"] = state.pop("_codes")
+
+        super().__setstate__(state)
+
+    @property
+    def nbytes(self) -> int:
+        return self._codes.nbytes + self.dtype.categories.values.nbytes
+
+    def memory_usage(self, deep: bool = False) -> int:
+        """
+        Memory usage of my values
+
+        Parameters
+        ----------
+        deep : bool
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption
+
+        Returns
+        -------
+        bytes used
+
+        Notes
+        -----
+        Memory usage does not include memory consumed by elements that
+        are not components of the array if deep=False
+
+        See Also
+        --------
+        numpy.ndarray.nbytes
+        """
+        return self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)
+
+    def isna(self) -> npt.NDArray[np.bool_]:
+        """
+        Detect missing values
+
+        Missing values (-1 in .codes) are detected.
+
+        Returns
+        -------
+        np.ndarray[bool] of whether my values are null
+
+        See Also
+        --------
+        isna : Top-level isna.
+        isnull : Alias of isna.
+        Categorical.notna : Boolean inverse of Categorical.isna.
+
+        """
+        return self._codes == -1
+
+    isnull = isna
+
+    def notna(self) -> npt.NDArray[np.bool_]:
+        """
+        Inverse of isna
+
+        Both missing values (-1 in .codes) and NA as a category are detected as
+        null.
+
+        Returns
+        -------
+        np.ndarray[bool] of whether my values are not null
+
+        See Also
+        --------
+        notna : Top-level notna.
+        notnull : Alias of notna.
+        Categorical.isna : Boolean inverse of Categorical.notna.
+
+        """
+        return ~self.isna()
+
+    notnull = notna
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of each category.
+
+        Every category will have an entry, even those with a count of 0.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NaN.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        from pandas import (
+            CategoricalIndex,
+            Series,
+        )
+
+        code, cat = self._codes, self.categories
+        ncat, mask = (len(cat), code >= 0)
+        ix, clean = np.arange(ncat), mask.all()
+
+        if dropna or clean:
+            obs = code if clean else code[mask]
+            count = np.bincount(obs, minlength=ncat or 0)
+        else:
+            count = np.bincount(np.where(mask, code, ncat))
+            ix = np.append(ix, -1)
+
+        ix = coerce_indexer_dtype(ix, self.dtype.categories)
+        ix = self._from_backing_data(ix)
+
+        return Series(
+            count, index=CategoricalIndex(ix), dtype="int64", name="count", copy=False
+        )
+
+    # error: Argument 2 of "_empty" is incompatible with supertype
+    # "NDArrayBackedExtensionArray"; supertype defines the argument type as
+    # "ExtensionDtype"
+    @classmethod
+    def _empty(  # type: ignore[override]
+        cls, shape: Shape, dtype: CategoricalDtype
+    ) -> Self:
+        """
+        Analogous to np.empty(shape, dtype=dtype)
+
+        Parameters
+        ----------
+        shape : tuple[int]
+        dtype : CategoricalDtype
+        """
+        arr = cls._from_sequence([], dtype=dtype)
+
+        # We have to use np.zeros instead of np.empty otherwise the resulting
+        #  ndarray may contain codes not supported by this dtype, in which
+        #  case repr(result) could segfault.
+        backing = np.zeros(shape, dtype=arr._ndarray.dtype)
+
+        return arr._from_backing_data(backing)
+
+    def _internal_get_values(self) -> ArrayLike:
+        """
+        Return the values.
+
+        For internal compatibility with pandas formatting.
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+            A numpy array or ExtensionArray of the same dtype as
+            categorical.categories.dtype.
+        """
+        # if we are a datetime and period index, return Index to keep metadata
+        if needs_i8_conversion(self.categories.dtype):
+            return self.categories.take(self._codes, fill_value=NaT)._values
+        elif is_integer_dtype(self.categories.dtype) and -1 in self._codes:
+            return (
+                self.categories.astype("object")
+                .take(self._codes, fill_value=np.nan)
+                ._values
+            )
+        return np.array(self)
+
+    def check_for_ordered(self, op) -> None:
+        """assert that we are ordered"""
+        if not self.ordered:
+            raise TypeError(
+                f"Categorical is not ordered for operation {op}\n"
+                "you can use .as_ordered() to change the "
+                "Categorical to an ordered one\n"
+            )
+
+    def argsort(
+        self, *, ascending: bool = True, kind: SortKind = "quicksort", **kwargs
+    ):
+        """
+        Return the indices that would sort the Categorical.
+
+        Missing values are sorted at the end.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            Whether the indices should result in an ascending
+            or descending sort.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+            Sorting algorithm.
+        **kwargs:
+            passed through to :func:`numpy.argsort`.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        numpy.ndarray.argsort
+
+        Notes
+        -----
+        While an ordering is applied to the category values, arg-sorting
+        in this context refers more to organizing and grouping together
+        based on matching category values. Thus, this function can be
+        called on an unordered Categorical instance unlike the functions
+        'Categorical.min' and 'Categorical.max'.
+
+        Examples
+        --------
+        >>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()
+        array([2, 0, 1, 3])
+
+        >>> cat = pd.Categorical(['b', 'b', 'a', 'c'],
+        ...                      categories=['c', 'b', 'a'],
+        ...                      ordered=True)
+        >>> cat.argsort()
+        array([3, 0, 1, 2])
+
+        Missing values are placed at the end
+
+        >>> cat = pd.Categorical([2, None, 1])
+        >>> cat.argsort()
+        array([2, 0, 1])
+        """
+        return super().argsort(ascending=ascending, kind=kind, **kwargs)
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        inplace: Literal[False] = ...,
+        ascending: bool = ...,
+        na_position: str = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self, *, inplace: Literal[True], ascending: bool = ..., na_position: str = ...
+    ) -> None:
+        ...
+
+    def sort_values(
+        self,
+        *,
+        inplace: bool = False,
+        ascending: bool = True,
+        na_position: str = "last",
+    ) -> Self | None:
+        """
+        Sort the Categorical by category value returning a new
+        Categorical by default.
+
+        While an ordering is applied to the category values, sorting in this
+        context refers more to organizing and grouping together based on
+        matching category values. Thus, this function can be called on an
+        unordered Categorical instance unlike the functions 'Categorical.min'
+        and 'Categorical.max'.
+
+        Parameters
+        ----------
+        inplace : bool, default False
+            Do operation in place.
+        ascending : bool, default True
+            Order ascending. Passing False orders descending. The
+            ordering parameter provides the method by which the
+            category values are organized.
+        na_position : {'first', 'last'} (optional, default='last')
+            'first' puts NaNs at the beginning
+            'last' puts NaNs at the end
+
+        Returns
+        -------
+        Categorical or None
+
+        See Also
+        --------
+        Categorical.sort
+        Series.sort_values
+
+        Examples
+        --------
+        >>> c = pd.Categorical([1, 2, 2, 1, 5])
+        >>> c
+        [1, 2, 2, 1, 5]
+        Categories (3, int64): [1, 2, 5]
+        >>> c.sort_values()
+        [1, 1, 2, 2, 5]
+        Categories (3, int64): [1, 2, 5]
+        >>> c.sort_values(ascending=False)
+        [5, 2, 2, 1, 1]
+        Categories (3, int64): [1, 2, 5]
+
+        >>> c = pd.Categorical([1, 2, 2, 1, 5])
+
+        'sort_values' behaviour with NaNs. Note that 'na_position'
+        is independent of the 'ascending' parameter:
+
+        >>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5])
+        >>> c
+        [NaN, 2, 2, NaN, 5]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values()
+        [2, 2, 5, NaN, NaN]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values(ascending=False)
+        [5, 2, 2, NaN, NaN]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values(na_position='first')
+        [NaN, NaN, 2, 2, 5]
+        Categories (2, int64): [2, 5]
+        >>> c.sort_values(ascending=False, na_position='first')
+        [NaN, NaN, 5, 2, 2]
+        Categories (2, int64): [2, 5]
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if na_position not in ["last", "first"]:
+            raise ValueError(f"invalid na_position: {repr(na_position)}")
+
+        sorted_idx = nargsort(self, ascending=ascending, na_position=na_position)
+
+        if not inplace:
+            codes = self._codes[sorted_idx]
+            return self._from_backing_data(codes)
+        self._codes[:] = self._codes[sorted_idx]
+        return None
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if axis != 0:
+            raise NotImplementedError
+        vff = self._values_for_rank()
+        return algorithms.rank(
+            vff,
+            axis=axis,
+            method=method,
+            na_option=na_option,
+            ascending=ascending,
+            pct=pct,
+        )
+
+    def _values_for_rank(self) -> np.ndarray:
+        """
+        For correctly ranking ordered categorical data. See GH#15420
+
+        Ordered categorical data should be ranked on the basis of
+        codes with -1 translated to NaN.
+
+        Returns
+        -------
+        numpy.array
+
+        """
+        from pandas import Series
+
+        if self.ordered:
+            values = self.codes
+            mask = values == -1
+            if mask.any():
+                values = values.astype("float64")
+                values[mask] = np.nan
+        elif is_any_real_numeric_dtype(self.categories.dtype):
+            values = np.array(self)
+        else:
+            #  reorder the categories (so rank can use the float codes)
+            #  instead of passing an object array to rank
+            values = np.array(
+                self.rename_categories(
+                    Series(self.categories, copy=False).rank().values
+                )
+            )
+        return values
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        """
+        Hash a Categorical by hashing its categories, and then mapping the codes
+        to the hashes.
+
+        Parameters
+        ----------
+        encoding : str
+        hash_key : str
+        categorize : bool
+            Ignored for Categorical.
+
+        Returns
+        -------
+        np.ndarray[uint64]
+        """
+        # Note we ignore categorize, as we are already Categorical.
+        from pandas.core.util.hashing import hash_array
+
+        # Convert ExtensionArrays to ndarrays
+        values = np.asarray(self.categories._values)
+        hashed = hash_array(values, encoding, hash_key, categorize=False)
+
+        # we have uint64, as we don't directly support missing values
+        # we don't want to use take_nd which will coerce to float
+        # instead, directly construct the result with a
+        # max(np.uint64) as the missing value indicator
+        #
+        # TODO: GH#15362
+
+        mask = self.isna()
+        if len(hashed):
+            result = hashed.take(self._codes)
+        else:
+            result = np.zeros(len(mask), dtype="uint64")
+
+        if mask.any():
+            result[mask] = lib.u8max
+
+        return result
+
+    # ------------------------------------------------------------------
+    # NDArrayBackedExtensionArray compat
+
+    @property
+    def _codes(self) -> np.ndarray:
+        return self._ndarray
+
+    def _box_func(self, i: int):
+        if i == -1:
+            return np.nan
+        return self.categories[i]
+
+    def _unbox_scalar(self, key) -> int:
+        # searchsorted is very performance sensitive. By converting codes
+        # to same dtype as self.codes, we get much faster performance.
+        code = self.categories.get_loc(key)
+        code = self._ndarray.dtype.type(code)
+        return code
+
+    # ------------------------------------------------------------------
+
+    def __iter__(self) -> Iterator:
+        """
+        Returns an Iterator over the values of this Categorical.
+        """
+        if self.ndim == 1:
+            return iter(self._internal_get_values().tolist())
+        else:
+            return (self[n] for n in range(len(self)))
+
+    def __contains__(self, key) -> bool:
+        """
+        Returns True if `key` is in this Categorical.
+        """
+        # if key is a NaN, check if any NaN is in self.
+        if is_valid_na_for_dtype(key, self.categories.dtype):
+            return bool(self.isna().any())
+
+        return contains(self, key, container=self._codes)
+
+    # ------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        # Returning None here will cause format_array to do inference.
+        return None
+
+    def _repr_categories(self) -> list[str]:
+        """
+        return the base repr for the categories
+        """
+        max_categories = (
+            10
+            if get_option("display.max_categories") == 0
+            else get_option("display.max_categories")
+        )
+        from pandas.io.formats import format as fmt
+
+        formatter = None
+        if self.categories.dtype == "str":
+            # the extension array formatter defaults to boxed=True in format_array
+            # override here to boxed=False to be consistent with QUOTE_NONNUMERIC
+            formatter = cast(ExtensionArray, self.categories._values)._formatter(
+                boxed=False
+            )
+
+        format_array = partial(
+            fmt.format_array, formatter=formatter, quoting=QUOTE_NONNUMERIC
+        )
+        if len(self.categories) > max_categories:
+            num = max_categories // 2
+            head = format_array(self.categories[:num]._values)
+            tail = format_array(self.categories[-num:]._values)
+            category_strs = head + ["..."] + tail
+        else:
+            category_strs = format_array(self.categories._values)
+
+        # Strip all leading spaces, which format_array adds for columns...
+        category_strs = [x.strip() for x in category_strs]
+        return category_strs
+
+    def _get_repr_footer(self) -> str:
+        """
+        Returns a string representation of the footer.
+        """
+        category_strs = self._repr_categories()
+        dtype = str(self.categories.dtype)
+        levheader = f"Categories ({len(self.categories)}, {dtype}): "
+        width, _ = get_terminal_size()
+        max_width = get_option("display.width") or width
+        if console.in_ipython_frontend():
+            # 0 = no breaks
+            max_width = 0
+        levstring = ""
+        start = True
+        cur_col_len = len(levheader)  # header
+        sep_len, sep = (3, " < ") if self.ordered else (2, ", ")
+        linesep = f"{sep.rstrip()}\n"  # remove whitespace
+        for val in category_strs:
+            if max_width != 0 and cur_col_len + sep_len + len(val) > max_width:
+                levstring += linesep + (" " * (len(levheader) + 1))
+                cur_col_len = len(levheader) + 1  # header + a whitespace
+            elif not start:
+                levstring += sep
+                cur_col_len += len(val)
+            levstring += val
+            start = False
+        # replace to simple save space by
+        return f"{levheader}[{levstring.replace(' < ... < ', ' ... ')}]"
+
+    def _get_values_repr(self) -> str:
+        from pandas.io.formats import format as fmt
+
+        assert len(self) > 0
+
+        vals = self._internal_get_values()
+        fmt_values = fmt.format_array(
+            vals,
+            None,
+            float_format=None,
+            na_rep="NaN",
+            quoting=QUOTE_NONNUMERIC,
+        )
+
+        fmt_values = [i.strip() for i in fmt_values]
+        joined = ", ".join(fmt_values)
+        result = "[" + joined + "]"
+        return result
+
+    def __repr__(self) -> str:
+        """
+        String representation.
+        """
+        footer = self._get_repr_footer()
+        length = len(self)
+        max_len = 10
+        if length > max_len:
+            # In long cases we do not display all entries, so we add Length
+            #  information to the __repr__.
+            num = max_len // 2
+            head = self[:num]._get_values_repr()
+            tail = self[-(max_len - num) :]._get_values_repr()
+            body = f"{head[:-1]}, ..., {tail[1:]}"
+            length_info = f"Length: {len(self)}"
+            result = f"{body}\n{length_info}\n{footer}"
+        elif length > 0:
+            body = self._get_values_repr()
+            result = f"{body}\n{footer}"
+        else:
+            # In the empty case we use a comma instead of newline to get
+            #  a more compact __repr__
+            body = "[]"
+            result = f"{body}, {footer}"
+
+        return result
+
+    # ------------------------------------------------------------------
+
+    def _validate_listlike(self, value):
+        # NB: here we assume scalar-like tuples have already been excluded
+        value = extract_array(value, extract_numpy=True)
+
+        # require identical categories set
+        if isinstance(value, Categorical):
+            if self.dtype != value.dtype:
+                raise TypeError(
+                    "Cannot set a Categorical with another, "
+                    "without identical categories"
+                )
+            # dtype equality implies categories_match_up_to_permutation
+            value = self._encode_with_my_categories(value)
+            return value._codes
+
+        from pandas import Index
+
+        # tupleize_cols=False for e.g. test_fillna_iterable_category GH#41914
+        to_add = Index._with_infer(value, tupleize_cols=False).difference(
+            self.categories
+        )
+
+        # no assignments of values not in categories, but it's always ok to set
+        # something to np.nan
+        if len(to_add) and not isna(to_add).all():
+            raise TypeError(
+                "Cannot setitem on a Categorical with a new "
+                "category, set the categories first"
+            )
+
+        codes = self.categories.get_indexer(value)
+        return codes.astype(self._ndarray.dtype, copy=False)
+
+    def _reverse_indexer(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        """
+        Compute the inverse of a categorical, returning
+        a dict of categories -> indexers.
+
+        *This is an internal function*
+
+        Returns
+        -------
+        Dict[Hashable, np.ndarray[np.intp]]
+            dict of categories -> indexers
+
+        Examples
+        --------
+        >>> c = pd.Categorical(list('aabca'))
+        >>> c
+        ['a', 'a', 'b', 'c', 'a']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> c.categories
+        Index(['a', 'b', 'c'], dtype='object')
+        >>> c.codes
+        array([0, 0, 1, 2, 0], dtype=int8)
+        >>> c._reverse_indexer()
+        {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}
+
+        """
+        categories = self.categories
+        r, counts = libalgos.groupsort_indexer(
+            ensure_platform_int(self.codes), categories.size
+        )
+        counts = ensure_int64(counts).cumsum()
+        _result = (r[start:end] for start, end in zip(counts, counts[1:]))
+        return dict(zip(categories, _result))
+
+    # ------------------------------------------------------------------
+    # Reductions
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        result = super()._reduce(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        if name in ["argmax", "argmin"]:
+            # don't wrap in Categorical!
+            return result
+        if keepdims:
+            return type(self)(result, dtype=self.dtype)
+        else:
+            return result
+
+    def min(self, *, skipna: bool = True, **kwargs):
+        """
+        The minimum value of the object.
+
+        Only ordered `Categoricals` have a minimum!
+
+        Raises
+        ------
+        TypeError
+            If the `Categorical` is not `ordered`.
+
+        Returns
+        -------
+        min : the minimum of this `Categorical`, NA value if empty
+        """
+        nv.validate_minmax_axis(kwargs.get("axis", 0))
+        nv.validate_min((), kwargs)
+        self.check_for_ordered("min")
+
+        if not len(self._codes):
+            return self.dtype.na_value
+
+        good = self._codes != -1
+        if not good.all():
+            if skipna and good.any():
+                pointer = self._codes[good].min()
+            else:
+                return np.nan
+        else:
+            pointer = self._codes.min()
+        return self._wrap_reduction_result(None, pointer)
+
+    def max(self, *, skipna: bool = True, **kwargs):
+        """
+        The maximum value of the object.
+
+        Only ordered `Categoricals` have a maximum!
+
+        Raises
+        ------
+        TypeError
+            If the `Categorical` is not `ordered`.
+
+        Returns
+        -------
+        max : the maximum of this `Categorical`, NA if array is empty
+        """
+        nv.validate_minmax_axis(kwargs.get("axis", 0))
+        nv.validate_max((), kwargs)
+        self.check_for_ordered("max")
+
+        if not len(self._codes):
+            return self.dtype.na_value
+
+        good = self._codes != -1
+        if not good.all():
+            if skipna and good.any():
+                pointer = self._codes[good].max()
+            else:
+                return np.nan
+        else:
+            pointer = self._codes.max()
+        return self._wrap_reduction_result(None, pointer)
+
+    def _mode(self, dropna: bool = True) -> Categorical:
+        codes = self._codes
+        mask = None
+        if dropna:
+            mask = self.isna()
+
+        res_codes = algorithms.mode(codes, mask=mask)
+        res_codes = cast(np.ndarray, res_codes)
+        assert res_codes.dtype == codes.dtype
+        res = self._from_backing_data(res_codes)
+        return res
+
+    # ------------------------------------------------------------------
+    # ExtensionArray Interface
+
+    def unique(self) -> Self:
+        """
+        Return the ``Categorical`` which ``categories`` and ``codes`` are
+        unique.
+
+        .. versionchanged:: 1.3.0
+
+            Previously, unused categories were dropped from the new categories.
+
+        Returns
+        -------
+        Categorical
+
+        See Also
+        --------
+        pandas.unique
+        CategoricalIndex.unique
+        Series.unique : Return unique values of Series object.
+
+        Examples
+        --------
+        >>> pd.Categorical(list("baabc")).unique()
+        ['b', 'a', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> pd.Categorical(list("baab"), categories=list("abc"), ordered=True).unique()
+        ['b', 'a']
+        Categories (3, object): ['a' < 'b' < 'c']
+        """
+        # pylint: disable=useless-parent-delegation
+        return super().unique()
+
+    def equals(self, other: object) -> bool:
+        """
+        Returns True if categorical arrays are equal.
+
+        Parameters
+        ----------
+        other : `Categorical`
+
+        Returns
+        -------
+        bool
+        """
+        if not isinstance(other, Categorical):
+            return False
+        elif self._categories_match_up_to_permutation(other):
+            other = self._encode_with_my_categories(other)
+            return np.array_equal(self._codes, other._codes)
+        return False
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[Self], axis: AxisInt = 0) -> Self:
+        from pandas.core.dtypes.concat import union_categoricals
+
+        first = to_concat[0]
+        if axis >= first.ndim:
+            raise ValueError(
+                f"axis {axis} is out of bounds for array of dimension {first.ndim}"
+            )
+
+        if axis == 1:
+            # Flatten, concatenate then reshape
+            if not all(x.ndim == 2 for x in to_concat):
+                raise ValueError
+
+            # pass correctly-shaped to union_categoricals
+            tc_flat = []
+            for obj in to_concat:
+                tc_flat.extend([obj[:, i] for i in range(obj.shape[1])])
+
+            res_flat = cls._concat_same_type(tc_flat, axis=0)
+
+            result = res_flat.reshape(len(first), -1, order="F")
+            return result
+
+        result = union_categoricals(to_concat)
+        return result
+
+    # ------------------------------------------------------------------
+
+    def _encode_with_my_categories(self, other: Categorical) -> Categorical:
+        """
+        Re-encode another categorical using this Categorical's categories.
+
+        Notes
+        -----
+        This assumes we have already checked
+        self._categories_match_up_to_permutation(other).
+        """
+        # Indexing on codes is more efficient if categories are the same,
+        #  so we can apply some optimizations based on the degree of
+        #  dtype-matching.
+        codes = recode_for_categories(
+            other.codes, other.categories, self.categories, copy=False
+        )
+        return self._from_backing_data(codes)
+
+    def _categories_match_up_to_permutation(self, other: Categorical) -> bool:
+        """
+        Returns True if categoricals are the same dtype
+          same categories, and same ordered
+
+        Parameters
+        ----------
+        other : Categorical
+
+        Returns
+        -------
+        bool
+        """
+        return hash(self.dtype) == hash(other.dtype)
+
+    def describe(self) -> DataFrame:
+        """
+        Describes this Categorical
+
+        Returns
+        -------
+        description: `DataFrame`
+            A dataframe with frequency and counts by category.
+        """
+        counts = self.value_counts(dropna=False)
+        freqs = counts / counts.sum()
+
+        from pandas import Index
+        from pandas.core.reshape.concat import concat
+
+        result = concat([counts, freqs], axis=1)
+        result.columns = Index(["counts", "freqs"])
+        result.index.name = "categories"
+
+        return result
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        """
+        Check whether `values` are contained in Categorical.
+
+        Return a boolean NumPy Array showing whether each element in
+        the Categorical matches an element in the passed sequence of
+        `values` exactly.
+
+        Parameters
+        ----------
+        values : np.ndarray or ExtensionArray
+            The sequence of values to test. Passing in a single string will
+            raise a ``TypeError``. Instead, turn a single string into a
+            list of one element.
+
+        Returns
+        -------
+        np.ndarray[bool]
+
+        Raises
+        ------
+        TypeError
+          * If `values` is not a set or list-like
+
+        See Also
+        --------
+        pandas.Series.isin : Equivalent method on Series.
+
+        Examples
+        --------
+        >>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',
+        ...                'hippo'])
+        >>> s.isin(['cow', 'lama'])
+        array([ True,  True,  True, False,  True, False])
+
+        Passing a single string as ``s.isin('lama')`` will raise an error. Use
+        a list of one element instead:
+
+        >>> s.isin(['lama'])
+        array([ True, False,  True, False,  True, False])
+        """
+        null_mask = np.asarray(isna(values))
+        code_values = self.categories.get_indexer_for(values)
+        code_values = code_values[null_mask | (code_values >= 0)]
+        return algorithms.isin(self.codes, code_values)
+
+    def _replace(self, *, to_replace, value, inplace: bool = False):
+        from pandas import Index
+
+        orig_dtype = self.dtype
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        cat = self if inplace else self.copy()
+
+        mask = isna(np.asarray(value))
+        if mask.any():
+            removals = np.asarray(to_replace)[mask]
+            removals = cat.categories[cat.categories.isin(removals)]
+            new_cat = cat.remove_categories(removals)
+            NDArrayBacked.__init__(cat, new_cat.codes, new_cat.dtype)
+
+        ser = cat.categories.to_series()
+        ser = ser.replace(to_replace=to_replace, value=value)
+
+        all_values = Index(ser)
+
+        # GH51016: maintain order of existing categories
+        idxr = cat.categories.get_indexer_for(all_values)
+        locs = np.arange(len(ser))
+        locs = np.where(idxr == -1, locs, idxr)
+        locs = locs.argsort()
+
+        new_categories = ser.take(locs)
+        new_categories = new_categories.drop_duplicates(keep="first")
+        new_categories = Index(new_categories)
+        new_codes = recode_for_categories(
+            cat._codes, all_values, new_categories, copy=False
+        )
+        new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered)
+        NDArrayBacked.__init__(cat, new_codes, new_dtype)
+
+        if new_dtype != orig_dtype:
+            warnings.warn(
+                # GH#55147
+                "The behavior of Series.replace (and DataFrame.replace) with "
+                "CategoricalDtype is deprecated. In a future version, replace "
+                "will only be used for cases that preserve the categories. "
+                "To change the categories, use ser.cat.rename_categories "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if not inplace:
+            return cat
+
+    # ------------------------------------------------------------------------
+    # String methods interface
+    def _str_map(
+        self, f, na_value=lib.no_default, dtype=np.dtype("object"), convert: bool = True
+    ):
+        # Optimization to apply the callable `f` to the categories once
+        # and rebuild the result by `take`ing from the result with the codes.
+        # Returns the same type as the object-dtype implementation though.
+        categories = self.categories
+        codes = self.codes
+        if categories.dtype == "string":
+            result = categories.array._str_map(f, na_value, dtype)  # type: ignore[attr-defined]
+            if (
+                categories.dtype.na_value is np.nan  # type: ignore[union-attr]
+                and is_bool_dtype(dtype)
+                and (na_value is lib.no_default or isna(na_value))
+            ):
+                # NaN propagates as False for functions with boolean return type
+                na_value = False
+        else:
+            from pandas.core.arrays import NumpyExtensionArray
+
+            result = NumpyExtensionArray(categories.to_numpy())._str_map(
+                f, na_value, dtype
+            )
+        return take_nd(result, codes, fill_value=na_value)
+
+    def _str_get_dummies(self, sep: str = "|"):
+        # sep may not be in categories. Just bail on this.
+        from pandas.core.arrays import NumpyExtensionArray
+
+        return NumpyExtensionArray(self.to_numpy(str, na_value="NaN"))._str_get_dummies(
+            sep
+        )
+
+    # ------------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        dtype = self.dtype
+        if how in ["sum", "prod", "cumsum", "cumprod", "skew"]:
+            raise TypeError(f"{dtype} type does not support {how} operations")
+        if how in ["min", "max", "rank", "idxmin", "idxmax"] and not dtype.ordered:
+            # raise TypeError instead of NotImplementedError to ensure we
+            #  don't go down a group-by-group path, since in the empty-groups
+            #  case that would fail to raise
+            raise TypeError(f"Cannot perform {how} with non-ordered Categorical")
+        if how not in [
+            "rank",
+            "any",
+            "all",
+            "first",
+            "last",
+            "min",
+            "max",
+            "idxmin",
+            "idxmax",
+        ]:
+            if kind == "transform":
+                raise TypeError(f"{dtype} type does not support {how} operations")
+            raise TypeError(f"{dtype} dtype does not support aggregation '{how}'")
+
+        result_mask = None
+        mask = self.isna()
+        if how == "rank":
+            assert self.ordered  # checked earlier
+            npvalues = self._ndarray
+        elif how in ["first", "last", "min", "max", "idxmin", "idxmax"]:
+            npvalues = self._ndarray
+            result_mask = np.zeros(ngroups, dtype=bool)
+        else:
+            # any/all
+            npvalues = self.astype(bool)
+
+        res_values = op._cython_op_ndim_compat(
+            npvalues,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=mask,
+            result_mask=result_mask,
+            **kwargs,
+        )
+
+        if how in op.cast_blocklist:
+            return res_values
+        elif how in ["first", "last", "min", "max"]:
+            res_values[result_mask == 1] = -1
+        return self._from_backing_data(res_values)
+
+
+# The Series.cat accessor
+
+
+@delegate_names(
+    delegate=Categorical, accessors=["categories", "ordered"], typ="property"
+)
+@delegate_names(
+    delegate=Categorical,
+    accessors=[
+        "rename_categories",
+        "reorder_categories",
+        "add_categories",
+        "remove_categories",
+        "remove_unused_categories",
+        "set_categories",
+        "as_ordered",
+        "as_unordered",
+    ],
+    typ="method",
+)
+class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    """
+    Accessor object for categorical properties of the Series values.
+
+    Parameters
+    ----------
+    data : Series or CategoricalIndex
+
+    Examples
+    --------
+    >>> s = pd.Series(list("abbccc")).astype("category")
+    >>> s
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a', 'b', 'c']
+
+    >>> s.cat.categories
+    Index(['a', 'b', 'c'], dtype='object')
+
+    >>> s.cat.rename_categories(list("cba"))
+    0    c
+    1    b
+    2    b
+    3    a
+    4    a
+    5    a
+    dtype: category
+    Categories (3, object): ['c', 'b', 'a']
+
+    >>> s.cat.reorder_categories(list("cba"))
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['c', 'b', 'a']
+
+    >>> s.cat.add_categories(["d", "e"])
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (5, object): ['a', 'b', 'c', 'd', 'e']
+
+    >>> s.cat.remove_categories(["a", "c"])
+    0    NaN
+    1      b
+    2      b
+    3    NaN
+    4    NaN
+    5    NaN
+    dtype: category
+    Categories (1, object): ['b']
+
+    >>> s1 = s.cat.add_categories(["d", "e"])
+    >>> s1.cat.remove_unused_categories()
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a', 'b', 'c']
+
+    >>> s.cat.set_categories(list("abcde"))
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (5, object): ['a', 'b', 'c', 'd', 'e']
+
+    >>> s.cat.as_ordered()
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a' < 'b' < 'c']
+
+    >>> s.cat.as_unordered()
+    0    a
+    1    b
+    2    b
+    3    c
+    4    c
+    5    c
+    dtype: category
+    Categories (3, object): ['a', 'b', 'c']
+    """
+
+    def __init__(self, data) -> None:
+        self._validate(data)
+        self._parent = data.values
+        self._index = data.index
+        self._name = data.name
+        self._freeze()
+
+    @staticmethod
+    def _validate(data):
+        if not isinstance(data.dtype, CategoricalDtype):
+            raise AttributeError("Can only use .cat accessor with a 'category' dtype")
+
+    def _delegate_property_get(self, name: str):
+        return getattr(self._parent, name)
+
+    # error: Signature of "_delegate_property_set" incompatible with supertype
+    # "PandasDelegate"
+    def _delegate_property_set(self, name: str, new_values):  # type: ignore[override]
+        return setattr(self._parent, name, new_values)
+
+    @property
+    def codes(self) -> Series:
+        """
+        Return Series of codes as well as the index.
+
+        Examples
+        --------
+        >>> raw_cate = pd.Categorical(["a", "b", "c", "a"], categories=["a", "b"])
+        >>> ser = pd.Series(raw_cate)
+        >>> ser.cat.codes
+        0   0
+        1   1
+        2  -1
+        3   0
+        dtype: int8
+        """
+        from pandas import Series
+
+        return Series(self._parent.codes, index=self._index)
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        from pandas import Series
+
+        method = getattr(self._parent, name)
+        res = method(*args, **kwargs)
+        if res is not None:
+            return Series(res, index=self._index, name=self._name)
+
+
+# utility routines
+
+
+def _get_codes_for_values(
+    values: Index | Series | ExtensionArray | np.ndarray,
+    categories: Index,
+) -> np.ndarray:
+    """
+    utility routine to turn values into codes given the specified categories
+
+    If `values` is known to be a Categorical, use recode_for_categories instead.
+    """
+    codes = categories.get_indexer_for(values)
+    return coerce_indexer_dtype(codes, categories)
+
+
+def recode_for_categories(
+    codes: np.ndarray, old_categories, new_categories, copy: bool = True
+) -> np.ndarray:
+    """
+    Convert a set of codes for to a new set of categories
+
+    Parameters
+    ----------
+    codes : np.ndarray
+    old_categories, new_categories : Index
+    copy: bool, default True
+        Whether to copy if the codes are unchanged.
+
+    Returns
+    -------
+    new_codes : np.ndarray[np.int64]
+
+    Examples
+    --------
+    >>> old_cat = pd.Index(['b', 'a', 'c'])
+    >>> new_cat = pd.Index(['a', 'b'])
+    >>> codes = np.array([0, 1, 1, 2])
+    >>> recode_for_categories(codes, old_cat, new_cat)
+    array([ 1,  0,  0, -1], dtype=int8)
+    """
+    if len(old_categories) == 0:
+        # All null anyway, so just retain the nulls
+        if copy:
+            return codes.copy()
+        return codes
+    elif new_categories.equals(old_categories):
+        # Same categories, so no need to actually recode
+        if copy:
+            return codes.copy()
+        return codes
+
+    indexer = coerce_indexer_dtype(
+        new_categories.get_indexer_for(old_categories), new_categories
+    )
+    new_codes = take_nd(indexer, codes, fill_value=-1)
+    return new_codes
+
+
+def factorize_from_iterable(values) -> tuple[np.ndarray, Index]:
+    """
+    Factorize an input `values` into `categories` and `codes`. Preserves
+    categorical dtype in `categories`.
+
+    Parameters
+    ----------
+    values : list-like
+
+    Returns
+    -------
+    codes : ndarray
+    categories : Index
+        If `values` has a categorical dtype, then `categories` is
+        a CategoricalIndex keeping the categories and order of `values`.
+    """
+    from pandas import CategoricalIndex
+
+    if not is_list_like(values):
+        raise TypeError("Input must be list-like")
+
+    categories: Index
+
+    vdtype = getattr(values, "dtype", None)
+    if isinstance(vdtype, CategoricalDtype):
+        values = extract_array(values)
+        # The Categorical we want to build has the same categories
+        # as values but its codes are by def [0, ..., len(n_categories) - 1]
+        cat_codes = np.arange(len(values.categories), dtype=values.codes.dtype)
+        cat = Categorical.from_codes(cat_codes, dtype=values.dtype, validate=False)
+
+        categories = CategoricalIndex(cat)
+        codes = values.codes
+    else:
+        # The value of ordered is irrelevant since we don't use cat as such,
+        # but only the resulting categories, the order of which is independent
+        # from ordered. Set ordered to False as default. See GH #15457
+        cat = Categorical(values, ordered=False)
+        categories = cat.categories
+        codes = cat.codes
+    return codes, categories
+
+
+def factorize_from_iterables(iterables) -> tuple[list[np.ndarray], list[Index]]:
+    """
+    A higher-level wrapper over `factorize_from_iterable`.
+
+    Parameters
+    ----------
+    iterables : list-like of list-likes
+
+    Returns
+    -------
+    codes : list of ndarrays
+    categories : list of Indexes
+
+    Notes
+    -----
+    See `factorize_from_iterable` for more info.
+    """
+    if len(iterables) == 0:
+        # For consistency, it should return two empty lists.
+        return [], []
+
+    codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
+    return list(codes), list(categories)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/datetimelike.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/datetimelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe1f3acd914344d5ec161b40a8cd494f03353dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/datetimelike.py
@@ -0,0 +1,2583 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+)
+from functools import wraps
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    Union,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    algos,
+    lib,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import (
+    BaseOffset,
+    IncompatibleFrequency,
+    NaT,
+    NaTType,
+    Period,
+    Resolution,
+    Tick,
+    Timedelta,
+    Timestamp,
+    add_overflowsafe,
+    astype_overflowsafe,
+    get_unit_from_dtype,
+    iNaT,
+    ints_to_pydatetime,
+    ints_to_pytimedelta,
+    periods_per_day,
+    to_offset,
+)
+from pandas._libs.tslibs.fields import (
+    RoundTo,
+    round_nsint64,
+)
+from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions
+from pandas._libs.tslibs.timedeltas import get_unit_for_round
+from pandas._libs.tslibs.timestamps import integer_op_not_supported
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    DatetimeLikeScalar,
+    Dtype,
+    DtypeObj,
+    F,
+    InterpolateOptions,
+    NpDtype,
+    PositionalIndexer2D,
+    PositionalIndexerTuple,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    TimeAmbiguous,
+    TimeNonexistent,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    AbstractMethodError,
+    InvalidComparison,
+    PerformanceWarning,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import (
+    is_all_strings,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCCategorical,
+    ABCMultiIndex,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core import (
+    algorithms,
+    missing,
+    nanops,
+    ops,
+)
+from pandas.core.algorithms import (
+    isin,
+    map_array,
+    unique1d,
+)
+from pandas.core.array_algos import datetimelike_accumulations
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._mixins import (
+    NDArrayBackedExtensionArray,
+    ravel_compat,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.integer import IntegerArray
+import pandas.core.common as com
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import (
+    check_array_indexer,
+    check_setitem_lengths,
+)
+from pandas.core.ops.common import unpack_zerodim_and_defer
+from pandas.core.ops.invalid import (
+    invalid_comparison,
+    make_invalid_op,
+)
+
+from pandas.tseries import frequencies
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+
+    from pandas import Index
+    from pandas.core.arrays import (
+        DatetimeArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+
+DTScalarOrNaT = Union[DatetimeLikeScalar, NaTType]
+
+
+def _make_unpacked_invalid_op(op_name: str):
+    op = make_invalid_op(op_name)
+    return unpack_zerodim_and_defer(op_name)(op)
+
+
+def _period_dispatch(meth: F) -> F:
+    """
+    For PeriodArray methods, dispatch to DatetimeArray and re-wrap the results
+    in PeriodArray.  We cannot use ._ndarray directly for the affected
+    methods because the i8 data has different semantics on NaT values.
+    """
+
+    @wraps(meth)
+    def new_meth(self, *args, **kwargs):
+        if not isinstance(self.dtype, PeriodDtype):
+            return meth(self, *args, **kwargs)
+
+        arr = self.view("M8[ns]")
+        result = meth(arr, *args, **kwargs)
+        if result is NaT:
+            return NaT
+        elif isinstance(result, Timestamp):
+            return self._box_func(result._value)
+
+        res_i8 = result.view("i8")
+        return self._from_backing_data(res_i8)
+
+    return cast(F, new_meth)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class DatetimeLikeArrayMixin(  # type: ignore[misc]
+    OpsMixin, NDArrayBackedExtensionArray
+):
+    """
+    Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray
+
+    Assumes that __new__/__init__ defines:
+        _ndarray
+
+    and that inheriting subclass implements:
+        freq
+    """
+
+    # _infer_matches -> which infer_dtype strings are close enough to our own
+    _infer_matches: tuple[str, ...]
+    _is_recognized_dtype: Callable[[DtypeObj], bool]
+    _recognized_scalars: tuple[type, ...]
+    _ndarray: np.ndarray
+    freq: BaseOffset | None
+
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        return True
+
+    def __init__(
+        self, data, dtype: Dtype | None = None, freq=None, copy: bool = False
+    ) -> None:
+        raise AbstractMethodError(self)
+
+    @property
+    def _scalar_type(self) -> type[DatetimeLikeScalar]:
+        """
+        The scalar associated with this datelike
+
+        * PeriodArray : Period
+        * DatetimeArray : Timestamp
+        * TimedeltaArray : Timedelta
+        """
+        raise AbstractMethodError(self)
+
+    def _scalar_from_string(self, value: str) -> DTScalarOrNaT:
+        """
+        Construct a scalar type from a string.
+
+        Parameters
+        ----------
+        value : str
+
+        Returns
+        -------
+        Period, Timestamp, or Timedelta, or NaT
+            Whatever the type of ``self._scalar_type`` is.
+
+        Notes
+        -----
+        This should call ``self._check_compatible_with`` before
+        unboxing the result.
+        """
+        raise AbstractMethodError(self)
+
+    def _unbox_scalar(
+        self, value: DTScalarOrNaT
+    ) -> np.int64 | np.datetime64 | np.timedelta64:
+        """
+        Unbox the integer value of a scalar `value`.
+
+        Parameters
+        ----------
+        value : Period, Timestamp, Timedelta, or NaT
+            Depending on subclass.
+
+        Returns
+        -------
+        int
+
+        Examples
+        --------
+        >>> arr = pd.array(np.array(['1970-01-01'], 'datetime64[ns]'))
+        >>> arr._unbox_scalar(arr[0])
+        numpy.datetime64('1970-01-01T00:00:00.000000000')
+        """
+        raise AbstractMethodError(self)
+
+    def _check_compatible_with(self, other: DTScalarOrNaT) -> None:
+        """
+        Verify that `self` and `other` are compatible.
+
+        * DatetimeArray verifies that the timezones (if any) match
+        * PeriodArray verifies that the freq matches
+        * Timedelta has no verification
+
+        In each case, NaT is considered compatible.
+
+        Parameters
+        ----------
+        other
+
+        Raises
+        ------
+        Exception
+        """
+        raise AbstractMethodError(self)
+
+    # ------------------------------------------------------------------
+
+    def _box_func(self, x):
+        """
+        box function to get object from internal representation
+        """
+        raise AbstractMethodError(self)
+
+    def _box_values(self, values) -> np.ndarray:
+        """
+        apply box func to passed values
+        """
+        return lib.map_infer(values, self._box_func, convert=False)
+
+    def __iter__(self) -> Iterator:
+        if self.ndim > 1:
+            return (self[n] for n in range(len(self)))
+        else:
+            return (self._box_func(v) for v in self.asi8)
+
+    @property
+    def asi8(self) -> npt.NDArray[np.int64]:
+        """
+        Integer representation of the values.
+
+        Returns
+        -------
+        ndarray
+            An ndarray with int64 dtype.
+        """
+        # do not cache or you'll create a memory leak
+        return self._ndarray.view("i8")
+
+    # ----------------------------------------------------------------
+    # Rendering Methods
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None
+    ) -> npt.NDArray[np.object_]:
+        """
+        Helper method for astype when converting to strings.
+
+        Returns
+        -------
+        ndarray[str]
+        """
+        raise AbstractMethodError(self)
+
+    def _formatter(self, boxed: bool = False):
+        # TODO: Remove Datetime & DatetimeTZ formatters.
+        return "'{}'".format
+
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        # used for Timedelta/DatetimeArray, overwritten by PeriodArray
+        if is_object_dtype(dtype):
+            if copy is False:
+                warnings.warn(
+                    "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                    "changed and passing 'copy=False' raises an error when returning "
+                    "a zero-copy NumPy array is not possible. pandas will follow this "
+                    "behavior starting with pandas 3.0.\nThis conversion to NumPy "
+                    "requires a copy, but 'copy=False' was passed. Consider using "
+                    "'np.asarray(..)' instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+            return np.array(list(self), dtype=object)
+
+        if copy is True:
+            return np.array(self._ndarray, dtype=dtype)
+        return self._ndarray
+
+    @overload
+    def __getitem__(self, item: ScalarIndexer) -> DTScalarOrNaT:
+        ...
+
+    @overload
+    def __getitem__(
+        self,
+        item: SequenceIndexer | PositionalIndexerTuple,
+    ) -> Self:
+        ...
+
+    def __getitem__(self, key: PositionalIndexer2D) -> Self | DTScalarOrNaT:
+        """
+        This getitem defers to the underlying array, which by-definition can
+        only handle list-likes, slices, and integer scalars
+        """
+        # Use cast as we know we will get back a DatetimeLikeArray or DTScalar,
+        # but skip evaluating the Union at runtime for performance
+        # (see https://github.com/pandas-dev/pandas/pull/44624)
+        result = cast("Union[Self, DTScalarOrNaT]", super().__getitem__(key))
+        if lib.is_scalar(result):
+            return result
+        else:
+            # At this point we know the result is an array.
+            result = cast(Self, result)
+        result._freq = self._get_getitem_freq(key)
+        return result
+
+    def _get_getitem_freq(self, key) -> BaseOffset | None:
+        """
+        Find the `freq` attribute to assign to the result of a __getitem__ lookup.
+        """
+        is_period = isinstance(self.dtype, PeriodDtype)
+        if is_period:
+            freq = self.freq
+        elif self.ndim != 1:
+            freq = None
+        else:
+            key = check_array_indexer(self, key)  # maybe ndarray[bool] -> slice
+            freq = None
+            if isinstance(key, slice):
+                if self.freq is not None and key.step is not None:
+                    freq = key.step * self.freq
+                else:
+                    freq = self.freq
+            elif key is Ellipsis:
+                # GH#21282 indexing with Ellipsis is similar to a full slice,
+                #  should preserve `freq` attribute
+                freq = self.freq
+            elif com.is_bool_indexer(key):
+                new_key = lib.maybe_booleans_to_slice(key.view(np.uint8))
+                if isinstance(new_key, slice):
+                    return self._get_getitem_freq(new_key)
+        return freq
+
+    # error: Argument 1 of "__setitem__" is incompatible with supertype
+    # "ExtensionArray"; supertype defines the argument type as "Union[int,
+    # ndarray]"
+    def __setitem__(
+        self,
+        key: int | Sequence[int] | Sequence[bool] | slice,
+        value: NaTType | Any | Sequence[Any],
+    ) -> None:
+        # I'm fudging the types a bit here. "Any" above really depends
+        # on type(self). For PeriodArray, it's Period (or stuff coercible
+        # to a period in from_sequence). For DatetimeArray, it's Timestamp...
+        # I don't know if mypy can do that, possibly with Generics.
+        # https://mypy.readthedocs.io/en/latest/generics.html
+
+        no_op = check_setitem_lengths(key, value, self)
+
+        # Calling super() before the no_op short-circuit means that we raise
+        #  on invalid 'value' even if this is a no-op, e.g. wrong-dtype empty array.
+        super().__setitem__(key, value)
+
+        if no_op:
+            return
+
+        self._maybe_clear_freq()
+
+    def _maybe_clear_freq(self) -> None:
+        # inplace operations like __setitem__ may invalidate the freq of
+        # DatetimeArray and TimedeltaArray
+        pass
+
+    def astype(self, dtype, copy: bool = True):
+        # Some notes on cases we don't have to handle here in the base class:
+        #   1. PeriodArray.astype handles period -> period
+        #   2. DatetimeArray.astype handles conversion between tz.
+        #   3. DatetimeArray.astype handles datetime -> period
+        dtype = pandas_dtype(dtype)
+
+        if dtype == object:
+            if self.dtype.kind == "M":
+                self = cast("DatetimeArray", self)
+                # *much* faster than self._box_values
+                #  for e.g. test_get_loc_tuple_monotonic_above_size_cutoff
+                i8data = self.asi8
+                converted = ints_to_pydatetime(
+                    i8data,
+                    tz=self.tz,
+                    box="timestamp",
+                    reso=self._creso,
+                )
+                return converted
+
+            elif self.dtype.kind == "m":
+                return ints_to_pytimedelta(self._ndarray, box=True)
+
+            return self._box_values(self.asi8.ravel()).reshape(self.shape)
+
+        elif is_string_dtype(dtype):
+            if isinstance(dtype, ExtensionDtype):
+                arr_object = self._format_native_types(na_rep=dtype.na_value)  # type: ignore[arg-type]
+                cls = dtype.construct_array_type()
+                return cls._from_sequence(arr_object, dtype=dtype, copy=False)
+            else:
+                return self._format_native_types()
+
+        elif isinstance(dtype, ExtensionDtype):
+            return super().astype(dtype, copy=copy)
+        elif dtype.kind in "iu":
+            # we deliberately ignore int32 vs. int64 here.
+            # See https://github.com/pandas-dev/pandas/issues/24381 for more.
+            values = self.asi8
+            if dtype != np.int64:
+                raise TypeError(
+                    f"Converting from {self.dtype} to {dtype} is not supported. "
+                    "Do obj.astype('int64').astype(dtype) instead"
+                )
+
+            if copy:
+                values = values.copy()
+            return values
+        elif (dtype.kind in "mM" and self.dtype != dtype) or dtype.kind == "f":
+            # disallow conversion between datetime/timedelta,
+            # and conversions for any datetimelike to float
+            msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
+            raise TypeError(msg)
+        else:
+            return np.asarray(self, dtype=dtype)
+
+    @overload
+    def view(self) -> Self:
+        ...
+
+    @overload
+    def view(self, dtype: Literal["M8[ns]"]) -> DatetimeArray:
+        ...
+
+    @overload
+    def view(self, dtype: Literal["m8[ns]"]) -> TimedeltaArray:
+        ...
+
+    @overload
+    def view(self, dtype: Dtype | None = ...) -> ArrayLike:
+        ...
+
+    # pylint: disable-next=useless-parent-delegation
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        # we need to explicitly call super() method as long as the `@overload`s
+        #  are present in this file.
+        return super().view(dtype)
+
+    # ------------------------------------------------------------------
+    # Validation Methods
+    # TODO: try to de-duplicate these, ensure identical behavior
+
+    def _validate_comparison_value(self, other):
+        if isinstance(other, str):
+            try:
+                # GH#18435 strings get a pass from tzawareness compat
+                other = self._scalar_from_string(other)
+            except (ValueError, IncompatibleFrequency):
+                # failed to parse as Timestamp/Timedelta/Period
+                raise InvalidComparison(other)
+
+        if isinstance(other, self._recognized_scalars) or other is NaT:
+            other = self._scalar_type(other)
+            try:
+                self._check_compatible_with(other)
+            except (TypeError, IncompatibleFrequency) as err:
+                # e.g. tzawareness mismatch
+                raise InvalidComparison(other) from err
+
+        elif not is_list_like(other):
+            raise InvalidComparison(other)
+
+        elif len(other) != len(self):
+            raise ValueError("Lengths must match")
+
+        else:
+            try:
+                other = self._validate_listlike(other, allow_object=True)
+                self._check_compatible_with(other)
+            except (TypeError, IncompatibleFrequency) as err:
+                if is_object_dtype(getattr(other, "dtype", None)):
+                    # We will have to operate element-wise
+                    pass
+                else:
+                    raise InvalidComparison(other) from err
+
+        return other
+
+    def _validate_scalar(
+        self,
+        value,
+        *,
+        allow_listlike: bool = False,
+        unbox: bool = True,
+    ):
+        """
+        Validate that the input value can be cast to our scalar_type.
+
+        Parameters
+        ----------
+        value : object
+        allow_listlike: bool, default False
+            When raising an exception, whether the message should say
+            listlike inputs are allowed.
+        unbox : bool, default True
+            Whether to unbox the result before returning.  Note: unbox=False
+            skips the setitem compatibility check.
+
+        Returns
+        -------
+        self._scalar_type or NaT
+        """
+        if isinstance(value, self._scalar_type):
+            pass
+
+        elif isinstance(value, str):
+            # NB: Careful about tzawareness
+            try:
+                value = self._scalar_from_string(value)
+            except ValueError as err:
+                msg = self._validation_error_message(value, allow_listlike)
+                raise TypeError(msg) from err
+
+        elif is_valid_na_for_dtype(value, self.dtype):
+            # GH#18295
+            value = NaT
+
+        elif isna(value):
+            # if we are dt64tz and value is dt64("NaT"), dont cast to NaT,
+            #  or else we'll fail to raise in _unbox_scalar
+            msg = self._validation_error_message(value, allow_listlike)
+            raise TypeError(msg)
+
+        elif isinstance(value, self._recognized_scalars):
+            # error: Argument 1 to "Timestamp" has incompatible type "object"; expected
+            # "integer[Any] | float | str | date | datetime | datetime64"
+            value = self._scalar_type(value)  # type: ignore[arg-type]
+
+        else:
+            msg = self._validation_error_message(value, allow_listlike)
+            raise TypeError(msg)
+
+        if not unbox:
+            # NB: In general NDArrayBackedExtensionArray will unbox here;
+            #  this option exists to prevent a performance hit in
+            #  TimedeltaIndex.get_loc
+            return value
+        return self._unbox_scalar(value)
+
+    def _validation_error_message(self, value, allow_listlike: bool = False) -> str:
+        """
+        Construct an exception message on validation error.
+
+        Some methods allow only scalar inputs, while others allow either scalar
+        or listlike.
+
+        Parameters
+        ----------
+        allow_listlike: bool, default False
+
+        Returns
+        -------
+        str
+        """
+        if hasattr(value, "dtype") and getattr(value, "ndim", 0) > 0:
+            msg_got = f"{value.dtype} array"
+        else:
+            msg_got = f"'{type(value).__name__}'"
+        if allow_listlike:
+            msg = (
+                f"value should be a '{self._scalar_type.__name__}', 'NaT', "
+                f"or array of those. Got {msg_got} instead."
+            )
+        else:
+            msg = (
+                f"value should be a '{self._scalar_type.__name__}' or 'NaT'. "
+                f"Got {msg_got} instead."
+            )
+        return msg
+
+    def _validate_listlike(self, value, allow_object: bool = False):
+        if isinstance(value, type(self)):
+            if self.dtype.kind in "mM" and not allow_object:
+                # error: "DatetimeLikeArrayMixin" has no attribute "as_unit"
+                value = value.as_unit(self.unit, round_ok=False)  # type: ignore[attr-defined]
+            return value
+
+        if isinstance(value, list) and len(value) == 0:
+            # We treat empty list as our own dtype.
+            return type(self)._from_sequence([], dtype=self.dtype)
+
+        if hasattr(value, "dtype") and value.dtype == object:
+            # `array` below won't do inference if value is an Index or Series.
+            #  so do so here.  in the Index case, inferred_type may be cached.
+            if lib.infer_dtype(value) in self._infer_matches:
+                try:
+                    value = type(self)._from_sequence(value)
+                except (ValueError, TypeError):
+                    if allow_object:
+                        return value
+                    msg = self._validation_error_message(value, True)
+                    raise TypeError(msg)
+
+        # Do type inference if necessary up front (after unpacking
+        # NumpyExtensionArray)
+        # e.g. we passed PeriodIndex.values and got an ndarray of Periods
+        value = extract_array(value, extract_numpy=True)
+        value = pd_array(value)
+        value = extract_array(value, extract_numpy=True)
+
+        if is_all_strings(value):
+            # We got a StringArray
+            try:
+                # TODO: Could use from_sequence_of_strings if implemented
+                # Note: passing dtype is necessary for PeriodArray tests
+                value = type(self)._from_sequence(value, dtype=self.dtype)
+            except ValueError:
+                pass
+
+        if isinstance(value.dtype, CategoricalDtype):
+            # e.g. we have a Categorical holding self.dtype
+            if value.categories.dtype == self.dtype:
+                # TODO: do we need equal dtype or just comparable?
+                value = value._internal_get_values()
+                value = extract_array(value, extract_numpy=True)
+
+        if allow_object and is_object_dtype(value.dtype):
+            pass
+
+        elif not type(self)._is_recognized_dtype(value.dtype):
+            msg = self._validation_error_message(value, True)
+            raise TypeError(msg)
+
+        if self.dtype.kind in "mM" and not allow_object:
+            # error: "DatetimeLikeArrayMixin" has no attribute "as_unit"
+            value = value.as_unit(self.unit, round_ok=False)  # type: ignore[attr-defined]
+        return value
+
+    def _validate_setitem_value(self, value):
+        if is_list_like(value):
+            value = self._validate_listlike(value)
+        else:
+            return self._validate_scalar(value, allow_listlike=True)
+
+        return self._unbox(value)
+
+    @final
+    def _unbox(self, other) -> np.int64 | np.datetime64 | np.timedelta64 | np.ndarray:
+        """
+        Unbox either a scalar with _unbox_scalar or an instance of our own type.
+        """
+        if lib.is_scalar(other):
+            other = self._unbox_scalar(other)
+        else:
+            # same type as self
+            self._check_compatible_with(other)
+            other = other._ndarray
+        return other
+
+    # ------------------------------------------------------------------
+    # Additional array methods
+    #  These are not part of the EA API, but we implement them because
+    #  pandas assumes they're there.
+
+    @ravel_compat
+    def map(self, mapper, na_action=None):
+        from pandas import Index
+
+        result = map_array(self, mapper, na_action=na_action)
+        result = Index(result)
+
+        if isinstance(result, ABCMultiIndex):
+            return result.to_numpy()
+        else:
+            return result.array
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        """
+        Compute boolean array of whether each value is found in the
+        passed set of values.
+
+        Parameters
+        ----------
+        values : np.ndarray or ExtensionArray
+
+        Returns
+        -------
+        ndarray[bool]
+        """
+        if values.dtype.kind in "fiuc":
+            # TODO: de-duplicate with equals, validate_comparison_value
+            return np.zeros(self.shape, dtype=bool)
+
+        values = ensure_wrapped_if_datetimelike(values)
+
+        if not isinstance(values, type(self)):
+            inferable = [
+                "timedelta",
+                "timedelta64",
+                "datetime",
+                "datetime64",
+                "date",
+                "period",
+            ]
+            if values.dtype == object:
+                values = lib.maybe_convert_objects(
+                    values,  # type: ignore[arg-type]
+                    convert_non_numeric=True,
+                    dtype_if_all_nat=self.dtype,
+                )
+                if values.dtype != object:
+                    return self.isin(values)
+
+                inferred = lib.infer_dtype(values, skipna=False)
+                if inferred not in inferable:
+                    if inferred == "string":
+                        pass
+
+                    elif "mixed" in inferred:
+                        return isin(self.astype(object), values)
+                    else:
+                        return np.zeros(self.shape, dtype=bool)
+
+            try:
+                values = type(self)._from_sequence(values)
+            except ValueError:
+                return isin(self.astype(object), values)
+            else:
+                warnings.warn(
+                    # GH#53111
+                    f"The behavior of 'isin' with dtype={self.dtype} and "
+                    "castable values (e.g. strings) is deprecated. In a "
+                    "future version, these will not be considered matching "
+                    "by isin. Explicitly cast to the appropriate dtype before "
+                    "calling isin instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        if self.dtype.kind in "mM":
+            self = cast("DatetimeArray | TimedeltaArray", self)
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "as_unit"
+            values = values.as_unit(self.unit)  # type: ignore[union-attr]
+
+        try:
+            # error: Argument 1 to "_check_compatible_with" of "DatetimeLikeArrayMixin"
+            # has incompatible type "ExtensionArray | ndarray[Any, Any]"; expected
+            # "Period | Timestamp | Timedelta | NaTType"
+            self._check_compatible_with(values)  # type: ignore[arg-type]
+        except (TypeError, ValueError):
+            # Includes tzawareness mismatch and IncompatibleFrequencyError
+            return np.zeros(self.shape, dtype=bool)
+
+        # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+        # has no attribute "asi8"
+        return isin(self.asi8, values.asi8)  # type: ignore[union-attr]
+
+    # ------------------------------------------------------------------
+    # Null Handling
+
+    def isna(self) -> npt.NDArray[np.bool_]:
+        return self._isnan
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _isnan(self) -> npt.NDArray[np.bool_]:
+        """
+        return if each value is nan
+        """
+        return self.asi8 == iNaT
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _hasna(self) -> bool:
+        """
+        return if I have any nans; enables various perf speedups
+        """
+        return bool(self._isnan.any())
+
+    def _maybe_mask_results(
+        self, result: np.ndarray, fill_value=iNaT, convert=None
+    ) -> np.ndarray:
+        """
+        Parameters
+        ----------
+        result : np.ndarray
+        fill_value : object, default iNaT
+        convert : str, dtype or None
+
+        Returns
+        -------
+        result : ndarray with values replace by the fill_value
+
+        mask the result if needed, convert to the provided dtype if its not
+        None
+
+        This is an internal routine.
+        """
+        if self._hasna:
+            if convert:
+                result = result.astype(convert)
+            if fill_value is None:
+                fill_value = np.nan
+            np.putmask(result, self._isnan, fill_value)
+        return result
+
+    # ------------------------------------------------------------------
+    # Frequency Properties/Methods
+
+    @property
+    def freqstr(self) -> str | None:
+        """
+        Return the frequency object as a string if it's set, otherwise None.
+
+        Examples
+        --------
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00"], freq="D")
+        >>> idx.freqstr
+        'D'
+
+        The frequency can be inferred if there are more than 2 points:
+
+        >>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"],
+        ...                        freq="infer")
+        >>> idx.freqstr
+        '2D'
+
+        For PeriodIndex:
+
+        >>> idx = pd.PeriodIndex(["2023-1", "2023-2", "2023-3"], freq="M")
+        >>> idx.freqstr
+        'M'
+        """
+        if self.freq is None:
+            return None
+        return self.freq.freqstr
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def inferred_freq(self) -> str | None:
+        """
+        Tries to return a string representing a frequency generated by infer_freq.
+
+        Returns None if it can't autodetect the frequency.
+
+        Examples
+        --------
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"])
+        >>> idx.inferred_freq
+        '2D'
+
+        For TimedeltaIndex:
+
+        >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
+        >>> tdelta_idx
+        TimedeltaIndex(['0 days', '10 days', '20 days'],
+                       dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.inferred_freq
+        '10D'
+        """
+        if self.ndim != 1:
+            return None
+        try:
+            return frequencies.infer_freq(self)
+        except ValueError:
+            return None
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _resolution_obj(self) -> Resolution | None:
+        freqstr = self.freqstr
+        if freqstr is None:
+            return None
+        try:
+            return Resolution.get_reso_from_freqstr(freqstr)
+        except KeyError:
+            return None
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def resolution(self) -> str:
+        """
+        Returns day, hour, minute, second, millisecond or microsecond
+        """
+        # error: Item "None" of "Optional[Any]" has no attribute "attrname"
+        return self._resolution_obj.attrname  # type: ignore[union-attr]
+
+    # monotonicity/uniqueness properties are called via frequencies.infer_freq,
+    #  see GH#23789
+
+    @property
+    def _is_monotonic_increasing(self) -> bool:
+        return algos.is_monotonic(self.asi8, timelike=True)[0]
+
+    @property
+    def _is_monotonic_decreasing(self) -> bool:
+        return algos.is_monotonic(self.asi8, timelike=True)[1]
+
+    @property
+    def _is_unique(self) -> bool:
+        return len(unique1d(self.asi8.ravel("K"))) == self.size
+
+    # ------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _cmp_method(self, other, op):
+        if self.ndim > 1 and getattr(other, "shape", None) == self.shape:
+            # TODO: handle 2D-like listlikes
+            return op(self.ravel(), other.ravel()).reshape(self.shape)
+
+        try:
+            other = self._validate_comparison_value(other)
+        except InvalidComparison:
+            return invalid_comparison(self, other, op)
+
+        dtype = getattr(other, "dtype", None)
+        if is_object_dtype(dtype):
+            # We have to use comp_method_OBJECT_ARRAY instead of numpy
+            #  comparison otherwise it would raise when comparing to None
+            result = ops.comp_method_OBJECT_ARRAY(
+                op, np.asarray(self.astype(object)), other
+            )
+            return result
+        if other is NaT:
+            if op is operator.ne:
+                result = np.ones(self.shape, dtype=bool)
+            else:
+                result = np.zeros(self.shape, dtype=bool)
+            return result
+
+        if not isinstance(self.dtype, PeriodDtype):
+            self = cast(TimelikeOps, self)
+            if self._creso != other._creso:
+                if not isinstance(other, type(self)):
+                    # i.e. Timedelta/Timestamp, cast to ndarray and let
+                    #  compare_mismatched_resolutions handle broadcasting
+                    try:
+                        # GH#52080 see if we can losslessly cast to shared unit
+                        other = other.as_unit(self.unit, round_ok=False)
+                    except ValueError:
+                        other_arr = np.array(other.asm8)
+                        return compare_mismatched_resolutions(
+                            self._ndarray, other_arr, op
+                        )
+                else:
+                    other_arr = other._ndarray
+                    return compare_mismatched_resolutions(self._ndarray, other_arr, op)
+
+        other_vals = self._unbox(other)
+        # GH#37462 comparison on i8 values is almost 2x faster than M8/m8
+        result = op(self._ndarray.view("i8"), other_vals.view("i8"))
+
+        o_mask = isna(other)
+        mask = self._isnan | o_mask
+        if mask.any():
+            nat_result = op is operator.ne
+            np.putmask(result, mask, nat_result)
+
+        return result
+
+    # pow is invalid for all three subclasses; TimedeltaArray will override
+    #  the multiplication and division ops
+    __pow__ = _make_unpacked_invalid_op("__pow__")
+    __rpow__ = _make_unpacked_invalid_op("__rpow__")
+    __mul__ = _make_unpacked_invalid_op("__mul__")
+    __rmul__ = _make_unpacked_invalid_op("__rmul__")
+    __truediv__ = _make_unpacked_invalid_op("__truediv__")
+    __rtruediv__ = _make_unpacked_invalid_op("__rtruediv__")
+    __floordiv__ = _make_unpacked_invalid_op("__floordiv__")
+    __rfloordiv__ = _make_unpacked_invalid_op("__rfloordiv__")
+    __mod__ = _make_unpacked_invalid_op("__mod__")
+    __rmod__ = _make_unpacked_invalid_op("__rmod__")
+    __divmod__ = _make_unpacked_invalid_op("__divmod__")
+    __rdivmod__ = _make_unpacked_invalid_op("__rdivmod__")
+
+    @final
+    def _get_i8_values_and_mask(
+        self, other
+    ) -> tuple[int | npt.NDArray[np.int64], None | npt.NDArray[np.bool_]]:
+        """
+        Get the int64 values and b_mask to pass to add_overflowsafe.
+        """
+        if isinstance(other, Period):
+            i8values = other.ordinal
+            mask = None
+        elif isinstance(other, (Timestamp, Timedelta)):
+            i8values = other._value
+            mask = None
+        else:
+            # PeriodArray, DatetimeArray, TimedeltaArray
+            mask = other._isnan
+            i8values = other.asi8
+        return i8values, mask
+
+    @final
+    def _get_arithmetic_result_freq(self, other) -> BaseOffset | None:
+        """
+        Check if we can preserve self.freq in addition or subtraction.
+        """
+        # Adding or subtracting a Timedelta/Timestamp scalar is freq-preserving
+        #  whenever self.freq is a Tick
+        if isinstance(self.dtype, PeriodDtype):
+            return self.freq
+        elif not lib.is_scalar(other):
+            return None
+        elif isinstance(self.freq, Tick):
+            # In these cases
+            return self.freq
+        return None
+
+    @final
+    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
+        if not lib.is_np_dtype(self.dtype, "m"):
+            raise TypeError(
+                f"cannot add {type(self).__name__} and {type(other).__name__}"
+            )
+
+        self = cast("TimedeltaArray", self)
+
+        from pandas.core.arrays import DatetimeArray
+        from pandas.core.arrays.datetimes import tz_to_dtype
+
+        assert other is not NaT
+        if isna(other):
+            # i.e. np.datetime64("NaT")
+            # In this case we specifically interpret NaT as a datetime, not
+            # the timedelta interpretation we would get by returning self + NaT
+            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self.unit}]")
+            # Preserve our resolution
+            return DatetimeArray._simple_new(result, dtype=result.dtype)
+
+        other = Timestamp(other)
+        self, other = self._ensure_matching_resos(other)
+        self = cast("TimedeltaArray", self)
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        result = add_overflowsafe(self.asi8, np.asarray(other_i8, dtype="i8"))
+        res_values = result.view(f"M8[{self.unit}]")
+
+        dtype = tz_to_dtype(tz=other.tz, unit=self.unit)
+        res_values = result.view(f"M8[{self.unit}]")
+        new_freq = self._get_arithmetic_result_freq(other)
+        return DatetimeArray._simple_new(res_values, dtype=dtype, freq=new_freq)
+
+    @final
+    def _add_datetime_arraylike(self, other: DatetimeArray) -> DatetimeArray:
+        if not lib.is_np_dtype(self.dtype, "m"):
+            raise TypeError(
+                f"cannot add {type(self).__name__} and {type(other).__name__}"
+            )
+
+        # defer to DatetimeArray.__add__
+        return other + self
+
+    @final
+    def _sub_datetimelike_scalar(
+        self, other: datetime | np.datetime64
+    ) -> TimedeltaArray:
+        if self.dtype.kind != "M":
+            raise TypeError(f"cannot subtract a datelike from a {type(self).__name__}")
+
+        self = cast("DatetimeArray", self)
+        # subtract a datetime from myself, yielding a ndarray[timedelta64[ns]]
+
+        if isna(other):
+            # i.e. np.datetime64("NaT")
+            return self - NaT
+
+        ts = Timestamp(other)
+
+        self, ts = self._ensure_matching_resos(ts)
+        return self._sub_datetimelike(ts)
+
+    @final
+    def _sub_datetime_arraylike(self, other: DatetimeArray) -> TimedeltaArray:
+        if self.dtype.kind != "M":
+            raise TypeError(f"cannot subtract a datelike from a {type(self).__name__}")
+
+        if len(self) != len(other):
+            raise ValueError("cannot add indices of unequal length")
+
+        self = cast("DatetimeArray", self)
+
+        self, other = self._ensure_matching_resos(other)
+        return self._sub_datetimelike(other)
+
+    @final
+    def _sub_datetimelike(self, other: Timestamp | DatetimeArray) -> TimedeltaArray:
+        self = cast("DatetimeArray", self)
+
+        from pandas.core.arrays import TimedeltaArray
+
+        try:
+            self._assert_tzawareness_compat(other)
+        except TypeError as err:
+            new_message = str(err).replace("compare", "subtract")
+            raise type(err)(new_message) from err
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        res_values = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
+        res_m8 = res_values.view(f"timedelta64[{self.unit}]")
+
+        new_freq = self._get_arithmetic_result_freq(other)
+        new_freq = cast("Tick | None", new_freq)
+        return TimedeltaArray._simple_new(res_m8, dtype=res_m8.dtype, freq=new_freq)
+
+    @final
+    def _add_period(self, other: Period) -> PeriodArray:
+        if not lib.is_np_dtype(self.dtype, "m"):
+            raise TypeError(f"cannot add Period to a {type(self).__name__}")
+
+        # We will wrap in a PeriodArray and defer to the reversed operation
+        from pandas.core.arrays.period import PeriodArray
+
+        i8vals = np.broadcast_to(other.ordinal, self.shape)
+        dtype = PeriodDtype(other.freq)
+        parr = PeriodArray(i8vals, dtype=dtype)
+        return parr + self
+
+    def _add_offset(self, offset):
+        raise AbstractMethodError(self)
+
+    def _add_timedeltalike_scalar(self, other):
+        """
+        Add a delta of a timedeltalike
+
+        Returns
+        -------
+        Same type as self
+        """
+        if isna(other):
+            # i.e np.timedelta64("NaT")
+            new_values = np.empty(self.shape, dtype="i8").view(self._ndarray.dtype)
+            new_values.fill(iNaT)
+            return type(self)._simple_new(new_values, dtype=self.dtype)
+
+        # PeriodArray overrides, so we only get here with DTA/TDA
+        self = cast("DatetimeArray | TimedeltaArray", self)
+        other = Timedelta(other)
+        self, other = self._ensure_matching_resos(other)
+        return self._add_timedeltalike(other)
+
+    def _add_timedelta_arraylike(self, other: TimedeltaArray):
+        """
+        Add a delta of a TimedeltaIndex
+
+        Returns
+        -------
+        Same type as self
+        """
+        # overridden by PeriodArray
+
+        if len(self) != len(other):
+            raise ValueError("cannot add indices of unequal length")
+
+        self = cast("DatetimeArray | TimedeltaArray", self)
+
+        self, other = self._ensure_matching_resos(other)
+        return self._add_timedeltalike(other)
+
+    @final
+    def _add_timedeltalike(self, other: Timedelta | TimedeltaArray):
+        self = cast("DatetimeArray | TimedeltaArray", self)
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        new_values = add_overflowsafe(self.asi8, np.asarray(other_i8, dtype="i8"))
+        res_values = new_values.view(self._ndarray.dtype)
+
+        new_freq = self._get_arithmetic_result_freq(other)
+
+        # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+        # incompatible type "Union[dtype[datetime64], DatetimeTZDtype,
+        # dtype[timedelta64]]"; expected "Union[dtype[datetime64], DatetimeTZDtype]"
+        return type(self)._simple_new(
+            res_values, dtype=self.dtype, freq=new_freq  # type: ignore[arg-type]
+        )
+
+    @final
+    def _add_nat(self):
+        """
+        Add pd.NaT to self
+        """
+        if isinstance(self.dtype, PeriodDtype):
+            raise TypeError(
+                f"Cannot add {type(self).__name__} and {type(NaT).__name__}"
+            )
+        self = cast("TimedeltaArray | DatetimeArray", self)
+
+        # GH#19124 pd.NaT is treated like a timedelta for both timedelta
+        # and datetime dtypes
+        result = np.empty(self.shape, dtype=np.int64)
+        result.fill(iNaT)
+        result = result.view(self._ndarray.dtype)  # preserve reso
+        # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+        # incompatible type "Union[dtype[timedelta64], dtype[datetime64],
+        # DatetimeTZDtype]"; expected "Union[dtype[datetime64], DatetimeTZDtype]"
+        return type(self)._simple_new(
+            result, dtype=self.dtype, freq=None  # type: ignore[arg-type]
+        )
+
+    @final
+    def _sub_nat(self):
+        """
+        Subtract pd.NaT from self
+        """
+        # GH#19124 Timedelta - datetime is not in general well-defined.
+        # We make an exception for pd.NaT, which in this case quacks
+        # like a timedelta.
+        # For datetime64 dtypes by convention we treat NaT as a datetime, so
+        # this subtraction returns a timedelta64 dtype.
+        # For period dtype, timedelta64 is a close-enough return dtype.
+        result = np.empty(self.shape, dtype=np.int64)
+        result.fill(iNaT)
+        if self.dtype.kind in "mM":
+            # We can retain unit in dtype
+            self = cast("DatetimeArray| TimedeltaArray", self)
+            return result.view(f"timedelta64[{self.unit}]")
+        else:
+            return result.view("timedelta64[ns]")
+
+    @final
+    def _sub_periodlike(self, other: Period | PeriodArray) -> npt.NDArray[np.object_]:
+        # If the operation is well-defined, we return an object-dtype ndarray
+        # of DateOffsets.  Null entries are filled with pd.NaT
+        if not isinstance(self.dtype, PeriodDtype):
+            raise TypeError(
+                f"cannot subtract {type(other).__name__} from {type(self).__name__}"
+            )
+
+        self = cast("PeriodArray", self)
+        self._check_compatible_with(other)
+
+        other_i8, o_mask = self._get_i8_values_and_mask(other)
+        new_i8_data = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
+        new_data = np.array([self.freq.base * x for x in new_i8_data])
+
+        if o_mask is None:
+            # i.e. Period scalar
+            mask = self._isnan
+        else:
+            # i.e. PeriodArray
+            mask = self._isnan | o_mask
+        new_data[mask] = NaT
+        return new_data
+
+    @final
+    def _addsub_object_array(self, other: npt.NDArray[np.object_], op):
+        """
+        Add or subtract array-like of DateOffset objects
+
+        Parameters
+        ----------
+        other : np.ndarray[object]
+        op : {operator.add, operator.sub}
+
+        Returns
+        -------
+        np.ndarray[object]
+            Except in fastpath case with length 1 where we operate on the
+            contained scalar.
+        """
+        assert op in [operator.add, operator.sub]
+        if len(other) == 1 and self.ndim == 1:
+            # Note: without this special case, we could annotate return type
+            #  as ndarray[object]
+            # If both 1D then broadcasting is unambiguous
+            return op(self, other[0])
+
+        warnings.warn(
+            "Adding/subtracting object-dtype array to "
+            f"{type(self).__name__} not vectorized.",
+            PerformanceWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        # Caller is responsible for broadcasting if necessary
+        assert self.shape == other.shape, (self.shape, other.shape)
+
+        res_values = op(self.astype("O"), np.asarray(other))
+        return res_values
+
+    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs) -> Self:
+        if name not in {"cummin", "cummax"}:
+            raise TypeError(f"Accumulation {name} not supported for {type(self)}")
+
+        op = getattr(datetimelike_accumulations, name)
+        result = op(self.copy(), skipna=skipna, **kwargs)
+
+        return type(self)._simple_new(result, dtype=self.dtype)
+
+    @unpack_zerodim_and_defer("__add__")
+    def __add__(self, other):
+        other_dtype = getattr(other, "dtype", None)
+        other = ensure_wrapped_if_datetimelike(other)
+
+        # scalar others
+        if other is NaT:
+            result = self._add_nat()
+        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
+            result = self._add_timedeltalike_scalar(other)
+        elif isinstance(other, BaseOffset):
+            # specifically _not_ a Tick
+            result = self._add_offset(other)
+        elif isinstance(other, (datetime, np.datetime64)):
+            result = self._add_datetimelike_scalar(other)
+        elif isinstance(other, Period) and lib.is_np_dtype(self.dtype, "m"):
+            result = self._add_period(other)
+        elif lib.is_integer(other):
+            # This check must come after the check for np.timedelta64
+            # as is_integer returns True for these
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.add)
+
+        # array-like others
+        elif lib.is_np_dtype(other_dtype, "m"):
+            # TimedeltaIndex, ndarray[timedelta64]
+            result = self._add_timedelta_arraylike(other)
+        elif is_object_dtype(other_dtype):
+            # e.g. Array/Index of DateOffset objects
+            result = self._addsub_object_array(other, operator.add)
+        elif lib.is_np_dtype(other_dtype, "M") or isinstance(
+            other_dtype, DatetimeTZDtype
+        ):
+            # DatetimeIndex, ndarray[datetime64]
+            return self._add_datetime_arraylike(other)
+        elif is_integer_dtype(other_dtype):
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.add)
+        else:
+            # Includes Categorical, other ExtensionArrays
+            # For PeriodDtype, if self is a TimedeltaArray and other is a
+            #  PeriodArray with  a timedelta-like (i.e. Tick) freq, this
+            #  operation is valid.  Defer to the PeriodArray implementation.
+            #  In remaining cases, this will end up raising TypeError.
+            return NotImplemented
+
+        if isinstance(result, np.ndarray) and lib.is_np_dtype(result.dtype, "m"):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._from_sequence(result)
+        return result
+
+    def __radd__(self, other):
+        # alias for __add__
+        return self.__add__(other)
+
+    @unpack_zerodim_and_defer("__sub__")
+    def __sub__(self, other):
+        other_dtype = getattr(other, "dtype", None)
+        other = ensure_wrapped_if_datetimelike(other)
+
+        # scalar others
+        if other is NaT:
+            result = self._sub_nat()
+        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
+            result = self._add_timedeltalike_scalar(-other)
+        elif isinstance(other, BaseOffset):
+            # specifically _not_ a Tick
+            result = self._add_offset(-other)
+        elif isinstance(other, (datetime, np.datetime64)):
+            result = self._sub_datetimelike_scalar(other)
+        elif lib.is_integer(other):
+            # This check must come after the check for np.timedelta64
+            # as is_integer returns True for these
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.sub)
+
+        elif isinstance(other, Period):
+            result = self._sub_periodlike(other)
+
+        # array-like others
+        elif lib.is_np_dtype(other_dtype, "m"):
+            # TimedeltaIndex, ndarray[timedelta64]
+            result = self._add_timedelta_arraylike(-other)
+        elif is_object_dtype(other_dtype):
+            # e.g. Array/Index of DateOffset objects
+            result = self._addsub_object_array(other, operator.sub)
+        elif lib.is_np_dtype(other_dtype, "M") or isinstance(
+            other_dtype, DatetimeTZDtype
+        ):
+            # DatetimeIndex, ndarray[datetime64]
+            result = self._sub_datetime_arraylike(other)
+        elif isinstance(other_dtype, PeriodDtype):
+            # PeriodIndex
+            result = self._sub_periodlike(other)
+        elif is_integer_dtype(other_dtype):
+            if not isinstance(self.dtype, PeriodDtype):
+                raise integer_op_not_supported(self)
+            obj = cast("PeriodArray", self)
+            result = obj._addsub_int_array_or_scalar(other * obj.dtype._n, operator.sub)
+        else:
+            # Includes ExtensionArrays, float_dtype
+            return NotImplemented
+
+        if isinstance(result, np.ndarray) and lib.is_np_dtype(result.dtype, "m"):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._from_sequence(result)
+        return result
+
+    def __rsub__(self, other):
+        other_dtype = getattr(other, "dtype", None)
+        other_is_dt64 = lib.is_np_dtype(other_dtype, "M") or isinstance(
+            other_dtype, DatetimeTZDtype
+        )
+
+        if other_is_dt64 and lib.is_np_dtype(self.dtype, "m"):
+            # ndarray[datetime64] cannot be subtracted from self, so
+            # we need to wrap in DatetimeArray/Index and flip the operation
+            if lib.is_scalar(other):
+                # i.e. np.datetime64 object
+                return Timestamp(other) - self
+            if not isinstance(other, DatetimeLikeArrayMixin):
+                # Avoid down-casting DatetimeIndex
+                from pandas.core.arrays import DatetimeArray
+
+                other = DatetimeArray._from_sequence(other)
+            return other - self
+        elif self.dtype.kind == "M" and hasattr(other, "dtype") and not other_is_dt64:
+            # GH#19959 datetime - datetime is well-defined as timedelta,
+            # but any other type - datetime is not well-defined.
+            raise TypeError(
+                f"cannot subtract {type(self).__name__} from {type(other).__name__}"
+            )
+        elif isinstance(self.dtype, PeriodDtype) and lib.is_np_dtype(other_dtype, "m"):
+            # TODO: Can we simplify/generalize these cases at all?
+            raise TypeError(f"cannot subtract {type(self).__name__} from {other.dtype}")
+        elif lib.is_np_dtype(self.dtype, "m"):
+            self = cast("TimedeltaArray", self)
+            return (-self) + other
+
+        # We get here with e.g. datetime objects
+        return -(self - other)
+
+    def __iadd__(self, other) -> Self:
+        result = self + other
+        self[:] = result[:]
+
+        if not isinstance(self.dtype, PeriodDtype):
+            # restore freq, which is invalidated by setitem
+            self._freq = result.freq
+        return self
+
+    def __isub__(self, other) -> Self:
+        result = self - other
+        self[:] = result[:]
+
+        if not isinstance(self.dtype, PeriodDtype):
+            # restore freq, which is invalidated by setitem
+            self._freq = result.freq
+        return self
+
+    # --------------------------------------------------------------
+    # Reductions
+
+    @_period_dispatch
+    def _quantile(
+        self,
+        qs: npt.NDArray[np.float64],
+        interpolation: str,
+    ) -> Self:
+        return super()._quantile(qs=qs, interpolation=interpolation)
+
+    @_period_dispatch
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
+        """
+        Return the minimum value of the Array or minimum along
+        an axis.
+
+        See Also
+        --------
+        numpy.ndarray.min
+        Index.min : Return the minimum value in an Index.
+        Series.min : Return the minimum value in a Series.
+        """
+        nv.validate_min((), kwargs)
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        result = nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    @_period_dispatch
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
+        """
+        Return the maximum value of the Array or maximum along
+        an axis.
+
+        See Also
+        --------
+        numpy.ndarray.max
+        Index.max : Return the maximum value in an Index.
+        Series.max : Return the maximum value in a Series.
+        """
+        nv.validate_max((), kwargs)
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        result = nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0):
+        """
+        Return the mean value of the Array.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Whether to ignore any NaT elements.
+        axis : int, optional, default 0
+
+        Returns
+        -------
+        scalar
+            Timestamp or Timedelta.
+
+        See Also
+        --------
+        numpy.ndarray.mean : Returns the average of array elements along a given axis.
+        Series.mean : Return the mean value in a Series.
+
+        Notes
+        -----
+        mean is only defined for Datetime and Timedelta dtypes, not for Period.
+
+        Examples
+        --------
+        For :class:`pandas.DatetimeIndex`:
+
+        >>> idx = pd.date_range('2001-01-01 00:00', periods=3)
+        >>> idx
+        DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.mean()
+        Timestamp('2001-01-02 00:00:00')
+
+        For :class:`pandas.TimedeltaIndex`:
+
+        >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days', '2 days', '3 days'],
+                        dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.mean()
+        Timedelta('2 days 00:00:00')
+        """
+        if isinstance(self.dtype, PeriodDtype):
+            # See discussion in GH#24757
+            raise TypeError(
+                f"mean is not implemented for {type(self).__name__} since the "
+                "meaning is ambiguous.  An alternative is "
+                "obj.to_timestamp(how='start').mean()"
+            )
+
+        result = nanops.nanmean(
+            self._ndarray, axis=axis, skipna=skipna, mask=self.isna()
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    @_period_dispatch
+    def median(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
+        nv.validate_median((), kwargs)
+
+        if axis is not None and abs(axis) >= self.ndim:
+            raise ValueError("abs(axis) must be less than ndim")
+
+        result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def _mode(self, dropna: bool = True):
+        mask = None
+        if dropna:
+            mask = self.isna()
+
+        i8modes = algorithms.mode(self.view("i8"), mask=mask)
+        npmodes = i8modes.view(self._ndarray.dtype)
+        npmodes = cast(np.ndarray, npmodes)
+        return self._from_backing_data(npmodes)
+
+    # ------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        dtype = self.dtype
+        if dtype.kind == "M":
+            # Adding/multiplying datetimes is not valid
+            if how in ["sum", "prod", "cumsum", "cumprod", "var", "skew"]:
+                raise TypeError(f"datetime64 type does not support {how} operations")
+            if how in ["any", "all"]:
+                # GH#34479
+                warnings.warn(
+                    f"'{how}' with datetime64 dtypes is deprecated and will raise in a "
+                    f"future version. Use (obj != pd.Timestamp(0)).{how}() instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        elif isinstance(dtype, PeriodDtype):
+            # Adding/multiplying Periods is not valid
+            if how in ["sum", "prod", "cumsum", "cumprod", "var", "skew"]:
+                raise TypeError(f"Period type does not support {how} operations")
+            if how in ["any", "all"]:
+                # GH#34479
+                warnings.warn(
+                    f"'{how}' with PeriodDtype is deprecated and will raise in a "
+                    f"future version. Use (obj != pd.Period(0, freq)).{how}() instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            # timedeltas we can add but not multiply
+            if how in ["prod", "cumprod", "skew", "var"]:
+                raise TypeError(f"timedelta64 type does not support {how} operations")
+
+        # All of the functions implemented here are ordinal, so we can
+        #  operate on the tz-naive equivalents
+        npvalues = self._ndarray.view("M8[ns]")
+
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        res_values = op._cython_op_ndim_compat(
+            npvalues,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=None,
+            **kwargs,
+        )
+
+        if op.how in op.cast_blocklist:
+            # i.e. how in ["rank"], since other cast_blocklist methods don't go
+            #  through cython_operation
+            return res_values
+
+        # We did a view to M8[ns] above, now we go the other direction
+        assert res_values.dtype == "M8[ns]"
+        if how in ["std", "sem"]:
+            from pandas.core.arrays import TimedeltaArray
+
+            if isinstance(self.dtype, PeriodDtype):
+                raise TypeError("'std' and 'sem' are not valid for PeriodDtype")
+            self = cast("DatetimeArray | TimedeltaArray", self)
+            new_dtype = f"m8[{self.unit}]"
+            res_values = res_values.view(new_dtype)
+            return TimedeltaArray._simple_new(res_values, dtype=res_values.dtype)
+
+        res_values = res_values.view(self._ndarray.dtype)
+        return self._from_backing_data(res_values)
+
+
+class DatelikeOps(DatetimeLikeArrayMixin):
+    """
+    Common ops for DatetimeIndex/PeriodIndex, but not TimedeltaIndex.
+    """
+
+    @Substitution(
+        URL="https://docs.python.org/3/library/datetime.html"
+        "#strftime-and-strptime-behavior"
+    )
+    def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
+        """
+        Convert to Index using specified date_format.
+
+        Return an Index of formatted strings specified by date_format, which
+        supports the same string format as the python standard library. Details
+        of the string format can be found in `python string format
+        doc <%(URL)s>`__.
+
+        Formats supported by the C `strftime` API but not by the python string format
+        doc (such as `"%%R"`, `"%%r"`) are not officially supported and should be
+        preferably replaced with their supported equivalents (such as `"%%H:%%M"`,
+        `"%%I:%%M:%%S %%p"`).
+
+        Note that `PeriodIndex` support additional directives, detailed in
+        `Period.strftime`.
+
+        Parameters
+        ----------
+        date_format : str
+            Date format string (e.g. "%%Y-%%m-%%d").
+
+        Returns
+        -------
+        ndarray[object]
+            NumPy ndarray of formatted strings.
+
+        See Also
+        --------
+        to_datetime : Convert the given argument to datetime.
+        DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
+        DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
+        DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
+        Timestamp.strftime : Format a single Timestamp.
+        Period.strftime : Format a single Period.
+
+        Examples
+        --------
+        >>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
+        ...                     periods=3, freq='s')
+        >>> rng.strftime('%%B %%d, %%Y, %%r')
+        Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
+               'March 10, 2018, 09:00:02 AM'],
+              dtype='object')
+        """
+        result = self._format_native_types(date_format=date_format, na_rep=np.nan)
+        if using_string_dtype():
+            from pandas import StringDtype
+
+            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
+        return result.astype(object, copy=False)
+
+
+_round_doc = """
+    Perform {op} operation on the data to the specified `freq`.
+
+    Parameters
+    ----------
+    freq : str or Offset
+        The frequency level to {op} the index to. Must be a fixed
+        frequency like 'S' (second) not 'ME' (month end). See
+        :ref:`frequency aliases <timeseries.offset_aliases>` for
+        a list of possible `freq` values.
+    ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+        Only relevant for DatetimeIndex:
+
+        - 'infer' will attempt to infer fall dst-transition hours based on
+          order
+        - bool-ndarray where True signifies a DST time, False designates
+          a non-DST time (note that this flag is only applicable for
+          ambiguous times)
+        - 'NaT' will return NaT where there are ambiguous times
+        - 'raise' will raise an AmbiguousTimeError if there are ambiguous
+          times.
+
+    nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise'
+        A nonexistent time does not exist in a particular timezone
+        where clocks moved forward due to DST.
+
+        - 'shift_forward' will shift the nonexistent time forward to the
+          closest existing time
+        - 'shift_backward' will shift the nonexistent time backward to the
+          closest existing time
+        - 'NaT' will return NaT where there are nonexistent times
+        - timedelta objects will shift nonexistent times by the timedelta
+        - 'raise' will raise an NonExistentTimeError if there are
+          nonexistent times.
+
+    Returns
+    -------
+    DatetimeIndex, TimedeltaIndex, or Series
+        Index of the same type for a DatetimeIndex or TimedeltaIndex,
+        or a Series with the same index for a Series.
+
+    Raises
+    ------
+    ValueError if the `freq` cannot be converted.
+
+    Notes
+    -----
+    If the timestamps have a timezone, {op}ing will take place relative to the
+    local ("wall") time and re-localized to the same timezone. When {op}ing
+    near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
+    control the re-localization behavior.
+
+    Examples
+    --------
+    **DatetimeIndex**
+
+    >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
+    >>> rng
+    DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
+                   '2018-01-01 12:01:00'],
+                  dtype='datetime64[ns]', freq='min')
+    """
+
+_round_example = """>>> rng.round('h')
+    DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
+                   '2018-01-01 12:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Series**
+
+    >>> pd.Series(rng).dt.round("h")
+    0   2018-01-01 12:00:00
+    1   2018-01-01 12:00:00
+    2   2018-01-01 12:00:00
+    dtype: datetime64[ns]
+
+    When rounding near a daylight savings time transition, use ``ambiguous`` or
+    ``nonexistent`` to control how the timestamp should be re-localized.
+
+    >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")
+
+    >>> rng_tz.floor("2h", ambiguous=False)
+    DatetimeIndex(['2021-10-31 02:00:00+01:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+
+    >>> rng_tz.floor("2h", ambiguous=True)
+    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+    """
+
+_floor_example = """>>> rng.floor('h')
+    DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
+                   '2018-01-01 12:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Series**
+
+    >>> pd.Series(rng).dt.floor("h")
+    0   2018-01-01 11:00:00
+    1   2018-01-01 12:00:00
+    2   2018-01-01 12:00:00
+    dtype: datetime64[ns]
+
+    When rounding near a daylight savings time transition, use ``ambiguous`` or
+    ``nonexistent`` to control how the timestamp should be re-localized.
+
+    >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")
+
+    >>> rng_tz.floor("2h", ambiguous=False)
+    DatetimeIndex(['2021-10-31 02:00:00+01:00'],
+                 dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+
+    >>> rng_tz.floor("2h", ambiguous=True)
+    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+    """
+
+_ceil_example = """>>> rng.ceil('h')
+    DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
+                   '2018-01-01 13:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Series**
+
+    >>> pd.Series(rng).dt.ceil("h")
+    0   2018-01-01 12:00:00
+    1   2018-01-01 12:00:00
+    2   2018-01-01 13:00:00
+    dtype: datetime64[ns]
+
+    When rounding near a daylight savings time transition, use ``ambiguous`` or
+    ``nonexistent`` to control how the timestamp should be re-localized.
+
+    >>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam")
+
+    >>> rng_tz.ceil("h", ambiguous=False)
+    DatetimeIndex(['2021-10-31 02:00:00+01:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+
+    >>> rng_tz.ceil("h", ambiguous=True)
+    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
+                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
+    """
+
+
+class TimelikeOps(DatetimeLikeArrayMixin):
+    """
+    Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.
+    """
+
+    _default_dtype: np.dtype
+
+    def __init__(
+        self, values, dtype=None, freq=lib.no_default, copy: bool = False
+    ) -> None:
+        warnings.warn(
+            # GH#55623
+            f"{type(self).__name__}.__init__ is deprecated and will be "
+            "removed in a future version. Use pd.array instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        values = extract_array(values, extract_numpy=True)
+        if isinstance(values, IntegerArray):
+            values = values.to_numpy("int64", na_value=iNaT)
+
+        inferred_freq = getattr(values, "_freq", None)
+        explicit_none = freq is None
+        freq = freq if freq is not lib.no_default else None
+
+        if isinstance(values, type(self)):
+            if explicit_none:
+                # don't inherit from values
+                pass
+            elif freq is None:
+                freq = values.freq
+            elif freq and values.freq:
+                freq = to_offset(freq)
+                freq = _validate_inferred_freq(freq, values.freq)
+
+            if dtype is not None and dtype != values.dtype:
+                # TODO: we only have tests for this for DTA, not TDA (2022-07-01)
+                raise TypeError(
+                    f"dtype={dtype} does not match data dtype {values.dtype}"
+                )
+
+            dtype = values.dtype
+            values = values._ndarray
+
+        elif dtype is None:
+            if isinstance(values, np.ndarray) and values.dtype.kind in "Mm":
+                dtype = values.dtype
+            else:
+                dtype = self._default_dtype
+                if isinstance(values, np.ndarray) and values.dtype == "i8":
+                    values = values.view(dtype)
+
+        if not isinstance(values, np.ndarray):
+            raise ValueError(
+                f"Unexpected type '{type(values).__name__}'. 'values' must be a "
+                f"{type(self).__name__}, ndarray, or Series or Index "
+                "containing one of those."
+            )
+        if values.ndim not in [1, 2]:
+            raise ValueError("Only 1-dimensional input arrays are supported.")
+
+        if values.dtype == "i8":
+            # for compat with datetime/timedelta/period shared methods,
+            #  we can sometimes get here with int64 values.  These represent
+            #  nanosecond UTC (or tz-naive) unix timestamps
+            if dtype is None:
+                dtype = self._default_dtype
+                values = values.view(self._default_dtype)
+            elif lib.is_np_dtype(dtype, "mM"):
+                values = values.view(dtype)
+            elif isinstance(dtype, DatetimeTZDtype):
+                kind = self._default_dtype.kind
+                new_dtype = f"{kind}8[{dtype.unit}]"
+                values = values.view(new_dtype)
+
+        dtype = self._validate_dtype(values, dtype)
+
+        if freq == "infer":
+            raise ValueError(
+                f"Frequency inference not allowed in {type(self).__name__}.__init__. "
+                "Use 'pd.array()' instead."
+            )
+
+        if copy:
+            values = values.copy()
+        if freq:
+            freq = to_offset(freq)
+            if values.dtype.kind == "m" and not isinstance(freq, Tick):
+                raise TypeError("TimedeltaArray/Index freq must be a Tick")
+
+        NDArrayBacked.__init__(self, values=values, dtype=dtype)
+        self._freq = freq
+
+        if inferred_freq is None and freq is not None:
+            type(self)._validate_frequency(self, freq)
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        raise AbstractMethodError(cls)
+
+    @property
+    def freq(self):
+        """
+        Return the frequency object if it is set, otherwise None.
+        """
+        return self._freq
+
+    @freq.setter
+    def freq(self, value) -> None:
+        if value is not None:
+            value = to_offset(value)
+            self._validate_frequency(self, value)
+            if self.dtype.kind == "m" and not isinstance(value, Tick):
+                raise TypeError("TimedeltaArray/Index freq must be a Tick")
+
+            if self.ndim > 1:
+                raise ValueError("Cannot set freq with ndim > 1")
+
+        self._freq = value
+
+    @final
+    def _maybe_pin_freq(self, freq, validate_kwds: dict):
+        """
+        Constructor helper to pin the appropriate `freq` attribute.  Assumes
+        that self._freq is currently set to any freq inferred in
+        _from_sequence_not_strict.
+        """
+        if freq is None:
+            # user explicitly passed None -> override any inferred_freq
+            self._freq = None
+        elif freq == "infer":
+            # if self._freq is *not* None then we already inferred a freq
+            #  and there is nothing left to do
+            if self._freq is None:
+                # Set _freq directly to bypass duplicative _validate_frequency
+                # check.
+                self._freq = to_offset(self.inferred_freq)
+        elif freq is lib.no_default:
+            # user did not specify anything, keep inferred freq if the original
+            #  data had one, otherwise do nothing
+            pass
+        elif self._freq is None:
+            # We cannot inherit a freq from the data, so we need to validate
+            #  the user-passed freq
+            freq = to_offset(freq)
+            type(self)._validate_frequency(self, freq, **validate_kwds)
+            self._freq = freq
+        else:
+            # Otherwise we just need to check that the user-passed freq
+            #  doesn't conflict with the one we already have.
+            freq = to_offset(freq)
+            _validate_inferred_freq(freq, self._freq)
+
+    @final
+    @classmethod
+    def _validate_frequency(cls, index, freq: BaseOffset, **kwargs):
+        """
+        Validate that a frequency is compatible with the values of a given
+        Datetime Array/Index or Timedelta Array/Index
+
+        Parameters
+        ----------
+        index : DatetimeIndex or TimedeltaIndex
+            The index on which to determine if the given frequency is valid
+        freq : DateOffset
+            The frequency to validate
+        """
+        inferred = index.inferred_freq
+        if index.size == 0 or inferred == freq.freqstr:
+            return None
+
+        try:
+            on_freq = cls._generate_range(
+                start=index[0],
+                end=None,
+                periods=len(index),
+                freq=freq,
+                unit=index.unit,
+                **kwargs,
+            )
+            if not np.array_equal(index.asi8, on_freq.asi8):
+                raise ValueError
+        except ValueError as err:
+            if "non-fixed" in str(err):
+                # non-fixed frequencies are not meaningful for timedelta64;
+                #  we retain that error message
+                raise err
+            # GH#11587 the main way this is reached is if the `np.array_equal`
+            #  check above is False.  This can also be reached if index[0]
+            #  is `NaT`, in which case the call to `cls._generate_range` will
+            #  raise a ValueError, which we re-raise with a more targeted
+            #  message.
+            raise ValueError(
+                f"Inferred frequency {inferred} from passed values "
+                f"does not conform to passed frequency {freq.freqstr}"
+            ) from err
+
+    @classmethod
+    def _generate_range(
+        cls, start, end, periods: int | None, freq, *args, **kwargs
+    ) -> Self:
+        raise AbstractMethodError(cls)
+
+    # --------------------------------------------------------------
+
+    @cache_readonly
+    def _creso(self) -> int:
+        return get_unit_from_dtype(self._ndarray.dtype)
+
+    @cache_readonly
+    def unit(self) -> str:
+        # e.g. "ns", "us", "ms"
+        # error: Argument 1 to "dtype_to_unit" has incompatible type
+        # "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]"
+        return dtype_to_unit(self.dtype)  # type: ignore[arg-type]
+
+    def as_unit(self, unit: str, round_ok: bool = True) -> Self:
+        if unit not in ["s", "ms", "us", "ns"]:
+            raise ValueError("Supported units are 's', 'ms', 'us', 'ns'")
+
+        dtype = np.dtype(f"{self.dtype.kind}8[{unit}]")
+        new_values = astype_overflowsafe(self._ndarray, dtype, round_ok=round_ok)
+
+        if isinstance(self.dtype, np.dtype):
+            new_dtype = new_values.dtype
+        else:
+            tz = cast("DatetimeArray", self).tz
+            new_dtype = DatetimeTZDtype(tz=tz, unit=unit)
+
+        # error: Unexpected keyword argument "freq" for "_simple_new" of
+        # "NDArrayBacked"  [call-arg]
+        return type(self)._simple_new(
+            new_values, dtype=new_dtype, freq=self.freq  # type: ignore[call-arg]
+        )
+
+    # TODO: annotate other as DatetimeArray | TimedeltaArray | Timestamp | Timedelta
+    #  with the return type matching input type.  TypeVar?
+    def _ensure_matching_resos(self, other):
+        if self._creso != other._creso:
+            # Just as with Timestamp/Timedelta, we cast to the higher resolution
+            if self._creso < other._creso:
+                self = self.as_unit(other.unit)
+            else:
+                other = other.as_unit(self.unit)
+        return self, other
+
+    # --------------------------------------------------------------
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        if (
+            ufunc in [np.isnan, np.isinf, np.isfinite]
+            and len(inputs) == 1
+            and inputs[0] is self
+        ):
+            # numpy 1.18 changed isinf and isnan to not raise on dt64/td64
+            return getattr(ufunc, method)(self._ndarray, **kwargs)
+
+        return super().__array_ufunc__(ufunc, method, *inputs, **kwargs)
+
+    def _round(self, freq, mode, ambiguous, nonexistent):
+        # round the local times
+        if isinstance(self.dtype, DatetimeTZDtype):
+            # operate on naive timestamps, then convert back to aware
+            self = cast("DatetimeArray", self)
+            naive = self.tz_localize(None)
+            result = naive._round(freq, mode, ambiguous, nonexistent)
+            return result.tz_localize(
+                self.tz, ambiguous=ambiguous, nonexistent=nonexistent
+            )
+
+        values = self.view("i8")
+        values = cast(np.ndarray, values)
+        nanos = get_unit_for_round(freq, self._creso)
+        if nanos == 0:
+            # GH 52761
+            return self.copy()
+        result_i8 = round_nsint64(values, mode, nanos)
+        result = self._maybe_mask_results(result_i8, fill_value=iNaT)
+        result = result.view(self._ndarray.dtype)
+        return self._simple_new(result, dtype=self.dtype)
+
+    @Appender((_round_doc + _round_example).format(op="round"))
+    def round(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent)
+
+    @Appender((_round_doc + _floor_example).format(op="floor"))
+    def floor(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent)
+
+    @Appender((_round_doc + _ceil_example).format(op="ceil"))
+    def ceil(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        return self._round(freq, RoundTo.PLUS_INFTY, ambiguous, nonexistent)
+
+    # --------------------------------------------------------------
+    # Reductions
+
+    def any(self, *, axis: AxisInt | None = None, skipna: bool = True) -> bool:
+        # GH#34479 the nanops call will issue a FutureWarning for non-td64 dtype
+        return nanops.nanany(self._ndarray, axis=axis, skipna=skipna, mask=self.isna())
+
+    def all(self, *, axis: AxisInt | None = None, skipna: bool = True) -> bool:
+        # GH#34479 the nanops call will issue a FutureWarning for non-td64 dtype
+
+        return nanops.nanall(self._ndarray, axis=axis, skipna=skipna, mask=self.isna())
+
+    # --------------------------------------------------------------
+    # Frequency Methods
+
+    def _maybe_clear_freq(self) -> None:
+        self._freq = None
+
+    def _with_freq(self, freq) -> Self:
+        """
+        Helper to get a view on the same data, with a new freq.
+
+        Parameters
+        ----------
+        freq : DateOffset, None, or "infer"
+
+        Returns
+        -------
+        Same type as self
+        """
+        # GH#29843
+        if freq is None:
+            # Always valid
+            pass
+        elif len(self) == 0 and isinstance(freq, BaseOffset):
+            # Always valid.  In the TimedeltaArray case, we require a Tick offset
+            if self.dtype.kind == "m" and not isinstance(freq, Tick):
+                raise TypeError("TimedeltaArray/Index freq must be a Tick")
+        else:
+            # As an internal method, we can ensure this assertion always holds
+            assert freq == "infer"
+            freq = to_offset(self.inferred_freq)
+
+        arr = self.view()
+        arr._freq = freq
+        return arr
+
+    # --------------------------------------------------------------
+    # ExtensionArray Interface
+
+    def _values_for_json(self) -> np.ndarray:
+        # Small performance bump vs the base class which calls np.asarray(self)
+        if isinstance(self.dtype, np.dtype):
+            return self._ndarray
+        return super()._values_for_json()
+
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+        sort: bool = False,
+    ):
+        if self.freq is not None:
+            # We must be unique, so can short-circuit (and retain freq)
+            codes = np.arange(len(self), dtype=np.intp)
+            uniques = self.copy()  # TODO: copy or view?
+            if sort and self.freq.n < 0:
+                codes = codes[::-1]
+                uniques = uniques[::-1]
+            return codes, uniques
+
+        if sort:
+            # algorithms.factorize only passes sort=True here when freq is
+            #  not None, so this should not be reached.
+            raise NotImplementedError(
+                f"The 'sort' keyword in {type(self).__name__}.factorize is "
+                "ignored unless arr.freq is not None. To factorize with sort, "
+                "call pd.factorize(obj, sort=True) instead."
+            )
+        return super().factorize(use_na_sentinel=use_na_sentinel)
+
+    @classmethod
+    def _concat_same_type(
+        cls,
+        to_concat: Sequence[Self],
+        axis: AxisInt = 0,
+    ) -> Self:
+        new_obj = super()._concat_same_type(to_concat, axis)
+
+        obj = to_concat[0]
+
+        if axis == 0:
+            # GH 3232: If the concat result is evenly spaced, we can retain the
+            # original frequency
+            to_concat = [x for x in to_concat if len(x)]
+
+            if obj.freq is not None and all(x.freq == obj.freq for x in to_concat):
+                pairs = zip(to_concat[:-1], to_concat[1:])
+                if all(pair[0][-1] + obj.freq == pair[1][0] for pair in pairs):
+                    new_freq = obj.freq
+                    new_obj._freq = new_freq
+        return new_obj
+
+    def copy(self, order: str = "C") -> Self:
+        new_obj = super().copy(order=order)
+        new_obj._freq = self.freq
+        return new_obj
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index: Index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if method != "linear":
+            raise NotImplementedError
+
+        if not copy:
+            out_data = self._ndarray
+        else:
+            out_data = self._ndarray.copy()
+
+        missing.interpolate_2d_inplace(
+            out_data,
+            method=method,
+            axis=axis,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            **kwargs,
+        )
+        if not copy:
+            return self
+        return type(self)._simple_new(out_data, dtype=self.dtype)
+
+    # --------------------------------------------------------------
+    # Unsorted
+
+    @property
+    def _is_dates_only(self) -> bool:
+        """
+        Check if we are round times at midnight (and no timezone), which will
+        be given a more compact __repr__ than other cases. For TimedeltaArray
+        we are checking for multiples of 24H.
+        """
+        if not lib.is_np_dtype(self.dtype):
+            # i.e. we have a timezone
+            return False
+
+        values_int = self.asi8
+        consider_values = values_int != iNaT
+        reso = get_unit_from_dtype(self.dtype)
+        ppd = periods_per_day(reso)
+
+        # TODO: can we reuse is_date_array_normalized?  would need a skipna kwd
+        #  (first attempt at this was less performant than this implementation)
+        even_days = np.logical_and(consider_values, values_int % ppd != 0).sum() == 0
+        return even_days
+
+
+# -------------------------------------------------------------------
+# Shared Constructor Helpers
+
+
+def ensure_arraylike_for_datetimelike(
+    data, copy: bool, cls_name: str
+) -> tuple[ArrayLike, bool]:
+    if not hasattr(data, "dtype"):
+        # e.g. list, tuple
+        if not isinstance(data, (list, tuple)) and np.ndim(data) == 0:
+            # i.e. generator
+            data = list(data)
+
+        data = construct_1d_object_array_from_listlike(data)
+        copy = False
+    elif isinstance(data, ABCMultiIndex):
+        raise TypeError(f"Cannot create a {cls_name} from a MultiIndex.")
+    else:
+        data = extract_array(data, extract_numpy=True)
+
+    if isinstance(data, IntegerArray) or (
+        isinstance(data, ArrowExtensionArray) and data.dtype.kind in "iu"
+    ):
+        data = data.to_numpy("int64", na_value=iNaT)
+        copy = False
+    elif isinstance(data, ArrowExtensionArray):
+        data = data._maybe_convert_datelike_array()
+        data = data.to_numpy()
+        copy = False
+    elif not isinstance(data, (np.ndarray, ExtensionArray)):
+        # GH#24539 e.g. xarray, dask object
+        data = np.asarray(data)
+
+    elif isinstance(data, ABCCategorical):
+        # GH#18664 preserve tz in going DTI->Categorical->DTI
+        # TODO: cases where we need to do another pass through maybe_convert_dtype,
+        #  e.g. the categories are timedelta64s
+        data = data.categories.take(data.codes, fill_value=NaT)._values
+        copy = False
+
+    return data, copy
+
+
+@overload
+def validate_periods(periods: None) -> None:
+    ...
+
+
+@overload
+def validate_periods(periods: int | float) -> int:
+    ...
+
+
+def validate_periods(periods: int | float | None) -> int | None:
+    """
+    If a `periods` argument is passed to the Datetime/Timedelta Array/Index
+    constructor, cast it to an integer.
+
+    Parameters
+    ----------
+    periods : None, float, int
+
+    Returns
+    -------
+    periods : None or int
+
+    Raises
+    ------
+    TypeError
+        if periods is None, float, or int
+    """
+    if periods is not None:
+        if lib.is_float(periods):
+            warnings.warn(
+                # GH#56036
+                "Non-integer 'periods' in pd.date_range, pd.timedelta_range, "
+                "pd.period_range, and pd.interval_range are deprecated and "
+                "will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            periods = int(periods)
+        elif not lib.is_integer(periods):
+            raise TypeError(f"periods must be a number, got {periods}")
+    return periods
+
+
+def _validate_inferred_freq(
+    freq: BaseOffset | None, inferred_freq: BaseOffset | None
+) -> BaseOffset | None:
+    """
+    If the user passes a freq and another freq is inferred from passed data,
+    require that they match.
+
+    Parameters
+    ----------
+    freq : DateOffset or None
+    inferred_freq : DateOffset or None
+
+    Returns
+    -------
+    freq : DateOffset or None
+    """
+    if inferred_freq is not None:
+        if freq is not None and freq != inferred_freq:
+            raise ValueError(
+                f"Inferred frequency {inferred_freq} from passed "
+                "values does not conform to passed frequency "
+                f"{freq.freqstr}"
+            )
+        if freq is None:
+            freq = inferred_freq
+
+    return freq
+
+
+def dtype_to_unit(dtype: DatetimeTZDtype | np.dtype | ArrowDtype) -> str:
+    """
+    Return the unit str corresponding to the dtype's resolution.
+
+    Parameters
+    ----------
+    dtype : DatetimeTZDtype or np.dtype
+        If np.dtype, we assume it is a datetime64 dtype.
+
+    Returns
+    -------
+    str
+    """
+    if isinstance(dtype, DatetimeTZDtype):
+        return dtype.unit
+    elif isinstance(dtype, ArrowDtype):
+        if dtype.kind not in "mM":
+            raise ValueError(f"{dtype=} does not have a resolution.")
+        return dtype.pyarrow_dtype.unit
+    return np.datetime_data(dtype)[0]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/datetimes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..0db25db02e75ad201e11ab0f6a6d10205060ea9a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/datetimes.py
@@ -0,0 +1,2837 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+    tzinfo,
+)
+from typing import (
+    TYPE_CHECKING,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    lib,
+    tslib,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    NaTType,
+    Resolution,
+    Timestamp,
+    astype_overflowsafe,
+    fields,
+    get_resolution,
+    get_supported_dtype,
+    get_unit_from_dtype,
+    ints_to_pydatetime,
+    is_date_array_normalized,
+    is_supported_dtype,
+    is_unitless,
+    normalize_i8_timestamps,
+    timezones,
+    to_offset,
+    tz_convert_from_utc,
+    tzconversion,
+)
+from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit
+from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_inclusive
+
+from pandas.core.dtypes.common import (
+    DT64NS_DTYPE,
+    INT64_DTYPE,
+    is_bool_dtype,
+    is_float_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays import datetimelike as dtl
+from pandas.core.arrays._ranges import generate_regular_range
+import pandas.core.common as com
+
+from pandas.tseries.frequencies import get_period_alias
+from pandas.tseries.offsets import (
+    Day,
+    Tick,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas._typing import (
+        ArrayLike,
+        DateTimeErrorChoices,
+        DtypeObj,
+        IntervalClosedType,
+        Self,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
+    )
+
+    from pandas import DataFrame
+    from pandas.core.arrays import PeriodArray
+
+
+_ITER_CHUNKSIZE = 10_000
+
+
+@overload
+def tz_to_dtype(tz: tzinfo, unit: str = ...) -> DatetimeTZDtype:
+    ...
+
+
+@overload
+def tz_to_dtype(tz: None, unit: str = ...) -> np.dtype[np.datetime64]:
+    ...
+
+
+def tz_to_dtype(
+    tz: tzinfo | None, unit: str = "ns"
+) -> np.dtype[np.datetime64] | DatetimeTZDtype:
+    """
+    Return a datetime64[ns] dtype appropriate for the given timezone.
+
+    Parameters
+    ----------
+    tz : tzinfo or None
+    unit : str, default "ns"
+
+    Returns
+    -------
+    np.dtype or Datetime64TZDType
+    """
+    if tz is None:
+        return np.dtype(f"M8[{unit}]")
+    else:
+        return DatetimeTZDtype(tz=tz, unit=unit)
+
+
+def _field_accessor(name: str, field: str, docstring: str | None = None):
+    def f(self):
+        values = self._local_timestamps()
+
+        if field in self._bool_ops:
+            result: np.ndarray
+
+            if field.endswith(("start", "end")):
+                freq = self.freq
+                month_kw = 12
+                if freq:
+                    kwds = freq.kwds
+                    month_kw = kwds.get("startingMonth", kwds.get("month", 12))
+
+                result = fields.get_start_end_field(
+                    values, field, self.freqstr, month_kw, reso=self._creso
+                )
+            else:
+                result = fields.get_date_field(values, field, reso=self._creso)
+
+            # these return a boolean by-definition
+            return result
+
+        if field in self._object_ops:
+            result = fields.get_date_name_field(values, field, reso=self._creso)
+            result = self._maybe_mask_results(result, fill_value=None)
+
+        else:
+            result = fields.get_date_field(values, field, reso=self._creso)
+            result = self._maybe_mask_results(
+                result, fill_value=None, convert="float64"
+            )
+
+        return result
+
+    f.__name__ = name
+    f.__doc__ = docstring
+    return property(f)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):  # type: ignore[misc]
+    """
+    Pandas ExtensionArray for tz-naive or tz-aware datetime data.
+
+    .. warning::
+
+       DatetimeArray is currently experimental, and its API may change
+       without warning. In particular, :attr:`DatetimeArray.dtype` is
+       expected to change to always be an instance of an ``ExtensionDtype``
+       subclass.
+
+    Parameters
+    ----------
+    values : Series, Index, DatetimeArray, ndarray
+        The datetime data.
+
+        For DatetimeArray `values` (or a Series or Index boxing one),
+        `dtype` and `freq` will be extracted from `values`.
+
+    dtype : numpy.dtype or DatetimeTZDtype
+        Note that the only NumPy dtype allowed is 'datetime64[ns]'.
+    freq : str or Offset, optional
+        The frequency.
+    copy : bool, default False
+        Whether to copy the underlying array of values.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.arrays.DatetimeArray._from_sequence(
+    ...    pd.DatetimeIndex(['2023-01-01', '2023-01-02'], freq='D'))
+    <DatetimeArray>
+    ['2023-01-01 00:00:00', '2023-01-02 00:00:00']
+    Length: 2, dtype: datetime64[ns]
+    """
+
+    _typ = "datetimearray"
+    _internal_fill_value = np.datetime64("NaT", "ns")
+    _recognized_scalars = (datetime, np.datetime64)
+    _is_recognized_dtype = lambda x: lib.is_np_dtype(x, "M") or isinstance(
+        x, DatetimeTZDtype
+    )
+    _infer_matches = ("datetime", "datetime64", "date")
+
+    @property
+    def _scalar_type(self) -> type[Timestamp]:
+        return Timestamp
+
+    # define my properties & methods for delegation
+    _bool_ops: list[str] = [
+        "is_month_start",
+        "is_month_end",
+        "is_quarter_start",
+        "is_quarter_end",
+        "is_year_start",
+        "is_year_end",
+        "is_leap_year",
+    ]
+    _object_ops: list[str] = ["freq", "tz"]
+    _field_ops: list[str] = [
+        "year",
+        "month",
+        "day",
+        "hour",
+        "minute",
+        "second",
+        "weekday",
+        "dayofweek",
+        "day_of_week",
+        "dayofyear",
+        "day_of_year",
+        "quarter",
+        "days_in_month",
+        "daysinmonth",
+        "microsecond",
+        "nanosecond",
+    ]
+    _other_ops: list[str] = ["date", "time", "timetz"]
+    _datetimelike_ops: list[str] = (
+        _field_ops + _object_ops + _bool_ops + _other_ops + ["unit"]
+    )
+    _datetimelike_methods: list[str] = [
+        "to_period",
+        "tz_localize",
+        "tz_convert",
+        "normalize",
+        "strftime",
+        "round",
+        "floor",
+        "ceil",
+        "month_name",
+        "day_name",
+        "as_unit",
+    ]
+
+    # ndim is inherited from ExtensionArray, must exist to ensure
+    #  Timestamp.__richcmp__(DateTimeArray) operates pointwise
+
+    # ensure that operations with numpy arrays defer to our implementation
+    __array_priority__ = 1000
+
+    # -----------------------------------------------------------------
+    # Constructors
+
+    _dtype: np.dtype[np.datetime64] | DatetimeTZDtype
+    _freq: BaseOffset | None = None
+    _default_dtype = DT64NS_DTYPE  # used in TimeLikeOps.__init__
+
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        if lib.infer_dtype(scalars, skipna=True) not in ["datetime", "datetime64"]:
+            # TODO: require any NAs be valid-for-DTA
+            # TODO: if dtype is passed, check for tzawareness compat?
+            raise ValueError
+        return cls._from_sequence(scalars, dtype=dtype)
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        # used in TimeLikeOps.__init__
+        dtype = _validate_dt64_dtype(dtype)
+        _validate_dt64_dtype(values.dtype)
+        if isinstance(dtype, np.dtype):
+            if values.dtype != dtype:
+                raise ValueError("Values resolution does not match dtype.")
+        else:
+            vunit = np.datetime_data(values.dtype)[0]
+            if vunit != dtype.unit:
+                raise ValueError("Values resolution does not match dtype.")
+        return dtype
+
+    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls,
+        values: npt.NDArray[np.datetime64],
+        freq: BaseOffset | None = None,
+        dtype: np.dtype[np.datetime64] | DatetimeTZDtype = DT64NS_DTYPE,
+    ) -> Self:
+        assert isinstance(values, np.ndarray)
+        assert dtype.kind == "M"
+        if isinstance(dtype, np.dtype):
+            assert dtype == values.dtype
+            assert not is_unitless(dtype)
+        else:
+            # DatetimeTZDtype. If we have e.g. DatetimeTZDtype[us, UTC],
+            #  then values.dtype should be M8[us].
+            assert dtype._creso == get_unit_from_dtype(values.dtype)
+
+        result = super()._simple_new(values, dtype)
+        result._freq = freq
+        return result
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False):
+        return cls._from_sequence_not_strict(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_sequence_not_strict(
+        cls,
+        data,
+        *,
+        dtype=None,
+        copy: bool = False,
+        tz=lib.no_default,
+        freq: str | BaseOffset | lib.NoDefault | None = lib.no_default,
+        dayfirst: bool = False,
+        yearfirst: bool = False,
+        ambiguous: TimeAmbiguous = "raise",
+    ) -> Self:
+        """
+        A non-strict version of _from_sequence, called from DatetimeIndex.__new__.
+        """
+
+        # if the user either explicitly passes tz=None or a tz-naive dtype, we
+        #  disallows inferring a tz.
+        explicit_tz_none = tz is None
+        if tz is lib.no_default:
+            tz = None
+        else:
+            tz = timezones.maybe_get_tz(tz)
+
+        dtype = _validate_dt64_dtype(dtype)
+        # if dtype has an embedded tz, capture it
+        tz = _validate_tz_from_dtype(dtype, tz, explicit_tz_none)
+
+        unit = None
+        if dtype is not None:
+            unit = dtl.dtype_to_unit(dtype)
+
+        data, copy = dtl.ensure_arraylike_for_datetimelike(
+            data, copy, cls_name="DatetimeArray"
+        )
+        inferred_freq = None
+        if isinstance(data, DatetimeArray):
+            inferred_freq = data.freq
+
+        subarr, tz = _sequence_to_dt64(
+            data,
+            copy=copy,
+            tz=tz,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            ambiguous=ambiguous,
+            out_unit=unit,
+        )
+        # We have to call this again after possibly inferring a tz above
+        _validate_tz_from_dtype(dtype, tz, explicit_tz_none)
+        if tz is not None and explicit_tz_none:
+            raise ValueError(
+                "Passed data is timezone-aware, incompatible with 'tz=None'. "
+                "Use obj.tz_localize(None) instead."
+            )
+
+        data_unit = np.datetime_data(subarr.dtype)[0]
+        data_dtype = tz_to_dtype(tz, data_unit)
+        result = cls._simple_new(subarr, freq=inferred_freq, dtype=data_dtype)
+        if unit is not None and unit != result.unit:
+            # If unit was specified in user-passed dtype, cast to it here
+            result = result.as_unit(unit)
+
+        validate_kwds = {"ambiguous": ambiguous}
+        result._maybe_pin_freq(freq, validate_kwds)
+        return result
+
+    @classmethod
+    def _generate_range(
+        cls,
+        start,
+        end,
+        periods: int | None,
+        freq,
+        tz=None,
+        normalize: bool = False,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+        inclusive: IntervalClosedType = "both",
+        *,
+        unit: str | None = None,
+    ) -> Self:
+        periods = dtl.validate_periods(periods)
+        if freq is None and any(x is None for x in [periods, start, end]):
+            raise ValueError("Must provide freq argument if no data is supplied")
+
+        if com.count_not_none(start, end, periods, freq) != 3:
+            raise ValueError(
+                "Of the four parameters: start, end, periods, "
+                "and freq, exactly three must be specified"
+            )
+        freq = to_offset(freq)
+
+        if start is not None:
+            start = Timestamp(start)
+
+        if end is not None:
+            end = Timestamp(end)
+
+        if start is NaT or end is NaT:
+            raise ValueError("Neither `start` nor `end` can be NaT")
+
+        if unit is not None:
+            if unit not in ["s", "ms", "us", "ns"]:
+                raise ValueError("'unit' must be one of 's', 'ms', 'us', 'ns'")
+        else:
+            unit = "ns"
+
+        if start is not None:
+            start = start.as_unit(unit, round_ok=False)
+        if end is not None:
+            end = end.as_unit(unit, round_ok=False)
+
+        left_inclusive, right_inclusive = validate_inclusive(inclusive)
+        start, end = _maybe_normalize_endpoints(start, end, normalize)
+        tz = _infer_tz_from_endpoints(start, end, tz)
+
+        if tz is not None:
+            # Localize the start and end arguments
+            start = _maybe_localize_point(start, freq, tz, ambiguous, nonexistent)
+            end = _maybe_localize_point(end, freq, tz, ambiguous, nonexistent)
+
+        if freq is not None:
+            # We break Day arithmetic (fixed 24 hour) here and opt for
+            # Day to mean calendar day (23/24/25 hour). Therefore, strip
+            # tz info from start and day to avoid DST arithmetic
+            if isinstance(freq, Day):
+                if start is not None:
+                    start = start.tz_localize(None)
+                if end is not None:
+                    end = end.tz_localize(None)
+
+            if isinstance(freq, Tick):
+                i8values = generate_regular_range(start, end, periods, freq, unit=unit)
+            else:
+                xdr = _generate_range(
+                    start=start, end=end, periods=periods, offset=freq, unit=unit
+                )
+                i8values = np.array([x._value for x in xdr], dtype=np.int64)
+
+            endpoint_tz = start.tz if start is not None else end.tz
+
+            if tz is not None and endpoint_tz is None:
+                if not timezones.is_utc(tz):
+                    # short-circuit tz_localize_to_utc which would make
+                    #  an unnecessary copy with UTC but be a no-op.
+                    creso = abbrev_to_npy_unit(unit)
+                    i8values = tzconversion.tz_localize_to_utc(
+                        i8values,
+                        tz,
+                        ambiguous=ambiguous,
+                        nonexistent=nonexistent,
+                        creso=creso,
+                    )
+
+                # i8values is localized datetime64 array -> have to convert
+                # start/end as well to compare
+                if start is not None:
+                    start = start.tz_localize(tz, ambiguous, nonexistent)
+                if end is not None:
+                    end = end.tz_localize(tz, ambiguous, nonexistent)
+        else:
+            # Create a linearly spaced date_range in local time
+            # Nanosecond-granularity timestamps aren't always correctly
+            # representable with doubles, so we limit the range that we
+            # pass to np.linspace as much as possible
+            periods = cast(int, periods)
+            i8values = (
+                np.linspace(0, end._value - start._value, periods, dtype="int64")
+                + start._value
+            )
+            if i8values.dtype != "i8":
+                # 2022-01-09 I (brock) am not sure if it is possible for this
+                #  to overflow and cast to e.g. f8, but if it does we need to cast
+                i8values = i8values.astype("i8")
+
+        if start == end:
+            if not left_inclusive and not right_inclusive:
+                i8values = i8values[1:-1]
+        else:
+            start_i8 = Timestamp(start)._value
+            end_i8 = Timestamp(end)._value
+            if not left_inclusive or not right_inclusive:
+                if not left_inclusive and len(i8values) and i8values[0] == start_i8:
+                    i8values = i8values[1:]
+                if not right_inclusive and len(i8values) and i8values[-1] == end_i8:
+                    i8values = i8values[:-1]
+
+        dt64_values = i8values.view(f"datetime64[{unit}]")
+        dtype = tz_to_dtype(tz, unit=unit)
+        return cls._simple_new(dt64_values, freq=freq, dtype=dtype)
+
+    # -----------------------------------------------------------------
+    # DatetimeLike Interface
+
+    def _unbox_scalar(self, value) -> np.datetime64:
+        if not isinstance(value, self._scalar_type) and value is not NaT:
+            raise ValueError("'value' should be a Timestamp.")
+        self._check_compatible_with(value)
+        if value is NaT:
+            return np.datetime64(value._value, self.unit)
+        else:
+            return value.as_unit(self.unit).asm8
+
+    def _scalar_from_string(self, value) -> Timestamp | NaTType:
+        return Timestamp(value, tz=self.tz)
+
+    def _check_compatible_with(self, other) -> None:
+        if other is NaT:
+            return
+        self._assert_tzawareness_compat(other)
+
+    # -----------------------------------------------------------------
+    # Descriptive Properties
+
+    def _box_func(self, x: np.datetime64) -> Timestamp | NaTType:
+        # GH#42228
+        value = x.view("i8")
+        ts = Timestamp._from_value_and_reso(value, reso=self._creso, tz=self.tz)
+        return ts
+
+    @property
+    # error: Return type "Union[dtype, DatetimeTZDtype]" of "dtype"
+    # incompatible with return type "ExtensionDtype" in supertype
+    # "ExtensionArray"
+    def dtype(self) -> np.dtype[np.datetime64] | DatetimeTZDtype:  # type: ignore[override]
+        """
+        The dtype for the DatetimeArray.
+
+        .. warning::
+
+           A future version of pandas will change dtype to never be a
+           ``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will
+           always be an instance of an ``ExtensionDtype`` subclass.
+
+        Returns
+        -------
+        numpy.dtype or DatetimeTZDtype
+            If the values are tz-naive, then ``np.dtype('datetime64[ns]')``
+            is returned.
+
+            If the values are tz-aware, then the ``DatetimeTZDtype``
+            is returned.
+        """
+        return self._dtype
+
+    @property
+    def tz(self) -> tzinfo | None:
+        """
+        Return the timezone.
+
+        Returns
+        -------
+        datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None
+            Returns None when the array is tz-naive.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.tz
+        datetime.timezone.utc
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.tz
+        datetime.timezone.utc
+        """
+        # GH 18595
+        return getattr(self.dtype, "tz", None)
+
+    @tz.setter
+    def tz(self, value):
+        # GH 3746: Prevent localizing or converting the index by setting tz
+        raise AttributeError(
+            "Cannot directly set timezone. Use tz_localize() "
+            "or tz_convert() as appropriate"
+        )
+
+    @property
+    def tzinfo(self) -> tzinfo | None:
+        """
+        Alias for tz attribute
+        """
+        return self.tz
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def is_normalized(self) -> bool:
+        """
+        Returns True if all of the dates are at midnight ("no time")
+        """
+        return is_date_array_normalized(self.asi8, self.tz, reso=self._creso)
+
+    @property  # NB: override with cache_readonly in immutable subclasses
+    def _resolution_obj(self) -> Resolution:
+        return get_resolution(self.asi8, self.tz, reso=self._creso)
+
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    def __array__(self, dtype=None, copy=None) -> np.ndarray:
+        if dtype is None and self.tz:
+            # The default for tz-aware is object, to preserve tz info
+            dtype = object
+
+        return super().__array__(dtype=dtype, copy=copy)
+
+    def __iter__(self) -> Iterator:
+        """
+        Return an iterator over the boxed values
+
+        Yields
+        ------
+        tstamp : Timestamp
+        """
+        if self.ndim > 1:
+            for i in range(len(self)):
+                yield self[i]
+        else:
+            # convert in chunks of 10k for efficiency
+            data = self.asi8
+            length = len(self)
+            chunksize = _ITER_CHUNKSIZE
+            chunks = (length // chunksize) + 1
+
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, length)
+                converted = ints_to_pydatetime(
+                    data[start_i:end_i],
+                    tz=self.tz,
+                    box="timestamp",
+                    reso=self._creso,
+                )
+                yield from converted
+
+    def astype(self, dtype, copy: bool = True):
+        # We handle
+        #   --> datetime
+        #   --> period
+        # DatetimeLikeArrayMixin Super handles the rest.
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        elif isinstance(dtype, ExtensionDtype):
+            if not isinstance(dtype, DatetimeTZDtype):
+                # e.g. Sparse[datetime64[ns]]
+                return super().astype(dtype, copy=copy)
+            elif self.tz is None:
+                # pre-2.0 this did self.tz_localize(dtype.tz), which did not match
+                #  the Series behavior which did
+                #  values.tz_localize("UTC").tz_convert(dtype.tz)
+                raise TypeError(
+                    "Cannot use .astype to convert from timezone-naive dtype to "
+                    "timezone-aware dtype. Use obj.tz_localize instead or "
+                    "series.dt.tz_localize instead"
+                )
+            else:
+                # tzaware unit conversion e.g. datetime64[s, UTC]
+                np_dtype = np.dtype(dtype.str)
+                res_values = astype_overflowsafe(self._ndarray, np_dtype, copy=copy)
+                return type(self)._simple_new(res_values, dtype=dtype, freq=self.freq)
+
+        elif (
+            self.tz is None
+            and lib.is_np_dtype(dtype, "M")
+            and not is_unitless(dtype)
+            and is_supported_dtype(dtype)
+        ):
+            # unit conversion e.g. datetime64[s]
+            res_values = astype_overflowsafe(self._ndarray, dtype, copy=True)
+            return type(self)._simple_new(res_values, dtype=res_values.dtype)
+            # TODO: preserve freq?
+
+        elif self.tz is not None and lib.is_np_dtype(dtype, "M"):
+            # pre-2.0 behavior for DTA/DTI was
+            #  values.tz_convert("UTC").tz_localize(None), which did not match
+            #  the Series behavior
+            raise TypeError(
+                "Cannot use .astype to convert from timezone-aware dtype to "
+                "timezone-naive dtype. Use obj.tz_localize(None) or "
+                "obj.tz_convert('UTC').tz_localize(None) instead."
+            )
+
+        elif (
+            self.tz is None
+            and lib.is_np_dtype(dtype, "M")
+            and dtype != self.dtype
+            and is_unitless(dtype)
+        ):
+            raise TypeError(
+                "Casting to unit-less dtype 'datetime64' is not supported. "
+                "Pass e.g. 'datetime64[ns]' instead."
+            )
+
+        elif isinstance(dtype, PeriodDtype):
+            return self.to_period(freq=dtype.freq)
+        return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy)
+
+    # -----------------------------------------------------------------
+    # Rendering Methods
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
+    ) -> npt.NDArray[np.object_]:
+        if date_format is None and self._is_dates_only:
+            # Only dates and no timezone: provide a default format
+            date_format = "%Y-%m-%d"
+
+        return tslib.format_array_from_datetime(
+            self.asi8, tz=self.tz, format=date_format, na_rep=na_rep, reso=self._creso
+        )
+
+    # -----------------------------------------------------------------
+    # Comparison Methods
+
+    def _has_same_tz(self, other) -> bool:
+        # vzone shouldn't be None if value is non-datetime like
+        if isinstance(other, np.datetime64):
+            # convert to Timestamp as np.datetime64 doesn't have tz attr
+            other = Timestamp(other)
+
+        if not hasattr(other, "tzinfo"):
+            return False
+        other_tz = other.tzinfo
+        return timezones.tz_compare(self.tzinfo, other_tz)
+
+    def _assert_tzawareness_compat(self, other) -> None:
+        # adapted from _Timestamp._assert_tzawareness_compat
+        other_tz = getattr(other, "tzinfo", None)
+        other_dtype = getattr(other, "dtype", None)
+
+        if isinstance(other_dtype, DatetimeTZDtype):
+            # Get tzinfo from Series dtype
+            other_tz = other.dtype.tz
+        if other is NaT:
+            # pd.NaT quacks both aware and naive
+            pass
+        elif self.tz is None:
+            if other_tz is not None:
+                raise TypeError(
+                    "Cannot compare tz-naive and tz-aware datetime-like objects."
+                )
+        elif other_tz is None:
+            raise TypeError(
+                "Cannot compare tz-naive and tz-aware datetime-like objects"
+            )
+
+    # -----------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _add_offset(self, offset: BaseOffset) -> Self:
+        assert not isinstance(offset, Tick)
+
+        if self.tz is not None:
+            values = self.tz_localize(None)
+        else:
+            values = self
+
+        try:
+            res_values = offset._apply_array(values._ndarray)
+            if res_values.dtype.kind == "i":
+                # error: Argument 1 to "view" of "ndarray" has incompatible type
+                # "dtype[datetime64] | DatetimeTZDtype"; expected
+                # "dtype[Any] | type[Any] | _SupportsDType[dtype[Any]]"
+                res_values = res_values.view(values.dtype)  # type: ignore[arg-type]
+        except NotImplementedError:
+            warnings.warn(
+                "Non-vectorized DateOffset being applied to Series or DatetimeIndex.",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+            res_values = self.astype("O") + offset
+            # TODO(GH#55564): as_unit will be unnecessary
+            result = type(self)._from_sequence(res_values).as_unit(self.unit)
+            if not len(self):
+                # GH#30336 _from_sequence won't be able to infer self.tz
+                return result.tz_localize(self.tz)
+
+        else:
+            result = type(self)._simple_new(res_values, dtype=res_values.dtype)
+            if offset.normalize:
+                result = result.normalize()
+                result._freq = None
+
+            if self.tz is not None:
+                result = result.tz_localize(self.tz)
+
+        return result
+
+    # -----------------------------------------------------------------
+    # Timezone Conversion and Localization Methods
+
+    def _local_timestamps(self) -> npt.NDArray[np.int64]:
+        """
+        Convert to an i8 (unix-like nanosecond timestamp) representation
+        while keeping the local timezone and not using UTC.
+        This is used to calculate time-of-day information as if the timestamps
+        were timezone-naive.
+        """
+        if self.tz is None or timezones.is_utc(self.tz):
+            # Avoid the copy that would be made in tzconversion
+            return self.asi8
+        return tz_convert_from_utc(self.asi8, self.tz, reso=self._creso)
+
+    def tz_convert(self, tz) -> Self:
+        """
+        Convert tz-aware Datetime Array/Index from one time zone to another.
+
+        Parameters
+        ----------
+        tz : str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None
+            Time zone for time. Corresponding timestamps would be converted
+            to this time zone of the Datetime Array/Index. A `tz` of None will
+            convert to UTC and remove the timezone information.
+
+        Returns
+        -------
+        Array or Index
+
+        Raises
+        ------
+        TypeError
+            If Datetime Array/Index is tz-naive.
+
+        See Also
+        --------
+        DatetimeIndex.tz : A timezone that has a variable offset from UTC.
+        DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a
+            given time zone, or remove timezone from a tz-aware DatetimeIndex.
+
+        Examples
+        --------
+        With the `tz` parameter, we can change the DatetimeIndex
+        to other time zones:
+
+        >>> dti = pd.date_range(start='2014-08-01 09:00',
+        ...                     freq='h', periods=3, tz='Europe/Berlin')
+
+        >>> dti
+        DatetimeIndex(['2014-08-01 09:00:00+02:00',
+                       '2014-08-01 10:00:00+02:00',
+                       '2014-08-01 11:00:00+02:00'],
+                      dtype='datetime64[ns, Europe/Berlin]', freq='h')
+
+        >>> dti.tz_convert('US/Central')
+        DatetimeIndex(['2014-08-01 02:00:00-05:00',
+                       '2014-08-01 03:00:00-05:00',
+                       '2014-08-01 04:00:00-05:00'],
+                      dtype='datetime64[ns, US/Central]', freq='h')
+
+        With the ``tz=None``, we can remove the timezone (after converting
+        to UTC if necessary):
+
+        >>> dti = pd.date_range(start='2014-08-01 09:00', freq='h',
+        ...                     periods=3, tz='Europe/Berlin')
+
+        >>> dti
+        DatetimeIndex(['2014-08-01 09:00:00+02:00',
+                       '2014-08-01 10:00:00+02:00',
+                       '2014-08-01 11:00:00+02:00'],
+                        dtype='datetime64[ns, Europe/Berlin]', freq='h')
+
+        >>> dti.tz_convert(None)
+        DatetimeIndex(['2014-08-01 07:00:00',
+                       '2014-08-01 08:00:00',
+                       '2014-08-01 09:00:00'],
+                        dtype='datetime64[ns]', freq='h')
+        """
+        tz = timezones.maybe_get_tz(tz)
+
+        if self.tz is None:
+            # tz naive, use tz_localize
+            raise TypeError(
+                "Cannot convert tz-naive timestamps, use tz_localize to localize"
+            )
+
+        # No conversion since timestamps are all UTC to begin with
+        dtype = tz_to_dtype(tz, unit=self.unit)
+        return self._simple_new(self._ndarray, dtype=dtype, freq=self.freq)
+
+    @dtl.ravel_compat
+    def tz_localize(
+        self,
+        tz,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        """
+        Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
+
+        This method takes a time zone (tz) naive Datetime Array/Index object
+        and makes this time zone aware. It does not move the time to another
+        time zone.
+
+        This method can also be used to do the inverse -- to create a time
+        zone unaware object from an aware object. To that end, pass `tz=None`.
+
+        Parameters
+        ----------
+        tz : str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None
+            Time zone to convert timestamps to. Passing ``None`` will
+            remove the time zone information preserving local time.
+        ambiguous : 'infer', 'NaT', bool array, default 'raise'
+            When clocks moved backward due to DST, ambiguous times may arise.
+            For example in Central European Time (UTC+01), when going from
+            03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
+            00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
+            `ambiguous` parameter dictates how ambiguous times should be
+            handled.
+
+            - 'infer' will attempt to infer fall dst-transition hours based on
+              order
+            - bool-ndarray where True signifies a DST time, False signifies a
+              non-DST time (note that this flag is only applicable for
+              ambiguous times)
+            - 'NaT' will return NaT where there are ambiguous times
+            - 'raise' will raise an AmbiguousTimeError if there are ambiguous
+              times.
+
+        nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, \
+default 'raise'
+            A nonexistent time does not exist in a particular timezone
+            where clocks moved forward due to DST.
+
+            - 'shift_forward' will shift the nonexistent time forward to the
+              closest existing time
+            - 'shift_backward' will shift the nonexistent time backward to the
+              closest existing time
+            - 'NaT' will return NaT where there are nonexistent times
+            - timedelta objects will shift nonexistent times by the timedelta
+            - 'raise' will raise an NonExistentTimeError if there are
+              nonexistent times.
+
+        Returns
+        -------
+        Same type as self
+            Array/Index converted to the specified time zone.
+
+        Raises
+        ------
+        TypeError
+            If the Datetime Array/Index is tz-aware and tz is not None.
+
+        See Also
+        --------
+        DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from
+            one time zone to another.
+
+        Examples
+        --------
+        >>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3)
+        >>> tz_naive
+        DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
+                       '2018-03-03 09:00:00'],
+                      dtype='datetime64[ns]', freq='D')
+
+        Localize DatetimeIndex in US/Eastern time zone:
+
+        >>> tz_aware = tz_naive.tz_localize(tz='US/Eastern')
+        >>> tz_aware
+        DatetimeIndex(['2018-03-01 09:00:00-05:00',
+                       '2018-03-02 09:00:00-05:00',
+                       '2018-03-03 09:00:00-05:00'],
+                      dtype='datetime64[ns, US/Eastern]', freq=None)
+
+        With the ``tz=None``, we can remove the time zone information
+        while keeping the local time (not converted to UTC):
+
+        >>> tz_aware.tz_localize(None)
+        DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
+                       '2018-03-03 09:00:00'],
+                      dtype='datetime64[ns]', freq=None)
+
+        Be careful with DST changes. When there is sequential data, pandas can
+        infer the DST time:
+
+        >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:30:00',
+        ...                               '2018-10-28 02:00:00',
+        ...                               '2018-10-28 02:30:00',
+        ...                               '2018-10-28 02:00:00',
+        ...                               '2018-10-28 02:30:00',
+        ...                               '2018-10-28 03:00:00',
+        ...                               '2018-10-28 03:30:00']))
+        >>> s.dt.tz_localize('CET', ambiguous='infer')
+        0   2018-10-28 01:30:00+02:00
+        1   2018-10-28 02:00:00+02:00
+        2   2018-10-28 02:30:00+02:00
+        3   2018-10-28 02:00:00+01:00
+        4   2018-10-28 02:30:00+01:00
+        5   2018-10-28 03:00:00+01:00
+        6   2018-10-28 03:30:00+01:00
+        dtype: datetime64[ns, CET]
+
+        In some cases, inferring the DST is impossible. In such cases, you can
+        pass an ndarray to the ambiguous parameter to set the DST explicitly
+
+        >>> s = pd.to_datetime(pd.Series(['2018-10-28 01:20:00',
+        ...                               '2018-10-28 02:36:00',
+        ...                               '2018-10-28 03:46:00']))
+        >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))
+        0   2018-10-28 01:20:00+02:00
+        1   2018-10-28 02:36:00+02:00
+        2   2018-10-28 03:46:00+01:00
+        dtype: datetime64[ns, CET]
+
+        If the DST transition causes nonexistent times, you can shift these
+        dates forward or backwards with a timedelta object or `'shift_forward'`
+        or `'shift_backwards'`.
+
+        >>> s = pd.to_datetime(pd.Series(['2015-03-29 02:30:00',
+        ...                               '2015-03-29 03:30:00']))
+        >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
+        0   2015-03-29 03:00:00+02:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
+
+        >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
+        0   2015-03-29 01:59:59.999999999+01:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
+
+        >>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
+        0   2015-03-29 03:30:00+02:00
+        1   2015-03-29 03:30:00+02:00
+        dtype: datetime64[ns, Europe/Warsaw]
+        """
+        nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward")
+        if nonexistent not in nonexistent_options and not isinstance(
+            nonexistent, timedelta
+        ):
+            raise ValueError(
+                "The nonexistent argument must be one of 'raise', "
+                "'NaT', 'shift_forward', 'shift_backward' or "
+                "a timedelta object"
+            )
+
+        if self.tz is not None:
+            if tz is None:
+                new_dates = tz_convert_from_utc(self.asi8, self.tz, reso=self._creso)
+            else:
+                raise TypeError("Already tz-aware, use tz_convert to convert.")
+        else:
+            tz = timezones.maybe_get_tz(tz)
+            # Convert to UTC
+
+            new_dates = tzconversion.tz_localize_to_utc(
+                self.asi8,
+                tz,
+                ambiguous=ambiguous,
+                nonexistent=nonexistent,
+                creso=self._creso,
+            )
+        new_dates_dt64 = new_dates.view(f"M8[{self.unit}]")
+        dtype = tz_to_dtype(tz, unit=self.unit)
+
+        freq = None
+        if timezones.is_utc(tz) or (len(self) == 1 and not isna(new_dates_dt64[0])):
+            # we can preserve freq
+            # TODO: Also for fixed-offsets
+            freq = self.freq
+        elif tz is None and self.tz is None:
+            # no-op
+            freq = self.freq
+        return self._simple_new(new_dates_dt64, dtype=dtype, freq=freq)
+
+    # ----------------------------------------------------------------
+    # Conversion Methods - Vectorized analogues of Timestamp methods
+
+    def to_pydatetime(self) -> npt.NDArray[np.object_]:
+        """
+        Return an ndarray of ``datetime.datetime`` objects.
+
+        Returns
+        -------
+        numpy.ndarray
+
+        Examples
+        --------
+        >>> idx = pd.date_range('2018-02-27', periods=3)
+        >>> idx.to_pydatetime()
+        array([datetime.datetime(2018, 2, 27, 0, 0),
+               datetime.datetime(2018, 2, 28, 0, 0),
+               datetime.datetime(2018, 3, 1, 0, 0)], dtype=object)
+        """
+        return ints_to_pydatetime(self.asi8, tz=self.tz, reso=self._creso)
+
+    def normalize(self) -> Self:
+        """
+        Convert times to midnight.
+
+        The time component of the date-time is converted to midnight i.e.
+        00:00:00. This is useful in cases, when the time does not matter.
+        Length is unaltered. The timezones are unaffected.
+
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on Datetime Array/Index.
+
+        Returns
+        -------
+        DatetimeArray, DatetimeIndex or Series
+            The same type as the original data. Series will have the same
+            name and index. DatetimeIndex will have the same name.
+
+        See Also
+        --------
+        floor : Floor the datetimes to the specified freq.
+        ceil : Ceil the datetimes to the specified freq.
+        round : Round the datetimes to the specified freq.
+
+        Examples
+        --------
+        >>> idx = pd.date_range(start='2014-08-01 10:00', freq='h',
+        ...                     periods=3, tz='Asia/Calcutta')
+        >>> idx
+        DatetimeIndex(['2014-08-01 10:00:00+05:30',
+                       '2014-08-01 11:00:00+05:30',
+                       '2014-08-01 12:00:00+05:30'],
+                        dtype='datetime64[ns, Asia/Calcutta]', freq='h')
+        >>> idx.normalize()
+        DatetimeIndex(['2014-08-01 00:00:00+05:30',
+                       '2014-08-01 00:00:00+05:30',
+                       '2014-08-01 00:00:00+05:30'],
+                       dtype='datetime64[ns, Asia/Calcutta]', freq=None)
+        """
+        new_values = normalize_i8_timestamps(self.asi8, self.tz, reso=self._creso)
+        dt64_values = new_values.view(self._ndarray.dtype)
+
+        dta = type(self)._simple_new(dt64_values, dtype=dt64_values.dtype)
+        dta = dta._with_freq("infer")
+        if self.tz is not None:
+            dta = dta.tz_localize(self.tz)
+        return dta
+
+    def to_period(self, freq=None) -> PeriodArray:
+        """
+        Cast to PeriodArray/PeriodIndex at a particular frequency.
+
+        Converts DatetimeArray/Index to PeriodArray/PeriodIndex.
+
+        Parameters
+        ----------
+        freq : str or Period, optional
+            One of pandas' :ref:`period aliases <timeseries.period_aliases>`
+            or an Period object. Will be inferred by default.
+
+        Returns
+        -------
+        PeriodArray/PeriodIndex
+
+        Raises
+        ------
+        ValueError
+            When converting a DatetimeArray/Index with non-regular values,
+            so that a frequency cannot be inferred.
+
+        See Also
+        --------
+        PeriodIndex: Immutable ndarray holding ordinal values.
+        DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"y": [1, 2, 3]},
+        ...                   index=pd.to_datetime(["2000-03-31 00:00:00",
+        ...                                         "2000-05-31 00:00:00",
+        ...                                         "2000-08-31 00:00:00"]))
+        >>> df.index.to_period("M")
+        PeriodIndex(['2000-03', '2000-05', '2000-08'],
+                    dtype='period[M]')
+
+        Infer the daily frequency
+
+        >>> idx = pd.date_range("2017-01-01", periods=2)
+        >>> idx.to_period()
+        PeriodIndex(['2017-01-01', '2017-01-02'],
+                    dtype='period[D]')
+        """
+        from pandas.core.arrays import PeriodArray
+
+        if self.tz is not None:
+            warnings.warn(
+                "Converting to PeriodArray/Index representation "
+                "will drop timezone information.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if freq is None:
+            freq = self.freqstr or self.inferred_freq
+            if isinstance(self.freq, BaseOffset) and hasattr(
+                self.freq, "_period_dtype_code"
+            ):
+                freq = PeriodDtype(self.freq)._freqstr
+
+            if freq is None:
+                raise ValueError(
+                    "You must pass a freq argument as current index has none."
+                )
+
+            res = get_period_alias(freq)
+
+            #  https://github.com/pandas-dev/pandas/issues/33358
+            if res is None:
+                res = freq
+
+            freq = res
+        return PeriodArray._from_datetime64(self._ndarray, freq, tz=self.tz)
+
+    # -----------------------------------------------------------------
+    # Properties - Vectorized Timestamp Properties/Methods
+
+    def month_name(self, locale=None) -> npt.NDArray[np.object_]:
+        """
+        Return the month names with specified locale.
+
+        Parameters
+        ----------
+        locale : str, optional
+            Locale determining the language in which to return the month name.
+            Default is English locale (``'en_US.utf8'``). Use the command
+            ``locale -a`` on your terminal on Unix systems to find your locale
+            language code.
+
+        Returns
+        -------
+        Series or Index
+            Series or Index of month names.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range(start='2018-01', freq='ME', periods=3))
+        >>> s
+        0   2018-01-31
+        1   2018-02-28
+        2   2018-03-31
+        dtype: datetime64[ns]
+        >>> s.dt.month_name()
+        0     January
+        1    February
+        2       March
+        dtype: object
+
+        >>> idx = pd.date_range(start='2018-01', freq='ME', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
+                      dtype='datetime64[ns]', freq='ME')
+        >>> idx.month_name()
+        Index(['January', 'February', 'March'], dtype='object')
+
+        Using the ``locale`` parameter you can set a different locale language,
+        for example: ``idx.month_name(locale='pt_BR.utf8')`` will return month
+        names in Brazilian Portuguese language.
+
+        >>> idx = pd.date_range(start='2018-01', freq='ME', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
+                      dtype='datetime64[ns]', freq='ME')
+        >>> idx.month_name(locale='pt_BR.utf8')  # doctest: +SKIP
+        Index(['Janeiro', 'Fevereiro', 'Março'], dtype='object')
+        """
+        values = self._local_timestamps()
+
+        result = fields.get_date_name_field(
+            values, "month_name", locale=locale, reso=self._creso
+        )
+        result = self._maybe_mask_results(result, fill_value=None)
+        if using_string_dtype():
+            from pandas import (
+                StringDtype,
+                array as pd_array,
+            )
+
+            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
+        return result
+
+    def day_name(self, locale=None) -> npt.NDArray[np.object_]:
+        """
+        Return the day names with specified locale.
+
+        Parameters
+        ----------
+        locale : str, optional
+            Locale determining the language in which to return the day name.
+            Default is English locale (``'en_US.utf8'``). Use the command
+            ``locale -a`` on your terminal on Unix systems to find your locale
+            language code.
+
+        Returns
+        -------
+        Series or Index
+            Series or Index of day names.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range(start='2018-01-01', freq='D', periods=3))
+        >>> s
+        0   2018-01-01
+        1   2018-01-02
+        2   2018-01-03
+        dtype: datetime64[ns]
+        >>> s.dt.day_name()
+        0       Monday
+        1      Tuesday
+        2    Wednesday
+        dtype: object
+
+        >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.day_name()
+        Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object')
+
+        Using the ``locale`` parameter you can set a different locale language,
+        for example: ``idx.day_name(locale='pt_BR.utf8')`` will return day
+        names in Brazilian Portuguese language.
+
+        >>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)
+        >>> idx
+        DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.day_name(locale='pt_BR.utf8') # doctest: +SKIP
+        Index(['Segunda', 'Terça', 'Quarta'], dtype='object')
+        """
+        values = self._local_timestamps()
+
+        result = fields.get_date_name_field(
+            values, "day_name", locale=locale, reso=self._creso
+        )
+        result = self._maybe_mask_results(result, fill_value=None)
+        if using_string_dtype():
+            # TODO: no tests that check for dtype of result as of 2024-08-15
+            from pandas import (
+                StringDtype,
+                array as pd_array,
+            )
+
+            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
+        return result
+
+    @property
+    def time(self) -> npt.NDArray[np.object_]:
+        """
+        Returns numpy array of :class:`datetime.time` objects.
+
+        The time part of the Timestamps.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.time
+        0    10:00:00
+        1    11:00:00
+        dtype: object
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.time
+        array([datetime.time(10, 0), datetime.time(11, 0)], dtype=object)
+        """
+        # If the Timestamps have a timezone that is not UTC,
+        # convert them into their i8 representation while
+        # keeping their timezone and not using UTC
+        timestamps = self._local_timestamps()
+
+        return ints_to_pydatetime(timestamps, box="time", reso=self._creso)
+
+    @property
+    def timetz(self) -> npt.NDArray[np.object_]:
+        """
+        Returns numpy array of :class:`datetime.time` objects with timezones.
+
+        The time part of the Timestamps.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.timetz
+        0    10:00:00+00:00
+        1    11:00:00+00:00
+        dtype: object
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.timetz
+        array([datetime.time(10, 0, tzinfo=datetime.timezone.utc),
+        datetime.time(11, 0, tzinfo=datetime.timezone.utc)], dtype=object)
+        """
+        return ints_to_pydatetime(self.asi8, self.tz, box="time", reso=self._creso)
+
+    @property
+    def date(self) -> npt.NDArray[np.object_]:
+        """
+        Returns numpy array of python :class:`datetime.date` objects.
+
+        Namely, the date part of Timestamps without time and
+        timezone information.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.date
+        0    2020-01-01
+        1    2020-02-01
+        dtype: object
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.date
+        array([datetime.date(2020, 1, 1), datetime.date(2020, 2, 1)], dtype=object)
+        """
+        # If the Timestamps have a timezone that is not UTC,
+        # convert them into their i8 representation while
+        # keeping their timezone and not using UTC
+        timestamps = self._local_timestamps()
+
+        return ints_to_pydatetime(timestamps, box="date", reso=self._creso)
+
+    def isocalendar(self) -> DataFrame:
+        """
+        Calculate year, week, and day according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+            With columns year, week and day.
+
+        See Also
+        --------
+        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
+            week number, and weekday for the given Timestamp object.
+        datetime.date.isocalendar : Return a named tuple object with
+            three components: year, week and weekday.
+
+        Examples
+        --------
+        >>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
+        >>> idx.isocalendar()
+                    year  week  day
+        2019-12-29  2019    52    7
+        2019-12-30  2020     1    1
+        2019-12-31  2020     1    2
+        2020-01-01  2020     1    3
+        >>> idx.isocalendar().week
+        2019-12-29    52
+        2019-12-30     1
+        2019-12-31     1
+        2020-01-01     1
+        Freq: D, Name: week, dtype: UInt32
+        """
+        from pandas import DataFrame
+
+        values = self._local_timestamps()
+        sarray = fields.build_isocalendar_sarray(values, reso=self._creso)
+        iso_calendar_df = DataFrame(
+            sarray, columns=["year", "week", "day"], dtype="UInt32"
+        )
+        if self._hasna:
+            iso_calendar_df.iloc[self._isnan] = None
+        return iso_calendar_df
+
+    year = _field_accessor(
+        "year",
+        "Y",
+        """
+        The year of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="YE")
+        ... )
+        >>> datetime_series
+        0   2000-12-31
+        1   2001-12-31
+        2   2002-12-31
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.year
+        0    2000
+        1    2001
+        2    2002
+        dtype: int32
+        """,
+    )
+    month = _field_accessor(
+        "month",
+        "M",
+        """
+        The month as January=1, December=12.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="ME")
+        ... )
+        >>> datetime_series
+        0   2000-01-31
+        1   2000-02-29
+        2   2000-03-31
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.month
+        0    1
+        1    2
+        2    3
+        dtype: int32
+        """,
+    )
+    day = _field_accessor(
+        "day",
+        "D",
+        """
+        The day of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="D")
+        ... )
+        >>> datetime_series
+        0   2000-01-01
+        1   2000-01-02
+        2   2000-01-03
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.day
+        0    1
+        1    2
+        2    3
+        dtype: int32
+        """,
+    )
+    hour = _field_accessor(
+        "hour",
+        "h",
+        """
+        The hours of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="h")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 01:00:00
+        2   2000-01-01 02:00:00
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.hour
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """,
+    )
+    minute = _field_accessor(
+        "minute",
+        "m",
+        """
+        The minutes of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="min")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 00:01:00
+        2   2000-01-01 00:02:00
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.minute
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """,
+    )
+    second = _field_accessor(
+        "second",
+        "s",
+        """
+        The seconds of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="s")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 00:00:01
+        2   2000-01-01 00:00:02
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.second
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """,
+    )
+    microsecond = _field_accessor(
+        "microsecond",
+        "us",
+        """
+        The microseconds of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="us")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00.000000
+        1   2000-01-01 00:00:00.000001
+        2   2000-01-01 00:00:00.000002
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.microsecond
+        0       0
+        1       1
+        2       2
+        dtype: int32
+        """,
+    )
+    nanosecond = _field_accessor(
+        "nanosecond",
+        "ns",
+        """
+        The nanoseconds of the datetime.
+
+        Examples
+        --------
+        >>> datetime_series = pd.Series(
+        ...     pd.date_range("2000-01-01", periods=3, freq="ns")
+        ... )
+        >>> datetime_series
+        0   2000-01-01 00:00:00.000000000
+        1   2000-01-01 00:00:00.000000001
+        2   2000-01-01 00:00:00.000000002
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.nanosecond
+        0       0
+        1       1
+        2       2
+        dtype: int32
+        """,
+    )
+    _dayofweek_doc = """
+    The day of the week with Monday=0, Sunday=6.
+
+    Return the day of the week. It is assumed the week starts on
+    Monday, which is denoted by 0 and ends on Sunday which is denoted
+    by 6. This method is available on both Series with datetime
+    values (using the `dt` accessor) or DatetimeIndex.
+
+    Returns
+    -------
+    Series or Index
+        Containing integers indicating the day number.
+
+    See Also
+    --------
+    Series.dt.dayofweek : Alias.
+    Series.dt.weekday : Alias.
+    Series.dt.day_name : Returns the name of the day of the week.
+
+    Examples
+    --------
+    >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()
+    >>> s.dt.dayofweek
+    2016-12-31    5
+    2017-01-01    6
+    2017-01-02    0
+    2017-01-03    1
+    2017-01-04    2
+    2017-01-05    3
+    2017-01-06    4
+    2017-01-07    5
+    2017-01-08    6
+    Freq: D, dtype: int32
+    """
+    day_of_week = _field_accessor("day_of_week", "dow", _dayofweek_doc)
+    dayofweek = day_of_week
+    weekday = day_of_week
+
+    day_of_year = _field_accessor(
+        "dayofyear",
+        "doy",
+        """
+        The ordinal day of the year.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.dayofyear
+        0    1
+        1   32
+        dtype: int32
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.dayofyear
+        Index([1, 32], dtype='int32')
+        """,
+    )
+    dayofyear = day_of_year
+    quarter = _field_accessor(
+        "quarter",
+        "q",
+        """
+        The quarter of the date.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "4/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-04-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.quarter
+        0    1
+        1    2
+        dtype: int32
+
+        For DatetimeIndex:
+
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00",
+        ...                         "2/1/2020 11:00:00+00:00"])
+        >>> idx.quarter
+        Index([1, 1], dtype='int32')
+        """,
+    )
+    days_in_month = _field_accessor(
+        "days_in_month",
+        "dim",
+        """
+        The number of days in the month.
+
+        Examples
+        --------
+        >>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+        >>> s = pd.to_datetime(s)
+        >>> s
+        0   2020-01-01 10:00:00+00:00
+        1   2020-02-01 11:00:00+00:00
+        dtype: datetime64[ns, UTC]
+        >>> s.dt.daysinmonth
+        0    31
+        1    29
+        dtype: int32
+        """,
+    )
+    daysinmonth = days_in_month
+    _is_month_doc = """
+        Indicates whether the date is the {first_or_last} day of the month.
+
+        Returns
+        -------
+        Series or array
+            For Series, returns a Series with boolean values.
+            For DatetimeIndex, returns a boolean array.
+
+        See Also
+        --------
+        is_month_start : Return a boolean indicating whether the date
+            is the first day of the month.
+        is_month_end : Return a boolean indicating whether the date
+            is the last day of the month.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> s = pd.Series(pd.date_range("2018-02-27", periods=3))
+        >>> s
+        0   2018-02-27
+        1   2018-02-28
+        2   2018-03-01
+        dtype: datetime64[ns]
+        >>> s.dt.is_month_start
+        0    False
+        1    False
+        2    True
+        dtype: bool
+        >>> s.dt.is_month_end
+        0    False
+        1    True
+        2    False
+        dtype: bool
+
+        >>> idx = pd.date_range("2018-02-27", periods=3)
+        >>> idx.is_month_start
+        array([False, False, True])
+        >>> idx.is_month_end
+        array([False, True, False])
+    """
+    is_month_start = _field_accessor(
+        "is_month_start", "is_month_start", _is_month_doc.format(first_or_last="first")
+    )
+
+    is_month_end = _field_accessor(
+        "is_month_end", "is_month_end", _is_month_doc.format(first_or_last="last")
+    )
+
+    is_quarter_start = _field_accessor(
+        "is_quarter_start",
+        "is_quarter_start",
+        """
+        Indicator for whether the date is the first day of a quarter.
+
+        Returns
+        -------
+        is_quarter_start : Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        quarter : Return the quarter of the date.
+        is_quarter_end : Similar property for indicating the quarter end.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30",
+        ...                   periods=4)})
+        >>> df.assign(quarter=df.dates.dt.quarter,
+        ...           is_quarter_start=df.dates.dt.is_quarter_start)
+               dates  quarter  is_quarter_start
+        0 2017-03-30        1             False
+        1 2017-03-31        1             False
+        2 2017-04-01        2              True
+        3 2017-04-02        2             False
+
+        >>> idx = pd.date_range('2017-03-30', periods=4)
+        >>> idx
+        DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_quarter_start
+        array([False, False,  True, False])
+        """,
+    )
+    is_quarter_end = _field_accessor(
+        "is_quarter_end",
+        "is_quarter_end",
+        """
+        Indicator for whether the date is the last day of a quarter.
+
+        Returns
+        -------
+        is_quarter_end : Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        quarter : Return the quarter of the date.
+        is_quarter_start : Similar property indicating the quarter start.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30",
+        ...                    periods=4)})
+        >>> df.assign(quarter=df.dates.dt.quarter,
+        ...           is_quarter_end=df.dates.dt.is_quarter_end)
+               dates  quarter    is_quarter_end
+        0 2017-03-30        1             False
+        1 2017-03-31        1              True
+        2 2017-04-01        2             False
+        3 2017-04-02        2             False
+
+        >>> idx = pd.date_range('2017-03-30', periods=4)
+        >>> idx
+        DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_quarter_end
+        array([False,  True, False, False])
+        """,
+    )
+    is_year_start = _field_accessor(
+        "is_year_start",
+        "is_year_start",
+        """
+        Indicate whether the date is the first day of a year.
+
+        Returns
+        -------
+        Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        is_year_end : Similar property indicating the last day of the year.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3))
+        >>> dates
+        0   2017-12-30
+        1   2017-12-31
+        2   2018-01-01
+        dtype: datetime64[ns]
+
+        >>> dates.dt.is_year_start
+        0    False
+        1    False
+        2    True
+        dtype: bool
+
+        >>> idx = pd.date_range("2017-12-30", periods=3)
+        >>> idx
+        DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_year_start
+        array([False, False,  True])
+        """,
+    )
+    is_year_end = _field_accessor(
+        "is_year_end",
+        "is_year_end",
+        """
+        Indicate whether the date is the last day of the year.
+
+        Returns
+        -------
+        Series or DatetimeIndex
+            The same type as the original data with boolean values. Series will
+            have the same name and index. DatetimeIndex will have the same
+            name.
+
+        See Also
+        --------
+        is_year_start : Similar property indicating the start of the year.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3))
+        >>> dates
+        0   2017-12-30
+        1   2017-12-31
+        2   2018-01-01
+        dtype: datetime64[ns]
+
+        >>> dates.dt.is_year_end
+        0    False
+        1     True
+        2    False
+        dtype: bool
+
+        >>> idx = pd.date_range("2017-12-30", periods=3)
+        >>> idx
+        DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
+                      dtype='datetime64[ns]', freq='D')
+
+        >>> idx.is_year_end
+        array([False,  True, False])
+        """,
+    )
+    is_leap_year = _field_accessor(
+        "is_leap_year",
+        "is_leap_year",
+        """
+        Boolean indicator if the date belongs to a leap year.
+
+        A leap year is a year, which has 366 days (instead of 365) including
+        29th of February as an intercalary day.
+        Leap years are years which are multiples of four with the exception
+        of years divisible by 100 but not by 400.
+
+        Returns
+        -------
+        Series or ndarray
+             Booleans indicating if dates belong to a leap year.
+
+        Examples
+        --------
+        This method is available on Series with datetime values under
+        the ``.dt`` accessor, and directly on DatetimeIndex.
+
+        >>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="YE")
+        >>> idx
+        DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],
+                      dtype='datetime64[ns]', freq='YE-DEC')
+        >>> idx.is_leap_year
+        array([ True, False, False])
+
+        >>> dates_series = pd.Series(idx)
+        >>> dates_series
+        0   2012-12-31
+        1   2013-12-31
+        2   2014-12-31
+        dtype: datetime64[ns]
+        >>> dates_series.dt.is_leap_year
+        0     True
+        1    False
+        2    False
+        dtype: bool
+        """,
+    )
+
+    def to_julian_date(self) -> npt.NDArray[np.float64]:
+        """
+        Convert Datetime Array to float64 ndarray of Julian Dates.
+        0 Julian date is noon January 1, 4713 BC.
+        https://en.wikipedia.org/wiki/Julian_day
+        """
+
+        # http://mysite.verizon.net/aesir_research/date/jdalg2.htm
+        year = np.asarray(self.year)
+        month = np.asarray(self.month)
+        day = np.asarray(self.day)
+        testarr = month < 3
+        year[testarr] -= 1
+        month[testarr] += 12
+        return (
+            day
+            + np.fix((153 * month - 457) / 5)
+            + 365 * year
+            + np.floor(year / 4)
+            - np.floor(year / 100)
+            + np.floor(year / 400)
+            + 1_721_118.5
+            + (
+                self.hour
+                + self.minute / 60
+                + self.second / 3600
+                + self.microsecond / 3600 / 10**6
+                + self.nanosecond / 3600 / 10**9
+            )
+            / 24
+        )
+
+    # -----------------------------------------------------------------
+    # Reductions
+
+    def std(
+        self,
+        axis=None,
+        dtype=None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        """
+        Return sample standard deviation over requested axis.
+
+        Normalized by `N-1` by default. This can be changed using ``ddof``.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis for the function to be applied on. For :class:`pandas.Series`
+            this parameter is unused and defaults to ``None``.
+        ddof : int, default 1
+            Degrees of Freedom. The divisor used in calculations is `N - ddof`,
+            where `N` represents the number of elements.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is ``NA``, the result
+            will be ``NA``.
+
+        Returns
+        -------
+        Timedelta
+
+        See Also
+        --------
+        numpy.ndarray.std : Returns the standard deviation of the array elements
+            along given axis.
+        Series.std : Return sample standard deviation over requested axis.
+
+        Examples
+        --------
+        For :class:`pandas.DatetimeIndex`:
+
+        >>> idx = pd.date_range('2001-01-01 00:00', periods=3)
+        >>> idx
+        DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
+                      dtype='datetime64[ns]', freq='D')
+        >>> idx.std()
+        Timedelta('1 days 00:00:00')
+        """
+        # Because std is translation-invariant, we can get self.std
+        #  by calculating (self - Timestamp(0)).std, and we can do it
+        #  without creating a copy by using a view on self._ndarray
+        from pandas.core.arrays import TimedeltaArray
+
+        # Find the td64 dtype with the same resolution as our dt64 dtype
+        dtype_str = self._ndarray.dtype.name.replace("datetime64", "timedelta64")
+        dtype = np.dtype(dtype_str)
+
+        tda = TimedeltaArray._simple_new(self._ndarray.view(dtype), dtype=dtype)
+
+        return tda.std(axis=axis, out=out, ddof=ddof, keepdims=keepdims, skipna=skipna)
+
+
+# -------------------------------------------------------------------
+# Constructor Helpers
+
+
+def _sequence_to_dt64(
+    data: ArrayLike,
+    *,
+    copy: bool = False,
+    tz: tzinfo | None = None,
+    dayfirst: bool = False,
+    yearfirst: bool = False,
+    ambiguous: TimeAmbiguous = "raise",
+    out_unit: str | None = None,
+):
+    """
+    Parameters
+    ----------
+    data : np.ndarray or ExtensionArray
+        dtl.ensure_arraylike_for_datetimelike has already been called.
+    copy : bool, default False
+    tz : tzinfo or None, default None
+    dayfirst : bool, default False
+    yearfirst : bool, default False
+    ambiguous : str, bool, or arraylike, default 'raise'
+        See pandas._libs.tslibs.tzconversion.tz_localize_to_utc.
+    out_unit : str or None, default None
+        Desired output resolution.
+
+    Returns
+    -------
+    result : numpy.ndarray
+        The sequence converted to a numpy array with dtype ``datetime64[unit]``.
+        Where `unit` is "ns" unless specified otherwise by `out_unit`.
+    tz : tzinfo or None
+        Either the user-provided tzinfo or one inferred from the data.
+
+    Raises
+    ------
+    TypeError : PeriodDType data is passed
+    """
+
+    # By this point we are assured to have either a numpy array or Index
+    data, copy = maybe_convert_dtype(data, copy, tz=tz)
+    data_dtype = getattr(data, "dtype", None)
+
+    if out_unit is None:
+        out_unit = "ns"
+    out_dtype = np.dtype(f"M8[{out_unit}]")
+
+    if data_dtype == object or is_string_dtype(data_dtype):
+        # TODO: We do not have tests specific to string-dtypes,
+        #  also complex or categorical or other extension
+        data = cast(np.ndarray, data)
+        copy = False
+        if lib.infer_dtype(data, skipna=False) == "integer":
+            # Much more performant than going through array_to_datetime
+            data = data.astype(np.int64)
+        elif tz is not None and ambiguous == "raise":
+            obj_data = np.asarray(data, dtype=object)
+            result = tslib.array_to_datetime_with_tz(
+                obj_data,
+                tz=tz,
+                dayfirst=dayfirst,
+                yearfirst=yearfirst,
+                creso=abbrev_to_npy_unit(out_unit),
+            )
+            return result, tz
+        else:
+            converted, inferred_tz = objects_to_datetime64(
+                data,
+                dayfirst=dayfirst,
+                yearfirst=yearfirst,
+                allow_object=False,
+                out_unit=out_unit or "ns",
+            )
+            copy = False
+            if tz and inferred_tz:
+                #  two timezones: convert to intended from base UTC repr
+                # GH#42505 by convention, these are _already_ UTC
+                result = converted
+
+            elif inferred_tz:
+                tz = inferred_tz
+                result = converted
+
+            else:
+                result, _ = _construct_from_dt64_naive(
+                    converted, tz=tz, copy=copy, ambiguous=ambiguous
+                )
+            return result, tz
+
+        data_dtype = data.dtype
+
+    # `data` may have originally been a Categorical[datetime64[ns, tz]],
+    # so we need to handle these types.
+    if isinstance(data_dtype, DatetimeTZDtype):
+        # DatetimeArray -> ndarray
+        data = cast(DatetimeArray, data)
+        tz = _maybe_infer_tz(tz, data.tz)
+        result = data._ndarray
+
+    elif lib.is_np_dtype(data_dtype, "M"):
+        # tz-naive DatetimeArray or ndarray[datetime64]
+        if isinstance(data, DatetimeArray):
+            data = data._ndarray
+
+        data = cast(np.ndarray, data)
+        result, copy = _construct_from_dt64_naive(
+            data, tz=tz, copy=copy, ambiguous=ambiguous
+        )
+
+    else:
+        # must be integer dtype otherwise
+        # assume this data are epoch timestamps
+        if data.dtype != INT64_DTYPE:
+            data = data.astype(np.int64, copy=False)
+            copy = False
+        data = cast(np.ndarray, data)
+        result = data.view(out_dtype)
+
+    if copy:
+        result = result.copy()
+
+    assert isinstance(result, np.ndarray), type(result)
+    assert result.dtype.kind == "M"
+    assert result.dtype != "M8"
+    assert is_supported_dtype(result.dtype)
+    return result, tz
+
+
+def _construct_from_dt64_naive(
+    data: np.ndarray, *, tz: tzinfo | None, copy: bool, ambiguous: TimeAmbiguous
+) -> tuple[np.ndarray, bool]:
+    """
+    Convert datetime64 data to a supported dtype, localizing if necessary.
+    """
+    # Caller is responsible for ensuring
+    #  lib.is_np_dtype(data.dtype)
+
+    new_dtype = data.dtype
+    if not is_supported_dtype(new_dtype):
+        # Cast to the nearest supported unit, generally "s"
+        new_dtype = get_supported_dtype(new_dtype)
+        data = astype_overflowsafe(data, dtype=new_dtype, copy=False)
+        copy = False
+
+    if data.dtype.byteorder == ">":
+        # TODO: better way to handle this?  non-copying alternative?
+        #  without this, test_constructor_datetime64_bigendian fails
+        data = data.astype(data.dtype.newbyteorder("<"))
+        new_dtype = data.dtype
+        copy = False
+
+    if tz is not None:
+        # Convert tz-naive to UTC
+        # TODO: if tz is UTC, are there situations where we *don't* want a
+        #  copy?  tz_localize_to_utc always makes one.
+        shape = data.shape
+        if data.ndim > 1:
+            data = data.ravel()
+
+        data_unit = get_unit_from_dtype(new_dtype)
+        data = tzconversion.tz_localize_to_utc(
+            data.view("i8"), tz, ambiguous=ambiguous, creso=data_unit
+        )
+        data = data.view(new_dtype)
+        data = data.reshape(shape)
+
+    assert data.dtype == new_dtype, data.dtype
+    result = data
+
+    return result, copy
+
+
+def objects_to_datetime64(
+    data: np.ndarray,
+    dayfirst,
+    yearfirst,
+    utc: bool = False,
+    errors: DateTimeErrorChoices = "raise",
+    allow_object: bool = False,
+    out_unit: str = "ns",
+):
+    """
+    Convert data to array of timestamps.
+
+    Parameters
+    ----------
+    data : np.ndarray[object]
+    dayfirst : bool
+    yearfirst : bool
+    utc : bool, default False
+        Whether to convert/localize timestamps to UTC.
+    errors : {'raise', 'ignore', 'coerce'}
+    allow_object : bool
+        Whether to return an object-dtype ndarray instead of raising if the
+        data contains more than one timezone.
+    out_unit : str, default "ns"
+
+    Returns
+    -------
+    result : ndarray
+        np.datetime64[out_unit] if returned values represent wall times or UTC
+        timestamps.
+        object if mixed timezones
+    inferred_tz : tzinfo or None
+        If not None, then the datetime64 values in `result` denote UTC timestamps.
+
+    Raises
+    ------
+    ValueError : if data cannot be converted to datetimes
+    TypeError  : When a type cannot be converted to datetime
+    """
+    assert errors in ["raise", "ignore", "coerce"]
+
+    # if str-dtype, convert
+    data = np.asarray(data, dtype=np.object_)
+
+    result, tz_parsed = tslib.array_to_datetime(
+        data,
+        errors=errors,
+        utc=utc,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        creso=abbrev_to_npy_unit(out_unit),
+    )
+
+    if tz_parsed is not None:
+        # We can take a shortcut since the datetime64 numpy array
+        #  is in UTC
+        return result, tz_parsed
+    elif result.dtype.kind == "M":
+        return result, tz_parsed
+    elif result.dtype == object:
+        # GH#23675 when called via `pd.to_datetime`, returning an object-dtype
+        #  array is allowed.  When called via `pd.DatetimeIndex`, we can
+        #  only accept datetime64 dtype, so raise TypeError if object-dtype
+        #  is returned, as that indicates the values can be recognized as
+        #  datetimes but they have conflicting timezones/awareness
+        if allow_object:
+            return result, tz_parsed
+        raise TypeError("DatetimeIndex has mixed timezones")
+    else:  # pragma: no cover
+        # GH#23675 this TypeError should never be hit, whereas the TypeError
+        #  in the object-dtype branch above is reachable.
+        raise TypeError(result)
+
+
+def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None):
+    """
+    Convert data based on dtype conventions, issuing
+    errors where appropriate.
+
+    Parameters
+    ----------
+    data : np.ndarray or pd.Index
+    copy : bool
+    tz : tzinfo or None, default None
+
+    Returns
+    -------
+    data : np.ndarray or pd.Index
+    copy : bool
+
+    Raises
+    ------
+    TypeError : PeriodDType data is passed
+    """
+    if not hasattr(data, "dtype"):
+        # e.g. collections.deque
+        return data, copy
+
+    if is_float_dtype(data.dtype):
+        # pre-2.0 we treated these as wall-times, inconsistent with ints
+        # GH#23675, GH#45573 deprecated to treat symmetrically with integer dtypes.
+        # Note: data.astype(np.int64) fails ARM tests, see
+        # https://github.com/pandas-dev/pandas/issues/49468.
+        data = data.astype(DT64NS_DTYPE).view("i8")
+        copy = False
+
+    elif lib.is_np_dtype(data.dtype, "m") or is_bool_dtype(data.dtype):
+        # GH#29794 enforcing deprecation introduced in GH#23539
+        raise TypeError(f"dtype {data.dtype} cannot be converted to datetime64[ns]")
+    elif isinstance(data.dtype, PeriodDtype):
+        # Note: without explicitly raising here, PeriodIndex
+        #  test_setops.test_join_does_not_recur fails
+        raise TypeError(
+            "Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead"
+        )
+
+    elif isinstance(data.dtype, ExtensionDtype) and not isinstance(
+        data.dtype, DatetimeTZDtype
+    ):
+        # TODO: We have no tests for these
+        data = np.array(data, dtype=np.object_)
+        copy = False
+
+    return data, copy
+
+
+# -------------------------------------------------------------------
+# Validation and Inference
+
+
+def _maybe_infer_tz(tz: tzinfo | None, inferred_tz: tzinfo | None) -> tzinfo | None:
+    """
+    If a timezone is inferred from data, check that it is compatible with
+    the user-provided timezone, if any.
+
+    Parameters
+    ----------
+    tz : tzinfo or None
+    inferred_tz : tzinfo or None
+
+    Returns
+    -------
+    tz : tzinfo or None
+
+    Raises
+    ------
+    TypeError : if both timezones are present but do not match
+    """
+    if tz is None:
+        tz = inferred_tz
+    elif inferred_tz is None:
+        pass
+    elif not timezones.tz_compare(tz, inferred_tz):
+        raise TypeError(
+            f"data is already tz-aware {inferred_tz}, unable to "
+            f"set specified tz: {tz}"
+        )
+    return tz
+
+
+def _validate_dt64_dtype(dtype):
+    """
+    Check that a dtype, if passed, represents either a numpy datetime64[ns]
+    dtype or a pandas DatetimeTZDtype.
+
+    Parameters
+    ----------
+    dtype : object
+
+    Returns
+    -------
+    dtype : None, numpy.dtype, or DatetimeTZDtype
+
+    Raises
+    ------
+    ValueError : invalid dtype
+
+    Notes
+    -----
+    Unlike _validate_tz_from_dtype, this does _not_ allow non-existent
+    tz errors to go through
+    """
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+        if dtype == np.dtype("M8"):
+            # no precision, disallowed GH#24806
+            msg = (
+                "Passing in 'datetime64' dtype with no precision is not allowed. "
+                "Please pass in 'datetime64[ns]' instead."
+            )
+            raise ValueError(msg)
+
+        if (
+            isinstance(dtype, np.dtype)
+            and (dtype.kind != "M" or not is_supported_dtype(dtype))
+        ) or not isinstance(dtype, (np.dtype, DatetimeTZDtype)):
+            raise ValueError(
+                f"Unexpected value for 'dtype': '{dtype}'. "
+                "Must be 'datetime64[s]', 'datetime64[ms]', 'datetime64[us]', "
+                "'datetime64[ns]' or DatetimeTZDtype'."
+            )
+
+        if getattr(dtype, "tz", None):
+            # https://github.com/pandas-dev/pandas/issues/18595
+            # Ensure that we have a standard timezone for pytz objects.
+            # Without this, things like adding an array of timedeltas and
+            # a  tz-aware Timestamp (with a tz specific to its datetime) will
+            # be incorrect(ish?) for the array as a whole
+            dtype = cast(DatetimeTZDtype, dtype)
+            dtype = DatetimeTZDtype(
+                unit=dtype.unit, tz=timezones.tz_standardize(dtype.tz)
+            )
+
+    return dtype
+
+
+def _validate_tz_from_dtype(
+    dtype, tz: tzinfo | None, explicit_tz_none: bool = False
+) -> tzinfo | None:
+    """
+    If the given dtype is a DatetimeTZDtype, extract the implied
+    tzinfo object from it and check that it does not conflict with the given
+    tz.
+
+    Parameters
+    ----------
+    dtype : dtype, str
+    tz : None, tzinfo
+    explicit_tz_none : bool, default False
+        Whether tz=None was passed explicitly, as opposed to lib.no_default.
+
+    Returns
+    -------
+    tz : consensus tzinfo
+
+    Raises
+    ------
+    ValueError : on tzinfo mismatch
+    """
+    if dtype is not None:
+        if isinstance(dtype, str):
+            try:
+                dtype = DatetimeTZDtype.construct_from_string(dtype)
+            except TypeError:
+                # Things like `datetime64[ns]`, which is OK for the
+                # constructors, but also nonsense, which should be validated
+                # but not by us. We *do* allow non-existent tz errors to
+                # go through
+                pass
+        dtz = getattr(dtype, "tz", None)
+        if dtz is not None:
+            if tz is not None and not timezones.tz_compare(tz, dtz):
+                raise ValueError("cannot supply both a tz and a dtype with a tz")
+            if explicit_tz_none:
+                raise ValueError("Cannot pass both a timezone-aware dtype and tz=None")
+            tz = dtz
+
+        if tz is not None and lib.is_np_dtype(dtype, "M"):
+            # We also need to check for the case where the user passed a
+            #  tz-naive dtype (i.e. datetime64[ns])
+            if tz is not None and not timezones.tz_compare(tz, dtz):
+                raise ValueError(
+                    "cannot supply both a tz and a "
+                    "timezone-naive dtype (i.e. datetime64[ns])"
+                )
+
+    return tz
+
+
+def _infer_tz_from_endpoints(
+    start: Timestamp, end: Timestamp, tz: tzinfo | None
+) -> tzinfo | None:
+    """
+    If a timezone is not explicitly given via `tz`, see if one can
+    be inferred from the `start` and `end` endpoints.  If more than one
+    of these inputs provides a timezone, require that they all agree.
+
+    Parameters
+    ----------
+    start : Timestamp
+    end : Timestamp
+    tz : tzinfo or None
+
+    Returns
+    -------
+    tz : tzinfo or None
+
+    Raises
+    ------
+    TypeError : if start and end timezones do not agree
+    """
+    try:
+        inferred_tz = timezones.infer_tzinfo(start, end)
+    except AssertionError as err:
+        # infer_tzinfo raises AssertionError if passed mismatched timezones
+        raise TypeError(
+            "Start and end cannot both be tz-aware with different timezones"
+        ) from err
+
+    inferred_tz = timezones.maybe_get_tz(inferred_tz)
+    tz = timezones.maybe_get_tz(tz)
+
+    if tz is not None and inferred_tz is not None:
+        if not timezones.tz_compare(inferred_tz, tz):
+            raise AssertionError("Inferred time zone not equal to passed time zone")
+
+    elif inferred_tz is not None:
+        tz = inferred_tz
+
+    return tz
+
+
+def _maybe_normalize_endpoints(
+    start: Timestamp | None, end: Timestamp | None, normalize: bool
+):
+    if normalize:
+        if start is not None:
+            start = start.normalize()
+
+        if end is not None:
+            end = end.normalize()
+
+    return start, end
+
+
+def _maybe_localize_point(
+    ts: Timestamp | None, freq, tz, ambiguous, nonexistent
+) -> Timestamp | None:
+    """
+    Localize a start or end Timestamp to the timezone of the corresponding
+    start or end Timestamp
+
+    Parameters
+    ----------
+    ts : start or end Timestamp to potentially localize
+    freq : Tick, DateOffset, or None
+    tz : str, timezone object or None
+    ambiguous: str, localization behavior for ambiguous times
+    nonexistent: str, localization behavior for nonexistent times
+
+    Returns
+    -------
+    ts : Timestamp
+    """
+    # Make sure start and end are timezone localized if:
+    # 1) freq = a Timedelta-like frequency (Tick)
+    # 2) freq = None i.e. generating a linspaced range
+    if ts is not None and ts.tzinfo is None:
+        # Note: We can't ambiguous='infer' a singular ambiguous time; however,
+        # we have historically defaulted ambiguous=False
+        ambiguous = ambiguous if ambiguous != "infer" else False
+        localize_args = {"ambiguous": ambiguous, "nonexistent": nonexistent, "tz": None}
+        if isinstance(freq, Tick) or freq is None:
+            localize_args["tz"] = tz
+        ts = ts.tz_localize(**localize_args)
+    return ts
+
+
+def _generate_range(
+    start: Timestamp | None,
+    end: Timestamp | None,
+    periods: int | None,
+    offset: BaseOffset,
+    *,
+    unit: str,
+):
+    """
+    Generates a sequence of dates corresponding to the specified time
+    offset. Similar to dateutil.rrule except uses pandas DateOffset
+    objects to represent time increments.
+
+    Parameters
+    ----------
+    start : Timestamp or None
+    end : Timestamp or None
+    periods : int or None
+    offset : DateOffset
+    unit : str
+
+    Notes
+    -----
+    * This method is faster for generating weekdays than dateutil.rrule
+    * At least two of (start, end, periods) must be specified.
+    * If both start and end are specified, the returned dates will
+    satisfy start <= date <= end.
+
+    Returns
+    -------
+    dates : generator object
+    """
+    offset = to_offset(offset)
+
+    # Argument 1 to "Timestamp" has incompatible type "Optional[Timestamp]";
+    # expected "Union[integer[Any], float, str, date, datetime64]"
+    start = Timestamp(start)  # type: ignore[arg-type]
+    if start is not NaT:
+        start = start.as_unit(unit)
+    else:
+        start = None
+
+    # Argument 1 to "Timestamp" has incompatible type "Optional[Timestamp]";
+    # expected "Union[integer[Any], float, str, date, datetime64]"
+    end = Timestamp(end)  # type: ignore[arg-type]
+    if end is not NaT:
+        end = end.as_unit(unit)
+    else:
+        end = None
+
+    if start and not offset.is_on_offset(start):
+        # Incompatible types in assignment (expression has type "datetime",
+        # variable has type "Optional[Timestamp]")
+        start = offset.rollforward(start)  # type: ignore[assignment]
+
+    elif end and not offset.is_on_offset(end):
+        # Incompatible types in assignment (expression has type "datetime",
+        # variable has type "Optional[Timestamp]")
+        end = offset.rollback(end)  # type: ignore[assignment]
+
+    # Unsupported operand types for < ("Timestamp" and "None")
+    if periods is None and end < start and offset.n >= 0:  # type: ignore[operator]
+        end = None
+        periods = 0
+
+    if end is None:
+        # error: No overload variant of "__radd__" of "BaseOffset" matches
+        # argument type "None"
+        end = start + (periods - 1) * offset  # type: ignore[operator]
+
+    if start is None:
+        # error: No overload variant of "__radd__" of "BaseOffset" matches
+        # argument type "None"
+        start = end - (periods - 1) * offset  # type: ignore[operator]
+
+    start = cast(Timestamp, start)
+    end = cast(Timestamp, end)
+
+    cur = start
+    if offset.n >= 0:
+        while cur <= end:
+            yield cur
+
+            if cur == end:
+                # GH#24252 avoid overflows by not performing the addition
+                # in offset.apply unless we have to
+                break
+
+            # faster than cur + offset
+            next_date = offset._apply(cur)
+            next_date = next_date.as_unit(unit)
+            if next_date <= cur:
+                raise ValueError(f"Offset {offset} did not increment date")
+            cur = next_date
+    else:
+        while cur >= end:
+            yield cur
+
+            if cur == end:
+                # GH#24252 avoid overflows by not performing the addition
+                # in offset.apply unless we have to
+                break
+
+            # faster than cur + offset
+            next_date = offset._apply(cur)
+            next_date = next_date.as_unit(unit)
+            if next_date >= cur:
+                raise ValueError(f"Offset {offset} did not decrement date")
+            cur = next_date
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/floating.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/floating.py
new file mode 100644
index 0000000000000000000000000000000000000000..74b8cfb65cbc7887b7d2a164121c90eda0833121
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/floating.py
@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+from typing import ClassVar
+
+import numpy as np
+
+from pandas.core.dtypes.base import register_extension_dtype
+from pandas.core.dtypes.common import is_float_dtype
+
+from pandas.core.arrays.numeric import (
+    NumericArray,
+    NumericDtype,
+)
+
+
+class FloatingDtype(NumericDtype):
+    """
+    An ExtensionDtype to hold a single size of floating dtype.
+
+    These specific implementations are subclasses of the non-public
+    FloatingDtype. For example we have Float32Dtype to represent float32.
+
+    The attributes name & type are set when these subclasses are created.
+    """
+
+    _default_np_dtype = np.dtype(np.float64)
+    _checker = is_float_dtype
+
+    @classmethod
+    def construct_array_type(cls) -> type[FloatingArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return FloatingArray
+
+    @classmethod
+    def _get_dtype_mapping(cls) -> dict[np.dtype, FloatingDtype]:
+        return NUMPY_FLOAT_TO_DTYPE
+
+    @classmethod
+    def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
+        """
+        Safely cast the values to the given dtype.
+
+        "safe" in this context means the casting is lossless.
+        """
+        # This is really only here for compatibility with IntegerDtype
+        # Here for compat with IntegerDtype
+        return values.astype(dtype, copy=copy)
+
+
+class FloatingArray(NumericArray):
+    """
+    Array of floating (optional missing) values.
+
+    .. warning::
+
+       FloatingArray is currently experimental, and its API or internal
+       implementation may change without warning. Especially the behaviour
+       regarding NaN (distinct from NA missing values) is subject to change.
+
+    We represent a FloatingArray with 2 numpy arrays:
+
+    - data: contains a numpy float array of the appropriate dtype
+    - mask: a boolean array holding a mask on the data, True is missing
+
+    To construct an FloatingArray from generic array-like input, use
+    :func:`pandas.array` with one of the float dtypes (see examples).
+
+    See :ref:`integer_na` for more.
+
+    Parameters
+    ----------
+    values : numpy.ndarray
+        A 1-d float-dtype array.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values.
+    copy : bool, default False
+        Whether to copy the `values` and `mask`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    FloatingArray
+
+    Examples
+    --------
+    Create an FloatingArray with :func:`pandas.array`:
+
+    >>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype())
+    <FloatingArray>
+    [0.1, <NA>, 0.3]
+    Length: 3, dtype: Float32
+
+    String aliases for the dtypes are also available. They are capitalized.
+
+    >>> pd.array([0.1, None, 0.3], dtype="Float32")
+    <FloatingArray>
+    [0.1, <NA>, 0.3]
+    Length: 3, dtype: Float32
+    """
+
+    _dtype_cls = FloatingDtype
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = np.nan
+    # Fill values used for any/all
+    # Incompatible types in assignment (expression has type "float", base class
+    # "BaseMaskedArray" defined the type as "<typing special form>")
+    _truthy_value = 1.0  # type: ignore[assignment]
+    _falsey_value = 0.0  # type: ignore[assignment]
+
+
+_dtype_docstring = """
+An ExtensionDtype for {dtype} data.
+
+This dtype uses ``pd.NA`` as missing value indicator.
+
+Attributes
+----------
+None
+
+Methods
+-------
+None
+
+Examples
+--------
+For Float32Dtype:
+
+>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype())
+>>> ser.dtype
+Float32Dtype()
+
+For Float64Dtype:
+
+>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype())
+>>> ser.dtype
+Float64Dtype()
+"""
+
+# create the Dtype
+
+
+@register_extension_dtype
+class Float32Dtype(FloatingDtype):
+    type = np.float32
+    name: ClassVar[str] = "Float32"
+    __doc__ = _dtype_docstring.format(dtype="float32")
+
+
+@register_extension_dtype
+class Float64Dtype(FloatingDtype):
+    type = np.float64
+    name: ClassVar[str] = "Float64"
+    __doc__ = _dtype_docstring.format(dtype="float64")
+
+
+NUMPY_FLOAT_TO_DTYPE: dict[np.dtype, FloatingDtype] = {
+    np.dtype(np.float32): Float32Dtype(),
+    np.dtype(np.float64): Float64Dtype(),
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/integer.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/integer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9384e25ba9d9f32caf826efc01b4eb58a454d65
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/integer.py
@@ -0,0 +1,272 @@
+from __future__ import annotations
+
+from typing import ClassVar
+
+import numpy as np
+
+from pandas.core.dtypes.base import register_extension_dtype
+from pandas.core.dtypes.common import is_integer_dtype
+
+from pandas.core.arrays.numeric import (
+    NumericArray,
+    NumericDtype,
+)
+
+
+class IntegerDtype(NumericDtype):
+    """
+    An ExtensionDtype to hold a single size & kind of integer dtype.
+
+    These specific implementations are subclasses of the non-public
+    IntegerDtype. For example, we have Int8Dtype to represent signed int 8s.
+
+    The attributes name & type are set when these subclasses are created.
+    """
+
+    _default_np_dtype = np.dtype(np.int64)
+    _checker = is_integer_dtype
+
+    @classmethod
+    def construct_array_type(cls) -> type[IntegerArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        return IntegerArray
+
+    @classmethod
+    def _get_dtype_mapping(cls) -> dict[np.dtype, IntegerDtype]:
+        return NUMPY_INT_TO_DTYPE
+
+    @classmethod
+    def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
+        """
+        Safely cast the values to the given dtype.
+
+        "safe" in this context means the casting is lossless. e.g. if 'values'
+        has a floating dtype, each value must be an integer.
+        """
+        try:
+            return values.astype(dtype, casting="safe", copy=copy)
+        except TypeError as err:
+            casted = values.astype(dtype, copy=copy)
+            if (casted == values).all():
+                return casted
+
+            raise TypeError(
+                f"cannot safely cast non-equivalent {values.dtype} to {np.dtype(dtype)}"
+            ) from err
+
+
+class IntegerArray(NumericArray):
+    """
+    Array of integer (optional missing) values.
+
+    Uses :attr:`pandas.NA` as the missing value.
+
+    .. warning::
+
+       IntegerArray is currently experimental, and its API or internal
+       implementation may change without warning.
+
+    We represent an IntegerArray with 2 numpy arrays:
+
+    - data: contains a numpy integer array of the appropriate dtype
+    - mask: a boolean array holding a mask on the data, True is missing
+
+    To construct an IntegerArray from generic array-like input, use
+    :func:`pandas.array` with one of the integer dtypes (see examples).
+
+    See :ref:`integer_na` for more.
+
+    Parameters
+    ----------
+    values : numpy.ndarray
+        A 1-d integer-dtype array.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values.
+    copy : bool, default False
+        Whether to copy the `values` and `mask`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    IntegerArray
+
+    Examples
+    --------
+    Create an IntegerArray with :func:`pandas.array`.
+
+    >>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
+    >>> int_array
+    <IntegerArray>
+    [1, <NA>, 3]
+    Length: 3, dtype: Int32
+
+    String aliases for the dtypes are also available. They are capitalized.
+
+    >>> pd.array([1, None, 3], dtype='Int32')
+    <IntegerArray>
+    [1, <NA>, 3]
+    Length: 3, dtype: Int32
+
+    >>> pd.array([1, None, 3], dtype='UInt16')
+    <IntegerArray>
+    [1, <NA>, 3]
+    Length: 3, dtype: UInt16
+    """
+
+    _dtype_cls = IntegerDtype
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = 1
+    # Fill values used for any/all
+    # Incompatible types in assignment (expression has type "int", base class
+    # "BaseMaskedArray" defined the type as "<typing special form>")
+    _truthy_value = 1  # type: ignore[assignment]
+    _falsey_value = 0  # type: ignore[assignment]
+
+
+_dtype_docstring = """
+An ExtensionDtype for {dtype} integer data.
+
+Uses :attr:`pandas.NA` as its missing value, rather than :attr:`numpy.nan`.
+
+Attributes
+----------
+None
+
+Methods
+-------
+None
+
+Examples
+--------
+For Int8Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())
+>>> ser.dtype
+Int8Dtype()
+
+For Int16Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())
+>>> ser.dtype
+Int16Dtype()
+
+For Int32Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())
+>>> ser.dtype
+Int32Dtype()
+
+For Int64Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())
+>>> ser.dtype
+Int64Dtype()
+
+For UInt8Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())
+>>> ser.dtype
+UInt8Dtype()
+
+For UInt16Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())
+>>> ser.dtype
+UInt16Dtype()
+
+For UInt32Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())
+>>> ser.dtype
+UInt32Dtype()
+
+For UInt64Dtype:
+
+>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())
+>>> ser.dtype
+UInt64Dtype()
+"""
+
+# create the Dtype
+
+
+@register_extension_dtype
+class Int8Dtype(IntegerDtype):
+    type = np.int8
+    name: ClassVar[str] = "Int8"
+    __doc__ = _dtype_docstring.format(dtype="int8")
+
+
+@register_extension_dtype
+class Int16Dtype(IntegerDtype):
+    type = np.int16
+    name: ClassVar[str] = "Int16"
+    __doc__ = _dtype_docstring.format(dtype="int16")
+
+
+@register_extension_dtype
+class Int32Dtype(IntegerDtype):
+    type = np.int32
+    name: ClassVar[str] = "Int32"
+    __doc__ = _dtype_docstring.format(dtype="int32")
+
+
+@register_extension_dtype
+class Int64Dtype(IntegerDtype):
+    type = np.int64
+    name: ClassVar[str] = "Int64"
+    __doc__ = _dtype_docstring.format(dtype="int64")
+
+
+@register_extension_dtype
+class UInt8Dtype(IntegerDtype):
+    type = np.uint8
+    name: ClassVar[str] = "UInt8"
+    __doc__ = _dtype_docstring.format(dtype="uint8")
+
+
+@register_extension_dtype
+class UInt16Dtype(IntegerDtype):
+    type = np.uint16
+    name: ClassVar[str] = "UInt16"
+    __doc__ = _dtype_docstring.format(dtype="uint16")
+
+
+@register_extension_dtype
+class UInt32Dtype(IntegerDtype):
+    type = np.uint32
+    name: ClassVar[str] = "UInt32"
+    __doc__ = _dtype_docstring.format(dtype="uint32")
+
+
+@register_extension_dtype
+class UInt64Dtype(IntegerDtype):
+    type = np.uint64
+    name: ClassVar[str] = "UInt64"
+    __doc__ = _dtype_docstring.format(dtype="uint64")
+
+
+NUMPY_INT_TO_DTYPE: dict[np.dtype, IntegerDtype] = {
+    np.dtype(np.int8): Int8Dtype(),
+    np.dtype(np.int16): Int16Dtype(),
+    np.dtype(np.int32): Int32Dtype(),
+    np.dtype(np.int64): Int64Dtype(),
+    np.dtype(np.uint8): UInt8Dtype(),
+    np.dtype(np.uint16): UInt16Dtype(),
+    np.dtype(np.uint32): UInt32Dtype(),
+    np.dtype(np.uint64): UInt64Dtype(),
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/interval.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..da57e4ceed87e3a63e63e5ac7172c807a5e5b683
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/interval.py
@@ -0,0 +1,1930 @@
+from __future__ import annotations
+
+import operator
+from operator import (
+    le,
+    lt,
+)
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    Union,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.interval import (
+    VALID_CLOSED,
+    Interval,
+    IntervalMixin,
+    intervals_to_interval_bounds,
+)
+from pandas._libs.missing import NA
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    Dtype,
+    FillnaOptions,
+    IntervalClosedType,
+    NpDtype,
+    PositionalIndexer,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    SortKind,
+    TimeArrayLike,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import IntCastingNaNError
+from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    maybe_upcast_numeric_to_64bit,
+)
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    IntervalDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCDatetimeIndex,
+    ABCIntervalIndex,
+    ABCPeriodIndex,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    notna,
+)
+
+from pandas.core.algorithms import (
+    isin,
+    take,
+    unique,
+    value_counts_internal as value_counts,
+)
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.base import (
+    ExtensionArray,
+    _extension_array_shared_docs,
+)
+from pandas.core.arrays.datetimes import DatetimeArray
+from pandas.core.arrays.timedeltas import TimedeltaArray
+import pandas.core.common as com
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import check_array_indexer
+from pandas.core.ops import (
+    invalid_comparison,
+    unpack_zerodim_and_defer,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+
+    from pandas import (
+        Index,
+        Series,
+    )
+
+
+IntervalSide = Union[TimeArrayLike, np.ndarray]
+IntervalOrNA = Union[Interval, float]
+
+_interval_shared_docs: dict[str, str] = {}
+
+_shared_docs_kwargs = {
+    "klass": "IntervalArray",
+    "qualname": "arrays.IntervalArray",
+    "name": "",
+}
+
+
+_interval_shared_docs[
+    "class"
+] = """
+%(summary)s
+
+Parameters
+----------
+data : array-like (1-dimensional)
+    Array-like (ndarray, :class:`DateTimeArray`, :class:`TimeDeltaArray`) containing
+    Interval objects from which to build the %(klass)s.
+closed : {'left', 'right', 'both', 'neither'}, default 'right'
+    Whether the intervals are closed on the left-side, right-side, both or
+    neither.
+dtype : dtype or None, default None
+    If None, dtype will be inferred.
+copy : bool, default False
+    Copy the input data.
+%(name)s\
+verify_integrity : bool, default True
+    Verify that the %(klass)s is valid.
+
+Attributes
+----------
+left
+right
+closed
+mid
+length
+is_empty
+is_non_overlapping_monotonic
+%(extra_attributes)s\
+
+Methods
+-------
+from_arrays
+from_tuples
+from_breaks
+contains
+overlaps
+set_closed
+to_tuples
+%(extra_methods)s\
+
+See Also
+--------
+Index : The base pandas Index type.
+Interval : A bounded slice-like interval; the elements of an %(klass)s.
+interval_range : Function to create a fixed frequency IntervalIndex.
+cut : Bin values into discrete Intervals.
+qcut : Bin values into equal-sized Intervals based on rank or sample quantiles.
+
+Notes
+-----
+See the `user guide
+<https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#intervalindex>`__
+for more.
+
+%(examples)s\
+"""
+
+
+@Appender(
+    _interval_shared_docs["class"]
+    % {
+        "klass": "IntervalArray",
+        "summary": "Pandas array for interval data that are closed on the same side.",
+        "name": "",
+        "extra_attributes": "",
+        "extra_methods": "",
+        "examples": textwrap.dedent(
+            """\
+    Examples
+    --------
+    A new ``IntervalArray`` can be constructed directly from an array-like of
+    ``Interval`` objects:
+
+    >>> pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+    <IntervalArray>
+    [(0, 1], (1, 5]]
+    Length: 2, dtype: interval[int64, right]
+
+    It may also be constructed using one of the constructor
+    methods: :meth:`IntervalArray.from_arrays`,
+    :meth:`IntervalArray.from_breaks`, and :meth:`IntervalArray.from_tuples`.
+    """
+        ),
+    }
+)
+class IntervalArray(IntervalMixin, ExtensionArray):
+    can_hold_na = True
+    _na_value = _fill_value = np.nan
+
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    # To make mypy recognize the fields
+    _left: IntervalSide
+    _right: IntervalSide
+    _dtype: IntervalDtype
+
+    # ---------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data,
+        closed: IntervalClosedType | None = None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        verify_integrity: bool = True,
+    ):
+        data = extract_array(data, extract_numpy=True)
+
+        if isinstance(data, cls):
+            left: IntervalSide = data._left
+            right: IntervalSide = data._right
+            closed = closed or data.closed
+            dtype = IntervalDtype(left.dtype, closed=closed)
+        else:
+            # don't allow scalars
+            if is_scalar(data):
+                msg = (
+                    f"{cls.__name__}(...) must be called with a collection "
+                    f"of some kind, {data} was passed"
+                )
+                raise TypeError(msg)
+
+            # might need to convert empty or purely na data
+            data = _maybe_convert_platform_interval(data)
+            left, right, infer_closed = intervals_to_interval_bounds(
+                data, validate_closed=closed is None
+            )
+            if left.dtype == object:
+                left = lib.maybe_convert_objects(left)
+                right = lib.maybe_convert_objects(right)
+            closed = closed or infer_closed
+
+            left, right, dtype = cls._ensure_simple_new_inputs(
+                left,
+                right,
+                closed=closed,
+                copy=copy,
+                dtype=dtype,
+            )
+
+        if verify_integrity:
+            cls._validate(left, right, dtype=dtype)
+
+        return cls._simple_new(
+            left,
+            right,
+            dtype=dtype,
+        )
+
+    @classmethod
+    def _simple_new(
+        cls,
+        left: IntervalSide,
+        right: IntervalSide,
+        dtype: IntervalDtype,
+    ) -> Self:
+        result = IntervalMixin.__new__(cls)
+        result._left = left
+        result._right = right
+        result._dtype = dtype
+
+        return result
+
+    @classmethod
+    def _ensure_simple_new_inputs(
+        cls,
+        left,
+        right,
+        closed: IntervalClosedType | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> tuple[IntervalSide, IntervalSide, IntervalDtype]:
+        """Ensure correctness of input parameters for cls._simple_new."""
+        from pandas.core.indexes.base import ensure_index
+
+        left = ensure_index(left, copy=copy)
+        left = maybe_upcast_numeric_to_64bit(left)
+
+        right = ensure_index(right, copy=copy)
+        right = maybe_upcast_numeric_to_64bit(right)
+
+        if closed is None and isinstance(dtype, IntervalDtype):
+            closed = dtype.closed
+
+        closed = closed or "right"
+
+        if dtype is not None:
+            # GH 19262: dtype must be an IntervalDtype to override inferred
+            dtype = pandas_dtype(dtype)
+            if isinstance(dtype, IntervalDtype):
+                if dtype.subtype is not None:
+                    left = left.astype(dtype.subtype)
+                    right = right.astype(dtype.subtype)
+            else:
+                msg = f"dtype must be an IntervalDtype, got {dtype}"
+                raise TypeError(msg)
+
+            if dtype.closed is None:
+                # possibly loading an old pickle
+                dtype = IntervalDtype(dtype.subtype, closed)
+            elif closed != dtype.closed:
+                raise ValueError("closed keyword does not match dtype.closed")
+
+        # coerce dtypes to match if needed
+        if is_float_dtype(left.dtype) and is_integer_dtype(right.dtype):
+            right = right.astype(left.dtype)
+        elif is_float_dtype(right.dtype) and is_integer_dtype(left.dtype):
+            left = left.astype(right.dtype)
+
+        if type(left) != type(right):
+            msg = (
+                f"must not have differing left [{type(left).__name__}] and "
+                f"right [{type(right).__name__}] types"
+            )
+            raise ValueError(msg)
+        if isinstance(left.dtype, CategoricalDtype) or is_string_dtype(left.dtype):
+            # GH 19016
+            msg = (
+                "category, object, and string subtypes are not supported "
+                "for IntervalArray"
+            )
+            raise TypeError(msg)
+        if isinstance(left, ABCPeriodIndex):
+            msg = "Period dtypes are not supported, use a PeriodIndex instead"
+            raise ValueError(msg)
+        if isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
+            msg = (
+                "left and right must have the same time zone, got "
+                f"'{left.tz}' and '{right.tz}'"
+            )
+            raise ValueError(msg)
+        elif needs_i8_conversion(left.dtype) and left.unit != right.unit:
+            # e.g. m8[s] vs m8[ms], try to cast to a common dtype GH#55714
+            left_arr, right_arr = left._data._ensure_matching_resos(right._data)
+            left = ensure_index(left_arr)
+            right = ensure_index(right_arr)
+
+        # For dt64/td64 we want DatetimeArray/TimedeltaArray instead of ndarray
+        left = ensure_wrapped_if_datetimelike(left)
+        left = extract_array(left, extract_numpy=True)
+        right = ensure_wrapped_if_datetimelike(right)
+        right = extract_array(right, extract_numpy=True)
+
+        if isinstance(left, ArrowExtensionArray) or isinstance(
+            right, ArrowExtensionArray
+        ):
+            pass
+        else:
+            lbase = getattr(left, "_ndarray", left)
+            lbase = getattr(lbase, "_data", lbase).base
+            rbase = getattr(right, "_ndarray", right)
+            rbase = getattr(rbase, "_data", rbase).base
+            if lbase is not None and lbase is rbase:
+                # If these share data, then setitem could corrupt our IA
+                right = right.copy()
+
+        dtype = IntervalDtype(left.dtype, closed=closed)
+
+        return left, right, dtype
+
+    @classmethod
+    def _from_sequence(
+        cls,
+        scalars,
+        *,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+    ) -> Self:
+        return cls(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_factorized(cls, values: np.ndarray, original: IntervalArray) -> Self:
+        return cls._from_sequence(values, dtype=original.dtype)
+
+    _interval_shared_docs["from_breaks"] = textwrap.dedent(
+        """
+        Construct an %(klass)s from an array of splits.
+
+        Parameters
+        ----------
+        breaks : array-like (1-dimensional)
+            Left and right bounds for each interval.
+        closed : {'left', 'right', 'both', 'neither'}, default 'right'
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.\
+        %(name)s
+        copy : bool, default False
+            Copy the data.
+        dtype : dtype or None, default None
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        %(klass)s
+
+        See Also
+        --------
+        interval_range : Function to create a fixed frequency IntervalIndex.
+        %(klass)s.from_arrays : Construct from a left and right array.
+        %(klass)s.from_tuples : Construct from a sequence of tuples.
+
+        %(examples)s\
+        """
+    )
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_breaks"]
+        % {
+            "klass": "IntervalArray",
+            "name": "",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3])
+        <IntervalArray>
+        [(0, 1], (1, 2], (2, 3]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def from_breaks(
+        cls,
+        breaks,
+        closed: IntervalClosedType | None = "right",
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> Self:
+        breaks = _maybe_convert_platform_interval(breaks)
+
+        return cls.from_arrays(breaks[:-1], breaks[1:], closed, copy=copy, dtype=dtype)
+
+    _interval_shared_docs["from_arrays"] = textwrap.dedent(
+        """
+        Construct from two arrays defining the left and right bounds.
+
+        Parameters
+        ----------
+        left : array-like (1-dimensional)
+            Left bounds for each interval.
+        right : array-like (1-dimensional)
+            Right bounds for each interval.
+        closed : {'left', 'right', 'both', 'neither'}, default 'right'
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.\
+        %(name)s
+        copy : bool, default False
+            Copy the data.
+        dtype : dtype, optional
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        %(klass)s
+
+        Raises
+        ------
+        ValueError
+            When a value is missing in only one of `left` or `right`.
+            When a value in `left` is greater than the corresponding value
+            in `right`.
+
+        See Also
+        --------
+        interval_range : Function to create a fixed frequency IntervalIndex.
+        %(klass)s.from_breaks : Construct an %(klass)s from an array of
+            splits.
+        %(klass)s.from_tuples : Construct an %(klass)s from an
+            array-like of tuples.
+
+        Notes
+        -----
+        Each element of `left` must be less than or equal to the `right`
+        element at the same position. If an element is missing, it must be
+        missing in both `left` and `right`. A TypeError is raised when
+        using an unsupported type for `left` or `right`. At the moment,
+        'category', 'object', and 'string' subtypes are not supported.
+
+        %(examples)s\
+        """
+    )
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_arrays"]
+        % {
+            "klass": "IntervalArray",
+            "name": "",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.arrays.IntervalArray.from_arrays([0, 1, 2], [1, 2, 3])
+        <IntervalArray>
+        [(0, 1], (1, 2], (2, 3]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def from_arrays(
+        cls,
+        left,
+        right,
+        closed: IntervalClosedType | None = "right",
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> Self:
+        left = _maybe_convert_platform_interval(left)
+        right = _maybe_convert_platform_interval(right)
+
+        left, right, dtype = cls._ensure_simple_new_inputs(
+            left,
+            right,
+            closed=closed,
+            copy=copy,
+            dtype=dtype,
+        )
+        cls._validate(left, right, dtype=dtype)
+
+        return cls._simple_new(left, right, dtype=dtype)
+
+    _interval_shared_docs["from_tuples"] = textwrap.dedent(
+        """
+        Construct an %(klass)s from an array-like of tuples.
+
+        Parameters
+        ----------
+        data : array-like (1-dimensional)
+            Array of tuples.
+        closed : {'left', 'right', 'both', 'neither'}, default 'right'
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.\
+        %(name)s
+        copy : bool, default False
+            By-default copy the data, this is compat only and ignored.
+        dtype : dtype or None, default None
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        %(klass)s
+
+        See Also
+        --------
+        interval_range : Function to create a fixed frequency IntervalIndex.
+        %(klass)s.from_arrays : Construct an %(klass)s from a left and
+                                    right array.
+        %(klass)s.from_breaks : Construct an %(klass)s from an array of
+                                    splits.
+
+        %(examples)s\
+        """
+    )
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_tuples"]
+        % {
+            "klass": "IntervalArray",
+            "name": "",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 2)])
+        <IntervalArray>
+        [(0, 1], (1, 2]]
+        Length: 2, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def from_tuples(
+        cls,
+        data,
+        closed: IntervalClosedType | None = "right",
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> Self:
+        if len(data):
+            left, right = [], []
+        else:
+            # ensure that empty data keeps input dtype
+            left = right = data
+
+        for d in data:
+            if not isinstance(d, tuple) and isna(d):
+                lhs = rhs = np.nan
+            else:
+                name = cls.__name__
+                try:
+                    # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
+                    lhs, rhs = d
+                except ValueError as err:
+                    msg = f"{name}.from_tuples requires tuples of length 2, got {d}"
+                    raise ValueError(msg) from err
+                except TypeError as err:
+                    msg = f"{name}.from_tuples received an invalid item, {d}"
+                    raise TypeError(msg) from err
+            left.append(lhs)
+            right.append(rhs)
+
+        return cls.from_arrays(left, right, closed, copy=False, dtype=dtype)
+
+    @classmethod
+    def _validate(cls, left, right, dtype: IntervalDtype) -> None:
+        """
+        Verify that the IntervalArray is valid.
+
+        Checks that
+
+        * dtype is correct
+        * left and right match lengths
+        * left and right have the same missing values
+        * left is always below right
+        """
+        if not isinstance(dtype, IntervalDtype):
+            msg = f"invalid dtype: {dtype}"
+            raise ValueError(msg)
+        if len(left) != len(right):
+            msg = "left and right must have the same length"
+            raise ValueError(msg)
+        left_mask = notna(left)
+        right_mask = notna(right)
+        if not (left_mask == right_mask).all():
+            msg = (
+                "missing values must be missing in the same "
+                "location both left and right sides"
+            )
+            raise ValueError(msg)
+        if not (left[left_mask] <= right[left_mask]).all():
+            msg = "left side of interval must be <= right side"
+            raise ValueError(msg)
+
+    def _shallow_copy(self, left, right) -> Self:
+        """
+        Return a new IntervalArray with the replacement attributes
+
+        Parameters
+        ----------
+        left : Index
+            Values to be used for the left-side of the intervals.
+        right : Index
+            Values to be used for the right-side of the intervals.
+        """
+        dtype = IntervalDtype(left.dtype, closed=self.closed)
+        left, right, dtype = self._ensure_simple_new_inputs(left, right, dtype=dtype)
+
+        return self._simple_new(left, right, dtype=dtype)
+
+    # ---------------------------------------------------------------------
+    # Descriptive
+
+    @property
+    def dtype(self) -> IntervalDtype:
+        return self._dtype
+
+    @property
+    def nbytes(self) -> int:
+        return self.left.nbytes + self.right.nbytes
+
+    @property
+    def size(self) -> int:
+        # Avoid materializing self.values
+        return self.left.size
+
+    # ---------------------------------------------------------------------
+    # EA Interface
+
+    def __iter__(self) -> Iterator:
+        return iter(np.asarray(self))
+
+    def __len__(self) -> int:
+        return len(self._left)
+
+    @overload
+    def __getitem__(self, key: ScalarIndexer) -> IntervalOrNA:
+        ...
+
+    @overload
+    def __getitem__(self, key: SequenceIndexer) -> Self:
+        ...
+
+    def __getitem__(self, key: PositionalIndexer) -> Self | IntervalOrNA:
+        key = check_array_indexer(self, key)
+        left = self._left[key]
+        right = self._right[key]
+
+        if not isinstance(left, (np.ndarray, ExtensionArray)):
+            # scalar
+            if is_scalar(left) and isna(left):
+                return self._fill_value
+            return Interval(left, right, self.closed)
+        if np.ndim(left) > 1:
+            # GH#30588 multi-dimensional indexer disallowed
+            raise ValueError("multi-dimensional indexing not allowed")
+        # Argument 2 to "_simple_new" of "IntervalArray" has incompatible type
+        # "Union[Period, Timestamp, Timedelta, NaTType, DatetimeArray, TimedeltaArray,
+        # ndarray[Any, Any]]"; expected "Union[Union[DatetimeArray, TimedeltaArray],
+        # ndarray[Any, Any]]"
+        return self._simple_new(left, right, dtype=self.dtype)  # type: ignore[arg-type]
+
+    def __setitem__(self, key, value) -> None:
+        value_left, value_right = self._validate_setitem_value(value)
+        key = check_array_indexer(self, key)
+
+        self._left[key] = value_left
+        self._right[key] = value_right
+
+    def _cmp_method(self, other, op):
+        # ensure pandas array for list-like and eliminate non-interval scalars
+        if is_list_like(other):
+            if len(self) != len(other):
+                raise ValueError("Lengths must match to compare")
+            other = pd_array(other)
+        elif not isinstance(other, Interval):
+            # non-interval scalar -> no matches
+            if other is NA:
+                # GH#31882
+                from pandas.core.arrays import BooleanArray
+
+                arr = np.empty(self.shape, dtype=bool)
+                mask = np.ones(self.shape, dtype=bool)
+                return BooleanArray(arr, mask)
+            return invalid_comparison(self, other, op)
+
+        # determine the dtype of the elements we want to compare
+        if isinstance(other, Interval):
+            other_dtype = pandas_dtype("interval")
+        elif not isinstance(other.dtype, CategoricalDtype):
+            other_dtype = other.dtype
+        else:
+            # for categorical defer to categories for dtype
+            other_dtype = other.categories.dtype
+
+            # extract intervals if we have interval categories with matching closed
+            if isinstance(other_dtype, IntervalDtype):
+                if self.closed != other.categories.closed:
+                    return invalid_comparison(self, other, op)
+
+                other = other.categories._values.take(
+                    other.codes, allow_fill=True, fill_value=other.categories._na_value
+                )
+
+        # interval-like -> need same closed and matching endpoints
+        if isinstance(other_dtype, IntervalDtype):
+            if self.closed != other.closed:
+                return invalid_comparison(self, other, op)
+            elif not isinstance(other, Interval):
+                other = type(self)(other)
+
+            if op is operator.eq:
+                return (self._left == other.left) & (self._right == other.right)
+            elif op is operator.ne:
+                return (self._left != other.left) | (self._right != other.right)
+            elif op is operator.gt:
+                return (self._left > other.left) | (
+                    (self._left == other.left) & (self._right > other.right)
+                )
+            elif op is operator.ge:
+                return (self == other) | (self > other)
+            elif op is operator.lt:
+                return (self._left < other.left) | (
+                    (self._left == other.left) & (self._right < other.right)
+                )
+            else:
+                # operator.lt
+                return (self == other) | (self < other)
+
+        # non-interval/non-object dtype -> no matches
+        if not is_object_dtype(other_dtype):
+            return invalid_comparison(self, other, op)
+
+        # object dtype -> iteratively check for intervals
+        result = np.zeros(len(self), dtype=bool)
+        for i, obj in enumerate(other):
+            try:
+                result[i] = op(self[i], obj)
+            except TypeError:
+                if obj is NA:
+                    # comparison with np.nan returns NA
+                    # github.com/pandas-dev/pandas/pull/37124#discussion_r509095092
+                    result = result.astype(object)
+                    result[i] = NA
+                else:
+                    raise
+        return result
+
+    @unpack_zerodim_and_defer("__eq__")
+    def __eq__(self, other):
+        return self._cmp_method(other, operator.eq)
+
+    @unpack_zerodim_and_defer("__ne__")
+    def __ne__(self, other):
+        return self._cmp_method(other, operator.ne)
+
+    @unpack_zerodim_and_defer("__gt__")
+    def __gt__(self, other):
+        return self._cmp_method(other, operator.gt)
+
+    @unpack_zerodim_and_defer("__ge__")
+    def __ge__(self, other):
+        return self._cmp_method(other, operator.ge)
+
+    @unpack_zerodim_and_defer("__lt__")
+    def __lt__(self, other):
+        return self._cmp_method(other, operator.lt)
+
+    @unpack_zerodim_and_defer("__le__")
+    def __le__(self, other):
+        return self._cmp_method(other, operator.le)
+
+    def argsort(
+        self,
+        *,
+        ascending: bool = True,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        **kwargs,
+    ) -> np.ndarray:
+        ascending = nv.validate_argsort_with_ascending(ascending, (), kwargs)
+
+        if ascending and kind == "quicksort" and na_position == "last":
+            # TODO: in an IntervalIndex we can reuse the cached
+            #  IntervalTree.left_sorter
+            return np.lexsort((self.right, self.left))
+
+        # TODO: other cases we can use lexsort for?  much more performant.
+        return super().argsort(
+            ascending=ascending, kind=kind, na_position=na_position, **kwargs
+        )
+
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOrNA:
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        if not len(self):
+            return self._na_value
+
+        mask = self.isna()
+        if mask.any():
+            if not skipna:
+                return self._na_value
+            obj = self[~mask]
+        else:
+            obj = self
+
+        indexer = obj.argsort()[0]
+        return obj[indexer]
+
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOrNA:
+        nv.validate_minmax_axis(axis, self.ndim)
+
+        if not len(self):
+            return self._na_value
+
+        mask = self.isna()
+        if mask.any():
+            if not skipna:
+                return self._na_value
+            obj = self[~mask]
+        else:
+            obj = self
+
+        indexer = obj.argsort()[-1]
+        return obj[indexer]
+
+    def _pad_or_backfill(  # pylint: disable=useless-parent-delegation
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        # TODO(3.0): after EA.fillna 'method' deprecation is enforced, we can remove
+        #  this method entirely.
+        return super()._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        """
+        Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series
+            If a scalar value is passed it is used to fill all missing values.
+            Alternatively, a Series or dict can be used to fill in different
+            values for each index. The value should not be a list. The
+            value(s) passed should be either Interval objects or NA/NaN.
+        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+            (Not implemented yet for IntervalArray)
+            Method to use for filling holes in reindexed Series
+        limit : int, default None
+            (Not implemented yet for IntervalArray)
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+        copy : bool, default True
+            Whether to make a copy of the data before filling. If False, then
+            the original should be modified and no new memory should be allocated.
+            For ExtensionArray subclasses that cannot do this, it is at the
+            author's discretion whether to ignore "copy=False" or to raise.
+
+        Returns
+        -------
+        filled : IntervalArray with NA/NaN filled
+        """
+        if copy is False:
+            raise NotImplementedError
+        if method is not None:
+            return super().fillna(value=value, method=method, limit=limit)
+
+        value_left, value_right = self._validate_scalar(value)
+
+        left = self.left.fillna(value=value_left)
+        right = self.right.fillna(value=value_right)
+        return self._shallow_copy(left, right)
+
+    def astype(self, dtype, copy: bool = True):
+        """
+        Cast to an ExtensionArray or NumPy array with dtype 'dtype'.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+
+        copy : bool, default True
+            Whether to copy the data, even if not necessary. If False,
+            a copy is made only if the old dtype does not match the
+            new dtype.
+
+        Returns
+        -------
+        array : ExtensionArray or ndarray
+            ExtensionArray or NumPy ndarray with 'dtype' for its dtype.
+        """
+        from pandas import Index
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        if isinstance(dtype, IntervalDtype):
+            if dtype == self.dtype:
+                return self.copy() if copy else self
+
+            if is_float_dtype(self.dtype.subtype) and needs_i8_conversion(
+                dtype.subtype
+            ):
+                # This is allowed on the Index.astype but we disallow it here
+                msg = (
+                    f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
+                )
+                raise TypeError(msg)
+
+            # need to cast to different subtype
+            try:
+                # We need to use Index rules for astype to prevent casting
+                #  np.nan entries to int subtypes
+                new_left = Index(self._left, copy=False).astype(dtype.subtype)
+                new_right = Index(self._right, copy=False).astype(dtype.subtype)
+            except IntCastingNaNError:
+                # e.g test_subtype_integer
+                raise
+            except (TypeError, ValueError) as err:
+                # e.g. test_subtype_integer_errors f8->u8 can be lossy
+                #  and raises ValueError
+                msg = (
+                    f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
+                )
+                raise TypeError(msg) from err
+            return self._shallow_copy(new_left, new_right)
+        else:
+            try:
+                return super().astype(dtype, copy=copy)
+            except (TypeError, ValueError) as err:
+                msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
+                raise TypeError(msg) from err
+
+    def equals(self, other) -> bool:
+        if type(self) != type(other):
+            return False
+
+        return bool(
+            self.closed == other.closed
+            and self.left.equals(other.left)
+            and self.right.equals(other.right)
+        )
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[IntervalArray]) -> Self:
+        """
+        Concatenate multiple IntervalArray
+
+        Parameters
+        ----------
+        to_concat : sequence of IntervalArray
+
+        Returns
+        -------
+        IntervalArray
+        """
+        closed_set = {interval.closed for interval in to_concat}
+        if len(closed_set) != 1:
+            raise ValueError("Intervals must all be closed on the same side.")
+        closed = closed_set.pop()
+
+        left: IntervalSide = np.concatenate([interval.left for interval in to_concat])
+        right: IntervalSide = np.concatenate([interval.right for interval in to_concat])
+
+        left, right, dtype = cls._ensure_simple_new_inputs(left, right, closed=closed)
+
+        return cls._simple_new(left, right, dtype=dtype)
+
+    def copy(self) -> Self:
+        """
+        Return a copy of the array.
+
+        Returns
+        -------
+        IntervalArray
+        """
+        left = self._left.copy()
+        right = self._right.copy()
+        dtype = self.dtype
+        return self._simple_new(left, right, dtype=dtype)
+
+    def isna(self) -> np.ndarray:
+        return isna(self._left)
+
+    def shift(self, periods: int = 1, fill_value: object = None) -> IntervalArray:
+        if not len(self) or periods == 0:
+            return self.copy()
+
+        self._validate_scalar(fill_value)
+
+        # ExtensionArray.shift doesn't work for two reasons
+        # 1. IntervalArray.dtype.na_value may not be correct for the dtype.
+        # 2. IntervalArray._from_sequence only accepts NaN for missing values,
+        #    not other values like NaT
+
+        empty_len = min(abs(periods), len(self))
+        if isna(fill_value):
+            from pandas import Index
+
+            fill_value = Index(self._left, copy=False)._na_value
+            empty = IntervalArray.from_breaks([fill_value] * (empty_len + 1))
+        else:
+            empty = self._from_sequence([fill_value] * empty_len, dtype=self.dtype)
+
+        if periods > 0:
+            a = empty
+            b = self[:-periods]
+        else:
+            a = self[abs(periods) :]
+            b = empty
+        return self._concat_same_type([a, b])
+
+    def take(
+        self,
+        indices,
+        *,
+        allow_fill: bool = False,
+        fill_value=None,
+        axis=None,
+        **kwargs,
+    ) -> Self:
+        """
+        Take elements from the IntervalArray.
+
+        Parameters
+        ----------
+        indices : sequence of integers
+            Indices to be taken.
+
+        allow_fill : bool, default False
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+              from the right (the default). This is similar to
+              :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+              missing values. These values are set to `fill_value`. Any other
+              other negative values raise a ``ValueError``.
+
+        fill_value : Interval or NA, optional
+            Fill value to use for NA-indices when `allow_fill` is True.
+            This may be ``None``, in which case the default NA value for
+            the type, ``self.dtype.na_value``, is used.
+
+            For many ExtensionArrays, there will be two representations of
+            `fill_value`: a user-facing "boxed" scalar, and a low-level
+            physical NA value. `fill_value` should be the user-facing version,
+            and the implementation should handle translating that to the
+            physical version for processing the take if necessary.
+
+        axis : any, default None
+            Present for compat with IntervalIndex; does nothing.
+
+        Returns
+        -------
+        IntervalArray
+
+        Raises
+        ------
+        IndexError
+            When the indices are out of bounds for the array.
+        ValueError
+            When `indices` contains negative values other than ``-1``
+            and `allow_fill` is True.
+        """
+        nv.validate_take((), kwargs)
+
+        fill_left = fill_right = fill_value
+        if allow_fill:
+            fill_left, fill_right = self._validate_scalar(fill_value)
+
+        left_take = take(
+            self._left, indices, allow_fill=allow_fill, fill_value=fill_left
+        )
+        right_take = take(
+            self._right, indices, allow_fill=allow_fill, fill_value=fill_right
+        )
+
+        return self._shallow_copy(left_take, right_take)
+
+    def _validate_listlike(self, value):
+        # list-like of intervals
+        try:
+            array = IntervalArray(value)
+            self._check_closed_matches(array, name="value")
+            value_left, value_right = array.left, array.right
+        except TypeError as err:
+            # wrong type: not interval or NA
+            msg = f"'value' should be an interval type, got {type(value)} instead."
+            raise TypeError(msg) from err
+
+        try:
+            self.left._validate_fill_value(value_left)
+        except (LossySetitemError, TypeError) as err:
+            msg = (
+                "'value' should be a compatible interval type, "
+                f"got {type(value)} instead."
+            )
+            raise TypeError(msg) from err
+
+        return value_left, value_right
+
+    def _validate_scalar(self, value):
+        if isinstance(value, Interval):
+            self._check_closed_matches(value, name="value")
+            left, right = value.left, value.right
+            # TODO: check subdtype match like _validate_setitem_value?
+        elif is_valid_na_for_dtype(value, self.left.dtype):
+            # GH#18295
+            left = right = self.left._na_value
+        else:
+            raise TypeError(
+                "can only insert Interval objects and NA into an IntervalArray"
+            )
+        return left, right
+
+    def _validate_setitem_value(self, value):
+        if is_valid_na_for_dtype(value, self.left.dtype):
+            # na value: need special casing to set directly on numpy arrays
+            value = self.left._na_value
+            if is_integer_dtype(self.dtype.subtype):
+                # can't set NaN on a numpy integer array
+                # GH#45484 TypeError, not ValueError, matches what we get with
+                #  non-NA un-holdable value.
+                raise TypeError("Cannot set float NaN to integer-backed IntervalArray")
+            value_left, value_right = value, value
+
+        elif isinstance(value, Interval):
+            # scalar interval
+            self._check_closed_matches(value, name="value")
+            value_left, value_right = value.left, value.right
+            self.left._validate_fill_value(value_left)
+            self.left._validate_fill_value(value_right)
+
+        else:
+            return self._validate_listlike(value)
+
+        return value_left, value_right
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Returns a Series containing counts of each interval.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NaN.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        # TODO: implement this is a non-naive way!
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "The behavior of value_counts with object-dtype is deprecated",
+                category=FutureWarning,
+            )
+            result = value_counts(np.asarray(self), dropna=dropna)
+            # Once the deprecation is enforced, we will need to do
+            #  `result.index = result.index.astype(self.dtype)`
+        return result
+
+    # ---------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        # returning 'str' here causes us to render as e.g. "(0, 1]" instead of
+        #  "Interval(0, 1, closed='right')"
+        return str
+
+    # ---------------------------------------------------------------------
+    # Vectorized Interval Properties/Attributes
+
+    @property
+    def left(self) -> Index:
+        """
+        Return the left endpoints of each Interval in the IntervalArray as an Index.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(2, 5)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0, 1], (2, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.left
+        Index([0, 2], dtype='int64')
+        """
+        from pandas import Index
+
+        return Index(self._left, copy=False)
+
+    @property
+    def right(self) -> Index:
+        """
+        Return the right endpoints of each Interval in the IntervalArray as an Index.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(2, 5)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0, 1], (2, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.right
+        Index([1, 5], dtype='int64')
+        """
+        from pandas import Index
+
+        return Index(self._right, copy=False)
+
+    @property
+    def length(self) -> Index:
+        """
+        Return an Index with entries denoting the length of each Interval.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.length
+        Index([1, 4], dtype='int64')
+        """
+        return self.right - self.left
+
+    @property
+    def mid(self) -> Index:
+        """
+        Return the midpoint of each Interval in the IntervalArray as an Index.
+
+        Examples
+        --------
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.mid
+        Index([0.5, 3.0], dtype='float64')
+        """
+        try:
+            return 0.5 * (self.left + self.right)
+        except TypeError:
+            # datetime safe version
+            return self.left + 0.5 * self.length
+
+    _interval_shared_docs["overlaps"] = textwrap.dedent(
+        """
+        Check elementwise if an Interval overlaps the values in the %(klass)s.
+
+        Two intervals overlap if they share a common point, including closed
+        endpoints. Intervals that only have an open endpoint in common do not
+        overlap.
+
+        Parameters
+        ----------
+        other : %(klass)s
+            Interval to check against for an overlap.
+
+        Returns
+        -------
+        ndarray
+            Boolean array positionally indicating where an overlap occurs.
+
+        See Also
+        --------
+        Interval.overlaps : Check whether two Interval objects overlap.
+
+        Examples
+        --------
+        %(examples)s
+        >>> intervals.overlaps(pd.Interval(0.5, 1.5))
+        array([ True,  True, False])
+
+        Intervals that share closed endpoints overlap:
+
+        >>> intervals.overlaps(pd.Interval(1, 3, closed='left'))
+        array([ True,  True, True])
+
+        Intervals that only have an open endpoint in common do not overlap:
+
+        >>> intervals.overlaps(pd.Interval(1, 2, closed='right'))
+        array([False,  True, False])
+        """
+    )
+
+    @Appender(
+        _interval_shared_docs["overlaps"]
+        % {
+            "klass": "IntervalArray",
+            "examples": textwrap.dedent(
+                """\
+        >>> data = [(0, 1), (1, 3), (2, 4)]
+        >>> intervals = pd.arrays.IntervalArray.from_tuples(data)
+        >>> intervals
+        <IntervalArray>
+        [(0, 1], (1, 3], (2, 4]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def overlaps(self, other):
+        if isinstance(other, (IntervalArray, ABCIntervalIndex)):
+            raise NotImplementedError
+        if not isinstance(other, Interval):
+            msg = f"`other` must be Interval-like, got {type(other).__name__}"
+            raise TypeError(msg)
+
+        # equality is okay if both endpoints are closed (overlap at a point)
+        op1 = le if (self.closed_left and other.closed_right) else lt
+        op2 = le if (other.closed_left and self.closed_right) else lt
+
+        # overlaps is equivalent negation of two interval being disjoint:
+        # disjoint = (A.left > B.right) or (B.left > A.right)
+        # (simplifying the negation allows this to be done in less operations)
+        return op1(self.left, other.right) & op2(other.left, self.right)
+
+    # ---------------------------------------------------------------------
+
+    @property
+    def closed(self) -> IntervalClosedType:
+        """
+        String describing the inclusive side the intervals.
+
+        Either ``left``, ``right``, ``both`` or ``neither``.
+
+        Examples
+        --------
+
+        For arrays:
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.closed
+        'right'
+
+        For Interval Index:
+
+        >>> interv_idx = pd.interval_range(start=0, end=2)
+        >>> interv_idx
+        IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]')
+        >>> interv_idx.closed
+        'right'
+        """
+        return self.dtype.closed
+
+    _interval_shared_docs["set_closed"] = textwrap.dedent(
+        """
+        Return an identical %(klass)s closed on the specified side.
+
+        Parameters
+        ----------
+        closed : {'left', 'right', 'both', 'neither'}
+            Whether the intervals are closed on the left-side, right-side, both
+            or neither.
+
+        Returns
+        -------
+        %(klass)s
+
+        %(examples)s\
+        """
+    )
+
+    @Appender(
+        _interval_shared_docs["set_closed"]
+        % {
+            "klass": "IntervalArray",
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> index = pd.arrays.IntervalArray.from_breaks(range(4))
+        >>> index
+        <IntervalArray>
+        [(0, 1], (1, 2], (2, 3]]
+        Length: 3, dtype: interval[int64, right]
+        >>> index.set_closed('both')
+        <IntervalArray>
+        [[0, 1], [1, 2], [2, 3]]
+        Length: 3, dtype: interval[int64, both]
+        """
+            ),
+        }
+    )
+    def set_closed(self, closed: IntervalClosedType) -> Self:
+        if closed not in VALID_CLOSED:
+            msg = f"invalid option for 'closed': {closed}"
+            raise ValueError(msg)
+
+        left, right = self._left, self._right
+        dtype = IntervalDtype(left.dtype, closed=closed)
+        return self._simple_new(left, right, dtype=dtype)
+
+    _interval_shared_docs[
+        "is_non_overlapping_monotonic"
+    ] = """
+        Return a boolean whether the %(klass)s is non-overlapping and monotonic.
+
+        Non-overlapping means (no Intervals share points), and monotonic means
+        either monotonic increasing or monotonic decreasing.
+
+        Examples
+        --------
+        For arrays:
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0, 1], (1, 5]]
+        Length: 2, dtype: interval[int64, right]
+        >>> interv_arr.is_non_overlapping_monotonic
+        True
+
+        >>> interv_arr = pd.arrays.IntervalArray([pd.Interval(0, 1),
+        ...                                       pd.Interval(-1, 0.1)])
+        >>> interv_arr
+        <IntervalArray>
+        [(0.0, 1.0], (-1.0, 0.1]]
+        Length: 2, dtype: interval[float64, right]
+        >>> interv_arr.is_non_overlapping_monotonic
+        False
+
+        For Interval Index:
+
+        >>> interv_idx = pd.interval_range(start=0, end=2)
+        >>> interv_idx
+        IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]')
+        >>> interv_idx.is_non_overlapping_monotonic
+        True
+
+        >>> interv_idx = pd.interval_range(start=0, end=2, closed='both')
+        >>> interv_idx
+        IntervalIndex([[0, 1], [1, 2]], dtype='interval[int64, both]')
+        >>> interv_idx.is_non_overlapping_monotonic
+        False
+        """
+
+    @property
+    @Appender(
+        _interval_shared_docs["is_non_overlapping_monotonic"] % _shared_docs_kwargs
+    )
+    def is_non_overlapping_monotonic(self) -> bool:
+        # must be increasing  (e.g., [0, 1), [1, 2), [2, 3), ... )
+        # or decreasing (e.g., [-1, 0), [-2, -1), [-3, -2), ...)
+        # we already require left <= right
+
+        # strict inequality for closed == 'both'; equality implies overlapping
+        # at a point when both sides of intervals are included
+        if self.closed == "both":
+            return bool(
+                (self._right[:-1] < self._left[1:]).all()
+                or (self._left[:-1] > self._right[1:]).all()
+            )
+
+        # non-strict inequality when closed != 'both'; at least one side is
+        # not included in the intervals, so equality does not imply overlapping
+        return bool(
+            (self._right[:-1] <= self._left[1:]).all()
+            or (self._left[:-1] >= self._right[1:]).all()
+        )
+
+    # ---------------------------------------------------------------------
+    # Conversion
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        Return the IntervalArray's data as a numpy array of Interval
+        objects (with dtype='object')
+        """
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        left = self._left
+        right = self._right
+        mask = self.isna()
+        closed = self.closed
+
+        result = np.empty(len(left), dtype=object)
+        for i, left_value in enumerate(left):
+            if mask[i]:
+                result[i] = np.nan
+            else:
+                result[i] = Interval(left_value, right[i], closed)
+        return result
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow
+
+        from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+
+        try:
+            subtype = pyarrow.from_numpy_dtype(self.dtype.subtype)
+        except TypeError as err:
+            raise TypeError(
+                f"Conversion to arrow with subtype '{self.dtype.subtype}' "
+                "is not supported"
+            ) from err
+        interval_type = ArrowIntervalType(subtype, self.closed)
+        storage_array = pyarrow.StructArray.from_arrays(
+            [
+                pyarrow.array(self._left, type=subtype, from_pandas=True),
+                pyarrow.array(self._right, type=subtype, from_pandas=True),
+            ],
+            names=["left", "right"],
+        )
+        mask = self.isna()
+        if mask.any():
+            # if there are missing values, set validity bitmap also on the array level
+            null_bitmap = pyarrow.array(~mask).buffers()[1]
+            storage_array = pyarrow.StructArray.from_buffers(
+                storage_array.type,
+                len(storage_array),
+                [null_bitmap],
+                children=[storage_array.field(0), storage_array.field(1)],
+            )
+
+        if type is not None:
+            if type.equals(interval_type.storage_type):
+                return storage_array
+            elif isinstance(type, ArrowIntervalType):
+                # ensure we have the same subtype and closed attributes
+                if not type.equals(interval_type):
+                    raise TypeError(
+                        "Not supported to convert IntervalArray to type with "
+                        f"different 'subtype' ({self.dtype.subtype} vs {type.subtype}) "
+                        f"and 'closed' ({self.closed} vs {type.closed}) attributes"
+                    )
+            else:
+                raise TypeError(
+                    f"Not supported to convert IntervalArray to '{type}' type"
+                )
+
+        return pyarrow.ExtensionArray.from_storage(interval_type, storage_array)
+
+    _interval_shared_docs["to_tuples"] = textwrap.dedent(
+        """
+        Return an %(return_type)s of tuples of the form (left, right).
+
+        Parameters
+        ----------
+        na_tuple : bool, default True
+            If ``True``, return ``NA`` as a tuple ``(nan, nan)``. If ``False``,
+            just return ``NA`` as ``nan``.
+
+        Returns
+        -------
+        tuples: %(return_type)s
+        %(examples)s\
+        """
+    )
+
+    @Appender(
+        _interval_shared_docs["to_tuples"]
+        % {
+            "return_type": (
+                "ndarray (if self is IntervalArray) or Index (if self is IntervalIndex)"
+            ),
+            "examples": textwrap.dedent(
+                """\
+
+         Examples
+         --------
+         For :class:`pandas.IntervalArray`:
+
+         >>> idx = pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 2)])
+         >>> idx
+         <IntervalArray>
+         [(0, 1], (1, 2]]
+         Length: 2, dtype: interval[int64, right]
+         >>> idx.to_tuples()
+         array([(0, 1), (1, 2)], dtype=object)
+
+         For :class:`pandas.IntervalIndex`:
+
+         >>> idx = pd.interval_range(start=0, end=2)
+         >>> idx
+         IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]')
+         >>> idx.to_tuples()
+         Index([(0, 1), (1, 2)], dtype='object')
+         """
+            ),
+        }
+    )
+    def to_tuples(self, na_tuple: bool = True) -> np.ndarray:
+        tuples = com.asarray_tuplesafe(zip(self._left, self._right))
+        if not na_tuple:
+            # GH 18756
+            tuples = np.where(~self.isna(), tuples, np.nan)
+        return tuples
+
+    # ---------------------------------------------------------------------
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        value_left, value_right = self._validate_setitem_value(value)
+
+        if isinstance(self._left, np.ndarray):
+            np.putmask(self._left, mask, value_left)
+            assert isinstance(self._right, np.ndarray)
+            np.putmask(self._right, mask, value_right)
+        else:
+            self._left._putmask(mask, value_left)
+            assert not isinstance(self._right, np.ndarray)
+            self._right._putmask(mask, value_right)
+
+    def insert(self, loc: int, item: Interval) -> Self:
+        """
+        Return a new IntervalArray inserting new item at location. Follows
+        Python numpy.insert semantics for negative values.  Only Interval
+        objects and NA can be inserted into an IntervalIndex
+
+        Parameters
+        ----------
+        loc : int
+        item : Interval
+
+        Returns
+        -------
+        IntervalArray
+        """
+        left_insert, right_insert = self._validate_scalar(item)
+
+        new_left = self.left.insert(loc, left_insert)
+        new_right = self.right.insert(loc, right_insert)
+
+        return self._shallow_copy(new_left, new_right)
+
+    def delete(self, loc) -> Self:
+        if isinstance(self._left, np.ndarray):
+            new_left = np.delete(self._left, loc)
+            assert isinstance(self._right, np.ndarray)
+            new_right = np.delete(self._right, loc)
+        else:
+            new_left = self._left.delete(loc)
+            assert not isinstance(self._right, np.ndarray)
+            new_right = self._right.delete(loc)
+        return self._shallow_copy(left=new_left, right=new_right)
+
+    @Appender(_extension_array_shared_docs["repeat"] % _shared_docs_kwargs)
+    def repeat(
+        self,
+        repeats: int | Sequence[int],
+        axis: AxisInt | None = None,
+    ) -> Self:
+        nv.validate_repeat((), {"axis": axis})
+        left_repeat = self.left.repeat(repeats)
+        right_repeat = self.right.repeat(repeats)
+        return self._shallow_copy(left=left_repeat, right=right_repeat)
+
+    _interval_shared_docs["contains"] = textwrap.dedent(
+        """
+        Check elementwise if the Intervals contain the value.
+
+        Return a boolean mask whether the value is contained in the Intervals
+        of the %(klass)s.
+
+        Parameters
+        ----------
+        other : scalar
+            The value to check whether it is contained in the Intervals.
+
+        Returns
+        -------
+        boolean array
+
+        See Also
+        --------
+        Interval.contains : Check whether Interval object contains value.
+        %(klass)s.overlaps : Check if an Interval overlaps the values in the
+            %(klass)s.
+
+        Examples
+        --------
+        %(examples)s
+        >>> intervals.contains(0.5)
+        array([ True, False, False])
+    """
+    )
+
+    @Appender(
+        _interval_shared_docs["contains"]
+        % {
+            "klass": "IntervalArray",
+            "examples": textwrap.dedent(
+                """\
+        >>> intervals = pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 3), (2, 4)])
+        >>> intervals
+        <IntervalArray>
+        [(0, 1], (1, 3], (2, 4]]
+        Length: 3, dtype: interval[int64, right]
+        """
+            ),
+        }
+    )
+    def contains(self, other):
+        if isinstance(other, Interval):
+            raise NotImplementedError("contains not implemented for two intervals")
+
+        return (self._left < other if self.open_left else self._left <= other) & (
+            other < self._right if self.open_right else other <= self._right
+        )
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        if isinstance(values, IntervalArray):
+            if self.closed != values.closed:
+                # not comparable -> no overlap
+                return np.zeros(self.shape, dtype=bool)
+
+            if self.dtype == values.dtype:
+                # GH#38353 instead of casting to object, operating on a
+                #  complex128 ndarray is much more performant.
+                left = self._combined.view("complex128")
+                right = values._combined.view("complex128")
+                # error: Argument 1 to "isin" has incompatible type
+                # "Union[ExtensionArray, ndarray[Any, Any],
+                # ndarray[Any, dtype[Any]]]"; expected
+                # "Union[_SupportsArray[dtype[Any]],
+                # _NestedSequence[_SupportsArray[dtype[Any]]], bool,
+                # int, float, complex, str, bytes, _NestedSequence[
+                # Union[bool, int, float, complex, str, bytes]]]"
+                return np.isin(left, right).ravel()  # type: ignore[arg-type]
+
+            elif needs_i8_conversion(self.left.dtype) ^ needs_i8_conversion(
+                values.left.dtype
+            ):
+                # not comparable -> no overlap
+                return np.zeros(self.shape, dtype=bool)
+
+        return isin(self.astype(object), values.astype(object))
+
+    @property
+    def _combined(self) -> IntervalSide:
+        # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+        # has no attribute "reshape"  [union-attr]
+        left = self.left._values.reshape(-1, 1)  # type: ignore[union-attr]
+        right = self.right._values.reshape(-1, 1)  # type: ignore[union-attr]
+        if needs_i8_conversion(left.dtype):
+            # error: Item "ndarray[Any, Any]" of "Any | ndarray[Any, Any]" has
+            # no attribute "_concat_same_type"
+            comb = left._concat_same_type(  # type: ignore[union-attr]
+                [left, right], axis=1
+            )
+        else:
+            comb = np.concatenate([left, right], axis=1)
+        return comb
+
+    def _from_combined(self, combined: np.ndarray) -> IntervalArray:
+        """
+        Create a new IntervalArray with our dtype from a 1D complex128 ndarray.
+        """
+        nc = combined.view("i8").reshape(-1, 2)
+
+        dtype = self._left.dtype
+        if needs_i8_conversion(dtype):
+            assert isinstance(self._left, (DatetimeArray, TimedeltaArray))
+            new_left = type(self._left)._from_sequence(nc[:, 0], dtype=dtype)
+            assert isinstance(self._right, (DatetimeArray, TimedeltaArray))
+            new_right = type(self._right)._from_sequence(nc[:, 1], dtype=dtype)
+        else:
+            assert isinstance(dtype, np.dtype)
+            new_left = nc[:, 0].view(dtype)
+            new_right = nc[:, 1].view(dtype)
+        return self._shallow_copy(left=new_left, right=new_right)
+
+    def unique(self) -> IntervalArray:
+        # No overload variant of "__getitem__" of "ExtensionArray" matches argument
+        # type "Tuple[slice, int]"
+        nc = unique(
+            self._combined.view("complex128")[:, 0]  # type: ignore[call-overload]
+        )
+        nc = nc[:, None]
+        return self._from_combined(nc)
+
+
+def _maybe_convert_platform_interval(values) -> ArrayLike:
+    """
+    Try to do platform conversion, with special casing for IntervalArray.
+    Wrapper around maybe_convert_platform that alters the default return
+    dtype in certain cases to be compatible with IntervalArray.  For example,
+    empty lists return with integer dtype instead of object dtype, which is
+    prohibited for IntervalArray.
+
+    Parameters
+    ----------
+    values : array-like
+
+    Returns
+    -------
+    array
+    """
+    if isinstance(values, (list, tuple)) and len(values) == 0:
+        # GH 19016
+        # empty lists/tuples get object dtype by default, but this is
+        # prohibited for IntervalArray, so coerce to integer instead
+        return np.array([], dtype=np.int64)
+    elif not is_list_like(values) or isinstance(values, ABCDataFrame):
+        # This will raise later, but we avoid passing to maybe_convert_platform
+        return values
+    elif isinstance(getattr(values, "dtype", None), CategoricalDtype):
+        values = np.asarray(values)
+    elif not hasattr(values, "dtype") and not isinstance(values, (list, tuple, range)):
+        # TODO: should we just cast these to list?
+        return values
+    else:
+        values = extract_array(values, extract_numpy=True)
+
+    if not hasattr(values, "dtype"):
+        values = np.asarray(values)
+        if values.dtype.kind in "iu" and values.dtype != np.int64:
+            values = values.astype(np.int64)
+    return values
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/masked.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/masked.py
new file mode 100644
index 0000000000000000000000000000000000000000..da656a2768901b572abd8b43810f0f496a293e1c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/masked.py
@@ -0,0 +1,1669 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas._libs.tslibs import is_supported_dtype
+from pandas._typing import (
+    ArrayLike,
+    AstypeArg,
+    AxisInt,
+    DtypeObj,
+    FillnaOptions,
+    InterpolateOptions,
+    NpDtype,
+    PositionalIndexer,
+    Scalar,
+    ScalarIndexer,
+    Self,
+    SequenceIndexer,
+    Shape,
+    npt,
+)
+from pandas.compat import (
+    IS64,
+    is_platform_windows,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_fillna_kwargs
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import BaseMaskedDtype
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    is_valid_na_for_dtype,
+    isna,
+    notna,
+)
+
+from pandas.core import (
+    algorithms as algos,
+    arraylike,
+    missing,
+    nanops,
+    ops,
+)
+from pandas.core.algorithms import (
+    factorize_array,
+    isin,
+    map_array,
+    mode,
+    take,
+)
+from pandas.core.array_algos import (
+    masked_accumulations,
+    masked_reductions,
+)
+from pandas.core.array_algos.quantile import quantile_with_mask
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._utils import to_numpy_dtype_inference
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import check_array_indexer
+from pandas.core.ops import invalid_comparison
+from pandas.core.util.hashing import hash_array
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Sequence,
+    )
+    from pandas import Series
+    from pandas.core.arrays import BooleanArray
+    from pandas._typing import (
+        NumpySorter,
+        NumpyValueArrayLike,
+    )
+    from pandas.core.arrays import FloatingArray
+
+from pandas.compat.numpy import function as nv
+
+
+class BaseMaskedArray(OpsMixin, ExtensionArray):
+    """
+    Base class for masked arrays (which use _data and _mask to store the data).
+
+    numpy based
+    """
+
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value: Scalar
+    # our underlying data and mask are each ndarrays
+    _data: np.ndarray
+    _mask: npt.NDArray[np.bool_]
+
+    # Fill values used for any/all
+    _truthy_value = Scalar  # bool(_truthy_value) = True
+    _falsey_value = Scalar  # bool(_falsey_value) = False
+
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, mask: npt.NDArray[np.bool_]) -> Self:
+        result = BaseMaskedArray.__new__(cls)
+        result._data = values
+        result._mask = mask
+        return result
+
+    def __init__(
+        self, values: np.ndarray, mask: npt.NDArray[np.bool_], copy: bool = False
+    ) -> None:
+        # values is supposed to already be validated in the subclass
+        if not (isinstance(mask, np.ndarray) and mask.dtype == np.bool_):
+            raise TypeError(
+                "mask should be boolean numpy array. Use "
+                "the 'pd.array' function instead"
+            )
+        if values.shape != mask.shape:
+            raise ValueError("values.shape must match mask.shape")
+
+        if copy:
+            values = values.copy()
+            mask = mask.copy()
+
+        self._data = values
+        self._mask = mask
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False) -> Self:
+        values, mask = cls._coerce_to_array(scalars, dtype=dtype, copy=copy)
+        return cls(values, mask)
+
+    @classmethod
+    @doc(ExtensionArray._empty)
+    def _empty(cls, shape: Shape, dtype: ExtensionDtype):
+        values = np.empty(shape, dtype=dtype.type)
+        values.fill(cls._internal_fill_value)
+        mask = np.ones(shape, dtype=bool)
+        result = cls(values, mask)
+        if not isinstance(result, cls) or dtype != result.dtype:
+            raise NotImplementedError(
+                f"Default 'empty' implementation is invalid for dtype='{dtype}'"
+            )
+        return result
+
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
+        # NEP 51: https://github.com/numpy/numpy/pull/22449
+        return str
+
+    @property
+    def dtype(self) -> BaseMaskedDtype:
+        raise AbstractMethodError(self)
+
+    @overload
+    def __getitem__(self, item: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(self, item: SequenceIndexer) -> Self:
+        ...
+
+    def __getitem__(self, item: PositionalIndexer) -> Self | Any:
+        item = check_array_indexer(self, item)
+
+        newmask = self._mask[item]
+        if is_bool(newmask):
+            # This is a scalar indexing
+            if newmask:
+                return self.dtype.na_value
+            return self._data[item]
+
+        return self._simple_new(self._data[item], newmask)
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        mask = self._mask
+
+        if mask.any():
+            func = missing.get_fill_func(method, ndim=self.ndim)
+
+            npvalues = self._data.T
+            new_mask = mask.T
+            if copy:
+                npvalues = npvalues.copy()
+                new_mask = new_mask.copy()
+            elif limit_area is not None:
+                mask = mask.copy()
+            func(npvalues, limit=limit, mask=new_mask)
+
+            if limit_area is not None and not mask.all():
+                mask = mask.T
+                neg_mask = ~mask
+                first = neg_mask.argmax()
+                last = len(neg_mask) - neg_mask[::-1].argmax() - 1
+                if limit_area == "inside":
+                    new_mask[:first] |= mask[:first]
+                    new_mask[last + 1 :] |= mask[last + 1 :]
+                elif limit_area == "outside":
+                    new_mask[first + 1 : last] |= mask[first + 1 : last]
+
+            if copy:
+                return self._simple_new(npvalues.T, new_mask.T)
+            else:
+                return self
+        else:
+            if copy:
+                new_values = self.copy()
+            else:
+                new_values = self
+        return new_values
+
+    @doc(ExtensionArray.fillna)
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        value, method = validate_fillna_kwargs(value, method)
+
+        mask = self._mask
+
+        value = missing.check_value_size(value, mask, len(self))
+
+        if mask.any():
+            if method is not None:
+                func = missing.get_fill_func(method, ndim=self.ndim)
+                npvalues = self._data.T
+                new_mask = mask.T
+                if copy:
+                    npvalues = npvalues.copy()
+                    new_mask = new_mask.copy()
+                func(npvalues, limit=limit, mask=new_mask)
+                return self._simple_new(npvalues.T, new_mask.T)
+            else:
+                # fill with value
+                if copy:
+                    new_values = self.copy()
+                else:
+                    new_values = self[:]
+                new_values[mask] = value
+        else:
+            if copy:
+                new_values = self.copy()
+            else:
+                new_values = self[:]
+        return new_values
+
+    @classmethod
+    def _coerce_to_array(
+        cls, values, *, dtype: DtypeObj, copy: bool = False
+    ) -> tuple[np.ndarray, np.ndarray]:
+        raise AbstractMethodError(cls)
+
+    def _validate_setitem_value(self, value):
+        """
+        Check if we have a scalar that we can cast losslessly.
+
+        Raises
+        ------
+        TypeError
+        """
+        kind = self.dtype.kind
+        # TODO: get this all from np_can_hold_element?
+        if kind == "b":
+            if lib.is_bool(value):
+                return value
+
+        elif kind == "f":
+            if lib.is_integer(value) or lib.is_float(value):
+                return value
+
+        else:
+            if lib.is_integer(value) or (lib.is_float(value) and value.is_integer()):
+                return value
+            # TODO: unsigned checks
+
+        # Note: without the "str" here, the f-string rendering raises in
+        #  py38 builds.
+        raise TypeError(f"Invalid value '{value!s}' for dtype '{self.dtype}'")
+
+    def __setitem__(self, key, value) -> None:
+        key = check_array_indexer(self, key)
+
+        if is_scalar(value):
+            if is_valid_na_for_dtype(value, self.dtype):
+                self._mask[key] = True
+            else:
+                value = self._validate_setitem_value(value)
+                self._data[key] = value
+                self._mask[key] = False
+            return
+
+        value, mask = self._coerce_to_array(value, dtype=self.dtype)
+
+        self._data[key] = value
+        self._mask[key] = mask
+
+    def __contains__(self, key) -> bool:
+        if isna(key) and key is not self.dtype.na_value:
+            # GH#52840
+            if self._data.dtype.kind == "f" and lib.is_float(key):
+                return bool((np.isnan(self._data) & ~self._mask).any())
+
+        return bool(super().__contains__(key))
+
+    def __iter__(self) -> Iterator:
+        if self.ndim == 1:
+            if not self._hasna:
+                for val in self._data:
+                    yield val
+            else:
+                na_value = self.dtype.na_value
+                for isna_, val in zip(self._mask, self._data):
+                    if isna_:
+                        yield na_value
+                    else:
+                        yield val
+        else:
+            for i in range(len(self)):
+                yield self[i]
+
+    def __len__(self) -> int:
+        return len(self._data)
+
+    @property
+    def shape(self) -> Shape:
+        return self._data.shape
+
+    @property
+    def ndim(self) -> int:
+        return self._data.ndim
+
+    def swapaxes(self, axis1, axis2) -> Self:
+        data = self._data.swapaxes(axis1, axis2)
+        mask = self._mask.swapaxes(axis1, axis2)
+        return self._simple_new(data, mask)
+
+    def delete(self, loc, axis: AxisInt = 0) -> Self:
+        data = np.delete(self._data, loc, axis=axis)
+        mask = np.delete(self._mask, loc, axis=axis)
+        return self._simple_new(data, mask)
+
+    def reshape(self, *args, **kwargs) -> Self:
+        data = self._data.reshape(*args, **kwargs)
+        mask = self._mask.reshape(*args, **kwargs)
+        return self._simple_new(data, mask)
+
+    def ravel(self, *args, **kwargs) -> Self:
+        # TODO: need to make sure we have the same order for data/mask
+        data = self._data.ravel(*args, **kwargs)
+        mask = self._mask.ravel(*args, **kwargs)
+        return type(self)(data, mask)
+
+    @property
+    def T(self) -> Self:
+        return self._simple_new(self._data.T, self._mask.T)
+
+    def round(self, decimals: int = 0, *args, **kwargs):
+        """
+        Round each value in the array a to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, default 0
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        NumericArray
+            Rounded values of the NumericArray.
+
+        See Also
+        --------
+        numpy.around : Round values of an np.array.
+        DataFrame.round : Round values of a DataFrame.
+        Series.round : Round values of a Series.
+        """
+        if self.dtype.kind == "b":
+            return self
+        nv.validate_round(args, kwargs)
+        values = np.round(self._data, decimals=decimals, **kwargs)
+
+        # Usually we'll get same type as self, but ndarray[bool] casts to float
+        return self._maybe_mask_result(values, self._mask.copy())
+
+    # ------------------------------------------------------------------
+    # Unary Methods
+
+    def __invert__(self) -> Self:
+        return self._simple_new(~self._data, self._mask.copy())
+
+    def __neg__(self) -> Self:
+        return self._simple_new(-self._data, self._mask.copy())
+
+    def __pos__(self) -> Self:
+        return self.copy()
+
+    def __abs__(self) -> Self:
+        return self._simple_new(abs(self._data), self._mask.copy())
+
+    # ------------------------------------------------------------------
+
+    def _values_for_json(self) -> np.ndarray:
+        return np.asarray(self, dtype=object)
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert to a NumPy Array.
+
+        By default converts to an object-dtype NumPy array. Specify the `dtype` and
+        `na_value` keywords to customize the conversion.
+
+        Parameters
+        ----------
+        dtype : dtype, default object
+            The numpy dtype to convert to.
+        copy : bool, default False
+            Whether to ensure that the returned value is a not a view on
+            the array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary. This is typically
+            only possible when no missing values are present and `dtype`
+            is the equivalent numpy dtype.
+        na_value : scalar, optional
+             Scalar missing value indicator to use in numpy array. Defaults
+             to the native missing value indicator of this array (pd.NA).
+
+        Returns
+        -------
+        numpy.ndarray
+
+        Examples
+        --------
+        An object-dtype is the default result
+
+        >>> a = pd.array([True, False, pd.NA], dtype="boolean")
+        >>> a.to_numpy()
+        array([True, False, <NA>], dtype=object)
+
+        When no missing values are present, an equivalent dtype can be used.
+
+        >>> pd.array([True, False], dtype="boolean").to_numpy(dtype="bool")
+        array([ True, False])
+        >>> pd.array([1, 2], dtype="Int64").to_numpy("int64")
+        array([1, 2])
+
+        However, requesting such dtype will raise a ValueError if
+        missing values are present and the default missing value :attr:`NA`
+        is used.
+
+        >>> a = pd.array([True, False, pd.NA], dtype="boolean")
+        >>> a
+        <BooleanArray>
+        [True, False, <NA>]
+        Length: 3, dtype: boolean
+
+        >>> a.to_numpy(dtype="bool")
+        Traceback (most recent call last):
+        ...
+        ValueError: cannot convert to bool numpy array in presence of missing values
+
+        Specify a valid `na_value` instead
+
+        >>> a.to_numpy(dtype="bool", na_value=False)
+        array([ True, False, False])
+        """
+        hasna = self._hasna
+        dtype, na_value = to_numpy_dtype_inference(self, dtype, na_value, hasna)
+        if dtype is None:
+            dtype = object
+
+        if hasna:
+            if (
+                dtype != object
+                and not is_string_dtype(dtype)
+                and na_value is libmissing.NA
+            ):
+                raise ValueError(
+                    f"cannot convert to '{dtype}'-dtype NumPy array "
+                    "with missing values. Specify an appropriate 'na_value' "
+                    "for this dtype."
+                )
+            # don't pass copy to astype -> always need a copy since we are mutating
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                data = self._data.astype(dtype)
+            data[self._mask] = na_value
+        else:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                data = self._data.astype(dtype, copy=copy)
+        return data
+
+    @doc(ExtensionArray.tolist)
+    def tolist(self):
+        if self.ndim > 1:
+            return [x.tolist() for x in self]
+        dtype = None if self._hasna else self._data.dtype
+        return self.to_numpy(dtype=dtype, na_value=libmissing.NA).tolist()
+
+    @overload
+    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
+        ...
+
+    @overload
+    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
+        ...
+
+    @overload
+    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
+        ...
+
+    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        # if we are astyping to another nullable masked dtype, we can fastpath
+        if isinstance(dtype, BaseMaskedDtype):
+            # TODO deal with NaNs for FloatingArray case
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                # TODO: Is rounding what we want long term?
+                data = self._data.astype(dtype.numpy_dtype, copy=copy)
+            # mask is copied depending on whether the data was copied, and
+            # not directly depending on the `copy` keyword
+            mask = self._mask if data is self._data else self._mask.copy()
+            cls = dtype.construct_array_type()
+            return cls(data, mask, copy=False)
+
+        if isinstance(dtype, ExtensionDtype):
+            eacls = dtype.construct_array_type()
+            return eacls._from_sequence(self, dtype=dtype, copy=copy)
+
+        na_value: float | np.datetime64 | lib.NoDefault
+
+        # coerce
+        if dtype.kind == "f":
+            # In astype, we consider dtype=float to also mean na_value=np.nan
+            na_value = np.nan
+        elif dtype.kind == "M":
+            na_value = np.datetime64("NaT")
+        else:
+            na_value = lib.no_default
+
+        # to_numpy will also raise, but we get somewhat nicer exception messages here
+        if dtype.kind in "iu" and self._hasna:
+            raise ValueError("cannot convert NA to integer")
+        if dtype.kind == "b" and self._hasna:
+            # careful: astype_nansafe converts np.nan to True
+            raise ValueError("cannot convert float NaN to bool")
+
+        data = self.to_numpy(dtype=dtype, na_value=na_value, copy=copy)
+        return data
+
+    __array_priority__ = 1000  # higher than ndarray so ops dispatch to us
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        the array interface, return my values
+        We return an object array here to preserve our scalar values
+        """
+        if copy is False:
+            if not self._hasna:
+                # special case, here we can simply return the underlying data
+                return np.array(self._data, dtype=dtype, copy=copy)
+
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if copy is None:
+            copy = False  # The NumPy copy=False meaning is different here.
+        return self.to_numpy(dtype=dtype, copy=copy)
+
+    _HANDLED_TYPES: tuple[type, ...]
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        # For MaskedArray inputs, we apply the ufunc to ._data
+        # and mask the result.
+
+        out = kwargs.get("out", ())
+
+        for x in inputs + out:
+            if not isinstance(x, self._HANDLED_TYPES + (BaseMaskedArray,)):
+                return NotImplemented
+
+        # for binary ops, use our custom dunder methods
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_ufunc_with_out
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        mask = np.zeros(len(self), dtype=bool)
+        inputs2 = []
+        for x in inputs:
+            if isinstance(x, BaseMaskedArray):
+                mask |= x._mask
+                inputs2.append(x._data)
+            else:
+                inputs2.append(x)
+
+        def reconstruct(x: np.ndarray):
+            # we don't worry about scalar `x` here, since we
+            # raise for reduce up above.
+            from pandas.core.arrays import (
+                BooleanArray,
+                FloatingArray,
+                IntegerArray,
+            )
+
+            if x.dtype.kind == "b":
+                m = mask.copy()
+                return BooleanArray(x, m)
+            elif x.dtype.kind in "iu":
+                m = mask.copy()
+                return IntegerArray(x, m)
+            elif x.dtype.kind == "f":
+                m = mask.copy()
+                if x.dtype == np.float16:
+                    # reached in e.g. np.sqrt on BooleanArray
+                    # we don't support float16
+                    x = x.astype(np.float32)
+                return FloatingArray(x, m)
+            else:
+                x[mask] = np.nan
+            return x
+
+        result = getattr(ufunc, method)(*inputs2, **kwargs)
+        if ufunc.nout > 1:
+            # e.g. np.divmod
+            return tuple(reconstruct(x) for x in result)
+        elif method == "reduce":
+            # e.g. np.add.reduce; test_ufunc_reduce_raises
+            if self._mask.any():
+                return self._na_value
+            return result
+        else:
+            return reconstruct(result)
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow as pa
+
+        return pa.array(self._data, mask=self._mask, type=type)
+
+    @property
+    def _hasna(self) -> bool:
+        # Note: this is expensive right now! The hope is that we can
+        # make this faster by having an optional mask, but not have to change
+        # source code using it..
+
+        # error: Incompatible return value type (got "bool_", expected "bool")
+        return self._mask.any()  # type: ignore[return-value]
+
+    def _propagate_mask(
+        self, mask: npt.NDArray[np.bool_] | None, other
+    ) -> npt.NDArray[np.bool_]:
+        if mask is None:
+            mask = self._mask.copy()  # TODO: need test for BooleanArray needing a copy
+            if other is libmissing.NA:
+                # GH#45421 don't alter inplace
+                mask = mask | True
+            elif is_list_like(other) and len(other) == len(mask):
+                mask = mask | isna(other)
+        else:
+            mask = self._mask | mask
+        # Incompatible return value type (got "Optional[ndarray[Any, dtype[bool_]]]",
+        # expected "ndarray[Any, dtype[bool_]]")
+        return mask  # type: ignore[return-value]
+
+    def _arith_method(self, other, op):
+        op_name = op.__name__
+        omask = None
+
+        if (
+            not hasattr(other, "dtype")
+            and is_list_like(other)
+            and len(other) == len(self)
+        ):
+            # Try inferring masked dtype instead of casting to object
+            other = pd_array(other)
+            other = extract_array(other, extract_numpy=True)
+
+        if isinstance(other, BaseMaskedArray):
+            other, omask = other._data, other._mask
+
+        elif is_list_like(other):
+            if not isinstance(other, ExtensionArray):
+                other = np.asarray(other)
+            if other.ndim > 1:
+                raise NotImplementedError("can only perform ops with 1-d structures")
+
+        # We wrap the non-masked arithmetic logic used for numpy dtypes
+        #  in Series/Index arithmetic ops.
+        other = ops.maybe_prepare_scalar_for_op(other, (len(self),))
+        pd_op = ops.get_array_op(op)
+        other = ensure_wrapped_if_datetimelike(other)
+
+        if op_name in {"pow", "rpow"} and isinstance(other, np.bool_):
+            # Avoid DeprecationWarning: In future, it will be an error
+            #  for 'np.bool_' scalars to be interpreted as an index
+            #  e.g. test_array_scalar_like_equivalence
+            other = bool(other)
+
+        mask = self._propagate_mask(omask, other)
+
+        if other is libmissing.NA:
+            result = np.ones_like(self._data)
+            if self.dtype.kind == "b":
+                if op_name in {
+                    "floordiv",
+                    "rfloordiv",
+                    "pow",
+                    "rpow",
+                    "truediv",
+                    "rtruediv",
+                }:
+                    # GH#41165 Try to match non-masked Series behavior
+                    #  This is still imperfect GH#46043
+                    raise NotImplementedError(
+                        f"operator '{op_name}' not implemented for bool dtypes"
+                    )
+                if op_name in {"mod", "rmod"}:
+                    dtype = "int8"
+                else:
+                    dtype = "bool"
+                result = result.astype(dtype)
+            elif "truediv" in op_name and self.dtype.kind != "f":
+                # The actual data here doesn't matter since the mask
+                #  will be all-True, but since this is division, we want
+                #  to end up with floating dtype.
+                result = result.astype(np.float64)
+        else:
+            # Make sure we do this before the "pow" mask checks
+            #  to get an expected exception message on shape mismatch.
+            if self.dtype.kind in "iu" and op_name in ["floordiv", "mod"]:
+                # TODO(GH#30188) ATM we don't match the behavior of non-masked
+                #  types with respect to floordiv-by-zero
+                pd_op = op
+
+            with np.errstate(all="ignore"):
+                result = pd_op(self._data, other)
+
+        if op_name == "pow":
+            # 1 ** x is 1.
+            mask = np.where((self._data == 1) & ~self._mask, False, mask)
+            # x ** 0 is 1.
+            if omask is not None:
+                mask = np.where((other == 0) & ~omask, False, mask)
+            elif other is not libmissing.NA:
+                mask = np.where(other == 0, False, mask)
+
+        elif op_name == "rpow":
+            # 1 ** x is 1.
+            if omask is not None:
+                mask = np.where((other == 1) & ~omask, False, mask)
+            elif other is not libmissing.NA:
+                mask = np.where(other == 1, False, mask)
+            # x ** 0 is 1.
+            mask = np.where((self._data == 0) & ~self._mask, False, mask)
+
+        return self._maybe_mask_result(result, mask)
+
+    _logical_method = _arith_method
+
+    def _cmp_method(self, other, op) -> BooleanArray:
+        from pandas.core.arrays import BooleanArray
+
+        mask = None
+
+        if isinstance(other, BaseMaskedArray):
+            other, mask = other._data, other._mask
+
+        elif is_list_like(other):
+            other = np.asarray(other)
+            if other.ndim > 1:
+                raise NotImplementedError("can only perform ops with 1-d structures")
+            if len(self) != len(other):
+                raise ValueError("Lengths must match to compare")
+
+        if other is libmissing.NA:
+            # numpy does not handle pd.NA well as "other" scalar (it returns
+            # a scalar False instead of an array)
+            # This may be fixed by NA.__array_ufunc__. Revisit this check
+            # once that's implemented.
+            result = np.zeros(self._data.shape, dtype="bool")
+            mask = np.ones(self._data.shape, dtype="bool")
+        else:
+            with warnings.catch_warnings():
+                # numpy may show a FutureWarning or DeprecationWarning:
+                #     elementwise comparison failed; returning scalar instead,
+                #     but in the future will perform elementwise comparison
+                # before returning NotImplemented. We fall back to the correct
+                # behavior today, so that should be fine to ignore.
+                warnings.filterwarnings("ignore", "elementwise", FutureWarning)
+                warnings.filterwarnings("ignore", "elementwise", DeprecationWarning)
+                method = getattr(self._data, f"__{op.__name__}__")
+                result = method(other)
+
+                if result is NotImplemented:
+                    result = invalid_comparison(self._data, other, op)
+
+        mask = self._propagate_mask(mask, other)
+        return BooleanArray(result, mask, copy=False)
+
+    def _maybe_mask_result(
+        self, result: np.ndarray | tuple[np.ndarray, np.ndarray], mask: np.ndarray
+    ):
+        """
+        Parameters
+        ----------
+        result : array-like or tuple[array-like]
+        mask : array-like bool
+        """
+        if isinstance(result, tuple):
+            # i.e. divmod
+            div, mod = result
+            return (
+                self._maybe_mask_result(div, mask),
+                self._maybe_mask_result(mod, mask),
+            )
+
+        if result.dtype.kind == "f":
+            from pandas.core.arrays import FloatingArray
+
+            return FloatingArray(result, mask, copy=False)
+
+        elif result.dtype.kind == "b":
+            from pandas.core.arrays import BooleanArray
+
+            return BooleanArray(result, mask, copy=False)
+
+        elif lib.is_np_dtype(result.dtype, "m") and is_supported_dtype(result.dtype):
+            # e.g. test_numeric_arr_mul_tdscalar_numexpr_path
+            from pandas.core.arrays import TimedeltaArray
+
+            result[mask] = result.dtype.type("NaT")
+
+            if not isinstance(result, TimedeltaArray):
+                return TimedeltaArray._simple_new(result, dtype=result.dtype)
+
+            return result
+
+        elif result.dtype.kind in "iu":
+            from pandas.core.arrays import IntegerArray
+
+            return IntegerArray(result, mask, copy=False)
+
+        else:
+            result[mask] = np.nan
+            return result
+
+    def isna(self) -> np.ndarray:
+        return self._mask.copy()
+
+    @property
+    def _na_value(self):
+        return self.dtype.na_value
+
+    @property
+    def nbytes(self) -> int:
+        return self._data.nbytes + self._mask.nbytes
+
+    @classmethod
+    def _concat_same_type(
+        cls,
+        to_concat: Sequence[Self],
+        axis: AxisInt = 0,
+    ) -> Self:
+        data = np.concatenate([x._data for x in to_concat], axis=axis)
+        mask = np.concatenate([x._mask for x in to_concat], axis=axis)
+        return cls(data, mask)
+
+    def _hash_pandas_object(
+        self, *, encoding: str, hash_key: str, categorize: bool
+    ) -> npt.NDArray[np.uint64]:
+        hashed_array = hash_array(
+            self._data, encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+        hashed_array[self.isna()] = hash(self.dtype.na_value)
+        return hashed_array
+
+    def take(
+        self,
+        indexer,
+        *,
+        allow_fill: bool = False,
+        fill_value: Scalar | None = None,
+        axis: AxisInt = 0,
+    ) -> Self:
+        # we always fill with 1 internally
+        # to avoid upcasting
+        data_fill_value = self._internal_fill_value if isna(fill_value) else fill_value
+        result = take(
+            self._data,
+            indexer,
+            fill_value=data_fill_value,
+            allow_fill=allow_fill,
+            axis=axis,
+        )
+
+        mask = take(
+            self._mask, indexer, fill_value=True, allow_fill=allow_fill, axis=axis
+        )
+
+        # if we are filling
+        # we only fill where the indexer is null
+        # not existing missing values
+        # TODO(jreback) what if we have a non-na float as a fill value?
+        if allow_fill and notna(fill_value):
+            fill_mask = np.asarray(indexer) == -1
+            result[fill_mask] = fill_value
+            mask = mask ^ fill_mask
+
+        return self._simple_new(result, mask)
+
+    # error: Return type "BooleanArray" of "isin" incompatible with return type
+    # "ndarray" in supertype "ExtensionArray"
+    def isin(self, values: ArrayLike) -> BooleanArray:  # type: ignore[override]
+        from pandas.core.arrays import BooleanArray
+
+        # algorithms.isin will eventually convert values to an ndarray, so no extra
+        # cost to doing it here first
+        values_arr = np.asarray(values)
+        result = isin(self._data, values_arr)
+
+        if self._hasna:
+            values_have_NA = values_arr.dtype == object and any(
+                val is self.dtype.na_value for val in values_arr
+            )
+
+            # For now, NA does not propagate so set result according to presence of NA,
+            # see https://github.com/pandas-dev/pandas/pull/38379 for some discussion
+            result[self._mask] = values_have_NA
+
+        mask = np.zeros(self._data.shape, dtype=bool)
+        return BooleanArray(result, mask, copy=False)
+
+    def copy(self) -> Self:
+        data = self._data.copy()
+        mask = self._mask.copy()
+        return self._simple_new(data, mask)
+
+    @doc(ExtensionArray.duplicated)
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        values = self._data
+        mask = self._mask
+        return algos.duplicated(values, keep=keep, mask=mask)
+
+    def unique(self) -> Self:
+        """
+        Compute the BaseMaskedArray of unique values.
+
+        Returns
+        -------
+        uniques : BaseMaskedArray
+        """
+        uniques, mask = algos.unique_with_mask(self._data, self._mask)
+        return self._simple_new(uniques, mask)
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if self._hasna:
+            raise ValueError(
+                "searchsorted requires array to be sorted, which is impossible "
+                "with NAs present."
+            )
+        if isinstance(value, ExtensionArray):
+            value = value.astype(object)
+        # Base class searchsorted would cast to object, which is *much* slower.
+        return self._data.searchsorted(value, side=side, sorter=sorter)
+
+    @doc(ExtensionArray.factorize)
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, ExtensionArray]:
+        arr = self._data
+        mask = self._mask
+
+        # Use a sentinel for na; recode and add NA to uniques if necessary below
+        codes, uniques = factorize_array(arr, use_na_sentinel=True, mask=mask)
+
+        # check that factorize_array correctly preserves dtype.
+        assert uniques.dtype == self.dtype.numpy_dtype, (uniques.dtype, self.dtype)
+
+        has_na = mask.any()
+        if use_na_sentinel or not has_na:
+            size = len(uniques)
+        else:
+            # Make room for an NA value
+            size = len(uniques) + 1
+        uniques_mask = np.zeros(size, dtype=bool)
+        if not use_na_sentinel and has_na:
+            na_index = mask.argmax()
+            # Insert na with the proper code
+            if na_index == 0:
+                na_code = np.intp(0)
+            else:
+                na_code = codes[:na_index].max() + 1
+            codes[codes >= na_code] += 1
+            codes[codes == -1] = na_code
+            # dummy value for uniques; not used since uniques_mask will be True
+            uniques = np.insert(uniques, na_code, 0)
+            uniques_mask[na_code] = True
+        uniques_ea = self._simple_new(uniques, uniques_mask)
+
+        return codes, uniques_ea
+
+    @doc(ExtensionArray._values_for_argsort)
+    def _values_for_argsort(self) -> np.ndarray:
+        return self._data
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Returns a Series containing counts of each unique value.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of missing values.
+
+        Returns
+        -------
+        counts : Series
+
+        See Also
+        --------
+        Series.value_counts
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+        from pandas.arrays import IntegerArray
+
+        keys, value_counts, na_counter = algos.value_counts_arraylike(
+            self._data, dropna=dropna, mask=self._mask
+        )
+        mask_index = np.zeros((len(value_counts),), dtype=np.bool_)
+        mask = mask_index.copy()
+
+        if na_counter > 0:
+            mask_index[-1] = True
+
+        arr = IntegerArray(value_counts, mask)
+        index = Index(
+            self.dtype.construct_array_type()(
+                keys, mask_index  # type: ignore[arg-type]
+            )
+        )
+        return Series(arr, index=index, name="count", copy=False)
+
+    def _mode(self, dropna: bool = True) -> Self:
+        if dropna:
+            result = mode(self._data, dropna=dropna, mask=self._mask)
+            res_mask = np.zeros(result.shape, dtype=np.bool_)
+        else:
+            result, res_mask = mode(self._data, dropna=dropna, mask=self._mask)
+        result = type(self)(result, res_mask)  # type: ignore[arg-type]
+        return result[result.argsort()]
+
+    @doc(ExtensionArray.equals)
+    def equals(self, other) -> bool:
+        if type(self) != type(other):
+            return False
+        if other.dtype != self.dtype:
+            return False
+
+        # GH#44382 if e.g. self[1] is np.nan and other[1] is pd.NA, we are NOT
+        #  equal.
+        if not np.array_equal(self._mask, other._mask):
+            return False
+
+        left = self._data[~self._mask]
+        right = other._data[~other._mask]
+        return array_equivalent(left, right, strict_nan=True, dtype_equal=True)
+
+    def _quantile(
+        self, qs: npt.NDArray[np.float64], interpolation: str
+    ) -> BaseMaskedArray:
+        """
+        Dispatch to quantile_with_mask, needed because we do not have
+        _from_factorized.
+
+        Notes
+        -----
+        We assume that all impacted cases are 1D-only.
+        """
+        res = quantile_with_mask(
+            self._data,
+            mask=self._mask,
+            # TODO(GH#40932): na_value_for_dtype(self.dtype.numpy_dtype)
+            #  instead of np.nan
+            fill_value=np.nan,
+            qs=qs,
+            interpolation=interpolation,
+        )
+
+        if self._hasna:
+            # Our result mask is all-False unless we are all-NA, in which
+            #  case it is all-True.
+            if self.ndim == 2:
+                # I think this should be out_mask=self.isna().all(axis=1)
+                #  but am holding off until we have tests
+                raise NotImplementedError
+            if self.isna().all():
+                out_mask = np.ones(res.shape, dtype=bool)
+
+                if is_integer_dtype(self.dtype):
+                    # We try to maintain int dtype if possible for not all-na case
+                    # as well
+                    res = np.zeros(res.shape, dtype=self.dtype.numpy_dtype)
+            else:
+                out_mask = np.zeros(res.shape, dtype=bool)
+        else:
+            out_mask = np.zeros(res.shape, dtype=bool)
+        return self._maybe_mask_result(res, mask=out_mask)
+
+    # ------------------------------------------------------------------
+    # Reductions
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        if name in {"any", "all", "min", "max", "sum", "prod", "mean", "var", "std"}:
+            result = getattr(self, name)(skipna=skipna, **kwargs)
+        else:
+            # median, skew, kurt, sem
+            data = self._data
+            mask = self._mask
+            op = getattr(nanops, f"nan{name}")
+            axis = kwargs.pop("axis", None)
+            result = op(data, axis=axis, skipna=skipna, mask=mask, **kwargs)
+
+        if keepdims:
+            if isna(result):
+                return self._wrap_na_result(name=name, axis=0, mask_size=(1,))
+            else:
+                result = result.reshape(1)
+                mask = np.zeros(1, dtype=bool)
+                return self._maybe_mask_result(result, mask)
+
+        if isna(result):
+            return libmissing.NA
+        else:
+            return result
+
+    def _wrap_reduction_result(self, name: str, result, *, skipna, axis):
+        if isinstance(result, np.ndarray):
+            if skipna:
+                # we only retain mask for all-NA rows/columns
+                mask = self._mask.all(axis=axis)
+            else:
+                mask = self._mask.any(axis=axis)
+
+            return self._maybe_mask_result(result, mask)
+        return result
+
+    def _wrap_na_result(self, *, name, axis, mask_size):
+        mask = np.ones(mask_size, dtype=bool)
+
+        float_dtyp = "float32" if self.dtype == "Float32" else "float64"
+        if name in ["mean", "median", "var", "std", "skew", "kurt"]:
+            np_dtype = float_dtyp
+        elif name in ["min", "max"] or self.dtype.itemsize == 8:
+            np_dtype = self.dtype.numpy_dtype.name
+        else:
+            is_windows_or_32bit = is_platform_windows() or not IS64
+            int_dtyp = "int32" if is_windows_or_32bit else "int64"
+            uint_dtyp = "uint32" if is_windows_or_32bit else "uint64"
+            np_dtype = {"b": int_dtyp, "i": int_dtyp, "u": uint_dtyp, "f": float_dtyp}[
+                self.dtype.kind
+            ]
+
+        value = np.array([1], dtype=np_dtype)
+        return self._maybe_mask_result(value, mask=mask)
+
+    def _wrap_min_count_reduction_result(
+        self, name: str, result, *, skipna, min_count, axis
+    ):
+        if min_count == 0 and isinstance(result, np.ndarray):
+            return self._maybe_mask_result(result, np.zeros(result.shape, dtype=bool))
+        return self._wrap_reduction_result(name, result, skipna=skipna, axis=axis)
+
+    def sum(
+        self,
+        *,
+        skipna: bool = True,
+        min_count: int = 0,
+        axis: AxisInt | None = 0,
+        **kwargs,
+    ):
+        nv.validate_sum((), kwargs)
+
+        result = masked_reductions.sum(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            min_count=min_count,
+            axis=axis,
+        )
+        return self._wrap_min_count_reduction_result(
+            "sum", result, skipna=skipna, min_count=min_count, axis=axis
+        )
+
+    def prod(
+        self,
+        *,
+        skipna: bool = True,
+        min_count: int = 0,
+        axis: AxisInt | None = 0,
+        **kwargs,
+    ):
+        nv.validate_prod((), kwargs)
+
+        result = masked_reductions.prod(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            min_count=min_count,
+            axis=axis,
+        )
+        return self._wrap_min_count_reduction_result(
+            "prod", result, skipna=skipna, min_count=min_count, axis=axis
+        )
+
+    def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        nv.validate_mean((), kwargs)
+        result = masked_reductions.mean(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result("mean", result, skipna=skipna, axis=axis)
+
+    def var(
+        self, *, skipna: bool = True, axis: AxisInt | None = 0, ddof: int = 1, **kwargs
+    ):
+        nv.validate_stat_ddof_func((), kwargs, fname="var")
+        result = masked_reductions.var(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+            ddof=ddof,
+        )
+        return self._wrap_reduction_result("var", result, skipna=skipna, axis=axis)
+
+    def std(
+        self, *, skipna: bool = True, axis: AxisInt | None = 0, ddof: int = 1, **kwargs
+    ):
+        nv.validate_stat_ddof_func((), kwargs, fname="std")
+        result = masked_reductions.std(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+            ddof=ddof,
+        )
+        return self._wrap_reduction_result("std", result, skipna=skipna, axis=axis)
+
+    def min(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        nv.validate_min((), kwargs)
+        result = masked_reductions.min(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result("min", result, skipna=skipna, axis=axis)
+
+    def max(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        nv.validate_max((), kwargs)
+        result = masked_reductions.max(
+            self._data,
+            self._mask,
+            skipna=skipna,
+            axis=axis,
+        )
+        return self._wrap_reduction_result("max", result, skipna=skipna, axis=axis)
+
+    def map(self, mapper, na_action=None):
+        return map_array(self.to_numpy(), mapper, na_action=na_action)
+
+    def any(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        """
+        Return whether any element is truthy.
+
+        Returns False unless there is at least one element that is truthy.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
+        is used as for logical operations.
+
+        .. versionchanged:: 1.4.0
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be False, as for an empty array.
+            If `skipna` is False, the result will still be True if there is
+            at least one element that is truthy, otherwise NA will be returned
+            if there are NA's present.
+        axis : int, optional, default 0
+        **kwargs : any, default None
+            Additional keywords have no effect but might be accepted for
+            compatibility with NumPy.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        numpy.any : Numpy version of this method.
+        BaseMaskedArray.all : Return whether all elements are truthy.
+
+        Examples
+        --------
+        The result indicates whether any element is truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, False, True]).any()
+        True
+        >>> pd.array([True, False, pd.NA]).any()
+        True
+        >>> pd.array([False, False, pd.NA]).any()
+        False
+        >>> pd.array([], dtype="boolean").any()
+        False
+        >>> pd.array([pd.NA], dtype="boolean").any()
+        False
+        >>> pd.array([pd.NA], dtype="Float64").any()
+        False
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, False, pd.NA]).any(skipna=False)
+        True
+        >>> pd.array([1, 0, pd.NA]).any(skipna=False)
+        True
+        >>> pd.array([False, False, pd.NA]).any(skipna=False)
+        <NA>
+        >>> pd.array([0, 0, pd.NA]).any(skipna=False)
+        <NA>
+        """
+        nv.validate_any((), kwargs)
+
+        values = self._data.copy()
+        # error: Argument 3 to "putmask" has incompatible type "object";
+        # expected "Union[_SupportsArray[dtype[Any]],
+        # _NestedSequence[_SupportsArray[dtype[Any]]],
+        # bool, int, float, complex, str, bytes,
+        # _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"
+        np.putmask(values, self._mask, self._falsey_value)  # type: ignore[arg-type]
+        result = values.any()
+        if skipna:
+            return result
+        else:
+            if result or len(self) == 0 or not self._mask.any():
+                return result
+            else:
+                return self.dtype.na_value
+
+    def all(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
+        """
+        Return whether all elements are truthy.
+
+        Returns True unless there is at least one element that is falsey.
+        By default, NAs are skipped. If ``skipna=False`` is specified and
+        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
+        is used as for logical operations.
+
+        .. versionchanged:: 1.4.0
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA values. If the entire array is NA and `skipna` is
+            True, then the result will be True, as for an empty array.
+            If `skipna` is False, the result will still be False if there is
+            at least one element that is falsey, otherwise NA will be returned
+            if there are NA's present.
+        axis : int, optional, default 0
+        **kwargs : any, default None
+            Additional keywords have no effect but might be accepted for
+            compatibility with NumPy.
+
+        Returns
+        -------
+        bool or :attr:`pandas.NA`
+
+        See Also
+        --------
+        numpy.all : Numpy version of this method.
+        BooleanArray.any : Return whether any element is truthy.
+
+        Examples
+        --------
+        The result indicates whether all elements are truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, True, pd.NA]).all()
+        True
+        >>> pd.array([1, 1, pd.NA]).all()
+        True
+        >>> pd.array([True, False, pd.NA]).all()
+        False
+        >>> pd.array([], dtype="boolean").all()
+        True
+        >>> pd.array([pd.NA], dtype="boolean").all()
+        True
+        >>> pd.array([pd.NA], dtype="Float64").all()
+        True
+
+        With ``skipna=False``, the result can be NA if this is logically
+        required (whether ``pd.NA`` is True or False influences the result):
+
+        >>> pd.array([True, True, pd.NA]).all(skipna=False)
+        <NA>
+        >>> pd.array([1, 1, pd.NA]).all(skipna=False)
+        <NA>
+        >>> pd.array([True, False, pd.NA]).all(skipna=False)
+        False
+        >>> pd.array([1, 0, pd.NA]).all(skipna=False)
+        False
+        """
+        nv.validate_all((), kwargs)
+
+        values = self._data.copy()
+        # error: Argument 3 to "putmask" has incompatible type "object";
+        # expected "Union[_SupportsArray[dtype[Any]],
+        # _NestedSequence[_SupportsArray[dtype[Any]]],
+        # bool, int, float, complex, str, bytes,
+        # _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"
+        np.putmask(values, self._mask, self._truthy_value)  # type: ignore[arg-type]
+        result = values.all(axis=axis)
+
+        if skipna:
+            return result
+        else:
+            if not result or len(self) == 0 or not self._mask.any():
+                return result
+            else:
+                return self.dtype.na_value
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> FloatingArray:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if self.dtype.kind == "f":
+            if copy:
+                data = self._data.copy()
+                mask = self._mask.copy()
+            else:
+                data = self._data
+                mask = self._mask
+        elif self.dtype.kind in "iu":
+            copy = True
+            data = self._data.astype("f8")
+            mask = self._mask.copy()
+        else:
+            raise NotImplementedError(
+                f"interpolate is not implemented for dtype={self.dtype}"
+            )
+
+        missing.interpolate_2d_inplace(
+            data,
+            method=method,
+            axis=0,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            mask=mask,
+            **kwargs,
+        )
+        if not copy:
+            return self  # type: ignore[return-value]
+        if self.dtype.kind == "f":
+            return type(self)._simple_new(data, mask)  # type: ignore[return-value]
+        else:
+            from pandas.core.arrays import FloatingArray
+
+            return FloatingArray._simple_new(data, mask)
+
+    def _accumulate(
+        self, name: str, *, skipna: bool = True, **kwargs
+    ) -> BaseMaskedArray:
+        data = self._data
+        mask = self._mask
+
+        op = getattr(masked_accumulations, name)
+        data, mask = op(data, mask, skipna=skipna, **kwargs)
+
+        return self._simple_new(data, mask)
+
+    # ------------------------------------------------------------------
+    # GroupBy Methods
+
+    def _groupby_op(
+        self,
+        *,
+        how: str,
+        has_dropped_na: bool,
+        min_count: int,
+        ngroups: int,
+        ids: npt.NDArray[np.intp],
+        **kwargs,
+    ):
+        from pandas.core.groupby.ops import WrappedCythonOp
+
+        kind = WrappedCythonOp.get_kind_from_how(how)
+        op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
+
+        # libgroupby functions are responsible for NOT altering mask
+        mask = self._mask
+        if op.kind != "aggregate":
+            result_mask = mask.copy()
+        else:
+            result_mask = np.zeros(ngroups, dtype=bool)
+
+        if how == "rank" and kwargs.get("na_option") in ["top", "bottom"]:
+            result_mask[:] = False
+
+        res_values = op._cython_op_ndim_compat(
+            self._data,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=ids,
+            mask=mask,
+            result_mask=result_mask,
+            **kwargs,
+        )
+
+        if op.how == "ohlc":
+            arity = op._cython_arity.get(op.how, 1)
+            result_mask = np.tile(result_mask, (arity, 1)).T
+
+        if op.how in ["idxmin", "idxmax"]:
+            # Result values are indexes to take, keep as ndarray
+            return res_values
+        else:
+            # res_values should already have the correct dtype, we just need to
+            #  wrap in a MaskedArray
+            return self._maybe_mask_result(res_values, result_mask)
+
+
+def transpose_homogeneous_masked_arrays(
+    masked_arrays: Sequence[BaseMaskedArray],
+) -> list[BaseMaskedArray]:
+    """Transpose masked arrays in a list, but faster.
+
+    Input should be a list of 1-dim masked arrays of equal length and all have the
+    same dtype. The caller is responsible for ensuring validity of input data.
+    """
+    masked_arrays = list(masked_arrays)
+    dtype = masked_arrays[0].dtype
+
+    values = [arr._data.reshape(1, -1) for arr in masked_arrays]
+    transposed_values = np.concatenate(
+        values,
+        axis=0,
+        out=np.empty(
+            (len(masked_arrays), len(masked_arrays[0])),
+            order="F",
+            dtype=dtype.numpy_dtype,
+        ),
+    )
+
+    masks = [arr._mask.reshape(1, -1) for arr in masked_arrays]
+    transposed_masks = np.concatenate(
+        masks, axis=0, out=np.empty_like(transposed_values, dtype=bool)
+    )
+
+    arr_type = dtype.construct_array_type()
+    transposed_arrays: list[BaseMaskedArray] = []
+    for i in range(transposed_values.shape[1]):
+        transposed_arr = arr_type(transposed_values[:, i], mask=transposed_masks[:, i])
+        transposed_arrays.append(transposed_arr)
+
+    return transposed_arrays
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/numeric.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..68fa7fcb6573c6b5ec754ca65263f8ddd6a6ba74
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/numeric.py
@@ -0,0 +1,286 @@
+from __future__ import annotations
+
+import numbers
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+
+from pandas.core.arrays.masked import (
+    BaseMaskedArray,
+    BaseMaskedDtype,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping
+
+    import pyarrow
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+    )
+
+
+class NumericDtype(BaseMaskedDtype):
+    _default_np_dtype: np.dtype
+    _checker: Callable[[Any], bool]  # is_foo_dtype
+
+    def __repr__(self) -> str:
+        return f"{self.name}Dtype()"
+
+    @cache_readonly
+    def is_signed_integer(self) -> bool:
+        return self.kind == "i"
+
+    @cache_readonly
+    def is_unsigned_integer(self) -> bool:
+        return self.kind == "u"
+
+    @property
+    def _is_numeric(self) -> bool:
+        return True
+
+    def __from_arrow__(
+        self, array: pyarrow.Array | pyarrow.ChunkedArray
+    ) -> BaseMaskedArray:
+        """
+        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays.arrow._arrow_utils import (
+            pyarrow_array_to_numpy_and_mask,
+        )
+
+        array_class = self.construct_array_type()
+
+        pyarrow_type = pyarrow.from_numpy_dtype(self.type)
+        if not array.type.equals(pyarrow_type) and not pyarrow.types.is_null(
+            array.type
+        ):
+            # test_from_arrow_type_error raise for string, but allow
+            #  through itemsize conversion GH#31896
+            rt_dtype = pandas_dtype(array.type.to_pandas_dtype())
+            if rt_dtype.kind not in "iuf":
+                # Could allow "c" or potentially disallow float<->int conversion,
+                #  but at the moment we specifically test that uint<->int works
+                raise TypeError(
+                    f"Expected array of {self} type, got {array.type} instead"
+                )
+
+            array = array.cast(pyarrow_type)
+
+        if isinstance(array, pyarrow.ChunkedArray):
+            # TODO this "if" can be removed when requiring pyarrow >= 10.0, which fixed
+            # combine_chunks for empty arrays https://github.com/apache/arrow/pull/13757
+            if array.num_chunks == 0:
+                array = pyarrow.array([], type=array.type)
+            else:
+                array = array.combine_chunks()
+
+        data, mask = pyarrow_array_to_numpy_and_mask(array, dtype=self.numpy_dtype)
+        return array_class(data.copy(), ~mask, copy=False)
+
+    @classmethod
+    def _get_dtype_mapping(cls) -> Mapping[np.dtype, NumericDtype]:
+        raise AbstractMethodError(cls)
+
+    @classmethod
+    def _standardize_dtype(cls, dtype: NumericDtype | str | np.dtype) -> NumericDtype:
+        """
+        Convert a string representation or a numpy dtype to NumericDtype.
+        """
+        if isinstance(dtype, str) and (dtype.startswith(("Int", "UInt", "Float"))):
+            # Avoid DeprecationWarning from NumPy about np.dtype("Int64")
+            # https://github.com/numpy/numpy/pull/7476
+            dtype = dtype.lower()
+
+        if not isinstance(dtype, NumericDtype):
+            mapping = cls._get_dtype_mapping()
+            try:
+                dtype = mapping[np.dtype(dtype)]
+            except KeyError as err:
+                raise ValueError(f"invalid dtype specified {dtype}") from err
+        return dtype
+
+    @classmethod
+    def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
+        """
+        Safely cast the values to the given dtype.
+
+        "safe" in this context means the casting is lossless.
+        """
+        raise AbstractMethodError(cls)
+
+
+def _coerce_to_data_and_mask(
+    values, dtype, copy: bool, dtype_cls: type[NumericDtype], default_dtype: np.dtype
+):
+    checker = dtype_cls._checker
+
+    mask = None
+    inferred_type = None
+
+    if dtype is None and hasattr(values, "dtype"):
+        if checker(values.dtype):
+            dtype = values.dtype
+
+    if dtype is not None:
+        dtype = dtype_cls._standardize_dtype(dtype)
+
+    cls = dtype_cls.construct_array_type()
+    if isinstance(values, cls):
+        values, mask = values._data, values._mask
+        if dtype is not None:
+            values = values.astype(dtype.numpy_dtype, copy=False)
+
+        if copy:
+            values = values.copy()
+            mask = mask.copy()
+        return values, mask, dtype, inferred_type
+
+    original = values
+    if not copy:
+        values = np.asarray(values)
+    else:
+        values = np.array(values, copy=copy)
+    inferred_type = None
+    if values.dtype == object or is_string_dtype(values.dtype):
+        inferred_type = lib.infer_dtype(values, skipna=True)
+        if inferred_type == "boolean" and dtype is None:
+            name = dtype_cls.__name__.strip("_")
+            raise TypeError(f"{values.dtype} cannot be converted to {name}")
+
+    elif values.dtype.kind == "b" and checker(dtype):
+        if not copy:
+            values = np.asarray(values, dtype=default_dtype)
+        else:
+            values = np.array(values, dtype=default_dtype, copy=copy)
+
+    elif values.dtype.kind not in "iuf":
+        name = dtype_cls.__name__.strip("_")
+        raise TypeError(f"{values.dtype} cannot be converted to {name}")
+
+    if values.ndim != 1:
+        raise TypeError("values must be a 1D list-like")
+
+    if mask is None:
+        if values.dtype.kind in "iu":
+            # fastpath
+            mask = np.zeros(len(values), dtype=np.bool_)
+        else:
+            mask = libmissing.is_numeric_na(values)
+    else:
+        assert len(mask) == len(values)
+
+    if mask.ndim != 1:
+        raise TypeError("mask must be a 1D list-like")
+
+    # infer dtype if needed
+    if dtype is None:
+        dtype = default_dtype
+    else:
+        dtype = dtype.numpy_dtype
+
+    if is_integer_dtype(dtype) and values.dtype.kind == "f" and len(values) > 0:
+        if mask.all():
+            values = np.ones(values.shape, dtype=dtype)
+        else:
+            idx = np.nanargmax(values)
+            if int(values[idx]) != original[idx]:
+                # We have ints that lost precision during the cast.
+                inferred_type = lib.infer_dtype(original, skipna=True)
+                if (
+                    inferred_type not in ["floating", "mixed-integer-float"]
+                    and not mask.any()
+                ):
+                    values = np.asarray(original, dtype=dtype)
+                else:
+                    values = np.asarray(original, dtype="object")
+
+    # we copy as need to coerce here
+    if mask.any():
+        values = values.copy()
+        values[mask] = cls._internal_fill_value
+    if inferred_type in ("string", "unicode"):
+        # casts from str are always safe since they raise
+        # a ValueError if the str cannot be parsed into a float
+        values = values.astype(dtype, copy=copy)
+    else:
+        values = dtype_cls._safe_cast(values, dtype, copy=False)
+
+    return values, mask, dtype, inferred_type
+
+
+class NumericArray(BaseMaskedArray):
+    """
+    Base class for IntegerArray and FloatingArray.
+    """
+
+    _dtype_cls: type[NumericDtype]
+
+    def __init__(
+        self, values: np.ndarray, mask: npt.NDArray[np.bool_], copy: bool = False
+    ) -> None:
+        checker = self._dtype_cls._checker
+        if not (isinstance(values, np.ndarray) and checker(values.dtype)):
+            descr = (
+                "floating"
+                if self._dtype_cls.kind == "f"  # type: ignore[comparison-overlap]
+                else "integer"
+            )
+            raise TypeError(
+                f"values should be {descr} numpy array. Use "
+                "the 'pd.array' function instead"
+            )
+        if values.dtype == np.float16:
+            # If we don't raise here, then accessing self.dtype would raise
+            raise TypeError("FloatingArray does not support np.float16 dtype.")
+
+        super().__init__(values, mask, copy=copy)
+
+    @cache_readonly
+    def dtype(self) -> NumericDtype:
+        mapping = self._dtype_cls._get_dtype_mapping()
+        return mapping[self._data.dtype]
+
+    @classmethod
+    def _coerce_to_array(
+        cls, value, *, dtype: DtypeObj, copy: bool = False
+    ) -> tuple[np.ndarray, np.ndarray]:
+        dtype_cls = cls._dtype_cls
+        default_dtype = dtype_cls._default_np_dtype
+        values, mask, _, _ = _coerce_to_data_and_mask(
+            value, dtype, copy, dtype_cls, default_dtype
+        )
+        return values, mask
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        from pandas.core.tools.numeric import to_numeric
+
+        scalars = to_numeric(strings, errors="raise", dtype_backend="numpy_nullable")
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
+
+    _HANDLED_TYPES = (np.ndarray, numbers.Number)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/numpy_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/numpy_.py
new file mode 100644
index 0000000000000000000000000000000000000000..07fd207933fc15bc26d0b001fff434f45bf0c3f6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/numpy_.py
@@ -0,0 +1,582 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import is_supported_dtype
+from pandas.compat.numpy import function as nv
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import NumpyEADtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    arraylike,
+    missing,
+    nanops,
+    ops,
+)
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.strings.object_array import ObjectStringArrayMixin
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from pandas._typing import (
+        AxisInt,
+        Dtype,
+        FillnaOptions,
+        InterpolateOptions,
+        NpDtype,
+        Scalar,
+        Self,
+        npt,
+    )
+
+    from pandas import Index
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class NumpyExtensionArray(  # type: ignore[misc]
+    OpsMixin,
+    NDArrayBackedExtensionArray,
+    ObjectStringArrayMixin,
+):
+    """
+    A pandas ExtensionArray for NumPy data.
+
+    This is mostly for internal compatibility, and is not especially
+    useful on its own.
+
+    Parameters
+    ----------
+    values : ndarray
+        The NumPy ndarray to wrap. Must be 1-dimensional.
+    copy : bool, default False
+        Whether to copy `values`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
+    <NumpyExtensionArray>
+    [0, 1, 2, 3]
+    Length: 4, dtype: int64
+    """
+
+    # If you're wondering why pd.Series(cls) doesn't put the array in an
+    # ExtensionBlock, search for `ABCNumpyExtensionArray`. We check for
+    # that _typ to ensure that users don't unnecessarily use EAs inside
+    # pandas internals, which turns off things like block consolidation.
+    _typ = "npy_extension"
+    __array_priority__ = 1000
+    _ndarray: np.ndarray
+    _dtype: NumpyEADtype
+    _internal_fill_value = np.nan
+
+    # ------------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self, values: np.ndarray | NumpyExtensionArray, copy: bool = False
+    ) -> None:
+        if isinstance(values, type(self)):
+            values = values._ndarray
+        if not isinstance(values, np.ndarray):
+            raise ValueError(
+                f"'values' must be a NumPy array, not {type(values).__name__}"
+            )
+
+        if values.ndim == 0:
+            # Technically we support 2, but do not advertise that fact.
+            raise ValueError("NumpyExtensionArray must be 1-dimensional.")
+
+        if copy:
+            values = values.copy()
+
+        dtype = NumpyEADtype(values.dtype)
+        super().__init__(values, dtype)
+
+    @classmethod
+    def _from_sequence(
+        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> NumpyExtensionArray:
+        if isinstance(dtype, NumpyEADtype):
+            dtype = dtype._dtype
+
+        # error: Argument "dtype" to "asarray" has incompatible type
+        # "Union[ExtensionDtype, str, dtype[Any], dtype[floating[_64Bit]], Type[object],
+        # None]"; expected "Union[dtype[Any], None, type, _SupportsDType, str,
+        # Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any],
+        # _DTypeDict, Tuple[Any, Any]]]"
+        result = np.asarray(scalars, dtype=dtype)  # type: ignore[arg-type]
+        if (
+            result.ndim > 1
+            and not hasattr(scalars, "dtype")
+            and (dtype is None or dtype == object)
+        ):
+            # e.g. list-of-tuples
+            result = construct_1d_object_array_from_listlike(scalars)
+
+        if copy and result is scalars:
+            result = result.copy()
+        return cls(result)
+
+    # ------------------------------------------------------------------------
+    # Data
+
+    @property
+    def dtype(self) -> NumpyEADtype:
+        return self._dtype
+
+    # ------------------------------------------------------------------------
+    # NumPy Array Interface
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        if copy is not None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            return np.array(self._ndarray, dtype=dtype, copy=copy)
+        return np.asarray(self._ndarray, dtype=dtype)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        # Lightly modified version of
+        # https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html
+        # The primary modification is not boxing scalar return values
+        # in NumpyExtensionArray, since pandas' ExtensionArrays are 1-d.
+        out = kwargs.get("out", ())
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_ufunc_unary
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                # e.g. tests.series.test_ufunc.TestNumpyReductions
+                return result
+
+        # Defer to the implementation of the ufunc on unwrapped values.
+        inputs = tuple(
+            x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in inputs
+        )
+        if out:
+            kwargs["out"] = tuple(
+                x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in out
+            )
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+
+        if ufunc.nout > 1:
+            # multiple return values; re-box array-like results
+            return tuple(type(self)(x) for x in result)
+        elif method == "at":
+            # no return value
+            return None
+        elif method == "reduce":
+            if isinstance(result, np.ndarray):
+                # e.g. test_np_reduce_2d
+                return type(self)(result)
+
+            # e.g. test_np_max_nested_tuples
+            return result
+        else:
+            if self.dtype.type is str:  # type: ignore[comparison-overlap]
+                # StringDtype
+                try:
+                    return type(self)(result)
+                except ValueError:
+                    # if validation of input fails (no strings)
+                    # -> fallback to returning raw numpy array
+                    return result
+            # one return value; re-box array-like results
+            return type(self)(result)
+
+    # ------------------------------------------------------------------------
+    # Pandas ExtensionArray Interface
+
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        result = astype_array(self._ndarray, dtype=dtype, copy=copy)
+        return result
+
+    def isna(self) -> np.ndarray:
+        return isna(self._ndarray)
+
+    def _validate_scalar(self, fill_value):
+        if fill_value is None:
+            # Primarily for subclasses
+            fill_value = self.dtype.na_value
+        return fill_value
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, float | None]:
+        if self.dtype.kind in "iub":
+            fv = None
+        else:
+            fv = np.nan
+        return self._ndarray, fv
+
+    # Base EA class (and all other EA classes) don't have limit_area keyword
+    # This can be removed here as well when the interpolate ffill/bfill method
+    # deprecation is enforced
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        ffill or bfill along axis=0.
+        """
+        if copy:
+            out_data = self._ndarray.copy()
+        else:
+            out_data = self._ndarray
+
+        meth = missing.clean_fill_method(method)
+        missing.pad_or_backfill_inplace(
+            out_data.T,
+            method=meth,
+            axis=0,
+            limit=limit,
+            limit_area=limit_area,
+        )
+
+        if not copy:
+            return self
+        return type(self)._simple_new(out_data, dtype=self.dtype)
+
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        axis: int,
+        index: Index,
+        limit,
+        limit_direction,
+        limit_area,
+        copy: bool,
+        **kwargs,
+    ) -> Self:
+        """
+        See NDFrame.interpolate.__doc__.
+        """
+        # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
+        if not copy:
+            out_data = self._ndarray
+        else:
+            out_data = self._ndarray.copy()
+
+        # TODO: assert we have floating dtype?
+        missing.interpolate_2d_inplace(
+            out_data,
+            method=method,
+            axis=axis,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            **kwargs,
+        )
+        if not copy:
+            return self
+        return type(self)._simple_new(out_data, dtype=self.dtype)
+
+    # ------------------------------------------------------------------------
+    # Reductions
+
+    def any(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_any((), {"out": out, "keepdims": keepdims})
+        result = nanops.nanany(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def all(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_all((), {"out": out, "keepdims": keepdims})
+        result = nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def min(
+        self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs
+    ) -> Scalar:
+        nv.validate_min((), kwargs)
+        result = nanops.nanmin(
+            values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def max(
+        self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs
+    ) -> Scalar:
+        nv.validate_max((), kwargs)
+        result = nanops.nanmax(
+            values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_sum((), kwargs)
+        result = nanops.nansum(
+            self._ndarray, axis=axis, skipna=skipna, min_count=min_count
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def prod(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_prod((), kwargs)
+        result = nanops.nanprod(
+            self._ndarray, axis=axis, skipna=skipna, min_count=min_count
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def mean(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_mean((), {"dtype": dtype, "out": out, "keepdims": keepdims})
+        result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def median(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        out=None,
+        overwrite_input: bool = False,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_median(
+            (), {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims}
+        )
+        result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def std(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
+        )
+        result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        return self._wrap_reduction_result(axis, result)
+
+    def var(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="var"
+        )
+        result = nanops.nanvar(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        return self._wrap_reduction_result(axis, result)
+
+    def sem(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="sem"
+        )
+        result = nanops.nansem(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        return self._wrap_reduction_result(axis, result)
+
+    def kurt(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="kurt"
+        )
+        result = nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    def skew(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="skew"
+        )
+        result = nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)
+        return self._wrap_reduction_result(axis, result)
+
+    # ------------------------------------------------------------------------
+    # Additional Methods
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        mask = self.isna()
+        if na_value is not lib.no_default and mask.any():
+            result = self._ndarray.copy()
+            result[mask] = na_value
+        else:
+            result = self._ndarray
+
+        result = np.asarray(result, dtype=dtype)
+
+        if copy and result is self._ndarray:
+            result = result.copy()
+
+        return result
+
+    # ------------------------------------------------------------------------
+    # Ops
+
+    def __invert__(self) -> NumpyExtensionArray:
+        return type(self)(~self._ndarray)
+
+    def __neg__(self) -> NumpyExtensionArray:
+        return type(self)(-self._ndarray)
+
+    def __pos__(self) -> NumpyExtensionArray:
+        return type(self)(+self._ndarray)
+
+    def __abs__(self) -> NumpyExtensionArray:
+        return type(self)(abs(self._ndarray))
+
+    def _cmp_method(self, other, op):
+        if isinstance(other, NumpyExtensionArray):
+            other = other._ndarray
+
+        other = ops.maybe_prepare_scalar_for_op(other, (len(self),))
+        pd_op = ops.get_array_op(op)
+        other = ensure_wrapped_if_datetimelike(other)
+        result = pd_op(self._ndarray, other)
+
+        if op is divmod or op is ops.rdivmod:
+            a, b = result
+            if isinstance(a, np.ndarray):
+                # for e.g. op vs TimedeltaArray, we may already
+                #  have an ExtensionArray, in which case we do not wrap
+                return self._wrap_ndarray_result(a), self._wrap_ndarray_result(b)
+            return a, b
+
+        if isinstance(result, np.ndarray):
+            # for e.g. multiplication vs TimedeltaArray, we may already
+            #  have an ExtensionArray, in which case we do not wrap
+            return self._wrap_ndarray_result(result)
+        return result
+
+    _arith_method = _cmp_method
+
+    def _wrap_ndarray_result(self, result: np.ndarray):
+        # If we have timedelta64[ns] result, return a TimedeltaArray instead
+        #  of a NumpyExtensionArray
+        if result.dtype.kind == "m" and is_supported_dtype(result.dtype):
+            from pandas.core.arrays import TimedeltaArray
+
+            return TimedeltaArray._simple_new(result, dtype=result.dtype)
+        return type(self)(result)
+
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
+        # NEP 51: https://github.com/numpy/numpy/pull/22449
+        if self.dtype.kind in "SU":
+            return "'{}'".format
+        elif self.dtype == "object":
+            return repr
+        else:
+            return str
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/period.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/period.py
new file mode 100644
index 0000000000000000000000000000000000000000..2947ba7b8c72ac09497f796aaaec8edfb133a948
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/period.py
@@ -0,0 +1,1331 @@
+from __future__ import annotations
+
+from datetime import timedelta
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    NaTType,
+    Timedelta,
+    add_overflowsafe,
+    astype_overflowsafe,
+    dt64arr_to_periodarr as c_dt64arr_to_periodarr,
+    get_unit_from_dtype,
+    iNaT,
+    parsing,
+    period as libperiod,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import (
+    FreqGroup,
+    PeriodDtypeBase,
+    freq_to_period_freqstr,
+)
+from pandas._libs.tslibs.fields import isleapyear_arr
+from pandas._libs.tslibs.offsets import (
+    Tick,
+    delta_to_tick,
+)
+from pandas._libs.tslibs.period import (
+    DIFFERENT_FREQ,
+    IncompatibleFrequency,
+    Period,
+    get_period_field_arr,
+    period_asfreq_arr,
+)
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCPeriodIndex,
+    ABCSeries,
+    ABCTimedeltaArray,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays import datetimelike as dtl
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        AnyArrayLike,
+        Dtype,
+        FillnaOptions,
+        NpDtype,
+        NumpySorter,
+        NumpyValueArrayLike,
+        Self,
+        npt,
+    )
+
+    from pandas.core.arrays import (
+        DatetimeArray,
+        TimedeltaArray,
+    )
+    from pandas.core.arrays.base import ExtensionArray
+
+
+BaseOffsetT = TypeVar("BaseOffsetT", bound=BaseOffset)
+
+
+_shared_doc_kwargs = {
+    "klass": "PeriodArray",
+}
+
+
+def _field_accessor(name: str, docstring: str | None = None):
+    def f(self):
+        base = self.dtype._dtype_code
+        result = get_period_field_arr(name, self.asi8, base)
+        return result
+
+    f.__name__ = name
+    f.__doc__ = docstring
+    return property(f)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class PeriodArray(dtl.DatelikeOps, libperiod.PeriodMixin):  # type: ignore[misc]
+    """
+    Pandas ExtensionArray for storing Period data.
+
+    Users should use :func:`~pandas.array` to create new instances.
+
+    Parameters
+    ----------
+    values : Union[PeriodArray, Series[period], ndarray[int], PeriodIndex]
+        The data to store. These should be arrays that can be directly
+        converted to ordinals without inference or copy (PeriodArray,
+        ndarray[int64]), or a box around such an array (Series[period],
+        PeriodIndex).
+    dtype : PeriodDtype, optional
+        A PeriodDtype instance from which to extract a `freq`. If both
+        `freq` and `dtype` are specified, then the frequencies must match.
+    freq : str or DateOffset
+        The `freq` to use for the array. Mostly applicable when `values`
+        is an ndarray of integers, when `freq` is required. When `values`
+        is a PeriodArray (or box around), it's checked that ``values.freq``
+        matches `freq`.
+    copy : bool, default False
+        Whether to copy the ordinals before storing.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    Period: Represents a period of time.
+    PeriodIndex : Immutable Index for period data.
+    period_range: Create a fixed-frequency PeriodArray.
+    array: Construct a pandas array.
+
+    Notes
+    -----
+    There are two components to a PeriodArray
+
+    - ordinals : integer ndarray
+    - freq : pd.tseries.offsets.Offset
+
+    The values are physically stored as a 1-D ndarray of integers. These are
+    called "ordinals" and represent some kind of offset from a base.
+
+    The `freq` indicates the span covered by each element of the array.
+    All elements in the PeriodArray have the same `freq`.
+
+    Examples
+    --------
+    >>> pd.arrays.PeriodArray(pd.PeriodIndex(['2023-01-01',
+    ...                                       '2023-01-02'], freq='D'))
+    <PeriodArray>
+    ['2023-01-01', '2023-01-02']
+    Length: 2, dtype: period[D]
+    """
+
+    # array priority higher than numpy scalars
+    __array_priority__ = 1000
+    _typ = "periodarray"  # ABCPeriodArray
+    _internal_fill_value = np.int64(iNaT)
+    _recognized_scalars = (Period,)
+    _is_recognized_dtype = lambda x: isinstance(
+        x, PeriodDtype
+    )  # check_compatible_with checks freq match
+    _infer_matches = ("period",)
+
+    @property
+    def _scalar_type(self) -> type[Period]:
+        return Period
+
+    # Names others delegate to us
+    _other_ops: list[str] = []
+    _bool_ops: list[str] = ["is_leap_year"]
+    _object_ops: list[str] = ["start_time", "end_time", "freq"]
+    _field_ops: list[str] = [
+        "year",
+        "month",
+        "day",
+        "hour",
+        "minute",
+        "second",
+        "weekofyear",
+        "weekday",
+        "week",
+        "dayofweek",
+        "day_of_week",
+        "dayofyear",
+        "day_of_year",
+        "quarter",
+        "qyear",
+        "days_in_month",
+        "daysinmonth",
+    ]
+    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops
+    _datetimelike_methods: list[str] = ["strftime", "to_timestamp", "asfreq"]
+
+    _dtype: PeriodDtype
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self, values, dtype: Dtype | None = None, freq=None, copy: bool = False
+    ) -> None:
+        if freq is not None:
+            # GH#52462
+            warnings.warn(
+                "The 'freq' keyword in the PeriodArray constructor is deprecated "
+                "and will be removed in a future version. Pass 'dtype' instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            freq = validate_dtype_freq(dtype, freq)
+            dtype = PeriodDtype(freq)
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+            if not isinstance(dtype, PeriodDtype):
+                raise ValueError(f"Invalid dtype {dtype} for PeriodArray")
+
+        if isinstance(values, ABCSeries):
+            values = values._values
+            if not isinstance(values, type(self)):
+                raise TypeError("Incorrect dtype")
+
+        elif isinstance(values, ABCPeriodIndex):
+            values = values._values
+
+        if isinstance(values, type(self)):
+            if dtype is not None and dtype != values.dtype:
+                raise raise_on_incompatible(values, dtype.freq)
+            values, dtype = values._ndarray, values.dtype
+
+        if not copy:
+            values = np.asarray(values, dtype="int64")
+        else:
+            values = np.array(values, dtype="int64", copy=copy)
+        if dtype is None:
+            raise ValueError("dtype is not specified and cannot be inferred")
+        dtype = cast(PeriodDtype, dtype)
+        NDArrayBacked.__init__(self, values, dtype)
+
+    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls,
+        values: npt.NDArray[np.int64],
+        dtype: PeriodDtype,
+    ) -> Self:
+        # alias for PeriodArray.__init__
+        assertion_msg = "Should be numpy array of type i8"
+        assert isinstance(values, np.ndarray) and values.dtype == "i8", assertion_msg
+        return cls(values, dtype=dtype)
+
+    @classmethod
+    def _from_sequence(
+        cls,
+        scalars,
+        *,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+    ) -> Self:
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+        if dtype and isinstance(dtype, PeriodDtype):
+            freq = dtype.freq
+        else:
+            freq = None
+
+        if isinstance(scalars, cls):
+            validate_dtype_freq(scalars.dtype, freq)
+            if copy:
+                scalars = scalars.copy()
+            return scalars
+
+        periods = np.asarray(scalars, dtype=object)
+
+        freq = freq or libperiod.extract_freq(periods)
+        ordinals = libperiod.extract_ordinals(periods, freq)
+        dtype = PeriodDtype(freq)
+        return cls(ordinals, dtype=dtype)
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        return cls._from_sequence(strings, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_datetime64(cls, data, freq, tz=None) -> Self:
+        """
+        Construct a PeriodArray from a datetime64 array
+
+        Parameters
+        ----------
+        data : ndarray[datetime64[ns], datetime64[ns, tz]]
+        freq : str or Tick
+        tz : tzinfo, optional
+
+        Returns
+        -------
+        PeriodArray[freq]
+        """
+        if isinstance(freq, BaseOffset):
+            freq = freq_to_period_freqstr(freq.n, freq.name)
+        data, freq = dt64arr_to_periodarr(data, freq, tz)
+        dtype = PeriodDtype(freq)
+        return cls(data, dtype=dtype)
+
+    @classmethod
+    def _generate_range(cls, start, end, periods, freq):
+        periods = dtl.validate_periods(periods)
+
+        if freq is not None:
+            freq = Period._maybe_convert_freq(freq)
+
+        if start is not None or end is not None:
+            subarr, freq = _get_ordinal_range(start, end, periods, freq)
+        else:
+            raise ValueError("Not enough parameters to construct Period range")
+
+        return subarr, freq
+
+    @classmethod
+    def _from_fields(cls, *, fields: dict, freq) -> Self:
+        subarr, freq = _range_from_fields(freq=freq, **fields)
+        dtype = PeriodDtype(freq)
+        return cls._simple_new(subarr, dtype=dtype)
+
+    # -----------------------------------------------------------------
+    # DatetimeLike Interface
+
+    # error: Argument 1 of "_unbox_scalar" is incompatible with supertype
+    # "DatetimeLikeArrayMixin"; supertype defines the argument type as
+    # "Union[Union[Period, Any, Timedelta], NaTType]"
+    def _unbox_scalar(  # type: ignore[override]
+        self,
+        value: Period | NaTType,
+    ) -> np.int64:
+        if value is NaT:
+            # error: Item "Period" of "Union[Period, NaTType]" has no attribute "value"
+            return np.int64(value._value)  # type: ignore[union-attr]
+        elif isinstance(value, self._scalar_type):
+            self._check_compatible_with(value)
+            return np.int64(value.ordinal)
+        else:
+            raise ValueError(f"'value' should be a Period. Got '{value}' instead.")
+
+    def _scalar_from_string(self, value: str) -> Period:
+        return Period(value, freq=self.freq)
+
+    # error: Argument 1 of "_check_compatible_with" is incompatible with
+    # supertype "DatetimeLikeArrayMixin"; supertype defines the argument type
+    # as "Period | Timestamp | Timedelta | NaTType"
+    def _check_compatible_with(self, other: Period | NaTType | PeriodArray) -> None:  # type: ignore[override]
+        if other is NaT:
+            return
+        # error: Item "NaTType" of "Period | NaTType | PeriodArray" has no
+        # attribute "freq"
+        self._require_matching_freq(other.freq)  # type: ignore[union-attr]
+
+    # --------------------------------------------------------------------
+    # Data / Attributes
+
+    @cache_readonly
+    def dtype(self) -> PeriodDtype:
+        return self._dtype
+
+    # error: Cannot override writeable attribute with read-only property
+    @property  # type: ignore[override]
+    def freq(self) -> BaseOffset:
+        """
+        Return the frequency object for this PeriodArray.
+        """
+        return self.dtype.freq
+
+    @property
+    def freqstr(self) -> str:
+        return freq_to_period_freqstr(self.freq.n, self.freq.name)
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        if dtype == "i8":
+            # For NumPy 1.x compatibility we cannot use copy=None.  And
+            # `copy=False` has the meaning of `copy=None` here:
+            if not copy:
+                return np.asarray(self.asi8, dtype=dtype)
+            else:
+                return np.array(self.asi8, dtype=dtype)
+
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if dtype == bool:
+            return ~self._isnan
+
+        # This will raise TypeError for non-object dtypes
+        return np.array(list(self), dtype=object)
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow
+
+        from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
+
+        if type is not None:
+            if pyarrow.types.is_integer(type):
+                return pyarrow.array(self._ndarray, mask=self.isna(), type=type)
+            elif isinstance(type, ArrowPeriodType):
+                # ensure we have the same freq
+                if self.freqstr != type.freq:
+                    raise TypeError(
+                        "Not supported to convert PeriodArray to array with different "
+                        f"'freq' ({self.freqstr} vs {type.freq})"
+                    )
+            else:
+                raise TypeError(
+                    f"Not supported to convert PeriodArray to '{type}' type"
+                )
+
+        period_type = ArrowPeriodType(self.freqstr)
+        storage_array = pyarrow.array(self._ndarray, mask=self.isna(), type="int64")
+        return pyarrow.ExtensionArray.from_storage(period_type, storage_array)
+
+    # --------------------------------------------------------------------
+    # Vectorized analogues of Period properties
+
+    year = _field_accessor(
+        "year",
+        """
+        The year of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
+        >>> idx.year
+        Index([2023, 2024, 2025], dtype='int64')
+        """,
+    )
+    month = _field_accessor(
+        "month",
+        """
+        The month as January=1, December=12.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.month
+        Index([1, 2, 3], dtype='int64')
+        """,
+    )
+    day = _field_accessor(
+        "day",
+        """
+        The days of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(['2020-01-31', '2020-02-28'], freq='D')
+        >>> idx.day
+        Index([31, 28], dtype='int64')
+        """,
+    )
+    hour = _field_accessor(
+        "hour",
+        """
+        The hour of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01 10:00", "2023-01-01 11:00"], freq='h')
+        >>> idx.hour
+        Index([10, 11], dtype='int64')
+        """,
+    )
+    minute = _field_accessor(
+        "minute",
+        """
+        The minute of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01 10:30:00",
+        ...                       "2023-01-01 11:50:00"], freq='min')
+        >>> idx.minute
+        Index([30, 50], dtype='int64')
+        """,
+    )
+    second = _field_accessor(
+        "second",
+        """
+        The second of the period.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01 10:00:30",
+        ...                       "2023-01-01 10:00:31"], freq='s')
+        >>> idx.second
+        Index([30, 31], dtype='int64')
+        """,
+    )
+    weekofyear = _field_accessor(
+        "week",
+        """
+        The week ordinal of the year.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.week  # It can be written `weekofyear`
+        Index([5, 9, 13], dtype='int64')
+        """,
+    )
+    week = weekofyear
+    day_of_week = _field_accessor(
+        "day_of_week",
+        """
+        The day of the week with Monday=0, Sunday=6.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-01", "2023-01-02", "2023-01-03"], freq="D")
+        >>> idx.weekday
+        Index([6, 0, 1], dtype='int64')
+        """,
+    )
+    dayofweek = day_of_week
+    weekday = dayofweek
+    dayofyear = day_of_year = _field_accessor(
+        "day_of_year",
+        """
+        The ordinal day of the year.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01-10", "2023-02-01", "2023-03-01"], freq="D")
+        >>> idx.dayofyear
+        Index([10, 32, 60], dtype='int64')
+
+        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
+        >>> idx
+        PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
+        >>> idx.dayofyear
+        Index([365, 366, 365], dtype='int64')
+        """,
+    )
+    quarter = _field_accessor(
+        "quarter",
+        """
+        The quarter of the date.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.quarter
+        Index([1, 1, 1], dtype='int64')
+        """,
+    )
+    qyear = _field_accessor("qyear")
+    days_in_month = _field_accessor(
+        "days_in_month",
+        """
+        The number of days in the month.
+
+        Examples
+        --------
+        For Series:
+
+        >>> period = pd.period_range('2020-1-1 00:00', '2020-3-1 00:00', freq='M')
+        >>> s = pd.Series(period)
+        >>> s
+        0   2020-01
+        1   2020-02
+        2   2020-03
+        dtype: period[M]
+        >>> s.dt.days_in_month
+        0    31
+        1    29
+        2    31
+        dtype: int64
+
+        For PeriodIndex:
+
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.days_in_month   # It can be also entered as `daysinmonth`
+        Index([31, 28, 31], dtype='int64')
+        """,
+    )
+    daysinmonth = days_in_month
+
+    @property
+    def is_leap_year(self) -> npt.NDArray[np.bool_]:
+        """
+        Logical indicating if the date belongs to a leap year.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
+        >>> idx.is_leap_year
+        array([False,  True, False])
+        """
+        return isleapyear_arr(np.asarray(self.year))
+
+    def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray:
+        """
+        Cast to DatetimeArray/Index.
+
+        Parameters
+        ----------
+        freq : str or DateOffset, optional
+            Target frequency. The default is 'D' for week or longer,
+            's' otherwise.
+        how : {'s', 'e', 'start', 'end'}
+            Whether to use the start or end of the time period being converted.
+
+        Returns
+        -------
+        DatetimeArray/Index
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
+        >>> idx.to_timestamp()
+        DatetimeIndex(['2023-01-01', '2023-02-01', '2023-03-01'],
+        dtype='datetime64[ns]', freq='MS')
+        """
+        from pandas.core.arrays import DatetimeArray
+
+        how = libperiod.validate_end_alias(how)
+
+        end = how == "E"
+        if end:
+            if freq == "B" or self.freq == "B":
+                # roll forward to ensure we land on B date
+                adjust = Timedelta(1, "D") - Timedelta(1, "ns")
+                return self.to_timestamp(how="start") + adjust
+            else:
+                adjust = Timedelta(1, "ns")
+                return (self + self.freq).to_timestamp(how="start") - adjust
+
+        if freq is None:
+            freq_code = self._dtype._get_to_timestamp_base()
+            dtype = PeriodDtypeBase(freq_code, 1)
+            freq = dtype._freqstr
+            base = freq_code
+        else:
+            freq = Period._maybe_convert_freq(freq)
+            base = freq._period_dtype_code
+
+        new_parr = self.asfreq(freq, how=how)
+
+        new_data = libperiod.periodarr_to_dt64arr(new_parr.asi8, base)
+        dta = DatetimeArray._from_sequence(new_data)
+
+        if self.freq.name == "B":
+            # See if we can retain BDay instead of Day in cases where
+            #  len(self) is too small for infer_freq to distinguish between them
+            diffs = libalgos.unique_deltas(self.asi8)
+            if len(diffs) == 1:
+                diff = diffs[0]
+                if diff == self.dtype._n:
+                    dta._freq = self.freq
+                elif diff == 1:
+                    dta._freq = self.freq.base
+                # TODO: other cases?
+            return dta
+        else:
+            return dta._with_freq("infer")
+
+    # --------------------------------------------------------------------
+
+    def _box_func(self, x) -> Period | NaTType:
+        return Period._from_ordinal(ordinal=x, freq=self.freq)
+
+    @doc(**_shared_doc_kwargs, other="PeriodIndex", other_name="PeriodIndex")
+    def asfreq(self, freq=None, how: str = "E") -> Self:
+        """
+        Convert the {klass} to the specified frequency `freq`.
+
+        Equivalent to applying :meth:`pandas.Period.asfreq` with the given arguments
+        to each :class:`~pandas.Period` in this {klass}.
+
+        Parameters
+        ----------
+        freq : str
+            A frequency.
+        how : str {{'E', 'S'}}, default 'E'
+            Whether the elements should be aligned to the end
+            or start within pa period.
+
+            * 'E', 'END', or 'FINISH' for end,
+            * 'S', 'START', or 'BEGIN' for start.
+
+            January 31st ('END') vs. January 1st ('START') for example.
+
+        Returns
+        -------
+        {klass}
+            The transformed {klass} with the new frequency.
+
+        See Also
+        --------
+        {other}.asfreq: Convert each Period in a {other_name} to the given frequency.
+        Period.asfreq : Convert a :class:`~pandas.Period` object to the given frequency.
+
+        Examples
+        --------
+        >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='Y')
+        >>> pidx
+        PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
+        dtype='period[Y-DEC]')
+
+        >>> pidx.asfreq('M')
+        PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
+        '2015-12'], dtype='period[M]')
+
+        >>> pidx.asfreq('M', how='S')
+        PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
+        '2015-01'], dtype='period[M]')
+        """
+        how = libperiod.validate_end_alias(how)
+        if isinstance(freq, BaseOffset) and hasattr(freq, "_period_dtype_code"):
+            freq = PeriodDtype(freq)._freqstr
+        freq = Period._maybe_convert_freq(freq)
+
+        base1 = self._dtype._dtype_code
+        base2 = freq._period_dtype_code
+
+        asi8 = self.asi8
+        # self.freq.n can't be negative or 0
+        end = how == "E"
+        if end:
+            ordinal = asi8 + self.dtype._n - 1
+        else:
+            ordinal = asi8
+
+        new_data = period_asfreq_arr(ordinal, base1, base2, end)
+
+        if self._hasna:
+            new_data[self._isnan] = iNaT
+
+        dtype = PeriodDtype(freq)
+        return type(self)(new_data, dtype=dtype)
+
+    # ------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        if boxed:
+            return str
+        return "'{}'".format
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
+    ) -> npt.NDArray[np.object_]:
+        """
+        actually format my specific types
+        """
+        return libperiod.period_array_strftime(
+            self.asi8, self.dtype._dtype_code, na_rep, date_format
+        )
+
+    # ------------------------------------------------------------------
+
+    def astype(self, dtype, copy: bool = True):
+        # We handle Period[T] -> Period[U]
+        # Our parent handles everything else.
+        dtype = pandas_dtype(dtype)
+        if dtype == self._dtype:
+            if not copy:
+                return self
+            else:
+                return self.copy()
+        if isinstance(dtype, PeriodDtype):
+            return self.asfreq(dtype.freq)
+
+        if lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
+            # GH#45038 match PeriodIndex behavior.
+            tz = getattr(dtype, "tz", None)
+            unit = dtl.dtype_to_unit(dtype)
+            return self.to_timestamp().tz_localize(tz).as_unit(unit)
+
+        return super().astype(dtype, copy=copy)
+
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        npvalue = self._validate_setitem_value(value).view("M8[ns]")
+
+        # Cast to M8 to get datetime-like NaT placement,
+        #  similar to dtl._period_dispatch
+        m8arr = self._ndarray.view("M8[ns]")
+        return m8arr.searchsorted(npvalue, side=side, sorter=sorter)
+
+    def _pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        # view as dt64 so we get treated as timelike in core.missing,
+        #  similar to dtl._period_dispatch
+        dta = self.view("M8[ns]")
+        result = dta._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+        if copy:
+            return cast("Self", result.view(self.dtype))
+        else:
+            return self
+
+    def fillna(
+        self, value=None, method=None, limit: int | None = None, copy: bool = True
+    ) -> Self:
+        if method is not None:
+            # view as dt64 so we get treated as timelike in core.missing,
+            #  similar to dtl._period_dispatch
+            dta = self.view("M8[ns]")
+            result = dta.fillna(value=value, method=method, limit=limit, copy=copy)
+            # error: Incompatible return value type (got "Union[ExtensionArray,
+            # ndarray[Any, Any]]", expected "PeriodArray")
+            return result.view(self.dtype)  # type: ignore[return-value]
+        return super().fillna(value=value, method=method, limit=limit, copy=copy)
+
+    # ------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _addsub_int_array_or_scalar(
+        self, other: np.ndarray | int, op: Callable[[Any, Any], Any]
+    ) -> Self:
+        """
+        Add or subtract array of integers.
+
+        Parameters
+        ----------
+        other : np.ndarray[int64] or int
+        op : {operator.add, operator.sub}
+
+        Returns
+        -------
+        result : PeriodArray
+        """
+        assert op in [operator.add, operator.sub]
+        if op is operator.sub:
+            other = -other
+        res_values = add_overflowsafe(self.asi8, np.asarray(other, dtype="i8"))
+        return type(self)(res_values, dtype=self.dtype)
+
+    def _add_offset(self, other: BaseOffset):
+        assert not isinstance(other, Tick)
+
+        self._require_matching_freq(other, base=True)
+        return self._addsub_int_array_or_scalar(other.n, operator.add)
+
+    # TODO: can we de-duplicate with Period._add_timedeltalike_scalar?
+    def _add_timedeltalike_scalar(self, other):
+        """
+        Parameters
+        ----------
+        other : timedelta, Tick, np.timedelta64
+
+        Returns
+        -------
+        PeriodArray
+        """
+        if not isinstance(self.freq, Tick):
+            # We cannot add timedelta-like to non-tick PeriodArray
+            raise raise_on_incompatible(self, other)
+
+        if isna(other):
+            # i.e. np.timedelta64("NaT")
+            return super()._add_timedeltalike_scalar(other)
+
+        td = np.asarray(Timedelta(other).asm8)
+        return self._add_timedelta_arraylike(td)
+
+    def _add_timedelta_arraylike(
+        self, other: TimedeltaArray | npt.NDArray[np.timedelta64]
+    ) -> Self:
+        """
+        Parameters
+        ----------
+        other : TimedeltaArray or ndarray[timedelta64]
+
+        Returns
+        -------
+        PeriodArray
+        """
+        if not self.dtype._is_tick_like():
+            # We cannot add timedelta-like to non-tick PeriodArray
+            raise TypeError(
+                f"Cannot add or subtract timedelta64[ns] dtype from {self.dtype}"
+            )
+
+        dtype = np.dtype(f"m8[{self.dtype._td64_unit}]")
+
+        # Similar to _check_timedeltalike_freq_compat, but we raise with a
+        #  more specific exception message if necessary.
+        try:
+            delta = astype_overflowsafe(
+                np.asarray(other), dtype=dtype, copy=False, round_ok=False
+            )
+        except ValueError as err:
+            # e.g. if we have minutes freq and try to add 30s
+            # "Cannot losslessly convert units"
+            raise IncompatibleFrequency(
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq."
+            ) from err
+
+        res_values = add_overflowsafe(self.asi8, np.asarray(delta.view("i8")))
+        return type(self)(res_values, dtype=self.dtype)
+
+    def _check_timedeltalike_freq_compat(self, other):
+        """
+        Arithmetic operations with timedelta-like scalars or array `other`
+        are only valid if `other` is an integer multiple of `self.freq`.
+        If the operation is valid, find that integer multiple.  Otherwise,
+        raise because the operation is invalid.
+
+        Parameters
+        ----------
+        other : timedelta, np.timedelta64, Tick,
+                ndarray[timedelta64], TimedeltaArray, TimedeltaIndex
+
+        Returns
+        -------
+        multiple : int or ndarray[int64]
+
+        Raises
+        ------
+        IncompatibleFrequency
+        """
+        assert self.dtype._is_tick_like()  # checked by calling function
+
+        dtype = np.dtype(f"m8[{self.dtype._td64_unit}]")
+
+        if isinstance(other, (timedelta, np.timedelta64, Tick)):
+            td = np.asarray(Timedelta(other).asm8)
+        else:
+            td = np.asarray(other)
+
+        try:
+            delta = astype_overflowsafe(td, dtype=dtype, copy=False, round_ok=False)
+        except ValueError as err:
+            raise raise_on_incompatible(self, other) from err
+
+        delta = delta.view("i8")
+        return lib.item_from_zerodim(delta)
+
+
+def raise_on_incompatible(left, right) -> IncompatibleFrequency:
+    """
+    Helper function to render a consistent error message when raising
+    IncompatibleFrequency.
+
+    Parameters
+    ----------
+    left : PeriodArray
+    right : None, DateOffset, Period, ndarray, or timedelta-like
+
+    Returns
+    -------
+    IncompatibleFrequency
+        Exception to be raised by the caller.
+    """
+    # GH#24283 error message format depends on whether right is scalar
+    if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None:
+        other_freq = None
+    elif isinstance(right, BaseOffset):
+        other_freq = freq_to_period_freqstr(right.n, right.name)
+    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period)):
+        other_freq = right.freqstr
+    else:
+        other_freq = delta_to_tick(Timedelta(right)).freqstr
+
+    own_freq = freq_to_period_freqstr(left.freq.n, left.freq.name)
+    msg = DIFFERENT_FREQ.format(
+        cls=type(left).__name__, own_freq=own_freq, other_freq=other_freq
+    )
+    return IncompatibleFrequency(msg)
+
+
+# -------------------------------------------------------------------
+# Constructor Helpers
+
+
+def period_array(
+    data: Sequence[Period | str | None] | AnyArrayLike,
+    freq: str | Tick | BaseOffset | None = None,
+    copy: bool = False,
+) -> PeriodArray:
+    """
+    Construct a new PeriodArray from a sequence of Period scalars.
+
+    Parameters
+    ----------
+    data : Sequence of Period objects
+        A sequence of Period objects. These are required to all have
+        the same ``freq.`` Missing values can be indicated by ``None``
+        or ``pandas.NaT``.
+    freq : str, Tick, or Offset
+        The frequency of every element of the array. This can be specified
+        to avoid inferring the `freq` from `data`.
+    copy : bool, default False
+        Whether to ensure a copy of the data is made.
+
+    Returns
+    -------
+    PeriodArray
+
+    See Also
+    --------
+    PeriodArray
+    pandas.PeriodIndex
+
+    Examples
+    --------
+    >>> period_array([pd.Period('2017', freq='Y'),
+    ...               pd.Period('2018', freq='Y')])
+    <PeriodArray>
+    ['2017', '2018']
+    Length: 2, dtype: period[Y-DEC]
+
+    >>> period_array([pd.Period('2017', freq='Y'),
+    ...               pd.Period('2018', freq='Y'),
+    ...               pd.NaT])
+    <PeriodArray>
+    ['2017', '2018', 'NaT']
+    Length: 3, dtype: period[Y-DEC]
+
+    Integers that look like years are handled
+
+    >>> period_array([2000, 2001, 2002], freq='D')
+    <PeriodArray>
+    ['2000-01-01', '2001-01-01', '2002-01-01']
+    Length: 3, dtype: period[D]
+
+    Datetime-like strings may also be passed
+
+    >>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
+    <PeriodArray>
+    ['2000Q1', '2000Q2', '2000Q3', '2000Q4']
+    Length: 4, dtype: period[Q-DEC]
+    """
+    data_dtype = getattr(data, "dtype", None)
+
+    if lib.is_np_dtype(data_dtype, "M"):
+        return PeriodArray._from_datetime64(data, freq)
+    if isinstance(data_dtype, PeriodDtype):
+        out = PeriodArray(data)
+        if freq is not None:
+            if freq == data_dtype.freq:
+                return out
+            return out.asfreq(freq)
+        return out
+
+    # other iterable of some kind
+    if not isinstance(data, (np.ndarray, list, tuple, ABCSeries)):
+        data = list(data)
+
+    arrdata = np.asarray(data)
+
+    dtype: PeriodDtype | None
+    if freq:
+        dtype = PeriodDtype(freq)
+    else:
+        dtype = None
+
+    if arrdata.dtype.kind == "f" and len(arrdata) > 0:
+        raise TypeError("PeriodIndex does not allow floating point in construction")
+
+    if arrdata.dtype.kind in "iu":
+        arr = arrdata.astype(np.int64, copy=False)
+        # error: Argument 2 to "from_ordinals" has incompatible type "Union[str,
+        # Tick, None]"; expected "Union[timedelta, BaseOffset, str]"
+        ordinals = libperiod.from_ordinals(arr, freq)  # type: ignore[arg-type]
+        return PeriodArray(ordinals, dtype=dtype)
+
+    data = ensure_object(arrdata)
+    if freq is None:
+        freq = libperiod.extract_freq(data)
+    dtype = PeriodDtype(freq)
+    return PeriodArray._from_sequence(data, dtype=dtype)
+
+
+@overload
+def validate_dtype_freq(dtype, freq: BaseOffsetT) -> BaseOffsetT:
+    ...
+
+
+@overload
+def validate_dtype_freq(dtype, freq: timedelta | str | None) -> BaseOffset:
+    ...
+
+
+def validate_dtype_freq(
+    dtype, freq: BaseOffsetT | BaseOffset | timedelta | str | None
+) -> BaseOffsetT:
+    """
+    If both a dtype and a freq are available, ensure they match.  If only
+    dtype is available, extract the implied freq.
+
+    Parameters
+    ----------
+    dtype : dtype
+    freq : DateOffset or None
+
+    Returns
+    -------
+    freq : DateOffset
+
+    Raises
+    ------
+    ValueError : non-period dtype
+    IncompatibleFrequency : mismatch between dtype and freq
+    """
+    if freq is not None:
+        freq = to_offset(freq, is_period=True)
+
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+        if not isinstance(dtype, PeriodDtype):
+            raise ValueError("dtype must be PeriodDtype")
+        if freq is None:
+            freq = dtype.freq
+        elif freq != dtype.freq:
+            raise IncompatibleFrequency("specified freq and dtype are different")
+    # error: Incompatible return value type (got "Union[BaseOffset, Any, None]",
+    # expected "BaseOffset")
+    return freq  # type: ignore[return-value]
+
+
+def dt64arr_to_periodarr(
+    data, freq, tz=None
+) -> tuple[npt.NDArray[np.int64], BaseOffset]:
+    """
+    Convert an datetime-like array to values Period ordinals.
+
+    Parameters
+    ----------
+    data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
+    freq : Optional[Union[str, Tick]]
+        Must match the `freq` on the `data` if `data` is a DatetimeIndex
+        or Series.
+    tz : Optional[tzinfo]
+
+    Returns
+    -------
+    ordinals : ndarray[int64]
+    freq : Tick
+        The frequency extracted from the Series or DatetimeIndex if that's
+        used.
+
+    """
+    if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M":
+        raise ValueError(f"Wrong dtype: {data.dtype}")
+
+    if freq is None:
+        if isinstance(data, ABCIndex):
+            data, freq = data._values, data.freq
+        elif isinstance(data, ABCSeries):
+            data, freq = data._values, data.dt.freq
+
+    elif isinstance(data, (ABCIndex, ABCSeries)):
+        data = data._values
+
+    reso = get_unit_from_dtype(data.dtype)
+    freq = Period._maybe_convert_freq(freq)
+    base = freq._period_dtype_code
+    return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq
+
+
+def _get_ordinal_range(start, end, periods, freq, mult: int = 1):
+    if com.count_not_none(start, end, periods) != 2:
+        raise ValueError(
+            "Of the three parameters: start, end, and periods, "
+            "exactly two must be specified"
+        )
+
+    if freq is not None:
+        freq = to_offset(freq, is_period=True)
+        mult = freq.n
+
+    if start is not None:
+        start = Period(start, freq)
+    if end is not None:
+        end = Period(end, freq)
+
+    is_start_per = isinstance(start, Period)
+    is_end_per = isinstance(end, Period)
+
+    if is_start_per and is_end_per and start.freq != end.freq:
+        raise ValueError("start and end must have same freq")
+    if start is NaT or end is NaT:
+        raise ValueError("start and end must not be NaT")
+
+    if freq is None:
+        if is_start_per:
+            freq = start.freq
+        elif is_end_per:
+            freq = end.freq
+        else:  # pragma: no cover
+            raise ValueError("Could not infer freq from start/end")
+        mult = freq.n
+
+    if periods is not None:
+        periods = periods * mult
+        if start is None:
+            data = np.arange(
+                end.ordinal - periods + mult, end.ordinal + 1, mult, dtype=np.int64
+            )
+        else:
+            data = np.arange(
+                start.ordinal, start.ordinal + periods, mult, dtype=np.int64
+            )
+    else:
+        data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64)
+
+    return data, freq
+
+
+def _range_from_fields(
+    year=None,
+    month=None,
+    quarter=None,
+    day=None,
+    hour=None,
+    minute=None,
+    second=None,
+    freq=None,
+) -> tuple[np.ndarray, BaseOffset]:
+    if hour is None:
+        hour = 0
+    if minute is None:
+        minute = 0
+    if second is None:
+        second = 0
+    if day is None:
+        day = 1
+
+    ordinals = []
+
+    if quarter is not None:
+        if freq is None:
+            freq = to_offset("Q", is_period=True)
+            base = FreqGroup.FR_QTR.value
+        else:
+            freq = to_offset(freq, is_period=True)
+            base = libperiod.freq_to_dtype_code(freq)
+            if base != FreqGroup.FR_QTR.value:
+                raise AssertionError("base must equal FR_QTR")
+
+        freqstr = freq.freqstr
+        year, quarter = _make_field_arrays(year, quarter)
+        for y, q in zip(year, quarter):
+            calendar_year, calendar_month = parsing.quarter_to_myear(y, q, freqstr)
+            val = libperiod.period_ordinal(
+                calendar_year, calendar_month, 1, 1, 1, 1, 0, 0, base
+            )
+            ordinals.append(val)
+    else:
+        freq = to_offset(freq, is_period=True)
+        base = libperiod.freq_to_dtype_code(freq)
+        arrays = _make_field_arrays(year, month, day, hour, minute, second)
+        for y, mth, d, h, mn, s in zip(*arrays):
+            ordinals.append(libperiod.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))
+
+    return np.array(ordinals, dtype=np.int64), freq
+
+
+def _make_field_arrays(*fields) -> list[np.ndarray]:
+    length = None
+    for x in fields:
+        if isinstance(x, (list, np.ndarray, ABCSeries)):
+            if length is not None and len(x) != length:
+                raise ValueError("Mismatched Period array lengths")
+            if length is None:
+                length = len(x)
+
+    # error: Argument 2 to "repeat" has incompatible type "Optional[int]"; expected
+    # "Union[Union[int, integer[Any]], Union[bool, bool_], ndarray, Sequence[Union[int,
+    # integer[Any]]], Sequence[Union[bool, bool_]], Sequence[Sequence[Any]]]"
+    return [
+        np.asarray(x)
+        if isinstance(x, (np.ndarray, list, ABCSeries))
+        else np.repeat(x, length)  # type: ignore[arg-type]
+        for x in fields
+    ]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..adf83963aca39e7d2ec2da55d21fc69aaca48977
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/__init__.py
@@ -0,0 +1,19 @@
+from pandas.core.arrays.sparse.accessor import (
+    SparseAccessor,
+    SparseFrameAccessor,
+)
+from pandas.core.arrays.sparse.array import (
+    BlockIndex,
+    IntIndex,
+    SparseArray,
+    make_sparse_index,
+)
+
+__all__ = [
+    "BlockIndex",
+    "IntIndex",
+    "make_sparse_index",
+    "SparseAccessor",
+    "SparseArray",
+    "SparseFrameAccessor",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..67bb41786547501c0cd7187e5ca6be8393b85ced
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/accessor.py
@@ -0,0 +1,414 @@
+"""Sparse accessor"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.dtypes import SparseDtype
+
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.arrays.sparse.array import SparseArray
+
+if TYPE_CHECKING:
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class BaseAccessor:
+    _validation_msg = "Can only use the '.sparse' accessor with Sparse data."
+
+    def __init__(self, data=None) -> None:
+        self._parent = data
+        self._validate(data)
+
+    def _validate(self, data):
+        raise NotImplementedError
+
+
+@delegate_names(
+    SparseArray, ["npoints", "density", "fill_value", "sp_values"], typ="property"
+)
+class SparseAccessor(BaseAccessor, PandasDelegate):
+    """
+    Accessor for SparseSparse from other sparse matrix data types.
+
+    Examples
+    --------
+    >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
+    >>> ser.sparse.density
+    0.6
+    >>> ser.sparse.sp_values
+    array([2, 2, 2])
+    """
+
+    def _validate(self, data):
+        if not isinstance(data.dtype, SparseDtype):
+            raise AttributeError(self._validation_msg)
+
+    def _delegate_property_get(self, name: str, *args, **kwargs):
+        return getattr(self._parent.array, name)
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        if name == "from_coo":
+            return self.from_coo(*args, **kwargs)
+        elif name == "to_coo":
+            return self.to_coo(*args, **kwargs)
+        else:
+            raise ValueError
+
+    @classmethod
+    def from_coo(cls, A, dense_index: bool = False) -> Series:
+        """
+        Create a Series with sparse values from a scipy.sparse.coo_matrix.
+
+        Parameters
+        ----------
+        A : scipy.sparse.coo_matrix
+        dense_index : bool, default False
+            If False (default), the index consists of only the
+            coords of the non-null entries of the original coo_matrix.
+            If True, the index consists of the full sorted
+            (row, col) coordinates of the coo_matrix.
+
+        Returns
+        -------
+        s : Series
+            A Series with sparse values.
+
+        Examples
+        --------
+        >>> from scipy import sparse
+
+        >>> A = sparse.coo_matrix(
+        ...     ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
+        ... )
+        >>> A
+        <COOrdinate sparse matrix of dtype 'float64'
+            with 3 stored elements and shape (3, 4)>
+
+        >>> A.todense()
+        matrix([[0., 0., 1., 2.],
+        [3., 0., 0., 0.],
+        [0., 0., 0., 0.]])
+
+        >>> ss = pd.Series.sparse.from_coo(A)
+        >>> ss
+        0  2    1.0
+           3    2.0
+        1  0    3.0
+        dtype: Sparse[float64, nan]
+        """
+        from pandas import Series
+        from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series
+
+        result = coo_to_sparse_series(A, dense_index=dense_index)
+        result = Series(result.array, index=result.index, copy=False)
+
+        return result
+
+    def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False):
+        """
+        Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
+
+        Use row_levels and column_levels to determine the row and column
+        coordinates respectively. row_levels and column_levels are the names
+        (labels) or numbers of the levels. {row_levels, column_levels} must be
+        a partition of the MultiIndex level names (or numbers).
+
+        Parameters
+        ----------
+        row_levels : tuple/list
+        column_levels : tuple/list
+        sort_labels : bool, default False
+            Sort the row and column labels before forming the sparse matrix.
+            When `row_levels` and/or `column_levels` refer to a single level,
+            set to `True` for a faster execution.
+
+        Returns
+        -------
+        y : scipy.sparse.coo_matrix
+        rows : list (row labels)
+        columns : list (column labels)
+
+        Examples
+        --------
+        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
+        >>> s.index = pd.MultiIndex.from_tuples(
+        ...     [
+        ...         (1, 2, "a", 0),
+        ...         (1, 2, "a", 1),
+        ...         (1, 1, "b", 0),
+        ...         (1, 1, "b", 1),
+        ...         (2, 1, "b", 0),
+        ...         (2, 1, "b", 1)
+        ...     ],
+        ...     names=["A", "B", "C", "D"],
+        ... )
+        >>> s
+        A  B  C  D
+        1  2  a  0    3.0
+                 1    NaN
+           1  b  0    1.0
+                 1    3.0
+        2  1  b  0    NaN
+                 1    NaN
+        dtype: float64
+
+        >>> ss = s.astype("Sparse")
+        >>> ss
+        A  B  C  D
+        1  2  a  0    3.0
+                 1    NaN
+           1  b  0    1.0
+                 1    3.0
+        2  1  b  0    NaN
+                 1    NaN
+        dtype: Sparse[float64, nan]
+
+        >>> A, rows, columns = ss.sparse.to_coo(
+        ...     row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
+        ... )
+        >>> A
+        <COOrdinate sparse matrix of dtype 'float64'
+            with 3 stored elements and shape (3, 4)>
+        >>> A.todense()
+        matrix([[0., 0., 1., 3.],
+        [3., 0., 0., 0.],
+        [0., 0., 0., 0.]])
+
+        >>> rows
+        [(1, 1), (1, 2), (2, 1)]
+        >>> columns
+        [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
+        """
+        from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo
+
+        A, rows, columns = sparse_series_to_coo(
+            self._parent, row_levels, column_levels, sort_labels=sort_labels
+        )
+        return A, rows, columns
+
+    def to_dense(self) -> Series:
+        """
+        Convert a Series from sparse values to dense.
+
+        Returns
+        -------
+        Series:
+            A Series with the same values, stored as a dense array.
+
+        Examples
+        --------
+        >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
+        >>> series
+        0    0
+        1    1
+        2    0
+        dtype: Sparse[int64, 0]
+
+        >>> series.sparse.to_dense()
+        0    0
+        1    1
+        2    0
+        dtype: int64
+        """
+        from pandas import Series
+
+        return Series(
+            self._parent.array.to_dense(),
+            index=self._parent.index,
+            name=self._parent.name,
+            copy=False,
+        )
+
+
+class SparseFrameAccessor(BaseAccessor, PandasDelegate):
+    """
+    DataFrame accessor for sparse data.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"a": [1, 2, 0, 0],
+    ...                   "b": [3, 0, 0, 4]}, dtype="Sparse[int]")
+    >>> df.sparse.density
+    0.5
+    """
+
+    def _validate(self, data):
+        dtypes = data.dtypes
+        if not all(isinstance(t, SparseDtype) for t in dtypes):
+            raise AttributeError(self._validation_msg)
+
+    @classmethod
+    def from_spmatrix(cls, data, index=None, columns=None) -> DataFrame:
+        """
+        Create a new DataFrame from a scipy sparse matrix.
+
+        Parameters
+        ----------
+        data : scipy.sparse.spmatrix
+            Must be convertible to csc format.
+        index, columns : Index, optional
+            Row and column labels to use for the resulting DataFrame.
+            Defaults to a RangeIndex.
+
+        Returns
+        -------
+        DataFrame
+            Each column of the DataFrame is stored as a
+            :class:`arrays.SparseArray`.
+
+        Examples
+        --------
+        >>> import scipy.sparse
+        >>> mat = scipy.sparse.eye(3, dtype=float)
+        >>> pd.DataFrame.sparse.from_spmatrix(mat)
+             0    1    2
+        0  1.0    0    0
+        1    0  1.0    0
+        2    0    0  1.0
+        """
+        from pandas._libs.sparse import IntIndex
+
+        from pandas import DataFrame
+
+        data = data.tocsc()
+        index, columns = cls._prep_index(data, index, columns)
+        n_rows, n_columns = data.shape
+        # We need to make sure indices are sorted, as we create
+        # IntIndex with no input validation (i.e. check_integrity=False ).
+        # Indices may already be sorted in scipy in which case this adds
+        # a small overhead.
+        data.sort_indices()
+        indices = data.indices
+        indptr = data.indptr
+        array_data = data.data
+        dtype = SparseDtype(array_data.dtype, 0)
+        arrays = []
+        for i in range(n_columns):
+            sl = slice(indptr[i], indptr[i + 1])
+            idx = IntIndex(n_rows, indices[sl], check_integrity=False)
+            arr = SparseArray._simple_new(array_data[sl], idx, dtype)
+            arrays.append(arr)
+        return DataFrame._from_arrays(
+            arrays, columns=columns, index=index, verify_integrity=False
+        )
+
+    def to_dense(self) -> DataFrame:
+        """
+        Convert a DataFrame with sparse values to dense.
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame with the same values stored as dense arrays.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})
+        >>> df.sparse.to_dense()
+           A
+        0  0
+        1  1
+        2  0
+        """
+        from pandas import DataFrame
+
+        data = {k: v.array.to_dense() for k, v in self._parent.items()}
+        return DataFrame(data, index=self._parent.index, columns=self._parent.columns)
+
+    def to_coo(self):
+        """
+        Return the contents of the frame as a sparse SciPy COO matrix.
+
+        Returns
+        -------
+        scipy.sparse.spmatrix
+            If the caller is heterogeneous and contains booleans or objects,
+            the result will be of dtype=object. See Notes.
+
+        Notes
+        -----
+        The dtype will be the lowest-common-denominator type (implicit
+        upcasting); that is to say if the dtypes (even of numeric types)
+        are mixed, the one that accommodates all will be chosen.
+
+        e.g. If the dtypes are float16 and float32, dtype will be upcast to
+        float32. By numpy.find_common_type convention, mixing int64 and
+        and uint64 will result in a float64 dtype.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
+        >>> df.sparse.to_coo()
+        <COOrdinate sparse matrix of dtype 'int64'
+            with 2 stored elements and shape (4, 1)>
+        """
+        import_optional_dependency("scipy")
+        from scipy.sparse import coo_matrix
+
+        dtype = find_common_type(self._parent.dtypes.to_list())
+        if isinstance(dtype, SparseDtype):
+            dtype = dtype.subtype
+
+        cols, rows, data = [], [], []
+        for col, (_, ser) in enumerate(self._parent.items()):
+            sp_arr = ser.array
+            if sp_arr.fill_value != 0:
+                raise ValueError("fill value must be 0 when converting to COO matrix")
+
+            row = sp_arr.sp_index.indices
+            cols.append(np.repeat(col, len(row)))
+            rows.append(row)
+            data.append(sp_arr.sp_values.astype(dtype, copy=False))
+
+        cols = np.concatenate(cols)
+        rows = np.concatenate(rows)
+        data = np.concatenate(data)
+        return coo_matrix((data, (rows, cols)), shape=self._parent.shape)
+
+    @property
+    def density(self) -> float:
+        """
+        Ratio of non-sparse points to total (dense) data points.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
+        >>> df.sparse.density
+        0.5
+        """
+        tmp = np.mean([column.array.density for _, column in self._parent.items()])
+        return tmp
+
+    @staticmethod
+    def _prep_index(data, index, columns):
+        from pandas.core.indexes.api import (
+            default_index,
+            ensure_index,
+        )
+
+        N, K = data.shape
+        if index is None:
+            index = default_index(N)
+        else:
+            index = ensure_index(index)
+        if columns is None:
+            columns = default_index(K)
+        else:
+            columns = ensure_index(columns)
+
+        if len(columns) != K:
+            raise ValueError(f"Column length mismatch: {len(columns)} vs. {K}")
+        if len(index) != N:
+            raise ValueError(f"Index length mismatch: {len(index)} vs. {N}")
+        return index, columns
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/array.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/array.py
new file mode 100644
index 0000000000000000000000000000000000000000..07ff592f491a82bebef7b158ec00aaee6c8df419
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/array.py
@@ -0,0 +1,1945 @@
+"""
+SparseArray data structure
+"""
+from __future__ import annotations
+
+from collections import abc
+import numbers
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+import pandas._libs.sparse as splib
+from pandas._libs.sparse import (
+    BlockIndex,
+    IntIndex,
+    SparseIndex,
+)
+from pandas._libs.tslibs import NaT
+from pandas.compat.numpy import function as nv
+from pandas.errors import PerformanceWarning
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    validate_bool_kwarg,
+    validate_insert_loc,
+)
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    find_common_type,
+    maybe_box_datetimelike,
+)
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+from pandas.core import arraylike
+import pandas.core.algorithms as algos
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.indexers import (
+    check_array_indexer,
+    unpack_tuple_and_ellipses,
+)
+from pandas.core.nanops import check_below_min_count
+
+from pandas.io.formats import printing
+
+# See https://github.com/python/typing/issues/684
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from enum import Enum
+
+    class ellipsis(Enum):
+        Ellipsis = "..."
+
+    Ellipsis = ellipsis.Ellipsis
+
+    from scipy.sparse import spmatrix
+
+    from pandas._typing import (
+        FillnaOptions,
+        NumpySorter,
+    )
+
+    SparseIndexKind = Literal["integer", "block"]
+
+    from pandas._typing import (
+        ArrayLike,
+        AstypeArg,
+        Axis,
+        AxisInt,
+        Dtype,
+        NpDtype,
+        PositionalIndexer,
+        Scalar,
+        ScalarIndexer,
+        Self,
+        SequenceIndexer,
+        npt,
+    )
+
+    from pandas import Series
+
+else:
+    ellipsis = type(Ellipsis)
+
+
+# ----------------------------------------------------------------------------
+# Array
+
+_sparray_doc_kwargs = {"klass": "SparseArray"}
+
+
+def _get_fill(arr: SparseArray) -> np.ndarray:
+    """
+    Create a 0-dim ndarray containing the fill value
+
+    Parameters
+    ----------
+    arr : SparseArray
+
+    Returns
+    -------
+    fill_value : ndarray
+        0-dim ndarray with just the fill value.
+
+    Notes
+    -----
+    coerce fill_value to arr dtype if possible
+    int64 SparseArray can have NaN as fill_value if there is no missing
+    """
+    try:
+        return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)
+    except ValueError:
+        return np.asarray(arr.fill_value)
+
+
+def _sparse_array_op(
+    left: SparseArray, right: SparseArray, op: Callable, name: str
+) -> SparseArray:
+    """
+    Perform a binary operation between two arrays.
+
+    Parameters
+    ----------
+    left : Union[SparseArray, ndarray]
+    right : Union[SparseArray, ndarray]
+    op : Callable
+        The binary operation to perform
+    name str
+        Name of the callable.
+
+    Returns
+    -------
+    SparseArray
+    """
+    if name.startswith("__"):
+        # For lookups in _libs.sparse we need non-dunder op name
+        name = name[2:-2]
+
+    # dtype used to find corresponding sparse method
+    ltype = left.dtype.subtype
+    rtype = right.dtype.subtype
+
+    if ltype != rtype:
+        subtype = find_common_type([ltype, rtype])
+        ltype = SparseDtype(subtype, left.fill_value)
+        rtype = SparseDtype(subtype, right.fill_value)
+
+        left = left.astype(ltype, copy=False)
+        right = right.astype(rtype, copy=False)
+        dtype = ltype.subtype
+    else:
+        dtype = ltype
+
+    # dtype the result must have
+    result_dtype = None
+
+    if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:
+        with np.errstate(all="ignore"):
+            result = op(left.to_dense(), right.to_dense())
+            fill = op(_get_fill(left), _get_fill(right))
+
+        if left.sp_index.ngaps == 0:
+            index = left.sp_index
+        else:
+            index = right.sp_index
+    elif left.sp_index.equals(right.sp_index):
+        with np.errstate(all="ignore"):
+            result = op(left.sp_values, right.sp_values)
+            fill = op(_get_fill(left), _get_fill(right))
+        index = left.sp_index
+    else:
+        if name[0] == "r":
+            left, right = right, left
+            name = name[1:]
+
+        if name in ("and", "or", "xor") and dtype == "bool":
+            opname = f"sparse_{name}_uint8"
+            # to make template simple, cast here
+            left_sp_values = left.sp_values.view(np.uint8)
+            right_sp_values = right.sp_values.view(np.uint8)
+            result_dtype = bool
+        else:
+            opname = f"sparse_{name}_{dtype}"
+            left_sp_values = left.sp_values
+            right_sp_values = right.sp_values
+
+        if (
+            name in ["floordiv", "mod"]
+            and (right == 0).any()
+            and left.dtype.kind in "iu"
+        ):
+            # Match the non-Sparse Series behavior
+            opname = f"sparse_{name}_float64"
+            left_sp_values = left_sp_values.astype("float64")
+            right_sp_values = right_sp_values.astype("float64")
+
+        sparse_op = getattr(splib, opname)
+
+        with np.errstate(all="ignore"):
+            result, index, fill = sparse_op(
+                left_sp_values,
+                left.sp_index,
+                left.fill_value,
+                right_sp_values,
+                right.sp_index,
+                right.fill_value,
+            )
+
+    if name == "divmod":
+        # result is a 2-tuple
+        # error: Incompatible return value type (got "Tuple[SparseArray,
+        # SparseArray]", expected "SparseArray")
+        return (  # type: ignore[return-value]
+            _wrap_result(name, result[0], index, fill[0], dtype=result_dtype),
+            _wrap_result(name, result[1], index, fill[1], dtype=result_dtype),
+        )
+
+    if result_dtype is None:
+        result_dtype = result.dtype
+
+    return _wrap_result(name, result, index, fill, dtype=result_dtype)
+
+
+def _wrap_result(
+    name: str, data, sparse_index, fill_value, dtype: Dtype | None = None
+) -> SparseArray:
+    """
+    wrap op result to have correct dtype
+    """
+    if name.startswith("__"):
+        # e.g. __eq__ --> eq
+        name = name[2:-2]
+
+    if name in ("eq", "ne", "lt", "gt", "le", "ge"):
+        dtype = bool
+
+    fill_value = lib.item_from_zerodim(fill_value)
+
+    if is_bool_dtype(dtype):
+        # fill_value may be np.bool_
+        fill_value = bool(fill_value)
+    return SparseArray(
+        data, sparse_index=sparse_index, fill_value=fill_value, dtype=dtype
+    )
+
+
+class SparseArray(OpsMixin, PandasObject, ExtensionArray):
+    """
+    An ExtensionArray for storing sparse data.
+
+    Parameters
+    ----------
+    data : array-like or scalar
+        A dense array of values to store in the SparseArray. This may contain
+        `fill_value`.
+    sparse_index : SparseIndex, optional
+    fill_value : scalar, optional
+        Elements in data that are ``fill_value`` are not stored in the
+        SparseArray. For memory savings, this should be the most common value
+        in `data`. By default, `fill_value` depends on the dtype of `data`:
+
+        =========== ==========
+        data.dtype  na_value
+        =========== ==========
+        float       ``np.nan``
+        int         ``0``
+        bool        False
+        datetime64  ``pd.NaT``
+        timedelta64 ``pd.NaT``
+        =========== ==========
+
+        The fill value is potentially specified in three ways. In order of
+        precedence, these are
+
+        1. The `fill_value` argument
+        2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is
+           a ``SparseDtype``
+        3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`
+           is not a ``SparseDtype`` and `data` is a ``SparseArray``.
+
+    kind : str
+        Can be 'integer' or 'block', default is 'integer'.
+        The type of storage for sparse locations.
+
+        * 'block': Stores a `block` and `block_length` for each
+          contiguous *span* of sparse values. This is best when
+          sparse data tends to be clumped together, with large
+          regions of ``fill-value`` values between sparse values.
+        * 'integer': uses an integer to store the location of
+          each sparse value.
+
+    dtype : np.dtype or SparseDtype, optional
+        The dtype to use for the SparseArray. For numpy dtypes, this
+        determines the dtype of ``self.sp_values``. For SparseDtype,
+        this determines ``self.sp_values`` and ``self.fill_value``.
+    copy : bool, default False
+        Whether to explicitly copy the incoming `data` array.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> from pandas.arrays import SparseArray
+    >>> arr = SparseArray([0, 0, 1, 2])
+    >>> arr
+    [0, 0, 1, 2]
+    Fill: 0
+    IntIndex
+    Indices: array([2, 3], dtype=int32)
+    """
+
+    _subtyp = "sparse_array"  # register ABCSparseArray
+    _hidden_attrs = PandasObject._hidden_attrs | frozenset([])
+    _sparse_index: SparseIndex
+    _sparse_values: np.ndarray
+    _dtype: SparseDtype
+
+    def __init__(
+        self,
+        data,
+        sparse_index=None,
+        fill_value=None,
+        kind: SparseIndexKind = "integer",
+        dtype: Dtype | None = None,
+        copy: bool = False,
+    ) -> None:
+        if fill_value is None and isinstance(dtype, SparseDtype):
+            fill_value = dtype.fill_value
+
+        if isinstance(data, type(self)):
+            # disable normal inference on dtype, sparse_index, & fill_value
+            if sparse_index is None:
+                sparse_index = data.sp_index
+            if fill_value is None:
+                fill_value = data.fill_value
+            if dtype is None:
+                dtype = data.dtype
+            # TODO: make kind=None, and use data.kind?
+            data = data.sp_values
+
+        # Handle use-provided dtype
+        if isinstance(dtype, str):
+            # Two options: dtype='int', regular numpy dtype
+            # or dtype='Sparse[int]', a sparse dtype
+            try:
+                dtype = SparseDtype.construct_from_string(dtype)
+            except TypeError:
+                dtype = pandas_dtype(dtype)
+
+        if isinstance(dtype, SparseDtype):
+            if fill_value is None:
+                fill_value = dtype.fill_value
+            dtype = dtype.subtype
+
+        if is_scalar(data):
+            warnings.warn(
+                f"Constructing {type(self).__name__} with scalar data is deprecated "
+                "and will raise in a future version. Pass a sequence instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            if sparse_index is None:
+                npoints = 1
+            else:
+                npoints = sparse_index.length
+
+            data = construct_1d_arraylike_from_scalar(data, npoints, dtype=None)
+            dtype = data.dtype
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        # TODO: disentangle the fill_value dtype inference from
+        # dtype inference
+        if data is None:
+            # TODO: What should the empty dtype be? Object or float?
+
+            # error: Argument "dtype" to "array" has incompatible type
+            # "Union[ExtensionDtype, dtype[Any], None]"; expected "Union[dtype[Any],
+            # None, type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any,
+            # Union[int, Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
+            data = np.array([], dtype=dtype)  # type: ignore[arg-type]
+
+        try:
+            data = sanitize_array(data, index=None)
+        except ValueError:
+            # NumPy may raise a ValueError on data like [1, []]
+            # we retry with object dtype here.
+            if dtype is None:
+                dtype = np.dtype(object)
+                data = np.atleast_1d(np.asarray(data, dtype=dtype))
+            else:
+                raise
+
+        if copy:
+            # TODO: avoid double copy when dtype forces cast.
+            data = data.copy()
+
+        if fill_value is None:
+            fill_value_dtype = data.dtype if dtype is None else dtype
+            if fill_value_dtype is None:
+                fill_value = np.nan
+            else:
+                fill_value = na_value_for_dtype(fill_value_dtype)
+
+        if isinstance(data, type(self)) and sparse_index is None:
+            sparse_index = data._sparse_index
+            # error: Argument "dtype" to "asarray" has incompatible type
+            # "Union[ExtensionDtype, dtype[Any], None]"; expected "None"
+            sparse_values = np.asarray(
+                data.sp_values, dtype=dtype  # type: ignore[arg-type]
+            )
+        elif sparse_index is None:
+            data = extract_array(data, extract_numpy=True)
+            if not isinstance(data, np.ndarray):
+                # EA
+                if isinstance(data.dtype, DatetimeTZDtype):
+                    warnings.warn(
+                        f"Creating SparseArray from {data.dtype} data "
+                        "loses timezone information. Cast to object before "
+                        "sparse to retain timezone information.",
+                        UserWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    data = np.asarray(data, dtype="datetime64[ns]")
+                    if fill_value is NaT:
+                        fill_value = np.datetime64("NaT", "ns")
+                data = np.asarray(data)
+            sparse_values, sparse_index, fill_value = _make_sparse(
+                # error: Argument "dtype" to "_make_sparse" has incompatible type
+                # "Union[ExtensionDtype, dtype[Any], None]"; expected
+                # "Optional[dtype[Any]]"
+                data,
+                kind=kind,
+                fill_value=fill_value,
+                dtype=dtype,  # type: ignore[arg-type]
+            )
+        else:
+            # error: Argument "dtype" to "asarray" has incompatible type
+            # "Union[ExtensionDtype, dtype[Any], None]"; expected "None"
+            sparse_values = np.asarray(data, dtype=dtype)  # type: ignore[arg-type]
+            if len(sparse_values) != sparse_index.npoints:
+                raise AssertionError(
+                    f"Non array-like type {type(sparse_values)} must "
+                    "have the same length as the index"
+                )
+        self._sparse_index = sparse_index
+        self._sparse_values = sparse_values
+        self._dtype = SparseDtype(sparse_values.dtype, fill_value)
+
+    @classmethod
+    def _simple_new(
+        cls,
+        sparse_array: np.ndarray,
+        sparse_index: SparseIndex,
+        dtype: SparseDtype,
+    ) -> Self:
+        new = object.__new__(cls)
+        new._sparse_index = sparse_index
+        new._sparse_values = sparse_array
+        new._dtype = dtype
+        return new
+
+    @classmethod
+    def from_spmatrix(cls, data: spmatrix) -> Self:
+        """
+        Create a SparseArray from a scipy.sparse matrix.
+
+        Parameters
+        ----------
+        data : scipy.sparse.sp_matrix
+            This should be a SciPy sparse matrix where the size
+            of the second dimension is 1. In other words, a
+            sparse matrix with a single column.
+
+        Returns
+        -------
+        SparseArray
+
+        Examples
+        --------
+        >>> import scipy.sparse
+        >>> mat = scipy.sparse.coo_matrix((4, 1))
+        >>> pd.arrays.SparseArray.from_spmatrix(mat)
+        [0.0, 0.0, 0.0, 0.0]
+        Fill: 0.0
+        IntIndex
+        Indices: array([], dtype=int32)
+        """
+        length, ncol = data.shape
+
+        if ncol != 1:
+            raise ValueError(f"'data' must have a single column, not '{ncol}'")
+
+        # our sparse index classes require that the positions be strictly
+        # increasing. So we need to sort loc, and arr accordingly.
+        data = data.tocsc()
+        data.sort_indices()
+        arr = data.data
+        idx = data.indices
+
+        zero = np.array(0, dtype=arr.dtype).item()
+        dtype = SparseDtype(arr.dtype, zero)
+        index = IntIndex(length, idx)
+
+        return cls._simple_new(arr, index, dtype)
+
+    def __array__(
+        self, dtype: NpDtype | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        if self.sp_index.ngaps == 0:
+            # Compat for na dtype and int values.
+            if copy is True:
+                return np.array(self.sp_values)
+            else:
+                return self.sp_values
+
+        if copy is False:
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        fill_value = self.fill_value
+
+        if dtype is None:
+            # Can NumPy represent this type?
+            # If not, `np.result_type` will raise. We catch that
+            # and return object.
+            if self.sp_values.dtype.kind == "M":
+                # However, we *do* special-case the common case of
+                # a datetime64 with pandas NaT.
+                if fill_value is NaT:
+                    # Can't put pd.NaT in a datetime64[ns]
+                    fill_value = np.datetime64("NaT")
+            try:
+                dtype = np.result_type(self.sp_values.dtype, type(fill_value))
+            except TypeError:
+                dtype = object
+
+        out = np.full(self.shape, fill_value, dtype=dtype)
+        out[self.sp_index.indices] = self.sp_values
+        return out
+
+    def __setitem__(self, key, value) -> None:
+        # I suppose we could allow setting of non-fill_value elements.
+        # TODO(SparseArray.__setitem__): remove special cases in
+        # ExtensionBlock.where
+        msg = "SparseArray does not support item assignment via setitem"
+        raise TypeError(msg)
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        return cls(scalars, dtype=dtype)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        return cls(values, dtype=original.dtype)
+
+    # ------------------------------------------------------------------------
+    # Data
+    # ------------------------------------------------------------------------
+    @property
+    def sp_index(self) -> SparseIndex:
+        """
+        The SparseIndex containing the location of non- ``fill_value`` points.
+        """
+        return self._sparse_index
+
+    @property
+    def sp_values(self) -> np.ndarray:
+        """
+        An ndarray containing the non- ``fill_value`` values.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
+        >>> s.sp_values
+        array([1, 2])
+        """
+        return self._sparse_values
+
+    @property
+    def dtype(self) -> SparseDtype:
+        return self._dtype
+
+    @property
+    def fill_value(self):
+        """
+        Elements in `data` that are `fill_value` are not stored.
+
+        For memory savings, this should be the most common value in the array.
+
+        Examples
+        --------
+        >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
+        >>> ser.sparse.fill_value
+        0
+        >>> spa_dtype = pd.SparseDtype(dtype=np.int32, fill_value=2)
+        >>> ser = pd.Series([0, 0, 2, 2, 2], dtype=spa_dtype)
+        >>> ser.sparse.fill_value
+        2
+        """
+        return self.dtype.fill_value
+
+    @fill_value.setter
+    def fill_value(self, value) -> None:
+        self._dtype = SparseDtype(self.dtype.subtype, value)
+
+    @property
+    def kind(self) -> SparseIndexKind:
+        """
+        The kind of sparse index for this array. One of {'integer', 'block'}.
+        """
+        if isinstance(self.sp_index, IntIndex):
+            return "integer"
+        else:
+            return "block"
+
+    @property
+    def _valid_sp_values(self) -> np.ndarray:
+        sp_vals = self.sp_values
+        mask = notna(sp_vals)
+        return sp_vals[mask]
+
+    def __len__(self) -> int:
+        return self.sp_index.length
+
+    @property
+    def _null_fill_value(self) -> bool:
+        return self._dtype._is_na_fill_value
+
+    def _fill_value_matches(self, fill_value) -> bool:
+        if self._null_fill_value:
+            return isna(fill_value)
+        else:
+            return self.fill_value == fill_value
+
+    @property
+    def nbytes(self) -> int:
+        return self.sp_values.nbytes + self.sp_index.nbytes
+
+    @property
+    def density(self) -> float:
+        """
+        The percent of non- ``fill_value`` points, as decimal.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
+        >>> s.density
+        0.6
+        """
+        return self.sp_index.npoints / self.sp_index.length
+
+    @property
+    def npoints(self) -> int:
+        """
+        The number of non- ``fill_value`` points.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
+        >>> s.npoints
+        3
+        """
+        return self.sp_index.npoints
+
+    # error: Return type "SparseArray" of "isna" incompatible with return type
+    # "ndarray[Any, Any] | ExtensionArraySupportsAnyAll" in supertype "ExtensionArray"
+    def isna(self) -> Self:  # type: ignore[override]
+        # If null fill value, we want SparseDtype[bool, true]
+        # to preserve the same memory usage.
+        dtype = SparseDtype(bool, self._null_fill_value)
+        if self._null_fill_value:
+            return type(self)._simple_new(isna(self.sp_values), self.sp_index, dtype)
+        mask = np.full(len(self), False, dtype=np.bool_)
+        mask[self.sp_index.indices] = isna(self.sp_values)
+        return type(self)(mask, fill_value=False, dtype=dtype)
+
+    def _pad_or_backfill(  # pylint: disable=useless-parent-delegation
+        self,
+        *,
+        method: FillnaOptions,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        copy: bool = True,
+    ) -> Self:
+        # TODO(3.0): We can remove this method once deprecation for fillna method
+        #  keyword is enforced.
+        return super()._pad_or_backfill(
+            method=method, limit=limit, limit_area=limit_area, copy=copy
+        )
+
+    def fillna(
+        self,
+        value=None,
+        method: FillnaOptions | None = None,
+        limit: int | None = None,
+        copy: bool = True,
+    ) -> Self:
+        """
+        Fill missing values with `value`.
+
+        Parameters
+        ----------
+        value : scalar, optional
+        method : str, optional
+
+            .. warning::
+
+               Using 'method' will result in high memory use,
+               as all `fill_value` methods will be converted to
+               an in-memory ndarray
+
+        limit : int, optional
+
+        copy: bool, default True
+            Ignored for SparseArray.
+
+        Returns
+        -------
+        SparseArray
+
+        Notes
+        -----
+        When `value` is specified, the result's ``fill_value`` depends on
+        ``self.fill_value``. The goal is to maintain low-memory use.
+
+        If ``self.fill_value`` is NA, the result dtype will be
+        ``SparseDtype(self.dtype, fill_value=value)``. This will preserve
+        amount of memory used before and after filling.
+
+        When ``self.fill_value`` is not NA, the result dtype will be
+        ``self.dtype``. Again, this preserves the amount of memory used.
+        """
+        if (method is None and value is None) or (
+            method is not None and value is not None
+        ):
+            raise ValueError("Must specify one of 'method' or 'value'.")
+
+        if method is not None:
+            return super().fillna(method=method, limit=limit)
+
+        else:
+            new_values = np.where(isna(self.sp_values), value, self.sp_values)
+
+            if self._null_fill_value:
+                # This is essentially just updating the dtype.
+                new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)
+            else:
+                new_dtype = self.dtype
+
+        return self._simple_new(new_values, self._sparse_index, new_dtype)
+
+    def shift(self, periods: int = 1, fill_value=None) -> Self:
+        if not len(self) or periods == 0:
+            return self.copy()
+
+        if isna(fill_value):
+            fill_value = self.dtype.na_value
+
+        subtype = np.result_type(fill_value, self.dtype.subtype)
+
+        if subtype != self.dtype.subtype:
+            # just coerce up front
+            arr = self.astype(SparseDtype(subtype, self.fill_value))
+        else:
+            arr = self
+
+        empty = self._from_sequence(
+            [fill_value] * min(abs(periods), len(self)), dtype=arr.dtype
+        )
+
+        if periods > 0:
+            a = empty
+            b = arr[:-periods]
+        else:
+            a = arr[abs(periods) :]
+            b = empty
+        return arr._concat_same_type([a, b])
+
+    def _first_fill_value_loc(self):
+        """
+        Get the location of the first fill value.
+
+        Returns
+        -------
+        int
+        """
+        if len(self) == 0 or self.sp_index.npoints == len(self):
+            return -1
+
+        indices = self.sp_index.indices
+        if not len(indices) or indices[0] > 0:
+            return 0
+
+        # a number larger than 1 should be appended to
+        # the last in case of fill value only appears
+        # in the tail of array
+        diff = np.r_[np.diff(indices), 2]
+        return indices[(diff > 1).argmax()] + 1
+
+    @doc(ExtensionArray.duplicated)
+    def duplicated(
+        self, keep: Literal["first", "last", False] = "first"
+    ) -> npt.NDArray[np.bool_]:
+        values = np.asarray(self)
+        mask = np.asarray(self.isna())
+        return algos.duplicated(values, keep=keep, mask=mask)
+
+    def unique(self) -> Self:
+        uniques = algos.unique(self.sp_values)
+        if len(self.sp_values) != len(self):
+            fill_loc = self._first_fill_value_loc()
+            # Inorder to align the behavior of pd.unique or
+            # pd.Series.unique, we should keep the original
+            # order, here we use unique again to find the
+            # insertion place. Since the length of sp_values
+            # is not large, maybe minor performance hurt
+            # is worthwhile to the correctness.
+            insert_loc = len(algos.unique(self.sp_values[:fill_loc]))
+            uniques = np.insert(uniques, insert_loc, self.fill_value)
+        return type(self)._from_sequence(uniques, dtype=self.dtype)
+
+    def _values_for_factorize(self):
+        # Still override this for hash_pandas_object
+        return np.asarray(self), self.fill_value
+
+    def factorize(
+        self,
+        use_na_sentinel: bool = True,
+    ) -> tuple[np.ndarray, SparseArray]:
+        # Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]
+        # The sparsity on this is backwards from what Sparse would want. Want
+        # ExtensionArray.factorize -> Tuple[EA, EA]
+        # Given that we have to return a dense array of codes, why bother
+        # implementing an efficient factorize?
+        codes, uniques = algos.factorize(
+            np.asarray(self), use_na_sentinel=use_na_sentinel
+        )
+        uniques_sp = SparseArray(uniques, dtype=self.dtype)
+        return codes, uniques_sp
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Returns a Series containing counts of unique values.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NaN, even if NaN is in sp_values.
+
+        Returns
+        -------
+        counts : Series
+        """
+        from pandas import (
+            Index,
+            Series,
+        )
+
+        keys, counts, _ = algos.value_counts_arraylike(self.sp_values, dropna=dropna)
+        fcounts = self.sp_index.ngaps
+        if fcounts > 0 and (not self._null_fill_value or not dropna):
+            mask = isna(keys) if self._null_fill_value else keys == self.fill_value
+            if mask.any():
+                counts[mask] += fcounts
+            else:
+                # error: Argument 1 to "insert" has incompatible type "Union[
+                # ExtensionArray,ndarray[Any, Any]]"; expected "Union[
+                # _SupportsArray[dtype[Any]], Sequence[_SupportsArray[dtype
+                # [Any]]], Sequence[Sequence[_SupportsArray[dtype[Any]]]],
+                # Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]], Sequence
+                # [Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]]]]"
+                keys = np.insert(keys, 0, self.fill_value)  # type: ignore[arg-type]
+                counts = np.insert(counts, 0, fcounts)
+
+        if not isinstance(keys, ABCIndex):
+            index = Index(keys)
+        else:
+            index = keys
+        return Series(counts, index=index, copy=False)
+
+    # --------
+    # Indexing
+    # --------
+    @overload
+    def __getitem__(self, key: ScalarIndexer) -> Any:
+        ...
+
+    @overload
+    def __getitem__(
+        self,
+        key: SequenceIndexer | tuple[int | ellipsis, ...],
+    ) -> Self:
+        ...
+
+    def __getitem__(
+        self,
+        key: PositionalIndexer | tuple[int | ellipsis, ...],
+    ) -> Self | Any:
+        if isinstance(key, tuple):
+            key = unpack_tuple_and_ellipses(key)
+            if key is Ellipsis:
+                raise ValueError("Cannot slice with Ellipsis")
+
+        if is_integer(key):
+            return self._get_val_at(key)
+        elif isinstance(key, tuple):
+            # error: Invalid index type "Tuple[Union[int, ellipsis], ...]"
+            # for "ndarray[Any, Any]"; expected type
+            # "Union[SupportsIndex, _SupportsArray[dtype[Union[bool_,
+            # integer[Any]]]], _NestedSequence[_SupportsArray[dtype[
+            # Union[bool_, integer[Any]]]]], _NestedSequence[Union[
+            # bool, int]], Tuple[Union[SupportsIndex, _SupportsArray[
+            # dtype[Union[bool_, integer[Any]]]], _NestedSequence[
+            # _SupportsArray[dtype[Union[bool_, integer[Any]]]]],
+            # _NestedSequence[Union[bool, int]]], ...]]"
+            data_slice = self.to_dense()[key]  # type: ignore[index]
+        elif isinstance(key, slice):
+            # Avoid densifying when handling contiguous slices
+            if key.step is None or key.step == 1:
+                start = 0 if key.start is None else key.start
+                if start < 0:
+                    start += len(self)
+
+                end = len(self) if key.stop is None else key.stop
+                if end < 0:
+                    end += len(self)
+
+                indices = self.sp_index.indices
+                keep_inds = np.flatnonzero((indices >= start) & (indices < end))
+                sp_vals = self.sp_values[keep_inds]
+
+                sp_index = indices[keep_inds].copy()
+
+                # If we've sliced to not include the start of the array, all our indices
+                # should be shifted. NB: here we are careful to also not shift by a
+                # negative value for a case like [0, 1][-100:] where the start index
+                # should be treated like 0
+                if start > 0:
+                    sp_index -= start
+
+                # Length of our result should match applying this slice to a range
+                # of the length of our original array
+                new_len = len(range(len(self))[key])
+                new_sp_index = make_sparse_index(new_len, sp_index, self.kind)
+                return type(self)._simple_new(sp_vals, new_sp_index, self.dtype)
+            else:
+                indices = np.arange(len(self), dtype=np.int32)[key]
+                return self.take(indices)
+
+        elif not is_list_like(key):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
+
+        else:
+            if isinstance(key, SparseArray):
+                # NOTE: If we guarantee that SparseDType(bool)
+                # has only fill_value - true, false or nan
+                # (see GH PR 44955)
+                # we can apply mask very fast:
+                if is_bool_dtype(key):
+                    if isna(key.fill_value):
+                        return self.take(key.sp_index.indices[key.sp_values])
+                    if not key.fill_value:
+                        return self.take(key.sp_index.indices)
+                    n = len(self)
+                    mask = np.full(n, True, dtype=np.bool_)
+                    mask[key.sp_index.indices] = False
+                    return self.take(np.arange(n)[mask])
+                else:
+                    key = np.asarray(key)
+
+            key = check_array_indexer(self, key)
+
+            if com.is_bool_indexer(key):
+                # mypy doesn't know we have an array here
+                key = cast(np.ndarray, key)
+                return self.take(np.arange(len(key), dtype=np.int32)[key])
+            elif hasattr(key, "__len__"):
+                return self.take(key)
+            else:
+                raise ValueError(f"Cannot slice with '{key}'")
+
+        return type(self)(data_slice, kind=self.kind)
+
+    def _get_val_at(self, loc):
+        loc = validate_insert_loc(loc, len(self))
+
+        sp_loc = self.sp_index.lookup(loc)
+        if sp_loc == -1:
+            return self.fill_value
+        else:
+            val = self.sp_values[sp_loc]
+            val = maybe_box_datetimelike(val, self.sp_values.dtype)
+            return val
+
+    def take(self, indices, *, allow_fill: bool = False, fill_value=None) -> Self:
+        if is_scalar(indices):
+            raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
+        indices = np.asarray(indices, dtype=np.int32)
+
+        dtype = None
+        if indices.size == 0:
+            result = np.array([], dtype="object")
+            dtype = self.dtype
+        elif allow_fill:
+            result = self._take_with_fill(indices, fill_value=fill_value)
+        else:
+            return self._take_without_fill(indices)
+
+        return type(self)(
+            result, fill_value=self.fill_value, kind=self.kind, dtype=dtype
+        )
+
+    def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
+        if fill_value is None:
+            fill_value = self.dtype.na_value
+
+        if indices.min() < -1:
+            raise ValueError(
+                "Invalid value in 'indices'. Must be between -1 "
+                "and the length of the array."
+            )
+
+        if indices.max() >= len(self):
+            raise IndexError("out of bounds value in 'indices'.")
+
+        if len(self) == 0:
+            # Empty... Allow taking only if all empty
+            if (indices == -1).all():
+                dtype = np.result_type(self.sp_values, type(fill_value))
+                taken = np.empty_like(indices, dtype=dtype)
+                taken.fill(fill_value)
+                return taken
+            else:
+                raise IndexError("cannot do a non-empty take from an empty axes.")
+
+        # sp_indexer may be -1 for two reasons
+        # 1.) we took for an index of -1 (new)
+        # 2.) we took a value that was self.fill_value (old)
+        sp_indexer = self.sp_index.lookup_array(indices)
+        new_fill_indices = indices == -1
+        old_fill_indices = (sp_indexer == -1) & ~new_fill_indices
+
+        if self.sp_index.npoints == 0 and old_fill_indices.all():
+            # We've looked up all valid points on an all-sparse array.
+            taken = np.full(
+                sp_indexer.shape, fill_value=self.fill_value, dtype=self.dtype.subtype
+            )
+
+        elif self.sp_index.npoints == 0:
+            # Use the old fill_value unless we took for an index of -1
+            _dtype = np.result_type(self.dtype.subtype, type(fill_value))
+            taken = np.full(sp_indexer.shape, fill_value=fill_value, dtype=_dtype)
+            taken[old_fill_indices] = self.fill_value
+        else:
+            taken = self.sp_values.take(sp_indexer)
+
+            # Fill in two steps.
+            # Old fill values
+            # New fill values
+            # potentially coercing to a new dtype at each stage.
+
+            m0 = sp_indexer[old_fill_indices] < 0
+            m1 = sp_indexer[new_fill_indices] < 0
+
+            result_type = taken.dtype
+
+            if m0.any():
+                result_type = np.result_type(result_type, type(self.fill_value))
+                taken = taken.astype(result_type)
+                taken[old_fill_indices] = self.fill_value
+
+            if m1.any():
+                result_type = np.result_type(result_type, type(fill_value))
+                taken = taken.astype(result_type)
+                taken[new_fill_indices] = fill_value
+
+        return taken
+
+    def _take_without_fill(self, indices) -> Self:
+        to_shift = indices < 0
+
+        n = len(self)
+
+        if (indices.max() >= n) or (indices.min() < -n):
+            if n == 0:
+                raise IndexError("cannot do a non-empty take from an empty axes.")
+            raise IndexError("out of bounds value in 'indices'.")
+
+        if to_shift.any():
+            indices = indices.copy()
+            indices[to_shift] += n
+
+        sp_indexer = self.sp_index.lookup_array(indices)
+        value_mask = sp_indexer != -1
+        new_sp_values = self.sp_values[sp_indexer[value_mask]]
+
+        value_indices = np.flatnonzero(value_mask).astype(np.int32, copy=False)
+
+        new_sp_index = make_sparse_index(len(indices), value_indices, kind=self.kind)
+        return type(self)._simple_new(new_sp_values, new_sp_index, dtype=self.dtype)
+
+    def searchsorted(
+        self,
+        v: ArrayLike | object,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        msg = "searchsorted requires high memory usage."
+        warnings.warn(msg, PerformanceWarning, stacklevel=find_stack_level())
+        v = np.asarray(v)
+        return np.asarray(self, dtype=self.dtype.subtype).searchsorted(v, side, sorter)
+
+    def copy(self) -> Self:
+        values = self.sp_values.copy()
+        return self._simple_new(values, self.sp_index, self.dtype)
+
+    @classmethod
+    def _concat_same_type(cls, to_concat: Sequence[Self]) -> Self:
+        fill_value = to_concat[0].fill_value
+
+        values = []
+        length = 0
+
+        if to_concat:
+            sp_kind = to_concat[0].kind
+        else:
+            sp_kind = "integer"
+
+        sp_index: SparseIndex
+        if sp_kind == "integer":
+            indices = []
+
+            for arr in to_concat:
+                int_idx = arr.sp_index.indices.copy()
+                int_idx += length  # TODO: wraparound
+                length += arr.sp_index.length
+
+                values.append(arr.sp_values)
+                indices.append(int_idx)
+
+            data = np.concatenate(values)
+            indices_arr = np.concatenate(indices)
+            # error: Argument 2 to "IntIndex" has incompatible type
+            # "ndarray[Any, dtype[signedinteger[_32Bit]]]";
+            # expected "Sequence[int]"
+            sp_index = IntIndex(length, indices_arr)  # type: ignore[arg-type]
+
+        else:
+            # when concatenating block indices, we don't claim that you'll
+            # get an identical index as concatenating the values and then
+            # creating a new index. We don't want to spend the time trying
+            # to merge blocks across arrays in `to_concat`, so the resulting
+            # BlockIndex may have more blocks.
+            blengths = []
+            blocs = []
+
+            for arr in to_concat:
+                block_idx = arr.sp_index.to_block_index()
+
+                values.append(arr.sp_values)
+                blocs.append(block_idx.blocs.copy() + length)
+                blengths.append(block_idx.blengths)
+                length += arr.sp_index.length
+
+            data = np.concatenate(values)
+            blocs_arr = np.concatenate(blocs)
+            blengths_arr = np.concatenate(blengths)
+
+            sp_index = BlockIndex(length, blocs_arr, blengths_arr)
+
+        return cls(data, sparse_index=sp_index, fill_value=fill_value)
+
+    def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
+        """
+        Change the dtype of a SparseArray.
+
+        The output will always be a SparseArray. To convert to a dense
+        ndarray with a certain dtype, use :meth:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : np.dtype or ExtensionDtype
+            For SparseDtype, this changes the dtype of
+            ``self.sp_values`` and the ``self.fill_value``.
+
+            For other dtypes, this only changes the dtype of
+            ``self.sp_values``.
+
+        copy : bool, default True
+            Whether to ensure a copy is made, even if not necessary.
+
+        Returns
+        -------
+        SparseArray
+
+        Examples
+        --------
+        >>> arr = pd.arrays.SparseArray([0, 0, 1, 2])
+        >>> arr
+        [0, 0, 1, 2]
+        Fill: 0
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+
+        >>> arr.astype(SparseDtype(np.dtype('int32')))
+        [0, 0, 1, 2]
+        Fill: 0
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+
+        Using a NumPy dtype with a different kind (e.g. float) will coerce
+        just ``self.sp_values``.
+
+        >>> arr.astype(SparseDtype(np.dtype('float64')))
+        ... # doctest: +NORMALIZE_WHITESPACE
+        [nan, nan, 1.0, 2.0]
+        Fill: nan
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+
+        Using a SparseDtype, you can also change the fill value as well.
+
+        >>> arr.astype(SparseDtype("float64", fill_value=0.0))
+        ... # doctest: +NORMALIZE_WHITESPACE
+        [0.0, 0.0, 1.0, 2.0]
+        Fill: 0.0
+        IntIndex
+        Indices: array([2, 3], dtype=int32)
+        """
+        if dtype == self._dtype:
+            if not copy:
+                return self
+            else:
+                return self.copy()
+
+        future_dtype = pandas_dtype(dtype)
+        if not isinstance(future_dtype, SparseDtype):
+            # GH#34457
+            values = np.asarray(self)
+            values = ensure_wrapped_if_datetimelike(values)
+            return astype_array(values, dtype=future_dtype, copy=False)
+
+        dtype = self.dtype.update_dtype(dtype)
+        subtype = pandas_dtype(dtype._subtype_with_str)
+        subtype = cast(np.dtype, subtype)  # ensured by update_dtype
+        values = ensure_wrapped_if_datetimelike(self.sp_values)
+        sp_values = astype_array(values, subtype, copy=copy)
+        sp_values = np.asarray(sp_values)
+
+        return self._simple_new(sp_values, self.sp_index, dtype)
+
+    def map(self, mapper, na_action=None) -> Self:
+        """
+        Map categories using an input mapping or function.
+
+        Parameters
+        ----------
+        mapper : dict, Series, callable
+            The correspondence from old values to new.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NA values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        SparseArray
+            The output array will have the same density as the input.
+            The output fill value will be the result of applying the
+            mapping to ``self.fill_value``
+
+        Examples
+        --------
+        >>> arr = pd.arrays.SparseArray([0, 1, 2])
+        >>> arr.map(lambda x: x + 10)
+        [10, 11, 12]
+        Fill: 10
+        IntIndex
+        Indices: array([1, 2], dtype=int32)
+
+        >>> arr.map({0: 10, 1: 11, 2: 12})
+        [10, 11, 12]
+        Fill: 10
+        IntIndex
+        Indices: array([1, 2], dtype=int32)
+
+        >>> arr.map(pd.Series([10, 11, 12], index=[0, 1, 2]))
+        [10, 11, 12]
+        Fill: 10
+        IntIndex
+        Indices: array([1, 2], dtype=int32)
+        """
+        is_map = isinstance(mapper, (abc.Mapping, ABCSeries))
+
+        fill_val = self.fill_value
+
+        if na_action is None or notna(fill_val):
+            fill_val = mapper.get(fill_val, fill_val) if is_map else mapper(fill_val)
+
+        def func(sp_val):
+            new_sp_val = mapper.get(sp_val, None) if is_map else mapper(sp_val)
+            # check identity and equality because nans are not equal to each other
+            if new_sp_val is fill_val or new_sp_val == fill_val:
+                msg = "fill value in the sparse values not supported"
+                raise ValueError(msg)
+            return new_sp_val
+
+        sp_values = [func(x) for x in self.sp_values]
+
+        return type(self)(sp_values, sparse_index=self.sp_index, fill_value=fill_val)
+
+    def to_dense(self) -> np.ndarray:
+        """
+        Convert SparseArray to a NumPy array.
+
+        Returns
+        -------
+        arr : NumPy array
+        """
+        return np.asarray(self, dtype=self.sp_values.dtype)
+
+    def _where(self, mask, value):
+        # NB: may not preserve dtype, e.g. result may be Sparse[float64]
+        #  while self is Sparse[int64]
+        naive_implementation = np.where(mask, self, value)
+        dtype = SparseDtype(naive_implementation.dtype, fill_value=self.fill_value)
+        result = type(self)._from_sequence(naive_implementation, dtype=dtype)
+        return result
+
+    # ------------------------------------------------------------------------
+    # IO
+    # ------------------------------------------------------------------------
+    def __setstate__(self, state) -> None:
+        """Necessary for making this object picklable"""
+        if isinstance(state, tuple):
+            # Compat for pandas < 0.24.0
+            nd_state, (fill_value, sp_index) = state
+            sparse_values = np.array([])
+            sparse_values.__setstate__(nd_state)
+
+            self._sparse_values = sparse_values
+            self._sparse_index = sp_index
+            self._dtype = SparseDtype(sparse_values.dtype, fill_value)
+        else:
+            self.__dict__.update(state)
+
+    def nonzero(self) -> tuple[npt.NDArray[np.int32]]:
+        if self.fill_value == 0:
+            return (self.sp_index.indices,)
+        else:
+            return (self.sp_index.indices[self.sp_values != 0],)
+
+    # ------------------------------------------------------------------------
+    # Reductions
+    # ------------------------------------------------------------------------
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        method = getattr(self, name, None)
+
+        if method is None:
+            raise TypeError(f"cannot perform {name} with type {self.dtype}")
+
+        if skipna:
+            arr = self
+        else:
+            arr = self.dropna()
+
+        result = getattr(arr, name)(**kwargs)
+
+        if keepdims:
+            return type(self)([result], dtype=self.dtype)
+        else:
+            return result
+
+    def all(self, axis=None, *args, **kwargs):
+        """
+        Tests whether all elements evaluate True
+
+        Returns
+        -------
+        all : bool
+
+        See Also
+        --------
+        numpy.all
+        """
+        nv.validate_all(args, kwargs)
+
+        values = self.sp_values
+
+        if len(values) != len(self) and not np.all(self.fill_value):
+            return False
+
+        return values.all()
+
+    def any(self, axis: AxisInt = 0, *args, **kwargs) -> bool:
+        """
+        Tests whether at least one of elements evaluate True
+
+        Returns
+        -------
+        any : bool
+
+        See Also
+        --------
+        numpy.any
+        """
+        nv.validate_any(args, kwargs)
+
+        values = self.sp_values
+
+        if len(values) != len(self) and np.any(self.fill_value):
+            return True
+
+        return values.any().item()
+
+    def sum(
+        self,
+        axis: AxisInt = 0,
+        min_count: int = 0,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ) -> Scalar:
+        """
+        Sum of non-NA/null values
+
+        Parameters
+        ----------
+        axis : int, default 0
+            Not Used. NumPy compatibility.
+        min_count : int, default 0
+            The required number of valid values to perform the summation. If fewer
+            than ``min_count`` valid values are present, the result will be the missing
+            value indicator for subarray type.
+        *args, **kwargs
+            Not Used. NumPy compatibility.
+
+        Returns
+        -------
+        scalar
+        """
+        nv.validate_sum(args, kwargs)
+        valid_vals = self._valid_sp_values
+        sp_sum = valid_vals.sum()
+        has_na = self.sp_index.ngaps > 0 and not self._null_fill_value
+
+        if has_na and not skipna:
+            return na_value_for_dtype(self.dtype.subtype, compat=False)
+
+        if self._null_fill_value:
+            if check_below_min_count(valid_vals.shape, None, min_count):
+                return na_value_for_dtype(self.dtype.subtype, compat=False)
+            return sp_sum
+        else:
+            nsparse = self.sp_index.ngaps
+            if check_below_min_count(valid_vals.shape, None, min_count - nsparse):
+                return na_value_for_dtype(self.dtype.subtype, compat=False)
+            return sp_sum + self.fill_value * nsparse
+
+    def cumsum(self, axis: AxisInt = 0, *args, **kwargs) -> SparseArray:
+        """
+        Cumulative sum of non-NA/null values.
+
+        When performing the cumulative summation, any non-NA/null values will
+        be skipped. The resulting SparseArray will preserve the locations of
+        NaN values, but the fill value will be `np.nan` regardless.
+
+        Parameters
+        ----------
+        axis : int or None
+            Axis over which to perform the cumulative summation. If None,
+            perform cumulative summation over flattened array.
+
+        Returns
+        -------
+        cumsum : SparseArray
+        """
+        nv.validate_cumsum(args, kwargs)
+
+        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.
+            raise ValueError(f"axis(={axis}) out of bounds")
+
+        if not self._null_fill_value:
+            return SparseArray(self.to_dense()).cumsum()
+
+        return SparseArray(
+            self.sp_values.cumsum(),
+            sparse_index=self.sp_index,
+            fill_value=self.fill_value,
+        )
+
+    def mean(self, axis: Axis = 0, *args, **kwargs):
+        """
+        Mean of non-NA/null values
+
+        Returns
+        -------
+        mean : float
+        """
+        nv.validate_mean(args, kwargs)
+        valid_vals = self._valid_sp_values
+        sp_sum = valid_vals.sum()
+        ct = len(valid_vals)
+
+        if self._null_fill_value:
+            return sp_sum / ct
+        else:
+            nsparse = self.sp_index.ngaps
+            return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
+
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True):
+        """
+        Max of array values, ignoring NA values if specified.
+
+        Parameters
+        ----------
+        axis : int, default 0
+            Not Used. NumPy compatibility.
+        skipna : bool, default True
+            Whether to ignore NA values.
+
+        Returns
+        -------
+        scalar
+        """
+        nv.validate_minmax_axis(axis, self.ndim)
+        return self._min_max("max", skipna=skipna)
+
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True):
+        """
+        Min of array values, ignoring NA values if specified.
+
+        Parameters
+        ----------
+        axis : int, default 0
+            Not Used. NumPy compatibility.
+        skipna : bool, default True
+            Whether to ignore NA values.
+
+        Returns
+        -------
+        scalar
+        """
+        nv.validate_minmax_axis(axis, self.ndim)
+        return self._min_max("min", skipna=skipna)
+
+    def _min_max(self, kind: Literal["min", "max"], skipna: bool) -> Scalar:
+        """
+        Min/max of non-NA/null values
+
+        Parameters
+        ----------
+        kind : {"min", "max"}
+        skipna : bool
+
+        Returns
+        -------
+        scalar
+        """
+        valid_vals = self._valid_sp_values
+        has_nonnull_fill_vals = not self._null_fill_value and self.sp_index.ngaps > 0
+
+        if len(valid_vals) > 0:
+            sp_min_max = getattr(valid_vals, kind)()
+
+            # If a non-null fill value is currently present, it might be the min/max
+            if has_nonnull_fill_vals:
+                func = max if kind == "max" else min
+                return func(sp_min_max, self.fill_value)
+            elif skipna:
+                return sp_min_max
+            elif self.sp_index.ngaps == 0:
+                # No NAs present
+                return sp_min_max
+            else:
+                return na_value_for_dtype(self.dtype.subtype, compat=False)
+        elif has_nonnull_fill_vals:
+            return self.fill_value
+        else:
+            return na_value_for_dtype(self.dtype.subtype, compat=False)
+
+    def _argmin_argmax(self, kind: Literal["argmin", "argmax"]) -> int:
+        values = self._sparse_values
+        index = self._sparse_index.indices
+        mask = np.asarray(isna(values))
+        func = np.argmax if kind == "argmax" else np.argmin
+
+        idx = np.arange(values.shape[0])
+        non_nans = values[~mask]
+        non_nan_idx = idx[~mask]
+
+        _candidate = non_nan_idx[func(non_nans)]
+        candidate = index[_candidate]
+
+        if isna(self.fill_value):
+            return candidate
+        if kind == "argmin" and self[candidate] < self.fill_value:
+            return candidate
+        if kind == "argmax" and self[candidate] > self.fill_value:
+            return candidate
+        _loc = self._first_fill_value_loc()
+        if _loc == -1:
+            # fill_value doesn't exist
+            return candidate
+        else:
+            return _loc
+
+    def argmax(self, skipna: bool = True) -> int:
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return self._argmin_argmax("argmax")
+
+    def argmin(self, skipna: bool = True) -> int:
+        validate_bool_kwarg(skipna, "skipna")
+        if not skipna and self._hasna:
+            raise NotImplementedError
+        return self._argmin_argmax("argmin")
+
+    # ------------------------------------------------------------------------
+    # Ufuncs
+    # ------------------------------------------------------------------------
+
+    _HANDLED_TYPES = (np.ndarray, numbers.Number)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+        out = kwargs.get("out", ())
+
+        for x in inputs + out:
+            if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):
+                return NotImplemented
+
+        # for binary ops, use our custom dunder methods
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. tests.arrays.sparse.test_arithmetics.test_ndarray_inplace
+            res = arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            return res
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                # e.g. tests.series.test_ufunc.TestNumpyReductions
+                return result
+
+        if len(inputs) == 1:
+            # No alignment necessary.
+            sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)
+            fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)
+
+            if ufunc.nout > 1:
+                # multiple outputs. e.g. modf
+                arrays = tuple(
+                    self._simple_new(
+                        sp_value, self.sp_index, SparseDtype(sp_value.dtype, fv)
+                    )
+                    for sp_value, fv in zip(sp_values, fill_value)
+                )
+                return arrays
+            elif method == "reduce":
+                # e.g. reductions
+                return sp_values
+
+            return self._simple_new(
+                sp_values, self.sp_index, SparseDtype(sp_values.dtype, fill_value)
+            )
+
+        new_inputs = tuple(np.asarray(x) for x in inputs)
+        result = getattr(ufunc, method)(*new_inputs, **kwargs)
+        if out:
+            if len(out) == 1:
+                out = out[0]
+            return out
+
+        if ufunc.nout > 1:
+            return tuple(type(self)(x) for x in result)
+        elif method == "at":
+            # no return value
+            return None
+        else:
+            return type(self)(result)
+
+    # ------------------------------------------------------------------------
+    # Ops
+    # ------------------------------------------------------------------------
+
+    def _arith_method(self, other, op):
+        op_name = op.__name__
+
+        if isinstance(other, SparseArray):
+            return _sparse_array_op(self, other, op, op_name)
+
+        elif is_scalar(other):
+            with np.errstate(all="ignore"):
+                fill = op(_get_fill(self), np.asarray(other))
+                result = op(self.sp_values, other)
+
+            if op_name == "divmod":
+                left, right = result
+                lfill, rfill = fill
+                return (
+                    _wrap_result(op_name, left, self.sp_index, lfill),
+                    _wrap_result(op_name, right, self.sp_index, rfill),
+                )
+
+            return _wrap_result(op_name, result, self.sp_index, fill)
+
+        else:
+            other = np.asarray(other)
+            with np.errstate(all="ignore"):
+                if len(self) != len(other):
+                    raise AssertionError(
+                        f"length mismatch: {len(self)} vs. {len(other)}"
+                    )
+                if not isinstance(other, SparseArray):
+                    dtype = getattr(other, "dtype", None)
+                    other = SparseArray(other, fill_value=self.fill_value, dtype=dtype)
+                return _sparse_array_op(self, other, op, op_name)
+
+    def _cmp_method(self, other, op) -> SparseArray:
+        if not is_scalar(other) and not isinstance(other, type(self)):
+            # convert list-like to ndarray
+            other = np.asarray(other)
+
+        if isinstance(other, np.ndarray):
+            # TODO: make this more flexible than just ndarray...
+            other = SparseArray(other, fill_value=self.fill_value)
+
+        if isinstance(other, SparseArray):
+            if len(self) != len(other):
+                raise ValueError(
+                    f"operands have mismatched length {len(self)} and {len(other)}"
+                )
+
+            op_name = op.__name__.strip("_")
+            return _sparse_array_op(self, other, op, op_name)
+        else:
+            # scalar
+            fill_value = op(self.fill_value, other)
+            result = np.full(len(self), fill_value, dtype=np.bool_)
+            result[self.sp_index.indices] = op(self.sp_values, other)
+
+            return type(self)(
+                result,
+                fill_value=fill_value,
+                dtype=np.bool_,
+            )
+
+    _logical_method = _cmp_method
+
+    def _unary_method(self, op) -> SparseArray:
+        fill_value = op(np.array(self.fill_value)).item()
+        dtype = SparseDtype(self.dtype.subtype, fill_value)
+        # NOTE: if fill_value doesn't change
+        # we just have to apply op to sp_values
+        if isna(self.fill_value) or fill_value == self.fill_value:
+            values = op(self.sp_values)
+            return type(self)._simple_new(values, self.sp_index, self.dtype)
+        # In the other case we have to recalc indexes
+        return type(self)(op(self.to_dense()), dtype=dtype)
+
+    def __pos__(self) -> SparseArray:
+        return self._unary_method(operator.pos)
+
+    def __neg__(self) -> SparseArray:
+        return self._unary_method(operator.neg)
+
+    def __invert__(self) -> SparseArray:
+        return self._unary_method(operator.invert)
+
+    def __abs__(self) -> SparseArray:
+        return self._unary_method(operator.abs)
+
+    # ----------
+    # Formatting
+    # -----------
+    def __repr__(self) -> str:
+        pp_str = printing.pprint_thing(self)
+        pp_fill = printing.pprint_thing(self.fill_value)
+        pp_index = printing.pprint_thing(self.sp_index)
+        return f"{pp_str}\nFill: {pp_fill}\n{pp_index}"
+
+    def _formatter(self, boxed: bool = False):
+        # Defer to the formatter from the GenericArrayFormatter calling us.
+        # This will infer the correct formatter from the dtype of the values.
+        return None
+
+
+def _make_sparse(
+    arr: np.ndarray,
+    kind: SparseIndexKind = "block",
+    fill_value=None,
+    dtype: np.dtype | None = None,
+):
+    """
+    Convert ndarray to sparse format
+
+    Parameters
+    ----------
+    arr : ndarray
+    kind : {'block', 'integer'}
+    fill_value : NaN or another value
+    dtype : np.dtype, optional
+    copy : bool, default False
+
+    Returns
+    -------
+    (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
+    """
+    assert isinstance(arr, np.ndarray)
+
+    if arr.ndim > 1:
+        raise TypeError("expected dimension <= 1 data")
+
+    if fill_value is None:
+        fill_value = na_value_for_dtype(arr.dtype)
+
+    if isna(fill_value):
+        mask = notna(arr)
+    else:
+        # cast to object comparison to be safe
+        if is_string_dtype(arr.dtype):
+            arr = arr.astype(object)
+
+        if is_object_dtype(arr.dtype):
+            # element-wise equality check method in numpy doesn't treat
+            # each element type, eg. 0, 0.0, and False are treated as
+            # same. So we have to check the both of its type and value.
+            mask = splib.make_mask_object_ndarray(arr, fill_value)
+        else:
+            mask = arr != fill_value
+
+    length = len(arr)
+    if length != len(mask):
+        # the arr is a SparseArray
+        indices = mask.sp_index.indices
+    else:
+        indices = mask.nonzero()[0].astype(np.int32)
+
+    index = make_sparse_index(length, indices, kind)
+    sparsified_values = arr[mask]
+    if dtype is not None:
+        sparsified_values = ensure_wrapped_if_datetimelike(sparsified_values)
+        sparsified_values = astype_array(sparsified_values, dtype=dtype)
+        sparsified_values = np.asarray(sparsified_values)
+
+    # TODO: copy
+    return sparsified_values, index, fill_value
+
+
+@overload
+def make_sparse_index(length: int, indices, kind: Literal["block"]) -> BlockIndex:
+    ...
+
+
+@overload
+def make_sparse_index(length: int, indices, kind: Literal["integer"]) -> IntIndex:
+    ...
+
+
+def make_sparse_index(length: int, indices, kind: SparseIndexKind) -> SparseIndex:
+    index: SparseIndex
+    if kind == "block":
+        locs, lens = splib.get_blocks(indices)
+        index = BlockIndex(length, locs, lens)
+    elif kind == "integer":
+        index = IntIndex(length, indices)
+    else:  # pragma: no cover
+        raise ValueError("must be block or integer type")
+    return index
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..71b71a9779da5c1a584e0ef98bc8320d81bc2a35
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/sparse/scipy_sparse.py
@@ -0,0 +1,207 @@
+"""
+Interaction with scipy.sparse matrices.
+
+Currently only includes to_coo helpers.
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.missing import notna
+
+from pandas.core.algorithms import factorize
+from pandas.core.indexes.api import MultiIndex
+from pandas.core.series import Series
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    import numpy as np
+    import scipy.sparse
+
+    from pandas._typing import (
+        IndexLabel,
+        npt,
+    )
+
+
+def _check_is_partition(parts: Iterable, whole: Iterable):
+    whole = set(whole)
+    parts = [set(x) for x in parts]
+    if set.intersection(*parts) != set():
+        raise ValueError("Is not a partition because intersection is not null.")
+    if set.union(*parts) != whole:
+        raise ValueError("Is not a partition because union is not the whole.")
+
+
+def _levels_to_axis(
+    ss,
+    levels: tuple[int] | list[int],
+    valid_ilocs: npt.NDArray[np.intp],
+    sort_labels: bool = False,
+) -> tuple[npt.NDArray[np.intp], list[IndexLabel]]:
+    """
+    For a MultiIndexed sparse Series `ss`, return `ax_coords` and `ax_labels`,
+    where `ax_coords` are the coordinates along one of the two axes of the
+    destination sparse matrix, and `ax_labels` are the labels from `ss`' Index
+    which correspond to these coordinates.
+
+    Parameters
+    ----------
+    ss : Series
+    levels : tuple/list
+    valid_ilocs : numpy.ndarray
+        Array of integer positions of valid values for the sparse matrix in ss.
+    sort_labels : bool, default False
+        Sort the axis labels before forming the sparse matrix. When `levels`
+        refers to a single level, set to True for a faster execution.
+
+    Returns
+    -------
+    ax_coords : numpy.ndarray (axis coordinates)
+    ax_labels : list (axis labels)
+    """
+    # Since the labels are sorted in `Index.levels`, when we wish to sort and
+    # there is only one level of the MultiIndex for this axis, the desired
+    # output can be obtained in the following simpler, more efficient way.
+    if sort_labels and len(levels) == 1:
+        ax_coords = ss.index.codes[levels[0]][valid_ilocs]
+        ax_labels = ss.index.levels[levels[0]]
+
+    else:
+        levels_values = lib.fast_zip(
+            [ss.index.get_level_values(lvl).to_numpy() for lvl in levels]
+        )
+        codes, ax_labels = factorize(levels_values, sort=sort_labels)
+        ax_coords = codes[valid_ilocs]
+
+    ax_labels = ax_labels.tolist()
+    return ax_coords, ax_labels
+
+
+def _to_ijv(
+    ss,
+    row_levels: tuple[int] | list[int] = (0,),
+    column_levels: tuple[int] | list[int] = (1,),
+    sort_labels: bool = False,
+) -> tuple[
+    np.ndarray,
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+    list[IndexLabel],
+    list[IndexLabel],
+]:
+    """
+    For an arbitrary MultiIndexed sparse Series return (v, i, j, ilabels,
+    jlabels) where (v, (i, j)) is suitable for passing to scipy.sparse.coo
+    constructor, and ilabels and jlabels are the row and column labels
+    respectively.
+
+    Parameters
+    ----------
+    ss : Series
+    row_levels : tuple/list
+    column_levels : tuple/list
+    sort_labels : bool, default False
+        Sort the row and column labels before forming the sparse matrix.
+        When `row_levels` and/or `column_levels` refer to a single level,
+        set to `True` for a faster execution.
+
+    Returns
+    -------
+    values : numpy.ndarray
+        Valid values to populate a sparse matrix, extracted from
+        ss.
+    i_coords : numpy.ndarray (row coordinates of the values)
+    j_coords : numpy.ndarray (column coordinates of the values)
+    i_labels : list (row labels)
+    j_labels : list (column labels)
+    """
+    # index and column levels must be a partition of the index
+    _check_is_partition([row_levels, column_levels], range(ss.index.nlevels))
+    # From the sparse Series, get the integer indices and data for valid sparse
+    # entries.
+    sp_vals = ss.array.sp_values
+    na_mask = notna(sp_vals)
+    values = sp_vals[na_mask]
+    valid_ilocs = ss.array.sp_index.indices[na_mask]
+
+    i_coords, i_labels = _levels_to_axis(
+        ss, row_levels, valid_ilocs, sort_labels=sort_labels
+    )
+
+    j_coords, j_labels = _levels_to_axis(
+        ss, column_levels, valid_ilocs, sort_labels=sort_labels
+    )
+
+    return values, i_coords, j_coords, i_labels, j_labels
+
+
+def sparse_series_to_coo(
+    ss: Series,
+    row_levels: Iterable[int] = (0,),
+    column_levels: Iterable[int] = (1,),
+    sort_labels: bool = False,
+) -> tuple[scipy.sparse.coo_matrix, list[IndexLabel], list[IndexLabel]]:
+    """
+    Convert a sparse Series to a scipy.sparse.coo_matrix using index
+    levels row_levels, column_levels as the row and column
+    labels respectively. Returns the sparse_matrix, row and column labels.
+    """
+    import scipy.sparse
+
+    if ss.index.nlevels < 2:
+        raise ValueError("to_coo requires MultiIndex with nlevels >= 2.")
+    if not ss.index.is_unique:
+        raise ValueError(
+            "Duplicate index entries are not allowed in to_coo transformation."
+        )
+
+    # to keep things simple, only rely on integer indexing (not labels)
+    row_levels = [ss.index._get_level_number(x) for x in row_levels]
+    column_levels = [ss.index._get_level_number(x) for x in column_levels]
+
+    v, i, j, rows, columns = _to_ijv(
+        ss, row_levels=row_levels, column_levels=column_levels, sort_labels=sort_labels
+    )
+    sparse_matrix = scipy.sparse.coo_matrix(
+        (v, (i, j)), shape=(len(rows), len(columns))
+    )
+    return sparse_matrix, rows, columns
+
+
+def coo_to_sparse_series(
+    A: scipy.sparse.coo_matrix, dense_index: bool = False
+) -> Series:
+    """
+    Convert a scipy.sparse.coo_matrix to a Series with type sparse.
+
+    Parameters
+    ----------
+    A : scipy.sparse.coo_matrix
+    dense_index : bool, default False
+
+    Returns
+    -------
+    Series
+
+    Raises
+    ------
+    TypeError if A is not a coo_matrix
+    """
+    from pandas import SparseDtype
+
+    try:
+        ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)), copy=False)
+    except AttributeError as err:
+        raise TypeError(
+            f"Expected coo_matrix. Got {type(A).__name__} instead."
+        ) from err
+    ser = ser.sort_index()
+    ser = ser.astype(SparseDtype(ser.dtype))
+    if dense_index:
+        ind = MultiIndex.from_product([A.row, A.col])
+        ser = ser.reindex(ind)
+    return ser
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/string_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/string_.py
new file mode 100644
index 0000000000000000000000000000000000000000..edc5529d0d78c78b4981ec953c4f202c23988914
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/string_.py
@@ -0,0 +1,1135 @@
+from __future__ import annotations
+
+from functools import partial
+import operator
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    get_option,
+    using_string_dtype,
+)
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.lib import ensure_string_array
+from pandas.compat import (
+    HAS_PYARROW,
+    pa_version_under10p1,
+)
+from pandas.compat.numpy import function as nv
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    StorageExtensionDtype,
+    register_extension_dtype,
+)
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_integer_dtype,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+
+from pandas.core import (
+    missing,
+    nanops,
+    ops,
+    roperator,
+)
+from pandas.core.algorithms import isin
+from pandas.core.array_algos import masked_reductions
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.floating import (
+    FloatingArray,
+    FloatingDtype,
+)
+from pandas.core.arrays.integer import (
+    IntegerArray,
+    IntegerDtype,
+)
+from pandas.core.arrays.numpy_ import NumpyExtensionArray
+from pandas.core.construction import extract_array
+from pandas.core.indexers import check_array_indexer
+from pandas.core.missing import isna
+
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from collections.abc import MutableMapping
+
+    import pyarrow
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        Dtype,
+        DtypeObj,
+        NumpySorter,
+        NumpyValueArrayLike,
+        Scalar,
+        Self,
+        npt,
+        type_t,
+    )
+
+    from pandas import Series
+
+
+@register_extension_dtype
+class StringDtype(StorageExtensionDtype):
+    """
+    Extension dtype for string data.
+
+    .. warning::
+
+       StringDtype is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    storage : {"python", "pyarrow"}, optional
+        If not given, the value of ``pd.options.mode.string_storage``.
+    na_value : {np.nan, pd.NA}, default pd.NA
+        Whether the dtype follows NaN or NA missing value semantics.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.StringDtype()
+    string[python]
+
+    >>> pd.StringDtype(storage="pyarrow")
+    string[pyarrow]
+    """
+
+    @property
+    def name(self) -> str:  # type: ignore[override]
+        if self._na_value is libmissing.NA:
+            return "string"
+        else:
+            return "str"
+
+    #: StringDtype().na_value uses pandas.NA except the implementation that
+    # follows NumPy semantics, which uses nan.
+    @property
+    def na_value(self) -> libmissing.NAType | float:  # type: ignore[override]
+        return self._na_value
+
+    _metadata = ("storage", "_na_value")  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        storage: str | None = None,
+        na_value: libmissing.NAType | float = libmissing.NA,
+    ) -> None:
+        # infer defaults
+        if storage is None:
+            if na_value is not libmissing.NA:
+                storage = get_option("mode.string_storage")
+                if storage == "auto":
+                    if HAS_PYARROW:
+                        storage = "pyarrow"
+                    else:
+                        storage = "python"
+            else:
+                storage = get_option("mode.string_storage")
+                if storage == "auto":
+                    storage = "python"
+
+        if storage == "pyarrow_numpy":
+            warnings.warn(
+                "The 'pyarrow_numpy' storage option name is deprecated and will be "
+                'removed in pandas 3.0. Use \'pd.StringDtype(storage="pyarrow", '
+                "na_value-np.nan)' to construct the same dtype.\nOr enable the "
+                "'pd.options.future.infer_string = True' option globally and use "
+                'the "str" alias as a shorthand notation to specify a dtype '
+                '(instead of "string[pyarrow_numpy]").',
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            storage = "pyarrow"
+            na_value = np.nan
+
+        # validate options
+        if storage not in {"python", "pyarrow"}:
+            raise ValueError(
+                f"Storage must be 'python' or 'pyarrow'. Got {storage} instead."
+            )
+        if storage == "pyarrow" and pa_version_under10p1:
+            raise ImportError(
+                "pyarrow>=10.0.1 is required for PyArrow backed StringArray."
+            )
+
+        if isinstance(na_value, float) and np.isnan(na_value):
+            # when passed a NaN value, always set to np.nan to ensure we use
+            # a consistent NaN value (and we can use `dtype.na_value is np.nan`)
+            na_value = np.nan
+        elif na_value is not libmissing.NA:
+            raise ValueError(f"'na_value' must be np.nan or pd.NA, got {na_value}")
+
+        self.storage = cast(str, storage)
+        self._na_value = na_value
+
+    def __repr__(self) -> str:
+        if self._na_value is libmissing.NA:
+            return f"{self.name}[{self.storage}]"
+        else:
+            storage = "" if self.storage == "pyarrow" else "storage='python', "
+            return f"<StringDtype({storage}na_value={self._na_value})>"
+
+    def __eq__(self, other: object) -> bool:
+        # we need to override the base class __eq__ because na_value (NA or NaN)
+        # cannot be checked with normal `==`
+        if isinstance(other, str):
+            # TODO should dtype == "string" work for the NaN variant?
+            if other == "string" or other == self.name:  # noqa: PLR1714
+                return True
+            try:
+                other = self.construct_from_string(other)
+            except (TypeError, ImportError):
+                # TypeError if `other` is not a valid string for StringDtype
+                # ImportError if pyarrow is not installed for "string[pyarrow]"
+                return False
+        if isinstance(other, type(self)):
+            return self.storage == other.storage and self.na_value is other.na_value
+        return False
+
+    def __setstate__(self, state: MutableMapping[str, Any]) -> None:
+        # back-compat for pandas < 2.3, where na_value did not yet exist
+        self.storage = state.pop("storage", "python")
+        self._na_value = state.pop("_na_value", libmissing.NA)
+
+    def __hash__(self) -> int:
+        # need to override __hash__ as well because of overriding __eq__
+        return super().__hash__()
+
+    def __reduce__(self):
+        return StringDtype, (self.storage, self.na_value)
+
+    @property
+    def type(self) -> type[str]:
+        return str
+
+    @classmethod
+    def construct_from_string(cls, string) -> Self:
+        """
+        Construct a StringDtype from a string.
+
+        Parameters
+        ----------
+        string : str
+            The type of the name. The storage type will be taking from `string`.
+            Valid options and their storage types are
+
+            ========================== ==============================================
+            string                     result storage
+            ========================== ==============================================
+            ``'string'``               pd.options.mode.string_storage, default python
+            ``'string[python]'``       python
+            ``'string[pyarrow]'``      pyarrow
+            ========================== ==============================================
+
+        Returns
+        -------
+        StringDtype
+
+        Raise
+        -----
+        TypeError
+            If the string is not a valid option.
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if string == "string":
+            return cls()
+        elif string == "str" and using_string_dtype():
+            return cls(na_value=np.nan)
+        elif string == "string[python]":
+            return cls(storage="python")
+        elif string == "string[pyarrow]":
+            return cls(storage="pyarrow")
+        elif string == "string[pyarrow_numpy]":
+            # this is deprecated in the dtype __init__, remove this in pandas 3.0
+            return cls(storage="pyarrow_numpy")
+        else:
+            raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
+
+    # https://github.com/pandas-dev/pandas/issues/36126
+    # error: Signature of "construct_array_type" incompatible with supertype
+    # "ExtensionDtype"
+    def construct_array_type(  # type: ignore[override]
+        self,
+    ) -> type_t[BaseStringArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays.string_arrow import (
+            ArrowStringArray,
+            ArrowStringArrayNumpySemantics,
+        )
+
+        if self.storage == "python" and self._na_value is libmissing.NA:
+            return StringArray
+        elif self.storage == "pyarrow" and self._na_value is libmissing.NA:
+            return ArrowStringArray
+        elif self.storage == "python":
+            return StringArrayNumpySemantics
+        else:
+            return ArrowStringArrayNumpySemantics
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        storages = set()
+        na_values = set()
+
+        for dtype in dtypes:
+            if isinstance(dtype, StringDtype):
+                storages.add(dtype.storage)
+                na_values.add(dtype.na_value)
+            elif isinstance(dtype, np.dtype) and dtype.kind in ("U", "T"):
+                continue
+            else:
+                return None
+
+        if len(storages) == 2:
+            # if both python and pyarrow storage -> priority to pyarrow
+            storage = "pyarrow"
+        else:
+            storage = next(iter(storages))  # type: ignore[assignment]
+
+        na_value: libmissing.NAType | float
+        if len(na_values) == 2:
+            # if both NaN and NA -> priority to NA
+            na_value = libmissing.NA
+        else:
+            na_value = next(iter(na_values))
+
+        return StringDtype(storage=storage, na_value=na_value)
+
+    def __from_arrow__(
+        self, array: pyarrow.Array | pyarrow.ChunkedArray
+    ) -> BaseStringArray:
+        """
+        Construct StringArray from pyarrow Array/ChunkedArray.
+        """
+        if self.storage == "pyarrow":
+            if self._na_value is libmissing.NA:
+                from pandas.core.arrays.string_arrow import ArrowStringArray
+
+                return ArrowStringArray(array)
+            else:
+                from pandas.core.arrays.string_arrow import (
+                    ArrowStringArrayNumpySemantics,
+                )
+
+                return ArrowStringArrayNumpySemantics(array)
+
+        else:
+            import pyarrow
+
+            if isinstance(array, pyarrow.Array):
+                chunks = [array]
+            else:
+                # pyarrow.ChunkedArray
+                chunks = array.chunks
+
+            results = []
+            for arr in chunks:
+                # convert chunk by chunk to numpy and concatenate then, to avoid
+                # overflow for large string data when concatenating the pyarrow arrays
+                arr = arr.to_numpy(zero_copy_only=False)
+                arr = ensure_string_array(arr, na_value=self.na_value)
+                results.append(arr)
+
+        if len(chunks) == 0:
+            arr = np.array([], dtype=object)
+        else:
+            arr = np.concatenate(results)
+
+        # Bypass validation inside StringArray constructor, see GH#47781
+        new_string_array = StringArray.__new__(StringArray)
+        NDArrayBacked.__init__(new_string_array, arr, self)
+        return new_string_array
+
+
+class BaseStringArray(ExtensionArray):
+    """
+    Mixin class for StringArray, ArrowStringArray.
+    """
+
+    dtype: StringDtype
+
+    # TODO(4.0): Once the deprecation here is enforced, this method can be
+    #  removed and we use the parent class method instead.
+    def _logical_method(self, other, op):
+        if (
+            op in (roperator.ror_, roperator.rand_, roperator.rxor)
+            and isinstance(other, np.ndarray)
+            and other.dtype == bool
+        ):
+            # GH#60234 backward compatibility for the move to StringDtype in 3.0
+            op_name = op.__name__[1:].strip("_")
+            warnings.warn(
+                f"'{op_name}' operations between boolean dtype and {self.dtype} are "
+                "deprecated and will raise in a future version. Explicitly "
+                "cast the strings to a boolean dtype before operating instead.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+            return op(other, self.astype(bool))
+        return NotImplemented
+
+    @doc(ExtensionArray.tolist)
+    def tolist(self):
+        if self.ndim > 1:
+            return [x.tolist() for x in self]
+        return list(self.to_numpy())
+
+    @classmethod
+    def _from_scalars(cls, scalars, dtype: DtypeObj) -> Self:
+        if lib.infer_dtype(scalars, skipna=True) not in ["string", "empty"]:
+            # TODO: require any NAs be valid-for-string
+            raise ValueError
+        return cls._from_sequence(scalars, dtype=dtype)
+
+    def _formatter(self, boxed: bool = False):
+        formatter = partial(
+            printing.pprint_thing,
+            escape_chars=("\t", "\r", "\n"),
+            quote_strings=not boxed,
+        )
+        return formatter
+
+    def _str_map(
+        self,
+        f,
+        na_value=lib.no_default,
+        dtype: Dtype | None = None,
+        convert: bool = True,
+    ):
+        if self.dtype.na_value is np.nan:
+            return self._str_map_nan_semantics(
+                f, na_value=na_value, dtype=dtype, convert=convert
+            )
+
+        from pandas.arrays import BooleanArray
+
+        if dtype is None:
+            dtype = self.dtype
+        if na_value is lib.no_default:
+            na_value = self.dtype.na_value
+
+        mask = isna(self)
+        arr = np.asarray(self)
+
+        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
+            constructor: type[IntegerArray | BooleanArray]
+            if is_integer_dtype(dtype):
+                constructor = IntegerArray
+            else:
+                constructor = BooleanArray
+
+            na_value_is_na = isna(na_value)
+            if na_value_is_na:
+                na_value = 1
+            elif dtype == np.dtype("bool"):
+                # GH#55736
+                na_value = bool(na_value)
+            result = lib.map_infer_mask(
+                arr,
+                f,
+                mask.view("uint8"),
+                convert=False,
+                na_value=na_value,
+                # error: Argument 1 to "dtype" has incompatible type
+                # "Union[ExtensionDtype, str, dtype[Any], Type[object]]"; expected
+                # "Type[object]"
+                dtype=np.dtype(cast(type, dtype)),
+            )
+
+            if not na_value_is_na:
+                mask[:] = False
+
+            return constructor(result, mask)
+
+        else:
+            return self._str_map_str_or_object(dtype, na_value, arr, f, mask)
+
+    def _str_map_str_or_object(
+        self,
+        dtype,
+        na_value,
+        arr: np.ndarray,
+        f,
+        mask: npt.NDArray[np.bool_],
+    ):
+        # _str_map helper for case where dtype is either string dtype or object
+        if is_string_dtype(dtype) and not is_object_dtype(dtype):
+            # i.e. StringDtype
+            result = lib.map_infer_mask(
+                arr, f, mask.view("uint8"), convert=False, na_value=na_value
+            )
+            if self.dtype.storage == "pyarrow":
+                import pyarrow as pa
+
+                result = pa.array(
+                    result, mask=mask, type=pa.large_string(), from_pandas=True
+                )
+            # error: Too many arguments for "BaseStringArray"
+            return type(self)(result)  # type: ignore[call-arg]
+
+        else:
+            # This is when the result type is object. We reach this when
+            # -> We know the result type is truly object (e.g. .encode returns bytes
+            #    or .findall returns a list).
+            # -> We don't know the result type. E.g. `.get` can return anything.
+            return lib.map_infer_mask(arr, f, mask.view("uint8"))
+
+    def _str_map_nan_semantics(
+        self,
+        f,
+        na_value=lib.no_default,
+        dtype: Dtype | None = None,
+        convert: bool = True,
+    ):
+        if dtype is None:
+            dtype = self.dtype
+        if na_value is lib.no_default:
+            if is_bool_dtype(dtype):
+                # NaN propagates as False
+                na_value = False
+            else:
+                na_value = self.dtype.na_value
+
+        mask = isna(self)
+        arr = np.asarray(self)
+
+        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
+            na_value_is_na = isna(na_value)
+            if na_value_is_na:
+                if is_integer_dtype(dtype):
+                    na_value = 0
+                else:
+                    # NaN propagates as False
+                    na_value = False
+
+            result = lib.map_infer_mask(
+                arr,
+                f,
+                mask.view("uint8"),
+                convert=False,
+                na_value=na_value,
+                dtype=np.dtype(cast(type, dtype)),
+            )
+            if na_value_is_na and is_integer_dtype(dtype) and mask.any():
+                # TODO: we could alternatively do this check before map_infer_mask
+                #  and adjust the dtype/na_value we pass there. Which is more
+                #  performant?
+                result = result.astype("float64")
+                result[mask] = np.nan
+
+            return result
+
+        else:
+            return self._str_map_str_or_object(dtype, na_value, arr, f, mask)
+
+    def view(self, dtype: Dtype | None = None) -> ArrayLike:
+        if dtype is not None:
+            raise TypeError("Cannot change data-type for string array.")
+        return super().view(dtype=dtype)
+
+
+# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
+# incompatible with definition in base class "ExtensionArray"
+class StringArray(BaseStringArray, NumpyExtensionArray):  # type: ignore[misc]
+    """
+    Extension array for string data.
+
+    .. warning::
+
+       StringArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : array-like
+        The array of data.
+
+        .. warning::
+
+           Currently, this expects an object-dtype ndarray
+           where the elements are Python strings
+           or nan-likes (``None``, ``np.nan``, ``NA``).
+           This may change without warning in the future. Use
+           :meth:`pandas.array` with ``dtype="string"`` for a stable way of
+           creating a `StringArray` from any sequence.
+
+        .. versionchanged:: 1.5.0
+
+           StringArray now accepts array-likes containing
+           nan-likes(``None``, ``np.nan``) for the ``values`` parameter
+           in addition to strings and :attr:`pandas.NA`
+
+    copy : bool, default False
+        Whether to copy the array of data.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    :func:`pandas.array`
+        The recommended function for creating a StringArray.
+    Series.str
+        The string methods are available on Series backed by
+        a StringArray.
+
+    Notes
+    -----
+    StringArray returns a BooleanArray for comparison methods.
+
+    Examples
+    --------
+    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string")
+    <StringArray>
+    ['This is', 'some text', <NA>, 'data.']
+    Length: 4, dtype: string
+
+    Unlike arrays instantiated with ``dtype="object"``, ``StringArray``
+    will convert the values to strings.
+
+    >>> pd.array(['1', 1], dtype="object")
+    <NumpyExtensionArray>
+    ['1', 1]
+    Length: 2, dtype: object
+    >>> pd.array(['1', 1], dtype="string")
+    <StringArray>
+    ['1', '1']
+    Length: 2, dtype: string
+
+    However, instantiating StringArrays directly with non-strings will raise an error.
+
+    For comparison methods, `StringArray` returns a :class:`pandas.BooleanArray`:
+
+    >>> pd.array(["a", None, "c"], dtype="string") == "a"
+    <BooleanArray>
+    [True, <NA>, False]
+    Length: 3, dtype: boolean
+    """
+
+    # undo the NumpyExtensionArray hack
+    _typ = "extension"
+    _storage = "python"
+    _na_value: libmissing.NAType | float = libmissing.NA
+
+    def __init__(self, values, copy: bool = False) -> None:
+        values = extract_array(values)
+
+        super().__init__(values, copy=copy)
+        if not isinstance(values, type(self)):
+            self._validate()
+        NDArrayBacked.__init__(
+            self,
+            self._ndarray,
+            StringDtype(storage=self._storage, na_value=self._na_value),
+        )
+
+    def _validate(self):
+        """Validate that we only store NA or strings."""
+        if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
+            raise ValueError("StringArray requires a sequence of strings or pandas.NA")
+        if self._ndarray.dtype != "object":
+            raise ValueError(
+                "StringArray requires a sequence of strings or pandas.NA. Got "
+                f"'{self._ndarray.dtype}' dtype instead."
+            )
+        # Check to see if need to convert Na values to pd.NA
+        if self._ndarray.ndim > 2:
+            # Ravel if ndims > 2 b/c no cythonized version available
+            lib.convert_nans_to_NA(self._ndarray.ravel("K"))
+        else:
+            lib.convert_nans_to_NA(self._ndarray)
+
+    def _validate_scalar(self, value):
+        # used by NDArrayBackedExtensionIndex.insert
+        if isna(value):
+            return self.dtype.na_value
+        elif not isinstance(value, str):
+            raise TypeError(
+                f"Invalid value '{value}' for dtype '{self.dtype}'. Value should be a "
+                f"string or missing value, got '{type(value).__name__}' instead."
+            )
+        return value
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        if dtype and not (isinstance(dtype, str) and dtype == "string"):
+            dtype = pandas_dtype(dtype)
+            assert isinstance(dtype, StringDtype) and dtype.storage == "python"
+        else:
+            if using_string_dtype():
+                dtype = StringDtype(storage="python", na_value=np.nan)
+            else:
+                dtype = StringDtype(storage="python")
+
+        from pandas.core.arrays.masked import BaseMaskedArray
+
+        na_value = dtype.na_value
+        if isinstance(scalars, BaseMaskedArray):
+            # avoid costly conversion to object dtype
+            na_values = scalars._mask
+            result = scalars._data
+            result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
+            result[na_values] = na_value
+
+        else:
+            if lib.is_pyarrow_array(scalars):
+                # pyarrow array; we cannot rely on the "to_numpy" check in
+                #  ensure_string_array because calling scalars.to_numpy would set
+                #  zero_copy_only to True which caused problems see GH#52076
+                scalars = np.array(scalars)
+            # convert non-na-likes to str, and nan-likes to StringDtype().na_value
+            result = lib.ensure_string_array(scalars, na_value=na_value, copy=copy)
+
+        # Manually creating new array avoids the validation step in the __init__, so is
+        # faster. Refactor need for validation?
+        new_string_array = cls.__new__(cls)
+        NDArrayBacked.__init__(new_string_array, result, dtype)
+
+        return new_string_array
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, *, dtype: Dtype | None = None, copy: bool = False
+    ):
+        return cls._from_sequence(strings, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _empty(cls, shape, dtype) -> StringArray:
+        values = np.empty(shape, dtype=object)
+        values[:] = libmissing.NA
+        return cls(values).astype(dtype, copy=False)
+
+    def __arrow_array__(self, type=None):
+        """
+        Convert myself into a pyarrow Array.
+        """
+        import pyarrow as pa
+
+        if type is None:
+            type = pa.string()
+
+        values = self._ndarray.copy()
+        values[self.isna()] = None
+        return pa.array(values, type=type, from_pandas=True)
+
+    def _values_for_factorize(self) -> tuple[np.ndarray, libmissing.NAType | float]:  # type: ignore[override]
+        arr = self._ndarray.copy()
+
+        return arr, self.dtype.na_value
+
+    def _maybe_convert_setitem_value(self, value):
+        """Maybe convert value to be pyarrow compatible."""
+        if lib.is_scalar(value):
+            if isna(value):
+                value = self.dtype.na_value
+            elif not isinstance(value, str):
+                raise TypeError(
+                    f"Invalid value '{value}' for dtype '{self.dtype}'. Value should "
+                    f"be a string or missing value, got '{type(value).__name__}' "
+                    "instead."
+                )
+        else:
+            value = extract_array(value, extract_numpy=True)
+            if not is_array_like(value):
+                value = np.asarray(value, dtype=object)
+            elif isinstance(value.dtype, type(self.dtype)):
+                return value
+            else:
+                # cast categories and friends to arrays to see if values are
+                # compatible, compatibility with arrow backed strings
+                value = np.asarray(value)
+            if len(value) and not lib.is_string_array(value, skipna=True):
+                raise TypeError(
+                    "Invalid value for dtype 'str'. Value should be a "
+                    "string or missing value (or array of those)."
+                )
+        return value
+
+    def __setitem__(self, key, value) -> None:
+        value = self._maybe_convert_setitem_value(value)
+
+        key = check_array_indexer(self, key)
+        scalar_key = lib.is_scalar(key)
+        scalar_value = lib.is_scalar(value)
+        if scalar_key and not scalar_value:
+            raise ValueError("setting an array element with a sequence.")
+
+        if not scalar_value:
+            if value.dtype == self.dtype:
+                value = value._ndarray
+            else:
+                value = np.asarray(value)
+                mask = isna(value)
+                if mask.any():
+                    value = value.copy()
+                    value[isna(value)] = self.dtype.na_value
+
+        super().__setitem__(key, value)
+
+    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
+        # the super() method NDArrayBackedExtensionArray._putmask uses
+        # np.putmask which doesn't properly handle None/pd.NA, so using the
+        # base class implementation that uses __setitem__
+        ExtensionArray._putmask(self, mask, value)
+
+    def _where(self, mask: npt.NDArray[np.bool_], value) -> Self:
+        # the super() method NDArrayBackedExtensionArray._where uses
+        # np.putmask which doesn't properly handle None/pd.NA, so using the
+        # base class implementation that uses __setitem__
+        return ExtensionArray._where(self, mask, value)
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        if isinstance(values, BaseStringArray) or (
+            isinstance(values, ExtensionArray) and is_string_dtype(values.dtype)
+        ):
+            values = values.astype(self.dtype, copy=False)
+        else:
+            if not lib.is_string_array(np.asarray(values), skipna=True):
+                values = np.array(
+                    [val for val in values if isinstance(val, str) or isna(val)],
+                    dtype=object,
+                )
+                if not len(values):
+                    return np.zeros(self.shape, dtype=bool)
+
+            values = self._from_sequence(values, dtype=self.dtype)
+
+        return isin(np.asarray(self), np.asarray(values))
+
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+
+        elif isinstance(dtype, IntegerDtype):
+            arr = self._ndarray.copy()
+            mask = self.isna()
+            arr[mask] = 0
+            values = arr.astype(dtype.numpy_dtype)
+            return IntegerArray(values, mask, copy=False)
+        elif isinstance(dtype, FloatingDtype):
+            arr = self.copy()
+            mask = self.isna()
+            arr[mask] = "0"
+            values = arr.astype(dtype.numpy_dtype)
+            return FloatingArray(values, mask, copy=False)
+        elif isinstance(dtype, ExtensionDtype):
+            # Skip the NumpyExtensionArray.astype method
+            return ExtensionArray.astype(self, dtype, copy)
+        elif np.issubdtype(dtype, np.floating):
+            arr = self._ndarray.copy()
+            mask = self.isna()
+            arr[mask] = 0
+            values = arr.astype(dtype)
+            values[mask] = np.nan
+            return values
+
+        return super().astype(dtype, copy)
+
+    def _reduce(
+        self,
+        name: str,
+        *,
+        skipna: bool = True,
+        keepdims: bool = False,
+        axis: AxisInt | None = 0,
+        **kwargs,
+    ):
+        if self.dtype.na_value is np.nan and name in ["any", "all"]:
+            if name == "any":
+                return nanops.nanany(self._ndarray, skipna=skipna)
+            else:
+                return nanops.nanall(self._ndarray, skipna=skipna)
+
+        if name in ["min", "max", "argmin", "argmax", "sum"]:
+            result = getattr(self, name)(skipna=skipna, axis=axis, **kwargs)
+            if keepdims:
+                return self._from_sequence([result], dtype=self.dtype)
+            return result
+        raise TypeError(f"Cannot perform reduction '{name}' with string dtype")
+
+    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs) -> StringArray:
+        """
+        Return an ExtensionArray performing an accumulation operation.
+
+        The underlying data type might change.
+
+        Parameters
+        ----------
+        name : str
+            Name of the function, supported values are:
+            - cummin
+            - cummax
+            - cumsum
+            - cumprod
+        skipna : bool, default True
+            If True, skip NA values.
+        **kwargs
+            Additional keyword arguments passed to the accumulation function.
+            Currently, there is no supported kwarg.
+
+        Returns
+        -------
+        array
+
+        Raises
+        ------
+        NotImplementedError : subclass does not define accumulations
+        """
+        if name == "cumprod":
+            msg = f"operation '{name}' not supported for dtype '{self.dtype}'"
+            raise TypeError(msg)
+
+        # We may need to strip out trailing NA values
+        tail: np.ndarray | None = None
+        na_mask: np.ndarray | None = None
+        ndarray = self._ndarray
+        np_func = {
+            "cumsum": np.cumsum,
+            "cummin": np.minimum.accumulate,
+            "cummax": np.maximum.accumulate,
+        }[name]
+
+        if self._hasna:
+            na_mask = cast("npt.NDArray[np.bool_]", isna(ndarray))
+            if np.all(na_mask):
+                return type(self)(ndarray)
+            if skipna:
+                if name == "cumsum":
+                    ndarray = np.where(na_mask, "", ndarray)
+                else:
+                    # We can retain the running min/max by forward/backward filling.
+                    ndarray = ndarray.copy()
+                    missing.pad_or_backfill_inplace(
+                        ndarray,
+                        method="pad",
+                        axis=0,
+                    )
+                    missing.pad_or_backfill_inplace(
+                        ndarray,
+                        method="backfill",
+                        axis=0,
+                    )
+            else:
+                # When not skipping NA values, the result should be null from
+                # the first NA value onward.
+                idx = np.argmax(na_mask)
+                tail = np.empty(len(ndarray) - idx, dtype="object")
+                tail[:] = self.dtype.na_value
+                ndarray = ndarray[:idx]
+
+        # mypy: Cannot call function of unknown type
+        np_result = np_func(ndarray)  # type: ignore[operator]
+
+        if tail is not None:
+            np_result = np.hstack((np_result, tail))
+        elif na_mask is not None:
+            # Argument 2 to "where" has incompatible type "NAType | float"
+            np_result = np.where(na_mask, self.dtype.na_value, np_result)  # type: ignore[arg-type]
+
+        result = type(self)(np_result)
+        return result
+
+    def _wrap_reduction_result(self, axis: AxisInt | None, result) -> Any:
+        if self.dtype.na_value is np.nan and result is libmissing.NA:
+            # the masked_reductions use pd.NA -> convert to np.nan
+            return np.nan
+        return super()._wrap_reduction_result(axis, result)
+
+    def min(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
+        nv.validate_min((), kwargs)
+        result = masked_reductions.min(
+            values=self.to_numpy(), mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def max(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
+        nv.validate_max((), kwargs)
+        result = masked_reductions.max(
+            values=self.to_numpy(), mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        min_count: int = 0,
+        **kwargs,
+    ) -> Scalar:
+        nv.validate_sum((), kwargs)
+        result = masked_reductions.sum(
+            values=self._ndarray, mask=self.isna(), skipna=skipna
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        from pandas.core.algorithms import value_counts_internal as value_counts
+
+        result = value_counts(self._ndarray, dropna=dropna).astype("Int64")
+        result = value_counts(self._ndarray, sort=False, dropna=dropna)
+        result.index = result.index.astype(self.dtype)
+
+        if self.dtype.na_value is libmissing.NA:
+            result = result.astype("Int64")
+        return result
+
+    def memory_usage(self, deep: bool = False) -> int:
+        result = self._ndarray.nbytes
+        if deep:
+            return result + lib.memory_usage_of_objects(self._ndarray)
+        return result
+
+    @doc(ExtensionArray.searchsorted)
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if self._hasna:
+            raise ValueError(
+                "searchsorted requires array to be sorted, which is impossible "
+                "with NAs present."
+            )
+        return super().searchsorted(value=value, side=side, sorter=sorter)
+
+    def _cmp_method(self, other, op):
+        from pandas.arrays import (
+            ArrowExtensionArray,
+            BooleanArray,
+        )
+
+        if (
+            isinstance(other, BaseStringArray)
+            and self.dtype.na_value is not libmissing.NA
+            and other.dtype.na_value is libmissing.NA
+        ):
+            # NA has priority of NaN semantics
+            return NotImplemented
+
+        if isinstance(other, ArrowExtensionArray):
+            if isinstance(other, BaseStringArray):
+                # pyarrow storage has priority over python storage
+                # (except if we have NA semantics and other not)
+                if not (
+                    self.dtype.na_value is libmissing.NA
+                    and other.dtype.na_value is not libmissing.NA
+                ):
+                    return NotImplemented
+            else:
+                return NotImplemented
+
+        if isinstance(other, StringArray):
+            other = other._ndarray
+
+        mask = isna(self) | isna(other)
+        valid = ~mask
+
+        if lib.is_list_like(other):
+            if len(other) != len(self):
+                # prevent improper broadcasting when other is 2D
+                raise ValueError(
+                    f"Lengths of operands do not match: {len(self)} != {len(other)}"
+                )
+
+            # for array-likes, first filter out NAs before converting to numpy
+            if not is_array_like(other):
+                other = np.asarray(other)
+            other = other[valid]
+
+        if op.__name__ in ops.ARITHMETIC_BINOPS:
+            result = np.empty_like(self._ndarray, dtype="object")
+            result[mask] = self.dtype.na_value
+            result[valid] = op(self._ndarray[valid], other)
+            if isinstance(other, Path):
+                # GH#61940
+                return result
+            return self._from_backing_data(result)
+        else:
+            # logical
+            result = np.zeros(len(self._ndarray), dtype="bool")
+            result[valid] = op(self._ndarray[valid], other)
+            res_arr = BooleanArray(result, mask)
+            if self.dtype.na_value is np.nan:
+                if op == operator.ne:
+                    return res_arr.to_numpy(np.bool_, na_value=True)
+                else:
+                    return res_arr.to_numpy(np.bool_, na_value=False)
+            return res_arr
+
+    _arith_method = _cmp_method
+
+
+class StringArrayNumpySemantics(StringArray):
+    _storage = "python"
+    _na_value = np.nan
+
+    def _validate(self) -> None:
+        """Validate that we only store NaN or strings."""
+        if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
+            raise ValueError(
+                "StringArrayNumpySemantics requires a sequence of strings or NaN"
+            )
+        if self._ndarray.dtype != "object":
+            raise ValueError(
+                "StringArrayNumpySemantics requires a sequence of strings or NaN. Got "
+                f"'{self._ndarray.dtype}' dtype instead."
+            )
+        # TODO validate or force NA/None to NaN
+
+    @classmethod
+    def _from_sequence(
+        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
+    ) -> Self:
+        if dtype is None:
+            dtype = StringDtype(storage="python", na_value=np.nan)
+        return super()._from_sequence(scalars, dtype=dtype, copy=copy)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/string_arrow.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/string_arrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9fd74cbd76b1262f6cc13f04486ba89026ab959
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/string_arrow.py
@@ -0,0 +1,555 @@
+from __future__ import annotations
+
+import operator
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Union,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under13p0,
+    pa_version_under16p0,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays._arrow_string_mixins import ArrowStringArrayMixin
+from pandas.core.arrays.arrow import ArrowExtensionArray
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.arrays.floating import Float64Dtype
+from pandas.core.arrays.integer import Int64Dtype
+from pandas.core.arrays.numeric import NumericDtype
+from pandas.core.arrays.string_ import (
+    BaseStringArray,
+    StringDtype,
+)
+from pandas.core.strings.object_array import ObjectStringArrayMixin
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+    import pyarrow.compute as pc
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        Dtype,
+        Scalar,
+        Self,
+        npt,
+    )
+
+    from pandas import Series
+
+
+ArrowStringScalarOrNAT = Union[str, libmissing.NAType]
+
+
+def _chk_pyarrow_available() -> None:
+    if pa_version_under10p1:
+        msg = "pyarrow>=10.0.1 is required for PyArrow backed ArrowExtensionArray."
+        raise ImportError(msg)
+
+
+def _is_string_view(typ):
+    return not pa_version_under16p0 and pa.types.is_string_view(typ)
+
+
+# TODO: Inherit directly from BaseStringArrayMethods. Currently we inherit from
+# ObjectStringArrayMixin because we want to have the object-dtype based methods as
+# fallback for the ones that pyarrow doesn't yet support
+
+
+class ArrowStringArray(ObjectStringArrayMixin, ArrowExtensionArray, BaseStringArray):
+    """
+    Extension array for string data in a ``pyarrow.ChunkedArray``.
+
+    .. warning::
+
+       ArrowStringArray is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    Parameters
+    ----------
+    values : pyarrow.Array or pyarrow.ChunkedArray
+        The array of data.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    :func:`pandas.array`
+        The recommended function for creating a ArrowStringArray.
+    Series.str
+        The string methods are available on Series backed by
+        a ArrowStringArray.
+
+    Notes
+    -----
+    ArrowStringArray returns a BooleanArray for comparison methods.
+
+    Examples
+    --------
+    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string[pyarrow]")
+    <ArrowStringArray>
+    ['This is', 'some text', <NA>, 'data.']
+    Length: 4, dtype: string
+    """
+
+    # error: Incompatible types in assignment (expression has type "StringDtype",
+    # base class "ArrowExtensionArray" defined the type as "ArrowDtype")
+    _dtype: StringDtype  # type: ignore[assignment]
+    _storage = "pyarrow"
+    _na_value: libmissing.NAType | float = libmissing.NA
+
+    def __init__(self, values) -> None:
+        _chk_pyarrow_available()
+        if isinstance(values, (pa.Array, pa.ChunkedArray)) and (
+            pa.types.is_string(values.type)
+            or _is_string_view(values.type)
+            or (
+                pa.types.is_dictionary(values.type)
+                and (
+                    pa.types.is_string(values.type.value_type)
+                    or pa.types.is_large_string(values.type.value_type)
+                    or _is_string_view(values.type.value_type)
+                )
+            )
+        ):
+            values = pc.cast(values, pa.large_string())
+
+        super().__init__(values)
+        self._dtype = StringDtype(storage=self._storage, na_value=self._na_value)
+
+        if not pa.types.is_large_string(self._pa_array.type):
+            raise ValueError(
+                "ArrowStringArray requires a PyArrow (chunked) array of "
+                "large_string type"
+            )
+
+    @classmethod
+    def _box_pa_scalar(cls, value, pa_type: pa.DataType | None = None) -> pa.Scalar:
+        pa_scalar = super()._box_pa_scalar(value, pa_type)
+        if pa.types.is_string(pa_scalar.type) and pa_type is None:
+            pa_scalar = pc.cast(pa_scalar, pa.large_string())
+        return pa_scalar
+
+    @classmethod
+    def _box_pa_array(
+        cls, value, pa_type: pa.DataType | None = None, copy: bool = False
+    ) -> pa.Array | pa.ChunkedArray:
+        pa_array = super()._box_pa_array(value, pa_type)
+        if pa.types.is_string(pa_array.type) and pa_type is None:
+            pa_array = pc.cast(pa_array, pa.large_string())
+        return pa_array
+
+    def __len__(self) -> int:
+        """
+        Length of this array.
+
+        Returns
+        -------
+        length : int
+        """
+        return len(self._pa_array)
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
+        from pandas.core.arrays.masked import BaseMaskedArray
+
+        _chk_pyarrow_available()
+
+        if dtype and not (isinstance(dtype, str) and dtype == "string"):
+            dtype = pandas_dtype(dtype)
+            assert isinstance(dtype, StringDtype) and dtype.storage == "pyarrow"
+
+        if isinstance(scalars, BaseMaskedArray):
+            # avoid costly conversion to object dtype in ensure_string_array and
+            # numerical issues with Float32Dtype
+            na_values = scalars._mask
+            result = scalars._data
+            result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
+            return cls(pa.array(result, mask=na_values, type=pa.large_string()))
+        elif isinstance(scalars, (pa.Array, pa.ChunkedArray)):
+            return cls(pc.cast(scalars, pa.large_string()))
+
+        # convert non-na-likes to str
+        result = lib.ensure_string_array(scalars, copy=copy)
+        return cls(pa.array(result, type=pa.large_string(), from_pandas=True))
+
+    @classmethod
+    def _from_sequence_of_strings(
+        cls, strings, dtype: Dtype | None = None, copy: bool = False
+    ):
+        return cls._from_sequence(strings, dtype=dtype, copy=copy)
+
+    @property
+    def dtype(self) -> StringDtype:  # type: ignore[override]
+        """
+        An instance of 'string[pyarrow]'.
+        """
+        return self._dtype
+
+    def insert(self, loc: int, item) -> ArrowStringArray:
+        if self.dtype.na_value is np.nan and item is np.nan:
+            item = libmissing.NA
+        if not isinstance(item, str) and item is not libmissing.NA:
+            raise TypeError(
+                f"Invalid value '{item}' for dtype 'str'. Value should be a "
+                f"string or missing value, got '{type(item).__name__}' instead."
+            )
+        return super().insert(loc, item)
+
+    def _convert_bool_result(self, values, na=lib.no_default, method_name=None):
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                f"Allowing a non-bool 'na' in obj.str.{method_name} is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            na = bool(na)
+
+        if self.dtype.na_value is np.nan:
+            if na is lib.no_default or isna(na):
+                # NaN propagates as False
+                values = values.fill_null(False)
+            else:
+                values = values.fill_null(na)
+            return values.to_numpy()
+        else:
+            if na is not lib.no_default and not isna(
+                na
+            ):  # pyright: ignore [reportGeneralTypeIssues]
+                values = values.fill_null(na)
+        return BooleanDtype().__from_arrow__(values)
+
+    def _maybe_convert_setitem_value(self, value):
+        """Maybe convert value to be pyarrow compatible."""
+        if is_scalar(value):
+            if isna(value):
+                value = None
+            elif not isinstance(value, str):
+                raise TypeError(
+                    f"Invalid value '{value}' for dtype 'str'. Value should be a "
+                    f"string or missing value, got '{type(value).__name__}' instead."
+                )
+        else:
+            value = np.array(value, dtype=object, copy=True)
+            value[isna(value)] = None
+            for v in value:
+                if not (v is None or isinstance(v, str)):
+                    raise TypeError(
+                        "Invalid value for dtype 'str'. Value should be a "
+                        "string or missing value (or array of those)."
+                    )
+        return super()._maybe_convert_setitem_value(value)
+
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        value_set = [
+            pa_scalar.as_py()
+            for pa_scalar in [pa.scalar(value, from_pandas=True) for value in values]
+            if pa_scalar.type in (pa.string(), pa.null(), pa.large_string())
+        ]
+
+        # short-circuit to return all False array.
+        if not len(value_set):
+            return np.zeros(len(self), dtype=bool)
+
+        result = pc.is_in(
+            self._pa_array, value_set=pa.array(value_set, type=self._pa_array.type)
+        )
+        # pyarrow 2.0.0 returned nulls, so we explicily specify dtype to convert nulls
+        # to False
+        return np.array(result, dtype=np.bool_)
+
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+
+        if dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+        elif isinstance(dtype, NumericDtype):
+            data = self._pa_array.cast(pa.from_numpy_dtype(dtype.numpy_dtype))
+            return dtype.__from_arrow__(data)
+        elif isinstance(dtype, np.dtype) and np.issubdtype(dtype, np.floating):
+            return self.to_numpy(dtype=dtype, na_value=np.nan)
+
+        return super().astype(dtype, copy=copy)
+
+    @property
+    def _data(self):
+        # dask accesses ._data directlys
+        warnings.warn(
+            f"{type(self).__name__}._data is a deprecated and will be removed "
+            "in a future version, use ._pa_array instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._pa_array
+
+    # ------------------------------------------------------------------------
+    # String methods interface
+
+    _str_isalnum = ArrowStringArrayMixin._str_isalnum
+    _str_isalpha = ArrowStringArrayMixin._str_isalpha
+    _str_isdecimal = ArrowStringArrayMixin._str_isdecimal
+    _str_isdigit = ArrowStringArrayMixin._str_isdigit
+    _str_islower = ArrowStringArrayMixin._str_islower
+    _str_isnumeric = ArrowStringArrayMixin._str_isnumeric
+    _str_isspace = ArrowStringArrayMixin._str_isspace
+    _str_istitle = ArrowStringArrayMixin._str_istitle
+    _str_isupper = ArrowStringArrayMixin._str_isupper
+
+    _str_map = BaseStringArray._str_map
+    _str_startswith = ArrowStringArrayMixin._str_startswith
+    _str_endswith = ArrowStringArrayMixin._str_endswith
+    _str_pad = ArrowStringArrayMixin._str_pad
+    _str_lower = ArrowStringArrayMixin._str_lower
+    _str_upper = ArrowStringArrayMixin._str_upper
+    _str_strip = ArrowStringArrayMixin._str_strip
+    _str_lstrip = ArrowStringArrayMixin._str_lstrip
+    _str_rstrip = ArrowStringArrayMixin._str_rstrip
+    _str_removesuffix = ArrowStringArrayMixin._str_removesuffix
+    _str_get = ArrowStringArrayMixin._str_get
+    _str_capitalize = ArrowStringArrayMixin._str_capitalize
+    _str_title = ArrowStringArrayMixin._str_title
+    _str_swapcase = ArrowStringArrayMixin._str_swapcase
+    _str_slice_replace = ArrowStringArrayMixin._str_slice_replace
+    _str_len = ArrowStringArrayMixin._str_len
+    _str_slice = ArrowStringArrayMixin._str_slice
+
+    @staticmethod
+    def _is_re_pattern_with_flags(pat: str | re.Pattern) -> bool:
+        # check if `pat` is a compiled regex pattern with flags that are not
+        # supported by pyarrow
+        return (
+            isinstance(pat, re.Pattern)
+            and (pat.flags & ~(re.IGNORECASE | re.UNICODE)) != 0
+        )
+
+    @staticmethod
+    def _preprocess_re_pattern(pat: re.Pattern, case: bool) -> tuple[str, bool, int]:
+        pattern = pat.pattern
+        flags = pat.flags
+        # flags is not supported by pyarrow, but `case` is -> extract and remove
+        if flags & re.IGNORECASE:
+            case = False
+            flags = flags & ~re.IGNORECASE
+        # when creating a pattern with re.compile and a string, it automatically
+        # gets a UNICODE flag, while pyarrow assumes unicode for strings anyway
+        flags = flags & ~re.UNICODE
+        return pattern, case, flags
+
+    def _str_contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na=lib.no_default,
+        regex: bool = True,
+    ):
+        if flags or self._is_re_pattern_with_flags(pat):
+            return super()._str_contains(pat, case, flags, na, regex)
+        if isinstance(pat, re.Pattern):
+            # TODO flags passed separately by user are ignored
+            pat, case, flags = self._preprocess_re_pattern(pat, case)
+
+        return ArrowStringArrayMixin._str_contains(self, pat, case, flags, na, regex)
+
+    def _str_match(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if flags or self._is_re_pattern_with_flags(pat):
+            return super()._str_match(pat, case, flags, na)
+        if isinstance(pat, re.Pattern):
+            pat, case, flags = self._preprocess_re_pattern(pat, case)
+
+        return ArrowStringArrayMixin._str_match(self, pat, case, flags, na)
+
+    def _str_fullmatch(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if flags or self._is_re_pattern_with_flags(pat):
+            return super()._str_fullmatch(pat, case, flags, na)
+        if isinstance(pat, re.Pattern):
+            pat, case, flags = self._preprocess_re_pattern(pat, case)
+
+        return ArrowStringArrayMixin._str_fullmatch(self, pat, case, flags, na)
+
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ):
+        if (
+            isinstance(pat, re.Pattern)
+            or callable(repl)
+            or not case
+            or flags
+            or (  # substitution contains a named group pattern
+                # https://docs.python.org/3/library/re.html
+                isinstance(repl, str)
+                and (r"\g<" in repl or re.search(r"\\\d", repl) is not None)
+            )
+        ):
+            return super()._str_replace(pat, repl, n, case, flags, regex)
+
+        return ArrowStringArrayMixin._str_replace(
+            self, pat, repl, n, case, flags, regex
+        )
+
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        if not isinstance(repeats, int):
+            return super()._str_repeat(repeats)
+        else:
+            return ArrowExtensionArray._str_repeat(self, repeats=repeats)
+
+    def _str_removeprefix(self, prefix: str):
+        if not pa_version_under13p0:
+            return ArrowStringArrayMixin._str_removeprefix(self, prefix)
+        return super()._str_removeprefix(prefix)
+
+    def _str_count(self, pat: str, flags: int = 0):
+        if flags:
+            return super()._str_count(pat, flags)
+        result = pc.count_substring_regex(self._pa_array, pat)
+        return self._convert_int_result(result)
+
+    def _str_find(self, sub: str, start: int = 0, end: int | None = None):
+        if (
+            pa_version_under13p0
+            and not (start != 0 and end is not None)
+            and not (start == 0 and end is None)
+        ):
+            # GH#59562
+            return super()._str_find(sub, start, end)
+        return ArrowStringArrayMixin._str_find(self, sub, start, end)
+
+    def _str_get_dummies(self, sep: str = "|"):
+        dummies_pa, labels = ArrowExtensionArray(self._pa_array)._str_get_dummies(sep)
+        if len(labels) == 0:
+            return np.empty(shape=(0, 0), dtype=np.int64), labels
+        dummies = np.vstack(dummies_pa.to_numpy())
+        return dummies.astype(np.int64, copy=False), labels
+
+    def _convert_int_result(self, result):
+        if self.dtype.na_value is np.nan:
+            if isinstance(result, pa.Array):
+                result = result.to_numpy(zero_copy_only=False)
+            else:
+                result = result.to_numpy()
+            if result.dtype == np.int32:
+                result = result.astype(np.int64)
+            return result
+
+        return Int64Dtype().__from_arrow__(result)
+
+    def _convert_rank_result(self, result):
+        if self.dtype.na_value is np.nan:
+            if isinstance(result, pa.Array):
+                result = result.to_numpy(zero_copy_only=False)
+            else:
+                result = result.to_numpy()
+            return result.astype("float64", copy=False)
+
+        return Float64Dtype().__from_arrow__(result)
+
+    def _reduce(
+        self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
+    ):
+        if self.dtype.na_value is np.nan and name in ["any", "all"]:
+            if not skipna:
+                nas = pc.is_null(self._pa_array)
+                arr = pc.or_kleene(nas, pc.not_equal(self._pa_array, ""))
+            else:
+                arr = pc.not_equal(self._pa_array, "")
+            result = ArrowExtensionArray(arr)._reduce(
+                name, skipna=skipna, keepdims=keepdims, **kwargs
+            )
+            if keepdims:
+                # ArrowExtensionArray will return a length-1 bool[pyarrow] array
+                return result.astype(np.bool_)
+            return result
+
+        if name in ("min", "max", "sum", "argmin", "argmax"):
+            result = self._reduce_calc(name, skipna=skipna, keepdims=keepdims, **kwargs)
+        else:
+            raise TypeError(f"Cannot perform reduction '{name}' with string dtype")
+
+        if name in ("argmin", "argmax") and isinstance(result, pa.Array):
+            return self._convert_int_result(result)
+        elif isinstance(result, pa.Array):
+            return type(self)(result)
+        else:
+            return result
+
+    def value_counts(self, dropna: bool = True) -> Series:
+        result = super().value_counts(dropna=dropna)
+        if self.dtype.na_value is np.nan:
+            res_values = result._values.to_numpy()
+            return result._constructor(
+                res_values, index=result.index, name=result.name, copy=False
+            )
+        return result
+
+    def _cmp_method(self, other, op):
+        if (
+            isinstance(other, (BaseStringArray, ArrowExtensionArray))
+            and self.dtype.na_value is not libmissing.NA
+            and other.dtype.na_value is libmissing.NA
+        ):
+            # NA has priority of NaN semantics
+            return NotImplemented
+
+        result = super()._cmp_method(other, op)
+        if self.dtype.na_value is np.nan:
+            if op == operator.ne:
+                return result.to_numpy(np.bool_, na_value=True)
+            else:
+                return result.to_numpy(np.bool_, na_value=False)
+        return result
+
+    def __pos__(self) -> Self:
+        raise TypeError(f"bad operand type for unary +: '{self.dtype}'")
+
+
+class ArrowStringArrayNumpySemantics(ArrowStringArray):
+    _na_value = np.nan
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/timedeltas.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4caec4bfd58a653c3d4af9e550dbda3dc50264a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/arrays/timedeltas.py
@@ -0,0 +1,1185 @@
+from __future__ import annotations
+
+from datetime import timedelta
+import operator
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    tslibs,
+)
+from pandas._libs.tslibs import (
+    NaT,
+    NaTType,
+    Tick,
+    Timedelta,
+    astype_overflowsafe,
+    get_supported_dtype,
+    iNaT,
+    is_supported_dtype,
+    periods_per_second,
+)
+from pandas._libs.tslibs.conversion import cast_from_unit_vectorized
+from pandas._libs.tslibs.fields import (
+    get_timedelta_days,
+    get_timedelta_field,
+)
+from pandas._libs.tslibs.timedeltas import (
+    array_to_timedelta64,
+    floordiv_object_array,
+    ints_to_pytimedelta,
+    parse_timedelta_unit,
+    truediv_object_array,
+)
+from pandas.compat.numpy import function as nv
+from pandas.util._validators import validate_endpoints
+
+from pandas.core.dtypes.common import (
+    TD64NS_DTYPE,
+    is_float_dtype,
+    is_integer_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import (
+    nanops,
+    roperator,
+)
+from pandas.core.array_algos import datetimelike_accumulations
+from pandas.core.arrays import datetimelike as dtl
+from pandas.core.arrays._ranges import generate_regular_range
+import pandas.core.common as com
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas._typing import (
+        AxisInt,
+        DateTimeErrorChoices,
+        DtypeObj,
+        NpDtype,
+        Self,
+        npt,
+    )
+
+    from pandas import DataFrame
+
+import textwrap
+
+
+def _field_accessor(name: str, alias: str, docstring: str):
+    def f(self) -> np.ndarray:
+        values = self.asi8
+        if alias == "days":
+            result = get_timedelta_days(values, reso=self._creso)
+        else:
+            # error: Incompatible types in assignment (
+            # expression has type "ndarray[Any, dtype[signedinteger[_32Bit]]]",
+            # variable has type "ndarray[Any, dtype[signedinteger[_64Bit]]]
+            result = get_timedelta_field(values, alias, reso=self._creso)  # type: ignore[assignment]
+        if self._hasna:
+            result = self._maybe_mask_results(
+                result, fill_value=None, convert="float64"
+            )
+
+        return result
+
+    f.__name__ = name
+    f.__doc__ = f"\n{docstring}\n"
+    return property(f)
+
+
+class TimedeltaArray(dtl.TimelikeOps):
+    """
+    Pandas ExtensionArray for timedelta data.
+
+    .. warning::
+
+       TimedeltaArray is currently experimental, and its API may change
+       without warning. In particular, :attr:`TimedeltaArray.dtype` is
+       expected to change to be an instance of an ``ExtensionDtype``
+       subclass.
+
+    Parameters
+    ----------
+    values : array-like
+        The timedelta data.
+
+    dtype : numpy.dtype
+        Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted.
+    freq : Offset, optional
+    copy : bool, default False
+        Whether to copy the underlying array of data.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.arrays.TimedeltaArray._from_sequence(pd.TimedeltaIndex(['1h', '2h']))
+    <TimedeltaArray>
+    ['0 days 01:00:00', '0 days 02:00:00']
+    Length: 2, dtype: timedelta64[ns]
+    """
+
+    _typ = "timedeltaarray"
+    _internal_fill_value = np.timedelta64("NaT", "ns")
+    _recognized_scalars = (timedelta, np.timedelta64, Tick)
+    _is_recognized_dtype = lambda x: lib.is_np_dtype(x, "m")
+    _infer_matches = ("timedelta", "timedelta64")
+
+    @property
+    def _scalar_type(self) -> type[Timedelta]:
+        return Timedelta
+
+    __array_priority__ = 1000
+    # define my properties & methods for delegation
+    _other_ops: list[str] = []
+    _bool_ops: list[str] = []
+    _object_ops: list[str] = ["freq"]
+    _field_ops: list[str] = ["days", "seconds", "microseconds", "nanoseconds"]
+    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + ["unit"]
+    _datetimelike_methods: list[str] = [
+        "to_pytimedelta",
+        "total_seconds",
+        "round",
+        "floor",
+        "ceil",
+        "as_unit",
+    ]
+
+    # Note: ndim must be defined to ensure NaT.__richcmp__(TimedeltaArray)
+    #  operates pointwise.
+
+    def _box_func(self, x: np.timedelta64) -> Timedelta | NaTType:
+        y = x.view("i8")
+        if y == NaT._value:
+            return NaT
+        return Timedelta._from_value_and_reso(y, reso=self._creso)
+
+    @property
+    # error: Return type "dtype" of "dtype" incompatible with return type
+    # "ExtensionDtype" in supertype "ExtensionArray"
+    def dtype(self) -> np.dtype[np.timedelta64]:  # type: ignore[override]
+        """
+        The dtype for the TimedeltaArray.
+
+        .. warning::
+
+           A future version of pandas will change dtype to be an instance
+           of a :class:`pandas.api.extensions.ExtensionDtype` subclass,
+           not a ``numpy.dtype``.
+
+        Returns
+        -------
+        numpy.dtype
+        """
+        return self._ndarray.dtype
+
+    # ----------------------------------------------------------------
+    # Constructors
+
+    _freq = None
+    _default_dtype = TD64NS_DTYPE  # used in TimeLikeOps.__init__
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        # used in TimeLikeOps.__init__
+        dtype = _validate_td64_dtype(dtype)
+        _validate_td64_dtype(values.dtype)
+        if dtype != values.dtype:
+            raise ValueError("Values resolution does not match dtype.")
+        return dtype
+
+    # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls,
+        values: npt.NDArray[np.timedelta64],
+        freq: Tick | None = None,
+        dtype: np.dtype[np.timedelta64] = TD64NS_DTYPE,
+    ) -> Self:
+        # Require td64 dtype, not unit-less, matching values.dtype
+        assert lib.is_np_dtype(dtype, "m")
+        assert not tslibs.is_unitless(dtype)
+        assert isinstance(values, np.ndarray), type(values)
+        assert dtype == values.dtype
+        assert freq is None or isinstance(freq, Tick)
+
+        result = super()._simple_new(values=values, dtype=dtype)
+        result._freq = freq
+        return result
+
+    @classmethod
+    def _from_sequence(cls, data, *, dtype=None, copy: bool = False) -> Self:
+        if dtype:
+            dtype = _validate_td64_dtype(dtype)
+
+        data, freq = sequence_to_td64ns(data, copy=copy, unit=None)
+
+        if dtype is not None:
+            data = astype_overflowsafe(data, dtype=dtype, copy=False)
+
+        return cls._simple_new(data, dtype=data.dtype, freq=freq)
+
+    @classmethod
+    def _from_sequence_not_strict(
+        cls,
+        data,
+        *,
+        dtype=None,
+        copy: bool = False,
+        freq=lib.no_default,
+        unit=None,
+    ) -> Self:
+        """
+        _from_sequence_not_strict but without responsibility for finding the
+        result's `freq`.
+        """
+        if dtype:
+            dtype = _validate_td64_dtype(dtype)
+
+        assert unit not in ["Y", "y", "M"]  # caller is responsible for checking
+
+        data, inferred_freq = sequence_to_td64ns(data, copy=copy, unit=unit)
+
+        if dtype is not None:
+            data = astype_overflowsafe(data, dtype=dtype, copy=False)
+
+        result = cls._simple_new(data, dtype=data.dtype, freq=inferred_freq)
+
+        result._maybe_pin_freq(freq, {})
+        return result
+
+    @classmethod
+    def _generate_range(
+        cls, start, end, periods, freq, closed=None, *, unit: str | None = None
+    ) -> Self:
+        periods = dtl.validate_periods(periods)
+        if freq is None and any(x is None for x in [periods, start, end]):
+            raise ValueError("Must provide freq argument if no data is supplied")
+
+        if com.count_not_none(start, end, periods, freq) != 3:
+            raise ValueError(
+                "Of the four parameters: start, end, periods, "
+                "and freq, exactly three must be specified"
+            )
+
+        if start is not None:
+            start = Timedelta(start).as_unit("ns")
+
+        if end is not None:
+            end = Timedelta(end).as_unit("ns")
+
+        if unit is not None:
+            if unit not in ["s", "ms", "us", "ns"]:
+                raise ValueError("'unit' must be one of 's', 'ms', 'us', 'ns'")
+        else:
+            unit = "ns"
+
+        if start is not None and unit is not None:
+            start = start.as_unit(unit, round_ok=False)
+        if end is not None and unit is not None:
+            end = end.as_unit(unit, round_ok=False)
+
+        left_closed, right_closed = validate_endpoints(closed)
+
+        if freq is not None:
+            index = generate_regular_range(start, end, periods, freq, unit=unit)
+        else:
+            index = np.linspace(start._value, end._value, periods).astype("i8")
+
+        if not left_closed:
+            index = index[1:]
+        if not right_closed:
+            index = index[:-1]
+
+        td64values = index.view(f"m8[{unit}]")
+        return cls._simple_new(td64values, dtype=td64values.dtype, freq=freq)
+
+    # ----------------------------------------------------------------
+    # DatetimeLike Interface
+
+    def _unbox_scalar(self, value) -> np.timedelta64:
+        if not isinstance(value, self._scalar_type) and value is not NaT:
+            raise ValueError("'value' should be a Timedelta.")
+        self._check_compatible_with(value)
+        if value is NaT:
+            return np.timedelta64(value._value, self.unit)
+        else:
+            return value.as_unit(self.unit).asm8
+
+    def _scalar_from_string(self, value) -> Timedelta | NaTType:
+        return Timedelta(value)
+
+    def _check_compatible_with(self, other) -> None:
+        # we don't have anything to validate.
+        pass
+
+    # ----------------------------------------------------------------
+    # Array-Like / EA-Interface Methods
+
+    def astype(self, dtype, copy: bool = True):
+        # We handle
+        #   --> timedelta64[ns]
+        #   --> timedelta64
+        # DatetimeLikeArrayMixin super call handles other cases
+        dtype = pandas_dtype(dtype)
+
+        if lib.is_np_dtype(dtype, "m"):
+            if dtype == self.dtype:
+                if copy:
+                    return self.copy()
+                return self
+
+            if is_supported_dtype(dtype):
+                # unit conversion e.g. timedelta64[s]
+                res_values = astype_overflowsafe(self._ndarray, dtype, copy=False)
+                return type(self)._simple_new(
+                    res_values, dtype=res_values.dtype, freq=self.freq
+                )
+            else:
+                raise ValueError(
+                    f"Cannot convert from {self.dtype} to {dtype}. "
+                    "Supported resolutions are 's', 'ms', 'us', 'ns'"
+                )
+
+        return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
+
+    def __iter__(self) -> Iterator:
+        if self.ndim > 1:
+            for i in range(len(self)):
+                yield self[i]
+        else:
+            # convert in chunks of 10k for efficiency
+            data = self._ndarray
+            length = len(self)
+            chunksize = 10000
+            chunks = (length // chunksize) + 1
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, length)
+                converted = ints_to_pytimedelta(data[start_i:end_i], box=True)
+                yield from converted
+
+    # ----------------------------------------------------------------
+    # Reductions
+
+    def sum(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        keepdims: bool = False,
+        initial=None,
+        skipna: bool = True,
+        min_count: int = 0,
+    ):
+        nv.validate_sum(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims, "initial": initial}
+        )
+
+        result = nanops.nansum(
+            self._ndarray, axis=axis, skipna=skipna, min_count=min_count
+        )
+        return self._wrap_reduction_result(axis, result)
+
+    def std(
+        self,
+        *,
+        axis: AxisInt | None = None,
+        dtype: NpDtype | None = None,
+        out=None,
+        ddof: int = 1,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        nv.validate_stat_ddof_func(
+            (), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
+        )
+
+        result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
+
+    # ----------------------------------------------------------------
+    # Accumulations
+
+    def _accumulate(self, name: str, *, skipna: bool = True, **kwargs):
+        if name == "cumsum":
+            op = getattr(datetimelike_accumulations, name)
+            result = op(self._ndarray.copy(), skipna=skipna, **kwargs)
+
+            return type(self)._simple_new(result, freq=None, dtype=self.dtype)
+        elif name == "cumprod":
+            raise TypeError("cumprod not supported for Timedelta.")
+
+        else:
+            return super()._accumulate(name, skipna=skipna, **kwargs)
+
+    # ----------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter(self, boxed: bool = False):
+        from pandas.io.formats.format import get_format_timedelta64
+
+        return get_format_timedelta64(self, box=True)
+
+    def _format_native_types(
+        self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
+    ) -> npt.NDArray[np.object_]:
+        from pandas.io.formats.format import get_format_timedelta64
+
+        # Relies on TimeDelta._repr_base
+        formatter = get_format_timedelta64(self, na_rep)
+        # equiv: np.array([formatter(x) for x in self._ndarray])
+        #  but independent of dimension
+        return np.frompyfunc(formatter, 1, 1)(self._ndarray)
+
+    # ----------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _add_offset(self, other):
+        assert not isinstance(other, Tick)
+        raise TypeError(
+            f"cannot add the type {type(other).__name__} to a {type(self).__name__}"
+        )
+
+    @unpack_zerodim_and_defer("__mul__")
+    def __mul__(self, other) -> Self:
+        if is_scalar(other):
+            # numpy will accept float and int, raise TypeError for others
+            result = self._ndarray * other
+            if result.dtype.kind != "m":
+                # numpy >= 2.1 may not raise a TypeError
+                # and seems to dispatch to others.__rmul__?
+                raise TypeError(f"Cannot multiply with {type(other).__name__}")
+            freq = None
+            if self.freq is not None and not isna(other):
+                freq = self.freq * other
+                if freq.n == 0:
+                    # GH#51575 Better to have no freq than an incorrect one
+                    freq = None
+            return type(self)._simple_new(result, dtype=result.dtype, freq=freq)
+
+        if not hasattr(other, "dtype"):
+            # list, tuple
+            other = np.array(other)
+        if len(other) != len(self) and not lib.is_np_dtype(other.dtype, "m"):
+            # Exclude timedelta64 here so we correctly raise TypeError
+            #  for that instead of ValueError
+            raise ValueError("Cannot multiply with unequal lengths")
+
+        if is_object_dtype(other.dtype):
+            # this multiplication will succeed only if all elements of other
+            #  are int or float scalars, so we will end up with
+            #  timedelta64[ns]-dtyped result
+            arr = self._ndarray
+            result = [arr[n] * other[n] for n in range(len(self))]
+            result = np.array(result)
+            return type(self)._simple_new(result, dtype=result.dtype)
+
+        # numpy will accept float or int dtype, raise TypeError for others
+        result = self._ndarray * other
+        if result.dtype.kind != "m":
+            # numpy >= 2.1 may not raise a TypeError
+            # and seems to dispatch to others.__rmul__?
+            raise TypeError(f"Cannot multiply with {type(other).__name__}")
+        return type(self)._simple_new(result, dtype=result.dtype)
+
+    __rmul__ = __mul__
+
+    def _scalar_divlike_op(self, other, op):
+        """
+        Shared logic for __truediv__, __rtruediv__, __floordiv__, __rfloordiv__
+        with scalar 'other'.
+        """
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+            # mypy assumes that __new__ returns an instance of the class
+            # github.com/python/mypy/issues/1020
+            if cast("Timedelta | NaTType", other) is NaT:
+                # specifically timedelta64-NaT
+                res = np.empty(self.shape, dtype=np.float64)
+                res.fill(np.nan)
+                return res
+
+            # otherwise, dispatch to Timedelta implementation
+            return op(self._ndarray, other)
+
+        else:
+            # caller is responsible for checking lib.is_scalar(other)
+            # assume other is numeric, otherwise numpy will raise
+
+            if op in [roperator.rtruediv, roperator.rfloordiv]:
+                raise TypeError(
+                    f"Cannot divide {type(other).__name__} by {type(self).__name__}"
+                )
+
+            result = op(self._ndarray, other)
+            freq = None
+
+            if self.freq is not None:
+                # Note: freq gets division, not floor-division, even if op
+                #  is floordiv.
+                freq = self.freq / other
+                if freq.nanos == 0 and self.freq.nanos != 0:
+                    # e.g. if self.freq is Nano(1) then dividing by 2
+                    #  rounds down to zero
+                    freq = None
+
+            return type(self)._simple_new(result, dtype=result.dtype, freq=freq)
+
+    def _cast_divlike_op(self, other):
+        if not hasattr(other, "dtype"):
+            # e.g. list, tuple
+            other = np.array(other)
+
+        if len(other) != len(self):
+            raise ValueError("Cannot divide vectors with unequal lengths")
+        return other
+
+    def _vector_divlike_op(self, other, op) -> np.ndarray | Self:
+        """
+        Shared logic for __truediv__, __floordiv__, and their reversed versions
+        with timedelta64-dtype ndarray other.
+        """
+        # Let numpy handle it
+        result = op(self._ndarray, np.asarray(other))
+
+        if (is_integer_dtype(other.dtype) or is_float_dtype(other.dtype)) and op in [
+            operator.truediv,
+            operator.floordiv,
+        ]:
+            return type(self)._simple_new(result, dtype=result.dtype)
+
+        if op in [operator.floordiv, roperator.rfloordiv]:
+            mask = self.isna() | isna(other)
+            if mask.any():
+                result = result.astype(np.float64)
+                np.putmask(result, mask, np.nan)
+
+        return result
+
+    @unpack_zerodim_and_defer("__truediv__")
+    def __truediv__(self, other):
+        # timedelta / X is well-defined for timedelta-like or numeric X
+        op = operator.truediv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if (
+            lib.is_np_dtype(other.dtype, "m")
+            or is_integer_dtype(other.dtype)
+            or is_float_dtype(other.dtype)
+        ):
+            return self._vector_divlike_op(other, op)
+
+        if is_object_dtype(other.dtype):
+            other = np.asarray(other)
+            if self.ndim > 1:
+                res_cols = [left / right for left, right in zip(self, other)]
+                res_cols2 = [x.reshape(1, -1) for x in res_cols]
+                result = np.concatenate(res_cols2, axis=0)
+            else:
+                result = truediv_object_array(self._ndarray, other)
+
+            return result
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__rtruediv__")
+    def __rtruediv__(self, other):
+        # X / timedelta is defined only for timedelta-like X
+        op = roperator.rtruediv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if lib.is_np_dtype(other.dtype, "m"):
+            return self._vector_divlike_op(other, op)
+
+        elif is_object_dtype(other.dtype):
+            # Note: unlike in __truediv__, we do not _need_ to do type
+            #  inference on the result.  It does not raise, a numeric array
+            #  is returned.  GH#23829
+            result_list = [other[n] / self[n] for n in range(len(self))]
+            return np.array(result_list)
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        op = operator.floordiv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if (
+            lib.is_np_dtype(other.dtype, "m")
+            or is_integer_dtype(other.dtype)
+            or is_float_dtype(other.dtype)
+        ):
+            return self._vector_divlike_op(other, op)
+
+        elif is_object_dtype(other.dtype):
+            other = np.asarray(other)
+            if self.ndim > 1:
+                res_cols = [left // right for left, right in zip(self, other)]
+                res_cols2 = [x.reshape(1, -1) for x in res_cols]
+                result = np.concatenate(res_cols2, axis=0)
+            else:
+                result = floordiv_object_array(self._ndarray, other)
+
+            assert result.dtype == object
+            return result
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__rfloordiv__")
+    def __rfloordiv__(self, other):
+        op = roperator.rfloordiv
+        if is_scalar(other):
+            return self._scalar_divlike_op(other, op)
+
+        other = self._cast_divlike_op(other)
+        if lib.is_np_dtype(other.dtype, "m"):
+            return self._vector_divlike_op(other, op)
+
+        elif is_object_dtype(other.dtype):
+            result_list = [other[n] // self[n] for n in range(len(self))]
+            result = np.array(result_list)
+            return result
+
+        else:
+            return NotImplemented
+
+    @unpack_zerodim_and_defer("__mod__")
+    def __mod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+        return self - (self // other) * other
+
+    @unpack_zerodim_and_defer("__rmod__")
+    def __rmod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+        return other - (other // self) * self
+
+    @unpack_zerodim_and_defer("__divmod__")
+    def __divmod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+
+        res1 = self // other
+        res2 = self - res1 * other
+        return res1, res2
+
+    @unpack_zerodim_and_defer("__rdivmod__")
+    def __rdivmod__(self, other):
+        # Note: This is a naive implementation, can likely be optimized
+        if isinstance(other, self._recognized_scalars):
+            other = Timedelta(other)
+
+        res1 = other // self
+        res2 = other - res1 * self
+        return res1, res2
+
+    def __neg__(self) -> TimedeltaArray:
+        freq = None
+        if self.freq is not None:
+            freq = -self.freq
+        return type(self)._simple_new(-self._ndarray, dtype=self.dtype, freq=freq)
+
+    def __pos__(self) -> TimedeltaArray:
+        return type(self)._simple_new(
+            self._ndarray.copy(), dtype=self.dtype, freq=self.freq
+        )
+
+    def __abs__(self) -> TimedeltaArray:
+        # Note: freq is not preserved
+        return type(self)._simple_new(np.abs(self._ndarray), dtype=self.dtype)
+
+    # ----------------------------------------------------------------
+    # Conversion Methods - Vectorized analogues of Timedelta methods
+
+    def total_seconds(self) -> npt.NDArray[np.float64]:
+        """
+        Return total duration of each element expressed in seconds.
+
+        This method is available directly on TimedeltaArray, TimedeltaIndex
+        and on Series containing timedelta values under the ``.dt`` namespace.
+
+        Returns
+        -------
+        ndarray, Index or Series
+            When the calling object is a TimedeltaArray, the return type
+            is ndarray.  When the calling object is a TimedeltaIndex,
+            the return type is an Index with a float64 dtype. When the calling object
+            is a Series, the return type is Series of type `float64` whose
+            index is the same as the original.
+
+        See Also
+        --------
+        datetime.timedelta.total_seconds : Standard library version
+            of this method.
+        TimedeltaIndex.components : Return a DataFrame with components of
+            each Timedelta.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
+        >>> s
+        0   0 days
+        1   1 days
+        2   2 days
+        3   3 days
+        4   4 days
+        dtype: timedelta64[ns]
+
+        >>> s.dt.total_seconds()
+        0         0.0
+        1     86400.0
+        2    172800.0
+        3    259200.0
+        4    345600.0
+        dtype: float64
+
+        **TimedeltaIndex**
+
+        >>> idx = pd.to_timedelta(np.arange(5), unit='d')
+        >>> idx
+        TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                       dtype='timedelta64[ns]', freq=None)
+
+        >>> idx.total_seconds()
+        Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64')
+        """
+        pps = periods_per_second(self._creso)
+        return self._maybe_mask_results(self.asi8 / pps, fill_value=None)
+
+    def to_pytimedelta(self) -> npt.NDArray[np.object_]:
+        """
+        Return an ndarray of datetime.timedelta objects.
+
+        Returns
+        -------
+        numpy.ndarray
+
+        Examples
+        --------
+        >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days', '2 days', '3 days'],
+                        dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.to_pytimedelta()
+        array([datetime.timedelta(days=1), datetime.timedelta(days=2),
+               datetime.timedelta(days=3)], dtype=object)
+        """
+        return ints_to_pytimedelta(self._ndarray)
+
+    days_docstring = textwrap.dedent(
+        """Number of days for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='d'))
+    >>> ser
+    0   1 days
+    1   2 days
+    2   3 days
+    dtype: timedelta64[ns]
+    >>> ser.dt.days
+    0    1
+    1    2
+    2    3
+    dtype: int64
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days', '10 days', '20 days'],
+                    dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.days
+    Index([0, 10, 20], dtype='int64')"""
+    )
+    days = _field_accessor("days", "days", days_docstring)
+
+    seconds_docstring = textwrap.dedent(
+        """Number of seconds (>= 0 and less than 1 day) for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='s'))
+    >>> ser
+    0   0 days 00:00:01
+    1   0 days 00:00:02
+    2   0 days 00:00:03
+    dtype: timedelta64[ns]
+    >>> ser.dt.seconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='s')
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.seconds
+    Index([1, 2, 3], dtype='int32')"""
+    )
+    seconds = _field_accessor(
+        "seconds",
+        "seconds",
+        seconds_docstring,
+    )
+
+    microseconds_docstring = textwrap.dedent(
+        """Number of microseconds (>= 0 and less than 1 second) for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='us'))
+    >>> ser
+    0   0 days 00:00:00.000001
+    1   0 days 00:00:00.000002
+    2   0 days 00:00:00.000003
+    dtype: timedelta64[ns]
+    >>> ser.dt.microseconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='us')
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002',
+                    '0 days 00:00:00.000003'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.microseconds
+    Index([1, 2, 3], dtype='int32')"""
+    )
+    microseconds = _field_accessor(
+        "microseconds",
+        "microseconds",
+        microseconds_docstring,
+    )
+
+    nanoseconds_docstring = textwrap.dedent(
+        """Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
+
+    Examples
+    --------
+    For Series:
+
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='ns'))
+    >>> ser
+    0   0 days 00:00:00.000000001
+    1   0 days 00:00:00.000000002
+    2   0 days 00:00:00.000000003
+    dtype: timedelta64[ns]
+    >>> ser.dt.nanoseconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    For TimedeltaIndex:
+
+    >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='ns')
+    >>> tdelta_idx
+    TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002',
+                    '0 days 00:00:00.000000003'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> tdelta_idx.nanoseconds
+    Index([1, 2, 3], dtype='int32')"""
+    )
+    nanoseconds = _field_accessor(
+        "nanoseconds",
+        "nanoseconds",
+        nanoseconds_docstring,
+    )
+
+    @property
+    def components(self) -> DataFrame:
+        """
+        Return a DataFrame of the individual resolution components of the Timedeltas.
+
+        The components (days, hours, minutes seconds, milliseconds, microseconds,
+        nanoseconds) are returned as columns in a DataFrame.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns'])
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days 00:03:00.000002042'],
+                       dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.components
+           days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0     1      0        3        0             0             2           42
+        """
+        from pandas import DataFrame
+
+        columns = [
+            "days",
+            "hours",
+            "minutes",
+            "seconds",
+            "milliseconds",
+            "microseconds",
+            "nanoseconds",
+        ]
+        hasnans = self._hasna
+        if hasnans:
+
+            def f(x):
+                if isna(x):
+                    return [np.nan] * len(columns)
+                return x.components
+
+        else:
+
+            def f(x):
+                return x.components
+
+        result = DataFrame([f(x) for x in self], columns=columns)
+        if not hasnans:
+            result = result.astype("int64")
+        return result
+
+
+# ---------------------------------------------------------------------
+# Constructor Helpers
+
+
+def sequence_to_td64ns(
+    data,
+    copy: bool = False,
+    unit=None,
+    errors: DateTimeErrorChoices = "raise",
+) -> tuple[np.ndarray, Tick | None]:
+    """
+    Parameters
+    ----------
+    data : list-like
+    copy : bool, default False
+    unit : str, optional
+        The timedelta unit to treat integers as multiples of. For numeric
+        data this defaults to ``'ns'``.
+        Must be un-specified if the data contains a str and ``errors=="raise"``.
+    errors : {"raise", "coerce", "ignore"}, default "raise"
+        How to handle elements that cannot be converted to timedelta64[ns].
+        See ``pandas.to_timedelta`` for details.
+
+    Returns
+    -------
+    converted : numpy.ndarray
+        The sequence converted to a numpy array with dtype ``timedelta64[ns]``.
+    inferred_freq : Tick or None
+        The inferred frequency of the sequence.
+
+    Raises
+    ------
+    ValueError : Data cannot be converted to timedelta64[ns].
+
+    Notes
+    -----
+    Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause
+    errors to be ignored; they are caught and subsequently ignored at a
+    higher level.
+    """
+    assert unit not in ["Y", "y", "M"]  # caller is responsible for checking
+
+    inferred_freq = None
+    if unit is not None:
+        unit = parse_timedelta_unit(unit)
+
+    data, copy = dtl.ensure_arraylike_for_datetimelike(
+        data, copy, cls_name="TimedeltaArray"
+    )
+
+    if isinstance(data, TimedeltaArray):
+        inferred_freq = data.freq
+
+    # Convert whatever we have into timedelta64[ns] dtype
+    if data.dtype == object or is_string_dtype(data.dtype):
+        # no need to make a copy, need to convert if string-dtyped
+        data = _objects_to_td64ns(data, unit=unit, errors=errors)
+        copy = False
+
+    elif is_integer_dtype(data.dtype):
+        # treat as multiples of the given unit
+        data, copy_made = _ints_to_td64ns(data, unit=unit)
+        copy = copy and not copy_made
+
+    elif is_float_dtype(data.dtype):
+        # cast the unit, multiply base/frac separately
+        # to avoid precision issues from float -> int
+        if isinstance(data.dtype, ExtensionDtype):
+            mask = data._mask
+            data = data._data
+        else:
+            mask = np.isnan(data)
+
+        data = cast_from_unit_vectorized(data, unit or "ns")
+        data[mask] = iNaT
+        data = data.view("m8[ns]")
+        copy = False
+
+    elif lib.is_np_dtype(data.dtype, "m"):
+        if not is_supported_dtype(data.dtype):
+            # cast to closest supported unit, i.e. s or ns
+            new_dtype = get_supported_dtype(data.dtype)
+            data = astype_overflowsafe(data, dtype=new_dtype, copy=False)
+            copy = False
+
+    else:
+        # This includes datetime64-dtype, see GH#23539, GH#29794
+        raise TypeError(f"dtype {data.dtype} cannot be converted to timedelta64[ns]")
+
+    if not copy:
+        data = np.asarray(data)
+    else:
+        data = np.array(data, copy=copy)
+
+    assert data.dtype.kind == "m"
+    assert data.dtype != "m8"  # i.e. not unit-less
+
+    return data, inferred_freq
+
+
+def _ints_to_td64ns(data, unit: str = "ns"):
+    """
+    Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating
+    the integers as multiples of the given timedelta unit.
+
+    Parameters
+    ----------
+    data : numpy.ndarray with integer-dtype
+    unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
+
+    Returns
+    -------
+    numpy.ndarray : timedelta64[ns] array converted from data
+    bool : whether a copy was made
+    """
+    copy_made = False
+    unit = unit if unit is not None else "ns"
+
+    if data.dtype != np.int64:
+        # converting to int64 makes a copy, so we can avoid
+        # re-copying later
+        data = data.astype(np.int64)
+        copy_made = True
+
+    if unit != "ns":
+        dtype_str = f"timedelta64[{unit}]"
+        data = data.view(dtype_str)
+
+        data = astype_overflowsafe(data, dtype=TD64NS_DTYPE)
+
+        # the astype conversion makes a copy, so we can avoid re-copying later
+        copy_made = True
+
+    else:
+        data = data.view("timedelta64[ns]")
+
+    return data, copy_made
+
+
+def _objects_to_td64ns(data, unit=None, errors: DateTimeErrorChoices = "raise"):
+    """
+    Convert a object-dtyped or string-dtyped array into an
+    timedelta64[ns]-dtyped array.
+
+    Parameters
+    ----------
+    data : ndarray or Index
+    unit : str, default "ns"
+        The timedelta unit to treat integers as multiples of.
+        Must not be specified if the data contains a str.
+    errors : {"raise", "coerce", "ignore"}, default "raise"
+        How to handle elements that cannot be converted to timedelta64[ns].
+        See ``pandas.to_timedelta`` for details.
+
+    Returns
+    -------
+    numpy.ndarray : timedelta64[ns] array converted from data
+
+    Raises
+    ------
+    ValueError : Data cannot be converted to timedelta64[ns].
+
+    Notes
+    -----
+    Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
+    errors to be ignored; they are caught and subsequently ignored at a
+    higher level.
+    """
+    # coerce Index to np.ndarray, converting string-dtype if necessary
+    values = np.asarray(data, dtype=np.object_)
+
+    result = array_to_timedelta64(values, unit=unit, errors=errors)
+    return result.view("timedelta64[ns]")
+
+
+def _validate_td64_dtype(dtype) -> DtypeObj:
+    dtype = pandas_dtype(dtype)
+    if dtype == np.dtype("m8"):
+        # no precision disallowed GH#24806
+        msg = (
+            "Passing in 'timedelta' dtype with no precision is not allowed. "
+            "Please pass in 'timedelta64[ns]' instead."
+        )
+        raise ValueError(msg)
+
+    if not lib.is_np_dtype(dtype, "m"):
+        raise ValueError(f"dtype '{dtype}' is invalid, should be np.timedelta64 dtype")
+    elif not is_supported_dtype(dtype):
+        raise ValueError("Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'")
+
+    return dtype
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..af8f80db6a34741b761991a4256e395100080171
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/base.py
@@ -0,0 +1,1400 @@
+"""
+Base and utility classes for pandas objects.
+"""
+
+from __future__ import annotations
+
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Generic,
+    Literal,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas._libs import lib
+from pandas._typing import (
+    AxisInt,
+    DtypeObj,
+    IndexLabel,
+    NDFrameT,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.compat import PYPY
+from pandas.compat.numpy import function as nv
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import can_hold_element
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    remove_na_arraylike,
+)
+
+from pandas.core import (
+    algorithms,
+    nanops,
+    ops,
+)
+from pandas.core.accessor import DirNamesMixin
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import ExtensionArray
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+    from pandas._typing import (
+        DropKeep,
+        NumpySorter,
+        NumpyValueArrayLike,
+        ScalarLike_co,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+_shared_docs: dict[str, str] = {}
+_indexops_doc_kwargs = {
+    "klass": "IndexOpsMixin",
+    "inplace": "",
+    "unique": "IndexOpsMixin",
+    "duplicated": "IndexOpsMixin",
+}
+
+
+class PandasObject(DirNamesMixin):
+    """
+    Baseclass for various pandas objects.
+    """
+
+    # results from calls to methods decorated with cache_readonly get added to _cache
+    _cache: dict[str, Any]
+
+    @property
+    def _constructor(self):
+        """
+        Class constructor (for this class it's just `__class__`).
+        """
+        return type(self)
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation for a particular object.
+        """
+        # Should be overwritten by base classes
+        return object.__repr__(self)
+
+    def _reset_cache(self, key: str | None = None) -> None:
+        """
+        Reset cached properties. If ``key`` is passed, only clears that key.
+        """
+        if not hasattr(self, "_cache"):
+            return
+        if key is None:
+            self._cache.clear()
+        else:
+            self._cache.pop(key, None)
+
+    def __sizeof__(self) -> int:
+        """
+        Generates the total memory usage for an object that returns
+        either a value or Series of values
+        """
+        memory_usage = getattr(self, "memory_usage", None)
+        if memory_usage:
+            mem = memory_usage(deep=True)  # pylint: disable=not-callable
+            return int(mem if is_scalar(mem) else mem.sum())
+
+        # no memory_usage attribute, so fall back to object's 'sizeof'
+        return super().__sizeof__()
+
+
+class NoNewAttributesMixin:
+    """
+    Mixin which prevents adding new attributes.
+
+    Prevents additional attributes via xxx.attribute = "something" after a
+    call to `self.__freeze()`. Mainly used to prevent the user from using
+    wrong attributes on an accessor (`Series.cat/.str/.dt`).
+
+    If you really want to add a new attribute at a later time, you need to use
+    `object.__setattr__(self, key, value)`.
+    """
+
+    def _freeze(self) -> None:
+        """
+        Prevents setting additional attributes.
+        """
+        object.__setattr__(self, "__frozen", True)
+
+    # prevent adding any attribute via s.xxx.new_attribute = ...
+    def __setattr__(self, key: str, value) -> None:
+        # _cache is used by a decorator
+        # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key)
+        # because
+        # 1.) getattr is false for attributes that raise errors
+        # 2.) cls.__dict__ doesn't traverse into base classes
+        if getattr(self, "__frozen", False) and not (
+            key == "_cache"
+            or key in type(self).__dict__
+            or getattr(self, key, None) is not None
+        ):
+            raise AttributeError(f"You cannot add any new attribute '{key}'")
+        object.__setattr__(self, key, value)
+
+
+class SelectionMixin(Generic[NDFrameT]):
+    """
+    mixin implementing the selection & aggregation interface on a group-like
+    object sub-classes need to define: obj, exclusions
+    """
+
+    obj: NDFrameT
+    _selection: IndexLabel | None = None
+    exclusions: frozenset[Hashable]
+    _internal_names = ["_cache", "__setstate__"]
+    _internal_names_set = set(_internal_names)
+
+    @final
+    @property
+    def _selection_list(self):
+        if not isinstance(
+            self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray)
+        ):
+            return [self._selection]
+        return self._selection
+
+    @cache_readonly
+    def _selected_obj(self):
+        if self._selection is None or isinstance(self.obj, ABCSeries):
+            return self.obj
+        else:
+            return self.obj[self._selection]
+
+    @final
+    @cache_readonly
+    def ndim(self) -> int:
+        return self._selected_obj.ndim
+
+    @final
+    @cache_readonly
+    def _obj_with_exclusions(self):
+        if isinstance(self.obj, ABCSeries):
+            return self.obj
+
+        if self._selection is not None:
+            return self.obj._getitem_nocopy(self._selection_list)
+
+        if len(self.exclusions) > 0:
+            # equivalent to `self.obj.drop(self.exclusions, axis=1)
+            #  but this avoids consolidating and making a copy
+            # TODO: following GH#45287 can we now use .drop directly without
+            #  making a copy?
+            return self.obj._drop_axis(self.exclusions, axis=1, only_slice=True)
+        else:
+            return self.obj
+
+    def __getitem__(self, key):
+        if self._selection is not None:
+            raise IndexError(f"Column(s) {self._selection} already selected")
+
+        if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)):
+            if len(self.obj.columns.intersection(key)) != len(set(key)):
+                bad_keys = list(set(key).difference(self.obj.columns))
+                raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}")
+            return self._gotitem(list(key), ndim=2)
+
+        else:
+            if key not in self.obj:
+                raise KeyError(f"Column not found: {key}")
+            ndim = self.obj[key].ndim
+            return self._gotitem(key, ndim=ndim)
+
+    def _gotitem(self, key, ndim: int, subset=None):
+        """
+        sub-classes to define
+        return a sliced object
+
+        Parameters
+        ----------
+        key : str / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def _infer_selection(self, key, subset: Series | DataFrame):
+        """
+        Infer the `selection` to pass to our constructor in _gotitem.
+        """
+        # Shared by Rolling and Resample
+        selection = None
+        if subset.ndim == 2 and (
+            (lib.is_scalar(key) and key in subset) or lib.is_list_like(key)
+        ):
+            selection = key
+        elif subset.ndim == 1 and lib.is_scalar(key) and key == subset.name:
+            selection = key
+        return selection
+
+    def aggregate(self, func, *args, **kwargs):
+        raise AbstractMethodError(self)
+
+    agg = aggregate
+
+
+class IndexOpsMixin(OpsMixin):
+    """
+    Common ops mixin to support a unified interface / docs for Series / Index
+    """
+
+    # ndarray compatibility
+    __array_priority__ = 1000
+    _hidden_attrs: frozenset[str] = frozenset(
+        ["tolist"]  # tolist is not deprecated, just suppressed in the __dir__
+    )
+
+    @property
+    def dtype(self) -> DtypeObj:
+        # must be defined here as a property for mypy
+        raise AbstractMethodError(self)
+
+    @property
+    def _values(self) -> ExtensionArray | np.ndarray:
+        # must be defined here as a property for mypy
+        raise AbstractMethodError(self)
+
+    @final
+    def transpose(self, *args, **kwargs) -> Self:
+        """
+        Return the transpose, which is by definition self.
+
+        Returns
+        -------
+        %(klass)s
+        """
+        nv.validate_transpose(args, kwargs)
+        return self
+
+    T = property(
+        transpose,
+        doc="""
+        Return the transpose, which is by definition self.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.T
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.T
+        Index([1, 2, 3], dtype='int64')
+        """,
+    )
+
+    @property
+    def shape(self) -> Shape:
+        """
+        Return a tuple of the shape of the underlying data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.shape
+        (3,)
+        """
+        return self._values.shape
+
+    def __len__(self) -> int:
+        # We need this defined here for mypy
+        raise AbstractMethodError(self)
+
+    # Temporarily avoid using `-> Literal[1]:` because of an IPython (jedi) bug
+    # https://github.com/ipython/ipython/issues/14412
+    # https://github.com/davidhalter/jedi/issues/1990
+    @property
+    def ndim(self) -> int:
+        """
+        Number of dimensions of the underlying data, by definition 1.
+
+        Examples
+        --------
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.ndim
+        1
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.ndim
+        1
+        """
+        return 1
+
+    @final
+    def item(self):
+        """
+        Return the first element of the underlying data as a Python scalar.
+
+        Returns
+        -------
+        scalar
+            The first element of Series or Index.
+
+        Raises
+        ------
+        ValueError
+            If the data is not length = 1.
+
+        Examples
+        --------
+        >>> s = pd.Series([1])
+        >>> s.item()
+        1
+
+        For an index:
+
+        >>> s = pd.Series([1], index=['a'])
+        >>> s.index.item()
+        'a'
+        """
+        if len(self) == 1:
+            return next(iter(self))
+        raise ValueError("can only convert an array of size 1 to a Python scalar")
+
+    @property
+    def nbytes(self) -> int:
+        """
+        Return the number of bytes in the underlying data.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.nbytes
+        24
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.nbytes
+        24
+        """
+        return self._values.nbytes
+
+    @property
+    def size(self) -> int:
+        """
+        Return the number of elements in the underlying data.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series(['Ant', 'Bear', 'Cow'])
+        >>> s
+        0     Ant
+        1    Bear
+        2     Cow
+        dtype: object
+        >>> s.size
+        3
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.size
+        3
+        """
+        return len(self._values)
+
+    @property
+    def array(self) -> ExtensionArray:
+        """
+        The ExtensionArray of the data backing this Series or Index.
+
+        Returns
+        -------
+        ExtensionArray
+            An ExtensionArray of the values stored within. For extension
+            types, this is the actual array. For NumPy native types, this
+            is a thin (no copy) wrapper around :class:`numpy.ndarray`.
+
+            ``.array`` differs from ``.values``, which may require converting
+            the data to a different form.
+
+        See Also
+        --------
+        Index.to_numpy : Similar method that always returns a NumPy array.
+        Series.to_numpy : Similar method that always returns a NumPy array.
+
+        Notes
+        -----
+        This table lays out the different array types for each extension
+        dtype within pandas.
+
+        ================== =============================
+        dtype              array type
+        ================== =============================
+        category           Categorical
+        period             PeriodArray
+        interval           IntervalArray
+        IntegerNA          IntegerArray
+        string             StringArray
+        boolean            BooleanArray
+        datetime64[ns, tz] DatetimeArray
+        ================== =============================
+
+        For any 3rd-party extension types, the array type will be an
+        ExtensionArray.
+
+        For all remaining dtypes ``.array`` will be a
+        :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
+        stored within. If you absolutely need a NumPy array (possibly with
+        copying / coercing data), then use :meth:`Series.to_numpy` instead.
+
+        Examples
+        --------
+        For regular NumPy types like int, and float, a NumpyExtensionArray
+        is returned.
+
+        >>> pd.Series([1, 2, 3]).array
+        <NumpyExtensionArray>
+        [1, 2, 3]
+        Length: 3, dtype: int64
+
+        For extension types, like Categorical, the actual ExtensionArray
+        is returned
+
+        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
+        >>> ser.array
+        ['a', 'b', 'a']
+        Categories (2, object): ['a', 'b']
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        A NumPy ndarray representing the values in this Series or Index.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to pass to :meth:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, optional
+            The value to use for missing values. The default value depends
+            on `dtype` and the type of the array.
+        **kwargs
+            Additional keywords passed through to the ``to_numpy`` method
+            of the underlying array (for extension arrays).
+
+        Returns
+        -------
+        numpy.ndarray
+
+        See Also
+        --------
+        Series.array : Get the actual data stored within.
+        Index.array : Get the actual data stored within.
+        DataFrame.to_numpy : Similar method for DataFrame.
+
+        Notes
+        -----
+        The returned array will be the same up to equality (values equal
+        in `self` will be equal in the returned array; likewise for values
+        that are not equal). When `self` contains an ExtensionArray, the
+        dtype may be different. For example, for a category-dtype Series,
+        ``to_numpy()`` will return a NumPy array and the categorical dtype
+        will be lost.
+
+        For NumPy dtypes, this will be a reference to the actual data stored
+        in this Series or Index (assuming ``copy=False``). Modifying the result
+        in place will modify the data stored in the Series or Index (not that
+        we recommend doing that).
+
+        For extension types, ``to_numpy()`` *may* require copying data and
+        coercing the result to a NumPy type (possibly object), which may be
+        expensive. When you need a no-copy reference to the underlying data,
+        :attr:`Series.array` should be used instead.
+
+        This table lays out the different dtypes and default return types of
+        ``to_numpy()`` for various dtypes within pandas.
+
+        ================== ================================
+        dtype              array type
+        ================== ================================
+        category[T]        ndarray[T] (same dtype as input)
+        period             ndarray[object] (Periods)
+        interval           ndarray[object] (Intervals)
+        IntegerNA          ndarray[object]
+        datetime64[ns]     datetime64[ns]
+        datetime64[ns, tz] ndarray[object] (Timestamps)
+        ================== ================================
+
+        Examples
+        --------
+        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
+        >>> ser.to_numpy()
+        array(['a', 'b', 'a'], dtype=object)
+
+        Specify the `dtype` to control how datetime-aware data is represented.
+        Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
+        objects, each with the correct ``tz``.
+
+        >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
+        >>> ser.to_numpy(dtype=object)
+        array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
+               Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
+              dtype=object)
+
+        Or ``dtype='datetime64[ns]'`` to return an ndarray of native
+        datetime64 values. The values are converted to UTC and the timezone
+        info is dropped.
+
+        >>> ser.to_numpy(dtype="datetime64[ns]")
+        ... # doctest: +ELLIPSIS
+        array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
+              dtype='datetime64[ns]')
+        """
+        if isinstance(self.dtype, ExtensionDtype):
+            return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
+        elif kwargs:
+            bad_keys = next(iter(kwargs.keys()))
+            raise TypeError(
+                f"to_numpy() got an unexpected keyword argument '{bad_keys}'"
+            )
+
+        fillna = (
+            na_value is not lib.no_default
+            # no need to fillna with np.nan if we already have a float dtype
+            and not (na_value is np.nan and np.issubdtype(self.dtype, np.floating))
+        )
+
+        values = self._values
+        if fillna:
+            if not can_hold_element(values, na_value):
+                # if we can't hold the na_value asarray either makes a copy or we
+                # error before modifying values. The asarray later on thus won't make
+                # another copy
+                values = np.asarray(values, dtype=dtype)
+            else:
+                values = values.copy()
+
+            values[np.asanyarray(isna(self))] = na_value
+
+        result = np.asarray(values, dtype=dtype)
+
+        if (copy and not fillna) or (not copy and using_copy_on_write()):
+            if np.shares_memory(self._values[:2], result[:2]):
+                # Take slices to improve performance of check
+                if using_copy_on_write() and not copy:
+                    result = result.view()
+                    result.flags.writeable = False
+                else:
+                    result = result.copy()
+
+        return result
+
+    @final
+    @property
+    def empty(self) -> bool:
+        return not self.size
+
+    @doc(op="max", oppose="min", value="largest")
+    def argmax(
+        self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
+    ) -> int:
+        """
+        Return int position of the {value} value in the Series.
+
+        If the {op}imum is achieved in multiple locations,
+        the first row position is returned.
+
+        Parameters
+        ----------
+        axis : {{None}}
+            Unused. Parameter needed for compatibility with DataFrame.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        int
+            Row position of the {op}imum value.
+
+        See Also
+        --------
+        Series.arg{op} : Return position of the {op}imum value.
+        Series.arg{oppose} : Return position of the {oppose}imum value.
+        numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
+        Series.idxmax : Return index label of the maximum values.
+        Series.idxmin : Return index label of the minimum values.
+
+        Examples
+        --------
+        Consider dataset containing cereal calories
+
+        >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
+        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
+        >>> s
+        Corn Flakes              100.0
+        Almond Delight           110.0
+        Cinnamon Toast Crunch    120.0
+        Cocoa Puff               110.0
+        dtype: float64
+
+        >>> s.argmax()
+        2
+        >>> s.argmin()
+        0
+
+        The maximum cereal calories is the third element and
+        the minimum cereal calories is the first element,
+        since series is zero-indexed.
+        """
+        delegate = self._values
+        nv.validate_minmax_axis(axis)
+        skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
+
+        if isinstance(delegate, ExtensionArray):
+            if not skipna and delegate.isna().any():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+            else:
+                return delegate.argmax()
+        else:
+            result = nanops.nanargmax(delegate, skipna=skipna)
+            if result == -1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            # error: Incompatible return value type (got "Union[int, ndarray]", expected
+            # "int")
+            return result  # type: ignore[return-value]
+
+    @doc(argmax, op="min", oppose="max", value="smallest")
+    def argmin(
+        self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
+    ) -> int:
+        delegate = self._values
+        nv.validate_minmax_axis(axis)
+        skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
+
+        if isinstance(delegate, ExtensionArray):
+            if not skipna and delegate.isna().any():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+            else:
+                return delegate.argmin()
+        else:
+            result = nanops.nanargmin(delegate, skipna=skipna)
+            if result == -1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            # error: Incompatible return value type (got "Union[int, ndarray]", expected
+            # "int")
+            return result  # type: ignore[return-value]
+
+    def tolist(self):
+        """
+        Return a list of the values.
+
+        These are each a scalar type, which is a Python scalar
+        (for str, int, float) or a pandas scalar
+        (for Timestamp/Timedelta/Interval/Period)
+
+        Returns
+        -------
+        list
+
+        See Also
+        --------
+        numpy.ndarray.tolist : Return the array as an a.ndim-levels deep
+            nested list of Python scalars.
+
+        Examples
+        --------
+        For Series
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.to_list()
+        [1, 2, 3]
+
+        For Index:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+
+        >>> idx.to_list()
+        [1, 2, 3]
+        """
+        return self._values.tolist()
+
+    to_list = tolist
+
+    def __iter__(self) -> Iterator:
+        """
+        Return an iterator of the values.
+
+        These are each a scalar type, which is a Python scalar
+        (for str, int, float) or a pandas scalar
+        (for Timestamp/Timedelta/Interval/Period)
+
+        Returns
+        -------
+        iterator
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> for x in s:
+        ...     print(x)
+        1
+        2
+        3
+        """
+        # We are explicitly making element iterators.
+        if not isinstance(self._values, np.ndarray):
+            # Check type instead of dtype to catch DTA/TDA
+            return iter(self._values)
+        else:
+            return map(self._values.item, range(self._values.size))
+
+    @cache_readonly
+    def hasnans(self) -> bool:
+        """
+        Return True if there are any NaNs.
+
+        Enables various performance speedups.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, None])
+        >>> s
+        0    1.0
+        1    2.0
+        2    3.0
+        3    NaN
+        dtype: float64
+        >>> s.hasnans
+        True
+        """
+        # error: Item "bool" of "Union[bool, ndarray[Any, dtype[bool_]], NDFrame]"
+        # has no attribute "any"
+        return bool(isna(self).any())  # type: ignore[union-attr]
+
+    @final
+    def _map_values(self, mapper, na_action=None, convert: bool = True):
+        """
+        An internal function that maps values using the input
+        correspondence (which can be a dict, Series, or function).
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            The input correspondence object
+        na_action : {None, 'ignore'}
+            If 'ignore', propagate NA values, without passing them to the
+            mapping function
+        convert : bool, default True
+            Try to find better dtype for elementwise function results. If
+            False, leave as dtype=object. Note that the dtype is always
+            preserved for some extension array dtypes, such as Categorical.
+
+        Returns
+        -------
+        Union[Index, MultiIndex], inferred
+            The output of the mapping function applied to the index.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+        """
+        arr = self._values
+
+        if isinstance(arr, ExtensionArray):
+            return arr.map(mapper, na_action=na_action)
+
+        return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
+
+    @final
+    def value_counts(
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
+    ) -> Series:
+        """
+        Return a Series containing counts of unique values.
+
+        The resulting object will be in descending order so that the
+        first element is the most frequently-occurring element.
+        Excludes NA values by default.
+
+        Parameters
+        ----------
+        normalize : bool, default False
+            If True then the object returned will contain the relative
+            frequencies of the unique values.
+        sort : bool, default True
+            Sort by frequencies when True. Preserve the order of the data when False.
+        ascending : bool, default False
+            Sort in ascending order.
+        bins : int, optional
+            Rather than count values, group them into half-open bins,
+            a convenience for ``pd.cut``, only works with numeric data.
+        dropna : bool, default True
+            Don't include counts of NaN.
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.count: Number of non-NA elements in a Series.
+        DataFrame.count: Number of non-NA elements in a DataFrame.
+        DataFrame.value_counts: Equivalent method on DataFrames.
+
+        Examples
+        --------
+        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
+        >>> index.value_counts()
+        3.0    2
+        1.0    1
+        2.0    1
+        4.0    1
+        Name: count, dtype: int64
+
+        With `normalize` set to `True`, returns the relative frequency by
+        dividing all values by the sum of values.
+
+        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
+        >>> s.value_counts(normalize=True)
+        3.0    0.4
+        1.0    0.2
+        2.0    0.2
+        4.0    0.2
+        Name: proportion, dtype: float64
+
+        **bins**
+
+        Bins can be useful for going from a continuous variable to a
+        categorical variable; instead of counting unique
+        apparitions of values, divide the index in the specified
+        number of half-open bins.
+
+        >>> s.value_counts(bins=3)
+        (0.996, 2.0]    2
+        (2.0, 3.0]      2
+        (3.0, 4.0]      1
+        Name: count, dtype: int64
+
+        **dropna**
+
+        With `dropna` set to `False` we can also see NaN index values.
+
+        >>> s.value_counts(dropna=False)
+        3.0    2
+        1.0    1
+        2.0    1
+        4.0    1
+        NaN    1
+        Name: count, dtype: int64
+        """
+        return algorithms.value_counts_internal(
+            self,
+            sort=sort,
+            ascending=ascending,
+            normalize=normalize,
+            bins=bins,
+            dropna=dropna,
+        )
+
+    def unique(self):
+        values = self._values
+        if not isinstance(values, np.ndarray):
+            # i.e. ExtensionArray
+            result = values.unique()
+        else:
+            result = algorithms.unique1d(values)
+        return result
+
+    @final
+    def nunique(self, dropna: bool = True) -> int:
+        """
+        Return number of unique elements in the object.
+
+        Excludes NA values by default.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include NaN in the count.
+
+        Returns
+        -------
+        int
+
+        See Also
+        --------
+        DataFrame.nunique: Method nunique for DataFrame.
+        Series.count: Count non-NA/null observations in the Series.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 3, 5, 7, 7])
+        >>> s
+        0    1
+        1    3
+        2    5
+        3    7
+        4    7
+        dtype: int64
+
+        >>> s.nunique()
+        4
+        """
+        uniqs = self.unique()
+        if dropna:
+            uniqs = remove_na_arraylike(uniqs)
+        return len(uniqs)
+
+    @property
+    def is_unique(self) -> bool:
+        """
+        Return boolean if values in the object are unique.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.is_unique
+        True
+
+        >>> s = pd.Series([1, 2, 3, 1])
+        >>> s.is_unique
+        False
+        """
+        return self.nunique(dropna=False) == len(self)
+
+    @property
+    def is_monotonic_increasing(self) -> bool:
+        """
+        Return boolean if values in the object are monotonically increasing.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 2])
+        >>> s.is_monotonic_increasing
+        True
+
+        >>> s = pd.Series([3, 2, 1])
+        >>> s.is_monotonic_increasing
+        False
+        """
+        from pandas import Index
+
+        return Index(self).is_monotonic_increasing
+
+    @property
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return boolean if values in the object are monotonically decreasing.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([3, 2, 2, 1])
+        >>> s.is_monotonic_decreasing
+        True
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.is_monotonic_decreasing
+        False
+        """
+        from pandas import Index
+
+        return Index(self).is_monotonic_decreasing
+
+    @final
+    def _memory_usage(self, deep: bool = False) -> int:
+        """
+        Memory usage of the values.
+
+        Parameters
+        ----------
+        deep : bool, default False
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption.
+
+        Returns
+        -------
+        bytes used
+
+        See Also
+        --------
+        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
+            array.
+
+        Notes
+        -----
+        Memory usage does not include memory consumed by elements that
+        are not components of the array if deep=False or if used on PyPy
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.memory_usage()
+        24
+        """
+        if hasattr(self.array, "memory_usage"):
+            return self.array.memory_usage(  # pyright: ignore[reportGeneralTypeIssues]
+                deep=deep,
+            )
+
+        v = self.array.nbytes
+        if deep and is_object_dtype(self.dtype) and not PYPY:
+            values = cast(np.ndarray, self._values)
+            v += lib.memory_usage_of_objects(values)
+        return v
+
+    @doc(
+        algorithms.factorize,
+        values="",
+        order="",
+        size_hint="",
+        sort=textwrap.dedent(
+            """\
+            sort : bool, default False
+                Sort `uniques` and shuffle `codes` to maintain the
+                relationship.
+            """
+        ),
+    )
+    def factorize(
+        self,
+        sort: bool = False,
+        use_na_sentinel: bool = True,
+    ) -> tuple[npt.NDArray[np.intp], Index]:
+        codes, uniques = algorithms.factorize(
+            self._values, sort=sort, use_na_sentinel=use_na_sentinel
+        )
+        if uniques.dtype == np.float16:
+            uniques = uniques.astype(np.float32)
+
+        if isinstance(self, ABCMultiIndex):
+            # preserve MultiIndex
+            uniques = self._constructor(uniques)
+        else:
+            from pandas import Index
+
+            try:
+                uniques = Index(uniques, dtype=self.dtype)
+            except NotImplementedError:
+                # not all dtypes are supported in Index that are allowed for Series
+                # e.g. float16 or bytes
+                uniques = Index(uniques)
+        return codes, uniques
+
+    _shared_docs[
+        "searchsorted"
+    ] = """
+        Find indices where elements should be inserted to maintain order.
+
+        Find the indices into a sorted {klass} `self` such that, if the
+        corresponding elements in `value` were inserted before the indices,
+        the order of `self` would be preserved.
+
+        .. note::
+
+            The {klass} *must* be monotonically sorted, otherwise
+            wrong locations will likely be returned. Pandas does *not*
+            check this for you.
+
+        Parameters
+        ----------
+        value : array-like or scalar
+            Values to insert into `self`.
+        side : {{'left', 'right'}}, optional
+            If 'left', the index of the first suitable location found is given.
+            If 'right', return the last such index.  If there is no suitable
+            index, return either 0 or N (where N is the length of `self`).
+        sorter : 1-D array-like, optional
+            Optional array of integer indices that sort `self` into ascending
+            order. They are typically the result of ``np.argsort``.
+
+        Returns
+        -------
+        int or array of int
+            A scalar or array of insertion points with the
+            same shape as `value`.
+
+        See Also
+        --------
+        sort_values : Sort by the values along either axis.
+        numpy.searchsorted : Similar method from NumPy.
+
+        Notes
+        -----
+        Binary search is used to find the required insertion points.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+        >>> ser
+        0    1
+        1    2
+        2    3
+        dtype: int64
+
+        >>> ser.searchsorted(4)
+        3
+
+        >>> ser.searchsorted([0, 4])
+        array([0, 3])
+
+        >>> ser.searchsorted([1, 3], side='left')
+        array([0, 2])
+
+        >>> ser.searchsorted([1, 3], side='right')
+        array([1, 3])
+
+        >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000']))
+        >>> ser
+        0   2000-03-11
+        1   2000-03-12
+        2   2000-03-13
+        dtype: datetime64[ns]
+
+        >>> ser.searchsorted('3/14/2000')
+        3
+
+        >>> ser = pd.Categorical(
+        ...     ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True
+        ... )
+        >>> ser
+        ['apple', 'bread', 'bread', 'cheese', 'milk']
+        Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
+
+        >>> ser.searchsorted('bread')
+        1
+
+        >>> ser.searchsorted(['bread'], side='right')
+        array([3])
+
+        If the values are not monotonically sorted, wrong locations
+        may be returned:
+
+        >>> ser = pd.Series([2, 1, 3])
+        >>> ser
+        0    2
+        1    1
+        2    3
+        dtype: int64
+
+        >>> ser.searchsorted(1)  # doctest: +SKIP
+        0  # wrong result, correct would be 1
+        """
+
+    # This overload is needed so that the call to searchsorted in
+    # pandas.core.resample.TimeGrouper._get_period_bins picks the correct result
+
+    # error: Overloaded function signatures 1 and 2 overlap with incompatible
+    # return types
+    @overload
+    def searchsorted(  # type: ignore[overload-overlap]
+        self,
+        value: ScalarLike_co,
+        side: Literal["left", "right"] = ...,
+        sorter: NumpySorter = ...,
+    ) -> np.intp:
+        ...
+
+    @overload
+    def searchsorted(
+        self,
+        value: npt.ArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = ...,
+        sorter: NumpySorter = ...,
+    ) -> npt.NDArray[np.intp]:
+        ...
+
+    @doc(_shared_docs["searchsorted"], klass="Index")
+    def searchsorted(
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if isinstance(value, ABCDataFrame):
+            msg = (
+                "Value must be 1-D array-like or scalar, "
+                f"{type(value).__name__} is not supported"
+            )
+            raise ValueError(msg)
+
+        values = self._values
+        if not isinstance(values, np.ndarray):
+            # Going through EA.searchsorted directly improves performance GH#38083
+            return values.searchsorted(value, side=side, sorter=sorter)
+
+        return algorithms.searchsorted(
+            values,
+            value,
+            side=side,
+            sorter=sorter,
+        )
+
+    def drop_duplicates(self, *, keep: DropKeep = "first"):
+        duplicated = self._duplicated(keep=keep)
+        # error: Value of type "IndexOpsMixin" is not indexable
+        return self[~duplicated]  # type: ignore[index]
+
+    @final
+    def _duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
+        arr = self._values
+        if isinstance(arr, ExtensionArray):
+            return arr.duplicated(keep=keep)
+        return algorithms.duplicated(arr, keep=keep)
+
+    def _arith_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
+        rvalues = ensure_wrapped_if_datetimelike(rvalues)
+        if isinstance(rvalues, range):
+            rvalues = np.arange(rvalues.start, rvalues.stop, rvalues.step)
+
+        with np.errstate(all="ignore"):
+            result = ops.arithmetic_op(lvalues, rvalues, op)
+
+        return self._construct_result(result, name=res_name)
+
+    def _construct_result(self, result, name):
+        """
+        Construct an appropriately-wrapped result from the ArrayLike result
+        of an arithmetic-like operation.
+        """
+        raise AbstractMethodError(self)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/common.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f024498d66edfe9cab7b6fb2e0802da79163786
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/common.py
@@ -0,0 +1,657 @@
+"""
+Misc tools for implementing data structures
+
+Note: pandas.core.common is *not* part of the public API.
+"""
+from __future__ import annotations
+
+import builtins
+from collections import (
+    abc,
+    defaultdict,
+)
+from collections.abc import (
+    Collection,
+    Generator,
+    Hashable,
+    Iterable,
+    Sequence,
+)
+import contextlib
+from functools import partial
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.compat.numpy import np_version_gte1p24
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_integer,
+)
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import iterable_not_string
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AnyArrayLike,
+        ArrayLike,
+        NpDtype,
+        RandomState,
+        T,
+    )
+
+    from pandas import Index
+
+
+def flatten(line):
+    """
+    Flatten an arbitrarily nested sequence.
+
+    Parameters
+    ----------
+    line : sequence
+        The non string sequence to flatten
+
+    Notes
+    -----
+    This doesn't consider strings sequences.
+
+    Returns
+    -------
+    flattened : generator
+    """
+    for element in line:
+        if iterable_not_string(element):
+            yield from flatten(element)
+        else:
+            yield element
+
+
+def consensus_name_attr(objs):
+    name = objs[0].name
+    for obj in objs[1:]:
+        try:
+            if obj.name != name:
+                name = None
+        except ValueError:
+            name = None
+    return name
+
+
+def is_bool_indexer(key: Any) -> bool:
+    """
+    Check whether `key` is a valid boolean indexer.
+
+    Parameters
+    ----------
+    key : Any
+        Only list-likes may be considered boolean indexers.
+        All other types are not considered a boolean indexer.
+        For array-like input, boolean ndarrays or ExtensionArrays
+        with ``_is_boolean`` set are considered boolean indexers.
+
+    Returns
+    -------
+    bool
+        Whether `key` is a valid boolean indexer.
+
+    Raises
+    ------
+    ValueError
+        When the array is an object-dtype ndarray or ExtensionArray
+        and contains missing values.
+
+    See Also
+    --------
+    check_array_indexer : Check that `key` is a valid array to index,
+        and convert to an ndarray.
+    """
+    if isinstance(
+        key, (ABCSeries, np.ndarray, ABCIndex, ABCExtensionArray)
+    ) and not isinstance(key, ABCMultiIndex):
+        if key.dtype == np.object_:
+            key_array = np.asarray(key)
+
+            if not lib.is_bool_array(key_array):
+                na_msg = "Cannot mask with non-boolean array containing NA / NaN values"
+                if lib.is_bool_array(key_array, skipna=True):
+                    # Don't raise on e.g. ["A", "B", np.nan], see
+                    #  test_loc_getitem_list_of_labels_categoricalindex_with_na
+                    raise ValueError(na_msg)
+                return False
+            return True
+        elif is_bool_dtype(key.dtype):
+            return True
+    elif isinstance(key, list):
+        # check if np.array(key).dtype would be bool
+        if len(key) > 0:
+            if type(key) is not list:  # noqa: E721
+                # GH#42461 cython will raise TypeError if we pass a subclass
+                key = list(key)
+            return lib.is_bool_list(key)
+
+    return False
+
+
+def cast_scalar_indexer(val):
+    """
+    Disallow indexing with a float key, even if that key is a round number.
+
+    Parameters
+    ----------
+    val : scalar
+
+    Returns
+    -------
+    outval : scalar
+    """
+    # assumes lib.is_scalar(val)
+    if lib.is_float(val) and val.is_integer():
+        raise IndexError(
+            # GH#34193
+            "Indexing with a float is no longer supported. Manually convert "
+            "to an integer key instead."
+        )
+    return val
+
+
+def not_none(*args):
+    """
+    Returns a generator consisting of the arguments that are not None.
+    """
+    return (arg for arg in args if arg is not None)
+
+
+def any_none(*args) -> bool:
+    """
+    Returns a boolean indicating if any argument is None.
+    """
+    return any(arg is None for arg in args)
+
+
+def all_none(*args) -> bool:
+    """
+    Returns a boolean indicating if all arguments are None.
+    """
+    return all(arg is None for arg in args)
+
+
+def any_not_none(*args) -> bool:
+    """
+    Returns a boolean indicating if any argument is not None.
+    """
+    return any(arg is not None for arg in args)
+
+
+def all_not_none(*args) -> bool:
+    """
+    Returns a boolean indicating if all arguments are not None.
+    """
+    return all(arg is not None for arg in args)
+
+
+def count_not_none(*args) -> int:
+    """
+    Returns the count of arguments that are not None.
+    """
+    return sum(x is not None for x in args)
+
+
+@overload
+def asarray_tuplesafe(
+    values: ArrayLike | list | tuple | zip, dtype: NpDtype | None = ...
+) -> np.ndarray:
+    # ExtensionArray can only be returned when values is an Index, all other iterables
+    # will return np.ndarray. Unfortunately "all other" cannot be encoded in a type
+    # signature, so instead we special-case some common types.
+    ...
+
+
+@overload
+def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = ...) -> ArrayLike:
+    ...
+
+
+def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = None) -> ArrayLike:
+    if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):
+        values = list(values)
+    elif isinstance(values, ABCIndex):
+        return values._values
+    elif isinstance(values, ABCSeries):
+        return values._values
+
+    if isinstance(values, list) and dtype in [np.object_, object]:
+        return construct_1d_object_array_from_listlike(values)
+
+    try:
+        with warnings.catch_warnings():
+            # Can remove warning filter once NumPy 1.24 is min version
+            if not np_version_gte1p24:
+                warnings.simplefilter("ignore", np.VisibleDeprecationWarning)
+            result = np.asarray(values, dtype=dtype)
+    except ValueError:
+        # Using try/except since it's more performant than checking is_list_like
+        # over each element
+        # error: Argument 1 to "construct_1d_object_array_from_listlike"
+        # has incompatible type "Iterable[Any]"; expected "Sized"
+        return construct_1d_object_array_from_listlike(values)  # type: ignore[arg-type]
+
+    if issubclass(result.dtype.type, str):
+        result = np.asarray(values, dtype=object)
+
+    if result.ndim == 2:
+        # Avoid building an array of arrays:
+        values = [tuple(x) for x in values]
+        result = construct_1d_object_array_from_listlike(values)
+
+    return result
+
+
+def index_labels_to_array(
+    labels: np.ndarray | Iterable, dtype: NpDtype | None = None
+) -> np.ndarray:
+    """
+    Transform label or iterable of labels to array, for use in Index.
+
+    Parameters
+    ----------
+    dtype : dtype
+        If specified, use as dtype of the resulting array, otherwise infer.
+
+    Returns
+    -------
+    array
+    """
+    if isinstance(labels, (str, tuple)):
+        labels = [labels]
+
+    if not isinstance(labels, (list, np.ndarray)):
+        try:
+            labels = list(labels)
+        except TypeError:  # non-iterable
+            labels = [labels]
+
+    labels = asarray_tuplesafe(labels, dtype=dtype)
+
+    return labels
+
+
+def maybe_make_list(obj):
+    if obj is not None and not isinstance(obj, (tuple, list)):
+        return [obj]
+    return obj
+
+
+def maybe_iterable_to_list(obj: Iterable[T] | T) -> Collection[T] | T:
+    """
+    If obj is Iterable but not list-like, consume into list.
+    """
+    if isinstance(obj, abc.Iterable) and not isinstance(obj, abc.Sized):
+        return list(obj)
+    obj = cast(Collection, obj)
+    return obj
+
+
+def is_null_slice(obj) -> bool:
+    """
+    We have a null slice.
+    """
+    return (
+        isinstance(obj, slice)
+        and obj.start is None
+        and obj.stop is None
+        and obj.step is None
+    )
+
+
+def is_empty_slice(obj) -> bool:
+    """
+    We have an empty slice, e.g. no values are selected.
+    """
+    return (
+        isinstance(obj, slice)
+        and obj.start is not None
+        and obj.stop is not None
+        and obj.start == obj.stop
+    )
+
+
+def is_true_slices(line) -> list[bool]:
+    """
+    Find non-trivial slices in "line": return a list of booleans with same length.
+    """
+    return [isinstance(k, slice) and not is_null_slice(k) for k in line]
+
+
+# TODO: used only once in indexing; belongs elsewhere?
+def is_full_slice(obj, line: int) -> bool:
+    """
+    We have a full length slice.
+    """
+    return (
+        isinstance(obj, slice)
+        and obj.start == 0
+        and obj.stop == line
+        and obj.step is None
+    )
+
+
+def get_callable_name(obj):
+    # typical case has name
+    if hasattr(obj, "__name__"):
+        return getattr(obj, "__name__")
+    # some objects don't; could recurse
+    if isinstance(obj, partial):
+        return get_callable_name(obj.func)
+    # fall back to class name
+    if callable(obj):
+        return type(obj).__name__
+    # everything failed (probably because the argument
+    # wasn't actually callable); we return None
+    # instead of the empty string in this case to allow
+    # distinguishing between no name and a name of ''
+    return None
+
+
+def apply_if_callable(maybe_callable, obj, **kwargs):
+    """
+    Evaluate possibly callable input using obj and kwargs if it is callable,
+    otherwise return as it is.
+
+    Parameters
+    ----------
+    maybe_callable : possibly a callable
+    obj : NDFrame
+    **kwargs
+    """
+    if callable(maybe_callable):
+        return maybe_callable(obj, **kwargs)
+
+    return maybe_callable
+
+
+def standardize_mapping(into):
+    """
+    Helper function to standardize a supplied mapping.
+
+    Parameters
+    ----------
+    into : instance or subclass of collections.abc.Mapping
+        Must be a class, an initialized collections.defaultdict,
+        or an instance of a collections.abc.Mapping subclass.
+
+    Returns
+    -------
+    mapping : a collections.abc.Mapping subclass or other constructor
+        a callable object that can accept an iterator to create
+        the desired Mapping.
+
+    See Also
+    --------
+    DataFrame.to_dict
+    Series.to_dict
+    """
+    if not inspect.isclass(into):
+        if isinstance(into, defaultdict):
+            return partial(defaultdict, into.default_factory)
+        into = type(into)
+    if not issubclass(into, abc.Mapping):
+        raise TypeError(f"unsupported type: {into}")
+    if into == defaultdict:
+        raise TypeError("to_dict() only accepts initialized defaultdicts")
+    return into
+
+
+@overload
+def random_state(state: np.random.Generator) -> np.random.Generator:
+    ...
+
+
+@overload
+def random_state(
+    state: int | np.ndarray | np.random.BitGenerator | np.random.RandomState | None,
+) -> np.random.RandomState:
+    ...
+
+
+def random_state(state: RandomState | None = None):
+    """
+    Helper function for processing random_state arguments.
+
+    Parameters
+    ----------
+    state : int, array-like, BitGenerator, Generator, np.random.RandomState, None.
+        If receives an int, array-like, or BitGenerator, passes to
+        np.random.RandomState() as seed.
+        If receives an np.random RandomState or Generator, just returns that unchanged.
+        If receives `None`, returns np.random.
+        If receives anything else, raises an informative ValueError.
+
+        Default None.
+
+    Returns
+    -------
+    np.random.RandomState or np.random.Generator. If state is None, returns np.random
+
+    """
+    if is_integer(state) or isinstance(state, (np.ndarray, np.random.BitGenerator)):
+        return np.random.RandomState(state)
+    elif isinstance(state, np.random.RandomState):
+        return state
+    elif isinstance(state, np.random.Generator):
+        return state
+    elif state is None:
+        return np.random
+    else:
+        raise ValueError(
+            "random_state must be an integer, array-like, a BitGenerator, Generator, "
+            "a numpy RandomState, or None"
+        )
+
+
+def pipe(
+    obj, func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs
+) -> T:
+    """
+    Apply a function ``func`` to object ``obj`` either by passing obj as the
+    first argument to the function or, in the case that the func is a tuple,
+    interpret the first element of the tuple as a function and pass the obj to
+    that function as a keyword argument whose key is the value of the second
+    element of the tuple.
+
+    Parameters
+    ----------
+    func : callable or tuple of (callable, str)
+        Function to apply to this object or, alternatively, a
+        ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
+        string indicating the keyword of ``callable`` that expects the
+        object.
+    *args : iterable, optional
+        Positional arguments passed into ``func``.
+    **kwargs : dict, optional
+        A dictionary of keyword arguments passed into ``func``.
+
+    Returns
+    -------
+    object : the return type of ``func``.
+    """
+    if isinstance(func, tuple):
+        func, target = func
+        if target in kwargs:
+            msg = f"{target} is both the pipe target and a keyword argument"
+            raise ValueError(msg)
+        kwargs[target] = obj
+        return func(*args, **kwargs)
+    else:
+        return func(obj, *args, **kwargs)
+
+
+def get_rename_function(mapper):
+    """
+    Returns a function that will map names/labels, dependent if mapper
+    is a dict, Series or just a function.
+    """
+
+    def f(x):
+        if x in mapper:
+            return mapper[x]
+        else:
+            return x
+
+    return f if isinstance(mapper, (abc.Mapping, ABCSeries)) else mapper
+
+
+def convert_to_list_like(
+    values: Hashable | Iterable | AnyArrayLike,
+) -> list | AnyArrayLike:
+    """
+    Convert list-like or scalar input to list-like. List, numpy and pandas array-like
+    inputs are returned unmodified whereas others are converted to list.
+    """
+    if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
+        return values
+    elif isinstance(values, abc.Iterable) and not isinstance(values, str):
+        return list(values)
+
+    return [values]
+
+
+@contextlib.contextmanager
+def temp_setattr(
+    obj, attr: str, value, condition: bool = True
+) -> Generator[None, None, None]:
+    """
+    Temporarily set attribute on an object.
+
+    Parameters
+    ----------
+    obj : object
+        Object whose attribute will be modified.
+    attr : str
+        Attribute to modify.
+    value : Any
+        Value to temporarily set attribute to.
+    condition : bool, default True
+        Whether to set the attribute. Provided in order to not have to
+        conditionally use this context manager.
+
+    Yields
+    ------
+    object : obj with modified attribute.
+    """
+    if condition:
+        old_value = getattr(obj, attr)
+        setattr(obj, attr, value)
+    try:
+        yield obj
+    finally:
+        if condition:
+            setattr(obj, attr, old_value)
+
+
+def require_length_match(data, index: Index) -> None:
+    """
+    Check the length of data matches the length of the index.
+    """
+    if len(data) != len(index):
+        raise ValueError(
+            "Length of values "
+            f"({len(data)}) "
+            "does not match length of index "
+            f"({len(index)})"
+        )
+
+
+# the ufuncs np.maximum.reduce and np.minimum.reduce default to axis=0,
+#  whereas np.min and np.max (which directly call obj.min and obj.max)
+#  default to axis=None.
+_builtin_table = {
+    builtins.sum: np.sum,
+    builtins.max: np.maximum.reduce,
+    builtins.min: np.minimum.reduce,
+}
+
+# GH#53425: Only for deprecation
+_builtin_table_alias = {
+    builtins.sum: "np.sum",
+    builtins.max: "np.maximum.reduce",
+    builtins.min: "np.minimum.reduce",
+}
+
+_cython_table = {
+    builtins.sum: "sum",
+    builtins.max: "max",
+    builtins.min: "min",
+    np.all: "all",
+    np.any: "any",
+    np.sum: "sum",
+    np.nansum: "sum",
+    np.mean: "mean",
+    np.nanmean: "mean",
+    np.prod: "prod",
+    np.nanprod: "prod",
+    np.std: "std",
+    np.nanstd: "std",
+    np.var: "var",
+    np.nanvar: "var",
+    np.median: "median",
+    np.nanmedian: "median",
+    np.max: "max",
+    np.nanmax: "max",
+    np.min: "min",
+    np.nanmin: "min",
+    np.cumprod: "cumprod",
+    np.nancumprod: "cumprod",
+    np.cumsum: "cumsum",
+    np.nancumsum: "cumsum",
+}
+
+
+def get_cython_func(arg: Callable) -> str | None:
+    """
+    if we define an internal function for this argument, return it
+    """
+    return _cython_table.get(arg)
+
+
+def is_builtin_func(arg):
+    """
+    if we define a builtin function for this argument, return it,
+    otherwise return the arg
+    """
+    return _builtin_table.get(arg, arg)
+
+
+def fill_missing_names(names: Sequence[Hashable | None]) -> list[Hashable]:
+    """
+    If a name is missing then replace it by level_n, where n is the count
+
+    .. versionadded:: 1.4.0
+
+    Parameters
+    ----------
+    names : list-like
+        list of column names or None values.
+
+    Returns
+    -------
+    list
+        list of column names with the None values replaced.
+    """
+    return [f"level_{i}" if name is None else name for i, name in enumerate(names)]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/align.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/align.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd852ba9249cf26ea39b7e51bbfa754ffe9c10ce
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/align.py
@@ -0,0 +1,213 @@
+"""
+Core eval alignment algorithms.
+"""
+from __future__ import annotations
+
+from functools import (
+    partial,
+    wraps,
+)
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+import numpy as np
+
+from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.computation.common import result_type_many
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import F
+
+    from pandas.core.generic import NDFrame
+    from pandas.core.indexes.api import Index
+
+
+def _align_core_single_unary_op(
+    term,
+) -> tuple[partial | type[NDFrame], dict[str, Index] | None]:
+    typ: partial | type[NDFrame]
+    axes: dict[str, Index] | None = None
+
+    if isinstance(term.value, np.ndarray):
+        typ = partial(np.asanyarray, dtype=term.value.dtype)
+    else:
+        typ = type(term.value)
+        if hasattr(term.value, "axes"):
+            axes = _zip_axes_from_type(typ, term.value.axes)
+
+    return typ, axes
+
+
+def _zip_axes_from_type(
+    typ: type[NDFrame], new_axes: Sequence[Index]
+) -> dict[str, Index]:
+    return {name: new_axes[i] for i, name in enumerate(typ._AXIS_ORDERS)}
+
+
+def _any_pandas_objects(terms) -> bool:
+    """
+    Check a sequence of terms for instances of PandasObject.
+    """
+    return any(isinstance(term.value, PandasObject) for term in terms)
+
+
+def _filter_special_cases(f) -> Callable[[F], F]:
+    @wraps(f)
+    def wrapper(terms):
+        # single unary operand
+        if len(terms) == 1:
+            return _align_core_single_unary_op(terms[0])
+
+        term_values = (term.value for term in terms)
+
+        # we don't have any pandas objects
+        if not _any_pandas_objects(terms):
+            return result_type_many(*term_values), None
+
+        return f(terms)
+
+    return wrapper
+
+
+@_filter_special_cases
+def _align_core(terms):
+    term_index = [i for i, term in enumerate(terms) if hasattr(term.value, "axes")]
+    term_dims = [terms[i].value.ndim for i in term_index]
+
+    from pandas import Series
+
+    ndims = Series(dict(zip(term_index, term_dims)))
+
+    # initial axes are the axes of the largest-axis'd term
+    biggest = terms[ndims.idxmax()].value
+    typ = biggest._constructor
+    axes = biggest.axes
+    naxes = len(axes)
+    gt_than_one_axis = naxes > 1
+
+    for value in (terms[i].value for i in term_index):
+        is_series = isinstance(value, ABCSeries)
+        is_series_and_gt_one_axis = is_series and gt_than_one_axis
+
+        for axis, items in enumerate(value.axes):
+            if is_series_and_gt_one_axis:
+                ax, itm = naxes - 1, value.index
+            else:
+                ax, itm = axis, items
+
+            if not axes[ax].is_(itm):
+                axes[ax] = axes[ax].union(itm)
+
+    for i, ndim in ndims.items():
+        for axis, items in zip(range(ndim), axes):
+            ti = terms[i].value
+
+            if hasattr(ti, "reindex"):
+                transpose = isinstance(ti, ABCSeries) and naxes > 1
+                reindexer = axes[naxes - 1] if transpose else items
+
+                term_axis_size = len(ti.axes[axis])
+                reindexer_size = len(reindexer)
+
+                ordm = np.log10(max(1, abs(reindexer_size - term_axis_size)))
+                if ordm >= 1 and reindexer_size >= 10000:
+                    w = (
+                        f"Alignment difference on axis {axis} is larger "
+                        f"than an order of magnitude on term {repr(terms[i].name)}, "
+                        f"by more than {ordm:.4g}; performance may suffer."
+                    )
+                    warnings.warn(
+                        w, category=PerformanceWarning, stacklevel=find_stack_level()
+                    )
+
+                obj = ti.reindex(reindexer, axis=axis, copy=False)
+                terms[i].update(obj)
+
+        terms[i].update(terms[i].value.values)
+
+    return typ, _zip_axes_from_type(typ, axes)
+
+
+def align_terms(terms):
+    """
+    Align a set of terms.
+    """
+    try:
+        # flatten the parse tree (a nested list, really)
+        terms = list(com.flatten(terms))
+    except TypeError:
+        # can't iterate so it must just be a constant or single variable
+        if isinstance(terms.value, (ABCSeries, ABCDataFrame)):
+            typ = type(terms.value)
+            return typ, _zip_axes_from_type(typ, terms.value.axes)
+        return np.result_type(terms.type), None
+
+    # if all resolved variables are numeric scalars
+    if all(term.is_scalar for term in terms):
+        return result_type_many(*(term.value for term in terms)).type, None
+
+    # perform the main alignment
+    typ, axes = _align_core(terms)
+    return typ, axes
+
+
+def reconstruct_object(typ, obj, axes, dtype):
+    """
+    Reconstruct an object given its type, raw value, and possibly empty
+    (None) axes.
+
+    Parameters
+    ----------
+    typ : object
+        A type
+    obj : object
+        The value to use in the type constructor
+    axes : dict
+        The axes to use to construct the resulting pandas object
+
+    Returns
+    -------
+    ret : typ
+        An object of type ``typ`` with the value `obj` and possible axes
+        `axes`.
+    """
+    try:
+        typ = typ.type
+    except AttributeError:
+        pass
+
+    res_t = np.result_type(obj.dtype, dtype)
+
+    if not isinstance(typ, partial) and issubclass(typ, PandasObject):
+        return typ(obj, dtype=res_t, **axes)
+
+    # special case for pathological things like ~True/~False
+    if hasattr(res_t, "type") and typ == np.bool_ and res_t != np.bool_:
+        ret_value = res_t.type(obj)
+    else:
+        ret_value = typ(obj).astype(res_t)
+        # The condition is to distinguish 0-dim array (returned in case of
+        # scalar) and 1 element array
+        # e.g. np.array(0) and np.array([0])
+        if (
+            len(obj.shape) == 1
+            and len(obj) == 1
+            and not isinstance(ret_value, np.ndarray)
+        ):
+            ret_value = np.array([ret_value]).astype(res_t)
+
+    return ret_value
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd3be5b3f8c42267c8a61421b7f0877a01b33d34
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/api.py
@@ -0,0 +1,2 @@
+__all__ = ["eval"]
+from pandas.core.computation.eval import eval
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/check.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/check.py
new file mode 100644
index 0000000000000000000000000000000000000000..caccf34f811112abbe04d965d6f6be1e21527e8b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/check.py
@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from pandas.compat._optional import import_optional_dependency
+
+ne = import_optional_dependency("numexpr", errors="warn")
+NUMEXPR_INSTALLED = ne is not None
+
+__all__ = ["NUMEXPR_INSTALLED"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/common.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..115191829f044a7d6d7f17c279025ccc26d44d04
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/common.py
@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from functools import reduce
+
+import numpy as np
+
+from pandas._config import get_option
+
+
+def ensure_decoded(s) -> str:
+    """
+    If we have bytes, decode them to unicode.
+    """
+    if isinstance(s, (np.bytes_, bytes)):
+        s = s.decode(get_option("display.encoding"))
+    return s
+
+
+def result_type_many(*arrays_and_dtypes):
+    """
+    Wrapper around numpy.result_type which overcomes the NPY_MAXARGS (32)
+    argument limit.
+    """
+    try:
+        return np.result_type(*arrays_and_dtypes)
+    except ValueError:
+        # we have > NPY_MAXARGS terms in our expression
+        return reduce(np.result_type, arrays_and_dtypes)
+    except TypeError:
+        from pandas.core.dtypes.cast import find_common_type
+        from pandas.core.dtypes.common import is_extension_array_dtype
+
+        arr_and_dtypes = list(arrays_and_dtypes)
+        ea_dtypes, non_ea_dtypes = [], []
+        for arr_or_dtype in arr_and_dtypes:
+            if is_extension_array_dtype(arr_or_dtype):
+                ea_dtypes.append(arr_or_dtype)
+            else:
+                non_ea_dtypes.append(arr_or_dtype)
+
+        if non_ea_dtypes:
+            try:
+                np_dtype = np.result_type(*non_ea_dtypes)
+            except ValueError:
+                np_dtype = reduce(np.result_type, arrays_and_dtypes)
+            return find_common_type(ea_dtypes + [np_dtype])
+
+        return find_common_type(ea_dtypes)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/engines.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/engines.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3a05a9d75c6ed6b80564a69ff5b6cf5a648c1b3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/engines.py
@@ -0,0 +1,143 @@
+"""
+Engine classes for :func:`~pandas.eval`
+"""
+from __future__ import annotations
+
+import abc
+from typing import TYPE_CHECKING
+
+from pandas.errors import NumExprClobberingError
+
+from pandas.core.computation.align import (
+    align_terms,
+    reconstruct_object,
+)
+from pandas.core.computation.ops import (
+    MATHOPS,
+    REDUCTIONS,
+)
+
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from pandas.core.computation.expr import Expr
+
+_ne_builtins = frozenset(MATHOPS + REDUCTIONS)
+
+
+def _check_ne_builtin_clash(expr: Expr) -> None:
+    """
+    Attempt to prevent foot-shooting in a helpful way.
+
+    Parameters
+    ----------
+    expr : Expr
+        Terms can contain
+    """
+    names = expr.names
+    overlap = names & _ne_builtins
+
+    if overlap:
+        s = ", ".join([repr(x) for x in overlap])
+        raise NumExprClobberingError(
+            f'Variables in expression "{expr}" overlap with builtins: ({s})'
+        )
+
+
+class AbstractEngine(metaclass=abc.ABCMeta):
+    """Object serving as a base class for all engines."""
+
+    has_neg_frac = False
+
+    def __init__(self, expr) -> None:
+        self.expr = expr
+        self.aligned_axes = None
+        self.result_type = None
+
+    def convert(self) -> str:
+        """
+        Convert an expression for evaluation.
+
+        Defaults to return the expression as a string.
+        """
+        return printing.pprint_thing(self.expr)
+
+    def evaluate(self) -> object:
+        """
+        Run the engine on the expression.
+
+        This method performs alignment which is necessary no matter what engine
+        is being used, thus its implementation is in the base class.
+
+        Returns
+        -------
+        object
+            The result of the passed expression.
+        """
+        if not self._is_aligned:
+            self.result_type, self.aligned_axes = align_terms(self.expr.terms)
+
+        # make sure no names in resolvers and locals/globals clash
+        res = self._evaluate()
+        return reconstruct_object(
+            self.result_type, res, self.aligned_axes, self.expr.terms.return_type
+        )
+
+    @property
+    def _is_aligned(self) -> bool:
+        return self.aligned_axes is not None and self.result_type is not None
+
+    @abc.abstractmethod
+    def _evaluate(self):
+        """
+        Return an evaluated expression.
+
+        Parameters
+        ----------
+        env : Scope
+            The local and global environment in which to evaluate an
+            expression.
+
+        Notes
+        -----
+        Must be implemented by subclasses.
+        """
+
+
+class NumExprEngine(AbstractEngine):
+    """NumExpr engine class"""
+
+    has_neg_frac = True
+
+    def _evaluate(self):
+        import numexpr as ne
+
+        # convert the expression to a valid numexpr expression
+        s = self.convert()
+
+        env = self.expr.env
+        scope = env.full_scope
+        _check_ne_builtin_clash(self.expr)
+        return ne.evaluate(s, local_dict=scope)
+
+
+class PythonEngine(AbstractEngine):
+    """
+    Evaluate an expression in Python space.
+
+    Mostly for testing purposes.
+    """
+
+    has_neg_frac = False
+
+    def evaluate(self):
+        return self.expr()
+
+    def _evaluate(self) -> None:
+        pass
+
+
+ENGINES: dict[str, type[AbstractEngine]] = {
+    "numexpr": NumExprEngine,
+    "python": PythonEngine,
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/eval.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bb623cba375539072e462cb1180f4ac85221ee1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/eval.py
@@ -0,0 +1,421 @@
+"""
+Top level ``eval`` module.
+"""
+from __future__ import annotations
+
+import tokenize
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.common import (
+    is_extension_array_dtype,
+    is_string_dtype,
+)
+
+from pandas.core.computation.engines import ENGINES
+from pandas.core.computation.expr import (
+    PARSERS,
+    Expr,
+)
+from pandas.core.computation.parsing import tokenize_string
+from pandas.core.computation.scope import ensure_scope
+from pandas.core.generic import NDFrame
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas.core.computation.ops import BinOp
+
+
+def _check_engine(engine: str | None) -> str:
+    """
+    Make sure a valid engine is passed.
+
+    Parameters
+    ----------
+    engine : str
+        String to validate.
+
+    Raises
+    ------
+    KeyError
+      * If an invalid engine is passed.
+    ImportError
+      * If numexpr was requested but doesn't exist.
+
+    Returns
+    -------
+    str
+        Engine name.
+    """
+    from pandas.core.computation.check import NUMEXPR_INSTALLED
+    from pandas.core.computation.expressions import USE_NUMEXPR
+
+    if engine is None:
+        engine = "numexpr" if USE_NUMEXPR else "python"
+
+    if engine not in ENGINES:
+        valid_engines = list(ENGINES.keys())
+        raise KeyError(
+            f"Invalid engine '{engine}' passed, valid engines are {valid_engines}"
+        )
+
+    # TODO: validate this in a more general way (thinking of future engines
+    # that won't necessarily be import-able)
+    # Could potentially be done on engine instantiation
+    if engine == "numexpr" and not NUMEXPR_INSTALLED:
+        raise ImportError(
+            "'numexpr' is not installed or an unsupported version. Cannot use "
+            "engine='numexpr' for query/eval if 'numexpr' is not installed"
+        )
+
+    return engine
+
+
+def _check_parser(parser: str):
+    """
+    Make sure a valid parser is passed.
+
+    Parameters
+    ----------
+    parser : str
+
+    Raises
+    ------
+    KeyError
+      * If an invalid parser is passed
+    """
+    if parser not in PARSERS:
+        raise KeyError(
+            f"Invalid parser '{parser}' passed, valid parsers are {PARSERS.keys()}"
+        )
+
+
+def _check_resolvers(resolvers):
+    if resolvers is not None:
+        for resolver in resolvers:
+            if not hasattr(resolver, "__getitem__"):
+                name = type(resolver).__name__
+                raise TypeError(
+                    f"Resolver of type '{name}' does not "
+                    "implement the __getitem__ method"
+                )
+
+
+def _check_expression(expr):
+    """
+    Make sure an expression is not an empty string
+
+    Parameters
+    ----------
+    expr : object
+        An object that can be converted to a string
+
+    Raises
+    ------
+    ValueError
+      * If expr is an empty string
+    """
+    if not expr:
+        raise ValueError("expr cannot be an empty string")
+
+
+def _convert_expression(expr) -> str:
+    """
+    Convert an object to an expression.
+
+    This function converts an object to an expression (a unicode string) and
+    checks to make sure it isn't empty after conversion. This is used to
+    convert operators to their string representation for recursive calls to
+    :func:`~pandas.eval`.
+
+    Parameters
+    ----------
+    expr : object
+        The object to be converted to a string.
+
+    Returns
+    -------
+    str
+        The string representation of an object.
+
+    Raises
+    ------
+    ValueError
+      * If the expression is empty.
+    """
+    s = pprint_thing(expr)
+    _check_expression(s)
+    return s
+
+
+def _check_for_locals(expr: str, stack_level: int, parser: str):
+    at_top_of_stack = stack_level == 0
+    not_pandas_parser = parser != "pandas"
+
+    if not_pandas_parser:
+        msg = "The '@' prefix is only supported by the pandas parser"
+    elif at_top_of_stack:
+        msg = (
+            "The '@' prefix is not allowed in top-level eval calls.\n"
+            "please refer to your variables by name without the '@' prefix."
+        )
+
+    if at_top_of_stack or not_pandas_parser:
+        for toknum, tokval in tokenize_string(expr):
+            if toknum == tokenize.OP and tokval == "@":
+                raise SyntaxError(msg)
+
+
+def eval(
+    expr: str | BinOp,  # we leave BinOp out of the docstr bc it isn't for users
+    parser: str = "pandas",
+    engine: str | None = None,
+    local_dict=None,
+    global_dict=None,
+    resolvers=(),
+    level: int = 0,
+    target=None,
+    inplace: bool = False,
+):
+    """
+    Evaluate a Python expression as a string using various backends.
+
+    The following arithmetic operations are supported: ``+``, ``-``, ``*``,
+    ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
+    boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
+    Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
+    :keyword:`or`, and :keyword:`not` with the same semantics as the
+    corresponding bitwise operators.  :class:`~pandas.Series` and
+    :class:`~pandas.DataFrame` objects are supported and behave as they would
+    with plain ol' Python evaluation.
+
+    Parameters
+    ----------
+    expr : str
+        The expression to evaluate. This string cannot contain any Python
+        `statements
+        <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__,
+        only Python `expressions
+        <https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__.
+    parser : {'pandas', 'python'}, default 'pandas'
+        The parser to use to construct the syntax tree from the expression. The
+        default of ``'pandas'`` parses code slightly different than standard
+        Python. Alternatively, you can parse an expression using the
+        ``'python'`` parser to retain strict Python semantics.  See the
+        :ref:`enhancing performance <enhancingperf.eval>` documentation for
+        more details.
+    engine : {'python', 'numexpr'}, default 'numexpr'
+
+        The engine used to evaluate the expression. Supported engines are
+
+        - None : tries to use ``numexpr``, falls back to ``python``
+        - ``'numexpr'`` : This default engine evaluates pandas objects using
+          numexpr for large speed ups in complex expressions with large frames.
+        - ``'python'`` : Performs operations as if you had ``eval``'d in top
+          level python. This engine is generally not that useful.
+
+        More backends may be available in the future.
+    local_dict : dict or None, optional
+        A dictionary of local variables, taken from locals() by default.
+    global_dict : dict or None, optional
+        A dictionary of global variables, taken from globals() by default.
+    resolvers : list of dict-like or None, optional
+        A list of objects implementing the ``__getitem__`` special method that
+        you can use to inject an additional collection of namespaces to use for
+        variable lookup. For example, this is used in the
+        :meth:`~DataFrame.query` method to inject the
+        ``DataFrame.index`` and ``DataFrame.columns``
+        variables that refer to their respective :class:`~pandas.DataFrame`
+        instance attributes.
+    level : int, optional
+        The number of prior stack frames to traverse and add to the current
+        scope. Most users will **not** need to change this parameter.
+    target : object, optional, default None
+        This is the target object for assignment. It is used when there is
+        variable assignment in the expression. If so, then `target` must
+        support item assignment with string keys, and if a copy is being
+        returned, it must also support `.copy()`.
+    inplace : bool, default False
+        If `target` is provided, and the expression mutates `target`, whether
+        to modify `target` inplace. Otherwise, return a copy of `target` with
+        the mutation.
+
+    Returns
+    -------
+    ndarray, numeric scalar, DataFrame, Series, or None
+        The completion value of evaluating the given code or None if ``inplace=True``.
+
+    Raises
+    ------
+    ValueError
+        There are many instances where such an error can be raised:
+
+        - `target=None`, but the expression is multiline.
+        - The expression is multiline, but not all them have item assignment.
+          An example of such an arrangement is this:
+
+          a = b + 1
+          a + 2
+
+          Here, there are expressions on different lines, making it multiline,
+          but the last line has no variable assigned to the output of `a + 2`.
+        - `inplace=True`, but the expression is missing item assignment.
+        - Item assignment is provided, but the `target` does not support
+          string item assignment.
+        - Item assignment is provided and `inplace=False`, but the `target`
+          does not support the `.copy()` method
+
+    See Also
+    --------
+    DataFrame.query : Evaluates a boolean expression to query the columns
+            of a frame.
+    DataFrame.eval : Evaluate a string describing operations on
+            DataFrame columns.
+
+    Notes
+    -----
+    The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
+    recursively cast to ``float64``.
+
+    See the :ref:`enhancing performance <enhancingperf.eval>` documentation for
+    more details.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]})
+    >>> df
+      animal  age
+    0    dog   10
+    1    pig   20
+
+    We can add a new column using ``pd.eval``:
+
+    >>> pd.eval("double_age = df.age * 2", target=df)
+      animal  age  double_age
+    0    dog   10          20
+    1    pig   20          40
+    """
+    inplace = validate_bool_kwarg(inplace, "inplace")
+
+    exprs: list[str | BinOp]
+    if isinstance(expr, str):
+        _check_expression(expr)
+        exprs = [e.strip() for e in expr.splitlines() if e.strip() != ""]
+    else:
+        # ops.BinOp; for internal compat, not intended to be passed by users
+        exprs = [expr]
+    multi_line = len(exprs) > 1
+
+    if multi_line and target is None:
+        raise ValueError(
+            "multi-line expressions are only valid in the "
+            "context of data, use DataFrame.eval"
+        )
+    engine = _check_engine(engine)
+    _check_parser(parser)
+    _check_resolvers(resolvers)
+
+    ret = None
+    first_expr = True
+    target_modified = False
+
+    for expr in exprs:
+        expr = _convert_expression(expr)
+        _check_for_locals(expr, level, parser)
+
+        # get our (possibly passed-in) scope
+        env = ensure_scope(
+            level + 1,
+            global_dict=global_dict,
+            local_dict=local_dict,
+            resolvers=resolvers,
+            target=target,
+        )
+
+        parsed_expr = Expr(expr, engine=engine, parser=parser, env=env)
+
+        if engine == "numexpr" and (
+            (
+                is_extension_array_dtype(parsed_expr.terms.return_type)
+                and not is_string_dtype(parsed_expr.terms.return_type)
+            )
+            or getattr(parsed_expr.terms, "operand_types", None) is not None
+            and any(
+                (is_extension_array_dtype(elem) and not is_string_dtype(elem))
+                for elem in parsed_expr.terms.operand_types
+            )
+        ):
+            warnings.warn(
+                "Engine has switched to 'python' because numexpr does not support "
+                "extension array dtypes. Please set your engine to python manually.",
+                RuntimeWarning,
+                stacklevel=find_stack_level(),
+            )
+            engine = "python"
+
+        # construct the engine and evaluate the parsed expression
+        eng = ENGINES[engine]
+        eng_inst = eng(parsed_expr)
+        ret = eng_inst.evaluate()
+
+        if parsed_expr.assigner is None:
+            if multi_line:
+                raise ValueError(
+                    "Multi-line expressions are only valid "
+                    "if all expressions contain an assignment"
+                )
+            if inplace:
+                raise ValueError("Cannot operate inplace if there is no assignment")
+
+        # assign if needed
+        assigner = parsed_expr.assigner
+        if env.target is not None and assigner is not None:
+            target_modified = True
+
+            # if returning a copy, copy only on the first assignment
+            if not inplace and first_expr:
+                try:
+                    target = env.target
+                    if isinstance(target, NDFrame):
+                        target = target.copy(deep=None)
+                    else:
+                        target = target.copy()
+                except AttributeError as err:
+                    raise ValueError("Cannot return a copy of the target") from err
+            else:
+                target = env.target
+
+            # TypeError is most commonly raised (e.g. int, list), but you
+            # get IndexError if you try to do this assignment on np.ndarray.
+            # we will ignore numpy warnings here; e.g. if trying
+            # to use a non-numeric indexer
+            try:
+                if inplace and isinstance(target, NDFrame):
+                    target.loc[:, assigner] = ret
+                else:
+                    target[assigner] = ret  # pyright: ignore[reportGeneralTypeIssues]
+            except (TypeError, IndexError) as err:
+                raise ValueError("Cannot assign expression output to target") from err
+
+            if not resolvers:
+                resolvers = ({assigner: ret},)
+            else:
+                # existing resolver needs updated to handle
+                # case of mutating existing column in copy
+                for resolver in resolvers:
+                    if assigner in resolver:
+                        resolver[assigner] = ret
+                        break
+                else:
+                    resolvers += ({assigner: ret},)
+
+            ret = None
+            first_expr = False
+
+    # We want to exclude `inplace=None` as being False.
+    if inplace is False:
+        return target if target_modified else ret
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/expr.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/expr.py
new file mode 100644
index 0000000000000000000000000000000000000000..34055d2177626dad7415c2109dedbe6a205b626b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/expr.py
@@ -0,0 +1,840 @@
+"""
+:func:`~pandas.eval` parsers.
+"""
+from __future__ import annotations
+
+import ast
+from functools import (
+    partial,
+    reduce,
+)
+from keyword import iskeyword
+import tokenize
+from typing import (
+    Callable,
+    ClassVar,
+    TypeVar,
+)
+
+import numpy as np
+
+from pandas.errors import UndefinedVariableError
+
+from pandas.core.dtypes.common import is_string_dtype
+
+import pandas.core.common as com
+from pandas.core.computation.ops import (
+    ARITH_OPS_SYMS,
+    BOOL_OPS_SYMS,
+    CMP_OPS_SYMS,
+    LOCAL_TAG,
+    MATHOPS,
+    REDUCTIONS,
+    UNARY_OPS_SYMS,
+    BinOp,
+    Constant,
+    FuncNode,
+    Op,
+    Term,
+    UnaryOp,
+    is_term,
+)
+from pandas.core.computation.parsing import (
+    clean_backtick_quoted_toks,
+    tokenize_string,
+)
+from pandas.core.computation.scope import Scope
+
+from pandas.io.formats import printing
+
+
+def _rewrite_assign(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Rewrite the assignment operator for PyTables expressions that use ``=``
+    as a substitute for ``==``.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tuple of int, str
+        Either the input or token or the replacement values
+    """
+    toknum, tokval = tok
+    return toknum, "==" if tokval == "=" else tokval
+
+
+def _replace_booleans(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise
+    precedence is changed to boolean precedence.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tuple of int, str
+        Either the input or token or the replacement values
+    """
+    toknum, tokval = tok
+    if toknum == tokenize.OP:
+        if tokval == "&":
+            return tokenize.NAME, "and"
+        elif tokval == "|":
+            return tokenize.NAME, "or"
+        return toknum, tokval
+    return toknum, tokval
+
+
+def _replace_locals(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Replace local variables with a syntactically valid name.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tuple of int, str
+        Either the input or token or the replacement values
+
+    Notes
+    -----
+    This is somewhat of a hack in that we rewrite a string such as ``'@a'`` as
+    ``'__pd_eval_local_a'`` by telling the tokenizer that ``__pd_eval_local_``
+    is a ``tokenize.OP`` and to replace the ``'@'`` symbol with it.
+    """
+    toknum, tokval = tok
+    if toknum == tokenize.OP and tokval == "@":
+        return tokenize.OP, LOCAL_TAG
+    return toknum, tokval
+
+
+def _compose2(f, g):
+    """
+    Compose 2 callables.
+    """
+    return lambda *args, **kwargs: f(g(*args, **kwargs))
+
+
+def _compose(*funcs):
+    """
+    Compose 2 or more callables.
+    """
+    assert len(funcs) > 1, "At least 2 callables must be passed to compose"
+    return reduce(_compose2, funcs)
+
+
+def _preparse(
+    source: str,
+    f=_compose(
+        _replace_locals, _replace_booleans, _rewrite_assign, clean_backtick_quoted_toks
+    ),
+) -> str:
+    """
+    Compose a collection of tokenization functions.
+
+    Parameters
+    ----------
+    source : str
+        A Python source code string
+    f : callable
+        This takes a tuple of (toknum, tokval) as its argument and returns a
+        tuple with the same structure but possibly different elements. Defaults
+        to the composition of ``_rewrite_assign``, ``_replace_booleans``, and
+        ``_replace_locals``.
+
+    Returns
+    -------
+    str
+        Valid Python source code
+
+    Notes
+    -----
+    The `f` parameter can be any callable that takes *and* returns input of the
+    form ``(toknum, tokval)``, where ``toknum`` is one of the constants from
+    the ``tokenize`` module and ``tokval`` is a string.
+    """
+    assert callable(f), "f must be callable"
+    return tokenize.untokenize(f(x) for x in tokenize_string(source))
+
+
+def _is_type(t):
+    """
+    Factory for a type checking function of type ``t`` or tuple of types.
+    """
+    return lambda x: isinstance(x.value, t)
+
+
+_is_list = _is_type(list)
+_is_str = _is_type(str)
+
+
+# partition all AST nodes
+_all_nodes = frozenset(
+    node
+    for node in (getattr(ast, name) for name in dir(ast))
+    if isinstance(node, type) and issubclass(node, ast.AST)
+)
+
+
+def _filter_nodes(superclass, all_nodes=_all_nodes):
+    """
+    Filter out AST nodes that are subclasses of ``superclass``.
+    """
+    node_names = (node.__name__ for node in all_nodes if issubclass(node, superclass))
+    return frozenset(node_names)
+
+
+_all_node_names = frozenset(x.__name__ for x in _all_nodes)
+_mod_nodes = _filter_nodes(ast.mod)
+_stmt_nodes = _filter_nodes(ast.stmt)
+_expr_nodes = _filter_nodes(ast.expr)
+_expr_context_nodes = _filter_nodes(ast.expr_context)
+_boolop_nodes = _filter_nodes(ast.boolop)
+_operator_nodes = _filter_nodes(ast.operator)
+_unary_op_nodes = _filter_nodes(ast.unaryop)
+_cmp_op_nodes = _filter_nodes(ast.cmpop)
+_comprehension_nodes = _filter_nodes(ast.comprehension)
+_handler_nodes = _filter_nodes(ast.excepthandler)
+_arguments_nodes = _filter_nodes(ast.arguments)
+_keyword_nodes = _filter_nodes(ast.keyword)
+_alias_nodes = _filter_nodes(ast.alias)
+
+
+# nodes that we don't support directly but are needed for parsing
+_hacked_nodes = frozenset(["Assign", "Module", "Expr"])
+
+
+_unsupported_expr_nodes = frozenset(
+    [
+        "Yield",
+        "GeneratorExp",
+        "IfExp",
+        "DictComp",
+        "SetComp",
+        "Repr",
+        "Lambda",
+        "Set",
+        "AST",
+        "Is",
+        "IsNot",
+    ]
+)
+
+# these nodes are low priority or won't ever be supported (e.g., AST)
+_unsupported_nodes = (
+    _stmt_nodes
+    | _mod_nodes
+    | _handler_nodes
+    | _arguments_nodes
+    | _keyword_nodes
+    | _alias_nodes
+    | _expr_context_nodes
+    | _unsupported_expr_nodes
+) - _hacked_nodes
+
+# we're adding a different assignment in some cases to be equality comparison
+# and we don't want `stmt` and friends in their so get only the class whose
+# names are capitalized
+_base_supported_nodes = (_all_node_names - _unsupported_nodes) | _hacked_nodes
+intersection = _unsupported_nodes & _base_supported_nodes
+_msg = f"cannot both support and not support {intersection}"
+assert not intersection, _msg
+
+
+def _node_not_implemented(node_name: str) -> Callable[..., None]:
+    """
+    Return a function that raises a NotImplementedError with a passed node name.
+    """
+
+    def f(self, *args, **kwargs):
+        raise NotImplementedError(f"'{node_name}' nodes are not implemented")
+
+    return f
+
+
+# should be bound by BaseExprVisitor but that creates a circular dependency:
+# _T is used in disallow, but disallow is used to define BaseExprVisitor
+# https://github.com/microsoft/pyright/issues/2315
+_T = TypeVar("_T")
+
+
+def disallow(nodes: set[str]) -> Callable[[type[_T]], type[_T]]:
+    """
+    Decorator to disallow certain nodes from parsing. Raises a
+    NotImplementedError instead.
+
+    Returns
+    -------
+    callable
+    """
+
+    def disallowed(cls: type[_T]) -> type[_T]:
+        # error: "Type[_T]" has no attribute "unsupported_nodes"
+        cls.unsupported_nodes = ()  # type: ignore[attr-defined]
+        for node in nodes:
+            new_method = _node_not_implemented(node)
+            name = f"visit_{node}"
+            # error: "Type[_T]" has no attribute "unsupported_nodes"
+            cls.unsupported_nodes += (name,)  # type: ignore[attr-defined]
+            setattr(cls, name, new_method)
+        return cls
+
+    return disallowed
+
+
+def _op_maker(op_class, op_symbol):
+    """
+    Return a function to create an op class with its symbol already passed.
+
+    Returns
+    -------
+    callable
+    """
+
+    def f(self, node, *args, **kwargs):
+        """
+        Return a partial function with an Op subclass with an operator already passed.
+
+        Returns
+        -------
+        callable
+        """
+        return partial(op_class, op_symbol, *args, **kwargs)
+
+    return f
+
+
+_op_classes = {"binary": BinOp, "unary": UnaryOp}
+
+
+def add_ops(op_classes):
+    """
+    Decorator to add default implementation of ops.
+    """
+
+    def f(cls):
+        for op_attr_name, op_class in op_classes.items():
+            ops = getattr(cls, f"{op_attr_name}_ops")
+            ops_map = getattr(cls, f"{op_attr_name}_op_nodes_map")
+            for op in ops:
+                op_node = ops_map[op]
+                if op_node is not None:
+                    made_op = _op_maker(op_class, op)
+                    setattr(cls, f"visit_{op_node}", made_op)
+        return cls
+
+    return f
+
+
+@disallow(_unsupported_nodes)
+@add_ops(_op_classes)
+class BaseExprVisitor(ast.NodeVisitor):
+    """
+    Custom ast walker. Parsers of other engines should subclass this class
+    if necessary.
+
+    Parameters
+    ----------
+    env : Scope
+    engine : str
+    parser : str
+    preparser : callable
+    """
+
+    const_type: ClassVar[type[Term]] = Constant
+    term_type: ClassVar[type[Term]] = Term
+
+    binary_ops = CMP_OPS_SYMS + BOOL_OPS_SYMS + ARITH_OPS_SYMS
+    binary_op_nodes = (
+        "Gt",
+        "Lt",
+        "GtE",
+        "LtE",
+        "Eq",
+        "NotEq",
+        "In",
+        "NotIn",
+        "BitAnd",
+        "BitOr",
+        "And",
+        "Or",
+        "Add",
+        "Sub",
+        "Mult",
+        "Div",
+        "Pow",
+        "FloorDiv",
+        "Mod",
+    )
+    binary_op_nodes_map = dict(zip(binary_ops, binary_op_nodes))
+
+    unary_ops = UNARY_OPS_SYMS
+    unary_op_nodes = "UAdd", "USub", "Invert", "Not"
+    unary_op_nodes_map = dict(zip(unary_ops, unary_op_nodes))
+
+    rewrite_map = {
+        ast.Eq: ast.In,
+        ast.NotEq: ast.NotIn,
+        ast.In: ast.In,
+        ast.NotIn: ast.NotIn,
+    }
+
+    unsupported_nodes: tuple[str, ...]
+
+    def __init__(self, env, engine, parser, preparser=_preparse) -> None:
+        self.env = env
+        self.engine = engine
+        self.parser = parser
+        self.preparser = preparser
+        self.assigner = None
+
+    def visit(self, node, **kwargs):
+        if isinstance(node, str):
+            clean = self.preparser(node)
+            try:
+                node = ast.fix_missing_locations(ast.parse(clean))
+            except SyntaxError as e:
+                if any(iskeyword(x) for x in clean.split()):
+                    e.msg = "Python keyword not valid identifier in numexpr query"
+                raise e
+
+        method = f"visit_{type(node).__name__}"
+        visitor = getattr(self, method)
+        return visitor(node, **kwargs)
+
+    def visit_Module(self, node, **kwargs):
+        if len(node.body) != 1:
+            raise SyntaxError("only a single expression is allowed")
+        expr = node.body[0]
+        return self.visit(expr, **kwargs)
+
+    def visit_Expr(self, node, **kwargs):
+        return self.visit(node.value, **kwargs)
+
+    def _rewrite_membership_op(self, node, left, right):
+        # the kind of the operator (is actually an instance)
+        op_instance = node.op
+        op_type = type(op_instance)
+
+        # must be two terms and the comparison operator must be ==/!=/in/not in
+        if is_term(left) and is_term(right) and op_type in self.rewrite_map:
+            left_list, right_list = map(_is_list, (left, right))
+            left_str, right_str = map(_is_str, (left, right))
+
+            # if there are any strings or lists in the expression
+            if left_list or right_list or left_str or right_str:
+                op_instance = self.rewrite_map[op_type]()
+
+            # pop the string variable out of locals and replace it with a list
+            # of one string, kind of a hack
+            if right_str:
+                name = self.env.add_tmp([right.value])
+                right = self.term_type(name, self.env)
+
+            if left_str:
+                name = self.env.add_tmp([left.value])
+                left = self.term_type(name, self.env)
+
+        op = self.visit(op_instance)
+        return op, op_instance, left, right
+
+    def _maybe_transform_eq_ne(self, node, left=None, right=None):
+        if left is None:
+            left = self.visit(node.left, side="left")
+        if right is None:
+            right = self.visit(node.right, side="right")
+        op, op_class, left, right = self._rewrite_membership_op(node, left, right)
+        return op, op_class, left, right
+
+    def _maybe_downcast_constants(self, left, right):
+        f32 = np.dtype(np.float32)
+        if (
+            left.is_scalar
+            and hasattr(left, "value")
+            and not right.is_scalar
+            and right.return_type == f32
+        ):
+            # right is a float32 array, left is a scalar
+            name = self.env.add_tmp(np.float32(left.value))
+            left = self.term_type(name, self.env)
+        if (
+            right.is_scalar
+            and hasattr(right, "value")
+            and not left.is_scalar
+            and left.return_type == f32
+        ):
+            # left is a float32 array, right is a scalar
+            name = self.env.add_tmp(np.float32(right.value))
+            right = self.term_type(name, self.env)
+
+        return left, right
+
+    def _maybe_eval(self, binop, eval_in_python):
+        # eval `in` and `not in` (for now) in "partial" python space
+        # things that can be evaluated in "eval" space will be turned into
+        # temporary variables. for example,
+        # [1,2] in a + 2 * b
+        # in that case a + 2 * b will be evaluated using numexpr, and the "in"
+        # call will be evaluated using isin (in python space)
+        return binop.evaluate(
+            self.env, self.engine, self.parser, self.term_type, eval_in_python
+        )
+
+    def _maybe_evaluate_binop(
+        self,
+        op,
+        op_class,
+        lhs,
+        rhs,
+        eval_in_python=("in", "not in"),
+        maybe_eval_in_python=("==", "!=", "<", ">", "<=", ">="),
+    ):
+        res = op(lhs, rhs)
+
+        if res.has_invalid_return_type:
+            raise TypeError(
+                f"unsupported operand type(s) for {res.op}: "
+                f"'{lhs.type}' and '{rhs.type}'"
+            )
+
+        if self.engine != "pytables" and (
+            res.op in CMP_OPS_SYMS
+            and getattr(lhs, "is_datetime", False)
+            or getattr(rhs, "is_datetime", False)
+        ):
+            # all date ops must be done in python bc numexpr doesn't work
+            # well with NaT
+            return self._maybe_eval(res, self.binary_ops)
+
+        if res.op in eval_in_python:
+            # "in"/"not in" ops are always evaluated in python
+            return self._maybe_eval(res, eval_in_python)
+        elif self.engine != "pytables":
+            if (
+                getattr(lhs, "return_type", None) == object
+                or is_string_dtype(getattr(lhs, "return_type", None))
+                or getattr(rhs, "return_type", None) == object
+                or is_string_dtype(getattr(rhs, "return_type", None))
+            ):
+                # evaluate "==" and "!=" in python if either of our operands
+                # has an object or string return type
+                return self._maybe_eval(res, eval_in_python + maybe_eval_in_python)
+        return res
+
+    def visit_BinOp(self, node, **kwargs):
+        op, op_class, left, right = self._maybe_transform_eq_ne(node)
+        left, right = self._maybe_downcast_constants(left, right)
+        return self._maybe_evaluate_binop(op, op_class, left, right)
+
+    def visit_UnaryOp(self, node, **kwargs):
+        op = self.visit(node.op)
+        operand = self.visit(node.operand)
+        return op(operand)
+
+    def visit_Name(self, node, **kwargs) -> Term:
+        return self.term_type(node.id, self.env, **kwargs)
+
+    # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min
+    def visit_NameConstant(self, node, **kwargs) -> Term:
+        return self.const_type(node.value, self.env)
+
+    # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min
+    def visit_Num(self, node, **kwargs) -> Term:
+        return self.const_type(node.value, self.env)
+
+    def visit_Constant(self, node, **kwargs) -> Term:
+        return self.const_type(node.value, self.env)
+
+    # TODO(py314): deprecated since Python 3.8. Remove after Python 3.14 is min
+    def visit_Str(self, node, **kwargs) -> Term:
+        name = self.env.add_tmp(node.s)
+        return self.term_type(name, self.env)
+
+    def visit_List(self, node, **kwargs) -> Term:
+        name = self.env.add_tmp([self.visit(e)(self.env) for e in node.elts])
+        return self.term_type(name, self.env)
+
+    visit_Tuple = visit_List
+
+    def visit_Index(self, node, **kwargs):
+        """df.index[4]"""
+        return self.visit(node.value)
+
+    def visit_Subscript(self, node, **kwargs) -> Term:
+        from pandas import eval as pd_eval
+
+        value = self.visit(node.value)
+        slobj = self.visit(node.slice)
+        result = pd_eval(
+            slobj, local_dict=self.env, engine=self.engine, parser=self.parser
+        )
+        try:
+            # a Term instance
+            v = value.value[result]
+        except AttributeError:
+            # an Op instance
+            lhs = pd_eval(
+                value, local_dict=self.env, engine=self.engine, parser=self.parser
+            )
+            v = lhs[result]
+        name = self.env.add_tmp(v)
+        return self.term_type(name, env=self.env)
+
+    def visit_Slice(self, node, **kwargs) -> slice:
+        """df.index[slice(4,6)]"""
+        lower = node.lower
+        if lower is not None:
+            lower = self.visit(lower).value
+        upper = node.upper
+        if upper is not None:
+            upper = self.visit(upper).value
+        step = node.step
+        if step is not None:
+            step = self.visit(step).value
+
+        return slice(lower, upper, step)
+
+    def visit_Assign(self, node, **kwargs):
+        """
+        support a single assignment node, like
+
+        c = a + b
+
+        set the assigner at the top level, must be a Name node which
+        might or might not exist in the resolvers
+
+        """
+        if len(node.targets) != 1:
+            raise SyntaxError("can only assign a single expression")
+        if not isinstance(node.targets[0], ast.Name):
+            raise SyntaxError("left hand side of an assignment must be a single name")
+        if self.env.target is None:
+            raise ValueError("cannot assign without a target object")
+
+        try:
+            assigner = self.visit(node.targets[0], **kwargs)
+        except UndefinedVariableError:
+            assigner = node.targets[0].id
+
+        self.assigner = getattr(assigner, "name", assigner)
+        if self.assigner is None:
+            raise SyntaxError(
+                "left hand side of an assignment must be a single resolvable name"
+            )
+
+        return self.visit(node.value, **kwargs)
+
+    def visit_Attribute(self, node, **kwargs):
+        attr = node.attr
+        value = node.value
+
+        ctx = node.ctx
+        if isinstance(ctx, ast.Load):
+            # resolve the value
+            resolved = self.visit(value).value
+            try:
+                v = getattr(resolved, attr)
+                name = self.env.add_tmp(v)
+                return self.term_type(name, self.env)
+            except AttributeError:
+                # something like datetime.datetime where scope is overridden
+                if isinstance(value, ast.Name) and value.id == attr:
+                    return resolved
+                raise
+
+        raise ValueError(f"Invalid Attribute context {type(ctx).__name__}")
+
+    def visit_Call(self, node, side=None, **kwargs):
+        if isinstance(node.func, ast.Attribute) and node.func.attr != "__call__":
+            res = self.visit_Attribute(node.func)
+        elif not isinstance(node.func, ast.Name):
+            raise TypeError("Only named functions are supported")
+        else:
+            try:
+                res = self.visit(node.func)
+            except UndefinedVariableError:
+                # Check if this is a supported function name
+                try:
+                    res = FuncNode(node.func.id)
+                except ValueError:
+                    # Raise original error
+                    raise
+
+        if res is None:
+            # error: "expr" has no attribute "id"
+            raise ValueError(
+                f"Invalid function call {node.func.id}"  # type: ignore[attr-defined]
+            )
+        if hasattr(res, "value"):
+            res = res.value
+
+        if isinstance(res, FuncNode):
+            new_args = [self.visit(arg) for arg in node.args]
+
+            if node.keywords:
+                raise TypeError(
+                    f'Function "{res.name}" does not support keyword arguments'
+                )
+
+            return res(*new_args)
+
+        else:
+            new_args = [self.visit(arg)(self.env) for arg in node.args]
+
+            for key in node.keywords:
+                if not isinstance(key, ast.keyword):
+                    # error: "expr" has no attribute "id"
+                    raise ValueError(
+                        "keyword error in function call "
+                        f"'{node.func.id}'"  # type: ignore[attr-defined]
+                    )
+
+                if key.arg:
+                    kwargs[key.arg] = self.visit(key.value)(self.env)
+
+            name = self.env.add_tmp(res(*new_args, **kwargs))
+            return self.term_type(name=name, env=self.env)
+
+    def translate_In(self, op):
+        return op
+
+    def visit_Compare(self, node, **kwargs):
+        ops = node.ops
+        comps = node.comparators
+
+        # base case: we have something like a CMP b
+        if len(comps) == 1:
+            op = self.translate_In(ops[0])
+            binop = ast.BinOp(op=op, left=node.left, right=comps[0])
+            return self.visit(binop)
+
+        # recursive case: we have a chained comparison, a CMP b CMP c, etc.
+        left = node.left
+        values = []
+        for op, comp in zip(ops, comps):
+            new_node = self.visit(
+                ast.Compare(comparators=[comp], left=left, ops=[self.translate_In(op)])
+            )
+            left = comp
+            values.append(new_node)
+        return self.visit(ast.BoolOp(op=ast.And(), values=values))
+
+    def _try_visit_binop(self, bop):
+        if isinstance(bop, (Op, Term)):
+            return bop
+        return self.visit(bop)
+
+    def visit_BoolOp(self, node, **kwargs):
+        def visitor(x, y):
+            lhs = self._try_visit_binop(x)
+            rhs = self._try_visit_binop(y)
+
+            op, op_class, lhs, rhs = self._maybe_transform_eq_ne(node, lhs, rhs)
+            return self._maybe_evaluate_binop(op, node.op, lhs, rhs)
+
+        operands = node.values
+        return reduce(visitor, operands)
+
+
+_python_not_supported = frozenset(["Dict", "BoolOp", "In", "NotIn"])
+_numexpr_supported_calls = frozenset(REDUCTIONS + MATHOPS)
+
+
+@disallow(
+    (_unsupported_nodes | _python_not_supported)
+    - (_boolop_nodes | frozenset(["BoolOp", "Attribute", "In", "NotIn", "Tuple"]))
+)
+class PandasExprVisitor(BaseExprVisitor):
+    def __init__(
+        self,
+        env,
+        engine,
+        parser,
+        preparser=partial(
+            _preparse,
+            f=_compose(_replace_locals, _replace_booleans, clean_backtick_quoted_toks),
+        ),
+    ) -> None:
+        super().__init__(env, engine, parser, preparser)
+
+
+@disallow(_unsupported_nodes | _python_not_supported | frozenset(["Not"]))
+class PythonExprVisitor(BaseExprVisitor):
+    def __init__(
+        self, env, engine, parser, preparser=lambda source, f=None: source
+    ) -> None:
+        super().__init__(env, engine, parser, preparser=preparser)
+
+
+class Expr:
+    """
+    Object encapsulating an expression.
+
+    Parameters
+    ----------
+    expr : str
+    engine : str, optional, default 'numexpr'
+    parser : str, optional, default 'pandas'
+    env : Scope, optional, default None
+    level : int, optional, default 2
+    """
+
+    env: Scope
+    engine: str
+    parser: str
+
+    def __init__(
+        self,
+        expr,
+        engine: str = "numexpr",
+        parser: str = "pandas",
+        env: Scope | None = None,
+        level: int = 0,
+    ) -> None:
+        self.expr = expr
+        self.env = env or Scope(level=level + 1)
+        self.engine = engine
+        self.parser = parser
+        self._visitor = PARSERS[parser](self.env, self.engine, self.parser)
+        self.terms = self.parse()
+
+    @property
+    def assigner(self):
+        return getattr(self._visitor, "assigner", None)
+
+    def __call__(self):
+        return self.terms(self.env)
+
+    def __repr__(self) -> str:
+        return printing.pprint_thing(self.terms)
+
+    def __len__(self) -> int:
+        return len(self.expr)
+
+    def parse(self):
+        """
+        Parse an expression.
+        """
+        return self._visitor.visit(self.expr)
+
+    @property
+    def names(self):
+        """
+        Get the names in an expression.
+        """
+        if is_term(self.terms):
+            return frozenset([self.terms.name])
+        return frozenset(term.name for term in com.flatten(self.terms))
+
+
+PARSERS = {"python": PythonExprVisitor, "pandas": PandasExprVisitor}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/expressions.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/expressions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6219cac4aeb16ee019551f95a03af59da44c9d06
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/expressions.py
@@ -0,0 +1,286 @@
+"""
+Expressions
+-----------
+
+Offer fast expression evaluation through numexpr
+
+"""
+from __future__ import annotations
+
+import operator
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core import roperator
+from pandas.core.computation.check import NUMEXPR_INSTALLED
+
+if NUMEXPR_INSTALLED:
+    import numexpr as ne
+
+if TYPE_CHECKING:
+    from pandas._typing import FuncType
+
+_TEST_MODE: bool | None = None
+_TEST_RESULT: list[bool] = []
+USE_NUMEXPR = NUMEXPR_INSTALLED
+_evaluate: FuncType | None = None
+_where: FuncType | None = None
+
+# the set of dtypes that we will allow pass to numexpr
+_ALLOWED_DTYPES = {
+    "evaluate": {"int64", "int32", "float64", "float32", "bool"},
+    "where": {"int64", "float64", "bool"},
+}
+
+# the minimum prod shape that we will use numexpr
+_MIN_ELEMENTS = 1_000_000
+
+
+def set_use_numexpr(v: bool = True) -> None:
+    # set/unset to use numexpr
+    global USE_NUMEXPR
+    if NUMEXPR_INSTALLED:
+        USE_NUMEXPR = v
+
+    # choose what we are going to do
+    global _evaluate, _where
+
+    _evaluate = _evaluate_numexpr if USE_NUMEXPR else _evaluate_standard
+    _where = _where_numexpr if USE_NUMEXPR else _where_standard
+
+
+def set_numexpr_threads(n=None) -> None:
+    # if we are using numexpr, set the threads to n
+    # otherwise reset
+    if NUMEXPR_INSTALLED and USE_NUMEXPR:
+        if n is None:
+            n = ne.detect_number_of_cores()
+        ne.set_num_threads(n)
+
+
+def _evaluate_standard(op, op_str, a, b):
+    """
+    Standard evaluation.
+    """
+    if _TEST_MODE:
+        _store_test_result(False)
+    return op(a, b)
+
+
+def _can_use_numexpr(op, op_str, a, b, dtype_check) -> bool:
+    """return a boolean if we WILL be using numexpr"""
+    if op_str is not None:
+        # required min elements (otherwise we are adding overhead)
+        if a.size > _MIN_ELEMENTS:
+            # check for dtype compatibility
+            dtypes: set[str] = set()
+            for o in [a, b]:
+                # ndarray and Series Case
+                if hasattr(o, "dtype"):
+                    dtypes |= {o.dtype.name}
+
+            # allowed are a superset
+            if not len(dtypes) or _ALLOWED_DTYPES[dtype_check] >= dtypes:
+                return True
+
+    return False
+
+
+def _evaluate_numexpr(op, op_str, a, b):
+    result = None
+
+    if _can_use_numexpr(op, op_str, a, b, "evaluate"):
+        is_reversed = op.__name__.strip("_").startswith("r")
+        if is_reversed:
+            # we were originally called by a reversed op method
+            a, b = b, a
+
+        a_value = a
+        b_value = b
+
+        try:
+            result = ne.evaluate(
+                f"a_value {op_str} b_value",
+                local_dict={"a_value": a_value, "b_value": b_value},
+                casting="safe",
+            )
+        except TypeError:
+            # numexpr raises eg for array ** array with integers
+            # (https://github.com/pydata/numexpr/issues/379)
+            pass
+        except NotImplementedError:
+            if _bool_arith_fallback(op_str, a, b):
+                pass
+            else:
+                raise
+
+        if is_reversed:
+            # reverse order to original for fallback
+            a, b = b, a
+
+    if _TEST_MODE:
+        _store_test_result(result is not None)
+
+    if result is None:
+        result = _evaluate_standard(op, op_str, a, b)
+
+    return result
+
+
+_op_str_mapping = {
+    operator.add: "+",
+    roperator.radd: "+",
+    operator.mul: "*",
+    roperator.rmul: "*",
+    operator.sub: "-",
+    roperator.rsub: "-",
+    operator.truediv: "/",
+    roperator.rtruediv: "/",
+    # floordiv not supported by numexpr 2.x
+    operator.floordiv: None,
+    roperator.rfloordiv: None,
+    # we require Python semantics for mod of negative for backwards compatibility
+    # see https://github.com/pydata/numexpr/issues/365
+    # so sticking with unaccelerated for now GH#36552
+    operator.mod: None,
+    roperator.rmod: None,
+    operator.pow: "**",
+    roperator.rpow: "**",
+    operator.eq: "==",
+    operator.ne: "!=",
+    operator.le: "<=",
+    operator.lt: "<",
+    operator.ge: ">=",
+    operator.gt: ">",
+    operator.and_: "&",
+    roperator.rand_: "&",
+    operator.or_: "|",
+    roperator.ror_: "|",
+    operator.xor: "^",
+    roperator.rxor: "^",
+    divmod: None,
+    roperator.rdivmod: None,
+}
+
+
+def _where_standard(cond, a, b):
+    # Caller is responsible for extracting ndarray if necessary
+    return np.where(cond, a, b)
+
+
+def _where_numexpr(cond, a, b):
+    # Caller is responsible for extracting ndarray if necessary
+    result = None
+
+    if _can_use_numexpr(None, "where", a, b, "where"):
+        result = ne.evaluate(
+            "where(cond_value, a_value, b_value)",
+            local_dict={"cond_value": cond, "a_value": a, "b_value": b},
+            casting="safe",
+        )
+
+    if result is None:
+        result = _where_standard(cond, a, b)
+
+    return result
+
+
+# turn myself on
+set_use_numexpr(get_option("compute.use_numexpr"))
+
+
+def _has_bool_dtype(x):
+    try:
+        return x.dtype == bool
+    except AttributeError:
+        return isinstance(x, (bool, np.bool_))
+
+
+_BOOL_OP_UNSUPPORTED = {"+": "|", "*": "&", "-": "^"}
+
+
+def _bool_arith_fallback(op_str, a, b) -> bool:
+    """
+    Check if we should fallback to the python `_evaluate_standard` in case
+    of an unsupported operation by numexpr, which is the case for some
+    boolean ops.
+    """
+    if _has_bool_dtype(a) and _has_bool_dtype(b):
+        if op_str in _BOOL_OP_UNSUPPORTED:
+            warnings.warn(
+                f"evaluating in Python space because the {repr(op_str)} "
+                "operator is not supported by numexpr for the bool dtype, "
+                f"use {repr(_BOOL_OP_UNSUPPORTED[op_str])} instead.",
+                stacklevel=find_stack_level(),
+            )
+            return True
+    return False
+
+
+def evaluate(op, a, b, use_numexpr: bool = True):
+    """
+    Evaluate and return the expression of the op on a and b.
+
+    Parameters
+    ----------
+    op : the actual operand
+    a : left operand
+    b : right operand
+    use_numexpr : bool, default True
+        Whether to try to use numexpr.
+    """
+    op_str = _op_str_mapping[op]
+    if op_str is not None:
+        if use_numexpr:
+            # error: "None" not callable
+            return _evaluate(op, op_str, a, b)  # type: ignore[misc]
+    return _evaluate_standard(op, op_str, a, b)
+
+
+def where(cond, a, b, use_numexpr: bool = True):
+    """
+    Evaluate the where condition cond on a and b.
+
+    Parameters
+    ----------
+    cond : np.ndarray[bool]
+    a : return if cond is True
+    b : return if cond is False
+    use_numexpr : bool, default True
+        Whether to try to use numexpr.
+    """
+    assert _where is not None
+    return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)
+
+
+def set_test_mode(v: bool = True) -> None:
+    """
+    Keeps track of whether numexpr was used.
+
+    Stores an additional ``True`` for every successful use of evaluate with
+    numexpr since the last ``get_test_result``.
+    """
+    global _TEST_MODE, _TEST_RESULT
+    _TEST_MODE = v
+    _TEST_RESULT = []
+
+
+def _store_test_result(used_numexpr: bool) -> None:
+    if used_numexpr:
+        _TEST_RESULT.append(used_numexpr)
+
+
+def get_test_result() -> list[bool]:
+    """
+    Get test result and reset test_results.
+    """
+    global _TEST_RESULT
+    res = _TEST_RESULT
+    _TEST_RESULT = []
+    return res
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/ops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8265456dfcedb5eb398cbf0517d78a1d976cd2a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/ops.py
@@ -0,0 +1,572 @@
+"""
+Operator classes for eval.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from functools import partial
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import Timestamp
+
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_scalar,
+)
+
+import pandas.core.common as com
+from pandas.core.computation.common import (
+    ensure_decoded,
+    result_type_many,
+)
+from pandas.core.computation.scope import DEFAULT_GLOBALS
+
+from pandas.io.formats.printing import (
+    pprint_thing,
+    pprint_thing_encoded,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Iterator,
+    )
+
+REDUCTIONS = ("sum", "prod", "min", "max")
+
+_unary_math_ops = (
+    "sin",
+    "cos",
+    "exp",
+    "log",
+    "expm1",
+    "log1p",
+    "sqrt",
+    "sinh",
+    "cosh",
+    "tanh",
+    "arcsin",
+    "arccos",
+    "arctan",
+    "arccosh",
+    "arcsinh",
+    "arctanh",
+    "abs",
+    "log10",
+    "floor",
+    "ceil",
+)
+_binary_math_ops = ("arctan2",)
+
+MATHOPS = _unary_math_ops + _binary_math_ops
+
+
+LOCAL_TAG = "__pd_eval_local_"
+
+
+class Term:
+    def __new__(cls, name, env, side=None, encoding=None):
+        klass = Constant if not isinstance(name, str) else cls
+        # error: Argument 2 for "super" not an instance of argument 1
+        supr_new = super(Term, klass).__new__  # type: ignore[misc]
+        return supr_new(klass)
+
+    is_local: bool
+
+    def __init__(self, name, env, side=None, encoding=None) -> None:
+        # name is a str for Term, but may be something else for subclasses
+        self._name = name
+        self.env = env
+        self.side = side
+        tname = str(name)
+        self.is_local = tname.startswith(LOCAL_TAG) or tname in DEFAULT_GLOBALS
+        self._value = self._resolve_name()
+        self.encoding = encoding
+
+    @property
+    def local_name(self) -> str:
+        return self.name.replace(LOCAL_TAG, "")
+
+    def __repr__(self) -> str:
+        return pprint_thing(self.name)
+
+    def __call__(self, *args, **kwargs):
+        return self.value
+
+    def evaluate(self, *args, **kwargs) -> Term:
+        return self
+
+    def _resolve_name(self):
+        local_name = str(self.local_name)
+        is_local = self.is_local
+        if local_name in self.env.scope and isinstance(
+            self.env.scope[local_name], type
+        ):
+            is_local = False
+
+        res = self.env.resolve(local_name, is_local=is_local)
+        self.update(res)
+
+        if hasattr(res, "ndim") and res.ndim > 2:
+            raise NotImplementedError(
+                "N-dimensional objects, where N > 2, are not supported with eval"
+            )
+        return res
+
+    def update(self, value) -> None:
+        """
+        search order for local (i.e., @variable) variables:
+
+        scope, key_variable
+        [('locals', 'local_name'),
+         ('globals', 'local_name'),
+         ('locals', 'key'),
+         ('globals', 'key')]
+        """
+        key = self.name
+
+        # if it's a variable name (otherwise a constant)
+        if isinstance(key, str):
+            self.env.swapkey(self.local_name, key, new_value=value)
+
+        self.value = value
+
+    @property
+    def is_scalar(self) -> bool:
+        return is_scalar(self._value)
+
+    @property
+    def type(self):
+        try:
+            # potentially very slow for large, mixed dtype frames
+            return self._value.values.dtype
+        except AttributeError:
+            try:
+                # ndarray
+                return self._value.dtype
+            except AttributeError:
+                # scalar
+                return type(self._value)
+
+    return_type = type
+
+    @property
+    def raw(self) -> str:
+        return f"{type(self).__name__}(name={repr(self.name)}, type={self.type})"
+
+    @property
+    def is_datetime(self) -> bool:
+        try:
+            t = self.type.type
+        except AttributeError:
+            t = self.type
+
+        return issubclass(t, (datetime, np.datetime64))
+
+    @property
+    def value(self):
+        return self._value
+
+    @value.setter
+    def value(self, new_value) -> None:
+        self._value = new_value
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def ndim(self) -> int:
+        return self._value.ndim
+
+
+class Constant(Term):
+    def _resolve_name(self):
+        return self._name
+
+    @property
+    def name(self):
+        return self.value
+
+    def __repr__(self) -> str:
+        # in python 2 str() of float
+        # can truncate shorter than repr()
+        return repr(self.name)
+
+
+_bool_op_map = {"not": "~", "and": "&", "or": "|"}
+
+
+class Op:
+    """
+    Hold an operator of arbitrary arity.
+    """
+
+    op: str
+
+    def __init__(self, op: str, operands: Iterable[Term | Op], encoding=None) -> None:
+        self.op = _bool_op_map.get(op, op)
+        self.operands = operands
+        self.encoding = encoding
+
+    def __iter__(self) -> Iterator:
+        return iter(self.operands)
+
+    def __repr__(self) -> str:
+        """
+        Print a generic n-ary operator and its operands using infix notation.
+        """
+        # recurse over the operands
+        parened = (f"({pprint_thing(opr)})" for opr in self.operands)
+        return pprint_thing(f" {self.op} ".join(parened))
+
+    @property
+    def return_type(self):
+        # clobber types to bool if the op is a boolean operator
+        if self.op in (CMP_OPS_SYMS + BOOL_OPS_SYMS):
+            return np.bool_
+        return result_type_many(*(term.type for term in com.flatten(self)))
+
+    @property
+    def has_invalid_return_type(self) -> bool:
+        types = self.operand_types
+        obj_dtype_set = frozenset([np.dtype("object")])
+        return self.return_type == object and types - obj_dtype_set
+
+    @property
+    def operand_types(self):
+        return frozenset(term.type for term in com.flatten(self))
+
+    @property
+    def is_scalar(self) -> bool:
+        return all(operand.is_scalar for operand in self.operands)
+
+    @property
+    def is_datetime(self) -> bool:
+        try:
+            t = self.return_type.type
+        except AttributeError:
+            t = self.return_type
+
+        return issubclass(t, (datetime, np.datetime64))
+
+
+def _in(x, y):
+    """
+    Compute the vectorized membership of ``x in y`` if possible, otherwise
+    use Python.
+    """
+    try:
+        return x.isin(y)
+    except AttributeError:
+        if is_list_like(x):
+            try:
+                return y.isin(x)
+            except AttributeError:
+                pass
+        return x in y
+
+
+def _not_in(x, y):
+    """
+    Compute the vectorized membership of ``x not in y`` if possible,
+    otherwise use Python.
+    """
+    try:
+        return ~x.isin(y)
+    except AttributeError:
+        if is_list_like(x):
+            try:
+                return ~y.isin(x)
+            except AttributeError:
+                pass
+        return x not in y
+
+
+CMP_OPS_SYMS = (">", "<", ">=", "<=", "==", "!=", "in", "not in")
+_cmp_ops_funcs = (
+    operator.gt,
+    operator.lt,
+    operator.ge,
+    operator.le,
+    operator.eq,
+    operator.ne,
+    _in,
+    _not_in,
+)
+_cmp_ops_dict = dict(zip(CMP_OPS_SYMS, _cmp_ops_funcs))
+
+BOOL_OPS_SYMS = ("&", "|", "and", "or")
+_bool_ops_funcs = (operator.and_, operator.or_, operator.and_, operator.or_)
+_bool_ops_dict = dict(zip(BOOL_OPS_SYMS, _bool_ops_funcs))
+
+ARITH_OPS_SYMS = ("+", "-", "*", "/", "**", "//", "%")
+_arith_ops_funcs = (
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.truediv,
+    operator.pow,
+    operator.floordiv,
+    operator.mod,
+)
+_arith_ops_dict = dict(zip(ARITH_OPS_SYMS, _arith_ops_funcs))
+
+SPECIAL_CASE_ARITH_OPS_SYMS = ("**", "//", "%")
+_special_case_arith_ops_funcs = (operator.pow, operator.floordiv, operator.mod)
+_special_case_arith_ops_dict = dict(
+    zip(SPECIAL_CASE_ARITH_OPS_SYMS, _special_case_arith_ops_funcs)
+)
+
+_binary_ops_dict = {}
+
+for d in (_cmp_ops_dict, _bool_ops_dict, _arith_ops_dict):
+    _binary_ops_dict.update(d)
+
+
+def is_term(obj) -> bool:
+    return isinstance(obj, Term)
+
+
+class BinOp(Op):
+    """
+    Hold a binary operator and its operands.
+
+    Parameters
+    ----------
+    op : str
+    lhs : Term or Op
+    rhs : Term or Op
+    """
+
+    def __init__(self, op: str, lhs, rhs) -> None:
+        super().__init__(op, (lhs, rhs))
+        self.lhs = lhs
+        self.rhs = rhs
+
+        self._disallow_scalar_only_bool_ops()
+
+        self.convert_values()
+
+        try:
+            self.func = _binary_ops_dict[op]
+        except KeyError as err:
+            # has to be made a list for python3
+            keys = list(_binary_ops_dict.keys())
+            raise ValueError(
+                f"Invalid binary operator {repr(op)}, valid operators are {keys}"
+            ) from err
+
+    def __call__(self, env):
+        """
+        Recursively evaluate an expression in Python space.
+
+        Parameters
+        ----------
+        env : Scope
+
+        Returns
+        -------
+        object
+            The result of an evaluated expression.
+        """
+        # recurse over the left/right nodes
+        left = self.lhs(env)
+        right = self.rhs(env)
+
+        return self.func(left, right)
+
+    def evaluate(self, env, engine: str, parser, term_type, eval_in_python):
+        """
+        Evaluate a binary operation *before* being passed to the engine.
+
+        Parameters
+        ----------
+        env : Scope
+        engine : str
+        parser : str
+        term_type : type
+        eval_in_python : list
+
+        Returns
+        -------
+        term_type
+            The "pre-evaluated" expression as an instance of ``term_type``
+        """
+        if engine == "python":
+            res = self(env)
+        else:
+            # recurse over the left/right nodes
+
+            left = self.lhs.evaluate(
+                env,
+                engine=engine,
+                parser=parser,
+                term_type=term_type,
+                eval_in_python=eval_in_python,
+            )
+
+            right = self.rhs.evaluate(
+                env,
+                engine=engine,
+                parser=parser,
+                term_type=term_type,
+                eval_in_python=eval_in_python,
+            )
+
+            # base cases
+            if self.op in eval_in_python:
+                res = self.func(left.value, right.value)
+            else:
+                from pandas.core.computation.eval import eval
+
+                res = eval(self, local_dict=env, engine=engine, parser=parser)
+
+        name = env.add_tmp(res)
+        return term_type(name, env=env)
+
+    def convert_values(self) -> None:
+        """
+        Convert datetimes to a comparable value in an expression.
+        """
+
+        def stringify(value):
+            encoder: Callable
+            if self.encoding is not None:
+                encoder = partial(pprint_thing_encoded, encoding=self.encoding)
+            else:
+                encoder = pprint_thing
+            return encoder(value)
+
+        lhs, rhs = self.lhs, self.rhs
+
+        if is_term(lhs) and lhs.is_datetime and is_term(rhs) and rhs.is_scalar:
+            v = rhs.value
+            if isinstance(v, (int, float)):
+                v = stringify(v)
+            v = Timestamp(ensure_decoded(v))
+            if v.tz is not None:
+                v = v.tz_convert("UTC")
+            self.rhs.update(v)
+
+        if is_term(rhs) and rhs.is_datetime and is_term(lhs) and lhs.is_scalar:
+            v = lhs.value
+            if isinstance(v, (int, float)):
+                v = stringify(v)
+            v = Timestamp(ensure_decoded(v))
+            if v.tz is not None:
+                v = v.tz_convert("UTC")
+            self.lhs.update(v)
+
+    def _disallow_scalar_only_bool_ops(self):
+        rhs = self.rhs
+        lhs = self.lhs
+
+        # GH#24883 unwrap dtype if necessary to ensure we have a type object
+        rhs_rt = rhs.return_type
+        rhs_rt = getattr(rhs_rt, "type", rhs_rt)
+        lhs_rt = lhs.return_type
+        lhs_rt = getattr(lhs_rt, "type", lhs_rt)
+        if (
+            (lhs.is_scalar or rhs.is_scalar)
+            and self.op in _bool_ops_dict
+            and (
+                not (
+                    issubclass(rhs_rt, (bool, np.bool_))
+                    and issubclass(lhs_rt, (bool, np.bool_))
+                )
+            )
+        ):
+            raise NotImplementedError("cannot evaluate scalar only bool ops")
+
+
+def isnumeric(dtype) -> bool:
+    return issubclass(np.dtype(dtype).type, np.number)
+
+
+UNARY_OPS_SYMS = ("+", "-", "~", "not")
+_unary_ops_funcs = (operator.pos, operator.neg, operator.invert, operator.invert)
+_unary_ops_dict = dict(zip(UNARY_OPS_SYMS, _unary_ops_funcs))
+
+
+class UnaryOp(Op):
+    """
+    Hold a unary operator and its operands.
+
+    Parameters
+    ----------
+    op : str
+        The token used to represent the operator.
+    operand : Term or Op
+        The Term or Op operand to the operator.
+
+    Raises
+    ------
+    ValueError
+        * If no function associated with the passed operator token is found.
+    """
+
+    def __init__(self, op: Literal["+", "-", "~", "not"], operand) -> None:
+        super().__init__(op, (operand,))
+        self.operand = operand
+
+        try:
+            self.func = _unary_ops_dict[op]
+        except KeyError as err:
+            raise ValueError(
+                f"Invalid unary operator {repr(op)}, "
+                f"valid operators are {UNARY_OPS_SYMS}"
+            ) from err
+
+    def __call__(self, env) -> MathCall:
+        operand = self.operand(env)
+        # error: Cannot call function of unknown type
+        return self.func(operand)  # type: ignore[operator]
+
+    def __repr__(self) -> str:
+        return pprint_thing(f"{self.op}({self.operand})")
+
+    @property
+    def return_type(self) -> np.dtype:
+        operand = self.operand
+        if operand.return_type == np.dtype("bool"):
+            return np.dtype("bool")
+        if isinstance(operand, Op) and (
+            operand.op in _cmp_ops_dict or operand.op in _bool_ops_dict
+        ):
+            return np.dtype("bool")
+        return np.dtype("int")
+
+
+class MathCall(Op):
+    def __init__(self, func, args) -> None:
+        super().__init__(func.name, args)
+        self.func = func
+
+    def __call__(self, env):
+        # error: "Op" not callable
+        operands = [op(env) for op in self.operands]  # type: ignore[operator]
+        return self.func.func(*operands)
+
+    def __repr__(self) -> str:
+        operands = map(str, self.operands)
+        return pprint_thing(f"{self.op}({','.join(operands)})")
+
+
+class FuncNode:
+    def __init__(self, name: str) -> None:
+        if name not in MATHOPS:
+            raise ValueError(f'"{name}" is not a supported function')
+        self.name = name
+        self.func = getattr(np, name)
+
+    def __call__(self, *args) -> MathCall:
+        return MathCall(self, args)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/parsing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/parsing.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cfa0f2baffd5ed45db19242c2afd00b6e5e23dc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/parsing.py
@@ -0,0 +1,198 @@
+"""
+:func:`~pandas.eval` source string parsing functions
+"""
+from __future__ import annotations
+
+from io import StringIO
+from keyword import iskeyword
+import token
+import tokenize
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+# A token value Python's tokenizer probably will never use.
+BACKTICK_QUOTED_STRING = 100
+
+
+def create_valid_python_identifier(name: str) -> str:
+    """
+    Create valid Python identifiers from any string.
+
+    Check if name contains any special characters. If it contains any
+    special characters, the special characters will be replaced by
+    a special string and a prefix is added.
+
+    Raises
+    ------
+    SyntaxError
+        If the returned name is not a Python valid identifier, raise an exception.
+        This can happen if there is a hashtag in the name, as the tokenizer will
+        than terminate and not find the backtick.
+        But also for characters that fall out of the range of (U+0001..U+007F).
+    """
+    if name.isidentifier() and not iskeyword(name):
+        return name
+
+    # Create a dict with the special characters and their replacement string.
+    # EXACT_TOKEN_TYPES contains these special characters
+    # token.tok_name contains a readable description of the replacement string.
+    special_characters_replacements = {
+        char: f"_{token.tok_name[tokval]}_"
+        for char, tokval in (tokenize.EXACT_TOKEN_TYPES.items())
+    }
+    special_characters_replacements.update(
+        {
+            " ": "_",
+            "?": "_QUESTIONMARK_",
+            "!": "_EXCLAMATIONMARK_",
+            "$": "_DOLLARSIGN_",
+            "€": "_EUROSIGN_",
+            "°": "_DEGREESIGN_",
+            # Including quotes works, but there are exceptions.
+            "'": "_SINGLEQUOTE_",
+            '"': "_DOUBLEQUOTE_",
+            # Currently not possible. Terminates parser and won't find backtick.
+            # "#": "_HASH_",
+        }
+    )
+
+    name = "".join([special_characters_replacements.get(char, char) for char in name])
+    name = f"BACKTICK_QUOTED_STRING_{name}"
+
+    if not name.isidentifier():
+        raise SyntaxError(f"Could not convert '{name}' to a valid Python identifier.")
+
+    return name
+
+
+def clean_backtick_quoted_toks(tok: tuple[int, str]) -> tuple[int, str]:
+    """
+    Clean up a column name if surrounded by backticks.
+
+    Backtick quoted string are indicated by a certain tokval value. If a string
+    is a backtick quoted token it will processed by
+    :func:`_create_valid_python_identifier` so that the parser can find this
+    string when the query is executed.
+    In this case the tok will get the NAME tokval.
+
+    Parameters
+    ----------
+    tok : tuple of int, str
+        ints correspond to the all caps constants in the tokenize module
+
+    Returns
+    -------
+    tok : Tuple[int, str]
+        Either the input or token or the replacement values
+    """
+    toknum, tokval = tok
+    if toknum == BACKTICK_QUOTED_STRING:
+        return tokenize.NAME, create_valid_python_identifier(tokval)
+    return toknum, tokval
+
+
+def clean_column_name(name: Hashable) -> Hashable:
+    """
+    Function to emulate the cleaning of a backtick quoted name.
+
+    The purpose for this function is to see what happens to the name of
+    identifier if it goes to the process of being parsed a Python code
+    inside a backtick quoted string and than being cleaned
+    (removed of any special characters).
+
+    Parameters
+    ----------
+    name : hashable
+        Name to be cleaned.
+
+    Returns
+    -------
+    name : hashable
+        Returns the name after tokenizing and cleaning.
+
+    Notes
+    -----
+        For some cases, a name cannot be converted to a valid Python identifier.
+        In that case :func:`tokenize_string` raises a SyntaxError.
+        In that case, we just return the name unmodified.
+
+        If this name was used in the query string (this makes the query call impossible)
+        an error will be raised by :func:`tokenize_backtick_quoted_string` instead,
+        which is not caught and propagates to the user level.
+    """
+    try:
+        tokenized = tokenize_string(f"`{name}`")
+        tokval = next(tokenized)[1]
+        return create_valid_python_identifier(tokval)
+    except SyntaxError:
+        return name
+
+
+def tokenize_backtick_quoted_string(
+    token_generator: Iterator[tokenize.TokenInfo], source: str, string_start: int
+) -> tuple[int, str]:
+    """
+    Creates a token from a backtick quoted string.
+
+    Moves the token_generator forwards till right after the next backtick.
+
+    Parameters
+    ----------
+    token_generator : Iterator[tokenize.TokenInfo]
+        The generator that yields the tokens of the source string (Tuple[int, str]).
+        The generator is at the first token after the backtick (`)
+
+    source : str
+        The Python source code string.
+
+    string_start : int
+        This is the start of backtick quoted string inside the source string.
+
+    Returns
+    -------
+    tok: Tuple[int, str]
+        The token that represents the backtick quoted string.
+        The integer is equal to BACKTICK_QUOTED_STRING (100).
+    """
+    for _, tokval, start, _, _ in token_generator:
+        if tokval == "`":
+            string_end = start[1]
+            break
+
+    return BACKTICK_QUOTED_STRING, source[string_start:string_end]
+
+
+def tokenize_string(source: str) -> Iterator[tuple[int, str]]:
+    """
+    Tokenize a Python source code string.
+
+    Parameters
+    ----------
+    source : str
+        The Python source code string.
+
+    Returns
+    -------
+    tok_generator : Iterator[Tuple[int, str]]
+        An iterator yielding all tokens with only toknum and tokval (Tuple[ing, str]).
+    """
+    line_reader = StringIO(source).readline
+    token_generator = tokenize.generate_tokens(line_reader)
+
+    # Loop over all tokens till a backtick (`) is found.
+    # Then, take all tokens till the next backtick to form a backtick quoted string
+    for toknum, tokval, start, _, _ in token_generator:
+        if tokval == "`":
+            try:
+                yield tokenize_backtick_quoted_string(
+                    token_generator, source, string_start=start[1] + 1
+                )
+            except Exception as err:
+                raise SyntaxError(f"Failed to parse backticks in '{source}'.") from err
+        else:
+            yield toknum, tokval
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/pytables.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/pytables.py
new file mode 100644
index 0000000000000000000000000000000000000000..04a8ad7ef0be6b044baf65b80cbf4161d45f8cac
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/pytables.py
@@ -0,0 +1,666 @@
+""" manage PyTables query interface via Expressions """
+from __future__ import annotations
+
+import ast
+from decimal import (
+    Decimal,
+    InvalidOperation,
+)
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    ClassVar,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    Timedelta,
+    Timestamp,
+)
+from pandas.errors import UndefinedVariableError
+
+from pandas.core.dtypes.common import is_list_like
+
+import pandas.core.common as com
+from pandas.core.computation import (
+    expr,
+    ops,
+    scope as _scope,
+)
+from pandas.core.computation.common import ensure_decoded
+from pandas.core.computation.expr import BaseExprVisitor
+from pandas.core.computation.ops import is_term
+from pandas.core.construction import extract_array
+from pandas.core.indexes.base import Index
+
+from pandas.io.formats.printing import (
+    pprint_thing,
+    pprint_thing_encoded,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Self,
+        npt,
+    )
+
+
+class PyTablesScope(_scope.Scope):
+    __slots__ = ("queryables",)
+
+    queryables: dict[str, Any]
+
+    def __init__(
+        self,
+        level: int,
+        global_dict=None,
+        local_dict=None,
+        queryables: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(level + 1, global_dict=global_dict, local_dict=local_dict)
+        self.queryables = queryables or {}
+
+
+class Term(ops.Term):
+    env: PyTablesScope
+
+    def __new__(cls, name, env, side=None, encoding=None):
+        if isinstance(name, str):
+            klass = cls
+        else:
+            klass = Constant
+        return object.__new__(klass)
+
+    def __init__(self, name, env: PyTablesScope, side=None, encoding=None) -> None:
+        super().__init__(name, env, side=side, encoding=encoding)
+
+    def _resolve_name(self):
+        # must be a queryables
+        if self.side == "left":
+            # Note: The behavior of __new__ ensures that self.name is a str here
+            if self.name not in self.env.queryables:
+                raise NameError(f"name {repr(self.name)} is not defined")
+            return self.name
+
+        # resolve the rhs (and allow it to be None)
+        try:
+            return self.env.resolve(self.name, is_local=False)
+        except UndefinedVariableError:
+            return self.name
+
+    # read-only property overwriting read/write property
+    @property  # type: ignore[misc]
+    def value(self):
+        return self._value
+
+
+class Constant(Term):
+    def __init__(self, name, env: PyTablesScope, side=None, encoding=None) -> None:
+        assert isinstance(env, PyTablesScope), type(env)
+        super().__init__(name, env, side=side, encoding=encoding)
+
+    def _resolve_name(self):
+        return self._name
+
+
+class BinOp(ops.BinOp):
+    _max_selectors = 31
+
+    op: str
+    queryables: dict[str, Any]
+    condition: str | None
+
+    def __init__(self, op: str, lhs, rhs, queryables: dict[str, Any], encoding) -> None:
+        super().__init__(op, lhs, rhs)
+        self.queryables = queryables
+        self.encoding = encoding
+        self.condition = None
+
+    def _disallow_scalar_only_bool_ops(self) -> None:
+        pass
+
+    def prune(self, klass):
+        def pr(left, right):
+            """create and return a new specialized BinOp from myself"""
+            if left is None:
+                return right
+            elif right is None:
+                return left
+
+            k = klass
+            if isinstance(left, ConditionBinOp):
+                if isinstance(right, ConditionBinOp):
+                    k = JointConditionBinOp
+                elif isinstance(left, k):
+                    return left
+                elif isinstance(right, k):
+                    return right
+
+            elif isinstance(left, FilterBinOp):
+                if isinstance(right, FilterBinOp):
+                    k = JointFilterBinOp
+                elif isinstance(left, k):
+                    return left
+                elif isinstance(right, k):
+                    return right
+
+            return k(
+                self.op, left, right, queryables=self.queryables, encoding=self.encoding
+            ).evaluate()
+
+        left, right = self.lhs, self.rhs
+
+        if is_term(left) and is_term(right):
+            res = pr(left.value, right.value)
+        elif not is_term(left) and is_term(right):
+            res = pr(left.prune(klass), right.value)
+        elif is_term(left) and not is_term(right):
+            res = pr(left.value, right.prune(klass))
+        elif not (is_term(left) or is_term(right)):
+            res = pr(left.prune(klass), right.prune(klass))
+
+        return res
+
+    def conform(self, rhs):
+        """inplace conform rhs"""
+        if not is_list_like(rhs):
+            rhs = [rhs]
+        if isinstance(rhs, np.ndarray):
+            rhs = rhs.ravel()
+        return rhs
+
+    @property
+    def is_valid(self) -> bool:
+        """return True if this is a valid field"""
+        return self.lhs in self.queryables
+
+    @property
+    def is_in_table(self) -> bool:
+        """
+        return True if this is a valid column name for generation (e.g. an
+        actual column in the table)
+        """
+        return self.queryables.get(self.lhs) is not None
+
+    @property
+    def kind(self):
+        """the kind of my field"""
+        return getattr(self.queryables.get(self.lhs), "kind", None)
+
+    @property
+    def meta(self):
+        """the meta of my field"""
+        return getattr(self.queryables.get(self.lhs), "meta", None)
+
+    @property
+    def metadata(self):
+        """the metadata of my field"""
+        return getattr(self.queryables.get(self.lhs), "metadata", None)
+
+    def generate(self, v) -> str:
+        """create and return the op string for this TermValue"""
+        val = v.tostring(self.encoding)
+        return f"({self.lhs} {self.op} {val})"
+
+    def convert_value(self, v) -> TermValue:
+        """
+        convert the expression that is in the term to something that is
+        accepted by pytables
+        """
+
+        def stringify(value):
+            if self.encoding is not None:
+                return pprint_thing_encoded(value, encoding=self.encoding)
+            return pprint_thing(value)
+
+        kind = ensure_decoded(self.kind)
+        meta = ensure_decoded(self.meta)
+        if kind == "datetime" or (kind and kind.startswith("datetime64")):
+            if isinstance(v, (int, float)):
+                v = stringify(v)
+            v = ensure_decoded(v)
+            v = Timestamp(v).as_unit("ns")
+            if v.tz is not None:
+                v = v.tz_convert("UTC")
+            return TermValue(v, v._value, kind)
+        elif kind in ("timedelta64", "timedelta"):
+            if isinstance(v, str):
+                v = Timedelta(v)
+            else:
+                v = Timedelta(v, unit="s")
+            v = v.as_unit("ns")._value
+            return TermValue(int(v), v, kind)
+        elif meta == "category":
+            metadata = extract_array(self.metadata, extract_numpy=True)
+            result: npt.NDArray[np.intp] | np.intp | int
+            if v not in metadata:
+                result = -1
+            else:
+                result = metadata.searchsorted(v, side="left")
+            return TermValue(result, result, "integer")
+        elif kind == "integer":
+            try:
+                v_dec = Decimal(v)
+            except InvalidOperation:
+                # GH 54186
+                # convert v to float to raise float's ValueError
+                float(v)
+            else:
+                v = int(v_dec.to_integral_exact(rounding="ROUND_HALF_EVEN"))
+            return TermValue(v, v, kind)
+        elif kind == "float":
+            v = float(v)
+            return TermValue(v, v, kind)
+        elif kind == "bool":
+            if isinstance(v, str):
+                v = v.strip().lower() not in [
+                    "false",
+                    "f",
+                    "no",
+                    "n",
+                    "none",
+                    "0",
+                    "[]",
+                    "{}",
+                    "",
+                ]
+            else:
+                v = bool(v)
+            return TermValue(v, v, kind)
+        elif isinstance(v, str):
+            # string quoting
+            return TermValue(v, stringify(v), "string")
+        else:
+            raise TypeError(f"Cannot compare {v} of type {type(v)} to {kind} column")
+
+    def convert_values(self) -> None:
+        pass
+
+
+class FilterBinOp(BinOp):
+    filter: tuple[Any, Any, Index] | None = None
+
+    def __repr__(self) -> str:
+        if self.filter is None:
+            return "Filter: Not Initialized"
+        return pprint_thing(f"[Filter : [{self.filter[0]}] -> [{self.filter[1]}]")
+
+    def invert(self) -> Self:
+        """invert the filter"""
+        if self.filter is not None:
+            self.filter = (
+                self.filter[0],
+                self.generate_filter_op(invert=True),
+                self.filter[2],
+            )
+        return self
+
+    def format(self):
+        """return the actual filter format"""
+        return [self.filter]
+
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self | None:  # type: ignore[override]
+        if not self.is_valid:
+            raise ValueError(f"query term is not valid [{self}]")
+
+        rhs = self.conform(self.rhs)
+        values = list(rhs)
+
+        if self.is_in_table:
+            # if too many values to create the expression, use a filter instead
+            if self.op in ["==", "!="] and len(values) > self._max_selectors:
+                filter_op = self.generate_filter_op()
+                self.filter = (self.lhs, filter_op, Index(values))
+
+                return self
+            return None
+
+        # equality conditions
+        if self.op in ["==", "!="]:
+            filter_op = self.generate_filter_op()
+            self.filter = (self.lhs, filter_op, Index(values))
+
+        else:
+            raise TypeError(
+                f"passing a filterable condition to a non-table indexer [{self}]"
+            )
+
+        return self
+
+    def generate_filter_op(self, invert: bool = False):
+        if (self.op == "!=" and not invert) or (self.op == "==" and invert):
+            return lambda axis, vals: ~axis.isin(vals)
+        else:
+            return lambda axis, vals: axis.isin(vals)
+
+
+class JointFilterBinOp(FilterBinOp):
+    def format(self):
+        raise NotImplementedError("unable to collapse Joint Filters")
+
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self:  # type: ignore[override]
+        return self
+
+
+class ConditionBinOp(BinOp):
+    def __repr__(self) -> str:
+        return pprint_thing(f"[Condition : [{self.condition}]]")
+
+    def invert(self):
+        """invert the condition"""
+        # if self.condition is not None:
+        #    self.condition = "~(%s)" % self.condition
+        # return self
+        raise NotImplementedError(
+            "cannot use an invert condition when passing to numexpr"
+        )
+
+    def format(self):
+        """return the actual ne format"""
+        return self.condition
+
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self | None:  # type: ignore[override]
+        if not self.is_valid:
+            raise ValueError(f"query term is not valid [{self}]")
+
+        # convert values if we are in the table
+        if not self.is_in_table:
+            return None
+
+        rhs = self.conform(self.rhs)
+        values = [self.convert_value(v) for v in rhs]
+
+        # equality conditions
+        if self.op in ["==", "!="]:
+            # too many values to create the expression?
+            if len(values) <= self._max_selectors:
+                vs = [self.generate(v) for v in values]
+                self.condition = f"({' | '.join(vs)})"
+
+            # use a filter after reading
+            else:
+                return None
+        else:
+            self.condition = self.generate(values[0])
+
+        return self
+
+
+class JointConditionBinOp(ConditionBinOp):
+    # error: Signature of "evaluate" incompatible with supertype "BinOp"
+    def evaluate(self) -> Self:  # type: ignore[override]
+        self.condition = f"({self.lhs.condition} {self.op} {self.rhs.condition})"
+        return self
+
+
+class UnaryOp(ops.UnaryOp):
+    def prune(self, klass):
+        if self.op != "~":
+            raise NotImplementedError("UnaryOp only support invert type ops")
+
+        operand = self.operand
+        operand = operand.prune(klass)
+
+        if operand is not None and (
+            issubclass(klass, ConditionBinOp)
+            and operand.condition is not None
+            or not issubclass(klass, ConditionBinOp)
+            and issubclass(klass, FilterBinOp)
+            and operand.filter is not None
+        ):
+            return operand.invert()
+        return None
+
+
+class PyTablesExprVisitor(BaseExprVisitor):
+    const_type: ClassVar[type[ops.Term]] = Constant
+    term_type: ClassVar[type[Term]] = Term
+
+    def __init__(self, env, engine, parser, **kwargs) -> None:
+        super().__init__(env, engine, parser)
+        for bin_op in self.binary_ops:
+            bin_node = self.binary_op_nodes_map[bin_op]
+            setattr(
+                self,
+                f"visit_{bin_node}",
+                lambda node, bin_op=bin_op: partial(BinOp, bin_op, **kwargs),
+            )
+
+    def visit_UnaryOp(self, node, **kwargs) -> ops.Term | UnaryOp | None:
+        if isinstance(node.op, (ast.Not, ast.Invert)):
+            return UnaryOp("~", self.visit(node.operand))
+        elif isinstance(node.op, ast.USub):
+            return self.const_type(-self.visit(node.operand).value, self.env)
+        elif isinstance(node.op, ast.UAdd):
+            raise NotImplementedError("Unary addition not supported")
+        # TODO: return None might never be reached
+        return None
+
+    def visit_Index(self, node, **kwargs):
+        return self.visit(node.value).value
+
+    def visit_Assign(self, node, **kwargs):
+        cmpr = ast.Compare(
+            ops=[ast.Eq()], left=node.targets[0], comparators=[node.value]
+        )
+        return self.visit(cmpr)
+
+    def visit_Subscript(self, node, **kwargs) -> ops.Term:
+        # only allow simple subscripts
+
+        value = self.visit(node.value)
+        slobj = self.visit(node.slice)
+        try:
+            value = value.value
+        except AttributeError:
+            pass
+
+        if isinstance(slobj, Term):
+            # In py39 np.ndarray lookups with Term containing int raise
+            slobj = slobj.value
+
+        try:
+            return self.const_type(value[slobj], self.env)
+        except TypeError as err:
+            raise ValueError(
+                f"cannot subscript {repr(value)} with {repr(slobj)}"
+            ) from err
+
+    def visit_Attribute(self, node, **kwargs):
+        attr = node.attr
+        value = node.value
+
+        ctx = type(node.ctx)
+        if ctx == ast.Load:
+            # resolve the value
+            resolved = self.visit(value)
+
+            # try to get the value to see if we are another expression
+            try:
+                resolved = resolved.value
+            except AttributeError:
+                pass
+
+            try:
+                return self.term_type(getattr(resolved, attr), self.env)
+            except AttributeError:
+                # something like datetime.datetime where scope is overridden
+                if isinstance(value, ast.Name) and value.id == attr:
+                    return resolved
+
+        raise ValueError(f"Invalid Attribute context {ctx.__name__}")
+
+    def translate_In(self, op):
+        return ast.Eq() if isinstance(op, ast.In) else op
+
+    def _rewrite_membership_op(self, node, left, right):
+        return self.visit(node.op), node.op, left, right
+
+
+def _validate_where(w):
+    """
+    Validate that the where statement is of the right type.
+
+    The type may either be String, Expr, or list-like of Exprs.
+
+    Parameters
+    ----------
+    w : String term expression, Expr, or list-like of Exprs.
+
+    Returns
+    -------
+    where : The original where clause if the check was successful.
+
+    Raises
+    ------
+    TypeError : An invalid data type was passed in for w (e.g. dict).
+    """
+    if not (isinstance(w, (PyTablesExpr, str)) or is_list_like(w)):
+        raise TypeError(
+            "where must be passed as a string, PyTablesExpr, "
+            "or list-like of PyTablesExpr"
+        )
+
+    return w
+
+
+class PyTablesExpr(expr.Expr):
+    """
+    Hold a pytables-like expression, comprised of possibly multiple 'terms'.
+
+    Parameters
+    ----------
+    where : string term expression, PyTablesExpr, or list-like of PyTablesExprs
+    queryables : a "kinds" map (dict of column name -> kind), or None if column
+        is non-indexable
+    encoding : an encoding that will encode the query terms
+
+    Returns
+    -------
+    a PyTablesExpr object
+
+    Examples
+    --------
+    'index>=date'
+    "columns=['A', 'D']"
+    'columns=A'
+    'columns==A'
+    "~(columns=['A','B'])"
+    'index>df.index[3] & string="bar"'
+    '(index>df.index[3] & index<=df.index[6]) | string="bar"'
+    "ts>=Timestamp('2012-02-01')"
+    "major_axis>=20130101"
+    """
+
+    _visitor: PyTablesExprVisitor | None
+    env: PyTablesScope
+    expr: str
+
+    def __init__(
+        self,
+        where,
+        queryables: dict[str, Any] | None = None,
+        encoding=None,
+        scope_level: int = 0,
+    ) -> None:
+        where = _validate_where(where)
+
+        self.encoding = encoding
+        self.condition = None
+        self.filter = None
+        self.terms = None
+        self._visitor = None
+
+        # capture the environment if needed
+        local_dict: _scope.DeepChainMap[Any, Any] | None = None
+
+        if isinstance(where, PyTablesExpr):
+            local_dict = where.env.scope
+            _where = where.expr
+
+        elif is_list_like(where):
+            where = list(where)
+            for idx, w in enumerate(where):
+                if isinstance(w, PyTablesExpr):
+                    local_dict = w.env.scope
+                else:
+                    where[idx] = _validate_where(w)
+            _where = " & ".join([f"({w})" for w in com.flatten(where)])
+        else:
+            # _validate_where ensures we otherwise have a string
+            _where = where
+
+        self.expr = _where
+        self.env = PyTablesScope(scope_level + 1, local_dict=local_dict)
+
+        if queryables is not None and isinstance(self.expr, str):
+            self.env.queryables.update(queryables)
+            self._visitor = PyTablesExprVisitor(
+                self.env,
+                queryables=queryables,
+                parser="pytables",
+                engine="pytables",
+                encoding=encoding,
+            )
+            self.terms = self.parse()
+
+    def __repr__(self) -> str:
+        if self.terms is not None:
+            return pprint_thing(self.terms)
+        return pprint_thing(self.expr)
+
+    def evaluate(self):
+        """create and return the numexpr condition and filter"""
+        try:
+            self.condition = self.terms.prune(ConditionBinOp)
+        except AttributeError as err:
+            raise ValueError(
+                f"cannot process expression [{self.expr}], [{self}] "
+                "is not a valid condition"
+            ) from err
+        try:
+            self.filter = self.terms.prune(FilterBinOp)
+        except AttributeError as err:
+            raise ValueError(
+                f"cannot process expression [{self.expr}], [{self}] "
+                "is not a valid filter"
+            ) from err
+
+        return self.condition, self.filter
+
+
+class TermValue:
+    """hold a term value the we use to construct a condition/filter"""
+
+    def __init__(self, value, converted, kind: str) -> None:
+        assert isinstance(kind, str), kind
+        self.value = value
+        self.converted = converted
+        self.kind = kind
+
+    def tostring(self, encoding) -> str:
+        """quote the string if not encoded else encode and return"""
+        if self.kind == "string":
+            if encoding is not None:
+                return str(self.converted)
+            return f'"{self.converted}"'
+        elif self.kind == "float":
+            # python 2 str(float) is not always
+            # round-trippable so use repr()
+            return repr(self.converted)
+        return str(self.converted)
+
+
+def maybe_expression(s) -> bool:
+    """loose checking if s is a pytables-acceptable expression"""
+    if not isinstance(s, str):
+        return False
+    operations = PyTablesExprVisitor.binary_ops + PyTablesExprVisitor.unary_ops + ("=",)
+
+    # make sure we have an op at least
+    return any(op in s for op in operations)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/scope.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/scope.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e553ca448218435eafe1fd7ca97dce6f739e2a3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/computation/scope.py
@@ -0,0 +1,355 @@
+"""
+Module for scope operations
+"""
+from __future__ import annotations
+
+from collections import ChainMap
+import datetime
+import inspect
+from io import StringIO
+import itertools
+import pprint
+import struct
+import sys
+from typing import TypeVar
+
+import numpy as np
+
+from pandas._libs.tslibs import Timestamp
+from pandas.errors import UndefinedVariableError
+
+_KT = TypeVar("_KT")
+_VT = TypeVar("_VT")
+
+
+# https://docs.python.org/3/library/collections.html#chainmap-examples-and-recipes
+class DeepChainMap(ChainMap[_KT, _VT]):
+    """
+    Variant of ChainMap that allows direct updates to inner scopes.
+
+    Only works when all passed mapping are mutable.
+    """
+
+    def __setitem__(self, key: _KT, value: _VT) -> None:
+        for mapping in self.maps:
+            if key in mapping:
+                mapping[key] = value
+                return
+        self.maps[0][key] = value
+
+    def __delitem__(self, key: _KT) -> None:
+        """
+        Raises
+        ------
+        KeyError
+            If `key` doesn't exist.
+        """
+        for mapping in self.maps:
+            if key in mapping:
+                del mapping[key]
+                return
+        raise KeyError(key)
+
+
+def ensure_scope(
+    level: int, global_dict=None, local_dict=None, resolvers=(), target=None
+) -> Scope:
+    """Ensure that we are grabbing the correct scope."""
+    return Scope(
+        level + 1,
+        global_dict=global_dict,
+        local_dict=local_dict,
+        resolvers=resolvers,
+        target=target,
+    )
+
+
+def _replacer(x) -> str:
+    """
+    Replace a number with its hexadecimal representation. Used to tag
+    temporary variables with their calling scope's id.
+    """
+    # get the hex repr of the binary char and remove 0x and pad by pad_size
+    # zeros
+    try:
+        hexin = ord(x)
+    except TypeError:
+        # bytes literals masquerade as ints when iterating in py3
+        hexin = x
+
+    return hex(hexin)
+
+
+def _raw_hex_id(obj) -> str:
+    """Return the padded hexadecimal id of ``obj``."""
+    # interpret as a pointer since that's what really what id returns
+    packed = struct.pack("@P", id(obj))
+    return "".join([_replacer(x) for x in packed])
+
+
+DEFAULT_GLOBALS = {
+    "Timestamp": Timestamp,
+    "datetime": datetime.datetime,
+    "True": True,
+    "False": False,
+    "list": list,
+    "tuple": tuple,
+    "inf": np.inf,
+    "Inf": np.inf,
+}
+
+
+def _get_pretty_string(obj) -> str:
+    """
+    Return a prettier version of obj.
+
+    Parameters
+    ----------
+    obj : object
+        Object to pretty print
+
+    Returns
+    -------
+    str
+        Pretty print object repr
+    """
+    sio = StringIO()
+    pprint.pprint(obj, stream=sio)
+    return sio.getvalue()
+
+
+class Scope:
+    """
+    Object to hold scope, with a few bells to deal with some custom syntax
+    and contexts added by pandas.
+
+    Parameters
+    ----------
+    level : int
+    global_dict : dict or None, optional, default None
+    local_dict : dict or Scope or None, optional, default None
+    resolvers : list-like or None, optional, default None
+    target : object
+
+    Attributes
+    ----------
+    level : int
+    scope : DeepChainMap
+    target : object
+    temps : dict
+    """
+
+    __slots__ = ["level", "scope", "target", "resolvers", "temps"]
+    level: int
+    scope: DeepChainMap
+    resolvers: DeepChainMap
+    temps: dict
+
+    def __init__(
+        self, level: int, global_dict=None, local_dict=None, resolvers=(), target=None
+    ) -> None:
+        self.level = level + 1
+
+        # shallow copy because we don't want to keep filling this up with what
+        # was there before if there are multiple calls to Scope/_ensure_scope
+        self.scope = DeepChainMap(DEFAULT_GLOBALS.copy())
+        self.target = target
+
+        if isinstance(local_dict, Scope):
+            self.scope.update(local_dict.scope)
+            if local_dict.target is not None:
+                self.target = local_dict.target
+            self._update(local_dict.level)
+
+        frame = sys._getframe(self.level)
+
+        try:
+            # shallow copy here because we don't want to replace what's in
+            # scope when we align terms (alignment accesses the underlying
+            # numpy array of pandas objects)
+            scope_global = self.scope.new_child(
+                (global_dict if global_dict is not None else frame.f_globals).copy()
+            )
+            self.scope = DeepChainMap(scope_global)
+            if not isinstance(local_dict, Scope):
+                scope_local = self.scope.new_child(
+                    (local_dict if local_dict is not None else frame.f_locals).copy()
+                )
+                self.scope = DeepChainMap(scope_local)
+        finally:
+            del frame
+
+        # assumes that resolvers are going from outermost scope to inner
+        if isinstance(local_dict, Scope):
+            resolvers += tuple(local_dict.resolvers.maps)
+        self.resolvers = DeepChainMap(*resolvers)
+        self.temps = {}
+
+    def __repr__(self) -> str:
+        scope_keys = _get_pretty_string(list(self.scope.keys()))
+        res_keys = _get_pretty_string(list(self.resolvers.keys()))
+        return f"{type(self).__name__}(scope={scope_keys}, resolvers={res_keys})"
+
+    @property
+    def has_resolvers(self) -> bool:
+        """
+        Return whether we have any extra scope.
+
+        For example, DataFrames pass Their columns as resolvers during calls to
+        ``DataFrame.eval()`` and ``DataFrame.query()``.
+
+        Returns
+        -------
+        hr : bool
+        """
+        return bool(len(self.resolvers))
+
+    def resolve(self, key: str, is_local: bool):
+        """
+        Resolve a variable name in a possibly local context.
+
+        Parameters
+        ----------
+        key : str
+            A variable name
+        is_local : bool
+            Flag indicating whether the variable is local or not (prefixed with
+            the '@' symbol)
+
+        Returns
+        -------
+        value : object
+            The value of a particular variable
+        """
+        try:
+            # only look for locals in outer scope
+            if is_local:
+                return self.scope[key]
+
+            # not a local variable so check in resolvers if we have them
+            if self.has_resolvers:
+                return self.resolvers[key]
+
+            # if we're here that means that we have no locals and we also have
+            # no resolvers
+            assert not is_local and not self.has_resolvers
+            return self.scope[key]
+        except KeyError:
+            try:
+                # last ditch effort we look in temporaries
+                # these are created when parsing indexing expressions
+                # e.g., df[df > 0]
+                return self.temps[key]
+            except KeyError as err:
+                raise UndefinedVariableError(key, is_local) from err
+
+    def swapkey(self, old_key: str, new_key: str, new_value=None) -> None:
+        """
+        Replace a variable name, with a potentially new value.
+
+        Parameters
+        ----------
+        old_key : str
+            Current variable name to replace
+        new_key : str
+            New variable name to replace `old_key` with
+        new_value : object
+            Value to be replaced along with the possible renaming
+        """
+        if self.has_resolvers:
+            maps = self.resolvers.maps + self.scope.maps
+        else:
+            maps = self.scope.maps
+
+        maps.append(self.temps)
+
+        for mapping in maps:
+            if old_key in mapping:
+                mapping[new_key] = new_value
+                return
+
+    def _get_vars(self, stack, scopes: list[str]) -> None:
+        """
+        Get specifically scoped variables from a list of stack frames.
+
+        Parameters
+        ----------
+        stack : list
+            A list of stack frames as returned by ``inspect.stack()``
+        scopes : sequence of strings
+            A sequence containing valid stack frame attribute names that
+            evaluate to a dictionary. For example, ('locals', 'globals')
+        """
+        variables = itertools.product(scopes, stack)
+        for scope, (frame, _, _, _, _, _) in variables:
+            try:
+                d = getattr(frame, f"f_{scope}")
+                self.scope = DeepChainMap(self.scope.new_child(d))
+            finally:
+                # won't remove it, but DECREF it
+                # in Py3 this probably isn't necessary since frame won't be
+                # scope after the loop
+                del frame
+
+    def _update(self, level: int) -> None:
+        """
+        Update the current scope by going back `level` levels.
+
+        Parameters
+        ----------
+        level : int
+        """
+        sl = level + 1
+
+        # add sl frames to the scope starting with the
+        # most distant and overwriting with more current
+        # makes sure that we can capture variable scope
+        stack = inspect.stack()
+
+        try:
+            self._get_vars(stack[:sl], scopes=["locals"])
+        finally:
+            del stack[:], stack
+
+    def add_tmp(self, value) -> str:
+        """
+        Add a temporary variable to the scope.
+
+        Parameters
+        ----------
+        value : object
+            An arbitrary object to be assigned to a temporary variable.
+
+        Returns
+        -------
+        str
+            The name of the temporary variable created.
+        """
+        name = f"{type(value).__name__}_{self.ntemps}_{_raw_hex_id(self)}"
+
+        # add to inner most scope
+        assert name not in self.temps
+        self.temps[name] = value
+        assert name in self.temps
+
+        # only increment if the variable gets put in the scope
+        return name
+
+    @property
+    def ntemps(self) -> int:
+        """The number of temporary variables in this scope"""
+        return len(self.temps)
+
+    @property
+    def full_scope(self) -> DeepChainMap:
+        """
+        Return the full scope for use with passing to engines transparently
+        as a mapping.
+
+        Returns
+        -------
+        vars : DeepChainMap
+            All variables in this scope.
+        """
+        maps = [self.temps] + self.resolvers.maps + self.scope.maps
+        return DeepChainMap(*maps)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/config_init.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/config_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1df455eebacf8868384ad383ee34c934d059df3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/config_init.py
@@ -0,0 +1,941 @@
+"""
+This module is imported from the pandas package __init__.py file
+in order to ensure that the core.config options registered here will
+be available as soon as the user loads the package. if register_option
+is invoked inside specific modules, they will not be registered until that
+module is imported, which may or may not be a problem.
+
+If you need to make sure options are available even before a certain
+module is imported, register them here rather than in the module.
+
+"""
+from __future__ import annotations
+
+import os
+from typing import (
+    Any,
+    Callable,
+)
+
+import pandas._config.config as cf
+from pandas._config.config import (
+    is_bool,
+    is_callable,
+    is_instance_factory,
+    is_int,
+    is_nonnegative_int,
+    is_one_of_factory,
+    is_str,
+    is_text,
+)
+
+# compute
+
+use_bottleneck_doc = """
+: bool
+    Use the bottleneck library to accelerate if it is installed,
+    the default is True
+    Valid values: False,True
+"""
+
+
+def use_bottleneck_cb(key) -> None:
+    from pandas.core import nanops
+
+    nanops.set_use_bottleneck(cf.get_option(key))
+
+
+use_numexpr_doc = """
+: bool
+    Use the numexpr library to accelerate computation if it is installed,
+    the default is True
+    Valid values: False,True
+"""
+
+
+def use_numexpr_cb(key) -> None:
+    from pandas.core.computation import expressions
+
+    expressions.set_use_numexpr(cf.get_option(key))
+
+
+use_numba_doc = """
+: bool
+    Use the numba engine option for select operations if it is installed,
+    the default is False
+    Valid values: False,True
+"""
+
+
+def use_numba_cb(key) -> None:
+    from pandas.core.util import numba_
+
+    numba_.set_use_numba(cf.get_option(key))
+
+
+with cf.config_prefix("compute"):
+    cf.register_option(
+        "use_bottleneck",
+        True,
+        use_bottleneck_doc,
+        validator=is_bool,
+        cb=use_bottleneck_cb,
+    )
+    cf.register_option(
+        "use_numexpr", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb
+    )
+    cf.register_option(
+        "use_numba", False, use_numba_doc, validator=is_bool, cb=use_numba_cb
+    )
+#
+# options from the "display" namespace
+
+pc_precision_doc = """
+: int
+    Floating point output precision in terms of number of places after the
+    decimal, for regular formatting as well as scientific notation. Similar
+    to ``precision`` in :meth:`numpy.set_printoptions`.
+"""
+
+pc_colspace_doc = """
+: int
+    Default space for DataFrame columns.
+"""
+
+pc_max_rows_doc = """
+: int
+    If max_rows is exceeded, switch to truncate view. Depending on
+    `large_repr`, objects are either centrally truncated or printed as
+    a summary view. 'None' value means unlimited.
+
+    In case python/IPython is running in a terminal and `large_repr`
+    equals 'truncate' this can be set to 0 and pandas will auto-detect
+    the height of the terminal and print a truncated object which fits
+    the screen height. The IPython notebook, IPython qtconsole, or
+    IDLE do not run in a terminal and hence it is not possible to do
+    correct auto-detection.
+"""
+
+pc_min_rows_doc = """
+: int
+    The numbers of rows to show in a truncated view (when `max_rows` is
+    exceeded). Ignored when `max_rows` is set to None or 0. When set to
+    None, follows the value of `max_rows`.
+"""
+
+pc_max_cols_doc = """
+: int
+    If max_cols is exceeded, switch to truncate view. Depending on
+    `large_repr`, objects are either centrally truncated or printed as
+    a summary view. 'None' value means unlimited.
+
+    In case python/IPython is running in a terminal and `large_repr`
+    equals 'truncate' this can be set to 0 or None and pandas will auto-detect
+    the width of the terminal and print a truncated object which fits
+    the screen width. The IPython notebook, IPython qtconsole, or IDLE
+    do not run in a terminal and hence it is not possible to do
+    correct auto-detection and defaults to 20.
+"""
+
+pc_max_categories_doc = """
+: int
+    This sets the maximum number of categories pandas should output when
+    printing out a `Categorical` or a Series of dtype "category".
+"""
+
+pc_max_info_cols_doc = """
+: int
+    max_info_columns is used in DataFrame.info method to decide if
+    per column information will be printed.
+"""
+
+pc_nb_repr_h_doc = """
+: boolean
+    When True, IPython notebook will use html representation for
+    pandas objects (if it is available).
+"""
+
+pc_pprint_nest_depth = """
+: int
+    Controls the number of nested levels to process when pretty-printing
+"""
+
+pc_multi_sparse_doc = """
+: boolean
+    "sparsify" MultiIndex display (don't display repeated
+    elements in outer levels within groups)
+"""
+
+float_format_doc = """
+: callable
+    The callable should accept a floating point number and return
+    a string with the desired format of the number. This is used
+    in some places like SeriesFormatter.
+    See formats.format.EngFormatter for an example.
+"""
+
+max_colwidth_doc = """
+: int or None
+    The maximum width in characters of a column in the repr of
+    a pandas data structure. When the column overflows, a "..."
+    placeholder is embedded in the output. A 'None' value means unlimited.
+"""
+
+colheader_justify_doc = """
+: 'left'/'right'
+    Controls the justification of column headers. used by DataFrameFormatter.
+"""
+
+pc_expand_repr_doc = """
+: boolean
+    Whether to print out the full DataFrame repr for wide DataFrames across
+    multiple lines, `max_columns` is still respected, but the output will
+    wrap-around across multiple "pages" if its width exceeds `display.width`.
+"""
+
+pc_show_dimensions_doc = """
+: boolean or 'truncate'
+    Whether to print out dimensions at the end of DataFrame repr.
+    If 'truncate' is specified, only print out the dimensions if the
+    frame is truncated (e.g. not display all rows and/or columns)
+"""
+
+pc_east_asian_width_doc = """
+: boolean
+    Whether to use the Unicode East Asian Width to calculate the display text
+    width.
+    Enabling this may affect to the performance (default: False)
+"""
+
+pc_ambiguous_as_wide_doc = """
+: boolean
+    Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)
+    (default: False)
+"""
+
+pc_table_schema_doc = """
+: boolean
+    Whether to publish a Table Schema representation for frontends
+    that support it.
+    (default: False)
+"""
+
+pc_html_border_doc = """
+: int
+    A ``border=value`` attribute is inserted in the ``<table>`` tag
+    for the DataFrame HTML repr.
+"""
+
+pc_html_use_mathjax_doc = """\
+: boolean
+    When True, Jupyter notebook will process table contents using MathJax,
+    rendering mathematical expressions enclosed by the dollar symbol.
+    (default: True)
+"""
+
+pc_max_dir_items = """\
+: int
+    The number of items that will be added to `dir(...)`. 'None' value means
+    unlimited. Because dir is cached, changing this option will not immediately
+    affect already existing dataframes until a column is deleted or added.
+
+    This is for instance used to suggest columns from a dataframe to tab
+    completion.
+"""
+
+pc_width_doc = """
+: int
+    Width of the display in characters. In case python/IPython is running in
+    a terminal this can be set to None and pandas will correctly auto-detect
+    the width.
+    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
+    terminal and hence it is not possible to correctly detect the width.
+"""
+
+pc_chop_threshold_doc = """
+: float or None
+    if set to a float value, all float values smaller than the given threshold
+    will be displayed as exactly 0 by repr and friends.
+"""
+
+pc_max_seq_items = """
+: int or None
+    When pretty-printing a long sequence, no more then `max_seq_items`
+    will be printed. If items are omitted, they will be denoted by the
+    addition of "..." to the resulting string.
+
+    If set to None, the number of items to be printed is unlimited.
+"""
+
+pc_max_info_rows_doc = """
+: int
+    df.info() will usually show null-counts for each column.
+    For large frames this can be quite slow. max_info_rows and max_info_cols
+    limit this null check only to frames with smaller dimensions than
+    specified.
+"""
+
+pc_large_repr_doc = """
+: 'truncate'/'info'
+    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
+    show a truncated table, or switch to the view from
+    df.info() (the behaviour in earlier versions of pandas).
+"""
+
+pc_memory_usage_doc = """
+: bool, string or None
+    This specifies if the memory usage of a DataFrame should be displayed when
+    df.info() is called. Valid values True,False,'deep'
+"""
+
+
+def table_schema_cb(key) -> None:
+    from pandas.io.formats.printing import enable_data_resource_formatter
+
+    enable_data_resource_formatter(cf.get_option(key))
+
+
+def is_terminal() -> bool:
+    """
+    Detect if Python is running in a terminal.
+
+    Returns True if Python is running in a terminal or False if not.
+    """
+    try:
+        # error: Name 'get_ipython' is not defined
+        ip = get_ipython()  # type: ignore[name-defined]
+    except NameError:  # assume standard Python interpreter in a terminal
+        return True
+    else:
+        if hasattr(ip, "kernel"):  # IPython as a Jupyter kernel
+            return False
+        else:  # IPython in a terminal
+            return True
+
+
+with cf.config_prefix("display"):
+    cf.register_option("precision", 6, pc_precision_doc, validator=is_nonnegative_int)
+    cf.register_option(
+        "float_format",
+        None,
+        float_format_doc,
+        validator=is_one_of_factory([None, is_callable]),
+    )
+    cf.register_option(
+        "max_info_rows",
+        1690785,
+        pc_max_info_rows_doc,
+        validator=is_int,
+    )
+    cf.register_option("max_rows", 60, pc_max_rows_doc, validator=is_nonnegative_int)
+    cf.register_option(
+        "min_rows",
+        10,
+        pc_min_rows_doc,
+        validator=is_instance_factory([type(None), int]),
+    )
+    cf.register_option("max_categories", 8, pc_max_categories_doc, validator=is_int)
+
+    cf.register_option(
+        "max_colwidth",
+        50,
+        max_colwidth_doc,
+        validator=is_nonnegative_int,
+    )
+    if is_terminal():
+        max_cols = 0  # automatically determine optimal number of columns
+    else:
+        max_cols = 20  # cannot determine optimal number of columns
+    cf.register_option(
+        "max_columns", max_cols, pc_max_cols_doc, validator=is_nonnegative_int
+    )
+    cf.register_option(
+        "large_repr",
+        "truncate",
+        pc_large_repr_doc,
+        validator=is_one_of_factory(["truncate", "info"]),
+    )
+    cf.register_option("max_info_columns", 100, pc_max_info_cols_doc, validator=is_int)
+    cf.register_option(
+        "colheader_justify", "right", colheader_justify_doc, validator=is_text
+    )
+    cf.register_option("notebook_repr_html", True, pc_nb_repr_h_doc, validator=is_bool)
+    cf.register_option("pprint_nest_depth", 3, pc_pprint_nest_depth, validator=is_int)
+    cf.register_option("multi_sparse", True, pc_multi_sparse_doc, validator=is_bool)
+    cf.register_option("expand_frame_repr", True, pc_expand_repr_doc)
+    cf.register_option(
+        "show_dimensions",
+        "truncate",
+        pc_show_dimensions_doc,
+        validator=is_one_of_factory([True, False, "truncate"]),
+    )
+    cf.register_option("chop_threshold", None, pc_chop_threshold_doc)
+    cf.register_option("max_seq_items", 100, pc_max_seq_items)
+    cf.register_option(
+        "width", 80, pc_width_doc, validator=is_instance_factory([type(None), int])
+    )
+    cf.register_option(
+        "memory_usage",
+        True,
+        pc_memory_usage_doc,
+        validator=is_one_of_factory([None, True, False, "deep"]),
+    )
+    cf.register_option(
+        "unicode.east_asian_width", False, pc_east_asian_width_doc, validator=is_bool
+    )
+    cf.register_option(
+        "unicode.ambiguous_as_wide", False, pc_east_asian_width_doc, validator=is_bool
+    )
+    cf.register_option(
+        "html.table_schema",
+        False,
+        pc_table_schema_doc,
+        validator=is_bool,
+        cb=table_schema_cb,
+    )
+    cf.register_option("html.border", 1, pc_html_border_doc, validator=is_int)
+    cf.register_option(
+        "html.use_mathjax", True, pc_html_use_mathjax_doc, validator=is_bool
+    )
+    cf.register_option(
+        "max_dir_items", 100, pc_max_dir_items, validator=is_nonnegative_int
+    )
+
+tc_sim_interactive_doc = """
+: boolean
+    Whether to simulate interactive mode for purposes of testing
+"""
+
+with cf.config_prefix("mode"):
+    cf.register_option("sim_interactive", False, tc_sim_interactive_doc)
+
+use_inf_as_na_doc = """
+: boolean
+    True means treat None, NaN, INF, -INF as NA (old way),
+    False means None and NaN are null, but INF, -INF are not NA
+    (new way).
+
+    This option is deprecated in pandas 2.1.0 and will be removed in 3.0.
+"""
+
+# We don't want to start importing everything at the global context level
+# or we'll hit circular deps.
+
+
+def use_inf_as_na_cb(key) -> None:
+    # TODO(3.0): enforcing this deprecation will close GH#52501
+    from pandas.core.dtypes.missing import _use_inf_as_na
+
+    _use_inf_as_na(key)
+
+
+with cf.config_prefix("mode"):
+    cf.register_option("use_inf_as_na", False, use_inf_as_na_doc, cb=use_inf_as_na_cb)
+
+cf.deprecate_option(
+    # GH#51684
+    "mode.use_inf_as_na",
+    "use_inf_as_na option is deprecated and will be removed in a future "
+    "version. Convert inf values to NaN before operating instead.",
+)
+
+data_manager_doc = """
+: string
+    Internal data manager type; can be "block" or "array". Defaults to "block",
+    unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs
+    to be set before pandas is imported).
+"""
+
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "data_manager",
+        # Get the default from an environment variable, if set, otherwise defaults
+        # to "block". This environment variable can be set for testing.
+        os.environ.get("PANDAS_DATA_MANAGER", "block"),
+        data_manager_doc,
+        validator=is_one_of_factory(["block", "array"]),
+    )
+
+cf.deprecate_option(
+    # GH#55043
+    "mode.data_manager",
+    "data_manager option is deprecated and will be removed in a future "
+    "version. Only the BlockManager will be available.",
+)
+
+
+# TODO better name?
+copy_on_write_doc = """
+: bool
+    Use new copy-view behaviour using Copy-on-Write. Defaults to False,
+    unless overridden by the 'PANDAS_COPY_ON_WRITE' environment variable
+    (if set to "1" for True, needs to be set before pandas is imported).
+"""
+
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "copy_on_write",
+        # Get the default from an environment variable, if set, otherwise defaults
+        # to False. This environment variable can be set for testing.
+        "warn"
+        if os.environ.get("PANDAS_COPY_ON_WRITE", "0") == "warn"
+        else os.environ.get("PANDAS_COPY_ON_WRITE", "0") == "1",
+        copy_on_write_doc,
+        validator=is_one_of_factory([True, False, "warn"]),
+    )
+
+
+# user warnings
+chained_assignment = """
+: string
+    Raise an exception, warn, or no action if trying to use chained assignment,
+    The default is warn
+"""
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "chained_assignment",
+        "warn",
+        chained_assignment,
+        validator=is_one_of_factory([None, "warn", "raise"]),
+    )
+
+
+string_storage_doc = """
+: string
+    The default storage for StringDtype.
+"""
+
+
+def is_valid_string_storage(value: Any) -> None:
+    legal_values = ["auto", "python", "pyarrow"]
+    if value not in legal_values:
+        msg = "Value must be one of python|pyarrow"
+        if value == "pyarrow_numpy":
+            # TODO: we can remove extra message after 3.0
+            msg += (
+                ". 'pyarrow_numpy' was specified, but this option should be "
+                "enabled using pandas.options.future.infer_string instead"
+            )
+        raise ValueError(msg)
+
+
+with cf.config_prefix("mode"):
+    cf.register_option(
+        "string_storage",
+        "auto",
+        string_storage_doc,
+        # validator=is_one_of_factory(["python", "pyarrow"]),
+        validator=is_valid_string_storage,
+    )
+
+
+# Set up the io.excel specific reader configuration.
+reader_engine_doc = """
+: string
+    The default Excel reader engine for '{ext}' files. Available options:
+    auto, {others}.
+"""
+
+_xls_options = ["xlrd", "calamine"]
+_xlsm_options = ["xlrd", "openpyxl", "calamine"]
+_xlsx_options = ["xlrd", "openpyxl", "calamine"]
+_ods_options = ["odf", "calamine"]
+_xlsb_options = ["pyxlsb", "calamine"]
+
+
+with cf.config_prefix("io.excel.xls"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
+        validator=is_one_of_factory(_xls_options + ["auto"]),
+    )
+
+with cf.config_prefix("io.excel.xlsm"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
+        validator=is_one_of_factory(_xlsm_options + ["auto"]),
+    )
+
+
+with cf.config_prefix("io.excel.xlsx"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
+        validator=is_one_of_factory(_xlsx_options + ["auto"]),
+    )
+
+
+with cf.config_prefix("io.excel.ods"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
+        validator=is_one_of_factory(_ods_options + ["auto"]),
+    )
+
+with cf.config_prefix("io.excel.xlsb"):
+    cf.register_option(
+        "reader",
+        "auto",
+        reader_engine_doc.format(ext="xlsb", others=", ".join(_xlsb_options)),
+        validator=is_one_of_factory(_xlsb_options + ["auto"]),
+    )
+
+# Set up the io.excel specific writer configuration.
+writer_engine_doc = """
+: string
+    The default Excel writer engine for '{ext}' files. Available options:
+    auto, {others}.
+"""
+
+_xlsm_options = ["openpyxl"]
+_xlsx_options = ["openpyxl", "xlsxwriter"]
+_ods_options = ["odf"]
+
+
+with cf.config_prefix("io.excel.xlsm"):
+    cf.register_option(
+        "writer",
+        "auto",
+        writer_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
+        validator=str,
+    )
+
+
+with cf.config_prefix("io.excel.xlsx"):
+    cf.register_option(
+        "writer",
+        "auto",
+        writer_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
+        validator=str,
+    )
+
+
+with cf.config_prefix("io.excel.ods"):
+    cf.register_option(
+        "writer",
+        "auto",
+        writer_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
+        validator=str,
+    )
+
+
+# Set up the io.parquet specific configuration.
+parquet_engine_doc = """
+: string
+    The default parquet reader/writer engine. Available options:
+    'auto', 'pyarrow', 'fastparquet', the default is 'auto'
+"""
+
+with cf.config_prefix("io.parquet"):
+    cf.register_option(
+        "engine",
+        "auto",
+        parquet_engine_doc,
+        validator=is_one_of_factory(["auto", "pyarrow", "fastparquet"]),
+    )
+
+
+# Set up the io.sql specific configuration.
+sql_engine_doc = """
+: string
+    The default sql reader/writer engine. Available options:
+    'auto', 'sqlalchemy', the default is 'auto'
+"""
+
+with cf.config_prefix("io.sql"):
+    cf.register_option(
+        "engine",
+        "auto",
+        sql_engine_doc,
+        validator=is_one_of_factory(["auto", "sqlalchemy"]),
+    )
+
+# --------
+# Plotting
+# ---------
+
+plotting_backend_doc = """
+: str
+    The plotting backend to use. The default value is "matplotlib", the
+    backend provided with pandas. Other backends can be specified by
+    providing the name of the module that implements the backend.
+"""
+
+
+def register_plotting_backend_cb(key) -> None:
+    if key == "matplotlib":
+        # We defer matplotlib validation, since it's the default
+        return
+    from pandas.plotting._core import _get_plot_backend
+
+    _get_plot_backend(key)
+
+
+with cf.config_prefix("plotting"):
+    cf.register_option(
+        "backend",
+        defval="matplotlib",
+        doc=plotting_backend_doc,
+        validator=register_plotting_backend_cb,
+    )
+
+
+register_converter_doc = """
+: bool or 'auto'.
+    Whether to register converters with matplotlib's units registry for
+    dates, times, datetimes, and Periods. Toggling to False will remove
+    the converters, restoring any converters that pandas overwrote.
+"""
+
+
+def register_converter_cb(key) -> None:
+    from pandas.plotting import (
+        deregister_matplotlib_converters,
+        register_matplotlib_converters,
+    )
+
+    if cf.get_option(key):
+        register_matplotlib_converters()
+    else:
+        deregister_matplotlib_converters()
+
+
+with cf.config_prefix("plotting.matplotlib"):
+    cf.register_option(
+        "register_converters",
+        "auto",
+        register_converter_doc,
+        validator=is_one_of_factory(["auto", True, False]),
+        cb=register_converter_cb,
+    )
+
+# ------
+# Styler
+# ------
+
+styler_sparse_index_doc = """
+: bool
+    Whether to sparsify the display of a hierarchical index. Setting to False will
+    display each explicit level element in a hierarchical key for each row.
+"""
+
+styler_sparse_columns_doc = """
+: bool
+    Whether to sparsify the display of hierarchical columns. Setting to False will
+    display each explicit level element in a hierarchical key for each column.
+"""
+
+styler_render_repr = """
+: str
+    Determine which output to use in Jupyter Notebook in {"html", "latex"}.
+"""
+
+styler_max_elements = """
+: int
+    The maximum number of data-cell (<td>) elements that will be rendered before
+    trimming will occur over columns, rows or both if needed.
+"""
+
+styler_max_rows = """
+: int, optional
+    The maximum number of rows that will be rendered. May still be reduced to
+    satisfy ``max_elements``, which takes precedence.
+"""
+
+styler_max_columns = """
+: int, optional
+    The maximum number of columns that will be rendered. May still be reduced to
+    satisfy ``max_elements``, which takes precedence.
+"""
+
+styler_precision = """
+: int
+    The precision for floats and complex numbers.
+"""
+
+styler_decimal = """
+: str
+    The character representation for the decimal separator for floats and complex.
+"""
+
+styler_thousands = """
+: str, optional
+    The character representation for thousands separator for floats, int and complex.
+"""
+
+styler_na_rep = """
+: str, optional
+    The string representation for values identified as missing.
+"""
+
+styler_escape = """
+: str, optional
+    Whether to escape certain characters according to the given context; html or latex.
+"""
+
+styler_formatter = """
+: str, callable, dict, optional
+    A formatter object to be used as default within ``Styler.format``.
+"""
+
+styler_multirow_align = """
+: {"c", "t", "b"}
+    The specifier for vertical alignment of sparsified LaTeX multirows.
+"""
+
+styler_multicol_align = r"""
+: {"r", "c", "l", "naive-l", "naive-r"}
+    The specifier for horizontal alignment of sparsified LaTeX multicolumns. Pipe
+    decorators can also be added to non-naive values to draw vertical
+    rules, e.g. "\|r" will draw a rule on the left side of right aligned merged cells.
+"""
+
+styler_hrules = """
+: bool
+    Whether to add horizontal rules on top and bottom and below the headers.
+"""
+
+styler_environment = """
+: str
+    The environment to replace ``\\begin{table}``. If "longtable" is used results
+    in a specific longtable environment format.
+"""
+
+styler_encoding = """
+: str
+    The encoding used for output HTML and LaTeX files.
+"""
+
+styler_mathjax = """
+: bool
+    If False will render special CSS classes to table attributes that indicate Mathjax
+    will not be used in Jupyter Notebook.
+"""
+
+with cf.config_prefix("styler"):
+    cf.register_option("sparse.index", True, styler_sparse_index_doc, validator=is_bool)
+
+    cf.register_option(
+        "sparse.columns", True, styler_sparse_columns_doc, validator=is_bool
+    )
+
+    cf.register_option(
+        "render.repr",
+        "html",
+        styler_render_repr,
+        validator=is_one_of_factory(["html", "latex"]),
+    )
+
+    cf.register_option(
+        "render.max_elements",
+        2**18,
+        styler_max_elements,
+        validator=is_nonnegative_int,
+    )
+
+    cf.register_option(
+        "render.max_rows",
+        None,
+        styler_max_rows,
+        validator=is_nonnegative_int,
+    )
+
+    cf.register_option(
+        "render.max_columns",
+        None,
+        styler_max_columns,
+        validator=is_nonnegative_int,
+    )
+
+    cf.register_option("render.encoding", "utf-8", styler_encoding, validator=is_str)
+
+    cf.register_option("format.decimal", ".", styler_decimal, validator=is_str)
+
+    cf.register_option(
+        "format.precision", 6, styler_precision, validator=is_nonnegative_int
+    )
+
+    cf.register_option(
+        "format.thousands",
+        None,
+        styler_thousands,
+        validator=is_instance_factory([type(None), str]),
+    )
+
+    cf.register_option(
+        "format.na_rep",
+        None,
+        styler_na_rep,
+        validator=is_instance_factory([type(None), str]),
+    )
+
+    cf.register_option(
+        "format.escape",
+        None,
+        styler_escape,
+        validator=is_one_of_factory([None, "html", "latex", "latex-math"]),
+    )
+
+    cf.register_option(
+        "format.formatter",
+        None,
+        styler_formatter,
+        validator=is_instance_factory([type(None), dict, Callable, str]),
+    )
+
+    cf.register_option("html.mathjax", True, styler_mathjax, validator=is_bool)
+
+    cf.register_option(
+        "latex.multirow_align",
+        "c",
+        styler_multirow_align,
+        validator=is_one_of_factory(["c", "t", "b", "naive"]),
+    )
+
+    val_mca = ["r", "|r|", "|r", "r|", "c", "|c|", "|c", "c|", "l", "|l|", "|l", "l|"]
+    val_mca += ["naive-l", "naive-r"]
+    cf.register_option(
+        "latex.multicol_align",
+        "r",
+        styler_multicol_align,
+        validator=is_one_of_factory(val_mca),
+    )
+
+    cf.register_option("latex.hrules", False, styler_hrules, validator=is_bool)
+
+    cf.register_option(
+        "latex.environment",
+        None,
+        styler_environment,
+        validator=is_instance_factory([type(None), str]),
+    )
+
+
+with cf.config_prefix("future"):
+    cf.register_option(
+        "infer_string",
+        True if os.environ.get("PANDAS_FUTURE_INFER_STRING", "0") == "1" else False,
+        "Whether to infer sequence of str objects as pyarrow string "
+        "dtype, which will be the default in pandas 3.0 "
+        "(at which point this option will be deprecated).",
+        validator=is_one_of_factory([True, False]),
+    )
+
+    cf.register_option(
+        "no_silent_downcasting",
+        False,
+        "Whether to opt-in to the future behavior which will *not* silently "
+        "downcast results from Series and DataFrame `where`, `mask`, and `clip` "
+        "methods. "
+        "Silent downcasting will be removed in pandas 3.0 "
+        "(at which point this option will be deprecated).",
+        validator=is_one_of_factory([True, False]),
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/construction.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..59e87f28a3dce82395abc16d3bb3ef63d155c820
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/construction.py
@@ -0,0 +1,821 @@
+"""
+Constructor functions intended to be shared by pd.array, Series.__init__,
+and Index.__new__.
+
+These should not depend on core.internals.
+"""
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import (
+    TYPE_CHECKING,
+    Optional,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+from numpy import ma
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    Period,
+    get_supported_dtype,
+    is_supported_dtype,
+)
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    Dtype,
+    DtypeObj,
+    T,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    construct_1d_object_array_from_listlike,
+    maybe_cast_to_datetime,
+    maybe_cast_to_integer_array,
+    maybe_convert_platform,
+    maybe_infer_to_datetimelike,
+    maybe_promote,
+)
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import NumpyEADtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from pandas import (
+        Index,
+        Series,
+    )
+    from pandas.core.arrays.base import ExtensionArray
+
+
+def array(
+    data: Sequence[object] | AnyArrayLike,
+    dtype: Dtype | None = None,
+    copy: bool = True,
+) -> ExtensionArray:
+    """
+    Create an array.
+
+    Parameters
+    ----------
+    data : Sequence of objects
+        The scalars inside `data` should be instances of the
+        scalar type for `dtype`. It's expected that `data`
+        represents a 1-dimensional array of data.
+
+        When `data` is an Index or Series, the underlying array
+        will be extracted from `data`.
+
+    dtype : str, np.dtype, or ExtensionDtype, optional
+        The dtype to use for the array. This may be a NumPy
+        dtype or an extension type registered with pandas using
+        :meth:`pandas.api.extensions.register_extension_dtype`.
+
+        If not specified, there are two possibilities:
+
+        1. When `data` is a :class:`Series`, :class:`Index`, or
+           :class:`ExtensionArray`, the `dtype` will be taken
+           from the data.
+        2. Otherwise, pandas will attempt to infer the `dtype`
+           from the data.
+
+        Note that when `data` is a NumPy array, ``data.dtype`` is
+        *not* used for inferring the array type. This is because
+        NumPy cannot represent all the types of data that can be
+        held in extension arrays.
+
+        Currently, pandas will infer an extension dtype for sequences of
+
+        ============================== =======================================
+        Scalar Type                    Array Type
+        ============================== =======================================
+        :class:`pandas.Interval`       :class:`pandas.arrays.IntervalArray`
+        :class:`pandas.Period`         :class:`pandas.arrays.PeriodArray`
+        :class:`datetime.datetime`     :class:`pandas.arrays.DatetimeArray`
+        :class:`datetime.timedelta`    :class:`pandas.arrays.TimedeltaArray`
+        :class:`int`                   :class:`pandas.arrays.IntegerArray`
+        :class:`float`                 :class:`pandas.arrays.FloatingArray`
+        :class:`str`                   :class:`pandas.arrays.StringArray` or
+                                       :class:`pandas.arrays.ArrowStringArray`
+        :class:`bool`                  :class:`pandas.arrays.BooleanArray`
+        ============================== =======================================
+
+        The ExtensionArray created when the scalar type is :class:`str` is determined by
+        ``pd.options.mode.string_storage`` if the dtype is not explicitly given.
+
+        For all other cases, NumPy's usual inference rules will be used.
+    copy : bool, default True
+        Whether to copy the data, even if not necessary. Depending
+        on the type of `data`, creating the new array may require
+        copying data, even if ``copy=False``.
+
+    Returns
+    -------
+    ExtensionArray
+        The newly created array.
+
+    Raises
+    ------
+    ValueError
+        When `data` is not 1-dimensional.
+
+    See Also
+    --------
+    numpy.array : Construct a NumPy array.
+    Series : Construct a pandas Series.
+    Index : Construct a pandas Index.
+    arrays.NumpyExtensionArray : ExtensionArray wrapping a NumPy array.
+    Series.array : Extract the array stored within a Series.
+
+    Notes
+    -----
+    Omitting the `dtype` argument means pandas will attempt to infer the
+    best array type from the values in the data. As new array types are
+    added by pandas and 3rd party libraries, the "best" array type may
+    change. We recommend specifying `dtype` to ensure that
+
+    1. the correct array type for the data is returned
+    2. the returned array type doesn't change as new extension types
+       are added by pandas and third-party libraries
+
+    Additionally, if the underlying memory representation of the returned
+    array matters, we recommend specifying the `dtype` as a concrete object
+    rather than a string alias or allowing it to be inferred. For example,
+    a future version of pandas or a 3rd-party library may include a
+    dedicated ExtensionArray for string data. In this event, the following
+    would no longer return a :class:`arrays.NumpyExtensionArray` backed by a
+    NumPy array.
+
+    >>> pd.array(['a', 'b'], dtype=str)
+    <NumpyExtensionArray>
+    ['a', 'b']
+    Length: 2, dtype: str32
+
+    This would instead return the new ExtensionArray dedicated for string
+    data. If you really need the new array to be backed by a  NumPy array,
+    specify that in the dtype.
+
+    >>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
+    <NumpyExtensionArray>
+    ['a', 'b']
+    Length: 2, dtype: str32
+
+    Finally, Pandas has arrays that mostly overlap with NumPy
+
+      * :class:`arrays.DatetimeArray`
+      * :class:`arrays.TimedeltaArray`
+
+    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
+    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
+    rather than a ``NumpyExtensionArray``. This is for symmetry with the case of
+    timezone-aware data, which NumPy does not natively support.
+
+    >>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
+    <DatetimeArray>
+    ['2015-01-01 00:00:00', '2016-01-01 00:00:00']
+    Length: 2, dtype: datetime64[ns]
+
+    >>> pd.array(["1h", "2h"], dtype='timedelta64[ns]')
+    <TimedeltaArray>
+    ['0 days 01:00:00', '0 days 02:00:00']
+    Length: 2, dtype: timedelta64[ns]
+
+    Examples
+    --------
+    If a dtype is not specified, pandas will infer the best dtype from the values.
+    See the description of `dtype` for the types pandas infers for.
+
+    >>> pd.array([1, 2])
+    <IntegerArray>
+    [1, 2]
+    Length: 2, dtype: Int64
+
+    >>> pd.array([1, 2, np.nan])
+    <IntegerArray>
+    [1, 2, <NA>]
+    Length: 3, dtype: Int64
+
+    >>> pd.array([1.1, 2.2])
+    <FloatingArray>
+    [1.1, 2.2]
+    Length: 2, dtype: Float64
+
+    >>> pd.array(["a", None, "c"])
+    <StringArray>
+    ['a', <NA>, 'c']
+    Length: 3, dtype: string
+
+    >>> with pd.option_context("string_storage", "pyarrow"):
+    ...     arr = pd.array(["a", None, "c"])
+    ...
+    >>> arr
+    <ArrowStringArray>
+    ['a', <NA>, 'c']
+    Length: 3, dtype: string
+
+    >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
+    <PeriodArray>
+    ['2000-01-01', '2000-01-01']
+    Length: 2, dtype: period[D]
+
+    You can use the string alias for `dtype`
+
+    >>> pd.array(['a', 'b', 'a'], dtype='category')
+    ['a', 'b', 'a']
+    Categories (2, object): ['a', 'b']
+
+    Or specify the actual dtype
+
+    >>> pd.array(['a', 'b', 'a'],
+    ...          dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
+    ['a', 'b', 'a']
+    Categories (3, object): ['a' < 'b' < 'c']
+
+    If pandas does not infer a dedicated extension type a
+    :class:`arrays.NumpyExtensionArray` is returned.
+
+    >>> pd.array([1 + 1j, 3 + 2j])
+    <NumpyExtensionArray>
+    [(1+1j), (3+2j)]
+    Length: 2, dtype: complex128
+
+    As mentioned in the "Notes" section, new extension types may be added
+    in the future (by pandas or 3rd party libraries), causing the return
+    value to no longer be a :class:`arrays.NumpyExtensionArray`. Specify the
+    `dtype` as a NumPy dtype if you need to ensure there's no future change in
+    behavior.
+
+    >>> pd.array([1, 2], dtype=np.dtype("int32"))
+    <NumpyExtensionArray>
+    [1, 2]
+    Length: 2, dtype: int32
+
+    `data` must be 1-dimensional. A ValueError is raised when the input
+    has the wrong dimensionality.
+
+    >>> pd.array(1)
+    Traceback (most recent call last):
+      ...
+    ValueError: Cannot pass scalar '1' to 'pandas.array'.
+    """
+    from pandas.core.arrays import (
+        BooleanArray,
+        DatetimeArray,
+        ExtensionArray,
+        FloatingArray,
+        IntegerArray,
+        IntervalArray,
+        NumpyExtensionArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+    from pandas.core.arrays.string_ import StringDtype
+
+    if lib.is_scalar(data):
+        msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
+        raise ValueError(msg)
+    elif isinstance(data, ABCDataFrame):
+        raise TypeError("Cannot pass DataFrame to 'pandas.array'")
+
+    if dtype is None and isinstance(data, (ABCSeries, ABCIndex, ExtensionArray)):
+        # Note: we exclude np.ndarray here, will do type inference on it
+        dtype = data.dtype
+
+    data = extract_array(data, extract_numpy=True)
+
+    # this returns None for not-found dtypes.
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+
+    if isinstance(data, ExtensionArray) and (dtype is None or data.dtype == dtype):
+        # e.g. TimedeltaArray[s], avoid casting to NumpyExtensionArray
+        if copy:
+            return data.copy()
+        return data
+
+    if isinstance(dtype, ExtensionDtype):
+        cls = dtype.construct_array_type()
+        return cls._from_sequence(data, dtype=dtype, copy=copy)
+
+    if dtype is None:
+        inferred_dtype = lib.infer_dtype(data, skipna=True)
+        if inferred_dtype == "period":
+            period_data = cast(Union[Sequence[Optional[Period]], AnyArrayLike], data)
+            return PeriodArray._from_sequence(period_data, copy=copy)
+
+        elif inferred_dtype == "interval":
+            return IntervalArray(data, copy=copy)
+
+        elif inferred_dtype.startswith("datetime"):
+            # datetime, datetime64
+            try:
+                return DatetimeArray._from_sequence(data, copy=copy)
+            except ValueError:
+                # Mixture of timezones, fall back to NumpyExtensionArray
+                pass
+
+        elif inferred_dtype.startswith("timedelta"):
+            # timedelta, timedelta64
+            return TimedeltaArray._from_sequence(data, copy=copy)
+
+        elif inferred_dtype == "string":
+            # StringArray/ArrowStringArray depending on pd.options.mode.string_storage
+            dtype = StringDtype()
+            cls = dtype.construct_array_type()
+            return cls._from_sequence(data, dtype=dtype, copy=copy)
+
+        elif inferred_dtype == "integer":
+            return IntegerArray._from_sequence(data, copy=copy)
+        elif inferred_dtype == "empty" and not hasattr(data, "dtype") and not len(data):
+            return FloatingArray._from_sequence(data, copy=copy)
+        elif (
+            inferred_dtype in ("floating", "mixed-integer-float")
+            and getattr(data, "dtype", None) != np.float16
+        ):
+            # GH#44715 Exclude np.float16 bc FloatingArray does not support it;
+            #  we will fall back to NumpyExtensionArray.
+            return FloatingArray._from_sequence(data, copy=copy)
+
+        elif inferred_dtype == "boolean":
+            return BooleanArray._from_sequence(data, dtype="boolean", copy=copy)
+
+    # Pandas overrides NumPy for
+    #   1. datetime64[ns,us,ms,s]
+    #   2. timedelta64[ns,us,ms,s]
+    # so that a DatetimeArray is returned.
+    if lib.is_np_dtype(dtype, "M") and is_supported_dtype(dtype):
+        return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
+    if lib.is_np_dtype(dtype, "m") and is_supported_dtype(dtype):
+        return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
+
+    elif lib.is_np_dtype(dtype, "mM"):
+        warnings.warn(
+            r"datetime64 and timedelta64 dtype resolutions other than "
+            r"'s', 'ms', 'us', and 'ns' are deprecated. "
+            r"In future releases passing unsupported resolutions will "
+            r"raise an exception.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    return NumpyExtensionArray._from_sequence(data, dtype=dtype, copy=copy)
+
+
+_typs = frozenset(
+    {
+        "index",
+        "rangeindex",
+        "multiindex",
+        "datetimeindex",
+        "timedeltaindex",
+        "periodindex",
+        "categoricalindex",
+        "intervalindex",
+        "series",
+    }
+)
+
+
+@overload
+def extract_array(
+    obj: Series | Index, extract_numpy: bool = ..., extract_range: bool = ...
+) -> ArrayLike:
+    ...
+
+
+@overload
+def extract_array(
+    obj: T, extract_numpy: bool = ..., extract_range: bool = ...
+) -> T | ArrayLike:
+    ...
+
+
+def extract_array(
+    obj: T, extract_numpy: bool = False, extract_range: bool = False
+) -> T | ArrayLike:
+    """
+    Extract the ndarray or ExtensionArray from a Series or Index.
+
+    For all other types, `obj` is just returned as is.
+
+    Parameters
+    ----------
+    obj : object
+        For Series / Index, the underlying ExtensionArray is unboxed.
+
+    extract_numpy : bool, default False
+        Whether to extract the ndarray from a NumpyExtensionArray.
+
+    extract_range : bool, default False
+        If we have a RangeIndex, return range._values if True
+        (which is a materialized integer ndarray), otherwise return unchanged.
+
+    Returns
+    -------
+    arr : object
+
+    Examples
+    --------
+    >>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category'))
+    ['a', 'b', 'c']
+    Categories (3, object): ['a', 'b', 'c']
+
+    Other objects like lists, arrays, and DataFrames are just passed through.
+
+    >>> extract_array([1, 2, 3])
+    [1, 2, 3]
+
+    For an ndarray-backed Series / Index the ndarray is returned.
+
+    >>> extract_array(pd.Series([1, 2, 3]))
+    array([1, 2, 3])
+
+    To extract all the way down to the ndarray, pass ``extract_numpy=True``.
+
+    >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
+    array([1, 2, 3])
+    """
+    typ = getattr(obj, "_typ", None)
+    if typ in _typs:
+        # i.e. isinstance(obj, (ABCIndex, ABCSeries))
+        if typ == "rangeindex":
+            if extract_range:
+                # error: "T" has no attribute "_values"
+                return obj._values  # type: ignore[attr-defined]
+            return obj
+
+        # error: "T" has no attribute "_values"
+        return obj._values  # type: ignore[attr-defined]
+
+    elif extract_numpy and typ == "npy_extension":
+        # i.e. isinstance(obj, ABCNumpyExtensionArray)
+        # error: "T" has no attribute "to_numpy"
+        return obj.to_numpy()  # type: ignore[attr-defined]
+
+    return obj
+
+
+def ensure_wrapped_if_datetimelike(arr):
+    """
+    Wrap datetime64 and timedelta64 ndarrays in DatetimeArray/TimedeltaArray.
+    """
+    if isinstance(arr, np.ndarray):
+        if arr.dtype.kind == "M":
+            from pandas.core.arrays import DatetimeArray
+
+            dtype = get_supported_dtype(arr.dtype)
+            return DatetimeArray._from_sequence(arr, dtype=dtype)
+
+        elif arr.dtype.kind == "m":
+            from pandas.core.arrays import TimedeltaArray
+
+            dtype = get_supported_dtype(arr.dtype)
+            return TimedeltaArray._from_sequence(arr, dtype=dtype)
+
+    return arr
+
+
+def sanitize_masked_array(data: ma.MaskedArray) -> np.ndarray:
+    """
+    Convert numpy MaskedArray to ensure mask is softened.
+    """
+    mask = ma.getmaskarray(data)
+    if mask.any():
+        dtype, fill_value = maybe_promote(data.dtype, np.nan)
+        dtype = cast(np.dtype, dtype)
+        data = ma.asarray(data.astype(dtype, copy=True))
+        data.soften_mask()  # set hardmask False if it was True
+        data[mask] = fill_value
+    else:
+        data = data.copy()
+    return data
+
+
+def sanitize_array(
+    data,
+    index: Index | None,
+    dtype: DtypeObj | None = None,
+    copy: bool = False,
+    *,
+    allow_2d: bool = False,
+) -> ArrayLike:
+    """
+    Sanitize input data to an ndarray or ExtensionArray, copy if specified,
+    coerce to the dtype if specified.
+
+    Parameters
+    ----------
+    data : Any
+    index : Index or None, default None
+    dtype : np.dtype, ExtensionDtype, or None, default None
+    copy : bool, default False
+    allow_2d : bool, default False
+        If False, raise if we have a 2D Arraylike.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    original_dtype = dtype
+    if isinstance(data, ma.MaskedArray):
+        data = sanitize_masked_array(data)
+
+    if isinstance(dtype, NumpyEADtype):
+        # Avoid ending up with a NumpyExtensionArray
+        dtype = dtype.numpy_dtype
+
+    object_index = False
+    if isinstance(data, ABCIndex) and data.dtype == object and dtype is None:
+        object_index = True
+
+    # extract ndarray or ExtensionArray, ensure we have no NumpyExtensionArray
+    data = extract_array(data, extract_numpy=True, extract_range=True)
+
+    if isinstance(data, np.ndarray) and data.ndim == 0:
+        if dtype is None:
+            dtype = data.dtype
+        data = lib.item_from_zerodim(data)
+    elif isinstance(data, range):
+        # GH#16804
+        data = range_to_ndarray(data)
+        copy = False
+
+    if not is_list_like(data):
+        if index is None:
+            raise ValueError("index must be specified when data is not list-like")
+        if isinstance(data, str) and using_string_dtype() and original_dtype is None:
+            from pandas.core.arrays.string_ import StringDtype
+
+            dtype = StringDtype(na_value=np.nan)
+        data = construct_1d_arraylike_from_scalar(data, len(index), dtype)
+
+        return data
+
+    elif isinstance(data, ABCExtensionArray):
+        # it is already ensured above this is not a NumpyExtensionArray
+        # Until GH#49309 is fixed this check needs to come before the
+        #  ExtensionDtype check
+        if dtype is not None:
+            subarr = data.astype(dtype, copy=copy)
+        elif copy:
+            subarr = data.copy()
+        else:
+            subarr = data
+
+    elif isinstance(dtype, ExtensionDtype):
+        # create an extension array from its dtype
+        _sanitize_non_ordered(data)
+        cls = dtype.construct_array_type()
+        if not hasattr(data, "__array__"):
+            data = list(data)
+        subarr = cls._from_sequence(data, dtype=dtype, copy=copy)
+
+    # GH#846
+    elif isinstance(data, np.ndarray):
+        if isinstance(data, np.matrix):
+            data = data.A
+
+        if dtype is None:
+            subarr = data
+            if data.dtype == object:
+                subarr = maybe_infer_to_datetimelike(data)
+                if object_index and using_string_dtype() and is_string_dtype(subarr):
+                    # Avoid inference when string option is set
+                    subarr = data
+            elif data.dtype.kind == "U" and using_string_dtype():
+                from pandas.core.arrays.string_ import StringDtype
+
+                dtype = StringDtype(na_value=np.nan)
+                subarr = dtype.construct_array_type()._from_sequence(data, dtype=dtype)
+
+            if (
+                subarr is data
+                or (subarr.dtype == "str" and subarr.dtype.storage == "python")  # type: ignore[union-attr]
+            ) and copy:
+                subarr = subarr.copy()
+
+        else:
+            # we will try to copy by-definition here
+            subarr = _try_cast(data, dtype, copy)
+
+    elif hasattr(data, "__array__"):
+        # e.g. dask array GH#38645
+        if not copy:
+            data = np.asarray(data)
+        else:
+            data = np.array(data, copy=copy)
+        return sanitize_array(
+            data,
+            index=index,
+            dtype=dtype,
+            copy=False,
+            allow_2d=allow_2d,
+        )
+
+    else:
+        _sanitize_non_ordered(data)
+        # materialize e.g. generators, convert e.g. tuples, abc.ValueView
+        data = list(data)
+
+        if len(data) == 0 and dtype is None:
+            # We default to float64, matching numpy
+            subarr = np.array([], dtype=np.float64)
+
+        elif dtype is not None:
+            subarr = _try_cast(data, dtype, copy)
+
+        else:
+            subarr = maybe_convert_platform(data)
+            if subarr.dtype == object:
+                subarr = cast(np.ndarray, subarr)
+                subarr = maybe_infer_to_datetimelike(subarr)
+
+    subarr = _sanitize_ndim(subarr, data, dtype, index, allow_2d=allow_2d)
+
+    if isinstance(subarr, np.ndarray):
+        # at this point we should have dtype be None or subarr.dtype == dtype
+        dtype = cast(np.dtype, dtype)
+        subarr = _sanitize_str_dtypes(subarr, data, dtype, copy)
+
+    return subarr
+
+
+def range_to_ndarray(rng: range) -> np.ndarray:
+    """
+    Cast a range object to ndarray.
+    """
+    # GH#30171 perf avoid realizing range as a list in np.array
+    try:
+        arr = np.arange(rng.start, rng.stop, rng.step, dtype="int64")
+    except OverflowError:
+        # GH#30173 handling for ranges that overflow int64
+        if (rng.start >= 0 and rng.step > 0) or (rng.step < 0 <= rng.stop):
+            try:
+                arr = np.arange(rng.start, rng.stop, rng.step, dtype="uint64")
+            except OverflowError:
+                arr = construct_1d_object_array_from_listlike(list(rng))
+        else:
+            arr = construct_1d_object_array_from_listlike(list(rng))
+    return arr
+
+
+def _sanitize_non_ordered(data) -> None:
+    """
+    Raise only for unordered sets, e.g., not for dict_keys
+    """
+    if isinstance(data, (set, frozenset)):
+        raise TypeError(f"'{type(data).__name__}' type is unordered")
+
+
+def _sanitize_ndim(
+    result: ArrayLike,
+    data,
+    dtype: DtypeObj | None,
+    index: Index | None,
+    *,
+    allow_2d: bool = False,
+) -> ArrayLike:
+    """
+    Ensure we have a 1-dimensional result array.
+    """
+    if getattr(result, "ndim", 0) == 0:
+        raise ValueError("result should be arraylike with ndim > 0")
+
+    if result.ndim == 1:
+        # the result that we want
+        result = _maybe_repeat(result, index)
+
+    elif result.ndim > 1:
+        if isinstance(data, np.ndarray):
+            if allow_2d:
+                return result
+            raise ValueError(
+                f"Data must be 1-dimensional, got ndarray of shape {data.shape} instead"
+            )
+        if is_object_dtype(dtype) and isinstance(dtype, ExtensionDtype):
+            # i.e. NumpyEADtype("O")
+
+            result = com.asarray_tuplesafe(data, dtype=np.dtype("object"))
+            cls = dtype.construct_array_type()
+            result = cls._from_sequence(result, dtype=dtype)
+        else:
+            # error: Argument "dtype" to "asarray_tuplesafe" has incompatible type
+            # "Union[dtype[Any], ExtensionDtype, None]"; expected "Union[str,
+            # dtype[Any], None]"
+            result = com.asarray_tuplesafe(data, dtype=dtype)  # type: ignore[arg-type]
+    return result
+
+
+def _sanitize_str_dtypes(
+    result: np.ndarray, data, dtype: np.dtype | None, copy: bool
+) -> np.ndarray:
+    """
+    Ensure we have a dtype that is supported by pandas.
+    """
+
+    # This is to prevent mixed-type Series getting all casted to
+    # NumPy string type, e.g. NaN --> '-1#IND'.
+    if issubclass(result.dtype.type, str):
+        # GH#16605
+        # If not empty convert the data to dtype
+        # GH#19853: If data is a scalar, result has already the result
+        if not lib.is_scalar(data):
+            if not np.all(isna(data)):
+                data = np.asarray(data, dtype=dtype)
+            if not copy:
+                result = np.asarray(data, dtype=object)
+            else:
+                result = np.array(data, dtype=object, copy=copy)
+    return result
+
+
+def _maybe_repeat(arr: ArrayLike, index: Index | None) -> ArrayLike:
+    """
+    If we have a length-1 array and an index describing how long we expect
+    the result to be, repeat the array.
+    """
+    if index is not None:
+        if 1 == len(arr) != len(index):
+            arr = arr.repeat(len(index))
+    return arr
+
+
+def _try_cast(
+    arr: list | np.ndarray,
+    dtype: np.dtype,
+    copy: bool,
+) -> ArrayLike:
+    """
+    Convert input to numpy ndarray and optionally cast to a given dtype.
+
+    Parameters
+    ----------
+    arr : ndarray or list
+        Excludes: ExtensionArray, Series, Index.
+    dtype : np.dtype
+    copy : bool
+        If False, don't copy the data if not needed.
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    is_ndarray = isinstance(arr, np.ndarray)
+
+    if dtype == object:
+        if not is_ndarray:
+            subarr = construct_1d_object_array_from_listlike(arr)
+            return subarr
+        return ensure_wrapped_if_datetimelike(arr).astype(dtype, copy=copy)
+
+    elif dtype.kind == "U":
+        # TODO: test cases with arr.dtype.kind in "mM"
+        if is_ndarray:
+            arr = cast(np.ndarray, arr)
+            shape = arr.shape
+            if arr.ndim > 1:
+                arr = arr.ravel()
+        else:
+            shape = (len(arr),)
+        return lib.ensure_string_array(arr, convert_na_value=False, copy=copy).reshape(
+            shape
+        )
+
+    elif dtype.kind in "mM":
+        return maybe_cast_to_datetime(arr, dtype)
+
+    # GH#15832: Check if we are requesting a numeric dtype and
+    # that we can convert the data to the requested dtype.
+    elif dtype.kind in "iu":
+        # this will raise if we have e.g. floats
+
+        subarr = maybe_cast_to_integer_array(arr, dtype)
+    elif not copy:
+        subarr = np.asarray(arr, dtype=dtype)
+    else:
+        subarr = np.array(arr, dtype=dtype, copy=copy)
+
+    return subarr
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..254abe330b8e7229d0c2a27519e82efbe902c537
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/api.py
@@ -0,0 +1,85 @@
+from pandas.core.dtypes.common import (
+    is_any_real_numeric_dtype,
+    is_array_like,
+    is_bool,
+    is_bool_dtype,
+    is_categorical_dtype,
+    is_complex,
+    is_complex_dtype,
+    is_datetime64_any_dtype,
+    is_datetime64_dtype,
+    is_datetime64_ns_dtype,
+    is_datetime64tz_dtype,
+    is_dict_like,
+    is_dtype_equal,
+    is_extension_array_dtype,
+    is_file_like,
+    is_float,
+    is_float_dtype,
+    is_hashable,
+    is_int64_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_interval,
+    is_interval_dtype,
+    is_iterator,
+    is_list_like,
+    is_named_tuple,
+    is_number,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_period_dtype,
+    is_re,
+    is_re_compilable,
+    is_scalar,
+    is_signed_integer_dtype,
+    is_sparse,
+    is_string_dtype,
+    is_timedelta64_dtype,
+    is_timedelta64_ns_dtype,
+    is_unsigned_integer_dtype,
+    pandas_dtype,
+)
+
+__all__ = [
+    "is_any_real_numeric_dtype",
+    "is_array_like",
+    "is_bool",
+    "is_bool_dtype",
+    "is_categorical_dtype",
+    "is_complex",
+    "is_complex_dtype",
+    "is_datetime64_any_dtype",
+    "is_datetime64_dtype",
+    "is_datetime64_ns_dtype",
+    "is_datetime64tz_dtype",
+    "is_dict_like",
+    "is_dtype_equal",
+    "is_extension_array_dtype",
+    "is_file_like",
+    "is_float",
+    "is_float_dtype",
+    "is_hashable",
+    "is_int64_dtype",
+    "is_integer",
+    "is_integer_dtype",
+    "is_interval",
+    "is_interval_dtype",
+    "is_iterator",
+    "is_list_like",
+    "is_named_tuple",
+    "is_number",
+    "is_numeric_dtype",
+    "is_object_dtype",
+    "is_period_dtype",
+    "is_re",
+    "is_re_compilable",
+    "is_scalar",
+    "is_signed_integer_dtype",
+    "is_sparse",
+    "is_string_dtype",
+    "is_timedelta64_dtype",
+    "is_timedelta64_ns_dtype",
+    "is_unsigned_integer_dtype",
+    "pandas_dtype",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/astype.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5579082c679bf131c056f3f2029b2485e88bd0d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/astype.py
@@ -0,0 +1,301 @@
+"""
+Functions for implementing 'astype' methods according to pandas conventions,
+particularly ones that differ from numpy.
+"""
+from __future__ import annotations
+
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs.timedeltas import array_to_timedelta64
+from pandas.errors import IntCastingNaNError
+
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    NumpyEADtype,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        IgnoreRaise,
+    )
+
+    from pandas.core.arrays import ExtensionArray
+
+_dtype_obj = np.dtype(object)
+
+
+@overload
+def _astype_nansafe(
+    arr: np.ndarray, dtype: np.dtype, copy: bool = ..., skipna: bool = ...
+) -> np.ndarray:
+    ...
+
+
+@overload
+def _astype_nansafe(
+    arr: np.ndarray, dtype: ExtensionDtype, copy: bool = ..., skipna: bool = ...
+) -> ExtensionArray:
+    ...
+
+
+def _astype_nansafe(
+    arr: np.ndarray, dtype: DtypeObj, copy: bool = True, skipna: bool = False
+) -> ArrayLike:
+    """
+    Cast the elements of an array to a given dtype a nan-safe manner.
+
+    Parameters
+    ----------
+    arr : ndarray
+    dtype : np.dtype or ExtensionDtype
+    copy : bool, default True
+        If False, a view will be attempted but may fail, if
+        e.g. the item sizes don't align.
+    skipna: bool, default False
+        Whether or not we should skip NaN when casting as a string-type.
+
+    Raises
+    ------
+    ValueError
+        The dtype was a datetime64/timedelta64 dtype, but it had no unit.
+    """
+
+    # dispatch on extension dtype if needed
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
+
+    elif not isinstance(dtype, np.dtype):  # pragma: no cover
+        raise ValueError("dtype must be np.dtype or ExtensionDtype")
+
+    if arr.dtype.kind in "mM":
+        from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+        arr = ensure_wrapped_if_datetimelike(arr)
+        res = arr.astype(dtype, copy=copy)
+        return np.asarray(res)
+
+    if issubclass(dtype.type, str):
+        shape = arr.shape
+        if arr.ndim > 1:
+            arr = arr.ravel()
+        return lib.ensure_string_array(
+            arr, skipna=skipna, convert_na_value=False
+        ).reshape(shape)
+
+    elif np.issubdtype(arr.dtype, np.floating) and dtype.kind in "iu":
+        return _astype_float_to_int_nansafe(arr, dtype, copy)
+
+    elif arr.dtype == object:
+        # if we have a datetime/timedelta array of objects
+        # then coerce to datetime64[ns] and use DatetimeArray.astype
+
+        if lib.is_np_dtype(dtype, "M"):
+            from pandas.core.arrays import DatetimeArray
+
+            dta = DatetimeArray._from_sequence(arr, dtype=dtype)
+            return dta._ndarray
+
+        elif lib.is_np_dtype(dtype, "m"):
+            from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+            # bc we know arr.dtype == object, this is equivalent to
+            #  `np.asarray(to_timedelta(arr))`, but using a lower-level API that
+            #  does not require a circular import.
+            tdvals = array_to_timedelta64(arr).view("m8[ns]")
+
+            tda = ensure_wrapped_if_datetimelike(tdvals)
+            return tda.astype(dtype, copy=False)._ndarray
+
+    if dtype.name in ("datetime64", "timedelta64"):
+        msg = (
+            f"The '{dtype.name}' dtype has no unit. Please pass in "
+            f"'{dtype.name}[ns]' instead."
+        )
+        raise ValueError(msg)
+
+    if copy or arr.dtype == object or dtype == object:
+        # Explicit copy, or required since NumPy can't view from / to object.
+        return arr.astype(dtype, copy=True)
+
+    return arr.astype(dtype, copy=copy)
+
+
+def _astype_float_to_int_nansafe(
+    values: np.ndarray, dtype: np.dtype, copy: bool
+) -> np.ndarray:
+    """
+    astype with a check preventing converting NaN to an meaningless integer value.
+    """
+    if not np.isfinite(values).all():
+        raise IntCastingNaNError(
+            "Cannot convert non-finite values (NA or inf) to integer"
+        )
+    if dtype.kind == "u":
+        # GH#45151
+        if not (values >= 0).all():
+            raise ValueError(f"Cannot losslessly cast from {values.dtype} to {dtype}")
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        return values.astype(dtype, copy=copy)
+
+
+def astype_array(values: ArrayLike, dtype: DtypeObj, copy: bool = False) -> ArrayLike:
+    """
+    Cast array (ndarray or ExtensionArray) to the new dtype.
+
+    Parameters
+    ----------
+    values : ndarray or ExtensionArray
+    dtype : dtype object
+    copy : bool, default False
+        copy if indicated
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    if values.dtype == dtype:
+        if copy:
+            return values.copy()
+        return values
+
+    if not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        values = values.astype(dtype, copy=copy)
+
+    else:
+        values = _astype_nansafe(values, dtype, copy=copy)
+
+    # in pandas we don't store numpy str dtypes, so convert to object
+    if isinstance(dtype, np.dtype) and issubclass(values.dtype.type, str):
+        values = np.array(values, dtype=object)
+
+    return values
+
+
+def astype_array_safe(
+    values: ArrayLike, dtype, copy: bool = False, errors: IgnoreRaise = "raise"
+) -> ArrayLike:
+    """
+    Cast array (ndarray or ExtensionArray) to the new dtype.
+
+    This basically is the implementation for DataFrame/Series.astype and
+    includes all custom logic for pandas (NaN-safety, converting str to object,
+    not allowing )
+
+    Parameters
+    ----------
+    values : ndarray or ExtensionArray
+    dtype : str, dtype convertible
+    copy : bool, default False
+        copy if indicated
+    errors : str, {'raise', 'ignore'}, default 'raise'
+        - ``raise`` : allow exceptions to be raised
+        - ``ignore`` : suppress exceptions. On error return original object
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    errors_legal_values = ("raise", "ignore")
+
+    if errors not in errors_legal_values:
+        invalid_arg = (
+            "Expected value of kwarg 'errors' to be one of "
+            f"{list(errors_legal_values)}. Supplied value is '{errors}'"
+        )
+        raise ValueError(invalid_arg)
+
+    if inspect.isclass(dtype) and issubclass(dtype, ExtensionDtype):
+        msg = (
+            f"Expected an instance of {dtype.__name__}, "
+            "but got the class instead. Try instantiating 'dtype'."
+        )
+        raise TypeError(msg)
+
+    dtype = pandas_dtype(dtype)
+    if isinstance(dtype, NumpyEADtype):
+        # Ensure we don't end up with a NumpyExtensionArray
+        dtype = dtype.numpy_dtype
+
+    try:
+        new_values = astype_array(values, dtype, copy=copy)
+    except (ValueError, TypeError):
+        # e.g. _astype_nansafe can fail on object-dtype of strings
+        #  trying to convert to float
+        if errors == "ignore":
+            new_values = values
+        else:
+            raise
+
+    return new_values
+
+
+def astype_is_view(dtype: DtypeObj, new_dtype: DtypeObj) -> bool:
+    """Checks if astype avoided copying the data.
+
+    Parameters
+    ----------
+    dtype : Original dtype
+    new_dtype : target dtype
+
+    Returns
+    -------
+    True if new data is a view or not guaranteed to be a copy, False otherwise
+    """
+    if isinstance(dtype, np.dtype) and not isinstance(new_dtype, np.dtype):
+        new_dtype, dtype = dtype, new_dtype
+
+    if dtype == new_dtype:
+        return True
+
+    elif isinstance(dtype, np.dtype) and isinstance(new_dtype, np.dtype):
+        # Only equal numpy dtypes avoid a copy
+        return False
+
+    elif is_string_dtype(dtype) and is_string_dtype(new_dtype):
+        # Potentially! a view when converting from object to string
+        return True
+
+    elif is_object_dtype(dtype) and new_dtype.kind == "O":
+        # When the underlying array has dtype object, we don't have to make a copy
+        return True
+
+    elif dtype.kind in "mM" and new_dtype.kind in "mM":
+        dtype = getattr(dtype, "numpy_dtype", dtype)
+        new_dtype = getattr(new_dtype, "numpy_dtype", new_dtype)
+        return getattr(dtype, "unit", None) == getattr(new_dtype, "unit", None)
+
+    numpy_dtype = getattr(dtype, "numpy_dtype", None)
+    new_numpy_dtype = getattr(new_dtype, "numpy_dtype", None)
+
+    if numpy_dtype is None and isinstance(dtype, np.dtype):
+        numpy_dtype = dtype
+
+    if new_numpy_dtype is None and isinstance(new_dtype, np.dtype):
+        new_numpy_dtype = new_dtype
+
+    if numpy_dtype is not None and new_numpy_dtype is not None:
+        # if both have NumPy dtype or one of them is a numpy dtype
+        # they are only a view when the numpy dtypes are equal, e.g.
+        # int64 -> Int64 or int64[pyarrow]
+        # int64 -> Int32 copies
+        return numpy_dtype == new_numpy_dtype
+
+    # Assume this is a view since we don't know for sure if a copy was made
+    return True
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b00a5284ec5b18809e233e9ef89e31771ac651e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/base.py
@@ -0,0 +1,583 @@
+"""
+Extend pandas with custom array types.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import missing as libmissing
+from pandas._libs.hashtable import object_hash
+from pandas._libs.properties import cache_readonly
+from pandas.errors import AbstractMethodError
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        Self,
+        Shape,
+        npt,
+        type_t,
+    )
+
+    from pandas import Index
+    from pandas.core.arrays import ExtensionArray
+
+    # To parameterize on same ExtensionDtype
+    ExtensionDtypeT = TypeVar("ExtensionDtypeT", bound="ExtensionDtype")
+
+
+class ExtensionDtype:
+    """
+    A custom data type, to be paired with an ExtensionArray.
+
+    See Also
+    --------
+    extensions.register_extension_dtype: Register an ExtensionType
+        with pandas as class decorator.
+    extensions.ExtensionArray: Abstract base class for custom 1-D array types.
+
+    Notes
+    -----
+    The interface includes the following abstract methods that must
+    be implemented by subclasses:
+
+    * type
+    * name
+    * construct_array_type
+
+    The following attributes and methods influence the behavior of the dtype in
+    pandas operations
+
+    * _is_numeric
+    * _is_boolean
+    * _get_common_dtype
+
+    The `na_value` class attribute can be used to set the default NA value
+    for this type. :attr:`numpy.nan` is used by default.
+
+    ExtensionDtypes are required to be hashable. The base class provides
+    a default implementation, which relies on the ``_metadata`` class
+    attribute. ``_metadata`` should be a tuple containing the strings
+    that define your data type. For example, with ``PeriodDtype`` that's
+    the ``freq`` attribute.
+
+    **If you have a parametrized dtype you should set the ``_metadata``
+    class property**.
+
+    Ideally, the attributes in ``_metadata`` will match the
+    parameters to your ``ExtensionDtype.__init__`` (if any). If any of
+    the attributes in ``_metadata`` don't implement the standard
+    ``__eq__`` or ``__hash__``, the default implementations here will not
+    work.
+
+    Examples
+    --------
+
+    For interaction with Apache Arrow (pyarrow), a ``__from_arrow__`` method
+    can be implemented: this method receives a pyarrow Array or ChunkedArray
+    as only argument and is expected to return the appropriate pandas
+    ExtensionArray for this dtype and the passed values:
+
+    >>> import pyarrow
+    >>> from pandas.api.extensions import ExtensionArray
+    >>> class ExtensionDtype:
+    ...     def __from_arrow__(
+    ...         self,
+    ...         array: pyarrow.Array | pyarrow.ChunkedArray
+    ...     ) -> ExtensionArray:
+    ...         ...
+
+    This class does not inherit from 'abc.ABCMeta' for performance reasons.
+    Methods and properties required by the interface raise
+    ``pandas.errors.AbstractMethodError`` and no ``register`` method is
+    provided for registering virtual subclasses.
+    """
+
+    _metadata: tuple[str, ...] = ()
+
+    def __str__(self) -> str:
+        return self.name
+
+    def __eq__(self, other: object) -> bool:
+        """
+        Check whether 'other' is equal to self.
+
+        By default, 'other' is considered equal if either
+
+        * it's a string matching 'self.name'.
+        * it's an instance of this type and all of the attributes
+          in ``self._metadata`` are equal between `self` and `other`.
+
+        Parameters
+        ----------
+        other : Any
+
+        Returns
+        -------
+        bool
+        """
+        if isinstance(other, str):
+            try:
+                other = self.construct_from_string(other)
+            except TypeError:
+                return False
+        if isinstance(other, type(self)):
+            return all(
+                getattr(self, attr) == getattr(other, attr) for attr in self._metadata
+            )
+        return False
+
+    def __hash__(self) -> int:
+        # for python>=3.10, different nan objects have different hashes
+        # we need to avoid that and thus use hash function with old behavior
+        return object_hash(tuple(getattr(self, attr) for attr in self._metadata))
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+    @property
+    def na_value(self) -> object:
+        """
+        Default NA value to use for this type.
+
+        This is used in e.g. ExtensionArray.take. This should be the
+        user-facing "boxed" version of the NA value, not the physical NA value
+        for storage.  e.g. for JSONArray, this is an empty dictionary.
+        """
+        return np.nan
+
+    @property
+    def type(self) -> type_t[Any]:
+        """
+        The scalar type for the array, e.g. ``int``
+
+        It's expected ``ExtensionArray[item]`` returns an instance
+        of ``ExtensionDtype.type`` for scalar ``item``, assuming
+        that value is valid (not NA). NA values do not need to be
+        instances of `type`.
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def kind(self) -> str:
+        """
+        A character code (one of 'biufcmMOSUV'), default 'O'
+
+        This should match the NumPy dtype used when the array is
+        converted to an ndarray, which is probably 'O' for object if
+        the extension type cannot be represented as a built-in NumPy
+        type.
+
+        See Also
+        --------
+        numpy.dtype.kind
+        """
+        return "O"
+
+    @property
+    def name(self) -> str:
+        """
+        A string identifying the data type.
+
+        Will be used for display in, e.g. ``Series.dtype``
+        """
+        raise AbstractMethodError(self)
+
+    @property
+    def names(self) -> list[str] | None:
+        """
+        Ordered list of field names, or None if there are no fields.
+
+        This is for compatibility with NumPy arrays, and may be removed in the
+        future.
+        """
+        return None
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[ExtensionArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        raise AbstractMethodError(cls)
+
+    def empty(self, shape: Shape) -> ExtensionArray:
+        """
+        Construct an ExtensionArray of this dtype with the given shape.
+
+        Analogous to numpy.empty.
+
+        Parameters
+        ----------
+        shape : int or tuple[int]
+
+        Returns
+        -------
+        ExtensionArray
+        """
+        cls = self.construct_array_type()
+        return cls._empty(shape, dtype=self)
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> Self:
+        r"""
+        Construct this type from a string.
+
+        This is useful mainly for data types that accept parameters.
+        For example, a period dtype accepts a frequency parameter that
+        can be set as ``period[h]`` (where H means hourly frequency).
+
+        By default, in the abstract class, just the name of the type is
+        expected. But subclasses can overwrite this method to accept
+        parameters.
+
+        Parameters
+        ----------
+        string : str
+            The name of the type, for example ``category``.
+
+        Returns
+        -------
+        ExtensionDtype
+            Instance of the dtype.
+
+        Raises
+        ------
+        TypeError
+            If a class cannot be constructed from this 'string'.
+
+        Examples
+        --------
+        For extension dtypes with arguments the following may be an
+        adequate implementation.
+
+        >>> import re
+        >>> @classmethod
+        ... def construct_from_string(cls, string):
+        ...     pattern = re.compile(r"^my_type\[(?P<arg_name>.+)\]$")
+        ...     match = pattern.match(string)
+        ...     if match:
+        ...         return cls(**match.groupdict())
+        ...     else:
+        ...         raise TypeError(
+        ...             f"Cannot construct a '{cls.__name__}' from '{string}'"
+        ...         )
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        # error: Non-overlapping equality check (left operand type: "str", right
+        #  operand type: "Callable[[ExtensionDtype], str]")  [comparison-overlap]
+        assert isinstance(cls.name, str), (cls, type(cls.name))
+        if string != cls.name:
+            raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")
+        return cls()
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        """
+        Check if we match 'dtype'.
+
+        Parameters
+        ----------
+        dtype : object
+            The object to check.
+
+        Returns
+        -------
+        bool
+
+        Notes
+        -----
+        The default implementation is True if
+
+        1. ``cls.construct_from_string(dtype)`` is an instance
+           of ``cls``.
+        2. ``dtype`` is an object and is an instance of ``cls``
+        3. ``dtype`` has a ``dtype`` attribute, and any of the above
+           conditions is true for ``dtype.dtype``.
+        """
+        dtype = getattr(dtype, "dtype", dtype)
+
+        if isinstance(dtype, (ABCSeries, ABCIndex, ABCDataFrame, np.dtype)):
+            # https://github.com/pandas-dev/pandas/issues/22960
+            # avoid passing data to `construct_from_string`. This could
+            # cause a FutureWarning from numpy about failing elementwise
+            # comparison from, e.g., comparing DataFrame == 'category'.
+            return False
+        elif dtype is None:
+            return False
+        elif isinstance(dtype, cls):
+            return True
+        if isinstance(dtype, str):
+            try:
+                return cls.construct_from_string(dtype) is not None
+            except TypeError:
+                return False
+        return False
+
+    @property
+    def _is_numeric(self) -> bool:
+        """
+        Whether columns with this dtype should be considered numeric.
+
+        By default ExtensionDtypes are assumed to be non-numeric.
+        They'll be excluded from operations that exclude non-numeric
+        columns, like (groupby) reductions, plotting, etc.
+        """
+        return False
+
+    @property
+    def _is_boolean(self) -> bool:
+        """
+        Whether this dtype should be considered boolean.
+
+        By default, ExtensionDtypes are assumed to be non-numeric.
+        Setting this to True will affect the behavior of several places,
+        e.g.
+
+        * is_bool
+        * boolean indexing
+
+        Returns
+        -------
+        bool
+        """
+        return False
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        """
+        Return the common dtype, if one exists.
+
+        Used in `find_common_type` implementation. This is for example used
+        to determine the resulting dtype in a concat operation.
+
+        If no common dtype exists, return None (which gives the other dtypes
+        the chance to determine a common dtype). If all dtypes in the list
+        return None, then the common dtype will be "object" dtype (this means
+        it is never needed to return "object" dtype from this method itself).
+
+        Parameters
+        ----------
+        dtypes : list of dtypes
+            The dtypes for which to determine a common dtype. This is a list
+            of np.dtype or ExtensionDtype instances.
+
+        Returns
+        -------
+        Common dtype (np.dtype or ExtensionDtype) or None
+        """
+        if len(set(dtypes)) == 1:
+            # only itself
+            return self
+        else:
+            return None
+
+    @property
+    def _can_hold_na(self) -> bool:
+        """
+        Can arrays of this dtype hold NA values?
+        """
+        return True
+
+    @property
+    def _is_immutable(self) -> bool:
+        """
+        Can arrays with this dtype be modified with __setitem__? If not, return
+        True.
+
+        Immutable arrays are expected to raise TypeError on __setitem__ calls.
+        """
+        return False
+
+    @cache_readonly
+    def index_class(self) -> type_t[Index]:
+        """
+        The Index subclass to return from Index.__new__ when this dtype is
+        encountered.
+        """
+        from pandas import Index
+
+        return Index
+
+    @property
+    def _supports_2d(self) -> bool:
+        """
+        Do ExtensionArrays with this dtype support 2D arrays?
+
+        Historically ExtensionArrays were limited to 1D. By returning True here,
+        authors can indicate that their arrays support 2D instances. This can
+        improve performance in some cases, particularly operations with `axis=1`.
+
+        Arrays that support 2D values should:
+
+            - implement Array.reshape
+            - subclass the Dim2CompatTests in tests.extension.base
+            - _concat_same_type should support `axis` keyword
+            - _reduce and reductions should support `axis` keyword
+        """
+        return False
+
+    @property
+    def _can_fast_transpose(self) -> bool:
+        """
+        Is transposing an array with this dtype zero-copy?
+
+        Only relevant for cases where _supports_2d is True.
+        """
+        return False
+
+
+class StorageExtensionDtype(ExtensionDtype):
+    """ExtensionDtype that may be backed by more than one implementation."""
+
+    name: str
+    _metadata = ("storage",)
+
+    def __init__(self, storage: str | None = None) -> None:
+        self.storage = storage
+
+    def __repr__(self) -> str:
+        return f"{self.name}[{self.storage}]"
+
+    def __str__(self) -> str:
+        return self.name
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str) and other == self.name:
+            return True
+        return super().__eq__(other)
+
+    def __hash__(self) -> int:
+        # custom __eq__ so have to override __hash__
+        return super().__hash__()
+
+    @property
+    def na_value(self) -> libmissing.NAType:
+        return libmissing.NA
+
+
+def register_extension_dtype(cls: type_t[ExtensionDtypeT]) -> type_t[ExtensionDtypeT]:
+    """
+    Register an ExtensionType with pandas as class decorator.
+
+    This enables operations like ``.astype(name)`` for the name
+    of the ExtensionDtype.
+
+    Returns
+    -------
+    callable
+        A class decorator.
+
+    Examples
+    --------
+    >>> from pandas.api.extensions import register_extension_dtype, ExtensionDtype
+    >>> @register_extension_dtype
+    ... class MyExtensionDtype(ExtensionDtype):
+    ...     name = "myextension"
+    """
+    _registry.register(cls)
+    return cls
+
+
+class Registry:
+    """
+    Registry for dtype inference.
+
+    The registry allows one to map a string repr of a extension
+    dtype to an extension dtype. The string alias can be used in several
+    places, including
+
+    * Series and Index constructors
+    * :meth:`pandas.array`
+    * :meth:`pandas.Series.astype`
+
+    Multiple extension types can be registered.
+    These are tried in order.
+    """
+
+    def __init__(self) -> None:
+        self.dtypes: list[type_t[ExtensionDtype]] = []
+
+    def register(self, dtype: type_t[ExtensionDtype]) -> None:
+        """
+        Parameters
+        ----------
+        dtype : ExtensionDtype class
+        """
+        if not issubclass(dtype, ExtensionDtype):
+            raise ValueError("can only register pandas extension dtypes")
+
+        self.dtypes.append(dtype)
+
+    @overload
+    def find(self, dtype: type_t[ExtensionDtypeT]) -> type_t[ExtensionDtypeT]:
+        ...
+
+    @overload
+    def find(self, dtype: ExtensionDtypeT) -> ExtensionDtypeT:
+        ...
+
+    @overload
+    def find(self, dtype: str) -> ExtensionDtype | None:
+        ...
+
+    @overload
+    def find(
+        self, dtype: npt.DTypeLike
+    ) -> type_t[ExtensionDtype] | ExtensionDtype | None:
+        ...
+
+    def find(
+        self, dtype: type_t[ExtensionDtype] | ExtensionDtype | npt.DTypeLike
+    ) -> type_t[ExtensionDtype] | ExtensionDtype | None:
+        """
+        Parameters
+        ----------
+        dtype : ExtensionDtype class or instance or str or numpy dtype or python type
+
+        Returns
+        -------
+        return the first matching dtype, otherwise return None
+        """
+        if not isinstance(dtype, str):
+            dtype_type: type_t
+            if not isinstance(dtype, type):
+                dtype_type = type(dtype)
+            else:
+                dtype_type = dtype
+            if issubclass(dtype_type, ExtensionDtype):
+                # cast needed here as mypy doesn't know we have figured
+                # out it is an ExtensionDtype or type_t[ExtensionDtype]
+                return cast("ExtensionDtype | type_t[ExtensionDtype]", dtype)
+
+            return None
+
+        for dtype_type in self.dtypes:
+            try:
+                return dtype_type.construct_from_string(dtype)
+            except TypeError:
+                pass
+
+        return None
+
+
+_registry = Registry()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/cast.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/cast.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a7cfc0dec84ee09e1948ee5af885ae6170aae31
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/cast.py
@@ -0,0 +1,1990 @@
+"""
+Routines for casting.
+"""
+
+from __future__ import annotations
+
+import datetime as dt
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    Interval,
+    Period,
+    lib,
+)
+from pandas._libs.missing import (
+    NA,
+    NAType,
+    checknull,
+)
+from pandas._libs.tslibs import (
+    NaT,
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+    Timedelta,
+    Timestamp,
+    is_supported_dtype,
+)
+from pandas._libs.tslibs.timedeltas import array_to_timedelta64
+from pandas.errors import (
+    IntCastingNaNError,
+    LossySetitemError,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_int8,
+    ensure_int16,
+    ensure_int32,
+    ensure_int64,
+    ensure_object,
+    ensure_str,
+    is_bool,
+    is_complex,
+    is_float,
+    is_integer,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype as pandas_dtype_func,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    BaseMaskedDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PandasExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_list_like
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+from pandas.io._util import _arrow_dtype_mapping
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Collection,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Dtype,
+        DtypeObj,
+        NumpyIndexT,
+        Scalar,
+        npt,
+    )
+
+    from pandas import Index
+    from pandas.core.arrays import (
+        Categorical,
+        DatetimeArray,
+        ExtensionArray,
+        IntervalArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+
+
+_int8_max = np.iinfo(np.int8).max
+_int16_max = np.iinfo(np.int16).max
+_int32_max = np.iinfo(np.int32).max
+
+_dtype_obj = np.dtype(object)
+
+NumpyArrayT = TypeVar("NumpyArrayT", bound=np.ndarray)
+
+
+def maybe_convert_platform(
+    values: list | tuple | range | np.ndarray | ExtensionArray,
+) -> ArrayLike:
+    """try to do platform conversion, allow ndarray or list here"""
+    arr: ArrayLike
+
+    if isinstance(values, (list, tuple, range)):
+        arr = construct_1d_object_array_from_listlike(values)
+    else:
+        # The caller is responsible for ensuring that we have np.ndarray
+        #  or ExtensionArray here.
+        arr = values
+
+    if arr.dtype == _dtype_obj:
+        arr = cast(np.ndarray, arr)
+        arr = lib.maybe_convert_objects(arr)
+
+    return arr
+
+
+def is_nested_object(obj) -> bool:
+    """
+    return a boolean if we have a nested object, e.g. a Series with 1 or
+    more Series elements
+
+    This may not be necessarily be performant.
+
+    """
+    return bool(
+        isinstance(obj, ABCSeries)
+        and is_object_dtype(obj.dtype)
+        and any(isinstance(v, ABCSeries) for v in obj._values)
+    )
+
+
+def maybe_box_datetimelike(value: Scalar, dtype: Dtype | None = None) -> Scalar:
+    """
+    Cast scalar to Timestamp or Timedelta if scalar is datetime-like
+    and dtype is not object.
+
+    Parameters
+    ----------
+    value : scalar
+    dtype : Dtype, optional
+
+    Returns
+    -------
+    scalar
+    """
+    if dtype == _dtype_obj:
+        pass
+    elif isinstance(value, (np.datetime64, dt.datetime)):
+        value = Timestamp(value)
+    elif isinstance(value, (np.timedelta64, dt.timedelta)):
+        value = Timedelta(value)
+
+    return value
+
+
+def maybe_box_native(value: Scalar | None | NAType) -> Scalar | None | NAType:
+    """
+    If passed a scalar cast the scalar to a python native type.
+
+    Parameters
+    ----------
+    value : scalar or Series
+
+    Returns
+    -------
+    scalar or Series
+    """
+    if is_float(value):
+        value = float(value)
+    elif is_integer(value):
+        value = int(value)
+    elif is_bool(value):
+        value = bool(value)
+    elif isinstance(value, (np.datetime64, np.timedelta64)):
+        value = maybe_box_datetimelike(value)
+    elif value is NA:
+        value = None
+    return value
+
+
+def _maybe_unbox_datetimelike(value: Scalar, dtype: DtypeObj) -> Scalar:
+    """
+    Convert a Timedelta or Timestamp to timedelta64 or datetime64 for setting
+    into a numpy array.  Failing to unbox would risk dropping nanoseconds.
+
+    Notes
+    -----
+    Caller is responsible for checking dtype.kind in "mM"
+    """
+    if is_valid_na_for_dtype(value, dtype):
+        # GH#36541: can't fill array directly with pd.NaT
+        # > np.empty(10, dtype="datetime64[ns]").fill(pd.NaT)
+        # ValueError: cannot convert float NaN to integer
+        value = dtype.type("NaT", "ns")
+    elif isinstance(value, Timestamp):
+        if value.tz is None:
+            value = value.to_datetime64()
+        elif not isinstance(dtype, DatetimeTZDtype):
+            raise TypeError("Cannot unbox tzaware Timestamp to tznaive dtype")
+    elif isinstance(value, Timedelta):
+        value = value.to_timedelta64()
+
+    _disallow_mismatched_datetimelike(value, dtype)
+    return value
+
+
+def _disallow_mismatched_datetimelike(value, dtype: DtypeObj):
+    """
+    numpy allows np.array(dt64values, dtype="timedelta64[ns]") and
+    vice-versa, but we do not want to allow this, so we need to
+    check explicitly
+    """
+    vdtype = getattr(value, "dtype", None)
+    if vdtype is None:
+        return
+    elif (vdtype.kind == "m" and dtype.kind == "M") or (
+        vdtype.kind == "M" and dtype.kind == "m"
+    ):
+        raise TypeError(f"Cannot cast {repr(value)} to {dtype}")
+
+
+@overload
+def maybe_downcast_to_dtype(result: np.ndarray, dtype: str | np.dtype) -> np.ndarray:
+    ...
+
+
+@overload
+def maybe_downcast_to_dtype(result: ExtensionArray, dtype: str | np.dtype) -> ArrayLike:
+    ...
+
+
+def maybe_downcast_to_dtype(result: ArrayLike, dtype: str | np.dtype) -> ArrayLike:
+    """
+    try to cast to the specified dtype (e.g. convert back to bool/int
+    or could be an astype of float64->float32
+    """
+    if isinstance(result, ABCSeries):
+        result = result._values
+    do_round = False
+
+    if isinstance(dtype, str):
+        if dtype == "infer":
+            inferred_type = lib.infer_dtype(result, skipna=False)
+            if inferred_type == "boolean":
+                dtype = "bool"
+            elif inferred_type == "integer":
+                dtype = "int64"
+            elif inferred_type == "datetime64":
+                dtype = "datetime64[ns]"
+            elif inferred_type in ["timedelta", "timedelta64"]:
+                dtype = "timedelta64[ns]"
+
+            # try to upcast here
+            elif inferred_type == "floating":
+                dtype = "int64"
+                if issubclass(result.dtype.type, np.number):
+                    do_round = True
+
+            else:
+                # TODO: complex?  what if result is already non-object?
+                dtype = "object"
+
+        dtype = np.dtype(dtype)
+
+    if not isinstance(dtype, np.dtype):
+        # enforce our signature annotation
+        raise TypeError(dtype)  # pragma: no cover
+
+    converted = maybe_downcast_numeric(result, dtype, do_round)
+    if converted is not result:
+        return converted
+
+    # a datetimelike
+    # GH12821, iNaT is cast to float
+    if dtype.kind in "mM" and result.dtype.kind in "if":
+        result = result.astype(dtype)
+
+    elif dtype.kind == "m" and result.dtype == _dtype_obj:
+        # test_where_downcast_to_td64
+        result = cast(np.ndarray, result)
+        result = array_to_timedelta64(result)
+
+    elif dtype == np.dtype("M8[ns]") and result.dtype == _dtype_obj:
+        result = cast(np.ndarray, result)
+        return np.asarray(maybe_cast_to_datetime(result, dtype=dtype))
+
+    return result
+
+
+@overload
+def maybe_downcast_numeric(
+    result: np.ndarray, dtype: np.dtype, do_round: bool = False
+) -> np.ndarray:
+    ...
+
+
+@overload
+def maybe_downcast_numeric(
+    result: ExtensionArray, dtype: DtypeObj, do_round: bool = False
+) -> ArrayLike:
+    ...
+
+
+def maybe_downcast_numeric(
+    result: ArrayLike, dtype: DtypeObj, do_round: bool = False
+) -> ArrayLike:
+    """
+    Subset of maybe_downcast_to_dtype restricted to numeric dtypes.
+
+    Parameters
+    ----------
+    result : ndarray or ExtensionArray
+    dtype : np.dtype or ExtensionDtype
+    do_round : bool
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    if not isinstance(dtype, np.dtype) or not isinstance(result.dtype, np.dtype):
+        # e.g. SparseDtype has no itemsize attr
+        return result
+
+    def trans(x):
+        if do_round:
+            return x.round()
+        return x
+
+    if dtype.kind == result.dtype.kind:
+        # don't allow upcasts here (except if empty)
+        if result.dtype.itemsize <= dtype.itemsize and result.size:
+            return result
+
+    if dtype.kind in "biu":
+        if not result.size:
+            # if we don't have any elements, just astype it
+            return trans(result).astype(dtype)
+
+        if isinstance(result, np.ndarray):
+            element = result.item(0)
+        else:
+            element = result.iloc[0]
+        if not isinstance(element, (np.integer, np.floating, int, float, bool)):
+            # a comparable, e.g. a Decimal may slip in here
+            return result
+
+        if (
+            issubclass(result.dtype.type, (np.object_, np.number))
+            and notna(result).all()
+        ):
+            new_result = trans(result).astype(dtype)
+            if new_result.dtype.kind == "O" or result.dtype.kind == "O":
+                # np.allclose may raise TypeError on object-dtype
+                if (new_result == result).all():
+                    return new_result
+            else:
+                if np.allclose(new_result, result, rtol=0):
+                    return new_result
+
+    elif (
+        issubclass(dtype.type, np.floating)
+        and result.dtype.kind != "b"
+        and not is_string_dtype(result.dtype)
+    ):
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "overflow encountered in cast", RuntimeWarning
+            )
+            new_result = result.astype(dtype)
+
+        # Adjust tolerances based on floating point size
+        size_tols = {4: 5e-4, 8: 5e-8, 16: 5e-16}
+
+        atol = size_tols.get(new_result.dtype.itemsize, 0.0)
+
+        # Check downcast float values are still equal within 7 digits when
+        # converting from float64 to float32
+        if np.allclose(new_result, result, equal_nan=True, rtol=0.0, atol=atol):
+            return new_result
+
+    elif dtype.kind == result.dtype.kind == "c":
+        new_result = result.astype(dtype)
+
+        if np.array_equal(new_result, result, equal_nan=True):
+            # TODO: use tolerance like we do for float?
+            return new_result
+
+    return result
+
+
+def maybe_upcast_numeric_to_64bit(arr: NumpyIndexT) -> NumpyIndexT:
+    """
+    If array is a int/uint/float bit size lower than 64 bit, upcast it to 64 bit.
+
+    Parameters
+    ----------
+    arr : ndarray or ExtensionArray
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    dtype = arr.dtype
+    if dtype.kind == "i" and dtype != np.int64:
+        return arr.astype(np.int64)
+    elif dtype.kind == "u" and dtype != np.uint64:
+        return arr.astype(np.uint64)
+    elif dtype.kind == "f" and dtype != np.float64:
+        return arr.astype(np.float64)
+    else:
+        return arr
+
+
+def maybe_cast_pointwise_result(
+    result: ArrayLike,
+    dtype: DtypeObj,
+    numeric_only: bool = False,
+    same_dtype: bool = True,
+) -> ArrayLike:
+    """
+    Try casting result of a pointwise operation back to the original dtype if
+    appropriate.
+
+    Parameters
+    ----------
+    result : array-like
+        Result to cast.
+    dtype : np.dtype or ExtensionDtype
+        Input Series from which result was calculated.
+    numeric_only : bool, default False
+        Whether to cast only numerics or datetimes as well.
+    same_dtype : bool, default True
+        Specify dtype when calling _from_sequence
+
+    Returns
+    -------
+    result : array-like
+        result maybe casted to the dtype.
+    """
+
+    if isinstance(dtype, ExtensionDtype):
+        cls = dtype.construct_array_type()
+        if same_dtype:
+            result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
+        else:
+            result = _maybe_cast_to_extension_array(cls, result)
+
+    elif (numeric_only and dtype.kind in "iufcb") or not numeric_only:
+        result = maybe_downcast_to_dtype(result, dtype)
+
+    return result
+
+
+def _maybe_cast_to_extension_array(
+    cls: type[ExtensionArray], obj: ArrayLike, dtype: ExtensionDtype | None = None
+) -> ArrayLike:
+    """
+    Call to `_from_sequence` that returns the object unchanged on Exception.
+
+    Parameters
+    ----------
+    cls : class, subclass of ExtensionArray
+    obj : arraylike
+        Values to pass to cls._from_sequence
+    dtype : ExtensionDtype, optional
+
+    Returns
+    -------
+    ExtensionArray or obj
+    """
+    result: ArrayLike
+
+    if dtype is not None:
+        try:
+            result = cls._from_scalars(obj, dtype=dtype)
+        except (TypeError, ValueError):
+            return obj
+        return result
+
+    try:
+        result = cls._from_sequence(obj, dtype=dtype)
+    except Exception:
+        # We can't predict what downstream EA constructors may raise
+        result = obj
+    return result
+
+
+@overload
+def ensure_dtype_can_hold_na(dtype: np.dtype) -> np.dtype:
+    ...
+
+
+@overload
+def ensure_dtype_can_hold_na(dtype: ExtensionDtype) -> ExtensionDtype:
+    ...
+
+
+def ensure_dtype_can_hold_na(dtype: DtypeObj) -> DtypeObj:
+    """
+    If we have a dtype that cannot hold NA values, find the best match that can.
+    """
+    if isinstance(dtype, ExtensionDtype):
+        if dtype._can_hold_na:
+            return dtype
+        elif isinstance(dtype, IntervalDtype):
+            # TODO(GH#45349): don't special-case IntervalDtype, allow
+            #  overriding instead of returning object below.
+            return IntervalDtype(np.float64, closed=dtype.closed)
+        return _dtype_obj
+    elif dtype.kind == "b":
+        return _dtype_obj
+    elif dtype.kind in "iu":
+        return np.dtype(np.float64)
+    return dtype
+
+
+_canonical_nans = {
+    np.datetime64: np.datetime64("NaT", "ns"),
+    np.timedelta64: np.timedelta64("NaT", "ns"),
+    type(np.nan): np.nan,
+}
+
+
+def maybe_promote(dtype: np.dtype, fill_value=np.nan):
+    """
+    Find the minimal dtype that can hold both the given dtype and fill_value.
+
+    Parameters
+    ----------
+    dtype : np.dtype
+    fill_value : scalar, default np.nan
+
+    Returns
+    -------
+    dtype
+        Upcasted from dtype argument if necessary.
+    fill_value
+        Upcasted from fill_value argument if necessary.
+
+    Raises
+    ------
+    ValueError
+        If fill_value is a non-scalar and dtype is not object.
+    """
+    orig = fill_value
+    orig_is_nat = False
+    if checknull(fill_value):
+        # https://github.com/pandas-dev/pandas/pull/39692#issuecomment-1441051740
+        #  avoid cache misses with NaN/NaT values that are not singletons
+        if fill_value is not NA:
+            try:
+                orig_is_nat = np.isnat(fill_value)
+            except TypeError:
+                pass
+
+        fill_value = _canonical_nans.get(type(fill_value), fill_value)
+
+    # for performance, we are using a cached version of the actual implementation
+    # of the function in _maybe_promote. However, this doesn't always work (in case
+    # of non-hashable arguments), so we fallback to the actual implementation if needed
+    try:
+        # error: Argument 3 to "__call__" of "_lru_cache_wrapper" has incompatible type
+        # "Type[Any]"; expected "Hashable"  [arg-type]
+        dtype, fill_value = _maybe_promote_cached(
+            dtype, fill_value, type(fill_value)  # type: ignore[arg-type]
+        )
+    except TypeError:
+        # if fill_value is not hashable (required for caching)
+        dtype, fill_value = _maybe_promote(dtype, fill_value)
+
+    if (dtype == _dtype_obj and orig is not None) or (
+        orig_is_nat and np.datetime_data(orig)[0] != "ns"
+    ):
+        # GH#51592,53497 restore our potentially non-canonical fill_value
+        fill_value = orig
+    return dtype, fill_value
+
+
+@functools.lru_cache
+def _maybe_promote_cached(dtype, fill_value, fill_value_type):
+    # The cached version of _maybe_promote below
+    # This also use fill_value_type as (unused) argument to use this in the
+    # cache lookup -> to differentiate 1 and True
+    return _maybe_promote(dtype, fill_value)
+
+
+def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
+    # The actual implementation of the function, use `maybe_promote` above for
+    # a cached version.
+    if not is_scalar(fill_value):
+        # with object dtype there is nothing to promote, and the user can
+        #  pass pretty much any weird fill_value they like
+        if dtype != object:
+            # with object dtype there is nothing to promote, and the user can
+            #  pass pretty much any weird fill_value they like
+            raise ValueError("fill_value must be a scalar")
+        dtype = _dtype_obj
+        return dtype, fill_value
+
+    if is_valid_na_for_dtype(fill_value, dtype) and dtype.kind in "iufcmM":
+        dtype = ensure_dtype_can_hold_na(dtype)
+        fv = na_value_for_dtype(dtype)
+        return dtype, fv
+
+    elif isinstance(dtype, CategoricalDtype):
+        if fill_value in dtype.categories or isna(fill_value):
+            return dtype, fill_value
+        else:
+            return object, ensure_object(fill_value)
+
+    elif isna(fill_value):
+        dtype = _dtype_obj
+        if fill_value is None:
+            # but we retain e.g. pd.NA
+            fill_value = np.nan
+        return dtype, fill_value
+
+    # returns tuple of (dtype, fill_value)
+    if issubclass(dtype.type, np.datetime64):
+        inferred, fv = infer_dtype_from_scalar(fill_value)
+        if inferred == dtype:
+            return dtype, fv
+
+        from pandas.core.arrays import DatetimeArray
+
+        dta = DatetimeArray._from_sequence([], dtype="M8[ns]")
+        try:
+            fv = dta._validate_setitem_value(fill_value)
+            return dta.dtype, fv
+        except (ValueError, TypeError):
+            return _dtype_obj, fill_value
+
+    elif issubclass(dtype.type, np.timedelta64):
+        inferred, fv = infer_dtype_from_scalar(fill_value)
+        if inferred == dtype:
+            return dtype, fv
+
+        elif inferred.kind == "m":
+            # different unit, e.g. passed np.timedelta64(24, "h") with dtype=m8[ns]
+            # see if we can losslessly cast it to our dtype
+            unit = np.datetime_data(dtype)[0]
+            try:
+                td = Timedelta(fill_value).as_unit(unit, round_ok=False)
+            except OutOfBoundsTimedelta:
+                return _dtype_obj, fill_value
+            else:
+                return dtype, td.asm8
+
+        return _dtype_obj, fill_value
+
+    elif is_float(fill_value):
+        if issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+        elif issubclass(dtype.type, np.integer):
+            dtype = np.dtype(np.float64)
+
+        elif dtype.kind == "f":
+            mst = np.min_scalar_type(fill_value)
+            if mst > dtype:
+                # e.g. mst is np.float64 and dtype is np.float32
+                dtype = mst
+
+        elif dtype.kind == "c":
+            mst = np.min_scalar_type(fill_value)
+            dtype = np.promote_types(dtype, mst)
+
+    elif is_bool(fill_value):
+        if not issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+    elif is_integer(fill_value):
+        if issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+        elif issubclass(dtype.type, np.integer):
+            if not np_can_cast_scalar(fill_value, dtype):  # type: ignore[arg-type]
+                # upcast to prevent overflow
+                mst = np.min_scalar_type(fill_value)
+                dtype = np.promote_types(dtype, mst)
+                if dtype.kind == "f":
+                    # Case where we disagree with numpy
+                    dtype = np.dtype(np.object_)
+
+    elif is_complex(fill_value):
+        if issubclass(dtype.type, np.bool_):
+            dtype = np.dtype(np.object_)
+
+        elif issubclass(dtype.type, (np.integer, np.floating)):
+            mst = np.min_scalar_type(fill_value)
+            dtype = np.promote_types(dtype, mst)
+
+        elif dtype.kind == "c":
+            mst = np.min_scalar_type(fill_value)
+            if mst > dtype:
+                # e.g. mst is np.complex128 and dtype is np.complex64
+                dtype = mst
+
+    else:
+        dtype = np.dtype(np.object_)
+
+    # in case we have a string that looked like a number
+    if issubclass(dtype.type, (bytes, str)):
+        dtype = np.dtype(np.object_)
+
+    fill_value = _ensure_dtype_type(fill_value, dtype)
+    return dtype, fill_value
+
+
+def _ensure_dtype_type(value, dtype: np.dtype):
+    """
+    Ensure that the given value is an instance of the given dtype.
+
+    e.g. if out dtype is np.complex64_, we should have an instance of that
+    as opposed to a python complex object.
+
+    Parameters
+    ----------
+    value : object
+    dtype : np.dtype
+
+    Returns
+    -------
+    object
+    """
+    # Start with exceptions in which we do _not_ cast to numpy types
+
+    if dtype == _dtype_obj:
+        return value
+
+    # Note: before we get here we have already excluded isna(value)
+    return dtype.type(value)
+
+
+def infer_dtype_from(val) -> tuple[DtypeObj, Any]:
+    """
+    Interpret the dtype from a scalar or array.
+
+    Parameters
+    ----------
+    val : object
+    """
+    if not is_list_like(val):
+        return infer_dtype_from_scalar(val)
+    return infer_dtype_from_array(val)
+
+
+def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
+    """
+    Interpret the dtype from a scalar.
+
+    Parameters
+    ----------
+    val : object
+    """
+    dtype: DtypeObj = _dtype_obj
+
+    # a 1-element ndarray
+    if isinstance(val, np.ndarray):
+        if val.ndim != 0:
+            msg = "invalid ndarray passed to infer_dtype_from_scalar"
+            raise ValueError(msg)
+
+        dtype = val.dtype
+        val = lib.item_from_zerodim(val)
+
+    elif isinstance(val, str):
+        # If we create an empty array using a string to infer
+        # the dtype, NumPy will only allocate one character per entry
+        # so this is kind of bad. Alternately we could use np.repeat
+        # instead of np.empty (but then you still don't want things
+        # coming out as np.str_!
+
+        dtype = _dtype_obj
+        if using_string_dtype():
+            from pandas.core.arrays.string_ import StringDtype
+
+            dtype = StringDtype(na_value=np.nan)
+
+    elif isinstance(val, (np.datetime64, dt.datetime)):
+        try:
+            val = Timestamp(val)
+        except OutOfBoundsDatetime:
+            return _dtype_obj, val
+
+        if val is NaT or val.tz is None:
+            val = val.to_datetime64()
+            dtype = val.dtype
+            # TODO: test with datetime(2920, 10, 1) based on test_replace_dtypes
+        else:
+            dtype = DatetimeTZDtype(unit=val.unit, tz=val.tz)
+
+    elif isinstance(val, (np.timedelta64, dt.timedelta)):
+        try:
+            val = Timedelta(val)
+        except (OutOfBoundsTimedelta, OverflowError):
+            dtype = _dtype_obj
+        else:
+            if val is NaT:
+                val = np.timedelta64("NaT", "ns")
+            else:
+                val = val.asm8
+            dtype = val.dtype
+
+    elif is_bool(val):
+        dtype = np.dtype(np.bool_)
+
+    elif is_integer(val):
+        if isinstance(val, np.integer):
+            dtype = np.dtype(type(val))
+        else:
+            dtype = np.dtype(np.int64)
+
+        try:
+            np.array(val, dtype=dtype)
+        except OverflowError:
+            dtype = np.array(val).dtype
+
+    elif is_float(val):
+        if isinstance(val, np.floating):
+            dtype = np.dtype(type(val))
+        else:
+            dtype = np.dtype(np.float64)
+
+    elif is_complex(val):
+        dtype = np.dtype(np.complex128)
+
+    if isinstance(val, Period):
+        dtype = PeriodDtype(freq=val.freq)
+    elif isinstance(val, Interval):
+        subtype = infer_dtype_from_scalar(val.left)[0]
+        dtype = IntervalDtype(subtype=subtype, closed=val.closed)
+
+    return dtype, val
+
+
+def dict_compat(d: dict[Scalar, Scalar]) -> dict[Scalar, Scalar]:
+    """
+    Convert datetimelike-keyed dicts to a Timestamp-keyed dict.
+
+    Parameters
+    ----------
+    d: dict-like object
+
+    Returns
+    -------
+    dict
+    """
+    return {maybe_box_datetimelike(key): value for key, value in d.items()}
+
+
+def infer_dtype_from_array(arr) -> tuple[DtypeObj, ArrayLike]:
+    """
+    Infer the dtype from an array.
+
+    Parameters
+    ----------
+    arr : array
+
+    Returns
+    -------
+    tuple (pandas-compat dtype, array)
+
+
+    Examples
+    --------
+    >>> np.asarray([1, '1'])
+    array(['1', '1'], dtype='<U21')
+
+    >>> infer_dtype_from_array([1, '1'])
+    (dtype('O'), [1, '1'])
+    """
+    if isinstance(arr, np.ndarray):
+        return arr.dtype, arr
+
+    if not is_list_like(arr):
+        raise TypeError("'arr' must be list-like")
+
+    arr_dtype = getattr(arr, "dtype", None)
+    if isinstance(arr_dtype, ExtensionDtype):
+        return arr.dtype, arr
+
+    elif isinstance(arr, ABCSeries):
+        return arr.dtype, np.asarray(arr)
+
+    # don't force numpy coerce with nan's
+    inferred = lib.infer_dtype(arr, skipna=False)
+    if inferred in ["string", "bytes", "mixed", "mixed-integer"]:
+        return (np.dtype(np.object_), arr)
+
+    arr = np.asarray(arr)
+    return arr.dtype, arr
+
+
+def _maybe_infer_dtype_type(element):
+    """
+    Try to infer an object's dtype, for use in arithmetic ops.
+
+    Uses `element.dtype` if that's available.
+    Objects implementing the iterator protocol are cast to a NumPy array,
+    and from there the array's type is used.
+
+    Parameters
+    ----------
+    element : object
+        Possibly has a `.dtype` attribute, and possibly the iterator
+        protocol.
+
+    Returns
+    -------
+    tipo : type
+
+    Examples
+    --------
+    >>> from collections import namedtuple
+    >>> Foo = namedtuple("Foo", "dtype")
+    >>> _maybe_infer_dtype_type(Foo(np.dtype("i8")))
+    dtype('int64')
+    """
+    tipo = None
+    if hasattr(element, "dtype"):
+        tipo = element.dtype
+    elif is_list_like(element):
+        element = np.asarray(element)
+        tipo = element.dtype
+    return tipo
+
+
+def invalidate_string_dtypes(dtype_set: set[DtypeObj]) -> None:
+    """
+    Change string like dtypes to object for
+    ``DataFrame.select_dtypes()``.
+    """
+    # error: Argument 1 to <set> has incompatible type "Type[generic]"; expected
+    # "Union[dtype[Any], ExtensionDtype, None]"
+    # error: Argument 2 to <set> has incompatible type "Type[generic]"; expected
+    # "Union[dtype[Any], ExtensionDtype, None]"
+    non_string_dtypes = dtype_set - {
+        np.dtype("S").type,  # type: ignore[arg-type]
+        np.dtype("<U").type,  # type: ignore[arg-type]
+    }
+    if non_string_dtypes != dtype_set:
+        raise TypeError(
+            "numpy string dtypes are not allowed, use 'str' or 'object' instead"
+        )
+
+
+def coerce_indexer_dtype(indexer, categories) -> np.ndarray:
+    """coerce the indexer input array to the smallest dtype possible"""
+    length = len(categories)
+    if length < _int8_max:
+        return ensure_int8(indexer)
+    elif length < _int16_max:
+        return ensure_int16(indexer)
+    elif length < _int32_max:
+        return ensure_int32(indexer)
+    return ensure_int64(indexer)
+
+
+def convert_dtypes(
+    input_array: ArrayLike,
+    convert_string: bool = True,
+    convert_integer: bool = True,
+    convert_boolean: bool = True,
+    convert_floating: bool = True,
+    infer_objects: bool = False,
+    dtype_backend: Literal["numpy_nullable", "pyarrow"] = "numpy_nullable",
+) -> DtypeObj:
+    """
+    Convert objects to best possible type, and optionally,
+    to types supporting ``pd.NA``.
+
+    Parameters
+    ----------
+    input_array : ExtensionArray or np.ndarray
+    convert_string : bool, default True
+        Whether object dtypes should be converted to ``StringDtype()``.
+    convert_integer : bool, default True
+        Whether, if possible, conversion can be done to integer extension types.
+    convert_boolean : bool, defaults True
+        Whether object dtypes should be converted to ``BooleanDtypes()``.
+    convert_floating : bool, defaults True
+        Whether, if possible, conversion can be done to floating extension types.
+        If `convert_integer` is also True, preference will be give to integer
+        dtypes if the floats can be faithfully casted to integers.
+    infer_objects : bool, defaults False
+        Whether to also infer objects to float/int if possible. Is only hit if the
+        object array contains pd.NA.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    np.dtype, or ExtensionDtype
+    """
+    from pandas.core.arrays.string_ import StringDtype
+
+    inferred_dtype: str | DtypeObj
+
+    if (
+        convert_string or convert_integer or convert_boolean or convert_floating
+    ) and isinstance(input_array, np.ndarray):
+        if input_array.dtype == object:
+            inferred_dtype = lib.infer_dtype(input_array)
+        else:
+            inferred_dtype = input_array.dtype
+
+        if is_string_dtype(inferred_dtype):
+            if not convert_string or inferred_dtype == "bytes":
+                inferred_dtype = input_array.dtype
+            else:
+                inferred_dtype = pandas_dtype_func("string")
+
+        if convert_integer:
+            target_int_dtype = pandas_dtype_func("Int64")
+
+            if input_array.dtype.kind in "iu":
+                from pandas.core.arrays.integer import NUMPY_INT_TO_DTYPE
+
+                inferred_dtype = NUMPY_INT_TO_DTYPE.get(
+                    input_array.dtype, target_int_dtype
+                )
+            elif input_array.dtype.kind in "fcb":
+                # TODO: de-dup with maybe_cast_to_integer_array?
+                arr = input_array[notna(input_array)]
+                if (arr.astype(int) == arr).all():
+                    inferred_dtype = target_int_dtype
+                else:
+                    inferred_dtype = input_array.dtype
+            elif (
+                infer_objects
+                and input_array.dtype == object
+                and (isinstance(inferred_dtype, str) and inferred_dtype == "integer")
+            ):
+                inferred_dtype = target_int_dtype
+
+        if convert_floating:
+            if input_array.dtype.kind in "fcb":
+                # i.e. numeric but not integer
+                from pandas.core.arrays.floating import NUMPY_FLOAT_TO_DTYPE
+
+                inferred_float_dtype: DtypeObj = NUMPY_FLOAT_TO_DTYPE.get(
+                    input_array.dtype, pandas_dtype_func("Float64")
+                )
+                # if we could also convert to integer, check if all floats
+                # are actually integers
+                if convert_integer:
+                    # TODO: de-dup with maybe_cast_to_integer_array?
+                    arr = input_array[notna(input_array)]
+                    if (arr.astype(int) == arr).all():
+                        inferred_dtype = pandas_dtype_func("Int64")
+                    else:
+                        inferred_dtype = inferred_float_dtype
+                else:
+                    inferred_dtype = inferred_float_dtype
+            elif (
+                infer_objects
+                and input_array.dtype == object
+                and (
+                    isinstance(inferred_dtype, str)
+                    and inferred_dtype == "mixed-integer-float"
+                )
+            ):
+                inferred_dtype = pandas_dtype_func("Float64")
+
+        if convert_boolean:
+            if input_array.dtype.kind == "b":
+                inferred_dtype = pandas_dtype_func("boolean")
+            elif isinstance(inferred_dtype, str) and inferred_dtype == "boolean":
+                inferred_dtype = pandas_dtype_func("boolean")
+
+        if isinstance(inferred_dtype, str):
+            # If we couldn't do anything else, then we retain the dtype
+            inferred_dtype = input_array.dtype
+
+    elif (
+        convert_string
+        and isinstance(input_array.dtype, StringDtype)
+        and input_array.dtype.na_value is np.nan
+    ):
+        inferred_dtype = pandas_dtype_func("string")
+
+    else:
+        inferred_dtype = input_array.dtype
+
+    if dtype_backend == "pyarrow":
+        from pandas.core.arrays.arrow.array import to_pyarrow_type
+
+        assert not isinstance(inferred_dtype, str)
+
+        if (
+            (convert_integer and inferred_dtype.kind in "iu")
+            or (convert_floating and inferred_dtype.kind in "fc")
+            or (convert_boolean and inferred_dtype.kind == "b")
+            or (convert_string and isinstance(inferred_dtype, StringDtype))
+            or (
+                inferred_dtype.kind not in "iufcb"
+                and not isinstance(inferred_dtype, StringDtype)
+            )
+        ):
+            if isinstance(inferred_dtype, PandasExtensionDtype) and not isinstance(
+                inferred_dtype, DatetimeTZDtype
+            ):
+                base_dtype = inferred_dtype.base
+            elif isinstance(inferred_dtype, (BaseMaskedDtype, ArrowDtype)):
+                base_dtype = inferred_dtype.numpy_dtype
+            elif isinstance(inferred_dtype, StringDtype):
+                base_dtype = np.dtype(str)
+            else:
+                base_dtype = inferred_dtype
+            if (
+                base_dtype.kind == "O"  # type: ignore[union-attr]
+                and input_array.size > 0
+                and isna(input_array).all()
+            ):
+                import pyarrow as pa
+
+                pa_type = pa.null()
+            else:
+                pa_type = to_pyarrow_type(base_dtype)
+            if pa_type is not None:
+                inferred_dtype = ArrowDtype(pa_type)
+    elif dtype_backend == "numpy_nullable" and isinstance(inferred_dtype, ArrowDtype):
+        # GH 53648
+        inferred_dtype = _arrow_dtype_mapping()[inferred_dtype.pyarrow_dtype]
+
+    # error: Incompatible return value type (got "Union[str, Union[dtype[Any],
+    # ExtensionDtype]]", expected "Union[dtype[Any], ExtensionDtype]")
+    return inferred_dtype  # type: ignore[return-value]
+
+
+def maybe_infer_to_datetimelike(
+    value: npt.NDArray[np.object_],
+    convert_to_nullable_dtype: bool = False,
+) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray | IntervalArray:
+    """
+    we might have a array (or single object) that is datetime like,
+    and no dtype is passed don't change the value unless we find a
+    datetime/timedelta set
+
+    this is pretty strict in that a datetime/timedelta is REQUIRED
+    in addition to possible nulls/string likes
+
+    Parameters
+    ----------
+    value : np.ndarray[object]
+
+    Returns
+    -------
+    np.ndarray, DatetimeArray, TimedeltaArray, PeriodArray, or IntervalArray
+
+    """
+    if not isinstance(value, np.ndarray) or value.dtype != object:
+        # Caller is responsible for passing only ndarray[object]
+        raise TypeError(type(value))  # pragma: no cover
+    if value.ndim != 1:
+        # Caller is responsible
+        raise ValueError(value.ndim)  # pragma: no cover
+
+    if not len(value):
+        return value
+
+    # error: Incompatible return value type (got "Union[ExtensionArray,
+    # ndarray[Any, Any]]", expected "Union[ndarray[Any, Any], DatetimeArray,
+    # TimedeltaArray, PeriodArray, IntervalArray]")
+    return lib.maybe_convert_objects(  # type: ignore[return-value]
+        value,
+        # Here we do not convert numeric dtypes, as if we wanted that,
+        #  numpy would have done it for us.
+        convert_numeric=False,
+        convert_non_numeric=True,
+        convert_to_nullable_dtype=convert_to_nullable_dtype,
+        dtype_if_all_nat=np.dtype("M8[ns]"),
+    )
+
+
+def maybe_cast_to_datetime(
+    value: np.ndarray | list, dtype: np.dtype
+) -> ExtensionArray | np.ndarray:
+    """
+    try to cast the array/value to a datetimelike dtype, converting float
+    nan to iNaT
+
+    Caller is responsible for handling ExtensionDtype cases and non dt64/td64
+    cases.
+    """
+    from pandas.core.arrays.datetimes import DatetimeArray
+    from pandas.core.arrays.timedeltas import TimedeltaArray
+
+    assert dtype.kind in "mM"
+    if not is_list_like(value):
+        raise TypeError("value must be listlike")
+
+    # TODO: _from_sequence would raise ValueError in cases where
+    #  _ensure_nanosecond_dtype raises TypeError
+    _ensure_nanosecond_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "m"):
+        res = TimedeltaArray._from_sequence(value, dtype=dtype)
+        return res
+    else:
+        try:
+            dta = DatetimeArray._from_sequence(value, dtype=dtype)
+        except ValueError as err:
+            # We can give a Series-specific exception message.
+            if "cannot supply both a tz and a timezone-naive dtype" in str(err):
+                raise ValueError(
+                    "Cannot convert timezone-aware data to "
+                    "timezone-naive dtype. Use "
+                    "pd.Series(values).dt.tz_localize(None) instead."
+                ) from err
+            raise
+
+        return dta
+
+
+def _ensure_nanosecond_dtype(dtype: DtypeObj) -> None:
+    """
+    Convert dtypes with granularity less than nanosecond to nanosecond
+
+    >>> _ensure_nanosecond_dtype(np.dtype("M8[us]"))
+
+    >>> _ensure_nanosecond_dtype(np.dtype("M8[D]"))
+    Traceback (most recent call last):
+        ...
+    TypeError: dtype=datetime64[D] is not supported. Supported resolutions are 's', 'ms', 'us', and 'ns'
+
+    >>> _ensure_nanosecond_dtype(np.dtype("m8[ps]"))
+    Traceback (most recent call last):
+        ...
+    TypeError: dtype=timedelta64[ps] is not supported. Supported resolutions are 's', 'ms', 'us', and 'ns'
+    """  # noqa: E501
+    msg = (
+        f"The '{dtype.name}' dtype has no unit. "
+        f"Please pass in '{dtype.name}[ns]' instead."
+    )
+
+    # unpack e.g. SparseDtype
+    dtype = getattr(dtype, "subtype", dtype)
+
+    if not isinstance(dtype, np.dtype):
+        # i.e. datetime64tz
+        pass
+
+    elif dtype.kind in "mM":
+        if not is_supported_dtype(dtype):
+            # pre-2.0 we would silently swap in nanos for lower-resolutions,
+            #  raise for above-nano resolutions
+            if dtype.name in ["datetime64", "timedelta64"]:
+                raise ValueError(msg)
+            # TODO: ValueError or TypeError? existing test
+            #  test_constructor_generic_timestamp_bad_frequency expects TypeError
+            raise TypeError(
+                f"dtype={dtype} is not supported. Supported resolutions are 's', "
+                "'ms', 'us', and 'ns'"
+            )
+
+
+# TODO: other value-dependent functions to standardize here include
+#  Index._find_common_type_compat
+def find_result_type(left_dtype: DtypeObj, right: Any) -> DtypeObj:
+    """
+    Find the type/dtype for the result of an operation between objects.
+
+    This is similar to find_common_type, but looks at the right object instead
+    of just its dtype. This can be useful in particular when the right
+    object does not have a `dtype`.
+
+    Parameters
+    ----------
+    left_dtype : np.dtype or ExtensionDtype
+    right : Any
+
+    Returns
+    -------
+    np.dtype or ExtensionDtype
+
+    See also
+    --------
+    find_common_type
+    numpy.result_type
+    """
+    new_dtype: DtypeObj
+
+    if (
+        isinstance(left_dtype, np.dtype)
+        and left_dtype.kind in "iuc"
+        and (lib.is_integer(right) or lib.is_float(right))
+    ):
+        # e.g. with int8 dtype and right=512, we want to end up with
+        # np.int16, whereas infer_dtype_from(512) gives np.int64,
+        #  which will make us upcast too far.
+        if lib.is_float(right) and right.is_integer() and left_dtype.kind != "f":
+            right = int(right)
+        # After NEP 50, numpy won't inspect Python scalars
+        # TODO: do we need to recreate numpy's inspection logic for floats too
+        # (this breaks some tests)
+        if isinstance(right, int) and not isinstance(right, np.integer):
+            # This gives an unsigned type by default
+            # (if our number is positive)
+
+            # If our left dtype is signed, we might not want this since
+            # this might give us 1 dtype too big
+            # We should check if the corresponding int dtype (e.g. int64 for uint64)
+            # can hold the number
+            right_dtype = np.min_scalar_type(right)
+            if right == 0:
+                # Special case 0
+                right = left_dtype
+            elif (
+                not np.issubdtype(left_dtype, np.unsignedinteger)
+                and 0 < right <= np.iinfo(right_dtype).max
+            ):
+                # If left dtype isn't unsigned, check if it fits in the signed dtype
+                right = np.dtype(f"i{right_dtype.itemsize}")
+            else:
+                right = right_dtype
+
+        new_dtype = np.result_type(left_dtype, right)
+
+    elif is_valid_na_for_dtype(right, left_dtype):
+        # e.g. IntervalDtype[int] and None/np.nan
+        new_dtype = ensure_dtype_can_hold_na(left_dtype)
+
+    else:
+        dtype, _ = infer_dtype_from(right)
+        new_dtype = find_common_type([left_dtype, dtype])
+
+    return new_dtype
+
+
+def common_dtype_categorical_compat(
+    objs: Sequence[Index | ArrayLike], dtype: DtypeObj
+) -> DtypeObj:
+    """
+    Update the result of find_common_type to account for NAs in a Categorical.
+
+    Parameters
+    ----------
+    objs : list[np.ndarray | ExtensionArray | Index]
+    dtype : np.dtype or ExtensionDtype
+
+    Returns
+    -------
+    np.dtype or ExtensionDtype
+    """
+    # GH#38240
+
+    # TODO: more generally, could do `not can_hold_na(dtype)`
+    if lib.is_np_dtype(dtype, "iu"):
+        for obj in objs:
+            # We don't want to accientally allow e.g. "categorical" str here
+            obj_dtype = getattr(obj, "dtype", None)
+            if isinstance(obj_dtype, CategoricalDtype):
+                if isinstance(obj, ABCIndex):
+                    # This check may already be cached
+                    hasnas = obj.hasnans
+                else:
+                    # Categorical
+                    hasnas = cast("Categorical", obj)._hasna
+
+                if hasnas:
+                    # see test_union_int_categorical_with_nan
+                    dtype = np.dtype(np.float64)
+                    break
+    return dtype
+
+
+def np_find_common_type(*dtypes: np.dtype) -> np.dtype:
+    """
+    np.find_common_type implementation pre-1.25 deprecation using np.result_type
+    https://github.com/pandas-dev/pandas/pull/49569#issuecomment-1308300065
+
+    Parameters
+    ----------
+    dtypes : np.dtypes
+
+    Returns
+    -------
+    np.dtype
+    """
+    try:
+        common_dtype = np.result_type(*dtypes)
+        if common_dtype.kind in "mMSU":
+            # NumPy promotion currently (1.25) misbehaves for for times and strings,
+            # so fall back to object (find_common_dtype did unless there
+            # was only one dtype)
+            common_dtype = np.dtype("O")
+
+    except TypeError:
+        common_dtype = np.dtype("O")
+    return common_dtype
+
+
+@overload
+def find_common_type(types: list[np.dtype]) -> np.dtype:
+    ...
+
+
+@overload
+def find_common_type(types: list[ExtensionDtype]) -> DtypeObj:
+    ...
+
+
+@overload
+def find_common_type(types: list[DtypeObj]) -> DtypeObj:
+    ...
+
+
+def find_common_type(types):
+    """
+    Find a common data type among the given dtypes.
+
+    Parameters
+    ----------
+    types : list of dtypes
+
+    Returns
+    -------
+    pandas extension or numpy dtype
+
+    See Also
+    --------
+    numpy.find_common_type
+
+    """
+    if not types:
+        raise ValueError("no types given")
+
+    first = types[0]
+
+    # workaround for find_common_type([np.dtype('datetime64[ns]')] * 2)
+    # => object
+    if lib.dtypes_all_equal(list(types)):
+        return first
+
+    # get unique types (dict.fromkeys is used as order-preserving set())
+    types = list(dict.fromkeys(types).keys())
+
+    if any(isinstance(t, ExtensionDtype) for t in types):
+        for t in types:
+            if isinstance(t, ExtensionDtype):
+                res = t._get_common_dtype(types)
+                if res is not None:
+                    return res
+        return np.dtype("object")
+
+    # take lowest unit
+    if all(lib.is_np_dtype(t, "M") for t in types):
+        return np.dtype(max(types))
+    if all(lib.is_np_dtype(t, "m") for t in types):
+        return np.dtype(max(types))
+
+    # don't mix bool / int or float or complex
+    # this is different from numpy, which casts bool with float/int as int
+    has_bools = any(t.kind == "b" for t in types)
+    if has_bools:
+        for t in types:
+            if t.kind in "iufc":
+                return np.dtype("object")
+
+    return np_find_common_type(*types)
+
+
+def construct_2d_arraylike_from_scalar(
+    value: Scalar, length: int, width: int, dtype: np.dtype, copy: bool
+) -> np.ndarray:
+    shape = (length, width)
+
+    if dtype.kind in "mM":
+        value = _maybe_box_and_unbox_datetimelike(value, dtype)
+    elif dtype == _dtype_obj:
+        if isinstance(value, (np.timedelta64, np.datetime64)):
+            # calling np.array below would cast to pytimedelta/pydatetime
+            out = np.empty(shape, dtype=object)
+            out.fill(value)
+            return out
+
+    # Attempt to coerce to a numpy array
+    try:
+        if not copy:
+            arr = np.asarray(value, dtype=dtype)
+        else:
+            arr = np.array(value, dtype=dtype, copy=copy)
+    except (ValueError, TypeError) as err:
+        raise TypeError(
+            f"DataFrame constructor called with incompatible data and dtype: {err}"
+        ) from err
+
+    if arr.ndim != 0:
+        raise ValueError("DataFrame constructor not properly called!")
+
+    return np.full(shape, arr)
+
+
+def construct_1d_arraylike_from_scalar(
+    value: Scalar, length: int, dtype: DtypeObj | None
+) -> ArrayLike:
+    """
+    create a np.ndarray / pandas type of specified shape and dtype
+    filled with values
+
+    Parameters
+    ----------
+    value : scalar value
+    length : int
+    dtype : pandas_dtype or np.dtype
+
+    Returns
+    -------
+    np.ndarray / pandas type of length, filled with value
+
+    """
+
+    if dtype is None:
+        try:
+            dtype, value = infer_dtype_from_scalar(value)
+        except OutOfBoundsDatetime:
+            dtype = _dtype_obj
+
+    if isinstance(dtype, ExtensionDtype):
+        cls = dtype.construct_array_type()
+        seq = [] if length == 0 else [value]
+        subarr = cls._from_sequence(seq, dtype=dtype).repeat(length)
+
+    else:
+        if length and dtype.kind in "iu" and isna(value):
+            # coerce if we have nan for an integer dtype
+            dtype = np.dtype("float64")
+        elif lib.is_np_dtype(dtype, "US"):
+            # we need to coerce to object dtype to avoid
+            # to allow numpy to take our string as a scalar value
+            dtype = np.dtype("object")
+            if not isna(value):
+                value = ensure_str(value)
+        elif dtype.kind in "mM":
+            value = _maybe_box_and_unbox_datetimelike(value, dtype)
+
+        subarr = np.empty(length, dtype=dtype)
+        if length:
+            # GH 47391: numpy > 1.24 will raise filling np.nan into int dtypes
+            subarr.fill(value)
+
+    return subarr
+
+
+def _maybe_box_and_unbox_datetimelike(value: Scalar, dtype: DtypeObj):
+    # Caller is responsible for checking dtype.kind in "mM"
+
+    if isinstance(value, dt.datetime):
+        # we dont want to box dt64, in particular datetime64("NaT")
+        value = maybe_box_datetimelike(value, dtype)
+
+    return _maybe_unbox_datetimelike(value, dtype)
+
+
+def construct_1d_object_array_from_listlike(values: Collection) -> np.ndarray:
+    """
+    Transform any list-like object in a 1-dimensional numpy array of object
+    dtype.
+
+    Parameters
+    ----------
+    values : any iterable which has a len()
+
+    Raises
+    ------
+    TypeError
+        * If `values` does not have a len()
+
+    Returns
+    -------
+    1-dimensional numpy array of dtype object
+    """
+    # numpy will try to interpret nested lists as further dimensions in np.array(),
+    # hence explicitly making a 1D array using np.fromiter
+    result = np.empty(len(values), dtype="object")
+    for i, obj in enumerate(values):
+        result[i] = obj
+    return result
+
+
+def maybe_cast_to_integer_array(arr: list | np.ndarray, dtype: np.dtype) -> np.ndarray:
+    """
+    Takes any dtype and returns the casted version, raising for when data is
+    incompatible with integer/unsigned integer dtypes.
+
+    Parameters
+    ----------
+    arr : np.ndarray or list
+        The array to cast.
+    dtype : np.dtype
+        The integer dtype to cast the array to.
+
+    Returns
+    -------
+    ndarray
+        Array of integer or unsigned integer dtype.
+
+    Raises
+    ------
+    OverflowError : the dtype is incompatible with the data
+    ValueError : loss of precision has occurred during casting
+
+    Examples
+    --------
+    If you try to coerce negative values to unsigned integers, it raises:
+
+    >>> pd.Series([-1], dtype="uint64")
+    Traceback (most recent call last):
+        ...
+    OverflowError: Trying to coerce negative values to unsigned integers
+
+    Also, if you try to coerce float values to integers, it raises:
+
+    >>> maybe_cast_to_integer_array([1, 2, 3.5], dtype=np.dtype("int64"))
+    Traceback (most recent call last):
+        ...
+    ValueError: Trying to coerce float values to integers
+    """
+    assert dtype.kind in "iu"
+
+    try:
+        if not isinstance(arr, np.ndarray):
+            with warnings.catch_warnings():
+                # We already disallow dtype=uint w/ negative numbers
+                # (test_constructor_coercion_signed_to_unsigned) so safe to ignore.
+                warnings.filterwarnings(
+                    "ignore",
+                    "NumPy will stop allowing conversion of out-of-bound Python int",
+                    DeprecationWarning,
+                )
+                casted = np.asarray(arr, dtype=dtype)
+        else:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                casted = arr.astype(dtype, copy=False)
+    except OverflowError as err:
+        raise OverflowError(
+            "The elements provided in the data cannot all be "
+            f"casted to the dtype {dtype}"
+        ) from err
+
+    if isinstance(arr, np.ndarray) and arr.dtype == dtype:
+        # avoid expensive array_equal check
+        return casted
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        warnings.filterwarnings(
+            "ignore", "elementwise comparison failed", FutureWarning
+        )
+        if np.array_equal(arr, casted):
+            return casted
+
+    # We do this casting to allow for proper
+    # data and dtype checking.
+    #
+    # We didn't do this earlier because NumPy
+    # doesn't handle `uint64` correctly.
+    arr = np.asarray(arr)
+
+    if np.issubdtype(arr.dtype, str):
+        # TODO(numpy-2.0 min): This case will raise an OverflowError above
+        if (casted.astype(str) == arr).all():
+            return casted
+        raise ValueError(f"string values cannot be losslessly cast to {dtype}")
+
+    if dtype.kind == "u" and (arr < 0).any():
+        # TODO: can this be hit anymore after numpy 2.0?
+        raise OverflowError("Trying to coerce negative values to unsigned integers")
+
+    if arr.dtype.kind == "f":
+        if not np.isfinite(arr).all():
+            raise IntCastingNaNError(
+                "Cannot convert non-finite values (NA or inf) to integer"
+            )
+        raise ValueError("Trying to coerce float values to integers")
+    if arr.dtype == object:
+        raise ValueError("Trying to coerce float values to integers")
+
+    if casted.dtype < arr.dtype:
+        # TODO: Can this path be hit anymore with numpy > 2
+        # GH#41734 e.g. [1, 200, 923442] and dtype="int8" -> overflows
+        raise ValueError(
+            f"Values are too large to be losslessly converted to {dtype}. "
+            f"To cast anyway, use pd.Series(values).astype({dtype})"
+        )
+
+    if arr.dtype.kind in "mM":
+        # test_constructor_maskedarray_nonfloat
+        raise TypeError(
+            f"Constructing a Series or DataFrame from {arr.dtype} values and "
+            f"dtype={dtype} is not supported. Use values.view({dtype}) instead."
+        )
+
+    # No known cases that get here, but raising explicitly to cover our bases.
+    raise ValueError(f"values cannot be losslessly cast to {dtype}")
+
+
+def can_hold_element(arr: ArrayLike, element: Any) -> bool:
+    """
+    Can we do an inplace setitem with this element in an array with this dtype?
+
+    Parameters
+    ----------
+    arr : np.ndarray or ExtensionArray
+    element : Any
+
+    Returns
+    -------
+    bool
+    """
+    dtype = arr.dtype
+    if not isinstance(dtype, np.dtype) or dtype.kind in "mM":
+        if isinstance(dtype, (PeriodDtype, IntervalDtype, DatetimeTZDtype, np.dtype)):
+            # np.dtype here catches datetime64ns and timedelta64ns; we assume
+            #  in this case that we have DatetimeArray/TimedeltaArray
+            arr = cast(
+                "PeriodArray | DatetimeArray | TimedeltaArray | IntervalArray", arr
+            )
+            try:
+                arr._validate_setitem_value(element)
+                return True
+            except (ValueError, TypeError):
+                return False
+
+        if dtype == "string":
+            try:
+                arr._maybe_convert_setitem_value(element)  # type: ignore[union-attr]
+                return True
+            except (ValueError, TypeError):
+                return False
+
+        # This is technically incorrect, but maintains the behavior of
+        # ExtensionBlock._can_hold_element
+        return True
+
+    try:
+        np_can_hold_element(dtype, element)
+        return True
+    except (TypeError, LossySetitemError):
+        return False
+
+
+def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
+    """
+    Raise if we cannot losslessly set this element into an ndarray with this dtype.
+
+    Specifically about places where we disagree with numpy.  i.e. there are
+    cases where numpy will raise in doing the setitem that we do not check
+    for here, e.g. setting str "X" into a numeric ndarray.
+
+    Returns
+    -------
+    Any
+        The element, potentially cast to the dtype.
+
+    Raises
+    ------
+    ValueError : If we cannot losslessly store this element with this dtype.
+    """
+    if dtype == _dtype_obj:
+        return element
+
+    tipo = _maybe_infer_dtype_type(element)
+
+    if dtype.kind in "iu":
+        if isinstance(element, range):
+            if _dtype_can_hold_range(element, dtype):
+                return element
+            raise LossySetitemError
+
+        if is_integer(element) or (is_float(element) and element.is_integer()):
+            # e.g. test_setitem_series_int8 if we have a python int 1
+            #  tipo may be np.int32, despite the fact that it will fit
+            #  in smaller int dtypes.
+            info = np.iinfo(dtype)
+            if info.min <= element <= info.max:
+                return dtype.type(element)
+            raise LossySetitemError
+
+        if tipo is not None:
+            if tipo.kind not in "iu":
+                if isinstance(element, np.ndarray) and element.dtype.kind == "f":
+                    # If all can be losslessly cast to integers, then we can hold them
+                    with np.errstate(invalid="ignore"):
+                        # We check afterwards if cast was losslessly, so no need to show
+                        # the warning
+                        casted = element.astype(dtype)
+                    comp = casted == element
+                    if comp.all():
+                        # Return the casted values bc they can be passed to
+                        #  np.putmask, whereas the raw values cannot.
+                        #  see TestSetitemFloatNDarrayIntoIntegerSeries
+                        return casted
+                    raise LossySetitemError
+
+                elif isinstance(element, ABCExtensionArray) and isinstance(
+                    element.dtype, CategoricalDtype
+                ):
+                    # GH#52927 setting Categorical value into non-EA frame
+                    # TODO: general-case for EAs?
+                    try:
+                        casted = element.astype(dtype)
+                    except (ValueError, TypeError):
+                        raise LossySetitemError
+                    # Check for cases of either
+                    #  a) lossy overflow/rounding or
+                    #  b) semantic changes like dt64->int64
+                    comp = casted == element
+                    if not comp.all():
+                        raise LossySetitemError
+                    return casted
+
+                # Anything other than integer we cannot hold
+                raise LossySetitemError
+            if (
+                dtype.kind == "u"
+                and isinstance(element, np.ndarray)
+                and element.dtype.kind == "i"
+            ):
+                # see test_where_uint64
+                casted = element.astype(dtype)
+                if (casted == element).all():
+                    # TODO: faster to check (element >=0).all()?  potential
+                    #  itemsize issues there?
+                    return casted
+                raise LossySetitemError
+            if dtype.itemsize < tipo.itemsize:
+                raise LossySetitemError
+            if not isinstance(tipo, np.dtype):
+                # i.e. nullable IntegerDtype; we can put this into an ndarray
+                #  losslessly iff it has no NAs
+                arr = element._values if isinstance(element, ABCSeries) else element
+                if arr._hasna:
+                    raise LossySetitemError
+                return element
+
+            return element
+
+        raise LossySetitemError
+
+    if dtype.kind == "f":
+        if lib.is_integer(element) or lib.is_float(element):
+            casted = dtype.type(element)
+            if np.isnan(casted) or casted == element:
+                return casted
+            # otherwise e.g. overflow see TestCoercionFloat32
+            raise LossySetitemError
+
+        if tipo is not None:
+            # TODO: itemsize check?
+            if tipo.kind not in "iuf":
+                # Anything other than float/integer we cannot hold
+                raise LossySetitemError
+            if not isinstance(tipo, np.dtype):
+                # i.e. nullable IntegerDtype or FloatingDtype;
+                #  we can put this into an ndarray losslessly iff it has no NAs
+                if element._hasna:
+                    raise LossySetitemError
+                return element
+            elif tipo.itemsize > dtype.itemsize or tipo.kind != dtype.kind:
+                if isinstance(element, np.ndarray):
+                    # e.g. TestDataFrameIndexingWhere::test_where_alignment
+                    casted = element.astype(dtype)
+                    if np.array_equal(casted, element, equal_nan=True):
+                        return casted
+                    raise LossySetitemError
+
+            return element
+
+        raise LossySetitemError
+
+    if dtype.kind == "c":
+        if lib.is_integer(element) or lib.is_complex(element) or lib.is_float(element):
+            if np.isnan(element):
+                # see test_where_complex GH#6345
+                return dtype.type(element)
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore")
+                casted = dtype.type(element)
+            if casted == element:
+                return casted
+            # otherwise e.g. overflow see test_32878_complex_itemsize
+            raise LossySetitemError
+
+        if tipo is not None:
+            if tipo.kind in "iufc":
+                return element
+            raise LossySetitemError
+        raise LossySetitemError
+
+    if dtype.kind == "b":
+        if tipo is not None:
+            if tipo.kind == "b":
+                if not isinstance(tipo, np.dtype):
+                    # i.e. we have a BooleanArray
+                    if element._hasna:
+                        # i.e. there are pd.NA elements
+                        raise LossySetitemError
+                return element
+            raise LossySetitemError
+        if lib.is_bool(element):
+            return element
+        raise LossySetitemError
+
+    if dtype.kind == "S":
+        # TODO: test tests.frame.methods.test_replace tests get here,
+        #  need more targeted tests.  xref phofl has a PR about this
+        if tipo is not None:
+            if tipo.kind == "S" and tipo.itemsize <= dtype.itemsize:
+                return element
+            raise LossySetitemError
+        if isinstance(element, bytes) and len(element) <= dtype.itemsize:
+            return element
+        raise LossySetitemError
+
+    if dtype.kind == "V":
+        # i.e. np.void, which cannot hold _anything_
+        raise LossySetitemError
+
+    raise NotImplementedError(dtype)
+
+
+def _dtype_can_hold_range(rng: range, dtype: np.dtype) -> bool:
+    """
+    _maybe_infer_dtype_type infers to int64 (and float64 for very large endpoints),
+    but in many cases a range can be held by a smaller integer dtype.
+    Check if this is one of those cases.
+    """
+    if not len(rng):
+        return True
+    return np_can_cast_scalar(rng.start, dtype) and np_can_cast_scalar(rng.stop, dtype)
+
+
+def np_can_cast_scalar(element: Scalar, dtype: np.dtype) -> bool:
+    """
+    np.can_cast pandas-equivalent for pre 2-0 behavior that allowed scalar
+    inference
+
+    Parameters
+    ----------
+    element : Scalar
+    dtype : np.dtype
+
+    Returns
+    -------
+    bool
+    """
+    try:
+        np_can_hold_element(dtype, element)
+        return True
+    except (LossySetitemError, NotImplementedError):
+        return False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/common.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dea15ac0bc2474b222762505b50efc0dbe680e5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/common.py
@@ -0,0 +1,1766 @@
+"""
+Common type operations.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    Interval,
+    Period,
+    algos,
+    lib,
+)
+from pandas._libs.tslibs import conversion
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import _registry as registry
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import ABCIndex
+from pandas.core.dtypes.inference import (
+    is_array_like,
+    is_bool,
+    is_complex,
+    is_dataclass,
+    is_decimal,
+    is_dict_like,
+    is_file_like,
+    is_float,
+    is_hashable,
+    is_integer,
+    is_interval,
+    is_iterator,
+    is_list_like,
+    is_named_tuple,
+    is_nested_list_like,
+    is_number,
+    is_re,
+    is_re_compilable,
+    is_scalar,
+    is_sequence,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+    )
+
+DT64NS_DTYPE = conversion.DT64NS_DTYPE
+TD64NS_DTYPE = conversion.TD64NS_DTYPE
+INT64_DTYPE = np.dtype(np.int64)
+
+# oh the troubles to reduce import time
+_is_scipy_sparse = None
+
+ensure_float64 = algos.ensure_float64
+ensure_int64 = algos.ensure_int64
+ensure_int32 = algos.ensure_int32
+ensure_int16 = algos.ensure_int16
+ensure_int8 = algos.ensure_int8
+ensure_platform_int = algos.ensure_platform_int
+ensure_object = algos.ensure_object
+ensure_uint64 = algos.ensure_uint64
+
+
+def ensure_str(value: bytes | Any) -> str:
+    """
+    Ensure that bytes and non-strings get converted into ``str`` objects.
+    """
+    if isinstance(value, bytes):
+        value = value.decode("utf-8")
+    elif not isinstance(value, str):
+        value = str(value)
+    return value
+
+
+def ensure_python_int(value: int | np.integer) -> int:
+    """
+    Ensure that a value is a python int.
+
+    Parameters
+    ----------
+    value: int or numpy.integer
+
+    Returns
+    -------
+    int
+
+    Raises
+    ------
+    TypeError: if the value isn't an int or can't be converted to one.
+    """
+    if not (is_integer(value) or is_float(value)):
+        if not is_scalar(value):
+            raise TypeError(
+                f"Value needs to be a scalar value, was type {type(value).__name__}"
+            )
+        raise TypeError(f"Wrong type {type(value)} for value {value}")
+    try:
+        new_value = int(value)
+        assert new_value == value
+    except (TypeError, ValueError, AssertionError) as err:
+        raise TypeError(f"Wrong type {type(value)} for value {value}") from err
+    return new_value
+
+
+def classes(*klasses) -> Callable:
+    """Evaluate if the tipo is a subclass of the klasses."""
+    return lambda tipo: issubclass(tipo, klasses)
+
+
+def _classes_and_not_datetimelike(*klasses) -> Callable:
+    """
+    Evaluate if the tipo is a subclass of the klasses
+    and not a datetimelike.
+    """
+    return lambda tipo: (
+        issubclass(tipo, klasses)
+        and not issubclass(tipo, (np.datetime64, np.timedelta64))
+    )
+
+
+def is_object_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the object dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the object dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_object_dtype
+    >>> is_object_dtype(object)
+    True
+    >>> is_object_dtype(int)
+    False
+    >>> is_object_dtype(np.array([], dtype=object))
+    True
+    >>> is_object_dtype(np.array([], dtype=int))
+    False
+    >>> is_object_dtype([1, 2, 3])
+    False
+    """
+    return _is_dtype_type(arr_or_dtype, classes(np.object_))
+
+
+def is_sparse(arr) -> bool:
+    """
+    Check whether an array-like is a 1-D pandas sparse array.
+
+    .. deprecated:: 2.1.0
+        Use isinstance(dtype, pd.SparseDtype) instead.
+
+    Check that the one-dimensional array-like is a pandas sparse array.
+    Returns True if it is a pandas sparse array, not another type of
+    sparse array.
+
+    Parameters
+    ----------
+    arr : array-like
+        Array-like to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like is a pandas sparse array.
+
+    Examples
+    --------
+    Returns `True` if the parameter is a 1-D pandas sparse array.
+
+    >>> from pandas.api.types import is_sparse
+    >>> is_sparse(pd.arrays.SparseArray([0, 0, 1, 0]))
+    True
+    >>> is_sparse(pd.Series(pd.arrays.SparseArray([0, 0, 1, 0])))
+    True
+
+    Returns `False` if the parameter is not sparse.
+
+    >>> is_sparse(np.array([0, 0, 1, 0]))
+    False
+    >>> is_sparse(pd.Series([0, 1, 0, 0]))
+    False
+
+    Returns `False` if the parameter is not a pandas sparse array.
+
+    >>> from scipy.sparse import bsr_matrix
+    >>> is_sparse(bsr_matrix([0, 1, 0, 0]))
+    False
+
+    Returns `False` if the parameter has more than one dimension.
+    """
+    warnings.warn(
+        "is_sparse is deprecated and will be removed in a future "
+        "version. Check `isinstance(dtype, pd.SparseDtype)` instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    dtype = getattr(arr, "dtype", arr)
+    return isinstance(dtype, SparseDtype)
+
+
+def is_scipy_sparse(arr) -> bool:
+    """
+    Check whether an array-like is a scipy.sparse.spmatrix instance.
+
+    Parameters
+    ----------
+    arr : array-like
+        The array-like to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like is a scipy.sparse.spmatrix instance.
+
+    Notes
+    -----
+    If scipy is not installed, this function will always return False.
+
+    Examples
+    --------
+    >>> from scipy.sparse import bsr_matrix
+    >>> is_scipy_sparse(bsr_matrix([1, 2, 3]))
+    True
+    >>> is_scipy_sparse(pd.arrays.SparseArray([1, 2, 3]))
+    False
+    """
+    global _is_scipy_sparse
+
+    if _is_scipy_sparse is None:  # pylint: disable=used-before-assignment
+        try:
+            from scipy.sparse import issparse as _is_scipy_sparse
+        except ImportError:
+            _is_scipy_sparse = lambda _: False
+
+    assert _is_scipy_sparse is not None
+    return _is_scipy_sparse(arr)
+
+
+def is_datetime64_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the datetime64 dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the datetime64 dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64_dtype
+    >>> is_datetime64_dtype(object)
+    False
+    >>> is_datetime64_dtype(np.datetime64)
+    True
+    >>> is_datetime64_dtype(np.array([], dtype=int))
+    False
+    >>> is_datetime64_dtype(np.array([], dtype=np.datetime64))
+    True
+    >>> is_datetime64_dtype([1, 2, 3])
+    False
+    """
+    if isinstance(arr_or_dtype, np.dtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.kind == "M"
+    return _is_dtype_type(arr_or_dtype, classes(np.datetime64))
+
+
+def is_datetime64tz_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
+
+    .. deprecated:: 2.1.0
+        Use isinstance(dtype, pd.DatetimeTZDtype) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of a DatetimeTZDtype dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64tz_dtype
+    >>> is_datetime64tz_dtype(object)
+    False
+    >>> is_datetime64tz_dtype([1, 2, 3])
+    False
+    >>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3]))  # tz-naive
+    False
+    >>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
+    True
+
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
+    >>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
+    >>> s = pd.Series([], dtype=dtype)
+    >>> is_datetime64tz_dtype(dtype)
+    True
+    >>> is_datetime64tz_dtype(s)
+    True
+    """
+    # GH#52607
+    warnings.warn(
+        "is_datetime64tz_dtype is deprecated and will be removed in a future "
+        "version. Check `isinstance(dtype, pd.DatetimeTZDtype)` instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, DatetimeTZDtype):
+        # GH#33400 fastpath for dtype object
+        # GH 34986
+        return True
+
+    if arr_or_dtype is None:
+        return False
+    return DatetimeTZDtype.is_dtype(arr_or_dtype)
+
+
+def is_timedelta64_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the timedelta64 dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the timedelta64 dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_timedelta64_dtype
+    >>> is_timedelta64_dtype(object)
+    False
+    >>> is_timedelta64_dtype(np.timedelta64)
+    True
+    >>> is_timedelta64_dtype([1, 2, 3])
+    False
+    >>> is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
+    True
+    >>> is_timedelta64_dtype('0 days')
+    False
+    """
+    if isinstance(arr_or_dtype, np.dtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.kind == "m"
+
+    return _is_dtype_type(arr_or_dtype, classes(np.timedelta64))
+
+
+def is_period_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the Period dtype.
+
+    .. deprecated:: 2.2.0
+        Use isinstance(dtype, pd.Period) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the Period dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_period_dtype
+    >>> is_period_dtype(object)
+    False
+    >>> is_period_dtype(pd.PeriodDtype(freq="D"))
+    True
+    >>> is_period_dtype([1, 2, 3])
+    False
+    >>> is_period_dtype(pd.Period("2017-01-01"))
+    False
+    >>> is_period_dtype(pd.PeriodIndex([], freq="Y"))
+    True
+    """
+    warnings.warn(
+        "is_period_dtype is deprecated and will be removed in a future version. "
+        "Use `isinstance(dtype, pd.PeriodDtype)` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, ExtensionDtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.type is Period
+
+    if arr_or_dtype is None:
+        return False
+    return PeriodDtype.is_dtype(arr_or_dtype)
+
+
+def is_interval_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the Interval dtype.
+
+    .. deprecated:: 2.2.0
+        Use isinstance(dtype, pd.IntervalDtype) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the Interval dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_interval_dtype
+    >>> is_interval_dtype(object)
+    False
+    >>> is_interval_dtype(pd.IntervalDtype())
+    True
+    >>> is_interval_dtype([1, 2, 3])
+    False
+    >>>
+    >>> interval = pd.Interval(1, 2, closed="right")
+    >>> is_interval_dtype(interval)
+    False
+    >>> is_interval_dtype(pd.IntervalIndex([interval]))
+    True
+    """
+    # GH#52607
+    warnings.warn(
+        "is_interval_dtype is deprecated and will be removed in a future version. "
+        "Use `isinstance(dtype, pd.IntervalDtype)` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, ExtensionDtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.type is Interval
+
+    if arr_or_dtype is None:
+        return False
+    return IntervalDtype.is_dtype(arr_or_dtype)
+
+
+def is_categorical_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether an array-like or dtype is of the Categorical dtype.
+
+    .. deprecated:: 2.2.0
+        Use isinstance(dtype, pd.CategoricalDtype) instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the Categorical dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_categorical_dtype
+    >>> from pandas import CategoricalDtype
+    >>> is_categorical_dtype(object)
+    False
+    >>> is_categorical_dtype(CategoricalDtype())
+    True
+    >>> is_categorical_dtype([1, 2, 3])
+    False
+    >>> is_categorical_dtype(pd.Categorical([1, 2, 3]))
+    True
+    >>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
+    True
+    """
+    # GH#52527
+    warnings.warn(
+        "is_categorical_dtype is deprecated and will be removed in a future "
+        "version. Use isinstance(dtype, pd.CategoricalDtype) instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if isinstance(arr_or_dtype, ExtensionDtype):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.name == "category"
+
+    if arr_or_dtype is None:
+        return False
+    return CategoricalDtype.is_dtype(arr_or_dtype)
+
+
+def is_string_or_object_np_dtype(dtype: np.dtype) -> bool:
+    """
+    Faster alternative to is_string_dtype, assumes we have a np.dtype object.
+    """
+    return dtype == object or dtype.kind in "SU"
+
+
+def is_string_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the string dtype.
+
+    If an array is passed with an object dtype, the elements must be
+    inferred as strings.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of the string dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_string_dtype
+    >>> is_string_dtype(str)
+    True
+    >>> is_string_dtype(object)
+    True
+    >>> is_string_dtype(int)
+    False
+    >>> is_string_dtype(np.array(['a', 'b']))
+    True
+    >>> is_string_dtype(pd.Series([1, 2]))
+    False
+    >>> is_string_dtype(pd.Series([1, 2], dtype=object))
+    False
+    """
+    if hasattr(arr_or_dtype, "dtype") and _get_dtype(arr_or_dtype).kind == "O":
+        return is_all_strings(arr_or_dtype)
+
+    def condition(dtype) -> bool:
+        if is_string_or_object_np_dtype(dtype):
+            return True
+        try:
+            return dtype == "string"
+        except TypeError:
+            return False
+
+    return _is_dtype(arr_or_dtype, condition)
+
+
+def is_dtype_equal(source, target) -> bool:
+    """
+    Check if two dtypes are equal.
+
+    Parameters
+    ----------
+    source : The first dtype to compare
+    target : The second dtype to compare
+
+    Returns
+    -------
+    boolean
+        Whether or not the two dtypes are equal.
+
+    Examples
+    --------
+    >>> is_dtype_equal(int, float)
+    False
+    >>> is_dtype_equal("int", int)
+    True
+    >>> is_dtype_equal(object, "category")
+    False
+    >>> is_dtype_equal(CategoricalDtype(), "category")
+    True
+    >>> is_dtype_equal(DatetimeTZDtype(tz="UTC"), "datetime64")
+    False
+    """
+    if isinstance(target, str):
+        if not isinstance(source, str):
+            # GH#38516 ensure we get the same behavior from
+            #  is_dtype_equal(CDT, "category") and CDT == "category"
+            try:
+                src = _get_dtype(source)
+                if isinstance(src, ExtensionDtype):
+                    return src == target
+            except (TypeError, AttributeError, ImportError):
+                return False
+    elif isinstance(source, str):
+        return is_dtype_equal(target, source)
+
+    try:
+        source = _get_dtype(source)
+        target = _get_dtype(target)
+        return source == target
+    except (TypeError, AttributeError, ImportError):
+        # invalid comparison
+        # object == category will hit this
+        return False
+
+
+def is_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of an integer dtype.
+
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
+
+    The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
+    as integer by this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of an integer dtype and
+        not an instance of timedelta64.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_integer_dtype
+    >>> is_integer_dtype(str)
+    False
+    >>> is_integer_dtype(int)
+    True
+    >>> is_integer_dtype(float)
+    False
+    >>> is_integer_dtype(np.uint64)
+    True
+    >>> is_integer_dtype('int8')
+    True
+    >>> is_integer_dtype('Int8')
+    True
+    >>> is_integer_dtype(pd.Int8Dtype)
+    True
+    >>> is_integer_dtype(np.datetime64)
+    False
+    >>> is_integer_dtype(np.timedelta64)
+    False
+    >>> is_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_integer_dtype(pd.Series([1, 2]))
+    True
+    >>> is_integer_dtype(np.array([], dtype=np.timedelta64))
+    False
+    >>> is_integer_dtype(pd.Index([1, 2.]))  # float
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.integer)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind in "iu"
+    )
+
+
+def is_signed_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a signed integer dtype.
+
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
+
+    The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
+    as integer by this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a signed integer dtype
+        and not an instance of timedelta64.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_signed_integer_dtype
+    >>> is_signed_integer_dtype(str)
+    False
+    >>> is_signed_integer_dtype(int)
+    True
+    >>> is_signed_integer_dtype(float)
+    False
+    >>> is_signed_integer_dtype(np.uint64)  # unsigned
+    False
+    >>> is_signed_integer_dtype('int8')
+    True
+    >>> is_signed_integer_dtype('Int8')
+    True
+    >>> is_signed_integer_dtype(pd.Int8Dtype)
+    True
+    >>> is_signed_integer_dtype(np.datetime64)
+    False
+    >>> is_signed_integer_dtype(np.timedelta64)
+    False
+    >>> is_signed_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_signed_integer_dtype(pd.Series([1, 2]))
+    True
+    >>> is_signed_integer_dtype(np.array([], dtype=np.timedelta64))
+    False
+    >>> is_signed_integer_dtype(pd.Index([1, 2.]))  # float
+    False
+    >>> is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.signedinteger)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind == "i"
+    )
+
+
+def is_unsigned_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of an unsigned integer dtype.
+
+    The nullable Integer dtypes (e.g. pandas.UInt64Dtype) are also
+    considered as integer by this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of an unsigned integer dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_unsigned_integer_dtype
+    >>> is_unsigned_integer_dtype(str)
+    False
+    >>> is_unsigned_integer_dtype(int)  # signed
+    False
+    >>> is_unsigned_integer_dtype(float)
+    False
+    >>> is_unsigned_integer_dtype(np.uint64)
+    True
+    >>> is_unsigned_integer_dtype('uint8')
+    True
+    >>> is_unsigned_integer_dtype('UInt8')
+    True
+    >>> is_unsigned_integer_dtype(pd.UInt8Dtype)
+    True
+    >>> is_unsigned_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_unsigned_integer_dtype(pd.Series([1, 2]))  # signed
+    False
+    >>> is_unsigned_integer_dtype(pd.Index([1, 2.]))  # float
+    False
+    >>> is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))
+    True
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.unsignedinteger)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind == "u"
+    )
+
+
+def is_int64_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the int64 dtype.
+
+    .. deprecated:: 2.1.0
+
+       is_int64_dtype is deprecated and will be removed in a future
+       version. Use dtype == np.int64 instead.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of the int64 dtype.
+
+    Notes
+    -----
+    Depending on system architecture, the return value of `is_int64_dtype(
+    int)` will be True if the OS uses 64-bit integers and False if the OS
+    uses 32-bit integers.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_int64_dtype
+    >>> is_int64_dtype(str)  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.int32)  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.int64)  # doctest: +SKIP
+    True
+    >>> is_int64_dtype('int8')  # doctest: +SKIP
+    False
+    >>> is_int64_dtype('Int8')  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(pd.Int64Dtype)  # doctest: +SKIP
+    True
+    >>> is_int64_dtype(float)  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.uint64)  # unsigned  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.array(['a', 'b']))  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.array([1, 2], dtype=np.int64))  # doctest: +SKIP
+    True
+    >>> is_int64_dtype(pd.Index([1, 2.]))  # float  # doctest: +SKIP
+    False
+    >>> is_int64_dtype(np.array([1, 2], dtype=np.uint32))  # unsigned  # doctest: +SKIP
+    False
+    """
+    # GH#52564
+    warnings.warn(
+        "is_int64_dtype is deprecated and will be removed in a future "
+        "version. Use dtype == np.int64 instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return _is_dtype_type(arr_or_dtype, classes(np.int64))
+
+
+def is_datetime64_any_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the datetime64 dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array or dtype is of the datetime64 dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64_any_dtype
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
+    >>> is_datetime64_any_dtype(str)
+    False
+    >>> is_datetime64_any_dtype(int)
+    False
+    >>> is_datetime64_any_dtype(np.datetime64)  # can be tz-naive
+    True
+    >>> is_datetime64_any_dtype(DatetimeTZDtype("ns", "US/Eastern"))
+    True
+    >>> is_datetime64_any_dtype(np.array(['a', 'b']))
+    False
+    >>> is_datetime64_any_dtype(np.array([1, 2]))
+    False
+    >>> is_datetime64_any_dtype(np.array([], dtype="datetime64[ns]"))
+    True
+    >>> is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
+    True
+    """
+    if isinstance(arr_or_dtype, (np.dtype, ExtensionDtype)):
+        # GH#33400 fastpath for dtype object
+        return arr_or_dtype.kind == "M"
+
+    if arr_or_dtype is None:
+        return False
+
+    try:
+        tipo = _get_dtype(arr_or_dtype)
+    except TypeError:
+        return False
+    return lib.is_np_dtype(tipo, "M") or isinstance(tipo, DatetimeTZDtype)
+
+
+def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the datetime64[ns] dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array or dtype is of the datetime64[ns] dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_datetime64_ns_dtype
+    >>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
+    >>> is_datetime64_ns_dtype(str)
+    False
+    >>> is_datetime64_ns_dtype(int)
+    False
+    >>> is_datetime64_ns_dtype(np.datetime64)  # no unit
+    False
+    >>> is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern"))
+    True
+    >>> is_datetime64_ns_dtype(np.array(['a', 'b']))
+    False
+    >>> is_datetime64_ns_dtype(np.array([1, 2]))
+    False
+    >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64"))  # no unit
+    False
+    >>> is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]"))  # wrong unit
+    False
+    >>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
+    True
+    """
+    if arr_or_dtype is None:
+        return False
+    try:
+        tipo = _get_dtype(arr_or_dtype)
+    except TypeError:
+        return False
+    return tipo == DT64NS_DTYPE or (
+        isinstance(tipo, DatetimeTZDtype) and tipo.unit == "ns"
+    )
+
+
+def is_timedelta64_ns_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of the timedelta64[ns] dtype.
+
+    This is a very specific dtype, so generic ones like `np.timedelta64`
+    will return False if passed into this function.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of the timedelta64[ns] dtype.
+
+    Examples
+    --------
+    >>> from pandas.core.dtypes.common import is_timedelta64_ns_dtype
+    >>> is_timedelta64_ns_dtype(np.dtype('m8[ns]'))
+    True
+    >>> is_timedelta64_ns_dtype(np.dtype('m8[ps]'))  # Wrong frequency
+    False
+    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))
+    True
+    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))
+    False
+    """
+    return _is_dtype(arr_or_dtype, lambda dtype: dtype == TD64NS_DTYPE)
+
+
+# This exists to silence numpy deprecation warnings, see GH#29553
+def is_numeric_v_string_like(a: ArrayLike, b) -> bool:
+    """
+    Check if we are comparing a string-like object to a numeric ndarray.
+    NumPy doesn't like to compare such objects, especially numeric arrays
+    and scalar string-likes.
+
+    Parameters
+    ----------
+    a : array-like, scalar
+        The first object to check.
+    b : array-like, scalar
+        The second object to check.
+
+    Returns
+    -------
+    boolean
+        Whether we return a comparing a string-like object to a numeric array.
+
+    Examples
+    --------
+    >>> is_numeric_v_string_like(np.array([1]), "foo")
+    True
+    >>> is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"]))
+    True
+    >>> is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
+    True
+    >>> is_numeric_v_string_like(np.array([1]), np.array([2]))
+    False
+    >>> is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"]))
+    False
+    """
+    is_a_array = isinstance(a, np.ndarray)
+    is_b_array = isinstance(b, np.ndarray)
+
+    is_a_numeric_array = is_a_array and a.dtype.kind in ("u", "i", "f", "c", "b")
+    is_b_numeric_array = is_b_array and b.dtype.kind in ("u", "i", "f", "c", "b")
+    is_a_string_array = is_a_array and a.dtype.kind in ("S", "U")
+    is_b_string_array = is_b_array and b.dtype.kind in ("S", "U")
+
+    is_b_scalar_string_like = not is_b_array and isinstance(b, str)
+
+    return (
+        (is_a_numeric_array and is_b_scalar_string_like)
+        or (is_a_numeric_array and is_b_string_array)
+        or (is_b_numeric_array and is_a_string_array)
+    )
+
+
+def needs_i8_conversion(dtype: DtypeObj | None) -> bool:
+    """
+    Check whether the dtype should be converted to int64.
+
+    Dtype "needs" such a conversion if the dtype is of a datetime-like dtype
+
+    Parameters
+    ----------
+    dtype : np.dtype, ExtensionDtype, or None
+
+    Returns
+    -------
+    boolean
+        Whether or not the dtype should be converted to int64.
+
+    Examples
+    --------
+    >>> needs_i8_conversion(str)
+    False
+    >>> needs_i8_conversion(np.int64)
+    False
+    >>> needs_i8_conversion(np.datetime64)
+    False
+    >>> needs_i8_conversion(np.dtype(np.datetime64))
+    True
+    >>> needs_i8_conversion(np.array(['a', 'b']))
+    False
+    >>> needs_i8_conversion(pd.Series([1, 2]))
+    False
+    >>> needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
+    False
+    >>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
+    False
+    >>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern").dtype)
+    True
+    """
+    if isinstance(dtype, np.dtype):
+        return dtype.kind in "mM"
+    return isinstance(dtype, (PeriodDtype, DatetimeTZDtype))
+
+
+def is_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a numeric dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a numeric dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_numeric_dtype
+    >>> is_numeric_dtype(str)
+    False
+    >>> is_numeric_dtype(int)
+    True
+    >>> is_numeric_dtype(float)
+    True
+    >>> is_numeric_dtype(np.uint64)
+    True
+    >>> is_numeric_dtype(np.datetime64)
+    False
+    >>> is_numeric_dtype(np.timedelta64)
+    False
+    >>> is_numeric_dtype(np.array(['a', 'b']))
+    False
+    >>> is_numeric_dtype(pd.Series([1, 2]))
+    True
+    >>> is_numeric_dtype(pd.Index([1, 2.]))
+    True
+    >>> is_numeric_dtype(np.array([], dtype=np.timedelta64))
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, _classes_and_not_datetimelike(np.number, np.bool_)
+    ) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ._is_numeric
+    )
+
+
+def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a real number dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a real number dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_any_real_numeric_dtype
+    >>> is_any_real_numeric_dtype(int)
+    True
+    >>> is_any_real_numeric_dtype(float)
+    True
+    >>> is_any_real_numeric_dtype(object)
+    False
+    >>> is_any_real_numeric_dtype(str)
+    False
+    >>> is_any_real_numeric_dtype(complex(1, 2))
+    False
+    >>> is_any_real_numeric_dtype(bool)
+    False
+    """
+    return (
+        is_numeric_dtype(arr_or_dtype)
+        and not is_complex_dtype(arr_or_dtype)
+        and not is_bool_dtype(arr_or_dtype)
+    )
+
+
+def is_float_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a float dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a float dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_float_dtype
+    >>> is_float_dtype(str)
+    False
+    >>> is_float_dtype(int)
+    False
+    >>> is_float_dtype(float)
+    True
+    >>> is_float_dtype(np.array(['a', 'b']))
+    False
+    >>> is_float_dtype(pd.Series([1, 2]))
+    False
+    >>> is_float_dtype(pd.Index([1, 2.]))
+    True
+    """
+    return _is_dtype_type(arr_or_dtype, classes(np.floating)) or _is_dtype(
+        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind in "f"
+    )
+
+
+def is_bool_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a boolean dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a boolean dtype.
+
+    Notes
+    -----
+    An ExtensionArray is considered boolean when the ``_is_boolean``
+    attribute is set to True.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_bool_dtype
+    >>> is_bool_dtype(str)
+    False
+    >>> is_bool_dtype(int)
+    False
+    >>> is_bool_dtype(bool)
+    True
+    >>> is_bool_dtype(np.bool_)
+    True
+    >>> is_bool_dtype(np.array(['a', 'b']))
+    False
+    >>> is_bool_dtype(pd.Series([1, 2]))
+    False
+    >>> is_bool_dtype(np.array([True, False]))
+    True
+    >>> is_bool_dtype(pd.Categorical([True, False]))
+    True
+    >>> is_bool_dtype(pd.arrays.SparseArray([True, False]))
+    True
+    """
+    if arr_or_dtype is None:
+        return False
+    try:
+        dtype = _get_dtype(arr_or_dtype)
+    except (TypeError, ValueError):
+        return False
+
+    if isinstance(dtype, CategoricalDtype):
+        arr_or_dtype = dtype.categories
+        # now we use the special definition for Index
+
+    if isinstance(arr_or_dtype, ABCIndex):
+        # Allow Index[object] that is all-bools or Index["boolean"]
+        if arr_or_dtype.inferred_type == "boolean":
+            if not is_bool_dtype(arr_or_dtype.dtype):
+                # GH#52680
+                warnings.warn(
+                    "The behavior of is_bool_dtype with an object-dtype Index "
+                    "of bool objects is deprecated. In a future version, "
+                    "this will return False. Cast the Index to a bool dtype instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+            return True
+        return False
+    elif isinstance(dtype, ExtensionDtype):
+        return getattr(dtype, "_is_boolean", False)
+
+    return issubclass(dtype.type, np.bool_)
+
+
+def is_1d_only_ea_dtype(dtype: DtypeObj | None) -> bool:
+    """
+    Analogue to is_extension_array_dtype but excluding DatetimeTZDtype.
+    """
+    return isinstance(dtype, ExtensionDtype) and not dtype._supports_2d
+
+
+def is_extension_array_dtype(arr_or_dtype) -> bool:
+    """
+    Check if an object is a pandas extension array type.
+
+    See the :ref:`Use Guide <extending.extension-types>` for more.
+
+    Parameters
+    ----------
+    arr_or_dtype : object
+        For array-like input, the ``.dtype`` attribute will
+        be extracted.
+
+    Returns
+    -------
+    bool
+        Whether the `arr_or_dtype` is an extension array type.
+
+    Notes
+    -----
+    This checks whether an object implements the pandas extension
+    array interface. In pandas, this includes:
+
+    * Categorical
+    * Sparse
+    * Interval
+    * Period
+    * DatetimeArray
+    * TimedeltaArray
+
+    Third-party libraries may implement arrays or types satisfying
+    this interface as well.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_extension_array_dtype
+    >>> arr = pd.Categorical(['a', 'b'])
+    >>> is_extension_array_dtype(arr)
+    True
+    >>> is_extension_array_dtype(arr.dtype)
+    True
+
+    >>> arr = np.array(['a', 'b'])
+    >>> is_extension_array_dtype(arr.dtype)
+    False
+    """
+    dtype = getattr(arr_or_dtype, "dtype", arr_or_dtype)
+    if isinstance(dtype, ExtensionDtype):
+        return True
+    elif isinstance(dtype, np.dtype):
+        return False
+    else:
+        try:
+            with warnings.catch_warnings():
+                # pandas_dtype(..) can raise UserWarning for class input
+                warnings.simplefilter("ignore", UserWarning)
+                dtype = pandas_dtype(dtype)
+        except (TypeError, ValueError):
+            # np.dtype(..) can raise ValueError
+            return False
+        return isinstance(dtype, ExtensionDtype)
+
+
+def is_ea_or_datetimelike_dtype(dtype: DtypeObj | None) -> bool:
+    """
+    Check for ExtensionDtype, datetime64 dtype, or timedelta64 dtype.
+
+    Notes
+    -----
+    Checks only for dtype objects, not dtype-castable strings or types.
+    """
+    return isinstance(dtype, ExtensionDtype) or (lib.is_np_dtype(dtype, "mM"))
+
+
+def is_complex_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a complex dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a complex dtype.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_complex_dtype
+    >>> is_complex_dtype(str)
+    False
+    >>> is_complex_dtype(int)
+    False
+    >>> is_complex_dtype(np.complex128)
+    True
+    >>> is_complex_dtype(np.array(['a', 'b']))
+    False
+    >>> is_complex_dtype(pd.Series([1, 2]))
+    False
+    >>> is_complex_dtype(np.array([1 + 1j, 5]))
+    True
+    """
+    return _is_dtype_type(arr_or_dtype, classes(np.complexfloating))
+
+
+def _is_dtype(arr_or_dtype, condition) -> bool:
+    """
+    Return true if the condition is satisfied for the arr_or_dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like, str, np.dtype, or ExtensionArrayType
+        The array-like or dtype object whose dtype we want to extract.
+    condition : callable[Union[np.dtype, ExtensionDtype]]
+
+    Returns
+    -------
+    bool
+
+    """
+    if arr_or_dtype is None:
+        return False
+    try:
+        dtype = _get_dtype(arr_or_dtype)
+    except (TypeError, ValueError):
+        return False
+    return condition(dtype)
+
+
+def _get_dtype(arr_or_dtype) -> DtypeObj:
+    """
+    Get the dtype instance associated with an array
+    or dtype object.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype object whose dtype we want to extract.
+
+    Returns
+    -------
+    obj_dtype : The extract dtype instance from the
+                passed in array or dtype object.
+
+    Raises
+    ------
+    TypeError : The passed in object is None.
+    """
+    if arr_or_dtype is None:
+        raise TypeError("Cannot deduce dtype from null object")
+
+    # fastpath
+    if isinstance(arr_or_dtype, np.dtype):
+        return arr_or_dtype
+    elif isinstance(arr_or_dtype, type):
+        return np.dtype(arr_or_dtype)
+
+    # if we have an array-like
+    elif hasattr(arr_or_dtype, "dtype"):
+        arr_or_dtype = arr_or_dtype.dtype
+
+    return pandas_dtype(arr_or_dtype)
+
+
+def _is_dtype_type(arr_or_dtype, condition) -> bool:
+    """
+    Return true if the condition is satisfied for the arr_or_dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype object whose dtype we want to extract.
+    condition : callable[Union[np.dtype, ExtensionDtypeType]]
+
+    Returns
+    -------
+    bool : if the condition is satisfied for the arr_or_dtype
+    """
+    if arr_or_dtype is None:
+        return condition(type(None))
+
+    # fastpath
+    if isinstance(arr_or_dtype, np.dtype):
+        return condition(arr_or_dtype.type)
+    elif isinstance(arr_or_dtype, type):
+        if issubclass(arr_or_dtype, ExtensionDtype):
+            arr_or_dtype = arr_or_dtype.type
+        return condition(np.dtype(arr_or_dtype).type)
+
+    # if we have an array-like
+    if hasattr(arr_or_dtype, "dtype"):
+        arr_or_dtype = arr_or_dtype.dtype
+
+    # we are not possibly a dtype
+    elif is_list_like(arr_or_dtype):
+        return condition(type(None))
+
+    try:
+        tipo = pandas_dtype(arr_or_dtype).type
+    except (TypeError, ValueError):
+        if is_scalar(arr_or_dtype):
+            return condition(type(None))
+
+        return False
+
+    return condition(tipo)
+
+
+def infer_dtype_from_object(dtype) -> type:
+    """
+    Get a numpy dtype.type-style object for a dtype object.
+
+    This methods also includes handling of the datetime64[ns] and
+    datetime64[ns, TZ] objects.
+
+    If no dtype can be found, we return ``object``.
+
+    Parameters
+    ----------
+    dtype : dtype, type
+        The dtype object whose numpy dtype.type-style
+        object we want to extract.
+
+    Returns
+    -------
+    type
+    """
+    if isinstance(dtype, type) and issubclass(dtype, np.generic):
+        # Type object from a dtype
+
+        return dtype
+    elif isinstance(dtype, (np.dtype, ExtensionDtype)):
+        # dtype object
+        try:
+            _validate_date_like_dtype(dtype)
+        except TypeError:
+            # Should still pass if we don't have a date-like
+            pass
+        if hasattr(dtype, "numpy_dtype"):
+            # TODO: Implement this properly
+            # https://github.com/pandas-dev/pandas/issues/52576
+            return dtype.numpy_dtype.type
+        return dtype.type
+
+    try:
+        dtype = pandas_dtype(dtype)
+    except TypeError:
+        pass
+
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.type
+    elif isinstance(dtype, str):
+        # TODO(jreback)
+        # should deprecate these
+        if dtype in ["datetimetz", "datetime64tz"]:
+            return DatetimeTZDtype.type
+        elif dtype in ["period"]:
+            raise NotImplementedError
+
+        if dtype in ["datetime", "timedelta"]:
+            dtype += "64"
+        try:
+            return infer_dtype_from_object(getattr(np, dtype))
+        except (AttributeError, TypeError):
+            # Handles cases like _get_dtype(int) i.e.,
+            # Python objects that are valid dtypes
+            # (unlike user-defined types, in general)
+            #
+            # TypeError handles the float16 type code of 'e'
+            # further handle internal types
+            pass
+
+    return infer_dtype_from_object(np.dtype(dtype))
+
+
+def _validate_date_like_dtype(dtype) -> None:
+    """
+    Check whether the dtype is a date-like dtype. Raises an error if invalid.
+
+    Parameters
+    ----------
+    dtype : dtype, type
+        The dtype to check.
+
+    Raises
+    ------
+    TypeError : The dtype could not be casted to a date-like dtype.
+    ValueError : The dtype is an illegal date-like dtype (e.g. the
+                 frequency provided is too specific)
+    """
+    try:
+        typ = np.datetime_data(dtype)[0]
+    except ValueError as e:
+        raise TypeError(e) from e
+    if typ not in ["generic", "ns"]:
+        raise ValueError(
+            f"{repr(dtype.name)} is too specific of a frequency, "
+            f"try passing {repr(dtype.type.__name__)}"
+        )
+
+
+def validate_all_hashable(*args, error_name: str | None = None) -> None:
+    """
+    Return None if all args are hashable, else raise a TypeError.
+
+    Parameters
+    ----------
+    *args
+        Arguments to validate.
+    error_name : str, optional
+        The name to use if error
+
+    Raises
+    ------
+    TypeError : If an argument is not hashable
+
+    Returns
+    -------
+    None
+    """
+    if not all(is_hashable(arg) for arg in args):
+        if error_name:
+            raise TypeError(f"{error_name} must be a hashable type")
+        raise TypeError("All elements must be hashable")
+
+
+def pandas_dtype(dtype) -> DtypeObj:
+    """
+    Convert input into a pandas only dtype object or a numpy dtype object.
+
+    Parameters
+    ----------
+    dtype : object to be converted
+
+    Returns
+    -------
+    np.dtype or a pandas dtype
+
+    Raises
+    ------
+    TypeError if not a dtype
+
+    Examples
+    --------
+    >>> pd.api.types.pandas_dtype(int)
+    dtype('int64')
+    """
+    # short-circuit
+    if isinstance(dtype, np.ndarray):
+        return dtype.dtype
+    elif isinstance(dtype, (np.dtype, ExtensionDtype)):
+        return dtype
+
+    # builtin aliases
+    if dtype is str and using_string_dtype():
+        from pandas.core.arrays.string_ import StringDtype
+
+        return StringDtype(na_value=np.nan)
+
+    # registered extension types
+    result = registry.find(dtype)
+    if result is not None:
+        if isinstance(result, type):
+            # GH 31356, GH 54592
+            warnings.warn(
+                f"Instantiating {result.__name__} without any arguments."
+                f"Pass a {result.__name__} instance to silence this warning.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            result = result()
+        return result
+
+    # try a numpy dtype
+    # raise a consistent TypeError if failed
+    try:
+        with warnings.catch_warnings():
+            # TODO: warnings.catch_warnings can be removed when numpy>2.3.0
+            # is the minimum version
+            # GH#51523 - Series.astype(np.integer) doesn't show
+            # numpy deprecation warning of np.integer
+            # Hence enabling DeprecationWarning
+            warnings.simplefilter("always", DeprecationWarning)
+            npdtype = np.dtype(dtype)
+    except SyntaxError as err:
+        # np.dtype uses `eval` which can raise SyntaxError
+        raise TypeError(f"data type '{dtype}' not understood") from err
+
+    # Any invalid dtype (such as pd.Timestamp) should raise an error.
+    # np.dtype(invalid_type).kind = 0 for such objects. However, this will
+    # also catch some valid dtypes such as object, np.object_ and 'object'
+    # which we safeguard against by catching them earlier and returning
+    # np.dtype(valid_dtype) before this condition is evaluated.
+    if is_hashable(dtype) and dtype in [
+        object,
+        np.object_,
+        "object",
+        "O",
+        "object_",
+    ]:
+        # check hashability to avoid errors/DeprecationWarning when we get
+        # here and `dtype` is an array
+        return npdtype
+    elif npdtype.kind == "O":
+        raise TypeError(f"dtype '{dtype}' not understood")
+
+    return npdtype
+
+
+def is_all_strings(value: ArrayLike) -> bool:
+    """
+    Check if this is an array of strings that we should try parsing.
+
+    Includes object-dtype ndarray containing all-strings, StringArray,
+    and Categorical with all-string categories.
+    Does not include numpy string dtypes.
+    """
+    dtype = value.dtype
+
+    if isinstance(dtype, np.dtype):
+        if len(value) == 0:
+            return dtype == np.dtype("object")
+        else:
+            return dtype == np.dtype("object") and lib.is_string_array(
+                np.asarray(value), skipna=False
+            )
+    elif isinstance(dtype, CategoricalDtype):
+        return dtype.categories.inferred_type == "string"
+    return dtype == "string"
+
+
+__all__ = [
+    "classes",
+    "DT64NS_DTYPE",
+    "ensure_float64",
+    "ensure_python_int",
+    "ensure_str",
+    "infer_dtype_from_object",
+    "INT64_DTYPE",
+    "is_1d_only_ea_dtype",
+    "is_all_strings",
+    "is_any_real_numeric_dtype",
+    "is_array_like",
+    "is_bool",
+    "is_bool_dtype",
+    "is_categorical_dtype",
+    "is_complex",
+    "is_complex_dtype",
+    "is_dataclass",
+    "is_datetime64_any_dtype",
+    "is_datetime64_dtype",
+    "is_datetime64_ns_dtype",
+    "is_datetime64tz_dtype",
+    "is_decimal",
+    "is_dict_like",
+    "is_dtype_equal",
+    "is_ea_or_datetimelike_dtype",
+    "is_extension_array_dtype",
+    "is_file_like",
+    "is_float_dtype",
+    "is_int64_dtype",
+    "is_integer_dtype",
+    "is_interval",
+    "is_interval_dtype",
+    "is_iterator",
+    "is_named_tuple",
+    "is_nested_list_like",
+    "is_number",
+    "is_numeric_dtype",
+    "is_object_dtype",
+    "is_period_dtype",
+    "is_re",
+    "is_re_compilable",
+    "is_scipy_sparse",
+    "is_sequence",
+    "is_signed_integer_dtype",
+    "is_sparse",
+    "is_string_dtype",
+    "is_string_or_object_np_dtype",
+    "is_timedelta64_dtype",
+    "is_timedelta64_ns_dtype",
+    "is_unsigned_integer_dtype",
+    "needs_i8_conversion",
+    "pandas_dtype",
+    "TD64NS_DTYPE",
+    "validate_all_hashable",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/concat.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ec662a6cd3520aaa49fdc96142ad1b02bb518d8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/concat.py
@@ -0,0 +1,348 @@
+"""
+Utility functions related to concat.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.astype import astype_array
+from pandas.core.dtypes.cast import (
+    common_dtype_categorical_compat,
+    find_common_type,
+    np_find_common_type,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.generic import (
+    ABCCategoricalIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+    )
+
+    from pandas.core.arrays import (
+        Categorical,
+        ExtensionArray,
+    )
+
+
+def _is_nonempty(x, axis) -> bool:
+    # filter empty arrays
+    # 1-d dtypes always are included here
+    if x.ndim <= axis:
+        return True
+    return x.shape[axis] > 0
+
+
+def concat_compat(
+    to_concat: Sequence[ArrayLike], axis: AxisInt = 0, ea_compat_axis: bool = False
+) -> ArrayLike:
+    """
+    provide concatenation of an array of arrays each of which is a single
+    'normalized' dtypes (in that for example, if it's object, then it is a
+    non-datetimelike and provide a combined dtype for the resulting array that
+    preserves the overall dtype if possible)
+
+    Parameters
+    ----------
+    to_concat : sequence of arrays
+    axis : axis to provide concatenation
+    ea_compat_axis : bool, default False
+        For ExtensionArray compat, behave as if axis == 1 when determining
+        whether to drop empty arrays.
+
+    Returns
+    -------
+    a single array, preserving the combined dtypes
+    """
+    if len(to_concat) and lib.dtypes_all_equal([obj.dtype for obj in to_concat]):
+        # fastpath!
+        obj = to_concat[0]
+        if isinstance(obj, np.ndarray):
+            to_concat_arrs = cast("Sequence[np.ndarray]", to_concat)
+            return np.concatenate(to_concat_arrs, axis=axis)
+
+        to_concat_eas = cast("Sequence[ExtensionArray]", to_concat)
+        if ea_compat_axis:
+            # We have 1D objects, that don't support axis keyword
+            return obj._concat_same_type(to_concat_eas)
+        elif axis == 0:
+            return obj._concat_same_type(to_concat_eas)
+        else:
+            # e.g. DatetimeArray
+            # NB: We are assuming here that ensure_wrapped_if_arraylike has
+            #  been called where relevant.
+            return obj._concat_same_type(
+                # error: Unexpected keyword argument "axis" for "_concat_same_type"
+                # of "ExtensionArray"
+                to_concat_eas,
+                axis=axis,  # type: ignore[call-arg]
+            )
+
+    # If all arrays are empty, there's nothing to convert, just short-cut to
+    # the concatenation, #3121.
+    #
+    # Creating an empty array directly is tempting, but the winnings would be
+    # marginal given that it would still require shape & dtype calculation and
+    # np.concatenate which has them both implemented is compiled.
+    orig = to_concat
+    non_empties = [x for x in to_concat if _is_nonempty(x, axis)]
+    if non_empties and axis == 0 and not ea_compat_axis:
+        # ea_compat_axis see GH#39574
+        to_concat = non_empties
+
+    any_ea, kinds, target_dtype = _get_result_dtype(to_concat, non_empties)
+
+    if len(to_concat) < len(orig):
+        _, _, alt_dtype = _get_result_dtype(orig, non_empties)
+        if alt_dtype != target_dtype:
+            # GH#39122
+            warnings.warn(
+                "The behavior of array concatenation with empty entries is "
+                "deprecated. In a future version, this will no longer exclude "
+                "empty items when determining the result dtype. "
+                "To retain the old behavior, exclude the empty entries before "
+                "the concat operation.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    if target_dtype is not None:
+        to_concat = [astype_array(arr, target_dtype, copy=False) for arr in to_concat]
+
+    if not isinstance(to_concat[0], np.ndarray):
+        # i.e. isinstance(to_concat[0], ExtensionArray)
+        to_concat_eas = cast("Sequence[ExtensionArray]", to_concat)
+        cls = type(to_concat[0])
+        # GH#53640: eg. for datetime array, axis=1 but 0 is default
+        # However, class method `_concat_same_type()` for some classes
+        # may not support the `axis` keyword
+        if ea_compat_axis or axis == 0:
+            return cls._concat_same_type(to_concat_eas)
+        else:
+            return cls._concat_same_type(
+                to_concat_eas,
+                axis=axis,  # type: ignore[call-arg]
+            )
+    else:
+        to_concat_arrs = cast("Sequence[np.ndarray]", to_concat)
+        result = np.concatenate(to_concat_arrs, axis=axis)
+
+        if not any_ea and "b" in kinds and result.dtype.kind in "iuf":
+            # GH#39817 cast to object instead of casting bools to numeric
+            result = result.astype(object, copy=False)
+    return result
+
+
+def _get_result_dtype(
+    to_concat: Sequence[ArrayLike], non_empties: Sequence[ArrayLike]
+) -> tuple[bool, set[str], DtypeObj | None]:
+    target_dtype = None
+
+    dtypes = {obj.dtype for obj in to_concat}
+    kinds = {obj.dtype.kind for obj in to_concat}
+
+    any_ea = any(not isinstance(x, np.ndarray) for x in to_concat)
+    if any_ea:
+        # i.e. any ExtensionArrays
+
+        # we ignore axis here, as internally concatting with EAs is always
+        # for axis=0
+        if len(dtypes) != 1:
+            target_dtype = find_common_type([x.dtype for x in to_concat])
+            target_dtype = common_dtype_categorical_compat(to_concat, target_dtype)
+
+    elif not len(non_empties):
+        # we have all empties, but may need to coerce the result dtype to
+        # object if we have non-numeric type operands (numpy would otherwise
+        # cast this to float)
+        if len(kinds) != 1:
+            if not len(kinds - {"i", "u", "f"}) or not len(kinds - {"b", "i", "u"}):
+                # let numpy coerce
+                pass
+            else:
+                # coerce to object
+                target_dtype = np.dtype(object)
+                kinds = {"o"}
+    else:
+        # error: Argument 1 to "np_find_common_type" has incompatible type
+        # "*Set[Union[ExtensionDtype, Any]]"; expected "dtype[Any]"
+        target_dtype = np_find_common_type(*dtypes)  # type: ignore[arg-type]
+
+    return any_ea, kinds, target_dtype
+
+
+def union_categoricals(
+    to_union, sort_categories: bool = False, ignore_order: bool = False
+) -> Categorical:
+    """
+    Combine list-like of Categorical-like, unioning categories.
+
+    All categories must have the same dtype.
+
+    Parameters
+    ----------
+    to_union : list-like
+        Categorical, CategoricalIndex, or Series with dtype='category'.
+    sort_categories : bool, default False
+        If true, resulting categories will be lexsorted, otherwise
+        they will be ordered as they appear in the data.
+    ignore_order : bool, default False
+        If true, the ordered attribute of the Categoricals will be ignored.
+        Results in an unordered categorical.
+
+    Returns
+    -------
+    Categorical
+
+    Raises
+    ------
+    TypeError
+        - all inputs do not have the same dtype
+        - all inputs do not have the same ordered property
+        - all inputs are ordered and their categories are not identical
+        - sort_categories=True and Categoricals are ordered
+    ValueError
+        Empty list of categoricals passed
+
+    Notes
+    -----
+    To learn more about categories, see `link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#unioning>`__
+
+    Examples
+    --------
+    If you want to combine categoricals that do not necessarily have
+    the same categories, `union_categoricals` will combine a list-like
+    of categoricals. The new categories will be the union of the
+    categories being combined.
+
+    >>> a = pd.Categorical(["b", "c"])
+    >>> b = pd.Categorical(["a", "b"])
+    >>> pd.api.types.union_categoricals([a, b])
+    ['b', 'c', 'a', 'b']
+    Categories (3, object): ['b', 'c', 'a']
+
+    By default, the resulting categories will be ordered as they appear
+    in the `categories` of the data. If you want the categories to be
+    lexsorted, use `sort_categories=True` argument.
+
+    >>> pd.api.types.union_categoricals([a, b], sort_categories=True)
+    ['b', 'c', 'a', 'b']
+    Categories (3, object): ['a', 'b', 'c']
+
+    `union_categoricals` also works with the case of combining two
+    categoricals of the same categories and order information (e.g. what
+    you could also `append` for).
+
+    >>> a = pd.Categorical(["a", "b"], ordered=True)
+    >>> b = pd.Categorical(["a", "b", "a"], ordered=True)
+    >>> pd.api.types.union_categoricals([a, b])
+    ['a', 'b', 'a', 'b', 'a']
+    Categories (2, object): ['a' < 'b']
+
+    Raises `TypeError` because the categories are ordered and not identical.
+
+    >>> a = pd.Categorical(["a", "b"], ordered=True)
+    >>> b = pd.Categorical(["a", "b", "c"], ordered=True)
+    >>> pd.api.types.union_categoricals([a, b])
+    Traceback (most recent call last):
+        ...
+    TypeError: to union ordered Categoricals, all categories must be the same
+
+    Ordered categoricals with different categories or orderings can be
+    combined by using the `ignore_ordered=True` argument.
+
+    >>> a = pd.Categorical(["a", "b", "c"], ordered=True)
+    >>> b = pd.Categorical(["c", "b", "a"], ordered=True)
+    >>> pd.api.types.union_categoricals([a, b], ignore_order=True)
+    ['a', 'b', 'c', 'c', 'b', 'a']
+    Categories (3, object): ['a', 'b', 'c']
+
+    `union_categoricals` also works with a `CategoricalIndex`, or `Series`
+    containing categorical data, but note that the resulting array will
+    always be a plain `Categorical`
+
+    >>> a = pd.Series(["b", "c"], dtype='category')
+    >>> b = pd.Series(["a", "b"], dtype='category')
+    >>> pd.api.types.union_categoricals([a, b])
+    ['b', 'c', 'a', 'b']
+    Categories (3, object): ['b', 'c', 'a']
+    """
+    from pandas import Categorical
+    from pandas.core.arrays.categorical import recode_for_categories
+
+    if len(to_union) == 0:
+        raise ValueError("No Categoricals to union")
+
+    def _maybe_unwrap(x):
+        if isinstance(x, (ABCCategoricalIndex, ABCSeries)):
+            return x._values
+        elif isinstance(x, Categorical):
+            return x
+        else:
+            raise TypeError("all components to combine must be Categorical")
+
+    to_union = [_maybe_unwrap(x) for x in to_union]
+    first = to_union[0]
+
+    if not lib.dtypes_all_equal([obj.categories.dtype for obj in to_union]):
+        raise TypeError("dtype of categories must be the same")
+
+    ordered = False
+    if all(first._categories_match_up_to_permutation(other) for other in to_union[1:]):
+        # identical categories - fastpath
+        categories = first.categories
+        ordered = first.ordered
+
+        all_codes = [first._encode_with_my_categories(x)._codes for x in to_union]
+        new_codes = np.concatenate(all_codes)
+
+        if sort_categories and not ignore_order and ordered:
+            raise TypeError("Cannot use sort_categories=True with ordered Categoricals")
+
+        if sort_categories and not categories.is_monotonic_increasing:
+            categories = categories.sort_values()
+            indexer = categories.get_indexer(first.categories)
+
+            from pandas.core.algorithms import take_nd
+
+            new_codes = take_nd(indexer, new_codes, fill_value=-1)
+    elif ignore_order or all(not c.ordered for c in to_union):
+        # different categories - union and recode
+        cats = first.categories.append([c.categories for c in to_union[1:]])
+        categories = cats.unique()
+        if sort_categories:
+            categories = categories.sort_values()
+
+        new_codes = [
+            recode_for_categories(c.codes, c.categories, categories) for c in to_union
+        ]
+        new_codes = np.concatenate(new_codes)
+    else:
+        # ordered - to show a proper error message
+        if all(c.ordered for c in to_union):
+            msg = "to union ordered Categoricals, all categories must be the same"
+            raise TypeError(msg)
+        raise TypeError("Categorical.ordered must be the same")
+
+    if ignore_order:
+        ordered = False
+
+    dtype = CategoricalDtype(categories=categories, ordered=ordered)
+    return Categorical._simple_new(new_codes, dtype=dtype)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/dtypes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..542bc85110cadfc777f82a6859899c129ef1d47f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/dtypes.py
@@ -0,0 +1,2348 @@
+"""
+Define extension dtypes.
+"""
+from __future__ import annotations
+
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from decimal import Decimal
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+import warnings
+
+import numpy as np
+import pytz
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas._libs.interval import Interval
+from pandas._libs.properties import cache_readonly
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    NaTType,
+    Period,
+    Timedelta,
+    Timestamp,
+    timezones,
+    to_offset,
+    tz_compare,
+)
+from pandas._libs.tslibs.dtypes import (
+    PeriodDtypeBase,
+    abbrev_to_npy_unit,
+)
+from pandas._libs.tslibs.offsets import BDay
+from pandas.compat import pa_version_under10p1
+from pandas.errors import PerformanceWarning
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import (
+    ExtensionDtype,
+    StorageExtensionDtype,
+    register_extension_dtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCCategoricalIndex,
+    ABCIndex,
+    ABCRangeIndex,
+)
+from pandas.core.dtypes.inference import (
+    is_bool,
+    is_list_like,
+)
+
+from pandas.util import capitalize_first_letter
+
+if not pa_version_under10p1:
+    import pyarrow as pa
+
+if TYPE_CHECKING:
+    from collections.abc import MutableMapping
+    from datetime import tzinfo
+
+    import pyarrow as pa  # noqa: TCH004
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        IntervalClosedType,
+        Ordered,
+        Self,
+        npt,
+        type_t,
+    )
+
+    from pandas import (
+        Categorical,
+        CategoricalIndex,
+        DatetimeIndex,
+        Index,
+        IntervalIndex,
+        PeriodIndex,
+    )
+    from pandas.core.arrays import (
+        BaseMaskedArray,
+        DatetimeArray,
+        IntervalArray,
+        NumpyExtensionArray,
+        PeriodArray,
+        SparseArray,
+    )
+    from pandas.core.arrays.arrow import ArrowExtensionArray
+
+str_type = str
+
+
+class PandasExtensionDtype(ExtensionDtype):
+    """
+    A np.dtype duck-typed class, suitable for holding a custom dtype.
+
+    THIS IS NOT A REAL NUMPY DTYPE
+    """
+
+    type: Any
+    kind: Any
+    # The Any type annotations above are here only because mypy seems to have a
+    # problem dealing with multiple inheritance from PandasExtensionDtype
+    # and ExtensionDtype's @properties in the subclasses below. The kind and
+    # type variables in those subclasses are explicitly typed below.
+    subdtype = None
+    str: str_type
+    num = 100
+    shape: tuple[int, ...] = ()
+    itemsize = 8
+    base: DtypeObj | None = None
+    isbuiltin = 0
+    isnative = 0
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+
+    def __repr__(self) -> str_type:
+        """
+        Return a string representation for a particular object.
+        """
+        return str(self)
+
+    def __hash__(self) -> int:
+        raise NotImplementedError("sub-classes should implement an __hash__ method")
+
+    def __getstate__(self) -> dict[str_type, Any]:
+        # pickle support; we don't want to pickle the cache
+        return {k: getattr(self, k, None) for k in self._metadata}
+
+    @classmethod
+    def reset_cache(cls) -> None:
+        """clear the cache"""
+        cls._cache_dtypes = {}
+
+
+class CategoricalDtypeType(type):
+    """
+    the type of CategoricalDtype, this metaclass determines subclass ability
+    """
+
+
+@register_extension_dtype
+class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
+    """
+    Type for categorical data with the categories and orderedness.
+
+    Parameters
+    ----------
+    categories : sequence, optional
+        Must be unique, and must not contain any nulls.
+        The categories are stored in an Index,
+        and if an index is provided the dtype of that index will be used.
+    ordered : bool or None, default False
+        Whether or not this categorical is treated as a ordered categorical.
+        None can be used to maintain the ordered value of existing categoricals when
+        used in operations that combine categoricals, e.g. astype, and will resolve to
+        False if there is no existing ordered to maintain.
+
+    Attributes
+    ----------
+    categories
+    ordered
+
+    Methods
+    -------
+    None
+
+    See Also
+    --------
+    Categorical : Represent a categorical variable in classic R / S-plus fashion.
+
+    Notes
+    -----
+    This class is useful for specifying the type of a ``Categorical``
+    independent of the values. See :ref:`categorical.categoricaldtype`
+    for more.
+
+    Examples
+    --------
+    >>> t = pd.CategoricalDtype(categories=['b', 'a'], ordered=True)
+    >>> pd.Series(['a', 'b', 'a', 'c'], dtype=t)
+    0      a
+    1      b
+    2      a
+    3    NaN
+    dtype: category
+    Categories (2, object): ['b' < 'a']
+
+    An empty CategoricalDtype with a specific dtype can be created
+    by providing an empty index. As follows,
+
+    >>> pd.CategoricalDtype(pd.DatetimeIndex([])).categories.dtype
+    dtype('<M8[ns]')
+    """
+
+    # TODO: Document public vs. private API
+    name = "category"
+    type: type[CategoricalDtypeType] = CategoricalDtypeType
+    kind: str_type = "O"
+    str = "|O08"
+    base = np.dtype("O")
+    _metadata = ("categories", "ordered")
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+    _supports_2d = False
+    _can_fast_transpose = False
+
+    def __init__(self, categories=None, ordered: Ordered = False) -> None:
+        self._finalize(categories, ordered, fastpath=False)
+
+    @classmethod
+    def _from_fastpath(
+        cls, categories=None, ordered: bool | None = None
+    ) -> CategoricalDtype:
+        self = cls.__new__(cls)
+        self._finalize(categories, ordered, fastpath=True)
+        return self
+
+    @classmethod
+    def _from_categorical_dtype(
+        cls, dtype: CategoricalDtype, categories=None, ordered: Ordered | None = None
+    ) -> CategoricalDtype:
+        if categories is ordered is None:
+            return dtype
+        if categories is None:
+            categories = dtype.categories
+        if ordered is None:
+            ordered = dtype.ordered
+        return cls(categories, ordered)
+
+    @classmethod
+    def _from_values_or_dtype(
+        cls,
+        values=None,
+        categories=None,
+        ordered: bool | None = None,
+        dtype: Dtype | None = None,
+    ) -> CategoricalDtype:
+        """
+        Construct dtype from the input parameters used in :class:`Categorical`.
+
+        This constructor method specifically does not do the factorization
+        step, if that is needed to find the categories. This constructor may
+        therefore return ``CategoricalDtype(categories=None, ordered=None)``,
+        which may not be useful. Additional steps may therefore have to be
+        taken to create the final dtype.
+
+        The return dtype is specified from the inputs in this prioritized
+        order:
+        1. if dtype is a CategoricalDtype, return dtype
+        2. if dtype is the string 'category', create a CategoricalDtype from
+           the supplied categories and ordered parameters, and return that.
+        3. if values is a categorical, use value.dtype, but override it with
+           categories and ordered if either/both of those are not None.
+        4. if dtype is None and values is not a categorical, construct the
+           dtype from categories and ordered, even if either of those is None.
+
+        Parameters
+        ----------
+        values : list-like, optional
+            The list-like must be 1-dimensional.
+        categories : list-like, optional
+            Categories for the CategoricalDtype.
+        ordered : bool, optional
+            Designating if the categories are ordered.
+        dtype : CategoricalDtype or the string "category", optional
+            If ``CategoricalDtype``, cannot be used together with
+            `categories` or `ordered`.
+
+        Returns
+        -------
+        CategoricalDtype
+
+        Examples
+        --------
+        >>> pd.CategoricalDtype._from_values_or_dtype()
+        CategoricalDtype(categories=None, ordered=None, categories_dtype=None)
+        >>> pd.CategoricalDtype._from_values_or_dtype(
+        ...     categories=['a', 'b'], ordered=True
+        ... )
+        CategoricalDtype(categories=['a', 'b'], ordered=True, categories_dtype=object)
+        >>> dtype1 = pd.CategoricalDtype(['a', 'b'], ordered=True)
+        >>> dtype2 = pd.CategoricalDtype(['x', 'y'], ordered=False)
+        >>> c = pd.Categorical([0, 1], dtype=dtype1)
+        >>> pd.CategoricalDtype._from_values_or_dtype(
+        ...     c, ['x', 'y'], ordered=True, dtype=dtype2
+        ... )
+        Traceback (most recent call last):
+            ...
+        ValueError: Cannot specify `categories` or `ordered` together with
+        `dtype`.
+
+        The supplied dtype takes precedence over values' dtype:
+
+        >>> pd.CategoricalDtype._from_values_or_dtype(c, dtype=dtype2)
+        CategoricalDtype(categories=['x', 'y'], ordered=False, categories_dtype=object)
+        """
+
+        if dtype is not None:
+            # The dtype argument takes precedence over values.dtype (if any)
+            if isinstance(dtype, str):
+                if dtype == "category":
+                    if ordered is None and cls.is_dtype(values):
+                        # GH#49309 preserve orderedness
+                        ordered = values.dtype.ordered
+
+                    dtype = CategoricalDtype(categories, ordered)
+                else:
+                    raise ValueError(f"Unknown dtype {repr(dtype)}")
+            elif categories is not None or ordered is not None:
+                raise ValueError(
+                    "Cannot specify `categories` or `ordered` together with `dtype`."
+                )
+            elif not isinstance(dtype, CategoricalDtype):
+                raise ValueError(f"Cannot not construct CategoricalDtype from {dtype}")
+        elif cls.is_dtype(values):
+            # If no "dtype" was passed, use the one from "values", but honor
+            # the "ordered" and "categories" arguments
+            dtype = values.dtype._from_categorical_dtype(
+                values.dtype, categories, ordered
+            )
+        else:
+            # If dtype=None and values is not categorical, create a new dtype.
+            # Note: This could potentially have categories=None and
+            # ordered=None.
+            dtype = CategoricalDtype(categories, ordered)
+
+        return cast(CategoricalDtype, dtype)
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> CategoricalDtype:
+        """
+        Construct a CategoricalDtype from a string.
+
+        Parameters
+        ----------
+        string : str
+            Must be the string "category" in order to be successfully constructed.
+
+        Returns
+        -------
+        CategoricalDtype
+            Instance of the dtype.
+
+        Raises
+        ------
+        TypeError
+            If a CategoricalDtype cannot be constructed from the input.
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if string != cls.name:
+            raise TypeError(f"Cannot construct a 'CategoricalDtype' from '{string}'")
+
+        # need ordered=None to ensure that operations specifying dtype="category" don't
+        # override the ordered value for existing categoricals
+        return cls(ordered=None)
+
+    def _finalize(self, categories, ordered: Ordered, fastpath: bool = False) -> None:
+        if ordered is not None:
+            self.validate_ordered(ordered)
+
+        if categories is not None:
+            categories = self.validate_categories(categories, fastpath=fastpath)
+
+        self._categories = categories
+        self._ordered = ordered
+
+    def __setstate__(self, state: MutableMapping[str_type, Any]) -> None:
+        # for pickle compat. __get_state__ is defined in the
+        # PandasExtensionDtype superclass and uses the public properties to
+        # pickle -> need to set the settable private ones here (see GH26067)
+        self._categories = state.pop("categories", None)
+        self._ordered = state.pop("ordered", False)
+
+    def __hash__(self) -> int:
+        # _hash_categories returns a uint64, so use the negative
+        # space for when we have unknown categories to avoid a conflict
+        if self.categories is None:
+            if self.ordered:
+                return -1
+            else:
+                return -2
+        # We *do* want to include the real self.ordered here
+        return int(self._hash_categories)
+
+    def __eq__(self, other: object) -> bool:
+        """
+        Rules for CDT equality:
+        1) Any CDT is equal to the string 'category'
+        2) Any CDT is equal to itself
+        3) Any CDT is equal to a CDT with categories=None regardless of ordered
+        4) A CDT with ordered=True is only equal to another CDT with
+           ordered=True and identical categories in the same order
+        5) A CDT with ordered={False, None} is only equal to another CDT with
+           ordered={False, None} and identical categories, but same order is
+           not required. There is no distinction between False/None.
+        6) Any other comparison returns False
+        """
+        if isinstance(other, str):
+            return other == self.name
+        elif other is self:
+            return True
+        elif not (hasattr(other, "ordered") and hasattr(other, "categories")):
+            return False
+        elif self.categories is None or other.categories is None:
+            # For non-fully-initialized dtypes, these are only equal to
+            #  - the string "category" (handled above)
+            #  - other CategoricalDtype with categories=None
+            return self.categories is other.categories
+        elif self.ordered or other.ordered:
+            # At least one has ordered=True; equal if both have ordered=True
+            # and the same values for categories in the same order.
+            return (self.ordered == other.ordered) and self.categories.equals(
+                other.categories
+            )
+        else:
+            # Neither has ordered=True; equal if both have the same categories,
+            # but same order is not necessary.  There is no distinction between
+            # ordered=False and ordered=None: CDT(., False) and CDT(., None)
+            # will be equal if they have the same categories.
+            left = self.categories
+            right = other.categories
+
+            # GH#36280 the ordering of checks here is for performance
+            if not left.dtype == right.dtype:
+                return False
+
+            if len(left) != len(right):
+                return False
+
+            if self.categories.equals(other.categories):
+                # Check and see if they happen to be identical categories
+                return True
+
+            if left.dtype != object:
+                # Faster than calculating hash
+                indexer = left.get_indexer(right)
+                # Because left and right have the same length and are unique,
+                #  `indexer` not having any -1s implies that there is a
+                #  bijection between `left` and `right`.
+                return bool((indexer != -1).all())
+
+            # With object-dtype we need a comparison that identifies
+            #  e.g. int(2) as distinct from float(2)
+            return set(left) == set(right)
+
+    def __repr__(self) -> str_type:
+        if self.categories is None:
+            data = "None"
+            dtype = "None"
+        else:
+            data = self.categories._format_data(name=type(self).__name__)
+            if isinstance(self.categories, ABCRangeIndex):
+                data = str(self.categories._range)
+            data = data.rstrip(", ")
+            dtype = self.categories.dtype
+
+        return (
+            f"CategoricalDtype(categories={data}, ordered={self.ordered}, "
+            f"categories_dtype={dtype})"
+        )
+
+    @cache_readonly
+    def _hash_categories(self) -> int:
+        from pandas.core.util.hashing import (
+            combine_hash_arrays,
+            hash_array,
+            hash_tuples,
+        )
+
+        categories = self.categories
+        ordered = self.ordered
+
+        if len(categories) and isinstance(categories[0], tuple):
+            # assumes if any individual category is a tuple, then all our. ATM
+            # I don't really want to support just some of the categories being
+            # tuples.
+            cat_list = list(categories)  # breaks if a np.array of categories
+            cat_array = hash_tuples(cat_list)
+        else:
+            if categories.dtype == "O" and len({type(x) for x in categories}) != 1:
+                # TODO: hash_array doesn't handle mixed types. It casts
+                # everything to a str first, which means we treat
+                # {'1', '2'} the same as {'1', 2}
+                # find a better solution
+                hashed = hash((tuple(categories), ordered))
+                return hashed
+
+            if DatetimeTZDtype.is_dtype(categories.dtype):
+                # Avoid future warning.
+                categories = categories.view("datetime64[ns]")
+
+            cat_array = hash_array(np.asarray(categories), categorize=False)
+        if ordered:
+            cat_array = np.vstack(
+                [cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)]
+            )
+        else:
+            cat_array = np.array([cat_array])
+        combined_hashed = combine_hash_arrays(iter(cat_array), num_items=len(cat_array))
+        return np.bitwise_xor.reduce(combined_hashed)
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[Categorical]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas import Categorical
+
+        return Categorical
+
+    @staticmethod
+    def validate_ordered(ordered: Ordered) -> None:
+        """
+        Validates that we have a valid ordered parameter. If
+        it is not a boolean, a TypeError will be raised.
+
+        Parameters
+        ----------
+        ordered : object
+            The parameter to be verified.
+
+        Raises
+        ------
+        TypeError
+            If 'ordered' is not a boolean.
+        """
+        if not is_bool(ordered):
+            raise TypeError("'ordered' must either be 'True' or 'False'")
+
+    @staticmethod
+    def validate_categories(categories, fastpath: bool = False) -> Index:
+        """
+        Validates that we have good categories
+
+        Parameters
+        ----------
+        categories : array-like
+        fastpath : bool
+            Whether to skip nan and uniqueness checks
+
+        Returns
+        -------
+        categories : Index
+        """
+        from pandas.core.indexes.base import Index
+
+        if not fastpath and not is_list_like(categories):
+            raise TypeError(
+                f"Parameter 'categories' must be list-like, was {repr(categories)}"
+            )
+        if not isinstance(categories, ABCIndex):
+            categories = Index._with_infer(categories, tupleize_cols=False)
+
+        if not fastpath:
+            if categories.hasnans:
+                raise ValueError("Categorical categories cannot be null")
+
+            if not categories.is_unique:
+                raise ValueError("Categorical categories must be unique")
+
+        if isinstance(categories, ABCCategoricalIndex):
+            categories = categories.categories
+
+        return categories
+
+    def update_dtype(self, dtype: str_type | CategoricalDtype) -> CategoricalDtype:
+        """
+        Returns a CategoricalDtype with categories and ordered taken from dtype
+        if specified, otherwise falling back to self if unspecified
+
+        Parameters
+        ----------
+        dtype : CategoricalDtype
+
+        Returns
+        -------
+        new_dtype : CategoricalDtype
+        """
+        if isinstance(dtype, str) and dtype == "category":
+            # dtype='category' should not change anything
+            return self
+        elif not self.is_dtype(dtype):
+            raise ValueError(
+                f"a CategoricalDtype must be passed to perform an update, "
+                f"got {repr(dtype)}"
+            )
+        else:
+            # from here on, dtype is a CategoricalDtype
+            dtype = cast(CategoricalDtype, dtype)
+
+        # update categories/ordered unless they've been explicitly passed as None
+        new_categories = (
+            dtype.categories if dtype.categories is not None else self.categories
+        )
+        new_ordered = dtype.ordered if dtype.ordered is not None else self.ordered
+
+        return CategoricalDtype(new_categories, new_ordered)
+
+    @property
+    def categories(self) -> Index:
+        """
+        An ``Index`` containing the unique categories allowed.
+
+        Examples
+        --------
+        >>> cat_type = pd.CategoricalDtype(categories=['a', 'b'], ordered=True)
+        >>> cat_type.categories
+        Index(['a', 'b'], dtype='object')
+        """
+        return self._categories
+
+    @property
+    def ordered(self) -> Ordered:
+        """
+        Whether the categories have an ordered relationship.
+
+        Examples
+        --------
+        >>> cat_type = pd.CategoricalDtype(categories=['a', 'b'], ordered=True)
+        >>> cat_type.ordered
+        True
+
+        >>> cat_type = pd.CategoricalDtype(categories=['a', 'b'], ordered=False)
+        >>> cat_type.ordered
+        False
+        """
+        return self._ordered
+
+    @property
+    def _is_boolean(self) -> bool:
+        from pandas.core.dtypes.common import is_bool_dtype
+
+        return is_bool_dtype(self.categories)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # check if we have all categorical dtype with identical categories
+        if all(isinstance(x, CategoricalDtype) for x in dtypes):
+            first = dtypes[0]
+            if all(first == other for other in dtypes[1:]):
+                return first
+
+        # special case non-initialized categorical
+        # TODO we should figure out the expected return value in general
+        non_init_cats = [
+            isinstance(x, CategoricalDtype) and x.categories is None for x in dtypes
+        ]
+        if all(non_init_cats):
+            return self
+        elif any(non_init_cats):
+            return None
+
+        # categorical is aware of Sparse -> extract sparse subdtypes
+        dtypes = [x.subtype if isinstance(x, SparseDtype) else x for x in dtypes]
+        # extract the categories' dtype
+        non_cat_dtypes = [
+            x.categories.dtype if isinstance(x, CategoricalDtype) else x for x in dtypes
+        ]
+        # TODO should categorical always give an answer?
+        from pandas.core.dtypes.cast import find_common_type
+
+        return find_common_type(non_cat_dtypes)
+
+    @cache_readonly
+    def index_class(self) -> type_t[CategoricalIndex]:
+        from pandas import CategoricalIndex
+
+        return CategoricalIndex
+
+
+@register_extension_dtype
+class DatetimeTZDtype(PandasExtensionDtype):
+    """
+    An ExtensionDtype for timezone-aware datetime data.
+
+    **This is not an actual numpy dtype**, but a duck type.
+
+    Parameters
+    ----------
+    unit : str, default "ns"
+        The precision of the datetime data. Currently limited
+        to ``"ns"``.
+    tz : str, int, or datetime.tzinfo
+        The timezone.
+
+    Attributes
+    ----------
+    unit
+    tz
+
+    Methods
+    -------
+    None
+
+    Raises
+    ------
+    ZoneInfoNotFoundError
+        When the requested timezone cannot be found.
+
+    Examples
+    --------
+    >>> from zoneinfo import ZoneInfo
+    >>> pd.DatetimeTZDtype(tz=ZoneInfo('UTC'))
+    datetime64[ns, UTC]
+
+    >>> pd.DatetimeTZDtype(tz=ZoneInfo('Europe/Paris'))
+    datetime64[ns, Europe/Paris]
+    """
+
+    type: type[Timestamp] = Timestamp
+    kind: str_type = "M"
+    num = 101
+    _metadata = ("unit", "tz")
+    _match = re.compile(r"(datetime64|M8)\[(?P<unit>.+), (?P<tz>.+)\]")
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+    _supports_2d = True
+    _can_fast_transpose = True
+
+    @property
+    def na_value(self) -> NaTType:
+        return NaT
+
+    @cache_readonly
+    def base(self) -> DtypeObj:  # type: ignore[override]
+        return np.dtype(f"M8[{self.unit}]")
+
+    # error: Signature of "str" incompatible with supertype "PandasExtensionDtype"
+    @cache_readonly
+    def str(self) -> str:  # type: ignore[override]
+        return f"|M8[{self.unit}]"
+
+    def __init__(self, unit: str_type | DatetimeTZDtype = "ns", tz=None) -> None:
+        if isinstance(unit, DatetimeTZDtype):
+            # error: "str" has no attribute "tz"
+            unit, tz = unit.unit, unit.tz  # type: ignore[attr-defined]
+
+        if unit != "ns":
+            if isinstance(unit, str) and tz is None:
+                # maybe a string like datetime64[ns, tz], which we support for
+                # now.
+                result = type(self).construct_from_string(unit)
+                unit = result.unit
+                tz = result.tz
+                msg = (
+                    f"Passing a dtype alias like 'datetime64[ns, {tz}]' "
+                    "to DatetimeTZDtype is no longer supported. Use "
+                    "'DatetimeTZDtype.construct_from_string()' instead."
+                )
+                raise ValueError(msg)
+            if unit not in ["s", "ms", "us", "ns"]:
+                raise ValueError("DatetimeTZDtype only supports s, ms, us, ns units")
+
+        if tz:
+            tz = timezones.maybe_get_tz(tz)
+            tz = timezones.tz_standardize(tz)
+        elif tz is not None:
+            raise pytz.UnknownTimeZoneError(tz)
+        if tz is None:
+            raise TypeError("A 'tz' is required.")
+
+        self._unit = unit
+        self._tz = tz
+
+    @cache_readonly
+    def _creso(self) -> int:
+        """
+        The NPY_DATETIMEUNIT corresponding to this dtype's resolution.
+        """
+        return abbrev_to_npy_unit(self.unit)
+
+    @property
+    def unit(self) -> str_type:
+        """
+        The precision of the datetime data.
+
+        Examples
+        --------
+        >>> from zoneinfo import ZoneInfo
+        >>> dtype = pd.DatetimeTZDtype(tz=ZoneInfo('America/Los_Angeles'))
+        >>> dtype.unit
+        'ns'
+        """
+        return self._unit
+
+    @property
+    def tz(self) -> tzinfo:
+        """
+        The timezone.
+
+        Examples
+        --------
+        >>> from zoneinfo import ZoneInfo
+        >>> dtype = pd.DatetimeTZDtype(tz=ZoneInfo('America/Los_Angeles'))
+        >>> dtype.tz
+        zoneinfo.ZoneInfo(key='America/Los_Angeles')
+        """
+        return self._tz
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[DatetimeArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import DatetimeArray
+
+        return DatetimeArray
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> DatetimeTZDtype:
+        """
+        Construct a DatetimeTZDtype from a string.
+
+        Parameters
+        ----------
+        string : str
+            The string alias for this DatetimeTZDtype.
+            Should be formatted like ``datetime64[ns, <tz>]``,
+            where ``<tz>`` is the timezone name.
+
+        Examples
+        --------
+        >>> DatetimeTZDtype.construct_from_string('datetime64[ns, UTC]')
+        datetime64[ns, UTC]
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+
+        msg = f"Cannot construct a 'DatetimeTZDtype' from '{string}'"
+        match = cls._match.match(string)
+        if match:
+            d = match.groupdict()
+            try:
+                return cls(unit=d["unit"], tz=d["tz"])
+            except (KeyError, TypeError, ValueError) as err:
+                # KeyError if maybe_get_tz tries and fails to get a
+                #  pytz timezone (actually pytz.UnknownTimeZoneError).
+                # TypeError if we pass a nonsense tz;
+                # ValueError if we pass a unit other than "ns"
+                raise TypeError(msg) from err
+        raise TypeError(msg)
+
+    def __str__(self) -> str_type:
+        return f"datetime64[{self.unit}, {self.tz}]"
+
+    @property
+    def name(self) -> str_type:
+        """A string representation of the dtype."""
+        return str(self)
+
+    def __hash__(self) -> int:
+        # make myself hashable
+        # TODO: update this.
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str):
+            if other.startswith("M8["):
+                other = f"datetime64[{other[3:]}"
+            return other == self.name
+
+        return (
+            isinstance(other, DatetimeTZDtype)
+            and self.unit == other.unit
+            and tz_compare(self.tz, other.tz)
+        )
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> DatetimeArray:
+        """
+        Construct DatetimeArray from pyarrow Array/ChunkedArray.
+
+        Note: If the units in the pyarrow Array are the same as this
+        DatetimeDtype, then values corresponding to the integer representation
+        of ``NaT`` (e.g. one nanosecond before :attr:`pandas.Timestamp.min`)
+        are converted to ``NaT``, regardless of the null indicator in the
+        pyarrow array.
+
+        Parameters
+        ----------
+        array : pyarrow.Array or pyarrow.ChunkedArray
+            The Arrow array to convert to DatetimeArray.
+
+        Returns
+        -------
+        extension array : DatetimeArray
+        """
+        import pyarrow
+
+        from pandas.core.arrays import DatetimeArray
+
+        array = array.cast(pyarrow.timestamp(unit=self._unit), safe=True)
+
+        if isinstance(array, pyarrow.Array):
+            np_arr = array.to_numpy(zero_copy_only=False)
+        else:
+            np_arr = array.to_numpy()
+
+        return DatetimeArray._simple_new(np_arr, dtype=self)
+
+    def __setstate__(self, state) -> None:
+        # for pickle compat. __get_state__ is defined in the
+        # PandasExtensionDtype superclass and uses the public properties to
+        # pickle -> need to set the settable private ones here (see GH26067)
+        self._tz = state["tz"]
+        self._unit = state["unit"]
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        if all(isinstance(t, DatetimeTZDtype) and t.tz == self.tz for t in dtypes):
+            np_dtype = np.max([cast(DatetimeTZDtype, t).base for t in [self, *dtypes]])
+            unit = np.datetime_data(np_dtype)[0]
+            return type(self)(unit=unit, tz=self.tz)
+        return super()._get_common_dtype(dtypes)
+
+    @cache_readonly
+    def index_class(self) -> type_t[DatetimeIndex]:
+        from pandas import DatetimeIndex
+
+        return DatetimeIndex
+
+
+@register_extension_dtype
+class PeriodDtype(PeriodDtypeBase, PandasExtensionDtype):
+    """
+    An ExtensionDtype for Period data.
+
+    **This is not an actual numpy dtype**, but a duck type.
+
+    Parameters
+    ----------
+    freq : str or DateOffset
+        The frequency of this PeriodDtype.
+
+    Attributes
+    ----------
+    freq
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.PeriodDtype(freq='D')
+    period[D]
+
+    >>> pd.PeriodDtype(freq=pd.offsets.MonthEnd())
+    period[M]
+    """
+
+    type: type[Period] = Period
+    kind: str_type = "O"
+    str = "|O08"
+    base = np.dtype("O")
+    num = 102
+    _metadata = ("freq",)
+    _match = re.compile(r"(P|p)eriod\[(?P<freq>.+)\]")
+    # error: Incompatible types in assignment (expression has type
+    # "Dict[int, PandasExtensionDtype]", base class "PandasExtensionDtype"
+    # defined the type as "Dict[str, PandasExtensionDtype]")  [assignment]
+    _cache_dtypes: dict[BaseOffset, int] = {}  # type: ignore[assignment]
+    __hash__ = PeriodDtypeBase.__hash__
+    _freq: BaseOffset
+    _supports_2d = True
+    _can_fast_transpose = True
+
+    def __new__(cls, freq) -> PeriodDtype:  # noqa: PYI034
+        """
+        Parameters
+        ----------
+        freq : PeriodDtype, BaseOffset, or string
+        """
+        if isinstance(freq, PeriodDtype):
+            return freq
+
+        if not isinstance(freq, BaseOffset):
+            freq = cls._parse_dtype_strict(freq)
+
+        if isinstance(freq, BDay):
+            # GH#53446
+            # TODO(3.0): enforcing this will close GH#10575
+            warnings.warn(
+                "PeriodDtype[B] is deprecated and will be removed in a future "
+                "version. Use a DatetimeIndex with freq='B' instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        try:
+            dtype_code = cls._cache_dtypes[freq]
+        except KeyError:
+            dtype_code = freq._period_dtype_code
+            cls._cache_dtypes[freq] = dtype_code
+        u = PeriodDtypeBase.__new__(cls, dtype_code, freq.n)
+        u._freq = freq
+        return u
+
+    def __reduce__(self) -> tuple[type_t[Self], tuple[str_type]]:
+        return type(self), (self.name,)
+
+    @property
+    def freq(self) -> BaseOffset:
+        """
+        The frequency object of this PeriodDtype.
+
+        Examples
+        --------
+        >>> dtype = pd.PeriodDtype(freq='D')
+        >>> dtype.freq
+        <Day>
+        """
+        return self._freq
+
+    @classmethod
+    def _parse_dtype_strict(cls, freq: str_type) -> BaseOffset:
+        if isinstance(freq, str):  # note: freq is already of type str!
+            if freq.startswith(("Period[", "period[")):
+                m = cls._match.search(freq)
+                if m is not None:
+                    freq = m.group("freq")
+
+            freq_offset = to_offset(freq, is_period=True)
+            if freq_offset is not None:
+                return freq_offset
+
+        raise TypeError(
+            "PeriodDtype argument should be string or BaseOffset, "
+            f"got {type(freq).__name__}"
+        )
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> PeriodDtype:
+        """
+        Strict construction from a string, raise a TypeError if not
+        possible
+        """
+        if (
+            isinstance(string, str)
+            and (string.startswith(("period[", "Period[")))
+            or isinstance(string, BaseOffset)
+        ):
+            # do not parse string like U as period[U]
+            # avoid tuple to be regarded as freq
+            try:
+                return cls(freq=string)
+            except ValueError:
+                pass
+        if isinstance(string, str):
+            msg = f"Cannot construct a 'PeriodDtype' from '{string}'"
+        else:
+            msg = f"'construct_from_string' expects a string, got {type(string)}"
+        raise TypeError(msg)
+
+    def __str__(self) -> str_type:
+        return self.name
+
+    @property
+    def name(self) -> str_type:
+        return f"period[{self._freqstr}]"
+
+    @property
+    def na_value(self) -> NaTType:
+        return NaT
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str):
+            return other in [self.name, capitalize_first_letter(self.name)]
+
+        return super().__eq__(other)
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        """
+        Return a boolean if we if the passed type is an actual dtype that we
+        can match (via string or type)
+        """
+        if isinstance(dtype, str):
+            # PeriodDtype can be instantiated from freq string like "U",
+            # but doesn't regard freq str like "U" as dtype.
+            if dtype.startswith(("period[", "Period[")):
+                try:
+                    return cls._parse_dtype_strict(dtype) is not None
+                except ValueError:
+                    return False
+            else:
+                return False
+        return super().is_dtype(dtype)
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[PeriodArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import PeriodArray
+
+        return PeriodArray
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> PeriodArray:
+        """
+        Construct PeriodArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays import PeriodArray
+        from pandas.core.arrays.arrow._arrow_utils import (
+            pyarrow_array_to_numpy_and_mask,
+        )
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+        else:
+            chunks = array.chunks
+
+        results = []
+        for arr in chunks:
+            data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=np.dtype(np.int64))
+            parr = PeriodArray(data.copy(), dtype=self, copy=False)
+            # error: Invalid index type "ndarray[Any, dtype[bool_]]" for "PeriodArray";
+            # expected type "Union[int, Sequence[int], Sequence[bool], slice]"
+            parr[~mask] = NaT  # type: ignore[index]
+            results.append(parr)
+
+        if not results:
+            return PeriodArray(np.array([], dtype="int64"), dtype=self, copy=False)
+        return PeriodArray._concat_same_type(results)
+
+    @cache_readonly
+    def index_class(self) -> type_t[PeriodIndex]:
+        from pandas import PeriodIndex
+
+        return PeriodIndex
+
+
+@register_extension_dtype
+class IntervalDtype(PandasExtensionDtype):
+    """
+    An ExtensionDtype for Interval data.
+
+    **This is not an actual numpy dtype**, but a duck type.
+
+    Parameters
+    ----------
+    subtype : str, np.dtype
+        The dtype of the Interval bounds.
+
+    Attributes
+    ----------
+    subtype
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> pd.IntervalDtype(subtype='int64', closed='both')
+    interval[int64, both]
+    """
+
+    name = "interval"
+    kind: str_type = "O"
+    str = "|O08"
+    base = np.dtype("O")
+    num = 103
+    _metadata = (
+        "subtype",
+        "closed",
+    )
+
+    _match = re.compile(
+        r"(I|i)nterval\[(?P<subtype>[^,]+(\[.+\])?)"
+        r"(, (?P<closed>(right|left|both|neither)))?\]"
+    )
+
+    _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
+    _subtype: None | np.dtype
+    _closed: IntervalClosedType | None
+
+    def __init__(self, subtype=None, closed: IntervalClosedType | None = None) -> None:
+        from pandas.core.dtypes.common import (
+            is_string_dtype,
+            pandas_dtype,
+        )
+
+        if closed is not None and closed not in {"right", "left", "both", "neither"}:
+            raise ValueError("closed must be one of 'right', 'left', 'both', 'neither'")
+
+        if isinstance(subtype, IntervalDtype):
+            if closed is not None and closed != subtype.closed:
+                raise ValueError(
+                    "dtype.closed and 'closed' do not match. "
+                    "Try IntervalDtype(dtype.subtype, closed) instead."
+                )
+            self._subtype = subtype._subtype
+            self._closed = subtype._closed
+        elif subtype is None:
+            # we are called as an empty constructor
+            # generally for pickle compat
+            self._subtype = None
+            self._closed = closed
+        elif isinstance(subtype, str) and subtype.lower() == "interval":
+            self._subtype = None
+            self._closed = closed
+        else:
+            if isinstance(subtype, str):
+                m = IntervalDtype._match.search(subtype)
+                if m is not None:
+                    gd = m.groupdict()
+                    subtype = gd["subtype"]
+                    if gd.get("closed", None) is not None:
+                        if closed is not None:
+                            if closed != gd["closed"]:
+                                raise ValueError(
+                                    "'closed' keyword does not match value "
+                                    "specified in dtype string"
+                                )
+                        closed = gd["closed"]  # type: ignore[assignment]
+
+            try:
+                subtype = pandas_dtype(subtype)
+            except TypeError as err:
+                raise TypeError("could not construct IntervalDtype") from err
+            if CategoricalDtype.is_dtype(subtype) or is_string_dtype(subtype):
+                # GH 19016
+                msg = (
+                    "category, object, and string subtypes are not supported "
+                    "for IntervalDtype"
+                )
+                raise TypeError(msg)
+            self._subtype = subtype
+            self._closed = closed
+
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        subtype = self._subtype
+        if subtype is None:
+            # partially-initialized
+            raise NotImplementedError(
+                "_can_hold_na is not defined for partially-initialized IntervalDtype"
+            )
+        if subtype.kind in "iu":
+            return False
+        return True
+
+    @property
+    def closed(self) -> IntervalClosedType:
+        return self._closed  # type: ignore[return-value]
+
+    @property
+    def subtype(self):
+        """
+        The dtype of the Interval bounds.
+
+        Examples
+        --------
+        >>> dtype = pd.IntervalDtype(subtype='int64', closed='both')
+        >>> dtype.subtype
+        dtype('int64')
+        """
+        return self._subtype
+
+    @classmethod
+    def construct_array_type(cls) -> type[IntervalArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import IntervalArray
+
+        return IntervalArray
+
+    @classmethod
+    def construct_from_string(cls, string: str_type) -> IntervalDtype:
+        """
+        attempt to construct this type from a string, raise a TypeError
+        if its not possible
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+
+        if string.lower() == "interval" or cls._match.search(string) is not None:
+            return cls(string)
+
+        msg = (
+            f"Cannot construct a 'IntervalDtype' from '{string}'.\n\n"
+            "Incorrectly formatted string passed to constructor. "
+            "Valid formats include Interval or Interval[dtype] "
+            "where dtype is numeric, datetime, or timedelta"
+        )
+        raise TypeError(msg)
+
+    @property
+    def type(self) -> type[Interval]:
+        return Interval
+
+    def __str__(self) -> str_type:
+        if self.subtype is None:
+            return "interval"
+        if self.closed is None:
+            # Only partially initialized GH#38394
+            return f"interval[{self.subtype}]"
+        return f"interval[{self.subtype}, {self.closed}]"
+
+    def __hash__(self) -> int:
+        # make myself hashable
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, str):
+            return other.lower() in (self.name.lower(), str(self).lower())
+        elif not isinstance(other, IntervalDtype):
+            return False
+        elif self.subtype is None or other.subtype is None:
+            # None should match any subtype
+            return True
+        elif self.closed != other.closed:
+            return False
+        else:
+            return self.subtype == other.subtype
+
+    def __setstate__(self, state) -> None:
+        # for pickle compat. __get_state__ is defined in the
+        # PandasExtensionDtype superclass and uses the public properties to
+        # pickle -> need to set the settable private ones here (see GH26067)
+        self._subtype = state["subtype"]
+
+        # backward-compat older pickles won't have "closed" key
+        self._closed = state.pop("closed", None)
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        """
+        Return a boolean if we if the passed type is an actual dtype that we
+        can match (via string or type)
+        """
+        if isinstance(dtype, str):
+            if dtype.lower().startswith("interval"):
+                try:
+                    return cls.construct_from_string(dtype) is not None
+                except (ValueError, TypeError):
+                    return False
+            else:
+                return False
+        return super().is_dtype(dtype)
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray) -> IntervalArray:
+        """
+        Construct IntervalArray from pyarrow Array/ChunkedArray.
+        """
+        import pyarrow
+
+        from pandas.core.arrays import IntervalArray
+
+        if isinstance(array, pyarrow.Array):
+            chunks = [array]
+        else:
+            chunks = array.chunks
+
+        results = []
+        for arr in chunks:
+            if isinstance(arr, pyarrow.ExtensionArray):
+                arr = arr.storage
+            left = np.asarray(arr.field("left"), dtype=self.subtype)
+            right = np.asarray(arr.field("right"), dtype=self.subtype)
+            iarr = IntervalArray.from_arrays(left, right, closed=self.closed)
+            results.append(iarr)
+
+        if not results:
+            return IntervalArray.from_arrays(
+                np.array([], dtype=self.subtype),
+                np.array([], dtype=self.subtype),
+                closed=self.closed,
+            )
+        return IntervalArray._concat_same_type(results)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        if not all(isinstance(x, IntervalDtype) for x in dtypes):
+            return None
+
+        closed = cast("IntervalDtype", dtypes[0]).closed
+        if not all(cast("IntervalDtype", x).closed == closed for x in dtypes):
+            return np.dtype(object)
+
+        from pandas.core.dtypes.cast import find_common_type
+
+        common = find_common_type([cast("IntervalDtype", x).subtype for x in dtypes])
+        if common == object:
+            return np.dtype(object)
+        return IntervalDtype(common, closed=closed)
+
+    @cache_readonly
+    def index_class(self) -> type_t[IntervalIndex]:
+        from pandas import IntervalIndex
+
+        return IntervalIndex
+
+
+class NumpyEADtype(ExtensionDtype):
+    """
+    A Pandas ExtensionDtype for NumPy dtypes.
+
+    This is mostly for internal compatibility, and is not especially
+    useful on its own.
+
+    Parameters
+    ----------
+    dtype : object
+        Object to be converted to a NumPy data type object.
+
+    See Also
+    --------
+    numpy.dtype
+    """
+
+    _metadata = ("_dtype",)
+    _supports_2d = False
+    _can_fast_transpose = False
+
+    def __init__(self, dtype: npt.DTypeLike | NumpyEADtype | None) -> None:
+        if isinstance(dtype, NumpyEADtype):
+            # make constructor idempotent
+            dtype = dtype.numpy_dtype
+        self._dtype = np.dtype(dtype)
+
+    def __repr__(self) -> str:
+        return f"NumpyEADtype({repr(self.name)})"
+
+    @property
+    def numpy_dtype(self) -> np.dtype:
+        """
+        The NumPy dtype this NumpyEADtype wraps.
+        """
+        return self._dtype
+
+    @property
+    def name(self) -> str:
+        """
+        A bit-width name for this data-type.
+        """
+        return self._dtype.name
+
+    @property
+    def type(self) -> type[np.generic]:
+        """
+        The type object used to instantiate a scalar of this NumPy data-type.
+        """
+        return self._dtype.type
+
+    @property
+    def _is_numeric(self) -> bool:
+        # exclude object, str, unicode, void.
+        return self.kind in set("biufc")
+
+    @property
+    def _is_boolean(self) -> bool:
+        return self.kind == "b"
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> NumpyEADtype:
+        try:
+            dtype = np.dtype(string)
+        except TypeError as err:
+            if not isinstance(string, str):
+                msg = f"'construct_from_string' expects a string, got {type(string)}"
+            else:
+                msg = f"Cannot construct a 'NumpyEADtype' from '{string}'"
+            raise TypeError(msg) from err
+        return cls(dtype)
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[NumpyExtensionArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays import NumpyExtensionArray
+
+        return NumpyExtensionArray
+
+    @property
+    def kind(self) -> str:
+        """
+        A character code (one of 'biufcmMOSUV') identifying the general kind of data.
+        """
+        return self._dtype.kind
+
+    @property
+    def itemsize(self) -> int:
+        """
+        The element size of this data-type object.
+        """
+        return self._dtype.itemsize
+
+
+class BaseMaskedDtype(ExtensionDtype):
+    """
+    Base class for dtypes for BaseMaskedArray subclasses.
+    """
+
+    base = None
+    type: type
+
+    @property
+    def na_value(self) -> libmissing.NAType:
+        return libmissing.NA
+
+    @cache_readonly
+    def numpy_dtype(self) -> np.dtype:
+        """Return an instance of our numpy dtype"""
+        return np.dtype(self.type)
+
+    @cache_readonly
+    def kind(self) -> str:
+        return self.numpy_dtype.kind
+
+    @cache_readonly
+    def itemsize(self) -> int:
+        """Return the number of bytes in this dtype"""
+        return self.numpy_dtype.itemsize
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[BaseMaskedArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def from_numpy_dtype(cls, dtype: np.dtype) -> BaseMaskedDtype:
+        """
+        Construct the MaskedDtype corresponding to the given numpy dtype.
+        """
+        if dtype.kind == "b":
+            from pandas.core.arrays.boolean import BooleanDtype
+
+            return BooleanDtype()
+        elif dtype.kind in "iu":
+            from pandas.core.arrays.integer import NUMPY_INT_TO_DTYPE
+
+            return NUMPY_INT_TO_DTYPE[dtype]
+        elif dtype.kind == "f":
+            from pandas.core.arrays.floating import NUMPY_FLOAT_TO_DTYPE
+
+            return NUMPY_FLOAT_TO_DTYPE[dtype]
+        else:
+            raise NotImplementedError(dtype)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # We unwrap any masked dtypes, find the common dtype we would use
+        #  for that, then re-mask the result.
+        from pandas.core.dtypes.cast import find_common_type
+
+        new_dtype = find_common_type(
+            [
+                dtype.numpy_dtype if isinstance(dtype, BaseMaskedDtype) else dtype
+                for dtype in dtypes
+            ]
+        )
+        if not isinstance(new_dtype, np.dtype):
+            # If we ever support e.g. Masked[DatetimeArray] then this will change
+            return None
+        try:
+            return type(self).from_numpy_dtype(new_dtype)
+        except (KeyError, NotImplementedError):
+            return None
+
+
+@register_extension_dtype
+class SparseDtype(ExtensionDtype):
+    """
+    Dtype for data stored in :class:`SparseArray`.
+
+    This dtype implements the pandas ExtensionDtype interface.
+
+    Parameters
+    ----------
+    dtype : str, ExtensionDtype, numpy.dtype, type, default numpy.float64
+        The dtype of the underlying array storing the non-fill value values.
+    fill_value : scalar, optional
+        The scalar value not stored in the SparseArray. By default, this
+        depends on `dtype`.
+
+        =========== ==========
+        dtype       na_value
+        =========== ==========
+        float       ``np.nan``
+        int         ``0``
+        bool        ``False``
+        datetime64  ``pd.NaT``
+        timedelta64 ``pd.NaT``
+        =========== ==========
+
+        The default value may be overridden by specifying a `fill_value`.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Examples
+    --------
+    >>> ser = pd.Series([1, 0, 0], dtype=pd.SparseDtype(dtype=int, fill_value=0))
+    >>> ser
+    0    1
+    1    0
+    2    0
+    dtype: Sparse[int64, 0]
+    >>> ser.sparse.density
+    0.3333333333333333
+    """
+
+    _is_immutable = True
+
+    # We include `_is_na_fill_value` in the metadata to avoid hash collisions
+    # between SparseDtype(float, 0.0) and SparseDtype(float, nan).
+    # Without is_na_fill_value in the comparison, those would be equal since
+    # hash(nan) is (sometimes?) 0.
+    _metadata = ("_dtype", "_fill_value", "_is_na_fill_value")
+
+    def __init__(self, dtype: Dtype = np.float64, fill_value: Any = None) -> None:
+        if isinstance(dtype, type(self)):
+            if fill_value is None:
+                fill_value = dtype.fill_value
+            dtype = dtype.subtype
+
+        from pandas.core.dtypes.common import (
+            is_string_dtype,
+            pandas_dtype,
+        )
+        from pandas.core.dtypes.missing import na_value_for_dtype
+
+        dtype = pandas_dtype(dtype)
+        if is_string_dtype(dtype):
+            dtype = np.dtype("object")
+        if not isinstance(dtype, np.dtype):
+            # GH#53160
+            raise TypeError("SparseDtype subtype must be a numpy dtype")
+
+        if fill_value is None:
+            fill_value = na_value_for_dtype(dtype)
+
+        self._dtype = dtype
+        self._fill_value = fill_value
+        self._check_fill_value()
+
+    def __hash__(self) -> int:
+        # Python3 doesn't inherit __hash__ when a base class overrides
+        # __eq__, so we explicitly do it here.
+        return super().__hash__()
+
+    def __eq__(self, other: object) -> bool:
+        # We have to override __eq__ to handle NA values in _metadata.
+        # The base class does simple == checks, which fail for NA.
+        if isinstance(other, str):
+            try:
+                other = self.construct_from_string(other)
+            except TypeError:
+                return False
+
+        if isinstance(other, type(self)):
+            subtype = self.subtype == other.subtype
+            if self._is_na_fill_value:
+                # this case is complicated by two things:
+                # SparseDtype(float, float(nan)) == SparseDtype(float, np.nan)
+                # SparseDtype(float, np.nan)     != SparseDtype(float, pd.NaT)
+                # i.e. we want to treat any floating-point NaN as equal, but
+                # not a floating-point NaN and a datetime NaT.
+                fill_value = (
+                    other._is_na_fill_value
+                    and isinstance(self.fill_value, type(other.fill_value))
+                    or isinstance(other.fill_value, type(self.fill_value))
+                )
+            else:
+                with warnings.catch_warnings():
+                    # Ignore spurious numpy warning
+                    warnings.filterwarnings(
+                        "ignore",
+                        "elementwise comparison failed",
+                        category=DeprecationWarning,
+                    )
+
+                    fill_value = self.fill_value == other.fill_value
+
+            return subtype and fill_value
+        return False
+
+    @property
+    def fill_value(self):
+        """
+        The fill value of the array.
+
+        Converting the SparseArray to a dense ndarray will fill the
+        array with this value.
+
+        .. warning::
+
+           It's possible to end up with a SparseArray that has ``fill_value``
+           values in ``sp_values``. This can occur, for example, when setting
+           ``SparseArray.fill_value`` directly.
+        """
+        return self._fill_value
+
+    def _check_fill_value(self) -> None:
+        if not lib.is_scalar(self._fill_value):
+            raise ValueError(
+                f"fill_value must be a scalar. Got {self._fill_value} instead"
+            )
+
+        from pandas.core.dtypes.cast import can_hold_element
+        from pandas.core.dtypes.missing import (
+            is_valid_na_for_dtype,
+            isna,
+        )
+
+        from pandas.core.construction import ensure_wrapped_if_datetimelike
+
+        # GH#23124 require fill_value and subtype to match
+        val = self._fill_value
+        if isna(val):
+            if not is_valid_na_for_dtype(val, self.subtype):
+                warnings.warn(
+                    "Allowing arbitrary scalar fill_value in SparseDtype is "
+                    "deprecated. In a future version, the fill_value must be "
+                    "a valid value for the SparseDtype.subtype.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            dummy = np.empty(0, dtype=self.subtype)
+            dummy = ensure_wrapped_if_datetimelike(dummy)
+
+            if not can_hold_element(dummy, val):
+                warnings.warn(
+                    "Allowing arbitrary scalar fill_value in SparseDtype is "
+                    "deprecated. In a future version, the fill_value must be "
+                    "a valid value for the SparseDtype.subtype.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    @property
+    def _is_na_fill_value(self) -> bool:
+        from pandas import isna
+
+        return isna(self.fill_value)
+
+    @property
+    def _is_numeric(self) -> bool:
+        return self.subtype != object
+
+    @property
+    def _is_boolean(self) -> bool:
+        return self.subtype.kind == "b"
+
+    @property
+    def kind(self) -> str:
+        """
+        The sparse kind. Either 'integer', or 'block'.
+        """
+        return self.subtype.kind
+
+    @property
+    def type(self):
+        return self.subtype.type
+
+    @property
+    def subtype(self):
+        return self._dtype
+
+    @property
+    def name(self) -> str:
+        return f"Sparse[{self.subtype.name}, {repr(self.fill_value)}]"
+
+    def __repr__(self) -> str:
+        return self.name
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[SparseArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays.sparse.array import SparseArray
+
+        return SparseArray
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> SparseDtype:
+        """
+        Construct a SparseDtype from a string form.
+
+        Parameters
+        ----------
+        string : str
+            Can take the following forms.
+
+            string           dtype
+            ================ ============================
+            'int'            SparseDtype[np.int64, 0]
+            'Sparse'         SparseDtype[np.float64, nan]
+            'Sparse[int]'    SparseDtype[np.int64, 0]
+            'Sparse[int, 0]' SparseDtype[np.int64, 0]
+            ================ ============================
+
+            It is not possible to specify non-default fill values
+            with a string. An argument like ``'Sparse[int, 1]'``
+            will raise a ``TypeError`` because the default fill value
+            for integers is 0.
+
+        Returns
+        -------
+        SparseDtype
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        msg = f"Cannot construct a 'SparseDtype' from '{string}'"
+        if string.startswith("Sparse"):
+            try:
+                sub_type, has_fill_value = cls._parse_subtype(string)
+            except ValueError as err:
+                raise TypeError(msg) from err
+            else:
+                result = SparseDtype(sub_type)
+                msg = (
+                    f"Cannot construct a 'SparseDtype' from '{string}'.\n\nIt "
+                    "looks like the fill_value in the string is not "
+                    "the default for the dtype. Non-default fill_values "
+                    "are not supported. Use the 'SparseDtype()' "
+                    "constructor instead."
+                )
+                if has_fill_value and str(result) != string:
+                    raise TypeError(msg)
+                return result
+        else:
+            raise TypeError(msg)
+
+    @staticmethod
+    def _parse_subtype(dtype: str) -> tuple[str, bool]:
+        """
+        Parse a string to get the subtype
+
+        Parameters
+        ----------
+        dtype : str
+            A string like
+
+            * Sparse[subtype]
+            * Sparse[subtype, fill_value]
+
+        Returns
+        -------
+        subtype : str
+
+        Raises
+        ------
+        ValueError
+            When the subtype cannot be extracted.
+        """
+        xpr = re.compile(r"Sparse\[(?P<subtype>[^,]*)(, )?(?P<fill_value>.*?)?\]$")
+        m = xpr.match(dtype)
+        has_fill_value = False
+        if m:
+            subtype = m.groupdict()["subtype"]
+            has_fill_value = bool(m.groupdict()["fill_value"])
+        elif dtype == "Sparse":
+            subtype = "float64"
+        else:
+            raise ValueError(f"Cannot parse {dtype}")
+        return subtype, has_fill_value
+
+    @classmethod
+    def is_dtype(cls, dtype: object) -> bool:
+        dtype = getattr(dtype, "dtype", dtype)
+        if isinstance(dtype, str) and dtype.startswith("Sparse"):
+            sub_type, _ = cls._parse_subtype(dtype)
+            dtype = np.dtype(sub_type)
+        elif isinstance(dtype, cls):
+            return True
+        return isinstance(dtype, np.dtype) or dtype == "Sparse"
+
+    def update_dtype(self, dtype) -> SparseDtype:
+        """
+        Convert the SparseDtype to a new dtype.
+
+        This takes care of converting the ``fill_value``.
+
+        Parameters
+        ----------
+        dtype : Union[str, numpy.dtype, SparseDtype]
+            The new dtype to use.
+
+            * For a SparseDtype, it is simply returned
+            * For a NumPy dtype (or str), the current fill value
+              is converted to the new dtype, and a SparseDtype
+              with `dtype` and the new fill value is returned.
+
+        Returns
+        -------
+        SparseDtype
+            A new SparseDtype with the correct `dtype` and fill value
+            for that `dtype`.
+
+        Raises
+        ------
+        ValueError
+            When the current fill value cannot be converted to the
+            new `dtype` (e.g. trying to convert ``np.nan`` to an
+            integer dtype).
+
+
+        Examples
+        --------
+        >>> SparseDtype(int, 0).update_dtype(float)
+        Sparse[float64, 0.0]
+
+        >>> SparseDtype(int, 1).update_dtype(SparseDtype(float, np.nan))
+        Sparse[float64, nan]
+        """
+        from pandas.core.dtypes.astype import astype_array
+        from pandas.core.dtypes.common import pandas_dtype
+
+        cls = type(self)
+        dtype = pandas_dtype(dtype)
+
+        if not isinstance(dtype, cls):
+            if not isinstance(dtype, np.dtype):
+                raise TypeError("sparse arrays of extension dtypes not supported")
+
+            fv_asarray = np.atleast_1d(np.array(self.fill_value))
+            fvarr = astype_array(fv_asarray, dtype)
+            # NB: not fv_0d.item(), as that casts dt64->int
+            fill_value = fvarr[0]
+            dtype = cls(dtype, fill_value=fill_value)
+
+        return dtype
+
+    @property
+    def _subtype_with_str(self):
+        """
+        Whether the SparseDtype's subtype should be considered ``str``.
+
+        Typically, pandas will store string data in an object-dtype array.
+        When converting values to a dtype, e.g. in ``.astype``, we need to
+        be more specific, we need the actual underlying type.
+
+        Returns
+        -------
+        >>> SparseDtype(int, 1)._subtype_with_str
+        dtype('int64')
+
+        >>> SparseDtype(object, 1)._subtype_with_str
+        dtype('O')
+
+        >>> dtype = SparseDtype(str, '')
+        >>> dtype.subtype
+        dtype('O')
+
+        >>> dtype._subtype_with_str
+        <class 'str'>
+        """
+        if isinstance(self.fill_value, str):
+            return type(self.fill_value)
+        return self.subtype
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # TODO for now only handle SparseDtypes and numpy dtypes => extend
+        # with other compatible extension dtypes
+        from pandas.core.dtypes.cast import np_find_common_type
+
+        if any(
+            isinstance(x, ExtensionDtype) and not isinstance(x, SparseDtype)
+            for x in dtypes
+        ):
+            return None
+
+        fill_values = [x.fill_value for x in dtypes if isinstance(x, SparseDtype)]
+        fill_value = fill_values[0]
+
+        from pandas import isna
+
+        # np.nan isn't a singleton, so we may end up with multiple
+        # NaNs here, so we ignore the all NA case too.
+        if not (len(set(fill_values)) == 1 or isna(fill_values).all()):
+            warnings.warn(
+                "Concatenating sparse arrays with multiple fill "
+                f"values: '{fill_values}'. Picking the first and "
+                "converting the rest.",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        np_dtypes = (x.subtype if isinstance(x, SparseDtype) else x for x in dtypes)
+        return SparseDtype(np_find_common_type(*np_dtypes), fill_value=fill_value)
+
+
+@register_extension_dtype
+class ArrowDtype(StorageExtensionDtype):
+    """
+    An ExtensionDtype for PyArrow data types.
+
+    .. warning::
+
+       ArrowDtype is considered experimental. The implementation and
+       parts of the API may change without warning.
+
+    While most ``dtype`` arguments can accept the "string"
+    constructor, e.g. ``"int64[pyarrow]"``, ArrowDtype is useful
+    if the data type contains parameters like ``pyarrow.timestamp``.
+
+    Parameters
+    ----------
+    pyarrow_dtype : pa.DataType
+        An instance of a `pyarrow.DataType <https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions>`__.
+
+    Attributes
+    ----------
+    pyarrow_dtype
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    ArrowDtype
+
+    Examples
+    --------
+    >>> import pyarrow as pa
+    >>> pd.ArrowDtype(pa.int64())
+    int64[pyarrow]
+
+    Types with parameters must be constructed with ArrowDtype.
+
+    >>> pd.ArrowDtype(pa.timestamp("s", tz="America/New_York"))
+    timestamp[s, tz=America/New_York][pyarrow]
+    >>> pd.ArrowDtype(pa.list_(pa.int64()))
+    list<item: int64>[pyarrow]
+    """
+
+    _metadata = ("storage", "pyarrow_dtype")  # type: ignore[assignment]
+
+    def __init__(self, pyarrow_dtype: pa.DataType) -> None:
+        super().__init__("pyarrow")
+        if pa_version_under10p1:
+            raise ImportError("pyarrow>=10.0.1 is required for ArrowDtype")
+        if not isinstance(pyarrow_dtype, pa.DataType):
+            raise ValueError(
+                f"pyarrow_dtype ({pyarrow_dtype}) must be an instance "
+                f"of a pyarrow.DataType. Got {type(pyarrow_dtype)} instead."
+            )
+        self.pyarrow_dtype = pyarrow_dtype
+
+    def __repr__(self) -> str:
+        return self.name
+
+    def __hash__(self) -> int:
+        # make myself hashable
+        return hash(str(self))
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, type(self)):
+            return super().__eq__(other)
+        return self.pyarrow_dtype == other.pyarrow_dtype
+
+    @property
+    def type(self):
+        """
+        Returns associated scalar type.
+        """
+        pa_type = self.pyarrow_dtype
+        if pa.types.is_integer(pa_type):
+            return int
+        elif pa.types.is_floating(pa_type):
+            return float
+        elif pa.types.is_string(pa_type) or pa.types.is_large_string(pa_type):
+            return str
+        elif (
+            pa.types.is_binary(pa_type)
+            or pa.types.is_fixed_size_binary(pa_type)
+            or pa.types.is_large_binary(pa_type)
+        ):
+            return bytes
+        elif pa.types.is_boolean(pa_type):
+            return bool
+        elif pa.types.is_duration(pa_type):
+            if pa_type.unit == "ns":
+                return Timedelta
+            else:
+                return timedelta
+        elif pa.types.is_timestamp(pa_type):
+            if pa_type.unit == "ns":
+                return Timestamp
+            else:
+                return datetime
+        elif pa.types.is_date(pa_type):
+            return date
+        elif pa.types.is_time(pa_type):
+            return time
+        elif pa.types.is_decimal(pa_type):
+            return Decimal
+        elif pa.types.is_dictionary(pa_type):
+            # TODO: Potentially change this & CategoricalDtype.type to
+            #  something more representative of the scalar
+            return CategoricalDtypeType
+        elif pa.types.is_list(pa_type) or pa.types.is_large_list(pa_type):
+            return list
+        elif pa.types.is_fixed_size_list(pa_type):
+            return list
+        elif pa.types.is_map(pa_type):
+            return list
+        elif pa.types.is_struct(pa_type):
+            return dict
+        elif pa.types.is_null(pa_type):
+            # TODO: None? pd.NA? pa.null?
+            return type(pa_type)
+        elif isinstance(pa_type, pa.ExtensionType):
+            return type(self)(pa_type.storage_type).type
+        raise NotImplementedError(pa_type)
+
+    @property
+    def name(self) -> str:  # type: ignore[override]
+        """
+        A string identifying the data type.
+        """
+        return f"{str(self.pyarrow_dtype)}[{self.storage}]"
+
+    @cache_readonly
+    def numpy_dtype(self) -> np.dtype:
+        """Return an instance of the related numpy dtype"""
+        if pa.types.is_timestamp(self.pyarrow_dtype):
+            # pa.timestamp(unit).to_pandas_dtype() returns ns units
+            # regardless of the pyarrow timestamp units.
+            # This can be removed if/when pyarrow addresses it:
+            # https://github.com/apache/arrow/issues/34462
+            return np.dtype(f"datetime64[{self.pyarrow_dtype.unit}]")
+        if pa.types.is_duration(self.pyarrow_dtype):
+            # pa.duration(unit).to_pandas_dtype() returns ns units
+            # regardless of the pyarrow duration units
+            # This can be removed if/when pyarrow addresses it:
+            # https://github.com/apache/arrow/issues/34462
+            return np.dtype(f"timedelta64[{self.pyarrow_dtype.unit}]")
+        if pa.types.is_string(self.pyarrow_dtype) or pa.types.is_large_string(
+            self.pyarrow_dtype
+        ):
+            # pa.string().to_pandas_dtype() = object which we don't want
+            return np.dtype(str)
+        try:
+            return np.dtype(self.pyarrow_dtype.to_pandas_dtype())
+        except (NotImplementedError, TypeError):
+            return np.dtype(object)
+
+    @cache_readonly
+    def kind(self) -> str:
+        if pa.types.is_timestamp(self.pyarrow_dtype):
+            # To mirror DatetimeTZDtype
+            return "M"
+        return self.numpy_dtype.kind
+
+    @cache_readonly
+    def itemsize(self) -> int:
+        """Return the number of bytes in this dtype"""
+        return self.numpy_dtype.itemsize
+
+    @classmethod
+    def construct_array_type(cls) -> type_t[ArrowExtensionArray]:
+        """
+        Return the array type associated with this dtype.
+
+        Returns
+        -------
+        type
+        """
+        from pandas.core.arrays.arrow import ArrowExtensionArray
+
+        return ArrowExtensionArray
+
+    @classmethod
+    def construct_from_string(cls, string: str) -> ArrowDtype:
+        """
+        Construct this type from a string.
+
+        Parameters
+        ----------
+        string : str
+            string should follow the format f"{pyarrow_type}[pyarrow]"
+            e.g. int64[pyarrow]
+        """
+        if not isinstance(string, str):
+            raise TypeError(
+                f"'construct_from_string' expects a string, got {type(string)}"
+            )
+        if not string.endswith("[pyarrow]"):
+            raise TypeError(f"'{string}' must end with '[pyarrow]'")
+        if string in ("string[pyarrow]", "str[pyarrow]"):
+            # Ensure Registry.find skips ArrowDtype to use StringDtype instead
+            raise TypeError("string[pyarrow] should be constructed by StringDtype")
+
+        base_type = string[:-9]  # get rid of "[pyarrow]"
+        try:
+            pa_dtype = pa.type_for_alias(base_type)
+        except ValueError as err:
+            has_parameters = re.search(r"[\[\(].*[\]\)]", base_type)
+            if has_parameters:
+                # Fallback to try common temporal types
+                try:
+                    return cls._parse_temporal_dtype_string(base_type)
+                except (NotImplementedError, ValueError):
+                    # Fall through to raise with nice exception message below
+                    pass
+
+                raise NotImplementedError(
+                    "Passing pyarrow type specific parameters "
+                    f"({has_parameters.group()}) in the string is not supported. "
+                    "Please construct an ArrowDtype object with a pyarrow_dtype "
+                    "instance with specific parameters."
+                ) from err
+            raise TypeError(f"'{base_type}' is not a valid pyarrow data type.") from err
+        return cls(pa_dtype)
+
+    # TODO(arrow#33642): This can be removed once supported by pyarrow
+    @classmethod
+    def _parse_temporal_dtype_string(cls, string: str) -> ArrowDtype:
+        """
+        Construct a temporal ArrowDtype from string.
+        """
+        # we assume
+        #  1) "[pyarrow]" has already been stripped from the end of our string.
+        #  2) we know "[" is present
+        head, tail = string.split("[", 1)
+
+        if not tail.endswith("]"):
+            raise ValueError
+        tail = tail[:-1]
+
+        if head == "timestamp":
+            assert "," in tail  # otherwise type_for_alias should work
+            unit, tz = tail.split(",", 1)
+            unit = unit.strip()
+            tz = tz.strip()
+            if tz.startswith("tz="):
+                tz = tz[3:]
+
+            pa_type = pa.timestamp(unit, tz=tz)
+            dtype = cls(pa_type)
+            return dtype
+
+        raise NotImplementedError(string)
+
+    @property
+    def _is_numeric(self) -> bool:
+        """
+        Whether columns with this dtype should be considered numeric.
+        """
+        # TODO: pa.types.is_boolean?
+        return (
+            pa.types.is_integer(self.pyarrow_dtype)
+            or pa.types.is_floating(self.pyarrow_dtype)
+            or pa.types.is_decimal(self.pyarrow_dtype)
+        )
+
+    @property
+    def _is_boolean(self) -> bool:
+        """
+        Whether this dtype should be considered boolean.
+        """
+        return pa.types.is_boolean(self.pyarrow_dtype)
+
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # We unwrap any masked dtypes, find the common dtype we would use
+        #  for that, then re-mask the result.
+        # Mirrors BaseMaskedDtype
+        from pandas.core.dtypes.cast import find_common_type
+
+        null_dtype = type(self)(pa.null())
+
+        new_dtype = find_common_type(
+            [
+                dtype.numpy_dtype if isinstance(dtype, ArrowDtype) else dtype
+                for dtype in dtypes
+                if dtype != null_dtype
+            ]
+        )
+        if not isinstance(new_dtype, np.dtype):
+            return None
+        try:
+            pa_dtype = pa.from_numpy_dtype(new_dtype)
+            return type(self)(pa_dtype)
+        except NotImplementedError:
+            return None
+
+    def __from_arrow__(self, array: pa.Array | pa.ChunkedArray):
+        """
+        Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
+        """
+        array_class = self.construct_array_type()
+        arr = array.cast(self.pyarrow_dtype, safe=True)
+        return array_class(arr)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/generic.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..9718ad600cb80b6e38f069a83aaf35ddb376fb00
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/generic.py
@@ -0,0 +1,147 @@
+""" define generic base classes for pandas objects """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Type,
+    cast,
+)
+
+if TYPE_CHECKING:
+    from pandas import (
+        Categorical,
+        CategoricalIndex,
+        DataFrame,
+        DatetimeIndex,
+        Index,
+        IntervalIndex,
+        MultiIndex,
+        PeriodIndex,
+        RangeIndex,
+        Series,
+        TimedeltaIndex,
+    )
+    from pandas.core.arrays import (
+        DatetimeArray,
+        ExtensionArray,
+        NumpyExtensionArray,
+        PeriodArray,
+        TimedeltaArray,
+    )
+    from pandas.core.generic import NDFrame
+
+
+# define abstract base classes to enable isinstance type checking on our
+# objects
+def create_pandas_abc_type(name, attr, comp):
+    def _check(inst) -> bool:
+        return getattr(inst, attr, "_typ") in comp
+
+    # https://github.com/python/mypy/issues/1006
+    # error: 'classmethod' used with a non-method
+    @classmethod  # type: ignore[misc]
+    def _instancecheck(cls, inst) -> bool:
+        return _check(inst) and not isinstance(inst, type)
+
+    @classmethod  # type: ignore[misc]
+    def _subclasscheck(cls, inst) -> bool:
+        # Raise instead of returning False
+        # This is consistent with default __subclasscheck__ behavior
+        if not isinstance(inst, type):
+            raise TypeError("issubclass() arg 1 must be a class")
+
+        return _check(inst)
+
+    dct = {"__instancecheck__": _instancecheck, "__subclasscheck__": _subclasscheck}
+    meta = type("ABCBase", (type,), dct)
+    return meta(name, (), dct)
+
+
+ABCRangeIndex = cast(
+    "Type[RangeIndex]",
+    create_pandas_abc_type("ABCRangeIndex", "_typ", ("rangeindex",)),
+)
+ABCMultiIndex = cast(
+    "Type[MultiIndex]",
+    create_pandas_abc_type("ABCMultiIndex", "_typ", ("multiindex",)),
+)
+ABCDatetimeIndex = cast(
+    "Type[DatetimeIndex]",
+    create_pandas_abc_type("ABCDatetimeIndex", "_typ", ("datetimeindex",)),
+)
+ABCTimedeltaIndex = cast(
+    "Type[TimedeltaIndex]",
+    create_pandas_abc_type("ABCTimedeltaIndex", "_typ", ("timedeltaindex",)),
+)
+ABCPeriodIndex = cast(
+    "Type[PeriodIndex]",
+    create_pandas_abc_type("ABCPeriodIndex", "_typ", ("periodindex",)),
+)
+ABCCategoricalIndex = cast(
+    "Type[CategoricalIndex]",
+    create_pandas_abc_type("ABCCategoricalIndex", "_typ", ("categoricalindex",)),
+)
+ABCIntervalIndex = cast(
+    "Type[IntervalIndex]",
+    create_pandas_abc_type("ABCIntervalIndex", "_typ", ("intervalindex",)),
+)
+ABCIndex = cast(
+    "Type[Index]",
+    create_pandas_abc_type(
+        "ABCIndex",
+        "_typ",
+        {
+            "index",
+            "rangeindex",
+            "multiindex",
+            "datetimeindex",
+            "timedeltaindex",
+            "periodindex",
+            "categoricalindex",
+            "intervalindex",
+        },
+    ),
+)
+
+
+ABCNDFrame = cast(
+    "Type[NDFrame]",
+    create_pandas_abc_type("ABCNDFrame", "_typ", ("series", "dataframe")),
+)
+ABCSeries = cast(
+    "Type[Series]",
+    create_pandas_abc_type("ABCSeries", "_typ", ("series",)),
+)
+ABCDataFrame = cast(
+    "Type[DataFrame]", create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe",))
+)
+
+ABCCategorical = cast(
+    "Type[Categorical]",
+    create_pandas_abc_type("ABCCategorical", "_typ", ("categorical")),
+)
+ABCDatetimeArray = cast(
+    "Type[DatetimeArray]",
+    create_pandas_abc_type("ABCDatetimeArray", "_typ", ("datetimearray")),
+)
+ABCTimedeltaArray = cast(
+    "Type[TimedeltaArray]",
+    create_pandas_abc_type("ABCTimedeltaArray", "_typ", ("timedeltaarray")),
+)
+ABCPeriodArray = cast(
+    "Type[PeriodArray]",
+    create_pandas_abc_type("ABCPeriodArray", "_typ", ("periodarray",)),
+)
+ABCExtensionArray = cast(
+    "Type[ExtensionArray]",
+    create_pandas_abc_type(
+        "ABCExtensionArray",
+        "_typ",
+        # Note: IntervalArray and SparseArray are included bc they have _typ="extension"
+        {"extension", "categorical", "periodarray", "datetimearray", "timedeltaarray"},
+    ),
+)
+ABCNumpyExtensionArray = cast(
+    "Type[NumpyExtensionArray]",
+    create_pandas_abc_type("ABCNumpyExtensionArray", "_typ", ("npy_extension",)),
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/inference.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..f551716772f61455a330bdb308cee830bd54fb03
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/inference.py
@@ -0,0 +1,437 @@
+""" basic inference routines """
+
+from __future__ import annotations
+
+from collections import abc
+from numbers import Number
+import re
+from re import Pattern
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs import lib
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import TypeGuard
+
+is_bool = lib.is_bool
+
+is_integer = lib.is_integer
+
+is_float = lib.is_float
+
+is_complex = lib.is_complex
+
+is_scalar = lib.is_scalar
+
+is_decimal = lib.is_decimal
+
+is_interval = lib.is_interval
+
+is_list_like = lib.is_list_like
+
+is_iterator = lib.is_iterator
+
+
+def is_number(obj) -> TypeGuard[Number | np.number]:
+    """
+    Check if the object is a number.
+
+    Returns True when the object is a number, and False if is not.
+
+    Parameters
+    ----------
+    obj : any type
+        The object to check if is a number.
+
+    Returns
+    -------
+    bool
+        Whether `obj` is a number or not.
+
+    See Also
+    --------
+    api.types.is_integer: Checks a subgroup of numbers.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_number
+    >>> is_number(1)
+    True
+    >>> is_number(7.15)
+    True
+
+    Booleans are valid because they are int subclass.
+
+    >>> is_number(False)
+    True
+
+    >>> is_number("foo")
+    False
+    >>> is_number("5")
+    False
+    """
+    return isinstance(obj, (Number, np.number))
+
+
+def iterable_not_string(obj) -> bool:
+    """
+    Check if the object is an iterable but not a string.
+
+    Parameters
+    ----------
+    obj : The object to check.
+
+    Returns
+    -------
+    is_iter_not_string : bool
+        Whether `obj` is a non-string iterable.
+
+    Examples
+    --------
+    >>> iterable_not_string([1, 2, 3])
+    True
+    >>> iterable_not_string("foo")
+    False
+    >>> iterable_not_string(1)
+    False
+    """
+    return isinstance(obj, abc.Iterable) and not isinstance(obj, str)
+
+
+def is_file_like(obj) -> bool:
+    """
+    Check if the object is a file-like object.
+
+    For objects to be considered file-like, they must
+    be an iterator AND have either a `read` and/or `write`
+    method as an attribute.
+
+    Note: file-like objects must be iterable, but
+    iterable objects need not be file-like.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` has file-like properties.
+
+    Examples
+    --------
+    >>> import io
+    >>> from pandas.api.types import is_file_like
+    >>> buffer = io.StringIO("data")
+    >>> is_file_like(buffer)
+    True
+    >>> is_file_like([1, 2, 3])
+    False
+    """
+    if not (hasattr(obj, "read") or hasattr(obj, "write")):
+        return False
+
+    return bool(hasattr(obj, "__iter__"))
+
+
+def is_re(obj) -> TypeGuard[Pattern]:
+    """
+    Check if the object is a regex pattern instance.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` is a regex pattern.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_re
+    >>> import re
+    >>> is_re(re.compile(".*"))
+    True
+    >>> is_re("foo")
+    False
+    """
+    return isinstance(obj, Pattern)
+
+
+def is_re_compilable(obj) -> bool:
+    """
+    Check if the object can be compiled into a regex pattern instance.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` can be compiled as a regex pattern.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_re_compilable
+    >>> is_re_compilable(".*")
+    True
+    >>> is_re_compilable(1)
+    False
+    """
+    try:
+        re.compile(obj)
+    except TypeError:
+        return False
+    else:
+        return True
+
+
+def is_array_like(obj) -> bool:
+    """
+    Check if the object is array-like.
+
+    For an object to be considered array-like, it must be list-like and
+    have a `dtype` attribute.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    is_array_like : bool
+        Whether `obj` has array-like properties.
+
+    Examples
+    --------
+    >>> is_array_like(np.array([1, 2, 3]))
+    True
+    >>> is_array_like(pd.Series(["a", "b"]))
+    True
+    >>> is_array_like(pd.Index(["2016-01-01"]))
+    True
+    >>> is_array_like([1, 2, 3])
+    False
+    >>> is_array_like(("a", "b"))
+    False
+    """
+    return is_list_like(obj) and hasattr(obj, "dtype")
+
+
+def is_nested_list_like(obj) -> bool:
+    """
+    Check if the object is list-like, and that all of its elements
+    are also list-like.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    is_list_like : bool
+        Whether `obj` has list-like properties.
+
+    Examples
+    --------
+    >>> is_nested_list_like([[1, 2, 3]])
+    True
+    >>> is_nested_list_like([{1, 2, 3}, {1, 2, 3}])
+    True
+    >>> is_nested_list_like(["foo"])
+    False
+    >>> is_nested_list_like([])
+    False
+    >>> is_nested_list_like([[1, 2, 3], 1])
+    False
+
+    Notes
+    -----
+    This won't reliably detect whether a consumable iterator (e. g.
+    a generator) is a nested-list-like without consuming the iterator.
+    To avoid consuming it, we always return False if the outer container
+    doesn't define `__len__`.
+
+    See Also
+    --------
+    is_list_like
+    """
+    return (
+        is_list_like(obj)
+        and hasattr(obj, "__len__")
+        and len(obj) > 0
+        and all(is_list_like(item) for item in obj)
+    )
+
+
+def is_dict_like(obj) -> bool:
+    """
+    Check if the object is dict-like.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` has dict-like properties.
+
+    Examples
+    --------
+    >>> from pandas.api.types import is_dict_like
+    >>> is_dict_like({1: 2})
+    True
+    >>> is_dict_like([1, 2, 3])
+    False
+    >>> is_dict_like(dict)
+    False
+    >>> is_dict_like(dict())
+    True
+    """
+    dict_like_attrs = ("__getitem__", "keys", "__contains__")
+    return (
+        all(hasattr(obj, attr) for attr in dict_like_attrs)
+        # [GH 25196] exclude classes
+        and not isinstance(obj, type)
+    )
+
+
+def is_named_tuple(obj) -> bool:
+    """
+    Check if the object is a named tuple.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    bool
+        Whether `obj` is a named tuple.
+
+    Examples
+    --------
+    >>> from collections import namedtuple
+    >>> from pandas.api.types import is_named_tuple
+    >>> Point = namedtuple("Point", ["x", "y"])
+    >>> p = Point(1, 2)
+    >>>
+    >>> is_named_tuple(p)
+    True
+    >>> is_named_tuple((1, 2))
+    False
+    """
+    return isinstance(obj, abc.Sequence) and hasattr(obj, "_fields")
+
+
+def is_hashable(obj) -> TypeGuard[Hashable]:
+    """
+    Return True if hash(obj) will succeed, False otherwise.
+
+    Some types will pass a test against collections.abc.Hashable but fail when
+    they are actually hashed with hash().
+
+    Distinguish between these and other types by trying the call to hash() and
+    seeing if they raise TypeError.
+
+    Returns
+    -------
+    bool
+
+    Examples
+    --------
+    >>> import collections
+    >>> from pandas.api.types import is_hashable
+    >>> a = ([],)
+    >>> isinstance(a, collections.abc.Hashable)
+    True
+    >>> is_hashable(a)
+    False
+    """
+    # Unfortunately, we can't use isinstance(obj, collections.abc.Hashable),
+    # which can be faster than calling hash. That is because numpy scalars
+    # fail this test.
+
+    # Reconsider this decision once this numpy bug is fixed:
+    # https://github.com/numpy/numpy/issues/5562
+
+    try:
+        hash(obj)
+    except TypeError:
+        return False
+    else:
+        return True
+
+
+def is_sequence(obj) -> bool:
+    """
+    Check if the object is a sequence of objects.
+    String types are not included as sequences here.
+
+    Parameters
+    ----------
+    obj : The object to check
+
+    Returns
+    -------
+    is_sequence : bool
+        Whether `obj` is a sequence of objects.
+
+    Examples
+    --------
+    >>> l = [1, 2, 3]
+    >>>
+    >>> is_sequence(l)
+    True
+    >>> is_sequence(iter(l))
+    False
+    """
+    try:
+        iter(obj)  # Can iterate over it.
+        len(obj)  # Has a length associated with it.
+        return not isinstance(obj, (str, bytes))
+    except (TypeError, AttributeError):
+        return False
+
+
+def is_dataclass(item) -> bool:
+    """
+    Checks if the object is a data-class instance
+
+    Parameters
+    ----------
+    item : object
+
+    Returns
+    --------
+    is_dataclass : bool
+        True if the item is an instance of a data-class,
+        will return false if you pass the data class itself
+
+    Examples
+    --------
+    >>> from dataclasses import dataclass
+    >>> @dataclass
+    ... class Point:
+    ...     x: int
+    ...     y: int
+
+    >>> is_dataclass(Point)
+    False
+    >>> is_dataclass(Point(0,2))
+    True
+
+    """
+    try:
+        import dataclasses
+
+        return dataclasses.is_dataclass(item) and not isinstance(item, type)
+    except ImportError:
+        return False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/missing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c341ff9dff7e613d8db2209efb5c10f170a9cd47
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/dtypes/missing.py
@@ -0,0 +1,810 @@
+"""
+missing types & inference
+"""
+from __future__ import annotations
+
+from decimal import Decimal
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+import pandas._libs.missing as libmissing
+from pandas._libs.tslibs import (
+    NaT,
+    iNaT,
+)
+
+from pandas.core.dtypes.common import (
+    DT64NS_DTYPE,
+    TD64NS_DTYPE,
+    ensure_object,
+    is_scalar,
+    is_string_or_object_np_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_list_like
+
+if TYPE_CHECKING:
+    from re import Pattern
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        NDFrame,
+        NDFrameT,
+        Scalar,
+        npt,
+    )
+
+    from pandas import Series
+    from pandas.core.indexes.base import Index
+
+
+isposinf_scalar = libmissing.isposinf_scalar
+isneginf_scalar = libmissing.isneginf_scalar
+
+nan_checker = np.isnan
+INF_AS_NA = False
+_dtype_object = np.dtype("object")
+_dtype_str = np.dtype(str)
+
+
+@overload
+def isna(obj: Scalar | Pattern) -> bool:
+    ...
+
+
+@overload
+def isna(
+    obj: ArrayLike | Index | list,
+) -> npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def isna(obj: NDFrameT) -> NDFrameT:
+    ...
+
+
+# handle unions
+@overload
+def isna(obj: NDFrameT | ArrayLike | Index | list) -> NDFrameT | npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    ...
+
+
+def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    """
+    Detect missing values for an array-like object.
+
+    This function takes a scalar or array-like object and indicates
+    whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
+    in object arrays, ``NaT`` in datetimelike).
+
+    Parameters
+    ----------
+    obj : scalar or array-like
+        Object to check for null or missing values.
+
+    Returns
+    -------
+    bool or array-like of bool
+        For scalar input, returns a scalar boolean.
+        For array input, returns an array of boolean indicating whether each
+        corresponding element is missing.
+
+    See Also
+    --------
+    notna : Boolean inverse of pandas.isna.
+    Series.isna : Detect missing values in a Series.
+    DataFrame.isna : Detect missing values in a DataFrame.
+    Index.isna : Detect missing values in an Index.
+
+    Examples
+    --------
+    Scalar arguments (including strings) result in a scalar boolean.
+
+    >>> pd.isna('dog')
+    False
+
+    >>> pd.isna(pd.NA)
+    True
+
+    >>> pd.isna(np.nan)
+    True
+
+    ndarrays result in an ndarray of booleans.
+
+    >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
+    >>> array
+    array([[ 1., nan,  3.],
+           [ 4.,  5., nan]])
+    >>> pd.isna(array)
+    array([[False,  True, False],
+           [False, False,  True]])
+
+    For indexes, an ndarray of booleans is returned.
+
+    >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
+    ...                           "2017-07-08"])
+    >>> index
+    DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
+                  dtype='datetime64[ns]', freq=None)
+    >>> pd.isna(index)
+    array([False, False,  True, False])
+
+    For Series and DataFrame, the same type is returned, containing booleans.
+
+    >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
+    >>> df
+         0     1    2
+    0  ant   bee  cat
+    1  dog  None  fly
+    >>> pd.isna(df)
+           0      1      2
+    0  False  False  False
+    1  False   True  False
+
+    >>> pd.isna(df[1])
+    0    False
+    1     True
+    Name: 1, dtype: bool
+    """
+    return _isna(obj)
+
+
+isnull = isna
+
+
+def _isna(obj, inf_as_na: bool = False):
+    """
+    Detect missing values, treating None, NaN or NA as null. Infinite
+    values will also be treated as null if inf_as_na is True.
+
+    Parameters
+    ----------
+    obj: ndarray or object value
+        Input array or scalar value.
+    inf_as_na: bool
+        Whether to treat infinity as null.
+
+    Returns
+    -------
+    boolean ndarray or boolean
+    """
+    if is_scalar(obj):
+        return libmissing.checknull(obj, inf_as_na=inf_as_na)
+    elif isinstance(obj, ABCMultiIndex):
+        raise NotImplementedError("isna is not defined for MultiIndex")
+    elif isinstance(obj, type):
+        return False
+    elif isinstance(obj, (np.ndarray, ABCExtensionArray)):
+        return _isna_array(obj, inf_as_na=inf_as_na)
+    elif isinstance(obj, ABCIndex):
+        # Try to use cached isna, which also short-circuits for integer dtypes
+        #  and avoids materializing RangeIndex._values
+        if not obj._can_hold_na:
+            return obj.isna()
+        return _isna_array(obj._values, inf_as_na=inf_as_na)
+
+    elif isinstance(obj, ABCSeries):
+        result = _isna_array(obj._values, inf_as_na=inf_as_na)
+        # box
+        result = obj._constructor(result, index=obj.index, name=obj.name, copy=False)
+        return result
+    elif isinstance(obj, ABCDataFrame):
+        return obj.isna()
+    elif isinstance(obj, list):
+        return _isna_array(np.asarray(obj, dtype=object), inf_as_na=inf_as_na)
+    elif hasattr(obj, "__array__"):
+        return _isna_array(np.asarray(obj), inf_as_na=inf_as_na)
+    else:
+        return False
+
+
+def _use_inf_as_na(key) -> None:
+    """
+    Option change callback for na/inf behaviour.
+
+    Choose which replacement for numpy.isnan / -numpy.isfinite is used.
+
+    Parameters
+    ----------
+    flag: bool
+        True means treat None, NaN, INF, -INF as null (old way),
+        False means None and NaN are null, but INF, -INF are not null
+        (new way).
+
+    Notes
+    -----
+    This approach to setting global module values is discussed and
+    approved here:
+
+    * https://stackoverflow.com/questions/4859217/
+      programmatically-creating-variables-in-python/4859312#4859312
+    """
+    inf_as_na = get_option(key)
+    globals()["_isna"] = partial(_isna, inf_as_na=inf_as_na)
+    if inf_as_na:
+        globals()["nan_checker"] = lambda x: ~np.isfinite(x)
+        globals()["INF_AS_NA"] = True
+    else:
+        globals()["nan_checker"] = np.isnan
+        globals()["INF_AS_NA"] = False
+
+
+def _isna_array(values: ArrayLike, inf_as_na: bool = False):
+    """
+    Return an array indicating which values of the input array are NaN / NA.
+
+    Parameters
+    ----------
+    obj: ndarray or ExtensionArray
+        The input array whose elements are to be checked.
+    inf_as_na: bool
+        Whether or not to treat infinite values as NA.
+
+    Returns
+    -------
+    array-like
+        Array of boolean values denoting the NA status of each element.
+    """
+    dtype = values.dtype
+
+    if not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        if inf_as_na and isinstance(dtype, CategoricalDtype):
+            result = libmissing.isnaobj(values.to_numpy(), inf_as_na=inf_as_na)
+        else:
+            # error: Incompatible types in assignment (expression has type
+            # "Union[ndarray[Any, Any], ExtensionArraySupportsAnyAll]", variable has
+            # type "ndarray[Any, dtype[bool_]]")
+            result = values.isna()  # type: ignore[assignment]
+    elif isinstance(values, np.rec.recarray):
+        # GH 48526
+        result = _isna_recarray_dtype(values, inf_as_na=inf_as_na)
+    elif is_string_or_object_np_dtype(values.dtype):
+        result = _isna_string_dtype(values, inf_as_na=inf_as_na)
+    elif dtype.kind in "mM":
+        # this is the NaT pattern
+        result = values.view("i8") == iNaT
+    else:
+        if inf_as_na:
+            result = ~np.isfinite(values)
+        else:
+            result = np.isnan(values)
+
+    return result
+
+
+def _isna_string_dtype(values: np.ndarray, inf_as_na: bool) -> npt.NDArray[np.bool_]:
+    # Working around NumPy ticket 1542
+    dtype = values.dtype
+
+    if dtype.kind in ("S", "U"):
+        result = np.zeros(values.shape, dtype=bool)
+    else:
+        if values.ndim in {1, 2}:
+            result = libmissing.isnaobj(values, inf_as_na=inf_as_na)
+        else:
+            # 0-D, reached via e.g. mask_missing
+            result = libmissing.isnaobj(values.ravel(), inf_as_na=inf_as_na)
+            result = result.reshape(values.shape)
+
+    return result
+
+
+def _has_record_inf_value(record_as_array: np.ndarray) -> np.bool_:
+    is_inf_in_record = np.zeros(len(record_as_array), dtype=bool)
+    for i, value in enumerate(record_as_array):
+        is_element_inf = False
+        try:
+            is_element_inf = np.isinf(value)
+        except TypeError:
+            is_element_inf = False
+        is_inf_in_record[i] = is_element_inf
+
+    return np.any(is_inf_in_record)
+
+
+def _isna_recarray_dtype(
+    values: np.rec.recarray, inf_as_na: bool
+) -> npt.NDArray[np.bool_]:
+    result = np.zeros(values.shape, dtype=bool)
+    for i, record in enumerate(values):
+        record_as_array = np.array(record.tolist())
+        does_record_contain_nan = isna_all(record_as_array)
+        does_record_contain_inf = False
+        if inf_as_na:
+            does_record_contain_inf = bool(_has_record_inf_value(record_as_array))
+        result[i] = np.any(
+            np.logical_or(does_record_contain_nan, does_record_contain_inf)
+        )
+
+    return result
+
+
+@overload
+def notna(obj: Scalar) -> bool:
+    ...
+
+
+@overload
+def notna(
+    obj: ArrayLike | Index | list,
+) -> npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def notna(obj: NDFrameT) -> NDFrameT:
+    ...
+
+
+# handle unions
+@overload
+def notna(obj: NDFrameT | ArrayLike | Index | list) -> NDFrameT | npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    ...
+
+
+def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    """
+    Detect non-missing values for an array-like object.
+
+    This function takes a scalar or array-like object and indicates
+    whether values are valid (not missing, which is ``NaN`` in numeric
+    arrays, ``None`` or ``NaN`` in object arrays, ``NaT`` in datetimelike).
+
+    Parameters
+    ----------
+    obj : array-like or object value
+        Object to check for *not* null or *non*-missing values.
+
+    Returns
+    -------
+    bool or array-like of bool
+        For scalar input, returns a scalar boolean.
+        For array input, returns an array of boolean indicating whether each
+        corresponding element is valid.
+
+    See Also
+    --------
+    isna : Boolean inverse of pandas.notna.
+    Series.notna : Detect valid values in a Series.
+    DataFrame.notna : Detect valid values in a DataFrame.
+    Index.notna : Detect valid values in an Index.
+
+    Examples
+    --------
+    Scalar arguments (including strings) result in a scalar boolean.
+
+    >>> pd.notna('dog')
+    True
+
+    >>> pd.notna(pd.NA)
+    False
+
+    >>> pd.notna(np.nan)
+    False
+
+    ndarrays result in an ndarray of booleans.
+
+    >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
+    >>> array
+    array([[ 1., nan,  3.],
+           [ 4.,  5., nan]])
+    >>> pd.notna(array)
+    array([[ True, False,  True],
+           [ True,  True, False]])
+
+    For indexes, an ndarray of booleans is returned.
+
+    >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
+    ...                          "2017-07-08"])
+    >>> index
+    DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
+                  dtype='datetime64[ns]', freq=None)
+    >>> pd.notna(index)
+    array([ True,  True, False,  True])
+
+    For Series and DataFrame, the same type is returned, containing booleans.
+
+    >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
+    >>> df
+         0     1    2
+    0  ant   bee  cat
+    1  dog  None  fly
+    >>> pd.notna(df)
+          0      1     2
+    0  True   True  True
+    1  True  False  True
+
+    >>> pd.notna(df[1])
+    0     True
+    1    False
+    Name: 1, dtype: bool
+    """
+    res = isna(obj)
+    if isinstance(res, bool):
+        return not res
+    return ~res
+
+
+notnull = notna
+
+
+def array_equivalent(
+    left,
+    right,
+    strict_nan: bool = False,
+    dtype_equal: bool = False,
+) -> bool:
+    """
+    True if two arrays, left and right, have equal non-NaN elements, and NaNs
+    in corresponding locations.  False otherwise. It is assumed that left and
+    right are NumPy arrays of the same dtype. The behavior of this function
+    (particularly with respect to NaNs) is not defined if the dtypes are
+    different.
+
+    Parameters
+    ----------
+    left, right : ndarrays
+    strict_nan : bool, default False
+        If True, consider NaN and None to be different.
+    dtype_equal : bool, default False
+        Whether `left` and `right` are known to have the same dtype
+        according to `is_dtype_equal`. Some methods like `BlockManager.equals`.
+        require that the dtypes match. Setting this to ``True`` can improve
+        performance, but will give different results for arrays that are
+        equal but different dtypes.
+
+    Returns
+    -------
+    b : bool
+        Returns True if the arrays are equivalent.
+
+    Examples
+    --------
+    >>> array_equivalent(
+    ...     np.array([1, 2, np.nan]),
+    ...     np.array([1, 2, np.nan]))
+    True
+    >>> array_equivalent(
+    ...     np.array([1, np.nan, 2]),
+    ...     np.array([1, 2, np.nan]))
+    False
+    """
+    left, right = np.asarray(left), np.asarray(right)
+
+    # shape compat
+    if left.shape != right.shape:
+        return False
+
+    if dtype_equal:
+        # fastpath when we require that the dtypes match (Block.equals)
+        if left.dtype.kind in "fc":
+            return _array_equivalent_float(left, right)
+        elif left.dtype.kind in "mM":
+            return _array_equivalent_datetimelike(left, right)
+        elif is_string_or_object_np_dtype(left.dtype):
+            # TODO: fastpath for pandas' StringDtype
+            return _array_equivalent_object(left, right, strict_nan)
+        else:
+            return np.array_equal(left, right)
+
+    # Slow path when we allow comparing different dtypes.
+    # Object arrays can contain None, NaN and NaT.
+    # string dtypes must be come to this path for NumPy 1.7.1 compat
+    if left.dtype.kind in "OSU" or right.dtype.kind in "OSU":
+        # Note: `in "OSU"` is non-trivially faster than `in ["O", "S", "U"]`
+        #  or `in ("O", "S", "U")`
+        return _array_equivalent_object(left, right, strict_nan)
+
+    # NaNs can occur in float and complex arrays.
+    if left.dtype.kind in "fc":
+        if not (left.size and right.size):
+            return True
+        return ((left == right) | (isna(left) & isna(right))).all()
+
+    elif left.dtype.kind in "mM" or right.dtype.kind in "mM":
+        # datetime64, timedelta64, Period
+        if left.dtype != right.dtype:
+            return False
+
+        left = left.view("i8")
+        right = right.view("i8")
+
+    # if we have structured dtypes, compare first
+    if (
+        left.dtype.type is np.void or right.dtype.type is np.void
+    ) and left.dtype != right.dtype:
+        return False
+
+    return np.array_equal(left, right)
+
+
+def _array_equivalent_float(left: np.ndarray, right: np.ndarray) -> bool:
+    return bool(((left == right) | (np.isnan(left) & np.isnan(right))).all())
+
+
+def _array_equivalent_datetimelike(left: np.ndarray, right: np.ndarray):
+    return np.array_equal(left.view("i8"), right.view("i8"))
+
+
+def _array_equivalent_object(left: np.ndarray, right: np.ndarray, strict_nan: bool):
+    left = ensure_object(left)
+    right = ensure_object(right)
+
+    mask: npt.NDArray[np.bool_] | None = None
+    if strict_nan:
+        mask = isna(left) & isna(right)
+        if not mask.any():
+            mask = None
+
+    try:
+        if mask is None:
+            return lib.array_equivalent_object(left, right)
+        if not lib.array_equivalent_object(left[~mask], right[~mask]):
+            return False
+        left_remaining = left[mask]
+        right_remaining = right[mask]
+    except ValueError:
+        # can raise a ValueError if left and right cannot be
+        # compared (e.g. nested arrays)
+        left_remaining = left
+        right_remaining = right
+
+    for left_value, right_value in zip(left_remaining, right_remaining):
+        if left_value is NaT and right_value is not NaT:
+            return False
+
+        elif left_value is libmissing.NA and right_value is not libmissing.NA:
+            return False
+
+        elif isinstance(left_value, float) and np.isnan(left_value):
+            if not isinstance(right_value, float) or not np.isnan(right_value):
+                return False
+        else:
+            with warnings.catch_warnings():
+                # suppress numpy's "elementwise comparison failed"
+                warnings.simplefilter("ignore", DeprecationWarning)
+                try:
+                    if np.any(np.asarray(left_value != right_value)):
+                        return False
+                except TypeError as err:
+                    if "boolean value of NA is ambiguous" in str(err):
+                        return False
+                    raise
+                except ValueError:
+                    # numpy can raise a ValueError if left and right cannot be
+                    # compared (e.g. nested arrays)
+                    return False
+    return True
+
+
+def array_equals(left: ArrayLike, right: ArrayLike) -> bool:
+    """
+    ExtensionArray-compatible implementation of array_equivalent.
+    """
+    if left.dtype != right.dtype:
+        return False
+    elif isinstance(left, ABCExtensionArray):
+        return left.equals(right)
+    else:
+        return array_equivalent(left, right, dtype_equal=True)
+
+
+def infer_fill_value(val):
+    """
+    infer the fill value for the nan/NaT from the provided
+    scalar/ndarray/list-like if we are a NaT, return the correct dtyped
+    element to provide proper block construction
+    """
+    if not is_list_like(val):
+        val = [val]
+    val = np.asarray(val)
+    if val.dtype.kind in "mM":
+        return np.array("NaT", dtype=val.dtype)
+    elif val.dtype == object:
+        dtype = lib.infer_dtype(ensure_object(val), skipna=False)
+        if dtype in ["datetime", "datetime64"]:
+            return np.array("NaT", dtype=DT64NS_DTYPE)
+        elif dtype in ["timedelta", "timedelta64"]:
+            return np.array("NaT", dtype=TD64NS_DTYPE)
+        return np.array(np.nan, dtype=object)
+    elif val.dtype.kind == "U":
+        return np.array(np.nan, dtype=val.dtype)
+    return np.nan
+
+
+def construct_1d_array_from_inferred_fill_value(
+    value: object, length: int
+) -> ArrayLike:
+    # Find our empty_value dtype by constructing an array
+    #  from our value and doing a .take on it
+    from pandas.core.algorithms import take_nd
+    from pandas.core.construction import sanitize_array
+    from pandas.core.indexes.base import Index
+
+    arr = sanitize_array(value, Index(range(1)), copy=False)
+    taker = -1 * np.ones(length, dtype=np.intp)
+    return take_nd(arr, taker)
+
+
+def maybe_fill(arr: np.ndarray) -> np.ndarray:
+    """
+    Fill numpy.ndarray with NaN, unless we have a integer or boolean dtype.
+    """
+    if arr.dtype.kind not in "iub":
+        arr.fill(np.nan)
+    return arr
+
+
+def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
+    """
+    Return a dtype compat na value
+
+    Parameters
+    ----------
+    dtype : string / dtype
+    compat : bool, default True
+
+    Returns
+    -------
+    np.dtype or a pandas dtype
+
+    Examples
+    --------
+    >>> na_value_for_dtype(np.dtype('int64'))
+    0
+    >>> na_value_for_dtype(np.dtype('int64'), compat=False)
+    nan
+    >>> na_value_for_dtype(np.dtype('float64'))
+    nan
+    >>> na_value_for_dtype(np.dtype('bool'))
+    False
+    >>> na_value_for_dtype(np.dtype('datetime64[ns]'))
+    numpy.datetime64('NaT')
+    """
+
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.na_value
+    elif dtype.kind in "mM":
+        unit = np.datetime_data(dtype)[0]
+        return dtype.type("NaT", unit)
+    elif dtype.kind == "f":
+        return np.nan
+    elif dtype.kind in "iu":
+        if compat:
+            return 0
+        return np.nan
+    elif dtype.kind == "b":
+        if compat:
+            return False
+        return np.nan
+    return np.nan
+
+
+def remove_na_arraylike(arr: Series | Index | np.ndarray):
+    """
+    Return array-like containing only true/non-NaN values, possibly empty.
+    """
+    if isinstance(arr.dtype, ExtensionDtype):
+        return arr[notna(arr)]
+    else:
+        return arr[notna(np.asarray(arr))]
+
+
+def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
+    """
+    isna check that excludes incompatible dtypes
+
+    Parameters
+    ----------
+    obj : object
+    dtype : np.datetime64, np.timedelta64, DatetimeTZDtype, or PeriodDtype
+
+    Returns
+    -------
+    bool
+    """
+    if not lib.is_scalar(obj) or not isna(obj):
+        return False
+    elif dtype.kind == "M":
+        if isinstance(dtype, np.dtype):
+            # i.e. not tzaware
+            return not isinstance(obj, (np.timedelta64, Decimal))
+        # we have to rule out tznaive dt64("NaT")
+        return not isinstance(obj, (np.timedelta64, np.datetime64, Decimal))
+    elif dtype.kind == "m":
+        return not isinstance(obj, (np.datetime64, Decimal))
+    elif dtype.kind in "iufc":
+        # Numeric
+        return obj is not NaT and not isinstance(obj, (np.datetime64, np.timedelta64))
+    elif dtype.kind == "b":
+        # We allow pd.NA, None, np.nan in BooleanArray (same as IntervalDtype)
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    elif dtype == _dtype_str:
+        # numpy string dtypes to avoid float np.nan
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal, float))
+
+    elif dtype == _dtype_object:
+        # This is needed for Categorical, but is kind of weird
+        return True
+
+    elif isinstance(dtype, PeriodDtype):
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+    elif isinstance(dtype, IntervalDtype):
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    elif isinstance(dtype, CategoricalDtype):
+        return is_valid_na_for_dtype(obj, dtype.categories.dtype)
+
+    # fallback, default to allowing NaN, None, NA, NaT
+    return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+
+def isna_all(arr: ArrayLike) -> bool:
+    """
+    Optimized equivalent to isna(arr).all()
+    """
+    total_len = len(arr)
+
+    # Usually it's enough to check but a small fraction of values to see if
+    #  a block is NOT null, chunks should help in such cases.
+    #  parameters 1000 and 40 were chosen arbitrarily
+    chunk_len = max(total_len // 40, 1000)
+
+    dtype = arr.dtype
+    if lib.is_np_dtype(dtype, "f"):
+        checker = nan_checker
+
+    elif (lib.is_np_dtype(dtype, "mM")) or isinstance(
+        dtype, (DatetimeTZDtype, PeriodDtype)
+    ):
+        # error: Incompatible types in assignment (expression has type
+        # "Callable[[Any], Any]", variable has type "ufunc")
+        checker = lambda x: np.asarray(x.view("i8")) == iNaT  # type: ignore[assignment]
+
+    else:
+        # error: Incompatible types in assignment (expression has type "Callable[[Any],
+        # Any]", variable has type "ufunc")
+        checker = lambda x: _isna_array(  # type: ignore[assignment]
+            x, inf_as_na=INF_AS_NA
+        )
+
+    return all(
+        checker(arr[i : i + chunk_len]).all() for i in range(0, total_len, chunk_len)
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/flags.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff7a15f283bafe1459173070e64df4caef0d45d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/flags.py
@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+import weakref
+
+if TYPE_CHECKING:
+    from pandas.core.generic import NDFrame
+
+
+class Flags:
+    """
+    Flags that apply to pandas objects.
+
+    Parameters
+    ----------
+    obj : Series or DataFrame
+        The object these flags are associated with.
+    allows_duplicate_labels : bool, default True
+        Whether to allow duplicate labels in this object. By default,
+        duplicate labels are permitted. Setting this to ``False`` will
+        cause an :class:`errors.DuplicateLabelError` to be raised when
+        `index` (or columns for DataFrame) is not unique, or any
+        subsequent operation on introduces duplicates.
+        See :ref:`duplicates.disallow` for more.
+
+        .. warning::
+
+           This is an experimental feature. Currently, many methods fail to
+           propagate the ``allows_duplicate_labels`` value. In future versions
+           it is expected that every method taking or returning one or more
+           DataFrame or Series objects will propagate ``allows_duplicate_labels``.
+
+    Examples
+    --------
+    Attributes can be set in two ways:
+
+    >>> df = pd.DataFrame()
+    >>> df.flags
+    <Flags(allows_duplicate_labels=True)>
+    >>> df.flags.allows_duplicate_labels = False
+    >>> df.flags
+    <Flags(allows_duplicate_labels=False)>
+
+    >>> df.flags['allows_duplicate_labels'] = True
+    >>> df.flags
+    <Flags(allows_duplicate_labels=True)>
+    """
+
+    _keys: set[str] = {"allows_duplicate_labels"}
+
+    def __init__(self, obj: NDFrame, *, allows_duplicate_labels: bool) -> None:
+        self._allows_duplicate_labels = allows_duplicate_labels
+        self._obj = weakref.ref(obj)
+
+    @property
+    def allows_duplicate_labels(self) -> bool:
+        """
+        Whether this object allows duplicate labels.
+
+        Setting ``allows_duplicate_labels=False`` ensures that the
+        index (and columns of a DataFrame) are unique. Most methods
+        that accept and return a Series or DataFrame will propagate
+        the value of ``allows_duplicate_labels``.
+
+        See :ref:`duplicates` for more.
+
+        See Also
+        --------
+        DataFrame.attrs : Set global metadata on this object.
+        DataFrame.set_flags : Set global flags on this object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2]}, index=['a', 'a'])
+        >>> df.flags.allows_duplicate_labels
+        True
+        >>> df.flags.allows_duplicate_labels = False
+        Traceback (most recent call last):
+            ...
+        pandas.errors.DuplicateLabelError: Index has duplicates.
+              positions
+        label
+        a        [0, 1]
+        """
+        return self._allows_duplicate_labels
+
+    @allows_duplicate_labels.setter
+    def allows_duplicate_labels(self, value: bool) -> None:
+        value = bool(value)
+        obj = self._obj()
+        if obj is None:
+            raise ValueError("This flag's object has been deleted.")
+
+        if not value:
+            for ax in obj.axes:
+                ax._maybe_check_unique()
+
+        self._allows_duplicate_labels = value
+
+    def __getitem__(self, key: str):
+        if key not in self._keys:
+            raise KeyError(key)
+
+        return getattr(self, key)
+
+    def __setitem__(self, key: str, value) -> None:
+        if key not in self._keys:
+            raise ValueError(f"Unknown flag {key}. Must be one of {self._keys}")
+        setattr(self, key, value)
+
+    def __repr__(self) -> str:
+        return f"<Flags(allows_duplicate_labels={self.allows_duplicate_labels})>"
+
+    def __eq__(self, other) -> bool:
+        if isinstance(other, type(self)):
+            return self.allows_duplicate_labels == other.allows_duplicate_labels
+        return False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/frame.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/frame.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc260770cbd78ffc0bf46d7d004cde56409df300
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/frame.py
@@ -0,0 +1,12724 @@
+"""
+DataFrame
+---------
+An efficient 2D container for potentially mixed-type time series or other
+labeled data series.
+
+Similar to its R counterpart, data.frame, except providing automatic data
+alignment and a host of useful data manipulation methods having to do with the
+labeling information
+"""
+from __future__ import annotations
+
+import collections
+from collections import abc
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Iterator,
+    Mapping,
+    Sequence,
+)
+import functools
+from inspect import signature
+from io import StringIO
+import itertools
+import operator
+import sys
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+from numpy import ma
+
+from pandas._config import (
+    get_option,
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+from pandas._config.config import _get_option
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+    properties,
+)
+from pandas._libs.hashtable import duplicated
+from pandas._libs.lib import is_range_indexer
+from pandas.compat import PYPY
+from pandas.compat._constants import (
+    REF_COUNT,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    ChainedAssignmentError,
+    InvalidIndexError,
+    _chained_assignment_method_msg,
+    _chained_assignment_msg,
+    _chained_assignment_warning_method_msg,
+    _chained_assignment_warning_msg,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
+from pandas.util._validators import (
+    validate_ascending,
+    validate_bool_kwarg,
+    validate_percentile,
+)
+
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    can_hold_element,
+    construct_1d_arraylike_from_scalar,
+    construct_2d_arraylike_from_scalar,
+    find_common_type,
+    infer_dtype_from_scalar,
+    invalidate_string_dtypes,
+    maybe_box_native,
+    maybe_downcast_to_dtype,
+)
+from pandas.core.dtypes.common import (
+    infer_dtype_from_object,
+    is_1d_only_ea_dtype,
+    is_array_like,
+    is_bool_dtype,
+    is_dataclass,
+    is_dict_like,
+    is_float,
+    is_float_dtype,
+    is_hashable,
+    is_integer,
+    is_integer_dtype,
+    is_iterator,
+    is_list_like,
+    is_scalar,
+    is_sequence,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    BaseMaskedDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import (
+    algorithms,
+    common as com,
+    nanops,
+    ops,
+    roperator,
+)
+from pandas.core.accessor import CachedAccessor
+from pandas.core.apply import reconstruct_and_relabel_result
+from pandas.core.array_algos.take import take_2d_multi
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import (
+    BaseMaskedArray,
+    DatetimeArray,
+    ExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.sparse import SparseFrameAccessor
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    sanitize_array,
+    sanitize_masked_array,
+)
+from pandas.core.generic import (
+    NDFrame,
+    make_doc,
+)
+from pandas.core.indexers import check_key_length
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    default_index,
+    ensure_index,
+    ensure_index_from_sequences,
+)
+from pandas.core.indexes.multi import (
+    MultiIndex,
+    maybe_droplevels,
+)
+from pandas.core.indexing import (
+    check_bool_indexer,
+    check_dict_or_set_indexers,
+)
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+)
+from pandas.core.internals.construction import (
+    arrays_to_mgr,
+    dataclasses_to_dicts,
+    dict_to_mgr,
+    mgr_to_mgr,
+    ndarray_to_mgr,
+    nested_data_to_arrays,
+    rec_array_to_mgr,
+    reorder_arrays,
+    to_arrays,
+    treat_as_nested,
+)
+from pandas.core.methods import selectn
+from pandas.core.reshape.melt import melt
+from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.sorting import (
+    get_group_index,
+    lexsort_indexer,
+    nargsort,
+)
+
+from pandas.io.common import get_handle
+from pandas.io.formats import (
+    console,
+    format as fmt,
+)
+from pandas.io.formats.info import (
+    INFO_DOCSTRING,
+    DataFrameInfo,
+    frame_sub_kwargs,
+)
+import pandas.plotting
+
+if TYPE_CHECKING:
+    import datetime
+
+    from pandas._libs.internals import BlockValuesRefs
+    from pandas._typing import (
+        AggFuncType,
+        AnyAll,
+        AnyArrayLike,
+        ArrayLike,
+        Axes,
+        Axis,
+        AxisInt,
+        ColspaceArgType,
+        CompressionOptions,
+        CorrelationMethod,
+        DropKeep,
+        Dtype,
+        DtypeObj,
+        FilePath,
+        FloatFormatType,
+        FormattersType,
+        Frequency,
+        FromDictOrient,
+        IgnoreRaise,
+        IndexKeyFunc,
+        IndexLabel,
+        JoinValidate,
+        Level,
+        MergeHow,
+        MergeValidate,
+        MutableMappingT,
+        NaAction,
+        NaPosition,
+        NsmallestNlargestKeep,
+        PythonFuncType,
+        QuantileInterpolation,
+        ReadBuffer,
+        ReindexMethod,
+        Renamer,
+        Scalar,
+        Self,
+        SequenceNotStr,
+        SortKind,
+        StorageOptions,
+        Suffixes,
+        ToGbqIfexist,
+        ToStataByteorder,
+        ToTimestampHow,
+        UpdateJoin,
+        ValueKeyFunc,
+        WriteBuffer,
+        XMLParsers,
+        npt,
+    )
+
+    from pandas.core.groupby.generic import DataFrameGroupBy
+    from pandas.core.interchange.dataframe_protocol import DataFrame as DataFrameXchg
+    from pandas.core.internals import SingleDataManager
+
+    from pandas.io.formats.style import Styler
+
+# ---------------------------------------------------------------------
+# Docstring templates
+
+_shared_doc_kwargs = {
+    "axes": "index, columns",
+    "klass": "DataFrame",
+    "axes_single_arg": "{0 or 'index', 1 or 'columns'}",
+    "axis": """axis : {0 or 'index', 1 or 'columns'}, default 0
+        If 0 or 'index': apply function to each column.
+        If 1 or 'columns': apply function to each row.""",
+    "inplace": """
+    inplace : bool, default False
+        Whether to modify the DataFrame rather than creating a new one.""",
+    "optional_by": """
+by : str or list of str
+    Name or list of names to sort by.
+
+    - if `axis` is 0 or `'index'` then `by` may contain index
+      levels and/or column labels.
+    - if `axis` is 1 or `'columns'` then `by` may contain column
+      levels and/or index labels.""",
+    "optional_reindex": """
+labels : array-like, optional
+    New labels / index to conform the axis specified by 'axis' to.
+index : array-like, optional
+    New labels for the index. Preferably an Index object to avoid
+    duplicating data.
+columns : array-like, optional
+    New labels for the columns. Preferably an Index object to avoid
+    duplicating data.
+axis : int or str, optional
+    Axis to target. Can be either the axis name ('index', 'columns')
+    or number (0, 1).""",
+}
+
+_merge_doc = """
+Merge DataFrame or named Series objects with a database-style join.
+
+A named Series object is treated as a DataFrame with a single named column.
+
+The join is done on columns or indexes. If joining columns on
+columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
+on indexes or indexes on a column or columns, the index will be passed on.
+When performing a cross merge, no column specifications to merge on are
+allowed.
+
+.. warning::
+
+    If both key columns contain rows where the key is a null value, those
+    rows will be matched against each other. This is different from usual SQL
+    join behaviour and can lead to unexpected results.
+
+Parameters
+----------%s
+right : DataFrame or named Series
+    Object to merge with.
+how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'inner'
+    Type of merge to be performed.
+
+    * left: use only keys from left frame, similar to a SQL left outer join;
+      preserve key order.
+    * right: use only keys from right frame, similar to a SQL right outer join;
+      preserve key order.
+    * outer: use union of keys from both frames, similar to a SQL full outer
+      join; sort keys lexicographically.
+    * inner: use intersection of keys from both frames, similar to a SQL inner
+      join; preserve the order of the left keys.
+    * cross: creates the cartesian product from both frames, preserves the order
+      of the left keys.
+on : label or list
+    Column or index level names to join on. These must be found in both
+    DataFrames. If `on` is None and not merging on indexes then this defaults
+    to the intersection of the columns in both DataFrames.
+left_on : label or list, or array-like
+    Column or index level names to join on in the left DataFrame. Can also
+    be an array or list of arrays of the length of the left DataFrame.
+    These arrays are treated as if they are columns.
+right_on : label or list, or array-like
+    Column or index level names to join on in the right DataFrame. Can also
+    be an array or list of arrays of the length of the right DataFrame.
+    These arrays are treated as if they are columns.
+left_index : bool, default False
+    Use the index from the left DataFrame as the join key(s). If it is a
+    MultiIndex, the number of keys in the other DataFrame (either the index
+    or a number of columns) must match the number of levels.
+right_index : bool, default False
+    Use the index from the right DataFrame as the join key. Same caveats as
+    left_index.
+sort : bool, default False
+    Sort the join keys lexicographically in the result DataFrame. If False,
+    the order of the join keys depends on the join type (how keyword).
+suffixes : list-like, default is ("_x", "_y")
+    A length-2 sequence where each element is optionally a string
+    indicating the suffix to add to overlapping column names in
+    `left` and `right` respectively. Pass a value of `None` instead
+    of a string to indicate that the column name from `left` or
+    `right` should be left as-is, with no suffix. At least one of the
+    values must not be None.
+copy : bool, default True
+    If False, avoid copy if possible.
+
+    .. note::
+        The `copy` keyword will change behavior in pandas 3.0.
+        `Copy-on-Write
+        <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+        will be enabled by default, which means that all methods with a
+        `copy` keyword will use a lazy copy mechanism to defer the copy and
+        ignore the `copy` keyword. The `copy` keyword will be removed in a
+        future version of pandas.
+
+        You can already get the future behavior and improvements through
+        enabling copy on write ``pd.options.mode.copy_on_write = True``
+indicator : bool or str, default False
+    If True, adds a column to the output DataFrame called "_merge" with
+    information on the source of each row. The column can be given a different
+    name by providing a string argument. The column will have a Categorical
+    type with the value of "left_only" for observations whose merge key only
+    appears in the left DataFrame, "right_only" for observations
+    whose merge key only appears in the right DataFrame, and "both"
+    if the observation's merge key is found in both DataFrames.
+
+validate : str, optional
+    If specified, checks if merge is of specified type.
+
+    * "one_to_one" or "1:1": check if merge keys are unique in both
+      left and right datasets.
+    * "one_to_many" or "1:m": check if merge keys are unique in left
+      dataset.
+    * "many_to_one" or "m:1": check if merge keys are unique in right
+      dataset.
+    * "many_to_many" or "m:m": allowed, but does not result in checks.
+
+Returns
+-------
+DataFrame
+    A DataFrame of the two merged objects.
+
+See Also
+--------
+merge_ordered : Merge with optional filling/interpolation.
+merge_asof : Merge on nearest keys.
+DataFrame.join : Similar method using indices.
+
+Examples
+--------
+>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
+...                     'value': [1, 2, 3, 5]})
+>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
+...                     'value': [5, 6, 7, 8]})
+>>> df1
+    lkey value
+0   foo      1
+1   bar      2
+2   baz      3
+3   foo      5
+>>> df2
+    rkey value
+0   foo      5
+1   bar      6
+2   baz      7
+3   foo      8
+
+Merge df1 and df2 on the lkey and rkey columns. The value columns have
+the default suffixes, _x and _y, appended.
+
+>>> df1.merge(df2, left_on='lkey', right_on='rkey')
+  lkey  value_x rkey  value_y
+0  foo        1  foo        5
+1  foo        1  foo        8
+2  bar        2  bar        6
+3  baz        3  baz        7
+4  foo        5  foo        5
+5  foo        5  foo        8
+
+Merge DataFrames df1 and df2 with specified left and right suffixes
+appended to any overlapping columns.
+
+>>> df1.merge(df2, left_on='lkey', right_on='rkey',
+...           suffixes=('_left', '_right'))
+  lkey  value_left rkey  value_right
+0  foo           1  foo            5
+1  foo           1  foo            8
+2  bar           2  bar            6
+3  baz           3  baz            7
+4  foo           5  foo            5
+5  foo           5  foo            8
+
+Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
+any overlapping columns.
+
+>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
+Traceback (most recent call last):
+...
+ValueError: columns overlap but no suffix specified:
+    Index(['value'], dtype='object')
+
+>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
+>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
+>>> df1
+      a  b
+0   foo  1
+1   bar  2
+>>> df2
+      a  c
+0   foo  3
+1   baz  4
+
+>>> df1.merge(df2, how='inner', on='a')
+      a  b  c
+0   foo  1  3
+
+>>> df1.merge(df2, how='left', on='a')
+      a  b  c
+0   foo  1  3.0
+1   bar  2  NaN
+
+>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
+>>> df2 = pd.DataFrame({'right': [7, 8]})
+>>> df1
+    left
+0   foo
+1   bar
+>>> df2
+    right
+0   7
+1   8
+
+>>> df1.merge(df2, how='cross')
+   left  right
+0   foo      7
+1   foo      8
+2   bar      7
+3   bar      8
+"""
+
+
+# -----------------------------------------------------------------------
+# DataFrame class
+
+
+class DataFrame(NDFrame, OpsMixin):
+    """
+    Two-dimensional, size-mutable, potentially heterogeneous tabular data.
+
+    Data structure also contains labeled axes (rows and columns).
+    Arithmetic operations align on both row and column labels. Can be
+    thought of as a dict-like container for Series objects. The primary
+    pandas data structure.
+
+    Parameters
+    ----------
+    data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
+        Dict can contain Series, arrays, constants, dataclass or list-like objects. If
+        data is a dict, column order follows insertion-order. If a dict contains Series
+        which have an index defined, it is aligned by its index. This alignment also
+        occurs if data is a Series or a DataFrame itself. Alignment is done on
+        Series/DataFrame inputs.
+
+        If data is a list of dicts, column order follows insertion-order.
+
+    index : Index or array-like
+        Index to use for resulting frame. Will default to RangeIndex if
+        no indexing information part of input data and no index provided.
+    columns : Index or array-like
+        Column labels to use for resulting frame when data does not have them,
+        defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,
+        will perform column selection instead.
+    dtype : dtype, default None
+        Data type to force. Only a single dtype is allowed. If None, infer.
+    copy : bool or None, default None
+        Copy data from inputs.
+        For dict data, the default of None behaves like ``copy=True``.  For DataFrame
+        or 2d ndarray input, the default of None behaves like ``copy=False``.
+        If data is a dict containing one or more Series (possibly of different dtypes),
+        ``copy=False`` will ensure that these inputs are not copied.
+
+        .. versionchanged:: 1.3.0
+
+    See Also
+    --------
+    DataFrame.from_records : Constructor from tuples, also record arrays.
+    DataFrame.from_dict : From dicts of Series, arrays, or dicts.
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+    read_table : Read general delimited file into DataFrame.
+    read_clipboard : Read text from clipboard into DataFrame.
+
+    Notes
+    -----
+    Please reference the :ref:`User Guide <basics.dataframe>` for more information.
+
+    Examples
+    --------
+    Constructing DataFrame from a dictionary.
+
+    >>> d = {'col1': [1, 2], 'col2': [3, 4]}
+    >>> df = pd.DataFrame(data=d)
+    >>> df
+       col1  col2
+    0     1     3
+    1     2     4
+
+    Notice that the inferred dtype is int64.
+
+    >>> df.dtypes
+    col1    int64
+    col2    int64
+    dtype: object
+
+    To enforce a single dtype:
+
+    >>> df = pd.DataFrame(data=d, dtype=np.int8)
+    >>> df.dtypes
+    col1    int8
+    col2    int8
+    dtype: object
+
+    Constructing DataFrame from a dictionary including Series:
+
+    >>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
+    >>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
+       col1  col2
+    0     0   NaN
+    1     1   NaN
+    2     2   2.0
+    3     3   3.0
+
+    Constructing DataFrame from numpy ndarray:
+
+    >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
+    ...                    columns=['a', 'b', 'c'])
+    >>> df2
+       a  b  c
+    0  1  2  3
+    1  4  5  6
+    2  7  8  9
+
+    Constructing DataFrame from a numpy ndarray that has labeled columns:
+
+    >>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
+    ...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
+    >>> df3 = pd.DataFrame(data, columns=['c', 'a'])
+    ...
+    >>> df3
+       c  a
+    0  3  1
+    1  6  4
+    2  9  7
+
+    Constructing DataFrame from dataclass:
+
+    >>> from dataclasses import make_dataclass
+    >>> Point = make_dataclass("Point", [("x", int), ("y", int)])
+    >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
+       x  y
+    0  0  0
+    1  0  3
+    2  2  3
+
+    Constructing DataFrame from Series/DataFrame:
+
+    >>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
+    >>> df = pd.DataFrame(data=ser, index=["a", "c"])
+    >>> df
+       0
+    a  1
+    c  3
+
+    >>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
+    >>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
+    >>> df2
+       x
+    a  1
+    c  3
+    """
+
+    _internal_names_set = {"columns", "index"} | NDFrame._internal_names_set
+    _typ = "dataframe"
+    _HANDLED_TYPES = (Series, Index, ExtensionArray, np.ndarray)
+    _accessors: set[str] = {"sparse"}
+    _hidden_attrs: frozenset[str] = NDFrame._hidden_attrs | frozenset([])
+    _mgr: BlockManager | ArrayManager
+
+    # similar to __array_priority__, positions DataFrame before Series, Index,
+    #  and ExtensionArray.  Should NOT be overridden by subclasses.
+    __pandas_priority__ = 4000
+
+    @property
+    def _constructor(self) -> Callable[..., DataFrame]:
+        return DataFrame
+
+    def _constructor_from_mgr(self, mgr, axes) -> DataFrame:
+        df = DataFrame._from_mgr(mgr, axes=axes)
+
+        if type(self) is DataFrame:
+            # This would also work `if self._constructor is DataFrame`, but
+            #  this check is slightly faster, benefiting the most-common case.
+            return df
+
+        elif type(self).__name__ == "GeoDataFrame":
+            # Shim until geopandas can override their _constructor_from_mgr
+            #  bc they have different behavior for Managers than for DataFrames
+            return self._constructor(mgr)
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.DataFrame object.
+        return self._constructor(df)
+
+    _constructor_sliced: Callable[..., Series] = Series
+
+    def _constructor_sliced_from_mgr(self, mgr, axes) -> Series:
+        ser = Series._from_mgr(mgr, axes)
+        ser._name = None  # caller is responsible for setting real name
+
+        if type(self) is DataFrame:
+            # This would also work `if self._constructor_sliced is Series`, but
+            #  this check is slightly faster, benefiting the most-common case.
+            return ser
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.Series object.
+        return self._constructor_sliced(ser)
+
+    # ----------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self,
+        data=None,
+        index: Axes | None = None,
+        columns: Axes | None = None,
+        dtype: Dtype | None = None,
+        copy: bool | None = None,
+    ) -> None:
+        allow_mgr = False
+        if dtype is not None:
+            dtype = self._validate_dtype(dtype)
+
+        if isinstance(data, DataFrame):
+            data = data._mgr
+            allow_mgr = True
+            if not copy:
+                # if not copying data, ensure to still return a shallow copy
+                # to avoid the result sharing the same Manager
+                data = data.copy(deep=False)
+
+        if isinstance(data, (BlockManager, ArrayManager)):
+            if not allow_mgr:
+                # GH#52419
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=1,  # bump to 2 once pyarrow 15.0 is released with fix
+                )
+
+            if using_copy_on_write():
+                data = data.copy(deep=False)
+            # first check if a Manager is passed without any other arguments
+            # -> use fastpath (without checking Manager type)
+            if index is None and columns is None and dtype is None and not copy:
+                # GH#33357 fastpath
+                NDFrame.__init__(self, data)
+                return
+
+        manager = _get_option("mode.data_manager", silent=True)
+
+        is_pandas_object = isinstance(data, (Series, Index, ExtensionArray))
+        data_dtype = getattr(data, "dtype", None)
+        original_dtype = dtype
+
+        # GH47215
+        if isinstance(index, set):
+            raise ValueError("index cannot be a set")
+        if isinstance(columns, set):
+            raise ValueError("columns cannot be a set")
+
+        if copy is None:
+            if isinstance(data, dict):
+                # retain pre-GH#38939 default behavior
+                copy = True
+            elif (
+                manager == "array"
+                and isinstance(data, (np.ndarray, ExtensionArray))
+                and data.ndim == 2
+            ):
+                # INFO(ArrayManager) by default copy the 2D input array to get
+                # contiguous 1D arrays
+                copy = True
+            elif using_copy_on_write() and not isinstance(
+                data, (Index, DataFrame, Series)
+            ):
+                copy = True
+            else:
+                copy = False
+
+        if data is None:
+            index = index if index is not None else default_index(0)
+            columns = columns if columns is not None else default_index(0)
+            dtype = dtype if dtype is not None else pandas_dtype(object)
+            data = []
+
+        if isinstance(data, (BlockManager, ArrayManager)):
+            mgr = self._init_mgr(
+                data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
+            )
+
+        elif isinstance(data, dict):
+            # GH#38939 de facto copy defaults to False only in non-dict cases
+            mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
+        elif isinstance(data, ma.MaskedArray):
+            from numpy.ma import mrecords
+
+            # masked recarray
+            if isinstance(data, mrecords.MaskedRecords):
+                raise TypeError(
+                    "MaskedRecords are not supported. Pass "
+                    "{name: data[name] for name in data.dtype.names} "
+                    "instead"
+                )
+
+            # a masked array
+            data = sanitize_masked_array(data)
+            mgr = ndarray_to_mgr(
+                data,
+                index,
+                columns,
+                dtype=dtype,
+                copy=copy,
+                typ=manager,
+            )
+
+        elif isinstance(data, (np.ndarray, Series, Index, ExtensionArray)):
+            if data.dtype.names:
+                # i.e. numpy structured array
+                data = cast(np.ndarray, data)
+                mgr = rec_array_to_mgr(
+                    data,
+                    index,
+                    columns,
+                    dtype,
+                    copy,
+                    typ=manager,
+                )
+            elif getattr(data, "name", None) is not None:
+                # i.e. Series/Index with non-None name
+                _copy = copy if using_copy_on_write() else True
+                mgr = dict_to_mgr(
+                    # error: Item "ndarray" of "Union[ndarray, Series, Index]" has no
+                    # attribute "name"
+                    {data.name: data},  # type: ignore[union-attr]
+                    index,
+                    columns,
+                    dtype=dtype,
+                    typ=manager,
+                    copy=_copy,
+                )
+            else:
+                mgr = ndarray_to_mgr(
+                    data,
+                    index,
+                    columns,
+                    dtype=dtype,
+                    copy=copy,
+                    typ=manager,
+                )
+
+        # For data is list-like, or Iterable (will consume into list)
+        elif is_list_like(data):
+            if not isinstance(data, abc.Sequence):
+                if hasattr(data, "__array__"):
+                    # GH#44616 big perf improvement for e.g. pytorch tensor
+                    data = np.asarray(data)
+                else:
+                    data = list(data)
+            if len(data) > 0:
+                if is_dataclass(data[0]):
+                    data = dataclasses_to_dicts(data)
+                if not isinstance(data, np.ndarray) and treat_as_nested(data):
+                    # exclude ndarray as we may have cast it a few lines above
+                    if columns is not None:
+                        columns = ensure_index(columns)
+                    arrays, columns, index = nested_data_to_arrays(
+                        # error: Argument 3 to "nested_data_to_arrays" has incompatible
+                        # type "Optional[Collection[Any]]"; expected "Optional[Index]"
+                        data,
+                        columns,
+                        index,  # type: ignore[arg-type]
+                        dtype,
+                    )
+                    mgr = arrays_to_mgr(
+                        arrays,
+                        columns,
+                        index,
+                        dtype=dtype,
+                        typ=manager,
+                    )
+                else:
+                    mgr = ndarray_to_mgr(
+                        data,
+                        index,
+                        columns,
+                        dtype=dtype,
+                        copy=copy,
+                        typ=manager,
+                    )
+            else:
+                mgr = dict_to_mgr(
+                    {},
+                    index,
+                    columns if columns is not None else default_index(0),
+                    dtype=dtype,
+                    typ=manager,
+                )
+        # For data is scalar
+        else:
+            if index is None or columns is None:
+                raise ValueError("DataFrame constructor not properly called!")
+
+            index = ensure_index(index)
+            columns = ensure_index(columns)
+
+            if not dtype:
+                dtype, _ = infer_dtype_from_scalar(data)
+
+            # For data is a scalar extension dtype
+            if isinstance(dtype, ExtensionDtype):
+                # TODO(EA2D): special case not needed with 2D EAs
+
+                values = [
+                    construct_1d_arraylike_from_scalar(data, len(index), dtype)
+                    for _ in range(len(columns))
+                ]
+                mgr = arrays_to_mgr(values, columns, index, dtype=None, typ=manager)
+            else:
+                arr2d = construct_2d_arraylike_from_scalar(
+                    data,
+                    len(index),
+                    len(columns),
+                    dtype,
+                    copy,
+                )
+
+                mgr = ndarray_to_mgr(
+                    arr2d,
+                    index,
+                    columns,
+                    dtype=arr2d.dtype,
+                    copy=False,
+                    typ=manager,
+                )
+
+        # ensure correct Manager type according to settings
+        mgr = mgr_to_mgr(mgr, typ=manager)
+
+        NDFrame.__init__(self, mgr)
+
+        if original_dtype is None and is_pandas_object and data_dtype == np.object_:
+            if self.dtypes.iloc[0] != data_dtype:
+                warnings.warn(
+                    "Dtype inference on a pandas object "
+                    "(Series, Index, ExtensionArray) is deprecated. The DataFrame "
+                    "constructor will keep the original dtype in the future. "
+                    "Call `infer_objects` on the result to get the old "
+                    "behavior.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+
+    # ----------------------------------------------------------------------
+
+    def __dataframe__(
+        self, nan_as_null: bool = False, allow_copy: bool = True
+    ) -> DataFrameXchg:
+        """
+        Return the dataframe interchange object implementing the interchange protocol.
+
+        Parameters
+        ----------
+        nan_as_null : bool, default False
+            `nan_as_null` is DEPRECATED and has no effect. Please avoid using
+            it; it will be removed in a future release.
+        allow_copy : bool, default True
+            Whether to allow memory copying when exporting. If set to False
+            it would cause non-zero-copy exports to fail.
+
+        Returns
+        -------
+        DataFrame interchange object
+            The object which consuming library can use to ingress the dataframe.
+
+        Notes
+        -----
+        Details on the interchange protocol:
+        https://data-apis.org/dataframe-protocol/latest/index.html
+
+        Examples
+        --------
+        >>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+        >>> interchange_object = df_not_necessarily_pandas.__dataframe__()
+        >>> interchange_object.column_names()
+        Index(['A', 'B'], dtype='object')
+        >>> df_pandas = (pd.api.interchange.from_dataframe
+        ...              (interchange_object.select_columns_by_name(['A'])))
+        >>> df_pandas
+             A
+        0    1
+        1    2
+
+        These methods (``column_names``, ``select_columns_by_name``) should work
+        for any dataframe library which implements the interchange protocol.
+        """
+
+        from pandas.core.interchange.dataframe import PandasDataFrameXchg
+
+        return PandasDataFrameXchg(self, allow_copy=allow_copy)
+
+    def __dataframe_consortium_standard__(
+        self, *, api_version: str | None = None
+    ) -> Any:
+        """
+        Provide entry point to the Consortium DataFrame Standard API.
+
+        This is developed and maintained outside of pandas.
+        Please report any issues to https://github.com/data-apis/dataframe-api-compat.
+        """
+        dataframe_api_compat = import_optional_dependency("dataframe_api_compat")
+        convert_to_standard_compliant_dataframe = (
+            dataframe_api_compat.pandas_standard.convert_to_standard_compliant_dataframe
+        )
+        return convert_to_standard_compliant_dataframe(self, api_version=api_version)
+
+    def __arrow_c_stream__(self, requested_schema=None):
+        """
+        Export the pandas DataFrame as an Arrow C stream PyCapsule.
+
+        This relies on pyarrow to convert the pandas DataFrame to the Arrow
+        format (and follows the default behaviour of ``pyarrow.Table.from_pandas``
+        in its handling of the index, i.e. store the index as a column except
+        for RangeIndex).
+        This conversion is not necessarily zero-copy.
+
+        Parameters
+        ----------
+        requested_schema : PyCapsule, default None
+            The schema to which the dataframe should be casted, passed as a
+            PyCapsule containing a C ArrowSchema representation of the
+            requested schema.
+
+        Returns
+        -------
+        PyCapsule
+        """
+        pa = import_optional_dependency("pyarrow", min_version="14.0.0")
+        if requested_schema is not None:
+            requested_schema = pa.Schema._import_from_c_capsule(requested_schema)
+        table = pa.Table.from_pandas(self, schema=requested_schema)
+        return table.__arrow_c_stream__()
+
+    # ----------------------------------------------------------------------
+
+    @property
+    def axes(self) -> list[Index]:
+        """
+        Return a list representing the axes of the DataFrame.
+
+        It has the row axis labels and column axis labels as the only members.
+        They are returned in that order.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.axes
+        [RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
+        dtype='object')]
+        """
+        return [self.index, self.columns]
+
+    @property
+    def shape(self) -> tuple[int, int]:
+        """
+        Return a tuple representing the dimensionality of the DataFrame.
+
+        See Also
+        --------
+        ndarray.shape : Tuple of array dimensions.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.shape
+        (2, 2)
+
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
+        ...                    'col3': [5, 6]})
+        >>> df.shape
+        (2, 3)
+        """
+        return len(self.index), len(self.columns)
+
+    @property
+    def _is_homogeneous_type(self) -> bool:
+        """
+        Whether all the columns in a DataFrame have the same type.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
+        True
+        >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
+        False
+
+        Items with the same type but different sizes are considered
+        different types.
+
+        >>> DataFrame({
+        ...    "A": np.array([1, 2], dtype=np.int32),
+        ...    "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
+        False
+        """
+        # The "<" part of "<=" here is for empty DataFrame cases
+        return len({arr.dtype for arr in self._mgr.arrays}) <= 1
+
+    @property
+    def _can_fast_transpose(self) -> bool:
+        """
+        Can we transpose this DataFrame without creating any new array objects.
+        """
+        if isinstance(self._mgr, ArrayManager):
+            return False
+        blocks = self._mgr.blocks
+        if len(blocks) != 1:
+            return False
+
+        dtype = blocks[0].dtype
+        # TODO(EA2D) special case would be unnecessary with 2D EAs
+        return not is_1d_only_ea_dtype(dtype)
+
+    @property
+    def _values(self) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray:
+        """
+        Analogue to ._values that may return a 2D ExtensionArray.
+        """
+        mgr = self._mgr
+
+        if isinstance(mgr, ArrayManager):
+            if len(mgr.arrays) == 1 and not is_1d_only_ea_dtype(mgr.arrays[0].dtype):
+                # error: Item "ExtensionArray" of "Union[ndarray, ExtensionArray]"
+                # has no attribute "reshape"
+                return mgr.arrays[0].reshape(-1, 1)  # type: ignore[union-attr]
+            return ensure_wrapped_if_datetimelike(self.values)
+
+        blocks = mgr.blocks
+        if len(blocks) != 1:
+            return ensure_wrapped_if_datetimelike(self.values)
+
+        arr = blocks[0].values
+        if arr.ndim == 1:
+            # non-2D ExtensionArray
+            return self.values
+
+        # more generally, whatever we allow in NDArrayBackedExtensionBlock
+        arr = cast("np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray", arr)
+        return arr.T
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def _repr_fits_vertical_(self) -> bool:
+        """
+        Check length against max_rows.
+        """
+        max_rows = get_option("display.max_rows")
+        return len(self) <= max_rows
+
+    def _repr_fits_horizontal_(self) -> bool:
+        """
+        Check if full repr fits in horizontal boundaries imposed by the display
+        options width and max_columns.
+        """
+        width, height = console.get_console_size()
+        max_columns = get_option("display.max_columns")
+        nb_columns = len(self.columns)
+
+        # exceed max columns
+        if (max_columns and nb_columns > max_columns) or (
+            width and nb_columns > (width // 2)
+        ):
+            return False
+
+        # used by repr_html under IPython notebook or scripts ignore terminal
+        # dims
+        if width is None or not console.in_interactive_session():
+            return True
+
+        if get_option("display.width") is not None or console.in_ipython_frontend():
+            # check at least the column row for excessive width
+            max_rows = 1
+        else:
+            max_rows = get_option("display.max_rows")
+
+        # when auto-detecting, so width=None and not in ipython front end
+        # check whether repr fits horizontal by actually checking
+        # the width of the rendered repr
+        buf = StringIO()
+
+        # only care about the stuff we'll actually print out
+        # and to_string on entire frame may be expensive
+        d = self
+
+        if max_rows is not None:  # unlimited rows
+            # min of two, where one may be None
+            d = d.iloc[: min(max_rows, len(d))]
+        else:
+            return True
+
+        d.to_string(buf=buf)
+        value = buf.getvalue()
+        repr_width = max(len(line) for line in value.split("\n"))
+
+        return repr_width < width
+
+    def _info_repr(self) -> bool:
+        """
+        True if the repr should show the info view.
+        """
+        info_repr_option = get_option("display.large_repr") == "info"
+        return info_repr_option and not (
+            self._repr_fits_horizontal_() and self._repr_fits_vertical_()
+        )
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation for a particular DataFrame.
+        """
+        if self._info_repr():
+            buf = StringIO()
+            self.info(buf=buf)
+            return buf.getvalue()
+
+        repr_params = fmt.get_dataframe_repr_params()
+        return self.to_string(**repr_params)
+
+    def _repr_html_(self) -> str | None:
+        """
+        Return a html representation for a particular DataFrame.
+
+        Mainly for IPython notebook.
+        """
+        if self._info_repr():
+            buf = StringIO()
+            self.info(buf=buf)
+            # need to escape the <class>, should be the first line.
+            val = buf.getvalue().replace("<", r"&lt;", 1)
+            val = val.replace(">", r"&gt;", 1)
+            return f"<pre>{val}</pre>"
+
+        if get_option("display.notebook_repr_html"):
+            max_rows = get_option("display.max_rows")
+            min_rows = get_option("display.min_rows")
+            max_cols = get_option("display.max_columns")
+            show_dimensions = get_option("display.show_dimensions")
+
+            formatter = fmt.DataFrameFormatter(
+                self,
+                columns=None,
+                col_space=None,
+                na_rep="NaN",
+                formatters=None,
+                float_format=None,
+                sparsify=None,
+                justify=None,
+                index_names=True,
+                header=True,
+                index=True,
+                bold_rows=True,
+                escape=True,
+                max_rows=max_rows,
+                min_rows=min_rows,
+                max_cols=max_cols,
+                show_dimensions=show_dimensions,
+                decimal=".",
+            )
+            return fmt.DataFrameRenderer(formatter).to_html(notebook=True)
+        else:
+            return None
+
+    @overload
+    def to_string(
+        self,
+        buf: None = ...,
+        columns: Axes | None = ...,
+        col_space: int | list[int] | dict[Hashable, int] | None = ...,
+        header: bool | SequenceNotStr[str] = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: fmt.FormattersType | None = ...,
+        float_format: fmt.FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool = ...,
+        decimal: str = ...,
+        line_width: int | None = ...,
+        min_rows: int | None = ...,
+        max_colwidth: int | None = ...,
+        encoding: str | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        columns: Axes | None = ...,
+        col_space: int | list[int] | dict[Hashable, int] | None = ...,
+        header: bool | SequenceNotStr[str] = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: fmt.FormattersType | None = ...,
+        float_format: fmt.FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool = ...,
+        decimal: str = ...,
+        line_width: int | None = ...,
+        min_rows: int | None = ...,
+        max_colwidth: int | None = ...,
+        encoding: str | None = ...,
+    ) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_string"
+    )
+    @Substitution(
+        header_type="bool or list of str",
+        header="Write out the column names. If a list of columns "
+        "is given, it is assumed to be aliases for the "
+        "column names",
+        col_space_type="int, list or dict of int",
+        col_space="The minimum width of each column. If a list of ints is given "
+        "every integers corresponds with one column. If a dict is given, the key "
+        "references the column, while the value defines the space to use.",
+    )
+    @Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        columns: Axes | None = None,
+        col_space: int | list[int] | dict[Hashable, int] | None = None,
+        header: bool | SequenceNotStr[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: fmt.FormattersType | None = None,
+        float_format: fmt.FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        justify: str | None = None,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool = False,
+        decimal: str = ".",
+        line_width: int | None = None,
+        min_rows: int | None = None,
+        max_colwidth: int | None = None,
+        encoding: str | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to a console-friendly tabular output.
+        %(shared_params)s
+        line_width : int, optional
+            Width to wrap a line in characters.
+        min_rows : int, optional
+            The number of rows to display in the console in a truncated repr
+            (when number of rows is above `max_rows`).
+        max_colwidth : int, optional
+            Max width to truncate each column in characters. By default, no limit.
+        encoding : str, default "utf-8"
+            Set character encoding.
+        %(returns)s
+        See Also
+        --------
+        to_html : Convert DataFrame to HTML.
+
+        Examples
+        --------
+        >>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
+        >>> df = pd.DataFrame(d)
+        >>> print(df.to_string())
+           col1  col2
+        0     1     4
+        1     2     5
+        2     3     6
+        """
+        from pandas import option_context
+
+        with option_context("display.max_colwidth", max_colwidth):
+            formatter = fmt.DataFrameFormatter(
+                self,
+                columns=columns,
+                col_space=col_space,
+                na_rep=na_rep,
+                formatters=formatters,
+                float_format=float_format,
+                sparsify=sparsify,
+                justify=justify,
+                index_names=index_names,
+                header=header,
+                index=index,
+                min_rows=min_rows,
+                max_rows=max_rows,
+                max_cols=max_cols,
+                show_dimensions=show_dimensions,
+                decimal=decimal,
+            )
+            return fmt.DataFrameRenderer(formatter).to_string(
+                buf=buf,
+                encoding=encoding,
+                line_width=line_width,
+            )
+
+    def _get_values_for_csv(
+        self,
+        *,
+        float_format: FloatFormatType | None,
+        date_format: str | None,
+        decimal: str,
+        na_rep: str,
+        quoting,  # int csv.QUOTE_FOO from stdlib
+    ) -> Self:
+        # helper used by to_csv
+        mgr = self._mgr.get_values_for_csv(
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+            na_rep=na_rep,
+            quoting=quoting,
+        )
+        # error: Incompatible return value type (got "DataFrame", expected "Self")
+        return self._constructor_from_mgr(mgr, axes=mgr.axes)  # type: ignore[return-value]
+
+    # ----------------------------------------------------------------------
+
+    @property
+    def style(self) -> Styler:
+        """
+        Returns a Styler object.
+
+        Contains methods for building a styled HTML representation of the DataFrame.
+
+        See Also
+        --------
+        io.formats.style.Styler : Helps style a DataFrame or Series according to the
+            data with HTML and CSS.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3]})
+        >>> df.style  # doctest: +SKIP
+
+        Please see
+        `Table Visualization <../../user_guide/style.ipynb>`_ for more examples.
+        """
+        # Raise AttributeError so that inspect works even if jinja2 is not installed.
+        has_jinja2 = import_optional_dependency("jinja2", errors="ignore")
+        if not has_jinja2:
+            raise AttributeError("The '.style' accessor requires jinja2")
+
+        from pandas.io.formats.style import Styler
+
+        return Styler(self)
+
+    _shared_docs[
+        "items"
+    ] = r"""
+        Iterate over (column name, Series) pairs.
+
+        Iterates over the DataFrame columns, returning a tuple with
+        the column name and the content as a Series.
+
+        Yields
+        ------
+        label : object
+            The column names for the DataFrame being iterated over.
+        content : Series
+            The column entries belonging to each label, as a Series.
+
+        See Also
+        --------
+        DataFrame.iterrows : Iterate over DataFrame rows as
+            (index, Series) pairs.
+        DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
+            of the values.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
+        ...                   'population': [1864, 22000, 80000]},
+        ...                   index=['panda', 'polar', 'koala'])
+        >>> df
+                species   population
+        panda   bear      1864
+        polar   bear      22000
+        koala   marsupial 80000
+        >>> for label, content in df.items():
+        ...     print(f'label: {label}')
+        ...     print(f'content: {content}', sep='\n')
+        ...
+        label: species
+        content:
+        panda         bear
+        polar         bear
+        koala    marsupial
+        Name: species, dtype: object
+        label: population
+        content:
+        panda     1864
+        polar    22000
+        koala    80000
+        Name: population, dtype: int64
+        """
+
+    @Appender(_shared_docs["items"])
+    def items(self) -> Iterable[tuple[Hashable, Series]]:
+        if self.columns.is_unique and hasattr(self, "_item_cache"):
+            for k in self.columns:
+                yield k, self._get_item_cache(k)
+        else:
+            for i, k in enumerate(self.columns):
+                yield k, self._ixs(i, axis=1)
+
+    def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
+        """
+        Iterate over DataFrame rows as (index, Series) pairs.
+
+        Yields
+        ------
+        index : label or tuple of label
+            The index of the row. A tuple for a `MultiIndex`.
+        data : Series
+            The data of the row as a Series.
+
+        See Also
+        --------
+        DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
+        DataFrame.items : Iterate over (column name, Series) pairs.
+
+        Notes
+        -----
+        1. Because ``iterrows`` returns a Series for each row,
+           it does **not** preserve dtypes across the rows (dtypes are
+           preserved across columns for DataFrames).
+
+           To preserve dtypes while iterating over the rows, it is better
+           to use :meth:`itertuples` which returns namedtuples of the values
+           and which is generally faster than ``iterrows``.
+
+        2. You should **never modify** something you are iterating over.
+           This is not guaranteed to work in all cases. Depending on the
+           data types, the iterator returns a copy and not a view, and writing
+           to it will have no effect.
+
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
+        >>> row = next(df.iterrows())[1]
+        >>> row
+        int      1.0
+        float    1.5
+        Name: 0, dtype: float64
+        >>> print(row['int'].dtype)
+        float64
+        >>> print(df['int'].dtype)
+        int64
+        """
+        columns = self.columns
+        klass = self._constructor_sliced
+        using_cow = using_copy_on_write()
+        for k, v in zip(self.index, self.values):
+            s = klass(v, index=columns, name=k).__finalize__(self)
+            if using_cow and self._mgr.is_single_block:
+                s._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+            yield k, s
+
+    def itertuples(
+        self, index: bool = True, name: str | None = "Pandas"
+    ) -> Iterable[tuple[Any, ...]]:
+        """
+        Iterate over DataFrame rows as namedtuples.
+
+        Parameters
+        ----------
+        index : bool, default True
+            If True, return the index as the first element of the tuple.
+        name : str or None, default "Pandas"
+            The name of the returned namedtuples or None to return regular
+            tuples.
+
+        Returns
+        -------
+        iterator
+            An object to iterate over namedtuples for each row in the
+            DataFrame with the first field possibly being the index and
+            following fields being the column values.
+
+        See Also
+        --------
+        DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
+            pairs.
+        DataFrame.items : Iterate over (column name, Series) pairs.
+
+        Notes
+        -----
+        The column names will be renamed to positional names if they are
+        invalid Python identifiers, repeated, or start with an underscore.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
+        ...                   index=['dog', 'hawk'])
+        >>> df
+              num_legs  num_wings
+        dog          4          0
+        hawk         2          2
+        >>> for row in df.itertuples():
+        ...     print(row)
+        ...
+        Pandas(Index='dog', num_legs=4, num_wings=0)
+        Pandas(Index='hawk', num_legs=2, num_wings=2)
+
+        By setting the `index` parameter to False we can remove the index
+        as the first element of the tuple:
+
+        >>> for row in df.itertuples(index=False):
+        ...     print(row)
+        ...
+        Pandas(num_legs=4, num_wings=0)
+        Pandas(num_legs=2, num_wings=2)
+
+        With the `name` parameter set we set a custom name for the yielded
+        namedtuples:
+
+        >>> for row in df.itertuples(name='Animal'):
+        ...     print(row)
+        ...
+        Animal(Index='dog', num_legs=4, num_wings=0)
+        Animal(Index='hawk', num_legs=2, num_wings=2)
+        """
+        arrays = []
+        fields = list(self.columns)
+        if index:
+            arrays.append(self.index)
+            fields.insert(0, "Index")
+
+        # use integer indexing because of possible duplicate column names
+        arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
+
+        if name is not None:
+            # https://github.com/python/mypy/issues/9046
+            # error: namedtuple() expects a string literal as the first argument
+            itertuple = collections.namedtuple(  # type: ignore[misc]
+                name, fields, rename=True
+            )
+            return map(itertuple._make, zip(*arrays))
+
+        # fallback to regular tuples
+        return zip(*arrays)
+
+    def __len__(self) -> int:
+        """
+        Returns length of info axis, but here we use the index.
+        """
+        return len(self.index)
+
+    @overload
+    def dot(self, other: Series) -> Series:
+        ...
+
+    @overload
+    def dot(self, other: DataFrame | Index | ArrayLike) -> DataFrame:
+        ...
+
+    def dot(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
+        """
+        Compute the matrix multiplication between the DataFrame and other.
+
+        This method computes the matrix product between the DataFrame and the
+        values of an other Series, DataFrame or a numpy array.
+
+        It can also be called using ``self @ other``.
+
+        Parameters
+        ----------
+        other : Series, DataFrame or array-like
+            The other object to compute the matrix product with.
+
+        Returns
+        -------
+        Series or DataFrame
+            If other is a Series, return the matrix product between self and
+            other as a Series. If other is a DataFrame or a numpy.array, return
+            the matrix product of self and other in a DataFrame of a np.array.
+
+        See Also
+        --------
+        Series.dot: Similar method for Series.
+
+        Notes
+        -----
+        The dimensions of DataFrame and other must be compatible in order to
+        compute the matrix multiplication. In addition, the column names of
+        DataFrame and the index of other must contain the same values, as they
+        will be aligned prior to the multiplication.
+
+        The dot method for Series computes the inner product, instead of the
+        matrix product here.
+
+        Examples
+        --------
+        Here we multiply a DataFrame with a Series.
+
+        >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
+        >>> s = pd.Series([1, 1, 2, 1])
+        >>> df.dot(s)
+        0    -4
+        1     5
+        dtype: int64
+
+        Here we multiply a DataFrame with another DataFrame.
+
+        >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
+        >>> df.dot(other)
+            0   1
+        0   1   4
+        1   2   2
+
+        Note that the dot method give the same result as @
+
+        >>> df @ other
+            0   1
+        0   1   4
+        1   2   2
+
+        The dot method works also if other is an np.array.
+
+        >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
+        >>> df.dot(arr)
+            0   1
+        0   1   4
+        1   2   2
+
+        Note how shuffling of the objects does not change the result.
+
+        >>> s2 = s.reindex([1, 0, 2, 3])
+        >>> df.dot(s2)
+        0    -4
+        1     5
+        dtype: int64
+        """
+        if isinstance(other, (Series, DataFrame)):
+            common = self.columns.union(other.index)
+            if len(common) > len(self.columns) or len(common) > len(other.index):
+                raise ValueError("matrices are not aligned")
+
+            left = self.reindex(columns=common, copy=False)
+            right = other.reindex(index=common, copy=False)
+            lvals = left.values
+            rvals = right._values
+        else:
+            left = self
+            lvals = self.values
+            rvals = np.asarray(other)
+            if lvals.shape[1] != rvals.shape[0]:
+                raise ValueError(
+                    f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
+                )
+
+        if isinstance(other, DataFrame):
+            common_type = find_common_type(list(self.dtypes) + list(other.dtypes))
+            return self._constructor(
+                np.dot(lvals, rvals),
+                index=left.index,
+                columns=other.columns,
+                copy=False,
+                dtype=common_type,
+            )
+        elif isinstance(other, Series):
+            common_type = find_common_type(list(self.dtypes) + [other.dtypes])
+            return self._constructor_sliced(
+                np.dot(lvals, rvals), index=left.index, copy=False, dtype=common_type
+            )
+        elif isinstance(rvals, (np.ndarray, Index)):
+            result = np.dot(lvals, rvals)
+            if result.ndim == 2:
+                return self._constructor(result, index=left.index, copy=False)
+            else:
+                return self._constructor_sliced(result, index=left.index, copy=False)
+        else:  # pragma: no cover
+            raise TypeError(f"unsupported type: {type(other)}")
+
+    @overload
+    def __matmul__(self, other: Series) -> Series:
+        ...
+
+    @overload
+    def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
+        ...
+
+    def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        return self.dot(other)
+
+    def __rmatmul__(self, other) -> DataFrame:
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        try:
+            return self.T.dot(np.transpose(other)).T
+        except ValueError as err:
+            if "shape mismatch" not in str(err):
+                raise
+            # GH#21581 give exception message for original shapes
+            msg = f"shapes {np.shape(other)} and {self.shape} not aligned"
+            raise ValueError(msg) from err
+
+    # ----------------------------------------------------------------------
+    # IO methods (to / from other formats)
+
+    @classmethod
+    def from_dict(
+        cls,
+        data: dict,
+        orient: FromDictOrient = "columns",
+        dtype: Dtype | None = None,
+        columns: Axes | None = None,
+    ) -> DataFrame:
+        """
+        Construct DataFrame from dict of array-like or dicts.
+
+        Creates DataFrame object from dictionary by columns or by index
+        allowing dtype specification.
+
+        Parameters
+        ----------
+        data : dict
+            Of the form {field : array-like} or {field : dict}.
+        orient : {'columns', 'index', 'tight'}, default 'columns'
+            The "orientation" of the data. If the keys of the passed dict
+            should be the columns of the resulting DataFrame, pass 'columns'
+            (default). Otherwise if the keys should be rows, pass 'index'.
+            If 'tight', assume a dict with keys ['index', 'columns', 'data',
+            'index_names', 'column_names'].
+
+            .. versionadded:: 1.4.0
+               'tight' as an allowed value for the ``orient`` argument
+
+        dtype : dtype, default None
+            Data type to force after DataFrame construction, otherwise infer.
+        columns : list, default None
+            Column labels to use when ``orient='index'``. Raises a ValueError
+            if used with ``orient='columns'`` or ``orient='tight'``.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.from_records : DataFrame from structured ndarray, sequence
+            of tuples or dicts, or DataFrame.
+        DataFrame : DataFrame object creation using constructor.
+        DataFrame.to_dict : Convert the DataFrame to a dictionary.
+
+        Examples
+        --------
+        By default the keys of the dict become the DataFrame columns:
+
+        >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
+        >>> pd.DataFrame.from_dict(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Specify ``orient='index'`` to create the DataFrame using dictionary
+        keys as rows:
+
+        >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
+        >>> pd.DataFrame.from_dict(data, orient='index')
+               0  1  2  3
+        row_1  3  2  1  0
+        row_2  a  b  c  d
+
+        When using the 'index' orientation, the column names can be
+        specified manually:
+
+        >>> pd.DataFrame.from_dict(data, orient='index',
+        ...                        columns=['A', 'B', 'C', 'D'])
+               A  B  C  D
+        row_1  3  2  1  0
+        row_2  a  b  c  d
+
+        Specify ``orient='tight'`` to create the DataFrame using a 'tight'
+        format:
+
+        >>> data = {'index': [('a', 'b'), ('a', 'c')],
+        ...         'columns': [('x', 1), ('y', 2)],
+        ...         'data': [[1, 3], [2, 4]],
+        ...         'index_names': ['n1', 'n2'],
+        ...         'column_names': ['z1', 'z2']}
+        >>> pd.DataFrame.from_dict(data, orient='tight')
+        z1     x  y
+        z2     1  2
+        n1 n2
+        a  b   1  3
+           c   2  4
+        """
+        index = None
+        orient = orient.lower()  # type: ignore[assignment]
+        if orient == "index":
+            if len(data) > 0:
+                # TODO speed up Series case
+                if isinstance(next(iter(data.values())), (Series, dict)):
+                    data = _from_nested_dict(data)
+                else:
+                    index = list(data.keys())
+                    # error: Incompatible types in assignment (expression has type
+                    # "List[Any]", variable has type "Dict[Any, Any]")
+                    data = list(data.values())  # type: ignore[assignment]
+        elif orient in ("columns", "tight"):
+            if columns is not None:
+                raise ValueError(f"cannot use columns parameter with orient='{orient}'")
+        else:  # pragma: no cover
+            raise ValueError(
+                f"Expected 'index', 'columns' or 'tight' for orient parameter. "
+                f"Got '{orient}' instead"
+            )
+
+        if orient != "tight":
+            return cls(data, index=index, columns=columns, dtype=dtype)
+        else:
+            realdata = data["data"]
+
+            def create_index(indexlist, namelist):
+                index: Index
+                if len(namelist) > 1:
+                    index = MultiIndex.from_tuples(indexlist, names=namelist)
+                else:
+                    index = Index(indexlist, name=namelist[0])
+                return index
+
+            index = create_index(data["index"], data["index_names"])
+            columns = create_index(data["columns"], data["column_names"])
+            return cls(realdata, index=index, columns=columns, dtype=dtype)
+
+    def to_numpy(
+        self,
+        dtype: npt.DTypeLike | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert the DataFrame to a NumPy array.
+
+        By default, the dtype of the returned array will be the common NumPy
+        dtype of all types in the DataFrame. For example, if the dtypes are
+        ``float16`` and ``float32``, the results dtype will be ``float32``.
+        This may require copying data and coercing values, which may be
+        expensive.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to pass to :meth:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, optional
+            The value to use for missing values. The default value depends
+            on `dtype` and the dtypes of the DataFrame columns.
+
+        Returns
+        -------
+        numpy.ndarray
+
+        See Also
+        --------
+        Series.to_numpy : Similar method for Series.
+
+        Examples
+        --------
+        >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
+        array([[1, 3],
+               [2, 4]])
+
+        With heterogeneous data, the lowest common type will have to
+        be used.
+
+        >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
+        >>> df.to_numpy()
+        array([[1. , 3. ],
+               [2. , 4.5]])
+
+        For a mix of numeric and non-numeric types, the output array will
+        have object dtype.
+
+        >>> df['C'] = pd.date_range('2000', periods=2)
+        >>> df.to_numpy()
+        array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
+               [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
+        """
+        if dtype is not None:
+            dtype = np.dtype(dtype)
+        result = self._mgr.as_array(dtype=dtype, copy=copy, na_value=na_value)
+        if result.dtype is not dtype:
+            result = np.asarray(result, dtype=dtype)
+
+        return result
+
+    def _create_data_for_split_and_tight_to_dict(
+        self, are_all_object_dtype_cols: bool, object_dtype_indices: list[int]
+    ) -> list:
+        """
+        Simple helper method to create data for to ``to_dict(orient="split")`` and
+        ``to_dict(orient="tight")`` to create the main output data
+        """
+        if are_all_object_dtype_cols:
+            data = [
+                list(map(maybe_box_native, t))
+                for t in self.itertuples(index=False, name=None)
+            ]
+        else:
+            data = [list(t) for t in self.itertuples(index=False, name=None)]
+            if object_dtype_indices:
+                # If we have object_dtype_cols, apply maybe_box_naive after list
+                # comprehension for perf
+                for row in data:
+                    for i in object_dtype_indices:
+                        row[i] = maybe_box_native(row[i])
+        return data
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+        *,
+        into: type[MutableMappingT] | MutableMappingT,
+        index: bool = ...,
+    ) -> MutableMappingT:
+        ...
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["records"],
+        *,
+        into: type[MutableMappingT] | MutableMappingT,
+        index: bool = ...,
+    ) -> list[MutableMappingT]:
+        ...
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+        *,
+        into: type[dict] = ...,
+        index: bool = ...,
+    ) -> dict:
+        ...
+
+    @overload
+    def to_dict(
+        self,
+        orient: Literal["records"],
+        *,
+        into: type[dict] = ...,
+        index: bool = ...,
+    ) -> list[dict]:
+        ...
+
+    # error: Incompatible default for argument "into" (default has type "type
+    # [dict[Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "orient"], name="to_dict"
+    )
+    def to_dict(
+        self,
+        orient: Literal[
+            "dict", "list", "series", "split", "tight", "records", "index"
+        ] = "dict",
+        into: type[MutableMappingT]
+        | MutableMappingT = dict,  # type: ignore[assignment]
+        index: bool = True,
+    ) -> MutableMappingT | list[MutableMappingT]:
+        """
+        Convert the DataFrame to a dictionary.
+
+        The type of the key-value pairs can be customized with the parameters
+        (see below).
+
+        Parameters
+        ----------
+        orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
+            Determines the type of the values of the dictionary.
+
+            - 'dict' (default) : dict like {column -> {index -> value}}
+            - 'list' : dict like {column -> [values]}
+            - 'series' : dict like {column -> Series(values)}
+            - 'split' : dict like
+              {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
+            - 'tight' : dict like
+              {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
+              'index_names' -> [index.names], 'column_names' -> [column.names]}
+            - 'records' : list like
+              [{column -> value}, ... , {column -> value}]
+            - 'index' : dict like {index -> {column -> value}}
+
+            .. versionadded:: 1.4.0
+                'tight' as an allowed value for the ``orient`` argument
+
+        into : class, default dict
+            The collections.abc.MutableMapping subclass used for all Mappings
+            in the return value.  Can be the actual class or an empty
+            instance of the mapping type you want.  If you want a
+            collections.defaultdict, you must pass it initialized.
+
+        index : bool, default True
+            Whether to include the index item (and index_names item if `orient`
+            is 'tight') in the returned dictionary. Can only be ``False``
+            when `orient` is 'split' or 'tight'.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        dict, list or collections.abc.MutableMapping
+            Return a collections.abc.MutableMapping object representing the
+            DataFrame. The resulting transformation depends on the `orient`
+            parameter.
+
+        See Also
+        --------
+        DataFrame.from_dict: Create a DataFrame from a dictionary.
+        DataFrame.to_json: Convert a DataFrame to JSON format.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2],
+        ...                    'col2': [0.5, 0.75]},
+        ...                   index=['row1', 'row2'])
+        >>> df
+              col1  col2
+        row1     1  0.50
+        row2     2  0.75
+        >>> df.to_dict()
+        {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
+
+        You can specify the return orientation.
+
+        >>> df.to_dict('series')
+        {'col1': row1    1
+                 row2    2
+        Name: col1, dtype: int64,
+        'col2': row1    0.50
+                row2    0.75
+        Name: col2, dtype: float64}
+
+        >>> df.to_dict('split')
+        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
+         'data': [[1, 0.5], [2, 0.75]]}
+
+        >>> df.to_dict('records')
+        [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
+
+        >>> df.to_dict('index')
+        {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
+
+        >>> df.to_dict('tight')
+        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
+         'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}
+
+        You can also specify the mapping type.
+
+        >>> from collections import OrderedDict, defaultdict
+        >>> df.to_dict(into=OrderedDict)
+        OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
+                     ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
+
+        If you want a `defaultdict`, you need to initialize it:
+
+        >>> dd = defaultdict(list)
+        >>> df.to_dict('records', into=dd)
+        [defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
+         defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
+        """
+        from pandas.core.methods.to_dict import to_dict
+
+        return to_dict(self, orient, into=into, index=index)
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "destination_table"], name="to_gbq"
+    )
+    def to_gbq(
+        self,
+        destination_table: str,
+        project_id: str | None = None,
+        chunksize: int | None = None,
+        reauth: bool = False,
+        if_exists: ToGbqIfexist = "fail",
+        auth_local_webserver: bool = True,
+        table_schema: list[dict[str, str]] | None = None,
+        location: str | None = None,
+        progress_bar: bool = True,
+        credentials=None,
+    ) -> None:
+        """
+        Write a DataFrame to a Google BigQuery table.
+
+        .. deprecated:: 2.2.0
+
+           Please use ``pandas_gbq.to_gbq`` instead.
+
+        This function requires the `pandas-gbq package
+        <https://pandas-gbq.readthedocs.io>`__.
+
+        See the `How to authenticate with Google BigQuery
+        <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
+        guide for authentication instructions.
+
+        Parameters
+        ----------
+        destination_table : str
+            Name of table to be written, in the form ``dataset.tablename``.
+        project_id : str, optional
+            Google BigQuery Account project ID. Optional when available from
+            the environment.
+        chunksize : int, optional
+            Number of rows to be inserted in each chunk from the dataframe.
+            Set to ``None`` to load the whole dataframe at once.
+        reauth : bool, default False
+            Force Google BigQuery to re-authenticate the user. This is useful
+            if multiple accounts are used.
+        if_exists : str, default 'fail'
+            Behavior when the destination table exists. Value can be one of:
+
+            ``'fail'``
+                If table exists raise pandas_gbq.gbq.TableCreationError.
+            ``'replace'``
+                If table exists, drop it, recreate it, and insert data.
+            ``'append'``
+                If table exists, insert data. Create if does not exist.
+        auth_local_webserver : bool, default True
+            Use the `local webserver flow`_ instead of the `console flow`_
+            when getting user credentials.
+
+            .. _local webserver flow:
+                https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
+            .. _console flow:
+                https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
+
+            *New in version 0.2.0 of pandas-gbq*.
+
+            .. versionchanged:: 1.5.0
+               Default value is changed to ``True``. Google has deprecated the
+               ``auth_local_webserver = False`` `"out of band" (copy-paste)
+               flow
+               <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
+        table_schema : list of dicts, optional
+            List of BigQuery table fields to which according DataFrame
+            columns conform to, e.g. ``[{'name': 'col1', 'type':
+            'STRING'},...]``. If schema is not provided, it will be
+            generated according to dtypes of DataFrame columns. See
+            BigQuery API documentation on available names of a field.
+
+            *New in version 0.3.1 of pandas-gbq*.
+        location : str, optional
+            Location where the load job should run. See the `BigQuery locations
+            documentation
+            <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
+            list of available locations. The location must match that of the
+            target dataset.
+
+            *New in version 0.5.0 of pandas-gbq*.
+        progress_bar : bool, default True
+            Use the library `tqdm` to show the progress bar for the upload,
+            chunk by chunk.
+
+            *New in version 0.5.0 of pandas-gbq*.
+        credentials : google.auth.credentials.Credentials, optional
+            Credentials for accessing Google APIs. Use this parameter to
+            override default credentials, such as to use Compute Engine
+            :class:`google.auth.compute_engine.Credentials` or Service
+            Account :class:`google.oauth2.service_account.Credentials`
+            directly.
+
+            *New in version 0.8.0 of pandas-gbq*.
+
+        See Also
+        --------
+        pandas_gbq.to_gbq : This function in the pandas-gbq library.
+        read_gbq : Read a DataFrame from Google BigQuery.
+
+        Examples
+        --------
+        Example taken from `Google BigQuery documentation
+        <https://cloud.google.com/bigquery/docs/samples/bigquery-pandas-gbq-to-gbq-simple>`_
+
+        >>> project_id = "my-project"
+        >>> table_id = 'my_dataset.my_table'
+        >>> df = pd.DataFrame({
+        ...                   "my_string": ["a", "b", "c"],
+        ...                   "my_int64": [1, 2, 3],
+        ...                   "my_float64": [4.0, 5.0, 6.0],
+        ...                   "my_bool1": [True, False, True],
+        ...                   "my_bool2": [False, True, False],
+        ...                   "my_dates": pd.date_range("now", periods=3),
+        ...                   }
+        ...                   )
+
+        >>> df.to_gbq(table_id, project_id=project_id)  # doctest: +SKIP
+        """
+        from pandas.io import gbq
+
+        gbq.to_gbq(
+            self,
+            destination_table,
+            project_id=project_id,
+            chunksize=chunksize,
+            reauth=reauth,
+            if_exists=if_exists,
+            auth_local_webserver=auth_local_webserver,
+            table_schema=table_schema,
+            location=location,
+            progress_bar=progress_bar,
+            credentials=credentials,
+        )
+
+    @classmethod
+    def from_records(
+        cls,
+        data,
+        index=None,
+        exclude=None,
+        columns=None,
+        coerce_float: bool = False,
+        nrows: int | None = None,
+    ) -> DataFrame:
+        """
+        Convert structured or record ndarray to DataFrame.
+
+        Creates a DataFrame object from a structured ndarray, sequence of
+        tuples or dicts, or DataFrame.
+
+        Parameters
+        ----------
+        data : structured ndarray, sequence of tuples or dicts, or DataFrame
+            Structured input data.
+
+            .. deprecated:: 2.1.0
+                Passing a DataFrame is deprecated.
+        index : str, list of fields, array-like
+            Field of array to use as the index, alternately a specific set of
+            input labels to use.
+        exclude : sequence, default None
+            Columns or fields to exclude.
+        columns : sequence, default None
+            Column names to use. If the passed data do not have names
+            associated with them, this argument provides names for the
+            columns. Otherwise this argument indicates the order of the columns
+            in the result (any names not found in the data will become all-NA
+            columns).
+        coerce_float : bool, default False
+            Attempt to convert values of non-string, non-numeric objects (like
+            decimal.Decimal) to floating point, useful for SQL result sets.
+        nrows : int, default None
+            Number of rows to read if data is an iterator.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.from_dict : DataFrame from dict of array-like or dicts.
+        DataFrame : DataFrame object creation using constructor.
+
+        Examples
+        --------
+        Data can be provided as a structured ndarray:
+
+        >>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
+        ...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
+        >>> pd.DataFrame.from_records(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Data can be provided as a list of dicts:
+
+        >>> data = [{'col_1': 3, 'col_2': 'a'},
+        ...         {'col_1': 2, 'col_2': 'b'},
+        ...         {'col_1': 1, 'col_2': 'c'},
+        ...         {'col_1': 0, 'col_2': 'd'}]
+        >>> pd.DataFrame.from_records(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Data can be provided as a list of tuples with corresponding columns:
+
+        >>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
+        >>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+        """
+        if isinstance(data, DataFrame):
+            warnings.warn(
+                "Passing a DataFrame to DataFrame.from_records is deprecated. Use "
+                "set_index and/or drop to modify the DataFrame instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            if columns is not None:
+                if is_scalar(columns):
+                    columns = [columns]
+                data = data[columns]
+            if index is not None:
+                data = data.set_index(index)
+            if exclude is not None:
+                data = data.drop(columns=exclude)
+            return data.copy(deep=False)
+
+        result_index = None
+
+        # Make a copy of the input columns so we can modify it
+        if columns is not None:
+            columns = ensure_index(columns)
+
+        def maybe_reorder(
+            arrays: list[ArrayLike], arr_columns: Index, columns: Index, index
+        ) -> tuple[list[ArrayLike], Index, Index | None]:
+            """
+            If our desired 'columns' do not match the data's pre-existing 'arr_columns',
+            we re-order our arrays.  This is like a pre-emptive (cheap) reindex.
+            """
+            if len(arrays):
+                length = len(arrays[0])
+            else:
+                length = 0
+
+            result_index = None
+            if len(arrays) == 0 and index is None and length == 0:
+                result_index = default_index(0)
+
+            arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns, length)
+            return arrays, arr_columns, result_index
+
+        if is_iterator(data):
+            if nrows == 0:
+                return cls()
+
+            try:
+                first_row = next(data)
+            except StopIteration:
+                return cls(index=index, columns=columns)
+
+            dtype = None
+            if hasattr(first_row, "dtype") and first_row.dtype.names:
+                dtype = first_row.dtype
+
+            values = [first_row]
+
+            if nrows is None:
+                values += data
+            else:
+                values.extend(itertools.islice(data, nrows - 1))
+
+            if dtype is not None:
+                data = np.array(values, dtype=dtype)
+            else:
+                data = values
+
+        if isinstance(data, dict):
+            if columns is None:
+                columns = arr_columns = ensure_index(sorted(data))
+                arrays = [data[k] for k in columns]
+            else:
+                arrays = []
+                arr_columns_list = []
+                for k, v in data.items():
+                    if k in columns:
+                        arr_columns_list.append(k)
+                        arrays.append(v)
+
+                arr_columns = Index(arr_columns_list)
+                arrays, arr_columns, result_index = maybe_reorder(
+                    arrays, arr_columns, columns, index
+                )
+
+        elif isinstance(data, np.ndarray):
+            arrays, columns = to_arrays(data, columns)
+            arr_columns = columns
+        else:
+            arrays, arr_columns = to_arrays(data, columns)
+            if coerce_float:
+                for i, arr in enumerate(arrays):
+                    if arr.dtype == object:
+                        # error: Argument 1 to "maybe_convert_objects" has
+                        # incompatible type "Union[ExtensionArray, ndarray]";
+                        # expected "ndarray"
+                        arrays[i] = lib.maybe_convert_objects(
+                            arr,  # type: ignore[arg-type]
+                            try_float=True,
+                        )
+
+            arr_columns = ensure_index(arr_columns)
+            if columns is None:
+                columns = arr_columns
+            else:
+                arrays, arr_columns, result_index = maybe_reorder(
+                    arrays, arr_columns, columns, index
+                )
+
+        if exclude is None:
+            exclude = set()
+        else:
+            exclude = set(exclude)
+
+        if index is not None:
+            if isinstance(index, str) or not hasattr(index, "__iter__"):
+                i = columns.get_loc(index)
+                exclude.add(index)
+                if len(arrays) > 0:
+                    result_index = Index(arrays[i], name=index)
+                else:
+                    result_index = Index([], name=index)
+            else:
+                try:
+                    index_data = [arrays[arr_columns.get_loc(field)] for field in index]
+                except (KeyError, TypeError):
+                    # raised by get_loc, see GH#29258
+                    result_index = index
+                else:
+                    result_index = ensure_index_from_sequences(index_data, names=index)
+                    exclude.update(index)
+
+        if any(exclude):
+            arr_exclude = [x for x in exclude if x in arr_columns]
+            to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
+            arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
+
+            columns = columns.drop(exclude)
+
+        manager = _get_option("mode.data_manager", silent=True)
+        mgr = arrays_to_mgr(arrays, columns, result_index, typ=manager)
+        df = DataFrame._from_mgr(mgr, axes=mgr.axes)
+        if cls is not DataFrame:
+            return cls(df, copy=False)
+        return df
+
+    def to_records(
+        self, index: bool = True, column_dtypes=None, index_dtypes=None
+    ) -> np.rec.recarray:
+        """
+        Convert DataFrame to a NumPy record array.
+
+        Index will be included as the first field of the record array if
+        requested.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Include index in resulting record array, stored in 'index'
+            field or using the index label, if set.
+        column_dtypes : str, type, dict, default None
+            If a string or type, the data type to store all columns. If
+            a dictionary, a mapping of column names and indices (zero-indexed)
+            to specific data types.
+        index_dtypes : str, type, dict, default None
+            If a string or type, the data type to store all index levels. If
+            a dictionary, a mapping of index level names and indices
+            (zero-indexed) to specific data types.
+
+            This mapping is applied only if `index=True`.
+
+        Returns
+        -------
+        numpy.rec.recarray
+            NumPy ndarray with the DataFrame labels as fields and each row
+            of the DataFrame as entries.
+
+        See Also
+        --------
+        DataFrame.from_records: Convert structured or record ndarray
+            to DataFrame.
+        numpy.rec.recarray: An ndarray that allows field access using
+            attributes, analogous to typed columns in a
+            spreadsheet.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
+        ...                   index=['a', 'b'])
+        >>> df
+           A     B
+        a  1  0.50
+        b  2  0.75
+        >>> df.to_records()
+        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
+                  dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
+
+        If the DataFrame index has no label then the recarray field name
+        is set to 'index'. If the index has a label then this is used as the
+        field name:
+
+        >>> df.index = df.index.rename("I")
+        >>> df.to_records()
+        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
+                  dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
+
+        The index can be excluded from the record array:
+
+        >>> df.to_records(index=False)
+        rec.array([(1, 0.5 ), (2, 0.75)],
+                  dtype=[('A', '<i8'), ('B', '<f8')])
+
+        Data types can be specified for the columns:
+
+        >>> df.to_records(column_dtypes={"A": "int32"})
+        rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
+                  dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
+
+        As well as for the index:
+
+        >>> df.to_records(index_dtypes="<S2")
+        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
+                  dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
+
+        >>> index_dtypes = f"<S{df.index.str.len().max()}"
+        >>> df.to_records(index_dtypes=index_dtypes)
+        rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
+                  dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
+        """
+        if index:
+            ix_vals = [
+                np.asarray(self.index.get_level_values(i))
+                for i in range(self.index.nlevels)
+            ]
+
+            arrays = ix_vals + [
+                np.asarray(self.iloc[:, i]) for i in range(len(self.columns))
+            ]
+
+            index_names = list(self.index.names)
+
+            if isinstance(self.index, MultiIndex):
+                index_names = com.fill_missing_names(index_names)
+            elif index_names[0] is None:
+                index_names = ["index"]
+
+            names = [str(name) for name in itertools.chain(index_names, self.columns)]
+        else:
+            arrays = [np.asarray(self.iloc[:, i]) for i in range(len(self.columns))]
+            names = [str(c) for c in self.columns]
+            index_names = []
+
+        index_len = len(index_names)
+        formats = []
+
+        for i, v in enumerate(arrays):
+            index_int = i
+
+            # When the names and arrays are collected, we
+            # first collect those in the DataFrame's index,
+            # followed by those in its columns.
+            #
+            # Thus, the total length of the array is:
+            # len(index_names) + len(DataFrame.columns).
+            #
+            # This check allows us to see whether we are
+            # handling a name / array in the index or column.
+            if index_int < index_len:
+                dtype_mapping = index_dtypes
+                name = index_names[index_int]
+            else:
+                index_int -= index_len
+                dtype_mapping = column_dtypes
+                name = self.columns[index_int]
+
+            # We have a dictionary, so we get the data type
+            # associated with the index or column (which can
+            # be denoted by its name in the DataFrame or its
+            # position in DataFrame's array of indices or
+            # columns, whichever is applicable.
+            if is_dict_like(dtype_mapping):
+                if name in dtype_mapping:
+                    dtype_mapping = dtype_mapping[name]
+                elif index_int in dtype_mapping:
+                    dtype_mapping = dtype_mapping[index_int]
+                else:
+                    dtype_mapping = None
+
+            # If no mapping can be found, use the array's
+            # dtype attribute for formatting.
+            #
+            # A valid dtype must either be a type or
+            # string naming a type.
+            if dtype_mapping is None:
+                formats.append(v.dtype)
+            elif isinstance(dtype_mapping, (type, np.dtype, str)):
+                # error: Argument 1 to "append" of "list" has incompatible
+                # type "Union[type, dtype[Any], str]"; expected "dtype[Any]"
+                formats.append(dtype_mapping)  # type: ignore[arg-type]
+            else:
+                element = "row" if i < index_len else "column"
+                msg = f"Invalid dtype {dtype_mapping} specified for {element} {name}"
+                raise ValueError(msg)
+
+        return np.rec.fromarrays(arrays, dtype={"names": names, "formats": formats})
+
+    @classmethod
+    def _from_arrays(
+        cls,
+        arrays,
+        columns,
+        index,
+        dtype: Dtype | None = None,
+        verify_integrity: bool = True,
+    ) -> Self:
+        """
+        Create DataFrame from a list of arrays corresponding to the columns.
+
+        Parameters
+        ----------
+        arrays : list-like of arrays
+            Each array in the list corresponds to one column, in order.
+        columns : list-like, Index
+            The column names for the resulting DataFrame.
+        index : list-like, Index
+            The rows labels for the resulting DataFrame.
+        dtype : dtype, optional
+            Optional dtype to enforce for all arrays.
+        verify_integrity : bool, default True
+            Validate and homogenize all input. If set to False, it is assumed
+            that all elements of `arrays` are actual arrays how they will be
+            stored in a block (numpy ndarray or ExtensionArray), have the same
+            length as and are aligned with the index, and that `columns` and
+            `index` are ensured to be an Index object.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        manager = _get_option("mode.data_manager", silent=True)
+        columns = ensure_index(columns)
+        if len(columns) != len(arrays):
+            raise ValueError("len(columns) must match len(arrays)")
+        mgr = arrays_to_mgr(
+            arrays,
+            columns,
+            index,
+            dtype=dtype,
+            verify_integrity=verify_integrity,
+            typ=manager,
+        )
+        return cls._from_mgr(mgr, axes=mgr.axes)
+
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path",
+    )
+    def to_stata(
+        self,
+        path: FilePath | WriteBuffer[bytes],
+        *,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: ToStataByteorder | None = None,
+        time_stamp: datetime.datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        version: int | None = 114,
+        convert_strl: Sequence[Hashable] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        """
+        Export DataFrame object to Stata dta format.
+
+        Writes the DataFrame to a Stata dataset file.
+        "dta" files contain a Stata dataset.
+
+        Parameters
+        ----------
+        path : str, path object, or buffer
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function.
+
+        convert_dates : dict
+            Dictionary mapping columns containing datetime types to stata
+            internal format to use when writing the dates. Options are 'tc',
+            'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
+            or a name. Datetime columns that do not have a conversion type
+            specified will be converted to 'tc'. Raises NotImplementedError if
+            a datetime column has timezone information.
+        write_index : bool
+            Write the index to Stata dataset.
+        byteorder : str
+            Can be ">", "<", "little", or "big". default is `sys.byteorder`.
+        time_stamp : datetime
+            A datetime to use as file creation date.  Default is the current
+            time.
+        data_label : str, optional
+            A label for the data set.  Must be 80 characters or smaller.
+        variable_labels : dict
+            Dictionary containing columns as keys and variable labels as
+            values. Each label must be 80 characters or smaller.
+        version : {{114, 117, 118, 119, None}}, default 114
+            Version to use in the output dta file. Set to None to let pandas
+            decide between 118 or 119 formats depending on the number of
+            columns in the frame. Version 114 can be read by Stata 10 and
+            later. Version 117 can be read by Stata 13 or later. Version 118
+            is supported in Stata 14 and later. Version 119 is supported in
+            Stata 15 and later. Version 114 limits string variables to 244
+            characters or fewer while versions 117 and later allow strings
+            with lengths up to 2,000,000 characters. Versions 118 and 119
+            support Unicode characters, and version 119 supports more than
+            32,767 variables.
+
+            Version 119 should usually only be used when the number of
+            variables exceeds the capacity of dta format 118. Exporting
+            smaller datasets in format 119 may have unintended consequences,
+            and, as of November 2020, Stata SE cannot read version 119 files.
+
+        convert_strl : list, optional
+            List of column names to convert to string columns to Stata StrL
+            format. Only available if version is 117.  Storing strings in the
+            StrL format can produce smaller dta files if strings have more than
+            8 characters and values are repeated.
+        {compression_options}
+
+            .. versionchanged:: 1.4.0 Zstandard support.
+
+        {storage_options}
+
+        value_labels : dict of dicts
+            Dictionary containing columns as keys and dictionaries of column value
+            to labels as values. Labels for a single variable must be 32,000
+            characters or smaller.
+
+            .. versionadded:: 1.4.0
+
+        Raises
+        ------
+        NotImplementedError
+            * If datetimes contain timezone information
+            * Column dtype is not representable in Stata
+        ValueError
+            * Columns listed in convert_dates are neither datetime64[ns]
+              or datetime.datetime
+            * Column listed in convert_dates is not in DataFrame
+            * Categorical label contains more than 32,000 characters
+
+        See Also
+        --------
+        read_stata : Import Stata data files.
+        io.stata.StataWriter : Low-level writer for Stata data files.
+        io.stata.StataWriter117 : Low-level writer for version 117 files.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',
+        ...                               'parrot'],
+        ...                    'speed': [350, 18, 361, 15]}})
+        >>> df.to_stata('animals.dta')  # doctest: +SKIP
+        """
+        if version not in (114, 117, 118, 119, None):
+            raise ValueError("Only formats 114, 117, 118 and 119 are supported.")
+        if version == 114:
+            if convert_strl is not None:
+                raise ValueError("strl is not supported in format 114")
+            from pandas.io.stata import StataWriter as statawriter
+        elif version == 117:
+            # Incompatible import of "statawriter" (imported name has type
+            # "Type[StataWriter117]", local name has type "Type[StataWriter]")
+            from pandas.io.stata import (  # type: ignore[assignment]
+                StataWriter117 as statawriter,
+            )
+        else:  # versions 118 and 119
+            # Incompatible import of "statawriter" (imported name has type
+            # "Type[StataWriter117]", local name has type "Type[StataWriter]")
+            from pandas.io.stata import (  # type: ignore[assignment]
+                StataWriterUTF8 as statawriter,
+            )
+
+        kwargs: dict[str, Any] = {}
+        if version is None or version >= 117:
+            # strl conversion is only supported >= 117
+            kwargs["convert_strl"] = convert_strl
+        if version is None or version >= 118:
+            # Specifying the version is only supported for UTF8 (118 or 119)
+            kwargs["version"] = version
+
+        writer = statawriter(
+            path,
+            self,
+            convert_dates=convert_dates,
+            byteorder=byteorder,
+            time_stamp=time_stamp,
+            data_label=data_label,
+            write_index=write_index,
+            variable_labels=variable_labels,
+            compression=compression,
+            storage_options=storage_options,
+            value_labels=value_labels,
+            **kwargs,
+        )
+        writer.write_file()
+
+    def to_feather(self, path: FilePath | WriteBuffer[bytes], **kwargs) -> None:
+        """
+        Write a DataFrame to the binary Feather format.
+
+        Parameters
+        ----------
+        path : str, path object, file-like object
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function. If a string or a path,
+            it will be used as Root Directory path when writing a partitioned dataset.
+        **kwargs :
+            Additional keywords passed to :func:`pyarrow.feather.write_feather`.
+            This includes the `compression`, `compression_level`, `chunksize`
+            and `version` keywords.
+
+        Notes
+        -----
+        This function writes the dataframe as a `feather file
+        <https://arrow.apache.org/docs/python/feather.html>`_. Requires a default
+        index. For saving the DataFrame with your custom index use a method that
+        supports custom indices e.g. `to_parquet`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
+        >>> df.to_feather("file.feather")  # doctest: +SKIP
+        """
+        from pandas.io.feather_format import to_feather
+
+        to_feather(self, path, **kwargs)
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_markdown"
+    )
+    @doc(
+        Series.to_markdown,
+        klass=_shared_doc_kwargs["klass"],
+        storage_options=_shared_docs["storage_options"],
+        examples="""Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
+        ... )
+        >>> print(df.to_markdown())
+        |    | animal_1   | animal_2   |
+        |---:|:-----------|:-----------|
+        |  0 | elk        | dog        |
+        |  1 | pig        | quetzal    |
+
+        Output markdown with a tabulate option.
+
+        >>> print(df.to_markdown(tablefmt="grid"))
+        +----+------------+------------+
+        |    | animal_1   | animal_2   |
+        +====+============+============+
+        |  0 | elk        | dog        |
+        +----+------------+------------+
+        |  1 | pig        | quetzal    |
+        +----+------------+------------+""",
+    )
+    def to_markdown(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        mode: str = "wt",
+        index: bool = True,
+        storage_options: StorageOptions | None = None,
+        **kwargs,
+    ) -> str | None:
+        if "showindex" in kwargs:
+            raise ValueError("Pass 'index' instead of 'showindex")
+
+        kwargs.setdefault("headers", "keys")
+        kwargs.setdefault("tablefmt", "pipe")
+        kwargs.setdefault("showindex", index)
+        tabulate = import_optional_dependency("tabulate")
+        result = tabulate.tabulate(self, **kwargs)
+        if buf is None:
+            return result
+
+        with get_handle(buf, mode, storage_options=storage_options) as handles:
+            handles.handle.write(result)
+        return None
+
+    @overload
+    def to_parquet(
+        self,
+        path: None = ...,
+        engine: Literal["auto", "pyarrow", "fastparquet"] = ...,
+        compression: str | None = ...,
+        index: bool | None = ...,
+        partition_cols: list[str] | None = ...,
+        storage_options: StorageOptions = ...,
+        **kwargs,
+    ) -> bytes:
+        ...
+
+    @overload
+    def to_parquet(
+        self,
+        path: FilePath | WriteBuffer[bytes],
+        engine: Literal["auto", "pyarrow", "fastparquet"] = ...,
+        compression: str | None = ...,
+        index: bool | None = ...,
+        partition_cols: list[str] | None = ...,
+        storage_options: StorageOptions = ...,
+        **kwargs,
+    ) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path"], name="to_parquet"
+    )
+    @doc(storage_options=_shared_docs["storage_options"])
+    def to_parquet(
+        self,
+        path: FilePath | WriteBuffer[bytes] | None = None,
+        engine: Literal["auto", "pyarrow", "fastparquet"] = "auto",
+        compression: str | None = "snappy",
+        index: bool | None = None,
+        partition_cols: list[str] | None = None,
+        storage_options: StorageOptions | None = None,
+        **kwargs,
+    ) -> bytes | None:
+        """
+        Write a DataFrame to the binary parquet format.
+
+        This function writes the dataframe as a `parquet file
+        <https://parquet.apache.org/>`_. You can choose different parquet
+        backends, and have the option of compression. See
+        :ref:`the user guide <io.parquet>` for more details.
+
+        Parameters
+        ----------
+        path : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function. If None, the result is
+            returned as bytes. If a string or path, it will be used as Root Directory
+            path when writing a partitioned dataset.
+        engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
+            Parquet library to use. If 'auto', then the option
+            ``io.parquet.engine`` is used. The default ``io.parquet.engine``
+            behavior is to try 'pyarrow', falling back to 'fastparquet' if
+            'pyarrow' is unavailable.
+        compression : str or None, default 'snappy'
+            Name of the compression to use. Use ``None`` for no compression.
+            Supported options: 'snappy', 'gzip', 'brotli', 'lz4', 'zstd'.
+        index : bool, default None
+            If ``True``, include the dataframe's index(es) in the file output.
+            If ``False``, they will not be written to the file.
+            If ``None``, similar to ``True`` the dataframe's index(es)
+            will be saved. However, instead of being saved as values,
+            the RangeIndex will be stored as a range in the metadata so it
+            doesn't require much space and is faster. Other indexes will
+            be included as columns in the file output.
+        partition_cols : list, optional, default None
+            Column names by which to partition the dataset.
+            Columns are partitioned in the order they are given.
+            Must be None if path is not a string.
+        {storage_options}
+
+        **kwargs
+            Additional arguments passed to the parquet library. See
+            :ref:`pandas io <io.parquet>` for more details.
+
+        Returns
+        -------
+        bytes if no path argument is provided else None
+
+        See Also
+        --------
+        read_parquet : Read a parquet file.
+        DataFrame.to_orc : Write an orc file.
+        DataFrame.to_csv : Write a csv file.
+        DataFrame.to_sql : Write to a sql table.
+        DataFrame.to_hdf : Write to hdf.
+
+        Notes
+        -----
+        This function requires either the `fastparquet
+        <https://pypi.org/project/fastparquet>`_ or `pyarrow
+        <https://arrow.apache.org/docs/python/>`_ library.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})
+        >>> df.to_parquet('df.parquet.gzip',
+        ...               compression='gzip')  # doctest: +SKIP
+        >>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
+           col1  col2
+        0     1     3
+        1     2     4
+
+        If you want to get a buffer to the parquet content you can use a io.BytesIO
+        object, as long as you don't use partition_cols, which creates multiple files.
+
+        >>> import io
+        >>> f = io.BytesIO()
+        >>> df.to_parquet(f)
+        >>> f.seek(0)
+        0
+        >>> content = f.read()
+        """
+        from pandas.io.parquet import to_parquet
+
+        return to_parquet(
+            self,
+            path,
+            engine,
+            compression=compression,
+            index=index,
+            partition_cols=partition_cols,
+            storage_options=storage_options,
+            **kwargs,
+        )
+
+    def to_orc(
+        self,
+        path: FilePath | WriteBuffer[bytes] | None = None,
+        *,
+        engine: Literal["pyarrow"] = "pyarrow",
+        index: bool | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> bytes | None:
+        """
+        Write a DataFrame to the ORC format.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        path : str, file-like object or None, default None
+            If a string, it will be used as Root Directory path
+            when writing a partitioned dataset. By file-like object,
+            we refer to objects with a write() method, such as a file handle
+            (e.g. via builtin open function). If path is None,
+            a bytes object is returned.
+        engine : {'pyarrow'}, default 'pyarrow'
+            ORC library to use.
+        index : bool, optional
+            If ``True``, include the dataframe's index(es) in the file output.
+            If ``False``, they will not be written to the file.
+            If ``None``, similar to ``infer`` the dataframe's index(es)
+            will be saved. However, instead of being saved as values,
+            the RangeIndex will be stored as a range in the metadata so it
+            doesn't require much space and is faster. Other indexes will
+            be included as columns in the file output.
+        engine_kwargs : dict[str, Any] or None, default None
+            Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.
+
+        Returns
+        -------
+        bytes if no path argument is provided else None
+
+        Raises
+        ------
+        NotImplementedError
+            Dtype of one or more columns is category, unsigned integers, interval,
+            period or sparse.
+        ValueError
+            engine is not pyarrow.
+
+        See Also
+        --------
+        read_orc : Read a ORC file.
+        DataFrame.to_parquet : Write a parquet file.
+        DataFrame.to_csv : Write a csv file.
+        DataFrame.to_sql : Write to a sql table.
+        DataFrame.to_hdf : Write to hdf.
+
+        Notes
+        -----
+        * Before using this function you should read the :ref:`user guide about
+          ORC <io.orc>` and :ref:`install optional dependencies <install.warn_orc>`.
+        * This function requires `pyarrow <https://arrow.apache.org/docs/python/>`_
+          library.
+        * For supported dtypes please refer to `supported ORC features in Arrow
+          <https://arrow.apache.org/docs/cpp/orc.html#data-types>`__.
+        * Currently timezones in datetime columns are not preserved when a
+          dataframe is converted into ORC files.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
+        >>> df.to_orc('df.orc')  # doctest: +SKIP
+        >>> pd.read_orc('df.orc')  # doctest: +SKIP
+           col1  col2
+        0     1     4
+        1     2     3
+
+        If you want to get a buffer to the orc content you can write it to io.BytesIO
+
+        >>> import io
+        >>> b = io.BytesIO(df.to_orc())  # doctest: +SKIP
+        >>> b.seek(0)  # doctest: +SKIP
+        0
+        >>> content = b.read()  # doctest: +SKIP
+        """
+        from pandas.io.orc import to_orc
+
+        return to_orc(
+            self, path, engine=engine, index=index, engine_kwargs=engine_kwargs
+        )
+
+    @overload
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        columns: Axes | None = ...,
+        col_space: ColspaceArgType | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool | str = ...,
+        decimal: str = ...,
+        bold_rows: bool = ...,
+        classes: str | list | tuple | None = ...,
+        escape: bool = ...,
+        notebook: bool = ...,
+        border: int | bool | None = ...,
+        table_id: str | None = ...,
+        render_links: bool = ...,
+        encoding: str | None = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def to_html(
+        self,
+        buf: None = ...,
+        columns: Axes | None = ...,
+        col_space: ColspaceArgType | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool | None = ...,
+        index_names: bool = ...,
+        justify: str | None = ...,
+        max_rows: int | None = ...,
+        max_cols: int | None = ...,
+        show_dimensions: bool | str = ...,
+        decimal: str = ...,
+        bold_rows: bool = ...,
+        classes: str | list | tuple | None = ...,
+        escape: bool = ...,
+        notebook: bool = ...,
+        border: int | bool | None = ...,
+        table_id: str | None = ...,
+        render_links: bool = ...,
+        encoding: str | None = ...,
+    ) -> str:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_html"
+    )
+    @Substitution(
+        header_type="bool",
+        header="Whether to print column labels, default True",
+        col_space_type="str or int, list or dict of int or str",
+        col_space="The minimum width of each column in CSS length "
+        "units.  An int is assumed to be px units.",
+    )
+    @Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        columns: Axes | None = None,
+        col_space: ColspaceArgType | None = None,
+        header: bool = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        justify: str | None = None,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool | str = False,
+        decimal: str = ".",
+        bold_rows: bool = True,
+        classes: str | list | tuple | None = None,
+        escape: bool = True,
+        notebook: bool = False,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+        encoding: str | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame as an HTML table.
+        %(shared_params)s
+        bold_rows : bool, default True
+            Make the row labels bold in the output.
+        classes : str or list or tuple, default None
+            CSS class(es) to apply to the resulting html table.
+        escape : bool, default True
+            Convert the characters <, >, and & to HTML-safe sequences.
+        notebook : {True, False}, default False
+            Whether the generated HTML is for IPython Notebook.
+        border : int
+            A ``border=border`` attribute is included in the opening
+            `<table>` tag. Default ``pd.options.display.html.border``.
+        table_id : str, optional
+            A css id is included in the opening `<table>` tag if specified.
+        render_links : bool, default False
+            Convert URLs to HTML links.
+        encoding : str, default "utf-8"
+            Set character encoding.
+        %(returns)s
+        See Also
+        --------
+        to_string : Convert DataFrame to a string.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
+        >>> html_string = '''<table border="1" class="dataframe">
+        ...   <thead>
+        ...     <tr style="text-align: right;">
+        ...       <th></th>
+        ...       <th>col1</th>
+        ...       <th>col2</th>
+        ...     </tr>
+        ...   </thead>
+        ...   <tbody>
+        ...     <tr>
+        ...       <th>0</th>
+        ...       <td>1</td>
+        ...       <td>4</td>
+        ...     </tr>
+        ...     <tr>
+        ...       <th>1</th>
+        ...       <td>2</td>
+        ...       <td>3</td>
+        ...     </tr>
+        ...   </tbody>
+        ... </table>'''
+        >>> assert html_string == df.to_html()
+        """
+        if justify is not None and justify not in fmt.VALID_JUSTIFY_PARAMETERS:
+            raise ValueError("Invalid value for justify parameter")
+
+        formatter = fmt.DataFrameFormatter(
+            self,
+            columns=columns,
+            col_space=col_space,
+            na_rep=na_rep,
+            header=header,
+            index=index,
+            formatters=formatters,
+            float_format=float_format,
+            bold_rows=bold_rows,
+            sparsify=sparsify,
+            justify=justify,
+            index_names=index_names,
+            escape=escape,
+            decimal=decimal,
+            max_rows=max_rows,
+            max_cols=max_cols,
+            show_dimensions=show_dimensions,
+        )
+        # TODO: a generic formatter wld b in DataFrameFormatter
+        return fmt.DataFrameRenderer(formatter).to_html(
+            buf=buf,
+            classes=classes,
+            notebook=notebook,
+            border=border,
+            encoding=encoding,
+            table_id=table_id,
+            render_links=render_links,
+        )
+
+    @overload
+    def to_xml(
+        self,
+        path_or_buffer: None = ...,
+        *,
+        index: bool = ...,
+        root_name: str | None = ...,
+        row_name: str | None = ...,
+        na_rep: str | None = ...,
+        attr_cols: list[str] | None = ...,
+        elem_cols: list[str] | None = ...,
+        namespaces: dict[str | None, str] | None = ...,
+        prefix: str | None = ...,
+        encoding: str = ...,
+        xml_declaration: bool | None = ...,
+        pretty_print: bool | None = ...,
+        parser: XMLParsers | None = ...,
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = ...,
+        compression: CompressionOptions = ...,
+        storage_options: StorageOptions | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_xml(
+        self,
+        path_or_buffer: FilePath | WriteBuffer[bytes] | WriteBuffer[str],
+        *,
+        index: bool = ...,
+        root_name: str | None = ...,
+        row_name: str | None = ...,
+        na_rep: str | None = ...,
+        attr_cols: list[str] | None = ...,
+        elem_cols: list[str] | None = ...,
+        namespaces: dict[str | None, str] | None = ...,
+        prefix: str | None = ...,
+        encoding: str = ...,
+        xml_declaration: bool | None = ...,
+        pretty_print: bool | None = ...,
+        parser: XMLParsers | None = ...,
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = ...,
+        compression: CompressionOptions = ...,
+        storage_options: StorageOptions | None = ...,
+    ) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buffer"], name="to_xml"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path_or_buffer",
+    )
+    def to_xml(
+        self,
+        path_or_buffer: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        index: bool = True,
+        root_name: str | None = "data",
+        row_name: str | None = "row",
+        na_rep: str | None = None,
+        attr_cols: list[str] | None = None,
+        elem_cols: list[str] | None = None,
+        namespaces: dict[str | None, str] | None = None,
+        prefix: str | None = None,
+        encoding: str = "utf-8",
+        xml_declaration: bool | None = True,
+        pretty_print: bool | None = True,
+        parser: XMLParsers | None = "lxml",
+        stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to an XML document.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        path_or_buffer : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a ``write()`` function. If None, the result is returned
+            as a string.
+        index : bool, default True
+            Whether to include index in XML document.
+        root_name : str, default 'data'
+            The name of root element in XML document.
+        row_name : str, default 'row'
+            The name of row element in XML document.
+        na_rep : str, optional
+            Missing data representation.
+        attr_cols : list-like, optional
+            List of columns to write as attributes in row element.
+            Hierarchical columns will be flattened with underscore
+            delimiting the different levels.
+        elem_cols : list-like, optional
+            List of columns to write as children in row element. By default,
+            all columns output as children of row element. Hierarchical
+            columns will be flattened with underscore delimiting the
+            different levels.
+        namespaces : dict, optional
+            All namespaces to be defined in root element. Keys of dict
+            should be prefix names and values of dict corresponding URIs.
+            Default namespaces should be given empty string key. For
+            example, ::
+
+                namespaces = {{"": "https://example.com"}}
+
+        prefix : str, optional
+            Namespace prefix to be used for every element and/or attribute
+            in document. This should be one of the keys in ``namespaces``
+            dict.
+        encoding : str, default 'utf-8'
+            Encoding of the resulting document.
+        xml_declaration : bool, default True
+            Whether to include the XML declaration at start of document.
+        pretty_print : bool, default True
+            Whether output should be pretty printed with indentation and
+            line breaks.
+        parser : {{'lxml','etree'}}, default 'lxml'
+            Parser module to use for building of tree. Only 'lxml' and
+            'etree' are supported. With 'lxml', the ability to use XSLT
+            stylesheet is supported.
+        stylesheet : str, path object or file-like object, optional
+            A URL, file-like object, or a raw string containing an XSLT
+            script used to transform the raw XML output. Script should use
+            layout of elements and attributes from original output. This
+            argument requires ``lxml`` to be installed. Only XSLT 1.0
+            scripts and not later versions is currently supported.
+        {compression_options}
+
+            .. versionchanged:: 1.4.0 Zstandard support.
+
+        {storage_options}
+
+        Returns
+        -------
+        None or str
+            If ``io`` is None, returns the resulting XML format as a
+            string. Otherwise returns None.
+
+        See Also
+        --------
+        to_json : Convert the pandas object to a JSON string.
+        to_html : Convert DataFrame to a html.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({{'shape': ['square', 'circle', 'triangle'],
+        ...                    'degrees': [360, 360, 180],
+        ...                    'sides': [4, np.nan, 3]}})
+
+        >>> df.to_xml()  # doctest: +SKIP
+        <?xml version='1.0' encoding='utf-8'?>
+        <data>
+          <row>
+            <index>0</index>
+            <shape>square</shape>
+            <degrees>360</degrees>
+            <sides>4.0</sides>
+          </row>
+          <row>
+            <index>1</index>
+            <shape>circle</shape>
+            <degrees>360</degrees>
+            <sides/>
+          </row>
+          <row>
+            <index>2</index>
+            <shape>triangle</shape>
+            <degrees>180</degrees>
+            <sides>3.0</sides>
+          </row>
+        </data>
+
+        >>> df.to_xml(attr_cols=[
+        ...           'index', 'shape', 'degrees', 'sides'
+        ...           ])  # doctest: +SKIP
+        <?xml version='1.0' encoding='utf-8'?>
+        <data>
+          <row index="0" shape="square" degrees="360" sides="4.0"/>
+          <row index="1" shape="circle" degrees="360"/>
+          <row index="2" shape="triangle" degrees="180" sides="3.0"/>
+        </data>
+
+        >>> df.to_xml(namespaces={{"doc": "https://example.com"}},
+        ...           prefix="doc")  # doctest: +SKIP
+        <?xml version='1.0' encoding='utf-8'?>
+        <doc:data xmlns:doc="https://example.com">
+          <doc:row>
+            <doc:index>0</doc:index>
+            <doc:shape>square</doc:shape>
+            <doc:degrees>360</doc:degrees>
+            <doc:sides>4.0</doc:sides>
+          </doc:row>
+          <doc:row>
+            <doc:index>1</doc:index>
+            <doc:shape>circle</doc:shape>
+            <doc:degrees>360</doc:degrees>
+            <doc:sides/>
+          </doc:row>
+          <doc:row>
+            <doc:index>2</doc:index>
+            <doc:shape>triangle</doc:shape>
+            <doc:degrees>180</doc:degrees>
+            <doc:sides>3.0</doc:sides>
+          </doc:row>
+        </doc:data>
+        """
+
+        from pandas.io.formats.xml import (
+            EtreeXMLFormatter,
+            LxmlXMLFormatter,
+        )
+
+        lxml = import_optional_dependency("lxml.etree", errors="ignore")
+
+        TreeBuilder: type[EtreeXMLFormatter | LxmlXMLFormatter]
+
+        if parser == "lxml":
+            if lxml is not None:
+                TreeBuilder = LxmlXMLFormatter
+            else:
+                raise ImportError(
+                    "lxml not found, please install or use the etree parser."
+                )
+
+        elif parser == "etree":
+            TreeBuilder = EtreeXMLFormatter
+
+        else:
+            raise ValueError("Values for parser can only be lxml or etree.")
+
+        xml_formatter = TreeBuilder(
+            self,
+            path_or_buffer=path_or_buffer,
+            index=index,
+            root_name=root_name,
+            row_name=row_name,
+            na_rep=na_rep,
+            attr_cols=attr_cols,
+            elem_cols=elem_cols,
+            namespaces=namespaces,
+            prefix=prefix,
+            encoding=encoding,
+            xml_declaration=xml_declaration,
+            pretty_print=pretty_print,
+            stylesheet=stylesheet,
+            compression=compression,
+            storage_options=storage_options,
+        )
+
+        return xml_formatter.write_output()
+
+    # ----------------------------------------------------------------------
+    @doc(INFO_DOCSTRING, **frame_sub_kwargs)
+    def info(
+        self,
+        verbose: bool | None = None,
+        buf: WriteBuffer[str] | None = None,
+        max_cols: int | None = None,
+        memory_usage: bool | str | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        info = DataFrameInfo(
+            data=self,
+            memory_usage=memory_usage,
+        )
+        info.render(
+            buf=buf,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+
+    def memory_usage(self, index: bool = True, deep: bool = False) -> Series:
+        """
+        Return the memory usage of each column in bytes.
+
+        The memory usage can optionally include the contribution of
+        the index and elements of `object` dtype.
+
+        This value is displayed in `DataFrame.info` by default. This can be
+        suppressed by setting ``pandas.options.display.memory_usage`` to False.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Specifies whether to include the memory usage of the DataFrame's
+            index in returned Series. If ``index=True``, the memory usage of
+            the index is the first item in the output.
+        deep : bool, default False
+            If True, introspect the data deeply by interrogating
+            `object` dtypes for system-level memory consumption, and include
+            it in the returned values.
+
+        Returns
+        -------
+        Series
+            A Series whose index is the original column names and whose values
+            is the memory usage of each column in bytes.
+
+        See Also
+        --------
+        numpy.ndarray.nbytes : Total bytes consumed by the elements of an
+            ndarray.
+        Series.memory_usage : Bytes consumed by a Series.
+        Categorical : Memory-efficient array for string values with
+            many repeated values.
+        DataFrame.info : Concise summary of a DataFrame.
+
+        Notes
+        -----
+        See the :ref:`Frequently Asked Questions <df-memory-usage>` for more
+        details.
+
+        Examples
+        --------
+        >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
+        >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
+        ...              for t in dtypes])
+        >>> df = pd.DataFrame(data)
+        >>> df.head()
+           int64  float64            complex128  object  bool
+        0      1      1.0              1.0+0.0j       1  True
+        1      1      1.0              1.0+0.0j       1  True
+        2      1      1.0              1.0+0.0j       1  True
+        3      1      1.0              1.0+0.0j       1  True
+        4      1      1.0              1.0+0.0j       1  True
+
+        >>> df.memory_usage()
+        Index           128
+        int64         40000
+        float64       40000
+        complex128    80000
+        object        40000
+        bool           5000
+        dtype: int64
+
+        >>> df.memory_usage(index=False)
+        int64         40000
+        float64       40000
+        complex128    80000
+        object        40000
+        bool           5000
+        dtype: int64
+
+        The memory footprint of `object` dtype columns is ignored by default:
+
+        >>> df.memory_usage(deep=True)
+        Index            128
+        int64          40000
+        float64        40000
+        complex128     80000
+        object        180000
+        bool            5000
+        dtype: int64
+
+        Use a Categorical for efficient storage of an object-dtype column with
+        many repeated values.
+
+        >>> df['object'].astype('category').memory_usage(deep=True)
+        5244
+        """
+        result = self._constructor_sliced(
+            [c.memory_usage(index=False, deep=deep) for col, c in self.items()],
+            index=self.columns,
+            dtype=np.intp,
+        )
+        if index:
+            index_memory_usage = self._constructor_sliced(
+                self.index.memory_usage(deep=deep), index=["Index"]
+            )
+            result = index_memory_usage._append(result)
+        return result
+
+    def transpose(self, *args, copy: bool = False) -> DataFrame:
+        """
+        Transpose index and columns.
+
+        Reflect the DataFrame over its main diagonal by writing rows as columns
+        and vice-versa. The property :attr:`.T` is an accessor to the method
+        :meth:`transpose`.
+
+        Parameters
+        ----------
+        *args : tuple, optional
+            Accepted for compatibility with NumPy.
+        copy : bool, default False
+            Whether to copy the data after transposing, even for DataFrames
+            with a single dtype.
+
+            Note that a copy is always required for mixed dtype DataFrames,
+            or for DataFrames with any extension types.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        DataFrame
+            The transposed DataFrame.
+
+        See Also
+        --------
+        numpy.transpose : Permute the dimensions of a given array.
+
+        Notes
+        -----
+        Transposing a DataFrame with mixed dtypes will result in a homogeneous
+        DataFrame with the `object` dtype. In such a case, a copy of the data
+        is always made.
+
+        Examples
+        --------
+        **Square DataFrame with homogeneous dtype**
+
+        >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
+        >>> df1 = pd.DataFrame(data=d1)
+        >>> df1
+           col1  col2
+        0     1     3
+        1     2     4
+
+        >>> df1_transposed = df1.T  # or df1.transpose()
+        >>> df1_transposed
+              0  1
+        col1  1  2
+        col2  3  4
+
+        When the dtype is homogeneous in the original DataFrame, we get a
+        transposed DataFrame with the same dtype:
+
+        >>> df1.dtypes
+        col1    int64
+        col2    int64
+        dtype: object
+        >>> df1_transposed.dtypes
+        0    int64
+        1    int64
+        dtype: object
+
+        **Non-square DataFrame with mixed dtypes**
+
+        >>> d2 = {'name': ['Alice', 'Bob'],
+        ...       'score': [9.5, 8],
+        ...       'employed': [False, True],
+        ...       'kids': [0, 0]}
+        >>> df2 = pd.DataFrame(data=d2)
+        >>> df2
+            name  score  employed  kids
+        0  Alice    9.5     False     0
+        1    Bob    8.0      True     0
+
+        >>> df2_transposed = df2.T  # or df2.transpose()
+        >>> df2_transposed
+                      0     1
+        name      Alice   Bob
+        score       9.5   8.0
+        employed  False  True
+        kids          0     0
+
+        When the DataFrame has mixed dtypes, we get a transposed DataFrame with
+        the `object` dtype:
+
+        >>> df2.dtypes
+        name         object
+        score       float64
+        employed       bool
+        kids          int64
+        dtype: object
+        >>> df2_transposed.dtypes
+        0    object
+        1    object
+        dtype: object
+        """
+        nv.validate_transpose(args, {})
+        # construct the args
+
+        dtypes = list(self.dtypes)
+
+        if self._can_fast_transpose:
+            # Note: tests pass without this, but this improves perf quite a bit.
+            new_vals = self._values.T
+            if copy and not using_copy_on_write():
+                new_vals = new_vals.copy()
+
+            result = self._constructor(
+                new_vals,
+                index=self.columns,
+                columns=self.index,
+                copy=False,
+                dtype=new_vals.dtype,
+            )
+            if using_copy_on_write() and len(self) > 0:
+                result._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+
+        elif (
+            self._is_homogeneous_type
+            and dtypes
+            and isinstance(dtypes[0], ExtensionDtype)
+        ):
+            new_values: list
+            if isinstance(dtypes[0], BaseMaskedDtype):
+                # We have masked arrays with the same dtype. We can transpose faster.
+                from pandas.core.arrays.masked import (
+                    transpose_homogeneous_masked_arrays,
+                )
+
+                new_values = transpose_homogeneous_masked_arrays(
+                    cast(Sequence[BaseMaskedArray], self._iter_column_arrays())
+                )
+            elif isinstance(dtypes[0], ArrowDtype):
+                # We have arrow EAs with the same dtype. We can transpose faster.
+                from pandas.core.arrays.arrow.array import (
+                    ArrowExtensionArray,
+                    transpose_homogeneous_pyarrow,
+                )
+
+                new_values = transpose_homogeneous_pyarrow(
+                    cast(Sequence[ArrowExtensionArray], self._iter_column_arrays())
+                )
+            else:
+                # We have other EAs with the same dtype. We preserve dtype in transpose.
+                dtyp = dtypes[0]
+                arr_typ = dtyp.construct_array_type()
+                values = self.values
+                new_values = [arr_typ._from_sequence(row, dtype=dtyp) for row in values]
+
+            result = type(self)._from_arrays(
+                new_values,
+                index=self.columns,
+                columns=self.index,
+                verify_integrity=False,
+            )
+
+        else:
+            new_arr = self.values.T
+            if copy and not using_copy_on_write():
+                new_arr = new_arr.copy()
+            result = self._constructor(
+                new_arr,
+                index=self.columns,
+                columns=self.index,
+                dtype=new_arr.dtype,
+                # We already made a copy (more than one block)
+                copy=False,
+            )
+
+        return result.__finalize__(self, method="transpose")
+
+    @property
+    def T(self) -> DataFrame:
+        """
+        The transpose of the DataFrame.
+
+        Returns
+        -------
+        DataFrame
+            The transposed DataFrame.
+
+        See Also
+        --------
+        DataFrame.transpose : Transpose index and columns.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df
+           col1  col2
+        0     1     3
+        1     2     4
+
+        >>> df.T
+              0  1
+        col1  1  2
+        col2  3  4
+        """
+        return self.transpose()
+
+    # ----------------------------------------------------------------------
+    # Indexing Methods
+
+    def _ixs(self, i: int, axis: AxisInt = 0) -> Series:
+        """
+        Parameters
+        ----------
+        i : int
+        axis : int
+
+        Returns
+        -------
+        Series
+        """
+        # irow
+        if axis == 0:
+            new_mgr = self._mgr.fast_xs(i)
+
+            # if we are a copy, mark as such
+            copy = isinstance(new_mgr.array, np.ndarray) and new_mgr.array.base is None
+            result = self._constructor_sliced_from_mgr(new_mgr, axes=new_mgr.axes)
+            result._name = self.index[i]
+            result = result.__finalize__(self)
+            result._set_is_copy(self, copy=copy)
+            return result
+
+        # icol
+        else:
+            label = self.columns[i]
+
+            col_mgr = self._mgr.iget(i)
+            result = self._box_col_values(col_mgr, i)
+
+            # this is a cached value, mark it so
+            result._set_as_cached(label, self)
+            return result
+
+    def _get_column_array(self, i: int) -> ArrayLike:
+        """
+        Get the values of the i'th column (ndarray or ExtensionArray, as stored
+        in the Block)
+
+        Warning! The returned array is a view but doesn't handle Copy-on-Write,
+        so this should be used with caution (for read-only purposes).
+        """
+        return self._mgr.iget_values(i)
+
+    def _iter_column_arrays(self) -> Iterator[ArrayLike]:
+        """
+        Iterate over the arrays of all columns in order.
+        This returns the values as stored in the Block (ndarray or ExtensionArray).
+
+        Warning! The returned array is a view but doesn't handle Copy-on-Write,
+        so this should be used with caution (for read-only purposes).
+        """
+        if isinstance(self._mgr, ArrayManager):
+            yield from self._mgr.arrays
+        else:
+            for i in range(len(self.columns)):
+                yield self._get_column_array(i)
+
+    def _getitem_nocopy(self, key: list):
+        """
+        Behaves like __getitem__, but returns a view in cases where __getitem__
+        would make a copy.
+        """
+        # TODO(CoW): can be removed if/when we are always Copy-on-Write
+        indexer = self.columns._get_indexer_strict(key, "columns")[1]
+        new_axis = self.columns[indexer]
+
+        new_mgr = self._mgr.reindex_indexer(
+            new_axis,
+            indexer,
+            axis=0,
+            allow_dups=True,
+            copy=False,
+            only_slice=True,
+        )
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        result = result.__finalize__(self)
+        return result
+
+    def __getitem__(self, key):
+        check_dict_or_set_indexers(key)
+        key = lib.item_from_zerodim(key)
+        key = com.apply_if_callable(key, self)
+
+        if is_hashable(key) and not is_iterator(key):
+            # is_iterator to exclude generator e.g. test_getitem_listlike
+            # shortcut if the key is in columns
+            is_mi = isinstance(self.columns, MultiIndex)
+            # GH#45316 Return view if key is not duplicated
+            # Only use drop_duplicates with duplicates for performance
+            if not is_mi and (
+                self.columns.is_unique
+                and key in self.columns
+                or key in self.columns.drop_duplicates(keep=False)
+            ):
+                return self._get_item_cache(key)
+
+            elif is_mi and self.columns.is_unique and key in self.columns:
+                return self._getitem_multilevel(key)
+
+        # Do we have a slicer (on rows)?
+        if isinstance(key, slice):
+            return self._getitem_slice(key)
+
+        # Do we have a (boolean) DataFrame?
+        if isinstance(key, DataFrame):
+            return self.where(key)
+
+        # Do we have a (boolean) 1d indexer?
+        if com.is_bool_indexer(key):
+            return self._getitem_bool_array(key)
+
+        # We are left with two options: a single key, and a collection of keys,
+        # We interpret tuples as collections only for non-MultiIndex
+        is_single_key = isinstance(key, tuple) or not is_list_like(key)
+
+        if is_single_key:
+            if self.columns.nlevels > 1:
+                return self._getitem_multilevel(key)
+            indexer = self.columns.get_loc(key)
+            if is_integer(indexer):
+                indexer = [indexer]
+        else:
+            if is_iterator(key):
+                key = list(key)
+            indexer = self.columns._get_indexer_strict(key, "columns")[1]
+
+        # take() does not accept boolean indexers
+        if getattr(indexer, "dtype", None) == bool:
+            indexer = np.where(indexer)[0]
+
+        if isinstance(indexer, slice):
+            return self._slice(indexer, axis=1)
+
+        data = self._take_with_is_copy(indexer, axis=1)
+
+        if is_single_key:
+            # What does looking for a single key in a non-unique index return?
+            # The behavior is inconsistent. It returns a Series, except when
+            # - the key itself is repeated (test on data.shape, #9519), or
+            # - we have a MultiIndex on columns (test on self.columns, #21309)
+            if data.shape[1] == 1 and not isinstance(self.columns, MultiIndex):
+                # GH#26490 using data[key] can cause RecursionError
+                return data._get_item_cache(key)
+
+        return data
+
+    def _getitem_bool_array(self, key):
+        # also raises Exception if object array with NA values
+        # warning here just in case -- previously __setitem__ was
+        # reindexing but __getitem__ was not; it seems more reasonable to
+        # go with the __setitem__ behavior since that is more consistent
+        # with all other indexing behavior
+        if isinstance(key, Series) and not key.index.equals(self.index):
+            warnings.warn(
+                "Boolean Series key will be reindexed to match DataFrame index.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif len(key) != len(self.index):
+            raise ValueError(
+                f"Item wrong length {len(key)} instead of {len(self.index)}."
+            )
+
+        # check_bool_indexer will throw exception if Series key cannot
+        # be reindexed to match DataFrame rows
+        key = check_bool_indexer(self.index, key)
+
+        if key.all():
+            return self.copy(deep=None)
+
+        indexer = key.nonzero()[0]
+        return self._take_with_is_copy(indexer, axis=0)
+
+    def _getitem_multilevel(self, key):
+        # self.columns is a MultiIndex
+        loc = self.columns.get_loc(key)
+        if isinstance(loc, (slice, np.ndarray)):
+            new_columns = self.columns[loc]
+            result_columns = maybe_droplevels(new_columns, key)
+            result = self.iloc[:, loc]
+            result.columns = result_columns
+
+            # If there is only one column being returned, and its name is
+            # either an empty string, or a tuple with an empty string as its
+            # first element, then treat the empty string as a placeholder
+            # and return the column as if the user had provided that empty
+            # string in the key. If the result is a Series, exclude the
+            # implied empty string from its name.
+            if len(result.columns) == 1:
+                # e.g. test_frame_getitem_multicolumn_empty_level,
+                #  test_frame_mixed_depth_get, test_loc_setitem_single_column_slice
+                top = result.columns[0]
+                if isinstance(top, tuple):
+                    top = top[0]
+                if top == "":
+                    result = result[""]
+                    if isinstance(result, Series):
+                        result = self._constructor_sliced(
+                            result, index=self.index, name=key
+                        )
+
+            result._set_is_copy(self)
+            return result
+        else:
+            # loc is neither a slice nor ndarray, so must be an int
+            return self._ixs(loc, axis=1)
+
+    def _get_value(self, index, col, takeable: bool = False) -> Scalar:
+        """
+        Quickly retrieve single value at passed column and index.
+
+        Parameters
+        ----------
+        index : row label
+        col : column label
+        takeable : interpret the index/col as indexers, default False
+
+        Returns
+        -------
+        scalar
+
+        Notes
+        -----
+        Assumes that both `self.index._index_as_unique` and
+        `self.columns._index_as_unique`; Caller is responsible for checking.
+        """
+        if takeable:
+            series = self._ixs(col, axis=1)
+            return series._values[index]
+
+        series = self._get_item_cache(col)
+        engine = self.index._engine
+
+        if not isinstance(self.index, MultiIndex):
+            # CategoricalIndex: Trying to use the engine fastpath may give incorrect
+            #  results if our categories are integers that dont match our codes
+            # IntervalIndex: IntervalTree has no get_loc
+            row = self.index.get_loc(index)
+            return series._values[row]
+
+        # For MultiIndex going through engine effectively restricts us to
+        #  same-length tuples; see test_get_set_value_no_partial_indexing
+        loc = engine.get_loc(index)
+        return series._values[loc]
+
+    def isetitem(self, loc, value) -> None:
+        """
+        Set the given value in the column with position `loc`.
+
+        This is a positional analogue to ``__setitem__``.
+
+        Parameters
+        ----------
+        loc : int or sequence of ints
+            Index position for the column.
+        value : scalar or arraylike
+            Value(s) for the column.
+
+        Notes
+        -----
+        ``frame.isetitem(loc, value)`` is an in-place method as it will
+        modify the DataFrame in place (not returning a new object). In contrast to
+        ``frame.iloc[:, i] = value`` which will try to update the existing values in
+        place, ``frame.isetitem(loc, value)`` will not update the values of the column
+        itself in place, it will instead insert a new array.
+
+        In cases where ``frame.columns`` is unique, this is equivalent to
+        ``frame[frame.columns[i]] = value``.
+        """
+        if isinstance(value, DataFrame):
+            if is_integer(loc):
+                loc = [loc]
+
+            if len(loc) != len(value.columns):
+                raise ValueError(
+                    f"Got {len(loc)} positions but value has {len(value.columns)} "
+                    f"columns."
+                )
+
+            for i, idx in enumerate(loc):
+                arraylike, refs = self._sanitize_column(value.iloc[:, i])
+                self._iset_item_mgr(idx, arraylike, inplace=False, refs=refs)
+            return
+
+        arraylike, refs = self._sanitize_column(value)
+        self._iset_item_mgr(loc, arraylike, inplace=False, refs=refs)
+
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= 3:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            if sys.getrefcount(self) <= 3 and (
+                warn_copy_on_write()
+                or (
+                    not warn_copy_on_write()
+                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
+                )
+            ):
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        key = com.apply_if_callable(key, self)
+
+        # see if we can slice the rows
+        if isinstance(key, slice):
+            slc = self.index._convert_slice_indexer(key, kind="getitem")
+            return self._setitem_slice(slc, value)
+
+        if isinstance(key, DataFrame) or getattr(key, "ndim", None) == 2:
+            self._setitem_frame(key, value)
+        elif isinstance(key, (Series, np.ndarray, list, Index)):
+            self._setitem_array(key, value)
+        elif isinstance(value, DataFrame):
+            self._set_item_frame_value(key, value)
+        elif (
+            is_list_like(value)
+            and not self.columns.is_unique
+            and 1 < len(self.columns.get_indexer_for([key])) == len(value)
+        ):
+            # Column to set is duplicated
+            self._setitem_array([key], value)
+        else:
+            # set column
+            self._set_item(key, value)
+
+    def _setitem_slice(self, key: slice, value) -> None:
+        # NB: we can't just use self.loc[key] = value because that
+        #  operates on labels and we need to operate positional for
+        #  backwards-compat, xref GH#31469
+        self._check_setitem_copy()
+        self.iloc[key] = value
+
+    def _setitem_array(self, key, value):
+        # also raises Exception if object array with NA values
+        if com.is_bool_indexer(key):
+            # bool indexer is indexing along rows
+            if len(key) != len(self.index):
+                raise ValueError(
+                    f"Item wrong length {len(key)} instead of {len(self.index)}!"
+                )
+            key = check_bool_indexer(self.index, key)
+            indexer = key.nonzero()[0]
+            self._check_setitem_copy()
+            if isinstance(value, DataFrame):
+                # GH#39931 reindex since iloc does not align
+                value = value.reindex(self.index.take(indexer))
+            self.iloc[indexer] = value
+
+        else:
+            # Note: unlike self.iloc[:, indexer] = value, this will
+            #  never try to overwrite values inplace
+
+            if isinstance(value, DataFrame):
+                check_key_length(self.columns, key, value)
+                for k1, k2 in zip(key, value.columns):
+                    self[k1] = value[k2]
+
+            elif not is_list_like(value):
+                for col in key:
+                    self[col] = value
+
+            elif isinstance(value, np.ndarray) and value.ndim == 2:
+                self._iset_not_inplace(key, value)
+
+            elif np.ndim(value) > 1:
+                # list of lists
+                value = DataFrame(value).values
+                return self._setitem_array(key, value)
+
+            else:
+                self._iset_not_inplace(key, value)
+
+    def _iset_not_inplace(self, key, value):
+        # GH#39510 when setting with df[key] = obj with a list-like key and
+        #  list-like value, we iterate over those listlikes and set columns
+        #  one at a time.  This is different from dispatching to
+        #  `self.loc[:, key]= value`  because loc.__setitem__ may overwrite
+        #  data inplace, whereas this will insert new arrays.
+
+        def igetitem(obj, i: int):
+            # Note: we catch DataFrame obj before getting here, but
+            #  hypothetically would return obj.iloc[:, i]
+            if isinstance(obj, np.ndarray):
+                return obj[..., i]
+            else:
+                return obj[i]
+
+        if self.columns.is_unique:
+            if np.shape(value)[-1] != len(key):
+                raise ValueError("Columns must be same length as key")
+
+            for i, col in enumerate(key):
+                self[col] = igetitem(value, i)
+
+        else:
+            ilocs = self.columns.get_indexer_non_unique(key)[0]
+            if (ilocs < 0).any():
+                # key entries not in self.columns
+                raise NotImplementedError
+
+            if np.shape(value)[-1] != len(ilocs):
+                raise ValueError("Columns must be same length as key")
+
+            assert np.ndim(value) <= 2
+
+            orig_columns = self.columns
+
+            # Using self.iloc[:, i] = ... may set values inplace, which
+            #  by convention we do not do in __setitem__
+            try:
+                self.columns = Index(range(len(self.columns)))
+                for i, iloc in enumerate(ilocs):
+                    self[iloc] = igetitem(value, i)
+            finally:
+                self.columns = orig_columns
+
+    def _setitem_frame(self, key, value):
+        # support boolean setting with DataFrame input, e.g.
+        # df[df > df2] = 0
+        if isinstance(key, np.ndarray):
+            if key.shape != self.shape:
+                raise ValueError("Array conditional must be same shape as self")
+            key = self._constructor(key, **self._construct_axes_dict(), copy=False)
+
+        if key.size and not all(is_bool_dtype(dtype) for dtype in key.dtypes):
+            raise TypeError(
+                "Must pass DataFrame or 2-d ndarray with boolean values only"
+            )
+
+        self._check_setitem_copy()
+        self._where(-key, value, inplace=True)
+
+    def _set_item_frame_value(self, key, value: DataFrame) -> None:
+        self._ensure_valid_index(value)
+
+        # align columns
+        if key in self.columns:
+            loc = self.columns.get_loc(key)
+            cols = self.columns[loc]
+            len_cols = 1 if is_scalar(cols) or isinstance(cols, tuple) else len(cols)
+            if len_cols != len(value.columns):
+                raise ValueError("Columns must be same length as key")
+
+            # align right-hand-side columns if self.columns
+            # is multi-index and self[key] is a sub-frame
+            if isinstance(self.columns, MultiIndex) and isinstance(
+                loc, (slice, Series, np.ndarray, Index)
+            ):
+                cols_droplevel = maybe_droplevels(cols, key)
+                if len(cols_droplevel) and not cols_droplevel.equals(value.columns):
+                    value = value.reindex(cols_droplevel, axis=1)
+
+                for col, col_droplevel in zip(cols, cols_droplevel):
+                    self[col] = value[col_droplevel]
+                return
+
+            if is_scalar(cols):
+                self[cols] = value[value.columns[0]]
+                return
+
+            locs: np.ndarray | list
+            if isinstance(loc, slice):
+                locs = np.arange(loc.start, loc.stop, loc.step)
+            elif is_scalar(loc):
+                locs = [loc]
+            else:
+                locs = loc.nonzero()[0]
+
+            return self.isetitem(locs, value)
+
+        if len(value.columns) > 1:
+            raise ValueError(
+                "Cannot set a DataFrame with multiple columns to the single "
+                f"column {key}"
+            )
+        elif len(value.columns) == 0:
+            raise ValueError(
+                f"Cannot set a DataFrame without columns to the column {key}"
+            )
+
+        self[key] = value[value.columns[0]]
+
+    def _iset_item_mgr(
+        self,
+        loc: int | slice | np.ndarray,
+        value,
+        inplace: bool = False,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        # when called from _set_item_mgr loc can be anything returned from get_loc
+        self._mgr.iset(loc, value, inplace=inplace, refs=refs)
+        self._clear_item_cache()
+
+    def _set_item_mgr(
+        self, key, value: ArrayLike, refs: BlockValuesRefs | None = None
+    ) -> None:
+        try:
+            loc = self._info_axis.get_loc(key)
+        except KeyError:
+            # This item wasn't present, just insert at end
+            self._mgr.insert(len(self._info_axis), key, value, refs)
+        else:
+            self._iset_item_mgr(loc, value, refs=refs)
+
+        # check if we are modifying a copy
+        # try to set first as we want an invalid
+        # value exception to occur first
+        if len(self):
+            self._check_setitem_copy()
+
+    def _iset_item(self, loc: int, value: Series, inplace: bool = True) -> None:
+        # We are only called from _replace_columnwise which guarantees that
+        # no reindex is necessary
+        if using_copy_on_write():
+            self._iset_item_mgr(
+                loc, value._values, inplace=inplace, refs=value._references
+            )
+        else:
+            self._iset_item_mgr(loc, value._values.copy(), inplace=True)
+
+        # check if we are modifying a copy
+        # try to set first as we want an invalid
+        # value exception to occur first
+        if len(self):
+            self._check_setitem_copy()
+
+    def _set_item(self, key, value) -> None:
+        """
+        Add series to DataFrame in specified column.
+
+        If series is a numpy-array (not a Series/TimeSeries), it must be the
+        same length as the DataFrames index or an error will be thrown.
+
+        Series/TimeSeries will be conformed to the DataFrames index to
+        ensure homogeneity.
+        """
+        value, refs = self._sanitize_column(value)
+
+        if (
+            key in self.columns
+            and value.ndim == 1
+            and not isinstance(value.dtype, ExtensionDtype)
+        ):
+            # broadcast across multiple columns if necessary
+            if not self.columns.is_unique or isinstance(self.columns, MultiIndex):
+                existing_piece = self[key]
+                if isinstance(existing_piece, DataFrame):
+                    value = np.tile(value, (len(existing_piece.columns), 1)).T
+                    refs = None
+
+        self._set_item_mgr(key, value, refs)
+
+    def _set_value(
+        self, index: IndexLabel, col, value: Scalar, takeable: bool = False
+    ) -> None:
+        """
+        Put single value at passed column and index.
+
+        Parameters
+        ----------
+        index : Label
+            row label
+        col : Label
+            column label
+        value : scalar
+        takeable : bool, default False
+            Sets whether or not index/col interpreted as indexers
+        """
+        try:
+            if takeable:
+                icol = col
+                iindex = cast(int, index)
+            else:
+                icol = self.columns.get_loc(col)
+                iindex = self.index.get_loc(index)
+            self._mgr.column_setitem(icol, iindex, value, inplace_only=True)
+            self._clear_item_cache()
+
+        except (KeyError, TypeError, ValueError, LossySetitemError):
+            # get_loc might raise a KeyError for missing labels (falling back
+            #  to (i)loc will do expansion of the index)
+            # column_setitem will do validation that may raise TypeError,
+            #  ValueError, or LossySetitemError
+            # set using a non-recursive method & reset the cache
+            if takeable:
+                self.iloc[index, col] = value
+            else:
+                self.loc[index, col] = value
+            self._item_cache.pop(col, None)
+
+        except InvalidIndexError as ii_err:
+            # GH48729: Seems like you are trying to assign a value to a
+            # row when only scalar options are permitted
+            raise InvalidIndexError(
+                f"You can only assign a scalar value not a {type(value)}"
+            ) from ii_err
+
+    def _ensure_valid_index(self, value) -> None:
+        """
+        Ensure that if we don't have an index, that we can create one from the
+        passed value.
+        """
+        # GH5632, make sure that we are a Series convertible
+        if not len(self.index) and is_list_like(value) and len(value):
+            if not isinstance(value, DataFrame):
+                try:
+                    value = Series(value)
+                except (ValueError, NotImplementedError, TypeError) as err:
+                    raise ValueError(
+                        "Cannot set a frame with no defined index "
+                        "and a value that cannot be converted to a Series"
+                    ) from err
+
+            # GH31368 preserve name of index
+            index_copy = value.index.copy()
+            if self.index.name is not None:
+                index_copy.name = self.index.name
+
+            self._mgr = self._mgr.reindex_axis(index_copy, axis=1, fill_value=np.nan)
+
+    def _box_col_values(self, values: SingleDataManager, loc: int) -> Series:
+        """
+        Provide boxed values for a column.
+        """
+        # Lookup in columns so that if e.g. a str datetime was passed
+        #  we attach the Timestamp object as the name.
+        name = self.columns[loc]
+        # We get index=self.index bc values is a SingleDataManager
+        obj = self._constructor_sliced_from_mgr(values, axes=values.axes)
+        obj._name = name
+        return obj.__finalize__(self)
+
+    # ----------------------------------------------------------------------
+    # Lookup Caching
+
+    def _clear_item_cache(self) -> None:
+        self._item_cache.clear()
+
+    def _get_item_cache(self, item: Hashable) -> Series:
+        """Return the cached item, item represents a label indexer."""
+        if using_copy_on_write() or warn_copy_on_write():
+            loc = self.columns.get_loc(item)
+            return self._ixs(loc, axis=1)
+
+        cache = self._item_cache
+        res = cache.get(item)
+        if res is None:
+            # All places that call _get_item_cache have unique columns,
+            #  pending resolution of GH#33047
+
+            loc = self.columns.get_loc(item)
+            res = self._ixs(loc, axis=1)
+
+            cache[item] = res
+
+            # for a chain
+            res._is_copy = self._is_copy
+        return res
+
+    def _reset_cacher(self) -> None:
+        # no-op for DataFrame
+        pass
+
+    def _maybe_cache_changed(self, item, value: Series, inplace: bool) -> None:
+        """
+        The object has called back to us saying maybe it has changed.
+        """
+        loc = self._info_axis.get_loc(item)
+        arraylike = value._values
+
+        old = self._ixs(loc, axis=1)
+        if old._values is value._values and inplace:
+            # GH#46149 avoid making unnecessary copies/block-splitting
+            return
+
+        self._mgr.iset(loc, arraylike, inplace=inplace)
+
+    # ----------------------------------------------------------------------
+    # Unsorted
+
+    @overload
+    def query(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> DataFrame:
+        ...
+
+    @overload
+    def query(self, expr: str, *, inplace: Literal[True], **kwargs) -> None:
+        ...
+
+    @overload
+    def query(self, expr: str, *, inplace: bool = ..., **kwargs) -> DataFrame | None:
+        ...
+
+    def query(self, expr: str, *, inplace: bool = False, **kwargs) -> DataFrame | None:
+        """
+        Query the columns of a DataFrame with a boolean expression.
+
+        Parameters
+        ----------
+        expr : str
+            The query string to evaluate.
+
+            You can refer to variables
+            in the environment by prefixing them with an '@' character like
+            ``@a + b``.
+
+            You can refer to column names that are not valid Python variable names
+            by surrounding them in backticks. Thus, column names containing spaces
+            or punctuations (besides underscores) or starting with digits must be
+            surrounded by backticks. (For example, a column named "Area (cm^2)" would
+            be referenced as ```Area (cm^2)```). Column names which are Python keywords
+            (like "list", "for", "import", etc) cannot be used.
+
+            For example, if one of your columns is called ``a a`` and you want
+            to sum it with ``b``, your query should be ```a a` + b``.
+
+        inplace : bool
+            Whether to modify the DataFrame rather than creating a new one.
+        **kwargs
+            See the documentation for :func:`eval` for complete details
+            on the keyword arguments accepted by :meth:`DataFrame.query`.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame resulting from the provided query expression or
+            None if ``inplace=True``.
+
+        See Also
+        --------
+        eval : Evaluate a string describing operations on
+            DataFrame columns.
+        DataFrame.eval : Evaluate a string describing operations on
+            DataFrame columns.
+
+        Notes
+        -----
+        The result of the evaluation of this expression is first passed to
+        :attr:`DataFrame.loc` and if that fails because of a
+        multidimensional key (e.g., a DataFrame) then the result will be passed
+        to :meth:`DataFrame.__getitem__`.
+
+        This method uses the top-level :func:`eval` function to
+        evaluate the passed query.
+
+        The :meth:`~pandas.DataFrame.query` method uses a slightly
+        modified Python syntax by default. For example, the ``&`` and ``|``
+        (bitwise) operators have the precedence of their boolean cousins,
+        :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
+        however the semantics are different.
+
+        You can change the semantics of the expression by passing the keyword
+        argument ``parser='python'``. This enforces the same semantics as
+        evaluation in Python space. Likewise, you can pass ``engine='python'``
+        to evaluate an expression using Python itself as a backend. This is not
+        recommended as it is inefficient compared to using ``numexpr`` as the
+        engine.
+
+        The :attr:`DataFrame.index` and
+        :attr:`DataFrame.columns` attributes of the
+        :class:`~pandas.DataFrame` instance are placed in the query namespace
+        by default, which allows you to treat both the index and columns of the
+        frame as a column in the frame.
+        The identifier ``index`` is used for the frame index; you can also
+        use the name of the index to identify it in a query. Please note that
+        Python keywords may not be used as identifiers.
+
+        For further details and examples see the ``query`` documentation in
+        :ref:`indexing <indexing.query>`.
+
+        *Backtick quoted variables*
+
+        Backtick quoted variables are parsed as literal Python code and
+        are converted internally to a Python valid identifier.
+        This can lead to the following problems.
+
+        During parsing a number of disallowed characters inside the backtick
+        quoted string are replaced by strings that are allowed as a Python identifier.
+        These characters include all operators in Python, the space character, the
+        question mark, the exclamation mark, the dollar sign, and the euro sign.
+        For other characters that fall outside the ASCII range (U+0001..U+007F)
+        and those that are not further specified in PEP 3131,
+        the query parser will raise an error.
+        This excludes whitespace different than the space character,
+        but also the hashtag (as it is used for comments) and the backtick
+        itself (backtick can also not be escaped).
+
+        In a special case, quotes that make a pair around a backtick can
+        confuse the parser.
+        For example, ```it's` > `that's``` will raise an error,
+        as it forms a quoted string (``'s > `that'``) with a backtick inside.
+
+        See also the Python documentation about lexical analysis
+        (https://docs.python.org/3/reference/lexical_analysis.html)
+        in combination with the source code in :mod:`pandas.core.computation.parsing`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': range(1, 6),
+        ...                    'B': range(10, 0, -2),
+        ...                    'C C': range(10, 5, -1)})
+        >>> df
+           A   B  C C
+        0  1  10   10
+        1  2   8    9
+        2  3   6    8
+        3  4   4    7
+        4  5   2    6
+        >>> df.query('A > B')
+           A  B  C C
+        4  5  2    6
+
+        The previous expression is equivalent to
+
+        >>> df[df.A > df.B]
+           A  B  C C
+        4  5  2    6
+
+        For columns with spaces in their name, you can use backtick quoting.
+
+        >>> df.query('B == `C C`')
+           A   B  C C
+        0  1  10   10
+
+        The previous expression is equivalent to
+
+        >>> df[df.B == df['C C']]
+           A   B  C C
+        0  1  10   10
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if not isinstance(expr, str):
+            msg = f"expr must be a string to be evaluated, {type(expr)} given"
+            raise ValueError(msg)
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        kwargs["target"] = None
+        res = self.eval(expr, **kwargs)
+
+        try:
+            result = self.loc[res]
+        except ValueError:
+            # when res is multi-dimensional loc raises, but this is sometimes a
+            # valid query
+            result = self[res]
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result
+
+    @overload
+    def eval(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> Any:
+        ...
+
+    @overload
+    def eval(self, expr: str, *, inplace: Literal[True], **kwargs) -> None:
+        ...
+
+    def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
+        """
+        Evaluate a string describing operations on DataFrame columns.
+
+        Operates on columns only, not specific rows or elements.  This allows
+        `eval` to run arbitrary code, which can make you vulnerable to code
+        injection if you pass user input to this function.
+
+        Parameters
+        ----------
+        expr : str
+            The expression string to evaluate.
+        inplace : bool, default False
+            If the expression contains an assignment, whether to perform the
+            operation inplace and mutate the existing DataFrame. Otherwise,
+            a new DataFrame is returned.
+        **kwargs
+            See the documentation for :func:`eval` for complete details
+            on the keyword arguments accepted by
+            :meth:`~pandas.DataFrame.query`.
+
+        Returns
+        -------
+        ndarray, scalar, pandas object, or None
+            The result of the evaluation or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.query : Evaluates a boolean expression to query the columns
+            of a frame.
+        DataFrame.assign : Can evaluate an expression or function to create new
+            values for a column.
+        eval : Evaluate a Python expression as a string using various
+            backends.
+
+        Notes
+        -----
+        For more details see the API documentation for :func:`~eval`.
+        For detailed examples see :ref:`enhancing performance with eval
+        <enhancingperf.eval>`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
+        >>> df
+           A   B
+        0  1  10
+        1  2   8
+        2  3   6
+        3  4   4
+        4  5   2
+        >>> df.eval('A + B')
+        0    11
+        1    10
+        2     9
+        3     8
+        4     7
+        dtype: int64
+
+        Assignment is allowed though by default the original DataFrame is not
+        modified.
+
+        >>> df.eval('C = A + B')
+           A   B   C
+        0  1  10  11
+        1  2   8  10
+        2  3   6   9
+        3  4   4   8
+        4  5   2   7
+        >>> df
+           A   B
+        0  1  10
+        1  2   8
+        2  3   6
+        3  4   4
+        4  5   2
+
+        Multiple columns can be assigned to using multi-line expressions:
+
+        >>> df.eval(
+        ...     '''
+        ... C = A + B
+        ... D = A - B
+        ... '''
+        ... )
+           A   B   C  D
+        0  1  10  11 -9
+        1  2   8  10 -6
+        2  3   6   9 -3
+        3  4   4   8  0
+        4  5   2   7  3
+        """
+        from pandas.core.computation.eval import eval as _eval
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        index_resolvers = self._get_index_resolvers()
+        column_resolvers = self._get_cleaned_column_resolvers()
+        resolvers = column_resolvers, index_resolvers
+        if "target" not in kwargs:
+            kwargs["target"] = self
+        kwargs["resolvers"] = tuple(kwargs.get("resolvers", ())) + resolvers
+
+        return _eval(expr, inplace=inplace, **kwargs)
+
+    def select_dtypes(self, include=None, exclude=None) -> Self:
+        """
+        Return a subset of the DataFrame's columns based on the column dtypes.
+
+        Parameters
+        ----------
+        include, exclude : scalar or list-like
+            A selection of dtypes or strings to be included/excluded. At least
+            one of these parameters must be supplied.
+
+        Returns
+        -------
+        DataFrame
+            The subset of the frame including the dtypes in ``include`` and
+            excluding the dtypes in ``exclude``.
+
+        Raises
+        ------
+        ValueError
+            * If both of ``include`` and ``exclude`` are empty
+            * If ``include`` and ``exclude`` have overlapping elements
+            * If any kind of string dtype is passed in.
+
+        See Also
+        --------
+        DataFrame.dtypes: Return Series with the data type of each column.
+
+        Notes
+        -----
+        * To select all *numeric* types, use ``np.number`` or ``'number'``
+        * To select strings you must use the ``object`` dtype, but note that
+          this will return *all* object dtype columns. With
+          ``pd.options.future.infer_string`` enabled, using ``"str"`` will
+          work to select all string columns.
+        * See the `numpy dtype hierarchy
+          <https://numpy.org/doc/stable/reference/arrays.scalars.html>`__
+        * To select datetimes, use ``np.datetime64``, ``'datetime'`` or
+          ``'datetime64'``
+        * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
+          ``'timedelta64'``
+        * To select Pandas categorical dtypes, use ``'category'``
+        * To select Pandas datetimetz dtypes, use ``'datetimetz'``
+          or ``'datetime64[ns, tz]'``
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'a': [1, 2] * 3,
+        ...                    'b': [True, False] * 3,
+        ...                    'c': [1.0, 2.0] * 3})
+        >>> df
+                a      b  c
+        0       1   True  1.0
+        1       2  False  2.0
+        2       1   True  1.0
+        3       2  False  2.0
+        4       1   True  1.0
+        5       2  False  2.0
+
+        >>> df.select_dtypes(include='bool')
+           b
+        0  True
+        1  False
+        2  True
+        3  False
+        4  True
+        5  False
+
+        >>> df.select_dtypes(include=['float64'])
+           c
+        0  1.0
+        1  2.0
+        2  1.0
+        3  2.0
+        4  1.0
+        5  2.0
+
+        >>> df.select_dtypes(exclude=['int64'])
+               b    c
+        0   True  1.0
+        1  False  2.0
+        2   True  1.0
+        3  False  2.0
+        4   True  1.0
+        5  False  2.0
+        """
+        if not is_list_like(include):
+            include = (include,) if include is not None else ()
+        if not is_list_like(exclude):
+            exclude = (exclude,) if exclude is not None else ()
+
+        selection = (frozenset(include), frozenset(exclude))
+
+        if not any(selection):
+            raise ValueError("at least one of include or exclude must be nonempty")
+
+        # convert the myriad valid dtypes object to a single representation
+        def check_int_infer_dtype(dtypes):
+            converted_dtypes: list[type] = []
+            for dtype in dtypes:
+                # Numpy maps int to different types (int32, in64) on Windows and Linux
+                # see https://github.com/numpy/numpy/issues/9464
+                if (isinstance(dtype, str) and dtype == "int") or (dtype is int):
+                    converted_dtypes.append(np.int32)
+                    converted_dtypes.append(np.int64)
+                elif dtype == "float" or dtype is float:
+                    # GH#42452 : np.dtype("float") coerces to np.float64 from Numpy 1.20
+                    converted_dtypes.extend([np.float64, np.float32])
+                else:
+                    converted_dtypes.append(infer_dtype_from_object(dtype))
+            return frozenset(converted_dtypes)
+
+        include = check_int_infer_dtype(include)
+        exclude = check_int_infer_dtype(exclude)
+
+        for dtypes in (include, exclude):
+            invalidate_string_dtypes(dtypes)
+
+        # can't both include AND exclude!
+        if not include.isdisjoint(exclude):
+            raise ValueError(f"include and exclude overlap on {(include & exclude)}")
+
+        def dtype_predicate(dtype: DtypeObj, dtypes_set) -> bool:
+            # GH 46870: BooleanDtype._is_numeric == True but should be excluded
+            dtype = dtype if not isinstance(dtype, ArrowDtype) else dtype.numpy_dtype
+            return (
+                issubclass(dtype.type, tuple(dtypes_set))
+                or (
+                    np.number in dtypes_set
+                    and getattr(dtype, "_is_numeric", False)
+                    and not is_bool_dtype(dtype)
+                )
+                # backwards compat for the default `str` dtype being selected by object
+                or (
+                    isinstance(dtype, StringDtype)
+                    and dtype.na_value is np.nan
+                    and np.object_ in dtypes_set
+                )
+            )
+
+        def predicate(arr: ArrayLike) -> bool:
+            dtype = arr.dtype
+            if include:
+                if not dtype_predicate(dtype, include):
+                    return False
+
+            if exclude:
+                if dtype_predicate(dtype, exclude):
+                    return False
+
+            return True
+
+        mgr = self._mgr._get_data_subset(predicate).copy(deep=None)
+        # error: Incompatible return value type (got "DataFrame", expected "Self")
+        return self._constructor_from_mgr(mgr, axes=mgr.axes).__finalize__(self)  # type: ignore[return-value]
+
+    def insert(
+        self,
+        loc: int,
+        column: Hashable,
+        value: Scalar | AnyArrayLike,
+        allow_duplicates: bool | lib.NoDefault = lib.no_default,
+    ) -> None:
+        """
+        Insert column into DataFrame at specified location.
+
+        Raises a ValueError if `column` is already contained in the DataFrame,
+        unless `allow_duplicates` is set to True.
+
+        Parameters
+        ----------
+        loc : int
+            Insertion index. Must verify 0 <= loc <= len(columns).
+        column : str, number, or hashable object
+            Label of the inserted column.
+        value : Scalar, Series, or array-like
+            Content of the inserted column.
+        allow_duplicates : bool, optional, default lib.no_default
+            Allow duplicate column labels to be created.
+
+        See Also
+        --------
+        Index.insert : Insert new item by index.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df
+           col1  col2
+        0     1     3
+        1     2     4
+        >>> df.insert(1, "newcol", [99, 99])
+        >>> df
+           col1  newcol  col2
+        0     1      99     3
+        1     2      99     4
+        >>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
+        >>> df
+           col1  col1  newcol  col2
+        0   100     1      99     3
+        1   100     2      99     4
+
+        Notice that pandas uses index alignment in case of `value` from type `Series`:
+
+        >>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
+        >>> df
+           col0  col1  col1  newcol  col2
+        0   NaN   100     1      99     3
+        1   5.0   100     2      99     4
+        """
+        if allow_duplicates is lib.no_default:
+            allow_duplicates = False
+        if allow_duplicates and not self.flags.allows_duplicate_labels:
+            raise ValueError(
+                "Cannot specify 'allow_duplicates=True' when "
+                "'self.flags.allows_duplicate_labels' is False."
+            )
+        if not allow_duplicates and column in self.columns:
+            # Should this be a different kind of error??
+            raise ValueError(f"cannot insert {column}, already exists")
+        if not is_integer(loc):
+            raise TypeError("loc must be int")
+        # convert non stdlib ints to satisfy typing checks
+        loc = int(loc)
+        if isinstance(value, DataFrame) and len(value.columns) > 1:
+            raise ValueError(
+                f"Expected a one-dimensional object, got a DataFrame with "
+                f"{len(value.columns)} columns instead."
+            )
+        elif isinstance(value, DataFrame):
+            value = value.iloc[:, 0]
+
+        value, refs = self._sanitize_column(value)
+        self._mgr.insert(loc, column, value, refs=refs)
+
+    def assign(self, **kwargs) -> DataFrame:
+        r"""
+        Assign new columns to a DataFrame.
+
+        Returns a new object with all original columns in addition to new ones.
+        Existing columns that are re-assigned will be overwritten.
+
+        Parameters
+        ----------
+        **kwargs : dict of {str: callable or Series}
+            The column names are keywords. If the values are
+            callable, they are computed on the DataFrame and
+            assigned to the new columns. The callable must not
+            change input DataFrame (though pandas doesn't check it).
+            If the values are not callable, (e.g. a Series, scalar, or array),
+            they are simply assigned.
+
+        Returns
+        -------
+        DataFrame
+            A new DataFrame with the new columns in addition to
+            all the existing columns.
+
+        Notes
+        -----
+        Assigning multiple columns within the same ``assign`` is possible.
+        Later items in '\*\*kwargs' may refer to newly created or modified
+        columns in 'df'; items are computed and assigned into 'df' in order.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
+        ...                   index=['Portland', 'Berkeley'])
+        >>> df
+                  temp_c
+        Portland    17.0
+        Berkeley    25.0
+
+        Where the value is a callable, evaluated on `df`:
+
+        >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
+                  temp_c  temp_f
+        Portland    17.0    62.6
+        Berkeley    25.0    77.0
+
+        Alternatively, the same behavior can be achieved by directly
+        referencing an existing Series or sequence:
+
+        >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
+                  temp_c  temp_f
+        Portland    17.0    62.6
+        Berkeley    25.0    77.0
+
+        You can create multiple columns within the same assign where one
+        of the columns depends on another one defined within the same assign:
+
+        >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
+        ...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
+                  temp_c  temp_f  temp_k
+        Portland    17.0    62.6  290.15
+        Berkeley    25.0    77.0  298.15
+        """
+        data = self.copy(deep=None)
+
+        for k, v in kwargs.items():
+            data[k] = com.apply_if_callable(v, data)
+        return data
+
+    def _sanitize_column(self, value) -> tuple[ArrayLike, BlockValuesRefs | None]:
+        """
+        Ensures new columns (which go into the BlockManager as new blocks) are
+        always copied (or a reference is being tracked to them under CoW)
+        and converted into an array.
+
+        Parameters
+        ----------
+        value : scalar, Series, or array-like
+
+        Returns
+        -------
+        tuple of numpy.ndarray or ExtensionArray and optional BlockValuesRefs
+        """
+        self._ensure_valid_index(value)
+
+        # Using a DataFrame would mean coercing values to one dtype
+        assert not isinstance(value, DataFrame)
+        if is_dict_like(value):
+            if not isinstance(value, Series):
+                value = Series(value)
+            return _reindex_for_setitem(value, self.index)
+
+        if is_list_like(value):
+            com.require_length_match(value, self.index)
+        arr = sanitize_array(value, self.index, copy=True, allow_2d=True)
+        if (
+            isinstance(value, Index)
+            and value.dtype == "object"
+            and arr.dtype != value.dtype
+        ):  #
+            # TODO: Remove kludge in sanitize_array for string mode when enforcing
+            # this deprecation
+            warnings.warn(
+                "Setting an Index with object dtype into a DataFrame will stop "
+                "inferring another dtype in a future version. Cast the Index "
+                "explicitly before setting it into the DataFrame.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return arr, None
+
+    @property
+    def _series(self):
+        return {item: self._ixs(idx, axis=1) for idx, item in enumerate(self.columns)}
+
+    # ----------------------------------------------------------------------
+    # Reindexing and alignment
+
+    def _reindex_multi(
+        self, axes: dict[str, Index], copy: bool, fill_value
+    ) -> DataFrame:
+        """
+        We are guaranteed non-Nones in the axes.
+        """
+
+        new_index, row_indexer = self.index.reindex(axes["index"])
+        new_columns, col_indexer = self.columns.reindex(axes["columns"])
+
+        if row_indexer is not None and col_indexer is not None:
+            # Fastpath. By doing two 'take's at once we avoid making an
+            #  unnecessary copy.
+            # We only get here with `self._can_fast_transpose`, which (almost)
+            #  ensures that self.values is cheap. It may be worth making this
+            #  condition more specific.
+            indexer = row_indexer, col_indexer
+            new_values = take_2d_multi(self.values, indexer, fill_value=fill_value)
+            return self._constructor(
+                new_values, index=new_index, columns=new_columns, copy=False
+            )
+        else:
+            return self._reindex_with_indexers(
+                {0: [new_index, row_indexer], 1: [new_columns, col_indexer]},
+                copy=copy,
+                fill_value=fill_value,
+            )
+
+    @Appender(
+        """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+
+        Change the row labels.
+
+        >>> df.set_axis(['a', 'b', 'c'], axis='index')
+           A  B
+        a  1  4
+        b  2  5
+        c  3  6
+
+        Change the column labels.
+
+        >>> df.set_axis(['I', 'II'], axis='columns')
+           I  II
+        0  1   4
+        1  2   5
+        2  3   6
+        """
+    )
+    @Substitution(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+        extended_summary_sub=" column or",
+        axis_description_sub=", and 1 identifies the columns",
+        see_also_sub=" or columns",
+    )
+    @Appender(NDFrame.set_axis.__doc__)
+    def set_axis(
+        self,
+        labels,
+        *,
+        axis: Axis = 0,
+        copy: bool | None = None,
+    ) -> DataFrame:
+        return super().set_axis(labels, axis=axis, copy=copy)
+
+    @doc(
+        NDFrame.reindex,
+        klass=_shared_doc_kwargs["klass"],
+        optional_reindex=_shared_doc_kwargs["optional_reindex"],
+    )
+    def reindex(
+        self,
+        labels=None,
+        *,
+        index=None,
+        columns=None,
+        axis: Axis | None = None,
+        method: ReindexMethod | None = None,
+        copy: bool | None = None,
+        level: Level | None = None,
+        fill_value: Scalar | None = np.nan,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> DataFrame:
+        return super().reindex(
+            labels=labels,
+            index=index,
+            columns=columns,
+            axis=axis,
+            method=method,
+            copy=copy,
+            level=level,
+            fill_value=fill_value,
+            limit=limit,
+            tolerance=tolerance,
+        )
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level = ...,
+        inplace: Literal[True],
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level = ...,
+        inplace: Literal[False] = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level = ...,
+        inplace: bool = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def drop(
+        self,
+        labels: IndexLabel | None = None,
+        *,
+        axis: Axis = 0,
+        index: IndexLabel | None = None,
+        columns: IndexLabel | None = None,
+        level: Level | None = None,
+        inplace: bool = False,
+        errors: IgnoreRaise = "raise",
+    ) -> DataFrame | None:
+        """
+        Drop specified labels from rows or columns.
+
+        Remove rows or columns by specifying label names and corresponding
+        axis, or by directly specifying index or column names. When using a
+        multi-index, labels on different levels can be removed by specifying
+        the level. See the :ref:`user guide <advanced.shown_levels>`
+        for more information about the now unused levels.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+            Index or column labels to drop. A tuple will be used as a single
+            label and not treated as a list-like.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Whether to drop labels from the index (0 or 'index') or
+            columns (1 or 'columns').
+        index : single label or list-like
+            Alternative to specifying axis (``labels, axis=0``
+            is equivalent to ``index=labels``).
+        columns : single label or list-like
+            Alternative to specifying axis (``labels, axis=1``
+            is equivalent to ``columns=labels``).
+        level : int or level name, optional
+            For MultiIndex, level from which the labels will be removed.
+        inplace : bool, default False
+            If False, return a copy. Otherwise, do operation
+            in place and return None.
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and only existing labels are
+            dropped.
+
+        Returns
+        -------
+        DataFrame or None
+            Returns DataFrame or None DataFrame with the specified
+            index or column labels removed or None if inplace=True.
+
+        Raises
+        ------
+        KeyError
+            If any of the labels is not found in the selected axis.
+
+        See Also
+        --------
+        DataFrame.loc : Label-location based indexer for selection by label.
+        DataFrame.dropna : Return DataFrame with labels on given axis omitted
+            where (all or any) data are missing.
+        DataFrame.drop_duplicates : Return DataFrame with duplicate rows
+            removed, optionally only considering certain columns.
+        Series.drop : Return Series with specified index labels removed.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
+        ...                   columns=['A', 'B', 'C', 'D'])
+        >>> df
+           A  B   C   D
+        0  0  1   2   3
+        1  4  5   6   7
+        2  8  9  10  11
+
+        Drop columns
+
+        >>> df.drop(['B', 'C'], axis=1)
+           A   D
+        0  0   3
+        1  4   7
+        2  8  11
+
+        >>> df.drop(columns=['B', 'C'])
+           A   D
+        0  0   3
+        1  4   7
+        2  8  11
+
+        Drop a row by index
+
+        >>> df.drop([0, 1])
+           A  B   C   D
+        2  8  9  10  11
+
+        Drop columns and/or rows of MultiIndex DataFrame
+
+        >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
+        ...                              ['speed', 'weight', 'length']],
+        ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
+        ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
+        >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
+        ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
+        ...                         [250, 150], [1.5, 0.8], [320, 250],
+        ...                         [1, 0.8], [0.3, 0.2]])
+        >>> df
+                        big     small
+        llama   speed   45.0    30.0
+                weight  200.0   100.0
+                length  1.5     1.0
+        cow     speed   30.0    20.0
+                weight  250.0   150.0
+                length  1.5     0.8
+        falcon  speed   320.0   250.0
+                weight  1.0     0.8
+                length  0.3     0.2
+
+        Drop a specific index combination from the MultiIndex
+        DataFrame, i.e., drop the combination ``'falcon'`` and
+        ``'weight'``, which deletes only the corresponding row
+
+        >>> df.drop(index=('falcon', 'weight'))
+                        big     small
+        llama   speed   45.0    30.0
+                weight  200.0   100.0
+                length  1.5     1.0
+        cow     speed   30.0    20.0
+                weight  250.0   150.0
+                length  1.5     0.8
+        falcon  speed   320.0   250.0
+                length  0.3     0.2
+
+        >>> df.drop(index='cow', columns='small')
+                        big
+        llama   speed   45.0
+                weight  200.0
+                length  1.5
+        falcon  speed   320.0
+                weight  1.0
+                length  0.3
+
+        >>> df.drop(index='length', level=1)
+                        big     small
+        llama   speed   45.0    30.0
+                weight  200.0   100.0
+        cow     speed   30.0    20.0
+                weight  250.0   150.0
+        falcon  speed   320.0   250.0
+                weight  1.0     0.8
+        """
+        return super().drop(
+            labels=labels,
+            axis=axis,
+            index=index,
+            columns=columns,
+            level=level,
+            inplace=inplace,
+            errors=errors,
+        )
+
+    @overload
+    def rename(
+        self,
+        mapper: Renamer | None = ...,
+        *,
+        index: Renamer | None = ...,
+        columns: Renamer | None = ...,
+        axis: Axis | None = ...,
+        copy: bool | None = ...,
+        inplace: Literal[True],
+        level: Level = ...,
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def rename(
+        self,
+        mapper: Renamer | None = ...,
+        *,
+        index: Renamer | None = ...,
+        columns: Renamer | None = ...,
+        axis: Axis | None = ...,
+        copy: bool | None = ...,
+        inplace: Literal[False] = ...,
+        level: Level = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def rename(
+        self,
+        mapper: Renamer | None = ...,
+        *,
+        index: Renamer | None = ...,
+        columns: Renamer | None = ...,
+        axis: Axis | None = ...,
+        copy: bool | None = ...,
+        inplace: bool = ...,
+        level: Level = ...,
+        errors: IgnoreRaise = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def rename(
+        self,
+        mapper: Renamer | None = None,
+        *,
+        index: Renamer | None = None,
+        columns: Renamer | None = None,
+        axis: Axis | None = None,
+        copy: bool | None = None,
+        inplace: bool = False,
+        level: Level | None = None,
+        errors: IgnoreRaise = "ignore",
+    ) -> DataFrame | None:
+        """
+        Rename columns or index labels.
+
+        Function / dict values must be unique (1-to-1). Labels not contained in
+        a dict / Series will be left as-is. Extra labels listed don't throw an
+        error.
+
+        See the :ref:`user guide <basics.rename>` for more.
+
+        Parameters
+        ----------
+        mapper : dict-like or function
+            Dict-like or function transformations to apply to
+            that axis' values. Use either ``mapper`` and ``axis`` to
+            specify the axis to target with ``mapper``, or ``index`` and
+            ``columns``.
+        index : dict-like or function
+            Alternative to specifying axis (``mapper, axis=0``
+            is equivalent to ``index=mapper``).
+        columns : dict-like or function
+            Alternative to specifying axis (``mapper, axis=1``
+            is equivalent to ``columns=mapper``).
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis to target with ``mapper``. Can be either the axis name
+            ('index', 'columns') or number (0, 1). The default is 'index'.
+        copy : bool, default True
+            Also copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+            If True then value of copy is ignored.
+        level : int or level name, default None
+            In case of a MultiIndex, only rename labels in the specified
+            level.
+        errors : {'ignore', 'raise'}, default 'ignore'
+            If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
+            or `columns` contains labels that are not present in the Index
+            being transformed.
+            If 'ignore', existing keys will be renamed and extra keys will be
+            ignored.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with the renamed axis labels or None if ``inplace=True``.
+
+        Raises
+        ------
+        KeyError
+            If any of the labels is not found in the selected axis and
+            "errors='raise'".
+
+        See Also
+        --------
+        DataFrame.rename_axis : Set the name of the axis.
+
+        Examples
+        --------
+        ``DataFrame.rename`` supports two calling conventions
+
+        * ``(index=index_mapper, columns=columns_mapper, ...)``
+        * ``(mapper, axis={'index', 'columns'}, ...)``
+
+        We *highly* recommend using keyword arguments to clarify your
+        intent.
+
+        Rename columns using a mapping:
+
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        >>> df.rename(columns={"A": "a", "B": "c"})
+           a  c
+        0  1  4
+        1  2  5
+        2  3  6
+
+        Rename index using a mapping:
+
+        >>> df.rename(index={0: "x", 1: "y", 2: "z"})
+           A  B
+        x  1  4
+        y  2  5
+        z  3  6
+
+        Cast index labels to a different type:
+
+        >>> df.index
+        RangeIndex(start=0, stop=3, step=1)
+        >>> df.rename(index=str).index
+        Index(['0', '1', '2'], dtype='object')
+
+        >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
+        Traceback (most recent call last):
+        KeyError: ['C'] not found in axis
+
+        Using axis-style parameters:
+
+        >>> df.rename(str.lower, axis='columns')
+           a  b
+        0  1  4
+        1  2  5
+        2  3  6
+
+        >>> df.rename({1: 2, 2: 4}, axis='index')
+           A  B
+        0  1  4
+        2  2  5
+        4  3  6
+        """
+        return super()._rename(
+            mapper=mapper,
+            index=index,
+            columns=columns,
+            axis=axis,
+            copy=copy,
+            inplace=inplace,
+            level=level,
+            errors=errors,
+        )
+
+    def pop(self, item: Hashable) -> Series:
+        """
+        Return item and drop from frame. Raise KeyError if not found.
+
+        Parameters
+        ----------
+        item : label
+            Label of column to be popped.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan)],
+        ...                   columns=('name', 'class', 'max_speed'))
+        >>> df
+             name   class  max_speed
+        0  falcon    bird      389.0
+        1  parrot    bird       24.0
+        2    lion  mammal       80.5
+        3  monkey  mammal        NaN
+
+        >>> df.pop('class')
+        0      bird
+        1      bird
+        2    mammal
+        3    mammal
+        Name: class, dtype: object
+
+        >>> df
+             name  max_speed
+        0  falcon      389.0
+        1  parrot       24.0
+        2    lion       80.5
+        3  monkey        NaN
+        """
+        return super().pop(item=item)
+
+    def _replace_columnwise(
+        self, mapping: dict[Hashable, tuple[Any, Any]], inplace: bool, regex
+    ):
+        """
+        Dispatch to Series.replace column-wise.
+
+        Parameters
+        ----------
+        mapping : dict
+            of the form {col: (target, value)}
+        inplace : bool
+        regex : bool or same types as `to_replace` in DataFrame.replace
+
+        Returns
+        -------
+        DataFrame or None
+        """
+        # Operate column-wise
+        res = self if inplace else self.copy(deep=None)
+        ax = self.columns
+
+        for i, ax_value in enumerate(ax):
+            if ax_value in mapping:
+                ser = self.iloc[:, i]
+
+                target, value = mapping[ax_value]
+                newobj = ser.replace(target, value, regex=regex)
+
+                res._iset_item(i, newobj, inplace=inplace)
+
+        if inplace:
+            return
+        return res.__finalize__(self)
+
+    @doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"])
+    def shift(
+        self,
+        periods: int | Sequence[int] = 1,
+        freq: Frequency | None = None,
+        axis: Axis = 0,
+        fill_value: Hashable = lib.no_default,
+        suffix: str | None = None,
+    ) -> DataFrame:
+        if freq is not None and fill_value is not lib.no_default:
+            # GH#53832
+            warnings.warn(
+                "Passing a 'freq' together with a 'fill_value' silently ignores "
+                "the fill_value and is deprecated. This will raise in a future "
+                "version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            fill_value = lib.no_default
+
+        if self.empty:
+            return self.copy()
+
+        axis = self._get_axis_number(axis)
+
+        if is_list_like(periods):
+            periods = cast(Sequence, periods)
+            if axis == 1:
+                raise ValueError(
+                    "If `periods` contains multiple shifts, `axis` cannot be 1."
+                )
+            if len(periods) == 0:
+                raise ValueError("If `periods` is an iterable, it cannot be empty.")
+            from pandas.core.reshape.concat import concat
+
+            shifted_dataframes = []
+            for period in periods:
+                if not is_integer(period):
+                    raise TypeError(
+                        f"Periods must be integer, but {period} is {type(period)}."
+                    )
+                period = cast(int, period)
+                shifted_dataframes.append(
+                    super()
+                    .shift(periods=period, freq=freq, axis=axis, fill_value=fill_value)
+                    .add_suffix(f"{suffix}_{period}" if suffix else f"_{period}")
+                )
+            return concat(shifted_dataframes, axis=1)
+        elif suffix:
+            raise ValueError("Cannot specify `suffix` if `periods` is an int.")
+        periods = cast(int, periods)
+
+        ncols = len(self.columns)
+        arrays = self._mgr.arrays
+        if axis == 1 and periods != 0 and ncols > 0 and freq is None:
+            if fill_value is lib.no_default:
+                # We will infer fill_value to match the closest column
+
+                # Use a column that we know is valid for our column's dtype GH#38434
+                label = self.columns[0]
+
+                if periods > 0:
+                    result = self.iloc[:, :-periods]
+                    for col in range(min(ncols, abs(periods))):
+                        # TODO(EA2D): doing this in a loop unnecessary with 2D EAs
+                        # Define filler inside loop so we get a copy
+                        filler = self.iloc[:, 0].shift(len(self))
+                        result.insert(0, label, filler, allow_duplicates=True)
+                else:
+                    result = self.iloc[:, -periods:]
+                    for col in range(min(ncols, abs(periods))):
+                        # Define filler inside loop so we get a copy
+                        filler = self.iloc[:, -1].shift(len(self))
+                        result.insert(
+                            len(result.columns), label, filler, allow_duplicates=True
+                        )
+
+                result.columns = self.columns.copy()
+                return result
+            elif len(arrays) > 1 or (
+                # If we only have one block and we know that we can't
+                #  keep the same dtype (i.e. the _can_hold_element check)
+                #  then we can go through the reindex_indexer path
+                #  (and avoid casting logic in the Block method).
+                not can_hold_element(arrays[0], fill_value)
+            ):
+                # GH#35488 we need to watch out for multi-block cases
+                # We only get here with fill_value not-lib.no_default
+                nper = abs(periods)
+                nper = min(nper, ncols)
+                if periods > 0:
+                    indexer = np.array(
+                        [-1] * nper + list(range(ncols - periods)), dtype=np.intp
+                    )
+                else:
+                    indexer = np.array(
+                        list(range(nper, ncols)) + [-1] * nper, dtype=np.intp
+                    )
+                mgr = self._mgr.reindex_indexer(
+                    self.columns,
+                    indexer,
+                    axis=0,
+                    fill_value=fill_value,
+                    allow_dups=True,
+                )
+                res_df = self._constructor_from_mgr(mgr, axes=mgr.axes)
+                return res_df.__finalize__(self, method="shift")
+            else:
+                return self.T.shift(periods=periods, fill_value=fill_value).T
+
+        return super().shift(
+            periods=periods, freq=freq, axis=axis, fill_value=fill_value
+        )
+
+    @overload
+    def set_index(
+        self,
+        keys,
+        *,
+        drop: bool = ...,
+        append: bool = ...,
+        inplace: Literal[False] = ...,
+        verify_integrity: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def set_index(
+        self,
+        keys,
+        *,
+        drop: bool = ...,
+        append: bool = ...,
+        inplace: Literal[True],
+        verify_integrity: bool = ...,
+    ) -> None:
+        ...
+
+    def set_index(
+        self,
+        keys,
+        *,
+        drop: bool = True,
+        append: bool = False,
+        inplace: bool = False,
+        verify_integrity: bool = False,
+    ) -> DataFrame | None:
+        """
+        Set the DataFrame index using existing columns.
+
+        Set the DataFrame index (row labels) using one or more existing
+        columns or arrays (of the correct length). The index can replace the
+        existing index or expand on it.
+
+        Parameters
+        ----------
+        keys : label or array-like or list of labels/arrays
+            This parameter can be either a single column key, a single array of
+            the same length as the calling DataFrame, or a list containing an
+            arbitrary combination of column keys and arrays. Here, "array"
+            encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
+            instances of :class:`~collections.abc.Iterator`.
+        drop : bool, default True
+            Delete columns to be used as the new index.
+        append : bool, default False
+            Whether to append columns to existing index.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        verify_integrity : bool, default False
+            Check the new index for duplicates. Otherwise defer the check until
+            necessary. Setting to False will improve the performance of this
+            method.
+
+        Returns
+        -------
+        DataFrame or None
+            Changed row labels or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.reset_index : Opposite of set_index.
+        DataFrame.reindex : Change to new indices or expand indices.
+        DataFrame.reindex_like : Change to same indices as other DataFrame.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
+        ...                    'year': [2012, 2014, 2013, 2014],
+        ...                    'sale': [55, 40, 84, 31]})
+        >>> df
+           month  year  sale
+        0      1  2012    55
+        1      4  2014    40
+        2      7  2013    84
+        3     10  2014    31
+
+        Set the index to become the 'month' column:
+
+        >>> df.set_index('month')
+               year  sale
+        month
+        1      2012    55
+        4      2014    40
+        7      2013    84
+        10     2014    31
+
+        Create a MultiIndex using columns 'year' and 'month':
+
+        >>> df.set_index(['year', 'month'])
+                    sale
+        year  month
+        2012  1     55
+        2014  4     40
+        2013  7     84
+        2014  10    31
+
+        Create a MultiIndex using an Index and a column:
+
+        >>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
+                 month  sale
+           year
+        1  2012  1      55
+        2  2014  4      40
+        3  2013  7      84
+        4  2014  10     31
+
+        Create a MultiIndex using two Series:
+
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> df.set_index([s, s**2])
+              month  year  sale
+        1 1       1  2012    55
+        2 4       4  2014    40
+        3 9       7  2013    84
+        4 16     10  2014    31
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        self._check_inplace_and_allows_duplicate_labels(inplace)
+        if not isinstance(keys, list):
+            keys = [keys]
+
+        err_msg = (
+            'The parameter "keys" may be a column key, one-dimensional '
+            "array, or a list containing only valid column keys and "
+            "one-dimensional arrays."
+        )
+
+        missing: list[Hashable] = []
+        for col in keys:
+            if isinstance(col, (Index, Series, np.ndarray, list, abc.Iterator)):
+                # arrays are fine as long as they are one-dimensional
+                # iterators get converted to list below
+                if getattr(col, "ndim", 1) != 1:
+                    raise ValueError(err_msg)
+            else:
+                # everything else gets tried as a key; see GH 24969
+                try:
+                    found = col in self.columns
+                except TypeError as err:
+                    raise TypeError(
+                        f"{err_msg}. Received column of type {type(col)}"
+                    ) from err
+                else:
+                    if not found:
+                        missing.append(col)
+
+        if missing:
+            raise KeyError(f"None of {missing} are in the columns")
+
+        if inplace:
+            frame = self
+        else:
+            # GH 49473 Use "lazy copy" with Copy-on-Write
+            frame = self.copy(deep=None)
+
+        arrays: list[Index] = []
+        names: list[Hashable] = []
+        if append:
+            names = list(self.index.names)
+            if isinstance(self.index, MultiIndex):
+                arrays.extend(
+                    self.index._get_level_values(i) for i in range(self.index.nlevels)
+                )
+            else:
+                arrays.append(self.index)
+
+        to_remove: list[Hashable] = []
+        for col in keys:
+            if isinstance(col, MultiIndex):
+                arrays.extend(col._get_level_values(n) for n in range(col.nlevels))
+                names.extend(col.names)
+            elif isinstance(col, (Index, Series)):
+                # if Index then not MultiIndex (treated above)
+
+                # error: Argument 1 to "append" of "list" has incompatible type
+                #  "Union[Index, Series]"; expected "Index"
+                arrays.append(col)  # type: ignore[arg-type]
+                names.append(col.name)
+            elif isinstance(col, (list, np.ndarray)):
+                # error: Argument 1 to "append" of "list" has incompatible type
+                # "Union[List[Any], ndarray]"; expected "Index"
+                arrays.append(col)  # type: ignore[arg-type]
+                names.append(None)
+            elif isinstance(col, abc.Iterator):
+                # error: Argument 1 to "append" of "list" has incompatible type
+                # "List[Any]"; expected "Index"
+                arrays.append(list(col))  # type: ignore[arg-type]
+                names.append(None)
+            # from here, col can only be a column label
+            else:
+                arrays.append(frame[col])
+                names.append(col)
+                if drop:
+                    to_remove.append(col)
+
+            if len(arrays[-1]) != len(self):
+                # check newest element against length of calling frame, since
+                # ensure_index_from_sequences would not raise for append=False.
+                raise ValueError(
+                    f"Length mismatch: Expected {len(self)} rows, "
+                    f"received array of length {len(arrays[-1])}"
+                )
+
+        index = ensure_index_from_sequences(arrays, names)
+
+        if verify_integrity and not index.is_unique:
+            duplicates = index[index.duplicated()].unique()
+            raise ValueError(f"Index has duplicate keys: {duplicates}")
+
+        # use set to handle duplicate column names gracefully in case of drop
+        for c in set(to_remove):
+            del frame[c]
+
+        # clear up memory usage
+        index._cleanup()
+
+        frame.index = index
+
+        if not inplace:
+            return frame
+        return None
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        inplace: Literal[False] = ...,
+        col_level: Hashable = ...,
+        col_fill: Hashable = ...,
+        allow_duplicates: bool | lib.NoDefault = ...,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        inplace: Literal[True],
+        col_level: Hashable = ...,
+        col_fill: Hashable = ...,
+        allow_duplicates: bool | lib.NoDefault = ...,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> None:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        inplace: bool = ...,
+        col_level: Hashable = ...,
+        col_fill: Hashable = ...,
+        allow_duplicates: bool | lib.NoDefault = ...,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> DataFrame | None:
+        ...
+
+    def reset_index(
+        self,
+        level: IndexLabel | None = None,
+        *,
+        drop: bool = False,
+        inplace: bool = False,
+        col_level: Hashable = 0,
+        col_fill: Hashable = "",
+        allow_duplicates: bool | lib.NoDefault = lib.no_default,
+        names: Hashable | Sequence[Hashable] | None = None,
+    ) -> DataFrame | None:
+        """
+        Reset the index, or a level of it.
+
+        Reset the index of the DataFrame, and use the default one instead.
+        If the DataFrame has a MultiIndex, this method can remove one or more
+        levels.
+
+        Parameters
+        ----------
+        level : int, str, tuple, or list, default None
+            Only remove the given levels from the index. Removes all levels by
+            default.
+        drop : bool, default False
+            Do not try to insert index into dataframe columns. This resets
+            the index to the default integer index.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        col_level : int or str, default 0
+            If the columns have multiple levels, determines which level the
+            labels are inserted into. By default it is inserted into the first
+            level.
+        col_fill : object, default ''
+            If the columns have multiple levels, determines how the other
+            levels are named. If None then the index name is repeated.
+        allow_duplicates : bool, optional, default lib.no_default
+            Allow duplicate column labels to be created.
+
+            .. versionadded:: 1.5.0
+
+        names : int, str or 1-dimensional list, default None
+            Using the given string, rename the DataFrame column which contains the
+            index data. If the DataFrame has a MultiIndex, this has to be a list or
+            tuple with length equal to the number of levels.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with the new index or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.set_index : Opposite of reset_index.
+        DataFrame.reindex : Change to new indices or expand indices.
+        DataFrame.reindex_like : Change to same indices as other DataFrame.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('bird', 389.0),
+        ...                    ('bird', 24.0),
+        ...                    ('mammal', 80.5),
+        ...                    ('mammal', np.nan)],
+        ...                   index=['falcon', 'parrot', 'lion', 'monkey'],
+        ...                   columns=('class', 'max_speed'))
+        >>> df
+                 class  max_speed
+        falcon    bird      389.0
+        parrot    bird       24.0
+        lion    mammal       80.5
+        monkey  mammal        NaN
+
+        When we reset the index, the old index is added as a column, and a
+        new sequential index is used:
+
+        >>> df.reset_index()
+            index   class  max_speed
+        0  falcon    bird      389.0
+        1  parrot    bird       24.0
+        2    lion  mammal       80.5
+        3  monkey  mammal        NaN
+
+        We can use the `drop` parameter to avoid the old index being added as
+        a column:
+
+        >>> df.reset_index(drop=True)
+            class  max_speed
+        0    bird      389.0
+        1    bird       24.0
+        2  mammal       80.5
+        3  mammal        NaN
+
+        You can also use `reset_index` with `MultiIndex`.
+
+        >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
+        ...                                    ('bird', 'parrot'),
+        ...                                    ('mammal', 'lion'),
+        ...                                    ('mammal', 'monkey')],
+        ...                                   names=['class', 'name'])
+        >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
+        ...                                      ('species', 'type')])
+        >>> df = pd.DataFrame([(389.0, 'fly'),
+        ...                    (24.0, 'fly'),
+        ...                    (80.5, 'run'),
+        ...                    (np.nan, 'jump')],
+        ...                   index=index,
+        ...                   columns=columns)
+        >>> df
+                       speed species
+                         max    type
+        class  name
+        bird   falcon  389.0     fly
+               parrot   24.0     fly
+        mammal lion     80.5     run
+               monkey    NaN    jump
+
+        Using the `names` parameter, choose a name for the index column:
+
+        >>> df.reset_index(names=['classes', 'names'])
+          classes   names  speed species
+                             max    type
+        0    bird  falcon  389.0     fly
+        1    bird  parrot   24.0     fly
+        2  mammal    lion   80.5     run
+        3  mammal  monkey    NaN    jump
+
+        If the index has multiple levels, we can reset a subset of them:
+
+        >>> df.reset_index(level='class')
+                 class  speed species
+                          max    type
+        name
+        falcon    bird  389.0     fly
+        parrot    bird   24.0     fly
+        lion    mammal   80.5     run
+        monkey  mammal    NaN    jump
+
+        If we are not dropping the index, by default, it is placed in the top
+        level. We can place it in another level:
+
+        >>> df.reset_index(level='class', col_level=1)
+                        speed species
+                 class    max    type
+        name
+        falcon    bird  389.0     fly
+        parrot    bird   24.0     fly
+        lion    mammal   80.5     run
+        monkey  mammal    NaN    jump
+
+        When the index is inserted under another level, we can specify under
+        which one with the parameter `col_fill`:
+
+        >>> df.reset_index(level='class', col_level=1, col_fill='species')
+                      species  speed species
+                        class    max    type
+        name
+        falcon           bird  389.0     fly
+        parrot           bird   24.0     fly
+        lion           mammal   80.5     run
+        monkey         mammal    NaN    jump
+
+        If we specify a nonexistent level for `col_fill`, it is created:
+
+        >>> df.reset_index(level='class', col_level=1, col_fill='genus')
+                        genus  speed species
+                        class    max    type
+        name
+        falcon           bird  389.0     fly
+        parrot           bird   24.0     fly
+        lion           mammal   80.5     run
+        monkey         mammal    NaN    jump
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        self._check_inplace_and_allows_duplicate_labels(inplace)
+        if inplace:
+            new_obj = self
+        else:
+            new_obj = self.copy(deep=None)
+        if allow_duplicates is not lib.no_default:
+            allow_duplicates = validate_bool_kwarg(allow_duplicates, "allow_duplicates")
+
+        new_index = default_index(len(new_obj))
+        if level is not None:
+            if not isinstance(level, (tuple, list)):
+                level = [level]
+            level = [self.index._get_level_number(lev) for lev in level]
+            if len(level) < self.index.nlevels:
+                new_index = self.index.droplevel(level)
+
+        if not drop:
+            to_insert: Iterable[tuple[Any, Any | None]]
+
+            default = "index" if "index" not in self else "level_0"
+            names = self.index._get_default_index_names(names, default)
+
+            if isinstance(self.index, MultiIndex):
+                to_insert = zip(self.index.levels, self.index.codes)
+            else:
+                to_insert = ((self.index, None),)
+
+            multi_col = isinstance(self.columns, MultiIndex)
+            for i, (lev, lab) in reversed(list(enumerate(to_insert))):
+                if level is not None and i not in level:
+                    continue
+                name = names[i]
+                if multi_col:
+                    col_name = list(name) if isinstance(name, tuple) else [name]
+                    if col_fill is None:
+                        if len(col_name) not in (1, self.columns.nlevels):
+                            raise ValueError(
+                                "col_fill=None is incompatible "
+                                f"with incomplete column name {name}"
+                            )
+                        col_fill = col_name[0]
+
+                    lev_num = self.columns._get_level_number(col_level)
+                    name_lst = [col_fill] * lev_num + col_name
+                    missing = self.columns.nlevels - len(name_lst)
+                    name_lst += [col_fill] * missing
+                    name = tuple(name_lst)
+
+                # to ndarray and maybe infer different dtype
+                level_values = lev._values
+                if level_values.dtype == np.object_:
+                    level_values = lib.maybe_convert_objects(level_values)
+
+                if lab is not None:
+                    # if we have the codes, extract the values with a mask
+                    level_values = algorithms.take(
+                        level_values, lab, allow_fill=True, fill_value=lev._na_value
+                    )
+
+                new_obj.insert(
+                    0,
+                    name,
+                    level_values,
+                    allow_duplicates=allow_duplicates,
+                )
+
+        new_obj.index = new_index
+        if not inplace:
+            return new_obj
+
+        return None
+
+    # ----------------------------------------------------------------------
+    # Reindex-based selection methods
+
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
+    def isna(self) -> DataFrame:
+        res_mgr = self._mgr.isna(func=isna)
+        result = self._constructor_from_mgr(res_mgr, axes=res_mgr.axes)
+        return result.__finalize__(self, method="isna")
+
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
+    def isnull(self) -> DataFrame:
+        """
+        DataFrame.isnull is an alias for DataFrame.isna.
+        """
+        return self.isna()
+
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
+    def notna(self) -> DataFrame:
+        return ~self.isna()
+
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
+    def notnull(self) -> DataFrame:
+        """
+        DataFrame.notnull is an alias for DataFrame.notna.
+        """
+        return ~self.isna()
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        how: AnyAll | lib.NoDefault = ...,
+        thresh: int | lib.NoDefault = ...,
+        subset: IndexLabel = ...,
+        inplace: Literal[False] = ...,
+        ignore_index: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        how: AnyAll | lib.NoDefault = ...,
+        thresh: int | lib.NoDefault = ...,
+        subset: IndexLabel = ...,
+        inplace: Literal[True],
+        ignore_index: bool = ...,
+    ) -> None:
+        ...
+
+    def dropna(
+        self,
+        *,
+        axis: Axis = 0,
+        how: AnyAll | lib.NoDefault = lib.no_default,
+        thresh: int | lib.NoDefault = lib.no_default,
+        subset: IndexLabel | None = None,
+        inplace: bool = False,
+        ignore_index: bool = False,
+    ) -> DataFrame | None:
+        """
+        Remove missing values.
+
+        See the :ref:`User Guide <missing_data>` for more on which values are
+        considered missing, and how to work with missing data.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Determine if rows or columns which contain missing values are
+            removed.
+
+            * 0, or 'index' : Drop rows which contain missing values.
+            * 1, or 'columns' : Drop columns which contain missing value.
+
+            Only a single axis is allowed.
+
+        how : {'any', 'all'}, default 'any'
+            Determine if row or column is removed from DataFrame, when we have
+            at least one NA or all NA.
+
+            * 'any' : If any NA values are present, drop that row or column.
+            * 'all' : If all values are NA, drop that row or column.
+
+        thresh : int, optional
+            Require that many non-NA values. Cannot be combined with how.
+        subset : column label or sequence of labels, optional
+            Labels along other axis to consider, e.g. if you are dropping rows
+            these would be a list of columns to include.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with NA entries dropped from it or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.isna: Indicate missing values.
+        DataFrame.notna : Indicate existing (non-missing) values.
+        DataFrame.fillna : Replace missing values.
+        Series.dropna : Drop missing values.
+        Index.dropna : Drop missing indices.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
+        ...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
+        ...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
+        ...                             pd.NaT]})
+        >>> df
+               name        toy       born
+        0    Alfred        NaN        NaT
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+
+        Drop the rows where at least one element is missing.
+
+        >>> df.dropna()
+             name        toy       born
+        1  Batman  Batmobile 1940-04-25
+
+        Drop the columns where at least one element is missing.
+
+        >>> df.dropna(axis='columns')
+               name
+        0    Alfred
+        1    Batman
+        2  Catwoman
+
+        Drop the rows where all elements are missing.
+
+        >>> df.dropna(how='all')
+               name        toy       born
+        0    Alfred        NaN        NaT
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+
+        Keep only the rows with at least 2 non-NA values.
+
+        >>> df.dropna(thresh=2)
+               name        toy       born
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+
+        Define in which columns to look for missing values.
+
+        >>> df.dropna(subset=['name', 'toy'])
+               name        toy       born
+        1    Batman  Batmobile 1940-04-25
+        2  Catwoman   Bullwhip        NaT
+        """
+        if (how is not lib.no_default) and (thresh is not lib.no_default):
+            raise TypeError(
+                "You cannot set both the how and thresh arguments at the same time."
+            )
+
+        if how is lib.no_default:
+            how = "any"
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if isinstance(axis, (tuple, list)):
+            # GH20987
+            raise TypeError("supplying multiple axes to axis is no longer supported.")
+
+        axis = self._get_axis_number(axis)
+        agg_axis = 1 - axis
+
+        agg_obj = self
+        if subset is not None:
+            # subset needs to be list
+            if not is_list_like(subset):
+                subset = [subset]
+            ax = self._get_axis(agg_axis)
+            indices = ax.get_indexer_for(subset)
+            check = indices == -1
+            if check.any():
+                raise KeyError(np.array(subset)[check].tolist())
+            agg_obj = self.take(indices, axis=agg_axis)
+
+        if thresh is not lib.no_default:
+            count = agg_obj.count(axis=agg_axis)
+            mask = count >= thresh
+        elif how == "any":
+            # faster equivalent to 'agg_obj.count(agg_axis) == self.shape[agg_axis]'
+            mask = notna(agg_obj).all(axis=agg_axis, bool_only=False)
+        elif how == "all":
+            # faster equivalent to 'agg_obj.count(agg_axis) > 0'
+            mask = notna(agg_obj).any(axis=agg_axis, bool_only=False)
+        else:
+            raise ValueError(f"invalid how option: {how}")
+
+        if np.all(mask):
+            result = self.copy(deep=None)
+        else:
+            result = self.loc(axis=axis)[mask]
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if not inplace:
+            return result
+        self._update_inplace(result)
+        return None
+
+    @overload
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = ...,
+        *,
+        keep: DropKeep = ...,
+        inplace: Literal[True],
+        ignore_index: bool = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = ...,
+        *,
+        keep: DropKeep = ...,
+        inplace: Literal[False] = ...,
+        ignore_index: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = ...,
+        *,
+        keep: DropKeep = ...,
+        inplace: bool = ...,
+        ignore_index: bool = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def drop_duplicates(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = None,
+        *,
+        keep: DropKeep = "first",
+        inplace: bool = False,
+        ignore_index: bool = False,
+    ) -> DataFrame | None:
+        """
+        Return DataFrame with duplicate rows removed.
+
+        Considering certain columns is optional. Indexes, including time indexes
+        are ignored.
+
+        Parameters
+        ----------
+        subset : column label or sequence of labels, optional
+            Only consider certain columns for identifying duplicates, by
+            default use all of the columns.
+        keep : {'first', 'last', ``False``}, default 'first'
+            Determines which duplicates (if any) to keep.
+
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        inplace : bool, default ``False``
+            Whether to modify the DataFrame rather than creating a new one.
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with duplicates removed or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.value_counts: Count unique combinations of columns.
+
+        Examples
+        --------
+        Consider dataset containing ramen rating.
+
+        >>> df = pd.DataFrame({
+        ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
+        ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
+        ...     'rating': [4, 4, 3.5, 15, 5]
+        ... })
+        >>> df
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        By default, it removes duplicate rows based on all columns.
+
+        >>> df.drop_duplicates()
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        To remove duplicates on specific column(s), use ``subset``.
+
+        >>> df.drop_duplicates(subset=['brand'])
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+
+        To remove duplicates and keep last occurrences, use ``keep``.
+
+        >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
+            brand style  rating
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        4  Indomie  pack     5.0
+        """
+        if self.empty:
+            return self.copy(deep=None)
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        ignore_index = validate_bool_kwarg(ignore_index, "ignore_index")
+
+        result = self[-self.duplicated(subset, keep=keep)]
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result
+
+    def duplicated(
+        self,
+        subset: Hashable | Sequence[Hashable] | None = None,
+        keep: DropKeep = "first",
+    ) -> Series:
+        """
+        Return boolean Series denoting duplicate rows.
+
+        Considering certain columns is optional.
+
+        Parameters
+        ----------
+        subset : column label or sequence of labels, optional
+            Only consider certain columns for identifying duplicates, by
+            default use all of the columns.
+        keep : {'first', 'last', False}, default 'first'
+            Determines which duplicates (if any) to mark.
+
+            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
+            - False : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        Series
+            Boolean series for each duplicated rows.
+
+        See Also
+        --------
+        Index.duplicated : Equivalent method on index.
+        Series.duplicated : Equivalent method on Series.
+        Series.drop_duplicates : Remove duplicate values from Series.
+        DataFrame.drop_duplicates : Remove duplicate values from DataFrame.
+
+        Examples
+        --------
+        Consider dataset containing ramen rating.
+
+        >>> df = pd.DataFrame({
+        ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
+        ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
+        ...     'rating': [4, 4, 3.5, 15, 5]
+        ... })
+        >>> df
+            brand style  rating
+        0  Yum Yum   cup     4.0
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        By default, for each set of duplicated values, the first occurrence
+        is set on False and all others on True.
+
+        >>> df.duplicated()
+        0    False
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True.
+
+        >>> df.duplicated(keep='last')
+        0     True
+        1    False
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        By setting ``keep`` on False, all duplicates are True.
+
+        >>> df.duplicated(keep=False)
+        0     True
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        To find duplicates on specific column(s), use ``subset``.
+
+        >>> df.duplicated(subset=['brand'])
+        0    False
+        1     True
+        2    False
+        3     True
+        4     True
+        dtype: bool
+        """
+
+        if self.empty:
+            return self._constructor_sliced(dtype=bool)
+
+        def f(vals) -> tuple[np.ndarray, int]:
+            labels, shape = algorithms.factorize(vals, size_hint=len(self))
+            return labels.astype("i8", copy=False), len(shape)
+
+        if subset is None:
+            # https://github.com/pandas-dev/pandas/issues/28770
+            # Incompatible types in assignment (expression has type "Index", variable
+            # has type "Sequence[Any]")
+            subset = self.columns  # type: ignore[assignment]
+        elif (
+            not np.iterable(subset)
+            or isinstance(subset, str)
+            or isinstance(subset, tuple)
+            and subset in self.columns
+        ):
+            subset = (subset,)
+
+        #  needed for mypy since can't narrow types using np.iterable
+        subset = cast(Sequence, subset)
+
+        # Verify all columns in subset exist in the queried dataframe
+        # Otherwise, raise a KeyError, same as if you try to __getitem__ with a
+        # key that doesn't exist.
+        diff = set(subset) - set(self.columns)
+        if diff:
+            raise KeyError(Index(diff))
+
+        if len(subset) == 1 and self.columns.is_unique:
+            # GH#45236 This is faster than get_group_index below
+            result = self[subset[0]].duplicated(keep)
+            result.name = None
+        else:
+            vals = (col.values for name, col in self.items() if name in subset)
+            labels, shape = map(list, zip(*map(f, vals)))
+
+            ids = get_group_index(labels, tuple(shape), sort=False, xnull=False)
+            result = self._constructor_sliced(duplicated(ids, keep), index=self.index)
+        return result.__finalize__(self, method="duplicated")
+
+    # ----------------------------------------------------------------------
+    # Sorting
+    # error: Signature of "sort_values" incompatible with supertype "NDFrame"
+    @overload  # type: ignore[override]
+    def sort_values(
+        self,
+        by: IndexLabel,
+        *,
+        axis: Axis = ...,
+        ascending=...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        by: IndexLabel,
+        *,
+        axis: Axis = ...,
+        ascending=...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: str = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> None:
+        ...
+
+    def sort_values(
+        self,
+        by: IndexLabel,
+        *,
+        axis: Axis = 0,
+        ascending: bool | list[bool] | tuple[bool, ...] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: str = "last",
+        ignore_index: bool = False,
+        key: ValueKeyFunc | None = None,
+    ) -> DataFrame | None:
+        """
+        Sort by the values along either axis.
+
+        Parameters
+        ----------
+        by : str or list of str
+            Name or list of names to sort by.
+
+            - if `axis` is 0 or `'index'` then `by` may contain index
+              levels and/or column labels.
+            - if `axis` is 1 or `'columns'` then `by` may contain column
+              levels and/or index labels.
+        axis : "{0 or 'index', 1 or 'columns'}", default 0
+             Axis to be sorted.
+        ascending : bool or list of bool, default True
+             Sort ascending vs. descending. Specify list for multiple sort
+             orders.  If this is a list of bools, must match the length of
+             the by.
+        inplace : bool, default False
+             If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+             Choice of sorting algorithm. See also :func:`numpy.sort` for more
+             information. `mergesort` and `stable` are the only stable algorithms. For
+             DataFrames, this option is only applied when sorting on a single
+             column or label.
+        na_position : {'first', 'last'}, default 'last'
+             Puts NaNs at the beginning if `first`; `last` puts NaNs at the
+             end.
+        ignore_index : bool, default False
+             If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            Apply the key function to the values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect a
+            ``Series`` and return a Series with the same shape as the input.
+            It will be applied to each column in `by` independently.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with sorted values or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.sort_index : Sort a DataFrame by the index.
+        Series.sort_values : Similar method for a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
+        ...     'col2': [2, 1, 9, 8, 7, 4],
+        ...     'col3': [0, 1, 9, 4, 2, 3],
+        ...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
+        ... })
+        >>> df
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Sort by col1
+
+        >>> df.sort_values(by=['col1'])
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort by multiple columns
+
+        >>> df.sort_values(by=['col1', 'col2'])
+          col1  col2  col3 col4
+        1    A     1     1    B
+        0    A     2     0    a
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort Descending
+
+        >>> df.sort_values(by='col1', ascending=False)
+          col1  col2  col3 col4
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+        3  NaN     8     4    D
+
+        Putting NAs first
+
+        >>> df.sort_values(by='col1', ascending=False, na_position='first')
+          col1  col2  col3 col4
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+
+        Sorting with a key function
+
+        >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
+           col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Natural sort with the key argument,
+        using the `natsort <https://github.com/SethMMorton/natsort>` package.
+
+        >>> df = pd.DataFrame({
+        ...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
+        ...    "value": [10, 20, 30, 40, 50]
+        ... })
+        >>> df
+            time  value
+        0    0hr     10
+        1  128hr     20
+        2   72hr     30
+        3   48hr     40
+        4   96hr     50
+        >>> from natsort import index_natsorted
+        >>> df.sort_values(
+        ...     by="time",
+        ...     key=lambda x: np.argsort(index_natsorted(df["time"]))
+        ... )
+            time  value
+        0    0hr     10
+        3   48hr     40
+        2   72hr     30
+        4   96hr     50
+        1  128hr     20
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        axis = self._get_axis_number(axis)
+        ascending = validate_ascending(ascending)
+        if not isinstance(by, list):
+            by = [by]
+        # error: Argument 1 to "len" has incompatible type "Union[bool, List[bool]]";
+        # expected "Sized"
+        if is_sequence(ascending) and (
+            len(by) != len(ascending)  # type: ignore[arg-type]
+        ):
+            # error: Argument 1 to "len" has incompatible type "Union[bool,
+            # List[bool]]"; expected "Sized"
+            raise ValueError(
+                f"Length of ascending ({len(ascending)})"  # type: ignore[arg-type]
+                f" != length of by ({len(by)})"
+            )
+        if len(by) > 1:
+            keys = [self._get_label_or_level_values(x, axis=axis) for x in by]
+
+            # need to rewrap columns in Series to apply key function
+            if key is not None:
+                # error: List comprehension has incompatible type List[Series];
+                # expected List[ndarray]
+                keys = [
+                    Series(k, name=name)  # type: ignore[misc]
+                    for (k, name) in zip(keys, by)
+                ]
+
+            indexer = lexsort_indexer(
+                keys, orders=ascending, na_position=na_position, key=key
+            )
+        elif len(by):
+            # len(by) == 1
+
+            k = self._get_label_or_level_values(by[0], axis=axis)
+
+            # need to rewrap column in Series to apply key function
+            if key is not None:
+                # error: Incompatible types in assignment (expression has type
+                # "Series", variable has type "ndarray")
+                k = Series(k, name=by[0])  # type: ignore[assignment]
+
+            if isinstance(ascending, (tuple, list)):
+                ascending = ascending[0]
+
+            indexer = nargsort(
+                k, kind=kind, ascending=ascending, na_position=na_position, key=key
+            )
+        else:
+            if inplace:
+                return self._update_inplace(self)
+            else:
+                return self.copy(deep=None)
+
+        if is_range_indexer(indexer, len(indexer)):
+            result = self.copy(deep=(not inplace and not using_copy_on_write()))
+            if ignore_index:
+                result.index = default_index(len(result))
+
+            if inplace:
+                return self._update_inplace(result)
+            else:
+                return result
+
+        new_data = self._mgr.take(
+            indexer, axis=self._get_block_manager_axis(axis), verify=False
+        )
+
+        if ignore_index:
+            new_data.set_axis(
+                self._get_block_manager_axis(axis), default_index(len(indexer))
+            )
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="sort_values")
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: bool = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> DataFrame | None:
+        ...
+
+    def sort_index(
+        self,
+        *,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        ascending: bool | Sequence[bool] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        sort_remaining: bool = True,
+        ignore_index: bool = False,
+        key: IndexKeyFunc | None = None,
+    ) -> DataFrame | None:
+        """
+        Sort object by labels (along an axis).
+
+        Returns a new DataFrame sorted by label if `inplace` argument is
+        ``False``, otherwise updates the original DataFrame and returns None.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis along which to sort.  The value 0 identifies the rows,
+            and 1 identifies the columns.
+        level : int or level name or list of ints or list of level names
+            If not None, sort on values in specified index level(s).
+        ascending : bool or list-like of bools, default True
+            Sort ascending vs. descending. When the index is a MultiIndex the
+            sort direction can be controlled for each level individually.
+        inplace : bool, default False
+            Whether to modify the DataFrame rather than creating a new one.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See also :func:`numpy.sort` for more
+            information. `mergesort` and `stable` are the only stable algorithms. For
+            DataFrames, this option is only applied when sorting on a single
+            column or label.
+        na_position : {'first', 'last'}, default 'last'
+            Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.
+            Not implemented for MultiIndex.
+        sort_remaining : bool, default True
+            If True and sorting by level and index is multilevel, sort by other
+            levels too (in order) after sorting by specified level.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            If not None, apply the key function to the index values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect an
+            ``Index`` and return an ``Index`` of the same shape. For MultiIndex
+            inputs, the key is applied *per level*.
+
+        Returns
+        -------
+        DataFrame or None
+            The original DataFrame sorted by the labels or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.sort_index : Sort Series by the index.
+        DataFrame.sort_values : Sort DataFrame by the value.
+        Series.sort_values : Sort Series by the value.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
+        ...                   columns=['A'])
+        >>> df.sort_index()
+             A
+        1    4
+        29   2
+        100  1
+        150  5
+        234  3
+
+        By default, it sorts in ascending order, to sort in descending order,
+        use ``ascending=False``
+
+        >>> df.sort_index(ascending=False)
+             A
+        234  3
+        150  5
+        100  1
+        29   2
+        1    4
+
+        A key function can be specified which is applied to the index before
+        sorting. For a ``MultiIndex`` this is applied to each level separately.
+
+        >>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
+        >>> df.sort_index(key=lambda x: x.str.lower())
+           a
+        A  1
+        b  2
+        C  3
+        d  4
+        """
+        return super().sort_index(
+            axis=axis,
+            level=level,
+            ascending=ascending,
+            inplace=inplace,
+            kind=kind,
+            na_position=na_position,
+            sort_remaining=sort_remaining,
+            ignore_index=ignore_index,
+            key=key,
+        )
+
+    def value_counts(
+        self,
+        subset: IndexLabel | None = None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> Series:
+        """
+        Return a Series containing the frequency of each distinct row in the Dataframe.
+
+        Parameters
+        ----------
+        subset : label or list of labels, optional
+            Columns to use when counting unique combinations.
+        normalize : bool, default False
+            Return proportions rather than frequencies.
+        sort : bool, default True
+            Sort by frequencies when True. Sort by DataFrame column values when False.
+        ascending : bool, default False
+            Sort in ascending order.
+        dropna : bool, default True
+            Don't include counts of rows that contain NA values.
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.value_counts: Equivalent method on Series.
+
+        Notes
+        -----
+        The returned Series will have a MultiIndex with one level per input
+        column but an Index (non-multi) for a single label. By default, rows
+        that contain any NA values are omitted from the result. By default,
+        the resulting Series will be in descending order so that the first
+        element is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
+        ...                    'num_wings': [2, 0, 0, 0]},
+        ...                   index=['falcon', 'dog', 'cat', 'ant'])
+        >>> df
+                num_legs  num_wings
+        falcon         2          2
+        dog            4          0
+        cat            4          0
+        ant            6          0
+
+        >>> df.value_counts()
+        num_legs  num_wings
+        4         0            2
+        2         2            1
+        6         0            1
+        Name: count, dtype: int64
+
+        >>> df.value_counts(sort=False)
+        num_legs  num_wings
+        2         2            1
+        4         0            2
+        6         0            1
+        Name: count, dtype: int64
+
+        >>> df.value_counts(ascending=True)
+        num_legs  num_wings
+        2         2            1
+        6         0            1
+        4         0            2
+        Name: count, dtype: int64
+
+        >>> df.value_counts(normalize=True)
+        num_legs  num_wings
+        4         0            0.50
+        2         2            0.25
+        6         0            0.25
+        Name: proportion, dtype: float64
+
+        With `dropna` set to `False` we can also count rows with NA values.
+
+        >>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
+        ...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
+        >>> df
+          first_name middle_name
+        0       John       Smith
+        1       Anne        <NA>
+        2       John        <NA>
+        3       Beth      Louise
+
+        >>> df.value_counts()
+        first_name  middle_name
+        Beth        Louise         1
+        John        Smith          1
+        Name: count, dtype: int64
+
+        >>> df.value_counts(dropna=False)
+        first_name  middle_name
+        Anne        NaN            1
+        Beth        Louise         1
+        John        Smith          1
+                    NaN            1
+        Name: count, dtype: int64
+
+        >>> df.value_counts("first_name")
+        first_name
+        John    2
+        Anne    1
+        Beth    1
+        Name: count, dtype: int64
+        """
+        if subset is None:
+            subset = self.columns.tolist()
+
+        name = "proportion" if normalize else "count"
+        counts = self.groupby(subset, dropna=dropna, observed=False)._grouper.size()
+        counts.name = name
+
+        if sort:
+            counts = counts.sort_values(ascending=ascending)
+        if normalize:
+            counts /= counts.sum()
+
+        # Force MultiIndex for a list_like subset with a single column
+        if is_list_like(subset) and len(subset) == 1:  # type: ignore[arg-type]
+            counts.index = MultiIndex.from_arrays(
+                [counts.index], names=[counts.index.name]
+            )
+
+        return counts
+
+    def nlargest(
+        self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = "first"
+    ) -> DataFrame:
+        """
+        Return the first `n` rows ordered by `columns` in descending order.
+
+        Return the first `n` rows with the largest values in `columns`, in
+        descending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        This method is equivalent to
+        ``df.sort_values(columns, ascending=False).head(n)``, but more
+        performant.
+
+        Parameters
+        ----------
+        n : int
+            Number of rows to return.
+        columns : label or list of labels
+            Column label(s) to order by.
+        keep : {'first', 'last', 'all'}, default 'first'
+            Where there are duplicate values:
+
+            - ``first`` : prioritize the first occurrence(s)
+            - ``last`` : prioritize the last occurrence(s)
+            - ``all`` : keep all the ties of the smallest item even if it means
+              selecting more than ``n`` items.
+
+        Returns
+        -------
+        DataFrame
+            The first `n` rows ordered by the given columns in descending
+            order.
+
+        See Also
+        --------
+        DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
+            ascending order.
+        DataFrame.sort_values : Sort DataFrame by the values.
+        DataFrame.head : Return the first `n` rows without re-ordering.
+
+        Notes
+        -----
+        This function cannot be used with all column types. For example, when
+        specifying columns with `object` or `category` dtypes, ``TypeError`` is
+        raised.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
+        ...                                   434000, 434000, 337000, 11300,
+        ...                                   11300, 11300],
+        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
+        ...                            17036, 182, 38, 311],
+        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
+        ...                                "IS", "NR", "TV", "AI"]},
+        ...                   index=["Italy", "France", "Malta",
+        ...                          "Maldives", "Brunei", "Iceland",
+        ...                          "Nauru", "Tuvalu", "Anguilla"])
+        >>> df
+                  population      GDP alpha-2
+        Italy       59000000  1937894      IT
+        France      65000000  2583560      FR
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+        Iceland       337000    17036      IS
+        Nauru          11300      182      NR
+        Tuvalu         11300       38      TV
+        Anguilla       11300      311      AI
+
+        In the following example, we will use ``nlargest`` to select the three
+        rows having the largest values in column "population".
+
+        >>> df.nlargest(3, 'population')
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Malta       434000    12011      MT
+
+        When using ``keep='last'``, ties are resolved in reverse order:
+
+        >>> df.nlargest(3, 'population', keep='last')
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Brunei      434000    12128      BN
+
+        When using ``keep='all'``, the number of element kept can go beyond ``n``
+        if there are duplicate values for the smallest element, all the
+        ties are kept:
+
+        >>> df.nlargest(3, 'population', keep='all')
+                  population      GDP alpha-2
+        France      65000000  2583560      FR
+        Italy       59000000  1937894      IT
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+
+        However, ``nlargest`` does not keep ``n`` distinct largest elements:
+
+        >>> df.nlargest(5, 'population', keep='all')
+                  population      GDP alpha-2
+        France      65000000  2583560      FR
+        Italy       59000000  1937894      IT
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+
+        To order by the largest values in column "population" and then "GDP",
+        we can specify multiple columns like in the next example.
+
+        >>> df.nlargest(3, ['population', 'GDP'])
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Brunei      434000    12128      BN
+        """
+        return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest()
+
+    def nsmallest(
+        self, n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = "first"
+    ) -> DataFrame:
+        """
+        Return the first `n` rows ordered by `columns` in ascending order.
+
+        Return the first `n` rows with the smallest values in `columns`, in
+        ascending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        This method is equivalent to
+        ``df.sort_values(columns, ascending=True).head(n)``, but more
+        performant.
+
+        Parameters
+        ----------
+        n : int
+            Number of items to retrieve.
+        columns : list or str
+            Column name or names to order by.
+        keep : {'first', 'last', 'all'}, default 'first'
+            Where there are duplicate values:
+
+            - ``first`` : take the first occurrence.
+            - ``last`` : take the last occurrence.
+            - ``all`` : keep all the ties of the largest item even if it means
+              selecting more than ``n`` items.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
+            descending order.
+        DataFrame.sort_values : Sort DataFrame by the values.
+        DataFrame.head : Return the first `n` rows without re-ordering.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
+        ...                                   434000, 434000, 337000, 337000,
+        ...                                   11300, 11300],
+        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
+        ...                            17036, 182, 38, 311],
+        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
+        ...                                "IS", "NR", "TV", "AI"]},
+        ...                   index=["Italy", "France", "Malta",
+        ...                          "Maldives", "Brunei", "Iceland",
+        ...                          "Nauru", "Tuvalu", "Anguilla"])
+        >>> df
+                  population      GDP alpha-2
+        Italy       59000000  1937894      IT
+        France      65000000  2583560      FR
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+        Iceland       337000    17036      IS
+        Nauru         337000      182      NR
+        Tuvalu         11300       38      TV
+        Anguilla       11300      311      AI
+
+        In the following example, we will use ``nsmallest`` to select the
+        three rows having the smallest values in column "population".
+
+        >>> df.nsmallest(3, 'population')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+
+        When using ``keep='last'``, ties are resolved in reverse order:
+
+        >>> df.nsmallest(3, 'population', keep='last')
+                  population  GDP alpha-2
+        Anguilla       11300  311      AI
+        Tuvalu         11300   38      TV
+        Nauru         337000  182      NR
+
+        When using ``keep='all'``, the number of element kept can go beyond ``n``
+        if there are duplicate values for the largest element, all the
+        ties are kept.
+
+        >>> df.nsmallest(3, 'population', keep='all')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+        Nauru         337000    182      NR
+
+        However, ``nsmallest`` does not keep ``n`` distinct
+        smallest elements:
+
+        >>> df.nsmallest(4, 'population', keep='all')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+        Nauru         337000    182      NR
+
+        To order by the smallest values in column "population" and then "GDP", we can
+        specify multiple columns like in the next example.
+
+        >>> df.nsmallest(3, ['population', 'GDP'])
+                  population  GDP alpha-2
+        Tuvalu         11300   38      TV
+        Anguilla       11300  311      AI
+        Nauru         337000  182      NR
+        """
+        return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nsmallest()
+
+    @doc(
+        Series.swaplevel,
+        klass=_shared_doc_kwargs["klass"],
+        extra_params=dedent(
+            """axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to swap levels on. 0 or 'index' for row-wise, 1 or
+            'columns' for column-wise."""
+        ),
+        examples=dedent(
+            """\
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {"Grade": ["A", "B", "A", "C"]},
+        ...     index=[
+        ...         ["Final exam", "Final exam", "Coursework", "Coursework"],
+        ...         ["History", "Geography", "History", "Geography"],
+        ...         ["January", "February", "March", "April"],
+        ...     ],
+        ... )
+        >>> df
+                                            Grade
+        Final exam  History     January      A
+                    Geography   February     B
+        Coursework  History     March        A
+                    Geography   April        C
+
+        In the following example, we will swap the levels of the indices.
+        Here, we will swap the levels column-wise, but levels can be swapped row-wise
+        in a similar manner. Note that column-wise is the default behaviour.
+        By not supplying any arguments for i and j, we swap the last and second to
+        last indices.
+
+        >>> df.swaplevel()
+                                            Grade
+        Final exam  January     History         A
+                    February    Geography       B
+        Coursework  March       History         A
+                    April       Geography       C
+
+        By supplying one argument, we can choose which index to swap the last
+        index with. We can for example swap the first index with the last one as
+        follows.
+
+        >>> df.swaplevel(0)
+                                            Grade
+        January     History     Final exam      A
+        February    Geography   Final exam      B
+        March       History     Coursework      A
+        April       Geography   Coursework      C
+
+        We can also define explicitly which indices we want to swap by supplying values
+        for both i and j. Here, we for example swap the first and second indices.
+
+        >>> df.swaplevel(0, 1)
+                                            Grade
+        History     Final exam  January         A
+        Geography   Final exam  February        B
+        History     Coursework  March           A
+        Geography   Coursework  April           C"""
+        ),
+    )
+    def swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) -> DataFrame:
+        result = self.copy(deep=None)
+
+        axis = self._get_axis_number(axis)
+
+        if not isinstance(result._get_axis(axis), MultiIndex):  # pragma: no cover
+            raise TypeError("Can only swap levels on a hierarchical axis.")
+
+        if axis == 0:
+            assert isinstance(result.index, MultiIndex)
+            result.index = result.index.swaplevel(i, j)
+        else:
+            assert isinstance(result.columns, MultiIndex)
+            result.columns = result.columns.swaplevel(i, j)
+        return result
+
+    def reorder_levels(self, order: Sequence[int | str], axis: Axis = 0) -> DataFrame:
+        """
+        Rearrange index levels using input order. May not drop or duplicate levels.
+
+        Parameters
+        ----------
+        order : list of int or list of str
+            List representing new level order. Reference level by number
+            (position) or by key (label).
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Where to reorder levels.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> data = {
+        ...     "class": ["Mammals", "Mammals", "Reptiles"],
+        ...     "diet": ["Omnivore", "Carnivore", "Carnivore"],
+        ...     "species": ["Humans", "Dogs", "Snakes"],
+        ... }
+        >>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
+        >>> df = df.set_index(["class", "diet"])
+        >>> df
+                                          species
+        class      diet
+        Mammals    Omnivore                Humans
+                   Carnivore                 Dogs
+        Reptiles   Carnivore               Snakes
+
+        Let's reorder the levels of the index:
+
+        >>> df.reorder_levels(["diet", "class"])
+                                          species
+        diet      class
+        Omnivore  Mammals                  Humans
+        Carnivore Mammals                    Dogs
+                  Reptiles                 Snakes
+        """
+        axis = self._get_axis_number(axis)
+        if not isinstance(self._get_axis(axis), MultiIndex):  # pragma: no cover
+            raise TypeError("Can only reorder levels on a hierarchical axis.")
+
+        result = self.copy(deep=None)
+
+        if axis == 0:
+            assert isinstance(result.index, MultiIndex)
+            result.index = result.index.reorder_levels(order)
+        else:
+            assert isinstance(result.columns, MultiIndex)
+            result.columns = result.columns.reorder_levels(order)
+        return result
+
+    # ----------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _cmp_method(self, other, op):
+        axis: Literal[1] = 1  # only relevant for Series other case
+
+        self, other = self._align_for_op(other, axis, flex=False, level=None)
+
+        # See GH#4537 for discussion of scalar op behavior
+        new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    def _arith_method(self, other, op):
+        if self._should_reindex_frame_op(other, op, 1, None, None):
+            return self._arith_method_with_reindex(other, op)
+
+        axis: Literal[1] = 1  # only relevant for Series other case
+        other = ops.maybe_prepare_scalar_for_op(other, (self.shape[axis],))
+
+        self, other = self._align_for_op(other, axis, flex=True, level=None)
+
+        with np.errstate(all="ignore"):
+            new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    _logical_method = _arith_method
+
+    def _dispatch_frame_op(
+        self, right, func: Callable, axis: AxisInt | None = None
+    ) -> DataFrame:
+        """
+        Evaluate the frame operation func(left, right) by evaluating
+        column-by-column, dispatching to the Series implementation.
+
+        Parameters
+        ----------
+        right : scalar, Series, or DataFrame
+        func : arithmetic or comparison operator
+        axis : {None, 0, 1}
+
+        Returns
+        -------
+        DataFrame
+
+        Notes
+        -----
+        Caller is responsible for setting np.errstate where relevant.
+        """
+        # Get the appropriate array-op to apply to each column/block's values.
+        array_op = ops.get_array_op(func)
+
+        right = lib.item_from_zerodim(right)
+        if not is_list_like(right):
+            # i.e. scalar, faster than checking np.ndim(right) == 0
+            bm = self._mgr.apply(array_op, right=right)
+            return self._constructor_from_mgr(bm, axes=bm.axes)
+
+        elif isinstance(right, DataFrame):
+            assert self.index.equals(right.index)
+            assert self.columns.equals(right.columns)
+            # TODO: The previous assertion `assert right._indexed_same(self)`
+            #  fails in cases with empty columns reached via
+            #  _frame_arith_method_with_reindex
+
+            # TODO operate_blockwise expects a manager of the same type
+            bm = self._mgr.operate_blockwise(
+                # error: Argument 1 to "operate_blockwise" of "ArrayManager" has
+                # incompatible type "Union[ArrayManager, BlockManager]"; expected
+                # "ArrayManager"
+                # error: Argument 1 to "operate_blockwise" of "BlockManager" has
+                # incompatible type "Union[ArrayManager, BlockManager]"; expected
+                # "BlockManager"
+                right._mgr,  # type: ignore[arg-type]
+                array_op,
+            )
+            return self._constructor_from_mgr(bm, axes=bm.axes)
+
+        elif isinstance(right, Series) and axis == 1:
+            # axis=1 means we want to operate row-by-row
+            assert right.index.equals(self.columns)
+
+            right = right._values
+            # maybe_align_as_frame ensures we do not have an ndarray here
+            assert not isinstance(right, np.ndarray)
+
+            arrays = [
+                array_op(_left, _right)
+                for _left, _right in zip(self._iter_column_arrays(), right)
+            ]
+
+        elif isinstance(right, Series):
+            assert right.index.equals(self.index)
+            right = right._values
+
+            arrays = [array_op(left, right) for left in self._iter_column_arrays()]
+
+        else:
+            raise NotImplementedError(right)
+
+        return type(self)._from_arrays(
+            arrays, self.columns, self.index, verify_integrity=False
+        )
+
+    def _combine_frame(self, other: DataFrame, func, fill_value=None):
+        # at this point we have `self._indexed_same(other)`
+
+        if fill_value is None:
+            # since _arith_op may be called in a loop, avoid function call
+            #  overhead if possible by doing this check once
+            _arith_op = func
+
+        else:
+
+            def _arith_op(left, right):
+                # for the mixed_type case where we iterate over columns,
+                # _arith_op(left, right) is equivalent to
+                # left._binop(right, func, fill_value=fill_value)
+                left, right = ops.fill_binop(left, right, fill_value)
+                return func(left, right)
+
+        new_data = self._dispatch_frame_op(other, _arith_op)
+        return new_data
+
+    def _arith_method_with_reindex(self, right: DataFrame, op) -> DataFrame:
+        """
+        For DataFrame-with-DataFrame operations that require reindexing,
+        operate only on shared columns, then reindex.
+
+        Parameters
+        ----------
+        right : DataFrame
+        op : binary operator
+
+        Returns
+        -------
+        DataFrame
+        """
+        left = self
+
+        # GH#31623, only operate on shared columns
+        cols, lcols, rcols = left.columns.join(
+            right.columns, how="inner", level=None, return_indexers=True
+        )
+
+        new_left = left.iloc[:, lcols]
+        new_right = right.iloc[:, rcols]
+        result = op(new_left, new_right)
+
+        # Do the join on the columns instead of using left._align_for_op
+        #  to avoid constructing two potentially large/sparse DataFrames
+        join_columns, _, _ = left.columns.join(
+            right.columns, how="outer", level=None, return_indexers=True
+        )
+
+        if result.columns.has_duplicates:
+            # Avoid reindexing with a duplicate axis.
+            # https://github.com/pandas-dev/pandas/issues/35194
+            indexer, _ = result.columns.get_indexer_non_unique(join_columns)
+            indexer = algorithms.unique1d(indexer)
+            result = result._reindex_with_indexers(
+                {1: [join_columns, indexer]}, allow_dups=True
+            )
+        else:
+            result = result.reindex(join_columns, axis=1)
+
+        return result
+
+    def _should_reindex_frame_op(self, right, op, axis: int, fill_value, level) -> bool:
+        """
+        Check if this is an operation between DataFrames that will need to reindex.
+        """
+        if op is operator.pow or op is roperator.rpow:
+            # GH#32685 pow has special semantics for operating with null values
+            return False
+
+        if not isinstance(right, DataFrame):
+            return False
+
+        if fill_value is None and level is None and axis == 1:
+            # TODO: any other cases we should handle here?
+
+            # Intersection is always unique so we have to check the unique columns
+            left_uniques = self.columns.unique()
+            right_uniques = right.columns.unique()
+            cols = left_uniques.intersection(right_uniques)
+            if len(cols) and not (
+                len(cols) == len(left_uniques) and len(cols) == len(right_uniques)
+            ):
+                # TODO: is there a shortcut available when len(cols) == 0?
+                return True
+
+        return False
+
+    def _align_for_op(
+        self,
+        other,
+        axis: AxisInt,
+        flex: bool | None = False,
+        level: Level | None = None,
+    ):
+        """
+        Convert rhs to meet lhs dims if input is list, tuple or np.ndarray.
+
+        Parameters
+        ----------
+        left : DataFrame
+        right : Any
+        axis : int
+        flex : bool or None, default False
+            Whether this is a flex op, in which case we reindex.
+            None indicates not to check for alignment.
+        level : int or level name, default None
+
+        Returns
+        -------
+        left : DataFrame
+        right : Any
+        """
+        left, right = self, other
+
+        def to_series(right):
+            msg = (
+                "Unable to coerce to Series, "
+                "length must be {req_len}: given {given_len}"
+            )
+
+            # pass dtype to avoid doing inference, which would break consistency
+            #  with Index/Series ops
+            dtype = None
+            if getattr(right, "dtype", None) == object:
+                # can't pass right.dtype unconditionally as that would break on e.g.
+                #  datetime64[h] ndarray
+                dtype = object
+
+            if axis == 0:
+                if len(left.index) != len(right):
+                    raise ValueError(
+                        msg.format(req_len=len(left.index), given_len=len(right))
+                    )
+                right = left._constructor_sliced(right, index=left.index, dtype=dtype)
+            else:
+                if len(left.columns) != len(right):
+                    raise ValueError(
+                        msg.format(req_len=len(left.columns), given_len=len(right))
+                    )
+                right = left._constructor_sliced(right, index=left.columns, dtype=dtype)
+            return right
+
+        if isinstance(right, np.ndarray):
+            if right.ndim == 1:
+                right = to_series(right)
+
+            elif right.ndim == 2:
+                # We need to pass dtype=right.dtype to retain object dtype
+                #  otherwise we lose consistency with Index and array ops
+                dtype = None
+                if right.dtype == object:
+                    # can't pass right.dtype unconditionally as that would break on e.g.
+                    #  datetime64[h] ndarray
+                    dtype = object
+
+                if right.shape == left.shape:
+                    right = left._constructor(
+                        right, index=left.index, columns=left.columns, dtype=dtype
+                    )
+
+                elif right.shape[0] == left.shape[0] and right.shape[1] == 1:
+                    # Broadcast across columns
+                    right = np.broadcast_to(right, left.shape)
+                    right = left._constructor(
+                        right, index=left.index, columns=left.columns, dtype=dtype
+                    )
+
+                elif right.shape[1] == left.shape[1] and right.shape[0] == 1:
+                    # Broadcast along rows
+                    right = to_series(right[0, :])
+
+                else:
+                    raise ValueError(
+                        "Unable to coerce to DataFrame, shape "
+                        f"must be {left.shape}: given {right.shape}"
+                    )
+
+            elif right.ndim > 2:
+                raise ValueError(
+                    "Unable to coerce to Series/DataFrame, "
+                    f"dimension must be <= 2: {right.shape}"
+                )
+
+        elif is_list_like(right) and not isinstance(right, (Series, DataFrame)):
+            # GH#36702. Raise when attempting arithmetic with list of array-like.
+            if any(is_array_like(el) for el in right):
+                raise ValueError(
+                    f"Unable to coerce list of {type(right[0])} to Series/DataFrame"
+                )
+            # GH#17901
+            right = to_series(right)
+
+        if flex is not None and isinstance(right, DataFrame):
+            if not left._indexed_same(right):
+                if flex:
+                    left, right = left.align(
+                        right, join="outer", level=level, copy=False
+                    )
+                else:
+                    raise ValueError(
+                        "Can only compare identically-labeled (both index and columns) "
+                        "DataFrame objects"
+                    )
+        elif isinstance(right, Series):
+            # axis=1 is default for DataFrame-with-Series op
+            axis = axis if axis is not None else 1
+            if not flex:
+                if not left.axes[axis].equals(right.index):
+                    raise ValueError(
+                        "Operands are not aligned. Do "
+                        "`left, right = left.align(right, axis=1, copy=False)` "
+                        "before operating."
+                    )
+
+            left, right = left.align(
+                right,
+                join="outer",
+                axis=axis,
+                level=level,
+                copy=False,
+            )
+            right = left._maybe_align_series_as_frame(right, axis)
+
+        return left, right
+
+    def _maybe_align_series_as_frame(self, series: Series, axis: AxisInt):
+        """
+        If the Series operand is not EA-dtype, we can broadcast to 2D and operate
+        blockwise.
+        """
+        rvalues = series._values
+        if not isinstance(rvalues, np.ndarray):
+            # TODO(EA2D): no need to special-case with 2D EAs
+            if rvalues.dtype in ("datetime64[ns]", "timedelta64[ns]"):
+                # We can losslessly+cheaply cast to ndarray
+                rvalues = np.asarray(rvalues)
+            else:
+                return series
+
+        if axis == 0:
+            rvalues = rvalues.reshape(-1, 1)
+        else:
+            rvalues = rvalues.reshape(1, -1)
+
+        rvalues = np.broadcast_to(rvalues, self.shape)
+        # pass dtype to avoid doing inference
+        return self._constructor(
+            rvalues,
+            index=self.index,
+            columns=self.columns,
+            dtype=rvalues.dtype,
+        )
+
+    def _flex_arith_method(
+        self, other, op, *, axis: Axis = "columns", level=None, fill_value=None
+    ):
+        axis = self._get_axis_number(axis) if axis is not None else 1
+
+        if self._should_reindex_frame_op(other, op, axis, fill_value, level):
+            return self._arith_method_with_reindex(other, op)
+
+        if isinstance(other, Series) and fill_value is not None:
+            # TODO: We could allow this in cases where we end up going
+            #  through the DataFrame path
+            raise NotImplementedError(f"fill_value {fill_value} not supported.")
+
+        other = ops.maybe_prepare_scalar_for_op(other, self.shape)
+        self, other = self._align_for_op(other, axis, flex=True, level=level)
+
+        with np.errstate(all="ignore"):
+            if isinstance(other, DataFrame):
+                # Another DataFrame
+                new_data = self._combine_frame(other, op, fill_value)
+
+            elif isinstance(other, Series):
+                new_data = self._dispatch_frame_op(other, op, axis=axis)
+            else:
+                # in this case we always have `np.ndim(other) == 0`
+                if fill_value is not None:
+                    self = self.fillna(fill_value)
+
+                new_data = self._dispatch_frame_op(other, op)
+
+        return self._construct_result(new_data)
+
+    def _construct_result(self, result) -> DataFrame:
+        """
+        Wrap the result of an arithmetic, comparison, or logical operation.
+
+        Parameters
+        ----------
+        result : DataFrame
+
+        Returns
+        -------
+        DataFrame
+        """
+        out = self._constructor(result, copy=False).__finalize__(self)
+        # Pin columns instead of passing to constructor for compat with
+        #  non-unique columns case
+        out.columns = self.columns
+        out.index = self.index
+        return out
+
+    def __divmod__(self, other) -> tuple[DataFrame, DataFrame]:
+        # Naive implementation, room for optimization
+        div = self // other
+        mod = self - div * other
+        return div, mod
+
+    def __rdivmod__(self, other) -> tuple[DataFrame, DataFrame]:
+        # Naive implementation, room for optimization
+        div = other // self
+        mod = other - div * self
+        return div, mod
+
+    def _flex_cmp_method(self, other, op, *, axis: Axis = "columns", level=None):
+        axis = self._get_axis_number(axis) if axis is not None else 1
+
+        self, other = self._align_for_op(other, axis, flex=True, level=level)
+
+        new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    @Appender(ops.make_flex_doc("eq", "dataframe"))
+    def eq(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.eq, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("ne", "dataframe"))
+    def ne(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.ne, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("le", "dataframe"))
+    def le(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.le, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("lt", "dataframe"))
+    def lt(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.lt, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("ge", "dataframe"))
+    def ge(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.ge, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("gt", "dataframe"))
+    def gt(self, other, axis: Axis = "columns", level=None) -> DataFrame:
+        return self._flex_cmp_method(other, operator.gt, axis=axis, level=level)
+
+    @Appender(ops.make_flex_doc("add", "dataframe"))
+    def add(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.add, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("radd", "dataframe"))
+    def radd(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.radd, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("sub", "dataframe"))
+    def sub(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.sub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    subtract = sub
+
+    @Appender(ops.make_flex_doc("rsub", "dataframe"))
+    def rsub(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rsub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mul", "dataframe"))
+    def mul(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.mul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    multiply = mul
+
+    @Appender(ops.make_flex_doc("rmul", "dataframe"))
+    def rmul(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rmul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("truediv", "dataframe"))
+    def truediv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.truediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    div = truediv
+    divide = truediv
+
+    @Appender(ops.make_flex_doc("rtruediv", "dataframe"))
+    def rtruediv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rtruediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    rdiv = rtruediv
+
+    @Appender(ops.make_flex_doc("floordiv", "dataframe"))
+    def floordiv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.floordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rfloordiv", "dataframe"))
+    def rfloordiv(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rfloordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mod", "dataframe"))
+    def mod(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.mod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rmod", "dataframe"))
+    def rmod(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("pow", "dataframe"))
+    def pow(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, operator.pow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rpow", "dataframe"))
+    def rpow(
+        self, other, axis: Axis = "columns", level=None, fill_value=None
+    ) -> DataFrame:
+        return self._flex_arith_method(
+            other, roperator.rpow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    # ----------------------------------------------------------------------
+    # Combination-Related
+
+    @doc(
+        _shared_docs["compare"],
+        dedent(
+            """
+        Returns
+        -------
+        DataFrame
+            DataFrame that shows the differences stacked side by side.
+
+            The resulting index will be a MultiIndex with 'self' and 'other'
+            stacked alternately at the inner level.
+
+        Raises
+        ------
+        ValueError
+            When the two DataFrames don't have identical labels or shape.
+
+        See Also
+        --------
+        Series.compare : Compare with another Series and show differences.
+        DataFrame.equals : Test whether two objects contain the same elements.
+
+        Notes
+        -----
+        Matching NaNs will not appear as a difference.
+
+        Can only compare identically-labeled
+        (i.e. same shape, identical row and column labels) DataFrames
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {{
+        ...         "col1": ["a", "a", "b", "b", "a"],
+        ...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
+        ...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
+        ...     }},
+        ...     columns=["col1", "col2", "col3"],
+        ... )
+        >>> df
+          col1  col2  col3
+        0    a   1.0   1.0
+        1    a   2.0   2.0
+        2    b   3.0   3.0
+        3    b   NaN   4.0
+        4    a   5.0   5.0
+
+        >>> df2 = df.copy()
+        >>> df2.loc[0, 'col1'] = 'c'
+        >>> df2.loc[2, 'col3'] = 4.0
+        >>> df2
+          col1  col2  col3
+        0    c   1.0   1.0
+        1    a   2.0   2.0
+        2    b   3.0   4.0
+        3    b   NaN   4.0
+        4    a   5.0   5.0
+
+        Align the differences on columns
+
+        >>> df.compare(df2)
+          col1       col3
+          self other self other
+        0    a     c  NaN   NaN
+        2  NaN   NaN  3.0   4.0
+
+        Assign result_names
+
+        >>> df.compare(df2, result_names=("left", "right"))
+          col1       col3
+          left right left right
+        0    a     c  NaN   NaN
+        2  NaN   NaN  3.0   4.0
+
+        Stack the differences on rows
+
+        >>> df.compare(df2, align_axis=0)
+                col1  col3
+        0 self     a   NaN
+          other    c   NaN
+        2 self   NaN   3.0
+          other  NaN   4.0
+
+        Keep the equal values
+
+        >>> df.compare(df2, keep_equal=True)
+          col1       col3
+          self other self other
+        0    a     c  1.0   1.0
+        2    b     b  3.0   4.0
+
+        Keep all original rows and columns
+
+        >>> df.compare(df2, keep_shape=True)
+          col1       col2       col3
+          self other self other self other
+        0    a     c  NaN   NaN  NaN   NaN
+        1  NaN   NaN  NaN   NaN  NaN   NaN
+        2  NaN   NaN  NaN   NaN  3.0   4.0
+        3  NaN   NaN  NaN   NaN  NaN   NaN
+        4  NaN   NaN  NaN   NaN  NaN   NaN
+
+        Keep all original rows and columns and also all original values
+
+        >>> df.compare(df2, keep_shape=True, keep_equal=True)
+          col1       col2       col3
+          self other self other self other
+        0    a     c  1.0   1.0  1.0   1.0
+        1    a     a  2.0   2.0  2.0   2.0
+        2    b     b  3.0   3.0  3.0   4.0
+        3    b     b  NaN   NaN  4.0   4.0
+        4    a     a  5.0   5.0  5.0   5.0
+        """
+        ),
+        klass=_shared_doc_kwargs["klass"],
+    )
+    def compare(
+        self,
+        other: DataFrame,
+        align_axis: Axis = 1,
+        keep_shape: bool = False,
+        keep_equal: bool = False,
+        result_names: Suffixes = ("self", "other"),
+    ) -> DataFrame:
+        return super().compare(
+            other=other,
+            align_axis=align_axis,
+            keep_shape=keep_shape,
+            keep_equal=keep_equal,
+            result_names=result_names,
+        )
+
+    def combine(
+        self,
+        other: DataFrame,
+        func: Callable[[Series, Series], Series | Hashable],
+        fill_value=None,
+        overwrite: bool = True,
+    ) -> DataFrame:
+        """
+        Perform column-wise combine with another DataFrame.
+
+        Combines a DataFrame with `other` DataFrame using `func`
+        to element-wise combine columns. The row and column indexes of the
+        resulting DataFrame will be the union of the two.
+
+        Parameters
+        ----------
+        other : DataFrame
+            The DataFrame to merge column-wise.
+        func : function
+            Function that takes two series as inputs and return a Series or a
+            scalar. Used to merge the two dataframes column by columns.
+        fill_value : scalar value, default None
+            The value to fill NaNs with prior to passing any column to the
+            merge func.
+        overwrite : bool, default True
+            If True, columns in `self` that do not exist in `other` will be
+            overwritten with NaNs.
+
+        Returns
+        -------
+        DataFrame
+            Combination of the provided DataFrames.
+
+        See Also
+        --------
+        DataFrame.combine_first : Combine two DataFrame objects and default to
+            non-null values in frame calling the method.
+
+        Examples
+        --------
+        Combine using a simple function that chooses the smaller column.
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
+        >>> df1.combine(df2, take_smaller)
+           A  B
+        0  0  3
+        1  0  3
+
+        Example using a true element-wise combine function.
+
+        >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1.combine(df2, np.minimum)
+           A  B
+        0  1  2
+        1  0  3
+
+        Using `fill_value` fills Nones prior to passing the column to the
+        merge function.
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1.combine(df2, take_smaller, fill_value=-5)
+           A    B
+        0  0 -5.0
+        1  0  4.0
+
+        However, if the same element in both dataframes is None, that None
+        is preserved
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
+        >>> df1.combine(df2, take_smaller, fill_value=-5)
+            A    B
+        0  0 -5.0
+        1  0  3.0
+
+        Example that demonstrates the use of `overwrite` and behavior when
+        the axis differ between the dataframes.
+
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
+        >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
+        >>> df1.combine(df2, take_smaller)
+             A    B     C
+        0  NaN  NaN   NaN
+        1  NaN  3.0 -10.0
+        2  NaN  3.0   1.0
+
+        >>> df1.combine(df2, take_smaller, overwrite=False)
+             A    B     C
+        0  0.0  NaN   NaN
+        1  0.0  3.0 -10.0
+        2  NaN  3.0   1.0
+
+        Demonstrating the preference of the passed in dataframe.
+
+        >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
+        >>> df2.combine(df1, take_smaller)
+           A    B   C
+        0  0.0  NaN NaN
+        1  0.0  3.0 NaN
+        2  NaN  3.0 NaN
+
+        >>> df2.combine(df1, take_smaller, overwrite=False)
+             A    B   C
+        0  0.0  NaN NaN
+        1  0.0  3.0 1.0
+        2  NaN  3.0 1.0
+        """
+        other_idxlen = len(other.index)  # save for compare
+
+        this, other = self.align(other, copy=False)
+        new_index = this.index
+
+        if other.empty and len(new_index) == len(self.index):
+            return self.copy()
+
+        if self.empty and len(other) == other_idxlen:
+            return other.copy()
+
+        # sorts if possible; otherwise align above ensures that these are set-equal
+        new_columns = this.columns.union(other.columns)
+        do_fill = fill_value is not None
+        result = {}
+        for col in new_columns:
+            series = this[col]
+            other_series = other[col]
+
+            this_dtype = series.dtype
+            other_dtype = other_series.dtype
+
+            this_mask = isna(series)
+            other_mask = isna(other_series)
+
+            # don't overwrite columns unnecessarily
+            # DO propagate if this column is not in the intersection
+            if not overwrite and other_mask.all():
+                result[col] = this[col].copy()
+                continue
+
+            if do_fill:
+                series = series.copy()
+                other_series = other_series.copy()
+                series[this_mask] = fill_value
+                other_series[other_mask] = fill_value
+
+            if col not in self.columns:
+                # If self DataFrame does not have col in other DataFrame,
+                # try to promote series, which is all NaN, as other_dtype.
+                new_dtype = other_dtype
+                try:
+                    series = series.astype(new_dtype, copy=False)
+                except ValueError:
+                    # e.g. new_dtype is integer types
+                    pass
+            else:
+                # if we have different dtypes, possibly promote
+                new_dtype = find_common_type([this_dtype, other_dtype])
+                series = series.astype(new_dtype, copy=False)
+                other_series = other_series.astype(new_dtype, copy=False)
+
+            arr = func(series, other_series)
+            if isinstance(new_dtype, np.dtype):
+                # if new_dtype is an EA Dtype, then `func` is expected to return
+                # the correct dtype without any additional casting
+                # error: No overload variant of "maybe_downcast_to_dtype" matches
+                # argument types "Union[Series, Hashable]", "dtype[Any]"
+                arr = maybe_downcast_to_dtype(  # type: ignore[call-overload]
+                    arr, new_dtype
+                )
+
+            result[col] = arr
+
+        # convert_objects just in case
+        frame_result = self._constructor(result, index=new_index, columns=new_columns)
+        return frame_result.__finalize__(self, method="combine")
+
+    def combine_first(self, other: DataFrame) -> DataFrame:
+        """
+        Update null elements with value in the same location in `other`.
+
+        Combine two DataFrame objects by filling null values in one DataFrame
+        with non-null values from other DataFrame. The row and column indexes
+        of the resulting DataFrame will be the union of the two. The resulting
+        dataframe contains the 'first' dataframe values and overrides the
+        second one values where both first.loc[index, col] and
+        second.loc[index, col] are not missing values, upon calling
+        first.combine_first(second).
+
+        Parameters
+        ----------
+        other : DataFrame
+            Provided DataFrame to use to fill null values.
+
+        Returns
+        -------
+        DataFrame
+            The result of combining the provided DataFrame with the other object.
+
+        See Also
+        --------
+        DataFrame.combine : Perform series-wise operation on two DataFrames
+            using a given function.
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1.combine_first(df2)
+             A    B
+        0  1.0  3.0
+        1  0.0  4.0
+
+        Null values still persist if the location of that null value
+        does not exist in `other`
+
+        >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
+        >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
+        >>> df1.combine_first(df2)
+             A    B    C
+        0  NaN  4.0  NaN
+        1  0.0  3.0  1.0
+        2  NaN  3.0  1.0
+        """
+        from pandas.core.computation import expressions
+
+        def combiner(x: Series, y: Series):
+            mask = x.isna()._values
+
+            x_values = x._values
+            y_values = y._values
+
+            # If the column y in other DataFrame is not in first DataFrame,
+            # just return y_values.
+            if y.name not in self.columns:
+                return y_values
+
+            return expressions.where(mask, y_values, x_values)
+
+        if len(other) == 0:
+            combined = self.reindex(
+                self.columns.append(other.columns.difference(self.columns)), axis=1
+            )
+            combined = combined.astype(other.dtypes)
+        else:
+            combined = self.combine(other, combiner, overwrite=False)
+
+        dtypes = {
+            col: find_common_type([self.dtypes[col], other.dtypes[col]])
+            for col in self.columns.intersection(other.columns)
+            if combined.dtypes[col] != self.dtypes[col]
+        }
+
+        if dtypes:
+            combined = combined.astype(dtypes)
+
+        return combined.__finalize__(self, method="combine_first")
+
+    def update(
+        self,
+        other,
+        join: UpdateJoin = "left",
+        overwrite: bool = True,
+        filter_func=None,
+        errors: IgnoreRaise = "ignore",
+    ) -> None:
+        """
+        Modify in place using non-NA values from another DataFrame.
+
+        Aligns on indices. There is no return value.
+
+        Parameters
+        ----------
+        other : DataFrame, or object coercible into a DataFrame
+            Should have at least one matching index/column label
+            with the original DataFrame. If a Series is passed,
+            its name attribute must be set, and that will be
+            used as the column name to align with the original DataFrame.
+        join : {'left'}, default 'left'
+            Only left join is implemented, keeping the index and columns of the
+            original object.
+        overwrite : bool, default True
+            How to handle non-NA values for overlapping keys:
+
+            * True: overwrite original DataFrame's values
+              with values from `other`.
+            * False: only update values that are NA in
+              the original DataFrame.
+
+        filter_func : callable(1d-array) -> bool 1d-array, optional
+            Can choose to replace values other than NA. Return True for values
+            that should be updated.
+        errors : {'raise', 'ignore'}, default 'ignore'
+            If 'raise', will raise a ValueError if the DataFrame and `other`
+            both contain non-NA data in the same place.
+
+        Returns
+        -------
+        None
+            This method directly changes calling object.
+
+        Raises
+        ------
+        ValueError
+            * When `errors='raise'` and there's overlapping non-NA data.
+            * When `errors` is not either `'ignore'` or `'raise'`
+        NotImplementedError
+            * If `join != 'left'`
+
+        See Also
+        --------
+        dict.update : Similar method for dictionaries.
+        DataFrame.merge : For column(s)-on-column(s) operations.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3],
+        ...                    'B': [400, 500, 600]})
+        >>> new_df = pd.DataFrame({'B': [4, 5, 6],
+        ...                        'C': [7, 8, 9]})
+        >>> df.update(new_df)
+        >>> df
+           A  B
+        0  1  4
+        1  2  5
+        2  3  6
+
+        The DataFrame's length does not increase as a result of the update,
+        only values at matching index/column labels are updated.
+
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
+        ...                    'B': ['x', 'y', 'z']})
+        >>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
+        >>> df.update(new_df)
+        >>> df
+           A  B
+        0  a  d
+        1  b  e
+        2  c  f
+
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
+        ...                    'B': ['x', 'y', 'z']})
+        >>> new_df = pd.DataFrame({'B': ['d', 'f']}, index=[0, 2])
+        >>> df.update(new_df)
+        >>> df
+           A  B
+        0  a  d
+        1  b  y
+        2  c  f
+
+        For Series, its name attribute must be set.
+
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
+        ...                    'B': ['x', 'y', 'z']})
+        >>> new_column = pd.Series(['d', 'e', 'f'], name='B')
+        >>> df.update(new_column)
+        >>> df
+           A  B
+        0  a  d
+        1  b  e
+        2  c  f
+
+        If `other` contains NaNs the corresponding values are not updated
+        in the original dataframe.
+
+        >>> df = pd.DataFrame({'A': [1, 2, 3],
+        ...                    'B': [400., 500., 600.]})
+        >>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
+        >>> df.update(new_df)
+        >>> df
+           A      B
+        0  1    4.0
+        1  2  500.0
+        2  3    6.0
+        """
+
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= REF_COUNT:
+                warnings.warn(
+                    _chained_assignment_method_msg,
+                    ChainedAssignmentError,
+                    stacklevel=2,
+                )
+        elif (
+            not PYPY
+            and not WARNING_CHECK_DISABLED
+            and not using_copy_on_write()
+            and self._is_view_after_cow_rules()
+        ):
+            if sys.getrefcount(self) <= REF_COUNT:
+                warnings.warn(
+                    _chained_assignment_warning_method_msg,
+                    FutureWarning,
+                    stacklevel=2,
+                )
+
+        # TODO: Support other joins
+        if join != "left":  # pragma: no cover
+            raise NotImplementedError("Only left join is supported")
+        if errors not in ["ignore", "raise"]:
+            raise ValueError("The parameter errors must be either 'ignore' or 'raise'")
+
+        if not isinstance(other, DataFrame):
+            other = DataFrame(other)
+
+        other = other.reindex(self.index)
+
+        for col in self.columns.intersection(other.columns):
+            this = self[col]._values
+            that = other[col]._values
+
+            if filter_func is not None:
+                mask = ~filter_func(this) | isna(that)
+            else:
+                if errors == "raise":
+                    mask_this = notna(that)
+                    mask_that = notna(this)
+                    if any(mask_this & mask_that):
+                        raise ValueError("Data overlaps.")
+
+                if overwrite:
+                    mask = isna(that)
+                else:
+                    mask = notna(this)
+
+            # don't overwrite columns unnecessarily
+            if mask.all():
+                continue
+
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore",
+                    message="Downcasting behavior",
+                    category=FutureWarning,
+                )
+                # GH#57124 - `that` might get upcasted because of NA values, and then
+                # downcasted in where because of the mask. Ignoring the warning
+                # is a stopgap, will replace with a new implementation of update
+                # in 3.0.
+                self.loc[:, col] = self[col].where(mask, that)
+
+    # ----------------------------------------------------------------------
+    # Data reshaping
+    @Appender(
+        dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
+        ...                               'Parrot', 'Parrot'],
+        ...                    'Max Speed': [380., 370., 24., 26.]})
+        >>> df
+           Animal  Max Speed
+        0  Falcon      380.0
+        1  Falcon      370.0
+        2  Parrot       24.0
+        3  Parrot       26.0
+        >>> df.groupby(['Animal']).mean()
+                Max Speed
+        Animal
+        Falcon      375.0
+        Parrot       25.0
+
+        **Hierarchical Indexes**
+
+        We can groupby different levels of a hierarchical index
+        using the `level` parameter:
+
+        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+        >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
+        ...                   index=index)
+        >>> df
+                        Max Speed
+        Animal Type
+        Falcon Captive      390.0
+               Wild         350.0
+        Parrot Captive       30.0
+               Wild          20.0
+        >>> df.groupby(level=0).mean()
+                Max Speed
+        Animal
+        Falcon      370.0
+        Parrot       25.0
+        >>> df.groupby(level="Type").mean()
+                 Max Speed
+        Type
+        Captive      210.0
+        Wild         185.0
+
+        We can also choose to include NA in group keys or not by setting
+        `dropna` parameter, the default setting is `True`.
+
+        >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
+        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
+
+        >>> df.groupby(by=["b"]).sum()
+            a   c
+        b
+        1.0 2   3
+        2.0 2   5
+
+        >>> df.groupby(by=["b"], dropna=False).sum()
+            a   c
+        b
+        1.0 2   3
+        2.0 2   5
+        NaN 1   4
+
+        >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
+        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
+
+        >>> df.groupby(by="a").sum()
+            b     c
+        a
+        a   13.0   13.0
+        b   12.3  123.0
+
+        >>> df.groupby(by="a", dropna=False).sum()
+            b     c
+        a
+        a   13.0   13.0
+        b   12.3  123.0
+        NaN 12.3   33.0
+
+        When using ``.apply()``, use ``group_keys`` to include or exclude the
+        group keys. The ``group_keys`` argument defaults to ``True`` (include).
+
+        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
+        ...                               'Parrot', 'Parrot'],
+        ...                    'Max Speed': [380., 370., 24., 26.]})
+        >>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
+                  Max Speed
+        Animal
+        Falcon 0      380.0
+               1      370.0
+        Parrot 2       24.0
+               3       26.0
+
+        >>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
+           Max Speed
+        0      380.0
+        1      370.0
+        2       24.0
+        3       26.0
+        """
+        )
+    )
+    @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
+    def groupby(
+        self,
+        by=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        level: IndexLabel | None = None,
+        as_index: bool = True,
+        sort: bool = True,
+        group_keys: bool = True,
+        observed: bool | lib.NoDefault = lib.no_default,
+        dropna: bool = True,
+    ) -> DataFrameGroupBy:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            if axis == 1:
+                warnings.warn(
+                    "DataFrame.groupby with axis=1 is deprecated. Do "
+                    "`frame.T.groupby(...)` without axis instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    "The 'axis' keyword in DataFrame.groupby is deprecated and "
+                    "will be removed in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        from pandas.core.groupby.generic import DataFrameGroupBy
+
+        if level is None and by is None:
+            raise TypeError("You have to supply one of 'by' and 'level'")
+
+        return DataFrameGroupBy(
+            obj=self,
+            keys=by,
+            axis=axis,
+            level=level,
+            as_index=as_index,
+            sort=sort,
+            group_keys=group_keys,
+            observed=observed,
+            dropna=dropna,
+        )
+
+    _shared_docs[
+        "pivot"
+    ] = """
+        Return reshaped DataFrame organized by given index / column values.
+
+        Reshape data (produce a "pivot" table) based on column values. Uses
+        unique values from specified `index` / `columns` to form axes of the
+        resulting DataFrame. This function does not support data
+        aggregation, multiple values will result in a MultiIndex in the
+        columns. See the :ref:`User Guide <reshaping>` for more on reshaping.
+
+        Parameters
+        ----------%s
+        columns : str or object or a list of str
+            Column to use to make new frame's columns.
+        index : str or object or a list of str, optional
+            Column to use to make new frame's index. If not given, uses existing index.
+        values : str, object or a list of the previous, optional
+            Column(s) to use for populating new frame's values. If not
+            specified, all remaining columns will be used and the result will
+            have hierarchically indexed columns.
+
+        Returns
+        -------
+        DataFrame
+            Returns reshaped DataFrame.
+
+        Raises
+        ------
+        ValueError:
+            When there are any `index`, `columns` combinations with multiple
+            values. `DataFrame.pivot_table` when you need to aggregate.
+
+        See Also
+        --------
+        DataFrame.pivot_table : Generalization of pivot that can handle
+            duplicate values for one index/column pair.
+        DataFrame.unstack : Pivot based on the index values instead of a
+            column.
+        wide_to_long : Wide panel to long format. Less flexible but more
+            user-friendly than melt.
+
+        Notes
+        -----
+        For finer-tuned control, see hierarchical indexing documentation along
+        with the related stack/unstack methods.
+
+        Reference :ref:`the user guide <reshaping.pivot>` for more examples.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
+        ...                            'two'],
+        ...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
+        ...                    'baz': [1, 2, 3, 4, 5, 6],
+        ...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
+        >>> df
+            foo   bar  baz  zoo
+        0   one   A    1    x
+        1   one   B    2    y
+        2   one   C    3    z
+        3   two   A    4    q
+        4   two   B    5    w
+        5   two   C    6    t
+
+        >>> df.pivot(index='foo', columns='bar', values='baz')
+        bar  A   B   C
+        foo
+        one  1   2   3
+        two  4   5   6
+
+        >>> df.pivot(index='foo', columns='bar')['baz']
+        bar  A   B   C
+        foo
+        one  1   2   3
+        two  4   5   6
+
+        >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
+              baz       zoo
+        bar   A  B  C   A  B  C
+        foo
+        one   1  2  3   x  y  z
+        two   4  5  6   q  w  t
+
+        You could also assign a list of column names or a list of index names.
+
+        >>> df = pd.DataFrame({
+        ...        "lev1": [1, 1, 1, 2, 2, 2],
+        ...        "lev2": [1, 1, 2, 1, 1, 2],
+        ...        "lev3": [1, 2, 1, 2, 1, 2],
+        ...        "lev4": [1, 2, 3, 4, 5, 6],
+        ...        "values": [0, 1, 2, 3, 4, 5]})
+        >>> df
+            lev1 lev2 lev3 lev4 values
+        0   1    1    1    1    0
+        1   1    1    2    2    1
+        2   1    2    1    3    2
+        3   2    1    2    4    3
+        4   2    1    1    5    4
+        5   2    2    2    6    5
+
+        >>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
+        lev2    1         2
+        lev3    1    2    1    2
+        lev1
+        1     0.0  1.0  2.0  NaN
+        2     4.0  3.0  NaN  5.0
+
+        >>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
+              lev3    1    2
+        lev1  lev2
+           1     1  0.0  1.0
+                 2  2.0  NaN
+           2     1  4.0  3.0
+                 2  NaN  5.0
+
+        A ValueError is raised if there are any duplicates.
+
+        >>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
+        ...                    "bar": ['A', 'A', 'B', 'C'],
+        ...                    "baz": [1, 2, 3, 4]})
+        >>> df
+           foo bar  baz
+        0  one   A    1
+        1  one   A    2
+        2  two   B    3
+        3  two   C    4
+
+        Notice that the first two rows are the same for our `index`
+        and `columns` arguments.
+
+        >>> df.pivot(index='foo', columns='bar', values='baz')
+        Traceback (most recent call last):
+           ...
+        ValueError: Index contains duplicate entries, cannot reshape
+        """
+
+    @Substitution("")
+    @Appender(_shared_docs["pivot"])
+    def pivot(
+        self, *, columns, index=lib.no_default, values=lib.no_default
+    ) -> DataFrame:
+        from pandas.core.reshape.pivot import pivot
+
+        return pivot(self, index=index, columns=columns, values=values)
+
+    _shared_docs[
+        "pivot_table"
+    ] = """
+        Create a spreadsheet-style pivot table as a DataFrame.
+
+        The levels in the pivot table will be stored in MultiIndex objects
+        (hierarchical indexes) on the index and columns of the result DataFrame.
+
+        Parameters
+        ----------%s
+        values : list-like or scalar, optional
+            Column or columns to aggregate.
+        index : column, Grouper, array, or list of the previous
+            Keys to group by on the pivot table index. If a list is passed,
+            it can contain any of the other types (except list). If an array is
+            passed, it must be the same length as the data and will be used in
+            the same manner as column values.
+        columns : column, Grouper, array, or list of the previous
+            Keys to group by on the pivot table column. If a list is passed,
+            it can contain any of the other types (except list). If an array is
+            passed, it must be the same length as the data and will be used in
+            the same manner as column values.
+        aggfunc : function, list of functions, dict, default "mean"
+            If a list of functions is passed, the resulting pivot table will have
+            hierarchical columns whose top level are the function names
+            (inferred from the function objects themselves).
+            If a dict is passed, the key is column to aggregate and the value is
+            function or list of functions. If ``margin=True``, aggfunc will be
+            used to calculate the partial aggregates.
+        fill_value : scalar, default None
+            Value to replace missing values with (in the resulting pivot table,
+            after aggregation).
+        margins : bool, default False
+            If ``margins=True``, special ``All`` columns and rows
+            will be added with partial group aggregates across the categories
+            on the rows and columns.
+        dropna : bool, default True
+            Do not include columns whose entries are all NaN. If True,
+            rows with a NaN value in any column will be omitted before
+            computing margins.
+        margins_name : str, default 'All'
+            Name of the row / column that will contain the totals
+            when margins is True.
+        observed : bool, default False
+            This only applies if any of the groupers are Categoricals.
+            If True: only show observed values for categorical groupers.
+            If False: show all values for categorical groupers.
+
+            .. deprecated:: 2.2.0
+
+                The default value of ``False`` is deprecated and will change to
+                ``True`` in a future version of pandas.
+
+        sort : bool, default True
+            Specifies if the result should be sorted.
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        DataFrame
+            An Excel style pivot table.
+
+        See Also
+        --------
+        DataFrame.pivot : Pivot without aggregation that can handle
+            non-numeric data.
+        DataFrame.melt: Unpivot a DataFrame from wide to long format,
+            optionally leaving identifiers set.
+        wide_to_long : Wide panel to long format. Less flexible but more
+            user-friendly than melt.
+
+        Notes
+        -----
+        Reference :ref:`the user guide <reshaping.pivot>` for more examples.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
+        ...                          "bar", "bar", "bar", "bar"],
+        ...                    "B": ["one", "one", "one", "two", "two",
+        ...                          "one", "one", "two", "two"],
+        ...                    "C": ["small", "large", "large", "small",
+        ...                          "small", "large", "small", "small",
+        ...                          "large"],
+        ...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+        ...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
+        >>> df
+             A    B      C  D  E
+        0  foo  one  small  1  2
+        1  foo  one  large  2  4
+        2  foo  one  large  2  5
+        3  foo  two  small  3  5
+        4  foo  two  small  3  6
+        5  bar  one  large  4  6
+        6  bar  one  small  5  8
+        7  bar  two  small  6  9
+        8  bar  two  large  7  9
+
+        This first example aggregates values by taking the sum.
+
+        >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
+        ...                        columns=['C'], aggfunc="sum")
+        >>> table
+        C        large  small
+        A   B
+        bar one    4.0    5.0
+            two    7.0    6.0
+        foo one    4.0    1.0
+            two    NaN    6.0
+
+        We can also fill missing values using the `fill_value` parameter.
+
+        >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
+        ...                        columns=['C'], aggfunc="sum", fill_value=0)
+        >>> table
+        C        large  small
+        A   B
+        bar one      4      5
+            two      7      6
+        foo one      4      1
+            two      0      6
+
+        The next example aggregates by taking the mean across multiple columns.
+
+        >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
+        ...                        aggfunc={'D': "mean", 'E': "mean"})
+        >>> table
+                        D         E
+        A   C
+        bar large  5.500000  7.500000
+            small  5.500000  8.500000
+        foo large  2.000000  4.500000
+            small  2.333333  4.333333
+
+        We can also calculate multiple types of aggregations for any given
+        value column.
+
+        >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
+        ...                        aggfunc={'D': "mean",
+        ...                                 'E': ["min", "max", "mean"]})
+        >>> table
+                          D   E
+                       mean max      mean  min
+        A   C
+        bar large  5.500000   9  7.500000    6
+            small  5.500000   9  8.500000    8
+        foo large  2.000000   5  4.500000    4
+            small  2.333333   6  4.333333    2
+        """
+
+    @Substitution("")
+    @Appender(_shared_docs["pivot_table"])
+    def pivot_table(
+        self,
+        values=None,
+        index=None,
+        columns=None,
+        aggfunc: AggFuncType = "mean",
+        fill_value=None,
+        margins: bool = False,
+        dropna: bool = True,
+        margins_name: Level = "All",
+        observed: bool | lib.NoDefault = lib.no_default,
+        sort: bool = True,
+    ) -> DataFrame:
+        from pandas.core.reshape.pivot import pivot_table
+
+        return pivot_table(
+            self,
+            values=values,
+            index=index,
+            columns=columns,
+            aggfunc=aggfunc,
+            fill_value=fill_value,
+            margins=margins,
+            dropna=dropna,
+            margins_name=margins_name,
+            observed=observed,
+            sort=sort,
+        )
+
+    def stack(
+        self,
+        level: IndexLabel = -1,
+        dropna: bool | lib.NoDefault = lib.no_default,
+        sort: bool | lib.NoDefault = lib.no_default,
+        future_stack: bool = False,
+    ):
+        """
+        Stack the prescribed level(s) from columns to index.
+
+        Return a reshaped DataFrame or Series having a multi-level
+        index with one or more new inner-most levels compared to the current
+        DataFrame. The new inner-most levels are created by pivoting the
+        columns of the current dataframe:
+
+          - if the columns have a single level, the output is a Series;
+          - if the columns have multiple levels, the new index
+            level(s) is (are) taken from the prescribed level(s) and
+            the output is a DataFrame.
+
+        Parameters
+        ----------
+        level : int, str, list, default -1
+            Level(s) to stack from the column axis onto the index
+            axis, defined as one index or label, or a list of indices
+            or labels.
+        dropna : bool, default True
+            Whether to drop rows in the resulting Frame/Series with
+            missing values. Stacking a column level onto the index
+            axis can create combinations of index and column values
+            that are missing from the original dataframe. See Examples
+            section.
+        sort : bool, default True
+            Whether to sort the levels of the resulting MultiIndex.
+        future_stack : bool, default False
+            Whether to use the new implementation that will replace the current
+            implementation in pandas 3.0. When True, dropna and sort have no impact
+            on the result and must remain unspecified. See :ref:`pandas 2.1.0 Release
+            notes <whatsnew_210.enhancements.new_stack>` for more details.
+
+        Returns
+        -------
+        DataFrame or Series
+            Stacked dataframe or series.
+
+        See Also
+        --------
+        DataFrame.unstack : Unstack prescribed level(s) from index axis
+             onto column axis.
+        DataFrame.pivot : Reshape dataframe from long format to wide
+             format.
+        DataFrame.pivot_table : Create a spreadsheet-style pivot table
+             as a DataFrame.
+
+        Notes
+        -----
+        The function is named by analogy with a collection of books
+        being reorganized from being side by side on a horizontal
+        position (the columns of the dataframe) to being stacked
+        vertically on top of each other (in the index of the
+        dataframe).
+
+        Reference :ref:`the user guide <reshaping.stacking>` for more examples.
+
+        Examples
+        --------
+        **Single level columns**
+
+        >>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=['weight', 'height'])
+
+        Stacking a dataframe with a single level column axis returns a Series:
+
+        >>> df_single_level_cols
+             weight height
+        cat       0      1
+        dog       2      3
+        >>> df_single_level_cols.stack(future_stack=True)
+        cat  weight    0
+             height    1
+        dog  weight    2
+             height    3
+        dtype: int64
+
+        **Multi level columns: simple case**
+
+        >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
+        ...                                        ('weight', 'pounds')])
+        >>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=multicol1)
+
+        Stacking a dataframe with a multi-level column axis:
+
+        >>> df_multi_level_cols1
+             weight
+                 kg    pounds
+        cat       1        2
+        dog       2        4
+        >>> df_multi_level_cols1.stack(future_stack=True)
+                    weight
+        cat kg           1
+            pounds       2
+        dog kg           2
+            pounds       4
+
+        **Missing values**
+
+        >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
+        ...                                        ('height', 'm')])
+        >>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=multicol2)
+
+        It is common to have missing values when stacking a dataframe
+        with multi-level columns, as the stacked dataframe typically
+        has more values than the original dataframe. Missing values
+        are filled with NaNs:
+
+        >>> df_multi_level_cols2
+            weight height
+                kg      m
+        cat    1.0    2.0
+        dog    3.0    4.0
+        >>> df_multi_level_cols2.stack(future_stack=True)
+                weight  height
+        cat kg     1.0     NaN
+            m      NaN     2.0
+        dog kg     3.0     NaN
+            m      NaN     4.0
+
+        **Prescribing the level(s) to be stacked**
+
+        The first parameter controls which level or levels are stacked:
+
+        >>> df_multi_level_cols2.stack(0, future_stack=True)
+                     kg    m
+        cat weight  1.0  NaN
+            height  NaN  2.0
+        dog weight  3.0  NaN
+            height  NaN  4.0
+        >>> df_multi_level_cols2.stack([0, 1], future_stack=True)
+        cat  weight  kg    1.0
+             height  m     2.0
+        dog  weight  kg    3.0
+             height  m     4.0
+        dtype: float64
+        """
+        if not future_stack:
+            from pandas.core.reshape.reshape import (
+                stack,
+                stack_multiple,
+            )
+
+            if (
+                dropna is not lib.no_default
+                or sort is not lib.no_default
+                or self.columns.nlevels > 1
+            ):
+                warnings.warn(
+                    "The previous implementation of stack is deprecated and will be "
+                    "removed in a future version of pandas. See the What's New notes "
+                    "for pandas 2.1.0 for details. Specify future_stack=True to adopt "
+                    "the new implementation and silence this warning.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+            if dropna is lib.no_default:
+                dropna = True
+            if sort is lib.no_default:
+                sort = True
+
+            if isinstance(level, (tuple, list)):
+                result = stack_multiple(self, level, dropna=dropna, sort=sort)
+            else:
+                result = stack(self, level, dropna=dropna, sort=sort)
+        else:
+            from pandas.core.reshape.reshape import stack_v3
+
+            if dropna is not lib.no_default:
+                raise ValueError(
+                    "dropna must be unspecified with future_stack=True as the new "
+                    "implementation does not introduce rows of NA values. This "
+                    "argument will be removed in a future version of pandas."
+                )
+
+            if sort is not lib.no_default:
+                raise ValueError(
+                    "Cannot specify sort with future_stack=True, this argument will be "
+                    "removed in a future version of pandas. Sort the result using "
+                    ".sort_index instead."
+                )
+
+            if (
+                isinstance(level, (tuple, list))
+                and not all(lev in self.columns.names for lev in level)
+                and not all(isinstance(lev, int) for lev in level)
+            ):
+                raise ValueError(
+                    "level should contain all level names or all level "
+                    "numbers, not a mixture of the two."
+                )
+
+            if not isinstance(level, (tuple, list)):
+                level = [level]
+            level = [self.columns._get_level_number(lev) for lev in level]
+            result = stack_v3(self, level)
+
+        return result.__finalize__(self, method="stack")
+
+    def explode(
+        self,
+        column: IndexLabel,
+        ignore_index: bool = False,
+    ) -> DataFrame:
+        """
+        Transform each element of a list-like to a row, replicating index values.
+
+        Parameters
+        ----------
+        column : IndexLabel
+            Column(s) to explode.
+            For multiple columns, specify a non-empty list with each element
+            be str or tuple, and all specified columns their list-like data
+            on same row of the frame must have matching length.
+
+            .. versionadded:: 1.3.0
+                Multi-column explode
+
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        DataFrame
+            Exploded lists to rows of the subset columns;
+            index will be duplicated for these rows.
+
+        Raises
+        ------
+        ValueError :
+            * If columns of the frame are not unique.
+            * If specified columns to explode is empty list.
+            * If specified columns to explode have not matching count of
+              elements rowwise in the frame.
+
+        See Also
+        --------
+        DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
+            index labels.
+        DataFrame.melt : Unpivot a DataFrame from wide format to long format.
+        Series.explode : Explode a DataFrame from list-like columns to long format.
+
+        Notes
+        -----
+        This routine will explode list-likes including lists, tuples, sets,
+        Series, and np.ndarray. The result dtype of the subset rows will
+        be object. Scalars will be returned unchanged, and empty list-likes will
+        result in a np.nan for that row. In addition, the ordering of rows in the
+        output will be non-deterministic when exploding sets.
+
+        Reference :ref:`the user guide <reshaping.explode>` for more examples.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
+        ...                    'B': 1,
+        ...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
+        >>> df
+                   A  B          C
+        0  [0, 1, 2]  1  [a, b, c]
+        1        foo  1        NaN
+        2         []  1         []
+        3     [3, 4]  1     [d, e]
+
+        Single-column explode.
+
+        >>> df.explode('A')
+             A  B          C
+        0    0  1  [a, b, c]
+        0    1  1  [a, b, c]
+        0    2  1  [a, b, c]
+        1  foo  1        NaN
+        2  NaN  1         []
+        3    3  1     [d, e]
+        3    4  1     [d, e]
+
+        Multi-column explode.
+
+        >>> df.explode(list('AC'))
+             A  B    C
+        0    0  1    a
+        0    1  1    b
+        0    2  1    c
+        1  foo  1  NaN
+        2  NaN  1  NaN
+        3    3  1    d
+        3    4  1    e
+        """
+        if not self.columns.is_unique:
+            duplicate_cols = self.columns[self.columns.duplicated()].tolist()
+            raise ValueError(
+                f"DataFrame columns must be unique. Duplicate columns: {duplicate_cols}"
+            )
+
+        columns: list[Hashable]
+        if is_scalar(column) or isinstance(column, tuple):
+            columns = [column]
+        elif isinstance(column, list) and all(
+            is_scalar(c) or isinstance(c, tuple) for c in column
+        ):
+            if not column:
+                raise ValueError("column must be nonempty")
+            if len(column) > len(set(column)):
+                raise ValueError("column must be unique")
+            columns = column
+        else:
+            raise ValueError("column must be a scalar, tuple, or list thereof")
+
+        df = self.reset_index(drop=True)
+        if len(columns) == 1:
+            result = df[columns[0]].explode()
+        else:
+            mylen = lambda x: len(x) if (is_list_like(x) and len(x) > 0) else 1
+            counts0 = self[columns[0]].apply(mylen)
+            for c in columns[1:]:
+                if not all(counts0 == self[c].apply(mylen)):
+                    raise ValueError("columns must have matching element counts")
+            result = DataFrame({c: df[c].explode() for c in columns})
+        result = df.drop(columns, axis=1).join(result)
+        if ignore_index:
+            result.index = default_index(len(result))
+        else:
+            result.index = self.index.take(result.index)
+        result = result.reindex(columns=self.columns, copy=False)
+
+        return result.__finalize__(self, method="explode")
+
+    def unstack(self, level: IndexLabel = -1, fill_value=None, sort: bool = True):
+        """
+        Pivot a level of the (necessarily hierarchical) index labels.
+
+        Returns a DataFrame having a new level of column labels whose inner-most level
+        consists of the pivoted index labels.
+
+        If the index is not a MultiIndex, the output will be a Series
+        (the analogue of stack when the columns are not a MultiIndex).
+
+        Parameters
+        ----------
+        level : int, str, or list of these, default -1 (last level)
+            Level(s) of index to unstack, can pass level name.
+        fill_value : int, str or dict
+            Replace NaN with this value if the unstack produces missing values.
+        sort : bool, default True
+            Sort the level(s) in the resulting MultiIndex columns.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        See Also
+        --------
+        DataFrame.pivot : Pivot a table based on column values.
+        DataFrame.stack : Pivot a level of the column labels (inverse operation
+            from `unstack`).
+
+        Notes
+        -----
+        Reference :ref:`the user guide <reshaping.stacking>` for more examples.
+
+        Examples
+        --------
+        >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
+        ...                                    ('two', 'a'), ('two', 'b')])
+        >>> s = pd.Series(np.arange(1.0, 5.0), index=index)
+        >>> s
+        one  a   1.0
+             b   2.0
+        two  a   3.0
+             b   4.0
+        dtype: float64
+
+        >>> s.unstack(level=-1)
+             a   b
+        one  1.0  2.0
+        two  3.0  4.0
+
+        >>> s.unstack(level=0)
+           one  two
+        a  1.0   3.0
+        b  2.0   4.0
+
+        >>> df = s.unstack(level=0)
+        >>> df.unstack()
+        one  a  1.0
+             b  2.0
+        two  a  3.0
+             b  4.0
+        dtype: float64
+        """
+        from pandas.core.reshape.reshape import unstack
+
+        result = unstack(self, level, fill_value, sort)
+
+        return result.__finalize__(self, method="unstack")
+
+    @Appender(_shared_docs["melt"] % {"caller": "df.melt(", "other": "melt"})
+    def melt(
+        self,
+        id_vars=None,
+        value_vars=None,
+        var_name=None,
+        value_name: Hashable = "value",
+        col_level: Level | None = None,
+        ignore_index: bool = True,
+    ) -> DataFrame:
+        return melt(
+            self,
+            id_vars=id_vars,
+            value_vars=value_vars,
+            var_name=var_name,
+            value_name=value_name,
+            col_level=col_level,
+            ignore_index=ignore_index,
+        ).__finalize__(self, method="melt")
+
+    # ----------------------------------------------------------------------
+    # Time series-related
+
+    @doc(
+        Series.diff,
+        klass="DataFrame",
+        extra_params="axis : {0 or 'index', 1 or 'columns'}, default 0\n    "
+        "Take difference over rows (0) or columns (1).\n",
+        other_klass="Series",
+        examples=dedent(
+            """
+        Difference with previous row
+
+        >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
+        ...                    'b': [1, 1, 2, 3, 5, 8],
+        ...                    'c': [1, 4, 9, 16, 25, 36]})
+        >>> df
+           a  b   c
+        0  1  1   1
+        1  2  1   4
+        2  3  2   9
+        3  4  3  16
+        4  5  5  25
+        5  6  8  36
+
+        >>> df.diff()
+             a    b     c
+        0  NaN  NaN   NaN
+        1  1.0  0.0   3.0
+        2  1.0  1.0   5.0
+        3  1.0  1.0   7.0
+        4  1.0  2.0   9.0
+        5  1.0  3.0  11.0
+
+        Difference with previous column
+
+        >>> df.diff(axis=1)
+            a  b   c
+        0 NaN  0   0
+        1 NaN -1   3
+        2 NaN -1   7
+        3 NaN -1  13
+        4 NaN  0  20
+        5 NaN  2  28
+
+        Difference with 3rd previous row
+
+        >>> df.diff(periods=3)
+             a    b     c
+        0  NaN  NaN   NaN
+        1  NaN  NaN   NaN
+        2  NaN  NaN   NaN
+        3  3.0  2.0  15.0
+        4  3.0  4.0  21.0
+        5  3.0  6.0  27.0
+
+        Difference with following row
+
+        >>> df.diff(periods=-1)
+             a    b     c
+        0 -1.0  0.0  -3.0
+        1 -1.0 -1.0  -5.0
+        2 -1.0 -1.0  -7.0
+        3 -1.0 -2.0  -9.0
+        4 -1.0 -3.0 -11.0
+        5  NaN  NaN   NaN
+
+        Overflow in input dtype
+
+        >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
+        >>> df.diff()
+               a
+        0    NaN
+        1  255.0"""
+        ),
+    )
+    def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
+        if not lib.is_integer(periods):
+            if not (is_float(periods) and periods.is_integer()):
+                raise ValueError("periods must be an integer")
+            periods = int(periods)
+
+        axis = self._get_axis_number(axis)
+        if axis == 1:
+            if periods != 0:
+                # in the periods == 0 case, this is equivalent diff of 0 periods
+                #  along axis=0, and the Manager method may be somewhat more
+                #  performant, so we dispatch in that case.
+                return self - self.shift(periods, axis=axis)
+            # With periods=0 this is equivalent to a diff with axis=0
+            axis = 0
+
+        new_data = self._mgr.diff(n=periods)
+        res_df = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res_df.__finalize__(self, "diff")
+
+    # ----------------------------------------------------------------------
+    # Function application
+
+    def _gotitem(
+        self,
+        key: IndexLabel,
+        ndim: int,
+        subset: DataFrame | Series | None = None,
+    ) -> DataFrame | Series:
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        if subset is None:
+            subset = self
+        elif subset.ndim == 1:  # is Series
+            return subset
+
+        # TODO: _shallow_copy(subset)?
+        return subset[key]
+
+    _agg_see_also_doc = dedent(
+        """
+    See Also
+    --------
+    DataFrame.apply : Perform any type of operations.
+    DataFrame.transform : Perform transformation type operations.
+    pandas.DataFrame.groupby : Perform operations over groups.
+    pandas.DataFrame.resample : Perform operations over resampled bins.
+    pandas.DataFrame.rolling : Perform operations over rolling window.
+    pandas.DataFrame.expanding : Perform operations over expanding window.
+    pandas.core.window.ewm.ExponentialMovingWindow : Perform operation over exponential
+        weighted window.
+    """
+    )
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 2, 3],
+    ...                    [4, 5, 6],
+    ...                    [7, 8, 9],
+    ...                    [np.nan, np.nan, np.nan]],
+    ...                   columns=['A', 'B', 'C'])
+
+    Aggregate these functions over the rows.
+
+    >>> df.agg(['sum', 'min'])
+            A     B     C
+    sum  12.0  15.0  18.0
+    min   1.0   2.0   3.0
+
+    Different aggregations per column.
+
+    >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
+            A    B
+    sum  12.0  NaN
+    min   1.0  2.0
+    max   NaN  8.0
+
+    Aggregate different functions over the columns and rename the index of the resulting
+    DataFrame.
+
+    >>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
+         A    B    C
+    x  7.0  NaN  NaN
+    y  NaN  2.0  NaN
+    z  NaN  NaN  6.0
+
+    Aggregate over the columns.
+
+    >>> df.agg("mean", axis="columns")
+    0    2.0
+    1    5.0
+    2    8.0
+    3    NaN
+    dtype: float64
+    """
+    )
+
+    @doc(
+        _shared_docs["aggregate"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+        see_also=_agg_see_also_doc,
+        examples=_agg_examples_doc,
+    )
+    def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
+        from pandas.core.apply import frame_apply
+
+        axis = self._get_axis_number(axis)
+
+        op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
+        result = op.agg()
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
+        return result
+
+    agg = aggregate
+
+    @doc(
+        _shared_docs["transform"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+    )
+    def transform(
+        self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
+    ) -> DataFrame:
+        from pandas.core.apply import frame_apply
+
+        op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
+        result = op.transform()
+        assert isinstance(result, DataFrame)
+        return result
+
+    def apply(
+        self,
+        func: AggFuncType,
+        axis: Axis = 0,
+        raw: bool = False,
+        result_type: Literal["expand", "reduce", "broadcast"] | None = None,
+        args=(),
+        by_row: Literal[False, "compat"] = "compat",
+        engine: Literal["python", "numba"] = "python",
+        engine_kwargs: dict[str, bool] | None = None,
+        **kwargs,
+    ):
+        """
+        Apply a function along an axis of the DataFrame.
+
+        Objects passed to the function are Series objects whose index is
+        either the DataFrame's index (``axis=0``) or the DataFrame's columns
+        (``axis=1``). By default (``result_type=None``), the final return type
+        is inferred from the return type of the applied function. Otherwise,
+        it depends on the `result_type` argument.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to each column or row.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis along which the function is applied:
+
+            * 0 or 'index': apply function to each column.
+            * 1 or 'columns': apply function to each row.
+
+        raw : bool, default False
+            Determines if row or column is passed as a Series or ndarray object:
+
+            * ``False`` : passes each row or column as a Series to the
+              function.
+            * ``True`` : the passed function will receive ndarray objects
+              instead.
+              If you are just applying a NumPy reduction function this will
+              achieve much better performance.
+
+        result_type : {'expand', 'reduce', 'broadcast', None}, default None
+            These only act when ``axis=1`` (columns):
+
+            * 'expand' : list-like results will be turned into columns.
+            * 'reduce' : returns a Series if possible rather than expanding
+              list-like results. This is the opposite of 'expand'.
+            * 'broadcast' : results will be broadcast to the original shape
+              of the DataFrame, the original index and columns will be
+              retained.
+
+            The default behaviour (None) depends on the return value of the
+            applied function: list-like results will be returned as a Series
+            of those. However if the apply function returns a Series these
+            are expanded to columns.
+        args : tuple
+            Positional arguments to pass to `func` in addition to the
+            array/series.
+        by_row : False or "compat", default "compat"
+            Only has an effect when ``func`` is a listlike or dictlike of funcs
+            and the func isn't a string.
+            If "compat", will if possible first translate the func into pandas
+            methods (e.g. ``Series().apply(np.sum)`` will be translated to
+            ``Series().sum()``). If that doesn't work, will try call to apply again with
+            ``by_row=True`` and if that fails, will call apply again with
+            ``by_row=False`` (backward compatible).
+            If False, the funcs will be passed the whole Series at once.
+
+            .. versionadded:: 2.1.0
+
+        engine : {'python', 'numba'}, default 'python'
+            Choose between the python (default) engine or the numba engine in apply.
+
+            The numba engine will attempt to JIT compile the passed function,
+            which may result in speedups for large DataFrames.
+            It also supports the following engine_kwargs :
+
+            - nopython (compile the function in nopython mode)
+            - nogil (release the GIL inside the JIT compiled function)
+            - parallel (try to apply the function in parallel over the DataFrame)
+
+              Note: Due to limitations within numba/how pandas interfaces with numba,
+              you should only use this if raw=True
+
+            Note: The numba compiler only supports a subset of
+            valid Python/numpy operations.
+
+            Please read more about the `supported python features
+            <https://numba.pydata.org/numba-doc/dev/reference/pysupported.html>`_
+            and `supported numpy features
+            <https://numba.pydata.org/numba-doc/dev/reference/numpysupported.html>`_
+            in numba to learn what you can or cannot use in the passed function.
+
+            .. versionadded:: 2.2.0
+
+        engine_kwargs : dict
+            Pass keyword arguments to the engine.
+            This is currently only used by the numba engine,
+            see the documentation for the engine argument for more information.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        Series or DataFrame
+            Result of applying ``func`` along the given axis of the
+            DataFrame.
+
+        See Also
+        --------
+        DataFrame.map: For elementwise operations.
+        DataFrame.aggregate: Only perform aggregating type operations.
+        DataFrame.transform: Only perform transforming type operations.
+
+        Notes
+        -----
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
+        >>> df
+           A  B
+        0  4  9
+        1  4  9
+        2  4  9
+
+        Using a numpy universal function (in this case the same as
+        ``np.sqrt(df)``):
+
+        >>> df.apply(np.sqrt)
+             A    B
+        0  2.0  3.0
+        1  2.0  3.0
+        2  2.0  3.0
+
+        Using a reducing function on either axis
+
+        >>> df.apply(np.sum, axis=0)
+        A    12
+        B    27
+        dtype: int64
+
+        >>> df.apply(np.sum, axis=1)
+        0    13
+        1    13
+        2    13
+        dtype: int64
+
+        Returning a list-like will result in a Series
+
+        >>> df.apply(lambda x: [1, 2], axis=1)
+        0    [1, 2]
+        1    [1, 2]
+        2    [1, 2]
+        dtype: object
+
+        Passing ``result_type='expand'`` will expand list-like results
+        to columns of a Dataframe
+
+        >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
+           0  1
+        0  1  2
+        1  1  2
+        2  1  2
+
+        Returning a Series inside the function is similar to passing
+        ``result_type='expand'``. The resulting column names
+        will be the Series index.
+
+        >>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
+           foo  bar
+        0    1    2
+        1    1    2
+        2    1    2
+
+        Passing ``result_type='broadcast'`` will ensure the same shape
+        result, whether list-like or scalar is returned by the function,
+        and broadcast it along the axis. The resulting column names will
+        be the originals.
+
+        >>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
+           A  B
+        0  1  2
+        1  1  2
+        2  1  2
+        """
+        from pandas.core.apply import frame_apply
+
+        op = frame_apply(
+            self,
+            func=func,
+            axis=axis,
+            raw=raw,
+            result_type=result_type,
+            by_row=by_row,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+        return op.apply().__finalize__(self, method="apply")
+
+    def map(
+        self, func: PythonFuncType, na_action: str | None = None, **kwargs
+    ) -> DataFrame:
+        """
+        Apply a function to a Dataframe elementwise.
+
+        .. versionadded:: 2.1.0
+
+           DataFrame.applymap was deprecated and renamed to DataFrame.map.
+
+        This method applies a function that accepts and returns a scalar
+        to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to func.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.replace: Replace values given in `to_replace` with `value`.
+        Series.map : Apply a function elementwise on a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
+        >>> df
+               0      1
+        0  1.000  2.120
+        1  3.356  4.567
+
+        >>> df.map(lambda x: len(str(x)))
+           0  1
+        0  3  4
+        1  5  5
+
+        Like Series.map, NA values can be ignored:
+
+        >>> df_copy = df.copy()
+        >>> df_copy.iloc[0, 0] = pd.NA
+        >>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
+             0  1
+        0  NaN  4
+        1  5.0  5
+
+        It is also possible to use `map` with functions that are not
+        `lambda` functions:
+
+        >>> df.map(round, ndigits=1)
+             0    1
+        0  1.0  2.1
+        1  3.4  4.6
+
+        Note that a vectorized version of `func` often exists, which will
+        be much faster. You could square each number elementwise.
+
+        >>> df.map(lambda x: x**2)
+                   0          1
+        0   1.000000   4.494400
+        1  11.262736  20.857489
+
+        But it's better to avoid map in that case.
+
+        >>> df ** 2
+                   0          1
+        0   1.000000   4.494400
+        1  11.262736  20.857489
+        """
+        if na_action not in {"ignore", None}:
+            raise ValueError(
+                f"na_action must be 'ignore' or None. Got {repr(na_action)}"
+            )
+
+        if self.empty:
+            return self.copy()
+
+        func = functools.partial(func, **kwargs)
+
+        def infer(x):
+            return x._map_values(func, na_action=na_action)
+
+        return self.apply(infer).__finalize__(self, "map")
+
+    def applymap(
+        self, func: PythonFuncType, na_action: NaAction | None = None, **kwargs
+    ) -> DataFrame:
+        """
+        Apply a function to a Dataframe elementwise.
+
+        .. deprecated:: 2.1.0
+
+           DataFrame.applymap has been deprecated. Use DataFrame.map instead.
+
+        This method applies a function that accepts and returns a scalar
+        to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to func.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to
+            `func`.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.map : Apply a function along input axis of DataFrame.
+        DataFrame.replace: Replace values given in `to_replace` with `value`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
+        >>> df
+               0      1
+        0  1.000  2.120
+        1  3.356  4.567
+
+        >>> df.map(lambda x: len(str(x)))
+           0  1
+        0  3  4
+        1  5  5
+        """
+        warnings.warn(
+            "DataFrame.applymap has been deprecated. Use DataFrame.map instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.map(func, na_action=na_action, **kwargs)
+
+    # ----------------------------------------------------------------------
+    # Merging / joining methods
+
+    def _append(
+        self,
+        other,
+        ignore_index: bool = False,
+        verify_integrity: bool = False,
+        sort: bool = False,
+    ) -> DataFrame:
+        if isinstance(other, (Series, dict)):
+            if isinstance(other, dict):
+                if not ignore_index:
+                    raise TypeError("Can only append a dict if ignore_index=True")
+                other = Series(other)
+            if other.name is None and not ignore_index:
+                raise TypeError(
+                    "Can only append a Series if ignore_index=True "
+                    "or if the Series has a name"
+                )
+
+            index = Index(
+                [other.name],
+                name=self.index.names
+                if isinstance(self.index, MultiIndex)
+                else self.index.name,
+            )
+            row_df = other.to_frame().T
+            # infer_objects is needed for
+            #  test_append_empty_frame_to_series_with_dateutil_tz
+            other = row_df.infer_objects(copy=False).rename_axis(
+                index.names, copy=False
+            )
+        elif isinstance(other, list):
+            if not other:
+                pass
+            elif not isinstance(other[0], DataFrame):
+                other = DataFrame(other)
+                if self.index.name is not None and not ignore_index:
+                    other.index.name = self.index.name
+
+        from pandas.core.reshape.concat import concat
+
+        if isinstance(other, (list, tuple)):
+            to_concat = [self, *other]
+        else:
+            to_concat = [self, other]
+
+        result = concat(
+            to_concat,
+            ignore_index=ignore_index,
+            verify_integrity=verify_integrity,
+            sort=sort,
+        )
+        return result.__finalize__(self, method="append")
+
+    def join(
+        self,
+        other: DataFrame | Series | Iterable[DataFrame | Series],
+        on: IndexLabel | None = None,
+        how: MergeHow = "left",
+        lsuffix: str = "",
+        rsuffix: str = "",
+        sort: bool = False,
+        validate: JoinValidate | None = None,
+    ) -> DataFrame:
+        """
+        Join columns of another DataFrame.
+
+        Join columns with `other` DataFrame either on index or on a key
+        column. Efficiently join multiple DataFrame objects by index at once by
+        passing a list.
+
+        Parameters
+        ----------
+        other : DataFrame, Series, or a list containing any combination of them
+            Index should be similar to one of the columns in this one. If a
+            Series is passed, its name attribute must be set, and that will be
+            used as the column name in the resulting joined DataFrame.
+        on : str, list of str, or array-like, optional
+            Column or index level name(s) in the caller to join on the index
+            in `other`, otherwise joins index-on-index. If multiple
+            values given, the `other` DataFrame must have a MultiIndex. Can
+            pass an array as the join key if it is not already contained in
+            the calling DataFrame. Like an Excel VLOOKUP operation.
+        how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'left'
+            How to handle the operation of the two objects.
+
+            * left: use calling frame's index (or column if on is specified)
+            * right: use `other`'s index.
+            * outer: form union of calling frame's index (or column if on is
+              specified) with `other`'s index, and sort it lexicographically.
+            * inner: form intersection of calling frame's index (or column if
+              on is specified) with `other`'s index, preserving the order
+              of the calling's one.
+            * cross: creates the cartesian product from both frames, preserves the order
+              of the left keys.
+        lsuffix : str, default ''
+            Suffix to use from left frame's overlapping columns.
+        rsuffix : str, default ''
+            Suffix to use from right frame's overlapping columns.
+        sort : bool, default False
+            Order result DataFrame lexicographically by the join key. If False,
+            the order of the join key depends on the join type (how keyword).
+        validate : str, optional
+            If specified, checks if join is of specified type.
+
+            * "one_to_one" or "1:1": check if join keys are unique in both left
+              and right datasets.
+            * "one_to_many" or "1:m": check if join keys are unique in left dataset.
+            * "many_to_one" or "m:1": check if join keys are unique in right dataset.
+            * "many_to_many" or "m:m": allowed, but does not result in checks.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        DataFrame
+            A dataframe containing columns from both the caller and `other`.
+
+        See Also
+        --------
+        DataFrame.merge : For column(s)-on-column(s) operations.
+
+        Notes
+        -----
+        Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
+        passing a list of `DataFrame` objects.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
+        ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
+
+        >>> df
+          key   A
+        0  K0  A0
+        1  K1  A1
+        2  K2  A2
+        3  K3  A3
+        4  K4  A4
+        5  K5  A5
+
+        >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
+        ...                       'B': ['B0', 'B1', 'B2']})
+
+        >>> other
+          key   B
+        0  K0  B0
+        1  K1  B1
+        2  K2  B2
+
+        Join DataFrames using their indexes.
+
+        >>> df.join(other, lsuffix='_caller', rsuffix='_other')
+          key_caller   A key_other    B
+        0         K0  A0        K0   B0
+        1         K1  A1        K1   B1
+        2         K2  A2        K2   B2
+        3         K3  A3       NaN  NaN
+        4         K4  A4       NaN  NaN
+        5         K5  A5       NaN  NaN
+
+        If we want to join using the key columns, we need to set key to be
+        the index in both `df` and `other`. The joined DataFrame will have
+        key as its index.
+
+        >>> df.set_index('key').join(other.set_index('key'))
+              A    B
+        key
+        K0   A0   B0
+        K1   A1   B1
+        K2   A2   B2
+        K3   A3  NaN
+        K4   A4  NaN
+        K5   A5  NaN
+
+        Another option to join using the key columns is to use the `on`
+        parameter. DataFrame.join always uses `other`'s index but we can use
+        any column in `df`. This method preserves the original DataFrame's
+        index in the result.
+
+        >>> df.join(other.set_index('key'), on='key')
+          key   A    B
+        0  K0  A0   B0
+        1  K1  A1   B1
+        2  K2  A2   B2
+        3  K3  A3  NaN
+        4  K4  A4  NaN
+        5  K5  A5  NaN
+
+        Using non-unique key values shows how they are matched.
+
+        >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
+        ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
+
+        >>> df
+          key   A
+        0  K0  A0
+        1  K1  A1
+        2  K1  A2
+        3  K3  A3
+        4  K0  A4
+        5  K1  A5
+
+        >>> df.join(other.set_index('key'), on='key', validate='m:1')
+          key   A    B
+        0  K0  A0   B0
+        1  K1  A1   B1
+        2  K1  A2   B1
+        3  K3  A3  NaN
+        4  K0  A4   B0
+        5  K1  A5   B1
+        """
+        from pandas.core.reshape.concat import concat
+        from pandas.core.reshape.merge import merge
+
+        if isinstance(other, Series):
+            if other.name is None:
+                raise ValueError("Other Series must have a name")
+            other = DataFrame({other.name: other})
+
+        if isinstance(other, DataFrame):
+            if how == "cross":
+                return merge(
+                    self,
+                    other,
+                    how=how,
+                    on=on,
+                    suffixes=(lsuffix, rsuffix),
+                    sort=sort,
+                    validate=validate,
+                )
+            return merge(
+                self,
+                other,
+                left_on=on,
+                how=how,
+                left_index=on is None,
+                right_index=True,
+                suffixes=(lsuffix, rsuffix),
+                sort=sort,
+                validate=validate,
+            )
+        else:
+            if on is not None:
+                raise ValueError(
+                    "Joining multiple DataFrames only supported for joining on index"
+                )
+
+            if rsuffix or lsuffix:
+                raise ValueError(
+                    "Suffixes not supported when joining multiple DataFrames"
+                )
+
+            # Mypy thinks the RHS is a
+            # "Union[DataFrame, Series, Iterable[Union[DataFrame, Series]]]" whereas
+            # the LHS is an "Iterable[DataFrame]", but in reality both types are
+            # "Iterable[Union[DataFrame, Series]]" due to the if statements
+            frames = [cast("DataFrame | Series", self)] + list(other)
+
+            can_concat = all(df.index.is_unique for df in frames)
+
+            # join indexes only using concat
+            if can_concat:
+                if how == "left":
+                    res = concat(
+                        frames, axis=1, join="outer", verify_integrity=True, sort=sort
+                    )
+                    return res.reindex(self.index, copy=False)
+                else:
+                    return concat(
+                        frames, axis=1, join=how, verify_integrity=True, sort=sort
+                    )
+
+            joined = frames[0]
+
+            for frame in frames[1:]:
+                joined = merge(
+                    joined,
+                    frame,
+                    how=how,
+                    left_index=True,
+                    right_index=True,
+                    validate=validate,
+                )
+
+            return joined
+
+    @Substitution("")
+    @Appender(_merge_doc, indents=2)
+    def merge(
+        self,
+        right: DataFrame | Series,
+        how: MergeHow = "inner",
+        on: IndexLabel | AnyArrayLike | None = None,
+        left_on: IndexLabel | AnyArrayLike | None = None,
+        right_on: IndexLabel | AnyArrayLike | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        sort: bool = False,
+        suffixes: Suffixes = ("_x", "_y"),
+        copy: bool | None = None,
+        indicator: str | bool = False,
+        validate: MergeValidate | None = None,
+    ) -> DataFrame:
+        from pandas.core.reshape.merge import merge
+
+        return merge(
+            self,
+            right,
+            how=how,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            sort=sort,
+            suffixes=suffixes,
+            copy=copy,
+            indicator=indicator,
+            validate=validate,
+        )
+
+    def round(
+        self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs
+    ) -> DataFrame:
+        """
+        Round a DataFrame to a variable number of decimal places.
+
+        Parameters
+        ----------
+        decimals : int, dict, Series
+            Number of decimal places to round each column to. If an int is
+            given, round each column to the same number of places.
+            Otherwise dict and Series round to variable numbers of places.
+            Column names should be in the keys if `decimals` is a
+            dict-like, or in the index if `decimals` is a Series. Any
+            columns not included in `decimals` will be left as is. Elements
+            of `decimals` which are not columns of the input will be
+            ignored.
+        *args
+            Additional keywords have no effect but might be accepted for
+            compatibility with numpy.
+        **kwargs
+            Additional keywords have no effect but might be accepted for
+            compatibility with numpy.
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame with the affected columns rounded to the specified
+            number of decimal places.
+
+        See Also
+        --------
+        numpy.around : Round a numpy array to the given number of decimals.
+        Series.round : Round a Series to the given number of decimals.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df
+            dogs  cats
+        0  0.21  0.32
+        1  0.01  0.67
+        2  0.66  0.03
+        3  0.21  0.18
+
+        By providing an integer each column is rounded to the same number
+        of decimal places
+
+        >>> df.round(1)
+            dogs  cats
+        0   0.2   0.3
+        1   0.0   0.7
+        2   0.7   0.0
+        3   0.2   0.2
+
+        With a dict, the number of places for specific columns can be
+        specified with the column names as key and the number of decimal
+        places as value
+
+        >>> df.round({'dogs': 1, 'cats': 0})
+            dogs  cats
+        0   0.2   0.0
+        1   0.0   1.0
+        2   0.7   0.0
+        3   0.2   0.0
+
+        Using a Series, the number of places for specific columns can be
+        specified with the column names as index and the number of
+        decimal places as value
+
+        >>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
+        >>> df.round(decimals)
+            dogs  cats
+        0   0.2   0.0
+        1   0.0   1.0
+        2   0.7   0.0
+        3   0.2   0.0
+        """
+        from pandas.core.reshape.concat import concat
+
+        def _dict_round(df: DataFrame, decimals):
+            for col, vals in df.items():
+                try:
+                    yield _series_round(vals, decimals[col])
+                except KeyError:
+                    yield vals
+
+        def _series_round(ser: Series, decimals: int) -> Series:
+            if is_integer_dtype(ser.dtype) or is_float_dtype(ser.dtype):
+                return ser.round(decimals)
+            return ser
+
+        nv.validate_round(args, kwargs)
+
+        if isinstance(decimals, (dict, Series)):
+            if isinstance(decimals, Series) and not decimals.index.is_unique:
+                raise ValueError("Index of decimals must be unique")
+            if is_dict_like(decimals) and not all(
+                is_integer(value) for _, value in decimals.items()
+            ):
+                raise TypeError("Values in decimals must be integers")
+            new_cols = list(_dict_round(self, decimals))
+        elif is_integer(decimals):
+            # Dispatch to Block.round
+            # Argument "decimals" to "round" of "BaseBlockManager" has incompatible
+            # type "Union[int, integer[Any]]"; expected "int"
+            new_mgr = self._mgr.round(
+                decimals=decimals,  # type: ignore[arg-type]
+                using_cow=using_copy_on_write(),
+            )
+            return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(
+                self, method="round"
+            )
+        else:
+            raise TypeError("decimals must be an integer, a dict-like or a Series")
+
+        if new_cols is not None and len(new_cols) > 0:
+            return self._constructor(
+                concat(new_cols, axis=1), index=self.index, columns=self.columns
+            ).__finalize__(self, method="round")
+        else:
+            return self.copy(deep=False)
+
+    # ----------------------------------------------------------------------
+    # Statistical methods, etc.
+
+    def corr(
+        self,
+        method: CorrelationMethod = "pearson",
+        min_periods: int = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Compute pairwise correlation of columns, excluding NA/null values.
+
+        Parameters
+        ----------
+        method : {'pearson', 'kendall', 'spearman'} or callable
+            Method of correlation:
+
+            * pearson : standard correlation coefficient
+            * kendall : Kendall Tau correlation coefficient
+            * spearman : Spearman rank correlation
+            * callable: callable with input two 1d ndarrays
+                and returning a float. Note that the returned matrix from corr
+                will have 1 along the diagonals and will be symmetric
+                regardless of the callable's behavior.
+        min_periods : int, optional
+            Minimum number of observations required per pair of columns
+            to have a valid result. Currently only available for Pearson
+            and Spearman correlation.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        Returns
+        -------
+        DataFrame
+            Correlation matrix.
+
+        See Also
+        --------
+        DataFrame.corrwith : Compute pairwise correlation with another
+            DataFrame or Series.
+        Series.corr : Compute the correlation between two Series.
+
+        Notes
+        -----
+        Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
+
+        * `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
+        * `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
+        * `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_
+
+        Examples
+        --------
+        >>> def histogram_intersection(a, b):
+        ...     v = np.minimum(a, b).sum().round(decimals=1)
+        ...     return v
+        >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df.corr(method=histogram_intersection)
+              dogs  cats
+        dogs   1.0   0.3
+        cats   0.3   1.0
+
+        >>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df.corr(min_periods=3)
+              dogs  cats
+        dogs   1.0   NaN
+        cats   NaN   1.0
+        """  # noqa: E501
+        data = self._get_numeric_data() if numeric_only else self
+        cols = data.columns
+        idx = cols.copy()
+        mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
+
+        if method == "pearson":
+            correl = libalgos.nancorr(mat, minp=min_periods)
+        elif method == "spearman":
+            correl = libalgos.nancorr_spearman(mat, minp=min_periods)
+        elif method == "kendall" or callable(method):
+            if min_periods is None:
+                min_periods = 1
+            mat = mat.T
+            corrf = nanops.get_corr_func(method)
+            K = len(cols)
+            correl = np.empty((K, K), dtype=float)
+            mask = np.isfinite(mat)
+            for i, ac in enumerate(mat):
+                for j, bc in enumerate(mat):
+                    if i > j:
+                        continue
+
+                    valid = mask[i] & mask[j]
+                    if valid.sum() < min_periods:
+                        c = np.nan
+                    elif i == j:
+                        c = 1.0
+                    elif not valid.all():
+                        c = corrf(ac[valid], bc[valid])
+                    else:
+                        c = corrf(ac, bc)
+                    correl[i, j] = c
+                    correl[j, i] = c
+        else:
+            raise ValueError(
+                "method must be either 'pearson', "
+                "'spearman', 'kendall', or a callable, "
+                f"'{method}' was supplied"
+            )
+
+        result = self._constructor(correl, index=idx, columns=cols, copy=False)
+        return result.__finalize__(self, method="corr")
+
+    def cov(
+        self,
+        min_periods: int | None = None,
+        ddof: int | None = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Compute pairwise covariance of columns, excluding NA/null values.
+
+        Compute the pairwise covariance among the series of a DataFrame.
+        The returned data frame is the `covariance matrix
+        <https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
+        of the DataFrame.
+
+        Both NA and null values are automatically excluded from the
+        calculation. (See the note below about bias from missing values.)
+        A threshold can be set for the minimum number of
+        observations for each value created. Comparisons with observations
+        below this threshold will be returned as ``NaN``.
+
+        This method is generally used for the analysis of time series data to
+        understand the relationship between different measures
+        across time.
+
+        Parameters
+        ----------
+        min_periods : int, optional
+            Minimum number of observations required per pair of columns
+            to have a valid result.
+
+        ddof : int, default 1
+            Delta degrees of freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+            This argument is applicable only when no ``nan`` is in the dataframe.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        Returns
+        -------
+        DataFrame
+            The covariance matrix of the series of the DataFrame.
+
+        See Also
+        --------
+        Series.cov : Compute covariance with another Series.
+        core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample
+            covariance.
+        core.window.expanding.Expanding.cov : Expanding sample covariance.
+        core.window.rolling.Rolling.cov : Rolling sample covariance.
+
+        Notes
+        -----
+        Returns the covariance matrix of the DataFrame's time series.
+        The covariance is normalized by N-ddof.
+
+        For DataFrames that have Series that are missing data (assuming that
+        data is `missing at random
+        <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
+        the returned covariance matrix will be an unbiased estimate
+        of the variance and covariance between the member Series.
+
+        However, for many applications this estimate may not be acceptable
+        because the estimate covariance matrix is not guaranteed to be positive
+        semi-definite. This could lead to estimate correlations having
+        absolute values which are greater than one, and/or a non-invertible
+        covariance matrix. See `Estimation of covariance matrices
+        <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
+        matrices>`__ for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
+        ...                   columns=['dogs', 'cats'])
+        >>> df.cov()
+                  dogs      cats
+        dogs  0.666667 -1.000000
+        cats -1.000000  1.666667
+
+        >>> np.random.seed(42)
+        >>> df = pd.DataFrame(np.random.randn(1000, 5),
+        ...                   columns=['a', 'b', 'c', 'd', 'e'])
+        >>> df.cov()
+                  a         b         c         d         e
+        a  0.998438 -0.020161  0.059277 -0.008943  0.014144
+        b -0.020161  1.059352 -0.008543 -0.024738  0.009826
+        c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
+        d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
+        e  0.014144  0.009826 -0.000271 -0.013692  0.977795
+
+        **Minimum number of periods**
+
+        This method also supports an optional ``min_periods`` keyword
+        that specifies the required minimum number of non-NA observations for
+        each column pair in order to have a valid result:
+
+        >>> np.random.seed(42)
+        >>> df = pd.DataFrame(np.random.randn(20, 3),
+        ...                   columns=['a', 'b', 'c'])
+        >>> df.loc[df.index[:5], 'a'] = np.nan
+        >>> df.loc[df.index[5:10], 'b'] = np.nan
+        >>> df.cov(min_periods=12)
+                  a         b         c
+        a  0.316741       NaN -0.150812
+        b       NaN  1.248003  0.191417
+        c -0.150812  0.191417  0.895202
+        """
+        data = self._get_numeric_data() if numeric_only else self
+        cols = data.columns
+        idx = cols.copy()
+        mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
+
+        if notna(mat).all():
+            if min_periods is not None and min_periods > len(mat):
+                base_cov = np.empty((mat.shape[1], mat.shape[1]))
+                base_cov.fill(np.nan)
+            else:
+                base_cov = np.cov(mat.T, ddof=ddof)
+            base_cov = base_cov.reshape((len(cols), len(cols)))
+        else:
+            base_cov = libalgos.nancorr(mat, cov=True, minp=min_periods)
+
+        result = self._constructor(base_cov, index=idx, columns=cols, copy=False)
+        return result.__finalize__(self, method="cov")
+
+    def corrwith(
+        self,
+        other: DataFrame | Series,
+        axis: Axis = 0,
+        drop: bool = False,
+        method: CorrelationMethod = "pearson",
+        numeric_only: bool = False,
+    ) -> Series:
+        """
+        Compute pairwise correlation.
+
+        Pairwise correlation is computed between rows or columns of
+        DataFrame with rows or columns of Series or DataFrame. DataFrames
+        are first aligned along both axes before computing the
+        correlations.
+
+        Parameters
+        ----------
+        other : DataFrame, Series
+            Object with which to compute correlations.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for
+            column-wise.
+        drop : bool, default False
+            Drop missing indices from result.
+        method : {'pearson', 'kendall', 'spearman'} or callable
+            Method of correlation:
+
+            * pearson : standard correlation coefficient
+            * kendall : Kendall Tau correlation coefficient
+            * spearman : Spearman rank correlation
+            * callable: callable with input two 1d ndarrays
+                and returning a float.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        Returns
+        -------
+        Series
+            Pairwise correlations.
+
+        See Also
+        --------
+        DataFrame.corr : Compute pairwise correlation of columns.
+
+        Examples
+        --------
+        >>> index = ["a", "b", "c", "d", "e"]
+        >>> columns = ["one", "two", "three", "four"]
+        >>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
+        >>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
+        >>> df1.corrwith(df2)
+        one      1.0
+        two      1.0
+        three    1.0
+        four     1.0
+        dtype: float64
+
+        >>> df2.corrwith(df1, axis=1)
+        a    1.0
+        b    1.0
+        c    1.0
+        d    1.0
+        e    NaN
+        dtype: float64
+        """  # noqa: E501
+        axis = self._get_axis_number(axis)
+        this = self._get_numeric_data() if numeric_only else self
+
+        if isinstance(other, Series):
+            return this.apply(lambda x: other.corr(x, method=method), axis=axis)
+
+        if numeric_only:
+            other = other._get_numeric_data()
+        left, right = this.align(other, join="inner", copy=False)
+
+        if axis == 1:
+            left = left.T
+            right = right.T
+
+        if method == "pearson":
+            # mask missing values
+            left = left + right * 0
+            right = right + left * 0
+
+            # demeaned data
+            ldem = left - left.mean(numeric_only=numeric_only)
+            rdem = right - right.mean(numeric_only=numeric_only)
+
+            num = (ldem * rdem).sum()
+            dom = (
+                (left.count() - 1)
+                * left.std(numeric_only=numeric_only)
+                * right.std(numeric_only=numeric_only)
+            )
+
+            correl = num / dom
+
+        elif method in ["kendall", "spearman"] or callable(method):
+
+            def c(x):
+                return nanops.nancorr(x[0], x[1], method=method)
+
+            correl = self._constructor_sliced(
+                map(c, zip(left.values.T, right.values.T)),
+                index=left.columns,
+                copy=False,
+            )
+
+        else:
+            raise ValueError(
+                f"Invalid method {method} was passed, "
+                "valid methods are: 'pearson', 'kendall', "
+                "'spearman', or callable"
+            )
+
+        if not drop:
+            # Find non-matching labels along the given axis
+            # and append missing correlations (GH 22375)
+            raxis: AxisInt = 1 if axis == 0 else 0
+            result_index = this._get_axis(raxis).union(other._get_axis(raxis))
+            idx_diff = result_index.difference(correl.index)
+
+            if len(idx_diff) > 0:
+                correl = correl._append(
+                    Series([np.nan] * len(idx_diff), index=idx_diff)
+                )
+
+        return correl
+
+    # ----------------------------------------------------------------------
+    # ndarray-like stats methods
+
+    def count(self, axis: Axis = 0, numeric_only: bool = False):
+        """
+        Count non-NA cells for each column or row.
+
+        The values `None`, `NaN`, `NaT`, ``pandas.NA`` are considered NA.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            If 0 or 'index' counts are generated for each column.
+            If 1 or 'columns' counts are generated for each row.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+        Returns
+        -------
+        Series
+            For each column/row the number of non-NA/null entries.
+
+        See Also
+        --------
+        Series.count: Number of non-NA elements in a Series.
+        DataFrame.value_counts: Count unique combinations of columns.
+        DataFrame.shape: Number of DataFrame rows and columns (including NA
+            elements).
+        DataFrame.isna: Boolean same-sized DataFrame showing places of NA
+            elements.
+
+        Examples
+        --------
+        Constructing DataFrame from a dictionary:
+
+        >>> df = pd.DataFrame({"Person":
+        ...                    ["John", "Myla", "Lewis", "John", "Myla"],
+        ...                    "Age": [24., np.nan, 21., 33, 26],
+        ...                    "Single": [False, True, True, True, False]})
+        >>> df
+           Person   Age  Single
+        0    John  24.0   False
+        1    Myla   NaN    True
+        2   Lewis  21.0    True
+        3    John  33.0    True
+        4    Myla  26.0   False
+
+        Notice the uncounted NA values:
+
+        >>> df.count()
+        Person    5
+        Age       4
+        Single    5
+        dtype: int64
+
+        Counts for each **row**:
+
+        >>> df.count(axis='columns')
+        0    3
+        1    2
+        2    3
+        3    3
+        4    3
+        dtype: int64
+        """
+        axis = self._get_axis_number(axis)
+
+        if numeric_only:
+            frame = self._get_numeric_data()
+        else:
+            frame = self
+
+        # GH #423
+        if len(frame._get_axis(axis)) == 0:
+            result = self._constructor_sliced(0, index=frame._get_agg_axis(axis))
+        else:
+            result = notna(frame).sum(axis=axis)
+
+        return result.astype("int64", copy=False).__finalize__(self, method="count")
+
+    def _reduce(
+        self,
+        op,
+        name: str,
+        *,
+        axis: Axis = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        filter_type=None,
+        **kwds,
+    ):
+        assert filter_type is None or filter_type == "bool", filter_type
+        out_dtype = "bool" if filter_type == "bool" else None
+
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        def func(values: np.ndarray):
+            # We only use this in the case that operates on self.values
+            return op(values, axis=axis, skipna=skipna, **kwds)
+
+        dtype_has_keepdims: dict[ExtensionDtype, bool] = {}
+
+        def blk_func(values, axis: Axis = 1):
+            if isinstance(values, ExtensionArray):
+                if not is_1d_only_ea_dtype(values.dtype) and not isinstance(
+                    self._mgr, ArrayManager
+                ):
+                    return values._reduce(name, axis=1, skipna=skipna, **kwds)
+                has_keepdims = dtype_has_keepdims.get(values.dtype)
+                if has_keepdims is None:
+                    sign = signature(values._reduce)
+                    has_keepdims = "keepdims" in sign.parameters
+                    dtype_has_keepdims[values.dtype] = has_keepdims
+                if has_keepdims:
+                    return values._reduce(name, skipna=skipna, keepdims=True, **kwds)
+                else:
+                    warnings.warn(
+                        f"{type(values)}._reduce will require a `keepdims` parameter "
+                        "in the future",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    result = values._reduce(name, skipna=skipna, **kwds)
+                    return np.array([result])
+            else:
+                return op(values, axis=axis, skipna=skipna, **kwds)
+
+        def _get_data() -> DataFrame:
+            if filter_type is None:
+                data = self._get_numeric_data()
+            else:
+                # GH#25101, GH#24434
+                assert filter_type == "bool"
+                data = self._get_bool_data()
+            return data
+
+        # Case with EAs see GH#35881
+        df = self
+        if numeric_only:
+            df = _get_data()
+        if axis is None:
+            dtype = find_common_type([arr.dtype for arr in df._mgr.arrays])
+            if isinstance(dtype, ExtensionDtype):
+                df = df.astype(dtype, copy=False)
+                arr = concat_compat(list(df._iter_column_arrays()))
+                return arr._reduce(name, skipna=skipna, keepdims=False, **kwds)
+            return func(df.values)
+        elif axis == 1:
+            if len(df.index) == 0:
+                # Taking a transpose would result in no columns, losing the dtype.
+                # In the empty case, reducing along axis 0 or 1 gives the same
+                # result dtype, so reduce with axis=0 and ignore values
+                result = df._reduce(
+                    op,
+                    name,
+                    axis=0,
+                    skipna=skipna,
+                    numeric_only=False,
+                    filter_type=filter_type,
+                    **kwds,
+                ).iloc[:0]
+                result.index = df.index
+                return result
+
+            # kurtosis excluded since groupby does not implement it
+            if df.shape[1] and name != "kurt":
+                dtype = find_common_type([arr.dtype for arr in df._mgr.arrays])
+                if isinstance(dtype, ExtensionDtype):
+                    # GH 54341: fastpath for EA-backed axis=1 reductions
+                    # This flattens the frame into a single 1D array while keeping
+                    # track of the row and column indices of the original frame. Once
+                    # flattened, grouping by the row indices and aggregating should
+                    # be equivalent to transposing the original frame and aggregating
+                    # with axis=0.
+                    name = {"argmax": "idxmax", "argmin": "idxmin"}.get(name, name)
+                    df = df.astype(dtype, copy=False)
+                    arr = concat_compat(list(df._iter_column_arrays()))
+                    nrows, ncols = df.shape
+                    row_index = np.tile(np.arange(nrows), ncols)
+                    col_index = np.repeat(np.arange(ncols), nrows)
+                    ser = Series(arr, index=col_index, copy=False)
+                    # GroupBy will raise a warning with SeriesGroupBy as the object,
+                    # likely confusing users
+                    with rewrite_warning(
+                        target_message=(
+                            f"The behavior of SeriesGroupBy.{name} with all-NA values"
+                        ),
+                        target_category=FutureWarning,
+                        new_message=(
+                            f"The behavior of {type(self).__name__}.{name} with all-NA "
+                            "values, or any-NA and skipna=False, is deprecated. In "
+                            "a future version this will raise ValueError"
+                        ),
+                    ):
+                        result = ser.groupby(row_index).agg(name, **kwds)
+                    result.index = df.index
+                    if not skipna and name not in ("any", "all"):
+                        mask = df.isna().to_numpy(dtype=np.bool_).any(axis=1)
+                        other = -1 if name in ("idxmax", "idxmin") else lib.no_default
+                        result = result.mask(mask, other)
+                    return result
+
+            df = df.T
+
+        # After possibly _get_data and transposing, we are now in the
+        #  simple case where we can use BlockManager.reduce
+        res = df._mgr.reduce(blk_func)
+        out = df._constructor_from_mgr(res, axes=res.axes).iloc[0]
+        if out_dtype is not None and out.dtype != "boolean":
+            out = out.astype(out_dtype)
+        elif (df._mgr.get_dtypes() == object).any() and name not in ["any", "all"]:
+            out = out.astype(object)
+        elif len(self) == 0 and out.dtype == object and name in ("sum", "prod"):
+            # Even if we are object dtype, follow numpy and return
+            #  float64, see test_apply_funcs_over_empty
+            out = out.astype(np.float64)
+
+        return out
+
+    def _reduce_axis1(self, name: str, func, skipna: bool) -> Series:
+        """
+        Special case for _reduce to try to avoid a potentially-expensive transpose.
+
+        Apply the reduction block-wise along axis=1 and then reduce the resulting
+        1D arrays.
+        """
+        if name == "all":
+            result = np.ones(len(self), dtype=bool)
+            ufunc = np.logical_and
+        elif name == "any":
+            result = np.zeros(len(self), dtype=bool)
+            # error: Incompatible types in assignment
+            # (expression has type "_UFunc_Nin2_Nout1[Literal['logical_or'],
+            # Literal[20], Literal[False]]", variable has type
+            # "_UFunc_Nin2_Nout1[Literal['logical_and'], Literal[20],
+            # Literal[True]]")
+            ufunc = np.logical_or  # type: ignore[assignment]
+        else:
+            raise NotImplementedError(name)
+
+        for arr in self._mgr.arrays:
+            middle = func(arr, axis=0, skipna=skipna)
+            result = ufunc(result, middle)
+
+        res_ser = self._constructor_sliced(result, index=self.index, copy=False)
+        return res_ser
+
+    @doc(make_doc("any", ndim=2))
+    # error: Signature of "any" incompatible with supertype "NDFrame"
+    def any(  # type: ignore[override]
+        self,
+        *,
+        axis: Axis | None = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> Series | bool:
+        result = self._logical_func(
+            "any", nanops.nanany, axis, bool_only, skipna, **kwargs
+        )
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="any")
+        return result
+
+    @doc(make_doc("all", ndim=2))
+    def all(
+        self,
+        axis: Axis | None = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> Series | bool:
+        result = self._logical_func(
+            "all", nanops.nanall, axis, bool_only, skipna, **kwargs
+        )
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="all")
+        return result
+
+    @doc(make_doc("min", ndim=2))
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().min(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="min")
+        return result
+
+    @doc(make_doc("max", ndim=2))
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().max(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="max")
+        return result
+
+    @doc(make_doc("sum", ndim=2))
+    def sum(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        result = super().sum(axis, skipna, numeric_only, min_count, **kwargs)
+        return result.__finalize__(self, method="sum")
+
+    @doc(make_doc("prod", ndim=2))
+    def prod(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        result = super().prod(axis, skipna, numeric_only, min_count, **kwargs)
+        return result.__finalize__(self, method="prod")
+
+    @doc(make_doc("mean", ndim=2))
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().mean(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="mean")
+        return result
+
+    @doc(make_doc("median", ndim=2))
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().median(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="median")
+        return result
+
+    @doc(make_doc("sem", ndim=2))
+    def sem(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().sem(axis, skipna, ddof, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="sem")
+        return result
+
+    @doc(make_doc("var", ndim=2))
+    def var(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().var(axis, skipna, ddof, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="var")
+        return result
+
+    @doc(make_doc("std", ndim=2))
+    def std(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().std(axis, skipna, ddof, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="std")
+        return result
+
+    @doc(make_doc("skew", ndim=2))
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().skew(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="skew")
+        return result
+
+    @doc(make_doc("kurt", ndim=2))
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        result = super().kurt(axis, skipna, numeric_only, **kwargs)
+        if isinstance(result, Series):
+            result = result.__finalize__(self, method="kurt")
+        return result
+
+    kurtosis = kurt
+    product = prod
+
+    @doc(make_doc("cummin", ndim=2))
+    def cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cummax", ndim=2))
+    def cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumsum", ndim=2))
+    def cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumprod", 2))
+    def cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
+
+    def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
+        """
+        Count number of distinct elements in specified axis.
+
+        Return Series with number of distinct elements. Can ignore NaN
+        values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
+            column-wise.
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.nunique: Method nunique for Series.
+        DataFrame.count: Count non-NA cells for each column or row.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
+        >>> df.nunique()
+        A    3
+        B    2
+        dtype: int64
+
+        >>> df.nunique(axis=1)
+        0    1
+        1    2
+        2    2
+        dtype: int64
+        """
+        return self.apply(Series.nunique, axis=axis, dropna=dropna)
+
+    @doc(_shared_docs["idxmin"], numeric_only_default="False")
+    def idxmin(
+        self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
+    ) -> Series:
+        axis = self._get_axis_number(axis)
+
+        if self.empty and len(self.axes[axis]):
+            axis_dtype = self.axes[axis].dtype
+            return self._constructor_sliced(dtype=axis_dtype)
+
+        if numeric_only:
+            data = self._get_numeric_data()
+        else:
+            data = self
+
+        res = data._reduce(
+            nanops.nanargmin, "argmin", axis=axis, skipna=skipna, numeric_only=False
+        )
+        indices = res._values
+        # indices will always be np.ndarray since axis is not N
+
+        if (indices == -1).any():
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmin with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        index = data._get_axis(axis)
+        result = algorithms.take(
+            index._values, indices, allow_fill=True, fill_value=index._na_value
+        )
+        final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
+        return final_result.__finalize__(self, method="idxmin")
+
+    @doc(_shared_docs["idxmax"], numeric_only_default="False")
+    def idxmax(
+        self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
+    ) -> Series:
+        axis = self._get_axis_number(axis)
+
+        if self.empty and len(self.axes[axis]):
+            axis_dtype = self.axes[axis].dtype
+            return self._constructor_sliced(dtype=axis_dtype)
+
+        if numeric_only:
+            data = self._get_numeric_data()
+        else:
+            data = self
+
+        res = data._reduce(
+            nanops.nanargmax, "argmax", axis=axis, skipna=skipna, numeric_only=False
+        )
+        indices = res._values
+        # indices will always be 1d array since axis is not None
+
+        if (indices == -1).any():
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmax with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        index = data._get_axis(axis)
+        result = algorithms.take(
+            index._values, indices, allow_fill=True, fill_value=index._na_value
+        )
+        final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
+        return final_result.__finalize__(self, method="idxmax")
+
+    def _get_agg_axis(self, axis_num: int) -> Index:
+        """
+        Let's be explicit about this.
+        """
+        if axis_num == 0:
+            return self.columns
+        elif axis_num == 1:
+            return self.index
+        else:
+            raise ValueError(f"Axis must be 0 or 1 (got {repr(axis_num)})")
+
+    def mode(
+        self, axis: Axis = 0, numeric_only: bool = False, dropna: bool = True
+    ) -> DataFrame:
+        """
+        Get the mode(s) of each element along the selected axis.
+
+        The mode of a set of values is the value that appears most often.
+        It can be multiple values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to iterate over while searching for the mode:
+
+            * 0 or 'index' : get mode of each column
+            * 1 or 'columns' : get mode of each row.
+
+        numeric_only : bool, default False
+            If True, only apply to numeric columns.
+        dropna : bool, default True
+            Don't consider counts of NaN/NaT.
+
+        Returns
+        -------
+        DataFrame
+            The modes of each column or row.
+
+        See Also
+        --------
+        Series.mode : Return the highest frequency value in a Series.
+        Series.value_counts : Return the counts of values in a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('bird', 2, 2),
+        ...                    ('mammal', 4, np.nan),
+        ...                    ('arthropod', 8, 0),
+        ...                    ('bird', 2, np.nan)],
+        ...                   index=('falcon', 'horse', 'spider', 'ostrich'),
+        ...                   columns=('species', 'legs', 'wings'))
+        >>> df
+                   species  legs  wings
+        falcon        bird     2    2.0
+        horse       mammal     4    NaN
+        spider   arthropod     8    0.0
+        ostrich       bird     2    NaN
+
+        By default, missing values are not considered, and the mode of wings
+        are both 0 and 2. Because the resulting DataFrame has two rows,
+        the second row of ``species`` and ``legs`` contains ``NaN``.
+
+        >>> df.mode()
+          species  legs  wings
+        0    bird   2.0    0.0
+        1     NaN   NaN    2.0
+
+        Setting ``dropna=False`` ``NaN`` values are considered and they can be
+        the mode (like for wings).
+
+        >>> df.mode(dropna=False)
+          species  legs  wings
+        0    bird     2    NaN
+
+        Setting ``numeric_only=True``, only the mode of numeric columns is
+        computed, and columns of other types are ignored.
+
+        >>> df.mode(numeric_only=True)
+           legs  wings
+        0   2.0    0.0
+        1   NaN    2.0
+
+        To compute the mode over columns and not rows, use the axis parameter:
+
+        >>> df.mode(axis='columns', numeric_only=True)
+                   0    1
+        falcon   2.0  NaN
+        horse    4.0  NaN
+        spider   0.0  8.0
+        ostrich  2.0  NaN
+        """
+        data = self if not numeric_only else self._get_numeric_data()
+
+        def f(s):
+            return s.mode(dropna=dropna)
+
+        data = data.apply(f, axis=axis)
+        # Ensure index is type stable (should always use int index)
+        if data.empty:
+            data.index = default_index(0)
+
+        return data
+
+    @overload
+    def quantile(
+        self,
+        q: float = ...,
+        axis: Axis = ...,
+        numeric_only: bool = ...,
+        interpolation: QuantileInterpolation = ...,
+        method: Literal["single", "table"] = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: AnyArrayLike | Sequence[float],
+        axis: Axis = ...,
+        numeric_only: bool = ...,
+        interpolation: QuantileInterpolation = ...,
+        method: Literal["single", "table"] = ...,
+    ) -> Series | DataFrame:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: float | AnyArrayLike | Sequence[float] = ...,
+        axis: Axis = ...,
+        numeric_only: bool = ...,
+        interpolation: QuantileInterpolation = ...,
+        method: Literal["single", "table"] = ...,
+    ) -> Series | DataFrame:
+        ...
+
+    def quantile(
+        self,
+        q: float | AnyArrayLike | Sequence[float] = 0.5,
+        axis: Axis = 0,
+        numeric_only: bool = False,
+        interpolation: QuantileInterpolation = "linear",
+        method: Literal["single", "table"] = "single",
+    ) -> Series | DataFrame:
+        """
+        Return values at the given quantile over requested axis.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            Value between 0 <= q <= 1, the quantile(s) to compute.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+            * linear: `i + (j - i) * fraction`, where `fraction` is the
+              fractional part of the index surrounded by `i` and `j`.
+            * lower: `i`.
+            * higher: `j`.
+            * nearest: `i` or `j` whichever is nearest.
+            * midpoint: (`i` + `j`) / 2.
+        method : {'single', 'table'}, default 'single'
+            Whether to compute quantiles per-column ('single') or over all columns
+            ('table'). When 'table', the only allowed interpolation methods are
+            'nearest', 'lower', and 'higher'.
+
+        Returns
+        -------
+        Series or DataFrame
+
+            If ``q`` is an array, a DataFrame will be returned where the
+              index is ``q``, the columns are the columns of self, and the
+              values are the quantiles.
+            If ``q`` is a float, a Series will be returned where the
+              index is the columns of self and the values are the quantiles.
+
+        See Also
+        --------
+        core.window.rolling.Rolling.quantile: Rolling quantile.
+        numpy.percentile: Numpy function to compute the percentile.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
+        ...                   columns=['a', 'b'])
+        >>> df.quantile(.1)
+        a    1.3
+        b    3.7
+        Name: 0.1, dtype: float64
+        >>> df.quantile([.1, .5])
+               a     b
+        0.1  1.3   3.7
+        0.5  2.5  55.0
+
+        Specifying `method='table'` will compute the quantile over all columns.
+
+        >>> df.quantile(.1, method="table", interpolation="nearest")
+        a    1
+        b    1
+        Name: 0.1, dtype: int64
+        >>> df.quantile([.1, .5], method="table", interpolation="nearest")
+             a    b
+        0.1  1    1
+        0.5  3  100
+
+        Specifying `numeric_only=False` will also compute the quantile of
+        datetime and timedelta data.
+
+        >>> df = pd.DataFrame({'A': [1, 2],
+        ...                    'B': [pd.Timestamp('2010'),
+        ...                          pd.Timestamp('2011')],
+        ...                    'C': [pd.Timedelta('1 days'),
+        ...                          pd.Timedelta('2 days')]})
+        >>> df.quantile(0.5, numeric_only=False)
+        A                    1.5
+        B    2010-07-02 12:00:00
+        C        1 days 12:00:00
+        Name: 0.5, dtype: object
+        """
+        validate_percentile(q)
+        axis = self._get_axis_number(axis)
+
+        if not is_list_like(q):
+            # BlockManager.quantile expects listlike, so we wrap and unwrap here
+            # error: List item 0 has incompatible type "float | ExtensionArray |
+            # ndarray[Any, Any] | Index | Series | Sequence[float]"; expected "float"
+            res_df = self.quantile(
+                [q],  # type: ignore[list-item]
+                axis=axis,
+                numeric_only=numeric_only,
+                interpolation=interpolation,
+                method=method,
+            )
+            if method == "single":
+                res = res_df.iloc[0]
+            else:
+                # cannot directly iloc over sparse arrays
+                res = res_df.T.iloc[:, 0]
+            if axis == 1 and len(self) == 0:
+                # GH#41544 try to get an appropriate dtype
+                dtype = find_common_type(list(self.dtypes))
+                if needs_i8_conversion(dtype):
+                    return res.astype(dtype)
+            return res
+
+        q = Index(q, dtype=np.float64)
+        data = self._get_numeric_data() if numeric_only else self
+
+        if axis == 1:
+            data = data.T
+
+        if len(data.columns) == 0:
+            # GH#23925 _get_numeric_data may have dropped all columns
+            cols = Index([], name=self.columns.name)
+
+            dtype = np.float64
+            if axis == 1:
+                # GH#41544 try to get an appropriate dtype
+                cdtype = find_common_type(list(self.dtypes))
+                if needs_i8_conversion(cdtype):
+                    dtype = cdtype
+
+            res = self._constructor([], index=q, columns=cols, dtype=dtype)
+            return res.__finalize__(self, method="quantile")
+
+        valid_method = {"single", "table"}
+        if method not in valid_method:
+            raise ValueError(
+                f"Invalid method: {method}. Method must be in {valid_method}."
+            )
+        if method == "single":
+            res = data._mgr.quantile(qs=q, interpolation=interpolation)
+        elif method == "table":
+            valid_interpolation = {"nearest", "lower", "higher"}
+            if interpolation not in valid_interpolation:
+                raise ValueError(
+                    f"Invalid interpolation: {interpolation}. "
+                    f"Interpolation must be in {valid_interpolation}"
+                )
+            # handle degenerate case
+            if len(data) == 0:
+                if data.ndim == 2:
+                    dtype = find_common_type(list(self.dtypes))
+                else:
+                    dtype = self.dtype
+                return self._constructor([], index=q, columns=data.columns, dtype=dtype)
+
+            q_idx = np.quantile(np.arange(len(data)), q, method=interpolation)
+
+            by = data.columns
+            if len(by) > 1:
+                keys = [data._get_label_or_level_values(x) for x in by]
+                indexer = lexsort_indexer(keys)
+            else:
+                k = data._get_label_or_level_values(by[0])
+                indexer = nargsort(k)
+
+            res = data._mgr.take(indexer[q_idx], verify=False)
+            res.axes[1] = q
+
+        result = self._constructor_from_mgr(res, axes=res.axes)
+        return result.__finalize__(self, method="quantile")
+
+    def to_timestamp(
+        self,
+        freq: Frequency | None = None,
+        how: ToTimestampHow = "start",
+        axis: Axis = 0,
+        copy: bool | None = None,
+    ) -> DataFrame:
+        """
+        Cast to DatetimeIndex of timestamps, at *beginning* of period.
+
+        Parameters
+        ----------
+        freq : str, default frequency of PeriodIndex
+            Desired frequency.
+        how : {'s', 'e', 'start', 'end'}
+            Convention for converting period to timestamp; start of period
+            vs. end.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to convert (the index by default).
+        copy : bool, default True
+            If False then underlying input data is not copied.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        DataFrame
+            The DataFrame has a DatetimeIndex.
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')
+        >>> d = {'col1': [1, 2], 'col2': [3, 4]}
+        >>> df1 = pd.DataFrame(data=d, index=idx)
+        >>> df1
+              col1   col2
+        2023     1      3
+        2024	 2      4
+
+        The resulting timestamps will be at the beginning of the year in this case
+
+        >>> df1 = df1.to_timestamp()
+        >>> df1
+                    col1   col2
+        2023-01-01     1      3
+        2024-01-01     2      4
+        >>> df1.index
+        DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)
+
+        Using `freq` which is the offset that the Timestamps will have
+
+        >>> df2 = pd.DataFrame(data=d, index=idx)
+        >>> df2 = df2.to_timestamp(freq='M')
+        >>> df2
+                    col1   col2
+        2023-01-31     1      3
+        2024-01-31     2      4
+        >>> df2.index
+        DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)
+        """
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+
+        axis_name = self._get_axis_name(axis)
+        old_ax = getattr(self, axis_name)
+        if not isinstance(old_ax, PeriodIndex):
+            raise TypeError(f"unsupported Type {type(old_ax).__name__}")
+
+        new_ax = old_ax.to_timestamp(freq=freq, how=how)
+
+        setattr(new_obj, axis_name, new_ax)
+        return new_obj
+
+    def to_period(
+        self, freq: Frequency | None = None, axis: Axis = 0, copy: bool | None = None
+    ) -> DataFrame:
+        """
+        Convert DataFrame from DatetimeIndex to PeriodIndex.
+
+        Convert DataFrame from DatetimeIndex to PeriodIndex with desired
+        frequency (inferred from index if not passed).
+
+        Parameters
+        ----------
+        freq : str, default
+            Frequency of the PeriodIndex.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to convert (the index by default).
+        copy : bool, default True
+            If False then underlying input data is not copied.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        DataFrame
+            The DataFrame has a PeriodIndex.
+
+        Examples
+        --------
+        >>> idx = pd.to_datetime(
+        ...     [
+        ...         "2001-03-31 00:00:00",
+        ...         "2002-05-31 00:00:00",
+        ...         "2003-08-31 00:00:00",
+        ...     ]
+        ... )
+
+        >>> idx
+        DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
+        dtype='datetime64[ns]', freq=None)
+
+        >>> idx.to_period("M")
+        PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')
+
+        For the yearly frequency
+
+        >>> idx.to_period("Y")
+        PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')
+        """
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+
+        axis_name = self._get_axis_name(axis)
+        old_ax = getattr(self, axis_name)
+        if not isinstance(old_ax, DatetimeIndex):
+            raise TypeError(f"unsupported Type {type(old_ax).__name__}")
+
+        new_ax = old_ax.to_period(freq=freq)
+
+        setattr(new_obj, axis_name, new_ax)
+        return new_obj
+
+    def isin(self, values: Series | DataFrame | Sequence | Mapping) -> DataFrame:
+        """
+        Whether each element in the DataFrame is contained in values.
+
+        Parameters
+        ----------
+        values : iterable, Series, DataFrame or dict
+            The result will only be true at a location if all the
+            labels match. If `values` is a Series, that's the index. If
+            `values` is a dict, the keys must be the column names,
+            which must match. If `values` is a DataFrame,
+            then both the index and column labels must match.
+
+        Returns
+        -------
+        DataFrame
+            DataFrame of booleans showing whether each element in the DataFrame
+            is contained in values.
+
+        See Also
+        --------
+        DataFrame.eq: Equality test for DataFrame.
+        Series.isin: Equivalent method on Series.
+        Series.str.contains: Test if pattern or regex is contained within a
+            string of a Series or Index.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
+        ...                   index=['falcon', 'dog'])
+        >>> df
+                num_legs  num_wings
+        falcon         2          2
+        dog            4          0
+
+        When ``values`` is a list check whether every value in the DataFrame
+        is present in the list (which animals have 0 or 2 legs or wings)
+
+        >>> df.isin([0, 2])
+                num_legs  num_wings
+        falcon      True       True
+        dog        False       True
+
+        To check if ``values`` is *not* in the DataFrame, use the ``~`` operator:
+
+        >>> ~df.isin([0, 2])
+                num_legs  num_wings
+        falcon     False      False
+        dog         True      False
+
+        When ``values`` is a dict, we can pass values to check for each
+        column separately:
+
+        >>> df.isin({'num_wings': [0, 3]})
+                num_legs  num_wings
+        falcon     False      False
+        dog        False       True
+
+        When ``values`` is a Series or DataFrame the index and column must
+        match. Note that 'falcon' does not match based on the number of legs
+        in other.
+
+        >>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
+        ...                      index=['spider', 'falcon'])
+        >>> df.isin(other)
+                num_legs  num_wings
+        falcon     False       True
+        dog        False      False
+        """
+        if isinstance(values, dict):
+            from pandas.core.reshape.concat import concat
+
+            values = collections.defaultdict(list, values)
+            result = concat(
+                (
+                    self.iloc[:, [i]].isin(values[col])
+                    for i, col in enumerate(self.columns)
+                ),
+                axis=1,
+            )
+        elif isinstance(values, Series):
+            if not values.index.is_unique:
+                raise ValueError("cannot compute isin with a duplicate axis.")
+            result = self.eq(values.reindex_like(self), axis="index")
+        elif isinstance(values, DataFrame):
+            if not (values.columns.is_unique and values.index.is_unique):
+                raise ValueError("cannot compute isin with a duplicate axis.")
+            result = self.eq(values.reindex_like(self))
+        else:
+            if not is_list_like(values):
+                raise TypeError(
+                    "only list-like or dict-like objects are allowed "
+                    "to be passed to DataFrame.isin(), "
+                    f"you passed a '{type(values).__name__}'"
+                )
+
+            def isin_(x):
+                # error: Argument 2 to "isin" has incompatible type "Union[Series,
+                # DataFrame, Sequence[Any], Mapping[Any, Any]]"; expected
+                # "Union[Union[Union[ExtensionArray, ndarray[Any, Any]], Index,
+                # Series], List[Any], range]"
+                result = algorithms.isin(
+                    x.ravel(),
+                    values,  # type: ignore[arg-type]
+                )
+                return result.reshape(x.shape)
+
+            res_mgr = self._mgr.apply(isin_)
+            result = self._constructor_from_mgr(
+                res_mgr,
+                axes=res_mgr.axes,
+            )
+        return result.__finalize__(self, method="isin")
+
+    # ----------------------------------------------------------------------
+    # Add index and columns
+    _AXIS_ORDERS: list[Literal["index", "columns"]] = ["index", "columns"]
+    _AXIS_TO_AXIS_NUMBER: dict[Axis, int] = {
+        **NDFrame._AXIS_TO_AXIS_NUMBER,
+        1: 1,
+        "columns": 1,
+    }
+    _AXIS_LEN = len(_AXIS_ORDERS)
+    _info_axis_number: Literal[1] = 1
+    _info_axis_name: Literal["columns"] = "columns"
+
+    index = properties.AxisProperty(
+        axis=1,
+        doc="""
+        The index (row labels) of the DataFrame.
+
+        The index of a DataFrame is a series of labels that identify each row.
+        The labels can be integers, strings, or any other hashable type. The index
+        is used for label-based access and alignment, and can be accessed or
+        modified using this attribute.
+
+        Returns
+        -------
+        pandas.Index
+            The index labels of the DataFrame.
+
+        See Also
+        --------
+        DataFrame.columns : The column labels of the DataFrame.
+        DataFrame.to_numpy : Convert the DataFrame to a NumPy array.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
+        ...                    'Age': [25, 30, 35],
+        ...                    'Location': ['Seattle', 'New York', 'Kona']},
+        ...                   index=([10, 20, 30]))
+        >>> df.index
+        Index([10, 20, 30], dtype='int64')
+
+        In this example, we create a DataFrame with 3 rows and 3 columns,
+        including Name, Age, and Location information. We set the index labels to
+        be the integers 10, 20, and 30. We then access the `index` attribute of the
+        DataFrame, which returns an `Index` object containing the index labels.
+
+        >>> df.index = [100, 200, 300]
+        >>> df
+            Name  Age Location
+        100  Alice   25  Seattle
+        200    Bob   30 New York
+        300  Aritra  35    Kona
+
+        In this example, we modify the index labels of the DataFrame by assigning
+        a new list of labels to the `index` attribute. The DataFrame is then
+        updated with the new labels, and the output shows the modified DataFrame.
+        """,
+    )
+    columns = properties.AxisProperty(
+        axis=0,
+        doc=dedent(
+            """
+                The column labels of the DataFrame.
+
+                Examples
+                --------
+                >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+                >>> df
+                     A  B
+                0    1  3
+                1    2  4
+                >>> df.columns
+                Index(['A', 'B'], dtype='object')
+                """
+        ),
+    )
+
+    # ----------------------------------------------------------------------
+    # Add plotting methods to DataFrame
+    plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
+    hist = pandas.plotting.hist_frame
+    boxplot = pandas.plotting.boxplot_frame
+    sparse = CachedAccessor("sparse", SparseFrameAccessor)
+
+    # ----------------------------------------------------------------------
+    # Internal Interface Methods
+
+    def _to_dict_of_blocks(self):
+        """
+        Return a dict of dtype -> Constructor Types that
+        each is a homogeneous dtype.
+
+        Internal ONLY - only works for BlockManager
+        """
+        mgr = self._mgr
+        # convert to BlockManager if needed -> this way support ArrayManager as well
+        mgr = cast(BlockManager, mgr_to_mgr(mgr, "block"))
+        return {
+            k: self._constructor_from_mgr(v, axes=v.axes).__finalize__(self)
+            for k, v, in mgr.to_dict().items()
+        }
+
+    @property
+    def values(self) -> np.ndarray:
+        """
+        Return a Numpy representation of the DataFrame.
+
+        .. warning::
+
+           We recommend using :meth:`DataFrame.to_numpy` instead.
+
+        Only the values in the DataFrame will be returned, the axes labels
+        will be removed.
+
+        Returns
+        -------
+        numpy.ndarray
+            The values of the DataFrame.
+
+        See Also
+        --------
+        DataFrame.to_numpy : Recommended alternative to this method.
+        DataFrame.index : Retrieve the index labels.
+        DataFrame.columns : Retrieving the column names.
+
+        Notes
+        -----
+        The dtype will be a lower-common-denominator dtype (implicit
+        upcasting); that is to say if the dtypes (even of numeric types)
+        are mixed, the one that accommodates all will be chosen. Use this
+        with care if you are not dealing with the blocks.
+
+        e.g. If the dtypes are float16 and float32, dtype will be upcast to
+        float32.  If dtypes are int32 and uint8, dtype will be upcast to
+        int32. By :func:`numpy.find_common_type` convention, mixing int64
+        and uint64 will result in a float64 dtype.
+
+        Examples
+        --------
+        A DataFrame where all columns are the same type (e.g., int64) results
+        in an array of the same type.
+
+        >>> df = pd.DataFrame({'age':    [ 3,  29],
+        ...                    'height': [94, 170],
+        ...                    'weight': [31, 115]})
+        >>> df
+           age  height  weight
+        0    3      94      31
+        1   29     170     115
+        >>> df.dtypes
+        age       int64
+        height    int64
+        weight    int64
+        dtype: object
+        >>> df.values
+        array([[  3,  94,  31],
+               [ 29, 170, 115]])
+
+        A DataFrame with mixed type columns(e.g., str/object, int64, float32)
+        results in an ndarray of the broadest type that accommodates these
+        mixed types (e.g., object).
+
+        >>> df2 = pd.DataFrame([('parrot',   24.0, 'second'),
+        ...                     ('lion',     80.5, 1),
+        ...                     ('monkey', np.nan, None)],
+        ...                   columns=('name', 'max_speed', 'rank'))
+        >>> df2.dtypes
+        name          object
+        max_speed    float64
+        rank          object
+        dtype: object
+        >>> df2.values
+        array([['parrot', 24.0, 'second'],
+               ['lion', 80.5, 1],
+               ['monkey', nan, None]], dtype=object)
+        """
+        return self._mgr.as_array()
+
+
+def _from_nested_dict(data) -> collections.defaultdict:
+    new_data: collections.defaultdict = collections.defaultdict(dict)
+    for index, s in data.items():
+        for col, v in s.items():
+            new_data[col][index] = v
+    return new_data
+
+
+def _reindex_for_setitem(
+    value: DataFrame | Series, index: Index
+) -> tuple[ArrayLike, BlockValuesRefs | None]:
+    # reindex if necessary
+
+    if value.index.equals(index) or not len(index):
+        if using_copy_on_write() and isinstance(value, Series):
+            return value._values, value._references
+        return value._values.copy(), None
+
+    # GH#4107
+    try:
+        reindexed_value = value.reindex(index)._values
+    except ValueError as err:
+        # raised in MultiIndex.from_tuples, see test_insert_error_msmgs
+        if not value.index.is_unique:
+            # duplicate axis
+            raise err
+
+        raise TypeError(
+            "incompatible index of inserted column with frame index"
+        ) from err
+    return reindexed_value, None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/generic.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd8c1723be54a91bf87f72c72e20607452ad65db
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/generic.py
@@ -0,0 +1,14025 @@
+# pyright: reportPropertyTypeMismatch=false
+from __future__ import annotations
+
+import collections
+from copy import deepcopy
+import datetime as dt
+from functools import partial
+import gc
+from json import loads
+import operator
+import pickle
+import re
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Literal,
+    NoReturn,
+    cast,
+    final,
+    overload,
+)
+import warnings
+import weakref
+
+import numpy as np
+
+from pandas._config import (
+    config,
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import lib
+from pandas._libs.lib import is_range_indexer
+from pandas._libs.tslibs import (
+    Period,
+    Tick,
+    Timestamp,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas._typing import (
+    AlignJoin,
+    AnyArrayLike,
+    ArrayLike,
+    Axes,
+    Axis,
+    AxisInt,
+    CompressionOptions,
+    DtypeArg,
+    DtypeBackend,
+    DtypeObj,
+    FilePath,
+    FillnaOptions,
+    FloatFormatType,
+    FormattersType,
+    Frequency,
+    IgnoreRaise,
+    IndexKeyFunc,
+    IndexLabel,
+    InterpolateOptions,
+    IntervalClosedType,
+    JSONSerializable,
+    Level,
+    Manager,
+    NaPosition,
+    NDFrameT,
+    OpenFileErrors,
+    RandomState,
+    ReindexMethod,
+    Renamer,
+    Scalar,
+    Self,
+    SequenceNotStr,
+    SortKind,
+    StorageOptions,
+    Suffixes,
+    T,
+    TimeAmbiguous,
+    TimedeltaConvertibleTypes,
+    TimeNonexistent,
+    TimestampConvertibleTypes,
+    TimeUnit,
+    ValueKeyFunc,
+    WriteBuffer,
+    WriteExcelBuffer,
+    npt,
+)
+from pandas.compat import PYPY
+from pandas.compat._constants import (
+    REF_COUNT,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    AbstractMethodError,
+    ChainedAssignmentError,
+    InvalidIndexError,
+    SettingWithCopyError,
+    SettingWithCopyWarning,
+    _chained_assignment_method_msg,
+    _chained_assignment_warning_method_msg,
+    _check_cacher,
+)
+from pandas.util._decorators import (
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    check_dtype_backend,
+    validate_ascending,
+    validate_bool_kwarg,
+    validate_fillna_kwargs,
+    validate_inclusive,
+)
+
+from pandas.core.dtypes.astype import astype_is_view
+from pandas.core.dtypes.common import (
+    ensure_object,
+    ensure_platform_int,
+    ensure_str,
+    is_bool,
+    is_bool_dtype,
+    is_dict_like,
+    is_extension_array_dtype,
+    is_list_like,
+    is_number,
+    is_numeric_dtype,
+    is_re_compilable,
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import (
+    is_hashable,
+    is_nested_list_like,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import (
+    algorithms as algos,
+    arraylike,
+    common,
+    indexing,
+    missing,
+    nanops,
+    sample,
+)
+from pandas.core.array_algos.replace import should_use_regex
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import PandasObject
+from pandas.core.construction import extract_array
+from pandas.core.flags import Flags
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    default_index,
+    ensure_index,
+)
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+    SingleArrayManager,
+)
+from pandas.core.internals.construction import (
+    mgr_to_mgr,
+    ndarray_to_mgr,
+)
+from pandas.core.methods.describe import describe_ndframe
+from pandas.core.missing import (
+    clean_fill_method,
+    clean_reindex_fill_method,
+    find_valid_index,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.sorting import get_indexer_indexer
+from pandas.core.window import (
+    Expanding,
+    ExponentialMovingWindow,
+    Rolling,
+    Window,
+)
+
+from pandas.io.formats.format import (
+    DataFrameFormatter,
+    DataFrameRenderer,
+)
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._libs.tslibs import BaseOffset
+
+    from pandas import (
+        DataFrame,
+        ExcelWriter,
+        HDFStore,
+        Series,
+    )
+    from pandas.core.indexers.objects import BaseIndexer
+    from pandas.core.resample import Resampler
+
+# goal is to be able to define the docs close to function, while still being
+# able to share
+_shared_docs = {**_shared_docs}
+_shared_doc_kwargs = {
+    "axes": "keywords for axes",
+    "klass": "Series/DataFrame",
+    "axes_single_arg": "{0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame",  # noqa: E501
+    "inplace": """
+    inplace : bool, default False
+        If True, performs operation inplace and returns None.""",
+    "optional_by": """
+        by : str or list of str
+            Name or list of names to sort by""",
+}
+
+
+bool_t = bool  # Need alias because NDFrame has def bool:
+
+
+class NDFrame(PandasObject, indexing.IndexingMixin):
+    """
+    N-dimensional analogue of DataFrame. Store multi-dimensional in a
+    size-mutable, labeled data structure
+
+    Parameters
+    ----------
+    data : BlockManager
+    axes : list
+    copy : bool, default False
+    """
+
+    _internal_names: list[str] = [
+        "_mgr",
+        "_cacher",
+        "_item_cache",
+        "_cache",
+        "_is_copy",
+        "_name",
+        "_metadata",
+        "_flags",
+    ]
+    _internal_names_set: set[str] = set(_internal_names)
+    _accessors: set[str] = set()
+    _hidden_attrs: frozenset[str] = frozenset([])
+    _metadata: list[str] = []
+    _is_copy: weakref.ReferenceType[NDFrame] | str | None = None
+    _mgr: Manager
+    _attrs: dict[Hashable, Any]
+    _typ: str
+
+    # ----------------------------------------------------------------------
+    # Constructors
+
+    def __init__(self, data: Manager) -> None:
+        object.__setattr__(self, "_is_copy", None)
+        object.__setattr__(self, "_mgr", data)
+        object.__setattr__(self, "_item_cache", {})
+        object.__setattr__(self, "_attrs", {})
+        object.__setattr__(self, "_flags", Flags(self, allows_duplicate_labels=True))
+
+    @final
+    @classmethod
+    def _init_mgr(
+        cls,
+        mgr: Manager,
+        axes: dict[Literal["index", "columns"], Axes | None],
+        dtype: DtypeObj | None = None,
+        copy: bool_t = False,
+    ) -> Manager:
+        """passed a manager and a axes dict"""
+        for a, axe in axes.items():
+            if axe is not None:
+                axe = ensure_index(axe)
+                bm_axis = cls._get_block_manager_axis(a)
+                mgr = mgr.reindex_axis(axe, axis=bm_axis)
+
+        # make a copy if explicitly requested
+        if copy:
+            mgr = mgr.copy()
+        if dtype is not None:
+            # avoid further copies if we can
+            if (
+                isinstance(mgr, BlockManager)
+                and len(mgr.blocks) == 1
+                and mgr.blocks[0].values.dtype == dtype
+            ):
+                pass
+            else:
+                mgr = mgr.astype(dtype=dtype)
+        return mgr
+
+    @final
+    def _as_manager(self, typ: str, copy: bool_t = True) -> Self:
+        """
+        Private helper function to create a DataFrame with specific manager.
+
+        Parameters
+        ----------
+        typ : {"block", "array"}
+        copy : bool, default True
+            Only controls whether the conversion from Block->ArrayManager
+            copies the 1D arrays (to ensure proper/contiguous memory layout).
+
+        Returns
+        -------
+        DataFrame
+            New DataFrame using specified manager type. Is not guaranteed
+            to be a copy or not.
+        """
+        new_mgr: Manager
+        new_mgr = mgr_to_mgr(self._mgr, typ=typ, copy=copy)
+        # fastpath of passing a manager doesn't check the option/manager class
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    @final
+    @classmethod
+    def _from_mgr(cls, mgr: Manager, axes: list[Index]) -> Self:
+        """
+        Construct a new object of this type from a Manager object and axes.
+
+        Parameters
+        ----------
+        mgr : Manager
+            Must have the same ndim as cls.
+        axes : list[Index]
+
+        Notes
+        -----
+        The axes must match mgr.axes, but are required for future-proofing
+        in the event that axes are refactored out of the Manager objects.
+        """
+        obj = cls.__new__(cls)
+        NDFrame.__init__(obj, mgr)
+        return obj
+
+    # ----------------------------------------------------------------------
+    # attrs and flags
+
+    @property
+    def attrs(self) -> dict[Hashable, Any]:
+        """
+        Dictionary of global attributes of this dataset.
+
+        .. warning::
+
+           attrs is experimental and may change without warning.
+
+        See Also
+        --------
+        DataFrame.flags : Global flags applying to this object.
+
+        Notes
+        -----
+        Many operations that create new datasets will copy ``attrs``. Copies
+        are always deep so that changing ``attrs`` will only affect the
+        present dataset. ``pandas.concat`` copies ``attrs`` only if all input
+        datasets have the same ``attrs``.
+
+        Examples
+        --------
+        For Series:
+
+        >>> ser = pd.Series([1, 2, 3])
+        >>> ser.attrs = {"A": [10, 20, 30]}
+        >>> ser.attrs
+        {'A': [10, 20, 30]}
+
+        For DataFrame:
+
+        >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+        >>> df.attrs = {"A": [10, 20, 30]}
+        >>> df.attrs
+        {'A': [10, 20, 30]}
+        """
+        return self._attrs
+
+    @attrs.setter
+    def attrs(self, value: Mapping[Hashable, Any]) -> None:
+        self._attrs = dict(value)
+
+    @final
+    @property
+    def flags(self) -> Flags:
+        """
+        Get the properties associated with this pandas object.
+
+        The available flags are
+
+        * :attr:`Flags.allows_duplicate_labels`
+
+        See Also
+        --------
+        Flags : Flags that apply to pandas objects.
+        DataFrame.attrs : Global metadata applying to this dataset.
+
+        Notes
+        -----
+        "Flags" differ from "metadata". Flags reflect properties of the
+        pandas object (the Series or DataFrame). Metadata refer to properties
+        of the dataset, and should be stored in :attr:`DataFrame.attrs`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2]})
+        >>> df.flags
+        <Flags(allows_duplicate_labels=True)>
+
+        Flags can be get or set using ``.``
+
+        >>> df.flags.allows_duplicate_labels
+        True
+        >>> df.flags.allows_duplicate_labels = False
+
+        Or by slicing with a key
+
+        >>> df.flags["allows_duplicate_labels"]
+        False
+        >>> df.flags["allows_duplicate_labels"] = True
+        """
+        return self._flags
+
+    @final
+    def set_flags(
+        self,
+        *,
+        copy: bool_t = False,
+        allows_duplicate_labels: bool_t | None = None,
+    ) -> Self:
+        """
+        Return a new object with updated flags.
+
+        Parameters
+        ----------
+        copy : bool, default False
+            Specify if a copy of the object should be made.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        allows_duplicate_labels : bool, optional
+            Whether the returned object allows duplicate labels.
+
+        Returns
+        -------
+        Series or DataFrame
+            The same type as the caller.
+
+        See Also
+        --------
+        DataFrame.attrs : Global metadata applying to this dataset.
+        DataFrame.flags : Global flags applying to this object.
+
+        Notes
+        -----
+        This method returns a new object that's a view on the same data
+        as the input. Mutating the input or the output values will be reflected
+        in the other.
+
+        This method is intended to be used in method chains.
+
+        "Flags" differ from "metadata". Flags reflect properties of the
+        pandas object (the Series or DataFrame). Metadata refer to properties
+        of the dataset, and should be stored in :attr:`DataFrame.attrs`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2]})
+        >>> df.flags.allows_duplicate_labels
+        True
+        >>> df2 = df.set_flags(allows_duplicate_labels=False)
+        >>> df2.flags.allows_duplicate_labels
+        False
+        """
+        df = self.copy(deep=copy and not using_copy_on_write())
+        if allows_duplicate_labels is not None:
+            df.flags["allows_duplicate_labels"] = allows_duplicate_labels
+        return df
+
+    @final
+    @classmethod
+    def _validate_dtype(cls, dtype) -> DtypeObj | None:
+        """validate the passed dtype"""
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+            # a compound dtype
+            if dtype.kind == "V":
+                raise NotImplementedError(
+                    "compound dtypes are not implemented "
+                    f"in the {cls.__name__} constructor"
+                )
+
+        return dtype
+
+    # ----------------------------------------------------------------------
+    # Construction
+
+    @property
+    def _constructor(self) -> Callable[..., Self]:
+        """
+        Used when a manipulation result has the same dimensions as the
+        original.
+        """
+        raise AbstractMethodError(self)
+
+    # ----------------------------------------------------------------------
+    # Internals
+
+    @final
+    @property
+    def _data(self):
+        # GH#33054 retained because some downstream packages uses this,
+        #  e.g. fastparquet
+        # GH#33333
+        warnings.warn(
+            f"{type(self).__name__}._data is deprecated and will be removed in "
+            "a future version. Use public APIs instead.",
+            DeprecationWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._mgr
+
+    # ----------------------------------------------------------------------
+    # Axis
+    _AXIS_ORDERS: list[Literal["index", "columns"]]
+    _AXIS_TO_AXIS_NUMBER: dict[Axis, AxisInt] = {0: 0, "index": 0, "rows": 0}
+    _info_axis_number: int
+    _info_axis_name: Literal["index", "columns"]
+    _AXIS_LEN: int
+
+    @final
+    def _construct_axes_dict(self, axes: Sequence[Axis] | None = None, **kwargs):
+        """Return an axes dictionary for myself."""
+        d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)}
+        # error: Argument 1 to "update" of "MutableMapping" has incompatible type
+        # "Dict[str, Any]"; expected "SupportsKeysAndGetItem[Union[int, str], Any]"
+        d.update(kwargs)  # type: ignore[arg-type]
+        return d
+
+    @final
+    @classmethod
+    def _get_axis_number(cls, axis: Axis) -> AxisInt:
+        try:
+            return cls._AXIS_TO_AXIS_NUMBER[axis]
+        except KeyError:
+            raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
+
+    @final
+    @classmethod
+    def _get_axis_name(cls, axis: Axis) -> Literal["index", "columns"]:
+        axis_number = cls._get_axis_number(axis)
+        return cls._AXIS_ORDERS[axis_number]
+
+    @final
+    def _get_axis(self, axis: Axis) -> Index:
+        axis_number = self._get_axis_number(axis)
+        assert axis_number in {0, 1}
+        return self.index if axis_number == 0 else self.columns
+
+    @final
+    @classmethod
+    def _get_block_manager_axis(cls, axis: Axis) -> AxisInt:
+        """Map the axis to the block_manager axis."""
+        axis = cls._get_axis_number(axis)
+        ndim = cls._AXIS_LEN
+        if ndim == 2:
+            # i.e. DataFrame
+            return 1 - axis
+        return axis
+
+    @final
+    def _get_axis_resolvers(self, axis: str) -> dict[str, Series | MultiIndex]:
+        # index or columns
+        axis_index = getattr(self, axis)
+        d = {}
+        prefix = axis[0]
+
+        for i, name in enumerate(axis_index.names):
+            if name is not None:
+                key = level = name
+            else:
+                # prefix with 'i' or 'c' depending on the input axis
+                # e.g., you must do ilevel_0 for the 0th level of an unnamed
+                # multiiindex
+                key = f"{prefix}level_{i}"
+                level = i
+
+            level_values = axis_index.get_level_values(level)
+            s = level_values.to_series()
+            s.index = axis_index
+            d[key] = s
+
+        # put the index/columns itself in the dict
+        if isinstance(axis_index, MultiIndex):
+            dindex = axis_index
+        else:
+            dindex = axis_index.to_series()
+
+        d[axis] = dindex
+        return d
+
+    @final
+    def _get_index_resolvers(self) -> dict[Hashable, Series | MultiIndex]:
+        from pandas.core.computation.parsing import clean_column_name
+
+        d: dict[str, Series | MultiIndex] = {}
+        for axis_name in self._AXIS_ORDERS:
+            d.update(self._get_axis_resolvers(axis_name))
+
+        return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)}
+
+    @final
+    def _get_cleaned_column_resolvers(self) -> dict[Hashable, Series]:
+        """
+        Return the special character free column resolvers of a dataframe.
+
+        Column names with special characters are 'cleaned up' so that they can
+        be referred to by backtick quoting.
+        Used in :meth:`DataFrame.eval`.
+        """
+        from pandas.core.computation.parsing import clean_column_name
+        from pandas.core.series import Series
+
+        if isinstance(self, ABCSeries):
+            return {clean_column_name(self.name): self}
+
+        return {
+            clean_column_name(k): Series(
+                v, copy=False, index=self.index, name=k, dtype=self.dtypes[k]
+            ).__finalize__(self)
+            for k, v in zip(self.columns, self._iter_column_arrays())
+            if not isinstance(k, int)
+        }
+
+    @final
+    @property
+    def _info_axis(self) -> Index:
+        return getattr(self, self._info_axis_name)
+
+    def _is_view_after_cow_rules(self):
+        # Only to be used in cases of chained assignment checks, this is a
+        # simplified check that assumes that either the whole object is a view
+        # or a copy
+        if len(self._mgr.blocks) == 0:  # type: ignore[union-attr]
+            return False
+        return self._mgr.blocks[0].refs.has_reference()  # type: ignore[union-attr]
+
+    @property
+    def shape(self) -> tuple[int, ...]:
+        """
+        Return a tuple of axis dimensions
+        """
+        return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
+
+    @property
+    def axes(self) -> list[Index]:
+        """
+        Return index label(s) of the internal NDFrame
+        """
+        # we do it this way because if we have reversed axes, then
+        # the block manager shows then reversed
+        return [self._get_axis(a) for a in self._AXIS_ORDERS]
+
+    @final
+    @property
+    def ndim(self) -> int:
+        """
+        Return an int representing the number of axes / array dimensions.
+
+        Return 1 if Series. Otherwise return 2 if DataFrame.
+
+        See Also
+        --------
+        ndarray.ndim : Number of array dimensions.
+
+        Examples
+        --------
+        >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
+        >>> s.ndim
+        1
+
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.ndim
+        2
+        """
+        return self._mgr.ndim
+
+    @final
+    @property
+    def size(self) -> int:
+        """
+        Return an int representing the number of elements in this object.
+
+        Return the number of rows if Series. Otherwise return the number of
+        rows times number of columns if DataFrame.
+
+        See Also
+        --------
+        ndarray.size : Number of elements in the array.
+
+        Examples
+        --------
+        >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
+        >>> s.size
+        3
+
+        >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
+        >>> df.size
+        4
+        """
+
+        return int(np.prod(self.shape))
+
+    def set_axis(
+        self,
+        labels,
+        *,
+        axis: Axis = 0,
+        copy: bool_t | None = None,
+    ) -> Self:
+        """
+        Assign desired index to given axis.
+
+        Indexes for%(extended_summary_sub)s row labels can be changed by assigning
+        a list-like or Index.
+
+        Parameters
+        ----------
+        labels : list-like, Index
+            The values for the new index.
+
+        axis : %(axes_single_arg)s, default 0
+            The axis to update. The value 0 identifies the rows. For `Series`
+            this parameter is unused and defaults to 0.
+
+        copy : bool, default True
+            Whether to make a copy of the underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        %(klass)s
+            An object of type %(klass)s.
+
+        See Also
+        --------
+        %(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s.
+        """
+        return self._set_axis_nocheck(labels, axis, inplace=False, copy=copy)
+
+    @final
+    def _set_axis_nocheck(
+        self, labels, axis: Axis, inplace: bool_t, copy: bool_t | None
+    ):
+        if inplace:
+            setattr(self, self._get_axis_name(axis), labels)
+        else:
+            # With copy=False, we create a new object but don't copy the
+            #  underlying data.
+            obj = self.copy(deep=copy and not using_copy_on_write())
+            setattr(obj, obj._get_axis_name(axis), labels)
+            return obj
+
+    @final
+    def _set_axis(self, axis: AxisInt, labels: AnyArrayLike | list) -> None:
+        """
+        This is called from the cython code when we set the `index` attribute
+        directly, e.g. `series.index = [1, 2, 3]`.
+        """
+        labels = ensure_index(labels)
+        self._mgr.set_axis(axis, labels)
+        self._clear_item_cache()
+
+    @final
+    def swapaxes(self, axis1: Axis, axis2: Axis, copy: bool_t | None = None) -> Self:
+        """
+        Interchange axes and swap values axes appropriately.
+
+        .. deprecated:: 2.1.0
+            ``swapaxes`` is deprecated and will be removed.
+            Please use ``transpose`` instead.
+
+        Returns
+        -------
+        same as input
+
+        Examples
+        --------
+        Please see examples for :meth:`DataFrame.transpose`.
+        """
+        warnings.warn(
+            # GH#51946
+            f"'{type(self).__name__}.swapaxes' is deprecated and "
+            "will be removed in a future version. "
+            f"Please use '{type(self).__name__}.transpose' instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        i = self._get_axis_number(axis1)
+        j = self._get_axis_number(axis2)
+
+        if i == j:
+            return self.copy(deep=copy and not using_copy_on_write())
+
+        mapping = {i: j, j: i}
+
+        new_axes = [self._get_axis(mapping.get(k, k)) for k in range(self._AXIS_LEN)]
+        new_values = self._values.swapaxes(i, j)  # type: ignore[union-attr]
+        if self._mgr.is_single_block and isinstance(self._mgr, BlockManager):
+            # This should only get hit in case of having a single block, otherwise a
+            # copy is made, we don't have to set up references.
+            new_mgr = ndarray_to_mgr(
+                new_values,
+                new_axes[0],
+                new_axes[1],
+                dtype=None,
+                copy=False,
+                typ="block",
+            )
+            assert isinstance(new_mgr, BlockManager)
+            assert isinstance(self._mgr, BlockManager)
+            new_mgr.blocks[0].refs = self._mgr.blocks[0].refs
+            new_mgr.blocks[0].refs.add_reference(new_mgr.blocks[0])
+            if not using_copy_on_write() and copy is not False:
+                new_mgr = new_mgr.copy(deep=True)
+
+            out = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+            return out.__finalize__(self, method="swapaxes")
+
+        return self._constructor(
+            new_values,
+            *new_axes,
+            # The no-copy case for CoW is handled above
+            copy=False,
+        ).__finalize__(self, method="swapaxes")
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def droplevel(self, level: IndexLabel, axis: Axis = 0) -> Self:
+        """
+        Return {klass} with requested index / column level(s) removed.
+
+        Parameters
+        ----------
+        level : int, str, or list-like
+            If a string is given, must be the name of a level
+            If list-like, elements must be names or positional indexes
+            of levels.
+
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            Axis along which the level(s) is removed:
+
+            * 0 or 'index': remove level(s) in column.
+            * 1 or 'columns': remove level(s) in row.
+
+            For `Series` this parameter is unused and defaults to 0.
+
+        Returns
+        -------
+        {klass}
+            {klass} with requested index / column level(s) removed.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([
+        ...     [1, 2, 3, 4],
+        ...     [5, 6, 7, 8],
+        ...     [9, 10, 11, 12]
+        ... ]).set_index([0, 1]).rename_axis(['a', 'b'])
+
+        >>> df.columns = pd.MultiIndex.from_tuples([
+        ...     ('c', 'e'), ('d', 'f')
+        ... ], names=['level_1', 'level_2'])
+
+        >>> df
+        level_1   c   d
+        level_2   e   f
+        a b
+        1 2      3   4
+        5 6      7   8
+        9 10    11  12
+
+        >>> df.droplevel('a')
+        level_1   c   d
+        level_2   e   f
+        b
+        2        3   4
+        6        7   8
+        10      11  12
+
+        >>> df.droplevel('level_2', axis=1)
+        level_1   c   d
+        a b
+        1 2      3   4
+        5 6      7   8
+        9 10    11  12
+        """
+        labels = self._get_axis(axis)
+        new_labels = labels.droplevel(level)
+        return self.set_axis(new_labels, axis=axis, copy=None)
+
+    def pop(self, item: Hashable) -> Series | Any:
+        result = self[item]
+        del self[item]
+
+        return result
+
+    @final
+    def squeeze(self, axis: Axis | None = None):
+        """
+        Squeeze 1 dimensional axis objects into scalars.
+
+        Series or DataFrames with a single element are squeezed to a scalar.
+        DataFrames with a single column or a single row are squeezed to a
+        Series. Otherwise the object is unchanged.
+
+        This method is most useful when you don't know if your
+        object is a Series or DataFrame, but you do know it has just a single
+        column. In that case you can safely call `squeeze` to ensure you have a
+        Series.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            A specific axis to squeeze. By default, all length-1 axes are
+            squeezed. For `Series` this parameter is unused and defaults to `None`.
+
+        Returns
+        -------
+        DataFrame, Series, or scalar
+            The projection after squeezing `axis` or all the axes.
+
+        See Also
+        --------
+        Series.iloc : Integer-location based indexing for selecting scalars.
+        DataFrame.iloc : Integer-location based indexing for selecting Series.
+        Series.to_frame : Inverse of DataFrame.squeeze for a
+            single-column DataFrame.
+
+        Examples
+        --------
+        >>> primes = pd.Series([2, 3, 5, 7])
+
+        Slicing might produce a Series with a single value:
+
+        >>> even_primes = primes[primes % 2 == 0]
+        >>> even_primes
+        0    2
+        dtype: int64
+
+        >>> even_primes.squeeze()
+        2
+
+        Squeezing objects with more than one value in every axis does nothing:
+
+        >>> odd_primes = primes[primes % 2 == 1]
+        >>> odd_primes
+        1    3
+        2    5
+        3    7
+        dtype: int64
+
+        >>> odd_primes.squeeze()
+        1    3
+        2    5
+        3    7
+        dtype: int64
+
+        Squeezing is even more effective when used with DataFrames.
+
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
+        >>> df
+           a  b
+        0  1  2
+        1  3  4
+
+        Slicing a single column will produce a DataFrame with the columns
+        having only one value:
+
+        >>> df_a = df[['a']]
+        >>> df_a
+           a
+        0  1
+        1  3
+
+        So the columns can be squeezed down, resulting in a Series:
+
+        >>> df_a.squeeze('columns')
+        0    1
+        1    3
+        Name: a, dtype: int64
+
+        Slicing a single row from a single column will produce a single
+        scalar DataFrame:
+
+        >>> df_0a = df.loc[df.index < 1, ['a']]
+        >>> df_0a
+           a
+        0  1
+
+        Squeezing the rows produces a single scalar Series:
+
+        >>> df_0a.squeeze('rows')
+        a    1
+        Name: 0, dtype: int64
+
+        Squeezing all axes will project directly into a scalar:
+
+        >>> df_0a.squeeze()
+        1
+        """
+        axes = range(self._AXIS_LEN) if axis is None else (self._get_axis_number(axis),)
+        result = self.iloc[
+            tuple(
+                0 if i in axes and len(a) == 1 else slice(None)
+                for i, a in enumerate(self.axes)
+            )
+        ]
+        if isinstance(result, NDFrame):
+            result = result.__finalize__(self, method="squeeze")
+        return result
+
+    # ----------------------------------------------------------------------
+    # Rename
+
+    @final
+    def _rename(
+        self,
+        mapper: Renamer | None = None,
+        *,
+        index: Renamer | None = None,
+        columns: Renamer | None = None,
+        axis: Axis | None = None,
+        copy: bool_t | None = None,
+        inplace: bool_t = False,
+        level: Level | None = None,
+        errors: str = "ignore",
+    ) -> Self | None:
+        # called by Series.rename and DataFrame.rename
+
+        if mapper is None and index is None and columns is None:
+            raise TypeError("must pass an index to rename")
+
+        if index is not None or columns is not None:
+            if axis is not None:
+                raise TypeError(
+                    "Cannot specify both 'axis' and any of 'index' or 'columns'"
+                )
+            if mapper is not None:
+                raise TypeError(
+                    "Cannot specify both 'mapper' and any of 'index' or 'columns'"
+                )
+        else:
+            # use the mapper argument
+            if axis and self._get_axis_number(axis) == 1:
+                columns = mapper
+            else:
+                index = mapper
+
+        self._check_inplace_and_allows_duplicate_labels(inplace)
+        result = self if inplace else self.copy(deep=copy and not using_copy_on_write())
+
+        for axis_no, replacements in enumerate((index, columns)):
+            if replacements is None:
+                continue
+
+            ax = self._get_axis(axis_no)
+            f = common.get_rename_function(replacements)
+
+            if level is not None:
+                level = ax._get_level_number(level)
+
+            # GH 13473
+            if not callable(replacements):
+                if ax._is_multi and level is not None:
+                    indexer = ax.get_level_values(level).get_indexer_for(replacements)
+                else:
+                    indexer = ax.get_indexer_for(replacements)
+
+                if errors == "raise" and len(indexer[indexer == -1]):
+                    missing_labels = [
+                        label
+                        for index, label in enumerate(replacements)
+                        if indexer[index] == -1
+                    ]
+                    raise KeyError(f"{missing_labels} not found in axis")
+
+            new_index = ax._transform_index(f, level=level)
+            result._set_axis_nocheck(new_index, axis=axis_no, inplace=True, copy=False)
+            result._clear_item_cache()
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result.__finalize__(self, method="rename")
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        columns=...,
+        axis: Axis = ...,
+        copy: bool_t | None = ...,
+        inplace: Literal[False] = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        columns=...,
+        axis: Axis = ...,
+        copy: bool_t | None = ...,
+        inplace: Literal[True],
+    ) -> None:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        columns=...,
+        axis: Axis = ...,
+        copy: bool_t | None = ...,
+        inplace: bool_t = ...,
+    ) -> Self | None:
+        ...
+
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = lib.no_default,
+        *,
+        index=lib.no_default,
+        columns=lib.no_default,
+        axis: Axis = 0,
+        copy: bool_t | None = None,
+        inplace: bool_t = False,
+    ) -> Self | None:
+        """
+        Set the name of the axis for the index or columns.
+
+        Parameters
+        ----------
+        mapper : scalar, list-like, optional
+            Value to set the axis name attribute.
+        index, columns : scalar, list-like, dict-like or function, optional
+            A scalar, list-like, dict-like or functions transformations to
+            apply to that axis' values.
+            Note that the ``columns`` parameter is not allowed if the
+            object is a Series. This parameter only apply for DataFrame
+            type objects.
+
+            Use either ``mapper`` and ``axis`` to
+            specify the axis to target with ``mapper``, or ``index``
+            and/or ``columns``.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to rename. For `Series` this parameter is unused and defaults to 0.
+        copy : bool, default None
+            Also copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Series
+            or DataFrame.
+
+        Returns
+        -------
+        Series, DataFrame, or None
+            The same type as the caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.rename : Alter Series index labels or name.
+        DataFrame.rename : Alter DataFrame index labels or name.
+        Index.rename : Set new names on index.
+
+        Notes
+        -----
+        ``DataFrame.rename_axis`` supports two calling conventions
+
+        * ``(index=index_mapper, columns=columns_mapper, ...)``
+        * ``(mapper, axis={'index', 'columns'}, ...)``
+
+        The first calling convention will only modify the names of
+        the index and/or the names of the Index object that is the columns.
+        In this case, the parameter ``copy`` is ignored.
+
+        The second calling convention will modify the names of the
+        corresponding index if mapper is a list or a scalar.
+        However, if mapper is dict-like or a function, it will use the
+        deprecated behavior of modifying the axis *labels*.
+
+        We *highly* recommend using keyword arguments to clarify your
+        intent.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = pd.Series(["dog", "cat", "monkey"])
+        >>> s
+        0       dog
+        1       cat
+        2    monkey
+        dtype: object
+        >>> s.rename_axis("animal")
+        animal
+        0    dog
+        1    cat
+        2    monkey
+        dtype: object
+
+        **DataFrame**
+
+        >>> df = pd.DataFrame({"num_legs": [4, 4, 2],
+        ...                    "num_arms": [0, 0, 2]},
+        ...                   ["dog", "cat", "monkey"])
+        >>> df
+                num_legs  num_arms
+        dog            4         0
+        cat            4         0
+        monkey         2         2
+        >>> df = df.rename_axis("animal")
+        >>> df
+                num_legs  num_arms
+        animal
+        dog            4         0
+        cat            4         0
+        monkey         2         2
+        >>> df = df.rename_axis("limbs", axis="columns")
+        >>> df
+        limbs   num_legs  num_arms
+        animal
+        dog            4         0
+        cat            4         0
+        monkey         2         2
+
+        **MultiIndex**
+
+        >>> df.index = pd.MultiIndex.from_product([['mammal'],
+        ...                                        ['dog', 'cat', 'monkey']],
+        ...                                       names=['type', 'name'])
+        >>> df
+        limbs          num_legs  num_arms
+        type   name
+        mammal dog            4         0
+               cat            4         0
+               monkey         2         2
+
+        >>> df.rename_axis(index={'type': 'class'})
+        limbs          num_legs  num_arms
+        class  name
+        mammal dog            4         0
+               cat            4         0
+               monkey         2         2
+
+        >>> df.rename_axis(columns=str.upper)
+        LIMBS          num_legs  num_arms
+        type   name
+        mammal dog            4         0
+               cat            4         0
+               monkey         2         2
+        """
+        axes = {"index": index, "columns": columns}
+
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if copy and using_copy_on_write():
+            copy = False
+
+        if mapper is not lib.no_default:
+            # Use v0.23 behavior if a scalar or list
+            non_mapper = is_scalar(mapper) or (
+                is_list_like(mapper) and not is_dict_like(mapper)
+            )
+            if non_mapper:
+                return self._set_axis_name(
+                    mapper, axis=axis, inplace=inplace, copy=copy
+                )
+            else:
+                raise ValueError("Use `.rename` to alter labels with a mapper.")
+        else:
+            # Use new behavior.  Means that index and/or columns
+            # is specified
+            result = self if inplace else self.copy(deep=copy)
+
+            for axis in range(self._AXIS_LEN):
+                v = axes.get(self._get_axis_name(axis))
+                if v is lib.no_default:
+                    continue
+                non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v))
+                if non_mapper:
+                    newnames = v
+                else:
+                    f = common.get_rename_function(v)
+                    curnames = self._get_axis(axis).names
+                    newnames = [f(name) for name in curnames]
+                result._set_axis_name(newnames, axis=axis, inplace=True, copy=copy)
+            if not inplace:
+                return result
+            return None
+
+    @final
+    def _set_axis_name(
+        self, name, axis: Axis = 0, inplace: bool_t = False, copy: bool_t | None = True
+    ):
+        """
+        Set the name(s) of the axis.
+
+        Parameters
+        ----------
+        name : str or list of str
+            Name(s) to set.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to set the label. The value 0 or 'index' specifies index,
+            and the value 1 or 'columns' specifies columns.
+        inplace : bool, default False
+            If `True`, do operation inplace and return None.
+        copy:
+            Whether to make a copy of the result.
+
+        Returns
+        -------
+        Series, DataFrame, or None
+            The same type as the caller or `None` if `inplace` is `True`.
+
+        See Also
+        --------
+        DataFrame.rename : Alter the axis labels of :class:`DataFrame`.
+        Series.rename : Alter the index labels or set the index name
+            of :class:`Series`.
+        Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
+        ...                   ["dog", "cat", "monkey"])
+        >>> df
+                num_legs
+        dog            4
+        cat            4
+        monkey         2
+        >>> df._set_axis_name("animal")
+                num_legs
+        animal
+        dog            4
+        cat            4
+        monkey         2
+        >>> df.index = pd.MultiIndex.from_product(
+        ...                [["mammal"], ['dog', 'cat', 'monkey']])
+        >>> df._set_axis_name(["type", "name"])
+                       num_legs
+        type   name
+        mammal dog        4
+               cat        4
+               monkey     2
+        """
+        axis = self._get_axis_number(axis)
+        idx = self._get_axis(axis).set_names(name)
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        renamed = self if inplace else self.copy(deep=copy)
+        if axis == 0:
+            renamed.index = idx
+        else:
+            renamed.columns = idx
+
+        if not inplace:
+            return renamed
+
+    # ----------------------------------------------------------------------
+    # Comparison Methods
+
+    @final
+    def _indexed_same(self, other) -> bool_t:
+        return all(
+            self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS
+        )
+
+    @final
+    def equals(self, other: object) -> bool_t:
+        """
+        Test whether two objects contain the same elements.
+
+        This function allows two Series or DataFrames to be compared against
+        each other to see if they have the same shape and elements. NaNs in
+        the same location are considered equal.
+
+        The row/column index do not need to have the same type, as long
+        as the values are considered equal. Corresponding columns and
+        index must be of the same dtype.
+
+        Parameters
+        ----------
+        other : Series or DataFrame
+            The other Series or DataFrame to be compared with the first.
+
+        Returns
+        -------
+        bool
+            True if all elements are the same in both objects, False
+            otherwise.
+
+        See Also
+        --------
+        Series.eq : Compare two Series objects of the same length
+            and return a Series where each element is True if the element
+            in each Series is equal, False otherwise.
+        DataFrame.eq : Compare two DataFrame objects of the same shape and
+            return a DataFrame where each element is True if the respective
+            element in each DataFrame is equal, False otherwise.
+        testing.assert_series_equal : Raises an AssertionError if left and
+            right are not equal. Provides an easy interface to ignore
+            inequality in dtypes, indexes and precision among others.
+        testing.assert_frame_equal : Like assert_series_equal, but targets
+            DataFrames.
+        numpy.array_equal : Return True if two arrays have the same shape
+            and elements, False otherwise.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({1: [10], 2: [20]})
+        >>> df
+            1   2
+        0  10  20
+
+        DataFrames df and exactly_equal have the same types and values for
+        their elements and column labels, which will return True.
+
+        >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
+        >>> exactly_equal
+            1   2
+        0  10  20
+        >>> df.equals(exactly_equal)
+        True
+
+        DataFrames df and different_column_type have the same element
+        types and values, but have different types for the column labels,
+        which will still return True.
+
+        >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
+        >>> different_column_type
+           1.0  2.0
+        0   10   20
+        >>> df.equals(different_column_type)
+        True
+
+        DataFrames df and different_data_type have different types for the
+        same values for their elements, and will return False even though
+        their column labels are the same values and types.
+
+        >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
+        >>> different_data_type
+              1     2
+        0  10.0  20.0
+        >>> df.equals(different_data_type)
+        False
+        """
+        if not (isinstance(other, type(self)) or isinstance(self, type(other))):
+            return False
+        other = cast(NDFrame, other)
+        return self._mgr.equals(other._mgr)
+
+    # -------------------------------------------------------------------------
+    # Unary Methods
+
+    @final
+    def __neg__(self) -> Self:
+        def blk_func(values: ArrayLike):
+            if is_bool_dtype(values.dtype):
+                # error: Argument 1 to "inv" has incompatible type "Union
+                # [ExtensionArray, ndarray[Any, Any]]"; expected
+                # "_SupportsInversion[ndarray[Any, dtype[bool_]]]"
+                return operator.inv(values)  # type: ignore[arg-type]
+            else:
+                # error: Argument 1 to "neg" has incompatible type "Union
+                # [ExtensionArray, ndarray[Any, Any]]"; expected
+                # "_SupportsNeg[ndarray[Any, dtype[Any]]]"
+                return operator.neg(values)  # type: ignore[arg-type]
+
+        new_data = self._mgr.apply(blk_func)
+        res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res.__finalize__(self, method="__neg__")
+
+    @final
+    def __pos__(self) -> Self:
+        def blk_func(values: ArrayLike):
+            if is_bool_dtype(values.dtype):
+                return values.copy()
+            else:
+                # error: Argument 1 to "pos" has incompatible type "Union
+                # [ExtensionArray, ndarray[Any, Any]]"; expected
+                # "_SupportsPos[ndarray[Any, dtype[Any]]]"
+                return operator.pos(values)  # type: ignore[arg-type]
+
+        new_data = self._mgr.apply(blk_func)
+        res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res.__finalize__(self, method="__pos__")
+
+    @final
+    def __invert__(self) -> Self:
+        if not self.size:
+            # inv fails with 0 len
+            return self.copy(deep=False)
+
+        new_data = self._mgr.apply(operator.invert)
+        res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        return res.__finalize__(self, method="__invert__")
+
+    @final
+    def __nonzero__(self) -> NoReturn:
+        raise ValueError(
+            f"The truth value of a {type(self).__name__} is ambiguous. "
+            "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
+        )
+
+    __bool__ = __nonzero__
+
+    @final
+    def bool(self) -> bool_t:
+        """
+        Return the bool of a single element Series or DataFrame.
+
+        .. deprecated:: 2.1.0
+
+           bool is deprecated and will be removed in future version of pandas.
+           For ``Series`` use ``pandas.Series.item``.
+
+        This must be a boolean scalar value, either True or False. It will raise a
+        ValueError if the Series or DataFrame does not have exactly 1 element, or that
+        element is not boolean (integer values 0 and 1 will also raise an exception).
+
+        Returns
+        -------
+        bool
+            The value in the Series or DataFrame.
+
+        See Also
+        --------
+        Series.astype : Change the data type of a Series, including to boolean.
+        DataFrame.astype : Change the data type of a DataFrame, including to boolean.
+        numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
+
+        Examples
+        --------
+        The method will only work for single element objects with a boolean value:
+
+        >>> pd.Series([True]).bool()  # doctest: +SKIP
+        True
+        >>> pd.Series([False]).bool()  # doctest: +SKIP
+        False
+
+        >>> pd.DataFrame({'col': [True]}).bool()  # doctest: +SKIP
+        True
+        >>> pd.DataFrame({'col': [False]}).bool()  # doctest: +SKIP
+        False
+
+        This is an alternative method and will only work
+        for single element objects with a boolean value:
+
+        >>> pd.Series([True]).item()  # doctest: +SKIP
+        True
+        >>> pd.Series([False]).item()  # doctest: +SKIP
+        False
+        """
+
+        warnings.warn(
+            f"{type(self).__name__}.bool is now deprecated and will be removed "
+            "in future version of pandas",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        v = self.squeeze()
+        if isinstance(v, (bool, np.bool_)):
+            return bool(v)
+        elif is_scalar(v):
+            raise ValueError(
+                "bool cannot act on a non-boolean single element "
+                f"{type(self).__name__}"
+            )
+
+        self.__nonzero__()
+        # for mypy (__nonzero__ raises)
+        return True
+
+    @final
+    def abs(self) -> Self:
+        """
+        Return a Series/DataFrame with absolute numeric value of each element.
+
+        This function only applies to elements that are all numeric.
+
+        Returns
+        -------
+        abs
+            Series/DataFrame containing the absolute value of each element.
+
+        See Also
+        --------
+        numpy.absolute : Calculate the absolute value element-wise.
+
+        Notes
+        -----
+        For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
+        :math:`\\sqrt{ a^2 + b^2 }`.
+
+        Examples
+        --------
+        Absolute numeric values in a Series.
+
+        >>> s = pd.Series([-1.10, 2, -3.33, 4])
+        >>> s.abs()
+        0    1.10
+        1    2.00
+        2    3.33
+        3    4.00
+        dtype: float64
+
+        Absolute numeric values in a Series with complex numbers.
+
+        >>> s = pd.Series([1.2 + 1j])
+        >>> s.abs()
+        0    1.56205
+        dtype: float64
+
+        Absolute numeric values in a Series with a Timedelta element.
+
+        >>> s = pd.Series([pd.Timedelta('1 days')])
+        >>> s.abs()
+        0   1 days
+        dtype: timedelta64[ns]
+
+        Select rows with data closest to certain value using argsort (from
+        `StackOverflow <https://stackoverflow.com/a/17758115>`__).
+
+        >>> df = pd.DataFrame({
+        ...     'a': [4, 5, 6, 7],
+        ...     'b': [10, 20, 30, 40],
+        ...     'c': [100, 50, -30, -50]
+        ... })
+        >>> df
+             a    b    c
+        0    4   10  100
+        1    5   20   50
+        2    6   30  -30
+        3    7   40  -50
+        >>> df.loc[(df.c - 43).abs().argsort()]
+             a    b    c
+        1    5   20   50
+        0    4   10  100
+        2    6   30  -30
+        3    7   40  -50
+        """
+        res_mgr = self._mgr.apply(np.abs)
+        return self._constructor_from_mgr(res_mgr, axes=res_mgr.axes).__finalize__(
+            self, name="abs"
+        )
+
+    @final
+    def __abs__(self) -> Self:
+        return self.abs()
+
+    @final
+    def __round__(self, decimals: int = 0) -> Self:
+        return self.round(decimals).__finalize__(self, method="__round__")
+
+    # -------------------------------------------------------------------------
+    # Label or Level Combination Helpers
+    #
+    # A collection of helper methods for DataFrame/Series operations that
+    # accept a combination of column/index labels and levels.  All such
+    # operations should utilize/extend these methods when possible so that we
+    # have consistent precedence and validation logic throughout the library.
+
+    @final
+    def _is_level_reference(self, key: Level, axis: Axis = 0) -> bool_t:
+        """
+        Test whether a key is a level reference for a given axis.
+
+        To be considered a level reference, `key` must be a string that:
+          - (axis=0): Matches the name of an index level and does NOT match
+            a column label.
+          - (axis=1): Matches the name of a column level and does NOT match
+            an index label.
+
+        Parameters
+        ----------
+        key : Hashable
+            Potential level name for the given axis
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        is_level : bool
+        """
+        axis_int = self._get_axis_number(axis)
+
+        return (
+            key is not None
+            and is_hashable(key)
+            and key in self.axes[axis_int].names
+            and not self._is_label_reference(key, axis=axis_int)
+        )
+
+    @final
+    def _is_label_reference(self, key: Level, axis: Axis = 0) -> bool_t:
+        """
+        Test whether a key is a label reference for a given axis.
+
+        To be considered a label reference, `key` must be a string that:
+          - (axis=0): Matches a column label
+          - (axis=1): Matches an index label
+
+        Parameters
+        ----------
+        key : Hashable
+            Potential label name, i.e. Index entry.
+        axis : int, default 0
+            Axis perpendicular to the axis that labels are associated with
+            (0 means search for column labels, 1 means search for index labels)
+
+        Returns
+        -------
+        is_label: bool
+        """
+        axis_int = self._get_axis_number(axis)
+        other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis_int)
+
+        return (
+            key is not None
+            and is_hashable(key)
+            and any(key in self.axes[ax] for ax in other_axes)
+        )
+
+    @final
+    def _is_label_or_level_reference(self, key: Level, axis: AxisInt = 0) -> bool_t:
+        """
+        Test whether a key is a label or level reference for a given axis.
+
+        To be considered either a label or a level reference, `key` must be a
+        string that:
+          - (axis=0): Matches a column label or an index level
+          - (axis=1): Matches an index label or a column level
+
+        Parameters
+        ----------
+        key : Hashable
+            Potential label or level name
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        bool
+        """
+        return self._is_level_reference(key, axis=axis) or self._is_label_reference(
+            key, axis=axis
+        )
+
+    @final
+    def _check_label_or_level_ambiguity(self, key: Level, axis: Axis = 0) -> None:
+        """
+        Check whether `key` is ambiguous.
+
+        By ambiguous, we mean that it matches both a level of the input
+        `axis` and a label of the other axis.
+
+        Parameters
+        ----------
+        key : Hashable
+            Label or level name.
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns).
+
+        Raises
+        ------
+        ValueError: `key` is ambiguous
+        """
+
+        axis_int = self._get_axis_number(axis)
+        other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis_int)
+
+        if (
+            key is not None
+            and is_hashable(key)
+            and key in self.axes[axis_int].names
+            and any(key in self.axes[ax] for ax in other_axes)
+        ):
+            # Build an informative and grammatical warning
+            level_article, level_type = (
+                ("an", "index") if axis_int == 0 else ("a", "column")
+            )
+
+            label_article, label_type = (
+                ("a", "column") if axis_int == 0 else ("an", "index")
+            )
+
+            msg = (
+                f"'{key}' is both {level_article} {level_type} level and "
+                f"{label_article} {label_type} label, which is ambiguous."
+            )
+            raise ValueError(msg)
+
+    @final
+    def _get_label_or_level_values(self, key: Level, axis: AxisInt = 0) -> ArrayLike:
+        """
+        Return a 1-D array of values associated with `key`, a label or level
+        from the given `axis`.
+
+        Retrieval logic:
+          - (axis=0): Return column values if `key` matches a column label.
+            Otherwise return index level values if `key` matches an index
+            level.
+          - (axis=1): Return row values if `key` matches an index label.
+            Otherwise return column level values if 'key' matches a column
+            level
+
+        Parameters
+        ----------
+        key : Hashable
+            Label or level name.
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+
+        Raises
+        ------
+        KeyError
+            if `key` matches neither a label nor a level
+        ValueError
+            if `key` matches multiple labels
+        """
+        axis = self._get_axis_number(axis)
+        other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
+
+        if self._is_label_reference(key, axis=axis):
+            self._check_label_or_level_ambiguity(key, axis=axis)
+            values = self.xs(key, axis=other_axes[0])._values
+        elif self._is_level_reference(key, axis=axis):
+            values = self.axes[axis].get_level_values(key)._values
+        else:
+            raise KeyError(key)
+
+        # Check for duplicates
+        if values.ndim > 1:
+            if other_axes and isinstance(self._get_axis(other_axes[0]), MultiIndex):
+                multi_message = (
+                    "\n"
+                    "For a multi-index, the label must be a "
+                    "tuple with elements corresponding to each level."
+                )
+            else:
+                multi_message = ""
+
+            label_axis_name = "column" if axis == 0 else "index"
+            raise ValueError(
+                f"The {label_axis_name} label '{key}' is not unique.{multi_message}"
+            )
+
+        return values
+
+    @final
+    def _drop_labels_or_levels(self, keys, axis: AxisInt = 0):
+        """
+        Drop labels and/or levels for the given `axis`.
+
+        For each key in `keys`:
+          - (axis=0): If key matches a column label then drop the column.
+            Otherwise if key matches an index level then drop the level.
+          - (axis=1): If key matches an index label then drop the row.
+            Otherwise if key matches a column level then drop the level.
+
+        Parameters
+        ----------
+        keys : str or list of str
+            labels or levels to drop
+        axis : int, default 0
+            Axis that levels are associated with (0 for index, 1 for columns)
+
+        Returns
+        -------
+        dropped: DataFrame
+
+        Raises
+        ------
+        ValueError
+            if any `keys` match neither a label nor a level
+        """
+        axis = self._get_axis_number(axis)
+
+        # Validate keys
+        keys = common.maybe_make_list(keys)
+        invalid_keys = [
+            k for k in keys if not self._is_label_or_level_reference(k, axis=axis)
+        ]
+
+        if invalid_keys:
+            raise ValueError(
+                "The following keys are not valid labels or "
+                f"levels for axis {axis}: {invalid_keys}"
+            )
+
+        # Compute levels and labels to drop
+        levels_to_drop = [k for k in keys if self._is_level_reference(k, axis=axis)]
+
+        labels_to_drop = [k for k in keys if not self._is_level_reference(k, axis=axis)]
+
+        # Perform copy upfront and then use inplace operations below.
+        # This ensures that we always perform exactly one copy.
+        # ``copy`` and/or ``inplace`` options could be added in the future.
+        dropped = self.copy(deep=False)
+
+        if axis == 0:
+            # Handle dropping index levels
+            if levels_to_drop:
+                dropped.reset_index(levels_to_drop, drop=True, inplace=True)
+
+            # Handle dropping columns labels
+            if labels_to_drop:
+                dropped.drop(labels_to_drop, axis=1, inplace=True)
+        else:
+            # Handle dropping column levels
+            if levels_to_drop:
+                if isinstance(dropped.columns, MultiIndex):
+                    # Drop the specified levels from the MultiIndex
+                    dropped.columns = dropped.columns.droplevel(levels_to_drop)
+                else:
+                    # Drop the last level of Index by replacing with
+                    # a RangeIndex
+                    dropped.columns = RangeIndex(dropped.columns.size)
+
+            # Handle dropping index labels
+            if labels_to_drop:
+                dropped.drop(labels_to_drop, axis=0, inplace=True)
+
+        return dropped
+
+    # ----------------------------------------------------------------------
+    # Iteration
+
+    # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
+    # Incompatible types in assignment (expression has type "None", base class
+    # "object" defined the type as "Callable[[object], int]")
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+
+    def __iter__(self) -> Iterator:
+        """
+        Iterate over info axis.
+
+        Returns
+        -------
+        iterator
+            Info axis as iterator.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
+        >>> for x in df:
+        ...     print(x)
+        A
+        B
+        """
+        return iter(self._info_axis)
+
+    # can we get a better explanation of this?
+    def keys(self) -> Index:
+        """
+        Get the 'info axis' (see Indexing for more).
+
+        This is index for Series, columns for DataFrame.
+
+        Returns
+        -------
+        Index
+            Info axis.
+
+        Examples
+        --------
+        >>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},
+        ...                  index=['a', 'b', 'c'])
+        >>> d
+           A  B
+        a  1  0
+        b  2  4
+        c  3  8
+        >>> d.keys()
+        Index(['A', 'B'], dtype='object')
+        """
+        return self._info_axis
+
+    def items(self):
+        """
+        Iterate over (label, values) on info axis
+
+        This is index for Series and columns for DataFrame.
+
+        Returns
+        -------
+        Generator
+        """
+        for h in self._info_axis:
+            yield h, self[h]
+
+    def __len__(self) -> int:
+        """Returns length of info axis"""
+        return len(self._info_axis)
+
+    @final
+    def __contains__(self, key) -> bool_t:
+        """True if the key is in the info axis"""
+        return key in self._info_axis
+
+    @property
+    def empty(self) -> bool_t:
+        """
+        Indicator whether Series/DataFrame is empty.
+
+        True if Series/DataFrame is entirely empty (no items), meaning any of the
+        axes are of length 0.
+
+        Returns
+        -------
+        bool
+            If Series/DataFrame is empty, return True, if not return False.
+
+        See Also
+        --------
+        Series.dropna : Return series without null values.
+        DataFrame.dropna : Return DataFrame with labels on given axis omitted
+            where (all or any) data are missing.
+
+        Notes
+        -----
+        If Series/DataFrame contains only NaNs, it is still not considered empty. See
+        the example below.
+
+        Examples
+        --------
+        An example of an actual empty DataFrame. Notice the index is empty:
+
+        >>> df_empty = pd.DataFrame({'A' : []})
+        >>> df_empty
+        Empty DataFrame
+        Columns: [A]
+        Index: []
+        >>> df_empty.empty
+        True
+
+        If we only have NaNs in our DataFrame, it is not considered empty! We
+        will need to drop the NaNs to make the DataFrame empty:
+
+        >>> df = pd.DataFrame({'A' : [np.nan]})
+        >>> df
+            A
+        0 NaN
+        >>> df.empty
+        False
+        >>> df.dropna().empty
+        True
+
+        >>> ser_empty = pd.Series({'A' : []})
+        >>> ser_empty
+        A    []
+        dtype: object
+        >>> ser_empty.empty
+        False
+        >>> ser_empty = pd.Series()
+        >>> ser_empty.empty
+        True
+        """
+        return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS)
+
+    # ----------------------------------------------------------------------
+    # Array Interface
+
+    # This is also set in IndexOpsMixin
+    # GH#23114 Ensure ndarray.__op__(DataFrame) returns NotImplemented
+    __array_priority__: int = 1000
+
+    def __array__(
+        self, dtype: npt.DTypeLike | None = None, copy: bool_t | None = None
+    ) -> np.ndarray:
+        if copy is False and not self._mgr.is_single_block and not self.empty:
+            # check this manually, otherwise ._values will already return a copy
+            # and np.array(values, copy=False) will not raise a warning
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        values = self._values
+        if copy is None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            arr = np.asarray(values, dtype=dtype)
+        else:
+            arr = np.array(values, dtype=dtype, copy=copy)
+
+        if (
+            copy is not True
+            and astype_is_view(values.dtype, arr.dtype)
+            and using_copy_on_write()
+            and self._mgr.is_single_block
+        ):
+            # Check if both conversions can be done without a copy
+            if astype_is_view(self.dtypes.iloc[0], values.dtype) and astype_is_view(
+                values.dtype, arr.dtype
+            ):
+                arr = arr.view()
+                arr.flags.writeable = False
+        return arr
+
+    @final
+    def __array_ufunc__(
+        self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any
+    ):
+        return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
+
+    # ----------------------------------------------------------------------
+    # Picklability
+
+    @final
+    def __getstate__(self) -> dict[str, Any]:
+        meta = {k: getattr(self, k, None) for k in self._metadata}
+        return {
+            "_mgr": self._mgr,
+            "_typ": self._typ,
+            "_metadata": self._metadata,
+            "attrs": self.attrs,
+            "_flags": {k: self.flags[k] for k in self.flags._keys},
+            **meta,
+        }
+
+    @final
+    def __setstate__(self, state) -> None:
+        if isinstance(state, BlockManager):
+            self._mgr = state
+        elif isinstance(state, dict):
+            if "_data" in state and "_mgr" not in state:
+                # compat for older pickles
+                state["_mgr"] = state.pop("_data")
+            typ = state.get("_typ")
+            if typ is not None:
+                attrs = state.get("_attrs", {})
+                if attrs is None:  # should not happen, but better be on the safe side
+                    attrs = {}
+                object.__setattr__(self, "_attrs", attrs)
+                flags = state.get("_flags", {"allows_duplicate_labels": True})
+                object.__setattr__(self, "_flags", Flags(self, **flags))
+
+                # set in the order of internal names
+                # to avoid definitional recursion
+                # e.g. say fill_value needing _mgr to be
+                # defined
+                meta = set(self._internal_names + self._metadata)
+                for k in list(meta):
+                    if k in state and k != "_flags":
+                        v = state[k]
+                        object.__setattr__(self, k, v)
+
+                for k, v in state.items():
+                    if k not in meta:
+                        object.__setattr__(self, k, v)
+
+            else:
+                raise NotImplementedError("Pre-0.12 pickles are no longer supported")
+        elif len(state) == 2:
+            raise NotImplementedError("Pre-0.12 pickles are no longer supported")
+
+        self._item_cache: dict[Hashable, Series] = {}
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def __repr__(self) -> str:
+        # string representation based upon iterating over self
+        # (since, by definition, `PandasContainers` are iterable)
+        prepr = f"[{','.join(map(pprint_thing, self))}]"
+        return f"{type(self).__name__}({prepr})"
+
+    @final
+    def _repr_latex_(self):
+        """
+        Returns a LaTeX representation for a particular object.
+        Mainly for use with nbconvert (jupyter notebook conversion to pdf).
+        """
+        if config.get_option("styler.render.repr") == "latex":
+            return self.to_latex()
+        else:
+            return None
+
+    @final
+    def _repr_data_resource_(self):
+        """
+        Not a real Jupyter special repr method, but we use the same
+        naming convention.
+        """
+        if config.get_option("display.html.table_schema"):
+            data = self.head(config.get_option("display.max_rows"))
+
+            as_json = data.to_json(orient="table")
+            as_json = cast(str, as_json)
+            return loads(as_json, object_pairs_hook=collections.OrderedDict)
+
+    # ----------------------------------------------------------------------
+    # I/O Methods
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "excel_writer"], name="to_excel"
+    )
+    @doc(
+        klass="object",
+        storage_options=_shared_docs["storage_options"],
+        storage_options_versionadded="1.2.0",
+    )
+    def to_excel(
+        self,
+        excel_writer: FilePath | WriteExcelBuffer | ExcelWriter,
+        sheet_name: str = "Sheet1",
+        na_rep: str = "",
+        float_format: str | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: Sequence[Hashable] | bool_t = True,
+        index: bool_t = True,
+        index_label: IndexLabel | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        engine: Literal["openpyxl", "xlsxwriter"] | None = None,
+        merge_cells: bool_t = True,
+        inf_rep: str = "inf",
+        freeze_panes: tuple[int, int] | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        """
+        Write {klass} to an Excel sheet.
+
+        To write a single {klass} to an Excel .xlsx file it is only necessary to
+        specify a target file name. To write to multiple sheets it is necessary to
+        create an `ExcelWriter` object with a target file name, and specify a sheet
+        in the file to write to.
+
+        Multiple sheets may be written to by specifying unique `sheet_name`.
+        With all data written to the file it is necessary to save the changes.
+        Note that creating an `ExcelWriter` object with a file name that already
+        exists will result in the contents of the existing file being erased.
+
+        Parameters
+        ----------
+        excel_writer : path-like, file-like, or ExcelWriter object
+            File path or existing ExcelWriter.
+        sheet_name : str, default 'Sheet1'
+            Name of sheet which will contain DataFrame.
+        na_rep : str, default ''
+            Missing data representation.
+        float_format : str, optional
+            Format string for floating point numbers. For example
+            ``float_format="%.2f"`` will format 0.1234 to 0.12.
+        columns : sequence or list of str, optional
+            Columns to write.
+        header : bool or list of str, default True
+            Write out the column names. If a list of string is given it is
+            assumed to be aliases for the column names.
+        index : bool, default True
+            Write row names (index).
+        index_label : str or sequence, optional
+            Column label for index column(s) if desired. If not specified, and
+            `header` and `index` are True, then the index names are used. A
+            sequence should be given if the DataFrame uses MultiIndex.
+        startrow : int, default 0
+            Upper left cell row to dump data frame.
+        startcol : int, default 0
+            Upper left cell column to dump data frame.
+        engine : str, optional
+            Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
+            via the options ``io.excel.xlsx.writer`` or
+            ``io.excel.xlsm.writer``.
+
+        merge_cells : bool, default True
+            Write MultiIndex and Hierarchical Rows as merged cells.
+        inf_rep : str, default 'inf'
+            Representation for infinity (there is no native representation for
+            infinity in Excel).
+        freeze_panes : tuple of int (length 2), optional
+            Specifies the one-based bottommost row and rightmost column that
+            is to be frozen.
+        {storage_options}
+
+            .. versionadded:: {storage_options_versionadded}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+
+        See Also
+        --------
+        to_csv : Write DataFrame to a comma-separated values (csv) file.
+        ExcelWriter : Class for writing DataFrame objects into excel sheets.
+        read_excel : Read an Excel file into a pandas DataFrame.
+        read_csv : Read a comma-separated values (csv) file into DataFrame.
+        io.formats.style.Styler.to_excel : Add styles to Excel sheet.
+
+        Notes
+        -----
+        For compatibility with :meth:`~DataFrame.to_csv`,
+        to_excel serializes lists and dicts to strings before writing.
+
+        Once a workbook has been saved it is not possible to write further
+        data without rewriting the whole workbook.
+
+        Examples
+        --------
+
+        Create, write to and save a workbook:
+
+        >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
+        ...                    index=['row 1', 'row 2'],
+        ...                    columns=['col 1', 'col 2'])
+        >>> df1.to_excel("output.xlsx")  # doctest: +SKIP
+
+        To specify the sheet name:
+
+        >>> df1.to_excel("output.xlsx",
+        ...              sheet_name='Sheet_name_1')  # doctest: +SKIP
+
+        If you wish to write to more than one sheet in the workbook, it is
+        necessary to specify an ExcelWriter object:
+
+        >>> df2 = df1.copy()
+        >>> with pd.ExcelWriter('output.xlsx') as writer:  # doctest: +SKIP
+        ...     df1.to_excel(writer, sheet_name='Sheet_name_1')
+        ...     df2.to_excel(writer, sheet_name='Sheet_name_2')
+
+        ExcelWriter can also be used to append to an existing Excel file:
+
+        >>> with pd.ExcelWriter('output.xlsx',
+        ...                     mode='a') as writer:  # doctest: +SKIP
+        ...     df1.to_excel(writer, sheet_name='Sheet_name_3')
+
+        To set the library that is used to write the Excel file,
+        you can pass the `engine` keyword (the default engine is
+        automatically chosen depending on the file extension):
+
+        >>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  # doctest: +SKIP
+        """
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        df = self if isinstance(self, ABCDataFrame) else self.to_frame()
+
+        from pandas.io.formats.excel import ExcelFormatter
+
+        formatter = ExcelFormatter(
+            df,
+            na_rep=na_rep,
+            cols=columns,
+            header=header,
+            float_format=float_format,
+            index=index,
+            index_label=index_label,
+            merge_cells=merge_cells,
+            inf_rep=inf_rep,
+        )
+        formatter.write(
+            excel_writer,
+            sheet_name=sheet_name,
+            startrow=startrow,
+            startcol=startcol,
+            freeze_panes=freeze_panes,
+            engine=engine,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buf"], name="to_json"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path_or_buf",
+    )
+    def to_json(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        orient: Literal["split", "records", "index", "table", "columns", "values"]
+        | None = None,
+        date_format: str | None = None,
+        double_precision: int = 10,
+        force_ascii: bool_t = True,
+        date_unit: TimeUnit = "ms",
+        default_handler: Callable[[Any], JSONSerializable] | None = None,
+        lines: bool_t = False,
+        compression: CompressionOptions = "infer",
+        index: bool_t | None = None,
+        indent: int | None = None,
+        storage_options: StorageOptions | None = None,
+        mode: Literal["a", "w"] = "w",
+    ) -> str | None:
+        """
+        Convert the object to a JSON string.
+
+        Note NaN's and None will be converted to null and datetime objects
+        will be converted to UNIX timestamps.
+
+        Parameters
+        ----------
+        path_or_buf : str, path object, file-like object, or None, default None
+            String, path object (implementing os.PathLike[str]), or file-like
+            object implementing a write() function. If None, the result is
+            returned as a string.
+        orient : str
+            Indication of expected JSON string format.
+
+            * Series:
+
+                - default is 'index'
+                - allowed values are: {{'split', 'records', 'index', 'table'}}.
+
+            * DataFrame:
+
+                - default is 'columns'
+                - allowed values are: {{'split', 'records', 'index', 'columns',
+                  'values', 'table'}}.
+
+            * The format of the JSON string:
+
+                - 'split' : dict like {{'index' -> [index], 'columns' -> [columns],
+                  'data' -> [values]}}
+                - 'records' : list like [{{column -> value}}, ... , {{column -> value}}]
+                - 'index' : dict like {{index -> {{column -> value}}}}
+                - 'columns' : dict like {{column -> {{index -> value}}}}
+                - 'values' : just the values array
+                - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}}
+
+                Describing the data, where data component is like ``orient='records'``.
+
+        date_format : {{None, 'epoch', 'iso'}}
+            Type of date conversion. 'epoch' = epoch milliseconds,
+            'iso' = ISO8601. The default depends on the `orient`. For
+            ``orient='table'``, the default is 'iso'. For all other orients,
+            the default is 'epoch'.
+        double_precision : int, default 10
+            The number of decimal places to use when encoding
+            floating point values. The possible maximal value is 15.
+            Passing double_precision greater than 15 will raise a ValueError.
+        force_ascii : bool, default True
+            Force encoded string to be ASCII.
+        date_unit : str, default 'ms' (milliseconds)
+            The time unit to encode to, governs timestamp and ISO8601
+            precision.  One of 's', 'ms', 'us', 'ns' for second, millisecond,
+            microsecond, and nanosecond respectively.
+        default_handler : callable, default None
+            Handler to call if object cannot otherwise be converted to a
+            suitable format for JSON. Should receive a single argument which is
+            the object to convert and return a serialisable object.
+        lines : bool, default False
+            If 'orient' is 'records' write out line-delimited json format. Will
+            throw ValueError if incorrect 'orient' since others are not
+            list-like.
+        {compression_options}
+
+            .. versionchanged:: 1.4.0 Zstandard support.
+
+        index : bool or None, default None
+            The index is only used when 'orient' is 'split', 'index', 'column',
+            or 'table'. Of these, 'index' and 'column' do not support
+            `index=False`.
+
+        indent : int, optional
+           Length of whitespace used to indent each record.
+
+        {storage_options}
+
+        mode : str, default 'w' (writing)
+            Specify the IO mode for output when supplying a path_or_buf.
+            Accepted args are 'w' (writing) and 'a' (append) only.
+            mode='a' is only supported when lines is True and orient is 'records'.
+
+        Returns
+        -------
+        None or str
+            If path_or_buf is None, returns the resulting json format as a
+            string. Otherwise returns None.
+
+        See Also
+        --------
+        read_json : Convert a JSON string to pandas object.
+
+        Notes
+        -----
+        The behavior of ``indent=0`` varies from the stdlib, which does not
+        indent the output but does insert newlines. Currently, ``indent=0``
+        and the default ``indent=None`` are equivalent in pandas, though this
+        may change in a future release.
+
+        ``orient='table'`` contains a 'pandas_version' field under 'schema'.
+        This stores the version of `pandas` used in the latest revision of the
+        schema.
+
+        Examples
+        --------
+        >>> from json import loads, dumps
+        >>> df = pd.DataFrame(
+        ...     [["a", "b"], ["c", "d"]],
+        ...     index=["row 1", "row 2"],
+        ...     columns=["col 1", "col 2"],
+        ... )
+
+        >>> result = df.to_json(orient="split")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "columns": [
+                "col 1",
+                "col 2"
+            ],
+            "index": [
+                "row 1",
+                "row 2"
+            ],
+            "data": [
+                [
+                    "a",
+                    "b"
+                ],
+                [
+                    "c",
+                    "d"
+                ]
+            ]
+        }}
+
+        Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
+        Note that index labels are not preserved with this encoding.
+
+        >>> result = df.to_json(orient="records")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        [
+            {{
+                "col 1": "a",
+                "col 2": "b"
+            }},
+            {{
+                "col 1": "c",
+                "col 2": "d"
+            }}
+        ]
+
+        Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
+
+        >>> result = df.to_json(orient="index")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "row 1": {{
+                "col 1": "a",
+                "col 2": "b"
+            }},
+            "row 2": {{
+                "col 1": "c",
+                "col 2": "d"
+            }}
+        }}
+
+        Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
+
+        >>> result = df.to_json(orient="columns")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "col 1": {{
+                "row 1": "a",
+                "row 2": "c"
+            }},
+            "col 2": {{
+                "row 1": "b",
+                "row 2": "d"
+            }}
+        }}
+
+        Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
+
+        >>> result = df.to_json(orient="values")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        [
+            [
+                "a",
+                "b"
+            ],
+            [
+                "c",
+                "d"
+            ]
+        ]
+
+        Encoding with Table Schema:
+
+        >>> result = df.to_json(orient="table")
+        >>> parsed = loads(result)
+        >>> dumps(parsed, indent=4)  # doctest: +SKIP
+        {{
+            "schema": {{
+                "fields": [
+                    {{
+                        "name": "index",
+                        "type": "string"
+                    }},
+                    {{
+                        "name": "col 1",
+                        "type": "string"
+                    }},
+                    {{
+                        "name": "col 2",
+                        "type": "string"
+                    }}
+                ],
+                "primaryKey": [
+                    "index"
+                ],
+                "pandas_version": "1.4.0"
+            }},
+            "data": [
+                {{
+                    "index": "row 1",
+                    "col 1": "a",
+                    "col 2": "b"
+                }},
+                {{
+                    "index": "row 2",
+                    "col 1": "c",
+                    "col 2": "d"
+                }}
+            ]
+        }}
+        """
+        from pandas.io import json
+
+        if date_format is None and orient == "table":
+            date_format = "iso"
+        elif date_format is None:
+            date_format = "epoch"
+
+        config.is_nonnegative_int(indent)
+        indent = indent or 0
+
+        return json.to_json(
+            path_or_buf=path_or_buf,
+            obj=self,
+            orient=orient,
+            date_format=date_format,
+            double_precision=double_precision,
+            force_ascii=force_ascii,
+            date_unit=date_unit,
+            default_handler=default_handler,
+            lines=lines,
+            compression=compression,
+            index=index,
+            indent=indent,
+            storage_options=storage_options,
+            mode=mode,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buf"], name="to_hdf"
+    )
+    def to_hdf(
+        self,
+        path_or_buf: FilePath | HDFStore,
+        key: str,
+        mode: Literal["a", "w", "r+"] = "a",
+        complevel: int | None = None,
+        complib: Literal["zlib", "lzo", "bzip2", "blosc"] | None = None,
+        append: bool_t = False,
+        format: Literal["fixed", "table"] | None = None,
+        index: bool_t = True,
+        min_itemsize: int | dict[str, int] | None = None,
+        nan_rep=None,
+        dropna: bool_t | None = None,
+        data_columns: Literal[True] | list[str] | None = None,
+        errors: OpenFileErrors = "strict",
+        encoding: str = "UTF-8",
+    ) -> None:
+        """
+        Write the contained data to an HDF5 file using HDFStore.
+
+        Hierarchical Data Format (HDF) is self-describing, allowing an
+        application to interpret the structure and contents of a file with
+        no outside information. One HDF file can hold a mix of related objects
+        which can be accessed as a group or as individual objects.
+
+        In order to add another DataFrame or Series to an existing HDF file
+        please use append mode and a different a key.
+
+        .. warning::
+
+           One can store a subclass of ``DataFrame`` or ``Series`` to HDF5,
+           but the type of the subclass is lost upon storing.
+
+        For more information see the :ref:`user guide <io.hdf5>`.
+
+        Parameters
+        ----------
+        path_or_buf : str or pandas.HDFStore
+            File path or HDFStore object.
+        key : str
+            Identifier for the group in the store.
+        mode : {'a', 'w', 'r+'}, default 'a'
+            Mode to open file:
+
+            - 'w': write, a new file is created (an existing file with
+              the same name would be deleted).
+            - 'a': append, an existing file is opened for reading and
+              writing, and if the file does not exist it is created.
+            - 'r+': similar to 'a', but the file must already exist.
+        complevel : {0-9}, default None
+            Specifies a compression level for data.
+            A value of 0 or None disables compression.
+        complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
+            Specifies the compression library to be used.
+            These additional compressors for Blosc are supported
+            (default if no compressor specified: 'blosc:blosclz'):
+            {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
+            'blosc:zlib', 'blosc:zstd'}.
+            Specifying a compression library which is not available issues
+            a ValueError.
+        append : bool, default False
+            For Table formats, append the input data to the existing.
+        format : {'fixed', 'table', None}, default 'fixed'
+            Possible values:
+
+            - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
+              nor searchable.
+            - 'table': Table format. Write as a PyTables Table structure
+              which may perform worse but allow more flexible operations
+              like searching / selecting subsets of the data.
+            - If None, pd.get_option('io.hdf.default_format') is checked,
+              followed by fallback to "fixed".
+        index : bool, default True
+            Write DataFrame index as a column.
+        min_itemsize : dict or int, optional
+            Map column names to minimum string sizes for columns.
+        nan_rep : Any, optional
+            How to represent null values as str.
+            Not allowed with append=True.
+        dropna : bool, default False, optional
+            Remove missing values.
+        data_columns : list of columns or True, optional
+            List of columns to create as indexed data columns for on-disk
+            queries, or True to use all columns. By default only the axes
+            of the object are indexed. See
+            :ref:`Query via data columns<io.hdf5-query-data-columns>`. for
+            more information.
+            Applicable only to format='table'.
+        errors : str, default 'strict'
+            Specifies how encoding and decoding errors are to be handled.
+            See the errors argument for :func:`open` for a full list
+            of options.
+        encoding : str, default "UTF-8"
+
+        See Also
+        --------
+        read_hdf : Read from HDF file.
+        DataFrame.to_orc : Write a DataFrame to the binary orc format.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+        DataFrame.to_sql : Write to a SQL table.
+        DataFrame.to_feather : Write out feather-format for DataFrames.
+        DataFrame.to_csv : Write out to a csv file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
+        ...                   index=['a', 'b', 'c'])  # doctest: +SKIP
+        >>> df.to_hdf('data.h5', key='df', mode='w')  # doctest: +SKIP
+
+        We can add another object to the same file:
+
+        >>> s = pd.Series([1, 2, 3, 4])  # doctest: +SKIP
+        >>> s.to_hdf('data.h5', key='s')  # doctest: +SKIP
+
+        Reading from HDF file:
+
+        >>> pd.read_hdf('data.h5', 'df')  # doctest: +SKIP
+        A  B
+        a  1  4
+        b  2  5
+        c  3  6
+        >>> pd.read_hdf('data.h5', 's')  # doctest: +SKIP
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        """
+        from pandas.io import pytables
+
+        # Argument 3 to "to_hdf" has incompatible type "NDFrame"; expected
+        # "Union[DataFrame, Series]" [arg-type]
+        pytables.to_hdf(
+            path_or_buf,
+            key,
+            self,  # type: ignore[arg-type]
+            mode=mode,
+            complevel=complevel,
+            complib=complib,
+            append=append,
+            format=format,
+            index=index,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            dropna=dropna,
+            data_columns=data_columns,
+            errors=errors,
+            encoding=encoding,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "name", "con"], name="to_sql"
+    )
+    def to_sql(
+        self,
+        name: str,
+        con,
+        schema: str | None = None,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool_t = True,
+        index_label: IndexLabel | None = None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Databases supported by SQLAlchemy [1]_ are supported. Tables can be
+        newly created, appended to, or overwritten.
+
+        Parameters
+        ----------
+        name : str
+            Name of SQL table.
+        con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection
+            Using SQLAlchemy makes it possible to use any DB supported by that
+            library. Legacy support is provided for sqlite3.Connection objects. The user
+            is responsible for engine disposal and connection closure for the SQLAlchemy
+            connectable. See `here \
+                <https://docs.sqlalchemy.org/en/20/core/connections.html>`_.
+            If passing a sqlalchemy.engine.Connection which is already in a transaction,
+            the transaction will not be committed.  If passing a sqlite3.Connection,
+            it will not be possible to roll back the record insertion.
+
+        schema : str, optional
+            Specify the schema (if database flavor supports this). If None, use
+            default schema.
+        if_exists : {'fail', 'replace', 'append'}, default 'fail'
+            How to behave if the table already exists.
+
+            * fail: Raise a ValueError.
+            * replace: Drop the table before inserting new values.
+            * append: Insert new values to the existing table.
+
+        index : bool, default True
+            Write DataFrame index as a column. Uses `index_label` as the column
+            name in the table. Creates a table index for this column.
+        index_label : str or sequence, default None
+            Column label for index column(s). If None is given (default) and
+            `index` is True, then the index names are used.
+            A sequence should be given if the DataFrame uses MultiIndex.
+        chunksize : int, optional
+            Specify the number of rows in each batch to be written at a time.
+            By default, all rows will be written at once.
+        dtype : dict or scalar, optional
+            Specifying the datatype for columns. If a dictionary is used, the
+            keys should be the column names and the values should be the
+            SQLAlchemy types or strings for the sqlite3 legacy mode. If a
+            scalar is provided, it will be applied to all columns.
+        method : {None, 'multi', callable}, optional
+            Controls the SQL insertion clause used:
+
+            * None : Uses standard SQL ``INSERT`` clause (one per row).
+            * 'multi': Pass multiple values in a single ``INSERT`` clause.
+            * callable with signature ``(pd_table, conn, keys, data_iter)``.
+
+            Details and a sample callable implementation can be found in the
+            section :ref:`insert method <io.sql.method>`.
+
+        Returns
+        -------
+        None or int
+            Number of rows affected by to_sql. None is returned if the callable
+            passed into ``method`` does not return an integer number of rows.
+
+            The number of returned rows affected is the sum of the ``rowcount``
+            attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable which may not
+            reflect the exact number of written rows as stipulated in the
+            `sqlite3 <https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.rowcount>`__ or
+            `SQLAlchemy <https://docs.sqlalchemy.org/en/20/core/connections.html#sqlalchemy.engine.CursorResult.rowcount>`__.
+
+            .. versionadded:: 1.4.0
+
+        Raises
+        ------
+        ValueError
+            When the table already exists and `if_exists` is 'fail' (the
+            default).
+
+        See Also
+        --------
+        read_sql : Read a DataFrame from a table.
+
+        Notes
+        -----
+        Timezone aware datetime columns will be written as
+        ``Timestamp with timezone`` type with SQLAlchemy if supported by the
+        database. Otherwise, the datetimes will be stored as timezone unaware
+        timestamps local to the original timezone.
+
+        Not all datastores support ``method="multi"``. Oracle, for example,
+        does not support multi-value insert.
+
+        References
+        ----------
+        .. [1] https://docs.sqlalchemy.org
+        .. [2] https://www.python.org/dev/peps/pep-0249/
+
+        Examples
+        --------
+        Create an in-memory SQLite database.
+
+        >>> from sqlalchemy import create_engine
+        >>> engine = create_engine('sqlite://', echo=False)
+
+        Create a table from scratch with 3 rows.
+
+        >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
+        >>> df
+             name
+        0  User 1
+        1  User 2
+        2  User 3
+
+        >>> df.to_sql(name='users', con=engine)
+        3
+        >>> from sqlalchemy import text
+        >>> with engine.connect() as conn:
+        ...    conn.execute(text("SELECT * FROM users")).fetchall()
+        [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
+
+        An `sqlalchemy.engine.Connection` can also be passed to `con`:
+
+        >>> with engine.begin() as connection:
+        ...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
+        ...     df1.to_sql(name='users', con=connection, if_exists='append')
+        2
+
+        This is allowed to support operations that require that the same
+        DBAPI connection is used for the entire operation.
+
+        >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
+        >>> df2.to_sql(name='users', con=engine, if_exists='append')
+        2
+        >>> with engine.connect() as conn:
+        ...    conn.execute(text("SELECT * FROM users")).fetchall()
+        [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
+         (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
+         (1, 'User 7')]
+
+        Overwrite the table with just ``df2``.
+
+        >>> df2.to_sql(name='users', con=engine, if_exists='replace',
+        ...            index_label='id')
+        2
+        >>> with engine.connect() as conn:
+        ...    conn.execute(text("SELECT * FROM users")).fetchall()
+        [(0, 'User 6'), (1, 'User 7')]
+
+        Use ``method`` to define a callable insertion method to do nothing
+        if there's a primary key conflict on a table in a PostgreSQL database.
+
+        >>> from sqlalchemy.dialects.postgresql import insert
+        >>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
+        ...     # "a" is the primary key in "conflict_table"
+        ...     data = [dict(zip(keys, row)) for row in data_iter]
+        ...     stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
+        ...     result = conn.execute(stmt)
+        ...     return result.rowcount
+        >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing)  # doctest: +SKIP
+        0
+
+        For MySQL, a callable to update columns ``b`` and ``c`` if there's a conflict
+        on a primary key.
+
+        >>> from sqlalchemy.dialects.mysql import insert
+        >>> def insert_on_conflict_update(table, conn, keys, data_iter):
+        ...     # update columns "b" and "c" on primary key conflict
+        ...     data = [dict(zip(keys, row)) for row in data_iter]
+        ...     stmt = (
+        ...         insert(table.table)
+        ...         .values(data)
+        ...     )
+        ...     stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
+        ...     result = conn.execute(stmt)
+        ...     return result.rowcount
+        >>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update)  # doctest: +SKIP
+        2
+
+        Specify the dtype (especially useful for integers with missing values).
+        Notice that while pandas is forced to store the data as floating point,
+        the database supports nullable integers. When fetching the data with
+        Python, we get back integer scalars.
+
+        >>> df = pd.DataFrame({"A": [1, None, 2]})
+        >>> df
+             A
+        0  1.0
+        1  NaN
+        2  2.0
+
+        >>> from sqlalchemy.types import Integer
+        >>> df.to_sql(name='integers', con=engine, index=False,
+        ...           dtype={"A": Integer()})
+        3
+
+        >>> with engine.connect() as conn:
+        ...   conn.execute(text("SELECT * FROM integers")).fetchall()
+        [(1,), (None,), (2,)]
+        """  # noqa: E501
+        from pandas.io import sql
+
+        return sql.to_sql(
+            self,
+            name,
+            con,
+            schema=schema,
+            if_exists=if_exists,
+            index=index,
+            index_label=index_label,
+            chunksize=chunksize,
+            dtype=dtype,
+            method=method,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path"], name="to_pickle"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path",
+    )
+    def to_pickle(
+        self,
+        path: FilePath | WriteBuffer[bytes],
+        compression: CompressionOptions = "infer",
+        protocol: int = pickle.HIGHEST_PROTOCOL,
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        """
+        Pickle (serialize) object to file.
+
+        Parameters
+        ----------
+        path : str, path object, or file-like object
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a binary ``write()`` function. File path where
+            the pickled object will be stored.
+        {compression_options}
+        protocol : int
+            Int which indicates which protocol should be used by the pickler,
+            default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
+            values are 0, 1, 2, 3, 4, 5. A negative value for the protocol
+            parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
+
+            .. [1] https://docs.python.org/3/library/pickle.html.
+
+        {storage_options}
+
+        See Also
+        --------
+        read_pickle : Load pickled pandas object (or any object) from file.
+        DataFrame.to_hdf : Write DataFrame to an HDF5 file.
+        DataFrame.to_sql : Write DataFrame to a SQL database.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+
+        Examples
+        --------
+        >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})  # doctest: +SKIP
+        >>> original_df  # doctest: +SKIP
+           foo  bar
+        0    0    5
+        1    1    6
+        2    2    7
+        3    3    8
+        4    4    9
+        >>> original_df.to_pickle("./dummy.pkl")  # doctest: +SKIP
+
+        >>> unpickled_df = pd.read_pickle("./dummy.pkl")  # doctest: +SKIP
+        >>> unpickled_df  # doctest: +SKIP
+           foo  bar
+        0    0    5
+        1    1    6
+        2    2    7
+        3    3    8
+        4    4    9
+        """  # noqa: E501
+        from pandas.io.pickle import to_pickle
+
+        to_pickle(
+            self,
+            path,
+            compression=compression,
+            protocol=protocol,
+            storage_options=storage_options,
+        )
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="to_clipboard"
+    )
+    def to_clipboard(
+        self, excel: bool_t = True, sep: str | None = None, **kwargs
+    ) -> None:
+        r"""
+        Copy object to the system clipboard.
+
+        Write a text representation of object to the system clipboard.
+        This can be pasted into Excel, for example.
+
+        Parameters
+        ----------
+        excel : bool, default True
+            Produce output in a csv format for easy pasting into excel.
+
+            - True, use the provided separator for csv pasting.
+            - False, write a string representation of the object to the clipboard.
+
+        sep : str, default ``'\t'``
+            Field delimiter.
+        **kwargs
+            These parameters will be passed to DataFrame.to_csv.
+
+        See Also
+        --------
+        DataFrame.to_csv : Write a DataFrame to a comma-separated values
+            (csv) file.
+        read_clipboard : Read text from clipboard and pass to read_csv.
+
+        Notes
+        -----
+        Requirements for your platform.
+
+          - Linux : `xclip`, or `xsel` (with `PyQt4` modules)
+          - Windows : none
+          - macOS : none
+
+        This method uses the processes developed for the package `pyperclip`. A
+        solution to render any output string format is given in the examples.
+
+        Examples
+        --------
+        Copy the contents of a DataFrame to the clipboard.
+
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+
+        >>> df.to_clipboard(sep=',')  # doctest: +SKIP
+        ... # Wrote the following to the system clipboard:
+        ... # ,A,B,C
+        ... # 0,1,2,3
+        ... # 1,4,5,6
+
+        We can omit the index by passing the keyword `index` and setting
+        it to false.
+
+        >>> df.to_clipboard(sep=',', index=False)  # doctest: +SKIP
+        ... # Wrote the following to the system clipboard:
+        ... # A,B,C
+        ... # 1,2,3
+        ... # 4,5,6
+
+        Using the original `pyperclip` package for any string output format.
+
+        .. code-block:: python
+
+           import pyperclip
+           html = df.style.to_html()
+           pyperclip.copy(html)
+        """
+        from pandas.io import clipboards
+
+        clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs)
+
+    @final
+    def to_xarray(self):
+        """
+        Return an xarray object from the pandas object.
+
+        Returns
+        -------
+        xarray.DataArray or xarray.Dataset
+            Data in the pandas structure converted to Dataset if the object is
+            a DataFrame, or a DataArray if the object is a Series.
+
+        See Also
+        --------
+        DataFrame.to_hdf : Write DataFrame to an HDF5 file.
+        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+
+        Notes
+        -----
+        See the `xarray docs <https://xarray.pydata.org/en/stable/>`__
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
+        ...                    ('parrot', 'bird', 24.0, 2),
+        ...                    ('lion', 'mammal', 80.5, 4),
+        ...                    ('monkey', 'mammal', np.nan, 4)],
+        ...                   columns=['name', 'class', 'max_speed',
+        ...                            'num_legs'])
+        >>> df
+             name   class  max_speed  num_legs
+        0  falcon    bird      389.0         2
+        1  parrot    bird       24.0         2
+        2    lion  mammal       80.5         4
+        3  monkey  mammal        NaN         4
+
+        >>> df.to_xarray()  # doctest: +SKIP
+        <xarray.Dataset>
+        Dimensions:    (index: 4)
+        Coordinates:
+          * index      (index) int64 32B 0 1 2 3
+        Data variables:
+            name       (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
+            class      (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
+            max_speed  (index) float64 32B 389.0 24.0 80.5 nan
+            num_legs   (index) int64 32B 2 2 4 4
+
+        >>> df['max_speed'].to_xarray()  # doctest: +SKIP
+        <xarray.DataArray 'max_speed' (index: 4)>
+        array([389. ,  24. ,  80.5,   nan])
+        Coordinates:
+          * index    (index) int64 0 1 2 3
+
+        >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
+        ...                         '2018-01-02', '2018-01-02'])
+        >>> df_multiindex = pd.DataFrame({'date': dates,
+        ...                               'animal': ['falcon', 'parrot',
+        ...                                          'falcon', 'parrot'],
+        ...                               'speed': [350, 18, 361, 15]})
+        >>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
+
+        >>> df_multiindex
+                           speed
+        date       animal
+        2018-01-01 falcon    350
+                   parrot     18
+        2018-01-02 falcon    361
+                   parrot     15
+
+        >>> df_multiindex.to_xarray()  # doctest: +SKIP
+        <xarray.Dataset>
+        Dimensions:  (date: 2, animal: 2)
+        Coordinates:
+          * date     (date) datetime64[ns] 2018-01-01 2018-01-02
+          * animal   (animal) object 'falcon' 'parrot'
+        Data variables:
+            speed    (date, animal) int64 350 18 361 15
+        """
+        xarray = import_optional_dependency("xarray")
+
+        if self.ndim == 1:
+            return xarray.DataArray.from_series(self)
+        else:
+            return xarray.Dataset.from_dataframe(self)
+
+    @overload
+    def to_latex(
+        self,
+        buf: None = ...,
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | SequenceNotStr[str] = ...,
+        index: bool_t = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool_t | None = ...,
+        index_names: bool_t = ...,
+        bold_rows: bool_t = ...,
+        column_format: str | None = ...,
+        longtable: bool_t | None = ...,
+        escape: bool_t | None = ...,
+        encoding: str | None = ...,
+        decimal: str = ...,
+        multicolumn: bool_t | None = ...,
+        multicolumn_format: str | None = ...,
+        multirow: bool_t | None = ...,
+        caption: str | tuple[str, str] | None = ...,
+        label: str | None = ...,
+        position: str | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_latex(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | SequenceNotStr[str] = ...,
+        index: bool_t = ...,
+        na_rep: str = ...,
+        formatters: FormattersType | None = ...,
+        float_format: FloatFormatType | None = ...,
+        sparsify: bool_t | None = ...,
+        index_names: bool_t = ...,
+        bold_rows: bool_t = ...,
+        column_format: str | None = ...,
+        longtable: bool_t | None = ...,
+        escape: bool_t | None = ...,
+        encoding: str | None = ...,
+        decimal: str = ...,
+        multicolumn: bool_t | None = ...,
+        multicolumn_format: str | None = ...,
+        multirow: bool_t | None = ...,
+        caption: str | tuple[str, str] | None = ...,
+        label: str | None = ...,
+        position: str | None = ...,
+    ) -> None:
+        ...
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "buf"], name="to_latex"
+    )
+    def to_latex(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: bool_t | SequenceNotStr[str] = True,
+        index: bool_t = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool_t | None = None,
+        index_names: bool_t = True,
+        bold_rows: bool_t = False,
+        column_format: str | None = None,
+        longtable: bool_t | None = None,
+        escape: bool_t | None = None,
+        encoding: str | None = None,
+        decimal: str = ".",
+        multicolumn: bool_t | None = None,
+        multicolumn_format: str | None = None,
+        multirow: bool_t | None = None,
+        caption: str | tuple[str, str] | None = None,
+        label: str | None = None,
+        position: str | None = None,
+    ) -> str | None:
+        r"""
+        Render object to a LaTeX tabular, longtable, or nested table.
+
+        Requires ``\usepackage{{booktabs}}``.  The output can be copy/pasted
+        into a main LaTeX document or read from an external file
+        with ``\input{{table.tex}}``.
+
+        .. versionchanged:: 2.0.0
+           Refactored to use the Styler implementation via jinja2 templating.
+
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        columns : list of label, optional
+            The subset of columns to write. Writes all columns by default.
+        header : bool or list of str, default True
+            Write out the column names. If a list of strings is given,
+            it is assumed to be aliases for the column names.
+        index : bool, default True
+            Write row names (index).
+        na_rep : str, default 'NaN'
+            Missing data representation.
+        formatters : list of functions or dict of {{str: function}}, optional
+            Formatter functions to apply to columns' elements by position or
+            name. The result of each function must be a unicode string.
+            List must be of length equal to the number of columns.
+        float_format : one-parameter function or str, optional, default None
+            Formatter for floating point numbers. For example
+            ``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will
+            both result in 0.1234 being formatted as 0.12.
+        sparsify : bool, optional
+            Set to False for a DataFrame with a hierarchical index to print
+            every multiindex key at each row. By default, the value will be
+            read from the config module.
+        index_names : bool, default True
+            Prints the names of the indexes.
+        bold_rows : bool, default False
+            Make the row labels bold in the output.
+        column_format : str, optional
+            The columns format as specified in `LaTeX table format
+            <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3
+            columns. By default, 'l' will be used for all columns except
+            columns of numbers, which default to 'r'.
+        longtable : bool, optional
+            Use a longtable environment instead of tabular. Requires
+            adding a \usepackage{{longtable}} to your LaTeX preamble.
+            By default, the value will be read from the pandas config
+            module, and set to `True` if the option ``styler.latex.environment`` is
+            `"longtable"`.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed.
+        escape : bool, optional
+            By default, the value will be read from the pandas config
+            module and set to `True` if the option ``styler.format.escape`` is
+            `"latex"`. When set to False prevents from escaping latex special
+            characters in column names.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed, as has the
+               default value to `False`.
+        encoding : str, optional
+            A string representing the encoding to use in the output file,
+            defaults to 'utf-8'.
+        decimal : str, default '.'
+            Character recognized as decimal separator, e.g. ',' in Europe.
+        multicolumn : bool, default True
+            Use \multicolumn to enhance MultiIndex columns.
+            The default will be read from the config module, and is set
+            as the option ``styler.sparse.columns``.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed.
+        multicolumn_format : str, default 'r'
+            The alignment for multicolumns, similar to `column_format`
+            The default will be read from the config module, and is set as the option
+            ``styler.latex.multicol_align``.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed, as has the
+               default value to "r".
+        multirow : bool, default True
+            Use \multirow to enhance MultiIndex rows. Requires adding a
+            \usepackage{{multirow}} to your LaTeX preamble. Will print
+            centered labels (instead of top-aligned) across the contained
+            rows, separating groups via clines. The default will be read
+            from the pandas config module, and is set as the option
+            ``styler.sparse.index``.
+
+            .. versionchanged:: 2.0.0
+               The pandas option affecting this argument has changed, as has the
+               default value to `True`.
+        caption : str or tuple, optional
+            Tuple (full_caption, short_caption),
+            which results in ``\caption[short_caption]{{full_caption}}``;
+            if a single string is passed, no short caption will be set.
+        label : str, optional
+            The LaTeX label to be placed inside ``\label{{}}`` in the output.
+            This is used with ``\ref{{}}`` in the main ``.tex`` file.
+
+        position : str, optional
+            The LaTeX positional argument for tables, to be placed after
+            ``\begin{{}}`` in the output.
+
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns None.
+
+        See Also
+        --------
+        io.formats.style.Styler.to_latex : Render a DataFrame to LaTeX
+            with conditional formatting.
+        DataFrame.to_string : Render a DataFrame to a console-friendly
+            tabular output.
+        DataFrame.to_html : Render a DataFrame as an HTML table.
+
+        Notes
+        -----
+        As of v2.0.0 this method has changed to use the Styler implementation as
+        part of :meth:`.Styler.to_latex` via ``jinja2`` templating. This means
+        that ``jinja2`` is a requirement, and needs to be installed, for this method
+        to function. It is advised that users switch to using Styler, since that
+        implementation is more frequently updated and contains much more
+        flexibility with the output.
+
+        Examples
+        --------
+        Convert a general DataFrame to LaTeX with formatting:
+
+        >>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
+        ...                        age=[26, 45],
+        ...                        height=[181.23, 177.65]))
+        >>> print(df.to_latex(index=False,
+        ...                   formatters={"name": str.upper},
+        ...                   float_format="{:.1f}".format,
+        ... ))  # doctest: +SKIP
+        \begin{tabular}{lrr}
+        \toprule
+        name & age & height \\
+        \midrule
+        RAPHAEL & 26 & 181.2 \\
+        DONATELLO & 45 & 177.7 \\
+        \bottomrule
+        \end{tabular}
+        """
+        # Get defaults from the pandas config
+        if self.ndim == 1:
+            self = self.to_frame()
+        if longtable is None:
+            longtable = config.get_option("styler.latex.environment") == "longtable"
+        if escape is None:
+            escape = config.get_option("styler.format.escape") == "latex"
+        if multicolumn is None:
+            multicolumn = config.get_option("styler.sparse.columns")
+        if multicolumn_format is None:
+            multicolumn_format = config.get_option("styler.latex.multicol_align")
+        if multirow is None:
+            multirow = config.get_option("styler.sparse.index")
+
+        if column_format is not None and not isinstance(column_format, str):
+            raise ValueError("`column_format` must be str or unicode")
+        length = len(self.columns) if columns is None else len(columns)
+        if isinstance(header, (list, tuple)) and len(header) != length:
+            raise ValueError(f"Writing {length} cols but got {len(header)} aliases")
+
+        # Refactor formatters/float_format/decimal/na_rep/escape to Styler structure
+        base_format_ = {
+            "na_rep": na_rep,
+            "escape": "latex" if escape else None,
+            "decimal": decimal,
+        }
+        index_format_: dict[str, Any] = {"axis": 0, **base_format_}
+        column_format_: dict[str, Any] = {"axis": 1, **base_format_}
+
+        if isinstance(float_format, str):
+            float_format_: Callable | None = lambda x: float_format % x
+        else:
+            float_format_ = float_format
+
+        def _wrap(x, alt_format_):
+            if isinstance(x, (float, complex)) and float_format_ is not None:
+                return float_format_(x)
+            else:
+                return alt_format_(x)
+
+        formatters_: list | tuple | dict | Callable | None = None
+        if isinstance(formatters, list):
+            formatters_ = {
+                c: partial(_wrap, alt_format_=formatters[i])
+                for i, c in enumerate(self.columns)
+            }
+        elif isinstance(formatters, dict):
+            index_formatter = formatters.pop("__index__", None)
+            column_formatter = formatters.pop("__columns__", None)
+            if index_formatter is not None:
+                index_format_.update({"formatter": index_formatter})
+            if column_formatter is not None:
+                column_format_.update({"formatter": column_formatter})
+
+            formatters_ = formatters
+            float_columns = self.select_dtypes(include="float").columns
+            for col in float_columns:
+                if col not in formatters.keys():
+                    formatters_.update({col: float_format_})
+        elif formatters is None and float_format is not None:
+            formatters_ = partial(_wrap, alt_format_=lambda v: v)
+        format_index_ = [index_format_, column_format_]
+
+        # Deal with hiding indexes and relabelling column names
+        hide_: list[dict] = []
+        relabel_index_: list[dict] = []
+        if columns:
+            hide_.append(
+                {
+                    "subset": [c for c in self.columns if c not in columns],
+                    "axis": "columns",
+                }
+            )
+        if header is False:
+            hide_.append({"axis": "columns"})
+        elif isinstance(header, (list, tuple)):
+            relabel_index_.append({"labels": header, "axis": "columns"})
+            format_index_ = [index_format_]  # column_format is overwritten
+
+        if index is False:
+            hide_.append({"axis": "index"})
+        if index_names is False:
+            hide_.append({"names": True, "axis": "index"})
+
+        render_kwargs_ = {
+            "hrules": True,
+            "sparse_index": sparsify,
+            "sparse_columns": sparsify,
+            "environment": "longtable" if longtable else None,
+            "multicol_align": multicolumn_format
+            if multicolumn
+            else f"naive-{multicolumn_format}",
+            "multirow_align": "t" if multirow else "naive",
+            "encoding": encoding,
+            "caption": caption,
+            "label": label,
+            "position": position,
+            "column_format": column_format,
+            "clines": "skip-last;data"
+            if (multirow and isinstance(self.index, MultiIndex))
+            else None,
+            "bold_rows": bold_rows,
+        }
+
+        return self._to_latex_via_styler(
+            buf,
+            hide=hide_,
+            relabel_index=relabel_index_,
+            format={"formatter": formatters_, **base_format_},
+            format_index=format_index_,
+            render_kwargs=render_kwargs_,
+        )
+
+    @final
+    def _to_latex_via_styler(
+        self,
+        buf=None,
+        *,
+        hide: dict | list[dict] | None = None,
+        relabel_index: dict | list[dict] | None = None,
+        format: dict | list[dict] | None = None,
+        format_index: dict | list[dict] | None = None,
+        render_kwargs: dict | None = None,
+    ):
+        """
+        Render object to a LaTeX tabular, longtable, or nested table.
+
+        Uses the ``Styler`` implementation with the following, ordered, method chaining:
+
+        .. code-block:: python
+           styler = Styler(DataFrame)
+           styler.hide(**hide)
+           styler.relabel_index(**relabel_index)
+           styler.format(**format)
+           styler.format_index(**format_index)
+           styler.to_latex(buf=buf, **render_kwargs)
+
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        hide : dict, list of dict
+            Keyword args to pass to the method call of ``Styler.hide``. If a list will
+            call the method numerous times.
+        relabel_index : dict, list of dict
+            Keyword args to pass to the method of ``Styler.relabel_index``. If a list
+            will call the method numerous times.
+        format : dict, list of dict
+            Keyword args to pass to the method call of ``Styler.format``. If a list will
+            call the method numerous times.
+        format_index : dict, list of dict
+            Keyword args to pass to the method call of ``Styler.format_index``. If a
+            list will call the method numerous times.
+        render_kwargs : dict
+            Keyword args to pass to the method call of ``Styler.to_latex``.
+
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns None.
+        """
+        from pandas.io.formats.style import Styler
+
+        self = cast("DataFrame", self)
+        styler = Styler(self, uuid="")
+
+        for kw_name in ["hide", "relabel_index", "format", "format_index"]:
+            kw = vars()[kw_name]
+            if isinstance(kw, dict):
+                getattr(styler, kw_name)(**kw)
+            elif isinstance(kw, list):
+                for sub_kw in kw:
+                    getattr(styler, kw_name)(**sub_kw)
+
+        # bold_rows is not a direct kwarg of Styler.to_latex
+        render_kwargs = {} if render_kwargs is None else render_kwargs
+        if render_kwargs.pop("bold_rows"):
+            styler.map_index(lambda v: "textbf:--rwrap;")
+
+        return styler.to_latex(buf=buf, **render_kwargs)
+
+    @overload
+    def to_csv(
+        self,
+        path_or_buf: None = ...,
+        sep: str = ...,
+        na_rep: str = ...,
+        float_format: str | Callable | None = ...,
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | list[str] = ...,
+        index: bool_t = ...,
+        index_label: IndexLabel | None = ...,
+        mode: str = ...,
+        encoding: str | None = ...,
+        compression: CompressionOptions = ...,
+        quoting: int | None = ...,
+        quotechar: str = ...,
+        lineterminator: str | None = ...,
+        chunksize: int | None = ...,
+        date_format: str | None = ...,
+        doublequote: bool_t = ...,
+        escapechar: str | None = ...,
+        decimal: str = ...,
+        errors: OpenFileErrors = ...,
+        storage_options: StorageOptions = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str],
+        sep: str = ...,
+        na_rep: str = ...,
+        float_format: str | Callable | None = ...,
+        columns: Sequence[Hashable] | None = ...,
+        header: bool_t | list[str] = ...,
+        index: bool_t = ...,
+        index_label: IndexLabel | None = ...,
+        mode: str = ...,
+        encoding: str | None = ...,
+        compression: CompressionOptions = ...,
+        quoting: int | None = ...,
+        quotechar: str = ...,
+        lineterminator: str | None = ...,
+        chunksize: int | None = ...,
+        date_format: str | None = ...,
+        doublequote: bool_t = ...,
+        escapechar: str | None = ...,
+        decimal: str = ...,
+        errors: OpenFileErrors = ...,
+        storage_options: StorageOptions = ...,
+    ) -> None:
+        ...
+
+    @final
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "path_or_buf"], name="to_csv"
+    )
+    @doc(
+        storage_options=_shared_docs["storage_options"],
+        compression_options=_shared_docs["compression_options"] % "path_or_buf",
+    )
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        sep: str = ",",
+        na_rep: str = "",
+        float_format: str | Callable | None = None,
+        columns: Sequence[Hashable] | None = None,
+        header: bool_t | list[str] = True,
+        index: bool_t = True,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        encoding: str | None = None,
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        quotechar: str = '"',
+        lineterminator: str | None = None,
+        chunksize: int | None = None,
+        date_format: str | None = None,
+        doublequote: bool_t = True,
+        escapechar: str | None = None,
+        decimal: str = ".",
+        errors: OpenFileErrors = "strict",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        r"""
+        Write object to a comma-separated values (csv) file.
+
+        Parameters
+        ----------
+        path_or_buf : str, path object, file-like object, or None, default None
+            String, path object (implementing os.PathLike[str]), or file-like
+            object implementing a write() function. If None, the result is
+            returned as a string. If a non-binary file object is passed, it should
+            be opened with `newline=''`, disabling universal newlines. If a binary
+            file object is passed, `mode` might need to contain a `'b'`.
+        sep : str, default ','
+            String of length 1. Field delimiter for the output file.
+        na_rep : str, default ''
+            Missing data representation.
+        float_format : str, Callable, default None
+            Format string for floating point numbers. If a Callable is given, it takes
+            precedence over other numeric formatting parameters, like decimal.
+        columns : sequence, optional
+            Columns to write.
+        header : bool or list of str, default True
+            Write out the column names. If a list of strings is given it is
+            assumed to be aliases for the column names.
+        index : bool, default True
+            Write row names (index).
+        index_label : str or sequence, or False, default None
+            Column label for index column(s) if desired. If None is given, and
+            `header` and `index` are True, then the index names are used. A
+            sequence should be given if the object uses MultiIndex. If
+            False do not print fields for index names. Use index_label=False
+            for easier importing in R.
+        mode : {{'w', 'x', 'a'}}, default 'w'
+            Forwarded to either `open(mode=)` or `fsspec.open(mode=)` to control
+            the file opening. Typical values include:
+
+            - 'w', truncate the file first.
+            - 'x', exclusive creation, failing if the file already exists.
+            - 'a', append to the end of file if it exists.
+
+        encoding : str, optional
+            A string representing the encoding to use in the output file,
+            defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
+            is a non-binary file object.
+        {compression_options}
+
+               May be a dict with key 'method' as compression mode
+               and other entries as additional compression options if
+               compression mode is 'zip'.
+
+               Passing compression options as keys in dict is
+               supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'.
+        quoting : optional constant from csv module
+            Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
+            then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
+            will treat them as non-numeric.
+        quotechar : str, default '\"'
+            String of length 1. Character used to quote fields.
+        lineterminator : str, optional
+            The newline character or character sequence to use in the output
+            file. Defaults to `os.linesep`, which depends on the OS in which
+            this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).
+
+            .. versionchanged:: 1.5.0
+
+                Previously was line_terminator, changed for consistency with
+                read_csv and the standard library 'csv' module.
+
+        chunksize : int or None
+            Rows to write at a time.
+        date_format : str, default None
+            Format string for datetime objects.
+        doublequote : bool, default True
+            Control quoting of `quotechar` inside a field.
+        escapechar : str, default None
+            String of length 1. Character used to escape `sep` and `quotechar`
+            when appropriate.
+        decimal : str, default '.'
+            Character recognized as decimal separator. E.g. use ',' for
+            European data.
+        errors : str, default 'strict'
+            Specifies how encoding and decoding errors are to be handled.
+            See the errors argument for :func:`open` for a full list
+            of options.
+
+        {storage_options}
+
+        Returns
+        -------
+        None or str
+            If path_or_buf is None, returns the resulting csv format as a
+            string. Otherwise returns None.
+
+        See Also
+        --------
+        read_csv : Load a CSV file into a DataFrame.
+        to_excel : Write DataFrame to an Excel file.
+
+        Examples
+        --------
+        Create 'out.csv' containing 'df' without indices
+
+        >>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'],
+        ...                    'mask': ['red', 'purple'],
+        ...                    'weapon': ['sai', 'bo staff']}})
+        >>> df.to_csv('out.csv', index=False)  # doctest: +SKIP
+
+        Create 'out.zip' containing 'out.csv'
+
+        >>> df.to_csv(index=False)
+        'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
+        >>> compression_opts = dict(method='zip',
+        ...                         archive_name='out.csv')  # doctest: +SKIP
+        >>> df.to_csv('out.zip', index=False,
+        ...           compression=compression_opts)  # doctest: +SKIP
+
+        To write a csv file to a new folder or nested folder you will first
+        need to create it using either Pathlib or os:
+
+        >>> from pathlib import Path  # doctest: +SKIP
+        >>> filepath = Path('folder/subfolder/out.csv')  # doctest: +SKIP
+        >>> filepath.parent.mkdir(parents=True, exist_ok=True)  # doctest: +SKIP
+        >>> df.to_csv(filepath)  # doctest: +SKIP
+
+        >>> import os  # doctest: +SKIP
+        >>> os.makedirs('folder/subfolder', exist_ok=True)  # doctest: +SKIP
+        >>> df.to_csv('folder/subfolder/out.csv')  # doctest: +SKIP
+        """
+        df = self if isinstance(self, ABCDataFrame) else self.to_frame()
+
+        formatter = DataFrameFormatter(
+            frame=df,
+            header=header,
+            index=index,
+            na_rep=na_rep,
+            float_format=float_format,
+            decimal=decimal,
+        )
+
+        return DataFrameRenderer(formatter).to_csv(
+            path_or_buf,
+            lineterminator=lineterminator,
+            sep=sep,
+            encoding=encoding,
+            errors=errors,
+            compression=compression,
+            quoting=quoting,
+            columns=columns,
+            index_label=index_label,
+            mode=mode,
+            chunksize=chunksize,
+            quotechar=quotechar,
+            date_format=date_format,
+            doublequote=doublequote,
+            escapechar=escapechar,
+            storage_options=storage_options,
+        )
+
+    # ----------------------------------------------------------------------
+    # Lookup Caching
+
+    def _reset_cacher(self) -> None:
+        """
+        Reset the cacher.
+        """
+        raise AbstractMethodError(self)
+
+    def _maybe_update_cacher(
+        self,
+        clear: bool_t = False,
+        verify_is_copy: bool_t = True,
+        inplace: bool_t = False,
+    ) -> None:
+        """
+        See if we need to update our parent cacher if clear, then clear our
+        cache.
+
+        Parameters
+        ----------
+        clear : bool, default False
+            Clear the item cache.
+        verify_is_copy : bool, default True
+            Provide is_copy checks.
+        """
+        if using_copy_on_write():
+            return
+
+        if verify_is_copy:
+            self._check_setitem_copy(t="referent")
+
+        if clear:
+            self._clear_item_cache()
+
+    def _clear_item_cache(self) -> None:
+        raise AbstractMethodError(self)
+
+    # ----------------------------------------------------------------------
+    # Indexing Methods
+
+    @final
+    def take(self, indices, axis: Axis = 0, **kwargs) -> Self:
+        """
+        Return the elements in the given *positional* indices along an axis.
+
+        This means that we are not indexing according to actual values in
+        the index attribute of the object. We are indexing according to the
+        actual position of the element in the object.
+
+        Parameters
+        ----------
+        indices : array-like
+            An array of ints indicating which positions to take.
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            The axis on which to select elements. ``0`` means that we are
+            selecting rows, ``1`` means that we are selecting columns.
+            For `Series` this parameter is unused and defaults to 0.
+        **kwargs
+            For compatibility with :meth:`numpy.take`. Has no effect on the
+            output.
+
+        Returns
+        -------
+        same type as caller
+            An array-like containing the elements taken from the object.
+
+        See Also
+        --------
+        DataFrame.loc : Select a subset of a DataFrame by labels.
+        DataFrame.iloc : Select a subset of a DataFrame by positions.
+        numpy.take : Take elements from an array along an axis.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan)],
+        ...                   columns=['name', 'class', 'max_speed'],
+        ...                   index=[0, 2, 3, 1])
+        >>> df
+             name   class  max_speed
+        0  falcon    bird      389.0
+        2  parrot    bird       24.0
+        3    lion  mammal       80.5
+        1  monkey  mammal        NaN
+
+        Take elements at positions 0 and 3 along the axis 0 (default).
+
+        Note how the actual indices selected (0 and 1) do not correspond to
+        our selected indices 0 and 3. That's because we are selecting the 0th
+        and 3rd rows, not rows whose indices equal 0 and 3.
+
+        >>> df.take([0, 3])
+             name   class  max_speed
+        0  falcon    bird      389.0
+        1  monkey  mammal        NaN
+
+        Take elements at indices 1 and 2 along the axis 1 (column selection).
+
+        >>> df.take([1, 2], axis=1)
+            class  max_speed
+        0    bird      389.0
+        2    bird       24.0
+        3  mammal       80.5
+        1  mammal        NaN
+
+        We may take elements using negative integers for positive indices,
+        starting from the end of the object, just like with Python lists.
+
+        >>> df.take([-1, -2])
+             name   class  max_speed
+        1  monkey  mammal        NaN
+        3    lion  mammal       80.5
+        """
+
+        nv.validate_take((), kwargs)
+
+        if not isinstance(indices, slice):
+            indices = np.asarray(indices, dtype=np.intp)
+            if (
+                axis == 0
+                and indices.ndim == 1
+                and using_copy_on_write()
+                and is_range_indexer(indices, len(self))
+            ):
+                return self.copy(deep=None)
+        elif self.ndim == 1:
+            raise TypeError(
+                f"{type(self).__name__}.take requires a sequence of integers, "
+                "not slice."
+            )
+        else:
+            warnings.warn(
+                # GH#51539
+                f"Passing a slice to {type(self).__name__}.take is deprecated "
+                "and will raise in a future version. Use `obj[slicer]` or pass "
+                "a sequence of integers instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            # We can get here with a slice via DataFrame.__getitem__
+            indices = np.arange(
+                indices.start, indices.stop, indices.step, dtype=np.intp
+            )
+
+        new_data = self._mgr.take(
+            indices,
+            axis=self._get_block_manager_axis(axis),
+            verify=True,
+        )
+        return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(
+            self, method="take"
+        )
+
+    @final
+    def _take_with_is_copy(self, indices, axis: Axis = 0) -> Self:
+        """
+        Internal version of the `take` method that sets the `_is_copy`
+        attribute to keep track of the parent dataframe (using in indexing
+        for the SettingWithCopyWarning).
+
+        For Series this does the same as the public take (it never sets `_is_copy`).
+
+        See the docstring of `take` for full explanation of the parameters.
+        """
+        result = self.take(indices=indices, axis=axis)
+        # Maybe set copy if we didn't actually change the index.
+        if self.ndim == 2 and not result._get_axis(axis).equals(self._get_axis(axis)):
+            result._set_is_copy(self)
+        return result
+
+    @final
+    def xs(
+        self,
+        key: IndexLabel,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        drop_level: bool_t = True,
+    ) -> Self:
+        """
+        Return cross-section from the Series/DataFrame.
+
+        This method takes a `key` argument to select data at a particular
+        level of a MultiIndex.
+
+        Parameters
+        ----------
+        key : label or tuple of label
+            Label contained in the index, or partially in a MultiIndex.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis to retrieve cross-section on.
+        level : object, defaults to first n levels (n=1 or len(key))
+            In case of a key partially contained in a MultiIndex, indicate
+            which levels are used. Levels can be referred by label or position.
+        drop_level : bool, default True
+            If False, returns object with same levels as self.
+
+        Returns
+        -------
+        Series or DataFrame
+            Cross-section from the original Series or DataFrame
+            corresponding to the selected index levels.
+
+        See Also
+        --------
+        DataFrame.loc : Access a group of rows and columns
+            by label(s) or a boolean array.
+        DataFrame.iloc : Purely integer-location based indexing
+            for selection by position.
+
+        Notes
+        -----
+        `xs` can not be used to set values.
+
+        MultiIndex Slicers is a generic way to get/set values on
+        any level or levels.
+        It is a superset of `xs` functionality, see
+        :ref:`MultiIndex Slicers <advanced.mi_slicers>`.
+
+        Examples
+        --------
+        >>> d = {'num_legs': [4, 4, 2, 2],
+        ...      'num_wings': [0, 0, 2, 2],
+        ...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
+        ...      'animal': ['cat', 'dog', 'bat', 'penguin'],
+        ...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
+        >>> df = pd.DataFrame(data=d)
+        >>> df = df.set_index(['class', 'animal', 'locomotion'])
+        >>> df
+                                   num_legs  num_wings
+        class  animal  locomotion
+        mammal cat     walks              4          0
+               dog     walks              4          0
+               bat     flies              2          2
+        bird   penguin walks              2          2
+
+        Get values at specified index
+
+        >>> df.xs('mammal')
+                           num_legs  num_wings
+        animal locomotion
+        cat    walks              4          0
+        dog    walks              4          0
+        bat    flies              2          2
+
+        Get values at several indexes
+
+        >>> df.xs(('mammal', 'dog', 'walks'))
+        num_legs     4
+        num_wings    0
+        Name: (mammal, dog, walks), dtype: int64
+
+        Get values at specified index and level
+
+        >>> df.xs('cat', level=1)
+                           num_legs  num_wings
+        class  locomotion
+        mammal walks              4          0
+
+        Get values at several indexes and levels
+
+        >>> df.xs(('bird', 'walks'),
+        ...       level=[0, 'locomotion'])
+                 num_legs  num_wings
+        animal
+        penguin         2          2
+
+        Get values at specified column and axis
+
+        >>> df.xs('num_wings', axis=1)
+        class   animal   locomotion
+        mammal  cat      walks         0
+                dog      walks         0
+                bat      flies         2
+        bird    penguin  walks         2
+        Name: num_wings, dtype: int64
+        """
+        axis = self._get_axis_number(axis)
+        labels = self._get_axis(axis)
+
+        if isinstance(key, list):
+            raise TypeError("list keys are not supported in xs, pass a tuple instead")
+
+        if level is not None:
+            if not isinstance(labels, MultiIndex):
+                raise TypeError("Index must be a MultiIndex")
+            loc, new_ax = labels.get_loc_level(key, level=level, drop_level=drop_level)
+
+            # create the tuple of the indexer
+            _indexer = [slice(None)] * self.ndim
+            _indexer[axis] = loc
+            indexer = tuple(_indexer)
+
+            result = self.iloc[indexer]
+            setattr(result, result._get_axis_name(axis), new_ax)
+            return result
+
+        if axis == 1:
+            if drop_level:
+                return self[key]
+            index = self.columns
+        else:
+            index = self.index
+
+        if isinstance(index, MultiIndex):
+            loc, new_index = index._get_loc_level(key, level=0)
+            if not drop_level:
+                if lib.is_integer(loc):
+                    # Slice index must be an integer or None
+                    new_index = index[loc : loc + 1]
+                else:
+                    new_index = index[loc]
+        else:
+            loc = index.get_loc(key)
+
+            if isinstance(loc, np.ndarray):
+                if loc.dtype == np.bool_:
+                    (inds,) = loc.nonzero()
+                    return self._take_with_is_copy(inds, axis=axis)
+                else:
+                    return self._take_with_is_copy(loc, axis=axis)
+
+            if not is_scalar(loc):
+                new_index = index[loc]
+
+        if is_scalar(loc) and axis == 0:
+            # In this case loc should be an integer
+            if self.ndim == 1:
+                # if we encounter an array-like and we only have 1 dim
+                # that means that their are list/ndarrays inside the Series!
+                # so just return them (GH 6394)
+                return self._values[loc]
+
+            new_mgr = self._mgr.fast_xs(loc)
+
+            result = self._constructor_sliced_from_mgr(new_mgr, axes=new_mgr.axes)
+            result._name = self.index[loc]
+            result = result.__finalize__(self)
+        elif is_scalar(loc):
+            result = self.iloc[:, slice(loc, loc + 1)]
+        elif axis == 1:
+            result = self.iloc[:, loc]
+        else:
+            result = self.iloc[loc]
+            result.index = new_index
+
+        # this could be a view
+        # but only in a single-dtyped view sliceable case
+        result._set_is_copy(self, copy=not result._is_view)
+        return result
+
+    def __getitem__(self, item):
+        raise AbstractMethodError(self)
+
+    @final
+    def _getitem_slice(self, key: slice) -> Self:
+        """
+        __getitem__ for the case where the key is a slice object.
+        """
+        # _convert_slice_indexer to determine if this slice is positional
+        #  or label based, and if the latter, convert to positional
+        slobj = self.index._convert_slice_indexer(key, kind="getitem")
+        if isinstance(slobj, np.ndarray):
+            # reachable with DatetimeIndex
+            indexer = lib.maybe_indices_to_slice(
+                slobj.astype(np.intp, copy=False), len(self)
+            )
+            if isinstance(indexer, np.ndarray):
+                # GH#43223 If we can not convert, use take
+                return self.take(indexer, axis=0)
+            slobj = indexer
+        return self._slice(slobj)
+
+    def _slice(self, slobj: slice, axis: AxisInt = 0) -> Self:
+        """
+        Construct a slice of this container.
+
+        Slicing with this method is *always* positional.
+        """
+        assert isinstance(slobj, slice), type(slobj)
+        axis = self._get_block_manager_axis(axis)
+        new_mgr = self._mgr.get_slice(slobj, axis=axis)
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        result = result.__finalize__(self)
+
+        # this could be a view
+        # but only in a single-dtyped view sliceable case
+        is_copy = axis != 0 or result._is_view
+        result._set_is_copy(self, copy=is_copy)
+        return result
+
+    @final
+    def _set_is_copy(self, ref: NDFrame, copy: bool_t = True) -> None:
+        if not copy:
+            self._is_copy = None
+        else:
+            assert ref is not None
+            self._is_copy = weakref.ref(ref)
+
+    def _check_is_chained_assignment_possible(self) -> bool_t:
+        """
+        Check if we are a view, have a cacher, and are of mixed type.
+        If so, then force a setitem_copy check.
+
+        Should be called just near setting a value
+
+        Will return a boolean if it we are a view and are cached, but a
+        single-dtype meaning that the cacher should be updated following
+        setting.
+        """
+        if self._is_copy:
+            self._check_setitem_copy(t="referent")
+        return False
+
+    @final
+    def _check_setitem_copy(self, t: str = "setting", force: bool_t = False):
+        """
+
+        Parameters
+        ----------
+        t : str, the type of setting error
+        force : bool, default False
+           If True, then force showing an error.
+
+        validate if we are doing a setitem on a chained copy.
+
+        It is technically possible to figure out that we are setting on
+        a copy even WITH a multi-dtyped pandas object. In other words, some
+        blocks may be views while other are not. Currently _is_view will ALWAYS
+        return False for multi-blocks to avoid having to handle this case.
+
+        df = DataFrame(np.arange(0,9), columns=['count'])
+        df['group'] = 'b'
+
+        # This technically need not raise SettingWithCopy if both are view
+        # (which is not generally guaranteed but is usually True.  However,
+        # this is in general not a good practice and we recommend using .loc.
+        df.iloc[0:5]['group'] = 'a'
+
+        """
+        if using_copy_on_write() or warn_copy_on_write():
+            return
+
+        # return early if the check is not needed
+        if not (force or self._is_copy):
+            return
+
+        value = config.get_option("mode.chained_assignment")
+        if value is None:
+            return
+
+        # see if the copy is not actually referred; if so, then dissolve
+        # the copy weakref
+        if self._is_copy is not None and not isinstance(self._is_copy, str):
+            r = self._is_copy()
+            if not gc.get_referents(r) or (r is not None and r.shape == self.shape):
+                self._is_copy = None
+                return
+
+        # a custom message
+        if isinstance(self._is_copy, str):
+            t = self._is_copy
+
+        elif t == "referent":
+            t = (
+                "\n"
+                "A value is trying to be set on a copy of a slice from a "
+                "DataFrame\n\n"
+                "See the caveats in the documentation: "
+                "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+                "indexing.html#returning-a-view-versus-a-copy"
+            )
+
+        else:
+            t = (
+                "\n"
+                "A value is trying to be set on a copy of a slice from a "
+                "DataFrame.\n"
+                "Try using .loc[row_indexer,col_indexer] = value "
+                "instead\n\nSee the caveats in the documentation: "
+                "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+                "indexing.html#returning-a-view-versus-a-copy"
+            )
+
+        if value == "raise":
+            raise SettingWithCopyError(t)
+        if value == "warn":
+            warnings.warn(t, SettingWithCopyWarning, stacklevel=find_stack_level())
+
+    @final
+    def __delitem__(self, key) -> None:
+        """
+        Delete item
+        """
+        deleted = False
+
+        maybe_shortcut = False
+        if self.ndim == 2 and isinstance(self.columns, MultiIndex):
+            try:
+                # By using engine's __contains__ we effectively
+                # restrict to same-length tuples
+                maybe_shortcut = key not in self.columns._engine
+            except TypeError:
+                pass
+
+        if maybe_shortcut:
+            # Allow shorthand to delete all columns whose first len(key)
+            # elements match key:
+            if not isinstance(key, tuple):
+                key = (key,)
+            for col in self.columns:
+                if isinstance(col, tuple) and col[: len(key)] == key:
+                    del self[col]
+                    deleted = True
+        if not deleted:
+            # If the above loop ran and didn't delete anything because
+            # there was no match, this call should raise the appropriate
+            # exception:
+            loc = self.axes[-1].get_loc(key)
+            self._mgr = self._mgr.idelete(loc)
+
+        # delete from the caches
+        try:
+            del self._item_cache[key]
+        except KeyError:
+            pass
+
+    # ----------------------------------------------------------------------
+    # Unsorted
+
+    @final
+    def _check_inplace_and_allows_duplicate_labels(self, inplace: bool_t):
+        if inplace and not self.flags.allows_duplicate_labels:
+            raise ValueError(
+                "Cannot specify 'inplace=True' when "
+                "'self.flags.allows_duplicate_labels' is False."
+            )
+
+    @final
+    def get(self, key, default=None):
+        """
+        Get item from object for given key (ex: DataFrame column).
+
+        Returns default value if not found.
+
+        Parameters
+        ----------
+        key : object
+
+        Returns
+        -------
+        same type as items contained in object
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     [
+        ...         [24.3, 75.7, "high"],
+        ...         [31, 87.8, "high"],
+        ...         [22, 71.6, "medium"],
+        ...         [35, 95, "medium"],
+        ...     ],
+        ...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
+        ...     index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
+        ... )
+
+        >>> df
+                    temp_celsius  temp_fahrenheit windspeed
+        2014-02-12          24.3             75.7      high
+        2014-02-13          31.0             87.8      high
+        2014-02-14          22.0             71.6    medium
+        2014-02-15          35.0             95.0    medium
+
+        >>> df.get(["temp_celsius", "windspeed"])
+                    temp_celsius windspeed
+        2014-02-12          24.3      high
+        2014-02-13          31.0      high
+        2014-02-14          22.0    medium
+        2014-02-15          35.0    medium
+
+        >>> ser = df['windspeed']
+        >>> ser.get('2014-02-13')
+        'high'
+
+        If the key isn't found, the default value will be used.
+
+        >>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
+        'default_value'
+
+        >>> ser.get('2014-02-10', '[unknown]')
+        '[unknown]'
+        """
+        try:
+            return self[key]
+        except (KeyError, ValueError, IndexError):
+            return default
+
+    @final
+    @property
+    def _is_view(self) -> bool_t:
+        """Return boolean indicating if self is view of another array"""
+        return self._mgr.is_view
+
+    @final
+    def reindex_like(
+        self,
+        other,
+        method: Literal["backfill", "bfill", "pad", "ffill", "nearest"] | None = None,
+        copy: bool_t | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> Self:
+        """
+        Return an object with matching indices as other object.
+
+        Conform the object to the same index on all axes. Optional
+        filling logic, placing NaN in locations having no value
+        in the previous index. A new object is produced unless the
+        new index is equivalent to the current one and copy=False.
+
+        Parameters
+        ----------
+        other : Object of the same data type
+            Its row and column indices are used to define the new indices
+            of this object.
+        method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
+            Method to use for filling holes in reindexed DataFrame.
+            Please note: this is only applicable to DataFrames/Series with a
+            monotonically increasing/decreasing index.
+
+            * None (default): don't fill gaps
+            * pad / ffill: propagate last valid observation forward to next
+              valid
+            * backfill / bfill: use next valid observation to fill gap
+            * nearest: use nearest valid observations to fill gap.
+
+        copy : bool, default True
+            Return a new object, even if the passed indexes are the same.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        limit : int, default None
+            Maximum number of consecutive labels to fill for inexact matches.
+        tolerance : optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations must
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        Series or DataFrame
+            Same type as caller, but with changed indices on each axis.
+
+        See Also
+        --------
+        DataFrame.set_index : Set row labels.
+        DataFrame.reset_index : Remove row labels or move them to new columns.
+        DataFrame.reindex : Change to new indices or expand indices.
+
+        Notes
+        -----
+        Same as calling
+        ``.reindex(index=other.index, columns=other.columns,...)``.
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
+        ...                     [31, 87.8, 'high'],
+        ...                     [22, 71.6, 'medium'],
+        ...                     [35, 95, 'medium']],
+        ...                    columns=['temp_celsius', 'temp_fahrenheit',
+        ...                             'windspeed'],
+        ...                    index=pd.date_range(start='2014-02-12',
+        ...                                        end='2014-02-15', freq='D'))
+
+        >>> df1
+                    temp_celsius  temp_fahrenheit windspeed
+        2014-02-12          24.3             75.7      high
+        2014-02-13          31.0             87.8      high
+        2014-02-14          22.0             71.6    medium
+        2014-02-15          35.0             95.0    medium
+
+        >>> df2 = pd.DataFrame([[28, 'low'],
+        ...                     [30, 'low'],
+        ...                     [35.1, 'medium']],
+        ...                    columns=['temp_celsius', 'windspeed'],
+        ...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
+        ...                                            '2014-02-15']))
+
+        >>> df2
+                    temp_celsius windspeed
+        2014-02-12          28.0       low
+        2014-02-13          30.0       low
+        2014-02-15          35.1    medium
+
+        >>> df2.reindex_like(df1)
+                    temp_celsius  temp_fahrenheit windspeed
+        2014-02-12          28.0              NaN       low
+        2014-02-13          30.0              NaN       low
+        2014-02-14           NaN              NaN       NaN
+        2014-02-15          35.1              NaN    medium
+        """
+        d = other._construct_axes_dict(
+            axes=self._AXIS_ORDERS,
+            method=method,
+            copy=copy,
+            limit=limit,
+            tolerance=tolerance,
+        )
+
+        return self.reindex(**d)
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[True],
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[False] = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: bool_t = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Self | None:
+        ...
+
+    def drop(
+        self,
+        labels: IndexLabel | None = None,
+        *,
+        axis: Axis = 0,
+        index: IndexLabel | None = None,
+        columns: IndexLabel | None = None,
+        level: Level | None = None,
+        inplace: bool_t = False,
+        errors: IgnoreRaise = "raise",
+    ) -> Self | None:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if labels is not None:
+            if index is not None or columns is not None:
+                raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
+            axis_name = self._get_axis_name(axis)
+            axes = {axis_name: labels}
+        elif index is not None or columns is not None:
+            axes = {"index": index}
+            if self.ndim == 2:
+                axes["columns"] = columns
+        else:
+            raise ValueError(
+                "Need to specify at least one of 'labels', 'index' or 'columns'"
+            )
+
+        obj = self
+
+        for axis, labels in axes.items():
+            if labels is not None:
+                obj = obj._drop_axis(labels, axis, level=level, errors=errors)
+
+        if inplace:
+            self._update_inplace(obj)
+            return None
+        else:
+            return obj
+
+    @final
+    def _drop_axis(
+        self,
+        labels,
+        axis,
+        level=None,
+        errors: IgnoreRaise = "raise",
+        only_slice: bool_t = False,
+    ) -> Self:
+        """
+        Drop labels from specified axis. Used in the ``drop`` method
+        internally.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+        axis : int or axis name
+        level : int or level name, default None
+            For MultiIndex
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and existing labels are dropped.
+        only_slice : bool, default False
+            Whether indexing along columns should be view-only.
+
+        """
+        axis_num = self._get_axis_number(axis)
+        axis = self._get_axis(axis)
+
+        if axis.is_unique:
+            if level is not None:
+                if not isinstance(axis, MultiIndex):
+                    raise AssertionError("axis must be a MultiIndex")
+                new_axis = axis.drop(labels, level=level, errors=errors)
+            else:
+                new_axis = axis.drop(labels, errors=errors)
+            indexer = axis.get_indexer(new_axis)
+
+        # Case for non-unique axis
+        else:
+            is_tuple_labels = is_nested_list_like(labels) or isinstance(labels, tuple)
+            labels = ensure_object(common.index_labels_to_array(labels))
+            if level is not None:
+                if not isinstance(axis, MultiIndex):
+                    raise AssertionError("axis must be a MultiIndex")
+                mask = ~axis.get_level_values(level).isin(labels)
+
+                # GH 18561 MultiIndex.drop should raise if label is absent
+                if errors == "raise" and mask.all():
+                    raise KeyError(f"{labels} not found in axis")
+            elif (
+                isinstance(axis, MultiIndex)
+                and labels.dtype == "object"
+                and not is_tuple_labels
+            ):
+                # Set level to zero in case of MultiIndex and label is string,
+                #  because isin can't handle strings for MultiIndexes GH#36293
+                # In case of tuples we get dtype object but have to use isin GH#42771
+                mask = ~axis.get_level_values(0).isin(labels)
+            else:
+                mask = ~axis.isin(labels)
+                # Check if label doesn't exist along axis
+                labels_missing = (axis.get_indexer_for(labels) == -1).any()
+                if errors == "raise" and labels_missing:
+                    raise KeyError(f"{labels} not found in axis")
+
+            if isinstance(mask.dtype, ExtensionDtype):
+                # GH#45860
+                mask = mask.to_numpy(dtype=bool)
+
+            indexer = mask.nonzero()[0]
+            new_axis = axis.take(indexer)
+
+        bm_axis = self.ndim - axis_num - 1
+        new_mgr = self._mgr.reindex_indexer(
+            new_axis,
+            indexer,
+            axis=bm_axis,
+            allow_dups=True,
+            copy=None,
+            only_slice=only_slice,
+        )
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        if self.ndim == 1:
+            result._name = self.name
+
+        return result.__finalize__(self)
+
+    @final
+    def _update_inplace(self, result, verify_is_copy: bool_t = True) -> None:
+        """
+        Replace self internals with result.
+
+        Parameters
+        ----------
+        result : same type as self
+        verify_is_copy : bool, default True
+            Provide is_copy checks.
+        """
+        # NOTE: This does *not* call __finalize__ and that's an explicit
+        # decision that we may revisit in the future.
+        self._reset_cache()
+        self._clear_item_cache()
+        self._mgr = result._mgr
+        self._maybe_update_cacher(verify_is_copy=verify_is_copy, inplace=True)
+
+    @final
+    def add_prefix(self, prefix: str, axis: Axis | None = None) -> Self:
+        """
+        Prefix labels with string `prefix`.
+
+        For Series, the row labels are prefixed.
+        For DataFrame, the column labels are prefixed.
+
+        Parameters
+        ----------
+        prefix : str
+            The string to add before each label.
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            Axis to add prefix on
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or DataFrame
+            New Series or DataFrame with updated labels.
+
+        See Also
+        --------
+        Series.add_suffix: Suffix row labels with string `suffix`.
+        DataFrame.add_suffix: Suffix column labels with string `suffix`.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+
+        >>> s.add_prefix('item_')
+        item_0    1
+        item_1    2
+        item_2    3
+        item_3    4
+        dtype: int64
+
+        >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
+        >>> df
+           A  B
+        0  1  3
+        1  2  4
+        2  3  5
+        3  4  6
+
+        >>> df.add_prefix('col_')
+             col_A  col_B
+        0       1       3
+        1       2       4
+        2       3       5
+        3       4       6
+        """
+        f = lambda x: f"{prefix}{x}"
+
+        axis_name = self._info_axis_name
+        if axis is not None:
+            axis_name = self._get_axis_name(axis)
+
+        mapper = {axis_name: f}
+
+        # error: Incompatible return value type (got "Optional[Self]",
+        # expected "Self")
+        # error: Argument 1 to "rename" of "NDFrame" has incompatible type
+        # "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
+        # error: Keywords must be strings
+        return self._rename(**mapper)  # type: ignore[return-value, arg-type, misc]
+
+    @final
+    def add_suffix(self, suffix: str, axis: Axis | None = None) -> Self:
+        """
+        Suffix labels with string `suffix`.
+
+        For Series, the row labels are suffixed.
+        For DataFrame, the column labels are suffixed.
+
+        Parameters
+        ----------
+        suffix : str
+            The string to add after each label.
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            Axis to add suffix on
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or DataFrame
+            New Series or DataFrame with updated labels.
+
+        See Also
+        --------
+        Series.add_prefix: Prefix row labels with string `prefix`.
+        DataFrame.add_prefix: Prefix column labels with string `prefix`.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+
+        >>> s.add_suffix('_item')
+        0_item    1
+        1_item    2
+        2_item    3
+        3_item    4
+        dtype: int64
+
+        >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
+        >>> df
+           A  B
+        0  1  3
+        1  2  4
+        2  3  5
+        3  4  6
+
+        >>> df.add_suffix('_col')
+             A_col  B_col
+        0       1       3
+        1       2       4
+        2       3       5
+        3       4       6
+        """
+        f = lambda x: f"{x}{suffix}"
+
+        axis_name = self._info_axis_name
+        if axis is not None:
+            axis_name = self._get_axis_name(axis)
+
+        mapper = {axis_name: f}
+        # error: Incompatible return value type (got "Optional[Self]",
+        # expected "Self")
+        # error: Argument 1 to "rename" of "NDFrame" has incompatible type
+        # "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
+        # error: Keywords must be strings
+        return self._rename(**mapper)  # type: ignore[return-value, arg-type, misc]
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool_t = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool_t = ...,
+        key: ValueKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: bool_t = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool_t = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Self | None:
+        ...
+
+    def sort_values(
+        self,
+        *,
+        axis: Axis = 0,
+        ascending: bool_t | Sequence[bool_t] = True,
+        inplace: bool_t = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        ignore_index: bool_t = False,
+        key: ValueKeyFunc | None = None,
+    ) -> Self | None:
+        """
+        Sort by the values along either axis.
+
+        Parameters
+        ----------%(optional_by)s
+        axis : %(axes_single_arg)s, default 0
+             Axis to be sorted.
+        ascending : bool or list of bool, default True
+             Sort ascending vs. descending. Specify list for multiple sort
+             orders.  If this is a list of bools, must match the length of
+             the by.
+        inplace : bool, default False
+             If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+             Choice of sorting algorithm. See also :func:`numpy.sort` for more
+             information. `mergesort` and `stable` are the only stable algorithms. For
+             DataFrames, this option is only applied when sorting on a single
+             column or label.
+        na_position : {'first', 'last'}, default 'last'
+             Puts NaNs at the beginning if `first`; `last` puts NaNs at the
+             end.
+        ignore_index : bool, default False
+             If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            Apply the key function to the values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect a
+            ``Series`` and return a Series with the same shape as the input.
+            It will be applied to each column in `by` independently.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with sorted values or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.sort_index : Sort a DataFrame by the index.
+        Series.sort_values : Similar method for a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
+        ...     'col2': [2, 1, 9, 8, 7, 4],
+        ...     'col3': [0, 1, 9, 4, 2, 3],
+        ...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
+        ... })
+        >>> df
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Sort by col1
+
+        >>> df.sort_values(by=['col1'])
+          col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort by multiple columns
+
+        >>> df.sort_values(by=['col1', 'col2'])
+          col1  col2  col3 col4
+        1    A     1     1    B
+        0    A     2     0    a
+        2    B     9     9    c
+        5    C     4     3    F
+        4    D     7     2    e
+        3  NaN     8     4    D
+
+        Sort Descending
+
+        >>> df.sort_values(by='col1', ascending=False)
+          col1  col2  col3 col4
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+        3  NaN     8     4    D
+
+        Putting NAs first
+
+        >>> df.sort_values(by='col1', ascending=False, na_position='first')
+          col1  col2  col3 col4
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+        2    B     9     9    c
+        0    A     2     0    a
+        1    A     1     1    B
+
+        Sorting with a key function
+
+        >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
+           col1  col2  col3 col4
+        0    A     2     0    a
+        1    A     1     1    B
+        2    B     9     9    c
+        3  NaN     8     4    D
+        4    D     7     2    e
+        5    C     4     3    F
+
+        Natural sort with the key argument,
+        using the `natsort <https://github.com/SethMMorton/natsort>` package.
+
+        >>> df = pd.DataFrame({
+        ...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
+        ...    "value": [10, 20, 30, 40, 50]
+        ... })
+        >>> df
+            time  value
+        0    0hr     10
+        1  128hr     20
+        2   72hr     30
+        3   48hr     40
+        4   96hr     50
+        >>> from natsort import index_natsorted
+        >>> df.sort_values(
+        ...     by="time",
+        ...     key=lambda x: np.argsort(index_natsorted(df["time"]))
+        ... )
+            time  value
+        0    0hr     10
+        3   48hr     40
+        2   72hr     30
+        4   96hr     50
+        1  128hr     20
+        """
+        raise AbstractMethodError(self)
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool_t = ...,
+        ignore_index: bool_t = ...,
+        key: IndexKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool_t = ...,
+        ignore_index: bool_t = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool_t | Sequence[bool_t] = ...,
+        inplace: bool_t = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool_t = ...,
+        ignore_index: bool_t = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Self | None:
+        ...
+
+    def sort_index(
+        self,
+        *,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        ascending: bool_t | Sequence[bool_t] = True,
+        inplace: bool_t = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        sort_remaining: bool_t = True,
+        ignore_index: bool_t = False,
+        key: IndexKeyFunc | None = None,
+    ) -> Self | None:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        axis = self._get_axis_number(axis)
+        ascending = validate_ascending(ascending)
+
+        target = self._get_axis(axis)
+
+        indexer = get_indexer_indexer(
+            target, level, ascending, kind, na_position, sort_remaining, key
+        )
+
+        if indexer is None:
+            if inplace:
+                result = self
+            else:
+                result = self.copy(deep=None)
+
+            if ignore_index:
+                result.index = default_index(len(self))
+            if inplace:
+                return None
+            else:
+                return result
+
+        baxis = self._get_block_manager_axis(axis)
+        new_data = self._mgr.take(indexer, axis=baxis, verify=False)
+
+        # reconstruct axis if needed
+        if not ignore_index:
+            new_axis = new_data.axes[baxis]._sort_levels_monotonic()
+        else:
+            new_axis = default_index(len(indexer))
+        new_data.set_axis(baxis, new_axis)
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="sort_index")
+
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        optional_reindex="",
+    )
+    def reindex(
+        self,
+        labels=None,
+        *,
+        index=None,
+        columns=None,
+        axis: Axis | None = None,
+        method: ReindexMethod | None = None,
+        copy: bool_t | None = None,
+        level: Level | None = None,
+        fill_value: Scalar | None = np.nan,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> Self:
+        """
+        Conform {klass} to new index with optional filling logic.
+
+        Places NA/NaN in locations having no value in the previous index. A new object
+        is produced unless the new index is equivalent to the current one and
+        ``copy=False``.
+
+        Parameters
+        ----------
+        {optional_reindex}
+        method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}}
+            Method to use for filling holes in reindexed DataFrame.
+            Please note: this is only applicable to DataFrames/Series with a
+            monotonically increasing/decreasing index.
+
+            * None (default): don't fill gaps
+            * pad / ffill: Propagate last valid observation forward to next
+              valid.
+            * backfill / bfill: Use next valid observation to fill gap.
+            * nearest: Use nearest valid observations to fill gap.
+
+        copy : bool, default True
+            Return a new object, even if the passed indexes are the same.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        level : int or name
+            Broadcast across a level, matching Index values on the
+            passed MultiIndex level.
+        fill_value : scalar, default np.nan
+            Value to use for missing values. Defaults to NaN, but can be any
+            "compatible" value.
+        limit : int, default None
+            Maximum number of consecutive elements to forward or backward fill.
+        tolerance : optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations most
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        {klass} with changed index.
+
+        See Also
+        --------
+        DataFrame.set_index : Set row labels.
+        DataFrame.reset_index : Remove row labels or move them to new columns.
+        DataFrame.reindex_like : Change to same indices as other DataFrame.
+
+        Examples
+        --------
+        ``DataFrame.reindex`` supports two calling conventions
+
+        * ``(index=index_labels, columns=column_labels, ...)``
+        * ``(labels, axis={{'index', 'columns'}}, ...)``
+
+        We *highly* recommend using keyword arguments to clarify your
+        intent.
+
+        Create a dataframe with some fictional data.
+
+        >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
+        >>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301],
+        ...                   'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}},
+        ...                   index=index)
+        >>> df
+                   http_status  response_time
+        Firefox            200           0.04
+        Chrome             200           0.02
+        Safari             404           0.07
+        IE10               404           0.08
+        Konqueror          301           1.00
+
+        Create a new index and reindex the dataframe. By default
+        values in the new index that do not have corresponding
+        records in the dataframe are assigned ``NaN``.
+
+        >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
+        ...              'Chrome']
+        >>> df.reindex(new_index)
+                       http_status  response_time
+        Safari               404.0           0.07
+        Iceweasel              NaN            NaN
+        Comodo Dragon          NaN            NaN
+        IE10                 404.0           0.08
+        Chrome               200.0           0.02
+
+        We can fill in the missing values by passing a value to
+        the keyword ``fill_value``. Because the index is not monotonically
+        increasing or decreasing, we cannot use arguments to the keyword
+        ``method`` to fill the ``NaN`` values.
+
+        >>> df.reindex(new_index, fill_value=0)
+                       http_status  response_time
+        Safari                 404           0.07
+        Iceweasel                0           0.00
+        Comodo Dragon            0           0.00
+        IE10                   404           0.08
+        Chrome                 200           0.02
+
+        >>> df.reindex(new_index, fill_value='missing')
+                      http_status response_time
+        Safari                404          0.07
+        Iceweasel         missing       missing
+        Comodo Dragon     missing       missing
+        IE10                  404          0.08
+        Chrome                200          0.02
+
+        We can also reindex the columns.
+
+        >>> df.reindex(columns=['http_status', 'user_agent'])
+                   http_status  user_agent
+        Firefox            200         NaN
+        Chrome             200         NaN
+        Safari             404         NaN
+        IE10               404         NaN
+        Konqueror          301         NaN
+
+        Or we can use "axis-style" keyword arguments
+
+        >>> df.reindex(['http_status', 'user_agent'], axis="columns")
+                   http_status  user_agent
+        Firefox            200         NaN
+        Chrome             200         NaN
+        Safari             404         NaN
+        IE10               404         NaN
+        Konqueror          301         NaN
+
+        To further illustrate the filling functionality in
+        ``reindex``, we will create a dataframe with a
+        monotonically increasing index (for example, a sequence
+        of dates).
+
+        >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
+        >>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}},
+        ...                    index=date_index)
+        >>> df2
+                    prices
+        2010-01-01   100.0
+        2010-01-02   101.0
+        2010-01-03     NaN
+        2010-01-04   100.0
+        2010-01-05    89.0
+        2010-01-06    88.0
+
+        Suppose we decide to expand the dataframe to cover a wider
+        date range.
+
+        >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
+        >>> df2.reindex(date_index2)
+                    prices
+        2009-12-29     NaN
+        2009-12-30     NaN
+        2009-12-31     NaN
+        2010-01-01   100.0
+        2010-01-02   101.0
+        2010-01-03     NaN
+        2010-01-04   100.0
+        2010-01-05    89.0
+        2010-01-06    88.0
+        2010-01-07     NaN
+
+        The index entries that did not have a value in the original data frame
+        (for example, '2009-12-29') are by default filled with ``NaN``.
+        If desired, we can fill in the missing values using one of several
+        options.
+
+        For example, to back-propagate the last valid value to fill the ``NaN``
+        values, pass ``bfill`` as an argument to the ``method`` keyword.
+
+        >>> df2.reindex(date_index2, method='bfill')
+                    prices
+        2009-12-29   100.0
+        2009-12-30   100.0
+        2009-12-31   100.0
+        2010-01-01   100.0
+        2010-01-02   101.0
+        2010-01-03     NaN
+        2010-01-04   100.0
+        2010-01-05    89.0
+        2010-01-06    88.0
+        2010-01-07     NaN
+
+        Please note that the ``NaN`` value present in the original dataframe
+        (at index value 2010-01-03) will not be filled by any of the
+        value propagation schemes. This is because filling while reindexing
+        does not look at dataframe values, but only compares the original and
+        desired indexes. If you do want to fill in the ``NaN`` values present
+        in the original dataframe, use the ``fillna()`` method.
+
+        See the :ref:`user guide <basics.reindexing>` for more.
+        """
+        # TODO: Decide if we care about having different examples for different
+        # kinds
+
+        if index is not None and columns is not None and labels is not None:
+            raise TypeError("Cannot specify all of 'labels', 'index', 'columns'.")
+        elif index is not None or columns is not None:
+            if axis is not None:
+                raise TypeError(
+                    "Cannot specify both 'axis' and any of 'index' or 'columns'"
+                )
+            if labels is not None:
+                if index is not None:
+                    columns = labels
+                else:
+                    index = labels
+        else:
+            if axis and self._get_axis_number(axis) == 1:
+                columns = labels
+            else:
+                index = labels
+        axes: dict[Literal["index", "columns"], Any] = {
+            "index": index,
+            "columns": columns,
+        }
+        method = clean_reindex_fill_method(method)
+
+        # if all axes that are requested to reindex are equal, then only copy
+        # if indicated must have index names equal here as well as values
+        if copy and using_copy_on_write():
+            copy = False
+        if all(
+            self._get_axis(axis_name).identical(ax)
+            for axis_name, ax in axes.items()
+            if ax is not None
+        ):
+            return self.copy(deep=copy)
+
+        # check if we are a multi reindex
+        if self._needs_reindex_multi(axes, method, level):
+            return self._reindex_multi(axes, copy, fill_value)
+
+        # perform the reindex on the axes
+        return self._reindex_axes(
+            axes, level, limit, tolerance, method, fill_value, copy
+        ).__finalize__(self, method="reindex")
+
+    @final
+    def _reindex_axes(
+        self,
+        axes,
+        level: Level | None,
+        limit: int | None,
+        tolerance,
+        method,
+        fill_value: Scalar | None,
+        copy: bool_t | None,
+    ) -> Self:
+        """Perform the reindex for all the axes."""
+        obj = self
+        for a in self._AXIS_ORDERS:
+            labels = axes[a]
+            if labels is None:
+                continue
+
+            ax = self._get_axis(a)
+            new_index, indexer = ax.reindex(
+                labels, level=level, limit=limit, tolerance=tolerance, method=method
+            )
+
+            axis = self._get_axis_number(a)
+            obj = obj._reindex_with_indexers(
+                {axis: [new_index, indexer]},
+                fill_value=fill_value,
+                copy=copy,
+                allow_dups=False,
+            )
+            # If we've made a copy once, no need to make another one
+            copy = False
+
+        return obj
+
+    def _needs_reindex_multi(self, axes, method, level: Level | None) -> bool_t:
+        """Check if we do need a multi reindex."""
+        return (
+            (common.count_not_none(*axes.values()) == self._AXIS_LEN)
+            and method is None
+            and level is None
+            # reindex_multi calls self.values, so we only want to go
+            #  down that path when doing so is cheap.
+            and self._can_fast_transpose
+        )
+
+    def _reindex_multi(self, axes, copy, fill_value):
+        raise AbstractMethodError(self)
+
+    @final
+    def _reindex_with_indexers(
+        self,
+        reindexers,
+        fill_value=None,
+        copy: bool_t | None = False,
+        allow_dups: bool_t = False,
+    ) -> Self:
+        """allow_dups indicates an internal call here"""
+        # reindex doing multiple operations on different axes if indicated
+        new_data = self._mgr
+        for axis in sorted(reindexers.keys()):
+            index, indexer = reindexers[axis]
+            baxis = self._get_block_manager_axis(axis)
+
+            if index is None:
+                continue
+
+            index = ensure_index(index)
+            if indexer is not None:
+                indexer = ensure_platform_int(indexer)
+
+            # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)
+            new_data = new_data.reindex_indexer(
+                index,
+                indexer,
+                axis=baxis,
+                fill_value=fill_value,
+                allow_dups=allow_dups,
+                copy=copy,
+            )
+            # If we've made a copy once, no need to make another one
+            copy = False
+
+        if (
+            (copy or copy is None)
+            and new_data is self._mgr
+            and not using_copy_on_write()
+        ):
+            new_data = new_data.copy(deep=copy)
+        elif using_copy_on_write() and new_data is self._mgr:
+            new_data = new_data.copy(deep=False)
+
+        return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(
+            self
+        )
+
+    def filter(
+        self,
+        items=None,
+        like: str | None = None,
+        regex: str | None = None,
+        axis: Axis | None = None,
+    ) -> Self:
+        """
+        Subset the dataframe rows or columns according to the specified index labels.
+
+        Note that this routine does not filter a dataframe on its
+        contents. The filter is applied to the labels of the index.
+
+        Parameters
+        ----------
+        items : list-like
+            Keep labels from axis which are in items.
+        like : str
+            Keep labels from axis for which "like in label == True".
+        regex : str (regular expression)
+            Keep labels from axis for which re.search(regex, label) == True.
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            The axis to filter on, expressed either as an index (int)
+            or axis name (str). By default this is the info axis, 'columns' for
+            DataFrame. For `Series` this parameter is unused and defaults to `None`.
+
+        Returns
+        -------
+        same type as input object
+
+        See Also
+        --------
+        DataFrame.loc : Access a group of rows and columns
+            by label(s) or a boolean array.
+
+        Notes
+        -----
+        The ``items``, ``like``, and ``regex`` parameters are
+        enforced to be mutually exclusive.
+
+        ``axis`` defaults to the info axis that is used when indexing
+        with ``[]``.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
+        ...                   index=['mouse', 'rabbit'],
+        ...                   columns=['one', 'two', 'three'])
+        >>> df
+                one  two  three
+        mouse     1    2      3
+        rabbit    4    5      6
+
+        >>> # select columns by name
+        >>> df.filter(items=['one', 'three'])
+                 one  three
+        mouse     1      3
+        rabbit    4      6
+
+        >>> # select columns by regular expression
+        >>> df.filter(regex='e$', axis=1)
+                 one  three
+        mouse     1      3
+        rabbit    4      6
+
+        >>> # select rows containing 'bbi'
+        >>> df.filter(like='bbi', axis=0)
+                 one  two  three
+        rabbit    4    5      6
+        """
+        nkw = common.count_not_none(items, like, regex)
+        if nkw > 1:
+            raise TypeError(
+                "Keyword arguments `items`, `like`, or `regex` "
+                "are mutually exclusive"
+            )
+
+        if axis is None:
+            axis = self._info_axis_name
+        labels = self._get_axis(axis)
+
+        if items is not None:
+            name = self._get_axis_name(axis)
+            items = Index(items).intersection(labels)
+            if len(items) == 0:
+                # Keep the dtype of labels when we are empty
+                items = items.astype(labels.dtype)
+            # error: Keywords must be strings
+            return self.reindex(**{name: items})  # type: ignore[misc]
+        elif like:
+
+            def f(x) -> bool_t:
+                assert like is not None  # needed for mypy
+                return like in ensure_str(x)
+
+            values = labels.map(f)
+            return self.loc(axis=axis)[values]
+        elif regex:
+
+            def f(x) -> bool_t:
+                return matcher.search(ensure_str(x)) is not None
+
+            matcher = re.compile(regex)
+            values = labels.map(f)
+            return self.loc(axis=axis)[values]
+        else:
+            raise TypeError("Must pass either `items`, `like`, or `regex`")
+
+    @final
+    def head(self, n: int = 5) -> Self:
+        """
+        Return the first `n` rows.
+
+        This function returns the first `n` rows for the object based
+        on position. It is useful for quickly testing if your object
+        has the right type of data in it.
+
+        For negative values of `n`, this function returns all rows except
+        the last `|n|` rows, equivalent to ``df[:n]``.
+
+        If n is larger than the number of rows, this function returns all rows.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Number of rows to select.
+
+        Returns
+        -------
+        same type as caller
+            The first `n` rows of the caller object.
+
+        See Also
+        --------
+        DataFrame.tail: Returns the last `n` rows.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
+        ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
+        >>> df
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+        5     parrot
+        6      shark
+        7      whale
+        8      zebra
+
+        Viewing the first 5 lines
+
+        >>> df.head()
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+
+        Viewing the first `n` lines (three in this case)
+
+        >>> df.head(3)
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+
+        For negative values of `n`
+
+        >>> df.head(-3)
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+        5     parrot
+        """
+        if using_copy_on_write():
+            return self.iloc[:n].copy()
+        return self.iloc[:n]
+
+    @final
+    def tail(self, n: int = 5) -> Self:
+        """
+        Return the last `n` rows.
+
+        This function returns last `n` rows from the object based on
+        position. It is useful for quickly verifying data, for example,
+        after sorting or appending rows.
+
+        For negative values of `n`, this function returns all rows except
+        the first `|n|` rows, equivalent to ``df[|n|:]``.
+
+        If n is larger than the number of rows, this function returns all rows.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Number of rows to select.
+
+        Returns
+        -------
+        type of caller
+            The last `n` rows of the caller object.
+
+        See Also
+        --------
+        DataFrame.head : The first `n` rows of the caller object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
+        ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
+        >>> df
+              animal
+        0  alligator
+        1        bee
+        2     falcon
+        3       lion
+        4     monkey
+        5     parrot
+        6      shark
+        7      whale
+        8      zebra
+
+        Viewing the last 5 lines
+
+        >>> df.tail()
+           animal
+        4  monkey
+        5  parrot
+        6   shark
+        7   whale
+        8   zebra
+
+        Viewing the last `n` lines (three in this case)
+
+        >>> df.tail(3)
+          animal
+        6  shark
+        7  whale
+        8  zebra
+
+        For negative values of `n`
+
+        >>> df.tail(-3)
+           animal
+        3    lion
+        4  monkey
+        5  parrot
+        6   shark
+        7   whale
+        8   zebra
+        """
+        if using_copy_on_write():
+            if n == 0:
+                return self.iloc[0:0].copy()
+            return self.iloc[-n:].copy()
+        if n == 0:
+            return self.iloc[0:0]
+        return self.iloc[-n:]
+
+    @final
+    def sample(
+        self,
+        n: int | None = None,
+        frac: float | None = None,
+        replace: bool_t = False,
+        weights=None,
+        random_state: RandomState | None = None,
+        axis: Axis | None = None,
+        ignore_index: bool_t = False,
+    ) -> Self:
+        """
+        Return a random sample of items from an axis of object.
+
+        You can use `random_state` for reproducibility.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of items from axis to return. Cannot be used with `frac`.
+            Default = 1 if `frac` = None.
+        frac : float, optional
+            Fraction of axis items to return. Cannot be used with `n`.
+        replace : bool, default False
+            Allow or disallow sampling of the same row more than once.
+        weights : str or ndarray-like, optional
+            Default 'None' results in equal probability weighting.
+            If passed a Series, will align with target object on index. Index
+            values in weights not found in sampled object will be ignored and
+            index values in sampled object not in weights will be assigned
+            weights of zero.
+            If called on a DataFrame, will accept the name of a column
+            when axis = 0.
+            Unless weights are a Series, weights must be same length as axis
+            being sampled.
+            If weights do not sum to 1, they will be normalized to sum to 1.
+            Missing values in the weights column will be treated as zero.
+            Infinite values not allowed.
+        random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
+            If int, array-like, or BitGenerator, seed for random number generator.
+            If np.random.RandomState or np.random.Generator, use as given.
+
+            .. versionchanged:: 1.4.0
+
+                np.random.Generator objects now accepted
+
+        axis : {0 or 'index', 1 or 'columns', None}, default None
+            Axis to sample. Accepts axis number or name. Default is stat axis
+            for given data type. For `Series` this parameter is unused and defaults to `None`.
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        Series or DataFrame
+            A new object of same type as caller containing `n` items randomly
+            sampled from the caller object.
+
+        See Also
+        --------
+        DataFrameGroupBy.sample: Generates random samples from each group of a
+            DataFrame object.
+        SeriesGroupBy.sample: Generates random samples from each group of a
+            Series object.
+        numpy.random.choice: Generates a random sample from a given 1-D numpy
+            array.
+
+        Notes
+        -----
+        If `frac` > 1, `replacement` should be set to `True`.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
+        ...                    'num_wings': [2, 0, 0, 0],
+        ...                    'num_specimen_seen': [10, 2, 1, 8]},
+        ...                   index=['falcon', 'dog', 'spider', 'fish'])
+        >>> df
+                num_legs  num_wings  num_specimen_seen
+        falcon         2          2                 10
+        dog            4          0                  2
+        spider         8          0                  1
+        fish           0          0                  8
+
+        Extract 3 random elements from the ``Series`` ``df['num_legs']``:
+        Note that we use `random_state` to ensure the reproducibility of
+        the examples.
+
+        >>> df['num_legs'].sample(n=3, random_state=1)
+        fish      0
+        spider    8
+        falcon    2
+        Name: num_legs, dtype: int64
+
+        A random 50% sample of the ``DataFrame`` with replacement:
+
+        >>> df.sample(frac=0.5, replace=True, random_state=1)
+              num_legs  num_wings  num_specimen_seen
+        dog          4          0                  2
+        fish         0          0                  8
+
+        An upsample sample of the ``DataFrame`` with replacement:
+        Note that `replace` parameter has to be `True` for `frac` parameter > 1.
+
+        >>> df.sample(frac=2, replace=True, random_state=1)
+                num_legs  num_wings  num_specimen_seen
+        dog            4          0                  2
+        fish           0          0                  8
+        falcon         2          2                 10
+        falcon         2          2                 10
+        fish           0          0                  8
+        dog            4          0                  2
+        fish           0          0                  8
+        dog            4          0                  2
+
+        Using a DataFrame column as weights. Rows with larger value in the
+        `num_specimen_seen` column are more likely to be sampled.
+
+        >>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
+                num_legs  num_wings  num_specimen_seen
+        falcon         2          2                 10
+        fish           0          0                  8
+        """  # noqa: E501
+        if axis is None:
+            axis = 0
+
+        axis = self._get_axis_number(axis)
+        obj_len = self.shape[axis]
+
+        # Process random_state argument
+        rs = common.random_state(random_state)
+
+        size = sample.process_sampling_size(n, frac, replace)
+        if size is None:
+            assert frac is not None
+            size = round(frac * obj_len)
+
+        if weights is not None:
+            weights = sample.preprocess_weights(self, weights, axis)
+
+        sampled_indices = sample.sample(obj_len, size, replace, weights, rs)
+        result = self.take(sampled_indices, axis=axis)
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        return result
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def pipe(
+        self,
+        func: Callable[..., T] | tuple[Callable[..., T], str],
+        *args,
+        **kwargs,
+    ) -> T:
+        r"""
+        Apply chainable functions that expect Series or DataFrames.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to the {klass}.
+            ``args``, and ``kwargs`` are passed into ``func``.
+            Alternatively a ``(callable, data_keyword)`` tuple where
+            ``data_keyword`` is a string indicating the keyword of
+            ``callable`` that expects the {klass}.
+        *args : iterable, optional
+            Positional arguments passed into ``func``.
+        **kwargs : mapping, optional
+            A dictionary of keyword arguments passed into ``func``.
+
+        Returns
+        -------
+        the return type of ``func``.
+
+        See Also
+        --------
+        DataFrame.apply : Apply a function along input axis of DataFrame.
+        DataFrame.map : Apply a function elementwise on a whole DataFrame.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+
+        Notes
+        -----
+        Use ``.pipe`` when chaining together functions that expect
+        Series, DataFrames or GroupBy objects.
+
+        Examples
+        --------
+        Constructing a income DataFrame from a dictionary.
+
+        >>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
+        >>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
+        >>> df
+           Salary  Others
+        0    8000  1000.0
+        1    9500     NaN
+        2    5000  2000.0
+
+        Functions that perform tax reductions on an income DataFrame.
+
+        >>> def subtract_federal_tax(df):
+        ...     return df * 0.9
+        >>> def subtract_state_tax(df, rate):
+        ...     return df * (1 - rate)
+        >>> def subtract_national_insurance(df, rate, rate_increase):
+        ...     new_rate = rate + rate_increase
+        ...     return df * (1 - new_rate)
+
+        Instead of writing
+
+        >>> subtract_national_insurance(
+        ...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
+        ...     rate=0.05,
+        ...     rate_increase=0.02)  # doctest: +SKIP
+
+        You can write
+
+        >>> (
+        ...     df.pipe(subtract_federal_tax)
+        ...     .pipe(subtract_state_tax, rate=0.12)
+        ...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
+        ... )
+            Salary   Others
+        0  5892.48   736.56
+        1  6997.32      NaN
+        2  3682.80  1473.12
+
+        If you have a function that takes the data as (say) the second
+        argument, pass a tuple indicating which keyword expects the
+        data. For example, suppose ``national_insurance`` takes its data as ``df``
+        in the second argument:
+
+        >>> def subtract_national_insurance(rate, df, rate_increase):
+        ...     new_rate = rate + rate_increase
+        ...     return df * (1 - new_rate)
+        >>> (
+        ...     df.pipe(subtract_federal_tax)
+        ...     .pipe(subtract_state_tax, rate=0.12)
+        ...     .pipe(
+        ...         (subtract_national_insurance, 'df'),
+        ...         rate=0.05,
+        ...         rate_increase=0.02
+        ...     )
+        ... )
+            Salary   Others
+        0  5892.48   736.56
+        1  6997.32      NaN
+        2  3682.80  1473.12
+        """
+        if using_copy_on_write():
+            return common.pipe(self.copy(deep=None), func, *args, **kwargs)
+        return common.pipe(self, func, *args, **kwargs)
+
+    # ----------------------------------------------------------------------
+    # Attribute access
+
+    @final
+    def __finalize__(self, other, method: str | None = None, **kwargs) -> Self:
+        """
+        Propagate metadata from other to self.
+
+        Parameters
+        ----------
+        other : the object from which to get the attributes that we are going
+            to propagate
+        method : str, optional
+            A passed method name providing context on where ``__finalize__``
+            was called.
+
+            .. warning::
+
+               The value passed as `method` are not currently considered
+               stable across pandas releases.
+        """
+        if isinstance(other, NDFrame):
+            if other.attrs:
+                # We want attrs propagation to have minimal performance
+                # impact if attrs are not used; i.e. attrs is an empty dict.
+                # One could make the deepcopy unconditionally, but a deepcopy
+                # of an empty dict is 50x more expensive than the empty check.
+                self.attrs = deepcopy(other.attrs)
+
+            self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
+            # For subclasses using _metadata.
+            for name in set(self._metadata) & set(other._metadata):
+                assert isinstance(name, str)
+                object.__setattr__(self, name, getattr(other, name, None))
+
+        if method == "concat":
+            # propagate attrs only if all concat arguments have the same attrs
+            if all(bool(obj.attrs) for obj in other.objs):
+                # all concatenate arguments have non-empty attrs
+                attrs = other.objs[0].attrs
+                have_same_attrs = all(obj.attrs == attrs for obj in other.objs[1:])
+                if have_same_attrs:
+                    self.attrs = deepcopy(attrs)
+
+            allows_duplicate_labels = all(
+                x.flags.allows_duplicate_labels for x in other.objs
+            )
+            self.flags.allows_duplicate_labels = allows_duplicate_labels
+
+        return self
+
+    @final
+    def __getattr__(self, name: str):
+        """
+        After regular attribute access, try looking up the name
+        This allows simpler access to columns for interactive use.
+        """
+        # Note: obj.x will always call obj.__getattribute__('x') prior to
+        # calling obj.__getattr__('x').
+        if (
+            name not in self._internal_names_set
+            and name not in self._metadata
+            and name not in self._accessors
+            and self._info_axis._can_hold_identifiers_and_holds_name(name)
+        ):
+            return self[name]
+        return object.__getattribute__(self, name)
+
+    @final
+    def __setattr__(self, name: str, value) -> None:
+        """
+        After regular attribute access, try setting the name
+        This allows simpler access to columns for interactive use.
+        """
+        # first try regular attribute access via __getattribute__, so that
+        # e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify
+        # the same attribute.
+
+        try:
+            object.__getattribute__(self, name)
+            return object.__setattr__(self, name, value)
+        except AttributeError:
+            pass
+
+        # if this fails, go on to more involved attribute setting
+        # (note that this matches __getattr__, above).
+        if name in self._internal_names_set:
+            object.__setattr__(self, name, value)
+        elif name in self._metadata:
+            object.__setattr__(self, name, value)
+        else:
+            try:
+                existing = getattr(self, name)
+                if isinstance(existing, Index):
+                    object.__setattr__(self, name, value)
+                elif name in self._info_axis:
+                    self[name] = value
+                else:
+                    object.__setattr__(self, name, value)
+            except (AttributeError, TypeError):
+                if isinstance(self, ABCDataFrame) and (is_list_like(value)):
+                    warnings.warn(
+                        "Pandas doesn't allow columns to be "
+                        "created via a new attribute name - see "
+                        "https://pandas.pydata.org/pandas-docs/"
+                        "stable/indexing.html#attribute-access",
+                        stacklevel=find_stack_level(),
+                    )
+                object.__setattr__(self, name, value)
+
+    @final
+    def _dir_additions(self) -> set[str]:
+        """
+        add the string-like attributes from the info_axis.
+        If info_axis is a MultiIndex, its first level values are used.
+        """
+        additions = super()._dir_additions()
+        if self._info_axis._can_hold_strings:
+            additions.update(self._info_axis._dir_additions_for_owner)
+        return additions
+
+    # ----------------------------------------------------------------------
+    # Consolidation of internals
+
+    @final
+    def _protect_consolidate(self, f):
+        """
+        Consolidate _mgr -- if the blocks have changed, then clear the
+        cache
+        """
+        if isinstance(self._mgr, (ArrayManager, SingleArrayManager)):
+            return f()
+        blocks_before = len(self._mgr.blocks)
+        result = f()
+        if len(self._mgr.blocks) != blocks_before:
+            self._clear_item_cache()
+        return result
+
+    @final
+    def _consolidate_inplace(self) -> None:
+        """Consolidate data in place and return None"""
+
+        def f() -> None:
+            self._mgr = self._mgr.consolidate()
+
+        self._protect_consolidate(f)
+
+    @final
+    def _consolidate(self):
+        """
+        Compute NDFrame with "consolidated" internals (data of each dtype
+        grouped together in a single ndarray).
+
+        Returns
+        -------
+        consolidated : same type as caller
+        """
+        f = lambda: self._mgr.consolidate()
+        cons_data = self._protect_consolidate(f)
+        return self._constructor_from_mgr(cons_data, axes=cons_data.axes).__finalize__(
+            self
+        )
+
+    @final
+    @property
+    def _is_mixed_type(self) -> bool_t:
+        if self._mgr.is_single_block:
+            # Includes all Series cases
+            return False
+
+        if self._mgr.any_extension_types:
+            # Even if they have the same dtype, we can't consolidate them,
+            #  so we pretend this is "mixed'"
+            return True
+
+        return self.dtypes.nunique() > 1
+
+    @final
+    def _get_numeric_data(self) -> Self:
+        new_mgr = self._mgr.get_numeric_data()
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    @final
+    def _get_bool_data(self):
+        new_mgr = self._mgr.get_bool_data()
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    # ----------------------------------------------------------------------
+    # Internal Interface Methods
+
+    @property
+    def values(self):
+        raise AbstractMethodError(self)
+
+    @property
+    def _values(self) -> ArrayLike:
+        """internal implementation"""
+        raise AbstractMethodError(self)
+
+    @property
+    def dtypes(self):
+        """
+        Return the dtypes in the DataFrame.
+
+        This returns a Series with the data type of each column.
+        The result's index is the original DataFrame's columns. Columns
+        with mixed types are stored with the ``object`` dtype. See
+        :ref:`the User Guide <basics.dtypes>` for more.
+
+        Returns
+        -------
+        pandas.Series
+            The data type of each column.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'float': [1.0],
+        ...                    'int': [1],
+        ...                    'datetime': [pd.Timestamp('20180310')],
+        ...                    'string': ['foo']})
+        >>> df.dtypes
+        float              float64
+        int                  int64
+        datetime    datetime64[ns]
+        string              object
+        dtype: object
+        """
+        data = self._mgr.get_dtypes()
+        return self._constructor_sliced(data, index=self._info_axis, dtype=np.object_)
+
+    @final
+    def astype(
+        self, dtype, copy: bool_t | None = None, errors: IgnoreRaise = "raise"
+    ) -> Self:
+        """
+        Cast a pandas object to a specified dtype ``dtype``.
+
+        Parameters
+        ----------
+        dtype : str, data type, Series or Mapping of column name -> data type
+            Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to
+            cast entire pandas object to the same type. Alternatively, use a
+            mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is
+            a numpy.dtype or Python type to cast one or more of the DataFrame's
+            columns to column-specific types.
+        copy : bool, default True
+            Return a copy when ``copy=True`` (be very careful setting
+            ``copy=False`` as changes to values then may propagate to other
+            pandas objects).
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        errors : {'raise', 'ignore'}, default 'raise'
+            Control raising of exceptions on invalid data for provided dtype.
+
+            - ``raise`` : allow exceptions to be raised
+            - ``ignore`` : suppress exceptions. On error return original object.
+
+        Returns
+        -------
+        same type as caller
+
+        See Also
+        --------
+        to_datetime : Convert argument to datetime.
+        to_timedelta : Convert argument to timedelta.
+        to_numeric : Convert argument to a numeric type.
+        numpy.ndarray.astype : Cast a numpy array to a specified type.
+
+        Notes
+        -----
+        .. versionchanged:: 2.0.0
+
+            Using ``astype`` to convert from timezone-naive dtype to
+            timezone-aware dtype will raise an exception.
+            Use :meth:`Series.dt.tz_localize` instead.
+
+        Examples
+        --------
+        Create a DataFrame:
+
+        >>> d = {'col1': [1, 2], 'col2': [3, 4]}
+        >>> df = pd.DataFrame(data=d)
+        >>> df.dtypes
+        col1    int64
+        col2    int64
+        dtype: object
+
+        Cast all columns to int32:
+
+        >>> df.astype('int32').dtypes
+        col1    int32
+        col2    int32
+        dtype: object
+
+        Cast col1 to int32 using a dictionary:
+
+        >>> df.astype({'col1': 'int32'}).dtypes
+        col1    int32
+        col2    int64
+        dtype: object
+
+        Create a series:
+
+        >>> ser = pd.Series([1, 2], dtype='int32')
+        >>> ser
+        0    1
+        1    2
+        dtype: int32
+        >>> ser.astype('int64')
+        0    1
+        1    2
+        dtype: int64
+
+        Convert to categorical type:
+
+        >>> ser.astype('category')
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int32): [1, 2]
+
+        Convert to ordered categorical type with custom ordering:
+
+        >>> from pandas.api.types import CategoricalDtype
+        >>> cat_dtype = CategoricalDtype(
+        ...     categories=[2, 1], ordered=True)
+        >>> ser.astype(cat_dtype)
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int64): [2 < 1]
+
+        Create a series of dates:
+
+        >>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
+        >>> ser_date
+        0   2020-01-01
+        1   2020-01-02
+        2   2020-01-03
+        dtype: datetime64[ns]
+        """
+        if copy and using_copy_on_write():
+            copy = False
+
+        if is_dict_like(dtype):
+            if self.ndim == 1:  # i.e. Series
+                if len(dtype) > 1 or self.name not in dtype:
+                    raise KeyError(
+                        "Only the Series name can be used for "
+                        "the key in Series dtype mappings."
+                    )
+                new_type = dtype[self.name]
+                return self.astype(new_type, copy, errors)
+
+            # GH#44417 cast to Series so we can use .iat below, which will be
+            #  robust in case we
+            from pandas import Series
+
+            dtype_ser = Series(dtype, dtype=object)
+
+            for col_name in dtype_ser.index:
+                if col_name not in self:
+                    raise KeyError(
+                        "Only a column name can be used for the "
+                        "key in a dtype mappings argument. "
+                        f"'{col_name}' not found in columns."
+                    )
+
+            dtype_ser = dtype_ser.reindex(self.columns, fill_value=None, copy=False)
+
+            results = []
+            for i, (col_name, col) in enumerate(self.items()):
+                cdt = dtype_ser.iat[i]
+                if isna(cdt):
+                    res_col = col.copy(deep=copy)
+                else:
+                    try:
+                        res_col = col.astype(dtype=cdt, copy=copy, errors=errors)
+                    except ValueError as ex:
+                        ex.args = (
+                            f"{ex}: Error while type casting for column '{col_name}'",
+                        )
+                        raise
+                results.append(res_col)
+
+        elif is_extension_array_dtype(dtype) and self.ndim > 1:
+            # TODO(EA2D): special case not needed with 2D EAs
+            dtype = pandas_dtype(dtype)
+            if isinstance(dtype, ExtensionDtype) and all(
+                arr.dtype == dtype for arr in self._mgr.arrays
+            ):
+                return self.copy(deep=copy)
+            # GH 18099/22869: columnwise conversion to extension dtype
+            # GH 24704: self.items handles duplicate column names
+            results = [
+                ser.astype(dtype, copy=copy, errors=errors) for _, ser in self.items()
+            ]
+
+        else:
+            # else, only a single dtype is given
+            new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
+            res = self._constructor_from_mgr(new_data, axes=new_data.axes)
+            return res.__finalize__(self, method="astype")
+
+        # GH 33113: handle empty frame or series
+        if not results:
+            return self.copy(deep=None)
+
+        # GH 19920: retain column metadata after concat
+        result = concat(results, axis=1, copy=False)
+        # GH#40810 retain subclass
+        # error: Incompatible types in assignment
+        # (expression has type "Self", variable has type "DataFrame")
+        result = self._constructor(result)  # type: ignore[assignment]
+        result.columns = self.columns
+        result = result.__finalize__(self, method="astype")
+        # https://github.com/python/mypy/issues/8354
+        return cast(Self, result)
+
+    @final
+    def copy(self, deep: bool_t | None = True) -> Self:
+        """
+        Make a copy of this object's indices and data.
+
+        When ``deep=True`` (default), a new object will be created with a
+        copy of the calling object's data and indices. Modifications to
+        the data or indices of the copy will not be reflected in the
+        original object (see notes below).
+
+        When ``deep=False``, a new object will be created without copying
+        the calling object's data or index (only references to the data
+        and index are copied). Any changes to the data of the original
+        will be reflected in the shallow copy (and vice versa).
+
+        .. note::
+            The ``deep=False`` behaviour as described above will change
+            in pandas 3.0. `Copy-on-Write
+            <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+            will be enabled by default, which means that the "shallow" copy
+            is that is returned with ``deep=False`` will still avoid making
+            an eager copy, but changes to the data of the original will *no*
+            longer be reflected in the shallow copy (or vice versa). Instead,
+            it makes use of a lazy (deferred) copy mechanism that will copy
+            the data only when any changes to the original or shallow copy is
+            made.
+
+            You can already get the future behavior and improvements through
+            enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Parameters
+        ----------
+        deep : bool, default True
+            Make a deep copy, including a copy of the data and the indices.
+            With ``deep=False`` neither the indices nor the data are copied.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object type matches caller.
+
+        Notes
+        -----
+        When ``deep=True``, data is copied but actual Python objects
+        will not be copied recursively, only the reference to the object.
+        This is in contrast to `copy.deepcopy` in the Standard Library,
+        which recursively copies object data (see examples below).
+
+        While ``Index`` objects are copied when ``deep=True``, the underlying
+        numpy array is not copied for performance reasons. Since ``Index`` is
+        immutable, the underlying data can be safely shared and a copy
+        is not needed.
+
+        Since pandas is not thread safe, see the
+        :ref:`gotchas <gotchas.thread-safety>` when copying in a threading
+        environment.
+
+        When ``copy_on_write`` in pandas config is set to ``True``, the
+        ``copy_on_write`` config takes effect even when ``deep=False``.
+        This means that any changes to the copied data would make a new copy
+        of the data upon write (and vice versa). Changes made to either the
+        original or copied variable would not be reflected in the counterpart.
+        See :ref:`Copy_on_Write <copy_on_write>` for more information.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2], index=["a", "b"])
+        >>> s
+        a    1
+        b    2
+        dtype: int64
+
+        >>> s_copy = s.copy()
+        >>> s_copy
+        a    1
+        b    2
+        dtype: int64
+
+        **Shallow copy versus default (deep) copy:**
+
+        >>> s = pd.Series([1, 2], index=["a", "b"])
+        >>> deep = s.copy()
+        >>> shallow = s.copy(deep=False)
+
+        Shallow copy shares data and index with original.
+
+        >>> s is shallow
+        False
+        >>> s.values is shallow.values and s.index is shallow.index
+        True
+
+        Deep copy has own copy of data and index.
+
+        >>> s is deep
+        False
+        >>> s.values is deep.values or s.index is deep.index
+        False
+
+        Updates to the data shared by shallow copy and original is reflected
+        in both (NOTE: this will no longer be true for pandas >= 3.0);
+        deep copy remains unchanged.
+
+        >>> s.iloc[0] = 3
+        >>> shallow.iloc[1] = 4
+        >>> s
+        a    3
+        b    4
+        dtype: int64
+        >>> shallow
+        a    3
+        b    4
+        dtype: int64
+        >>> deep
+        a    1
+        b    2
+        dtype: int64
+
+        Note that when copying an object containing Python objects, a deep copy
+        will copy the data, but will not do so recursively. Updating a nested
+        data object will be reflected in the deep copy.
+
+        >>> s = pd.Series([[1, 2], [3, 4]])
+        >>> deep = s.copy()
+        >>> s[0][0] = 10
+        >>> s
+        0    [10, 2]
+        1     [3, 4]
+        dtype: object
+        >>> deep
+        0    [10, 2]
+        1     [3, 4]
+        dtype: object
+
+        **Copy-on-Write is set to true**, the shallow copy is not modified
+        when the original data is changed:
+
+        >>> with pd.option_context("mode.copy_on_write", True):
+        ...     s = pd.Series([1, 2], index=["a", "b"])
+        ...     copy = s.copy(deep=False)
+        ...     s.iloc[0] = 100
+        ...     s
+        a    100
+        b      2
+        dtype: int64
+        >>> copy
+        a    1
+        b    2
+        dtype: int64
+        """
+        data = self._mgr.copy(deep=deep)
+        self._clear_item_cache()
+        return self._constructor_from_mgr(data, axes=data.axes).__finalize__(
+            self, method="copy"
+        )
+
+    @final
+    def __copy__(self, deep: bool_t = True) -> Self:
+        return self.copy(deep=deep)
+
+    @final
+    def __deepcopy__(self, memo=None) -> Self:
+        """
+        Parameters
+        ----------
+        memo, default None
+            Standard signature. Unused
+        """
+        return self.copy(deep=True)
+
+    @final
+    def infer_objects(self, copy: bool_t | None = None) -> Self:
+        """
+        Attempt to infer better dtypes for object columns.
+
+        Attempts soft conversion of object-dtyped
+        columns, leaving non-object and unconvertible
+        columns unchanged. The inference rules are the
+        same as during normal Series/DataFrame construction.
+
+        Parameters
+        ----------
+        copy : bool, default True
+            Whether to make a copy for non-object or non-inferable columns
+            or Series.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        same type as input object
+
+        See Also
+        --------
+        to_datetime : Convert argument to datetime.
+        to_timedelta : Convert argument to timedelta.
+        to_numeric : Convert argument to numeric type.
+        convert_dtypes : Convert argument to best possible dtype.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
+        >>> df = df.iloc[1:]
+        >>> df
+           A
+        1  1
+        2  2
+        3  3
+
+        >>> df.dtypes
+        A    object
+        dtype: object
+
+        >>> df.infer_objects().dtypes
+        A    int64
+        dtype: object
+        """
+        new_mgr = self._mgr.convert(copy=copy)
+        res = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        return res.__finalize__(self, method="infer_objects")
+
+    @final
+    def convert_dtypes(
+        self,
+        infer_objects: bool_t = True,
+        convert_string: bool_t = True,
+        convert_integer: bool_t = True,
+        convert_boolean: bool_t = True,
+        convert_floating: bool_t = True,
+        dtype_backend: DtypeBackend = "numpy_nullable",
+    ) -> Self:
+        """
+        Convert columns to the best possible dtypes using dtypes supporting ``pd.NA``.
+
+        Parameters
+        ----------
+        infer_objects : bool, default True
+            Whether object dtypes should be converted to the best possible types.
+        convert_string : bool, default True
+            Whether object dtypes should be converted to ``StringDtype()``.
+        convert_integer : bool, default True
+            Whether, if possible, conversion can be done to integer extension types.
+        convert_boolean : bool, defaults True
+            Whether object dtypes should be converted to ``BooleanDtypes()``.
+        convert_floating : bool, defaults True
+            Whether, if possible, conversion can be done to floating extension types.
+            If `convert_integer` is also True, preference will be give to integer
+            dtypes if the floats can be faithfully casted to integers.
+        dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+            Back-end data type applied to the resultant :class:`DataFrame`
+            (still experimental). Behaviour is as follows:
+
+            * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+              (default).
+            * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+              DataFrame.
+
+            .. versionadded:: 2.0
+
+        Returns
+        -------
+        Series or DataFrame
+            Copy of input object with new dtype.
+
+        See Also
+        --------
+        infer_objects : Infer dtypes of objects.
+        to_datetime : Convert argument to datetime.
+        to_timedelta : Convert argument to timedelta.
+        to_numeric : Convert argument to a numeric type.
+
+        Notes
+        -----
+        By default, ``convert_dtypes`` will attempt to convert a Series (or each
+        Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
+        ``convert_string``, ``convert_integer``, ``convert_boolean`` and
+        ``convert_floating``, it is possible to turn off individual conversions
+        to ``StringDtype``, the integer extension types, ``BooleanDtype``
+        or floating extension types, respectively.
+
+        For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
+        rules as during normal Series/DataFrame construction.  Then, if possible,
+        convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
+        or floating extension type, otherwise leave as ``object``.
+
+        If the dtype is integer, convert to an appropriate integer extension type.
+
+        If the dtype is numeric, and consists of all integers, convert to an
+        appropriate integer extension type. Otherwise, convert to an
+        appropriate floating extension type.
+
+        In the future, as new dtypes are added that support ``pd.NA``, the results
+        of this method will change to support those new dtypes.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
+        ...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
+        ...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
+        ...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
+        ...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
+        ...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
+        ...     }
+        ... )
+
+        Start with a DataFrame with default dtypes.
+
+        >>> df
+           a  b      c    d     e      f
+        0  1  x   True    h  10.0    NaN
+        1  2  y  False    i   NaN  100.5
+        2  3  z    NaN  NaN  20.0  200.0
+
+        >>> df.dtypes
+        a      int32
+        b     object
+        c     object
+        d     object
+        e    float64
+        f    float64
+        dtype: object
+
+        Convert the DataFrame to use best possible dtypes.
+
+        >>> dfn = df.convert_dtypes()
+        >>> dfn
+           a  b      c     d     e      f
+        0  1  x   True     h    10   <NA>
+        1  2  y  False     i  <NA>  100.5
+        2  3  z   <NA>  <NA>    20  200.0
+
+        >>> dfn.dtypes
+        a             Int32
+        b    string[python]
+        c           boolean
+        d    string[python]
+        e             Int64
+        f           Float64
+        dtype: object
+
+        Start with a Series of strings and missing data represented by ``np.nan``.
+
+        >>> s = pd.Series(["a", "b", np.nan])
+        >>> s
+        0      a
+        1      b
+        2    NaN
+        dtype: object
+
+        Obtain a Series with dtype ``StringDtype``.
+
+        >>> s.convert_dtypes()
+        0       a
+        1       b
+        2    <NA>
+        dtype: string
+        """
+        check_dtype_backend(dtype_backend)
+        new_mgr = self._mgr.convert_dtypes(  # type: ignore[union-attr]
+            infer_objects=infer_objects,
+            convert_string=convert_string,
+            convert_integer=convert_integer,
+            convert_boolean=convert_boolean,
+            convert_floating=convert_floating,
+            dtype_backend=dtype_backend,
+        )
+        res = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        return res.__finalize__(self, method="convert_dtypes")
+
+    # ----------------------------------------------------------------------
+    # Filling NA's
+
+    def _deprecate_downcast(self, downcast, method_name: str):
+        # GH#40988
+        if downcast is not lib.no_default:
+            warnings.warn(
+                f"The 'downcast' keyword in {method_name} is deprecated and "
+                "will be removed in a future version. Use "
+                "res.infer_objects(copy=False) to infer non-object dtype, or "
+                "pd.to_numeric with the 'downcast' keyword to downcast numeric "
+                "results.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            downcast = None
+        return downcast
+
+    @final
+    def _pad_or_backfill(
+        self,
+        method: Literal["ffill", "bfill", "pad", "backfill"],
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: dict | None = None,
+    ):
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+        method = clean_fill_method(method)
+
+        if not self._mgr.is_single_block and axis == 1:
+            # e.g. test_align_fill_method
+            # TODO(3.0): once downcast is removed, we can do the .T
+            #  in all axis=1 cases, and remove axis kward from mgr.pad_or_backfill.
+            if inplace:
+                raise NotImplementedError()
+            result = self.T._pad_or_backfill(
+                method=method, limit=limit, limit_area=limit_area
+            ).T
+
+            return result
+
+        new_mgr = self._mgr.pad_or_backfill(
+            method=method,
+            axis=self._get_block_manager_axis(axis),
+            limit=limit,
+            limit_area=limit_area,
+            inplace=inplace,
+            downcast=downcast,
+        )
+        result = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="fillna")
+
+    @overload
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame = ...,
+        *,
+        method: FillnaOptions | None = ...,
+        axis: Axis | None = ...,
+        inplace: Literal[False] = ...,
+        limit: int | None = ...,
+        downcast: dict | None = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame = ...,
+        *,
+        method: FillnaOptions | None = ...,
+        axis: Axis | None = ...,
+        inplace: Literal[True],
+        limit: int | None = ...,
+        downcast: dict | None = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame = ...,
+        *,
+        method: FillnaOptions | None = ...,
+        axis: Axis | None = ...,
+        inplace: bool_t = ...,
+        limit: int | None = ...,
+        downcast: dict | None = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame | None = None,
+        *,
+        method: FillnaOptions | None = None,
+        axis: Axis | None = None,
+        inplace: bool_t = False,
+        limit: int | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series, or DataFrame
+            Value to use to fill holes (e.g. 0), alternately a
+            dict/Series/DataFrame of values specifying which value to use for
+            each index (for a Series) or column (for a DataFrame).  Values not
+            in the dict/Series/DataFrame will not be filled. This value cannot
+            be a list.
+        method : {{'backfill', 'bfill', 'ffill', None}}, default None
+            Method to use for filling holes in reindexed Series:
+
+            * ffill: propagate last valid observation forward to next valid.
+            * backfill / bfill: use next valid observation to fill gap.
+
+            .. deprecated:: 2.1.0
+                Use ffill or bfill instead.
+
+        axis : {axes_single_arg}
+            Axis along which to fill missing values. For `Series`
+            this parameter is unused and defaults to 0.
+        inplace : bool, default False
+            If True, fill in-place. Note: this will modify any
+            other views on this object (e.g., a no-copy slice for a column in a
+            DataFrame).
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.2.0
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        See Also
+        --------
+        ffill : Fill values by propagating the last valid observation to next valid.
+        bfill : Fill values by using the next valid observation to fill the gap.
+        interpolate : Fill NaN values using interpolation.
+        reindex : Conform object to new index.
+        asfreq : Convert TimeSeries to specified frequency.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
+        ...                    [3, 4, np.nan, 1],
+        ...                    [np.nan, np.nan, np.nan, np.nan],
+        ...                    [np.nan, 3, np.nan, 4]],
+        ...                   columns=list("ABCD"))
+        >>> df
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  NaN  NaN NaN  NaN
+        3  NaN  3.0 NaN  4.0
+
+        Replace all NaN elements with 0s.
+
+        >>> df.fillna(0)
+             A    B    C    D
+        0  0.0  2.0  0.0  0.0
+        1  3.0  4.0  0.0  1.0
+        2  0.0  0.0  0.0  0.0
+        3  0.0  3.0  0.0  4.0
+
+        Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
+        2, and 3 respectively.
+
+        >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}}
+        >>> df.fillna(value=values)
+             A    B    C    D
+        0  0.0  2.0  2.0  0.0
+        1  3.0  4.0  2.0  1.0
+        2  0.0  1.0  2.0  3.0
+        3  0.0  3.0  2.0  4.0
+
+        Only replace the first NaN element.
+
+        >>> df.fillna(value=values, limit=1)
+             A    B    C    D
+        0  0.0  2.0  2.0  0.0
+        1  3.0  4.0  NaN  1.0
+        2  NaN  1.0  NaN  3.0
+        3  NaN  3.0  NaN  4.0
+
+        When filling using a DataFrame, replacement happens along
+        the same column names and same indices
+
+        >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
+        >>> df.fillna(df2)
+             A    B    C    D
+        0  0.0  2.0  0.0  0.0
+        1  3.0  4.0  0.0  1.0
+        2  0.0  0.0  0.0  NaN
+        3  0.0  3.0  0.0  4.0
+
+        Note that column D is not affected since it is not present in df2.
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        value, method = validate_fillna_kwargs(value, method)
+        if method is not None:
+            warnings.warn(
+                f"{type(self).__name__}.fillna with 'method' is deprecated and "
+                "will raise in a future version. Use obj.ffill() or obj.bfill() "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        was_no_default = downcast is lib.no_default
+        downcast = self._deprecate_downcast(downcast, "fillna")
+
+        # set the default here, so functions examining the signaure
+        # can detect if something was set (e.g. in groupby) (GH9221)
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+
+        if value is None:
+            return self._pad_or_backfill(
+                # error: Argument 1 to "_pad_or_backfill" of "NDFrame" has
+                # incompatible type "Optional[Literal['backfill', 'bfill', 'ffill',
+                # 'pad']]"; expected "Literal['ffill', 'bfill', 'pad', 'backfill']"
+                method,  # type: ignore[arg-type]
+                axis=axis,
+                limit=limit,
+                inplace=inplace,
+                # error: Argument "downcast" to "_fillna_with_method" of "NDFrame"
+                # has incompatible type "Union[Dict[Any, Any], None,
+                # Literal[_NoDefault.no_default]]"; expected
+                # "Optional[Dict[Any, Any]]"
+                downcast=downcast,  # type: ignore[arg-type]
+            )
+        else:
+            if self.ndim == 1:
+                if isinstance(value, (dict, ABCSeries)):
+                    if not len(value):
+                        # test_fillna_nonscalar
+                        if inplace:
+                            return None
+                        return self.copy(deep=None)
+                    from pandas import Series
+
+                    value = Series(value)
+                    value = value.reindex(self.index, copy=False)
+                    value = value._values
+                elif not is_list_like(value):
+                    pass
+                else:
+                    raise TypeError(
+                        '"value" parameter must be a scalar, dict '
+                        "or Series, but you passed a "
+                        f'"{type(value).__name__}"'
+                    )
+
+                new_data = self._mgr.fillna(
+                    value=value, limit=limit, inplace=inplace, downcast=downcast
+                )
+
+            elif isinstance(value, (dict, ABCSeries)):
+                if axis == 1:
+                    raise NotImplementedError(
+                        "Currently only can fill "
+                        "with dict/Series column "
+                        "by column"
+                    )
+                if using_copy_on_write():
+                    result = self.copy(deep=None)
+                else:
+                    result = self if inplace else self.copy()
+                is_dict = isinstance(downcast, dict)
+                for k, v in value.items():
+                    if k not in result:
+                        continue
+
+                    if was_no_default:
+                        downcast_k = lib.no_default
+                    else:
+                        downcast_k = (
+                            # error: Incompatible types in assignment (expression
+                            # has type "Union[Dict[Any, Any], None,
+                            # Literal[_NoDefault.no_default], Any]", variable has
+                            # type "_NoDefault")
+                            downcast  # type: ignore[assignment]
+                            if not is_dict
+                            # error: Item "None" of "Optional[Dict[Any, Any]]" has
+                            # no attribute "get"
+                            else downcast.get(k)  # type: ignore[union-attr]
+                        )
+
+                    res_k = result[k].fillna(v, limit=limit, downcast=downcast_k)
+
+                    if not inplace:
+                        result[k] = res_k
+                    else:
+                        # We can write into our existing column(s) iff dtype
+                        #  was preserved.
+                        if isinstance(res_k, ABCSeries):
+                            # i.e. 'k' only shows up once in self.columns
+                            if res_k.dtype == result[k].dtype:
+                                result.loc[:, k] = res_k
+                            else:
+                                # Different dtype -> no way to do inplace.
+                                result[k] = res_k
+                        else:
+                            # see test_fillna_dict_inplace_nonunique_columns
+                            locs = result.columns.get_loc(k)
+                            if isinstance(locs, slice):
+                                locs = np.arange(self.shape[1])[locs]
+                            elif (
+                                isinstance(locs, np.ndarray) and locs.dtype.kind == "b"
+                            ):
+                                locs = locs.nonzero()[0]
+                            elif not (
+                                isinstance(locs, np.ndarray) and locs.dtype.kind == "i"
+                            ):
+                                # Should never be reached, but let's cover our bases
+                                raise NotImplementedError(
+                                    "Unexpected get_loc result, please report a bug at "
+                                    "https://github.com/pandas-dev/pandas"
+                                )
+
+                            for i, loc in enumerate(locs):
+                                res_loc = res_k.iloc[:, i]
+                                target = self.iloc[:, loc]
+
+                                if res_loc.dtype == target.dtype:
+                                    result.iloc[:, loc] = res_loc
+                                else:
+                                    result.isetitem(loc, res_loc)
+                if inplace:
+                    return self._update_inplace(result)
+                else:
+                    return result
+
+            elif not is_list_like(value):
+                if axis == 1:
+                    result = self.T.fillna(value=value, limit=limit).T
+                    new_data = result._mgr
+                else:
+                    new_data = self._mgr.fillna(
+                        value=value, limit=limit, inplace=inplace, downcast=downcast
+                    )
+            elif isinstance(value, ABCDataFrame) and self.ndim == 2:
+                new_data = self.where(self.notna(), value)._mgr
+            else:
+                raise ValueError(f"invalid fill value with a {type(value)}")
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="fillna")
+
+    @overload
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[False] = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[True],
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: bool_t = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def ffill(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by propagating the last valid observation to next valid.
+
+        Parameters
+        ----------
+        axis : {axes_single_arg}
+            Axis along which to fill missing values. For `Series`
+            this parameter is unused and defaults to 0.
+        inplace : bool, default False
+            If True, fill in-place. Note: this will modify any
+            other views on this object (e.g., a no-copy slice for a column in a
+            DataFrame).
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+            .. versionadded:: 2.2.0
+
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.2.0
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
+        ...                    [3, 4, np.nan, 1],
+        ...                    [np.nan, np.nan, np.nan, np.nan],
+        ...                    [np.nan, 3, np.nan, 4]],
+        ...                   columns=list("ABCD"))
+        >>> df
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  NaN  NaN NaN  NaN
+        3  NaN  3.0 NaN  4.0
+
+        >>> df.ffill()
+             A    B   C    D
+        0  NaN  2.0 NaN  0.0
+        1  3.0  4.0 NaN  1.0
+        2  3.0  4.0 NaN  1.0
+        3  3.0  3.0 NaN  4.0
+
+        >>> ser = pd.Series([1, np.nan, 2, 3])
+        >>> ser.ffill()
+        0   1.0
+        1   1.0
+        2   2.0
+        3   3.0
+        dtype: float64
+        """
+        downcast = self._deprecate_downcast(downcast, "ffill")
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        return self._pad_or_backfill(
+            "ffill",
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            limit_area=limit_area,
+            # error: Argument "downcast" to "_fillna_with_method" of "NDFrame"
+            # has incompatible type "Union[Dict[Any, Any], None,
+            # Literal[_NoDefault.no_default]]"; expected "Optional[Dict[Any, Any]]"
+            downcast=downcast,  # type: ignore[arg-type]
+        )
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def pad(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by propagating the last valid observation to next valid.
+
+        .. deprecated:: 2.0
+
+            {klass}.pad is deprecated. Use {klass}.ffill instead.
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        Please see examples for :meth:`DataFrame.ffill` or :meth:`Series.ffill`.
+        """
+        warnings.warn(
+            "DataFrame.pad/Series.pad is deprecated. Use "
+            "DataFrame.ffill/Series.ffill instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.ffill(axis=axis, inplace=inplace, limit=limit, downcast=downcast)
+
+    @overload
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[False] = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: Literal[True],
+        limit: None | int = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = ...,
+        inplace: bool_t = ...,
+        limit: None | int = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: dict | None | lib.NoDefault = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def bfill(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by using the next valid observation to fill the gap.
+
+        Parameters
+        ----------
+        axis : {axes_single_arg}
+            Axis along which to fill missing values. For `Series`
+            this parameter is unused and defaults to 0.
+        inplace : bool, default False
+            If True, fill in-place. Note: this will modify any
+            other views on this object (e.g., a no-copy slice for a column in a
+            DataFrame).
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+            .. versionadded:: 2.2.0
+
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.2.0
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series([1, None, None, 2])
+        >>> s.bfill()
+        0    1.0
+        1    2.0
+        2    2.0
+        3    2.0
+        dtype: float64
+        >>> s.bfill(limit=1)
+        0    1.0
+        1    NaN
+        2    2.0
+        3    2.0
+        dtype: float64
+
+        With DataFrame:
+
+        >>> df = pd.DataFrame({{'A': [1, None, None, 4], 'B': [None, 5, None, 7]}})
+        >>> df
+              A     B
+        0   1.0	  NaN
+        1   NaN	  5.0
+        2   NaN   NaN
+        3   4.0   7.0
+        >>> df.bfill()
+              A     B
+        0   1.0   5.0
+        1   4.0   5.0
+        2   4.0   7.0
+        3   4.0   7.0
+        >>> df.bfill(limit=1)
+              A     B
+        0   1.0   5.0
+        1   NaN   5.0
+        2   4.0   7.0
+        3   4.0   7.0
+        """
+        downcast = self._deprecate_downcast(downcast, "bfill")
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        return self._pad_or_backfill(
+            "bfill",
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            limit_area=limit_area,
+            # error: Argument "downcast" to "_fillna_with_method" of "NDFrame"
+            # has incompatible type "Union[Dict[Any, Any], None,
+            # Literal[_NoDefault.no_default]]"; expected "Optional[Dict[Any, Any]]"
+            downcast=downcast,  # type: ignore[arg-type]
+        )
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def backfill(
+        self,
+        *,
+        axis: None | Axis = None,
+        inplace: bool_t = False,
+        limit: None | int = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        """
+        Fill NA/NaN values by using the next valid observation to fill the gap.
+
+        .. deprecated:: 2.0
+
+            {klass}.backfill is deprecated. Use {klass}.bfill instead.
+
+        Returns
+        -------
+        {klass} or None
+            Object with missing values filled or None if ``inplace=True``.
+
+        Examples
+        --------
+        Please see examples for :meth:`DataFrame.bfill` or :meth:`Series.bfill`.
+        """
+        warnings.warn(
+            "DataFrame.backfill/Series.backfill is deprecated. Use "
+            "DataFrame.bfill/Series.bfill instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.bfill(axis=axis, inplace=inplace, limit=limit, downcast=downcast)
+
+    @overload
+    def replace(
+        self,
+        to_replace=...,
+        value=...,
+        *,
+        inplace: Literal[False] = ...,
+        limit: int | None = ...,
+        regex: bool_t = ...,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def replace(
+        self,
+        to_replace=...,
+        value=...,
+        *,
+        inplace: Literal[True],
+        limit: int | None = ...,
+        regex: bool_t = ...,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def replace(
+        self,
+        to_replace=...,
+        value=...,
+        *,
+        inplace: bool_t = ...,
+        limit: int | None = ...,
+        regex: bool_t = ...,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        _shared_docs["replace"],
+        klass=_shared_doc_kwargs["klass"],
+        inplace=_shared_doc_kwargs["inplace"],
+    )
+    def replace(
+        self,
+        to_replace=None,
+        value=lib.no_default,
+        *,
+        inplace: bool_t = False,
+        limit: int | None = None,
+        regex: bool_t = False,
+        method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default,
+    ) -> Self | None:
+        if method is not lib.no_default:
+            warnings.warn(
+                # GH#33302
+                f"The 'method' keyword in {type(self).__name__}.replace is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif limit is not None:
+            warnings.warn(
+                # GH#33302
+                f"The 'limit' keyword in {type(self).__name__}.replace is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if (
+            value is lib.no_default
+            and method is lib.no_default
+            and not is_dict_like(to_replace)
+            and regex is False
+        ):
+            # case that goes through _replace_single and defaults to method="pad"
+            warnings.warn(
+                # GH#33302
+                f"{type(self).__name__}.replace without 'value' and with "
+                "non-dict-like 'to_replace' is deprecated "
+                "and will raise in a future version. "
+                "Explicitly specify the new values instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if not (
+            is_scalar(to_replace)
+            or is_re_compilable(to_replace)
+            or is_list_like(to_replace)
+        ):
+            raise TypeError(
+                "Expecting 'to_replace' to be either a scalar, array-like, "
+                "dict or None, got invalid type "
+                f"{repr(type(to_replace).__name__)}"
+            )
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # in non-CoW mode, chained Series access will populate the
+                    # `_item_cache` which results in an increased ref count not below
+                    # the threshold, while we still need to warn. We detect this case
+                    # of a Series derived from a DataFrame through the presence of
+                    # checking the `_cacher`
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        if not is_bool(regex) and to_replace is not None:
+            raise ValueError("'to_replace' must be 'None' if 'regex' is not a bool")
+
+        if value is lib.no_default or method is not lib.no_default:
+            # GH#36984 if the user explicitly passes value=None we want to
+            #  respect that. We have the corner case where the user explicitly
+            #  passes value=None *and* a method, which we interpret as meaning
+            #  they want the (documented) default behavior.
+            if method is lib.no_default:
+                # TODO: get this to show up as the default in the docs?
+                method = "pad"
+
+            # passing a single value that is scalar like
+            # when value is None (GH5319), for compat
+            if not is_dict_like(to_replace) and not is_dict_like(regex):
+                to_replace = [to_replace]
+
+            if isinstance(to_replace, (tuple, list)):
+                # TODO: Consider copy-on-write for non-replaced columns's here
+                if isinstance(self, ABCDataFrame):
+                    from pandas import Series
+
+                    result = self.apply(
+                        Series._replace_single,
+                        args=(to_replace, method, inplace, limit),
+                    )
+                    if inplace:
+                        return None
+                    return result
+                return self._replace_single(to_replace, method, inplace, limit)
+
+            if not is_dict_like(to_replace):
+                if not is_dict_like(regex):
+                    raise TypeError(
+                        'If "to_replace" and "value" are both None '
+                        'and "to_replace" is not a list, then '
+                        "regex must be a mapping"
+                    )
+                to_replace = regex
+                regex = True
+
+            items = list(to_replace.items())
+            if items:
+                keys, values = zip(*items)
+            else:
+                # error: Incompatible types in assignment (expression has type
+                # "list[Never]", variable has type "tuple[Any, ...]")
+                keys, values = ([], [])  # type: ignore[assignment]
+
+            are_mappings = [is_dict_like(v) for v in values]
+
+            if any(are_mappings):
+                if not all(are_mappings):
+                    raise TypeError(
+                        "If a nested mapping is passed, all values "
+                        "of the top level mapping must be mappings"
+                    )
+                # passed a nested dict/Series
+                to_rep_dict = {}
+                value_dict = {}
+
+                for k, v in items:
+                    # error: Incompatible types in assignment (expression has type
+                    # "list[Never]", variable has type "tuple[Any, ...]")
+                    keys, values = list(zip(*v.items())) or (  # type: ignore[assignment]
+                        [],
+                        [],
+                    )
+
+                    to_rep_dict[k] = list(keys)
+                    value_dict[k] = list(values)
+
+                to_replace, value = to_rep_dict, value_dict
+            else:
+                to_replace, value = keys, values
+
+            return self.replace(
+                to_replace, value, inplace=inplace, limit=limit, regex=regex
+            )
+        else:
+            # need a non-zero len on all axes
+            if not self.size:
+                if inplace:
+                    return None
+                return self.copy(deep=None)
+
+            if is_dict_like(to_replace):
+                if is_dict_like(value):  # {'A' : NA} -> {'A' : 0}
+                    # Note: Checking below for `in foo.keys()` instead of
+                    #  `in foo` is needed for when we have a Series and not dict
+                    mapping = {
+                        col: (to_replace[col], value[col])
+                        for col in to_replace.keys()
+                        if col in value.keys() and col in self
+                    }
+                    return self._replace_columnwise(mapping, inplace, regex)
+
+                # {'A': NA} -> 0
+                elif not is_list_like(value):
+                    # Operate column-wise
+                    if self.ndim == 1:
+                        raise ValueError(
+                            "Series.replace cannot use dict-like to_replace "
+                            "and non-None value"
+                        )
+                    mapping = {
+                        col: (to_rep, value) for col, to_rep in to_replace.items()
+                    }
+                    return self._replace_columnwise(mapping, inplace, regex)
+                else:
+                    raise TypeError("value argument must be scalar, dict, or Series")
+
+            elif is_list_like(to_replace):
+                if not is_list_like(value):
+                    # e.g. to_replace = [NA, ''] and value is 0,
+                    #  so we replace NA with 0 and then replace '' with 0
+                    value = [value] * len(to_replace)
+
+                # e.g. we have to_replace = [NA, ''] and value = [0, 'missing']
+                if len(to_replace) != len(value):
+                    raise ValueError(
+                        f"Replacement lists must match in length. "
+                        f"Expecting {len(to_replace)} got {len(value)} "
+                    )
+                new_data = self._mgr.replace_list(
+                    src_list=to_replace,
+                    dest_list=value,
+                    inplace=inplace,
+                    regex=regex,
+                )
+
+            elif to_replace is None:
+                if not (
+                    is_re_compilable(regex)
+                    or is_list_like(regex)
+                    or is_dict_like(regex)
+                ):
+                    raise TypeError(
+                        f"'regex' must be a string or a compiled regular expression "
+                        f"or a list or dict of strings or regular expressions, "
+                        f"you passed a {repr(type(regex).__name__)}"
+                    )
+                return self.replace(
+                    regex, value, inplace=inplace, limit=limit, regex=True
+                )
+            else:
+                # dest iterable dict-like
+                if is_dict_like(value):  # NA -> {'A' : 0, 'B' : -1}
+                    # Operate column-wise
+                    if self.ndim == 1:
+                        raise ValueError(
+                            "Series.replace cannot use dict-value and "
+                            "non-None to_replace"
+                        )
+                    mapping = {col: (to_replace, val) for col, val in value.items()}
+                    return self._replace_columnwise(mapping, inplace, regex)
+
+                elif not is_list_like(value):  # NA -> 0
+                    regex = should_use_regex(regex, to_replace)
+                    if regex:
+                        new_data = self._mgr.replace_regex(
+                            to_replace=to_replace,
+                            value=value,
+                            inplace=inplace,
+                        )
+                    else:
+                        new_data = self._mgr.replace(
+                            to_replace=to_replace, value=value, inplace=inplace
+                        )
+                else:
+                    raise TypeError(
+                        f'Invalid "to_replace" type: {repr(type(to_replace).__name__)}'
+                    )
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="replace")
+
+    @overload
+    def interpolate(
+        self,
+        method: InterpolateOptions = ...,
+        *,
+        axis: Axis = ...,
+        limit: int | None = ...,
+        inplace: Literal[False] = ...,
+        limit_direction: Literal["forward", "backward", "both"] | None = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: Literal["infer"] | None | lib.NoDefault = ...,
+        **kwargs,
+    ) -> Self:
+        ...
+
+    @overload
+    def interpolate(
+        self,
+        method: InterpolateOptions = ...,
+        *,
+        axis: Axis = ...,
+        limit: int | None = ...,
+        inplace: Literal[True],
+        limit_direction: Literal["forward", "backward", "both"] | None = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: Literal["infer"] | None | lib.NoDefault = ...,
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def interpolate(
+        self,
+        method: InterpolateOptions = ...,
+        *,
+        axis: Axis = ...,
+        limit: int | None = ...,
+        inplace: bool_t = ...,
+        limit_direction: Literal["forward", "backward", "both"] | None = ...,
+        limit_area: Literal["inside", "outside"] | None = ...,
+        downcast: Literal["infer"] | None | lib.NoDefault = ...,
+        **kwargs,
+    ) -> Self | None:
+        ...
+
+    @final
+    def interpolate(
+        self,
+        method: InterpolateOptions = "linear",
+        *,
+        axis: Axis = 0,
+        limit: int | None = None,
+        inplace: bool_t = False,
+        limit_direction: Literal["forward", "backward", "both"] | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None | lib.NoDefault = lib.no_default,
+        **kwargs,
+    ) -> Self | None:
+        """
+        Fill NaN values using an interpolation method.
+
+        Please note that only ``method='linear'`` is supported for
+        DataFrame/Series with a MultiIndex.
+
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. One of:
+
+            * 'linear': Ignore the index and treat the values as equally
+              spaced. This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate
+              given length of interval.
+            * 'index', 'values': use the actual numerical values of the index.
+            * 'pad': Fill in NaNs using existing values.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+              'barycentric', 'polynomial': Passed to
+              `scipy.interpolate.interp1d`, whereas 'spline' is passed to
+              `scipy.interpolate.UnivariateSpline`. These methods use the numerical
+              values of the index.  Both 'polynomial' and 'spline' require that
+              you also specify an `order` (int), e.g.
+              ``df.interpolate(method='polynomial', order=5)``. Note that,
+              `slinear` method in Pandas refers to the Scipy first order `spline`
+              instead of Pandas first order `spline`.
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
+              'cubicspline': Wrappers around the SciPy interpolation methods of
+              similar names. See `Notes`.
+            * 'from_derivatives': Refers to
+              `scipy.interpolate.BPoly.from_derivatives`.
+
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Axis to interpolate along. For `Series` this parameter is unused
+            and defaults to 0.
+        limit : int, optional
+            Maximum number of consecutive NaNs to fill. Must be greater than
+            0.
+        inplace : bool, default False
+            Update the data in place if possible.
+        limit_direction : {{'forward', 'backward', 'both'}}, Optional
+            Consecutive NaNs will be filled in this direction.
+
+            If limit is specified:
+                * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
+                * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
+                  'backwards'.
+
+            If 'limit' is not specified:
+                * If 'method' is 'backfill' or 'bfill', the default is 'backward'
+                * else the default is 'forward'
+
+            raises ValueError if `limit_direction` is 'forward' or 'both' and
+                method is 'backfill' or 'bfill'.
+            raises ValueError if `limit_direction` is 'backward' or 'both' and
+                method is 'pad' or 'ffill'.
+
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+        downcast : optional, 'infer' or None, defaults to None
+            Downcast dtypes if possible.
+
+            .. deprecated:: 2.1.0
+
+        ``**kwargs`` : optional
+            Keyword arguments to pass on to the interpolating function.
+
+        Returns
+        -------
+        Series or DataFrame or None
+            Returns the same object type as the caller, interpolated at
+            some or all ``NaN`` values or None if ``inplace=True``.
+
+        See Also
+        --------
+        fillna : Fill missing values using different methods.
+        scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials
+            (Akima interpolator).
+        scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the
+            Bernstein basis.
+        scipy.interpolate.interp1d : Interpolate a 1-D function.
+        scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh
+            interpolator).
+        scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic
+            interpolation.
+        scipy.interpolate.CubicSpline : Cubic spline data interpolator.
+
+        Notes
+        -----
+        The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
+        methods are wrappers around the respective SciPy implementations of
+        similar names. These use the actual numerical values of the index.
+        For more information on their behavior, see the
+        `SciPy documentation
+        <https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__.
+
+        Examples
+        --------
+        Filling in ``NaN`` in a :class:`~pandas.Series` via linear
+        interpolation.
+
+        >>> s = pd.Series([0, 1, np.nan, 3])
+        >>> s
+        0    0.0
+        1    1.0
+        2    NaN
+        3    3.0
+        dtype: float64
+        >>> s.interpolate()
+        0    0.0
+        1    1.0
+        2    2.0
+        3    3.0
+        dtype: float64
+
+        Filling in ``NaN`` in a Series via polynomial interpolation or splines:
+        Both 'polynomial' and 'spline' methods require that you also specify
+        an ``order`` (int).
+
+        >>> s = pd.Series([0, 2, np.nan, 8])
+        >>> s.interpolate(method='polynomial', order=2)
+        0    0.000000
+        1    2.000000
+        2    4.666667
+        3    8.000000
+        dtype: float64
+
+        Fill the DataFrame forward (that is, going down) along each column
+        using linear interpolation.
+
+        Note how the last entry in column 'a' is interpolated differently,
+        because there is no entry after it to use for interpolation.
+        Note how the first entry in column 'b' remains ``NaN``, because there
+        is no entry before it to use for interpolation.
+
+        >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
+        ...                    (np.nan, 2.0, np.nan, np.nan),
+        ...                    (2.0, 3.0, np.nan, 9.0),
+        ...                    (np.nan, 4.0, -4.0, 16.0)],
+        ...                   columns=list('abcd'))
+        >>> df
+             a    b    c     d
+        0  0.0  NaN -1.0   1.0
+        1  NaN  2.0  NaN   NaN
+        2  2.0  3.0  NaN   9.0
+        3  NaN  4.0 -4.0  16.0
+        >>> df.interpolate(method='linear', limit_direction='forward', axis=0)
+             a    b    c     d
+        0  0.0  NaN -1.0   1.0
+        1  1.0  2.0 -2.0   5.0
+        2  2.0  3.0 -3.0   9.0
+        3  2.0  4.0 -4.0  16.0
+
+        Using polynomial interpolation.
+
+        >>> df['d'].interpolate(method='polynomial', order=2)
+        0     1.0
+        1     4.0
+        2     9.0
+        3    16.0
+        Name: d, dtype: float64
+        """
+        if downcast is not lib.no_default:
+            # GH#40988
+            warnings.warn(
+                f"The 'downcast' keyword in {type(self).__name__}.interpolate "
+                "is deprecated and will be removed in a future version. "
+                "Call result.infer_objects(copy=False) on the result instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            downcast = None
+        if downcast is not None and downcast != "infer":
+            raise ValueError("downcast must be either None or 'infer'")
+
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and _check_cacher(self):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        axis = self._get_axis_number(axis)
+
+        if self.empty:
+            if inplace:
+                return None
+            return self.copy()
+
+        if not isinstance(method, str):
+            raise ValueError("'method' should be a string, not None.")
+
+        fillna_methods = ["ffill", "bfill", "pad", "backfill"]
+        if method.lower() in fillna_methods:
+            # GH#53581
+            warnings.warn(
+                f"{type(self).__name__}.interpolate with method={method} is "
+                "deprecated and will raise in a future version. "
+                "Use obj.ffill() or obj.bfill() instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            obj, should_transpose = self, False
+        else:
+            obj, should_transpose = (self.T, True) if axis == 1 else (self, False)
+            if np.any(obj.dtypes == object):
+                # GH#53631
+                if not (obj.ndim == 2 and np.all(obj.dtypes == object)):
+                    # don't warn in cases that already raise
+                    warnings.warn(
+                        f"{type(self).__name__}.interpolate with object dtype is "
+                        "deprecated and will raise in a future version. Call "
+                        "obj.infer_objects(copy=False) before interpolating instead.",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+        if method in fillna_methods and "fill_value" in kwargs:
+            raise ValueError(
+                "'fill_value' is not a valid keyword for "
+                f"{type(self).__name__}.interpolate with method from "
+                f"{fillna_methods}"
+            )
+
+        if isinstance(obj.index, MultiIndex) and method != "linear":
+            raise ValueError(
+                "Only `method=linear` interpolation is supported on MultiIndexes."
+            )
+
+        limit_direction = missing.infer_limit_direction(limit_direction, method)
+
+        if obj.ndim == 2 and np.all(obj.dtypes == object):
+            raise TypeError(
+                "Cannot interpolate with all object-dtype columns "
+                "in the DataFrame. Try setting at least one "
+                "column to a numeric dtype."
+            )
+
+        if method.lower() in fillna_methods:
+            # TODO(3.0): remove this case
+            # TODO: warn/raise on limit_direction or kwargs which are ignored?
+            #  as of 2023-06-26 no tests get here with either
+            if not self._mgr.is_single_block and axis == 1:
+                # GH#53898
+                if inplace:
+                    raise NotImplementedError()
+                obj, axis, should_transpose = self.T, 1 - axis, True
+
+            new_data = obj._mgr.pad_or_backfill(
+                method=method,
+                axis=self._get_block_manager_axis(axis),
+                limit=limit,
+                limit_area=limit_area,
+                inplace=inplace,
+                downcast=downcast,
+            )
+        else:
+            index = missing.get_interp_index(method, obj.index)
+            new_data = obj._mgr.interpolate(
+                method=method,
+                index=index,
+                limit=limit,
+                limit_direction=limit_direction,
+                limit_area=limit_area,
+                inplace=inplace,
+                downcast=downcast,
+                **kwargs,
+            )
+
+        result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+        if should_transpose:
+            result = result.T
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result.__finalize__(self, method="interpolate")
+
+    # ----------------------------------------------------------------------
+    # Timeseries methods Methods
+
+    @final
+    def asof(self, where, subset=None):
+        """
+        Return the last row(s) without any NaNs before `where`.
+
+        The last row (for each element in `where`, if list) without any
+        NaN is taken.
+        In case of a :class:`~pandas.DataFrame`, the last row without NaN
+        considering only the subset of columns (if not `None`)
+
+        If there is no good value, NaN is returned for a Series or
+        a Series of NaN values for a DataFrame
+
+        Parameters
+        ----------
+        where : date or array-like of dates
+            Date(s) before which the last row(s) are returned.
+        subset : str or array-like of str, default `None`
+            For DataFrame, if not `None`, only use these columns to
+            check for NaNs.
+
+        Returns
+        -------
+        scalar, Series, or DataFrame
+
+            The return can be:
+
+            * scalar : when `self` is a Series and `where` is a scalar
+            * Series: when `self` is a Series and `where` is an array-like,
+              or when `self` is a DataFrame and `where` is a scalar
+            * DataFrame : when `self` is a DataFrame and `where` is an
+              array-like
+
+        See Also
+        --------
+        merge_asof : Perform an asof merge. Similar to left join.
+
+        Notes
+        -----
+        Dates are assumed to be sorted. Raises if this is not the case.
+
+        Examples
+        --------
+        A Series and a scalar `where`.
+
+        >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
+        >>> s
+        10    1.0
+        20    2.0
+        30    NaN
+        40    4.0
+        dtype: float64
+
+        >>> s.asof(20)
+        2.0
+
+        For a sequence `where`, a Series is returned. The first value is
+        NaN, because the first element of `where` is before the first
+        index value.
+
+        >>> s.asof([5, 20])
+        5     NaN
+        20    2.0
+        dtype: float64
+
+        Missing values are not considered. The following is ``2.0``, not
+        NaN, even though NaN is at the index location for ``30``.
+
+        >>> s.asof(30)
+        2.0
+
+        Take all columns into consideration
+
+        >>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
+        ...                    'b': [None, None, None, None, 500]},
+        ...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
+        ...                                           '2018-02-27 09:02:00',
+        ...                                           '2018-02-27 09:03:00',
+        ...                                           '2018-02-27 09:04:00',
+        ...                                           '2018-02-27 09:05:00']))
+        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
+        ...                           '2018-02-27 09:04:30']))
+                              a   b
+        2018-02-27 09:03:30 NaN NaN
+        2018-02-27 09:04:30 NaN NaN
+
+        Take a single column into consideration
+
+        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
+        ...                           '2018-02-27 09:04:30']),
+        ...         subset=['a'])
+                                a   b
+        2018-02-27 09:03:30  30.0 NaN
+        2018-02-27 09:04:30  40.0 NaN
+        """
+        if isinstance(where, str):
+            where = Timestamp(where)
+
+        if not self.index.is_monotonic_increasing:
+            raise ValueError("asof requires a sorted index")
+
+        is_series = isinstance(self, ABCSeries)
+        if is_series:
+            if subset is not None:
+                raise ValueError("subset is not valid for Series")
+        else:
+            if subset is None:
+                subset = self.columns
+            if not is_list_like(subset):
+                subset = [subset]
+
+        is_list = is_list_like(where)
+        if not is_list:
+            start = self.index[0]
+            if isinstance(self.index, PeriodIndex):
+                where = Period(where, freq=self.index.freq)
+
+            if where < start:
+                if not is_series:
+                    return self._constructor_sliced(
+                        index=self.columns, name=where, dtype=np.float64
+                    )
+                return np.nan
+
+            # It's always much faster to use a *while* loop here for
+            # Series than pre-computing all the NAs. However a
+            # *while* loop is extremely expensive for DataFrame
+            # so we later pre-compute all the NAs and use the same
+            # code path whether *where* is a scalar or list.
+            # See PR: https://github.com/pandas-dev/pandas/pull/14476
+            if is_series:
+                loc = self.index.searchsorted(where, side="right")
+                if loc > 0:
+                    loc -= 1
+
+                values = self._values
+                while loc > 0 and isna(values[loc]):
+                    loc -= 1
+                return values[loc]
+
+        if not isinstance(where, Index):
+            where = Index(where) if is_list else Index([where])
+
+        nulls = self.isna() if is_series else self[subset].isna().any(axis=1)
+        if nulls.all():
+            if is_series:
+                self = cast("Series", self)
+                return self._constructor(np.nan, index=where, name=self.name)
+            elif is_list:
+                self = cast("DataFrame", self)
+                return self._constructor(np.nan, index=where, columns=self.columns)
+            else:
+                self = cast("DataFrame", self)
+                return self._constructor_sliced(
+                    np.nan, index=self.columns, name=where[0]
+                )
+
+        locs = self.index.asof_locs(where, ~(nulls._values))
+
+        # mask the missing
+        mask = locs == -1
+        data = self.take(locs)
+        data.index = where
+        if mask.any():
+            # GH#16063 only do this setting when necessary, otherwise
+            #  we'd cast e.g. bools to floats
+            data.loc[mask] = np.nan
+        return data if is_list else data.iloc[-1]
+
+    # ----------------------------------------------------------------------
+    # Action Methods
+
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def isna(self) -> Self:
+        """
+        Detect missing values.
+
+        Return a boolean same-sized object indicating if the values are NA.
+        NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
+        values.
+        Everything else gets mapped to False values. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values
+        (unless you set ``pandas.options.mode.use_inf_as_na = True``).
+
+        Returns
+        -------
+        {klass}
+            Mask of bool values for each element in {klass} that
+            indicates whether an element is an NA value.
+
+        See Also
+        --------
+        {klass}.isnull : Alias of isna.
+        {klass}.notna : Boolean inverse of isna.
+        {klass}.dropna : Omit axes labels with missing values.
+        isna : Top-level isna.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are NA.
+
+        >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
+        ...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
+        ...                              pd.Timestamp('1940-04-25')],
+        ...                        name=['Alfred', 'Batman', ''],
+        ...                        toy=[None, 'Batmobile', 'Joker']))
+        >>> df
+           age       born    name        toy
+        0  5.0        NaT  Alfred       None
+        1  6.0 1939-05-27  Batman  Batmobile
+        2  NaN 1940-04-25              Joker
+
+        >>> df.isna()
+             age   born   name    toy
+        0  False   True  False   True
+        1  False  False  False  False
+        2   True  False  False  False
+
+        Show which entries in a Series are NA.
+
+        >>> ser = pd.Series([5, 6, np.nan])
+        >>> ser
+        0    5.0
+        1    6.0
+        2    NaN
+        dtype: float64
+
+        >>> ser.isna()
+        0    False
+        1    False
+        2     True
+        dtype: bool
+        """
+        return isna(self).__finalize__(self, method="isna")
+
+    @doc(isna, klass=_shared_doc_kwargs["klass"])
+    def isnull(self) -> Self:
+        return isna(self).__finalize__(self, method="isnull")
+
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def notna(self) -> Self:
+        """
+        Detect existing (non-missing) values.
+
+        Return a boolean same-sized object indicating if the values are not NA.
+        Non-missing values get mapped to True. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values
+        (unless you set ``pandas.options.mode.use_inf_as_na = True``).
+        NA values, such as None or :attr:`numpy.NaN`, get mapped to False
+        values.
+
+        Returns
+        -------
+        {klass}
+            Mask of bool values for each element in {klass} that
+            indicates whether an element is not an NA value.
+
+        See Also
+        --------
+        {klass}.notnull : Alias of notna.
+        {klass}.isna : Boolean inverse of notna.
+        {klass}.dropna : Omit axes labels with missing values.
+        notna : Top-level notna.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are not NA.
+
+        >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
+        ...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
+        ...                              pd.Timestamp('1940-04-25')],
+        ...                        name=['Alfred', 'Batman', ''],
+        ...                        toy=[None, 'Batmobile', 'Joker']))
+        >>> df
+           age       born    name        toy
+        0  5.0        NaT  Alfred       None
+        1  6.0 1939-05-27  Batman  Batmobile
+        2  NaN 1940-04-25              Joker
+
+        >>> df.notna()
+             age   born  name    toy
+        0   True  False  True  False
+        1   True   True  True   True
+        2  False   True  True   True
+
+        Show which entries in a Series are not NA.
+
+        >>> ser = pd.Series([5, 6, np.nan])
+        >>> ser
+        0    5.0
+        1    6.0
+        2    NaN
+        dtype: float64
+
+        >>> ser.notna()
+        0     True
+        1     True
+        2    False
+        dtype: bool
+        """
+        return notna(self).__finalize__(self, method="notna")
+
+    @doc(notna, klass=_shared_doc_kwargs["klass"])
+    def notnull(self) -> Self:
+        return notna(self).__finalize__(self, method="notnull")
+
+    @final
+    def _clip_with_scalar(self, lower, upper, inplace: bool_t = False):
+        if (lower is not None and np.any(isna(lower))) or (
+            upper is not None and np.any(isna(upper))
+        ):
+            raise ValueError("Cannot use an NA value as a clip threshold")
+
+        result = self
+        mask = self.isna()
+
+        if lower is not None:
+            cond = mask | (self >= lower)
+            result = result.where(
+                cond, lower, inplace=inplace
+            )  # type: ignore[assignment]
+        if upper is not None:
+            cond = mask | (self <= upper)
+            result = self if inplace else result
+            result = result.where(
+                cond, upper, inplace=inplace
+            )  # type: ignore[assignment]
+
+        return result
+
+    @final
+    def _clip_with_one_bound(self, threshold, method, axis, inplace):
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        # method is self.le for upper bound and self.ge for lower bound
+        if is_scalar(threshold) and is_number(threshold):
+            if method.__name__ == "le":
+                return self._clip_with_scalar(None, threshold, inplace=inplace)
+            return self._clip_with_scalar(threshold, None, inplace=inplace)
+
+        # GH #15390
+        # In order for where method to work, the threshold must
+        # be transformed to NDFrame from other array like structure.
+        if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold):
+            if isinstance(self, ABCSeries):
+                threshold = self._constructor(threshold, index=self.index)
+            else:
+                threshold = self._align_for_op(threshold, axis, flex=None)[1]
+
+        # GH 40420
+        # Treat missing thresholds as no bounds, not clipping the values
+        if is_list_like(threshold):
+            fill_value = np.inf if method.__name__ == "le" else -np.inf
+            threshold_inf = threshold.fillna(fill_value)
+        else:
+            threshold_inf = threshold
+
+        subset = method(threshold_inf, axis=axis) | isna(self)
+
+        # GH 40420
+        return self.where(subset, threshold, axis=axis, inplace=inplace)
+
+    @overload
+    def clip(
+        self,
+        lower=...,
+        upper=...,
+        *,
+        axis: Axis | None = ...,
+        inplace: Literal[False] = ...,
+        **kwargs,
+    ) -> Self:
+        ...
+
+    @overload
+    def clip(
+        self,
+        lower=...,
+        upper=...,
+        *,
+        axis: Axis | None = ...,
+        inplace: Literal[True],
+        **kwargs,
+    ) -> None:
+        ...
+
+    @overload
+    def clip(
+        self,
+        lower=...,
+        upper=...,
+        *,
+        axis: Axis | None = ...,
+        inplace: bool_t = ...,
+        **kwargs,
+    ) -> Self | None:
+        ...
+
+    @final
+    def clip(
+        self,
+        lower=None,
+        upper=None,
+        *,
+        axis: Axis | None = None,
+        inplace: bool_t = False,
+        **kwargs,
+    ) -> Self | None:
+        """
+        Trim values at input threshold(s).
+
+        Assigns values outside boundary to boundary values. Thresholds
+        can be singular values or array like, and in the latter case
+        the clipping is performed element-wise in the specified axis.
+
+        Parameters
+        ----------
+        lower : float or array-like, default None
+            Minimum threshold value. All values below this
+            threshold will be set to it. A missing
+            threshold (e.g `NA`) will not clip the value.
+        upper : float or array-like, default None
+            Maximum threshold value. All values above this
+            threshold will be set to it. A missing
+            threshold (e.g `NA`) will not clip the value.
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Align object with lower and upper along the given axis.
+            For `Series` this parameter is unused and defaults to `None`.
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+        *args, **kwargs
+            Additional keywords have no effect but might be accepted
+            for compatibility with numpy.
+
+        Returns
+        -------
+        Series or DataFrame or None
+            Same type as calling object with the values outside the
+            clip boundaries replaced or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.clip : Trim values at input threshold in series.
+        DataFrame.clip : Trim values at input threshold in dataframe.
+        numpy.clip : Clip (limit) the values in an array.
+
+        Examples
+        --------
+        >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
+        >>> df = pd.DataFrame(data)
+        >>> df
+           col_0  col_1
+        0      9     -2
+        1     -3     -7
+        2      0      6
+        3     -1      8
+        4      5     -5
+
+        Clips per column using lower and upper thresholds:
+
+        >>> df.clip(-4, 6)
+           col_0  col_1
+        0      6     -2
+        1     -3     -4
+        2      0      6
+        3     -1      6
+        4      5     -4
+
+        Clips using specific lower and upper thresholds per column:
+
+        >>> df.clip([-2, -1], [4, 5])
+            col_0  col_1
+        0      4     -1
+        1     -2     -1
+        2      0      5
+        3     -1      5
+        4      4     -1
+
+        Clips using specific lower and upper thresholds per column element:
+
+        >>> t = pd.Series([2, -4, -1, 6, 3])
+        >>> t
+        0    2
+        1   -4
+        2   -1
+        3    6
+        4    3
+        dtype: int64
+
+        >>> df.clip(t, t + 4, axis=0)
+           col_0  col_1
+        0      6      2
+        1     -3     -4
+        2      0      3
+        3      6      8
+        4      5      3
+
+        Clips using specific lower threshold per column element, with missing values:
+
+        >>> t = pd.Series([2, -4, np.nan, 6, 3])
+        >>> t
+        0    2.0
+        1   -4.0
+        2    NaN
+        3    6.0
+        4    3.0
+        dtype: float64
+
+        >>> df.clip(t, axis=0)
+        col_0  col_1
+        0      9      2
+        1     -3     -4
+        2      0      6
+        3      6      8
+        4      5      3
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and hasattr(self, "_cacher"):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        axis = nv.validate_clip_with_axis(axis, (), kwargs)
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        # GH 17276
+        # numpy doesn't like NaN as a clip value
+        # so ignore
+        # GH 19992
+        # numpy doesn't drop a list-like bound containing NaN
+        isna_lower = isna(lower)
+        if not is_list_like(lower):
+            if np.any(isna_lower):
+                lower = None
+        elif np.all(isna_lower):
+            lower = None
+        isna_upper = isna(upper)
+        if not is_list_like(upper):
+            if np.any(isna_upper):
+                upper = None
+        elif np.all(isna_upper):
+            upper = None
+
+        # GH 2747 (arguments were reversed)
+        if (
+            lower is not None
+            and upper is not None
+            and is_scalar(lower)
+            and is_scalar(upper)
+        ):
+            lower, upper = min(lower, upper), max(lower, upper)
+
+        # fast-path for scalars
+        if (lower is None or is_number(lower)) and (upper is None or is_number(upper)):
+            return self._clip_with_scalar(lower, upper, inplace=inplace)
+
+        result = self
+        if lower is not None:
+            result = result._clip_with_one_bound(
+                lower, method=self.ge, axis=axis, inplace=inplace
+            )
+        if upper is not None:
+            if inplace:
+                result = self
+            result = result._clip_with_one_bound(
+                upper, method=self.le, axis=axis, inplace=inplace
+            )
+
+        return result
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def asfreq(
+        self,
+        freq: Frequency,
+        method: FillnaOptions | None = None,
+        how: Literal["start", "end"] | None = None,
+        normalize: bool_t = False,
+        fill_value: Hashable | None = None,
+    ) -> Self:
+        """
+        Convert time series to specified frequency.
+
+        Returns the original data conformed to a new index with the specified
+        frequency.
+
+        If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index
+        is the result of transforming the original index with
+        :meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index
+        will map one-to-one to the new index).
+
+        Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
+        freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
+        last entries in the original index (see :func:`pandas.date_range`). The
+        values corresponding to any timesteps in the new index which were not present
+        in the original index will be null (``NaN``), unless a method for filling
+        such unknowns is provided (see the ``method`` parameter below).
+
+        The :meth:`resample` method is more appropriate if an operation on each group of
+        timesteps (such as an aggregate) is necessary to represent the data at the new
+        frequency.
+
+        Parameters
+        ----------
+        freq : DateOffset or str
+            Frequency DateOffset or string.
+        method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None
+            Method to use for filling holes in reindexed Series (note this
+            does not fill NaNs that already were present):
+
+            * 'pad' / 'ffill': propagate last valid observation forward to next
+              valid
+            * 'backfill' / 'bfill': use NEXT valid observation to fill.
+        how : {{'start', 'end'}}, default end
+            For PeriodIndex only (see PeriodIndex.asfreq).
+        normalize : bool, default False
+            Whether to reset output index to midnight.
+        fill_value : scalar, optional
+            Value to use for missing values, applied during upsampling (note
+            this does not fill NaNs that already were present).
+
+        Returns
+        -------
+        {klass}
+            {klass} object reindexed to the specified frequency.
+
+        See Also
+        --------
+        reindex : Conform DataFrame to new index with optional filling logic.
+
+        Notes
+        -----
+        To learn more about the frequency strings, please see `this link
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+        Examples
+        --------
+        Start by creating a series with 4 one minute timestamps.
+
+        >>> index = pd.date_range('1/1/2000', periods=4, freq='min')
+        >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
+        >>> df = pd.DataFrame({{'s': series}})
+        >>> df
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:03:00    3.0
+
+        Upsample the series into 30 second bins.
+
+        >>> df.asfreq(freq='30s')
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:00:30    NaN
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:01:30    NaN
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:02:30    NaN
+        2000-01-01 00:03:00    3.0
+
+        Upsample again, providing a ``fill value``.
+
+        >>> df.asfreq(freq='30s', fill_value=9.0)
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:00:30    9.0
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:01:30    9.0
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:02:30    9.0
+        2000-01-01 00:03:00    3.0
+
+        Upsample again, providing a ``method``.
+
+        >>> df.asfreq(freq='30s', method='bfill')
+                               s
+        2000-01-01 00:00:00    0.0
+        2000-01-01 00:00:30    NaN
+        2000-01-01 00:01:00    NaN
+        2000-01-01 00:01:30    2.0
+        2000-01-01 00:02:00    2.0
+        2000-01-01 00:02:30    3.0
+        2000-01-01 00:03:00    3.0
+        """
+        from pandas.core.resample import asfreq
+
+        return asfreq(
+            self,
+            freq,
+            method=method,
+            how=how,
+            normalize=normalize,
+            fill_value=fill_value,
+        )
+
+    @final
+    def at_time(self, time, asof: bool_t = False, axis: Axis | None = None) -> Self:
+        """
+        Select values at particular time of day (e.g., 9:30AM).
+
+        Parameters
+        ----------
+        time : datetime.time or str
+            The values to select.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            For `Series` this parameter is unused and defaults to 0.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        between_time : Select values between particular times of the day.
+        first : Select initial periods of time series based on a date offset.
+        last : Select final periods of time series based on a date offset.
+        DatetimeIndex.indexer_at_time : Get just the index locations for
+            values at particular time of the day.
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='12h')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                             A
+        2018-04-09 00:00:00  1
+        2018-04-09 12:00:00  2
+        2018-04-10 00:00:00  3
+        2018-04-10 12:00:00  4
+
+        >>> ts.at_time('12:00')
+                             A
+        2018-04-09 12:00:00  2
+        2018-04-10 12:00:00  4
+        """
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+
+        index = self._get_axis(axis)
+
+        if not isinstance(index, DatetimeIndex):
+            raise TypeError("Index must be DatetimeIndex")
+
+        indexer = index.indexer_at_time(time, asof=asof)
+        return self._take_with_is_copy(indexer, axis=axis)
+
+    @final
+    def between_time(
+        self,
+        start_time,
+        end_time,
+        inclusive: IntervalClosedType = "both",
+        axis: Axis | None = None,
+    ) -> Self:
+        """
+        Select values between particular times of the day (e.g., 9:00-9:30 AM).
+
+        By setting ``start_time`` to be later than ``end_time``,
+        you can get the times that are *not* between the two times.
+
+        Parameters
+        ----------
+        start_time : datetime.time or str
+            Initial time as a time filter limit.
+        end_time : datetime.time or str
+            End time as a time filter limit.
+        inclusive : {"both", "neither", "left", "right"}, default "both"
+            Include boundaries; whether to set each bound as closed or open.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Determine range time on index or columns value.
+            For `Series` this parameter is unused and defaults to 0.
+
+        Returns
+        -------
+        Series or DataFrame
+            Data from the original object filtered to the specified dates range.
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        at_time : Select values at a particular time of the day.
+        first : Select initial periods of time series based on a date offset.
+        last : Select final periods of time series based on a date offset.
+        DatetimeIndex.indexer_between_time : Get just the index locations for
+            values between particular times of the day.
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                             A
+        2018-04-09 00:00:00  1
+        2018-04-10 00:20:00  2
+        2018-04-11 00:40:00  3
+        2018-04-12 01:00:00  4
+
+        >>> ts.between_time('0:15', '0:45')
+                             A
+        2018-04-10 00:20:00  2
+        2018-04-11 00:40:00  3
+
+        You get the times that are *not* between two times by setting
+        ``start_time`` later than ``end_time``:
+
+        >>> ts.between_time('0:45', '0:15')
+                             A
+        2018-04-09 00:00:00  1
+        2018-04-12 01:00:00  4
+        """
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+
+        index = self._get_axis(axis)
+        if not isinstance(index, DatetimeIndex):
+            raise TypeError("Index must be DatetimeIndex")
+
+        left_inclusive, right_inclusive = validate_inclusive(inclusive)
+        indexer = index.indexer_between_time(
+            start_time,
+            end_time,
+            include_start=left_inclusive,
+            include_end=right_inclusive,
+        )
+        return self._take_with_is_copy(indexer, axis=axis)
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def resample(
+        self,
+        rule,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        closed: Literal["right", "left"] | None = None,
+        label: Literal["right", "left"] | None = None,
+        convention: Literal["start", "end", "s", "e"] = "start",
+        kind: Literal["timestamp", "period"] | None | lib.NoDefault = lib.no_default,
+        on: Level | None = None,
+        level: Level | None = None,
+        origin: str | TimestampConvertibleTypes = "start_day",
+        offset: TimedeltaConvertibleTypes | None = None,
+        group_keys: bool_t = False,
+    ) -> Resampler:
+        """
+        Resample time-series data.
+
+        Convenience method for frequency conversion and resampling of time series.
+        The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`,
+        or `TimedeltaIndex`), or the caller must pass the label of a datetime-like
+        series/index to the ``on``/``level`` keyword parameter.
+
+        Parameters
+        ----------
+        rule : DateOffset, Timedelta or str
+            The offset string or object representing target conversion.
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            Which axis to use for up- or down-sampling. For `Series` this parameter
+            is unused and defaults to 0. Must be
+            `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
+
+            .. deprecated:: 2.0.0
+                Use frame.T.resample(...) instead.
+        closed : {{'right', 'left'}}, default None
+            Which side of bin interval is closed. The default is 'left'
+            for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
+            'BA', 'BQE', and 'W' which all have a default of 'right'.
+        label : {{'right', 'left'}}, default None
+            Which bin edge label to label bucket with. The default is 'left'
+            for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
+            'BA', 'BQE', and 'W' which all have a default of 'right'.
+        convention : {{'start', 'end', 's', 'e'}}, default 'start'
+            For `PeriodIndex` only, controls whether to use the start or
+            end of `rule`.
+
+        kind : {{'timestamp', 'period'}}, optional, default None
+            Pass 'timestamp' to convert the resulting index to a
+            `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
+            By default the input representation is retained.
+
+            .. deprecated:: 2.2.0
+                Convert index to desired type explicitly instead.
+
+        on : str, optional
+            For a DataFrame, column to use instead of index for resampling.
+            Column must be datetime-like.
+        level : str or int, optional
+            For a MultiIndex, level (name or number) to use for
+            resampling. `level` must be datetime-like.
+        origin : Timestamp or str, default 'start_day'
+            The timestamp on which to adjust the grouping. The timezone of origin
+            must match the timezone of the index.
+            If string, must be one of the following:
+
+            - 'epoch': `origin` is 1970-01-01
+            - 'start': `origin` is the first value of the timeseries
+            - 'start_day': `origin` is the first day at midnight of the timeseries
+
+            - 'end': `origin` is the last value of the timeseries
+            - 'end_day': `origin` is the ceiling midnight of the last day
+
+            .. versionadded:: 1.3.0
+
+            .. note::
+
+                Only takes effect for Tick-frequencies (i.e. fixed frequencies like
+                days, hours, and minutes, rather than months or quarters).
+        offset : Timedelta or str, default is None
+            An offset timedelta added to the origin.
+
+        group_keys : bool, default False
+            Whether to include the group keys in the result index when using
+            ``.apply()`` on the resampled object.
+
+            .. versionadded:: 1.5.0
+
+                Not specifying ``group_keys`` will retain values-dependent behavior
+                from pandas 1.4 and earlier (see :ref:`pandas 1.5.0 Release notes
+                <whatsnew_150.enhancements.resample_group_keys>` for examples).
+
+            .. versionchanged:: 2.0.0
+
+                ``group_keys`` now defaults to ``False``.
+
+        Returns
+        -------
+        pandas.api.typing.Resampler
+            :class:`~pandas.core.Resampler` object.
+
+        See Also
+        --------
+        Series.resample : Resample a Series.
+        DataFrame.resample : Resample a DataFrame.
+        groupby : Group {klass} by mapping, function, label, or list of labels.
+        asfreq : Reindex a {klass} with the given frequency without grouping.
+
+        Notes
+        -----
+        See the `user guide
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`__
+        for more.
+
+        To learn more about the offset strings, please see `this link
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__.
+
+        Examples
+        --------
+        Start by creating a series with 9 one minute timestamps.
+
+        >>> index = pd.date_range('1/1/2000', periods=9, freq='min')
+        >>> series = pd.Series(range(9), index=index)
+        >>> series
+        2000-01-01 00:00:00    0
+        2000-01-01 00:01:00    1
+        2000-01-01 00:02:00    2
+        2000-01-01 00:03:00    3
+        2000-01-01 00:04:00    4
+        2000-01-01 00:05:00    5
+        2000-01-01 00:06:00    6
+        2000-01-01 00:07:00    7
+        2000-01-01 00:08:00    8
+        Freq: min, dtype: int64
+
+        Downsample the series into 3 minute bins and sum the values
+        of the timestamps falling into a bin.
+
+        >>> series.resample('3min').sum()
+        2000-01-01 00:00:00     3
+        2000-01-01 00:03:00    12
+        2000-01-01 00:06:00    21
+        Freq: 3min, dtype: int64
+
+        Downsample the series into 3 minute bins as above, but label each
+        bin using the right edge instead of the left. Please note that the
+        value in the bucket used as the label is not included in the bucket,
+        which it labels. For example, in the original series the
+        bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
+        value in the resampled bucket with the label ``2000-01-01 00:03:00``
+        does not include 3 (if it did, the summed value would be 6, not 3).
+
+        >>> series.resample('3min', label='right').sum()
+        2000-01-01 00:03:00     3
+        2000-01-01 00:06:00    12
+        2000-01-01 00:09:00    21
+        Freq: 3min, dtype: int64
+
+        To include this value close the right side of the bin interval,
+        as shown below.
+
+        >>> series.resample('3min', label='right', closed='right').sum()
+        2000-01-01 00:00:00     0
+        2000-01-01 00:03:00     6
+        2000-01-01 00:06:00    15
+        2000-01-01 00:09:00    15
+        Freq: 3min, dtype: int64
+
+        Upsample the series into 30 second bins.
+
+        >>> series.resample('30s').asfreq()[0:5]   # Select first 5 rows
+        2000-01-01 00:00:00   0.0
+        2000-01-01 00:00:30   NaN
+        2000-01-01 00:01:00   1.0
+        2000-01-01 00:01:30   NaN
+        2000-01-01 00:02:00   2.0
+        Freq: 30s, dtype: float64
+
+        Upsample the series into 30 second bins and fill the ``NaN``
+        values using the ``ffill`` method.
+
+        >>> series.resample('30s').ffill()[0:5]
+        2000-01-01 00:00:00    0
+        2000-01-01 00:00:30    0
+        2000-01-01 00:01:00    1
+        2000-01-01 00:01:30    1
+        2000-01-01 00:02:00    2
+        Freq: 30s, dtype: int64
+
+        Upsample the series into 30 second bins and fill the
+        ``NaN`` values using the ``bfill`` method.
+
+        >>> series.resample('30s').bfill()[0:5]
+        2000-01-01 00:00:00    0
+        2000-01-01 00:00:30    1
+        2000-01-01 00:01:00    1
+        2000-01-01 00:01:30    2
+        2000-01-01 00:02:00    2
+        Freq: 30s, dtype: int64
+
+        Pass a custom function via ``apply``
+
+        >>> def custom_resampler(arraylike):
+        ...     return np.sum(arraylike) + 5
+        ...
+        >>> series.resample('3min').apply(custom_resampler)
+        2000-01-01 00:00:00     8
+        2000-01-01 00:03:00    17
+        2000-01-01 00:06:00    26
+        Freq: 3min, dtype: int64
+
+        For a Series with a PeriodIndex, the keyword `convention` can be
+        used to control whether to use the start or end of `rule`.
+
+        Resample a year by quarter using 'start' `convention`. Values are
+        assigned to the first quarter of the period.
+
+        >>> s = pd.Series(
+        ...     [1, 2], index=pd.period_range("2012-01-01", freq="Y", periods=2)
+        ... )
+        >>> s
+        2012    1
+        2013    2
+        Freq: Y-DEC, dtype: int64
+        >>> s.resample("Q", convention="start").asfreq()
+        2012Q1    1.0
+        2012Q2    NaN
+        2012Q3    NaN
+        2012Q4    NaN
+        2013Q1    2.0
+        2013Q2    NaN
+        2013Q3    NaN
+        2013Q4    NaN
+        Freq: Q-DEC, dtype: float64
+
+        Resample quarters by month using 'end' `convention`. Values are
+        assigned to the last month of the period.
+
+        >>> q = pd.Series(
+        ...     [1, 2, 3, 4], index=pd.period_range("2018-01-01", freq="Q", periods=4)
+        ... )
+        >>> q
+        2018Q1    1
+        2018Q2    2
+        2018Q3    3
+        2018Q4    4
+        Freq: Q-DEC, dtype: int64
+        >>> q.resample("M", convention="end").asfreq()
+        2018-03    1.0
+        2018-04    NaN
+        2018-05    NaN
+        2018-06    2.0
+        2018-07    NaN
+        2018-08    NaN
+        2018-09    3.0
+        2018-10    NaN
+        2018-11    NaN
+        2018-12    4.0
+        Freq: M, dtype: float64
+
+        For DataFrame objects, the keyword `on` can be used to specify the
+        column instead of the index for resampling.
+
+        >>> d = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
+        ...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
+        >>> df = pd.DataFrame(d)
+        >>> df['week_starting'] = pd.date_range('01/01/2018',
+        ...                                     periods=8,
+        ...                                     freq='W')
+        >>> df
+           price  volume week_starting
+        0     10      50    2018-01-07
+        1     11      60    2018-01-14
+        2      9      40    2018-01-21
+        3     13     100    2018-01-28
+        4     14      50    2018-02-04
+        5     18     100    2018-02-11
+        6     17      40    2018-02-18
+        7     19      50    2018-02-25
+        >>> df.resample('ME', on='week_starting').mean()
+                       price  volume
+        week_starting
+        2018-01-31     10.75    62.5
+        2018-02-28     17.00    60.0
+
+        For a DataFrame with MultiIndex, the keyword `level` can be used to
+        specify on which level the resampling needs to take place.
+
+        >>> days = pd.date_range('1/1/2000', periods=4, freq='D')
+        >>> d2 = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
+        ...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
+        >>> df2 = pd.DataFrame(
+        ...     d2,
+        ...     index=pd.MultiIndex.from_product(
+        ...         [days, ['morning', 'afternoon']]
+        ...     )
+        ... )
+        >>> df2
+                              price  volume
+        2000-01-01 morning       10      50
+                   afternoon     11      60
+        2000-01-02 morning        9      40
+                   afternoon     13     100
+        2000-01-03 morning       14      50
+                   afternoon     18     100
+        2000-01-04 morning       17      40
+                   afternoon     19      50
+        >>> df2.resample('D', level=0).sum()
+                    price  volume
+        2000-01-01     21     110
+        2000-01-02     22     140
+        2000-01-03     32     150
+        2000-01-04     36      90
+
+        If you want to adjust the start of the bins based on a fixed timestamp:
+
+        >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
+        >>> rng = pd.date_range(start, end, freq='7min')
+        >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
+        >>> ts
+        2000-10-01 23:30:00     0
+        2000-10-01 23:37:00     3
+        2000-10-01 23:44:00     6
+        2000-10-01 23:51:00     9
+        2000-10-01 23:58:00    12
+        2000-10-02 00:05:00    15
+        2000-10-02 00:12:00    18
+        2000-10-02 00:19:00    21
+        2000-10-02 00:26:00    24
+        Freq: 7min, dtype: int64
+
+        >>> ts.resample('17min').sum()
+        2000-10-01 23:14:00     0
+        2000-10-01 23:31:00     9
+        2000-10-01 23:48:00    21
+        2000-10-02 00:05:00    54
+        2000-10-02 00:22:00    24
+        Freq: 17min, dtype: int64
+
+        >>> ts.resample('17min', origin='epoch').sum()
+        2000-10-01 23:18:00     0
+        2000-10-01 23:35:00    18
+        2000-10-01 23:52:00    27
+        2000-10-02 00:09:00    39
+        2000-10-02 00:26:00    24
+        Freq: 17min, dtype: int64
+
+        >>> ts.resample('17min', origin='2000-01-01').sum()
+        2000-10-01 23:24:00     3
+        2000-10-01 23:41:00    15
+        2000-10-01 23:58:00    45
+        2000-10-02 00:15:00    45
+        Freq: 17min, dtype: int64
+
+        If you want to adjust the start of the bins with an `offset` Timedelta, the two
+        following lines are equivalent:
+
+        >>> ts.resample('17min', origin='start').sum()
+        2000-10-01 23:30:00     9
+        2000-10-01 23:47:00    21
+        2000-10-02 00:04:00    54
+        2000-10-02 00:21:00    24
+        Freq: 17min, dtype: int64
+
+        >>> ts.resample('17min', offset='23h30min').sum()
+        2000-10-01 23:30:00     9
+        2000-10-01 23:47:00    21
+        2000-10-02 00:04:00    54
+        2000-10-02 00:21:00    24
+        Freq: 17min, dtype: int64
+
+        If you want to take the largest Timestamp as the end of the bins:
+
+        >>> ts.resample('17min', origin='end').sum()
+        2000-10-01 23:35:00     0
+        2000-10-01 23:52:00    18
+        2000-10-02 00:09:00    27
+        2000-10-02 00:26:00    63
+        Freq: 17min, dtype: int64
+
+        In contrast with the `start_day`, you can use `end_day` to take the ceiling
+        midnight of the largest Timestamp as the end of the bins and drop the bins
+        not containing data:
+
+        >>> ts.resample('17min', origin='end_day').sum()
+        2000-10-01 23:38:00     3
+        2000-10-01 23:55:00    15
+        2000-10-02 00:12:00    45
+        2000-10-02 00:29:00    45
+        Freq: 17min, dtype: int64
+        """
+        from pandas.core.resample import get_resampler
+
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            if axis == 1:
+                warnings.warn(
+                    "DataFrame.resample with axis=1 is deprecated. Do "
+                    "`frame.T.resample(...)` without axis instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.resample is "
+                    "deprecated and will be removed in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        if kind is not lib.no_default:
+            # GH#55895
+            warnings.warn(
+                f"The 'kind' keyword in {type(self).__name__}.resample is "
+                "deprecated and will be removed in a future version. "
+                "Explicitly cast the index to the desired type instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            kind = None
+
+        return get_resampler(
+            cast("Series | DataFrame", self),
+            freq=rule,
+            label=label,
+            closed=closed,
+            axis=axis,
+            kind=kind,
+            convention=convention,
+            key=on,
+            level=level,
+            origin=origin,
+            offset=offset,
+            group_keys=group_keys,
+        )
+
+    @final
+    def first(self, offset) -> Self:
+        """
+        Select initial periods of time series data based on a date offset.
+
+        .. deprecated:: 2.1
+            :meth:`.first` is deprecated and will be removed in a future version.
+            Please create a mask and filter using `.loc` instead.
+
+        For a DataFrame with a sorted DatetimeIndex, this function can
+        select the first few rows based on a date offset.
+
+        Parameters
+        ----------
+        offset : str, DateOffset or dateutil.relativedelta
+            The offset length of the data that will be selected. For instance,
+            '1ME' will display all the rows having their index within the first month.
+
+        Returns
+        -------
+        Series or DataFrame
+            A subset of the caller.
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        last : Select final periods of time series based on a date offset.
+        at_time : Select values at a particular time of the day.
+        between_time : Select values between particular times of the day.
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        2018-04-13  3
+        2018-04-15  4
+
+        Get the rows for the first 3 days:
+
+        >>> ts.first('3D')
+                    A
+        2018-04-09  1
+        2018-04-11  2
+
+        Notice the data for 3 first calendar days were returned, not the first
+        3 days observed in the dataset, and therefore data for 2018-04-13 was
+        not returned.
+        """
+        warnings.warn(
+            "first is deprecated and will be removed in a future version. "
+            "Please create a mask and filter using `.loc` instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        if not isinstance(self.index, DatetimeIndex):
+            raise TypeError("'first' only supports a DatetimeIndex index")
+
+        if len(self.index) == 0:
+            return self.copy(deep=False)
+
+        offset = to_offset(offset)
+        if not isinstance(offset, Tick) and offset.is_on_offset(self.index[0]):
+            # GH#29623 if first value is end of period, remove offset with n = 1
+            #  before adding the real offset
+            end_date = end = self.index[0] - offset.base + offset
+        else:
+            end_date = end = self.index[0] + offset
+
+        # Tick-like, e.g. 3 weeks
+        if isinstance(offset, Tick) and end_date in self.index:
+            end = self.index.searchsorted(end_date, side="left")
+            return self.iloc[:end]
+
+        return self.loc[:end]
+
+    @final
+    def last(self, offset) -> Self:
+        """
+        Select final periods of time series data based on a date offset.
+
+        .. deprecated:: 2.1
+            :meth:`.last` is deprecated and will be removed in a future version.
+            Please create a mask and filter using `.loc` instead.
+
+        For a DataFrame with a sorted DatetimeIndex, this function
+        selects the last few rows based on a date offset.
+
+        Parameters
+        ----------
+        offset : str, DateOffset, dateutil.relativedelta
+            The offset length of the data that will be selected. For instance,
+            '3D' will display all the rows having their index within the last 3 days.
+
+        Returns
+        -------
+        Series or DataFrame
+            A subset of the caller.
+
+        Raises
+        ------
+        TypeError
+            If the index is not  a :class:`DatetimeIndex`
+
+        See Also
+        --------
+        first : Select initial periods of time series based on a date offset.
+        at_time : Select values at a particular time of the day.
+        between_time : Select values between particular times of the day.
+
+        Notes
+        -----
+        .. deprecated:: 2.1.0
+            Please create a mask and filter using `.loc` instead
+
+        Examples
+        --------
+        >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
+        >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        2018-04-13  3
+        2018-04-15  4
+
+        Get the rows for the last 3 days:
+
+        >>> ts.last('3D')  # doctest: +SKIP
+                    A
+        2018-04-13  3
+        2018-04-15  4
+
+        Notice the data for 3 last calendar days were returned, not the last
+        3 observed days in the dataset, and therefore data for 2018-04-11 was
+        not returned.
+        """
+        warnings.warn(
+            "last is deprecated and will be removed in a future version. "
+            "Please create a mask and filter using `.loc` instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        if not isinstance(self.index, DatetimeIndex):
+            raise TypeError("'last' only supports a DatetimeIndex index")
+
+        if len(self.index) == 0:
+            return self.copy(deep=False)
+
+        offset = to_offset(offset)
+
+        start_date = self.index[-1] - offset
+        start = self.index.searchsorted(start_date, side="right")
+        return self.iloc[start:]
+
+    @final
+    def rank(
+        self,
+        axis: Axis = 0,
+        method: Literal["average", "min", "max", "first", "dense"] = "average",
+        numeric_only: bool_t = False,
+        na_option: Literal["keep", "top", "bottom"] = "keep",
+        ascending: bool_t = True,
+        pct: bool_t = False,
+    ) -> Self:
+        """
+        Compute numerical data ranks (1 through n) along axis.
+
+        By default, equal values are assigned a rank that is the average of the
+        ranks of those values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Index to direct ranking.
+            For `Series` this parameter is unused and defaults to 0.
+        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+            * first: ranks assigned in order they appear in the array
+            * dense: like 'min', but rank always increases by 1 between groups.
+
+        numeric_only : bool, default False
+            For DataFrame objects, rank only numeric columns if set to True.
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+            How to rank NaN values:
+
+            * keep: assign NaN rank to NaN values
+            * top: assign lowest rank to NaN values
+            * bottom: assign highest rank to NaN values
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+
+        Returns
+        -------
+        same type as caller
+            Return a Series or DataFrame with data ranks as values.
+
+        See Also
+        --------
+        core.groupby.DataFrameGroupBy.rank : Rank of values within each group.
+        core.groupby.SeriesGroupBy.rank : Rank of values within each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
+        ...                                    'spider', 'snake'],
+        ...                         'Number_legs': [4, 2, 4, 8, np.nan]})
+        >>> df
+            Animal  Number_legs
+        0      cat          4.0
+        1  penguin          2.0
+        2      dog          4.0
+        3   spider          8.0
+        4    snake          NaN
+
+        Ties are assigned the mean of the ranks (by default) for the group.
+
+        >>> s = pd.Series(range(5), index=list("abcde"))
+        >>> s["d"] = s["b"]
+        >>> s.rank()
+        a    1.0
+        b    2.5
+        c    4.0
+        d    2.5
+        e    5.0
+        dtype: float64
+
+        The following example shows how the method behaves with the above
+        parameters:
+
+        * default_rank: this is the default behaviour obtained without using
+          any parameter.
+        * max_rank: setting ``method = 'max'`` the records that have the
+          same values are ranked using the highest rank (e.g.: since 'cat'
+          and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
+        * NA_bottom: choosing ``na_option = 'bottom'``, if there are records
+          with NaN values they are placed at the bottom of the ranking.
+        * pct_rank: when setting ``pct = True``, the ranking is expressed as
+          percentile rank.
+
+        >>> df['default_rank'] = df['Number_legs'].rank()
+        >>> df['max_rank'] = df['Number_legs'].rank(method='max')
+        >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
+        >>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
+        >>> df
+            Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
+        0      cat          4.0           2.5       3.0        2.5     0.625
+        1  penguin          2.0           1.0       1.0        1.0     0.250
+        2      dog          4.0           2.5       3.0        2.5     0.625
+        3   spider          8.0           4.0       4.0        4.0     1.000
+        4    snake          NaN           NaN       NaN        5.0       NaN
+        """
+        axis_int = self._get_axis_number(axis)
+
+        if na_option not in {"keep", "top", "bottom"}:
+            msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+            raise ValueError(msg)
+
+        def ranker(data):
+            if data.ndim == 2:
+                # i.e. DataFrame, we cast to ndarray
+                values = data.values
+            else:
+                # i.e. Series, can dispatch to EA
+                values = data._values
+
+            if isinstance(values, ExtensionArray):
+                ranks = values._rank(
+                    axis=axis_int,
+                    method=method,
+                    ascending=ascending,
+                    na_option=na_option,
+                    pct=pct,
+                )
+            else:
+                ranks = algos.rank(
+                    values,
+                    axis=axis_int,
+                    method=method,
+                    ascending=ascending,
+                    na_option=na_option,
+                    pct=pct,
+                )
+
+            ranks_obj = self._constructor(ranks, **data._construct_axes_dict())
+            return ranks_obj.__finalize__(self, method="rank")
+
+        if numeric_only:
+            if self.ndim == 1 and not is_numeric_dtype(self.dtype):
+                # GH#47500
+                raise TypeError(
+                    "Series.rank does not allow numeric_only=True with "
+                    "non-numeric dtype."
+                )
+            data = self._get_numeric_data()
+        else:
+            data = self
+
+        return ranker(data)
+
+    @doc(_shared_docs["compare"], klass=_shared_doc_kwargs["klass"])
+    def compare(
+        self,
+        other,
+        align_axis: Axis = 1,
+        keep_shape: bool_t = False,
+        keep_equal: bool_t = False,
+        result_names: Suffixes = ("self", "other"),
+    ):
+        if type(self) is not type(other):
+            cls_self, cls_other = type(self).__name__, type(other).__name__
+            raise TypeError(
+                f"can only compare '{cls_self}' (not '{cls_other}') with '{cls_self}'"
+            )
+
+        mask = ~((self == other) | (self.isna() & other.isna()))
+        mask.fillna(True, inplace=True)
+
+        if not keep_equal:
+            self = self.where(mask)
+            other = other.where(mask)
+
+        if not keep_shape:
+            if isinstance(self, ABCDataFrame):
+                cmask = mask.any()
+                rmask = mask.any(axis=1)
+                self = self.loc[rmask, cmask]
+                other = other.loc[rmask, cmask]
+            else:
+                self = self[mask]
+                other = other[mask]
+        if not isinstance(result_names, tuple):
+            raise TypeError(
+                f"Passing 'result_names' as a {type(result_names)} is not "
+                "supported. Provide 'result_names' as a tuple instead."
+            )
+
+        if align_axis in (1, "columns"):  # This is needed for Series
+            axis = 1
+        else:
+            axis = self._get_axis_number(align_axis)
+
+        # error: List item 0 has incompatible type "NDFrame"; expected
+        #  "Union[Series, DataFrame]"
+        diff = concat(
+            [self, other],  # type: ignore[list-item]
+            axis=axis,
+            keys=result_names,
+        )
+
+        if axis >= self.ndim:
+            # No need to reorganize data if stacking on new axis
+            # This currently applies for stacking two Series on columns
+            return diff
+
+        ax = diff._get_axis(axis)
+        ax_names = np.array(ax.names)
+
+        # set index names to positions to avoid confusion
+        ax.names = np.arange(len(ax_names))
+
+        # bring self-other to inner level
+        order = list(range(1, ax.nlevels)) + [0]
+        if isinstance(diff, ABCDataFrame):
+            diff = diff.reorder_levels(order, axis=axis)
+        else:
+            diff = diff.reorder_levels(order)
+
+        # restore the index names in order
+        diff._get_axis(axis=axis).names = ax_names[order]
+
+        # reorder axis to keep things organized
+        indices = (
+            np.arange(diff.shape[axis]).reshape([2, diff.shape[axis] // 2]).T.flatten()
+        )
+        diff = diff.take(indices, axis=axis)
+
+        return diff
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+    )
+    def align(
+        self,
+        other: NDFrameT,
+        join: AlignJoin = "outer",
+        axis: Axis | None = None,
+        level: Level | None = None,
+        copy: bool_t | None = None,
+        fill_value: Hashable | None = None,
+        method: FillnaOptions | None | lib.NoDefault = lib.no_default,
+        limit: int | None | lib.NoDefault = lib.no_default,
+        fill_axis: Axis | lib.NoDefault = lib.no_default,
+        broadcast_axis: Axis | None | lib.NoDefault = lib.no_default,
+    ) -> tuple[Self, NDFrameT]:
+        """
+        Align two objects on their axes with the specified join method.
+
+        Join method is specified for each axis Index.
+
+        Parameters
+        ----------
+        other : DataFrame or Series
+        join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
+            Type of alignment to be performed.
+
+            * left: use only keys from left frame, preserve key order.
+            * right: use only keys from right frame, preserve key order.
+            * outer: use union of keys from both frames, sort keys lexicographically.
+            * inner: use intersection of keys from both frames,
+              preserve the order of the left keys.
+
+        axis : allowed axis of the other object, default None
+            Align on index (0), columns (1), or both (None).
+        level : int or level name, default None
+            Broadcast across a level, matching Index values on the
+            passed MultiIndex level.
+        copy : bool, default True
+            Always returns new objects. If copy=False and no reindexing is
+            required then original objects are returned.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        fill_value : scalar, default np.nan
+            Value to use for missing values. Defaults to NaN, but can be any
+            "compatible" value.
+        method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
+            Method to use for filling holes in reindexed Series:
+
+            - pad / ffill: propagate last valid observation forward to next valid.
+            - backfill / bfill: use NEXT valid observation to fill gap.
+
+            .. deprecated:: 2.1
+
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+
+            .. deprecated:: 2.1
+
+        fill_axis : {axes_single_arg}, default 0
+            Filling axis, method and limit.
+
+            .. deprecated:: 2.1
+
+        broadcast_axis : {axes_single_arg}, default None
+            Broadcast values along this axis, if aligning two objects of
+            different dimensions.
+
+            .. deprecated:: 2.1
+
+        Returns
+        -------
+        tuple of ({klass}, type of other)
+            Aligned objects.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
+        ... )
+        >>> other = pd.DataFrame(
+        ...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
+        ...     columns=["A", "B", "C", "D"],
+        ...     index=[2, 3, 4],
+        ... )
+        >>> df
+           D  B  E  A
+        1  1  2  3  4
+        2  6  7  8  9
+        >>> other
+            A    B    C    D
+        2   10   20   30   40
+        3   60   70   80   90
+        4  600  700  800  900
+
+        Align on columns:
+
+        >>> left, right = df.align(other, join="outer", axis=1)
+        >>> left
+           A  B   C  D  E
+        1  4  2 NaN  1  3
+        2  9  7 NaN  6  8
+        >>> right
+            A    B    C    D   E
+        2   10   20   30   40 NaN
+        3   60   70   80   90 NaN
+        4  600  700  800  900 NaN
+
+        We can also align on the index:
+
+        >>> left, right = df.align(other, join="outer", axis=0)
+        >>> left
+            D    B    E    A
+        1  1.0  2.0  3.0  4.0
+        2  6.0  7.0  8.0  9.0
+        3  NaN  NaN  NaN  NaN
+        4  NaN  NaN  NaN  NaN
+        >>> right
+            A      B      C      D
+        1    NaN    NaN    NaN    NaN
+        2   10.0   20.0   30.0   40.0
+        3   60.0   70.0   80.0   90.0
+        4  600.0  700.0  800.0  900.0
+
+        Finally, the default `axis=None` will align on both index and columns:
+
+        >>> left, right = df.align(other, join="outer", axis=None)
+        >>> left
+             A    B   C    D    E
+        1  4.0  2.0 NaN  1.0  3.0
+        2  9.0  7.0 NaN  6.0  8.0
+        3  NaN  NaN NaN  NaN  NaN
+        4  NaN  NaN NaN  NaN  NaN
+        >>> right
+               A      B      C      D   E
+        1    NaN    NaN    NaN    NaN NaN
+        2   10.0   20.0   30.0   40.0 NaN
+        3   60.0   70.0   80.0   90.0 NaN
+        4  600.0  700.0  800.0  900.0 NaN
+        """
+        if (
+            method is not lib.no_default
+            or limit is not lib.no_default
+            or fill_axis is not lib.no_default
+        ):
+            # GH#51856
+            warnings.warn(
+                "The 'method', 'limit', and 'fill_axis' keywords in "
+                f"{type(self).__name__}.align are deprecated and will be removed "
+                "in a future version. Call fillna directly on the returned objects "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if fill_axis is lib.no_default:
+            fill_axis = 0
+        if method is lib.no_default:
+            method = None
+        if limit is lib.no_default:
+            limit = None
+
+        if method is not None:
+            method = clean_fill_method(method)
+
+        if broadcast_axis is not lib.no_default:
+            # GH#51856
+            # TODO(3.0): enforcing this deprecation will close GH#13194
+            msg = (
+                f"The 'broadcast_axis' keyword in {type(self).__name__}.align is "
+                "deprecated and will be removed in a future version."
+            )
+            if broadcast_axis is not None:
+                if self.ndim == 1 and other.ndim == 2:
+                    msg += (
+                        " Use left = DataFrame({col: left for col in right.columns}, "
+                        "index=right.index) before calling `left.align(right)` instead."
+                    )
+                elif self.ndim == 2 and other.ndim == 1:
+                    msg += (
+                        " Use right = DataFrame({col: right for col in left.columns}, "
+                        "index=left.index) before calling `left.align(right)` instead"
+                    )
+            warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+        else:
+            broadcast_axis = None
+
+        if broadcast_axis == 1 and self.ndim != other.ndim:
+            if isinstance(self, ABCSeries):
+                # this means other is a DataFrame, and we need to broadcast
+                # self
+                cons = self._constructor_expanddim
+                df = cons(
+                    {c: self for c in other.columns}, **other._construct_axes_dict()
+                )
+                # error: Incompatible return value type (got "Tuple[DataFrame,
+                # DataFrame]", expected "Tuple[Self, NDFrameT]")
+                return df._align_frame(  # type: ignore[return-value]
+                    other,  # type: ignore[arg-type]
+                    join=join,
+                    axis=axis,
+                    level=level,
+                    copy=copy,
+                    fill_value=fill_value,
+                    method=method,
+                    limit=limit,
+                    fill_axis=fill_axis,
+                )[:2]
+            elif isinstance(other, ABCSeries):
+                # this means self is a DataFrame, and we need to broadcast
+                # other
+                cons = other._constructor_expanddim
+                df = cons(
+                    {c: other for c in self.columns}, **self._construct_axes_dict()
+                )
+                # error: Incompatible return value type (got "Tuple[NDFrameT,
+                # DataFrame]", expected "Tuple[Self, NDFrameT]")
+                return self._align_frame(  # type: ignore[return-value]
+                    df,
+                    join=join,
+                    axis=axis,
+                    level=level,
+                    copy=copy,
+                    fill_value=fill_value,
+                    method=method,
+                    limit=limit,
+                    fill_axis=fill_axis,
+                )[:2]
+
+        _right: DataFrame | Series
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+        if isinstance(other, ABCDataFrame):
+            left, _right, join_index = self._align_frame(
+                other,
+                join=join,
+                axis=axis,
+                level=level,
+                copy=copy,
+                fill_value=fill_value,
+                method=method,
+                limit=limit,
+                fill_axis=fill_axis,
+            )
+
+        elif isinstance(other, ABCSeries):
+            left, _right, join_index = self._align_series(
+                other,
+                join=join,
+                axis=axis,
+                level=level,
+                copy=copy,
+                fill_value=fill_value,
+                method=method,
+                limit=limit,
+                fill_axis=fill_axis,
+            )
+        else:  # pragma: no cover
+            raise TypeError(f"unsupported type: {type(other)}")
+
+        right = cast(NDFrameT, _right)
+        if self.ndim == 1 or axis == 0:
+            # If we are aligning timezone-aware DatetimeIndexes and the timezones
+            #  do not match, convert both to UTC.
+            if isinstance(left.index.dtype, DatetimeTZDtype):
+                if left.index.tz != right.index.tz:
+                    if join_index is not None:
+                        # GH#33671 copy to ensure we don't change the index on
+                        #  our original Series
+                        left = left.copy(deep=False)
+                        right = right.copy(deep=False)
+                        left.index = join_index
+                        right.index = join_index
+
+        left = left.__finalize__(self)
+        right = right.__finalize__(other)
+        return left, right
+
+    @final
+    def _align_frame(
+        self,
+        other: DataFrame,
+        join: AlignJoin = "outer",
+        axis: Axis | None = None,
+        level=None,
+        copy: bool_t | None = None,
+        fill_value=None,
+        method=None,
+        limit: int | None = None,
+        fill_axis: Axis = 0,
+    ) -> tuple[Self, DataFrame, Index | None]:
+        # defaults
+        join_index, join_columns = None, None
+        ilidx, iridx = None, None
+        clidx, cridx = None, None
+
+        is_series = isinstance(self, ABCSeries)
+
+        if (axis is None or axis == 0) and not self.index.equals(other.index):
+            join_index, ilidx, iridx = self.index.join(
+                other.index, how=join, level=level, return_indexers=True
+            )
+
+        if (
+            (axis is None or axis == 1)
+            and not is_series
+            and not self.columns.equals(other.columns)
+        ):
+            join_columns, clidx, cridx = self.columns.join(
+                other.columns, how=join, level=level, return_indexers=True
+            )
+
+        if is_series:
+            reindexers = {0: [join_index, ilidx]}
+        else:
+            reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]}
+
+        left = self._reindex_with_indexers(
+            reindexers, copy=copy, fill_value=fill_value, allow_dups=True
+        )
+        # other must be always DataFrame
+        right = other._reindex_with_indexers(
+            {0: [join_index, iridx], 1: [join_columns, cridx]},
+            copy=copy,
+            fill_value=fill_value,
+            allow_dups=True,
+        )
+
+        if method is not None:
+            left = left._pad_or_backfill(method, axis=fill_axis, limit=limit)
+            right = right._pad_or_backfill(method, axis=fill_axis, limit=limit)
+
+        return left, right, join_index
+
+    @final
+    def _align_series(
+        self,
+        other: Series,
+        join: AlignJoin = "outer",
+        axis: Axis | None = None,
+        level=None,
+        copy: bool_t | None = None,
+        fill_value=None,
+        method=None,
+        limit: int | None = None,
+        fill_axis: Axis = 0,
+    ) -> tuple[Self, Series, Index | None]:
+        is_series = isinstance(self, ABCSeries)
+        if copy and using_copy_on_write():
+            copy = False
+
+        if (not is_series and axis is None) or axis not in [None, 0, 1]:
+            raise ValueError("Must specify axis=0 or 1")
+
+        if is_series and axis == 1:
+            raise ValueError("cannot align series to a series other than axis 0")
+
+        # series/series compat, other must always be a Series
+        if not axis:
+            # equal
+            if self.index.equals(other.index):
+                join_index, lidx, ridx = None, None, None
+            else:
+                join_index, lidx, ridx = self.index.join(
+                    other.index, how=join, level=level, return_indexers=True
+                )
+
+            if is_series:
+                left = self._reindex_indexer(join_index, lidx, copy)
+            elif lidx is None or join_index is None:
+                left = self.copy(deep=copy)
+            else:
+                new_mgr = self._mgr.reindex_indexer(join_index, lidx, axis=1, copy=copy)
+                left = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
+
+            right = other._reindex_indexer(join_index, ridx, copy)
+
+        else:
+            # one has > 1 ndim
+            fdata = self._mgr
+            join_index = self.axes[1]
+            lidx, ridx = None, None
+            if not join_index.equals(other.index):
+                join_index, lidx, ridx = join_index.join(
+                    other.index, how=join, level=level, return_indexers=True
+                )
+
+            if lidx is not None:
+                bm_axis = self._get_block_manager_axis(1)
+                fdata = fdata.reindex_indexer(join_index, lidx, axis=bm_axis)
+
+            if copy and fdata is self._mgr:
+                fdata = fdata.copy()
+
+            left = self._constructor_from_mgr(fdata, axes=fdata.axes)
+
+            if ridx is None:
+                right = other.copy(deep=copy)
+            else:
+                right = other.reindex(join_index, level=level)
+
+        # fill
+        fill_na = notna(fill_value) or (method is not None)
+        if fill_na:
+            fill_value, method = validate_fillna_kwargs(fill_value, method)
+            if method is not None:
+                left = left._pad_or_backfill(method, limit=limit, axis=fill_axis)
+                right = right._pad_or_backfill(method, limit=limit)
+            else:
+                left = left.fillna(fill_value, limit=limit, axis=fill_axis)
+                right = right.fillna(fill_value, limit=limit)
+
+        return left, right, join_index
+
+    @final
+    def _where(
+        self,
+        cond,
+        other=lib.no_default,
+        inplace: bool_t = False,
+        axis: Axis | None = None,
+        level=None,
+        warn: bool_t = True,
+    ):
+        """
+        Equivalent to public method `where`, except that `other` is not
+        applied as a function even if callable. Used in __setitem__.
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if axis is not None:
+            axis = self._get_axis_number(axis)
+
+        # align the cond to same shape as myself
+        cond = common.apply_if_callable(cond, self)
+        if isinstance(cond, NDFrame):
+            # CoW: Make sure reference is not kept alive
+            if cond.ndim == 1 and self.ndim == 2:
+                cond = cond._constructor_expanddim(
+                    {i: cond for i in range(len(self.columns))},
+                    copy=False,
+                )
+                cond.columns = self.columns
+            cond = cond.align(self, join="right", copy=False)[0]
+        else:
+            if not hasattr(cond, "shape"):
+                cond = np.asanyarray(cond)
+            if cond.shape != self.shape:
+                raise ValueError("Array conditional must be same shape as self")
+            cond = self._constructor(cond, **self._construct_axes_dict(), copy=False)
+
+        # make sure we are boolean
+        fill_value = bool(inplace)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "Downcasting object dtype arrays",
+                category=FutureWarning,
+            )
+            cond = cond.fillna(fill_value)
+        cond = cond.infer_objects(copy=False)
+
+        msg = "Boolean array expected for the condition, not {dtype}"
+
+        if not cond.empty:
+            if not isinstance(cond, ABCDataFrame):
+                # This is a single-dimensional object.
+                if not is_bool_dtype(cond):
+                    raise ValueError(msg.format(dtype=cond.dtype))
+            else:
+                for _dt in cond.dtypes:
+                    if not is_bool_dtype(_dt):
+                        raise ValueError(msg.format(dtype=_dt))
+                if cond._mgr.any_extension_types:
+                    # GH51574: avoid object ndarray conversion later on
+                    cond = cond._constructor(
+                        cond.to_numpy(dtype=bool, na_value=fill_value),
+                        **cond._construct_axes_dict(),
+                    )
+        else:
+            # GH#21947 we have an empty DataFrame/Series, could be object-dtype
+            cond = cond.astype(bool)
+
+        cond = -cond if inplace else cond
+        cond = cond.reindex(self._info_axis, axis=self._info_axis_number, copy=False)
+
+        # try to align with other
+        if isinstance(other, NDFrame):
+            # align with me
+            if other.ndim <= self.ndim:
+                # CoW: Make sure reference is not kept alive
+                other = self.align(
+                    other,
+                    join="left",
+                    axis=axis,
+                    level=level,
+                    fill_value=None,
+                    copy=False,
+                )[1]
+
+                # if we are NOT aligned, raise as we cannot where index
+                if axis is None and not other._indexed_same(self):
+                    raise InvalidIndexError
+
+                if other.ndim < self.ndim:
+                    # TODO(EA2D): avoid object-dtype cast in EA case GH#38729
+                    other = other._values
+                    if axis == 0:
+                        other = np.reshape(other, (-1, 1))
+                    elif axis == 1:
+                        other = np.reshape(other, (1, -1))
+
+                    other = np.broadcast_to(other, self.shape)
+
+            # slice me out of the other
+            else:
+                raise NotImplementedError(
+                    "cannot align with a higher dimensional NDFrame"
+                )
+
+        elif not isinstance(other, (MultiIndex, NDFrame)):
+            # mainly just catching Index here
+            other = extract_array(other, extract_numpy=True)
+
+        if isinstance(other, (np.ndarray, ExtensionArray)):
+            if other.shape != self.shape:
+                if self.ndim != 1:
+                    # In the ndim == 1 case we may have
+                    #  other length 1, which we treat as scalar (GH#2745, GH#4192)
+                    #  or len(other) == icond.sum(), which we treat like
+                    #  __setitem__ (GH#3235)
+                    raise ValueError(
+                        "other must be the same shape as self when an ndarray"
+                    )
+
+            # we are the same shape, so create an actual object for alignment
+            else:
+                other = self._constructor(
+                    other, **self._construct_axes_dict(), copy=False
+                )
+
+        if axis is None:
+            axis = 0
+
+        if self.ndim == getattr(other, "ndim", 0):
+            align = True
+        else:
+            align = self._get_axis_number(axis) == 1
+
+        if inplace:
+            # we may have different type blocks come out of putmask, so
+            # reconstruct the block manager
+
+            new_data = self._mgr.putmask(mask=cond, new=other, align=align, warn=warn)
+            result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+            return self._update_inplace(result)
+
+        else:
+            new_data = self._mgr.where(
+                other=other,
+                cond=cond,
+                align=align,
+            )
+            result = self._constructor_from_mgr(new_data, axes=new_data.axes)
+            return result.__finalize__(self)
+
+    @overload
+    def where(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[False] = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def where(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[True],
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def where(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: bool_t = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        cond="True",
+        cond_rev="False",
+        name="where",
+        name_other="mask",
+    )
+    def where(
+        self,
+        cond,
+        other=np.nan,
+        *,
+        inplace: bool_t = False,
+        axis: Axis | None = None,
+        level: Level | None = None,
+    ) -> Self | None:
+        """
+        Replace values where the condition is {cond_rev}.
+
+        Parameters
+        ----------
+        cond : bool {klass}, array-like, or callable
+            Where `cond` is {cond}, keep the original value. Where
+            {cond_rev}, replace with corresponding value from `other`.
+            If `cond` is callable, it is computed on the {klass} and
+            should return boolean {klass} or array. The callable must
+            not change input {klass} (though pandas doesn't check it).
+        other : scalar, {klass}, or callable
+            Entries where `cond` is {cond_rev} are replaced with
+            corresponding value from `other`.
+            If other is callable, it is computed on the {klass} and
+            should return scalar or {klass}. The callable must not
+            change input {klass} (though pandas doesn't check it).
+            If not specified, entries will be filled with the corresponding
+            NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension
+            dtypes).
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+        axis : int, default None
+            Alignment axis if needed. For `Series` this parameter is
+            unused and defaults to 0.
+        level : int, default None
+            Alignment level if needed.
+
+        Returns
+        -------
+        Same type as caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        :func:`DataFrame.{name_other}` : Return an object of same shape as
+            self.
+
+        Notes
+        -----
+        The {name} method is an application of the if-then idiom. For each
+        element in the calling DataFrame, if ``cond`` is ``{cond}`` the
+        element is used; otherwise the corresponding element from the DataFrame
+        ``other`` is used. If the axis of ``other`` does not align with axis of
+        ``cond`` {klass}, the misaligned index positions will be filled with
+        {cond_rev}.
+
+        The signature for :func:`DataFrame.where` differs from
+        :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
+        ``np.where(m, df1, df2)``.
+
+        For further details and examples see the ``{name}`` documentation in
+        :ref:`indexing <indexing.where_mask>`.
+
+        The dtype of the object takes precedence. The fill value is casted to
+        the object's dtype, if this can be done losslessly.
+
+        Examples
+        --------
+        >>> s = pd.Series(range(5))
+        >>> s.where(s > 0)
+        0    NaN
+        1    1.0
+        2    2.0
+        3    3.0
+        4    4.0
+        dtype: float64
+        >>> s.mask(s > 0)
+        0    0.0
+        1    NaN
+        2    NaN
+        3    NaN
+        4    NaN
+        dtype: float64
+
+        >>> s = pd.Series(range(5))
+        >>> t = pd.Series([True, False])
+        >>> s.where(t, 99)
+        0     0
+        1    99
+        2    99
+        3    99
+        4    99
+        dtype: int64
+        >>> s.mask(t, 99)
+        0    99
+        1     1
+        2    99
+        3    99
+        4    99
+        dtype: int64
+
+        >>> s.where(s > 1, 10)
+        0    10
+        1    10
+        2    2
+        3    3
+        4    4
+        dtype: int64
+        >>> s.mask(s > 1, 10)
+        0     0
+        1     1
+        2    10
+        3    10
+        4    10
+        dtype: int64
+
+        >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
+        >>> df
+           A  B
+        0  0  1
+        1  2  3
+        2  4  5
+        3  6  7
+        4  8  9
+        >>> m = df % 3 == 0
+        >>> df.where(m, -df)
+           A  B
+        0  0 -1
+        1 -2  3
+        2 -4 -5
+        3  6 -7
+        4 -8  9
+        >>> df.where(m, -df) == np.where(m, df, -df)
+              A     B
+        0  True  True
+        1  True  True
+        2  True  True
+        3  True  True
+        4  True  True
+        >>> df.where(m, -df) == df.mask(~m, -df)
+              A     B
+        0  True  True
+        1  True  True
+        2  True  True
+        3  True  True
+        4  True  True
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and hasattr(self, "_cacher"):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        other = common.apply_if_callable(other, self)
+        return self._where(cond, other, inplace, axis, level)
+
+    @overload
+    def mask(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[False] = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def mask(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: Literal[True],
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def mask(
+        self,
+        cond,
+        other=...,
+        *,
+        inplace: bool_t = ...,
+        axis: Axis | None = ...,
+        level: Level = ...,
+    ) -> Self | None:
+        ...
+
+    @final
+    @doc(
+        where,
+        klass=_shared_doc_kwargs["klass"],
+        cond="False",
+        cond_rev="True",
+        name="mask",
+        name_other="where",
+    )
+    def mask(
+        self,
+        cond,
+        other=lib.no_default,
+        *,
+        inplace: bool_t = False,
+        axis: Axis | None = None,
+        level: Level | None = None,
+    ) -> Self | None:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+                if sys.getrefcount(self) <= REF_COUNT:
+                    warnings.warn(
+                        _chained_assignment_method_msg,
+                        ChainedAssignmentError,
+                        stacklevel=2,
+                    )
+            elif (
+                not PYPY
+                and not WARNING_CHECK_DISABLED
+                and not using_copy_on_write()
+                and self._is_view_after_cow_rules()
+            ):
+                ctr = sys.getrefcount(self)
+                ref_count = REF_COUNT
+                if isinstance(self, ABCSeries) and hasattr(self, "_cacher"):
+                    # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                    ref_count += 1
+                if ctr <= ref_count:
+                    warnings.warn(
+                        _chained_assignment_warning_method_msg,
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+
+        cond = common.apply_if_callable(cond, self)
+        other = common.apply_if_callable(other, self)
+
+        # see gh-21891
+        if not hasattr(cond, "__invert__"):
+            cond = np.array(cond)
+
+        return self._where(
+            ~cond,
+            other=other,
+            inplace=inplace,
+            axis=axis,
+            level=level,
+        )
+
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def shift(
+        self,
+        periods: int | Sequence[int] = 1,
+        freq=None,
+        axis: Axis = 0,
+        fill_value: Hashable = lib.no_default,
+        suffix: str | None = None,
+    ) -> Self | DataFrame:
+        """
+        Shift index by desired number of periods with an optional time `freq`.
+
+        When `freq` is not passed, shift the index without realigning the data.
+        If `freq` is passed (in this case, the index must be date or datetime,
+        or it will raise a `NotImplementedError`), the index will be
+        increased using the periods and the `freq`. `freq` can be inferred
+        when specified as "infer" as long as either freq or inferred_freq
+        attribute is set in the index.
+
+        Parameters
+        ----------
+        periods : int or Sequence
+            Number of periods to shift. Can be positive or negative.
+            If an iterable of ints, the data will be shifted once by each int.
+            This is equivalent to shifting by one value at a time and
+            concatenating all resulting frames. The resulting columns will have
+            the shift suffixed to their column names. For multiple periods,
+            axis must not be 1.
+        freq : DateOffset, tseries.offsets, timedelta, or str, optional
+            Offset to use from the tseries module or time rule (e.g. 'EOM').
+            If `freq` is specified then the index values are shifted but the
+            data is not realigned. That is, use `freq` if you would like to
+            extend the index when shifting and preserve the original data.
+            If `freq` is specified as "infer" then it will be inferred from
+            the freq or inferred_freq attributes of the index. If neither of
+            those attributes exist, a ValueError is thrown.
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Shift direction. For `Series` this parameter is unused and defaults to 0.
+        fill_value : object, optional
+            The scalar value to use for newly introduced missing values.
+            the default depends on the dtype of `self`.
+            For numeric data, ``np.nan`` is used.
+            For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
+            For extension dtypes, ``self.dtype.na_value`` is used.
+        suffix : str, optional
+            If str and periods is an iterable, this is added after the column
+            name and before the shift value for each shifted column name.
+
+        Returns
+        -------
+        {klass}
+            Copy of input object, shifted.
+
+        See Also
+        --------
+        Index.shift : Shift values of Index.
+        DatetimeIndex.shift : Shift values of DatetimeIndex.
+        PeriodIndex.shift : Shift values of PeriodIndex.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45],
+        ...                    "Col2": [13, 23, 18, 33, 48],
+        ...                    "Col3": [17, 27, 22, 37, 52]}},
+        ...                   index=pd.date_range("2020-01-01", "2020-01-05"))
+        >>> df
+                    Col1  Col2  Col3
+        2020-01-01    10    13    17
+        2020-01-02    20    23    27
+        2020-01-03    15    18    22
+        2020-01-04    30    33    37
+        2020-01-05    45    48    52
+
+        >>> df.shift(periods=3)
+                    Col1  Col2  Col3
+        2020-01-01   NaN   NaN   NaN
+        2020-01-02   NaN   NaN   NaN
+        2020-01-03   NaN   NaN   NaN
+        2020-01-04  10.0  13.0  17.0
+        2020-01-05  20.0  23.0  27.0
+
+        >>> df.shift(periods=1, axis="columns")
+                    Col1  Col2  Col3
+        2020-01-01   NaN    10    13
+        2020-01-02   NaN    20    23
+        2020-01-03   NaN    15    18
+        2020-01-04   NaN    30    33
+        2020-01-05   NaN    45    48
+
+        >>> df.shift(periods=3, fill_value=0)
+                    Col1  Col2  Col3
+        2020-01-01     0     0     0
+        2020-01-02     0     0     0
+        2020-01-03     0     0     0
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+
+        >>> df.shift(periods=3, freq="D")
+                    Col1  Col2  Col3
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+        2020-01-06    15    18    22
+        2020-01-07    30    33    37
+        2020-01-08    45    48    52
+
+        >>> df.shift(periods=3, freq="infer")
+                    Col1  Col2  Col3
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+        2020-01-06    15    18    22
+        2020-01-07    30    33    37
+        2020-01-08    45    48    52
+
+        >>> df['Col1'].shift(periods=[0, 1, 2])
+                    Col1_0  Col1_1  Col1_2
+        2020-01-01      10     NaN     NaN
+        2020-01-02      20    10.0     NaN
+        2020-01-03      15    20.0    10.0
+        2020-01-04      30    15.0    20.0
+        2020-01-05      45    30.0    15.0
+        """
+        axis = self._get_axis_number(axis)
+
+        if freq is not None and fill_value is not lib.no_default:
+            # GH#53832
+            warnings.warn(
+                "Passing a 'freq' together with a 'fill_value' silently ignores "
+                "the fill_value and is deprecated. This will raise in a future "
+                "version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            fill_value = lib.no_default
+
+        if periods == 0:
+            return self.copy(deep=None)
+
+        if is_list_like(periods) and isinstance(self, ABCSeries):
+            return self.to_frame().shift(
+                periods=periods, freq=freq, axis=axis, fill_value=fill_value
+            )
+        periods = cast(int, periods)
+
+        if freq is None:
+            # when freq is None, data is shifted, index is not
+            axis = self._get_axis_number(axis)
+            assert axis == 0  # axis == 1 cases handled in DataFrame.shift
+            new_data = self._mgr.shift(periods=periods, fill_value=fill_value)
+            return self._constructor_from_mgr(
+                new_data, axes=new_data.axes
+            ).__finalize__(self, method="shift")
+
+        return self._shift_with_freq(periods, axis, freq)
+
+    @final
+    def _shift_with_freq(self, periods: int, axis: int, freq) -> Self:
+        # see shift.__doc__
+        # when freq is given, index is shifted, data is not
+        index = self._get_axis(axis)
+
+        if freq == "infer":
+            freq = getattr(index, "freq", None)
+
+            if freq is None:
+                freq = getattr(index, "inferred_freq", None)
+
+            if freq is None:
+                msg = "Freq was not set in the index hence cannot be inferred"
+                raise ValueError(msg)
+
+        elif isinstance(freq, str):
+            is_period = isinstance(index, PeriodIndex)
+            freq = to_offset(freq, is_period=is_period)
+
+        if isinstance(index, PeriodIndex):
+            orig_freq = to_offset(index.freq)
+            if freq != orig_freq:
+                assert orig_freq is not None  # for mypy
+                raise ValueError(
+                    f"Given freq {freq_to_period_freqstr(freq.n, freq.name)} "
+                    f"does not match PeriodIndex freq "
+                    f"{freq_to_period_freqstr(orig_freq.n, orig_freq.name)}"
+                )
+            new_ax = index.shift(periods)
+        else:
+            new_ax = index.shift(periods, freq)
+
+        result = self.set_axis(new_ax, axis=axis)
+        return result.__finalize__(self, method="shift")
+
+    @final
+    def truncate(
+        self,
+        before=None,
+        after=None,
+        axis: Axis | None = None,
+        copy: bool_t | None = None,
+    ) -> Self:
+        """
+        Truncate a Series or DataFrame before and after some index value.
+
+        This is a useful shorthand for boolean indexing based on index
+        values above or below certain thresholds.
+
+        Parameters
+        ----------
+        before : date, str, int
+            Truncate all rows before this index value.
+        after : date, str, int
+            Truncate all rows after this index value.
+        axis : {0 or 'index', 1 or 'columns'}, optional
+            Axis to truncate. Truncates the index (rows) by default.
+            For `Series` this parameter is unused and defaults to 0.
+        copy : bool, default is True,
+            Return a copy of the truncated section.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        type of caller
+            The truncated Series or DataFrame.
+
+        See Also
+        --------
+        DataFrame.loc : Select a subset of a DataFrame by label.
+        DataFrame.iloc : Select a subset of a DataFrame by position.
+
+        Notes
+        -----
+        If the index being truncated contains only datetime values,
+        `before` and `after` may be specified as strings instead of
+        Timestamps.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
+        ...                    'B': ['f', 'g', 'h', 'i', 'j'],
+        ...                    'C': ['k', 'l', 'm', 'n', 'o']},
+        ...                   index=[1, 2, 3, 4, 5])
+        >>> df
+           A  B  C
+        1  a  f  k
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+        5  e  j  o
+
+        >>> df.truncate(before=2, after=4)
+           A  B  C
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+
+        The columns of a DataFrame can be truncated.
+
+        >>> df.truncate(before="A", after="B", axis="columns")
+           A  B
+        1  a  f
+        2  b  g
+        3  c  h
+        4  d  i
+        5  e  j
+
+        For Series, only rows can be truncated.
+
+        >>> df['A'].truncate(before=2, after=4)
+        2    b
+        3    c
+        4    d
+        Name: A, dtype: object
+
+        The index values in ``truncate`` can be datetimes or string
+        dates.
+
+        >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
+        >>> df = pd.DataFrame(index=dates, data={'A': 1})
+        >>> df.tail()
+                             A
+        2016-01-31 23:59:56  1
+        2016-01-31 23:59:57  1
+        2016-01-31 23:59:58  1
+        2016-01-31 23:59:59  1
+        2016-02-01 00:00:00  1
+
+        >>> df.truncate(before=pd.Timestamp('2016-01-05'),
+        ...             after=pd.Timestamp('2016-01-10')).tail()
+                             A
+        2016-01-09 23:59:56  1
+        2016-01-09 23:59:57  1
+        2016-01-09 23:59:58  1
+        2016-01-09 23:59:59  1
+        2016-01-10 00:00:00  1
+
+        Because the index is a DatetimeIndex containing only dates, we can
+        specify `before` and `after` as strings. They will be coerced to
+        Timestamps before truncation.
+
+        >>> df.truncate('2016-01-05', '2016-01-10').tail()
+                             A
+        2016-01-09 23:59:56  1
+        2016-01-09 23:59:57  1
+        2016-01-09 23:59:58  1
+        2016-01-09 23:59:59  1
+        2016-01-10 00:00:00  1
+
+        Note that ``truncate`` assumes a 0 value for any unspecified time
+        component (midnight). This differs from partial string slicing, which
+        returns any partially matching dates.
+
+        >>> df.loc['2016-01-05':'2016-01-10', :].tail()
+                             A
+        2016-01-10 23:59:55  1
+        2016-01-10 23:59:56  1
+        2016-01-10 23:59:57  1
+        2016-01-10 23:59:58  1
+        2016-01-10 23:59:59  1
+        """
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_number(axis)
+        ax = self._get_axis(axis)
+
+        # GH 17935
+        # Check that index is sorted
+        if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
+            raise ValueError("truncate requires a sorted index")
+
+        # if we have a date index, convert to dates, otherwise
+        # treat like a slice
+        if ax._is_all_dates:
+            from pandas.core.tools.datetimes import to_datetime
+
+            before = to_datetime(before)
+            after = to_datetime(after)
+
+        if before is not None and after is not None and before > after:
+            raise ValueError(f"Truncate: {after} must be after {before}")
+
+        if len(ax) > 1 and ax.is_monotonic_decreasing and ax.nunique() > 1:
+            before, after = after, before
+
+        slicer = [slice(None, None)] * self._AXIS_LEN
+        slicer[axis] = slice(before, after)
+        result = self.loc[tuple(slicer)]
+
+        if isinstance(ax, MultiIndex):
+            setattr(result, self._get_axis_name(axis), ax.truncate(before, after))
+
+        result = result.copy(deep=copy and not using_copy_on_write())
+
+        return result
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def tz_convert(
+        self, tz, axis: Axis = 0, level=None, copy: bool_t | None = None
+    ) -> Self:
+        """
+        Convert tz-aware axis to target time zone.
+
+        Parameters
+        ----------
+        tz : str or tzinfo object or None
+            Target time zone. Passing ``None`` will convert to
+            UTC and remove the timezone information.
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            The axis to convert
+        level : int, str, default None
+            If axis is a MultiIndex, convert a specific level. Otherwise
+            must be None.
+        copy : bool, default True
+            Also make a copy of the underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        {klass}
+            Object with time zone converted axis.
+
+        Raises
+        ------
+        TypeError
+            If the axis is tz-naive.
+
+        Examples
+        --------
+        Change to another time zone:
+
+        >>> s = pd.Series(
+        ...     [1],
+        ...     index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
+        ... )
+        >>> s.tz_convert('Asia/Shanghai')
+        2018-09-15 07:30:00+08:00    1
+        dtype: int64
+
+        Pass None to convert to UTC and get a tz-naive index:
+
+        >>> s = pd.Series([1],
+        ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
+        >>> s.tz_convert(None)
+        2018-09-14 23:30:00    1
+        dtype: int64
+        """
+        axis = self._get_axis_number(axis)
+        ax = self._get_axis(axis)
+
+        def _tz_convert(ax, tz):
+            if not hasattr(ax, "tz_convert"):
+                if len(ax) > 0:
+                    ax_name = self._get_axis_name(axis)
+                    raise TypeError(
+                        f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
+                    )
+                ax = DatetimeIndex([], tz=tz)
+            else:
+                ax = ax.tz_convert(tz)
+            return ax
+
+        # if a level is given it must be a MultiIndex level or
+        # equivalent to the axis name
+        if isinstance(ax, MultiIndex):
+            level = ax._get_level_number(level)
+            new_level = _tz_convert(ax.levels[level], tz)
+            ax = ax.set_levels(new_level, level=level)
+        else:
+            if level not in (None, 0, ax.name):
+                raise ValueError(f"The level {level} is not valid")
+            ax = _tz_convert(ax, tz)
+
+        result = self.copy(deep=copy and not using_copy_on_write())
+        result = result.set_axis(ax, axis=axis, copy=False)
+        return result.__finalize__(self, method="tz_convert")
+
+    @final
+    @doc(klass=_shared_doc_kwargs["klass"])
+    def tz_localize(
+        self,
+        tz,
+        axis: Axis = 0,
+        level=None,
+        copy: bool_t | None = None,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        """
+        Localize tz-naive index of a Series or DataFrame to target time zone.
+
+        This operation localizes the Index. To localize the values in a
+        timezone-naive Series, use :meth:`Series.dt.tz_localize`.
+
+        Parameters
+        ----------
+        tz : str or tzinfo or None
+            Time zone to localize. Passing ``None`` will remove the
+            time zone information and preserve local time.
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            The axis to localize
+        level : int, str, default None
+            If axis ia a MultiIndex, localize a specific level. Otherwise
+            must be None.
+        copy : bool, default True
+            Also make a copy of the underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+            When clocks moved backward due to DST, ambiguous times may arise.
+            For example in Central European Time (UTC+01), when going from
+            03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
+            00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
+            `ambiguous` parameter dictates how ambiguous times should be
+            handled.
+
+            - 'infer' will attempt to infer fall dst-transition hours based on
+              order
+            - bool-ndarray where True signifies a DST time, False designates
+              a non-DST time (note that this flag is only applicable for
+              ambiguous times)
+            - 'NaT' will return NaT where there are ambiguous times
+            - 'raise' will raise an AmbiguousTimeError if there are ambiguous
+              times.
+        nonexistent : str, default 'raise'
+            A nonexistent time does not exist in a particular timezone
+            where clocks moved forward due to DST. Valid values are:
+
+            - 'shift_forward' will shift the nonexistent time forward to the
+              closest existing time
+            - 'shift_backward' will shift the nonexistent time backward to the
+              closest existing time
+            - 'NaT' will return NaT where there are nonexistent times
+            - timedelta objects will shift nonexistent times by the timedelta
+            - 'raise' will raise an NonExistentTimeError if there are
+              nonexistent times.
+
+        Returns
+        -------
+        {klass}
+            Same type as the input.
+
+        Raises
+        ------
+        TypeError
+            If the TimeSeries is tz-aware and tz is not None.
+
+        Examples
+        --------
+        Localize local times:
+
+        >>> s = pd.Series(
+        ...     [1],
+        ...     index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
+        ... )
+        >>> s.tz_localize('CET')
+        2018-09-15 01:30:00+02:00    1
+        dtype: int64
+
+        Pass None to convert to tz-naive index and preserve local time:
+
+        >>> s = pd.Series([1],
+        ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
+        >>> s.tz_localize(None)
+        2018-09-15 01:30:00    1
+        dtype: int64
+
+        Be careful with DST changes. When there is sequential data, pandas
+        can infer the DST time:
+
+        >>> s = pd.Series(range(7),
+        ...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
+        ...                                       '2018-10-28 02:00:00',
+        ...                                       '2018-10-28 02:30:00',
+        ...                                       '2018-10-28 02:00:00',
+        ...                                       '2018-10-28 02:30:00',
+        ...                                       '2018-10-28 03:00:00',
+        ...                                       '2018-10-28 03:30:00']))
+        >>> s.tz_localize('CET', ambiguous='infer')
+        2018-10-28 01:30:00+02:00    0
+        2018-10-28 02:00:00+02:00    1
+        2018-10-28 02:30:00+02:00    2
+        2018-10-28 02:00:00+01:00    3
+        2018-10-28 02:30:00+01:00    4
+        2018-10-28 03:00:00+01:00    5
+        2018-10-28 03:30:00+01:00    6
+        dtype: int64
+
+        In some cases, inferring the DST is impossible. In such cases, you can
+        pass an ndarray to the ambiguous parameter to set the DST explicitly
+
+        >>> s = pd.Series(range(3),
+        ...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
+        ...                                       '2018-10-28 02:36:00',
+        ...                                       '2018-10-28 03:46:00']))
+        >>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
+        2018-10-28 01:20:00+02:00    0
+        2018-10-28 02:36:00+02:00    1
+        2018-10-28 03:46:00+01:00    2
+        dtype: int64
+
+        If the DST transition causes nonexistent times, you can shift these
+        dates forward or backward with a timedelta object or `'shift_forward'`
+        or `'shift_backward'`.
+
+        >>> s = pd.Series(range(2),
+        ...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
+        ...                                       '2015-03-29 03:30:00']))
+        >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
+        2015-03-29 03:00:00+02:00    0
+        2015-03-29 03:30:00+02:00    1
+        dtype: int64
+        >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
+        2015-03-29 01:59:59.999999999+01:00    0
+        2015-03-29 03:30:00+02:00              1
+        dtype: int64
+        >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
+        2015-03-29 03:30:00+02:00    0
+        2015-03-29 03:30:00+02:00    1
+        dtype: int64
+        """
+        nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward")
+        if nonexistent not in nonexistent_options and not isinstance(
+            nonexistent, dt.timedelta
+        ):
+            raise ValueError(
+                "The nonexistent argument must be one of 'raise', "
+                "'NaT', 'shift_forward', 'shift_backward' or "
+                "a timedelta object"
+            )
+
+        axis = self._get_axis_number(axis)
+        ax = self._get_axis(axis)
+
+        def _tz_localize(ax, tz, ambiguous, nonexistent):
+            if not hasattr(ax, "tz_localize"):
+                if len(ax) > 0:
+                    ax_name = self._get_axis_name(axis)
+                    raise TypeError(
+                        f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
+                    )
+                ax = DatetimeIndex([], tz=tz)
+            else:
+                ax = ax.tz_localize(tz, ambiguous=ambiguous, nonexistent=nonexistent)
+            return ax
+
+        # if a level is given it must be a MultiIndex level or
+        # equivalent to the axis name
+        if isinstance(ax, MultiIndex):
+            level = ax._get_level_number(level)
+            new_level = _tz_localize(ax.levels[level], tz, ambiguous, nonexistent)
+            ax = ax.set_levels(new_level, level=level)
+        else:
+            if level not in (None, 0, ax.name):
+                raise ValueError(f"The level {level} is not valid")
+            ax = _tz_localize(ax, tz, ambiguous, nonexistent)
+
+        result = self.copy(deep=copy and not using_copy_on_write())
+        result = result.set_axis(ax, axis=axis, copy=False)
+        return result.__finalize__(self, method="tz_localize")
+
+    # ----------------------------------------------------------------------
+    # Numeric Methods
+
+    @final
+    def describe(
+        self,
+        percentiles=None,
+        include=None,
+        exclude=None,
+    ) -> Self:
+        """
+        Generate descriptive statistics.
+
+        Descriptive statistics include those that summarize the central
+        tendency, dispersion and shape of a
+        dataset's distribution, excluding ``NaN`` values.
+
+        Analyzes both numeric and object series, as well
+        as ``DataFrame`` column sets of mixed data types. The output
+        will vary depending on what is provided. Refer to the notes
+        below for more detail.
+
+        Parameters
+        ----------
+        percentiles : list-like of numbers, optional
+            The percentiles to include in the output. All should
+            fall between 0 and 1. The default is
+            ``[.25, .5, .75]``, which returns the 25th, 50th, and
+            75th percentiles.
+        include : 'all', list-like of dtypes or None (default), optional
+            A white list of data types to include in the result. Ignored
+            for ``Series``. Here are the options:
+
+            - 'all' : All columns of the input will be included in the output.
+            - A list-like of dtypes : Limits the results to the
+              provided data types.
+              To limit the result to numeric types submit
+              ``numpy.number``. To limit it instead to object columns submit
+              the ``numpy.object`` data type. Strings
+              can also be used in the style of
+              ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
+              select pandas categorical columns, use ``'category'``
+            - None (default) : The result will include all numeric columns.
+        exclude : list-like of dtypes or None (default), optional,
+            A black list of data types to omit from the result. Ignored
+            for ``Series``. Here are the options:
+
+            - A list-like of dtypes : Excludes the provided data types
+              from the result. To exclude numeric types submit
+              ``numpy.number``. To exclude object columns submit the data
+              type ``numpy.object``. Strings can also be used in the style of
+              ``select_dtypes`` (e.g. ``df.describe(exclude=['O'])``). To
+              exclude pandas categorical columns, use ``'category'``
+            - None (default) : The result will exclude nothing.
+
+        Returns
+        -------
+        Series or DataFrame
+            Summary statistics of the Series or Dataframe provided.
+
+        See Also
+        --------
+        DataFrame.count: Count number of non-NA/null observations.
+        DataFrame.max: Maximum of the values in the object.
+        DataFrame.min: Minimum of the values in the object.
+        DataFrame.mean: Mean of the values.
+        DataFrame.std: Standard deviation of the observations.
+        DataFrame.select_dtypes: Subset of a DataFrame including/excluding
+            columns based on their dtype.
+
+        Notes
+        -----
+        For numeric data, the result's index will include ``count``,
+        ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
+        upper percentiles. By default the lower percentile is ``25`` and the
+        upper percentile is ``75``. The ``50`` percentile is the
+        same as the median.
+
+        For object data (e.g. strings or timestamps), the result's index
+        will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
+        is the most common value. The ``freq`` is the most common value's
+        frequency. Timestamps also include the ``first`` and ``last`` items.
+
+        If multiple object values have the highest count, then the
+        ``count`` and ``top`` results will be arbitrarily chosen from
+        among those with the highest count.
+
+        For mixed data types provided via a ``DataFrame``, the default is to
+        return only an analysis of numeric columns. If the dataframe consists
+        only of object and categorical data without any numeric columns, the
+        default is to return an analysis of both the object and categorical
+        columns. If ``include='all'`` is provided as an option, the result
+        will include a union of attributes of each type.
+
+        The `include` and `exclude` parameters can be used to limit
+        which columns in a ``DataFrame`` are analyzed for the output.
+        The parameters are ignored when analyzing a ``Series``.
+
+        Examples
+        --------
+        Describing a numeric ``Series``.
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.describe()
+        count    3.0
+        mean     2.0
+        std      1.0
+        min      1.0
+        25%      1.5
+        50%      2.0
+        75%      2.5
+        max      3.0
+        dtype: float64
+
+        Describing a categorical ``Series``.
+
+        >>> s = pd.Series(['a', 'a', 'b', 'c'])
+        >>> s.describe()
+        count     4
+        unique    3
+        top       a
+        freq      2
+        dtype: object
+
+        Describing a timestamp ``Series``.
+
+        >>> s = pd.Series([
+        ...     np.datetime64("2000-01-01"),
+        ...     np.datetime64("2010-01-01"),
+        ...     np.datetime64("2010-01-01")
+        ... ])
+        >>> s.describe()
+        count                      3
+        mean     2006-09-01 08:00:00
+        min      2000-01-01 00:00:00
+        25%      2004-12-31 12:00:00
+        50%      2010-01-01 00:00:00
+        75%      2010-01-01 00:00:00
+        max      2010-01-01 00:00:00
+        dtype: object
+
+        Describing a ``DataFrame``. By default only numeric fields
+        are returned.
+
+        >>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
+        ...                    'numeric': [1, 2, 3],
+        ...                    'object': ['a', 'b', 'c']
+        ...                    })
+        >>> df.describe()
+               numeric
+        count      3.0
+        mean       2.0
+        std        1.0
+        min        1.0
+        25%        1.5
+        50%        2.0
+        75%        2.5
+        max        3.0
+
+        Describing all columns of a ``DataFrame`` regardless of data type.
+
+        >>> df.describe(include='all')  # doctest: +SKIP
+               categorical  numeric object
+        count            3      3.0      3
+        unique           3      NaN      3
+        top              f      NaN      a
+        freq             1      NaN      1
+        mean           NaN      2.0    NaN
+        std            NaN      1.0    NaN
+        min            NaN      1.0    NaN
+        25%            NaN      1.5    NaN
+        50%            NaN      2.0    NaN
+        75%            NaN      2.5    NaN
+        max            NaN      3.0    NaN
+
+        Describing a column from a ``DataFrame`` by accessing it as
+        an attribute.
+
+        >>> df.numeric.describe()
+        count    3.0
+        mean     2.0
+        std      1.0
+        min      1.0
+        25%      1.5
+        50%      2.0
+        75%      2.5
+        max      3.0
+        Name: numeric, dtype: float64
+
+        Including only numeric columns in a ``DataFrame`` description.
+
+        >>> df.describe(include=[np.number])
+               numeric
+        count      3.0
+        mean       2.0
+        std        1.0
+        min        1.0
+        25%        1.5
+        50%        2.0
+        75%        2.5
+        max        3.0
+
+        Including only string columns in a ``DataFrame`` description.
+
+        >>> df.describe(include=[object])  # doctest: +SKIP
+               object
+        count       3
+        unique      3
+        top         a
+        freq        1
+
+        Including only categorical columns from a ``DataFrame`` description.
+
+        >>> df.describe(include=['category'])
+               categorical
+        count            3
+        unique           3
+        top              d
+        freq             1
+
+        Excluding numeric columns from a ``DataFrame`` description.
+
+        >>> df.describe(exclude=[np.number])  # doctest: +SKIP
+               categorical object
+        count            3      3
+        unique           3      3
+        top              f      a
+        freq             1      1
+
+        Excluding object columns from a ``DataFrame`` description.
+
+        >>> df.describe(exclude=[object])  # doctest: +SKIP
+               categorical  numeric
+        count            3      3.0
+        unique           3      NaN
+        top              f      NaN
+        freq             1      NaN
+        mean           NaN      2.0
+        std            NaN      1.0
+        min            NaN      1.0
+        25%            NaN      1.5
+        50%            NaN      2.0
+        75%            NaN      2.5
+        max            NaN      3.0
+        """
+        return describe_ndframe(
+            obj=self,
+            include=include,
+            exclude=exclude,
+            percentiles=percentiles,
+        ).__finalize__(self, method="describe")
+
+    @final
+    def pct_change(
+        self,
+        periods: int = 1,
+        fill_method: FillnaOptions | None | lib.NoDefault = lib.no_default,
+        limit: int | None | lib.NoDefault = lib.no_default,
+        freq=None,
+        **kwargs,
+    ) -> Self:
+        """
+        Fractional change between the current and a prior element.
+
+        Computes the fractional change from the immediately previous row by
+        default. This is useful in comparing the fraction of change in a time
+        series of elements.
+
+        .. note::
+
+            Despite the name of this method, it calculates fractional change
+            (also known as per unit change or relative change) and not
+            percentage change. If you need the percentage change, multiply
+            these values by 100.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for forming percent change.
+        fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad'
+            How to handle NAs **before** computing percent changes.
+
+            .. deprecated:: 2.1
+                All options of `fill_method` are deprecated except `fill_method=None`.
+
+        limit : int, default None
+            The number of consecutive NAs to fill before stopping.
+
+            .. deprecated:: 2.1
+
+        freq : DateOffset, timedelta, or str, optional
+            Increment to use from time series API (e.g. 'ME' or BDay()).
+        **kwargs
+            Additional keyword arguments are passed into
+            `DataFrame.shift` or `Series.shift`.
+
+        Returns
+        -------
+        Series or DataFrame
+            The same type as the calling object.
+
+        See Also
+        --------
+        Series.diff : Compute the difference of two elements in a Series.
+        DataFrame.diff : Compute the difference of two elements in a DataFrame.
+        Series.shift : Shift the index by some number of periods.
+        DataFrame.shift : Shift the index by some number of periods.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = pd.Series([90, 91, 85])
+        >>> s
+        0    90
+        1    91
+        2    85
+        dtype: int64
+
+        >>> s.pct_change()
+        0         NaN
+        1    0.011111
+        2   -0.065934
+        dtype: float64
+
+        >>> s.pct_change(periods=2)
+        0         NaN
+        1         NaN
+        2   -0.055556
+        dtype: float64
+
+        See the percentage change in a Series where filling NAs with last
+        valid observation forward to next valid.
+
+        >>> s = pd.Series([90, 91, None, 85])
+        >>> s
+        0    90.0
+        1    91.0
+        2     NaN
+        3    85.0
+        dtype: float64
+
+        >>> s.ffill().pct_change()
+        0         NaN
+        1    0.011111
+        2    0.000000
+        3   -0.065934
+        dtype: float64
+
+        **DataFrame**
+
+        Percentage change in French franc, Deutsche Mark, and Italian lira from
+        1980-01-01 to 1980-03-01.
+
+        >>> df = pd.DataFrame({
+        ...     'FR': [4.0405, 4.0963, 4.3149],
+        ...     'GR': [1.7246, 1.7482, 1.8519],
+        ...     'IT': [804.74, 810.01, 860.13]},
+        ...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
+        >>> df
+                        FR      GR      IT
+        1980-01-01  4.0405  1.7246  804.74
+        1980-02-01  4.0963  1.7482  810.01
+        1980-03-01  4.3149  1.8519  860.13
+
+        >>> df.pct_change()
+                          FR        GR        IT
+        1980-01-01       NaN       NaN       NaN
+        1980-02-01  0.013810  0.013684  0.006549
+        1980-03-01  0.053365  0.059318  0.061876
+
+        Percentage of change in GOOG and APPL stock volume. Shows computing
+        the percentage change between columns.
+
+        >>> df = pd.DataFrame({
+        ...     '2016': [1769950, 30586265],
+        ...     '2015': [1500923, 40912316],
+        ...     '2014': [1371819, 41403351]},
+        ...     index=['GOOG', 'APPL'])
+        >>> df
+                  2016      2015      2014
+        GOOG   1769950   1500923   1371819
+        APPL  30586265  40912316  41403351
+
+        >>> df.pct_change(axis='columns', periods=-1)
+                  2016      2015  2014
+        GOOG  0.179241  0.094112   NaN
+        APPL -0.252395 -0.011860   NaN
+        """
+        # GH#53491
+        if fill_method not in (lib.no_default, None) or limit is not lib.no_default:
+            warnings.warn(
+                "The 'fill_method' keyword being not None and the 'limit' keyword in "
+                f"{type(self).__name__}.pct_change are deprecated and will be removed "
+                "in a future version. Either fill in any non-leading NA values prior "
+                "to calling pct_change or specify 'fill_method=None' to not fill NA "
+                "values.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if fill_method is lib.no_default:
+            if limit is lib.no_default:
+                cols = self.items() if self.ndim == 2 else [(None, self)]
+                for _, col in cols:
+                    if len(col) > 0:
+                        mask = col.isna().values
+                        mask = mask[np.argmax(~mask) :]
+                        if mask.any():
+                            warnings.warn(
+                                "The default fill_method='pad' in "
+                                f"{type(self).__name__}.pct_change is deprecated and "
+                                "will be removed in a future version. Either fill in "
+                                "any non-leading NA values prior to calling pct_change "
+                                "or specify 'fill_method=None' to not fill NA values.",
+                                FutureWarning,
+                                stacklevel=find_stack_level(),
+                            )
+                            break
+            fill_method = "pad"
+        if limit is lib.no_default:
+            limit = None
+
+        axis = self._get_axis_number(kwargs.pop("axis", "index"))
+        if fill_method is None:
+            data = self
+        else:
+            data = self._pad_or_backfill(fill_method, axis=axis, limit=limit)
+
+        shifted = data.shift(periods=periods, freq=freq, axis=axis, **kwargs)
+        # Unsupported left operand type for / ("Self")
+        rs = data / shifted - 1  # type: ignore[operator]
+        if freq is not None:
+            # Shift method is implemented differently when freq is not None
+            # We want to restore the original index
+            rs = rs.loc[~rs.index.duplicated()]
+            rs = rs.reindex_like(data)
+        return rs.__finalize__(self, method="pct_change")
+
+    @final
+    def _logical_func(
+        self,
+        name: str,
+        func,
+        axis: Axis | None = 0,
+        bool_only: bool_t = False,
+        skipna: bool_t = True,
+        **kwargs,
+    ) -> Series | bool_t:
+        nv.validate_logical_func((), kwargs, fname=name)
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        if self.ndim > 1 and axis is None:
+            # Reduce along one dimension then the other, to simplify DataFrame._reduce
+            res = self._logical_func(
+                name, func, axis=0, bool_only=bool_only, skipna=skipna, **kwargs
+            )
+            # error: Item "bool" of "Series | bool" has no attribute "_logical_func"
+            return res._logical_func(  # type: ignore[union-attr]
+                name, func, skipna=skipna, **kwargs
+            )
+        elif axis is None:
+            axis = 0
+
+        if (
+            self.ndim > 1
+            and axis == 1
+            and len(self._mgr.arrays) > 1
+            # TODO(EA2D): special-case not needed
+            and all(x.ndim == 2 for x in self._mgr.arrays)
+            and not kwargs
+        ):
+            # Fastpath avoiding potentially expensive transpose
+            obj = self
+            if bool_only:
+                obj = self._get_bool_data()
+            return obj._reduce_axis1(name, func, skipna=skipna)
+
+        return self._reduce(
+            func,
+            name=name,
+            axis=axis,
+            skipna=skipna,
+            numeric_only=bool_only,
+            filter_type="bool",
+        )
+
+    def any(
+        self,
+        axis: Axis | None = 0,
+        bool_only: bool_t = False,
+        skipna: bool_t = True,
+        **kwargs,
+    ) -> Series | bool_t:
+        return self._logical_func(
+            "any", nanops.nanany, axis, bool_only, skipna, **kwargs
+        )
+
+    def all(
+        self,
+        axis: Axis = 0,
+        bool_only: bool_t = False,
+        skipna: bool_t = True,
+        **kwargs,
+    ) -> Series | bool_t:
+        return self._logical_func(
+            "all", nanops.nanall, axis, bool_only, skipna, **kwargs
+        )
+
+    @final
+    def _accum_func(
+        self,
+        name: str,
+        func,
+        axis: Axis | None = None,
+        skipna: bool_t = True,
+        *args,
+        **kwargs,
+    ):
+        skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
+        if axis is None:
+            axis = 0
+        else:
+            axis = self._get_axis_number(axis)
+
+        if axis == 1:
+            return self.T._accum_func(
+                name, func, axis=0, skipna=skipna, *args, **kwargs  # noqa: B026
+            ).T
+
+        def block_accum_func(blk_values):
+            values = blk_values.T if hasattr(blk_values, "T") else blk_values
+
+            result: np.ndarray | ExtensionArray
+            if isinstance(values, ExtensionArray):
+                result = values._accumulate(name, skipna=skipna, **kwargs)
+            else:
+                result = nanops.na_accum_func(values, func, skipna=skipna)
+
+            result = result.T if hasattr(result, "T") else result
+            return result
+
+        result = self._mgr.apply(block_accum_func)
+
+        return self._constructor_from_mgr(result, axes=result.axes).__finalize__(
+            self, method=name
+        )
+
+    def cummax(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func(
+            "cummax", np.maximum.accumulate, axis, skipna, *args, **kwargs
+        )
+
+    def cummin(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func(
+            "cummin", np.minimum.accumulate, axis, skipna, *args, **kwargs
+        )
+
+    def cumsum(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func("cumsum", np.cumsum, axis, skipna, *args, **kwargs)
+
+    def cumprod(self, axis: Axis | None = None, skipna: bool_t = True, *args, **kwargs):
+        return self._accum_func("cumprod", np.cumprod, axis, skipna, *args, **kwargs)
+
+    @final
+    def _stat_function_ddof(
+        self,
+        name: str,
+        func,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        nv.validate_stat_ddof_func((), kwargs, fname=name)
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        if axis is None:
+            if self.ndim > 1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.{name} with axis=None "
+                    "is deprecated, in a future version this will reduce over both "
+                    "axes and return a scalar. To retain the old behavior, pass "
+                    "axis=0 (or do not pass axis)",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            axis = 0
+        elif axis is lib.no_default:
+            axis = 0
+
+        return self._reduce(
+            func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
+        )
+
+    def sem(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function_ddof(
+            "sem", nanops.nansem, axis, skipna, ddof, numeric_only, **kwargs
+        )
+
+    def var(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function_ddof(
+            "var", nanops.nanvar, axis, skipna, ddof, numeric_only, **kwargs
+        )
+
+    def std(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        ddof: int = 1,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function_ddof(
+            "std", nanops.nanstd, axis, skipna, ddof, numeric_only, **kwargs
+        )
+
+    @final
+    def _stat_function(
+        self,
+        name: str,
+        func,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ):
+        assert name in ["median", "mean", "min", "max", "kurt", "skew"], name
+        nv.validate_func(name, (), kwargs)
+
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        return self._reduce(
+            func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
+        )
+
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ):
+        return self._stat_function(
+            "min",
+            nanops.nanmin,
+            axis,
+            skipna,
+            numeric_only,
+            **kwargs,
+        )
+
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ):
+        return self._stat_function(
+            "max",
+            nanops.nanmax,
+            axis,
+            skipna,
+            numeric_only,
+            **kwargs,
+        )
+
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
+        )
+
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "median", nanops.nanmedian, axis, skipna, numeric_only, **kwargs
+        )
+
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "skew", nanops.nanskew, axis, skipna, numeric_only, **kwargs
+        )
+
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        **kwargs,
+    ) -> Series | float:
+        return self._stat_function(
+            "kurt", nanops.nankurt, axis, skipna, numeric_only, **kwargs
+        )
+
+    kurtosis = kurt
+
+    @final
+    def _min_count_stat_function(
+        self,
+        name: str,
+        func,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        assert name in ["sum", "prod"], name
+        nv.validate_func(name, (), kwargs)
+
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+
+        if axis is None:
+            if self.ndim > 1:
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.{name} with axis=None "
+                    "is deprecated, in a future version this will reduce over both "
+                    "axes and return a scalar. To retain the old behavior, pass "
+                    "axis=0 (or do not pass axis)",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            axis = 0
+        elif axis is lib.no_default:
+            axis = 0
+
+        return self._reduce(
+            func,
+            name=name,
+            axis=axis,
+            skipna=skipna,
+            numeric_only=numeric_only,
+            min_count=min_count,
+        )
+
+    def sum(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return self._min_count_stat_function(
+            "sum", nanops.nansum, axis, skipna, numeric_only, min_count, **kwargs
+        )
+
+    def prod(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool_t = True,
+        numeric_only: bool_t = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return self._min_count_stat_function(
+            "prod",
+            nanops.nanprod,
+            axis,
+            skipna,
+            numeric_only,
+            min_count,
+            **kwargs,
+        )
+
+    product = prod
+
+    @final
+    @doc(Rolling)
+    def rolling(
+        self,
+        window: int | dt.timedelta | str | BaseOffset | BaseIndexer,
+        min_periods: int | None = None,
+        center: bool_t = False,
+        win_type: str | None = None,
+        on: str | None = None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        closed: IntervalClosedType | None = None,
+        step: int | None = None,
+        method: str = "single",
+    ) -> Window | Rolling:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            name = "rolling"
+            if axis == 1:
+                warnings.warn(
+                    f"Support for axis=1 in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    f"Use obj.T.{name}(...) instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    "Call the method without the axis keyword instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        if win_type is not None:
+            return Window(
+                self,
+                window=window,
+                min_periods=min_periods,
+                center=center,
+                win_type=win_type,
+                on=on,
+                axis=axis,
+                closed=closed,
+                step=step,
+                method=method,
+            )
+
+        return Rolling(
+            self,
+            window=window,
+            min_periods=min_periods,
+            center=center,
+            win_type=win_type,
+            on=on,
+            axis=axis,
+            closed=closed,
+            step=step,
+            method=method,
+        )
+
+    @final
+    @doc(Expanding)
+    def expanding(
+        self,
+        min_periods: int = 1,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        method: Literal["single", "table"] = "single",
+    ) -> Expanding:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            name = "expanding"
+            if axis == 1:
+                warnings.warn(
+                    f"Support for axis=1 in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    f"Use obj.T.{name}(...) instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    "Call the method without the axis keyword instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+        return Expanding(self, min_periods=min_periods, axis=axis, method=method)
+
+    @final
+    @doc(ExponentialMovingWindow)
+    def ewm(
+        self,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | TimedeltaConvertibleTypes | None = None,
+        alpha: float | None = None,
+        min_periods: int | None = 0,
+        adjust: bool_t = True,
+        ignore_na: bool_t = False,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        times: np.ndarray | DataFrame | Series | None = None,
+        method: Literal["single", "table"] = "single",
+    ) -> ExponentialMovingWindow:
+        if axis is not lib.no_default:
+            axis = self._get_axis_number(axis)
+            name = "ewm"
+            if axis == 1:
+                warnings.warn(
+                    f"Support for axis=1 in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    f"Use obj.T.{name}(...) instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                warnings.warn(
+                    f"The 'axis' keyword in {type(self).__name__}.{name} is "
+                    "deprecated and will be removed in a future version. "
+                    "Call the method without the axis keyword instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+        else:
+            axis = 0
+
+        return ExponentialMovingWindow(
+            self,
+            com=com,
+            span=span,
+            halflife=halflife,
+            alpha=alpha,
+            min_periods=min_periods,
+            adjust=adjust,
+            ignore_na=ignore_na,
+            axis=axis,
+            times=times,
+            method=method,
+        )
+
+    # ----------------------------------------------------------------------
+    # Arithmetic Methods
+
+    @final
+    def _inplace_method(self, other, op) -> Self:
+        """
+        Wrap arithmetic method to operate inplace.
+        """
+        warn = True
+        if not PYPY and warn_copy_on_write():
+            if sys.getrefcount(self) <= REF_COUNT + 2:
+                # we are probably in an inplace setitem context (e.g. df['a'] += 1)
+                warn = False
+
+        result = op(self, other)
+
+        if (
+            self.ndim == 1
+            and result._indexed_same(self)
+            and result.dtype == self.dtype
+            and not using_copy_on_write()
+            and not (warn_copy_on_write() and not warn)
+        ):
+            # GH#36498 this inplace op can _actually_ be inplace.
+            # Item "ArrayManager" of "Union[ArrayManager, SingleArrayManager,
+            # BlockManager, SingleBlockManager]" has no attribute "setitem_inplace"
+            self._mgr.setitem_inplace(  # type: ignore[union-attr]
+                slice(None), result._values, warn=warn
+            )
+            return self
+
+        # Delete cacher
+        self._reset_cacher()
+
+        # this makes sure that we are aligned like the input
+        # we are updating inplace so we want to ignore is_copy
+        self._update_inplace(
+            result.reindex_like(self, copy=False), verify_is_copy=False
+        )
+        return self
+
+    @final
+    def __iadd__(self, other) -> Self:
+        # error: Unsupported left operand type for + ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__add__)  # type: ignore[operator]
+
+    @final
+    def __isub__(self, other) -> Self:
+        # error: Unsupported left operand type for - ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__sub__)  # type: ignore[operator]
+
+    @final
+    def __imul__(self, other) -> Self:
+        # error: Unsupported left operand type for * ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__mul__)  # type: ignore[operator]
+
+    @final
+    def __itruediv__(self, other) -> Self:
+        # error: Unsupported left operand type for / ("Type[NDFrame]")
+        return self._inplace_method(
+            other, type(self).__truediv__  # type: ignore[operator]
+        )
+
+    @final
+    def __ifloordiv__(self, other) -> Self:
+        # error: Unsupported left operand type for // ("Type[NDFrame]")
+        return self._inplace_method(
+            other, type(self).__floordiv__  # type: ignore[operator]
+        )
+
+    @final
+    def __imod__(self, other) -> Self:
+        # error: Unsupported left operand type for % ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__mod__)  # type: ignore[operator]
+
+    @final
+    def __ipow__(self, other) -> Self:
+        # error: Unsupported left operand type for ** ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__pow__)  # type: ignore[operator]
+
+    @final
+    def __iand__(self, other) -> Self:
+        # error: Unsupported left operand type for & ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__and__)  # type: ignore[operator]
+
+    @final
+    def __ior__(self, other) -> Self:
+        return self._inplace_method(other, type(self).__or__)
+
+    @final
+    def __ixor__(self, other) -> Self:
+        # error: Unsupported left operand type for ^ ("Type[NDFrame]")
+        return self._inplace_method(other, type(self).__xor__)  # type: ignore[operator]
+
+    # ----------------------------------------------------------------------
+    # Misc methods
+
+    @final
+    def _find_valid_index(self, *, how: str) -> Hashable | None:
+        """
+        Retrieves the index of the first valid value.
+
+        Parameters
+        ----------
+        how : {'first', 'last'}
+            Use this parameter to change between the first or last valid index.
+
+        Returns
+        -------
+        idx_first_valid : type of index
+        """
+        is_valid = self.notna().values
+        idxpos = find_valid_index(how=how, is_valid=is_valid)
+        if idxpos is None:
+            return None
+        return self.index[idxpos]
+
+    @final
+    @doc(position="first", klass=_shared_doc_kwargs["klass"])
+    def first_valid_index(self) -> Hashable | None:
+        """
+        Return index for {position} non-NA value or None, if no non-NA value is found.
+
+        Returns
+        -------
+        type of index
+
+        Examples
+        --------
+        For Series:
+
+        >>> s = pd.Series([None, 3, 4])
+        >>> s.first_valid_index()
+        1
+        >>> s.last_valid_index()
+        2
+
+        >>> s = pd.Series([None, None])
+        >>> print(s.first_valid_index())
+        None
+        >>> print(s.last_valid_index())
+        None
+
+        If all elements in Series are NA/null, returns None.
+
+        >>> s = pd.Series()
+        >>> print(s.first_valid_index())
+        None
+        >>> print(s.last_valid_index())
+        None
+
+        If Series is empty, returns None.
+
+        For DataFrame:
+
+        >>> df = pd.DataFrame({{'A': [None, None, 2], 'B': [None, 3, 4]}})
+        >>> df
+             A      B
+        0  NaN    NaN
+        1  NaN    3.0
+        2  2.0    4.0
+        >>> df.first_valid_index()
+        1
+        >>> df.last_valid_index()
+        2
+
+        >>> df = pd.DataFrame({{'A': [None, None, None], 'B': [None, None, None]}})
+        >>> df
+             A      B
+        0  None   None
+        1  None   None
+        2  None   None
+        >>> print(df.first_valid_index())
+        None
+        >>> print(df.last_valid_index())
+        None
+
+        If all elements in DataFrame are NA/null, returns None.
+
+        >>> df = pd.DataFrame()
+        >>> df
+        Empty DataFrame
+        Columns: []
+        Index: []
+        >>> print(df.first_valid_index())
+        None
+        >>> print(df.last_valid_index())
+        None
+
+        If DataFrame is empty, returns None.
+        """
+        return self._find_valid_index(how="first")
+
+    @final
+    @doc(first_valid_index, position="last", klass=_shared_doc_kwargs["klass"])
+    def last_valid_index(self) -> Hashable | None:
+        return self._find_valid_index(how="last")
+
+
+_num_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+    Axis for the function to be applied on.
+    For `Series` this parameter is unused and defaults to 0.
+
+    For DataFrames, specifying ``axis=None`` will apply the aggregation
+    across both axes.
+
+    .. versionadded:: 2.0.0
+
+skipna : bool, default True
+    Exclude NA/null values when computing the result.
+numeric_only : bool, default False
+    Include only float, int, boolean columns. Not implemented for Series.
+
+{min_count}\
+**kwargs
+    Additional keyword arguments to be passed to the function.
+
+Returns
+-------
+{name1} or scalar\
+{see_also}\
+{examples}
+"""
+
+_sum_prod_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+    Axis for the function to be applied on.
+    For `Series` this parameter is unused and defaults to 0.
+
+    .. warning::
+
+        The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
+        in a future version this will reduce over both axes and return a scalar
+        To retain the old behavior, pass axis=0 (or do not pass axis).
+
+    .. versionadded:: 2.0.0
+
+skipna : bool, default True
+    Exclude NA/null values when computing the result.
+numeric_only : bool, default False
+    Include only float, int, boolean columns. Not implemented for Series.
+
+{min_count}\
+**kwargs
+    Additional keyword arguments to be passed to the function.
+
+Returns
+-------
+{name1} or scalar\
+{see_also}\
+{examples}
+"""
+
+_num_ddof_doc = """
+{desc}
+
+Parameters
+----------
+axis : {axis_descr}
+    For `Series` this parameter is unused and defaults to 0.
+
+    .. warning::
+
+        The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
+        in a future version this will reduce over both axes and return a scalar
+        To retain the old behavior, pass axis=0 (or do not pass axis).
+
+skipna : bool, default True
+    Exclude NA/null values. If an entire row/column is NA, the result
+    will be NA.
+ddof : int, default 1
+    Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+    where N represents the number of elements.
+numeric_only : bool, default False
+    Include only float, int, boolean columns. Not implemented for Series.
+
+Returns
+-------
+{name1} or {name2} (if level specified) \
+{notes}\
+{examples}
+"""
+
+_std_notes = """
+
+Notes
+-----
+To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
+default `ddof=1`)"""
+
+_std_examples = """
+
+Examples
+--------
+>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
+...                    'age': [21, 25, 62, 43],
+...                    'height': [1.61, 1.87, 1.49, 2.01]}
+...                   ).set_index('person_id')
+>>> df
+           age  height
+person_id
+0           21    1.61
+1           25    1.87
+2           62    1.49
+3           43    2.01
+
+The standard deviation of the columns can be found as follows:
+
+>>> df.std()
+age       18.786076
+height     0.237417
+dtype: float64
+
+Alternatively, `ddof=0` can be set to normalize by N instead of N-1:
+
+>>> df.std(ddof=0)
+age       16.269219
+height     0.205609
+dtype: float64"""
+
+_var_examples = """
+
+Examples
+--------
+>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
+...                    'age': [21, 25, 62, 43],
+...                    'height': [1.61, 1.87, 1.49, 2.01]}
+...                   ).set_index('person_id')
+>>> df
+           age  height
+person_id
+0           21    1.61
+1           25    1.87
+2           62    1.49
+3           43    2.01
+
+>>> df.var()
+age       352.916667
+height      0.056367
+dtype: float64
+
+Alternatively, ``ddof=0`` can be set to normalize by N instead of N-1:
+
+>>> df.var(ddof=0)
+age       264.687500
+height      0.042275
+dtype: float64"""
+
+_bool_doc = """
+{desc}
+
+Parameters
+----------
+axis : {{0 or 'index', 1 or 'columns', None}}, default 0
+    Indicate which axis or axes should be reduced. For `Series` this parameter
+    is unused and defaults to 0.
+
+    * 0 / 'index' : reduce the index, return a Series whose index is the
+      original column labels.
+    * 1 / 'columns' : reduce the columns, return a Series whose index is the
+      original index.
+    * None : reduce all axes, return a scalar.
+
+bool_only : bool, default False
+    Include only boolean columns. Not implemented for Series.
+skipna : bool, default True
+    Exclude NA/null values. If the entire row/column is NA and skipna is
+    True, then the result will be {empty_value}, as for an empty row/column.
+    If skipna is False, then NA are treated as True, because these are not
+    equal to zero.
+**kwargs : any, default None
+    Additional keywords have no effect but might be accepted for
+    compatibility with NumPy.
+
+Returns
+-------
+{name1} or {name2}
+    If level is specified, then, {name2} is returned; otherwise, {name1}
+    is returned.
+
+{see_also}
+{examples}"""
+
+_all_desc = """\
+Return whether all elements are True, potentially over an axis.
+
+Returns True unless there at least one element within a series or
+along a Dataframe axis that is False or equivalent (e.g. zero or
+empty)."""
+
+_all_examples = """\
+Examples
+--------
+**Series**
+
+>>> pd.Series([True, True]).all()
+True
+>>> pd.Series([True, False]).all()
+False
+>>> pd.Series([], dtype="float64").all()
+True
+>>> pd.Series([np.nan]).all()
+True
+>>> pd.Series([np.nan]).all(skipna=False)
+True
+
+**DataFrames**
+
+Create a dataframe from a dictionary.
+
+>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
+>>> df
+   col1   col2
+0  True   True
+1  True  False
+
+Default behaviour checks if values in each column all return True.
+
+>>> df.all()
+col1     True
+col2    False
+dtype: bool
+
+Specify ``axis='columns'`` to check if values in each row all return True.
+
+>>> df.all(axis='columns')
+0     True
+1    False
+dtype: bool
+
+Or ``axis=None`` for whether every value is True.
+
+>>> df.all(axis=None)
+False
+"""
+
+_all_see_also = """\
+See Also
+--------
+Series.all : Return True if all elements are True.
+DataFrame.any : Return True if one (or more) elements are True.
+"""
+
+_cnum_doc = """
+Return cumulative {desc} over a DataFrame or Series axis.
+
+Returns a DataFrame or Series of the same size containing the cumulative
+{desc}.
+
+Parameters
+----------
+axis : {{0 or 'index', 1 or 'columns'}}, default 0
+    The index or the name of the axis. 0 is equivalent to None or 'index'.
+    For `Series` this parameter is unused and defaults to 0.
+skipna : bool, default True
+    Exclude NA/null values. If an entire row/column is NA, the result
+    will be NA.
+*args, **kwargs
+    Additional keywords have no effect but might be accepted for
+    compatibility with NumPy.
+
+Returns
+-------
+{name1} or {name2}
+    Return cumulative {desc} of {name1} or {name2}.
+
+See Also
+--------
+core.window.expanding.Expanding.{accum_func_name} : Similar functionality
+    but ignores ``NaN`` values.
+{name2}.{accum_func_name} : Return the {desc} over
+    {name2} axis.
+{name2}.cummax : Return cumulative maximum over {name2} axis.
+{name2}.cummin : Return cumulative minimum over {name2} axis.
+{name2}.cumsum : Return cumulative sum over {name2} axis.
+{name2}.cumprod : Return cumulative product over {name2} axis.
+
+{examples}"""
+
+_cummin_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cummin()
+0    2.0
+1    NaN
+2    2.0
+3   -1.0
+4   -1.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cummin(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the minimum
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cummin()
+     A    B
+0  2.0  1.0
+1  2.0  NaN
+2  1.0  0.0
+
+To iterate over columns and find the minimum in each row,
+use ``axis=1``
+
+>>> df.cummin(axis=1)
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+"""
+
+_cumsum_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cumsum()
+0    2.0
+1    NaN
+2    7.0
+3    6.0
+4    6.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cumsum(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the sum
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cumsum()
+     A    B
+0  2.0  1.0
+1  5.0  NaN
+2  6.0  1.0
+
+To iterate over columns and find the sum in each row,
+use ``axis=1``
+
+>>> df.cumsum(axis=1)
+     A    B
+0  2.0  3.0
+1  3.0  NaN
+2  1.0  1.0
+"""
+
+_cumprod_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cumprod()
+0     2.0
+1     NaN
+2    10.0
+3   -10.0
+4    -0.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cumprod(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the product
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cumprod()
+     A    B
+0  2.0  1.0
+1  6.0  NaN
+2  6.0  0.0
+
+To iterate over columns and find the product in each row,
+use ``axis=1``
+
+>>> df.cumprod(axis=1)
+     A    B
+0  2.0  2.0
+1  3.0  NaN
+2  1.0  0.0
+"""
+
+_cummax_examples = """\
+Examples
+--------
+**Series**
+
+>>> s = pd.Series([2, np.nan, 5, -1, 0])
+>>> s
+0    2.0
+1    NaN
+2    5.0
+3   -1.0
+4    0.0
+dtype: float64
+
+By default, NA values are ignored.
+
+>>> s.cummax()
+0    2.0
+1    NaN
+2    5.0
+3    5.0
+4    5.0
+dtype: float64
+
+To include NA values in the operation, use ``skipna=False``
+
+>>> s.cummax(skipna=False)
+0    2.0
+1    NaN
+2    NaN
+3    NaN
+4    NaN
+dtype: float64
+
+**DataFrame**
+
+>>> df = pd.DataFrame([[2.0, 1.0],
+...                    [3.0, np.nan],
+...                    [1.0, 0.0]],
+...                   columns=list('AB'))
+>>> df
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  1.0  0.0
+
+By default, iterates over rows and finds the maximum
+in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
+
+>>> df.cummax()
+     A    B
+0  2.0  1.0
+1  3.0  NaN
+2  3.0  1.0
+
+To iterate over columns and find the maximum in each row,
+use ``axis=1``
+
+>>> df.cummax(axis=1)
+     A    B
+0  2.0  2.0
+1  3.0  NaN
+2  1.0  1.0
+"""
+
+_any_see_also = """\
+See Also
+--------
+numpy.any : Numpy version of this method.
+Series.any : Return whether any element is True.
+Series.all : Return whether all elements are True.
+DataFrame.any : Return whether any element is True over requested axis.
+DataFrame.all : Return whether all elements are True over requested axis.
+"""
+
+_any_desc = """\
+Return whether any element is True, potentially over an axis.
+
+Returns False unless there is at least one element within a series or
+along a Dataframe axis that is True or equivalent (e.g. non-zero or
+non-empty)."""
+
+_any_examples = """\
+Examples
+--------
+**Series**
+
+For Series input, the output is a scalar indicating whether any element
+is True.
+
+>>> pd.Series([False, False]).any()
+False
+>>> pd.Series([True, False]).any()
+True
+>>> pd.Series([], dtype="float64").any()
+False
+>>> pd.Series([np.nan]).any()
+False
+>>> pd.Series([np.nan]).any(skipna=False)
+True
+
+**DataFrame**
+
+Whether each column contains at least one True element (the default).
+
+>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
+>>> df
+   A  B  C
+0  1  0  0
+1  2  2  0
+
+>>> df.any()
+A     True
+B     True
+C    False
+dtype: bool
+
+Aggregating over the columns.
+
+>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
+>>> df
+       A  B
+0   True  1
+1  False  2
+
+>>> df.any(axis='columns')
+0    True
+1    True
+dtype: bool
+
+>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
+>>> df
+       A  B
+0   True  1
+1  False  0
+
+>>> df.any(axis='columns')
+0    True
+1    False
+dtype: bool
+
+Aggregating over the entire DataFrame with ``axis=None``.
+
+>>> df.any(axis=None)
+True
+
+`any` for an empty DataFrame is an empty Series.
+
+>>> pd.DataFrame([]).any()
+Series([], dtype: bool)
+"""
+
+_shared_docs[
+    "stat_func_example"
+] = """
+
+Examples
+--------
+>>> idx = pd.MultiIndex.from_arrays([
+...     ['warm', 'warm', 'cold', 'cold'],
+...     ['dog', 'falcon', 'fish', 'spider']],
+...     names=['blooded', 'animal'])
+>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
+>>> s
+blooded  animal
+warm     dog       4
+         falcon    2
+cold     fish      0
+         spider    8
+Name: legs, dtype: int64
+
+>>> s.{stat_func}()
+{default_output}"""
+
+_sum_examples = _shared_docs["stat_func_example"].format(
+    stat_func="sum", verb="Sum", default_output=14, level_output_0=6, level_output_1=8
+)
+
+_sum_examples += """
+
+By default, the sum of an empty or all-NA Series is ``0``.
+
+>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
+0.0
+
+This can be controlled with the ``min_count`` parameter. For example, if
+you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
+
+>>> pd.Series([], dtype="float64").sum(min_count=1)
+nan
+
+Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
+empty series identically.
+
+>>> pd.Series([np.nan]).sum()
+0.0
+
+>>> pd.Series([np.nan]).sum(min_count=1)
+nan"""
+
+_max_examples: str = _shared_docs["stat_func_example"].format(
+    stat_func="max", verb="Max", default_output=8, level_output_0=4, level_output_1=8
+)
+
+_min_examples: str = _shared_docs["stat_func_example"].format(
+    stat_func="min", verb="Min", default_output=0, level_output_0=2, level_output_1=0
+)
+
+_stat_func_see_also = """
+
+See Also
+--------
+Series.sum : Return the sum.
+Series.min : Return the minimum.
+Series.max : Return the maximum.
+Series.idxmin : Return the index of the minimum.
+Series.idxmax : Return the index of the maximum.
+DataFrame.sum : Return the sum over the requested axis.
+DataFrame.min : Return the minimum over the requested axis.
+DataFrame.max : Return the maximum over the requested axis.
+DataFrame.idxmin : Return the index of the minimum over the requested axis.
+DataFrame.idxmax : Return the index of the maximum over the requested axis."""
+
+_prod_examples = """
+
+Examples
+--------
+By default, the product of an empty or all-NA Series is ``1``
+
+>>> pd.Series([], dtype="float64").prod()
+1.0
+
+This can be controlled with the ``min_count`` parameter
+
+>>> pd.Series([], dtype="float64").prod(min_count=1)
+nan
+
+Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
+empty series identically.
+
+>>> pd.Series([np.nan]).prod()
+1.0
+
+>>> pd.Series([np.nan]).prod(min_count=1)
+nan"""
+
+_min_count_stub = """\
+min_count : int, default 0
+    The required number of valid values to perform the operation. If fewer than
+    ``min_count`` non-NA values are present the result will be NA.
+"""
+
+
+def make_doc(name: str, ndim: int) -> str:
+    """
+    Generate the docstring for a Series/DataFrame reduction.
+    """
+    if ndim == 1:
+        name1 = "scalar"
+        name2 = "Series"
+        axis_descr = "{index (0)}"
+    else:
+        name1 = "Series"
+        name2 = "DataFrame"
+        axis_descr = "{index (0), columns (1)}"
+
+    if name == "any":
+        base_doc = _bool_doc
+        desc = _any_desc
+        see_also = _any_see_also
+        examples = _any_examples
+        kwargs = {"empty_value": "False"}
+    elif name == "all":
+        base_doc = _bool_doc
+        desc = _all_desc
+        see_also = _all_see_also
+        examples = _all_examples
+        kwargs = {"empty_value": "True"}
+    elif name == "min":
+        base_doc = _num_doc
+        desc = (
+            "Return the minimum of the values over the requested axis.\n\n"
+            "If you want the *index* of the minimum, use ``idxmin``. This is "
+            "the equivalent of the ``numpy.ndarray`` method ``argmin``."
+        )
+        see_also = _stat_func_see_also
+        examples = _min_examples
+        kwargs = {"min_count": ""}
+    elif name == "max":
+        base_doc = _num_doc
+        desc = (
+            "Return the maximum of the values over the requested axis.\n\n"
+            "If you want the *index* of the maximum, use ``idxmax``. This is "
+            "the equivalent of the ``numpy.ndarray`` method ``argmax``."
+        )
+        see_also = _stat_func_see_also
+        examples = _max_examples
+        kwargs = {"min_count": ""}
+
+    elif name == "sum":
+        base_doc = _sum_prod_doc
+        desc = (
+            "Return the sum of the values over the requested axis.\n\n"
+            "This is equivalent to the method ``numpy.sum``."
+        )
+        see_also = _stat_func_see_also
+        examples = _sum_examples
+        kwargs = {"min_count": _min_count_stub}
+
+    elif name == "prod":
+        base_doc = _sum_prod_doc
+        desc = "Return the product of the values over the requested axis."
+        see_also = _stat_func_see_also
+        examples = _prod_examples
+        kwargs = {"min_count": _min_count_stub}
+
+    elif name == "median":
+        base_doc = _num_doc
+        desc = "Return the median of the values over the requested axis."
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.median()
+            2.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.median()
+            a   1.5
+            b   2.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.median(axis=1)
+            tiger   1.5
+            zebra   2.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True`
+            to avoid getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.median(numeric_only=True)
+            a   1.5
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+
+    elif name == "mean":
+        base_doc = _num_doc
+        desc = "Return the mean of the values over the requested axis."
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.mean()
+            2.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.mean()
+            a   1.5
+            b   2.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.mean(axis=1)
+            tiger   1.5
+            zebra   2.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True` to avoid
+            getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.mean(numeric_only=True)
+            a   1.5
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+
+    elif name == "var":
+        base_doc = _num_ddof_doc
+        desc = (
+            "Return unbiased variance over requested axis.\n\nNormalized by "
+            "N-1 by default. This can be changed using the ddof argument."
+        )
+        examples = _var_examples
+        see_also = ""
+        kwargs = {"notes": ""}
+
+    elif name == "std":
+        base_doc = _num_ddof_doc
+        desc = (
+            "Return sample standard deviation over requested axis."
+            "\n\nNormalized by N-1 by default. This can be changed using the "
+            "ddof argument."
+        )
+        examples = _std_examples
+        see_also = ""
+        kwargs = {"notes": _std_notes}
+
+    elif name == "sem":
+        base_doc = _num_ddof_doc
+        desc = (
+            "Return unbiased standard error of the mean over requested "
+            "axis.\n\nNormalized by N-1 by default. This can be changed "
+            "using the ddof argument"
+        )
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.sem().round(6)
+            0.57735
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
+            >>> df
+                   a   b
+            tiger  1   2
+            zebra  2   3
+            >>> df.sem()
+            a   0.5
+            b   0.5
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.sem(axis=1)
+            tiger   0.5
+            zebra   0.5
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True`
+            to avoid getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
+            ...                   index=['tiger', 'zebra'])
+            >>> df.sem(numeric_only=True)
+            a   0.5
+            dtype: float64"""
+        see_also = ""
+        kwargs = {"notes": ""}
+
+    elif name == "skew":
+        base_doc = _num_doc
+        desc = "Return unbiased skew over requested axis.\n\nNormalized by N-1."
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 3])
+            >>> s.skew()
+            0.0
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
+            ...                   index=['tiger', 'zebra', 'cow'])
+            >>> df
+                    a   b   c
+            tiger   1   2   1
+            zebra   2   3   3
+            cow     3   4   5
+            >>> df.skew()
+            a   0.0
+            b   0.0
+            c   0.0
+            dtype: float64
+
+            Using axis=1
+
+            >>> df.skew(axis=1)
+            tiger   1.732051
+            zebra  -1.732051
+            cow     0.000000
+            dtype: float64
+
+            In this case, `numeric_only` should be set to `True` to avoid
+            getting an error.
+
+            >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
+            ...                   index=['tiger', 'zebra', 'cow'])
+            >>> df.skew(numeric_only=True)
+            a   0.0
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+    elif name == "kurt":
+        base_doc = _num_doc
+        desc = (
+            "Return unbiased kurtosis over requested axis.\n\n"
+            "Kurtosis obtained using Fisher's definition of\n"
+            "kurtosis (kurtosis of normal == 0.0). Normalized "
+            "by N-1."
+        )
+        see_also = ""
+        examples = """
+
+            Examples
+            --------
+            >>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
+            >>> s
+            cat    1
+            dog    2
+            dog    2
+            mouse  3
+            dtype: int64
+            >>> s.kurt()
+            1.5
+
+            With a DataFrame
+
+            >>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
+            ...                   index=['cat', 'dog', 'dog', 'mouse'])
+            >>> df
+                   a   b
+              cat  1   3
+              dog  2   4
+              dog  2   4
+            mouse  3   4
+            >>> df.kurt()
+            a   1.5
+            b   4.0
+            dtype: float64
+
+            With axis=None
+
+            >>> df.kurt(axis=None).round(6)
+            -0.988693
+
+            Using axis=1
+
+            >>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
+            ...                   index=['cat', 'dog'])
+            >>> df.kurt(axis=1)
+            cat   -6.0
+            dog   -6.0
+            dtype: float64"""
+        kwargs = {"min_count": ""}
+
+    elif name == "cumsum":
+        base_doc = _cnum_doc
+        desc = "sum"
+        see_also = ""
+        examples = _cumsum_examples
+        kwargs = {"accum_func_name": "sum"}
+
+    elif name == "cumprod":
+        base_doc = _cnum_doc
+        desc = "product"
+        see_also = ""
+        examples = _cumprod_examples
+        kwargs = {"accum_func_name": "prod"}
+
+    elif name == "cummin":
+        base_doc = _cnum_doc
+        desc = "minimum"
+        see_also = ""
+        examples = _cummin_examples
+        kwargs = {"accum_func_name": "min"}
+
+    elif name == "cummax":
+        base_doc = _cnum_doc
+        desc = "maximum"
+        see_also = ""
+        examples = _cummax_examples
+        kwargs = {"accum_func_name": "max"}
+
+    else:
+        raise NotImplementedError
+
+    docstr = base_doc.format(
+        desc=desc,
+        name=name,
+        name1=name1,
+        name2=name2,
+        axis_descr=axis_descr,
+        see_also=see_also,
+        examples=examples,
+        **kwargs,
+    )
+    return docstr
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8248f378e2c1acea37bdc2d41065c591360b902a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/__init__.py
@@ -0,0 +1,15 @@
+from pandas.core.groupby.generic import (
+    DataFrameGroupBy,
+    NamedAgg,
+    SeriesGroupBy,
+)
+from pandas.core.groupby.groupby import GroupBy
+from pandas.core.groupby.grouper import Grouper
+
+__all__ = [
+    "DataFrameGroupBy",
+    "NamedAgg",
+    "SeriesGroupBy",
+    "GroupBy",
+    "Grouper",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..a443597347283887deb9cbd3eafb5f6d3bb6d9a6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/base.py
@@ -0,0 +1,121 @@
+"""
+Provide basic components for groupby.
+"""
+from __future__ import annotations
+
+import dataclasses
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+
+@dataclasses.dataclass(order=True, frozen=True)
+class OutputKey:
+    label: Hashable
+    position: int
+
+
+# special case to prevent duplicate plots when catching exceptions when
+# forwarding methods from NDFrames
+plotting_methods = frozenset(["plot", "hist"])
+
+# cythonized transformations or canned "agg+broadcast", which do not
+# require postprocessing of the result by transform.
+cythonized_kernels = frozenset(["cumprod", "cumsum", "shift", "cummin", "cummax"])
+
+# List of aggregation/reduction functions.
+# These map each group to a single numeric value
+reduction_kernels = frozenset(
+    [
+        "all",
+        "any",
+        "corrwith",
+        "count",
+        "first",
+        "idxmax",
+        "idxmin",
+        "last",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "nunique",
+        "prod",
+        # as long as `quantile`'s signature accepts only
+        # a single quantile value, it's a reduction.
+        # GH#27526 might change that.
+        "quantile",
+        "sem",
+        "size",
+        "skew",
+        "std",
+        "sum",
+        "var",
+    ]
+)
+
+# List of transformation functions.
+# a transformation is a function that, for each group,
+# produces a result that has the same shape as the group.
+
+
+transformation_kernels = frozenset(
+    [
+        "bfill",
+        "cumcount",
+        "cummax",
+        "cummin",
+        "cumprod",
+        "cumsum",
+        "diff",
+        "ffill",
+        "fillna",
+        "ngroup",
+        "pct_change",
+        "rank",
+        "shift",
+    ]
+)
+
+# these are all the public methods on Grouper which don't belong
+# in either of the above lists
+groupby_other_methods = frozenset(
+    [
+        "agg",
+        "aggregate",
+        "apply",
+        "boxplot",
+        # corr and cov return ngroups*ncolumns rows, so they
+        # are neither a transformation nor a reduction
+        "corr",
+        "cov",
+        "describe",
+        "dtypes",
+        "expanding",
+        "ewm",
+        "filter",
+        "get_group",
+        "groups",
+        "head",
+        "hist",
+        "indices",
+        "ndim",
+        "ngroups",
+        "nth",
+        "ohlc",
+        "pipe",
+        "plot",
+        "resample",
+        "rolling",
+        "tail",
+        "take",
+        "transform",
+        "sample",
+        "value_counts",
+    ]
+)
+# Valid values  of `name` for `groupby.transform(name)`
+# NOTE: do NOT edit this directly. New additions should be inserted
+# into the appropriate list above.
+transform_kernel_allowlist = reduction_kernels | transformation_kernels
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/categorical.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ab98cf4fe55e9b064db99e61d1245cb83b63dc1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/categorical.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+import numpy as np
+
+from pandas.core.algorithms import unique1d
+from pandas.core.arrays.categorical import (
+    Categorical,
+    CategoricalDtype,
+    recode_for_categories,
+)
+
+
+def recode_for_groupby(
+    c: Categorical, sort: bool, observed: bool
+) -> tuple[Categorical, Categorical | None]:
+    """
+    Code the categories to ensure we can groupby for categoricals.
+
+    If observed=True, we return a new Categorical with the observed
+    categories only.
+
+    If sort=False, return a copy of self, coded with categories as
+    returned by .unique(), followed by any categories not appearing in
+    the data. If sort=True, return self.
+
+    This method is needed solely to ensure the categorical index of the
+    GroupBy result has categories in the order of appearance in the data
+    (GH-8868).
+
+    Parameters
+    ----------
+    c : Categorical
+    sort : bool
+        The value of the sort parameter groupby was called with.
+    observed : bool
+        Account only for the observed values
+
+    Returns
+    -------
+    Categorical
+        If sort=False, the new categories are set to the order of
+        appearance in codes (unless ordered=True, in which case the
+        original order is preserved), followed by any unrepresented
+        categories in the original order.
+    Categorical or None
+        If we are observed, return the original categorical, otherwise None
+    """
+    # we only care about observed values
+    if observed:
+        # In cases with c.ordered, this is equivalent to
+        #  return c.remove_unused_categories(), c
+
+        unique_codes = unique1d(c.codes)
+
+        take_codes = unique_codes[unique_codes != -1]
+        if sort:
+            take_codes = np.sort(take_codes)
+
+        # we recode according to the uniques
+        categories = c.categories.take(take_codes)
+        codes = recode_for_categories(c.codes, c.categories, categories)
+
+        # return a new categorical that maps our new codes
+        # and categories
+        dtype = CategoricalDtype(categories, ordered=c.ordered)
+        return Categorical._simple_new(codes, dtype=dtype), c
+
+    # Already sorted according to c.categories; all is fine
+    if sort:
+        return c, None
+
+    # sort=False should order groups in as-encountered order (GH-8868)
+
+    # xref GH:46909: Re-ordering codes faster than using (set|add|reorder)_categories
+    all_codes = np.arange(c.categories.nunique())
+    # GH 38140: exclude nan from indexer for categories
+    unique_notnan_codes = unique1d(c.codes[c.codes != -1])
+    if sort:
+        unique_notnan_codes = np.sort(unique_notnan_codes)
+    if len(all_codes) > len(unique_notnan_codes):
+        # GH 13179: All categories need to be present, even if missing from the data
+        missing_codes = np.setdiff1d(all_codes, unique_notnan_codes, assume_unique=True)
+        take_codes = np.concatenate((unique_notnan_codes, missing_codes))
+    else:
+        take_codes = unique_notnan_codes
+
+    return Categorical(c, c.unique().categories.take(take_codes)), None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/generic.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2e314046fb749e7d32bf7fc76ca22eb1194a328
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/generic.py
@@ -0,0 +1,2852 @@
+"""
+Define the SeriesGroupBy and DataFrameGroupBy
+classes that hold the groupby interfaces (and some implementations).
+
+These are user facing as the result of the ``df.groupby(...)`` operations,
+which here returns a DataFrameGroupBy object.
+"""
+from __future__ import annotations
+
+from collections import abc
+from functools import partial
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    NamedTuple,
+    TypeVar,
+    Union,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    Interval,
+    lib,
+)
+from pandas._libs.hashtable import duplicated
+from pandas.errors import SpecificationError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    is_bool,
+    is_dict_like,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    IntervalDtype,
+)
+from pandas.core.dtypes.inference import is_hashable
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import algorithms
+from pandas.core.apply import (
+    GroupByApply,
+    maybe_mangle_lambdas,
+    reconstruct_func,
+    validate_func_kwargs,
+    warn_alias_replacement,
+)
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.core.groupby import (
+    base,
+    ops,
+)
+from pandas.core.groupby.groupby import (
+    GroupBy,
+    GroupByPlot,
+    _agg_template_frame,
+    _agg_template_series,
+    _apply_docs,
+    _transform_template,
+)
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    all_indexes_same,
+    default_index,
+)
+from pandas.core.series import Series
+from pandas.core.sorting import get_group_index
+from pandas.core.util.numba_ import maybe_use_numba
+
+from pandas.plotting import boxplot_frame_groupby
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Axis,
+        AxisInt,
+        CorrelationMethod,
+        FillnaOptions,
+        IndexLabel,
+        Manager,
+        Manager2D,
+        SingleManager,
+        TakeIndexer,
+    )
+
+    from pandas import Categorical
+    from pandas.core.generic import NDFrame
+
+# TODO(typing) the return value on this callable should be any *scalar*.
+AggScalar = Union[str, Callable[..., Any]]
+# TODO: validate types on ScalarResult and move to _typing
+# Blocked from using by https://github.com/python/mypy/issues/1484
+# See note at _mangle_lambda_list
+ScalarResult = TypeVar("ScalarResult")
+
+
+class NamedAgg(NamedTuple):
+    """
+    Helper for column specific aggregation with control over output column names.
+
+    Subclass of typing.NamedTuple.
+
+    Parameters
+    ----------
+    column : Hashable
+        Column label in the DataFrame to apply aggfunc.
+    aggfunc : function or str
+        Function to apply to the provided column. If string, the name of a built-in
+        pandas function.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"key": [1, 1, 2], "a": [-1, 0, 1], 1: [10, 11, 12]})
+    >>> agg_a = pd.NamedAgg(column="a", aggfunc="min")
+    >>> agg_1 = pd.NamedAgg(column=1, aggfunc=lambda x: np.mean(x))
+    >>> df.groupby("key").agg(result_a=agg_a, result_1=agg_1)
+         result_a  result_1
+    key
+    1          -1      10.5
+    2           1      12.0
+    """
+
+    column: Hashable
+    aggfunc: AggScalar
+
+
+class SeriesGroupBy(GroupBy[Series]):
+    def _wrap_agged_manager(self, mgr: Manager) -> Series:
+        out = self.obj._constructor_from_mgr(mgr, axes=mgr.axes)
+        out._name = self.obj.name
+        return out
+
+    def _get_data_to_aggregate(
+        self, *, numeric_only: bool = False, name: str | None = None
+    ) -> SingleManager:
+        ser = self._obj_with_exclusions
+        single = ser._mgr
+        if numeric_only and not is_numeric_dtype(ser.dtype):
+            # GH#41291 match Series behavior
+            kwd_name = "numeric_only"
+            raise TypeError(
+                f"Cannot use {kwd_name}=True with "
+                f"{type(self).__name__}.{name} and non-numeric dtypes."
+            )
+        return single
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> s = pd.Series([1, 2, 3, 4])
+
+    >>> s
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+
+    >>> s.groupby([1, 1, 2, 2]).min()
+    1    1
+    2    3
+    dtype: int64
+
+    >>> s.groupby([1, 1, 2, 2]).agg('min')
+    1    1
+    2    3
+    dtype: int64
+
+    >>> s.groupby([1, 1, 2, 2]).agg(['min', 'max'])
+       min  max
+    1    1    2
+    2    3    4
+
+    The output column names can be controlled by passing
+    the desired column names and aggregations as keyword arguments.
+
+    >>> s.groupby([1, 1, 2, 2]).agg(
+    ...     minimum='min',
+    ...     maximum='max',
+    ... )
+       minimum  maximum
+    1        1        2
+    2        3        4
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the aggregating function.
+
+    >>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
+    1    1.0
+    2    3.0
+    dtype: float64
+    """
+    )
+
+    @Appender(
+        _apply_docs["template"].format(
+            input="series", examples=_apply_docs["series_examples"]
+        )
+    )
+    def apply(self, func, *args, **kwargs) -> Series:
+        return super().apply(func, *args, **kwargs)
+
+    @doc(_agg_template_series, examples=_agg_examples_doc, klass="Series")
+    def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
+        relabeling = func is None
+        columns = None
+        if relabeling:
+            columns, func = validate_func_kwargs(kwargs)
+            kwargs = {}
+
+        if isinstance(func, str):
+            if maybe_use_numba(engine) and engine is not None:
+                # Not all agg functions support numba, only propagate numba kwargs
+                # if user asks for numba, and engine is not None
+                # (if engine is None, the called function will handle the case where
+                # numba is requested via the global option)
+                kwargs["engine"] = engine
+            if engine_kwargs is not None:
+                kwargs["engine_kwargs"] = engine_kwargs
+            return getattr(self, func)(*args, **kwargs)
+
+        elif isinstance(func, abc.Iterable):
+            # Catch instances of lists / tuples
+            # but not the class list / tuple itself.
+            func = maybe_mangle_lambdas(func)
+            kwargs["engine"] = engine
+            kwargs["engine_kwargs"] = engine_kwargs
+            ret = self._aggregate_multiple_funcs(func, *args, **kwargs)
+            if relabeling:
+                # columns is not narrowed by mypy from relabeling flag
+                assert columns is not None  # for mypy
+                ret.columns = columns
+            if not self.as_index:
+                ret = ret.reset_index()
+            return ret
+
+        else:
+            cyfunc = com.get_cython_func(func)
+            if cyfunc and not args and not kwargs:
+                warn_alias_replacement(self, func, cyfunc)
+                return getattr(self, cyfunc)()
+
+            if maybe_use_numba(engine):
+                return self._aggregate_with_numba(
+                    func, *args, engine_kwargs=engine_kwargs, **kwargs
+                )
+
+            if self.ngroups == 0:
+                # e.g. test_evaluate_with_empty_groups without any groups to
+                #  iterate over, we have no output on which to do dtype
+                #  inference. We default to using the existing dtype.
+                #  xref GH#51445
+                obj = self._obj_with_exclusions
+                return self.obj._constructor(
+                    [],
+                    name=self.obj.name,
+                    index=self._grouper.result_index,
+                    dtype=obj.dtype,
+                )
+
+            if self._grouper.nkeys > 1:
+                return self._python_agg_general(func, *args, **kwargs)
+
+            try:
+                return self._python_agg_general(func, *args, **kwargs)
+            except KeyError:
+                # KeyError raised in test_groupby.test_basic is bc the func does
+                #  a dictionary lookup on group.name, but group name is not
+                #  pinned in _python_agg_general, only in _aggregate_named
+                result = self._aggregate_named(func, *args, **kwargs)
+
+                warnings.warn(
+                    "Pinning the groupby key to each group in "
+                    f"{type(self).__name__}.agg is deprecated, and cases that "
+                    "relied on it will raise in a future version. "
+                    "If your operation requires utilizing the groupby keys, "
+                    "iterate over the groupby object instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+                # result is a dict whose keys are the elements of result_index
+                result = Series(result, index=self._grouper.result_index)
+                result = self._wrap_aggregated_output(result)
+                return result
+
+    agg = aggregate
+
+    def _python_agg_general(self, func, *args, **kwargs):
+        orig_func = func
+        func = com.is_builtin_func(func)
+        if orig_func != func:
+            alias = com._builtin_table_alias[func]
+            warn_alias_replacement(self, orig_func, alias)
+        f = lambda x: func(x, *args, **kwargs)
+
+        obj = self._obj_with_exclusions
+        result = self._grouper.agg_series(obj, f)
+        res = obj._constructor(result, name=obj.name)
+        return self._wrap_aggregated_output(res)
+
+    def _aggregate_multiple_funcs(self, arg, *args, **kwargs) -> DataFrame:
+        if isinstance(arg, dict):
+            if self.as_index:
+                # GH 15931
+                raise SpecificationError("nested renamer is not supported")
+            else:
+                # GH#50684 - This accidentally worked in 1.x
+                msg = (
+                    "Passing a dictionary to SeriesGroupBy.agg is deprecated "
+                    "and will raise in a future version of pandas. Pass a list "
+                    "of aggregations instead."
+                )
+                warnings.warn(
+                    message=msg,
+                    category=FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                arg = list(arg.items())
+        elif any(isinstance(x, (tuple, list)) for x in arg):
+            arg = [(x, x) if not isinstance(x, (tuple, list)) else x for x in arg]
+        else:
+            # list of functions / function names
+            columns = (com.get_callable_name(f) or f for f in arg)
+            arg = zip(columns, arg)
+
+        results: dict[base.OutputKey, DataFrame | Series] = {}
+        with com.temp_setattr(self, "as_index", True):
+            # Combine results using the index, need to adjust index after
+            # if as_index=False (GH#50724)
+            for idx, (name, func) in enumerate(arg):
+                key = base.OutputKey(label=name, position=idx)
+                results[key] = self.aggregate(func, *args, **kwargs)
+
+        if any(isinstance(x, DataFrame) for x in results.values()):
+            from pandas import concat
+
+            res_df = concat(
+                results.values(), axis=1, keys=[key.label for key in results]
+            )
+            return res_df
+
+        indexed_output = {key.position: val for key, val in results.items()}
+        output = self.obj._constructor_expanddim(indexed_output, index=None)
+        output.columns = Index(key.label for key in results)
+
+        return output
+
+    def _wrap_applied_output(
+        self,
+        data: Series,
+        values: list[Any],
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ) -> DataFrame | Series:
+        """
+        Wrap the output of SeriesGroupBy.apply into the expected result.
+
+        Parameters
+        ----------
+        data : Series
+            Input data for groupby operation.
+        values : List[Any]
+            Applied output for each group.
+        not_indexed_same : bool, default False
+            Whether the applied outputs are not indexed the same as the group axes.
+
+        Returns
+        -------
+        DataFrame or Series
+        """
+        if len(values) == 0:
+            # GH #6265
+            if is_transform:
+                # GH#47787 see test_group_on_empty_multiindex
+                res_index = data.index
+            else:
+                res_index = self._grouper.result_index
+
+            return self.obj._constructor(
+                [],
+                name=self.obj.name,
+                index=res_index,
+                dtype=data.dtype,
+            )
+        assert values is not None
+
+        if isinstance(values[0], dict):
+            # GH #823 #24880
+            index = self._grouper.result_index
+            res_df = self.obj._constructor_expanddim(values, index=index)
+            res_df = self._reindex_output(res_df)
+            # if self.observed is False,
+            # keep all-NaN rows created while re-indexing
+            res_ser = res_df.stack(future_stack=True)
+            res_ser.name = self.obj.name
+            return res_ser
+        elif isinstance(values[0], (Series, DataFrame)):
+            result = self._concat_objects(
+                values,
+                not_indexed_same=not_indexed_same,
+                is_transform=is_transform,
+            )
+            if isinstance(result, Series):
+                result.name = self.obj.name
+            if not self.as_index and not_indexed_same:
+                result = self._insert_inaxis_grouper(result)
+                result.index = default_index(len(result))
+            return result
+        else:
+            # GH #6265 #24880
+            result = self.obj._constructor(
+                data=values, index=self._grouper.result_index, name=self.obj.name
+            )
+            if not self.as_index:
+                result = self._insert_inaxis_grouper(result)
+                result.index = default_index(len(result))
+            return self._reindex_output(result)
+
+    def _aggregate_named(self, func, *args, **kwargs):
+        # Note: this is very similar to _aggregate_series_pure_python,
+        #  but that does not pin group.name
+        result = {}
+        initialized = False
+
+        for name, group in self._grouper.get_iterator(
+            self._obj_with_exclusions, axis=self.axis
+        ):
+            # needed for pandas/tests/groupby/test_groupby.py::test_basic_aggregations
+            object.__setattr__(group, "name", name)
+
+            output = func(group, *args, **kwargs)
+            output = ops.extract_result(output)
+            if not initialized:
+                # We only do this validation on the first iteration
+                ops.check_result_array(output, group.dtype)
+                initialized = True
+            result[name] = output
+
+        return result
+
+    __examples_series_doc = dedent(
+        """
+    >>> ser = pd.Series([390.0, 350.0, 30.0, 20.0],
+    ...                 index=["Falcon", "Falcon", "Parrot", "Parrot"],
+    ...                 name="Max Speed")
+    >>> grouped = ser.groupby([1, 1, 2, 2])
+    >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
+        Falcon    0.707107
+        Falcon   -0.707107
+        Parrot    0.707107
+        Parrot   -0.707107
+        Name: Max Speed, dtype: float64
+
+    Broadcast result of the transformation
+
+    >>> grouped.transform(lambda x: x.max() - x.min())
+    Falcon    40.0
+    Falcon    40.0
+    Parrot    10.0
+    Parrot    10.0
+    Name: Max Speed, dtype: float64
+
+    >>> grouped.transform("mean")
+    Falcon    370.0
+    Falcon    370.0
+    Parrot     25.0
+    Parrot     25.0
+    Name: Max Speed, dtype: float64
+
+    .. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    for example:
+
+    >>> grouped.transform(lambda x: x.astype(int).max())
+    Falcon    390
+    Falcon    390
+    Parrot     30
+    Parrot     30
+    Name: Max Speed, dtype: int64
+    """
+    )
+
+    @Substitution(klass="Series", example=__examples_series_doc)
+    @Appender(_transform_template)
+    def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        return self._transform(
+            func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
+        )
+
+    def _cython_transform(
+        self, how: str, numeric_only: bool = False, axis: AxisInt = 0, **kwargs
+    ):
+        assert axis == 0  # handled by caller
+
+        obj = self._obj_with_exclusions
+
+        try:
+            result = self._grouper._cython_operation(
+                "transform", obj._values, how, axis, **kwargs
+            )
+        except NotImplementedError as err:
+            # e.g. test_groupby_raises_string
+            raise TypeError(f"{how} is not supported for {obj.dtype} dtype") from err
+
+        return obj._constructor(result, index=self.obj.index, name=obj.name)
+
+    def _transform_general(
+        self, func: Callable, engine, engine_kwargs, *args, **kwargs
+    ) -> Series:
+        """
+        Transform with a callable `func`.
+        """
+        if maybe_use_numba(engine):
+            return self._transform_with_numba(
+                func, *args, engine_kwargs=engine_kwargs, **kwargs
+            )
+        assert callable(func)
+        klass = type(self.obj)
+
+        results = []
+        for name, group in self._grouper.get_iterator(
+            self._obj_with_exclusions, axis=self.axis
+        ):
+            # this setattr is needed for test_transform_lambda_with_datetimetz
+            object.__setattr__(group, "name", name)
+            res = func(group, *args, **kwargs)
+
+            results.append(klass(res, index=group.index))
+
+        # check for empty "results" to avoid concat ValueError
+        if results:
+            from pandas.core.reshape.concat import concat
+
+            concatenated = concat(results)
+            result = self._set_result_index_ordered(concatenated)
+        else:
+            result = self.obj._constructor(dtype=np.float64)
+
+        result.name = self.obj.name
+        return result
+
+    def filter(self, func, dropna: bool = True, *args, **kwargs):
+        """
+        Filter elements from groups that don't satisfy a criterion.
+
+        Elements from groups are filtered if they do not satisfy the
+        boolean criterion specified by func.
+
+        Parameters
+        ----------
+        func : function
+            Criterion to apply to each group. Should return True or False.
+        dropna : bool
+            Drop groups that do not pass the filter. True by default; if False,
+            groups that evaluate False are filled with NaNs.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+        ...                           'foo', 'bar'],
+        ...                    'B' : [1, 2, 3, 4, 5, 6],
+        ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
+        >>> grouped = df.groupby('A')
+        >>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
+        1    2
+        3    4
+        5    6
+        Name: B, dtype: int64
+        """
+        if isinstance(func, str):
+            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
+        else:
+            wrapper = lambda x: func(x, *args, **kwargs)
+
+        # Interpret np.nan as False.
+        def true_and_notna(x) -> bool:
+            b = wrapper(x)
+            return notna(b) and b
+
+        try:
+            indices = [
+                self._get_index(name)
+                for name, group in self._grouper.get_iterator(
+                    self._obj_with_exclusions, axis=self.axis
+                )
+                if true_and_notna(group)
+            ]
+        except (ValueError, TypeError) as err:
+            raise TypeError("the filter must return a boolean result") from err
+
+        filtered = self._apply_filter(indices, dropna)
+        return filtered
+
+    def nunique(self, dropna: bool = True) -> Series | DataFrame:
+        """
+        Return number of unique elements in the group.
+
+        Returns
+        -------
+        Series
+            Number of unique values within each group.
+
+        Examples
+        --------
+        For SeriesGroupby:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).nunique()
+        a    2
+        b    1
+        dtype: int64
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 3], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    3
+        dtype: int64
+        >>> ser.resample('MS').nunique()
+        2023-01-01    2
+        2023-02-01    1
+        Freq: MS, dtype: int64
+        """
+        ids, _, ngroups = self._grouper.group_info
+        val = self.obj._values
+        codes, uniques = algorithms.factorize(val, use_na_sentinel=dropna, sort=False)
+
+        if self._grouper.has_dropped_na:
+            mask = ids >= 0
+            ids = ids[mask]
+            codes = codes[mask]
+
+        group_index = get_group_index(
+            labels=[ids, codes],
+            shape=(ngroups, len(uniques)),
+            sort=False,
+            xnull=dropna,
+        )
+
+        if dropna:
+            mask = group_index >= 0
+            if (~mask).any():
+                ids = ids[mask]
+                group_index = group_index[mask]
+
+        mask = duplicated(group_index, "first")
+        res = np.bincount(ids[~mask], minlength=ngroups)
+        res = ensure_int64(res)
+
+        ri = self._grouper.result_index
+        result: Series | DataFrame = self.obj._constructor(
+            res, index=ri, name=self.obj.name
+        )
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+        return self._reindex_output(result, fill_value=0)
+
+    @doc(Series.describe)
+    def describe(self, percentiles=None, include=None, exclude=None) -> Series:
+        return super().describe(
+            percentiles=percentiles, include=include, exclude=exclude
+        )
+
+    def value_counts(
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
+    ) -> Series | DataFrame:
+        name = "proportion" if normalize else "count"
+
+        if bins is None:
+            result = self._value_counts(
+                normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+            )
+            result.name = name
+            return result
+
+        from pandas.core.reshape.merge import get_join_indexers
+        from pandas.core.reshape.tile import cut
+
+        ids, _, _ = self._grouper.group_info
+        val = self.obj._values
+
+        index_names = self._grouper.names + [self.obj.name]
+
+        if isinstance(val.dtype, CategoricalDtype) or (
+            bins is not None and not np.iterable(bins)
+        ):
+            # scalar bins cannot be done at top level
+            # in a backward compatible way
+            # GH38672 relates to categorical dtype
+            ser = self.apply(
+                Series.value_counts,
+                normalize=normalize,
+                sort=sort,
+                ascending=ascending,
+                bins=bins,
+            )
+            ser.name = name
+            ser.index.names = index_names
+            return ser
+
+        # groupby removes null keys from groupings
+        mask = ids != -1
+        ids, val = ids[mask], val[mask]
+
+        lab: Index | np.ndarray
+        if bins is None:
+            lab, lev = algorithms.factorize(val, sort=True)
+            llab = lambda lab, inc: lab[inc]
+        else:
+            # lab is a Categorical with categories an IntervalIndex
+            cat_ser = cut(Series(val, copy=False), bins, include_lowest=True)
+            cat_obj = cast("Categorical", cat_ser._values)
+            lev = cat_obj.categories
+            lab = lev.take(
+                cat_obj.codes,
+                allow_fill=True,
+                fill_value=lev._na_value,
+            )
+            llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]
+
+        if isinstance(lab.dtype, IntervalDtype):
+            # TODO: should we do this inside II?
+            lab_interval = cast(Interval, lab)
+
+            sorter = np.lexsort((lab_interval.left, lab_interval.right, ids))
+        else:
+            sorter = np.lexsort((lab, ids))
+
+        ids, lab = ids[sorter], lab[sorter]
+
+        # group boundaries are where group ids change
+        idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
+        idx = np.r_[0, idchanges]
+        if not len(ids):
+            idx = idchanges
+
+        # new values are where sorted labels change
+        lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
+        inc = np.r_[True, lchanges]
+        if not len(val):
+            inc = lchanges
+        inc[idx] = True  # group boundaries are also new values
+        out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts
+
+        # num. of times each group should be repeated
+        rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))
+
+        # multi-index components
+        codes = self._grouper.reconstructed_codes
+        codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
+        levels = [ping._group_index for ping in self._grouper.groupings] + [lev]
+
+        if dropna:
+            mask = codes[-1] != -1
+            if mask.all():
+                dropna = False
+            else:
+                out, codes = out[mask], [level_codes[mask] for level_codes in codes]
+
+        if normalize:
+            out = out.astype("float")
+            d = np.diff(np.r_[idx, len(ids)])
+            if dropna:
+                m = ids[lab == -1]
+                np.add.at(d, m, -1)
+                acc = rep(d)[mask]
+            else:
+                acc = rep(d)
+            out /= acc
+
+        if sort and bins is None:
+            cat = ids[inc][mask] if dropna else ids[inc]
+            sorter = np.lexsort((out if ascending else -out, cat))
+            out, codes[-1] = out[sorter], codes[-1][sorter]
+
+        if bins is not None:
+            # for compat. with libgroupby.value_counts need to ensure every
+            # bin is present at every index level, null filled with zeros
+            diff = np.zeros(len(out), dtype="bool")
+            for level_codes in codes[:-1]:
+                diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]
+
+            ncat, nbin = diff.sum(), len(levels[-1])
+
+            left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]
+
+            right = [diff.cumsum() - 1, codes[-1]]
+
+            # error: Argument 1 to "get_join_indexers" has incompatible type
+            # "List[ndarray[Any, Any]]"; expected "List[Union[Union[ExtensionArray,
+            # ndarray[Any, Any]], Index, Series]]
+            _, idx = get_join_indexers(
+                left, right, sort=False, how="left"  # type: ignore[arg-type]
+            )
+            if idx is not None:
+                out = np.where(idx != -1, out[idx], 0)
+
+            if sort:
+                sorter = np.lexsort((out if ascending else -out, left[0]))
+                out, left[-1] = out[sorter], left[-1][sorter]
+
+            # build the multi-index w/ full levels
+            def build_codes(lev_codes: np.ndarray) -> np.ndarray:
+                return np.repeat(lev_codes[diff], nbin)
+
+            codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
+            codes.append(left[-1])
+
+        mi = MultiIndex(
+            levels=levels, codes=codes, names=index_names, verify_integrity=False
+        )
+
+        if is_integer_dtype(out.dtype):
+            out = ensure_int64(out)
+        result = self.obj._constructor(out, index=mi, name=name)
+        if not self.as_index:
+            result = result.reset_index()
+        return result
+
+    def fillna(
+        self,
+        value: object | ArrayLike | None = None,
+        method: FillnaOptions | None = None,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        inplace: bool = False,
+        limit: int | None = None,
+        downcast: dict | None | lib.NoDefault = lib.no_default,
+    ) -> Series | None:
+        """
+        Fill NA/NaN values using the specified method within groups.
+
+        .. deprecated:: 2.2.0
+            This method is deprecated and will be removed in a future version.
+            Use the :meth:`.SeriesGroupBy.ffill` or :meth:`.SeriesGroupBy.bfill`
+            for forward or backward filling instead. If you want to fill with a
+            single value, use :meth:`Series.fillna` instead.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series, or DataFrame
+            Value to use to fill holes (e.g. 0), alternately a
+            dict/Series/DataFrame of values specifying which value to use for
+            each index (for a Series) or column (for a DataFrame).  Values not
+            in the dict/Series/DataFrame will not be filled. This value cannot
+            be a list. Users wanting to use the ``value`` argument and not ``method``
+            should prefer :meth:`.Series.fillna` as this
+            will produce the same result and be more performant.
+        method : {{'bfill', 'ffill', None}}, default None
+            Method to use for filling holes. ``'ffill'`` will propagate
+            the last valid observation forward within a group.
+            ``'bfill'`` will use next valid observation to fill the gap.
+        axis : {0 or 'index', 1 or 'columns'}
+            Unused, only for compatibility with :meth:`DataFrameGroupBy.fillna`.
+        inplace : bool, default False
+            Broken. Do not set to True.
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill within a group. In other words,
+            if there is a gap with more than this number of consecutive NaNs,
+            it will only be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+        Returns
+        -------
+        Series
+            Object with missing values filled within groups.
+
+        See Also
+        --------
+        ffill : Forward fill values within a group.
+        bfill : Backward fill values within a group.
+
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['cat', 'cat', 'cat', 'mouse', 'mouse']
+        >>> ser = pd.Series([1, None, None, 2, None], index=lst)
+        >>> ser
+        cat    1.0
+        cat    NaN
+        cat    NaN
+        mouse  2.0
+        mouse  NaN
+        dtype: float64
+        >>> ser.groupby(level=0).fillna(0, limit=1)
+        cat    1.0
+        cat    0.0
+        cat    NaN
+        mouse  2.0
+        mouse  0.0
+        dtype: float64
+        """
+        warnings.warn(
+            f"{type(self).__name__}.fillna is deprecated and "
+            "will be removed in a future version. Use obj.ffill() or obj.bfill() "
+            "for forward or backward filling instead. If you want to fill with a "
+            f"single value, use {type(self.obj).__name__}.fillna instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        result = self._op_via_apply(
+            "fillna",
+            value=value,
+            method=method,
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            downcast=downcast,
+        )
+        return result
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        **kwargs,
+    ) -> Series:
+        """
+        Return the elements in the given *positional* indices in each group.
+
+        This means that we are not indexing according to actual values in
+        the index attribute of the object. We are indexing according to the
+        actual position of the element in the object.
+
+        If a requested index does not exist for some group, this method will raise.
+        To get similar behavior that ignores indices that don't exist, see
+        :meth:`.SeriesGroupBy.nth`.
+
+        Parameters
+        ----------
+        indices : array-like
+            An array of ints indicating which positions to take in each group.
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            The axis on which to select elements. ``0`` means that we are
+            selecting rows, ``1`` means that we are selecting columns.
+            For `SeriesGroupBy` this parameter is unused and defaults to 0.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        **kwargs
+            For compatibility with :meth:`numpy.take`. Has no effect on the
+            output.
+
+        Returns
+        -------
+        Series
+            A Series containing the elements taken from each group.
+
+        See Also
+        --------
+        Series.take : Take elements from a Series along an axis.
+        Series.loc : Select a subset of a DataFrame by labels.
+        Series.iloc : Select a subset of a DataFrame by positions.
+        numpy.take : Take elements from an array along an axis.
+        SeriesGroupBy.nth : Similar to take, won't raise if indices don't exist.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan),
+        ...                    ('rabbit', 'mammal', 15.0)],
+        ...                   columns=['name', 'class', 'max_speed'],
+        ...                   index=[4, 3, 2, 1, 0])
+        >>> df
+             name   class  max_speed
+        4  falcon    bird      389.0
+        3  parrot    bird       24.0
+        2    lion  mammal       80.5
+        1  monkey  mammal        NaN
+        0  rabbit  mammal       15.0
+        >>> gb = df["name"].groupby([1, 1, 2, 2, 2])
+
+        Take elements at positions 0 and 1 along the axis 0 in each group (default).
+
+        >>> gb.take([0, 1])
+        1  4    falcon
+           3    parrot
+        2  2      lion
+           1    monkey
+        Name: name, dtype: object
+
+        We may take elements using negative integers for positive indices,
+        starting from the end of the object, just like with Python lists.
+
+        >>> gb.take([-1, -2])
+        1  3    parrot
+           4    falcon
+        2  0    rabbit
+           1    monkey
+        Name: name, dtype: object
+        """
+        result = self._op_via_apply("take", indices=indices, axis=axis, **kwargs)
+        return result
+
+    def skew(
+        self,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> Series:
+        """
+        Return unbiased skew within groups.
+
+        Normalized by N-1.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Axis for the function to be applied on.
+            This parameter is only for compatibility with DataFrame and is unused.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+
+        numeric_only : bool, default False
+            Include only float, int, boolean columns. Not implemented for Series.
+
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.skew : Return unbiased skew over requested axis.
+
+        Examples
+        --------
+        >>> ser = pd.Series([390., 350., 357., np.nan, 22., 20., 30.],
+        ...                 index=['Falcon', 'Falcon', 'Falcon', 'Falcon',
+        ...                        'Parrot', 'Parrot', 'Parrot'],
+        ...                 name="Max Speed")
+        >>> ser
+        Falcon    390.0
+        Falcon    350.0
+        Falcon    357.0
+        Falcon      NaN
+        Parrot     22.0
+        Parrot     20.0
+        Parrot     30.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).skew()
+        Falcon    1.525174
+        Parrot    1.457863
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).skew(skipna=False)
+        Falcon         NaN
+        Parrot    1.457863
+        Name: Max Speed, dtype: float64
+        """
+        if axis is lib.no_default:
+            axis = 0
+
+        if axis != 0:
+            result = self._op_via_apply(
+                "skew",
+                axis=axis,
+                skipna=skipna,
+                numeric_only=numeric_only,
+                **kwargs,
+            )
+            return result
+
+        def alt(obj):
+            # This should not be reached since the cython path should raise
+            #  TypeError and not NotImplementedError.
+            raise TypeError(f"'skew' is not supported for dtype={obj.dtype}")
+
+        return self._cython_agg_general(
+            "skew", alt=alt, skipna=skipna, numeric_only=numeric_only, **kwargs
+        )
+
+    @property
+    @doc(Series.plot.__doc__)
+    def plot(self) -> GroupByPlot:
+        result = GroupByPlot(self)
+        return result
+
+    @doc(Series.nlargest.__doc__)
+    def nlargest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        f = partial(Series.nlargest, n=n, keep=keep)
+        data = self._obj_with_exclusions
+        # Don't change behavior if result index happens to be the same, i.e.
+        # already ordered and n >= all group sizes.
+        result = self._python_apply_general(f, data, not_indexed_same=True)
+        return result
+
+    @doc(Series.nsmallest.__doc__)
+    def nsmallest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        f = partial(Series.nsmallest, n=n, keep=keep)
+        data = self._obj_with_exclusions
+        # Don't change behavior if result index happens to be the same, i.e.
+        # already ordered and n >= all group sizes.
+        result = self._python_apply_general(f, data, not_indexed_same=True)
+        return result
+
+    @doc(Series.idxmin.__doc__)
+    def idxmin(
+        self, axis: Axis | lib.NoDefault = lib.no_default, skipna: bool = True
+    ) -> Series:
+        return self._idxmax_idxmin("idxmin", axis=axis, skipna=skipna)
+
+    @doc(Series.idxmax.__doc__)
+    def idxmax(
+        self, axis: Axis | lib.NoDefault = lib.no_default, skipna: bool = True
+    ) -> Series:
+        return self._idxmax_idxmin("idxmax", axis=axis, skipna=skipna)
+
+    @doc(Series.corr.__doc__)
+    def corr(
+        self,
+        other: Series,
+        method: CorrelationMethod = "pearson",
+        min_periods: int | None = None,
+    ) -> Series:
+        result = self._op_via_apply(
+            "corr", other=other, method=method, min_periods=min_periods
+        )
+        return result
+
+    @doc(Series.cov.__doc__)
+    def cov(
+        self, other: Series, min_periods: int | None = None, ddof: int | None = 1
+    ) -> Series:
+        result = self._op_via_apply(
+            "cov", other=other, min_periods=min_periods, ddof=ddof
+        )
+        return result
+
+    @property
+    def is_monotonic_increasing(self) -> Series:
+        """
+        Return whether each group's values are monotonically increasing.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 1, 3, 4], index=['Falcon', 'Falcon', 'Parrot', 'Parrot'])
+        >>> s.groupby(level=0).is_monotonic_increasing
+        Falcon    False
+        Parrot     True
+        dtype: bool
+        """
+        return self.apply(lambda ser: ser.is_monotonic_increasing)
+
+    @property
+    def is_monotonic_decreasing(self) -> Series:
+        """
+        Return whether each group's values are monotonically decreasing.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 1, 3, 4], index=['Falcon', 'Falcon', 'Parrot', 'Parrot'])
+        >>> s.groupby(level=0).is_monotonic_decreasing
+        Falcon     True
+        Parrot    False
+        dtype: bool
+        """
+        return self.apply(lambda ser: ser.is_monotonic_decreasing)
+
+    @doc(Series.hist.__doc__)
+    def hist(
+        self,
+        by=None,
+        ax=None,
+        grid: bool = True,
+        xlabelsize: int | None = None,
+        xrot: float | None = None,
+        ylabelsize: int | None = None,
+        yrot: float | None = None,
+        figsize: tuple[int, int] | None = None,
+        bins: int | Sequence[int] = 10,
+        backend: str | None = None,
+        legend: bool = False,
+        **kwargs,
+    ):
+        result = self._op_via_apply(
+            "hist",
+            by=by,
+            ax=ax,
+            grid=grid,
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+            figsize=figsize,
+            bins=bins,
+            backend=backend,
+            legend=legend,
+            **kwargs,
+        )
+        return result
+
+    @property
+    @doc(Series.dtype.__doc__)
+    def dtype(self) -> Series:
+        return self.apply(lambda ser: ser.dtype)
+
+    def unique(self) -> Series:
+        """
+        Return unique values for each group.
+
+        It returns unique values for each of the grouped values. Returned in
+        order of appearance. Hash table-based unique, therefore does NOT sort.
+
+        Returns
+        -------
+        Series
+            Unique values for each of the grouped values.
+
+        See Also
+        --------
+        Series.unique : Return unique values of Series object.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('Chihuahua', 'dog', 6.1),
+        ...                    ('Beagle', 'dog', 15.2),
+        ...                    ('Chihuahua', 'dog', 6.9),
+        ...                    ('Persian', 'cat', 9.2),
+        ...                    ('Chihuahua', 'dog', 7),
+        ...                    ('Persian', 'cat', 8.8)],
+        ...                   columns=['breed', 'animal', 'height_in'])
+        >>> df
+               breed     animal   height_in
+        0  Chihuahua        dog         6.1
+        1     Beagle        dog        15.2
+        2  Chihuahua        dog         6.9
+        3    Persian        cat         9.2
+        4  Chihuahua        dog         7.0
+        5    Persian        cat         8.8
+        >>> ser = df.groupby('animal')['breed'].unique()
+        >>> ser
+        animal
+        cat              [Persian]
+        dog    [Chihuahua, Beagle]
+        Name: breed, dtype: object
+        """
+        result = self._op_via_apply("unique")
+        return result
+
+
+class DataFrameGroupBy(GroupBy[DataFrame]):
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> data = {"A": [1, 1, 2, 2],
+    ...         "B": [1, 2, 3, 4],
+    ...         "C": [0.362838, 0.227877, 1.267767, -0.562860]}
+    >>> df = pd.DataFrame(data)
+    >>> df
+       A  B         C
+    0  1  1  0.362838
+    1  1  2  0.227877
+    2  2  3  1.267767
+    3  2  4 -0.562860
+
+    The aggregation is for each column.
+
+    >>> df.groupby('A').agg('min')
+       B         C
+    A
+    1  1  0.227877
+    2  3 -0.562860
+
+    Multiple aggregations
+
+    >>> df.groupby('A').agg(['min', 'max'])
+        B             C
+      min max       min       max
+    A
+    1   1   2  0.227877  0.362838
+    2   3   4 -0.562860  1.267767
+
+    Select a column for aggregation
+
+    >>> df.groupby('A').B.agg(['min', 'max'])
+       min  max
+    A
+    1    1    2
+    2    3    4
+
+    User-defined function for aggregation
+
+    >>> df.groupby('A').agg(lambda x: sum(x) + 2)
+        B	       C
+    A
+    1	5	2.590715
+    2	9	2.704907
+
+    Different aggregations per column
+
+    >>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
+        B             C
+      min max       sum
+    A
+    1   1   2  0.590715
+    2   3   4  0.704907
+
+    To control the output names with different aggregations per column,
+    pandas supports "named aggregation"
+
+    >>> df.groupby("A").agg(
+    ...     b_min=pd.NamedAgg(column="B", aggfunc="min"),
+    ...     c_sum=pd.NamedAgg(column="C", aggfunc="sum")
+    ... )
+       b_min     c_sum
+    A
+    1      1  0.590715
+    2      3  0.704907
+
+    - The keywords are the *output* column names
+    - The values are tuples whose first element is the column to select
+      and the second element is the aggregation to apply to that column.
+      Pandas provides the ``pandas.NamedAgg`` namedtuple with the fields
+      ``['column', 'aggfunc']`` to make it clearer what the arguments are.
+      As usual, the aggregation can be a callable or a string alias.
+
+    See :ref:`groupby.aggregate.named` for more.
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the aggregating function.
+
+    >>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
+          B
+    A
+    1   1.0
+    2   3.0
+    """
+    )
+
+    @doc(_agg_template_frame, examples=_agg_examples_doc, klass="DataFrame")
+    def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
+        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+        func = maybe_mangle_lambdas(func)
+
+        if maybe_use_numba(engine):
+            # Not all agg functions support numba, only propagate numba kwargs
+            # if user asks for numba
+            kwargs["engine"] = engine
+            kwargs["engine_kwargs"] = engine_kwargs
+
+        op = GroupByApply(self, func, args=args, kwargs=kwargs)
+        result = op.agg()
+        if not is_dict_like(func) and result is not None:
+            # GH #52849
+            if not self.as_index and is_list_like(func):
+                return result.reset_index()
+            else:
+                return result
+        elif relabeling:
+            # this should be the only (non-raising) case with relabeling
+            # used reordered index of columns
+            result = cast(DataFrame, result)
+            result = result.iloc[:, order]
+            result = cast(DataFrame, result)
+            # error: Incompatible types in assignment (expression has type
+            # "Optional[List[str]]", variable has type
+            # "Union[Union[Union[ExtensionArray, ndarray[Any, Any]],
+            # Index, Series], Sequence[Any]]")
+            result.columns = columns  # type: ignore[assignment]
+
+        if result is None:
+            # Remove the kwargs we inserted
+            # (already stored in engine, engine_kwargs arguments)
+            if "engine" in kwargs:
+                del kwargs["engine"]
+                del kwargs["engine_kwargs"]
+            # at this point func is not a str, list-like, dict-like,
+            # or a known callable(e.g. sum)
+            if maybe_use_numba(engine):
+                return self._aggregate_with_numba(
+                    func, *args, engine_kwargs=engine_kwargs, **kwargs
+                )
+            # grouper specific aggregations
+            if self._grouper.nkeys > 1:
+                # test_groupby_as_index_series_scalar gets here with 'not self.as_index'
+                return self._python_agg_general(func, *args, **kwargs)
+            elif args or kwargs:
+                # test_pass_args_kwargs gets here (with and without as_index)
+                # can't return early
+                result = self._aggregate_frame(func, *args, **kwargs)
+
+            elif self.axis == 1:
+                # _aggregate_multiple_funcs does not allow self.axis == 1
+                # Note: axis == 1 precludes 'not self.as_index', see __init__
+                result = self._aggregate_frame(func)
+                return result
+
+            else:
+                # try to treat as if we are passing a list
+                gba = GroupByApply(self, [func], args=(), kwargs={})
+                try:
+                    result = gba.agg()
+
+                except ValueError as err:
+                    if "No objects to concatenate" not in str(err):
+                        raise
+                    # _aggregate_frame can fail with e.g. func=Series.mode,
+                    # where it expects 1D values but would be getting 2D values
+                    # In other tests, using aggregate_frame instead of GroupByApply
+                    #  would give correct values but incorrect dtypes
+                    #  object vs float64 in test_cython_agg_empty_buckets
+                    #  float64 vs int64 in test_category_order_apply
+                    result = self._aggregate_frame(func)
+
+                else:
+                    # GH#32040, GH#35246
+                    # e.g. test_groupby_as_index_select_column_sum_empty_df
+                    result = cast(DataFrame, result)
+                    result.columns = self._obj_with_exclusions.columns.copy()
+
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+
+        return result
+
+    agg = aggregate
+
+    def _python_agg_general(self, func, *args, **kwargs):
+        orig_func = func
+        func = com.is_builtin_func(func)
+        if orig_func != func:
+            alias = com._builtin_table_alias[func]
+            warn_alias_replacement(self, orig_func, alias)
+        f = lambda x: func(x, *args, **kwargs)
+
+        if self.ngroups == 0:
+            # e.g. test_evaluate_with_empty_groups different path gets different
+            #  result dtype in empty case.
+            return self._python_apply_general(f, self._selected_obj, is_agg=True)
+
+        obj = self._obj_with_exclusions
+        if self.axis == 1:
+            obj = obj.T
+
+        if not len(obj.columns):
+            # e.g. test_margins_no_values_no_cols
+            return self._python_apply_general(f, self._selected_obj)
+
+        output: dict[int, ArrayLike] = {}
+        for idx, (name, ser) in enumerate(obj.items()):
+            result = self._grouper.agg_series(ser, f)
+            output[idx] = result
+
+        res = self.obj._constructor(output)
+        res.columns = obj.columns.copy(deep=False)
+        return self._wrap_aggregated_output(res)
+
+    def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
+        if self._grouper.nkeys != 1:
+            raise AssertionError("Number of keys must be 1")
+
+        obj = self._obj_with_exclusions
+
+        result: dict[Hashable, NDFrame | np.ndarray] = {}
+        for name, grp_df in self._grouper.get_iterator(obj, self.axis):
+            fres = func(grp_df, *args, **kwargs)
+            result[name] = fres
+
+        result_index = self._grouper.result_index
+        other_ax = obj.axes[1 - self.axis]
+        out = self.obj._constructor(result, index=other_ax, columns=result_index)
+        if self.axis == 0:
+            out = out.T
+
+        return out
+
+    def _wrap_applied_output(
+        self,
+        data: DataFrame,
+        values: list,
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ):
+        if len(values) == 0:
+            if is_transform:
+                # GH#47787 see test_group_on_empty_multiindex
+                res_index = data.index
+            else:
+                res_index = self._grouper.result_index
+
+            result = self.obj._constructor(index=res_index, columns=data.columns)
+            result = result.astype(data.dtypes, copy=False)
+            return result
+
+        # GH12824
+        # using values[0] here breaks test_groupby_apply_none_first
+        first_not_none = next(com.not_none(*values), None)
+
+        if first_not_none is None:
+            # GH9684 - All values are None, return an empty frame.
+            return self.obj._constructor()
+        elif isinstance(first_not_none, DataFrame):
+            return self._concat_objects(
+                values,
+                not_indexed_same=not_indexed_same,
+                is_transform=is_transform,
+            )
+
+        key_index = self._grouper.result_index if self.as_index else None
+
+        if isinstance(first_not_none, (np.ndarray, Index)):
+            # GH#1738: values is list of arrays of unequal lengths
+            #  fall through to the outer else clause
+            # TODO: sure this is right?  we used to do this
+            #  after raising AttributeError above
+            # GH 18930
+            if not is_hashable(self._selection):
+                # error: Need type annotation for "name"
+                name = tuple(self._selection)  # type: ignore[var-annotated, arg-type]
+            else:
+                # error: Incompatible types in assignment
+                # (expression has type "Hashable", variable
+                # has type "Tuple[Any, ...]")
+                name = self._selection  # type: ignore[assignment]
+            return self.obj._constructor_sliced(values, index=key_index, name=name)
+        elif not isinstance(first_not_none, Series):
+            # values are not series or array-like but scalars
+            # self._selection not passed through to Series as the
+            # result should not take the name of original selection
+            # of columns
+            if self.as_index:
+                return self.obj._constructor_sliced(values, index=key_index)
+            else:
+                result = self.obj._constructor(values, columns=[self._selection])
+                result = self._insert_inaxis_grouper(result)
+                return result
+        else:
+            # values are Series
+            return self._wrap_applied_output_series(
+                values,
+                not_indexed_same,
+                first_not_none,
+                key_index,
+                is_transform,
+            )
+
+    def _wrap_applied_output_series(
+        self,
+        values: list[Series],
+        not_indexed_same: bool,
+        first_not_none,
+        key_index: Index | None,
+        is_transform: bool,
+    ) -> DataFrame | Series:
+        kwargs = first_not_none._construct_axes_dict()
+        backup = Series(**kwargs)
+        values = [x if (x is not None) else backup for x in values]
+
+        all_indexed_same = all_indexes_same(x.index for x in values)
+
+        if not all_indexed_same:
+            # GH 8467
+            return self._concat_objects(
+                values,
+                not_indexed_same=True,
+                is_transform=is_transform,
+            )
+
+        # Combine values
+        # vstack+constructor is faster than concat and handles MI-columns
+        stacked_values = np.vstack([np.asarray(v) for v in values])
+
+        if self.axis == 0:
+            index = key_index
+            columns = first_not_none.index.copy()
+            if columns.name is None:
+                # GH6124 - propagate name of Series when it's consistent
+                names = {v.name for v in values}
+                if len(names) == 1:
+                    columns.name = next(iter(names))
+        else:
+            index = first_not_none.index
+            columns = key_index
+            stacked_values = stacked_values.T
+
+        if stacked_values.dtype == object:
+            # We'll have the DataFrame constructor do inference
+            stacked_values = stacked_values.tolist()
+        result = self.obj._constructor(stacked_values, index=index, columns=columns)
+
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+
+        return self._reindex_output(result)
+
+    def _cython_transform(
+        self,
+        how: str,
+        numeric_only: bool = False,
+        axis: AxisInt = 0,
+        **kwargs,
+    ) -> DataFrame:
+        assert axis == 0  # handled by caller
+
+        # With self.axis == 0, we have multi-block tests
+        #  e.g. test_rank_min_int, test_cython_transform_frame
+        #  test_transform_numeric_ret
+        # With self.axis == 1, _get_data_to_aggregate does a transpose
+        #  so we always have a single block.
+        mgr: Manager2D = self._get_data_to_aggregate(
+            numeric_only=numeric_only, name=how
+        )
+
+        def arr_func(bvalues: ArrayLike) -> ArrayLike:
+            return self._grouper._cython_operation(
+                "transform", bvalues, how, 1, **kwargs
+            )
+
+        # We could use `mgr.apply` here and not have to set_axis, but
+        #  we would have to do shape gymnastics for ArrayManager compat
+        res_mgr = mgr.grouped_reduce(arr_func)
+        res_mgr.set_axis(1, mgr.axes[1])
+
+        res_df = self.obj._constructor_from_mgr(res_mgr, axes=res_mgr.axes)
+        res_df = self._maybe_transpose_result(res_df)
+        return res_df
+
+    def _transform_general(self, func, engine, engine_kwargs, *args, **kwargs):
+        if maybe_use_numba(engine):
+            return self._transform_with_numba(
+                func, *args, engine_kwargs=engine_kwargs, **kwargs
+            )
+        from pandas.core.reshape.concat import concat
+
+        applied = []
+        obj = self._obj_with_exclusions
+        gen = self._grouper.get_iterator(obj, axis=self.axis)
+        fast_path, slow_path = self._define_paths(func, *args, **kwargs)
+
+        # Determine whether to use slow or fast path by evaluating on the first group.
+        # Need to handle the case of an empty generator and process the result so that
+        # it does not need to be computed again.
+        try:
+            name, group = next(gen)
+        except StopIteration:
+            pass
+        else:
+            # 2023-02-27 No tests broken by disabling this pinning
+            object.__setattr__(group, "name", name)
+            try:
+                path, res = self._choose_path(fast_path, slow_path, group)
+            except ValueError as err:
+                # e.g. test_transform_with_non_scalar_group
+                msg = "transform must return a scalar value for each group"
+                raise ValueError(msg) from err
+            if group.size > 0:
+                res = _wrap_transform_general_frame(self.obj, group, res)
+                applied.append(res)
+
+        # Compute and process with the remaining groups
+        for name, group in gen:
+            if group.size == 0:
+                continue
+            # 2023-02-27 No tests broken by disabling this pinning
+            object.__setattr__(group, "name", name)
+            res = path(group)
+
+            res = _wrap_transform_general_frame(self.obj, group, res)
+            applied.append(res)
+
+        concat_index = obj.columns if self.axis == 0 else obj.index
+        other_axis = 1 if self.axis == 0 else 0  # switches between 0 & 1
+        concatenated = concat(applied, axis=self.axis, verify_integrity=False)
+        concatenated = concatenated.reindex(concat_index, axis=other_axis, copy=False)
+        return self._set_result_index_ordered(concatenated)
+
+    __examples_dataframe_doc = dedent(
+        """
+    >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+    ...                           'foo', 'bar'],
+    ...                    'B' : ['one', 'one', 'two', 'three',
+    ...                           'two', 'two'],
+    ...                    'C' : [1, 5, 5, 2, 5, 5],
+    ...                    'D' : [2.0, 5., 8., 1., 2., 9.]})
+    >>> grouped = df.groupby('A')[['C', 'D']]
+    >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
+            C         D
+    0 -1.154701 -0.577350
+    1  0.577350  0.000000
+    2  0.577350  1.154701
+    3 -1.154701 -1.000000
+    4  0.577350 -0.577350
+    5  0.577350  1.000000
+
+    Broadcast result of the transformation
+
+    >>> grouped.transform(lambda x: x.max() - x.min())
+        C    D
+    0  4.0  6.0
+    1  3.0  8.0
+    2  4.0  6.0
+    3  3.0  8.0
+    4  4.0  6.0
+    5  3.0  8.0
+
+    >>> grouped.transform("mean")
+        C    D
+    0  3.666667  4.0
+    1  4.000000  5.0
+    2  3.666667  4.0
+    3  4.000000  5.0
+    4  3.666667  4.0
+    5  4.000000  5.0
+
+    .. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    for example:
+
+    >>> grouped.transform(lambda x: x.astype(int).max())
+    C  D
+    0  5  8
+    1  5  9
+    2  5  8
+    3  5  9
+    4  5  8
+    5  5  9
+    """
+    )
+
+    @Substitution(klass="DataFrame", example=__examples_dataframe_doc)
+    @Appender(_transform_template)
+    def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        return self._transform(
+            func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
+        )
+
+    def _define_paths(self, func, *args, **kwargs):
+        if isinstance(func, str):
+            fast_path = lambda group: getattr(group, func)(*args, **kwargs)
+            slow_path = lambda group: group.apply(
+                lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis
+            )
+        else:
+            fast_path = lambda group: func(group, *args, **kwargs)
+            slow_path = lambda group: group.apply(
+                lambda x: func(x, *args, **kwargs), axis=self.axis
+            )
+        return fast_path, slow_path
+
+    def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFrame):
+        path = slow_path
+        res = slow_path(group)
+
+        if self.ngroups == 1:
+            # no need to evaluate multiple paths when only
+            # a single group exists
+            return path, res
+
+        # if we make it here, test if we can use the fast path
+        try:
+            res_fast = fast_path(group)
+        except AssertionError:
+            raise  # pragma: no cover
+        except Exception:
+            # GH#29631 For user-defined function, we can't predict what may be
+            #  raised; see test_transform.test_transform_fastpath_raises
+            return path, res
+
+        # verify fast path returns either:
+        # a DataFrame with columns equal to group.columns
+        # OR a Series with index equal to group.columns
+        if isinstance(res_fast, DataFrame):
+            if not res_fast.columns.equals(group.columns):
+                return path, res
+        elif isinstance(res_fast, Series):
+            if not res_fast.index.equals(group.columns):
+                return path, res
+        else:
+            return path, res
+
+        if res_fast.equals(res):
+            path = fast_path
+
+        return path, res
+
+    def filter(self, func, dropna: bool = True, *args, **kwargs):
+        """
+        Filter elements from groups that don't satisfy a criterion.
+
+        Elements from groups are filtered if they do not satisfy the
+        boolean criterion specified by func.
+
+        Parameters
+        ----------
+        func : function
+            Criterion to apply to each group. Should return True or False.
+        dropna : bool
+            Drop groups that do not pass the filter. True by default; if False,
+            groups that evaluate False are filled with NaNs.
+
+        Returns
+        -------
+        DataFrame
+
+        Notes
+        -----
+        Each subframe is endowed the attribute 'name' in case you need to know
+        which group you are working on.
+
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
+        ...                           'foo', 'bar'],
+        ...                    'B' : [1, 2, 3, 4, 5, 6],
+        ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
+        >>> grouped = df.groupby('A')
+        >>> grouped.filter(lambda x: x['B'].mean() > 3.)
+             A  B    C
+        1  bar  2  5.0
+        3  bar  4  1.0
+        5  bar  6  9.0
+        """
+        indices = []
+
+        obj = self._selected_obj
+        gen = self._grouper.get_iterator(obj, axis=self.axis)
+
+        for name, group in gen:
+            # 2023-02-27 no tests are broken this pinning, but it is documented in the
+            #  docstring above.
+            object.__setattr__(group, "name", name)
+
+            res = func(group, *args, **kwargs)
+
+            try:
+                res = res.squeeze()
+            except AttributeError:  # allow e.g., scalars and frames to pass
+                pass
+
+            # interpret the result of the filter
+            if is_bool(res) or (is_scalar(res) and isna(res)):
+                if notna(res) and res:
+                    indices.append(self._get_index(name))
+            else:
+                # non scalars aren't allowed
+                raise TypeError(
+                    f"filter function returned a {type(res).__name__}, "
+                    "but expected a scalar bool"
+                )
+
+        return self._apply_filter(indices, dropna)
+
+    def __getitem__(self, key) -> DataFrameGroupBy | SeriesGroupBy:
+        if self.axis == 1:
+            # GH 37725
+            raise ValueError("Cannot subset columns when using axis=1")
+        # per GH 23566
+        if isinstance(key, tuple) and len(key) > 1:
+            # if len == 1, then it becomes a SeriesGroupBy and this is actually
+            # valid syntax, so don't raise
+            raise ValueError(
+                "Cannot subset columns with a tuple with more than one element. "
+                "Use a list instead."
+            )
+        return super().__getitem__(key)
+
+    def _gotitem(self, key, ndim: int, subset=None):
+        """
+        sub-classes to define
+        return a sliced object
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        if ndim == 2:
+            if subset is None:
+                subset = self.obj
+            return DataFrameGroupBy(
+                subset,
+                self.keys,
+                axis=self.axis,
+                level=self.level,
+                grouper=self._grouper,
+                exclusions=self.exclusions,
+                selection=key,
+                as_index=self.as_index,
+                sort=self.sort,
+                group_keys=self.group_keys,
+                observed=self.observed,
+                dropna=self.dropna,
+            )
+        elif ndim == 1:
+            if subset is None:
+                subset = self.obj[key]
+            return SeriesGroupBy(
+                subset,
+                self.keys,
+                level=self.level,
+                grouper=self._grouper,
+                exclusions=self.exclusions,
+                selection=key,
+                as_index=self.as_index,
+                sort=self.sort,
+                group_keys=self.group_keys,
+                observed=self.observed,
+                dropna=self.dropna,
+            )
+
+        raise AssertionError("invalid ndim for _gotitem")
+
+    def _get_data_to_aggregate(
+        self, *, numeric_only: bool = False, name: str | None = None
+    ) -> Manager2D:
+        obj = self._obj_with_exclusions
+        if self.axis == 1:
+            mgr = obj.T._mgr
+        else:
+            mgr = obj._mgr
+
+        if numeric_only:
+            mgr = mgr.get_numeric_data()
+        return mgr
+
+    def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
+        return self.obj._constructor_from_mgr(mgr, axes=mgr.axes)
+
+    def _apply_to_column_groupbys(self, func) -> DataFrame:
+        from pandas.core.reshape.concat import concat
+
+        obj = self._obj_with_exclusions
+        columns = obj.columns
+        sgbs = [
+            SeriesGroupBy(
+                obj.iloc[:, i],
+                selection=colname,
+                grouper=self._grouper,
+                exclusions=self.exclusions,
+                observed=self.observed,
+            )
+            for i, colname in enumerate(obj.columns)
+        ]
+        results = [func(sgb) for sgb in sgbs]
+
+        if not len(results):
+            # concat would raise
+            res_df = DataFrame([], columns=columns, index=self._grouper.result_index)
+        else:
+            res_df = concat(results, keys=columns, axis=1)
+
+        if not self.as_index:
+            res_df.index = default_index(len(res_df))
+            res_df = self._insert_inaxis_grouper(res_df)
+        return res_df
+
+    def nunique(self, dropna: bool = True) -> DataFrame:
+        """
+        Return DataFrame with counts of unique elements in each position.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        nunique: DataFrame
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
+        ...                           'ham', 'ham'],
+        ...                    'value1': [1, 5, 5, 2, 5, 5],
+        ...                    'value2': list('abbaxy')})
+        >>> df
+             id  value1 value2
+        0  spam       1      a
+        1   egg       5      b
+        2   egg       5      b
+        3  spam       2      a
+        4   ham       5      x
+        5   ham       5      y
+
+        >>> df.groupby('id').nunique()
+              value1  value2
+        id
+        egg        1       1
+        ham        1       2
+        spam       2       1
+
+        Check for rows with the same id but conflicting values:
+
+        >>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any())
+             id  value1 value2
+        0  spam       1      a
+        3  spam       2      a
+        4   ham       5      x
+        5   ham       5      y
+        """
+
+        if self.axis != 0:
+            # see test_groupby_crash_on_nunique
+            return self._python_apply_general(
+                lambda sgb: sgb.nunique(dropna), self._obj_with_exclusions, is_agg=True
+            )
+
+        return self._apply_to_column_groupbys(lambda sgb: sgb.nunique(dropna))
+
+    def idxmax(
+        self,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Return index of first occurrence of maximum over requested axis.
+
+        NA/null values are excluded.
+
+        Parameters
+        ----------
+        axis : {{0 or 'index', 1 or 'columns'}}, default None
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+            If axis is not provided, grouper's axis is used.
+
+            .. versionchanged:: 2.0.0
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series
+            Indexes of maxima along the specified axis.
+
+        Raises
+        ------
+        ValueError
+            * If the row/column is empty
+
+        See Also
+        --------
+        Series.idxmax : Return index of the maximum element.
+
+        Notes
+        -----
+        This method is the DataFrame version of ``ndarray.argmax``.
+
+        Examples
+        --------
+        Consider a dataset containing food consumption in Argentina.
+
+        >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
+        ...                    'co2_emissions': [37.2, 19.66, 1712]},
+        ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+        >>> df
+                        consumption  co2_emissions
+        Pork                  10.51         37.20
+        Wheat Products       103.11         19.66
+        Beef                  55.48       1712.00
+
+        By default, it returns the index for the maximum value in each column.
+
+        >>> df.idxmax()
+        consumption     Wheat Products
+        co2_emissions             Beef
+        dtype: object
+
+        To return the index for the maximum value in each row, use ``axis="columns"``.
+
+        >>> df.idxmax(axis="columns")
+        Pork              co2_emissions
+        Wheat Products     consumption
+        Beef              co2_emissions
+        dtype: object
+        """
+        return self._idxmax_idxmin(
+            "idxmax", axis=axis, numeric_only=numeric_only, skipna=skipna
+        )
+
+    def idxmin(
+        self,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        """
+        Return index of first occurrence of minimum over requested axis.
+
+        NA/null values are excluded.
+
+        Parameters
+        ----------
+        axis : {{0 or 'index', 1 or 'columns'}}, default None
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+            If axis is not provided, grouper's axis is used.
+
+            .. versionchanged:: 2.0.0
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series
+            Indexes of minima along the specified axis.
+
+        Raises
+        ------
+        ValueError
+            * If the row/column is empty
+
+        See Also
+        --------
+        Series.idxmin : Return index of the minimum element.
+
+        Notes
+        -----
+        This method is the DataFrame version of ``ndarray.argmin``.
+
+        Examples
+        --------
+        Consider a dataset containing food consumption in Argentina.
+
+        >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
+        ...                    'co2_emissions': [37.2, 19.66, 1712]},
+        ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+        >>> df
+                        consumption  co2_emissions
+        Pork                  10.51         37.20
+        Wheat Products       103.11         19.66
+        Beef                  55.48       1712.00
+
+        By default, it returns the index for the minimum value in each column.
+
+        >>> df.idxmin()
+        consumption                Pork
+        co2_emissions    Wheat Products
+        dtype: object
+
+        To return the index for the minimum value in each row, use ``axis="columns"``.
+
+        >>> df.idxmin(axis="columns")
+        Pork                consumption
+        Wheat Products    co2_emissions
+        Beef                consumption
+        dtype: object
+        """
+        return self._idxmax_idxmin(
+            "idxmin", axis=axis, numeric_only=numeric_only, skipna=skipna
+        )
+
+    boxplot = boxplot_frame_groupby
+
+    def value_counts(
+        self,
+        subset: Sequence[Hashable] | None = None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> DataFrame | Series:
+        """
+        Return a Series or DataFrame containing counts of unique rows.
+
+        .. versionadded:: 1.4.0
+
+        Parameters
+        ----------
+        subset : list-like, optional
+            Columns to use when counting unique combinations.
+        normalize : bool, default False
+            Return proportions rather than frequencies.
+        sort : bool, default True
+            Sort by frequencies.
+        ascending : bool, default False
+            Sort in ascending order.
+        dropna : bool, default True
+            Don't include counts of rows that contain NA values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Series if the groupby as_index is True, otherwise DataFrame.
+
+        See Also
+        --------
+        Series.value_counts: Equivalent method on Series.
+        DataFrame.value_counts: Equivalent method on DataFrame.
+        SeriesGroupBy.value_counts: Equivalent method on SeriesGroupBy.
+
+        Notes
+        -----
+        - If the groupby as_index is True then the returned Series will have a
+          MultiIndex with one level per input column.
+        - If the groupby as_index is False then the returned DataFrame will have an
+          additional column with the value_counts. The column is labelled 'count' or
+          'proportion', depending on the ``normalize`` parameter.
+
+        By default, rows that contain any NA values are omitted from
+        the result.
+
+        By default, the result will be in descending order so that the
+        first element of each group is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
+        ...     'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
+        ...     'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
+        ... })
+
+        >>> df
+                gender  education   country
+        0       male    low         US
+        1       male    medium      FR
+        2       female  high        US
+        3       male    low         FR
+        4       female  high        FR
+        5       male    low         FR
+
+        >>> df.groupby('gender').value_counts()
+        gender  education  country
+        female  high       FR         1
+                           US         1
+        male    low        FR         2
+                           US         1
+                medium     FR         1
+        Name: count, dtype: int64
+
+        >>> df.groupby('gender').value_counts(ascending=True)
+        gender  education  country
+        female  high       FR         1
+                           US         1
+        male    low        US         1
+                medium     FR         1
+                low        FR         2
+        Name: count, dtype: int64
+
+        >>> df.groupby('gender').value_counts(normalize=True)
+        gender  education  country
+        female  high       FR         0.50
+                           US         0.50
+        male    low        FR         0.50
+                           US         0.25
+                medium     FR         0.25
+        Name: proportion, dtype: float64
+
+        >>> df.groupby('gender', as_index=False).value_counts()
+           gender education country  count
+        0  female      high      FR      1
+        1  female      high      US      1
+        2    male       low      FR      2
+        3    male       low      US      1
+        4    male    medium      FR      1
+
+        >>> df.groupby('gender', as_index=False).value_counts(normalize=True)
+           gender education country  proportion
+        0  female      high      FR        0.50
+        1  female      high      US        0.50
+        2    male       low      FR        0.50
+        3    male       low      US        0.25
+        4    male    medium      FR        0.25
+        """
+        return self._value_counts(subset, normalize, sort, ascending, dropna)
+
+    def fillna(
+        self,
+        value: Hashable | Mapping | Series | DataFrame | None = None,
+        method: FillnaOptions | None = None,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        inplace: bool = False,
+        limit: int | None = None,
+        downcast=lib.no_default,
+    ) -> DataFrame | None:
+        """
+        Fill NA/NaN values using the specified method within groups.
+
+        .. deprecated:: 2.2.0
+            This method is deprecated and will be removed in a future version.
+            Use the :meth:`.DataFrameGroupBy.ffill` or :meth:`.DataFrameGroupBy.bfill`
+            for forward or backward filling instead. If you want to fill with a
+            single value, use :meth:`DataFrame.fillna` instead.
+
+        Parameters
+        ----------
+        value : scalar, dict, Series, or DataFrame
+            Value to use to fill holes (e.g. 0), alternately a
+            dict/Series/DataFrame of values specifying which value to use for
+            each index (for a Series) or column (for a DataFrame).  Values not
+            in the dict/Series/DataFrame will not be filled. This value cannot
+            be a list. Users wanting to use the ``value`` argument and not ``method``
+            should prefer :meth:`.DataFrame.fillna` as this
+            will produce the same result and be more performant.
+        method : {{'bfill', 'ffill', None}}, default None
+            Method to use for filling holes. ``'ffill'`` will propagate
+            the last valid observation forward within a group.
+            ``'bfill'`` will use next valid observation to fill the gap.
+        axis : {0 or 'index', 1 or 'columns'}
+            Axis along which to fill missing values. When the :class:`DataFrameGroupBy`
+            ``axis`` argument is ``0``, using ``axis=1`` here will produce
+            the same results as :meth:`.DataFrame.fillna`. When the
+            :class:`DataFrameGroupBy` ``axis`` argument is ``1``, using ``axis=0``
+            or ``axis=1`` here will produce the same results.
+        inplace : bool, default False
+            Broken. Do not set to True.
+        limit : int, default None
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill within a group. In other words,
+            if there is a gap with more than this number of consecutive NaNs,
+            it will only be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled. Must be greater than 0 if not None.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+        Returns
+        -------
+        DataFrame
+            Object with missing values filled.
+
+        See Also
+        --------
+        ffill : Forward fill values within a group.
+        bfill : Backward fill values within a group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "key": [0, 0, 1, 1, 1],
+        ...         "A": [np.nan, 2, np.nan, 3, np.nan],
+        ...         "B": [2, 3, np.nan, np.nan, np.nan],
+        ...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
+        ...     }
+        ... )
+        >>> df
+           key    A    B   C
+        0    0  NaN  2.0 NaN
+        1    0  2.0  3.0 NaN
+        2    1  NaN  NaN 2.0
+        3    1  3.0  NaN NaN
+        4    1  NaN  NaN NaN
+
+        Propagate non-null values forward or backward within each group along columns.
+
+        >>> df.groupby("key").fillna(method="ffill")
+             A    B   C
+        0  NaN  2.0 NaN
+        1  2.0  3.0 NaN
+        2  NaN  NaN 2.0
+        3  3.0  NaN 2.0
+        4  3.0  NaN 2.0
+
+        >>> df.groupby("key").fillna(method="bfill")
+             A    B   C
+        0  2.0  2.0 NaN
+        1  2.0  3.0 NaN
+        2  3.0  NaN 2.0
+        3  3.0  NaN NaN
+        4  NaN  NaN NaN
+
+        Propagate non-null values forward or backward within each group along rows.
+
+        >>> df.T.groupby(np.array([0, 0, 1, 1])).fillna(method="ffill").T
+           key    A    B    C
+        0  0.0  0.0  2.0  2.0
+        1  0.0  2.0  3.0  3.0
+        2  1.0  1.0  NaN  2.0
+        3  1.0  3.0  NaN  NaN
+        4  1.0  1.0  NaN  NaN
+
+        >>> df.T.groupby(np.array([0, 0, 1, 1])).fillna(method="bfill").T
+           key    A    B    C
+        0  0.0  NaN  2.0  NaN
+        1  0.0  2.0  3.0  NaN
+        2  1.0  NaN  2.0  2.0
+        3  1.0  3.0  NaN  NaN
+        4  1.0  NaN  NaN  NaN
+
+        Only replace the first NaN element within a group along rows.
+
+        >>> df.groupby("key").fillna(method="ffill", limit=1)
+             A    B    C
+        0  NaN  2.0  NaN
+        1  2.0  3.0  NaN
+        2  NaN  NaN  2.0
+        3  3.0  NaN  2.0
+        4  3.0  NaN  NaN
+        """
+        warnings.warn(
+            f"{type(self).__name__}.fillna is deprecated and "
+            "will be removed in a future version. Use obj.ffill() or obj.bfill() "
+            "for forward or backward filling instead. If you want to fill with a "
+            f"single value, use {type(self.obj).__name__}.fillna instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        result = self._op_via_apply(
+            "fillna",
+            value=value,
+            method=method,
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+            downcast=downcast,
+        )
+        return result
+
+    def take(
+        self,
+        indices: TakeIndexer,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        **kwargs,
+    ) -> DataFrame:
+        """
+        Return the elements in the given *positional* indices in each group.
+
+        This means that we are not indexing according to actual values in
+        the index attribute of the object. We are indexing according to the
+        actual position of the element in the object.
+
+        If a requested index does not exist for some group, this method will raise.
+        To get similar behavior that ignores indices that don't exist, see
+        :meth:`.DataFrameGroupBy.nth`.
+
+        Parameters
+        ----------
+        indices : array-like
+            An array of ints indicating which positions to take.
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            The axis on which to select elements. ``0`` means that we are
+            selecting rows, ``1`` means that we are selecting columns.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        **kwargs
+            For compatibility with :meth:`numpy.take`. Has no effect on the
+            output.
+
+        Returns
+        -------
+        DataFrame
+            An DataFrame containing the elements taken from each group.
+
+        See Also
+        --------
+        DataFrame.take : Take elements from a Series along an axis.
+        DataFrame.loc : Select a subset of a DataFrame by labels.
+        DataFrame.iloc : Select a subset of a DataFrame by positions.
+        numpy.take : Take elements from an array along an axis.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
+        ...                    ('parrot', 'bird', 24.0),
+        ...                    ('lion', 'mammal', 80.5),
+        ...                    ('monkey', 'mammal', np.nan),
+        ...                    ('rabbit', 'mammal', 15.0)],
+        ...                   columns=['name', 'class', 'max_speed'],
+        ...                   index=[4, 3, 2, 1, 0])
+        >>> df
+             name   class  max_speed
+        4  falcon    bird      389.0
+        3  parrot    bird       24.0
+        2    lion  mammal       80.5
+        1  monkey  mammal        NaN
+        0  rabbit  mammal       15.0
+        >>> gb = df.groupby([1, 1, 2, 2, 2])
+
+        Take elements at positions 0 and 1 along the axis 0 (default).
+
+        Note how the indices selected in the result do not correspond to
+        our input indices 0 and 1. That's because we are selecting the 0th
+        and 1st rows, not rows whose indices equal 0 and 1.
+
+        >>> gb.take([0, 1])
+               name   class  max_speed
+        1 4  falcon    bird      389.0
+          3  parrot    bird       24.0
+        2 2    lion  mammal       80.5
+          1  monkey  mammal        NaN
+
+        The order of the specified indices influences the order in the result.
+        Here, the order is swapped from the previous example.
+
+        >>> gb.take([1, 0])
+               name   class  max_speed
+        1 3  parrot    bird       24.0
+          4  falcon    bird      389.0
+        2 1  monkey  mammal        NaN
+          2    lion  mammal       80.5
+
+        Take elements at indices 1 and 2 along the axis 1 (column selection).
+
+        We may take elements using negative integers for positive indices,
+        starting from the end of the object, just like with Python lists.
+
+        >>> gb.take([-1, -2])
+               name   class  max_speed
+        1 3  parrot    bird       24.0
+          4  falcon    bird      389.0
+        2 0  rabbit  mammal       15.0
+          1  monkey  mammal        NaN
+        """
+        result = self._op_via_apply("take", indices=indices, axis=axis, **kwargs)
+        return result
+
+    def skew(
+        self,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> DataFrame:
+        """
+        Return unbiased skew within groups.
+
+        Normalized by N-1.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Axis for the function to be applied on.
+
+            Specifying ``axis=None`` will apply the aggregation across both axes.
+
+            .. versionadded:: 2.0.0
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.skew : Return unbiased skew over requested axis.
+
+        Examples
+        --------
+        >>> arrays = [['falcon', 'parrot', 'cockatoo', 'kiwi',
+        ...            'lion', 'monkey', 'rabbit'],
+        ...           ['bird', 'bird', 'bird', 'bird',
+        ...            'mammal', 'mammal', 'mammal']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('name', 'class'))
+        >>> df = pd.DataFrame({'max_speed': [389.0, 24.0, 70.0, np.nan,
+        ...                                  80.5, 21.5, 15.0]},
+        ...                   index=index)
+        >>> df
+                        max_speed
+        name     class
+        falcon   bird        389.0
+        parrot   bird         24.0
+        cockatoo bird         70.0
+        kiwi     bird          NaN
+        lion     mammal       80.5
+        monkey   mammal       21.5
+        rabbit   mammal       15.0
+        >>> gb = df.groupby(["class"])
+        >>> gb.skew()
+                max_speed
+        class
+        bird     1.628296
+        mammal   1.669046
+        >>> gb.skew(skipna=False)
+                max_speed
+        class
+        bird          NaN
+        mammal   1.669046
+        """
+        if axis is lib.no_default:
+            axis = 0
+
+        if axis != 0:
+            result = self._op_via_apply(
+                "skew",
+                axis=axis,
+                skipna=skipna,
+                numeric_only=numeric_only,
+                **kwargs,
+            )
+            return result
+
+        def alt(obj):
+            # This should not be reached since the cython path should raise
+            #  TypeError and not NotImplementedError.
+            raise TypeError(f"'skew' is not supported for dtype={obj.dtype}")
+
+        return self._cython_agg_general(
+            "skew", alt=alt, skipna=skipna, numeric_only=numeric_only, **kwargs
+        )
+
+    @property
+    @doc(DataFrame.plot.__doc__)
+    def plot(self) -> GroupByPlot:
+        result = GroupByPlot(self)
+        return result
+
+    @doc(DataFrame.corr.__doc__)
+    def corr(
+        self,
+        method: str | Callable[[np.ndarray, np.ndarray], float] = "pearson",
+        min_periods: int = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        result = self._op_via_apply(
+            "corr", method=method, min_periods=min_periods, numeric_only=numeric_only
+        )
+        return result
+
+    @doc(DataFrame.cov.__doc__)
+    def cov(
+        self,
+        min_periods: int | None = None,
+        ddof: int | None = 1,
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        result = self._op_via_apply(
+            "cov", min_periods=min_periods, ddof=ddof, numeric_only=numeric_only
+        )
+        return result
+
+    @doc(DataFrame.hist.__doc__)
+    def hist(
+        self,
+        column: IndexLabel | None = None,
+        by=None,
+        grid: bool = True,
+        xlabelsize: int | None = None,
+        xrot: float | None = None,
+        ylabelsize: int | None = None,
+        yrot: float | None = None,
+        ax=None,
+        sharex: bool = False,
+        sharey: bool = False,
+        figsize: tuple[int, int] | None = None,
+        layout: tuple[int, int] | None = None,
+        bins: int | Sequence[int] = 10,
+        backend: str | None = None,
+        legend: bool = False,
+        **kwargs,
+    ):
+        result = self._op_via_apply(
+            "hist",
+            column=column,
+            by=by,
+            grid=grid,
+            xlabelsize=xlabelsize,
+            xrot=xrot,
+            ylabelsize=ylabelsize,
+            yrot=yrot,
+            ax=ax,
+            sharex=sharex,
+            sharey=sharey,
+            figsize=figsize,
+            layout=layout,
+            bins=bins,
+            backend=backend,
+            legend=legend,
+            **kwargs,
+        )
+        return result
+
+    @property
+    @doc(DataFrame.dtypes.__doc__)
+    def dtypes(self) -> Series:
+        # GH#51045
+        warnings.warn(
+            f"{type(self).__name__}.dtypes is deprecated and will be removed in "
+            "a future version. Check the dtypes on the base object instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        # error: Incompatible return value type (got "DataFrame", expected "Series")
+        return self._python_apply_general(  # type: ignore[return-value]
+            lambda df: df.dtypes, self._selected_obj
+        )
+
+    @doc(DataFrame.corrwith.__doc__)
+    def corrwith(
+        self,
+        other: DataFrame | Series,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        drop: bool = False,
+        method: CorrelationMethod = "pearson",
+        numeric_only: bool = False,
+    ) -> DataFrame:
+        result = self._op_via_apply(
+            "corrwith",
+            other=other,
+            axis=axis,
+            drop=drop,
+            method=method,
+            numeric_only=numeric_only,
+        )
+        return result
+
+
+def _wrap_transform_general_frame(
+    obj: DataFrame, group: DataFrame, res: DataFrame | Series
+) -> DataFrame:
+    from pandas import concat
+
+    if isinstance(res, Series):
+        # we need to broadcast across the
+        # other dimension; this will preserve dtypes
+        # GH14457
+        if res.index.is_(obj.index):
+            res_frame = concat([res] * len(group.columns), axis=1)
+            res_frame.columns = group.columns
+            res_frame.index = group.index
+        else:
+            res_frame = obj._constructor(
+                np.tile(res.values, (len(group.index), 1)),
+                columns=group.columns,
+                index=group.index,
+            )
+        assert isinstance(res_frame, DataFrame)
+        return res_frame
+    elif isinstance(res, DataFrame) and not res.index.is_(group.index):
+        return res._align_frame(group)[0]
+    else:
+        return res
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/groupby.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/groupby.py
new file mode 100644
index 0000000000000000000000000000000000000000..5baa2a9b703cc4100aa6a342b3b9f5a3d6775e64
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/groupby.py
@@ -0,0 +1,6023 @@
+"""
+Provide the groupby split-apply-combine paradigm. Define the GroupBy
+class providing the base-class of operations.
+
+The SeriesGroupBy and DataFrameGroupBy sub-class
+(defined in pandas.core.groupby.generic)
+expose these user-facing objects to provide specific functionality.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterator,
+    Mapping,
+    Sequence,
+)
+import datetime
+from functools import (
+    partial,
+    wraps,
+)
+import inspect
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    TypeVar,
+    Union,
+    cast,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+from pandas._config.config import option_context
+
+from pandas._libs import (
+    Timestamp,
+    lib,
+)
+from pandas._libs.algos import rank_1d
+import pandas._libs.groupby as libgroupby
+from pandas._libs.missing import NA
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    Axis,
+    AxisInt,
+    DtypeObj,
+    FillnaOptions,
+    IndexLabel,
+    NDFrameT,
+    PositionalIndexer,
+    RandomState,
+    Scalar,
+    T,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    AbstractMethodError,
+    DataError,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    coerce_indexer_dtype,
+    ensure_dtype_can_hold_na,
+)
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_float_dtype,
+    is_hashable,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+from pandas.core import (
+    algorithms,
+    sample,
+)
+from pandas.core._numba import executor
+from pandas.core.apply import warn_alias_replacement
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    Categorical,
+    ExtensionArray,
+    FloatingArray,
+    IntegerArray,
+    SparseArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.arrays.string_arrow import (
+    ArrowStringArray,
+    ArrowStringArrayNumpySemantics,
+)
+from pandas.core.base import (
+    PandasObject,
+    SelectionMixin,
+)
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.core.generic import NDFrame
+from pandas.core.groupby import (
+    base,
+    numba_,
+    ops,
+)
+from pandas.core.groupby.grouper import get_grouper
+from pandas.core.groupby.indexing import (
+    GroupByIndexingMixin,
+    GroupByNthSelector,
+)
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    default_index,
+)
+from pandas.core.internals.blocks import ensure_block_shape
+from pandas.core.series import Series
+from pandas.core.sorting import get_group_index_sorter
+from pandas.core.util.numba_ import (
+    get_jit_arguments,
+    maybe_use_numba,
+)
+
+if TYPE_CHECKING:
+    from typing import Any
+
+    from pandas.core.resample import Resampler
+    from pandas.core.window import (
+        ExpandingGroupby,
+        ExponentialMovingWindowGroupby,
+        RollingGroupby,
+    )
+
+_common_see_also = """
+        See Also
+        --------
+        Series.%(name)s : Apply a function %(name)s to a Series.
+        DataFrame.%(name)s : Apply a function %(name)s
+            to each row or column of a DataFrame.
+"""
+
+_apply_docs = {
+    "template": """
+    Apply function ``func`` group-wise and combine the results together.
+
+    The function passed to ``apply`` must take a {input} as its first
+    argument and return a DataFrame, Series or scalar. ``apply`` will
+    then take care of combining the results back together into a single
+    dataframe or series. ``apply`` is therefore a highly flexible
+    grouping method.
+
+    While ``apply`` is a very flexible method, its downside is that
+    using it can be quite a bit slower than using more specific methods
+    like ``agg`` or ``transform``. Pandas offers a wide range of method that will
+    be much faster than using ``apply`` for their specific purposes, so try to
+    use them before reaching for ``apply``.
+
+    Parameters
+    ----------
+    func : callable
+        A callable that takes a {input} as its first argument, and
+        returns a dataframe, a series or a scalar. In addition the
+        callable may take positional and keyword arguments.
+    include_groups : bool, default True
+        When True, will attempt to apply ``func`` to the groupings in
+        the case that they are columns of the DataFrame. If this raises a
+        TypeError, the result will be computed with the groupings excluded.
+        When False, the groupings will be excluded when applying ``func``.
+
+        .. versionadded:: 2.2.0
+
+        .. deprecated:: 2.2.0
+
+           Setting include_groups to True is deprecated. Only the value
+           False will be allowed in a future version of pandas.
+
+    args, kwargs : tuple and dict
+        Optional positional and keyword arguments to pass to ``func``.
+
+    Returns
+    -------
+    Series or DataFrame
+
+    See Also
+    --------
+    pipe : Apply function to the full GroupBy object instead of to each
+        group.
+    aggregate : Apply aggregate function to the GroupBy object.
+    transform : Apply function column-by-column to the GroupBy object.
+    Series.apply : Apply a function to a Series.
+    DataFrame.apply : Apply a function to each row or column of a DataFrame.
+
+    Notes
+    -----
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the passed ``func``,
+        see the examples below.
+
+    Functions that mutate the passed object can produce unexpected
+    behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+    for more details.
+
+    Examples
+    --------
+    {examples}
+    """,
+    "dataframe_examples": """
+    >>> df = pd.DataFrame({'A': 'a a b'.split(),
+    ...                    'B': [1, 2, 3],
+    ...                    'C': [4, 6, 5]})
+    >>> g1 = df.groupby('A', group_keys=False)
+    >>> g2 = df.groupby('A', group_keys=True)
+
+    Notice that ``g1`` and ``g2`` have two groups, ``a`` and ``b``, and only
+    differ in their ``group_keys`` argument. Calling `apply` in various ways,
+    we can get different grouping results:
+
+    Example 1: below the function passed to `apply` takes a DataFrame as
+    its argument and returns a DataFrame. `apply` combines the result for
+    each group together into a new DataFrame:
+
+    >>> g1[['B', 'C']].apply(lambda x: x / x.sum())
+              B    C
+    0  0.333333  0.4
+    1  0.666667  0.6
+    2  1.000000  1.0
+
+    In the above, the groups are not part of the index. We can have them included
+    by using ``g2`` where ``group_keys=True``:
+
+    >>> g2[['B', 'C']].apply(lambda x: x / x.sum())
+                B    C
+    A
+    a 0  0.333333  0.4
+      1  0.666667  0.6
+    b 2  1.000000  1.0
+
+    Example 2: The function passed to `apply` takes a DataFrame as
+    its argument and returns a Series.  `apply` combines the result for
+    each group together into a new DataFrame.
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the passed ``func``.
+
+    >>> g1[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min())
+         B    C
+    A
+    a  1.0  2.0
+    b  0.0  0.0
+
+    >>> g2[['B', 'C']].apply(lambda x: x.astype(float).max() - x.min())
+         B    C
+    A
+    a  1.0  2.0
+    b  0.0  0.0
+
+    The ``group_keys`` argument has no effect here because the result is not
+    like-indexed (i.e. :ref:`a transform <groupby.transform>`) when compared
+    to the input.
+
+    Example 3: The function passed to `apply` takes a DataFrame as
+    its argument and returns a scalar. `apply` combines the result for
+    each group together into a Series, including setting the index as
+    appropriate:
+
+    >>> g1.apply(lambda x: x.C.max() - x.B.min(), include_groups=False)
+    A
+    a    5
+    b    2
+    dtype: int64""",
+    "series_examples": """
+    >>> s = pd.Series([0, 1, 2], index='a a b'.split())
+    >>> g1 = s.groupby(s.index, group_keys=False)
+    >>> g2 = s.groupby(s.index, group_keys=True)
+
+    From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``.
+    Notice that ``g1`` have ``g2`` have two groups, ``a`` and ``b``, and only
+    differ in their ``group_keys`` argument. Calling `apply` in various ways,
+    we can get different grouping results:
+
+    Example 1: The function passed to `apply` takes a Series as
+    its argument and returns a Series.  `apply` combines the result for
+    each group together into a new Series.
+
+    .. versionchanged:: 1.3.0
+
+        The resulting dtype will reflect the return value of the passed ``func``.
+
+    >>> g1.apply(lambda x: x * 2 if x.name == 'a' else x / 2)
+    a    0.0
+    a    2.0
+    b    1.0
+    dtype: float64
+
+    In the above, the groups are not part of the index. We can have them included
+    by using ``g2`` where ``group_keys=True``:
+
+    >>> g2.apply(lambda x: x * 2 if x.name == 'a' else x / 2)
+    a  a    0.0
+       a    2.0
+    b  b    1.0
+    dtype: float64
+
+    Example 2: The function passed to `apply` takes a Series as
+    its argument and returns a scalar. `apply` combines the result for
+    each group together into a Series, including setting the index as
+    appropriate:
+
+    >>> g1.apply(lambda x: x.max() - x.min())
+    a    1
+    b    0
+    dtype: int64
+
+    The ``group_keys`` argument has no effect here because the result is not
+    like-indexed (i.e. :ref:`a transform <groupby.transform>`) when compared
+    to the input.
+
+    >>> g2.apply(lambda x: x.max() - x.min())
+    a    1
+    b    0
+    dtype: int64""",
+}
+
+_groupby_agg_method_template = """
+Compute {fname} of group values.
+
+Parameters
+----------
+numeric_only : bool, default {no}
+    Include only float, int, boolean columns.
+
+    .. versionchanged:: 2.0.0
+
+        numeric_only no longer accepts ``None``.
+
+min_count : int, default {mc}
+    The required number of valid values to perform the operation. If fewer
+    than ``min_count`` non-NA values are present the result will be NA.
+
+Returns
+-------
+Series or DataFrame
+    Computed {fname} of values within each group.
+
+Examples
+--------
+{example}
+"""
+
+_groupby_agg_method_engine_template = """
+Compute {fname} of group values.
+
+Parameters
+----------
+numeric_only : bool, default {no}
+    Include only float, int, boolean columns.
+
+    .. versionchanged:: 2.0.0
+
+        numeric_only no longer accepts ``None``.
+
+min_count : int, default {mc}
+    The required number of valid values to perform the operation. If fewer
+    than ``min_count`` non-NA values are present the result will be NA.
+
+engine : str, default None {e}
+    * ``'cython'`` : Runs rolling apply through C-extensions from cython.
+    * ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
+        Only available when ``raw`` is set to ``True``.
+    * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None {ek}
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+        and ``parallel`` dictionary keys. The values must either be ``True`` or
+        ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+        ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+        applied to both the ``func`` and the ``apply`` groupby aggregation.
+
+Returns
+-------
+Series or DataFrame
+    Computed {fname} of values within each group.
+
+Examples
+--------
+{example}
+"""
+
+_pipe_template = """
+Apply a ``func`` with arguments to this %(klass)s object and return its result.
+
+Use `.pipe` when you want to improve readability by chaining together
+functions that expect Series, DataFrames, GroupBy or Resampler objects.
+Instead of writing
+
+>>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3
+>>> g = lambda x, arg1: x * 5 / arg1
+>>> f = lambda x: x ** 4
+>>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"])
+>>> h(g(f(df.groupby('group')), arg1=1), arg2=2, arg3=3)  # doctest: +SKIP
+
+You can write
+
+>>> (df.groupby('group')
+...    .pipe(f)
+...    .pipe(g, arg1=1)
+...    .pipe(h, arg2=2, arg3=3))  # doctest: +SKIP
+
+which is much more readable.
+
+Parameters
+----------
+func : callable or tuple of (callable, str)
+    Function to apply to this %(klass)s object or, alternatively,
+    a `(callable, data_keyword)` tuple where `data_keyword` is a
+    string indicating the keyword of `callable` that expects the
+    %(klass)s object.
+args : iterable, optional
+       Positional arguments passed into `func`.
+kwargs : dict, optional
+         A dictionary of keyword arguments passed into `func`.
+
+Returns
+-------
+the return type of `func`.
+
+See Also
+--------
+Series.pipe : Apply a function with arguments to a series.
+DataFrame.pipe: Apply a function with arguments to a dataframe.
+apply : Apply function to each group instead of to the
+    full %(klass)s object.
+
+Notes
+-----
+See more `here
+<https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#piping-function-calls>`_
+
+Examples
+--------
+%(examples)s
+"""
+
+_transform_template = """
+Call function producing a same-indexed %(klass)s on each group.
+
+Returns a %(klass)s having the same indexes as the original object
+filled with the transformed values.
+
+Parameters
+----------
+f : function, str
+    Function to apply to each group. See the Notes section below for requirements.
+
+    Accepted inputs are:
+
+    - String
+    - Python function
+    - Numba JIT function with ``engine='numba'`` specified.
+
+    Only passing a single function is supported with this engine.
+    If the ``'numba'`` engine is chosen, the function must be
+    a user defined function with ``values`` and ``index`` as the
+    first and second arguments respectively in the function signature.
+    Each group's index will be passed to the user defined function
+    and optionally available for use.
+
+    If a string is chosen, then it needs to be the name
+    of the groupby method you want to use.
+*args
+    Positional arguments to pass to func.
+engine : str, default None
+    * ``'cython'`` : Runs the function through C-extensions from cython.
+    * ``'numba'`` : Runs the function through JIT compiled code from numba.
+    * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba``
+
+engine_kwargs : dict, default None
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+      and ``parallel`` dictionary keys. The values must either be ``True`` or
+      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+      ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be
+      applied to the function
+
+**kwargs
+    Keyword arguments to be passed into func.
+
+Returns
+-------
+%(klass)s
+
+See Also
+--------
+%(klass)s.groupby.apply : Apply function ``func`` group-wise and combine
+    the results together.
+%(klass)s.groupby.aggregate : Aggregate using one or more
+    operations over the specified axis.
+%(klass)s.transform : Call ``func`` on self producing a %(klass)s with the
+    same axis shape as self.
+
+Notes
+-----
+Each group is endowed the attribute 'name' in case you need to know
+which group you are working on.
+
+The current implementation imposes three requirements on f:
+
+* f must return a value that either has the same shape as the input
+  subframe or can be broadcast to the shape of the input subframe.
+  For example, if `f` returns a scalar it will be broadcast to have the
+  same shape as the input subframe.
+* if this is a DataFrame, f must support application column-by-column
+  in the subframe. If f also supports application to the entire subframe,
+  then a fast path is used starting from the second chunk.
+* f must not mutate groups. Mutation is not supported and may
+  produce unexpected results. See :ref:`gotchas.udf-mutation` for more details.
+
+When using ``engine='numba'``, there will be no "fall back" behavior internally.
+The group data and group index will be passed as numpy arrays to the JITed
+user defined function, and no alternative execution attempts will be tried.
+
+.. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    see the examples below.
+
+.. versionchanged:: 2.0.0
+
+    When using ``.transform`` on a grouped DataFrame and the transformation function
+    returns a DataFrame, pandas now aligns the result's index
+    with the input's index. You can call ``.to_numpy()`` on the
+    result of the transformation function to avoid alignment.
+
+Examples
+--------
+%(example)s"""
+
+_agg_template_series = """
+Aggregate using one or more operations over the specified axis.
+
+Parameters
+----------
+func : function, str, list, dict or None
+    Function to use for aggregating the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
+    - None, in which case ``**kwargs`` are used with Named Aggregation. Here the
+      output has one column for each element in ``**kwargs``. The name of the
+      column is keyword, whereas the value determines the aggregation used to compute
+      the values in the column.
+
+      Can also accept a Numba JIT function with
+      ``engine='numba'`` specified. Only passing a single function is supported
+      with this engine.
+
+      If the ``'numba'`` engine is chosen, the function must be
+      a user defined function with ``values`` and ``index`` as the
+      first and second arguments respectively in the function signature.
+      Each group's index will be passed to the user defined function
+      and optionally available for use.
+
+    .. deprecated:: 2.1.0
+
+        Passing a dictionary is deprecated and will raise in a future version
+        of pandas. Pass a list of aggregations instead.
+*args
+    Positional arguments to pass to func.
+engine : str, default None
+    * ``'cython'`` : Runs the function through C-extensions from cython.
+    * ``'numba'`` : Runs the function through JIT compiled code from numba.
+    * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+      and ``parallel`` dictionary keys. The values must either be ``True`` or
+      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+      ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+      applied to the function
+
+**kwargs
+    * If ``func`` is None, ``**kwargs`` are used to define the output names and
+      aggregations via Named Aggregation. See ``func`` entry.
+    * Otherwise, keyword arguments to be passed into func.
+
+Returns
+-------
+{klass}
+
+See Also
+--------
+{klass}.groupby.apply : Apply function func group-wise
+    and combine the results together.
+{klass}.groupby.transform : Transforms the Series on each group
+    based on the given function.
+{klass}.aggregate : Aggregate using one or more
+    operations over the specified axis.
+
+Notes
+-----
+When using ``engine='numba'``, there will be no "fall back" behavior internally.
+The group data and group index will be passed as numpy arrays to the JITed
+user defined function, and no alternative execution attempts will be tried.
+
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+.. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    see the examples below.
+{examples}"""
+
+_agg_template_frame = """
+Aggregate using one or more operations over the specified axis.
+
+Parameters
+----------
+func : function, str, list, dict or None
+    Function to use for aggregating the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
+    - dict of axis labels -> functions, function names or list of such.
+    - None, in which case ``**kwargs`` are used with Named Aggregation. Here the
+      output has one column for each element in ``**kwargs``. The name of the
+      column is keyword, whereas the value determines the aggregation used to compute
+      the values in the column.
+
+      Can also accept a Numba JIT function with
+      ``engine='numba'`` specified. Only passing a single function is supported
+      with this engine.
+
+      If the ``'numba'`` engine is chosen, the function must be
+      a user defined function with ``values`` and ``index`` as the
+      first and second arguments respectively in the function signature.
+      Each group's index will be passed to the user defined function
+      and optionally available for use.
+
+*args
+    Positional arguments to pass to func.
+engine : str, default None
+    * ``'cython'`` : Runs the function through C-extensions from cython.
+    * ``'numba'`` : Runs the function through JIT compiled code from numba.
+    * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+engine_kwargs : dict, default None
+    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+      and ``parallel`` dictionary keys. The values must either be ``True`` or
+      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+      ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+      applied to the function
+
+**kwargs
+    * If ``func`` is None, ``**kwargs`` are used to define the output names and
+      aggregations via Named Aggregation. See ``func`` entry.
+    * Otherwise, keyword arguments to be passed into func.
+
+Returns
+-------
+{klass}
+
+See Also
+--------
+{klass}.groupby.apply : Apply function func group-wise
+    and combine the results together.
+{klass}.groupby.transform : Transforms the Series on each group
+    based on the given function.
+{klass}.aggregate : Aggregate using one or more
+    operations over the specified axis.
+
+Notes
+-----
+When using ``engine='numba'``, there will be no "fall back" behavior internally.
+The group data and group index will be passed as numpy arrays to the JITed
+user defined function, and no alternative execution attempts will be tried.
+
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+.. versionchanged:: 1.3.0
+
+    The resulting dtype will reflect the return value of the passed ``func``,
+    see the examples below.
+{examples}"""
+
+
+@final
+class GroupByPlot(PandasObject):
+    """
+    Class implementing the .plot attribute for groupby objects.
+    """
+
+    def __init__(self, groupby: GroupBy) -> None:
+        self._groupby = groupby
+
+    def __call__(self, *args, **kwargs):
+        def f(self):
+            return self.plot(*args, **kwargs)
+
+        f.__name__ = "plot"
+        return self._groupby._python_apply_general(f, self._groupby._selected_obj)
+
+    def __getattr__(self, name: str):
+        def attr(*args, **kwargs):
+            def f(self):
+                return getattr(self.plot, name)(*args, **kwargs)
+
+            return self._groupby._python_apply_general(f, self._groupby._selected_obj)
+
+        return attr
+
+
+_KeysArgType = Union[
+    Hashable,
+    list[Hashable],
+    Callable[[Hashable], Hashable],
+    list[Callable[[Hashable], Hashable]],
+    Mapping[Hashable, Hashable],
+]
+
+
+class BaseGroupBy(PandasObject, SelectionMixin[NDFrameT], GroupByIndexingMixin):
+    _hidden_attrs = PandasObject._hidden_attrs | {
+        "as_index",
+        "axis",
+        "dropna",
+        "exclusions",
+        "grouper",
+        "group_keys",
+        "keys",
+        "level",
+        "obj",
+        "observed",
+        "sort",
+    }
+
+    axis: AxisInt
+    _grouper: ops.BaseGrouper
+    keys: _KeysArgType | None = None
+    level: IndexLabel | None = None
+    group_keys: bool
+
+    @final
+    def __len__(self) -> int:
+        return len(self.groups)
+
+    @final
+    def __repr__(self) -> str:
+        # TODO: Better repr for GroupBy object
+        return object.__repr__(self)
+
+    @final
+    @property
+    def grouper(self) -> ops.BaseGrouper:
+        warnings.warn(
+            f"{type(self).__name__}.grouper is deprecated and will be removed in a "
+            "future version of pandas.",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._grouper
+
+    @final
+    @property
+    def groups(self) -> dict[Hashable, np.ndarray]:
+        """
+        Dict {group name -> group labels}.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).groups
+        {'a': ['a', 'a'], 'b': ['b']}
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"])
+        >>> df
+           a  b  c
+        0  1  2  3
+        1  1  5  6
+        2  7  8  9
+        >>> df.groupby(by=["a"]).groups
+        {1: [0, 1], 7: [2]}
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').groups
+        {Timestamp('2023-01-01 00:00:00'): 2, Timestamp('2023-02-01 00:00:00'): 4}
+        """
+        return self._grouper.groups
+
+    @final
+    @property
+    def ngroups(self) -> int:
+        return self._grouper.ngroups
+
+    @final
+    @property
+    def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        """
+        Dict {group name -> group indices}.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).indices
+        {'a': array([0, 1]), 'b': array([2])}
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["owl", "toucan", "eagle"])
+        >>> df
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+        eagle   7  8  9
+        >>> df.groupby(by=["a"]).indices
+        {1: array([0, 1]), 7: array([2])}
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').indices
+        defaultdict(<class 'list'>, {Timestamp('2023-01-01 00:00:00'): [0, 1],
+        Timestamp('2023-02-01 00:00:00'): [2, 3]})
+        """
+        return self._grouper.indices
+
+    @final
+    def _get_indices(self, names):
+        """
+        Safe get multiple indices, translate keys for
+        datelike to underlying repr.
+        """
+
+        def get_converter(s):
+            # possibly convert to the actual key types
+            # in the indices, could be a Timestamp or a np.datetime64
+            if isinstance(s, datetime.datetime):
+                return lambda key: Timestamp(key)
+            elif isinstance(s, np.datetime64):
+                return lambda key: Timestamp(key).asm8
+            else:
+                return lambda key: key
+
+        if len(names) == 0:
+            return []
+
+        if len(self.indices) > 0:
+            index_sample = next(iter(self.indices))
+        else:
+            index_sample = None  # Dummy sample
+
+        name_sample = names[0]
+        if isinstance(index_sample, tuple):
+            if not isinstance(name_sample, tuple):
+                msg = "must supply a tuple to get_group with multiple grouping keys"
+                raise ValueError(msg)
+            if not len(name_sample) == len(index_sample):
+                try:
+                    # If the original grouper was a tuple
+                    return [self.indices[name] for name in names]
+                except KeyError as err:
+                    # turns out it wasn't a tuple
+                    msg = (
+                        "must supply a same-length tuple to get_group "
+                        "with multiple grouping keys"
+                    )
+                    raise ValueError(msg) from err
+
+            converters = [get_converter(s) for s in index_sample]
+            names = (tuple(f(n) for f, n in zip(converters, name)) for name in names)
+
+        else:
+            converter = get_converter(index_sample)
+            names = (converter(name) for name in names)
+
+        return [self.indices.get(name, []) for name in names]
+
+    @final
+    def _get_index(self, name):
+        """
+        Safe get index, translate keys for datelike to underlying repr.
+        """
+        return self._get_indices([name])[0]
+
+    @final
+    @cache_readonly
+    def _selected_obj(self):
+        # Note: _selected_obj is always just `self.obj` for SeriesGroupBy
+        if isinstance(self.obj, Series):
+            return self.obj
+
+        if self._selection is not None:
+            if is_hashable(self._selection):
+                # i.e. a single key, so selecting it will return a Series.
+                #  In this case, _obj_with_exclusions would wrap the key
+                #  in a list and return a single-column DataFrame.
+                return self.obj[self._selection]
+
+            # Otherwise _selection is equivalent to _selection_list, so
+            #  _selected_obj matches _obj_with_exclusions, so we can reuse
+            #  that and avoid making a copy.
+            return self._obj_with_exclusions
+
+        return self.obj
+
+    @final
+    def _dir_additions(self) -> set[str]:
+        return self.obj._dir_additions()
+
+    @Substitution(
+        klass="GroupBy",
+        examples=dedent(
+            """\
+        >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})
+        >>> df
+           A  B
+        0  a  1
+        1  b  2
+        2  a  3
+        3  b  4
+
+        To get the difference between each groups maximum and minimum value in one
+        pass, you can do
+
+        >>> df.groupby('A').pipe(lambda x: x.max() - x.min())
+           B
+        A
+        a  2
+        b  2"""
+        ),
+    )
+    @Appender(_pipe_template)
+    def pipe(
+        self,
+        func: Callable[..., T] | tuple[Callable[..., T], str],
+        *args,
+        **kwargs,
+    ) -> T:
+        return com.pipe(self, func, *args, **kwargs)
+
+    @final
+    def get_group(self, name, obj=None) -> DataFrame | Series:
+        """
+        Construct DataFrame from group with provided name.
+
+        Parameters
+        ----------
+        name : object
+            The name of the group to get as a DataFrame.
+        obj : DataFrame, default None
+            The DataFrame to take the DataFrame out of.  If
+            it is None, the object groupby was called on will
+            be used.
+
+            .. deprecated:: 2.1.0
+                The obj is deprecated and will be removed in a future version.
+                Do ``df.iloc[gb.indices.get(name)]``
+                instead of ``gb.get_group(name, obj=df)``.
+
+        Returns
+        -------
+        same type as obj
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> ser.groupby(level=0).get_group("a")
+        a    1
+        a    2
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["owl", "toucan", "eagle"])
+        >>> df
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+        eagle   7  8  9
+        >>> df.groupby(by=["a"]).get_group((1,))
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').get_group('2023-01-01')
+        2023-01-01    1
+        2023-01-15    2
+        dtype: int64
+        """
+        keys = self.keys
+        level = self.level
+        # mypy doesn't recognize level/keys as being sized when passed to len
+        if (is_list_like(level) and len(level) == 1) or (  # type: ignore[arg-type]
+            is_list_like(keys) and len(keys) == 1  # type: ignore[arg-type]
+        ):
+            # GH#25971
+            if isinstance(name, tuple) and len(name) == 1:
+                # Allow users to pass tuples of length 1 to silence warning
+                name = name[0]
+            elif not isinstance(name, tuple):
+                warnings.warn(
+                    "When grouping with a length-1 list-like, "
+                    "you will need to pass a length-1 tuple to get_group in a future "
+                    "version of pandas. Pass `(name,)` instead of `name` to silence "
+                    "this warning.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        inds = self._get_index(name)
+        if not len(inds):
+            raise KeyError(name)
+
+        if obj is None:
+            indexer = inds if self.axis == 0 else (slice(None), inds)
+            return self._selected_obj.iloc[indexer]
+        else:
+            warnings.warn(
+                "obj is deprecated and will be removed in a future version. "
+                "Do ``df.iloc[gb.indices.get(name)]`` "
+                "instead of ``gb.get_group(name, obj=df)``.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return obj._take_with_is_copy(inds, axis=self.axis)
+
+    @final
+    def __iter__(self) -> Iterator[tuple[Hashable, NDFrameT]]:
+        """
+        Groupby iterator.
+
+        Returns
+        -------
+        Generator yielding sequence of (name, subsetted object)
+        for each group
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        dtype: int64
+        >>> for x, y in ser.groupby(level=0):
+        ...     print(f'{x}\\n{y}\\n')
+        a
+        a    1
+        a    2
+        dtype: int64
+        b
+        b    3
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"])
+        >>> df
+           a  b  c
+        0  1  2  3
+        1  1  5  6
+        2  7  8  9
+        >>> for x, y in df.groupby(by=["a"]):
+        ...     print(f'{x}\\n{y}\\n')
+        (1,)
+           a  b  c
+        0  1  2  3
+        1  1  5  6
+        (7,)
+           a  b  c
+        2  7  8  9
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> for x, y in ser.resample('MS'):
+        ...     print(f'{x}\\n{y}\\n')
+        2023-01-01 00:00:00
+        2023-01-01    1
+        2023-01-15    2
+        dtype: int64
+        2023-02-01 00:00:00
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        """
+        keys = self.keys
+        level = self.level
+        result = self._grouper.get_iterator(self._selected_obj, axis=self.axis)
+        # error: Argument 1 to "len" has incompatible type "Hashable"; expected "Sized"
+        if is_list_like(level) and len(level) == 1:  # type: ignore[arg-type]
+            # GH 51583
+            warnings.warn(
+                "Creating a Groupby object with a length-1 list-like "
+                "level parameter will yield indexes as tuples in a future version. "
+                "To keep indexes as scalars, create Groupby objects with "
+                "a scalar level parameter instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if isinstance(keys, list) and len(keys) == 1:
+            # GH#42795 - when keys is a list, return tuples even when length is 1
+            result = (((key,), group) for key, group in result)
+        return result
+
+
+# To track operations that expand dimensions, like ohlc
+OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame)
+
+
+class GroupBy(BaseGroupBy[NDFrameT]):
+    """
+    Class for grouping and aggregating relational data.
+
+    See aggregate, transform, and apply functions on this object.
+
+    It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:
+
+    ::
+
+        grouped = groupby(obj, ...)
+
+    Parameters
+    ----------
+    obj : pandas object
+    axis : int, default 0
+    level : int, default None
+        Level of MultiIndex
+    groupings : list of Grouping objects
+        Most users should ignore this
+    exclusions : array-like, optional
+        List of columns to exclude
+    name : str
+        Most users should ignore this
+
+    Returns
+    -------
+    **Attributes**
+    groups : dict
+        {group name -> group labels}
+    len(grouped) : int
+        Number of groups
+
+    Notes
+    -----
+    After grouping, see aggregate, apply, and transform functions. Here are
+    some other brief notes about usage. When grouping by multiple groups, the
+    result index will be a MultiIndex (hierarchical) by default.
+
+    Iteration produces (key, group) tuples, i.e. chunking the data by group. So
+    you can write code like:
+
+    ::
+
+        grouped = obj.groupby(keys, axis=axis)
+        for key, group in grouped:
+            # do something with the data
+
+    Function calls on GroupBy, if not specially implemented, "dispatch" to the
+    grouped data. So if you group a DataFrame and wish to invoke the std()
+    method on each group, you can simply do:
+
+    ::
+
+        df.groupby(mapper).std()
+
+    rather than
+
+    ::
+
+        df.groupby(mapper).aggregate(np.std)
+
+    You can pass arguments to these "wrapped" functions, too.
+
+    See the online documentation for full exposition on these topics and much
+    more
+    """
+
+    _grouper: ops.BaseGrouper
+    as_index: bool
+
+    @final
+    def __init__(
+        self,
+        obj: NDFrameT,
+        keys: _KeysArgType | None = None,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        grouper: ops.BaseGrouper | None = None,
+        exclusions: frozenset[Hashable] | None = None,
+        selection: IndexLabel | None = None,
+        as_index: bool = True,
+        sort: bool = True,
+        group_keys: bool = True,
+        observed: bool | lib.NoDefault = lib.no_default,
+        dropna: bool = True,
+    ) -> None:
+        self._selection = selection
+
+        assert isinstance(obj, NDFrame), type(obj)
+
+        self.level = level
+
+        if not as_index:
+            if axis != 0:
+                raise ValueError("as_index=False only valid for axis=0")
+
+        self.as_index = as_index
+        self.keys = keys
+        self.sort = sort
+        self.group_keys = group_keys
+        self.dropna = dropna
+
+        if grouper is None:
+            grouper, exclusions, obj = get_grouper(
+                obj,
+                keys,
+                axis=axis,
+                level=level,
+                sort=sort,
+                observed=False if observed is lib.no_default else observed,
+                dropna=self.dropna,
+            )
+
+        if observed is lib.no_default:
+            if any(ping._passed_categorical for ping in grouper.groupings):
+                warnings.warn(
+                    "The default of observed=False is deprecated and will be changed "
+                    "to True in a future version of pandas. Pass observed=False to "
+                    "retain current behavior or observed=True to adopt the future "
+                    "default and silence this warning.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            observed = False
+        self.observed = observed
+
+        self.obj = obj
+        self.axis = obj._get_axis_number(axis)
+        self._grouper = grouper
+        self.exclusions = frozenset(exclusions) if exclusions else frozenset()
+
+    def __getattr__(self, attr: str):
+        if attr in self._internal_names_set:
+            return object.__getattribute__(self, attr)
+        if attr in self.obj:
+            return self[attr]
+
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{attr}'"
+        )
+
+    @final
+    def _deprecate_axis(self, axis: int, name: str) -> None:
+        if axis == 1:
+            warnings.warn(
+                f"{type(self).__name__}.{name} with axis=1 is deprecated and "
+                "will be removed in a future version. Operate on the un-grouped "
+                "DataFrame instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            warnings.warn(
+                f"The 'axis' keyword in {type(self).__name__}.{name} is deprecated "
+                "and will be removed in a future version. "
+                "Call without passing 'axis' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    @final
+    def _op_via_apply(self, name: str, *args, **kwargs):
+        """Compute the result of an operation by using GroupBy's apply."""
+        f = getattr(type(self._obj_with_exclusions), name)
+        sig = inspect.signature(f)
+
+        if "axis" in kwargs and kwargs["axis"] is not lib.no_default:
+            axis = self.obj._get_axis_number(kwargs["axis"])
+            self._deprecate_axis(axis, name)
+        elif "axis" in kwargs:
+            # exclude skew here because that was already defaulting to lib.no_default
+            #  before this deprecation was instituted
+            if name == "skew":
+                pass
+            elif name == "fillna":
+                # maintain the behavior from before the deprecation
+                kwargs["axis"] = None
+            else:
+                kwargs["axis"] = 0
+
+        # a little trickery for aggregation functions that need an axis
+        # argument
+        if "axis" in sig.parameters:
+            if kwargs.get("axis", None) is None or kwargs.get("axis") is lib.no_default:
+                kwargs["axis"] = self.axis
+
+        def curried(x):
+            return f(x, *args, **kwargs)
+
+        # preserve the name so we can detect it when calling plot methods,
+        # to avoid duplicates
+        curried.__name__ = name
+
+        # special case otherwise extra plots are created when catching the
+        # exception below
+        if name in base.plotting_methods:
+            return self._python_apply_general(curried, self._selected_obj)
+
+        is_transform = name in base.transformation_kernels
+        result = self._python_apply_general(
+            curried,
+            self._obj_with_exclusions,
+            is_transform=is_transform,
+            not_indexed_same=not is_transform,
+        )
+
+        if self._grouper.has_dropped_na and is_transform:
+            # result will have dropped rows due to nans, fill with null
+            # and ensure index is ordered same as the input
+            result = self._set_result_index_ordered(result)
+        return result
+
+    # -----------------------------------------------------------------
+    # Dispatch/Wrapping
+
+    @final
+    def _concat_objects(
+        self,
+        values,
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ):
+        from pandas.core.reshape.concat import concat
+
+        if self.group_keys and not is_transform:
+            if self.as_index:
+                # possible MI return case
+                group_keys = self._grouper.result_index
+                group_levels = self._grouper.levels
+                group_names = self._grouper.names
+
+                result = concat(
+                    values,
+                    axis=self.axis,
+                    keys=group_keys,
+                    levels=group_levels,
+                    names=group_names,
+                    sort=False,
+                )
+            else:
+                # GH5610, returns a MI, with the first level being a
+                # range index
+                keys = list(range(len(values)))
+                result = concat(values, axis=self.axis, keys=keys)
+
+        elif not not_indexed_same:
+            result = concat(values, axis=self.axis)
+
+            ax = self._selected_obj._get_axis(self.axis)
+            if self.dropna:
+                labels = self._grouper.group_info[0]
+                mask = labels != -1
+                ax = ax[mask]
+
+            # this is a very unfortunate situation
+            # we can't use reindex to restore the original order
+            # when the ax has duplicates
+            # so we resort to this
+            # GH 14776, 30667
+            # TODO: can we reuse e.g. _reindex_non_unique?
+            if ax.has_duplicates and not result.axes[self.axis].equals(ax):
+                # e.g. test_category_order_transformer
+                target = algorithms.unique1d(ax._values)
+                indexer, _ = result.index.get_indexer_non_unique(target)
+                result = result.take(indexer, axis=self.axis)
+            else:
+                result = result.reindex(ax, axis=self.axis, copy=False)
+
+        else:
+            result = concat(values, axis=self.axis)
+
+        if self.obj.ndim == 1:
+            name = self.obj.name
+        elif is_hashable(self._selection):
+            name = self._selection
+        else:
+            name = None
+
+        if isinstance(result, Series) and name is not None:
+            result.name = name
+
+        return result
+
+    @final
+    def _set_result_index_ordered(
+        self, result: OutputFrameOrSeries
+    ) -> OutputFrameOrSeries:
+        # set the result index on the passed values object and
+        # return the new object, xref 8046
+
+        obj_axis = self.obj._get_axis(self.axis)
+
+        if self._grouper.is_monotonic and not self._grouper.has_dropped_na:
+            # shortcut if we have an already ordered grouper
+            result = result.set_axis(obj_axis, axis=self.axis, copy=False)
+            return result
+
+        # row order is scrambled => sort the rows by position in original index
+        original_positions = Index(self._grouper.result_ilocs())
+        result = result.set_axis(original_positions, axis=self.axis, copy=False)
+        result = result.sort_index(axis=self.axis)
+        if self._grouper.has_dropped_na:
+            # Add back in any missing rows due to dropna - index here is integral
+            # with values referring to the row of the input so can use RangeIndex
+            result = result.reindex(RangeIndex(len(obj_axis)), axis=self.axis)
+        result = result.set_axis(obj_axis, axis=self.axis, copy=False)
+
+        return result
+
+    @final
+    def _insert_inaxis_grouper(self, result: Series | DataFrame) -> DataFrame:
+        if isinstance(result, Series):
+            result = result.to_frame()
+
+        # zip in reverse so we can always insert at loc 0
+        columns = result.columns
+        for name, lev, in_axis in zip(
+            reversed(self._grouper.names),
+            reversed(self._grouper.get_group_levels()),
+            reversed([grp.in_axis for grp in self._grouper.groupings]),
+        ):
+            # GH #28549
+            # When using .apply(-), name will be in columns already
+            if name not in columns:
+                if in_axis:
+                    result.insert(0, name, lev)
+                else:
+                    msg = (
+                        "A grouping was used that is not in the columns of the "
+                        "DataFrame and so was excluded from the result. This grouping "
+                        "will be included in a future version of pandas. Add the "
+                        "grouping as a column of the DataFrame to silence this warning."
+                    )
+                    warnings.warn(
+                        message=msg,
+                        category=FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+        return result
+
+    @final
+    def _maybe_transpose_result(self, result: NDFrameT) -> NDFrameT:
+        if self.axis == 1:
+            # Only relevant for DataFrameGroupBy, no-op for SeriesGroupBy
+            result = result.T
+            if result.index.equals(self.obj.index):
+                # Retain e.g. DatetimeIndex/TimedeltaIndex freq
+                # e.g. test_groupby_crash_on_nunique
+                result.index = self.obj.index.copy()
+        return result
+
+    @final
+    def _wrap_aggregated_output(
+        self,
+        result: Series | DataFrame,
+        qs: npt.NDArray[np.float64] | None = None,
+    ):
+        """
+        Wraps the output of GroupBy aggregations into the expected result.
+
+        Parameters
+        ----------
+        result : Series, DataFrame
+
+        Returns
+        -------
+        Series or DataFrame
+        """
+        # ATM we do not get here for SeriesGroupBy; when we do, we will
+        #  need to require that result.name already match self.obj.name
+
+        if not self.as_index:
+            # `not self.as_index` is only relevant for DataFrameGroupBy,
+            #   enforced in __init__
+            result = self._insert_inaxis_grouper(result)
+            result = result._consolidate()
+            index = Index(range(self._grouper.ngroups))
+
+        else:
+            index = self._grouper.result_index
+
+        if qs is not None:
+            # We get here with len(qs) != 1 and not self.as_index
+            #  in test_pass_args_kwargs
+            index = _insert_quantile_level(index, qs)
+
+        result.index = index
+
+        # error: Argument 1 to "_maybe_transpose_result" of "GroupBy" has
+        # incompatible type "Union[Series, DataFrame]"; expected "NDFrameT"
+        res = self._maybe_transpose_result(result)  # type: ignore[arg-type]
+        return self._reindex_output(res, qs=qs)
+
+    def _wrap_applied_output(
+        self,
+        data,
+        values: list,
+        not_indexed_same: bool = False,
+        is_transform: bool = False,
+    ):
+        raise AbstractMethodError(self)
+
+    # -----------------------------------------------------------------
+    # numba
+
+    @final
+    def _numba_prep(self, data: DataFrame):
+        ids, _, ngroups = self._grouper.group_info
+        sorted_index = self._grouper._sort_idx
+        sorted_ids = self._grouper._sorted_ids
+
+        sorted_data = data.take(sorted_index, axis=self.axis).to_numpy()
+        # GH 46867
+        index_data = data.index
+        if isinstance(index_data, MultiIndex):
+            if len(self._grouper.groupings) > 1:
+                raise NotImplementedError(
+                    "Grouping with more than 1 grouping labels and "
+                    "a MultiIndex is not supported with engine='numba'"
+                )
+            group_key = self._grouper.groupings[0].name
+            index_data = index_data.get_level_values(group_key)
+        sorted_index_data = index_data.take(sorted_index).to_numpy()
+
+        starts, ends = lib.generate_slices(sorted_ids, ngroups)
+        return (
+            starts,
+            ends,
+            sorted_index_data,
+            sorted_data,
+        )
+
+    def _numba_agg_general(
+        self,
+        func: Callable,
+        dtype_mapping: dict[np.dtype, Any],
+        engine_kwargs: dict[str, bool] | None,
+        **aggregator_kwargs,
+    ):
+        """
+        Perform groupby with a standard numerical aggregation function (e.g. mean)
+        with Numba.
+        """
+        if not self.as_index:
+            raise NotImplementedError(
+                "as_index=False is not supported. Use .reset_index() instead."
+            )
+        if self.axis == 1:
+            raise NotImplementedError("axis=1 is not supported.")
+
+        data = self._obj_with_exclusions
+        df = data if data.ndim == 2 else data.to_frame()
+
+        aggregator = executor.generate_shared_aggregator(
+            func,
+            dtype_mapping,
+            True,  # is_grouped_kernel
+            **get_jit_arguments(engine_kwargs),
+        )
+        # Pass group ids to kernel directly if it can handle it
+        # (This is faster since it doesn't require a sort)
+        ids, _, _ = self._grouper.group_info
+        ngroups = self._grouper.ngroups
+
+        res_mgr = df._mgr.apply(
+            aggregator, labels=ids, ngroups=ngroups, **aggregator_kwargs
+        )
+        res_mgr.axes[1] = self._grouper.result_index
+        result = df._constructor_from_mgr(res_mgr, axes=res_mgr.axes)
+
+        if data.ndim == 1:
+            result = result.squeeze("columns")
+            result.name = data.name
+        else:
+            result.columns = data.columns
+        return result
+
+    @final
+    def _transform_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
+        """
+        Perform groupby transform routine with the numba engine.
+
+        This routine mimics the data splitting routine of the DataSplitter class
+        to generate the indices of each group in the sorted data and then passes the
+        data and indices into a Numba jitted function.
+        """
+        data = self._obj_with_exclusions
+        df = data if data.ndim == 2 else data.to_frame()
+
+        starts, ends, sorted_index, sorted_data = self._numba_prep(df)
+        numba_.validate_udf(func)
+        numba_transform_func = numba_.generate_numba_transform_func(
+            func, **get_jit_arguments(engine_kwargs, kwargs)
+        )
+        result = numba_transform_func(
+            sorted_data,
+            sorted_index,
+            starts,
+            ends,
+            len(df.columns),
+            *args,
+        )
+        # result values needs to be resorted to their original positions since we
+        # evaluated the data sorted by group
+        result = result.take(np.argsort(sorted_index), axis=0)
+        index = data.index
+        if data.ndim == 1:
+            result_kwargs = {"name": data.name}
+            result = result.ravel()
+        else:
+            result_kwargs = {"columns": data.columns}
+        return data._constructor(result, index=index, **result_kwargs)
+
+    @final
+    def _aggregate_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
+        """
+        Perform groupby aggregation routine with the numba engine.
+
+        This routine mimics the data splitting routine of the DataSplitter class
+        to generate the indices of each group in the sorted data and then passes the
+        data and indices into a Numba jitted function.
+        """
+        data = self._obj_with_exclusions
+        df = data if data.ndim == 2 else data.to_frame()
+
+        starts, ends, sorted_index, sorted_data = self._numba_prep(df)
+        numba_.validate_udf(func)
+        numba_agg_func = numba_.generate_numba_agg_func(
+            func, **get_jit_arguments(engine_kwargs, kwargs)
+        )
+        result = numba_agg_func(
+            sorted_data,
+            sorted_index,
+            starts,
+            ends,
+            len(df.columns),
+            *args,
+        )
+        index = self._grouper.result_index
+        if data.ndim == 1:
+            result_kwargs = {"name": data.name}
+            result = result.ravel()
+        else:
+            result_kwargs = {"columns": data.columns}
+        res = data._constructor(result, index=index, **result_kwargs)
+        if not self.as_index:
+            res = self._insert_inaxis_grouper(res)
+            res.index = default_index(len(res))
+        return res
+
+    # -----------------------------------------------------------------
+    # apply/agg/transform
+
+    @Appender(
+        _apply_docs["template"].format(
+            input="dataframe", examples=_apply_docs["dataframe_examples"]
+        )
+    )
+    def apply(self, func, *args, include_groups: bool = True, **kwargs) -> NDFrameT:
+        orig_func = func
+        func = com.is_builtin_func(func)
+        if orig_func != func:
+            alias = com._builtin_table_alias[orig_func]
+            warn_alias_replacement(self, orig_func, alias)
+
+        if isinstance(func, str):
+            if hasattr(self, func):
+                res = getattr(self, func)
+                if callable(res):
+                    return res(*args, **kwargs)
+                elif args or kwargs:
+                    raise ValueError(f"Cannot pass arguments to property {func}")
+                return res
+
+            else:
+                raise TypeError(f"apply func should be callable, not '{func}'")
+
+        elif args or kwargs:
+            if callable(func):
+
+                @wraps(func)
+                def f(g):
+                    return func(g, *args, **kwargs)
+
+            else:
+                raise ValueError(
+                    "func must be a callable if args or kwargs are supplied"
+                )
+        else:
+            f = func
+
+        if not include_groups:
+            return self._python_apply_general(f, self._obj_with_exclusions)
+
+        # ignore SettingWithCopy here in case the user mutates
+        with option_context("mode.chained_assignment", None):
+            try:
+                result = self._python_apply_general(f, self._selected_obj)
+                if (
+                    not isinstance(self.obj, Series)
+                    and self._selection is None
+                    and self._selected_obj.shape != self._obj_with_exclusions.shape
+                ):
+                    warnings.warn(
+                        message=_apply_groupings_depr.format(
+                            type(self).__name__, "apply"
+                        ),
+                        category=FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+            except TypeError:
+                # gh-20949
+                # try again, with .apply acting as a filtering
+                # operation, by excluding the grouping column
+                # This would normally not be triggered
+                # except if the udf is trying an operation that
+                # fails on *some* columns, e.g. a numeric operation
+                # on a string grouper column
+
+                return self._python_apply_general(f, self._obj_with_exclusions)
+
+        return result
+
+    @final
+    def _python_apply_general(
+        self,
+        f: Callable,
+        data: DataFrame | Series,
+        not_indexed_same: bool | None = None,
+        is_transform: bool = False,
+        is_agg: bool = False,
+    ) -> NDFrameT:
+        """
+        Apply function f in python space
+
+        Parameters
+        ----------
+        f : callable
+            Function to apply
+        data : Series or DataFrame
+            Data to apply f to
+        not_indexed_same: bool, optional
+            When specified, overrides the value of not_indexed_same. Apply behaves
+            differently when the result index is equal to the input index, but
+            this can be coincidental leading to value-dependent behavior.
+        is_transform : bool, default False
+            Indicator for whether the function is actually a transform
+            and should not have group keys prepended.
+        is_agg : bool, default False
+            Indicator for whether the function is an aggregation. When the
+            result is empty, we don't want to warn for this case.
+            See _GroupBy._python_agg_general.
+
+        Returns
+        -------
+        Series or DataFrame
+            data after applying f
+        """
+        values, mutated = self._grouper.apply_groupwise(f, data, self.axis)
+        if not_indexed_same is None:
+            not_indexed_same = mutated
+
+        return self._wrap_applied_output(
+            data,
+            values,
+            not_indexed_same,
+            is_transform,
+        )
+
+    @final
+    def _agg_general(
+        self,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        *,
+        alias: str,
+        npfunc: Callable | None = None,
+        **kwargs,
+    ):
+        result = self._cython_agg_general(
+            how=alias,
+            alt=npfunc,
+            numeric_only=numeric_only,
+            min_count=min_count,
+            **kwargs,
+        )
+        return result.__finalize__(self.obj, method="groupby")
+
+    def _agg_py_fallback(
+        self, how: str, values: ArrayLike, ndim: int, alt: Callable
+    ) -> ArrayLike:
+        """
+        Fallback to pure-python aggregation if _cython_operation raises
+        NotImplementedError.
+        """
+        # We get here with a) EADtypes and b) object dtype
+        assert alt is not None
+
+        if values.ndim == 1:
+            # For DataFrameGroupBy we only get here with ExtensionArray
+            ser = Series(values, copy=False)
+        else:
+            # We only get here with values.dtype == object
+            df = DataFrame(values.T, dtype=values.dtype)
+            # bc we split object blocks in grouped_reduce, we have only 1 col
+            # otherwise we'd have to worry about block-splitting GH#39329
+            assert df.shape[1] == 1
+            # Avoid call to self.values that can occur in DataFrame
+            #  reductions; see GH#28949
+            ser = df.iloc[:, 0]
+
+        # We do not get here with UDFs, so we know that our dtype
+        #  should always be preserved by the implemented aggregations
+        # TODO: Is this exactly right; see WrappedCythonOp get_result_dtype?
+        try:
+            res_values = self._grouper.agg_series(ser, alt, preserve_dtype=True)
+        except Exception as err:
+            msg = f"agg function failed [how->{how},dtype->{ser.dtype}]"
+            # preserve the kind of exception that raised
+            raise type(err)(msg) from err
+
+        dtype = ser.dtype
+        if dtype == object:
+            res_values = res_values.astype(object, copy=False)
+        elif is_string_dtype(dtype):
+            # mypy doesn't infer dtype is an ExtensionDtype
+            string_array_cls = dtype.construct_array_type()  # type: ignore[union-attr]
+            res_values = string_array_cls._from_sequence(res_values, dtype=dtype)
+
+        # If we are DataFrameGroupBy and went through a SeriesGroupByPath
+        # then we need to reshape
+        # GH#32223 includes case with IntegerArray values, ndarray res_values
+        # test_groupby_duplicate_columns with object dtype values
+        return ensure_block_shape(res_values, ndim=ndim)
+
+    @final
+    def _cython_agg_general(
+        self,
+        how: str,
+        alt: Callable | None = None,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        **kwargs,
+    ):
+        # Note: we never get here with how="ohlc" for DataFrameGroupBy;
+        #  that goes through SeriesGroupBy
+
+        data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how)
+
+        def array_func(values: ArrayLike) -> ArrayLike:
+            try:
+                result = self._grouper._cython_operation(
+                    "aggregate",
+                    values,
+                    how,
+                    axis=data.ndim - 1,
+                    min_count=min_count,
+                    **kwargs,
+                )
+            except NotImplementedError:
+                # generally if we have numeric_only=False
+                # and non-applicable functions
+                # try to python agg
+                # TODO: shouldn't min_count matter?
+                # TODO: avoid special casing SparseArray here
+                if how in ["any", "all"] and isinstance(values, SparseArray):
+                    pass
+                elif alt is None or how in ["any", "all", "std", "sem"]:
+                    raise  # TODO: re-raise as TypeError?  should not be reached
+            else:
+                return result
+
+            assert alt is not None
+            result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
+            return result
+
+        new_mgr = data.grouped_reduce(array_func)
+        res = self._wrap_agged_manager(new_mgr)
+        if how in ["idxmin", "idxmax"]:
+            res = self._wrap_idxmax_idxmin(res)
+        out = self._wrap_aggregated_output(res)
+        if self.axis == 1:
+            out = out.infer_objects(copy=False)
+        return out
+
+    def _cython_transform(
+        self, how: str, numeric_only: bool = False, axis: AxisInt = 0, **kwargs
+    ):
+        raise AbstractMethodError(self)
+
+    @final
+    def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        # optimized transforms
+        orig_func = func
+        func = com.get_cython_func(func) or func
+        if orig_func != func:
+            warn_alias_replacement(self, orig_func, func)
+
+        if not isinstance(func, str):
+            return self._transform_general(func, engine, engine_kwargs, *args, **kwargs)
+
+        elif func not in base.transform_kernel_allowlist:
+            msg = f"'{func}' is not a valid function name for transform(name)"
+            raise ValueError(msg)
+        elif func in base.cythonized_kernels or func in base.transformation_kernels:
+            # cythonized transform or canned "agg+broadcast"
+            if engine is not None:
+                kwargs["engine"] = engine
+                kwargs["engine_kwargs"] = engine_kwargs
+            return getattr(self, func)(*args, **kwargs)
+
+        else:
+            # i.e. func in base.reduction_kernels
+
+            # GH#30918 Use _transform_fast only when we know func is an aggregation
+            # If func is a reduction, we need to broadcast the
+            # result to the whole group. Compute func result
+            # and deal with possible broadcasting below.
+            with com.temp_setattr(self, "as_index", True):
+                # GH#49834 - result needs groups in the index for
+                # _wrap_transform_fast_result
+                if func in ["idxmin", "idxmax"]:
+                    func = cast(Literal["idxmin", "idxmax"], func)
+                    result = self._idxmax_idxmin(func, True, *args, **kwargs)
+                else:
+                    if engine is not None:
+                        kwargs["engine"] = engine
+                        kwargs["engine_kwargs"] = engine_kwargs
+                    result = getattr(self, func)(*args, **kwargs)
+
+            return self._wrap_transform_fast_result(result)
+
+    @final
+    def _wrap_transform_fast_result(self, result: NDFrameT) -> NDFrameT:
+        """
+        Fast transform path for aggregations.
+        """
+        obj = self._obj_with_exclusions
+
+        # for each col, reshape to size of original frame by take operation
+        ids, _, _ = self._grouper.group_info
+        result = result.reindex(self._grouper.result_index, axis=self.axis, copy=False)
+
+        if self.obj.ndim == 1:
+            # i.e. SeriesGroupBy
+            out = algorithms.take_nd(result._values, ids)
+            output = obj._constructor(out, index=obj.index, name=obj.name)
+        else:
+            # `.size()` gives Series output on DataFrame input, need axis 0
+            axis = 0 if result.ndim == 1 else self.axis
+            # GH#46209
+            # Don't convert indices: negative indices need to give rise
+            # to null values in the result
+            new_ax = result.axes[axis].take(ids)
+            output = result._reindex_with_indexers(
+                {axis: (new_ax, ids)}, allow_dups=True, copy=False
+            )
+            output = output.set_axis(obj._get_axis(self.axis), axis=axis)
+        return output
+
+    # -----------------------------------------------------------------
+    # Utilities
+
+    @final
+    def _apply_filter(self, indices, dropna):
+        if len(indices) == 0:
+            indices = np.array([], dtype="int64")
+        else:
+            indices = np.sort(np.concatenate(indices))
+        if dropna:
+            filtered = self._selected_obj.take(indices, axis=self.axis)
+        else:
+            mask = np.empty(len(self._selected_obj.index), dtype=bool)
+            mask.fill(False)
+            mask[indices.astype(int)] = True
+            # mask fails to broadcast when passed to where; broadcast manually.
+            mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T
+            filtered = self._selected_obj.where(mask)  # Fill with NaNs.
+        return filtered
+
+    @final
+    def _cumcount_array(self, ascending: bool = True) -> np.ndarray:
+        """
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from length of group - 1 to 0.
+
+        Notes
+        -----
+        this is currently implementing sort=False
+        (though the default is sort=True) for groupby in general
+        """
+        ids, _, ngroups = self._grouper.group_info
+        sorter = get_group_index_sorter(ids, ngroups)
+        ids, count = ids[sorter], len(ids)
+
+        if count == 0:
+            return np.empty(0, dtype=np.int64)
+
+        run = np.r_[True, ids[:-1] != ids[1:]]
+        rep = np.diff(np.r_[np.nonzero(run)[0], count])
+        out = (~run).cumsum()
+
+        if ascending:
+            out -= np.repeat(out[run], rep)
+        else:
+            out = np.repeat(out[np.r_[run[1:], True]], rep) - out
+
+        if self._grouper.has_dropped_na:
+            out = np.where(ids == -1, np.nan, out.astype(np.float64, copy=False))
+        else:
+            out = out.astype(np.int64, copy=False)
+
+        rev = np.empty(count, dtype=np.intp)
+        rev[sorter] = np.arange(count, dtype=np.intp)
+        return out[rev]
+
+    # -----------------------------------------------------------------
+
+    @final
+    @property
+    def _obj_1d_constructor(self) -> Callable:
+        # GH28330 preserve subclassed Series/DataFrames
+        if isinstance(self.obj, DataFrame):
+            return self.obj._constructor_sliced
+        assert isinstance(self.obj, Series)
+        return self.obj._constructor
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def any(self, skipna: bool = True) -> NDFrameT:
+        """
+        Return True if any value in the group is truthful, else False.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Flag to ignore nan values during truth testing.
+
+        Returns
+        -------
+        Series or DataFrame
+            DataFrame or Series of boolean values, where a value is True if any element
+            is True within its respective group, False otherwise.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 0], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).any()
+        a     True
+        b    False
+        dtype: bool
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["ostrich", "penguin", "parrot"])
+        >>> df
+                 a  b  c
+        ostrich  1  0  3
+        penguin  1  0  6
+        parrot   7  1  9
+        >>> df.groupby(by=["a"]).any()
+               b      c
+        a
+        1  False   True
+        7   True   True
+        """
+        return self._cython_agg_general(
+            "any",
+            alt=lambda x: Series(x, copy=False).any(skipna=skipna),
+            skipna=skipna,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def all(self, skipna: bool = True) -> NDFrameT:
+        """
+        Return True if all values in the group are truthful, else False.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Flag to ignore nan values during truth testing.
+
+        Returns
+        -------
+        Series or DataFrame
+            DataFrame or Series of boolean values, where a value is True if all elements
+            are True within its respective group, False otherwise.
+        %(see_also)s
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 0], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).all()
+        a     True
+        b    False
+        dtype: bool
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["ostrich", "penguin", "parrot"])
+        >>> df
+                 a  b  c
+        ostrich  1  0  3
+        penguin  1  5  6
+        parrot   7  8  9
+        >>> df.groupby(by=["a"]).all()
+               b      c
+        a
+        1  False   True
+        7   True   True
+        """
+        return self._cython_agg_general(
+            "all",
+            alt=lambda x: Series(x, copy=False).all(skipna=skipna),
+            skipna=skipna,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def count(self) -> NDFrameT:
+        """
+        Compute count of group, excluding missing values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Count of values within each group.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, np.nan], index=lst)
+        >>> ser
+        a    1.0
+        a    2.0
+        b    NaN
+        dtype: float64
+        >>> ser.groupby(level=0).count()
+        a    2
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["cow", "horse", "bull"])
+        >>> df
+                a	  b	c
+        cow     1	NaN	3
+        horse	1	NaN	6
+        bull	7	8.0	9
+        >>> df.groupby("a").count()
+            b   c
+        a
+        1   0   2
+        7   1   1
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').count()
+        2023-01-01    2
+        2023-02-01    2
+        Freq: MS, dtype: int64
+        """
+        data = self._get_data_to_aggregate()
+        ids, _, ngroups = self._grouper.group_info
+        mask = ids != -1
+
+        is_series = data.ndim == 1
+
+        def hfunc(bvalues: ArrayLike) -> ArrayLike:
+            # TODO(EA2D): reshape would not be necessary with 2D EAs
+            if bvalues.ndim == 1:
+                # EA
+                masked = mask & ~isna(bvalues).reshape(1, -1)
+            else:
+                masked = mask & ~isna(bvalues)
+
+            counted = lib.count_level_2d(masked, labels=ids, max_bin=ngroups)
+            if isinstance(bvalues, BaseMaskedArray):
+                return IntegerArray(
+                    counted[0], mask=np.zeros(counted.shape[1], dtype=np.bool_)
+                )
+            elif isinstance(bvalues, ArrowExtensionArray) and not isinstance(
+                bvalues.dtype, StringDtype
+            ):
+                dtype = pandas_dtype("int64[pyarrow]")
+                return type(bvalues)._from_sequence(counted[0], dtype=dtype)
+            if is_series:
+                assert counted.ndim == 2
+                assert counted.shape[0] == 1
+                return counted[0]
+            return counted
+
+        new_mgr = data.grouped_reduce(hfunc)
+        new_obj = self._wrap_agged_manager(new_mgr)
+
+        # If we are grouping on categoricals we want unobserved categories to
+        # return zero, rather than the default of NaN which the reindexing in
+        # _wrap_aggregated_output() returns. GH 35028
+        # e.g. test_dataframe_groupby_on_2_categoricals_when_observed_is_false
+        with com.temp_setattr(self, "observed", True):
+            result = self._wrap_aggregated_output(new_obj)
+
+        return self._reindex_output(result, fill_value=0)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        """
+        Compute mean of groups, excluding missing values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None`` and defaults to ``False``.
+
+        engine : str, default None
+            * ``'cython'`` : Runs the operation through C-extensions from cython.
+            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+            * ``None`` : Defaults to ``'cython'`` or globally setting
+              ``compute.use_numba``
+
+            .. versionadded:: 1.4.0
+
+        engine_kwargs : dict, default None
+            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+            .. versionadded:: 1.4.0
+
+        Returns
+        -------
+        pandas.Series or pandas.DataFrame
+        %(see_also)s
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
+        ...                    'B': [np.nan, 2, 3, 4, 5],
+        ...                    'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
+
+        Groupby one column and return the mean of the remaining columns in
+        each group.
+
+        >>> df.groupby('A').mean()
+             B         C
+        A
+        1  3.0  1.333333
+        2  4.0  1.500000
+
+        Groupby two columns and return the mean of the remaining column.
+
+        >>> df.groupby(['A', 'B']).mean()
+                 C
+        A B
+        1 2.0  2.0
+          4.0  1.0
+        2 3.0  1.0
+          5.0  2.0
+
+        Groupby one column and return the mean of only particular column in
+        the group.
+
+        >>> df.groupby('A')['B'].mean()
+        A
+        1    3.0
+        2    4.0
+        Name: B, dtype: float64
+        """
+
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_mean
+
+            return self._numba_agg_general(
+                grouped_mean,
+                executor.float_dtype_mapping,
+                engine_kwargs,
+                min_periods=0,
+            )
+        else:
+            result = self._cython_agg_general(
+                "mean",
+                alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only),
+                numeric_only=numeric_only,
+            )
+            return result.__finalize__(self.obj, method="groupby")
+
+    @final
+    def median(self, numeric_only: bool = False) -> NDFrameT:
+        """
+        Compute median of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None`` and defaults to False.
+
+        Returns
+        -------
+        Series or DataFrame
+            Median of values within each group.
+
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).median()
+        a    7.0
+        b    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).median()
+                 a    b
+        dog    3.0  4.0
+        mouse  7.0  3.0
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3, 3, 4, 5],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').median()
+        2023-01-01    2.0
+        2023-02-01    4.0
+        Freq: MS, dtype: float64
+        """
+        result = self._cython_agg_general(
+            "median",
+            alt=lambda x: Series(x, copy=False).median(numeric_only=numeric_only),
+            numeric_only=numeric_only,
+        )
+        return result.__finalize__(self.obj, method="groupby")
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def std(
+        self,
+        ddof: int = 1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        numeric_only: bool = False,
+    ):
+        """
+        Compute standard deviation of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        engine : str, default None
+            * ``'cython'`` : Runs the operation through C-extensions from cython.
+            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+            * ``None`` : Defaults to ``'cython'`` or globally setting
+              ``compute.use_numba``
+
+            .. versionadded:: 1.4.0
+
+        engine_kwargs : dict, default None
+            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+            .. versionadded:: 1.4.0
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Standard deviation of values within each group.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).std()
+        a    3.21455
+        b    0.57735
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).std()
+                      a         b
+        dog    2.000000  3.511885
+        mouse  2.217356  1.500000
+        """
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_var
+
+            return np.sqrt(
+                self._numba_agg_general(
+                    grouped_var,
+                    executor.float_dtype_mapping,
+                    engine_kwargs,
+                    min_periods=0,
+                    ddof=ddof,
+                )
+            )
+        else:
+            return self._cython_agg_general(
+                "std",
+                alt=lambda x: Series(x, copy=False).std(ddof=ddof),
+                numeric_only=numeric_only,
+                ddof=ddof,
+            )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def var(
+        self,
+        ddof: int = 1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        numeric_only: bool = False,
+    ):
+        """
+        Compute variance of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        engine : str, default None
+            * ``'cython'`` : Runs the operation through C-extensions from cython.
+            * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+            * ``None`` : Defaults to ``'cython'`` or globally setting
+              ``compute.use_numba``
+
+            .. versionadded:: 1.4.0
+
+        engine_kwargs : dict, default None
+            * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+            .. versionadded:: 1.4.0
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Variance of values within each group.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).var()
+        a    10.333333
+        b     0.333333
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).var()
+                      a          b
+        dog    4.000000  12.333333
+        mouse  4.916667   2.250000
+        """
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_var
+
+            return self._numba_agg_general(
+                grouped_var,
+                executor.float_dtype_mapping,
+                engine_kwargs,
+                min_periods=0,
+                ddof=ddof,
+            )
+        else:
+            return self._cython_agg_general(
+                "var",
+                alt=lambda x: Series(x, copy=False).var(ddof=ddof),
+                numeric_only=numeric_only,
+                ddof=ddof,
+            )
+
+    @final
+    def _value_counts(
+        self,
+        subset: Sequence[Hashable] | None = None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> DataFrame | Series:
+        """
+        Shared implementation of value_counts for SeriesGroupBy and DataFrameGroupBy.
+
+        SeriesGroupBy additionally supports a bins argument. See the docstring of
+        DataFrameGroupBy.value_counts for a description of arguments.
+        """
+        if self.axis == 1:
+            raise NotImplementedError(
+                "DataFrameGroupBy.value_counts only handles axis=0"
+            )
+        name = "proportion" if normalize else "count"
+
+        df = self.obj
+        obj = self._obj_with_exclusions
+
+        in_axis_names = {
+            grouping.name for grouping in self._grouper.groupings if grouping.in_axis
+        }
+        if isinstance(obj, Series):
+            _name = obj.name
+            keys = [] if _name in in_axis_names else [obj]
+        else:
+            unique_cols = set(obj.columns)
+            if subset is not None:
+                subsetted = set(subset)
+                clashing = subsetted & set(in_axis_names)
+                if clashing:
+                    raise ValueError(
+                        f"Keys {clashing} in subset cannot be in "
+                        "the groupby column keys."
+                    )
+                doesnt_exist = subsetted - unique_cols
+                if doesnt_exist:
+                    raise ValueError(
+                        f"Keys {doesnt_exist} in subset do not "
+                        f"exist in the DataFrame."
+                    )
+            else:
+                subsetted = unique_cols
+
+            keys = [
+                # Can't use .values because the column label needs to be preserved
+                obj.iloc[:, idx]
+                for idx, _name in enumerate(obj.columns)
+                if _name not in in_axis_names and _name in subsetted
+            ]
+
+        groupings = list(self._grouper.groupings)
+        for key in keys:
+            grouper, _, _ = get_grouper(
+                df,
+                key=key,
+                axis=self.axis,
+                sort=self.sort,
+                observed=False,
+                dropna=dropna,
+            )
+            groupings += list(grouper.groupings)
+
+        # Take the size of the overall columns
+        gb = df.groupby(
+            groupings,
+            sort=self.sort,
+            observed=self.observed,
+            dropna=self.dropna,
+        )
+        result_series = cast(Series, gb.size())
+        result_series.name = name
+
+        # GH-46357 Include non-observed categories
+        # of non-grouping columns regardless of `observed`
+        if any(
+            isinstance(grouping.grouping_vector, (Categorical, CategoricalIndex))
+            and not grouping._observed
+            for grouping in groupings
+        ):
+            levels_list = [ping._result_index for ping in groupings]
+            multi_index = MultiIndex.from_product(
+                levels_list, names=[ping.name for ping in groupings]
+            )
+            result_series = result_series.reindex(multi_index, fill_value=0)
+
+        if sort:
+            # Sort by the values
+            result_series = result_series.sort_values(
+                ascending=ascending, kind="stable"
+            )
+        if self.sort:
+            # Sort by the groupings
+            names = result_series.index.names
+            # GH#55951 - Temporarily replace names in case they are integers
+            result_series.index.names = range(len(names))
+            index_level = list(range(len(self._grouper.groupings)))
+            result_series = result_series.sort_index(
+                level=index_level, sort_remaining=False
+            )
+            result_series.index.names = names
+
+        if normalize:
+            # Normalize the results by dividing by the original group sizes.
+            # We are guaranteed to have the first N levels be the
+            # user-requested grouping.
+            levels = list(
+                range(len(self._grouper.groupings), result_series.index.nlevels)
+            )
+            indexed_group_size = result_series.groupby(
+                result_series.index.droplevel(levels),
+                sort=self.sort,
+                dropna=self.dropna,
+                # GH#43999 - deprecation of observed=False
+                observed=False,
+            ).transform("sum")
+            result_series /= indexed_group_size
+
+            # Handle groups of non-observed categories
+            result_series = result_series.fillna(0.0)
+
+        result: Series | DataFrame
+        if self.as_index:
+            result = result_series
+        else:
+            # Convert to frame
+            index = result_series.index
+            columns = com.fill_missing_names(index.names)
+            if name in columns:
+                raise ValueError(f"Column label '{name}' is duplicate of result column")
+            result_series.name = name
+            result_series.index = index.set_names(range(len(columns)))
+            result_frame = result_series.reset_index()
+            orig_dtype = self._grouper.groupings[0].obj.columns.dtype  # type: ignore[union-attr]
+            cols = Index(columns, dtype=orig_dtype).insert(len(columns), name)
+            result_frame.columns = cols
+            result = result_frame
+        return result.__finalize__(self.obj, method="value_counts")
+
+    @final
+    def sem(self, ddof: int = 1, numeric_only: bool = False) -> NDFrameT:
+        """
+        Compute standard error of the mean of groups, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Standard error of the mean of values within each group.
+
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([5, 10, 8, 14], index=lst)
+        >>> ser
+        a     5
+        a    10
+        b     8
+        b    14
+        dtype: int64
+        >>> ser.groupby(level=0).sem()
+        a    2.5
+        b    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tuna", "salmon", "catfish", "goldfish"])
+        >>> df
+                   a   b   c
+            tuna   1  12  11
+          salmon   1  15   2
+         catfish   2   5   8
+        goldfish   2   6  12
+        >>> df.groupby("a").sem()
+              b  c
+        a
+        1    1.5  4.5
+        2    0.5  2.0
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').sem()
+        2023-01-01    0.577350
+        2023-02-01    1.527525
+        Freq: MS, dtype: float64
+        """
+        if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype):
+            raise TypeError(
+                f"{type(self).__name__}.sem called with "
+                f"numeric_only={numeric_only} and dtype {self.obj.dtype}"
+            )
+        return self._cython_agg_general(
+            "sem",
+            alt=lambda x: Series(x, copy=False).sem(ddof=ddof),
+            numeric_only=numeric_only,
+            ddof=ddof,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def size(self) -> DataFrame | Series:
+        """
+        Compute group sizes.
+
+        Returns
+        -------
+        DataFrame or Series
+            Number of rows in each group as a Series if as_index is True
+            or a DataFrame if as_index is False.
+        %(see_also)s
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([1, 2, 3], index=lst)
+        >>> ser
+        a     1
+        a     2
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).size()
+        a    2
+        b    1
+        dtype: int64
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["owl", "toucan", "eagle"])
+        >>> df
+                a  b  c
+        owl     1  2  3
+        toucan  1  5  6
+        eagle   7  8  9
+        >>> df.groupby("a").size()
+        a
+        1    2
+        7    1
+        dtype: int64
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 2, 3], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        dtype: int64
+        >>> ser.resample('MS').size()
+        2023-01-01    2
+        2023-02-01    1
+        Freq: MS, dtype: int64
+        """
+        result = self._grouper.size()
+        dtype_backend: None | Literal["pyarrow", "numpy_nullable"] = None
+        if isinstance(self.obj, Series):
+            if isinstance(self.obj.array, ArrowExtensionArray):
+                if isinstance(self.obj.array, ArrowStringArrayNumpySemantics):
+                    dtype_backend = None
+                elif isinstance(self.obj.array, ArrowStringArray):
+                    dtype_backend = "numpy_nullable"
+                else:
+                    dtype_backend = "pyarrow"
+            elif isinstance(self.obj.array, BaseMaskedArray):
+                dtype_backend = "numpy_nullable"
+        # TODO: For DataFrames what if columns are mixed arrow/numpy/masked?
+
+        # GH28330 preserve subclassed Series/DataFrames through calls
+        if isinstance(self.obj, Series):
+            result = self._obj_1d_constructor(result, name=self.obj.name)
+        else:
+            result = self._obj_1d_constructor(result)
+
+        if dtype_backend is not None:
+            result = result.convert_dtypes(
+                infer_objects=False,
+                convert_string=False,
+                convert_boolean=False,
+                convert_floating=False,
+                dtype_backend=dtype_backend,
+            )
+
+        with com.temp_setattr(self, "as_index", True):
+            # size already has the desired behavior in GH#49519, but this makes the
+            # as_index=False path of _reindex_output fail on categorical groupers.
+            result = self._reindex_output(result, fill_value=0)
+        if not self.as_index:
+            # error: Incompatible types in assignment (expression has
+            # type "DataFrame", variable has type "Series")
+            result = result.rename("size").reset_index()  # type: ignore[assignment]
+        return result
+
+    @final
+    @doc(
+        _groupby_agg_method_engine_template,
+        fname="sum",
+        no=False,
+        mc=0,
+        e=None,
+        ek=None,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).sum()
+        a    3
+        b    7
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").sum()
+             b   c
+        a
+        1   10   7
+        2   11  17"""
+        ),
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_sum
+
+            return self._numba_agg_general(
+                grouped_sum,
+                executor.default_dtype_mapping,
+                engine_kwargs,
+                min_periods=min_count,
+            )
+        else:
+            # If we are grouping on categoricals we want unobserved categories to
+            # return zero, rather than the default of NaN which the reindexing in
+            # _agg_general() returns. GH #31422
+            with com.temp_setattr(self, "observed", True):
+                result = self._agg_general(
+                    numeric_only=numeric_only,
+                    min_count=min_count,
+                    alias="sum",
+                    npfunc=np.sum,
+                )
+
+            return self._reindex_output(result, fill_value=0, method="sum")
+
+    @final
+    @doc(
+        _groupby_agg_method_template,
+        fname="prod",
+        no=False,
+        mc=0,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).prod()
+        a    2
+        b   12
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").prod()
+             b    c
+        a
+        1   16   10
+        2   30   72"""
+        ),
+    )
+    def prod(self, numeric_only: bool = False, min_count: int = 0) -> NDFrameT:
+        return self._agg_general(
+            numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
+        )
+
+    @final
+    @doc(
+        _groupby_agg_method_engine_template,
+        fname="min",
+        no=False,
+        mc=-1,
+        e=None,
+        ek=None,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).min()
+        a    1
+        b    3
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").min()
+            b  c
+        a
+        1   2  2
+        2   5  8"""
+        ),
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_min_max
+
+            return self._numba_agg_general(
+                grouped_min_max,
+                executor.identity_dtype_mapping,
+                engine_kwargs,
+                min_periods=min_count,
+                is_max=False,
+            )
+        else:
+            return self._agg_general(
+                numeric_only=numeric_only,
+                min_count=min_count,
+                alias="min",
+                npfunc=np.min,
+            )
+
+    @final
+    @doc(
+        _groupby_agg_method_engine_template,
+        fname="max",
+        no=False,
+        mc=-1,
+        e=None,
+        ek=None,
+        example=dedent(
+            """\
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).max()
+        a    2
+        b    4
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df
+                  a  b  c
+          tiger   1  8  2
+        leopard   1  2  5
+        cheetah   2  5  8
+           lion   2  6  9
+        >>> df.groupby("a").max()
+            b  c
+        a
+        1   8  5
+        2   6  9"""
+        ),
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        min_count: int = -1,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            from pandas.core._numba.kernels import grouped_min_max
+
+            return self._numba_agg_general(
+                grouped_min_max,
+                executor.identity_dtype_mapping,
+                engine_kwargs,
+                min_periods=min_count,
+                is_max=True,
+            )
+        else:
+            return self._agg_general(
+                numeric_only=numeric_only,
+                min_count=min_count,
+                alias="max",
+                npfunc=np.max,
+            )
+
+    @final
+    def first(
+        self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True
+    ) -> NDFrameT:
+        """
+        Compute the first entry of each column within each group.
+
+        Defaults to skipping NA elements.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+        min_count : int, default -1
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` valid values are present the result will be NA.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+
+            .. versionadded:: 2.2.1
+
+        Returns
+        -------
+        Series or DataFrame
+            First values within each group.
+
+        See Also
+        --------
+        DataFrame.groupby : Apply a function groupby to each row or column of a
+            DataFrame.
+        pandas.core.groupby.DataFrameGroupBy.last : Compute the last non-null entry
+            of each column.
+        pandas.core.groupby.DataFrameGroupBy.nth : Take the nth row from each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3],
+        ...                        D=['3/11/2000', '3/12/2000', '3/13/2000']))
+        >>> df['D'] = pd.to_datetime(df['D'])
+        >>> df.groupby("A").first()
+             B  C          D
+        A
+        1  5.0  1 2000-03-11
+        3  6.0  3 2000-03-13
+        >>> df.groupby("A").first(min_count=2)
+            B    C          D
+        A
+        1 NaN  1.0 2000-03-11
+        3 NaN  NaN        NaT
+        >>> df.groupby("A").first(numeric_only=True)
+             B  C
+        A
+        1  5.0  1
+        3  6.0  3
+        """
+
+        def first_compat(obj: NDFrameT, axis: AxisInt = 0):
+            def first(x: Series):
+                """Helper function for first item that isn't NA."""
+                arr = x.array[notna(x.array)]
+                if not len(arr):
+                    return x.array.dtype.na_value
+                return arr[0]
+
+            if isinstance(obj, DataFrame):
+                return obj.apply(first, axis=axis)
+            elif isinstance(obj, Series):
+                return first(obj)
+            else:  # pragma: no cover
+                raise TypeError(type(obj))
+
+        return self._agg_general(
+            numeric_only=numeric_only,
+            min_count=min_count,
+            alias="first",
+            npfunc=first_compat,
+            skipna=skipna,
+        )
+
+    @final
+    def last(
+        self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True
+    ) -> NDFrameT:
+        """
+        Compute the last entry of each column within each group.
+
+        Defaults to skipping NA elements.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns. If None, will attempt to use
+            everything, then use only numeric data.
+        min_count : int, default -1
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` valid values are present the result will be NA.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+
+            .. versionadded:: 2.2.1
+
+        Returns
+        -------
+        Series or DataFrame
+            Last of values within each group.
+
+        See Also
+        --------
+        DataFrame.groupby : Apply a function groupby to each row or column of a
+            DataFrame.
+        pandas.core.groupby.DataFrameGroupBy.first : Compute the first non-null entry
+            of each column.
+        pandas.core.groupby.DataFrameGroupBy.nth : Take the nth row from each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3]))
+        >>> df.groupby("A").last()
+             B  C
+        A
+        1  5.0  2
+        3  6.0  3
+        """
+
+        def last_compat(obj: NDFrameT, axis: AxisInt = 0):
+            def last(x: Series):
+                """Helper function for last item that isn't NA."""
+                arr = x.array[notna(x.array)]
+                if not len(arr):
+                    return x.array.dtype.na_value
+                return arr[-1]
+
+            if isinstance(obj, DataFrame):
+                return obj.apply(last, axis=axis)
+            elif isinstance(obj, Series):
+                return last(obj)
+            else:  # pragma: no cover
+                raise TypeError(type(obj))
+
+        return self._agg_general(
+            numeric_only=numeric_only,
+            min_count=min_count,
+            alias="last",
+            npfunc=last_compat,
+            skipna=skipna,
+        )
+
+    @final
+    def ohlc(self) -> DataFrame:
+        """
+        Compute open, high, low and close values of a group, excluding missing values.
+
+        For multiple groupings, the result index will be a MultiIndex
+
+        Returns
+        -------
+        DataFrame
+            Open, high, low and close values within each group.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['SPX', 'CAC', 'SPX', 'CAC', 'SPX', 'CAC', 'SPX', 'CAC',]
+        >>> ser = pd.Series([3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 0.1, 0.5], index=lst)
+        >>> ser
+        SPX     3.4
+        CAC     9.0
+        SPX     7.2
+        CAC     5.2
+        SPX     8.8
+        CAC     9.4
+        SPX     0.1
+        CAC     0.5
+        dtype: float64
+        >>> ser.groupby(level=0).ohlc()
+             open  high  low  close
+        CAC   9.0   9.4  0.5    0.5
+        SPX   3.4   8.8  0.1    0.1
+
+        For DataFrameGroupBy:
+
+        >>> data = {2022: [1.2, 2.3, 8.9, 4.5, 4.4, 3, 2 , 1],
+        ...         2023: [3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 8.2, 1.0]}
+        >>> df = pd.DataFrame(data, index=['SPX', 'CAC', 'SPX', 'CAC',
+        ...                   'SPX', 'CAC', 'SPX', 'CAC'])
+        >>> df
+             2022  2023
+        SPX   1.2   3.4
+        CAC   2.3   9.0
+        SPX   8.9   7.2
+        CAC   4.5   5.2
+        SPX   4.4   8.8
+        CAC   3.0   9.4
+        SPX   2.0   8.2
+        CAC   1.0   1.0
+        >>> df.groupby(level=0).ohlc()
+            2022                 2023
+            open high  low close open high  low close
+        CAC  2.3  4.5  1.0   1.0  9.0  9.4  1.0   1.0
+        SPX  1.2  8.9  1.2   2.0  3.4  8.8  3.4   8.2
+
+        For Resampler:
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 5],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').ohlc()
+                    open  high  low  close
+        2023-01-01     1     3    1      2
+        2023-02-01     4     5    3      5
+        """
+        if self.obj.ndim == 1:
+            obj = self._selected_obj
+
+            is_numeric = is_numeric_dtype(obj.dtype)
+            if not is_numeric:
+                raise DataError("No numeric types to aggregate")
+
+            res_values = self._grouper._cython_operation(
+                "aggregate", obj._values, "ohlc", axis=0, min_count=-1
+            )
+
+            agg_names = ["open", "high", "low", "close"]
+            result = self.obj._constructor_expanddim(
+                res_values, index=self._grouper.result_index, columns=agg_names
+            )
+            return self._reindex_output(result)
+
+        result = self._apply_to_column_groupbys(lambda sgb: sgb.ohlc())
+        return result
+
+    @doc(DataFrame.describe)
+    def describe(
+        self,
+        percentiles=None,
+        include=None,
+        exclude=None,
+    ) -> NDFrameT:
+        obj = self._obj_with_exclusions
+
+        if len(obj) == 0:
+            described = obj.describe(
+                percentiles=percentiles, include=include, exclude=exclude
+            )
+            if obj.ndim == 1:
+                result = described
+            else:
+                result = described.unstack()
+            return result.to_frame().T.iloc[:0]
+
+        with com.temp_setattr(self, "as_index", True):
+            result = self._python_apply_general(
+                lambda x: x.describe(
+                    percentiles=percentiles, include=include, exclude=exclude
+                ),
+                obj,
+                not_indexed_same=True,
+            )
+        if self.axis == 1:
+            return result.T
+
+        # GH#49256 - properly handle the grouping column(s)
+        result = result.unstack()
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+
+        return result
+
+    @final
+    def resample(self, rule, *args, include_groups: bool = True, **kwargs) -> Resampler:
+        """
+        Provide resampling when using a TimeGrouper.
+
+        Given a grouper, the function resamples it according to a string
+        "string" -> "frequency".
+
+        See the :ref:`frequency aliases <timeseries.offset_aliases>`
+        documentation for more details.
+
+        Parameters
+        ----------
+        rule : str or DateOffset
+            The offset string or object representing target grouper conversion.
+        *args
+            Possible arguments are `how`, `fill_method`, `limit`, `kind` and
+            `on`, and other arguments of `TimeGrouper`.
+        include_groups : bool, default True
+            When True, will attempt to include the groupings in the operation in
+            the case that they are columns of the DataFrame. If this raises a
+            TypeError, the result will be computed with the groupings excluded.
+            When False, the groupings will be excluded when applying ``func``.
+
+            .. versionadded:: 2.2.0
+
+            .. deprecated:: 2.2.0
+
+               Setting include_groups to True is deprecated. Only the value
+               False will be allowed in a future version of pandas.
+
+        **kwargs
+            Possible arguments are `how`, `fill_method`, `limit`, `kind` and
+            `on`, and other arguments of `TimeGrouper`.
+
+        Returns
+        -------
+        pandas.api.typing.DatetimeIndexResamplerGroupby,
+        pandas.api.typing.PeriodIndexResamplerGroupby, or
+        pandas.api.typing.TimedeltaIndexResamplerGroupby
+            Return a new groupby object, with type depending on the data
+            being resampled.
+
+        See Also
+        --------
+        Grouper : Specify a frequency to resample with when
+            grouping by a key.
+        DatetimeIndex.resample : Frequency conversion and resampling of
+            time series.
+
+        Examples
+        --------
+        >>> idx = pd.date_range('1/1/2000', periods=4, freq='min')
+        >>> df = pd.DataFrame(data=4 * [range(2)],
+        ...                   index=idx,
+        ...                   columns=['a', 'b'])
+        >>> df.iloc[2, 0] = 5
+        >>> df
+                            a  b
+        2000-01-01 00:00:00  0  1
+        2000-01-01 00:01:00  0  1
+        2000-01-01 00:02:00  5  1
+        2000-01-01 00:03:00  0  1
+
+        Downsample the DataFrame into 3 minute bins and sum the values of
+        the timestamps falling into a bin.
+
+        >>> df.groupby('a').resample('3min', include_groups=False).sum()
+                                 b
+        a
+        0   2000-01-01 00:00:00  2
+            2000-01-01 00:03:00  1
+        5   2000-01-01 00:00:00  1
+
+        Upsample the series into 30 second bins.
+
+        >>> df.groupby('a').resample('30s', include_groups=False).sum()
+                            b
+        a
+        0   2000-01-01 00:00:00  1
+            2000-01-01 00:00:30  0
+            2000-01-01 00:01:00  1
+            2000-01-01 00:01:30  0
+            2000-01-01 00:02:00  0
+            2000-01-01 00:02:30  0
+            2000-01-01 00:03:00  1
+        5   2000-01-01 00:02:00  1
+
+        Resample by month. Values are assigned to the month of the period.
+
+        >>> df.groupby('a').resample('ME', include_groups=False).sum()
+                    b
+        a
+        0   2000-01-31  3
+        5   2000-01-31  1
+
+        Downsample the series into 3 minute bins as above, but close the right
+        side of the bin interval.
+
+        >>> (
+        ...     df.groupby('a')
+        ...     .resample('3min', closed='right', include_groups=False)
+        ...     .sum()
+        ... )
+                                 b
+        a
+        0   1999-12-31 23:57:00  1
+            2000-01-01 00:00:00  2
+        5   2000-01-01 00:00:00  1
+
+        Downsample the series into 3 minute bins and close the right side of
+        the bin interval, but label each bin using the right edge instead of
+        the left.
+
+        >>> (
+        ...     df.groupby('a')
+        ...     .resample('3min', closed='right', label='right', include_groups=False)
+        ...     .sum()
+        ... )
+                                 b
+        a
+        0   2000-01-01 00:00:00  1
+            2000-01-01 00:03:00  2
+        5   2000-01-01 00:03:00  1
+        """
+        from pandas.core.resample import get_resampler_for_grouping
+
+        # mypy flags that include_groups could be specified via `*args` or `**kwargs`
+        # GH#54961 would resolve.
+        return get_resampler_for_grouping(  # type: ignore[misc]
+            self, rule, *args, include_groups=include_groups, **kwargs
+        )
+
+    @final
+    def rolling(self, *args, **kwargs) -> RollingGroupby:
+        """
+        Return a rolling grouper, providing rolling functionality per group.
+
+        Parameters
+        ----------
+        window : int, timedelta, str, offset, or BaseIndexer subclass
+            Size of the moving window.
+
+            If an integer, the fixed number of observations used for
+            each window.
+
+            If a timedelta, str, or offset, the time period of each window. Each
+            window will be a variable sized based on the observations included in
+            the time-period. This is only valid for datetimelike indexes.
+            To learn more about the offsets & frequency strings, please see `this link
+            <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+            If a BaseIndexer subclass, the window boundaries
+            based on the defined ``get_window_bounds`` method. Additional rolling
+            keyword arguments, namely ``min_periods``, ``center``, ``closed`` and
+            ``step`` will be passed to ``get_window_bounds``.
+
+        min_periods : int, default None
+            Minimum number of observations in window required to have a value;
+            otherwise, result is ``np.nan``.
+
+            For a window that is specified by an offset,
+            ``min_periods`` will default to 1.
+
+            For a window that is specified by an integer, ``min_periods`` will default
+            to the size of the window.
+
+        center : bool, default False
+            If False, set the window labels as the right edge of the window index.
+
+            If True, set the window labels as the center of the window index.
+
+        win_type : str, default None
+            If ``None``, all points are evenly weighted.
+
+            If a string, it must be a valid `scipy.signal window function
+            <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>`__.
+
+            Certain Scipy window types require additional parameters to be passed
+            in the aggregation function. The additional parameters must match
+            the keywords specified in the Scipy window type method signature.
+
+        on : str, optional
+            For a DataFrame, a column label or Index level on which
+            to calculate the rolling window, rather than the DataFrame's index.
+
+            Provided integer column is ignored and excluded from result since
+            an integer index is not used to calculate the rolling window.
+
+        axis : int or str, default 0
+            If ``0`` or ``'index'``, roll across the rows.
+
+            If ``1`` or ``'columns'``, roll across the columns.
+
+            For `Series` this parameter is unused and defaults to 0.
+
+        closed : str, default None
+            If ``'right'``, the first point in the window is excluded from calculations.
+
+            If ``'left'``, the last point in the window is excluded from calculations.
+
+            If ``'both'``, no points in the window are excluded from calculations.
+
+            If ``'neither'``, the first and last points in the window are excluded
+            from calculations.
+
+            Default ``None`` (``'right'``).
+
+        method : str {'single', 'table'}, default 'single'
+            Execute the rolling operation per single column or row (``'single'``)
+            or over the entire object (``'table'``).
+
+            This argument is only implemented when specifying ``engine='numba'``
+            in the method call.
+
+        Returns
+        -------
+        pandas.api.typing.RollingGroupby
+            Return a new grouper with our rolling appended.
+
+        See Also
+        --------
+        Series.rolling : Calling object with Series data.
+        DataFrame.rolling : Calling object with DataFrames.
+        Series.groupby : Apply a function groupby to a Series.
+        DataFrame.groupby : Apply a function groupby.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 2],
+        ...                    'B': [1, 2, 3, 4],
+        ...                    'C': [0.362, 0.227, 1.267, -0.562]})
+        >>> df
+              A  B      C
+        0     1  1  0.362
+        1     1  2  0.227
+        2     2  3  1.267
+        3     2  4 -0.562
+
+        >>> df.groupby('A').rolling(2).sum()
+            B      C
+        A
+        1 0  NaN    NaN
+          1  3.0  0.589
+        2 2  NaN    NaN
+          3  7.0  0.705
+
+        >>> df.groupby('A').rolling(2, min_periods=1).sum()
+            B      C
+        A
+        1 0  1.0  0.362
+          1  3.0  0.589
+        2 2  3.0  1.267
+          3  7.0  0.705
+
+        >>> df.groupby('A').rolling(2, on='B').sum()
+            B      C
+        A
+        1 0  1    NaN
+          1  2  0.589
+        2 2  3    NaN
+          3  4  0.705
+        """
+        from pandas.core.window import RollingGroupby
+
+        return RollingGroupby(
+            self._selected_obj,
+            *args,
+            _grouper=self._grouper,
+            _as_index=self.as_index,
+            **kwargs,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Appender(_common_see_also)
+    def expanding(self, *args, **kwargs) -> ExpandingGroupby:
+        """
+        Return an expanding grouper, providing expanding
+        functionality per group.
+
+        Returns
+        -------
+        pandas.api.typing.ExpandingGroupby
+        """
+        from pandas.core.window import ExpandingGroupby
+
+        return ExpandingGroupby(
+            self._selected_obj,
+            *args,
+            _grouper=self._grouper,
+            **kwargs,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Appender(_common_see_also)
+    def ewm(self, *args, **kwargs) -> ExponentialMovingWindowGroupby:
+        """
+        Return an ewm grouper, providing ewm functionality per group.
+
+        Returns
+        -------
+        pandas.api.typing.ExponentialMovingWindowGroupby
+        """
+        from pandas.core.window import ExponentialMovingWindowGroupby
+
+        return ExponentialMovingWindowGroupby(
+            self._selected_obj,
+            *args,
+            _grouper=self._grouper,
+            **kwargs,
+        )
+
+    @final
+    def _fill(self, direction: Literal["ffill", "bfill"], limit: int | None = None):
+        """
+        Shared function for `pad` and `backfill` to call Cython method.
+
+        Parameters
+        ----------
+        direction : {'ffill', 'bfill'}
+            Direction passed to underlying Cython function. `bfill` will cause
+            values to be filled backwards. `ffill` and any other values will
+            default to a forward fill
+        limit : int, default None
+            Maximum number of consecutive values to fill. If `None`, this
+            method will convert to -1 prior to passing to Cython
+
+        Returns
+        -------
+        `Series` or `DataFrame` with filled values
+
+        See Also
+        --------
+        pad : Returns Series with minimum number of char in object.
+        backfill : Backward fill the missing values in the dataset.
+        """
+        # Need int value for Cython
+        if limit is None:
+            limit = -1
+
+        ids, _, _ = self._grouper.group_info
+        sorted_labels = np.argsort(ids, kind="mergesort").astype(np.intp, copy=False)
+        if direction == "bfill":
+            sorted_labels = sorted_labels[::-1]
+
+        col_func = partial(
+            libgroupby.group_fillna_indexer,
+            labels=ids,
+            sorted_labels=sorted_labels,
+            limit=limit,
+            dropna=self.dropna,
+        )
+
+        def blk_func(values: ArrayLike) -> ArrayLike:
+            mask = isna(values)
+            if values.ndim == 1:
+                indexer = np.empty(values.shape, dtype=np.intp)
+                col_func(out=indexer, mask=mask)
+                return algorithms.take_nd(values, indexer)
+
+            else:
+                # We broadcast algorithms.take_nd analogous to
+                #  np.take_along_axis
+                if isinstance(values, np.ndarray):
+                    dtype = values.dtype
+                    if self._grouper.has_dropped_na:
+                        # dropped null groups give rise to nan in the result
+                        dtype = ensure_dtype_can_hold_na(values.dtype)
+                    out = np.empty(values.shape, dtype=dtype)
+                else:
+                    # Note: we only get here with backfill/pad,
+                    #  so if we have a dtype that cannot hold NAs,
+                    #  then there will be no -1s in indexer, so we can use
+                    #  the original dtype (no need to ensure_dtype_can_hold_na)
+                    out = type(values)._empty(values.shape, dtype=values.dtype)
+
+                for i, value_element in enumerate(values):
+                    # call group_fillna_indexer column-wise
+                    indexer = np.empty(values.shape[1], dtype=np.intp)
+                    col_func(out=indexer, mask=mask[i])
+                    out[i, :] = algorithms.take_nd(value_element, indexer)
+                return out
+
+        mgr = self._get_data_to_aggregate()
+        res_mgr = mgr.apply(blk_func)
+
+        new_obj = self._wrap_agged_manager(res_mgr)
+
+        if self.axis == 1:
+            # Only relevant for DataFrameGroupBy
+            new_obj = new_obj.T
+            new_obj.columns = self.obj.columns
+
+        new_obj.index = self.obj.index
+        return new_obj
+
+    @final
+    @Substitution(name="groupby")
+    def ffill(self, limit: int | None = None):
+        """
+        Forward fill the values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object with missing values filled.
+
+        See Also
+        --------
+        Series.ffill: Returns Series with minimum number of char in object.
+        DataFrame.ffill: Object with missing values filled or None if inplace=True.
+        Series.fillna: Fill NaN values of a Series.
+        DataFrame.fillna: Fill NaN values of a DataFrame.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> key = [0, 0, 1, 1]
+        >>> ser = pd.Series([np.nan, 2, 3, np.nan], index=key)
+        >>> ser
+        0    NaN
+        0    2.0
+        1    3.0
+        1    NaN
+        dtype: float64
+        >>> ser.groupby(level=0).ffill()
+        0    NaN
+        0    2.0
+        1    3.0
+        1    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "key": [0, 0, 1, 1, 1],
+        ...         "A": [np.nan, 2, np.nan, 3, np.nan],
+        ...         "B": [2, 3, np.nan, np.nan, np.nan],
+        ...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
+        ...     }
+        ... )
+        >>> df
+           key    A    B   C
+        0    0  NaN  2.0 NaN
+        1    0  2.0  3.0 NaN
+        2    1  NaN  NaN 2.0
+        3    1  3.0  NaN NaN
+        4    1  NaN  NaN NaN
+
+        Propagate non-null values forward or backward within each group along columns.
+
+        >>> df.groupby("key").ffill()
+             A    B   C
+        0  NaN  2.0 NaN
+        1  2.0  3.0 NaN
+        2  NaN  NaN 2.0
+        3  3.0  NaN 2.0
+        4  3.0  NaN 2.0
+
+        Propagate non-null values forward or backward within each group along rows.
+
+        >>> df.T.groupby(np.array([0, 0, 1, 1])).ffill().T
+           key    A    B    C
+        0  0.0  0.0  2.0  2.0
+        1  0.0  2.0  3.0  3.0
+        2  1.0  1.0  NaN  2.0
+        3  1.0  3.0  NaN  NaN
+        4  1.0  1.0  NaN  NaN
+
+        Only replace the first NaN element within a group along rows.
+
+        >>> df.groupby("key").ffill(limit=1)
+             A    B    C
+        0  NaN  2.0  NaN
+        1  2.0  3.0  NaN
+        2  NaN  NaN  2.0
+        3  3.0  NaN  2.0
+        4  3.0  NaN  NaN
+        """
+        return self._fill("ffill", limit=limit)
+
+    @final
+    @Substitution(name="groupby")
+    def bfill(self, limit: int | None = None):
+        """
+        Backward fill the values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object with missing values filled.
+
+        See Also
+        --------
+        Series.bfill :  Backward fill the missing values in the dataset.
+        DataFrame.bfill:  Backward fill the missing values in the dataset.
+        Series.fillna: Fill NaN values of a Series.
+        DataFrame.fillna: Fill NaN values of a DataFrame.
+
+        Examples
+        --------
+
+        With Series:
+
+        >>> index = ['Falcon', 'Falcon', 'Parrot', 'Parrot', 'Parrot']
+        >>> s = pd.Series([None, 1, None, None, 3], index=index)
+        >>> s
+        Falcon    NaN
+        Falcon    1.0
+        Parrot    NaN
+        Parrot    NaN
+        Parrot    3.0
+        dtype: float64
+        >>> s.groupby(level=0).bfill()
+        Falcon    1.0
+        Falcon    1.0
+        Parrot    3.0
+        Parrot    3.0
+        Parrot    3.0
+        dtype: float64
+        >>> s.groupby(level=0).bfill(limit=1)
+        Falcon    1.0
+        Falcon    1.0
+        Parrot    NaN
+        Parrot    3.0
+        Parrot    3.0
+        dtype: float64
+
+        With DataFrame:
+
+        >>> df = pd.DataFrame({'A': [1, None, None, None, 4],
+        ...                    'B': [None, None, 5, None, 7]}, index=index)
+        >>> df
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	NaN	  5.0
+        Parrot	NaN	  NaN
+        Parrot	4.0	  7.0
+        >>> df.groupby(level=0).bfill()
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	4.0	  5.0
+        Parrot	4.0	  7.0
+        Parrot	4.0	  7.0
+        >>> df.groupby(level=0).bfill(limit=1)
+                  A	    B
+        Falcon	1.0	  NaN
+        Falcon	NaN	  NaN
+        Parrot	NaN	  5.0
+        Parrot	4.0	  7.0
+        Parrot	4.0	  7.0
+        """
+        return self._fill("bfill", limit=limit)
+
+    @final
+    @property
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def nth(self) -> GroupByNthSelector:
+        """
+        Take the nth row from each group if n is an int, otherwise a subset of rows.
+
+        Can be either a call or an index. dropna is not available with index notation.
+        Index notation accepts a comma separated list of integers and slices.
+
+        If dropna, will take the nth non-null row, dropna is either
+        'all' or 'any'; this is equivalent to calling dropna(how=dropna)
+        before the groupby.
+
+        Parameters
+        ----------
+        n : int, slice or list of ints and slices
+            A single nth value for the row or a list of nth values or slices.
+
+            .. versionchanged:: 1.4.0
+                Added slice and lists containing slices.
+                Added index notation.
+
+        dropna : {'any', 'all', None}, default None
+            Apply the specified dropna operation before counting which row is
+            the nth row. Only supported if n is an int.
+
+        Returns
+        -------
+        Series or DataFrame
+            N-th value within each group.
+        %(see_also)s
+        Examples
+        --------
+
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
+        ...                    'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
+        >>> g = df.groupby('A')
+        >>> g.nth(0)
+           A   B
+        0  1 NaN
+        2  2 3.0
+        >>> g.nth(1)
+           A   B
+        1  1 2.0
+        4  2 5.0
+        >>> g.nth(-1)
+           A   B
+        3  1 4.0
+        4  2 5.0
+        >>> g.nth([0, 1])
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+        4  2 5.0
+        >>> g.nth(slice(None, -1))
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+
+        Index notation may also be used
+
+        >>> g.nth[0, 1]
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+        4  2 5.0
+        >>> g.nth[:-1]
+           A   B
+        0  1 NaN
+        1  1 2.0
+        2  2 3.0
+
+        Specifying `dropna` allows ignoring ``NaN`` values
+
+        >>> g.nth(0, dropna='any')
+           A   B
+        1  1 2.0
+        2  2 3.0
+
+        When the specified ``n`` is larger than any of the groups, an
+        empty DataFrame is returned
+
+        >>> g.nth(3, dropna='any')
+        Empty DataFrame
+        Columns: [A, B]
+        Index: []
+        """
+        return GroupByNthSelector(self)
+
+    def _nth(
+        self,
+        n: PositionalIndexer | tuple,
+        dropna: Literal["any", "all", None] = None,
+    ) -> NDFrameT:
+        if not dropna:
+            mask = self._make_mask_from_positional_indexer(n)
+
+            ids, _, _ = self._grouper.group_info
+
+            # Drop NA values in grouping
+            mask = mask & (ids != -1)
+
+            out = self._mask_selected_obj(mask)
+            return out
+
+        # dropna is truthy
+        if not is_integer(n):
+            raise ValueError("dropna option only supported for an integer argument")
+
+        if dropna not in ["any", "all"]:
+            # Note: when agg-ing picker doesn't raise this, just returns NaN
+            raise ValueError(
+                "For a DataFrame or Series groupby.nth, dropna must be "
+                "either None, 'any' or 'all', "
+                f"(was passed {dropna})."
+            )
+
+        # old behaviour, but with all and any support for DataFrames.
+        # modified in GH 7559 to have better perf
+        n = cast(int, n)
+        dropped = self._selected_obj.dropna(how=dropna, axis=self.axis)
+
+        # get a new grouper for our dropped obj
+        grouper: np.ndarray | Index | ops.BaseGrouper
+        if len(dropped) == len(self._selected_obj):
+            # Nothing was dropped, can use the same grouper
+            grouper = self._grouper
+        else:
+            # we don't have the grouper info available
+            # (e.g. we have selected out
+            # a column that is not in the current object)
+            axis = self._grouper.axis
+            grouper = self._grouper.codes_info[axis.isin(dropped.index)]
+            if self._grouper.has_dropped_na:
+                # Null groups need to still be encoded as -1 when passed to groupby
+                nulls = grouper == -1
+                # error: No overload variant of "where" matches argument types
+                #        "Any", "NAType", "Any"
+                values = np.where(nulls, NA, grouper)  # type: ignore[call-overload]
+                grouper = Index(values, dtype="Int64")
+
+        if self.axis == 1:
+            grb = dropped.T.groupby(grouper, as_index=self.as_index, sort=self.sort)
+        else:
+            grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
+        return grb.nth(n)
+
+    @final
+    def quantile(
+        self,
+        q: float | AnyArrayLike = 0.5,
+        interpolation: str = "linear",
+        numeric_only: bool = False,
+    ):
+        """
+        Return group values at the given quantile, a la numpy.percentile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            Value(s) between 0 and 1 providing the quantile(s) to compute.
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            Method to use when the desired quantile falls between two points.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        Series or DataFrame
+            Return type determined by caller of GroupBy object.
+
+        See Also
+        --------
+        Series.quantile : Similar method for Series.
+        DataFrame.quantile : Similar method for DataFrame.
+        numpy.percentile : NumPy method to compute qth percentile.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([
+        ...     ['a', 1], ['a', 2], ['a', 3],
+        ...     ['b', 1], ['b', 3], ['b', 5]
+        ... ], columns=['key', 'val'])
+        >>> df.groupby('key').quantile()
+            val
+        key
+        a    2.0
+        b    3.0
+        """
+        mgr = self._get_data_to_aggregate(numeric_only=numeric_only, name="quantile")
+        obj = self._wrap_agged_manager(mgr)
+        if self.axis == 1:
+            splitter = self._grouper._get_splitter(obj.T, axis=self.axis)
+            sdata = splitter._sorted_data.T
+        else:
+            splitter = self._grouper._get_splitter(obj, axis=self.axis)
+            sdata = splitter._sorted_data
+
+        starts, ends = lib.generate_slices(splitter._slabels, splitter.ngroups)
+
+        def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, DtypeObj | None]:
+            if isinstance(vals.dtype, StringDtype) or is_object_dtype(vals.dtype):
+                raise TypeError(
+                    f"dtype '{vals.dtype}' does not support operation 'quantile'"
+                )
+
+            inference: DtypeObj | None = None
+            if isinstance(vals, BaseMaskedArray) and is_numeric_dtype(vals.dtype):
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+                inference = vals.dtype
+            elif is_integer_dtype(vals.dtype):
+                if isinstance(vals, ExtensionArray):
+                    out = vals.to_numpy(dtype=float, na_value=np.nan)
+                else:
+                    out = vals
+                inference = np.dtype(np.int64)
+            elif is_bool_dtype(vals.dtype) and isinstance(vals, ExtensionArray):
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+            elif is_bool_dtype(vals.dtype):
+                # GH#51424 deprecate to match Series/DataFrame behavior
+                warnings.warn(
+                    f"Allowing bool dtype in {type(self).__name__}.quantile is "
+                    "deprecated and will raise in a future version, matching "
+                    "the Series/DataFrame behavior. Cast to uint8 dtype before "
+                    "calling quantile instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                out = np.asarray(vals)
+            elif needs_i8_conversion(vals.dtype):
+                inference = vals.dtype
+                # In this case we need to delay the casting until after the
+                #  np.lexsort below.
+                # error: Incompatible return value type (got
+                # "Tuple[Union[ExtensionArray, ndarray[Any, Any]], Union[Any,
+                # ExtensionDtype]]", expected "Tuple[ndarray[Any, Any],
+                # Optional[Union[dtype[Any], ExtensionDtype]]]")
+                return vals, inference  # type: ignore[return-value]
+            elif isinstance(vals, ExtensionArray) and is_float_dtype(vals.dtype):
+                inference = np.dtype(np.float64)
+                out = vals.to_numpy(dtype=float, na_value=np.nan)
+            else:
+                out = np.asarray(vals)
+
+            return out, inference
+
+        def post_processor(
+            vals: np.ndarray,
+            inference: DtypeObj | None,
+            result_mask: np.ndarray | None,
+            orig_vals: ArrayLike,
+        ) -> ArrayLike:
+            if inference:
+                # Check for edge case
+                if isinstance(orig_vals, BaseMaskedArray):
+                    assert result_mask is not None  # for mypy
+
+                    if interpolation in {"linear", "midpoint"} and not is_float_dtype(
+                        orig_vals
+                    ):
+                        return FloatingArray(vals, result_mask)
+                    else:
+                        # Item "ExtensionDtype" of "Union[ExtensionDtype, str,
+                        # dtype[Any], Type[object]]" has no attribute "numpy_dtype"
+                        # [union-attr]
+                        with warnings.catch_warnings():
+                            # vals.astype with nan can warn with numpy >1.24
+                            warnings.filterwarnings("ignore", category=RuntimeWarning)
+                            return type(orig_vals)(
+                                vals.astype(
+                                    inference.numpy_dtype  # type: ignore[union-attr]
+                                ),
+                                result_mask,
+                            )
+
+                elif not (
+                    is_integer_dtype(inference)
+                    and interpolation in {"linear", "midpoint"}
+                ):
+                    if needs_i8_conversion(inference):
+                        # error: Item "ExtensionArray" of "Union[ExtensionArray,
+                        # ndarray[Any, Any]]" has no attribute "_ndarray"
+                        vals = vals.astype("i8").view(
+                            orig_vals._ndarray.dtype  # type: ignore[union-attr]
+                        )
+                        # error: Item "ExtensionArray" of "Union[ExtensionArray,
+                        # ndarray[Any, Any]]" has no attribute "_from_backing_data"
+                        return orig_vals._from_backing_data(  # type: ignore[union-attr]
+                            vals
+                        )
+
+                    assert isinstance(inference, np.dtype)  # for mypy
+                    return vals.astype(inference)
+
+            return vals
+
+        qs = np.array(q, dtype=np.float64)
+        pass_qs: np.ndarray | None = qs
+        if is_scalar(q):
+            qs = np.array([q], dtype=np.float64)
+            pass_qs = None
+
+        ids, _, ngroups = self._grouper.group_info
+        nqs = len(qs)
+
+        func = partial(
+            libgroupby.group_quantile,
+            labels=ids,
+            qs=qs,
+            interpolation=interpolation,
+            starts=starts,
+            ends=ends,
+        )
+
+        def blk_func(values: ArrayLike) -> ArrayLike:
+            orig_vals = values
+            if isinstance(values, BaseMaskedArray):
+                mask = values._mask
+                result_mask = np.zeros((ngroups, nqs), dtype=np.bool_)
+            else:
+                mask = isna(values)
+                result_mask = None
+
+            is_datetimelike = needs_i8_conversion(values.dtype)
+
+            vals, inference = pre_processor(values)
+
+            ncols = 1
+            if vals.ndim == 2:
+                ncols = vals.shape[0]
+
+            out = np.empty((ncols, ngroups, nqs), dtype=np.float64)
+
+            if is_datetimelike:
+                vals = vals.view("i8")
+
+            if vals.ndim == 1:
+                # EA is always 1d
+                func(
+                    out[0],
+                    values=vals,
+                    mask=mask,
+                    result_mask=result_mask,
+                    is_datetimelike=is_datetimelike,
+                )
+            else:
+                for i in range(ncols):
+                    func(
+                        out[i],
+                        values=vals[i],
+                        mask=mask[i],
+                        result_mask=None,
+                        is_datetimelike=is_datetimelike,
+                    )
+
+            if vals.ndim == 1:
+                out = out.ravel("K")
+                if result_mask is not None:
+                    result_mask = result_mask.ravel("K")
+            else:
+                out = out.reshape(ncols, ngroups * nqs)
+
+            return post_processor(out, inference, result_mask, orig_vals)
+
+        res_mgr = sdata._mgr.grouped_reduce(blk_func)
+
+        res = self._wrap_agged_manager(res_mgr)
+        return self._wrap_aggregated_output(res, qs=pass_qs)
+
+    @final
+    @Substitution(name="groupby")
+    def ngroup(self, ascending: bool = True):
+        """
+        Number each group from 0 to the number of groups - 1.
+
+        This is the enumerative complement of cumcount.  Note that the
+        numbers given to the groups match the order in which the groups
+        would be seen when iterating over the groupby object, not the
+        order they are first observed.
+
+        Groups with missing keys (where `pd.isna()` is True) will be labeled with `NaN`
+        and will be skipped from the count.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from number of group - 1 to 0.
+
+        Returns
+        -------
+        Series
+            Unique numbers for each group.
+
+        See Also
+        --------
+        .cumcount : Number the rows in each group.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]})
+        >>> df
+           color
+        0    red
+        1   None
+        2    red
+        3   blue
+        4   blue
+        5    red
+        >>> df.groupby("color").ngroup()
+        0    1.0
+        1    NaN
+        2    1.0
+        3    0.0
+        4    0.0
+        5    1.0
+        dtype: float64
+        >>> df.groupby("color", dropna=False).ngroup()
+        0    1
+        1    2
+        2    1
+        3    0
+        4    0
+        5    1
+        dtype: int64
+        >>> df.groupby("color", dropna=False).ngroup(ascending=False)
+        0    1
+        1    0
+        2    1
+        3    2
+        4    2
+        5    1
+        dtype: int64
+        """
+        obj = self._obj_with_exclusions
+        index = obj._get_axis(self.axis)
+        comp_ids = self._grouper.group_info[0]
+
+        dtype: type
+        if self._grouper.has_dropped_na:
+            comp_ids = np.where(comp_ids == -1, np.nan, comp_ids)
+            dtype = np.float64
+        else:
+            dtype = np.int64
+
+        if any(ping._passed_categorical for ping in self._grouper.groupings):
+            # comp_ids reflect non-observed groups, we need only observed
+            comp_ids = rank_1d(comp_ids, ties_method="dense") - 1
+
+        result = self._obj_1d_constructor(comp_ids, index, dtype=dtype)
+        if not ascending:
+            result = self.ngroups - 1 - result
+        return result
+
+    @final
+    @Substitution(name="groupby")
+    def cumcount(self, ascending: bool = True):
+        """
+        Number each item in each group from 0 to the length of that group - 1.
+
+        Essentially this is equivalent to
+
+        .. code-block:: python
+
+            self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from length of group - 1 to 0.
+
+        Returns
+        -------
+        Series
+            Sequence number of each element within each group.
+
+        See Also
+        --------
+        .ngroup : Number the groups themselves.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
+        ...                   columns=['A'])
+        >>> df
+           A
+        0  a
+        1  a
+        2  a
+        3  b
+        4  b
+        5  a
+        >>> df.groupby('A').cumcount()
+        0    0
+        1    1
+        2    2
+        3    0
+        4    1
+        5    3
+        dtype: int64
+        >>> df.groupby('A').cumcount(ascending=False)
+        0    3
+        1    2
+        2    1
+        3    1
+        4    0
+        5    0
+        dtype: int64
+        """
+        index = self._obj_with_exclusions._get_axis(self.axis)
+        cumcounts = self._cumcount_array(ascending=ascending)
+        return self._obj_1d_constructor(cumcounts, index)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def rank(
+        self,
+        method: str = "average",
+        ascending: bool = True,
+        na_option: str = "keep",
+        pct: bool = False,
+        axis: AxisInt | lib.NoDefault = lib.no_default,
+    ) -> NDFrameT:
+        """
+        Provide the rank of values within each group.
+
+        Parameters
+        ----------
+        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+            * average: average rank of group.
+            * min: lowest rank in group.
+            * max: highest rank in group.
+            * first: ranks assigned in order they appear in the array.
+            * dense: like 'min', but rank always increases by 1 between groups.
+        ascending : bool, default True
+            False for ranks by high (1) to low (N).
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+            * keep: leave NA values where they are.
+            * top: smallest rank if ascending.
+            * bottom: smallest rank if descending.
+        pct : bool, default False
+            Compute percentage rank of data within each group.
+        axis : int, default 0
+            The axis of the object over which to compute the rank.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        Returns
+        -------
+        DataFrame with ranking of values within each group
+        %(see_also)s
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],
+        ...         "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5],
+        ...     }
+        ... )
+        >>> df
+          group  value
+        0     a      2
+        1     a      4
+        2     a      2
+        3     a      3
+        4     a      5
+        5     b      1
+        6     b      2
+        7     b      4
+        8     b      1
+        9     b      5
+        >>> for method in ['average', 'min', 'max', 'dense', 'first']:
+        ...     df[f'{method}_rank'] = df.groupby('group')['value'].rank(method)
+        >>> df
+          group  value  average_rank  min_rank  max_rank  dense_rank  first_rank
+        0     a      2           1.5       1.0       2.0         1.0         1.0
+        1     a      4           4.0       4.0       4.0         3.0         4.0
+        2     a      2           1.5       1.0       2.0         1.0         2.0
+        3     a      3           3.0       3.0       3.0         2.0         3.0
+        4     a      5           5.0       5.0       5.0         4.0         5.0
+        5     b      1           1.5       1.0       2.0         1.0         1.0
+        6     b      2           3.0       3.0       3.0         2.0         3.0
+        7     b      4           4.0       4.0       4.0         3.0         4.0
+        8     b      1           1.5       1.0       2.0         1.0         2.0
+        9     b      5           5.0       5.0       5.0         4.0         5.0
+        """
+        if na_option not in {"keep", "top", "bottom"}:
+            msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+            raise ValueError(msg)
+
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "rank")
+        else:
+            axis = 0
+
+        kwargs = {
+            "ties_method": method,
+            "ascending": ascending,
+            "na_option": na_option,
+            "pct": pct,
+        }
+        if axis != 0:
+            # DataFrame uses different keyword name
+            kwargs["method"] = kwargs.pop("ties_method")
+            f = lambda x: x.rank(axis=axis, numeric_only=False, **kwargs)
+            result = self._python_apply_general(
+                f, self._selected_obj, is_transform=True
+            )
+            return result
+
+        return self._cython_transform(
+            "rank",
+            numeric_only=False,
+            axis=axis,
+            **kwargs,
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cumprod(
+        self, axis: Axis | lib.NoDefault = lib.no_default, *args, **kwargs
+    ) -> NDFrameT:
+        """
+        Cumulative product for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([6, 2, 0], index=lst)
+        >>> ser
+        a    6
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).cumprod()
+        a    6
+        a   12
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["cow", "horse", "bull"])
+        >>> df
+                a   b   c
+        cow     1   8   2
+        horse   1   2   5
+        bull    2   6   9
+        >>> df.groupby("a").groups
+        {1: ['cow', 'horse'], 2: ['bull']}
+        >>> df.groupby("a").cumprod()
+                b   c
+        cow     8   2
+        horse  16  10
+        bull    6   9
+        """
+        nv.validate_groupby_func("cumprod", args, kwargs, ["numeric_only", "skipna"])
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cumprod")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: x.cumprod(axis=axis, **kwargs)
+            return self._python_apply_general(f, self._selected_obj, is_transform=True)
+
+        return self._cython_transform("cumprod", **kwargs)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cumsum(
+        self, axis: Axis | lib.NoDefault = lib.no_default, *args, **kwargs
+    ) -> NDFrameT:
+        """
+        Cumulative sum for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b']
+        >>> ser = pd.Series([6, 2, 0], index=lst)
+        >>> ser
+        a    6
+        a    2
+        b    0
+        dtype: int64
+        >>> ser.groupby(level=0).cumsum()
+        a    6
+        a    8
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["fox", "gorilla", "lion"])
+        >>> df
+                  a   b   c
+        fox       1   8   2
+        gorilla   1   2   5
+        lion      2   6   9
+        >>> df.groupby("a").groups
+        {1: ['fox', 'gorilla'], 2: ['lion']}
+        >>> df.groupby("a").cumsum()
+                  b   c
+        fox       8   2
+        gorilla  10   7
+        lion      6   9
+        """
+        nv.validate_groupby_func("cumsum", args, kwargs, ["numeric_only", "skipna"])
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cumsum")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: x.cumsum(axis=axis, **kwargs)
+            return self._python_apply_general(f, self._selected_obj, is_transform=True)
+
+        return self._cython_transform("cumsum", **kwargs)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cummin(
+        self,
+        axis: AxisInt | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> NDFrameT:
+        """
+        Cumulative min for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 6, 2, 3, 0, 4], index=lst)
+        >>> ser
+        a    1
+        a    6
+        a    2
+        b    3
+        b    0
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).cummin()
+        a    1
+        a    1
+        a    1
+        b    3
+        b    0
+        b    0
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 0, 2], [1, 1, 5], [6, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["snake", "rabbit", "turtle"])
+        >>> df
+                a   b   c
+        snake   1   0   2
+        rabbit  1   1   5
+        turtle  6   6   9
+        >>> df.groupby("a").groups
+        {1: ['snake', 'rabbit'], 6: ['turtle']}
+        >>> df.groupby("a").cummin()
+                b   c
+        snake   0   2
+        rabbit  0   2
+        turtle  6   9
+        """
+        skipna = kwargs.get("skipna", True)
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cummin")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: np.minimum.accumulate(x, axis)
+            obj = self._selected_obj
+            if numeric_only:
+                obj = obj._get_numeric_data()
+            return self._python_apply_general(f, obj, is_transform=True)
+
+        return self._cython_transform(
+            "cummin", numeric_only=numeric_only, skipna=skipna
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def cummax(
+        self,
+        axis: AxisInt | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
+        **kwargs,
+    ) -> NDFrameT:
+        """
+        Cumulative max for each group.
+
+        Returns
+        -------
+        Series or DataFrame
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 6, 2, 3, 1, 4], index=lst)
+        >>> ser
+        a    1
+        a    6
+        a    2
+        b    3
+        b    1
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).cummax()
+        a    1
+        a    6
+        a    6
+        b    3
+        b    3
+        b    4
+        dtype: int64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["cow", "horse", "bull"])
+        >>> df
+                a   b   c
+        cow     1   8   2
+        horse   1   1   0
+        bull    2   6   9
+        >>> df.groupby("a").groups
+        {1: ['cow', 'horse'], 2: ['bull']}
+        >>> df.groupby("a").cummax()
+                b   c
+        cow     8   2
+        horse   8   2
+        bull    6   9
+        """
+        skipna = kwargs.get("skipna", True)
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "cummax")
+        else:
+            axis = 0
+
+        if axis != 0:
+            f = lambda x: np.maximum.accumulate(x, axis)
+            obj = self._selected_obj
+            if numeric_only:
+                obj = obj._get_numeric_data()
+            return self._python_apply_general(f, obj, is_transform=True)
+
+        return self._cython_transform(
+            "cummax", numeric_only=numeric_only, skipna=skipna
+        )
+
+    @final
+    @Substitution(name="groupby")
+    def shift(
+        self,
+        periods: int | Sequence[int] = 1,
+        freq=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        fill_value=lib.no_default,
+        suffix: str | None = None,
+    ):
+        """
+        Shift each group by periods observations.
+
+        If freq is passed, the index will be increased using the periods and the freq.
+
+        Parameters
+        ----------
+        periods : int | Sequence[int], default 1
+            Number of periods to shift. If a list of values, shift each group by
+            each period.
+        freq : str, optional
+            Frequency string.
+        axis : axis to shift, default 0
+            Shift direction.
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        fill_value : optional
+            The scalar value to use for newly introduced missing values.
+
+            .. versionchanged:: 2.1.0
+                Will raise a ``ValueError`` if ``freq`` is provided too.
+
+        suffix : str, optional
+            A string to add to each shifted column if there are multiple periods.
+            Ignored otherwise.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object shifted within each group.
+
+        See Also
+        --------
+        Index.shift : Shift values of Index.
+
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).shift(1)
+        a    NaN
+        a    1.0
+        b    NaN
+        b    3.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tuna", "salmon", "catfish", "goldfish"])
+        >>> df
+                   a  b  c
+            tuna   1  2  3
+          salmon   1  5  6
+         catfish   2  5  8
+        goldfish   2  6  9
+        >>> df.groupby("a").shift(1)
+                      b    c
+            tuna    NaN  NaN
+          salmon    2.0  3.0
+         catfish    NaN  NaN
+        goldfish    5.0  8.0
+        """
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "shift")
+        else:
+            axis = 0
+
+        if is_list_like(periods):
+            if axis == 1:
+                raise ValueError(
+                    "If `periods` contains multiple shifts, `axis` cannot be 1."
+                )
+            periods = cast(Sequence, periods)
+            if len(periods) == 0:
+                raise ValueError("If `periods` is an iterable, it cannot be empty.")
+            from pandas.core.reshape.concat import concat
+
+            add_suffix = True
+        else:
+            if not is_integer(periods):
+                raise TypeError(
+                    f"Periods must be integer, but {periods} is {type(periods)}."
+                )
+            if suffix:
+                raise ValueError("Cannot specify `suffix` if `periods` is an int.")
+            periods = [cast(int, periods)]
+            add_suffix = False
+
+        shifted_dataframes = []
+        for period in periods:
+            if not is_integer(period):
+                raise TypeError(
+                    f"Periods must be integer, but {period} is {type(period)}."
+                )
+            period = cast(int, period)
+            if freq is not None or axis != 0:
+                f = lambda x: x.shift(
+                    period, freq, axis, fill_value  # pylint: disable=cell-var-from-loop
+                )
+                shifted = self._python_apply_general(
+                    f, self._selected_obj, is_transform=True
+                )
+            else:
+                if fill_value is lib.no_default:
+                    fill_value = None
+                ids, _, ngroups = self._grouper.group_info
+                res_indexer = np.zeros(len(ids), dtype=np.int64)
+
+                libgroupby.group_shift_indexer(res_indexer, ids, ngroups, period)
+
+                obj = self._obj_with_exclusions
+
+                shifted = obj._reindex_with_indexers(
+                    {self.axis: (obj.axes[self.axis], res_indexer)},
+                    fill_value=fill_value,
+                    allow_dups=True,
+                )
+
+            if add_suffix:
+                if isinstance(shifted, Series):
+                    shifted = cast(NDFrameT, shifted.to_frame())
+                shifted = shifted.add_suffix(
+                    f"{suffix}_{period}" if suffix else f"_{period}"
+                )
+            shifted_dataframes.append(cast(Union[Series, DataFrame], shifted))
+
+        return (
+            shifted_dataframes[0]
+            if len(shifted_dataframes) == 1
+            else concat(shifted_dataframes, axis=1)
+        )
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def diff(
+        self, periods: int = 1, axis: AxisInt | lib.NoDefault = lib.no_default
+    ) -> NDFrameT:
+        """
+        First discrete difference of element.
+
+        Calculates the difference of each element compared with another
+        element in the group (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference, accepts negative values.
+        axis : axis to shift, default 0
+            Take difference over rows (0) or columns (1).
+
+            .. deprecated:: 2.1.0
+                For axis=1, operate on the underlying object instead. Otherwise
+                the axis keyword is not necessary.
+
+        Returns
+        -------
+        Series or DataFrame
+            First differences.
+        %(see_also)s
+        Examples
+        --------
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
+        >>> ser
+        a     7
+        a     2
+        a     8
+        b     4
+        b     3
+        b     3
+        dtype: int64
+        >>> ser.groupby(level=0).diff()
+        a    NaN
+        a   -5.0
+        a    6.0
+        b    NaN
+        b   -1.0
+        b    0.0
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
+        >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
+        ...                   'mouse', 'mouse', 'mouse', 'mouse'])
+        >>> df
+                 a  b
+          dog    1  1
+          dog    3  4
+          dog    5  8
+        mouse    7  4
+        mouse    7  4
+        mouse    8  2
+        mouse    3  1
+        >>> df.groupby(level=0).diff()
+                 a    b
+          dog  NaN  NaN
+          dog  2.0  3.0
+          dog  2.0  4.0
+        mouse  NaN  NaN
+        mouse  0.0  0.0
+        mouse  1.0 -2.0
+        mouse -5.0 -1.0
+        """
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "diff")
+        else:
+            axis = 0
+
+        if axis != 0:
+            return self.apply(lambda x: x.diff(periods=periods, axis=axis))
+
+        obj = self._obj_with_exclusions
+        shifted = self.shift(periods=periods)
+
+        # GH45562 - to retain existing behavior and match behavior of Series.diff(),
+        # int8 and int16 are coerced to float32 rather than float64.
+        dtypes_to_f32 = ["int8", "int16"]
+        if obj.ndim == 1:
+            if obj.dtype in dtypes_to_f32:
+                shifted = shifted.astype("float32")
+        else:
+            to_coerce = [c for c, dtype in obj.dtypes.items() if dtype in dtypes_to_f32]
+            if len(to_coerce):
+                shifted = shifted.astype({c: "float32" for c in to_coerce})
+
+        return obj - shifted
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def pct_change(
+        self,
+        periods: int = 1,
+        fill_method: FillnaOptions | None | lib.NoDefault = lib.no_default,
+        limit: int | None | lib.NoDefault = lib.no_default,
+        freq=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+    ):
+        """
+        Calculate pct_change of each value to previous entry in group.
+
+        Returns
+        -------
+        Series or DataFrame
+            Percentage changes within each group.
+        %(see_also)s
+        Examples
+        --------
+
+        For SeriesGroupBy:
+
+        >>> lst = ['a', 'a', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 3, 4], index=lst)
+        >>> ser
+        a    1
+        a    2
+        b    3
+        b    4
+        dtype: int64
+        >>> ser.groupby(level=0).pct_change()
+        a         NaN
+        a    1.000000
+        b         NaN
+        b    0.333333
+        dtype: float64
+
+        For DataFrameGroupBy:
+
+        >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
+        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
+        ...                   index=["tuna", "salmon", "catfish", "goldfish"])
+        >>> df
+                   a  b  c
+            tuna   1  2  3
+          salmon   1  5  6
+         catfish   2  5  8
+        goldfish   2  6  9
+        >>> df.groupby("a").pct_change()
+                    b  c
+            tuna    NaN    NaN
+          salmon    1.5  1.000
+         catfish    NaN    NaN
+        goldfish    0.2  0.125
+        """
+        # GH#53491
+        if fill_method not in (lib.no_default, None) or limit is not lib.no_default:
+            warnings.warn(
+                "The 'fill_method' keyword being not None and the 'limit' keyword in "
+                f"{type(self).__name__}.pct_change are deprecated and will be removed "
+                "in a future version. Either fill in any non-leading NA values prior "
+                "to calling pct_change or specify 'fill_method=None' to not fill NA "
+                "values.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if fill_method is lib.no_default:
+            if limit is lib.no_default and any(
+                grp.isna().values.any() for _, grp in self
+            ):
+                warnings.warn(
+                    "The default fill_method='ffill' in "
+                    f"{type(self).__name__}.pct_change is deprecated and will "
+                    "be removed in a future version. Either fill in any "
+                    "non-leading NA values prior to calling pct_change or "
+                    "specify 'fill_method=None' to not fill NA values.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            fill_method = "ffill"
+        if limit is lib.no_default:
+            limit = None
+
+        if axis is not lib.no_default:
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, "pct_change")
+        else:
+            axis = 0
+
+        # TODO(GH#23918): Remove this conditional for SeriesGroupBy when
+        #  GH#23918 is fixed
+        if freq is not None or axis != 0:
+            f = lambda x: x.pct_change(
+                periods=periods,
+                fill_method=fill_method,
+                limit=limit,
+                freq=freq,
+                axis=axis,
+            )
+            return self._python_apply_general(f, self._selected_obj, is_transform=True)
+
+        if fill_method is None:  # GH30463
+            fill_method = "ffill"
+            limit = 0
+        filled = getattr(self, fill_method)(limit=limit)
+        if self.axis == 0:
+            fill_grp = filled.groupby(self._grouper.codes, group_keys=self.group_keys)
+        else:
+            fill_grp = filled.T.groupby(self._grouper.codes, group_keys=self.group_keys)
+        shifted = fill_grp.shift(periods=periods, freq=freq)
+        if self.axis == 1:
+            shifted = shifted.T
+        return (filled / shifted) - 1
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def head(self, n: int = 5) -> NDFrameT:
+        """
+        Return first n rows of each group.
+
+        Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows
+        from the original DataFrame with original index and order preserved
+        (``as_index`` flag is ignored).
+
+        Parameters
+        ----------
+        n : int
+            If positive: number of entries to include from start of each group.
+            If negative: number of entries to exclude from end of each group.
+
+        Returns
+        -------
+        Series or DataFrame
+            Subset of original Series or DataFrame as determined by n.
+        %(see_also)s
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
+        ...                   columns=['A', 'B'])
+        >>> df.groupby('A').head(1)
+           A  B
+        0  1  2
+        2  5  6
+        >>> df.groupby('A').head(-1)
+           A  B
+        0  1  2
+        """
+        mask = self._make_mask_from_positional_indexer(slice(None, n))
+        return self._mask_selected_obj(mask)
+
+    @final
+    @Substitution(name="groupby")
+    @Substitution(see_also=_common_see_also)
+    def tail(self, n: int = 5) -> NDFrameT:
+        """
+        Return last n rows of each group.
+
+        Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows
+        from the original DataFrame with original index and order preserved
+        (``as_index`` flag is ignored).
+
+        Parameters
+        ----------
+        n : int
+            If positive: number of entries to include from end of each group.
+            If negative: number of entries to exclude from start of each group.
+
+        Returns
+        -------
+        Series or DataFrame
+            Subset of original Series or DataFrame as determined by n.
+        %(see_also)s
+        Examples
+        --------
+
+        >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
+        ...                   columns=['A', 'B'])
+        >>> df.groupby('A').tail(1)
+           A  B
+        1  a  2
+        3  b  2
+        >>> df.groupby('A').tail(-1)
+           A  B
+        1  a  2
+        3  b  2
+        """
+        if n:
+            mask = self._make_mask_from_positional_indexer(slice(-n, None))
+        else:
+            mask = self._make_mask_from_positional_indexer([])
+
+        return self._mask_selected_obj(mask)
+
+    @final
+    def _mask_selected_obj(self, mask: npt.NDArray[np.bool_]) -> NDFrameT:
+        """
+        Return _selected_obj with mask applied to the correct axis.
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool]
+            Boolean mask to apply.
+
+        Returns
+        -------
+        Series or DataFrame
+            Filtered _selected_obj.
+        """
+        ids = self._grouper.group_info[0]
+        mask = mask & (ids != -1)
+
+        if self.axis == 0:
+            return self._selected_obj[mask]
+        else:
+            return self._selected_obj.iloc[:, mask]
+
+    @final
+    def _reindex_output(
+        self,
+        output: OutputFrameOrSeries,
+        fill_value: Scalar = np.nan,
+        qs: npt.NDArray[np.float64] | None = None,
+        method: str | None = None,
+    ) -> OutputFrameOrSeries:
+        """
+        If we have categorical groupers, then we might want to make sure that
+        we have a fully re-indexed output to the levels. This means expanding
+        the output space to accommodate all values in the cartesian product of
+        our groups, regardless of whether they were observed in the data or
+        not. This will expand the output space if there are missing groups.
+
+        The method returns early without modifying the input if the number of
+        groupings is less than 2, self.observed == True or none of the groupers
+        are categorical.
+
+        Parameters
+        ----------
+        output : Series or DataFrame
+            Object resulting from grouping and applying an operation.
+        fill_value : scalar, default np.nan
+            Value to use for unobserved categories if self.observed is False.
+        qs : np.ndarray[float64] or None, default None
+            quantile values, only relevant for quantile.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object (potentially) re-indexed to include all possible groups.
+        """
+        groupings = self._grouper.groupings
+        if len(groupings) == 1:
+            return output
+
+        # if we only care about the observed values
+        # we are done
+        elif self.observed:
+            return output
+
+        # reindexing only applies to a Categorical grouper
+        elif not any(
+            isinstance(ping.grouping_vector, (Categorical, CategoricalIndex))
+            for ping in groupings
+        ):
+            return output
+
+        levels_list = [ping._group_index for ping in groupings]
+        names = self._grouper.names
+        if qs is not None:
+            # error: Argument 1 to "append" of "list" has incompatible type
+            # "ndarray[Any, dtype[floating[_64Bit]]]"; expected "Index"
+            levels_list.append(qs)  # type: ignore[arg-type]
+            names = names + [None]
+        index = MultiIndex.from_product(levels_list, names=names)
+        if self.sort:
+            index = index.sort_values()
+
+        if self.as_index:
+            # Always holds for SeriesGroupBy unless GH#36507 is implemented
+            d = {
+                self.obj._get_axis_name(self.axis): index,
+                "copy": False,
+                "fill_value": fill_value,
+            }
+            if using_string_dtype() and method == "sum":
+                if isinstance(output, Series) and isinstance(output.dtype, StringDtype):
+                    d["fill_value"] = ""
+                    return output.reindex(**d)  # type: ignore[return-value, arg-type]
+                elif isinstance(output, DataFrame) and any(
+                    isinstance(dtype, StringDtype) for dtype in output.dtypes
+                ):
+                    orig_dtypes = output.dtypes
+                    indices = np.nonzero(output.dtypes == "string")[0]
+                    for idx in indices:
+                        output.isetitem(idx, output.iloc[:, idx].astype(object))
+                    output = output.reindex(**d)  # type: ignore[assignment, arg-type]
+                    for idx in indices:
+                        col = output.iloc[:, idx]
+                        output.isetitem(
+                            idx, col.mask(col == 0, "").astype(orig_dtypes.iloc[idx])
+                        )
+                    return output  # type: ignore[return-value]
+            return output.reindex(**d)  # type: ignore[arg-type]
+
+        # GH 13204
+        # Here, the categorical in-axis groupers, which need to be fully
+        # expanded, are columns in `output`. An idea is to do:
+        # output = output.set_index(self._grouper.names)
+        #                .reindex(index).reset_index()
+        # but special care has to be taken because of possible not-in-axis
+        # groupers.
+        # So, we manually select and drop the in-axis grouper columns,
+        # reindex `output`, and then reset the in-axis grouper columns.
+
+        # Select in-axis groupers
+        in_axis_grps = [
+            (i, ping.name) for (i, ping) in enumerate(groupings) if ping.in_axis
+        ]
+        if len(in_axis_grps) > 0:
+            g_nums, g_names = zip(*in_axis_grps)
+            output = output.drop(labels=list(g_names), axis=1)
+
+        # Set a temp index and reindex (possibly expanding)
+        output = output.set_index(self._grouper.result_index).reindex(
+            index, copy=False, fill_value=fill_value
+        )
+
+        # Reset in-axis grouper columns
+        # (using level numbers `g_nums` because level names may not be unique)
+        if len(in_axis_grps) > 0:
+            output = output.reset_index(level=g_nums)
+
+        return output.reset_index(drop=True)
+
+    @final
+    def sample(
+        self,
+        n: int | None = None,
+        frac: float | None = None,
+        replace: bool = False,
+        weights: Sequence | Series | None = None,
+        random_state: RandomState | None = None,
+    ):
+        """
+        Return a random sample of items from each group.
+
+        You can use `random_state` for reproducibility.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of items to return for each group. Cannot be used with
+            `frac` and must be no larger than the smallest group unless
+            `replace` is True. Default is one if `frac` is None.
+        frac : float, optional
+            Fraction of items to return. Cannot be used with `n`.
+        replace : bool, default False
+            Allow or disallow sampling of the same row more than once.
+        weights : list-like, optional
+            Default None results in equal probability weighting.
+            If passed a list-like then values must have the same length as
+            the underlying DataFrame or Series object and will be used as
+            sampling probabilities after normalization within each group.
+            Values must be non-negative with at least one positive element
+            within each group.
+        random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
+            If int, array-like, or BitGenerator, seed for random number generator.
+            If np.random.RandomState or np.random.Generator, use as given.
+
+            .. versionchanged:: 1.4.0
+
+                np.random.Generator objects now accepted
+
+        Returns
+        -------
+        Series or DataFrame
+            A new object of same type as caller containing items randomly
+            sampled within each group from the caller object.
+
+        See Also
+        --------
+        DataFrame.sample: Generate random samples from a DataFrame object.
+        numpy.random.choice: Generate a random sample from a given 1-D numpy
+            array.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)}
+        ... )
+        >>> df
+               a  b
+        0    red  0
+        1    red  1
+        2   blue  2
+        3   blue  3
+        4  black  4
+        5  black  5
+
+        Select one row at random for each distinct value in column a. The
+        `random_state` argument can be used to guarantee reproducibility:
+
+        >>> df.groupby("a").sample(n=1, random_state=1)
+               a  b
+        4  black  4
+        2   blue  2
+        1    red  1
+
+        Set `frac` to sample fixed proportions rather than counts:
+
+        >>> df.groupby("a")["b"].sample(frac=0.5, random_state=2)
+        5    5
+        2    2
+        0    0
+        Name: b, dtype: int64
+
+        Control sample probabilities within groups by setting weights:
+
+        >>> df.groupby("a").sample(
+        ...     n=1,
+        ...     weights=[1, 1, 1, 0, 0, 1],
+        ...     random_state=1,
+        ... )
+               a  b
+        5  black  5
+        2   blue  2
+        0    red  0
+        """  # noqa: E501
+        if self._selected_obj.empty:
+            # GH48459 prevent ValueError when object is empty
+            return self._selected_obj
+        size = sample.process_sampling_size(n, frac, replace)
+        if weights is not None:
+            weights_arr = sample.preprocess_weights(
+                self._selected_obj, weights, axis=self.axis
+            )
+
+        random_state = com.random_state(random_state)
+
+        group_iterator = self._grouper.get_iterator(self._selected_obj, self.axis)
+
+        sampled_indices = []
+        for labels, obj in group_iterator:
+            grp_indices = self.indices[labels]
+            group_size = len(grp_indices)
+            if size is not None:
+                sample_size = size
+            else:
+                assert frac is not None
+                sample_size = round(frac * group_size)
+
+            grp_sample = sample.sample(
+                group_size,
+                size=sample_size,
+                replace=replace,
+                weights=None if weights is None else weights_arr[grp_indices],
+                random_state=random_state,
+            )
+            sampled_indices.append(grp_indices[grp_sample])
+
+        sampled_indices = np.concatenate(sampled_indices)
+        return self._selected_obj.take(sampled_indices, axis=self.axis)
+
+    def _idxmax_idxmin(
+        self,
+        how: Literal["idxmax", "idxmin"],
+        ignore_unobserved: bool = False,
+        axis: Axis | None | lib.NoDefault = lib.no_default,
+        skipna: bool = True,
+        numeric_only: bool = False,
+    ) -> NDFrameT:
+        """Compute idxmax/idxmin.
+
+        Parameters
+        ----------
+        how : {'idxmin', 'idxmax'}
+            Whether to compute idxmin or idxmax.
+        axis : {{0 or 'index', 1 or 'columns'}}, default None
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+            If axis is not provided, grouper's axis is used.
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+        skipna : bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        ignore_unobserved : bool, default False
+            When True and an unobserved group is encountered, do not raise. This used
+            for transform where unobserved groups do not play an impact on the result.
+
+        Returns
+        -------
+        Series or DataFrame
+            idxmax or idxmin for the groupby operation.
+        """
+        if axis is not lib.no_default:
+            if axis is None:
+                axis = self.axis
+            axis = self.obj._get_axis_number(axis)
+            self._deprecate_axis(axis, how)
+        else:
+            axis = self.axis
+
+        if not self.observed and any(
+            ping._passed_categorical for ping in self._grouper.groupings
+        ):
+            expected_len = np.prod(
+                [len(ping._group_index) for ping in self._grouper.groupings]
+            )
+            if len(self._grouper.groupings) == 1:
+                result_len = len(self._grouper.groupings[0].grouping_vector.unique())
+            else:
+                # result_index only contains observed groups in this case
+                result_len = len(self._grouper.result_index)
+            assert result_len <= expected_len
+            has_unobserved = result_len < expected_len
+
+            raise_err: bool | np.bool_ = not ignore_unobserved and has_unobserved
+            # Only raise an error if there are columns to compute; otherwise we return
+            # an empty DataFrame with an index (possibly including unobserved) but no
+            # columns
+            data = self._obj_with_exclusions
+            if raise_err and isinstance(data, DataFrame):
+                if numeric_only:
+                    data = data._get_numeric_data()
+                raise_err = len(data.columns) > 0
+
+            if raise_err:
+                raise ValueError(
+                    f"Can't get {how} of an empty group due to unobserved categories. "
+                    "Specify observed=True in groupby instead."
+                )
+        elif not skipna:
+            if self._obj_with_exclusions.isna().any(axis=None):
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.{how} with all-NA "
+                    "values, or any-NA and skipna=False, is deprecated. In a future "
+                    "version this will raise ValueError",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        if axis == 1:
+            try:
+
+                def func(df):
+                    method = getattr(df, how)
+                    return method(axis=axis, skipna=skipna, numeric_only=numeric_only)
+
+                func.__name__ = how
+                result = self._python_apply_general(
+                    func, self._obj_with_exclusions, not_indexed_same=True
+                )
+            except ValueError as err:
+                name = "argmax" if how == "idxmax" else "argmin"
+                if f"attempt to get {name} of an empty sequence" in str(err):
+                    raise ValueError(
+                        f"Can't get {how} of an empty group due to unobserved "
+                        "categories. Specify observed=True in groupby instead."
+                    ) from None
+                raise
+            return result
+
+        result = self._agg_general(
+            numeric_only=numeric_only,
+            min_count=1,
+            alias=how,
+            skipna=skipna,
+        )
+        return result
+
+    def _wrap_idxmax_idxmin(self, res: NDFrameT) -> NDFrameT:
+        index = self.obj._get_axis(self.axis)
+        if res.size == 0:
+            result = res.astype(index.dtype)
+        else:
+            if isinstance(index, MultiIndex):
+                index = index.to_flat_index()
+            values = res._values
+            assert isinstance(values, np.ndarray)
+            na_value = na_value_for_dtype(index.dtype, compat=False)
+            if isinstance(res, Series):
+                # mypy: expression has type "Series", variable has type "NDFrameT"
+                result = res._constructor(  # type: ignore[assignment]
+                    index.array.take(values, allow_fill=True, fill_value=na_value),
+                    index=res.index,
+                    name=res.name,
+                )
+            else:
+                data = {}
+                for k, column_values in enumerate(values.T):
+                    data[k] = index.array.take(
+                        column_values, allow_fill=True, fill_value=na_value
+                    )
+                result = self.obj._constructor(data, index=res.index)
+                result.columns = res.columns
+        return result
+
+
+@doc(GroupBy)
+def get_groupby(
+    obj: NDFrame,
+    by: _KeysArgType | None = None,
+    axis: AxisInt = 0,
+    grouper: ops.BaseGrouper | None = None,
+    group_keys: bool = True,
+) -> GroupBy:
+    klass: type[GroupBy]
+    if isinstance(obj, Series):
+        from pandas.core.groupby.generic import SeriesGroupBy
+
+        klass = SeriesGroupBy
+    elif isinstance(obj, DataFrame):
+        from pandas.core.groupby.generic import DataFrameGroupBy
+
+        klass = DataFrameGroupBy
+    else:  # pragma: no cover
+        raise TypeError(f"invalid type: {obj}")
+
+    return klass(
+        obj=obj,
+        keys=by,
+        axis=axis,
+        grouper=grouper,
+        group_keys=group_keys,
+    )
+
+
+def _insert_quantile_level(idx: Index, qs: npt.NDArray[np.float64]) -> MultiIndex:
+    """
+    Insert the sequence 'qs' of quantiles as the inner-most level of a MultiIndex.
+
+    The quantile level in the MultiIndex is a repeated copy of 'qs'.
+
+    Parameters
+    ----------
+    idx : Index
+    qs : np.ndarray[float64]
+
+    Returns
+    -------
+    MultiIndex
+    """
+    nqs = len(qs)
+    lev_codes, lev = Index(qs).factorize()
+    lev_codes = coerce_indexer_dtype(lev_codes, lev)
+
+    if idx._is_multi:
+        idx = cast(MultiIndex, idx)
+        levels = list(idx.levels) + [lev]
+        codes = [np.repeat(x, nqs) for x in idx.codes] + [np.tile(lev_codes, len(idx))]
+        mi = MultiIndex(levels=levels, codes=codes, names=idx.names + [None])
+    else:
+        nidx = len(idx)
+        idx_codes = coerce_indexer_dtype(np.arange(nidx), idx)
+        levels = [idx, lev]
+        codes = [np.repeat(idx_codes, nqs), np.tile(lev_codes, nidx)]
+        mi = MultiIndex(levels=levels, codes=codes, names=[idx.name, None])
+
+    return mi
+
+
+# GH#7155
+_apply_groupings_depr = (
+    "{}.{} operated on the grouping columns. This behavior is deprecated, "
+    "and in a future version of pandas the grouping columns will be excluded "
+    "from the operation. Either pass `include_groups=False` to exclude the "
+    "groupings or explicitly select the grouping columns after groupby to silence "
+    "this warning."
+)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/grouper.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/grouper.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bf2e8b90a0b09146f41975aa801929c77fd5fce
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/grouper.py
@@ -0,0 +1,1102 @@
+"""
+Provide user facing operators for doing the split part of the
+split-apply-combine paradigm.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import lib
+from pandas._libs.tslibs import OutOfBoundsDatetime
+from pandas.errors import InvalidIndexError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas.core import algorithms
+from pandas.core.arrays import (
+    Categorical,
+    ExtensionArray,
+)
+import pandas.core.common as com
+from pandas.core.frame import DataFrame
+from pandas.core.groupby import ops
+from pandas.core.groupby.categorical import recode_for_groupby
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+)
+from pandas.core.series import Series
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Axis,
+        NDFrameT,
+        npt,
+    )
+
+    from pandas.core.generic import NDFrame
+
+
+class Grouper:
+    """
+    A Grouper allows the user to specify a groupby instruction for an object.
+
+    This specification will select a column via the key parameter, or if the
+    level and/or axis parameters are given, a level of the index of the target
+    object.
+
+    If `axis` and/or `level` are passed as keywords to both `Grouper` and
+    `groupby`, the values passed to `Grouper` take precedence.
+
+    Parameters
+    ----------
+    key : str, defaults to None
+        Groupby key, which selects the grouping column of the target.
+    level : name/number, defaults to None
+        The level for the target index.
+    freq : str / frequency object, defaults to None
+        This will groupby the specified frequency if the target selection
+        (via key or level) is a datetime-like object. For full specification
+        of available frequencies, please see `here
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_.
+    axis : str, int, defaults to 0
+        Number/name of the axis.
+    sort : bool, default to False
+        Whether to sort the resulting labels.
+    closed : {'left' or 'right'}
+        Closed end of interval. Only when `freq` parameter is passed.
+    label : {'left' or 'right'}
+        Interval boundary to use for labeling.
+        Only when `freq` parameter is passed.
+    convention : {'start', 'end', 'e', 's'}
+        If grouper is PeriodIndex and `freq` parameter is passed.
+
+    origin : Timestamp or str, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin must
+        match the timezone of the index.
+        If string, must be one of the following:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+
+        - 'end': `origin` is the last value of the timeseries
+        - 'end_day': `origin` is the ceiling midnight of the last day
+
+        .. versionadded:: 1.3.0
+
+    offset : Timedelta or str, default is None
+        An offset timedelta added to the origin.
+
+    dropna : bool, default True
+        If True, and if group keys contain NA values, NA values together with
+        row/column will be dropped. If False, NA values will also be treated as
+        the key in groups.
+
+    Returns
+    -------
+    Grouper or pandas.api.typing.TimeGrouper
+        A TimeGrouper is returned if ``freq`` is not ``None``. Otherwise, a Grouper
+        is returned.
+
+    Examples
+    --------
+    ``df.groupby(pd.Grouper(key="Animal"))`` is equivalent to ``df.groupby('Animal')``
+
+    >>> df = pd.DataFrame(
+    ...     {
+    ...         "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"],
+    ...         "Speed": [100, 5, 200, 300, 15],
+    ...     }
+    ... )
+    >>> df
+       Animal  Speed
+    0  Falcon    100
+    1  Parrot      5
+    2  Falcon    200
+    3  Falcon    300
+    4  Parrot     15
+    >>> df.groupby(pd.Grouper(key="Animal")).mean()
+            Speed
+    Animal
+    Falcon  200.0
+    Parrot   10.0
+
+    Specify a resample operation on the column 'Publish date'
+
+    >>> df = pd.DataFrame(
+    ...    {
+    ...        "Publish date": [
+    ...             pd.Timestamp("2000-01-02"),
+    ...             pd.Timestamp("2000-01-02"),
+    ...             pd.Timestamp("2000-01-09"),
+    ...             pd.Timestamp("2000-01-16")
+    ...         ],
+    ...         "ID": [0, 1, 2, 3],
+    ...         "Price": [10, 20, 30, 40]
+    ...     }
+    ... )
+    >>> df
+      Publish date  ID  Price
+    0   2000-01-02   0     10
+    1   2000-01-02   1     20
+    2   2000-01-09   2     30
+    3   2000-01-16   3     40
+    >>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean()
+                   ID  Price
+    Publish date
+    2000-01-02    0.5   15.0
+    2000-01-09    2.0   30.0
+    2000-01-16    3.0   40.0
+
+    If you want to adjust the start of the bins based on a fixed timestamp:
+
+    >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
+    >>> rng = pd.date_range(start, end, freq='7min')
+    >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
+    >>> ts
+    2000-10-01 23:30:00     0
+    2000-10-01 23:37:00     3
+    2000-10-01 23:44:00     6
+    2000-10-01 23:51:00     9
+    2000-10-01 23:58:00    12
+    2000-10-02 00:05:00    15
+    2000-10-02 00:12:00    18
+    2000-10-02 00:19:00    21
+    2000-10-02 00:26:00    24
+    Freq: 7min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min')).sum()
+    2000-10-01 23:14:00     0
+    2000-10-01 23:31:00     9
+    2000-10-01 23:48:00    21
+    2000-10-02 00:05:00    54
+    2000-10-02 00:22:00    24
+    Freq: 17min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min', origin='epoch')).sum()
+    2000-10-01 23:18:00     0
+    2000-10-01 23:35:00    18
+    2000-10-01 23:52:00    27
+    2000-10-02 00:09:00    39
+    2000-10-02 00:26:00    24
+    Freq: 17min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum()
+    2000-10-01 23:24:00     3
+    2000-10-01 23:41:00    15
+    2000-10-01 23:58:00    45
+    2000-10-02 00:15:00    45
+    Freq: 17min, dtype: int64
+
+    If you want to adjust the start of the bins with an `offset` Timedelta, the two
+    following lines are equivalent:
+
+    >>> ts.groupby(pd.Grouper(freq='17min', origin='start')).sum()
+    2000-10-01 23:30:00     9
+    2000-10-01 23:47:00    21
+    2000-10-02 00:04:00    54
+    2000-10-02 00:21:00    24
+    Freq: 17min, dtype: int64
+
+    >>> ts.groupby(pd.Grouper(freq='17min', offset='23h30min')).sum()
+    2000-10-01 23:30:00     9
+    2000-10-01 23:47:00    21
+    2000-10-02 00:04:00    54
+    2000-10-02 00:21:00    24
+    Freq: 17min, dtype: int64
+
+    To replace the use of the deprecated `base` argument, you can now use `offset`,
+    in this example it is equivalent to have `base=2`:
+
+    >>> ts.groupby(pd.Grouper(freq='17min', offset='2min')).sum()
+    2000-10-01 23:16:00     0
+    2000-10-01 23:33:00     9
+    2000-10-01 23:50:00    36
+    2000-10-02 00:07:00    39
+    2000-10-02 00:24:00    24
+    Freq: 17min, dtype: int64
+    """
+
+    sort: bool
+    dropna: bool
+    _gpr_index: Index | None
+    _grouper: Index | None
+
+    _attributes: tuple[str, ...] = ("key", "level", "freq", "axis", "sort", "dropna")
+
+    def __new__(cls, *args, **kwargs):
+        if kwargs.get("freq") is not None:
+            from pandas.core.resample import TimeGrouper
+
+            cls = TimeGrouper
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        key=None,
+        level=None,
+        freq=None,
+        axis: Axis | lib.NoDefault = lib.no_default,
+        sort: bool = False,
+        dropna: bool = True,
+    ) -> None:
+        if type(self) is Grouper:
+            # i.e. not TimeGrouper
+            if axis is not lib.no_default:
+                warnings.warn(
+                    "Grouper axis keyword is deprecated and will be removed in a "
+                    "future version. To group on axis=1, use obj.T.groupby(...) "
+                    "instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                axis = 0
+        if axis is lib.no_default:
+            axis = 0
+
+        self.key = key
+        self.level = level
+        self.freq = freq
+        self.axis = axis
+        self.sort = sort
+        self.dropna = dropna
+
+        self._grouper_deprecated = None
+        self._indexer_deprecated: npt.NDArray[np.intp] | None = None
+        self._obj_deprecated = None
+        self._gpr_index = None
+        self.binner = None
+        self._grouper = None
+        self._indexer: npt.NDArray[np.intp] | None = None
+
+    def _get_grouper(
+        self, obj: NDFrameT, validate: bool = True
+    ) -> tuple[ops.BaseGrouper, NDFrameT]:
+        """
+        Parameters
+        ----------
+        obj : Series or DataFrame
+        validate : bool, default True
+            if True, validate the grouper
+
+        Returns
+        -------
+        a tuple of grouper, obj (possibly sorted)
+        """
+        obj, _, _ = self._set_grouper(obj)
+        grouper, _, obj = get_grouper(
+            obj,
+            [self.key],
+            axis=self.axis,
+            level=self.level,
+            sort=self.sort,
+            validate=validate,
+            dropna=self.dropna,
+        )
+        # Without setting this, subsequent lookups to .groups raise
+        # error: Incompatible types in assignment (expression has type "BaseGrouper",
+        # variable has type "None")
+        self._grouper_deprecated = grouper  # type: ignore[assignment]
+
+        return grouper, obj
+
+    def _set_grouper(
+        self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
+    ) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
+        """
+        given an object and the specifications, setup the internal grouper
+        for this particular specification
+
+        Parameters
+        ----------
+        obj : Series or DataFrame
+        sort : bool, default False
+            whether the resulting grouper should be sorted
+        gpr_index : Index or None, default None
+
+        Returns
+        -------
+        NDFrame
+        Index
+        np.ndarray[np.intp] | None
+        """
+        assert obj is not None
+
+        if self.key is not None and self.level is not None:
+            raise ValueError("The Grouper cannot specify both a key and a level!")
+
+        # Keep self._grouper value before overriding
+        if self._grouper is None:
+            # TODO: What are we assuming about subsequent calls?
+            self._grouper = gpr_index
+            self._indexer = self._indexer_deprecated
+
+        # the key must be a valid info item
+        if self.key is not None:
+            key = self.key
+            # The 'on' is already defined
+            if getattr(gpr_index, "name", None) == key and isinstance(obj, Series):
+                # Sometimes self._grouper will have been resorted while
+                # obj has not. In this case there is a mismatch when we
+                # call self._grouper.take(obj.index) so we need to undo the sorting
+                # before we call _grouper.take.
+                assert self._grouper is not None
+                if self._indexer is not None:
+                    reverse_indexer = self._indexer.argsort()
+                    unsorted_ax = self._grouper.take(reverse_indexer)
+                    ax = unsorted_ax.take(obj.index)
+                else:
+                    ax = self._grouper.take(obj.index)
+            else:
+                if key not in obj._info_axis:
+                    raise KeyError(f"The grouper name {key} is not found")
+                ax = Index(obj[key], name=key)
+
+        else:
+            ax = obj._get_axis(self.axis)
+            if self.level is not None:
+                level = self.level
+
+                # if a level is given it must be a mi level or
+                # equivalent to the axis name
+                if isinstance(ax, MultiIndex):
+                    level = ax._get_level_number(level)
+                    ax = Index(ax._get_level_values(level), name=ax.names[level])
+
+                else:
+                    if level not in (0, ax.name):
+                        raise ValueError(f"The level {level} is not valid")
+
+        # possibly sort
+        indexer: npt.NDArray[np.intp] | None = None
+        if (self.sort or sort) and not ax.is_monotonic_increasing:
+            # use stable sort to support first, last, nth
+            # TODO: why does putting na_position="first" fix datetimelike cases?
+            indexer = self._indexer_deprecated = ax.array.argsort(
+                kind="mergesort", na_position="first"
+            )
+            ax = ax.take(indexer)
+            obj = obj.take(indexer, axis=self.axis)
+
+        # error: Incompatible types in assignment (expression has type
+        # "NDFrameT", variable has type "None")
+        self._obj_deprecated = obj  # type: ignore[assignment]
+        self._gpr_index = ax
+        return obj, ax, indexer
+
+    @final
+    @property
+    def ax(self) -> Index:
+        warnings.warn(
+            f"{type(self).__name__}.ax is deprecated and will be removed in a "
+            "future version. Use Resampler.ax instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        index = self._gpr_index
+        if index is None:
+            raise ValueError("_set_grouper must be called before ax is accessed")
+        return index
+
+    @final
+    @property
+    def indexer(self):
+        warnings.warn(
+            f"{type(self).__name__}.indexer is deprecated and will be removed "
+            "in a future version. Use Resampler.indexer instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._indexer_deprecated
+
+    @final
+    @property
+    def obj(self):
+        # TODO(3.0): enforcing these deprecations on Grouper should close
+        #  GH#25564, GH#41930
+        warnings.warn(
+            f"{type(self).__name__}.obj is deprecated and will be removed "
+            "in a future version. Use GroupBy.indexer instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._obj_deprecated
+
+    @final
+    @property
+    def grouper(self):
+        warnings.warn(
+            f"{type(self).__name__}.grouper is deprecated and will be removed "
+            "in a future version. Use GroupBy.grouper instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._grouper_deprecated
+
+    @final
+    @property
+    def groups(self):
+        warnings.warn(
+            f"{type(self).__name__}.groups is deprecated and will be removed "
+            "in a future version. Use GroupBy.groups instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        # error: "None" has no attribute "groups"
+        return self._grouper_deprecated.groups  # type: ignore[attr-defined]
+
+    @final
+    def __repr__(self) -> str:
+        attrs_list = (
+            f"{attr_name}={repr(getattr(self, attr_name))}"
+            for attr_name in self._attributes
+            if getattr(self, attr_name) is not None
+        )
+        attrs = ", ".join(attrs_list)
+        cls_name = type(self).__name__
+        return f"{cls_name}({attrs})"
+
+
+@final
+class Grouping:
+    """
+    Holds the grouping information for a single key
+
+    Parameters
+    ----------
+    index : Index
+    grouper :
+    obj : DataFrame or Series
+    name : Label
+    level :
+    observed : bool, default False
+        If we are a Categorical, use the observed values
+    in_axis : if the Grouping is a column in self.obj and hence among
+        Groupby.exclusions list
+    dropna : bool, default True
+        Whether to drop NA groups.
+    uniques : Array-like, optional
+        When specified, will be used for unique values. Enables including empty groups
+        in the result for a BinGrouper. Must not contain duplicates.
+
+    Attributes
+    -------
+    indices : dict
+        Mapping of {group -> index_list}
+    codes : ndarray
+        Group codes
+    group_index : Index or None
+        unique groups
+    groups : dict
+        Mapping of {group -> label_list}
+    """
+
+    _codes: npt.NDArray[np.signedinteger] | None = None
+    _all_grouper: Categorical | None
+    _orig_cats: Index | None
+    _index: Index
+
+    def __init__(
+        self,
+        index: Index,
+        grouper=None,
+        obj: NDFrame | None = None,
+        level=None,
+        sort: bool = True,
+        observed: bool = False,
+        in_axis: bool = False,
+        dropna: bool = True,
+        uniques: ArrayLike | None = None,
+    ) -> None:
+        self.level = level
+        self._orig_grouper = grouper
+        grouping_vector = _convert_grouper(index, grouper)
+        self._all_grouper = None
+        self._orig_cats = None
+        self._index = index
+        self._sort = sort
+        self.obj = obj
+        self._observed = observed
+        self.in_axis = in_axis
+        self._dropna = dropna
+        self._uniques = uniques
+
+        # we have a single grouper which may be a myriad of things,
+        # some of which are dependent on the passing in level
+
+        ilevel = self._ilevel
+        if ilevel is not None:
+            # In extant tests, the new self.grouping_vector matches
+            #  `index.get_level_values(ilevel)` whenever
+            #  mapper is None and isinstance(index, MultiIndex)
+            if isinstance(index, MultiIndex):
+                index_level = index.get_level_values(ilevel)
+            else:
+                index_level = index
+
+            if grouping_vector is None:
+                grouping_vector = index_level
+            else:
+                mapper = grouping_vector
+                grouping_vector = index_level.map(mapper)
+
+        # a passed Grouper like, directly get the grouper in the same way
+        # as single grouper groupby, use the group_info to get codes
+        elif isinstance(grouping_vector, Grouper):
+            # get the new grouper; we already have disambiguated
+            # what key/level refer to exactly, don't need to
+            # check again as we have by this point converted these
+            # to an actual value (rather than a pd.Grouper)
+            assert self.obj is not None  # for mypy
+            newgrouper, newobj = grouping_vector._get_grouper(self.obj, validate=False)
+            self.obj = newobj
+
+            if isinstance(newgrouper, ops.BinGrouper):
+                # TODO: can we unwrap this and get a tighter typing
+                #  for self.grouping_vector?
+                grouping_vector = newgrouper
+            else:
+                # ops.BaseGrouper
+                # TODO: 2023-02-03 no test cases with len(newgrouper.groupings) > 1.
+                #  If that were to occur, would we be throwing out information?
+                # error: Cannot determine type of "grouping_vector"  [has-type]
+                ng = newgrouper.groupings[0].grouping_vector  # type: ignore[has-type]
+                # use Index instead of ndarray so we can recover the name
+                grouping_vector = Index(ng, name=newgrouper.result_index.name)
+
+        elif not isinstance(
+            grouping_vector, (Series, Index, ExtensionArray, np.ndarray)
+        ):
+            # no level passed
+            if getattr(grouping_vector, "ndim", 1) != 1:
+                t = str(type(grouping_vector))
+                raise ValueError(f"Grouper for '{t}' not 1-dimensional")
+
+            grouping_vector = index.map(grouping_vector)
+
+            if not (
+                hasattr(grouping_vector, "__len__")
+                and len(grouping_vector) == len(index)
+            ):
+                grper = pprint_thing(grouping_vector)
+                errmsg = (
+                    "Grouper result violates len(labels) == "
+                    f"len(data)\nresult: {grper}"
+                )
+                raise AssertionError(errmsg)
+
+        if isinstance(grouping_vector, np.ndarray):
+            if grouping_vector.dtype.kind in "mM":
+                # if we have a date/time-like grouper, make sure that we have
+                # Timestamps like
+                # TODO 2022-10-08 we only have one test that gets here and
+                #  values are already in nanoseconds in that case.
+                grouping_vector = Series(grouping_vector).to_numpy()
+        elif isinstance(getattr(grouping_vector, "dtype", None), CategoricalDtype):
+            # a passed Categorical
+            self._orig_cats = grouping_vector.categories
+            grouping_vector, self._all_grouper = recode_for_groupby(
+                grouping_vector, sort, observed
+            )
+
+        self.grouping_vector = grouping_vector
+
+    def __repr__(self) -> str:
+        return f"Grouping({self.name})"
+
+    def __iter__(self) -> Iterator:
+        return iter(self.indices)
+
+    @cache_readonly
+    def _passed_categorical(self) -> bool:
+        dtype = getattr(self.grouping_vector, "dtype", None)
+        return isinstance(dtype, CategoricalDtype)
+
+    @cache_readonly
+    def name(self) -> Hashable:
+        ilevel = self._ilevel
+        if ilevel is not None:
+            return self._index.names[ilevel]
+
+        if isinstance(self._orig_grouper, (Index, Series)):
+            return self._orig_grouper.name
+
+        elif isinstance(self.grouping_vector, ops.BaseGrouper):
+            return self.grouping_vector.result_index.name
+
+        elif isinstance(self.grouping_vector, Index):
+            return self.grouping_vector.name
+
+        # otherwise we have ndarray or ExtensionArray -> no name
+        return None
+
+    @cache_readonly
+    def _ilevel(self) -> int | None:
+        """
+        If necessary, converted index level name to index level position.
+        """
+        level = self.level
+        if level is None:
+            return None
+        if not isinstance(level, int):
+            index = self._index
+            if level not in index.names:
+                raise AssertionError(f"Level {level} not in index")
+            return index.names.index(level)
+        return level
+
+    @property
+    def ngroups(self) -> int:
+        return len(self._group_index)
+
+    @cache_readonly
+    def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        # we have a list of groupers
+        if isinstance(self.grouping_vector, ops.BaseGrouper):
+            return self.grouping_vector.indices
+
+        values = Categorical(self.grouping_vector)
+        return values._reverse_indexer()
+
+    @property
+    def codes(self) -> npt.NDArray[np.signedinteger]:
+        return self._codes_and_uniques[0]
+
+    @cache_readonly
+    def _group_arraylike(self) -> ArrayLike:
+        """
+        Analogous to result_index, but holding an ArrayLike to ensure
+        we can retain ExtensionDtypes.
+        """
+        if self._all_grouper is not None:
+            # retain dtype for categories, including unobserved ones
+            return self._result_index._values
+
+        elif self._passed_categorical:
+            return self._group_index._values
+
+        return self._codes_and_uniques[1]
+
+    @property
+    def group_arraylike(self) -> ArrayLike:
+        """
+        Analogous to result_index, but holding an ArrayLike to ensure
+        we can retain ExtensionDtypes.
+        """
+        warnings.warn(
+            "group_arraylike is deprecated and will be removed in a future "
+            "version of pandas",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._group_arraylike
+
+    @cache_readonly
+    def _result_index(self) -> Index:
+        # result_index retains dtype for categories, including unobserved ones,
+        #  which group_index does not
+        if self._all_grouper is not None:
+            group_idx = self._group_index
+            assert isinstance(group_idx, CategoricalIndex)
+            cats = self._orig_cats
+            # set_categories is dynamically added
+            return group_idx.set_categories(cats)  # type: ignore[attr-defined]
+        return self._group_index
+
+    @property
+    def result_index(self) -> Index:
+        warnings.warn(
+            "result_index is deprecated and will be removed in a future "
+            "version of pandas",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._result_index
+
+    @cache_readonly
+    def _group_index(self) -> Index:
+        codes, uniques = self._codes_and_uniques
+        if not self._dropna and self._passed_categorical:
+            assert isinstance(uniques, Categorical)
+            if self._sort and (codes == len(uniques)).any():
+                # Add NA value on the end when sorting
+                uniques = Categorical.from_codes(
+                    np.append(uniques.codes, [-1]), uniques.categories, validate=False
+                )
+            elif len(codes) > 0:
+                # Need to determine proper placement of NA value when not sorting
+                cat = self.grouping_vector
+                na_idx = (cat.codes < 0).argmax()
+                if cat.codes[na_idx] < 0:
+                    # count number of unique codes that comes before the nan value
+                    na_unique_idx = algorithms.nunique_ints(cat.codes[:na_idx])
+                    new_codes = np.insert(uniques.codes, na_unique_idx, -1)
+                    uniques = Categorical.from_codes(
+                        new_codes, uniques.categories, validate=False
+                    )
+        return Index._with_infer(uniques, name=self.name)
+
+    @property
+    def group_index(self) -> Index:
+        warnings.warn(
+            "group_index is deprecated and will be removed in a future "
+            "version of pandas",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._group_index
+
+    @cache_readonly
+    def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
+        uniques: ArrayLike
+        if self._passed_categorical:
+            # we make a CategoricalIndex out of the cat grouper
+            # preserving the categories / ordered attributes;
+            # doesn't (yet - GH#46909) handle dropna=False
+            cat = self.grouping_vector
+            categories = cat.categories
+
+            if self._observed:
+                ucodes = algorithms.unique1d(cat.codes)
+                ucodes = ucodes[ucodes != -1]
+                if self._sort:
+                    ucodes = np.sort(ucodes)
+            else:
+                ucodes = np.arange(len(categories))
+
+            uniques = Categorical.from_codes(
+                codes=ucodes, categories=categories, ordered=cat.ordered, validate=False
+            )
+
+            codes = cat.codes
+            if not self._dropna:
+                na_mask = codes < 0
+                if np.any(na_mask):
+                    if self._sort:
+                        # Replace NA codes with `largest code + 1`
+                        na_code = len(categories)
+                        codes = np.where(na_mask, na_code, codes)
+                    else:
+                        # Insert NA code into the codes based on first appearance
+                        # A negative code must exist, no need to check codes[na_idx] < 0
+                        na_idx = na_mask.argmax()
+                        # count number of unique codes that comes before the nan value
+                        na_code = algorithms.nunique_ints(codes[:na_idx])
+                        codes = np.where(codes >= na_code, codes + 1, codes)
+                        codes = np.where(na_mask, na_code, codes)
+
+            if not self._observed:
+                uniques = uniques.reorder_categories(self._orig_cats)
+
+            return codes, uniques
+
+        elif isinstance(self.grouping_vector, ops.BaseGrouper):
+            # we have a list of groupers
+            codes = self.grouping_vector.codes_info
+            uniques = self.grouping_vector.result_index._values
+        elif self._uniques is not None:
+            # GH#50486 Code grouping_vector using _uniques; allows
+            # including uniques that are not present in grouping_vector.
+            cat = Categorical(self.grouping_vector, categories=self._uniques)
+            codes = cat.codes
+            uniques = self._uniques
+        else:
+            # GH35667, replace dropna=False with use_na_sentinel=False
+            # error: Incompatible types in assignment (expression has type "Union[
+            # ndarray[Any, Any], Index]", variable has type "Categorical")
+            codes, uniques = algorithms.factorize(  # type: ignore[assignment]
+                self.grouping_vector, sort=self._sort, use_na_sentinel=self._dropna
+            )
+        return codes, uniques
+
+    @cache_readonly
+    def groups(self) -> dict[Hashable, np.ndarray]:
+        cats = Categorical.from_codes(self.codes, self._group_index, validate=False)
+        return self._index.groupby(cats)
+
+
+def get_grouper(
+    obj: NDFrameT,
+    key=None,
+    axis: Axis = 0,
+    level=None,
+    sort: bool = True,
+    observed: bool = False,
+    validate: bool = True,
+    dropna: bool = True,
+) -> tuple[ops.BaseGrouper, frozenset[Hashable], NDFrameT]:
+    """
+    Create and return a BaseGrouper, which is an internal
+    mapping of how to create the grouper indexers.
+    This may be composed of multiple Grouping objects, indicating
+    multiple groupers
+
+    Groupers are ultimately index mappings. They can originate as:
+    index mappings, keys to columns, functions, or Groupers
+
+    Groupers enable local references to axis,level,sort, while
+    the passed in axis, level, and sort are 'global'.
+
+    This routine tries to figure out what the passing in references
+    are and then creates a Grouping for each one, combined into
+    a BaseGrouper.
+
+    If observed & we have a categorical grouper, only show the observed
+    values.
+
+    If validate, then check for key/level overlaps.
+
+    """
+    group_axis = obj._get_axis(axis)
+
+    # validate that the passed single level is compatible with the passed
+    # axis of the object
+    if level is not None:
+        # TODO: These if-block and else-block are almost same.
+        # MultiIndex instance check is removable, but it seems that there are
+        # some processes only for non-MultiIndex in else-block,
+        # eg. `obj.index.name != level`. We have to consider carefully whether
+        # these are applicable for MultiIndex. Even if these are applicable,
+        # we need to check if it makes no side effect to subsequent processes
+        # on the outside of this condition.
+        # (GH 17621)
+        if isinstance(group_axis, MultiIndex):
+            if is_list_like(level) and len(level) == 1:
+                level = level[0]
+
+            if key is None and is_scalar(level):
+                # Get the level values from group_axis
+                key = group_axis.get_level_values(level)
+                level = None
+
+        else:
+            # allow level to be a length-one list-like object
+            # (e.g., level=[0])
+            # GH 13901
+            if is_list_like(level):
+                nlevels = len(level)
+                if nlevels == 1:
+                    level = level[0]
+                elif nlevels == 0:
+                    raise ValueError("No group keys passed!")
+                else:
+                    raise ValueError("multiple levels only valid with MultiIndex")
+
+            if isinstance(level, str):
+                if obj._get_axis(axis).name != level:
+                    raise ValueError(
+                        f"level name {level} is not the name "
+                        f"of the {obj._get_axis_name(axis)}"
+                    )
+            elif level > 0 or level < -1:
+                raise ValueError("level > 0 or level < -1 only valid with MultiIndex")
+
+            # NOTE: `group_axis` and `group_axis.get_level_values(level)`
+            # are same in this section.
+            level = None
+            key = group_axis
+
+    # a passed-in Grouper, directly convert
+    if isinstance(key, Grouper):
+        grouper, obj = key._get_grouper(obj, validate=False)
+        if key.key is None:
+            return grouper, frozenset(), obj
+        else:
+            return grouper, frozenset({key.key}), obj
+
+    # already have a BaseGrouper, just return it
+    elif isinstance(key, ops.BaseGrouper):
+        return key, frozenset(), obj
+
+    if not isinstance(key, list):
+        keys = [key]
+        match_axis_length = False
+    else:
+        keys = key
+        match_axis_length = len(keys) == len(group_axis)
+
+    # what are we after, exactly?
+    any_callable = any(callable(g) or isinstance(g, dict) for g in keys)
+    any_groupers = any(isinstance(g, (Grouper, Grouping)) for g in keys)
+    any_arraylike = any(
+        isinstance(g, (list, tuple, Series, Index, np.ndarray)) for g in keys
+    )
+
+    # is this an index replacement?
+    if (
+        not any_callable
+        and not any_arraylike
+        and not any_groupers
+        and match_axis_length
+        and level is None
+    ):
+        if isinstance(obj, DataFrame):
+            all_in_columns_index = all(
+                g in obj.columns or g in obj.index.names for g in keys
+            )
+        else:
+            assert isinstance(obj, Series)
+            all_in_columns_index = all(g in obj.index.names for g in keys)
+
+        if not all_in_columns_index:
+            keys = [com.asarray_tuplesafe(keys)]
+
+    if isinstance(level, (tuple, list)):
+        if key is None:
+            keys = [None] * len(level)
+        levels = level
+    else:
+        levels = [level] * len(keys)
+
+    groupings: list[Grouping] = []
+    exclusions: set[Hashable] = set()
+
+    # if the actual grouper should be obj[key]
+    def is_in_axis(key) -> bool:
+        if not _is_label_like(key):
+            if obj.ndim == 1:
+                return False
+
+            # items -> .columns for DataFrame, .index for Series
+            items = obj.axes[-1]
+            try:
+                items.get_loc(key)
+            except (KeyError, TypeError, InvalidIndexError):
+                # TypeError shows up here if we pass e.g. an Index
+                return False
+
+        return True
+
+    # if the grouper is obj[name]
+    def is_in_obj(gpr) -> bool:
+        if not hasattr(gpr, "name"):
+            return False
+        if using_copy_on_write() or warn_copy_on_write():
+            # For the CoW case, we check the references to determine if the
+            # series is part of the object
+            try:
+                obj_gpr_column = obj[gpr.name]
+            except (KeyError, IndexError, InvalidIndexError, OutOfBoundsDatetime):
+                return False
+            if isinstance(gpr, Series) and isinstance(obj_gpr_column, Series):
+                return gpr._mgr.references_same_values(  # type: ignore[union-attr]
+                    obj_gpr_column._mgr, 0  # type: ignore[arg-type]
+                )
+            return False
+        try:
+            return gpr is obj[gpr.name]
+        except (KeyError, IndexError, InvalidIndexError, OutOfBoundsDatetime):
+            # IndexError reached in e.g. test_skip_group_keys when we pass
+            #  lambda here
+            # InvalidIndexError raised on key-types inappropriate for index,
+            #  e.g. DatetimeIndex.get_loc(tuple())
+            # OutOfBoundsDatetime raised when obj is a Series with DatetimeIndex
+            # and gpr.name is month str
+            return False
+
+    for gpr, level in zip(keys, levels):
+        if isinstance(obj, DataFrame) and is_in_obj(gpr):  # df.groupby(df['name'])
+            in_axis = True
+            exclusions.add(gpr.name)
+
+        elif is_in_axis(gpr):  # df.groupby('name')
+            if obj.ndim != 1 and gpr in obj:
+                if validate:
+                    obj._check_label_or_level_ambiguity(gpr, axis=axis)
+                in_axis, name, gpr = True, gpr, obj[gpr]
+                if gpr.ndim != 1:
+                    # non-unique columns; raise here to get the name in the
+                    # exception message
+                    raise ValueError(f"Grouper for '{name}' not 1-dimensional")
+                exclusions.add(name)
+            elif obj._is_level_reference(gpr, axis=axis):
+                in_axis, level, gpr = False, gpr, None
+            else:
+                raise KeyError(gpr)
+        elif isinstance(gpr, Grouper) and gpr.key is not None:
+            # Add key to exclusions
+            exclusions.add(gpr.key)
+            in_axis = True
+        else:
+            in_axis = False
+
+        # create the Grouping
+        # allow us to passing the actual Grouping as the gpr
+        ping = (
+            Grouping(
+                group_axis,
+                gpr,
+                obj=obj,
+                level=level,
+                sort=sort,
+                observed=observed,
+                in_axis=in_axis,
+                dropna=dropna,
+            )
+            if not isinstance(gpr, Grouping)
+            else gpr
+        )
+
+        groupings.append(ping)
+
+    if len(groupings) == 0 and len(obj):
+        raise ValueError("No group keys passed!")
+    if len(groupings) == 0:
+        groupings.append(Grouping(Index([], dtype="int"), np.array([], dtype=np.intp)))
+
+    # create the internals grouper
+    grouper = ops.BaseGrouper(group_axis, groupings, sort=sort, dropna=dropna)
+    return grouper, frozenset(exclusions), obj
+
+
+def _is_label_like(val) -> bool:
+    return isinstance(val, (str, tuple)) or (val is not None and is_scalar(val))
+
+
+def _convert_grouper(axis: Index, grouper):
+    if isinstance(grouper, dict):
+        return grouper.get
+    elif isinstance(grouper, Series):
+        if grouper.index.equals(axis):
+            return grouper._values
+        else:
+            return grouper.reindex(axis)._values
+    elif isinstance(grouper, MultiIndex):
+        return grouper._values
+    elif isinstance(grouper, (list, tuple, Index, Categorical, np.ndarray)):
+        if len(grouper) != len(axis):
+            raise ValueError("Grouper and axis must be same length")
+
+        if isinstance(grouper, (list, tuple)):
+            grouper = com.asarray_tuplesafe(grouper)
+        return grouper
+    else:
+        return grouper
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/indexing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3c5ab8edc94e4f91175891282252d0e8cdfd3ec
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/indexing.py
@@ -0,0 +1,304 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+
+import numpy as np
+
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import PositionalIndexer
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.groupby import groupby
+
+
+class GroupByIndexingMixin:
+    """
+    Mixin for adding ._positional_selector to GroupBy.
+    """
+
+    @cache_readonly
+    def _positional_selector(self) -> GroupByPositionalSelector:
+        """
+        Return positional selection for each group.
+
+        ``groupby._positional_selector[i:j]`` is similar to
+        ``groupby.apply(lambda x: x.iloc[i:j])``
+        but much faster and preserves the original index and order.
+
+        ``_positional_selector[]`` is compatible with and extends :meth:`~GroupBy.head`
+        and :meth:`~GroupBy.tail`. For example:
+
+        - ``head(5)``
+        - ``_positional_selector[5:-5]``
+        - ``tail(5)``
+
+        together return all the rows.
+
+        Allowed inputs for the index are:
+
+        - An integer valued iterable, e.g. ``range(2, 4)``.
+        - A comma separated list of integers and slices, e.g. ``5``, ``2, 4``, ``2:4``.
+
+        The output format is the same as :meth:`~GroupBy.head` and
+        :meth:`~GroupBy.tail`, namely
+        a subset of the ``DataFrame`` or ``Series`` with the index and order preserved.
+
+        Returns
+        -------
+        Series
+            The filtered subset of the original Series.
+        DataFrame
+            The filtered subset of the original DataFrame.
+
+        See Also
+        --------
+        DataFrame.iloc : Purely integer-location based indexing for selection by
+            position.
+        GroupBy.head : Return first n rows of each group.
+        GroupBy.tail : Return last n rows of each group.
+        GroupBy.nth : Take the nth row from each group if n is an int, or a
+            subset of rows, if n is a list of ints.
+
+        Notes
+        -----
+        - The slice step cannot be negative.
+        - If the index specification results in overlaps, the item is not duplicated.
+        - If the index specification changes the order of items, then
+          they are returned in their original order.
+          By contrast, ``DataFrame.iloc`` can change the row order.
+        - ``groupby()`` parameters such as as_index and dropna are ignored.
+
+        The differences between ``_positional_selector[]`` and :meth:`~GroupBy.nth`
+        with ``as_index=False`` are:
+
+        - Input to ``_positional_selector`` can include
+          one or more slices whereas ``nth``
+          just handles an integer or a list of integers.
+        - ``_positional_selector`` can  accept a slice relative to the
+          last row of each group.
+        - ``_positional_selector`` does not have an equivalent to the
+          ``nth()`` ``dropna`` parameter.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([["a", 1], ["a", 2], ["a", 3], ["b", 4], ["b", 5]],
+        ...                   columns=["A", "B"])
+        >>> df.groupby("A")._positional_selector[1:2]
+           A  B
+        1  a  2
+        4  b  5
+
+        >>> df.groupby("A")._positional_selector[1, -1]
+           A  B
+        1  a  2
+        2  a  3
+        4  b  5
+        """
+        if TYPE_CHECKING:
+            # pylint: disable-next=used-before-assignment
+            groupby_self = cast(groupby.GroupBy, self)
+        else:
+            groupby_self = self
+
+        return GroupByPositionalSelector(groupby_self)
+
+    def _make_mask_from_positional_indexer(
+        self,
+        arg: PositionalIndexer | tuple,
+    ) -> np.ndarray:
+        if is_list_like(arg):
+            if all(is_integer(i) for i in cast(Iterable, arg)):
+                mask = self._make_mask_from_list(cast(Iterable[int], arg))
+            else:
+                mask = self._make_mask_from_tuple(cast(tuple, arg))
+
+        elif isinstance(arg, slice):
+            mask = self._make_mask_from_slice(arg)
+        elif is_integer(arg):
+            mask = self._make_mask_from_int(cast(int, arg))
+        else:
+            raise TypeError(
+                f"Invalid index {type(arg)}. "
+                "Must be integer, list-like, slice or a tuple of "
+                "integers and slices"
+            )
+
+        if isinstance(mask, bool):
+            if mask:
+                mask = self._ascending_count >= 0
+            else:
+                mask = self._ascending_count < 0
+
+        return cast(np.ndarray, mask)
+
+    def _make_mask_from_int(self, arg: int) -> np.ndarray:
+        if arg >= 0:
+            return self._ascending_count == arg
+        else:
+            return self._descending_count == (-arg - 1)
+
+    def _make_mask_from_list(self, args: Iterable[int]) -> bool | np.ndarray:
+        positive = [arg for arg in args if arg >= 0]
+        negative = [-arg - 1 for arg in args if arg < 0]
+
+        mask: bool | np.ndarray = False
+
+        if positive:
+            mask |= np.isin(self._ascending_count, positive)
+
+        if negative:
+            mask |= np.isin(self._descending_count, negative)
+
+        return mask
+
+    def _make_mask_from_tuple(self, args: tuple) -> bool | np.ndarray:
+        mask: bool | np.ndarray = False
+
+        for arg in args:
+            if is_integer(arg):
+                mask |= self._make_mask_from_int(cast(int, arg))
+            elif isinstance(arg, slice):
+                mask |= self._make_mask_from_slice(arg)
+            else:
+                raise ValueError(
+                    f"Invalid argument {type(arg)}. Should be int or slice."
+                )
+
+        return mask
+
+    def _make_mask_from_slice(self, arg: slice) -> bool | np.ndarray:
+        start = arg.start
+        stop = arg.stop
+        step = arg.step
+
+        if step is not None and step < 0:
+            raise ValueError(f"Invalid step {step}. Must be non-negative")
+
+        mask: bool | np.ndarray = True
+
+        if step is None:
+            step = 1
+
+        if start is None:
+            if step > 1:
+                mask &= self._ascending_count % step == 0
+
+        elif start >= 0:
+            mask &= self._ascending_count >= start
+
+            if step > 1:
+                mask &= (self._ascending_count - start) % step == 0
+
+        else:
+            mask &= self._descending_count < -start
+
+            offset_array = self._descending_count + start + 1
+            limit_array = (
+                self._ascending_count + self._descending_count + (start + 1)
+            ) < 0
+            offset_array = np.where(limit_array, self._ascending_count, offset_array)
+
+            mask &= offset_array % step == 0
+
+        if stop is not None:
+            if stop >= 0:
+                mask &= self._ascending_count < stop
+            else:
+                mask &= self._descending_count >= -stop
+
+        return mask
+
+    @cache_readonly
+    def _ascending_count(self) -> np.ndarray:
+        if TYPE_CHECKING:
+            groupby_self = cast(groupby.GroupBy, self)
+        else:
+            groupby_self = self
+
+        return groupby_self._cumcount_array()
+
+    @cache_readonly
+    def _descending_count(self) -> np.ndarray:
+        if TYPE_CHECKING:
+            groupby_self = cast(groupby.GroupBy, self)
+        else:
+            groupby_self = self
+
+        return groupby_self._cumcount_array(ascending=False)
+
+
+@doc(GroupByIndexingMixin._positional_selector)
+class GroupByPositionalSelector:
+    def __init__(self, groupby_object: groupby.GroupBy) -> None:
+        self.groupby_object = groupby_object
+
+    def __getitem__(self, arg: PositionalIndexer | tuple) -> DataFrame | Series:
+        """
+        Select by positional index per group.
+
+        Implements GroupBy._positional_selector
+
+        Parameters
+        ----------
+        arg : PositionalIndexer | tuple
+            Allowed values are:
+            - int
+            - int valued iterable such as list or range
+            - slice with step either None or positive
+            - tuple of integers and slices
+
+        Returns
+        -------
+        Series
+            The filtered subset of the original groupby Series.
+        DataFrame
+            The filtered subset of the original groupby DataFrame.
+
+        See Also
+        --------
+        DataFrame.iloc : Integer-location based indexing for selection by position.
+        GroupBy.head : Return first n rows of each group.
+        GroupBy.tail : Return last n rows of each group.
+        GroupBy._positional_selector : Return positional selection for each group.
+        GroupBy.nth : Take the nth row from each group if n is an int, or a
+            subset of rows, if n is a list of ints.
+        """
+        mask = self.groupby_object._make_mask_from_positional_indexer(arg)
+        return self.groupby_object._mask_selected_obj(mask)
+
+
+class GroupByNthSelector:
+    """
+    Dynamically substituted for GroupBy.nth to enable both call and index
+    """
+
+    def __init__(self, groupby_object: groupby.GroupBy) -> None:
+        self.groupby_object = groupby_object
+
+    def __call__(
+        self,
+        n: PositionalIndexer | tuple,
+        dropna: Literal["any", "all", None] = None,
+    ) -> DataFrame | Series:
+        return self.groupby_object._nth(n, dropna)
+
+    def __getitem__(self, n: PositionalIndexer | tuple) -> DataFrame | Series:
+        return self.groupby_object._nth(n)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/numba_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/numba_.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b7a58e87603e578216c4c80e8c88e06828d5dfa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/numba_.py
@@ -0,0 +1,181 @@
+"""Common utilities for Numba operations with groupby ops"""
+from __future__ import annotations
+
+import functools
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.util.numba_ import (
+    NumbaUtilError,
+    jit_user_function,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import Scalar
+
+
+def validate_udf(func: Callable) -> None:
+    """
+    Validate user defined function for ops when using Numba with groupby ops.
+
+    The first signature arguments should include:
+
+    def f(values, index, ...):
+        ...
+
+    Parameters
+    ----------
+    func : function, default False
+        user defined function
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    NumbaUtilError
+    """
+    if not callable(func):
+        raise NotImplementedError(
+            "Numba engine can only be used with a single function."
+        )
+    udf_signature = list(inspect.signature(func).parameters.keys())
+    expected_args = ["values", "index"]
+    min_number_args = len(expected_args)
+    if (
+        len(udf_signature) < min_number_args
+        or udf_signature[:min_number_args] != expected_args
+    ):
+        raise NumbaUtilError(
+            f"The first {min_number_args} arguments to {func.__name__} must be "
+            f"{expected_args}"
+        )
+
+
+@functools.cache
+def generate_numba_agg_func(
+    func: Callable[..., Scalar],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, Any], np.ndarray]:
+    """
+    Generate a numba jitted agg function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a groupby agg function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the groupby evaluation loop.
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each group and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def group_agg(
+        values: np.ndarray,
+        index: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        num_columns: int,
+        *args: Any,
+    ) -> np.ndarray:
+        assert len(begin) == len(end)
+        num_groups = len(begin)
+
+        result = np.empty((num_groups, num_columns))
+        for i in numba.prange(num_groups):
+            group_index = index[begin[i] : end[i]]
+            for j in numba.prange(num_columns):
+                group = values[begin[i] : end[i], j]
+                result[i, j] = numba_func(group, group_index, *args)
+        return result
+
+    return group_agg
+
+
+@functools.cache
+def generate_numba_transform_func(
+    func: Callable[..., np.ndarray],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, Any], np.ndarray]:
+    """
+    Generate a numba jitted transform function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a groupby transform function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the groupby evaluation loop.
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each window and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def group_transform(
+        values: np.ndarray,
+        index: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        num_columns: int,
+        *args: Any,
+    ) -> np.ndarray:
+        assert len(begin) == len(end)
+        num_groups = len(begin)
+
+        result = np.empty((len(values), num_columns))
+        for i in numba.prange(num_groups):
+            group_index = index[begin[i] : end[i]]
+            for j in numba.prange(num_columns):
+                group = values[begin[i] : end[i], j]
+                result[begin[i] : end[i], j] = numba_func(group, group_index, *args)
+        return result
+
+    return group_transform
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/ops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..e44c28f656eccf5744e0db8aa70dfbc9ac076f15
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/groupby/ops.py
@@ -0,0 +1,1217 @@
+"""
+Provide classes to perform the groupby aggregate operations.
+
+These are not exposed to the user and provide implementations of the grouping
+operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
+are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
+"""
+from __future__ import annotations
+
+import collections
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    final,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    lib,
+)
+import pandas._libs.groupby as libgroupby
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    NDFrameT,
+    Shape,
+    npt,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.cast import (
+    maybe_cast_pointwise_result,
+    maybe_downcast_to_dtype,
+)
+from pandas.core.dtypes.common import (
+    ensure_float64,
+    ensure_int64,
+    ensure_platform_int,
+    ensure_uint64,
+    is_1d_only_ea_dtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    maybe_fill,
+)
+
+from pandas.core.frame import DataFrame
+from pandas.core.groupby import grouper
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    ensure_index,
+)
+from pandas.core.series import Series
+from pandas.core.sorting import (
+    compress_group_index,
+    decons_obs_group_ids,
+    get_flattened_list,
+    get_group_index,
+    get_group_index_sorter,
+    get_indexer_dict,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sequence,
+    )
+
+    from pandas.core.generic import NDFrame
+
+
+def check_result_array(obj, dtype) -> None:
+    # Our operation is supposed to be an aggregation/reduction. If
+    #  it returns an ndarray, this likely means an invalid operation has
+    #  been passed. See test_apply_without_aggregation, test_agg_must_agg
+    if isinstance(obj, np.ndarray):
+        if dtype != object:
+            # If it is object dtype, the function can be a reduction/aggregation
+            #  and still return an ndarray e.g. test_agg_over_numpy_arrays
+            raise ValueError("Must produce aggregated value")
+
+
+def extract_result(res):
+    """
+    Extract the result object, it might be a 0-dim ndarray
+    or a len-1 0-dim, or a scalar
+    """
+    if hasattr(res, "_values"):
+        # Preserve EA
+        res = res._values
+        if res.ndim == 1 and len(res) == 1:
+            # see test_agg_lambda_with_timezone, test_resampler_grouper.py::test_apply
+            res = res[0]
+    return res
+
+
+class WrappedCythonOp:
+    """
+    Dispatch logic for functions defined in _libs.groupby
+
+    Parameters
+    ----------
+    kind: str
+        Whether the operation is an aggregate or transform.
+    how: str
+        Operation name, e.g. "mean".
+    has_dropped_na: bool
+        True precisely when dropna=True and the grouper contains a null value.
+    """
+
+    # Functions for which we do _not_ attempt to cast the cython result
+    #  back to the original dtype.
+    cast_blocklist = frozenset(
+        ["any", "all", "rank", "count", "size", "idxmin", "idxmax"]
+    )
+
+    def __init__(self, kind: str, how: str, has_dropped_na: bool) -> None:
+        self.kind = kind
+        self.how = how
+        self.has_dropped_na = has_dropped_na
+
+    _CYTHON_FUNCTIONS: dict[str, dict] = {
+        "aggregate": {
+            "any": functools.partial(libgroupby.group_any_all, val_test="any"),
+            "all": functools.partial(libgroupby.group_any_all, val_test="all"),
+            "sum": "group_sum",
+            "prod": "group_prod",
+            "idxmin": functools.partial(libgroupby.group_idxmin_idxmax, name="idxmin"),
+            "idxmax": functools.partial(libgroupby.group_idxmin_idxmax, name="idxmax"),
+            "min": "group_min",
+            "max": "group_max",
+            "mean": "group_mean",
+            "median": "group_median_float64",
+            "var": "group_var",
+            "std": functools.partial(libgroupby.group_var, name="std"),
+            "sem": functools.partial(libgroupby.group_var, name="sem"),
+            "skew": "group_skew",
+            "first": "group_nth",
+            "last": "group_last",
+            "ohlc": "group_ohlc",
+        },
+        "transform": {
+            "cumprod": "group_cumprod",
+            "cumsum": "group_cumsum",
+            "cummin": "group_cummin",
+            "cummax": "group_cummax",
+            "rank": "group_rank",
+        },
+    }
+
+    _cython_arity = {"ohlc": 4}  # OHLC
+
+    @classmethod
+    def get_kind_from_how(cls, how: str) -> str:
+        if how in cls._CYTHON_FUNCTIONS["aggregate"]:
+            return "aggregate"
+        return "transform"
+
+    # Note: we make this a classmethod and pass kind+how so that caching
+    #  works at the class level and not the instance level
+    @classmethod
+    @functools.cache
+    def _get_cython_function(
+        cls, kind: str, how: str, dtype: np.dtype, is_numeric: bool
+    ):
+        dtype_str = dtype.name
+        ftype = cls._CYTHON_FUNCTIONS[kind][how]
+
+        # see if there is a fused-type version of function
+        # only valid for numeric
+        if callable(ftype):
+            f = ftype
+        else:
+            f = getattr(libgroupby, ftype)
+        if is_numeric:
+            return f
+        elif dtype == np.dtype(object):
+            if how in ["median", "cumprod"]:
+                # no fused types -> no __signatures__
+                raise NotImplementedError(
+                    f"function is not implemented for this dtype: "
+                    f"[how->{how},dtype->{dtype_str}]"
+                )
+            elif how in ["std", "sem", "idxmin", "idxmax"]:
+                # We have a partial object that does not have __signatures__
+                return f
+            elif how == "skew":
+                # _get_cython_vals will convert to float64
+                pass
+            elif "object" not in f.__signatures__:
+                # raise NotImplementedError here rather than TypeError later
+                raise NotImplementedError(
+                    f"function is not implemented for this dtype: "
+                    f"[how->{how},dtype->{dtype_str}]"
+                )
+            return f
+        else:
+            raise NotImplementedError(
+                "This should not be reached. Please report a bug at "
+                "github.com/pandas-dev/pandas/",
+                dtype,
+            )
+
+    def _get_cython_vals(self, values: np.ndarray) -> np.ndarray:
+        """
+        Cast numeric dtypes to float64 for functions that only support that.
+
+        Parameters
+        ----------
+        values : np.ndarray
+
+        Returns
+        -------
+        values : np.ndarray
+        """
+        how = self.how
+
+        if how in ["median", "std", "sem", "skew"]:
+            # median only has a float64 implementation
+            # We should only get here with is_numeric, as non-numeric cases
+            #  should raise in _get_cython_function
+            values = ensure_float64(values)
+
+        elif values.dtype.kind in "iu":
+            if how in ["var", "mean"] or (
+                self.kind == "transform" and self.has_dropped_na
+            ):
+                # has_dropped_na check need for test_null_group_str_transformer
+                # result may still include NaN, so we have to cast
+                values = ensure_float64(values)
+
+            elif how in ["sum", "ohlc", "prod", "cumsum", "cumprod"]:
+                # Avoid overflow during group op
+                if values.dtype.kind == "i":
+                    values = ensure_int64(values)
+                else:
+                    values = ensure_uint64(values)
+
+        return values
+
+    def _get_output_shape(self, ngroups: int, values: np.ndarray) -> Shape:
+        how = self.how
+        kind = self.kind
+
+        arity = self._cython_arity.get(how, 1)
+
+        out_shape: Shape
+        if how == "ohlc":
+            out_shape = (ngroups, arity)
+        elif arity > 1:
+            raise NotImplementedError(
+                "arity of more than 1 is not supported for the 'how' argument"
+            )
+        elif kind == "transform":
+            out_shape = values.shape
+        else:
+            out_shape = (ngroups,) + values.shape[1:]
+        return out_shape
+
+    def _get_out_dtype(self, dtype: np.dtype) -> np.dtype:
+        how = self.how
+
+        if how == "rank":
+            out_dtype = "float64"
+        elif how in ["idxmin", "idxmax"]:
+            # The Cython implementation only produces the row number; we'll take
+            # from the index using this in post processing
+            out_dtype = "intp"
+        else:
+            if dtype.kind in "iufcb":
+                out_dtype = f"{dtype.kind}{dtype.itemsize}"
+            else:
+                out_dtype = "object"
+        return np.dtype(out_dtype)
+
+    def _get_result_dtype(self, dtype: np.dtype) -> np.dtype:
+        """
+        Get the desired dtype of a result based on the
+        input dtype and how it was computed.
+
+        Parameters
+        ----------
+        dtype : np.dtype
+
+        Returns
+        -------
+        np.dtype
+            The desired dtype of the result.
+        """
+        how = self.how
+
+        if how in ["sum", "cumsum", "sum", "prod", "cumprod"]:
+            if dtype == np.dtype(bool):
+                return np.dtype(np.int64)
+        elif how in ["mean", "median", "var", "std", "sem"]:
+            if dtype.kind in "fc":
+                return dtype
+            elif dtype.kind in "iub":
+                return np.dtype(np.float64)
+        return dtype
+
+    @final
+    def _cython_op_ndim_compat(
+        self,
+        values: np.ndarray,
+        *,
+        min_count: int,
+        ngroups: int,
+        comp_ids: np.ndarray,
+        mask: npt.NDArray[np.bool_] | None = None,
+        result_mask: npt.NDArray[np.bool_] | None = None,
+        initial: Any = 0,
+        **kwargs,
+    ) -> np.ndarray:
+        if values.ndim == 1:
+            # expand to 2d, dispatch, then squeeze if appropriate
+            values2d = values[None, :]
+            if mask is not None:
+                mask = mask[None, :]
+            if result_mask is not None:
+                result_mask = result_mask[None, :]
+            res = self._call_cython_op(
+                values2d,
+                min_count=min_count,
+                ngroups=ngroups,
+                comp_ids=comp_ids,
+                mask=mask,
+                result_mask=result_mask,
+                initial=initial,
+                **kwargs,
+            )
+            if res.shape[0] == 1:
+                return res[0]
+
+            # otherwise we have OHLC
+            return res.T
+
+        return self._call_cython_op(
+            values,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=comp_ids,
+            mask=mask,
+            result_mask=result_mask,
+            initial=initial,
+            **kwargs,
+        )
+
+    @final
+    def _call_cython_op(
+        self,
+        values: np.ndarray,  # np.ndarray[ndim=2]
+        *,
+        min_count: int,
+        ngroups: int,
+        comp_ids: np.ndarray,
+        mask: npt.NDArray[np.bool_] | None,
+        result_mask: npt.NDArray[np.bool_] | None,
+        initial: Any = 0,
+        **kwargs,
+    ) -> np.ndarray:  # np.ndarray[ndim=2]
+        orig_values = values
+
+        dtype = values.dtype
+        is_numeric = dtype.kind in "iufcb"
+
+        is_datetimelike = dtype.kind in "mM"
+
+        if is_datetimelike:
+            values = values.view("int64")
+            is_numeric = True
+        elif dtype.kind == "b":
+            values = values.view("uint8")
+        if values.dtype == "float16":
+            values = values.astype(np.float32)
+
+        if self.how in ["any", "all"]:
+            if mask is None:
+                mask = isna(values)
+            if dtype == object:
+                if kwargs["skipna"]:
+                    # GH#37501: don't raise on pd.NA when skipna=True
+                    if mask.any():
+                        # mask on original values computed separately
+                        values = values.copy()
+                        values[mask] = True
+            values = values.astype(bool, copy=False).view(np.int8)
+            is_numeric = True
+
+        values = values.T
+        if mask is not None:
+            mask = mask.T
+            if result_mask is not None:
+                result_mask = result_mask.T
+
+        out_shape = self._get_output_shape(ngroups, values)
+        func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
+        values = self._get_cython_vals(values)
+        out_dtype = self._get_out_dtype(values.dtype)
+
+        result = maybe_fill(np.empty(out_shape, dtype=out_dtype))
+        if self.kind == "aggregate":
+            counts = np.zeros(ngroups, dtype=np.int64)
+            if self.how in [
+                "idxmin",
+                "idxmax",
+                "min",
+                "max",
+                "mean",
+                "last",
+                "first",
+                "sum",
+            ]:
+                if self.how == "sum":
+                    # pass in through kwargs only for sum (other functions don't have
+                    # the keyword)
+                    kwargs["initial"] = initial
+                func(
+                    out=result,
+                    counts=counts,
+                    values=values,
+                    labels=comp_ids,
+                    min_count=min_count,
+                    mask=mask,
+                    result_mask=result_mask,
+                    is_datetimelike=is_datetimelike,
+                    **kwargs,
+                )
+            elif self.how in ["sem", "std", "var", "ohlc", "prod", "median"]:
+                if self.how in ["std", "sem"]:
+                    kwargs["is_datetimelike"] = is_datetimelike
+                func(
+                    result,
+                    counts,
+                    values,
+                    comp_ids,
+                    min_count=min_count,
+                    mask=mask,
+                    result_mask=result_mask,
+                    **kwargs,
+                )
+            elif self.how in ["any", "all"]:
+                func(
+                    out=result,
+                    values=values,
+                    labels=comp_ids,
+                    mask=mask,
+                    result_mask=result_mask,
+                    **kwargs,
+                )
+                result = result.astype(bool, copy=False)
+            elif self.how in ["skew"]:
+                func(
+                    out=result,
+                    counts=counts,
+                    values=values,
+                    labels=comp_ids,
+                    mask=mask,
+                    result_mask=result_mask,
+                    **kwargs,
+                )
+                if dtype == object:
+                    result = result.astype(object)
+
+            else:
+                raise NotImplementedError(f"{self.how} is not implemented")
+        else:
+            # TODO: min_count
+            if self.how != "rank":
+                # TODO: should rank take result_mask?
+                kwargs["result_mask"] = result_mask
+            func(
+                out=result,
+                values=values,
+                labels=comp_ids,
+                ngroups=ngroups,
+                is_datetimelike=is_datetimelike,
+                mask=mask,
+                **kwargs,
+            )
+
+        if self.kind == "aggregate" and self.how not in ["idxmin", "idxmax"]:
+            # i.e. counts is defined.  Locations where count<min_count
+            # need to have the result set to np.nan, which may require casting,
+            # see GH#40767. For idxmin/idxmax is handled specially via post-processing
+            if result.dtype.kind in "iu" and not is_datetimelike:
+                # if the op keeps the int dtypes, we have to use 0
+                cutoff = max(0 if self.how in ["sum", "prod"] else 1, min_count)
+                empty_groups = counts < cutoff
+                if empty_groups.any():
+                    if result_mask is not None:
+                        assert result_mask[empty_groups].all()
+                    else:
+                        # Note: this conversion could be lossy, see GH#40767
+                        result = result.astype("float64")
+                        result[empty_groups] = np.nan
+
+        result = result.T
+
+        if self.how not in self.cast_blocklist:
+            # e.g. if we are int64 and need to restore to datetime64/timedelta64
+            # "rank" is the only member of cast_blocklist we get here
+            # Casting only needed for float16, bool, datetimelike,
+            #  and self.how in ["sum", "prod", "ohlc", "cumprod"]
+            res_dtype = self._get_result_dtype(orig_values.dtype)
+            op_result = maybe_downcast_to_dtype(result, res_dtype)
+        else:
+            op_result = result
+
+        return op_result
+
+    @final
+    def _validate_axis(self, axis: AxisInt, values: ArrayLike) -> None:
+        if values.ndim > 2:
+            raise NotImplementedError("number of dimensions is currently limited to 2")
+        if values.ndim == 2:
+            assert axis == 1, axis
+        elif not is_1d_only_ea_dtype(values.dtype):
+            # Note: it is *not* the case that axis is always 0 for 1-dim values,
+            #  as we can have 1D ExtensionArrays that we need to treat as 2D
+            assert axis == 0
+
+    @final
+    def cython_operation(
+        self,
+        *,
+        values: ArrayLike,
+        axis: AxisInt,
+        min_count: int = -1,
+        comp_ids: np.ndarray,
+        ngroups: int,
+        **kwargs,
+    ) -> ArrayLike:
+        """
+        Call our cython function, with appropriate pre- and post- processing.
+        """
+        self._validate_axis(axis, values)
+
+        if not isinstance(values, np.ndarray):
+            # i.e. ExtensionArray
+            return values._groupby_op(
+                how=self.how,
+                has_dropped_na=self.has_dropped_na,
+                min_count=min_count,
+                ngroups=ngroups,
+                ids=comp_ids,
+                **kwargs,
+            )
+
+        return self._cython_op_ndim_compat(
+            values,
+            min_count=min_count,
+            ngroups=ngroups,
+            comp_ids=comp_ids,
+            mask=None,
+            **kwargs,
+        )
+
+
+class BaseGrouper:
+    """
+    This is an internal Grouper class, which actually holds
+    the generated groups
+
+    Parameters
+    ----------
+    axis : Index
+    groupings : Sequence[Grouping]
+        all the grouping instances to handle in this grouper
+        for example for grouper list to groupby, need to pass the list
+    sort : bool, default True
+        whether this grouper will give sorted result or not
+
+    """
+
+    axis: Index
+
+    def __init__(
+        self,
+        axis: Index,
+        groupings: Sequence[grouper.Grouping],
+        sort: bool = True,
+        dropna: bool = True,
+    ) -> None:
+        assert isinstance(axis, Index), axis
+
+        self.axis = axis
+        self._groupings: list[grouper.Grouping] = list(groupings)
+        self._sort = sort
+        self.dropna = dropna
+
+    @property
+    def groupings(self) -> list[grouper.Grouping]:
+        return self._groupings
+
+    @property
+    def shape(self) -> Shape:
+        return tuple(ping.ngroups for ping in self.groupings)
+
+    def __iter__(self) -> Iterator[Hashable]:
+        return iter(self.indices)
+
+    @property
+    def nkeys(self) -> int:
+        return len(self.groupings)
+
+    def get_iterator(
+        self, data: NDFrameT, axis: AxisInt = 0
+    ) -> Iterator[tuple[Hashable, NDFrameT]]:
+        """
+        Groupby iterator
+
+        Returns
+        -------
+        Generator yielding sequence of (name, subsetted object)
+        for each group
+        """
+        splitter = self._get_splitter(data, axis=axis)
+        keys = self.group_keys_seq
+        yield from zip(keys, splitter)
+
+    @final
+    def _get_splitter(self, data: NDFrame, axis: AxisInt = 0) -> DataSplitter:
+        """
+        Returns
+        -------
+        Generator yielding subsetted objects
+        """
+        ids, _, ngroups = self.group_info
+        return _get_splitter(
+            data,
+            ids,
+            ngroups,
+            sorted_ids=self._sorted_ids,
+            sort_idx=self._sort_idx,
+            axis=axis,
+        )
+
+    @final
+    @cache_readonly
+    def group_keys_seq(self):
+        if len(self.groupings) == 1:
+            return self.levels[0]
+        else:
+            ids, _, ngroups = self.group_info
+
+            # provide "flattened" iterator for multi-group setting
+            return get_flattened_list(ids, ngroups, self.levels, self.codes)
+
+    @cache_readonly
+    def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
+        """dict {group name -> group indices}"""
+        if len(self.groupings) == 1 and isinstance(self.result_index, CategoricalIndex):
+            # This shows unused categories in indices GH#38642
+            return self.groupings[0].indices
+        codes_list = [ping.codes for ping in self.groupings]
+        keys = [ping._group_index for ping in self.groupings]
+        return get_indexer_dict(codes_list, keys)
+
+    @final
+    def result_ilocs(self) -> npt.NDArray[np.intp]:
+        """
+        Get the original integer locations of result_index in the input.
+        """
+        # Original indices are where group_index would go via sorting.
+        # But when dropna is true, we need to remove null values while accounting for
+        # any gaps that then occur because of them.
+        group_index = get_group_index(
+            self.codes, self.shape, sort=self._sort, xnull=True
+        )
+        group_index, _ = compress_group_index(group_index, sort=self._sort)
+
+        if self.has_dropped_na:
+            mask = np.where(group_index >= 0)
+            # Count how many gaps are caused by previous null values for each position
+            null_gaps = np.cumsum(group_index == -1)[mask]
+            group_index = group_index[mask]
+
+        result = get_group_index_sorter(group_index, self.ngroups)
+
+        if self.has_dropped_na:
+            # Shift by the number of prior null gaps
+            result += np.take(null_gaps, result)
+
+        return result
+
+    @final
+    @property
+    def codes(self) -> list[npt.NDArray[np.signedinteger]]:
+        return [ping.codes for ping in self.groupings]
+
+    @property
+    def levels(self) -> list[Index]:
+        return [ping._group_index for ping in self.groupings]
+
+    @property
+    def names(self) -> list[Hashable]:
+        return [ping.name for ping in self.groupings]
+
+    @final
+    def size(self) -> Series:
+        """
+        Compute group sizes.
+        """
+        ids, _, ngroups = self.group_info
+        out: np.ndarray | list
+        if ngroups:
+            out = np.bincount(ids[ids != -1], minlength=ngroups)
+        else:
+            out = []
+        return Series(out, index=self.result_index, dtype="int64", copy=False)
+
+    @cache_readonly
+    def groups(self) -> dict[Hashable, np.ndarray]:
+        """dict {group name -> group labels}"""
+        if len(self.groupings) == 1:
+            return self.groupings[0].groups
+        else:
+            to_groupby = []
+            for ping in self.groupings:
+                gv = ping.grouping_vector
+                if not isinstance(gv, BaseGrouper):
+                    to_groupby.append(gv)
+                else:
+                    to_groupby.append(gv.groupings[0].grouping_vector)
+            index = MultiIndex.from_arrays(to_groupby)
+            return self.axis.groupby(index)
+
+    @final
+    @cache_readonly
+    def is_monotonic(self) -> bool:
+        # return if my group orderings are monotonic
+        return Index(self.group_info[0]).is_monotonic_increasing
+
+    @final
+    @cache_readonly
+    def has_dropped_na(self) -> bool:
+        """
+        Whether grouper has null value(s) that are dropped.
+        """
+        return bool((self.group_info[0] < 0).any())
+
+    @cache_readonly
+    def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
+        comp_ids, obs_group_ids = self._get_compressed_codes()
+
+        ngroups = len(obs_group_ids)
+        comp_ids = ensure_platform_int(comp_ids)
+
+        return comp_ids, obs_group_ids, ngroups
+
+    @cache_readonly
+    def codes_info(self) -> npt.NDArray[np.intp]:
+        # return the codes of items in original grouped axis
+        ids, _, _ = self.group_info
+        return ids
+
+    @final
+    def _get_compressed_codes(
+        self,
+    ) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]:
+        # The first returned ndarray may have any signed integer dtype
+        if len(self.groupings) > 1:
+            group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)
+            return compress_group_index(group_index, sort=self._sort)
+            # FIXME: compress_group_index's second return value is int64, not intp
+
+        ping = self.groupings[0]
+        return ping.codes, np.arange(len(ping._group_index), dtype=np.intp)
+
+    @final
+    @cache_readonly
+    def ngroups(self) -> int:
+        return len(self.result_index)
+
+    @property
+    def reconstructed_codes(self) -> list[npt.NDArray[np.intp]]:
+        codes = self.codes
+        ids, obs_ids, _ = self.group_info
+        return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)
+
+    @cache_readonly
+    def result_index(self) -> Index:
+        if len(self.groupings) == 1:
+            return self.groupings[0]._result_index.rename(self.names[0])
+
+        codes = self.reconstructed_codes
+        levels = [ping._result_index for ping in self.groupings]
+        return MultiIndex(
+            levels=levels, codes=codes, verify_integrity=False, names=self.names
+        )
+
+    @final
+    def get_group_levels(self) -> list[ArrayLike]:
+        # Note: only called from _insert_inaxis_grouper, which
+        #  is only called for BaseGrouper, never for BinGrouper
+        if len(self.groupings) == 1:
+            return [self.groupings[0]._group_arraylike]
+
+        name_list = []
+        for ping, codes in zip(self.groupings, self.reconstructed_codes):
+            codes = ensure_platform_int(codes)
+            levels = ping._group_arraylike.take(codes)
+
+            name_list.append(levels)
+
+        return name_list
+
+    # ------------------------------------------------------------
+    # Aggregation functions
+
+    @final
+    def _cython_operation(
+        self,
+        kind: str,
+        values,
+        how: str,
+        axis: AxisInt,
+        min_count: int = -1,
+        **kwargs,
+    ) -> ArrayLike:
+        """
+        Returns the values of a cython operation.
+        """
+        assert kind in ["transform", "aggregate"]
+
+        cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)
+
+        ids, _, _ = self.group_info
+        ngroups = self.ngroups
+        return cy_op.cython_operation(
+            values=values,
+            axis=axis,
+            min_count=min_count,
+            comp_ids=ids,
+            ngroups=ngroups,
+            **kwargs,
+        )
+
+    @final
+    def agg_series(
+        self, obj: Series, func: Callable, preserve_dtype: bool = False
+    ) -> ArrayLike:
+        """
+        Parameters
+        ----------
+        obj : Series
+        func : function taking a Series and returning a scalar-like
+        preserve_dtype : bool
+            Whether the aggregation is known to be dtype-preserving.
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+
+        if not isinstance(obj._values, np.ndarray):
+            # we can preserve a little bit more aggressively with EA dtype
+            #  because maybe_cast_pointwise_result will do a try/except
+            #  with _from_sequence.  NB we are assuming here that _from_sequence
+            #  is sufficiently strict that it casts appropriately.
+            preserve_dtype = True
+
+        result = self._aggregate_series_pure_python(obj, func)
+
+        npvalues = lib.maybe_convert_objects(result, try_float=False)
+        if preserve_dtype:
+            out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True)
+        else:
+            out = npvalues
+        return out
+
+    @final
+    def _aggregate_series_pure_python(
+        self, obj: Series, func: Callable
+    ) -> npt.NDArray[np.object_]:
+        _, _, ngroups = self.group_info
+
+        result = np.empty(ngroups, dtype="O")
+        initialized = False
+
+        splitter = self._get_splitter(obj, axis=0)
+
+        for i, group in enumerate(splitter):
+            res = func(group)
+            res = extract_result(res)
+
+            if not initialized:
+                # We only do this validation on the first iteration
+                check_result_array(res, group.dtype)
+                initialized = True
+
+            result[i] = res
+
+        return result
+
+    @final
+    def apply_groupwise(
+        self, f: Callable, data: DataFrame | Series, axis: AxisInt = 0
+    ) -> tuple[list, bool]:
+        mutated = False
+        splitter = self._get_splitter(data, axis=axis)
+        group_keys = self.group_keys_seq
+        result_values = []
+
+        # This calls DataSplitter.__iter__
+        zipped = zip(group_keys, splitter)
+
+        for key, group in zipped:
+            # Pinning name is needed for
+            #  test_group_apply_once_per_group,
+            #  test_inconsistent_return_type, test_set_group_name,
+            #  test_group_name_available_in_inference_pass,
+            #  test_groupby_multi_timezone
+            object.__setattr__(group, "name", key)
+
+            # group might be modified
+            group_axes = group.axes
+            res = f(group)
+            if not mutated and not _is_indexed_like(res, group_axes, axis):
+                mutated = True
+            result_values.append(res)
+        # getattr pattern for __name__ is needed for functools.partial objects
+        if len(group_keys) == 0 and getattr(f, "__name__", None) in [
+            "skew",
+            "sum",
+            "prod",
+        ]:
+            #  If group_keys is empty, then no function calls have been made,
+            #  so we will not have raised even if this is an invalid dtype.
+            #  So do one dummy call here to raise appropriate TypeError.
+            f(data.iloc[:0])
+
+        return result_values, mutated
+
+    # ------------------------------------------------------------
+    # Methods for sorting subsets of our GroupBy's object
+
+    @final
+    @cache_readonly
+    def _sort_idx(self) -> npt.NDArray[np.intp]:
+        # Counting sort indexer
+        ids, _, ngroups = self.group_info
+        return get_group_index_sorter(ids, ngroups)
+
+    @final
+    @cache_readonly
+    def _sorted_ids(self) -> npt.NDArray[np.intp]:
+        ids, _, _ = self.group_info
+        return ids.take(self._sort_idx)
+
+
+class BinGrouper(BaseGrouper):
+    """
+    This is an internal Grouper class
+
+    Parameters
+    ----------
+    bins : the split index of binlabels to group the item of axis
+    binlabels : the label list
+    indexer : np.ndarray[np.intp], optional
+        the indexer created by Grouper
+        some groupers (TimeGrouper) will sort its axis and its
+        group_info is also sorted, so need the indexer to reorder
+
+    Examples
+    --------
+    bins: [2, 4, 6, 8, 10]
+    binlabels: DatetimeIndex(['2005-01-01', '2005-01-03',
+        '2005-01-05', '2005-01-07', '2005-01-09'],
+        dtype='datetime64[ns]', freq='2D')
+
+    the group_info, which contains the label of each item in grouped
+    axis, the index of label in label list, group number, is
+
+    (array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), array([0, 1, 2, 3, 4]), 5)
+
+    means that, the grouped axis has 10 items, can be grouped into 5
+    labels, the first and second items belong to the first label, the
+    third and forth items belong to the second label, and so on
+
+    """
+
+    bins: npt.NDArray[np.int64]
+    binlabels: Index
+
+    def __init__(
+        self,
+        bins,
+        binlabels,
+        indexer=None,
+    ) -> None:
+        self.bins = ensure_int64(bins)
+        self.binlabels = ensure_index(binlabels)
+        self.indexer = indexer
+
+        # These lengths must match, otherwise we could call agg_series
+        #  with empty self.bins, which would raise later.
+        assert len(self.binlabels) == len(self.bins)
+
+    @cache_readonly
+    def groups(self):
+        """dict {group name -> group labels}"""
+        # this is mainly for compat
+        # GH 3881
+        result = {
+            key: value
+            for key, value in zip(self.binlabels, self.bins)
+            if key is not NaT
+        }
+        return result
+
+    @property
+    def nkeys(self) -> int:
+        # still matches len(self.groupings), but we can hard-code
+        return 1
+
+    @cache_readonly
+    def codes_info(self) -> npt.NDArray[np.intp]:
+        # return the codes of items in original grouped axis
+        ids, _, _ = self.group_info
+        if self.indexer is not None:
+            sorter = np.lexsort((ids, self.indexer))
+            ids = ids[sorter]
+        return ids
+
+    def get_iterator(self, data: NDFrame, axis: AxisInt = 0):
+        """
+        Groupby iterator
+
+        Returns
+        -------
+        Generator yielding sequence of (name, subsetted object)
+        for each group
+        """
+        if axis == 0:
+            slicer = lambda start, edge: data.iloc[start:edge]
+        else:
+            slicer = lambda start, edge: data.iloc[:, start:edge]
+
+        length = len(data.axes[axis])
+
+        start = 0
+        for edge, label in zip(self.bins, self.binlabels):
+            if label is not NaT:
+                yield label, slicer(start, edge)
+            start = edge
+
+        if start < length:
+            yield self.binlabels[-1], slicer(start, None)
+
+    @cache_readonly
+    def indices(self):
+        indices = collections.defaultdict(list)
+
+        i = 0
+        for label, bin in zip(self.binlabels, self.bins):
+            if i < bin:
+                if label is not NaT:
+                    indices[label] = list(range(i, bin))
+                i = bin
+        return indices
+
+    @cache_readonly
+    def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
+        ngroups = self.ngroups
+        obs_group_ids = np.arange(ngroups, dtype=np.intp)
+        rep = np.diff(np.r_[0, self.bins])
+
+        rep = ensure_platform_int(rep)
+        if ngroups == len(self.bins):
+            comp_ids = np.repeat(np.arange(ngroups), rep)
+        else:
+            comp_ids = np.repeat(np.r_[-1, np.arange(ngroups)], rep)
+
+        return (
+            ensure_platform_int(comp_ids),
+            obs_group_ids,
+            ngroups,
+        )
+
+    @cache_readonly
+    def reconstructed_codes(self) -> list[np.ndarray]:
+        # get unique result indices, and prepend 0 as groupby starts from the first
+        return [np.r_[0, np.flatnonzero(self.bins[1:] != self.bins[:-1]) + 1]]
+
+    @cache_readonly
+    def result_index(self) -> Index:
+        if len(self.binlabels) != 0 and isna(self.binlabels[0]):
+            return self.binlabels[1:]
+
+        return self.binlabels
+
+    @property
+    def levels(self) -> list[Index]:
+        return [self.binlabels]
+
+    @property
+    def names(self) -> list[Hashable]:
+        return [self.binlabels.name]
+
+    @property
+    def groupings(self) -> list[grouper.Grouping]:
+        lev = self.binlabels
+        codes = self.group_info[0]
+        labels = lev.take(codes)
+        ping = grouper.Grouping(
+            labels, labels, in_axis=False, level=None, uniques=lev._values
+        )
+        return [ping]
+
+
+def _is_indexed_like(obj, axes, axis: AxisInt) -> bool:
+    if isinstance(obj, Series):
+        if len(axes) > 1:
+            return False
+        return obj.axes[axis].equals(axes[axis])
+    elif isinstance(obj, DataFrame):
+        return obj.axes[axis].equals(axes[axis])
+
+    return False
+
+
+# ----------------------------------------------------------------------
+# Splitting / application
+
+
+class DataSplitter(Generic[NDFrameT]):
+    def __init__(
+        self,
+        data: NDFrameT,
+        labels: npt.NDArray[np.intp],
+        ngroups: int,
+        *,
+        sort_idx: npt.NDArray[np.intp],
+        sorted_ids: npt.NDArray[np.intp],
+        axis: AxisInt = 0,
+    ) -> None:
+        self.data = data
+        self.labels = ensure_platform_int(labels)  # _should_ already be np.intp
+        self.ngroups = ngroups
+
+        self._slabels = sorted_ids
+        self._sort_idx = sort_idx
+
+        self.axis = axis
+        assert isinstance(axis, int), axis
+
+    def __iter__(self) -> Iterator:
+        sdata = self._sorted_data
+
+        if self.ngroups == 0:
+            # we are inside a generator, rather than raise StopIteration
+            # we merely return signal the end
+            return
+
+        starts, ends = lib.generate_slices(self._slabels, self.ngroups)
+
+        for start, end in zip(starts, ends):
+            yield self._chop(sdata, slice(start, end))
+
+    @cache_readonly
+    def _sorted_data(self) -> NDFrameT:
+        return self.data.take(self._sort_idx, axis=self.axis)
+
+    def _chop(self, sdata, slice_obj: slice) -> NDFrame:
+        raise AbstractMethodError(self)
+
+
+class SeriesSplitter(DataSplitter):
+    def _chop(self, sdata: Series, slice_obj: slice) -> Series:
+        # fastpath equivalent to `sdata.iloc[slice_obj]`
+        mgr = sdata._mgr.get_slice(slice_obj)
+        ser = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
+        ser._name = sdata.name
+        return ser.__finalize__(sdata, method="groupby")
+
+
+class FrameSplitter(DataSplitter):
+    def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
+        # Fastpath equivalent to:
+        # if self.axis == 0:
+        #     return sdata.iloc[slice_obj]
+        # else:
+        #     return sdata.iloc[:, slice_obj]
+        mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis)
+        df = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
+        return df.__finalize__(sdata, method="groupby")
+
+
+def _get_splitter(
+    data: NDFrame,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    *,
+    sort_idx: npt.NDArray[np.intp],
+    sorted_ids: npt.NDArray[np.intp],
+    axis: AxisInt = 0,
+) -> DataSplitter:
+    if isinstance(data, Series):
+        klass: type[DataSplitter] = SeriesSplitter
+    else:
+        # i.e. DataFrame
+        klass = FrameSplitter
+
+    return klass(
+        data, labels, ngroups, sort_idx=sort_idx, sorted_ids=sorted_ids, axis=axis
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba8a4f1d0ee7adb668c6b0ac49b2360d3c0dc356
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/__init__.py
@@ -0,0 +1,31 @@
+from pandas.core.indexers.utils import (
+    check_array_indexer,
+    check_key_length,
+    check_setitem_lengths,
+    disallow_ndim_indexing,
+    is_empty_indexer,
+    is_list_like_indexer,
+    is_scalar_indexer,
+    is_valid_positional_slice,
+    length_of_indexer,
+    maybe_convert_indices,
+    unpack_1tuple,
+    unpack_tuple_and_ellipses,
+    validate_indices,
+)
+
+__all__ = [
+    "is_valid_positional_slice",
+    "is_list_like_indexer",
+    "is_scalar_indexer",
+    "is_empty_indexer",
+    "check_setitem_lengths",
+    "validate_indices",
+    "maybe_convert_indices",
+    "length_of_indexer",
+    "disallow_ndim_indexing",
+    "unpack_1tuple",
+    "check_key_length",
+    "check_array_indexer",
+    "unpack_tuple_and_ellipses",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/objects.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/objects.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2db4886a559017422ed41bb8bd2246d6a3f0fb0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/objects.py
@@ -0,0 +1,453 @@
+"""Indexer objects for computing start/end window bounds for rolling operations"""
+from __future__ import annotations
+
+from datetime import timedelta
+
+import numpy as np
+
+from pandas._libs.tslibs import BaseOffset
+from pandas._libs.window.indexers import calculate_variable_window_bounds
+from pandas.util._decorators import Appender
+
+from pandas.core.dtypes.common import ensure_platform_int
+
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+from pandas.tseries.offsets import Nano
+
+get_window_bounds_doc = """
+Computes the bounds of a window.
+
+Parameters
+----------
+num_values : int, default 0
+    number of values that will be aggregated over
+window_size : int, default 0
+    the number of rows in a window
+min_periods : int, default None
+    min_periods passed from the top level rolling API
+center : bool, default None
+    center passed from the top level rolling API
+closed : str, default None
+    closed passed from the top level rolling API
+step : int, default None
+    step passed from the top level rolling API
+    .. versionadded:: 1.5
+win_type : str, default None
+    win_type passed from the top level rolling API
+
+Returns
+-------
+A tuple of ndarray[int64]s, indicating the boundaries of each
+window
+"""
+
+
+class BaseIndexer:
+    """
+    Base class for window bounds calculations.
+
+    Examples
+    --------
+    >>> from pandas.api.indexers import BaseIndexer
+    >>> class CustomIndexer(BaseIndexer):
+    ...     def get_window_bounds(self, num_values, min_periods, center, closed, step):
+    ...         start = np.empty(num_values, dtype=np.int64)
+    ...         end = np.empty(num_values, dtype=np.int64)
+    ...         for i in range(num_values):
+    ...             start[i] = i
+    ...             end[i] = i + self.window_size
+    ...         return start, end
+    >>> df = pd.DataFrame({"values": range(5)})
+    >>> indexer = CustomIndexer(window_size=2)
+    >>> df.rolling(indexer).sum()
+        values
+    0	1.0
+    1	3.0
+    2	5.0
+    3	7.0
+    4	4.0
+    """
+
+    def __init__(
+        self, index_array: np.ndarray | None = None, window_size: int = 0, **kwargs
+    ) -> None:
+        self.index_array = index_array
+        self.window_size = window_size
+        # Set user defined kwargs as attributes that can be used in get_window_bounds
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        raise NotImplementedError
+
+
+class FixedWindowIndexer(BaseIndexer):
+    """Creates window boundaries that are of fixed length."""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if center or self.window_size == 0:
+            offset = (self.window_size - 1) // 2
+        else:
+            offset = 0
+
+        end = np.arange(1 + offset, num_values + 1 + offset, step, dtype="int64")
+        start = end - self.window_size
+        if closed in ["left", "both"]:
+            start -= 1
+        if closed in ["left", "neither"]:
+            end -= 1
+
+        end = np.clip(end, 0, num_values)
+        start = np.clip(start, 0, num_values)
+
+        return start, end
+
+
+class VariableWindowIndexer(BaseIndexer):
+    """Creates window boundaries that are of variable length, namely for time series."""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        # error: Argument 4 to "calculate_variable_window_bounds" has incompatible
+        # type "Optional[bool]"; expected "bool"
+        # error: Argument 6 to "calculate_variable_window_bounds" has incompatible
+        # type "Optional[ndarray]"; expected "ndarray"
+        return calculate_variable_window_bounds(
+            num_values,
+            self.window_size,
+            min_periods,
+            center,  # type: ignore[arg-type]
+            closed,
+            self.index_array,  # type: ignore[arg-type]
+        )
+
+
+class VariableOffsetWindowIndexer(BaseIndexer):
+    """
+    Calculate window boundaries based on a non-fixed offset such as a BusinessDay.
+
+    Examples
+    --------
+    >>> from pandas.api.indexers import VariableOffsetWindowIndexer
+    >>> df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10))
+    >>> offset = pd.offsets.BDay(1)
+    >>> indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset)
+    >>> df
+                0
+    2020-01-01  0
+    2020-01-02  1
+    2020-01-03  2
+    2020-01-04  3
+    2020-01-05  4
+    2020-01-06  5
+    2020-01-07  6
+    2020-01-08  7
+    2020-01-09  8
+    2020-01-10  9
+    >>> df.rolling(indexer).sum()
+                   0
+    2020-01-01   0.0
+    2020-01-02   1.0
+    2020-01-03   2.0
+    2020-01-04   3.0
+    2020-01-05   7.0
+    2020-01-06  12.0
+    2020-01-07   6.0
+    2020-01-08   7.0
+    2020-01-09   8.0
+    2020-01-10   9.0
+    """
+
+    def __init__(
+        self,
+        index_array: np.ndarray | None = None,
+        window_size: int = 0,
+        index: DatetimeIndex | None = None,
+        offset: BaseOffset | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(index_array, window_size, **kwargs)
+        if not isinstance(index, DatetimeIndex):
+            raise ValueError("index must be a DatetimeIndex.")
+        self.index = index
+        if not isinstance(offset, BaseOffset):
+            raise ValueError("offset must be a DateOffset-like object.")
+        self.offset = offset
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if step is not None:
+            raise NotImplementedError("step not implemented for variable offset window")
+        if num_values <= 0:
+            return np.empty(0, dtype="int64"), np.empty(0, dtype="int64")
+
+        # if windows is variable, default is 'right', otherwise default is 'both'
+        if closed is None:
+            closed = "right" if self.index is not None else "both"
+
+        right_closed = closed in ["right", "both"]
+        left_closed = closed in ["left", "both"]
+
+        if self.index[num_values - 1] < self.index[0]:
+            index_growth_sign = -1
+        else:
+            index_growth_sign = 1
+        offset_diff = index_growth_sign * self.offset
+
+        start = np.empty(num_values, dtype="int64")
+        start.fill(-1)
+        end = np.empty(num_values, dtype="int64")
+        end.fill(-1)
+
+        start[0] = 0
+
+        # right endpoint is closed
+        if right_closed:
+            end[0] = 1
+        # right endpoint is open
+        else:
+            end[0] = 0
+
+        zero = timedelta(0)
+        # start is start of slice interval (including)
+        # end is end of slice interval (not including)
+        for i in range(1, num_values):
+            end_bound = self.index[i]
+            start_bound = end_bound - offset_diff
+
+            # left endpoint is closed
+            if left_closed:
+                start_bound -= Nano(1)
+
+            # advance the start bound until we are
+            # within the constraint
+            start[i] = i
+            for j in range(start[i - 1], i):
+                start_diff = (self.index[j] - start_bound) * index_growth_sign
+                if start_diff > zero:
+                    start[i] = j
+                    break
+
+            # end bound is previous end
+            # or current index
+            end_diff = (self.index[end[i - 1]] - end_bound) * index_growth_sign
+            if end_diff == zero and not right_closed:
+                end[i] = end[i - 1] + 1
+            elif end_diff <= zero:
+                end[i] = i + 1
+            else:
+                end[i] = end[i - 1]
+
+            # right endpoint is open
+            if not right_closed:
+                end[i] -= 1
+
+        return start, end
+
+
+class ExpandingIndexer(BaseIndexer):
+    """Calculate expanding window bounds, mimicking df.expanding()"""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        return (
+            np.zeros(num_values, dtype=np.int64),
+            np.arange(1, num_values + 1, dtype=np.int64),
+        )
+
+
+class FixedForwardWindowIndexer(BaseIndexer):
+    """
+    Creates window boundaries for fixed-length windows that include the current row.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
+    >>> df.rolling(window=indexer, min_periods=1).sum()
+         B
+    0  1.0
+    1  3.0
+    2  2.0
+    3  4.0
+    4  4.0
+    """
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if center:
+            raise ValueError("Forward-looking windows can't have center=True")
+        if closed is not None:
+            raise ValueError(
+                "Forward-looking windows don't support setting the closed argument"
+            )
+        if step is None:
+            step = 1
+
+        start = np.arange(0, num_values, step, dtype="int64")
+        end = start + self.window_size
+        if self.window_size:
+            end = np.clip(end, 0, num_values)
+
+        return start, end
+
+
+class GroupbyIndexer(BaseIndexer):
+    """Calculate bounds to compute groupby rolling, mimicking df.groupby().rolling()"""
+
+    def __init__(
+        self,
+        index_array: np.ndarray | None = None,
+        window_size: int | BaseIndexer = 0,
+        groupby_indices: dict | None = None,
+        window_indexer: type[BaseIndexer] = BaseIndexer,
+        indexer_kwargs: dict | None = None,
+        **kwargs,
+    ) -> None:
+        """
+        Parameters
+        ----------
+        index_array : np.ndarray or None
+            np.ndarray of the index of the original object that we are performing
+            a chained groupby operation over. This index has been pre-sorted relative to
+            the groups
+        window_size : int or BaseIndexer
+            window size during the windowing operation
+        groupby_indices : dict or None
+            dict of {group label: [positional index of rows belonging to the group]}
+        window_indexer : BaseIndexer
+            BaseIndexer class determining the start and end bounds of each group
+        indexer_kwargs : dict or None
+            Custom kwargs to be passed to window_indexer
+        **kwargs :
+            keyword arguments that will be available when get_window_bounds is called
+        """
+        self.groupby_indices = groupby_indices or {}
+        self.window_indexer = window_indexer
+        self.indexer_kwargs = indexer_kwargs.copy() if indexer_kwargs else {}
+        super().__init__(
+            index_array=index_array,
+            window_size=self.indexer_kwargs.pop("window_size", window_size),
+            **kwargs,
+        )
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        # 1) For each group, get the indices that belong to the group
+        # 2) Use the indices to calculate the start & end bounds of the window
+        # 3) Append the window bounds in group order
+        start_arrays = []
+        end_arrays = []
+        window_indices_start = 0
+        for key, indices in self.groupby_indices.items():
+            index_array: np.ndarray | None
+
+            if self.index_array is not None:
+                index_array = self.index_array.take(ensure_platform_int(indices))
+            else:
+                index_array = self.index_array
+            indexer = self.window_indexer(
+                index_array=index_array,
+                window_size=self.window_size,
+                **self.indexer_kwargs,
+            )
+            start, end = indexer.get_window_bounds(
+                len(indices), min_periods, center, closed, step
+            )
+            start = start.astype(np.int64)
+            end = end.astype(np.int64)
+            assert len(start) == len(
+                end
+            ), "these should be equal in length from get_window_bounds"
+            # Cannot use groupby_indices as they might not be monotonic with the object
+            # we're rolling over
+            window_indices = np.arange(
+                window_indices_start, window_indices_start + len(indices)
+            )
+            window_indices_start += len(indices)
+            # Extend as we'll be slicing window like [start, end)
+            window_indices = np.append(window_indices, [window_indices[-1] + 1]).astype(
+                np.int64, copy=False
+            )
+            start_arrays.append(window_indices.take(ensure_platform_int(start)))
+            end_arrays.append(window_indices.take(ensure_platform_int(end)))
+        if len(start_arrays) == 0:
+            return np.array([], dtype=np.int64), np.array([], dtype=np.int64)
+        start = np.concatenate(start_arrays)
+        end = np.concatenate(end_arrays)
+        return start, end
+
+
+class ExponentialMovingWindowIndexer(BaseIndexer):
+    """Calculate ewm window bounds (the entire window)"""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        return np.array([0], dtype=np.int64), np.array([num_values], dtype=np.int64)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..55bb58f3108c3d7004058494284ea6fb4b2fca7f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexers/utils.py
@@ -0,0 +1,553 @@
+"""
+Low-dependency indexing utilities.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AnyArrayLike
+
+    from pandas.core.frame import DataFrame
+    from pandas.core.indexes.base import Index
+
+# -----------------------------------------------------------
+# Indexer Identification
+
+
+def is_valid_positional_slice(slc: slice) -> bool:
+    """
+    Check if a slice object can be interpreted as a positional indexer.
+
+    Parameters
+    ----------
+    slc : slice
+
+    Returns
+    -------
+    bool
+
+    Notes
+    -----
+    A valid positional slice may also be interpreted as a label-based slice
+    depending on the index being sliced.
+    """
+    return (
+        lib.is_int_or_none(slc.start)
+        and lib.is_int_or_none(slc.stop)
+        and lib.is_int_or_none(slc.step)
+    )
+
+
+def is_list_like_indexer(key) -> bool:
+    """
+    Check if we have a list-like indexer that is *not* a NamedTuple.
+
+    Parameters
+    ----------
+    key : object
+
+    Returns
+    -------
+    bool
+    """
+    # allow a list_like, but exclude NamedTuples which can be indexers
+    return is_list_like(key) and not (isinstance(key, tuple) and type(key) is not tuple)
+
+
+def is_scalar_indexer(indexer, ndim: int) -> bool:
+    """
+    Return True if we are all scalar indexers.
+
+    Parameters
+    ----------
+    indexer : object
+    ndim : int
+        Number of dimensions in the object being indexed.
+
+    Returns
+    -------
+    bool
+    """
+    if ndim == 1 and is_integer(indexer):
+        # GH37748: allow indexer to be an integer for Series
+        return True
+    if isinstance(indexer, tuple) and len(indexer) == ndim:
+        return all(is_integer(x) for x in indexer)
+    return False
+
+
+def is_empty_indexer(indexer) -> bool:
+    """
+    Check if we have an empty indexer.
+
+    Parameters
+    ----------
+    indexer : object
+
+    Returns
+    -------
+    bool
+    """
+    if is_list_like(indexer) and not len(indexer):
+        return True
+    if not isinstance(indexer, tuple):
+        indexer = (indexer,)
+    return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer)
+
+
+# -----------------------------------------------------------
+# Indexer Validation
+
+
+def check_setitem_lengths(indexer, value, values) -> bool:
+    """
+    Validate that value and indexer are the same length.
+
+    An special-case is allowed for when the indexer is a boolean array
+    and the number of true values equals the length of ``value``. In
+    this case, no exception is raised.
+
+    Parameters
+    ----------
+    indexer : sequence
+        Key for the setitem.
+    value : array-like
+        Value for the setitem.
+    values : array-like
+        Values being set into.
+
+    Returns
+    -------
+    bool
+        Whether this is an empty listlike setting which is a no-op.
+
+    Raises
+    ------
+    ValueError
+        When the indexer is an ndarray or list and the lengths don't match.
+    """
+    no_op = False
+
+    if isinstance(indexer, (np.ndarray, list)):
+        # We can ignore other listlikes because they are either
+        #  a) not necessarily 1-D indexers, e.g. tuple
+        #  b) boolean indexers e.g. BoolArray
+        if is_list_like(value):
+            if len(indexer) != len(value) and values.ndim == 1:
+                # boolean with truth values == len of the value is ok too
+                if isinstance(indexer, list):
+                    indexer = np.array(indexer)
+                if not (
+                    isinstance(indexer, np.ndarray)
+                    and indexer.dtype == np.bool_
+                    and indexer.sum() == len(value)
+                ):
+                    raise ValueError(
+                        "cannot set using a list-like indexer "
+                        "with a different length than the value"
+                    )
+            if not len(indexer):
+                no_op = True
+
+    elif isinstance(indexer, slice):
+        if is_list_like(value):
+            if len(value) != length_of_indexer(indexer, values) and values.ndim == 1:
+                # In case of two dimensional value is used row-wise and broadcasted
+                raise ValueError(
+                    "cannot set using a slice indexer with a "
+                    "different length than the value"
+                )
+            if not len(value):
+                no_op = True
+
+    return no_op
+
+
+def validate_indices(indices: np.ndarray, n: int) -> None:
+    """
+    Perform bounds-checking for an indexer.
+
+    -1 is allowed for indicating missing values.
+
+    Parameters
+    ----------
+    indices : ndarray
+    n : int
+        Length of the array being indexed.
+
+    Raises
+    ------
+    ValueError
+
+    Examples
+    --------
+    >>> validate_indices(np.array([1, 2]), 3) # OK
+
+    >>> validate_indices(np.array([1, -2]), 3)
+    Traceback (most recent call last):
+        ...
+    ValueError: negative dimensions are not allowed
+
+    >>> validate_indices(np.array([1, 2, 3]), 3)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+
+    >>> validate_indices(np.array([-1, -1]), 0) # OK
+
+    >>> validate_indices(np.array([0, 1]), 0)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+    """
+    if len(indices):
+        min_idx = indices.min()
+        if min_idx < -1:
+            msg = f"'indices' contains values less than allowed ({min_idx} < -1)"
+            raise ValueError(msg)
+
+        max_idx = indices.max()
+        if max_idx >= n:
+            raise IndexError("indices are out-of-bounds")
+
+
+# -----------------------------------------------------------
+# Indexer Conversion
+
+
+def maybe_convert_indices(indices, n: int, verify: bool = True) -> np.ndarray:
+    """
+    Attempt to convert indices into valid, positive indices.
+
+    If we have negative indices, translate to positive here.
+    If we have indices that are out-of-bounds, raise an IndexError.
+
+    Parameters
+    ----------
+    indices : array-like
+        Array of indices that we are to convert.
+    n : int
+        Number of elements in the array that we are indexing.
+    verify : bool, default True
+        Check that all entries are between 0 and n - 1, inclusive.
+
+    Returns
+    -------
+    array-like
+        An array-like of positive indices that correspond to the ones
+        that were passed in initially to this function.
+
+    Raises
+    ------
+    IndexError
+        One of the converted indices either exceeded the number of,
+        elements (specified by `n`), or was still negative.
+    """
+    if isinstance(indices, list):
+        indices = np.array(indices)
+        if len(indices) == 0:
+            # If `indices` is empty, np.array will return a float,
+            # and will cause indexing errors.
+            return np.empty(0, dtype=np.intp)
+
+    mask = indices < 0
+    if mask.any():
+        indices = indices.copy()
+        indices[mask] += n
+
+    if verify:
+        mask = (indices >= n) | (indices < 0)
+        if mask.any():
+            raise IndexError("indices are out-of-bounds")
+    return indices
+
+
+# -----------------------------------------------------------
+# Unsorted
+
+
+def length_of_indexer(indexer, target=None) -> int:
+    """
+    Return the expected length of target[indexer]
+
+    Returns
+    -------
+    int
+    """
+    if target is not None and isinstance(indexer, slice):
+        target_len = len(target)
+        start = indexer.start
+        stop = indexer.stop
+        step = indexer.step
+        if start is None:
+            start = 0
+        elif start < 0:
+            start += target_len
+        if stop is None or stop > target_len:
+            stop = target_len
+        elif stop < 0:
+            stop += target_len
+        if step is None:
+            step = 1
+        elif step < 0:
+            start, stop = stop + 1, start + 1
+            step = -step
+        return (stop - start + step - 1) // step
+    elif isinstance(indexer, (ABCSeries, ABCIndex, np.ndarray, list)):
+        if isinstance(indexer, list):
+            indexer = np.array(indexer)
+
+        if indexer.dtype == bool:
+            # GH#25774
+            return indexer.sum()
+        return len(indexer)
+    elif isinstance(indexer, range):
+        return (indexer.stop - indexer.start) // indexer.step
+    elif not is_list_like_indexer(indexer):
+        return 1
+    raise AssertionError("cannot find the length of the indexer")
+
+
+def disallow_ndim_indexing(result) -> None:
+    """
+    Helper function to disallow multi-dimensional indexing on 1D Series/Index.
+
+    GH#27125 indexer like idx[:, None] expands dim, but we cannot do that
+    and keep an index, so we used to return ndarray, which was deprecated
+    in GH#30588.
+    """
+    if np.ndim(result) > 1:
+        raise ValueError(
+            "Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer "
+            "supported. Convert to a numpy array before indexing instead."
+        )
+
+
+def unpack_1tuple(tup):
+    """
+    If we have a length-1 tuple/list that contains a slice, unpack to just
+    the slice.
+
+    Notes
+    -----
+    The list case is deprecated.
+    """
+    if len(tup) == 1 and isinstance(tup[0], slice):
+        # if we don't have a MultiIndex, we may still be able to handle
+        #  a 1-tuple.  see test_1tuple_without_multiindex
+
+        if isinstance(tup, list):
+            # GH#31299
+            raise ValueError(
+                "Indexing with a single-item list containing a "
+                "slice is not allowed. Pass a tuple instead.",
+            )
+
+        return tup[0]
+    return tup
+
+
+def check_key_length(columns: Index, key, value: DataFrame) -> None:
+    """
+    Checks if a key used as indexer has the same length as the columns it is
+    associated with.
+
+    Parameters
+    ----------
+    columns : Index The columns of the DataFrame to index.
+    key : A list-like of keys to index with.
+    value : DataFrame The value to set for the keys.
+
+    Raises
+    ------
+    ValueError: If the length of key is not equal to the number of columns in value
+                or if the number of columns referenced by key is not equal to number
+                of columns.
+    """
+    if columns.is_unique:
+        if len(value.columns) != len(key):
+            raise ValueError("Columns must be same length as key")
+    else:
+        # Missing keys in columns are represented as -1
+        if len(columns.get_indexer_non_unique(key)[0]) != len(value.columns):
+            raise ValueError("Columns must be same length as key")
+
+
+def unpack_tuple_and_ellipses(item: tuple):
+    """
+    Possibly unpack arr[..., n] to arr[n]
+    """
+    if len(item) > 1:
+        # Note: we are assuming this indexing is being done on a 1D arraylike
+        if item[0] is Ellipsis:
+            item = item[1:]
+        elif item[-1] is Ellipsis:
+            item = item[:-1]
+
+    if len(item) > 1:
+        raise IndexError("too many indices for array.")
+
+    item = item[0]
+    return item
+
+
+# -----------------------------------------------------------
+# Public indexer validation
+
+
+def check_array_indexer(array: AnyArrayLike, indexer: Any) -> Any:
+    """
+    Check if `indexer` is a valid array indexer for `array`.
+
+    For a boolean mask, `array` and `indexer` are checked to have the same
+    length. The dtype is validated, and if it is an integer or boolean
+    ExtensionArray, it is checked if there are missing values present, and
+    it is converted to the appropriate numpy array. Other dtypes will raise
+    an error.
+
+    Non-array indexers (integer, slice, Ellipsis, tuples, ..) are passed
+    through as is.
+
+    Parameters
+    ----------
+    array : array-like
+        The array that is being indexed (only used for the length).
+    indexer : array-like or list-like
+        The array-like that's used to index. List-like input that is not yet
+        a numpy array or an ExtensionArray is converted to one. Other input
+        types are passed through as is.
+
+    Returns
+    -------
+    numpy.ndarray
+        The validated indexer as a numpy array that can be used to index.
+
+    Raises
+    ------
+    IndexError
+        When the lengths don't match.
+    ValueError
+        When `indexer` cannot be converted to a numpy ndarray to index
+        (e.g. presence of missing values).
+
+    See Also
+    --------
+    api.types.is_bool_dtype : Check if `key` is of boolean dtype.
+
+    Examples
+    --------
+    When checking a boolean mask, a boolean ndarray is returned when the
+    arguments are all valid.
+
+    >>> mask = pd.array([True, False])
+    >>> arr = pd.array([1, 2])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    An IndexError is raised when the lengths don't match.
+
+    >>> mask = pd.array([True, False, True])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    Traceback (most recent call last):
+    ...
+    IndexError: Boolean index has wrong length: 3 instead of 2.
+
+    NA values in a boolean array are treated as False.
+
+    >>> mask = pd.array([True, pd.NA])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    A numpy boolean mask will get passed through (if the length is correct):
+
+    >>> mask = np.array([True, False])
+    >>> pd.api.indexers.check_array_indexer(arr, mask)
+    array([ True, False])
+
+    Similarly for integer indexers, an integer ndarray is returned when it is
+    a valid indexer, otherwise an error is  (for integer indexers, a matching
+    length is not required):
+
+    >>> indexer = pd.array([0, 2], dtype="Int64")
+    >>> arr = pd.array([1, 2, 3])
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    array([0, 2])
+
+    >>> indexer = pd.array([0, pd.NA], dtype="Int64")
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    Traceback (most recent call last):
+    ...
+    ValueError: Cannot index with an integer indexer containing NA values
+
+    For non-integer/boolean dtypes, an appropriate error is raised:
+
+    >>> indexer = np.array([0., 2.], dtype="float64")
+    >>> pd.api.indexers.check_array_indexer(arr, indexer)
+    Traceback (most recent call last):
+    ...
+    IndexError: arrays used as indices must be of integer or boolean type
+    """
+    from pandas.core.construction import array as pd_array
+
+    # whatever is not an array-like is returned as-is (possible valid array
+    # indexers that are not array-like: integer, slice, Ellipsis, None)
+    # In this context, tuples are not considered as array-like, as they have
+    # a specific meaning in indexing (multi-dimensional indexing)
+    if is_list_like(indexer):
+        if isinstance(indexer, tuple):
+            return indexer
+    else:
+        return indexer
+
+    # convert list-likes to array
+    if not is_array_like(indexer):
+        indexer = pd_array(indexer)
+        if len(indexer) == 0:
+            # empty list is converted to float array by pd.array
+            indexer = np.array([], dtype=np.intp)
+
+    dtype = indexer.dtype
+    if is_bool_dtype(dtype):
+        if isinstance(dtype, ExtensionDtype):
+            indexer = indexer.to_numpy(dtype=bool, na_value=False)
+        else:
+            indexer = np.asarray(indexer, dtype=bool)
+
+        # GH26658
+        if len(indexer) != len(array):
+            raise IndexError(
+                f"Boolean index has wrong length: "
+                f"{len(indexer)} instead of {len(array)}"
+            )
+    elif is_integer_dtype(dtype):
+        try:
+            indexer = np.asarray(indexer, dtype=np.intp)
+        except ValueError as err:
+            raise ValueError(
+                "Cannot index with an integer indexer containing NA values"
+            ) from err
+    else:
+        raise IndexError("arrays used as indices must be of integer or boolean type")
+
+    return indexer
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/accessors.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/accessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e3ba4089ff60e9ab226f7536c6be1843be8ea59
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/accessors.py
@@ -0,0 +1,643 @@
+"""
+datetimelike delegation
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.base import (
+    NoNewAttributesMixin,
+    PandasObject,
+)
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+
+if TYPE_CHECKING:
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class Properties(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    _hidden_attrs = PandasObject._hidden_attrs | {
+        "orig",
+        "name",
+    }
+
+    def __init__(self, data: Series, orig) -> None:
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        self._parent = data
+        self.orig = orig
+        self.name = getattr(data, "name", None)
+        self._freeze()
+
+    def _get_values(self):
+        data = self._parent
+        if lib.is_np_dtype(data.dtype, "M"):
+            return DatetimeIndex(data, copy=False, name=self.name)
+
+        elif isinstance(data.dtype, DatetimeTZDtype):
+            return DatetimeIndex(data, copy=False, name=self.name)
+
+        elif lib.is_np_dtype(data.dtype, "m"):
+            return TimedeltaIndex(data, copy=False, name=self.name)
+
+        elif isinstance(data.dtype, PeriodDtype):
+            return PeriodArray(data, copy=False)
+
+        raise TypeError(
+            f"cannot convert an object of type {type(data)} to a datetimelike index"
+        )
+
+    def _delegate_property_get(self, name: str):
+        from pandas import Series
+
+        values = self._get_values()
+
+        result = getattr(values, name)
+
+        # maybe need to upcast (ints)
+        if isinstance(result, np.ndarray):
+            if is_integer_dtype(result):
+                result = result.astype("int64")
+        elif not is_list_like(result):
+            return result
+
+        result = np.asarray(result)
+
+        if self.orig is not None:
+            index = self.orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series
+        result = Series(result, index=index, name=self.name).__finalize__(self._parent)
+
+        # setting this object will show a SettingWithCopyWarning/Error
+        result._is_copy = (
+            "modifications to a property of a datetimelike "
+            "object are not supported and are discarded. "
+            "Change values on the original."
+        )
+
+        return result
+
+    def _delegate_property_set(self, name: str, value, *args, **kwargs):
+        raise ValueError(
+            "modifications to a property of a datetimelike object are not supported. "
+            "Change values on the original."
+        )
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        from pandas import Series
+
+        values = self._get_values()
+
+        method = getattr(values, name)
+        result = method(*args, **kwargs)
+
+        if not is_list_like(result):
+            return result
+
+        result = Series(result, index=self._parent.index, name=self.name).__finalize__(
+            self._parent
+        )
+
+        # setting this object will show a SettingWithCopyWarning/Error
+        result._is_copy = (
+            "modifications to a method of a datetimelike "
+            "object are not supported and are discarded. "
+            "Change values on the original."
+        )
+
+        return result
+
+
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=TimedeltaArray._datetimelike_ops,
+    typ="property",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=TimedeltaArray._datetimelike_methods,
+    typ="method",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=DatetimeArray._datetimelike_ops,
+    typ="property",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=DatetimeArray._datetimelike_methods,
+    typ="method",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+class ArrowTemporalProperties(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    def __init__(self, data: Series, orig) -> None:
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        self._parent = data
+        self._orig = orig
+        self._freeze()
+
+    def _delegate_property_get(self, name: str):
+        if not hasattr(self._parent.array, f"_dt_{name}"):
+            raise NotImplementedError(
+                f"dt.{name} is not supported for {self._parent.dtype}"
+            )
+        result = getattr(self._parent.array, f"_dt_{name}")
+
+        if not is_list_like(result):
+            return result
+
+        if self._orig is not None:
+            index = self._orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series, which is by definition a copy
+        result = type(self._parent)(
+            result, index=index, name=self._parent.name
+        ).__finalize__(self._parent)
+
+        return result
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        if not hasattr(self._parent.array, f"_dt_{name}"):
+            raise NotImplementedError(
+                f"dt.{name} is not supported for {self._parent.dtype}"
+            )
+
+        result = getattr(self._parent.array, f"_dt_{name}")(*args, **kwargs)
+
+        if self._orig is not None:
+            index = self._orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series, which is by definition a copy
+        result = type(self._parent)(
+            result, index=index, name=self._parent.name
+        ).__finalize__(self._parent)
+
+        return result
+
+    def to_pytimedelta(self):
+        return cast(ArrowExtensionArray, self._parent.array)._dt_to_pytimedelta()
+
+    def to_pydatetime(self):
+        # GH#20306
+        warnings.warn(
+            f"The behavior of {type(self).__name__}.to_pydatetime is deprecated, "
+            "in a future version this will return a Series containing python "
+            "datetime objects instead of an ndarray. To retain the old behavior, "
+            "call `np.array` on the result",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return cast(ArrowExtensionArray, self._parent.array)._dt_to_pydatetime()
+
+    def isocalendar(self) -> DataFrame:
+        from pandas import DataFrame
+
+        result = (
+            cast(ArrowExtensionArray, self._parent.array)
+            ._dt_isocalendar()
+            ._pa_array.combine_chunks()
+        )
+        iso_calendar_df = DataFrame(
+            {
+                col: type(self._parent.array)(result.field(i))  # type: ignore[call-arg]
+                for i, col in enumerate(["year", "week", "day"])
+            }
+        )
+        return iso_calendar_df
+
+    @property
+    def components(self) -> DataFrame:
+        from pandas import DataFrame
+
+        components_df = DataFrame(
+            {
+                col: getattr(self._parent.array, f"_dt_{col}")
+                for col in [
+                    "days",
+                    "hours",
+                    "minutes",
+                    "seconds",
+                    "milliseconds",
+                    "microseconds",
+                    "nanoseconds",
+                ]
+            }
+        )
+        return components_df
+
+
+@delegate_names(
+    delegate=DatetimeArray,
+    accessors=DatetimeArray._datetimelike_ops + ["unit"],
+    typ="property",
+)
+@delegate_names(
+    delegate=DatetimeArray,
+    accessors=DatetimeArray._datetimelike_methods + ["as_unit"],
+    typ="method",
+)
+class DatetimeProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
+    >>> seconds_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 00:00:01
+    2   2000-01-01 00:00:02
+    dtype: datetime64[ns]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    dtype: int32
+
+    >>> hours_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="h"))
+    >>> hours_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 01:00:00
+    2   2000-01-01 02:00:00
+    dtype: datetime64[ns]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    dtype: int32
+
+    >>> quarters_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="QE"))
+    >>> quarters_series
+    0   2000-03-31
+    1   2000-06-30
+    2   2000-09-30
+    dtype: datetime64[ns]
+    >>> quarters_series.dt.quarter
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+    """
+
+    def to_pydatetime(self) -> np.ndarray:
+        """
+        Return the data as an array of :class:`datetime.datetime` objects.
+
+        .. deprecated:: 2.1.0
+
+            The current behavior of dt.to_pydatetime is deprecated.
+            In a future version this will return a Series containing python
+            datetime objects instead of a ndarray.
+
+        Timezone information is retained if present.
+
+        .. warning::
+
+           Python's datetime uses microsecond resolution, which is lower than
+           pandas (nanosecond). The values are truncated.
+
+        Returns
+        -------
+        numpy.ndarray
+            Object dtype array containing native Python datetime objects.
+
+        See Also
+        --------
+        datetime.datetime : Standard library value for a datetime.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range('20180310', periods=2))
+        >>> s
+        0   2018-03-10
+        1   2018-03-11
+        dtype: datetime64[ns]
+
+        >>> s.dt.to_pydatetime()
+        array([datetime.datetime(2018, 3, 10, 0, 0),
+               datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)
+
+        pandas' nanosecond precision is truncated to microseconds.
+
+        >>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
+        >>> s
+        0   2018-03-10 00:00:00.000000000
+        1   2018-03-10 00:00:00.000000001
+        dtype: datetime64[ns]
+
+        >>> s.dt.to_pydatetime()
+        array([datetime.datetime(2018, 3, 10, 0, 0),
+               datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)
+        """
+        # GH#20306
+        warnings.warn(
+            f"The behavior of {type(self).__name__}.to_pydatetime is deprecated, "
+            "in a future version this will return a Series containing python "
+            "datetime objects instead of an ndarray. To retain the old behavior, "
+            "call `np.array` on the result",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._get_values().to_pydatetime()
+
+    @property
+    def freq(self):
+        return self._get_values().inferred_freq
+
+    def isocalendar(self) -> DataFrame:
+        """
+        Calculate year, week, and day according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+            With columns year, week and day.
+
+        See Also
+        --------
+        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
+            week number, and weekday for the given Timestamp object.
+        datetime.date.isocalendar : Return a named tuple object with
+            three components: year, week and weekday.
+
+        Examples
+        --------
+        >>> ser = pd.to_datetime(pd.Series(["2010-01-01", pd.NaT]))
+        >>> ser.dt.isocalendar()
+           year  week  day
+        0  2009    53     5
+        1  <NA>  <NA>  <NA>
+        >>> ser.dt.isocalendar().week
+        0      53
+        1    <NA>
+        Name: week, dtype: UInt32
+        """
+        return self._get_values().isocalendar().set_index(self._parent.index)
+
+
+@delegate_names(
+    delegate=TimedeltaArray, accessors=TimedeltaArray._datetimelike_ops, typ="property"
+)
+@delegate_names(
+    delegate=TimedeltaArray,
+    accessors=TimedeltaArray._datetimelike_methods,
+    typ="method",
+)
+class TimedeltaProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(
+    ...     pd.timedelta_range(start="1 second", periods=3, freq="s")
+    ... )
+    >>> seconds_series
+    0   0 days 00:00:01
+    1   0 days 00:00:02
+    2   0 days 00:00:03
+    dtype: timedelta64[ns]
+    >>> seconds_series.dt.seconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+    """
+
+    def to_pytimedelta(self) -> np.ndarray:
+        """
+        Return an array of native :class:`datetime.timedelta` objects.
+
+        Python's standard `datetime` library uses a different representation
+        timedelta's. This method converts a Series of pandas Timedeltas
+        to `datetime.timedelta` format with the same length as the original
+        Series.
+
+        Returns
+        -------
+        numpy.ndarray
+            Array of 1D containing data with `datetime.timedelta` type.
+
+        See Also
+        --------
+        datetime.timedelta : A duration expressing the difference
+            between two date, time, or datetime.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
+        >>> s
+        0   0 days
+        1   1 days
+        2   2 days
+        3   3 days
+        4   4 days
+        dtype: timedelta64[ns]
+
+        >>> s.dt.to_pytimedelta()
+        array([datetime.timedelta(0), datetime.timedelta(days=1),
+        datetime.timedelta(days=2), datetime.timedelta(days=3),
+        datetime.timedelta(days=4)], dtype=object)
+        """
+        return self._get_values().to_pytimedelta()
+
+    @property
+    def components(self):
+        """
+        Return a Dataframe of the components of the Timedeltas.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
+        >>> s
+        0   0 days 00:00:00
+        1   0 days 00:00:01
+        2   0 days 00:00:02
+        3   0 days 00:00:03
+        4   0 days 00:00:04
+        dtype: timedelta64[ns]
+        >>> s.dt.components
+           days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0     0      0        0        0             0             0            0
+        1     0      0        0        1             0             0            0
+        2     0      0        0        2             0             0            0
+        3     0      0        0        3             0             0            0
+        4     0      0        0        4             0             0            0
+        """
+        return (
+            self._get_values()
+            .components.set_index(self._parent.index)
+            .__finalize__(self._parent)
+        )
+
+    @property
+    def freq(self):
+        return self._get_values().inferred_freq
+
+
+@delegate_names(
+    delegate=PeriodArray, accessors=PeriodArray._datetimelike_ops, typ="property"
+)
+@delegate_names(
+    delegate=PeriodArray, accessors=PeriodArray._datetimelike_methods, typ="method"
+)
+class PeriodProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(
+    ...     pd.period_range(
+    ...         start="2000-01-01 00:00:00", end="2000-01-01 00:00:03", freq="s"
+    ...     )
+    ... )
+    >>> seconds_series
+    0    2000-01-01 00:00:00
+    1    2000-01-01 00:00:01
+    2    2000-01-01 00:00:02
+    3    2000-01-01 00:00:03
+    dtype: period[s]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    3    3
+    dtype: int64
+
+    >>> hours_series = pd.Series(
+    ...     pd.period_range(start="2000-01-01 00:00", end="2000-01-01 03:00", freq="h")
+    ... )
+    >>> hours_series
+    0    2000-01-01 00:00
+    1    2000-01-01 01:00
+    2    2000-01-01 02:00
+    3    2000-01-01 03:00
+    dtype: period[h]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    3    3
+    dtype: int64
+
+    >>> quarters_series = pd.Series(
+    ...     pd.period_range(start="2000-01-01", end="2000-12-31", freq="Q-DEC")
+    ... )
+    >>> quarters_series
+    0    2000Q1
+    1    2000Q2
+    2    2000Q3
+    3    2000Q4
+    dtype: period[Q-DEC]
+    >>> quarters_series.dt.quarter
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+    """
+
+
+class CombinedDatetimelikeProperties(
+    DatetimeProperties, TimedeltaProperties, PeriodProperties
+):
+    def __new__(cls, data: Series):  # pyright: ignore[reportInconsistentConstructor]
+        # CombinedDatetimelikeProperties isn't really instantiated. Instead
+        # we need to choose which parent (datetime or timedelta) is
+        # appropriate. Since we're checking the dtypes anyway, we'll just
+        # do all the validation here.
+
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        orig = data if isinstance(data.dtype, CategoricalDtype) else None
+        if orig is not None:
+            data = data._constructor(
+                orig.array,
+                name=orig.name,
+                copy=False,
+                dtype=orig._values.categories.dtype,
+                index=orig.index,
+            )
+
+        if isinstance(data.dtype, ArrowDtype) and data.dtype.kind in "Mm":
+            return ArrowTemporalProperties(data, orig)
+        if lib.is_np_dtype(data.dtype, "M"):
+            return DatetimeProperties(data, orig)
+        elif isinstance(data.dtype, DatetimeTZDtype):
+            return DatetimeProperties(data, orig)
+        elif lib.is_np_dtype(data.dtype, "m"):
+            return TimedeltaProperties(data, orig)
+        elif isinstance(data.dtype, PeriodDtype):
+            return PeriodProperties(data, orig)
+
+        raise AttributeError("Can only use .dt accessor with datetimelike values")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..15292953e72d00a8f57c34d2e2cc8a43f6863d39
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/api.py
@@ -0,0 +1,388 @@
+from __future__ import annotations
+
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    lib,
+)
+from pandas.errors import InvalidIndexError
+
+from pandas.core.dtypes.cast import find_common_type
+
+from pandas.core.algorithms import safe_sort
+from pandas.core.indexes.base import (
+    Index,
+    _new_Index,
+    ensure_index,
+    ensure_index_from_sequences,
+    get_unanimous_names,
+)
+from pandas.core.indexes.category import CategoricalIndex
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.interval import IntervalIndex
+from pandas.core.indexes.multi import MultiIndex
+from pandas.core.indexes.period import PeriodIndex
+from pandas.core.indexes.range import RangeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+
+if TYPE_CHECKING:
+    from pandas._typing import Axis
+_sort_msg = textwrap.dedent(
+    """\
+Sorting because non-concatenation axis is not aligned. A future version
+of pandas will change to not sort by default.
+
+To accept the future behavior, pass 'sort=False'.
+
+To retain the current behavior and silence the warning, pass 'sort=True'.
+"""
+)
+
+
+__all__ = [
+    "Index",
+    "MultiIndex",
+    "CategoricalIndex",
+    "IntervalIndex",
+    "RangeIndex",
+    "InvalidIndexError",
+    "TimedeltaIndex",
+    "PeriodIndex",
+    "DatetimeIndex",
+    "_new_Index",
+    "NaT",
+    "ensure_index",
+    "ensure_index_from_sequences",
+    "get_objs_combined_axis",
+    "union_indexes",
+    "get_unanimous_names",
+    "all_indexes_same",
+    "default_index",
+    "safe_sort_index",
+]
+
+
+def get_objs_combined_axis(
+    objs,
+    intersect: bool = False,
+    axis: Axis = 0,
+    sort: bool = True,
+    copy: bool = False,
+) -> Index:
+    """
+    Extract combined index: return intersection or union (depending on the
+    value of "intersect") of indexes on given axis, or None if all objects
+    lack indexes (e.g. they are numpy arrays).
+
+    Parameters
+    ----------
+    objs : list
+        Series or DataFrame objects, may be mix of the two.
+    intersect : bool, default False
+        If True, calculate the intersection between indexes. Otherwise,
+        calculate the union.
+    axis : {0 or 'index', 1 or 'outer'}, default 0
+        The axis to extract indexes from.
+    sort : bool, default True
+        Whether the result index should come out sorted or not.
+    copy : bool, default False
+        If True, return a copy of the combined index.
+
+    Returns
+    -------
+    Index
+    """
+    obs_idxes = [obj._get_axis(axis) for obj in objs]
+    return _get_combined_index(obs_idxes, intersect=intersect, sort=sort, copy=copy)
+
+
+def _get_distinct_objs(objs: list[Index]) -> list[Index]:
+    """
+    Return a list with distinct elements of "objs" (different ids).
+    Preserves order.
+    """
+    ids: set[int] = set()
+    res = []
+    for obj in objs:
+        if id(obj) not in ids:
+            ids.add(id(obj))
+            res.append(obj)
+    return res
+
+
+def _get_combined_index(
+    indexes: list[Index],
+    intersect: bool = False,
+    sort: bool = False,
+    copy: bool = False,
+) -> Index:
+    """
+    Return the union or intersection of indexes.
+
+    Parameters
+    ----------
+    indexes : list of Index or list objects
+        When intersect=True, do not accept list of lists.
+    intersect : bool, default False
+        If True, calculate the intersection between indexes. Otherwise,
+        calculate the union.
+    sort : bool, default False
+        Whether the result index should come out sorted or not.
+    copy : bool, default False
+        If True, return a copy of the combined index.
+
+    Returns
+    -------
+    Index
+    """
+    # TODO: handle index names!
+    indexes = _get_distinct_objs(indexes)
+    if len(indexes) == 0:
+        index = Index([])
+    elif len(indexes) == 1:
+        index = indexes[0]
+    elif intersect:
+        index = indexes[0]
+        for other in indexes[1:]:
+            index = index.intersection(other)
+    else:
+        index = union_indexes(indexes, sort=False)
+        index = ensure_index(index)
+
+    if sort:
+        index = safe_sort_index(index)
+    # GH 29879
+    if copy:
+        index = index.copy()
+
+    return index
+
+
+def safe_sort_index(index: Index) -> Index:
+    """
+    Returns the sorted index
+
+    We keep the dtypes and the name attributes.
+
+    Parameters
+    ----------
+    index : an Index
+
+    Returns
+    -------
+    Index
+    """
+    if index.is_monotonic_increasing:
+        return index
+
+    try:
+        array_sorted = safe_sort(index)
+    except TypeError:
+        pass
+    else:
+        if isinstance(array_sorted, Index):
+            return array_sorted
+
+        array_sorted = cast(np.ndarray, array_sorted)
+        if isinstance(index, MultiIndex):
+            index = MultiIndex.from_tuples(array_sorted, names=index.names)
+        else:
+            index = Index(array_sorted, name=index.name, dtype=index.dtype)
+
+    return index
+
+
+def union_indexes(indexes, sort: bool | None = True) -> Index:
+    """
+    Return the union of indexes.
+
+    The behavior of sort and names is not consistent.
+
+    Parameters
+    ----------
+    indexes : list of Index or list objects
+    sort : bool, default True
+        Whether the result index should come out sorted or not.
+
+    Returns
+    -------
+    Index
+    """
+    if len(indexes) == 0:
+        raise AssertionError("Must have at least 1 Index to union")
+    if len(indexes) == 1:
+        result = indexes[0]
+        if isinstance(result, list):
+            if not sort:
+                result = Index(result)
+            else:
+                result = Index(sorted(result))
+        return result
+
+    indexes, kind = _sanitize_and_check(indexes)
+
+    def _unique_indices(inds, dtype) -> Index:
+        """
+        Concatenate indices and remove duplicates.
+
+        Parameters
+        ----------
+        inds : list of Index or list objects
+        dtype : dtype to set for the resulting Index
+
+        Returns
+        -------
+        Index
+        """
+        if all(isinstance(ind, Index) for ind in inds):
+            inds = [ind.astype(dtype, copy=False) for ind in inds]
+            result = inds[0].unique()
+            other = inds[1].append(inds[2:])
+            diff = other[result.get_indexer_for(other) == -1]
+            if len(diff):
+                result = result.append(diff.unique())
+            if sort:
+                result = result.sort_values()
+            return result
+
+        def conv(i):
+            if isinstance(i, Index):
+                i = i.tolist()
+            return i
+
+        return Index(
+            lib.fast_unique_multiple_list([conv(i) for i in inds], sort=sort),
+            dtype=dtype,
+        )
+
+    def _find_common_index_dtype(inds):
+        """
+        Finds a common type for the indexes to pass through to resulting index.
+
+        Parameters
+        ----------
+        inds: list of Index or list objects
+
+        Returns
+        -------
+        The common type or None if no indexes were given
+        """
+        dtypes = [idx.dtype for idx in indexes if isinstance(idx, Index)]
+        if dtypes:
+            dtype = find_common_type(dtypes)
+        else:
+            dtype = None
+
+        return dtype
+
+    if kind == "special":
+        result = indexes[0]
+
+        dtis = [x for x in indexes if isinstance(x, DatetimeIndex)]
+        dti_tzs = [x for x in dtis if x.tz is not None]
+        if len(dti_tzs) not in [0, len(dtis)]:
+            # TODO: this behavior is not tested (so may not be desired),
+            #  but is kept in order to keep behavior the same when
+            #  deprecating union_many
+            # test_frame_from_dict_with_mixed_indexes
+            raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
+
+        if len(dtis) == len(indexes):
+            sort = True
+            result = indexes[0]
+
+        elif len(dtis) > 1:
+            # If we have mixed timezones, our casting behavior may depend on
+            #  the order of indexes, which we don't want.
+            sort = False
+
+            # TODO: what about Categorical[dt64]?
+            # test_frame_from_dict_with_mixed_indexes
+            indexes = [x.astype(object, copy=False) for x in indexes]
+            result = indexes[0]
+
+        for other in indexes[1:]:
+            result = result.union(other, sort=None if sort else False)
+        return result
+
+    elif kind == "array":
+        dtype = _find_common_index_dtype(indexes)
+        index = indexes[0]
+        if not all(index.equals(other) for other in indexes[1:]):
+            index = _unique_indices(indexes, dtype)
+
+        name = get_unanimous_names(*indexes)[0]
+        if name != index.name:
+            index = index.rename(name)
+        return index
+    else:  # kind='list'
+        dtype = _find_common_index_dtype(indexes)
+        return _unique_indices(indexes, dtype)
+
+
+def _sanitize_and_check(indexes):
+    """
+    Verify the type of indexes and convert lists to Index.
+
+    Cases:
+
+    - [list, list, ...]: Return ([list, list, ...], 'list')
+    - [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
+        Lists are sorted and converted to Index.
+    - [Index, Index, ...]: Return ([Index, Index, ...], TYPE)
+        TYPE = 'special' if at least one special type, 'array' otherwise.
+
+    Parameters
+    ----------
+    indexes : list of Index or list objects
+
+    Returns
+    -------
+    sanitized_indexes : list of Index or list objects
+    type : {'list', 'array', 'special'}
+    """
+    kinds = list({type(index) for index in indexes})
+
+    if list in kinds:
+        if len(kinds) > 1:
+            indexes = [
+                Index(list(x)) if not isinstance(x, Index) else x for x in indexes
+            ]
+            kinds.remove(list)
+        else:
+            return indexes, "list"
+
+    if len(kinds) > 1 or Index not in kinds:
+        return indexes, "special"
+    else:
+        return indexes, "array"
+
+
+def all_indexes_same(indexes) -> bool:
+    """
+    Determine if all indexes contain the same elements.
+
+    Parameters
+    ----------
+    indexes : iterable of Index objects
+
+    Returns
+    -------
+    bool
+        True if all indexes contain the same elements, False otherwise.
+    """
+    itr = iter(indexes)
+    first = next(itr)
+    return all(first.equals(index) for index in itr)
+
+
+def default_index(n: int) -> RangeIndex:
+    rng = range(n)
+    return RangeIndex._simple_new(rng, name=None)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab3eaac852f7a506a35abab154dcc10321f8b5fb
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/base.py
@@ -0,0 +1,7943 @@
+from __future__ import annotations
+
+from collections import abc
+from datetime import datetime
+import functools
+from itertools import zip_longest
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Literal,
+    NoReturn,
+    cast,
+    final,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    get_option,
+    using_copy_on_write,
+    using_string_dtype,
+)
+
+from pandas._libs import (
+    NaT,
+    algos as libalgos,
+    index as libindex,
+    lib,
+    writers,
+)
+from pandas._libs.internals import BlockValuesRefs
+import pandas._libs.join as libjoin
+from pandas._libs.lib import (
+    is_datetime_array,
+    no_default,
+)
+from pandas._libs.tslibs import (
+    IncompatibleFrequency,
+    OutOfBoundsDatetime,
+    Timestamp,
+    tz_compare,
+)
+from pandas._typing import (
+    AnyAll,
+    ArrayLike,
+    Axes,
+    Axis,
+    DropKeep,
+    DtypeObj,
+    F,
+    IgnoreRaise,
+    IndexLabel,
+    JoinHow,
+    Level,
+    NaPosition,
+    ReindexMethod,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    DuplicateLabelError,
+    InvalidIndexError,
+)
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_exception,
+)
+
+from pandas.core.dtypes.astype import (
+    astype_array,
+    astype_is_view,
+)
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    can_hold_element,
+    common_dtype_categorical_compat,
+    find_result_type,
+    infer_dtype_from,
+    maybe_cast_pointwise_result,
+    np_can_hold_element,
+)
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_object,
+    ensure_platform_int,
+    is_any_real_numeric_dtype,
+    is_bool_dtype,
+    is_ea_or_datetimelike_dtype,
+    is_float,
+    is_hashable,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_signed_integer_dtype,
+    is_string_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+    validate_all_hashable,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCCategoricalIndex,
+    ABCDataFrame,
+    ABCDatetimeIndex,
+    ABCIntervalIndex,
+    ABCMultiIndex,
+    ABCPeriodIndex,
+    ABCRangeIndex,
+    ABCSeries,
+    ABCTimedeltaIndex,
+)
+from pandas.core.dtypes.inference import is_dict_like
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core import (
+    arraylike,
+    nanops,
+    ops,
+)
+from pandas.core.accessor import CachedAccessor
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.putmask import (
+    setitem_datetimelike_compat,
+    validate_putmask,
+)
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import (
+    StringArray,
+    StringDtype,
+)
+from pandas.core.base import (
+    IndexOpsMixin,
+    PandasObject,
+)
+import pandas.core.common as com
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.indexers import (
+    disallow_ndim_indexing,
+    is_valid_positional_slice,
+)
+from pandas.core.indexes.frozen import FrozenList
+from pandas.core.missing import clean_reindex_fill_method
+from pandas.core.ops import get_op_result_name
+from pandas.core.ops.invalid import make_invalid_op
+from pandas.core.sorting import (
+    ensure_key_mapped,
+    get_group_index_sorter,
+    nargsort,
+)
+from pandas.core.strings.accessor import StringMethods
+
+from pandas.io.formats.printing import (
+    PrettyDict,
+    default_pprint,
+    format_object_summary,
+    pprint_thing,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Sequence,
+    )
+
+    from pandas import (
+        CategoricalIndex,
+        DataFrame,
+        MultiIndex,
+        Series,
+    )
+    from pandas.core.arrays import (
+        IntervalArray,
+        PeriodArray,
+    )
+
+__all__ = ["Index"]
+
+_unsortable_types = frozenset(("mixed", "mixed-integer"))
+
+_index_doc_kwargs: dict[str, str] = {
+    "klass": "Index",
+    "inplace": "",
+    "target_klass": "Index",
+    "raises_section": "",
+    "unique": "Index",
+    "duplicated": "np.ndarray",
+}
+_index_shared_docs: dict[str, str] = {}
+str_t = str
+
+_dtype_obj = np.dtype("object")
+
+_masked_engines = {
+    "Complex128": libindex.MaskedComplex128Engine,
+    "Complex64": libindex.MaskedComplex64Engine,
+    "Float64": libindex.MaskedFloat64Engine,
+    "Float32": libindex.MaskedFloat32Engine,
+    "UInt64": libindex.MaskedUInt64Engine,
+    "UInt32": libindex.MaskedUInt32Engine,
+    "UInt16": libindex.MaskedUInt16Engine,
+    "UInt8": libindex.MaskedUInt8Engine,
+    "Int64": libindex.MaskedInt64Engine,
+    "Int32": libindex.MaskedInt32Engine,
+    "Int16": libindex.MaskedInt16Engine,
+    "Int8": libindex.MaskedInt8Engine,
+    "boolean": libindex.MaskedBoolEngine,
+    "double[pyarrow]": libindex.MaskedFloat64Engine,
+    "float64[pyarrow]": libindex.MaskedFloat64Engine,
+    "float32[pyarrow]": libindex.MaskedFloat32Engine,
+    "float[pyarrow]": libindex.MaskedFloat32Engine,
+    "uint64[pyarrow]": libindex.MaskedUInt64Engine,
+    "uint32[pyarrow]": libindex.MaskedUInt32Engine,
+    "uint16[pyarrow]": libindex.MaskedUInt16Engine,
+    "uint8[pyarrow]": libindex.MaskedUInt8Engine,
+    "int64[pyarrow]": libindex.MaskedInt64Engine,
+    "int32[pyarrow]": libindex.MaskedInt32Engine,
+    "int16[pyarrow]": libindex.MaskedInt16Engine,
+    "int8[pyarrow]": libindex.MaskedInt8Engine,
+    "bool[pyarrow]": libindex.MaskedBoolEngine,
+}
+
+
+def _maybe_return_indexers(meth: F) -> F:
+    """
+    Decorator to simplify 'return_indexers' checks in Index.join.
+    """
+
+    @functools.wraps(meth)
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = "left",
+        level=None,
+        return_indexers: bool = False,
+        sort: bool = False,
+    ):
+        join_index, lidx, ridx = meth(self, other, how=how, level=level, sort=sort)
+        if not return_indexers:
+            return join_index
+
+        if lidx is not None:
+            lidx = ensure_platform_int(lidx)
+        if ridx is not None:
+            ridx = ensure_platform_int(ridx)
+        return join_index, lidx, ridx
+
+    return cast(F, join)
+
+
+def _new_Index(cls, d):
+    """
+    This is called upon unpickling, rather than the default which doesn't
+    have arguments and breaks __new__.
+    """
+    # required for backward compat, because PI can't be instantiated with
+    # ordinals through __new__ GH #13277
+    if issubclass(cls, ABCPeriodIndex):
+        from pandas.core.indexes.period import _new_PeriodIndex
+
+        return _new_PeriodIndex(cls, **d)
+
+    if issubclass(cls, ABCMultiIndex):
+        if "labels" in d and "codes" not in d:
+            # GH#23752 "labels" kwarg has been replaced with "codes"
+            d["codes"] = d.pop("labels")
+
+        # Since this was a valid MultiIndex at pickle-time, we don't need to
+        #  check validty at un-pickle time.
+        d["verify_integrity"] = False
+
+    elif "dtype" not in d and "data" in d:
+        # Prevent Index.__new__ from conducting inference;
+        #  "data" key not in RangeIndex
+        d["dtype"] = d["data"].dtype
+    return cls.__new__(cls, **d)
+
+
+class Index(IndexOpsMixin, PandasObject):
+    """
+    Immutable sequence used for indexing and alignment.
+
+    The basic object storing axis labels for all pandas objects.
+
+    .. versionchanged:: 2.0.0
+
+       Index can hold all numpy numeric dtypes (except float16). Previously only
+       int64/uint64/float64 dtypes were accepted.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : str, numpy.dtype, or ExtensionDtype, optional
+        Data type for the output Index. If not specified, this will be
+        inferred from `data`.
+        See the :ref:`user guide <basics.dtypes>` for more usages.
+    copy : bool, default False
+        Copy input data.
+    name : object
+        Name to be stored in the index.
+    tupleize_cols : bool (default: True)
+        When True, attempt to create a MultiIndex if possible.
+
+    See Also
+    --------
+    RangeIndex : Index implementing a monotonic integer range.
+    CategoricalIndex : Index of :class:`Categorical` s.
+    MultiIndex : A multi-level, or hierarchical Index.
+    IntervalIndex : An Index of :class:`Interval` s.
+    DatetimeIndex : Index of datetime64 data.
+    TimedeltaIndex : Index of timedelta64 data.
+    PeriodIndex : Index of Period data.
+
+    Notes
+    -----
+    An Index instance can **only** contain hashable objects.
+    An Index instance *can not* hold numpy float16 dtype.
+
+    Examples
+    --------
+    >>> pd.Index([1, 2, 3])
+    Index([1, 2, 3], dtype='int64')
+
+    >>> pd.Index(list('abc'))
+    Index(['a', 'b', 'c'], dtype='object')
+
+    >>> pd.Index([1, 2, 3], dtype="uint8")
+    Index([1, 2, 3], dtype='uint8')
+    """
+
+    # similar to __array_priority__, positions Index after Series and DataFrame
+    #  but before ExtensionArray.  Should NOT be overridden by subclasses.
+    __pandas_priority__ = 2000
+
+    # Cython methods; see github.com/cython/cython/issues/2647
+    #  for why we need to wrap these instead of making them class attributes
+    # Moreover, cython will choose the appropriate-dtyped sub-function
+    #  given the dtypes of the passed arguments
+
+    @final
+    def _left_indexer_unique(self, other: Self) -> npt.NDArray[np.intp]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        # similar but not identical to ov.searchsorted(sv)
+        return libjoin.left_join_indexer_unique(sv, ov)
+
+    @final
+    def _left_indexer(
+        self, other: Self
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        joined_ndarray, lidx, ridx = libjoin.left_join_indexer(sv, ov)
+        joined = self._from_join_target(joined_ndarray)
+        return joined, lidx, ridx
+
+    @final
+    def _inner_indexer(
+        self, other: Self
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        joined_ndarray, lidx, ridx = libjoin.inner_join_indexer(sv, ov)
+        joined = self._from_join_target(joined_ndarray)
+        return joined, lidx, ridx
+
+    @final
+    def _outer_indexer(
+        self, other: Self
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        # Caller is responsible for ensuring other.dtype == self.dtype
+        sv = self._get_join_target()
+        ov = other._get_join_target()
+        joined_ndarray, lidx, ridx = libjoin.outer_join_indexer(sv, ov)
+        joined = self._from_join_target(joined_ndarray)
+        return joined, lidx, ridx
+
+    _typ: str = "index"
+    _data: ExtensionArray | np.ndarray
+    _data_cls: type[ExtensionArray] | tuple[type[np.ndarray], type[ExtensionArray]] = (
+        np.ndarray,
+        ExtensionArray,
+    )
+    _id: object | None = None
+    _name: Hashable = None
+    # MultiIndex.levels previously allowed setting the index name. We
+    # don't allow this anymore, and raise if it happens rather than
+    # failing silently.
+    _no_setting_name: bool = False
+    _comparables: list[str] = ["name"]
+    _attributes: list[str] = ["name"]
+
+    @cache_readonly
+    def _can_hold_strings(self) -> bool:
+        return not is_numeric_dtype(self.dtype)
+
+    _engine_types: dict[np.dtype | ExtensionDtype, type[libindex.IndexEngine]] = {
+        np.dtype(np.int8): libindex.Int8Engine,
+        np.dtype(np.int16): libindex.Int16Engine,
+        np.dtype(np.int32): libindex.Int32Engine,
+        np.dtype(np.int64): libindex.Int64Engine,
+        np.dtype(np.uint8): libindex.UInt8Engine,
+        np.dtype(np.uint16): libindex.UInt16Engine,
+        np.dtype(np.uint32): libindex.UInt32Engine,
+        np.dtype(np.uint64): libindex.UInt64Engine,
+        np.dtype(np.float32): libindex.Float32Engine,
+        np.dtype(np.float64): libindex.Float64Engine,
+        np.dtype(np.complex64): libindex.Complex64Engine,
+        np.dtype(np.complex128): libindex.Complex128Engine,
+    }
+
+    @property
+    def _engine_type(
+        self,
+    ) -> type[libindex.IndexEngine | libindex.ExtensionEngine]:
+        return self._engine_types.get(self.dtype, libindex.ObjectEngine)
+
+    # whether we support partial string indexing. Overridden
+    # in DatetimeIndex and PeriodIndex
+    _supports_partial_string_indexing = False
+
+    _accessors = {"str"}
+
+    str = CachedAccessor("str", StringMethods)
+
+    _references = None
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        dtype=None,
+        copy: bool = False,
+        name=None,
+        tupleize_cols: bool = True,
+    ) -> Self:
+        from pandas.core.indexes.range import RangeIndex
+
+        name = maybe_extract_name(name, data, cls)
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        data_dtype = getattr(data, "dtype", None)
+
+        refs = None
+        if not copy and isinstance(data, (ABCSeries, Index)):
+            refs = data._references
+
+        is_pandas_object = isinstance(data, (ABCSeries, Index, ExtensionArray))
+
+        # range
+        if isinstance(data, (range, RangeIndex)):
+            result = RangeIndex(start=data, copy=copy, name=name)
+            if dtype is not None:
+                return result.astype(dtype, copy=False)
+            # error: Incompatible return value type (got "MultiIndex",
+            # expected "Self")
+            return result  # type: ignore[return-value]
+
+        elif is_ea_or_datetimelike_dtype(dtype):
+            # non-EA dtype indexes have special casting logic, so we punt here
+            if isinstance(data, (set, frozenset)):
+                data = list(data)
+
+        elif is_ea_or_datetimelike_dtype(data_dtype):
+            pass
+
+        elif isinstance(data, (np.ndarray, Index, ABCSeries)):
+            if isinstance(data, ABCMultiIndex):
+                data = data._values
+
+            if data.dtype.kind not in "iufcbmM":
+                # GH#11836 we need to avoid having numpy coerce
+                # things that look like ints/floats to ints unless
+                # they are actually ints, e.g. '0' and 0.0
+                # should not be coerced
+                data = com.asarray_tuplesafe(data, dtype=_dtype_obj)
+
+        elif is_scalar(data):
+            raise cls._raise_scalar_data_error(data)
+        elif hasattr(data, "__array__"):
+            return cls(np.asarray(data), dtype=dtype, copy=copy, name=name)
+        elif not is_list_like(data) and not isinstance(data, memoryview):
+            # 2022-11-16 the memoryview check is only necessary on some CI
+            #  builds, not clear why
+            raise cls._raise_scalar_data_error(data)
+
+        else:
+            if tupleize_cols:
+                # GH21470: convert iterable to list before determining if empty
+                if is_iterator(data):
+                    data = list(data)
+
+                if data and all(isinstance(e, tuple) for e in data):
+                    # we must be all tuples, otherwise don't construct
+                    # 10697
+                    from pandas.core.indexes.multi import MultiIndex
+
+                    # error: Incompatible return value type (got "MultiIndex",
+                    # expected "Self")
+                    return MultiIndex.from_tuples(  # type: ignore[return-value]
+                        data, names=name
+                    )
+            # other iterable of some kind
+
+            if not isinstance(data, (list, tuple)):
+                # we allow set/frozenset, which Series/sanitize_array does not, so
+                #  cast to list here
+                data = list(data)
+            if len(data) == 0:
+                # unlike Series, we default to object dtype:
+                data = np.array(data, dtype=object)
+
+            if len(data) and isinstance(data[0], tuple):
+                # Ensure we get 1-D array of tuples instead of 2D array.
+                data = com.asarray_tuplesafe(data, dtype=_dtype_obj)
+
+        try:
+            arr = sanitize_array(data, None, dtype=dtype, copy=copy)
+        except ValueError as err:
+            if "index must be specified when data is not list-like" in str(err):
+                raise cls._raise_scalar_data_error(data) from err
+            if "Data must be 1-dimensional" in str(err):
+                raise ValueError("Index data must be 1-dimensional") from err
+            raise
+        arr = ensure_wrapped_if_datetimelike(arr)
+
+        klass = cls._dtype_to_subclass(arr.dtype)
+
+        arr = klass._ensure_array(arr, arr.dtype, copy=False)
+        result = klass._simple_new(arr, name, refs=refs)
+        if dtype is None and is_pandas_object and data_dtype == np.object_:
+            if result.dtype != data_dtype:
+                warnings.warn(
+                    "Dtype inference on a pandas object "
+                    "(Series, Index, ExtensionArray) is deprecated. The Index "
+                    "constructor will keep the original dtype in the future. "
+                    "Call `infer_objects` on the result to get the old "
+                    "behavior.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+        return result  # type: ignore[return-value]
+
+    @classmethod
+    def _ensure_array(cls, data, dtype, copy: bool):
+        """
+        Ensure we have a valid array to pass to _simple_new.
+        """
+        if data.ndim > 1:
+            # GH#13601, GH#20285, GH#27125
+            raise ValueError("Index data must be 1-dimensional")
+        elif dtype == np.float16:
+            # float16 not supported (no indexing engine)
+            raise NotImplementedError("float16 indexes are not supported")
+
+        if copy:
+            # asarray_tuplesafe does not always copy underlying data,
+            #  so need to make sure that this happens
+            data = data.copy()
+        return data
+
+    @final
+    @classmethod
+    def _dtype_to_subclass(cls, dtype: DtypeObj):
+        # Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
+
+        if isinstance(dtype, ExtensionDtype):
+            return dtype.index_class
+
+        if dtype.kind == "M":
+            from pandas import DatetimeIndex
+
+            return DatetimeIndex
+
+        elif dtype.kind == "m":
+            from pandas import TimedeltaIndex
+
+            return TimedeltaIndex
+
+        elif dtype.kind == "O":
+            # NB: assuming away MultiIndex
+            return Index
+
+        elif issubclass(dtype.type, str) or is_numeric_dtype(dtype):
+            return Index
+
+        raise NotImplementedError(dtype)
+
+    # NOTE for new Index creation:
+
+    # - _simple_new: It returns new Index with the same type as the caller.
+    #   All metadata (such as name) must be provided by caller's responsibility.
+    #   Using _shallow_copy is recommended because it fills these metadata
+    #   otherwise specified.
+
+    # - _shallow_copy: It returns new Index with the same type (using
+    #   _simple_new), but fills caller's metadata otherwise specified. Passed
+    #   kwargs will overwrite corresponding metadata.
+
+    # See each method's docstring.
+
+    @classmethod
+    def _simple_new(
+        cls, values: ArrayLike, name: Hashable | None = None, refs=None
+    ) -> Self:
+        """
+        We require that we have a dtype compat for the values. If we are passed
+        a non-dtype compat, then coerce using the constructor.
+
+        Must be careful not to recurse.
+        """
+        assert isinstance(values, cls._data_cls), type(values)
+
+        result = object.__new__(cls)
+        result._data = values
+        result._name = name
+        result._cache = {}
+        result._reset_identity()
+        if refs is not None:
+            result._references = refs
+        else:
+            result._references = BlockValuesRefs()
+        result._references.add_index_reference(result)
+
+        return result
+
+    @classmethod
+    def _with_infer(cls, *args, **kwargs):
+        """
+        Constructor that uses the 1.0.x behavior inferring numeric dtypes
+        for ndarray[object] inputs.
+        """
+        result = cls(*args, **kwargs)
+
+        if result.dtype == _dtype_obj and not result._is_multi:
+            # error: Argument 1 to "maybe_convert_objects" has incompatible type
+            # "Union[ExtensionArray, ndarray[Any, Any]]"; expected
+            # "ndarray[Any, Any]"
+            values = lib.maybe_convert_objects(result._values)  # type: ignore[arg-type]
+            if values.dtype.kind in "iufb":
+                return Index(values, name=result.name)
+
+        return result
+
+    @cache_readonly
+    def _constructor(self) -> type[Self]:
+        return type(self)
+
+    @final
+    def _maybe_check_unique(self) -> None:
+        """
+        Check that an Index has no duplicates.
+
+        This is typically only called via
+        `NDFrame.flags.allows_duplicate_labels.setter` when it's set to
+        True (duplicates aren't allowed).
+
+        Raises
+        ------
+        DuplicateLabelError
+            When the index is not unique.
+        """
+        if not self.is_unique:
+            msg = """Index has duplicates."""
+            duplicates = self._format_duplicate_message()
+            msg += f"\n{duplicates}"
+
+            raise DuplicateLabelError(msg)
+
+    @final
+    def _format_duplicate_message(self) -> DataFrame:
+        """
+        Construct the DataFrame for a DuplicateLabelError.
+
+        This returns a DataFrame indicating the labels and positions
+        of duplicates in an index. This should only be called when it's
+        already known that duplicates are present.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'a'])
+        >>> idx._format_duplicate_message()
+            positions
+        label
+        a        [0, 2]
+        """
+        from pandas import Series
+
+        duplicates = self[self.duplicated(keep="first")].unique()
+        assert len(duplicates)
+
+        out = (
+            Series(np.arange(len(self)), copy=False)
+            .groupby(self, observed=False)
+            .agg(list)[duplicates]
+        )
+        if self._is_multi:
+            # test_format_duplicate_labels_message_multi
+            # error: "Type[Index]" has no attribute "from_tuples"  [attr-defined]
+            out.index = type(self).from_tuples(out.index)  # type: ignore[attr-defined]
+
+        if self.nlevels == 1:
+            out = out.rename_axis("label")
+        return out.to_frame(name="positions")
+
+    # --------------------------------------------------------------------
+    # Index Internals Methods
+
+    def _shallow_copy(self, values, name: Hashable = no_default) -> Self:
+        """
+        Create a new Index with the same class as the caller, don't copy the
+        data, use the same object attributes with passed in attributes taking
+        precedence.
+
+        *this is an internal non-public method*
+
+        Parameters
+        ----------
+        values : the values to create the new Index, optional
+        name : Label, defaults to self.name
+        """
+        name = self._name if name is no_default else name
+
+        return self._simple_new(values, name=name, refs=self._references)
+
+    def _view(self) -> Self:
+        """
+        fastpath to make a shallow copy, i.e. new object with same data.
+        """
+        result = self._simple_new(self._values, name=self._name, refs=self._references)
+
+        result._cache = self._cache
+        return result
+
+    @final
+    def _rename(self, name: Hashable) -> Self:
+        """
+        fastpath for rename if new name is already validated.
+        """
+        result = self._view()
+        result._name = name
+        return result
+
+    @final
+    def is_(self, other) -> bool:
+        """
+        More flexible, faster check like ``is`` but that works through views.
+
+        Note: this is *not* the same as ``Index.identical()``, which checks
+        that metadata is also the same.
+
+        Parameters
+        ----------
+        other : object
+            Other object to compare against.
+
+        Returns
+        -------
+        bool
+            True if both have same underlying data, False otherwise.
+
+        See Also
+        --------
+        Index.identical : Works like ``Index.is_`` but also checks metadata.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index(['1', '2', '3'])
+        >>> idx1.is_(idx1.view())
+        True
+
+        >>> idx1.is_(idx1.copy())
+        False
+        """
+        if self is other:
+            return True
+        elif not hasattr(other, "_id"):
+            return False
+        elif self._id is None or other._id is None:
+            return False
+        else:
+            return self._id is other._id
+
+    @final
+    def _reset_identity(self) -> None:
+        """
+        Initializes or resets ``_id`` attribute with new object.
+        """
+        self._id = object()
+
+    @final
+    def _cleanup(self) -> None:
+        self._engine.clear_mapping()
+
+    @cache_readonly
+    def _engine(
+        self,
+    ) -> libindex.IndexEngine | libindex.ExtensionEngine | libindex.MaskedIndexEngine:
+        # For base class (object dtype) we get ObjectEngine
+        target_values = self._get_engine_target()
+
+        if isinstance(self._values, ArrowExtensionArray) and self.dtype.kind in "Mm":
+            import pyarrow as pa
+
+            pa_type = self._values._pa_array.type
+            if pa.types.is_timestamp(pa_type):
+                target_values = self._values._to_datetimearray()
+                return libindex.DatetimeEngine(target_values._ndarray)
+            elif pa.types.is_duration(pa_type):
+                target_values = self._values._to_timedeltaarray()
+                return libindex.TimedeltaEngine(target_values._ndarray)
+
+        if isinstance(target_values, ExtensionArray):
+            if isinstance(target_values, (BaseMaskedArray, ArrowExtensionArray)):
+                try:
+                    return _masked_engines[target_values.dtype.name](target_values)
+                except KeyError:
+                    # Not supported yet e.g. decimal
+                    pass
+            elif self._engine_type is libindex.ObjectEngine:
+                return libindex.ExtensionEngine(target_values)
+
+        target_values = cast(np.ndarray, target_values)
+        # to avoid a reference cycle, bind `target_values` to a local variable, so
+        # `self` is not passed into the lambda.
+        if target_values.dtype == bool:
+            return libindex.BoolEngine(target_values)
+        elif target_values.dtype == np.complex64:
+            return libindex.Complex64Engine(target_values)
+        elif target_values.dtype == np.complex128:
+            return libindex.Complex128Engine(target_values)
+        elif needs_i8_conversion(self.dtype):
+            # We need to keep M8/m8 dtype when initializing the Engine,
+            #  but don't want to change _get_engine_target bc it is used
+            #  elsewhere
+            # error: Item "ExtensionArray" of "Union[ExtensionArray,
+            # ndarray[Any, Any]]" has no attribute "_ndarray"  [union-attr]
+            target_values = self._data._ndarray  # type: ignore[union-attr]
+        elif is_string_dtype(self.dtype) and not is_object_dtype(self.dtype):
+            return libindex.StringObjectEngine(target_values, self.dtype.na_value)  # type: ignore[union-attr]
+
+        # error: Argument 1 to "ExtensionEngine" has incompatible type
+        # "ndarray[Any, Any]"; expected "ExtensionArray"
+        return self._engine_type(target_values)  # type: ignore[arg-type]
+
+    @final
+    @cache_readonly
+    def _dir_additions_for_owner(self) -> set[str_t]:
+        """
+        Add the string-like labels to the owner dataframe/series dir output.
+
+        If this is a MultiIndex, it's first level values are used.
+        """
+        return {
+            c
+            for c in self.unique(level=0)[: get_option("display.max_dir_items")]
+            if isinstance(c, str) and c.isidentifier()
+        }
+
+    # --------------------------------------------------------------------
+    # Array-Like Methods
+
+    # ndarray compat
+    def __len__(self) -> int:
+        """
+        Return the length of the Index.
+        """
+        return len(self._data)
+
+    def __array__(self, dtype=None, copy=None) -> np.ndarray:
+        """
+        The array interface, return my values.
+        """
+        if copy is None:
+            # Note, that the if branch exists for NumPy 1.x support
+            return np.asarray(self._data, dtype=dtype)
+
+        return np.array(self._data, dtype=dtype, copy=copy)
+
+    def __array_ufunc__(self, ufunc: np.ufunc, method: str_t, *inputs, **kwargs):
+        if any(isinstance(other, (ABCSeries, ABCDataFrame)) for other in inputs):
+            return NotImplemented
+
+        result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
+            self, ufunc, method, *inputs, **kwargs
+        )
+        if result is not NotImplemented:
+            return result
+
+        if "out" in kwargs:
+            # e.g. test_dti_isub_tdi
+            return arraylike.dispatch_ufunc_with_out(
+                self, ufunc, method, *inputs, **kwargs
+            )
+
+        if method == "reduce":
+            result = arraylike.dispatch_reduction_ufunc(
+                self, ufunc, method, *inputs, **kwargs
+            )
+            if result is not NotImplemented:
+                return result
+
+        new_inputs = [x if x is not self else x._values for x in inputs]
+        result = getattr(ufunc, method)(*new_inputs, **kwargs)
+        if ufunc.nout == 2:
+            # i.e. np.divmod, np.modf, np.frexp
+            return tuple(self.__array_wrap__(x) for x in result)
+        elif method == "reduce":
+            result = lib.item_from_zerodim(result)
+            return result
+
+        if result.dtype == np.float16:
+            result = result.astype(np.float32)
+
+        return self.__array_wrap__(result)
+
+    @final
+    def __array_wrap__(self, result, context=None, return_scalar=False):
+        """
+        Gets called after a ufunc and other functions e.g. np.split.
+        """
+        result = lib.item_from_zerodim(result)
+        if (not isinstance(result, Index) and is_bool_dtype(result.dtype)) or np.ndim(
+            result
+        ) > 1:
+            # exclude Index to avoid warning from is_bool_dtype deprecation;
+            #  in the Index case it doesn't matter which path we go down.
+            # reached in plotting tests with e.g. np.nonzero(index)
+            return result
+
+        return Index(result, name=self.name)
+
+    @cache_readonly
+    def dtype(self) -> DtypeObj:
+        """
+        Return the dtype object of the underlying data.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.dtype
+        dtype('int64')
+        """
+        return self._data.dtype
+
+    @final
+    def ravel(self, order: str_t = "C") -> Self:
+        """
+        Return a view on self.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        numpy.ndarray.ravel : Return a flattened array.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
+        >>> s.index.ravel()
+        Index(['a', 'b', 'c'], dtype='object')
+        """
+        return self[:]
+
+    def view(self, cls=None):
+        # we need to see if we are subclassing an
+        # index type here
+        if cls is not None and not hasattr(cls, "_typ"):
+            dtype = cls
+            if isinstance(cls, str):
+                dtype = pandas_dtype(cls)
+
+            if needs_i8_conversion(dtype):
+                idx_cls = self._dtype_to_subclass(dtype)
+                arr = self.array.view(dtype)
+                if isinstance(arr, ExtensionArray):
+                    # here we exclude non-supported dt64/td64 dtypes
+                    return idx_cls._simple_new(
+                        arr, name=self.name, refs=self._references
+                    )
+                return arr
+
+            result = self._data.view(cls)
+        else:
+            if cls is not None:
+                warnings.warn(
+                    # GH#55709
+                    f"Passing a type in {type(self).__name__}.view is deprecated "
+                    "and will raise in a future version. "
+                    "Call view without any argument to retain the old behavior.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+            result = self._view()
+        if isinstance(result, Index):
+            result._id = self._id
+        return result
+
+    def astype(self, dtype, copy: bool = True):
+        """
+        Create an Index with values cast to dtypes.
+
+        The class of a new Index is determined by dtype. When conversion is
+        impossible, a TypeError exception is raised.
+
+        Parameters
+        ----------
+        dtype : numpy dtype or pandas type
+            Note that any signed integer `dtype` is treated as ``'int64'``,
+            and any unsigned integer `dtype` is treated as ``'uint64'``,
+            regardless of the size.
+        copy : bool, default True
+            By default, astype always returns a newly allocated object.
+            If copy is set to False and internal requirements on dtype are
+            satisfied, the original data is used to create a new Index
+            or the original Index is returned.
+
+        Returns
+        -------
+        Index
+            Index with values cast to specified dtype.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.astype('float')
+        Index([1.0, 2.0, 3.0], dtype='float64')
+        """
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        if self.dtype == dtype:
+            # Ensure that self.astype(self.dtype) is self
+            return self.copy() if copy else self
+
+        values = self._data
+        if isinstance(values, ExtensionArray):
+            with rewrite_exception(type(values).__name__, type(self).__name__):
+                new_values = values.astype(dtype, copy=copy)
+
+        elif isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            # Note: for RangeIndex and CategoricalDtype self vs self._values
+            #  behaves differently here.
+            new_values = cls._from_sequence(self, dtype=dtype, copy=copy)
+
+        else:
+            # GH#13149 specifically use astype_array instead of astype
+            new_values = astype_array(values, dtype=dtype, copy=copy)
+
+        # pass copy=False because any copying will be done in the astype above
+        result = Index(new_values, name=self.name, dtype=new_values.dtype, copy=False)
+        if (
+            not copy
+            and self._references is not None
+            and astype_is_view(self.dtype, dtype)
+        ):
+            result._references = self._references
+            result._references.add_index_reference(result)
+        return result
+
+    _index_shared_docs[
+        "take"
+    ] = """
+        Return a new %(klass)s of the values selected by the indices.
+
+        For internal compatibility with numpy arrays.
+
+        Parameters
+        ----------
+        indices : array-like
+            Indices to be taken.
+        axis : int, optional
+            The axis over which to select values, always 0.
+        allow_fill : bool, default True
+        fill_value : scalar, default None
+            If allow_fill=True and fill_value is not None, indices specified by
+            -1 are regarded as NA. If Index doesn't hold NA, raise ValueError.
+
+        Returns
+        -------
+        Index
+            An index formed of elements at the given indices. Will be the same
+            type as self, except for RangeIndex.
+
+        See Also
+        --------
+        numpy.ndarray.take: Return an array formed from the
+            elements of a at the given indices.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.take([2, 2, 1, 2])
+        Index(['c', 'c', 'b', 'c'], dtype='object')
+        """
+
+    @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
+    def take(
+        self,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> Self:
+        if kwargs:
+            nv.validate_take((), kwargs)
+        if is_scalar(indices):
+            raise TypeError("Expected indices to be array-like")
+        indices = ensure_platform_int(indices)
+        allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
+
+        # Note: we discard fill_value and use self._na_value, only relevant
+        #  in the case where allow_fill is True and fill_value is not None
+        values = self._values
+        if isinstance(values, np.ndarray):
+            taken = algos.take(
+                values, indices, allow_fill=allow_fill, fill_value=self._na_value
+            )
+        else:
+            # algos.take passes 'axis' keyword which not all EAs accept
+            taken = values.take(
+                indices, allow_fill=allow_fill, fill_value=self._na_value
+            )
+        return self._constructor._simple_new(taken, name=self.name)
+
+    @final
+    def _maybe_disallow_fill(self, allow_fill: bool, fill_value, indices) -> bool:
+        """
+        We only use pandas-style take when allow_fill is True _and_
+        fill_value is not None.
+        """
+        if allow_fill and fill_value is not None:
+            # only fill if we are passing a non-None fill_value
+            if self._can_hold_na:
+                if (indices < -1).any():
+                    raise ValueError(
+                        "When allow_fill=True and fill_value is not None, "
+                        "all indices must be >= -1"
+                    )
+            else:
+                cls_name = type(self).__name__
+                raise ValueError(
+                    f"Unable to fill values because {cls_name} cannot contain NA"
+                )
+        else:
+            allow_fill = False
+        return allow_fill
+
+    _index_shared_docs[
+        "repeat"
+    ] = """
+        Repeat elements of a %(klass)s.
+
+        Returns a new %(klass)s where each element of the current %(klass)s
+        is repeated consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int or array of ints
+            The number of repetitions for each element. This should be a
+            non-negative integer. Repeating 0 times will return an empty
+            %(klass)s.
+        axis : None
+            Must be ``None``. Has no effect but is accepted for compatibility
+            with numpy.
+
+        Returns
+        -------
+        %(klass)s
+            Newly created %(klass)s with repeated elements.
+
+        See Also
+        --------
+        Series.repeat : Equivalent function for Series.
+        numpy.repeat : Similar method for :class:`numpy.ndarray`.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx
+        Index(['a', 'b', 'c'], dtype='object')
+        >>> idx.repeat(2)
+        Index(['a', 'a', 'b', 'b', 'c', 'c'], dtype='object')
+        >>> idx.repeat([1, 2, 3])
+        Index(['a', 'b', 'b', 'c', 'c', 'c'], dtype='object')
+        """
+
+    @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
+    def repeat(self, repeats, axis: None = None) -> Self:
+        repeats = ensure_platform_int(repeats)
+        nv.validate_repeat((), {"axis": axis})
+        res_values = self._values.repeat(repeats)
+
+        # _constructor so RangeIndex-> Index with an int64 dtype
+        return self._constructor._simple_new(res_values, name=self.name)
+
+    # --------------------------------------------------------------------
+    # Copying Methods
+
+    def copy(
+        self,
+        name: Hashable | None = None,
+        deep: bool = False,
+    ) -> Self:
+        """
+        Make a copy of this object.
+
+        Name is set on the new object.
+
+        Parameters
+        ----------
+        name : Label, optional
+            Set name for new object.
+        deep : bool, default False
+
+        Returns
+        -------
+        Index
+            Index refer to new object which is a copy of this object.
+
+        Notes
+        -----
+        In most cases, there should be no functional difference from using
+        ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> new_idx = idx.copy()
+        >>> idx is new_idx
+        False
+        """
+
+        name = self._validate_names(name=name, deep=deep)[0]
+        if deep:
+            new_data = self._data.copy()
+            new_index = type(self)._simple_new(new_data, name=name)
+        else:
+            new_index = self._rename(name=name)
+        return new_index
+
+    @final
+    def __copy__(self, **kwargs) -> Self:
+        return self.copy(**kwargs)
+
+    @final
+    def __deepcopy__(self, memo=None) -> Self:
+        """
+        Parameters
+        ----------
+        memo, default None
+            Standard signature. Unused
+        """
+        return self.copy(deep=True)
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    @final
+    def __repr__(self) -> str_t:
+        """
+        Return a string representation for this object.
+        """
+        klass_name = type(self).__name__
+        data = self._format_data()
+        attrs = self._format_attrs()
+        attrs_str = [f"{k}={v}" for k, v in attrs]
+        prepr = ", ".join(attrs_str)
+
+        return f"{klass_name}({data}{prepr})"
+
+    @property
+    def _formatter_func(self):
+        """
+        Return the formatter function.
+        """
+        return default_pprint
+
+    @final
+    def _format_data(self, name=None) -> str_t:
+        """
+        Return the formatted data as a unicode string.
+        """
+        # do we want to justify (only do so for non-objects)
+        is_justify = True
+
+        if self.inferred_type == "string":
+            is_justify = False
+        elif isinstance(self.dtype, CategoricalDtype):
+            self = cast("CategoricalIndex", self)
+            if is_object_dtype(self.categories.dtype):
+                is_justify = False
+        elif isinstance(self, ABCRangeIndex):
+            # We will do the relevant formatting via attrs
+            return ""
+
+        return format_object_summary(
+            self,
+            self._formatter_func,
+            is_justify=is_justify,
+            name=name,
+            line_break_each_value=self._is_multi,
+        )
+
+    def _format_attrs(self) -> list[tuple[str_t, str_t | int | bool | None]]:
+        """
+        Return a list of tuples of the (attr,formatted_value).
+        """
+        attrs: list[tuple[str_t, str_t | int | bool | None]] = []
+
+        if not self._is_multi:
+            attrs.append(("dtype", f"'{self.dtype}'"))
+
+        if self.name is not None:
+            attrs.append(("name", default_pprint(self.name)))
+        elif self._is_multi and any(x is not None for x in self.names):
+            attrs.append(("names", default_pprint(self.names)))
+
+        max_seq_items = get_option("display.max_seq_items") or len(self)
+        if len(self) > max_seq_items:
+            attrs.append(("length", len(self)))
+        return attrs
+
+    @final
+    def _get_level_names(self) -> Hashable | Sequence[Hashable]:
+        """
+        Return a name or list of names with None replaced by the level number.
+        """
+        if self._is_multi:
+            return [
+                level if name is None else name for level, name in enumerate(self.names)
+            ]
+        else:
+            return 0 if self.name is None else self.name
+
+    @final
+    def _mpl_repr(self) -> np.ndarray:
+        # how to represent ourselves to matplotlib
+        if isinstance(self.dtype, np.dtype) and self.dtype.kind != "M":
+            return cast(np.ndarray, self.values)
+        return self.astype(object, copy=False)._values
+
+    def format(
+        self,
+        name: bool = False,
+        formatter: Callable | None = None,
+        na_rep: str_t = "NaN",
+    ) -> list[str_t]:
+        """
+        Render a string representation of the Index.
+        """
+        warnings.warn(
+            # GH#55413
+            f"{type(self).__name__}.format is deprecated and will be removed "
+            "in a future version. Convert using index.astype(str) or "
+            "index.map(formatter) instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        header = []
+        if name:
+            header.append(
+                pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
+                if self.name is not None
+                else ""
+            )
+
+        if formatter is not None:
+            return header + list(self.map(formatter))
+
+        return self._format_with_header(header=header, na_rep=na_rep)
+
+    _default_na_rep = "NaN"
+
+    @final
+    def _format_flat(
+        self,
+        *,
+        include_name: bool,
+        formatter: Callable | None = None,
+    ) -> list[str_t]:
+        """
+        Render a string representation of the Index.
+        """
+        header = []
+        if include_name:
+            header.append(
+                pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
+                if self.name is not None
+                else ""
+            )
+
+        if formatter is not None:
+            return header + list(self.map(formatter))
+
+        return self._format_with_header(header=header, na_rep=self._default_na_rep)
+
+    def _format_with_header(self, *, header: list[str_t], na_rep: str_t) -> list[str_t]:
+        from pandas.io.formats.format import format_array
+
+        values = self._values
+
+        if (
+            is_object_dtype(values.dtype)
+            or is_string_dtype(values.dtype)
+            or isinstance(self.dtype, (IntervalDtype, CategoricalDtype))
+        ):
+            # TODO: why do we need different justify for these cases?
+            justify = "all"
+        else:
+            justify = "left"
+        # passing leading_space=False breaks test_format_missing,
+        #  test_index_repr_in_frame_with_nan, but would otherwise make
+        #  trim_front unnecessary
+        formatted = format_array(values, None, justify=justify)
+        result = trim_front(formatted)
+        return header + result
+
+    def _get_values_for_csv(
+        self,
+        *,
+        na_rep: str_t = "",
+        decimal: str_t = ".",
+        float_format=None,
+        date_format=None,
+        quoting=None,
+    ) -> npt.NDArray[np.object_]:
+        return get_values_for_csv(
+            self._values,
+            na_rep=na_rep,
+            decimal=decimal,
+            float_format=float_format,
+            date_format=date_format,
+            quoting=quoting,
+        )
+
+    def _summary(self, name=None) -> str_t:
+        """
+        Return a summarized representation.
+
+        Parameters
+        ----------
+        name : str
+            name to use in the summary representation
+
+        Returns
+        -------
+        String with a summarized representation of the index
+        """
+        if len(self) > 0:
+            head = self[0]
+            if hasattr(head, "format") and not isinstance(head, str):
+                head = head.format()
+            elif needs_i8_conversion(self.dtype):
+                # e.g. Timedelta, display as values, not quoted
+                head = self._formatter_func(head).replace("'", "")
+            tail = self[-1]
+            if hasattr(tail, "format") and not isinstance(tail, str):
+                tail = tail.format()
+            elif needs_i8_conversion(self.dtype):
+                # e.g. Timedelta, display as values, not quoted
+                tail = self._formatter_func(tail).replace("'", "")
+
+            index_summary = f", {head} to {tail}"
+        else:
+            index_summary = ""
+
+        if name is None:
+            name = type(self).__name__
+        return f"{name}: {len(self)} entries{index_summary}"
+
+    # --------------------------------------------------------------------
+    # Conversion Methods
+
+    def to_flat_index(self) -> Self:
+        """
+        Identity method.
+
+        This is implemented for compatibility with subclass implementations
+        when chaining.
+
+        Returns
+        -------
+        pd.Index
+            Caller.
+
+        See Also
+        --------
+        MultiIndex.to_flat_index : Subclass implementation.
+        """
+        return self
+
+    @final
+    def to_series(self, index=None, name: Hashable | None = None) -> Series:
+        """
+        Create a Series with both index and values equal to the index keys.
+
+        Useful with map for returning an indexer based on an index.
+
+        Parameters
+        ----------
+        index : Index, optional
+            Index of resulting Series. If None, defaults to original index.
+        name : str, optional
+            Name of resulting Series. If None, defaults to name of original
+            index.
+
+        Returns
+        -------
+        Series
+            The dtype will be based on the type of the Index values.
+
+        See Also
+        --------
+        Index.to_frame : Convert an Index to a DataFrame.
+        Series.to_frame : Convert Series to DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
+
+        By default, the original index and original name is reused.
+
+        >>> idx.to_series()
+        animal
+        Ant      Ant
+        Bear    Bear
+        Cow      Cow
+        Name: animal, dtype: object
+
+        To enforce a new index, specify new labels to ``index``:
+
+        >>> idx.to_series(index=[0, 1, 2])
+        0     Ant
+        1    Bear
+        2     Cow
+        Name: animal, dtype: object
+
+        To override the name of the resulting column, specify ``name``:
+
+        >>> idx.to_series(name='zoo')
+        animal
+        Ant      Ant
+        Bear    Bear
+        Cow      Cow
+        Name: zoo, dtype: object
+        """
+        from pandas import Series
+
+        if index is None:
+            index = self._view()
+        if name is None:
+            name = self.name
+
+        return Series(self._values.copy(), index=index, name=name)
+
+    def to_frame(
+        self, index: bool = True, name: Hashable = lib.no_default
+    ) -> DataFrame:
+        """
+        Create a DataFrame with a column containing the Index.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Set the index of the returned DataFrame as the original Index.
+
+        name : object, defaults to index.name
+            The passed name should substitute for the index name (if it has
+            one).
+
+        Returns
+        -------
+        DataFrame
+            DataFrame containing the original Index data.
+
+        See Also
+        --------
+        Index.to_series : Convert an Index to a Series.
+        Series.to_frame : Convert Series to DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
+        >>> idx.to_frame()
+               animal
+        animal
+        Ant       Ant
+        Bear     Bear
+        Cow       Cow
+
+        By default, the original Index is reused. To enforce a new Index:
+
+        >>> idx.to_frame(index=False)
+            animal
+        0   Ant
+        1  Bear
+        2   Cow
+
+        To override the name of the resulting column, specify `name`:
+
+        >>> idx.to_frame(index=False, name='zoo')
+            zoo
+        0   Ant
+        1  Bear
+        2   Cow
+        """
+        from pandas import DataFrame
+
+        if name is lib.no_default:
+            name = self._get_level_names()
+        result = DataFrame({name: self}, copy=not using_copy_on_write())
+
+        if index:
+            result.index = self
+        return result
+
+    # --------------------------------------------------------------------
+    # Name-Centric Methods
+
+    @property
+    def name(self) -> Hashable:
+        """
+        Return Index or MultiIndex name.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3], name='x')
+        >>> idx
+        Index([1, 2, 3], dtype='int64',  name='x')
+        >>> idx.name
+        'x'
+        """
+        return self._name
+
+    @name.setter
+    def name(self, value: Hashable) -> None:
+        if self._no_setting_name:
+            # Used in MultiIndex.levels to avoid silently ignoring name updates.
+            raise RuntimeError(
+                "Cannot set name on a level of a MultiIndex. Use "
+                "'MultiIndex.set_names' instead."
+            )
+        maybe_extract_name(value, None, type(self))
+        self._name = value
+
+    @final
+    def _validate_names(
+        self, name=None, names=None, deep: bool = False
+    ) -> list[Hashable]:
+        """
+        Handles the quirks of having a singular 'name' parameter for general
+        Index and plural 'names' parameter for MultiIndex.
+        """
+        from copy import deepcopy
+
+        if names is not None and name is not None:
+            raise TypeError("Can only provide one of `names` and `name`")
+        if names is None and name is None:
+            new_names = deepcopy(self.names) if deep else self.names
+        elif names is not None:
+            if not is_list_like(names):
+                raise TypeError("Must pass list-like as `names`.")
+            new_names = names
+        elif not is_list_like(name):
+            new_names = [name]
+        else:
+            new_names = name
+
+        if len(new_names) != len(self.names):
+            raise ValueError(
+                f"Length of new names must be {len(self.names)}, got {len(new_names)}"
+            )
+
+        # All items in 'new_names' need to be hashable
+        validate_all_hashable(*new_names, error_name=f"{type(self).__name__}.name")
+
+        return new_names
+
+    def _get_default_index_names(
+        self, names: Hashable | Sequence[Hashable] | None = None, default=None
+    ) -> list[Hashable]:
+        """
+        Get names of index.
+
+        Parameters
+        ----------
+        names : int, str or 1-dimensional list, default None
+            Index names to set.
+        default : str
+            Default name of index.
+
+        Raises
+        ------
+        TypeError
+            if names not str or list-like
+        """
+        from pandas.core.indexes.multi import MultiIndex
+
+        if names is not None:
+            if isinstance(names, (int, str)):
+                names = [names]
+
+        if not isinstance(names, list) and names is not None:
+            raise ValueError("Index names must be str or 1-dimensional list")
+
+        if not names:
+            if isinstance(self, MultiIndex):
+                names = com.fill_missing_names(self.names)
+            else:
+                names = [default] if self.name is None else [self.name]
+
+        return names
+
+    def _get_names(self) -> FrozenList:
+        return FrozenList((self.name,))
+
+    def _set_names(self, values, *, level=None) -> None:
+        """
+        Set new names on index. Each name has to be a hashable type.
+
+        Parameters
+        ----------
+        values : str or sequence
+            name(s) to set
+        level : int, level name, or sequence of int/level names (default None)
+            If the index is a MultiIndex (hierarchical), level(s) to set (None
+            for all levels).  Otherwise level must be None
+
+        Raises
+        ------
+        TypeError if each name is not hashable.
+        """
+        if not is_list_like(values):
+            raise ValueError("Names must be a list-like")
+        if len(values) != 1:
+            raise ValueError(f"Length of new names must be 1, got {len(values)}")
+
+        # GH 20527
+        # All items in 'name' need to be hashable:
+        validate_all_hashable(*values, error_name=f"{type(self).__name__}.name")
+
+        self._name = values[0]
+
+    names = property(fset=_set_names, fget=_get_names)
+
+    @overload
+    def set_names(self, names, *, level=..., inplace: Literal[False] = ...) -> Self:
+        ...
+
+    @overload
+    def set_names(self, names, *, level=..., inplace: Literal[True]) -> None:
+        ...
+
+    @overload
+    def set_names(self, names, *, level=..., inplace: bool = ...) -> Self | None:
+        ...
+
+    def set_names(self, names, *, level=None, inplace: bool = False) -> Self | None:
+        """
+        Set Index or MultiIndex name.
+
+        Able to set new names partially and by level.
+
+        Parameters
+        ----------
+
+        names : label or list of label or dict-like for MultiIndex
+            Name(s) to set.
+
+            .. versionchanged:: 1.3.0
+
+        level : int, label or list of int or label, optional
+            If the index is a MultiIndex and names is not dict-like, level(s) to set
+            (None for all levels). Otherwise level must be None.
+
+            .. versionchanged:: 1.3.0
+
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Index or
+            MultiIndex.
+
+        Returns
+        -------
+        Index or None
+            The same type as the caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        Index.rename : Able to set new names without level.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx
+        Index([1, 2, 3, 4], dtype='int64')
+        >>> idx.set_names('quarter')
+        Index([1, 2, 3, 4], dtype='int64', name='quarter')
+
+        >>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
+        ...                                   [2018, 2019]])
+        >>> idx
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   )
+        >>> idx = idx.set_names(['kind', 'year'])
+        >>> idx.set_names('species', level=0)
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['species', 'year'])
+
+        When renaming levels with a dict, levels can not be passed.
+
+        >>> idx.set_names({'kind': 'snake'})
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['snake', 'year'])
+        """
+        if level is not None and not isinstance(self, ABCMultiIndex):
+            raise ValueError("Level must be None for non-MultiIndex")
+
+        if level is not None and not is_list_like(level) and is_list_like(names):
+            raise TypeError("Names must be a string when a single level is provided.")
+
+        if not is_list_like(names) and level is None and self.nlevels > 1:
+            raise TypeError("Must pass list-like as `names`.")
+
+        if is_dict_like(names) and not isinstance(self, ABCMultiIndex):
+            raise TypeError("Can only pass dict-like as `names` for MultiIndex.")
+
+        if is_dict_like(names) and level is not None:
+            raise TypeError("Can not pass level for dictlike `names`.")
+
+        if isinstance(self, ABCMultiIndex) and is_dict_like(names) and level is None:
+            # Transform dict to list of new names and corresponding levels
+            level, names_adjusted = [], []
+            for i, name in enumerate(self.names):
+                if name in names.keys():
+                    level.append(i)
+                    names_adjusted.append(names[name])
+            names = names_adjusted
+
+        if not is_list_like(names):
+            names = [names]
+        if level is not None and not is_list_like(level):
+            level = [level]
+
+        if inplace:
+            idx = self
+        else:
+            idx = self._view()
+
+        idx._set_names(names, level=level)
+        if not inplace:
+            return idx
+        return None
+
+    @overload
+    def rename(self, name, *, inplace: Literal[False] = ...) -> Self:
+        ...
+
+    @overload
+    def rename(self, name, *, inplace: Literal[True]) -> None:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self", "name"], name="rename"
+    )
+    def rename(self, name, inplace: bool = False) -> Self | None:
+        """
+        Alter Index or MultiIndex name.
+
+        Able to set new names without level. Defaults to returning new index.
+        Length of names must match number of levels in MultiIndex.
+
+        Parameters
+        ----------
+        name : label or list of labels
+            Name(s) to set.
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Index or
+            MultiIndex.
+
+        Returns
+        -------
+        Index or None
+            The same type as the caller or None if ``inplace=True``.
+
+        See Also
+        --------
+        Index.set_names : Able to set new names partially and by level.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['A', 'C', 'A', 'B'], name='score')
+        >>> idx.rename('grade')
+        Index(['A', 'C', 'A', 'B'], dtype='object', name='grade')
+
+        >>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
+        ...                                   [2018, 2019]],
+        ...                                   names=['kind', 'year'])
+        >>> idx
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['kind', 'year'])
+        >>> idx.rename(['species', 'year'])
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   names=['species', 'year'])
+        >>> idx.rename('species')
+        Traceback (most recent call last):
+        TypeError: Must pass list-like as `names`.
+        """
+        return self.set_names([name], inplace=inplace)
+
+    # --------------------------------------------------------------------
+    # Level-Centric Methods
+
+    @property
+    def nlevels(self) -> int:
+        """
+        Number of levels.
+        """
+        return 1
+
+    def _sort_levels_monotonic(self) -> Self:
+        """
+        Compat with MultiIndex.
+        """
+        return self
+
+    @final
+    def _validate_index_level(self, level) -> None:
+        """
+        Validate index level.
+
+        For single-level Index getting level number is a no-op, but some
+        verification must be done like in MultiIndex.
+
+        """
+        if isinstance(level, int):
+            if level < 0 and level != -1:
+                raise IndexError(
+                    "Too many levels: Index has only 1 level, "
+                    f"{level} is not a valid level number"
+                )
+            if level > 0:
+                raise IndexError(
+                    f"Too many levels: Index has only 1 level, not {level + 1}"
+                )
+        elif level != self.name:
+            raise KeyError(
+                f"Requested level ({level}) does not match index name ({self.name})"
+            )
+
+    def _get_level_number(self, level) -> int:
+        self._validate_index_level(level)
+        return 0
+
+    def sortlevel(
+        self,
+        level=None,
+        ascending: bool | list[bool] = True,
+        sort_remaining=None,
+        na_position: NaPosition = "first",
+    ):
+        """
+        For internal compatibility with the Index API.
+
+        Sort the Index. This is for compat with MultiIndex
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            False to sort in descending order
+        na_position : {'first' or 'last'}, default 'first'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+
+            .. versionadded:: 2.1.0
+
+        level, sort_remaining are compat parameters
+
+        Returns
+        -------
+        Index
+        """
+        if not isinstance(ascending, (list, bool)):
+            raise TypeError(
+                "ascending must be a single bool value or"
+                "a list of bool values of length 1"
+            )
+
+        if isinstance(ascending, list):
+            if len(ascending) != 1:
+                raise TypeError("ascending must be a list of bool values of length 1")
+            ascending = ascending[0]
+
+        if not isinstance(ascending, bool):
+            raise TypeError("ascending must be a bool value")
+
+        return self.sort_values(
+            return_indexer=True, ascending=ascending, na_position=na_position
+        )
+
+    def _get_level_values(self, level) -> Index:
+        """
+        Return an Index of values for requested level.
+
+        This is primarily useful to get an individual level of values from a
+        MultiIndex, but is provided on Index as well for compatibility.
+
+        Parameters
+        ----------
+        level : int or str
+            It is either the integer position or the name of the level.
+
+        Returns
+        -------
+        Index
+            Calling object, as there is only one level in the Index.
+
+        See Also
+        --------
+        MultiIndex.get_level_values : Get values for a level of a MultiIndex.
+
+        Notes
+        -----
+        For Index, level should be 0, since there are no multiple levels.
+
+        Examples
+        --------
+        >>> idx = pd.Index(list('abc'))
+        >>> idx
+        Index(['a', 'b', 'c'], dtype='object')
+
+        Get level values by supplying `level` as integer:
+
+        >>> idx.get_level_values(0)
+        Index(['a', 'b', 'c'], dtype='object')
+        """
+        self._validate_index_level(level)
+        return self
+
+    get_level_values = _get_level_values
+
+    @final
+    def droplevel(self, level: IndexLabel = 0):
+        """
+        Return index with requested level(s) removed.
+
+        If resulting index has only 1 level left, the result will be
+        of Index type, not MultiIndex. The original index is not modified inplace.
+
+        Parameters
+        ----------
+        level : int, str, or list-like, default 0
+            If a string is given, must be the name of a level
+            If list-like, elements must be names or indexes of levels.
+
+        Returns
+        -------
+        Index or MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays(
+        ... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z'])
+        >>> mi
+        MultiIndex([(1, 3, 5),
+                    (2, 4, 6)],
+                   names=['x', 'y', 'z'])
+
+        >>> mi.droplevel()
+        MultiIndex([(3, 5),
+                    (4, 6)],
+                   names=['y', 'z'])
+
+        >>> mi.droplevel(2)
+        MultiIndex([(1, 3),
+                    (2, 4)],
+                   names=['x', 'y'])
+
+        >>> mi.droplevel('z')
+        MultiIndex([(1, 3),
+                    (2, 4)],
+                   names=['x', 'y'])
+
+        >>> mi.droplevel(['x', 'y'])
+        Index([5, 6], dtype='int64', name='z')
+        """
+        if not isinstance(level, (tuple, list)):
+            level = [level]
+
+        levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
+
+        return self._drop_level_numbers(levnums)
+
+    @final
+    def _drop_level_numbers(self, levnums: list[int]):
+        """
+        Drop MultiIndex levels by level _number_, not name.
+        """
+
+        if not levnums and not isinstance(self, ABCMultiIndex):
+            return self
+        if len(levnums) >= self.nlevels:
+            raise ValueError(
+                f"Cannot remove {len(levnums)} levels from an index with "
+                f"{self.nlevels} levels: at least one level must be left."
+            )
+        # The two checks above guarantee that here self is a MultiIndex
+        self = cast("MultiIndex", self)
+
+        new_levels = list(self.levels)
+        new_codes = list(self.codes)
+        new_names = list(self.names)
+
+        for i in levnums:
+            new_levels.pop(i)
+            new_codes.pop(i)
+            new_names.pop(i)
+
+        if len(new_levels) == 1:
+            lev = new_levels[0]
+
+            if len(lev) == 0:
+                # If lev is empty, lev.take will fail GH#42055
+                if len(new_codes[0]) == 0:
+                    # GH#45230 preserve RangeIndex here
+                    #  see test_reset_index_empty_rangeindex
+                    result = lev[:0]
+                else:
+                    res_values = algos.take(lev._values, new_codes[0], allow_fill=True)
+                    # _constructor instead of type(lev) for RangeIndex compat GH#35230
+                    result = lev._constructor._simple_new(res_values, name=new_names[0])
+            else:
+                # set nan if needed
+                mask = new_codes[0] == -1
+                result = new_levels[0].take(new_codes[0])
+                if mask.any():
+                    result = result.putmask(mask, np.nan)
+
+                result._name = new_names[0]
+
+            return result
+        else:
+            from pandas.core.indexes.multi import MultiIndex
+
+            return MultiIndex(
+                levels=new_levels,
+                codes=new_codes,
+                names=new_names,
+                verify_integrity=False,
+            )
+
+    # --------------------------------------------------------------------
+    # Introspection Methods
+
+    @cache_readonly
+    @final
+    def _can_hold_na(self) -> bool:
+        if isinstance(self.dtype, ExtensionDtype):
+            return self.dtype._can_hold_na
+        if self.dtype.kind in "iub":
+            return False
+        return True
+
+    @property
+    def is_monotonic_increasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or increasing.
+
+        Returns
+        -------
+        bool
+
+        See Also
+        --------
+        Index.is_monotonic_decreasing : Check if the values are equal or decreasing.
+
+        Examples
+        --------
+        >>> pd.Index([1, 2, 3]).is_monotonic_increasing
+        True
+        >>> pd.Index([1, 2, 2]).is_monotonic_increasing
+        True
+        >>> pd.Index([1, 3, 2]).is_monotonic_increasing
+        False
+        """
+        return self._engine.is_monotonic_increasing
+
+    @property
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or decreasing.
+
+        Returns
+        -------
+        bool
+
+        See Also
+        --------
+        Index.is_monotonic_increasing : Check if the values are equal or increasing.
+
+        Examples
+        --------
+        >>> pd.Index([3, 2, 1]).is_monotonic_decreasing
+        True
+        >>> pd.Index([3, 2, 2]).is_monotonic_decreasing
+        True
+        >>> pd.Index([3, 1, 2]).is_monotonic_decreasing
+        False
+        """
+        return self._engine.is_monotonic_decreasing
+
+    @final
+    @property
+    def _is_strictly_monotonic_increasing(self) -> bool:
+        """
+        Return if the index is strictly monotonic increasing
+        (only increasing) values.
+
+        Examples
+        --------
+        >>> Index([1, 2, 3])._is_strictly_monotonic_increasing
+        True
+        >>> Index([1, 2, 2])._is_strictly_monotonic_increasing
+        False
+        >>> Index([1, 3, 2])._is_strictly_monotonic_increasing
+        False
+        """
+        return self.is_unique and self.is_monotonic_increasing
+
+    @final
+    @property
+    def _is_strictly_monotonic_decreasing(self) -> bool:
+        """
+        Return if the index is strictly monotonic decreasing
+        (only decreasing) values.
+
+        Examples
+        --------
+        >>> Index([3, 2, 1])._is_strictly_monotonic_decreasing
+        True
+        >>> Index([3, 2, 2])._is_strictly_monotonic_decreasing
+        False
+        >>> Index([3, 1, 2])._is_strictly_monotonic_decreasing
+        False
+        """
+        return self.is_unique and self.is_monotonic_decreasing
+
+    @cache_readonly
+    def is_unique(self) -> bool:
+        """
+        Return if the index has unique values.
+
+        Returns
+        -------
+        bool
+
+        See Also
+        --------
+        Index.has_duplicates : Inverse method that checks if it has duplicate values.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 5, 7, 7])
+        >>> idx.is_unique
+        False
+
+        >>> idx = pd.Index([1, 5, 7])
+        >>> idx.is_unique
+        True
+
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_unique
+        False
+
+        >>> idx = pd.Index(["Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_unique
+        True
+        """
+        return self._engine.is_unique
+
+    @final
+    @property
+    def has_duplicates(self) -> bool:
+        """
+        Check if the Index has duplicate values.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index has duplicate values.
+
+        See Also
+        --------
+        Index.is_unique : Inverse method that checks if it has unique values.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 5, 7, 7])
+        >>> idx.has_duplicates
+        True
+
+        >>> idx = pd.Index([1, 5, 7])
+        >>> idx.has_duplicates
+        False
+
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.has_duplicates
+        True
+
+        >>> idx = pd.Index(["Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.has_duplicates
+        False
+        """
+        return not self.is_unique
+
+    @final
+    def is_boolean(self) -> bool:
+        """
+        Check if the Index only consists of booleans.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_bool_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of booleans.
+
+        See Also
+        --------
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype (deprecated).
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([True, False, True])
+        >>> idx.is_boolean()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index(["True", "False", "True"])
+        >>> idx.is_boolean()  # doctest: +SKIP
+        False
+
+        >>> idx = pd.Index([True, False, "True"])
+        >>> idx.is_boolean()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_boolean is deprecated. "
+            "Use pandas.api.types.is_bool_type instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["boolean"]
+
+    @final
+    def is_integer(self) -> bool:
+        """
+        Check if the Index only consists of integers.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_integer_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of integers.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx.is_integer()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_integer()  # doctest: +SKIP
+        False
+
+        >>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
+        >>> idx.is_integer()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_integer is deprecated. "
+            "Use pandas.api.types.is_integer_dtype instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["integer"]
+
+    @final
+    def is_floating(self) -> bool:
+        """
+        Check if the Index is a floating type.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_float_dtype` instead
+
+        The Index may consist of only floats, NaNs, or a mix of floats,
+        integers, or NaNs.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of only consists of floats, NaNs, or
+            a mix of floats, integers, or NaNs.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_floating()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1.0, 2.0, np.nan, 4.0])
+        >>> idx.is_floating()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4, np.nan])
+        >>> idx.is_floating()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx.is_floating()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_floating is deprecated. "
+            "Use pandas.api.types.is_float_dtype instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["floating", "mixed-integer-float", "integer-na"]
+
+    @final
+    def is_numeric(self) -> bool:
+        """
+        Check if the Index only consists of numeric data.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_numeric_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of numeric data.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4.0])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4.0, np.nan])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 2, 3, 4.0, np.nan, "Apple"])
+        >>> idx.is_numeric()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_numeric is deprecated. "
+            "Use pandas.api.types.is_any_real_numeric_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["integer", "floating"]
+
+    @final
+    def is_object(self) -> bool:
+        """
+        Check if the Index is of the object dtype.
+
+        .. deprecated:: 2.0.0
+           Use `pandas.api.types.is_object_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index is of the object dtype.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
+        >>> idx.is_object()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index(["Apple", "Mango", 2.0])
+        >>> idx.is_object()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_object()  # doctest: +SKIP
+        False
+
+        >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_object()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_object is deprecated."
+            "Use pandas.api.types.is_object_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return is_object_dtype(self.dtype)
+
+    @final
+    def is_categorical(self) -> bool:
+        """
+        Check if the Index holds categorical data.
+
+        .. deprecated:: 2.0.0
+              Use `isinstance(index.dtype, pd.CategoricalDtype)` instead.
+
+        Returns
+        -------
+        bool
+            True if the Index is categorical.
+
+        See Also
+        --------
+        CategoricalIndex : Index for categorical data.
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_interval : Check if the Index holds Interval objects (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_categorical()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 3, 5, 7])
+        >>> idx.is_categorical()  # doctest: +SKIP
+        False
+
+        >>> s = pd.Series(["Peter", "Victor", "Elisabeth", "Mar"])
+        >>> s
+        0        Peter
+        1       Victor
+        2    Elisabeth
+        3          Mar
+        dtype: object
+        >>> s.index.is_categorical()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_categorical is deprecated."
+            "Use pandas.api.types.is_categorical_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        return self.inferred_type in ["categorical"]
+
+    @final
+    def is_interval(self) -> bool:
+        """
+        Check if the Index holds Interval objects.
+
+        .. deprecated:: 2.0.0
+            Use `isinstance(index.dtype, pd.IntervalDtype)` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index holds Interval objects.
+
+        See Also
+        --------
+        IntervalIndex : Index for Interval objects.
+        is_boolean : Check if the Index only consists of booleans (deprecated).
+        is_integer : Check if the Index only consists of integers (deprecated).
+        is_floating : Check if the Index is a floating type (deprecated).
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
+        is_object : Check if the Index is of the object dtype. (deprecated).
+        is_categorical : Check if the Index holds categorical data (deprecated).
+
+        Examples
+        --------
+        >>> idx = pd.Index([pd.Interval(left=0, right=5),
+        ...                 pd.Interval(left=5, right=10)])
+        >>> idx.is_interval()  # doctest: +SKIP
+        True
+
+        >>> idx = pd.Index([1, 3, 5, 7])
+        >>> idx.is_interval()  # doctest: +SKIP
+        False
+        """
+        warnings.warn(
+            f"{type(self).__name__}.is_interval is deprecated."
+            "Use pandas.api.types.is_interval_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.inferred_type in ["interval"]
+
+    @final
+    def _holds_integer(self) -> bool:
+        """
+        Whether the type is an integer type.
+        """
+        return self.inferred_type in ["integer", "mixed-integer"]
+
+    @final
+    def holds_integer(self) -> bool:
+        """
+        Whether the type is an integer type.
+
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.infer_dtype` instead
+        """
+        warnings.warn(
+            f"{type(self).__name__}.holds_integer is deprecated. "
+            "Use pandas.api.types.infer_dtype instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._holds_integer()
+
+    @cache_readonly
+    def inferred_type(self) -> str_t:
+        """
+        Return a string of the type inferred from the values.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.inferred_type
+        'integer'
+        """
+        return lib.infer_dtype(self._values, skipna=False)
+
+    @cache_readonly
+    @final
+    def _is_all_dates(self) -> bool:
+        """
+        Whether or not the index values only consist of dates.
+        """
+        if needs_i8_conversion(self.dtype):
+            return True
+        elif self.dtype != _dtype_obj:
+            # TODO(ExtensionIndex): 3rd party EA might override?
+            # Note: this includes IntervalIndex, even when the left/right
+            #  contain datetime-like objects.
+            return False
+        elif self._is_multi:
+            return False
+        return is_datetime_array(ensure_object(self._values))
+
+    @final
+    @cache_readonly
+    def _is_multi(self) -> bool:
+        """
+        Cached check equivalent to isinstance(self, MultiIndex)
+        """
+        return isinstance(self, ABCMultiIndex)
+
+    # --------------------------------------------------------------------
+    # Pickle Methods
+
+    def __reduce__(self):
+        d = {"data": self._data, "name": self.name}
+        return _new_Index, (type(self), d), None
+
+    # --------------------------------------------------------------------
+    # Null Handling Methods
+
+    @cache_readonly
+    def _na_value(self):
+        """The expected NA value to use with this index."""
+        dtype = self.dtype
+        if isinstance(dtype, np.dtype):
+            if dtype.kind in "mM":
+                return NaT
+            return np.nan
+        return dtype.na_value
+
+    @cache_readonly
+    def _isnan(self) -> npt.NDArray[np.bool_]:
+        """
+        Return if each value is NaN.
+        """
+        if self._can_hold_na:
+            return isna(self)
+        else:
+            # shouldn't reach to this condition by checking hasnans beforehand
+            values = np.empty(len(self), dtype=np.bool_)
+            values.fill(False)
+            return values
+
+    @cache_readonly
+    def hasnans(self) -> bool:
+        """
+        Return True if there are any NaNs.
+
+        Enables various performance speedups.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=['a', 'b', None])
+        >>> s
+        a    1
+        b    2
+        None 3
+        dtype: int64
+        >>> s.index.hasnans
+        True
+        """
+        if self._can_hold_na:
+            return bool(self._isnan.any())
+        else:
+            return False
+
+    @final
+    def isna(self) -> npt.NDArray[np.bool_]:
+        """
+        Detect missing values.
+
+        Return a boolean same-sized object indicating if the values are NA.
+        NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
+        mapped to ``True`` values.
+        Everything else get mapped to ``False`` values. Characters such as
+        empty strings `''` or :attr:`numpy.inf` are not considered NA values.
+
+        Returns
+        -------
+        numpy.ndarray[bool]
+            A boolean array of whether my values are NA.
+
+        See Also
+        --------
+        Index.notna : Boolean inverse of isna.
+        Index.dropna : Omit entries with missing values.
+        isna : Top-level isna.
+        Series.isna : Detect missing values in Series object.
+
+        Examples
+        --------
+        Show which entries in a pandas.Index are NA. The result is an
+        array.
+
+        >>> idx = pd.Index([5.2, 6.0, np.nan])
+        >>> idx
+        Index([5.2, 6.0, nan], dtype='float64')
+        >>> idx.isna()
+        array([False, False,  True])
+
+        Empty strings are not considered NA values. None is considered an NA
+        value.
+
+        >>> idx = pd.Index(['black', '', 'red', None])
+        >>> idx
+        Index(['black', '', 'red', None], dtype='object')
+        >>> idx.isna()
+        array([False, False, False,  True])
+
+        For datetimes, `NaT` (Not a Time) is considered as an NA value.
+
+        >>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
+        ...                         pd.Timestamp(''), None, pd.NaT])
+        >>> idx
+        DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
+                      dtype='datetime64[ns]', freq=None)
+        >>> idx.isna()
+        array([False,  True,  True,  True])
+        """
+        return self._isnan
+
+    isnull = isna
+
+    @final
+    def notna(self) -> npt.NDArray[np.bool_]:
+        """
+        Detect existing (non-missing) values.
+
+        Return a boolean same-sized object indicating if the values are not NA.
+        Non-missing values get mapped to ``True``. Characters such as empty
+        strings ``''`` or :attr:`numpy.inf` are not considered NA values.
+        NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``
+        values.
+
+        Returns
+        -------
+        numpy.ndarray[bool]
+            Boolean array to indicate which entries are not NA.
+
+        See Also
+        --------
+        Index.notnull : Alias of notna.
+        Index.isna: Inverse of notna.
+        notna : Top-level notna.
+
+        Examples
+        --------
+        Show which entries in an Index are not NA. The result is an
+        array.
+
+        >>> idx = pd.Index([5.2, 6.0, np.nan])
+        >>> idx
+        Index([5.2, 6.0, nan], dtype='float64')
+        >>> idx.notna()
+        array([ True,  True, False])
+
+        Empty strings are not considered NA values. None is considered a NA
+        value.
+
+        >>> idx = pd.Index(['black', '', 'red', None])
+        >>> idx
+        Index(['black', '', 'red', None], dtype='object')
+        >>> idx.notna()
+        array([ True,  True,  True, False])
+        """
+        return ~self.isna()
+
+    notnull = notna
+
+    def fillna(self, value=None, downcast=lib.no_default):
+        """
+        Fill NA/NaN values with the specified value.
+
+        Parameters
+        ----------
+        value : scalar
+            Scalar value to use to fill holes (e.g. 0).
+            This value cannot be a list-likes.
+        downcast : dict, default is None
+            A dict of item->dtype of what to downcast if possible,
+            or the string 'infer' which will try to downcast to an appropriate
+            equal type (e.g. float64 to int64 if possible).
+
+            .. deprecated:: 2.1.0
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        DataFrame.fillna : Fill NaN values of a DataFrame.
+        Series.fillna : Fill NaN Values of a Series.
+
+        Examples
+        --------
+        >>> idx = pd.Index([np.nan, np.nan, 3])
+        >>> idx.fillna(0)
+        Index([0.0, 0.0, 3.0], dtype='float64')
+        """
+        if not is_scalar(value):
+            raise TypeError(f"'value' must be a scalar, passed: {type(value).__name__}")
+        if downcast is not lib.no_default:
+            warnings.warn(
+                f"The 'downcast' keyword in {type(self).__name__}.fillna is "
+                "deprecated and will be removed in a future version. "
+                "It was previously silently ignored.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            downcast = None
+
+        if self.hasnans:
+            result = self.putmask(self._isnan, value)
+            if downcast is None:
+                # no need to care metadata other than name
+                # because it can't have freq if it has NaTs
+                # _with_infer needed for test_fillna_categorical
+                return Index._with_infer(result, name=self.name)
+            raise NotImplementedError(
+                f"{type(self).__name__}.fillna does not support 'downcast' "
+                "argument values other than 'None'."
+            )
+        return self._view()
+
+    def dropna(self, how: AnyAll = "any") -> Self:
+        """
+        Return Index without NA/NaN values.
+
+        Parameters
+        ----------
+        how : {'any', 'all'}, default 'any'
+            If the Index is a MultiIndex, drop the value when any or all levels
+            are NaN.
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, np.nan, 3])
+        >>> idx.dropna()
+        Index([1.0, 3.0], dtype='float64')
+        """
+        if how not in ("any", "all"):
+            raise ValueError(f"invalid how option: {how}")
+
+        if self.hasnans:
+            res_values = self._values[~self._isnan]
+            return type(self)._simple_new(res_values, name=self.name)
+        return self._view()
+
+    # --------------------------------------------------------------------
+    # Uniqueness Methods
+
+    def unique(self, level: Hashable | None = None) -> Self:
+        """
+        Return unique values in the index.
+
+        Unique values are returned in order of appearance, this does NOT sort.
+
+        Parameters
+        ----------
+        level : int or hashable, optional
+            Only return values from specified level (for MultiIndex).
+            If int, gets the level by integer position, else by level name.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        unique : Numpy array of unique values in that column.
+        Series.unique : Return unique values of Series object.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 1, 2, 3, 3])
+        >>> idx.unique()
+        Index([1, 2, 3], dtype='int64')
+        """
+        if level is not None:
+            self._validate_index_level(level)
+
+        if self.is_unique:
+            return self._view()
+
+        result = super().unique()
+        return self._shallow_copy(result)
+
+    def drop_duplicates(self, *, keep: DropKeep = "first") -> Self:
+        """
+        Return Index with duplicate values removed.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        Series.drop_duplicates : Equivalent method on Series.
+        DataFrame.drop_duplicates : Equivalent method on DataFrame.
+        Index.duplicated : Related method on Index, indicating duplicate
+            Index values.
+
+        Examples
+        --------
+        Generate an pandas.Index with duplicate values.
+
+        >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
+
+        The `keep` parameter controls  which duplicate values are removed.
+        The value 'first' keeps the first occurrence for each
+        set of duplicated entries. The default value of keep is 'first'.
+
+        >>> idx.drop_duplicates(keep='first')
+        Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')
+
+        The value 'last' keeps the last occurrence for each set of duplicated
+        entries.
+
+        >>> idx.drop_duplicates(keep='last')
+        Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object')
+
+        The value ``False`` discards all sets of duplicated entries.
+
+        >>> idx.drop_duplicates(keep=False)
+        Index(['cow', 'beetle', 'hippo'], dtype='object')
+        """
+        if self.is_unique:
+            return self._view()
+
+        return super().drop_duplicates(keep=keep)
+
+    def duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
+        """
+        Indicate duplicate index values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        array. Either all duplicates, all except the first, or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            The value or values in a set of duplicates to mark as missing.
+
+            - 'first' : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - 'last' : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        np.ndarray[bool]
+
+        See Also
+        --------
+        Series.duplicated : Equivalent method on pandas.Series.
+        DataFrame.duplicated : Equivalent method on pandas.DataFrame.
+        Index.drop_duplicates : Remove duplicate values from Index.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set to False and all others to True:
+
+        >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> idx.duplicated()
+        array([False, False,  True, False,  True])
+
+        which is equivalent to
+
+        >>> idx.duplicated(keep='first')
+        array([False, False,  True, False,  True])
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> idx.duplicated(keep='last')
+        array([ True, False,  True, False, False])
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> idx.duplicated(keep=False)
+        array([ True, False,  True, False,  True])
+        """
+        if self.is_unique:
+            # fastpath available bc we are immutable
+            return np.zeros(len(self), dtype=bool)
+        return self._duplicated(keep=keep)
+
+    # --------------------------------------------------------------------
+    # Arithmetic & Logical Methods
+
+    def __iadd__(self, other):
+        # alias for __add__
+        return self + other
+
+    @final
+    def __nonzero__(self) -> NoReturn:
+        raise ValueError(
+            f"The truth value of a {type(self).__name__} is ambiguous. "
+            "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
+        )
+
+    __bool__ = __nonzero__
+
+    # --------------------------------------------------------------------
+    # Set Operation Methods
+
+    def _get_reconciled_name_object(self, other):
+        """
+        If the result of a set operation will be self,
+        return self, unless the name changes, in which
+        case make a shallow copy of self.
+        """
+        name = get_op_result_name(self, other)
+        if self.name is not name:
+            return self.rename(name)
+        return self
+
+    @final
+    def _validate_sort_keyword(self, sort):
+        if sort not in [None, False, True]:
+            raise ValueError(
+                "The 'sort' keyword only takes the values of "
+                f"None, True, or False; {sort} was passed."
+            )
+
+    @final
+    def _dti_setop_align_tzs(self, other: Index, setop: str_t) -> tuple[Index, Index]:
+        """
+        With mismatched timezones, cast both to UTC.
+        """
+        # Caller is responsibelf or checking
+        #  `self.dtype != other.dtype`
+        if (
+            isinstance(self, ABCDatetimeIndex)
+            and isinstance(other, ABCDatetimeIndex)
+            and self.tz is not None
+            and other.tz is not None
+        ):
+            # GH#39328, GH#45357
+            left = self.tz_convert("UTC")
+            right = other.tz_convert("UTC")
+            return left, right
+        return self, other
+
+    @final
+    def union(self, other, sort=None):
+        """
+        Form the union of two Index objects.
+
+        If the Index objects are incompatible, both Index objects will be
+        cast to dtype('object') first.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : bool or None, default None
+            Whether to sort the resulting Index.
+
+            * None : Sort the result, except when
+
+              1. `self` and `other` are equal.
+              2. `self` or `other` has length 0.
+              3. Some values in `self` or `other` cannot be compared.
+                 A RuntimeWarning is issued in this case.
+
+            * False : do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        Union matching dtypes
+
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([3, 4, 5, 6])
+        >>> idx1.union(idx2)
+        Index([1, 2, 3, 4, 5, 6], dtype='int64')
+
+        Union mismatched dtypes
+
+        >>> idx1 = pd.Index(['a', 'b', 'c', 'd'])
+        >>> idx2 = pd.Index([1, 2, 3, 4])
+        >>> idx1.union(idx2)
+        Index(['a', 'b', 'c', 'd', 1, 2, 3, 4], dtype='object')
+
+        MultiIndex case
+
+        >>> idx1 = pd.MultiIndex.from_arrays(
+        ...     [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+        ... )
+        >>> idx1
+        MultiIndex([(1,  'Red'),
+            (1, 'Blue'),
+            (2,  'Red'),
+            (2, 'Blue')],
+           )
+        >>> idx2 = pd.MultiIndex.from_arrays(
+        ...     [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
+        ... )
+        >>> idx2
+        MultiIndex([(3,   'Red'),
+            (3, 'Green'),
+            (2,   'Red'),
+            (2, 'Green')],
+           )
+        >>> idx1.union(idx2)
+        MultiIndex([(1,  'Blue'),
+            (1,   'Red'),
+            (2,  'Blue'),
+            (2, 'Green'),
+            (2,   'Red'),
+            (3, 'Green'),
+            (3,   'Red')],
+           )
+        >>> idx1.union(idx2, sort=False)
+        MultiIndex([(1,   'Red'),
+            (1,  'Blue'),
+            (2,   'Red'),
+            (2,  'Blue'),
+            (3,   'Red'),
+            (3, 'Green'),
+            (2, 'Green')],
+           )
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        if self.dtype != other.dtype:
+            if (
+                isinstance(self, ABCMultiIndex)
+                and not is_object_dtype(_unpack_nested_dtype(other))
+                and len(other) > 0
+            ):
+                raise NotImplementedError(
+                    "Can only union MultiIndex with MultiIndex or Index of tuples, "
+                    "try mi.to_flat_index().union(other) instead."
+                )
+            self, other = self._dti_setop_align_tzs(other, "union")
+
+            dtype = self._find_common_type_compat(other)
+            left = self.astype(dtype, copy=False)
+            right = other.astype(dtype, copy=False)
+            return left.union(right, sort=sort)
+
+        elif not len(other) or self.equals(other):
+            # NB: whether this (and the `if not len(self)` check below) come before
+            #  or after the dtype equality check above affects the returned dtype
+            result = self._get_reconciled_name_object(other)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        elif not len(self):
+            result = other._get_reconciled_name_object(self)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        result = self._union(other, sort=sort)
+
+        return self._wrap_setop_result(other, result)
+
+    def _union(self, other: Index, sort: bool | None):
+        """
+        Specific union logic should go here. In subclasses, union behavior
+        should be overwritten here rather than in `self.union`.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : False or None, default False
+            Whether to sort the resulting index.
+
+            * True : sort the result
+            * False : do not sort the result.
+            * None : sort the result, except when `self` and `other` are equal
+              or when the values cannot be compared.
+
+        Returns
+        -------
+        Index
+        """
+        lvals = self._values
+        rvals = other._values
+
+        if (
+            sort in (None, True)
+            and self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and not (self.has_duplicates and other.has_duplicates)
+            and self._can_use_libjoin
+            and other._can_use_libjoin
+        ):
+            # Both are monotonic and at least one is unique, so can use outer join
+            #  (actually don't need either unique, but without this restriction
+            #  test_union_same_value_duplicated_in_both fails)
+            try:
+                return self._outer_indexer(other)[0]
+            except (TypeError, IncompatibleFrequency):
+                # incomparable objects; should only be for object dtype
+                value_list = list(lvals)
+
+                # worth making this faster? a very unusual case
+                value_set = set(lvals)
+                value_list.extend([x for x in rvals if x not in value_set])
+                # If objects are unorderable, we must have object dtype.
+                return np.array(value_list, dtype=object)
+
+        elif not other.is_unique:
+            # other has duplicates
+            result_dups = algos.union_with_duplicates(self, other)
+            return _maybe_try_sort(result_dups, sort)
+
+        # The rest of this method is analogous to Index._intersection_via_get_indexer
+
+        # Self may have duplicates; other already checked as unique
+        # find indexes of things in "other" that are not in "self"
+        if self._index_as_unique:
+            indexer = self.get_indexer(other)
+            missing = (indexer == -1).nonzero()[0]
+        else:
+            missing = algos.unique1d(self.get_indexer_non_unique(other)[1])
+
+        result: Index | MultiIndex | ArrayLike
+        if self._is_multi:
+            # Preserve MultiIndex to avoid losing dtypes
+            result = self.append(other.take(missing))
+
+        else:
+            if len(missing) > 0:
+                other_diff = rvals.take(missing)
+                result = concat_compat((lvals, other_diff))
+            else:
+                result = lvals
+
+        if not self.is_monotonic_increasing or not other.is_monotonic_increasing:
+            # if both are monotonic then result should already be sorted
+            result = _maybe_try_sort(result, sort)
+
+        return result
+
+    @final
+    def _wrap_setop_result(self, other: Index, result) -> Index:
+        name = get_op_result_name(self, other)
+        if isinstance(result, Index):
+            if result.name != name:
+                result = result.rename(name)
+        else:
+            result = self._shallow_copy(result, name=name)
+        return result
+
+    @final
+    def intersection(self, other, sort: bool = False):
+        # default sort keyword is different here from other setops intentionally
+        #  done in GH#25063
+        """
+        Form the intersection of two Index objects.
+
+        This returns a new Index with elements common to the index and `other`.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : True, False or None, default False
+            Whether to sort the resulting index.
+
+            * None : sort the result, except when `self` and `other` are equal
+              or when the values cannot be compared.
+            * False : do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([3, 4, 5, 6])
+        >>> idx1.intersection(idx2)
+        Index([3, 4], dtype='int64')
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        if self.dtype != other.dtype:
+            self, other = self._dti_setop_align_tzs(other, "intersection")
+
+        if self.equals(other):
+            if not self.is_unique:
+                result = self.unique()._get_reconciled_name_object(other)
+            else:
+                result = self._get_reconciled_name_object(other)
+            if sort is True:
+                result = result.sort_values()
+            return result
+
+        if len(self) == 0 or len(other) == 0:
+            # fastpath; we need to be careful about having commutativity
+
+            if self._is_multi or other._is_multi:
+                # _convert_can_do_setop ensures that we have both or neither
+                # We retain self.levels
+                return self[:0].rename(result_name)
+
+            dtype = self._find_common_type_compat(other)
+            if self.dtype == dtype:
+                # Slicing allows us to retain DTI/TDI.freq, RangeIndex
+
+                # Note: self[:0] vs other[:0] affects
+                #  1) which index's `freq` we get in DTI/TDI cases
+                #     This may be a historical artifact, i.e. no documented
+                #     reason for this choice.
+                #  2) The `step` we get in RangeIndex cases
+                if len(self) == 0:
+                    return self[:0].rename(result_name)
+                else:
+                    return other[:0].rename(result_name)
+
+            return Index([], dtype=dtype, name=result_name)
+
+        elif not self._should_compare(other):
+            # We can infer that the intersection is empty.
+            if isinstance(self, ABCMultiIndex):
+                return self[:0].rename(result_name)
+            return Index([], name=result_name)
+
+        elif self.dtype != other.dtype:
+            dtype = self._find_common_type_compat(other)
+            this = self.astype(dtype, copy=False)
+            other = other.astype(dtype, copy=False)
+            return this.intersection(other, sort=sort)
+
+        result = self._intersection(other, sort=sort)
+        return self._wrap_intersection_result(other, result)
+
+    def _intersection(self, other: Index, sort: bool = False):
+        """
+        intersection specialized to the case with matching dtypes.
+        """
+        if (
+            self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and self._can_use_libjoin
+            and other._can_use_libjoin
+        ):
+            try:
+                res_indexer, indexer, _ = self._inner_indexer(other)
+            except TypeError:
+                # non-comparable; should only be for object dtype
+                pass
+            else:
+                # TODO: algos.unique1d should preserve DTA/TDA
+                if is_numeric_dtype(self.dtype):
+                    # This is faster, because Index.unique() checks for uniqueness
+                    # before calculating the unique values.
+                    res = algos.unique1d(res_indexer)
+                else:
+                    result = self.take(indexer)
+                    res = result.drop_duplicates()
+                return ensure_wrapped_if_datetimelike(res)
+
+        res_values = self._intersection_via_get_indexer(other, sort=sort)
+        res_values = _maybe_try_sort(res_values, sort)
+        return res_values
+
+    def _wrap_intersection_result(self, other, result):
+        # We will override for MultiIndex to handle empty results
+        return self._wrap_setop_result(other, result)
+
+    @final
+    def _intersection_via_get_indexer(
+        self, other: Index | MultiIndex, sort
+    ) -> ArrayLike | MultiIndex:
+        """
+        Find the intersection of two Indexes using get_indexer.
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray or MultiIndex
+            The returned array will be unique.
+        """
+        left_unique = self.unique()
+        right_unique = other.unique()
+
+        # even though we are unique, we need get_indexer_for for IntervalIndex
+        indexer = left_unique.get_indexer_for(right_unique)
+
+        mask = indexer != -1
+
+        taker = indexer.take(mask.nonzero()[0])
+        if sort is False:
+            # sort bc we want the elements in the same order they are in self
+            # unnecessary in the case with sort=None bc we will sort later
+            taker = np.sort(taker)
+
+        result: MultiIndex | ExtensionArray | np.ndarray
+        if isinstance(left_unique, ABCMultiIndex):
+            result = left_unique.take(taker)
+        else:
+            result = left_unique.take(taker)._values
+        return result
+
+    @final
+    def difference(self, other, sort=None):
+        """
+        Return a new Index with elements of index not in `other`.
+
+        This is the set difference of two Index objects.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : bool or None, default None
+            Whether to sort the resulting index. By default, the
+            values are attempted to be sorted, but any TypeError from
+            incomparable elements is caught by pandas.
+
+            * None : Attempt to sort the result, but catch any TypeErrors
+              from comparing incomparable elements.
+            * False : Do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([2, 1, 3, 4])
+        >>> idx2 = pd.Index([3, 4, 5, 6])
+        >>> idx1.difference(idx2)
+        Index([1, 2], dtype='int64')
+        >>> idx1.difference(idx2, sort=False)
+        Index([2, 1], dtype='int64')
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        # Note: we do NOT call _dti_setop_align_tzs here, as there
+        #  is no requirement that .difference be commutative, so it does
+        #  not cast to object.
+
+        if self.equals(other):
+            # Note: we do not (yet) sort even if sort=None GH#24959
+            return self[:0].rename(result_name)
+
+        if len(other) == 0:
+            # Note: we do not (yet) sort even if sort=None GH#24959
+            result = self.unique().rename(result_name)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        if not self._should_compare(other):
+            # Nothing matches -> difference is everything
+            result = self.unique().rename(result_name)
+            if sort is True:
+                return result.sort_values()
+            return result
+
+        result = self._difference(other, sort=sort)
+        return self._wrap_difference_result(other, result)
+
+    def _difference(self, other, sort):
+        # overridden by RangeIndex
+        this = self
+        if isinstance(self, ABCCategoricalIndex) and self.hasnans and other.hasnans:
+            this = this.dropna()
+        other = other.unique()
+        the_diff = this[other.get_indexer_for(this) == -1]
+        the_diff = the_diff if this.is_unique else the_diff.unique()
+        the_diff = _maybe_try_sort(the_diff, sort)
+        return the_diff
+
+    def _wrap_difference_result(self, other, result):
+        # We will override for MultiIndex to handle empty results
+        return self._wrap_setop_result(other, result)
+
+    def symmetric_difference(self, other, result_name=None, sort=None):
+        """
+        Compute the symmetric difference of two Index objects.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        result_name : str
+        sort : bool or None, default None
+            Whether to sort the resulting index. By default, the
+            values are attempted to be sorted, but any TypeError from
+            incomparable elements is caught by pandas.
+
+            * None : Attempt to sort the result, but catch any TypeErrors
+              from comparing incomparable elements.
+            * False : Do not sort the result.
+            * True : Sort the result (which may raise TypeError).
+
+        Returns
+        -------
+        Index
+
+        Notes
+        -----
+        ``symmetric_difference`` contains elements that appear in either
+        ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
+        ``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
+        dropped.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([2, 3, 4, 5])
+        >>> idx1.symmetric_difference(idx2)
+        Index([1, 5], dtype='int64')
+        """
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name_update = self._convert_can_do_setop(other)
+        if result_name is None:
+            result_name = result_name_update
+
+        if self.dtype != other.dtype:
+            self, other = self._dti_setop_align_tzs(other, "symmetric_difference")
+
+        if not self._should_compare(other):
+            return self.union(other, sort=sort).rename(result_name)
+
+        elif self.dtype != other.dtype:
+            dtype = self._find_common_type_compat(other)
+            this = self.astype(dtype, copy=False)
+            that = other.astype(dtype, copy=False)
+            return this.symmetric_difference(that, sort=sort).rename(result_name)
+
+        this = self.unique()
+        other = other.unique()
+        indexer = this.get_indexer_for(other)
+
+        # {this} minus {other}
+        common_indexer = indexer.take((indexer != -1).nonzero()[0])
+        left_indexer = np.setdiff1d(
+            np.arange(this.size), common_indexer, assume_unique=True
+        )
+        left_diff = this.take(left_indexer)
+
+        # {other} minus {this}
+        right_indexer = (indexer == -1).nonzero()[0]
+        right_diff = other.take(right_indexer)
+
+        res_values = left_diff.append(right_diff)
+        result = _maybe_try_sort(res_values, sort)
+
+        if not self._is_multi:
+            return Index(result, name=result_name, dtype=res_values.dtype)
+        else:
+            left_diff = cast("MultiIndex", left_diff)
+            if len(result) == 0:
+                # result might be an Index, if other was an Index
+                return left_diff.remove_unused_levels().set_names(result_name)
+            return result.set_names(result_name)
+
+    @final
+    def _assert_can_do_setop(self, other) -> bool:
+        if not is_list_like(other):
+            raise TypeError("Input must be Index or array-like")
+        return True
+
+    def _convert_can_do_setop(self, other) -> tuple[Index, Hashable]:
+        if not isinstance(other, Index):
+            other = Index(other, name=self.name)
+            result_name = self.name
+        else:
+            result_name = get_op_result_name(self, other)
+        return other, result_name
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def get_loc(self, key):
+        """
+        Get integer location, slice or boolean mask for requested label.
+
+        Parameters
+        ----------
+        key : label
+
+        Returns
+        -------
+        int if unique index, slice if monotonic index, else mask
+
+        Examples
+        --------
+        >>> unique_index = pd.Index(list('abc'))
+        >>> unique_index.get_loc('b')
+        1
+
+        >>> monotonic_index = pd.Index(list('abbc'))
+        >>> monotonic_index.get_loc('b')
+        slice(1, 3, None)
+
+        >>> non_monotonic_index = pd.Index(list('abcb'))
+        >>> non_monotonic_index.get_loc('b')
+        array([False,  True, False,  True])
+        """
+        casted_key = self._maybe_cast_indexer(key)
+        try:
+            return self._engine.get_loc(casted_key)
+        except KeyError as err:
+            if isinstance(casted_key, slice) or (
+                isinstance(casted_key, abc.Iterable)
+                and any(isinstance(x, slice) for x in casted_key)
+            ):
+                raise InvalidIndexError(key)
+            raise KeyError(key) from err
+        except TypeError:
+            # If we have a listlike key, _check_indexing_error will raise
+            #  InvalidIndexError. Otherwise we fall through and re-raise
+            #  the TypeError.
+            self._check_indexing_error(key)
+            raise
+
+    @final
+    def get_indexer(
+        self,
+        target,
+        method: ReindexMethod | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> npt.NDArray[np.intp]:
+        """
+        Compute indexer and mask for new index given the current index.
+
+        The indexer should be then used as an input to ndarray.take to align the
+        current data to the new index.
+
+        Parameters
+        ----------
+        target : Index
+        method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
+            * default: exact matches only.
+            * pad / ffill: find the PREVIOUS index value if no exact match.
+            * backfill / bfill: use NEXT index value if no exact match
+            * nearest: use the NEAREST index value if no exact match. Tied
+              distances are broken by preferring the larger index value.
+        limit : int, optional
+            Maximum number of consecutive labels in ``target`` to match for
+            inexact matches.
+        tolerance : optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations must
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            Integers from 0 to n - 1 indicating that the index at these
+            positions matches the corresponding target values. Missing values
+            in the target are marked by -1.
+
+        Notes
+        -----
+        Returns -1 for unmatched values, for further explanation see the
+        example below.
+
+        Examples
+        --------
+        >>> index = pd.Index(['c', 'a', 'b'])
+        >>> index.get_indexer(['a', 'b', 'x'])
+        array([ 1,  2, -1])
+
+        Notice that the return value is an array of locations in ``index``
+        and ``x`` is marked by -1, as it is not in ``index``.
+        """
+        method = clean_reindex_fill_method(method)
+        orig_target = target
+        target = self._maybe_cast_listlike_indexer(target)
+
+        self._check_indexing_method(method, limit, tolerance)
+
+        if not self._index_as_unique:
+            raise InvalidIndexError(self._requires_unique_msg)
+
+        if len(target) == 0:
+            return np.array([], dtype=np.intp)
+
+        if not self._should_compare(target) and not self._should_partial_index(target):
+            # IntervalIndex get special treatment bc numeric scalars can be
+            #  matched to Interval scalars
+            return self._get_indexer_non_comparable(target, method=method, unique=True)
+
+        if isinstance(self.dtype, CategoricalDtype):
+            # _maybe_cast_listlike_indexer ensures target has our dtype
+            #  (could improve perf by doing _should_compare check earlier?)
+            assert self.dtype == target.dtype
+
+            indexer = self._engine.get_indexer(target.codes)
+            if self.hasnans and target.hasnans:
+                # After _maybe_cast_listlike_indexer, target elements which do not
+                # belong to some category are changed to NaNs
+                # Mask to track actual NaN values compared to inserted NaN values
+                # GH#45361
+                target_nans = isna(orig_target)
+                loc = self.get_loc(np.nan)
+                mask = target.isna()
+                indexer[target_nans] = loc
+                indexer[mask & ~target_nans] = -1
+            return indexer
+
+        if isinstance(target.dtype, CategoricalDtype):
+            # potential fastpath
+            # get an indexer for unique categories then propagate to codes via take_nd
+            # get_indexer instead of _get_indexer needed for MultiIndex cases
+            #  e.g. test_append_different_columns_types
+            categories_indexer = self.get_indexer(target.categories)
+
+            indexer = algos.take_nd(categories_indexer, target.codes, fill_value=-1)
+
+            if (not self._is_multi and self.hasnans) and target.hasnans:
+                # Exclude MultiIndex because hasnans raises NotImplementedError
+                # we should only get here if we are unique, so loc is an integer
+                # GH#41934
+                loc = self.get_loc(np.nan)
+                mask = target.isna()
+                indexer[mask] = loc
+
+            return ensure_platform_int(indexer)
+
+        pself, ptarget = self._maybe_downcast_for_indexing(target)
+        if pself is not self or ptarget is not target:
+            return pself.get_indexer(
+                ptarget, method=method, limit=limit, tolerance=tolerance
+            )
+
+        if self.dtype == target.dtype and self.equals(target):
+            # Only call equals if we have same dtype to avoid inference/casting
+            return np.arange(len(target), dtype=np.intp)
+
+        if self.dtype != target.dtype and not self._should_partial_index(target):
+            # _should_partial_index e.g. IntervalIndex with numeric scalars
+            #  that can be matched to Interval scalars.
+            dtype = self._find_common_type_compat(target)
+
+            this = self.astype(dtype, copy=False)
+            target = target.astype(dtype, copy=False)
+            return this._get_indexer(
+                target, method=method, limit=limit, tolerance=tolerance
+            )
+
+        return self._get_indexer(target, method, limit, tolerance)
+
+    def _get_indexer(
+        self,
+        target: Index,
+        method: str_t | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> npt.NDArray[np.intp]:
+        if tolerance is not None:
+            tolerance = self._convert_tolerance(tolerance, target)
+
+        if method in ["pad", "backfill"]:
+            indexer = self._get_fill_indexer(target, method, limit, tolerance)
+        elif method == "nearest":
+            indexer = self._get_nearest_indexer(target, limit, tolerance)
+        else:
+            if target._is_multi and self._is_multi:
+                engine = self._engine
+                # error: Item "IndexEngine" of "Union[IndexEngine, ExtensionEngine]"
+                # has no attribute "_extract_level_codes"
+                tgt_values = engine._extract_level_codes(  # type: ignore[union-attr]
+                    target
+                )
+            else:
+                tgt_values = target._get_engine_target()
+
+            indexer = self._engine.get_indexer(tgt_values)
+
+        return ensure_platform_int(indexer)
+
+    @final
+    def _should_partial_index(self, target: Index) -> bool:
+        """
+        Should we attempt partial-matching indexing?
+        """
+        if isinstance(self.dtype, IntervalDtype):
+            if isinstance(target.dtype, IntervalDtype):
+                return False
+            # "Index" has no attribute "left"
+            return self.left._should_compare(target)  # type: ignore[attr-defined]
+        return False
+
+    @final
+    def _check_indexing_method(
+        self,
+        method: str_t | None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> None:
+        """
+        Raise if we have a get_indexer `method` that is not supported or valid.
+        """
+        if method not in [None, "bfill", "backfill", "pad", "ffill", "nearest"]:
+            # in practice the clean_reindex_fill_method call would raise
+            #  before we get here
+            raise ValueError("Invalid fill method")  # pragma: no cover
+
+        if self._is_multi:
+            if method == "nearest":
+                raise NotImplementedError(
+                    "method='nearest' not implemented yet "
+                    "for MultiIndex; see GitHub issue 9365"
+                )
+            if method in ("pad", "backfill"):
+                if tolerance is not None:
+                    raise NotImplementedError(
+                        "tolerance not implemented yet for MultiIndex"
+                    )
+
+        if isinstance(self.dtype, (IntervalDtype, CategoricalDtype)):
+            # GH#37871 for now this is only for IntervalIndex and CategoricalIndex
+            if method is not None:
+                raise NotImplementedError(
+                    f"method {method} not yet implemented for {type(self).__name__}"
+                )
+
+        if method is None:
+            if tolerance is not None:
+                raise ValueError(
+                    "tolerance argument only valid if doing pad, "
+                    "backfill or nearest reindexing"
+                )
+            if limit is not None:
+                raise ValueError(
+                    "limit argument only valid if doing pad, "
+                    "backfill or nearest reindexing"
+                )
+
+    def _convert_tolerance(self, tolerance, target: np.ndarray | Index) -> np.ndarray:
+        # override this method on subclasses
+        tolerance = np.asarray(tolerance)
+        if target.size != tolerance.size and tolerance.size > 1:
+            raise ValueError("list-like tolerance size must match target index size")
+        elif is_numeric_dtype(self) and not np.issubdtype(tolerance.dtype, np.number):
+            if tolerance.ndim > 0:
+                raise ValueError(
+                    f"tolerance argument for {type(self).__name__} with dtype "
+                    f"{self.dtype} must contain numeric elements if it is list type"
+                )
+
+            raise ValueError(
+                f"tolerance argument for {type(self).__name__} with dtype {self.dtype} "
+                f"must be numeric if it is a scalar: {repr(tolerance)}"
+            )
+        return tolerance
+
+    @final
+    def _get_fill_indexer(
+        self, target: Index, method: str_t, limit: int | None = None, tolerance=None
+    ) -> npt.NDArray[np.intp]:
+        if self._is_multi:
+            if not (self.is_monotonic_increasing or self.is_monotonic_decreasing):
+                raise ValueError("index must be monotonic increasing or decreasing")
+            encoded = self.append(target)._engine.values  # type: ignore[union-attr]
+            self_encoded = Index(encoded[: len(self)])
+            target_encoded = Index(encoded[len(self) :])
+            return self_encoded._get_fill_indexer(
+                target_encoded, method, limit, tolerance
+            )
+
+        if self.is_monotonic_increasing and target.is_monotonic_increasing:
+            target_values = target._get_engine_target()
+            own_values = self._get_engine_target()
+            if not isinstance(target_values, np.ndarray) or not isinstance(
+                own_values, np.ndarray
+            ):
+                raise NotImplementedError
+
+            if method == "pad":
+                indexer = libalgos.pad(own_values, target_values, limit=limit)
+            else:
+                # i.e. "backfill"
+                indexer = libalgos.backfill(own_values, target_values, limit=limit)
+        else:
+            indexer = self._get_fill_indexer_searchsorted(target, method, limit)
+        if tolerance is not None and len(self):
+            indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
+        return indexer
+
+    @final
+    def _get_fill_indexer_searchsorted(
+        self, target: Index, method: str_t, limit: int | None = None
+    ) -> npt.NDArray[np.intp]:
+        """
+        Fallback pad/backfill get_indexer that works for monotonic decreasing
+        indexes and non-monotonic targets.
+        """
+        if limit is not None:
+            raise ValueError(
+                f"limit argument for {repr(method)} method only well-defined "
+                "if index and target are monotonic"
+            )
+
+        side: Literal["left", "right"] = "left" if method == "pad" else "right"
+
+        # find exact matches first (this simplifies the algorithm)
+        indexer = self.get_indexer(target)
+        nonexact = indexer == -1
+        indexer[nonexact] = self._searchsorted_monotonic(target[nonexact], side)
+        if side == "left":
+            # searchsorted returns "indices into a sorted array such that,
+            # if the corresponding elements in v were inserted before the
+            # indices, the order of a would be preserved".
+            # Thus, we need to subtract 1 to find values to the left.
+            indexer[nonexact] -= 1
+            # This also mapped not found values (values of 0 from
+            # np.searchsorted) to -1, which conveniently is also our
+            # sentinel for missing values
+        else:
+            # Mark indices to the right of the largest value as not found
+            indexer[indexer == len(self)] = -1
+        return indexer
+
+    @final
+    def _get_nearest_indexer(
+        self, target: Index, limit: int | None, tolerance
+    ) -> npt.NDArray[np.intp]:
+        """
+        Get the indexer for the nearest index labels; requires an index with
+        values that can be subtracted from each other (e.g., not strings or
+        tuples).
+        """
+        if not len(self):
+            return self._get_fill_indexer(target, "pad")
+
+        left_indexer = self.get_indexer(target, "pad", limit=limit)
+        right_indexer = self.get_indexer(target, "backfill", limit=limit)
+
+        left_distances = self._difference_compat(target, left_indexer)
+        right_distances = self._difference_compat(target, right_indexer)
+
+        op = operator.lt if self.is_monotonic_increasing else operator.le
+        indexer = np.where(
+            # error: Argument 1&2 has incompatible type "Union[ExtensionArray,
+            # ndarray[Any, Any]]"; expected "Union[SupportsDunderLE,
+            # SupportsDunderGE, SupportsDunderGT, SupportsDunderLT]"
+            op(left_distances, right_distances)  # type: ignore[arg-type]
+            | (right_indexer == -1),
+            left_indexer,
+            right_indexer,
+        )
+        if tolerance is not None:
+            indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
+        return indexer
+
+    @final
+    def _filter_indexer_tolerance(
+        self,
+        target: Index,
+        indexer: npt.NDArray[np.intp],
+        tolerance,
+    ) -> npt.NDArray[np.intp]:
+        distance = self._difference_compat(target, indexer)
+
+        return np.where(distance <= tolerance, indexer, -1)
+
+    @final
+    def _difference_compat(
+        self, target: Index, indexer: npt.NDArray[np.intp]
+    ) -> ArrayLike:
+        # Compatibility for PeriodArray, for which __sub__ returns an ndarray[object]
+        #  of DateOffset objects, which do not support __abs__ (and would be slow
+        #  if they did)
+
+        if isinstance(self.dtype, PeriodDtype):
+            # Note: we only get here with matching dtypes
+            own_values = cast("PeriodArray", self._data)._ndarray
+            target_values = cast("PeriodArray", target._data)._ndarray
+            diff = own_values[indexer] - target_values
+        else:
+            # error: Unsupported left operand type for - ("ExtensionArray")
+            diff = self._values[indexer] - target._values  # type: ignore[operator]
+        return abs(diff)
+
+    # --------------------------------------------------------------------
+    # Indexer Conversion Methods
+
+    @final
+    def _validate_positional_slice(self, key: slice) -> None:
+        """
+        For positional indexing, a slice must have either int or None
+        for each of start, stop, and step.
+        """
+        self._validate_indexer("positional", key.start, "iloc")
+        self._validate_indexer("positional", key.stop, "iloc")
+        self._validate_indexer("positional", key.step, "iloc")
+
+    def _convert_slice_indexer(self, key: slice, kind: Literal["loc", "getitem"]):
+        """
+        Convert a slice indexer.
+
+        By definition, these are labels unless 'iloc' is passed in.
+        Floats are not allowed as the start, step, or stop of the slice.
+
+        Parameters
+        ----------
+        key : label of the slice bound
+        kind : {'loc', 'getitem'}
+        """
+
+        # potentially cast the bounds to integers
+        start, stop, step = key.start, key.stop, key.step
+
+        # figure out if this is a positional indexer
+        is_index_slice = is_valid_positional_slice(key)
+
+        # TODO(GH#50617): once Series.__[gs]etitem__ is removed we should be able
+        #  to simplify this.
+        if lib.is_np_dtype(self.dtype, "f"):
+            # We always treat __getitem__ slicing as label-based
+            # translate to locations
+            if kind == "getitem" and is_index_slice and not start == stop and step != 0:
+                # exclude step=0 from the warning because it will raise anyway
+                # start/stop both None e.g. [:] or [::-1] won't change.
+                # exclude start==stop since it will be empty either way, or
+                # will be [:] or [::-1] which won't change
+                warnings.warn(
+                    # GH#49612
+                    "The behavior of obj[i:j] with a float-dtype index is "
+                    "deprecated. In a future version, this will be treated as "
+                    "positional instead of label-based. For label-based slicing, "
+                    "use obj.loc[i:j] instead",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            return self.slice_indexer(start, stop, step)
+
+        if kind == "getitem":
+            # called from the getitem slicers, validate that we are in fact integers
+            if is_index_slice:
+                # In this case the _validate_indexer checks below are redundant
+                return key
+            elif self.dtype.kind in "iu":
+                # Note: these checks are redundant if we know is_index_slice
+                self._validate_indexer("slice", key.start, "getitem")
+                self._validate_indexer("slice", key.stop, "getitem")
+                self._validate_indexer("slice", key.step, "getitem")
+                return key
+
+        # convert the slice to an indexer here; checking that the user didn't
+        #  pass a positional slice to loc
+        is_positional = is_index_slice and self._should_fallback_to_positional
+
+        # if we are mixed and have integers
+        if is_positional:
+            try:
+                # Validate start & stop
+                if start is not None:
+                    self.get_loc(start)
+                if stop is not None:
+                    self.get_loc(stop)
+                is_positional = False
+            except KeyError:
+                pass
+
+        if com.is_null_slice(key):
+            # It doesn't matter if we are positional or label based
+            indexer = key
+        elif is_positional:
+            if kind == "loc":
+                # GH#16121, GH#24612, GH#31810
+                raise TypeError(
+                    "Slicing a positional slice with .loc is not allowed, "
+                    "Use .loc with labels or .iloc with positions instead.",
+                )
+            indexer = key
+        else:
+            indexer = self.slice_indexer(start, stop, step)
+
+        return indexer
+
+    @final
+    def _raise_invalid_indexer(
+        self,
+        form: Literal["slice", "positional"],
+        key,
+        reraise: lib.NoDefault | None | Exception = lib.no_default,
+    ) -> None:
+        """
+        Raise consistent invalid indexer message.
+        """
+        msg = (
+            f"cannot do {form} indexing on {type(self).__name__} with these "
+            f"indexers [{key}] of type {type(key).__name__}"
+        )
+        if reraise is not lib.no_default:
+            raise TypeError(msg) from reraise
+        raise TypeError(msg)
+
+    # --------------------------------------------------------------------
+    # Reindex Methods
+
+    @final
+    def _validate_can_reindex(self, indexer: np.ndarray) -> None:
+        """
+        Check if we are allowing reindexing with this particular indexer.
+
+        Parameters
+        ----------
+        indexer : an integer ndarray
+
+        Raises
+        ------
+        ValueError if its a duplicate axis
+        """
+        # trying to reindex on an axis with duplicates
+        if not self._index_as_unique and len(indexer):
+            raise ValueError("cannot reindex on an axis with duplicate labels")
+
+    def reindex(
+        self,
+        target,
+        method: ReindexMethod | None = None,
+        level=None,
+        limit: int | None = None,
+        tolerance: float | None = None,
+    ) -> tuple[Index, npt.NDArray[np.intp] | None]:
+        """
+        Create index with target's values.
+
+        Parameters
+        ----------
+        target : an iterable
+        method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
+            * default: exact matches only.
+            * pad / ffill: find the PREVIOUS index value if no exact match.
+            * backfill / bfill: use NEXT index value if no exact match
+            * nearest: use the NEAREST index value if no exact match. Tied
+              distances are broken by preferring the larger index value.
+        level : int, optional
+            Level of multiindex.
+        limit : int, optional
+            Maximum number of consecutive labels in ``target`` to match for
+            inexact matches.
+        tolerance : int or float, optional
+            Maximum distance between original and new labels for inexact
+            matches. The values of the index at the matching locations must
+            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
+
+            Tolerance may be a scalar value, which applies the same tolerance
+            to all values, or list-like, which applies variable tolerance per
+            element. List-like includes list, tuple, array, Series, and must be
+            the same size as the index and its dtype must exactly match the
+            index's type.
+
+        Returns
+        -------
+        new_index : pd.Index
+            Resulting index.
+        indexer : np.ndarray[np.intp] or None
+            Indices of output values in original index.
+
+        Raises
+        ------
+        TypeError
+            If ``method`` passed along with ``level``.
+        ValueError
+            If non-unique multi-index
+        ValueError
+            If non-unique index and ``method`` or ``limit`` passed.
+
+        See Also
+        --------
+        Series.reindex : Conform Series to new index with optional filling logic.
+        DataFrame.reindex : Conform DataFrame to new index with optional filling logic.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
+        >>> idx
+        Index(['car', 'bike', 'train', 'tractor'], dtype='object')
+        >>> idx.reindex(['car', 'bike'])
+        (Index(['car', 'bike'], dtype='object'), array([0, 1]))
+        """
+        # GH6552: preserve names when reindexing to non-named target
+        # (i.e. neither Index nor Series).
+        preserve_names = not hasattr(target, "name")
+
+        # GH7774: preserve dtype/tz if target is empty and not an Index.
+        target = ensure_has_len(target)  # target may be an iterator
+
+        if not isinstance(target, Index) and len(target) == 0:
+            if level is not None and self._is_multi:
+                # "Index" has no attribute "levels"; maybe "nlevels"?
+                idx = self.levels[level]  # type: ignore[attr-defined]
+            else:
+                idx = self
+            target = idx[:0]
+        else:
+            target = ensure_index(target)
+
+        if level is not None and (
+            isinstance(self, ABCMultiIndex) or isinstance(target, ABCMultiIndex)
+        ):
+            if method is not None:
+                raise TypeError("Fill method not supported if level passed")
+
+            # TODO: tests where passing `keep_order=not self._is_multi`
+            #  makes a difference for non-MultiIndex case
+            target, indexer, _ = self._join_level(
+                target, level, how="right", keep_order=not self._is_multi
+            )
+
+        else:
+            if self.equals(target):
+                indexer = None
+            else:
+                if self._index_as_unique:
+                    indexer = self.get_indexer(
+                        target, method=method, limit=limit, tolerance=tolerance
+                    )
+                elif self._is_multi:
+                    raise ValueError("cannot handle a non-unique multi-index!")
+                elif not self.is_unique:
+                    # GH#42568
+                    raise ValueError("cannot reindex on an axis with duplicate labels")
+                else:
+                    indexer, _ = self.get_indexer_non_unique(target)
+
+        target = self._wrap_reindex_result(target, indexer, preserve_names)
+        return target, indexer
+
+    def _wrap_reindex_result(self, target, indexer, preserve_names: bool):
+        target = self._maybe_preserve_names(target, preserve_names)
+        return target
+
+    def _maybe_preserve_names(self, target: Index, preserve_names: bool):
+        if preserve_names and target.nlevels == 1 and target.name != self.name:
+            target = target.copy(deep=False)
+            target.name = self.name
+        return target
+
+    @final
+    def _reindex_non_unique(
+        self, target: Index
+    ) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp] | None]:
+        """
+        Create a new index with target's values (move/add/delete values as
+        necessary) use with non-unique Index and a possibly non-unique target.
+
+        Parameters
+        ----------
+        target : an iterable
+
+        Returns
+        -------
+        new_index : pd.Index
+            Resulting index.
+        indexer : np.ndarray[np.intp]
+            Indices of output values in original index.
+        new_indexer : np.ndarray[np.intp] or None
+
+        """
+        target = ensure_index(target)
+        if len(target) == 0:
+            # GH#13691
+            return self[:0], np.array([], dtype=np.intp), None
+
+        indexer, missing = self.get_indexer_non_unique(target)
+        check = indexer != -1
+        new_labels: Index | np.ndarray = self.take(indexer[check])
+        new_indexer = None
+
+        if len(missing):
+            length = np.arange(len(indexer), dtype=np.intp)
+
+            missing = ensure_platform_int(missing)
+            missing_labels = target.take(missing)
+            missing_indexer = length[~check]
+            cur_labels = self.take(indexer[check]).values
+            cur_indexer = length[check]
+
+            # Index constructor below will do inference
+            new_labels = np.empty((len(indexer),), dtype=object)
+            new_labels[cur_indexer] = cur_labels
+            new_labels[missing_indexer] = missing_labels
+
+            # GH#38906
+            if not len(self):
+                new_indexer = np.arange(0, dtype=np.intp)
+
+            # a unique indexer
+            elif target.is_unique:
+                # see GH5553, make sure we use the right indexer
+                new_indexer = np.arange(len(indexer), dtype=np.intp)
+                new_indexer[cur_indexer] = np.arange(len(cur_labels))
+                new_indexer[missing_indexer] = -1
+
+            # we have a non_unique selector, need to use the original
+            # indexer here
+            else:
+                # need to retake to have the same size as the indexer
+                indexer[~check] = -1
+
+                # reset the new indexer to account for the new size
+                new_indexer = np.arange(len(self.take(indexer)), dtype=np.intp)
+                new_indexer[~check] = -1
+
+        if not isinstance(self, ABCMultiIndex):
+            new_index = Index(new_labels, name=self.name)
+        else:
+            new_index = type(self).from_tuples(new_labels, names=self.names)
+        return new_index, indexer, new_indexer
+
+    # --------------------------------------------------------------------
+    # Join Methods
+
+    @overload
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = ...,
+        level: Level = ...,
+        return_indexers: Literal[True],
+        sort: bool = ...,
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        ...
+
+    @overload
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = ...,
+        level: Level = ...,
+        return_indexers: Literal[False] = ...,
+        sort: bool = ...,
+    ) -> Index:
+        ...
+
+    @overload
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = ...,
+        level: Level = ...,
+        return_indexers: bool = ...,
+        sort: bool = ...,
+    ) -> Index | tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        ...
+
+    @final
+    @_maybe_return_indexers
+    def join(
+        self,
+        other: Index,
+        *,
+        how: JoinHow = "left",
+        level: Level | None = None,
+        return_indexers: bool = False,
+        sort: bool = False,
+    ) -> Index | tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        """
+        Compute join_index and indexers to conform data structures to the new index.
+
+        Parameters
+        ----------
+        other : Index
+        how : {'left', 'right', 'inner', 'outer'}
+        level : int or level name, default None
+        return_indexers : bool, default False
+        sort : bool, default False
+            Sort the join keys lexicographically in the result Index. If False,
+            the order of the join keys depends on the join type (how keyword).
+
+        Returns
+        -------
+        join_index, (left_indexer, right_indexer)
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3])
+        >>> idx2 = pd.Index([4, 5, 6])
+        >>> idx1.join(idx2, how='outer')
+        Index([1, 2, 3, 4, 5, 6], dtype='int64')
+        """
+        other = ensure_index(other)
+        sort = sort or how == "outer"
+
+        if isinstance(self, ABCDatetimeIndex) and isinstance(other, ABCDatetimeIndex):
+            if (self.tz is None) ^ (other.tz is None):
+                # Raise instead of casting to object below.
+                raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
+
+        if not self._is_multi and not other._is_multi:
+            # We have specific handling for MultiIndex below
+            pself, pother = self._maybe_downcast_for_indexing(other)
+            if pself is not self or pother is not other:
+                return pself.join(
+                    pother, how=how, level=level, return_indexers=True, sort=sort
+                )
+
+        # try to figure out the join level
+        # GH3662
+        if level is None and (self._is_multi or other._is_multi):
+            # have the same levels/names so a simple join
+            if self.names == other.names:
+                pass
+            else:
+                return self._join_multi(other, how=how)
+
+        # join on the level
+        if level is not None and (self._is_multi or other._is_multi):
+            return self._join_level(other, level, how=how)
+
+        if len(self) == 0 or len(other) == 0:
+            try:
+                return self._join_empty(other, how, sort)
+            except TypeError:
+                # object dtype; non-comparable objects
+                pass
+
+        if self.dtype != other.dtype:
+            dtype = self._find_common_type_compat(other)
+            this = self.astype(dtype, copy=False)
+            other = other.astype(dtype, copy=False)
+            return this.join(other, how=how, return_indexers=True)
+        elif (
+            isinstance(self, ABCCategoricalIndex)
+            and isinstance(other, ABCCategoricalIndex)
+            and not self.ordered
+            and not self.categories.equals(other.categories)
+        ):
+            # dtypes are "equal" but categories are in different order
+            other = Index(other._values.reorder_categories(self.categories))
+
+        _validate_join_method(how)
+
+        if (
+            self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and self._can_use_libjoin
+            and other._can_use_libjoin
+            and (self.is_unique or other.is_unique)
+        ):
+            try:
+                return self._join_monotonic(other, how=how)
+            except TypeError:
+                # object dtype; non-comparable objects
+                pass
+        elif not self.is_unique or not other.is_unique:
+            return self._join_non_unique(other, how=how, sort=sort)
+
+        return self._join_via_get_indexer(other, how, sort)
+
+    @final
+    def _join_empty(
+        self, other: Index, how: JoinHow, sort: bool
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        assert len(self) == 0 or len(other) == 0
+        _validate_join_method(how)
+
+        lidx: np.ndarray | None
+        ridx: np.ndarray | None
+
+        if len(other):
+            how = cast(JoinHow, {"left": "right", "right": "left"}.get(how, how))
+            join_index, ridx, lidx = other._join_empty(self, how, sort)
+        elif how in ["left", "outer"]:
+            if sort and not self.is_monotonic_increasing:
+                lidx = self.argsort()
+                join_index = self.take(lidx)
+            else:
+                lidx = None
+                join_index = self._view()
+            ridx = np.broadcast_to(np.intp(-1), len(join_index))
+        else:
+            join_index = other._view()
+            lidx = np.array([], dtype=np.intp)
+            ridx = None
+        return join_index, lidx, ridx
+
+    @final
+    def _join_via_get_indexer(
+        self, other: Index, how: JoinHow, sort: bool
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        # Fallback if we do not have any fastpaths available based on
+        #  uniqueness/monotonicity
+
+        # Note: at this point we have checked matching dtypes
+
+        if how == "left":
+            join_index = self.sort_values() if sort else self
+        elif how == "right":
+            join_index = other.sort_values() if sort else other
+        elif how == "inner":
+            join_index = self.intersection(other, sort=sort)
+        elif how == "outer":
+            try:
+                join_index = self.union(other, sort=sort)
+            except TypeError:
+                join_index = self.union(other)
+                try:
+                    join_index = _maybe_try_sort(join_index, sort)
+                except TypeError:
+                    pass
+
+        if join_index is self:
+            lindexer = None
+        else:
+            lindexer = self.get_indexer_for(join_index)
+        if join_index is other:
+            rindexer = None
+        else:
+            rindexer = other.get_indexer_for(join_index)
+        return join_index, lindexer, rindexer
+
+    @final
+    def _join_multi(self, other: Index, how: JoinHow):
+        from pandas.core.indexes.multi import MultiIndex
+        from pandas.core.reshape.merge import restore_dropped_levels_multijoin
+
+        # figure out join names
+        self_names_list = list(com.not_none(*self.names))
+        other_names_list = list(com.not_none(*other.names))
+        self_names_order = self_names_list.index
+        other_names_order = other_names_list.index
+        self_names = set(self_names_list)
+        other_names = set(other_names_list)
+        overlap = self_names & other_names
+
+        # need at least 1 in common
+        if not overlap:
+            raise ValueError("cannot join with no overlapping index names")
+
+        if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
+            # Drop the non-matching levels from left and right respectively
+            ldrop_names = sorted(self_names - overlap, key=self_names_order)
+            rdrop_names = sorted(other_names - overlap, key=other_names_order)
+
+            # if only the order differs
+            if not len(ldrop_names + rdrop_names):
+                self_jnlevels = self
+                other_jnlevels = other.reorder_levels(self.names)
+            else:
+                self_jnlevels = self.droplevel(ldrop_names)
+                other_jnlevels = other.droplevel(rdrop_names)
+
+            # Join left and right
+            # Join on same leveled multi-index frames is supported
+            join_idx, lidx, ridx = self_jnlevels.join(
+                other_jnlevels, how=how, return_indexers=True
+            )
+
+            # Restore the dropped levels
+            # Returned index level order is
+            # common levels, ldrop_names, rdrop_names
+            dropped_names = ldrop_names + rdrop_names
+
+            # error: Argument 5/6 to "restore_dropped_levels_multijoin" has
+            # incompatible type "Optional[ndarray[Any, dtype[signedinteger[Any
+            # ]]]]"; expected "ndarray[Any, dtype[signedinteger[Any]]]"
+            levels, codes, names = restore_dropped_levels_multijoin(
+                self,
+                other,
+                dropped_names,
+                join_idx,
+                lidx,  # type: ignore[arg-type]
+                ridx,  # type: ignore[arg-type]
+            )
+
+            # Re-create the multi-index
+            multi_join_idx = MultiIndex(
+                levels=levels, codes=codes, names=names, verify_integrity=False
+            )
+
+            multi_join_idx = multi_join_idx.remove_unused_levels()
+
+            # maintain the order of the index levels
+            if how == "right":
+                level_order = other_names_list + ldrop_names
+            else:
+                level_order = self_names_list + rdrop_names
+            multi_join_idx = multi_join_idx.reorder_levels(level_order)
+
+            return multi_join_idx, lidx, ridx
+
+        jl = next(iter(overlap))
+
+        # Case where only one index is multi
+        # make the indices into mi's that match
+        flip_order = False
+        if isinstance(self, MultiIndex):
+            self, other = other, self
+            flip_order = True
+            # flip if join method is right or left
+            flip: dict[JoinHow, JoinHow] = {"right": "left", "left": "right"}
+            how = flip.get(how, how)
+
+        level = other.names.index(jl)
+        result = self._join_level(other, level, how=how)
+
+        if flip_order:
+            return result[0], result[2], result[1]
+        return result
+
+    @final
+    def _join_non_unique(
+        self, other: Index, how: JoinHow = "left", sort: bool = False
+    ) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        from pandas.core.reshape.merge import get_join_indexers_non_unique
+
+        # We only get here if dtypes match
+        assert self.dtype == other.dtype
+
+        left_idx, right_idx = get_join_indexers_non_unique(
+            self._values, other._values, how=how, sort=sort
+        )
+        mask = left_idx == -1
+
+        join_idx = self.take(left_idx)
+        right = other.take(right_idx)
+        join_index = join_idx.putmask(mask, right)
+        if isinstance(join_index, ABCMultiIndex) and how == "outer":
+            # test_join_index_levels
+            join_index = join_index._sort_levels_monotonic()
+        return join_index, left_idx, right_idx
+
+    @final
+    def _join_level(
+        self, other: Index, level, how: JoinHow = "left", keep_order: bool = True
+    ) -> tuple[MultiIndex, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        """
+        The join method *only* affects the level of the resulting
+        MultiIndex. Otherwise it just exactly aligns the Index data to the
+        labels of the level in the MultiIndex.
+
+        If ```keep_order == True```, the order of the data indexed by the
+        MultiIndex will not be changed; otherwise, it will tie out
+        with `other`.
+        """
+        from pandas.core.indexes.multi import MultiIndex
+
+        def _get_leaf_sorter(labels: list[np.ndarray]) -> npt.NDArray[np.intp]:
+            """
+            Returns sorter for the inner most level while preserving the
+            order of higher levels.
+
+            Parameters
+            ----------
+            labels : list[np.ndarray]
+                Each ndarray has signed integer dtype, not necessarily identical.
+
+            Returns
+            -------
+            np.ndarray[np.intp]
+            """
+            if labels[0].size == 0:
+                return np.empty(0, dtype=np.intp)
+
+            if len(labels) == 1:
+                return get_group_index_sorter(ensure_platform_int(labels[0]))
+
+            # find indexers of beginning of each set of
+            # same-key labels w.r.t all but last level
+            tic = labels[0][:-1] != labels[0][1:]
+            for lab in labels[1:-1]:
+                tic |= lab[:-1] != lab[1:]
+
+            starts = np.hstack(([True], tic, [True])).nonzero()[0]
+            lab = ensure_int64(labels[-1])
+            return lib.get_level_sorter(lab, ensure_platform_int(starts))
+
+        if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
+            raise TypeError("Join on level between two MultiIndex objects is ambiguous")
+
+        left, right = self, other
+
+        flip_order = not isinstance(self, MultiIndex)
+        if flip_order:
+            left, right = right, left
+            flip: dict[JoinHow, JoinHow] = {"right": "left", "left": "right"}
+            how = flip.get(how, how)
+
+        assert isinstance(left, MultiIndex)
+
+        level = left._get_level_number(level)
+        old_level = left.levels[level]
+
+        if not right.is_unique:
+            raise NotImplementedError(
+                "Index._join_level on non-unique index is not implemented"
+            )
+
+        new_level, left_lev_indexer, right_lev_indexer = old_level.join(
+            right, how=how, return_indexers=True
+        )
+
+        if left_lev_indexer is None:
+            if keep_order or len(left) == 0:
+                left_indexer = None
+                join_index = left
+            else:  # sort the leaves
+                left_indexer = _get_leaf_sorter(left.codes[: level + 1])
+                join_index = left[left_indexer]
+
+        else:
+            left_lev_indexer = ensure_platform_int(left_lev_indexer)
+            rev_indexer = lib.get_reverse_indexer(left_lev_indexer, len(old_level))
+            old_codes = left.codes[level]
+
+            taker = old_codes[old_codes != -1]
+            new_lev_codes = rev_indexer.take(taker)
+
+            new_codes = list(left.codes)
+            new_codes[level] = new_lev_codes
+
+            new_levels = list(left.levels)
+            new_levels[level] = new_level
+
+            if keep_order:  # just drop missing values. o.w. keep order
+                left_indexer = np.arange(len(left), dtype=np.intp)
+                left_indexer = cast(np.ndarray, left_indexer)
+                mask = new_lev_codes != -1
+                if not mask.all():
+                    new_codes = [lab[mask] for lab in new_codes]
+                    left_indexer = left_indexer[mask]
+
+            else:  # tie out the order with other
+                if level == 0:  # outer most level, take the fast route
+                    max_new_lev = 0 if len(new_lev_codes) == 0 else new_lev_codes.max()
+                    ngroups = 1 + max_new_lev
+                    left_indexer, counts = libalgos.groupsort_indexer(
+                        new_lev_codes, ngroups
+                    )
+
+                    # missing values are placed first; drop them!
+                    left_indexer = left_indexer[counts[0] :]
+                    new_codes = [lab[left_indexer] for lab in new_codes]
+
+                else:  # sort the leaves
+                    mask = new_lev_codes != -1
+                    mask_all = mask.all()
+                    if not mask_all:
+                        new_codes = [lab[mask] for lab in new_codes]
+
+                    left_indexer = _get_leaf_sorter(new_codes[: level + 1])
+                    new_codes = [lab[left_indexer] for lab in new_codes]
+
+                    # left_indexers are w.r.t masked frame.
+                    # reverse to original frame!
+                    if not mask_all:
+                        left_indexer = mask.nonzero()[0][left_indexer]
+
+            join_index = MultiIndex(
+                levels=new_levels,
+                codes=new_codes,
+                names=left.names,
+                verify_integrity=False,
+            )
+
+        if right_lev_indexer is not None:
+            right_indexer = right_lev_indexer.take(join_index.codes[level])
+        else:
+            right_indexer = join_index.codes[level]
+
+        if flip_order:
+            left_indexer, right_indexer = right_indexer, left_indexer
+
+        left_indexer = (
+            None if left_indexer is None else ensure_platform_int(left_indexer)
+        )
+        right_indexer = (
+            None if right_indexer is None else ensure_platform_int(right_indexer)
+        )
+        return join_index, left_indexer, right_indexer
+
+    @final
+    def _join_monotonic(
+        self, other: Index, how: JoinHow = "left"
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        # We only get here with matching dtypes and both monotonic increasing
+        assert other.dtype == self.dtype
+        assert self._can_use_libjoin and other._can_use_libjoin
+
+        if self.equals(other):
+            # This is a convenient place for this check, but its correctness
+            #  does not depend on monotonicity, so it could go earlier
+            #  in the calling method.
+            ret_index = other if how == "right" else self
+            return ret_index, None, None
+
+        ridx: npt.NDArray[np.intp] | None
+        lidx: npt.NDArray[np.intp] | None
+
+        if self.is_unique and other.is_unique:
+            # We can perform much better than the general case
+            if how == "left":
+                join_index = self
+                lidx = None
+                ridx = self._left_indexer_unique(other)
+            elif how == "right":
+                join_index = other
+                lidx = other._left_indexer_unique(self)
+                ridx = None
+            elif how == "inner":
+                join_array, lidx, ridx = self._inner_indexer(other)
+                join_index = self._wrap_joined_index(join_array, other, lidx, ridx)
+            elif how == "outer":
+                join_array, lidx, ridx = self._outer_indexer(other)
+                join_index = self._wrap_joined_index(join_array, other, lidx, ridx)
+        else:
+            if how == "left":
+                join_array, lidx, ridx = self._left_indexer(other)
+            elif how == "right":
+                join_array, ridx, lidx = other._left_indexer(self)
+            elif how == "inner":
+                join_array, lidx, ridx = self._inner_indexer(other)
+            elif how == "outer":
+                join_array, lidx, ridx = self._outer_indexer(other)
+
+            assert lidx is not None
+            assert ridx is not None
+
+            join_index = self._wrap_joined_index(join_array, other, lidx, ridx)
+
+        lidx = None if lidx is None else ensure_platform_int(lidx)
+        ridx = None if ridx is None else ensure_platform_int(ridx)
+        return join_index, lidx, ridx
+
+    def _wrap_joined_index(
+        self,
+        joined: ArrayLike,
+        other: Self,
+        lidx: npt.NDArray[np.intp],
+        ridx: npt.NDArray[np.intp],
+    ) -> Self:
+        assert other.dtype == self.dtype
+
+        if isinstance(self, ABCMultiIndex):
+            name = self.names if self.names == other.names else None
+            # error: Incompatible return value type (got "MultiIndex",
+            # expected "Self")
+            mask = lidx == -1
+            join_idx = self.take(lidx)
+            right = cast("MultiIndex", other.take(ridx))
+            join_index = join_idx.putmask(mask, right)._sort_levels_monotonic()
+            return join_index.set_names(name)  # type: ignore[return-value]
+        else:
+            name = get_op_result_name(self, other)
+            return self._constructor._with_infer(joined, name=name, dtype=self.dtype)
+
+    @final
+    @cache_readonly
+    def _can_use_libjoin(self) -> bool:
+        """
+        Whether we can use the fastpaths implemented in _libs.join.
+
+        This is driven by whether (in monotonic increasing cases that are
+        guaranteed not to have NAs) we can convert to a np.ndarray without
+        making a copy. If we cannot, this negates the performance benefit
+        of using libjoin.
+        """
+        if type(self) is Index:
+            # excludes EAs, but include masks, we get here with monotonic
+            # values only, meaning no NA
+            return (
+                isinstance(self.dtype, np.dtype)
+                or isinstance(self._values, (ArrowExtensionArray, BaseMaskedArray))
+                or (
+                    isinstance(self.dtype, StringDtype)
+                    and self.dtype.storage == "python"
+                )
+            )
+        # Exclude index types where the conversion to numpy converts to object dtype,
+        #  which negates the performance benefit of libjoin
+        # Subclasses should override to return False if _get_join_target is
+        #  not zero-copy.
+        # TODO: exclude RangeIndex (which allocates memory)?
+        #  Doing so seems to break test_concat_datetime_timezone
+        return not isinstance(self, (ABCIntervalIndex, ABCMultiIndex))
+
+    # --------------------------------------------------------------------
+    # Uncategorized Methods
+
+    @property
+    def values(self) -> ArrayLike:
+        """
+        Return an array representing the data in the Index.
+
+        .. warning::
+
+           We recommend using :attr:`Index.array` or
+           :meth:`Index.to_numpy`, depending on whether you need
+           a reference to the underlying data or a NumPy array.
+
+        Returns
+        -------
+        array: numpy.ndarray or ExtensionArray
+
+        See Also
+        --------
+        Index.array : Reference to the underlying data.
+        Index.to_numpy : A NumPy array representing the underlying data.
+
+        Examples
+        --------
+        For :class:`pandas.Index`:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.values
+        array([1, 2, 3])
+
+        For :class:`pandas.IntervalIndex`:
+
+        >>> idx = pd.interval_range(start=0, end=5)
+        >>> idx.values
+        <IntervalArray>
+        [(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]]
+        Length: 5, dtype: interval[int64, right]
+        """
+        if using_copy_on_write():
+            data = self._data
+            if isinstance(data, np.ndarray):
+                data = data.view()
+                data.flags.writeable = False
+            return data
+        return self._data
+
+    @cache_readonly
+    @doc(IndexOpsMixin.array)
+    def array(self) -> ExtensionArray:
+        array = self._data
+        if isinstance(array, np.ndarray):
+            from pandas.core.arrays.numpy_ import NumpyExtensionArray
+
+            array = NumpyExtensionArray(array)
+        return array
+
+    @property
+    def _values(self) -> ExtensionArray | np.ndarray:
+        """
+        The best array representation.
+
+        This is an ndarray or ExtensionArray.
+
+        ``_values`` are consistent between ``Series`` and ``Index``.
+
+        It may differ from the public '.values' method.
+
+        index             | values          | _values       |
+        ----------------- | --------------- | ------------- |
+        Index             | ndarray         | ndarray       |
+        CategoricalIndex  | Categorical     | Categorical   |
+        DatetimeIndex     | ndarray[M8ns]   | DatetimeArray |
+        DatetimeIndex[tz] | ndarray[M8ns]   | DatetimeArray |
+        PeriodIndex       | ndarray[object] | PeriodArray   |
+        IntervalIndex     | IntervalArray   | IntervalArray |
+
+        See Also
+        --------
+        values : Values
+        """
+        return self._data
+
+    def _get_engine_target(self) -> ArrayLike:
+        """
+        Get the ndarray or ExtensionArray that we can pass to the IndexEngine
+        constructor.
+        """
+        vals = self._values
+        if isinstance(vals, StringArray):
+            # GH#45652 much more performant than ExtensionEngine
+            return vals._ndarray
+        if isinstance(vals, ArrowExtensionArray) and self.dtype.kind in "Mm":
+            import pyarrow as pa
+
+            pa_type = vals._pa_array.type
+            if pa.types.is_timestamp(pa_type):
+                vals = vals._to_datetimearray()
+                return vals._ndarray.view("i8")
+            elif pa.types.is_duration(pa_type):
+                vals = vals._to_timedeltaarray()
+                return vals._ndarray.view("i8")
+        if (
+            type(self) is Index
+            and isinstance(self._values, ExtensionArray)
+            and not isinstance(self._values, BaseMaskedArray)
+            and not (
+                isinstance(self._values, ArrowExtensionArray)
+                and is_numeric_dtype(self.dtype)
+                # Exclude decimal
+                and self.dtype.kind != "O"
+            )
+        ):
+            # TODO(ExtensionIndex): remove special-case, just use self._values
+            return self._values.astype(object)
+        return vals
+
+    @final
+    def _get_join_target(self) -> np.ndarray:
+        """
+        Get the ndarray or ExtensionArray that we can pass to the join
+        functions.
+        """
+        if isinstance(self._values, BaseMaskedArray):
+            # This is only used if our array is monotonic, so no NAs present
+            return self._values._data
+        elif isinstance(self._values, ArrowExtensionArray):
+            # This is only used if our array is monotonic, so no missing values
+            # present
+            return self._values.to_numpy()
+
+        # TODO: exclude ABCRangeIndex case here as it copies
+        target = self._get_engine_target()
+        if not isinstance(target, np.ndarray):
+            raise ValueError("_can_use_libjoin should return False.")
+        return target
+
+    def _from_join_target(self, result: np.ndarray) -> ArrayLike:
+        """
+        Cast the ndarray returned from one of the libjoin.foo_indexer functions
+        back to type(self._data).
+        """
+        if isinstance(self.values, BaseMaskedArray):
+            return type(self.values)(result, np.zeros(result.shape, dtype=np.bool_))
+        elif isinstance(self.values, (ArrowExtensionArray, StringArray)):
+            return type(self.values)._from_sequence(result, dtype=self.dtype)
+        return result
+
+    @doc(IndexOpsMixin._memory_usage)
+    def memory_usage(self, deep: bool = False) -> int:
+        result = self._memory_usage(deep=deep)
+
+        # include our engine hashtable
+        result += self._engine.sizeof(deep=deep)
+        return result
+
+    @final
+    def where(self, cond, other=None) -> Index:
+        """
+        Replace values where the condition is False.
+
+        The replacement is taken from other.
+
+        Parameters
+        ----------
+        cond : bool array-like with the same length as self
+            Condition to select the values on.
+        other : scalar, or array-like, default None
+            Replacement if the condition is False.
+
+        Returns
+        -------
+        pandas.Index
+            A copy of self with values replaced from other
+            where the condition is False.
+
+        See Also
+        --------
+        Series.where : Same method for Series.
+        DataFrame.where : Same method for DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
+        >>> idx
+        Index(['car', 'bike', 'train', 'tractor'], dtype='object')
+        >>> idx.where(idx.isin(['car', 'train']), 'other')
+        Index(['car', 'other', 'train', 'other'], dtype='object')
+        """
+        if isinstance(self, ABCMultiIndex):
+            raise NotImplementedError(
+                ".where is not supported for MultiIndex operations"
+            )
+        cond = np.asarray(cond, dtype=bool)
+        return self.putmask(~cond, other)
+
+    # construction helpers
+    @final
+    @classmethod
+    def _raise_scalar_data_error(cls, data):
+        # We return the TypeError so that we can raise it from the constructor
+        #  in order to keep mypy happy
+        raise TypeError(
+            f"{cls.__name__}(...) must be called with a collection of some "
+            f"kind, {repr(data) if not isinstance(data, np.generic) else str(data)} "
+            "was passed"
+        )
+
+    def _validate_fill_value(self, value):
+        """
+        Check if the value can be inserted into our array without casting,
+        and convert it to an appropriate native type if necessary.
+
+        Raises
+        ------
+        TypeError
+            If the value cannot be inserted into an array of this dtype.
+        """
+        dtype = self.dtype
+        if isinstance(dtype, np.dtype) and dtype.kind not in "mM":
+            # return np_can_hold_element(dtype, value)
+            try:
+                return np_can_hold_element(dtype, value)
+            except LossySetitemError as err:
+                # re-raise as TypeError for consistency
+                raise TypeError from err
+        elif not can_hold_element(self._values, value):
+            raise TypeError
+        return value
+
+    def _is_memory_usage_qualified(self) -> bool:
+        """
+        Return a boolean if we need a qualified .info display.
+        """
+        return is_object_dtype(self.dtype) or (
+            is_string_dtype(self.dtype) and self.dtype.storage == "python"  # type: ignore[union-attr]
+        )
+
+    def __contains__(self, key: Any) -> bool:
+        """
+        Return a boolean indicating whether the provided key is in the index.
+
+        Parameters
+        ----------
+        key : label
+            The key to check if it is present in the index.
+
+        Returns
+        -------
+        bool
+            Whether the key search is in the index.
+
+        Raises
+        ------
+        TypeError
+            If the key is not hashable.
+
+        See Also
+        --------
+        Index.isin : Returns an ndarray of boolean dtype indicating whether the
+            list-like key is in the index.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3, 4])
+        >>> idx
+        Index([1, 2, 3, 4], dtype='int64')
+
+        >>> 2 in idx
+        True
+        >>> 6 in idx
+        False
+        """
+        hash(key)
+        try:
+            return key in self._engine
+        except (OverflowError, TypeError, ValueError):
+            return False
+
+    # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
+    # Incompatible types in assignment (expression has type "None", base class
+    # "object" defined the type as "Callable[[object], int]")
+    __hash__: ClassVar[None]  # type: ignore[assignment]
+
+    @final
+    def __setitem__(self, key, value) -> None:
+        raise TypeError("Index does not support mutable operations")
+
+    def __getitem__(self, key):
+        """
+        Override numpy.ndarray's __getitem__ method to work as desired.
+
+        This function adds lists and Series as valid boolean indexers
+        (ndarrays only supports ndarray with dtype=bool).
+
+        If resulting ndim != 1, plain ndarray is returned instead of
+        corresponding `Index` subclass.
+
+        """
+        getitem = self._data.__getitem__
+
+        if is_integer(key) or is_float(key):
+            # GH#44051 exclude bool, which would return a 2d ndarray
+            key = com.cast_scalar_indexer(key)
+            return getitem(key)
+
+        if isinstance(key, slice):
+            # This case is separated from the conditional above to avoid
+            # pessimization com.is_bool_indexer and ndim checks.
+            return self._getitem_slice(key)
+
+        if com.is_bool_indexer(key):
+            # if we have list[bools, length=1e5] then doing this check+convert
+            #  takes 166 µs + 2.1 ms and cuts the ndarray.__getitem__
+            #  time below from 3.8 ms to 496 µs
+            # if we already have ndarray[bool], the overhead is 1.4 µs or .25%
+            if isinstance(getattr(key, "dtype", None), ExtensionDtype):
+                key = key.to_numpy(dtype=bool, na_value=False)
+            else:
+                key = np.asarray(key, dtype=bool)
+
+            if not isinstance(self.dtype, ExtensionDtype):
+                if len(key) == 0 and len(key) != len(self):
+                    warnings.warn(
+                        "Using a boolean indexer with length 0 on an Index with "
+                        "length greater than 0 is deprecated and will raise in a "
+                        "future version.",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+        result = getitem(key)
+        # Because we ruled out integer above, we always get an arraylike here
+        if result.ndim > 1:
+            disallow_ndim_indexing(result)
+
+        # NB: Using _constructor._simple_new would break if MultiIndex
+        #  didn't override __getitem__
+        return self._constructor._simple_new(result, name=self._name)
+
+    def _getitem_slice(self, slobj: slice) -> Self:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        res = self._data[slobj]
+        result = type(self)._simple_new(res, name=self._name, refs=self._references)
+        if "_engine" in self._cache:
+            reverse = slobj.step is not None and slobj.step < 0
+            result._engine._update_from_sliced(self._engine, reverse=reverse)  # type: ignore[union-attr]
+
+        return result
+
+    @final
+    def _can_hold_identifiers_and_holds_name(self, name) -> bool:
+        """
+        Faster check for ``name in self`` when we know `name` is a Python
+        identifier (e.g. in NDFrame.__getattr__, which hits this to support
+        . key lookup). For indexes that can't hold identifiers (everything
+        but object & categorical) we just return False.
+
+        https://github.com/pandas-dev/pandas/issues/19764
+        """
+        if (
+            is_object_dtype(self.dtype)
+            or is_string_dtype(self.dtype)
+            or isinstance(self.dtype, CategoricalDtype)
+        ):
+            return name in self
+        return False
+
+    def append(self, other: Index | Sequence[Index]) -> Index:
+        """
+        Append a collection of Index options together.
+
+        Parameters
+        ----------
+        other : Index or list/tuple of indices
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.append(pd.Index([4]))
+        Index([1, 2, 3, 4], dtype='int64')
+        """
+        to_concat = [self]
+
+        if isinstance(other, (list, tuple)):
+            to_concat += list(other)
+        else:
+            # error: Argument 1 to "append" of "list" has incompatible type
+            # "Union[Index, Sequence[Index]]"; expected "Index"
+            to_concat.append(other)  # type: ignore[arg-type]
+
+        for obj in to_concat:
+            if not isinstance(obj, Index):
+                raise TypeError("all inputs must be Index")
+
+        names = {obj.name for obj in to_concat}
+        name = None if len(names) > 1 else self.name
+
+        return self._concat(to_concat, name)
+
+    def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
+        """
+        Concatenate multiple Index objects.
+        """
+        to_concat_vals = [x._values for x in to_concat]
+
+        result = concat_compat(to_concat_vals)
+
+        return Index._with_infer(result, name=name)
+
+    def putmask(self, mask, value) -> Index:
+        """
+        Return a new Index of the values set with the mask.
+
+        Returns
+        -------
+        Index
+
+        See Also
+        --------
+        numpy.ndarray.putmask : Changes elements of an array
+            based on conditional and input values.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3])
+        >>> idx2 = pd.Index([5, 6, 7])
+        >>> idx1.putmask([True, False, False], idx2)
+        Index([5, 2, 3], dtype='int64')
+        """
+        mask, noop = validate_putmask(self._values, mask)
+        if noop:
+            return self.copy()
+
+        if self.dtype != object and is_valid_na_for_dtype(value, self.dtype):
+            # e.g. None -> np.nan, see also Block._standardize_fill_value
+            value = self._na_value
+
+        try:
+            converted = self._validate_fill_value(value)
+        except (LossySetitemError, ValueError, TypeError) as err:
+            if is_object_dtype(self.dtype):  # pragma: no cover
+                raise err
+
+            # See also: Block.coerce_to_target_dtype
+            dtype = self._find_common_type_compat(value)
+            return self.astype(dtype).putmask(mask, value)
+
+        values = self._values.copy()
+
+        if isinstance(values, np.ndarray):
+            converted = setitem_datetimelike_compat(values, mask.sum(), converted)
+            np.putmask(values, mask, converted)
+
+        else:
+            # Note: we use the original value here, not converted, as
+            #  _validate_fill_value is not idempotent
+            values._putmask(mask, value)
+
+        return self._shallow_copy(values)
+
+    def equals(self, other: Any) -> bool:
+        """
+        Determine if two Index object are equal.
+
+        The things that are being compared are:
+
+        * The elements inside the Index object.
+        * The order of the elements inside the Index object.
+
+        Parameters
+        ----------
+        other : Any
+            The other object to compare against.
+
+        Returns
+        -------
+        bool
+            True if "other" is an Index and it has the same elements and order
+            as the calling index; False otherwise.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index([1, 2, 3])
+        >>> idx1
+        Index([1, 2, 3], dtype='int64')
+        >>> idx1.equals(pd.Index([1, 2, 3]))
+        True
+
+        The elements inside are compared
+
+        >>> idx2 = pd.Index(["1", "2", "3"])
+        >>> idx2
+        Index(['1', '2', '3'], dtype='object')
+
+        >>> idx1.equals(idx2)
+        False
+
+        The order is compared
+
+        >>> ascending_idx = pd.Index([1, 2, 3])
+        >>> ascending_idx
+        Index([1, 2, 3], dtype='int64')
+        >>> descending_idx = pd.Index([3, 2, 1])
+        >>> descending_idx
+        Index([3, 2, 1], dtype='int64')
+        >>> ascending_idx.equals(descending_idx)
+        False
+
+        The dtype is *not* compared
+
+        >>> int64_idx = pd.Index([1, 2, 3], dtype='int64')
+        >>> int64_idx
+        Index([1, 2, 3], dtype='int64')
+        >>> uint64_idx = pd.Index([1, 2, 3], dtype='uint64')
+        >>> uint64_idx
+        Index([1, 2, 3], dtype='uint64')
+        >>> int64_idx.equals(uint64_idx)
+        True
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+
+        if len(self) != len(other):
+            # quickly return if the lengths are different
+            return False
+
+        if (
+            isinstance(self.dtype, StringDtype)
+            and self.dtype.na_value is np.nan
+            and other.dtype != self.dtype
+        ):
+            # TODO(infer_string) can we avoid this special case?
+            # special case for object behavior
+            return other.equals(self.astype(object))
+
+        if is_object_dtype(self.dtype) and not is_object_dtype(other.dtype):
+            # if other is not object, use other's logic for coercion
+            return other.equals(self)
+
+        if isinstance(other, ABCMultiIndex):
+            # d-level MultiIndex can equal d-tuple Index
+            return other.equals(self)
+
+        if isinstance(self._values, ExtensionArray):
+            # Dispatch to the ExtensionArray's .equals method.
+            if not isinstance(other, type(self)):
+                return False
+
+            earr = cast(ExtensionArray, self._data)
+            return earr.equals(other._data)
+
+        if isinstance(other.dtype, ExtensionDtype):
+            # All EA-backed Index subclasses override equals
+            return other.equals(self)
+
+        return array_equivalent(self._values, other._values)
+
+    @final
+    def identical(self, other) -> bool:
+        """
+        Similar to equals, but checks that object attributes and types are also equal.
+
+        Returns
+        -------
+        bool
+            If two Index objects have equal elements and same type True,
+            otherwise False.
+
+        Examples
+        --------
+        >>> idx1 = pd.Index(['1', '2', '3'])
+        >>> idx2 = pd.Index(['1', '2', '3'])
+        >>> idx2.identical(idx1)
+        True
+
+        >>> idx1 = pd.Index(['1', '2', '3'], name="A")
+        >>> idx2 = pd.Index(['1', '2', '3'], name="B")
+        >>> idx2.identical(idx1)
+        False
+        """
+        return (
+            self.equals(other)
+            and all(
+                getattr(self, c, None) == getattr(other, c, None)
+                for c in self._comparables
+            )
+            and type(self) == type(other)
+            and self.dtype == other.dtype
+        )
+
+    @final
+    def asof(self, label):
+        """
+        Return the label from the index, or, if not present, the previous one.
+
+        Assuming that the index is sorted, return the passed index label if it
+        is in the index, or return the previous index label if the passed one
+        is not in the index.
+
+        Parameters
+        ----------
+        label : object
+            The label up to which the method returns the latest index label.
+
+        Returns
+        -------
+        object
+            The passed label if it is in the index. The previous label if the
+            passed label is not in the sorted index or `NaN` if there is no
+            such label.
+
+        See Also
+        --------
+        Series.asof : Return the latest value in a Series up to the
+            passed index.
+        merge_asof : Perform an asof merge (similar to left join but it
+            matches on nearest key rather than equal key).
+        Index.get_loc : An `asof` is a thin wrapper around `get_loc`
+            with method='pad'.
+
+        Examples
+        --------
+        `Index.asof` returns the latest index label up to the passed label.
+
+        >>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03'])
+        >>> idx.asof('2014-01-01')
+        '2013-12-31'
+
+        If the label is in the index, the method returns the passed label.
+
+        >>> idx.asof('2014-01-02')
+        '2014-01-02'
+
+        If all of the labels in the index are later than the passed label,
+        NaN is returned.
+
+        >>> idx.asof('1999-01-02')
+        nan
+
+        If the index is not sorted, an error is raised.
+
+        >>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02',
+        ...                            '2014-01-03'])
+        >>> idx_not_sorted.asof('2013-12-31')
+        Traceback (most recent call last):
+        ValueError: index must be monotonic increasing or decreasing
+        """
+        self._searchsorted_monotonic(label)  # validate sortedness
+        try:
+            loc = self.get_loc(label)
+        except (KeyError, TypeError):
+            # KeyError -> No exact match, try for padded
+            # TypeError -> passed e.g. non-hashable, fall through to get
+            #  the tested exception message
+            indexer = self.get_indexer([label], method="pad")
+            if indexer.ndim > 1 or indexer.size > 1:
+                raise TypeError("asof requires scalar valued input")
+            loc = indexer.item()
+            if loc == -1:
+                return self._na_value
+        else:
+            if isinstance(loc, slice):
+                loc = loc.indices(len(self))[-1]
+
+        return self[loc]
+
+    def asof_locs(
+        self, where: Index, mask: npt.NDArray[np.bool_]
+    ) -> npt.NDArray[np.intp]:
+        """
+        Return the locations (indices) of labels in the index.
+
+        As in the :meth:`pandas.Index.asof`, if the label (a particular entry in
+        ``where``) is not in the index, the latest index label up to the
+        passed label is chosen and its index returned.
+
+        If all of the labels in the index are later than a label in ``where``,
+        -1 is returned.
+
+        ``mask`` is used to ignore ``NA`` values in the index during calculation.
+
+        Parameters
+        ----------
+        where : Index
+            An Index consisting of an array of timestamps.
+        mask : np.ndarray[bool]
+            Array of booleans denoting where values in the original
+            data are not ``NA``.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            An array of locations (indices) of the labels from the index
+            which correspond to the return values of :meth:`pandas.Index.asof`
+            for every element in ``where``.
+
+        See Also
+        --------
+        Index.asof : Return the label from the index, or, if not present, the
+            previous one.
+
+        Examples
+        --------
+        >>> idx = pd.date_range('2023-06-01', periods=3, freq='D')
+        >>> where = pd.DatetimeIndex(['2023-05-30 00:12:00', '2023-06-01 00:00:00',
+        ...                           '2023-06-02 23:59:59'])
+        >>> mask = np.ones(3, dtype=bool)
+        >>> idx.asof_locs(where, mask)
+        array([-1,  0,  1])
+
+        We can use ``mask`` to ignore certain values in the index during calculation.
+
+        >>> mask[1] = False
+        >>> idx.asof_locs(where, mask)
+        array([-1,  0,  0])
+        """
+        # error: No overload variant of "searchsorted" of "ndarray" matches argument
+        # types "Union[ExtensionArray, ndarray[Any, Any]]", "str"
+        # TODO: will be fixed when ExtensionArray.searchsorted() is fixed
+        locs = self._values[mask].searchsorted(
+            where._values, side="right"  # type: ignore[call-overload]
+        )
+        locs = np.where(locs > 0, locs - 1, 0)
+
+        result = np.arange(len(self), dtype=np.intp)[mask].take(locs)
+
+        first_value = self._values[mask.argmax()]
+        result[(locs == 0) & (where._values < first_value)] = -1
+
+        return result
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[False] = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[True],
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> tuple[Self, np.ndarray]:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: bool = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self | tuple[Self, np.ndarray]:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="sort_values"
+    )
+    def sort_values(
+        self,
+        return_indexer: bool = False,
+        ascending: bool = True,
+        na_position: NaPosition = "last",
+        key: Callable | None = None,
+    ) -> Self | tuple[Self, np.ndarray]:
+        """
+        Return a sorted copy of the index.
+
+        Return a sorted copy of the index, and optionally return the indices
+        that sorted the index itself.
+
+        Parameters
+        ----------
+        return_indexer : bool, default False
+            Should the indices that would sort the index be returned.
+        ascending : bool, default True
+            Should the index values be sorted in an ascending order.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+        key : callable, optional
+            If not None, apply the key function to the index values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect an
+            ``Index`` and return an ``Index`` of the same shape.
+
+        Returns
+        -------
+        sorted_index : pandas.Index
+            Sorted copy of the index.
+        indexer : numpy.ndarray, optional
+            The indices that the index itself was sorted by.
+
+        See Also
+        --------
+        Series.sort_values : Sort values of a Series.
+        DataFrame.sort_values : Sort values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([10, 100, 1, 1000])
+        >>> idx
+        Index([10, 100, 1, 1000], dtype='int64')
+
+        Sort values in ascending order (default behavior).
+
+        >>> idx.sort_values()
+        Index([1, 10, 100, 1000], dtype='int64')
+
+        Sort values in descending order, and also get the indices `idx` was
+        sorted by.
+
+        >>> idx.sort_values(ascending=False, return_indexer=True)
+        (Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))
+        """
+        if key is None and (
+            (ascending and self.is_monotonic_increasing)
+            or (not ascending and self.is_monotonic_decreasing)
+        ):
+            if return_indexer:
+                indexer = np.arange(len(self), dtype=np.intp)
+                return self.copy(), indexer
+            else:
+                return self.copy()
+
+        # GH 35584. Sort missing values according to na_position kwarg
+        # ignore na_position for MultiIndex
+        if not isinstance(self, ABCMultiIndex):
+            _as = nargsort(
+                items=self, ascending=ascending, na_position=na_position, key=key
+            )
+        else:
+            idx = cast(Index, ensure_key_mapped(self, key))
+            _as = idx.argsort(na_position=na_position)
+            if not ascending:
+                _as = _as[::-1]
+
+        sorted_index = self.take(_as)
+
+        if return_indexer:
+            return sorted_index, _as
+        else:
+            return sorted_index
+
+    @final
+    def sort(self, *args, **kwargs):
+        """
+        Use sort_values instead.
+        """
+        raise TypeError("cannot sort an Index object in-place, use sort_values instead")
+
+    def shift(self, periods: int = 1, freq=None):
+        """
+        Shift index by desired number of time frequency increments.
+
+        This method is for shifting the values of datetime-like indexes
+        by a specified time increment a given number of times.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Number of periods (or increments) to shift by,
+            can be positive or negative.
+        freq : pandas.DateOffset, pandas.Timedelta or str, optional
+            Frequency increment to shift by.
+            If None, the index is shifted by its own `freq` attribute.
+            Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
+
+        Returns
+        -------
+        pandas.Index
+            Shifted index.
+
+        See Also
+        --------
+        Series.shift : Shift values of Series.
+
+        Notes
+        -----
+        This method is only implemented for datetime-like index classes,
+        i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
+
+        Examples
+        --------
+        Put the first 5 month starts of 2011 into an index.
+
+        >>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
+        >>> month_starts
+        DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
+                       '2011-05-01'],
+                      dtype='datetime64[ns]', freq='MS')
+
+        Shift the index by 10 days.
+
+        >>> month_starts.shift(10, freq='D')
+        DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
+                       '2011-05-11'],
+                      dtype='datetime64[ns]', freq=None)
+
+        The default value of `freq` is the `freq` attribute of the index,
+        which is 'MS' (month start) in this example.
+
+        >>> month_starts.shift(10)
+        DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
+                       '2012-03-01'],
+                      dtype='datetime64[ns]', freq='MS')
+        """
+        raise NotImplementedError(
+            f"This method is only implemented for DatetimeIndex, PeriodIndex and "
+            f"TimedeltaIndex; Got type {type(self).__name__}"
+        )
+
+    def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
+        """
+        Return the integer indices that would sort the index.
+
+        Parameters
+        ----------
+        *args
+            Passed to `numpy.ndarray.argsort`.
+        **kwargs
+            Passed to `numpy.ndarray.argsort`.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            Integer indices that would sort the index if used as
+            an indexer.
+
+        See Also
+        --------
+        numpy.argsort : Similar method for NumPy arrays.
+        Index.sort_values : Return sorted copy of Index.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['b', 'a', 'd', 'c'])
+        >>> idx
+        Index(['b', 'a', 'd', 'c'], dtype='object')
+
+        >>> order = idx.argsort()
+        >>> order
+        array([1, 0, 3, 2])
+
+        >>> idx[order]
+        Index(['a', 'b', 'c', 'd'], dtype='object')
+        """
+        # This works for either ndarray or EA, is overridden
+        #  by RangeIndex, MultIIndex
+        return self._data.argsort(*args, **kwargs)
+
+    def _check_indexing_error(self, key):
+        if not is_scalar(key):
+            # if key is not a scalar, directly raise an error (the code below
+            # would convert to numpy arrays and raise later any way) - GH29926
+            raise InvalidIndexError(key)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        """
+        Should an integer key be treated as positional?
+        """
+        return self.inferred_type not in {
+            "integer",
+            "mixed-integer",
+            "floating",
+            "complex",
+        }
+
+    _index_shared_docs[
+        "get_indexer_non_unique"
+    ] = """
+        Compute indexer and mask for new index given the current index.
+
+        The indexer should be then used as an input to ndarray.take to align the
+        current data to the new index.
+
+        Parameters
+        ----------
+        target : %(target_klass)s
+
+        Returns
+        -------
+        indexer : np.ndarray[np.intp]
+            Integers from 0 to n - 1 indicating that the index at these
+            positions matches the corresponding target values. Missing values
+            in the target are marked by -1.
+        missing : np.ndarray[np.intp]
+            An indexer into the target of the values not found.
+            These correspond to the -1 in the indexer array.
+
+        Examples
+        --------
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['b', 'b'])
+        (array([1, 3, 4, 1, 3, 4]), array([], dtype=int64))
+
+        In the example below there are no matched values.
+
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['q', 'r', 't'])
+        (array([-1, -1, -1]), array([0, 1, 2]))
+
+        For this reason, the returned ``indexer`` contains only integers equal to -1.
+        It demonstrates that there's no match between the index and the ``target``
+        values at these positions. The mask [0, 1, 2] in the return value shows that
+        the first, second, and third elements are missing.
+
+        Notice that the return value is a tuple contains two items. In the example
+        below the first item is an array of locations in ``index``. The second
+        item is a mask shows that the first and third elements are missing.
+
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['f', 'b', 's'])
+        (array([-1,  1,  3,  4, -1]), array([0, 2]))
+        """
+
+    @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
+    def get_indexer_non_unique(
+        self, target
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        target = self._maybe_cast_listlike_indexer(target)
+
+        if not self._should_compare(target) and not self._should_partial_index(target):
+            # _should_partial_index e.g. IntervalIndex with numeric scalars
+            #  that can be matched to Interval scalars.
+            return self._get_indexer_non_comparable(target, method=None, unique=False)
+
+        pself, ptarget = self._maybe_downcast_for_indexing(target)
+        if pself is not self or ptarget is not target:
+            return pself.get_indexer_non_unique(ptarget)
+
+        if self.dtype != target.dtype:
+            # TODO: if object, could use infer_dtype to preempt costly
+            #  conversion if still non-comparable?
+            dtype = self._find_common_type_compat(target)
+
+            this = self.astype(dtype, copy=False)
+            that = target.astype(dtype, copy=False)
+            return this.get_indexer_non_unique(that)
+
+        # TODO: get_indexer has fastpaths for both Categorical-self and
+        #  Categorical-target. Can we do something similar here?
+
+        # Note: _maybe_downcast_for_indexing ensures we never get here
+        #  with MultiIndex self and non-Multi target
+        if self._is_multi and target._is_multi:
+            engine = self._engine
+            # Item "IndexEngine" of "Union[IndexEngine, ExtensionEngine]" has
+            # no attribute "_extract_level_codes"
+            tgt_values = engine._extract_level_codes(target)  # type: ignore[union-attr]
+        else:
+            tgt_values = target._get_engine_target()
+
+        indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
+        return ensure_platform_int(indexer), ensure_platform_int(missing)
+
+    @final
+    def get_indexer_for(self, target) -> npt.NDArray[np.intp]:
+        """
+        Guaranteed return of an indexer even when non-unique.
+
+        This dispatches to get_indexer or get_indexer_non_unique
+        as appropriate.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+            List of indices.
+
+        Examples
+        --------
+        >>> idx = pd.Index([np.nan, 'var1', np.nan])
+        >>> idx.get_indexer_for([np.nan])
+        array([0, 2])
+        """
+        if self._index_as_unique:
+            return self.get_indexer(target)
+        indexer, _ = self.get_indexer_non_unique(target)
+        return indexer
+
+    def _get_indexer_strict(self, key, axis_name: str_t) -> tuple[Index, np.ndarray]:
+        """
+        Analogue to get_indexer that raises if any elements are missing.
+        """
+        keyarr = key
+        if not isinstance(keyarr, Index):
+            keyarr = com.asarray_tuplesafe(keyarr)
+
+        if self._index_as_unique:
+            indexer = self.get_indexer_for(keyarr)
+            keyarr = self.reindex(keyarr)[0]
+        else:
+            keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
+
+        self._raise_if_missing(keyarr, indexer, axis_name)
+
+        keyarr = self.take(indexer)
+        if isinstance(key, Index):
+            # GH 42790 - Preserve name from an Index
+            keyarr.name = key.name
+        if lib.is_np_dtype(keyarr.dtype, "mM") or isinstance(
+            keyarr.dtype, DatetimeTZDtype
+        ):
+            # DTI/TDI.take can infer a freq in some cases when we dont want one
+            if isinstance(key, list) or (
+                isinstance(key, type(self))
+                # "Index" has no attribute "freq"
+                and key.freq is None  # type: ignore[attr-defined]
+            ):
+                keyarr = keyarr._with_freq(None)
+
+        return keyarr, indexer
+
+    def _raise_if_missing(self, key, indexer, axis_name: str_t) -> None:
+        """
+        Check that indexer can be used to return a result.
+
+        e.g. at least one element was found,
+        unless the list of keys was actually empty.
+
+        Parameters
+        ----------
+        key : list-like
+            Targeted labels (only used to show correct error message).
+        indexer: array-like of booleans
+            Indices corresponding to the key,
+            (with -1 indicating not found).
+        axis_name : str
+
+        Raises
+        ------
+        KeyError
+            If at least one key was requested but none was found.
+        """
+        if len(key) == 0:
+            return
+
+        # Count missing values
+        missing_mask = indexer < 0
+        nmissing = missing_mask.sum()
+
+        if nmissing:
+            if nmissing == len(indexer):
+                raise KeyError(f"None of [{key}] are in the [{axis_name}]")
+
+            not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
+            raise KeyError(f"{not_found} not in index")
+
+    @overload
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: Literal[True] = ...
+    ) -> npt.NDArray[np.intp]:
+        ...
+
+    @overload
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: Literal[False]
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        ...
+
+    @overload
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: bool = True
+    ) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        ...
+
+    @final
+    def _get_indexer_non_comparable(
+        self, target: Index, method, unique: bool = True
+    ) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        """
+        Called from get_indexer or get_indexer_non_unique when the target
+        is of a non-comparable dtype.
+
+        For get_indexer lookups with method=None, get_indexer is an _equality_
+        check, so non-comparable dtypes mean we will always have no matches.
+
+        For get_indexer lookups with a method, get_indexer is an _inequality_
+        check, so non-comparable dtypes mean we will always raise TypeError.
+
+        Parameters
+        ----------
+        target : Index
+        method : str or None
+        unique : bool, default True
+            * True if called from get_indexer.
+            * False if called from get_indexer_non_unique.
+
+        Raises
+        ------
+        TypeError
+            If doing an inequality check, i.e. method is not None.
+        """
+        if method is not None:
+            other_dtype = _unpack_nested_dtype(target)
+            raise TypeError(f"Cannot compare dtypes {self.dtype} and {other_dtype}")
+
+        no_matches = -1 * np.ones(target.shape, dtype=np.intp)
+        if unique:
+            # This is for get_indexer
+            return no_matches
+        else:
+            # This is for get_indexer_non_unique
+            missing = np.arange(len(target), dtype=np.intp)
+            return no_matches, missing
+
+    @property
+    def _index_as_unique(self) -> bool:
+        """
+        Whether we should treat this as unique for the sake of
+        get_indexer vs get_indexer_non_unique.
+
+        For IntervalIndex compat.
+        """
+        return self.is_unique
+
+    _requires_unique_msg = "Reindexing only valid with uniquely valued Index objects"
+
+    @final
+    def _maybe_downcast_for_indexing(self, other: Index) -> tuple[Index, Index]:
+        """
+        When dealing with an object-dtype Index and a non-object Index, see
+        if we can upcast the object-dtype one to improve performance.
+        """
+
+        if isinstance(self, ABCDatetimeIndex) and isinstance(other, ABCDatetimeIndex):
+            if (
+                self.tz is not None
+                and other.tz is not None
+                and not tz_compare(self.tz, other.tz)
+            ):
+                # standardize on UTC
+                return self.tz_convert("UTC"), other.tz_convert("UTC")
+
+        elif self.inferred_type == "date" and isinstance(other, ABCDatetimeIndex):
+            try:
+                return type(other)(self), other
+            except OutOfBoundsDatetime:
+                return self, other
+        elif self.inferred_type == "timedelta" and isinstance(other, ABCTimedeltaIndex):
+            # TODO: we dont have tests that get here
+            return type(other)(self), other
+
+        elif self.dtype.kind == "u" and other.dtype.kind == "i":
+            # GH#41873
+            if other.min() >= 0:
+                # lookup min as it may be cached
+                # TODO: may need itemsize check if we have non-64-bit Indexes
+                return self, other.astype(self.dtype)
+
+        elif self._is_multi and not other._is_multi:
+            try:
+                # "Type[Index]" has no attribute "from_tuples"
+                other = type(self).from_tuples(other)  # type: ignore[attr-defined]
+            except (TypeError, ValueError):
+                # let's instead try with a straight Index
+                self = Index(self._values)
+
+        if not is_object_dtype(self.dtype) and is_object_dtype(other.dtype):
+            # Reverse op so we dont need to re-implement on the subclasses
+            other, self = other._maybe_downcast_for_indexing(self)
+
+        return self, other
+
+    @final
+    def _find_common_type_compat(self, target) -> DtypeObj:
+        """
+        Implementation of find_common_type that adjusts for Index-specific
+        special cases.
+        """
+        target_dtype, _ = infer_dtype_from(target)
+
+        if using_string_dtype():
+            # special case: if left or right is a zero-length RangeIndex or
+            # Index[object], those can be created by the default empty constructors
+            # -> for that case ignore this dtype and always return the other
+            # (https://github.com/pandas-dev/pandas/pull/60797)
+            from pandas.core.indexes.range import RangeIndex
+
+            if len(self) == 0 and (
+                isinstance(self, RangeIndex) or self.dtype == np.object_
+            ):
+                if target_dtype.kind == "M":
+                    if hasattr(target_dtype, "tz"):
+                        target_dtype_ns = DatetimeTZDtype("ns", tz=target_dtype.tz)
+                    else:
+                        target_dtype_ns = np.dtype("datetime64[ns]")  # type: ignore[assignment]
+                    try:
+                        Index(target, dtype=target_dtype_ns, copy=False)
+                    except OutOfBoundsDatetime:
+                        return np.dtype(object)
+                    except Exception:
+                        pass
+                    return target_dtype_ns
+                return target_dtype
+            if (
+                isinstance(target, Index)
+                and len(target) == 0
+                and (isinstance(target, RangeIndex) or target_dtype == np.object_)
+            ):
+                return self.dtype
+
+        # special case: if one dtype is uint64 and the other a signed int, return object
+        # See https://github.com/pandas-dev/pandas/issues/26778 for discussion
+        # Now it's:
+        # * float | [u]int -> float
+        # * uint64 | signed int  -> object
+        # We may change union(float | [u]int) to go to object.
+        if self.dtype == "uint64" or target_dtype == "uint64":
+            if is_signed_integer_dtype(self.dtype) or is_signed_integer_dtype(
+                target_dtype
+            ):
+                return _dtype_obj
+
+        dtype = find_result_type(self.dtype, target)
+        dtype = common_dtype_categorical_compat([self, target], dtype)
+        return dtype
+
+    @final
+    def _should_compare(self, other: Index) -> bool:
+        """
+        Check if `self == other` can ever have non-False entries.
+        """
+
+        # NB: we use inferred_type rather than is_bool_dtype to catch
+        #  object_dtype_of_bool and categorical[object_dtype_of_bool] cases
+        if (
+            other.inferred_type == "boolean" and is_any_real_numeric_dtype(self.dtype)
+        ) or (
+            self.inferred_type == "boolean" and is_any_real_numeric_dtype(other.dtype)
+        ):
+            # GH#16877 Treat boolean labels passed to a numeric index as not
+            #  found. Without this fix False and True would be treated as 0 and 1
+            #  respectively.
+            return False
+
+        dtype = _unpack_nested_dtype(other)
+        return (
+            self._is_comparable_dtype(dtype)
+            or is_object_dtype(dtype)
+            or is_string_dtype(dtype)
+        )
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        if self.dtype.kind == "b":
+            return dtype.kind == "b"
+        elif is_numeric_dtype(self.dtype):
+            return is_numeric_dtype(dtype)
+        # TODO: this was written assuming we only get here with object-dtype,
+        #  which is no longer correct. Can we specialize for EA?
+        return True
+
+    @final
+    def groupby(self, values) -> PrettyDict[Hashable, np.ndarray]:
+        """
+        Group the index labels by a given array of values.
+
+        Parameters
+        ----------
+        values : array
+            Values used to determine the groups.
+
+        Returns
+        -------
+        dict
+            {group name -> group labels}
+        """
+        # TODO: if we are a MultiIndex, we can do better
+        # that converting to tuples
+        if isinstance(values, ABCMultiIndex):
+            values = values._values
+        values = Categorical(values)
+        result = values._reverse_indexer()
+
+        # map to the label
+        result = {k: self.take(v) for k, v in result.items()}
+
+        return PrettyDict(result)
+
+    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+        """
+        Map values using an input mapping or function.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}
+            If 'ignore', propagate NA values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        Union[Index, MultiIndex]
+            The output of the mapping function applied to the index.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.map({1: 'a', 2: 'b', 3: 'c'})
+        Index(['a', 'b', 'c'], dtype='object')
+
+        Using `map` with a function:
+
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx.map('I am a {}'.format)
+        Index(['I am a 1', 'I am a 2', 'I am a 3'], dtype='object')
+
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.map(lambda x: x.upper())
+        Index(['A', 'B', 'C'], dtype='object')
+        """
+        from pandas.core.indexes.multi import MultiIndex
+
+        new_values = self._map_values(mapper, na_action=na_action)
+
+        # we can return a MultiIndex
+        if new_values.size and isinstance(new_values[0], tuple):
+            if isinstance(self, MultiIndex):
+                names = self.names
+            elif self.name:
+                names = [self.name] * len(new_values[0])
+            else:
+                names = None
+            return MultiIndex.from_tuples(new_values, names=names)
+
+        dtype = None
+        if not new_values.size:
+            # empty
+            dtype = self.dtype
+
+        # e.g. if we are floating and new_values is all ints, then we
+        #  don't want to cast back to floating.  But if we are UInt64
+        #  and new_values is all ints, we want to try.
+        same_dtype = lib.infer_dtype(new_values, skipna=False) == self.inferred_type
+        if same_dtype:
+            new_values = maybe_cast_pointwise_result(
+                new_values, self.dtype, same_dtype=same_dtype
+            )
+
+        return Index._with_infer(new_values, dtype=dtype, copy=False, name=self.name)
+
+    # TODO: De-duplicate with map, xref GH#32349
+    @final
+    def _transform_index(self, func, *, level=None) -> Index:
+        """
+        Apply function to all values found in index.
+
+        This includes transforming multiindex entries separately.
+        Only apply function to one level of the MultiIndex if level is specified.
+        """
+        if isinstance(self, ABCMultiIndex):
+            values = [
+                self.get_level_values(i).map(func)
+                if i == level or level is None
+                else self.get_level_values(i)
+                for i in range(self.nlevels)
+            ]
+            return type(self).from_arrays(values)
+        else:
+            items = [func(x) for x in self]
+            return Index(items, name=self.name, tupleize_cols=False)
+
+    def isin(self, values, level=None) -> npt.NDArray[np.bool_]:
+        """
+        Return a boolean array where the index values are in `values`.
+
+        Compute boolean array of whether each index value is found in the
+        passed set of values. The length of the returned boolean array matches
+        the length of the index.
+
+        Parameters
+        ----------
+        values : set or list-like
+            Sought values.
+        level : str or int, optional
+            Name or position of the index level to use (if the index is a
+            `MultiIndex`).
+
+        Returns
+        -------
+        np.ndarray[bool]
+            NumPy array of boolean values.
+
+        See Also
+        --------
+        Series.isin : Same for Series.
+        DataFrame.isin : Same method for DataFrames.
+
+        Notes
+        -----
+        In the case of `MultiIndex` you must either specify `values` as a
+        list-like object containing tuples that are the same length as the
+        number of levels, or specify `level`. Otherwise it will raise a
+        ``ValueError``.
+
+        If `level` is specified:
+
+        - if it is the name of one *and only one* index level, use that level;
+        - otherwise it should be a number indicating level position.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1,2,3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+
+        Check whether each index value in a list of values.
+
+        >>> idx.isin([1, 4])
+        array([ True, False, False])
+
+        >>> midx = pd.MultiIndex.from_arrays([[1,2,3],
+        ...                                  ['red', 'blue', 'green']],
+        ...                                  names=('number', 'color'))
+        >>> midx
+        MultiIndex([(1,   'red'),
+                    (2,  'blue'),
+                    (3, 'green')],
+                   names=['number', 'color'])
+
+        Check whether the strings in the 'color' level of the MultiIndex
+        are in a list of colors.
+
+        >>> midx.isin(['red', 'orange', 'yellow'], level='color')
+        array([ True, False, False])
+
+        To check across the levels of a MultiIndex, pass a list of tuples:
+
+        >>> midx.isin([(1, 'red'), (3, 'red')])
+        array([ True, False, False])
+        """
+        if level is not None:
+            self._validate_index_level(level)
+        return algos.isin(self._values, values)
+
+    def _get_string_slice(self, key: str_t):
+        # this is for partial string indexing,
+        # overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
+        raise NotImplementedError
+
+    def slice_indexer(
+        self,
+        start: Hashable | None = None,
+        end: Hashable | None = None,
+        step: int | None = None,
+    ) -> slice:
+        """
+        Compute the slice indexer for input labels and step.
+
+        Index needs to be ordered and unique.
+
+        Parameters
+        ----------
+        start : label, default None
+            If None, defaults to the beginning.
+        end : label, default None
+            If None, defaults to the end.
+        step : int, default None
+
+        Returns
+        -------
+        slice
+
+        Raises
+        ------
+        KeyError : If key does not exist, or key is not unique and index is
+            not ordered.
+
+        Notes
+        -----
+        This function assumes that the data is sorted, so use at your own peril
+
+        Examples
+        --------
+        This is a method on all index types. For example you can do:
+
+        >>> idx = pd.Index(list('abcd'))
+        >>> idx.slice_indexer(start='b', end='c')
+        slice(1, 3, None)
+
+        >>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])
+        >>> idx.slice_indexer(start='b', end=('c', 'g'))
+        slice(1, 3, None)
+        """
+        start_slice, end_slice = self.slice_locs(start, end, step=step)
+
+        # return a slice
+        if not is_scalar(start_slice):
+            raise AssertionError("Start slice bound is non-scalar")
+        if not is_scalar(end_slice):
+            raise AssertionError("End slice bound is non-scalar")
+
+        return slice(start_slice, end_slice, step)
+
+    def _maybe_cast_indexer(self, key):
+        """
+        If we have a float key and are not a floating index, then try to cast
+        to an int if equivalent.
+        """
+        return key
+
+    def _maybe_cast_listlike_indexer(self, target) -> Index:
+        """
+        Analogue to maybe_cast_indexer for get_indexer instead of get_loc.
+        """
+        target_index = ensure_index(target)
+        if (
+            not hasattr(target, "dtype")
+            and self.dtype == object
+            and target_index.dtype == "string"
+        ):
+            # If we started with a list-like, avoid inference to string dtype if self
+            # is object dtype (coercing to string dtype will alter the missing values)
+            target_index = Index(target, dtype=self.dtype)
+        return target_index
+
+    @final
+    def _validate_indexer(
+        self,
+        form: Literal["positional", "slice"],
+        key,
+        kind: Literal["getitem", "iloc"],
+    ) -> None:
+        """
+        If we are positional indexer, validate that we have appropriate
+        typed bounds must be an integer.
+        """
+        if not lib.is_int_or_none(key):
+            self._raise_invalid_indexer(form, key)
+
+    def _maybe_cast_slice_bound(self, label, side: str_t):
+        """
+        This function should be overloaded in subclasses that allow non-trivial
+        casting on label-slice bounds, e.g. datetime-like indices allowing
+        strings containing formatted datetimes.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        label : object
+
+        Notes
+        -----
+        Value of `side` parameter should be validated in caller.
+        """
+
+        # We are a plain index here (sub-class override this method if they
+        # wish to have special treatment for floats/ints, e.g. datetimelike Indexes
+
+        if is_numeric_dtype(self.dtype):
+            return self._maybe_cast_indexer(label)
+
+        # reject them, if index does not contain label
+        if (is_float(label) or is_integer(label)) and label not in self:
+            self._raise_invalid_indexer("slice", label)
+
+        return label
+
+    def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
+        if self.is_monotonic_increasing:
+            return self.searchsorted(label, side=side)
+        elif self.is_monotonic_decreasing:
+            # np.searchsorted expects ascending sort order, have to reverse
+            # everything for it to work (element ordering, search side and
+            # resulting value).
+            pos = self[::-1].searchsorted(
+                label, side="right" if side == "left" else "left"
+            )
+            return len(self) - pos
+
+        raise ValueError("index must be monotonic increasing or decreasing")
+
+    def get_slice_bound(self, label, side: Literal["left", "right"]) -> int:
+        """
+        Calculate slice bound that corresponds to given label.
+
+        Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
+        of given label.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        int
+            Index of label.
+
+        See Also
+        --------
+        Index.get_loc : Get integer location, slice or boolean mask for requested
+            label.
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.get_slice_bound(3, 'left')
+        3
+
+        >>> idx.get_slice_bound(3, 'right')
+        4
+
+        If ``label`` is non-unique in the index, an error will be raised.
+
+        >>> idx_duplicate = pd.Index(['a', 'b', 'a', 'c', 'd'])
+        >>> idx_duplicate.get_slice_bound('a', 'left')
+        Traceback (most recent call last):
+        KeyError: Cannot get left slice bound for non-unique label: 'a'
+        """
+
+        if side not in ("left", "right"):
+            raise ValueError(
+                "Invalid value for side kwarg, must be either "
+                f"'left' or 'right': {side}"
+            )
+
+        original_label = label
+
+        # For datetime indices label may be a string that has to be converted
+        # to datetime boundary according to its resolution.
+        label = self._maybe_cast_slice_bound(label, side)
+
+        # we need to look up the label
+        try:
+            slc = self.get_loc(label)
+        except KeyError as err:
+            try:
+                return self._searchsorted_monotonic(label, side)
+            except ValueError:
+                # raise the original KeyError
+                raise err
+
+        if isinstance(slc, np.ndarray):
+            # get_loc may return a boolean array, which
+            # is OK as long as they are representable by a slice.
+            assert is_bool_dtype(slc.dtype)
+            slc = lib.maybe_booleans_to_slice(slc.view("u1"))
+            if isinstance(slc, np.ndarray):
+                raise KeyError(
+                    f"Cannot get {side} slice bound for non-unique "
+                    f"label: {repr(original_label)}"
+                )
+
+        if isinstance(slc, slice):
+            if side == "left":
+                return slc.start
+            else:
+                return slc.stop
+        else:
+            if side == "right":
+                return slc + 1
+            else:
+                return slc
+
+    def slice_locs(self, start=None, end=None, step=None) -> tuple[int, int]:
+        """
+        Compute slice locations for input labels.
+
+        Parameters
+        ----------
+        start : label, default None
+            If None, defaults to the beginning.
+        end : label, default None
+            If None, defaults to the end.
+        step : int, defaults None
+            If None, defaults to 1.
+
+        Returns
+        -------
+        tuple[int, int]
+
+        See Also
+        --------
+        Index.get_loc : Get location for a single label.
+
+        Notes
+        -----
+        This method only works if the index is monotonic or unique.
+
+        Examples
+        --------
+        >>> idx = pd.Index(list('abcd'))
+        >>> idx.slice_locs(start='b', end='c')
+        (1, 3)
+        """
+        inc = step is None or step >= 0
+
+        if not inc:
+            # If it's a reverse slice, temporarily swap bounds.
+            start, end = end, start
+
+        # GH 16785: If start and end happen to be date strings with UTC offsets
+        # attempt to parse and check that the offsets are the same
+        if isinstance(start, (str, datetime)) and isinstance(end, (str, datetime)):
+            try:
+                ts_start = Timestamp(start)
+                ts_end = Timestamp(end)
+            except (ValueError, TypeError):
+                pass
+            else:
+                if not tz_compare(ts_start.tzinfo, ts_end.tzinfo):
+                    raise ValueError("Both dates must have the same UTC offset")
+
+        start_slice = None
+        if start is not None:
+            start_slice = self.get_slice_bound(start, "left")
+        if start_slice is None:
+            start_slice = 0
+
+        end_slice = None
+        if end is not None:
+            end_slice = self.get_slice_bound(end, "right")
+        if end_slice is None:
+            end_slice = len(self)
+
+        if not inc:
+            # Bounds at this moment are swapped, swap them back and shift by 1.
+            #
+            # slice_locs('B', 'A', step=-1): s='B', e='A'
+            #
+            #              s='A'                 e='B'
+            # AFTER SWAP:    |                     |
+            #                v ------------------> V
+            #           -----------------------------------
+            #           | | |A|A|A|A| | | | | |B|B| | | | |
+            #           -----------------------------------
+            #              ^ <------------------ ^
+            # SHOULD BE:   |                     |
+            #           end=s-1              start=e-1
+            #
+            end_slice, start_slice = start_slice - 1, end_slice - 1
+
+            # i == -1 triggers ``len(self) + i`` selection that points to the
+            # last element, not before-the-first one, subtracting len(self)
+            # compensates that.
+            if end_slice == -1:
+                end_slice -= len(self)
+            if start_slice == -1:
+                start_slice -= len(self)
+
+        return start_slice, end_slice
+
+    def delete(self, loc) -> Self:
+        """
+        Make new Index with passed location(-s) deleted.
+
+        Parameters
+        ----------
+        loc : int or list of int
+            Location of item(-s) which will be deleted.
+            Use a list of locations to delete more than one value at the same time.
+
+        Returns
+        -------
+        Index
+            Will be same type as self, except for RangeIndex.
+
+        See Also
+        --------
+        numpy.delete : Delete any rows and column from NumPy array (ndarray).
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.delete(1)
+        Index(['a', 'c'], dtype='object')
+
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.delete([0, 2])
+        Index(['b'], dtype='object')
+        """
+        values = self._values
+        res_values: ArrayLike
+        if isinstance(values, np.ndarray):
+            # TODO(__array_function__): special casing will be unnecessary
+            res_values = np.delete(values, loc)
+        else:
+            res_values = values.delete(loc)
+
+        # _constructor so RangeIndex-> Index with an int64 dtype
+        return self._constructor._simple_new(res_values, name=self.name)
+
+    def insert(self, loc: int, item) -> Index:
+        """
+        Make new Index inserting new item at location.
+
+        Follows Python numpy.insert semantics for negative values.
+
+        Parameters
+        ----------
+        loc : int
+        item : object
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.insert(1, 'x')
+        Index(['a', 'x', 'b', 'c'], dtype='object')
+        """
+        item = lib.item_from_zerodim(item)
+        if is_valid_na_for_dtype(item, self.dtype) and self.dtype != object:
+            item = self._na_value
+
+        arr = self._values
+
+        if using_string_dtype() and len(self) == 0 and self.dtype == np.object_:
+            # special case: if we are an empty object-dtype Index, also
+            # take into account the inserted item for the resulting dtype
+            # (https://github.com/pandas-dev/pandas/pull/60797)
+            dtype = self._find_common_type_compat(item)
+            if dtype != self.dtype:
+                return self.astype(dtype).insert(loc, item)
+
+        try:
+            if isinstance(arr, ExtensionArray):
+                res_values = arr.insert(loc, item)
+                return type(self)._simple_new(res_values, name=self.name)
+            else:
+                item = self._validate_fill_value(item)
+        except (TypeError, ValueError, LossySetitemError):
+            # e.g. trying to insert an integer into a DatetimeIndex
+            #  We cannot keep the same dtype, so cast to the (often object)
+            #  minimal shared dtype before doing the insert.
+            dtype = self._find_common_type_compat(item)
+            if dtype == self.dtype:
+                # EA's might run into recursion errors if loc is invalid
+                raise
+            return self.astype(dtype).insert(loc, item)
+
+        if arr.dtype != object or not isinstance(
+            item, (tuple, np.datetime64, np.timedelta64)
+        ):
+            # with object-dtype we need to worry about numpy incorrectly casting
+            # dt64/td64 to integer, also about treating tuples as sequences
+            # special-casing dt64/td64 https://github.com/numpy/numpy/issues/12550
+            casted = arr.dtype.type(item)
+            new_values = np.insert(arr, loc, casted)
+
+        else:
+            # error: No overload variant of "insert" matches argument types
+            # "ndarray[Any, Any]", "int", "None"
+            new_values = np.insert(arr, loc, None)  # type: ignore[call-overload]
+            loc = loc if loc >= 0 else loc - 1
+            new_values[loc] = item
+
+        out = Index._with_infer(new_values, name=self.name)
+        if (
+            using_string_dtype()
+            and is_string_dtype(out.dtype)
+            and new_values.dtype == object
+        ):
+            out = out.astype(new_values.dtype)
+        if self.dtype == object and out.dtype != object:
+            # GH#51363
+            warnings.warn(
+                "The behavior of Index.insert with object-dtype is deprecated, "
+                "in a future version this will return an object-dtype Index "
+                "instead of inferring a non-object dtype. To retain the old "
+                "behavior, do `idx.insert(loc, item).infer_objects(copy=False)`",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return out
+
+    def drop(
+        self,
+        labels: Index | np.ndarray | Iterable[Hashable],
+        errors: IgnoreRaise = "raise",
+    ) -> Index:
+        """
+        Make new Index with passed list of labels deleted.
+
+        Parameters
+        ----------
+        labels : array-like or scalar
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and existing labels are dropped.
+
+        Returns
+        -------
+        Index
+            Will be same type as self, except for RangeIndex.
+
+        Raises
+        ------
+        KeyError
+            If not all of the labels are found in the selected axis
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.drop(['a'])
+        Index(['b', 'c'], dtype='object')
+        """
+        if not isinstance(labels, Index):
+            # avoid materializing e.g. RangeIndex
+            arr_dtype = "object" if self.dtype == "object" else None
+            labels = com.index_labels_to_array(labels, dtype=arr_dtype)
+
+        indexer = self.get_indexer_for(labels)
+        mask = indexer == -1
+        if mask.any():
+            if errors != "ignore":
+                raise KeyError(f"{labels[mask].tolist()} not found in axis")
+            indexer = indexer[~mask]
+        return self.delete(indexer)
+
+    @final
+    def infer_objects(self, copy: bool = True) -> Index:
+        """
+        If we have an object dtype, try to infer a non-object dtype.
+
+        Parameters
+        ----------
+        copy : bool, default True
+            Whether to make a copy in cases where no inference occurs.
+        """
+        if self._is_multi:
+            raise NotImplementedError(
+                "infer_objects is not implemented for MultiIndex. "
+                "Use index.to_frame().infer_objects() instead."
+            )
+        if self.dtype != object:
+            return self.copy() if copy else self
+
+        values = self._values
+        values = cast("npt.NDArray[np.object_]", values)
+        res_values = lib.maybe_convert_objects(
+            values,
+            convert_non_numeric=True,
+        )
+        if copy and res_values is values:
+            return self.copy()
+        result = Index(res_values, name=self.name)
+        if not copy and res_values is values and self._references is not None:
+            result._references = self._references
+            result._references.add_index_reference(result)
+        return result
+
+    @final
+    def diff(self, periods: int = 1) -> Index:
+        """
+        Computes the difference between consecutive values in the Index object.
+
+        If periods is greater than 1, computes the difference between values that
+        are `periods` number of positions apart.
+
+        Parameters
+        ----------
+        periods : int, optional
+            The number of positions between the current and previous
+            value to compute the difference with. Default is 1.
+
+        Returns
+        -------
+        Index
+            A new Index object with the computed differences.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> idx = pd.Index([10, 20, 30, 40, 50])
+        >>> idx.diff()
+        Index([nan, 10.0, 10.0, 10.0, 10.0], dtype='float64')
+
+        """
+        return Index(self.to_series().diff(periods))
+
+    @final
+    def round(self, decimals: int = 0) -> Self:
+        """
+        Round each value in the Index to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, optional
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+
+        Returns
+        -------
+        Index
+            A new Index with the rounded values.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> idx = pd.Index([10.1234, 20.5678, 30.9123, 40.4567, 50.7890])
+        >>> idx.round(decimals=2)
+        Index([10.12, 20.57, 30.91, 40.46, 50.79], dtype='float64')
+
+        """
+        return self._constructor(self.to_series().round(decimals))
+
+    # --------------------------------------------------------------------
+    # Generated Arithmetic, Comparison, and Unary Methods
+
+    def _cmp_method(self, other, op):
+        """
+        Wrapper used to dispatch comparison operations.
+        """
+        if self.is_(other):
+            # fastpath
+            if op in {operator.eq, operator.le, operator.ge}:
+                arr = np.ones(len(self), dtype=bool)
+                if self._can_hold_na and not isinstance(self, ABCMultiIndex):
+                    # TODO: should set MultiIndex._can_hold_na = False?
+                    arr[self.isna()] = False
+                return arr
+            elif op is operator.ne:
+                arr = np.zeros(len(self), dtype=bool)
+                if self._can_hold_na and not isinstance(self, ABCMultiIndex):
+                    arr[self.isna()] = True
+                return arr
+
+        if isinstance(other, (np.ndarray, Index, ABCSeries, ExtensionArray)) and len(
+            self
+        ) != len(other):
+            raise ValueError("Lengths must match to compare")
+
+        if not isinstance(other, ABCMultiIndex):
+            other = extract_array(other, extract_numpy=True)
+        else:
+            other = np.asarray(other)
+
+        if is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
+            # e.g. PeriodArray, Categorical
+            result = op(self._values, other)
+
+        elif isinstance(self._values, ExtensionArray):
+            result = op(self._values, other)
+
+        elif is_object_dtype(self.dtype) and not isinstance(self, ABCMultiIndex):
+            # don't pass MultiIndex
+            result = ops.comp_method_OBJECT_ARRAY(op, self._values, other)
+
+        else:
+            result = ops.comparison_op(self._values, other, op)
+
+        return result
+
+    @final
+    def _logical_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+
+        res_values = ops.logical_op(lvalues, rvalues, op)
+        return self._construct_result(res_values, name=res_name)
+
+    @final
+    def _construct_result(self, result, name):
+        if isinstance(result, tuple):
+            return (
+                Index(result[0], name=name, dtype=result[0].dtype),
+                Index(result[1], name=name, dtype=result[1].dtype),
+            )
+        return Index(result, name=name, dtype=result.dtype)
+
+    def _arith_method(self, other, op):
+        if (
+            isinstance(other, Index)
+            and is_object_dtype(other.dtype)
+            and type(other) is not Index
+        ):
+            # We return NotImplemented for object-dtype index *subclasses* so they have
+            # a chance to implement ops before we unwrap them.
+            # See https://github.com/pandas-dev/pandas/issues/31109
+            return NotImplemented
+
+        return super()._arith_method(other, op)
+
+    @final
+    def _unary_method(self, op):
+        result = op(self._values)
+        return Index(result, name=self.name)
+
+    def __abs__(self) -> Index:
+        return self._unary_method(operator.abs)
+
+    def __neg__(self) -> Index:
+        return self._unary_method(operator.neg)
+
+    def __pos__(self) -> Index:
+        return self._unary_method(operator.pos)
+
+    def __invert__(self) -> Index:
+        # GH#8875
+        return self._unary_method(operator.inv)
+
+    # --------------------------------------------------------------------
+    # Reductions
+
+    def any(self, *args, **kwargs):
+        """
+        Return whether any element is Truthy.
+
+        Parameters
+        ----------
+        *args
+            Required for compatibility with numpy.
+        **kwargs
+            Required for compatibility with numpy.
+
+        Returns
+        -------
+        bool or array-like (if axis is specified)
+            A single element array-like may be converted to bool.
+
+        See Also
+        --------
+        Index.all : Return whether all elements are True.
+        Series.all : Return whether all elements are True.
+
+        Notes
+        -----
+        Not a Number (NaN), positive infinity and negative infinity
+        evaluate to True because these are not equal to zero.
+
+        Examples
+        --------
+        >>> index = pd.Index([0, 1, 2])
+        >>> index.any()
+        True
+
+        >>> index = pd.Index([0, 0, 0])
+        >>> index.any()
+        False
+        """
+        nv.validate_any(args, kwargs)
+        self._maybe_disable_logical_methods("any")
+        vals = self._values
+        if not isinstance(vals, np.ndarray):
+            # i.e. EA, call _reduce instead of "any" to get TypeError instead
+            #  of AttributeError
+            return vals._reduce("any")
+        return np.any(vals)
+
+    def all(self, *args, **kwargs):
+        """
+        Return whether all elements are Truthy.
+
+        Parameters
+        ----------
+        *args
+            Required for compatibility with numpy.
+        **kwargs
+            Required for compatibility with numpy.
+
+        Returns
+        -------
+        bool or array-like (if axis is specified)
+            A single element array-like may be converted to bool.
+
+        See Also
+        --------
+        Index.any : Return whether any element in an Index is True.
+        Series.any : Return whether any element in a Series is True.
+        Series.all : Return whether all elements in a Series are True.
+
+        Notes
+        -----
+        Not a Number (NaN), positive infinity and negative infinity
+        evaluate to True because these are not equal to zero.
+
+        Examples
+        --------
+        True, because nonzero integers are considered True.
+
+        >>> pd.Index([1, 2, 3]).all()
+        True
+
+        False, because ``0`` is considered False.
+
+        >>> pd.Index([0, 1, 2]).all()
+        False
+        """
+        nv.validate_all(args, kwargs)
+        self._maybe_disable_logical_methods("all")
+        vals = self._values
+        if not isinstance(vals, np.ndarray):
+            # i.e. EA, call _reduce instead of "all" to get TypeError instead
+            #  of AttributeError
+            return vals._reduce("all")
+        return np.all(vals)
+
+    @final
+    def _maybe_disable_logical_methods(self, opname: str_t) -> None:
+        """
+        raise if this Index subclass does not support any or all.
+        """
+        if (
+            isinstance(self, ABCMultiIndex)
+            # TODO(3.0): PeriodArray and DatetimeArray any/all will raise,
+            #  so checking needs_i8_conversion will be unnecessary
+            or (needs_i8_conversion(self.dtype) and self.dtype.kind != "m")
+        ):
+            # This call will raise
+            make_invalid_op(opname)(self)
+
+    @Appender(IndexOpsMixin.argmin.__doc__)
+    def argmin(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        nv.validate_argmin(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+        return super().argmin(skipna=skipna)
+
+    @Appender(IndexOpsMixin.argmax.__doc__)
+    def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        nv.validate_argmax(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                warnings.warn(
+                    f"The behavior of {type(self).__name__}.argmax/argmin "
+                    "with skipna=False and NAs, or with all-NAs is deprecated. "
+                    "In a future version this will raise ValueError.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return -1
+        return super().argmax(skipna=skipna)
+
+    def min(self, axis=None, skipna: bool = True, *args, **kwargs):
+        """
+        Return the minimum value of the Index.
+
+        Parameters
+        ----------
+        axis : {None}
+            Dummy argument for consistency with Series.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        scalar
+            Minimum value.
+
+        See Also
+        --------
+        Index.max : Return the maximum value of the object.
+        Series.min : Return the minimum value in a Series.
+        DataFrame.min : Return the minimum values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([3, 2, 1])
+        >>> idx.min()
+        1
+
+        >>> idx = pd.Index(['c', 'b', 'a'])
+        >>> idx.min()
+        'a'
+
+        For a MultiIndex, the minimum is determined lexicographically.
+
+        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
+        >>> idx.min()
+        ('a', 1)
+        """
+        nv.validate_min(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not len(self):
+            return self._na_value
+
+        if len(self) and self.is_monotonic_increasing:
+            # quick check
+            first = self[0]
+            if not isna(first):
+                return first
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return self._na_value
+
+        if not self._is_multi and not isinstance(self._values, np.ndarray):
+            return self._values._reduce(name="min", skipna=skipna)
+
+        return nanops.nanmin(self._values, skipna=skipna)
+
+    def max(self, axis=None, skipna: bool = True, *args, **kwargs):
+        """
+        Return the maximum value of the Index.
+
+        Parameters
+        ----------
+        axis : int, optional
+            For compatibility with NumPy. Only 0 or None are allowed.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        scalar
+            Maximum value.
+
+        See Also
+        --------
+        Index.min : Return the minimum value in an Index.
+        Series.max : Return the maximum value in a Series.
+        DataFrame.max : Return the maximum values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([3, 2, 1])
+        >>> idx.max()
+        3
+
+        >>> idx = pd.Index(['c', 'b', 'a'])
+        >>> idx.max()
+        'c'
+
+        For a MultiIndex, the maximum is determined lexicographically.
+
+        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
+        >>> idx.max()
+        ('b', 2)
+        """
+
+        nv.validate_max(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not len(self):
+            return self._na_value
+
+        if len(self) and self.is_monotonic_increasing:
+            # quick check
+            last = self[-1]
+            if not isna(last):
+                return last
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return self._na_value
+
+        if not self._is_multi and not isinstance(self._values, np.ndarray):
+            return self._values._reduce(name="max", skipna=skipna)
+
+        return nanops.nanmax(self._values, skipna=skipna)
+
+    # --------------------------------------------------------------------
+
+    @final
+    @property
+    def shape(self) -> Shape:
+        """
+        Return a tuple of the shape of the underlying data.
+
+        Examples
+        --------
+        >>> idx = pd.Index([1, 2, 3])
+        >>> idx
+        Index([1, 2, 3], dtype='int64')
+        >>> idx.shape
+        (3,)
+        """
+        # See GH#27775, GH#27384 for history/reasoning in how this is defined.
+        return (len(self),)
+
+
+def ensure_index_from_sequences(sequences, names=None) -> Index:
+    """
+    Construct an index from sequences of data.
+
+    A single sequence returns an Index. Many sequences returns a
+    MultiIndex.
+
+    Parameters
+    ----------
+    sequences : sequence of sequences
+    names : sequence of str
+
+    Returns
+    -------
+    index : Index or MultiIndex
+
+    Examples
+    --------
+    >>> ensure_index_from_sequences([[1, 2, 3]], names=["name"])
+    Index([1, 2, 3], dtype='int64', name='name')
+
+    >>> ensure_index_from_sequences([["a", "a"], ["a", "b"]], names=["L1", "L2"])
+    MultiIndex([('a', 'a'),
+                ('a', 'b')],
+               names=['L1', 'L2'])
+
+    See Also
+    --------
+    ensure_index
+    """
+    from pandas.core.indexes.multi import MultiIndex
+
+    if len(sequences) == 1:
+        if names is not None:
+            names = names[0]
+        return Index(sequences[0], name=names)
+    else:
+        return MultiIndex.from_arrays(sequences, names=names)
+
+
+def ensure_index(index_like: Axes, copy: bool = False) -> Index:
+    """
+    Ensure that we have an index from some index-like object.
+
+    Parameters
+    ----------
+    index_like : sequence
+        An Index or other sequence
+    copy : bool, default False
+
+    Returns
+    -------
+    index : Index or MultiIndex
+
+    See Also
+    --------
+    ensure_index_from_sequences
+
+    Examples
+    --------
+    >>> ensure_index(['a', 'b'])
+    Index(['a', 'b'], dtype='object')
+
+    >>> ensure_index([('a', 'a'),  ('b', 'c')])
+    Index([('a', 'a'), ('b', 'c')], dtype='object')
+
+    >>> ensure_index([['a', 'a'], ['b', 'c']])
+    MultiIndex([('a', 'b'),
+            ('a', 'c')],
+           )
+    """
+    if isinstance(index_like, Index):
+        if copy:
+            index_like = index_like.copy()
+        return index_like
+
+    if isinstance(index_like, ABCSeries):
+        name = index_like.name
+        return Index(index_like, name=name, copy=copy)
+
+    if is_iterator(index_like):
+        index_like = list(index_like)
+
+    if isinstance(index_like, list):
+        if type(index_like) is not list:  # noqa: E721
+            # must check for exactly list here because of strict type
+            # check in clean_index_list
+            index_like = list(index_like)
+
+        if len(index_like) and lib.is_all_arraylike(index_like):
+            from pandas.core.indexes.multi import MultiIndex
+
+            return MultiIndex.from_arrays(index_like)
+        else:
+            return Index(index_like, copy=copy, tupleize_cols=False)
+    else:
+        return Index(index_like, copy=copy)
+
+
+def ensure_has_len(seq):
+    """
+    If seq is an iterator, put its values into a list.
+    """
+    try:
+        len(seq)
+    except TypeError:
+        return list(seq)
+    else:
+        return seq
+
+
+def trim_front(strings: list[str]) -> list[str]:
+    """
+    Trims zeros and decimal points.
+
+    Examples
+    --------
+    >>> trim_front([" a", " b"])
+    ['a', 'b']
+
+    >>> trim_front([" a", " "])
+    ['a', '']
+    """
+    if not strings:
+        return strings
+    while all(strings) and all(x[0] == " " for x in strings):
+        strings = [x[1:] for x in strings]
+    return strings
+
+
+def _validate_join_method(method: str) -> None:
+    if method not in ["left", "right", "inner", "outer"]:
+        raise ValueError(f"do not recognize join method {method}")
+
+
+def maybe_extract_name(name, obj, cls) -> Hashable:
+    """
+    If no name is passed, then extract it from data, validating hashability.
+    """
+    if name is None and isinstance(obj, (Index, ABCSeries)):
+        # Note we don't just check for "name" attribute since that would
+        #  pick up e.g. dtype.name
+        name = obj.name
+
+    # GH#29069
+    if not is_hashable(name):
+        raise TypeError(f"{cls.__name__}.name must be a hashable type")
+
+    return name
+
+
+def get_unanimous_names(*indexes: Index) -> tuple[Hashable, ...]:
+    """
+    Return common name if all indices agree, otherwise None (level-by-level).
+
+    Parameters
+    ----------
+    indexes : list of Index objects
+
+    Returns
+    -------
+    list
+        A list representing the unanimous 'names' found.
+    """
+    name_tups = [tuple(i.names) for i in indexes]
+    name_sets = [{*ns} for ns in zip_longest(*name_tups)]
+    names = tuple(ns.pop() if len(ns) == 1 else None for ns in name_sets)
+    return names
+
+
+def _unpack_nested_dtype(other: Index) -> DtypeObj:
+    """
+    When checking if our dtype is comparable with another, we need
+    to unpack CategoricalDtype to look at its categories.dtype.
+
+    Parameters
+    ----------
+    other : Index
+
+    Returns
+    -------
+    np.dtype or ExtensionDtype
+    """
+    dtype = other.dtype
+    if isinstance(dtype, CategoricalDtype):
+        # If there is ever a SparseIndex, this could get dispatched
+        #  here too.
+        return dtype.categories.dtype
+    elif isinstance(dtype, ArrowDtype):
+        # GH 53617
+        import pyarrow as pa
+
+        if pa.types.is_dictionary(dtype.pyarrow_dtype):
+            other = other[:0].astype(ArrowDtype(dtype.pyarrow_dtype.value_type))
+    return other.dtype
+
+
+def _maybe_try_sort(result: Index | ArrayLike, sort: bool | None):
+    if sort is not False:
+        try:
+            # error: Incompatible types in assignment (expression has type
+            # "Union[ExtensionArray, ndarray[Any, Any], Index, Series,
+            # Tuple[Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series],
+            # ndarray[Any, Any]]]", variable has type "Union[Index,
+            # Union[ExtensionArray, ndarray[Any, Any]]]")
+            result = algos.safe_sort(result)  # type: ignore[assignment]
+        except TypeError as err:
+            if sort is True:
+                raise
+            warnings.warn(
+                f"{err}, sort order is undefined for incomparable objects.",
+                RuntimeWarning,
+                stacklevel=find_stack_level(),
+            )
+    return result
+
+
+def get_values_for_csv(
+    values: ArrayLike,
+    *,
+    date_format,
+    na_rep: str = "nan",
+    quoting=None,
+    float_format=None,
+    decimal: str = ".",
+) -> npt.NDArray[np.object_]:
+    """
+    Convert to types which can be consumed by the standard library's
+    csv.writer.writerows.
+    """
+    if isinstance(values, Categorical) and values.categories.dtype.kind in "Mm":
+        # GH#40754 Convert categorical datetimes to datetime array
+        values = algos.take_nd(
+            values.categories._values,
+            ensure_platform_int(values._codes),
+            fill_value=na_rep,
+        )
+
+    values = ensure_wrapped_if_datetimelike(values)
+
+    if isinstance(values, (DatetimeArray, TimedeltaArray)):
+        if values.ndim == 1:
+            result = values._format_native_types(na_rep=na_rep, date_format=date_format)
+            result = result.astype(object, copy=False)
+            return result
+
+        # GH#21734 Process every column separately, they might have different formats
+        results_converted = []
+        for i in range(len(values)):
+            result = values[i, :]._format_native_types(
+                na_rep=na_rep, date_format=date_format
+            )
+            results_converted.append(result.astype(object, copy=False))
+        return np.vstack(results_converted)
+
+    elif isinstance(values.dtype, PeriodDtype):
+        # TODO: tests that get here in column path
+        values = cast("PeriodArray", values)
+        res = values._format_native_types(na_rep=na_rep, date_format=date_format)
+        return res
+
+    elif isinstance(values.dtype, IntervalDtype):
+        # TODO: tests that get here in column path
+        values = cast("IntervalArray", values)
+        mask = values.isna()
+        if not quoting:
+            result = np.asarray(values).astype(str)
+        else:
+            result = np.array(values, dtype=object, copy=True)
+
+        result[mask] = na_rep
+        return result
+
+    elif values.dtype.kind == "f" and not isinstance(values.dtype, SparseDtype):
+        # see GH#13418: no special formatting is desired at the
+        # output (important for appropriate 'quoting' behaviour),
+        # so do not pass it through the FloatArrayFormatter
+        if float_format is None and decimal == ".":
+            mask = isna(values)
+
+            if not quoting:
+                values = values.astype(str)
+            else:
+                values = np.array(values, dtype="object")
+
+            values[mask] = na_rep
+            values = values.astype(object, copy=False)
+            return values
+
+        from pandas.io.formats.format import FloatArrayFormatter
+
+        formatter = FloatArrayFormatter(
+            values,
+            na_rep=na_rep,
+            float_format=float_format,
+            decimal=decimal,
+            quoting=quoting,
+            fixed_width=False,
+        )
+        res = formatter.get_result_as_array()
+        res = res.astype(object, copy=False)
+        return res
+
+    elif isinstance(values, ExtensionArray):
+        mask = isna(values)
+
+        new_values = np.asarray(values.astype(object))
+        new_values[mask] = na_rep
+        return new_values
+
+    else:
+        mask = isna(values)
+        itemsize = writers.word_len(na_rep)
+
+        if values.dtype != _dtype_obj and not quoting and itemsize:
+            values = values.astype(str)
+            if values.dtype.itemsize / np.dtype("U1").itemsize < itemsize:
+                # enlarge for the na_rep
+                values = values.astype(f"<U{itemsize}")
+        else:
+            values = np.array(values, dtype="object")
+
+        values[mask] = na_rep
+        values = values.astype(object, copy=False)
+        return values
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/category.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/category.py
new file mode 100644
index 0000000000000000000000000000000000000000..b307be004ad6e889d74cd3c5a98453a044dbf644
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/category.py
@@ -0,0 +1,513 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import index as libindex
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+
+from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+)
+
+from pandas.core.arrays.categorical import (
+    Categorical,
+    contains,
+)
+from pandas.core.construction import extract_array
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.extension import (
+    NDArrayBackedExtensionIndex,
+    inherit_names,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+    )
+
+
+@inherit_names(
+    [
+        "argsort",
+        "tolist",
+        "codes",
+        "categories",
+        "ordered",
+        "_reverse_indexer",
+        "searchsorted",
+        "min",
+        "max",
+    ],
+    Categorical,
+)
+@inherit_names(
+    [
+        "rename_categories",
+        "reorder_categories",
+        "add_categories",
+        "remove_categories",
+        "remove_unused_categories",
+        "set_categories",
+        "as_ordered",
+        "as_unordered",
+    ],
+    Categorical,
+    wrap=True,
+)
+class CategoricalIndex(NDArrayBackedExtensionIndex):
+    """
+    Index based on an underlying :class:`Categorical`.
+
+    CategoricalIndex, like Categorical, can only take on a limited,
+    and usually fixed, number of possible values (`categories`). Also,
+    like Categorical, it might have an order, but numerical operations
+    (additions, divisions, ...) are not possible.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+        The values of the categorical. If `categories` are given, values not in
+        `categories` will be replaced with NaN.
+    categories : index-like, optional
+        The categories for the categorical. Items need to be unique.
+        If the categories are not given here (and also not in `dtype`), they
+        will be inferred from the `data`.
+    ordered : bool, optional
+        Whether or not this categorical is treated as an ordered
+        categorical. If not given here or in `dtype`, the resulting
+        categorical will be unordered.
+    dtype : CategoricalDtype or "category", optional
+        If :class:`CategoricalDtype`, cannot be used together with
+        `categories` or `ordered`.
+    copy : bool, default False
+        Make a copy of input ndarray.
+    name : object, optional
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    codes
+    categories
+    ordered
+
+    Methods
+    -------
+    rename_categories
+    reorder_categories
+    add_categories
+    remove_categories
+    remove_unused_categories
+    set_categories
+    as_ordered
+    as_unordered
+    map
+
+    Raises
+    ------
+    ValueError
+        If the categories do not validate.
+    TypeError
+        If an explicit ``ordered=True`` is given but no `categories` and the
+        `values` are not sortable.
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    Categorical : A categorical array.
+    CategoricalDtype : Type for categorical data.
+
+    Notes
+    -----
+    See the `user guide
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#categoricalindex>`__
+    for more.
+
+    Examples
+    --------
+    >>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"])
+    CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+                     categories=['a', 'b', 'c'], ordered=False, dtype='category')
+
+    ``CategoricalIndex`` can also be instantiated from a ``Categorical``:
+
+    >>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
+    >>> pd.CategoricalIndex(c)
+    CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+                     categories=['a', 'b', 'c'], ordered=False, dtype='category')
+
+    Ordered ``CategoricalIndex`` can have a min and max value.
+
+    >>> ci = pd.CategoricalIndex(
+    ...     ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
+    ... )
+    >>> ci
+    CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+                     categories=['c', 'b', 'a'], ordered=True, dtype='category')
+    >>> ci.min()
+    'c'
+    """
+
+    _typ = "categoricalindex"
+    _data_cls = Categorical
+
+    @property
+    def _can_hold_strings(self):
+        return self.categories._can_hold_strings
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        return self.categories._should_fallback_to_positional
+
+    codes: np.ndarray
+    categories: Index
+    ordered: bool | None
+    _data: Categorical
+    _values: Categorical
+
+    @property
+    def _engine_type(self) -> type[libindex.IndexEngine]:
+        # self.codes can have dtype int8, int16, int32 or int64, so we need
+        # to return the corresponding engine type (libindex.Int8Engine, etc.).
+        return {
+            np.int8: libindex.Int8Engine,
+            np.int16: libindex.Int16Engine,
+            np.int32: libindex.Int32Engine,
+            np.int64: libindex.Int64Engine,
+        }[self.codes.dtype.type]
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        categories=None,
+        ordered=None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+    ) -> Self:
+        name = maybe_extract_name(name, data, cls)
+
+        if is_scalar(data):
+            # GH#38944 include None here, which pre-2.0 subbed in []
+            cls._raise_scalar_data_error(data)
+
+        data = Categorical(
+            data, categories=categories, ordered=ordered, dtype=dtype, copy=copy
+        )
+
+        return cls._simple_new(data, name=name)
+
+    # --------------------------------------------------------------------
+
+    def _is_dtype_compat(self, other: Index) -> Categorical:
+        """
+        *this is an internal non-public method*
+
+        provide a comparison between the dtype of self and other (coercing if
+        needed)
+
+        Parameters
+        ----------
+        other : Index
+
+        Returns
+        -------
+        Categorical
+
+        Raises
+        ------
+        TypeError if the dtypes are not compatible
+        """
+        if isinstance(other.dtype, CategoricalDtype):
+            cat = extract_array(other)
+            cat = cast(Categorical, cat)
+            if not cat._categories_match_up_to_permutation(self._values):
+                raise TypeError(
+                    "categories must match existing categories when appending"
+                )
+
+        elif other._is_multi:
+            # preempt raising NotImplementedError in isna call
+            raise TypeError("MultiIndex is not dtype-compatible with CategoricalIndex")
+        else:
+            values = other
+
+            cat = Categorical(other, dtype=self.dtype)
+            other = CategoricalIndex(cat)
+            if not other.isin(values).all():
+                raise TypeError(
+                    "cannot append a non-category item to a CategoricalIndex"
+                )
+            cat = other._values
+
+            if not ((cat == values) | (isna(cat) & isna(values))).all():
+                # GH#37667 see test_equals_non_category
+                raise TypeError(
+                    "categories must match existing categories when appending"
+                )
+
+        return cat
+
+    def equals(self, other: object) -> bool:
+        """
+        Determine if two CategoricalIndex objects contain the same elements.
+
+        Returns
+        -------
+        bool
+            ``True`` if two :class:`pandas.CategoricalIndex` objects have equal
+            elements, ``False`` otherwise.
+
+        Examples
+        --------
+        >>> ci = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'])
+        >>> ci2 = pd.CategoricalIndex(pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c']))
+        >>> ci.equals(ci2)
+        True
+
+        The order of elements matters.
+
+        >>> ci3 = pd.CategoricalIndex(['c', 'b', 'a', 'a', 'b', 'c'])
+        >>> ci.equals(ci3)
+        False
+
+        The orderedness also matters.
+
+        >>> ci4 = ci.as_ordered()
+        >>> ci.equals(ci4)
+        False
+
+        The categories matter, but the order of the categories matters only when
+        ``ordered=True``.
+
+        >>> ci5 = ci.set_categories(['a', 'b', 'c', 'd'])
+        >>> ci.equals(ci5)
+        False
+
+        >>> ci6 = ci.set_categories(['b', 'c', 'a'])
+        >>> ci.equals(ci6)
+        True
+        >>> ci_ordered = pd.CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
+        ...                                  ordered=True)
+        >>> ci2_ordered = ci_ordered.set_categories(['b', 'c', 'a'])
+        >>> ci_ordered.equals(ci2_ordered)
+        False
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+
+        try:
+            other = self._is_dtype_compat(other)
+        except (TypeError, ValueError):
+            return False
+
+        return self._data.equals(other)
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    @property
+    def _formatter_func(self):
+        return self.categories._formatter_func
+
+    def _format_attrs(self):
+        """
+        Return a list of tuples of the (attr,formatted_value)
+        """
+        attrs: list[tuple[str, str | int | bool | None]]
+
+        attrs = [
+            (
+                "categories",
+                f"[{', '.join(self._data._repr_categories())}]",
+            ),
+            ("ordered", self.ordered),
+        ]
+        extra = super()._format_attrs()
+        return attrs + extra
+
+    # --------------------------------------------------------------------
+
+    @property
+    def inferred_type(self) -> str:
+        return "categorical"
+
+    @doc(Index.__contains__)
+    def __contains__(self, key: Any) -> bool:
+        # if key is a NaN, check if any NaN is in self.
+        if is_valid_na_for_dtype(key, self.categories.dtype):
+            return self.hasnans
+
+        return contains(self, key, container=self._engine)
+
+    def reindex(
+        self, target, method=None, level=None, limit: int | None = None, tolerance=None
+    ) -> tuple[Index, npt.NDArray[np.intp] | None]:
+        """
+        Create index with target's values (move/add/delete values as necessary)
+
+        Returns
+        -------
+        new_index : pd.Index
+            Resulting index
+        indexer : np.ndarray[np.intp] or None
+            Indices of output values in original index
+
+        """
+        if method is not None:
+            raise NotImplementedError(
+                "argument method is not implemented for CategoricalIndex.reindex"
+            )
+        if level is not None:
+            raise NotImplementedError(
+                "argument level is not implemented for CategoricalIndex.reindex"
+            )
+        if limit is not None:
+            raise NotImplementedError(
+                "argument limit is not implemented for CategoricalIndex.reindex"
+            )
+        return super().reindex(target)
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def _maybe_cast_indexer(self, key) -> int:
+        # GH#41933: we have to do this instead of self._data._validate_scalar
+        #  because this will correctly get partial-indexing on Interval categories
+        try:
+            return self._data._unbox_scalar(key)
+        except KeyError:
+            if is_valid_na_for_dtype(key, self.categories.dtype):
+                return -1
+            raise
+
+    def _maybe_cast_listlike_indexer(self, values) -> CategoricalIndex:
+        if isinstance(values, CategoricalIndex):
+            values = values._data
+        if isinstance(values, Categorical):
+            # Indexing on codes is more efficient if categories are the same,
+            #  so we can apply some optimizations based on the degree of
+            #  dtype-matching.
+            cat = self._data._encode_with_my_categories(values)
+            codes = cat._codes
+        else:
+            codes = self.categories.get_indexer(values)
+            codes = codes.astype(self.codes.dtype, copy=False)
+            cat = self._data._from_backing_data(codes)
+        return type(self)._simple_new(cat)
+
+    # --------------------------------------------------------------------
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        return self.categories._is_comparable_dtype(dtype)
+
+    def map(self, mapper, na_action: Literal["ignore"] | None = None):
+        """
+        Map values using input an input mapping or function.
+
+        Maps the values (their categories, not the codes) of the index to new
+        categories. If the mapping correspondence is one-to-one the result is a
+        :class:`~pandas.CategoricalIndex` which has the same order property as
+        the original, otherwise an :class:`~pandas.Index` is returned.
+
+        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
+        mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
+        will be returned.
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            Mapping correspondence.
+
+        Returns
+        -------
+        pandas.CategoricalIndex or pandas.Index
+            Mapped index.
+
+        See Also
+        --------
+        Index.map : Apply a mapping correspondence on an
+            :class:`~pandas.Index`.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+        Series.apply : Apply more complex functions on a
+            :class:`~pandas.Series`.
+
+        Examples
+        --------
+        >>> idx = pd.CategoricalIndex(['a', 'b', 'c'])
+        >>> idx
+        CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
+                          ordered=False, dtype='category')
+        >>> idx.map(lambda x: x.upper())
+        CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],
+                         ordered=False, dtype='category')
+        >>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})
+        CategoricalIndex(['first', 'second', 'third'], categories=['first',
+                         'second', 'third'], ordered=False, dtype='category')
+
+        If the mapping is one-to-one the ordering of the categories is
+        preserved:
+
+        >>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)
+        >>> idx
+        CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
+                         ordered=True, dtype='category')
+        >>> idx.map({'a': 3, 'b': 2, 'c': 1})
+        CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,
+                         dtype='category')
+
+        If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
+
+        >>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})
+        Index(['first', 'second', 'first'], dtype='object')
+
+        If a `dict` is used, all unmapped categories are mapped to `NaN` and
+        the result is an :class:`~pandas.Index`:
+
+        >>> idx.map({'a': 'first', 'b': 'second'})
+        Index(['first', 'second', nan], dtype='object')
+        """
+        mapped = self._values.map(mapper, na_action=na_action)
+        return Index(mapped, name=self.name)
+
+    def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
+        # if calling index is category, don't check dtype of others
+        try:
+            cat = Categorical._concat_same_type(
+                [self._is_dtype_compat(c) for c in to_concat]
+            )
+        except TypeError:
+            # not all to_concat elements are among our categories (or NA)
+
+            res = concat_compat([x._values for x in to_concat])
+            return Index(res, name=name)
+        else:
+            return type(self)._simple_new(cat, name=name)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/datetimelike.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/datetimelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..cad8737a987d44f23518a8b6fa88e9a686755c65
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/datetimelike.py
@@ -0,0 +1,843 @@
+"""
+Base and utility classes for tseries type pandas objects.
+"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas._libs import (
+    NaT,
+    Timedelta,
+    lib,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Resolution,
+    Tick,
+    parsing,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    InvalidIndexError,
+    NullFrequencyError,
+)
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
+import pandas.core.common as com
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    _index_shared_docs,
+)
+from pandas.core.indexes.extension import NDArrayBackedExtensionIndex
+from pandas.core.indexes.range import RangeIndex
+from pandas.core.tools.timedeltas import to_timedelta
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from datetime import datetime
+
+    from pandas._typing import (
+        Axis,
+        Self,
+        npt,
+    )
+
+    from pandas import CategoricalIndex
+
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+
+
+class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex, ABC):
+    """
+    Common ops mixin to support a unified interface datetimelike Index.
+    """
+
+    _can_hold_strings = False
+    _data: DatetimeArray | TimedeltaArray | PeriodArray
+
+    @doc(DatetimeLikeArrayMixin.mean)
+    def mean(self, *, skipna: bool = True, axis: int | None = 0):
+        return self._data.mean(skipna=skipna, axis=axis)
+
+    @property
+    def freq(self) -> BaseOffset | None:
+        return self._data.freq
+
+    @freq.setter
+    def freq(self, value) -> None:
+        # error: Property "freq" defined in "PeriodArray" is read-only  [misc]
+        self._data.freq = value  # type: ignore[misc]
+
+    @property
+    def asi8(self) -> npt.NDArray[np.int64]:
+        return self._data.asi8
+
+    @property
+    @doc(DatetimeLikeArrayMixin.freqstr)
+    def freqstr(self) -> str:
+        from pandas import PeriodIndex
+
+        if self._data.freqstr is not None and isinstance(
+            self._data, (PeriodArray, PeriodIndex)
+        ):
+            freq = freq_to_period_freqstr(self._data.freq.n, self._data.freq.name)
+            return freq
+        else:
+            return self._data.freqstr  # type: ignore[return-value]
+
+    @cache_readonly
+    @abstractmethod
+    def _resolution_obj(self) -> Resolution:
+        ...
+
+    @cache_readonly
+    @doc(DatetimeLikeArrayMixin.resolution)
+    def resolution(self) -> str:
+        return self._data.resolution
+
+    # ------------------------------------------------------------------------
+
+    @cache_readonly
+    def hasnans(self) -> bool:
+        return self._data._hasna
+
+    def equals(self, other: Any) -> bool:
+        """
+        Determines if two Index objects contain the same elements.
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+        elif other.dtype.kind in "iufc":
+            return False
+        elif not isinstance(other, type(self)):
+            should_try = False
+            inferable = self._data._infer_matches
+            if other.dtype == object:
+                should_try = other.inferred_type in inferable
+            elif isinstance(other.dtype, CategoricalDtype):
+                other = cast("CategoricalIndex", other)
+                should_try = other.categories.inferred_type in inferable
+
+            if should_try:
+                try:
+                    other = type(self)(other)
+                except (ValueError, TypeError, OverflowError):
+                    # e.g.
+                    #  ValueError -> cannot parse str entry, or OutOfBoundsDatetime
+                    #  TypeError  -> trying to convert IntervalIndex to DatetimeIndex
+                    #  OverflowError -> Index([very_large_timedeltas])
+                    return False
+
+        if self.dtype != other.dtype:
+            # have different timezone
+            return False
+
+        return np.array_equal(self.asi8, other.asi8)
+
+    @Appender(Index.__contains__.__doc__)
+    def __contains__(self, key: Any) -> bool:
+        hash(key)
+        try:
+            self.get_loc(key)
+        except (KeyError, TypeError, ValueError, InvalidIndexError):
+            return False
+        return True
+
+    def _convert_tolerance(self, tolerance, target):
+        tolerance = np.asarray(to_timedelta(tolerance).to_numpy())
+        return super()._convert_tolerance(tolerance, target)
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+    _default_na_rep = "NaT"
+
+    def format(
+        self,
+        name: bool = False,
+        formatter: Callable | None = None,
+        na_rep: str = "NaT",
+        date_format: str | None = None,
+    ) -> list[str]:
+        """
+        Render a string representation of the Index.
+        """
+        warnings.warn(
+            # GH#55413
+            f"{type(self).__name__}.format is deprecated and will be removed "
+            "in a future version. Convert using index.astype(str) or "
+            "index.map(formatter) instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        header = []
+        if name:
+            header.append(
+                ibase.pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
+                if self.name is not None
+                else ""
+            )
+
+        if formatter is not None:
+            return header + list(self.map(formatter))
+
+        return self._format_with_header(
+            header=header, na_rep=na_rep, date_format=date_format
+        )
+
+    def _format_with_header(
+        self, *, header: list[str], na_rep: str, date_format: str | None = None
+    ) -> list[str]:
+        # TODO: not reached in tests 2023-10-11
+        # matches base class except for whitespace padding and date_format
+        return header + list(
+            self._get_values_for_csv(na_rep=na_rep, date_format=date_format)
+        )
+
+    @property
+    def _formatter_func(self):
+        return self._data._formatter()
+
+    def _format_attrs(self):
+        """
+        Return a list of tuples of the (attr,formatted_value).
+        """
+        attrs = super()._format_attrs()
+        for attrib in self._attributes:
+            # iterating over _attributes prevents us from doing this for PeriodIndex
+            if attrib == "freq":
+                freq = self.freqstr
+                if freq is not None:
+                    freq = repr(freq)  # e.g. D -> 'D'
+                attrs.append(("freq", freq))
+        return attrs
+
+    @Appender(Index._summary.__doc__)
+    def _summary(self, name=None) -> str:
+        result = super()._summary(name=name)
+        if self.freq:
+            result += f"\nFreq: {self.freqstr}"
+
+        return result
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    @final
+    def _can_partial_date_slice(self, reso: Resolution) -> bool:
+        # e.g. test_getitem_setitem_periodindex
+        # History of conversation GH#3452, GH#3931, GH#2369, GH#14826
+        return reso > self._resolution_obj
+        # NB: for DTI/PI, not TDI
+
+    def _parsed_string_to_bounds(self, reso: Resolution, parsed):
+        raise NotImplementedError
+
+    def _parse_with_reso(self, label: str):
+        # overridden by TimedeltaIndex
+        try:
+            if self.freq is None or hasattr(self.freq, "rule_code"):
+                freq = self.freq
+        except NotImplementedError:
+            freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
+
+        freqstr: str | None
+        if freq is not None and not isinstance(freq, str):
+            freqstr = freq.rule_code
+        else:
+            freqstr = freq
+
+        if isinstance(label, np.str_):
+            # GH#45580
+            label = str(label)
+
+        parsed, reso_str = parsing.parse_datetime_string_with_reso(label, freqstr)
+        reso = Resolution.from_attrname(reso_str)
+        return parsed, reso
+
+    def _get_string_slice(self, key: str):
+        # overridden by TimedeltaIndex
+        parsed, reso = self._parse_with_reso(key)
+        try:
+            return self._partial_date_slice(reso, parsed)
+        except KeyError as err:
+            raise KeyError(key) from err
+
+    @final
+    def _partial_date_slice(
+        self,
+        reso: Resolution,
+        parsed: datetime,
+    ) -> slice | npt.NDArray[np.intp]:
+        """
+        Parameters
+        ----------
+        reso : Resolution
+        parsed : datetime
+
+        Returns
+        -------
+        slice or ndarray[intp]
+        """
+        if not self._can_partial_date_slice(reso):
+            raise ValueError
+
+        t1, t2 = self._parsed_string_to_bounds(reso, parsed)
+        vals = self._data._ndarray
+        unbox = self._data._unbox
+
+        if self.is_monotonic_increasing:
+            if len(self) and (
+                (t1 < self[0] and t2 < self[0]) or (t1 > self[-1] and t2 > self[-1])
+            ):
+                # we are out of range
+                raise KeyError
+
+            # TODO: does this depend on being monotonic _increasing_?
+
+            # a monotonic (sorted) series can be sliced
+            left = vals.searchsorted(unbox(t1), side="left")
+            right = vals.searchsorted(unbox(t2), side="right")
+            return slice(left, right)
+
+        else:
+            lhs_mask = vals >= unbox(t1)
+            rhs_mask = vals <= unbox(t2)
+
+            # try to find the dates
+            return (lhs_mask & rhs_mask).nonzero()[0]
+
+    def _maybe_cast_slice_bound(self, label, side: str):
+        """
+        If label is a string, cast it to scalar type according to resolution.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        label : object
+
+        Notes
+        -----
+        Value of `side` parameter should be validated in caller.
+        """
+        if isinstance(label, str):
+            try:
+                parsed, reso = self._parse_with_reso(label)
+            except ValueError as err:
+                # DTI -> parsing.DateParseError
+                # TDI -> 'unit abbreviation w/o a number'
+                # PI -> string cannot be parsed as datetime-like
+                self._raise_invalid_indexer("slice", label, err)
+
+            lower, upper = self._parsed_string_to_bounds(reso, parsed)
+            return lower if side == "left" else upper
+        elif not isinstance(label, self._data._recognized_scalars):
+            self._raise_invalid_indexer("slice", label)
+
+        return label
+
+    # --------------------------------------------------------------------
+    # Arithmetic Methods
+
+    def shift(self, periods: int = 1, freq=None) -> Self:
+        """
+        Shift index by desired number of time frequency increments.
+
+        This method is for shifting the values of datetime-like indexes
+        by a specified time increment a given number of times.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Number of periods (or increments) to shift by,
+            can be positive or negative.
+        freq : pandas.DateOffset, pandas.Timedelta or string, optional
+            Frequency increment to shift by.
+            If None, the index is shifted by its own `freq` attribute.
+            Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
+
+        Returns
+        -------
+        pandas.DatetimeIndex
+            Shifted index.
+
+        See Also
+        --------
+        Index.shift : Shift values of Index.
+        PeriodIndex.shift : Shift values of PeriodIndex.
+        """
+        raise NotImplementedError
+
+    # --------------------------------------------------------------------
+
+    @doc(Index._maybe_cast_listlike_indexer)
+    def _maybe_cast_listlike_indexer(self, keyarr):
+        try:
+            res = self._data._validate_listlike(keyarr, allow_object=True)
+        except (ValueError, TypeError):
+            if not isinstance(keyarr, ExtensionArray):
+                # e.g. we don't want to cast DTA to ndarray[object]
+                res = com.asarray_tuplesafe(keyarr)
+                # TODO: com.asarray_tuplesafe shouldn't cast e.g. DatetimeArray
+            else:
+                res = keyarr
+        return Index(res, dtype=res.dtype)
+
+
+class DatetimeTimedeltaMixin(DatetimeIndexOpsMixin, ABC):
+    """
+    Mixin class for methods shared by DatetimeIndex and TimedeltaIndex,
+    but not PeriodIndex
+    """
+
+    _data: DatetimeArray | TimedeltaArray
+    _comparables = ["name", "freq"]
+    _attributes = ["name", "freq"]
+
+    # Compat for frequency inference, see GH#23789
+    _is_monotonic_increasing = Index.is_monotonic_increasing
+    _is_monotonic_decreasing = Index.is_monotonic_decreasing
+    _is_unique = Index.is_unique
+
+    @property
+    def unit(self) -> str:
+        return self._data.unit
+
+    def as_unit(self, unit: str) -> Self:
+        """
+        Convert to a dtype with the given unit resolution.
+
+        Parameters
+        ----------
+        unit : {'s', 'ms', 'us', 'ns'}
+
+        Returns
+        -------
+        same type as self
+
+        Examples
+        --------
+        For :class:`pandas.DatetimeIndex`:
+
+        >>> idx = pd.DatetimeIndex(['2020-01-02 01:02:03.004005006'])
+        >>> idx
+        DatetimeIndex(['2020-01-02 01:02:03.004005006'],
+                      dtype='datetime64[ns]', freq=None)
+        >>> idx.as_unit('s')
+        DatetimeIndex(['2020-01-02 01:02:03'], dtype='datetime64[s]', freq=None)
+
+        For :class:`pandas.TimedeltaIndex`:
+
+        >>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns'])
+        >>> tdelta_idx
+        TimedeltaIndex(['1 days 00:03:00.000002042'],
+                        dtype='timedelta64[ns]', freq=None)
+        >>> tdelta_idx.as_unit('s')
+        TimedeltaIndex(['1 days 00:03:00'], dtype='timedelta64[s]', freq=None)
+        """
+        arr = self._data.as_unit(unit)
+        return type(self)._simple_new(arr, name=self.name)
+
+    def _with_freq(self, freq):
+        arr = self._data._with_freq(freq)
+        return type(self)._simple_new(arr, name=self._name)
+
+    @property
+    def values(self) -> np.ndarray:
+        # NB: For Datetime64TZ this is lossy
+        data = self._data._ndarray
+        if using_copy_on_write():
+            data = data.view()
+            data.flags.writeable = False
+        return data
+
+    @doc(DatetimeIndexOpsMixin.shift)
+    def shift(self, periods: int = 1, freq=None) -> Self:
+        if freq is not None and freq != self.freq:
+            if isinstance(freq, str):
+                freq = to_offset(freq)
+            offset = periods * freq
+            return self + offset
+
+        if periods == 0 or len(self) == 0:
+            # GH#14811 empty case
+            return self.copy()
+
+        if self.freq is None:
+            raise NullFrequencyError("Cannot shift with no freq")
+
+        start = self[0] + periods * self.freq
+        end = self[-1] + periods * self.freq
+
+        # Note: in the DatetimeTZ case, _generate_range will infer the
+        #  appropriate timezone from `start` and `end`, so tz does not need
+        #  to be passed explicitly.
+        result = self._data._generate_range(
+            start=start, end=end, periods=None, freq=self.freq, unit=self.unit
+        )
+        return type(self)._simple_new(result, name=self.name)
+
+    @cache_readonly
+    @doc(DatetimeLikeArrayMixin.inferred_freq)
+    def inferred_freq(self) -> str | None:
+        return self._data.inferred_freq
+
+    # --------------------------------------------------------------------
+    # Set Operation Methods
+
+    @cache_readonly
+    def _as_range_index(self) -> RangeIndex:
+        # Convert our i8 representations to RangeIndex
+        # Caller is responsible for checking isinstance(self.freq, Tick)
+        freq = cast(Tick, self.freq)
+        tick = Timedelta(freq).as_unit("ns")._value
+        rng = range(self[0]._value, self[-1]._value + tick, tick)
+        return RangeIndex(rng)
+
+    def _can_range_setop(self, other) -> bool:
+        return isinstance(self.freq, Tick) and isinstance(other.freq, Tick)
+
+    def _wrap_range_setop(self, other, res_i8) -> Self:
+        new_freq = None
+        if not len(res_i8):
+            # RangeIndex defaults to step=1, which we don't want.
+            new_freq = self.freq
+        elif isinstance(res_i8, RangeIndex):
+            new_freq = to_offset(Timedelta(res_i8.step))
+
+        # TODO(GH#41493): we cannot just do
+        #  type(self._data)(res_i8.values, dtype=self.dtype, freq=new_freq)
+        # because test_setops_preserve_freq fails with _validate_frequency raising.
+        # This raising is incorrect, as 'on_freq' is incorrect. This will
+        # be fixed by GH#41493
+        res_values = res_i8.values.view(self._data._ndarray.dtype)
+        result = type(self._data)._simple_new(
+            # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+            # incompatible type "Union[dtype[Any], ExtensionDtype]"; expected
+            # "Union[dtype[datetime64], DatetimeTZDtype]"
+            res_values,
+            dtype=self.dtype,  # type: ignore[arg-type]
+            freq=new_freq,  # type: ignore[arg-type]
+        )
+        return cast("Self", self._wrap_setop_result(other, result))
+
+    def _range_intersect(self, other, sort) -> Self:
+        # Dispatch to RangeIndex intersection logic.
+        left = self._as_range_index
+        right = other._as_range_index
+        res_i8 = left.intersection(right, sort=sort)
+        return self._wrap_range_setop(other, res_i8)
+
+    def _range_union(self, other, sort) -> Self:
+        # Dispatch to RangeIndex union logic.
+        left = self._as_range_index
+        right = other._as_range_index
+        res_i8 = left.union(right, sort=sort)
+        return self._wrap_range_setop(other, res_i8)
+
+    def _intersection(self, other: Index, sort: bool = False) -> Index:
+        """
+        intersection specialized to the case with matching dtypes and both non-empty.
+        """
+        other = cast("DatetimeTimedeltaMixin", other)
+
+        if self._can_range_setop(other):
+            return self._range_intersect(other, sort=sort)
+
+        if not self._can_fast_intersect(other):
+            result = Index._intersection(self, other, sort=sort)
+            # We need to invalidate the freq because Index._intersection
+            #  uses _shallow_copy on a view of self._data, which will preserve
+            #  self.freq if we're not careful.
+            # At this point we should have result.dtype == self.dtype
+            #  and type(result) is type(self._data)
+            result = self._wrap_setop_result(other, result)
+            return result._with_freq(None)._with_freq("infer")
+
+        else:
+            return self._fast_intersect(other, sort)
+
+    def _fast_intersect(self, other, sort):
+        # to make our life easier, "sort" the two ranges
+        if self[0] <= other[0]:
+            left, right = self, other
+        else:
+            left, right = other, self
+
+        # after sorting, the intersection always starts with the right index
+        # and ends with the index of which the last elements is smallest
+        end = min(left[-1], right[-1])
+        start = right[0]
+
+        if end < start:
+            result = self[:0]
+        else:
+            lslice = slice(*left.slice_locs(start, end))
+            result = left._values[lslice]
+
+        return result
+
+    def _can_fast_intersect(self, other: Self) -> bool:
+        # Note: we only get here with len(self) > 0 and len(other) > 0
+        if self.freq is None:
+            return False
+
+        elif other.freq != self.freq:
+            return False
+
+        elif not self.is_monotonic_increasing:
+            # Because freq is not None, we must then be monotonic decreasing
+            return False
+
+        # this along with matching freqs ensure that we "line up",
+        #  so intersection will preserve freq
+        # Note we are assuming away Ticks, as those go through _range_intersect
+        # GH#42104
+        return self.freq.n == 1
+
+    def _can_fast_union(self, other: Self) -> bool:
+        # Assumes that type(self) == type(other), as per the annotation
+        # The ability to fast_union also implies that `freq` should be
+        #  retained on union.
+        freq = self.freq
+
+        if freq is None or freq != other.freq:
+            return False
+
+        if not self.is_monotonic_increasing:
+            # Because freq is not None, we must then be monotonic decreasing
+            # TODO: do union on the reversed indexes?
+            return False
+
+        if len(self) == 0 or len(other) == 0:
+            # only reached via union_many
+            return True
+
+        # to make our life easier, "sort" the two ranges
+        if self[0] <= other[0]:
+            left, right = self, other
+        else:
+            left, right = other, self
+
+        right_start = right[0]
+        left_end = left[-1]
+
+        # Only need to "adjoin", not overlap
+        return (right_start == left_end + freq) or right_start in left
+
+    def _fast_union(self, other: Self, sort=None) -> Self:
+        # Caller is responsible for ensuring self and other are non-empty
+
+        # to make our life easier, "sort" the two ranges
+        if self[0] <= other[0]:
+            left, right = self, other
+        elif sort is False:
+            # TDIs are not in the "correct" order and we don't want
+            #  to sort but want to remove overlaps
+            left, right = self, other
+            left_start = left[0]
+            loc = right.searchsorted(left_start, side="left")
+            right_chunk = right._values[:loc]
+            dates = concat_compat((left._values, right_chunk))
+            result = type(self)._simple_new(dates, name=self.name)
+            return result
+        else:
+            left, right = other, self
+
+        left_end = left[-1]
+        right_end = right[-1]
+
+        # concatenate
+        if left_end < right_end:
+            loc = right.searchsorted(left_end, side="right")
+            right_chunk = right._values[loc:]
+            dates = concat_compat([left._values, right_chunk])
+            # The can_fast_union check ensures that the result.freq
+            #  should match self.freq
+            assert isinstance(dates, type(self._data))
+            # error: Item "ExtensionArray" of "ExtensionArray |
+            # ndarray[Any, Any]" has no attribute "_freq"
+            assert dates._freq == self.freq  # type: ignore[union-attr]
+            result = type(self)._simple_new(dates)
+            return result
+        else:
+            return left
+
+    def _union(self, other, sort):
+        # We are called by `union`, which is responsible for this validation
+        assert isinstance(other, type(self))
+        assert self.dtype == other.dtype
+
+        if self._can_range_setop(other):
+            return self._range_union(other, sort=sort)
+
+        if self._can_fast_union(other):
+            result = self._fast_union(other, sort=sort)
+            # in the case with sort=None, the _can_fast_union check ensures
+            #  that result.freq == self.freq
+            return result
+        else:
+            return super()._union(other, sort)._with_freq("infer")
+
+    # --------------------------------------------------------------------
+    # Join Methods
+
+    def _get_join_freq(self, other):
+        """
+        Get the freq to attach to the result of a join operation.
+        """
+        freq = None
+        if self._can_fast_union(other):
+            freq = self.freq
+        return freq
+
+    def _wrap_joined_index(
+        self, joined, other, lidx: npt.NDArray[np.intp], ridx: npt.NDArray[np.intp]
+    ):
+        assert other.dtype == self.dtype, (other.dtype, self.dtype)
+        result = super()._wrap_joined_index(joined, other, lidx, ridx)
+        result._data._freq = self._get_join_freq(other)
+        return result
+
+    def _get_engine_target(self) -> np.ndarray:
+        # engine methods and libjoin methods need dt64/td64 values cast to i8
+        return self._data._ndarray.view("i8")
+
+    def _from_join_target(self, result: np.ndarray):
+        # view e.g. i8 back to M8[ns]
+        result = result.view(self._data._ndarray.dtype)
+        return self._data._from_backing_data(result)
+
+    # --------------------------------------------------------------------
+    # List-like Methods
+
+    def _get_delete_freq(self, loc: int | slice | Sequence[int]):
+        """
+        Find the `freq` for self.delete(loc).
+        """
+        freq = None
+        if self.freq is not None:
+            if is_integer(loc):
+                if loc in (0, -len(self), -1, len(self) - 1):
+                    freq = self.freq
+            else:
+                if is_list_like(loc):
+                    # error: Incompatible types in assignment (expression has
+                    # type "Union[slice, ndarray]", variable has type
+                    # "Union[int, slice, Sequence[int]]")
+                    loc = lib.maybe_indices_to_slice(  # type: ignore[assignment]
+                        np.asarray(loc, dtype=np.intp), len(self)
+                    )
+                if isinstance(loc, slice) and loc.step in (1, None):
+                    if loc.start in (0, None) or loc.stop in (len(self), None):
+                        freq = self.freq
+        return freq
+
+    def _get_insert_freq(self, loc: int, item):
+        """
+        Find the `freq` for self.insert(loc, item).
+        """
+        value = self._data._validate_scalar(item)
+        item = self._data._box_func(value)
+
+        freq = None
+        if self.freq is not None:
+            # freq can be preserved on edge cases
+            if self.size:
+                if item is NaT:
+                    pass
+                elif loc in (0, -len(self)) and item + self.freq == self[0]:
+                    freq = self.freq
+                elif (loc == len(self)) and item - self.freq == self[-1]:
+                    freq = self.freq
+            else:
+                # Adding a single item to an empty index may preserve freq
+                if isinstance(self.freq, Tick):
+                    # all TimedeltaIndex cases go through here; is_on_offset
+                    #  would raise TypeError
+                    freq = self.freq
+                elif self.freq.is_on_offset(item):
+                    freq = self.freq
+        return freq
+
+    @doc(NDArrayBackedExtensionIndex.delete)
+    def delete(self, loc) -> Self:
+        result = super().delete(loc)
+        result._data._freq = self._get_delete_freq(loc)
+        return result
+
+    @doc(NDArrayBackedExtensionIndex.insert)
+    def insert(self, loc: int, item):
+        result = super().insert(loc, item)
+        if isinstance(result, type(self)):
+            # i.e. parent class method did not cast
+            result._data._freq = self._get_insert_freq(loc, item)
+        return result
+
+    # --------------------------------------------------------------------
+    # NDArray-Like Methods
+
+    @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
+    def take(
+        self,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> Self:
+        nv.validate_take((), kwargs)
+        indices = np.asarray(indices, dtype=np.intp)
+
+        result = NDArrayBackedExtensionIndex.take(
+            self, indices, axis, allow_fill, fill_value, **kwargs
+        )
+
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(self))
+        if isinstance(maybe_slice, slice):
+            freq = self._data._get_getitem_freq(maybe_slice)
+            result._data._freq = freq
+        return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/datetimes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..3204a9c97ee73fa873a4cbe461e2f3c4690c781f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/datetimes.py
@@ -0,0 +1,1127 @@
+from __future__ import annotations
+
+import datetime as dt
+import operator
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+import pytz
+
+from pandas._libs import (
+    NaT,
+    Period,
+    Timestamp,
+    index as libindex,
+    lib,
+)
+from pandas._libs.tslibs import (
+    Resolution,
+    Tick,
+    Timedelta,
+    periods_per_day,
+    timezones,
+    to_offset,
+)
+from pandas._libs.tslibs.offsets import prefix_mapping
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
+
+from pandas.core.arrays.datetimes import (
+    DatetimeArray,
+    tz_to_dtype,
+)
+import pandas.core.common as com
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
+from pandas.core.indexes.extension import inherit_names
+from pandas.core.tools.times import to_time
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Frequency,
+        IntervalClosedType,
+        Self,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
+    )
+
+    from pandas.core.api import (
+        DataFrame,
+        PeriodIndex,
+    )
+
+from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR
+
+
+def _new_DatetimeIndex(cls, d):
+    """
+    This is called upon unpickling, rather than the default which doesn't
+    have arguments and breaks __new__
+    """
+    if "data" in d and not isinstance(d["data"], DatetimeIndex):
+        # Avoid need to verify integrity by calling simple_new directly
+        data = d.pop("data")
+        if not isinstance(data, DatetimeArray):
+            # For backward compat with older pickles, we may need to construct
+            #  a DatetimeArray to adapt to the newer _simple_new signature
+            tz = d.pop("tz")
+            freq = d.pop("freq")
+            dta = DatetimeArray._simple_new(data, dtype=tz_to_dtype(tz), freq=freq)
+        else:
+            dta = data
+            for key in ["tz", "freq"]:
+                # These are already stored in our DatetimeArray; if they are
+                #  also in the pickle and don't match, we have a problem.
+                if key in d:
+                    assert d[key] == getattr(dta, key)
+                    d.pop(key)
+        result = cls._simple_new(dta, **d)
+    else:
+        with warnings.catch_warnings():
+            # TODO: If we knew what was going in to **d, we might be able to
+            #  go through _simple_new instead
+            warnings.simplefilter("ignore")
+            result = cls.__new__(cls, **d)
+
+    return result
+
+
+@inherit_names(
+    DatetimeArray._field_ops
+    + [
+        method
+        for method in DatetimeArray._datetimelike_methods
+        if method not in ("tz_localize", "tz_convert", "strftime")
+    ],
+    DatetimeArray,
+    wrap=True,
+)
+@inherit_names(["is_normalized"], DatetimeArray, cache=True)
+@inherit_names(
+    [
+        "tz",
+        "tzinfo",
+        "dtype",
+        "to_pydatetime",
+        "date",
+        "time",
+        "timetz",
+        "std",
+    ]
+    + DatetimeArray._bool_ops,
+    DatetimeArray,
+)
+class DatetimeIndex(DatetimeTimedeltaMixin):
+    """
+    Immutable ndarray-like of datetime64 data.
+
+    Represented internally as int64, and which can be boxed to Timestamp objects
+    that are subclasses of datetime and carry metadata.
+
+    .. versionchanged:: 2.0.0
+        The various numeric date/time attributes (:attr:`~DatetimeIndex.day`,
+        :attr:`~DatetimeIndex.month`, :attr:`~DatetimeIndex.year` etc.) now have dtype
+        ``int32``. Previously they had dtype ``int64``.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+        Datetime-like data to construct index with.
+    freq : str or pandas offset object, optional
+        One of pandas date offset strings or corresponding objects. The string
+        'infer' can be passed in order to set the frequency of the index as the
+        inferred frequency upon creation.
+    tz : pytz.timezone or dateutil.tz.tzfile or datetime.tzinfo or str
+        Set the Timezone of the data.
+    normalize : bool, default False
+        Normalize start/end dates to midnight before generating date range.
+
+        .. deprecated:: 2.1.0
+
+    closed : {'left', 'right'}, optional
+        Set whether to include `start` and `end` that are on the
+        boundary. The default includes boundary points on either end.
+
+        .. deprecated:: 2.1.0
+
+    ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+        When clocks moved backward due to DST, ambiguous times may arise.
+        For example in Central European Time (UTC+01), when going from 03:00
+        DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC
+        and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter
+        dictates how ambiguous times should be handled.
+
+        - 'infer' will attempt to infer fall dst-transition hours based on
+          order
+        - bool-ndarray where True signifies a DST time, False signifies a
+          non-DST time (note that this flag is only applicable for ambiguous
+          times)
+        - 'NaT' will return NaT where there are ambiguous times
+        - 'raise' will raise an AmbiguousTimeError if there are ambiguous times.
+    dayfirst : bool, default False
+        If True, parse dates in `data` with the day first order.
+    yearfirst : bool, default False
+        If True parse dates in `data` with the year first order.
+    dtype : numpy.dtype or DatetimeTZDtype or str, default None
+        Note that the only NumPy dtype allowed is `datetime64[ns]`.
+    copy : bool, default False
+        Make a copy of input ndarray.
+    name : label, default None
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    year
+    month
+    day
+    hour
+    minute
+    second
+    microsecond
+    nanosecond
+    date
+    time
+    timetz
+    dayofyear
+    day_of_year
+    dayofweek
+    day_of_week
+    weekday
+    quarter
+    tz
+    freq
+    freqstr
+    is_month_start
+    is_month_end
+    is_quarter_start
+    is_quarter_end
+    is_year_start
+    is_year_end
+    is_leap_year
+    inferred_freq
+
+    Methods
+    -------
+    normalize
+    strftime
+    snap
+    tz_convert
+    tz_localize
+    round
+    floor
+    ceil
+    to_period
+    to_pydatetime
+    to_series
+    to_frame
+    month_name
+    day_name
+    mean
+    std
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    TimedeltaIndex : Index of timedelta64 data.
+    PeriodIndex : Index of Period data.
+    to_datetime : Convert argument to datetime.
+    date_range : Create a fixed-frequency DatetimeIndex.
+
+    Notes
+    -----
+    To learn more about the frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    >>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
+    >>> idx
+    DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'],
+    dtype='datetime64[ns, UTC]', freq=None)
+    """
+
+    _typ = "datetimeindex"
+
+    _data_cls = DatetimeArray
+    _supports_partial_string_indexing = True
+
+    @property
+    def _engine_type(self) -> type[libindex.DatetimeEngine]:
+        return libindex.DatetimeEngine
+
+    _data: DatetimeArray
+    _values: DatetimeArray
+    tz: dt.tzinfo | None
+
+    # --------------------------------------------------------------------
+    # methods that dispatch to DatetimeArray and wrap result
+
+    @doc(DatetimeArray.strftime)
+    def strftime(self, date_format) -> Index:
+        arr = self._data.strftime(date_format)
+        return Index(arr, name=self.name, dtype=arr.dtype)
+
+    @doc(DatetimeArray.tz_convert)
+    def tz_convert(self, tz) -> Self:
+        arr = self._data.tz_convert(tz)
+        return type(self)._simple_new(arr, name=self.name, refs=self._references)
+
+    @doc(DatetimeArray.tz_localize)
+    def tz_localize(
+        self,
+        tz,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ) -> Self:
+        arr = self._data.tz_localize(tz, ambiguous, nonexistent)
+        return type(self)._simple_new(arr, name=self.name)
+
+    @doc(DatetimeArray.to_period)
+    def to_period(self, freq=None) -> PeriodIndex:
+        from pandas.core.indexes.api import PeriodIndex
+
+        arr = self._data.to_period(freq)
+        return PeriodIndex._simple_new(arr, name=self.name)
+
+    @doc(DatetimeArray.to_julian_date)
+    def to_julian_date(self) -> Index:
+        arr = self._data.to_julian_date()
+        return Index._simple_new(arr, name=self.name)
+
+    @doc(DatetimeArray.isocalendar)
+    def isocalendar(self) -> DataFrame:
+        df = self._data.isocalendar()
+        return df.set_index(self)
+
+    @cache_readonly
+    def _resolution_obj(self) -> Resolution:
+        return self._data._resolution_obj
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        freq: Frequency | lib.NoDefault = lib.no_default,
+        tz=lib.no_default,
+        normalize: bool | lib.NoDefault = lib.no_default,
+        closed=lib.no_default,
+        ambiguous: TimeAmbiguous = "raise",
+        dayfirst: bool = False,
+        yearfirst: bool = False,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+    ) -> Self:
+        if closed is not lib.no_default:
+            # GH#52628
+            warnings.warn(
+                f"The 'closed' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if normalize is not lib.no_default:
+            # GH#52628
+            warnings.warn(
+                f"The 'normalize' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if is_scalar(data):
+            cls._raise_scalar_data_error(data)
+
+        # - Cases checked above all return/raise before reaching here - #
+
+        name = maybe_extract_name(name, data, cls)
+
+        if (
+            isinstance(data, DatetimeArray)
+            and freq is lib.no_default
+            and tz is lib.no_default
+            and dtype is None
+        ):
+            # fastpath, similar logic in TimedeltaIndex.__new__;
+            # Note in this particular case we retain non-nano.
+            if copy:
+                data = data.copy()
+            return cls._simple_new(data, name=name)
+
+        dtarr = DatetimeArray._from_sequence_not_strict(
+            data,
+            dtype=dtype,
+            copy=copy,
+            tz=tz,
+            freq=freq,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            ambiguous=ambiguous,
+        )
+        refs = None
+        if not copy and isinstance(data, (Index, ABCSeries)):
+            refs = data._references
+
+        subarr = cls._simple_new(dtarr, name=name, refs=refs)
+        return subarr
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def _is_dates_only(self) -> bool:
+        """
+        Return a boolean if we are only dates (and don't have a timezone)
+
+        Returns
+        -------
+        bool
+        """
+        if isinstance(self.freq, Tick):
+            delta = Timedelta(self.freq)
+
+            if delta % dt.timedelta(days=1) != dt.timedelta(days=0):
+                return False
+
+        return self._values._is_dates_only
+
+    def __reduce__(self):
+        d = {"data": self._data, "name": self.name}
+        return _new_DatetimeIndex, (type(self), d), None
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        if self.tz is not None:
+            # If we have tz, we can compare to tzaware
+            return isinstance(dtype, DatetimeTZDtype)
+        # if we dont have tz, we can only compare to tznaive
+        return lib.is_np_dtype(dtype, "M")
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    @cache_readonly
+    def _formatter_func(self):
+        # Note this is equivalent to the DatetimeIndexOpsMixin method but
+        #  uses the maybe-cached self._is_dates_only instead of re-computing it.
+        from pandas.io.formats.format import get_format_datetime64
+
+        formatter = get_format_datetime64(is_dates_only=self._is_dates_only)
+        return lambda x: f"'{formatter(x)}'"
+
+    # --------------------------------------------------------------------
+    # Set Operation Methods
+
+    def _can_range_setop(self, other) -> bool:
+        # GH 46702: If self or other have non-UTC tzs, DST transitions prevent
+        # range representation due to no singular step
+        if (
+            self.tz is not None
+            and not timezones.is_utc(self.tz)
+            and not timezones.is_fixed_offset(self.tz)
+        ):
+            return False
+        if (
+            other.tz is not None
+            and not timezones.is_utc(other.tz)
+            and not timezones.is_fixed_offset(other.tz)
+        ):
+            return False
+        return super()._can_range_setop(other)
+
+    # --------------------------------------------------------------------
+
+    def _get_time_micros(self) -> npt.NDArray[np.int64]:
+        """
+        Return the number of microseconds since midnight.
+
+        Returns
+        -------
+        ndarray[int64_t]
+        """
+        values = self._data._local_timestamps()
+
+        ppd = periods_per_day(self._data._creso)
+
+        frac = values % ppd
+        if self.unit == "ns":
+            micros = frac // 1000
+        elif self.unit == "us":
+            micros = frac
+        elif self.unit == "ms":
+            micros = frac * 1000
+        elif self.unit == "s":
+            micros = frac * 1_000_000
+        else:  # pragma: no cover
+            raise NotImplementedError(self.unit)
+
+        micros[self._isnan] = -1
+        return micros
+
+    def snap(self, freq: Frequency = "S") -> DatetimeIndex:
+        """
+        Snap time stamps to nearest occurring frequency.
+
+        Returns
+        -------
+        DatetimeIndex
+
+        Examples
+        --------
+        >>> idx = pd.DatetimeIndex(['2023-01-01', '2023-01-02',
+        ...                        '2023-02-01', '2023-02-02'])
+        >>> idx
+        DatetimeIndex(['2023-01-01', '2023-01-02', '2023-02-01', '2023-02-02'],
+        dtype='datetime64[ns]', freq=None)
+        >>> idx.snap('MS')
+        DatetimeIndex(['2023-01-01', '2023-01-01', '2023-02-01', '2023-02-01'],
+        dtype='datetime64[ns]', freq=None)
+        """
+        # Superdumb, punting on any optimizing
+        freq = to_offset(freq)
+
+        dta = self._data.copy()
+
+        for i, v in enumerate(self):
+            s = v
+            if not freq.is_on_offset(s):
+                t0 = freq.rollback(s)
+                t1 = freq.rollforward(s)
+                if abs(s - t0) < abs(t1 - s):
+                    s = t0
+                else:
+                    s = t1
+            dta[i] = s
+
+        return DatetimeIndex._simple_new(dta, name=self.name)
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def _parsed_string_to_bounds(self, reso: Resolution, parsed: dt.datetime):
+        """
+        Calculate datetime bounds for parsed time string and its resolution.
+
+        Parameters
+        ----------
+        reso : Resolution
+            Resolution provided by parsed string.
+        parsed : datetime
+            Datetime from parsed string.
+
+        Returns
+        -------
+        lower, upper: pd.Timestamp
+        """
+        freq = OFFSET_TO_PERIOD_FREQSTR.get(reso.attr_abbrev, reso.attr_abbrev)
+        per = Period(parsed, freq=freq)
+        start, end = per.start_time, per.end_time
+
+        # GH 24076
+        # If an incoming date string contained a UTC offset, need to localize
+        # the parsed date to this offset first before aligning with the index's
+        # timezone
+        start = start.tz_localize(parsed.tzinfo)
+        end = end.tz_localize(parsed.tzinfo)
+
+        if parsed.tzinfo is not None:
+            if self.tz is None:
+                raise ValueError(
+                    "The index must be timezone aware when indexing "
+                    "with a date string with a UTC offset"
+                )
+        # The flipped case with parsed.tz is None and self.tz is not None
+        #  is ruled out bc parsed and reso are produced by _parse_with_reso,
+        #  which localizes parsed.
+        return start, end
+
+    def _parse_with_reso(self, label: str):
+        parsed, reso = super()._parse_with_reso(label)
+
+        parsed = Timestamp(parsed)
+
+        if self.tz is not None and parsed.tzinfo is None:
+            # we special-case timezone-naive strings and timezone-aware
+            #  DatetimeIndex
+            # https://github.com/pandas-dev/pandas/pull/36148#issuecomment-687883081
+            parsed = parsed.tz_localize(self.tz)
+
+        return parsed, reso
+
+    def _disallow_mismatched_indexing(self, key) -> None:
+        """
+        Check for mismatched-tzawareness indexing and re-raise as KeyError.
+        """
+        # we get here with isinstance(key, self._data._recognized_scalars)
+        try:
+            # GH#36148
+            self._data._assert_tzawareness_compat(key)
+        except TypeError as err:
+            raise KeyError(key) from err
+
+    def get_loc(self, key):
+        """
+        Get integer location for requested label
+
+        Returns
+        -------
+        loc : int
+        """
+        self._check_indexing_error(key)
+
+        orig_key = key
+        if is_valid_na_for_dtype(key, self.dtype):
+            key = NaT
+
+        if isinstance(key, self._data._recognized_scalars):
+            # needed to localize naive datetimes
+            self._disallow_mismatched_indexing(key)
+            key = Timestamp(key)
+
+        elif isinstance(key, str):
+            try:
+                parsed, reso = self._parse_with_reso(key)
+            except (ValueError, pytz.NonExistentTimeError) as err:
+                raise KeyError(key) from err
+            self._disallow_mismatched_indexing(parsed)
+
+            if self._can_partial_date_slice(reso):
+                try:
+                    return self._partial_date_slice(reso, parsed)
+                except KeyError as err:
+                    raise KeyError(key) from err
+
+            key = parsed
+
+        elif isinstance(key, dt.timedelta):
+            # GH#20464
+            raise TypeError(
+                f"Cannot index {type(self).__name__} with {type(key).__name__}"
+            )
+
+        elif isinstance(key, dt.time):
+            return self.indexer_at_time(key)
+
+        else:
+            # unrecognized type
+            raise KeyError(key)
+
+        try:
+            return Index.get_loc(self, key)
+        except KeyError as err:
+            raise KeyError(orig_key) from err
+
+    @doc(DatetimeTimedeltaMixin._maybe_cast_slice_bound)
+    def _maybe_cast_slice_bound(self, label, side: str):
+        # GH#42855 handle date here instead of get_slice_bound
+        if isinstance(label, dt.date) and not isinstance(label, dt.datetime):
+            # Pandas supports slicing with dates, treated as datetimes at midnight.
+            # https://github.com/pandas-dev/pandas/issues/31501
+            label = Timestamp(label).to_pydatetime()
+
+        label = super()._maybe_cast_slice_bound(label, side)
+        self._data._assert_tzawareness_compat(label)
+        return Timestamp(label)
+
+    def slice_indexer(self, start=None, end=None, step=None):
+        """
+        Return indexer for specified label slice.
+        Index.slice_indexer, customized to handle time slicing.
+
+        In addition to functionality provided by Index.slice_indexer, does the
+        following:
+
+        - if both `start` and `end` are instances of `datetime.time`, it
+          invokes `indexer_between_time`
+        - if `start` and `end` are both either string or None perform
+          value-based selection in non-monotonic cases.
+
+        """
+        # For historical reasons DatetimeIndex supports slices between two
+        # instances of datetime.time as if it were applying a slice mask to
+        # an array of (self.hour, self.minute, self.seconds, self.microsecond).
+        if isinstance(start, dt.time) and isinstance(end, dt.time):
+            if step is not None and step != 1:
+                raise ValueError("Must have step size of 1 with time slices")
+            return self.indexer_between_time(start, end)
+
+        if isinstance(start, dt.time) or isinstance(end, dt.time):
+            raise KeyError("Cannot mix time and non-time slice keys")
+
+        def check_str_or_none(point) -> bool:
+            return point is not None and not isinstance(point, str)
+
+        # GH#33146 if start and end are combinations of str and None and Index is not
+        # monotonic, we can not use Index.slice_indexer because it does not honor the
+        # actual elements, is only searching for start and end
+        if (
+            check_str_or_none(start)
+            or check_str_or_none(end)
+            or self.is_monotonic_increasing
+        ):
+            return Index.slice_indexer(self, start, end, step)
+
+        mask = np.array(True)
+        in_index = True
+        if start is not None:
+            start_casted = self._maybe_cast_slice_bound(start, "left")
+            mask = start_casted <= self
+            in_index &= (start_casted == self).any()
+
+        if end is not None:
+            end_casted = self._maybe_cast_slice_bound(end, "right")
+            mask = (self <= end_casted) & mask
+            in_index &= (end_casted == self).any()
+
+        if not in_index:
+            raise KeyError(
+                "Value based partial slicing on non-monotonic DatetimeIndexes "
+                "with non-existing keys is not allowed.",
+            )
+        indexer = mask.nonzero()[0][::step]
+        if len(indexer) == len(self):
+            return slice(None)
+        else:
+            return indexer
+
+    # --------------------------------------------------------------------
+
+    @property
+    def inferred_type(self) -> str:
+        # b/c datetime is represented as microseconds since the epoch, make
+        # sure we can't have ambiguous indexing
+        return "datetime64"
+
+    def indexer_at_time(self, time, asof: bool = False) -> npt.NDArray[np.intp]:
+        """
+        Return index locations of values at particular time of day.
+
+        Parameters
+        ----------
+        time : datetime.time or str
+            Time passed in either as object (datetime.time) or as string in
+            appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
+            "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p").
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        indexer_between_time : Get index locations of values between particular
+            times of day.
+        DataFrame.at_time : Select values at particular time of day.
+
+        Examples
+        --------
+        >>> idx = pd.DatetimeIndex(["1/1/2020 10:00", "2/1/2020 11:00",
+        ...                         "3/1/2020 10:00"])
+        >>> idx.indexer_at_time("10:00")
+        array([0, 2])
+        """
+        if asof:
+            raise NotImplementedError("'asof' argument is not supported")
+
+        if isinstance(time, str):
+            from dateutil.parser import parse
+
+            time = parse(time).time()
+
+        if time.tzinfo:
+            if self.tz is None:
+                raise ValueError("Index must be timezone aware.")
+            time_micros = self.tz_convert(time.tzinfo)._get_time_micros()
+        else:
+            time_micros = self._get_time_micros()
+        micros = _time_to_micros(time)
+        return (time_micros == micros).nonzero()[0]
+
+    def indexer_between_time(
+        self, start_time, end_time, include_start: bool = True, include_end: bool = True
+    ) -> npt.NDArray[np.intp]:
+        """
+        Return index locations of values between particular times of day.
+
+        Parameters
+        ----------
+        start_time, end_time : datetime.time, str
+            Time passed either as object (datetime.time) or as string in
+            appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
+            "%H:%M:%S", "%H%M%S", "%I:%M:%S%p","%I%M%S%p").
+        include_start : bool, default True
+        include_end : bool, default True
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        indexer_at_time : Get index locations of values at particular time of day.
+        DataFrame.between_time : Select values between particular times of day.
+
+        Examples
+        --------
+        >>> idx = pd.date_range("2023-01-01", periods=4, freq="h")
+        >>> idx
+        DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
+                           '2023-01-01 02:00:00', '2023-01-01 03:00:00'],
+                          dtype='datetime64[ns]', freq='h')
+        >>> idx.indexer_between_time("00:00", "2:00", include_end=False)
+        array([0, 1])
+        """
+        start_time = to_time(start_time)
+        end_time = to_time(end_time)
+        time_micros = self._get_time_micros()
+        start_micros = _time_to_micros(start_time)
+        end_micros = _time_to_micros(end_time)
+
+        if include_start and include_end:
+            lop = rop = operator.le
+        elif include_start:
+            lop = operator.le
+            rop = operator.lt
+        elif include_end:
+            lop = operator.lt
+            rop = operator.le
+        else:
+            lop = rop = operator.lt
+
+        if start_time <= end_time:
+            join_op = operator.and_
+        else:
+            join_op = operator.or_
+
+        mask = join_op(lop(start_micros, time_micros), rop(time_micros, end_micros))
+
+        return mask.nonzero()[0]
+
+
+def date_range(
+    start=None,
+    end=None,
+    periods=None,
+    freq=None,
+    tz=None,
+    normalize: bool = False,
+    name: Hashable | None = None,
+    inclusive: IntervalClosedType = "both",
+    *,
+    unit: str | None = None,
+    **kwargs,
+) -> DatetimeIndex:
+    """
+    Return a fixed frequency DatetimeIndex.
+
+    Returns the range of equally spaced time points (where the difference between any
+    two adjacent points is specified by the given frequency) such that they all
+    satisfy `start <[=] x <[=] end`, where the first one and the last one are, resp.,
+    the first and last time points in that range that fall on the boundary of ``freq``
+    (if given as a frequency string) or that are valid for ``freq`` (if given as a
+    :class:`pandas.tseries.offsets.DateOffset`). (If exactly one of ``start``,
+    ``end``, or ``freq`` is *not* specified, this missing parameter can be computed
+    given ``periods``, the number of timesteps in the range. See the note below.)
+
+    Parameters
+    ----------
+    start : str or datetime-like, optional
+        Left bound for generating dates.
+    end : str or datetime-like, optional
+        Right bound for generating dates.
+    periods : int, optional
+        Number of periods to generate.
+    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D'
+        Frequency strings can have multiples, e.g. '5h'. See
+        :ref:`here <timeseries.offset_aliases>` for a list of
+        frequency aliases.
+    tz : str or tzinfo, optional
+        Time zone name for returning localized DatetimeIndex, for example
+        'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
+        timezone-naive unless timezone-aware datetime-likes are passed.
+    normalize : bool, default False
+        Normalize start/end dates to midnight before generating date range.
+    name : str, default None
+        Name of the resulting DatetimeIndex.
+    inclusive : {"both", "neither", "left", "right"}, default "both"
+        Include boundaries; Whether to set each bound as closed or open.
+
+        .. versionadded:: 1.4.0
+    unit : str, default None
+        Specify the desired resolution of the result.
+
+        .. versionadded:: 2.0.0
+    **kwargs
+        For compatibility. Has no effect on the result.
+
+    Returns
+    -------
+    DatetimeIndex
+
+    See Also
+    --------
+    DatetimeIndex : An immutable container for datetimes.
+    timedelta_range : Return a fixed frequency TimedeltaIndex.
+    period_range : Return a fixed frequency PeriodIndex.
+    interval_range : Return a fixed frequency IntervalIndex.
+
+    Notes
+    -----
+    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified. If ``freq`` is omitted, the resulting
+    ``DatetimeIndex`` will have ``periods`` linearly spaced elements between
+    ``start`` and ``end`` (closed on both sides).
+
+    To learn more about the frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    **Specifying the values**
+
+    The next four examples generate the same `DatetimeIndex`, but vary
+    the combination of `start`, `end` and `periods`.
+
+    Specify `start` and `end`, with the default daily frequency.
+
+    >>> pd.date_range(start='1/1/2018', end='1/08/2018')
+    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
+                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Specify timezone-aware `start` and `end`, with the default daily frequency.
+
+    >>> pd.date_range(
+    ...     start=pd.to_datetime("1/1/2018").tz_localize("Europe/Berlin"),
+    ...     end=pd.to_datetime("1/08/2018").tz_localize("Europe/Berlin"),
+    ... )
+    DatetimeIndex(['2018-01-01 00:00:00+01:00', '2018-01-02 00:00:00+01:00',
+                   '2018-01-03 00:00:00+01:00', '2018-01-04 00:00:00+01:00',
+                   '2018-01-05 00:00:00+01:00', '2018-01-06 00:00:00+01:00',
+                   '2018-01-07 00:00:00+01:00', '2018-01-08 00:00:00+01:00'],
+                  dtype='datetime64[ns, Europe/Berlin]', freq='D')
+
+    Specify `start` and `periods`, the number of periods (days).
+
+    >>> pd.date_range(start='1/1/2018', periods=8)
+    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
+                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Specify `end` and `periods`, the number of periods (days).
+
+    >>> pd.date_range(end='1/1/2018', periods=8)
+    DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
+                   '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Specify `start`, `end`, and `periods`; the frequency is generated
+    automatically (linearly spaced).
+
+    >>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
+    DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
+                   '2018-04-27 00:00:00'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Other Parameters**
+
+    Changed the `freq` (frequency) to ``'ME'`` (month end frequency).
+
+    >>> pd.date_range(start='1/1/2018', periods=5, freq='ME')
+    DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
+                   '2018-05-31'],
+                  dtype='datetime64[ns]', freq='ME')
+
+    Multiples are allowed
+
+    >>> pd.date_range(start='1/1/2018', periods=5, freq='3ME')
+    DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
+                   '2019-01-31'],
+                  dtype='datetime64[ns]', freq='3ME')
+
+    `freq` can also be specified as an Offset object.
+
+    >>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
+    DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
+                   '2019-01-31'],
+                  dtype='datetime64[ns]', freq='3ME')
+
+    Specify `tz` to set the timezone.
+
+    >>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
+    DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
+                   '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
+                   '2018-01-05 00:00:00+09:00'],
+                  dtype='datetime64[ns, Asia/Tokyo]', freq='D')
+
+    `inclusive` controls whether to include `start` and `end` that are on the
+    boundary. The default, "both", includes boundary points on either end.
+
+    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive="both")
+    DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Use ``inclusive='left'`` to exclude `end` if it falls on the boundary.
+
+    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='left')
+    DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
+                  dtype='datetime64[ns]', freq='D')
+
+    Use ``inclusive='right'`` to exclude `start` if it falls on the boundary, and
+    similarly ``inclusive='neither'`` will exclude both `start` and `end`.
+
+    >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='right')
+    DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
+                  dtype='datetime64[ns]', freq='D')
+
+    **Specify a unit**
+
+    >>> pd.date_range(start="2017-01-01", periods=10, freq="100YS", unit="s")
+    DatetimeIndex(['2017-01-01', '2117-01-01', '2217-01-01', '2317-01-01',
+                   '2417-01-01', '2517-01-01', '2617-01-01', '2717-01-01',
+                   '2817-01-01', '2917-01-01'],
+                  dtype='datetime64[s]', freq='100YS-JAN')
+    """
+    if freq is None and com.any_none(periods, start, end):
+        freq = "D"
+
+    dtarr = DatetimeArray._generate_range(
+        start=start,
+        end=end,
+        periods=periods,
+        freq=freq,
+        tz=tz,
+        normalize=normalize,
+        inclusive=inclusive,
+        unit=unit,
+        **kwargs,
+    )
+    return DatetimeIndex._simple_new(dtarr, name=name)
+
+
+def bdate_range(
+    start=None,
+    end=None,
+    periods: int | None = None,
+    freq: Frequency | dt.timedelta = "B",
+    tz=None,
+    normalize: bool = True,
+    name: Hashable | None = None,
+    weekmask=None,
+    holidays=None,
+    inclusive: IntervalClosedType = "both",
+    **kwargs,
+) -> DatetimeIndex:
+    """
+    Return a fixed frequency DatetimeIndex with business day as the default.
+
+    Parameters
+    ----------
+    start : str or datetime-like, default None
+        Left bound for generating dates.
+    end : str or datetime-like, default None
+        Right bound for generating dates.
+    periods : int, default None
+        Number of periods to generate.
+    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'B'
+        Frequency strings can have multiples, e.g. '5h'. The default is
+        business daily ('B').
+    tz : str or None
+        Time zone name for returning localized DatetimeIndex, for example
+        Asia/Beijing.
+    normalize : bool, default False
+        Normalize start/end dates to midnight before generating date range.
+    name : str, default None
+        Name of the resulting DatetimeIndex.
+    weekmask : str or None, default None
+        Weekmask of valid business days, passed to ``numpy.busdaycalendar``,
+        only used when custom frequency strings are passed.  The default
+        value None is equivalent to 'Mon Tue Wed Thu Fri'.
+    holidays : list-like or None, default None
+        Dates to exclude from the set of valid business days, passed to
+        ``numpy.busdaycalendar``, only used when custom frequency strings
+        are passed.
+    inclusive : {"both", "neither", "left", "right"}, default "both"
+        Include boundaries; Whether to set each bound as closed or open.
+
+        .. versionadded:: 1.4.0
+    **kwargs
+        For compatibility. Has no effect on the result.
+
+    Returns
+    -------
+    DatetimeIndex
+
+    Notes
+    -----
+    Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified.  Specifying ``freq`` is a requirement
+    for ``bdate_range``.  Use ``date_range`` if specifying ``freq`` is not
+    desired.
+
+    To learn more about the frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    Note how the two weekend days are skipped in the result.
+
+    >>> pd.bdate_range(start='1/1/2018', end='1/08/2018')
+    DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
+               '2018-01-05', '2018-01-08'],
+              dtype='datetime64[ns]', freq='B')
+    """
+    if freq is None:
+        msg = "freq must be specified for bdate_range; use date_range instead"
+        raise TypeError(msg)
+
+    if isinstance(freq, str) and freq.startswith("C"):
+        try:
+            weekmask = weekmask or "Mon Tue Wed Thu Fri"
+            freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask)
+        except (KeyError, TypeError) as err:
+            msg = f"invalid custom frequency string: {freq}"
+            raise ValueError(msg) from err
+    elif holidays or weekmask:
+        msg = (
+            "a custom frequency string is required when holidays or "
+            f"weekmask are passed, got frequency {freq}"
+        )
+        raise ValueError(msg)
+
+    return date_range(
+        start=start,
+        end=end,
+        periods=periods,
+        freq=freq,
+        tz=tz,
+        normalize=normalize,
+        name=name,
+        inclusive=inclusive,
+        **kwargs,
+    )
+
+
+def _time_to_micros(time_obj: dt.time) -> int:
+    seconds = time_obj.hour * 60 * 60 + 60 * time_obj.minute + time_obj.second
+    return 1_000_000 * seconds + time_obj.microsecond
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/extension.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..371d3c811e772ba10af5071e6cc9cb97ba9f3f58
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/extension.py
@@ -0,0 +1,172 @@
+"""
+Shared methods for Index subclasses backed by ExtensionArray.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    TypeVar,
+)
+
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.generic import ABCDataFrame
+
+from pandas.core.indexes.base import Index
+
+if TYPE_CHECKING:
+    import numpy as np
+
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+    from pandas.core.arrays import IntervalArray
+    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+
+_ExtensionIndexT = TypeVar("_ExtensionIndexT", bound="ExtensionIndex")
+
+
+def _inherit_from_data(
+    name: str, delegate: type, cache: bool = False, wrap: bool = False
+):
+    """
+    Make an alias for a method of the underlying ExtensionArray.
+
+    Parameters
+    ----------
+    name : str
+        Name of an attribute the class should inherit from its EA parent.
+    delegate : class
+    cache : bool, default False
+        Whether to convert wrapped properties into cache_readonly
+    wrap : bool, default False
+        Whether to wrap the inherited result in an Index.
+
+    Returns
+    -------
+    attribute, method, property, or cache_readonly
+    """
+    attr = getattr(delegate, name)
+
+    if isinstance(attr, property) or type(attr).__name__ == "getset_descriptor":
+        # getset_descriptor i.e. property defined in cython class
+        if cache:
+
+            def cached(self):
+                return getattr(self._data, name)
+
+            cached.__name__ = name
+            cached.__doc__ = attr.__doc__
+            method = cache_readonly(cached)
+
+        else:
+
+            def fget(self):
+                result = getattr(self._data, name)
+                if wrap:
+                    if isinstance(result, type(self._data)):
+                        return type(self)._simple_new(result, name=self.name)
+                    elif isinstance(result, ABCDataFrame):
+                        return result.set_index(self)
+                    return Index(result, name=self.name, dtype=result.dtype)
+                return result
+
+            def fset(self, value) -> None:
+                setattr(self._data, name, value)
+
+            fget.__name__ = name
+            fget.__doc__ = attr.__doc__
+
+            method = property(fget, fset)
+
+    elif not callable(attr):
+        # just a normal attribute, no wrapping
+        method = attr
+
+    else:
+        # error: Incompatible redefinition (redefinition with type "Callable[[Any,
+        # VarArg(Any), KwArg(Any)], Any]", original type "property")
+        def method(self, *args, **kwargs):  # type: ignore[misc]
+            if "inplace" in kwargs:
+                raise ValueError(f"cannot use inplace with {type(self).__name__}")
+            result = attr(self._data, *args, **kwargs)
+            if wrap:
+                if isinstance(result, type(self._data)):
+                    return type(self)._simple_new(result, name=self.name)
+                elif isinstance(result, ABCDataFrame):
+                    return result.set_index(self)
+                return Index(result, name=self.name, dtype=result.dtype)
+            return result
+
+        # error: "property" has no attribute "__name__"
+        method.__name__ = name  # type: ignore[attr-defined]
+        method.__doc__ = attr.__doc__
+    return method
+
+
+def inherit_names(
+    names: list[str], delegate: type, cache: bool = False, wrap: bool = False
+) -> Callable[[type[_ExtensionIndexT]], type[_ExtensionIndexT]]:
+    """
+    Class decorator to pin attributes from an ExtensionArray to a Index subclass.
+
+    Parameters
+    ----------
+    names : List[str]
+    delegate : class
+    cache : bool, default False
+    wrap : bool, default False
+        Whether to wrap the inherited result in an Index.
+    """
+
+    def wrapper(cls: type[_ExtensionIndexT]) -> type[_ExtensionIndexT]:
+        for name in names:
+            meth = _inherit_from_data(name, delegate, cache=cache, wrap=wrap)
+            setattr(cls, name, meth)
+
+        return cls
+
+    return wrapper
+
+
+class ExtensionIndex(Index):
+    """
+    Index subclass for indexes backed by ExtensionArray.
+    """
+
+    # The base class already passes through to _data:
+    #  size, __len__, dtype
+
+    _data: IntervalArray | NDArrayBackedExtensionArray
+
+    # ---------------------------------------------------------------------
+
+    def _validate_fill_value(self, value):
+        """
+        Convert value to be insertable to underlying array.
+        """
+        return self._data._validate_setitem_value(value)
+
+    @cache_readonly
+    def _isnan(self) -> npt.NDArray[np.bool_]:
+        # error: Incompatible return value type (got "ExtensionArray", expected
+        # "ndarray")
+        return self._data.isna()  # type: ignore[return-value]
+
+
+class NDArrayBackedExtensionIndex(ExtensionIndex):
+    """
+    Index subclass for indexes backed by NDArrayBackedExtensionArray.
+    """
+
+    _data: NDArrayBackedExtensionArray
+
+    def _get_engine_target(self) -> np.ndarray:
+        return self._data._ndarray
+
+    def _from_join_target(self, result: np.ndarray) -> ArrayLike:
+        assert result.dtype == self._data._ndarray.dtype
+        return self._data._from_backing_data(result)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/frozen.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/frozen.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d528d34e36845efd44126c087cd15cd81e1e02e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/frozen.py
@@ -0,0 +1,120 @@
+"""
+frozen (immutable) data structures to support MultiIndexing
+
+These are used for:
+
+- .names (FrozenList)
+
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    NoReturn,
+)
+
+from pandas.core.base import PandasObject
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import Self
+
+
+class FrozenList(PandasObject, list):
+    """
+    Container that doesn't allow setting item *but*
+    because it's technically hashable, will be used
+    for lookups, appropriately, etc.
+    """
+
+    # Side note: This has to be of type list. Otherwise,
+    #            it messes up PyTables type checks.
+
+    def union(self, other) -> FrozenList:
+        """
+        Returns a FrozenList with other concatenated to the end of self.
+
+        Parameters
+        ----------
+        other : array-like
+            The array-like whose elements we are concatenating.
+
+        Returns
+        -------
+        FrozenList
+            The collection difference between self and other.
+        """
+        if isinstance(other, tuple):
+            other = list(other)
+        return type(self)(super().__add__(other))
+
+    def difference(self, other) -> FrozenList:
+        """
+        Returns a FrozenList with elements from other removed from self.
+
+        Parameters
+        ----------
+        other : array-like
+            The array-like whose elements we are removing self.
+
+        Returns
+        -------
+        FrozenList
+            The collection difference between self and other.
+        """
+        other = set(other)
+        temp = [x for x in self if x not in other]
+        return type(self)(temp)
+
+    # TODO: Consider deprecating these in favor of `union` (xref gh-15506)
+    # error: Incompatible types in assignment (expression has type
+    # "Callable[[FrozenList, Any], FrozenList]", base class "list" defined the
+    # type as overloaded function)
+    __add__ = __iadd__ = union  # type: ignore[assignment]
+
+    def __getitem__(self, n):
+        if isinstance(n, slice):
+            return type(self)(super().__getitem__(n))
+        return super().__getitem__(n)
+
+    def __radd__(self, other) -> Self:
+        if isinstance(other, tuple):
+            other = list(other)
+        return type(self)(other + list(self))
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, (tuple, FrozenList)):
+            other = list(other)
+        return super().__eq__(other)
+
+    __req__ = __eq__
+
+    def __mul__(self, other) -> Self:
+        return type(self)(super().__mul__(other))
+
+    __imul__ = __mul__
+
+    def __reduce__(self):
+        return type(self), (list(self),)
+
+    # error: Signature of "__hash__" incompatible with supertype "list"
+    def __hash__(self) -> int:  # type: ignore[override]
+        return hash(tuple(self))
+
+    def _disabled(self, *args, **kwargs) -> NoReturn:
+        """
+        This method will not function because object is immutable.
+        """
+        raise TypeError(f"'{type(self).__name__}' does not support mutable operations.")
+
+    def __str__(self) -> str:
+        return pprint_thing(self, quote_strings=True, escape_chars=("\t", "\r", "\n"))
+
+    def __repr__(self) -> str:
+        return f"{type(self).__name__}({str(self)})"
+
+    __setitem__ = __setslice__ = _disabled  # type: ignore[assignment]
+    __delitem__ = __delslice__ = _disabled
+    pop = append = extend = _disabled
+    remove = sort = insert = _disabled  # type: ignore[assignment]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/interval.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/interval.py
new file mode 100644
index 0000000000000000000000000000000000000000..635924674d9f4aec5537f77e5fbcf5e81180e2d4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/interval.py
@@ -0,0 +1,1137 @@
+""" define the IntervalIndex """
+from __future__ import annotations
+
+from operator import (
+    le,
+    lt,
+)
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.interval import (
+    Interval,
+    IntervalMixin,
+    IntervalTree,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Period,
+    Timedelta,
+    Timestamp,
+    to_offset,
+)
+from pandas.errors import InvalidIndexError
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+)
+from pandas.util._exceptions import rewrite_exception
+
+from pandas.core.dtypes.cast import (
+    find_common_type,
+    infer_dtype_from_scalar,
+    maybe_box_datetimelike,
+    maybe_downcast_numeric,
+    maybe_upcast_numeric_to_64bit,
+)
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_number,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    IntervalDtype,
+)
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
+
+from pandas.core.algorithms import unique
+from pandas.core.arrays.datetimelike import validate_periods
+from pandas.core.arrays.interval import (
+    IntervalArray,
+    _interval_shared_docs,
+)
+import pandas.core.common as com
+from pandas.core.indexers import is_valid_positional_slice
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    _index_shared_docs,
+    ensure_index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.datetimes import (
+    DatetimeIndex,
+    date_range,
+)
+from pandas.core.indexes.extension import (
+    ExtensionIndex,
+    inherit_names,
+)
+from pandas.core.indexes.multi import MultiIndex
+from pandas.core.indexes.timedeltas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        IntervalClosedType,
+        Self,
+        npt,
+    )
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+
+_index_doc_kwargs.update(
+    {
+        "klass": "IntervalIndex",
+        "qualname": "IntervalIndex",
+        "target_klass": "IntervalIndex or list of Intervals",
+        "name": textwrap.dedent(
+            """\
+         name : object, optional
+              Name to be stored in the index.
+         """
+        ),
+    }
+)
+
+
+def _get_next_label(label):
+    # see test_slice_locs_with_ints_and_floats_succeeds
+    dtype = getattr(label, "dtype", type(label))
+    if isinstance(label, (Timestamp, Timedelta)):
+        dtype = "datetime64[ns]"
+    dtype = pandas_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "mM") or isinstance(dtype, DatetimeTZDtype):
+        return label + np.timedelta64(1, "ns")
+    elif is_integer_dtype(dtype):
+        return label + 1
+    elif is_float_dtype(dtype):
+        return np.nextafter(label, np.inf)
+    else:
+        raise TypeError(f"cannot determine next label for type {repr(type(label))}")
+
+
+def _get_prev_label(label):
+    # see test_slice_locs_with_ints_and_floats_succeeds
+    dtype = getattr(label, "dtype", type(label))
+    if isinstance(label, (Timestamp, Timedelta)):
+        dtype = "datetime64[ns]"
+    dtype = pandas_dtype(dtype)
+
+    if lib.is_np_dtype(dtype, "mM") or isinstance(dtype, DatetimeTZDtype):
+        return label - np.timedelta64(1, "ns")
+    elif is_integer_dtype(dtype):
+        return label - 1
+    elif is_float_dtype(dtype):
+        return np.nextafter(label, -np.inf)
+    else:
+        raise TypeError(f"cannot determine next label for type {repr(type(label))}")
+
+
+def _new_IntervalIndex(cls, d):
+    """
+    This is called upon unpickling, rather than the default which doesn't have
+    arguments and breaks __new__.
+    """
+    return cls.from_arrays(**d)
+
+
+@Appender(
+    _interval_shared_docs["class"]
+    % {
+        "klass": "IntervalIndex",
+        "summary": "Immutable index of intervals that are closed on the same side.",
+        "name": _index_doc_kwargs["name"],
+        "extra_attributes": "is_overlapping\nvalues\n",
+        "extra_methods": "",
+        "examples": textwrap.dedent(
+            """\
+    Examples
+    --------
+    A new ``IntervalIndex`` is typically constructed using
+    :func:`interval_range`:
+
+    >>> pd.interval_range(start=0, end=5)
+    IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
+                  dtype='interval[int64, right]')
+
+    It may also be constructed using one of the constructor
+    methods: :meth:`IntervalIndex.from_arrays`,
+    :meth:`IntervalIndex.from_breaks`, and :meth:`IntervalIndex.from_tuples`.
+
+    See further examples in the doc strings of ``interval_range`` and the
+    mentioned constructor methods.
+    """
+        ),
+    }
+)
+@inherit_names(["set_closed", "to_tuples"], IntervalArray, wrap=True)
+@inherit_names(
+    [
+        "__array__",
+        "overlaps",
+        "contains",
+        "closed_left",
+        "closed_right",
+        "open_left",
+        "open_right",
+        "is_empty",
+    ],
+    IntervalArray,
+)
+@inherit_names(["is_non_overlapping_monotonic", "closed"], IntervalArray, cache=True)
+class IntervalIndex(ExtensionIndex):
+    _typ = "intervalindex"
+
+    # annotate properties pinned via inherit_names
+    closed: IntervalClosedType
+    is_non_overlapping_monotonic: bool
+    closed_left: bool
+    closed_right: bool
+    open_left: bool
+    open_right: bool
+
+    _data: IntervalArray
+    _values: IntervalArray
+    _can_hold_strings = False
+    _data_cls = IntervalArray
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data,
+        closed: IntervalClosedType | None = None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+        verify_integrity: bool = True,
+    ) -> Self:
+        name = maybe_extract_name(name, data, cls)
+
+        with rewrite_exception("IntervalArray", cls.__name__):
+            array = IntervalArray(
+                data,
+                closed=closed,
+                copy=copy,
+                dtype=dtype,
+                verify_integrity=verify_integrity,
+            )
+
+        return cls._simple_new(array, name)
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_breaks"]
+        % {
+            "klass": "IntervalIndex",
+            "name": textwrap.dedent(
+                """
+             name : str, optional
+                  Name of the resulting IntervalIndex."""
+            ),
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.IntervalIndex.from_breaks([0, 1, 2, 3])
+        IntervalIndex([(0, 1], (1, 2], (2, 3]],
+                      dtype='interval[int64, right]')
+        """
+            ),
+        }
+    )
+    def from_breaks(
+        cls,
+        breaks,
+        closed: IntervalClosedType | None = "right",
+        name: Hashable | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> IntervalIndex:
+        with rewrite_exception("IntervalArray", cls.__name__):
+            array = IntervalArray.from_breaks(
+                breaks, closed=closed, copy=copy, dtype=dtype
+            )
+        return cls._simple_new(array, name=name)
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_arrays"]
+        % {
+            "klass": "IntervalIndex",
+            "name": textwrap.dedent(
+                """
+             name : str, optional
+                  Name of the resulting IntervalIndex."""
+            ),
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])
+        IntervalIndex([(0, 1], (1, 2], (2, 3]],
+                      dtype='interval[int64, right]')
+        """
+            ),
+        }
+    )
+    def from_arrays(
+        cls,
+        left,
+        right,
+        closed: IntervalClosedType = "right",
+        name: Hashable | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> IntervalIndex:
+        with rewrite_exception("IntervalArray", cls.__name__):
+            array = IntervalArray.from_arrays(
+                left, right, closed, copy=copy, dtype=dtype
+            )
+        return cls._simple_new(array, name=name)
+
+    @classmethod
+    @Appender(
+        _interval_shared_docs["from_tuples"]
+        % {
+            "klass": "IntervalIndex",
+            "name": textwrap.dedent(
+                """
+             name : str, optional
+                  Name of the resulting IntervalIndex."""
+            ),
+            "examples": textwrap.dedent(
+                """\
+        Examples
+        --------
+        >>> pd.IntervalIndex.from_tuples([(0, 1), (1, 2)])
+        IntervalIndex([(0, 1], (1, 2]],
+                       dtype='interval[int64, right]')
+        """
+            ),
+        }
+    )
+    def from_tuples(
+        cls,
+        data,
+        closed: IntervalClosedType = "right",
+        name: Hashable | None = None,
+        copy: bool = False,
+        dtype: Dtype | None = None,
+    ) -> IntervalIndex:
+        with rewrite_exception("IntervalArray", cls.__name__):
+            arr = IntervalArray.from_tuples(data, closed=closed, copy=copy, dtype=dtype)
+        return cls._simple_new(arr, name=name)
+
+    # --------------------------------------------------------------------
+    # error: Return type "IntervalTree" of "_engine" incompatible with return type
+    # "Union[IndexEngine, ExtensionEngine]" in supertype "Index"
+    @cache_readonly
+    def _engine(self) -> IntervalTree:  # type: ignore[override]
+        # IntervalTree does not supports numpy array unless they are 64 bit
+        left = self._maybe_convert_i8(self.left)
+        left = maybe_upcast_numeric_to_64bit(left)
+        right = self._maybe_convert_i8(self.right)
+        right = maybe_upcast_numeric_to_64bit(right)
+        return IntervalTree(left, right, closed=self.closed)
+
+    def __contains__(self, key: Any) -> bool:
+        """
+        return a boolean if this key is IN the index
+        We *only* accept an Interval
+
+        Parameters
+        ----------
+        key : Interval
+
+        Returns
+        -------
+        bool
+        """
+        hash(key)
+        if not isinstance(key, Interval):
+            if is_valid_na_for_dtype(key, self.dtype):
+                return self.hasnans
+            return False
+
+        try:
+            self.get_loc(key)
+            return True
+        except KeyError:
+            return False
+
+    def _getitem_slice(self, slobj: slice) -> IntervalIndex:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        res = self._data[slobj]
+        return type(self)._simple_new(res, name=self._name)
+
+    @cache_readonly
+    def _multiindex(self) -> MultiIndex:
+        return MultiIndex.from_arrays([self.left, self.right], names=["left", "right"])
+
+    def __reduce__(self):
+        d = {
+            "left": self.left,
+            "right": self.right,
+            "closed": self.closed,
+            "name": self.name,
+        }
+        return _new_IntervalIndex, (type(self), d), None
+
+    @property
+    def inferred_type(self) -> str:
+        """Return a string of the type inferred from the values"""
+        return "interval"
+
+    # Cannot determine type of "memory_usage"
+    @Appender(Index.memory_usage.__doc__)  # type: ignore[has-type]
+    def memory_usage(self, deep: bool = False) -> int:
+        # we don't use an explicit engine
+        # so return the bytes here
+        return self.left.memory_usage(deep=deep) + self.right.memory_usage(deep=deep)
+
+    # IntervalTree doesn't have a is_monotonic_decreasing, so have to override
+    #  the Index implementation
+    @cache_readonly
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return True if the IntervalIndex is monotonic decreasing (only equal or
+        decreasing values), else False
+        """
+        return self[::-1].is_monotonic_increasing
+
+    @cache_readonly
+    def is_unique(self) -> bool:
+        """
+        Return True if the IntervalIndex contains unique elements, else False.
+        """
+        left = self.left
+        right = self.right
+
+        if self.isna().sum() > 1:
+            return False
+
+        if left.is_unique or right.is_unique:
+            return True
+
+        seen_pairs = set()
+        check_idx = np.where(left.duplicated(keep=False))[0]
+        for idx in check_idx:
+            pair = (left[idx], right[idx])
+            if pair in seen_pairs:
+                return False
+            seen_pairs.add(pair)
+
+        return True
+
+    @property
+    def is_overlapping(self) -> bool:
+        """
+        Return True if the IntervalIndex has overlapping intervals, else False.
+
+        Two intervals overlap if they share a common point, including closed
+        endpoints. Intervals that only have an open endpoint in common do not
+        overlap.
+
+        Returns
+        -------
+        bool
+            Boolean indicating if the IntervalIndex has overlapping intervals.
+
+        See Also
+        --------
+        Interval.overlaps : Check whether two Interval objects overlap.
+        IntervalIndex.overlaps : Check an IntervalIndex elementwise for
+            overlaps.
+
+        Examples
+        --------
+        >>> index = pd.IntervalIndex.from_tuples([(0, 2), (1, 3), (4, 5)])
+        >>> index
+        IntervalIndex([(0, 2], (1, 3], (4, 5]],
+              dtype='interval[int64, right]')
+        >>> index.is_overlapping
+        True
+
+        Intervals that share closed endpoints overlap:
+
+        >>> index = pd.interval_range(0, 3, closed='both')
+        >>> index
+        IntervalIndex([[0, 1], [1, 2], [2, 3]],
+              dtype='interval[int64, both]')
+        >>> index.is_overlapping
+        True
+
+        Intervals that only have an open endpoint in common do not overlap:
+
+        >>> index = pd.interval_range(0, 3, closed='left')
+        >>> index
+        IntervalIndex([[0, 1), [1, 2), [2, 3)],
+              dtype='interval[int64, left]')
+        >>> index.is_overlapping
+        False
+        """
+        # GH 23309
+        return self._engine.is_overlapping
+
+    def _needs_i8_conversion(self, key) -> bool:
+        """
+        Check if a given key needs i8 conversion. Conversion is necessary for
+        Timestamp, Timedelta, DatetimeIndex, and TimedeltaIndex keys. An
+        Interval-like requires conversion if its endpoints are one of the
+        aforementioned types.
+
+        Assumes that any list-like data has already been cast to an Index.
+
+        Parameters
+        ----------
+        key : scalar or Index-like
+            The key that should be checked for i8 conversion
+
+        Returns
+        -------
+        bool
+        """
+        key_dtype = getattr(key, "dtype", None)
+        if isinstance(key_dtype, IntervalDtype) or isinstance(key, Interval):
+            return self._needs_i8_conversion(key.left)
+
+        i8_types = (Timestamp, Timedelta, DatetimeIndex, TimedeltaIndex)
+        return isinstance(key, i8_types)
+
+    def _maybe_convert_i8(self, key):
+        """
+        Maybe convert a given key to its equivalent i8 value(s). Used as a
+        preprocessing step prior to IntervalTree queries (self._engine), which
+        expects numeric data.
+
+        Parameters
+        ----------
+        key : scalar or list-like
+            The key that should maybe be converted to i8.
+
+        Returns
+        -------
+        scalar or list-like
+            The original key if no conversion occurred, int if converted scalar,
+            Index with an int64 dtype if converted list-like.
+        """
+        if is_list_like(key):
+            key = ensure_index(key)
+            key = maybe_upcast_numeric_to_64bit(key)
+
+        if not self._needs_i8_conversion(key):
+            return key
+
+        scalar = is_scalar(key)
+        key_dtype = getattr(key, "dtype", None)
+        if isinstance(key_dtype, IntervalDtype) or isinstance(key, Interval):
+            # convert left/right and reconstruct
+            left = self._maybe_convert_i8(key.left)
+            right = self._maybe_convert_i8(key.right)
+            constructor = Interval if scalar else IntervalIndex.from_arrays
+            # error: "object" not callable
+            return constructor(
+                left, right, closed=self.closed
+            )  # type: ignore[operator]
+
+        if scalar:
+            # Timestamp/Timedelta
+            key_dtype, key_i8 = infer_dtype_from_scalar(key)
+            if isinstance(key, Period):
+                key_i8 = key.ordinal
+            elif isinstance(key_i8, Timestamp):
+                key_i8 = key_i8._value
+            elif isinstance(key_i8, (np.datetime64, np.timedelta64)):
+                key_i8 = key_i8.view("i8")
+        else:
+            # DatetimeIndex/TimedeltaIndex
+            key_dtype, key_i8 = key.dtype, Index(key.asi8)
+            if key.hasnans:
+                # convert NaT from its i8 value to np.nan so it's not viewed
+                # as a valid value, maybe causing errors (e.g. is_overlapping)
+                key_i8 = key_i8.where(~key._isnan)
+
+        # ensure consistency with IntervalIndex subtype
+        # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "subtype"
+        subtype = self.dtype.subtype  # type: ignore[union-attr]
+
+        if subtype != key_dtype:
+            raise ValueError(
+                f"Cannot index an IntervalIndex of subtype {subtype} with "
+                f"values of dtype {key_dtype}"
+            )
+
+        return key_i8
+
+    def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
+        if not self.is_non_overlapping_monotonic:
+            raise KeyError(
+                "can only get slices from an IntervalIndex if bounds are "
+                "non-overlapping and all monotonic increasing or decreasing"
+            )
+
+        if isinstance(label, (IntervalMixin, IntervalIndex)):
+            raise NotImplementedError("Interval objects are not currently supported")
+
+        # GH 20921: "not is_monotonic_increasing" for the second condition
+        # instead of "is_monotonic_decreasing" to account for single element
+        # indexes being both increasing and decreasing
+        if (side == "left" and self.left.is_monotonic_increasing) or (
+            side == "right" and not self.left.is_monotonic_increasing
+        ):
+            sub_idx = self.right
+            if self.open_right:
+                label = _get_next_label(label)
+        else:
+            sub_idx = self.left
+            if self.open_left:
+                label = _get_prev_label(label)
+
+        return sub_idx._searchsorted_monotonic(label, side)
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def get_loc(self, key) -> int | slice | np.ndarray:
+        """
+        Get integer location, slice or boolean mask for requested label.
+
+        Parameters
+        ----------
+        key : label
+
+        Returns
+        -------
+        int if unique index, slice if monotonic index, else mask
+
+        Examples
+        --------
+        >>> i1, i2 = pd.Interval(0, 1), pd.Interval(1, 2)
+        >>> index = pd.IntervalIndex([i1, i2])
+        >>> index.get_loc(1)
+        0
+
+        You can also supply a point inside an interval.
+
+        >>> index.get_loc(1.5)
+        1
+
+        If a label is in several intervals, you get the locations of all the
+        relevant intervals.
+
+        >>> i3 = pd.Interval(0, 2)
+        >>> overlapping_index = pd.IntervalIndex([i1, i2, i3])
+        >>> overlapping_index.get_loc(0.5)
+        array([ True, False,  True])
+
+        Only exact matches will be returned if an interval is provided.
+
+        >>> index.get_loc(pd.Interval(0, 1))
+        0
+        """
+        self._check_indexing_error(key)
+
+        if isinstance(key, Interval):
+            if self.closed != key.closed:
+                raise KeyError(key)
+            mask = (self.left == key.left) & (self.right == key.right)
+        elif is_valid_na_for_dtype(key, self.dtype):
+            mask = self.isna()
+        else:
+            # assume scalar
+            op_left = le if self.closed_left else lt
+            op_right = le if self.closed_right else lt
+            try:
+                mask = op_left(self.left, key) & op_right(key, self.right)
+            except TypeError as err:
+                # scalar is not comparable to II subtype --> invalid label
+                raise KeyError(key) from err
+
+        matches = mask.sum()
+        if matches == 0:
+            raise KeyError(key)
+        if matches == 1:
+            return mask.argmax()
+
+        res = lib.maybe_booleans_to_slice(mask.view("u1"))
+        if isinstance(res, slice) and res.stop is None:
+            # TODO: DO this in maybe_booleans_to_slice?
+            res = slice(res.start, len(self), res.step)
+        return res
+
+    def _get_indexer(
+        self,
+        target: Index,
+        method: str | None = None,
+        limit: int | None = None,
+        tolerance: Any | None = None,
+    ) -> npt.NDArray[np.intp]:
+        if isinstance(target, IntervalIndex):
+            # We only get here with not self.is_overlapping
+            # -> at most one match per interval in target
+            # want exact matches -> need both left/right to match, so defer to
+            # left/right get_indexer, compare elementwise, equality -> match
+            indexer = self._get_indexer_unique_sides(target)
+
+        elif not (is_object_dtype(target.dtype) or is_string_dtype(target.dtype)):
+            # homogeneous scalar index: use IntervalTree
+            # we should always have self._should_partial_index(target) here
+            target = self._maybe_convert_i8(target)
+            indexer = self._engine.get_indexer(target.values)
+        else:
+            # heterogeneous scalar index: defer elementwise to get_loc
+            # we should always have self._should_partial_index(target) here
+            return self._get_indexer_pointwise(target)[0]
+
+        return ensure_platform_int(indexer)
+
+    @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
+    def get_indexer_non_unique(
+        self, target: Index
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        target = ensure_index(target)
+
+        if not self._should_compare(target) and not self._should_partial_index(target):
+            # e.g. IntervalIndex with different closed or incompatible subtype
+            #  -> no matches
+            return self._get_indexer_non_comparable(target, None, unique=False)
+
+        elif isinstance(target, IntervalIndex):
+            if self.left.is_unique and self.right.is_unique:
+                # fastpath available even if we don't have self._index_as_unique
+                indexer = self._get_indexer_unique_sides(target)
+                missing = (indexer == -1).nonzero()[0]
+            else:
+                return self._get_indexer_pointwise(target)
+
+        elif is_object_dtype(target.dtype) or not self._should_partial_index(target):
+            # target might contain intervals: defer elementwise to get_loc
+            return self._get_indexer_pointwise(target)
+
+        else:
+            # Note: this case behaves differently from other Index subclasses
+            #  because IntervalIndex does partial-int indexing
+            target = self._maybe_convert_i8(target)
+            indexer, missing = self._engine.get_indexer_non_unique(target.values)
+
+        return ensure_platform_int(indexer), ensure_platform_int(missing)
+
+    def _get_indexer_unique_sides(self, target: IntervalIndex) -> npt.NDArray[np.intp]:
+        """
+        _get_indexer specialized to the case where both of our sides are unique.
+        """
+        # Caller is responsible for checking
+        #  `self.left.is_unique and self.right.is_unique`
+
+        left_indexer = self.left.get_indexer(target.left)
+        right_indexer = self.right.get_indexer(target.right)
+        indexer = np.where(left_indexer == right_indexer, left_indexer, -1)
+        return indexer
+
+    def _get_indexer_pointwise(
+        self, target: Index
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        """
+        pointwise implementation for get_indexer and get_indexer_non_unique.
+        """
+        indexer, missing = [], []
+        for i, key in enumerate(target):
+            try:
+                locs = self.get_loc(key)
+                if isinstance(locs, slice):
+                    # Only needed for get_indexer_non_unique
+                    locs = np.arange(locs.start, locs.stop, locs.step, dtype="intp")
+                elif lib.is_integer(locs):
+                    locs = np.array(locs, ndmin=1)
+                else:
+                    # otherwise we have ndarray[bool]
+                    locs = np.where(locs)[0]
+            except KeyError:
+                missing.append(i)
+                locs = np.array([-1])
+            except InvalidIndexError:
+                # i.e. non-scalar key e.g. a tuple.
+                # see test_append_different_columns_types_raises
+                missing.append(i)
+                locs = np.array([-1])
+
+            indexer.append(locs)
+
+        indexer = np.concatenate(indexer)
+        return ensure_platform_int(indexer), ensure_platform_int(missing)
+
+    @cache_readonly
+    def _index_as_unique(self) -> bool:
+        return not self.is_overlapping and self._engine._na_count < 2
+
+    _requires_unique_msg = (
+        "cannot handle overlapping indices; use IntervalIndex.get_indexer_non_unique"
+    )
+
+    def _convert_slice_indexer(self, key: slice, kind: Literal["loc", "getitem"]):
+        if not (key.step is None or key.step == 1):
+            # GH#31658 if label-based, we require step == 1,
+            #  if positional, we disallow float start/stop
+            msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
+            if kind == "loc":
+                raise ValueError(msg)
+            if kind == "getitem":
+                if not is_valid_positional_slice(key):
+                    # i.e. this cannot be interpreted as a positional slice
+                    raise ValueError(msg)
+
+        return super()._convert_slice_indexer(key, kind)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        # integer lookups in Series.__getitem__ are unambiguously
+        #  positional in this case
+        # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
+        # ExtensionDtype]" has no attribute "subtype"
+        return self.dtype.subtype.kind in "mM"  # type: ignore[union-attr]
+
+    def _maybe_cast_slice_bound(self, label, side: str):
+        return getattr(self, side)._maybe_cast_slice_bound(label, side)
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        if not isinstance(dtype, IntervalDtype):
+            return False
+        common_subtype = find_common_type([self.dtype, dtype])
+        return not is_object_dtype(common_subtype)
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def left(self) -> Index:
+        return Index(self._data.left, copy=False)
+
+    @cache_readonly
+    def right(self) -> Index:
+        return Index(self._data.right, copy=False)
+
+    @cache_readonly
+    def mid(self) -> Index:
+        return Index(self._data.mid, copy=False)
+
+    @property
+    def length(self) -> Index:
+        return Index(self._data.length, copy=False)
+
+    # --------------------------------------------------------------------
+    # Set Operations
+
+    def _intersection(self, other, sort):
+        """
+        intersection specialized to the case with matching dtypes.
+        """
+        # For IntervalIndex we also know other.closed == self.closed
+        if self.left.is_unique and self.right.is_unique:
+            taken = self._intersection_unique(other)
+        elif other.left.is_unique and other.right.is_unique and self.isna().sum() <= 1:
+            # Swap other/self if other is unique and self does not have
+            # multiple NaNs
+            taken = other._intersection_unique(self)
+        else:
+            # duplicates
+            taken = self._intersection_non_unique(other)
+
+        if sort is None:
+            taken = taken.sort_values()
+
+        return taken
+
+    def _intersection_unique(self, other: IntervalIndex) -> IntervalIndex:
+        """
+        Used when the IntervalIndex does not have any common endpoint,
+        no matter left or right.
+        Return the intersection with another IntervalIndex.
+        Parameters
+        ----------
+        other : IntervalIndex
+        Returns
+        -------
+        IntervalIndex
+        """
+        # Note: this is much more performant than super()._intersection(other)
+        lindexer = self.left.get_indexer(other.left)
+        rindexer = self.right.get_indexer(other.right)
+
+        match = (lindexer == rindexer) & (lindexer != -1)
+        indexer = lindexer.take(match.nonzero()[0])
+        indexer = unique(indexer)
+
+        return self.take(indexer)
+
+    def _intersection_non_unique(self, other: IntervalIndex) -> IntervalIndex:
+        """
+        Used when the IntervalIndex does have some common endpoints,
+        on either sides.
+        Return the intersection with another IntervalIndex.
+
+        Parameters
+        ----------
+        other : IntervalIndex
+
+        Returns
+        -------
+        IntervalIndex
+        """
+        # Note: this is about 3.25x faster than super()._intersection(other)
+        #  in IntervalIndexMethod.time_intersection_both_duplicate(1000)
+        mask = np.zeros(len(self), dtype=bool)
+
+        if self.hasnans and other.hasnans:
+            first_nan_loc = np.arange(len(self))[self.isna()][0]
+            mask[first_nan_loc] = True
+
+        other_tups = set(zip(other.left, other.right))
+        for i, tup in enumerate(zip(self.left, self.right)):
+            if tup in other_tups:
+                mask[i] = True
+
+        return self[mask]
+
+    # --------------------------------------------------------------------
+
+    def _get_engine_target(self) -> np.ndarray:
+        # Note: we _could_ use libjoin functions by either casting to object
+        #  dtype or constructing tuples (faster than constructing Intervals)
+        #  but the libjoin fastpaths are no longer fast in these cases.
+        raise NotImplementedError(
+            "IntervalIndex does not use libjoin fastpaths or pass values to "
+            "IndexEngine objects"
+        )
+
+    def _from_join_target(self, result):
+        raise NotImplementedError("IntervalIndex does not use libjoin fastpaths")
+
+    # TODO: arithmetic operations
+
+
+def _is_valid_endpoint(endpoint) -> bool:
+    """
+    Helper for interval_range to check if start/end are valid types.
+    """
+    return any(
+        [
+            is_number(endpoint),
+            isinstance(endpoint, Timestamp),
+            isinstance(endpoint, Timedelta),
+            endpoint is None,
+        ]
+    )
+
+
+def _is_type_compatible(a, b) -> bool:
+    """
+    Helper for interval_range to check type compat of start/end/freq.
+    """
+    is_ts_compat = lambda x: isinstance(x, (Timestamp, BaseOffset))
+    is_td_compat = lambda x: isinstance(x, (Timedelta, BaseOffset))
+    return (
+        (is_number(a) and is_number(b))
+        or (is_ts_compat(a) and is_ts_compat(b))
+        or (is_td_compat(a) and is_td_compat(b))
+        or com.any_none(a, b)
+    )
+
+
+def interval_range(
+    start=None,
+    end=None,
+    periods=None,
+    freq=None,
+    name: Hashable | None = None,
+    closed: IntervalClosedType = "right",
+) -> IntervalIndex:
+    """
+    Return a fixed frequency IntervalIndex.
+
+    Parameters
+    ----------
+    start : numeric or datetime-like, default None
+        Left bound for generating intervals.
+    end : numeric or datetime-like, default None
+        Right bound for generating intervals.
+    periods : int, default None
+        Number of periods to generate.
+    freq : numeric, str, Timedelta, datetime.timedelta, or DateOffset, default None
+        The length of each interval. Must be consistent with the type of start
+        and end, e.g. 2 for numeric, or '5H' for datetime-like.  Default is 1
+        for numeric and 'D' for datetime-like.
+    name : str, default None
+        Name of the resulting IntervalIndex.
+    closed : {'left', 'right', 'both', 'neither'}, default 'right'
+        Whether the intervals are closed on the left-side, right-side, both
+        or neither.
+
+    Returns
+    -------
+    IntervalIndex
+
+    See Also
+    --------
+    IntervalIndex : An Index of intervals that are all closed on the same side.
+
+    Notes
+    -----
+    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified. If ``freq`` is omitted, the resulting
+    ``IntervalIndex`` will have ``periods`` linearly spaced elements between
+    ``start`` and ``end``, inclusively.
+
+    To learn more about datetime-like frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    Numeric ``start`` and  ``end`` is supported.
+
+    >>> pd.interval_range(start=0, end=5)
+    IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
+                  dtype='interval[int64, right]')
+
+    Additionally, datetime-like input is also supported.
+
+    >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
+    ...                   end=pd.Timestamp('2017-01-04'))
+    IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
+                   (2017-01-02 00:00:00, 2017-01-03 00:00:00],
+                   (2017-01-03 00:00:00, 2017-01-04 00:00:00]],
+                  dtype='interval[datetime64[ns], right]')
+
+    The ``freq`` parameter specifies the frequency between the left and right.
+    endpoints of the individual intervals within the ``IntervalIndex``.  For
+    numeric ``start`` and ``end``, the frequency must also be numeric.
+
+    >>> pd.interval_range(start=0, periods=4, freq=1.5)
+    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
+                  dtype='interval[float64, right]')
+
+    Similarly, for datetime-like ``start`` and ``end``, the frequency must be
+    convertible to a DateOffset.
+
+    >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
+    ...                   periods=3, freq='MS')
+    IntervalIndex([(2017-01-01 00:00:00, 2017-02-01 00:00:00],
+                   (2017-02-01 00:00:00, 2017-03-01 00:00:00],
+                   (2017-03-01 00:00:00, 2017-04-01 00:00:00]],
+                  dtype='interval[datetime64[ns], right]')
+
+    Specify ``start``, ``end``, and ``periods``; the frequency is generated
+    automatically (linearly spaced).
+
+    >>> pd.interval_range(start=0, end=6, periods=4)
+    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
+              dtype='interval[float64, right]')
+
+    The ``closed`` parameter specifies which endpoints of the individual
+    intervals within the ``IntervalIndex`` are closed.
+
+    >>> pd.interval_range(end=5, periods=4, closed='both')
+    IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
+                  dtype='interval[int64, both]')
+    """
+    start = maybe_box_datetimelike(start)
+    end = maybe_box_datetimelike(end)
+    endpoint = start if start is not None else end
+
+    if freq is None and com.any_none(periods, start, end):
+        freq = 1 if is_number(endpoint) else "D"
+
+    if com.count_not_none(start, end, periods, freq) != 3:
+        raise ValueError(
+            "Of the four parameters: start, end, periods, and "
+            "freq, exactly three must be specified"
+        )
+
+    if not _is_valid_endpoint(start):
+        raise ValueError(f"start must be numeric or datetime-like, got {start}")
+    if not _is_valid_endpoint(end):
+        raise ValueError(f"end must be numeric or datetime-like, got {end}")
+
+    periods = validate_periods(periods)
+
+    if freq is not None and not is_number(freq):
+        try:
+            freq = to_offset(freq)
+        except ValueError as err:
+            raise ValueError(
+                f"freq must be numeric or convertible to DateOffset, got {freq}"
+            ) from err
+
+    # verify type compatibility
+    if not all(
+        [
+            _is_type_compatible(start, end),
+            _is_type_compatible(start, freq),
+            _is_type_compatible(end, freq),
+        ]
+    ):
+        raise TypeError("start, end, freq need to be type compatible")
+
+    # +1 to convert interval count to breaks count (n breaks = n-1 intervals)
+    if periods is not None:
+        periods += 1
+
+    breaks: np.ndarray | TimedeltaIndex | DatetimeIndex
+
+    if is_number(endpoint):
+        if com.all_not_none(start, end, freq):
+            # 0.1 ensures we capture end
+            breaks = np.arange(start, end + (freq * 0.1), freq)
+        else:
+            # compute the period/start/end if unspecified (at most one)
+            if periods is None:
+                periods = int((end - start) // freq) + 1
+            elif start is None:
+                start = end - (periods - 1) * freq
+            elif end is None:
+                end = start + (periods - 1) * freq
+
+            breaks = np.linspace(start, end, periods)
+        if all(is_integer(x) for x in com.not_none(start, end, freq)):
+            # np.linspace always produces float output
+
+            # error: Argument 1 to "maybe_downcast_numeric" has incompatible type
+            # "Union[ndarray[Any, Any], TimedeltaIndex, DatetimeIndex]";
+            # expected "ndarray[Any, Any]"  [
+            breaks = maybe_downcast_numeric(
+                breaks,  # type: ignore[arg-type]
+                np.dtype("int64"),
+            )
+    else:
+        # delegate to the appropriate range function
+        if isinstance(endpoint, Timestamp):
+            breaks = date_range(start=start, end=end, periods=periods, freq=freq)
+        else:
+            breaks = timedelta_range(start=start, end=end, periods=periods, freq=freq)
+
+    return IntervalIndex.from_breaks(breaks, name=name, closed=closed)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/multi.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..8954d49649a2b132927980f5874a9006a65301f4
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/multi.py
@@ -0,0 +1,4176 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Collection,
+    Generator,
+    Hashable,
+    Iterable,
+    Sequence,
+)
+from functools import wraps
+from sys import getsizeof
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import (
+    algos as libalgos,
+    index as libindex,
+    lib,
+)
+from pandas._libs.hashtable import duplicated
+from pandas._typing import (
+    AnyAll,
+    AnyArrayLike,
+    Axis,
+    DropKeep,
+    DtypeObj,
+    F,
+    IgnoreRaise,
+    IndexLabel,
+    Scalar,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    InvalidIndexError,
+    PerformanceWarning,
+    UnsortedIndexError,
+)
+from pandas.util._decorators import (
+    Appender,
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import coerce_indexer_dtype
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
+    is_hashable,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_array_like
+from pandas.core.dtypes.missing import (
+    array_equivalent,
+    isna,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.putmask import validate_putmask
+from pandas.core.arrays import (
+    Categorical,
+    ExtensionArray,
+)
+from pandas.core.arrays.categorical import (
+    factorize_from_iterables,
+    recode_for_categories,
+)
+import pandas.core.common as com
+from pandas.core.construction import sanitize_array
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    _index_shared_docs,
+    ensure_index,
+    get_unanimous_names,
+)
+from pandas.core.indexes.frozen import FrozenList
+from pandas.core.ops.invalid import make_invalid_op
+from pandas.core.sorting import (
+    get_group_index,
+    lexsort_indexer,
+)
+
+from pandas.io.formats.printing import (
+    get_adjustment,
+    pprint_thing,
+)
+
+if TYPE_CHECKING:
+    from pandas import (
+        CategoricalIndex,
+        DataFrame,
+        Series,
+    )
+
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+_index_doc_kwargs.update(
+    {"klass": "MultiIndex", "target_klass": "MultiIndex or list of tuples"}
+)
+
+
+class MultiIndexUIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.UInt64Engine):
+    """
+    This class manages a MultiIndex by mapping label combinations to positive
+    integers.
+    """
+
+    _base = libindex.UInt64Engine
+
+    def _codes_to_ints(self, codes):
+        """
+        Transform combination(s) of uint64 in one uint64 (each), in a strictly
+        monotonic way (i.e. respecting the lexicographic order of integer
+        combinations): see BaseMultiIndexCodesEngine documentation.
+
+        Parameters
+        ----------
+        codes : 1- or 2-dimensional array of dtype uint64
+            Combinations of integers (one per row)
+
+        Returns
+        -------
+        scalar or 1-dimensional array, of dtype uint64
+            Integer(s) representing one combination (each).
+        """
+        # Shift the representation of each level by the pre-calculated number
+        # of bits:
+        codes <<= self.offsets
+
+        # Now sum and OR are in fact interchangeable. This is a simple
+        # composition of the (disjunct) significant bits of each level (i.e.
+        # each column in "codes") in a single positive integer:
+        if codes.ndim == 1:
+            # Single key
+            return np.bitwise_or.reduce(codes)
+
+        # Multiple keys
+        return np.bitwise_or.reduce(codes, axis=1)
+
+
+class MultiIndexPyIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.ObjectEngine):
+    """
+    This class manages those (extreme) cases in which the number of possible
+    label combinations overflows the 64 bits integers, and uses an ObjectEngine
+    containing Python integers.
+    """
+
+    _base = libindex.ObjectEngine
+
+    def _codes_to_ints(self, codes):
+        """
+        Transform combination(s) of uint64 in one Python integer (each), in a
+        strictly monotonic way (i.e. respecting the lexicographic order of
+        integer combinations): see BaseMultiIndexCodesEngine documentation.
+
+        Parameters
+        ----------
+        codes : 1- or 2-dimensional array of dtype uint64
+            Combinations of integers (one per row)
+
+        Returns
+        -------
+        int, or 1-dimensional array of dtype object
+            Integer(s) representing one combination (each).
+        """
+        # Shift the representation of each level by the pre-calculated number
+        # of bits. Since this can overflow uint64, first make sure we are
+        # working with Python integers:
+        codes = codes.astype("object") << self.offsets
+
+        # Now sum and OR are in fact interchangeable. This is a simple
+        # composition of the (disjunct) significant bits of each level (i.e.
+        # each column in "codes") in a single positive integer (per row):
+        if codes.ndim == 1:
+            # Single key
+            return np.bitwise_or.reduce(codes)
+
+        # Multiple keys
+        return np.bitwise_or.reduce(codes, axis=1)
+
+
+def names_compat(meth: F) -> F:
+    """
+    A decorator to allow either `name` or `names` keyword but not both.
+
+    This makes it easier to share code with base class.
+    """
+
+    @wraps(meth)
+    def new_meth(self_or_cls, *args, **kwargs):
+        if "name" in kwargs and "names" in kwargs:
+            raise TypeError("Can only provide one of `names` and `name`")
+        if "name" in kwargs:
+            kwargs["names"] = kwargs.pop("name")
+
+        return meth(self_or_cls, *args, **kwargs)
+
+    return cast(F, new_meth)
+
+
+class MultiIndex(Index):
+    """
+    A multi-level, or hierarchical, index object for pandas objects.
+
+    Parameters
+    ----------
+    levels : sequence of arrays
+        The unique labels for each level.
+    codes : sequence of arrays
+        Integers for each level designating which label at each location.
+    sortorder : optional int
+        Level of sortedness (must be lexicographically sorted by that
+        level).
+    names : optional sequence of objects
+        Names for each of the index levels. (name is accepted for compat).
+    copy : bool, default False
+        Copy the meta-data.
+    verify_integrity : bool, default True
+        Check that the levels/codes are consistent and valid.
+
+    Attributes
+    ----------
+    names
+    levels
+    codes
+    nlevels
+    levshape
+    dtypes
+
+    Methods
+    -------
+    from_arrays
+    from_tuples
+    from_product
+    from_frame
+    set_levels
+    set_codes
+    to_frame
+    to_flat_index
+    sortlevel
+    droplevel
+    swaplevel
+    reorder_levels
+    remove_unused_levels
+    get_level_values
+    get_indexer
+    get_loc
+    get_locs
+    get_loc_level
+    drop
+
+    See Also
+    --------
+    MultiIndex.from_arrays  : Convert list of arrays to MultiIndex.
+    MultiIndex.from_product : Create a MultiIndex from the cartesian product
+                              of iterables.
+    MultiIndex.from_tuples  : Convert list of tuples to a MultiIndex.
+    MultiIndex.from_frame   : Make a MultiIndex from a DataFrame.
+    Index : The base pandas Index type.
+
+    Notes
+    -----
+    See the `user guide
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html>`__
+    for more.
+
+    Examples
+    --------
+    A new ``MultiIndex`` is typically constructed using one of the helper
+    methods :meth:`MultiIndex.from_arrays`, :meth:`MultiIndex.from_product`
+    and :meth:`MultiIndex.from_tuples`. For example (using ``.from_arrays``):
+
+    >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
+    >>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
+    MultiIndex([(1,  'red'),
+                (1, 'blue'),
+                (2,  'red'),
+                (2, 'blue')],
+               names=['number', 'color'])
+
+    See further examples for how to construct a MultiIndex in the doc strings
+    of the mentioned helper methods.
+    """
+
+    _hidden_attrs = Index._hidden_attrs | frozenset()
+
+    # initialize to zero-length tuples to make everything work
+    _typ = "multiindex"
+    _names: list[Hashable | None] = []
+    _levels = FrozenList()
+    _codes = FrozenList()
+    _comparables = ["names"]
+
+    sortorder: int | None
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        levels=None,
+        codes=None,
+        sortorder=None,
+        names=None,
+        dtype=None,
+        copy: bool = False,
+        name=None,
+        verify_integrity: bool = True,
+    ) -> Self:
+        # compat with Index
+        if name is not None:
+            names = name
+        if levels is None or codes is None:
+            raise TypeError("Must pass both levels and codes")
+        if len(levels) != len(codes):
+            raise ValueError("Length of levels and codes must be the same.")
+        if len(levels) == 0:
+            raise ValueError("Must pass non-zero number of levels/codes")
+
+        result = object.__new__(cls)
+        result._cache = {}
+
+        # we've already validated levels and codes, so shortcut here
+        result._set_levels(levels, copy=copy, validate=False)
+        result._set_codes(codes, copy=copy, validate=False)
+
+        result._names = [None] * len(levels)
+        if names is not None:
+            # handles name validation
+            result._set_names(names)
+
+        if sortorder is not None:
+            result.sortorder = int(sortorder)
+        else:
+            result.sortorder = sortorder
+
+        if verify_integrity:
+            new_codes = result._verify_integrity()
+            result._codes = new_codes
+
+        result._reset_identity()
+        result._references = None
+
+        return result
+
+    def _validate_codes(self, level: list, code: list):
+        """
+        Reassign code values as -1 if their corresponding levels are NaN.
+
+        Parameters
+        ----------
+        code : list
+            Code to reassign.
+        level : list
+            Level to check for missing values (NaN, NaT, None).
+
+        Returns
+        -------
+        new code where code value = -1 if it corresponds
+        to a level with missing values (NaN, NaT, None).
+        """
+        null_mask = isna(level)
+        if np.any(null_mask):
+            # error: Incompatible types in assignment
+            # (expression has type "ndarray[Any, dtype[Any]]",
+            # variable has type "List[Any]")
+            code = np.where(null_mask[code], -1, code)  # type: ignore[assignment]
+        return code
+
+    def _verify_integrity(
+        self,
+        codes: list | None = None,
+        levels: list | None = None,
+        levels_to_verify: list[int] | range | None = None,
+    ):
+        """
+        Parameters
+        ----------
+        codes : optional list
+            Codes to check for validity. Defaults to current codes.
+        levels : optional list
+            Levels to check for validity. Defaults to current levels.
+        levels_to_validate: optional list
+            Specifies the levels to verify.
+
+        Raises
+        ------
+        ValueError
+            If length of levels and codes don't match, if the codes for any
+            level would exceed level bounds, or there are any duplicate levels.
+
+        Returns
+        -------
+        new codes where code value = -1 if it corresponds to a
+        NaN level.
+        """
+        # NOTE: Currently does not check, among other things, that cached
+        # nlevels matches nor that sortorder matches actually sortorder.
+        codes = codes or self.codes
+        levels = levels or self.levels
+        if levels_to_verify is None:
+            levels_to_verify = range(len(levels))
+
+        if len(levels) != len(codes):
+            raise ValueError(
+                "Length of levels and codes must match. NOTE: "
+                "this index is in an inconsistent state."
+            )
+        codes_length = len(codes[0])
+        for i in levels_to_verify:
+            level = levels[i]
+            level_codes = codes[i]
+
+            if len(level_codes) != codes_length:
+                raise ValueError(
+                    f"Unequal code lengths: {[len(code_) for code_ in codes]}"
+                )
+            if len(level_codes) and level_codes.max() >= len(level):
+                raise ValueError(
+                    f"On level {i}, code max ({level_codes.max()}) >= length of "
+                    f"level ({len(level)}). NOTE: this index is in an "
+                    "inconsistent state"
+                )
+            if len(level_codes) and level_codes.min() < -1:
+                raise ValueError(f"On level {i}, code value ({level_codes.min()}) < -1")
+            if not level.is_unique:
+                raise ValueError(
+                    f"Level values must be unique: {list(level)} on level {i}"
+                )
+        if self.sortorder is not None:
+            if self.sortorder > _lexsort_depth(self.codes, self.nlevels):
+                raise ValueError(
+                    "Value for sortorder must be inferior or equal to actual "
+                    f"lexsort_depth: sortorder {self.sortorder} "
+                    f"with lexsort_depth {_lexsort_depth(self.codes, self.nlevels)}"
+                )
+
+        result_codes = []
+        for i in range(len(levels)):
+            if i in levels_to_verify:
+                result_codes.append(self._validate_codes(levels[i], codes[i]))
+            else:
+                result_codes.append(codes[i])
+
+        new_codes = FrozenList(result_codes)
+        return new_codes
+
+    @classmethod
+    def from_arrays(
+        cls,
+        arrays,
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | lib.NoDefault = lib.no_default,
+    ) -> MultiIndex:
+        """
+        Convert arrays to MultiIndex.
+
+        Parameters
+        ----------
+        arrays : list / sequence of array-likes
+            Each array-like gives one level's value for each data point.
+            len(arrays) is the number of levels.
+        sortorder : int or None
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
+        >>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue')],
+                   names=['number', 'color'])
+        """
+        error_msg = "Input must be a list / sequence of array-likes."
+        if not is_list_like(arrays):
+            raise TypeError(error_msg)
+        if is_iterator(arrays):
+            arrays = list(arrays)
+
+        # Check if elements of array are list-like
+        for array in arrays:
+            if not is_list_like(array):
+                raise TypeError(error_msg)
+
+        # Check if lengths of all arrays are equal or not,
+        # raise ValueError, if not
+        for i in range(1, len(arrays)):
+            if len(arrays[i]) != len(arrays[i - 1]):
+                raise ValueError("all arrays must be same length")
+
+        codes, levels = factorize_from_iterables(arrays)
+        if names is lib.no_default:
+            names = [getattr(arr, "name", None) for arr in arrays]
+
+        return cls(
+            levels=levels,
+            codes=codes,
+            sortorder=sortorder,
+            names=names,
+            verify_integrity=False,
+        )
+
+    @classmethod
+    @names_compat
+    def from_tuples(
+        cls,
+        tuples: Iterable[tuple[Hashable, ...]],
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | None = None,
+    ) -> MultiIndex:
+        """
+        Convert list of tuples to MultiIndex.
+
+        Parameters
+        ----------
+        tuples : list / sequence of tuple-likes
+            Each tuple is the index of one row/column.
+        sortorder : int or None
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> tuples = [(1, 'red'), (1, 'blue'),
+        ...           (2, 'red'), (2, 'blue')]
+        >>> pd.MultiIndex.from_tuples(tuples, names=('number', 'color'))
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue')],
+                   names=['number', 'color'])
+        """
+        if not is_list_like(tuples):
+            raise TypeError("Input must be a list / sequence of tuple-likes.")
+        if is_iterator(tuples):
+            tuples = list(tuples)
+        tuples = cast(Collection[tuple[Hashable, ...]], tuples)
+
+        # handling the empty tuple cases
+        if len(tuples) and all(isinstance(e, tuple) and not e for e in tuples):
+            codes = [np.zeros(len(tuples))]
+            levels = [Index(com.asarray_tuplesafe(tuples, dtype=np.dtype("object")))]
+            return cls(
+                levels=levels,
+                codes=codes,
+                sortorder=sortorder,
+                names=names,
+                verify_integrity=False,
+            )
+
+        arrays: list[Sequence[Hashable]]
+        if len(tuples) == 0:
+            if names is None:
+                raise TypeError("Cannot infer number of levels from empty list")
+            # error: Argument 1 to "len" has incompatible type "Hashable";
+            # expected "Sized"
+            arrays = [[]] * len(names)  # type: ignore[arg-type]
+        elif isinstance(tuples, (np.ndarray, Index)):
+            if isinstance(tuples, Index):
+                tuples = np.asarray(tuples._values)
+
+            arrays = list(lib.tuples_to_object_array(tuples).T)
+        elif isinstance(tuples, list):
+            arrays = list(lib.to_object_array_tuples(tuples).T)
+        else:
+            arrs = zip(*tuples)
+            arrays = cast(list[Sequence[Hashable]], arrs)
+
+        return cls.from_arrays(arrays, sortorder=sortorder, names=names)
+
+    @classmethod
+    def from_product(
+        cls,
+        iterables: Sequence[Iterable[Hashable]],
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | lib.NoDefault = lib.no_default,
+    ) -> MultiIndex:
+        """
+        Make a MultiIndex from the cartesian product of multiple iterables.
+
+        Parameters
+        ----------
+        iterables : list / sequence of iterables
+            Each iterable has unique labels for each level of the index.
+        sortorder : int or None
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+            If not explicitly provided, names will be inferred from the
+            elements of iterables if an element has a name attribute.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> numbers = [0, 1, 2]
+        >>> colors = ['green', 'purple']
+        >>> pd.MultiIndex.from_product([numbers, colors],
+        ...                            names=['number', 'color'])
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        """
+        from pandas.core.reshape.util import cartesian_product
+
+        if not is_list_like(iterables):
+            raise TypeError("Input must be a list / sequence of iterables.")
+        if is_iterator(iterables):
+            iterables = list(iterables)
+
+        codes, levels = factorize_from_iterables(iterables)
+        if names is lib.no_default:
+            names = [getattr(it, "name", None) for it in iterables]
+
+        # codes are all ndarrays, so cartesian_product is lossless
+        codes = cartesian_product(codes)
+        return cls(levels, codes, sortorder=sortorder, names=names)
+
+    @classmethod
+    def from_frame(
+        cls,
+        df: DataFrame,
+        sortorder: int | None = None,
+        names: Sequence[Hashable] | Hashable | None = None,
+    ) -> MultiIndex:
+        """
+        Make a MultiIndex from a DataFrame.
+
+        Parameters
+        ----------
+        df : DataFrame
+            DataFrame to be converted to MultiIndex.
+        sortorder : int, optional
+            Level of sortedness (must be lexicographically sorted by that
+            level).
+        names : list-like, optional
+            If no names are provided, use the column names, or tuple of column
+            names if the columns is a MultiIndex. If a sequence, overwrite
+            names with the given sequence.
+
+        Returns
+        -------
+        MultiIndex
+            The MultiIndex representation of the given DataFrame.
+
+        See Also
+        --------
+        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([['HI', 'Temp'], ['HI', 'Precip'],
+        ...                    ['NJ', 'Temp'], ['NJ', 'Precip']],
+        ...                   columns=['a', 'b'])
+        >>> df
+              a       b
+        0    HI    Temp
+        1    HI  Precip
+        2    NJ    Temp
+        3    NJ  Precip
+
+        >>> pd.MultiIndex.from_frame(df)
+        MultiIndex([('HI',   'Temp'),
+                    ('HI', 'Precip'),
+                    ('NJ',   'Temp'),
+                    ('NJ', 'Precip')],
+                   names=['a', 'b'])
+
+        Using explicit names, instead of the column names
+
+        >>> pd.MultiIndex.from_frame(df, names=['state', 'observation'])
+        MultiIndex([('HI',   'Temp'),
+                    ('HI', 'Precip'),
+                    ('NJ',   'Temp'),
+                    ('NJ', 'Precip')],
+                   names=['state', 'observation'])
+        """
+        if not isinstance(df, ABCDataFrame):
+            raise TypeError("Input must be a DataFrame")
+
+        column_names, columns = zip(*df.items())
+        names = column_names if names is None else names
+        return cls.from_arrays(columns, sortorder=sortorder, names=names)
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def _values(self) -> np.ndarray:
+        # We override here, since our parent uses _data, which we don't use.
+        values = []
+
+        for i in range(self.nlevels):
+            index = self.levels[i]
+            codes = self.codes[i]
+
+            vals = index
+            if isinstance(vals.dtype, CategoricalDtype):
+                vals = cast("CategoricalIndex", vals)
+                vals = vals._data._internal_get_values()
+
+            if isinstance(vals.dtype, ExtensionDtype) or lib.is_np_dtype(
+                vals.dtype, "mM"
+            ):
+                vals = vals.astype(object)
+
+            vals = np.asarray(vals)
+            vals = algos.take_nd(vals, codes, fill_value=index._na_value)
+            values.append(vals)
+
+        arr = lib.fast_zip(values)
+        return arr
+
+    @property
+    def values(self) -> np.ndarray:
+        return self._values
+
+    @property
+    def array(self):
+        """
+        Raises a ValueError for `MultiIndex` because there's no single
+        array backing a MultiIndex.
+
+        Raises
+        ------
+        ValueError
+        """
+        raise ValueError(
+            "MultiIndex has no single backing array. Use "
+            "'MultiIndex.to_numpy()' to get a NumPy array of tuples."
+        )
+
+    @cache_readonly
+    def dtypes(self) -> Series:
+        """
+        Return the dtypes as a Series for the underlying MultiIndex.
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_product([(0, 1, 2), ('green', 'purple')],
+        ...                                  names=['number', 'color'])
+        >>> idx
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        >>> idx.dtypes
+        number     int64
+        color     object
+        dtype: object
+        """
+        from pandas import Series
+
+        names = com.fill_missing_names([level.name for level in self.levels])
+        return Series([level.dtype for level in self.levels], index=Index(names))
+
+    def __len__(self) -> int:
+        return len(self.codes[0])
+
+    @property
+    def size(self) -> int:
+        """
+        Return the number of elements in the underlying data.
+        """
+        # override Index.size to avoid materializing _values
+        return len(self)
+
+    # --------------------------------------------------------------------
+    # Levels Methods
+
+    @cache_readonly
+    def levels(self) -> FrozenList:
+        """
+        Levels of the MultiIndex.
+
+        Levels refer to the different hierarchical levels or layers in a MultiIndex.
+        In a MultiIndex, each level represents a distinct dimension or category of
+        the index.
+
+        To access the levels, you can use the levels attribute of the MultiIndex,
+        which returns a tuple of Index objects. Each Index object represents a
+        level in the MultiIndex and contains the unique values found in that
+        specific level.
+
+        If a MultiIndex is created with levels A, B, C, and the DataFrame using
+        it filters out all rows of the level C, MultiIndex.levels will still
+        return A, B, C.
+
+        Examples
+        --------
+        >>> index = pd.MultiIndex.from_product([['mammal'],
+        ...                                     ('goat', 'human', 'cat', 'dog')],
+        ...                                    names=['Category', 'Animals'])
+        >>> leg_num = pd.DataFrame(data=(4, 2, 4, 4), index=index, columns=['Legs'])
+        >>> leg_num
+                          Legs
+        Category Animals
+        mammal   goat        4
+                 human       2
+                 cat         4
+                 dog         4
+
+        >>> leg_num.index.levels
+        FrozenList([['mammal'], ['cat', 'dog', 'goat', 'human']])
+
+        MultiIndex levels will not change even if the DataFrame using the MultiIndex
+        does not contain all them anymore.
+        See how "human" is not in the DataFrame, but it is still in levels:
+
+        >>> large_leg_num = leg_num[leg_num.Legs > 2]
+        >>> large_leg_num
+                          Legs
+        Category Animals
+        mammal   goat        4
+                 cat         4
+                 dog         4
+
+        >>> large_leg_num.index.levels
+        FrozenList([['mammal'], ['cat', 'dog', 'goat', 'human']])
+        """
+        # Use cache_readonly to ensure that self.get_locs doesn't repeatedly
+        # create new IndexEngine
+        # https://github.com/pandas-dev/pandas/issues/31648
+        result = [x._rename(name=name) for x, name in zip(self._levels, self._names)]
+        for level in result:
+            # disallow midx.levels[0].name = "foo"
+            level._no_setting_name = True
+        return FrozenList(result)
+
+    def _set_levels(
+        self,
+        levels,
+        *,
+        level=None,
+        copy: bool = False,
+        validate: bool = True,
+        verify_integrity: bool = False,
+    ) -> None:
+        # This is NOT part of the levels property because it should be
+        # externally not allowed to set levels. User beware if you change
+        # _levels directly
+        if validate:
+            if len(levels) == 0:
+                raise ValueError("Must set non-zero number of levels.")
+            if level is None and len(levels) != self.nlevels:
+                raise ValueError("Length of levels must match number of levels.")
+            if level is not None and len(levels) != len(level):
+                raise ValueError("Length of levels must match length of level.")
+
+        if level is None:
+            new_levels = FrozenList(
+                ensure_index(lev, copy=copy)._view() for lev in levels
+            )
+            level_numbers = list(range(len(new_levels)))
+        else:
+            level_numbers = [self._get_level_number(lev) for lev in level]
+            new_levels_list = list(self._levels)
+            for lev_num, lev in zip(level_numbers, levels):
+                new_levels_list[lev_num] = ensure_index(lev, copy=copy)._view()
+            new_levels = FrozenList(new_levels_list)
+
+        if verify_integrity:
+            new_codes = self._verify_integrity(
+                levels=new_levels, levels_to_verify=level_numbers
+            )
+            self._codes = new_codes
+
+        names = self.names
+        self._levels = new_levels
+        if any(names):
+            self._set_names(names)
+
+        self._reset_cache()
+
+    def set_levels(
+        self, levels, *, level=None, verify_integrity: bool = True
+    ) -> MultiIndex:
+        """
+        Set new levels on MultiIndex. Defaults to returning new index.
+
+        Parameters
+        ----------
+        levels : sequence or list of sequence
+            New level(s) to apply.
+        level : int, level name, or sequence of int/level names (default None)
+            Level(s) to set (None for all levels).
+        verify_integrity : bool, default True
+            If True, checks that levels and codes are compatible.
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_tuples(
+        ...     [
+        ...         (1, "one"),
+        ...         (1, "two"),
+        ...         (2, "one"),
+        ...         (2, "two"),
+        ...         (3, "one"),
+        ...         (3, "two")
+        ...     ],
+        ...     names=["foo", "bar"]
+        ... )
+        >>> idx
+        MultiIndex([(1, 'one'),
+            (1, 'two'),
+            (2, 'one'),
+            (2, 'two'),
+            (3, 'one'),
+            (3, 'two')],
+           names=['foo', 'bar'])
+
+        >>> idx.set_levels([['a', 'b', 'c'], [1, 2]])
+        MultiIndex([('a', 1),
+                    ('a', 2),
+                    ('b', 1),
+                    ('b', 2),
+                    ('c', 1),
+                    ('c', 2)],
+                   names=['foo', 'bar'])
+        >>> idx.set_levels(['a', 'b', 'c'], level=0)
+        MultiIndex([('a', 'one'),
+                    ('a', 'two'),
+                    ('b', 'one'),
+                    ('b', 'two'),
+                    ('c', 'one'),
+                    ('c', 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_levels(['a', 'b'], level='bar')
+        MultiIndex([(1, 'a'),
+                    (1, 'b'),
+                    (2, 'a'),
+                    (2, 'b'),
+                    (3, 'a'),
+                    (3, 'b')],
+                   names=['foo', 'bar'])
+
+        If any of the levels passed to ``set_levels()`` exceeds the
+        existing length, all of the values from that argument will
+        be stored in the MultiIndex levels, though the values will
+        be truncated in the MultiIndex output.
+
+        >>> idx.set_levels([['a', 'b', 'c'], [1, 2, 3, 4]], level=[0, 1])
+        MultiIndex([('a', 1),
+            ('a', 2),
+            ('b', 1),
+            ('b', 2),
+            ('c', 1),
+            ('c', 2)],
+           names=['foo', 'bar'])
+        >>> idx.set_levels([['a', 'b', 'c'], [1, 2, 3, 4]], level=[0, 1]).levels
+        FrozenList([['a', 'b', 'c'], [1, 2, 3, 4]])
+        """
+
+        if isinstance(levels, Index):
+            pass
+        elif is_array_like(levels):
+            levels = Index(levels)
+        elif is_list_like(levels):
+            levels = list(levels)
+
+        level, levels = _require_listlike(level, levels, "Levels")
+        idx = self._view()
+        idx._reset_identity()
+        idx._set_levels(
+            levels, level=level, validate=True, verify_integrity=verify_integrity
+        )
+        return idx
+
+    @property
+    def nlevels(self) -> int:
+        """
+        Integer number of levels in this MultiIndex.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
+        >>> mi
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        >>> mi.nlevels
+        3
+        """
+        return len(self._levels)
+
+    @property
+    def levshape(self) -> Shape:
+        """
+        A tuple with the length of each level.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
+        >>> mi
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        >>> mi.levshape
+        (1, 1, 1)
+        """
+        return tuple(len(x) for x in self.levels)
+
+    # --------------------------------------------------------------------
+    # Codes Methods
+
+    @property
+    def codes(self) -> FrozenList:
+        return self._codes
+
+    def _set_codes(
+        self,
+        codes,
+        *,
+        level=None,
+        copy: bool = False,
+        validate: bool = True,
+        verify_integrity: bool = False,
+    ) -> None:
+        if validate:
+            if level is None and len(codes) != self.nlevels:
+                raise ValueError("Length of codes must match number of levels")
+            if level is not None and len(codes) != len(level):
+                raise ValueError("Length of codes must match length of levels.")
+
+        level_numbers: list[int] | range
+        if level is None:
+            new_codes = FrozenList(
+                _coerce_indexer_frozen(level_codes, lev, copy=copy).view()
+                for lev, level_codes in zip(self._levels, codes)
+            )
+            level_numbers = range(len(new_codes))
+        else:
+            level_numbers = [self._get_level_number(lev) for lev in level]
+            new_codes_list = list(self._codes)
+            for lev_num, level_codes in zip(level_numbers, codes):
+                lev = self.levels[lev_num]
+                new_codes_list[lev_num] = _coerce_indexer_frozen(
+                    level_codes, lev, copy=copy
+                )
+            new_codes = FrozenList(new_codes_list)
+
+        if verify_integrity:
+            new_codes = self._verify_integrity(
+                codes=new_codes, levels_to_verify=level_numbers
+            )
+
+        self._codes = new_codes
+
+        self._reset_cache()
+
+    def set_codes(
+        self, codes, *, level=None, verify_integrity: bool = True
+    ) -> MultiIndex:
+        """
+        Set new codes on MultiIndex. Defaults to returning new index.
+
+        Parameters
+        ----------
+        codes : sequence or list of sequence
+            New codes to apply.
+        level : int, level name, or sequence of int/level names (default None)
+            Level(s) to set (None for all levels).
+        verify_integrity : bool, default True
+            If True, checks that levels and codes are compatible.
+
+        Returns
+        -------
+        new index (of same type and class...etc) or None
+            The same type as the caller or None if ``inplace=True``.
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_tuples(
+        ...     [(1, "one"), (1, "two"), (2, "one"), (2, "two")], names=["foo", "bar"]
+        ... )
+        >>> idx
+        MultiIndex([(1, 'one'),
+            (1, 'two'),
+            (2, 'one'),
+            (2, 'two')],
+           names=['foo', 'bar'])
+
+        >>> idx.set_codes([[1, 0, 1, 0], [0, 0, 1, 1]])
+        MultiIndex([(2, 'one'),
+                    (1, 'one'),
+                    (2, 'two'),
+                    (1, 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_codes([1, 0, 1, 0], level=0)
+        MultiIndex([(2, 'one'),
+                    (1, 'two'),
+                    (2, 'one'),
+                    (1, 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_codes([0, 0, 1, 1], level='bar')
+        MultiIndex([(1, 'one'),
+                    (1, 'one'),
+                    (2, 'two'),
+                    (2, 'two')],
+                   names=['foo', 'bar'])
+        >>> idx.set_codes([[1, 0, 1, 0], [0, 0, 1, 1]], level=[0, 1])
+        MultiIndex([(2, 'one'),
+                    (1, 'one'),
+                    (2, 'two'),
+                    (1, 'two')],
+                   names=['foo', 'bar'])
+        """
+
+        level, codes = _require_listlike(level, codes, "Codes")
+        idx = self._view()
+        idx._reset_identity()
+        idx._set_codes(codes, level=level, verify_integrity=verify_integrity)
+        return idx
+
+    # --------------------------------------------------------------------
+    # Index Internals
+
+    @cache_readonly
+    def _engine(self):
+        # Calculate the number of bits needed to represent labels in each
+        # level, as log2 of their sizes:
+        # NaN values are shifted to 1 and missing values in other while
+        # calculating the indexer are shifted to 0
+        sizes = np.ceil(
+            np.log2(
+                [len(level) + libindex.multiindex_nulls_shift for level in self.levels]
+            )
+        )
+
+        # Sum bit counts, starting from the _right_....
+        lev_bits = np.cumsum(sizes[::-1])[::-1]
+
+        # ... in order to obtain offsets such that sorting the combination of
+        # shifted codes (one for each level, resulting in a unique integer) is
+        # equivalent to sorting lexicographically the codes themselves. Notice
+        # that each level needs to be shifted by the number of bits needed to
+        # represent the _previous_ ones:
+        offsets = np.concatenate([lev_bits[1:], [0]]).astype("uint64")
+
+        # Check the total number of bits needed for our representation:
+        if lev_bits[0] > 64:
+            # The levels would overflow a 64 bit uint - use Python integers:
+            return MultiIndexPyIntEngine(self.levels, self.codes, offsets)
+        return MultiIndexUIntEngine(self.levels, self.codes, offsets)
+
+    # Return type "Callable[..., MultiIndex]" of "_constructor" incompatible with return
+    # type "Type[MultiIndex]" in supertype "Index"
+    @property
+    def _constructor(self) -> Callable[..., MultiIndex]:  # type: ignore[override]
+        return type(self).from_tuples
+
+    @doc(Index._shallow_copy)
+    def _shallow_copy(self, values: np.ndarray, name=lib.no_default) -> MultiIndex:
+        names = name if name is not lib.no_default else self.names
+
+        return type(self).from_tuples(values, sortorder=None, names=names)
+
+    def _view(self) -> MultiIndex:
+        result = type(self)(
+            levels=self.levels,
+            codes=self.codes,
+            sortorder=self.sortorder,
+            names=self.names,
+            verify_integrity=False,
+        )
+        result._cache = self._cache.copy()
+        result._cache.pop("levels", None)  # GH32669
+        return result
+
+    # --------------------------------------------------------------------
+
+    # error: Signature of "copy" incompatible with supertype "Index"
+    def copy(  # type: ignore[override]
+        self,
+        names=None,
+        deep: bool = False,
+        name=None,
+    ) -> Self:
+        """
+        Make a copy of this object.
+
+        Names, dtype, levels and codes can be passed and will be set on new copy.
+
+        Parameters
+        ----------
+        names : sequence, optional
+        deep : bool, default False
+        name : Label
+            Kept for compatibility with 1-dimensional Index. Should not be used.
+
+        Returns
+        -------
+        MultiIndex
+
+        Notes
+        -----
+        In most cases, there should be no functional difference from using
+        ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
+        This could be potentially expensive on large MultiIndex objects.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
+        >>> mi
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        >>> mi.copy()
+        MultiIndex([('a', 'b', 'c')],
+                   )
+        """
+        names = self._validate_names(name=name, names=names, deep=deep)
+        keep_id = not deep
+        levels, codes = None, None
+
+        if deep:
+            from copy import deepcopy
+
+            levels = deepcopy(self.levels)
+            codes = deepcopy(self.codes)
+
+        levels = levels if levels is not None else self.levels
+        codes = codes if codes is not None else self.codes
+
+        new_index = type(self)(
+            levels=levels,
+            codes=codes,
+            sortorder=self.sortorder,
+            names=names,
+            verify_integrity=False,
+        )
+        new_index._cache = self._cache.copy()
+        new_index._cache.pop("levels", None)  # GH32669
+        if keep_id:
+            new_index._id = self._id
+        return new_index
+
+    def __array__(self, dtype=None, copy=None) -> np.ndarray:
+        """the array interface, return my values"""
+        if copy is False:
+            # self.values is always a newly construct array, so raise.
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if copy is True:
+            # explicit np.array call to ensure a copy is made and unique objects
+            # are returned, because self.values is cached
+            return np.array(self.values, dtype=dtype)
+        return self.values
+
+    def view(self, cls=None) -> Self:
+        """this is defined as a copy with the same identity"""
+        result = self.copy()
+        result._id = self._id
+        return result
+
+    @doc(Index.__contains__)
+    def __contains__(self, key: Any) -> bool:
+        hash(key)
+        try:
+            self.get_loc(key)
+            return True
+        except (LookupError, TypeError, ValueError):
+            return False
+
+    @cache_readonly
+    def dtype(self) -> np.dtype:
+        return np.dtype("O")
+
+    def _is_memory_usage_qualified(self) -> bool:
+        """return a boolean if we need a qualified .info display"""
+
+        def f(dtype) -> bool:
+            return is_object_dtype(dtype) or (
+                is_string_dtype(dtype) and dtype.storage == "python"
+            )
+
+        return any(f(level.dtype) for level in self.levels)
+
+    # Cannot determine type of "memory_usage"
+    @doc(Index.memory_usage)  # type: ignore[has-type]
+    def memory_usage(self, deep: bool = False) -> int:
+        # we are overwriting our base class to avoid
+        # computing .values here which could materialize
+        # a tuple representation unnecessarily
+        return self._nbytes(deep)
+
+    @cache_readonly
+    def nbytes(self) -> int:
+        """return the number of bytes in the underlying data"""
+        return self._nbytes(False)
+
+    def _nbytes(self, deep: bool = False) -> int:
+        """
+        return the number of bytes in the underlying data
+        deeply introspect the level data if deep=True
+
+        include the engine hashtable
+
+        *this is in internal routine*
+
+        """
+        # for implementations with no useful getsizeof (PyPy)
+        objsize = 24
+
+        level_nbytes = sum(i.memory_usage(deep=deep) for i in self.levels)
+        label_nbytes = sum(i.nbytes for i in self.codes)
+        names_nbytes = sum(getsizeof(i, objsize) for i in self.names)
+        result = level_nbytes + label_nbytes + names_nbytes
+
+        # include our engine hashtable
+        result += self._engine.sizeof(deep=deep)
+        return result
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    def _formatter_func(self, tup):
+        """
+        Formats each item in tup according to its level's formatter function.
+        """
+        formatter_funcs = [level._formatter_func for level in self.levels]
+        return tuple(func(val) for func, val in zip(formatter_funcs, tup))
+
+    def _get_values_for_csv(
+        self, *, na_rep: str = "nan", **kwargs
+    ) -> npt.NDArray[np.object_]:
+        new_levels = []
+        new_codes = []
+
+        # go through the levels and format them
+        for level, level_codes in zip(self.levels, self.codes):
+            level_strs = level._get_values_for_csv(na_rep=na_rep, **kwargs)
+            # add nan values, if there are any
+            mask = level_codes == -1
+            if mask.any():
+                nan_index = len(level_strs)
+                # numpy 1.21 deprecated implicit string casting
+                level_strs = level_strs.astype(str)
+                level_strs = np.append(level_strs, na_rep)
+                assert not level_codes.flags.writeable  # i.e. copy is needed
+                level_codes = level_codes.copy()  # make writeable
+                level_codes[mask] = nan_index
+            new_levels.append(level_strs)
+            new_codes.append(level_codes)
+
+        if len(new_levels) == 1:
+            # a single-level multi-index
+            return Index(new_levels[0].take(new_codes[0]))._get_values_for_csv()
+        else:
+            # reconstruct the multi-index
+            mi = MultiIndex(
+                levels=new_levels,
+                codes=new_codes,
+                names=self.names,
+                sortorder=self.sortorder,
+                verify_integrity=False,
+            )
+            return mi._values
+
+    def format(
+        self,
+        name: bool | None = None,
+        formatter: Callable | None = None,
+        na_rep: str | None = None,
+        names: bool = False,
+        space: int = 2,
+        sparsify=None,
+        adjoin: bool = True,
+    ) -> list:
+        warnings.warn(
+            # GH#55413
+            f"{type(self).__name__}.format is deprecated and will be removed "
+            "in a future version. Convert using index.astype(str) or "
+            "index.map(formatter) instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        if name is not None:
+            names = name
+
+        if len(self) == 0:
+            return []
+
+        stringified_levels = []
+        for lev, level_codes in zip(self.levels, self.codes):
+            na = na_rep if na_rep is not None else _get_na_rep(lev.dtype)
+
+            if len(lev) > 0:
+                formatted = lev.take(level_codes).format(formatter=formatter)
+
+                # we have some NA
+                mask = level_codes == -1
+                if mask.any():
+                    formatted = np.array(formatted, dtype=object)
+                    formatted[mask] = na
+                    formatted = formatted.tolist()
+
+            else:
+                # weird all NA case
+                formatted = [
+                    pprint_thing(na if isna(x) else x, escape_chars=("\t", "\r", "\n"))
+                    for x in algos.take_nd(lev._values, level_codes)
+                ]
+            stringified_levels.append(formatted)
+
+        result_levels = []
+        for lev, lev_name in zip(stringified_levels, self.names):
+            level = []
+
+            if names:
+                level.append(
+                    pprint_thing(lev_name, escape_chars=("\t", "\r", "\n"))
+                    if lev_name is not None
+                    else ""
+                )
+
+            level.extend(np.array(lev, dtype=object))
+            result_levels.append(level)
+
+        if sparsify is None:
+            sparsify = get_option("display.multi_sparse")
+
+        if sparsify:
+            sentinel: Literal[""] | bool | lib.NoDefault = ""
+            # GH3547 use value of sparsify as sentinel if it's "Falsey"
+            assert isinstance(sparsify, bool) or sparsify is lib.no_default
+            if sparsify in [False, lib.no_default]:
+                sentinel = sparsify
+            # little bit of a kludge job for #1217
+            result_levels = sparsify_labels(
+                result_levels, start=int(names), sentinel=sentinel
+            )
+
+        if adjoin:
+            adj = get_adjustment()
+            return adj.adjoin(space, *result_levels).split("\n")
+        else:
+            return result_levels
+
+    def _format_multi(
+        self,
+        *,
+        include_names: bool,
+        sparsify: bool | None | lib.NoDefault,
+        formatter: Callable | None = None,
+    ) -> list:
+        if len(self) == 0:
+            return []
+
+        stringified_levels = []
+        for lev, level_codes in zip(self.levels, self.codes):
+            na = _get_na_rep(lev.dtype)
+
+            if len(lev) > 0:
+                taken = formatted = lev.take(level_codes)
+                formatted = taken._format_flat(include_name=False, formatter=formatter)
+
+                # we have some NA
+                mask = level_codes == -1
+                if mask.any():
+                    formatted = np.array(formatted, dtype=object)
+                    formatted[mask] = na
+                    formatted = formatted.tolist()
+
+            else:
+                # weird all NA case
+                formatted = [
+                    pprint_thing(na if isna(x) else x, escape_chars=("\t", "\r", "\n"))
+                    for x in algos.take_nd(lev._values, level_codes)
+                ]
+            stringified_levels.append(formatted)
+
+        result_levels = []
+        for lev, lev_name in zip(stringified_levels, self.names):
+            level = []
+
+            if include_names:
+                level.append(
+                    pprint_thing(lev_name, escape_chars=("\t", "\r", "\n"))
+                    if lev_name is not None
+                    else ""
+                )
+
+            level.extend(np.array(lev, dtype=object))
+            result_levels.append(level)
+
+        if sparsify is None:
+            sparsify = get_option("display.multi_sparse")
+
+        if sparsify:
+            sentinel: Literal[""] | bool | lib.NoDefault = ""
+            # GH3547 use value of sparsify as sentinel if it's "Falsey"
+            assert isinstance(sparsify, bool) or sparsify is lib.no_default
+            if sparsify is lib.no_default:
+                sentinel = sparsify
+            # little bit of a kludge job for #1217
+            result_levels = sparsify_labels(
+                result_levels, start=int(include_names), sentinel=sentinel
+            )
+
+        return result_levels
+
+    # --------------------------------------------------------------------
+    # Names Methods
+
+    def _get_names(self) -> FrozenList:
+        return FrozenList(self._names)
+
+    def _set_names(self, names, *, level=None, validate: bool = True):
+        """
+        Set new names on index. Each name has to be a hashable type.
+
+        Parameters
+        ----------
+        values : str or sequence
+            name(s) to set
+        level : int, level name, or sequence of int/level names (default None)
+            If the index is a MultiIndex (hierarchical), level(s) to set (None
+            for all levels).  Otherwise level must be None
+        validate : bool, default True
+            validate that the names match level lengths
+
+        Raises
+        ------
+        TypeError if each name is not hashable.
+
+        Notes
+        -----
+        sets names on levels. WARNING: mutates!
+
+        Note that you generally want to set this *after* changing levels, so
+        that it only acts on copies
+        """
+        # GH 15110
+        # Don't allow a single string for names in a MultiIndex
+        if names is not None and not is_list_like(names):
+            raise ValueError("Names should be list-like for a MultiIndex")
+        names = list(names)
+
+        if validate:
+            if level is not None and len(names) != len(level):
+                raise ValueError("Length of names must match length of level.")
+            if level is None and len(names) != self.nlevels:
+                raise ValueError(
+                    "Length of names must match number of levels in MultiIndex."
+                )
+
+        if level is None:
+            level = range(self.nlevels)
+        else:
+            level = [self._get_level_number(lev) for lev in level]
+
+        # set the name
+        for lev, name in zip(level, names):
+            if name is not None:
+                # GH 20527
+                # All items in 'names' need to be hashable:
+                if not is_hashable(name):
+                    raise TypeError(
+                        f"{type(self).__name__}.name must be a hashable type"
+                    )
+            self._names[lev] = name
+
+        # If .levels has been accessed, the names in our cache will be stale.
+        self._reset_cache()
+
+    names = property(
+        fset=_set_names,
+        fget=_get_names,
+        doc="""
+        Names of levels in MultiIndex.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays(
+        ... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z'])
+        >>> mi
+        MultiIndex([(1, 3, 5),
+                    (2, 4, 6)],
+                   names=['x', 'y', 'z'])
+        >>> mi.names
+        FrozenList(['x', 'y', 'z'])
+        """,
+    )
+
+    # --------------------------------------------------------------------
+
+    @cache_readonly
+    def inferred_type(self) -> str:
+        return "mixed"
+
+    def _get_level_number(self, level) -> int:
+        count = self.names.count(level)
+        if (count > 1) and not is_integer(level):
+            raise ValueError(
+                f"The name {level} occurs multiple times, use a level number"
+            )
+        try:
+            level = self.names.index(level)
+        except ValueError as err:
+            if not is_integer(level):
+                raise KeyError(f"Level {level} not found") from err
+            if level < 0:
+                level += self.nlevels
+                if level < 0:
+                    orig_level = level - self.nlevels
+                    raise IndexError(
+                        f"Too many levels: Index has only {self.nlevels} levels, "
+                        f"{orig_level} is not a valid level number"
+                    ) from err
+            # Note: levels are zero-based
+            elif level >= self.nlevels:
+                raise IndexError(
+                    f"Too many levels: Index has only {self.nlevels} levels, "
+                    f"not {level + 1}"
+                ) from err
+        return level
+
+    @cache_readonly
+    def is_monotonic_increasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or increasing.
+        """
+        if any(-1 in code for code in self.codes):
+            return False
+
+        if all(level.is_monotonic_increasing for level in self.levels):
+            # If each level is sorted, we can operate on the codes directly. GH27495
+            return libalgos.is_lexsorted(
+                [x.astype("int64", copy=False) for x in self.codes]
+            )
+
+        # reversed() because lexsort() wants the most significant key last.
+        values = [
+            self._get_level_values(i)._values for i in reversed(range(len(self.levels)))
+        ]
+        try:
+            # error: Argument 1 to "lexsort" has incompatible type
+            # "List[Union[ExtensionArray, ndarray[Any, Any]]]";
+            # expected "Union[_SupportsArray[dtype[Any]],
+            # _NestedSequence[_SupportsArray[dtype[Any]]], bool,
+            # int, float, complex, str, bytes, _NestedSequence[Union
+            # [bool, int, float, complex, str, bytes]]]"
+            sort_order = np.lexsort(values)  # type: ignore[arg-type]
+            return Index(sort_order).is_monotonic_increasing
+        except TypeError:
+            # we have mixed types and np.lexsort is not happy
+            return Index(self._values).is_monotonic_increasing
+
+    @cache_readonly
+    def is_monotonic_decreasing(self) -> bool:
+        """
+        Return a boolean if the values are equal or decreasing.
+        """
+        # monotonic decreasing if and only if reverse is monotonic increasing
+        return self[::-1].is_monotonic_increasing
+
+    @cache_readonly
+    def _inferred_type_levels(self) -> list[str]:
+        """return a list of the inferred types, one for each level"""
+        return [i.inferred_type for i in self.levels]
+
+    @doc(Index.duplicated)
+    def duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
+        shape = tuple(len(lev) for lev in self.levels)
+        ids = get_group_index(self.codes, shape, sort=False, xnull=False)
+
+        return duplicated(ids, keep)
+
+    # error: Cannot override final attribute "_duplicated"
+    # (previously declared in base class "IndexOpsMixin")
+    _duplicated = duplicated  # type: ignore[misc]
+
+    def fillna(self, value=None, downcast=None):
+        """
+        fillna is not implemented for MultiIndex
+        """
+        raise NotImplementedError("isna is not defined for MultiIndex")
+
+    @doc(Index.dropna)
+    def dropna(self, how: AnyAll = "any") -> MultiIndex:
+        nans = [level_codes == -1 for level_codes in self.codes]
+        if how == "any":
+            indexer = np.any(nans, axis=0)
+        elif how == "all":
+            indexer = np.all(nans, axis=0)
+        else:
+            raise ValueError(f"invalid how option: {how}")
+
+        new_codes = [level_codes[~indexer] for level_codes in self.codes]
+        return self.set_codes(codes=new_codes)
+
+    def _get_level_values(self, level: int, unique: bool = False) -> Index:
+        """
+        Return vector of label values for requested level,
+        equal to the length of the index
+
+        **this is an internal method**
+
+        Parameters
+        ----------
+        level : int
+        unique : bool, default False
+            if True, drop duplicated values
+
+        Returns
+        -------
+        Index
+        """
+        lev = self.levels[level]
+        level_codes = self.codes[level]
+        name = self._names[level]
+        if unique:
+            level_codes = algos.unique(level_codes)
+        filled = algos.take_nd(lev._values, level_codes, fill_value=lev._na_value)
+        return lev._shallow_copy(filled, name=name)
+
+    # error: Signature of "get_level_values" incompatible with supertype "Index"
+    def get_level_values(self, level) -> Index:  # type: ignore[override]
+        """
+        Return vector of label values for requested level.
+
+        Length of returned vector is equal to the length of the index.
+
+        Parameters
+        ----------
+        level : int or str
+            ``level`` is either the integer position of the level in the
+            MultiIndex, or the name of the level.
+
+        Returns
+        -------
+        Index
+            Values is a level of this MultiIndex converted to
+            a single :class:`Index` (or subclass thereof).
+
+        Notes
+        -----
+        If the level contains missing values, the result may be casted to
+        ``float`` with missing values specified as ``NaN``. This is because
+        the level is converted to a regular ``Index``.
+
+        Examples
+        --------
+        Create a MultiIndex:
+
+        >>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def')))
+        >>> mi.names = ['level_1', 'level_2']
+
+        Get level values by supplying level as either integer or name:
+
+        >>> mi.get_level_values(0)
+        Index(['a', 'b', 'c'], dtype='object', name='level_1')
+        >>> mi.get_level_values('level_2')
+        Index(['d', 'e', 'f'], dtype='object', name='level_2')
+
+        If a level contains missing values, the return type of the level
+        may be cast to ``float``.
+
+        >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).dtypes
+        level_0    int64
+        level_1    int64
+        dtype: object
+        >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).get_level_values(0)
+        Index([1.0, nan, 2.0], dtype='float64')
+        """
+        level = self._get_level_number(level)
+        values = self._get_level_values(level)
+        return values
+
+    @doc(Index.unique)
+    def unique(self, level=None):
+        if level is None:
+            return self.drop_duplicates()
+        else:
+            level = self._get_level_number(level)
+            return self._get_level_values(level=level, unique=True)
+
+    def to_frame(
+        self,
+        index: bool = True,
+        name=lib.no_default,
+        allow_duplicates: bool = False,
+    ) -> DataFrame:
+        """
+        Create a DataFrame with the levels of the MultiIndex as columns.
+
+        Column ordering is determined by the DataFrame constructor with data as
+        a dict.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Set the index of the returned DataFrame as the original MultiIndex.
+
+        name : list / sequence of str, optional
+            The passed names should substitute index level names.
+
+        allow_duplicates : bool, optional default False
+            Allow duplicate column labels to be created.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame : Two-dimensional, size-mutable, potentially heterogeneous
+            tabular data.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a', 'b'], ['c', 'd']])
+        >>> mi
+        MultiIndex([('a', 'c'),
+                    ('b', 'd')],
+                   )
+
+        >>> df = mi.to_frame()
+        >>> df
+             0  1
+        a c  a  c
+        b d  b  d
+
+        >>> df = mi.to_frame(index=False)
+        >>> df
+           0  1
+        0  a  c
+        1  b  d
+
+        >>> df = mi.to_frame(name=['x', 'y'])
+        >>> df
+             x  y
+        a c  a  c
+        b d  b  d
+        """
+        from pandas import DataFrame
+
+        if name is not lib.no_default:
+            if not is_list_like(name):
+                raise TypeError("'name' must be a list / sequence of column names.")
+
+            if len(name) != len(self.levels):
+                raise ValueError(
+                    "'name' should have same length as number of levels on index."
+                )
+            idx_names = name
+        else:
+            idx_names = self._get_level_names()
+
+        if not allow_duplicates and len(set(idx_names)) != len(idx_names):
+            raise ValueError(
+                "Cannot create duplicate column labels if allow_duplicates is False"
+            )
+
+        # Guarantee resulting column order - PY36+ dict maintains insertion order
+        result = DataFrame(
+            {level: self._get_level_values(level) for level in range(len(self.levels))},
+            copy=False,
+        )
+        result.columns = idx_names
+
+        if index:
+            result.index = self
+        return result
+
+    # error: Return type "Index" of "to_flat_index" incompatible with return type
+    # "MultiIndex" in supertype "Index"
+    def to_flat_index(self) -> Index:  # type: ignore[override]
+        """
+        Convert a MultiIndex to an Index of Tuples containing the level values.
+
+        Returns
+        -------
+        pd.Index
+            Index with the MultiIndex data represented in Tuples.
+
+        See Also
+        --------
+        MultiIndex.from_tuples : Convert flat index back to MultiIndex.
+
+        Notes
+        -----
+        This method will simply return the caller if called by anything other
+        than a MultiIndex.
+
+        Examples
+        --------
+        >>> index = pd.MultiIndex.from_product(
+        ...     [['foo', 'bar'], ['baz', 'qux']],
+        ...     names=['a', 'b'])
+        >>> index.to_flat_index()
+        Index([('foo', 'baz'), ('foo', 'qux'),
+               ('bar', 'baz'), ('bar', 'qux')],
+              dtype='object')
+        """
+        return Index(self._values, tupleize_cols=False)
+
+    def _is_lexsorted(self) -> bool:
+        """
+        Return True if the codes are lexicographically sorted.
+
+        Returns
+        -------
+        bool
+
+        Examples
+        --------
+        In the below examples, the first level of the MultiIndex is sorted because
+        a<b<c, so there is no need to look at the next level.
+
+        >>> pd.MultiIndex.from_arrays([['a', 'b', 'c'],
+        ...                            ['d', 'e', 'f']])._is_lexsorted()
+        True
+        >>> pd.MultiIndex.from_arrays([['a', 'b', 'c'],
+        ...                            ['d', 'f', 'e']])._is_lexsorted()
+        True
+
+        In case there is a tie, the lexicographical sorting looks
+        at the next level of the MultiIndex.
+
+        >>> pd.MultiIndex.from_arrays([[0, 1, 1], ['a', 'b', 'c']])._is_lexsorted()
+        True
+        >>> pd.MultiIndex.from_arrays([[0, 1, 1], ['a', 'c', 'b']])._is_lexsorted()
+        False
+        >>> pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'],
+        ...                            ['aa', 'bb', 'aa', 'bb']])._is_lexsorted()
+        True
+        >>> pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'],
+        ...                            ['bb', 'aa', 'aa', 'bb']])._is_lexsorted()
+        False
+        """
+        return self._lexsort_depth == self.nlevels
+
+    @cache_readonly
+    def _lexsort_depth(self) -> int:
+        """
+        Compute and return the lexsort_depth, the number of levels of the
+        MultiIndex that are sorted lexically
+
+        Returns
+        -------
+        int
+        """
+        if self.sortorder is not None:
+            return self.sortorder
+        return _lexsort_depth(self.codes, self.nlevels)
+
+    def _sort_levels_monotonic(self, raise_if_incomparable: bool = False) -> MultiIndex:
+        """
+        This is an *internal* function.
+
+        Create a new MultiIndex from the current to monotonically sorted
+        items IN the levels. This does not actually make the entire MultiIndex
+        monotonic, JUST the levels.
+
+        The resulting MultiIndex will have the same outward
+        appearance, meaning the same .values and ordering. It will also
+        be .equals() to the original.
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
+        ...                    codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        >>> mi
+        MultiIndex([('a', 'bb'),
+                    ('a', 'aa'),
+                    ('b', 'bb'),
+                    ('b', 'aa')],
+                   )
+
+        >>> mi.sort_values()
+        MultiIndex([('a', 'aa'),
+                    ('a', 'bb'),
+                    ('b', 'aa'),
+                    ('b', 'bb')],
+                   )
+        """
+        if self._is_lexsorted() and self.is_monotonic_increasing:
+            return self
+
+        new_levels = []
+        new_codes = []
+
+        for lev, level_codes in zip(self.levels, self.codes):
+            if not lev.is_monotonic_increasing:
+                try:
+                    # indexer to reorder the levels
+                    indexer = lev.argsort()
+                except TypeError:
+                    if raise_if_incomparable:
+                        raise
+                else:
+                    lev = lev.take(indexer)
+
+                    # indexer to reorder the level codes
+                    indexer = ensure_platform_int(indexer)
+                    ri = lib.get_reverse_indexer(indexer, len(indexer))
+                    level_codes = algos.take_nd(ri, level_codes, fill_value=-1)
+
+            new_levels.append(lev)
+            new_codes.append(level_codes)
+
+        return MultiIndex(
+            new_levels,
+            new_codes,
+            names=self.names,
+            sortorder=self.sortorder,
+            verify_integrity=False,
+        )
+
+    def remove_unused_levels(self) -> MultiIndex:
+        """
+        Create new MultiIndex from current that removes unused levels.
+
+        Unused level(s) means levels that are not expressed in the
+        labels. The resulting MultiIndex will have the same outward
+        appearance, meaning the same .values and ordering. It will
+        also be .equals() to the original.
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_product([range(2), list('ab')])
+        >>> mi
+        MultiIndex([(0, 'a'),
+                    (0, 'b'),
+                    (1, 'a'),
+                    (1, 'b')],
+                   )
+
+        >>> mi[2:]
+        MultiIndex([(1, 'a'),
+                    (1, 'b')],
+                   )
+
+        The 0 from the first level is not represented
+        and can be removed
+
+        >>> mi2 = mi[2:].remove_unused_levels()
+        >>> mi2.levels
+        FrozenList([[1], ['a', 'b']])
+        """
+        new_levels = []
+        new_codes = []
+
+        changed = False
+        for lev, level_codes in zip(self.levels, self.codes):
+            # Since few levels are typically unused, bincount() is more
+            # efficient than unique() - however it only accepts positive values
+            # (and drops order):
+            uniques = np.where(np.bincount(level_codes + 1) > 0)[0] - 1
+            has_na = int(len(uniques) and (uniques[0] == -1))
+
+            if len(uniques) != len(lev) + has_na:
+                if lev.isna().any() and len(uniques) == len(lev):
+                    break
+                # We have unused levels
+                changed = True
+
+                # Recalculate uniques, now preserving order.
+                # Can easily be cythonized by exploiting the already existing
+                # "uniques" and stop parsing "level_codes" when all items
+                # are found:
+                uniques = algos.unique(level_codes)
+                if has_na:
+                    na_idx = np.where(uniques == -1)[0]
+                    # Just ensure that -1 is in first position:
+                    uniques[[0, na_idx[0]]] = uniques[[na_idx[0], 0]]
+
+                # codes get mapped from uniques to 0:len(uniques)
+                # -1 (if present) is mapped to last position
+                code_mapping = np.zeros(len(lev) + has_na)
+                # ... and reassigned value -1:
+                code_mapping[uniques] = np.arange(len(uniques)) - has_na
+
+                level_codes = code_mapping[level_codes]
+
+                # new levels are simple
+                lev = lev.take(uniques[has_na:])
+
+            new_levels.append(lev)
+            new_codes.append(level_codes)
+
+        result = self.view()
+
+        if changed:
+            result._reset_identity()
+            result._set_levels(new_levels, validate=False)
+            result._set_codes(new_codes, validate=False)
+
+        return result
+
+    # --------------------------------------------------------------------
+    # Pickling Methods
+
+    def __reduce__(self):
+        """Necessary for making this object picklable"""
+        d = {
+            "levels": list(self.levels),
+            "codes": list(self.codes),
+            "sortorder": self.sortorder,
+            "names": list(self.names),
+        }
+        return ibase._new_Index, (type(self), d), None
+
+    # --------------------------------------------------------------------
+
+    def __getitem__(self, key):
+        if is_scalar(key):
+            key = com.cast_scalar_indexer(key)
+
+            retval = []
+            for lev, level_codes in zip(self.levels, self.codes):
+                if level_codes[key] == -1:
+                    retval.append(np.nan)
+                else:
+                    retval.append(lev[level_codes[key]])
+
+            return tuple(retval)
+        else:
+            # in general cannot be sure whether the result will be sorted
+            sortorder = None
+            if com.is_bool_indexer(key):
+                key = np.asarray(key, dtype=bool)
+                sortorder = self.sortorder
+            elif isinstance(key, slice):
+                if key.step is None or key.step > 0:
+                    sortorder = self.sortorder
+            elif isinstance(key, Index):
+                key = np.asarray(key)
+
+            new_codes = [level_codes[key] for level_codes in self.codes]
+
+            return MultiIndex(
+                levels=self.levels,
+                codes=new_codes,
+                names=self.names,
+                sortorder=sortorder,
+                verify_integrity=False,
+            )
+
+    def _getitem_slice(self: MultiIndex, slobj: slice) -> MultiIndex:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        sortorder = None
+        if slobj.step is None or slobj.step > 0:
+            sortorder = self.sortorder
+
+        new_codes = [level_codes[slobj] for level_codes in self.codes]
+
+        return type(self)(
+            levels=self.levels,
+            codes=new_codes,
+            names=self._names,
+            sortorder=sortorder,
+            verify_integrity=False,
+        )
+
+    @Appender(_index_shared_docs["take"] % _index_doc_kwargs)
+    def take(
+        self: MultiIndex,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> MultiIndex:
+        nv.validate_take((), kwargs)
+        indices = ensure_platform_int(indices)
+
+        # only fill if we are passing a non-None fill_value
+        allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
+
+        na_value = -1
+
+        taken = [lab.take(indices) for lab in self.codes]
+        if allow_fill:
+            mask = indices == -1
+            if mask.any():
+                masked = []
+                for new_label in taken:
+                    label_values = new_label
+                    label_values[mask] = na_value
+                    masked.append(np.asarray(label_values))
+                taken = masked
+
+        return MultiIndex(
+            levels=self.levels, codes=taken, names=self.names, verify_integrity=False
+        )
+
+    def append(self, other):
+        """
+        Append a collection of Index options together.
+
+        Parameters
+        ----------
+        other : Index or list/tuple of indices
+
+        Returns
+        -------
+        Index
+            The combined index.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a'], ['b']])
+        >>> mi
+        MultiIndex([('a', 'b')],
+                   )
+        >>> mi.append(mi)
+        MultiIndex([('a', 'b'), ('a', 'b')],
+                   )
+        """
+        if not isinstance(other, (list, tuple)):
+            other = [other]
+
+        if all(
+            (isinstance(o, MultiIndex) and o.nlevels >= self.nlevels) for o in other
+        ):
+            codes = []
+            levels = []
+            names = []
+            for i in range(self.nlevels):
+                level_values = self.levels[i]
+                for mi in other:
+                    level_values = level_values.union(mi.levels[i])
+                level_codes = [
+                    recode_for_categories(
+                        mi.codes[i], mi.levels[i], level_values, copy=False
+                    )
+                    for mi in ([self, *other])
+                ]
+                level_name = self.names[i]
+                if any(mi.names[i] != level_name for mi in other):
+                    level_name = None
+                codes.append(np.concatenate(level_codes))
+                levels.append(level_values)
+                names.append(level_name)
+            return MultiIndex(
+                codes=codes, levels=levels, names=names, verify_integrity=False
+            )
+
+        to_concat = (self._values,) + tuple(k._values for k in other)
+        new_tuples = np.concatenate(to_concat)
+
+        # if all(isinstance(x, MultiIndex) for x in other):
+        try:
+            # We only get here if other contains at least one index with tuples,
+            # setting names to None automatically
+            return MultiIndex.from_tuples(new_tuples)
+        except (TypeError, IndexError):
+            return Index(new_tuples)
+
+    def argsort(
+        self, *args, na_position: str = "last", **kwargs
+    ) -> npt.NDArray[np.intp]:
+        target = self._sort_levels_monotonic(raise_if_incomparable=True)
+        keys = [lev.codes for lev in target._get_codes_for_sorting()]
+        return lexsort_indexer(keys, na_position=na_position, codes_given=True)
+
+    @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
+    def repeat(self, repeats: int, axis=None) -> MultiIndex:
+        nv.validate_repeat((), {"axis": axis})
+        # error: Incompatible types in assignment (expression has type "ndarray",
+        # variable has type "int")
+        repeats = ensure_platform_int(repeats)  # type: ignore[assignment]
+        return MultiIndex(
+            levels=self.levels,
+            codes=[
+                level_codes.view(np.ndarray).astype(np.intp, copy=False).repeat(repeats)
+                for level_codes in self.codes
+            ],
+            names=self.names,
+            sortorder=self.sortorder,
+            verify_integrity=False,
+        )
+
+    # error: Signature of "drop" incompatible with supertype "Index"
+    def drop(  # type: ignore[override]
+        self,
+        codes,
+        level: Index | np.ndarray | Iterable[Hashable] | None = None,
+        errors: IgnoreRaise = "raise",
+    ) -> MultiIndex:
+        """
+        Make a new :class:`pandas.MultiIndex` with the passed list of codes deleted.
+
+        Parameters
+        ----------
+        codes : array-like
+            Must be a list of tuples when ``level`` is not specified.
+        level : int or level name, default None
+        errors : str, default 'raise'
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> idx = pd.MultiIndex.from_product([(0, 1, 2), ('green', 'purple')],
+        ...                                  names=["number", "color"])
+        >>> idx
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        >>> idx.drop([(1, 'green'), (2, 'purple')])
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1, 'purple'),
+                    (2,  'green')],
+                   names=['number', 'color'])
+
+        We can also drop from a specific level.
+
+        >>> idx.drop('green', level='color')
+        MultiIndex([(0, 'purple'),
+                    (1, 'purple'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+
+        >>> idx.drop([1, 2], level=0)
+        MultiIndex([(0,  'green'),
+                    (0, 'purple')],
+                   names=['number', 'color'])
+        """
+        if level is not None:
+            return self._drop_from_level(codes, level, errors)
+
+        if not isinstance(codes, (np.ndarray, Index)):
+            try:
+                codes = com.index_labels_to_array(codes, dtype=np.dtype("object"))
+            except ValueError:
+                pass
+
+        inds = []
+        for level_codes in codes:
+            try:
+                loc = self.get_loc(level_codes)
+                # get_loc returns either an integer, a slice, or a boolean
+                # mask
+                if isinstance(loc, int):
+                    inds.append(loc)
+                elif isinstance(loc, slice):
+                    step = loc.step if loc.step is not None else 1
+                    inds.extend(range(loc.start, loc.stop, step))
+                elif com.is_bool_indexer(loc):
+                    if self._lexsort_depth == 0:
+                        warnings.warn(
+                            "dropping on a non-lexsorted multi-index "
+                            "without a level parameter may impact performance.",
+                            PerformanceWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    loc = loc.nonzero()[0]
+                    inds.extend(loc)
+                else:
+                    msg = f"unsupported indexer of type {type(loc)}"
+                    raise AssertionError(msg)
+            except KeyError:
+                if errors != "ignore":
+                    raise
+
+        return self.delete(inds)
+
+    def _drop_from_level(
+        self, codes, level, errors: IgnoreRaise = "raise"
+    ) -> MultiIndex:
+        codes = com.index_labels_to_array(codes)
+        i = self._get_level_number(level)
+        index = self.levels[i]
+        values = index.get_indexer(codes)
+        # If nan should be dropped it will equal -1 here. We have to check which values
+        # are not nan and equal -1, this means they are missing in the index
+        nan_codes = isna(codes)
+        values[(np.equal(nan_codes, False)) & (values == -1)] = -2
+        if index.shape[0] == self.shape[0]:
+            values[np.equal(nan_codes, True)] = -2
+
+        not_found = codes[values == -2]
+        if len(not_found) != 0 and errors != "ignore":
+            raise KeyError(f"labels {not_found} not found in level")
+        mask = ~algos.isin(self.codes[i], values)
+
+        return self[mask]
+
+    def swaplevel(self, i=-2, j=-1) -> MultiIndex:
+        """
+        Swap level i with level j.
+
+        Calling this method does not change the ordering of the values.
+
+        Parameters
+        ----------
+        i : int, str, default -2
+            First level of index to be swapped. Can pass level name as string.
+            Type of parameters can be mixed.
+        j : int, str, default -1
+            Second level of index to be swapped. Can pass level name as string.
+            Type of parameters can be mixed.
+
+        Returns
+        -------
+        MultiIndex
+            A new MultiIndex.
+
+        See Also
+        --------
+        Series.swaplevel : Swap levels i and j in a MultiIndex.
+        DataFrame.swaplevel : Swap levels i and j in a MultiIndex on a
+            particular axis.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
+        ...                    codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        >>> mi
+        MultiIndex([('a', 'bb'),
+                    ('a', 'aa'),
+                    ('b', 'bb'),
+                    ('b', 'aa')],
+                   )
+        >>> mi.swaplevel(0, 1)
+        MultiIndex([('bb', 'a'),
+                    ('aa', 'a'),
+                    ('bb', 'b'),
+                    ('aa', 'b')],
+                   )
+        """
+        new_levels = list(self.levels)
+        new_codes = list(self.codes)
+        new_names = list(self.names)
+
+        i = self._get_level_number(i)
+        j = self._get_level_number(j)
+
+        new_levels[i], new_levels[j] = new_levels[j], new_levels[i]
+        new_codes[i], new_codes[j] = new_codes[j], new_codes[i]
+        new_names[i], new_names[j] = new_names[j], new_names[i]
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+
+    def reorder_levels(self, order) -> MultiIndex:
+        """
+        Rearrange levels using input order. May not drop or duplicate levels.
+
+        Parameters
+        ----------
+        order : list of int or list of str
+            List representing new level order. Reference level by number
+            (position) or by key (label).
+
+        Returns
+        -------
+        MultiIndex
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([[1, 2], [3, 4]], names=['x', 'y'])
+        >>> mi
+        MultiIndex([(1, 3),
+                    (2, 4)],
+                   names=['x', 'y'])
+
+        >>> mi.reorder_levels(order=[1, 0])
+        MultiIndex([(3, 1),
+                    (4, 2)],
+                   names=['y', 'x'])
+
+        >>> mi.reorder_levels(order=['y', 'x'])
+        MultiIndex([(3, 1),
+                    (4, 2)],
+                   names=['y', 'x'])
+        """
+        order = [self._get_level_number(i) for i in order]
+        result = self._reorder_ilevels(order)
+        return result
+
+    def _reorder_ilevels(self, order) -> MultiIndex:
+        if len(order) != self.nlevels:
+            raise AssertionError(
+                f"Length of order must be same as number of levels ({self.nlevels}), "
+                f"got {len(order)}"
+            )
+        new_levels = [self.levels[i] for i in order]
+        new_codes = [self.codes[i] for i in order]
+        new_names = [self.names[i] for i in order]
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+
+    def _recode_for_new_levels(
+        self, new_levels, copy: bool = True
+    ) -> Generator[np.ndarray, None, None]:
+        if len(new_levels) > self.nlevels:
+            raise AssertionError(
+                f"Length of new_levels ({len(new_levels)}) "
+                f"must be <= self.nlevels ({self.nlevels})"
+            )
+        for i in range(len(new_levels)):
+            yield recode_for_categories(
+                self.codes[i], self.levels[i], new_levels[i], copy=copy
+            )
+
+    def _get_codes_for_sorting(self) -> list[Categorical]:
+        """
+        we are categorizing our codes by using the
+        available categories (all, not just observed)
+        excluding any missing ones (-1); this is in preparation
+        for sorting, where we need to disambiguate that -1 is not
+        a valid valid
+        """
+
+        def cats(level_codes):
+            return np.arange(
+                np.array(level_codes).max() + 1 if len(level_codes) else 0,
+                dtype=level_codes.dtype,
+            )
+
+        return [
+            Categorical.from_codes(level_codes, cats(level_codes), True, validate=False)
+            for level_codes in self.codes
+        ]
+
+    def sortlevel(
+        self,
+        level: IndexLabel = 0,
+        ascending: bool | list[bool] = True,
+        sort_remaining: bool = True,
+        na_position: str = "first",
+    ) -> tuple[MultiIndex, npt.NDArray[np.intp]]:
+        """
+        Sort MultiIndex at the requested level.
+
+        The result will respect the original ordering of the associated
+        factor at that level.
+
+        Parameters
+        ----------
+        level : list-like, int or str, default 0
+            If a string is given, must be a name of the level.
+            If list-like must be names or ints of levels.
+        ascending : bool, default True
+            False to sort in descending order.
+            Can also be a list to specify a directed ordering.
+        sort_remaining : sort by the remaining levels after level
+        na_position : {'first' or 'last'}, default 'first'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+
+            .. versionadded:: 2.1.0
+
+        Returns
+        -------
+        sorted_index : pd.MultiIndex
+            Resulting index.
+        indexer : np.ndarray[np.intp]
+            Indices of output values in original index.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([[0, 0], [2, 1]])
+        >>> mi
+        MultiIndex([(0, 2),
+                    (0, 1)],
+                   )
+
+        >>> mi.sortlevel()
+        (MultiIndex([(0, 1),
+                    (0, 2)],
+                   ), array([1, 0]))
+
+        >>> mi.sortlevel(sort_remaining=False)
+        (MultiIndex([(0, 2),
+                    (0, 1)],
+                   ), array([0, 1]))
+
+        >>> mi.sortlevel(1)
+        (MultiIndex([(0, 1),
+                    (0, 2)],
+                   ), array([1, 0]))
+
+        >>> mi.sortlevel(1, ascending=False)
+        (MultiIndex([(0, 2),
+                    (0, 1)],
+                   ), array([0, 1]))
+        """
+        if not is_list_like(level):
+            level = [level]
+        # error: Item "Hashable" of "Union[Hashable, Sequence[Hashable]]" has
+        # no attribute "__iter__" (not iterable)
+        level = [
+            self._get_level_number(lev) for lev in level  # type: ignore[union-attr]
+        ]
+        sortorder = None
+
+        codes = [self.codes[lev] for lev in level]
+        # we have a directed ordering via ascending
+        if isinstance(ascending, list):
+            if not len(level) == len(ascending):
+                raise ValueError("level must have same length as ascending")
+        elif sort_remaining:
+            codes.extend(
+                [self.codes[lev] for lev in range(len(self.levels)) if lev not in level]
+            )
+        else:
+            sortorder = level[0]
+
+        indexer = lexsort_indexer(
+            codes, orders=ascending, na_position=na_position, codes_given=True
+        )
+
+        indexer = ensure_platform_int(indexer)
+        new_codes = [level_codes.take(indexer) for level_codes in self.codes]
+
+        new_index = MultiIndex(
+            codes=new_codes,
+            levels=self.levels,
+            names=self.names,
+            sortorder=sortorder,
+            verify_integrity=False,
+        )
+
+        return new_index, indexer
+
+    def _wrap_reindex_result(self, target, indexer, preserve_names: bool):
+        if not isinstance(target, MultiIndex):
+            if indexer is None:
+                target = self
+            elif (indexer >= 0).all():
+                target = self.take(indexer)
+            else:
+                try:
+                    target = MultiIndex.from_tuples(target)
+                except TypeError:
+                    # not all tuples, see test_constructor_dict_multiindex_reindex_flat
+                    return target
+
+        target = self._maybe_preserve_names(target, preserve_names)
+        return target
+
+    def _maybe_preserve_names(self, target: Index, preserve_names: bool) -> Index:
+        if (
+            preserve_names
+            and target.nlevels == self.nlevels
+            and target.names != self.names
+        ):
+            target = target.copy(deep=False)
+            target.names = self.names
+        return target
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    def _check_indexing_error(self, key) -> None:
+        if not is_hashable(key) or is_iterator(key):
+            # We allow tuples if they are hashable, whereas other Index
+            #  subclasses require scalar.
+            # We have to explicitly exclude generators, as these are hashable.
+            raise InvalidIndexError(key)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        """
+        Should integer key(s) be treated as positional?
+        """
+        # GH#33355
+        return self.levels[0]._should_fallback_to_positional
+
+    def _get_indexer_strict(
+        self, key, axis_name: str
+    ) -> tuple[Index, npt.NDArray[np.intp]]:
+        keyarr = key
+        if not isinstance(keyarr, Index):
+            keyarr = com.asarray_tuplesafe(keyarr)
+
+        if len(keyarr) and not isinstance(keyarr[0], tuple):
+            indexer = self._get_indexer_level_0(keyarr)
+
+            self._raise_if_missing(key, indexer, axis_name)
+            return self[indexer], indexer
+
+        return super()._get_indexer_strict(key, axis_name)
+
+    def _raise_if_missing(self, key, indexer, axis_name: str) -> None:
+        keyarr = key
+        if not isinstance(key, Index):
+            keyarr = com.asarray_tuplesafe(key)
+
+        if len(keyarr) and not isinstance(keyarr[0], tuple):
+            # i.e. same condition for special case in MultiIndex._get_indexer_strict
+
+            mask = indexer == -1
+            if mask.any():
+                check = self.levels[0].get_indexer(keyarr)
+                cmask = check == -1
+                if cmask.any():
+                    raise KeyError(f"{keyarr[cmask]} not in index")
+                # We get here when levels still contain values which are not
+                # actually in Index anymore
+                raise KeyError(f"{keyarr} not in index")
+        else:
+            return super()._raise_if_missing(key, indexer, axis_name)
+
+    def _get_indexer_level_0(self, target) -> npt.NDArray[np.intp]:
+        """
+        Optimized equivalent to `self.get_level_values(0).get_indexer_for(target)`.
+        """
+        lev = self.levels[0]
+        codes = self._codes[0]
+        cat = Categorical.from_codes(codes=codes, categories=lev, validate=False)
+        ci = Index(cat)
+        return ci.get_indexer_for(target)
+
+    def get_slice_bound(
+        self,
+        label: Hashable | Sequence[Hashable],
+        side: Literal["left", "right"],
+    ) -> int:
+        """
+        For an ordered MultiIndex, compute slice bound
+        that corresponds to given label.
+
+        Returns leftmost (one-past-the-rightmost if `side=='right') position
+        of given label.
+
+        Parameters
+        ----------
+        label : object or tuple of objects
+        side : {'left', 'right'}
+
+        Returns
+        -------
+        int
+            Index of label.
+
+        Notes
+        -----
+        This method only works if level 0 index of the MultiIndex is lexsorted.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abbc'), list('gefd')])
+
+        Get the locations from the leftmost 'b' in the first level
+        until the end of the multiindex:
+
+        >>> mi.get_slice_bound('b', side="left")
+        1
+
+        Like above, but if you get the locations from the rightmost
+        'b' in the first level and 'f' in the second level:
+
+        >>> mi.get_slice_bound(('b','f'), side="right")
+        3
+
+        See Also
+        --------
+        MultiIndex.get_loc : Get location for a label or a tuple of labels.
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+        """
+        if not isinstance(label, tuple):
+            label = (label,)
+        return self._partial_tup_index(label, side=side)
+
+    # pylint: disable-next=useless-parent-delegation
+    def slice_locs(self, start=None, end=None, step=None) -> tuple[int, int]:
+        """
+        For an ordered MultiIndex, compute the slice locations for input
+        labels.
+
+        The input labels can be tuples representing partial levels, e.g. for a
+        MultiIndex with 3 levels, you can pass a single value (corresponding to
+        the first level), or a 1-, 2-, or 3-tuple.
+
+        Parameters
+        ----------
+        start : label or tuple, default None
+            If None, defaults to the beginning
+        end : label or tuple
+            If None, defaults to the end
+        step : int or None
+            Slice step
+
+        Returns
+        -------
+        (start, end) : (int, int)
+
+        Notes
+        -----
+        This method only works if the MultiIndex is properly lexsorted. So,
+        if only the first 2 levels of a 3-level MultiIndex are lexsorted,
+        you can only pass two levels to ``.slice_locs``.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abbd'), list('deff')],
+        ...                                names=['A', 'B'])
+
+        Get the slice locations from the beginning of 'b' in the first level
+        until the end of the multiindex:
+
+        >>> mi.slice_locs(start='b')
+        (1, 4)
+
+        Like above, but stop at the end of 'b' in the first level and 'f' in
+        the second level:
+
+        >>> mi.slice_locs(start='b', end=('b', 'f'))
+        (1, 3)
+
+        See Also
+        --------
+        MultiIndex.get_loc : Get location for a label or a tuple of labels.
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+        """
+        # This function adds nothing to its parent implementation (the magic
+        # happens in get_slice_bound method), but it adds meaningful doc.
+        return super().slice_locs(start, end, step)
+
+    def _partial_tup_index(self, tup: tuple, side: Literal["left", "right"] = "left"):
+        if len(tup) > self._lexsort_depth:
+            raise UnsortedIndexError(
+                f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth "
+                f"({self._lexsort_depth})"
+            )
+
+        n = len(tup)
+        start, end = 0, len(self)
+        zipped = zip(tup, self.levels, self.codes)
+        for k, (lab, lev, level_codes) in enumerate(zipped):
+            section = level_codes[start:end]
+
+            loc: npt.NDArray[np.intp] | np.intp | int
+            if lab not in lev and not isna(lab):
+                # short circuit
+                try:
+                    loc = algos.searchsorted(lev, lab, side=side)
+                except TypeError as err:
+                    # non-comparable e.g. test_slice_locs_with_type_mismatch
+                    raise TypeError(f"Level type mismatch: {lab}") from err
+                if not is_integer(loc):
+                    # non-comparable level, e.g. test_groupby_example
+                    raise TypeError(f"Level type mismatch: {lab}")
+                if side == "right" and loc >= 0:
+                    loc -= 1
+                return start + algos.searchsorted(section, loc, side=side)
+
+            idx = self._get_loc_single_level_index(lev, lab)
+            if isinstance(idx, slice) and k < n - 1:
+                # Get start and end value from slice, necessary when a non-integer
+                # interval is given as input GH#37707
+                start = idx.start
+                end = idx.stop
+            elif k < n - 1:
+                # error: Incompatible types in assignment (expression has type
+                # "Union[ndarray[Any, dtype[signedinteger[Any]]]
+                end = start + algos.searchsorted(  # type: ignore[assignment]
+                    section, idx, side="right"
+                )
+                # error: Incompatible types in assignment (expression has type
+                # "Union[ndarray[Any, dtype[signedinteger[Any]]]
+                start = start + algos.searchsorted(  # type: ignore[assignment]
+                    section, idx, side="left"
+                )
+            elif isinstance(idx, slice):
+                idx = idx.start
+                return start + algos.searchsorted(section, idx, side=side)
+            else:
+                return start + algos.searchsorted(section, idx, side=side)
+
+    def _get_loc_single_level_index(self, level_index: Index, key: Hashable) -> int:
+        """
+        If key is NA value, location of index unify as -1.
+
+        Parameters
+        ----------
+        level_index: Index
+        key : label
+
+        Returns
+        -------
+        loc : int
+            If key is NA value, loc is -1
+            Else, location of key in index.
+
+        See Also
+        --------
+        Index.get_loc : The get_loc method for (single-level) index.
+        """
+        if is_scalar(key) and isna(key):
+            # TODO: need is_valid_na_for_dtype(key, level_index.dtype)
+            return -1
+        else:
+            return level_index.get_loc(key)
+
+    def get_loc(self, key):
+        """
+        Get location for a label or a tuple of labels.
+
+        The location is returned as an integer/slice or boolean
+        mask.
+
+        Parameters
+        ----------
+        key : label or tuple of labels (one for each level)
+
+        Returns
+        -------
+        int, slice object or boolean mask
+            If the key is past the lexsort depth, the return may be a
+            boolean mask array, otherwise it is always a slice or int.
+
+        See Also
+        --------
+        Index.get_loc : The get_loc method for (single-level) index.
+        MultiIndex.slice_locs : Get slice location given start label(s) and
+                                end label(s).
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+
+        Notes
+        -----
+        The key cannot be a slice, list of same-level labels, a boolean mask,
+        or a sequence of such. If you want to use those, use
+        :meth:`MultiIndex.get_locs` instead.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
+
+        >>> mi.get_loc('b')
+        slice(1, 3, None)
+
+        >>> mi.get_loc(('b', 'e'))
+        1
+        """
+        self._check_indexing_error(key)
+
+        def _maybe_to_slice(loc):
+            """convert integer indexer to boolean mask or slice if possible"""
+            if not isinstance(loc, np.ndarray) or loc.dtype != np.intp:
+                return loc
+
+            loc = lib.maybe_indices_to_slice(loc, len(self))
+            if isinstance(loc, slice):
+                return loc
+
+            mask = np.empty(len(self), dtype="bool")
+            mask.fill(False)
+            mask[loc] = True
+            return mask
+
+        if not isinstance(key, tuple):
+            loc = self._get_level_indexer(key, level=0)
+            return _maybe_to_slice(loc)
+
+        keylen = len(key)
+        if self.nlevels < keylen:
+            raise KeyError(
+                f"Key length ({keylen}) exceeds index depth ({self.nlevels})"
+            )
+
+        if keylen == self.nlevels and self.is_unique:
+            # TODO: what if we have an IntervalIndex level?
+            #  i.e. do we need _index_as_unique on that level?
+            try:
+                return self._engine.get_loc(key)
+            except KeyError as err:
+                raise KeyError(key) from err
+            except TypeError:
+                # e.g. test_partial_slicing_with_multiindex partial string slicing
+                loc, _ = self.get_loc_level(key, list(range(self.nlevels)))
+                return loc
+
+        # -- partial selection or non-unique index
+        # break the key into 2 parts based on the lexsort_depth of the index;
+        # the first part returns a continuous slice of the index; the 2nd part
+        # needs linear search within the slice
+        i = self._lexsort_depth
+        lead_key, follow_key = key[:i], key[i:]
+
+        if not lead_key:
+            start = 0
+            stop = len(self)
+        else:
+            try:
+                start, stop = self.slice_locs(lead_key, lead_key)
+            except TypeError as err:
+                # e.g. test_groupby_example key = ((0, 0, 1, 2), "new_col")
+                #  when self has 5 integer levels
+                raise KeyError(key) from err
+
+        if start == stop:
+            raise KeyError(key)
+
+        if not follow_key:
+            return slice(start, stop)
+
+        warnings.warn(
+            "indexing past lexsort depth may impact performance.",
+            PerformanceWarning,
+            stacklevel=find_stack_level(),
+        )
+
+        loc = np.arange(start, stop, dtype=np.intp)
+
+        for i, k in enumerate(follow_key, len(lead_key)):
+            mask = self.codes[i][loc] == self._get_loc_single_level_index(
+                self.levels[i], k
+            )
+            if not mask.all():
+                loc = loc[mask]
+            if not len(loc):
+                raise KeyError(key)
+
+        return _maybe_to_slice(loc) if len(loc) != stop - start else slice(start, stop)
+
+    def get_loc_level(self, key, level: IndexLabel = 0, drop_level: bool = True):
+        """
+        Get location and sliced index for requested label(s)/level(s).
+
+        Parameters
+        ----------
+        key : label or sequence of labels
+        level : int/level name or list thereof, optional
+        drop_level : bool, default True
+            If ``False``, the resulting index will not drop any level.
+
+        Returns
+        -------
+        tuple
+            A 2-tuple where the elements :
+
+            Element 0: int, slice object or boolean array.
+
+            Element 1: The resulting sliced multiindex/index. If the key
+            contains all levels, this will be ``None``.
+
+        See Also
+        --------
+        MultiIndex.get_loc  : Get location for a label or a tuple of labels.
+        MultiIndex.get_locs : Get location for a label/slice/list/mask or a
+                              sequence of such.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')],
+        ...                                names=['A', 'B'])
+
+        >>> mi.get_loc_level('b')
+        (slice(1, 3, None), Index(['e', 'f'], dtype='object', name='B'))
+
+        >>> mi.get_loc_level('e', level='B')
+        (array([False,  True, False]), Index(['b'], dtype='object', name='A'))
+
+        >>> mi.get_loc_level(['b', 'e'])
+        (1, None)
+        """
+        if not isinstance(level, (list, tuple)):
+            level = self._get_level_number(level)
+        else:
+            level = [self._get_level_number(lev) for lev in level]
+
+        loc, mi = self._get_loc_level(key, level=level)
+        if not drop_level:
+            if lib.is_integer(loc):
+                # Slice index must be an integer or None
+                mi = self[loc : loc + 1]
+            else:
+                mi = self[loc]
+        return loc, mi
+
+    def _get_loc_level(self, key, level: int | list[int] = 0):
+        """
+        get_loc_level but with `level` known to be positional, not name-based.
+        """
+
+        # different name to distinguish from maybe_droplevels
+        def maybe_mi_droplevels(indexer, levels):
+            """
+            If level does not exist or all levels were dropped, the exception
+            has to be handled outside.
+            """
+            new_index = self[indexer]
+
+            for i in sorted(levels, reverse=True):
+                new_index = new_index._drop_level_numbers([i])
+
+            return new_index
+
+        if isinstance(level, (tuple, list)):
+            if len(key) != len(level):
+                raise AssertionError(
+                    "Key for location must have same length as number of levels"
+                )
+            result = None
+            for lev, k in zip(level, key):
+                loc, new_index = self._get_loc_level(k, level=lev)
+                if isinstance(loc, slice):
+                    mask = np.zeros(len(self), dtype=bool)
+                    mask[loc] = True
+                    loc = mask
+                result = loc if result is None else result & loc
+
+            try:
+                # FIXME: we should be only dropping levels on which we are
+                #  scalar-indexing
+                mi = maybe_mi_droplevels(result, level)
+            except ValueError:
+                # droplevel failed because we tried to drop all levels,
+                #  i.e. len(level) == self.nlevels
+                mi = self[result]
+
+            return result, mi
+
+        # kludge for #1796
+        if isinstance(key, list):
+            key = tuple(key)
+
+        if isinstance(key, tuple) and level == 0:
+            try:
+                # Check if this tuple is a single key in our first level
+                if key in self.levels[0]:
+                    indexer = self._get_level_indexer(key, level=level)
+                    new_index = maybe_mi_droplevels(indexer, [0])
+                    return indexer, new_index
+            except (TypeError, InvalidIndexError):
+                pass
+
+            if not any(isinstance(k, slice) for k in key):
+                if len(key) == self.nlevels and self.is_unique:
+                    # Complete key in unique index -> standard get_loc
+                    try:
+                        return (self._engine.get_loc(key), None)
+                    except KeyError as err:
+                        raise KeyError(key) from err
+                    except TypeError:
+                        # e.g. partial string indexing
+                        #  test_partial_string_timestamp_multiindex
+                        pass
+
+                # partial selection
+                indexer = self.get_loc(key)
+                ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)]
+                if len(ilevels) == self.nlevels:
+                    if is_integer(indexer):
+                        # we are dropping all levels
+                        return indexer, None
+
+                    # TODO: in some cases we still need to drop some levels,
+                    #  e.g. test_multiindex_perf_warn
+                    # test_partial_string_timestamp_multiindex
+                    ilevels = [
+                        i
+                        for i in range(len(key))
+                        if (
+                            not isinstance(key[i], str)
+                            or not self.levels[i]._supports_partial_string_indexing
+                        )
+                        and key[i] != slice(None, None)
+                    ]
+                    if len(ilevels) == self.nlevels:
+                        # TODO: why?
+                        ilevels = []
+                return indexer, maybe_mi_droplevels(indexer, ilevels)
+
+            else:
+                indexer = None
+                for i, k in enumerate(key):
+                    if not isinstance(k, slice):
+                        loc_level = self._get_level_indexer(k, level=i)
+                        if isinstance(loc_level, slice):
+                            if com.is_null_slice(loc_level) or com.is_full_slice(
+                                loc_level, len(self)
+                            ):
+                                # everything
+                                continue
+
+                            # e.g. test_xs_IndexSlice_argument_not_implemented
+                            k_index = np.zeros(len(self), dtype=bool)
+                            k_index[loc_level] = True
+
+                        else:
+                            k_index = loc_level
+
+                    elif com.is_null_slice(k):
+                        # taking everything, does not affect `indexer` below
+                        continue
+
+                    else:
+                        # FIXME: this message can be inaccurate, e.g.
+                        #  test_series_varied_multiindex_alignment
+                        raise TypeError(f"Expected label or tuple of labels, got {key}")
+
+                    if indexer is None:
+                        indexer = k_index
+                    else:
+                        indexer &= k_index
+                if indexer is None:
+                    indexer = slice(None, None)
+                ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)]
+                return indexer, maybe_mi_droplevels(indexer, ilevels)
+        else:
+            indexer = self._get_level_indexer(key, level=level)
+            if (
+                isinstance(key, str)
+                and self.levels[level]._supports_partial_string_indexing
+            ):
+                # check to see if we did an exact lookup vs sliced
+                check = self.levels[level].get_loc(key)
+                if not is_integer(check):
+                    # e.g. test_partial_string_timestamp_multiindex
+                    return indexer, self[indexer]
+
+            try:
+                result_index = maybe_mi_droplevels(indexer, [level])
+            except ValueError:
+                result_index = self[indexer]
+
+            return indexer, result_index
+
+    def _get_level_indexer(
+        self, key, level: int = 0, indexer: npt.NDArray[np.bool_] | None = None
+    ):
+        # `level` kwarg is _always_ positional, never name
+        # return a boolean array or slice showing where the key is
+        # in the totality of values
+        # if the indexer is provided, then use this
+
+        level_index = self.levels[level]
+        level_codes = self.codes[level]
+
+        def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
+            # Compute a bool indexer to identify the positions to take.
+            # If we have an existing indexer, we only need to examine the
+            # subset of positions where the existing indexer is True.
+            if indexer is not None:
+                # we only need to look at the subset of codes where the
+                # existing indexer equals True
+                codes = codes[indexer]
+
+            if step is None or step == 1:
+                new_indexer = (codes >= start) & (codes < stop)
+            else:
+                r = np.arange(start, stop, step, dtype=codes.dtype)
+                new_indexer = algos.isin(codes, r)
+
+            if indexer is None:
+                return new_indexer
+
+            indexer = indexer.copy()
+            indexer[indexer] = new_indexer
+            return indexer
+
+        if isinstance(key, slice):
+            # handle a slice, returning a slice if we can
+            # otherwise a boolean indexer
+            step = key.step
+            is_negative_step = step is not None and step < 0
+
+            try:
+                if key.start is not None:
+                    start = level_index.get_loc(key.start)
+                elif is_negative_step:
+                    start = len(level_index) - 1
+                else:
+                    start = 0
+
+                if key.stop is not None:
+                    stop = level_index.get_loc(key.stop)
+                elif is_negative_step:
+                    stop = 0
+                elif isinstance(start, slice):
+                    stop = len(level_index)
+                else:
+                    stop = len(level_index) - 1
+            except KeyError:
+                # we have a partial slice (like looking up a partial date
+                # string)
+                start = stop = level_index.slice_indexer(key.start, key.stop, key.step)
+                step = start.step
+
+            if isinstance(start, slice) or isinstance(stop, slice):
+                # we have a slice for start and/or stop
+                # a partial date slicer on a DatetimeIndex generates a slice
+                # note that the stop ALREADY includes the stopped point (if
+                # it was a string sliced)
+                start = getattr(start, "start", start)
+                stop = getattr(stop, "stop", stop)
+                return convert_indexer(start, stop, step)
+
+            elif level > 0 or self._lexsort_depth == 0 or step is not None:
+                # need to have like semantics here to right
+                # searching as when we are using a slice
+                # so adjust the stop by 1 (so we include stop)
+                stop = (stop - 1) if is_negative_step else (stop + 1)
+                return convert_indexer(start, stop, step)
+            else:
+                # sorted, so can return slice object -> view
+                i = algos.searchsorted(level_codes, start, side="left")
+                j = algos.searchsorted(level_codes, stop, side="right")
+                return slice(i, j, step)
+
+        else:
+            idx = self._get_loc_single_level_index(level_index, key)
+
+            if level > 0 or self._lexsort_depth == 0:
+                # Desired level is not sorted
+                if isinstance(idx, slice):
+                    # test_get_loc_partial_timestamp_multiindex
+                    locs = (level_codes >= idx.start) & (level_codes < idx.stop)
+                    return locs
+
+                locs = np.asarray(level_codes == idx, dtype=bool)
+
+                if not locs.any():
+                    # The label is present in self.levels[level] but unused:
+                    raise KeyError(key)
+                return locs
+
+            if isinstance(idx, slice):
+                # e.g. test_partial_string_timestamp_multiindex
+                start = algos.searchsorted(level_codes, idx.start, side="left")
+                # NB: "left" here bc of slice semantics
+                end = algos.searchsorted(level_codes, idx.stop, side="left")
+            else:
+                start = algos.searchsorted(level_codes, idx, side="left")
+                end = algos.searchsorted(level_codes, idx, side="right")
+
+            if start == end:
+                # The label is present in self.levels[level] but unused:
+                raise KeyError(key)
+            return slice(start, end)
+
+    def get_locs(self, seq) -> npt.NDArray[np.intp]:
+        """
+        Get location for a sequence of labels.
+
+        Parameters
+        ----------
+        seq : label, slice, list, mask or a sequence of such
+           You should use one of the above for each level.
+           If a level should not be used, set it to ``slice(None)``.
+
+        Returns
+        -------
+        numpy.ndarray
+            NumPy array of integers suitable for passing to iloc.
+
+        See Also
+        --------
+        MultiIndex.get_loc : Get location for a label or a tuple of labels.
+        MultiIndex.slice_locs : Get slice location given start label(s) and
+                                end label(s).
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
+
+        >>> mi.get_locs('b')  # doctest: +SKIP
+        array([1, 2], dtype=int64)
+
+        >>> mi.get_locs([slice(None), ['e', 'f']])  # doctest: +SKIP
+        array([1, 2], dtype=int64)
+
+        >>> mi.get_locs([[True, False, True], slice('e', 'f')])  # doctest: +SKIP
+        array([2], dtype=int64)
+        """
+
+        # must be lexsorted to at least as many levels
+        true_slices = [i for (i, s) in enumerate(com.is_true_slices(seq)) if s]
+        if true_slices and true_slices[-1] >= self._lexsort_depth:
+            raise UnsortedIndexError(
+                "MultiIndex slicing requires the index to be lexsorted: slicing "
+                f"on levels {true_slices}, lexsort depth {self._lexsort_depth}"
+            )
+
+        if any(x is Ellipsis for x in seq):
+            raise NotImplementedError(
+                "MultiIndex does not support indexing with Ellipsis"
+            )
+
+        n = len(self)
+
+        def _to_bool_indexer(indexer) -> npt.NDArray[np.bool_]:
+            if isinstance(indexer, slice):
+                new_indexer = np.zeros(n, dtype=np.bool_)
+                new_indexer[indexer] = True
+                return new_indexer
+            return indexer
+
+        # a bool indexer for the positions we want to take
+        indexer: npt.NDArray[np.bool_] | None = None
+
+        for i, k in enumerate(seq):
+            lvl_indexer: npt.NDArray[np.bool_] | slice | None = None
+
+            if com.is_bool_indexer(k):
+                if len(k) != n:
+                    raise ValueError(
+                        "cannot index with a boolean indexer that "
+                        "is not the same length as the index"
+                    )
+                lvl_indexer = np.asarray(k)
+                if indexer is None:
+                    lvl_indexer = lvl_indexer.copy()
+
+            elif is_list_like(k):
+                # a collection of labels to include from this level (these are or'd)
+
+                # GH#27591 check if this is a single tuple key in the level
+                try:
+                    lvl_indexer = self._get_level_indexer(k, level=i, indexer=indexer)
+                except (InvalidIndexError, TypeError, KeyError) as err:
+                    # InvalidIndexError e.g. non-hashable, fall back to treating
+                    #  this as a sequence of labels
+                    # KeyError it can be ambiguous if this is a label or sequence
+                    #  of labels
+                    #  github.com/pandas-dev/pandas/issues/39424#issuecomment-871626708
+                    for x in k:
+                        if not is_hashable(x):
+                            # e.g. slice
+                            raise err
+                        # GH 39424: Ignore not founds
+                        # GH 42351: No longer ignore not founds & enforced in 2.0
+                        # TODO: how to handle IntervalIndex level? (no test cases)
+                        item_indexer = self._get_level_indexer(
+                            x, level=i, indexer=indexer
+                        )
+                        if lvl_indexer is None:
+                            lvl_indexer = _to_bool_indexer(item_indexer)
+                        elif isinstance(item_indexer, slice):
+                            lvl_indexer[item_indexer] = True  # type: ignore[index]
+                        else:
+                            lvl_indexer |= item_indexer
+
+                if lvl_indexer is None:
+                    # no matches we are done
+                    # test_loc_getitem_duplicates_multiindex_empty_indexer
+                    return np.array([], dtype=np.intp)
+
+            elif com.is_null_slice(k):
+                # empty slice
+                if indexer is None and i == len(seq) - 1:
+                    return np.arange(n, dtype=np.intp)
+                continue
+
+            else:
+                # a slice or a single label
+                lvl_indexer = self._get_level_indexer(k, level=i, indexer=indexer)
+
+            # update indexer
+            lvl_indexer = _to_bool_indexer(lvl_indexer)
+            if indexer is None:
+                indexer = lvl_indexer
+            else:
+                indexer &= lvl_indexer
+                if not np.any(indexer) and np.any(lvl_indexer):
+                    raise KeyError(seq)
+
+        # empty indexer
+        if indexer is None:
+            return np.array([], dtype=np.intp)
+
+        pos_indexer = indexer.nonzero()[0]
+        return self._reorder_indexer(seq, pos_indexer)
+
+    # --------------------------------------------------------------------
+
+    def _reorder_indexer(
+        self,
+        seq: tuple[Scalar | Iterable | AnyArrayLike, ...],
+        indexer: npt.NDArray[np.intp],
+    ) -> npt.NDArray[np.intp]:
+        """
+        Reorder an indexer of a MultiIndex (self) so that the labels are in the
+        same order as given in seq
+
+        Parameters
+        ----------
+        seq : label/slice/list/mask or a sequence of such
+        indexer: a position indexer of self
+
+        Returns
+        -------
+        indexer : a sorted position indexer of self ordered as seq
+        """
+
+        # check if sorting is necessary
+        need_sort = False
+        for i, k in enumerate(seq):
+            if com.is_null_slice(k) or com.is_bool_indexer(k) or is_scalar(k):
+                pass
+            elif is_list_like(k):
+                if len(k) <= 1:  # type: ignore[arg-type]
+                    pass
+                elif self._is_lexsorted():
+                    # If the index is lexsorted and the list_like label
+                    # in seq are sorted then we do not need to sort
+                    k_codes = self.levels[i].get_indexer(k)
+                    k_codes = k_codes[k_codes >= 0]  # Filter absent keys
+                    # True if the given codes are not ordered
+                    need_sort = (k_codes[:-1] > k_codes[1:]).any()
+                else:
+                    need_sort = True
+            elif isinstance(k, slice):
+                if self._is_lexsorted():
+                    need_sort = k.step is not None and k.step < 0
+                else:
+                    need_sort = True
+            else:
+                need_sort = True
+            if need_sort:
+                break
+        if not need_sort:
+            return indexer
+
+        n = len(self)
+        keys: tuple[np.ndarray, ...] = ()
+        # For each level of the sequence in seq, map the level codes with the
+        # order they appears in a list-like sequence
+        # This mapping is then use to reorder the indexer
+        for i, k in enumerate(seq):
+            if is_scalar(k):
+                # GH#34603 we want to treat a scalar the same as an all equal list
+                k = [k]
+            if com.is_bool_indexer(k):
+                new_order = np.arange(n)[indexer]
+            elif is_list_like(k):
+                # Generate a map with all level codes as sorted initially
+                if not isinstance(k, (np.ndarray, ExtensionArray, Index, ABCSeries)):
+                    k = sanitize_array(k, None)
+                k = algos.unique(k)
+                key_order_map = np.ones(len(self.levels[i]), dtype=np.uint64) * len(
+                    self.levels[i]
+                )
+                # Set order as given in the indexer list
+                level_indexer = self.levels[i].get_indexer(k)
+                level_indexer = level_indexer[level_indexer >= 0]  # Filter absent keys
+                key_order_map[level_indexer] = np.arange(len(level_indexer))
+
+                new_order = key_order_map[self.codes[i][indexer]]
+            elif isinstance(k, slice) and k.step is not None and k.step < 0:
+                # flip order for negative step
+                new_order = np.arange(n)[::-1][indexer]
+            elif isinstance(k, slice) and k.start is None and k.stop is None:
+                # slice(None) should not determine order GH#31330
+                new_order = np.ones((n,), dtype=np.intp)[indexer]
+            else:
+                # For all other case, use the same order as the level
+                new_order = np.arange(n)[indexer]
+            keys = (new_order,) + keys
+
+        # Find the reordering using lexsort on the keys mapping
+        ind = np.lexsort(keys)
+        return indexer[ind]
+
+    def truncate(self, before=None, after=None) -> MultiIndex:
+        """
+        Slice index between two labels / tuples, return new MultiIndex.
+
+        Parameters
+        ----------
+        before : label or tuple, can be partial. Default None
+            None defaults to start.
+        after : label or tuple, can be partial. Default None
+            None defaults to end.
+
+        Returns
+        -------
+        MultiIndex
+            The truncated MultiIndex.
+
+        Examples
+        --------
+        >>> mi = pd.MultiIndex.from_arrays([['a', 'b', 'c'], ['x', 'y', 'z']])
+        >>> mi
+        MultiIndex([('a', 'x'), ('b', 'y'), ('c', 'z')],
+                   )
+        >>> mi.truncate(before='a', after='b')
+        MultiIndex([('a', 'x'), ('b', 'y')],
+                   )
+        """
+        if after and before and after < before:
+            raise ValueError("after < before")
+
+        i, j = self.levels[0].slice_locs(before, after)
+        left, right = self.slice_locs(before, after)
+
+        new_levels = list(self.levels)
+        new_levels[0] = new_levels[0][i:j]
+
+        new_codes = [level_codes[left:right] for level_codes in self.codes]
+        new_codes[0] = new_codes[0] - i
+
+        return MultiIndex(
+            levels=new_levels,
+            codes=new_codes,
+            names=self._names,
+            verify_integrity=False,
+        )
+
+    def equals(self, other: object) -> bool:
+        """
+        Determines if two MultiIndex objects have the same labeling information
+        (the levels themselves do not necessarily have to be the same)
+
+        See Also
+        --------
+        equal_levels
+        """
+        if self.is_(other):
+            return True
+
+        if not isinstance(other, Index):
+            return False
+
+        if len(self) != len(other):
+            return False
+
+        if not isinstance(other, MultiIndex):
+            # d-level MultiIndex can equal d-tuple Index
+            if not self._should_compare(other):
+                # object Index or Categorical[object] may contain tuples
+                return False
+            return array_equivalent(self._values, other._values)
+
+        if self.nlevels != other.nlevels:
+            return False
+
+        for i in range(self.nlevels):
+            self_codes = self.codes[i]
+            other_codes = other.codes[i]
+            self_mask = self_codes == -1
+            other_mask = other_codes == -1
+            if not np.array_equal(self_mask, other_mask):
+                return False
+            self_codes = self_codes[~self_mask]
+            self_values = self.levels[i]._values.take(self_codes)
+
+            other_codes = other_codes[~other_mask]
+            other_values = other.levels[i]._values.take(other_codes)
+
+            # since we use NaT both datetime64 and timedelta64 we can have a
+            # situation where a level is typed say timedelta64 in self (IOW it
+            # has other values than NaT) but types datetime64 in other (where
+            # its all NaT) but these are equivalent
+            if len(self_values) == 0 and len(other_values) == 0:
+                continue
+
+            if not isinstance(self_values, np.ndarray):
+                # i.e. ExtensionArray
+                if not self_values.equals(other_values):
+                    return False
+            elif not isinstance(other_values, np.ndarray):
+                # i.e. other is ExtensionArray
+                if not other_values.equals(self_values):
+                    return False
+            else:
+                if not array_equivalent(self_values, other_values):
+                    return False
+
+        return True
+
+    def equal_levels(self, other: MultiIndex) -> bool:
+        """
+        Return True if the levels of both MultiIndex objects are the same
+
+        """
+        if self.nlevels != other.nlevels:
+            return False
+
+        for i in range(self.nlevels):
+            if not self.levels[i].equals(other.levels[i]):
+                return False
+        return True
+
+    # --------------------------------------------------------------------
+    # Set Methods
+
+    def _union(self, other, sort) -> MultiIndex:
+        other, result_names = self._convert_can_do_setop(other)
+        if other.has_duplicates:
+            # This is only necessary if other has dupes,
+            # otherwise difference is faster
+            result = super()._union(other, sort)
+
+            if isinstance(result, MultiIndex):
+                return result
+            return MultiIndex.from_arrays(
+                zip(*result), sortorder=None, names=result_names
+            )
+
+        else:
+            right_missing = other.difference(self, sort=False)
+            if len(right_missing):
+                result = self.append(right_missing)
+            else:
+                result = self._get_reconciled_name_object(other)
+
+            if sort is not False:
+                try:
+                    result = result.sort_values()
+                except TypeError:
+                    if sort is True:
+                        raise
+                    warnings.warn(
+                        "The values in the array are unorderable. "
+                        "Pass `sort=False` to suppress this warning.",
+                        RuntimeWarning,
+                        stacklevel=find_stack_level(),
+                    )
+            return result
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        return is_object_dtype(dtype)
+
+    def _get_reconciled_name_object(self, other) -> MultiIndex:
+        """
+        If the result of a set operation will be self,
+        return self, unless the names change, in which
+        case make a shallow copy of self.
+        """
+        names = self._maybe_match_names(other)
+        if self.names != names:
+            # error: Cannot determine type of "rename"
+            return self.rename(names)  # type: ignore[has-type]
+        return self
+
+    def _maybe_match_names(self, other):
+        """
+        Try to find common names to attach to the result of an operation between
+        a and b. Return a consensus list of names if they match at least partly
+        or list of None if they have completely different names.
+        """
+        if len(self.names) != len(other.names):
+            return [None] * len(self.names)
+        names = []
+        for a_name, b_name in zip(self.names, other.names):
+            if a_name == b_name:
+                names.append(a_name)
+            else:
+                # TODO: what if they both have np.nan for their names?
+                names.append(None)
+        return names
+
+    def _wrap_intersection_result(self, other, result) -> MultiIndex:
+        _, result_names = self._convert_can_do_setop(other)
+        return result.set_names(result_names)
+
+    def _wrap_difference_result(self, other, result: MultiIndex) -> MultiIndex:
+        _, result_names = self._convert_can_do_setop(other)
+
+        if len(result) == 0:
+            return result.remove_unused_levels().set_names(result_names)
+        else:
+            return result.set_names(result_names)
+
+    def _convert_can_do_setop(self, other):
+        result_names = self.names
+
+        if not isinstance(other, Index):
+            if len(other) == 0:
+                return self[:0], self.names
+            else:
+                msg = "other must be a MultiIndex or a list of tuples"
+                try:
+                    other = MultiIndex.from_tuples(other, names=self.names)
+                except (ValueError, TypeError) as err:
+                    # ValueError raised by tuples_to_object_array if we
+                    #  have non-object dtype
+                    raise TypeError(msg) from err
+        else:
+            result_names = get_unanimous_names(self, other)
+
+        return other, result_names
+
+    # --------------------------------------------------------------------
+
+    @doc(Index.astype)
+    def astype(self, dtype, copy: bool = True):
+        dtype = pandas_dtype(dtype)
+        if isinstance(dtype, CategoricalDtype):
+            msg = "> 1 ndim Categorical are not supported at this time"
+            raise NotImplementedError(msg)
+        if not is_object_dtype(dtype):
+            raise TypeError(
+                "Setting a MultiIndex dtype to anything other than object "
+                "is not supported"
+            )
+        if copy is True:
+            return self._view()
+        return self
+
+    def _validate_fill_value(self, item):
+        if isinstance(item, MultiIndex):
+            # GH#43212
+            if item.nlevels != self.nlevels:
+                raise ValueError("Item must have length equal to number of levels.")
+            return item._values
+        elif not isinstance(item, tuple):
+            # Pad the key with empty strings if lower levels of the key
+            # aren't specified:
+            item = (item,) + ("",) * (self.nlevels - 1)
+        elif len(item) != self.nlevels:
+            raise ValueError("Item must have length equal to number of levels.")
+        return item
+
+    def putmask(self, mask, value: MultiIndex) -> MultiIndex:
+        """
+        Return a new MultiIndex of the values set with the mask.
+
+        Parameters
+        ----------
+        mask : array like
+        value : MultiIndex
+            Must either be the same length as self or length one
+
+        Returns
+        -------
+        MultiIndex
+        """
+        mask, noop = validate_putmask(self, mask)
+        if noop:
+            return self.copy()
+
+        if len(mask) == len(value):
+            subset = value[mask].remove_unused_levels()
+        else:
+            subset = value.remove_unused_levels()
+
+        new_levels = []
+        new_codes = []
+
+        for i, (value_level, level, level_codes) in enumerate(
+            zip(subset.levels, self.levels, self.codes)
+        ):
+            new_level = level.union(value_level, sort=False)
+            value_codes = new_level.get_indexer_for(subset.get_level_values(i))
+            new_code = ensure_int64(level_codes)
+            new_code[mask] = value_codes
+            new_levels.append(new_level)
+            new_codes.append(new_code)
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=self.names, verify_integrity=False
+        )
+
+    def insert(self, loc: int, item) -> MultiIndex:
+        """
+        Make new MultiIndex inserting new item at location
+
+        Parameters
+        ----------
+        loc : int
+        item : tuple
+            Must be same length as number of levels in the MultiIndex
+
+        Returns
+        -------
+        new_index : Index
+        """
+        item = self._validate_fill_value(item)
+
+        new_levels = []
+        new_codes = []
+        for k, level, level_codes in zip(item, self.levels, self.codes):
+            if k not in level:
+                # have to insert into level
+                # must insert at end otherwise you have to recompute all the
+                # other codes
+                lev_loc = len(level)
+                level = level.insert(lev_loc, k)
+            else:
+                lev_loc = level.get_loc(k)
+
+            new_levels.append(level)
+            new_codes.append(np.insert(ensure_int64(level_codes), loc, lev_loc))
+
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=self.names, verify_integrity=False
+        )
+
+    def delete(self, loc) -> MultiIndex:
+        """
+        Make new index with passed location deleted
+
+        Returns
+        -------
+        new_index : MultiIndex
+        """
+        new_codes = [np.delete(level_codes, loc) for level_codes in self.codes]
+        return MultiIndex(
+            levels=self.levels,
+            codes=new_codes,
+            names=self.names,
+            verify_integrity=False,
+        )
+
+    @doc(Index.isin)
+    def isin(self, values, level=None) -> npt.NDArray[np.bool_]:
+        if isinstance(values, Generator):
+            values = list(values)
+
+        if level is None:
+            if len(values) == 0:
+                return np.zeros((len(self),), dtype=np.bool_)
+            if not isinstance(values, MultiIndex):
+                values = MultiIndex.from_tuples(values)
+            return values.unique().get_indexer_for(self) != -1
+        else:
+            num = self._get_level_number(level)
+            levs = self.get_level_values(num)
+
+            if levs.size == 0:
+                return np.zeros(len(levs), dtype=np.bool_)
+            return levs.isin(values)
+
+    # error: Incompatible types in assignment (expression has type overloaded function,
+    # base class "Index" defined the type as "Callable[[Index, Any, bool], Any]")
+    rename = Index.set_names  # type: ignore[assignment]
+
+    # ---------------------------------------------------------------
+    # Arithmetic/Numeric Methods - Disabled
+
+    __add__ = make_invalid_op("__add__")
+    __radd__ = make_invalid_op("__radd__")
+    __iadd__ = make_invalid_op("__iadd__")
+    __sub__ = make_invalid_op("__sub__")
+    __rsub__ = make_invalid_op("__rsub__")
+    __isub__ = make_invalid_op("__isub__")
+    __pow__ = make_invalid_op("__pow__")
+    __rpow__ = make_invalid_op("__rpow__")
+    __mul__ = make_invalid_op("__mul__")
+    __rmul__ = make_invalid_op("__rmul__")
+    __floordiv__ = make_invalid_op("__floordiv__")
+    __rfloordiv__ = make_invalid_op("__rfloordiv__")
+    __truediv__ = make_invalid_op("__truediv__")
+    __rtruediv__ = make_invalid_op("__rtruediv__")
+    __mod__ = make_invalid_op("__mod__")
+    __rmod__ = make_invalid_op("__rmod__")
+    __divmod__ = make_invalid_op("__divmod__")
+    __rdivmod__ = make_invalid_op("__rdivmod__")
+    # Unary methods disabled
+    __neg__ = make_invalid_op("__neg__")
+    __pos__ = make_invalid_op("__pos__")
+    __abs__ = make_invalid_op("__abs__")
+    __invert__ = make_invalid_op("__invert__")
+
+
+def _lexsort_depth(codes: list[np.ndarray], nlevels: int) -> int:
+    """Count depth (up to a maximum of `nlevels`) with which codes are lexsorted."""
+    int64_codes = [ensure_int64(level_codes) for level_codes in codes]
+    for k in range(nlevels, 0, -1):
+        if libalgos.is_lexsorted(int64_codes[:k]):
+            return k
+    return 0
+
+
+def sparsify_labels(label_list, start: int = 0, sentinel: object = ""):
+    pivoted = list(zip(*label_list))
+    k = len(label_list)
+
+    result = pivoted[: start + 1]
+    prev = pivoted[start]
+
+    for cur in pivoted[start + 1 :]:
+        sparse_cur = []
+
+        for i, (p, t) in enumerate(zip(prev, cur)):
+            if i == k - 1:
+                sparse_cur.append(t)
+                # error: Argument 1 to "append" of "list" has incompatible
+                # type "list[Any]"; expected "tuple[Any, ...]"
+                result.append(sparse_cur)  # type: ignore[arg-type]
+                break
+
+            if p == t:
+                sparse_cur.append(sentinel)
+            else:
+                sparse_cur.extend(cur[i:])
+                # error: Argument 1 to "append" of "list" has incompatible
+                # type "list[Any]"; expected "tuple[Any, ...]"
+                result.append(sparse_cur)  # type: ignore[arg-type]
+                break
+
+        prev = cur
+
+    return list(zip(*result))
+
+
+def _get_na_rep(dtype: DtypeObj) -> str:
+    if isinstance(dtype, ExtensionDtype):
+        return f"{dtype.na_value}"
+    else:
+        dtype_type = dtype.type
+
+    return {np.datetime64: "NaT", np.timedelta64: "NaT"}.get(dtype_type, "NaN")
+
+
+def maybe_droplevels(index: Index, key) -> Index:
+    """
+    Attempt to drop level or levels from the given index.
+
+    Parameters
+    ----------
+    index: Index
+    key : scalar or tuple
+
+    Returns
+    -------
+    Index
+    """
+    # drop levels
+    original_index = index
+    if isinstance(key, tuple):
+        # Caller is responsible for ensuring the key is not an entry in the first
+        #  level of the MultiIndex.
+        for _ in key:
+            try:
+                index = index._drop_level_numbers([0])
+            except ValueError:
+                # we have dropped too much, so back out
+                return original_index
+    else:
+        try:
+            index = index._drop_level_numbers([0])
+        except ValueError:
+            pass
+
+    return index
+
+
+def _coerce_indexer_frozen(array_like, categories, copy: bool = False) -> np.ndarray:
+    """
+    Coerce the array-like indexer to the smallest integer dtype that can encode all
+    of the given categories.
+
+    Parameters
+    ----------
+    array_like : array-like
+    categories : array-like
+    copy : bool
+
+    Returns
+    -------
+    np.ndarray
+        Non-writeable.
+    """
+    array_like = coerce_indexer_dtype(array_like, categories)
+    if copy:
+        array_like = array_like.copy()
+    array_like.flags.writeable = False
+    return array_like
+
+
+def _require_listlike(level, arr, arrname: str):
+    """
+    Ensure that level is either None or listlike, and arr is list-of-listlike.
+    """
+    if level is not None and not is_list_like(level):
+        if not is_list_like(arr):
+            raise TypeError(f"{arrname} must be list-like")
+        if len(arr) > 0 and is_list_like(arr[0]):
+            raise TypeError(f"{arrname} must be list-like")
+        level = [level]
+        arr = [arr]
+    elif level is None or is_list_like(level):
+        if not is_list_like(arr) or not is_list_like(arr[0]):
+            raise TypeError(f"{arrname} must be list of lists-like")
+    return level, arr
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/period.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/period.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f1933800fd383df9dc52a211b54190985fc32e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/period.py
@@ -0,0 +1,614 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    timedelta,
+)
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._libs import index as libindex
+from pandas._libs.tslibs import (
+    BaseOffset,
+    NaT,
+    Period,
+    Resolution,
+    Tick,
+)
+from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR
+from pandas.util._decorators import (
+    cache_readonly,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_integer
+from pandas.core.dtypes.dtypes import PeriodDtype
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import is_valid_na_for_dtype
+
+from pandas.core.arrays.period import (
+    PeriodArray,
+    period_array,
+    raise_on_incompatible,
+    validate_dtype_freq,
+)
+import pandas.core.common as com
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import maybe_extract_name
+from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
+from pandas.core.indexes.datetimes import (
+    DatetimeIndex,
+    Index,
+)
+from pandas.core.indexes.extension import inherit_names
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        Dtype,
+        DtypeObj,
+        Self,
+        npt,
+    )
+
+
+_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+_index_doc_kwargs.update({"target_klass": "PeriodIndex or list of Periods"})
+_shared_doc_kwargs = {
+    "klass": "PeriodArray",
+}
+
+# --- Period index sketch
+
+
+def _new_PeriodIndex(cls, **d):
+    # GH13277 for unpickling
+    values = d.pop("data")
+    if values.dtype == "int64":
+        freq = d.pop("freq", None)
+        dtype = PeriodDtype(freq)
+        values = PeriodArray(values, dtype=dtype)
+        return cls._simple_new(values, **d)
+    else:
+        return cls(values, **d)
+
+
+@inherit_names(
+    ["strftime", "start_time", "end_time"] + PeriodArray._field_ops,
+    PeriodArray,
+    wrap=True,
+)
+@inherit_names(["is_leap_year"], PeriodArray)
+class PeriodIndex(DatetimeIndexOpsMixin):
+    """
+    Immutable ndarray holding ordinal values indicating regular periods in time.
+
+    Index keys are boxed to Period objects which carries the metadata (eg,
+    frequency information).
+
+    Parameters
+    ----------
+    data : array-like (1d int np.ndarray or PeriodArray), optional
+        Optional period-like data to construct index with.
+    copy : bool
+        Make a copy of input ndarray.
+    freq : str or period object, optional
+        One of pandas period strings or corresponding objects.
+    year : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    month : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    quarter : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    day : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    hour : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    minute : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    second : int, array, or Series, default None
+
+        .. deprecated:: 2.2.0
+           Use PeriodIndex.from_fields instead.
+    dtype : str or PeriodDtype, default None
+
+    Attributes
+    ----------
+    day
+    dayofweek
+    day_of_week
+    dayofyear
+    day_of_year
+    days_in_month
+    daysinmonth
+    end_time
+    freq
+    freqstr
+    hour
+    is_leap_year
+    minute
+    month
+    quarter
+    qyear
+    second
+    start_time
+    week
+    weekday
+    weekofyear
+    year
+
+    Methods
+    -------
+    asfreq
+    strftime
+    to_timestamp
+    from_fields
+    from_ordinals
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    Period : Represents a period of time.
+    DatetimeIndex : Index with datetime64 data.
+    TimedeltaIndex : Index of timedelta64 data.
+    period_range : Create a fixed-frequency PeriodIndex.
+
+    Examples
+    --------
+    >>> idx = pd.PeriodIndex.from_fields(year=[2000, 2002], quarter=[1, 3])
+    >>> idx
+    PeriodIndex(['2000Q1', '2002Q3'], dtype='period[Q-DEC]')
+    """
+
+    _typ = "periodindex"
+
+    _data: PeriodArray
+    freq: BaseOffset
+    dtype: PeriodDtype
+
+    _data_cls = PeriodArray
+    _supports_partial_string_indexing = True
+
+    @property
+    def _engine_type(self) -> type[libindex.PeriodEngine]:
+        return libindex.PeriodEngine
+
+    @cache_readonly
+    def _resolution_obj(self) -> Resolution:
+        # for compat with DatetimeIndex
+        return self.dtype._resolution_obj
+
+    # --------------------------------------------------------------------
+    # methods that dispatch to array and wrap result in Index
+    # These are defined here instead of via inherit_names for mypy
+
+    @doc(
+        PeriodArray.asfreq,
+        other="pandas.arrays.PeriodArray",
+        other_name="PeriodArray",
+        **_shared_doc_kwargs,
+    )
+    def asfreq(self, freq=None, how: str = "E") -> Self:
+        arr = self._data.asfreq(freq, how)
+        return type(self)._simple_new(arr, name=self.name)
+
+    @doc(PeriodArray.to_timestamp)
+    def to_timestamp(self, freq=None, how: str = "start") -> DatetimeIndex:
+        arr = self._data.to_timestamp(freq, how)
+        return DatetimeIndex._simple_new(arr, name=self.name)
+
+    @property
+    @doc(PeriodArray.hour.fget)
+    def hour(self) -> Index:
+        return Index(self._data.hour, name=self.name)
+
+    @property
+    @doc(PeriodArray.minute.fget)
+    def minute(self) -> Index:
+        return Index(self._data.minute, name=self.name)
+
+    @property
+    @doc(PeriodArray.second.fget)
+    def second(self) -> Index:
+        return Index(self._data.second, name=self.name)
+
+    # ------------------------------------------------------------------------
+    # Index Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        ordinal=None,
+        freq=None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+        **fields,
+    ) -> Self:
+        valid_field_set = {
+            "year",
+            "month",
+            "day",
+            "quarter",
+            "hour",
+            "minute",
+            "second",
+        }
+
+        refs = None
+        if not copy and isinstance(data, (Index, ABCSeries)):
+            refs = data._references
+
+        if not set(fields).issubset(valid_field_set):
+            argument = next(iter(set(fields) - valid_field_set))
+            raise TypeError(f"__new__() got an unexpected keyword argument {argument}")
+        elif len(fields):
+            # GH#55960
+            warnings.warn(
+                "Constructing PeriodIndex from fields is deprecated. Use "
+                "PeriodIndex.from_fields instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if ordinal is not None:
+            # GH#55960
+            warnings.warn(
+                "The 'ordinal' keyword in PeriodIndex is deprecated and will "
+                "be removed in a future version. Use PeriodIndex.from_ordinals "
+                "instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        name = maybe_extract_name(name, data, cls)
+
+        if data is None and ordinal is None:
+            # range-based.
+            if not fields:
+                # test_pickle_compat_construction
+                cls._raise_scalar_data_error(None)
+            data = cls.from_fields(**fields, freq=freq)._data
+            copy = False
+
+        elif fields:
+            if data is not None:
+                raise ValueError("Cannot pass both data and fields")
+            raise ValueError("Cannot pass both ordinal and fields")
+
+        else:
+            freq = validate_dtype_freq(dtype, freq)
+
+            # PeriodIndex allow PeriodIndex(period_index, freq=different)
+            # Let's not encourage that kind of behavior in PeriodArray.
+
+            if freq and isinstance(data, cls) and data.freq != freq:
+                # TODO: We can do some of these with no-copy / coercion?
+                # e.g. D -> 2D seems to be OK
+                data = data.asfreq(freq)
+
+            if data is None and ordinal is not None:
+                ordinal = np.asarray(ordinal, dtype=np.int64)
+                dtype = PeriodDtype(freq)
+                data = PeriodArray(ordinal, dtype=dtype)
+            elif data is not None and ordinal is not None:
+                raise ValueError("Cannot pass both data and ordinal")
+            else:
+                # don't pass copy here, since we copy later.
+                data = period_array(data=data, freq=freq)
+
+        if copy:
+            data = data.copy()
+
+        return cls._simple_new(data, name=name, refs=refs)
+
+    @classmethod
+    def from_fields(
+        cls,
+        *,
+        year=None,
+        quarter=None,
+        month=None,
+        day=None,
+        hour=None,
+        minute=None,
+        second=None,
+        freq=None,
+    ) -> Self:
+        fields = {
+            "year": year,
+            "quarter": quarter,
+            "month": month,
+            "day": day,
+            "hour": hour,
+            "minute": minute,
+            "second": second,
+        }
+        fields = {key: value for key, value in fields.items() if value is not None}
+        arr = PeriodArray._from_fields(fields=fields, freq=freq)
+        return cls._simple_new(arr)
+
+    @classmethod
+    def from_ordinals(cls, ordinals, *, freq, name=None) -> Self:
+        ordinals = np.asarray(ordinals, dtype=np.int64)
+        dtype = PeriodDtype(freq)
+        data = PeriodArray._simple_new(ordinals, dtype=dtype)
+        return cls._simple_new(data, name=name)
+
+    # ------------------------------------------------------------------------
+    # Data
+
+    @property
+    def values(self) -> npt.NDArray[np.object_]:
+        return np.asarray(self, dtype=object)
+
+    def _maybe_convert_timedelta(self, other) -> int | npt.NDArray[np.int64]:
+        """
+        Convert timedelta-like input to an integer multiple of self.freq
+
+        Parameters
+        ----------
+        other : timedelta, np.timedelta64, DateOffset, int, np.ndarray
+
+        Returns
+        -------
+        converted : int, np.ndarray[int64]
+
+        Raises
+        ------
+        IncompatibleFrequency : if the input cannot be written as a multiple
+            of self.freq.  Note IncompatibleFrequency subclasses ValueError.
+        """
+        if isinstance(other, (timedelta, np.timedelta64, Tick, np.ndarray)):
+            if isinstance(self.freq, Tick):
+                # _check_timedeltalike_freq_compat will raise if incompatible
+                delta = self._data._check_timedeltalike_freq_compat(other)
+                return delta
+        elif isinstance(other, BaseOffset):
+            if other.base == self.freq.base:
+                return other.n
+
+            raise raise_on_incompatible(self, other)
+        elif is_integer(other):
+            assert isinstance(other, int)
+            return other
+
+        # raise when input doesn't have freq
+        raise raise_on_incompatible(self, None)
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        return self.dtype == dtype
+
+    # ------------------------------------------------------------------------
+    # Index Methods
+
+    def asof_locs(self, where: Index, mask: npt.NDArray[np.bool_]) -> np.ndarray:
+        """
+        where : array of timestamps
+        mask : np.ndarray[bool]
+            Array of booleans where data is not NA.
+        """
+        if isinstance(where, DatetimeIndex):
+            where = PeriodIndex(where._values, freq=self.freq)
+        elif not isinstance(where, PeriodIndex):
+            raise TypeError("asof_locs `where` must be DatetimeIndex or PeriodIndex")
+
+        return super().asof_locs(where, mask)
+
+    @property
+    def is_full(self) -> bool:
+        """
+        Returns True if this PeriodIndex is range-like in that all Periods
+        between start and end are present, in order.
+        """
+        if len(self) == 0:
+            return True
+        if not self.is_monotonic_increasing:
+            raise ValueError("Index is not monotonic")
+        values = self.asi8
+        return bool(((values[1:] - values[:-1]) < 2).all())
+
+    @property
+    def inferred_type(self) -> str:
+        # b/c data is represented as ints make sure we can't have ambiguous
+        # indexing
+        return "period"
+
+    # ------------------------------------------------------------------------
+    # Indexing Methods
+
+    def _convert_tolerance(self, tolerance, target):
+        # Returned tolerance must be in dtype/units so that
+        #  `|self._get_engine_target() - target._engine_target()| <= tolerance`
+        #  is meaningful.  Since PeriodIndex returns int64 for engine_target,
+        #  we may need to convert timedelta64 tolerance to int64.
+        tolerance = super()._convert_tolerance(tolerance, target)
+
+        if self.dtype == target.dtype:
+            # convert tolerance to i8
+            tolerance = self._maybe_convert_timedelta(tolerance)
+
+        return tolerance
+
+    def get_loc(self, key):
+        """
+        Get integer location for requested label.
+
+        Parameters
+        ----------
+        key : Period, NaT, str, or datetime
+            String or datetime key must be parsable as Period.
+
+        Returns
+        -------
+        loc : int or ndarray[int64]
+
+        Raises
+        ------
+        KeyError
+            Key is not present in the index.
+        TypeError
+            If key is listlike or otherwise not hashable.
+        """
+        orig_key = key
+
+        self._check_indexing_error(key)
+
+        if is_valid_na_for_dtype(key, self.dtype):
+            key = NaT
+
+        elif isinstance(key, str):
+            try:
+                parsed, reso = self._parse_with_reso(key)
+            except ValueError as err:
+                # A string with invalid format
+                raise KeyError(f"Cannot interpret '{key}' as period") from err
+
+            if self._can_partial_date_slice(reso):
+                try:
+                    return self._partial_date_slice(reso, parsed)
+                except KeyError as err:
+                    raise KeyError(key) from err
+
+            if reso == self._resolution_obj:
+                # the reso < self._resolution_obj case goes
+                #  through _get_string_slice
+                key = self._cast_partial_indexing_scalar(parsed)
+            else:
+                raise KeyError(key)
+
+        elif isinstance(key, Period):
+            self._disallow_mismatched_indexing(key)
+
+        elif isinstance(key, datetime):
+            key = self._cast_partial_indexing_scalar(key)
+
+        else:
+            # in particular integer, which Period constructor would cast to string
+            raise KeyError(key)
+
+        try:
+            return Index.get_loc(self, key)
+        except KeyError as err:
+            raise KeyError(orig_key) from err
+
+    def _disallow_mismatched_indexing(self, key: Period) -> None:
+        if key._dtype != self.dtype:
+            raise KeyError(key)
+
+    def _cast_partial_indexing_scalar(self, label: datetime) -> Period:
+        try:
+            period = Period(label, freq=self.freq)
+        except ValueError as err:
+            # we cannot construct the Period
+            raise KeyError(label) from err
+        return period
+
+    @doc(DatetimeIndexOpsMixin._maybe_cast_slice_bound)
+    def _maybe_cast_slice_bound(self, label, side: str):
+        if isinstance(label, datetime):
+            label = self._cast_partial_indexing_scalar(label)
+
+        return super()._maybe_cast_slice_bound(label, side)
+
+    def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime):
+        freq = OFFSET_TO_PERIOD_FREQSTR.get(reso.attr_abbrev, reso.attr_abbrev)
+        iv = Period(parsed, freq=freq)
+        return (iv.asfreq(self.freq, how="start"), iv.asfreq(self.freq, how="end"))
+
+    @doc(DatetimeIndexOpsMixin.shift)
+    def shift(self, periods: int = 1, freq=None) -> Self:
+        if freq is not None:
+            raise TypeError(
+                f"`freq` argument is not supported for {type(self).__name__}.shift"
+            )
+        return self + periods
+
+
+def period_range(
+    start=None,
+    end=None,
+    periods: int | None = None,
+    freq=None,
+    name: Hashable | None = None,
+) -> PeriodIndex:
+    """
+    Return a fixed frequency PeriodIndex.
+
+    The day (calendar) is the default frequency.
+
+    Parameters
+    ----------
+    start : str, datetime, date, pandas.Timestamp, or period-like, default None
+        Left bound for generating periods.
+    end : str, datetime, date, pandas.Timestamp, or period-like, default None
+        Right bound for generating periods.
+    periods : int, default None
+        Number of periods to generate.
+    freq : str or DateOffset, optional
+        Frequency alias. By default the freq is taken from `start` or `end`
+        if those are Period objects. Otherwise, the default is ``"D"`` for
+        daily frequency.
+    name : str, default None
+        Name of the resulting PeriodIndex.
+
+    Returns
+    -------
+    PeriodIndex
+
+    Notes
+    -----
+    Of the three parameters: ``start``, ``end``, and ``periods``, exactly two
+    must be specified.
+
+    To learn more about the frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    >>> pd.period_range(start='2017-01-01', end='2018-01-01', freq='M')
+    PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06',
+             '2017-07', '2017-08', '2017-09', '2017-10', '2017-11', '2017-12',
+             '2018-01'],
+            dtype='period[M]')
+
+    If ``start`` or ``end`` are ``Period`` objects, they will be used as anchor
+    endpoints for a ``PeriodIndex`` with frequency matching that of the
+    ``period_range`` constructor.
+
+    >>> pd.period_range(start=pd.Period('2017Q1', freq='Q'),
+    ...                 end=pd.Period('2017Q2', freq='Q'), freq='M')
+    PeriodIndex(['2017-03', '2017-04', '2017-05', '2017-06'],
+                dtype='period[M]')
+    """
+    if com.count_not_none(start, end, periods) != 2:
+        raise ValueError(
+            "Of the three parameters: start, end, and periods, "
+            "exactly two must be specified"
+        )
+    if freq is None and (not isinstance(start, Period) and not isinstance(end, Period)):
+        freq = "D"
+
+    data, freq = PeriodArray._generate_range(start, end, periods, freq)
+    dtype = PeriodDtype(freq)
+    data = PeriodArray(data, dtype=dtype)
+    return PeriodIndex(data, name=name)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/range.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/range.py
new file mode 100644
index 0000000000000000000000000000000000000000..62afcf8badb50d95f2cc006bf8d89e79f6fe8615
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/range.py
@@ -0,0 +1,1187 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterator,
+)
+from datetime import timedelta
+import operator
+from sys import getsizeof
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    index as libindex,
+    lib,
+)
+from pandas._libs.algos import unique_deltas
+from pandas._libs.lib import no_default
+from pandas.compat.numpy import function as nv
+from pandas.util._decorators import (
+    cache_readonly,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    ensure_python_int,
+    is_float,
+    is_integer,
+    is_scalar,
+    is_signed_integer_dtype,
+)
+from pandas.core.dtypes.generic import ABCTimedeltaIndex
+
+from pandas.core import ops
+import pandas.core.common as com
+from pandas.core.construction import extract_array
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        Dtype,
+        NaPosition,
+        Self,
+        npt,
+    )
+_empty_range = range(0)
+_dtype_int64 = np.dtype(np.int64)
+
+
+class RangeIndex(Index):
+    """
+    Immutable Index implementing a monotonic integer range.
+
+    RangeIndex is a memory-saving special case of an Index limited to representing
+    monotonic ranges with a 64-bit dtype. Using RangeIndex may in some instances
+    improve computing speed.
+
+    This is the default index type used
+    by DataFrame and Series when no explicit index is provided by the user.
+
+    Parameters
+    ----------
+    start : int (default: 0), range, or other RangeIndex instance
+        If int and "stop" is not given, interpreted as "stop" instead.
+    stop : int (default: 0)
+    step : int (default: 1)
+    dtype : np.int64
+        Unused, accepted for homogeneity with other index types.
+    copy : bool, default False
+        Unused, accepted for homogeneity with other index types.
+    name : object, optional
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    start
+    stop
+    step
+
+    Methods
+    -------
+    from_range
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+
+    Examples
+    --------
+    >>> list(pd.RangeIndex(5))
+    [0, 1, 2, 3, 4]
+
+    >>> list(pd.RangeIndex(-2, 4))
+    [-2, -1, 0, 1, 2, 3]
+
+    >>> list(pd.RangeIndex(0, 10, 2))
+    [0, 2, 4, 6, 8]
+
+    >>> list(pd.RangeIndex(2, -10, -3))
+    [2, -1, -4, -7]
+
+    >>> list(pd.RangeIndex(0))
+    []
+
+    >>> list(pd.RangeIndex(1, 0))
+    []
+    """
+
+    _typ = "rangeindex"
+    _dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
+    _range: range
+    _values: np.ndarray
+
+    @property
+    def _engine_type(self) -> type[libindex.Int64Engine]:
+        return libindex.Int64Engine
+
+    # --------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        start=None,
+        stop=None,
+        step=None,
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable | None = None,
+    ) -> Self:
+        cls._validate_dtype(dtype)
+        name = maybe_extract_name(name, start, cls)
+
+        # RangeIndex
+        if isinstance(start, cls):
+            return start.copy(name=name)
+        elif isinstance(start, range):
+            return cls._simple_new(start, name=name)
+
+        # validate the arguments
+        if com.all_none(start, stop, step):
+            raise TypeError("RangeIndex(...) must be called with integers")
+
+        start = ensure_python_int(start) if start is not None else 0
+
+        if stop is None:
+            start, stop = 0, start
+        else:
+            stop = ensure_python_int(stop)
+
+        step = ensure_python_int(step) if step is not None else 1
+        if step == 0:
+            raise ValueError("Step must not be zero")
+
+        rng = range(start, stop, step)
+        return cls._simple_new(rng, name=name)
+
+    @classmethod
+    def from_range(cls, data: range, name=None, dtype: Dtype | None = None) -> Self:
+        """
+        Create :class:`pandas.RangeIndex` from a ``range`` object.
+
+        Returns
+        -------
+        RangeIndex
+
+        Examples
+        --------
+        >>> pd.RangeIndex.from_range(range(5))
+        RangeIndex(start=0, stop=5, step=1)
+
+        >>> pd.RangeIndex.from_range(range(2, -10, -3))
+        RangeIndex(start=2, stop=-10, step=-3)
+        """
+        if not isinstance(data, range):
+            raise TypeError(
+                f"{cls.__name__}(...) must be called with object coercible to a "
+                f"range, {repr(data)} was passed"
+            )
+        cls._validate_dtype(dtype)
+        return cls._simple_new(data, name=name)
+
+    #  error: Argument 1 of "_simple_new" is incompatible with supertype "Index";
+    #  supertype defines the argument type as
+    #  "Union[ExtensionArray, ndarray[Any, Any]]"  [override]
+    @classmethod
+    def _simple_new(  # type: ignore[override]
+        cls, values: range, name: Hashable | None = None
+    ) -> Self:
+        result = object.__new__(cls)
+
+        assert isinstance(values, range)
+
+        result._range = values
+        result._name = name
+        result._cache = {}
+        result._reset_identity()
+        result._references = None
+        return result
+
+    @classmethod
+    def _validate_dtype(cls, dtype: Dtype | None) -> None:
+        if dtype is None:
+            return
+
+        validation_func, expected = cls._dtype_validation_metadata
+        if not validation_func(dtype):
+            raise ValueError(
+                f"Incorrect `dtype` passed: expected {expected}, received {dtype}"
+            )
+
+    # --------------------------------------------------------------------
+
+    # error: Return type "Type[Index]" of "_constructor" incompatible with return
+    # type "Type[RangeIndex]" in supertype "Index"
+    @cache_readonly
+    def _constructor(self) -> type[Index]:  # type: ignore[override]
+        """return the class to use for construction"""
+        return Index
+
+    # error: Signature of "_data" incompatible with supertype "Index"
+    @cache_readonly
+    def _data(self) -> np.ndarray:  # type: ignore[override]
+        """
+        An int array that for performance reasons is created only when needed.
+
+        The constructed array is saved in ``_cache``.
+        """
+        return np.arange(self.start, self.stop, self.step, dtype=np.int64)
+
+    def _get_data_as_items(self) -> list[tuple[str, int]]:
+        """return a list of tuples of start, stop, step"""
+        rng = self._range
+        return [("start", rng.start), ("stop", rng.stop), ("step", rng.step)]
+
+    def __reduce__(self):
+        d = {"name": self._name}
+        d.update(dict(self._get_data_as_items()))
+        return ibase._new_Index, (type(self), d), None
+
+    # --------------------------------------------------------------------
+    # Rendering Methods
+
+    def _format_attrs(self):
+        """
+        Return a list of tuples of the (attr, formatted_value)
+        """
+        attrs = cast("list[tuple[str, str | int]]", self._get_data_as_items())
+        if self._name is not None:
+            attrs.append(("name", ibase.default_pprint(self._name)))
+        return attrs
+
+    def _format_with_header(self, *, header: list[str], na_rep: str) -> list[str]:
+        # Equivalent to Index implementation, but faster
+        if not len(self._range):
+            return header
+        first_val_str = str(self._range[0])
+        last_val_str = str(self._range[-1])
+        max_length = max(len(first_val_str), len(last_val_str))
+
+        return header + [f"{x:<{max_length}}" for x in self._range]
+
+    # --------------------------------------------------------------------
+
+    @property
+    def start(self) -> int:
+        """
+        The value of the `start` parameter (``0`` if this was not supplied).
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.start
+        0
+
+        >>> idx = pd.RangeIndex(2, -10, -3)
+        >>> idx.start
+        2
+        """
+        # GH 25710
+        return self._range.start
+
+    @property
+    def stop(self) -> int:
+        """
+        The value of the `stop` parameter.
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.stop
+        5
+
+        >>> idx = pd.RangeIndex(2, -10, -3)
+        >>> idx.stop
+        -10
+        """
+        return self._range.stop
+
+    @property
+    def step(self) -> int:
+        """
+        The value of the `step` parameter (``1`` if this was not supplied).
+
+        Examples
+        --------
+        >>> idx = pd.RangeIndex(5)
+        >>> idx.step
+        1
+
+        >>> idx = pd.RangeIndex(2, -10, -3)
+        >>> idx.step
+        -3
+
+        Even if :class:`pandas.RangeIndex` is empty, ``step`` is still ``1`` if
+        not supplied.
+
+        >>> idx = pd.RangeIndex(1, 0)
+        >>> idx.step
+        1
+        """
+        # GH 25710
+        return self._range.step
+
+    @cache_readonly
+    def nbytes(self) -> int:
+        """
+        Return the number of bytes in the underlying data.
+        """
+        rng = self._range
+        return getsizeof(rng) + sum(
+            getsizeof(getattr(rng, attr_name))
+            for attr_name in ["start", "stop", "step"]
+        )
+
+    def memory_usage(self, deep: bool = False) -> int:
+        """
+        Memory usage of my values
+
+        Parameters
+        ----------
+        deep : bool
+            Introspect the data deeply, interrogate
+            `object` dtypes for system-level memory consumption
+
+        Returns
+        -------
+        bytes used
+
+        Notes
+        -----
+        Memory usage does not include memory consumed by elements that
+        are not components of the array if deep=False
+
+        See Also
+        --------
+        numpy.ndarray.nbytes
+        """
+        return self.nbytes
+
+    @property
+    def dtype(self) -> np.dtype:
+        return _dtype_int64
+
+    @property
+    def is_unique(self) -> bool:
+        """return if the index has unique values"""
+        return True
+
+    @cache_readonly
+    def is_monotonic_increasing(self) -> bool:
+        return self._range.step > 0 or len(self) <= 1
+
+    @cache_readonly
+    def is_monotonic_decreasing(self) -> bool:
+        return self._range.step < 0 or len(self) <= 1
+
+    def __contains__(self, key: Any) -> bool:
+        hash(key)
+        try:
+            key = ensure_python_int(key)
+        except TypeError:
+            return False
+        return key in self._range
+
+    @property
+    def inferred_type(self) -> str:
+        return "integer"
+
+    # --------------------------------------------------------------------
+    # Indexing Methods
+
+    @doc(Index.get_loc)
+    def get_loc(self, key) -> int:
+        if is_integer(key) or (is_float(key) and key.is_integer()):
+            new_key = int(key)
+            try:
+                return self._range.index(new_key)
+            except ValueError as err:
+                raise KeyError(key) from err
+        if isinstance(key, Hashable):
+            raise KeyError(key)
+        self._check_indexing_error(key)
+        raise KeyError(key)
+
+    def _get_indexer(
+        self,
+        target: Index,
+        method: str | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> npt.NDArray[np.intp]:
+        if com.any_not_none(method, tolerance, limit):
+            return super()._get_indexer(
+                target, method=method, tolerance=tolerance, limit=limit
+            )
+
+        if self.step > 0:
+            start, stop, step = self.start, self.stop, self.step
+        else:
+            # GH 28678: work on reversed range for simplicity
+            reverse = self._range[::-1]
+            start, stop, step = reverse.start, reverse.stop, reverse.step
+
+        target_array = np.asarray(target)
+        locs = target_array - start
+        valid = (locs % step == 0) & (locs >= 0) & (target_array < stop)
+        locs[~valid] = -1
+        locs[valid] = locs[valid] / step
+
+        if step != self.step:
+            # We reversed this range: transform to original locs
+            locs[valid] = len(self) - 1 - locs[valid]
+        return ensure_platform_int(locs)
+
+    @cache_readonly
+    def _should_fallback_to_positional(self) -> bool:
+        """
+        Should an integer key be treated as positional?
+        """
+        return False
+
+    # --------------------------------------------------------------------
+
+    def tolist(self) -> list[int]:
+        return list(self._range)
+
+    @doc(Index.__iter__)
+    def __iter__(self) -> Iterator[int]:
+        yield from self._range
+
+    @doc(Index._shallow_copy)
+    def _shallow_copy(self, values, name: Hashable = no_default):
+        name = self._name if name is no_default else name
+
+        if values.dtype.kind == "f":
+            return Index(values, name=name, dtype=np.float64)
+        # GH 46675 & 43885: If values is equally spaced, return a
+        # more memory-compact RangeIndex instead of Index with 64-bit dtype
+        unique_diffs = unique_deltas(values)
+        if len(unique_diffs) == 1 and unique_diffs[0] != 0:
+            diff = unique_diffs[0]
+            new_range = range(values[0], values[-1] + diff, diff)
+            return type(self)._simple_new(new_range, name=name)
+        else:
+            return self._constructor._simple_new(values, name=name)
+
+    def _view(self) -> Self:
+        result = type(self)._simple_new(self._range, name=self._name)
+        result._cache = self._cache
+        return result
+
+    @doc(Index.copy)
+    def copy(self, name: Hashable | None = None, deep: bool = False) -> Self:
+        name = self._validate_names(name=name, deep=deep)[0]
+        new_index = self._rename(name=name)
+        return new_index
+
+    def _minmax(self, meth: str):
+        no_steps = len(self) - 1
+        if no_steps == -1:
+            return np.nan
+        elif (meth == "min" and self.step > 0) or (meth == "max" and self.step < 0):
+            return self.start
+
+        return self.start + self.step * no_steps
+
+    def min(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        """The minimum value of the RangeIndex"""
+        nv.validate_minmax_axis(axis)
+        nv.validate_min(args, kwargs)
+        return self._minmax("min")
+
+    def max(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
+        """The maximum value of the RangeIndex"""
+        nv.validate_minmax_axis(axis)
+        nv.validate_max(args, kwargs)
+        return self._minmax("max")
+
+    def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
+        """
+        Returns the indices that would sort the index and its
+        underlying data.
+
+        Returns
+        -------
+        np.ndarray[np.intp]
+
+        See Also
+        --------
+        numpy.ndarray.argsort
+        """
+        ascending = kwargs.pop("ascending", True)  # EA compat
+        kwargs.pop("kind", None)  # e.g. "mergesort" is irrelevant
+        nv.validate_argsort(args, kwargs)
+
+        if self._range.step > 0:
+            result = np.arange(len(self), dtype=np.intp)
+        else:
+            result = np.arange(len(self) - 1, -1, -1, dtype=np.intp)
+
+        if not ascending:
+            result = result[::-1]
+        return result
+
+    def factorize(
+        self,
+        sort: bool = False,
+        use_na_sentinel: bool = True,
+    ) -> tuple[npt.NDArray[np.intp], RangeIndex]:
+        codes = np.arange(len(self), dtype=np.intp)
+        uniques = self
+        if sort and self.step < 0:
+            codes = codes[::-1]
+            uniques = uniques[::-1]
+        return codes, uniques
+
+    def equals(self, other: object) -> bool:
+        """
+        Determines if two Index objects contain the same elements.
+        """
+        if isinstance(other, RangeIndex):
+            return self._range == other._range
+        return super().equals(other)
+
+    # error: Signature of "sort_values" incompatible with supertype "Index"
+    @overload  # type: ignore[override]
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[False] = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: Literal[True],
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> tuple[Self, np.ndarray | RangeIndex]:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        return_indexer: bool = ...,
+        ascending: bool = ...,
+        na_position: NaPosition = ...,
+        key: Callable | None = ...,
+    ) -> Self | tuple[Self, np.ndarray | RangeIndex]:
+        ...
+
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="sort_values"
+    )
+    def sort_values(
+        self,
+        return_indexer: bool = False,
+        ascending: bool = True,
+        na_position: NaPosition = "last",
+        key: Callable | None = None,
+    ) -> Self | tuple[Self, np.ndarray | RangeIndex]:
+        if key is not None:
+            return super().sort_values(
+                return_indexer=return_indexer,
+                ascending=ascending,
+                na_position=na_position,
+                key=key,
+            )
+        else:
+            sorted_index = self
+            inverse_indexer = False
+            if ascending:
+                if self.step < 0:
+                    sorted_index = self[::-1]
+                    inverse_indexer = True
+            else:
+                if self.step > 0:
+                    sorted_index = self[::-1]
+                    inverse_indexer = True
+
+        if return_indexer:
+            if inverse_indexer:
+                rng = range(len(self) - 1, -1, -1)
+            else:
+                rng = range(len(self))
+            return sorted_index, RangeIndex(rng)
+        else:
+            return sorted_index
+
+    # --------------------------------------------------------------------
+    # Set Operations
+
+    def _intersection(self, other: Index, sort: bool = False):
+        # caller is responsible for checking self and other are both non-empty
+
+        if not isinstance(other, RangeIndex):
+            return super()._intersection(other, sort=sort)
+
+        first = self._range[::-1] if self.step < 0 else self._range
+        second = other._range[::-1] if other.step < 0 else other._range
+
+        # check whether intervals intersect
+        # deals with in- and decreasing ranges
+        int_low = max(first.start, second.start)
+        int_high = min(first.stop, second.stop)
+        if int_high <= int_low:
+            return self._simple_new(_empty_range)
+
+        # Method hint: linear Diophantine equation
+        # solve intersection problem
+        # performance hint: for identical step sizes, could use
+        # cheaper alternative
+        gcd, s, _ = self._extended_gcd(first.step, second.step)
+
+        # check whether element sets intersect
+        if (first.start - second.start) % gcd:
+            return self._simple_new(_empty_range)
+
+        # calculate parameters for the RangeIndex describing the
+        # intersection disregarding the lower bounds
+        tmp_start = first.start + (second.start - first.start) * first.step // gcd * s
+        new_step = first.step * second.step // gcd
+        new_range = range(tmp_start, int_high, new_step)
+        new_index = self._simple_new(new_range)
+
+        # adjust index to limiting interval
+        new_start = new_index._min_fitting_element(int_low)
+        new_range = range(new_start, new_index.stop, new_index.step)
+        new_index = self._simple_new(new_range)
+
+        if (self.step < 0 and other.step < 0) is not (new_index.step < 0):
+            new_index = new_index[::-1]
+
+        if sort is None:
+            new_index = new_index.sort_values()
+
+        return new_index
+
+    def _min_fitting_element(self, lower_limit: int) -> int:
+        """Returns the smallest element greater than or equal to the limit"""
+        no_steps = -(-(lower_limit - self.start) // abs(self.step))
+        return self.start + abs(self.step) * no_steps
+
+    def _extended_gcd(self, a: int, b: int) -> tuple[int, int, int]:
+        """
+        Extended Euclidean algorithms to solve Bezout's identity:
+           a*x + b*y = gcd(x, y)
+        Finds one particular solution for x, y: s, t
+        Returns: gcd, s, t
+        """
+        s, old_s = 0, 1
+        t, old_t = 1, 0
+        r, old_r = b, a
+        while r:
+            quotient = old_r // r
+            old_r, r = r, old_r - quotient * r
+            old_s, s = s, old_s - quotient * s
+            old_t, t = t, old_t - quotient * t
+        return old_r, old_s, old_t
+
+    def _range_in_self(self, other: range) -> bool:
+        """Check if other range is contained in self"""
+        # https://stackoverflow.com/a/32481015
+        if not other:
+            return True
+        if not self._range:
+            return False
+        if len(other) > 1 and other.step % self._range.step:
+            return False
+        return other.start in self._range and other[-1] in self._range
+
+    def _union(self, other: Index, sort: bool | None):
+        """
+        Form the union of two Index objects and sorts if possible
+
+        Parameters
+        ----------
+        other : Index or array-like
+
+        sort : bool or None, default None
+            Whether to sort (monotonically increasing) the resulting index.
+            ``sort=None|True`` returns a ``RangeIndex`` if possible or a sorted
+            ``Index`` with a int64 dtype if not.
+            ``sort=False`` can return a ``RangeIndex`` if self is monotonically
+            increasing and other is fully contained in self. Otherwise, returns
+            an unsorted ``Index`` with an int64 dtype.
+
+        Returns
+        -------
+        union : Index
+        """
+        if isinstance(other, RangeIndex):
+            if sort in (None, True) or (
+                sort is False and self.step > 0 and self._range_in_self(other._range)
+            ):
+                # GH 47557: Can still return a RangeIndex
+                # if other range in self and sort=False
+                start_s, step_s = self.start, self.step
+                end_s = self.start + self.step * (len(self) - 1)
+                start_o, step_o = other.start, other.step
+                end_o = other.start + other.step * (len(other) - 1)
+                if self.step < 0:
+                    start_s, step_s, end_s = end_s, -step_s, start_s
+                if other.step < 0:
+                    start_o, step_o, end_o = end_o, -step_o, start_o
+                if len(self) == 1 and len(other) == 1:
+                    step_s = step_o = abs(self.start - other.start)
+                elif len(self) == 1:
+                    step_s = step_o
+                elif len(other) == 1:
+                    step_o = step_s
+                start_r = min(start_s, start_o)
+                end_r = max(end_s, end_o)
+                if step_o == step_s:
+                    if (
+                        (start_s - start_o) % step_s == 0
+                        and (start_s - end_o) <= step_s
+                        and (start_o - end_s) <= step_s
+                    ):
+                        return type(self)(start_r, end_r + step_s, step_s)
+                    if (
+                        (step_s % 2 == 0)
+                        and (abs(start_s - start_o) == step_s / 2)
+                        and (abs(end_s - end_o) == step_s / 2)
+                    ):
+                        # e.g. range(0, 10, 2) and range(1, 11, 2)
+                        #  but not range(0, 20, 4) and range(1, 21, 4) GH#44019
+                        return type(self)(start_r, end_r + step_s / 2, step_s / 2)
+
+                elif step_o % step_s == 0:
+                    if (
+                        (start_o - start_s) % step_s == 0
+                        and (start_o + step_s >= start_s)
+                        and (end_o - step_s <= end_s)
+                    ):
+                        return type(self)(start_r, end_r + step_s, step_s)
+                elif step_s % step_o == 0:
+                    if (
+                        (start_s - start_o) % step_o == 0
+                        and (start_s + step_o >= start_o)
+                        and (end_s - step_o <= end_o)
+                    ):
+                        return type(self)(start_r, end_r + step_o, step_o)
+
+        return super()._union(other, sort=sort)
+
+    def _difference(self, other, sort=None):
+        # optimized set operation if we have another RangeIndex
+        self._validate_sort_keyword(sort)
+        self._assert_can_do_setop(other)
+        other, result_name = self._convert_can_do_setop(other)
+
+        if not isinstance(other, RangeIndex):
+            return super()._difference(other, sort=sort)
+
+        if sort is not False and self.step < 0:
+            return self[::-1]._difference(other)
+
+        res_name = ops.get_op_result_name(self, other)
+
+        first = self._range[::-1] if self.step < 0 else self._range
+        overlap = self.intersection(other)
+        if overlap.step < 0:
+            overlap = overlap[::-1]
+
+        if len(overlap) == 0:
+            return self.rename(name=res_name)
+        if len(overlap) == len(self):
+            return self[:0].rename(res_name)
+
+        # overlap.step will always be a multiple of self.step (see _intersection)
+
+        if len(overlap) == 1:
+            if overlap[0] == self[0]:
+                return self[1:]
+
+            elif overlap[0] == self[-1]:
+                return self[:-1]
+
+            elif len(self) == 3 and overlap[0] == self[1]:
+                return self[::2]
+
+            else:
+                return super()._difference(other, sort=sort)
+
+        elif len(overlap) == 2 and overlap[0] == first[0] and overlap[-1] == first[-1]:
+            # e.g. range(-8, 20, 7) and range(13, -9, -3)
+            return self[1:-1]
+
+        if overlap.step == first.step:
+            if overlap[0] == first.start:
+                # The difference is everything after the intersection
+                new_rng = range(overlap[-1] + first.step, first.stop, first.step)
+            elif overlap[-1] == first[-1]:
+                # The difference is everything before the intersection
+                new_rng = range(first.start, overlap[0], first.step)
+            elif overlap._range == first[1:-1]:
+                # e.g. range(4) and range(1, 3)
+                step = len(first) - 1
+                new_rng = first[::step]
+            else:
+                # The difference is not range-like
+                # e.g. range(1, 10, 1) and range(3, 7, 1)
+                return super()._difference(other, sort=sort)
+
+        else:
+            # We must have len(self) > 1, bc we ruled out above
+            #  len(overlap) == 0 and len(overlap) == len(self)
+            assert len(self) > 1
+
+            if overlap.step == first.step * 2:
+                if overlap[0] == first[0] and overlap[-1] in (first[-1], first[-2]):
+                    # e.g. range(1, 10, 1) and range(1, 10, 2)
+                    new_rng = first[1::2]
+
+                elif overlap[0] == first[1] and overlap[-1] in (first[-1], first[-2]):
+                    # e.g. range(1, 10, 1) and range(2, 10, 2)
+                    new_rng = first[::2]
+
+                else:
+                    # We can get here with  e.g. range(20) and range(0, 10, 2)
+                    return super()._difference(other, sort=sort)
+
+            else:
+                # e.g. range(10) and range(0, 10, 3)
+                return super()._difference(other, sort=sort)
+
+        new_index = type(self)._simple_new(new_rng, name=res_name)
+        if first is not self._range:
+            new_index = new_index[::-1]
+
+        return new_index
+
+    def symmetric_difference(
+        self, other, result_name: Hashable | None = None, sort=None
+    ):
+        if not isinstance(other, RangeIndex) or sort is not None:
+            return super().symmetric_difference(other, result_name, sort)
+
+        left = self.difference(other)
+        right = other.difference(self)
+        result = left.union(right)
+
+        if result_name is not None:
+            result = result.rename(result_name)
+        return result
+
+    # --------------------------------------------------------------------
+
+    # error: Return type "Index" of "delete" incompatible with return type
+    #  "RangeIndex" in supertype "Index"
+    def delete(self, loc) -> Index:  # type: ignore[override]
+        # In some cases we can retain RangeIndex, see also
+        #  DatetimeTimedeltaMixin._get_delete_Freq
+        if is_integer(loc):
+            if loc in (0, -len(self)):
+                return self[1:]
+            if loc in (-1, len(self) - 1):
+                return self[:-1]
+            if len(self) == 3 and loc in (1, -2):
+                return self[::2]
+
+        elif lib.is_list_like(loc):
+            slc = lib.maybe_indices_to_slice(np.asarray(loc, dtype=np.intp), len(self))
+
+            if isinstance(slc, slice):
+                # defer to RangeIndex._difference, which is optimized to return
+                #  a RangeIndex whenever possible
+                other = self[slc]
+                return self.difference(other, sort=False)
+
+        return super().delete(loc)
+
+    def insert(self, loc: int, item) -> Index:
+        if len(self) and (is_integer(item) or is_float(item)):
+            # We can retain RangeIndex is inserting at the beginning or end,
+            #  or right in the middle.
+            rng = self._range
+            if loc == 0 and item == self[0] - self.step:
+                new_rng = range(rng.start - rng.step, rng.stop, rng.step)
+                return type(self)._simple_new(new_rng, name=self._name)
+
+            elif loc == len(self) and item == self[-1] + self.step:
+                new_rng = range(rng.start, rng.stop + rng.step, rng.step)
+                return type(self)._simple_new(new_rng, name=self._name)
+
+            elif len(self) == 2 and item == self[0] + self.step / 2:
+                # e.g. inserting 1 into [0, 2]
+                step = int(self.step / 2)
+                new_rng = range(self.start, self.stop, step)
+                return type(self)._simple_new(new_rng, name=self._name)
+
+        return super().insert(loc, item)
+
+    def _concat(self, indexes: list[Index], name: Hashable) -> Index:
+        """
+        Overriding parent method for the case of all RangeIndex instances.
+
+        When all members of "indexes" are of type RangeIndex: result will be
+        RangeIndex if possible, Index with a int64 dtype otherwise. E.g.:
+        indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6)
+        indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Index([0,1,2,4,5], dtype='int64')
+        """
+        if not all(isinstance(x, RangeIndex) for x in indexes):
+            return super()._concat(indexes, name)
+
+        elif len(indexes) == 1:
+            return indexes[0]
+
+        rng_indexes = cast(list[RangeIndex], indexes)
+
+        start = step = next_ = None
+
+        # Filter the empty indexes
+        non_empty_indexes = [obj for obj in rng_indexes if len(obj)]
+
+        for obj in non_empty_indexes:
+            rng = obj._range
+
+            if start is None:
+                # This is set by the first non-empty index
+                start = rng.start
+                if step is None and len(rng) > 1:
+                    step = rng.step
+            elif step is None:
+                # First non-empty index had only one element
+                if rng.start == start:
+                    values = np.concatenate([x._values for x in rng_indexes])
+                    result = self._constructor(values)
+                    return result.rename(name)
+
+                step = rng.start - start
+
+            non_consecutive = (step != rng.step and len(rng) > 1) or (
+                next_ is not None and rng.start != next_
+            )
+            if non_consecutive:
+                result = self._constructor(
+                    np.concatenate([x._values for x in rng_indexes])
+                )
+                return result.rename(name)
+
+            if step is not None:
+                next_ = rng[-1] + step
+
+        if non_empty_indexes:
+            # Get the stop value from "next" or alternatively
+            # from the last non-empty index
+            stop = non_empty_indexes[-1].stop if next_ is None else next_
+            return RangeIndex(start, stop, step).rename(name)
+
+        # Here all "indexes" had 0 length, i.e. were empty.
+        # In this case return an empty range index.
+        return RangeIndex(0, 0).rename(name)
+
+    def __len__(self) -> int:
+        """
+        return the length of the RangeIndex
+        """
+        return len(self._range)
+
+    @property
+    def size(self) -> int:
+        return len(self)
+
+    def __getitem__(self, key):
+        """
+        Conserve RangeIndex type for scalar and slice keys.
+        """
+        if isinstance(key, slice):
+            return self._getitem_slice(key)
+        elif is_integer(key):
+            new_key = int(key)
+            try:
+                return self._range[new_key]
+            except IndexError as err:
+                raise IndexError(
+                    f"index {key} is out of bounds for axis 0 with size {len(self)}"
+                ) from err
+        elif is_scalar(key):
+            raise IndexError(
+                "only integers, slices (`:`), "
+                "ellipsis (`...`), numpy.newaxis (`None`) "
+                "and integer or boolean "
+                "arrays are valid indices"
+            )
+        return super().__getitem__(key)
+
+    def _getitem_slice(self, slobj: slice) -> Self:
+        """
+        Fastpath for __getitem__ when we know we have a slice.
+        """
+        res = self._range[slobj]
+        return type(self)._simple_new(res, name=self._name)
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        if is_integer(other) and other != 0:
+            if len(self) == 0 or self.start % other == 0 and self.step % other == 0:
+                start = self.start // other
+                step = self.step // other
+                stop = start + len(self) * step
+                new_range = range(start, stop, step or 1)
+                return self._simple_new(new_range, name=self._name)
+            if len(self) == 1:
+                start = self.start // other
+                new_range = range(start, start + 1, 1)
+                return self._simple_new(new_range, name=self._name)
+
+        return super().__floordiv__(other)
+
+    # --------------------------------------------------------------------
+    # Reductions
+
+    def all(self, *args, **kwargs) -> bool:
+        return 0 not in self._range
+
+    def any(self, *args, **kwargs) -> bool:
+        return any(self._range)
+
+    # --------------------------------------------------------------------
+
+    def _cmp_method(self, other, op):
+        if isinstance(other, RangeIndex) and self._range == other._range:
+            # Both are immutable so if ._range attr. are equal, shortcut is possible
+            return super()._cmp_method(self, op)
+        return super()._cmp_method(other, op)
+
+    def _arith_method(self, other, op):
+        """
+        Parameters
+        ----------
+        other : Any
+        op : callable that accepts 2 params
+            perform the binary op
+        """
+
+        if isinstance(other, ABCTimedeltaIndex):
+            # Defer to TimedeltaIndex implementation
+            return NotImplemented
+        elif isinstance(other, (timedelta, np.timedelta64)):
+            # GH#19333 is_integer evaluated True on timedelta64,
+            # so we need to catch these explicitly
+            return super()._arith_method(other, op)
+        elif lib.is_np_dtype(getattr(other, "dtype", None), "m"):
+            # Must be an np.ndarray; GH#22390
+            return super()._arith_method(other, op)
+
+        if op in [
+            operator.pow,
+            ops.rpow,
+            operator.mod,
+            ops.rmod,
+            operator.floordiv,
+            ops.rfloordiv,
+            divmod,
+            ops.rdivmod,
+        ]:
+            return super()._arith_method(other, op)
+
+        step: Callable | None = None
+        if op in [operator.mul, ops.rmul, operator.truediv, ops.rtruediv]:
+            step = op
+
+        # TODO: if other is a RangeIndex we may have more efficient options
+        right = extract_array(other, extract_numpy=True, extract_range=True)
+        left = self
+
+        try:
+            # apply if we have an override
+            if step:
+                with np.errstate(all="ignore"):
+                    rstep = step(left.step, right)
+
+                # we don't have a representable op
+                # so return a base index
+                if not is_integer(rstep) or not rstep:
+                    raise ValueError
+
+            # GH#53255
+            else:
+                rstep = -left.step if op == ops.rsub else left.step
+
+            with np.errstate(all="ignore"):
+                rstart = op(left.start, right)
+                rstop = op(left.stop, right)
+
+            res_name = ops.get_op_result_name(self, other)
+            result = type(self)(rstart, rstop, rstep, name=res_name)
+
+            # for compat with numpy / Index with int64 dtype
+            # even if we can represent as a RangeIndex, return
+            # as a float64 Index if we have float-like descriptors
+            if not all(is_integer(x) for x in [rstart, rstop, rstep]):
+                result = result.astype("float64")
+
+            return result
+
+        except (ValueError, TypeError, ZeroDivisionError):
+            # test_arithmetic_explicit_conversions
+            return super()._arith_method(other, op)
+
+    # error: Return type "Index" of "take" incompatible with return type
+    # "RangeIndex" in supertype "Index"
+    def take(  # type: ignore[override]
+        self,
+        indices,
+        axis: Axis = 0,
+        allow_fill: bool = True,
+        fill_value=None,
+        **kwargs,
+    ) -> Index:
+        if kwargs:
+            nv.validate_take((), kwargs)
+        if is_scalar(indices):
+            raise TypeError("Expected indices to be array-like")
+        indices = ensure_platform_int(indices)
+
+        # raise an exception if allow_fill is True and fill_value is not None
+        self._maybe_disallow_fill(allow_fill, fill_value, indices)
+
+        if len(indices) == 0:
+            taken = np.array([], dtype=self.dtype)
+        else:
+            ind_max = indices.max()
+            if ind_max >= len(self):
+                raise IndexError(
+                    f"index {ind_max} is out of bounds for axis 0 with size {len(self)}"
+                )
+            ind_min = indices.min()
+            if ind_min < -len(self):
+                raise IndexError(
+                    f"index {ind_min} is out of bounds for axis 0 with size {len(self)}"
+                )
+            taken = indices.astype(self.dtype, casting="safe")
+            if ind_min < 0:
+                taken %= len(self)
+            if self.step != 1:
+                taken *= self.step
+            if self.start != 0:
+                taken += self.start
+
+        # _constructor so RangeIndex-> Index with an int64 dtype
+        return self._constructor._simple_new(taken, name=self.name)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/timedeltas.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..08a265ba4764892fde0bc50670b6706ff788c8bc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexes/timedeltas.py
@@ -0,0 +1,356 @@
+""" implement the TimedeltaIndex """
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas._libs import (
+    index as libindex,
+    lib,
+)
+from pandas._libs.tslibs import (
+    Resolution,
+    Timedelta,
+    to_offset,
+)
+from pandas._libs.tslibs.timedeltas import disallow_ambiguous_unit
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_scalar,
+    pandas_dtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas.core.arrays.timedeltas import TimedeltaArray
+import pandas.core.common as com
+from pandas.core.indexes.base import (
+    Index,
+    maybe_extract_name,
+)
+from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
+from pandas.core.indexes.extension import inherit_names
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+@inherit_names(
+    ["__neg__", "__pos__", "__abs__", "total_seconds", "round", "floor", "ceil"]
+    + TimedeltaArray._field_ops,
+    TimedeltaArray,
+    wrap=True,
+)
+@inherit_names(
+    [
+        "components",
+        "to_pytimedelta",
+        "sum",
+        "std",
+        "median",
+    ],
+    TimedeltaArray,
+)
+class TimedeltaIndex(DatetimeTimedeltaMixin):
+    """
+    Immutable Index of timedelta64 data.
+
+    Represented internally as int64, and scalars returned Timedelta objects.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional), optional
+        Optional timedelta-like data to construct index with.
+    unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional
+        The unit of ``data``.
+
+        .. deprecated:: 2.2.0
+         Use ``pd.to_timedelta`` instead.
+
+    freq : str or pandas offset object, optional
+        One of pandas date offset strings or corresponding objects. The string
+        ``'infer'`` can be passed in order to set the frequency of the index as
+        the inferred frequency upon creation.
+    dtype : numpy.dtype or str, default None
+        Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``,
+        ``timedelta64[ms]``, and ``timedelta64[s]``.
+    copy : bool
+        Make a copy of input array.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    days
+    seconds
+    microseconds
+    nanoseconds
+    components
+    inferred_freq
+
+    Methods
+    -------
+    to_pytimedelta
+    to_series
+    round
+    floor
+    ceil
+    to_frame
+    mean
+
+    See Also
+    --------
+    Index : The base pandas Index type.
+    Timedelta : Represents a duration between two dates or times.
+    DatetimeIndex : Index of datetime64 data.
+    PeriodIndex : Index of Period data.
+    timedelta_range : Create a fixed-frequency TimedeltaIndex.
+
+    Notes
+    -----
+    To learn more about the frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    >>> pd.TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'])
+    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq=None)
+
+    We can also let pandas infer the frequency when possible.
+
+    >>> pd.TimedeltaIndex(np.arange(5) * 24 * 3600 * 1e9, freq='infer')
+    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq='D')
+    """
+
+    _typ = "timedeltaindex"
+
+    _data_cls = TimedeltaArray
+
+    @property
+    def _engine_type(self) -> type[libindex.TimedeltaEngine]:
+        return libindex.TimedeltaEngine
+
+    _data: TimedeltaArray
+
+    # Use base class method instead of DatetimeTimedeltaMixin._get_string_slice
+    _get_string_slice = Index._get_string_slice
+
+    # error: Signature of "_resolution_obj" incompatible with supertype
+    # "DatetimeIndexOpsMixin"
+    @property
+    def _resolution_obj(self) -> Resolution | None:  # type: ignore[override]
+        return self._data._resolution_obj
+
+    # -------------------------------------------------------------------
+    # Constructors
+
+    def __new__(
+        cls,
+        data=None,
+        unit=lib.no_default,
+        freq=lib.no_default,
+        closed=lib.no_default,
+        dtype=None,
+        copy: bool = False,
+        name=None,
+    ):
+        if closed is not lib.no_default:
+            # GH#52628
+            warnings.warn(
+                f"The 'closed' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        if unit is not lib.no_default:
+            # GH#55499
+            warnings.warn(
+                f"The 'unit' keyword in {cls.__name__} construction is "
+                "deprecated and will be removed in a future version. "
+                "Use pd.to_timedelta instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            unit = None
+
+        name = maybe_extract_name(name, data, cls)
+
+        if is_scalar(data):
+            cls._raise_scalar_data_error(data)
+
+        disallow_ambiguous_unit(unit)
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+
+        if (
+            isinstance(data, TimedeltaArray)
+            and freq is lib.no_default
+            and (dtype is None or dtype == data.dtype)
+        ):
+            if copy:
+                data = data.copy()
+            return cls._simple_new(data, name=name)
+
+        if (
+            isinstance(data, TimedeltaIndex)
+            and freq is lib.no_default
+            and name is None
+            and (dtype is None or dtype == data.dtype)
+        ):
+            if copy:
+                return data.copy()
+            else:
+                return data._view()
+
+        # - Cases checked above all return/raise before reaching here - #
+
+        tdarr = TimedeltaArray._from_sequence_not_strict(
+            data, freq=freq, unit=unit, dtype=dtype, copy=copy
+        )
+        refs = None
+        if not copy and isinstance(data, (ABCSeries, Index)):
+            refs = data._references
+
+        return cls._simple_new(tdarr, name=name, refs=refs)
+
+    # -------------------------------------------------------------------
+
+    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
+        """
+        Can we compare values of the given dtype to our own?
+        """
+        return lib.is_np_dtype(dtype, "m")  # aka self._data._is_recognized_dtype
+
+    # -------------------------------------------------------------------
+    # Indexing Methods
+
+    def get_loc(self, key):
+        """
+        Get integer location for requested label
+
+        Returns
+        -------
+        loc : int, slice, or ndarray[int]
+        """
+        self._check_indexing_error(key)
+
+        try:
+            key = self._data._validate_scalar(key, unbox=False)
+        except TypeError as err:
+            raise KeyError(key) from err
+
+        return Index.get_loc(self, key)
+
+    def _parse_with_reso(self, label: str):
+        # the "with_reso" is a no-op for TimedeltaIndex
+        parsed = Timedelta(label)
+        return parsed, None
+
+    def _parsed_string_to_bounds(self, reso, parsed: Timedelta):
+        # reso is unused, included to match signature of DTI/PI
+        lbound = parsed.round(parsed.resolution_string)
+        rbound = lbound + to_offset(parsed.resolution_string) - Timedelta(1, "ns")
+        return lbound, rbound
+
+    # -------------------------------------------------------------------
+
+    @property
+    def inferred_type(self) -> str:
+        return "timedelta64"
+
+
+def timedelta_range(
+    start=None,
+    end=None,
+    periods: int | None = None,
+    freq=None,
+    name=None,
+    closed=None,
+    *,
+    unit: str | None = None,
+) -> TimedeltaIndex:
+    """
+    Return a fixed frequency TimedeltaIndex with day as the default.
+
+    Parameters
+    ----------
+    start : str or timedelta-like, default None
+        Left bound for generating timedeltas.
+    end : str or timedelta-like, default None
+        Right bound for generating timedeltas.
+    periods : int, default None
+        Number of periods to generate.
+    freq : str, Timedelta, datetime.timedelta, or DateOffset, default 'D'
+        Frequency strings can have multiples, e.g. '5h'.
+    name : str, default None
+        Name of the resulting TimedeltaIndex.
+    closed : str, default None
+        Make the interval closed with respect to the given frequency to
+        the 'left', 'right', or both sides (None).
+    unit : str, default None
+        Specify the desired resolution of the result.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    TimedeltaIndex
+
+    Notes
+    -----
+    Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
+    exactly three must be specified. If ``freq`` is omitted, the resulting
+    ``TimedeltaIndex`` will have ``periods`` linearly spaced elements between
+    ``start`` and ``end`` (closed on both sides).
+
+    To learn more about the frequency strings, please see `this link
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+    Examples
+    --------
+    >>> pd.timedelta_range(start='1 day', periods=4)
+    TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq='D')
+
+    The ``closed`` parameter specifies which endpoint is included.  The default
+    behavior is to include both endpoints.
+
+    >>> pd.timedelta_range(start='1 day', periods=4, closed='right')
+    TimedeltaIndex(['2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq='D')
+
+    The ``freq`` parameter specifies the frequency of the TimedeltaIndex.
+    Only fixed frequencies can be passed, non-fixed frequencies such as
+    'M' (month end) will raise.
+
+    >>> pd.timedelta_range(start='1 day', end='2 days', freq='6h')
+    TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
+                    '1 days 18:00:00', '2 days 00:00:00'],
+                   dtype='timedelta64[ns]', freq='6h')
+
+    Specify ``start``, ``end``, and ``periods``; the frequency is generated
+    automatically (linearly spaced).
+
+    >>> pd.timedelta_range(start='1 day', end='5 days', periods=4)
+    TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00',
+                    '5 days 00:00:00'],
+                   dtype='timedelta64[ns]', freq=None)
+
+    **Specify a unit**
+
+    >>> pd.timedelta_range("1 Day", periods=3, freq="100000D", unit="s")
+    TimedeltaIndex(['1 days', '100001 days', '200001 days'],
+                   dtype='timedelta64[s]', freq='100000D')
+    """
+    if freq is None and com.any_none(periods, start, end):
+        freq = "D"
+
+    freq = to_offset(freq)
+    tdarr = TimedeltaArray._generate_range(
+        start, end, periods, freq, closed=closed, unit=unit
+    )
+    return TimedeltaIndex._simple_new(tdarr, name=name)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7bab4860411f37c177366177fb5d53d16837d17
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/indexing.py
@@ -0,0 +1,2821 @@
+from __future__ import annotations
+
+from contextlib import suppress
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    final,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs.indexing import NDFrameIndexerBase
+from pandas._libs.lib import item_from_zerodim
+from pandas.compat import PYPY
+from pandas.compat._constants import WARNING_CHECK_DISABLED
+from pandas.errors import (
+    AbstractMethodError,
+    ChainedAssignmentError,
+    IndexingError,
+    InvalidIndexError,
+    LossySetitemError,
+    _chained_assignment_msg,
+    _chained_assignment_warning_msg,
+    _check_cacher,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    can_hold_element,
+    maybe_promote,
+)
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_hashable,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_sequence,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    construct_1d_array_from_inferred_fill_value,
+    infer_fill_value,
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas.core import algorithms as algos
+import pandas.core.common as com
+from pandas.core.construction import (
+    array as pd_array,
+    extract_array,
+)
+from pandas.core.indexers import (
+    check_array_indexer,
+    is_list_like_indexer,
+    is_scalar_indexer,
+    length_of_indexer,
+)
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+        Self,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+T = TypeVar("T")
+# "null slice"
+_NS = slice(None, None)
+_one_ellipsis_message = "indexer may only contain one '...' entry"
+
+
+# the public IndexSlicerMaker
+class _IndexSlice:
+    """
+    Create an object to more easily perform multi-index slicing.
+
+    See Also
+    --------
+    MultiIndex.remove_unused_levels : New MultiIndex with no unused levels.
+
+    Notes
+    -----
+    See :ref:`Defined Levels <advanced.shown_levels>`
+    for further info on slicing a MultiIndex.
+
+    Examples
+    --------
+    >>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])
+    >>> columns = ['foo', 'bar']
+    >>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),
+    ...                     index=midx, columns=columns)
+
+    Using the default slice command:
+
+    >>> dfmi.loc[(slice(None), slice('B0', 'B1')), :]
+               foo  bar
+        A0 B0    0    1
+           B1    2    3
+        A1 B0    8    9
+           B1   10   11
+
+    Using the IndexSlice class for a more intuitive command:
+
+    >>> idx = pd.IndexSlice
+    >>> dfmi.loc[idx[:, 'B0':'B1'], :]
+               foo  bar
+        A0 B0    0    1
+           B1    2    3
+        A1 B0    8    9
+           B1   10   11
+    """
+
+    def __getitem__(self, arg):
+        return arg
+
+
+IndexSlice = _IndexSlice()
+
+
+class IndexingMixin:
+    """
+    Mixin for adding .loc/.iloc/.at/.iat to Dataframes and Series.
+    """
+
+    @property
+    def iloc(self) -> _iLocIndexer:
+        """
+        Purely integer-location based indexing for selection by position.
+
+        .. deprecated:: 2.2.0
+
+           Returning a tuple from a callable is deprecated.
+
+        ``.iloc[]`` is primarily integer position based (from ``0`` to
+        ``length-1`` of the axis), but may also be used with a boolean
+        array.
+
+        Allowed inputs are:
+
+        - An integer, e.g. ``5``.
+        - A list or array of integers, e.g. ``[4, 3, 0]``.
+        - A slice object with ints, e.g. ``1:7``.
+        - A boolean array.
+        - A ``callable`` function with one argument (the calling Series or
+          DataFrame) and that returns valid output for indexing (one of the above).
+          This is useful in method chains, when you don't have a reference to the
+          calling object, but would like to base your selection on
+          some value.
+        - A tuple of row and column indexes. The tuple elements consist of one of the
+          above inputs, e.g. ``(0, 1)``.
+
+        ``.iloc`` will raise ``IndexError`` if a requested indexer is
+        out-of-bounds, except *slice* indexers which allow out-of-bounds
+        indexing (this conforms with python/numpy *slice* semantics).
+
+        See more at :ref:`Selection by Position <indexing.integer>`.
+
+        See Also
+        --------
+        DataFrame.iat : Fast integer location scalar accessor.
+        DataFrame.loc : Purely label-location based indexer for selection by label.
+        Series.iloc : Purely integer-location based indexing for
+                       selection by position.
+
+        Examples
+        --------
+        >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
+        ...           {'a': 100, 'b': 200, 'c': 300, 'd': 400},
+        ...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
+        >>> df = pd.DataFrame(mydict)
+        >>> df
+              a     b     c     d
+        0     1     2     3     4
+        1   100   200   300   400
+        2  1000  2000  3000  4000
+
+        **Indexing just the rows**
+
+        With a scalar integer.
+
+        >>> type(df.iloc[0])
+        <class 'pandas.core.series.Series'>
+        >>> df.iloc[0]
+        a    1
+        b    2
+        c    3
+        d    4
+        Name: 0, dtype: int64
+
+        With a list of integers.
+
+        >>> df.iloc[[0]]
+           a  b  c  d
+        0  1  2  3  4
+        >>> type(df.iloc[[0]])
+        <class 'pandas.core.frame.DataFrame'>
+
+        >>> df.iloc[[0, 1]]
+             a    b    c    d
+        0    1    2    3    4
+        1  100  200  300  400
+
+        With a `slice` object.
+
+        >>> df.iloc[:3]
+              a     b     c     d
+        0     1     2     3     4
+        1   100   200   300   400
+        2  1000  2000  3000  4000
+
+        With a boolean mask the same length as the index.
+
+        >>> df.iloc[[True, False, True]]
+              a     b     c     d
+        0     1     2     3     4
+        2  1000  2000  3000  4000
+
+        With a callable, useful in method chains. The `x` passed
+        to the ``lambda`` is the DataFrame being sliced. This selects
+        the rows whose index label even.
+
+        >>> df.iloc[lambda x: x.index % 2 == 0]
+              a     b     c     d
+        0     1     2     3     4
+        2  1000  2000  3000  4000
+
+        **Indexing both axes**
+
+        You can mix the indexer types for the index and columns. Use ``:`` to
+        select the entire axis.
+
+        With scalar integers.
+
+        >>> df.iloc[0, 1]
+        2
+
+        With lists of integers.
+
+        >>> df.iloc[[0, 2], [1, 3]]
+              b     d
+        0     2     4
+        2  2000  4000
+
+        With `slice` objects.
+
+        >>> df.iloc[1:3, 0:3]
+              a     b     c
+        1   100   200   300
+        2  1000  2000  3000
+
+        With a boolean array whose length matches the columns.
+
+        >>> df.iloc[:, [True, False, True, False]]
+              a     c
+        0     1     3
+        1   100   300
+        2  1000  3000
+
+        With a callable function that expects the Series or DataFrame.
+
+        >>> df.iloc[:, lambda df: [0, 2]]
+              a     c
+        0     1     3
+        1   100   300
+        2  1000  3000
+        """
+        return _iLocIndexer("iloc", self)
+
+    @property
+    def loc(self) -> _LocIndexer:
+        """
+        Access a group of rows and columns by label(s) or a boolean array.
+
+        ``.loc[]`` is primarily label based, but may also be used with a
+        boolean array.
+
+        Allowed inputs are:
+
+        - A single label, e.g. ``5`` or ``'a'``, (note that ``5`` is
+          interpreted as a *label* of the index, and **never** as an
+          integer position along the index).
+        - A list or array of labels, e.g. ``['a', 'b', 'c']``.
+        - A slice object with labels, e.g. ``'a':'f'``.
+
+          .. warning:: Note that contrary to usual python slices, **both** the
+              start and the stop are included
+
+        - A boolean array of the same length as the axis being sliced,
+          e.g. ``[True, False, True]``.
+        - An alignable boolean Series. The index of the key will be aligned before
+          masking.
+        - An alignable Index. The Index of the returned selection will be the input.
+        - A ``callable`` function with one argument (the calling Series or
+          DataFrame) and that returns valid output for indexing (one of the above)
+
+        See more at :ref:`Selection by Label <indexing.label>`.
+
+        Raises
+        ------
+        KeyError
+            If any items are not found.
+        IndexingError
+            If an indexed key is passed and its index is unalignable to the frame index.
+
+        See Also
+        --------
+        DataFrame.at : Access a single value for a row/column label pair.
+        DataFrame.iloc : Access group of rows and columns by integer position(s).
+        DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
+                       Series/DataFrame.
+        Series.loc : Access group of values using labels.
+
+        Examples
+        --------
+        **Getting values**
+
+        >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
+        ...                   index=['cobra', 'viper', 'sidewinder'],
+        ...                   columns=['max_speed', 'shield'])
+        >>> df
+                    max_speed  shield
+        cobra               1       2
+        viper               4       5
+        sidewinder          7       8
+
+        Single label. Note this returns the row as a Series.
+
+        >>> df.loc['viper']
+        max_speed    4
+        shield       5
+        Name: viper, dtype: int64
+
+        List of labels. Note using ``[[]]`` returns a DataFrame.
+
+        >>> df.loc[['viper', 'sidewinder']]
+                    max_speed  shield
+        viper               4       5
+        sidewinder          7       8
+
+        Single label for row and column
+
+        >>> df.loc['cobra', 'shield']
+        2
+
+        Slice with labels for row and single label for column. As mentioned
+        above, note that both the start and stop of the slice are included.
+
+        >>> df.loc['cobra':'viper', 'max_speed']
+        cobra    1
+        viper    4
+        Name: max_speed, dtype: int64
+
+        Boolean list with the same length as the row axis
+
+        >>> df.loc[[False, False, True]]
+                    max_speed  shield
+        sidewinder          7       8
+
+        Alignable boolean Series:
+
+        >>> df.loc[pd.Series([False, True, False],
+        ...                  index=['viper', 'sidewinder', 'cobra'])]
+                             max_speed  shield
+        sidewinder          7       8
+
+        Index (same behavior as ``df.reindex``)
+
+        >>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
+               max_speed  shield
+        foo
+        cobra          1       2
+        viper          4       5
+
+        Conditional that returns a boolean Series
+
+        >>> df.loc[df['shield'] > 6]
+                    max_speed  shield
+        sidewinder          7       8
+
+        Conditional that returns a boolean Series with column labels specified
+
+        >>> df.loc[df['shield'] > 6, ['max_speed']]
+                    max_speed
+        sidewinder          7
+
+        Multiple conditional using ``&`` that returns a boolean Series
+
+        >>> df.loc[(df['max_speed'] > 1) & (df['shield'] < 8)]
+                    max_speed  shield
+        viper          4       5
+
+        Multiple conditional using ``|`` that returns a boolean Series
+
+        >>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)]
+                    max_speed  shield
+        cobra               1       2
+        sidewinder          7       8
+
+        Please ensure that each condition is wrapped in parentheses ``()``.
+        See the :ref:`user guide<indexing.boolean>`
+        for more details and explanations of Boolean indexing.
+
+        .. note::
+            If you find yourself using 3 or more conditionals in ``.loc[]``,
+            consider using :ref:`advanced indexing<advanced.advanced_hierarchical>`.
+
+            See below for using ``.loc[]`` on MultiIndex DataFrames.
+
+        Callable that returns a boolean Series
+
+        >>> df.loc[lambda df: df['shield'] == 8]
+                    max_speed  shield
+        sidewinder          7       8
+
+        **Setting values**
+
+        Set value for all items matching the list of labels
+
+        >>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
+        >>> df
+                    max_speed  shield
+        cobra               1       2
+        viper               4      50
+        sidewinder          7      50
+
+        Set value for an entire row
+
+        >>> df.loc['cobra'] = 10
+        >>> df
+                    max_speed  shield
+        cobra              10      10
+        viper               4      50
+        sidewinder          7      50
+
+        Set value for an entire column
+
+        >>> df.loc[:, 'max_speed'] = 30
+        >>> df
+                    max_speed  shield
+        cobra              30      10
+        viper              30      50
+        sidewinder         30      50
+
+        Set value for rows matching callable condition
+
+        >>> df.loc[df['shield'] > 35] = 0
+        >>> df
+                    max_speed  shield
+        cobra              30      10
+        viper               0       0
+        sidewinder          0       0
+
+        Add value matching location
+
+        >>> df.loc["viper", "shield"] += 5
+        >>> df
+                    max_speed  shield
+        cobra              30      10
+        viper               0       5
+        sidewinder          0       0
+
+        Setting using a ``Series`` or a ``DataFrame`` sets the values matching the
+        index labels, not the index positions.
+
+        >>> shuffled_df = df.loc[["viper", "cobra", "sidewinder"]]
+        >>> df.loc[:] += shuffled_df
+        >>> df
+                    max_speed  shield
+        cobra              60      20
+        viper               0      10
+        sidewinder          0       0
+
+        **Getting values on a DataFrame with an index that has integer labels**
+
+        Another example using integers for the index
+
+        >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
+        ...                   index=[7, 8, 9], columns=['max_speed', 'shield'])
+        >>> df
+           max_speed  shield
+        7          1       2
+        8          4       5
+        9          7       8
+
+        Slice with integer labels for rows. As mentioned above, note that both
+        the start and stop of the slice are included.
+
+        >>> df.loc[7:9]
+           max_speed  shield
+        7          1       2
+        8          4       5
+        9          7       8
+
+        **Getting values with a MultiIndex**
+
+        A number of examples using a DataFrame with a MultiIndex
+
+        >>> tuples = [
+        ...     ('cobra', 'mark i'), ('cobra', 'mark ii'),
+        ...     ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
+        ...     ('viper', 'mark ii'), ('viper', 'mark iii')
+        ... ]
+        >>> index = pd.MultiIndex.from_tuples(tuples)
+        >>> values = [[12, 2], [0, 4], [10, 20],
+        ...           [1, 4], [7, 1], [16, 36]]
+        >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
+        >>> df
+                             max_speed  shield
+        cobra      mark i           12       2
+                   mark ii           0       4
+        sidewinder mark i           10      20
+                   mark ii           1       4
+        viper      mark ii           7       1
+                   mark iii         16      36
+
+        Single label. Note this returns a DataFrame with a single index.
+
+        >>> df.loc['cobra']
+                 max_speed  shield
+        mark i          12       2
+        mark ii          0       4
+
+        Single index tuple. Note this returns a Series.
+
+        >>> df.loc[('cobra', 'mark ii')]
+        max_speed    0
+        shield       4
+        Name: (cobra, mark ii), dtype: int64
+
+        Single label for row and column. Similar to passing in a tuple, this
+        returns a Series.
+
+        >>> df.loc['cobra', 'mark i']
+        max_speed    12
+        shield        2
+        Name: (cobra, mark i), dtype: int64
+
+        Single tuple. Note using ``[[]]`` returns a DataFrame.
+
+        >>> df.loc[[('cobra', 'mark ii')]]
+                       max_speed  shield
+        cobra mark ii          0       4
+
+        Single tuple for the index with a single label for the column
+
+        >>> df.loc[('cobra', 'mark i'), 'shield']
+        2
+
+        Slice from index tuple to single label
+
+        >>> df.loc[('cobra', 'mark i'):'viper']
+                             max_speed  shield
+        cobra      mark i           12       2
+                   mark ii           0       4
+        sidewinder mark i           10      20
+                   mark ii           1       4
+        viper      mark ii           7       1
+                   mark iii         16      36
+
+        Slice from index tuple to index tuple
+
+        >>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
+                            max_speed  shield
+        cobra      mark i          12       2
+                   mark ii          0       4
+        sidewinder mark i          10      20
+                   mark ii          1       4
+        viper      mark ii          7       1
+
+        Please see the :ref:`user guide<advanced.advanced_hierarchical>`
+        for more details and explanations of advanced indexing.
+        """
+        return _LocIndexer("loc", self)
+
+    @property
+    def at(self) -> _AtIndexer:
+        """
+        Access a single value for a row/column label pair.
+
+        Similar to ``loc``, in that both provide label-based lookups. Use
+        ``at`` if you only need to get or set a single value in a DataFrame
+        or Series.
+
+        Raises
+        ------
+        KeyError
+            If getting a value and 'label' does not exist in a DataFrame or Series.
+
+        ValueError
+            If row/column label pair is not a tuple or if any label
+            from the pair is not a scalar for DataFrame.
+            If label is list-like (*excluding* NamedTuple) for Series.
+
+        See Also
+        --------
+        DataFrame.at : Access a single value for a row/column pair by label.
+        DataFrame.iat : Access a single value for a row/column pair by integer
+            position.
+        DataFrame.loc : Access a group of rows and columns by label(s).
+        DataFrame.iloc : Access a group of rows and columns by integer
+            position(s).
+        Series.at : Access a single value by label.
+        Series.iat : Access a single value by integer position.
+        Series.loc : Access a group of rows by label(s).
+        Series.iloc : Access a group of rows by integer position(s).
+
+        Notes
+        -----
+        See :ref:`Fast scalar value getting and setting <indexing.basics.get_value>`
+        for more details.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
+        ...                   index=[4, 5, 6], columns=['A', 'B', 'C'])
+        >>> df
+            A   B   C
+        4   0   2   3
+        5   0   4   1
+        6  10  20  30
+
+        Get value at specified row/column pair
+
+        >>> df.at[4, 'B']
+        2
+
+        Set value at specified row/column pair
+
+        >>> df.at[4, 'B'] = 10
+        >>> df.at[4, 'B']
+        10
+
+        Get value within a Series
+
+        >>> df.loc[5].at['B']
+        4
+        """
+        return _AtIndexer("at", self)
+
+    @property
+    def iat(self) -> _iAtIndexer:
+        """
+        Access a single value for a row/column pair by integer position.
+
+        Similar to ``iloc``, in that both provide integer-based lookups. Use
+        ``iat`` if you only need to get or set a single value in a DataFrame
+        or Series.
+
+        Raises
+        ------
+        IndexError
+            When integer position is out of bounds.
+
+        See Also
+        --------
+        DataFrame.at : Access a single value for a row/column label pair.
+        DataFrame.loc : Access a group of rows and columns by label(s).
+        DataFrame.iloc : Access a group of rows and columns by integer position(s).
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
+        ...                   columns=['A', 'B', 'C'])
+        >>> df
+            A   B   C
+        0   0   2   3
+        1   0   4   1
+        2  10  20  30
+
+        Get value at specified row/column pair
+
+        >>> df.iat[1, 2]
+        1
+
+        Set value at specified row/column pair
+
+        >>> df.iat[1, 2] = 10
+        >>> df.iat[1, 2]
+        10
+
+        Get value within a series
+
+        >>> df.loc[0].iat[1]
+        2
+        """
+        return _iAtIndexer("iat", self)
+
+
+class _LocationIndexer(NDFrameIndexerBase):
+    _valid_types: str
+    axis: AxisInt | None = None
+
+    # sub-classes need to set _takeable
+    _takeable: bool
+
+    @final
+    def __call__(self, axis: Axis | None = None) -> Self:
+        # we need to return a copy of ourselves
+        new_self = type(self)(self.name, self.obj)
+
+        if axis is not None:
+            axis_int_none = self.obj._get_axis_number(axis)
+        else:
+            axis_int_none = axis
+        new_self.axis = axis_int_none
+        return new_self
+
+    def _get_setitem_indexer(self, key):
+        """
+        Convert a potentially-label-based key into a positional indexer.
+        """
+        if self.name == "loc":
+            # always holds here bc iloc overrides _get_setitem_indexer
+            self._ensure_listlike_indexer(key)
+
+        if isinstance(key, tuple):
+            for x in key:
+                check_dict_or_set_indexers(x)
+
+        if self.axis is not None:
+            key = _tupleize_axis_indexer(self.ndim, self.axis, key)
+
+        ax = self.obj._get_axis(0)
+
+        if (
+            isinstance(ax, MultiIndex)
+            and self.name != "iloc"
+            and is_hashable(key)
+            and not isinstance(key, slice)
+        ):
+            with suppress(KeyError, InvalidIndexError):
+                # TypeError e.g. passed a bool
+                return ax.get_loc(key)
+
+        if isinstance(key, tuple):
+            with suppress(IndexingError):
+                # suppress "Too many indexers"
+                return self._convert_tuple(key)
+
+        if isinstance(key, range):
+            # GH#45479 test_loc_setitem_range_key
+            key = list(key)
+
+        return self._convert_to_indexer(key, axis=0)
+
+    @final
+    def _maybe_mask_setitem_value(self, indexer, value):
+        """
+        If we have obj.iloc[mask] = series_or_frame and series_or_frame has the
+        same length as obj, we treat this as obj.iloc[mask] = series_or_frame[mask],
+        similar to Series.__setitem__.
+
+        Note this is only for loc, not iloc.
+        """
+
+        if (
+            isinstance(indexer, tuple)
+            and len(indexer) == 2
+            and isinstance(value, (ABCSeries, ABCDataFrame))
+        ):
+            pi, icols = indexer
+            ndim = value.ndim
+            if com.is_bool_indexer(pi) and len(value) == len(pi):
+                newkey = pi.nonzero()[0]
+
+                if is_scalar_indexer(icols, self.ndim - 1) and ndim == 1:
+                    # e.g. test_loc_setitem_boolean_mask_allfalse
+                    # test_loc_setitem_ndframe_values_alignment
+                    value = self.obj.iloc._align_series(indexer, value)
+                    indexer = (newkey, icols)
+
+                elif (
+                    isinstance(icols, np.ndarray)
+                    and icols.dtype.kind == "i"
+                    and len(icols) == 1
+                ):
+                    if ndim == 1:
+                        # We implicitly broadcast, though numpy does not, see
+                        # github.com/pandas-dev/pandas/pull/45501#discussion_r789071825
+                        # test_loc_setitem_ndframe_values_alignment
+                        value = self.obj.iloc._align_series(indexer, value)
+                        indexer = (newkey, icols)
+
+                    elif ndim == 2 and value.shape[1] == 1:
+                        # test_loc_setitem_ndframe_values_alignment
+                        value = self.obj.iloc._align_frame(indexer, value)
+                        indexer = (newkey, icols)
+        elif com.is_bool_indexer(indexer):
+            indexer = indexer.nonzero()[0]
+
+        return indexer, value
+
+    @final
+    def _ensure_listlike_indexer(self, key, axis=None, value=None) -> None:
+        """
+        Ensure that a list-like of column labels are all present by adding them if
+        they do not already exist.
+
+        Parameters
+        ----------
+        key : list-like of column labels
+            Target labels.
+        axis : key axis if known
+        """
+        column_axis = 1
+
+        # column only exists in 2-dimensional DataFrame
+        if self.ndim != 2:
+            return
+
+        if isinstance(key, tuple) and len(key) > 1:
+            # key may be a tuple if we are .loc
+            # if length of key is > 1 set key to column part
+            key = key[column_axis]
+            axis = column_axis
+
+        if (
+            axis == column_axis
+            and not isinstance(self.obj.columns, MultiIndex)
+            and is_list_like_indexer(key)
+            and not com.is_bool_indexer(key)
+            and all(is_hashable(k) for k in key)
+        ):
+            # GH#38148
+            keys = self.obj.columns.union(key, sort=False)
+            diff = Index(key).difference(self.obj.columns, sort=False)
+
+            if len(diff):
+                # e.g. if we are doing df.loc[:, ["A", "B"]] = 7 and "B"
+                #  is a new column, add the new columns with dtype=np.void
+                #  so that later when we go through setitem_single_column
+                #  we will use isetitem. Without this, the reindex_axis
+                #  below would create float64 columns in this example, which
+                #  would successfully hold 7, so we would end up with the wrong
+                #  dtype.
+                indexer = np.arange(len(keys), dtype=np.intp)
+                indexer[len(self.obj.columns) :] = -1
+                new_mgr = self.obj._mgr.reindex_indexer(
+                    keys, indexer=indexer, axis=0, only_slice=True, use_na_proxy=True
+                )
+                self.obj._mgr = new_mgr
+                return
+
+            self.obj._mgr = self.obj._mgr.reindex_axis(keys, axis=0, only_slice=True)
+
+    @final
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self.obj) <= 2:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self.obj)
+            ref_count = 2
+            if not warn_copy_on_write() and _check_cacher(self.obj):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        check_dict_or_set_indexers(key)
+        if isinstance(key, tuple):
+            key = tuple(list(x) if is_iterator(x) else x for x in key)
+            key = tuple(com.apply_if_callable(x, self.obj) for x in key)
+        else:
+            maybe_callable = com.apply_if_callable(key, self.obj)
+            key = self._check_deprecated_callable_usage(key, maybe_callable)
+        indexer = self._get_setitem_indexer(key)
+        self._has_valid_setitem_indexer(key)
+
+        iloc = self if self.name == "iloc" else self.obj.iloc
+        iloc._setitem_with_indexer(indexer, value, self.name)
+
+    def _validate_key(self, key, axis: AxisInt):
+        """
+        Ensure that key is valid for current indexer.
+
+        Parameters
+        ----------
+        key : scalar, slice or list-like
+            Key requested.
+        axis : int
+            Dimension on which the indexing is being made.
+
+        Raises
+        ------
+        TypeError
+            If the key (or some element of it) has wrong type.
+        IndexError
+            If the key (or some element of it) is out of bounds.
+        KeyError
+            If the key was not found.
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def _expand_ellipsis(self, tup: tuple) -> tuple:
+        """
+        If a tuple key includes an Ellipsis, replace it with an appropriate
+        number of null slices.
+        """
+        if any(x is Ellipsis for x in tup):
+            if tup.count(Ellipsis) > 1:
+                raise IndexingError(_one_ellipsis_message)
+
+            if len(tup) == self.ndim:
+                # It is unambiguous what axis this Ellipsis is indexing,
+                #  treat as a single null slice.
+                i = tup.index(Ellipsis)
+                # FIXME: this assumes only one Ellipsis
+                new_key = tup[:i] + (_NS,) + tup[i + 1 :]
+                return new_key
+
+            # TODO: other cases?  only one test gets here, and that is covered
+            #  by _validate_key_length
+        return tup
+
+    @final
+    def _validate_tuple_indexer(self, key: tuple) -> tuple:
+        """
+        Check the key for valid keys across my indexer.
+        """
+        key = self._validate_key_length(key)
+        key = self._expand_ellipsis(key)
+        for i, k in enumerate(key):
+            try:
+                self._validate_key(k, i)
+            except ValueError as err:
+                raise ValueError(
+                    "Location based indexing can only have "
+                    f"[{self._valid_types}] types"
+                ) from err
+        return key
+
+    @final
+    def _is_nested_tuple_indexer(self, tup: tuple) -> bool:
+        """
+        Returns
+        -------
+        bool
+        """
+        if any(isinstance(ax, MultiIndex) for ax in self.obj.axes):
+            return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
+        return False
+
+    @final
+    def _convert_tuple(self, key: tuple) -> tuple:
+        # Note: we assume _tupleize_axis_indexer has been called, if necessary.
+        self._validate_key_length(key)
+        keyidx = [self._convert_to_indexer(k, axis=i) for i, k in enumerate(key)]
+        return tuple(keyidx)
+
+    @final
+    def _validate_key_length(self, key: tuple) -> tuple:
+        if len(key) > self.ndim:
+            if key[0] is Ellipsis:
+                # e.g. Series.iloc[..., 3] reduces to just Series.iloc[3]
+                key = key[1:]
+                if Ellipsis in key:
+                    raise IndexingError(_one_ellipsis_message)
+                return self._validate_key_length(key)
+            raise IndexingError("Too many indexers")
+        return key
+
+    @final
+    def _getitem_tuple_same_dim(self, tup: tuple):
+        """
+        Index with indexers that should return an object of the same dimension
+        as self.obj.
+
+        This is only called after a failed call to _getitem_lowerdim.
+        """
+        retval = self.obj
+        # Selecting columns before rows is significantly faster
+        start_val = (self.ndim - len(tup)) + 1
+        for i, key in enumerate(reversed(tup)):
+            i = self.ndim - i - start_val
+            if com.is_null_slice(key):
+                continue
+
+            retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
+            # We should never have retval.ndim < self.ndim, as that should
+            #  be handled by the _getitem_lowerdim call above.
+            assert retval.ndim == self.ndim
+
+        if retval is self.obj:
+            # if all axes were a null slice (`df.loc[:, :]`), ensure we still
+            # return a new object (https://github.com/pandas-dev/pandas/pull/49469)
+            retval = retval.copy(deep=False)
+
+        return retval
+
+    @final
+    def _getitem_lowerdim(self, tup: tuple):
+        # we can directly get the axis result since the axis is specified
+        if self.axis is not None:
+            axis = self.obj._get_axis_number(self.axis)
+            return self._getitem_axis(tup, axis=axis)
+
+        # we may have a nested tuples indexer here
+        if self._is_nested_tuple_indexer(tup):
+            return self._getitem_nested_tuple(tup)
+
+        # we maybe be using a tuple to represent multiple dimensions here
+        ax0 = self.obj._get_axis(0)
+        # ...but iloc should handle the tuple as simple integer-location
+        # instead of checking it as multiindex representation (GH 13797)
+        if (
+            isinstance(ax0, MultiIndex)
+            and self.name != "iloc"
+            and not any(isinstance(x, slice) for x in tup)
+        ):
+            # Note: in all extant test cases, replacing the slice condition with
+            #  `all(is_hashable(x) or com.is_null_slice(x) for x in tup)`
+            #  is equivalent.
+            #  (see the other place where we call _handle_lowerdim_multi_index_axis0)
+            with suppress(IndexingError):
+                return cast(_LocIndexer, self)._handle_lowerdim_multi_index_axis0(tup)
+
+        tup = self._validate_key_length(tup)
+
+        for i, key in enumerate(tup):
+            if is_label_like(key):
+                # We don't need to check for tuples here because those are
+                #  caught by the _is_nested_tuple_indexer check above.
+                section = self._getitem_axis(key, axis=i)
+
+                # We should never have a scalar section here, because
+                #  _getitem_lowerdim is only called after a check for
+                #  is_scalar_access, which that would be.
+                if section.ndim == self.ndim:
+                    # we're in the middle of slicing through a MultiIndex
+                    # revise the key wrt to `section` by inserting an _NS
+                    new_key = tup[:i] + (_NS,) + tup[i + 1 :]
+
+                else:
+                    # Note: the section.ndim == self.ndim check above
+                    #  rules out having DataFrame here, so we dont need to worry
+                    #  about transposing.
+                    new_key = tup[:i] + tup[i + 1 :]
+
+                    if len(new_key) == 1:
+                        new_key = new_key[0]
+
+                # Slices should return views, but calling iloc/loc with a null
+                # slice returns a new object.
+                if com.is_null_slice(new_key):
+                    return section
+                # This is an elided recursive call to iloc/loc
+                return getattr(section, self.name)[new_key]
+
+        raise IndexingError("not applicable")
+
+    @final
+    def _getitem_nested_tuple(self, tup: tuple):
+        # we have a nested tuple so have at least 1 multi-index level
+        # we should be able to match up the dimensionality here
+
+        def _contains_slice(x: object) -> bool:
+            # Check if object is a slice or a tuple containing a slice
+            if isinstance(x, tuple):
+                return any(isinstance(v, slice) for v in x)
+            elif isinstance(x, slice):
+                return True
+            return False
+
+        for key in tup:
+            check_dict_or_set_indexers(key)
+
+        # we have too many indexers for our dim, but have at least 1
+        # multi-index dimension, try to see if we have something like
+        # a tuple passed to a series with a multi-index
+        if len(tup) > self.ndim:
+            if self.name != "loc":
+                # This should never be reached, but let's be explicit about it
+                raise ValueError("Too many indices")  # pragma: no cover
+            if all(
+                (is_hashable(x) and not _contains_slice(x)) or com.is_null_slice(x)
+                for x in tup
+            ):
+                # GH#10521 Series should reduce MultiIndex dimensions instead of
+                #  DataFrame, IndexingError is not raised when slice(None,None,None)
+                #  with one row.
+                with suppress(IndexingError):
+                    return cast(_LocIndexer, self)._handle_lowerdim_multi_index_axis0(
+                        tup
+                    )
+            elif isinstance(self.obj, ABCSeries) and any(
+                isinstance(k, tuple) for k in tup
+            ):
+                # GH#35349 Raise if tuple in tuple for series
+                # Do this after the all-hashable-or-null-slice check so that
+                #  we are only getting non-hashable tuples, in particular ones
+                #  that themselves contain a slice entry
+                # See test_loc_series_getitem_too_many_dimensions
+                raise IndexingError("Too many indexers")
+
+            # this is a series with a multi-index specified a tuple of
+            # selectors
+            axis = self.axis or 0
+            return self._getitem_axis(tup, axis=axis)
+
+        # handle the multi-axis by taking sections and reducing
+        # this is iterative
+        obj = self.obj
+        # GH#41369 Loop in reverse order ensures indexing along columns before rows
+        # which selects only necessary blocks which avoids dtype conversion if possible
+        axis = len(tup) - 1
+        for key in tup[::-1]:
+            if com.is_null_slice(key):
+                axis -= 1
+                continue
+
+            obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
+            axis -= 1
+
+            # if we have a scalar, we are done
+            if is_scalar(obj) or not hasattr(obj, "ndim"):
+                break
+
+        return obj
+
+    def _convert_to_indexer(self, key, axis: AxisInt):
+        raise AbstractMethodError(self)
+
+    def _check_deprecated_callable_usage(self, key: Any, maybe_callable: T) -> T:
+        # GH53533
+        if self.name == "iloc" and callable(key) and isinstance(maybe_callable, tuple):
+            warnings.warn(
+                "Returning a tuple from a callable with iloc "
+                "is deprecated and will be removed in a future version",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return maybe_callable
+
+    @final
+    def __getitem__(self, key):
+        check_dict_or_set_indexers(key)
+        if type(key) is tuple:
+            key = tuple(list(x) if is_iterator(x) else x for x in key)
+            key = tuple(com.apply_if_callable(x, self.obj) for x in key)
+            if self._is_scalar_access(key):
+                return self.obj._get_value(*key, takeable=self._takeable)
+            return self._getitem_tuple(key)
+        else:
+            # we by definition only have the 0th axis
+            axis = self.axis or 0
+
+            maybe_callable = com.apply_if_callable(key, self.obj)
+            maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable)
+            return self._getitem_axis(maybe_callable, axis=axis)
+
+    def _is_scalar_access(self, key: tuple):
+        raise NotImplementedError()
+
+    def _getitem_tuple(self, tup: tuple):
+        raise AbstractMethodError(self)
+
+    def _getitem_axis(self, key, axis: AxisInt):
+        raise NotImplementedError()
+
+    def _has_valid_setitem_indexer(self, indexer) -> bool:
+        raise AbstractMethodError(self)
+
+    @final
+    def _getbool_axis(self, key, axis: AxisInt):
+        # caller is responsible for ensuring non-None axis
+        labels = self.obj._get_axis(axis)
+        key = check_bool_indexer(labels, key)
+        inds = key.nonzero()[0]
+        return self.obj._take_with_is_copy(inds, axis=axis)
+
+
+@doc(IndexingMixin.loc)
+class _LocIndexer(_LocationIndexer):
+    _takeable: bool = False
+    _valid_types = (
+        "labels (MUST BE IN THE INDEX), slices of labels (BOTH "
+        "endpoints included! Can be slices of integers if the "
+        "index is integers), listlike of labels, boolean"
+    )
+
+    # -------------------------------------------------------------------
+    # Key Checks
+
+    @doc(_LocationIndexer._validate_key)
+    def _validate_key(self, key, axis: Axis):
+        # valid for a collection of labels (we check their presence later)
+        # slice of labels (where start-end in labels)
+        # slice of integers (only if in the labels)
+        # boolean not in slice and with boolean index
+        ax = self.obj._get_axis(axis)
+        if isinstance(key, bool) and not (
+            is_bool_dtype(ax.dtype)
+            or ax.dtype.name == "boolean"
+            or isinstance(ax, MultiIndex)
+            and is_bool_dtype(ax.get_level_values(0).dtype)
+        ):
+            raise KeyError(
+                f"{key}: boolean label can not be used without a boolean index"
+            )
+
+        if isinstance(key, slice) and (
+            isinstance(key.start, bool) or isinstance(key.stop, bool)
+        ):
+            raise TypeError(f"{key}: boolean values can not be used in a slice")
+
+    def _has_valid_setitem_indexer(self, indexer) -> bool:
+        return True
+
+    def _is_scalar_access(self, key: tuple) -> bool:
+        """
+        Returns
+        -------
+        bool
+        """
+        # this is a shortcut accessor to both .loc and .iloc
+        # that provide the equivalent access of .at and .iat
+        # a) avoid getting things via sections and (to minimize dtype changes)
+        # b) provide a performant path
+        if len(key) != self.ndim:
+            return False
+
+        for i, k in enumerate(key):
+            if not is_scalar(k):
+                return False
+
+            ax = self.obj.axes[i]
+            if isinstance(ax, MultiIndex):
+                return False
+
+            if isinstance(k, str) and ax._supports_partial_string_indexing:
+                # partial string indexing, df.loc['2000', 'A']
+                # should not be considered scalar
+                return False
+
+            if not ax._index_as_unique:
+                return False
+
+        return True
+
+    # -------------------------------------------------------------------
+    # MultiIndex Handling
+
+    def _multi_take_opportunity(self, tup: tuple) -> bool:
+        """
+        Check whether there is the possibility to use ``_multi_take``.
+
+        Currently the limit is that all axes being indexed, must be indexed with
+        list-likes.
+
+        Parameters
+        ----------
+        tup : tuple
+            Tuple of indexers, one per axis.
+
+        Returns
+        -------
+        bool
+            Whether the current indexing,
+            can be passed through `_multi_take`.
+        """
+        if not all(is_list_like_indexer(x) for x in tup):
+            return False
+
+        # just too complicated
+        return not any(com.is_bool_indexer(x) for x in tup)
+
+    def _multi_take(self, tup: tuple):
+        """
+        Create the indexers for the passed tuple of keys, and
+        executes the take operation. This allows the take operation to be
+        executed all at once, rather than once for each dimension.
+        Improving efficiency.
+
+        Parameters
+        ----------
+        tup : tuple
+            Tuple of indexers, one per axis.
+
+        Returns
+        -------
+        values: same type as the object being indexed
+        """
+        # GH 836
+        d = {
+            axis: self._get_listlike_indexer(key, axis)
+            for (key, axis) in zip(tup, self.obj._AXIS_ORDERS)
+        }
+        return self.obj._reindex_with_indexers(d, copy=True, allow_dups=True)
+
+    # -------------------------------------------------------------------
+
+    def _getitem_iterable(self, key, axis: AxisInt):
+        """
+        Index current object with an iterable collection of keys.
+
+        Parameters
+        ----------
+        key : iterable
+            Targeted labels.
+        axis : int
+            Dimension on which the indexing is being made.
+
+        Raises
+        ------
+        KeyError
+            If no key was found. Will change in the future to raise if not all
+            keys were found.
+
+        Returns
+        -------
+        scalar, DataFrame, or Series: indexed value(s).
+        """
+        # we assume that not com.is_bool_indexer(key), as that is
+        #  handled before we get here.
+        self._validate_key(key, axis)
+
+        # A collection of keys
+        keyarr, indexer = self._get_listlike_indexer(key, axis)
+        return self.obj._reindex_with_indexers(
+            {axis: [keyarr, indexer]}, copy=True, allow_dups=True
+        )
+
+    def _getitem_tuple(self, tup: tuple):
+        with suppress(IndexingError):
+            tup = self._expand_ellipsis(tup)
+            return self._getitem_lowerdim(tup)
+
+        # no multi-index, so validate all of the indexers
+        tup = self._validate_tuple_indexer(tup)
+
+        # ugly hack for GH #836
+        if self._multi_take_opportunity(tup):
+            return self._multi_take(tup)
+
+        return self._getitem_tuple_same_dim(tup)
+
+    def _get_label(self, label, axis: AxisInt):
+        # GH#5567 this will fail if the label is not present in the axis.
+        return self.obj.xs(label, axis=axis)
+
+    def _handle_lowerdim_multi_index_axis0(self, tup: tuple):
+        # we have an axis0 multi-index, handle or raise
+        axis = self.axis or 0
+        try:
+            # fast path for series or for tup devoid of slices
+            return self._get_label(tup, axis=axis)
+
+        except KeyError as ek:
+            # raise KeyError if number of indexers match
+            # else IndexingError will be raised
+            if self.ndim < len(tup) <= self.obj.index.nlevels:
+                raise ek
+            raise IndexingError("No label returned") from ek
+
+    def _getitem_axis(self, key, axis: AxisInt):
+        key = item_from_zerodim(key)
+        if is_iterator(key):
+            key = list(key)
+        if key is Ellipsis:
+            key = slice(None)
+
+        labels = self.obj._get_axis(axis)
+
+        if isinstance(key, tuple) and isinstance(labels, MultiIndex):
+            key = tuple(key)
+
+        if isinstance(key, slice):
+            self._validate_key(key, axis)
+            return self._get_slice_axis(key, axis=axis)
+        elif com.is_bool_indexer(key):
+            return self._getbool_axis(key, axis=axis)
+        elif is_list_like_indexer(key):
+            # an iterable multi-selection
+            if not (isinstance(key, tuple) and isinstance(labels, MultiIndex)):
+                if hasattr(key, "ndim") and key.ndim > 1:
+                    raise ValueError("Cannot index with multidimensional key")
+
+                return self._getitem_iterable(key, axis=axis)
+
+            # nested tuple slicing
+            if is_nested_tuple(key, labels):
+                locs = labels.get_locs(key)
+                indexer: list[slice | npt.NDArray[np.intp]] = [slice(None)] * self.ndim
+                indexer[axis] = locs
+                return self.obj.iloc[tuple(indexer)]
+
+        # fall thru to straight lookup
+        self._validate_key(key, axis)
+        return self._get_label(key, axis=axis)
+
+    def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
+        """
+        This is pretty simple as we just have to deal with labels.
+        """
+        # caller is responsible for ensuring non-None axis
+        obj = self.obj
+        if not need_slice(slice_obj):
+            return obj.copy(deep=False)
+
+        labels = obj._get_axis(axis)
+        indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
+
+        if isinstance(indexer, slice):
+            return self.obj._slice(indexer, axis=axis)
+        else:
+            # DatetimeIndex overrides Index.slice_indexer and may
+            #  return a DatetimeIndex instead of a slice object.
+            return self.obj.take(indexer, axis=axis)
+
+    def _convert_to_indexer(self, key, axis: AxisInt):
+        """
+        Convert indexing key into something we can use to do actual fancy
+        indexing on a ndarray.
+
+        Examples
+        ix[:5] -> slice(0, 5)
+        ix[[1,2,3]] -> [1,2,3]
+        ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz)
+
+        Going by Zen of Python?
+        'In the face of ambiguity, refuse the temptation to guess.'
+        raise AmbiguousIndexError with integer labels?
+        - No, prefer label-based indexing
+        """
+        labels = self.obj._get_axis(axis)
+
+        if isinstance(key, slice):
+            return labels._convert_slice_indexer(key, kind="loc")
+
+        if (
+            isinstance(key, tuple)
+            and not isinstance(labels, MultiIndex)
+            and self.ndim < 2
+            and len(key) > 1
+        ):
+            raise IndexingError("Too many indexers")
+
+        # Slices are not valid keys passed in by the user,
+        # even though they are hashable in Python 3.12
+        contains_slice = False
+        if isinstance(key, tuple):
+            contains_slice = any(isinstance(v, slice) for v in key)
+
+        if is_scalar(key) or (
+            isinstance(labels, MultiIndex) and is_hashable(key) and not contains_slice
+        ):
+            # Otherwise get_loc will raise InvalidIndexError
+
+            # if we are a label return me
+            try:
+                return labels.get_loc(key)
+            except LookupError:
+                if isinstance(key, tuple) and isinstance(labels, MultiIndex):
+                    if len(key) == labels.nlevels:
+                        return {"key": key}
+                    raise
+            except InvalidIndexError:
+                # GH35015, using datetime as column indices raises exception
+                if not isinstance(labels, MultiIndex):
+                    raise
+            except ValueError:
+                if not is_integer(key):
+                    raise
+                return {"key": key}
+
+        if is_nested_tuple(key, labels):
+            if self.ndim == 1 and any(isinstance(k, tuple) for k in key):
+                # GH#35349 Raise if tuple in tuple for series
+                raise IndexingError("Too many indexers")
+            return labels.get_locs(key)
+
+        elif is_list_like_indexer(key):
+            if is_iterator(key):
+                key = list(key)
+
+            if com.is_bool_indexer(key):
+                key = check_bool_indexer(labels, key)
+                return key
+            else:
+                return self._get_listlike_indexer(key, axis)[1]
+        else:
+            try:
+                return labels.get_loc(key)
+            except LookupError:
+                # allow a not found key only if we are a setter
+                if not is_list_like_indexer(key):
+                    return {"key": key}
+                raise
+
+    def _get_listlike_indexer(self, key, axis: AxisInt):
+        """
+        Transform a list-like of keys into a new index and an indexer.
+
+        Parameters
+        ----------
+        key : list-like
+            Targeted labels.
+        axis:  int
+            Dimension on which the indexing is being made.
+
+        Raises
+        ------
+        KeyError
+            If at least one key was requested but none was found.
+
+        Returns
+        -------
+        keyarr: Index
+            New index (coinciding with 'key' if the axis is unique).
+        values : array-like
+            Indexer for the return object, -1 denotes keys not found.
+        """
+        ax = self.obj._get_axis(axis)
+        axis_name = self.obj._get_axis_name(axis)
+
+        keyarr, indexer = ax._get_indexer_strict(key, axis_name)
+
+        return keyarr, indexer
+
+
+@doc(IndexingMixin.iloc)
+class _iLocIndexer(_LocationIndexer):
+    _valid_types = (
+        "integer, integer slice (START point is INCLUDED, END "
+        "point is EXCLUDED), listlike of integers, boolean array"
+    )
+    _takeable = True
+
+    # -------------------------------------------------------------------
+    # Key Checks
+
+    def _validate_key(self, key, axis: AxisInt):
+        if com.is_bool_indexer(key):
+            if hasattr(key, "index") and isinstance(key.index, Index):
+                if key.index.inferred_type == "integer":
+                    raise NotImplementedError(
+                        "iLocation based boolean "
+                        "indexing on an integer type "
+                        "is not available"
+                    )
+                raise ValueError(
+                    "iLocation based boolean indexing cannot use "
+                    "an indexable as a mask"
+                )
+            return
+
+        if isinstance(key, slice):
+            return
+        elif is_integer(key):
+            self._validate_integer(key, axis)
+        elif isinstance(key, tuple):
+            # a tuple should already have been caught by this point
+            # so don't treat a tuple as a valid indexer
+            raise IndexingError("Too many indexers")
+        elif is_list_like_indexer(key):
+            if isinstance(key, ABCSeries):
+                arr = key._values
+            elif is_array_like(key):
+                arr = key
+            else:
+                arr = np.array(key)
+            len_axis = len(self.obj._get_axis(axis))
+
+            # check that the key has a numeric dtype
+            if not is_numeric_dtype(arr.dtype):
+                raise IndexError(f".iloc requires numeric indexers, got {arr}")
+
+            # check that the key does not exceed the maximum size of the index
+            if len(arr) and (arr.max() >= len_axis or arr.min() < -len_axis):
+                raise IndexError("positional indexers are out-of-bounds")
+        else:
+            raise ValueError(f"Can only index by location with a [{self._valid_types}]")
+
+    def _has_valid_setitem_indexer(self, indexer) -> bool:
+        """
+        Validate that a positional indexer cannot enlarge its target
+        will raise if needed, does not modify the indexer externally.
+
+        Returns
+        -------
+        bool
+        """
+        if isinstance(indexer, dict):
+            raise IndexError("iloc cannot enlarge its target object")
+
+        if isinstance(indexer, ABCDataFrame):
+            raise TypeError(
+                "DataFrame indexer for .iloc is not supported. "
+                "Consider using .loc with a DataFrame indexer for automatic alignment.",
+            )
+
+        if not isinstance(indexer, tuple):
+            indexer = _tuplify(self.ndim, indexer)
+
+        for ax, i in zip(self.obj.axes, indexer):
+            if isinstance(i, slice):
+                # should check the stop slice?
+                pass
+            elif is_list_like_indexer(i):
+                # should check the elements?
+                pass
+            elif is_integer(i):
+                if i >= len(ax):
+                    raise IndexError("iloc cannot enlarge its target object")
+            elif isinstance(i, dict):
+                raise IndexError("iloc cannot enlarge its target object")
+
+        return True
+
+    def _is_scalar_access(self, key: tuple) -> bool:
+        """
+        Returns
+        -------
+        bool
+        """
+        # this is a shortcut accessor to both .loc and .iloc
+        # that provide the equivalent access of .at and .iat
+        # a) avoid getting things via sections and (to minimize dtype changes)
+        # b) provide a performant path
+        if len(key) != self.ndim:
+            return False
+
+        return all(is_integer(k) for k in key)
+
+    def _validate_integer(self, key: int | np.integer, axis: AxisInt) -> None:
+        """
+        Check that 'key' is a valid position in the desired axis.
+
+        Parameters
+        ----------
+        key : int
+            Requested position.
+        axis : int
+            Desired axis.
+
+        Raises
+        ------
+        IndexError
+            If 'key' is not a valid position in axis 'axis'.
+        """
+        len_axis = len(self.obj._get_axis(axis))
+        if key >= len_axis or key < -len_axis:
+            raise IndexError("single positional indexer is out-of-bounds")
+
+    # -------------------------------------------------------------------
+
+    def _getitem_tuple(self, tup: tuple):
+        tup = self._validate_tuple_indexer(tup)
+        with suppress(IndexingError):
+            return self._getitem_lowerdim(tup)
+
+        return self._getitem_tuple_same_dim(tup)
+
+    def _get_list_axis(self, key, axis: AxisInt):
+        """
+        Return Series values by list or array of integers.
+
+        Parameters
+        ----------
+        key : list-like positional indexer
+        axis : int
+
+        Returns
+        -------
+        Series object
+
+        Notes
+        -----
+        `axis` can only be zero.
+        """
+        try:
+            return self.obj._take_with_is_copy(key, axis=axis)
+        except IndexError as err:
+            # re-raise with different error message, e.g. test_getitem_ndarray_3d
+            raise IndexError("positional indexers are out-of-bounds") from err
+
+    def _getitem_axis(self, key, axis: AxisInt):
+        if key is Ellipsis:
+            key = slice(None)
+        elif isinstance(key, ABCDataFrame):
+            raise IndexError(
+                "DataFrame indexer is not allowed for .iloc\n"
+                "Consider using .loc for automatic alignment."
+            )
+
+        if isinstance(key, slice):
+            return self._get_slice_axis(key, axis=axis)
+
+        if is_iterator(key):
+            key = list(key)
+
+        if isinstance(key, list):
+            key = np.asarray(key)
+
+        if com.is_bool_indexer(key):
+            self._validate_key(key, axis)
+            return self._getbool_axis(key, axis=axis)
+
+        # a list of integers
+        elif is_list_like_indexer(key):
+            return self._get_list_axis(key, axis=axis)
+
+        # a single integer
+        else:
+            key = item_from_zerodim(key)
+            if not is_integer(key):
+                raise TypeError("Cannot index by location index with a non-integer key")
+
+            # validate the location
+            self._validate_integer(key, axis)
+
+            return self.obj._ixs(key, axis=axis)
+
+    def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
+        # caller is responsible for ensuring non-None axis
+        obj = self.obj
+
+        if not need_slice(slice_obj):
+            return obj.copy(deep=False)
+
+        labels = obj._get_axis(axis)
+        labels._validate_positional_slice(slice_obj)
+        return self.obj._slice(slice_obj, axis=axis)
+
+    def _convert_to_indexer(self, key, axis: AxisInt):
+        """
+        Much simpler as we only have to deal with our valid types.
+        """
+        return key
+
+    def _get_setitem_indexer(self, key):
+        # GH#32257 Fall through to let numpy do validation
+        if is_iterator(key):
+            key = list(key)
+
+        if self.axis is not None:
+            key = _tupleize_axis_indexer(self.ndim, self.axis, key)
+
+        return key
+
+    # -------------------------------------------------------------------
+
+    def _setitem_with_indexer(self, indexer, value, name: str = "iloc"):
+        """
+        _setitem_with_indexer is for setting values on a Series/DataFrame
+        using positional indexers.
+
+        If the relevant keys are not present, the Series/DataFrame may be
+        expanded.
+
+        This method is currently broken when dealing with non-unique Indexes,
+        since it goes from positional indexers back to labels when calling
+        BlockManager methods, see GH#12991, GH#22046, GH#15686.
+        """
+        info_axis = self.obj._info_axis_number
+
+        # maybe partial set
+        take_split_path = not self.obj._mgr.is_single_block
+
+        if not take_split_path and isinstance(value, ABCDataFrame):
+            # Avoid cast of values
+            take_split_path = not value._mgr.is_single_block
+
+        # if there is only one block/type, still have to take split path
+        # unless the block is one-dimensional or it can hold the value
+        if not take_split_path and len(self.obj._mgr.arrays) and self.ndim > 1:
+            # in case of dict, keys are indices
+            val = list(value.values()) if isinstance(value, dict) else value
+            arr = self.obj._mgr.arrays[0]
+            take_split_path = not can_hold_element(
+                arr, extract_array(val, extract_numpy=True)
+            )
+
+        # if we have any multi-indexes that have non-trivial slices
+        # (not null slices) then we must take the split path, xref
+        # GH 10360, GH 27841
+        if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
+            for i, ax in zip(indexer, self.obj.axes):
+                if isinstance(ax, MultiIndex) and not (
+                    is_integer(i) or com.is_null_slice(i)
+                ):
+                    take_split_path = True
+                    break
+
+        if isinstance(indexer, tuple):
+            nindexer = []
+            for i, idx in enumerate(indexer):
+                if isinstance(idx, dict):
+                    # reindex the axis to the new value
+                    # and set inplace
+                    key, _ = convert_missing_indexer(idx)
+
+                    # if this is the items axes, then take the main missing
+                    # path first
+                    # this correctly sets the dtype and avoids cache issues
+                    # essentially this separates out the block that is needed
+                    # to possibly be modified
+                    if self.ndim > 1 and i == info_axis:
+                        # add the new item, and set the value
+                        # must have all defined axes if we have a scalar
+                        # or a list-like on the non-info axes if we have a
+                        # list-like
+                        if not len(self.obj):
+                            if not is_list_like_indexer(value):
+                                raise ValueError(
+                                    "cannot set a frame with no "
+                                    "defined index and a scalar"
+                                )
+                            self.obj[key] = value
+                            return
+
+                        # add a new item with the dtype setup
+                        if com.is_null_slice(indexer[0]):
+                            # We are setting an entire column
+                            self.obj[key] = value
+                            return
+                        elif is_array_like(value):
+                            # GH#42099
+                            arr = extract_array(value, extract_numpy=True)
+                            taker = -1 * np.ones(len(self.obj), dtype=np.intp)
+                            empty_value = algos.take_nd(arr, taker)
+                            if not isinstance(value, ABCSeries):
+                                # if not Series (in which case we need to align),
+                                #  we can short-circuit
+                                if (
+                                    isinstance(arr, np.ndarray)
+                                    and arr.ndim == 1
+                                    and len(arr) == 1
+                                ):
+                                    # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+                                    arr = arr[0, ...]
+                                empty_value[indexer[0]] = arr
+                                self.obj[key] = empty_value
+                                return
+
+                            self.obj[key] = empty_value
+                        elif not is_list_like(value):
+                            self.obj[key] = construct_1d_array_from_inferred_fill_value(
+                                value, len(self.obj)
+                            )
+                        else:
+                            # FIXME: GH#42099#issuecomment-864326014
+                            self.obj[key] = infer_fill_value(value)
+
+                        new_indexer = convert_from_missing_indexer_tuple(
+                            indexer, self.obj.axes
+                        )
+                        self._setitem_with_indexer(new_indexer, value, name)
+
+                        return
+
+                    # reindex the axis
+                    # make sure to clear the cache because we are
+                    # just replacing the block manager here
+                    # so the object is the same
+                    index = self.obj._get_axis(i)
+                    with warnings.catch_warnings():
+                        # TODO: re-issue this with setitem-specific message?
+                        warnings.filterwarnings(
+                            "ignore",
+                            "The behavior of Index.insert with object-dtype "
+                            "is deprecated",
+                            category=FutureWarning,
+                        )
+                        labels = index.insert(len(index), key)
+
+                    # We are expanding the Series/DataFrame values to match
+                    #  the length of thenew index `labels`.  GH#40096 ensure
+                    #  this is valid even if the index has duplicates.
+                    taker = np.arange(len(index) + 1, dtype=np.intp)
+                    taker[-1] = -1
+                    reindexers = {i: (labels, taker)}
+                    new_obj = self.obj._reindex_with_indexers(
+                        reindexers, allow_dups=True
+                    )
+                    self.obj._mgr = new_obj._mgr
+                    self.obj._maybe_update_cacher(clear=True)
+                    self.obj._is_copy = None
+
+                    nindexer.append(labels.get_loc(key))
+
+                else:
+                    nindexer.append(idx)
+
+            indexer = tuple(nindexer)
+        else:
+            indexer, missing = convert_missing_indexer(indexer)
+
+            if missing:
+                self._setitem_with_indexer_missing(indexer, value)
+                return
+
+        if name == "loc":
+            # must come after setting of missing
+            indexer, value = self._maybe_mask_setitem_value(indexer, value)
+
+        # align and set the values
+        if take_split_path:
+            # We have to operate column-wise
+            self._setitem_with_indexer_split_path(indexer, value, name)
+        else:
+            self._setitem_single_block(indexer, value, name)
+
+    def _setitem_with_indexer_split_path(self, indexer, value, name: str):
+        """
+        Setitem column-wise.
+        """
+        # Above we only set take_split_path to True for 2D cases
+        assert self.ndim == 2
+
+        if not isinstance(indexer, tuple):
+            indexer = _tuplify(self.ndim, indexer)
+        if len(indexer) > self.ndim:
+            raise IndexError("too many indices for array")
+        if isinstance(indexer[0], np.ndarray) and indexer[0].ndim > 2:
+            raise ValueError(r"Cannot set values with ndim > 2")
+
+        if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
+            from pandas import Series
+
+            value = self._align_series(indexer, Series(value))
+
+        # Ensure we have something we can iterate over
+        info_axis = indexer[1]
+        ilocs = self._ensure_iterable_column_indexer(info_axis)
+
+        pi = indexer[0]
+        lplane_indexer = length_of_indexer(pi, self.obj.index)
+        # lplane_indexer gives the expected length of obj[indexer[0]]
+
+        # we need an iterable, with a ndim of at least 1
+        # eg. don't pass through np.array(0)
+        if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:
+            if isinstance(value, ABCDataFrame):
+                self._setitem_with_indexer_frame_value(indexer, value, name)
+
+            elif np.ndim(value) == 2:
+                # TODO: avoid np.ndim call in case it isn't an ndarray, since
+                #  that will construct an ndarray, which will be wasteful
+                self._setitem_with_indexer_2d_value(indexer, value)
+
+            elif len(ilocs) == 1 and lplane_indexer == len(value) and not is_scalar(pi):
+                # We are setting multiple rows in a single column.
+                self._setitem_single_column(ilocs[0], value, pi)
+
+            elif len(ilocs) == 1 and 0 != lplane_indexer != len(value):
+                # We are trying to set N values into M entries of a single
+                #  column, which is invalid for N != M
+                # Exclude zero-len for e.g. boolean masking that is all-false
+
+                if len(value) == 1 and not is_integer(info_axis):
+                    # This is a case like df.iloc[:3, [1]] = [0]
+                    #  where we treat as df.iloc[:3, 1] = 0
+                    return self._setitem_with_indexer((pi, info_axis[0]), value[0])
+
+                raise ValueError(
+                    "Must have equal len keys and value "
+                    "when setting with an iterable"
+                )
+
+            elif lplane_indexer == 0 and len(value) == len(self.obj.index):
+                # We get here in one case via .loc with a all-False mask
+                pass
+
+            elif self._is_scalar_access(indexer) and is_object_dtype(
+                self.obj.dtypes._values[ilocs[0]]
+            ):
+                # We are setting nested data, only possible for object dtype data
+                self._setitem_single_column(indexer[1], value, pi)
+
+            elif len(ilocs) == len(value):
+                # We are setting multiple columns in a single row.
+                for loc, v in zip(ilocs, value):
+                    self._setitem_single_column(loc, v, pi)
+
+            elif len(ilocs) == 1 and com.is_null_slice(pi) and len(self.obj) == 0:
+                # This is a setitem-with-expansion, see
+                #  test_loc_setitem_empty_append_expands_rows_mixed_dtype
+                # e.g. df = DataFrame(columns=["x", "y"])
+                #  df["x"] = df["x"].astype(np.int64)
+                #  df.loc[:, "x"] = [1, 2, 3]
+                self._setitem_single_column(ilocs[0], value, pi)
+
+            else:
+                raise ValueError(
+                    "Must have equal len keys and value "
+                    "when setting with an iterable"
+                )
+
+        else:
+            # scalar value
+            for loc in ilocs:
+                self._setitem_single_column(loc, value, pi)
+
+    def _setitem_with_indexer_2d_value(self, indexer, value):
+        # We get here with np.ndim(value) == 2, excluding DataFrame,
+        #  which goes through _setitem_with_indexer_frame_value
+        pi = indexer[0]
+
+        ilocs = self._ensure_iterable_column_indexer(indexer[1])
+
+        if not is_array_like(value):
+            # cast lists to array
+            value = np.array(value, dtype=object)
+        if len(ilocs) != value.shape[1]:
+            raise ValueError(
+                "Must have equal len keys and value when setting with an ndarray"
+            )
+
+        for i, loc in enumerate(ilocs):
+            value_col = value[:, i]
+            if is_object_dtype(value_col.dtype):
+                # casting to list so that we do type inference in setitem_single_column
+                value_col = value_col.tolist()
+            self._setitem_single_column(loc, value_col, pi)
+
+    def _setitem_with_indexer_frame_value(self, indexer, value: DataFrame, name: str):
+        ilocs = self._ensure_iterable_column_indexer(indexer[1])
+
+        sub_indexer = list(indexer)
+        pi = indexer[0]
+
+        multiindex_indexer = isinstance(self.obj.columns, MultiIndex)
+
+        unique_cols = value.columns.is_unique
+
+        # We do not want to align the value in case of iloc GH#37728
+        if name == "iloc":
+            for i, loc in enumerate(ilocs):
+                val = value.iloc[:, i]
+                self._setitem_single_column(loc, val, pi)
+
+        elif not unique_cols and value.columns.equals(self.obj.columns):
+            # We assume we are already aligned, see
+            # test_iloc_setitem_frame_duplicate_columns_multiple_blocks
+            for loc in ilocs:
+                item = self.obj.columns[loc]
+                if item in value:
+                    sub_indexer[1] = item
+                    val = self._align_series(
+                        tuple(sub_indexer),
+                        value.iloc[:, loc],
+                        multiindex_indexer,
+                    )
+                else:
+                    val = np.nan
+
+                self._setitem_single_column(loc, val, pi)
+
+        elif not unique_cols:
+            raise ValueError("Setting with non-unique columns is not allowed.")
+
+        else:
+            for loc in ilocs:
+                item = self.obj.columns[loc]
+                if item in value:
+                    sub_indexer[1] = item
+                    val = self._align_series(
+                        tuple(sub_indexer),
+                        value[item],
+                        multiindex_indexer,
+                        using_cow=using_copy_on_write(),
+                    )
+                else:
+                    val = np.nan
+
+                self._setitem_single_column(loc, val, pi)
+
+    def _setitem_single_column(self, loc: int, value, plane_indexer) -> None:
+        """
+
+        Parameters
+        ----------
+        loc : int
+            Indexer for column position
+        plane_indexer : int, slice, listlike[int]
+            The indexer we use for setitem along axis=0.
+        """
+        pi = plane_indexer
+
+        is_full_setter = com.is_null_slice(pi) or com.is_full_slice(pi, len(self.obj))
+
+        is_null_setter = com.is_empty_slice(pi) or is_array_like(pi) and len(pi) == 0
+
+        if is_null_setter:
+            # no-op, don't cast dtype later
+            return
+
+        elif is_full_setter:
+            try:
+                self.obj._mgr.column_setitem(
+                    loc, plane_indexer, value, inplace_only=True
+                )
+            except (ValueError, TypeError, LossySetitemError):
+                # If we're setting an entire column and we can't do it inplace,
+                #  then we can use value's dtype (or inferred dtype)
+                #  instead of object
+                dtype = self.obj.dtypes.iloc[loc]
+                if dtype not in (np.void, object) and not self.obj.empty:
+                    # - Exclude np.void, as that is a special case for expansion.
+                    #   We want to warn for
+                    #       df = pd.DataFrame({'a': [1, 2]})
+                    #       df.loc[:, 'a'] = .3
+                    #   but not for
+                    #       df = pd.DataFrame({'a': [1, 2]})
+                    #       df.loc[:, 'b'] = .3
+                    # - Exclude `object`, as then no upcasting happens.
+                    # - Exclude empty initial object with enlargement,
+                    #   as then there's nothing to be inconsistent with.
+                    warnings.warn(
+                        f"Setting an item of incompatible dtype is deprecated "
+                        "and will raise in a future error of pandas. "
+                        f"Value '{value}' has dtype incompatible with {dtype}, "
+                        "please explicitly cast to a compatible dtype first.",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                self.obj.isetitem(loc, value)
+        else:
+            # set value into the column (first attempting to operate inplace, then
+            #  falling back to casting if necessary)
+            dtype = self.obj.dtypes.iloc[loc]
+            if dtype == np.void:
+                # This means we're expanding, with multiple columns, e.g.
+                #     df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})
+                #     df.loc[df.index <= 2, ['F', 'G']] = (1, 'abc')
+                # Columns F and G will initially be set to np.void.
+                # Here, we replace those temporary `np.void` columns with
+                # columns of the appropriate dtype, based on `value`.
+                self.obj.iloc[:, loc] = construct_1d_array_from_inferred_fill_value(
+                    value, len(self.obj)
+                )
+            self.obj._mgr.column_setitem(loc, plane_indexer, value)
+
+        self.obj._clear_item_cache()
+
+    def _setitem_single_block(self, indexer, value, name: str) -> None:
+        """
+        _setitem_with_indexer for the case when we have a single Block.
+        """
+        from pandas import Series
+
+        if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
+            # TODO(EA): ExtensionBlock.setitem this causes issues with
+            # setting for extensionarrays that store dicts. Need to decide
+            # if it's worth supporting that.
+            value = self._align_series(indexer, Series(value))
+
+        info_axis = self.obj._info_axis_number
+        item_labels = self.obj._get_axis(info_axis)
+        if isinstance(indexer, tuple):
+            # if we are setting on the info axis ONLY
+            # set using those methods to avoid block-splitting
+            # logic here
+            if (
+                self.ndim == len(indexer) == 2
+                and is_integer(indexer[1])
+                and com.is_null_slice(indexer[0])
+            ):
+                col = item_labels[indexer[info_axis]]
+                if len(item_labels.get_indexer_for([col])) == 1:
+                    # e.g. test_loc_setitem_empty_append_expands_rows
+                    loc = item_labels.get_loc(col)
+                    self._setitem_single_column(loc, value, indexer[0])
+                    return
+
+            indexer = maybe_convert_ix(*indexer)  # e.g. test_setitem_frame_align
+
+        if isinstance(value, ABCDataFrame) and name != "iloc":
+            value = self._align_frame(indexer, value)._values
+
+        # check for chained assignment
+        self.obj._check_is_chained_assignment_possible()
+
+        # actually do the set
+        self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
+        self.obj._maybe_update_cacher(clear=True, inplace=True)
+
+    def _setitem_with_indexer_missing(self, indexer, value):
+        """
+        Insert new row(s) or column(s) into the Series or DataFrame.
+        """
+        from pandas import Series
+
+        # reindex the axis to the new value
+        # and set inplace
+        if self.ndim == 1:
+            index = self.obj.index
+            with warnings.catch_warnings():
+                # TODO: re-issue this with setitem-specific message?
+                warnings.filterwarnings(
+                    "ignore",
+                    "The behavior of Index.insert with object-dtype is deprecated",
+                    category=FutureWarning,
+                )
+                new_index = index.insert(len(index), indexer)
+
+            # we have a coerced indexer, e.g. a float
+            # that matches in an int64 Index, so
+            # we will not create a duplicate index, rather
+            # index to that element
+            # e.g. 0.0 -> 0
+            # GH#12246
+            if index.is_unique:
+                # pass new_index[-1:] instead if [new_index[-1]]
+                #  so that we retain dtype
+                new_indexer = index.get_indexer(new_index[-1:])
+                if (new_indexer != -1).any():
+                    # We get only here with loc, so can hard code
+                    return self._setitem_with_indexer(new_indexer, value, "loc")
+
+            # this preserves dtype of the value and of the object
+            if not is_scalar(value):
+                new_dtype = None
+
+            elif is_valid_na_for_dtype(value, self.obj.dtype):
+                if not is_object_dtype(self.obj.dtype):
+                    # Every NA value is suitable for object, no conversion needed
+                    value = na_value_for_dtype(self.obj.dtype, compat=False)
+
+                new_dtype = maybe_promote(self.obj.dtype, value)[0]
+
+            elif isna(value):
+                new_dtype = None
+            elif not self.obj.empty and not is_object_dtype(self.obj.dtype):
+                # We should not cast, if we have object dtype because we can
+                # set timedeltas into object series
+                curr_dtype = self.obj.dtype
+                curr_dtype = getattr(curr_dtype, "numpy_dtype", curr_dtype)
+                new_dtype = maybe_promote(curr_dtype, value)[0]
+            else:
+                new_dtype = None
+
+            new_values = Series([value], dtype=new_dtype)._values
+
+            if len(self.obj._values):
+                # GH#22717 handle casting compatibility that np.concatenate
+                #  does incorrectly
+                new_values = concat_compat([self.obj._values, new_values])
+            self.obj._mgr = self.obj._constructor(
+                new_values, index=new_index, name=self.obj.name
+            )._mgr
+            self.obj._maybe_update_cacher(clear=True)
+
+        elif self.ndim == 2:
+            if not len(self.obj.columns):
+                # no columns and scalar
+                raise ValueError("cannot set a frame with no defined columns")
+
+            has_dtype = hasattr(value, "dtype")
+            if isinstance(value, ABCSeries):
+                # append a Series
+                value = value.reindex(index=self.obj.columns, copy=True)
+                value.name = indexer
+            elif isinstance(value, dict):
+                value = Series(
+                    value, index=self.obj.columns, name=indexer, dtype=object
+                )
+            else:
+                # a list-list
+                if is_list_like_indexer(value):
+                    # must have conforming columns
+                    if len(value) != len(self.obj.columns):
+                        raise ValueError("cannot set a row with mismatched columns")
+
+                value = Series(value, index=self.obj.columns, name=indexer)
+
+            if not len(self.obj):
+                # We will ignore the existing dtypes instead of using
+                #  internals.concat logic
+                df = value.to_frame().T
+
+                idx = self.obj.index
+                if isinstance(idx, MultiIndex):
+                    name = idx.names
+                else:
+                    name = idx.name
+
+                df.index = Index([indexer], name=name)
+                if not has_dtype:
+                    # i.e. if we already had a Series or ndarray, keep that
+                    #  dtype.  But if we had a list or dict, then do inference
+                    df = df.infer_objects(copy=False)
+                self.obj._mgr = df._mgr
+            else:
+                self.obj._mgr = self.obj._append(value)._mgr
+            self.obj._maybe_update_cacher(clear=True)
+
+    def _ensure_iterable_column_indexer(self, column_indexer):
+        """
+        Ensure that our column indexer is something that can be iterated over.
+        """
+        ilocs: Sequence[int | np.integer] | np.ndarray
+        if is_integer(column_indexer):
+            ilocs = [column_indexer]
+        elif isinstance(column_indexer, slice):
+            ilocs = np.arange(len(self.obj.columns))[column_indexer]
+        elif (
+            isinstance(column_indexer, np.ndarray) and column_indexer.dtype.kind == "b"
+        ):
+            ilocs = np.arange(len(column_indexer))[column_indexer]
+        else:
+            ilocs = column_indexer
+        return ilocs
+
+    def _align_series(
+        self,
+        indexer,
+        ser: Series,
+        multiindex_indexer: bool = False,
+        using_cow: bool = False,
+    ):
+        """
+        Parameters
+        ----------
+        indexer : tuple, slice, scalar
+            Indexer used to get the locations that will be set to `ser`.
+        ser : pd.Series
+            Values to assign to the locations specified by `indexer`.
+        multiindex_indexer : bool, optional
+            Defaults to False. Should be set to True if `indexer` was from
+            a `pd.MultiIndex`, to avoid unnecessary broadcasting.
+
+        Returns
+        -------
+        `np.array` of `ser` broadcast to the appropriate shape for assignment
+        to the locations selected by `indexer`
+        """
+        if isinstance(indexer, (slice, np.ndarray, list, Index)):
+            indexer = (indexer,)
+
+        if isinstance(indexer, tuple):
+            # flatten np.ndarray indexers
+            def ravel(i):
+                return i.ravel() if isinstance(i, np.ndarray) else i
+
+            indexer = tuple(map(ravel, indexer))
+
+            aligners = [not com.is_null_slice(idx) for idx in indexer]
+            sum_aligners = sum(aligners)
+            single_aligner = sum_aligners == 1
+            is_frame = self.ndim == 2
+            obj = self.obj
+
+            # are we a single alignable value on a non-primary
+            # dim (e.g. panel: 1,2, or frame: 0) ?
+            # hence need to align to a single axis dimension
+            # rather that find all valid dims
+
+            # frame
+            if is_frame:
+                single_aligner = single_aligner and aligners[0]
+
+            # we have a frame, with multiple indexers on both axes; and a
+            # series, so need to broadcast (see GH5206)
+            if sum_aligners == self.ndim and all(is_sequence(_) for _ in indexer):
+                ser_values = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values
+
+                # single indexer
+                if len(indexer) > 1 and not multiindex_indexer:
+                    len_indexer = len(indexer[1])
+                    ser_values = (
+                        np.tile(ser_values, len_indexer).reshape(len_indexer, -1).T
+                    )
+
+                return ser_values
+
+            for i, idx in enumerate(indexer):
+                ax = obj.axes[i]
+
+                # multiple aligners (or null slices)
+                if is_sequence(idx) or isinstance(idx, slice):
+                    if single_aligner and com.is_null_slice(idx):
+                        continue
+                    new_ix = ax[idx]
+                    if not is_list_like_indexer(new_ix):
+                        new_ix = Index([new_ix])
+                    else:
+                        new_ix = Index(new_ix)
+                    if ser.index.equals(new_ix):
+                        if using_cow:
+                            return ser
+                        return ser._values.copy()
+
+                    return ser.reindex(new_ix)._values
+
+                # 2 dims
+                elif single_aligner:
+                    # reindex along index
+                    ax = self.obj.axes[1]
+                    if ser.index.equals(ax) or not len(ax):
+                        return ser._values.copy()
+                    return ser.reindex(ax)._values
+
+        elif is_integer(indexer) and self.ndim == 1:
+            if is_object_dtype(self.obj.dtype):
+                return ser
+            ax = self.obj._get_axis(0)
+
+            if ser.index.equals(ax):
+                return ser._values.copy()
+
+            return ser.reindex(ax)._values[indexer]
+
+        elif is_integer(indexer):
+            ax = self.obj._get_axis(1)
+
+            if ser.index.equals(ax):
+                return ser._values.copy()
+
+            return ser.reindex(ax)._values
+
+        raise ValueError("Incompatible indexer with Series")
+
+    def _align_frame(self, indexer, df: DataFrame) -> DataFrame:
+        is_frame = self.ndim == 2
+
+        if isinstance(indexer, tuple):
+            idx, cols = None, None
+            sindexers = []
+            for i, ix in enumerate(indexer):
+                ax = self.obj.axes[i]
+                if is_sequence(ix) or isinstance(ix, slice):
+                    if isinstance(ix, np.ndarray):
+                        ix = ix.ravel()
+                    if idx is None:
+                        idx = ax[ix]
+                    elif cols is None:
+                        cols = ax[ix]
+                    else:
+                        break
+                else:
+                    sindexers.append(i)
+
+            if idx is not None and cols is not None:
+                if df.index.equals(idx) and df.columns.equals(cols):
+                    val = df.copy()
+                else:
+                    val = df.reindex(idx, columns=cols)
+                return val
+
+        elif (isinstance(indexer, slice) or is_list_like_indexer(indexer)) and is_frame:
+            ax = self.obj.index[indexer]
+            if df.index.equals(ax):
+                val = df.copy()
+            else:
+                # we have a multi-index and are trying to align
+                # with a particular, level GH3738
+                if (
+                    isinstance(ax, MultiIndex)
+                    and isinstance(df.index, MultiIndex)
+                    and ax.nlevels != df.index.nlevels
+                ):
+                    raise TypeError(
+                        "cannot align on a multi-index with out "
+                        "specifying the join levels"
+                    )
+
+                val = df.reindex(index=ax)
+            return val
+
+        raise ValueError("Incompatible indexer with DataFrame")
+
+
+class _ScalarAccessIndexer(NDFrameIndexerBase):
+    """
+    Access scalars quickly.
+    """
+
+    # sub-classes need to set _takeable
+    _takeable: bool
+
+    def _convert_key(self, key):
+        raise AbstractMethodError(self)
+
+    def __getitem__(self, key):
+        if not isinstance(key, tuple):
+            # we could have a convertible item here (e.g. Timestamp)
+            if not is_list_like_indexer(key):
+                key = (key,)
+            else:
+                raise ValueError("Invalid call for scalar access (getting)!")
+
+        key = self._convert_key(key)
+        return self.obj._get_value(*key, takeable=self._takeable)
+
+    def __setitem__(self, key, value) -> None:
+        if isinstance(key, tuple):
+            key = tuple(com.apply_if_callable(x, self.obj) for x in key)
+        else:
+            # scalar callable may return tuple
+            key = com.apply_if_callable(key, self.obj)
+
+        if not isinstance(key, tuple):
+            key = _tuplify(self.ndim, key)
+        key = list(self._convert_key(key))
+        if len(key) != self.ndim:
+            raise ValueError("Not enough indexers for scalar access (setting)!")
+
+        self.obj._set_value(*key, value=value, takeable=self._takeable)
+
+
+@doc(IndexingMixin.at)
+class _AtIndexer(_ScalarAccessIndexer):
+    _takeable = False
+
+    def _convert_key(self, key):
+        """
+        Require they keys to be the same type as the index. (so we don't
+        fallback)
+        """
+        # GH 26989
+        # For series, unpacking key needs to result in the label.
+        # This is already the case for len(key) == 1; e.g. (1,)
+        if self.ndim == 1 and len(key) > 1:
+            key = (key,)
+
+        return key
+
+    @property
+    def _axes_are_unique(self) -> bool:
+        # Only relevant for self.ndim == 2
+        assert self.ndim == 2
+        return self.obj.index.is_unique and self.obj.columns.is_unique
+
+    def __getitem__(self, key):
+        if self.ndim == 2 and not self._axes_are_unique:
+            # GH#33041 fall back to .loc
+            if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
+                raise ValueError("Invalid call for scalar access (getting)!")
+            return self.obj.loc[key]
+
+        return super().__getitem__(key)
+
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self.obj) <= 2:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self.obj)
+            ref_count = 2
+            if not warn_copy_on_write() and _check_cacher(self.obj):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        if self.ndim == 2 and not self._axes_are_unique:
+            # GH#33041 fall back to .loc
+            if not isinstance(key, tuple) or not all(is_scalar(x) for x in key):
+                raise ValueError("Invalid call for scalar access (setting)!")
+
+            self.obj.loc[key] = value
+            return
+
+        return super().__setitem__(key, value)
+
+
+@doc(IndexingMixin.iat)
+class _iAtIndexer(_ScalarAccessIndexer):
+    _takeable = True
+
+    def _convert_key(self, key):
+        """
+        Require integer args. (and convert to label arguments)
+        """
+        for i in key:
+            if not is_integer(i):
+                raise ValueError("iAt based indexing can only have integer indexers")
+        return key
+
+    def __setitem__(self, key, value) -> None:
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self.obj) <= 2:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self.obj)
+            ref_count = 2
+            if not warn_copy_on_write() and _check_cacher(self.obj):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        return super().__setitem__(key, value)
+
+
+def _tuplify(ndim: int, loc: Hashable) -> tuple[Hashable | slice, ...]:
+    """
+    Given an indexer for the first dimension, create an equivalent tuple
+    for indexing over all dimensions.
+
+    Parameters
+    ----------
+    ndim : int
+    loc : object
+
+    Returns
+    -------
+    tuple
+    """
+    _tup: list[Hashable | slice]
+    _tup = [slice(None, None) for _ in range(ndim)]
+    _tup[0] = loc
+    return tuple(_tup)
+
+
+def _tupleize_axis_indexer(ndim: int, axis: AxisInt, key) -> tuple:
+    """
+    If we have an axis, adapt the given key to be axis-independent.
+    """
+    new_key = [slice(None)] * ndim
+    new_key[axis] = key
+    return tuple(new_key)
+
+
+def check_bool_indexer(index: Index, key) -> np.ndarray:
+    """
+    Check if key is a valid boolean indexer for an object with such index and
+    perform reindexing or conversion if needed.
+
+    This function assumes that is_bool_indexer(key) == True.
+
+    Parameters
+    ----------
+    index : Index
+        Index of the object on which the indexing is done.
+    key : list-like
+        Boolean indexer to check.
+
+    Returns
+    -------
+    np.array
+        Resulting key.
+
+    Raises
+    ------
+    IndexError
+        If the key does not have the same length as index.
+    IndexingError
+        If the index of the key is unalignable to index.
+    """
+    result = key
+    if isinstance(key, ABCSeries) and not key.index.equals(index):
+        indexer = result.index.get_indexer_for(index)
+        if -1 in indexer:
+            raise IndexingError(
+                "Unalignable boolean Series provided as "
+                "indexer (index of the boolean Series and of "
+                "the indexed object do not match)."
+            )
+
+        result = result.take(indexer)
+
+        # fall through for boolean
+        if not isinstance(result.dtype, ExtensionDtype):
+            return result.astype(bool)._values
+
+    if is_object_dtype(key):
+        # key might be object-dtype bool, check_array_indexer needs bool array
+        result = np.asarray(result, dtype=bool)
+    elif not is_array_like(result):
+        # GH 33924
+        # key may contain nan elements, check_array_indexer needs bool array
+        result = pd_array(result, dtype=bool)
+    return check_array_indexer(index, result)
+
+
+def convert_missing_indexer(indexer):
+    """
+    Reverse convert a missing indexer, which is a dict
+    return the scalar indexer and a boolean indicating if we converted
+    """
+    if isinstance(indexer, dict):
+        # a missing key (but not a tuple indexer)
+        indexer = indexer["key"]
+
+        if isinstance(indexer, bool):
+            raise KeyError("cannot use a single bool to index into setitem")
+        return indexer, True
+
+    return indexer, False
+
+
+def convert_from_missing_indexer_tuple(indexer, axes):
+    """
+    Create a filtered indexer that doesn't have any missing indexers.
+    """
+
+    def get_indexer(_i, _idx):
+        return axes[_i].get_loc(_idx["key"]) if isinstance(_idx, dict) else _idx
+
+    return tuple(get_indexer(_i, _idx) for _i, _idx in enumerate(indexer))
+
+
+def maybe_convert_ix(*args):
+    """
+    We likely want to take the cross-product.
+    """
+    for arg in args:
+        if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)):
+            return args
+    return np.ix_(*args)
+
+
+def is_nested_tuple(tup, labels) -> bool:
+    """
+    Returns
+    -------
+    bool
+    """
+    # check for a compatible nested tuple and multiindexes among the axes
+    if not isinstance(tup, tuple):
+        return False
+
+    for k in tup:
+        if is_list_like(k) or isinstance(k, slice):
+            return isinstance(labels, MultiIndex)
+
+    return False
+
+
+def is_label_like(key) -> bool:
+    """
+    Returns
+    -------
+    bool
+    """
+    # select a label or row
+    return (
+        not isinstance(key, slice)
+        and not is_list_like_indexer(key)
+        and key is not Ellipsis
+    )
+
+
+def need_slice(obj: slice) -> bool:
+    """
+    Returns
+    -------
+    bool
+    """
+    return (
+        obj.start is not None
+        or obj.stop is not None
+        or (obj.step is not None and obj.step != 1)
+    )
+
+
+def check_dict_or_set_indexers(key) -> None:
+    """
+    Check if the indexer is or contains a dict or set, which is no longer allowed.
+    """
+    if (
+        isinstance(key, set)
+        or isinstance(key, tuple)
+        and any(isinstance(x, set) for x in key)
+    ):
+        raise TypeError(
+            "Passing a set as an indexer is not supported. Use a list instead."
+        )
+
+    if (
+        isinstance(key, dict)
+        or isinstance(key, tuple)
+        and any(isinstance(x, dict) for x in key)
+    ):
+        raise TypeError(
+            "Passing a dict as an indexer is not supported. Use a list instead."
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/buffer.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d24325e67f62a5da0fa3863d81576dd61f86869
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/buffer.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.core.interchange.dataframe_protocol import (
+    Buffer,
+    DlpackDeviceType,
+)
+
+if TYPE_CHECKING:
+    import numpy as np
+    import pyarrow as pa
+
+
+class PandasBuffer(Buffer):
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+    """
+
+    def __init__(self, x: np.ndarray, allow_copy: bool = True) -> None:
+        """
+        Handle only regular columns (= numpy arrays) for now.
+        """
+        if x.strides[0] and not x.strides == (x.dtype.itemsize,):
+            # The protocol does not support strided buffers, so a copy is
+            # necessary. If that's not allowed, we need to raise an exception.
+            if allow_copy:
+                x = x.copy()
+            else:
+                raise RuntimeError(
+                    "Exports cannot be zero-copy in the case "
+                    "of a non-contiguous buffer"
+                )
+
+        # Store the numpy array in which the data resides as a private
+        # attribute, so we can use it to retrieve the public attributes
+        self._x = x
+
+    @property
+    def bufsize(self) -> int:
+        """
+        Buffer size in bytes.
+        """
+        return self._x.size * self._x.dtype.itemsize
+
+    @property
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+        return self._x.__array_interface__["data"][0]
+
+    def __dlpack__(self) -> Any:
+        """
+        Represent this structure as DLPack interface.
+        """
+        return self._x.__dlpack__()
+
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
+        """
+        Device type and device ID for where the data in the buffer resides.
+        """
+        return (DlpackDeviceType.CPU, None)
+
+    def __repr__(self) -> str:
+        return (
+            "PandasBuffer("
+            + str(
+                {
+                    "bufsize": self.bufsize,
+                    "ptr": self.ptr,
+                    "device": self.__dlpack_device__()[0].name,
+                }
+            )
+            + ")"
+        )
+
+
+class PandasBufferPyarrow(Buffer):
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+    """
+
+    def __init__(
+        self,
+        buffer: pa.Buffer,
+        *,
+        length: int,
+    ) -> None:
+        """
+        Handle pyarrow chunked arrays.
+        """
+        self._buffer = buffer
+        self._length = length
+
+    @property
+    def bufsize(self) -> int:
+        """
+        Buffer size in bytes.
+        """
+        return self._buffer.size
+
+    @property
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+        return self._buffer.address
+
+    def __dlpack__(self) -> Any:
+        """
+        Represent this structure as DLPack interface.
+        """
+        raise NotImplementedError()
+
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
+        """
+        Device type and device ID for where the data in the buffer resides.
+        """
+        return (DlpackDeviceType.CPU, None)
+
+    def __repr__(self) -> str:
+        return (
+            "PandasBuffer[pyarrow]("
+            + str(
+                {
+                    "bufsize": self.bufsize,
+                    "ptr": self.ptr,
+                    "device": "CPU",
+                }
+            )
+            + ")"
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/column.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/column.py
new file mode 100644
index 0000000000000000000000000000000000000000..d59a3df694bb3f6ab5716c25592704d49737a215
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/column.py
@@ -0,0 +1,461 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs.lib import infer_dtype
+from pandas._libs.tslibs import iNaT
+from pandas.errors import NoBufferPresent
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.dtypes import BaseMaskedDtype
+
+import pandas as pd
+from pandas import (
+    ArrowDtype,
+    DatetimeTZDtype,
+)
+from pandas.api.types import is_string_dtype
+from pandas.core.interchange.buffer import (
+    PandasBuffer,
+    PandasBufferPyarrow,
+)
+from pandas.core.interchange.dataframe_protocol import (
+    Column,
+    ColumnBuffers,
+    ColumnNullType,
+    DtypeKind,
+)
+from pandas.core.interchange.utils import (
+    ArrowCTypes,
+    Endianness,
+    dtype_to_arrow_c_fmt,
+)
+
+if TYPE_CHECKING:
+    from pandas.core.interchange.dataframe_protocol import Buffer
+
+_NP_KINDS = {
+    "i": DtypeKind.INT,
+    "u": DtypeKind.UINT,
+    "f": DtypeKind.FLOAT,
+    "b": DtypeKind.BOOL,
+    "U": DtypeKind.STRING,
+    "M": DtypeKind.DATETIME,
+    "m": DtypeKind.DATETIME,
+}
+
+_NULL_DESCRIPTION = {
+    DtypeKind.FLOAT: (ColumnNullType.USE_NAN, None),
+    DtypeKind.DATETIME: (ColumnNullType.USE_SENTINEL, iNaT),
+    DtypeKind.INT: (ColumnNullType.NON_NULLABLE, None),
+    DtypeKind.UINT: (ColumnNullType.NON_NULLABLE, None),
+    DtypeKind.BOOL: (ColumnNullType.NON_NULLABLE, None),
+    # Null values for categoricals are stored as `-1` sentinel values
+    # in the category date (e.g., `col.values.codes` is int8 np.ndarray)
+    DtypeKind.CATEGORICAL: (ColumnNullType.USE_SENTINEL, -1),
+    # follow Arrow in using 1 as valid value and 0 for missing/null value
+    DtypeKind.STRING: (ColumnNullType.USE_BYTEMASK, 0),
+}
+
+_NO_VALIDITY_BUFFER = {
+    ColumnNullType.NON_NULLABLE: "This column is non-nullable",
+    ColumnNullType.USE_NAN: "This column uses NaN as null",
+    ColumnNullType.USE_SENTINEL: "This column uses a sentinel value",
+}
+
+
+class PandasColumn(Column):
+    """
+    A column object, with only the methods and properties required by the
+    interchange protocol defined.
+    A column can contain one or more chunks. Each chunk can contain up to three
+    buffers - a data buffer, a mask buffer (depending on null representation),
+    and an offsets buffer (if variable-size binary; e.g., variable-length
+    strings).
+    Note: this Column object can only be produced by ``__dataframe__``, so
+          doesn't need its own version or ``__column__`` protocol.
+    """
+
+    def __init__(self, column: pd.Series, allow_copy: bool = True) -> None:
+        """
+        Note: doesn't deal with extension arrays yet, just assume a regular
+        Series/ndarray for now.
+        """
+        if isinstance(column, pd.DataFrame):
+            raise TypeError(
+                "Expected a Series, got a DataFrame. This likely happened "
+                "because you called __dataframe__ on a DataFrame which, "
+                "after converting column names to string, resulted in duplicated "
+                f"names: {column.columns}. Please rename these columns before "
+                "using the interchange protocol."
+            )
+        if not isinstance(column, pd.Series):
+            raise NotImplementedError(f"Columns of type {type(column)} not handled yet")
+
+        # Store the column as a private attribute
+        self._col = column
+        self._allow_copy = allow_copy
+
+    def size(self) -> int:
+        """
+        Size of the column, in elements.
+        """
+        return self._col.size
+
+    @property
+    def offset(self) -> int:
+        """
+        Offset of first element. Always zero.
+        """
+        # TODO: chunks are implemented now, probably this should return something
+        return 0
+
+    @cache_readonly
+    def dtype(self) -> tuple[DtypeKind, int, str, str]:
+        dtype = self._col.dtype
+
+        if isinstance(dtype, pd.CategoricalDtype):
+            codes = self._col.values.codes
+            (
+                _,
+                bitwidth,
+                c_arrow_dtype_f_str,
+                _,
+            ) = self._dtype_from_pandasdtype(codes.dtype)
+            return (
+                DtypeKind.CATEGORICAL,
+                bitwidth,
+                c_arrow_dtype_f_str,
+                Endianness.NATIVE,
+            )
+        elif is_string_dtype(dtype):
+            if infer_dtype(self._col) in ("string", "empty"):
+                return (
+                    DtypeKind.STRING,
+                    8,
+                    dtype_to_arrow_c_fmt(dtype),
+                    Endianness.NATIVE,
+                )
+            raise NotImplementedError("Non-string object dtypes are not supported yet")
+        else:
+            return self._dtype_from_pandasdtype(dtype)
+
+    def _dtype_from_pandasdtype(self, dtype) -> tuple[DtypeKind, int, str, str]:
+        """
+        See `self.dtype` for details.
+        """
+        # Note: 'c' (complex) not handled yet (not in array spec v1).
+        #       'b', 'B' (bytes), 'S', 'a', (old-style string) 'V' (void) not handled
+        #       datetime and timedelta both map to datetime (is timedelta handled?)
+
+        kind = _NP_KINDS.get(dtype.kind, None)
+        if kind is None:
+            # Not a NumPy dtype. Check if it's a categorical maybe
+            raise ValueError(f"Data type {dtype} not supported by interchange protocol")
+        if isinstance(dtype, ArrowDtype):
+            byteorder = dtype.numpy_dtype.byteorder
+        elif isinstance(dtype, DatetimeTZDtype):
+            byteorder = dtype.base.byteorder  # type: ignore[union-attr]
+        elif isinstance(dtype, BaseMaskedDtype):
+            byteorder = dtype.numpy_dtype.byteorder
+        else:
+            byteorder = dtype.byteorder
+
+        if dtype == "bool[pyarrow]":
+            # return early to avoid the `* 8` below, as this is a bitmask
+            # rather than a bytemask
+            return (
+                kind,
+                dtype.itemsize,  # pyright: ignore[reportGeneralTypeIssues]
+                ArrowCTypes.BOOL,
+                byteorder,
+            )
+
+        return kind, dtype.itemsize * 8, dtype_to_arrow_c_fmt(dtype), byteorder
+
+    @property
+    def describe_categorical(self):
+        """
+        If the dtype is categorical, there are two options:
+        - There are only values in the data buffer.
+        - There is a separate non-categorical Column encoding for categorical values.
+
+        Raises TypeError if the dtype is not categorical
+
+        Content of returned dict:
+            - "is_ordered" : bool, whether the ordering of dictionary indices is
+                             semantically meaningful.
+            - "is_dictionary" : bool, whether a dictionary-style mapping of
+                                categorical values to other objects exists
+            - "categories" : Column representing the (implicit) mapping of indices to
+                             category values (e.g. an array of cat1, cat2, ...).
+                             None if not a dictionary-style categorical.
+        """
+        if not self.dtype[0] == DtypeKind.CATEGORICAL:
+            raise TypeError(
+                "describe_categorical only works on a column with categorical dtype!"
+            )
+
+        return {
+            "is_ordered": self._col.cat.ordered,
+            "is_dictionary": True,
+            "categories": PandasColumn(pd.Series(self._col.cat.categories)),
+        }
+
+    @property
+    def describe_null(self):
+        if isinstance(self._col.dtype, BaseMaskedDtype):
+            column_null_dtype = ColumnNullType.USE_BYTEMASK
+            null_value = 1
+            return column_null_dtype, null_value
+        if isinstance(self._col.dtype, ArrowDtype):
+            # We already rechunk (if necessary / allowed) upon initialization, so this
+            # is already single-chunk by the time we get here.
+            if self._col.array._pa_array.chunks[0].buffers()[0] is None:  # type: ignore[attr-defined]
+                return ColumnNullType.NON_NULLABLE, None
+            return ColumnNullType.USE_BITMASK, 0
+        kind = self.dtype[0]
+        try:
+            null, value = _NULL_DESCRIPTION[kind]
+        except KeyError:
+            raise NotImplementedError(f"Data type {kind} not yet supported")
+
+        return null, value
+
+    @cache_readonly
+    def null_count(self) -> int:
+        """
+        Number of null elements. Should always be known.
+        """
+        return self._col.isna().sum().item()
+
+    @property
+    def metadata(self) -> dict[str, pd.Index]:
+        """
+        Store specific metadata of the column.
+        """
+        return {"pandas.index": self._col.index}
+
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the column consists of.
+        """
+        return 1
+
+    def get_chunks(self, n_chunks: int | None = None):
+        """
+        Return an iterator yielding the chunks.
+        See `DataFrame.get_chunks` for details on ``n_chunks``.
+        """
+        if n_chunks and n_chunks > 1:
+            size = len(self._col)
+            step = size // n_chunks
+            if size % n_chunks != 0:
+                step += 1
+            for start in range(0, step * n_chunks, step):
+                yield PandasColumn(
+                    self._col.iloc[start : start + step], self._allow_copy
+                )
+        else:
+            yield self
+
+    def get_buffers(self) -> ColumnBuffers:
+        """
+        Return a dictionary containing the underlying buffers.
+        The returned dictionary has the following contents:
+            - "data": a two-element tuple whose first element is a buffer
+                      containing the data and whose second element is the data
+                      buffer's associated dtype.
+            - "validity": a two-element tuple whose first element is a buffer
+                          containing mask values indicating missing data and
+                          whose second element is the mask value buffer's
+                          associated dtype. None if the null representation is
+                          not a bit or byte mask.
+            - "offsets": a two-element tuple whose first element is a buffer
+                         containing the offset values for variable-size binary
+                         data (e.g., variable-length strings) and whose second
+                         element is the offsets buffer's associated dtype. None
+                         if the data buffer does not have an associated offsets
+                         buffer.
+        """
+        buffers: ColumnBuffers = {
+            "data": self._get_data_buffer(),
+            "validity": None,
+            "offsets": None,
+        }
+
+        try:
+            buffers["validity"] = self._get_validity_buffer()
+        except NoBufferPresent:
+            pass
+
+        try:
+            buffers["offsets"] = self._get_offsets_buffer()
+        except NoBufferPresent:
+            pass
+
+        return buffers
+
+    def _get_data_buffer(
+        self,
+    ) -> tuple[Buffer, tuple[DtypeKind, int, str, str]]:
+        """
+        Return the buffer containing the data and the buffer's associated dtype.
+        """
+        buffer: Buffer
+        if self.dtype[0] in (
+            DtypeKind.INT,
+            DtypeKind.UINT,
+            DtypeKind.FLOAT,
+            DtypeKind.BOOL,
+            DtypeKind.DATETIME,
+        ):
+            # self.dtype[2] is an ArrowCTypes.TIMESTAMP where the tz will make
+            # it longer than 4 characters
+            dtype = self.dtype
+            if self.dtype[0] == DtypeKind.DATETIME and len(self.dtype[2]) > 4:
+                np_arr = self._col.dt.tz_convert(None).to_numpy()
+            else:
+                arr = self._col.array
+                if isinstance(self._col.dtype, BaseMaskedDtype):
+                    np_arr = arr._data  # type: ignore[attr-defined]
+                elif isinstance(self._col.dtype, ArrowDtype):
+                    # We already rechunk (if necessary / allowed) upon initialization,
+                    # so this is already single-chunk by the time we get here.
+                    arr = arr._pa_array.chunks[0]  # type: ignore[attr-defined]
+                    buffer = PandasBufferPyarrow(
+                        arr.buffers()[1],  # type: ignore[attr-defined]
+                        length=len(arr),
+                    )
+                    return buffer, dtype
+                else:
+                    np_arr = arr._ndarray  # type: ignore[attr-defined]
+            buffer = PandasBuffer(np_arr, allow_copy=self._allow_copy)
+        elif self.dtype[0] == DtypeKind.CATEGORICAL:
+            codes = self._col.values._codes
+            buffer = PandasBuffer(codes, allow_copy=self._allow_copy)
+            dtype = self._dtype_from_pandasdtype(codes.dtype)
+        elif self.dtype[0] == DtypeKind.STRING:
+            # Marshal the strings from a NumPy object array into a byte array
+            buf = self._col.to_numpy()
+            b = bytearray()
+
+            # TODO: this for-loop is slow; can be implemented in Cython/C/C++ later
+            for obj in buf:
+                if isinstance(obj, str):
+                    b.extend(obj.encode(encoding="utf-8"))
+
+            # Convert the byte array to a Pandas "buffer" using
+            # a NumPy array as the backing store
+            buffer = PandasBuffer(np.frombuffer(b, dtype="uint8"))
+
+            # Define the dtype for the returned buffer
+            # TODO: this will need correcting
+            # https://github.com/pandas-dev/pandas/issues/54781
+            dtype = self.dtype
+        else:
+            raise NotImplementedError(f"Data type {self._col.dtype} not handled yet")
+
+        return buffer, dtype
+
+    def _get_validity_buffer(self) -> tuple[Buffer, Any] | None:
+        """
+        Return the buffer containing the mask values indicating missing data and
+        the buffer's associated dtype.
+        Raises NoBufferPresent if null representation is not a bit or byte mask.
+        """
+        null, invalid = self.describe_null
+        buffer: Buffer
+        if isinstance(self._col.dtype, ArrowDtype):
+            # We already rechunk (if necessary / allowed) upon initialization, so this
+            # is already single-chunk by the time we get here.
+            arr = self._col.array._pa_array.chunks[0]  # type: ignore[attr-defined]
+            dtype = (DtypeKind.BOOL, 1, ArrowCTypes.BOOL, Endianness.NATIVE)
+            if arr.buffers()[0] is None:
+                return None
+            buffer = PandasBufferPyarrow(
+                arr.buffers()[0],
+                length=len(arr),
+            )
+            return buffer, dtype
+
+        if isinstance(self._col.dtype, BaseMaskedDtype):
+            mask = self._col.array._mask  # type: ignore[attr-defined]
+            buffer = PandasBuffer(mask)
+            dtype = (DtypeKind.BOOL, 8, ArrowCTypes.BOOL, Endianness.NATIVE)
+            return buffer, dtype
+
+        if self.dtype[0] == DtypeKind.STRING:
+            # For now, use byte array as the mask.
+            # TODO: maybe store as bit array to save space?..
+            buf = self._col.to_numpy()
+
+            # Determine the encoding for valid values
+            valid = invalid == 0
+            invalid = not valid
+
+            mask = np.zeros(shape=(len(buf),), dtype=np.bool_)
+            for i, obj in enumerate(buf):
+                mask[i] = valid if isinstance(obj, str) else invalid
+
+            # Convert the mask array to a Pandas "buffer" using
+            # a NumPy array as the backing store
+            buffer = PandasBuffer(mask)
+
+            # Define the dtype of the returned buffer
+            dtype = (DtypeKind.BOOL, 8, ArrowCTypes.BOOL, Endianness.NATIVE)
+
+            return buffer, dtype
+
+        try:
+            msg = f"{_NO_VALIDITY_BUFFER[null]} so does not have a separate mask"
+        except KeyError:
+            # TODO: implement for other bit/byte masks?
+            raise NotImplementedError("See self.describe_null")
+
+        raise NoBufferPresent(msg)
+
+    def _get_offsets_buffer(self) -> tuple[PandasBuffer, Any]:
+        """
+        Return the buffer containing the offset values for variable-size binary
+        data (e.g., variable-length strings) and the buffer's associated dtype.
+        Raises NoBufferPresent if the data buffer does not have an associated
+        offsets buffer.
+        """
+        if self.dtype[0] == DtypeKind.STRING:
+            # For each string, we need to manually determine the next offset
+            values = self._col.to_numpy()
+            ptr = 0
+            offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64)
+            for i, v in enumerate(values):
+                # For missing values (in this case, `np.nan` values)
+                # we don't increment the pointer
+                if isinstance(v, str):
+                    b = v.encode(encoding="utf-8")
+                    ptr += len(b)
+
+                offsets[i + 1] = ptr
+
+            # Convert the offsets to a Pandas "buffer" using
+            # the NumPy array as the backing store
+            buffer = PandasBuffer(offsets)
+
+            # Assemble the buffer dtype info
+            dtype = (
+                DtypeKind.INT,
+                64,
+                ArrowCTypes.INT64,
+                Endianness.NATIVE,
+            )  # note: currently only support native endianness
+        else:
+            raise NoBufferPresent(
+                "This column has a fixed-length dtype so "
+                "it does not have an offsets buffer"
+            )
+
+        return buffer, dtype
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/dataframe.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/dataframe.py
new file mode 100644
index 0000000000000000000000000000000000000000..1abacddfc7e3b7035a9446cd4118097c6accd385
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/dataframe.py
@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+from collections import abc
+from typing import TYPE_CHECKING
+
+from pandas.core.interchange.column import PandasColumn
+from pandas.core.interchange.dataframe_protocol import DataFrame as DataFrameXchg
+from pandas.core.interchange.utils import maybe_rechunk
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+    )
+
+
+class PandasDataFrameXchg(DataFrameXchg):
+    """
+    A data frame class, with only the methods required by the interchange
+    protocol defined.
+    Instances of this (private) class are returned from
+    ``pd.DataFrame.__dataframe__`` as objects with the methods and
+    attributes defined on this class.
+    """
+
+    def __init__(self, df: DataFrame, allow_copy: bool = True) -> None:
+        """
+        Constructor - an instance of this (private) class is returned from
+        `pd.DataFrame.__dataframe__`.
+        """
+        self._df = df.rename(columns=str, copy=False)
+        self._allow_copy = allow_copy
+        for i, _col in enumerate(self._df.columns):
+            rechunked = maybe_rechunk(self._df.iloc[:, i], allow_copy=allow_copy)
+            if rechunked is not None:
+                self._df.isetitem(i, rechunked)
+
+    def __dataframe__(
+        self, nan_as_null: bool = False, allow_copy: bool = True
+    ) -> PandasDataFrameXchg:
+        # `nan_as_null` can be removed here once it's removed from
+        # Dataframe.__dataframe__
+        return PandasDataFrameXchg(self._df, allow_copy)
+
+    @property
+    def metadata(self) -> dict[str, Index]:
+        # `index` isn't a regular column, and the protocol doesn't support row
+        # labels - so we export it as Pandas-specific metadata here.
+        return {"pandas.index": self._df.index}
+
+    def num_columns(self) -> int:
+        return len(self._df.columns)
+
+    def num_rows(self) -> int:
+        return len(self._df)
+
+    def num_chunks(self) -> int:
+        return 1
+
+    def column_names(self) -> Index:
+        return self._df.columns
+
+    def get_column(self, i: int) -> PandasColumn:
+        return PandasColumn(self._df.iloc[:, i], allow_copy=self._allow_copy)
+
+    def get_column_by_name(self, name: str) -> PandasColumn:
+        return PandasColumn(self._df[name], allow_copy=self._allow_copy)
+
+    def get_columns(self) -> list[PandasColumn]:
+        return [
+            PandasColumn(self._df[name], allow_copy=self._allow_copy)
+            for name in self._df.columns
+        ]
+
+    def select_columns(self, indices: Sequence[int]) -> PandasDataFrameXchg:
+        if not isinstance(indices, abc.Sequence):
+            raise ValueError("`indices` is not a sequence")
+        if not isinstance(indices, list):
+            indices = list(indices)
+
+        return PandasDataFrameXchg(
+            self._df.iloc[:, indices], allow_copy=self._allow_copy
+        )
+
+    def select_columns_by_name(self, names: list[str]) -> PandasDataFrameXchg:  # type: ignore[override]
+        if not isinstance(names, abc.Sequence):
+            raise ValueError("`names` is not a sequence")
+        if not isinstance(names, list):
+            names = list(names)
+
+        return PandasDataFrameXchg(self._df.loc[:, names], allow_copy=self._allow_copy)
+
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[PandasDataFrameXchg]:
+        """
+        Return an iterator yielding the chunks.
+        """
+        if n_chunks and n_chunks > 1:
+            size = len(self._df)
+            step = size // n_chunks
+            if size % n_chunks != 0:
+                step += 1
+            for start in range(0, step * n_chunks, step):
+                yield PandasDataFrameXchg(
+                    self._df.iloc[start : start + step, :],
+                    allow_copy=self._allow_copy,
+                )
+        else:
+            yield self
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/dataframe_protocol.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/dataframe_protocol.py
new file mode 100644
index 0000000000000000000000000000000000000000..95e7b6a26f93a8cd10048076bd6906190e04d2ba
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/dataframe_protocol.py
@@ -0,0 +1,465 @@
+"""
+A verbatim copy (vendored) of the spec from https://github.com/data-apis/dataframe-api
+"""
+
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import enum
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypedDict,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+
+class DlpackDeviceType(enum.IntEnum):
+    """Integer enum for device type codes matching DLPack."""
+
+    CPU = 1
+    CUDA = 2
+    CPU_PINNED = 3
+    OPENCL = 4
+    VULKAN = 7
+    METAL = 8
+    VPI = 9
+    ROCM = 10
+
+
+class DtypeKind(enum.IntEnum):
+    """
+    Integer enum for data types.
+
+    Attributes
+    ----------
+    INT : int
+        Matches to signed integer data type.
+    UINT : int
+        Matches to unsigned integer data type.
+    FLOAT : int
+        Matches to floating point data type.
+    BOOL : int
+        Matches to boolean data type.
+    STRING : int
+        Matches to string data type (UTF-8 encoded).
+    DATETIME : int
+        Matches to datetime data type.
+    CATEGORICAL : int
+        Matches to categorical data type.
+    """
+
+    INT = 0
+    UINT = 1
+    FLOAT = 2
+    BOOL = 20
+    STRING = 21  # UTF-8
+    DATETIME = 22
+    CATEGORICAL = 23
+
+
+class ColumnNullType(enum.IntEnum):
+    """
+    Integer enum for null type representation.
+
+    Attributes
+    ----------
+    NON_NULLABLE : int
+        Non-nullable column.
+    USE_NAN : int
+        Use explicit float NaN value.
+    USE_SENTINEL : int
+        Sentinel value besides NaN/NaT.
+    USE_BITMASK : int
+        The bit is set/unset representing a null on a certain position.
+    USE_BYTEMASK : int
+        The byte is set/unset representing a null on a certain position.
+    """
+
+    NON_NULLABLE = 0
+    USE_NAN = 1
+    USE_SENTINEL = 2
+    USE_BITMASK = 3
+    USE_BYTEMASK = 4
+
+
+class ColumnBuffers(TypedDict):
+    # first element is a buffer containing the column data;
+    # second element is the data buffer's associated dtype
+    data: tuple[Buffer, Any]
+
+    # first element is a buffer containing mask values indicating missing data;
+    # second element is the mask value buffer's associated dtype.
+    # None if the null representation is not a bit or byte mask
+    validity: tuple[Buffer, Any] | None
+
+    # first element is a buffer containing the offset values for
+    # variable-size binary data (e.g., variable-length strings);
+    # second element is the offsets buffer's associated dtype.
+    # None if the data buffer does not have an associated offsets buffer
+    offsets: tuple[Buffer, Any] | None
+
+
+class CategoricalDescription(TypedDict):
+    # whether the ordering of dictionary indices is semantically meaningful
+    is_ordered: bool
+    # whether a dictionary-style mapping of categorical values to other objects exists
+    is_dictionary: bool
+    # Python-level only (e.g. ``{int: str}``).
+    # None if not a dictionary-style categorical.
+    categories: Column | None
+
+
+class Buffer(ABC):
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+
+    Note that there is no dtype attribute present, a buffer can be thought of
+    as simply a block of memory. However, if the column that the buffer is
+    attached to has a dtype that's supported by DLPack and ``__dlpack__`` is
+    implemented, then that dtype information will be contained in the return
+    value from ``__dlpack__``.
+
+    This distinction is useful to support both data exchange via DLPack on a
+    buffer and (b) dtypes like variable-length strings which do not have a
+    fixed number of bytes per element.
+    """
+
+    @property
+    @abstractmethod
+    def bufsize(self) -> int:
+        """
+        Buffer size in bytes.
+        """
+
+    @property
+    @abstractmethod
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+
+    @abstractmethod
+    def __dlpack__(self):
+        """
+        Produce DLPack capsule (see array API standard).
+
+        Raises:
+
+            - TypeError : if the buffer contains unsupported dtypes.
+            - NotImplementedError : if DLPack support is not implemented
+
+        Useful to have to connect to array libraries. Support optional because
+        it's not completely trivial to implement for a Python-only library.
+        """
+        raise NotImplementedError("__dlpack__")
+
+    @abstractmethod
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
+        """
+        Device type and device ID for where the data in the buffer resides.
+        Uses device type codes matching DLPack.
+        Note: must be implemented even if ``__dlpack__`` is not.
+        """
+
+
+class Column(ABC):
+    """
+    A column object, with only the methods and properties required by the
+    interchange protocol defined.
+
+    A column can contain one or more chunks. Each chunk can contain up to three
+    buffers - a data buffer, a mask buffer (depending on null representation),
+    and an offsets buffer (if variable-size binary; e.g., variable-length
+    strings).
+
+    TBD: Arrow has a separate "null" dtype, and has no separate mask concept.
+         Instead, it seems to use "children" for both columns with a bit mask,
+         and for nested dtypes. Unclear whether this is elegant or confusing.
+         This design requires checking the null representation explicitly.
+
+         The Arrow design requires checking:
+         1. the ARROW_FLAG_NULLABLE (for sentinel values)
+         2. if a column has two children, combined with one of those children
+            having a null dtype.
+
+         Making the mask concept explicit seems useful. One null dtype would
+         not be enough to cover both bit and byte masks, so that would mean
+         even more checking if we did it the Arrow way.
+
+    TBD: there's also the "chunk" concept here, which is implicit in Arrow as
+         multiple buffers per array (= column here). Semantically it may make
+         sense to have both: chunks were meant for example for lazy evaluation
+         of data which doesn't fit in memory, while multiple buffers per column
+         could also come from doing a selection operation on a single
+         contiguous buffer.
+
+         Given these concepts, one would expect chunks to be all of the same
+         size (say a 10,000 row dataframe could have 10 chunks of 1,000 rows),
+         while multiple buffers could have data-dependent lengths. Not an issue
+         in pandas if one column is backed by a single NumPy array, but in
+         Arrow it seems possible.
+         Are multiple chunks *and* multiple buffers per column necessary for
+         the purposes of this interchange protocol, or must producers either
+         reuse the chunk concept for this or copy the data?
+
+    Note: this Column object can only be produced by ``__dataframe__``, so
+          doesn't need its own version or ``__column__`` protocol.
+    """
+
+    @abstractmethod
+    def size(self) -> int:
+        """
+        Size of the column, in elements.
+
+        Corresponds to DataFrame.num_rows() if column is a single chunk;
+        equal to size of this current chunk otherwise.
+        """
+
+    @property
+    @abstractmethod
+    def offset(self) -> int:
+        """
+        Offset of first element.
+
+        May be > 0 if using chunks; for example for a column with N chunks of
+        equal size M (only the last chunk may be shorter),
+        ``offset = n * M``, ``n = 0 .. N-1``.
+        """
+
+    @property
+    @abstractmethod
+    def dtype(self) -> tuple[DtypeKind, int, str, str]:
+        """
+        Dtype description as a tuple ``(kind, bit-width, format string, endianness)``.
+
+        Bit-width : the number of bits as an integer
+        Format string : data type description format string in Apache Arrow C
+                        Data Interface format.
+        Endianness : current only native endianness (``=``) is supported
+
+        Notes:
+            - Kind specifiers are aligned with DLPack where possible (hence the
+              jump to 20, leave enough room for future extension)
+            - Masks must be specified as boolean with either bit width 1 (for bit
+              masks) or 8 (for byte masks).
+            - Dtype width in bits was preferred over bytes
+            - Endianness isn't too useful, but included now in case in the future
+              we need to support non-native endianness
+            - Went with Apache Arrow format strings over NumPy format strings
+              because they're more complete from a dataframe perspective
+            - Format strings are mostly useful for datetime specification, and
+              for categoricals.
+            - For categoricals, the format string describes the type of the
+              categorical in the data buffer. In case of a separate encoding of
+              the categorical (e.g. an integer to string mapping), this can
+              be derived from ``self.describe_categorical``.
+            - Data types not included: complex, Arrow-style null, binary, decimal,
+              and nested (list, struct, map, union) dtypes.
+        """
+
+    @property
+    @abstractmethod
+    def describe_categorical(self) -> CategoricalDescription:
+        """
+        If the dtype is categorical, there are two options:
+        - There are only values in the data buffer.
+        - There is a separate non-categorical Column encoding for categorical values.
+
+        Raises TypeError if the dtype is not categorical
+
+        Returns the dictionary with description on how to interpret the data buffer:
+            - "is_ordered" : bool, whether the ordering of dictionary indices is
+                             semantically meaningful.
+            - "is_dictionary" : bool, whether a mapping of
+                                categorical values to other objects exists
+            - "categories" : Column representing the (implicit) mapping of indices to
+                             category values (e.g. an array of cat1, cat2, ...).
+                             None if not a dictionary-style categorical.
+
+        TBD: are there any other in-memory representations that are needed?
+        """
+
+    @property
+    @abstractmethod
+    def describe_null(self) -> tuple[ColumnNullType, Any]:
+        """
+        Return the missing value (or "null") representation the column dtype
+        uses, as a tuple ``(kind, value)``.
+
+        Value : if kind is "sentinel value", the actual value. If kind is a bit
+        mask or a byte mask, the value (0 or 1) indicating a missing value. None
+        otherwise.
+        """
+
+    @property
+    @abstractmethod
+    def null_count(self) -> int | None:
+        """
+        Number of null elements, if known.
+
+        Note: Arrow uses -1 to indicate "unknown", but None seems cleaner.
+        """
+
+    @property
+    @abstractmethod
+    def metadata(self) -> dict[str, Any]:
+        """
+        The metadata for the column. See `DataFrame.metadata` for more details.
+        """
+
+    @abstractmethod
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the column consists of.
+        """
+
+    @abstractmethod
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[Column]:
+        """
+        Return an iterator yielding the chunks.
+
+        See `DataFrame.get_chunks` for details on ``n_chunks``.
+        """
+
+    @abstractmethod
+    def get_buffers(self) -> ColumnBuffers:
+        """
+        Return a dictionary containing the underlying buffers.
+
+        The returned dictionary has the following contents:
+
+            - "data": a two-element tuple whose first element is a buffer
+                      containing the data and whose second element is the data
+                      buffer's associated dtype.
+            - "validity": a two-element tuple whose first element is a buffer
+                          containing mask values indicating missing data and
+                          whose second element is the mask value buffer's
+                          associated dtype. None if the null representation is
+                          not a bit or byte mask.
+            - "offsets": a two-element tuple whose first element is a buffer
+                         containing the offset values for variable-size binary
+                         data (e.g., variable-length strings) and whose second
+                         element is the offsets buffer's associated dtype. None
+                         if the data buffer does not have an associated offsets
+                         buffer.
+        """
+
+
+#    def get_children(self) -> Iterable[Column]:
+#        """
+#        Children columns underneath the column, each object in this iterator
+#        must adhere to the column specification.
+#        """
+#        pass
+
+
+class DataFrame(ABC):
+    """
+    A data frame class, with only the methods required by the interchange
+    protocol defined.
+
+    A "data frame" represents an ordered collection of named columns.
+    A column's "name" must be a unique string.
+    Columns may be accessed by name or by position.
+
+    This could be a public data frame class, or an object with the methods and
+    attributes defined on this DataFrame class could be returned from the
+    ``__dataframe__`` method of a public data frame class in a library adhering
+    to the dataframe interchange protocol specification.
+    """
+
+    version = 0  # version of the protocol
+
+    @abstractmethod
+    def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True):
+        """Construct a new interchange object, potentially changing the parameters."""
+
+    @property
+    @abstractmethod
+    def metadata(self) -> dict[str, Any]:
+        """
+        The metadata for the data frame, as a dictionary with string keys. The
+        contents of `metadata` may be anything, they are meant for a library
+        to store information that it needs to, e.g., roundtrip losslessly or
+        for two implementations to share data that is not (yet) part of the
+        interchange protocol specification. For avoiding collisions with other
+        entries, please add name the keys with the name of the library
+        followed by a period and the desired name, e.g, ``pandas.indexcol``.
+        """
+
+    @abstractmethod
+    def num_columns(self) -> int:
+        """
+        Return the number of columns in the DataFrame.
+        """
+
+    @abstractmethod
+    def num_rows(self) -> int | None:
+        # TODO: not happy with Optional, but need to flag it may be expensive
+        #       why include it if it may be None - what do we expect consumers
+        #       to do here?
+        """
+        Return the number of rows in the DataFrame, if available.
+        """
+
+    @abstractmethod
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the DataFrame consists of.
+        """
+
+    @abstractmethod
+    def column_names(self) -> Iterable[str]:
+        """
+        Return an iterator yielding the column names.
+        """
+
+    @abstractmethod
+    def get_column(self, i: int) -> Column:
+        """
+        Return the column at the indicated position.
+        """
+
+    @abstractmethod
+    def get_column_by_name(self, name: str) -> Column:
+        """
+        Return the column whose name is the indicated name.
+        """
+
+    @abstractmethod
+    def get_columns(self) -> Iterable[Column]:
+        """
+        Return an iterator yielding the columns.
+        """
+
+    @abstractmethod
+    def select_columns(self, indices: Sequence[int]) -> DataFrame:
+        """
+        Create a new DataFrame by selecting a subset of columns by index.
+        """
+
+    @abstractmethod
+    def select_columns_by_name(self, names: Sequence[str]) -> DataFrame:
+        """
+        Create a new DataFrame by selecting a subset of columns by name.
+        """
+
+    @abstractmethod
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[DataFrame]:
+        """
+        Return an iterator yielding the chunks.
+
+        By default (None), yields the chunks that the data is stored as by the
+        producer. If given, ``n_chunks`` must be a multiple of
+        ``self.num_chunks()``, meaning the producer must subdivide each chunk
+        before yielding it.
+        """
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/from_dataframe.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/from_dataframe.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0df1c17e3a7c5276ff055e4fe23b5fca90988ca
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/from_dataframe.py
@@ -0,0 +1,557 @@
+from __future__ import annotations
+
+import ctypes
+import re
+from typing import Any
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import SettingWithCopyError
+
+import pandas as pd
+from pandas.core.interchange.dataframe_protocol import (
+    Buffer,
+    Column,
+    ColumnNullType,
+    DataFrame as DataFrameXchg,
+    DtypeKind,
+)
+from pandas.core.interchange.utils import (
+    ArrowCTypes,
+    Endianness,
+)
+
+_NP_DTYPES: dict[DtypeKind, dict[int, Any]] = {
+    DtypeKind.INT: {8: np.int8, 16: np.int16, 32: np.int32, 64: np.int64},
+    DtypeKind.UINT: {8: np.uint8, 16: np.uint16, 32: np.uint32, 64: np.uint64},
+    DtypeKind.FLOAT: {32: np.float32, 64: np.float64},
+    DtypeKind.BOOL: {1: bool, 8: bool},
+}
+
+
+def from_dataframe(df, allow_copy: bool = True) -> pd.DataFrame:
+    """
+    Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.
+
+    .. note::
+
+       For new development, we highly recommend using the Arrow C Data Interface
+       alongside the Arrow PyCapsule Interface instead of the interchange protocol.
+       From pandas 2.3 onwards, `from_dataframe` uses the PyCapsule Interface,
+       only falling back to the interchange protocol if that fails.
+
+    .. warning::
+
+        Due to severe implementation issues, we recommend only considering using the
+        interchange protocol in the following cases:
+
+        - converting to pandas: for pandas >= 2.0.3
+        - converting from pandas: for pandas >= 3.0.0
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+        Object supporting the interchange protocol, i.e. `__dataframe__` method.
+    allow_copy : bool, default: True
+        Whether to allow copying the memory to perform the conversion
+        (if false then zero-copy approach is requested).
+
+    Returns
+    -------
+    pd.DataFrame
+
+    Examples
+    --------
+    >>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+    >>> interchange_object = df_not_necessarily_pandas.__dataframe__()
+    >>> interchange_object.column_names()
+    Index(['A', 'B'], dtype='object')
+    >>> df_pandas = (pd.api.interchange.from_dataframe
+    ...              (interchange_object.select_columns_by_name(['A'])))
+    >>> df_pandas
+         A
+    0    1
+    1    2
+
+    These methods (``column_names``, ``select_columns_by_name``) should work
+    for any dataframe library which implements the interchange protocol.
+    """
+    if isinstance(df, pd.DataFrame):
+        return df
+
+    if hasattr(df, "__arrow_c_stream__"):
+        try:
+            pa = import_optional_dependency("pyarrow", min_version="14.0.0")
+        except ImportError:
+            # fallback to _from_dataframe
+            pass
+        else:
+            try:
+                return pa.table(df).to_pandas(zero_copy_only=not allow_copy)
+            except pa.ArrowInvalid as e:
+                raise RuntimeError(e) from e
+
+    if not hasattr(df, "__dataframe__"):
+        raise ValueError("`df` does not support __dataframe__")
+
+    return _from_dataframe(
+        df.__dataframe__(allow_copy=allow_copy), allow_copy=allow_copy
+    )
+
+
+def _from_dataframe(df: DataFrameXchg, allow_copy: bool = True):
+    """
+    Build a ``pd.DataFrame`` from the DataFrame interchange object.
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+        Object supporting the interchange protocol, i.e. `__dataframe__` method.
+    allow_copy : bool, default: True
+        Whether to allow copying the memory to perform the conversion
+        (if false then zero-copy approach is requested).
+
+    Returns
+    -------
+    pd.DataFrame
+    """
+    pandas_dfs = []
+    for chunk in df.get_chunks():
+        pandas_df = protocol_df_chunk_to_pandas(chunk)
+        pandas_dfs.append(pandas_df)
+
+    if not allow_copy and len(pandas_dfs) > 1:
+        raise RuntimeError(
+            "To join chunks a copy is required which is forbidden by allow_copy=False"
+        )
+    if not pandas_dfs:
+        pandas_df = protocol_df_chunk_to_pandas(df)
+    elif len(pandas_dfs) == 1:
+        pandas_df = pandas_dfs[0]
+    else:
+        pandas_df = pd.concat(pandas_dfs, axis=0, ignore_index=True, copy=False)
+
+    index_obj = df.metadata.get("pandas.index", None)
+    if index_obj is not None:
+        pandas_df.index = index_obj
+
+    return pandas_df
+
+
+def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
+    """
+    Convert interchange protocol chunk to ``pd.DataFrame``.
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+
+    Returns
+    -------
+    pd.DataFrame
+    """
+    columns: dict[str, Any] = {}
+    buffers = []  # hold on to buffers, keeps memory alive
+    for name in df.column_names():
+        if not isinstance(name, str):
+            raise ValueError(f"Column {name} is not a string")
+        if name in columns:
+            raise ValueError(f"Column {name} is not unique")
+        col = df.get_column_by_name(name)
+        dtype = col.dtype[0]
+        if dtype in (
+            DtypeKind.INT,
+            DtypeKind.UINT,
+            DtypeKind.FLOAT,
+            DtypeKind.BOOL,
+        ):
+            columns[name], buf = primitive_column_to_ndarray(col)
+        elif dtype == DtypeKind.CATEGORICAL:
+            columns[name], buf = categorical_column_to_series(col)
+        elif dtype == DtypeKind.STRING:
+            columns[name], buf = string_column_to_ndarray(col)
+        elif dtype == DtypeKind.DATETIME:
+            columns[name], buf = datetime_column_to_ndarray(col)
+        else:
+            raise NotImplementedError(f"Data type {dtype} not handled yet")
+
+        buffers.append(buf)
+
+    pandas_df = pd.DataFrame(columns)
+    pandas_df.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"] = buffers
+    return pandas_df
+
+
+def primitive_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
+    """
+    Convert a column holding one of the primitive dtypes to a NumPy array.
+
+    A primitive type is one of: int, uint, float, bool.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    buffers = col.get_buffers()
+
+    data_buff, data_dtype = buffers["data"]
+    data = buffer_to_ndarray(
+        data_buff, data_dtype, offset=col.offset, length=col.size()
+    )
+
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def categorical_column_to_series(col: Column) -> tuple[pd.Series, Any]:
+    """
+    Convert a column holding categorical data to a pandas Series.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of pd.Series holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    categorical = col.describe_categorical
+
+    if not categorical["is_dictionary"]:
+        raise NotImplementedError("Non-dictionary categoricals not supported yet")
+
+    cat_column = categorical["categories"]
+    if hasattr(cat_column, "_col"):
+        # Item "Column" of "Optional[Column]" has no attribute "_col"
+        # Item "None" of "Optional[Column]" has no attribute "_col"
+        categories = np.array(cat_column._col)  # type: ignore[union-attr]
+    else:
+        raise NotImplementedError(
+            "Interchanging categorical columns isn't supported yet, and our "
+            "fallback of using the `col._col` attribute (a ndarray) failed."
+        )
+    buffers = col.get_buffers()
+
+    codes_buff, codes_dtype = buffers["data"]
+    codes = buffer_to_ndarray(
+        codes_buff, codes_dtype, offset=col.offset, length=col.size()
+    )
+
+    # Doing module in order to not get ``IndexError`` for
+    # out-of-bounds sentinel values in `codes`
+    if len(categories) > 0:
+        values = categories[codes % len(categories)]
+    else:
+        values = codes
+
+    cat = pd.Categorical(
+        values, categories=categories, ordered=categorical["is_ordered"]
+    )
+    data = pd.Series(cat)
+
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
+    """
+    Convert a column holding string data to a NumPy array.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    null_kind, sentinel_val = col.describe_null
+
+    if null_kind not in (
+        ColumnNullType.NON_NULLABLE,
+        ColumnNullType.USE_BITMASK,
+        ColumnNullType.USE_BYTEMASK,
+    ):
+        raise NotImplementedError(
+            f"{null_kind} null kind is not yet supported for string columns."
+        )
+
+    buffers = col.get_buffers()
+
+    assert buffers["offsets"], "String buffers must contain offsets"
+    # Retrieve the data buffer containing the UTF-8 code units
+    data_buff, _ = buffers["data"]
+    # We're going to reinterpret the buffer as uint8, so make sure we can do it safely
+    assert col.dtype[2] in (
+        ArrowCTypes.STRING,
+        ArrowCTypes.LARGE_STRING,
+    )  # format_str == utf-8
+    # Convert the buffers to NumPy arrays. In order to go from STRING to
+    # an equivalent ndarray, we claim that the buffer is uint8 (i.e., a byte array)
+    data_dtype = (
+        DtypeKind.UINT,
+        8,
+        ArrowCTypes.UINT8,
+        Endianness.NATIVE,
+    )
+    # Specify zero offset as we don't want to chunk the string data
+    data = buffer_to_ndarray(data_buff, data_dtype, offset=0, length=data_buff.bufsize)
+
+    # Retrieve the offsets buffer containing the index offsets demarcating
+    # the beginning and the ending of each string
+    offset_buff, offset_dtype = buffers["offsets"]
+    # Offsets buffer contains start-stop positions of strings in the data buffer,
+    # meaning that it has more elements than in the data buffer, do `col.size() + 1`
+    # here to pass a proper offsets buffer size
+    offsets = buffer_to_ndarray(
+        offset_buff, offset_dtype, offset=col.offset, length=col.size() + 1
+    )
+
+    null_pos = None
+    if null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
+        validity = buffers["validity"]
+        if validity is not None:
+            valid_buff, valid_dtype = validity
+            null_pos = buffer_to_ndarray(
+                valid_buff, valid_dtype, offset=col.offset, length=col.size()
+            )
+            if sentinel_val == 0:
+                null_pos = ~null_pos
+
+    # Assemble the strings from the code units
+    str_list: list[None | float | str] = [None] * col.size()
+    for i in range(col.size()):
+        # Check for missing values
+        if null_pos is not None and null_pos[i]:
+            str_list[i] = np.nan
+            continue
+
+        # Extract a range of code units
+        units = data[offsets[i] : offsets[i + 1]]
+
+        # Convert the list of code units to bytes
+        str_bytes = bytes(units)
+
+        # Create the string
+        string = str_bytes.decode(encoding="utf-8")
+
+        # Add to our list of strings
+        str_list[i] = string
+
+    if using_string_dtype():
+        res = pd.Series(str_list, dtype="str")
+    else:
+        res = np.asarray(str_list, dtype="object")  # type: ignore[assignment]
+
+    return res, buffers  # type: ignore[return-value]
+
+
+def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:
+    """Parse datetime `format_str` to interpret the `data`."""
+    # timestamp 'ts{unit}:tz'
+    timestamp_meta = re.match(r"ts([smun]):(.*)", format_str)
+    if timestamp_meta:
+        unit, tz = timestamp_meta.group(1), timestamp_meta.group(2)
+        if unit != "s":
+            # the format string describes only a first letter of the unit, so
+            # add one extra letter to convert the unit to numpy-style:
+            # 'm' -> 'ms', 'u' -> 'us', 'n' -> 'ns'
+            unit += "s"
+        data = data.astype(f"datetime64[{unit}]")
+        if tz != "":
+            data = pd.Series(data).dt.tz_localize("UTC").dt.tz_convert(tz)
+        return data
+
+    # date 'td{Days/Ms}'
+    date_meta = re.match(r"td([Dm])", format_str)
+    if date_meta:
+        unit = date_meta.group(1)
+        if unit == "D":
+            # NumPy doesn't support DAY unit, so converting days to seconds
+            # (converting to uint64 to avoid overflow)
+            data = (data.astype(np.uint64) * (24 * 60 * 60)).astype("datetime64[s]")
+        elif unit == "m":
+            data = data.astype("datetime64[ms]")
+        else:
+            raise NotImplementedError(f"Date unit is not supported: {unit}")
+        return data
+
+    raise NotImplementedError(f"DateTime kind is not supported: {format_str}")
+
+
+def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray | pd.Series, Any]:
+    """
+    Convert a column holding DateTime data to a NumPy array.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    buffers = col.get_buffers()
+
+    _, col_bit_width, format_str, _ = col.dtype
+    dbuf, _ = buffers["data"]
+    # Consider dtype being `uint` to get number of units passed since the 01.01.1970
+
+    data = buffer_to_ndarray(
+        dbuf,
+        (
+            DtypeKind.INT,
+            col_bit_width,
+            getattr(ArrowCTypes, f"INT{col_bit_width}"),
+            Endianness.NATIVE,
+        ),
+        offset=col.offset,
+        length=col.size(),
+    )
+
+    data = parse_datetime_format_str(format_str, data)  # type: ignore[assignment]
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def buffer_to_ndarray(
+    buffer: Buffer,
+    dtype: tuple[DtypeKind, int, str, str],
+    *,
+    length: int,
+    offset: int = 0,
+) -> np.ndarray:
+    """
+    Build a NumPy array from the passed buffer.
+
+    Parameters
+    ----------
+    buffer : Buffer
+        Buffer to build a NumPy array from.
+    dtype : tuple
+        Data type of the buffer conforming protocol dtypes format.
+    offset : int, default: 0
+        Number of elements to offset from the start of the buffer.
+    length : int, optional
+        If the buffer is a bit-mask, specifies a number of bits to read
+        from the buffer. Has no effect otherwise.
+
+    Returns
+    -------
+    np.ndarray
+
+    Notes
+    -----
+    The returned array doesn't own the memory. The caller of this function is
+    responsible for keeping the memory owner object alive as long as
+    the returned NumPy array is being used.
+    """
+    kind, bit_width, _, _ = dtype
+
+    column_dtype = _NP_DTYPES.get(kind, {}).get(bit_width, None)
+    if column_dtype is None:
+        raise NotImplementedError(f"Conversion for {dtype} is not yet supported.")
+
+    # TODO: No DLPack yet, so need to construct a new ndarray from the data pointer
+    # and size in the buffer plus the dtype on the column. Use DLPack as NumPy supports
+    # it since https://github.com/numpy/numpy/pull/19083
+    ctypes_type = np.ctypeslib.as_ctypes_type(column_dtype)
+
+    if bit_width == 1:
+        assert length is not None, "`length` must be specified for a bit-mask buffer."
+        pa = import_optional_dependency("pyarrow")
+        arr = pa.BooleanArray.from_buffers(
+            pa.bool_(),
+            length,
+            [None, pa.foreign_buffer(buffer.ptr, length)],
+            offset=offset,
+        )
+        return np.asarray(arr)
+    else:
+        data_pointer = ctypes.cast(
+            buffer.ptr + (offset * bit_width // 8), ctypes.POINTER(ctypes_type)
+        )
+        if length > 0:
+            return np.ctypeslib.as_array(data_pointer, shape=(length,))
+        return np.array([], dtype=ctypes_type)
+
+
+def set_nulls(
+    data: np.ndarray | pd.Series,
+    col: Column,
+    validity: tuple[Buffer, tuple[DtypeKind, int, str, str]] | None,
+    allow_modify_inplace: bool = True,
+):
+    """
+    Set null values for the data according to the column null kind.
+
+    Parameters
+    ----------
+    data : np.ndarray or pd.Series
+        Data to set nulls in.
+    col : Column
+        Column object that describes the `data`.
+    validity : tuple(Buffer, dtype) or None
+        The return value of ``col.buffers()``. We do not access the ``col.buffers()``
+        here to not take the ownership of the memory of buffer objects.
+    allow_modify_inplace : bool, default: True
+        Whether to modify the `data` inplace when zero-copy is possible (True) or always
+        modify a copy of the `data` (False).
+
+    Returns
+    -------
+    np.ndarray or pd.Series
+        Data with the nulls being set.
+    """
+    if validity is None:
+        return data
+    null_kind, sentinel_val = col.describe_null
+    null_pos = None
+
+    if null_kind == ColumnNullType.USE_SENTINEL:
+        null_pos = pd.Series(data) == sentinel_val
+    elif null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
+        assert validity, "Expected to have a validity buffer for the mask"
+        valid_buff, valid_dtype = validity
+        null_pos = buffer_to_ndarray(
+            valid_buff, valid_dtype, offset=col.offset, length=col.size()
+        )
+        if sentinel_val == 0:
+            null_pos = ~null_pos
+    elif null_kind in (ColumnNullType.NON_NULLABLE, ColumnNullType.USE_NAN):
+        pass
+    else:
+        raise NotImplementedError(f"Null kind {null_kind} is not yet supported.")
+
+    if null_pos is not None and np.any(null_pos):
+        if not allow_modify_inplace:
+            data = data.copy()
+        try:
+            data[null_pos] = None
+        except TypeError:
+            # TypeError happens if the `data` dtype appears to be non-nullable
+            # in numpy notation (bool, int, uint). If this happens,
+            # cast the `data` to nullable float dtype.
+            data = data.astype(float)
+            data[null_pos] = None
+        except SettingWithCopyError:
+            # `SettingWithCopyError` may happen for datetime-like with missing values.
+            data = data.copy()
+            data[null_pos] = None
+
+    return data
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/utils.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..035a1f8abdbc5d07ccec000d38c3fc159ccf94b6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/interchange/utils.py
@@ -0,0 +1,183 @@
+"""
+Utility functions and objects for implementing the interchange API.
+"""
+
+from __future__ import annotations
+
+import typing
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+)
+
+import pandas as pd
+
+if typing.TYPE_CHECKING:
+    from pandas._typing import DtypeObj
+
+
+# Maps str(pyarrow.DataType) = C type format string
+# Currently, no pyarrow API for this
+PYARROW_CTYPES = {
+    "null": "n",
+    "bool": "b",
+    "uint8": "C",
+    "uint16": "S",
+    "uint32": "I",
+    "uint64": "L",
+    "int8": "c",
+    "int16": "S",
+    "int32": "i",
+    "int64": "l",
+    "halffloat": "e",  # float16
+    "float": "f",  # float32
+    "double": "g",  # float64
+    "string": "u",
+    "large_string": "U",
+    "binary": "z",
+    "time32[s]": "tts",
+    "time32[ms]": "ttm",
+    "time64[us]": "ttu",
+    "time64[ns]": "ttn",
+    "date32[day]": "tdD",
+    "date64[ms]": "tdm",
+    "timestamp[s]": "tss:",
+    "timestamp[ms]": "tsm:",
+    "timestamp[us]": "tsu:",
+    "timestamp[ns]": "tsn:",
+    "duration[s]": "tDs",
+    "duration[ms]": "tDm",
+    "duration[us]": "tDu",
+    "duration[ns]": "tDn",
+}
+
+
+class ArrowCTypes:
+    """
+    Enum for Apache Arrow C type format strings.
+
+    The Arrow C data interface:
+    https://arrow.apache.org/docs/format/CDataInterface.html#data-type-description-format-strings
+    """
+
+    NULL = "n"
+    BOOL = "b"
+    INT8 = "c"
+    UINT8 = "C"
+    INT16 = "s"
+    UINT16 = "S"
+    INT32 = "i"
+    UINT32 = "I"
+    INT64 = "l"
+    UINT64 = "L"
+    FLOAT16 = "e"
+    FLOAT32 = "f"
+    FLOAT64 = "g"
+    STRING = "u"  # utf-8
+    LARGE_STRING = "U"  # utf-8
+    DATE32 = "tdD"
+    DATE64 = "tdm"
+    # Resoulution:
+    #   - seconds -> 's'
+    #   - milliseconds -> 'm'
+    #   - microseconds -> 'u'
+    #   - nanoseconds -> 'n'
+    TIMESTAMP = "ts{resolution}:{tz}"
+    TIME = "tt{resolution}"
+
+
+class Endianness:
+    """Enum indicating the byte-order of a data-type."""
+
+    LITTLE = "<"
+    BIG = ">"
+    NATIVE = "="
+    NA = "|"
+
+
+def dtype_to_arrow_c_fmt(dtype: DtypeObj) -> str:
+    """
+    Represent pandas `dtype` as a format string in Apache Arrow C notation.
+
+    Parameters
+    ----------
+    dtype : np.dtype
+        Datatype of pandas DataFrame to represent.
+
+    Returns
+    -------
+    str
+        Format string in Apache Arrow C notation of the given `dtype`.
+    """
+    if isinstance(dtype, CategoricalDtype):
+        return ArrowCTypes.INT64
+    elif dtype == np.dtype("O"):
+        return ArrowCTypes.STRING
+    elif isinstance(dtype, ArrowDtype):
+        import pyarrow as pa
+
+        pa_type = dtype.pyarrow_dtype
+        if pa.types.is_decimal(pa_type):
+            return f"d:{pa_type.precision},{pa_type.scale}"
+        elif pa.types.is_timestamp(pa_type) and pa_type.tz is not None:
+            return f"ts{pa_type.unit[0]}:{pa_type.tz}"
+        format_str = PYARROW_CTYPES.get(str(pa_type), None)
+        if format_str is not None:
+            return format_str
+
+    format_str = getattr(ArrowCTypes, dtype.name.upper(), None)
+    if format_str is not None:
+        return format_str
+
+    if isinstance(dtype, pd.StringDtype):
+        # TODO(infer_string) this should be LARGE_STRING for pyarrow storage,
+        # but current tests don't cover this distinction
+        return ArrowCTypes.STRING
+
+    elif lib.is_np_dtype(dtype, "M"):
+        # Selecting the first char of resolution string:
+        # dtype.str -> '<M8[ns]' -> 'n'
+        resolution = np.datetime_data(dtype)[0][0]
+        return ArrowCTypes.TIMESTAMP.format(resolution=resolution, tz="")
+
+    elif isinstance(dtype, DatetimeTZDtype):
+        return ArrowCTypes.TIMESTAMP.format(resolution=dtype.unit[0], tz=dtype.tz)
+
+    elif isinstance(dtype, pd.BooleanDtype):
+        return ArrowCTypes.BOOL
+
+    raise NotImplementedError(
+        f"Conversion of {dtype} to Arrow C format string is not implemented."
+    )
+
+
+def maybe_rechunk(series: pd.Series, *, allow_copy: bool) -> pd.Series | None:
+    """
+    Rechunk a multi-chunk pyarrow array into a single-chunk array, if necessary.
+
+    - Returns `None` if the input series is not backed by a multi-chunk pyarrow array
+      (and so doesn't need rechunking)
+    - Returns a single-chunk-backed-Series if the input is backed by a multi-chunk
+      pyarrow array and `allow_copy` is `True`.
+    - Raises a `RuntimeError` if `allow_copy` is `False` and input is a
+      based by a multi-chunk pyarrow array.
+    """
+    if not isinstance(series.dtype, pd.ArrowDtype):
+        return None
+    chunked_array = series.array._pa_array  # type: ignore[attr-defined]
+    if len(chunked_array.chunks) == 1:
+        return None
+    if not allow_copy:
+        raise RuntimeError(
+            "Found multi-chunk pyarrow array, but `allow_copy` is False. "
+            "Please rechunk the array before calling this function, or set "
+            "`allow_copy=True`."
+        )
+    arr = chunked_array.combine_chunks()
+    return pd.Series(arr, dtype=series.dtype, name=series.name, index=series.index)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eb413440ba9c1ef4c016cd874d19c2aba6d791e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/__init__.py
@@ -0,0 +1,85 @@
+from pandas.core.internals.api import make_block  # 2023-09-18 pyarrow uses this
+from pandas.core.internals.array_manager import (
+    ArrayManager,
+    SingleArrayManager,
+)
+from pandas.core.internals.base import (
+    DataManager,
+    SingleDataManager,
+)
+from pandas.core.internals.concat import concatenate_managers
+from pandas.core.internals.managers import (
+    BlockManager,
+    SingleBlockManager,
+)
+
+__all__ = [
+    "Block",  # pylint: disable=undefined-all-variable
+    "DatetimeTZBlock",  # pylint: disable=undefined-all-variable
+    "ExtensionBlock",  # pylint: disable=undefined-all-variable
+    "make_block",
+    "DataManager",
+    "ArrayManager",
+    "BlockManager",
+    "SingleDataManager",
+    "SingleBlockManager",
+    "SingleArrayManager",
+    "concatenate_managers",
+]
+
+
+def __getattr__(name: str):
+    # GH#55139
+    import warnings
+
+    if name == "create_block_manager_from_blocks":
+        # GH#33892
+        warnings.warn(
+            f"{name} is deprecated and will be removed in a future version. "
+            "Use public APIs instead.",
+            DeprecationWarning,
+            # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+            # on hard-coding stacklevel
+            stacklevel=2,
+        )
+        from pandas.core.internals.managers import create_block_manager_from_blocks
+
+        return create_block_manager_from_blocks
+
+    if name in [
+        "NumericBlock",
+        "ObjectBlock",
+        "Block",
+        "ExtensionBlock",
+        "DatetimeTZBlock",
+    ]:
+        warnings.warn(
+            f"{name} is deprecated and will be removed in a future version. "
+            "Use public APIs instead.",
+            DeprecationWarning,
+            # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+            # on hard-coding stacklevel
+            stacklevel=2,
+        )
+        if name == "NumericBlock":
+            from pandas.core.internals.blocks import NumericBlock
+
+            return NumericBlock
+        elif name == "DatetimeTZBlock":
+            from pandas.core.internals.blocks import DatetimeTZBlock
+
+            return DatetimeTZBlock
+        elif name == "ExtensionBlock":
+            from pandas.core.internals.blocks import ExtensionBlock
+
+            return ExtensionBlock
+        elif name == "Block":
+            from pandas.core.internals.blocks import Block
+
+            return Block
+        else:
+            from pandas.core.internals.blocks import ObjectBlock
+
+            return ObjectBlock
+
+    raise AttributeError(f"module 'pandas.core.internals' has no attribute '{name}'")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0b3937ca47ea06c42b4b51964f6a74830a5d9ee
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/api.py
@@ -0,0 +1,156 @@
+"""
+This is a pseudo-public API for downstream libraries.  We ask that downstream
+authors
+
+1) Try to avoid using internals directly altogether, and failing that,
+2) Use only functions exposed here (or in core.internals)
+
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.internals import BlockPlacement
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+
+from pandas.core.arrays import DatetimeArray
+from pandas.core.construction import extract_array
+from pandas.core.internals.blocks import (
+    check_ndim,
+    ensure_block_shape,
+    extract_pandas_array,
+    get_block_type,
+    maybe_coerce_values,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import Dtype
+
+    from pandas.core.internals.blocks import Block
+
+
+def make_block(
+    values, placement, klass=None, ndim=None, dtype: Dtype | None = None
+) -> Block:
+    """
+    This is a pseudo-public analogue to blocks.new_block.
+
+    We ask that downstream libraries use this rather than any fully-internal
+    APIs, including but not limited to:
+
+    - core.internals.blocks.make_block
+    - Block.make_block
+    - Block.make_block_same_class
+    - Block.__init__
+    """
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+
+    values, dtype = extract_pandas_array(values, dtype, ndim)
+
+    from pandas.core.internals.blocks import (
+        DatetimeTZBlock,
+        ExtensionBlock,
+    )
+
+    if klass is ExtensionBlock and isinstance(values.dtype, PeriodDtype):
+        # GH-44681 changed PeriodArray to be stored in the 2D
+        # NDArrayBackedExtensionBlock instead of ExtensionBlock
+        # -> still allow ExtensionBlock to be passed in this case for back compat
+        klass = None
+
+    if klass is None:
+        dtype = dtype or values.dtype
+        klass = get_block_type(dtype)
+
+    elif klass is DatetimeTZBlock and not isinstance(values.dtype, DatetimeTZDtype):
+        # pyarrow calls get here
+        values = DatetimeArray._simple_new(
+            # error: Argument "dtype" to "_simple_new" of "DatetimeArray" has
+            # incompatible type "Union[ExtensionDtype, dtype[Any], None]";
+            # expected "Union[dtype[datetime64], DatetimeTZDtype]"
+            values,
+            dtype=dtype,  # type: ignore[arg-type]
+        )
+
+    if not isinstance(placement, BlockPlacement):
+        placement = BlockPlacement(placement)
+
+    ndim = maybe_infer_ndim(values, placement, ndim)
+    if isinstance(values.dtype, (PeriodDtype, DatetimeTZDtype)):
+        # GH#41168 ensure we can pass 1D dt64tz values
+        # More generally, any EA dtype that isn't is_1d_only_ea_dtype
+        values = extract_array(values, extract_numpy=True)
+        values = ensure_block_shape(values, ndim)
+
+    check_ndim(values, placement, ndim)
+    values = maybe_coerce_values(values)
+    return klass(values, ndim=ndim, placement=placement)
+
+
+def maybe_infer_ndim(values, placement: BlockPlacement, ndim: int | None) -> int:
+    """
+    If `ndim` is not provided, infer it from placement and values.
+    """
+    if ndim is None:
+        # GH#38134 Block constructor now assumes ndim is not None
+        if not isinstance(values.dtype, np.dtype):
+            if len(placement) != 1:
+                ndim = 1
+            else:
+                ndim = 2
+        else:
+            ndim = values.ndim
+    return ndim
+
+
+def __getattr__(name: str):
+    # GH#55139
+    import warnings
+
+    if name in [
+        "Block",
+        "ExtensionBlock",
+        "DatetimeTZBlock",
+        "create_block_manager_from_blocks",
+    ]:
+        # GH#33892
+        warnings.warn(
+            f"{name} is deprecated and will be removed in a future version. "
+            "Use public APIs instead.",
+            DeprecationWarning,
+            # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+            # on hard-coding stacklevel
+            stacklevel=2,
+        )
+
+        if name == "create_block_manager_from_blocks":
+            from pandas.core.internals.managers import create_block_manager_from_blocks
+
+            return create_block_manager_from_blocks
+
+        elif name == "Block":
+            from pandas.core.internals.blocks import Block
+
+            return Block
+
+        elif name == "DatetimeTZBlock":
+            from pandas.core.internals.blocks import DatetimeTZBlock
+
+            return DatetimeTZBlock
+
+        elif name == "ExtensionBlock":
+            from pandas.core.internals.blocks import ExtensionBlock
+
+            return ExtensionBlock
+
+    raise AttributeError(
+        f"module 'pandas.core.internals.api' has no attribute '{name}'"
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/array_manager.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/array_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..e253f82256a5f6dd8b277b576a33597355d69dcc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/array_manager.py
@@ -0,0 +1,1340 @@
+"""
+Experimental manager based on storing a collection of 1D arrays
+"""
+from __future__ import annotations
+
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    lib,
+)
+
+from pandas.core.dtypes.astype import (
+    astype_array,
+    astype_array_safe,
+)
+from pandas.core.dtypes.cast import (
+    ensure_dtype_can_hold_na,
+    find_common_type,
+    infer_dtype_from_scalar,
+    np_find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_datetime64_ns_dtype,
+    is_integer,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_timedelta64_ns_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    array_equals,
+    isna,
+    na_value_for_dtype,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.quantile import quantile_compat
+from pandas.core.array_algos.take import take_1d
+from pandas.core.arrays import (
+    DatetimeArray,
+    ExtensionArray,
+    NumpyExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.indexers import (
+    maybe_convert_indices,
+    validate_indices,
+)
+from pandas.core.indexes.api import (
+    Index,
+    ensure_index,
+)
+from pandas.core.indexes.base import get_values_for_csv
+from pandas.core.internals.base import (
+    DataManager,
+    SingleDataManager,
+    ensure_np_dtype,
+    interleaved_dtype,
+)
+from pandas.core.internals.blocks import (
+    BlockPlacement,
+    ensure_block_shape,
+    external_values,
+    extract_pandas_array,
+    maybe_coerce_values,
+    new_block,
+)
+from pandas.core.internals.managers import make_na_array
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        QuantileInterpolation,
+        Self,
+        npt,
+    )
+
+
+class BaseArrayManager(DataManager):
+    """
+    Core internal data structure to implement DataFrame and Series.
+
+    Alternative to the BlockManager, storing a list of 1D arrays instead of
+    Blocks.
+
+    This is *not* a public API class
+
+    Parameters
+    ----------
+    arrays : Sequence of arrays
+    axes : Sequence of Index
+    verify_integrity : bool, default True
+
+    """
+
+    __slots__ = [
+        "_axes",  # private attribute, because 'axes' has different order, see below
+        "arrays",
+    ]
+
+    arrays: list[np.ndarray | ExtensionArray]
+    _axes: list[Index]
+
+    def __init__(
+        self,
+        arrays: list[np.ndarray | ExtensionArray],
+        axes: list[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        raise NotImplementedError
+
+    def make_empty(self, axes=None) -> Self:
+        """Return an empty ArrayManager with the items axis of len 0 (no columns)"""
+        if axes is None:
+            axes = [self.axes[1:], Index([])]
+
+        arrays: list[np.ndarray | ExtensionArray] = []
+        return type(self)(arrays, axes)
+
+    @property
+    def items(self) -> Index:
+        return self._axes[-1]
+
+    @property
+    # error: Signature of "axes" incompatible with supertype "DataManager"
+    def axes(self) -> list[Index]:  # type: ignore[override]
+        # mypy doesn't work to override attribute with property
+        # see https://github.com/python/mypy/issues/4125
+        """Axes is BlockManager-compatible order (columns, rows)"""
+        return [self._axes[1], self._axes[0]]
+
+    @property
+    def shape_proper(self) -> tuple[int, ...]:
+        # this returns (n_rows, n_columns)
+        return tuple(len(ax) for ax in self._axes)
+
+    @staticmethod
+    def _normalize_axis(axis: AxisInt) -> int:
+        # switch axis
+        axis = 1 if axis == 0 else 0
+        return axis
+
+    def set_axis(self, axis: AxisInt, new_labels: Index) -> None:
+        # Caller is responsible for ensuring we have an Index object.
+        self._validate_set_axis(axis, new_labels)
+        axis = self._normalize_axis(axis)
+        self._axes[axis] = new_labels
+
+    def get_dtypes(self) -> npt.NDArray[np.object_]:
+        return np.array([arr.dtype for arr in self.arrays], dtype="object")
+
+    def add_references(self, mgr: BaseArrayManager) -> None:
+        """
+        Only implemented on the BlockManager level
+        """
+        return
+
+    def __getstate__(self):
+        return self.arrays, self._axes
+
+    def __setstate__(self, state) -> None:
+        self.arrays = state[0]
+        self._axes = state[1]
+
+    def __repr__(self) -> str:
+        output = type(self).__name__
+        output += f"\nIndex: {self._axes[0]}"
+        if self.ndim == 2:
+            output += f"\nColumns: {self._axes[1]}"
+        output += f"\n{len(self.arrays)} arrays:"
+        for arr in self.arrays:
+            output += f"\n{arr.dtype}"
+        return output
+
+    def apply(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        """
+        Iterate over the arrays, collect and create a new ArrayManager.
+
+        Parameters
+        ----------
+        f : str or callable
+            Name of the Array method to apply.
+        align_keys: List[str] or None, default None
+        **kwargs
+            Keywords to pass to `f`
+
+        Returns
+        -------
+        ArrayManager
+        """
+        assert "filter" not in kwargs
+
+        align_keys = align_keys or []
+        result_arrays: list[ArrayLike] = []
+        # fillna: Series/DataFrame is responsible for making sure value is aligned
+
+        aligned_args = {k: kwargs[k] for k in align_keys}
+
+        if f == "apply":
+            f = kwargs.pop("func")
+
+        for i, arr in enumerate(self.arrays):
+            if aligned_args:
+                for k, obj in aligned_args.items():
+                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
+                        # The caller is responsible for ensuring that
+                        #  obj.axes[-1].equals(self.items)
+                        if obj.ndim == 1:
+                            kwargs[k] = obj.iloc[i]
+                        else:
+                            kwargs[k] = obj.iloc[:, i]._values
+                    else:
+                        # otherwise we have an array-like
+                        kwargs[k] = obj[i]
+
+            if callable(f):
+                applied = f(arr, **kwargs)
+            else:
+                applied = getattr(arr, f)(**kwargs)
+
+            result_arrays.append(applied)
+
+        new_axes = self._axes
+        return type(self)(result_arrays, new_axes)
+
+    def apply_with_block(self, f, align_keys=None, **kwargs) -> Self:
+        # switch axis to follow BlockManager logic
+        swap_axis = True
+        if f == "interpolate":
+            swap_axis = False
+        if swap_axis and "axis" in kwargs and self.ndim == 2:
+            kwargs["axis"] = 1 if kwargs["axis"] == 0 else 0
+
+        align_keys = align_keys or []
+        aligned_args = {k: kwargs[k] for k in align_keys}
+
+        result_arrays = []
+
+        for i, arr in enumerate(self.arrays):
+            if aligned_args:
+                for k, obj in aligned_args.items():
+                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
+                        # The caller is responsible for ensuring that
+                        #  obj.axes[-1].equals(self.items)
+                        if obj.ndim == 1:
+                            if self.ndim == 2:
+                                kwargs[k] = obj.iloc[slice(i, i + 1)]._values
+                            else:
+                                kwargs[k] = obj.iloc[:]._values
+                        else:
+                            kwargs[k] = obj.iloc[:, [i]]._values
+                    else:
+                        # otherwise we have an ndarray
+                        if obj.ndim == 2:
+                            kwargs[k] = obj[[i]]
+
+            if isinstance(arr.dtype, np.dtype) and not isinstance(arr, np.ndarray):
+                # i.e. TimedeltaArray, DatetimeArray with tz=None. Need to
+                #  convert for the Block constructors.
+                arr = np.asarray(arr)
+
+            arr = maybe_coerce_values(arr)
+            if self.ndim == 2:
+                arr = ensure_block_shape(arr, 2)
+                bp = BlockPlacement(slice(0, 1, 1))
+                block = new_block(arr, placement=bp, ndim=2)
+            else:
+                bp = BlockPlacement(slice(0, len(self), 1))
+                block = new_block(arr, placement=bp, ndim=1)
+
+            applied = getattr(block, f)(**kwargs)
+            if isinstance(applied, list):
+                applied = applied[0]
+            arr = applied.values
+            if self.ndim == 2 and arr.ndim == 2:
+                # 2D for np.ndarray or DatetimeArray/TimedeltaArray
+                assert len(arr) == 1
+                # error: No overload variant of "__getitem__" of "ExtensionArray"
+                # matches argument type "Tuple[int, slice]"
+                arr = arr[0, :]  # type: ignore[call-overload]
+            result_arrays.append(arr)
+
+        return type(self)(result_arrays, self._axes)
+
+    def setitem(self, indexer, value, warn: bool = True) -> Self:
+        return self.apply_with_block("setitem", indexer=indexer, value=value)
+
+    def diff(self, n: int) -> Self:
+        assert self.ndim == 2  # caller ensures
+        return self.apply(algos.diff, n=n)
+
+    def astype(self, dtype, copy: bool | None = False, errors: str = "raise") -> Self:
+        if copy is None:
+            copy = True
+
+        return self.apply(astype_array_safe, dtype=dtype, copy=copy, errors=errors)
+
+    def convert(self, copy: bool | None) -> Self:
+        if copy is None:
+            copy = True
+
+        def _convert(arr):
+            if is_object_dtype(arr.dtype):
+                # extract NumpyExtensionArray for tests that patch
+                #  NumpyExtensionArray._typ
+                arr = np.asarray(arr)
+                result = lib.maybe_convert_objects(
+                    arr,
+                    convert_non_numeric=True,
+                )
+                if result is arr and copy:
+                    return arr.copy()
+                return result
+            else:
+                return arr.copy() if copy else arr
+
+        return self.apply(_convert)
+
+    def get_values_for_csv(
+        self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None
+    ) -> Self:
+        return self.apply(
+            get_values_for_csv,
+            na_rep=na_rep,
+            quoting=quoting,
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+        )
+
+    @property
+    def any_extension_types(self) -> bool:
+        """Whether any of the blocks in this manager are extension blocks"""
+        return False  # any(block.is_extension for block in self.blocks)
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if we are a single block and are a view"""
+        # TODO what is this used for?
+        return False
+
+    @property
+    def is_single_block(self) -> bool:
+        return len(self.arrays) == 1
+
+    def _get_data_subset(self, predicate: Callable) -> Self:
+        indices = [i for i, arr in enumerate(self.arrays) if predicate(arr)]
+        arrays = [self.arrays[i] for i in indices]
+        # TODO copy?
+        # Note: using Index.take ensures we can retain e.g. DatetimeIndex.freq,
+        #  see test_describe_datetime_columns
+        taker = np.array(indices, dtype="intp")
+        new_cols = self._axes[1].take(taker)
+        new_axes = [self._axes[0], new_cols]
+        return type(self)(arrays, new_axes, verify_integrity=False)
+
+    def get_bool_data(self, copy: bool = False) -> Self:
+        """
+        Select columns that are bool-dtype and object-dtype columns that are all-bool.
+
+        Parameters
+        ----------
+        copy : bool, default False
+            Whether to copy the blocks
+        """
+        return self._get_data_subset(lambda x: x.dtype == np.dtype(bool))
+
+    def get_numeric_data(self, copy: bool = False) -> Self:
+        """
+        Select columns that have a numeric dtype.
+
+        Parameters
+        ----------
+        copy : bool, default False
+            Whether to copy the blocks
+        """
+        return self._get_data_subset(
+            lambda arr: is_numeric_dtype(arr.dtype)
+            or getattr(arr.dtype, "_is_numeric", False)
+        )
+
+    def copy(self, deep: bool | Literal["all"] | None = True) -> Self:
+        """
+        Make deep or shallow copy of ArrayManager
+
+        Parameters
+        ----------
+        deep : bool or string, default True
+            If False, return shallow copy (do not copy data)
+            If 'all', copy data and a deep copy of the index
+
+        Returns
+        -------
+        BlockManager
+        """
+        if deep is None:
+            # ArrayManager does not yet support CoW, so deep=None always means
+            # deep=True for now
+            deep = True
+
+        # this preserves the notion of view copying of axes
+        if deep:
+            # hit in e.g. tests.io.json.test_pandas
+
+            def copy_func(ax):
+                return ax.copy(deep=True) if deep == "all" else ax.view()
+
+            new_axes = [copy_func(ax) for ax in self._axes]
+        else:
+            new_axes = list(self._axes)
+
+        if deep:
+            new_arrays = [arr.copy() for arr in self.arrays]
+        else:
+            new_arrays = list(self.arrays)
+        return type(self)(new_arrays, new_axes, verify_integrity=False)
+
+    def reindex_indexer(
+        self,
+        new_axis,
+        indexer,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool | None = True,
+        # ignored keywords
+        only_slice: bool = False,
+        # ArrayManager specific keywords
+        use_na_proxy: bool = False,
+    ) -> Self:
+        axis = self._normalize_axis(axis)
+        return self._reindex_indexer(
+            new_axis,
+            indexer,
+            axis,
+            fill_value,
+            allow_dups,
+            copy,
+            use_na_proxy,
+        )
+
+    def _reindex_indexer(
+        self,
+        new_axis,
+        indexer: npt.NDArray[np.intp] | None,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool | None = True,
+        use_na_proxy: bool = False,
+    ) -> Self:
+        """
+        Parameters
+        ----------
+        new_axis : Index
+        indexer : ndarray[intp] or None
+        axis : int
+        fill_value : object, default None
+        allow_dups : bool, default False
+        copy : bool, default True
+
+
+        pandas-indexer with -1's only.
+        """
+        if copy is None:
+            # ArrayManager does not yet support CoW, so deep=None always means
+            # deep=True for now
+            copy = True
+
+        if indexer is None:
+            if new_axis is self._axes[axis] and not copy:
+                return self
+
+            result = self.copy(deep=copy)
+            result._axes = list(self._axes)
+            result._axes[axis] = new_axis
+            return result
+
+        # some axes don't allow reindexing with dups
+        if not allow_dups:
+            self._axes[axis]._validate_can_reindex(indexer)
+
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        if axis == 1:
+            new_arrays = []
+            for i in indexer:
+                if i == -1:
+                    arr = self._make_na_array(
+                        fill_value=fill_value, use_na_proxy=use_na_proxy
+                    )
+                else:
+                    arr = self.arrays[i]
+                    if copy:
+                        arr = arr.copy()
+                new_arrays.append(arr)
+
+        else:
+            validate_indices(indexer, len(self._axes[0]))
+            indexer = ensure_platform_int(indexer)
+            mask = indexer == -1
+            needs_masking = mask.any()
+            new_arrays = [
+                take_1d(
+                    arr,
+                    indexer,
+                    allow_fill=needs_masking,
+                    fill_value=fill_value,
+                    mask=mask,
+                    # if fill_value is not None else blk.fill_value
+                )
+                for arr in self.arrays
+            ]
+
+        new_axes = list(self._axes)
+        new_axes[axis] = new_axis
+
+        return type(self)(new_arrays, new_axes, verify_integrity=False)
+
+    def take(
+        self,
+        indexer: npt.NDArray[np.intp],
+        axis: AxisInt = 1,
+        verify: bool = True,
+    ) -> Self:
+        """
+        Take items along any axis.
+        """
+        assert isinstance(indexer, np.ndarray), type(indexer)
+        assert indexer.dtype == np.intp, indexer.dtype
+
+        axis = self._normalize_axis(axis)
+
+        if not indexer.ndim == 1:
+            raise ValueError("indexer should be 1-dimensional")
+
+        n = self.shape_proper[axis]
+        indexer = maybe_convert_indices(indexer, n, verify=verify)
+
+        new_labels = self._axes[axis].take(indexer)
+        return self._reindex_indexer(
+            new_axis=new_labels, indexer=indexer, axis=axis, allow_dups=True
+        )
+
+    def _make_na_array(self, fill_value=None, use_na_proxy: bool = False):
+        if use_na_proxy:
+            assert fill_value is None
+            return NullArrayProxy(self.shape_proper[0])
+
+        if fill_value is None:
+            fill_value = np.nan
+
+        dtype, fill_value = infer_dtype_from_scalar(fill_value)
+        array_values = make_na_array(dtype, self.shape_proper[:1], fill_value)
+        return array_values
+
+    def _equal_values(self, other) -> bool:
+        """
+        Used in .equals defined in base class. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        for left, right in zip(self.arrays, other.arrays):
+            if not array_equals(left, right):
+                return False
+        return True
+
+    # TODO
+    # to_dict
+
+
+class ArrayManager(BaseArrayManager):
+    @property
+    def ndim(self) -> Literal[2]:
+        return 2
+
+    def __init__(
+        self,
+        arrays: list[np.ndarray | ExtensionArray],
+        axes: list[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        # Note: we are storing the axes in "_axes" in the (row, columns) order
+        # which contrasts the order how it is stored in BlockManager
+        self._axes = axes
+        self.arrays = arrays
+
+        if verify_integrity:
+            self._axes = [ensure_index(ax) for ax in axes]
+            arrays = [extract_pandas_array(x, None, 1)[0] for x in arrays]
+            self.arrays = [maybe_coerce_values(arr) for arr in arrays]
+            self._verify_integrity()
+
+    def _verify_integrity(self) -> None:
+        n_rows, n_columns = self.shape_proper
+        if not len(self.arrays) == n_columns:
+            raise ValueError(
+                "Number of passed arrays must equal the size of the column Index: "
+                f"{len(self.arrays)} arrays vs {n_columns} columns."
+            )
+        for arr in self.arrays:
+            if not len(arr) == n_rows:
+                raise ValueError(
+                    "Passed arrays should have the same length as the rows Index: "
+                    f"{len(arr)} vs {n_rows} rows"
+                )
+            if not isinstance(arr, (np.ndarray, ExtensionArray)):
+                raise ValueError(
+                    "Passed arrays should be np.ndarray or ExtensionArray instances, "
+                    f"got {type(arr)} instead"
+                )
+            if not arr.ndim == 1:
+                raise ValueError(
+                    "Passed arrays should be 1-dimensional, got array with "
+                    f"{arr.ndim} dimensions instead."
+                )
+
+    # --------------------------------------------------------------------
+    # Indexing
+
+    def fast_xs(self, loc: int) -> SingleArrayManager:
+        """
+        Return the array corresponding to `frame.iloc[loc]`.
+
+        Parameters
+        ----------
+        loc : int
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        dtype = interleaved_dtype([arr.dtype for arr in self.arrays])
+
+        values = [arr[loc] for arr in self.arrays]
+        if isinstance(dtype, ExtensionDtype):
+            result = dtype.construct_array_type()._from_sequence(values, dtype=dtype)
+        # for datetime64/timedelta64, the np.ndarray constructor cannot handle pd.NaT
+        elif is_datetime64_ns_dtype(dtype):
+            result = DatetimeArray._from_sequence(values, dtype=dtype)._ndarray
+        elif is_timedelta64_ns_dtype(dtype):
+            result = TimedeltaArray._from_sequence(values, dtype=dtype)._ndarray
+        else:
+            result = np.array(values, dtype=dtype)
+        return SingleArrayManager([result], [self._axes[1]])
+
+    def get_slice(self, slobj: slice, axis: AxisInt = 0) -> ArrayManager:
+        axis = self._normalize_axis(axis)
+
+        if axis == 0:
+            arrays = [arr[slobj] for arr in self.arrays]
+        elif axis == 1:
+            arrays = self.arrays[slobj]
+
+        new_axes = list(self._axes)
+        new_axes[axis] = new_axes[axis]._getitem_slice(slobj)
+
+        return type(self)(arrays, new_axes, verify_integrity=False)
+
+    def iget(self, i: int) -> SingleArrayManager:
+        """
+        Return the data as a SingleArrayManager.
+        """
+        values = self.arrays[i]
+        return SingleArrayManager([values], [self._axes[0]])
+
+    def iget_values(self, i: int) -> ArrayLike:
+        """
+        Return the data for column i as the values (ndarray or ExtensionArray).
+        """
+        return self.arrays[i]
+
+    @property
+    def column_arrays(self) -> list[ArrayLike]:
+        """
+        Used in the JSON C code to access column arrays.
+        """
+
+        return [np.asarray(arr) for arr in self.arrays]
+
+    def iset(
+        self,
+        loc: int | slice | np.ndarray,
+        value: ArrayLike,
+        inplace: bool = False,
+        refs=None,
+    ) -> None:
+        """
+        Set new column(s).
+
+        This changes the ArrayManager in-place, but replaces (an) existing
+        column(s), not changing column values in-place).
+
+        Parameters
+        ----------
+        loc : integer, slice or boolean mask
+            Positional location (already bounds checked)
+        value : np.ndarray or ExtensionArray
+        inplace : bool, default False
+            Whether overwrite existing array as opposed to replacing it.
+        """
+        # single column -> single integer index
+        if lib.is_integer(loc):
+            # TODO can we avoid needing to unpack this here? That means converting
+            # DataFrame into 1D array when loc is an integer
+            if isinstance(value, np.ndarray) and value.ndim == 2:
+                assert value.shape[1] == 1
+                value = value[:, 0]
+
+            # TODO we receive a datetime/timedelta64 ndarray from DataFrame._iset_item
+            # but we should avoid that and pass directly the proper array
+            value = maybe_coerce_values(value)
+
+            assert isinstance(value, (np.ndarray, ExtensionArray))
+            assert value.ndim == 1
+            assert len(value) == len(self._axes[0])
+            self.arrays[loc] = value
+            return
+
+        # multiple columns -> convert slice or array to integer indices
+        elif isinstance(loc, slice):
+            indices: range | np.ndarray = range(
+                loc.start if loc.start is not None else 0,
+                loc.stop if loc.stop is not None else self.shape_proper[1],
+                loc.step if loc.step is not None else 1,
+            )
+        else:
+            assert isinstance(loc, np.ndarray)
+            assert loc.dtype == "bool"
+            indices = np.nonzero(loc)[0]
+
+        assert value.ndim == 2
+        assert value.shape[0] == len(self._axes[0])
+
+        for value_idx, mgr_idx in enumerate(indices):
+            # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+            # argument type "Tuple[slice, int]"
+            value_arr = value[:, value_idx]  # type: ignore[call-overload]
+            self.arrays[mgr_idx] = value_arr
+        return
+
+    def column_setitem(
+        self, loc: int, idx: int | slice | np.ndarray, value, inplace_only: bool = False
+    ) -> None:
+        """
+        Set values ("setitem") into a single column (not setting the full column).
+
+        This is a method on the ArrayManager level, to avoid creating an
+        intermediate Series at the DataFrame level (`s = df[loc]; s[idx] = value`)
+        """
+        if not is_integer(loc):
+            raise TypeError("The column index should be an integer")
+        arr = self.arrays[loc]
+        mgr = SingleArrayManager([arr], [self._axes[0]])
+        if inplace_only:
+            mgr.setitem_inplace(idx, value)
+        else:
+            new_mgr = mgr.setitem((idx,), value)
+            # update existing ArrayManager in-place
+            self.arrays[loc] = new_mgr.arrays[0]
+
+    def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
+        """
+        Insert item at selected position.
+
+        Parameters
+        ----------
+        loc : int
+        item : hashable
+        value : np.ndarray or ExtensionArray
+        """
+        # insert to the axis; this could possibly raise a TypeError
+        new_axis = self.items.insert(loc, item)
+
+        value = extract_array(value, extract_numpy=True)
+        if value.ndim == 2:
+            if value.shape[0] == 1:
+                # error: No overload variant of "__getitem__" of "ExtensionArray"
+                # matches argument type "Tuple[int, slice]"
+                value = value[0, :]  # type: ignore[call-overload]
+            else:
+                raise ValueError(
+                    f"Expected a 1D array, got an array with shape {value.shape}"
+                )
+        value = maybe_coerce_values(value)
+
+        # TODO self.arrays can be empty
+        # assert len(value) == len(self.arrays[0])
+
+        # TODO is this copy needed?
+        arrays = self.arrays.copy()
+        arrays.insert(loc, value)
+
+        self.arrays = arrays
+        self._axes[1] = new_axis
+
+    def idelete(self, indexer) -> ArrayManager:
+        """
+        Delete selected locations in-place (new block and array, same BlockManager)
+        """
+        to_keep = np.ones(self.shape[0], dtype=np.bool_)
+        to_keep[indexer] = False
+
+        self.arrays = [self.arrays[i] for i in np.nonzero(to_keep)[0]]
+        self._axes = [self._axes[0], self._axes[1][to_keep]]
+        return self
+
+    # --------------------------------------------------------------------
+    # Array-wise Operation
+
+    def grouped_reduce(self, func: Callable) -> Self:
+        """
+        Apply grouped reduction function columnwise, returning a new ArrayManager.
+
+        Parameters
+        ----------
+        func : grouped reduction function
+
+        Returns
+        -------
+        ArrayManager
+        """
+        result_arrays: list[np.ndarray] = []
+        result_indices: list[int] = []
+
+        for i, arr in enumerate(self.arrays):
+            # grouped_reduce functions all expect 2D arrays
+            arr = ensure_block_shape(arr, ndim=2)
+            res = func(arr)
+            if res.ndim == 2:
+                # reverse of ensure_block_shape
+                assert res.shape[0] == 1
+                res = res[0]
+
+            result_arrays.append(res)
+            result_indices.append(i)
+
+        if len(result_arrays) == 0:
+            nrows = 0
+        else:
+            nrows = result_arrays[0].shape[0]
+        index = Index(range(nrows))
+
+        columns = self.items
+
+        # error: Argument 1 to "ArrayManager" has incompatible type "List[ndarray]";
+        # expected "List[Union[ndarray, ExtensionArray]]"
+        return type(self)(result_arrays, [index, columns])  # type: ignore[arg-type]
+
+    def reduce(self, func: Callable) -> Self:
+        """
+        Apply reduction function column-wise, returning a single-row ArrayManager.
+
+        Parameters
+        ----------
+        func : reduction function
+
+        Returns
+        -------
+        ArrayManager
+        """
+        result_arrays: list[np.ndarray] = []
+        for i, arr in enumerate(self.arrays):
+            res = func(arr, axis=0)
+
+            # TODO NaT doesn't preserve dtype, so we need to ensure to create
+            # a timedelta result array if original was timedelta
+            # what if datetime results in timedelta? (eg std)
+            dtype = arr.dtype if res is NaT else None
+            result_arrays.append(
+                sanitize_array([res], None, dtype=dtype)  # type: ignore[arg-type]
+            )
+
+        index = Index._simple_new(np.array([None], dtype=object))  # placeholder
+        columns = self.items
+
+        # error: Argument 1 to "ArrayManager" has incompatible type "List[ndarray]";
+        # expected "List[Union[ndarray, ExtensionArray]]"
+        new_mgr = type(self)(result_arrays, [index, columns])  # type: ignore[arg-type]
+        return new_mgr
+
+    def operate_blockwise(self, other: ArrayManager, array_op) -> ArrayManager:
+        """
+        Apply array_op blockwise with another (aligned) BlockManager.
+        """
+        # TODO what if `other` is BlockManager ?
+        left_arrays = self.arrays
+        right_arrays = other.arrays
+        result_arrays = [
+            array_op(left, right) for left, right in zip(left_arrays, right_arrays)
+        ]
+        return type(self)(result_arrays, self._axes)
+
+    def quantile(
+        self,
+        *,
+        qs: Index,  # with dtype float64
+        transposed: bool = False,
+        interpolation: QuantileInterpolation = "linear",
+    ) -> ArrayManager:
+        arrs = [ensure_block_shape(x, 2) for x in self.arrays]
+        new_arrs = [
+            quantile_compat(x, np.asarray(qs._values), interpolation) for x in arrs
+        ]
+        for i, arr in enumerate(new_arrs):
+            if arr.ndim == 2:
+                assert arr.shape[0] == 1, arr.shape
+                new_arrs[i] = arr[0]
+
+        axes = [qs, self._axes[1]]
+        return type(self)(new_arrs, axes)
+
+    # ----------------------------------------------------------------
+
+    def unstack(self, unstacker, fill_value) -> ArrayManager:
+        """
+        Return a BlockManager with all blocks unstacked.
+
+        Parameters
+        ----------
+        unstacker : reshape._Unstacker
+        fill_value : Any
+            fill_value for newly introduced missing values.
+
+        Returns
+        -------
+        unstacked : BlockManager
+        """
+        indexer, _ = unstacker._indexer_and_to_sort
+        if unstacker.mask.all():
+            new_indexer = indexer
+            allow_fill = False
+            new_mask2D = None
+            needs_masking = None
+        else:
+            new_indexer = np.full(unstacker.mask.shape, -1)
+            new_indexer[unstacker.mask] = indexer
+            allow_fill = True
+            # calculating the full mask once and passing it to take_1d is faster
+            # than letting take_1d calculate it in each repeated call
+            new_mask2D = (~unstacker.mask).reshape(*unstacker.full_shape)
+            needs_masking = new_mask2D.any(axis=0)
+        new_indexer2D = new_indexer.reshape(*unstacker.full_shape)
+        new_indexer2D = ensure_platform_int(new_indexer2D)
+
+        new_arrays = []
+        for arr in self.arrays:
+            for i in range(unstacker.full_shape[1]):
+                if allow_fill:
+                    # error: Value of type "Optional[Any]" is not indexable  [index]
+                    new_arr = take_1d(
+                        arr,
+                        new_indexer2D[:, i],
+                        allow_fill=needs_masking[i],  # type: ignore[index]
+                        fill_value=fill_value,
+                        mask=new_mask2D[:, i],  # type: ignore[index]
+                    )
+                else:
+                    new_arr = take_1d(arr, new_indexer2D[:, i], allow_fill=False)
+                new_arrays.append(new_arr)
+
+        new_index = unstacker.new_index
+        new_columns = unstacker.get_new_columns(self._axes[1])
+        new_axes = [new_index, new_columns]
+
+        return type(self)(new_arrays, new_axes, verify_integrity=False)
+
+    def as_array(
+        self,
+        dtype=None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert the blockmanager data into an numpy array.
+
+        Parameters
+        ----------
+        dtype : object, default None
+            Data type of the return array.
+        copy : bool, default False
+            If True then guarantee that a copy is returned. A value of
+            False does not guarantee that the underlying data is not
+            copied.
+        na_value : object, default lib.no_default
+            Value to be used as the missing value sentinel.
+
+        Returns
+        -------
+        arr : ndarray
+        """
+        if len(self.arrays) == 0:
+            empty_arr = np.empty(self.shape, dtype=float)
+            return empty_arr.transpose()
+
+        # We want to copy when na_value is provided to avoid
+        # mutating the original object
+        copy = copy or na_value is not lib.no_default
+
+        if not dtype:
+            dtype = interleaved_dtype([arr.dtype for arr in self.arrays])
+
+        dtype = ensure_np_dtype(dtype)
+
+        result = np.empty(self.shape_proper, dtype=dtype)
+
+        for i, arr in enumerate(self.arrays):
+            arr = arr.astype(dtype, copy=copy)
+            result[:, i] = arr
+
+        if na_value is not lib.no_default:
+            result[isna(result)] = na_value
+
+        return result
+
+    @classmethod
+    def concat_horizontal(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed ArrayManagers horizontally.
+        """
+        # concatting along the columns -> combine reindexed arrays in a single manager
+        arrays = list(itertools.chain.from_iterable([mgr.arrays for mgr in mgrs]))
+        new_mgr = cls(arrays, [axes[1], axes[0]], verify_integrity=False)
+        return new_mgr
+
+    @classmethod
+    def concat_vertical(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed ArrayManagers vertically.
+        """
+        # concatting along the rows -> concat the reindexed arrays
+        # TODO(ArrayManager) doesn't yet preserve the correct dtype
+        arrays = [
+            concat_arrays([mgrs[i].arrays[j] for i in range(len(mgrs))])
+            for j in range(len(mgrs[0].arrays))
+        ]
+        new_mgr = cls(arrays, [axes[1], axes[0]], verify_integrity=False)
+        return new_mgr
+
+
+class SingleArrayManager(BaseArrayManager, SingleDataManager):
+    __slots__ = [
+        "_axes",  # private attribute, because 'axes' has different order, see below
+        "arrays",
+    ]
+
+    arrays: list[np.ndarray | ExtensionArray]
+    _axes: list[Index]
+
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    def __init__(
+        self,
+        arrays: list[np.ndarray | ExtensionArray],
+        axes: list[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        self._axes = axes
+        self.arrays = arrays
+
+        if verify_integrity:
+            assert len(axes) == 1
+            assert len(arrays) == 1
+            self._axes = [ensure_index(ax) for ax in self._axes]
+            arr = arrays[0]
+            arr = maybe_coerce_values(arr)
+            arr = extract_pandas_array(arr, None, 1)[0]
+            self.arrays = [arr]
+            self._verify_integrity()
+
+    def _verify_integrity(self) -> None:
+        (n_rows,) = self.shape
+        assert len(self.arrays) == 1
+        arr = self.arrays[0]
+        assert len(arr) == n_rows
+        if not arr.ndim == 1:
+            raise ValueError(
+                "Passed array should be 1-dimensional, got array with "
+                f"{arr.ndim} dimensions instead."
+            )
+
+    @staticmethod
+    def _normalize_axis(axis):
+        return axis
+
+    def make_empty(self, axes=None) -> Self:
+        """Return an empty ArrayManager with index/array of length 0"""
+        if axes is None:
+            axes = [Index([], dtype=object)]
+        array: np.ndarray = np.array([], dtype=self.dtype)
+        return type(self)([array], axes)
+
+    @classmethod
+    def from_array(cls, array, index) -> SingleArrayManager:
+        return cls([array], [index])
+
+    # error: Cannot override writeable attribute with read-only property
+    @property
+    def axes(self) -> list[Index]:  # type: ignore[override]
+        return self._axes
+
+    @property
+    def index(self) -> Index:
+        return self._axes[0]
+
+    @property
+    def dtype(self):
+        return self.array.dtype
+
+    def external_values(self):
+        """The array that Series.values returns"""
+        return external_values(self.array)
+
+    def internal_values(self):
+        """The array that Series._values returns"""
+        return self.array
+
+    def array_values(self):
+        """The array that Series.array returns"""
+        arr = self.array
+        if isinstance(arr, np.ndarray):
+            arr = NumpyExtensionArray(arr)
+        return arr
+
+    @property
+    def _can_hold_na(self) -> bool:
+        if isinstance(self.array, np.ndarray):
+            return self.array.dtype.kind not in "iub"
+        else:
+            # ExtensionArray
+            return self.array._can_hold_na
+
+    @property
+    def is_single_block(self) -> bool:
+        return True
+
+    def fast_xs(self, loc: int) -> SingleArrayManager:
+        raise NotImplementedError("Use series._values[loc] instead")
+
+    def get_slice(self, slobj: slice, axis: AxisInt = 0) -> SingleArrayManager:
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        new_array = self.array[slobj]
+        new_index = self.index._getitem_slice(slobj)
+        return type(self)([new_array], [new_index], verify_integrity=False)
+
+    def get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> SingleArrayManager:
+        new_array = self.array[indexer]
+        new_index = self.index[indexer]
+        return type(self)([new_array], [new_index])
+
+    # error: Signature of "apply" incompatible with supertype "BaseArrayManager"
+    def apply(self, func, **kwargs) -> Self:  # type: ignore[override]
+        if callable(func):
+            new_array = func(self.array, **kwargs)
+        else:
+            new_array = getattr(self.array, func)(**kwargs)
+        return type(self)([new_array], self._axes)
+
+    def setitem(self, indexer, value, warn: bool = True) -> SingleArrayManager:
+        """
+        Set values with indexer.
+
+        For SingleArrayManager, this backs s[indexer] = value
+
+        See `setitem_inplace` for a version that works inplace and doesn't
+        return a new Manager.
+        """
+        if isinstance(indexer, np.ndarray) and indexer.ndim > self.ndim:
+            raise ValueError(f"Cannot set values with ndim > {self.ndim}")
+        return self.apply_with_block("setitem", indexer=indexer, value=value)
+
+    def idelete(self, indexer) -> SingleArrayManager:
+        """
+        Delete selected locations in-place (new array, same ArrayManager)
+        """
+        to_keep = np.ones(self.shape[0], dtype=np.bool_)
+        to_keep[indexer] = False
+
+        self.arrays = [self.arrays[0][to_keep]]
+        self._axes = [self._axes[0][to_keep]]
+        return self
+
+    def _get_data_subset(self, predicate: Callable) -> SingleArrayManager:
+        # used in get_numeric_data / get_bool_data
+        if predicate(self.array):
+            return type(self)(self.arrays, self._axes, verify_integrity=False)
+        else:
+            return self.make_empty()
+
+    def set_values(self, values: ArrayLike) -> None:
+        """
+        Set (replace) the values of the SingleArrayManager in place.
+
+        Use at your own risk! This does not check if the passed values are
+        valid for the current SingleArrayManager (length, dtype, etc).
+        """
+        self.arrays[0] = values
+
+    def to_2d_mgr(self, columns: Index) -> ArrayManager:
+        """
+        Manager analogue of Series.to_frame
+        """
+        arrays = [self.arrays[0]]
+        axes = [self.axes[0], columns]
+
+        return ArrayManager(arrays, axes, verify_integrity=False)
+
+
+class NullArrayProxy:
+    """
+    Proxy object for an all-NA array.
+
+    Only stores the length of the array, and not the dtype. The dtype
+    will only be known when actually concatenating (after determining the
+    common dtype, for which this proxy is ignored).
+    Using this object avoids that the internals/concat.py needs to determine
+    the proper dtype and array type.
+    """
+
+    ndim = 1
+
+    def __init__(self, n: int) -> None:
+        self.n = n
+
+    @property
+    def shape(self) -> tuple[int]:
+        return (self.n,)
+
+    def to_array(self, dtype: DtypeObj) -> ArrayLike:
+        """
+        Helper function to create the actual all-NA array from the NullArrayProxy
+        object.
+
+        Parameters
+        ----------
+        arr : NullArrayProxy
+        dtype : the dtype for the resulting array
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        if isinstance(dtype, ExtensionDtype):
+            empty = dtype.construct_array_type()._from_sequence([], dtype=dtype)
+            indexer = -np.ones(self.n, dtype=np.intp)
+            return empty.take(indexer, allow_fill=True)
+        else:
+            # when introducing missing values, int becomes float, bool becomes object
+            dtype = ensure_dtype_can_hold_na(dtype)
+            fill_value = na_value_for_dtype(dtype)
+            arr = np.empty(self.n, dtype=dtype)
+            arr.fill(fill_value)
+            return ensure_wrapped_if_datetimelike(arr)
+
+
+def concat_arrays(to_concat: list) -> ArrayLike:
+    """
+    Alternative for concat_compat but specialized for use in the ArrayManager.
+
+    Differences: only deals with 1D arrays (no axis keyword), assumes
+    ensure_wrapped_if_datetimelike and does not skip empty arrays to determine
+    the dtype.
+    In addition ensures that all NullArrayProxies get replaced with actual
+    arrays.
+
+    Parameters
+    ----------
+    to_concat : list of arrays
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    # ignore the all-NA proxies to determine the resulting dtype
+    to_concat_no_proxy = [x for x in to_concat if not isinstance(x, NullArrayProxy)]
+
+    dtypes = {x.dtype for x in to_concat_no_proxy}
+    single_dtype = len(dtypes) == 1
+
+    if single_dtype:
+        target_dtype = to_concat_no_proxy[0].dtype
+    elif all(lib.is_np_dtype(x, "iub") for x in dtypes):
+        # GH#42092
+        target_dtype = np_find_common_type(*dtypes)
+    else:
+        target_dtype = find_common_type([arr.dtype for arr in to_concat_no_proxy])
+
+    to_concat = [
+        arr.to_array(target_dtype)
+        if isinstance(arr, NullArrayProxy)
+        else astype_array(arr, target_dtype, copy=False)
+        for arr in to_concat
+    ]
+
+    if isinstance(to_concat[0], ExtensionArray):
+        cls = type(to_concat[0])
+        return cls._concat_same_type(to_concat)
+
+    result = np.concatenate(to_concat)
+
+    # TODO decide on exact behaviour (we shouldn't do this only for empty result)
+    # see https://github.com/pandas-dev/pandas/issues/39817
+    if len(result) == 0:
+        # all empties -> check for bool to not coerce to float
+        kinds = {obj.dtype.kind for obj in to_concat_no_proxy}
+        if len(kinds) != 1:
+            if "b" in kinds:
+                result = result.astype(object)
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae91f167205a0628c4bcf9b61ce58e888fe6ec8e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/base.py
@@ -0,0 +1,407 @@
+"""
+Base class for the internal managers. Both BlockManager and ArrayManager
+inherit from this class.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    final,
+)
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import (
+    algos as libalgos,
+    lib,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.cast import (
+    find_common_type,
+    np_can_hold_element,
+)
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    SparseDtype,
+)
+
+from pandas.core.base import PandasObject
+from pandas.core.construction import extract_array
+from pandas.core.indexes.api import (
+    Index,
+    default_index,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        Self,
+        Shape,
+    )
+
+
+class _AlreadyWarned:
+    def __init__(self):
+        # This class is used on the manager level to the block level to
+        # ensure that we warn only once. The block method can update the
+        # warned_already option without returning a value to keep the
+        # interface consistent. This is only a temporary solution for
+        # CoW warnings.
+        self.warned_already = False
+
+
+class DataManager(PandasObject):
+    # TODO share more methods/attributes
+
+    axes: list[Index]
+
+    @property
+    def items(self) -> Index:
+        raise AbstractMethodError(self)
+
+    @final
+    def __len__(self) -> int:
+        return len(self.items)
+
+    @property
+    def ndim(self) -> int:
+        return len(self.axes)
+
+    @property
+    def shape(self) -> Shape:
+        return tuple(len(ax) for ax in self.axes)
+
+    @final
+    def _validate_set_axis(self, axis: AxisInt, new_labels: Index) -> None:
+        # Caller is responsible for ensuring we have an Index object.
+        old_len = len(self.axes[axis])
+        new_len = len(new_labels)
+
+        if axis == 1 and len(self.items) == 0:
+            # If we are setting the index on a DataFrame with no columns,
+            #  it is OK to change the length.
+            pass
+
+        elif new_len != old_len:
+            raise ValueError(
+                f"Length mismatch: Expected axis has {old_len} elements, new "
+                f"values have {new_len} elements"
+            )
+
+    def reindex_indexer(
+        self,
+        new_axis,
+        indexer,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool = True,
+        only_slice: bool = False,
+    ) -> Self:
+        raise AbstractMethodError(self)
+
+    @final
+    def reindex_axis(
+        self,
+        new_index: Index,
+        axis: AxisInt,
+        fill_value=None,
+        only_slice: bool = False,
+    ) -> Self:
+        """
+        Conform data manager to new index.
+        """
+        new_index, indexer = self.axes[axis].reindex(new_index)
+
+        return self.reindex_indexer(
+            new_index,
+            indexer,
+            axis=axis,
+            fill_value=fill_value,
+            copy=False,
+            only_slice=only_slice,
+        )
+
+    def _equal_values(self, other: Self) -> bool:
+        """
+        To be implemented by the subclasses. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        raise AbstractMethodError(self)
+
+    @final
+    def equals(self, other: object) -> bool:
+        """
+        Implementation for DataFrame.equals
+        """
+        if not isinstance(other, type(self)):
+            return False
+
+        self_axes, other_axes = self.axes, other.axes
+        if len(self_axes) != len(other_axes):
+            return False
+        if not all(ax1.equals(ax2) for ax1, ax2 in zip(self_axes, other_axes)):
+            return False
+
+        return self._equal_values(other)
+
+    def apply(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        raise AbstractMethodError(self)
+
+    def apply_with_block(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        raise AbstractMethodError(self)
+
+    @final
+    def isna(self, func) -> Self:
+        return self.apply("apply", func=func)
+
+    @final
+    def fillna(self, value, limit: int | None, inplace: bool, downcast) -> Self:
+        if limit is not None:
+            # Do this validation even if we go through one of the no-op paths
+            limit = libalgos.validate_limit(None, limit=limit)
+
+        return self.apply_with_block(
+            "fillna",
+            value=value,
+            limit=limit,
+            inplace=inplace,
+            downcast=downcast,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    @final
+    def where(self, other, cond, align: bool) -> Self:
+        if align:
+            align_keys = ["other", "cond"]
+        else:
+            align_keys = ["cond"]
+            other = extract_array(other, extract_numpy=True)
+
+        return self.apply_with_block(
+            "where",
+            align_keys=align_keys,
+            other=other,
+            cond=cond,
+            using_cow=using_copy_on_write(),
+        )
+
+    @final
+    def putmask(self, mask, new, align: bool = True, warn: bool = True) -> Self:
+        if align:
+            align_keys = ["new", "mask"]
+        else:
+            align_keys = ["mask"]
+            new = extract_array(new, extract_numpy=True)
+
+        already_warned = None
+        if warn_copy_on_write():
+            already_warned = _AlreadyWarned()
+            if not warn:
+                already_warned.warned_already = True
+
+        return self.apply_with_block(
+            "putmask",
+            align_keys=align_keys,
+            mask=mask,
+            new=new,
+            using_cow=using_copy_on_write(),
+            already_warned=already_warned,
+        )
+
+    @final
+    def round(self, decimals: int, using_cow: bool = False) -> Self:
+        return self.apply_with_block(
+            "round",
+            decimals=decimals,
+            using_cow=using_cow,
+        )
+
+    @final
+    def replace(self, to_replace, value, inplace: bool) -> Self:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        # NDFrame.replace ensures the not-is_list_likes here
+        assert not lib.is_list_like(to_replace)
+        assert not lib.is_list_like(value)
+        return self.apply_with_block(
+            "replace",
+            to_replace=to_replace,
+            value=value,
+            inplace=inplace,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    @final
+    def replace_regex(self, **kwargs) -> Self:
+        return self.apply_with_block(
+            "_replace_regex",
+            **kwargs,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    @final
+    def replace_list(
+        self,
+        src_list: list[Any],
+        dest_list: list[Any],
+        inplace: bool = False,
+        regex: bool = False,
+    ) -> Self:
+        """do a list replace"""
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        bm = self.apply_with_block(
+            "replace_list",
+            src_list=src_list,
+            dest_list=dest_list,
+            inplace=inplace,
+            regex=regex,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+        bm._consolidate_inplace()
+        return bm
+
+    def interpolate(self, inplace: bool, **kwargs) -> Self:
+        return self.apply_with_block(
+            "interpolate",
+            inplace=inplace,
+            **kwargs,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    def pad_or_backfill(self, inplace: bool, **kwargs) -> Self:
+        return self.apply_with_block(
+            "pad_or_backfill",
+            inplace=inplace,
+            **kwargs,
+            using_cow=using_copy_on_write(),
+            already_warned=_AlreadyWarned(),
+        )
+
+    def shift(self, periods: int, fill_value) -> Self:
+        if fill_value is lib.no_default:
+            fill_value = None
+
+        return self.apply_with_block("shift", periods=periods, fill_value=fill_value)
+
+    # --------------------------------------------------------------------
+    # Consolidation: No-ops for all but BlockManager
+
+    def is_consolidated(self) -> bool:
+        return True
+
+    def consolidate(self) -> Self:
+        return self
+
+    def _consolidate_inplace(self) -> None:
+        return
+
+
+class SingleDataManager(DataManager):
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    @final
+    @property
+    def array(self) -> ArrayLike:
+        """
+        Quick access to the backing array of the Block or SingleArrayManager.
+        """
+        # error: "SingleDataManager" has no attribute "arrays"; maybe "array"
+        return self.arrays[0]  # type: ignore[attr-defined]
+
+    def setitem_inplace(self, indexer, value, warn: bool = True) -> None:
+        """
+        Set values with indexer.
+
+        For Single[Block/Array]Manager, this backs s[indexer] = value
+
+        This is an inplace version of `setitem()`, mutating the manager/values
+        in place, not returning a new Manager (and Block), and thus never changing
+        the dtype.
+        """
+        arr = self.array
+
+        # EAs will do this validation in their own __setitem__ methods.
+        if isinstance(arr, np.ndarray):
+            # Note: checking for ndarray instead of np.dtype means we exclude
+            #  dt64/td64, which do their own validation.
+            value = np_can_hold_element(arr.dtype, value)
+
+        if isinstance(value, np.ndarray) and value.ndim == 1 and len(value) == 1:
+            # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+            value = value[0, ...]
+
+        arr[indexer] = value
+
+    def grouped_reduce(self, func):
+        arr = self.array
+        res = func(arr)
+        index = default_index(len(res))
+
+        mgr = type(self).from_array(res, index)
+        return mgr
+
+    @classmethod
+    def from_array(cls, arr: ArrayLike, index: Index):
+        raise AbstractMethodError(cls)
+
+
+def interleaved_dtype(dtypes: list[DtypeObj]) -> DtypeObj | None:
+    """
+    Find the common dtype for `blocks`.
+
+    Parameters
+    ----------
+    blocks : List[DtypeObj]
+
+    Returns
+    -------
+    dtype : np.dtype, ExtensionDtype, or None
+        None is returned when `blocks` is empty.
+    """
+    if not len(dtypes):
+        return None
+
+    return find_common_type(dtypes)
+
+
+def ensure_np_dtype(dtype: DtypeObj) -> np.dtype:
+    # TODO: https://github.com/pandas-dev/pandas/issues/22791
+    # Give EAs some input on what happens here. Sparse needs this.
+    if isinstance(dtype, SparseDtype):
+        dtype = dtype.subtype
+        dtype = cast(np.dtype, dtype)
+    elif isinstance(dtype, ExtensionDtype):
+        dtype = np.dtype("object")
+    elif dtype == np.dtype(str):
+        dtype = np.dtype("object")
+    return dtype
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/blocks.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..452c919449ec40c042bdb68b249801f2267b2b19
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/blocks.py
@@ -0,0 +1,2923 @@
+from __future__ import annotations
+
+from functools import wraps
+import inspect
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    final,
+)
+import warnings
+import weakref
+
+import numpy as np
+
+from pandas._config import (
+    get_option,
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import (
+    NaT,
+    internals as libinternals,
+    lib,
+)
+from pandas._libs.internals import (
+    BlockPlacement,
+    BlockValuesRefs,
+)
+from pandas._libs.missing import NA
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    DtypeBackend,
+    DtypeObj,
+    F,
+    FillnaOptions,
+    IgnoreRaise,
+    InterpolateOptions,
+    QuantileInterpolation,
+    Self,
+    Shape,
+    npt,
+)
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
+
+from pandas.core.dtypes.astype import (
+    astype_array_safe,
+    astype_is_view,
+)
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    can_hold_element,
+    convert_dtypes,
+    find_result_type,
+    maybe_downcast_to_dtype,
+    np_can_hold_element,
+)
+from pandas.core.dtypes.common import (
+    is_1d_only_ea_dtype,
+    is_float_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    NumpyEADtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCNumpyExtensionArray,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_re
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas.core import missing
+import pandas.core.algorithms as algos
+from pandas.core.array_algos.putmask import (
+    extract_bool_array,
+    putmask_inplace,
+    putmask_without_repeat,
+    setitem_datetimelike_compat,
+    validate_putmask,
+)
+from pandas.core.array_algos.quantile import quantile_compat
+from pandas.core.array_algos.replace import (
+    compare_or_regex_search,
+    replace_regex,
+    should_use_regex,
+)
+from pandas.core.array_algos.transforms import shift
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    IntervalArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.computation import expressions
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import check_setitem_lengths
+from pandas.core.indexes.base import get_values_for_csv
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from pandas.core.api import Index
+    from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+
+# comparison is faster than is_object_dtype
+_dtype_obj = np.dtype("object")
+
+
+COW_WARNING_GENERAL_MSG = """\
+Setting a value on a view: behaviour will change in pandas 3.0.
+You are mutating a Series or DataFrame object, and currently this mutation will
+also have effect on other Series or DataFrame objects that share data with this
+object. In pandas 3.0 (with Copy-on-Write), updating one Series or DataFrame object
+will never modify another.
+"""
+
+
+COW_WARNING_SETITEM_MSG = """\
+Setting a value on a view: behaviour will change in pandas 3.0.
+Currently, the mutation will also have effect on the object that shares data
+with this object. For example, when setting a value in a Series that was
+extracted from a column of a DataFrame, that DataFrame will also be updated:
+
+    ser = df["col"]
+    ser[0] = 0     <--- in pandas 2, this also updates `df`
+
+In pandas 3.0 (with Copy-on-Write), updating one Series/DataFrame will never
+modify another, and thus in the example above, `df` will not be changed.
+"""
+
+
+def maybe_split(meth: F) -> F:
+    """
+    If we have a multi-column block, split and operate block-wise.  Otherwise
+    use the original method.
+    """
+
+    @wraps(meth)
+    def newfunc(self, *args, **kwargs) -> list[Block]:
+        if self.ndim == 1 or self.shape[0] == 1:
+            return meth(self, *args, **kwargs)
+        else:
+            # Split and operate column-by-column
+            return self.split_and_operate(meth, *args, **kwargs)
+
+    return cast(F, newfunc)
+
+
+class Block(PandasObject, libinternals.Block):
+    """
+    Canonical n-dimensional unit of homogeneous dtype contained in a pandas
+    data structure
+
+    Index-ignorant; let the container take care of that
+    """
+
+    values: np.ndarray | ExtensionArray
+    ndim: int
+    refs: BlockValuesRefs
+    __init__: Callable
+
+    __slots__ = ()
+    is_numeric = False
+
+    @final
+    @cache_readonly
+    def _validate_ndim(self) -> bool:
+        """
+        We validate dimension for blocks that can hold 2D values, which for now
+        means numpy dtypes or DatetimeTZDtype.
+        """
+        dtype = self.dtype
+        return not isinstance(dtype, ExtensionDtype) or isinstance(
+            dtype, DatetimeTZDtype
+        )
+
+    @final
+    @cache_readonly
+    def is_object(self) -> bool:
+        return self.values.dtype == _dtype_obj
+
+    @final
+    @cache_readonly
+    def is_extension(self) -> bool:
+        return not lib.is_np_dtype(self.values.dtype)
+
+    @final
+    @cache_readonly
+    def _can_consolidate(self) -> bool:
+        # We _could_ consolidate for DatetimeTZDtype but don't for now.
+        return not self.is_extension
+
+    @final
+    @cache_readonly
+    def _consolidate_key(self):
+        return self._can_consolidate, self.dtype.name
+
+    @final
+    @cache_readonly
+    def _can_hold_na(self) -> bool:
+        """
+        Can we store NA values in this Block?
+        """
+        dtype = self.dtype
+        if isinstance(dtype, np.dtype):
+            return dtype.kind not in "iub"
+        return dtype._can_hold_na
+
+    @final
+    @property
+    def is_bool(self) -> bool:
+        """
+        We can be bool if a) we are bool dtype or b) object dtype with bool objects.
+        """
+        return self.values.dtype == np.dtype(bool)
+
+    @final
+    def external_values(self):
+        return external_values(self.values)
+
+    @final
+    @cache_readonly
+    def fill_value(self):
+        # Used in reindex_indexer
+        return na_value_for_dtype(self.dtype, compat=False)
+
+    @final
+    def _standardize_fill_value(self, value):
+        # if we are passed a scalar None, convert it here
+        if self.dtype != _dtype_obj and is_valid_na_for_dtype(value, self.dtype):
+            value = self.fill_value
+        return value
+
+    @property
+    def mgr_locs(self) -> BlockPlacement:
+        return self._mgr_locs
+
+    @mgr_locs.setter
+    def mgr_locs(self, new_mgr_locs: BlockPlacement) -> None:
+        self._mgr_locs = new_mgr_locs
+
+    @final
+    def make_block(
+        self,
+        values,
+        placement: BlockPlacement | None = None,
+        refs: BlockValuesRefs | None = None,
+    ) -> Block:
+        """
+        Create a new block, with type inference propagate any values that are
+        not specified
+        """
+        if placement is None:
+            placement = self._mgr_locs
+        if self.is_extension:
+            values = ensure_block_shape(values, ndim=self.ndim)
+
+        return new_block(values, placement=placement, ndim=self.ndim, refs=refs)
+
+    @final
+    def make_block_same_class(
+        self,
+        values,
+        placement: BlockPlacement | None = None,
+        refs: BlockValuesRefs | None = None,
+    ) -> Self:
+        """Wrap given values in a block of same type as self."""
+        # Pre-2.0 we called ensure_wrapped_if_datetimelike because fastparquet
+        #  relied on it, as of 2.0 the caller is responsible for this.
+        if placement is None:
+            placement = self._mgr_locs
+
+        # We assume maybe_coerce_values has already been called
+        return type(self)(values, placement=placement, ndim=self.ndim, refs=refs)
+
+    @final
+    def __repr__(self) -> str:
+        # don't want to print out all of the items here
+        name = type(self).__name__
+        if self.ndim == 1:
+            result = f"{name}: {len(self)} dtype: {self.dtype}"
+        else:
+            shape = " x ".join([str(s) for s in self.shape])
+            result = f"{name}: {self.mgr_locs.indexer}, {shape}, dtype: {self.dtype}"
+
+        return result
+
+    @final
+    def __len__(self) -> int:
+        return len(self.values)
+
+    @final
+    def slice_block_columns(self, slc: slice) -> Self:
+        """
+        Perform __getitem__-like, return result as block.
+        """
+        new_mgr_locs = self._mgr_locs[slc]
+
+        new_values = self._slice(slc)
+        refs = self.refs
+        return type(self)(new_values, new_mgr_locs, self.ndim, refs=refs)
+
+    @final
+    def take_block_columns(self, indices: npt.NDArray[np.intp]) -> Self:
+        """
+        Perform __getitem__-like, return result as block.
+
+        Only supports slices that preserve dimensionality.
+        """
+        # Note: only called from is from internals.concat, and we can verify
+        #  that never happens with 1-column blocks, i.e. never for ExtensionBlock.
+
+        new_mgr_locs = self._mgr_locs[indices]
+
+        new_values = self._slice(indices)
+        return type(self)(new_values, new_mgr_locs, self.ndim, refs=None)
+
+    @final
+    def getitem_block_columns(
+        self, slicer: slice, new_mgr_locs: BlockPlacement, ref_inplace_op: bool = False
+    ) -> Self:
+        """
+        Perform __getitem__-like, return result as block.
+
+        Only supports slices that preserve dimensionality.
+        """
+        new_values = self._slice(slicer)
+        refs = self.refs if not ref_inplace_op or self.refs.has_reference() else None
+        return type(self)(new_values, new_mgr_locs, self.ndim, refs=refs)
+
+    @final
+    def _can_hold_element(self, element: Any) -> bool:
+        """require the same dtype as ourselves"""
+        element = extract_array(element, extract_numpy=True)
+        return can_hold_element(self.values, element)
+
+    @final
+    def should_store(self, value: ArrayLike) -> bool:
+        """
+        Should we set self.values[indexer] = value inplace or do we need to cast?
+
+        Parameters
+        ----------
+        value : np.ndarray or ExtensionArray
+
+        Returns
+        -------
+        bool
+        """
+        return value.dtype == self.dtype
+
+    # ---------------------------------------------------------------------
+    # Apply/Reduce and Helpers
+
+    @final
+    def apply(self, func, **kwargs) -> list[Block]:
+        """
+        apply the function to my values; return a block if we are not
+        one
+        """
+        result = func(self.values, **kwargs)
+
+        result = maybe_coerce_values(result)
+        return self._split_op_result(result)
+
+    @final
+    def reduce(self, func) -> list[Block]:
+        # We will apply the function and reshape the result into a single-row
+        #  Block with the same mgr_locs; squeezing will be done at a higher level
+        assert self.ndim == 2
+
+        result = func(self.values)
+
+        if self.values.ndim == 1:
+            res_values = result
+        else:
+            res_values = result.reshape(-1, 1)
+
+        nb = self.make_block(res_values)
+        return [nb]
+
+    @final
+    def _split_op_result(self, result: ArrayLike) -> list[Block]:
+        # See also: split_and_operate
+        if result.ndim > 1 and isinstance(result.dtype, ExtensionDtype):
+            # TODO(EA2D): unnecessary with 2D EAs
+            # if we get a 2D ExtensionArray, we need to split it into 1D pieces
+            nbs = []
+            for i, loc in enumerate(self._mgr_locs):
+                if not is_1d_only_ea_dtype(result.dtype):
+                    vals = result[i : i + 1]
+                else:
+                    vals = result[i]
+
+                bp = BlockPlacement(loc)
+                block = self.make_block(values=vals, placement=bp)
+                nbs.append(block)
+            return nbs
+
+        nb = self.make_block(result)
+
+        return [nb]
+
+    @final
+    def _split(self) -> list[Block]:
+        """
+        Split a block into a list of single-column blocks.
+        """
+        assert self.ndim == 2
+
+        new_blocks = []
+        for i, ref_loc in enumerate(self._mgr_locs):
+            vals = self.values[slice(i, i + 1)]
+
+            bp = BlockPlacement(ref_loc)
+            nb = type(self)(vals, placement=bp, ndim=2, refs=self.refs)
+            new_blocks.append(nb)
+        return new_blocks
+
+    @final
+    def split_and_operate(self, func, *args, **kwargs) -> list[Block]:
+        """
+        Split the block and apply func column-by-column.
+
+        Parameters
+        ----------
+        func : Block method
+        *args
+        **kwargs
+
+        Returns
+        -------
+        List[Block]
+        """
+        assert self.ndim == 2 and self.shape[0] != 1
+
+        res_blocks = []
+        for nb in self._split():
+            rbs = func(nb, *args, **kwargs)
+            res_blocks.extend(rbs)
+        return res_blocks
+
+    # ---------------------------------------------------------------------
+    # Up/Down-casting
+
+    @final
+    def coerce_to_target_dtype(
+        self, other, warn_on_upcast: bool = False, using_cow: bool = False
+    ) -> Block:
+        """
+        coerce the current block to a dtype compat for other
+        we will return a block, possibly object, and not raise
+
+        we can also safely try to coerce to the same dtype
+        and will receive the same block
+        """
+        new_dtype = find_result_type(self.values.dtype, other)
+        if new_dtype == self.dtype:
+            # GH#52927 avoid RecursionError
+            raise AssertionError(
+                "Something has gone wrong, please report a bug at "
+                "https://github.com/pandas-dev/pandas/issues"
+            )
+
+        # In a future version of pandas, the default will be that
+        # setting `nan` into an integer series won't raise.
+        if (
+            is_scalar(other)
+            and is_integer_dtype(self.values.dtype)
+            and isna(other)
+            and other is not NaT
+            and not (
+                isinstance(other, (np.datetime64, np.timedelta64)) and np.isnat(other)
+            )
+        ):
+            warn_on_upcast = False
+        elif (
+            isinstance(other, np.ndarray)
+            and other.ndim == 1
+            and is_integer_dtype(self.values.dtype)
+            and is_float_dtype(other.dtype)
+            and lib.has_only_ints_or_nan(other)
+        ):
+            warn_on_upcast = False
+
+        if warn_on_upcast:
+            warnings.warn(
+                f"Setting an item of incompatible dtype is deprecated "
+                "and will raise an error in a future version of pandas. "
+                f"Value '{other}' has dtype incompatible with {self.values.dtype}, "
+                "please explicitly cast to a compatible dtype first.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        if self.values.dtype == new_dtype:
+            raise AssertionError(
+                f"Did not expect new dtype {new_dtype} to equal self.dtype "
+                f"{self.values.dtype}. Please report a bug at "
+                "https://github.com/pandas-dev/pandas/issues."
+            )
+        copy = False
+        if (
+            not using_cow
+            and isinstance(self.dtype, StringDtype)
+            and self.dtype.storage == "python"
+        ):
+            copy = True
+        return self.astype(new_dtype, copy=copy, using_cow=using_cow)
+
+    @final
+    def _maybe_downcast(
+        self,
+        blocks: list[Block],
+        downcast,
+        using_cow: bool,
+        caller: str,
+    ) -> list[Block]:
+        if downcast is False:
+            return blocks
+
+        if self.dtype == _dtype_obj:
+            # TODO: does it matter that self.dtype might not match blocks[i].dtype?
+            # GH#44241 We downcast regardless of the argument;
+            #  respecting 'downcast=None' may be worthwhile at some point,
+            #  but ATM it breaks too much existing code.
+            # split and convert the blocks
+
+            if caller == "fillna" and get_option("future.no_silent_downcasting"):
+                return blocks
+
+            nbs = extend_blocks(
+                [
+                    blk.convert(
+                        using_cow=using_cow, copy=not using_cow, convert_string=False
+                    )
+                    for blk in blocks
+                ]
+            )
+            if caller == "fillna":
+                if len(nbs) != len(blocks) or not all(
+                    x.dtype == y.dtype for x, y in zip(nbs, blocks)
+                ):
+                    # GH#54261
+                    warnings.warn(
+                        "Downcasting object dtype arrays on .fillna, .ffill, .bfill "
+                        "is deprecated and will change in a future version. "
+                        "Call result.infer_objects(copy=False) instead. "
+                        "To opt-in to the future "
+                        "behavior, set "
+                        "`pd.set_option('future.no_silent_downcasting', True)`",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+
+            return nbs
+
+        elif downcast is None:
+            return blocks
+        elif caller == "where" and get_option("future.no_silent_downcasting") is True:
+            return blocks
+        else:
+            nbs = extend_blocks([b._downcast_2d(downcast, using_cow) for b in blocks])
+
+        # When _maybe_downcast is called with caller="where", it is either
+        #  a) with downcast=False, which is a no-op (the desired future behavior)
+        #  b) with downcast="infer", which is _not_ passed by the user.
+        # In the latter case the future behavior is to stop doing inference,
+        #  so we issue a warning if and only if some inference occurred.
+        if caller == "where":
+            # GH#53656
+            if len(blocks) != len(nbs) or any(
+                left.dtype != right.dtype for left, right in zip(blocks, nbs)
+            ):
+                # In this case _maybe_downcast was _not_ a no-op, so the behavior
+                #  will change, so we issue a warning.
+                warnings.warn(
+                    "Downcasting behavior in Series and DataFrame methods 'where', "
+                    "'mask', and 'clip' is deprecated. In a future "
+                    "version this will not infer object dtypes or cast all-round "
+                    "floats to integers. Instead call "
+                    "result.infer_objects(copy=False) for object inference, "
+                    "or cast round floats explicitly. To opt-in to the future "
+                    "behavior, set "
+                    "`pd.set_option('future.no_silent_downcasting', True)`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        return nbs
+
+    @final
+    @maybe_split
+    def _downcast_2d(self, dtype, using_cow: bool = False) -> list[Block]:
+        """
+        downcast specialized to 2D case post-validation.
+
+        Refactored to allow use of maybe_split.
+        """
+        new_values = maybe_downcast_to_dtype(self.values, dtype=dtype)
+        new_values = maybe_coerce_values(new_values)
+        refs = self.refs if new_values is self.values else None
+        return [self.make_block(new_values, refs=refs)]
+
+    @final
+    def convert(
+        self,
+        *,
+        copy: bool = True,
+        using_cow: bool = False,
+        convert_string: bool = True,
+    ) -> list[Block]:
+        """
+        Attempt to coerce any object types to better types. Return a copy
+        of the block (if copy = True).
+        """
+        if not self.is_object:
+            if not copy and using_cow:
+                return [self.copy(deep=False)]
+            return [self.copy()] if copy else [self]
+
+        if self.ndim != 1 and self.shape[0] != 1:
+            blocks = self.split_and_operate(
+                Block.convert,
+                copy=copy,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+            if all(blk.dtype.kind == "O" for blk in blocks):
+                # Avoid fragmenting the block if convert is a no-op
+                if using_cow:
+                    return [self.copy(deep=False)]
+                return [self.copy()] if copy else [self]
+            return blocks
+
+        values = self.values
+        if values.ndim == 2:
+            # the check above ensures we only get here with values.shape[0] == 1,
+            # avoid doing .ravel as that might make a copy
+            values = values[0]
+
+        res_values = lib.maybe_convert_objects(
+            values,  # type: ignore[arg-type]
+            convert_non_numeric=True,
+            convert_string=convert_string,
+        )
+        refs = None
+        if (
+            copy
+            and res_values is values
+            or isinstance(res_values, NumpyExtensionArray)
+            and res_values._ndarray is values
+        ):
+            res_values = res_values.copy()
+        elif res_values is values:
+            refs = self.refs
+
+        res_values = ensure_block_shape(res_values, self.ndim)
+        res_values = maybe_coerce_values(res_values)
+        return [self.make_block(res_values, refs=refs)]
+
+    def convert_dtypes(
+        self,
+        copy: bool,
+        using_cow: bool,
+        infer_objects: bool = True,
+        convert_string: bool = True,
+        convert_integer: bool = True,
+        convert_boolean: bool = True,
+        convert_floating: bool = True,
+        dtype_backend: DtypeBackend = "numpy_nullable",
+    ) -> list[Block]:
+        if infer_objects and self.is_object:
+            blks = self.convert(copy=False, using_cow=using_cow)
+        else:
+            blks = [self]
+
+        if not any(
+            [convert_floating, convert_integer, convert_boolean, convert_string]
+        ):
+            return [b.copy(deep=copy) for b in blks]
+
+        rbs = []
+        for blk in blks:
+            # Determine dtype column by column
+            sub_blks = [blk] if blk.ndim == 1 or self.shape[0] == 1 else blk._split()
+            dtypes = [
+                convert_dtypes(
+                    b.values,
+                    convert_string,
+                    convert_integer,
+                    convert_boolean,
+                    convert_floating,
+                    infer_objects,
+                    dtype_backend,
+                )
+                for b in sub_blks
+            ]
+            if all(dtype == self.dtype for dtype in dtypes):
+                # Avoid block splitting if no dtype changes
+                rbs.append(blk.copy(deep=copy))
+                continue
+
+            for dtype, b in zip(dtypes, sub_blks):
+                rbs.append(b.astype(dtype=dtype, copy=copy, squeeze=b.ndim != 1))
+        return rbs
+
+    # ---------------------------------------------------------------------
+    # Array-Like Methods
+
+    @final
+    @cache_readonly
+    def dtype(self) -> DtypeObj:
+        return self.values.dtype
+
+    @final
+    def astype(
+        self,
+        dtype: DtypeObj,
+        copy: bool = False,
+        errors: IgnoreRaise = "raise",
+        using_cow: bool = False,
+        squeeze: bool = False,
+    ) -> Block:
+        """
+        Coerce to the new dtype.
+
+        Parameters
+        ----------
+        dtype : np.dtype or ExtensionDtype
+        copy : bool, default False
+            copy if indicated
+        errors : str, {'raise', 'ignore'}, default 'raise'
+            - ``raise`` : allow exceptions to be raised
+            - ``ignore`` : suppress exceptions. On error return original object
+        using_cow: bool, default False
+            Signaling if copy on write copy logic is used.
+        squeeze : bool, default False
+            squeeze values to ndim=1 if only one column is given
+
+        Returns
+        -------
+        Block
+        """
+        values = self.values
+        if squeeze and values.ndim == 2 and is_1d_only_ea_dtype(dtype):
+            if values.shape[0] != 1:
+                raise ValueError("Can not squeeze with more than one column.")
+            values = values[0, :]  # type: ignore[call-overload]
+
+        new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
+
+        new_values = maybe_coerce_values(new_values)
+
+        refs = None
+        if (using_cow or not copy) and astype_is_view(values.dtype, new_values.dtype):
+            refs = self.refs
+
+        newb = self.make_block(new_values, refs=refs)
+        if newb.shape != self.shape:
+            raise TypeError(
+                f"cannot set astype for copy = [{copy}] for dtype "
+                f"({self.dtype.name} [{self.shape}]) to different shape "
+                f"({newb.dtype.name} [{newb.shape}])"
+            )
+        return newb
+
+    @final
+    def get_values_for_csv(
+        self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None
+    ) -> Block:
+        """convert to our native types format"""
+        result = get_values_for_csv(
+            self.values,
+            na_rep=na_rep,
+            quoting=quoting,
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+        )
+        return self.make_block(result)
+
+    @final
+    def copy(self, deep: bool = True) -> Self:
+        """copy constructor"""
+        values = self.values
+        refs: BlockValuesRefs | None
+        if deep:
+            values = values.copy()
+            refs = None
+        else:
+            refs = self.refs
+        return type(self)(values, placement=self._mgr_locs, ndim=self.ndim, refs=refs)
+
+    # ---------------------------------------------------------------------
+    # Copy-on-Write Helpers
+
+    @final
+    def _maybe_copy(self, using_cow: bool, inplace: bool) -> Self:
+        if using_cow and inplace:
+            deep = self.refs.has_reference()
+            blk = self.copy(deep=deep)
+        else:
+            blk = self if inplace else self.copy()
+        return blk
+
+    @final
+    def _get_refs_and_copy(self, using_cow: bool, inplace: bool):
+        refs = None
+        copy = not inplace
+        if inplace:
+            if using_cow and self.refs.has_reference():
+                copy = True
+            else:
+                refs = self.refs
+        return copy, refs
+
+    # ---------------------------------------------------------------------
+    # Replace
+
+    @final
+    def replace(
+        self,
+        to_replace,
+        value,
+        inplace: bool = False,
+        # mask may be pre-computed if we're called from replace_list
+        mask: npt.NDArray[np.bool_] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+        convert_string=None,
+    ) -> list[Block]:
+        """
+        replace the to_replace value with value, possible to create new
+        blocks here this is just a call to putmask.
+        """
+
+        # Note: the checks we do in NDFrame.replace ensure we never get
+        #  here with listlike to_replace or value, as those cases
+        #  go through replace_list
+        values = self.values
+
+        if isinstance(values, Categorical):
+            # TODO: avoid special-casing
+            # GH49404
+            blk = self._maybe_copy(using_cow, inplace)
+            values = cast(Categorical, blk.values)
+            values._replace(to_replace=to_replace, value=value, inplace=True)
+            return [blk]
+
+        if not self._can_hold_element(to_replace):
+            # We cannot hold `to_replace`, so we know immediately that
+            #  replacing it is a no-op.
+            # Note: If to_replace were a list, NDFrame.replace would call
+            #  replace_list instead of replace.
+            if using_cow:
+                return [self.copy(deep=False)]
+            else:
+                return [self] if inplace else [self.copy()]
+
+        if mask is None:
+            mask = missing.mask_missing(values, to_replace)
+        if not mask.any():
+            # Note: we get here with test_replace_extension_other incorrectly
+            #  bc _can_hold_element is incorrect.
+            if using_cow:
+                return [self.copy(deep=False)]
+            else:
+                return [self] if inplace else [self.copy()]
+
+        elif self._can_hold_element(value) or (self.dtype == "string" and is_re(value)):
+            # TODO(CoW): Maybe split here as well into columns where mask has True
+            # and rest?
+            blk = self._maybe_copy(using_cow, inplace)
+            putmask_inplace(blk.values, mask, value)
+            if (
+                inplace
+                and warn_copy_on_write()
+                and already_warned is not None
+                and not already_warned.warned_already
+            ):
+                if self.refs.has_reference():
+                    warnings.warn(
+                        COW_WARNING_GENERAL_MSG,
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    already_warned.warned_already = True
+
+            if not (self.is_object and value is None):
+                # if the user *explicitly* gave None, we keep None, otherwise
+                #  may downcast to NaN
+                if get_option("future.no_silent_downcasting") is True:
+                    blocks = [blk]
+                else:
+                    blocks = blk.convert(
+                        copy=False,
+                        using_cow=using_cow,
+                        convert_string=convert_string or self.dtype == "string",
+                    )
+                    if len(blocks) > 1 or blocks[0].dtype != blk.dtype:
+                        warnings.warn(
+                            # GH#54710
+                            "Downcasting behavior in `replace` is deprecated and "
+                            "will be removed in a future version. To retain the old "
+                            "behavior, explicitly call "
+                            "`result.infer_objects(copy=False)`. "
+                            "To opt-in to the future "
+                            "behavior, set "
+                            "`pd.set_option('future.no_silent_downcasting', True)`",
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
+            else:
+                blocks = [blk]
+            return blocks
+
+        elif self.ndim == 1 or self.shape[0] == 1:
+            if value is None or value is NA:
+                blk = self.astype(np.dtype(object))
+            else:
+                blk = self.coerce_to_target_dtype(value, using_cow=using_cow)
+            return blk.replace(
+                to_replace=to_replace,
+                value=value,
+                inplace=True,
+                mask=mask,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+
+        else:
+            # split so that we only upcast where necessary
+            blocks = []
+            for i, nb in enumerate(self._split()):
+                blocks.extend(
+                    type(self).replace(
+                        nb,
+                        to_replace=to_replace,
+                        value=value,
+                        inplace=True,
+                        mask=mask[i : i + 1],
+                        using_cow=using_cow,
+                        convert_string=convert_string,
+                    )
+                )
+            return blocks
+
+    @final
+    def _replace_regex(
+        self,
+        to_replace,
+        value,
+        inplace: bool = False,
+        mask=None,
+        using_cow: bool = False,
+        convert_string=None,
+        already_warned=None,
+    ) -> list[Block]:
+        """
+        Replace elements by the given value.
+
+        Parameters
+        ----------
+        to_replace : object or pattern
+            Scalar to replace or regular expression to match.
+        value : object
+            Replacement object.
+        inplace : bool, default False
+            Perform inplace modification.
+        mask : array-like of bool, optional
+            True indicate corresponding element is ignored.
+        using_cow: bool, default False
+            Specifying if copy on write is enabled.
+
+        Returns
+        -------
+        List[Block]
+        """
+        if not is_re(to_replace) and not self._can_hold_element(to_replace):
+            # i.e. only if self.is_object is True, but could in principle include a
+            #  String ExtensionBlock
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        if is_re(to_replace) and self.dtype not in [object, "string"]:
+            # only object or string dtype can hold strings, and a regex object
+            # will only match strings
+            return [self.copy(deep=False)]
+
+        if not (
+            self._can_hold_element(value) or (self.dtype == "string" and is_re(value))
+        ):
+            block = self.astype(np.dtype(object))
+        else:
+            block = self._maybe_copy(using_cow, inplace)
+
+        rx = re.compile(to_replace)
+
+        replace_regex(block.values, rx, value, mask)
+
+        if (
+            inplace
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        nbs = block.convert(
+            copy=False,
+            using_cow=using_cow,
+            convert_string=convert_string or self.dtype == "string",
+        )
+        opt = get_option("future.no_silent_downcasting")
+        if (
+            len(nbs) > 1
+            or (
+                nbs[0].dtype != block.dtype
+                and not (self.dtype == "string" and nbs[0].dtype == "string")
+            )
+        ) and not opt:
+            warnings.warn(
+                # GH#54710
+                "Downcasting behavior in `replace` is deprecated and "
+                "will be removed in a future version. To retain the old "
+                "behavior, explicitly call `result.infer_objects(copy=False)`. "
+                "To opt-in to the future "
+                "behavior, set "
+                "`pd.set_option('future.no_silent_downcasting', True)`",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return nbs
+
+    @final
+    def replace_list(
+        self,
+        src_list: Iterable[Any],
+        dest_list: Sequence[Any],
+        inplace: bool = False,
+        regex: bool = False,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        """
+        See BlockManager.replace_list docstring.
+        """
+        values = self.values
+
+        if isinstance(values, Categorical):
+            # TODO: avoid special-casing
+            # GH49404
+            blk = self._maybe_copy(using_cow, inplace)
+            values = cast(Categorical, blk.values)
+            values._replace(to_replace=src_list, value=dest_list, inplace=True)
+            return [blk]
+
+        convert_string = self.dtype == "string"
+
+        # Exclude anything that we know we won't contain
+        pairs = [
+            (x, y)
+            for x, y in zip(src_list, dest_list)
+            if (self._can_hold_element(x) or (self.dtype == "string" and is_re(x)))
+        ]
+        if not len(pairs):
+            if using_cow:
+                return [self.copy(deep=False)]
+            # shortcut, nothing to replace
+            return [self] if inplace else [self.copy()]
+
+        src_len = len(pairs) - 1
+
+        if is_string_dtype(values.dtype):
+            # Calculate the mask once, prior to the call of comp
+            # in order to avoid repeating the same computations
+            na_mask = ~isna(values)
+            masks: Iterable[npt.NDArray[np.bool_]] = (
+                extract_bool_array(
+                    cast(
+                        ArrayLike,
+                        compare_or_regex_search(
+                            values, s[0], regex=regex, mask=na_mask
+                        ),
+                    )
+                )
+                for s in pairs
+            )
+        else:
+            # GH#38086 faster if we know we dont need to check for regex
+            masks = (missing.mask_missing(values, s[0]) for s in pairs)
+        # Materialize if inplace = True, since the masks can change
+        # as we replace
+        if inplace:
+            masks = list(masks)
+
+        if using_cow:
+            # Don't set up refs here, otherwise we will think that we have
+            # references when we check again later
+            rb = [self]
+        else:
+            rb = [self if inplace else self.copy()]
+
+        if (
+            inplace
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        opt = get_option("future.no_silent_downcasting")
+        for i, ((src, dest), mask) in enumerate(zip(pairs, masks)):
+            convert = i == src_len  # only convert once at the end
+            new_rb: list[Block] = []
+
+            # GH-39338: _replace_coerce can split a block into
+            # single-column blocks, so track the index so we know
+            # where to index into the mask
+            for blk_num, blk in enumerate(rb):
+                if len(rb) == 1:
+                    m = mask
+                else:
+                    mib = mask
+                    assert not isinstance(mib, bool)
+                    m = mib[blk_num : blk_num + 1]
+
+                # error: Argument "mask" to "_replace_coerce" of "Block" has
+                # incompatible type "Union[ExtensionArray, ndarray[Any, Any], bool]";
+                # expected "ndarray[Any, dtype[bool_]]"
+                result = blk._replace_coerce(
+                    to_replace=src,
+                    value=dest,
+                    mask=m,
+                    inplace=inplace,
+                    regex=regex,
+                    using_cow=using_cow,
+                    convert_string=convert_string,
+                )
+
+                if using_cow and i != src_len:
+                    # This is ugly, but we have to get rid of intermediate refs
+                    # that did not go out of scope yet, otherwise we will trigger
+                    # many unnecessary copies
+                    for b in result:
+                        ref = weakref.ref(b)
+                        b.refs.referenced_blocks.pop(
+                            b.refs.referenced_blocks.index(ref)
+                        )
+
+                if (
+                    not opt
+                    and convert
+                    and blk.is_object
+                    and not all(x is None for x in dest_list)
+                ):
+                    # GH#44498 avoid unwanted cast-back
+                    nbs = []
+                    for res_blk in result:
+                        converted = res_blk.convert(
+                            copy=True and not using_cow,
+                            using_cow=using_cow,
+                            convert_string=convert_string,
+                        )
+                        if len(converted) > 1 or converted[0].dtype != res_blk.dtype:
+                            warnings.warn(
+                                # GH#54710
+                                "Downcasting behavior in `replace` is deprecated "
+                                "and will be removed in a future version. To "
+                                "retain the old behavior, explicitly call "
+                                "`result.infer_objects(copy=False)`. "
+                                "To opt-in to the future "
+                                "behavior, set "
+                                "`pd.set_option('future.no_silent_downcasting', True)`",
+                                FutureWarning,
+                                stacklevel=find_stack_level(),
+                            )
+                        nbs.extend(converted)
+                    result = nbs
+                new_rb.extend(result)
+            rb = new_rb
+        return rb
+
+    @final
+    def _replace_coerce(
+        self,
+        to_replace,
+        value,
+        mask: npt.NDArray[np.bool_],
+        inplace: bool = True,
+        regex: bool = False,
+        using_cow: bool = False,
+        convert_string: bool = True,
+    ) -> list[Block]:
+        """
+        Replace value corresponding to the given boolean array with another
+        value.
+
+        Parameters
+        ----------
+        to_replace : object or pattern
+            Scalar to replace or regular expression to match.
+        value : object
+            Replacement object.
+        mask : np.ndarray[bool]
+            True indicate corresponding element is ignored.
+        inplace : bool, default True
+            Perform inplace modification.
+        regex : bool, default False
+            If true, perform regular expression substitution.
+
+        Returns
+        -------
+        List[Block]
+        """
+        if should_use_regex(regex, to_replace):
+            return self._replace_regex(
+                to_replace,
+                value,
+                inplace=inplace,
+                mask=mask,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+        else:
+            if value is None:
+                # gh-45601, gh-45836, gh-46634
+                if mask.any():
+                    has_ref = self.refs.has_reference()
+                    nb = self.astype(np.dtype(object), copy=False, using_cow=using_cow)
+                    if (nb is self or using_cow) and not inplace:
+                        nb = nb.copy()
+                    elif inplace and has_ref and nb.refs.has_reference() and using_cow:
+                        # no copy in astype and we had refs before
+                        nb = nb.copy()
+                    putmask_inplace(nb.values, mask, value)
+                    return [nb]
+                if using_cow:
+                    return [self.copy(deep=False)]
+                return [self] if inplace else [self.copy()]
+            return self.replace(
+                to_replace=to_replace,
+                value=value,
+                inplace=inplace,
+                mask=mask,
+                using_cow=using_cow,
+                convert_string=convert_string,
+            )
+
+    # ---------------------------------------------------------------------
+    # 2D Methods - Shared by NumpyBlock and NDArrayBackedExtensionBlock
+    #  but not ExtensionBlock
+
+    def _maybe_squeeze_arg(self, arg: np.ndarray) -> np.ndarray:
+        """
+        For compatibility with 1D-only ExtensionArrays.
+        """
+        return arg
+
+    def _unwrap_setitem_indexer(self, indexer):
+        """
+        For compatibility with 1D-only ExtensionArrays.
+        """
+        return indexer
+
+    # NB: this cannot be made cache_readonly because in mgr.set_values we pin
+    #  new .values that can have different shape GH#42631
+    @property
+    def shape(self) -> Shape:
+        return self.values.shape
+
+    def iget(self, i: int | tuple[int, int] | tuple[slice, int]) -> np.ndarray:
+        # In the case where we have a tuple[slice, int], the slice will always
+        #  be slice(None)
+        # Note: only reached with self.ndim == 2
+        # Invalid index type "Union[int, Tuple[int, int], Tuple[slice, int]]"
+        # for "Union[ndarray[Any, Any], ExtensionArray]"; expected type
+        # "Union[int, integer[Any]]"
+        return self.values[i]  # type: ignore[index]
+
+    def _slice(
+        self, slicer: slice | npt.NDArray[np.bool_] | npt.NDArray[np.intp]
+    ) -> ArrayLike:
+        """return a slice of my values"""
+
+        return self.values[slicer]
+
+    def set_inplace(self, locs, values: ArrayLike, copy: bool = False) -> None:
+        """
+        Modify block values in-place with new item value.
+
+        If copy=True, first copy the underlying values in place before modifying
+        (for Copy-on-Write).
+
+        Notes
+        -----
+        `set_inplace` never creates a new array or new Block, whereas `setitem`
+        _may_ create a new array and always creates a new Block.
+
+        Caller is responsible for checking values.dtype == self.dtype.
+        """
+        if copy:
+            self.values = self.values.copy()
+        self.values[locs] = values
+
+    @final
+    def take_nd(
+        self,
+        indexer: npt.NDArray[np.intp],
+        axis: AxisInt,
+        new_mgr_locs: BlockPlacement | None = None,
+        fill_value=lib.no_default,
+    ) -> Block:
+        """
+        Take values according to indexer and return them as a block.
+        """
+        values = self.values
+
+        if fill_value is lib.no_default:
+            fill_value = self.fill_value
+            allow_fill = False
+        else:
+            allow_fill = True
+
+        # Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype
+        new_values = algos.take_nd(
+            values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value
+        )
+
+        # Called from three places in managers, all of which satisfy
+        #  these assertions
+        if isinstance(self, ExtensionBlock):
+            # NB: in this case, the 'axis' kwarg will be ignored in the
+            #  algos.take_nd call above.
+            assert not (self.ndim == 1 and new_mgr_locs is None)
+        assert not (axis == 0 and new_mgr_locs is None)
+
+        if new_mgr_locs is None:
+            new_mgr_locs = self._mgr_locs
+
+        if new_values.dtype != self.dtype:
+            return self.make_block(new_values, new_mgr_locs)
+        else:
+            return self.make_block_same_class(new_values, new_mgr_locs)
+
+    def _unstack(
+        self,
+        unstacker,
+        fill_value,
+        new_placement: npt.NDArray[np.intp],
+        needs_masking: npt.NDArray[np.bool_],
+    ):
+        """
+        Return a list of unstacked blocks of self
+
+        Parameters
+        ----------
+        unstacker : reshape._Unstacker
+        fill_value : int
+            Only used in ExtensionBlock._unstack
+        new_placement : np.ndarray[np.intp]
+        allow_fill : bool
+        needs_masking : np.ndarray[bool]
+
+        Returns
+        -------
+        blocks : list of Block
+            New blocks of unstacked values.
+        mask : array-like of bool
+            The mask of columns of `blocks` we should keep.
+        """
+        new_values, mask = unstacker.get_new_values(
+            self.values.T, fill_value=fill_value
+        )
+
+        mask = mask.any(0)
+        # TODO: in all tests we have mask.all(); can we rely on that?
+
+        # Note: these next two lines ensure that
+        #  mask.sum() == sum(len(nb.mgr_locs) for nb in blocks)
+        #  which the calling function needs in order to pass verify_integrity=False
+        #  to the BlockManager constructor
+        new_values = new_values.T[mask]
+        new_placement = new_placement[mask]
+
+        bp = BlockPlacement(new_placement)
+        blocks = [new_block_2d(new_values, placement=bp)]
+        return blocks, mask
+
+    # ---------------------------------------------------------------------
+
+    def setitem(self, indexer, value, using_cow: bool = False) -> Block:
+        """
+        Attempt self.values[indexer] = value, possibly creating a new array.
+
+        Parameters
+        ----------
+        indexer : tuple, list-like, array-like, slice, int
+            The subset of self.values to set
+        value : object
+            The value being set
+        using_cow: bool, default False
+            Signaling if CoW is used.
+
+        Returns
+        -------
+        Block
+
+        Notes
+        -----
+        `indexer` is a direct slice/positional indexer. `value` must
+        be a compatible shape.
+        """
+
+        value = self._standardize_fill_value(value)
+
+        values = cast(np.ndarray, self.values)
+        if self.ndim == 2:
+            values = values.T
+
+        # length checking
+        check_setitem_lengths(indexer, value, values)
+
+        if self.dtype != _dtype_obj:
+            # GH48933: extract_array would convert a pd.Series value to np.ndarray
+            value = extract_array(value, extract_numpy=True)
+        try:
+            casted = np_can_hold_element(values.dtype, value)
+        except LossySetitemError:
+            # current dtype cannot store value, coerce to common dtype
+            nb = self.coerce_to_target_dtype(value, warn_on_upcast=True)
+            return nb.setitem(indexer, value)
+        else:
+            if self.dtype == _dtype_obj:
+                # TODO: avoid having to construct values[indexer]
+                vi = values[indexer]
+                if lib.is_list_like(vi):
+                    # checking lib.is_scalar here fails on
+                    #  test_iloc_setitem_custom_object
+                    casted = setitem_datetimelike_compat(values, len(vi), casted)
+
+            self = self._maybe_copy(using_cow, inplace=True)
+            values = cast(np.ndarray, self.values.T)
+            if isinstance(casted, np.ndarray) and casted.ndim == 1 and len(casted) == 1:
+                # NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
+                casted = casted[0, ...]
+            try:
+                values[indexer] = casted
+            except (TypeError, ValueError) as err:
+                if is_list_like(casted):
+                    raise ValueError(
+                        "setting an array element with a sequence."
+                    ) from err
+                raise
+        return self
+
+    def putmask(
+        self, mask, new, using_cow: bool = False, already_warned=None
+    ) -> list[Block]:
+        """
+        putmask the data to the block; it is possible that we may create a
+        new dtype of block
+
+        Return the resulting block(s).
+
+        Parameters
+        ----------
+        mask : np.ndarray[bool], SparseArray[bool], or BooleanArray
+        new : a ndarray/object
+        using_cow: bool, default False
+
+        Returns
+        -------
+        List[Block]
+        """
+        orig_mask = mask
+        values = cast(np.ndarray, self.values)
+        mask, noop = validate_putmask(values.T, mask)
+        assert not isinstance(new, (ABCIndex, ABCSeries, ABCDataFrame))
+
+        if new is lib.no_default:
+            new = self.fill_value
+
+        new = self._standardize_fill_value(new)
+        new = extract_array(new, extract_numpy=True)
+
+        if noop:
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self]
+
+        if (
+            warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        try:
+            casted = np_can_hold_element(values.dtype, new)
+
+            self = self._maybe_copy(using_cow, inplace=True)
+            values = cast(np.ndarray, self.values)
+
+            putmask_without_repeat(values.T, mask, casted)
+            return [self]
+        except LossySetitemError:
+            if self.ndim == 1 or self.shape[0] == 1:
+                # no need to split columns
+
+                if not is_list_like(new):
+                    # using just new[indexer] can't save us the need to cast
+                    return self.coerce_to_target_dtype(
+                        new, warn_on_upcast=True
+                    ).putmask(mask, new)
+                else:
+                    indexer = mask.nonzero()[0]
+                    nb = self.setitem(indexer, new[indexer], using_cow=using_cow)
+                    return [nb]
+
+            else:
+                is_array = isinstance(new, np.ndarray)
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = new
+                    if is_array:
+                        # we have a different value per-column
+                        n = new[:, i : i + 1]
+
+                    submask = orig_mask[:, i : i + 1]
+                    rbs = nb.putmask(submask, n, using_cow=using_cow)
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+    def where(
+        self, other, cond, _downcast: str | bool = "infer", using_cow: bool = False
+    ) -> list[Block]:
+        """
+        evaluate the block; return result block(s) from the result
+
+        Parameters
+        ----------
+        other : a ndarray/object
+        cond : np.ndarray[bool], SparseArray[bool], or BooleanArray
+        _downcast : str or None, default "infer"
+            Private because we only specify it when calling from fillna.
+
+        Returns
+        -------
+        List[Block]
+        """
+        assert cond.ndim == self.ndim
+        assert not isinstance(other, (ABCIndex, ABCSeries, ABCDataFrame))
+
+        transpose = self.ndim == 2
+
+        cond = extract_bool_array(cond)
+
+        # EABlocks override where
+        values = cast(np.ndarray, self.values)
+        orig_other = other
+        if transpose:
+            values = values.T
+
+        icond, noop = validate_putmask(values, ~cond)
+        if noop:
+            # GH-39595: Always return a copy; short-circuit up/downcasting
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self.copy()]
+
+        if other is lib.no_default:
+            other = self.fill_value
+
+        other = self._standardize_fill_value(other)
+
+        try:
+            # try/except here is equivalent to a self._can_hold_element check,
+            #  but this gets us back 'casted' which we will reuse below;
+            #  without using 'casted', expressions.where may do unwanted upcasts.
+            casted = np_can_hold_element(values.dtype, other)
+        except (ValueError, TypeError, LossySetitemError):
+            # we cannot coerce, return a compat dtype
+
+            if self.ndim == 1 or self.shape[0] == 1:
+                # no need to split columns
+
+                block = self.coerce_to_target_dtype(other)
+                blocks = block.where(orig_other, cond, using_cow=using_cow)
+                return self._maybe_downcast(
+                    blocks, downcast=_downcast, using_cow=using_cow, caller="where"
+                )
+
+            else:
+                # since _maybe_downcast would split blocks anyway, we
+                #  can avoid some potential upcast/downcast by splitting
+                #  on the front end.
+                is_array = isinstance(other, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    oth = other
+                    if is_array:
+                        # we have a different value per-column
+                        oth = other[:, i : i + 1]
+
+                    submask = cond[:, i : i + 1]
+                    rbs = nb.where(
+                        oth, submask, _downcast=_downcast, using_cow=using_cow
+                    )
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+        else:
+            other = casted
+            alt = setitem_datetimelike_compat(values, icond.sum(), other)
+            if alt is not other:
+                if is_list_like(other) and len(other) < len(values):
+                    # call np.where with other to get the appropriate ValueError
+                    np.where(~icond, values, other)
+                    raise NotImplementedError(
+                        "This should not be reached; call to np.where above is "
+                        "expected to raise ValueError. Please report a bug at "
+                        "github.com/pandas-dev/pandas"
+                    )
+                result = values.copy()
+                np.putmask(result, icond, alt)
+            else:
+                # By the time we get here, we should have all Series/Index
+                #  args extracted to ndarray
+                if (
+                    is_list_like(other)
+                    and not isinstance(other, np.ndarray)
+                    and len(other) == self.shape[-1]
+                ):
+                    # If we don't do this broadcasting here, then expressions.where
+                    #  will broadcast a 1D other to be row-like instead of
+                    #  column-like.
+                    other = np.array(other).reshape(values.shape)
+                    # If lengths don't match (or len(other)==1), we will raise
+                    #  inside expressions.where, see test_series_where
+
+                # Note: expressions.where may upcast.
+                result = expressions.where(~icond, values, other)
+                # The np_can_hold_element check _should_ ensure that we always
+                #  have result.dtype == self.dtype here.
+
+        if transpose:
+            result = result.T
+
+        return [self.make_block(result)]
+
+    def fillna(
+        self,
+        value,
+        limit: int | None = None,
+        inplace: bool = False,
+        downcast=None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        """
+        fillna on the block with the value. If we fail, then convert to
+        block to hold objects instead and try again
+        """
+        # Caller is responsible for validating limit; if int it is strictly positive
+        inplace = validate_bool_kwarg(inplace, "inplace")
+
+        if not self._can_hold_na:
+            # can short-circuit the isna call
+            noop = True
+        else:
+            mask = isna(self.values)
+            mask, noop = validate_putmask(self.values, mask)
+
+        if noop:
+            # we can't process the value, but nothing to do
+            if inplace:
+                if using_cow:
+                    return [self.copy(deep=False)]
+                # Arbitrarily imposing the convention that we ignore downcast
+                #  on no-op when inplace=True
+                return [self]
+            else:
+                # GH#45423 consistent downcasting on no-ops.
+                nb = self.copy(deep=not using_cow)
+                nbs = nb._maybe_downcast(
+                    [nb], downcast=downcast, using_cow=using_cow, caller="fillna"
+                )
+                return nbs
+
+        if limit is not None:
+            mask[mask.cumsum(self.values.ndim - 1) > limit] = False
+
+        if inplace:
+            nbs = self.putmask(
+                mask.T, value, using_cow=using_cow, already_warned=already_warned
+            )
+        else:
+            # without _downcast, we would break
+            #  test_fillna_dtype_conversion_equiv_replace
+            nbs = self.where(value, ~mask.T, _downcast=False)
+
+        # Note: blk._maybe_downcast vs self._maybe_downcast(nbs)
+        #  makes a difference bc blk may have object dtype, which has
+        #  different behavior in _maybe_downcast.
+        return extend_blocks(
+            [
+                blk._maybe_downcast(
+                    [blk], downcast=downcast, using_cow=using_cow, caller="fillna"
+                )
+                for blk in nbs
+            ]
+        )
+
+    def pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        axis: AxisInt = 0,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        if not self._can_hold_na:
+            # If there are no NAs, then interpolate is a no-op
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        copy, refs = self._get_refs_and_copy(using_cow, inplace)
+
+        # Dispatch to the NumpyExtensionArray method.
+        # We know self.array_values is a NumpyExtensionArray bc EABlock overrides
+        vals = cast(NumpyExtensionArray, self.array_values)
+        if axis == 1:
+            vals = vals.T
+        new_values = vals._pad_or_backfill(
+            method=method,
+            limit=limit,
+            limit_area=limit_area,
+            copy=copy,
+        )
+        if (
+            not copy
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+        if axis == 1:
+            new_values = new_values.T
+
+        data = extract_array(new_values, extract_numpy=True)
+
+        nb = self.make_block_same_class(data, refs=refs)
+        return nb._maybe_downcast([nb], downcast, using_cow, caller="fillna")
+
+    @final
+    def interpolate(
+        self,
+        *,
+        method: InterpolateOptions,
+        index: Index,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_direction: Literal["forward", "backward", "both"] = "forward",
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+        **kwargs,
+    ) -> list[Block]:
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        # error: Non-overlapping equality check [...]
+        if method == "asfreq":  # type: ignore[comparison-overlap]
+            # clean_fill_method used to allow this
+            missing.clean_fill_method(method)
+
+        if not self._can_hold_na:
+            # If there are no NAs, then interpolate is a no-op
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        # TODO(3.0): this case will not be reachable once GH#53638 is enforced
+        if self.dtype == _dtype_obj:
+            # only deal with floats
+            # bc we already checked that can_hold_na, we don't have int dtype here
+            # test_interp_basic checks that we make a copy here
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self] if inplace else [self.copy()]
+
+        copy, refs = self._get_refs_and_copy(using_cow, inplace)
+
+        # Dispatch to the EA method.
+        new_values = self.array_values.interpolate(
+            method=method,
+            axis=self.ndim - 1,
+            index=index,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            copy=copy,
+            **kwargs,
+        )
+        data = extract_array(new_values, extract_numpy=True)
+
+        if (
+            not copy
+            and warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        nb = self.make_block_same_class(data, refs=refs)
+        return nb._maybe_downcast([nb], downcast, using_cow, caller="interpolate")
+
+    @final
+    def diff(self, n: int) -> list[Block]:
+        """return block for the diff of the values"""
+        # only reached with ndim == 2
+        # TODO(EA2D): transpose will be unnecessary with 2D EAs
+        new_values = algos.diff(self.values.T, n, axis=0).T
+        return [self.make_block(values=new_values)]
+
+    def shift(self, periods: int, fill_value: Any = None) -> list[Block]:
+        """shift the block by periods, possibly upcast"""
+        # convert integer to float if necessary. need to do a lot more than
+        # that, handle boolean etc also
+        axis = self.ndim - 1
+
+        # Note: periods is never 0 here, as that is handled at the top of
+        #  NDFrame.shift.  If that ever changes, we can do a check for periods=0
+        #  and possibly avoid coercing.
+
+        if not lib.is_scalar(fill_value) and self.dtype != _dtype_obj:
+            # with object dtype there is nothing to promote, and the user can
+            #  pass pretty much any weird fill_value they like
+            # see test_shift_object_non_scalar_fill
+            raise ValueError("fill_value must be a scalar")
+
+        fill_value = self._standardize_fill_value(fill_value)
+
+        try:
+            # error: Argument 1 to "np_can_hold_element" has incompatible type
+            # "Union[dtype[Any], ExtensionDtype]"; expected "dtype[Any]"
+            casted = np_can_hold_element(
+                self.dtype, fill_value  # type: ignore[arg-type]
+            )
+        except LossySetitemError:
+            nb = self.coerce_to_target_dtype(fill_value)
+            return nb.shift(periods, fill_value=fill_value)
+
+        else:
+            values = cast(np.ndarray, self.values)
+            new_values = shift(values, periods, axis, casted)
+            return [self.make_block_same_class(new_values)]
+
+    @final
+    def quantile(
+        self,
+        qs: Index,  # with dtype float64
+        interpolation: QuantileInterpolation = "linear",
+    ) -> Block:
+        """
+        compute the quantiles of the
+
+        Parameters
+        ----------
+        qs : Index
+            The quantiles to be computed in float64.
+        interpolation : str, default 'linear'
+            Type of interpolation.
+
+        Returns
+        -------
+        Block
+        """
+        # We should always have ndim == 2 because Series dispatches to DataFrame
+        assert self.ndim == 2
+        assert is_list_like(qs)  # caller is responsible for this
+
+        result = quantile_compat(self.values, np.asarray(qs._values), interpolation)
+        # ensure_block_shape needed for cases where we start with EA and result
+        #  is ndarray, e.g. IntegerArray, SparseArray
+        result = ensure_block_shape(result, ndim=2)
+        return new_block_2d(result, placement=self._mgr_locs)
+
+    @final
+    def round(self, decimals: int, using_cow: bool = False) -> Self:
+        """
+        Rounds the values.
+        If the block is not of an integer or float dtype, nothing happens.
+        This is consistent with DataFrame.round behavivor.
+        (Note: Series.round would raise)
+
+        Parameters
+        ----------
+        decimals: int,
+            Number of decimal places to round to.
+            Caller is responsible for validating this
+        using_cow: bool,
+            Whether Copy on Write is enabled right now
+        """
+        if not self.is_numeric or self.is_bool:
+            return self.copy(deep=not using_cow)
+        refs = None
+        # TODO: round only defined on BaseMaskedArray
+        # Series also does this, so would need to fix both places
+        # error: Item "ExtensionArray" of "Union[ndarray[Any, Any], ExtensionArray]"
+        # has no attribute "round"
+        values = self.values.round(decimals)  # type: ignore[union-attr]
+        if values is self.values:
+            if not using_cow:
+                # Normally would need to do this before, but
+                # numpy only returns same array when round operation
+                # is no-op
+                # https://github.com/numpy/numpy/blob/486878b37fc7439a3b2b87747f50db9b62fea8eb/numpy/core/src/multiarray/calculation.c#L625-L636
+                values = values.copy()
+            else:
+                refs = self.refs
+        return self.make_block_same_class(values, refs=refs)
+
+    # ---------------------------------------------------------------------
+    # Abstract Methods Overridden By EABackedBlock and NumpyBlock
+
+    def delete(self, loc) -> list[Block]:
+        """Deletes the locs from the block.
+
+        We split the block to avoid copying the underlying data. We create new
+        blocks for every connected segment of the initial block that is not deleted.
+        The new blocks point to the initial array.
+        """
+        if not is_list_like(loc):
+            loc = [loc]
+
+        if self.ndim == 1:
+            values = cast(np.ndarray, self.values)
+            values = np.delete(values, loc)
+            mgr_locs = self._mgr_locs.delete(loc)
+            return [type(self)(values, placement=mgr_locs, ndim=self.ndim)]
+
+        if np.max(loc) >= self.values.shape[0]:
+            raise IndexError
+
+        # Add one out-of-bounds indexer as maximum to collect
+        # all columns after our last indexer if any
+        loc = np.concatenate([loc, [self.values.shape[0]]])
+        mgr_locs_arr = self._mgr_locs.as_array
+        new_blocks: list[Block] = []
+
+        previous_loc = -1
+        # TODO(CoW): This is tricky, if parent block goes out of scope
+        # all split blocks are referencing each other even though they
+        # don't share data
+        refs = self.refs if self.refs.has_reference() else None
+        for idx in loc:
+            if idx == previous_loc + 1:
+                # There is no column between current and last idx
+                pass
+            else:
+                # No overload variant of "__getitem__" of "ExtensionArray" matches
+                # argument type "Tuple[slice, slice]"
+                values = self.values[previous_loc + 1 : idx, :]  # type: ignore[call-overload]
+                locs = mgr_locs_arr[previous_loc + 1 : idx]
+                nb = type(self)(
+                    values, placement=BlockPlacement(locs), ndim=self.ndim, refs=refs
+                )
+                new_blocks.append(nb)
+
+            previous_loc = idx
+
+        return new_blocks
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if I am possibly a view"""
+        raise AbstractMethodError(self)
+
+    @property
+    def array_values(self) -> ExtensionArray:
+        """
+        The array that Series.array returns. Always an ExtensionArray.
+        """
+        raise AbstractMethodError(self)
+
+    def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
+        """
+        return an internal format, currently just the ndarray
+        this is often overridden to handle to_dense like operations
+        """
+        raise AbstractMethodError(self)
+
+
+class EABackedBlock(Block):
+    """
+    Mixin for Block subclasses backed by ExtensionArray.
+    """
+
+    values: ExtensionArray
+
+    @final
+    def shift(self, periods: int, fill_value: Any = None) -> list[Block]:
+        """
+        Shift the block by `periods`.
+
+        Dispatches to underlying ExtensionArray and re-boxes in an
+        ExtensionBlock.
+        """
+        # Transpose since EA.shift is always along axis=0, while we want to shift
+        #  along rows.
+        new_values = self.values.T.shift(periods=periods, fill_value=fill_value).T
+        return [self.make_block_same_class(new_values)]
+
+    @final
+    def setitem(self, indexer, value, using_cow: bool = False):
+        """
+        Attempt self.values[indexer] = value, possibly creating a new array.
+
+        This differs from Block.setitem by not allowing setitem to change
+        the dtype of the Block.
+
+        Parameters
+        ----------
+        indexer : tuple, list-like, array-like, slice, int
+            The subset of self.values to set
+        value : object
+            The value being set
+        using_cow: bool, default False
+            Signaling if CoW is used.
+
+        Returns
+        -------
+        Block
+
+        Notes
+        -----
+        `indexer` is a direct slice/positional indexer. `value` must
+        be a compatible shape.
+        """
+        orig_indexer = indexer
+        orig_value = value
+
+        indexer = self._unwrap_setitem_indexer(indexer)
+        value = self._maybe_squeeze_arg(value)
+
+        values = self.values
+        if values.ndim == 2:
+            # TODO(GH#45419): string[pyarrow] tests break if we transpose
+            #  unconditionally
+            values = values.T
+        check_setitem_lengths(indexer, value, values)
+
+        try:
+            values[indexer] = value
+        except (ValueError, TypeError):
+            if isinstance(self.dtype, IntervalDtype):
+                # see TestSetitemFloatIntervalWithIntIntervalValues
+                nb = self.coerce_to_target_dtype(orig_value, warn_on_upcast=True)
+                return nb.setitem(orig_indexer, orig_value)
+
+            elif isinstance(self, NDArrayBackedExtensionBlock):
+                nb = self.coerce_to_target_dtype(orig_value, warn_on_upcast=True)
+                return nb.setitem(orig_indexer, orig_value)
+
+            else:
+                raise
+
+        else:
+            return self
+
+    @final
+    def where(
+        self, other, cond, _downcast: str | bool = "infer", using_cow: bool = False
+    ) -> list[Block]:
+        # _downcast private bc we only specify it when calling from fillna
+        arr = self.values.T
+
+        cond = extract_bool_array(cond)
+
+        orig_other = other
+        orig_cond = cond
+        other = self._maybe_squeeze_arg(other)
+        cond = self._maybe_squeeze_arg(cond)
+
+        if other is lib.no_default:
+            other = self.fill_value
+
+        icond, noop = validate_putmask(arr, ~cond)
+        if noop:
+            # GH#44181, GH#45135
+            # Avoid a) raising for Interval/PeriodDtype and b) unnecessary object upcast
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self.copy()]
+
+        try:
+            res_values = arr._where(cond, other).T
+        except (ValueError, TypeError):
+            if self.ndim == 1 or self.shape[0] == 1:
+                if isinstance(self.dtype, (IntervalDtype, StringDtype)):
+                    # TestSetitemFloatIntervalWithIntIntervalValues
+                    blk = self.coerce_to_target_dtype(orig_other)
+                    if (
+                        self.ndim == 2
+                        and isinstance(orig_cond, np.ndarray)
+                        and orig_cond.ndim == 1
+                        and not is_1d_only_ea_dtype(blk.dtype)
+                    ):
+                        orig_cond = orig_cond[:, None]
+                    nbs = blk.where(orig_other, orig_cond, using_cow=using_cow)
+                    return self._maybe_downcast(
+                        nbs, downcast=_downcast, using_cow=using_cow, caller="where"
+                    )
+
+                elif isinstance(self, NDArrayBackedExtensionBlock):
+                    # NB: not (yet) the same as
+                    #  isinstance(values, NDArrayBackedExtensionArray)
+                    blk = self.coerce_to_target_dtype(orig_other)
+                    nbs = blk.where(orig_other, orig_cond, using_cow=using_cow)
+                    return self._maybe_downcast(
+                        nbs, downcast=_downcast, using_cow=using_cow, caller="where"
+                    )
+
+                else:
+                    raise
+
+            else:
+                # Same pattern we use in Block.putmask
+                is_array = isinstance(orig_other, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = orig_other
+                    if is_array:
+                        # we have a different value per-column
+                        n = orig_other[:, i : i + 1]
+
+                    submask = orig_cond[:, i : i + 1]
+                    rbs = nb.where(n, submask, using_cow=using_cow)
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+        nb = self.make_block_same_class(res_values)
+        return [nb]
+
+    @final
+    def putmask(
+        self, mask, new, using_cow: bool = False, already_warned=None
+    ) -> list[Block]:
+        """
+        See Block.putmask.__doc__
+        """
+        mask = extract_bool_array(mask)
+        if new is lib.no_default:
+            new = self.fill_value
+
+        orig_new = new
+        orig_mask = mask
+        new = self._maybe_squeeze_arg(new)
+        mask = self._maybe_squeeze_arg(mask)
+
+        if not mask.any():
+            if using_cow:
+                return [self.copy(deep=False)]
+            return [self]
+
+        if (
+            warn_copy_on_write()
+            and already_warned is not None
+            and not already_warned.warned_already
+        ):
+            if self.refs.has_reference():
+                warnings.warn(
+                    COW_WARNING_GENERAL_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                already_warned.warned_already = True
+
+        self = self._maybe_copy(using_cow, inplace=True)
+        values = self.values
+        if values.ndim == 2:
+            values = values.T
+
+        try:
+            # Caller is responsible for ensuring matching lengths
+            values._putmask(mask, new)
+        except (TypeError, ValueError):
+            if self.ndim == 1 or self.shape[0] == 1:
+                if isinstance(self.dtype, IntervalDtype):
+                    # Discussion about what we want to support in the general
+                    #  case GH#39584
+                    blk = self.coerce_to_target_dtype(orig_new, warn_on_upcast=True)
+                    return blk.putmask(orig_mask, orig_new)
+
+                elif isinstance(self, NDArrayBackedExtensionBlock):
+                    # NB: not (yet) the same as
+                    #  isinstance(values, NDArrayBackedExtensionArray)
+                    blk = self.coerce_to_target_dtype(orig_new, warn_on_upcast=True)
+                    return blk.putmask(orig_mask, orig_new)
+
+                else:
+                    raise
+
+            else:
+                # Same pattern we use in Block.putmask
+                is_array = isinstance(orig_new, (np.ndarray, ExtensionArray))
+
+                res_blocks = []
+                nbs = self._split()
+                for i, nb in enumerate(nbs):
+                    n = orig_new
+                    if is_array:
+                        # we have a different value per-column
+                        n = orig_new[:, i : i + 1]
+
+                    submask = orig_mask[:, i : i + 1]
+                    rbs = nb.putmask(submask, n)
+                    res_blocks.extend(rbs)
+                return res_blocks
+
+        return [self]
+
+    @final
+    def delete(self, loc) -> list[Block]:
+        # This will be unnecessary if/when __array_function__ is implemented
+        if self.ndim == 1:
+            values = self.values.delete(loc)
+            mgr_locs = self._mgr_locs.delete(loc)
+            return [type(self)(values, placement=mgr_locs, ndim=self.ndim)]
+        elif self.values.ndim == 1:
+            # We get here through to_stata
+            return []
+        return super().delete(loc)
+
+    @final
+    @cache_readonly
+    def array_values(self) -> ExtensionArray:
+        return self.values
+
+    @final
+    def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
+        """
+        return object dtype as boxed values, such as Timestamps/Timedelta
+        """
+        values: ArrayLike = self.values
+        if dtype == _dtype_obj:
+            values = values.astype(object)
+        # TODO(EA2D): reshape not needed with 2D EAs
+        return np.asarray(values).reshape(self.shape)
+
+    @final
+    def pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions,
+        axis: AxisInt = 0,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        values = self.values
+
+        kwargs: dict[str, Any] = {"method": method, "limit": limit}
+        if "limit_area" in inspect.signature(values._pad_or_backfill).parameters:
+            kwargs["limit_area"] = limit_area
+        elif limit_area is not None:
+            raise NotImplementedError(
+                f"{type(values).__name__} does not implement limit_area "
+                "(added in pandas 2.2). 3rd-party ExtnsionArray authors "
+                "need to add this argument to _pad_or_backfill."
+            )
+
+        if values.ndim == 2 and axis == 1:
+            # NDArrayBackedExtensionArray.fillna assumes axis=0
+            new_values = values.T._pad_or_backfill(**kwargs).T
+        else:
+            new_values = values._pad_or_backfill(**kwargs)
+        return [self.make_block_same_class(new_values)]
+
+
+class ExtensionBlock(EABackedBlock):
+    """
+    Block for holding extension types.
+
+    Notes
+    -----
+    This holds all 3rd-party extension array types. It's also the immediate
+    parent class for our internal extension types' blocks.
+
+    ExtensionArrays are limited to 1-D.
+    """
+
+    values: ExtensionArray
+
+    def fillna(
+        self,
+        value,
+        limit: int | None = None,
+        inplace: bool = False,
+        downcast=None,
+        using_cow: bool = False,
+        already_warned=None,
+    ) -> list[Block]:
+        if isinstance(self.dtype, (IntervalDtype, StringDtype)):
+            # Block.fillna handles coercion (test_fillna_interval)
+            return super().fillna(
+                value=value,
+                limit=limit,
+                inplace=inplace,
+                downcast=downcast,
+                using_cow=using_cow,
+                already_warned=already_warned,
+            )
+        if using_cow and self._can_hold_na and not self.values._hasna:
+            refs = self.refs
+            new_values = self.values
+        else:
+            copy, refs = self._get_refs_and_copy(using_cow, inplace)
+
+            try:
+                new_values = self.values.fillna(
+                    value=value, method=None, limit=limit, copy=copy
+                )
+            except TypeError:
+                # 3rd party EA that has not implemented copy keyword yet
+                refs = None
+                new_values = self.values.fillna(value=value, method=None, limit=limit)
+                # issue the warning *after* retrying, in case the TypeError
+                #  was caused by an invalid fill_value
+                warnings.warn(
+                    # GH#53278
+                    "ExtensionArray.fillna added a 'copy' keyword in pandas "
+                    "2.1.0. In a future version, ExtensionArray subclasses will "
+                    "need to implement this keyword or an exception will be "
+                    "raised. In the interim, the keyword is ignored by "
+                    f"{type(self.values).__name__}.",
+                    DeprecationWarning,
+                    stacklevel=find_stack_level(),
+                )
+            else:
+                if (
+                    not copy
+                    and warn_copy_on_write()
+                    and already_warned is not None
+                    and not already_warned.warned_already
+                ):
+                    if self.refs.has_reference():
+                        warnings.warn(
+                            COW_WARNING_GENERAL_MSG,
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                        already_warned.warned_already = True
+
+        nb = self.make_block_same_class(new_values, refs=refs)
+        return nb._maybe_downcast([nb], downcast, using_cow=using_cow, caller="fillna")
+
+    @cache_readonly
+    def shape(self) -> Shape:
+        # TODO(EA2D): override unnecessary with 2D EAs
+        if self.ndim == 1:
+            return (len(self.values),)
+        return len(self._mgr_locs), len(self.values)
+
+    def iget(self, i: int | tuple[int, int] | tuple[slice, int]):
+        # In the case where we have a tuple[slice, int], the slice will always
+        #  be slice(None)
+        # We _could_ make the annotation more specific, but mypy would
+        #  complain about override mismatch:
+        #  Literal[0] | tuple[Literal[0], int] | tuple[slice, int]
+
+        # Note: only reached with self.ndim == 2
+
+        if isinstance(i, tuple):
+            # TODO(EA2D): unnecessary with 2D EAs
+            col, loc = i
+            if not com.is_null_slice(col) and col != 0:
+                raise IndexError(f"{self} only contains one item")
+            if isinstance(col, slice):
+                # the is_null_slice check above assures that col is slice(None)
+                #  so what we want is a view on all our columns and row loc
+                if loc < 0:
+                    loc += len(self.values)
+                # Note: loc:loc+1 vs [[loc]] makes a difference when called
+                #  from fast_xs because we want to get a view back.
+                return self.values[loc : loc + 1]
+            return self.values[loc]
+        else:
+            if i != 0:
+                raise IndexError(f"{self} only contains one item")
+            return self.values
+
+    def set_inplace(self, locs, values: ArrayLike, copy: bool = False) -> None:
+        # When an ndarray, we should have locs.tolist() == [0]
+        # When a BlockPlacement we should have list(locs) == [0]
+        if copy:
+            self.values = self.values.copy()
+        self.values[:] = values
+
+    def _maybe_squeeze_arg(self, arg):
+        """
+        If necessary, squeeze a (N, 1) ndarray to (N,)
+        """
+        # e.g. if we are passed a 2D mask for putmask
+        if (
+            isinstance(arg, (np.ndarray, ExtensionArray))
+            and arg.ndim == self.values.ndim + 1
+        ):
+            # TODO(EA2D): unnecessary with 2D EAs
+            assert arg.shape[1] == 1
+            # error: No overload variant of "__getitem__" of "ExtensionArray"
+            # matches argument type "Tuple[slice, int]"
+            arg = arg[:, 0]  # type: ignore[call-overload]
+        elif isinstance(arg, ABCDataFrame):
+            # 2022-01-06 only reached for setitem
+            # TODO: should we avoid getting here with DataFrame?
+            assert arg.shape[1] == 1
+            arg = arg._ixs(0, axis=1)._values
+
+        return arg
+
+    def _unwrap_setitem_indexer(self, indexer):
+        """
+        Adapt a 2D-indexer to our 1D values.
+
+        This is intended for 'setitem', not 'iget' or '_slice'.
+        """
+        # TODO: ATM this doesn't work for iget/_slice, can we change that?
+
+        if isinstance(indexer, tuple) and len(indexer) == 2:
+            # TODO(EA2D): not needed with 2D EAs
+            #  Should never have length > 2.  Caller is responsible for checking.
+            #  Length 1 is reached vis setitem_single_block and setitem_single_column
+            #  each of which pass indexer=(pi,)
+            if all(isinstance(x, np.ndarray) and x.ndim == 2 for x in indexer):
+                # GH#44703 went through indexing.maybe_convert_ix
+                first, second = indexer
+                if not (
+                    second.size == 1 and (second == 0).all() and first.shape[1] == 1
+                ):
+                    raise NotImplementedError(
+                        "This should not be reached. Please report a bug at "
+                        "github.com/pandas-dev/pandas/"
+                    )
+                indexer = first[:, 0]
+
+            elif lib.is_integer(indexer[1]) and indexer[1] == 0:
+                # reached via setitem_single_block passing the whole indexer
+                indexer = indexer[0]
+
+            elif com.is_null_slice(indexer[1]):
+                indexer = indexer[0]
+
+            elif is_list_like(indexer[1]) and indexer[1][0] == 0:
+                indexer = indexer[0]
+
+            else:
+                raise NotImplementedError(
+                    "This should not be reached. Please report a bug at "
+                    "github.com/pandas-dev/pandas/"
+                )
+        return indexer
+
+    @property
+    def is_view(self) -> bool:
+        """Extension arrays are never treated as views."""
+        return False
+
+    # error: Cannot override writeable attribute with read-only property
+    @cache_readonly
+    def is_numeric(self) -> bool:  # type: ignore[override]
+        return self.values.dtype._is_numeric
+
+    def _slice(
+        self, slicer: slice | npt.NDArray[np.bool_] | npt.NDArray[np.intp]
+    ) -> ExtensionArray:
+        """
+        Return a slice of my values.
+
+        Parameters
+        ----------
+        slicer : slice, ndarray[int], or ndarray[bool]
+            Valid (non-reducing) indexer for self.values.
+
+        Returns
+        -------
+        ExtensionArray
+        """
+        # Notes: ndarray[bool] is only reachable when via get_rows_with_mask, which
+        #  is only for Series, i.e. self.ndim == 1.
+
+        # return same dims as we currently have
+        if self.ndim == 2:
+            # reached via getitem_block via _slice_take_blocks_ax0
+            # TODO(EA2D): won't be necessary with 2D EAs
+
+            if not isinstance(slicer, slice):
+                raise AssertionError(
+                    "invalid slicing for a 1-ndim ExtensionArray", slicer
+                )
+            # GH#32959 only full-slicers along fake-dim0 are valid
+            # TODO(EA2D): won't be necessary with 2D EAs
+            # range(1) instead of self._mgr_locs to avoid exception on [::-1]
+            #  see test_iloc_getitem_slice_negative_step_ea_block
+            new_locs = range(1)[slicer]
+            if not len(new_locs):
+                raise AssertionError(
+                    "invalid slicing for a 1-ndim ExtensionArray", slicer
+                )
+            slicer = slice(None)
+
+        return self.values[slicer]
+
+    @final
+    def slice_block_rows(self, slicer: slice) -> Self:
+        """
+        Perform __getitem__-like specialized to slicing along index.
+        """
+        # GH#42787 in principle this is equivalent to values[..., slicer], but we don't
+        # require subclasses of ExtensionArray to support that form (for now).
+        new_values = self.values[slicer]
+        return type(self)(new_values, self._mgr_locs, ndim=self.ndim, refs=self.refs)
+
+    def _unstack(
+        self,
+        unstacker,
+        fill_value,
+        new_placement: npt.NDArray[np.intp],
+        needs_masking: npt.NDArray[np.bool_],
+    ):
+        # ExtensionArray-safe unstack.
+        # We override Block._unstack, which unstacks directly on the
+        # values of the array. For EA-backed blocks, this would require
+        # converting to a 2-D ndarray of objects.
+        # Instead, we unstack an ndarray of integer positions, followed by
+        # a `take` on the actual values.
+
+        # Caller is responsible for ensuring self.shape[-1] == len(unstacker.index)
+        new_values, mask = unstacker.arange_result
+
+        # Note: these next two lines ensure that
+        #  mask.sum() == sum(len(nb.mgr_locs) for nb in blocks)
+        #  which the calling function needs in order to pass verify_integrity=False
+        #  to the BlockManager constructor
+        new_values = new_values.T[mask]
+        new_placement = new_placement[mask]
+
+        # needs_masking[i] calculated once in BlockManager.unstack tells
+        #  us if there are any -1s in the relevant indices.  When False,
+        #  that allows us to go through a faster path in 'take', among
+        #  other things avoiding e.g. Categorical._validate_scalar.
+        blocks = [
+            # TODO: could cast to object depending on fill_value?
+            type(self)(
+                self.values.take(
+                    indices, allow_fill=needs_masking[i], fill_value=fill_value
+                ),
+                BlockPlacement(place),
+                ndim=2,
+            )
+            for i, (indices, place) in enumerate(zip(new_values, new_placement))
+        ]
+        return blocks, mask
+
+
+class NumpyBlock(Block):
+    values: np.ndarray
+    __slots__ = ()
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if I am possibly a view"""
+        return self.values.base is not None
+
+    @property
+    def array_values(self) -> ExtensionArray:
+        return NumpyExtensionArray(self.values)
+
+    def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
+        if dtype == _dtype_obj:
+            return self.values.astype(_dtype_obj)
+        return self.values
+
+    @cache_readonly
+    def is_numeric(self) -> bool:  # type: ignore[override]
+        dtype = self.values.dtype
+        kind = dtype.kind
+
+        return kind in "fciub"
+
+
+class NumericBlock(NumpyBlock):
+    # this Block type is kept for backwards-compatibility
+    # TODO(3.0): delete and remove deprecation in __init__.py.
+    __slots__ = ()
+
+
+class ObjectBlock(NumpyBlock):
+    # this Block type is kept for backwards-compatibility
+    # TODO(3.0): delete and remove deprecation in __init__.py.
+    __slots__ = ()
+
+
+class NDArrayBackedExtensionBlock(EABackedBlock):
+    """
+    Block backed by an NDArrayBackedExtensionArray
+    """
+
+    values: NDArrayBackedExtensionArray
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if I am possibly a view"""
+        # check the ndarray values of the DatetimeIndex values
+        return self.values._ndarray.base is not None
+
+
+class DatetimeLikeBlock(NDArrayBackedExtensionBlock):
+    """Block for datetime64[ns], timedelta64[ns]."""
+
+    __slots__ = ()
+    is_numeric = False
+    values: DatetimeArray | TimedeltaArray
+
+
+class DatetimeTZBlock(DatetimeLikeBlock):
+    """implement a datetime64 block with a tz attribute"""
+
+    values: DatetimeArray
+
+    __slots__ = ()
+
+
+# -----------------------------------------------------------------
+# Constructor Helpers
+
+
+def maybe_coerce_values(values: ArrayLike) -> ArrayLike:
+    """
+    Input validation for values passed to __init__. Ensure that
+    any datetime64/timedelta64 dtypes are in nanoseconds.  Ensure
+    that we do not have string dtypes.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+
+    Returns
+    -------
+    values : np.ndarray or ExtensionArray
+    """
+    # Caller is responsible for ensuring NumpyExtensionArray is already extracted.
+
+    if isinstance(values, np.ndarray):
+        values = ensure_wrapped_if_datetimelike(values)
+
+        if issubclass(values.dtype.type, str):
+            values = np.array(values, dtype=object)
+
+    if isinstance(values, (DatetimeArray, TimedeltaArray)) and values.freq is not None:
+        # freq is only stored in DatetimeIndex/TimedeltaIndex, not in Series/DataFrame
+        values = values._with_freq(None)
+
+    return values
+
+
+def get_block_type(dtype: DtypeObj) -> type[Block]:
+    """
+    Find the appropriate Block subclass to use for the given values and dtype.
+
+    Parameters
+    ----------
+    dtype : numpy or pandas dtype
+
+    Returns
+    -------
+    cls : class, subclass of Block
+    """
+    if isinstance(dtype, DatetimeTZDtype):
+        return DatetimeTZBlock
+    elif isinstance(dtype, PeriodDtype):
+        return NDArrayBackedExtensionBlock
+    elif isinstance(dtype, ExtensionDtype):
+        # Note: need to be sure NumpyExtensionArray is unwrapped before we get here
+        return ExtensionBlock
+
+    # We use kind checks because it is much more performant
+    #  than is_foo_dtype
+    kind = dtype.kind
+    if kind in "Mm":
+        return DatetimeLikeBlock
+
+    return NumpyBlock
+
+
+def new_block_2d(
+    values: ArrayLike, placement: BlockPlacement, refs: BlockValuesRefs | None = None
+):
+    # new_block specialized to case with
+    #  ndim=2
+    #  isinstance(placement, BlockPlacement)
+    #  check_ndim/ensure_block_shape already checked
+    klass = get_block_type(values.dtype)
+
+    values = maybe_coerce_values(values)
+    return klass(values, ndim=2, placement=placement, refs=refs)
+
+
+def new_block(
+    values,
+    placement: BlockPlacement,
+    *,
+    ndim: int,
+    refs: BlockValuesRefs | None = None,
+) -> Block:
+    # caller is responsible for ensuring:
+    # - values is NOT a NumpyExtensionArray
+    # - check_ndim/ensure_block_shape already checked
+    # - maybe_coerce_values already called/unnecessary
+    klass = get_block_type(values.dtype)
+    return klass(values, ndim=ndim, placement=placement, refs=refs)
+
+
+def check_ndim(values, placement: BlockPlacement, ndim: int) -> None:
+    """
+    ndim inference and validation.
+
+    Validates that values.ndim and ndim are consistent.
+    Validates that len(values) and len(placement) are consistent.
+
+    Parameters
+    ----------
+    values : array-like
+    placement : BlockPlacement
+    ndim : int
+
+    Raises
+    ------
+    ValueError : the number of dimensions do not match
+    """
+
+    if values.ndim > ndim:
+        # Check for both np.ndarray and ExtensionArray
+        raise ValueError(
+            "Wrong number of dimensions. "
+            f"values.ndim > ndim [{values.ndim} > {ndim}]"
+        )
+
+    if not is_1d_only_ea_dtype(values.dtype):
+        # TODO(EA2D): special case not needed with 2D EAs
+        if values.ndim != ndim:
+            raise ValueError(
+                "Wrong number of dimensions. "
+                f"values.ndim != ndim [{values.ndim} != {ndim}]"
+            )
+        if len(placement) != len(values):
+            raise ValueError(
+                f"Wrong number of items passed {len(values)}, "
+                f"placement implies {len(placement)}"
+            )
+    elif ndim == 2 and len(placement) != 1:
+        # TODO(EA2D): special case unnecessary with 2D EAs
+        raise ValueError("need to split")
+
+
+def extract_pandas_array(
+    values: ArrayLike, dtype: DtypeObj | None, ndim: int
+) -> tuple[ArrayLike, DtypeObj | None]:
+    """
+    Ensure that we don't allow NumpyExtensionArray / NumpyEADtype in internals.
+    """
+    # For now, blocks should be backed by ndarrays when possible.
+    if isinstance(values, ABCNumpyExtensionArray):
+        values = values.to_numpy()
+        if ndim and ndim > 1:
+            # TODO(EA2D): special case not needed with 2D EAs
+            values = np.atleast_2d(values)
+
+    if isinstance(dtype, NumpyEADtype):
+        dtype = dtype.numpy_dtype
+
+    return values, dtype
+
+
+# -----------------------------------------------------------------
+
+
+def extend_blocks(result, blocks=None) -> list[Block]:
+    """return a new extended blocks, given the result"""
+    if blocks is None:
+        blocks = []
+    if isinstance(result, list):
+        for r in result:
+            if isinstance(r, list):
+                blocks.extend(r)
+            else:
+                blocks.append(r)
+    else:
+        assert isinstance(result, Block), type(result)
+        blocks.append(result)
+    return blocks
+
+
+def ensure_block_shape(values: ArrayLike, ndim: int = 1) -> ArrayLike:
+    """
+    Reshape if possible to have values.ndim == ndim.
+    """
+
+    if values.ndim < ndim:
+        if not is_1d_only_ea_dtype(values.dtype):
+            # TODO(EA2D): https://github.com/pandas-dev/pandas/issues/23023
+            # block.shape is incorrect for "2D" ExtensionArrays
+            # We can't, and don't need to, reshape.
+            values = cast("np.ndarray | DatetimeArray | TimedeltaArray", values)
+            values = values.reshape(1, -1)
+
+    return values
+
+
+def external_values(values: ArrayLike) -> ArrayLike:
+    """
+    The array that Series.values returns (public attribute).
+
+    This has some historical constraints, and is overridden in block
+    subclasses to return the correct array (e.g. period returns
+    object ndarray and datetimetz a datetime64[ns] ndarray instead of
+    proper extension array).
+    """
+    if isinstance(values, (PeriodArray, IntervalArray)):
+        return values.astype(object)
+    elif isinstance(values, (DatetimeArray, TimedeltaArray)):
+        # NB: for datetime64tz this is different from np.asarray(values), since
+        #  that returns an object-dtype ndarray of Timestamps.
+        # Avoid raising in .astype in casting from dt64tz to dt64
+        values = values._ndarray
+
+    if isinstance(values, np.ndarray) and using_copy_on_write():
+        values = values.view()
+        values.flags.writeable = False
+
+    # TODO(CoW) we should also mark our ExtensionArrays as read-only
+
+    return values
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/concat.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2d463a8c6c26f62ded5a06283f29275612c9b40
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/concat.py
@@ -0,0 +1,598 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    algos as libalgos,
+    internals as libinternals,
+    lib,
+)
+from pandas._libs.missing import NA
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    ensure_dtype_can_hold_na,
+    find_common_type,
+)
+from pandas.core.dtypes.common import (
+    is_1d_only_ea_dtype,
+    is_scalar,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    isna_all,
+)
+
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.internals.array_manager import ArrayManager
+from pandas.core.internals.blocks import (
+    ensure_block_shape,
+    new_block_2d,
+)
+from pandas.core.internals.managers import (
+    BlockManager,
+    make_na_array,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        Manager2D,
+        Shape,
+    )
+
+    from pandas import Index
+    from pandas.core.internals.blocks import (
+        Block,
+        BlockPlacement,
+    )
+
+
+def _concatenate_array_managers(
+    mgrs: list[ArrayManager], axes: list[Index], concat_axis: AxisInt
+) -> Manager2D:
+    """
+    Concatenate array managers into one.
+
+    Parameters
+    ----------
+    mgrs_indexers : list of (ArrayManager, {axis: indexer,...}) tuples
+    axes : list of Index
+    concat_axis : int
+
+    Returns
+    -------
+    ArrayManager
+    """
+    if concat_axis == 1:
+        return mgrs[0].concat_vertical(mgrs, axes)
+    else:
+        # concatting along the columns -> combine reindexed arrays in a single manager
+        assert concat_axis == 0
+        return mgrs[0].concat_horizontal(mgrs, axes)
+
+
+def concatenate_managers(
+    mgrs_indexers, axes: list[Index], concat_axis: AxisInt, copy: bool
+) -> Manager2D:
+    """
+    Concatenate block managers into one.
+
+    Parameters
+    ----------
+    mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
+    axes : list of Index
+    concat_axis : int
+    copy : bool
+
+    Returns
+    -------
+    BlockManager
+    """
+
+    needs_copy = copy and concat_axis == 0
+
+    # TODO(ArrayManager) this assumes that all managers are of the same type
+    if isinstance(mgrs_indexers[0][0], ArrayManager):
+        mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)
+        # error: Argument 1 to "_concatenate_array_managers" has incompatible
+        # type "List[BlockManager]"; expected "List[Union[ArrayManager,
+        # SingleArrayManager, BlockManager, SingleBlockManager]]"
+        return _concatenate_array_managers(
+            mgrs, axes, concat_axis  # type: ignore[arg-type]
+        )
+
+    # Assertions disabled for performance
+    # for tup in mgrs_indexers:
+    #    # caller is responsible for ensuring this
+    #    indexers = tup[1]
+    #    assert concat_axis not in indexers
+
+    if concat_axis == 0:
+        mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)
+        return mgrs[0].concat_horizontal(mgrs, axes)
+
+    if len(mgrs_indexers) > 0 and mgrs_indexers[0][0].nblocks > 0:
+        first_dtype = mgrs_indexers[0][0].blocks[0].dtype
+        if first_dtype in [np.float64, np.float32]:
+            # TODO: support more dtypes here.  This will be simpler once
+            #  JoinUnit.is_na behavior is deprecated.
+            if (
+                all(_is_homogeneous_mgr(mgr, first_dtype) for mgr, _ in mgrs_indexers)
+                and len(mgrs_indexers) > 1
+            ):
+                # Fastpath!
+                # Length restriction is just to avoid having to worry about 'copy'
+                shape = tuple(len(x) for x in axes)
+                nb = _concat_homogeneous_fastpath(mgrs_indexers, shape, first_dtype)
+                return BlockManager((nb,), axes)
+
+    mgrs = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)
+
+    if len(mgrs) == 1:
+        mgr = mgrs[0]
+        out = mgr.copy(deep=False)
+        out.axes = axes
+        return out
+
+    concat_plan = _get_combined_plan(mgrs)
+
+    blocks = []
+    values: ArrayLike
+
+    for placement, join_units in concat_plan:
+        unit = join_units[0]
+        blk = unit.block
+
+        if _is_uniform_join_units(join_units):
+            vals = [ju.block.values for ju in join_units]
+
+            if not blk.is_extension:
+                # _is_uniform_join_units ensures a single dtype, so
+                #  we can use np.concatenate, which is more performant
+                #  than concat_compat
+                # error: Argument 1 to "concatenate" has incompatible type
+                # "List[Union[ndarray[Any, Any], ExtensionArray]]";
+                # expected "Union[_SupportsArray[dtype[Any]],
+                # _NestedSequence[_SupportsArray[dtype[Any]]]]"
+                values = np.concatenate(vals, axis=1)  # type: ignore[arg-type]
+            elif is_1d_only_ea_dtype(blk.dtype):
+                # TODO(EA2D): special-casing not needed with 2D EAs
+                values = concat_compat(vals, axis=0, ea_compat_axis=True)
+                values = ensure_block_shape(values, ndim=2)
+            else:
+                values = concat_compat(vals, axis=1)
+
+            values = ensure_wrapped_if_datetimelike(values)
+
+            fastpath = blk.values.dtype == values.dtype
+        else:
+            values = _concatenate_join_units(join_units, copy=copy)
+            fastpath = False
+
+        if fastpath:
+            b = blk.make_block_same_class(values, placement=placement)
+        else:
+            b = new_block_2d(values, placement=placement)
+
+        blocks.append(b)
+
+    return BlockManager(tuple(blocks), axes)
+
+
+def _maybe_reindex_columns_na_proxy(
+    axes: list[Index],
+    mgrs_indexers: list[tuple[BlockManager, dict[int, np.ndarray]]],
+    needs_copy: bool,
+) -> list[BlockManager]:
+    """
+    Reindex along columns so that all of the BlockManagers being concatenated
+    have matching columns.
+
+    Columns added in this reindexing have dtype=np.void, indicating they
+    should be ignored when choosing a column's final dtype.
+    """
+    new_mgrs = []
+
+    for mgr, indexers in mgrs_indexers:
+        # For axis=0 (i.e. columns) we use_na_proxy and only_slice, so this
+        #  is a cheap reindexing.
+        for i, indexer in indexers.items():
+            mgr = mgr.reindex_indexer(
+                axes[i],
+                indexers[i],
+                axis=i,
+                copy=False,
+                only_slice=True,  # only relevant for i==0
+                allow_dups=True,
+                use_na_proxy=True,  # only relevant for i==0
+            )
+        if needs_copy and not indexers:
+            mgr = mgr.copy()
+
+        new_mgrs.append(mgr)
+    return new_mgrs
+
+
+def _is_homogeneous_mgr(mgr: BlockManager, first_dtype: DtypeObj) -> bool:
+    """
+    Check if this Manager can be treated as a single ndarray.
+    """
+    if mgr.nblocks != 1:
+        return False
+    blk = mgr.blocks[0]
+    if not (blk.mgr_locs.is_slice_like and blk.mgr_locs.as_slice.step == 1):
+        return False
+
+    return blk.dtype == first_dtype
+
+
+def _concat_homogeneous_fastpath(
+    mgrs_indexers, shape: Shape, first_dtype: np.dtype
+) -> Block:
+    """
+    With single-Block managers with homogeneous dtypes (that can already hold nan),
+    we avoid [...]
+    """
+    # assumes
+    #  all(_is_homogeneous_mgr(mgr, first_dtype) for mgr, _ in in mgrs_indexers)
+
+    if all(not indexers for _, indexers in mgrs_indexers):
+        # https://github.com/pandas-dev/pandas/pull/52685#issuecomment-1523287739
+        arrs = [mgr.blocks[0].values.T for mgr, _ in mgrs_indexers]
+        arr = np.concatenate(arrs).T
+        bp = libinternals.BlockPlacement(slice(shape[0]))
+        nb = new_block_2d(arr, bp)
+        return nb
+
+    arr = np.empty(shape, dtype=first_dtype)
+
+    if first_dtype == np.float64:
+        take_func = libalgos.take_2d_axis0_float64_float64
+    else:
+        take_func = libalgos.take_2d_axis0_float32_float32
+
+    start = 0
+    for mgr, indexers in mgrs_indexers:
+        mgr_len = mgr.shape[1]
+        end = start + mgr_len
+
+        if 0 in indexers:
+            take_func(
+                mgr.blocks[0].values,
+                indexers[0],
+                arr[:, start:end],
+            )
+        else:
+            # No reindexing necessary, we can copy values directly
+            arr[:, start:end] = mgr.blocks[0].values
+
+        start += mgr_len
+
+    bp = libinternals.BlockPlacement(slice(shape[0]))
+    nb = new_block_2d(arr, bp)
+    return nb
+
+
+def _get_combined_plan(
+    mgrs: list[BlockManager],
+) -> list[tuple[BlockPlacement, list[JoinUnit]]]:
+    plan = []
+
+    max_len = mgrs[0].shape[0]
+
+    blknos_list = [mgr.blknos for mgr in mgrs]
+    pairs = libinternals.get_concat_blkno_indexers(blknos_list)
+    for ind, (blknos, bp) in enumerate(pairs):
+        # assert bp.is_slice_like
+        # assert len(bp) > 0
+
+        units_for_bp = []
+        for k, mgr in enumerate(mgrs):
+            blkno = blknos[k]
+
+            nb = _get_block_for_concat_plan(mgr, bp, blkno, max_len=max_len)
+            unit = JoinUnit(nb)
+            units_for_bp.append(unit)
+
+        plan.append((bp, units_for_bp))
+
+    return plan
+
+
+def _get_block_for_concat_plan(
+    mgr: BlockManager, bp: BlockPlacement, blkno: int, *, max_len: int
+) -> Block:
+    blk = mgr.blocks[blkno]
+    # Assertions disabled for performance:
+    #  assert bp.is_slice_like
+    #  assert blkno != -1
+    #  assert (mgr.blknos[bp] == blkno).all()
+
+    if len(bp) == len(blk.mgr_locs) and (
+        blk.mgr_locs.is_slice_like and blk.mgr_locs.as_slice.step == 1
+    ):
+        nb = blk
+    else:
+        ax0_blk_indexer = mgr.blklocs[bp.indexer]
+
+        slc = lib.maybe_indices_to_slice(ax0_blk_indexer, max_len)
+        # TODO: in all extant test cases 2023-04-08 we have a slice here.
+        #  Will this always be the case?
+        if isinstance(slc, slice):
+            nb = blk.slice_block_columns(slc)
+        else:
+            nb = blk.take_block_columns(slc)
+
+    # assert nb.shape == (len(bp), mgr.shape[1])
+    return nb
+
+
+class JoinUnit:
+    def __init__(self, block: Block) -> None:
+        self.block = block
+
+    def __repr__(self) -> str:
+        return f"{type(self).__name__}({repr(self.block)})"
+
+    def _is_valid_na_for(self, dtype: DtypeObj) -> bool:
+        """
+        Check that we are all-NA of a type/dtype that is compatible with this dtype.
+        Augments `self.is_na` with an additional check of the type of NA values.
+        """
+        if not self.is_na:
+            return False
+
+        blk = self.block
+        if blk.dtype.kind == "V":
+            return True
+
+        if blk.dtype == object:
+            values = blk.values
+            return all(is_valid_na_for_dtype(x, dtype) for x in values.ravel(order="K"))
+
+        na_value = blk.fill_value
+        if na_value is NaT and blk.dtype != dtype:
+            # e.g. we are dt64 and other is td64
+            # fill_values match but we should not cast blk.values to dtype
+            # TODO: this will need updating if we ever have non-nano dt64/td64
+            return False
+
+        if na_value is NA and needs_i8_conversion(dtype):
+            # FIXME: kludge; test_append_empty_frame_with_timedelta64ns_nat
+            #  e.g. blk.dtype == "Int64" and dtype is td64, we dont want
+            #  to consider these as matching
+            return False
+
+        # TODO: better to use can_hold_element?
+        return is_valid_na_for_dtype(na_value, dtype)
+
+    @cache_readonly
+    def is_na(self) -> bool:
+        blk = self.block
+        if blk.dtype.kind == "V":
+            return True
+
+        if not blk._can_hold_na:
+            return False
+
+        values = blk.values
+        if values.size == 0:
+            # GH#39122 this case will return False once deprecation is enforced
+            return True
+
+        if isinstance(values.dtype, SparseDtype):
+            return False
+
+        if values.ndim == 1:
+            # TODO(EA2D): no need for special case with 2D EAs
+            val = values[0]
+            if not is_scalar(val) or not isna(val):
+                # ideally isna_all would do this short-circuiting
+                return False
+            return isna_all(values)
+        else:
+            val = values[0][0]
+            if not is_scalar(val) or not isna(val):
+                # ideally isna_all would do this short-circuiting
+                return False
+            return all(isna_all(row) for row in values)
+
+    @cache_readonly
+    def is_na_after_size_and_isna_all_deprecation(self) -> bool:
+        """
+        Will self.is_na be True after values.size == 0 deprecation and isna_all
+        deprecation are enforced?
+        """
+        blk = self.block
+        if blk.dtype.kind == "V":
+            return True
+        return False
+
+    def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
+        values: ArrayLike
+
+        if upcasted_na is None and self.block.dtype.kind != "V":
+            # No upcasting is necessary
+            return self.block.values
+        else:
+            fill_value = upcasted_na
+
+            if self._is_valid_na_for(empty_dtype):
+                # note: always holds when self.block.dtype.kind == "V"
+                blk_dtype = self.block.dtype
+
+                if blk_dtype == np.dtype("object"):
+                    # we want to avoid filling with np.nan if we are
+                    # using None; we already know that we are all
+                    # nulls
+                    values = cast(np.ndarray, self.block.values)
+                    if values.size and values[0, 0] is None:
+                        fill_value = None
+
+                return make_na_array(empty_dtype, self.block.shape, fill_value)
+
+            return self.block.values
+
+
+def _concatenate_join_units(join_units: list[JoinUnit], copy: bool) -> ArrayLike:
+    """
+    Concatenate values from several join units along axis=1.
+    """
+    empty_dtype, empty_dtype_future = _get_empty_dtype(join_units)
+
+    has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
+    upcasted_na = _dtype_to_na_value(empty_dtype, has_none_blocks)
+
+    to_concat = [
+        ju.get_reindexed_values(empty_dtype=empty_dtype, upcasted_na=upcasted_na)
+        for ju in join_units
+    ]
+
+    if any(is_1d_only_ea_dtype(t.dtype) for t in to_concat):
+        # TODO(EA2D): special case not needed if all EAs used HybridBlocks
+
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[int, slice]"
+        to_concat = [
+            t
+            if is_1d_only_ea_dtype(t.dtype)
+            else t[0, :]  # type: ignore[call-overload]
+            for t in to_concat
+        ]
+        concat_values = concat_compat(to_concat, axis=0, ea_compat_axis=True)
+        concat_values = ensure_block_shape(concat_values, 2)
+
+    else:
+        concat_values = concat_compat(to_concat, axis=1)
+
+    if empty_dtype != empty_dtype_future:
+        if empty_dtype == concat_values.dtype:
+            # GH#39122, GH#40893
+            warnings.warn(
+                "The behavior of DataFrame concatenation with empty or all-NA "
+                "entries is deprecated. In a future version, this will no longer "
+                "exclude empty or all-NA columns when determining the result dtypes. "
+                "To retain the old behavior, exclude the relevant entries before "
+                "the concat operation.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+    return concat_values
+
+
+def _dtype_to_na_value(dtype: DtypeObj, has_none_blocks: bool):
+    """
+    Find the NA value to go with this dtype.
+    """
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.na_value
+    elif dtype.kind in "mM":
+        return dtype.type("NaT")
+    elif dtype.kind in "fc":
+        return dtype.type("NaN")
+    elif dtype.kind == "b":
+        # different from missing.na_value_for_dtype
+        return None
+    elif dtype.kind in "iu":
+        if not has_none_blocks:
+            # different from missing.na_value_for_dtype
+            return None
+        return np.nan
+    elif dtype.kind == "O":
+        return np.nan
+    raise NotImplementedError
+
+
+def _get_empty_dtype(join_units: Sequence[JoinUnit]) -> tuple[DtypeObj, DtypeObj]:
+    """
+    Return dtype and N/A values to use when concatenating specified units.
+
+    Returned N/A value may be None which means there was no casting involved.
+
+    Returns
+    -------
+    dtype
+    """
+    if lib.dtypes_all_equal([ju.block.dtype for ju in join_units]):
+        empty_dtype = join_units[0].block.dtype
+        return empty_dtype, empty_dtype
+
+    has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
+
+    dtypes = [unit.block.dtype for unit in join_units if not unit.is_na]
+    if not len(dtypes):
+        dtypes = [
+            unit.block.dtype for unit in join_units if unit.block.dtype.kind != "V"
+        ]
+
+    dtype = find_common_type(dtypes)
+    if has_none_blocks:
+        dtype = ensure_dtype_can_hold_na(dtype)
+
+    dtype_future = dtype
+    if len(dtypes) != len(join_units):
+        dtypes_future = [
+            unit.block.dtype
+            for unit in join_units
+            if not unit.is_na_after_size_and_isna_all_deprecation
+        ]
+        if not len(dtypes_future):
+            dtypes_future = [
+                unit.block.dtype for unit in join_units if unit.block.dtype.kind != "V"
+            ]
+
+        if len(dtypes) != len(dtypes_future):
+            dtype_future = find_common_type(dtypes_future)
+            if has_none_blocks:
+                dtype_future = ensure_dtype_can_hold_na(dtype_future)
+
+    return dtype, dtype_future
+
+
+def _is_uniform_join_units(join_units: list[JoinUnit]) -> bool:
+    """
+    Check if the join units consist of blocks of uniform type that can
+    be concatenated using Block.concat_same_type instead of the generic
+    _concatenate_join_units (which uses `concat_compat`).
+
+    """
+    first = join_units[0].block
+    if first.dtype.kind == "V":
+        return False
+    return (
+        # exclude cases where a) ju.block is None or b) we have e.g. Int64+int64
+        all(type(ju.block) is type(first) for ju in join_units)
+        and
+        # e.g. DatetimeLikeBlock can be dt64 or td64, but these are not uniform
+        all(
+            ju.block.dtype == first.dtype
+            # GH#42092 we only want the dtype_equal check for non-numeric blocks
+            #  (for now, may change but that would need a deprecation)
+            or ju.block.dtype.kind in "iub"
+            for ju in join_units
+        )
+        and
+        # no blocks that would get missing values (can lead to type upcasts)
+        # unless we're an extension dtype.
+        all(not ju.is_na or ju.block.is_extension for ju in join_units)
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/construction.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/construction.py
new file mode 100644
index 0000000000000000000000000000000000000000..64fac5fcfcdc29aab451cce31b3591e56da744a0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/construction.py
@@ -0,0 +1,1073 @@
+"""
+Functions for preparing various inputs passed to the DataFrame or Series
+constructors before passing them to a BlockManager.
+"""
+from __future__ import annotations
+
+from collections import abc
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+from numpy import ma
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.astype import astype_is_view
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    dict_compat,
+    maybe_cast_to_datetime,
+    maybe_convert_platform,
+    maybe_infer_to_datetimelike,
+)
+from pandas.core.dtypes.common import (
+    is_1d_only_ea_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_named_tuple,
+    is_object_dtype,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core import (
+    algorithms,
+    common as com,
+)
+from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+    range_to_ndarray,
+    sanitize_array,
+)
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    TimedeltaIndex,
+    default_index,
+    ensure_index,
+    get_objs_combined_axis,
+    union_indexes,
+)
+from pandas.core.internals.array_manager import (
+    ArrayManager,
+    SingleArrayManager,
+)
+from pandas.core.internals.blocks import (
+    BlockPlacement,
+    ensure_block_shape,
+    new_block,
+    new_block_2d,
+)
+from pandas.core.internals.managers import (
+    BlockManager,
+    SingleBlockManager,
+    create_block_manager_from_blocks,
+    create_block_manager_from_column_arrays,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        Manager,
+        npt,
+    )
+# ---------------------------------------------------------------------
+# BlockManager Interface
+
+
+def arrays_to_mgr(
+    arrays,
+    columns: Index,
+    index,
+    *,
+    dtype: DtypeObj | None = None,
+    verify_integrity: bool = True,
+    typ: str | None = None,
+    consolidate: bool = True,
+) -> Manager:
+    """
+    Segregate Series based on type and coerce into matrices.
+
+    Needs to handle a lot of exceptional cases.
+    """
+    if verify_integrity:
+        # figure out the index, if necessary
+        if index is None:
+            index = _extract_index(arrays)
+        else:
+            index = ensure_index(index)
+
+        # don't force copy because getting jammed in an ndarray anyway
+        arrays, refs = _homogenize(arrays, index, dtype)
+        # _homogenize ensures
+        #  - all(len(x) == len(index) for x in arrays)
+        #  - all(x.ndim == 1 for x in arrays)
+        #  - all(isinstance(x, (np.ndarray, ExtensionArray)) for x in arrays)
+        #  - all(type(x) is not NumpyExtensionArray for x in arrays)
+
+    else:
+        index = ensure_index(index)
+        arrays = [extract_array(x, extract_numpy=True) for x in arrays]
+        # with _from_arrays, the passed arrays should never be Series objects
+        refs = [None] * len(arrays)
+
+        # Reached via DataFrame._from_arrays; we do minimal validation here
+        for arr in arrays:
+            if (
+                not isinstance(arr, (np.ndarray, ExtensionArray))
+                or arr.ndim != 1
+                or len(arr) != len(index)
+            ):
+                raise ValueError(
+                    "Arrays must be 1-dimensional np.ndarray or ExtensionArray "
+                    "with length matching len(index)"
+                )
+
+    columns = ensure_index(columns)
+    if len(columns) != len(arrays):
+        raise ValueError("len(arrays) must match len(columns)")
+
+    # from BlockManager perspective
+    axes = [columns, index]
+
+    if typ == "block":
+        return create_block_manager_from_column_arrays(
+            arrays, axes, consolidate=consolidate, refs=refs
+        )
+    elif typ == "array":
+        return ArrayManager(arrays, [index, columns])
+    else:
+        raise ValueError(f"'typ' needs to be one of {{'block', 'array'}}, got '{typ}'")
+
+
+def rec_array_to_mgr(
+    data: np.rec.recarray | np.ndarray,
+    index,
+    columns,
+    dtype: DtypeObj | None,
+    copy: bool,
+    typ: str,
+) -> Manager:
+    """
+    Extract from a masked rec array and create the manager.
+    """
+    # essentially process a record array then fill it
+    fdata = ma.getdata(data)
+    if index is None:
+        index = default_index(len(fdata))
+    else:
+        index = ensure_index(index)
+
+    if columns is not None:
+        columns = ensure_index(columns)
+    arrays, arr_columns = to_arrays(fdata, columns)
+
+    # create the manager
+
+    arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns, len(index))
+    if columns is None:
+        columns = arr_columns
+
+    mgr = arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ)
+
+    if copy:
+        mgr = mgr.copy()
+    return mgr
+
+
+def mgr_to_mgr(mgr, typ: str, copy: bool = True) -> Manager:
+    """
+    Convert to specific type of Manager. Does not copy if the type is already
+    correct. Does not guarantee a copy otherwise. `copy` keyword only controls
+    whether conversion from Block->ArrayManager copies the 1D arrays.
+    """
+    new_mgr: Manager
+
+    if typ == "block":
+        if isinstance(mgr, BlockManager):
+            new_mgr = mgr
+        else:
+            if mgr.ndim == 2:
+                new_mgr = arrays_to_mgr(
+                    mgr.arrays, mgr.axes[0], mgr.axes[1], typ="block"
+                )
+            else:
+                new_mgr = SingleBlockManager.from_array(mgr.arrays[0], mgr.index)
+    elif typ == "array":
+        if isinstance(mgr, ArrayManager):
+            new_mgr = mgr
+        else:
+            if mgr.ndim == 2:
+                arrays = [mgr.iget_values(i) for i in range(len(mgr.axes[0]))]
+                if copy:
+                    arrays = [arr.copy() for arr in arrays]
+                new_mgr = ArrayManager(arrays, [mgr.axes[1], mgr.axes[0]])
+            else:
+                array = mgr.internal_values()
+                if copy:
+                    array = array.copy()
+                new_mgr = SingleArrayManager([array], [mgr.index])
+    else:
+        raise ValueError(f"'typ' needs to be one of {{'block', 'array'}}, got '{typ}'")
+    return new_mgr
+
+
+# ---------------------------------------------------------------------
+# DataFrame Constructor Interface
+
+
+def ndarray_to_mgr(
+    values, index, columns, dtype: DtypeObj | None, copy: bool, typ: str
+) -> Manager:
+    # used in DataFrame.__init__
+    # input must be a ndarray, list, Series, Index, ExtensionArray
+
+    if isinstance(values, ABCSeries):
+        if columns is None:
+            if values.name is not None:
+                columns = Index([values.name])
+        if index is None:
+            index = values.index
+        else:
+            values = values.reindex(index)
+
+        # zero len case (GH #2234)
+        if not len(values) and columns is not None and len(columns):
+            values = np.empty((0, 1), dtype=object)
+
+    # if the array preparation does a copy -> avoid this for ArrayManager,
+    # since the copy is done on conversion to 1D arrays
+    copy_on_sanitize = False if typ == "array" else copy
+
+    vdtype = getattr(values, "dtype", None)
+    refs = None
+    if is_1d_only_ea_dtype(vdtype) or is_1d_only_ea_dtype(dtype):
+        # GH#19157
+
+        if isinstance(values, (np.ndarray, ExtensionArray)) and values.ndim > 1:
+            # GH#12513 a EA dtype passed with a 2D array, split into
+            #  multiple EAs that view the values
+            # error: No overload variant of "__getitem__" of "ExtensionArray"
+            # matches argument type "Tuple[slice, int]"
+            values = [
+                values[:, n]  # type: ignore[call-overload]
+                for n in range(values.shape[1])
+            ]
+        else:
+            values = [values]
+
+        if columns is None:
+            columns = Index(range(len(values)))
+        else:
+            columns = ensure_index(columns)
+
+        return arrays_to_mgr(values, columns, index, dtype=dtype, typ=typ)
+
+    elif isinstance(vdtype, ExtensionDtype):
+        # i.e. Datetime64TZ, PeriodDtype; cases with is_1d_only_ea_dtype(vdtype)
+        #  are already caught above
+        values = extract_array(values, extract_numpy=True)
+        if copy:
+            values = values.copy()
+        if values.ndim == 1:
+            values = values.reshape(-1, 1)
+
+    elif isinstance(values, (ABCSeries, Index)):
+        if not copy_on_sanitize and (
+            dtype is None or astype_is_view(values.dtype, dtype)
+        ):
+            refs = values._references
+
+        if copy_on_sanitize:
+            values = values._values.copy()
+        else:
+            values = values._values
+
+        values = _ensure_2d(values)
+
+    elif isinstance(values, (np.ndarray, ExtensionArray)):
+        # drop subclass info
+        if copy_on_sanitize and (dtype is None or astype_is_view(values.dtype, dtype)):
+            # only force a copy now if copy=True was requested
+            # and a subsequent `astype` will not already result in a copy
+            values = np.array(values, copy=True, order="F")
+        else:
+            values = np.asarray(values)
+        values = _ensure_2d(values)
+
+    else:
+        # by definition an array here
+        # the dtypes will be coerced to a single dtype
+        values = _prep_ndarraylike(values, copy=copy_on_sanitize)
+
+    if dtype is not None and values.dtype != dtype:
+        # GH#40110 see similar check inside sanitize_array
+        values = sanitize_array(
+            values,
+            None,
+            dtype=dtype,
+            copy=copy_on_sanitize,
+            allow_2d=True,
+        )
+
+    # _prep_ndarraylike ensures that values.ndim == 2 at this point
+    index, columns = _get_axes(
+        values.shape[0], values.shape[1], index=index, columns=columns
+    )
+
+    _check_values_indices_shape_match(values, index, columns)
+
+    if typ == "array":
+        if issubclass(values.dtype.type, str):
+            values = np.array(values, dtype=object)
+
+        if dtype is None and is_object_dtype(values.dtype):
+            arrays = [
+                ensure_wrapped_if_datetimelike(
+                    maybe_infer_to_datetimelike(values[:, i])
+                )
+                for i in range(values.shape[1])
+            ]
+        else:
+            if lib.is_np_dtype(values.dtype, "mM"):
+                values = ensure_wrapped_if_datetimelike(values)
+            arrays = [values[:, i] for i in range(values.shape[1])]
+
+        if copy:
+            arrays = [arr.copy() for arr in arrays]
+
+        return ArrayManager(arrays, [index, columns], verify_integrity=False)
+
+    values = values.T
+
+    # if we don't have a dtype specified, then try to convert objects
+    # on the entire block; this is to convert if we have datetimelike's
+    # embedded in an object type
+    if dtype is None and is_object_dtype(values.dtype):
+        obj_columns = list(values)
+        maybe_datetime = [maybe_infer_to_datetimelike(x) for x in obj_columns]
+        # don't convert (and copy) the objects if no type inference occurs
+        if any(x is not y for x, y in zip(obj_columns, maybe_datetime)):
+            dvals_list = [ensure_block_shape(dval, 2) for dval in maybe_datetime]
+            block_values = [
+                new_block_2d(dvals_list[n], placement=BlockPlacement(n))
+                for n in range(len(dvals_list))
+            ]
+        else:
+            bp = BlockPlacement(slice(len(columns)))
+            nb = new_block_2d(values, placement=bp, refs=refs)
+            block_values = [nb]
+    elif dtype is None and values.dtype.kind == "U" and using_string_dtype():
+        dtype = StringDtype(na_value=np.nan)
+
+        obj_columns = list(values)
+        block_values = [
+            new_block(
+                dtype.construct_array_type()._from_sequence(data, dtype=dtype),
+                BlockPlacement(slice(i, i + 1)),
+                ndim=2,
+            )
+            for i, data in enumerate(obj_columns)
+        ]
+
+    else:
+        bp = BlockPlacement(slice(len(columns)))
+        nb = new_block_2d(values, placement=bp, refs=refs)
+        block_values = [nb]
+
+    if len(columns) == 0:
+        # TODO: check len(values) == 0?
+        block_values = []
+
+    return create_block_manager_from_blocks(
+        block_values, [columns, index], verify_integrity=False
+    )
+
+
+def _check_values_indices_shape_match(
+    values: np.ndarray, index: Index, columns: Index
+) -> None:
+    """
+    Check that the shape implied by our axes matches the actual shape of the
+    data.
+    """
+    if values.shape[1] != len(columns) or values.shape[0] != len(index):
+        # Could let this raise in Block constructor, but we get a more
+        #  helpful exception message this way.
+        if values.shape[0] == 0 < len(index):
+            raise ValueError("Empty data passed with indices specified.")
+
+        passed = values.shape
+        implied = (len(index), len(columns))
+        raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
+
+
+def dict_to_mgr(
+    data: dict,
+    index,
+    columns,
+    *,
+    dtype: DtypeObj | None = None,
+    typ: str = "block",
+    copy: bool = True,
+) -> Manager:
+    """
+    Segregate Series based on type and coerce into matrices.
+    Needs to handle a lot of exceptional cases.
+
+    Used in DataFrame.__init__
+    """
+    arrays: Sequence[Any] | Series
+
+    if columns is not None:
+        from pandas.core.series import Series
+
+        arrays = Series(data, index=columns, dtype=object)
+        missing = arrays.isna()
+        if index is None:
+            # GH10856
+            # raise ValueError if only scalars in dict
+            index = _extract_index(arrays[~missing])
+        else:
+            index = ensure_index(index)
+
+        # no obvious "empty" int column
+        if missing.any() and not is_integer_dtype(dtype):
+            nan_dtype: DtypeObj
+
+            if dtype is not None:
+                # calling sanitize_array ensures we don't mix-and-match
+                #  NA dtypes
+                midxs = missing.values.nonzero()[0]
+                for i in midxs:
+                    arr = sanitize_array(arrays.iat[i], index, dtype=dtype)
+                    arrays.iat[i] = arr
+            else:
+                # GH#1783
+                nan_dtype = np.dtype("object")
+                val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype)
+                nmissing = missing.sum()
+                if copy:
+                    rhs = [val] * nmissing
+                else:
+                    # GH#45369
+                    rhs = [val.copy() for _ in range(nmissing)]
+                arrays.loc[missing] = rhs
+
+        arrays = list(arrays)
+        columns = ensure_index(columns)
+
+    else:
+        keys = list(data.keys())
+        columns = Index(keys) if keys else default_index(0)
+        arrays = [com.maybe_iterable_to_list(data[k]) for k in keys]
+
+    if copy:
+        if typ == "block":
+            # We only need to copy arrays that will not get consolidated, i.e.
+            #  only EA arrays
+            arrays = [
+                x.copy()
+                if isinstance(x, ExtensionArray)
+                else x.copy(deep=True)
+                if (
+                    isinstance(x, Index)
+                    or isinstance(x, ABCSeries)
+                    and is_1d_only_ea_dtype(x.dtype)
+                )
+                else x
+                for x in arrays
+            ]
+        else:
+            # dtype check to exclude e.g. range objects, scalars
+            arrays = [x.copy() if hasattr(x, "dtype") else x for x in arrays]
+
+    return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
+
+
+def nested_data_to_arrays(
+    data: Sequence,
+    columns: Index | None,
+    index: Index | None,
+    dtype: DtypeObj | None,
+) -> tuple[list[ArrayLike], Index, Index]:
+    """
+    Convert a single sequence of arrays to multiple arrays.
+    """
+    # By the time we get here we have already checked treat_as_nested(data)
+
+    if is_named_tuple(data[0]) and columns is None:
+        columns = ensure_index(data[0]._fields)
+
+    arrays, columns = to_arrays(data, columns, dtype=dtype)
+    columns = ensure_index(columns)
+
+    if index is None:
+        if isinstance(data[0], ABCSeries):
+            index = _get_names_from_index(data)
+        else:
+            index = default_index(len(data))
+
+    return arrays, columns, index
+
+
+def treat_as_nested(data) -> bool:
+    """
+    Check if we should use nested_data_to_arrays.
+    """
+    return (
+        len(data) > 0
+        and is_list_like(data[0])
+        and getattr(data[0], "ndim", 1) == 1
+        and not (isinstance(data, ExtensionArray) and data.ndim == 2)
+    )
+
+
+# ---------------------------------------------------------------------
+
+
+def _prep_ndarraylike(values, copy: bool = True) -> np.ndarray:
+    # values is specifically _not_ ndarray, EA, Index, or Series
+    # We only get here with `not treat_as_nested(values)`
+
+    if len(values) == 0:
+        # TODO: check for length-zero range, in which case return int64 dtype?
+        # TODO: reuse anything in try_cast?
+        return np.empty((0, 0), dtype=object)
+    elif isinstance(values, range):
+        arr = range_to_ndarray(values)
+        return arr[..., np.newaxis]
+
+    def convert(v):
+        if not is_list_like(v) or isinstance(v, ABCDataFrame):
+            return v
+
+        v = extract_array(v, extract_numpy=True)
+        res = maybe_convert_platform(v)
+        # We don't do maybe_infer_to_datetimelike here bc we will end up doing
+        #  it column-by-column in ndarray_to_mgr
+        return res
+
+    # we could have a 1-dim or 2-dim list here
+    # this is equiv of np.asarray, but does object conversion
+    # and platform dtype preservation
+    # does not convert e.g. [1, "a", True] to ["1", "a", "True"] like
+    #  np.asarray would
+    if is_list_like(values[0]):
+        values = np.array([convert(v) for v in values])
+    elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
+        # GH#21861 see test_constructor_list_of_lists
+        values = np.array([convert(v) for v in values])
+    else:
+        values = convert(values)
+
+    return _ensure_2d(values)
+
+
+def _ensure_2d(values: np.ndarray) -> np.ndarray:
+    """
+    Reshape 1D values, raise on anything else other than 2D.
+    """
+    if values.ndim == 1:
+        values = values.reshape((values.shape[0], 1))
+    elif values.ndim != 2:
+        raise ValueError(f"Must pass 2-d input. shape={values.shape}")
+    return values
+
+
+def _homogenize(
+    data, index: Index, dtype: DtypeObj | None
+) -> tuple[list[ArrayLike], list[Any]]:
+    oindex = None
+    homogenized = []
+    # if the original array-like in `data` is a Series, keep track of this Series' refs
+    refs: list[Any] = []
+
+    for val in data:
+        if isinstance(val, (ABCSeries, Index)):
+            if dtype is not None:
+                val = val.astype(dtype, copy=False)
+            if isinstance(val, ABCSeries) and val.index is not index:
+                # Forces alignment. No need to copy data since we
+                # are putting it into an ndarray later
+                val = val.reindex(index, copy=False)
+            refs.append(val._references)
+            val = val._values
+        else:
+            if isinstance(val, dict):
+                # GH#41785 this _should_ be equivalent to (but faster than)
+                #  val = Series(val, index=index)._values
+                if oindex is None:
+                    oindex = index.astype("O")
+
+                if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+                    # see test_constructor_dict_datetime64_index
+                    val = dict_compat(val)
+                else:
+                    # see test_constructor_subclass_dict
+                    val = dict(val)
+                val = lib.fast_multiget(val, oindex._values, default=np.nan)
+
+            val = sanitize_array(val, index, dtype=dtype, copy=False)
+            com.require_length_match(val, index)
+            refs.append(None)
+
+        homogenized.append(val)
+
+    return homogenized, refs
+
+
+def _extract_index(data) -> Index:
+    """
+    Try to infer an Index from the passed data, raise ValueError on failure.
+    """
+    index: Index
+    if len(data) == 0:
+        return default_index(0)
+
+    raw_lengths = []
+    indexes: list[list[Hashable] | Index] = []
+
+    have_raw_arrays = False
+    have_series = False
+    have_dicts = False
+
+    for val in data:
+        if isinstance(val, ABCSeries):
+            have_series = True
+            indexes.append(val.index)
+        elif isinstance(val, dict):
+            have_dicts = True
+            indexes.append(list(val.keys()))
+        elif is_list_like(val) and getattr(val, "ndim", 1) == 1:
+            have_raw_arrays = True
+            raw_lengths.append(len(val))
+        elif isinstance(val, np.ndarray) and val.ndim > 1:
+            raise ValueError("Per-column arrays must each be 1-dimensional")
+
+    if not indexes and not raw_lengths:
+        raise ValueError("If using all scalar values, you must pass an index")
+
+    if have_series:
+        index = union_indexes(indexes)
+    elif have_dicts:
+        index = union_indexes(indexes, sort=False)
+
+    if have_raw_arrays:
+        lengths = list(set(raw_lengths))
+        if len(lengths) > 1:
+            raise ValueError("All arrays must be of the same length")
+
+        if have_dicts:
+            raise ValueError(
+                "Mixing dicts with non-Series may lead to ambiguous ordering."
+            )
+
+        if have_series:
+            if lengths[0] != len(index):
+                msg = (
+                    f"array length {lengths[0]} does not match index "
+                    f"length {len(index)}"
+                )
+                raise ValueError(msg)
+        else:
+            index = default_index(lengths[0])
+
+    return ensure_index(index)
+
+
+def reorder_arrays(
+    arrays: list[ArrayLike], arr_columns: Index, columns: Index | None, length: int
+) -> tuple[list[ArrayLike], Index]:
+    """
+    Pre-emptively (cheaply) reindex arrays with new columns.
+    """
+    # reorder according to the columns
+    if columns is not None:
+        if not columns.equals(arr_columns):
+            # if they are equal, there is nothing to do
+            new_arrays: list[ArrayLike] = []
+            indexer = arr_columns.get_indexer(columns)
+            for i, k in enumerate(indexer):
+                if k == -1:
+                    # by convention default is all-NaN object dtype
+                    arr = np.empty(length, dtype=object)
+                    arr.fill(np.nan)
+                else:
+                    arr = arrays[k]
+                new_arrays.append(arr)
+
+            arrays = new_arrays
+            arr_columns = columns
+
+    return arrays, arr_columns
+
+
+def _get_names_from_index(data) -> Index:
+    has_some_name = any(getattr(s, "name", None) is not None for s in data)
+    if not has_some_name:
+        return default_index(len(data))
+
+    index: list[Hashable] = list(range(len(data)))
+    count = 0
+    for i, s in enumerate(data):
+        n = getattr(s, "name", None)
+        if n is not None:
+            index[i] = n
+        else:
+            index[i] = f"Unnamed {count}"
+            count += 1
+
+    return Index(index)
+
+
+def _get_axes(
+    N: int, K: int, index: Index | None, columns: Index | None
+) -> tuple[Index, Index]:
+    # helper to create the axes as indexes
+    # return axes or defaults
+
+    if index is None:
+        index = default_index(N)
+    else:
+        index = ensure_index(index)
+
+    if columns is None:
+        columns = default_index(K)
+    else:
+        columns = ensure_index(columns)
+    return index, columns
+
+
+def dataclasses_to_dicts(data):
+    """
+    Converts a list of dataclass instances to a list of dictionaries.
+
+    Parameters
+    ----------
+    data : List[Type[dataclass]]
+
+    Returns
+    --------
+    list_dict : List[dict]
+
+    Examples
+    --------
+    >>> from dataclasses import dataclass
+    >>> @dataclass
+    ... class Point:
+    ...     x: int
+    ...     y: int
+
+    >>> dataclasses_to_dicts([Point(1, 2), Point(2, 3)])
+    [{'x': 1, 'y': 2}, {'x': 2, 'y': 3}]
+
+    """
+    from dataclasses import asdict
+
+    return list(map(asdict, data))
+
+
+# ---------------------------------------------------------------------
+# Conversion of Inputs to Arrays
+
+
+def to_arrays(
+    data, columns: Index | None, dtype: DtypeObj | None = None
+) -> tuple[list[ArrayLike], Index]:
+    """
+    Return list of arrays, columns.
+
+    Returns
+    -------
+    list[ArrayLike]
+        These will become columns in a DataFrame.
+    Index
+        This will become frame.columns.
+
+    Notes
+    -----
+    Ensures that len(result_arrays) == len(result_index).
+    """
+
+    if not len(data):
+        if isinstance(data, np.ndarray):
+            if data.dtype.names is not None:
+                # i.e. numpy structured array
+                columns = ensure_index(data.dtype.names)
+                arrays = [data[name] for name in columns]
+
+                if len(data) == 0:
+                    # GH#42456 the indexing above results in list of 2D ndarrays
+                    # TODO: is that an issue with numpy?
+                    for i, arr in enumerate(arrays):
+                        if arr.ndim == 2:
+                            arrays[i] = arr[:, 0]
+
+                return arrays, columns
+        return [], ensure_index([])
+
+    elif isinstance(data, np.ndarray) and data.dtype.names is not None:
+        # e.g. recarray
+        columns = Index(list(data.dtype.names))
+        arrays = [data[k] for k in columns]
+        return arrays, columns
+
+    if isinstance(data[0], (list, tuple)):
+        arr = _list_to_arrays(data)
+    elif isinstance(data[0], abc.Mapping):
+        arr, columns = _list_of_dict_to_arrays(data, columns)
+    elif isinstance(data[0], ABCSeries):
+        arr, columns = _list_of_series_to_arrays(data, columns)
+    else:
+        # last ditch effort
+        data = [tuple(x) for x in data]
+        arr = _list_to_arrays(data)
+
+    content, columns = _finalize_columns_and_data(arr, columns, dtype)
+    return content, columns
+
+
+def _list_to_arrays(data: list[tuple | list]) -> np.ndarray:
+    # Returned np.ndarray has ndim = 2
+    # Note: we already check len(data) > 0 before getting hre
+    if isinstance(data[0], tuple):
+        content = lib.to_object_array_tuples(data)
+    else:
+        # list of lists
+        content = lib.to_object_array(data)
+    return content
+
+
+def _list_of_series_to_arrays(
+    data: list,
+    columns: Index | None,
+) -> tuple[np.ndarray, Index]:
+    # returned np.ndarray has ndim == 2
+
+    if columns is None:
+        # We know pass_data is non-empty because data[0] is a Series
+        pass_data = [x for x in data if isinstance(x, (ABCSeries, ABCDataFrame))]
+        columns = get_objs_combined_axis(pass_data, sort=False)
+
+    indexer_cache: dict[int, np.ndarray] = {}
+
+    aligned_values = []
+    for s in data:
+        index = getattr(s, "index", None)
+        if index is None:
+            index = default_index(len(s))
+
+        if id(index) in indexer_cache:
+            indexer = indexer_cache[id(index)]
+        else:
+            indexer = indexer_cache[id(index)] = index.get_indexer(columns)
+
+        values = extract_array(s, extract_numpy=True)
+        aligned_values.append(algorithms.take_nd(values, indexer))
+
+    content = np.vstack(aligned_values)
+    return content, columns
+
+
+def _list_of_dict_to_arrays(
+    data: list[dict],
+    columns: Index | None,
+) -> tuple[np.ndarray, Index]:
+    """
+    Convert list of dicts to numpy arrays
+
+    if `columns` is not passed, column names are inferred from the records
+    - for OrderedDict and dicts, the column names match
+      the key insertion-order from the first record to the last.
+    - For other kinds of dict-likes, the keys are lexically sorted.
+
+    Parameters
+    ----------
+    data : iterable
+        collection of records (OrderedDict, dict)
+    columns: iterables or None
+
+    Returns
+    -------
+    content : np.ndarray[object, ndim=2]
+    columns : Index
+    """
+    if columns is None:
+        gen = (list(x.keys()) for x in data)
+        sort = not any(isinstance(d, dict) for d in data)
+        pre_cols = lib.fast_unique_multiple_list_gen(gen, sort=sort)
+        columns = ensure_index(pre_cols)
+
+    # assure that they are of the base dict class and not of derived
+    # classes
+    data = [d if type(d) is dict else dict(d) for d in data]  # noqa: E721
+
+    content = lib.dicts_to_array(data, list(columns))
+    return content, columns
+
+
+def _finalize_columns_and_data(
+    content: np.ndarray,  # ndim == 2
+    columns: Index | None,
+    dtype: DtypeObj | None,
+) -> tuple[list[ArrayLike], Index]:
+    """
+    Ensure we have valid columns, cast object dtypes if possible.
+    """
+    contents = list(content.T)
+
+    try:
+        columns = _validate_or_indexify_columns(contents, columns)
+    except AssertionError as err:
+        # GH#26429 do not raise user-facing AssertionError
+        raise ValueError(err) from err
+
+    if len(contents) and contents[0].dtype == np.object_:
+        contents = convert_object_array(contents, dtype=dtype)
+
+    return contents, columns
+
+
+def _validate_or_indexify_columns(
+    content: list[np.ndarray], columns: Index | None
+) -> Index:
+    """
+    If columns is None, make numbers as column names; Otherwise, validate that
+    columns have valid length.
+
+    Parameters
+    ----------
+    content : list of np.ndarrays
+    columns : Index or None
+
+    Returns
+    -------
+    Index
+        If columns is None, assign positional column index value as columns.
+
+    Raises
+    ------
+    1. AssertionError when content is not composed of list of lists, and if
+        length of columns is not equal to length of content.
+    2. ValueError when content is list of lists, but length of each sub-list
+        is not equal
+    3. ValueError when content is list of lists, but length of sub-list is
+        not equal to length of content
+    """
+    if columns is None:
+        columns = default_index(len(content))
+    else:
+        # Add mask for data which is composed of list of lists
+        is_mi_list = isinstance(columns, list) and all(
+            isinstance(col, list) for col in columns
+        )
+
+        if not is_mi_list and len(columns) != len(content):  # pragma: no cover
+            # caller's responsibility to check for this...
+            raise AssertionError(
+                f"{len(columns)} columns passed, passed data had "
+                f"{len(content)} columns"
+            )
+        if is_mi_list:
+            # check if nested list column, length of each sub-list should be equal
+            if len({len(col) for col in columns}) > 1:
+                raise ValueError(
+                    "Length of columns passed for MultiIndex columns is different"
+                )
+
+            # if columns is not empty and length of sublist is not equal to content
+            if columns and len(columns[0]) != len(content):
+                raise ValueError(
+                    f"{len(columns[0])} columns passed, passed data had "
+                    f"{len(content)} columns"
+                )
+    return columns
+
+
+def convert_object_array(
+    content: list[npt.NDArray[np.object_]],
+    dtype: DtypeObj | None,
+    dtype_backend: str = "numpy",
+    coerce_float: bool = False,
+) -> list[ArrayLike]:
+    """
+    Internal function to convert object array.
+
+    Parameters
+    ----------
+    content: List[np.ndarray]
+    dtype: np.dtype or ExtensionDtype
+    dtype_backend: Controls if nullable/pyarrow dtypes are returned.
+    coerce_float: Cast floats that are integers to int.
+
+    Returns
+    -------
+    List[ArrayLike]
+    """
+    # provide soft conversion of object dtypes
+
+    def convert(arr):
+        if dtype != np.dtype("O"):
+            arr = lib.maybe_convert_objects(
+                arr,
+                try_float=coerce_float,
+                convert_to_nullable_dtype=dtype_backend != "numpy",
+            )
+            # Notes on cases that get here 2023-02-15
+            # 1) we DO get here when arr is all Timestamps and dtype=None
+            # 2) disabling this doesn't break the world, so this must be
+            #    getting caught at a higher level
+            # 3) passing convert_non_numeric to maybe_convert_objects get this right
+            # 4) convert_non_numeric?
+
+            if dtype is None:
+                if arr.dtype == np.dtype("O"):
+                    # i.e. maybe_convert_objects didn't convert
+                    convert_to_nullable_dtype = dtype_backend != "numpy"
+                    arr = maybe_infer_to_datetimelike(arr, convert_to_nullable_dtype)
+                    if convert_to_nullable_dtype and arr.dtype == np.dtype("O"):
+                        new_dtype = StringDtype()
+                        arr_cls = new_dtype.construct_array_type()
+                        arr = arr_cls._from_sequence(arr, dtype=new_dtype)
+                elif dtype_backend != "numpy" and isinstance(arr, np.ndarray):
+                    if arr.dtype.kind in "iufb":
+                        arr = pd_array(arr, copy=False)
+
+            elif isinstance(dtype, ExtensionDtype):
+                # TODO: test(s) that get here
+                # TODO: try to de-duplicate this convert function with
+                #  core.construction functions
+                cls = dtype.construct_array_type()
+                arr = cls._from_sequence(arr, dtype=dtype, copy=False)
+            elif dtype.kind in "mM":
+                # This restriction is harmless bc these are the only cases
+                #  where maybe_cast_to_datetime is not a no-op.
+                # Here we know:
+                #  1) dtype.kind in "mM" and
+                #  2) arr is either object or numeric dtype
+                arr = maybe_cast_to_datetime(arr, dtype)
+
+        return arr
+
+    arrays = [convert(arr) for arr in content]
+
+    return arrays
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/managers.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/managers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e3a67d5363fdd7333217df1256c29df58df8665
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/managers.py
@@ -0,0 +1,2394 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+
+from pandas._libs import (
+    internals as libinternals,
+    lib,
+)
+from pandas._libs.internals import (
+    BlockPlacement,
+    BlockValuesRefs,
+)
+from pandas._libs.tslibs import Timestamp
+from pandas.errors import PerformanceWarning
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import infer_dtype_from_scalar
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_1d_only_ea_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    array_equals,
+    isna,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    ArrowStringArray,
+    DatetimeArray,
+)
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.indexers import maybe_convert_indices
+from pandas.core.indexes.api import (
+    Index,
+    ensure_index,
+)
+from pandas.core.internals.base import (
+    DataManager,
+    SingleDataManager,
+    ensure_np_dtype,
+    interleaved_dtype,
+)
+from pandas.core.internals.blocks import (
+    COW_WARNING_GENERAL_MSG,
+    COW_WARNING_SETITEM_MSG,
+    Block,
+    NumpyBlock,
+    ensure_block_shape,
+    extend_blocks,
+    get_block_type,
+    maybe_coerce_values,
+    new_block,
+    new_block_2d,
+)
+from pandas.core.internals.ops import (
+    blockwise_all,
+    operate_blockwise,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        DtypeObj,
+        QuantileInterpolation,
+        Self,
+        Shape,
+        npt,
+    )
+
+    from pandas.api.extensions import ExtensionArray
+
+
+class BaseBlockManager(DataManager):
+    """
+    Core internal data structure to implement DataFrame, Series, etc.
+
+    Manage a bunch of labeled 2D mixed-type ndarrays. Essentially it's a
+    lightweight blocked set of labeled data to be manipulated by the DataFrame
+    public API class
+
+    Attributes
+    ----------
+    shape
+    ndim
+    axes
+    values
+    items
+
+    Methods
+    -------
+    set_axis(axis, new_labels)
+    copy(deep=True)
+
+    get_dtypes
+
+    apply(func, axes, block_filter_fn)
+
+    get_bool_data
+    get_numeric_data
+
+    get_slice(slice_like, axis)
+    get(label)
+    iget(loc)
+
+    take(indexer, axis)
+    reindex_axis(new_labels, axis)
+    reindex_indexer(new_labels, indexer, axis)
+
+    delete(label)
+    insert(loc, label, value)
+    set(label, value)
+
+    Parameters
+    ----------
+    blocks: Sequence of Block
+    axes: Sequence of Index
+    verify_integrity: bool, default True
+
+    Notes
+    -----
+    This is *not* a public API class
+    """
+
+    __slots__ = ()
+
+    _blknos: npt.NDArray[np.intp]
+    _blklocs: npt.NDArray[np.intp]
+    blocks: tuple[Block, ...]
+    axes: list[Index]
+
+    @property
+    def ndim(self) -> int:
+        raise NotImplementedError
+
+    _known_consolidated: bool
+    _is_consolidated: bool
+
+    def __init__(self, blocks, axes, verify_integrity: bool = True) -> None:
+        raise NotImplementedError
+
+    @classmethod
+    def from_blocks(cls, blocks: list[Block], axes: list[Index]) -> Self:
+        raise NotImplementedError
+
+    @property
+    def blknos(self) -> npt.NDArray[np.intp]:
+        """
+        Suppose we want to find the array corresponding to our i'th column.
+
+        blknos[i] identifies the block from self.blocks that contains this column.
+
+        blklocs[i] identifies the column of interest within
+        self.blocks[self.blknos[i]]
+        """
+        if self._blknos is None:
+            # Note: these can be altered by other BlockManager methods.
+            self._rebuild_blknos_and_blklocs()
+
+        return self._blknos
+
+    @property
+    def blklocs(self) -> npt.NDArray[np.intp]:
+        """
+        See blknos.__doc__
+        """
+        if self._blklocs is None:
+            # Note: these can be altered by other BlockManager methods.
+            self._rebuild_blknos_and_blklocs()
+
+        return self._blklocs
+
+    def make_empty(self, axes=None) -> Self:
+        """return an empty BlockManager with the items axis of len 0"""
+        if axes is None:
+            axes = [Index([])] + self.axes[1:]
+
+        # preserve dtype if possible
+        if self.ndim == 1:
+            assert isinstance(self, SingleBlockManager)  # for mypy
+            blk = self.blocks[0]
+            arr = blk.values[:0]
+            bp = BlockPlacement(slice(0, 0))
+            nb = blk.make_block_same_class(arr, placement=bp)
+            blocks = [nb]
+        else:
+            blocks = []
+        return type(self).from_blocks(blocks, axes)
+
+    def __nonzero__(self) -> bool:
+        return True
+
+    # Python3 compat
+    __bool__ = __nonzero__
+
+    def _normalize_axis(self, axis: AxisInt) -> int:
+        # switch axis to follow BlockManager logic
+        if self.ndim == 2:
+            axis = 1 if axis == 0 else 0
+        return axis
+
+    def set_axis(self, axis: AxisInt, new_labels: Index) -> None:
+        # Caller is responsible for ensuring we have an Index object.
+        self._validate_set_axis(axis, new_labels)
+        self.axes[axis] = new_labels
+
+    @property
+    def is_single_block(self) -> bool:
+        # Assumes we are 2D; overridden by SingleBlockManager
+        return len(self.blocks) == 1
+
+    @property
+    def items(self) -> Index:
+        return self.axes[0]
+
+    def _has_no_reference(self, i: int) -> bool:
+        """
+        Check for column `i` if it has references.
+        (whether it references another array or is itself being referenced)
+        Returns True if the column has no references.
+        """
+        blkno = self.blknos[i]
+        return self._has_no_reference_block(blkno)
+
+    def _has_no_reference_block(self, blkno: int) -> bool:
+        """
+        Check for block `i` if it has references.
+        (whether it references another array or is itself being referenced)
+        Returns True if the block has no references.
+        """
+        return not self.blocks[blkno].refs.has_reference()
+
+    def add_references(self, mgr: BaseBlockManager) -> None:
+        """
+        Adds the references from one manager to another. We assume that both
+        managers have the same block structure.
+        """
+        if len(self.blocks) != len(mgr.blocks):
+            # If block structure changes, then we made a copy
+            return
+        for i, blk in enumerate(self.blocks):
+            blk.refs = mgr.blocks[i].refs
+            blk.refs.add_reference(blk)
+
+    def references_same_values(self, mgr: BaseBlockManager, blkno: int) -> bool:
+        """
+        Checks if two blocks from two different block managers reference the
+        same underlying values.
+        """
+        blk = self.blocks[blkno]
+        return any(blk is ref() for ref in mgr.blocks[blkno].refs.referenced_blocks)
+
+    def get_dtypes(self) -> npt.NDArray[np.object_]:
+        dtypes = np.array([blk.dtype for blk in self.blocks], dtype=object)
+        return dtypes.take(self.blknos)
+
+    @property
+    def arrays(self) -> list[ArrayLike]:
+        """
+        Quick access to the backing arrays of the Blocks.
+
+        Only for compatibility with ArrayManager for testing convenience.
+        Not to be used in actual code, and return value is not the same as the
+        ArrayManager method (list of 1D arrays vs iterator of 2D ndarrays / 1D EAs).
+
+        Warning! The returned arrays don't handle Copy-on-Write, so this should
+        be used with caution (only in read-mode).
+        """
+        return [blk.values for blk in self.blocks]
+
+    def __repr__(self) -> str:
+        output = type(self).__name__
+        for i, ax in enumerate(self.axes):
+            if i == 0:
+                output += f"\nItems: {ax}"
+            else:
+                output += f"\nAxis {i}: {ax}"
+
+        for block in self.blocks:
+            output += f"\n{block}"
+        return output
+
+    def apply(
+        self,
+        f,
+        align_keys: list[str] | None = None,
+        **kwargs,
+    ) -> Self:
+        """
+        Iterate over the blocks, collect and create a new BlockManager.
+
+        Parameters
+        ----------
+        f : str or callable
+            Name of the Block method to apply.
+        align_keys: List[str] or None, default None
+        **kwargs
+            Keywords to pass to `f`
+
+        Returns
+        -------
+        BlockManager
+        """
+        assert "filter" not in kwargs
+
+        align_keys = align_keys or []
+        result_blocks: list[Block] = []
+        # fillna: Series/DataFrame is responsible for making sure value is aligned
+
+        aligned_args = {k: kwargs[k] for k in align_keys}
+
+        for b in self.blocks:
+            if aligned_args:
+                for k, obj in aligned_args.items():
+                    if isinstance(obj, (ABCSeries, ABCDataFrame)):
+                        # The caller is responsible for ensuring that
+                        #  obj.axes[-1].equals(self.items)
+                        if obj.ndim == 1:
+                            kwargs[k] = obj.iloc[b.mgr_locs.indexer]._values
+                        else:
+                            kwargs[k] = obj.iloc[:, b.mgr_locs.indexer]._values
+                    else:
+                        # otherwise we have an ndarray
+                        kwargs[k] = obj[b.mgr_locs.indexer]
+
+            if callable(f):
+                applied = b.apply(f, **kwargs)
+            else:
+                applied = getattr(b, f)(**kwargs)
+            result_blocks = extend_blocks(applied, result_blocks)
+
+        out = type(self).from_blocks(result_blocks, self.axes)
+        return out
+
+    # Alias so we can share code with ArrayManager
+    apply_with_block = apply
+
+    def setitem(self, indexer, value, warn: bool = True) -> Self:
+        """
+        Set values with indexer.
+
+        For SingleBlockManager, this backs s[indexer] = value
+        """
+        if isinstance(indexer, np.ndarray) and indexer.ndim > self.ndim:
+            raise ValueError(f"Cannot set values with ndim > {self.ndim}")
+
+        if warn and warn_copy_on_write() and not self._has_no_reference(0):
+            warnings.warn(
+                COW_WARNING_GENERAL_MSG,
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        elif using_copy_on_write() and not self._has_no_reference(0):
+            # this method is only called if there is a single block -> hardcoded 0
+            # Split blocks to only copy the columns we want to modify
+            if self.ndim == 2 and isinstance(indexer, tuple):
+                blk_loc = self.blklocs[indexer[1]]
+                if is_list_like(blk_loc) and blk_loc.ndim == 2:
+                    blk_loc = np.squeeze(blk_loc, axis=0)
+                elif not is_list_like(blk_loc):
+                    # Keep dimension and copy data later
+                    blk_loc = [blk_loc]  # type: ignore[assignment]
+                if len(blk_loc) == 0:
+                    return self.copy(deep=False)
+
+                values = self.blocks[0].values
+                if values.ndim == 2:
+                    values = values[blk_loc]
+                    # "T" has no attribute "_iset_split_block"
+                    self._iset_split_block(  # type: ignore[attr-defined]
+                        0, blk_loc, values
+                    )
+
+                    indexer = list(indexer)
+                    # first block equals values we are setting to -> set to all columns
+                    if lib.is_integer(indexer[1]):
+                        col_indexer = 0
+                    elif len(blk_loc) > 1:
+                        col_indexer = slice(None)  # type: ignore[assignment]
+                    else:
+                        col_indexer = np.arange(len(blk_loc))  # type: ignore[assignment]
+                    indexer[1] = col_indexer
+
+                    row_indexer = indexer[0]
+                    if isinstance(row_indexer, np.ndarray) and row_indexer.ndim == 2:
+                        # numpy cannot handle a 2d indexer in combo with a slice
+                        row_indexer = np.squeeze(row_indexer, axis=1)
+                    if isinstance(row_indexer, np.ndarray) and len(row_indexer) == 0:
+                        # numpy does not like empty indexer combined with slice
+                        # and we are setting nothing anyway
+                        return self
+                    indexer[0] = row_indexer
+                    self.blocks[0].setitem(tuple(indexer), value)
+                    return self
+            # No need to split if we either set all columns or on a single block
+            # manager
+            self = self.copy()
+
+        return self.apply("setitem", indexer=indexer, value=value)
+
+    def diff(self, n: int) -> Self:
+        # only reached with self.ndim == 2
+        return self.apply("diff", n=n)
+
+    def astype(self, dtype, copy: bool | None = False, errors: str = "raise") -> Self:
+        if copy is None:
+            if using_copy_on_write():
+                copy = False
+            else:
+                copy = True
+        elif using_copy_on_write():
+            copy = False
+
+        return self.apply(
+            "astype",
+            dtype=dtype,
+            copy=copy,
+            errors=errors,
+            using_cow=using_copy_on_write(),
+        )
+
+    def convert(self, copy: bool | None) -> Self:
+        if copy is None:
+            if using_copy_on_write():
+                copy = False
+            else:
+                copy = True
+        elif using_copy_on_write():
+            copy = False
+
+        return self.apply("convert", copy=copy, using_cow=using_copy_on_write())
+
+    def convert_dtypes(self, **kwargs):
+        if using_copy_on_write():
+            copy = False
+        else:
+            copy = True
+
+        return self.apply(
+            "convert_dtypes", copy=copy, using_cow=using_copy_on_write(), **kwargs
+        )
+
+    def get_values_for_csv(
+        self, *, float_format, date_format, decimal, na_rep: str = "nan", quoting=None
+    ) -> Self:
+        """
+        Convert values to native types (strings / python objects) that are used
+        in formatting (repr / csv).
+        """
+        return self.apply(
+            "get_values_for_csv",
+            na_rep=na_rep,
+            quoting=quoting,
+            float_format=float_format,
+            date_format=date_format,
+            decimal=decimal,
+        )
+
+    @property
+    def any_extension_types(self) -> bool:
+        """Whether any of the blocks in this manager are extension blocks"""
+        return any(block.is_extension for block in self.blocks)
+
+    @property
+    def is_view(self) -> bool:
+        """return a boolean if we are a single block and are a view"""
+        if len(self.blocks) == 1:
+            return self.blocks[0].is_view
+
+        # It is technically possible to figure out which blocks are views
+        # e.g. [ b.values.base is not None for b in self.blocks ]
+        # but then we have the case of possibly some blocks being a view
+        # and some blocks not. setting in theory is possible on the non-view
+        # blocks w/o causing a SettingWithCopy raise/warn. But this is a bit
+        # complicated
+
+        return False
+
+    def _get_data_subset(self, predicate: Callable) -> Self:
+        blocks = [blk for blk in self.blocks if predicate(blk.values)]
+        return self._combine(blocks)
+
+    def get_bool_data(self) -> Self:
+        """
+        Select blocks that are bool-dtype and columns from object-dtype blocks
+        that are all-bool.
+        """
+
+        new_blocks = []
+
+        for blk in self.blocks:
+            if blk.dtype == bool:
+                new_blocks.append(blk)
+
+            elif blk.is_object:
+                nbs = blk._split()
+                new_blocks.extend(nb for nb in nbs if nb.is_bool)
+
+        return self._combine(new_blocks)
+
+    def get_numeric_data(self) -> Self:
+        numeric_blocks = [blk for blk in self.blocks if blk.is_numeric]
+        if len(numeric_blocks) == len(self.blocks):
+            # Avoid somewhat expensive _combine
+            return self
+        return self._combine(numeric_blocks)
+
+    def _combine(self, blocks: list[Block], index: Index | None = None) -> Self:
+        """return a new manager with the blocks"""
+        if len(blocks) == 0:
+            if self.ndim == 2:
+                # retain our own Index dtype
+                if index is not None:
+                    axes = [self.items[:0], index]
+                else:
+                    axes = [self.items[:0]] + self.axes[1:]
+                return self.make_empty(axes)
+            return self.make_empty()
+
+        # FIXME: optimization potential
+        indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks]))
+        inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])
+
+        new_blocks: list[Block] = []
+        for b in blocks:
+            nb = b.copy(deep=False)
+            nb.mgr_locs = BlockPlacement(inv_indexer[nb.mgr_locs.indexer])
+            new_blocks.append(nb)
+
+        axes = list(self.axes)
+        if index is not None:
+            axes[-1] = index
+        axes[0] = self.items.take(indexer)
+
+        return type(self).from_blocks(new_blocks, axes)
+
+    @property
+    def nblocks(self) -> int:
+        return len(self.blocks)
+
+    def copy(self, deep: bool | None | Literal["all"] = True) -> Self:
+        """
+        Make deep or shallow copy of BlockManager
+
+        Parameters
+        ----------
+        deep : bool, string or None, default True
+            If False or None, return a shallow copy (do not copy data)
+            If 'all', copy data and a deep copy of the index
+
+        Returns
+        -------
+        BlockManager
+        """
+        if deep is None:
+            if using_copy_on_write():
+                # use shallow copy
+                deep = False
+            else:
+                # preserve deep copy for BlockManager with copy=None
+                deep = True
+
+        # this preserves the notion of view copying of axes
+        if deep:
+            # hit in e.g. tests.io.json.test_pandas
+
+            def copy_func(ax):
+                return ax.copy(deep=True) if deep == "all" else ax.view()
+
+            new_axes = [copy_func(ax) for ax in self.axes]
+        else:
+            if using_copy_on_write():
+                new_axes = [ax.view() for ax in self.axes]
+            else:
+                new_axes = list(self.axes)
+
+        res = self.apply("copy", deep=deep)
+        res.axes = new_axes
+
+        if self.ndim > 1:
+            # Avoid needing to re-compute these
+            blknos = self._blknos
+            if blknos is not None:
+                res._blknos = blknos.copy()
+                res._blklocs = self._blklocs.copy()
+
+        if deep:
+            res._consolidate_inplace()
+        return res
+
+    def consolidate(self) -> Self:
+        """
+        Join together blocks having same dtype
+
+        Returns
+        -------
+        y : BlockManager
+        """
+        if self.is_consolidated():
+            return self
+
+        bm = type(self)(self.blocks, self.axes, verify_integrity=False)
+        bm._is_consolidated = False
+        bm._consolidate_inplace()
+        return bm
+
+    def reindex_indexer(
+        self,
+        new_axis: Index,
+        indexer: npt.NDArray[np.intp] | None,
+        axis: AxisInt,
+        fill_value=None,
+        allow_dups: bool = False,
+        copy: bool | None = True,
+        only_slice: bool = False,
+        *,
+        use_na_proxy: bool = False,
+    ) -> Self:
+        """
+        Parameters
+        ----------
+        new_axis : Index
+        indexer : ndarray[intp] or None
+        axis : int
+        fill_value : object, default None
+        allow_dups : bool, default False
+        copy : bool or None, default True
+            If None, regard as False to get shallow copy.
+        only_slice : bool, default False
+            Whether to take views, not copies, along columns.
+        use_na_proxy : bool, default False
+            Whether to use a np.void ndarray for newly introduced columns.
+
+        pandas-indexer with -1's only.
+        """
+        if copy is None:
+            if using_copy_on_write():
+                # use shallow copy
+                copy = False
+            else:
+                # preserve deep copy for BlockManager with copy=None
+                copy = True
+
+        if indexer is None:
+            if new_axis is self.axes[axis] and not copy:
+                return self
+
+            result = self.copy(deep=copy)
+            result.axes = list(self.axes)
+            result.axes[axis] = new_axis
+            return result
+
+        # Should be intp, but in some cases we get int64 on 32bit builds
+        assert isinstance(indexer, np.ndarray)
+
+        # some axes don't allow reindexing with dups
+        if not allow_dups:
+            self.axes[axis]._validate_can_reindex(indexer)
+
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        if axis == 0:
+            new_blocks = self._slice_take_blocks_ax0(
+                indexer,
+                fill_value=fill_value,
+                only_slice=only_slice,
+                use_na_proxy=use_na_proxy,
+            )
+        else:
+            new_blocks = [
+                blk.take_nd(
+                    indexer,
+                    axis=1,
+                    fill_value=(
+                        fill_value if fill_value is not None else blk.fill_value
+                    ),
+                )
+                for blk in self.blocks
+            ]
+
+        new_axes = list(self.axes)
+        new_axes[axis] = new_axis
+
+        new_mgr = type(self).from_blocks(new_blocks, new_axes)
+        if axis == 1:
+            # We can avoid the need to rebuild these
+            new_mgr._blknos = self.blknos.copy()
+            new_mgr._blklocs = self.blklocs.copy()
+        return new_mgr
+
+    def _slice_take_blocks_ax0(
+        self,
+        slice_or_indexer: slice | np.ndarray,
+        fill_value=lib.no_default,
+        only_slice: bool = False,
+        *,
+        use_na_proxy: bool = False,
+        ref_inplace_op: bool = False,
+    ) -> list[Block]:
+        """
+        Slice/take blocks along axis=0.
+
+        Overloaded for SingleBlock
+
+        Parameters
+        ----------
+        slice_or_indexer : slice or np.ndarray[int64]
+        fill_value : scalar, default lib.no_default
+        only_slice : bool, default False
+            If True, we always return views on existing arrays, never copies.
+            This is used when called from ops.blockwise.operate_blockwise.
+        use_na_proxy : bool, default False
+            Whether to use a np.void ndarray for newly introduced columns.
+        ref_inplace_op: bool, default False
+            Don't track refs if True because we operate inplace
+
+        Returns
+        -------
+        new_blocks : list of Block
+        """
+        allow_fill = fill_value is not lib.no_default
+
+        sl_type, slobj, sllen = _preprocess_slice_or_indexer(
+            slice_or_indexer, self.shape[0], allow_fill=allow_fill
+        )
+
+        if self.is_single_block:
+            blk = self.blocks[0]
+
+            if sl_type == "slice":
+                # GH#32959 EABlock would fail since we can't make 0-width
+                # TODO(EA2D): special casing unnecessary with 2D EAs
+                if sllen == 0:
+                    return []
+                bp = BlockPlacement(slice(0, sllen))
+                return [blk.getitem_block_columns(slobj, new_mgr_locs=bp)]
+            elif not allow_fill or self.ndim == 1:
+                if allow_fill and fill_value is None:
+                    fill_value = blk.fill_value
+
+                if not allow_fill and only_slice:
+                    # GH#33597 slice instead of take, so we get
+                    #  views instead of copies
+                    blocks = [
+                        blk.getitem_block_columns(
+                            slice(ml, ml + 1),
+                            new_mgr_locs=BlockPlacement(i),
+                            ref_inplace_op=ref_inplace_op,
+                        )
+                        for i, ml in enumerate(slobj)
+                    ]
+                    return blocks
+                else:
+                    bp = BlockPlacement(slice(0, sllen))
+                    return [
+                        blk.take_nd(
+                            slobj,
+                            axis=0,
+                            new_mgr_locs=bp,
+                            fill_value=fill_value,
+                        )
+                    ]
+
+        if sl_type == "slice":
+            blknos = self.blknos[slobj]
+            blklocs = self.blklocs[slobj]
+        else:
+            blknos = algos.take_nd(
+                self.blknos, slobj, fill_value=-1, allow_fill=allow_fill
+            )
+            blklocs = algos.take_nd(
+                self.blklocs, slobj, fill_value=-1, allow_fill=allow_fill
+            )
+
+        # When filling blknos, make sure blknos is updated before appending to
+        # blocks list, that way new blkno is exactly len(blocks).
+        blocks = []
+        group = not only_slice
+        for blkno, mgr_locs in libinternals.get_blkno_placements(blknos, group=group):
+            if blkno == -1:
+                # If we've got here, fill_value was not lib.no_default
+
+                blocks.append(
+                    self._make_na_block(
+                        placement=mgr_locs,
+                        fill_value=fill_value,
+                        use_na_proxy=use_na_proxy,
+                    )
+                )
+            else:
+                blk = self.blocks[blkno]
+
+                # Otherwise, slicing along items axis is necessary.
+                if not blk._can_consolidate and not blk._validate_ndim:
+                    # i.e. we dont go through here for DatetimeTZBlock
+                    # A non-consolidatable block, it's easy, because there's
+                    # only one item and each mgr loc is a copy of that single
+                    # item.
+                    deep = not (only_slice or using_copy_on_write())
+                    for mgr_loc in mgr_locs:
+                        newblk = blk.copy(deep=deep)
+                        newblk.mgr_locs = BlockPlacement(slice(mgr_loc, mgr_loc + 1))
+                        blocks.append(newblk)
+
+                else:
+                    # GH#32779 to avoid the performance penalty of copying,
+                    #  we may try to only slice
+                    taker = blklocs[mgr_locs.indexer]
+                    max_len = max(len(mgr_locs), taker.max() + 1)
+                    if only_slice or using_copy_on_write():
+                        taker = lib.maybe_indices_to_slice(taker, max_len)
+
+                    if isinstance(taker, slice):
+                        nb = blk.getitem_block_columns(taker, new_mgr_locs=mgr_locs)
+                        blocks.append(nb)
+                    elif only_slice:
+                        # GH#33597 slice instead of take, so we get
+                        #  views instead of copies
+                        for i, ml in zip(taker, mgr_locs):
+                            slc = slice(i, i + 1)
+                            bp = BlockPlacement(ml)
+                            nb = blk.getitem_block_columns(slc, new_mgr_locs=bp)
+                            # We have np.shares_memory(nb.values, blk.values)
+                            blocks.append(nb)
+                    else:
+                        nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)
+                        blocks.append(nb)
+
+        return blocks
+
+    def _make_na_block(
+        self, placement: BlockPlacement, fill_value=None, use_na_proxy: bool = False
+    ) -> Block:
+        # Note: we only get here with self.ndim == 2
+
+        if use_na_proxy:
+            assert fill_value is None
+            shape = (len(placement), self.shape[1])
+            vals = np.empty(shape, dtype=np.void)
+            nb = NumpyBlock(vals, placement, ndim=2)
+            return nb
+
+        if fill_value is None:
+            fill_value = np.nan
+
+        shape = (len(placement), self.shape[1])
+
+        dtype, fill_value = infer_dtype_from_scalar(fill_value)
+        block_values = make_na_array(dtype, shape, fill_value)
+        return new_block_2d(block_values, placement=placement)
+
+    def take(
+        self,
+        indexer: npt.NDArray[np.intp],
+        axis: AxisInt = 1,
+        verify: bool = True,
+    ) -> Self:
+        """
+        Take items along any axis.
+
+        indexer : np.ndarray[np.intp]
+        axis : int, default 1
+        verify : bool, default True
+            Check that all entries are between 0 and len(self) - 1, inclusive.
+            Pass verify=False if this check has been done by the caller.
+
+        Returns
+        -------
+        BlockManager
+        """
+        # Caller is responsible for ensuring indexer annotation is accurate
+
+        n = self.shape[axis]
+        indexer = maybe_convert_indices(indexer, n, verify=verify)
+
+        new_labels = self.axes[axis].take(indexer)
+        return self.reindex_indexer(
+            new_axis=new_labels,
+            indexer=indexer,
+            axis=axis,
+            allow_dups=True,
+            copy=None,
+        )
+
+
+class BlockManager(libinternals.BlockManager, BaseBlockManager):
+    """
+    BaseBlockManager that holds 2D blocks.
+    """
+
+    ndim = 2
+
+    # ----------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self,
+        blocks: Sequence[Block],
+        axes: Sequence[Index],
+        verify_integrity: bool = True,
+    ) -> None:
+        if verify_integrity:
+            # Assertion disabled for performance
+            # assert all(isinstance(x, Index) for x in axes)
+
+            for block in blocks:
+                if self.ndim != block.ndim:
+                    raise AssertionError(
+                        f"Number of Block dimensions ({block.ndim}) must equal "
+                        f"number of axes ({self.ndim})"
+                    )
+                # As of 2.0, the caller is responsible for ensuring that
+                #  DatetimeTZBlock with block.ndim == 2 has block.values.ndim ==2;
+                #  previously there was a special check for fastparquet compat.
+
+            self._verify_integrity()
+
+    def _verify_integrity(self) -> None:
+        mgr_shape = self.shape
+        tot_items = sum(len(x.mgr_locs) for x in self.blocks)
+        for block in self.blocks:
+            if block.shape[1:] != mgr_shape[1:]:
+                raise_construction_error(tot_items, block.shape[1:], self.axes)
+        if len(self.items) != tot_items:
+            raise AssertionError(
+                "Number of manager items must equal union of "
+                f"block items\n# manager items: {len(self.items)}, # "
+                f"tot_items: {tot_items}"
+            )
+
+    @classmethod
+    def from_blocks(cls, blocks: list[Block], axes: list[Index]) -> Self:
+        """
+        Constructor for BlockManager and SingleBlockManager with same signature.
+        """
+        return cls(blocks, axes, verify_integrity=False)
+
+    # ----------------------------------------------------------------
+    # Indexing
+
+    def fast_xs(self, loc: int) -> SingleBlockManager:
+        """
+        Return the array corresponding to `frame.iloc[loc]`.
+
+        Parameters
+        ----------
+        loc : int
+
+        Returns
+        -------
+        np.ndarray or ExtensionArray
+        """
+        if len(self.blocks) == 1:
+            # TODO: this could be wrong if blk.mgr_locs is not slice(None)-like;
+            #  is this ruled out in the general case?
+            result = self.blocks[0].iget((slice(None), loc))
+            # in the case of a single block, the new block is a view
+            bp = BlockPlacement(slice(0, len(result)))
+            block = new_block(
+                result,
+                placement=bp,
+                ndim=1,
+                refs=self.blocks[0].refs,
+            )
+            return SingleBlockManager(block, self.axes[0])
+
+        dtype = interleaved_dtype([blk.dtype for blk in self.blocks])
+
+        n = len(self)
+
+        if isinstance(dtype, ExtensionDtype):
+            # TODO: use object dtype as workaround for non-performant
+            #  EA.__setitem__ methods. (primarily ArrowExtensionArray.__setitem__
+            #  when iteratively setting individual values)
+            #  https://github.com/pandas-dev/pandas/pull/54508#issuecomment-1675827918
+            result = np.empty(n, dtype=object)
+        else:
+            result = np.empty(n, dtype=dtype)
+            result = ensure_wrapped_if_datetimelike(result)
+
+        for blk in self.blocks:
+            # Such assignment may incorrectly coerce NaT to None
+            # result[blk.mgr_locs] = blk._slice((slice(None), loc))
+            for i, rl in enumerate(blk.mgr_locs):
+                result[rl] = blk.iget((i, loc))
+
+        if isinstance(dtype, ExtensionDtype):
+            cls = dtype.construct_array_type()
+            result = cls._from_sequence(result, dtype=dtype)
+
+        bp = BlockPlacement(slice(0, len(result)))
+        block = new_block(result, placement=bp, ndim=1)
+        return SingleBlockManager(block, self.axes[0])
+
+    def iget(self, i: int, track_ref: bool = True) -> SingleBlockManager:
+        """
+        Return the data as a SingleBlockManager.
+        """
+        block = self.blocks[self.blknos[i]]
+        values = block.iget(self.blklocs[i])
+
+        # shortcut for select a single-dim from a 2-dim BM
+        bp = BlockPlacement(slice(0, len(values)))
+        nb = type(block)(
+            values, placement=bp, ndim=1, refs=block.refs if track_ref else None
+        )
+        return SingleBlockManager(nb, self.axes[1])
+
+    def iget_values(self, i: int) -> ArrayLike:
+        """
+        Return the data for column i as the values (ndarray or ExtensionArray).
+
+        Warning! The returned array is a view but doesn't handle Copy-on-Write,
+        so this should be used with caution.
+        """
+        # TODO(CoW) making the arrays read-only might make this safer to use?
+        block = self.blocks[self.blknos[i]]
+        values = block.iget(self.blklocs[i])
+        return values
+
+    @property
+    def column_arrays(self) -> list[np.ndarray]:
+        """
+        Used in the JSON C code to access column arrays.
+        This optimizes compared to using `iget_values` by converting each
+
+        Warning! This doesn't handle Copy-on-Write, so should be used with
+        caution (current use case of consuming this in the JSON code is fine).
+        """
+        # This is an optimized equivalent to
+        #  result = [self.iget_values(i) for i in range(len(self.items))]
+        result: list[np.ndarray | None] = [None] * len(self.items)
+
+        for blk in self.blocks:
+            mgr_locs = blk._mgr_locs
+            values = blk.array_values._values_for_json()
+            if values.ndim == 1:
+                # TODO(EA2D): special casing not needed with 2D EAs
+                result[mgr_locs[0]] = values
+
+            else:
+                for i, loc in enumerate(mgr_locs):
+                    result[loc] = values[i]
+
+        # error: Incompatible return value type (got "List[None]",
+        # expected "List[ndarray[Any, Any]]")
+        return result  # type: ignore[return-value]
+
+    def iset(
+        self,
+        loc: int | slice | np.ndarray,
+        value: ArrayLike,
+        inplace: bool = False,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        """
+        Set new item in-place. Does not consolidate. Adds new Block if not
+        contained in the current set of items
+        """
+
+        # FIXME: refactor, clearly separate broadcasting & zip-like assignment
+        #        can prob also fix the various if tests for sparse/categorical
+        if self._blklocs is None and self.ndim > 1:
+            self._rebuild_blknos_and_blklocs()
+
+        # Note: we exclude DTA/TDA here
+        value_is_extension_type = is_1d_only_ea_dtype(value.dtype)
+        if not value_is_extension_type:
+            if value.ndim == 2:
+                value = value.T
+            else:
+                value = ensure_block_shape(value, ndim=2)
+
+            if value.shape[1:] != self.shape[1:]:
+                raise AssertionError(
+                    "Shape of new values must be compatible with manager shape"
+                )
+
+        if lib.is_integer(loc):
+            # We have 6 tests where loc is _not_ an int.
+            # In this case, get_blkno_placements will yield only one tuple,
+            #  containing (self._blknos[loc], BlockPlacement(slice(0, 1, 1)))
+
+            # Check if we can use _iset_single fastpath
+            loc = cast(int, loc)
+            blkno = self.blknos[loc]
+            blk = self.blocks[blkno]
+            if len(blk._mgr_locs) == 1:  # TODO: fastest way to check this?
+                return self._iset_single(
+                    loc,
+                    value,
+                    inplace=inplace,
+                    blkno=blkno,
+                    blk=blk,
+                    refs=refs,
+                )
+
+            # error: Incompatible types in assignment (expression has type
+            # "List[Union[int, slice, ndarray]]", variable has type "Union[int,
+            # slice, ndarray]")
+            loc = [loc]  # type: ignore[assignment]
+
+        # categorical/sparse/datetimetz
+        if value_is_extension_type:
+
+            def value_getitem(placement):
+                return value
+
+        else:
+
+            def value_getitem(placement):
+                return value[placement.indexer]
+
+        # Accessing public blknos ensures the public versions are initialized
+        blknos = self.blknos[loc]
+        blklocs = self.blklocs[loc].copy()
+
+        unfit_mgr_locs = []
+        unfit_val_locs = []
+        removed_blknos = []
+        for blkno_l, val_locs in libinternals.get_blkno_placements(blknos, group=True):
+            blk = self.blocks[blkno_l]
+            blk_locs = blklocs[val_locs.indexer]
+            if inplace and blk.should_store(value):
+                # Updating inplace -> check if we need to do Copy-on-Write
+                if using_copy_on_write() and not self._has_no_reference_block(blkno_l):
+                    self._iset_split_block(
+                        blkno_l, blk_locs, value_getitem(val_locs), refs=refs
+                    )
+                else:
+                    blk.set_inplace(blk_locs, value_getitem(val_locs))
+                    continue
+            else:
+                unfit_mgr_locs.append(blk.mgr_locs.as_array[blk_locs])
+                unfit_val_locs.append(val_locs)
+
+                # If all block items are unfit, schedule the block for removal.
+                if len(val_locs) == len(blk.mgr_locs):
+                    removed_blknos.append(blkno_l)
+                    continue
+                else:
+                    # Defer setting the new values to enable consolidation
+                    self._iset_split_block(blkno_l, blk_locs, refs=refs)
+
+        if len(removed_blknos):
+            # Remove blocks & update blknos accordingly
+            is_deleted = np.zeros(self.nblocks, dtype=np.bool_)
+            is_deleted[removed_blknos] = True
+
+            new_blknos = np.empty(self.nblocks, dtype=np.intp)
+            new_blknos.fill(-1)
+            new_blknos[~is_deleted] = np.arange(self.nblocks - len(removed_blknos))
+            self._blknos = new_blknos[self._blknos]
+            self.blocks = tuple(
+                blk for i, blk in enumerate(self.blocks) if i not in set(removed_blknos)
+            )
+
+        if unfit_val_locs:
+            unfit_idxr = np.concatenate(unfit_mgr_locs)
+            unfit_count = len(unfit_idxr)
+
+            new_blocks: list[Block] = []
+            if value_is_extension_type:
+                # This code (ab-)uses the fact that EA blocks contain only
+                # one item.
+                # TODO(EA2D): special casing unnecessary with 2D EAs
+                new_blocks.extend(
+                    new_block_2d(
+                        values=value,
+                        placement=BlockPlacement(slice(mgr_loc, mgr_loc + 1)),
+                        refs=refs,
+                    )
+                    for mgr_loc in unfit_idxr
+                )
+
+                self._blknos[unfit_idxr] = np.arange(unfit_count) + len(self.blocks)
+                self._blklocs[unfit_idxr] = 0
+
+            else:
+                # unfit_val_locs contains BlockPlacement objects
+                unfit_val_items = unfit_val_locs[0].append(unfit_val_locs[1:])
+
+                new_blocks.append(
+                    new_block_2d(
+                        values=value_getitem(unfit_val_items),
+                        placement=BlockPlacement(unfit_idxr),
+                        refs=refs,
+                    )
+                )
+
+                self._blknos[unfit_idxr] = len(self.blocks)
+                self._blklocs[unfit_idxr] = np.arange(unfit_count)
+
+            self.blocks += tuple(new_blocks)
+
+            # Newly created block's dtype may already be present.
+            self._known_consolidated = False
+
+    def _iset_split_block(
+        self,
+        blkno_l: int,
+        blk_locs: np.ndarray | list[int],
+        value: ArrayLike | None = None,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        """Removes columns from a block by splitting the block.
+
+        Avoids copying the whole block through slicing and updates the manager
+        after determinint the new block structure. Optionally adds a new block,
+        otherwise has to be done by the caller.
+
+        Parameters
+        ----------
+        blkno_l: The block number to operate on, relevant for updating the manager
+        blk_locs: The locations of our block that should be deleted.
+        value: The value to set as a replacement.
+        refs: The reference tracking object of the value to set.
+        """
+        blk = self.blocks[blkno_l]
+
+        if self._blklocs is None:
+            self._rebuild_blknos_and_blklocs()
+
+        nbs_tup = tuple(blk.delete(blk_locs))
+        if value is not None:
+            locs = blk.mgr_locs.as_array[blk_locs]
+            first_nb = new_block_2d(value, BlockPlacement(locs), refs=refs)
+        else:
+            first_nb = nbs_tup[0]
+            nbs_tup = tuple(nbs_tup[1:])
+
+        nr_blocks = len(self.blocks)
+        blocks_tup = (
+            self.blocks[:blkno_l] + (first_nb,) + self.blocks[blkno_l + 1 :] + nbs_tup
+        )
+        self.blocks = blocks_tup
+
+        if not nbs_tup and value is not None:
+            # No need to update anything if split did not happen
+            return
+
+        self._blklocs[first_nb.mgr_locs.indexer] = np.arange(len(first_nb))
+
+        for i, nb in enumerate(nbs_tup):
+            self._blklocs[nb.mgr_locs.indexer] = np.arange(len(nb))
+            self._blknos[nb.mgr_locs.indexer] = i + nr_blocks
+
+    def _iset_single(
+        self,
+        loc: int,
+        value: ArrayLike,
+        inplace: bool,
+        blkno: int,
+        blk: Block,
+        refs: BlockValuesRefs | None = None,
+    ) -> None:
+        """
+        Fastpath for iset when we are only setting a single position and
+        the Block currently in that position is itself single-column.
+
+        In this case we can swap out the entire Block and blklocs and blknos
+        are unaffected.
+        """
+        # Caller is responsible for verifying value.shape
+
+        if inplace and blk.should_store(value):
+            copy = False
+            if using_copy_on_write() and not self._has_no_reference_block(blkno):
+                # perform Copy-on-Write and clear the reference
+                copy = True
+            iloc = self.blklocs[loc]
+            blk.set_inplace(slice(iloc, iloc + 1), value, copy=copy)
+            return
+
+        nb = new_block_2d(value, placement=blk._mgr_locs, refs=refs)
+        old_blocks = self.blocks
+        new_blocks = old_blocks[:blkno] + (nb,) + old_blocks[blkno + 1 :]
+        self.blocks = new_blocks
+        return
+
+    def column_setitem(
+        self, loc: int, idx: int | slice | np.ndarray, value, inplace_only: bool = False
+    ) -> None:
+        """
+        Set values ("setitem") into a single column (not setting the full column).
+
+        This is a method on the BlockManager level, to avoid creating an
+        intermediate Series at the DataFrame level (`s = df[loc]; s[idx] = value`)
+        """
+        needs_to_warn = False
+        if warn_copy_on_write() and not self._has_no_reference(loc):
+            if not isinstance(
+                self.blocks[self.blknos[loc]].values,
+                (ArrowExtensionArray, ArrowStringArray),
+            ):
+                # We might raise if we are in an expansion case, so defer
+                # warning till we actually updated
+                needs_to_warn = True
+
+        elif using_copy_on_write() and not self._has_no_reference(loc):
+            blkno = self.blknos[loc]
+            # Split blocks to only copy the column we want to modify
+            blk_loc = self.blklocs[loc]
+            # Copy our values
+            values = self.blocks[blkno].values
+            if values.ndim == 1:
+                values = values.copy()
+            else:
+                # Use [blk_loc] as indexer to keep ndim=2, this already results in a
+                # copy
+                values = values[[blk_loc]]
+            self._iset_split_block(blkno, [blk_loc], values)
+
+        # this manager is only created temporarily to mutate the values in place
+        # so don't track references, otherwise the `setitem` would perform CoW again
+        col_mgr = self.iget(loc, track_ref=False)
+        if inplace_only:
+            col_mgr.setitem_inplace(idx, value)
+        else:
+            new_mgr = col_mgr.setitem((idx,), value)
+            self.iset(loc, new_mgr._block.values, inplace=True)
+
+        if needs_to_warn:
+            warnings.warn(
+                COW_WARNING_GENERAL_MSG,
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
+        """
+        Insert item at selected position.
+
+        Parameters
+        ----------
+        loc : int
+        item : hashable
+        value : np.ndarray or ExtensionArray
+        refs : The reference tracking object of the value to set.
+        """
+        with warnings.catch_warnings():
+            # TODO: re-issue this with setitem-specific message?
+            warnings.filterwarnings(
+                "ignore",
+                "The behavior of Index.insert with object-dtype is deprecated",
+                category=FutureWarning,
+            )
+            new_axis = self.items.insert(loc, item)
+
+        if value.ndim == 2:
+            value = value.T
+            if len(value) > 1:
+                raise ValueError(
+                    f"Expected a 1D array, got an array with shape {value.T.shape}"
+                )
+        else:
+            value = ensure_block_shape(value, ndim=self.ndim)
+
+        bp = BlockPlacement(slice(loc, loc + 1))
+        block = new_block_2d(values=value, placement=bp, refs=refs)
+
+        if not len(self.blocks):
+            # Fastpath
+            self._blklocs = np.array([0], dtype=np.intp)
+            self._blknos = np.array([0], dtype=np.intp)
+        else:
+            self._insert_update_mgr_locs(loc)
+            self._insert_update_blklocs_and_blknos(loc)
+
+        self.axes[0] = new_axis
+        self.blocks += (block,)
+
+        self._known_consolidated = False
+
+        if sum(not block.is_extension for block in self.blocks) > 100:
+            warnings.warn(
+                "DataFrame is highly fragmented.  This is usually the result "
+                "of calling `frame.insert` many times, which has poor performance.  "
+                "Consider joining all columns at once using pd.concat(axis=1) "
+                "instead. To get a de-fragmented frame, use `newframe = frame.copy()`",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+
+    def _insert_update_mgr_locs(self, loc) -> None:
+        """
+        When inserting a new Block at location 'loc', we increment
+        all of the mgr_locs of blocks above that by one.
+        """
+        for blkno, count in _fast_count_smallints(self.blknos[loc:]):
+            # .620 this way, .326 of which is in increment_above
+            blk = self.blocks[blkno]
+            blk._mgr_locs = blk._mgr_locs.increment_above(loc)
+
+    def _insert_update_blklocs_and_blknos(self, loc) -> None:
+        """
+        When inserting a new Block at location 'loc', we update our
+        _blklocs and _blknos.
+        """
+
+        # Accessing public blklocs ensures the public versions are initialized
+        if loc == self.blklocs.shape[0]:
+            # np.append is a lot faster, let's use it if we can.
+            self._blklocs = np.append(self._blklocs, 0)
+            self._blknos = np.append(self._blknos, len(self.blocks))
+        elif loc == 0:
+            # np.append is a lot faster, let's use it if we can.
+            self._blklocs = np.append(self._blklocs[::-1], 0)[::-1]
+            self._blknos = np.append(self._blknos[::-1], len(self.blocks))[::-1]
+        else:
+            new_blklocs, new_blknos = libinternals.update_blklocs_and_blknos(
+                self.blklocs, self.blknos, loc, len(self.blocks)
+            )
+            self._blklocs = new_blklocs
+            self._blknos = new_blknos
+
+    def idelete(self, indexer) -> BlockManager:
+        """
+        Delete selected locations, returning a new BlockManager.
+        """
+        is_deleted = np.zeros(self.shape[0], dtype=np.bool_)
+        is_deleted[indexer] = True
+        taker = (~is_deleted).nonzero()[0]
+
+        nbs = self._slice_take_blocks_ax0(taker, only_slice=True, ref_inplace_op=True)
+        new_columns = self.items[~is_deleted]
+        axes = [new_columns, self.axes[1]]
+        return type(self)(tuple(nbs), axes, verify_integrity=False)
+
+    # ----------------------------------------------------------------
+    # Block-wise Operation
+
+    def grouped_reduce(self, func: Callable) -> Self:
+        """
+        Apply grouped reduction function blockwise, returning a new BlockManager.
+
+        Parameters
+        ----------
+        func : grouped reduction function
+
+        Returns
+        -------
+        BlockManager
+        """
+        result_blocks: list[Block] = []
+
+        for blk in self.blocks:
+            if blk.is_object:
+                # split on object-dtype blocks bc some columns may raise
+                #  while others do not.
+                for sb in blk._split():
+                    applied = sb.apply(func)
+                    result_blocks = extend_blocks(applied, result_blocks)
+            else:
+                applied = blk.apply(func)
+                result_blocks = extend_blocks(applied, result_blocks)
+
+        if len(result_blocks) == 0:
+            nrows = 0
+        else:
+            nrows = result_blocks[0].values.shape[-1]
+        index = Index(range(nrows))
+
+        return type(self).from_blocks(result_blocks, [self.axes[0], index])
+
+    def reduce(self, func: Callable) -> Self:
+        """
+        Apply reduction function blockwise, returning a single-row BlockManager.
+
+        Parameters
+        ----------
+        func : reduction function
+
+        Returns
+        -------
+        BlockManager
+        """
+        # If 2D, we assume that we're operating column-wise
+        assert self.ndim == 2
+
+        res_blocks: list[Block] = []
+        for blk in self.blocks:
+            nbs = blk.reduce(func)
+            res_blocks.extend(nbs)
+
+        index = Index([None])  # placeholder
+        new_mgr = type(self).from_blocks(res_blocks, [self.items, index])
+        return new_mgr
+
+    def operate_blockwise(self, other: BlockManager, array_op) -> BlockManager:
+        """
+        Apply array_op blockwise with another (aligned) BlockManager.
+        """
+        return operate_blockwise(self, other, array_op)
+
+    def _equal_values(self: BlockManager, other: BlockManager) -> bool:
+        """
+        Used in .equals defined in base class. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        return blockwise_all(self, other, array_equals)
+
+    def quantile(
+        self,
+        *,
+        qs: Index,  # with dtype float 64
+        interpolation: QuantileInterpolation = "linear",
+    ) -> Self:
+        """
+        Iterate over blocks applying quantile reduction.
+        This routine is intended for reduction type operations and
+        will do inference on the generated blocks.
+
+        Parameters
+        ----------
+        interpolation : type of interpolation, default 'linear'
+        qs : list of the quantiles to be computed
+
+        Returns
+        -------
+        BlockManager
+        """
+        # Series dispatches to DataFrame for quantile, which allows us to
+        #  simplify some of the code here and in the blocks
+        assert self.ndim >= 2
+        assert is_list_like(qs)  # caller is responsible for this
+
+        new_axes = list(self.axes)
+        new_axes[1] = Index(qs, dtype=np.float64)
+
+        blocks = [
+            blk.quantile(qs=qs, interpolation=interpolation) for blk in self.blocks
+        ]
+
+        return type(self)(blocks, new_axes)
+
+    # ----------------------------------------------------------------
+
+    def unstack(self, unstacker, fill_value) -> BlockManager:
+        """
+        Return a BlockManager with all blocks unstacked.
+
+        Parameters
+        ----------
+        unstacker : reshape._Unstacker
+        fill_value : Any
+            fill_value for newly introduced missing values.
+
+        Returns
+        -------
+        unstacked : BlockManager
+        """
+        new_columns = unstacker.get_new_columns(self.items)
+        new_index = unstacker.new_index
+
+        allow_fill = not unstacker.mask_all
+        if allow_fill:
+            # calculating the full mask once and passing it to Block._unstack is
+            #  faster than letting calculating it in each repeated call
+            new_mask2D = (~unstacker.mask).reshape(*unstacker.full_shape)
+            needs_masking = new_mask2D.any(axis=0)
+        else:
+            needs_masking = np.zeros(unstacker.full_shape[1], dtype=bool)
+
+        new_blocks: list[Block] = []
+        columns_mask: list[np.ndarray] = []
+
+        if len(self.items) == 0:
+            factor = 1
+        else:
+            fac = len(new_columns) / len(self.items)
+            assert fac == int(fac)
+            factor = int(fac)
+
+        for blk in self.blocks:
+            mgr_locs = blk.mgr_locs
+            new_placement = mgr_locs.tile_for_unstack(factor)
+
+            blocks, mask = blk._unstack(
+                unstacker,
+                fill_value,
+                new_placement=new_placement,
+                needs_masking=needs_masking,
+            )
+
+            new_blocks.extend(blocks)
+            columns_mask.extend(mask)
+
+            # Block._unstack should ensure this holds,
+            assert mask.sum() == sum(len(nb._mgr_locs) for nb in blocks)
+            # In turn this ensures that in the BlockManager call below
+            #  we have len(new_columns) == sum(x.shape[0] for x in new_blocks)
+            #  which suffices to allow us to pass verify_inegrity=False
+
+        new_columns = new_columns[columns_mask]
+
+        bm = BlockManager(new_blocks, [new_columns, new_index], verify_integrity=False)
+        return bm
+
+    def to_dict(self) -> dict[str, Self]:
+        """
+        Return a dict of str(dtype) -> BlockManager
+
+        Returns
+        -------
+        values : a dict of dtype -> BlockManager
+        """
+
+        bd: dict[str, list[Block]] = {}
+        for b in self.blocks:
+            bd.setdefault(str(b.dtype), []).append(b)
+
+        # TODO(EA2D): the combine will be unnecessary with 2D EAs
+        return {dtype: self._combine(blocks) for dtype, blocks in bd.items()}
+
+    def as_array(
+        self,
+        dtype: np.dtype | None = None,
+        copy: bool = False,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Convert the blockmanager data into an numpy array.
+
+        Parameters
+        ----------
+        dtype : np.dtype or None, default None
+            Data type of the return array.
+        copy : bool, default False
+            If True then guarantee that a copy is returned. A value of
+            False does not guarantee that the underlying data is not
+            copied.
+        na_value : object, default lib.no_default
+            Value to be used as the missing value sentinel.
+
+        Returns
+        -------
+        arr : ndarray
+        """
+        passed_nan = lib.is_float(na_value) and isna(na_value)
+
+        if len(self.blocks) == 0:
+            arr = np.empty(self.shape, dtype=float)
+            return arr.transpose()
+
+        if self.is_single_block:
+            blk = self.blocks[0]
+
+            if na_value is not lib.no_default:
+                # We want to copy when na_value is provided to avoid
+                # mutating the original object
+                if lib.is_np_dtype(blk.dtype, "f") and passed_nan:
+                    # We are already numpy-float and na_value=np.nan
+                    pass
+                else:
+                    copy = True
+
+            if blk.is_extension:
+                # Avoid implicit conversion of extension blocks to object
+
+                # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no
+                # attribute "to_numpy"
+                arr = blk.values.to_numpy(  # type: ignore[union-attr]
+                    dtype=dtype,
+                    na_value=na_value,
+                    copy=copy,
+                ).reshape(blk.shape)
+            elif not copy:
+                arr = np.asarray(blk.values, dtype=dtype)
+            else:
+                arr = np.array(blk.values, dtype=dtype, copy=copy)
+
+            if using_copy_on_write() and not copy:
+                arr = arr.view()
+                arr.flags.writeable = False
+        else:
+            arr = self._interleave(dtype=dtype, na_value=na_value)
+            # The underlying data was copied within _interleave, so no need
+            # to further copy if copy=True or setting na_value
+
+        if na_value is lib.no_default:
+            pass
+        elif arr.dtype.kind == "f" and passed_nan:
+            pass
+        else:
+            arr[isna(arr)] = na_value
+
+        return arr.transpose()
+
+    def _interleave(
+        self,
+        dtype: np.dtype | None = None,
+        na_value: object = lib.no_default,
+    ) -> np.ndarray:
+        """
+        Return ndarray from blocks with specified item order
+        Items must be contained in the blocks
+        """
+        if not dtype:
+            # Incompatible types in assignment (expression has type
+            # "Optional[Union[dtype[Any], ExtensionDtype]]", variable has
+            # type "Optional[dtype[Any]]")
+            dtype = interleaved_dtype(  # type: ignore[assignment]
+                [blk.dtype for blk in self.blocks]
+            )
+
+        # error: Argument 1 to "ensure_np_dtype" has incompatible type
+        # "Optional[dtype[Any]]"; expected "Union[dtype[Any], ExtensionDtype]"
+        dtype = ensure_np_dtype(dtype)  # type: ignore[arg-type]
+        result = np.empty(self.shape, dtype=dtype)
+
+        itemmask = np.zeros(self.shape[0])
+
+        if dtype == np.dtype("object") and na_value is lib.no_default:
+            # much more performant than using to_numpy below
+            for blk in self.blocks:
+                rl = blk.mgr_locs
+                arr = blk.get_values(dtype)
+                result[rl.indexer] = arr
+                itemmask[rl.indexer] = 1
+            return result
+
+        for blk in self.blocks:
+            rl = blk.mgr_locs
+            if blk.is_extension:
+                # Avoid implicit conversion of extension blocks to object
+
+                # error: Item "ndarray" of "Union[ndarray, ExtensionArray]" has no
+                # attribute "to_numpy"
+                arr = blk.values.to_numpy(  # type: ignore[union-attr]
+                    dtype=dtype,
+                    na_value=na_value,
+                )
+            else:
+                arr = blk.get_values(dtype)
+            result[rl.indexer] = arr
+            itemmask[rl.indexer] = 1
+
+        if not itemmask.all():
+            raise AssertionError("Some items were not contained in blocks")
+
+        return result
+
+    # ----------------------------------------------------------------
+    # Consolidation
+
+    def is_consolidated(self) -> bool:
+        """
+        Return True if more than one block with the same dtype
+        """
+        if not self._known_consolidated:
+            self._consolidate_check()
+        return self._is_consolidated
+
+    def _consolidate_check(self) -> None:
+        if len(self.blocks) == 1:
+            # fastpath
+            self._is_consolidated = True
+            self._known_consolidated = True
+            return
+        dtypes = [blk.dtype for blk in self.blocks if blk._can_consolidate]
+        self._is_consolidated = len(dtypes) == len(set(dtypes))
+        self._known_consolidated = True
+
+    def _consolidate_inplace(self) -> None:
+        # In general, _consolidate_inplace should only be called via
+        #  DataFrame._consolidate_inplace, otherwise we will fail to invalidate
+        #  the DataFrame's _item_cache. The exception is for newly-created
+        #  BlockManager objects not yet attached to a DataFrame.
+        if not self.is_consolidated():
+            self.blocks = _consolidate(self.blocks)
+            self._is_consolidated = True
+            self._known_consolidated = True
+            self._rebuild_blknos_and_blklocs()
+
+    # ----------------------------------------------------------------
+    # Concatenation
+
+    @classmethod
+    def concat_horizontal(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed BlockManagers horizontally.
+        """
+        offset = 0
+        blocks: list[Block] = []
+        for mgr in mgrs:
+            for blk in mgr.blocks:
+                # We need to do getitem_block here otherwise we would be altering
+                #  blk.mgr_locs in place, which would render it invalid. This is only
+                #  relevant in the copy=False case.
+                nb = blk.slice_block_columns(slice(None))
+                nb._mgr_locs = nb._mgr_locs.add(offset)
+                blocks.append(nb)
+
+            offset += len(mgr.items)
+
+        new_mgr = cls(tuple(blocks), axes)
+        return new_mgr
+
+    @classmethod
+    def concat_vertical(cls, mgrs: list[Self], axes: list[Index]) -> Self:
+        """
+        Concatenate uniformly-indexed BlockManagers vertically.
+        """
+        raise NotImplementedError("This logic lives (for now) in internals.concat")
+
+
+class SingleBlockManager(BaseBlockManager, SingleDataManager):
+    """manage a single block with"""
+
+    @property
+    def ndim(self) -> Literal[1]:
+        return 1
+
+    _is_consolidated = True
+    _known_consolidated = True
+    __slots__ = ()
+    is_single_block = True
+
+    def __init__(
+        self,
+        block: Block,
+        axis: Index,
+        verify_integrity: bool = False,
+    ) -> None:
+        # Assertions disabled for performance
+        # assert isinstance(block, Block), type(block)
+        # assert isinstance(axis, Index), type(axis)
+
+        self.axes = [axis]
+        self.blocks = (block,)
+
+    @classmethod
+    def from_blocks(
+        cls,
+        blocks: list[Block],
+        axes: list[Index],
+    ) -> Self:
+        """
+        Constructor for BlockManager and SingleBlockManager with same signature.
+        """
+        assert len(blocks) == 1
+        assert len(axes) == 1
+        return cls(blocks[0], axes[0], verify_integrity=False)
+
+    @classmethod
+    def from_array(
+        cls, array: ArrayLike, index: Index, refs: BlockValuesRefs | None = None
+    ) -> SingleBlockManager:
+        """
+        Constructor for if we have an array that is not yet a Block.
+        """
+        array = maybe_coerce_values(array)
+        bp = BlockPlacement(slice(0, len(index)))
+        block = new_block(array, placement=bp, ndim=1, refs=refs)
+        return cls(block, index)
+
+    def to_2d_mgr(self, columns: Index) -> BlockManager:
+        """
+        Manager analogue of Series.to_frame
+        """
+        blk = self.blocks[0]
+        arr = ensure_block_shape(blk.values, ndim=2)
+        bp = BlockPlacement(0)
+        new_blk = type(blk)(arr, placement=bp, ndim=2, refs=blk.refs)
+        axes = [columns, self.axes[0]]
+        return BlockManager([new_blk], axes=axes, verify_integrity=False)
+
+    def _has_no_reference(self, i: int = 0) -> bool:
+        """
+        Check for column `i` if it has references.
+        (whether it references another array or is itself being referenced)
+        Returns True if the column has no references.
+        """
+        return not self.blocks[0].refs.has_reference()
+
+    def __getstate__(self):
+        block_values = [b.values for b in self.blocks]
+        block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]
+        axes_array = list(self.axes)
+
+        extra_state = {
+            "0.14.1": {
+                "axes": axes_array,
+                "blocks": [
+                    {"values": b.values, "mgr_locs": b.mgr_locs.indexer}
+                    for b in self.blocks
+                ],
+            }
+        }
+
+        # First three elements of the state are to maintain forward
+        # compatibility with 0.13.1.
+        return axes_array, block_values, block_items, extra_state
+
+    def __setstate__(self, state) -> None:
+        def unpickle_block(values, mgr_locs, ndim: int) -> Block:
+            # TODO(EA2D): ndim would be unnecessary with 2D EAs
+            # older pickles may store e.g. DatetimeIndex instead of DatetimeArray
+            values = extract_array(values, extract_numpy=True)
+            if not isinstance(mgr_locs, BlockPlacement):
+                mgr_locs = BlockPlacement(mgr_locs)
+
+            values = maybe_coerce_values(values)
+            return new_block(values, placement=mgr_locs, ndim=ndim)
+
+        if isinstance(state, tuple) and len(state) >= 4 and "0.14.1" in state[3]:
+            state = state[3]["0.14.1"]
+            self.axes = [ensure_index(ax) for ax in state["axes"]]
+            ndim = len(self.axes)
+            self.blocks = tuple(
+                unpickle_block(b["values"], b["mgr_locs"], ndim=ndim)
+                for b in state["blocks"]
+            )
+        else:
+            raise NotImplementedError("pre-0.14.1 pickles are no longer supported")
+
+        self._post_setstate()
+
+    def _post_setstate(self) -> None:
+        pass
+
+    @cache_readonly
+    def _block(self) -> Block:
+        return self.blocks[0]
+
+    @property
+    def _blknos(self):
+        """compat with BlockManager"""
+        return None
+
+    @property
+    def _blklocs(self):
+        """compat with BlockManager"""
+        return None
+
+    def get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> Self:
+        # similar to get_slice, but not restricted to slice indexer
+        blk = self._block
+        if using_copy_on_write() and len(indexer) > 0 and indexer.all():
+            return type(self)(blk.copy(deep=False), self.index)
+        array = blk.values[indexer]
+
+        if isinstance(indexer, np.ndarray) and indexer.dtype.kind == "b":
+            # boolean indexing always gives a copy with numpy
+            refs = None
+        else:
+            # TODO(CoW) in theory only need to track reference if new_array is a view
+            refs = blk.refs
+
+        bp = BlockPlacement(slice(0, len(array)))
+        block = type(blk)(array, placement=bp, ndim=1, refs=refs)
+
+        new_idx = self.index[indexer]
+        return type(self)(block, new_idx)
+
+    def get_slice(self, slobj: slice, axis: AxisInt = 0) -> SingleBlockManager:
+        # Assertion disabled for performance
+        # assert isinstance(slobj, slice), type(slobj)
+        if axis >= self.ndim:
+            raise IndexError("Requested axis not found in manager")
+
+        blk = self._block
+        array = blk.values[slobj]
+        bp = BlockPlacement(slice(0, len(array)))
+        # TODO this method is only used in groupby SeriesSplitter at the moment,
+        # so passing refs is not yet covered by the tests
+        block = type(blk)(array, placement=bp, ndim=1, refs=blk.refs)
+        new_index = self.index._getitem_slice(slobj)
+        return type(self)(block, new_index)
+
+    @property
+    def index(self) -> Index:
+        return self.axes[0]
+
+    @property
+    def dtype(self) -> DtypeObj:
+        return self._block.dtype
+
+    def get_dtypes(self) -> npt.NDArray[np.object_]:
+        return np.array([self._block.dtype], dtype=object)
+
+    def external_values(self):
+        """The array that Series.values returns"""
+        return self._block.external_values()
+
+    def internal_values(self):
+        """The array that Series._values returns"""
+        return self._block.values
+
+    def array_values(self) -> ExtensionArray:
+        """The array that Series.array returns"""
+        return self._block.array_values
+
+    def get_numeric_data(self) -> Self:
+        if self._block.is_numeric:
+            return self.copy(deep=False)
+        return self.make_empty()
+
+    @property
+    def _can_hold_na(self) -> bool:
+        return self._block._can_hold_na
+
+    def setitem_inplace(self, indexer, value, warn: bool = True) -> None:
+        """
+        Set values with indexer.
+
+        For Single[Block/Array]Manager, this backs s[indexer] = value
+
+        This is an inplace version of `setitem()`, mutating the manager/values
+        in place, not returning a new Manager (and Block), and thus never changing
+        the dtype.
+        """
+        using_cow = using_copy_on_write()
+        warn_cow = warn_copy_on_write()
+        if (using_cow or warn_cow) and not self._has_no_reference(0):
+            if using_cow:
+                self.blocks = (self._block.copy(),)
+                self._cache.clear()
+            elif warn_cow and warn:
+                warnings.warn(
+                    COW_WARNING_SETITEM_MSG,
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+        super().setitem_inplace(indexer, value)
+
+    def idelete(self, indexer) -> SingleBlockManager:
+        """
+        Delete single location from SingleBlockManager.
+
+        Ensures that self.blocks doesn't become empty.
+        """
+        nb = self._block.delete(indexer)[0]
+        self.blocks = (nb,)
+        self.axes[0] = self.axes[0].delete(indexer)
+        self._cache.clear()
+        return self
+
+    def fast_xs(self, loc):
+        """
+        fast path for getting a cross-section
+        return a view of the data
+        """
+        raise NotImplementedError("Use series._values[loc] instead")
+
+    def set_values(self, values: ArrayLike) -> None:
+        """
+        Set the values of the single block in place.
+
+        Use at your own risk! This does not check if the passed values are
+        valid for the current Block/SingleBlockManager (length, dtype, etc),
+        and this does not properly keep track of references.
+        """
+        # NOTE(CoW) Currently this is only used for FrameColumnApply.series_generator
+        # which handles CoW by setting the refs manually if necessary
+        self.blocks[0].values = values
+        self.blocks[0]._mgr_locs = BlockPlacement(slice(len(values)))
+
+    def _equal_values(self, other: Self) -> bool:
+        """
+        Used in .equals defined in base class. Only check the column values
+        assuming shape and indexes have already been checked.
+        """
+        # For SingleBlockManager (i.e.Series)
+        if other.ndim != 1:
+            return False
+        left = self.blocks[0].values
+        right = other.blocks[0].values
+        return array_equals(left, right)
+
+
+# --------------------------------------------------------------------
+# Constructor Helpers
+
+
+def create_block_manager_from_blocks(
+    blocks: list[Block],
+    axes: list[Index],
+    consolidate: bool = True,
+    verify_integrity: bool = True,
+) -> BlockManager:
+    # If verify_integrity=False, then caller is responsible for checking
+    #  all(x.shape[-1] == len(axes[1]) for x in blocks)
+    #  sum(x.shape[0] for x in blocks) == len(axes[0])
+    #  set(x for blk in blocks for x in blk.mgr_locs) == set(range(len(axes[0])))
+    #  all(blk.ndim == 2 for blk in blocks)
+    # This allows us to safely pass verify_integrity=False
+
+    try:
+        mgr = BlockManager(blocks, axes, verify_integrity=verify_integrity)
+
+    except ValueError as err:
+        arrays = [blk.values for blk in blocks]
+        tot_items = sum(arr.shape[0] for arr in arrays)
+        raise_construction_error(tot_items, arrays[0].shape[1:], axes, err)
+
+    if consolidate:
+        mgr._consolidate_inplace()
+    return mgr
+
+
+def create_block_manager_from_column_arrays(
+    arrays: list[ArrayLike],
+    axes: list[Index],
+    consolidate: bool,
+    refs: list,
+) -> BlockManager:
+    # Assertions disabled for performance (caller is responsible for verifying)
+    # assert isinstance(axes, list)
+    # assert all(isinstance(x, Index) for x in axes)
+    # assert all(isinstance(x, (np.ndarray, ExtensionArray)) for x in arrays)
+    # assert all(type(x) is not NumpyExtensionArray for x in arrays)
+    # assert all(x.ndim == 1 for x in arrays)
+    # assert all(len(x) == len(axes[1]) for x in arrays)
+    # assert len(arrays) == len(axes[0])
+    # These last three are sufficient to allow us to safely pass
+    #  verify_integrity=False below.
+
+    try:
+        blocks = _form_blocks(arrays, consolidate, refs)
+        mgr = BlockManager(blocks, axes, verify_integrity=False)
+    except ValueError as e:
+        raise_construction_error(len(arrays), arrays[0].shape, axes, e)
+    if consolidate:
+        mgr._consolidate_inplace()
+    return mgr
+
+
+def raise_construction_error(
+    tot_items: int,
+    block_shape: Shape,
+    axes: list[Index],
+    e: ValueError | None = None,
+):
+    """raise a helpful message about our construction"""
+    passed = tuple(map(int, [tot_items] + list(block_shape)))
+    # Correcting the user facing error message during dataframe construction
+    if len(passed) <= 2:
+        passed = passed[::-1]
+
+    implied = tuple(len(ax) for ax in axes)
+    # Correcting the user facing error message during dataframe construction
+    if len(implied) <= 2:
+        implied = implied[::-1]
+
+    # We return the exception object instead of raising it so that we
+    #  can raise it in the caller; mypy plays better with that
+    if passed == implied and e is not None:
+        raise e
+    if block_shape[0] == 0:
+        raise ValueError("Empty data passed with indices specified.")
+    raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
+
+
+# -----------------------------------------------------------------------
+
+
+def _grouping_func(tup: tuple[int, ArrayLike]) -> tuple[int, DtypeObj]:
+    dtype = tup[1].dtype
+
+    if is_1d_only_ea_dtype(dtype):
+        # We know these won't be consolidated, so don't need to group these.
+        # This avoids expensive comparisons of CategoricalDtype objects
+        sep = id(dtype)
+    else:
+        sep = 0
+
+    return sep, dtype
+
+
+def _form_blocks(arrays: list[ArrayLike], consolidate: bool, refs: list) -> list[Block]:
+    tuples = list(enumerate(arrays))
+
+    if not consolidate:
+        return _tuples_to_blocks_no_consolidate(tuples, refs)
+
+    # when consolidating, we can ignore refs (either stacking always copies,
+    # or the EA is already copied in the calling dict_to_mgr)
+
+    # group by dtype
+    grouper = itertools.groupby(tuples, _grouping_func)
+
+    nbs: list[Block] = []
+    for (_, dtype), tup_block in grouper:
+        block_type = get_block_type(dtype)
+
+        if isinstance(dtype, np.dtype):
+            is_dtlike = dtype.kind in "mM"
+
+            if issubclass(dtype.type, (str, bytes)):
+                dtype = np.dtype(object)
+
+            values, placement = _stack_arrays(list(tup_block), dtype)
+            if is_dtlike:
+                values = ensure_wrapped_if_datetimelike(values)
+            blk = block_type(values, placement=BlockPlacement(placement), ndim=2)
+            nbs.append(blk)
+
+        elif is_1d_only_ea_dtype(dtype):
+            dtype_blocks = [
+                block_type(x[1], placement=BlockPlacement(x[0]), ndim=2)
+                for x in tup_block
+            ]
+            nbs.extend(dtype_blocks)
+
+        else:
+            dtype_blocks = [
+                block_type(
+                    ensure_block_shape(x[1], 2), placement=BlockPlacement(x[0]), ndim=2
+                )
+                for x in tup_block
+            ]
+            nbs.extend(dtype_blocks)
+    return nbs
+
+
+def _tuples_to_blocks_no_consolidate(tuples, refs) -> list[Block]:
+    # tuples produced within _form_blocks are of the form (placement, array)
+    return [
+        new_block_2d(
+            ensure_block_shape(arr, ndim=2), placement=BlockPlacement(i), refs=ref
+        )
+        for ((i, arr), ref) in zip(tuples, refs)
+    ]
+
+
+def _stack_arrays(tuples, dtype: np.dtype):
+    placement, arrays = zip(*tuples)
+
+    first = arrays[0]
+    shape = (len(arrays),) + first.shape
+
+    stacked = np.empty(shape, dtype=dtype)
+    for i, arr in enumerate(arrays):
+        stacked[i] = arr
+
+    return stacked, placement
+
+
+def _consolidate(blocks: tuple[Block, ...]) -> tuple[Block, ...]:
+    """
+    Merge blocks having same dtype, exclude non-consolidating blocks
+    """
+    # sort by _can_consolidate, dtype
+    gkey = lambda x: x._consolidate_key
+    grouper = itertools.groupby(sorted(blocks, key=gkey), gkey)
+
+    new_blocks: list[Block] = []
+    for (_can_consolidate, dtype), group_blocks in grouper:
+        merged_blocks, _ = _merge_blocks(
+            list(group_blocks), dtype=dtype, can_consolidate=_can_consolidate
+        )
+        new_blocks = extend_blocks(merged_blocks, new_blocks)
+    return tuple(new_blocks)
+
+
+def _merge_blocks(
+    blocks: list[Block], dtype: DtypeObj, can_consolidate: bool
+) -> tuple[list[Block], bool]:
+    if len(blocks) == 1:
+        return blocks, False
+
+    if can_consolidate:
+        # TODO: optimization potential in case all mgrs contain slices and
+        # combination of those slices is a slice, too.
+        new_mgr_locs = np.concatenate([b.mgr_locs.as_array for b in blocks])
+
+        new_values: ArrayLike
+
+        if isinstance(blocks[0].dtype, np.dtype):
+            # error: List comprehension has incompatible type List[Union[ndarray,
+            # ExtensionArray]]; expected List[Union[complex, generic,
+            # Sequence[Union[int, float, complex, str, bytes, generic]],
+            # Sequence[Sequence[Any]], SupportsArray]]
+            new_values = np.vstack([b.values for b in blocks])  # type: ignore[misc]
+        else:
+            bvals = [blk.values for blk in blocks]
+            bvals2 = cast(Sequence[NDArrayBackedExtensionArray], bvals)
+            new_values = bvals2[0]._concat_same_type(bvals2, axis=0)
+
+        argsort = np.argsort(new_mgr_locs)
+        new_values = new_values[argsort]
+        new_mgr_locs = new_mgr_locs[argsort]
+
+        bp = BlockPlacement(new_mgr_locs)
+        return [new_block_2d(new_values, placement=bp)], True
+
+    # can't consolidate --> no merge
+    return blocks, False
+
+
+def _fast_count_smallints(arr: npt.NDArray[np.intp]):
+    """Faster version of set(arr) for sequences of small numbers."""
+    counts = np.bincount(arr)
+    nz = counts.nonzero()[0]
+    # Note: list(zip(...) outperforms list(np.c_[nz, counts[nz]]) here,
+    #  in one benchmark by a factor of 11
+    return zip(nz, counts[nz])
+
+
+def _preprocess_slice_or_indexer(
+    slice_or_indexer: slice | np.ndarray, length: int, allow_fill: bool
+):
+    if isinstance(slice_or_indexer, slice):
+        return (
+            "slice",
+            slice_or_indexer,
+            libinternals.slice_len(slice_or_indexer, length),
+        )
+    else:
+        if (
+            not isinstance(slice_or_indexer, np.ndarray)
+            or slice_or_indexer.dtype.kind != "i"
+        ):
+            dtype = getattr(slice_or_indexer, "dtype", None)
+            raise TypeError(type(slice_or_indexer), dtype)
+
+        indexer = ensure_platform_int(slice_or_indexer)
+        if not allow_fill:
+            indexer = maybe_convert_indices(indexer, length)
+        return "fancy", indexer, len(indexer)
+
+
+def make_na_array(dtype: DtypeObj, shape: Shape, fill_value) -> ArrayLike:
+    if isinstance(dtype, DatetimeTZDtype):
+        # NB: exclude e.g. pyarrow[dt64tz] dtypes
+        ts = Timestamp(fill_value).as_unit(dtype.unit)
+        i8values = np.full(shape, ts._value)
+        dt64values = i8values.view(f"M8[{dtype.unit}]")
+        return DatetimeArray._simple_new(dt64values, dtype=dtype)
+
+    elif is_1d_only_ea_dtype(dtype):
+        dtype = cast(ExtensionDtype, dtype)
+        cls = dtype.construct_array_type()
+
+        missing_arr = cls._from_sequence([], dtype=dtype)
+        ncols, nrows = shape
+        assert ncols == 1, ncols
+        empty_arr = -1 * np.ones((nrows,), dtype=np.intp)
+        return missing_arr.take(empty_arr, allow_fill=True, fill_value=fill_value)
+    elif isinstance(dtype, ExtensionDtype):
+        # TODO: no tests get here, a handful would if we disabled
+        #  the dt64tz special-case above (which is faster)
+        cls = dtype.construct_array_type()
+        missing_arr = cls._empty(shape=shape, dtype=dtype)
+        missing_arr[:] = fill_value
+        return missing_arr
+    else:
+        # NB: we should never get here with dtype integer or bool;
+        #  if we did, the missing_arr.fill would cast to gibberish
+        missing_arr = np.empty(shape, dtype=dtype)
+        missing_arr.fill(fill_value)
+
+        if dtype.kind in "mM":
+            missing_arr = ensure_wrapped_if_datetimelike(missing_arr)
+        return missing_arr
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/ops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9466c0bdf0bf4df623e2d819faf3ea7b36c878
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/internals/ops.py
@@ -0,0 +1,154 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    NamedTuple,
+)
+
+from pandas.core.dtypes.common import is_1d_only_ea_dtype
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    from pandas._libs.internals import BlockPlacement
+    from pandas._typing import ArrayLike
+
+    from pandas.core.internals.blocks import Block
+    from pandas.core.internals.managers import BlockManager
+
+
+class BlockPairInfo(NamedTuple):
+    lvals: ArrayLike
+    rvals: ArrayLike
+    locs: BlockPlacement
+    left_ea: bool
+    right_ea: bool
+    rblk: Block
+
+
+def _iter_block_pairs(
+    left: BlockManager, right: BlockManager
+) -> Iterator[BlockPairInfo]:
+    # At this point we have already checked the parent DataFrames for
+    #  assert rframe._indexed_same(lframe)
+
+    for blk in left.blocks:
+        locs = blk.mgr_locs
+        blk_vals = blk.values
+
+        left_ea = blk_vals.ndim == 1
+
+        rblks = right._slice_take_blocks_ax0(locs.indexer, only_slice=True)
+
+        # Assertions are disabled for performance, but should hold:
+        # if left_ea:
+        #    assert len(locs) == 1, locs
+        #    assert len(rblks) == 1, rblks
+        #    assert rblks[0].shape[0] == 1, rblks[0].shape
+
+        for rblk in rblks:
+            right_ea = rblk.values.ndim == 1
+
+            lvals, rvals = _get_same_shape_values(blk, rblk, left_ea, right_ea)
+            info = BlockPairInfo(lvals, rvals, locs, left_ea, right_ea, rblk)
+            yield info
+
+
+def operate_blockwise(
+    left: BlockManager, right: BlockManager, array_op
+) -> BlockManager:
+    # At this point we have already checked the parent DataFrames for
+    #  assert rframe._indexed_same(lframe)
+
+    res_blks: list[Block] = []
+    for lvals, rvals, locs, left_ea, right_ea, rblk in _iter_block_pairs(left, right):
+        res_values = array_op(lvals, rvals)
+        if (
+            left_ea
+            and not right_ea
+            and hasattr(res_values, "reshape")
+            and not is_1d_only_ea_dtype(res_values.dtype)
+        ):
+            res_values = res_values.reshape(1, -1)
+        nbs = rblk._split_op_result(res_values)
+
+        # Assertions are disabled for performance, but should hold:
+        # if right_ea or left_ea:
+        #    assert len(nbs) == 1
+        # else:
+        #    assert res_values.shape == lvals.shape, (res_values.shape, lvals.shape)
+
+        _reset_block_mgr_locs(nbs, locs)
+
+        res_blks.extend(nbs)
+
+    # Assertions are disabled for performance, but should hold:
+    #  slocs = {y for nb in res_blks for y in nb.mgr_locs.as_array}
+    #  nlocs = sum(len(nb.mgr_locs.as_array) for nb in res_blks)
+    #  assert nlocs == len(left.items), (nlocs, len(left.items))
+    #  assert len(slocs) == nlocs, (len(slocs), nlocs)
+    #  assert slocs == set(range(nlocs)), slocs
+
+    new_mgr = type(right)(tuple(res_blks), axes=right.axes, verify_integrity=False)
+    return new_mgr
+
+
+def _reset_block_mgr_locs(nbs: list[Block], locs) -> None:
+    """
+    Reset mgr_locs to correspond to our original DataFrame.
+    """
+    for nb in nbs:
+        nblocs = locs[nb.mgr_locs.indexer]
+        nb.mgr_locs = nblocs
+        # Assertions are disabled for performance, but should hold:
+        #  assert len(nblocs) == nb.shape[0], (len(nblocs), nb.shape)
+        #  assert all(x in locs.as_array for x in nb.mgr_locs.as_array)
+
+
+def _get_same_shape_values(
+    lblk: Block, rblk: Block, left_ea: bool, right_ea: bool
+) -> tuple[ArrayLike, ArrayLike]:
+    """
+    Slice lblk.values to align with rblk.  Squeeze if we have EAs.
+    """
+    lvals = lblk.values
+    rvals = rblk.values
+
+    # Require that the indexing into lvals be slice-like
+    assert rblk.mgr_locs.is_slice_like, rblk.mgr_locs
+
+    # TODO(EA2D): with 2D EAs only this first clause would be needed
+    if not (left_ea or right_ea):
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[Union[ndarray, slice], slice]"
+        lvals = lvals[rblk.mgr_locs.indexer, :]  # type: ignore[call-overload]
+        assert lvals.shape == rvals.shape, (lvals.shape, rvals.shape)
+    elif left_ea and right_ea:
+        assert lvals.shape == rvals.shape, (lvals.shape, rvals.shape)
+    elif right_ea:
+        # lvals are 2D, rvals are 1D
+
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[Union[ndarray, slice], slice]"
+        lvals = lvals[rblk.mgr_locs.indexer, :]  # type: ignore[call-overload]
+        assert lvals.shape[0] == 1, lvals.shape
+        lvals = lvals[0, :]
+    else:
+        # lvals are 1D, rvals are 2D
+        assert rvals.shape[0] == 1, rvals.shape
+        # error: No overload variant of "__getitem__" of "ExtensionArray" matches
+        # argument type "Tuple[int, slice]"
+        rvals = rvals[0, :]  # type: ignore[call-overload]
+
+    return lvals, rvals
+
+
+def blockwise_all(left: BlockManager, right: BlockManager, op) -> bool:
+    """
+    Blockwise `all` reduction.
+    """
+    for info in _iter_block_pairs(left, right):
+        res = op(info.lvals, info.rvals)
+        if not res:
+            return False
+    return True
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/describe.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/describe.py
new file mode 100644
index 0000000000000000000000000000000000000000..c620bb9d17976a9deeb682ff323476c484f283ff
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/describe.py
@@ -0,0 +1,416 @@
+"""
+Module responsible for execution of NDFrame.describe() method.
+
+Method NDFrame.describe() delegates actual execution to function describe_ndframe().
+"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import Timestamp
+from pandas._typing import (
+    DtypeObj,
+    NDFrameT,
+    npt,
+)
+from pandas.util._validators import validate_percentile
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+
+from pandas.core.arrays.floating import Float64Dtype
+from pandas.core.reshape.concat import concat
+
+from pandas.io.formats.format import format_percentiles
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+def describe_ndframe(
+    *,
+    obj: NDFrameT,
+    include: str | Sequence[str] | None,
+    exclude: str | Sequence[str] | None,
+    percentiles: Sequence[float] | np.ndarray | None,
+) -> NDFrameT:
+    """Describe series or dataframe.
+
+    Called from pandas.core.generic.NDFrame.describe()
+
+    Parameters
+    ----------
+    obj: DataFrame or Series
+        Either dataframe or series to be described.
+    include : 'all', list-like of dtypes or None (default), optional
+        A white list of data types to include in the result. Ignored for ``Series``.
+    exclude : list-like of dtypes or None (default), optional,
+        A black list of data types to omit from the result. Ignored for ``Series``.
+    percentiles : list-like of numbers, optional
+        The percentiles to include in the output. All should fall between 0 and 1.
+        The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and
+        75th percentiles.
+
+    Returns
+    -------
+    Dataframe or series description.
+    """
+    percentiles = _refine_percentiles(percentiles)
+
+    describer: NDFrameDescriberAbstract
+    if obj.ndim == 1:
+        describer = SeriesDescriber(
+            obj=cast("Series", obj),
+        )
+    else:
+        describer = DataFrameDescriber(
+            obj=cast("DataFrame", obj),
+            include=include,
+            exclude=exclude,
+        )
+
+    result = describer.describe(percentiles=percentiles)
+    return cast(NDFrameT, result)
+
+
+class NDFrameDescriberAbstract(ABC):
+    """Abstract class for describing dataframe or series.
+
+    Parameters
+    ----------
+    obj : Series or DataFrame
+        Object to be described.
+    """
+
+    def __init__(self, obj: DataFrame | Series) -> None:
+        self.obj = obj
+
+    @abstractmethod
+    def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame | Series:
+        """Do describe either series or dataframe.
+
+        Parameters
+        ----------
+        percentiles : list-like of numbers
+            The percentiles to include in the output.
+        """
+
+
+class SeriesDescriber(NDFrameDescriberAbstract):
+    """Class responsible for creating series description."""
+
+    obj: Series
+
+    def describe(self, percentiles: Sequence[float] | np.ndarray) -> Series:
+        describe_func = select_describe_func(
+            self.obj,
+        )
+        return describe_func(self.obj, percentiles)
+
+
+class DataFrameDescriber(NDFrameDescriberAbstract):
+    """Class responsible for creating dataobj description.
+
+    Parameters
+    ----------
+    obj : DataFrame
+        DataFrame to be described.
+    include : 'all', list-like of dtypes or None
+        A white list of data types to include in the result.
+    exclude : list-like of dtypes or None
+        A black list of data types to omit from the result.
+    """
+
+    obj: DataFrame
+
+    def __init__(
+        self,
+        obj: DataFrame,
+        *,
+        include: str | Sequence[str] | None,
+        exclude: str | Sequence[str] | None,
+    ) -> None:
+        self.include = include
+        self.exclude = exclude
+
+        if obj.ndim == 2 and obj.columns.size == 0:
+            raise ValueError("Cannot describe a DataFrame without columns")
+
+        super().__init__(obj)
+
+    def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame:
+        data = self._select_data()
+
+        ldesc: list[Series] = []
+        for _, series in data.items():
+            describe_func = select_describe_func(series)
+            ldesc.append(describe_func(series, percentiles))
+
+        col_names = reorder_columns(ldesc)
+        d = concat(
+            [x.reindex(col_names, copy=False) for x in ldesc],
+            axis=1,
+            sort=False,
+        )
+        d.columns = data.columns.copy()
+        return d
+
+    def _select_data(self) -> DataFrame:
+        """Select columns to be described."""
+        if (self.include is None) and (self.exclude is None):
+            # when some numerics are found, keep only numerics
+            default_include: list[npt.DTypeLike] = [np.number, "datetime"]
+            data = self.obj.select_dtypes(include=default_include)
+            if len(data.columns) == 0:
+                data = self.obj
+        elif self.include == "all":
+            if self.exclude is not None:
+                msg = "exclude must be None when include is 'all'"
+                raise ValueError(msg)
+            data = self.obj
+        else:
+            data = self.obj.select_dtypes(
+                include=self.include,
+                exclude=self.exclude,
+            )
+        return data
+
+
+def reorder_columns(ldesc: Sequence[Series]) -> list[Hashable]:
+    """Set a convenient order for rows for display."""
+    names: list[Hashable] = []
+    seen_names: set[Hashable] = set()
+    ldesc_indexes = sorted((x.index for x in ldesc), key=len)
+    for idxnames in ldesc_indexes:
+        for name in idxnames:
+            if name not in seen_names:
+                seen_names.add(name)
+                names.append(name)
+    return names
+
+
+def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
+    """Describe series containing numerical data.
+
+    Parameters
+    ----------
+    series : Series
+        Series to be described.
+    percentiles : list-like of numbers
+        The percentiles to include in the output.
+    """
+    from pandas import Series
+
+    formatted_percentiles = format_percentiles(percentiles)
+
+    stat_index = ["count", "mean", "std", "min"] + formatted_percentiles + ["max"]
+    d = (
+        [series.count(), series.mean(), series.std(), series.min()]
+        + series.quantile(percentiles).tolist()
+        + [series.max()]
+    )
+    # GH#48340 - always return float on non-complex numeric data
+    dtype: DtypeObj | None
+    if isinstance(series.dtype, ExtensionDtype):
+        if isinstance(series.dtype, ArrowDtype):
+            if series.dtype.kind == "m":
+                # GH53001: describe timedeltas with object dtype
+                dtype = None
+            else:
+                import pyarrow as pa
+
+                dtype = ArrowDtype(pa.float64())
+        else:
+            dtype = Float64Dtype()
+    elif series.dtype.kind in "iufb":
+        # i.e. numeric but exclude complex dtype
+        dtype = np.dtype("float")
+    else:
+        dtype = None
+    return Series(d, index=stat_index, name=series.name, dtype=dtype)
+
+
+def describe_categorical_1d(
+    data: Series,
+    percentiles_ignored: Sequence[float],
+) -> Series:
+    """Describe series containing categorical data.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    percentiles_ignored : list-like of numbers
+        Ignored, but in place to unify interface.
+    """
+    names = ["count", "unique", "top", "freq"]
+    objcounts = data.value_counts()
+    count_unique = len(objcounts[objcounts != 0])
+    if count_unique > 0:
+        top, freq = objcounts.index[0], objcounts.iloc[0]
+        dtype = None
+    else:
+        # If the DataFrame is empty, set 'top' and 'freq' to None
+        # to maintain output shape consistency
+        top, freq = np.nan, np.nan
+        dtype = "object"
+
+    result = [data.count(), count_unique, top, freq]
+
+    from pandas import Series
+
+    return Series(result, index=names, name=data.name, dtype=dtype)
+
+
+def describe_timestamp_as_categorical_1d(
+    data: Series,
+    percentiles_ignored: Sequence[float],
+) -> Series:
+    """Describe series containing timestamp data treated as categorical.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    percentiles_ignored : list-like of numbers
+        Ignored, but in place to unify interface.
+    """
+    names = ["count", "unique"]
+    objcounts = data.value_counts()
+    count_unique = len(objcounts[objcounts != 0])
+    result: list[float | Timestamp] = [data.count(), count_unique]
+    dtype = None
+    if count_unique > 0:
+        top, freq = objcounts.index[0], objcounts.iloc[0]
+        tz = data.dt.tz
+        asint = data.dropna().values.view("i8")
+        top = Timestamp(top)
+        if top.tzinfo is not None and tz is not None:
+            # Don't tz_localize(None) if key is already tz-aware
+            top = top.tz_convert(tz)
+        else:
+            top = top.tz_localize(tz)
+        names += ["top", "freq", "first", "last"]
+        result += [
+            top,
+            freq,
+            Timestamp(asint.min(), tz=tz),
+            Timestamp(asint.max(), tz=tz),
+        ]
+
+    # If the DataFrame is empty, set 'top' and 'freq' to None
+    # to maintain output shape consistency
+    else:
+        names += ["top", "freq"]
+        result += [np.nan, np.nan]
+        dtype = "object"
+
+    from pandas import Series
+
+    return Series(result, index=names, name=data.name, dtype=dtype)
+
+
+def describe_timestamp_1d(data: Series, percentiles: Sequence[float]) -> Series:
+    """Describe series containing datetime64 dtype.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    percentiles : list-like of numbers
+        The percentiles to include in the output.
+    """
+    # GH-30164
+    from pandas import Series
+
+    formatted_percentiles = format_percentiles(percentiles)
+
+    stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"]
+    d = (
+        [data.count(), data.mean(), data.min()]
+        + data.quantile(percentiles).tolist()
+        + [data.max()]
+    )
+    return Series(d, index=stat_index, name=data.name)
+
+
+def select_describe_func(
+    data: Series,
+) -> Callable:
+    """Select proper function for describing series based on data type.
+
+    Parameters
+    ----------
+    data : Series
+        Series to be described.
+    """
+    if is_bool_dtype(data.dtype):
+        return describe_categorical_1d
+    elif is_numeric_dtype(data):
+        return describe_numeric_1d
+    elif data.dtype.kind == "M" or isinstance(data.dtype, DatetimeTZDtype):
+        return describe_timestamp_1d
+    elif data.dtype.kind == "m":
+        return describe_numeric_1d
+    else:
+        return describe_categorical_1d
+
+
+def _refine_percentiles(
+    percentiles: Sequence[float] | np.ndarray | None,
+) -> npt.NDArray[np.float64]:
+    """
+    Ensure that percentiles are unique and sorted.
+
+    Parameters
+    ----------
+    percentiles : list-like of numbers, optional
+        The percentiles to include in the output.
+    """
+    if percentiles is None:
+        return np.array([0.25, 0.5, 0.75])
+
+    # explicit conversion of `percentiles` to list
+    percentiles = list(percentiles)
+
+    # get them all to be in [0, 1]
+    validate_percentile(percentiles)
+
+    # median should always be included
+    if 0.5 not in percentiles:
+        percentiles.append(0.5)
+
+    percentiles = np.asarray(percentiles)
+
+    # sort and check for duplicates
+    unique_pcts = np.unique(percentiles)
+    assert percentiles is not None
+    if len(unique_pcts) < len(percentiles):
+        raise ValueError("percentiles cannot contain duplicates")
+
+    return unique_pcts
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/selectn.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/selectn.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2f8ca94134b897bd3f7824a6cb45ca13516e5ff
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/selectn.py
@@ -0,0 +1,269 @@
+"""
+Implementation of nlargest and nsmallest.
+"""
+
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    cast,
+    final,
+)
+
+import numpy as np
+
+from pandas._libs import algos as libalgos
+
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_complex_dtype,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import BaseMaskedDtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        IndexLabel,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class SelectN:
+    def __init__(self, obj, n: int, keep: str) -> None:
+        self.obj = obj
+        self.n = n
+        self.keep = keep
+
+        if self.keep not in ("first", "last", "all"):
+            raise ValueError('keep must be either "first", "last" or "all"')
+
+    def compute(self, method: str) -> DataFrame | Series:
+        raise NotImplementedError
+
+    @final
+    def nlargest(self):
+        return self.compute("nlargest")
+
+    @final
+    def nsmallest(self):
+        return self.compute("nsmallest")
+
+    @final
+    @staticmethod
+    def is_valid_dtype_n_method(dtype: DtypeObj) -> bool:
+        """
+        Helper function to determine if dtype is valid for
+        nsmallest/nlargest methods
+        """
+        if is_numeric_dtype(dtype):
+            return not is_complex_dtype(dtype)
+        return needs_i8_conversion(dtype)
+
+
+class SelectNSeries(SelectN):
+    """
+    Implement n largest/smallest for Series
+
+    Parameters
+    ----------
+    obj : Series
+    n : int
+    keep : {'first', 'last'}, default 'first'
+
+    Returns
+    -------
+    nordered : Series
+    """
+
+    def compute(self, method: str) -> Series:
+        from pandas.core.reshape.concat import concat
+
+        n = self.n
+        dtype = self.obj.dtype
+        if not self.is_valid_dtype_n_method(dtype):
+            raise TypeError(f"Cannot use method '{method}' with dtype {dtype}")
+
+        if n <= 0:
+            return self.obj[[]]
+
+        dropped = self.obj.dropna()
+        nan_index = self.obj.drop(dropped.index)
+
+        # slow method
+        if n >= len(self.obj):
+            ascending = method == "nsmallest"
+            return self.obj.sort_values(ascending=ascending).head(n)
+
+        # fast method
+        new_dtype = dropped.dtype
+
+        # Similar to algorithms._ensure_data
+        arr = dropped._values
+        if needs_i8_conversion(arr.dtype):
+            arr = arr.view("i8")
+        elif isinstance(arr.dtype, BaseMaskedDtype):
+            arr = arr._data
+        else:
+            arr = np.asarray(arr)
+        if arr.dtype.kind == "b":
+            arr = arr.view(np.uint8)
+
+        if method == "nlargest":
+            arr = -arr
+            if is_integer_dtype(new_dtype):
+                # GH 21426: ensure reverse ordering at boundaries
+                arr -= 1
+
+            elif is_bool_dtype(new_dtype):
+                # GH 26154: ensure False is smaller than True
+                arr = 1 - (-arr)
+
+        if self.keep == "last":
+            arr = arr[::-1]
+
+        nbase = n
+        narr = len(arr)
+        n = min(n, narr)
+
+        # arr passed into kth_smallest must be contiguous. We copy
+        # here because kth_smallest will modify its input
+        # avoid OOB access with kth_smallest_c when n <= 0
+        if len(arr) > 0:
+            kth_val = libalgos.kth_smallest(arr.copy(order="C"), n - 1)
+        else:
+            kth_val = np.nan
+        (ns,) = np.nonzero(arr <= kth_val)
+        inds = ns[arr[ns].argsort(kind="mergesort")]
+
+        if self.keep != "all":
+            inds = inds[:n]
+            findex = nbase
+        else:
+            if len(inds) < nbase <= len(nan_index) + len(inds):
+                findex = len(nan_index) + len(inds)
+            else:
+                findex = len(inds)
+
+        if self.keep == "last":
+            # reverse indices
+            inds = narr - 1 - inds
+
+        return concat([dropped.iloc[inds], nan_index]).iloc[:findex]
+
+
+class SelectNFrame(SelectN):
+    """
+    Implement n largest/smallest for DataFrame
+
+    Parameters
+    ----------
+    obj : DataFrame
+    n : int
+    keep : {'first', 'last'}, default 'first'
+    columns : list or str
+
+    Returns
+    -------
+    nordered : DataFrame
+    """
+
+    def __init__(self, obj: DataFrame, n: int, keep: str, columns: IndexLabel) -> None:
+        super().__init__(obj, n, keep)
+        if not is_list_like(columns) or isinstance(columns, tuple):
+            columns = [columns]
+
+        columns = cast(Sequence[Hashable], columns)
+        columns = list(columns)
+        self.columns = columns
+
+    def compute(self, method: str) -> DataFrame:
+        from pandas.core.api import Index
+
+        n = self.n
+        frame = self.obj
+        columns = self.columns
+
+        for column in columns:
+            dtype = frame[column].dtype
+            if not self.is_valid_dtype_n_method(dtype):
+                raise TypeError(
+                    f"Column {repr(column)} has dtype {dtype}, "
+                    f"cannot use method {repr(method)} with this dtype"
+                )
+
+        def get_indexer(current_indexer, other_indexer):
+            """
+            Helper function to concat `current_indexer` and `other_indexer`
+            depending on `method`
+            """
+            if method == "nsmallest":
+                return current_indexer.append(other_indexer)
+            else:
+                return other_indexer.append(current_indexer)
+
+        # Below we save and reset the index in case index contains duplicates
+        original_index = frame.index
+        cur_frame = frame = frame.reset_index(drop=True)
+        cur_n = n
+        indexer = Index([], dtype=np.int64)
+
+        for i, column in enumerate(columns):
+            # For each column we apply method to cur_frame[column].
+            # If it's the last column or if we have the number of
+            # results desired we are done.
+            # Otherwise there are duplicates of the largest/smallest
+            # value and we need to look at the rest of the columns
+            # to determine which of the rows with the largest/smallest
+            # value in the column to keep.
+            series = cur_frame[column]
+            is_last_column = len(columns) - 1 == i
+            values = getattr(series, method)(
+                cur_n, keep=self.keep if is_last_column else "all"
+            )
+
+            if is_last_column or len(values) <= cur_n:
+                indexer = get_indexer(indexer, values.index)
+                break
+
+            # Now find all values which are equal to
+            # the (nsmallest: largest)/(nlargest: smallest)
+            # from our series.
+            border_value = values == values[values.index[-1]]
+
+            # Some of these values are among the top-n
+            # some aren't.
+            unsafe_values = values[border_value]
+
+            # These values are definitely among the top-n
+            safe_values = values[~border_value]
+            indexer = get_indexer(indexer, safe_values.index)
+
+            # Go on and separate the unsafe_values on the remaining
+            # columns.
+            cur_frame = cur_frame.loc[unsafe_values.index]
+            cur_n = n - len(indexer)
+
+        frame = frame.take(indexer)
+
+        # Restore the index on frame
+        frame.index = original_index.take(indexer)
+
+        # If there is only one column, the frame is already sorted.
+        if len(columns) == 1:
+            return frame
+
+        ascending = method == "nsmallest"
+
+        return frame.sort_values(columns, ascending=ascending, kind="mergesort")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/to_dict.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/to_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..accbd92a91ed66fe4dfd1e3939fe8c498184285d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/methods/to_dict.py
@@ -0,0 +1,272 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import maybe_box_native
+from pandas.core.dtypes.dtypes import (
+    BaseMaskedDtype,
+    ExtensionDtype,
+)
+
+from pandas.core import common as com
+
+if TYPE_CHECKING:
+    from pandas._typing import MutableMappingT
+
+    from pandas import DataFrame
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+    *,
+    into: type[MutableMappingT] | MutableMappingT,
+    index: bool = ...,
+) -> MutableMappingT:
+    ...
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["records"],
+    *,
+    into: type[MutableMappingT] | MutableMappingT,
+    index: bool = ...,
+) -> list[MutableMappingT]:
+    ...
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+    *,
+    into: type[dict] = ...,
+    index: bool = ...,
+) -> dict:
+    ...
+
+
+@overload
+def to_dict(
+    df: DataFrame,
+    orient: Literal["records"],
+    *,
+    into: type[dict] = ...,
+    index: bool = ...,
+) -> list[dict]:
+    ...
+
+
+# error: Incompatible default for argument "into" (default has type "type[dict
+# [Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+def to_dict(
+    df: DataFrame,
+    orient: Literal[
+        "dict", "list", "series", "split", "tight", "records", "index"
+    ] = "dict",
+    *,
+    into: type[MutableMappingT] | MutableMappingT = dict,  # type: ignore[assignment]
+    index: bool = True,
+) -> MutableMappingT | list[MutableMappingT]:
+    """
+    Convert the DataFrame to a dictionary.
+
+    The type of the key-value pairs can be customized with the parameters
+    (see below).
+
+    Parameters
+    ----------
+    orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
+        Determines the type of the values of the dictionary.
+
+        - 'dict' (default) : dict like {column -> {index -> value}}
+        - 'list' : dict like {column -> [values]}
+        - 'series' : dict like {column -> Series(values)}
+        - 'split' : dict like
+          {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
+        - 'tight' : dict like
+          {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
+          'index_names' -> [index.names], 'column_names' -> [column.names]}
+        - 'records' : list like
+          [{column -> value}, ... , {column -> value}]
+        - 'index' : dict like {index -> {column -> value}}
+
+        .. versionadded:: 1.4.0
+            'tight' as an allowed value for the ``orient`` argument
+
+    into : class, default dict
+        The collections.abc.MutableMapping subclass used for all Mappings
+        in the return value.  Can be the actual class or an empty
+        instance of the mapping type you want.  If you want a
+        collections.defaultdict, you must pass it initialized.
+
+    index : bool, default True
+        Whether to include the index item (and index_names item if `orient`
+        is 'tight') in the returned dictionary. Can only be ``False``
+        when `orient` is 'split' or 'tight'.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    dict, list or collections.abc.Mapping
+        Return a collections.abc.MutableMapping object representing the
+        DataFrame. The resulting transformation depends on the `orient` parameter.
+    """
+    if not df.columns.is_unique:
+        warnings.warn(
+            "DataFrame columns are not unique, some columns will be omitted.",
+            UserWarning,
+            stacklevel=find_stack_level(),
+        )
+    # GH16122
+    into_c = com.standardize_mapping(into)
+
+    #  error: Incompatible types in assignment (expression has type "str",
+    # variable has type "Literal['dict', 'list', 'series', 'split', 'tight',
+    # 'records', 'index']")
+    orient = orient.lower()  # type: ignore[assignment]
+
+    if not index and orient not in ["split", "tight"]:
+        raise ValueError(
+            "'index=False' is only valid when 'orient' is 'split' or 'tight'"
+        )
+
+    if orient == "series":
+        # GH46470 Return quickly if orient series to avoid creating dtype objects
+        return into_c((k, v) for k, v in df.items())
+
+    box_native_indices = [
+        i
+        for i, col_dtype in enumerate(df.dtypes.values)
+        if col_dtype == np.dtype(object) or isinstance(col_dtype, ExtensionDtype)
+    ]
+    box_na_values = [
+        lib.no_default if not isinstance(col_dtype, BaseMaskedDtype) else libmissing.NA
+        for i, col_dtype in enumerate(df.dtypes.values)
+    ]
+    are_all_object_dtype_cols = len(box_native_indices) == len(df.dtypes)
+
+    if orient == "dict":
+        return into_c((k, v.to_dict(into=into)) for k, v in df.items())
+
+    elif orient == "list":
+        object_dtype_indices_as_set: set[int] = set(box_native_indices)
+        return into_c(
+            (
+                k,
+                list(map(maybe_box_native, v.to_numpy(na_value=box_na_values[i])))
+                if i in object_dtype_indices_as_set
+                else list(map(maybe_box_native, v.to_numpy())),
+            )
+            for i, (k, v) in enumerate(df.items())
+        )
+
+    elif orient == "split":
+        data = df._create_data_for_split_and_tight_to_dict(
+            are_all_object_dtype_cols, box_native_indices
+        )
+
+        return into_c(
+            ((("index", df.index.tolist()),) if index else ())
+            + (
+                ("columns", df.columns.tolist()),
+                ("data", data),
+            )
+        )
+
+    elif orient == "tight":
+        data = df._create_data_for_split_and_tight_to_dict(
+            are_all_object_dtype_cols, box_native_indices
+        )
+
+        return into_c(
+            ((("index", df.index.tolist()),) if index else ())
+            + (
+                ("columns", df.columns.tolist()),
+                (
+                    "data",
+                    [
+                        list(map(maybe_box_native, t))
+                        for t in df.itertuples(index=False, name=None)
+                    ],
+                ),
+            )
+            + ((("index_names", list(df.index.names)),) if index else ())
+            + (("column_names", list(df.columns.names)),)
+        )
+
+    elif orient == "records":
+        columns = df.columns.tolist()
+        if are_all_object_dtype_cols:
+            rows = (
+                dict(zip(columns, row)) for row in df.itertuples(index=False, name=None)
+            )
+            return [
+                into_c((k, maybe_box_native(v)) for k, v in row.items()) for row in rows
+            ]
+        else:
+            data = [
+                into_c(zip(columns, t)) for t in df.itertuples(index=False, name=None)
+            ]
+            if box_native_indices:
+                object_dtype_indices_as_set = set(box_native_indices)
+                object_dtype_cols = {
+                    col
+                    for i, col in enumerate(df.columns)
+                    if i in object_dtype_indices_as_set
+                }
+                for row in data:
+                    for col in object_dtype_cols:
+                        row[col] = maybe_box_native(row[col])
+            return data
+
+    elif orient == "index":
+        if not df.index.is_unique:
+            raise ValueError("DataFrame index must be unique for orient='index'.")
+        columns = df.columns.tolist()
+        if are_all_object_dtype_cols:
+            return into_c(
+                (t[0], dict(zip(df.columns, map(maybe_box_native, t[1:]))))
+                for t in df.itertuples(name=None)
+            )
+        elif box_native_indices:
+            object_dtype_indices_as_set = set(box_native_indices)
+            is_object_dtype_by_index = [
+                i in object_dtype_indices_as_set for i in range(len(df.columns))
+            ]
+            return into_c(
+                (
+                    t[0],
+                    {
+                        columns[i]: maybe_box_native(v)
+                        if is_object_dtype_by_index[i]
+                        else v
+                        for i, v in enumerate(t[1:])
+                    },
+                )
+                for t in df.itertuples(name=None)
+            )
+        else:
+            return into_c(
+                (t[0], dict(zip(df.columns, t[1:]))) for t in df.itertuples(name=None)
+            )
+
+    else:
+        raise ValueError(f"orient '{orient}' not understood")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/missing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c016aab8ad0748220d04d047f84d3aa14564ab80
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/missing.py
@@ -0,0 +1,1158 @@
+"""
+Routines for filling missing data.
+"""
+from __future__ import annotations
+
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+    cast,
+    overload,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    algos,
+    lib,
+)
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    F,
+    ReindexMethod,
+    npt,
+)
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.cast import infer_dtype_from
+from pandas.core.dtypes.common import (
+    is_array_like,
+    is_bool_dtype,
+    is_numeric_dtype,
+    is_numeric_v_string_like,
+    is_object_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import (
+    is_valid_na_for_dtype,
+    isna,
+    na_value_for_dtype,
+)
+
+if TYPE_CHECKING:
+    from pandas import Index
+
+
+def check_value_size(value, mask: npt.NDArray[np.bool_], length: int):
+    """
+    Validate the size of the values passed to ExtensionArray.fillna.
+    """
+    if is_array_like(value):
+        if len(value) != length:
+            raise ValueError(
+                f"Length of 'value' does not match. Got ({len(value)}) "
+                f" expected {length}"
+            )
+        value = value[mask]
+
+    return value
+
+
+def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
+    """
+    Return a masking array of same size/shape as arr
+    with entries equaling any member of values_to_mask set to True
+
+    Parameters
+    ----------
+    arr : ArrayLike
+    values_to_mask: list, tuple, or scalar
+
+    Returns
+    -------
+    np.ndarray[bool]
+    """
+    # When called from Block.replace/replace_list, values_to_mask is a scalar
+    #  known to be holdable by arr.
+    # When called from Series._single_replace, values_to_mask is tuple or list
+    dtype, values_to_mask = infer_dtype_from(values_to_mask)
+
+    if isinstance(dtype, np.dtype):
+        values_to_mask = np.array(values_to_mask, dtype=dtype)
+    else:
+        cls = dtype.construct_array_type()
+        if not lib.is_list_like(values_to_mask):
+            values_to_mask = [values_to_mask]
+        values_to_mask = cls._from_sequence(values_to_mask, dtype=dtype, copy=False)
+
+    potential_na = False
+    if is_object_dtype(arr.dtype):
+        # pre-compute mask to avoid comparison to NA
+        potential_na = True
+        arr_mask = ~isna(arr)
+
+    na_mask = isna(values_to_mask)
+    nonna = values_to_mask[~na_mask]
+
+    # GH 21977
+    mask = np.zeros(arr.shape, dtype=bool)
+    if (
+        is_numeric_dtype(arr.dtype)
+        and not is_bool_dtype(arr.dtype)
+        and is_bool_dtype(nonna.dtype)
+    ):
+        pass
+    elif (
+        is_bool_dtype(arr.dtype)
+        and is_numeric_dtype(nonna.dtype)
+        and not is_bool_dtype(nonna.dtype)
+    ):
+        pass
+    else:
+        for x in nonna:
+            if is_numeric_v_string_like(arr, x):
+                # GH#29553 prevent numpy deprecation warnings
+                pass
+            else:
+                if potential_na:
+                    new_mask = np.zeros(arr.shape, dtype=np.bool_)
+                    new_mask[arr_mask] = arr[arr_mask] == x
+                else:
+                    new_mask = arr == x
+
+                    if not isinstance(new_mask, np.ndarray):
+                        # usually BooleanArray
+                        new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
+                mask |= new_mask
+
+    if na_mask.any():
+        mask |= isna(arr)
+
+    return mask
+
+
+@overload
+def clean_fill_method(
+    method: Literal["ffill", "pad", "bfill", "backfill"],
+    *,
+    allow_nearest: Literal[False] = ...,
+) -> Literal["pad", "backfill"]:
+    ...
+
+
+@overload
+def clean_fill_method(
+    method: Literal["ffill", "pad", "bfill", "backfill", "nearest"],
+    *,
+    allow_nearest: Literal[True],
+) -> Literal["pad", "backfill", "nearest"]:
+    ...
+
+
+def clean_fill_method(
+    method: Literal["ffill", "pad", "bfill", "backfill", "nearest"],
+    *,
+    allow_nearest: bool = False,
+) -> Literal["pad", "backfill", "nearest"]:
+    if isinstance(method, str):
+        # error: Incompatible types in assignment (expression has type "str", variable
+        # has type "Literal['ffill', 'pad', 'bfill', 'backfill', 'nearest']")
+        method = method.lower()  # type: ignore[assignment]
+        if method == "ffill":
+            method = "pad"
+        elif method == "bfill":
+            method = "backfill"
+
+    valid_methods = ["pad", "backfill"]
+    expecting = "pad (ffill) or backfill (bfill)"
+    if allow_nearest:
+        valid_methods.append("nearest")
+        expecting = "pad (ffill), backfill (bfill) or nearest"
+    if method not in valid_methods:
+        raise ValueError(f"Invalid fill method. Expecting {expecting}. Got {method}")
+    return method
+
+
+# interpolation methods that dispatch to np.interp
+
+NP_METHODS = ["linear", "time", "index", "values"]
+
+# interpolation methods that dispatch to _interpolate_scipy_wrapper
+
+SP_METHODS = [
+    "nearest",
+    "zero",
+    "slinear",
+    "quadratic",
+    "cubic",
+    "barycentric",
+    "krogh",
+    "spline",
+    "polynomial",
+    "from_derivatives",
+    "piecewise_polynomial",
+    "pchip",
+    "akima",
+    "cubicspline",
+]
+
+
+def clean_interp_method(method: str, index: Index, **kwargs) -> str:
+    order = kwargs.get("order")
+
+    if method in ("spline", "polynomial") and order is None:
+        raise ValueError("You must specify the order of the spline or polynomial.")
+
+    valid = NP_METHODS + SP_METHODS
+    if method not in valid:
+        raise ValueError(f"method must be one of {valid}. Got '{method}' instead.")
+
+    if method in ("krogh", "piecewise_polynomial", "pchip"):
+        if not index.is_monotonic_increasing:
+            raise ValueError(
+                f"{method} interpolation requires that the index be monotonic."
+            )
+
+    return method
+
+
+def find_valid_index(how: str, is_valid: npt.NDArray[np.bool_]) -> int | None:
+    """
+    Retrieves the positional index of the first valid value.
+
+    Parameters
+    ----------
+    how : {'first', 'last'}
+        Use this parameter to change between the first or last valid index.
+    is_valid: np.ndarray
+        Mask to find na_values.
+
+    Returns
+    -------
+    int or None
+    """
+    assert how in ["first", "last"]
+
+    if len(is_valid) == 0:  # early stop
+        return None
+
+    if is_valid.ndim == 2:
+        is_valid = is_valid.any(axis=1)  # reduce axis 1
+
+    if how == "first":
+        idxpos = is_valid[::].argmax()
+
+    elif how == "last":
+        idxpos = len(is_valid) - 1 - is_valid[::-1].argmax()
+
+    chk_notna = is_valid[idxpos]
+
+    if not chk_notna:
+        return None
+    # Incompatible return value type (got "signedinteger[Any]",
+    # expected "Optional[int]")
+    return idxpos  # type: ignore[return-value]
+
+
+def validate_limit_direction(
+    limit_direction: str,
+) -> Literal["forward", "backward", "both"]:
+    valid_limit_directions = ["forward", "backward", "both"]
+    limit_direction = limit_direction.lower()
+    if limit_direction not in valid_limit_directions:
+        raise ValueError(
+            "Invalid limit_direction: expecting one of "
+            f"{valid_limit_directions}, got '{limit_direction}'."
+        )
+    # error: Incompatible return value type (got "str", expected
+    # "Literal['forward', 'backward', 'both']")
+    return limit_direction  # type: ignore[return-value]
+
+
+def validate_limit_area(limit_area: str | None) -> Literal["inside", "outside"] | None:
+    if limit_area is not None:
+        valid_limit_areas = ["inside", "outside"]
+        limit_area = limit_area.lower()
+        if limit_area not in valid_limit_areas:
+            raise ValueError(
+                f"Invalid limit_area: expecting one of {valid_limit_areas}, got "
+                f"{limit_area}."
+            )
+    # error: Incompatible return value type (got "Optional[str]", expected
+    # "Optional[Literal['inside', 'outside']]")
+    return limit_area  # type: ignore[return-value]
+
+
+def infer_limit_direction(
+    limit_direction: Literal["backward", "forward", "both"] | None, method: str
+) -> Literal["backward", "forward", "both"]:
+    # Set `limit_direction` depending on `method`
+    if limit_direction is None:
+        if method in ("backfill", "bfill"):
+            limit_direction = "backward"
+        else:
+            limit_direction = "forward"
+    else:
+        if method in ("pad", "ffill") and limit_direction != "forward":
+            raise ValueError(
+                f"`limit_direction` must be 'forward' for method `{method}`"
+            )
+        if method in ("backfill", "bfill") and limit_direction != "backward":
+            raise ValueError(
+                f"`limit_direction` must be 'backward' for method `{method}`"
+            )
+    return limit_direction
+
+
+def get_interp_index(method, index: Index) -> Index:
+    # create/use the index
+    if method == "linear":
+        # prior default
+        from pandas import Index
+
+        index = Index(np.arange(len(index)))
+    else:
+        methods = {"index", "values", "nearest", "time"}
+        is_numeric_or_datetime = (
+            is_numeric_dtype(index.dtype)
+            or isinstance(index.dtype, DatetimeTZDtype)
+            or lib.is_np_dtype(index.dtype, "mM")
+        )
+        if method not in methods and not is_numeric_or_datetime:
+            raise ValueError(
+                "Index column must be numeric or datetime type when "
+                f"using {method} method other than linear. "
+                "Try setting a numeric or datetime index column before "
+                "interpolating."
+            )
+
+    if isna(index).any():
+        raise NotImplementedError(
+            "Interpolation with NaNs in the index "
+            "has not been implemented. Try filling "
+            "those NaNs before interpolating."
+        )
+    return index
+
+
+def interpolate_2d_inplace(
+    data: np.ndarray,  # floating dtype
+    index: Index,
+    axis: AxisInt,
+    method: str = "linear",
+    limit: int | None = None,
+    limit_direction: str = "forward",
+    limit_area: str | None = None,
+    fill_value: Any | None = None,
+    mask=None,
+    **kwargs,
+) -> None:
+    """
+    Column-wise application of _interpolate_1d.
+
+    Notes
+    -----
+    Alters 'data' in-place.
+
+    The signature does differ from _interpolate_1d because it only
+    includes what is needed for Block.interpolate.
+    """
+    # validate the interp method
+    clean_interp_method(method, index, **kwargs)
+
+    if is_valid_na_for_dtype(fill_value, data.dtype):
+        fill_value = na_value_for_dtype(data.dtype, compat=False)
+
+    if method == "time":
+        if not needs_i8_conversion(index.dtype):
+            raise ValueError(
+                "time-weighted interpolation only works "
+                "on Series or DataFrames with a "
+                "DatetimeIndex"
+            )
+        method = "values"
+
+    limit_direction = validate_limit_direction(limit_direction)
+    limit_area_validated = validate_limit_area(limit_area)
+
+    # default limit is unlimited GH #16282
+    limit = algos.validate_limit(nobs=None, limit=limit)
+
+    indices = _index_to_interp_indices(index, method)
+
+    def func(yvalues: np.ndarray) -> None:
+        # process 1-d slices in the axis direction
+
+        _interpolate_1d(
+            indices=indices,
+            yvalues=yvalues,
+            method=method,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area_validated,
+            fill_value=fill_value,
+            bounds_error=False,
+            mask=mask,
+            **kwargs,
+        )
+
+    # error: Argument 1 to "apply_along_axis" has incompatible type
+    # "Callable[[ndarray[Any, Any]], None]"; expected "Callable[...,
+    # Union[_SupportsArray[dtype[<nothing>]], Sequence[_SupportsArray
+    # [dtype[<nothing>]]], Sequence[Sequence[_SupportsArray[dtype[<nothing>]]]],
+    # Sequence[Sequence[Sequence[_SupportsArray[dtype[<nothing>]]]]],
+    # Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[<nothing>]]]]]]]]"
+    np.apply_along_axis(func, axis, data)  # type: ignore[arg-type]
+
+
+def _index_to_interp_indices(index: Index, method: str) -> np.ndarray:
+    """
+    Convert Index to ndarray of indices to pass to NumPy/SciPy.
+    """
+    xarr = index._values
+    if needs_i8_conversion(xarr.dtype):
+        # GH#1646 for dt64tz
+        xarr = xarr.view("i8")
+
+    if method == "linear":
+        inds = xarr
+        inds = cast(np.ndarray, inds)
+    else:
+        inds = np.asarray(xarr)
+
+        if method in ("values", "index"):
+            if inds.dtype == np.object_:
+                inds = lib.maybe_convert_objects(inds)
+
+    return inds
+
+
+def _interpolate_1d(
+    indices: np.ndarray,
+    yvalues: np.ndarray,
+    method: str = "linear",
+    limit: int | None = None,
+    limit_direction: str = "forward",
+    limit_area: Literal["inside", "outside"] | None = None,
+    fill_value: Any | None = None,
+    bounds_error: bool = False,
+    order: int | None = None,
+    mask=None,
+    **kwargs,
+) -> None:
+    """
+    Logic for the 1-d interpolation.  The input
+    indices and yvalues will each be 1-d arrays of the same length.
+
+    Bounds_error is currently hardcoded to False since non-scipy ones don't
+    take it as an argument.
+
+    Notes
+    -----
+    Fills 'yvalues' in-place.
+    """
+    if mask is not None:
+        invalid = mask
+    else:
+        invalid = isna(yvalues)
+    valid = ~invalid
+
+    if not valid.any():
+        return
+
+    if valid.all():
+        return
+
+    # These are sets of index pointers to invalid values... i.e. {0, 1, etc...
+    all_nans = set(np.flatnonzero(invalid))
+
+    first_valid_index = find_valid_index(how="first", is_valid=valid)
+    if first_valid_index is None:  # no nan found in start
+        first_valid_index = 0
+    start_nans = set(range(first_valid_index))
+
+    last_valid_index = find_valid_index(how="last", is_valid=valid)
+    if last_valid_index is None:  # no nan found in end
+        last_valid_index = len(yvalues)
+    end_nans = set(range(1 + last_valid_index, len(valid)))
+
+    # Like the sets above, preserve_nans contains indices of invalid values,
+    # but in this case, it is the final set of indices that need to be
+    # preserved as NaN after the interpolation.
+
+    # For example if limit_direction='forward' then preserve_nans will
+    # contain indices of NaNs at the beginning of the series, and NaNs that
+    # are more than 'limit' away from the prior non-NaN.
+
+    # set preserve_nans based on direction using _interp_limit
+    preserve_nans: list | set
+    if limit_direction == "forward":
+        preserve_nans = start_nans | set(_interp_limit(invalid, limit, 0))
+    elif limit_direction == "backward":
+        preserve_nans = end_nans | set(_interp_limit(invalid, 0, limit))
+    else:
+        # both directions... just use _interp_limit
+        preserve_nans = set(_interp_limit(invalid, limit, limit))
+
+    # if limit_area is set, add either mid or outside indices
+    # to preserve_nans GH #16284
+    if limit_area == "inside":
+        # preserve NaNs on the outside
+        preserve_nans |= start_nans | end_nans
+    elif limit_area == "outside":
+        # preserve NaNs on the inside
+        mid_nans = all_nans - start_nans - end_nans
+        preserve_nans |= mid_nans
+
+    # sort preserve_nans and convert to list
+    preserve_nans = sorted(preserve_nans)
+
+    is_datetimelike = yvalues.dtype.kind in "mM"
+
+    if is_datetimelike:
+        yvalues = yvalues.view("i8")
+
+    if method in NP_METHODS:
+        # np.interp requires sorted X values, #21037
+
+        indexer = np.argsort(indices[valid])
+        yvalues[invalid] = np.interp(
+            indices[invalid], indices[valid][indexer], yvalues[valid][indexer]
+        )
+    else:
+        yvalues[invalid] = _interpolate_scipy_wrapper(
+            indices[valid],
+            yvalues[valid],
+            indices[invalid],
+            method=method,
+            fill_value=fill_value,
+            bounds_error=bounds_error,
+            order=order,
+            **kwargs,
+        )
+
+    if mask is not None:
+        mask[:] = False
+        mask[preserve_nans] = True
+    elif is_datetimelike:
+        yvalues[preserve_nans] = NaT.value
+    else:
+        yvalues[preserve_nans] = np.nan
+    return
+
+
+def _interpolate_scipy_wrapper(
+    x: np.ndarray,
+    y: np.ndarray,
+    new_x: np.ndarray,
+    method: str,
+    fill_value=None,
+    bounds_error: bool = False,
+    order=None,
+    **kwargs,
+):
+    """
+    Passed off to scipy.interpolate.interp1d. method is scipy's kind.
+    Returns an array interpolated at new_x.  Add any new methods to
+    the list in _clean_interp_method.
+    """
+    extra = f"{method} interpolation requires SciPy."
+    import_optional_dependency("scipy", extra=extra)
+    from scipy import interpolate
+
+    new_x = np.asarray(new_x)
+
+    # ignores some kwargs that could be passed along.
+    alt_methods = {
+        "barycentric": interpolate.barycentric_interpolate,
+        "krogh": interpolate.krogh_interpolate,
+        "from_derivatives": _from_derivatives,
+        "piecewise_polynomial": _from_derivatives,
+        "cubicspline": _cubicspline_interpolate,
+        "akima": _akima_interpolate,
+        "pchip": interpolate.pchip_interpolate,
+    }
+
+    interp1d_methods = [
+        "nearest",
+        "zero",
+        "slinear",
+        "quadratic",
+        "cubic",
+        "polynomial",
+    ]
+    if method in interp1d_methods:
+        if method == "polynomial":
+            kind = order
+        else:
+            kind = method
+        terp = interpolate.interp1d(
+            x, y, kind=kind, fill_value=fill_value, bounds_error=bounds_error
+        )
+        new_y = terp(new_x)
+    elif method == "spline":
+        # GH #10633, #24014
+        if isna(order) or (order <= 0):
+            raise ValueError(
+                f"order needs to be specified and greater than 0; got order: {order}"
+            )
+        terp = interpolate.UnivariateSpline(x, y, k=order, **kwargs)
+        new_y = terp(new_x)
+    else:
+        # GH 7295: need to be able to write for some reason
+        # in some circumstances: check all three
+        if not x.flags.writeable:
+            x = x.copy()
+        if not y.flags.writeable:
+            y = y.copy()
+        if not new_x.flags.writeable:
+            new_x = new_x.copy()
+        terp = alt_methods[method]
+        new_y = terp(x, y, new_x, **kwargs)
+    return new_y
+
+
+def _from_derivatives(
+    xi: np.ndarray,
+    yi: np.ndarray,
+    x: np.ndarray,
+    order=None,
+    der: int | list[int] | None = 0,
+    extrapolate: bool = False,
+):
+    """
+    Convenience function for interpolate.BPoly.from_derivatives.
+
+    Construct a piecewise polynomial in the Bernstein basis, compatible
+    with the specified values and derivatives at breakpoints.
+
+    Parameters
+    ----------
+    xi : array-like
+        sorted 1D array of x-coordinates
+    yi : array-like or list of array-likes
+        yi[i][j] is the j-th derivative known at xi[i]
+    order: None or int or array-like of ints. Default: None.
+        Specifies the degree of local polynomials. If not None, some
+        derivatives are ignored.
+    der : int or list
+        How many derivatives to extract; None for all potentially nonzero
+        derivatives (that is a number equal to the number of points), or a
+        list of derivatives to extract. This number includes the function
+        value as 0th derivative.
+     extrapolate : bool, optional
+        Whether to extrapolate to ouf-of-bounds points based on first and last
+        intervals, or to return NaNs. Default: True.
+
+    See Also
+    --------
+    scipy.interpolate.BPoly.from_derivatives
+
+    Returns
+    -------
+    y : scalar or array-like
+        The result, of length R or length M or M by R.
+    """
+    from scipy import interpolate
+
+    # return the method for compat with scipy version & backwards compat
+    method = interpolate.BPoly.from_derivatives
+    m = method(xi, yi.reshape(-1, 1), orders=order, extrapolate=extrapolate)
+
+    return m(x)
+
+
+def _akima_interpolate(
+    xi: np.ndarray,
+    yi: np.ndarray,
+    x: np.ndarray,
+    der: int | list[int] | None = 0,
+    axis: AxisInt = 0,
+):
+    """
+    Convenience function for akima interpolation.
+    xi and yi are arrays of values used to approximate some function f,
+    with ``yi = f(xi)``.
+
+    See `Akima1DInterpolator` for details.
+
+    Parameters
+    ----------
+    xi : np.ndarray
+        A sorted list of x-coordinates, of length N.
+    yi : np.ndarray
+        A 1-D array of real values.  `yi`'s length along the interpolation
+        axis must be equal to the length of `xi`. If N-D array, use axis
+        parameter to select correct axis.
+    x : np.ndarray
+        Of length M.
+    der : int, optional
+        How many derivatives to extract; None for all potentially
+        nonzero derivatives (that is a number equal to the number
+        of points), or a list of derivatives to extract. This number
+        includes the function value as 0th derivative.
+    axis : int, optional
+        Axis in the yi array corresponding to the x-coordinate values.
+
+    See Also
+    --------
+    scipy.interpolate.Akima1DInterpolator
+
+    Returns
+    -------
+    y : scalar or array-like
+        The result, of length R or length M or M by R,
+
+    """
+    from scipy import interpolate
+
+    P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
+
+    return P(x, nu=der)
+
+
+def _cubicspline_interpolate(
+    xi: np.ndarray,
+    yi: np.ndarray,
+    x: np.ndarray,
+    axis: AxisInt = 0,
+    bc_type: str | tuple[Any, Any] = "not-a-knot",
+    extrapolate=None,
+):
+    """
+    Convenience function for cubic spline data interpolator.
+
+    See `scipy.interpolate.CubicSpline` for details.
+
+    Parameters
+    ----------
+    xi : np.ndarray, shape (n,)
+        1-d array containing values of the independent variable.
+        Values must be real, finite and in strictly increasing order.
+    yi : np.ndarray
+        Array containing values of the dependent variable. It can have
+        arbitrary number of dimensions, but the length along ``axis``
+        (see below) must match the length of ``x``. Values must be finite.
+    x : np.ndarray, shape (m,)
+    axis : int, optional
+        Axis along which `y` is assumed to be varying. Meaning that for
+        ``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``.
+        Default is 0.
+    bc_type : string or 2-tuple, optional
+        Boundary condition type. Two additional equations, given by the
+        boundary conditions, are required to determine all coefficients of
+        polynomials on each segment [2]_.
+        If `bc_type` is a string, then the specified condition will be applied
+        at both ends of a spline. Available conditions are:
+        * 'not-a-knot' (default): The first and second segment at a curve end
+          are the same polynomial. It is a good default when there is no
+          information on boundary conditions.
+        * 'periodic': The interpolated functions is assumed to be periodic
+          of period ``x[-1] - x[0]``. The first and last value of `y` must be
+          identical: ``y[0] == y[-1]``. This boundary condition will result in
+          ``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``.
+        * 'clamped': The first derivative at curves ends are zero. Assuming
+          a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition.
+        * 'natural': The second derivative at curve ends are zero. Assuming
+          a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition.
+        If `bc_type` is a 2-tuple, the first and the second value will be
+        applied at the curve start and end respectively. The tuple values can
+        be one of the previously mentioned strings (except 'periodic') or a
+        tuple `(order, deriv_values)` allowing to specify arbitrary
+        derivatives at curve ends:
+        * `order`: the derivative order, 1 or 2.
+        * `deriv_value`: array-like containing derivative values, shape must
+          be the same as `y`, excluding ``axis`` dimension. For example, if
+          `y` is 1D, then `deriv_value` must be a scalar. If `y` is 3D with
+          the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D
+          and have the shape (n0, n1).
+    extrapolate : {bool, 'periodic', None}, optional
+        If bool, determines whether to extrapolate to out-of-bounds points
+        based on first and last intervals, or to return NaNs. If 'periodic',
+        periodic extrapolation is used. If None (default), ``extrapolate`` is
+        set to 'periodic' for ``bc_type='periodic'`` and to True otherwise.
+
+    See Also
+    --------
+    scipy.interpolate.CubicHermiteSpline
+
+    Returns
+    -------
+    y : scalar or array-like
+        The result, of shape (m,)
+
+    References
+    ----------
+    .. [1] `Cubic Spline Interpolation
+            <https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation>`_
+            on Wikiversity.
+    .. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978.
+    """
+    from scipy import interpolate
+
+    P = interpolate.CubicSpline(
+        xi, yi, axis=axis, bc_type=bc_type, extrapolate=extrapolate
+    )
+
+    return P(x)
+
+
+def _interpolate_with_limit_area(
+    values: np.ndarray,
+    method: Literal["pad", "backfill"],
+    limit: int | None,
+    limit_area: Literal["inside", "outside"],
+) -> None:
+    """
+    Apply interpolation and limit_area logic to values along a to-be-specified axis.
+
+    Parameters
+    ----------
+    values: np.ndarray
+        Input array.
+    method: str
+        Interpolation method. Could be "bfill" or "pad"
+    limit: int, optional
+        Index limit on interpolation.
+    limit_area: {'inside', 'outside'}
+        Limit area for interpolation.
+
+    Notes
+    -----
+    Modifies values in-place.
+    """
+
+    invalid = isna(values)
+    is_valid = ~invalid
+
+    if not invalid.all():
+        first = find_valid_index(how="first", is_valid=is_valid)
+        if first is None:
+            first = 0
+        last = find_valid_index(how="last", is_valid=is_valid)
+        if last is None:
+            last = len(values)
+
+        pad_or_backfill_inplace(
+            values,
+            method=method,
+            limit=limit,
+            limit_area=limit_area,
+        )
+
+        if limit_area == "inside":
+            invalid[first : last + 1] = False
+        elif limit_area == "outside":
+            invalid[:first] = invalid[last + 1 :] = False
+        else:
+            raise ValueError("limit_area should be 'inside' or 'outside'")
+
+        values[invalid] = np.nan
+
+
+def pad_or_backfill_inplace(
+    values: np.ndarray,
+    method: Literal["pad", "backfill"] = "pad",
+    axis: AxisInt = 0,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+) -> None:
+    """
+    Perform an actual interpolation of values, values will be make 2-d if
+    needed fills inplace, returns the result.
+
+    Parameters
+    ----------
+    values: np.ndarray
+        Input array.
+    method: str, default "pad"
+        Interpolation method. Could be "bfill" or "pad"
+    axis: 0 or 1
+        Interpolation axis
+    limit: int, optional
+        Index limit on interpolation.
+    limit_area: str, optional
+        Limit area for interpolation. Can be "inside" or "outside"
+
+    Notes
+    -----
+    Modifies values in-place.
+    """
+    transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
+
+    # reshape a 1 dim if needed
+    if values.ndim == 1:
+        if axis != 0:  # pragma: no cover
+            raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0")
+        values = values.reshape(tuple((1,) + values.shape))
+
+    method = clean_fill_method(method)
+    tvalues = transf(values)
+
+    func = get_fill_func(method, ndim=2)
+    # _pad_2d and _backfill_2d both modify tvalues inplace
+    func(tvalues, limit=limit, limit_area=limit_area)
+
+
+def _fillna_prep(
+    values, mask: npt.NDArray[np.bool_] | None = None
+) -> npt.NDArray[np.bool_]:
+    # boilerplate for _pad_1d, _backfill_1d, _pad_2d, _backfill_2d
+
+    if mask is None:
+        mask = isna(values)
+
+    return mask
+
+
+def _datetimelike_compat(func: F) -> F:
+    """
+    Wrapper to handle datetime64 and timedelta64 dtypes.
+    """
+
+    @wraps(func)
+    def new_func(
+        values,
+        limit: int | None = None,
+        limit_area: Literal["inside", "outside"] | None = None,
+        mask=None,
+    ):
+        if needs_i8_conversion(values.dtype):
+            if mask is None:
+                # This needs to occur before casting to int64
+                mask = isna(values)
+
+            result, mask = func(
+                values.view("i8"), limit=limit, limit_area=limit_area, mask=mask
+            )
+            return result.view(values.dtype), mask
+
+        return func(values, limit=limit, limit_area=limit_area, mask=mask)
+
+    return cast(F, new_func)
+
+
+@_datetimelike_compat
+def _pad_1d(
+    values: np.ndarray,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None and not mask.all():
+        _fill_limit_area_1d(mask, limit_area)
+    algos.pad_inplace(values, mask, limit=limit)
+    return values, mask
+
+
+@_datetimelike_compat
+def _backfill_1d(
+    values: np.ndarray,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None and not mask.all():
+        _fill_limit_area_1d(mask, limit_area)
+    algos.backfill_inplace(values, mask, limit=limit)
+    return values, mask
+
+
+@_datetimelike_compat
+def _pad_2d(
+    values: np.ndarray,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+):
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None:
+        _fill_limit_area_2d(mask, limit_area)
+
+    if values.size:
+        algos.pad_2d_inplace(values, mask, limit=limit)
+    else:
+        # for test coverage
+        pass
+    return values, mask
+
+
+@_datetimelike_compat
+def _backfill_2d(
+    values,
+    limit: int | None = None,
+    limit_area: Literal["inside", "outside"] | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+):
+    mask = _fillna_prep(values, mask)
+    if limit_area is not None:
+        _fill_limit_area_2d(mask, limit_area)
+
+    if values.size:
+        algos.backfill_2d_inplace(values, mask, limit=limit)
+    else:
+        # for test coverage
+        pass
+    return values, mask
+
+
+def _fill_limit_area_1d(
+    mask: npt.NDArray[np.bool_], limit_area: Literal["outside", "inside"]
+) -> None:
+    """Prepare 1d mask for ffill/bfill with limit_area.
+
+    Caller is responsible for checking at least one value of mask is False.
+    When called, mask will no longer faithfully represent when
+    the corresponding are NA or not.
+
+    Parameters
+    ----------
+    mask : np.ndarray[bool, ndim=1]
+        Mask representing NA values when filling.
+    limit_area : { "outside", "inside" }
+        Whether to limit filling to outside or inside the outer most non-NA value.
+    """
+    neg_mask = ~mask
+    first = neg_mask.argmax()
+    last = len(neg_mask) - neg_mask[::-1].argmax() - 1
+    if limit_area == "inside":
+        mask[:first] = False
+        mask[last + 1 :] = False
+    elif limit_area == "outside":
+        mask[first + 1 : last] = False
+
+
+def _fill_limit_area_2d(
+    mask: npt.NDArray[np.bool_], limit_area: Literal["outside", "inside"]
+) -> None:
+    """Prepare 2d mask for ffill/bfill with limit_area.
+
+    When called, mask will no longer faithfully represent when
+    the corresponding are NA or not.
+
+    Parameters
+    ----------
+    mask : np.ndarray[bool, ndim=1]
+        Mask representing NA values when filling.
+    limit_area : { "outside", "inside" }
+        Whether to limit filling to outside or inside the outer most non-NA value.
+    """
+    neg_mask = ~mask.T
+    if limit_area == "outside":
+        # Identify inside
+        la_mask = (
+            np.maximum.accumulate(neg_mask, axis=0)
+            & np.maximum.accumulate(neg_mask[::-1], axis=0)[::-1]
+        )
+    else:
+        # Identify outside
+        la_mask = (
+            ~np.maximum.accumulate(neg_mask, axis=0)
+            | ~np.maximum.accumulate(neg_mask[::-1], axis=0)[::-1]
+        )
+    mask[la_mask.T] = False
+
+
+_fill_methods = {"pad": _pad_1d, "backfill": _backfill_1d}
+
+
+def get_fill_func(method, ndim: int = 1):
+    method = clean_fill_method(method)
+    if ndim == 1:
+        return _fill_methods[method]
+    return {"pad": _pad_2d, "backfill": _backfill_2d}[method]
+
+
+def clean_reindex_fill_method(method) -> ReindexMethod | None:
+    if method is None:
+        return None
+    return clean_fill_method(method, allow_nearest=True)
+
+
+def _interp_limit(
+    invalid: npt.NDArray[np.bool_], fw_limit: int | None, bw_limit: int | None
+):
+    """
+    Get indexers of values that won't be filled
+    because they exceed the limits.
+
+    Parameters
+    ----------
+    invalid : np.ndarray[bool]
+    fw_limit : int or None
+        forward limit to index
+    bw_limit : int or None
+        backward limit to index
+
+    Returns
+    -------
+    set of indexers
+
+    Notes
+    -----
+    This is equivalent to the more readable, but slower
+
+    .. code-block:: python
+
+        def _interp_limit(invalid, fw_limit, bw_limit):
+            for x in np.where(invalid)[0]:
+                if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
+                    yield x
+    """
+    # handle forward first; the backward direction is the same except
+    # 1. operate on the reversed array
+    # 2. subtract the returned indices from N - 1
+    N = len(invalid)
+    f_idx = set()
+    b_idx = set()
+
+    def inner(invalid, limit: int):
+        limit = min(limit, N)
+        windowed = _rolling_window(invalid, limit + 1).all(1)
+        idx = set(np.where(windowed)[0] + limit) | set(
+            np.where((~invalid[: limit + 1]).cumsum() == 0)[0]
+        )
+        return idx
+
+    if fw_limit is not None:
+        if fw_limit == 0:
+            f_idx = set(np.where(invalid)[0])
+        else:
+            f_idx = inner(invalid, fw_limit)
+
+    if bw_limit is not None:
+        if bw_limit == 0:
+            # then we don't even need to care about backwards
+            # just use forwards
+            return f_idx
+        else:
+            b_idx_inv = list(inner(invalid[::-1], bw_limit))
+            b_idx = set(N - 1 - np.asarray(b_idx_inv))
+            if fw_limit == 0:
+                return b_idx
+
+    return f_idx & b_idx
+
+
+def _rolling_window(a: npt.NDArray[np.bool_], window: int) -> npt.NDArray[np.bool_]:
+    """
+    [True, True, False, True, False], 2 ->
+
+    [
+        [True,  True],
+        [True, False],
+        [False, True],
+        [True, False],
+    ]
+    """
+    # https://stackoverflow.com/a/6811241
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/nanops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/nanops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cb825e9b79a25742d6f9f622b7226dbd68d88fe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/nanops.py
@@ -0,0 +1,1748 @@
+from __future__ import annotations
+
+import functools
+import itertools
+from typing import (
+    Any,
+    Callable,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import (
+    NaT,
+    NaTType,
+    iNaT,
+    lib,
+)
+from pandas._typing import (
+    ArrayLike,
+    AxisInt,
+    CorrelationMethod,
+    Dtype,
+    DtypeObj,
+    F,
+    Scalar,
+    Shape,
+    npt,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_complex,
+    is_float,
+    is_float_dtype,
+    is_integer,
+    is_numeric_dtype,
+    is_object_dtype,
+    needs_i8_conversion,
+    pandas_dtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+)
+
+bn = import_optional_dependency("bottleneck", errors="warn")
+_BOTTLENECK_INSTALLED = bn is not None
+_USE_BOTTLENECK = False
+
+
+def set_use_bottleneck(v: bool = True) -> None:
+    # set/unset to use bottleneck
+    global _USE_BOTTLENECK
+    if _BOTTLENECK_INSTALLED:
+        _USE_BOTTLENECK = v
+
+
+set_use_bottleneck(get_option("compute.use_bottleneck"))
+
+
+class disallow:
+    def __init__(self, *dtypes: Dtype) -> None:
+        super().__init__()
+        self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
+
+    def check(self, obj) -> bool:
+        return hasattr(obj, "dtype") and issubclass(obj.dtype.type, self.dtypes)
+
+    def __call__(self, f: F) -> F:
+        @functools.wraps(f)
+        def _f(*args, **kwargs):
+            obj_iter = itertools.chain(args, kwargs.values())
+            if any(self.check(obj) for obj in obj_iter):
+                f_name = f.__name__.replace("nan", "")
+                raise TypeError(
+                    f"reduction operation '{f_name}' not allowed for this dtype"
+                )
+            try:
+                return f(*args, **kwargs)
+            except ValueError as e:
+                # we want to transform an object array
+                # ValueError message to the more typical TypeError
+                # e.g. this is normally a disallowed function on
+                # object arrays that contain strings
+                if is_object_dtype(args[0]):
+                    raise TypeError(e) from e
+                raise
+
+        return cast(F, _f)
+
+
+class bottleneck_switch:
+    def __init__(self, name=None, **kwargs) -> None:
+        self.name = name
+        self.kwargs = kwargs
+
+    def __call__(self, alt: F) -> F:
+        bn_name = self.name or alt.__name__
+
+        try:
+            bn_func = getattr(bn, bn_name)
+        except (AttributeError, NameError):  # pragma: no cover
+            bn_func = None
+
+        @functools.wraps(alt)
+        def f(
+            values: np.ndarray,
+            *,
+            axis: AxisInt | None = None,
+            skipna: bool = True,
+            **kwds,
+        ):
+            if len(self.kwargs) > 0:
+                for k, v in self.kwargs.items():
+                    if k not in kwds:
+                        kwds[k] = v
+
+            if values.size == 0 and kwds.get("min_count") is None:
+                # We are empty, returning NA for our type
+                # Only applies for the default `min_count` of None
+                # since that affects how empty arrays are handled.
+                # TODO(GH-18976) update all the nanops methods to
+                # correctly handle empty inputs and remove this check.
+                # It *may* just be `var`
+                return _na_for_min_count(values, axis)
+
+            if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype, bn_name):
+                if kwds.get("mask", None) is None:
+                    # `mask` is not recognised by bottleneck, would raise
+                    #  TypeError if called
+                    kwds.pop("mask", None)
+                    result = bn_func(values, axis=axis, **kwds)
+
+                    # prefer to treat inf/-inf as NA, but must compute the func
+                    # twice :(
+                    if _has_infs(result):
+                        result = alt(values, axis=axis, skipna=skipna, **kwds)
+                else:
+                    result = alt(values, axis=axis, skipna=skipna, **kwds)
+            else:
+                result = alt(values, axis=axis, skipna=skipna, **kwds)
+
+            return result
+
+        return cast(F, f)
+
+
+def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
+    # Bottleneck chokes on datetime64, PeriodDtype (or and EA)
+    if dtype != object and not needs_i8_conversion(dtype):
+        # GH 42878
+        # Bottleneck uses naive summation leading to O(n) loss of precision
+        # unlike numpy which implements pairwise summation, which has O(log(n)) loss
+        # crossref: https://github.com/pydata/bottleneck/issues/379
+
+        # GH 15507
+        # bottleneck does not properly upcast during the sum
+        # so can overflow
+
+        # GH 9422
+        # further we also want to preserve NaN when all elements
+        # are NaN, unlike bottleneck/numpy which consider this
+        # to be 0
+        return name not in ["nansum", "nanprod", "nanmean"]
+    return False
+
+
+def _has_infs(result) -> bool:
+    if isinstance(result, np.ndarray):
+        if result.dtype in ("f8", "f4"):
+            # Note: outside of an nanops-specific test, we always have
+            #  result.ndim == 1, so there is no risk of this ravel making a copy.
+            return lib.has_infs(result.ravel("K"))
+    try:
+        return np.isinf(result).any()
+    except (TypeError, NotImplementedError):
+        # if it doesn't support infs, then it can't have infs
+        return False
+
+
+def _get_fill_value(
+    dtype: DtypeObj, fill_value: Scalar | None = None, fill_value_typ=None
+):
+    """return the correct fill value for the dtype of the values"""
+    if fill_value is not None:
+        return fill_value
+    if _na_ok_dtype(dtype):
+        if fill_value_typ is None:
+            return np.nan
+        else:
+            if fill_value_typ == "+inf":
+                return np.inf
+            else:
+                return -np.inf
+    else:
+        if fill_value_typ == "+inf":
+            # need the max int here
+            return lib.i8max
+        else:
+            return iNaT
+
+
+def _maybe_get_mask(
+    values: np.ndarray, skipna: bool, mask: npt.NDArray[np.bool_] | None
+) -> npt.NDArray[np.bool_] | None:
+    """
+    Compute a mask if and only if necessary.
+
+    This function will compute a mask iff it is necessary. Otherwise,
+    return the provided mask (potentially None) when a mask does not need to be
+    computed.
+
+    A mask is never necessary if the values array is of boolean or integer
+    dtypes, as these are incapable of storing NaNs. If passing a NaN-capable
+    dtype that is interpretable as either boolean or integer data (eg,
+    timedelta64), a mask must be provided.
+
+    If the skipna parameter is False, a new mask will not be computed.
+
+    The mask is computed using isna() by default. Setting invert=True selects
+    notna() as the masking function.
+
+    Parameters
+    ----------
+    values : ndarray
+        input array to potentially compute mask for
+    skipna : bool
+        boolean for whether NaNs should be skipped
+    mask : Optional[ndarray]
+        nan-mask if known
+
+    Returns
+    -------
+    Optional[np.ndarray[bool]]
+    """
+    if mask is None:
+        if values.dtype.kind in "biu":
+            # Boolean data cannot contain nulls, so signal via mask being None
+            return None
+
+        if skipna or values.dtype.kind in "mM":
+            mask = isna(values)
+
+    return mask
+
+
+def _get_values(
+    values: np.ndarray,
+    skipna: bool,
+    fill_value: Any = None,
+    fill_value_typ: str | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> tuple[np.ndarray, npt.NDArray[np.bool_] | None]:
+    """
+    Utility to get the values view, mask, dtype, dtype_max, and fill_value.
+
+    If both mask and fill_value/fill_value_typ are not None and skipna is True,
+    the values array will be copied.
+
+    For input arrays of boolean or integer dtypes, copies will only occur if a
+    precomputed mask, a fill_value/fill_value_typ, and skipna=True are
+    provided.
+
+    Parameters
+    ----------
+    values : ndarray
+        input array to potentially compute mask for
+    skipna : bool
+        boolean for whether NaNs should be skipped
+    fill_value : Any
+        value to fill NaNs with
+    fill_value_typ : str
+        Set to '+inf' or '-inf' to handle dtype-specific infinities
+    mask : Optional[np.ndarray[bool]]
+        nan-mask if known
+
+    Returns
+    -------
+    values : ndarray
+        Potential copy of input value array
+    mask : Optional[ndarray[bool]]
+        Mask for values, if deemed necessary to compute
+    """
+    # In _get_values is only called from within nanops, and in all cases
+    #  with scalar fill_value.  This guarantee is important for the
+    #  np.where call below
+
+    mask = _maybe_get_mask(values, skipna, mask)
+
+    dtype = values.dtype
+
+    datetimelike = False
+    if values.dtype.kind in "mM":
+        # changing timedelta64/datetime64 to int64 needs to happen after
+        #  finding `mask` above
+        values = np.asarray(values.view("i8"))
+        datetimelike = True
+
+    if skipna and (mask is not None):
+        # get our fill value (in case we need to provide an alternative
+        # dtype for it)
+        fill_value = _get_fill_value(
+            dtype, fill_value=fill_value, fill_value_typ=fill_value_typ
+        )
+
+        if fill_value is not None:
+            if mask.any():
+                if datetimelike or _na_ok_dtype(dtype):
+                    values = values.copy()
+                    np.putmask(values, mask, fill_value)
+                else:
+                    # np.where will promote if needed
+                    values = np.where(~mask, values, fill_value)
+
+    return values, mask
+
+
+def _get_dtype_max(dtype: np.dtype) -> np.dtype:
+    # return a platform independent precision dtype
+    dtype_max = dtype
+    if dtype.kind in "bi":
+        dtype_max = np.dtype(np.int64)
+    elif dtype.kind == "u":
+        dtype_max = np.dtype(np.uint64)
+    elif dtype.kind == "f":
+        dtype_max = np.dtype(np.float64)
+    return dtype_max
+
+
+def _na_ok_dtype(dtype: DtypeObj) -> bool:
+    if needs_i8_conversion(dtype):
+        return False
+    return not issubclass(dtype.type, np.integer)
+
+
+def _wrap_results(result, dtype: np.dtype, fill_value=None):
+    """wrap our results if needed"""
+    if result is NaT:
+        pass
+
+    elif dtype.kind == "M":
+        if fill_value is None:
+            # GH#24293
+            fill_value = iNaT
+        if not isinstance(result, np.ndarray):
+            assert not isna(fill_value), "Expected non-null fill_value"
+            if result == fill_value:
+                result = np.nan
+
+            if isna(result):
+                result = np.datetime64("NaT", "ns").astype(dtype)
+            else:
+                result = np.int64(result).view(dtype)
+            # retain original unit
+            result = result.astype(dtype, copy=False)
+        else:
+            # If we have float dtype, taking a view will give the wrong result
+            result = result.astype(dtype)
+    elif dtype.kind == "m":
+        if not isinstance(result, np.ndarray):
+            if result == fill_value or np.isnan(result):
+                result = np.timedelta64("NaT").astype(dtype)
+
+            elif np.fabs(result) > lib.i8max:
+                # raise if we have a timedelta64[ns] which is too large
+                raise ValueError("overflow in timedelta operation")
+            else:
+                # return a timedelta64 with the original unit
+                result = np.int64(result).astype(dtype, copy=False)
+
+        else:
+            result = result.astype("m8[ns]").view(dtype)
+
+    return result
+
+
+def _datetimelike_compat(func: F) -> F:
+    """
+    If we have datetime64 or timedelta64 values, ensure we have a correct
+    mask before calling the wrapped function, then cast back afterwards.
+    """
+
+    @functools.wraps(func)
+    def new_func(
+        values: np.ndarray,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        mask: npt.NDArray[np.bool_] | None = None,
+        **kwargs,
+    ):
+        orig_values = values
+
+        datetimelike = values.dtype.kind in "mM"
+        if datetimelike and mask is None:
+            mask = isna(values)
+
+        result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
+
+        if datetimelike:
+            result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)
+            if not skipna:
+                assert mask is not None  # checked above
+                result = _mask_datetimelike_result(result, axis, mask, orig_values)
+
+        return result
+
+    return cast(F, new_func)
+
+
+def _na_for_min_count(values: np.ndarray, axis: AxisInt | None) -> Scalar | np.ndarray:
+    """
+    Return the missing value for `values`.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int or None
+        axis for the reduction, required if values.ndim > 1.
+
+    Returns
+    -------
+    result : scalar or ndarray
+        For 1-D values, returns a scalar of the correct missing type.
+        For 2-D values, returns a 1-D array where each element is missing.
+    """
+    # we either return np.nan or pd.NaT
+    if values.dtype.kind in "iufcb":
+        values = values.astype("float64")
+    fill_value = na_value_for_dtype(values.dtype)
+
+    if values.ndim == 1:
+        return fill_value
+    elif axis is None:
+        return fill_value
+    else:
+        result_shape = values.shape[:axis] + values.shape[axis + 1 :]
+
+        return np.full(result_shape, fill_value, dtype=values.dtype)
+
+
+def maybe_operate_rowwise(func: F) -> F:
+    """
+    NumPy operations on C-contiguous ndarrays with axis=1 can be
+    very slow if axis 1 >> axis 0.
+    Operate row-by-row and concatenate the results.
+    """
+
+    @functools.wraps(func)
+    def newfunc(values: np.ndarray, *, axis: AxisInt | None = None, **kwargs):
+        if (
+            axis == 1
+            and values.ndim == 2
+            and values.flags["C_CONTIGUOUS"]
+            # only takes this path for wide arrays (long dataframes), for threshold see
+            # https://github.com/pandas-dev/pandas/pull/43311#issuecomment-974891737
+            and (values.shape[1] / 1000) > values.shape[0]
+            and values.dtype != object
+            and values.dtype != bool
+        ):
+            arrs = list(values)
+            if kwargs.get("mask") is not None:
+                mask = kwargs.pop("mask")
+                results = [
+                    func(arrs[i], mask=mask[i], **kwargs) for i in range(len(arrs))
+                ]
+            else:
+                results = [func(x, **kwargs) for x in arrs]
+            return np.array(results)
+
+        return func(values, axis=axis, **kwargs)
+
+    return cast(F, newfunc)
+
+
+def nanany(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> bool:
+    """
+    Check if any elements along an axis evaluate to True.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : bool
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2])
+    >>> nanops.nanany(s.values)
+    True
+
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([np.nan])
+    >>> nanops.nanany(s.values)
+    False
+    """
+    if values.dtype.kind in "iub" and mask is None:
+        # GH#26032 fastpath
+        # error: Incompatible return value type (got "Union[bool_, ndarray]",
+        # expected "bool")
+        return values.any(axis)  # type: ignore[return-value]
+
+    if values.dtype.kind == "M":
+        # GH#34479
+        warnings.warn(
+            "'any' with datetime64 dtypes is deprecated and will raise in a "
+            "future version. Use (obj != pd.Timestamp(0)).any() instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    values, _ = _get_values(values, skipna, fill_value=False, mask=mask)
+
+    # For object type, any won't necessarily return
+    # boolean values (numpy/numpy#4352)
+    if values.dtype == object:
+        values = values.astype(bool)
+
+    # error: Incompatible return value type (got "Union[bool_, ndarray]", expected
+    # "bool")
+    return values.any(axis)  # type: ignore[return-value]
+
+
+def nanall(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> bool:
+    """
+    Check if all elements along an axis evaluate to True.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : bool
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nanall(s.values)
+    True
+
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 0])
+    >>> nanops.nanall(s.values)
+    False
+    """
+    if values.dtype.kind in "iub" and mask is None:
+        # GH#26032 fastpath
+        # error: Incompatible return value type (got "Union[bool_, ndarray]",
+        # expected "bool")
+        return values.all(axis)  # type: ignore[return-value]
+
+    if values.dtype.kind == "M":
+        # GH#34479
+        warnings.warn(
+            "'all' with datetime64 dtypes is deprecated and will raise in a "
+            "future version. Use (obj != pd.Timestamp(0)).all() instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    values, _ = _get_values(values, skipna, fill_value=True, mask=mask)
+
+    # For object type, all won't necessarily return
+    # boolean values (numpy/numpy#4352)
+    if values.dtype == object:
+        values = values.astype(bool)
+
+    # error: Incompatible return value type (got "Union[bool_, ndarray]", expected
+    # "bool")
+    return values.all(axis)  # type: ignore[return-value]
+
+
+@disallow("M8")
+@_datetimelike_compat
+@maybe_operate_rowwise
+def nansum(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    min_count: int = 0,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Sum the elements along an axis ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray[dtype]
+    axis : int, optional
+    skipna : bool, default True
+    min_count: int, default 0
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : dtype
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nansum(s.values)
+    3.0
+    """
+    dtype = values.dtype
+    values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
+    dtype_sum = _get_dtype_max(dtype)
+    if dtype.kind == "f":
+        dtype_sum = dtype
+    elif dtype.kind == "m":
+        dtype_sum = np.dtype(np.float64)
+
+    the_sum = values.sum(axis, dtype=dtype_sum)
+    the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
+
+    return the_sum
+
+
+def _mask_datetimelike_result(
+    result: np.ndarray | np.datetime64 | np.timedelta64,
+    axis: AxisInt | None,
+    mask: npt.NDArray[np.bool_],
+    orig_values: np.ndarray,
+) -> np.ndarray | np.datetime64 | np.timedelta64 | NaTType:
+    if isinstance(result, np.ndarray):
+        # we need to apply the mask
+        result = result.astype("i8").view(orig_values.dtype)
+        axis_mask = mask.any(axis=axis)
+        # error: Unsupported target for indexed assignment ("Union[ndarray[Any, Any],
+        # datetime64, timedelta64]")
+        result[axis_mask] = iNaT  # type: ignore[index]
+    else:
+        if mask.any():
+            return np.int64(iNaT).view(orig_values.dtype)
+    return result
+
+
+@bottleneck_switch()
+@_datetimelike_compat
+def nanmean(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the mean of the element along an axis ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nanmean(s.values)
+    1.5
+    """
+    dtype = values.dtype
+    values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
+    dtype_sum = _get_dtype_max(dtype)
+    dtype_count = np.dtype(np.float64)
+
+    # not using needs_i8_conversion because that includes period
+    if dtype.kind in "mM":
+        dtype_sum = np.dtype(np.float64)
+    elif dtype.kind in "iu":
+        dtype_sum = np.dtype(np.float64)
+    elif dtype.kind == "f":
+        dtype_sum = dtype
+        dtype_count = dtype
+
+    count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
+    the_sum = values.sum(axis, dtype=dtype_sum)
+    the_sum = _ensure_numeric(the_sum)
+
+    if axis is not None and getattr(the_sum, "ndim", False):
+        count = cast(np.ndarray, count)
+        with np.errstate(all="ignore"):
+            # suppress division by zero warnings
+            the_mean = the_sum / count
+        ct_mask = count == 0
+        if ct_mask.any():
+            the_mean[ct_mask] = np.nan
+    else:
+        the_mean = the_sum / count if count > 0 else np.nan
+
+    return the_mean
+
+
+@bottleneck_switch()
+def nanmedian(values, *, axis: AxisInt | None = None, skipna: bool = True, mask=None):
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 2])
+    >>> nanops.nanmedian(s.values)
+    2.0
+    """
+    # for floats without mask, the data already uses NaN as missing value
+    # indicator, and `mask` will be calculated from that below -> in those
+    # cases we never need to set NaN to the masked values
+    using_nan_sentinel = values.dtype.kind == "f" and mask is None
+
+    def get_median(x, _mask=None):
+        if _mask is None:
+            _mask = notna(x)
+        else:
+            _mask = ~_mask
+        if not skipna and not _mask.all():
+            return np.nan
+        with warnings.catch_warnings():
+            # Suppress RuntimeWarning about All-NaN slice
+            warnings.filterwarnings(
+                "ignore", "All-NaN slice encountered", RuntimeWarning
+            )
+            res = np.nanmedian(x[_mask])
+        return res
+
+    dtype = values.dtype
+    values, mask = _get_values(values, skipna, mask=mask, fill_value=None)
+    if values.dtype.kind != "f":
+        if values.dtype == object:
+            # GH#34671 avoid casting strings to numeric
+            inferred = lib.infer_dtype(values)
+            if inferred in ["string", "mixed"]:
+                raise TypeError(f"Cannot convert {values} to numeric")
+        try:
+            values = values.astype("f8")
+        except ValueError as err:
+            # e.g. "could not convert string to float: 'a'"
+            raise TypeError(str(err)) from err
+    if not using_nan_sentinel and mask is not None:
+        if not values.flags.writeable:
+            values = values.copy()
+        values[mask] = np.nan
+
+    notempty = values.size
+
+    # an array from a frame
+    if values.ndim > 1 and axis is not None:
+        # there's a non-empty array to apply over otherwise numpy raises
+        if notempty:
+            if not skipna:
+                res = np.apply_along_axis(get_median, axis, values)
+
+            else:
+                # fastpath for the skipna case
+                with warnings.catch_warnings():
+                    # Suppress RuntimeWarning about All-NaN slice
+                    warnings.filterwarnings(
+                        "ignore", "All-NaN slice encountered", RuntimeWarning
+                    )
+                    if (values.shape[1] == 1 and axis == 0) or (
+                        values.shape[0] == 1 and axis == 1
+                    ):
+                        # GH52788: fastpath when squeezable, nanmedian for 2D array slow
+                        res = np.nanmedian(np.squeeze(values), keepdims=True)
+                    else:
+                        res = np.nanmedian(values, axis=axis)
+
+        else:
+            # must return the correct shape, but median is not defined for the
+            # empty set so return nans of shape "everything but the passed axis"
+            # since "axis" is where the reduction would occur if we had a nonempty
+            # array
+            res = _get_empty_reduction_result(values.shape, axis)
+
+    else:
+        # otherwise return a scalar value
+        res = get_median(values, mask) if notempty else np.nan
+    return _wrap_results(res, dtype)
+
+
+def _get_empty_reduction_result(
+    shape: Shape,
+    axis: AxisInt,
+) -> np.ndarray:
+    """
+    The result from a reduction on an empty ndarray.
+
+    Parameters
+    ----------
+    shape : Tuple[int, ...]
+    axis : int
+
+    Returns
+    -------
+    np.ndarray
+    """
+    shp = np.array(shape)
+    dims = np.arange(len(shape))
+    ret = np.empty(shp[dims != axis], dtype=np.float64)
+    ret.fill(np.nan)
+    return ret
+
+
+def _get_counts_nanvar(
+    values_shape: Shape,
+    mask: npt.NDArray[np.bool_] | None,
+    axis: AxisInt | None,
+    ddof: int,
+    dtype: np.dtype = np.dtype(np.float64),
+) -> tuple[float | np.ndarray, float | np.ndarray]:
+    """
+    Get the count of non-null values along an axis, accounting
+    for degrees of freedom.
+
+    Parameters
+    ----------
+    values_shape : Tuple[int, ...]
+        shape tuple from values ndarray, used if mask is None
+    mask : Optional[ndarray[bool]]
+        locations in values that should be considered missing
+    axis : Optional[int]
+        axis to count along
+    ddof : int
+        degrees of freedom
+    dtype : type, optional
+        type to use for count
+
+    Returns
+    -------
+    count : int, np.nan or np.ndarray
+    d : int, np.nan or np.ndarray
+    """
+    count = _get_counts(values_shape, mask, axis, dtype=dtype)
+    d = count - dtype.type(ddof)
+
+    # always return NaN, never inf
+    if is_float(count):
+        if count <= ddof:
+            # error: Incompatible types in assignment (expression has type
+            # "float", variable has type "Union[floating[Any], ndarray[Any,
+            # dtype[floating[Any]]]]")
+            count = np.nan  # type: ignore[assignment]
+            d = np.nan
+    else:
+        # count is not narrowed by is_float check
+        count = cast(np.ndarray, count)
+        mask = count <= ddof
+        if mask.any():
+            np.putmask(d, mask, np.nan)
+            np.putmask(count, mask, np.nan)
+    return count, d
+
+
+@bottleneck_switch(ddof=1)
+def nanstd(
+    values,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    ddof: int = 1,
+    mask=None,
+):
+    """
+    Compute the standard deviation along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nanstd(s.values)
+    1.0
+    """
+    if values.dtype == "M8[ns]":
+        values = values.view("m8[ns]")
+
+    orig_dtype = values.dtype
+    values, mask = _get_values(values, skipna, mask=mask)
+
+    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask))
+    return _wrap_results(result, orig_dtype)
+
+
+@disallow("M8", "m8")
+@bottleneck_switch(ddof=1)
+def nanvar(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    ddof: int = 1,
+    mask=None,
+):
+    """
+    Compute the variance along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nanvar(s.values)
+    1.0
+    """
+    dtype = values.dtype
+    mask = _maybe_get_mask(values, skipna, mask)
+    if dtype.kind in "iu":
+        values = values.astype("f8")
+        if mask is not None:
+            values[mask] = np.nan
+
+    if values.dtype.kind == "f":
+        count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
+    else:
+        count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        np.putmask(values, mask, 0)
+
+    # xref GH10242
+    # Compute variance via two-pass algorithm, which is stable against
+    # cancellation errors and relatively accurate for small numbers of
+    # observations.
+    #
+    # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+    avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
+    if axis is not None:
+        avg = np.expand_dims(avg, axis)
+    sqr = _ensure_numeric((avg - values) ** 2)
+    if mask is not None:
+        np.putmask(sqr, mask, 0)
+    result = sqr.sum(axis=axis, dtype=np.float64) / d
+
+    # Return variance as np.float64 (the datatype used in the accumulator),
+    # unless we were dealing with a float array, in which case use the same
+    # precision as the original values array.
+    if dtype.kind == "f":
+        result = result.astype(dtype, copy=False)
+    return result
+
+
+@disallow("M8", "m8")
+def nansem(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    ddof: int = 1,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the standard error in the mean along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nansem(s.values)
+     0.5773502691896258
+    """
+    # This checks if non-numeric-like data is passed with numeric_only=False
+    # and raises a TypeError otherwise
+    nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
+
+    mask = _maybe_get_mask(values, skipna, mask)
+    if values.dtype.kind != "f":
+        values = values.astype("f8")
+
+    if not skipna and mask is not None and mask.any():
+        return np.nan
+
+    count, _ = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
+    var = nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
+
+    return np.sqrt(var) / np.sqrt(count)
+
+
+def _nanminmax(meth, fill_value_typ):
+    @bottleneck_switch(name=f"nan{meth}")
+    @_datetimelike_compat
+    def reduction(
+        values: np.ndarray,
+        *,
+        axis: AxisInt | None = None,
+        skipna: bool = True,
+        mask: npt.NDArray[np.bool_] | None = None,
+    ):
+        if values.size == 0:
+            return _na_for_min_count(values, axis)
+
+        values, mask = _get_values(
+            values, skipna, fill_value_typ=fill_value_typ, mask=mask
+        )
+        result = getattr(values, meth)(axis)
+        result = _maybe_null_out(result, axis, mask, values.shape)
+        return result
+
+    return reduction
+
+
+nanmin = _nanminmax("min", fill_value_typ="+inf")
+nanmax = _nanminmax("max", fill_value_typ="-inf")
+
+
+def nanargmax(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> int | np.ndarray:
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : int or ndarray[int]
+        The index/indices  of max value in specified axis or -1 in the NA case
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> arr = np.array([1, 2, 3, np.nan, 4])
+    >>> nanops.nanargmax(arr)
+    4
+
+    >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
+    >>> arr[2:, 2] = np.nan
+    >>> arr
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4.,  5.],
+           [ 6.,  7., nan],
+           [ 9., 10., nan]])
+    >>> nanops.nanargmax(arr, axis=1)
+    array([2, 2, 1, 1])
+    """
+    values, mask = _get_values(values, True, fill_value_typ="-inf", mask=mask)
+    result = values.argmax(axis)
+    # error: Argument 1 to "_maybe_arg_null_out" has incompatible type "Any |
+    # signedinteger[Any]"; expected "ndarray[Any, Any]"
+    result = _maybe_arg_null_out(result, axis, mask, skipna)  # type: ignore[arg-type]
+    return result
+
+
+def nanargmin(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> int | np.ndarray:
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : int or ndarray[int]
+        The index/indices of min value in specified axis or -1 in the NA case
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> arr = np.array([1, 2, 3, np.nan, 4])
+    >>> nanops.nanargmin(arr)
+    0
+
+    >>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
+    >>> arr[2:, 0] = np.nan
+    >>> arr
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4.,  5.],
+           [nan,  7.,  8.],
+           [nan, 10., 11.]])
+    >>> nanops.nanargmin(arr, axis=1)
+    array([0, 0, 1, 1])
+    """
+    values, mask = _get_values(values, True, fill_value_typ="+inf", mask=mask)
+    result = values.argmin(axis)
+    # error: Argument 1 to "_maybe_arg_null_out" has incompatible type "Any |
+    # signedinteger[Any]"; expected "ndarray[Any, Any]"
+    result = _maybe_arg_null_out(result, axis, mask, skipna)  # type: ignore[arg-type]
+    return result
+
+
+@disallow("M8", "m8")
+@maybe_operate_rowwise
+def nanskew(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the sample skewness.
+
+    The statistic computed here is the adjusted Fisher-Pearson standardized
+    moment coefficient G1. The algorithm computes this coefficient directly
+    from the second and third central moment.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 1, 2])
+    >>> nanops.nanskew(s.values)
+    1.7320508075688787
+    """
+    mask = _maybe_get_mask(values, skipna, mask)
+    if values.dtype.kind != "f":
+        values = values.astype("f8")
+        count = _get_counts(values.shape, mask, axis)
+    else:
+        count = _get_counts(values.shape, mask, axis, dtype=values.dtype)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        np.putmask(values, mask, 0)
+    elif not skipna and mask is not None and mask.any():
+        return np.nan
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        mean = values.sum(axis, dtype=np.float64) / count
+    if axis is not None:
+        mean = np.expand_dims(mean, axis)
+
+    adjusted = values - mean
+    if skipna and mask is not None:
+        np.putmask(adjusted, mask, 0)
+    adjusted2 = adjusted**2
+    adjusted3 = adjusted2 * adjusted
+    m2 = adjusted2.sum(axis, dtype=np.float64)
+    m3 = adjusted3.sum(axis, dtype=np.float64)
+
+    # floating point error
+    #
+    # #18044 in _libs/windows.pyx calc_skew follow this behavior
+    # to fix the fperr to treat m2 <1e-14 as zero
+    m2 = _zero_out_fperr(m2)
+    m3 = _zero_out_fperr(m3)
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        result = (count * (count - 1) ** 0.5 / (count - 2)) * (m3 / m2**1.5)
+
+    dtype = values.dtype
+    if dtype.kind == "f":
+        result = result.astype(dtype, copy=False)
+
+    if isinstance(result, np.ndarray):
+        result = np.where(m2 == 0, 0, result)
+        result[count < 3] = np.nan
+    else:
+        result = dtype.type(0) if m2 == 0 else result
+        if count < 3:
+            return np.nan
+
+    return result
+
+
+@disallow("M8", "m8")
+@maybe_operate_rowwise
+def nankurt(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Compute the sample excess kurtosis
+
+    The statistic computed here is the adjusted Fisher-Pearson standardized
+    moment coefficient G2, computed directly from the second and fourth
+    central moment.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, np.nan, 1, 3, 2])
+    >>> nanops.nankurt(s.values)
+    -1.2892561983471076
+    """
+    mask = _maybe_get_mask(values, skipna, mask)
+    if values.dtype.kind != "f":
+        values = values.astype("f8")
+        count = _get_counts(values.shape, mask, axis)
+    else:
+        count = _get_counts(values.shape, mask, axis, dtype=values.dtype)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        np.putmask(values, mask, 0)
+    elif not skipna and mask is not None and mask.any():
+        return np.nan
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        mean = values.sum(axis, dtype=np.float64) / count
+    if axis is not None:
+        mean = np.expand_dims(mean, axis)
+
+    adjusted = values - mean
+    if skipna and mask is not None:
+        np.putmask(adjusted, mask, 0)
+    adjusted2 = adjusted**2
+    adjusted4 = adjusted2**2
+    m2 = adjusted2.sum(axis, dtype=np.float64)
+    m4 = adjusted4.sum(axis, dtype=np.float64)
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
+        numerator = count * (count + 1) * (count - 1) * m4
+        denominator = (count - 2) * (count - 3) * m2**2
+
+    # floating point error
+    #
+    # #18044 in _libs/windows.pyx calc_kurt follow this behavior
+    # to fix the fperr to treat denom <1e-14 as zero
+    numerator = _zero_out_fperr(numerator)
+    denominator = _zero_out_fperr(denominator)
+
+    if not isinstance(denominator, np.ndarray):
+        # if ``denom`` is a scalar, check these corner cases first before
+        # doing division
+        if count < 4:
+            return np.nan
+        if denominator == 0:
+            return values.dtype.type(0)
+
+    with np.errstate(invalid="ignore", divide="ignore"):
+        result = numerator / denominator - adj
+
+    dtype = values.dtype
+    if dtype.kind == "f":
+        result = result.astype(dtype, copy=False)
+
+    if isinstance(result, np.ndarray):
+        result = np.where(denominator == 0, 0, result)
+        result[count < 4] = np.nan
+
+    return result
+
+
+@disallow("M8", "m8")
+@maybe_operate_rowwise
+def nanprod(
+    values: np.ndarray,
+    *,
+    axis: AxisInt | None = None,
+    skipna: bool = True,
+    min_count: int = 0,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> float:
+    """
+    Parameters
+    ----------
+    values : ndarray[dtype]
+    axis : int, optional
+    skipna : bool, default True
+    min_count: int, default 0
+    mask : ndarray[bool], optional
+        nan-mask if known
+
+    Returns
+    -------
+    Dtype
+        The product of all elements on a given axis. ( NaNs are treated as 1)
+
+    Examples
+    --------
+    >>> from pandas.core import nanops
+    >>> s = pd.Series([1, 2, 3, np.nan])
+    >>> nanops.nanprod(s.values)
+    6.0
+    """
+    mask = _maybe_get_mask(values, skipna, mask)
+
+    if skipna and mask is not None:
+        values = values.copy()
+        values[mask] = 1
+    result = values.prod(axis)
+    # error: Incompatible return value type (got "Union[ndarray, float]", expected
+    # "float")
+    return _maybe_null_out(  # type: ignore[return-value]
+        result, axis, mask, values.shape, min_count=min_count
+    )
+
+
+def _maybe_arg_null_out(
+    result: np.ndarray,
+    axis: AxisInt | None,
+    mask: npt.NDArray[np.bool_] | None,
+    skipna: bool,
+) -> np.ndarray | int:
+    # helper function for nanargmin/nanargmax
+    if mask is None:
+        return result
+
+    if axis is None or not getattr(result, "ndim", False):
+        if skipna:
+            if mask.all():
+                return -1
+        else:
+            if mask.any():
+                return -1
+    else:
+        if skipna:
+            na_mask = mask.all(axis)
+        else:
+            na_mask = mask.any(axis)
+        if na_mask.any():
+            result[na_mask] = -1
+    return result
+
+
+def _get_counts(
+    values_shape: Shape,
+    mask: npt.NDArray[np.bool_] | None,
+    axis: AxisInt | None,
+    dtype: np.dtype[np.floating] = np.dtype(np.float64),
+) -> np.floating | npt.NDArray[np.floating]:
+    """
+    Get the count of non-null values along an axis
+
+    Parameters
+    ----------
+    values_shape : tuple of int
+        shape tuple from values ndarray, used if mask is None
+    mask : Optional[ndarray[bool]]
+        locations in values that should be considered missing
+    axis : Optional[int]
+        axis to count along
+    dtype : type, optional
+        type to use for count
+
+    Returns
+    -------
+    count : scalar or array
+    """
+    if axis is None:
+        if mask is not None:
+            n = mask.size - mask.sum()
+        else:
+            n = np.prod(values_shape)
+        return dtype.type(n)
+
+    if mask is not None:
+        count = mask.shape[axis] - mask.sum(axis)
+    else:
+        count = values_shape[axis]
+
+    if is_integer(count):
+        return dtype.type(count)
+    return count.astype(dtype, copy=False)
+
+
+def _maybe_null_out(
+    result: np.ndarray | float | NaTType,
+    axis: AxisInt | None,
+    mask: npt.NDArray[np.bool_] | None,
+    shape: tuple[int, ...],
+    min_count: int = 1,
+) -> np.ndarray | float | NaTType:
+    """
+    Returns
+    -------
+    Dtype
+        The product of all elements on a given axis. ( NaNs are treated as 1)
+    """
+    if mask is None and min_count == 0:
+        # nothing to check; short-circuit
+        return result
+
+    if axis is not None and isinstance(result, np.ndarray):
+        if mask is not None:
+            null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
+        else:
+            # we have no nulls, kept mask=None in _maybe_get_mask
+            below_count = shape[axis] - min_count < 0
+            new_shape = shape[:axis] + shape[axis + 1 :]
+            null_mask = np.broadcast_to(below_count, new_shape)
+
+        if np.any(null_mask):
+            if is_numeric_dtype(result):
+                if np.iscomplexobj(result):
+                    result = result.astype("c16")
+                elif not is_float_dtype(result):
+                    result = result.astype("f8", copy=False)
+                result[null_mask] = np.nan
+            else:
+                # GH12941, use None to auto cast null
+                result[null_mask] = None
+    elif result is not NaT:
+        if check_below_min_count(shape, mask, min_count):
+            result_dtype = getattr(result, "dtype", None)
+            if is_float_dtype(result_dtype):
+                # error: Item "None" of "Optional[Any]" has no attribute "type"
+                result = result_dtype.type("nan")  # type: ignore[union-attr]
+            else:
+                result = np.nan
+
+    return result
+
+
+def check_below_min_count(
+    shape: tuple[int, ...], mask: npt.NDArray[np.bool_] | None, min_count: int
+) -> bool:
+    """
+    Check for the `min_count` keyword. Returns True if below `min_count` (when
+    missing value should be returned from the reduction).
+
+    Parameters
+    ----------
+    shape : tuple
+        The shape of the values (`values.shape`).
+    mask : ndarray[bool] or None
+        Boolean numpy array (typically of same shape as `shape`) or None.
+    min_count : int
+        Keyword passed through from sum/prod call.
+
+    Returns
+    -------
+    bool
+    """
+    if min_count > 0:
+        if mask is None:
+            # no missing values, only check size
+            non_nulls = np.prod(shape)
+        else:
+            non_nulls = mask.size - mask.sum()
+        if non_nulls < min_count:
+            return True
+    return False
+
+
+def _zero_out_fperr(arg):
+    # #18044 reference this behavior to fix rolling skew/kurt issue
+    if isinstance(arg, np.ndarray):
+        return np.where(np.abs(arg) < 1e-14, 0, arg)
+    else:
+        return arg.dtype.type(0) if np.abs(arg) < 1e-14 else arg
+
+
+@disallow("M8", "m8")
+def nancorr(
+    a: np.ndarray,
+    b: np.ndarray,
+    *,
+    method: CorrelationMethod = "pearson",
+    min_periods: int | None = None,
+) -> float:
+    """
+    a, b: ndarrays
+    """
+    if len(a) != len(b):
+        raise AssertionError("Operands to nancorr must have same size")
+
+    if min_periods is None:
+        min_periods = 1
+
+    valid = notna(a) & notna(b)
+    if not valid.all():
+        a = a[valid]
+        b = b[valid]
+
+    if len(a) < min_periods:
+        return np.nan
+
+    a = _ensure_numeric(a)
+    b = _ensure_numeric(b)
+
+    f = get_corr_func(method)
+    return f(a, b)
+
+
+def get_corr_func(
+    method: CorrelationMethod,
+) -> Callable[[np.ndarray, np.ndarray], float]:
+    if method == "kendall":
+        from scipy.stats import kendalltau
+
+        def func(a, b):
+            return kendalltau(a, b)[0]
+
+        return func
+    elif method == "spearman":
+        from scipy.stats import spearmanr
+
+        def func(a, b):
+            return spearmanr(a, b)[0]
+
+        return func
+    elif method == "pearson":
+
+        def func(a, b):
+            return np.corrcoef(a, b)[0, 1]
+
+        return func
+    elif callable(method):
+        return method
+
+    raise ValueError(
+        f"Unknown method '{method}', expected one of "
+        "'kendall', 'spearman', 'pearson', or callable"
+    )
+
+
+@disallow("M8", "m8")
+def nancov(
+    a: np.ndarray,
+    b: np.ndarray,
+    *,
+    min_periods: int | None = None,
+    ddof: int | None = 1,
+) -> float:
+    if len(a) != len(b):
+        raise AssertionError("Operands to nancov must have same size")
+
+    if min_periods is None:
+        min_periods = 1
+
+    valid = notna(a) & notna(b)
+    if not valid.all():
+        a = a[valid]
+        b = b[valid]
+
+    if len(a) < min_periods:
+        return np.nan
+
+    a = _ensure_numeric(a)
+    b = _ensure_numeric(b)
+
+    return np.cov(a, b, ddof=ddof)[0, 1]
+
+
+def _ensure_numeric(x):
+    if isinstance(x, np.ndarray):
+        if x.dtype.kind in "biu":
+            x = x.astype(np.float64)
+        elif x.dtype == object:
+            inferred = lib.infer_dtype(x)
+            if inferred in ["string", "mixed"]:
+                # GH#44008, GH#36703 avoid casting e.g. strings to numeric
+                raise TypeError(f"Could not convert {x} to numeric")
+            try:
+                x = x.astype(np.complex128)
+            except (TypeError, ValueError):
+                try:
+                    x = x.astype(np.float64)
+                except ValueError as err:
+                    # GH#29941 we get here with object arrays containing strs
+                    raise TypeError(f"Could not convert {x} to numeric") from err
+            else:
+                if not np.any(np.imag(x)):
+                    x = x.real
+    elif not (is_float(x) or is_integer(x) or is_complex(x)):
+        if isinstance(x, str):
+            # GH#44008, GH#36703 avoid casting e.g. strings to numeric
+            raise TypeError(f"Could not convert string '{x}' to numeric")
+        try:
+            x = float(x)
+        except (TypeError, ValueError):
+            # e.g. "1+1j" or "foo"
+            try:
+                x = complex(x)
+            except ValueError as err:
+                # e.g. "foo"
+                raise TypeError(f"Could not convert {x} to numeric") from err
+    return x
+
+
+def na_accum_func(values: ArrayLike, accum_func, *, skipna: bool) -> ArrayLike:
+    """
+    Cumulative function with skipna support.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    accum_func : {np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate}
+    skipna : bool
+
+    Returns
+    -------
+    np.ndarray or ExtensionArray
+    """
+    mask_a, mask_b = {
+        np.cumprod: (1.0, np.nan),
+        np.maximum.accumulate: (-np.inf, np.nan),
+        np.cumsum: (0.0, np.nan),
+        np.minimum.accumulate: (np.inf, np.nan),
+    }[accum_func]
+
+    # This should go through ea interface
+    assert values.dtype.kind not in "mM"
+
+    # We will be applying this function to block values
+    if skipna and not issubclass(values.dtype.type, (np.integer, np.bool_)):
+        vals = values.copy()
+        mask = isna(vals)
+        vals[mask] = mask_a
+        result = accum_func(vals, axis=0)
+        result[mask] = mask_b
+    else:
+        result = accum_func(values, axis=0)
+
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae889a7fdbc24935c0884e8bbcdf56bde8946460
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/__init__.py
@@ -0,0 +1,93 @@
+"""
+Arithmetic operations for PandasObjects
+
+This is not a public API.
+"""
+from __future__ import annotations
+
+from pandas.core.ops.array_ops import (
+    arithmetic_op,
+    comp_method_OBJECT_ARRAY,
+    comparison_op,
+    fill_binop,
+    get_array_op,
+    logical_op,
+    maybe_prepare_scalar_for_op,
+)
+from pandas.core.ops.common import (
+    get_op_result_name,
+    unpack_zerodim_and_defer,
+)
+from pandas.core.ops.docstrings import make_flex_doc
+from pandas.core.ops.invalid import invalid_comparison
+from pandas.core.ops.mask_ops import (
+    kleene_and,
+    kleene_or,
+    kleene_xor,
+)
+from pandas.core.roperator import (
+    radd,
+    rand_,
+    rdiv,
+    rdivmod,
+    rfloordiv,
+    rmod,
+    rmul,
+    ror_,
+    rpow,
+    rsub,
+    rtruediv,
+    rxor,
+)
+
+# -----------------------------------------------------------------------------
+# constants
+ARITHMETIC_BINOPS: set[str] = {
+    "add",
+    "sub",
+    "mul",
+    "pow",
+    "mod",
+    "floordiv",
+    "truediv",
+    "divmod",
+    "radd",
+    "rsub",
+    "rmul",
+    "rpow",
+    "rmod",
+    "rfloordiv",
+    "rtruediv",
+    "rdivmod",
+}
+
+
+__all__ = [
+    "ARITHMETIC_BINOPS",
+    "arithmetic_op",
+    "comparison_op",
+    "comp_method_OBJECT_ARRAY",
+    "invalid_comparison",
+    "fill_binop",
+    "kleene_and",
+    "kleene_or",
+    "kleene_xor",
+    "logical_op",
+    "make_flex_doc",
+    "radd",
+    "rand_",
+    "rdiv",
+    "rdivmod",
+    "rfloordiv",
+    "rmod",
+    "rmul",
+    "ror_",
+    "rpow",
+    "rsub",
+    "rtruediv",
+    "rxor",
+    "unpack_zerodim_and_defer",
+    "get_op_result_name",
+    "maybe_prepare_scalar_for_op",
+    "get_array_op",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/array_ops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/array_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b762a359d321ea61660e78ec63d392f8436939d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/array_ops.py
@@ -0,0 +1,604 @@
+"""
+Functions for arithmetic and comparison operations on NumPy arrays and
+ExtensionArrays.
+"""
+from __future__ import annotations
+
+import datetime
+from functools import partial
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+    lib,
+    ops as libops,
+)
+from pandas._libs.tslibs import (
+    BaseOffset,
+    get_supported_dtype,
+    is_supported_dtype,
+    is_unitless,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    construct_1d_object_array_from_listlike,
+    find_common_type,
+)
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_list_like,
+    is_numeric_v_string_like,
+    is_object_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core import roperator
+from pandas.core.computation import expressions
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.ops import missing
+from pandas.core.ops.dispatch import should_extension_dispatch
+from pandas.core.ops.invalid import invalid_comparison
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Shape,
+    )
+
+# -----------------------------------------------------------------------------
+# Masking NA values and fallbacks for operations numpy does not support
+
+
+def fill_binop(left, right, fill_value):
+    """
+    If a non-None fill_value is given, replace null entries in left and right
+    with this value, but only in positions where _one_ of left/right is null,
+    not both.
+
+    Parameters
+    ----------
+    left : array-like
+    right : array-like
+    fill_value : object
+
+    Returns
+    -------
+    left : array-like
+    right : array-like
+
+    Notes
+    -----
+    Makes copies if fill_value is not None and NAs are present.
+    """
+    if fill_value is not None:
+        left_mask = isna(left)
+        right_mask = isna(right)
+
+        # one but not both
+        mask = left_mask ^ right_mask
+
+        if left_mask.any():
+            # Avoid making a copy if we can
+            left = left.copy()
+            left[left_mask & mask] = fill_value
+
+        if right_mask.any():
+            # Avoid making a copy if we can
+            right = right.copy()
+            right[right_mask & mask] = fill_value
+
+    return left, right
+
+
+def comp_method_OBJECT_ARRAY(op, x, y):
+    if isinstance(y, list):
+        # e.g. test_tuple_categories
+        y = construct_1d_object_array_from_listlike(y)
+
+    if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
+        if not is_object_dtype(y.dtype):
+            y = y.astype(np.object_)
+
+        if isinstance(y, (ABCSeries, ABCIndex)):
+            y = y._values
+
+        if x.shape != y.shape:
+            raise ValueError("Shapes must match", x.shape, y.shape)
+        result = libops.vec_compare(x.ravel(), y.ravel(), op)
+    else:
+        result = libops.scalar_compare(x.ravel(), y, op)
+    return result.reshape(x.shape)
+
+
+def _masked_arith_op(x: np.ndarray, y, op):
+    """
+    If the given arithmetic operation fails, attempt it again on
+    only the non-null elements of the input array(s).
+
+    Parameters
+    ----------
+    x : np.ndarray
+    y : np.ndarray, Series, Index
+    op : binary operator
+    """
+    # For Series `x` is 1D so ravel() is a no-op; calling it anyway makes
+    # the logic valid for both Series and DataFrame ops.
+    xrav = x.ravel()
+
+    if isinstance(y, np.ndarray):
+        dtype = find_common_type([x.dtype, y.dtype])
+        result = np.empty(x.size, dtype=dtype)
+
+        if len(x) != len(y):
+            raise ValueError(x.shape, y.shape)
+        ymask = notna(y)
+
+        # NB: ravel() is only safe since y is ndarray; for e.g. PeriodIndex
+        #  we would get int64 dtype, see GH#19956
+        yrav = y.ravel()
+        mask = notna(xrav) & ymask.ravel()
+
+        # See GH#5284, GH#5035, GH#19448 for historical reference
+        if mask.any():
+            result[mask] = op(xrav[mask], yrav[mask])
+
+    else:
+        if not is_scalar(y):
+            raise TypeError(
+                f"Cannot broadcast np.ndarray with operand of type { type(y) }"
+            )
+
+        # mask is only meaningful for x
+        result = np.empty(x.size, dtype=x.dtype)
+        mask = notna(xrav)
+
+        # 1 ** np.nan is 1. So we have to unmask those.
+        if op is pow:
+            mask = np.where(x == 1, False, mask)
+        elif op is roperator.rpow:
+            mask = np.where(y == 1, False, mask)
+
+        if mask.any():
+            result[mask] = op(xrav[mask], y)
+
+    np.putmask(result, ~mask, np.nan)
+    result = result.reshape(x.shape)  # 2D compat
+    return result
+
+
+def _na_arithmetic_op(left: np.ndarray, right, op, is_cmp: bool = False):
+    """
+    Return the result of evaluating op on the passed in values.
+
+    If native types are not compatible, try coercion to object dtype.
+
+    Parameters
+    ----------
+    left : np.ndarray
+    right : np.ndarray or scalar
+        Excludes DataFrame, Series, Index, ExtensionArray.
+    is_cmp : bool, default False
+        If this a comparison operation.
+
+    Returns
+    -------
+    array-like
+
+    Raises
+    ------
+    TypeError : invalid operation
+    """
+    if isinstance(right, str):
+        # can never use numexpr
+        func = op
+    else:
+        func = partial(expressions.evaluate, op)
+
+    try:
+        result = func(left, right)
+    except TypeError:
+        if not is_cmp and (
+            left.dtype == object or getattr(right, "dtype", None) == object
+        ):
+            # For object dtype, fallback to a masked operation (only operating
+            #  on the non-missing values)
+            # Don't do this for comparisons, as that will handle complex numbers
+            #  incorrectly, see GH#32047
+            result = _masked_arith_op(left, right, op)
+        else:
+            raise
+
+    if is_cmp and (is_scalar(result) or result is NotImplemented):
+        # numpy returned a scalar instead of operating element-wise
+        # e.g. numeric array vs str
+        # TODO: can remove this after dropping some future numpy version?
+        return invalid_comparison(left, right, op)
+
+    return missing.dispatch_fill_zeros(op, left, right, result)
+
+
+def arithmetic_op(left: ArrayLike, right: Any, op):
+    """
+    Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
+
+    Note: the caller is responsible for ensuring that numpy warnings are
+    suppressed (with np.errstate(all="ignore")) if needed.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+        Cannot be a DataFrame or Index.  Series is *not* excluded.
+    op : {operator.add, operator.sub, ...}
+        Or one of the reversed variants from roperator.
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+        Or a 2-tuple of these in the case of divmod or rdivmod.
+    """
+    # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
+    #  have already been called on `left` and `right`,
+    #  and `maybe_prepare_scalar_for_op` has already been called on `right`
+    # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
+    # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
+
+    if (
+        should_extension_dispatch(left, right)
+        or isinstance(right, (Timedelta, BaseOffset, Timestamp))
+        or right is NaT
+    ):
+        # Timedelta/Timestamp and other custom scalars are included in the check
+        # because numexpr will fail on it, see GH#31457
+        res_values = op(left, right)
+    else:
+        # TODO we should handle EAs consistently and move this check before the if/else
+        # (https://github.com/pandas-dev/pandas/issues/41165)
+        # error: Argument 2 to "_bool_arith_check" has incompatible type
+        # "Union[ExtensionArray, ndarray[Any, Any]]"; expected "ndarray[Any, Any]"
+        _bool_arith_check(op, left, right)  # type: ignore[arg-type]
+
+        # error: Argument 1 to "_na_arithmetic_op" has incompatible type
+        # "Union[ExtensionArray, ndarray[Any, Any]]"; expected "ndarray[Any, Any]"
+        res_values = _na_arithmetic_op(left, right, op)  # type: ignore[arg-type]
+
+    return res_values
+
+
+def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
+    """
+    Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`.
+
+    Note: the caller is responsible for ensuring that numpy warnings are
+    suppressed (with np.errstate(all="ignore")) if needed.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+        Cannot be a DataFrame, Series, or Index.
+    op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le}
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    # NB: We assume extract_array has already been called on left and right
+    lvalues = ensure_wrapped_if_datetimelike(left)
+    rvalues = ensure_wrapped_if_datetimelike(right)
+
+    rvalues = lib.item_from_zerodim(rvalues)
+    if isinstance(rvalues, list):
+        # We don't catch tuple here bc we may be comparing e.g. MultiIndex
+        #  to a tuple that represents a single entry, see test_compare_tuple_strs
+        rvalues = np.asarray(rvalues)
+
+    if isinstance(rvalues, (np.ndarray, ABCExtensionArray)):
+        # TODO: make this treatment consistent across ops and classes.
+        #  We are not catching all listlikes here (e.g. frozenset, tuple)
+        #  The ambiguous case is object-dtype.  See GH#27803
+        if len(lvalues) != len(rvalues):
+            raise ValueError(
+                "Lengths must match to compare", lvalues.shape, rvalues.shape
+            )
+
+    if should_extension_dispatch(lvalues, rvalues) or (
+        (isinstance(rvalues, (Timedelta, BaseOffset, Timestamp)) or right is NaT)
+        and lvalues.dtype != object
+    ):
+        # Call the method on lvalues
+        res_values = op(lvalues, rvalues)
+
+    elif is_scalar(rvalues) and isna(rvalues):  # TODO: but not pd.NA?
+        # numpy does not like comparisons vs None
+        if op is operator.ne:
+            res_values = np.ones(lvalues.shape, dtype=bool)
+        else:
+            res_values = np.zeros(lvalues.shape, dtype=bool)
+
+    elif is_numeric_v_string_like(lvalues, rvalues):
+        # GH#36377 going through the numexpr path would incorrectly raise
+        return invalid_comparison(lvalues, rvalues, op)
+
+    elif lvalues.dtype == object or isinstance(rvalues, str):
+        res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues)
+
+    else:
+        res_values = _na_arithmetic_op(lvalues, rvalues, op, is_cmp=True)
+
+    return res_values
+
+
+def na_logical_op(x: np.ndarray, y, op):
+    try:
+        # For exposition, write:
+        #  yarr = isinstance(y, np.ndarray)
+        #  yint = is_integer(y) or (yarr and y.dtype.kind == "i")
+        #  ybool = is_bool(y) or (yarr and y.dtype.kind == "b")
+        #  xint = x.dtype.kind == "i"
+        #  xbool = x.dtype.kind == "b"
+        # Then Cases where this goes through without raising include:
+        #  (xint or xbool) and (yint or bool)
+        result = op(x, y)
+    except TypeError:
+        if isinstance(y, np.ndarray):
+            # bool-bool dtype operations should be OK, should not get here
+            assert not (x.dtype.kind == "b" and y.dtype.kind == "b")
+            x = ensure_object(x)
+            y = ensure_object(y)
+            result = libops.vec_binop(x.ravel(), y.ravel(), op)
+        else:
+            # let null fall thru
+            assert lib.is_scalar(y)
+            if not isna(y):
+                y = bool(y)
+            try:
+                result = libops.scalar_binop(x, y, op)
+            except (
+                TypeError,
+                ValueError,
+                AttributeError,
+                OverflowError,
+                NotImplementedError,
+            ) as err:
+                typ = type(y).__name__
+                raise TypeError(
+                    f"Cannot perform '{op.__name__}' with a dtyped [{x.dtype}] array "
+                    f"and scalar of type [{typ}]"
+                ) from err
+
+    return result.reshape(x.shape)
+
+
+def logical_op(left: ArrayLike, right: Any, op) -> ArrayLike:
+    """
+    Evaluate a logical operation `|`, `&`, or `^`.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+        Cannot be a DataFrame, Series, or Index.
+    op : {operator.and_, operator.or_, operator.xor}
+        Or one of the reversed variants from roperator.
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+
+    def fill_bool(x, left=None):
+        # if `left` is specifically not-boolean, we do not cast to bool
+        if x.dtype.kind in "cfO":
+            # dtypes that can hold NA
+            mask = isna(x)
+            if mask.any():
+                x = x.astype(object)
+                x[mask] = False
+
+        if left is None or left.dtype.kind == "b":
+            x = x.astype(bool)
+        return x
+
+    right = lib.item_from_zerodim(right)
+    if is_list_like(right) and not hasattr(right, "dtype"):
+        # e.g. list, tuple
+        warnings.warn(
+            "Logical ops (and, or, xor) between Pandas objects and dtype-less "
+            "sequences (e.g. list, tuple) are deprecated and will raise in a "
+            "future version. Wrap the object in a Series, Index, or np.array "
+            "before operating instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        right = construct_1d_object_array_from_listlike(right)
+
+    # NB: We assume extract_array has already been called on left and right
+    lvalues = ensure_wrapped_if_datetimelike(left)
+    rvalues = right
+
+    if should_extension_dispatch(lvalues, rvalues):
+        # Call the method on lvalues
+        res_values = op(lvalues, rvalues)
+
+    else:
+        if isinstance(rvalues, np.ndarray):
+            is_other_int_dtype = rvalues.dtype.kind in "iu"
+            if not is_other_int_dtype:
+                rvalues = fill_bool(rvalues, lvalues)
+
+        else:
+            # i.e. scalar
+            is_other_int_dtype = lib.is_integer(rvalues)
+
+        res_values = na_logical_op(lvalues, rvalues, op)
+
+        # For int vs int `^`, `|`, `&` are bitwise operators and return
+        #   integer dtypes.  Otherwise these are boolean ops
+        if not (left.dtype.kind in "iu" and is_other_int_dtype):
+            res_values = fill_bool(res_values)
+
+    return res_values
+
+
+def get_array_op(op):
+    """
+    Return a binary array operation corresponding to the given operator op.
+
+    Parameters
+    ----------
+    op : function
+        Binary operator from operator or roperator module.
+
+    Returns
+    -------
+    functools.partial
+    """
+    if isinstance(op, partial):
+        # We get here via dispatch_to_series in DataFrame case
+        # e.g. test_rolling_consistency_var_debiasing_factors
+        return op
+
+    op_name = op.__name__.strip("_").lstrip("r")
+    if op_name == "arith_op":
+        # Reached via DataFrame._combine_frame i.e. flex methods
+        # e.g. test_df_add_flex_filled_mixed_dtypes
+        return op
+
+    if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}:
+        return partial(comparison_op, op=op)
+    elif op_name in {"and", "or", "xor", "rand", "ror", "rxor"}:
+        return partial(logical_op, op=op)
+    elif op_name in {
+        "add",
+        "sub",
+        "mul",
+        "truediv",
+        "floordiv",
+        "mod",
+        "divmod",
+        "pow",
+    }:
+        return partial(arithmetic_op, op=op)
+    else:
+        raise NotImplementedError(op_name)
+
+
+def maybe_prepare_scalar_for_op(obj, shape: Shape):
+    """
+    Cast non-pandas objects to pandas types to unify behavior of arithmetic
+    and comparison operations.
+
+    Parameters
+    ----------
+    obj: object
+    shape : tuple[int]
+
+    Returns
+    -------
+    out : object
+
+    Notes
+    -----
+    Be careful to call this *after* determining the `name` attribute to be
+    attached to the result of the arithmetic operation.
+    """
+    if type(obj) is datetime.timedelta:
+        # GH#22390  cast up to Timedelta to rely on Timedelta
+        # implementation; otherwise operation against numeric-dtype
+        # raises TypeError
+        return Timedelta(obj)
+    elif type(obj) is datetime.datetime:
+        # cast up to Timestamp to rely on Timestamp implementation, see Timedelta above
+        return Timestamp(obj)
+    elif isinstance(obj, np.datetime64):
+        # GH#28080 numpy casts integer-dtype to datetime64 when doing
+        #  array[int] + datetime64, which we do not allow
+        if isna(obj):
+            from pandas.core.arrays import DatetimeArray
+
+            # Avoid possible ambiguities with pd.NaT
+            # GH 52295
+            if is_unitless(obj.dtype):
+                obj = obj.astype("datetime64[ns]")
+            elif not is_supported_dtype(obj.dtype):
+                new_dtype = get_supported_dtype(obj.dtype)
+                obj = obj.astype(new_dtype)
+            right = np.broadcast_to(obj, shape)
+            return DatetimeArray._simple_new(right, dtype=right.dtype)
+
+        return Timestamp(obj)
+
+    elif isinstance(obj, np.timedelta64):
+        if isna(obj):
+            from pandas.core.arrays import TimedeltaArray
+
+            # wrapping timedelta64("NaT") in Timedelta returns NaT,
+            #  which would incorrectly be treated as a datetime-NaT, so
+            #  we broadcast and wrap in a TimedeltaArray
+            # GH 52295
+            if is_unitless(obj.dtype):
+                obj = obj.astype("timedelta64[ns]")
+            elif not is_supported_dtype(obj.dtype):
+                new_dtype = get_supported_dtype(obj.dtype)
+                obj = obj.astype(new_dtype)
+            right = np.broadcast_to(obj, shape)
+            return TimedeltaArray._simple_new(right, dtype=right.dtype)
+
+        # In particular non-nanosecond timedelta64 needs to be cast to
+        #  nanoseconds, or else we get undesired behavior like
+        #  np.timedelta64(3, 'D') / 2 == np.timedelta64(1, 'D')
+        return Timedelta(obj)
+
+    # We want NumPy numeric scalars to behave like Python scalars
+    # post NEP 50
+    elif isinstance(obj, np.integer):
+        return int(obj)
+
+    elif isinstance(obj, np.floating):
+        return float(obj)
+
+    return obj
+
+
+_BOOL_OP_NOT_ALLOWED = {
+    operator.truediv,
+    roperator.rtruediv,
+    operator.floordiv,
+    roperator.rfloordiv,
+    operator.pow,
+    roperator.rpow,
+}
+
+
+def _bool_arith_check(op, a: np.ndarray, b):
+    """
+    In contrast to numpy, pandas raises an error for certain operations
+    with booleans.
+    """
+    if op in _BOOL_OP_NOT_ALLOWED:
+        if a.dtype.kind == "b" and (is_bool_dtype(b) or lib.is_bool(b)):
+            op_name = op.__name__.strip("_").lstrip("r")
+            raise NotImplementedError(
+                f"operator '{op_name}' not implemented for bool dtypes"
+            )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/common.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..559977bacf881552d546e7704d4cf4b12b4a32fe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/common.py
@@ -0,0 +1,146 @@
+"""
+Boilerplate functions used in defining binary operations.
+"""
+from __future__ import annotations
+
+from functools import wraps
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+from pandas._libs.lib import item_from_zerodim
+from pandas._libs.missing import is_matching_na
+
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def unpack_zerodim_and_defer(name: str) -> Callable[[F], F]:
+    """
+    Boilerplate for pandas conventions in arithmetic and comparison methods.
+
+    Parameters
+    ----------
+    name : str
+
+    Returns
+    -------
+    decorator
+    """
+
+    def wrapper(method: F) -> F:
+        return _unpack_zerodim_and_defer(method, name)
+
+    return wrapper
+
+
+def _unpack_zerodim_and_defer(method, name: str):
+    """
+    Boilerplate for pandas conventions in arithmetic and comparison methods.
+
+    Ensure method returns NotImplemented when operating against "senior"
+    classes.  Ensure zero-dimensional ndarrays are always unpacked.
+
+    Parameters
+    ----------
+    method : binary method
+    name : str
+
+    Returns
+    -------
+    method
+    """
+    stripped_name = name.removeprefix("__").removesuffix("__")
+    is_cmp = stripped_name in {"eq", "ne", "lt", "le", "gt", "ge"}
+
+    @wraps(method)
+    def new_method(self, other):
+        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
+            # For comparison ops, Index does *not* defer to Series
+            pass
+        else:
+            prio = getattr(other, "__pandas_priority__", None)
+            if prio is not None:
+                if prio > self.__pandas_priority__:
+                    # e.g. other is DataFrame while self is Index/Series/EA
+                    return NotImplemented
+
+        other = item_from_zerodim(other)
+
+        return method(self, other)
+
+    return new_method
+
+
+def get_op_result_name(left, right):
+    """
+    Find the appropriate name to pin to an operation result.  This result
+    should always be either an Index or a Series.
+
+    Parameters
+    ----------
+    left : {Series, Index}
+    right : object
+
+    Returns
+    -------
+    name : object
+        Usually a string
+    """
+    if isinstance(right, (ABCSeries, ABCIndex)):
+        name = _maybe_match_name(left, right)
+    else:
+        name = left.name
+    return name
+
+
+def _maybe_match_name(a, b):
+    """
+    Try to find a name to attach to the result of an operation between
+    a and b.  If only one of these has a `name` attribute, return that
+    name.  Otherwise return a consensus name if they match or None if
+    they have different names.
+
+    Parameters
+    ----------
+    a : object
+    b : object
+
+    Returns
+    -------
+    name : str or None
+
+    See Also
+    --------
+    pandas.core.common.consensus_name_attr
+    """
+    a_has = hasattr(a, "name")
+    b_has = hasattr(b, "name")
+    if a_has and b_has:
+        try:
+            if a.name == b.name:
+                return a.name
+            elif is_matching_na(a.name, b.name):
+                # e.g. both are np.nan
+                return a.name
+            else:
+                return None
+        except TypeError:
+            # pd.NA
+            if is_matching_na(a.name, b.name):
+                return a.name
+            return None
+        except ValueError:
+            # e.g. np.int64(1) vs (np.int64(1), np.int64(2))
+            return None
+    elif a_has:
+        return a.name
+    elif b_has:
+        return b.name
+    return None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/dispatch.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a939fdd3d041e9f99dde7ea40fd7aa0572d0d9b7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/dispatch.py
@@ -0,0 +1,30 @@
+"""
+Functions for defining unary operations.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.core.dtypes.generic import ABCExtensionArray
+
+if TYPE_CHECKING:
+    from pandas._typing import ArrayLike
+
+
+def should_extension_dispatch(left: ArrayLike, right: Any) -> bool:
+    """
+    Identify cases where Series operation should dispatch to ExtensionArray method.
+
+    Parameters
+    ----------
+    left : np.ndarray or ExtensionArray
+    right : object
+
+    Returns
+    -------
+    bool
+    """
+    return isinstance(left, ABCExtensionArray) or isinstance(right, ABCExtensionArray)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/docstrings.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/docstrings.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd2e532536d8491af44631e52982217a04ef5b17
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/docstrings.py
@@ -0,0 +1,772 @@
+"""
+Templating for ops docstrings
+"""
+from __future__ import annotations
+
+
+def make_flex_doc(op_name: str, typ: str) -> str:
+    """
+    Make the appropriate substitutions for the given operation and class-typ
+    into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
+    to attach to a generated method.
+
+    Parameters
+    ----------
+    op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
+    typ : str {series, 'dataframe']}
+
+    Returns
+    -------
+    doc : str
+    """
+    op_name = op_name.replace("__", "")
+    op_desc = _op_descriptions[op_name]
+
+    op_desc_op = op_desc["op"]
+    assert op_desc_op is not None  # for mypy
+    if op_name.startswith("r"):
+        equiv = f"other {op_desc_op} {typ}"
+    elif op_name == "divmod":
+        equiv = f"{op_name}({typ}, other)"
+    else:
+        equiv = f"{typ} {op_desc_op} other"
+
+    if typ == "series":
+        base_doc = _flex_doc_SERIES
+        if op_desc["reverse"]:
+            base_doc += _see_also_reverse_SERIES.format(
+                reverse=op_desc["reverse"], see_also_desc=op_desc["see_also_desc"]
+            )
+        doc_no_examples = base_doc.format(
+            desc=op_desc["desc"],
+            op_name=op_name,
+            equiv=equiv,
+            series_returns=op_desc["series_returns"],
+        )
+        ser_example = op_desc["series_examples"]
+        if ser_example:
+            doc = doc_no_examples + ser_example
+        else:
+            doc = doc_no_examples
+    elif typ == "dataframe":
+        if op_name in ["eq", "ne", "le", "lt", "ge", "gt"]:
+            base_doc = _flex_comp_doc_FRAME
+            doc = _flex_comp_doc_FRAME.format(
+                op_name=op_name,
+                desc=op_desc["desc"],
+            )
+        else:
+            base_doc = _flex_doc_FRAME
+            doc = base_doc.format(
+                desc=op_desc["desc"],
+                op_name=op_name,
+                equiv=equiv,
+                reverse=op_desc["reverse"],
+            )
+    else:
+        raise AssertionError("Invalid typ argument.")
+    return doc
+
+
+_common_examples_algebra_SERIES = """
+Examples
+--------
+>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
+>>> a
+a    1.0
+b    1.0
+c    1.0
+d    NaN
+dtype: float64
+>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
+>>> b
+a    1.0
+b    NaN
+d    1.0
+e    NaN
+dtype: float64"""
+
+_common_examples_comparison_SERIES = """
+Examples
+--------
+>>> a = pd.Series([1, 1, 1, np.nan, 1], index=['a', 'b', 'c', 'd', 'e'])
+>>> a
+a    1.0
+b    1.0
+c    1.0
+d    NaN
+e    1.0
+dtype: float64
+>>> b = pd.Series([0, 1, 2, np.nan, 1], index=['a', 'b', 'c', 'd', 'f'])
+>>> b
+a    0.0
+b    1.0
+c    2.0
+d    NaN
+f    1.0
+dtype: float64"""
+
+_add_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.add(b, fill_value=0)
+a    2.0
+b    1.0
+c    1.0
+d    1.0
+e    NaN
+dtype: float64
+"""
+)
+
+_sub_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.subtract(b, fill_value=0)
+a    0.0
+b    1.0
+c    1.0
+d   -1.0
+e    NaN
+dtype: float64
+"""
+)
+
+_mul_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.multiply(b, fill_value=0)
+a    1.0
+b    0.0
+c    0.0
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_div_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.divide(b, fill_value=0)
+a    1.0
+b    inf
+c    inf
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_floordiv_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.floordiv(b, fill_value=0)
+a    1.0
+b    inf
+c    inf
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_divmod_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.divmod(b, fill_value=0)
+(a    1.0
+ b    inf
+ c    inf
+ d    0.0
+ e    NaN
+ dtype: float64,
+ a    0.0
+ b    NaN
+ c    NaN
+ d    0.0
+ e    NaN
+ dtype: float64)
+"""
+)
+
+_mod_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.mod(b, fill_value=0)
+a    0.0
+b    NaN
+c    NaN
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+_pow_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.pow(b, fill_value=0)
+a    1.0
+b    1.0
+c    1.0
+d    0.0
+e    NaN
+dtype: float64
+"""
+)
+
+_ne_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.ne(b, fill_value=0)
+a    False
+b     True
+c     True
+d     True
+e     True
+dtype: bool
+"""
+)
+
+_eq_example_SERIES = (
+    _common_examples_algebra_SERIES
+    + """
+>>> a.eq(b, fill_value=0)
+a     True
+b    False
+c    False
+d    False
+e    False
+dtype: bool
+"""
+)
+
+_lt_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.lt(b, fill_value=0)
+a    False
+b    False
+c     True
+d    False
+e    False
+f     True
+dtype: bool
+"""
+)
+
+_le_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.le(b, fill_value=0)
+a    False
+b     True
+c     True
+d    False
+e    False
+f     True
+dtype: bool
+"""
+)
+
+_gt_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.gt(b, fill_value=0)
+a     True
+b    False
+c    False
+d    False
+e     True
+f    False
+dtype: bool
+"""
+)
+
+_ge_example_SERIES = (
+    _common_examples_comparison_SERIES
+    + """
+>>> a.ge(b, fill_value=0)
+a     True
+b     True
+c    False
+d    False
+e     True
+f    False
+dtype: bool
+"""
+)
+
+_returns_series = """Series\n    The result of the operation."""
+
+_returns_tuple = """2-Tuple of Series\n    The result of the operation."""
+
+_op_descriptions: dict[str, dict[str, str | None]] = {
+    # Arithmetic Operators
+    "add": {
+        "op": "+",
+        "desc": "Addition",
+        "reverse": "radd",
+        "series_examples": _add_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "sub": {
+        "op": "-",
+        "desc": "Subtraction",
+        "reverse": "rsub",
+        "series_examples": _sub_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "mul": {
+        "op": "*",
+        "desc": "Multiplication",
+        "reverse": "rmul",
+        "series_examples": _mul_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "mod": {
+        "op": "%",
+        "desc": "Modulo",
+        "reverse": "rmod",
+        "series_examples": _mod_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "pow": {
+        "op": "**",
+        "desc": "Exponential power",
+        "reverse": "rpow",
+        "series_examples": _pow_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "truediv": {
+        "op": "/",
+        "desc": "Floating division",
+        "reverse": "rtruediv",
+        "series_examples": _div_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "floordiv": {
+        "op": "//",
+        "desc": "Integer division",
+        "reverse": "rfloordiv",
+        "series_examples": _floordiv_example_SERIES,
+        "series_returns": _returns_series,
+        "df_examples": None,
+    },
+    "divmod": {
+        "op": "divmod",
+        "desc": "Integer division and modulo",
+        "reverse": "rdivmod",
+        "series_examples": _divmod_example_SERIES,
+        "series_returns": _returns_tuple,
+        "df_examples": None,
+    },
+    # Comparison Operators
+    "eq": {
+        "op": "==",
+        "desc": "Equal to",
+        "reverse": None,
+        "series_examples": _eq_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "ne": {
+        "op": "!=",
+        "desc": "Not equal to",
+        "reverse": None,
+        "series_examples": _ne_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "lt": {
+        "op": "<",
+        "desc": "Less than",
+        "reverse": None,
+        "series_examples": _lt_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "le": {
+        "op": "<=",
+        "desc": "Less than or equal to",
+        "reverse": None,
+        "series_examples": _le_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "gt": {
+        "op": ">",
+        "desc": "Greater than",
+        "reverse": None,
+        "series_examples": _gt_example_SERIES,
+        "series_returns": _returns_series,
+    },
+    "ge": {
+        "op": ">=",
+        "desc": "Greater than or equal to",
+        "reverse": None,
+        "series_examples": _ge_example_SERIES,
+        "series_returns": _returns_series,
+    },
+}
+
+_py_num_ref = """see
+    `Python documentation
+    <https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types>`_
+    for more details"""
+_op_names = list(_op_descriptions.keys())
+for key in _op_names:
+    reverse_op = _op_descriptions[key]["reverse"]
+    if reverse_op is not None:
+        _op_descriptions[reverse_op] = _op_descriptions[key].copy()
+        _op_descriptions[reverse_op]["reverse"] = key
+        _op_descriptions[key][
+            "see_also_desc"
+        ] = f"Reverse of the {_op_descriptions[key]['desc']} operator, {_py_num_ref}"
+        _op_descriptions[reverse_op][
+            "see_also_desc"
+        ] = f"Element-wise {_op_descriptions[key]['desc']}, {_py_num_ref}"
+
+_flex_doc_SERIES = """
+Return {desc} of series and other, element-wise (binary operator `{op_name}`).
+
+Equivalent to ``{equiv}``, but with support to substitute a fill_value for
+missing data in either one of the inputs.
+
+Parameters
+----------
+other : Series or scalar value
+level : int or name
+    Broadcast across a level, matching Index values on the
+    passed MultiIndex level.
+fill_value : None or float value, default None (NaN)
+    Fill existing missing (NaN) values, and any new element needed for
+    successful Series alignment, with this value before computation.
+    If data in both corresponding Series locations is missing
+    the result of filling (at that location) will be missing.
+axis : {{0 or 'index'}}
+    Unused. Parameter needed for compatibility with DataFrame.
+
+Returns
+-------
+{series_returns}
+"""
+
+_see_also_reverse_SERIES = """
+See Also
+--------
+Series.{reverse} : {see_also_desc}.
+"""
+
+_flex_doc_FRAME = """
+Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
+
+Equivalent to ``{equiv}``, but with support to substitute a fill_value
+for missing data in one of the inputs. With reverse version, `{reverse}`.
+
+Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to
+arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`.
+
+Parameters
+----------
+other : scalar, sequence, Series, dict or DataFrame
+    Any single or multiple element data structure, or list-like object.
+axis : {{0 or 'index', 1 or 'columns'}}
+    Whether to compare by the index (0 or 'index') or columns.
+    (1 or 'columns'). For Series input, axis to match Series index on.
+level : int or label
+    Broadcast across a level, matching Index values on the
+    passed MultiIndex level.
+fill_value : float or None, default None
+    Fill existing missing (NaN) values, and any new element needed for
+    successful DataFrame alignment, with this value before computation.
+    If data in both corresponding DataFrame locations is missing
+    the result will be missing.
+
+Returns
+-------
+DataFrame
+    Result of the arithmetic operation.
+
+See Also
+--------
+DataFrame.add : Add DataFrames.
+DataFrame.sub : Subtract DataFrames.
+DataFrame.mul : Multiply DataFrames.
+DataFrame.div : Divide DataFrames (float division).
+DataFrame.truediv : Divide DataFrames (float division).
+DataFrame.floordiv : Divide DataFrames (integer division).
+DataFrame.mod : Calculate modulo (remainder after division).
+DataFrame.pow : Calculate exponential power.
+
+Notes
+-----
+Mismatched indices will be unioned together.
+
+Examples
+--------
+>>> df = pd.DataFrame({{'angles': [0, 3, 4],
+...                    'degrees': [360, 180, 360]}},
+...                   index=['circle', 'triangle', 'rectangle'])
+>>> df
+           angles  degrees
+circle          0      360
+triangle        3      180
+rectangle       4      360
+
+Add a scalar with operator version which return the same
+results.
+
+>>> df + 1
+           angles  degrees
+circle          1      361
+triangle        4      181
+rectangle       5      361
+
+>>> df.add(1)
+           angles  degrees
+circle          1      361
+triangle        4      181
+rectangle       5      361
+
+Divide by constant with reverse version.
+
+>>> df.div(10)
+           angles  degrees
+circle        0.0     36.0
+triangle      0.3     18.0
+rectangle     0.4     36.0
+
+>>> df.rdiv(10)
+             angles   degrees
+circle          inf  0.027778
+triangle   3.333333  0.055556
+rectangle  2.500000  0.027778
+
+Subtract a list and Series by axis with operator version.
+
+>>> df - [1, 2]
+           angles  degrees
+circle         -1      358
+triangle        2      178
+rectangle       3      358
+
+>>> df.sub([1, 2], axis='columns')
+           angles  degrees
+circle         -1      358
+triangle        2      178
+rectangle       3      358
+
+>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
+...        axis='index')
+           angles  degrees
+circle         -1      359
+triangle        2      179
+rectangle       3      359
+
+Multiply a dictionary by axis.
+
+>>> df.mul({{'angles': 0, 'degrees': 2}})
+            angles  degrees
+circle           0      720
+triangle         0      360
+rectangle        0      720
+
+>>> df.mul({{'circle': 0, 'triangle': 2, 'rectangle': 3}}, axis='index')
+            angles  degrees
+circle           0        0
+triangle         6      360
+rectangle       12     1080
+
+Multiply a DataFrame of different shape with operator version.
+
+>>> other = pd.DataFrame({{'angles': [0, 3, 4]}},
+...                      index=['circle', 'triangle', 'rectangle'])
+>>> other
+           angles
+circle          0
+triangle        3
+rectangle       4
+
+>>> df * other
+           angles  degrees
+circle          0      NaN
+triangle        9      NaN
+rectangle      16      NaN
+
+>>> df.mul(other, fill_value=0)
+           angles  degrees
+circle          0      0.0
+triangle        9      0.0
+rectangle      16      0.0
+
+Divide by a MultiIndex by level.
+
+>>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6],
+...                              'degrees': [360, 180, 360, 360, 540, 720]}},
+...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
+...                                    ['circle', 'triangle', 'rectangle',
+...                                     'square', 'pentagon', 'hexagon']])
+>>> df_multindex
+             angles  degrees
+A circle          0      360
+  triangle        3      180
+  rectangle       4      360
+B square          4      360
+  pentagon        5      540
+  hexagon         6      720
+
+>>> df.div(df_multindex, level=1, fill_value=0)
+             angles  degrees
+A circle        NaN      1.0
+  triangle      1.0      1.0
+  rectangle     1.0      1.0
+B square        0.0      0.0
+  pentagon      0.0      0.0
+  hexagon       0.0      0.0
+"""
+
+_flex_comp_doc_FRAME = """
+Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
+
+Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison
+operators.
+
+Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
+(rows or columns) and level for comparison.
+
+Parameters
+----------
+other : scalar, sequence, Series, or DataFrame
+    Any single or multiple element data structure, or list-like object.
+axis : {{0 or 'index', 1 or 'columns'}}, default 'columns'
+    Whether to compare by the index (0 or 'index') or columns
+    (1 or 'columns').
+level : int or label
+    Broadcast across a level, matching Index values on the passed
+    MultiIndex level.
+
+Returns
+-------
+DataFrame of bool
+    Result of the comparison.
+
+See Also
+--------
+DataFrame.eq : Compare DataFrames for equality elementwise.
+DataFrame.ne : Compare DataFrames for inequality elementwise.
+DataFrame.le : Compare DataFrames for less than inequality
+    or equality elementwise.
+DataFrame.lt : Compare DataFrames for strictly less than
+    inequality elementwise.
+DataFrame.ge : Compare DataFrames for greater than inequality
+    or equality elementwise.
+DataFrame.gt : Compare DataFrames for strictly greater than
+    inequality elementwise.
+
+Notes
+-----
+Mismatched indices will be unioned together.
+`NaN` values are considered different (i.e. `NaN` != `NaN`).
+
+Examples
+--------
+>>> df = pd.DataFrame({{'cost': [250, 150, 100],
+...                    'revenue': [100, 250, 300]}},
+...                   index=['A', 'B', 'C'])
+>>> df
+   cost  revenue
+A   250      100
+B   150      250
+C   100      300
+
+Comparison with a scalar, using either the operator or method:
+
+>>> df == 100
+    cost  revenue
+A  False     True
+B  False    False
+C   True    False
+
+>>> df.eq(100)
+    cost  revenue
+A  False     True
+B  False    False
+C   True    False
+
+When `other` is a :class:`Series`, the columns of a DataFrame are aligned
+with the index of `other` and broadcast:
+
+>>> df != pd.Series([100, 250], index=["cost", "revenue"])
+    cost  revenue
+A   True     True
+B   True    False
+C  False     True
+
+Use the method to control the broadcast axis:
+
+>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
+   cost  revenue
+A  True    False
+B  True     True
+C  True     True
+D  True     True
+
+When comparing to an arbitrary sequence, the number of columns must
+match the number elements in `other`:
+
+>>> df == [250, 100]
+    cost  revenue
+A   True     True
+B  False    False
+C  False    False
+
+Use the method to control the axis:
+
+>>> df.eq([250, 250, 100], axis='index')
+    cost  revenue
+A   True    False
+B  False     True
+C   True    False
+
+Compare to a DataFrame of different shape.
+
+>>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}},
+...                      index=['A', 'B', 'C', 'D'])
+>>> other
+   revenue
+A      300
+B      250
+C      100
+D      150
+
+>>> df.gt(other)
+    cost  revenue
+A  False    False
+B  False    False
+C  False     True
+D  False    False
+
+Compare to a MultiIndex by level.
+
+>>> df_multindex = pd.DataFrame({{'cost': [250, 150, 100, 150, 300, 220],
+...                              'revenue': [100, 250, 300, 200, 175, 225]}},
+...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
+...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
+>>> df_multindex
+      cost  revenue
+Q1 A   250      100
+   B   150      250
+   C   100      300
+Q2 A   150      200
+   B   300      175
+   C   220      225
+
+>>> df.le(df_multindex, level=1)
+       cost  revenue
+Q1 A   True     True
+   B   True     True
+   C   True     True
+Q2 A  False     True
+   B   True    False
+   C   True    False
+"""
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/invalid.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/invalid.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5ae6d359ac2205b01706211382d116b29176c7a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/invalid.py
@@ -0,0 +1,62 @@
+"""
+Templates for invalid operations.
+"""
+from __future__ import annotations
+
+import operator
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+def invalid_comparison(left, right, op) -> npt.NDArray[np.bool_]:
+    """
+    If a comparison has mismatched types and is not necessarily meaningful,
+    follow python3 conventions by:
+
+        - returning all-False for equality
+        - returning all-True for inequality
+        - raising TypeError otherwise
+
+    Parameters
+    ----------
+    left : array-like
+    right : scalar, array-like
+    op : operator.{eq, ne, lt, le, gt}
+
+    Raises
+    ------
+    TypeError : on inequality comparisons
+    """
+    if op is operator.eq:
+        res_values = np.zeros(left.shape, dtype=bool)
+    elif op is operator.ne:
+        res_values = np.ones(left.shape, dtype=bool)
+    else:
+        typ = type(right).__name__
+        raise TypeError(f"Invalid comparison between dtype={left.dtype} and {typ}")
+    return res_values
+
+
+def make_invalid_op(name: str):
+    """
+    Return a binary method that always raises a TypeError.
+
+    Parameters
+    ----------
+    name : str
+
+    Returns
+    -------
+    invalid_op : function
+    """
+
+    def invalid_op(self, other=None):
+        typ = type(self).__name__
+        raise TypeError(f"cannot perform {name} with this index type: {typ}")
+
+    invalid_op.__name__ = name
+    return invalid_op
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/mask_ops.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/mask_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc1f63c568bf579f31b13446f4614435d443df1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/mask_ops.py
@@ -0,0 +1,189 @@
+"""
+Ops for masked arrays.
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+
+
+def kleene_or(
+    left: bool | np.ndarray | libmissing.NAType,
+    right: bool | np.ndarray | libmissing.NAType,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
+):
+    """
+    Boolean ``or`` using Kleene logic.
+
+    Values are NA where we have ``NA | NA`` or ``NA | False``.
+    ``NA | True`` is considered True.
+
+    Parameters
+    ----------
+    left, right : ndarray, NA, or bool
+        The values of the array.
+    left_mask, right_mask : ndarray, optional
+        The masks. Only one of these may be None, which implies that
+        the associated `left` or `right` value is a scalar.
+
+    Returns
+    -------
+    result, mask: ndarray[bool]
+        The result of the logical or, and the new mask.
+    """
+    # To reduce the number of cases, we ensure that `left` & `left_mask`
+    # always come from an array, not a scalar. This is safe, since
+    # A | B == B | A
+    if left_mask is None:
+        return kleene_or(right, left, right_mask, left_mask)
+
+    if not isinstance(left, np.ndarray):
+        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+
+    raise_for_nan(right, method="or")
+
+    if right is libmissing.NA:
+        result = left.copy()
+    else:
+        result = left | right
+
+    if right_mask is not None:
+        # output is unknown where (False & NA), (NA & False), (NA & NA)
+        left_false = ~(left | left_mask)
+        right_false = ~(right | right_mask)
+        mask = (
+            (left_false & right_mask)
+            | (right_false & left_mask)
+            | (left_mask & right_mask)
+        )
+    else:
+        if right is True:
+            mask = np.zeros_like(left_mask)
+        elif right is libmissing.NA:
+            mask = (~left & ~left_mask) | left_mask
+        else:
+            # False
+            mask = left_mask.copy()
+
+    return result, mask
+
+
+def kleene_xor(
+    left: bool | np.ndarray | libmissing.NAType,
+    right: bool | np.ndarray | libmissing.NAType,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
+):
+    """
+    Boolean ``xor`` using Kleene logic.
+
+    This is the same as ``or``, with the following adjustments
+
+    * True, True -> False
+    * True, NA   -> NA
+
+    Parameters
+    ----------
+    left, right : ndarray, NA, or bool
+        The values of the array.
+    left_mask, right_mask : ndarray, optional
+        The masks. Only one of these may be None, which implies that
+        the associated `left` or `right` value is a scalar.
+
+    Returns
+    -------
+    result, mask: ndarray[bool]
+        The result of the logical xor, and the new mask.
+    """
+    # To reduce the number of cases, we ensure that `left` & `left_mask`
+    # always come from an array, not a scalar. This is safe, since
+    # A ^ B == B ^ A
+    if left_mask is None:
+        return kleene_xor(right, left, right_mask, left_mask)
+
+    if not isinstance(left, np.ndarray):
+        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+
+    raise_for_nan(right, method="xor")
+    if right is libmissing.NA:
+        result = np.zeros_like(left)
+    else:
+        result = left ^ right
+
+    if right_mask is None:
+        if right is libmissing.NA:
+            mask = np.ones_like(left_mask)
+        else:
+            mask = left_mask.copy()
+    else:
+        mask = left_mask | right_mask
+
+    return result, mask
+
+
+def kleene_and(
+    left: bool | libmissing.NAType | np.ndarray,
+    right: bool | libmissing.NAType | np.ndarray,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
+):
+    """
+    Boolean ``and`` using Kleene logic.
+
+    Values are ``NA`` for ``NA & NA`` or ``True & NA``.
+
+    Parameters
+    ----------
+    left, right : ndarray, NA, or bool
+        The values of the array.
+    left_mask, right_mask : ndarray, optional
+        The masks. Only one of these may be None, which implies that
+        the associated `left` or `right` value is a scalar.
+
+    Returns
+    -------
+    result, mask: ndarray[bool]
+        The result of the logical xor, and the new mask.
+    """
+    # To reduce the number of cases, we ensure that `left` & `left_mask`
+    # always come from an array, not a scalar. This is safe, since
+    # A & B == B & A
+    if left_mask is None:
+        return kleene_and(right, left, right_mask, left_mask)
+
+    if not isinstance(left, np.ndarray):
+        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+    raise_for_nan(right, method="and")
+
+    if right is libmissing.NA:
+        result = np.zeros_like(left)
+    else:
+        result = left & right
+
+    if right_mask is None:
+        # Scalar `right`
+        if right is libmissing.NA:
+            mask = (left & ~left_mask) | left_mask
+
+        else:
+            mask = left_mask.copy()
+            if right is False:
+                # unmask everything
+                mask[:] = False
+    else:
+        # unmask where either left or right is False
+        left_false = ~(left | left_mask)
+        right_false = ~(right | right_mask)
+        mask = (left_mask & ~right_false) | (right_mask & ~left_false)
+
+    return result, mask
+
+
+def raise_for_nan(value, method: str) -> None:
+    if lib.is_float(value) and np.isnan(value):
+        raise ValueError(f"Cannot perform logical '{method}' with floating NaN")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/missing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc685935a35fceab74012912d3c3cae65b9c1818
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/ops/missing.py
@@ -0,0 +1,176 @@
+"""
+Missing data handling for arithmetic operations.
+
+In particular, pandas conventions regarding division by zero differ
+from numpy in the following ways:
+    1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2)
+       gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for
+       the remaining pairs
+       (the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN).
+
+       pandas convention is to return [-inf, nan, inf] for all dtype
+       combinations.
+
+       Note: the numpy behavior described here is py3-specific.
+
+    2) np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2)
+       gives precisely the same results as the // operation.
+
+       pandas convention is to return [nan, nan, nan] for all dtype
+       combinations.
+
+    3) divmod behavior consistent with 1) and 2).
+"""
+from __future__ import annotations
+
+import operator
+
+import numpy as np
+
+from pandas.core import roperator
+
+
+def _fill_zeros(result: np.ndarray, x, y):
+    """
+    If this is a reversed op, then flip x,y
+
+    If we have an integer value (or array in y)
+    and we have 0's, fill them with np.nan,
+    return the result.
+
+    Mask the nan's from x.
+    """
+    if result.dtype.kind == "f":
+        return result
+
+    is_variable_type = hasattr(y, "dtype")
+    is_scalar_type = not isinstance(y, np.ndarray)
+
+    if not is_variable_type and not is_scalar_type:
+        # e.g. test_series_ops_name_retention with mod we get here with list/tuple
+        return result
+
+    if is_scalar_type:
+        y = np.array(y)
+
+    if y.dtype.kind in "iu":
+        ymask = y == 0
+        if ymask.any():
+            # GH#7325, mask and nans must be broadcastable
+            mask = ymask & ~np.isnan(result)
+
+            # GH#9308 doing ravel on result and mask can improve putmask perf,
+            #  but can also make unwanted copies.
+            result = result.astype("float64", copy=False)
+
+            np.putmask(result, mask, np.nan)
+
+    return result
+
+
+def mask_zero_div_zero(x, y, result: np.ndarray) -> np.ndarray:
+    """
+    Set results of  0 // 0 to np.nan, regardless of the dtypes
+    of the numerator or the denominator.
+
+    Parameters
+    ----------
+    x : ndarray
+    y : ndarray
+    result : ndarray
+
+    Returns
+    -------
+    ndarray
+        The filled result.
+
+    Examples
+    --------
+    >>> x = np.array([1, 0, -1], dtype=np.int64)
+    >>> x
+    array([ 1,  0, -1])
+    >>> y = 0       # int 0; numpy behavior is different with float
+    >>> result = x // y
+    >>> result      # raw numpy result does not fill division by zero
+    array([0, 0, 0])
+    >>> mask_zero_div_zero(x, y, result)
+    array([ inf,  nan, -inf])
+    """
+
+    if not hasattr(y, "dtype"):
+        # e.g. scalar, tuple
+        y = np.array(y)
+    if not hasattr(x, "dtype"):
+        # e.g scalar, tuple
+        x = np.array(x)
+
+    zmask = y == 0
+
+    if zmask.any():
+        # Flip sign if necessary for -0.0
+        zneg_mask = zmask & np.signbit(y)
+        zpos_mask = zmask & ~zneg_mask
+
+        x_lt0 = x < 0
+        x_gt0 = x > 0
+        nan_mask = zmask & (x == 0)
+        neginf_mask = (zpos_mask & x_lt0) | (zneg_mask & x_gt0)
+        posinf_mask = (zpos_mask & x_gt0) | (zneg_mask & x_lt0)
+
+        if nan_mask.any() or neginf_mask.any() or posinf_mask.any():
+            # Fill negative/0 with -inf, positive/0 with +inf, 0/0 with NaN
+            result = result.astype("float64", copy=False)
+
+            result[nan_mask] = np.nan
+            result[posinf_mask] = np.inf
+            result[neginf_mask] = -np.inf
+
+    return result
+
+
+def dispatch_fill_zeros(op, left, right, result):
+    """
+    Call _fill_zeros with the appropriate fill value depending on the operation,
+    with special logic for divmod and rdivmod.
+
+    Parameters
+    ----------
+    op : function (operator.add, operator.div, ...)
+    left : object (np.ndarray for non-reversed ops)
+        We have excluded ExtensionArrays here
+    right : object (np.ndarray for reversed ops)
+        We have excluded ExtensionArrays here
+    result : ndarray
+
+    Returns
+    -------
+    result : np.ndarray
+
+    Notes
+    -----
+    For divmod and rdivmod, the `result` parameter and returned `result`
+    is a 2-tuple of ndarray objects.
+    """
+    if op is divmod:
+        result = (
+            mask_zero_div_zero(left, right, result[0]),
+            _fill_zeros(result[1], left, right),
+        )
+    elif op is roperator.rdivmod:
+        result = (
+            mask_zero_div_zero(right, left, result[0]),
+            _fill_zeros(result[1], right, left),
+        )
+    elif op is operator.floordiv:
+        # Note: no need to do this for truediv; in py3 numpy behaves the way
+        #  we want.
+        result = mask_zero_div_zero(left, right, result)
+    elif op is roperator.rfloordiv:
+        # Note: no need to do this for rtruediv; in py3 numpy behaves the way
+        #  we want.
+        result = mask_zero_div_zero(right, left, result)
+    elif op is operator.mod:
+        result = _fill_zeros(result, left, right)
+    elif op is roperator.rmod:
+        result = _fill_zeros(result, right, left)
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/resample.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c5da08a86fdc883ed9f2b66bf07d0e743b1d63b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/resample.py
@@ -0,0 +1,2906 @@
+from __future__ import annotations
+
+import copy
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+    final,
+    no_type_check,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    BaseOffset,
+    IncompatibleFrequency,
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas._typing import NDFrameT
+from pandas.compat.numpy import function as nv
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    doc,
+)
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
+
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+import pandas.core.algorithms as algos
+from pandas.core.apply import (
+    ResamplerWindowApply,
+    warn_alias_replacement,
+)
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.base import (
+    PandasObject,
+    SelectionMixin,
+)
+import pandas.core.common as com
+from pandas.core.generic import (
+    NDFrame,
+    _shared_docs,
+)
+from pandas.core.groupby.generic import SeriesGroupBy
+from pandas.core.groupby.groupby import (
+    BaseGroupBy,
+    GroupBy,
+    _apply_groupings_depr,
+    _pipe_template,
+    get_groupby,
+)
+from pandas.core.groupby.grouper import Grouper
+from pandas.core.groupby.ops import BinGrouper
+from pandas.core.indexes.api import MultiIndex
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.datetimes import (
+    DatetimeIndex,
+    date_range,
+)
+from pandas.core.indexes.period import (
+    PeriodIndex,
+    period_range,
+)
+from pandas.core.indexes.timedeltas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+
+from pandas.tseries.frequencies import (
+    is_subperiod,
+    is_superperiod,
+)
+from pandas.tseries.offsets import (
+    Day,
+    Tick,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import (
+        AnyArrayLike,
+        Axis,
+        AxisInt,
+        Frequency,
+        IndexLabel,
+        InterpolateOptions,
+        T,
+        TimedeltaConvertibleTypes,
+        TimeGrouperOrigin,
+        TimestampConvertibleTypes,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+_shared_docs_kwargs: dict[str, str] = {}
+
+
+class Resampler(BaseGroupBy, PandasObject):
+    """
+    Class for resampling datetimelike data, a groupby-like operation.
+    See aggregate, transform, and apply functions on this object.
+
+    It's easiest to use obj.resample(...) to use Resampler.
+
+    Parameters
+    ----------
+    obj : Series or DataFrame
+    groupby : TimeGrouper
+    axis : int, default 0
+    kind : str or None
+        'period', 'timestamp' to override default index treatment
+
+    Returns
+    -------
+    a Resampler of the appropriate type
+
+    Notes
+    -----
+    After resampling, see aggregate, apply, and transform functions.
+    """
+
+    _grouper: BinGrouper
+    _timegrouper: TimeGrouper
+    binner: DatetimeIndex | TimedeltaIndex | PeriodIndex  # depends on subclass
+    exclusions: frozenset[Hashable] = frozenset()  # for SelectionMixin compat
+    _internal_names_set = set({"obj", "ax", "_indexer"})
+
+    # to the groupby descriptor
+    _attributes = [
+        "freq",
+        "axis",
+        "closed",
+        "label",
+        "convention",
+        "kind",
+        "origin",
+        "offset",
+    ]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        timegrouper: TimeGrouper,
+        axis: Axis = 0,
+        kind=None,
+        *,
+        gpr_index: Index,
+        group_keys: bool = False,
+        selection=None,
+        include_groups: bool = True,
+    ) -> None:
+        self._timegrouper = timegrouper
+        self.keys = None
+        self.sort = True
+        self.axis = obj._get_axis_number(axis)
+        self.kind = kind
+        self.group_keys = group_keys
+        self.as_index = True
+        self.include_groups = include_groups
+
+        self.obj, self.ax, self._indexer = self._timegrouper._set_grouper(
+            self._convert_obj(obj), sort=True, gpr_index=gpr_index
+        )
+        self.binner, self._grouper = self._get_binner()
+        self._selection = selection
+        if self._timegrouper.key is not None:
+            self.exclusions = frozenset([self._timegrouper.key])
+        else:
+            self.exclusions = frozenset()
+
+    @final
+    def __str__(self) -> str:
+        """
+        Provide a nice str repr of our rolling object.
+        """
+        attrs = (
+            f"{k}={getattr(self._timegrouper, k)}"
+            for k in self._attributes
+            if getattr(self._timegrouper, k, None) is not None
+        )
+        return f"{type(self).__name__} [{', '.join(attrs)}]"
+
+    @final
+    def __getattr__(self, attr: str):
+        if attr in self._internal_names_set:
+            return object.__getattribute__(self, attr)
+        if attr in self._attributes:
+            return getattr(self._timegrouper, attr)
+        if attr in self.obj:
+            return self[attr]
+
+        return object.__getattribute__(self, attr)
+
+    @final
+    @property
+    def _from_selection(self) -> bool:
+        """
+        Is the resampling from a DataFrame column or MultiIndex level.
+        """
+        # upsampling and PeriodIndex resampling do not work
+        # with selection, this state used to catch and raise an error
+        return self._timegrouper is not None and (
+            self._timegrouper.key is not None or self._timegrouper.level is not None
+        )
+
+    def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
+        """
+        Provide any conversions for the object in order to correctly handle.
+
+        Parameters
+        ----------
+        obj : Series or DataFrame
+
+        Returns
+        -------
+        Series or DataFrame
+        """
+        return obj._consolidate()
+
+    def _get_binner_for_time(self):
+        raise AbstractMethodError(self)
+
+    @final
+    def _get_binner(self):
+        """
+        Create the BinGrouper, assume that self.set_grouper(obj)
+        has already been called.
+        """
+        binner, bins, binlabels = self._get_binner_for_time()
+        assert len(bins) == len(binlabels)
+        bin_grouper = BinGrouper(bins, binlabels, indexer=self._indexer)
+        return binner, bin_grouper
+
+    @final
+    @Substitution(
+        klass="Resampler",
+        examples="""
+    >>> df = pd.DataFrame({'A': [1, 2, 3, 4]},
+    ...                   index=pd.date_range('2012-08-02', periods=4))
+    >>> df
+                A
+    2012-08-02  1
+    2012-08-03  2
+    2012-08-04  3
+    2012-08-05  4
+
+    To get the difference between each 2-day period's maximum and minimum
+    value in one pass, you can do
+
+    >>> df.resample('2D').pipe(lambda x: x.max() - x.min())
+                A
+    2012-08-02  1
+    2012-08-04  1""",
+    )
+    @Appender(_pipe_template)
+    def pipe(
+        self,
+        func: Callable[..., T] | tuple[Callable[..., T], str],
+        *args,
+        **kwargs,
+    ) -> T:
+        return super().pipe(func, *args, **kwargs)
+
+    _agg_see_also_doc = dedent(
+        """
+    See Also
+    --------
+    DataFrame.groupby.aggregate : Aggregate using callable, string, dict,
+        or list of string/callables.
+    DataFrame.resample.transform : Transforms the Series on each group
+        based on the given function.
+    DataFrame.aggregate: Aggregate using one or more
+        operations over the specified axis.
+    """
+    )
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> s = pd.Series([1, 2, 3, 4, 5],
+    ...               index=pd.date_range('20130101', periods=5, freq='s'))
+    >>> s
+    2013-01-01 00:00:00    1
+    2013-01-01 00:00:01    2
+    2013-01-01 00:00:02    3
+    2013-01-01 00:00:03    4
+    2013-01-01 00:00:04    5
+    Freq: s, dtype: int64
+
+    >>> r = s.resample('2s')
+
+    >>> r.agg("sum")
+    2013-01-01 00:00:00    3
+    2013-01-01 00:00:02    7
+    2013-01-01 00:00:04    5
+    Freq: 2s, dtype: int64
+
+    >>> r.agg(['sum', 'mean', 'max'])
+                         sum  mean  max
+    2013-01-01 00:00:00    3   1.5    2
+    2013-01-01 00:00:02    7   3.5    4
+    2013-01-01 00:00:04    5   5.0    5
+
+    >>> r.agg({'result': lambda x: x.mean() / x.std(),
+    ...        'total': "sum"})
+                           result  total
+    2013-01-01 00:00:00  2.121320      3
+    2013-01-01 00:00:02  4.949747      7
+    2013-01-01 00:00:04       NaN      5
+
+    >>> r.agg(average="mean", total="sum")
+                             average  total
+    2013-01-01 00:00:00      1.5      3
+    2013-01-01 00:00:02      3.5      7
+    2013-01-01 00:00:04      5.0      5
+    """
+    )
+
+    @final
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=_agg_see_also_doc,
+        examples=_agg_examples_doc,
+        klass="DataFrame",
+        axis="",
+    )
+    def aggregate(self, func=None, *args, **kwargs):
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if result is None:
+            how = func
+            result = self._groupby_and_aggregate(how, *args, **kwargs)
+
+        return result
+
+    agg = aggregate
+    apply = aggregate
+
+    @final
+    def transform(self, arg, *args, **kwargs):
+        """
+        Call function producing a like-indexed Series on each group.
+
+        Return a Series with the transformed values.
+
+        Parameters
+        ----------
+        arg : function
+            To apply to each group. Should return a Series with the same index.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2],
+        ...               index=pd.date_range('20180101',
+        ...                                   periods=2,
+        ...                                   freq='1h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        Freq: h, dtype: int64
+
+        >>> resampled = s.resample('15min')
+        >>> resampled.transform(lambda x: (x - x.mean()) / x.std())
+        2018-01-01 00:00:00   NaN
+        2018-01-01 01:00:00   NaN
+        Freq: h, dtype: float64
+        """
+        return self._selected_obj.groupby(self._timegrouper).transform(
+            arg, *args, **kwargs
+        )
+
+    def _downsample(self, f, **kwargs):
+        raise AbstractMethodError(self)
+
+    def _upsample(self, f, limit: int | None = None, fill_value=None):
+        raise AbstractMethodError(self)
+
+    def _gotitem(self, key, ndim: int, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        grouper = self._grouper
+        if subset is None:
+            subset = self.obj
+            if key is not None:
+                subset = subset[key]
+            else:
+                # reached via Apply.agg_dict_like with selection=None and ndim=1
+                assert subset.ndim == 1
+        if ndim == 1:
+            assert subset.ndim == 1
+
+        grouped = get_groupby(
+            subset, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
+        )
+        return grouped
+
+    def _groupby_and_aggregate(self, how, *args, **kwargs):
+        """
+        Re-evaluate the obj with a groupby aggregation.
+        """
+        grouper = self._grouper
+
+        # Excludes `on` column when provided
+        obj = self._obj_with_exclusions
+
+        grouped = get_groupby(
+            obj, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
+        )
+
+        try:
+            if callable(how):
+                # TODO: test_resample_apply_with_additional_args fails if we go
+                #  through the non-lambda path, not clear that it should.
+                func = lambda x: how(x, *args, **kwargs)
+                result = grouped.aggregate(func)
+            else:
+                result = grouped.aggregate(how, *args, **kwargs)
+        except (AttributeError, KeyError):
+            # we have a non-reducing function; try to evaluate
+            # alternatively we want to evaluate only a column of the input
+
+            # test_apply_to_one_column_of_df the function being applied references
+            #  a DataFrame column, but aggregate_item_by_item operates column-wise
+            #  on Series, raising AttributeError or KeyError
+            #  (depending on whether the column lookup uses getattr/__getitem__)
+            result = _apply(
+                grouped, how, *args, include_groups=self.include_groups, **kwargs
+            )
+
+        except ValueError as err:
+            if "Must produce aggregated value" in str(err):
+                # raised in _aggregate_named
+                # see test_apply_without_aggregation, test_apply_with_mutated_index
+                pass
+            else:
+                raise
+
+            # we have a non-reducing function
+            # try to evaluate
+            result = _apply(
+                grouped, how, *args, include_groups=self.include_groups, **kwargs
+            )
+
+        return self._wrap_result(result)
+
+    @final
+    def _get_resampler_for_grouping(
+        self, groupby: GroupBy, key, include_groups: bool = True
+    ):
+        """
+        Return the correct class for resampling with groupby.
+        """
+        return self._resampler_for_grouping(
+            groupby=groupby, key=key, parent=self, include_groups=include_groups
+        )
+
+    def _wrap_result(self, result):
+        """
+        Potentially wrap any results.
+        """
+        # GH 47705
+        obj = self.obj
+        if (
+            isinstance(result, ABCDataFrame)
+            and len(result) == 0
+            and not isinstance(result.index, PeriodIndex)
+        ):
+            result = result.set_index(
+                _asfreq_compat(obj.index[:0], freq=self.freq), append=True
+            )
+
+        if isinstance(result, ABCSeries) and self._selection is not None:
+            result.name = self._selection
+
+        if isinstance(result, ABCSeries) and result.empty:
+            # When index is all NaT, result is empty but index is not
+            result.index = _asfreq_compat(obj.index[:0], freq=self.freq)
+            result.name = getattr(obj, "name", None)
+
+        if self._timegrouper._arrow_dtype is not None:
+            result.index = result.index.astype(self._timegrouper._arrow_dtype)
+
+        return result
+
+    @final
+    def ffill(self, limit: int | None = None):
+        """
+        Forward fill the values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        An upsampled Series.
+
+        See Also
+        --------
+        Series.fillna: Fill NA/NaN values using the specified method.
+        DataFrame.fillna: Fill NA/NaN values using the specified method.
+
+        Examples
+        --------
+        Here we only create a ``Series``.
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+
+        Example for ``ffill`` with downsampling (we have fewer dates after resampling):
+
+        >>> ser.resample('MS').ffill()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
+
+        Example for ``ffill`` with upsampling (fill the new dates with
+        the previous value):
+
+        >>> ser.resample('W').ffill()
+        2023-01-01    1
+        2023-01-08    1
+        2023-01-15    2
+        2023-01-22    2
+        2023-01-29    2
+        2023-02-05    3
+        2023-02-12    3
+        2023-02-19    4
+        Freq: W-SUN, dtype: int64
+
+        With upsampling and limiting (only fill the first new date with the
+        previous value):
+
+        >>> ser.resample('W').ffill(limit=1)
+        2023-01-01    1.0
+        2023-01-08    1.0
+        2023-01-15    2.0
+        2023-01-22    2.0
+        2023-01-29    NaN
+        2023-02-05    3.0
+        2023-02-12    NaN
+        2023-02-19    4.0
+        Freq: W-SUN, dtype: float64
+        """
+        return self._upsample("ffill", limit=limit)
+
+    @final
+    def nearest(self, limit: int | None = None):
+        """
+        Resample by using the nearest value.
+
+        When resampling data, missing values may appear (e.g., when the
+        resampling frequency is higher than the original frequency).
+        The `nearest` method will replace ``NaN`` values that appeared in
+        the resampled data with the value from the nearest member of the
+        sequence, based on the index value.
+        Missing values that existed in the original data will not be modified.
+        If `limit` is given, fill only this many values in each direction for
+        each of the original values.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            An upsampled Series or DataFrame with ``NaN`` values filled with
+            their nearest value.
+
+        See Also
+        --------
+        backfill : Backward fill the new missing values in the resampled data.
+        pad : Forward fill ``NaN`` values.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2],
+        ...               index=pd.date_range('20180101',
+        ...                                   periods=2,
+        ...                                   freq='1h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        Freq: h, dtype: int64
+
+        >>> s.resample('15min').nearest()
+        2018-01-01 00:00:00    1
+        2018-01-01 00:15:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 00:45:00    2
+        2018-01-01 01:00:00    2
+        Freq: 15min, dtype: int64
+
+        Limit the number of upsampled values imputed by the nearest:
+
+        >>> s.resample('15min').nearest(limit=1)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        Freq: 15min, dtype: float64
+        """
+        return self._upsample("nearest", limit=limit)
+
+    @final
+    def bfill(self, limit: int | None = None):
+        """
+        Backward fill the new missing values in the resampled data.
+
+        In statistics, imputation is the process of replacing missing data with
+        substituted values [1]_. When resampling data, missing values may
+        appear (e.g., when the resampling frequency is higher than the original
+        frequency). The backward fill will replace NaN values that appeared in
+        the resampled data with the next value in the original sequence.
+        Missing values that existed in the original data will not be modified.
+
+        Parameters
+        ----------
+        limit : int, optional
+            Limit of how many values to fill.
+
+        Returns
+        -------
+        Series, DataFrame
+            An upsampled Series or DataFrame with backward filled NaN values.
+
+        See Also
+        --------
+        bfill : Alias of backfill.
+        fillna : Fill NaN values using the specified method, which can be
+            'backfill'.
+        nearest : Fill NaN values with nearest neighbor starting from center.
+        ffill : Forward fill NaN values.
+        Series.fillna : Fill NaN values in the Series using the
+            specified method, which can be 'backfill'.
+        DataFrame.fillna : Fill NaN values in the DataFrame using the
+            specified method, which can be 'backfill'.
+
+        References
+        ----------
+        .. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
+
+        Examples
+        --------
+        Resampling a Series:
+
+        >>> s = pd.Series([1, 2, 3],
+        ...               index=pd.date_range('20180101', periods=3, freq='h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        2018-01-01 02:00:00    3
+        Freq: h, dtype: int64
+
+        >>> s.resample('30min').bfill()
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    3
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        >>> s.resample('15min').bfill(limit=2)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    NaN
+        2018-01-01 00:30:00    2.0
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        2018-01-01 01:15:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 01:45:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 15min, dtype: float64
+
+        Resampling a DataFrame that has missing values:
+
+        >>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
+        ...                   index=pd.date_range('20180101', periods=3,
+        ...                                       freq='h'))
+        >>> df
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 02:00:00  6.0  5
+
+        >>> df.resample('30min').bfill()
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 00:30:00  NaN  3
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 01:30:00  6.0  5
+        2018-01-01 02:00:00  6.0  5
+
+        >>> df.resample('15min').bfill(limit=2)
+                               a    b
+        2018-01-01 00:00:00  2.0  1.0
+        2018-01-01 00:15:00  NaN  NaN
+        2018-01-01 00:30:00  NaN  3.0
+        2018-01-01 00:45:00  NaN  3.0
+        2018-01-01 01:00:00  NaN  3.0
+        2018-01-01 01:15:00  NaN  NaN
+        2018-01-01 01:30:00  6.0  5.0
+        2018-01-01 01:45:00  6.0  5.0
+        2018-01-01 02:00:00  6.0  5.0
+        """
+        return self._upsample("bfill", limit=limit)
+
+    @final
+    def fillna(self, method, limit: int | None = None):
+        """
+        Fill missing values introduced by upsampling.
+
+        In statistics, imputation is the process of replacing missing data with
+        substituted values [1]_. When resampling data, missing values may
+        appear (e.g., when the resampling frequency is higher than the original
+        frequency).
+
+        Missing values that existed in the original data will
+        not be modified.
+
+        Parameters
+        ----------
+        method : {'pad', 'backfill', 'ffill', 'bfill', 'nearest'}
+            Method to use for filling holes in resampled data
+
+            * 'pad' or 'ffill': use previous valid observation to fill gap
+              (forward fill).
+            * 'backfill' or 'bfill': use next valid observation to fill gap.
+            * 'nearest': use nearest valid observation to fill gap.
+
+        limit : int, optional
+            Limit of how many consecutive missing values to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            An upsampled Series or DataFrame with missing values filled.
+
+        See Also
+        --------
+        bfill : Backward fill NaN values in the resampled data.
+        ffill : Forward fill NaN values in the resampled data.
+        nearest : Fill NaN values in the resampled data
+            with nearest neighbor starting from center.
+        interpolate : Fill NaN values using interpolation.
+        Series.fillna : Fill NaN values in the Series using the
+            specified method, which can be 'bfill' and 'ffill'.
+        DataFrame.fillna : Fill NaN values in the DataFrame using the
+            specified method, which can be 'bfill' and 'ffill'.
+
+        References
+        ----------
+        .. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
+
+        Examples
+        --------
+        Resampling a Series:
+
+        >>> s = pd.Series([1, 2, 3],
+        ...               index=pd.date_range('20180101', periods=3, freq='h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        2018-01-01 02:00:00    3
+        Freq: h, dtype: int64
+
+        Without filling the missing values you get:
+
+        >>> s.resample("30min").asfreq()
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 01:00:00    2.0
+        2018-01-01 01:30:00    NaN
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        >>> s.resample('30min').fillna("backfill")
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    3
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        >>> s.resample('15min').fillna("backfill", limit=2)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    NaN
+        2018-01-01 00:30:00    2.0
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        2018-01-01 01:15:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 01:45:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 15min, dtype: float64
+
+        >>> s.resample('30min').fillna("pad")
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    1
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    2
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        >>> s.resample('30min').fillna("nearest")
+        2018-01-01 00:00:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 01:00:00    2
+        2018-01-01 01:30:00    3
+        2018-01-01 02:00:00    3
+        Freq: 30min, dtype: int64
+
+        Missing values present before the upsampling are not affected.
+
+        >>> sm = pd.Series([1, None, 3],
+        ...                index=pd.date_range('20180101', periods=3, freq='h'))
+        >>> sm
+        2018-01-01 00:00:00    1.0
+        2018-01-01 01:00:00    NaN
+        2018-01-01 02:00:00    3.0
+        Freq: h, dtype: float64
+
+        >>> sm.resample('30min').fillna('backfill')
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 01:00:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        >>> sm.resample('30min').fillna('pad')
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    1.0
+        2018-01-01 01:00:00    NaN
+        2018-01-01 01:30:00    NaN
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        >>> sm.resample('30min').fillna('nearest')
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 01:00:00    NaN
+        2018-01-01 01:30:00    3.0
+        2018-01-01 02:00:00    3.0
+        Freq: 30min, dtype: float64
+
+        DataFrame resampling is done column-wise. All the same options are
+        available.
+
+        >>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
+        ...                   index=pd.date_range('20180101', periods=3,
+        ...                                       freq='h'))
+        >>> df
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 02:00:00  6.0  5
+
+        >>> df.resample('30min').fillna("bfill")
+                               a  b
+        2018-01-01 00:00:00  2.0  1
+        2018-01-01 00:30:00  NaN  3
+        2018-01-01 01:00:00  NaN  3
+        2018-01-01 01:30:00  6.0  5
+        2018-01-01 02:00:00  6.0  5
+        """
+        warnings.warn(
+            f"{type(self).__name__}.fillna is deprecated and will be removed "
+            "in a future version. Use obj.ffill(), obj.bfill(), "
+            "or obj.nearest() instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._upsample(method, limit=limit)
+
+    @final
+    def interpolate(
+        self,
+        method: InterpolateOptions = "linear",
+        *,
+        axis: Axis = 0,
+        limit: int | None = None,
+        inplace: bool = False,
+        limit_direction: Literal["forward", "backward", "both"] = "forward",
+        limit_area=None,
+        downcast=lib.no_default,
+        **kwargs,
+    ):
+        """
+        Interpolate values between target timestamps according to different methods.
+
+        The original index is first reindexed to target timestamps
+        (see :meth:`core.resample.Resampler.asfreq`),
+        then the interpolation of ``NaN`` values via :meth:`DataFrame.interpolate`
+        happens.
+
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. One of:
+
+            * 'linear': Ignore the index and treat the values as equally
+              spaced. This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate
+              given length of interval.
+            * 'index', 'values': use the actual numerical values of the index.
+            * 'pad': Fill in NaNs using existing values.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+              'barycentric', 'polynomial': Passed to
+              `scipy.interpolate.interp1d`, whereas 'spline' is passed to
+              `scipy.interpolate.UnivariateSpline`. These methods use the numerical
+              values of the index.  Both 'polynomial' and 'spline' require that
+              you also specify an `order` (int), e.g.
+              ``df.interpolate(method='polynomial', order=5)``. Note that,
+              `slinear` method in Pandas refers to the Scipy first order `spline`
+              instead of Pandas first order `spline`.
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
+              'cubicspline': Wrappers around the SciPy interpolation methods of
+              similar names. See `Notes`.
+            * 'from_derivatives': Refers to
+              `scipy.interpolate.BPoly.from_derivatives`.
+
+        axis : {{0 or 'index', 1 or 'columns', None}}, default None
+            Axis to interpolate along. For `Series` this parameter is unused
+            and defaults to 0.
+        limit : int, optional
+            Maximum number of consecutive NaNs to fill. Must be greater than
+            0.
+        inplace : bool, default False
+            Update the data in place if possible.
+        limit_direction : {{'forward', 'backward', 'both'}}, Optional
+            Consecutive NaNs will be filled in this direction.
+
+            If limit is specified:
+                * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
+                * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
+                  'backwards'.
+
+            If 'limit' is not specified:
+                * If 'method' is 'backfill' or 'bfill', the default is 'backward'
+                * else the default is 'forward'
+
+                raises ValueError if `limit_direction` is 'forward' or 'both' and
+                    method is 'backfill' or 'bfill'.
+                raises ValueError if `limit_direction` is 'backward' or 'both' and
+                    method is 'pad' or 'ffill'.
+
+        limit_area : {{`None`, 'inside', 'outside'}}, default None
+            If limit is specified, consecutive NaNs will be filled with this
+            restriction.
+
+            * ``None``: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values
+              (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+
+        downcast : optional, 'infer' or None, defaults to None
+            Downcast dtypes if possible.
+
+            .. deprecated:: 2.1.0
+
+        ``**kwargs`` : optional
+            Keyword arguments to pass on to the interpolating function.
+
+        Returns
+        -------
+        DataFrame or Series
+            Interpolated values at the specified freq.
+
+        See Also
+        --------
+        core.resample.Resampler.asfreq: Return the values at the new freq,
+            essentially a reindex.
+        DataFrame.interpolate: Fill NaN values using an interpolation method.
+
+        Notes
+        -----
+        For high-frequent or non-equidistant time-series with timestamps
+        the reindexing followed by interpolation may lead to information loss
+        as shown in the last example.
+
+        Examples
+        --------
+
+        >>> start = "2023-03-01T07:00:00"
+        >>> timesteps = pd.date_range(start, periods=5, freq="s")
+        >>> series = pd.Series(data=[1, -1, 2, 1, 3], index=timesteps)
+        >>> series
+        2023-03-01 07:00:00    1
+        2023-03-01 07:00:01   -1
+        2023-03-01 07:00:02    2
+        2023-03-01 07:00:03    1
+        2023-03-01 07:00:04    3
+        Freq: s, dtype: int64
+
+        Upsample the dataframe to 0.5Hz by providing the period time of 2s.
+
+        >>> series.resample("2s").interpolate("linear")
+        2023-03-01 07:00:00    1
+        2023-03-01 07:00:02    2
+        2023-03-01 07:00:04    3
+        Freq: 2s, dtype: int64
+
+        Downsample the dataframe to 2Hz by providing the period time of 500ms.
+
+        >>> series.resample("500ms").interpolate("linear")
+        2023-03-01 07:00:00.000    1.0
+        2023-03-01 07:00:00.500    0.0
+        2023-03-01 07:00:01.000   -1.0
+        2023-03-01 07:00:01.500    0.5
+        2023-03-01 07:00:02.000    2.0
+        2023-03-01 07:00:02.500    1.5
+        2023-03-01 07:00:03.000    1.0
+        2023-03-01 07:00:03.500    2.0
+        2023-03-01 07:00:04.000    3.0
+        Freq: 500ms, dtype: float64
+
+        Internal reindexing with ``asfreq()`` prior to interpolation leads to
+        an interpolated timeseries on the basis the reindexed timestamps (anchors).
+        Since not all datapoints from original series become anchors,
+        it can lead to misleading interpolation results as in the following example:
+
+        >>> series.resample("400ms").interpolate("linear")
+        2023-03-01 07:00:00.000    1.0
+        2023-03-01 07:00:00.400    1.2
+        2023-03-01 07:00:00.800    1.4
+        2023-03-01 07:00:01.200    1.6
+        2023-03-01 07:00:01.600    1.8
+        2023-03-01 07:00:02.000    2.0
+        2023-03-01 07:00:02.400    2.2
+        2023-03-01 07:00:02.800    2.4
+        2023-03-01 07:00:03.200    2.6
+        2023-03-01 07:00:03.600    2.8
+        2023-03-01 07:00:04.000    3.0
+        Freq: 400ms, dtype: float64
+
+        Note that the series erroneously increases between two anchors
+        ``07:00:00`` and ``07:00:02``.
+        """
+        assert downcast is lib.no_default  # just checking coverage
+        result = self._upsample("asfreq")
+        return result.interpolate(
+            method=method,
+            axis=axis,
+            limit=limit,
+            inplace=inplace,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            downcast=downcast,
+            **kwargs,
+        )
+
+    @final
+    def asfreq(self, fill_value=None):
+        """
+        Return the values at the new freq, essentially a reindex.
+
+        Parameters
+        ----------
+        fill_value : scalar, optional
+            Value to use for missing values, applied during upsampling (note
+            this does not fill NaNs that already were present).
+
+        Returns
+        -------
+        DataFrame or Series
+            Values at the specified freq.
+
+        See Also
+        --------
+        Series.asfreq: Convert TimeSeries to specified frequency.
+        DataFrame.asfreq: Convert TimeSeries to specified frequency.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-31', '2023-02-01', '2023-02-28']))
+        >>> ser
+        2023-01-01    1
+        2023-01-31    2
+        2023-02-01    3
+        2023-02-28    4
+        dtype: int64
+        >>> ser.resample('MS').asfreq()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
+        """
+        return self._upsample("asfreq", fill_value=fill_value)
+
+    @final
+    def sum(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute sum of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed sum of values within each group.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').sum()
+        2023-01-01    3
+        2023-02-01    7
+        Freq: MS, dtype: int64
+        """
+        maybe_warn_args_and_kwargs(type(self), "sum", args, kwargs)
+        nv.validate_resampler_func("sum", args, kwargs)
+        return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    def prod(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute prod of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed prod of values within each group.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').prod()
+        2023-01-01    2
+        2023-02-01   12
+        Freq: MS, dtype: int64
+        """
+        maybe_warn_args_and_kwargs(type(self), "prod", args, kwargs)
+        nv.validate_resampler_func("prod", args, kwargs)
+        return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    def min(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute min value of group.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').min()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
+        """
+
+        maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
+        nv.validate_resampler_func("min", args, kwargs)
+        return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    def max(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute max value of group.
+
+        Returns
+        -------
+        Series or DataFrame
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').max()
+        2023-01-01    2
+        2023-02-01    4
+        Freq: MS, dtype: int64
+        """
+        maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
+        nv.validate_resampler_func("max", args, kwargs)
+        return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
+
+    @final
+    @doc(GroupBy.first)
+    def first(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "first", args, kwargs)
+        nv.validate_resampler_func("first", args, kwargs)
+        return self._downsample(
+            "first", numeric_only=numeric_only, min_count=min_count, skipna=skipna
+        )
+
+    @final
+    @doc(GroupBy.last)
+    def last(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "last", args, kwargs)
+        nv.validate_resampler_func("last", args, kwargs)
+        return self._downsample(
+            "last", numeric_only=numeric_only, min_count=min_count, skipna=skipna
+        )
+
+    @final
+    @doc(GroupBy.median)
+    def median(self, numeric_only: bool = False, *args, **kwargs):
+        maybe_warn_args_and_kwargs(type(self), "median", args, kwargs)
+        nv.validate_resampler_func("median", args, kwargs)
+        return self._downsample("median", numeric_only=numeric_only)
+
+    @final
+    def mean(
+        self,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute mean of groups, excluding missing values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        DataFrame or Series
+            Mean of values within each group.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
+        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
+        >>> ser
+        2023-01-01    1
+        2023-01-15    2
+        2023-02-01    3
+        2023-02-15    4
+        dtype: int64
+        >>> ser.resample('MS').mean()
+        2023-01-01    1.5
+        2023-02-01    3.5
+        Freq: MS, dtype: float64
+        """
+        maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
+        nv.validate_resampler_func("mean", args, kwargs)
+        return self._downsample("mean", numeric_only=numeric_only)
+
+    @final
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute standard deviation of groups, excluding missing values.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        DataFrame or Series
+            Standard deviation of values within each group.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').std()
+        2023-01-01    1.000000
+        2023-02-01    2.645751
+        Freq: MS, dtype: float64
+        """
+        maybe_warn_args_and_kwargs(type(self), "std", args, kwargs)
+        nv.validate_resampler_func("std", args, kwargs)
+        return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
+
+    @final
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """
+        Compute variance of groups, excluding missing values.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Degrees of freedom.
+
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
+        Returns
+        -------
+        DataFrame or Series
+            Variance of values within each group.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').var()
+        2023-01-01    1.0
+        2023-02-01    7.0
+        Freq: MS, dtype: float64
+
+        >>> ser.resample('MS').var(ddof=0)
+        2023-01-01    0.666667
+        2023-02-01    4.666667
+        Freq: MS, dtype: float64
+        """
+        maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
+        nv.validate_resampler_func("var", args, kwargs)
+        return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
+
+    @final
+    @doc(GroupBy.sem)
+    def sem(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "sem", args, kwargs)
+        nv.validate_resampler_func("sem", args, kwargs)
+        return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
+
+    @final
+    @doc(GroupBy.ohlc)
+    def ohlc(
+        self,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "ohlc", args, kwargs)
+        nv.validate_resampler_func("ohlc", args, kwargs)
+
+        ax = self.ax
+        obj = self._obj_with_exclusions
+        if len(ax) == 0:
+            # GH#42902
+            obj = obj.copy()
+            obj.index = _asfreq_compat(obj.index, self.freq)
+            if obj.ndim == 1:
+                obj = obj.to_frame()
+                obj = obj.reindex(["open", "high", "low", "close"], axis=1)
+            else:
+                mi = MultiIndex.from_product(
+                    [obj.columns, ["open", "high", "low", "close"]]
+                )
+                obj = obj.reindex(mi, axis=1)
+            return obj
+
+        return self._downsample("ohlc")
+
+    @final
+    @doc(SeriesGroupBy.nunique)
+    def nunique(
+        self,
+        *args,
+        **kwargs,
+    ):
+        maybe_warn_args_and_kwargs(type(self), "nunique", args, kwargs)
+        nv.validate_resampler_func("nunique", args, kwargs)
+        return self._downsample("nunique")
+
+    @final
+    @doc(GroupBy.size)
+    def size(self):
+        result = self._downsample("size")
+
+        # If the result is a non-empty DataFrame we stack to get a Series
+        # GH 46826
+        if isinstance(result, ABCDataFrame) and not result.empty:
+            result = result.stack(future_stack=True)
+
+        if not len(self.ax):
+            from pandas import Series
+
+            if self._selected_obj.ndim == 1:
+                name = self._selected_obj.name
+            else:
+                name = None
+            result = Series([], index=result.index, dtype="int64", name=name)
+        return result
+
+    @final
+    @doc(GroupBy.count)
+    def count(self):
+        result = self._downsample("count")
+        if not len(self.ax):
+            if self._selected_obj.ndim == 1:
+                result = type(self._selected_obj)(
+                    [], index=result.index, dtype="int64", name=self._selected_obj.name
+                )
+            else:
+                from pandas import DataFrame
+
+                result = DataFrame(
+                    [], index=result.index, columns=result.columns, dtype="int64"
+                )
+
+        return result
+
+    @final
+    def quantile(self, q: float | list[float] | AnyArrayLike = 0.5, **kwargs):
+        """
+        Return value at the given quantile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+
+        Returns
+        -------
+        DataFrame or Series
+            Quantile of values within each group.
+
+        See Also
+        --------
+        Series.quantile
+            Return a series, where the index is q and the values are the quantiles.
+        DataFrame.quantile
+            Return a DataFrame, where the columns are the columns of self,
+            and the values are the quantiles.
+        DataFrameGroupBy.quantile
+            Return a DataFrame, where the columns are groupby columns,
+            and the values are its quantiles.
+
+        Examples
+        --------
+
+        >>> ser = pd.Series([1, 3, 2, 4, 3, 8],
+        ...                 index=pd.DatetimeIndex(['2023-01-01',
+        ...                                         '2023-01-10',
+        ...                                         '2023-01-15',
+        ...                                         '2023-02-01',
+        ...                                         '2023-02-10',
+        ...                                         '2023-02-15']))
+        >>> ser.resample('MS').quantile()
+        2023-01-01    2.0
+        2023-02-01    4.0
+        Freq: MS, dtype: float64
+
+        >>> ser.resample('MS').quantile(.25)
+        2023-01-01    1.5
+        2023-02-01    3.5
+        Freq: MS, dtype: float64
+        """
+        return self._downsample("quantile", q=q, **kwargs)
+
+
+class _GroupByMixin(PandasObject, SelectionMixin):
+    """
+    Provide the groupby facilities.
+    """
+
+    _attributes: list[str]  # in practice the same as Resampler._attributes
+    _selection: IndexLabel | None = None
+    _groupby: GroupBy
+    _timegrouper: TimeGrouper
+
+    def __init__(
+        self,
+        *,
+        parent: Resampler,
+        groupby: GroupBy,
+        key=None,
+        selection: IndexLabel | None = None,
+        include_groups: bool = False,
+    ) -> None:
+        # reached via ._gotitem and _get_resampler_for_grouping
+
+        assert isinstance(groupby, GroupBy), type(groupby)
+
+        # parent is always a Resampler, sometimes a _GroupByMixin
+        assert isinstance(parent, Resampler), type(parent)
+
+        # initialize our GroupByMixin object with
+        # the resampler attributes
+        for attr in self._attributes:
+            setattr(self, attr, getattr(parent, attr))
+        self._selection = selection
+
+        self.binner = parent.binner
+        self.key = key
+
+        self._groupby = groupby
+        self._timegrouper = copy.copy(parent._timegrouper)
+
+        self.ax = parent.ax
+        self.obj = parent.obj
+        self.include_groups = include_groups
+
+    @no_type_check
+    def _apply(self, f, *args, **kwargs):
+        """
+        Dispatch to _upsample; we are stripping all of the _upsample kwargs and
+        performing the original function call on the grouped object.
+        """
+
+        def func(x):
+            x = self._resampler_cls(x, timegrouper=self._timegrouper, gpr_index=self.ax)
+
+            if isinstance(f, str):
+                return getattr(x, f)(**kwargs)
+
+            return x.apply(f, *args, **kwargs)
+
+        result = _apply(self._groupby, func, include_groups=self.include_groups)
+        return self._wrap_result(result)
+
+    _upsample = _apply
+    _downsample = _apply
+    _groupby_and_aggregate = _apply
+
+    @final
+    def _gotitem(self, key, ndim, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        # create a new object to prevent aliasing
+        if subset is None:
+            subset = self.obj
+            if key is not None:
+                subset = subset[key]
+            else:
+                # reached via Apply.agg_dict_like with selection=None, ndim=1
+                assert subset.ndim == 1
+
+        # Try to select from a DataFrame, falling back to a Series
+        try:
+            if isinstance(key, list) and self.key not in key and self.key is not None:
+                key.append(self.key)
+            groupby = self._groupby[key]
+        except IndexError:
+            groupby = self._groupby
+
+        selection = self._infer_selection(key, subset)
+
+        new_rs = type(self)(
+            groupby=groupby,
+            parent=cast(Resampler, self),
+            selection=selection,
+        )
+        return new_rs
+
+
+class DatetimeIndexResampler(Resampler):
+    ax: DatetimeIndex
+
+    @property
+    def _resampler_for_grouping(self):
+        return DatetimeIndexResamplerGroupby
+
+    def _get_binner_for_time(self):
+        # this is how we are actually creating the bins
+        if self.kind == "period":
+            return self._timegrouper._get_time_period_bins(self.ax)
+        return self._timegrouper._get_time_bins(self.ax)
+
+    def _downsample(self, how, **kwargs):
+        """
+        Downsample the cython defined function.
+
+        Parameters
+        ----------
+        how : string / cython mapped function
+        **kwargs : kw args passed to how function
+        """
+        orig_how = how
+        how = com.get_cython_func(how) or how
+        if orig_how != how:
+            warn_alias_replacement(self, orig_how, how)
+        ax = self.ax
+
+        # Excludes `on` column when provided
+        obj = self._obj_with_exclusions
+
+        if not len(ax):
+            # reset to the new freq
+            obj = obj.copy()
+            obj.index = obj.index._with_freq(self.freq)
+            assert obj.index.freq == self.freq, (obj.index.freq, self.freq)
+            return obj
+
+        # do we have a regular frequency
+
+        # error: Item "None" of "Optional[Any]" has no attribute "binlabels"
+        if (
+            (ax.freq is not None or ax.inferred_freq is not None)
+            and len(self._grouper.binlabels) > len(ax)
+            and how is None
+        ):
+            # let's do an asfreq
+            return self.asfreq()
+
+        # we are downsampling
+        # we want to call the actual grouper method here
+        if self.axis == 0:
+            result = obj.groupby(self._grouper).aggregate(how, **kwargs)
+        else:
+            # test_resample_axis1
+            result = obj.T.groupby(self._grouper).aggregate(how, **kwargs).T
+
+        return self._wrap_result(result)
+
+    def _adjust_binner_for_upsample(self, binner):
+        """
+        Adjust our binner when upsampling.
+
+        The range of a new index should not be outside specified range
+        """
+        if self.closed == "right":
+            binner = binner[1:]
+        else:
+            binner = binner[:-1]
+        return binner
+
+    def _upsample(self, method, limit: int | None = None, fill_value=None):
+        """
+        Parameters
+        ----------
+        method : string {'backfill', 'bfill', 'pad',
+            'ffill', 'asfreq'} method for upsampling
+        limit : int, default None
+            Maximum size gap to fill when reindexing
+        fill_value : scalar, default None
+            Value to use for missing values
+
+        See Also
+        --------
+        .fillna: Fill NA/NaN values using the specified method.
+
+        """
+        if self.axis:
+            raise AssertionError("axis must be 0")
+        if self._from_selection:
+            raise ValueError(
+                "Upsampling from level= or on= selection "
+                "is not supported, use .set_index(...) "
+                "to explicitly set index to datetime-like"
+            )
+
+        ax = self.ax
+        obj = self._selected_obj
+        binner = self.binner
+        res_index = self._adjust_binner_for_upsample(binner)
+
+        # if we have the same frequency as our axis, then we are equal sampling
+        if (
+            limit is None
+            and to_offset(ax.inferred_freq) == self.freq
+            and len(obj) == len(res_index)
+        ):
+            result = obj.copy()
+            result.index = res_index
+        else:
+            if method == "asfreq":
+                method = None
+            result = obj.reindex(
+                res_index, method=method, limit=limit, fill_value=fill_value
+            )
+
+        return self._wrap_result(result)
+
+    def _wrap_result(self, result):
+        result = super()._wrap_result(result)
+
+        # we may have a different kind that we were asked originally
+        # convert if needed
+        if self.kind == "period" and not isinstance(result.index, PeriodIndex):
+            if isinstance(result.index, MultiIndex):
+                # GH 24103 - e.g. groupby resample
+                if not isinstance(result.index.levels[-1], PeriodIndex):
+                    new_level = result.index.levels[-1].to_period(self.freq)
+                    result.index = result.index.set_levels(new_level, level=-1)
+            else:
+                result.index = result.index.to_period(self.freq)
+        return result
+
+
+# error: Definition of "ax" in base class "_GroupByMixin" is incompatible
+# with definition in base class "DatetimeIndexResampler"
+class DatetimeIndexResamplerGroupby(  # type: ignore[misc]
+    _GroupByMixin, DatetimeIndexResampler
+):
+    """
+    Provides a resample of a groupby implementation
+    """
+
+    @property
+    def _resampler_cls(self):
+        return DatetimeIndexResampler
+
+
+class PeriodIndexResampler(DatetimeIndexResampler):
+    # error: Incompatible types in assignment (expression has type "PeriodIndex", base
+    # class "DatetimeIndexResampler" defined the type as "DatetimeIndex")
+    ax: PeriodIndex  # type: ignore[assignment]
+
+    @property
+    def _resampler_for_grouping(self):
+        return PeriodIndexResamplerGroupby
+
+    def _get_binner_for_time(self):
+        if self.kind == "timestamp":
+            return super()._get_binner_for_time()
+        return self._timegrouper._get_period_bins(self.ax)
+
+    def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
+        obj = super()._convert_obj(obj)
+
+        if self._from_selection:
+            # see GH 14008, GH 12871
+            msg = (
+                "Resampling from level= or on= selection "
+                "with a PeriodIndex is not currently supported, "
+                "use .set_index(...) to explicitly set index"
+            )
+            raise NotImplementedError(msg)
+
+        # convert to timestamp
+        if self.kind == "timestamp":
+            obj = obj.to_timestamp(how=self.convention)
+
+        return obj
+
+    def _downsample(self, how, **kwargs):
+        """
+        Downsample the cython defined function.
+
+        Parameters
+        ----------
+        how : string / cython mapped function
+        **kwargs : kw args passed to how function
+        """
+        # we may need to actually resample as if we are timestamps
+        if self.kind == "timestamp":
+            return super()._downsample(how, **kwargs)
+
+        orig_how = how
+        how = com.get_cython_func(how) or how
+        if orig_how != how:
+            warn_alias_replacement(self, orig_how, how)
+        ax = self.ax
+
+        if is_subperiod(ax.freq, self.freq):
+            # Downsampling
+            return self._groupby_and_aggregate(how, **kwargs)
+        elif is_superperiod(ax.freq, self.freq):
+            if how == "ohlc":
+                # GH #13083
+                # upsampling to subperiods is handled as an asfreq, which works
+                # for pure aggregating/reducing methods
+                # OHLC reduces along the time dimension, but creates multiple
+                # values for each period -> handle by _groupby_and_aggregate()
+                return self._groupby_and_aggregate(how)
+            return self.asfreq()
+        elif ax.freq == self.freq:
+            return self.asfreq()
+
+        raise IncompatibleFrequency(
+            f"Frequency {ax.freq} cannot be resampled to {self.freq}, "
+            "as they are not sub or super periods"
+        )
+
+    def _upsample(self, method, limit: int | None = None, fill_value=None):
+        """
+        Parameters
+        ----------
+        method : {'backfill', 'bfill', 'pad', 'ffill'}
+            Method for upsampling.
+        limit : int, default None
+            Maximum size gap to fill when reindexing.
+        fill_value : scalar, default None
+            Value to use for missing values.
+
+        See Also
+        --------
+        .fillna: Fill NA/NaN values using the specified method.
+
+        """
+        # we may need to actually resample as if we are timestamps
+        if self.kind == "timestamp":
+            return super()._upsample(method, limit=limit, fill_value=fill_value)
+
+        ax = self.ax
+        obj = self.obj
+        new_index = self.binner
+
+        # Start vs. end of period
+        memb = ax.asfreq(self.freq, how=self.convention)
+
+        # Get the fill indexer
+        if method == "asfreq":
+            method = None
+        indexer = memb.get_indexer(new_index, method=method, limit=limit)
+        new_obj = _take_new_index(
+            obj,
+            indexer,
+            new_index,
+            axis=self.axis,
+        )
+        return self._wrap_result(new_obj)
+
+
+# error: Definition of "ax" in base class "_GroupByMixin" is incompatible with
+# definition in base class "PeriodIndexResampler"
+class PeriodIndexResamplerGroupby(  # type: ignore[misc]
+    _GroupByMixin, PeriodIndexResampler
+):
+    """
+    Provides a resample of a groupby implementation.
+    """
+
+    @property
+    def _resampler_cls(self):
+        return PeriodIndexResampler
+
+
+class TimedeltaIndexResampler(DatetimeIndexResampler):
+    # error: Incompatible types in assignment (expression has type "TimedeltaIndex",
+    # base class "DatetimeIndexResampler" defined the type as "DatetimeIndex")
+    ax: TimedeltaIndex  # type: ignore[assignment]
+
+    @property
+    def _resampler_for_grouping(self):
+        return TimedeltaIndexResamplerGroupby
+
+    def _get_binner_for_time(self):
+        return self._timegrouper._get_time_delta_bins(self.ax)
+
+    def _adjust_binner_for_upsample(self, binner):
+        """
+        Adjust our binner when upsampling.
+
+        The range of a new index is allowed to be greater than original range
+        so we don't need to change the length of a binner, GH 13022
+        """
+        return binner
+
+
+# error: Definition of "ax" in base class "_GroupByMixin" is incompatible with
+# definition in base class "DatetimeIndexResampler"
+class TimedeltaIndexResamplerGroupby(  # type: ignore[misc]
+    _GroupByMixin, TimedeltaIndexResampler
+):
+    """
+    Provides a resample of a groupby implementation.
+    """
+
+    @property
+    def _resampler_cls(self):
+        return TimedeltaIndexResampler
+
+
+def get_resampler(obj: Series | DataFrame, kind=None, **kwds) -> Resampler:
+    """
+    Create a TimeGrouper and return our resampler.
+    """
+    tg = TimeGrouper(obj, **kwds)  # type: ignore[arg-type]
+    return tg._get_resampler(obj, kind=kind)
+
+
+get_resampler.__doc__ = Resampler.__doc__
+
+
+def get_resampler_for_grouping(
+    groupby: GroupBy,
+    rule,
+    how=None,
+    fill_method=None,
+    limit: int | None = None,
+    kind=None,
+    on=None,
+    include_groups: bool = True,
+    **kwargs,
+) -> Resampler:
+    """
+    Return our appropriate resampler when grouping as well.
+    """
+    # .resample uses 'on' similar to how .groupby uses 'key'
+    tg = TimeGrouper(freq=rule, key=on, **kwargs)
+    resampler = tg._get_resampler(groupby.obj, kind=kind)
+    return resampler._get_resampler_for_grouping(
+        groupby=groupby, include_groups=include_groups, key=tg.key
+    )
+
+
+class TimeGrouper(Grouper):
+    """
+    Custom groupby class for time-interval grouping.
+
+    Parameters
+    ----------
+    freq : pandas date offset or offset alias for identifying bin edges
+    closed : closed end of interval; 'left' or 'right'
+    label : interval boundary to use for labeling; 'left' or 'right'
+    convention : {'start', 'end', 'e', 's'}
+        If axis is PeriodIndex
+    """
+
+    _attributes = Grouper._attributes + (
+        "closed",
+        "label",
+        "how",
+        "kind",
+        "convention",
+        "origin",
+        "offset",
+    )
+
+    origin: TimeGrouperOrigin
+
+    def __init__(
+        self,
+        obj: Grouper | None = None,
+        freq: Frequency = "Min",
+        key: str | None = None,
+        closed: Literal["left", "right"] | None = None,
+        label: Literal["left", "right"] | None = None,
+        how: str = "mean",
+        axis: Axis = 0,
+        fill_method=None,
+        limit: int | None = None,
+        kind: str | None = None,
+        convention: Literal["start", "end", "e", "s"] | None = None,
+        origin: Literal["epoch", "start", "start_day", "end", "end_day"]
+        | TimestampConvertibleTypes = "start_day",
+        offset: TimedeltaConvertibleTypes | None = None,
+        group_keys: bool = False,
+        **kwargs,
+    ) -> None:
+        # Check for correctness of the keyword arguments which would
+        # otherwise silently use the default if misspelled
+        if label not in {None, "left", "right"}:
+            raise ValueError(f"Unsupported value {label} for `label`")
+        if closed not in {None, "left", "right"}:
+            raise ValueError(f"Unsupported value {closed} for `closed`")
+        if convention not in {None, "start", "end", "e", "s"}:
+            raise ValueError(f"Unsupported value {convention} for `convention`")
+
+        if (
+            key is None
+            and obj is not None
+            and isinstance(obj.index, PeriodIndex)  # type: ignore[attr-defined]
+            or (
+                key is not None
+                and obj is not None
+                and getattr(obj[key], "dtype", None) == "period"  # type: ignore[index]
+            )
+        ):
+            freq = to_offset(freq, is_period=True)
+        else:
+            freq = to_offset(freq)
+
+        end_types = {"ME", "YE", "QE", "BME", "BYE", "BQE", "W"}
+        rule = freq.rule_code
+        if rule in end_types or ("-" in rule and rule[: rule.find("-")] in end_types):
+            if closed is None:
+                closed = "right"
+            if label is None:
+                label = "right"
+        else:
+            # The backward resample sets ``closed`` to ``'right'`` by default
+            # since the last value should be considered as the edge point for
+            # the last bin. When origin in "end" or "end_day", the value for a
+            # specific ``Timestamp`` index stands for the resample result from
+            # the current ``Timestamp`` minus ``freq`` to the current
+            # ``Timestamp`` with a right close.
+            if origin in ["end", "end_day"]:
+                if closed is None:
+                    closed = "right"
+                if label is None:
+                    label = "right"
+            else:
+                if closed is None:
+                    closed = "left"
+                if label is None:
+                    label = "left"
+
+        self.closed = closed
+        self.label = label
+        self.kind = kind
+        self.convention = convention if convention is not None else "e"
+        self.how = how
+        self.fill_method = fill_method
+        self.limit = limit
+        self.group_keys = group_keys
+        self._arrow_dtype: ArrowDtype | None = None
+
+        if origin in ("epoch", "start", "start_day", "end", "end_day"):
+            # error: Incompatible types in assignment (expression has type "Union[Union[
+            # Timestamp, datetime, datetime64, signedinteger[_64Bit], float, str],
+            # Literal['epoch', 'start', 'start_day', 'end', 'end_day']]", variable has
+            # type "Union[Timestamp, Literal['epoch', 'start', 'start_day', 'end',
+            # 'end_day']]")
+            self.origin = origin  # type: ignore[assignment]
+        else:
+            try:
+                self.origin = Timestamp(origin)
+            except (ValueError, TypeError) as err:
+                raise ValueError(
+                    "'origin' should be equal to 'epoch', 'start', 'start_day', "
+                    "'end', 'end_day' or "
+                    f"should be a Timestamp convertible type. Got '{origin}' instead."
+                ) from err
+
+        try:
+            self.offset = Timedelta(offset) if offset is not None else None
+        except (ValueError, TypeError) as err:
+            raise ValueError(
+                "'offset' should be a Timedelta convertible type. "
+                f"Got '{offset}' instead."
+            ) from err
+
+        # always sort time groupers
+        kwargs["sort"] = True
+
+        super().__init__(freq=freq, key=key, axis=axis, **kwargs)
+
+    def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
+        """
+        Return my resampler or raise if we have an invalid axis.
+
+        Parameters
+        ----------
+        obj : Series or DataFrame
+        kind : string, optional
+            'period','timestamp','timedelta' are valid
+
+        Returns
+        -------
+        Resampler
+
+        Raises
+        ------
+        TypeError if incompatible axis
+
+        """
+        _, ax, _ = self._set_grouper(obj, gpr_index=None)
+        if isinstance(ax, DatetimeIndex):
+            return DatetimeIndexResampler(
+                obj,
+                timegrouper=self,
+                kind=kind,
+                axis=self.axis,
+                group_keys=self.group_keys,
+                gpr_index=ax,
+            )
+        elif isinstance(ax, PeriodIndex) or kind == "period":
+            if not isinstance(ax, PeriodIndex):
+                warnings.warn(
+                    "Resampling with kind='period' is deprecated.  "
+                    "Use datetime paths instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            return PeriodIndexResampler(
+                obj,
+                timegrouper=self,
+                kind=kind,
+                axis=self.axis,
+                group_keys=self.group_keys,
+                gpr_index=ax,
+            )
+        elif isinstance(ax, TimedeltaIndex):
+            return TimedeltaIndexResampler(
+                obj,
+                timegrouper=self,
+                axis=self.axis,
+                group_keys=self.group_keys,
+                gpr_index=ax,
+            )
+
+        raise TypeError(
+            "Only valid with DatetimeIndex, "
+            "TimedeltaIndex or PeriodIndex, "
+            f"but got an instance of '{type(ax).__name__}'"
+        )
+
+    def _get_grouper(
+        self, obj: NDFrameT, validate: bool = True
+    ) -> tuple[BinGrouper, NDFrameT]:
+        # create the resampler and return our binner
+        r = self._get_resampler(obj)
+        return r._grouper, cast(NDFrameT, r.obj)
+
+    def _get_time_bins(self, ax: DatetimeIndex):
+        if not isinstance(ax, DatetimeIndex):
+            raise TypeError(
+                "axis must be a DatetimeIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        if len(ax) == 0:
+            binner = labels = DatetimeIndex(
+                data=[], freq=self.freq, name=ax.name, dtype=ax.dtype
+            )
+            return binner, [], labels
+
+        first, last = _get_timestamp_range_edges(
+            ax.min(),
+            ax.max(),
+            self.freq,
+            unit=ax.unit,
+            closed=self.closed,
+            origin=self.origin,
+            offset=self.offset,
+        )
+        # GH #12037
+        # use first/last directly instead of call replace() on them
+        # because replace() will swallow the nanosecond part
+        # thus last bin maybe slightly before the end if the end contains
+        # nanosecond part and lead to `Values falls after last bin` error
+        # GH 25758: If DST lands at midnight (e.g. 'America/Havana'), user feedback
+        # has noted that ambiguous=True provides the most sensible result
+        binner = labels = date_range(
+            freq=self.freq,
+            start=first,
+            end=last,
+            tz=ax.tz,
+            name=ax.name,
+            ambiguous=True,
+            nonexistent="shift_forward",
+            unit=ax.unit,
+        )
+
+        ax_values = ax.asi8
+        binner, bin_edges = self._adjust_bin_edges(binner, ax_values)
+
+        # general version, knowing nothing about relative frequencies
+        bins = lib.generate_bins_dt64(
+            ax_values, bin_edges, self.closed, hasnans=ax.hasnans
+        )
+
+        if self.closed == "right":
+            labels = binner
+            if self.label == "right":
+                labels = labels[1:]
+        elif self.label == "right":
+            labels = labels[1:]
+
+        if ax.hasnans:
+            binner = binner.insert(0, NaT)
+            labels = labels.insert(0, NaT)
+
+        # if we end up with more labels than bins
+        # adjust the labels
+        # GH4076
+        if len(bins) < len(labels):
+            labels = labels[: len(bins)]
+
+        return binner, bins, labels
+
+    def _adjust_bin_edges(
+        self, binner: DatetimeIndex, ax_values: npt.NDArray[np.int64]
+    ) -> tuple[DatetimeIndex, npt.NDArray[np.int64]]:
+        # Some hacks for > daily data, see #1471, #1458, #1483
+
+        if self.freq.name in ("BME", "ME", "W") or self.freq.name.split("-")[0] in (
+            "BQE",
+            "BYE",
+            "QE",
+            "YE",
+            "W",
+        ):
+            # If the right end-point is on the last day of the month, roll forwards
+            # until the last moment of that day. Note that we only do this for offsets
+            # which correspond to the end of a super-daily period - "month start", for
+            # example, is excluded.
+            if self.closed == "right":
+                # GH 21459, GH 9119: Adjust the bins relative to the wall time
+                edges_dti = binner.tz_localize(None)
+                edges_dti = (
+                    edges_dti
+                    + Timedelta(days=1, unit=edges_dti.unit).as_unit(edges_dti.unit)
+                    - Timedelta(1, unit=edges_dti.unit).as_unit(edges_dti.unit)
+                )
+                bin_edges = edges_dti.tz_localize(binner.tz).asi8
+            else:
+                bin_edges = binner.asi8
+
+            # intraday values on last day
+            if bin_edges[-2] > ax_values.max():
+                bin_edges = bin_edges[:-1]
+                binner = binner[:-1]
+        else:
+            bin_edges = binner.asi8
+        return binner, bin_edges
+
+    def _get_time_delta_bins(self, ax: TimedeltaIndex):
+        if not isinstance(ax, TimedeltaIndex):
+            raise TypeError(
+                "axis must be a TimedeltaIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        if not isinstance(self.freq, Tick):
+            # GH#51896
+            raise ValueError(
+                "Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
+                f"e.g. '24h' or '3D', not {self.freq}"
+            )
+
+        if not len(ax):
+            binner = labels = TimedeltaIndex(data=[], freq=self.freq, name=ax.name)
+            return binner, [], labels
+
+        start, end = ax.min(), ax.max()
+
+        if self.closed == "right":
+            end += self.freq
+
+        labels = binner = timedelta_range(
+            start=start, end=end, freq=self.freq, name=ax.name
+        )
+
+        end_stamps = labels
+        if self.closed == "left":
+            end_stamps += self.freq
+
+        bins = ax.searchsorted(end_stamps, side=self.closed)
+
+        if self.offset:
+            # GH 10530 & 31809
+            labels += self.offset
+
+        return binner, bins, labels
+
+    def _get_time_period_bins(self, ax: DatetimeIndex):
+        if not isinstance(ax, DatetimeIndex):
+            raise TypeError(
+                "axis must be a DatetimeIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        freq = self.freq
+
+        if len(ax) == 0:
+            binner = labels = PeriodIndex(
+                data=[], freq=freq, name=ax.name, dtype=ax.dtype
+            )
+            return binner, [], labels
+
+        labels = binner = period_range(start=ax[0], end=ax[-1], freq=freq, name=ax.name)
+
+        end_stamps = (labels + freq).asfreq(freq, "s").to_timestamp()
+        if ax.tz:
+            end_stamps = end_stamps.tz_localize(ax.tz)
+        bins = ax.searchsorted(end_stamps, side="left")
+
+        return binner, bins, labels
+
+    def _get_period_bins(self, ax: PeriodIndex):
+        if not isinstance(ax, PeriodIndex):
+            raise TypeError(
+                "axis must be a PeriodIndex, but got "
+                f"an instance of {type(ax).__name__}"
+            )
+
+        memb = ax.asfreq(self.freq, how=self.convention)
+
+        # NaT handling as in pandas._lib.lib.generate_bins_dt64()
+        nat_count = 0
+        if memb.hasnans:
+            # error: Incompatible types in assignment (expression has type
+            # "bool_", variable has type "int")  [assignment]
+            nat_count = np.sum(memb._isnan)  # type: ignore[assignment]
+            memb = memb[~memb._isnan]
+
+        if not len(memb):
+            # index contains no valid (non-NaT) values
+            bins = np.array([], dtype=np.int64)
+            binner = labels = PeriodIndex(data=[], freq=self.freq, name=ax.name)
+            if len(ax) > 0:
+                # index is all NaT
+                binner, bins, labels = _insert_nat_bin(binner, bins, labels, len(ax))
+            return binner, bins, labels
+
+        freq_mult = self.freq.n
+
+        start = ax.min().asfreq(self.freq, how=self.convention)
+        end = ax.max().asfreq(self.freq, how="end")
+        bin_shift = 0
+
+        if isinstance(self.freq, Tick):
+            # GH 23882 & 31809: get adjusted bin edge labels with 'origin'
+            # and 'origin' support. This call only makes sense if the freq is a
+            # Tick since offset and origin are only used in those cases.
+            # Not doing this check could create an extra empty bin.
+            p_start, end = _get_period_range_edges(
+                start,
+                end,
+                self.freq,
+                closed=self.closed,
+                origin=self.origin,
+                offset=self.offset,
+            )
+
+            # Get offset for bin edge (not label edge) adjustment
+            start_offset = Period(start, self.freq) - Period(p_start, self.freq)
+            # error: Item "Period" of "Union[Period, Any]" has no attribute "n"
+            bin_shift = start_offset.n % freq_mult  # type: ignore[union-attr]
+            start = p_start
+
+        labels = binner = period_range(
+            start=start, end=end, freq=self.freq, name=ax.name
+        )
+
+        i8 = memb.asi8
+
+        # when upsampling to subperiods, we need to generate enough bins
+        expected_bins_count = len(binner) * freq_mult
+        i8_extend = expected_bins_count - (i8[-1] - i8[0])
+        rng = np.arange(i8[0], i8[-1] + i8_extend, freq_mult)
+        rng += freq_mult
+        # adjust bin edge indexes to account for base
+        rng -= bin_shift
+
+        # Wrap in PeriodArray for PeriodArray.searchsorted
+        prng = type(memb._data)(rng, dtype=memb.dtype)
+        bins = memb.searchsorted(prng, side="left")
+
+        if nat_count > 0:
+            binner, bins, labels = _insert_nat_bin(binner, bins, labels, nat_count)
+
+        return binner, bins, labels
+
+    def _set_grouper(
+        self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
+    ) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
+        obj, ax, indexer = super()._set_grouper(obj, sort, gpr_index=gpr_index)
+        if isinstance(ax.dtype, ArrowDtype) and ax.dtype.kind in "Mm":
+            self._arrow_dtype = ax.dtype
+            ax = Index(
+                cast(ArrowExtensionArray, ax.array)._maybe_convert_datelike_array()
+            )
+        return obj, ax, indexer
+
+
+def _take_new_index(
+    obj: NDFrameT, indexer: npt.NDArray[np.intp], new_index: Index, axis: AxisInt = 0
+) -> NDFrameT:
+    if isinstance(obj, ABCSeries):
+        new_values = algos.take_nd(obj._values, indexer)
+        # error: Incompatible return value type (got "Series", expected "NDFrameT")
+        return obj._constructor(  # type: ignore[return-value]
+            new_values, index=new_index, name=obj.name
+        )
+    elif isinstance(obj, ABCDataFrame):
+        if axis == 1:
+            raise NotImplementedError("axis 1 is not supported")
+        new_mgr = obj._mgr.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
+        # error: Incompatible return value type (got "DataFrame", expected "NDFrameT")
+        return obj._constructor_from_mgr(new_mgr, axes=new_mgr.axes)  # type: ignore[return-value]
+    else:
+        raise ValueError("'obj' should be either a Series or a DataFrame")
+
+
+def _get_timestamp_range_edges(
+    first: Timestamp,
+    last: Timestamp,
+    freq: BaseOffset,
+    unit: str,
+    closed: Literal["right", "left"] = "left",
+    origin: TimeGrouperOrigin = "start_day",
+    offset: Timedelta | None = None,
+) -> tuple[Timestamp, Timestamp]:
+    """
+    Adjust the `first` Timestamp to the preceding Timestamp that resides on
+    the provided offset. Adjust the `last` Timestamp to the following
+    Timestamp that resides on the provided offset. Input Timestamps that
+    already reside on the offset will be adjusted depending on the type of
+    offset and the `closed` parameter.
+
+    Parameters
+    ----------
+    first : pd.Timestamp
+        The beginning Timestamp of the range to be adjusted.
+    last : pd.Timestamp
+        The ending Timestamp of the range to be adjusted.
+    freq : pd.DateOffset
+        The dateoffset to which the Timestamps will be adjusted.
+    closed : {'right', 'left'}, default "left"
+        Which side of bin interval is closed.
+    origin : {'epoch', 'start', 'start_day'} or Timestamp, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin must
+        match the timezone of the index.
+        If a timestamp is not used, these values are also supported:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+    offset : pd.Timedelta, default is None
+        An offset timedelta added to the origin.
+
+    Returns
+    -------
+    A tuple of length 2, containing the adjusted pd.Timestamp objects.
+    """
+    if isinstance(freq, Tick):
+        index_tz = first.tz
+        if isinstance(origin, Timestamp) and (origin.tz is None) != (index_tz is None):
+            raise ValueError("The origin must have the same timezone as the index.")
+        if origin == "epoch":
+            # set the epoch based on the timezone to have similar bins results when
+            # resampling on the same kind of indexes on different timezones
+            origin = Timestamp("1970-01-01", tz=index_tz)
+
+        if isinstance(freq, Day):
+            # _adjust_dates_anchored assumes 'D' means 24h, but first/last
+            # might contain a DST transition (23h, 24h, or 25h).
+            # So "pretend" the dates are naive when adjusting the endpoints
+            first = first.tz_localize(None)
+            last = last.tz_localize(None)
+            if isinstance(origin, Timestamp):
+                origin = origin.tz_localize(None)
+
+        first, last = _adjust_dates_anchored(
+            first, last, freq, closed=closed, origin=origin, offset=offset, unit=unit
+        )
+        if isinstance(freq, Day):
+            first = first.tz_localize(index_tz)
+            last = last.tz_localize(index_tz)
+    else:
+        first = first.normalize()
+        last = last.normalize()
+
+        if closed == "left":
+            first = Timestamp(freq.rollback(first))
+        else:
+            first = Timestamp(first - freq)
+
+        last = Timestamp(last + freq)
+
+    return first, last
+
+
+def _get_period_range_edges(
+    first: Period,
+    last: Period,
+    freq: BaseOffset,
+    closed: Literal["right", "left"] = "left",
+    origin: TimeGrouperOrigin = "start_day",
+    offset: Timedelta | None = None,
+) -> tuple[Period, Period]:
+    """
+    Adjust the provided `first` and `last` Periods to the respective Period of
+    the given offset that encompasses them.
+
+    Parameters
+    ----------
+    first : pd.Period
+        The beginning Period of the range to be adjusted.
+    last : pd.Period
+        The ending Period of the range to be adjusted.
+    freq : pd.DateOffset
+        The freq to which the Periods will be adjusted.
+    closed : {'right', 'left'}, default "left"
+        Which side of bin interval is closed.
+    origin : {'epoch', 'start', 'start_day'}, Timestamp, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin must
+        match the timezone of the index.
+
+        If a timestamp is not used, these values are also supported:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+    offset : pd.Timedelta, default is None
+        An offset timedelta added to the origin.
+
+    Returns
+    -------
+    A tuple of length 2, containing the adjusted pd.Period objects.
+    """
+    if not all(isinstance(obj, Period) for obj in [first, last]):
+        raise TypeError("'first' and 'last' must be instances of type Period")
+
+    # GH 23882
+    first_ts = first.to_timestamp()
+    last_ts = last.to_timestamp()
+    adjust_first = not freq.is_on_offset(first_ts)
+    adjust_last = freq.is_on_offset(last_ts)
+
+    first_ts, last_ts = _get_timestamp_range_edges(
+        first_ts, last_ts, freq, unit="ns", closed=closed, origin=origin, offset=offset
+    )
+
+    first = (first_ts + int(adjust_first) * freq).to_period(freq)
+    last = (last_ts - int(adjust_last) * freq).to_period(freq)
+    return first, last
+
+
+def _insert_nat_bin(
+    binner: PeriodIndex, bins: np.ndarray, labels: PeriodIndex, nat_count: int
+) -> tuple[PeriodIndex, np.ndarray, PeriodIndex]:
+    # NaT handling as in pandas._lib.lib.generate_bins_dt64()
+    # shift bins by the number of NaT
+    assert nat_count > 0
+    bins += nat_count
+    bins = np.insert(bins, 0, nat_count)
+
+    # Incompatible types in assignment (expression has type "Index", variable
+    # has type "PeriodIndex")
+    binner = binner.insert(0, NaT)  # type: ignore[assignment]
+    # Incompatible types in assignment (expression has type "Index", variable
+    # has type "PeriodIndex")
+    labels = labels.insert(0, NaT)  # type: ignore[assignment]
+    return binner, bins, labels
+
+
+def _adjust_dates_anchored(
+    first: Timestamp,
+    last: Timestamp,
+    freq: Tick,
+    closed: Literal["right", "left"] = "right",
+    origin: TimeGrouperOrigin = "start_day",
+    offset: Timedelta | None = None,
+    unit: str = "ns",
+) -> tuple[Timestamp, Timestamp]:
+    # First and last offsets should be calculated from the start day to fix an
+    # error cause by resampling across multiple days when a one day period is
+    # not a multiple of the frequency. See GH 8683
+    # To handle frequencies that are not multiple or divisible by a day we let
+    # the possibility to define a fixed origin timestamp. See GH 31809
+    first = first.as_unit(unit)
+    last = last.as_unit(unit)
+    if offset is not None:
+        offset = offset.as_unit(unit)
+
+    freq_value = Timedelta(freq).as_unit(unit)._value
+
+    origin_timestamp = 0  # origin == "epoch"
+    if origin == "start_day":
+        origin_timestamp = first.normalize()._value
+    elif origin == "start":
+        origin_timestamp = first._value
+    elif isinstance(origin, Timestamp):
+        origin_timestamp = origin.as_unit(unit)._value
+    elif origin in ["end", "end_day"]:
+        origin_last = last if origin == "end" else last.ceil("D")
+        sub_freq_times = (origin_last._value - first._value) // freq_value
+        if closed == "left":
+            sub_freq_times += 1
+        first = origin_last - sub_freq_times * freq
+        origin_timestamp = first._value
+    origin_timestamp += offset._value if offset else 0
+
+    # GH 10117 & GH 19375. If first and last contain timezone information,
+    # Perform the calculation in UTC in order to avoid localizing on an
+    # Ambiguous or Nonexistent time.
+    first_tzinfo = first.tzinfo
+    last_tzinfo = last.tzinfo
+    if first_tzinfo is not None:
+        first = first.tz_convert("UTC")
+    if last_tzinfo is not None:
+        last = last.tz_convert("UTC")
+
+    foffset = (first._value - origin_timestamp) % freq_value
+    loffset = (last._value - origin_timestamp) % freq_value
+
+    if closed == "right":
+        if foffset > 0:
+            # roll back
+            fresult_int = first._value - foffset
+        else:
+            fresult_int = first._value - freq_value
+
+        if loffset > 0:
+            # roll forward
+            lresult_int = last._value + (freq_value - loffset)
+        else:
+            # already the end of the road
+            lresult_int = last._value
+    else:  # closed == 'left'
+        if foffset > 0:
+            fresult_int = first._value - foffset
+        else:
+            # start of the road
+            fresult_int = first._value
+
+        if loffset > 0:
+            # roll forward
+            lresult_int = last._value + (freq_value - loffset)
+        else:
+            lresult_int = last._value + freq_value
+    fresult = Timestamp(fresult_int, unit=unit)
+    lresult = Timestamp(lresult_int, unit=unit)
+    if first_tzinfo is not None:
+        fresult = fresult.tz_localize("UTC").tz_convert(first_tzinfo)
+    if last_tzinfo is not None:
+        lresult = lresult.tz_localize("UTC").tz_convert(last_tzinfo)
+    return fresult, lresult
+
+
+def asfreq(
+    obj: NDFrameT,
+    freq,
+    method=None,
+    how=None,
+    normalize: bool = False,
+    fill_value=None,
+) -> NDFrameT:
+    """
+    Utility frequency conversion method for Series/DataFrame.
+
+    See :meth:`pandas.NDFrame.asfreq` for full documentation.
+    """
+    if isinstance(obj.index, PeriodIndex):
+        if method is not None:
+            raise NotImplementedError("'method' argument is not supported")
+
+        if how is None:
+            how = "E"
+
+        if isinstance(freq, BaseOffset):
+            if hasattr(freq, "_period_dtype_code"):
+                freq = freq_to_period_freqstr(freq.n, freq.name)
+            else:
+                raise ValueError(
+                    f"Invalid offset: '{freq.base}' for converting time series "
+                    f"with PeriodIndex."
+                )
+
+        new_obj = obj.copy()
+        new_obj.index = obj.index.asfreq(freq, how=how)
+
+    elif len(obj.index) == 0:
+        new_obj = obj.copy()
+
+        new_obj.index = _asfreq_compat(obj.index, freq)
+    else:
+        unit = None
+        if isinstance(obj.index, DatetimeIndex):
+            # TODO: should we disallow non-DatetimeIndex?
+            unit = obj.index.unit
+        dti = date_range(obj.index.min(), obj.index.max(), freq=freq, unit=unit)
+        dti.name = obj.index.name
+        new_obj = obj.reindex(dti, method=method, fill_value=fill_value)
+        if normalize:
+            new_obj.index = new_obj.index.normalize()
+
+    return new_obj
+
+
+def _asfreq_compat(index: DatetimeIndex | PeriodIndex | TimedeltaIndex, freq):
+    """
+    Helper to mimic asfreq on (empty) DatetimeIndex and TimedeltaIndex.
+
+    Parameters
+    ----------
+    index : PeriodIndex, DatetimeIndex, or TimedeltaIndex
+    freq : DateOffset
+
+    Returns
+    -------
+    same type as index
+    """
+    if len(index) != 0:
+        # This should never be reached, always checked by the caller
+        raise ValueError(
+            "Can only set arbitrary freq for empty DatetimeIndex or TimedeltaIndex"
+        )
+    new_index: Index
+    if isinstance(index, PeriodIndex):
+        new_index = index.asfreq(freq=freq)
+    elif isinstance(index, DatetimeIndex):
+        new_index = DatetimeIndex([], dtype=index.dtype, freq=freq, name=index.name)
+    elif isinstance(index, TimedeltaIndex):
+        new_index = TimedeltaIndex([], dtype=index.dtype, freq=freq, name=index.name)
+    else:  # pragma: no cover
+        raise TypeError(type(index))
+    return new_index
+
+
+def maybe_warn_args_and_kwargs(cls, kernel: str, args, kwargs) -> None:
+    """
+    Warn for deprecation of args and kwargs in resample functions.
+
+    Parameters
+    ----------
+    cls : type
+        Class to warn about.
+    kernel : str
+        Operation name.
+    args : tuple or None
+        args passed by user. Will be None if and only if kernel does not have args.
+    kwargs : dict or None
+        kwargs passed by user. Will be None if and only if kernel does not have kwargs.
+    """
+    warn_args = args is not None and len(args) > 0
+    warn_kwargs = kwargs is not None and len(kwargs) > 0
+    if warn_args and warn_kwargs:
+        msg = "args and kwargs"
+    elif warn_args:
+        msg = "args"
+    elif warn_kwargs:
+        msg = "kwargs"
+    else:
+        return
+    warnings.warn(
+        f"Passing additional {msg} to {cls.__name__}.{kernel} has "
+        "no impact on the result and is deprecated. This will "
+        "raise a TypeError in a future version of pandas.",
+        category=FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+
+
+def _apply(
+    grouped: GroupBy, how: Callable, *args, include_groups: bool, **kwargs
+) -> DataFrame:
+    # GH#7155 - rewrite warning to appear as if it came from `.resample`
+    target_message = "DataFrameGroupBy.apply operated on the grouping columns"
+    new_message = _apply_groupings_depr.format("DataFrameGroupBy", "resample")
+    with rewrite_warning(
+        target_message=target_message,
+        target_category=FutureWarning,
+        new_message=new_message,
+    ):
+        result = grouped.apply(how, *args, include_groups=include_groups, **kwargs)
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1884c497f0ad7000351e131ef11dadab4a7c700
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/api.py
@@ -0,0 +1,41 @@
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.encoding import (
+    from_dummies,
+    get_dummies,
+)
+from pandas.core.reshape.melt import (
+    lreshape,
+    melt,
+    wide_to_long,
+)
+from pandas.core.reshape.merge import (
+    merge,
+    merge_asof,
+    merge_ordered,
+)
+from pandas.core.reshape.pivot import (
+    crosstab,
+    pivot,
+    pivot_table,
+)
+from pandas.core.reshape.tile import (
+    cut,
+    qcut,
+)
+
+__all__ = [
+    "concat",
+    "crosstab",
+    "cut",
+    "from_dummies",
+    "get_dummies",
+    "lreshape",
+    "melt",
+    "merge",
+    "merge_asof",
+    "merge_ordered",
+    "pivot",
+    "pivot_table",
+    "qcut",
+    "wide_to_long",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/concat.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc18bb65b35bcfa5c3789b35e7d41690923b50a7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/concat.py
@@ -0,0 +1,894 @@
+"""
+Concat routines.
+"""
+from __future__ import annotations
+
+from collections import abc
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_copy_on_write
+
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_iterator,
+)
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays.categorical import (
+    factorize_from_iterable,
+    factorize_from_iterables,
+)
+import pandas.core.common as com
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    all_indexes_same,
+    default_index,
+    ensure_index,
+    get_objs_combined_axis,
+    get_unanimous_names,
+)
+from pandas.core.internals import concatenate_managers
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+    )
+
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+        HashableT,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ---------------------------------------------------------------------
+# Concatenate DataFrame objects
+
+
+@overload
+def concat(
+    objs: Iterable[DataFrame] | Mapping[HashableT, DataFrame],
+    *,
+    axis: Literal[0, "index"] = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series] | Mapping[HashableT, Series],
+    *,
+    axis: Literal[0, "index"] = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> Series:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Literal[0, "index"] = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame | Series:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Literal[1, "columns"],
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Axis = ...,
+    join: str = ...,
+    ignore_index: bool = ...,
+    keys: Iterable[Hashable] | None = ...,
+    levels=...,
+    names: list[HashableT] | None = ...,
+    verify_integrity: bool = ...,
+    sort: bool = ...,
+    copy: bool | None = ...,
+) -> DataFrame | Series:
+    ...
+
+
+def concat(
+    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+    *,
+    axis: Axis = 0,
+    join: str = "outer",
+    ignore_index: bool = False,
+    keys: Iterable[Hashable] | None = None,
+    levels=None,
+    names: list[HashableT] | None = None,
+    verify_integrity: bool = False,
+    sort: bool = False,
+    copy: bool | None = None,
+) -> DataFrame | Series:
+    """
+    Concatenate pandas objects along a particular axis.
+
+    Allows optional set logic along the other axes.
+
+    Can also add a layer of hierarchical indexing on the concatenation axis,
+    which may be useful if the labels are the same (or overlapping) on
+    the passed axis number.
+
+    Parameters
+    ----------
+    objs : a sequence or mapping of Series or DataFrame objects
+        If a mapping is passed, the sorted keys will be used as the `keys`
+        argument, unless it is passed, in which case the values will be
+        selected (see below). Any None objects will be dropped silently unless
+        they are all None in which case a ValueError will be raised.
+    axis : {0/'index', 1/'columns'}, default 0
+        The axis to concatenate along.
+    join : {'inner', 'outer'}, default 'outer'
+        How to handle indexes on other axis (or axes).
+    ignore_index : bool, default False
+        If True, do not use the index values along the concatenation axis. The
+        resulting axis will be labeled 0, ..., n - 1. This is useful if you are
+        concatenating objects where the concatenation axis does not have
+        meaningful indexing information. Note the index values on the other
+        axes are still respected in the join.
+    keys : sequence, default None
+        If multiple levels passed, should contain tuples. Construct
+        hierarchical index using the passed keys as the outermost level.
+    levels : list of sequences, default None
+        Specific levels (unique values) to use for constructing a
+        MultiIndex. Otherwise they will be inferred from the keys.
+    names : list, default None
+        Names for the levels in the resulting hierarchical index.
+    verify_integrity : bool, default False
+        Check whether the new concatenated axis contains duplicates. This can
+        be very expensive relative to the actual data concatenation.
+    sort : bool, default False
+        Sort non-concatenation axis if it is not already aligned. One exception to
+        this is when the non-concatentation axis is a DatetimeIndex and join='outer'
+        and the axis is not already aligned. In that case, the non-concatenation
+        axis is always sorted lexicographically.
+    copy : bool, default True
+        If False, do not copy data unnecessarily.
+
+    Returns
+    -------
+    object, type of objs
+        When concatenating all ``Series`` along the index (axis=0), a
+        ``Series`` is returned. When ``objs`` contains at least one
+        ``DataFrame``, a ``DataFrame`` is returned. When concatenating along
+        the columns (axis=1), a ``DataFrame`` is returned.
+
+    See Also
+    --------
+    DataFrame.join : Join DataFrames using indexes.
+    DataFrame.merge : Merge DataFrames by indexes or columns.
+
+    Notes
+    -----
+    The keys, levels, and names arguments are all optional.
+
+    A walkthrough of how this method fits in with other tools for combining
+    pandas objects can be found `here
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.
+
+    It is not recommended to build DataFrames by adding single rows in a
+    for loop. Build a list of rows and make a DataFrame in a single concat.
+
+    Examples
+    --------
+    Combine two ``Series``.
+
+    >>> s1 = pd.Series(['a', 'b'])
+    >>> s2 = pd.Series(['c', 'd'])
+    >>> pd.concat([s1, s2])
+    0    a
+    1    b
+    0    c
+    1    d
+    dtype: object
+
+    Clear the existing index and reset it in the result
+    by setting the ``ignore_index`` option to ``True``.
+
+    >>> pd.concat([s1, s2], ignore_index=True)
+    0    a
+    1    b
+    2    c
+    3    d
+    dtype: object
+
+    Add a hierarchical index at the outermost level of
+    the data with the ``keys`` option.
+
+    >>> pd.concat([s1, s2], keys=['s1', 's2'])
+    s1  0    a
+        1    b
+    s2  0    c
+        1    d
+    dtype: object
+
+    Label the index keys you create with the ``names`` option.
+
+    >>> pd.concat([s1, s2], keys=['s1', 's2'],
+    ...           names=['Series name', 'Row ID'])
+    Series name  Row ID
+    s1           0         a
+                 1         b
+    s2           0         c
+                 1         d
+    dtype: object
+
+    Combine two ``DataFrame`` objects with identical columns.
+
+    >>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
+    ...                    columns=['letter', 'number'])
+    >>> df1
+      letter  number
+    0      a       1
+    1      b       2
+    >>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
+    ...                    columns=['letter', 'number'])
+    >>> df2
+      letter  number
+    0      c       3
+    1      d       4
+    >>> pd.concat([df1, df2])
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return everything. Columns outside the intersection will
+    be filled with ``NaN`` values.
+
+    >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
+    ...                    columns=['letter', 'number', 'animal'])
+    >>> df3
+      letter  number animal
+    0      c       3    cat
+    1      d       4    dog
+    >>> pd.concat([df1, df3], sort=False)
+      letter  number animal
+    0      a       1    NaN
+    1      b       2    NaN
+    0      c       3    cat
+    1      d       4    dog
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return only those that are shared by passing ``inner`` to
+    the ``join`` keyword argument.
+
+    >>> pd.concat([df1, df3], join="inner")
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects horizontally along the x axis by
+    passing in ``axis=1``.
+
+    >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
+    ...                    columns=['animal', 'name'])
+    >>> pd.concat([df1, df4], axis=1)
+      letter  number  animal    name
+    0      a       1    bird   polly
+    1      b       2  monkey  george
+
+    Prevent the result from including duplicate index values with the
+    ``verify_integrity`` option.
+
+    >>> df5 = pd.DataFrame([1], index=['a'])
+    >>> df5
+       0
+    a  1
+    >>> df6 = pd.DataFrame([2], index=['a'])
+    >>> df6
+       0
+    a  2
+    >>> pd.concat([df5, df6], verify_integrity=True)
+    Traceback (most recent call last):
+        ...
+    ValueError: Indexes have overlapping values: ['a']
+
+    Append a single row to the end of a ``DataFrame`` object.
+
+    >>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
+    >>> df7
+        a   b
+    0   1   2
+    >>> new_row = pd.Series({'a': 3, 'b': 4})
+    >>> new_row
+    a    3
+    b    4
+    dtype: int64
+    >>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
+        a   b
+    0   1   2
+    1   3   4
+    """
+    if copy is None:
+        if using_copy_on_write():
+            copy = False
+        else:
+            copy = True
+    elif copy and using_copy_on_write():
+        copy = False
+
+    op = _Concatenator(
+        objs,
+        axis=axis,
+        ignore_index=ignore_index,
+        join=join,
+        keys=keys,
+        levels=levels,
+        names=names,
+        verify_integrity=verify_integrity,
+        copy=copy,
+        sort=sort,
+    )
+
+    return op.get_result()
+
+
+class _Concatenator:
+    """
+    Orchestrates a concatenation operation for BlockManagers
+    """
+
+    sort: bool
+
+    def __init__(
+        self,
+        objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+        axis: Axis = 0,
+        join: str = "outer",
+        keys: Iterable[Hashable] | None = None,
+        levels=None,
+        names: list[HashableT] | None = None,
+        ignore_index: bool = False,
+        verify_integrity: bool = False,
+        copy: bool = True,
+        sort: bool = False,
+    ) -> None:
+        if isinstance(objs, (ABCSeries, ABCDataFrame, str)):
+            raise TypeError(
+                "first argument must be an iterable of pandas "
+                f'objects, you passed an object of type "{type(objs).__name__}"'
+            )
+
+        if join == "outer":
+            self.intersect = False
+        elif join == "inner":
+            self.intersect = True
+        else:  # pragma: no cover
+            raise ValueError(
+                "Only can inner (intersect) or outer (union) join the other axis"
+            )
+
+        if not is_bool(sort):
+            raise ValueError(
+                f"The 'sort' keyword only accepts boolean values; {sort} was passed."
+            )
+        # Incompatible types in assignment (expression has type "Union[bool, bool_]",
+        # variable has type "bool")
+        self.sort = sort  # type: ignore[assignment]
+
+        self.ignore_index = ignore_index
+        self.verify_integrity = verify_integrity
+        self.copy = copy
+
+        objs, keys = self._clean_keys_and_objs(objs, keys)
+
+        # figure out what our result ndim is going to be
+        ndims = self._get_ndims(objs)
+        sample, objs = self._get_sample_object(objs, ndims, keys, names, levels)
+
+        # Standardize axis parameter to int
+        if sample.ndim == 1:
+            from pandas import DataFrame
+
+            axis = DataFrame._get_axis_number(axis)
+            self._is_frame = False
+            self._is_series = True
+        else:
+            axis = sample._get_axis_number(axis)
+            self._is_frame = True
+            self._is_series = False
+
+            # Need to flip BlockManager axis in the DataFrame special case
+            axis = sample._get_block_manager_axis(axis)
+
+        # if we have mixed ndims, then convert to highest ndim
+        # creating column numbers as needed
+        if len(ndims) > 1:
+            objs = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
+
+        self.objs = objs
+
+        # note: this is the BlockManager axis (since DataFrame is transposed)
+        self.bm_axis = axis
+        self.axis = 1 - self.bm_axis if self._is_frame else 0
+        self.keys = keys
+        self.names = names or getattr(keys, "names", None)
+        self.levels = levels
+
+    def _get_ndims(self, objs: list[Series | DataFrame]) -> set[int]:
+        # figure out what our result ndim is going to be
+        ndims = set()
+        for obj in objs:
+            if not isinstance(obj, (ABCSeries, ABCDataFrame)):
+                msg = (
+                    f"cannot concatenate object of type '{type(obj)}'; "
+                    "only Series and DataFrame objs are valid"
+                )
+                raise TypeError(msg)
+
+            ndims.add(obj.ndim)
+        return ndims
+
+    def _clean_keys_and_objs(
+        self,
+        objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
+        keys,
+    ) -> tuple[list[Series | DataFrame], Index | None]:
+        if isinstance(objs, abc.Mapping):
+            if keys is None:
+                keys = list(objs.keys())
+            objs_list = [objs[k] for k in keys]
+        else:
+            objs_list = list(objs)
+
+        if len(objs_list) == 0:
+            raise ValueError("No objects to concatenate")
+
+        if keys is None:
+            objs_list = list(com.not_none(*objs_list))
+        else:
+            # GH#1649
+            clean_keys = []
+            clean_objs = []
+            if is_iterator(keys):
+                keys = list(keys)
+            if len(keys) != len(objs_list):
+                # GH#43485
+                warnings.warn(
+                    "The behavior of pd.concat with len(keys) != len(objs) is "
+                    "deprecated. In a future version this will raise instead of "
+                    "truncating to the smaller of the two sequences",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            for k, v in zip(keys, objs_list):
+                if v is None:
+                    continue
+                clean_keys.append(k)
+                clean_objs.append(v)
+            objs_list = clean_objs
+
+            if isinstance(keys, MultiIndex):
+                # TODO: retain levels?
+                keys = type(keys).from_tuples(clean_keys, names=keys.names)
+            else:
+                name = getattr(keys, "name", None)
+                keys = Index(clean_keys, name=name, dtype=getattr(keys, "dtype", None))
+
+        if len(objs_list) == 0:
+            raise ValueError("All objects passed were None")
+
+        return objs_list, keys
+
+    def _get_sample_object(
+        self,
+        objs: list[Series | DataFrame],
+        ndims: set[int],
+        keys,
+        names,
+        levels,
+    ) -> tuple[Series | DataFrame, list[Series | DataFrame]]:
+        # get the sample
+        # want the highest ndim that we have, and must be non-empty
+        # unless all objs are empty
+        sample: Series | DataFrame | None = None
+        if len(ndims) > 1:
+            max_ndim = max(ndims)
+            for obj in objs:
+                if obj.ndim == max_ndim and np.sum(obj.shape):
+                    sample = obj
+                    break
+
+        else:
+            # filter out the empties if we have not multi-index possibilities
+            # note to keep empty Series as it affect to result columns / name
+            non_empties = [obj for obj in objs if sum(obj.shape) > 0 or obj.ndim == 1]
+
+            if len(non_empties) and (
+                keys is None and names is None and levels is None and not self.intersect
+            ):
+                objs = non_empties
+                sample = objs[0]
+
+        if sample is None:
+            sample = objs[0]
+        return sample, objs
+
+    def _sanitize_mixed_ndim(
+        self,
+        objs: list[Series | DataFrame],
+        sample: Series | DataFrame,
+        ignore_index: bool,
+        axis: AxisInt,
+    ) -> list[Series | DataFrame]:
+        # if we have mixed ndims, then convert to highest ndim
+        # creating column numbers as needed
+
+        new_objs = []
+
+        current_column = 0
+        max_ndim = sample.ndim
+        for obj in objs:
+            ndim = obj.ndim
+            if ndim == max_ndim:
+                pass
+
+            elif ndim != max_ndim - 1:
+                raise ValueError(
+                    "cannot concatenate unaligned mixed dimensional NDFrame objects"
+                )
+
+            else:
+                name = getattr(obj, "name", None)
+                if ignore_index or name is None:
+                    if axis == 1:
+                        # doing a row-wise concatenation so need everything
+                        # to line up
+                        name = 0
+                    else:
+                        # doing a column-wise concatenation so need series
+                        # to have unique names
+                        name = current_column
+                        current_column += 1
+
+                obj = sample._constructor({name: obj}, copy=False)
+
+            new_objs.append(obj)
+
+        return new_objs
+
+    def get_result(self):
+        cons: Callable[..., DataFrame | Series]
+        sample: DataFrame | Series
+
+        # series only
+        if self._is_series:
+            sample = cast("Series", self.objs[0])
+
+            # stack blocks
+            if self.bm_axis == 0:
+                name = com.consensus_name_attr(self.objs)
+                cons = sample._constructor
+
+                arrs = [ser._values for ser in self.objs]
+
+                res = concat_compat(arrs, axis=0)
+
+                new_index: Index
+                if self.ignore_index:
+                    # We can avoid surprisingly-expensive _get_concat_axis
+                    new_index = default_index(len(res))
+                else:
+                    new_index = self.new_axes[0]
+
+                mgr = type(sample._mgr).from_array(res, index=new_index)
+
+                result = sample._constructor_from_mgr(mgr, axes=mgr.axes)
+                result._name = name
+                return result.__finalize__(self, method="concat")
+
+            # combine as columns in a frame
+            else:
+                data = dict(zip(range(len(self.objs)), self.objs))
+
+                # GH28330 Preserves subclassed objects through concat
+                cons = sample._constructor_expanddim
+
+                index, columns = self.new_axes
+                df = cons(data, index=index, copy=self.copy)
+                df.columns = columns
+                return df.__finalize__(self, method="concat")
+
+        # combine block managers
+        else:
+            sample = cast("DataFrame", self.objs[0])
+
+            mgrs_indexers = []
+            for obj in self.objs:
+                indexers = {}
+                for ax, new_labels in enumerate(self.new_axes):
+                    # ::-1 to convert BlockManager ax to DataFrame ax
+                    if ax == self.bm_axis:
+                        # Suppress reindexing on concat axis
+                        continue
+
+                    # 1-ax to convert BlockManager axis to DataFrame axis
+                    obj_labels = obj.axes[1 - ax]
+                    if not new_labels.equals(obj_labels):
+                        indexers[ax] = obj_labels.get_indexer(new_labels)
+
+                mgrs_indexers.append((obj._mgr, indexers))
+
+            new_data = concatenate_managers(
+                mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
+            )
+            if not self.copy and not using_copy_on_write():
+                new_data._consolidate_inplace()
+
+            out = sample._constructor_from_mgr(new_data, axes=new_data.axes)
+            return out.__finalize__(self, method="concat")
+
+    def _get_result_dim(self) -> int:
+        if self._is_series and self.bm_axis == 1:
+            return 2
+        else:
+            return self.objs[0].ndim
+
+    @cache_readonly
+    def new_axes(self) -> list[Index]:
+        ndim = self._get_result_dim()
+        return [
+            self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)
+            for i in range(ndim)
+        ]
+
+    def _get_comb_axis(self, i: AxisInt) -> Index:
+        data_axis = self.objs[0]._get_block_manager_axis(i)
+        return get_objs_combined_axis(
+            self.objs,
+            axis=data_axis,
+            intersect=self.intersect,
+            sort=self.sort,
+            copy=self.copy,
+        )
+
+    @cache_readonly
+    def _get_concat_axis(self) -> Index:
+        """
+        Return index to be used along concatenation axis.
+        """
+        if self._is_series:
+            if self.bm_axis == 0:
+                indexes = [x.index for x in self.objs]
+            elif self.ignore_index:
+                idx = default_index(len(self.objs))
+                return idx
+            elif self.keys is None:
+                names: list[Hashable] = [None] * len(self.objs)
+                num = 0
+                has_names = False
+                for i, x in enumerate(self.objs):
+                    if x.ndim != 1:
+                        raise TypeError(
+                            f"Cannot concatenate type 'Series' with "
+                            f"object of type '{type(x).__name__}'"
+                        )
+                    if x.name is not None:
+                        names[i] = x.name
+                        has_names = True
+                    else:
+                        names[i] = num
+                        num += 1
+                if has_names:
+                    return Index(names)
+                else:
+                    return default_index(len(self.objs))
+            else:
+                return ensure_index(self.keys).set_names(self.names)
+        else:
+            indexes = [x.axes[self.axis] for x in self.objs]
+
+        if self.ignore_index:
+            idx = default_index(sum(len(i) for i in indexes))
+            return idx
+
+        if self.keys is None:
+            if self.levels is not None:
+                raise ValueError("levels supported only when keys is not None")
+            concat_axis = _concat_indexes(indexes)
+        else:
+            concat_axis = _make_concat_multiindex(
+                indexes, self.keys, self.levels, self.names
+            )
+
+        self._maybe_check_integrity(concat_axis)
+
+        return concat_axis
+
+    def _maybe_check_integrity(self, concat_index: Index):
+        if self.verify_integrity:
+            if not concat_index.is_unique:
+                overlap = concat_index[concat_index.duplicated()].unique()
+                raise ValueError(f"Indexes have overlapping values: {overlap}")
+
+
+def _concat_indexes(indexes) -> Index:
+    return indexes[0].append(indexes[1:])
+
+
+def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiIndex:
+    if (levels is None and isinstance(keys[0], tuple)) or (
+        levels is not None and len(levels) > 1
+    ):
+        zipped = list(zip(*keys))
+        if names is None:
+            names = [None] * len(zipped)
+
+        if levels is None:
+            _, levels = factorize_from_iterables(zipped)
+        else:
+            levels = [ensure_index(x) for x in levels]
+    else:
+        zipped = [keys]
+        if names is None:
+            names = [None]
+
+        if levels is None:
+            levels = [ensure_index(keys).unique()]
+        else:
+            levels = [ensure_index(x) for x in levels]
+
+    for level in levels:
+        if not level.is_unique:
+            raise ValueError(f"Level values not unique: {level.tolist()}")
+
+    if not all_indexes_same(indexes) or not all(level.is_unique for level in levels):
+        codes_list = []
+
+        # things are potentially different sizes, so compute the exact codes
+        # for each level and pass those to MultiIndex.from_arrays
+
+        for hlevel, level in zip(zipped, levels):
+            to_concat = []
+            if isinstance(hlevel, Index) and hlevel.equals(level):
+                lens = [len(idx) for idx in indexes]
+                codes_list.append(np.repeat(np.arange(len(hlevel)), lens))
+            else:
+                for key, index in zip(hlevel, indexes):
+                    # Find matching codes, include matching nan values as equal.
+                    mask = (isna(level) & isna(key)) | (level == key)
+                    if not mask.any():
+                        raise ValueError(f"Key {key} not in level {level}")
+                    i = np.nonzero(mask)[0][0]
+
+                    to_concat.append(np.repeat(i, len(index)))
+                codes_list.append(np.concatenate(to_concat))
+
+        concat_index = _concat_indexes(indexes)
+
+        # these go at the end
+        if isinstance(concat_index, MultiIndex):
+            levels.extend(concat_index.levels)
+            codes_list.extend(concat_index.codes)
+        else:
+            codes, categories = factorize_from_iterable(concat_index)
+            levels.append(categories)
+            codes_list.append(codes)
+
+        if len(names) == len(levels):
+            names = list(names)
+        else:
+            # make sure that all of the passed indices have the same nlevels
+            if not len({idx.nlevels for idx in indexes}) == 1:
+                raise AssertionError(
+                    "Cannot concat indices that do not have the same number of levels"
+                )
+
+            # also copies
+            names = list(names) + list(get_unanimous_names(*indexes))
+
+        return MultiIndex(
+            levels=levels, codes=codes_list, names=names, verify_integrity=False
+        )
+
+    new_index = indexes[0]
+    n = len(new_index)
+    kpieces = len(indexes)
+
+    # also copies
+    new_names = list(names)
+    new_levels = list(levels)
+
+    # construct codes
+    new_codes = []
+
+    # do something a bit more speedy
+
+    for hlevel, level in zip(zipped, levels):
+        hlevel_index = ensure_index(hlevel)
+        mapped = level.get_indexer(hlevel_index)
+
+        mask = mapped == -1
+        if mask.any():
+            raise ValueError(
+                f"Values not found in passed level: {hlevel_index[mask]!s}"
+            )
+
+        new_codes.append(np.repeat(mapped, n))
+
+    if isinstance(new_index, MultiIndex):
+        new_levels.extend(new_index.levels)
+        new_codes.extend([np.tile(lab, kpieces) for lab in new_index.codes])
+    else:
+        new_levels.append(new_index.unique())
+        single_codes = new_index.unique().get_indexer(new_index)
+        new_codes.append(np.tile(single_codes, kpieces))
+
+    if len(new_names) < len(new_levels):
+        new_names.extend(new_index.names)
+
+    return MultiIndex(
+        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/encoding.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..85c10f11665776680b3249fcec304f9c029a5599
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/encoding.py
@@ -0,0 +1,571 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import (
+    Hashable,
+    Iterable,
+)
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import missing as libmissing
+from pandas._libs.sparse import IntIndex
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+    is_object_dtype,
+    pandas_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+)
+
+from pandas.core.arrays import SparseArray
+from pandas.core.arrays.categorical import factorize_from_iterable
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.api import (
+    Index,
+    default_index,
+)
+from pandas.core.series import Series
+
+if TYPE_CHECKING:
+    from pandas._typing import NpDtype
+
+
+def get_dummies(
+    data,
+    prefix=None,
+    prefix_sep: str | Iterable[str] | dict[str, str] = "_",
+    dummy_na: bool = False,
+    columns=None,
+    sparse: bool = False,
+    drop_first: bool = False,
+    dtype: NpDtype | None = None,
+) -> DataFrame:
+    """
+    Convert categorical variable into dummy/indicator variables.
+
+    Each variable is converted in as many 0/1 variables as there are different
+    values. Columns in the output are each named after a value; if the input is
+    a DataFrame, the name of the original variable is prepended to the value.
+
+    Parameters
+    ----------
+    data : array-like, Series, or DataFrame
+        Data of which to get dummy indicators.
+    prefix : str, list of str, or dict of str, default None
+        String to append DataFrame column names.
+        Pass a list with length equal to the number of columns
+        when calling get_dummies on a DataFrame. Alternatively, `prefix`
+        can be a dictionary mapping column names to prefixes.
+    prefix_sep : str, default '_'
+        If appending prefix, separator/delimiter to use. Or pass a
+        list or dictionary as with `prefix`.
+    dummy_na : bool, default False
+        Add a column to indicate NaNs, if False NaNs are ignored.
+    columns : list-like, default None
+        Column names in the DataFrame to be encoded.
+        If `columns` is None then all the columns with
+        `object`, `string`, or `category` dtype will be converted.
+    sparse : bool, default False
+        Whether the dummy-encoded columns should be backed by
+        a :class:`SparseArray` (True) or a regular NumPy array (False).
+    drop_first : bool, default False
+        Whether to get k-1 dummies out of k categorical levels by removing the
+        first level.
+    dtype : dtype, default bool
+        Data type for new columns. Only a single dtype is allowed.
+
+    Returns
+    -------
+    DataFrame
+        Dummy-coded data. If `data` contains other columns than the
+        dummy-coded one(s), these will be prepended, unaltered, to the result.
+
+    See Also
+    --------
+    Series.str.get_dummies : Convert Series of strings to dummy codes.
+    :func:`~pandas.from_dummies` : Convert dummy codes to categorical ``DataFrame``.
+
+    Notes
+    -----
+    Reference :ref:`the user guide <reshaping.dummies>` for more examples.
+
+    Examples
+    --------
+    >>> s = pd.Series(list('abca'))
+
+    >>> pd.get_dummies(s)
+           a      b      c
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+    3   True  False  False
+
+    >>> s1 = ['a', 'b', np.nan]
+
+    >>> pd.get_dummies(s1)
+           a      b
+    0   True  False
+    1  False   True
+    2  False  False
+
+    >>> pd.get_dummies(s1, dummy_na=True)
+           a      b    NaN
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+
+    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
+    ...                    'C': [1, 2, 3]})
+
+    >>> pd.get_dummies(df, prefix=['col1', 'col2'])
+       C  col1_a  col1_b  col2_a  col2_b  col2_c
+    0  1    True   False   False    True   False
+    1  2   False    True    True   False   False
+    2  3    True   False   False   False    True
+
+    >>> pd.get_dummies(pd.Series(list('abcaa')))
+           a      b      c
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+    3   True  False  False
+    4   True  False  False
+
+    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
+           b      c
+    0  False  False
+    1   True  False
+    2  False   True
+    3  False  False
+    4  False  False
+
+    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
+         a    b    c
+    0  1.0  0.0  0.0
+    1  0.0  1.0  0.0
+    2  0.0  0.0  1.0
+    """
+    from pandas.core.reshape.concat import concat
+
+    dtypes_to_encode = ["object", "string", "category"]
+
+    if isinstance(data, DataFrame):
+        # determine columns being encoded
+        if columns is None:
+            data_to_encode = data.select_dtypes(include=dtypes_to_encode)
+        elif not is_list_like(columns):
+            raise TypeError("Input must be a list-like for parameter `columns`")
+        else:
+            data_to_encode = data[columns]
+
+        # validate prefixes and separator to avoid silently dropping cols
+        def check_len(item, name: str):
+            if is_list_like(item):
+                if not len(item) == data_to_encode.shape[1]:
+                    len_msg = (
+                        f"Length of '{name}' ({len(item)}) did not match the "
+                        "length of the columns being encoded "
+                        f"({data_to_encode.shape[1]})."
+                    )
+                    raise ValueError(len_msg)
+
+        check_len(prefix, "prefix")
+        check_len(prefix_sep, "prefix_sep")
+
+        if isinstance(prefix, str):
+            prefix = itertools.cycle([prefix])
+        if isinstance(prefix, dict):
+            prefix = [prefix[col] for col in data_to_encode.columns]
+
+        if prefix is None:
+            prefix = data_to_encode.columns
+
+        # validate separators
+        if isinstance(prefix_sep, str):
+            prefix_sep = itertools.cycle([prefix_sep])
+        elif isinstance(prefix_sep, dict):
+            prefix_sep = [prefix_sep[col] for col in data_to_encode.columns]
+
+        with_dummies: list[DataFrame]
+        if data_to_encode.shape == data.shape:
+            # Encoding the entire df, do not prepend any dropped columns
+            with_dummies = []
+        elif columns is not None:
+            # Encoding only cols specified in columns. Get all cols not in
+            # columns to prepend to result.
+            with_dummies = [data.drop(columns, axis=1)]
+        else:
+            # Encoding only object and category dtype columns. Get remaining
+            # columns to prepend to result.
+            with_dummies = [data.select_dtypes(exclude=dtypes_to_encode)]
+
+        for col, pre, sep in zip(data_to_encode.items(), prefix, prefix_sep):
+            # col is (column_name, column), use just column data here
+            dummy = _get_dummies_1d(
+                col[1],
+                prefix=pre,
+                prefix_sep=sep,
+                dummy_na=dummy_na,
+                sparse=sparse,
+                drop_first=drop_first,
+                dtype=dtype,
+            )
+            with_dummies.append(dummy)
+        result = concat(with_dummies, axis=1)
+    else:
+        result = _get_dummies_1d(
+            data,
+            prefix,
+            prefix_sep,
+            dummy_na,
+            sparse=sparse,
+            drop_first=drop_first,
+            dtype=dtype,
+        )
+    return result
+
+
+def _get_dummies_1d(
+    data,
+    prefix,
+    prefix_sep: str | Iterable[str] | dict[str, str] = "_",
+    dummy_na: bool = False,
+    sparse: bool = False,
+    drop_first: bool = False,
+    dtype: NpDtype | None = None,
+) -> DataFrame:
+    from pandas.core.reshape.concat import concat
+
+    # Series avoids inconsistent NaN handling
+    codes, levels = factorize_from_iterable(Series(data, copy=False))
+
+    if dtype is None and hasattr(data, "dtype"):
+        input_dtype = data.dtype
+        if isinstance(input_dtype, CategoricalDtype):
+            input_dtype = input_dtype.categories.dtype
+
+        if isinstance(input_dtype, ArrowDtype):
+            import pyarrow as pa
+
+            dtype = ArrowDtype(pa.bool_())  # type: ignore[assignment]
+        elif (
+            isinstance(input_dtype, StringDtype)
+            and input_dtype.na_value is libmissing.NA
+        ):
+            dtype = pandas_dtype("boolean")  # type: ignore[assignment]
+        else:
+            dtype = np.dtype(bool)
+    elif dtype is None:
+        dtype = np.dtype(bool)
+
+    _dtype = pandas_dtype(dtype)
+
+    if is_object_dtype(_dtype):
+        raise ValueError("dtype=object is not a valid dtype for get_dummies")
+
+    def get_empty_frame(data) -> DataFrame:
+        index: Index | np.ndarray
+        if isinstance(data, Series):
+            index = data.index
+        else:
+            index = default_index(len(data))
+        return DataFrame(index=index)
+
+    # if all NaN
+    if not dummy_na and len(levels) == 0:
+        return get_empty_frame(data)
+
+    codes = codes.copy()
+    if dummy_na:
+        codes[codes == -1] = len(levels)
+        levels = levels.insert(len(levels), np.nan)
+
+    # if dummy_na, we just fake a nan level. drop_first will drop it again
+    if drop_first and len(levels) == 1:
+        return get_empty_frame(data)
+
+    number_of_cols = len(levels)
+
+    if prefix is None:
+        dummy_cols = levels
+    else:
+        dummy_cols = Index([f"{prefix}{prefix_sep}{level}" for level in levels])
+
+    index: Index | None
+    if isinstance(data, Series):
+        index = data.index
+    else:
+        index = None
+
+    if sparse:
+        fill_value: bool | float
+        if is_integer_dtype(dtype):
+            fill_value = 0
+        elif dtype == np.dtype(bool):
+            fill_value = False
+        else:
+            fill_value = 0.0
+
+        sparse_series = []
+        N = len(data)
+        sp_indices: list[list] = [[] for _ in range(len(dummy_cols))]
+        mask = codes != -1
+        codes = codes[mask]
+        n_idx = np.arange(N)[mask]
+
+        for ndx, code in zip(n_idx, codes):
+            sp_indices[code].append(ndx)
+
+        if drop_first:
+            # remove first categorical level to avoid perfect collinearity
+            # GH12042
+            sp_indices = sp_indices[1:]
+            dummy_cols = dummy_cols[1:]
+        for col, ixs in zip(dummy_cols, sp_indices):
+            sarr = SparseArray(
+                np.ones(len(ixs), dtype=dtype),
+                sparse_index=IntIndex(N, ixs),
+                fill_value=fill_value,
+                dtype=dtype,
+            )
+            sparse_series.append(Series(data=sarr, index=index, name=col, copy=False))
+
+        return concat(sparse_series, axis=1, copy=False)
+
+    else:
+        # ensure ndarray layout is column-major
+        shape = len(codes), number_of_cols
+        dummy_dtype: NpDtype
+        if isinstance(_dtype, np.dtype):
+            dummy_dtype = _dtype
+        else:
+            dummy_dtype = np.bool_
+        dummy_mat = np.zeros(shape=shape, dtype=dummy_dtype, order="F")
+        dummy_mat[np.arange(len(codes)), codes] = 1
+
+        if not dummy_na:
+            # reset NaN GH4446
+            dummy_mat[codes == -1] = 0
+
+        if drop_first:
+            # remove first GH12042
+            dummy_mat = dummy_mat[:, 1:]
+            dummy_cols = dummy_cols[1:]
+        return DataFrame(dummy_mat, index=index, columns=dummy_cols, dtype=_dtype)
+
+
+def from_dummies(
+    data: DataFrame,
+    sep: None | str = None,
+    default_category: None | Hashable | dict[str, Hashable] = None,
+) -> DataFrame:
+    """
+    Create a categorical ``DataFrame`` from a ``DataFrame`` of dummy variables.
+
+    Inverts the operation performed by :func:`~pandas.get_dummies`.
+
+    .. versionadded:: 1.5.0
+
+    Parameters
+    ----------
+    data : DataFrame
+        Data which contains dummy-coded variables in form of integer columns of
+        1's and 0's.
+    sep : str, default None
+        Separator used in the column names of the dummy categories they are
+        character indicating the separation of the categorical names from the prefixes.
+        For example, if your column names are 'prefix_A' and 'prefix_B',
+        you can strip the underscore by specifying sep='_'.
+    default_category : None, Hashable or dict of Hashables, default None
+        The default category is the implied category when a value has none of the
+        listed categories specified with a one, i.e. if all dummies in a row are
+        zero. Can be a single value for all variables or a dict directly mapping
+        the default categories to a prefix of a variable.
+
+    Returns
+    -------
+    DataFrame
+        Categorical data decoded from the dummy input-data.
+
+    Raises
+    ------
+    ValueError
+        * When the input ``DataFrame`` ``data`` contains NA values.
+        * When the input ``DataFrame`` ``data`` contains column names with separators
+          that do not match the separator specified with ``sep``.
+        * When a ``dict`` passed to ``default_category`` does not include an implied
+          category for each prefix.
+        * When a value in ``data`` has more than one category assigned to it.
+        * When ``default_category=None`` and a value in ``data`` has no category
+          assigned to it.
+    TypeError
+        * When the input ``data`` is not of type ``DataFrame``.
+        * When the input ``DataFrame`` ``data`` contains non-dummy data.
+        * When the passed ``sep`` is of a wrong data type.
+        * When the passed ``default_category`` is of a wrong data type.
+
+    See Also
+    --------
+    :func:`~pandas.get_dummies` : Convert ``Series`` or ``DataFrame`` to dummy codes.
+    :class:`~pandas.Categorical` : Represent a categorical variable in classic.
+
+    Notes
+    -----
+    The columns of the passed dummy data should only include 1's and 0's,
+    or boolean values.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"a": [1, 0, 0, 1], "b": [0, 1, 0, 0],
+    ...                    "c": [0, 0, 1, 0]})
+
+    >>> df
+       a  b  c
+    0  1  0  0
+    1  0  1  0
+    2  0  0  1
+    3  1  0  0
+
+    >>> pd.from_dummies(df)
+    0     a
+    1     b
+    2     c
+    3     a
+
+    >>> df = pd.DataFrame({"col1_a": [1, 0, 1], "col1_b": [0, 1, 0],
+    ...                    "col2_a": [0, 1, 0], "col2_b": [1, 0, 0],
+    ...                    "col2_c": [0, 0, 1]})
+
+    >>> df
+          col1_a  col1_b  col2_a  col2_b  col2_c
+    0       1       0       0       1       0
+    1       0       1       1       0       0
+    2       1       0       0       0       1
+
+    >>> pd.from_dummies(df, sep="_")
+        col1    col2
+    0    a       b
+    1    b       a
+    2    a       c
+
+    >>> df = pd.DataFrame({"col1_a": [1, 0, 0], "col1_b": [0, 1, 0],
+    ...                    "col2_a": [0, 1, 0], "col2_b": [1, 0, 0],
+    ...                    "col2_c": [0, 0, 0]})
+
+    >>> df
+          col1_a  col1_b  col2_a  col2_b  col2_c
+    0       1       0       0       1       0
+    1       0       1       1       0       0
+    2       0       0       0       0       0
+
+    >>> pd.from_dummies(df, sep="_", default_category={"col1": "d", "col2": "e"})
+        col1    col2
+    0    a       b
+    1    b       a
+    2    d       e
+    """
+    from pandas.core.reshape.concat import concat
+
+    if not isinstance(data, DataFrame):
+        raise TypeError(
+            "Expected 'data' to be a 'DataFrame'; "
+            f"Received 'data' of type: {type(data).__name__}"
+        )
+
+    col_isna_mask = cast(Series, data.isna().any())
+
+    if col_isna_mask.any():
+        raise ValueError(
+            "Dummy DataFrame contains NA value in column: "
+            f"'{col_isna_mask.idxmax()}'"
+        )
+
+    # index data with a list of all columns that are dummies
+    try:
+        data_to_decode = data.astype("boolean", copy=False)
+    except TypeError:
+        raise TypeError("Passed DataFrame contains non-dummy data")
+
+    # collect prefixes and get lists to slice data for each prefix
+    variables_slice = defaultdict(list)
+    if sep is None:
+        variables_slice[""] = list(data.columns)
+    elif isinstance(sep, str):
+        for col in data_to_decode.columns:
+            prefix = col.split(sep)[0]
+            if len(prefix) == len(col):
+                raise ValueError(f"Separator not specified for column: {col}")
+            variables_slice[prefix].append(col)
+    else:
+        raise TypeError(
+            "Expected 'sep' to be of type 'str' or 'None'; "
+            f"Received 'sep' of type: {type(sep).__name__}"
+        )
+
+    if default_category is not None:
+        if isinstance(default_category, dict):
+            if not len(default_category) == len(variables_slice):
+                len_msg = (
+                    f"Length of 'default_category' ({len(default_category)}) "
+                    f"did not match the length of the columns being encoded "
+                    f"({len(variables_slice)})"
+                )
+                raise ValueError(len_msg)
+        elif isinstance(default_category, Hashable):
+            default_category = dict(
+                zip(variables_slice, [default_category] * len(variables_slice))
+            )
+        else:
+            raise TypeError(
+                "Expected 'default_category' to be of type "
+                "'None', 'Hashable', or 'dict'; "
+                "Received 'default_category' of type: "
+                f"{type(default_category).__name__}"
+            )
+
+    cat_data = {}
+    for prefix, prefix_slice in variables_slice.items():
+        if sep is None:
+            cats = prefix_slice.copy()
+        else:
+            cats = [col[len(prefix + sep) :] for col in prefix_slice]
+        assigned = data_to_decode.loc[:, prefix_slice].sum(axis=1)
+        if any(assigned > 1):
+            raise ValueError(
+                "Dummy DataFrame contains multi-assignment(s); "
+                f"First instance in row: {assigned.idxmax()}"
+            )
+        if any(assigned == 0):
+            if isinstance(default_category, dict):
+                cats.append(default_category[prefix])
+            else:
+                raise ValueError(
+                    "Dummy DataFrame contains unassigned value(s); "
+                    f"First instance in row: {assigned.idxmin()}"
+                )
+            data_slice = concat(
+                (data_to_decode.loc[:, prefix_slice], assigned == 0), axis=1
+            )
+        else:
+            data_slice = data_to_decode.loc[:, prefix_slice]
+        cats_array = data._constructor_sliced(cats, dtype=data.columns.dtype)
+        # get indices of True entries along axis=1
+        true_values = data_slice.idxmax(axis=1)
+        indexer = data_slice.columns.get_indexer_for(true_values)
+        cat_data[prefix] = cats_array.take(indexer).set_axis(data.index)
+
+    result = DataFrame(cat_data)
+    if sep is not None:
+        result.columns = result.columns.astype(data.columns.dtype)
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/melt.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/melt.py
new file mode 100644
index 0000000000000000000000000000000000000000..e54f847895f1a42cf1782392da684f2bcfa7e81c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/melt.py
@@ -0,0 +1,512 @@
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.util._decorators import Appender
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.missing import notna
+
+import pandas.core.algorithms as algos
+from pandas.core.indexes.api import MultiIndex
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.util import tile_compat
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.tools.numeric import to_numeric
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._typing import AnyArrayLike
+
+    from pandas import DataFrame
+
+
+def ensure_list_vars(arg_vars, variable: str, columns) -> list:
+    if arg_vars is not None:
+        if not is_list_like(arg_vars):
+            return [arg_vars]
+        elif isinstance(columns, MultiIndex) and not isinstance(arg_vars, list):
+            raise ValueError(
+                f"{variable} must be a list of tuples when columns are a MultiIndex"
+            )
+        else:
+            return list(arg_vars)
+    else:
+        return []
+
+
+@Appender(_shared_docs["melt"] % {"caller": "pd.melt(df, ", "other": "DataFrame.melt"})
+def melt(
+    frame: DataFrame,
+    id_vars=None,
+    value_vars=None,
+    var_name=None,
+    value_name: Hashable = "value",
+    col_level=None,
+    ignore_index: bool = True,
+) -> DataFrame:
+    if value_name in frame.columns:
+        raise ValueError(
+            f"value_name ({value_name}) cannot match an element in "
+            "the DataFrame columns."
+        )
+    id_vars = ensure_list_vars(id_vars, "id_vars", frame.columns)
+    value_vars_was_not_none = value_vars is not None
+    value_vars = ensure_list_vars(value_vars, "value_vars", frame.columns)
+
+    if id_vars or value_vars:
+        if col_level is not None:
+            level = frame.columns.get_level_values(col_level)
+        else:
+            level = frame.columns
+        labels = id_vars + value_vars
+        idx = level.get_indexer_for(labels)
+        missing = idx == -1
+        if missing.any():
+            missing_labels = [
+                lab for lab, not_found in zip(labels, missing) if not_found
+            ]
+            raise KeyError(
+                "The following id_vars or value_vars are not present in "
+                f"the DataFrame: {missing_labels}"
+            )
+        if value_vars_was_not_none:
+            frame = frame.iloc[:, algos.unique(idx)]
+        else:
+            frame = frame.copy()
+    else:
+        frame = frame.copy()
+
+    if col_level is not None:  # allow list or other?
+        # frame is a copy
+        frame.columns = frame.columns.get_level_values(col_level)
+
+    if var_name is None:
+        if isinstance(frame.columns, MultiIndex):
+            if len(frame.columns.names) == len(set(frame.columns.names)):
+                var_name = frame.columns.names
+            else:
+                var_name = [f"variable_{i}" for i in range(len(frame.columns.names))]
+        else:
+            var_name = [
+                frame.columns.name if frame.columns.name is not None else "variable"
+            ]
+    elif is_list_like(var_name):
+        raise ValueError(f"{var_name=} must be a scalar.")
+    else:
+        var_name = [var_name]
+
+    num_rows, K = frame.shape
+    num_cols_adjusted = K - len(id_vars)
+
+    mdata: dict[Hashable, AnyArrayLike] = {}
+    for col in id_vars:
+        id_data = frame.pop(col)
+        if not isinstance(id_data.dtype, np.dtype):
+            # i.e. ExtensionDtype
+            if num_cols_adjusted > 0:
+                mdata[col] = concat([id_data] * num_cols_adjusted, ignore_index=True)
+            else:
+                # We can't concat empty list. (GH 46044)
+                mdata[col] = type(id_data)([], name=id_data.name, dtype=id_data.dtype)
+        else:
+            mdata[col] = np.tile(id_data._values, num_cols_adjusted)
+
+    mcolumns = id_vars + var_name + [value_name]
+
+    if frame.shape[1] > 0 and not any(
+        not isinstance(dt, np.dtype) and dt._supports_2d for dt in frame.dtypes
+    ):
+        mdata[value_name] = concat(
+            [frame.iloc[:, i] for i in range(frame.shape[1])]
+        ).values
+    else:
+        mdata[value_name] = frame._values.ravel("F")
+    for i, col in enumerate(var_name):
+        mdata[col] = frame.columns._get_level_values(i).repeat(num_rows)
+
+    result = frame._constructor(mdata, columns=mcolumns)
+
+    if not ignore_index:
+        result.index = tile_compat(frame.index, num_cols_adjusted)
+
+    return result
+
+
+def lreshape(data: DataFrame, groups: dict, dropna: bool = True) -> DataFrame:
+    """
+    Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.
+
+    Accepts a dictionary, ``groups``, in which each key is a new column name
+    and each value is a list of old column names that will be "melted" under
+    the new column name as part of the reshape.
+
+    Parameters
+    ----------
+    data : DataFrame
+        The wide-format DataFrame.
+    groups : dict
+        {new_name : list_of_columns}.
+    dropna : bool, default True
+        Do not include columns whose entries are all NaN.
+
+    Returns
+    -------
+    DataFrame
+        Reshaped DataFrame.
+
+    See Also
+    --------
+    melt : Unpivot a DataFrame from wide to long format, optionally leaving
+        identifiers set.
+    pivot : Create a spreadsheet-style pivot table as a DataFrame.
+    DataFrame.pivot : Pivot without aggregation that can handle
+        non-numeric data.
+    DataFrame.pivot_table : Generalization of pivot that can handle
+        duplicate values for one index/column pair.
+    DataFrame.unstack : Pivot based on the index values instead of a
+        column.
+    wide_to_long : Wide panel to long format. Less flexible but more
+        user-friendly than melt.
+
+    Examples
+    --------
+    >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
+    ...                      'team': ['Red Sox', 'Yankees'],
+    ...                      'year1': [2007, 2007], 'year2': [2008, 2008]})
+    >>> data
+       hr1  hr2     team  year1  year2
+    0  514  545  Red Sox   2007   2008
+    1  573  526  Yankees   2007   2008
+
+    >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
+          team  year   hr
+    0  Red Sox  2007  514
+    1  Yankees  2007  573
+    2  Red Sox  2008  545
+    3  Yankees  2008  526
+    """
+    mdata = {}
+    pivot_cols = []
+    all_cols: set[Hashable] = set()
+    K = len(next(iter(groups.values())))
+    for target, names in groups.items():
+        if len(names) != K:
+            raise ValueError("All column lists must be same length")
+        to_concat = [data[col]._values for col in names]
+
+        mdata[target] = concat_compat(to_concat)
+        pivot_cols.append(target)
+        all_cols = all_cols.union(names)
+
+    id_cols = list(data.columns.difference(all_cols))
+    for col in id_cols:
+        mdata[col] = np.tile(data[col]._values, K)
+
+    if dropna:
+        mask = np.ones(len(mdata[pivot_cols[0]]), dtype=bool)
+        for c in pivot_cols:
+            mask &= notna(mdata[c])
+        if not mask.all():
+            mdata = {k: v[mask] for k, v in mdata.items()}
+
+    return data._constructor(mdata, columns=id_cols + pivot_cols)
+
+
+def wide_to_long(
+    df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
+) -> DataFrame:
+    r"""
+    Unpivot a DataFrame from wide to long format.
+
+    Less flexible but more user-friendly than melt.
+
+    With stubnames ['A', 'B'], this function expects to find one or more
+    group of columns with format
+    A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
+    You specify what you want to call this suffix in the resulting long format
+    with `j` (for example `j='year'`)
+
+    Each row of these wide variables are assumed to be uniquely identified by
+    `i` (can be a single column name or a list of column names)
+
+    All remaining variables in the data frame are left intact.
+
+    Parameters
+    ----------
+    df : DataFrame
+        The wide-format DataFrame.
+    stubnames : str or list-like
+        The stub name(s). The wide format variables are assumed to
+        start with the stub names.
+    i : str or list-like
+        Column(s) to use as id variable(s).
+    j : str
+        The name of the sub-observation variable. What you wish to name your
+        suffix in the long format.
+    sep : str, default ""
+        A character indicating the separation of the variable names
+        in the wide format, to be stripped from the names in the long format.
+        For example, if your column names are A-suffix1, A-suffix2, you
+        can strip the hyphen by specifying `sep='-'`.
+    suffix : str, default '\\d+'
+        A regular expression capturing the wanted suffixes. '\\d+' captures
+        numeric suffixes. Suffixes with no numbers could be specified with the
+        negated character class '\\D+'. You can also further disambiguate
+        suffixes, for example, if your wide variables are of the form A-one,
+        B-two,.., and you have an unrelated column A-rating, you can ignore the
+        last one by specifying `suffix='(!?one|two)'`. When all suffixes are
+        numeric, they are cast to int64/float64.
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame that contains each stub name as a variable, with new index
+        (i, j).
+
+    See Also
+    --------
+    melt : Unpivot a DataFrame from wide to long format, optionally leaving
+        identifiers set.
+    pivot : Create a spreadsheet-style pivot table as a DataFrame.
+    DataFrame.pivot : Pivot without aggregation that can handle
+        non-numeric data.
+    DataFrame.pivot_table : Generalization of pivot that can handle
+        duplicate values for one index/column pair.
+    DataFrame.unstack : Pivot based on the index values instead of a
+        column.
+
+    Notes
+    -----
+    All extra variables are left untouched. This simply uses
+    `pandas.melt` under the hood, but is hard-coded to "do the right thing"
+    in a typical case.
+
+    Examples
+    --------
+    >>> np.random.seed(123)
+    >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
+    ...                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},
+    ...                    "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
+    ...                    "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
+    ...                    "X"     : dict(zip(range(3), np.random.randn(3)))
+    ...                   })
+    >>> df["id"] = df.index
+    >>> df
+      A1970 A1980  B1970  B1980         X  id
+    0     a     d    2.5    3.2 -1.085631   0
+    1     b     e    1.2    1.3  0.997345   1
+    2     c     f    0.7    0.1  0.282978   2
+    >>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
+    ... # doctest: +NORMALIZE_WHITESPACE
+                    X  A    B
+    id year
+    0  1970 -1.085631  a  2.5
+    1  1970  0.997345  b  1.2
+    2  1970  0.282978  c  0.7
+    0  1980 -1.085631  d  3.2
+    1  1980  0.997345  e  1.3
+    2  1980  0.282978  f  0.1
+
+    With multiple id columns
+
+    >>> df = pd.DataFrame({
+    ...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+    ...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+    ...     'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
+    ...     'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
+    ... })
+    >>> df
+       famid  birth  ht1  ht2
+    0      1      1  2.8  3.4
+    1      1      2  2.9  3.8
+    2      1      3  2.2  2.9
+    3      2      1  2.0  3.2
+    4      2      2  1.8  2.8
+    5      2      3  1.9  2.4
+    6      3      1  2.2  3.3
+    7      3      2  2.3  3.4
+    8      3      3  2.1  2.9
+    >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
+    >>> l
+    ... # doctest: +NORMALIZE_WHITESPACE
+                      ht
+    famid birth age
+    1     1     1    2.8
+                2    3.4
+          2     1    2.9
+                2    3.8
+          3     1    2.2
+                2    2.9
+    2     1     1    2.0
+                2    3.2
+          2     1    1.8
+                2    2.8
+          3     1    1.9
+                2    2.4
+    3     1     1    2.2
+                2    3.3
+          2     1    2.3
+                2    3.4
+          3     1    2.1
+                2    2.9
+
+    Going from long back to wide just takes some creative use of `unstack`
+
+    >>> w = l.unstack()
+    >>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
+    >>> w.reset_index()
+       famid  birth  ht1  ht2
+    0      1      1  2.8  3.4
+    1      1      2  2.9  3.8
+    2      1      3  2.2  2.9
+    3      2      1  2.0  3.2
+    4      2      2  1.8  2.8
+    5      2      3  1.9  2.4
+    6      3      1  2.2  3.3
+    7      3      2  2.3  3.4
+    8      3      3  2.1  2.9
+
+    Less wieldy column names are also handled
+
+    >>> np.random.seed(0)
+    >>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),
+    ...                    'A(weekly)-2011': np.random.rand(3),
+    ...                    'B(weekly)-2010': np.random.rand(3),
+    ...                    'B(weekly)-2011': np.random.rand(3),
+    ...                    'X' : np.random.randint(3, size=3)})
+    >>> df['id'] = df.index
+    >>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
+       A(weekly)-2010  A(weekly)-2011  B(weekly)-2010  B(weekly)-2011  X  id
+    0        0.548814        0.544883        0.437587        0.383442  0   0
+    1        0.715189        0.423655        0.891773        0.791725  1   1
+    2        0.602763        0.645894        0.963663        0.528895  1   2
+
+    >>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',
+    ...                 j='year', sep='-')
+    ... # doctest: +NORMALIZE_WHITESPACE
+             X  A(weekly)  B(weekly)
+    id year
+    0  2010  0   0.548814   0.437587
+    1  2010  1   0.715189   0.891773
+    2  2010  1   0.602763   0.963663
+    0  2011  0   0.544883   0.383442
+    1  2011  1   0.423655   0.791725
+    2  2011  1   0.645894   0.528895
+
+    If we have many columns, we could also use a regex to find our
+    stubnames and pass that list on to wide_to_long
+
+    >>> stubnames = sorted(
+    ...     set([match[0] for match in df.columns.str.findall(
+    ...         r'[A-B]\(.*\)').values if match != []])
+    ... )
+    >>> list(stubnames)
+    ['A(weekly)', 'B(weekly)']
+
+    All of the above examples have integers as suffixes. It is possible to
+    have non-integers as suffixes.
+
+    >>> df = pd.DataFrame({
+    ...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+    ...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
+    ...     'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
+    ...     'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
+    ... })
+    >>> df
+       famid  birth  ht_one  ht_two
+    0      1      1     2.8     3.4
+    1      1      2     2.9     3.8
+    2      1      3     2.2     2.9
+    3      2      1     2.0     3.2
+    4      2      2     1.8     2.8
+    5      2      3     1.9     2.4
+    6      3      1     2.2     3.3
+    7      3      2     2.3     3.4
+    8      3      3     2.1     2.9
+
+    >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
+    ...                     sep='_', suffix=r'\w+')
+    >>> l
+    ... # doctest: +NORMALIZE_WHITESPACE
+                      ht
+    famid birth age
+    1     1     one  2.8
+                two  3.4
+          2     one  2.9
+                two  3.8
+          3     one  2.2
+                two  2.9
+    2     1     one  2.0
+                two  3.2
+          2     one  1.8
+                two  2.8
+          3     one  1.9
+                two  2.4
+    3     1     one  2.2
+                two  3.3
+          2     one  2.3
+                two  3.4
+          3     one  2.1
+                two  2.9
+    """
+
+    def get_var_names(df, stub: str, sep: str, suffix: str):
+        regex = rf"^{re.escape(stub)}{re.escape(sep)}{suffix}$"
+        return df.columns[df.columns.str.match(regex)]
+
+    def melt_stub(df, stub: str, i, j, value_vars, sep: str):
+        newdf = melt(
+            df,
+            id_vars=i,
+            value_vars=value_vars,
+            value_name=stub.rstrip(sep),
+            var_name=j,
+        )
+        newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "", regex=True)
+
+        # GH17627 Cast numerics suffixes to int/float
+        try:
+            newdf[j] = to_numeric(newdf[j])
+        except (TypeError, ValueError, OverflowError):
+            # TODO: anything else to catch?
+            pass
+
+        return newdf.set_index(i + [j])
+
+    if not is_list_like(stubnames):
+        stubnames = [stubnames]
+    else:
+        stubnames = list(stubnames)
+
+    if df.columns.isin(stubnames).any():
+        raise ValueError("stubname can't be identical to a column name")
+
+    if not is_list_like(i):
+        i = [i]
+    else:
+        i = list(i)
+
+    if df[i].duplicated().any():
+        raise ValueError("the id variables need to uniquely identify each row")
+
+    _melted = []
+    value_vars_flattened = []
+    for stub in stubnames:
+        value_var = get_var_names(df, stub, sep, suffix)
+        value_vars_flattened.extend(value_var)
+        _melted.append(melt_stub(df, stub, i, j, value_var, sep))
+
+    melted = concat(_melted, axis=1)
+    id_vars = df.columns.difference(value_vars_flattened)
+    new = df[id_vars]
+
+    if len(i) == 1:
+        return new.set_index(i).join(melted)
+    else:
+        return new.merge(melted.reset_index(), on=i).set_index(i + [j])
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/merge.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1e003c2001d24de56a066f30f17b7b60a8ed8f8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/merge.py
@@ -0,0 +1,2762 @@
+"""
+SQL-style merge routines
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+import datetime
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+    final,
+)
+import uuid
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    Timedelta,
+    hashtable as libhashtable,
+    join as libjoin,
+    lib,
+)
+from pandas._libs.lib import is_range_indexer
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    IndexLabel,
+    JoinHow,
+    MergeHow,
+    Shape,
+    Suffixes,
+    npt,
+)
+from pandas.errors import MergeError
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_object,
+    is_bool,
+    is_bool_dtype,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_number,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_string_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+)
+
+from pandas import (
+    ArrowDtype,
+    Categorical,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    BaseMaskedArray,
+    ExtensionArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+import pandas.core.common as com
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
+from pandas.core.frame import _merge_doc
+from pandas.core.indexes.api import default_index
+from pandas.core.sorting import (
+    get_group_index,
+    is_int64_overflow_possible,
+)
+
+if TYPE_CHECKING:
+    from pandas import DataFrame
+    from pandas.core import groupby
+    from pandas.core.arrays import DatetimeArray
+    from pandas.core.indexes.frozen import FrozenList
+
+_factorizers = {
+    np.int64: libhashtable.Int64Factorizer,
+    np.longlong: libhashtable.Int64Factorizer,
+    np.int32: libhashtable.Int32Factorizer,
+    np.int16: libhashtable.Int16Factorizer,
+    np.int8: libhashtable.Int8Factorizer,
+    np.uint64: libhashtable.UInt64Factorizer,
+    np.uint32: libhashtable.UInt32Factorizer,
+    np.uint16: libhashtable.UInt16Factorizer,
+    np.uint8: libhashtable.UInt8Factorizer,
+    np.bool_: libhashtable.UInt8Factorizer,
+    np.float64: libhashtable.Float64Factorizer,
+    np.float32: libhashtable.Float32Factorizer,
+    np.complex64: libhashtable.Complex64Factorizer,
+    np.complex128: libhashtable.Complex128Factorizer,
+    np.object_: libhashtable.ObjectFactorizer,
+}
+
+# See https://github.com/pandas-dev/pandas/issues/52451
+if np.intc is not np.int32:
+    _factorizers[np.intc] = libhashtable.Int64Factorizer
+
+_known = (np.ndarray, ExtensionArray, Index, ABCSeries)
+
+
+@Substitution("\nleft : DataFrame or named Series")
+@Appender(_merge_doc, indents=0)
+def merge(
+    left: DataFrame | Series,
+    right: DataFrame | Series,
+    how: MergeHow = "inner",
+    on: IndexLabel | AnyArrayLike | None = None,
+    left_on: IndexLabel | AnyArrayLike | None = None,
+    right_on: IndexLabel | AnyArrayLike | None = None,
+    left_index: bool = False,
+    right_index: bool = False,
+    sort: bool = False,
+    suffixes: Suffixes = ("_x", "_y"),
+    copy: bool | None = None,
+    indicator: str | bool = False,
+    validate: str | None = None,
+) -> DataFrame:
+    left_df = _validate_operand(left)
+    right_df = _validate_operand(right)
+    if how == "cross":
+        return _cross_merge(
+            left_df,
+            right_df,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            sort=sort,
+            suffixes=suffixes,
+            indicator=indicator,
+            validate=validate,
+            copy=copy,
+        )
+    else:
+        op = _MergeOperation(
+            left_df,
+            right_df,
+            how=how,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            sort=sort,
+            suffixes=suffixes,
+            indicator=indicator,
+            validate=validate,
+        )
+        return op.get_result(copy=copy)
+
+
+def _cross_merge(
+    left: DataFrame,
+    right: DataFrame,
+    on: IndexLabel | AnyArrayLike | None = None,
+    left_on: IndexLabel | AnyArrayLike | None = None,
+    right_on: IndexLabel | AnyArrayLike | None = None,
+    left_index: bool = False,
+    right_index: bool = False,
+    sort: bool = False,
+    suffixes: Suffixes = ("_x", "_y"),
+    copy: bool | None = None,
+    indicator: str | bool = False,
+    validate: str | None = None,
+) -> DataFrame:
+    """
+    See merge.__doc__ with how='cross'
+    """
+
+    if (
+        left_index
+        or right_index
+        or right_on is not None
+        or left_on is not None
+        or on is not None
+    ):
+        raise MergeError(
+            "Can not pass on, right_on, left_on or set right_index=True or "
+            "left_index=True"
+        )
+
+    cross_col = f"_cross_{uuid.uuid4()}"
+    left = left.assign(**{cross_col: 1})
+    right = right.assign(**{cross_col: 1})
+
+    left_on = right_on = [cross_col]
+
+    res = merge(
+        left,
+        right,
+        how="inner",
+        on=on,
+        left_on=left_on,
+        right_on=right_on,
+        left_index=left_index,
+        right_index=right_index,
+        sort=sort,
+        suffixes=suffixes,
+        indicator=indicator,
+        validate=validate,
+        copy=copy,
+    )
+    del res[cross_col]
+    return res
+
+
+def _groupby_and_merge(
+    by, left: DataFrame | Series, right: DataFrame | Series, merge_pieces
+):
+    """
+    groupby & merge; we are always performing a left-by type operation
+
+    Parameters
+    ----------
+    by: field to group
+    left: DataFrame
+    right: DataFrame
+    merge_pieces: function for merging
+    """
+    pieces = []
+    if not isinstance(by, (list, tuple)):
+        by = [by]
+
+    lby = left.groupby(by, sort=False)
+    rby: groupby.DataFrameGroupBy | groupby.SeriesGroupBy | None = None
+
+    # if we can groupby the rhs
+    # then we can get vastly better perf
+    if all(item in right.columns for item in by):
+        rby = right.groupby(by, sort=False)
+
+    for key, lhs in lby._grouper.get_iterator(lby._selected_obj, axis=lby.axis):
+        if rby is None:
+            rhs = right
+        else:
+            try:
+                rhs = right.take(rby.indices[key])
+            except KeyError:
+                # key doesn't exist in left
+                lcols = lhs.columns.tolist()
+                cols = lcols + [r for r in right.columns if r not in set(lcols)]
+                merged = lhs.reindex(columns=cols)
+                merged.index = range(len(merged))
+                pieces.append(merged)
+                continue
+
+        merged = merge_pieces(lhs, rhs)
+
+        # make sure join keys are in the merged
+        # TODO, should merge_pieces do this?
+        merged[by] = key
+
+        pieces.append(merged)
+
+    # preserve the original order
+    # if we have a missing piece this can be reset
+    from pandas.core.reshape.concat import concat
+
+    result = concat(pieces, ignore_index=True)
+    result = result.reindex(columns=pieces[0].columns, copy=False)
+    return result, lby
+
+
+def merge_ordered(
+    left: DataFrame | Series,
+    right: DataFrame | Series,
+    on: IndexLabel | None = None,
+    left_on: IndexLabel | None = None,
+    right_on: IndexLabel | None = None,
+    left_by=None,
+    right_by=None,
+    fill_method: str | None = None,
+    suffixes: Suffixes = ("_x", "_y"),
+    how: JoinHow = "outer",
+) -> DataFrame:
+    """
+    Perform a merge for ordered data with optional filling/interpolation.
+
+    Designed for ordered data like time series data. Optionally
+    perform group-wise merge (see examples).
+
+    Parameters
+    ----------
+    left : DataFrame or named Series
+    right : DataFrame or named Series
+    on : label or list
+        Field names to join on. Must be found in both DataFrames.
+    left_on : label or list, or array-like
+        Field names to join on in left DataFrame. Can be a vector or list of
+        vectors of the length of the DataFrame to use a particular vector as
+        the join key instead of columns.
+    right_on : label or list, or array-like
+        Field names to join on in right DataFrame or vector/list of vectors per
+        left_on docs.
+    left_by : column name or list of column names
+        Group left DataFrame by group columns and merge piece by piece with
+        right DataFrame. Must be None if either left or right are a Series.
+    right_by : column name or list of column names
+        Group right DataFrame by group columns and merge piece by piece with
+        left DataFrame. Must be None if either left or right are a Series.
+    fill_method : {'ffill', None}, default None
+        Interpolation method for data.
+    suffixes : list-like, default is ("_x", "_y")
+        A length-2 sequence where each element is optionally a string
+        indicating the suffix to add to overlapping column names in
+        `left` and `right` respectively. Pass a value of `None` instead
+        of a string to indicate that the column name from `left` or
+        `right` should be left as-is, with no suffix. At least one of the
+        values must not be None.
+
+    how : {'left', 'right', 'outer', 'inner'}, default 'outer'
+        * left: use only keys from left frame (SQL: left outer join)
+        * right: use only keys from right frame (SQL: right outer join)
+        * outer: use union of keys from both frames (SQL: full outer join)
+        * inner: use intersection of keys from both frames (SQL: inner join).
+
+    Returns
+    -------
+    DataFrame
+        The merged DataFrame output type will be the same as
+        'left', if it is a subclass of DataFrame.
+
+    See Also
+    --------
+    merge : Merge with a database-style join.
+    merge_asof : Merge on nearest keys.
+
+    Examples
+    --------
+    >>> from pandas import merge_ordered
+    >>> df1 = pd.DataFrame(
+    ...     {
+    ...         "key": ["a", "c", "e", "a", "c", "e"],
+    ...         "lvalue": [1, 2, 3, 1, 2, 3],
+    ...         "group": ["a", "a", "a", "b", "b", "b"]
+    ...     }
+    ... )
+    >>> df1
+      key  lvalue group
+    0   a       1     a
+    1   c       2     a
+    2   e       3     a
+    3   a       1     b
+    4   c       2     b
+    5   e       3     b
+
+    >>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
+    >>> df2
+      key  rvalue
+    0   b       1
+    1   c       2
+    2   d       3
+
+    >>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
+      key  lvalue group  rvalue
+    0   a       1     a     NaN
+    1   b       1     a     1.0
+    2   c       2     a     2.0
+    3   d       2     a     3.0
+    4   e       3     a     3.0
+    5   a       1     b     NaN
+    6   b       1     b     1.0
+    7   c       2     b     2.0
+    8   d       2     b     3.0
+    9   e       3     b     3.0
+    """
+
+    def _merger(x, y) -> DataFrame:
+        # perform the ordered merge operation
+        op = _OrderedMerge(
+            x,
+            y,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            suffixes=suffixes,
+            fill_method=fill_method,
+            how=how,
+        )
+        return op.get_result()
+
+    if left_by is not None and right_by is not None:
+        raise ValueError("Can only group either left or right frames")
+    if left_by is not None:
+        if isinstance(left_by, str):
+            left_by = [left_by]
+        check = set(left_by).difference(left.columns)
+        if len(check) != 0:
+            raise KeyError(f"{check} not found in left columns")
+        result, _ = _groupby_and_merge(left_by, left, right, lambda x, y: _merger(x, y))
+    elif right_by is not None:
+        if isinstance(right_by, str):
+            right_by = [right_by]
+        check = set(right_by).difference(right.columns)
+        if len(check) != 0:
+            raise KeyError(f"{check} not found in right columns")
+        result, _ = _groupby_and_merge(
+            right_by, right, left, lambda x, y: _merger(y, x)
+        )
+    else:
+        result = _merger(left, right)
+    return result
+
+
+def merge_asof(
+    left: DataFrame | Series,
+    right: DataFrame | Series,
+    on: IndexLabel | None = None,
+    left_on: IndexLabel | None = None,
+    right_on: IndexLabel | None = None,
+    left_index: bool = False,
+    right_index: bool = False,
+    by=None,
+    left_by=None,
+    right_by=None,
+    suffixes: Suffixes = ("_x", "_y"),
+    tolerance: int | Timedelta | None = None,
+    allow_exact_matches: bool = True,
+    direction: str = "backward",
+) -> DataFrame:
+    """
+    Perform a merge by key distance.
+
+    This is similar to a left-join except that we match on nearest
+    key rather than equal keys. Both DataFrames must be sorted by the key.
+
+    For each row in the left DataFrame:
+
+      - A "backward" search selects the last row in the right DataFrame whose
+        'on' key is less than or equal to the left's key.
+
+      - A "forward" search selects the first row in the right DataFrame whose
+        'on' key is greater than or equal to the left's key.
+
+      - A "nearest" search selects the row in the right DataFrame whose 'on'
+        key is closest in absolute distance to the left's key.
+
+    Optionally match on equivalent keys with 'by' before searching with 'on'.
+
+    Parameters
+    ----------
+    left : DataFrame or named Series
+    right : DataFrame or named Series
+    on : label
+        Field name to join on. Must be found in both DataFrames.
+        The data MUST be ordered. Furthermore this must be a numeric column,
+        such as datetimelike, integer, or float. On or left_on/right_on
+        must be given.
+    left_on : label
+        Field name to join on in left DataFrame.
+    right_on : label
+        Field name to join on in right DataFrame.
+    left_index : bool
+        Use the index of the left DataFrame as the join key.
+    right_index : bool
+        Use the index of the right DataFrame as the join key.
+    by : column name or list of column names
+        Match on these columns before performing merge operation.
+    left_by : column name
+        Field names to match on in the left DataFrame.
+    right_by : column name
+        Field names to match on in the right DataFrame.
+    suffixes : 2-length sequence (tuple, list, ...)
+        Suffix to apply to overlapping column names in the left and right
+        side, respectively.
+    tolerance : int or Timedelta, optional, default None
+        Select asof tolerance within this range; must be compatible
+        with the merge index.
+    allow_exact_matches : bool, default True
+
+        - If True, allow matching with the same 'on' value
+          (i.e. less-than-or-equal-to / greater-than-or-equal-to)
+        - If False, don't match the same 'on' value
+          (i.e., strictly less-than / strictly greater-than).
+
+    direction : 'backward' (default), 'forward', or 'nearest'
+        Whether to search for prior, subsequent, or closest matches.
+
+    Returns
+    -------
+    DataFrame
+
+    See Also
+    --------
+    merge : Merge with a database-style join.
+    merge_ordered : Merge with optional filling/interpolation.
+
+    Examples
+    --------
+    >>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+    >>> left
+        a left_val
+    0   1        a
+    1   5        b
+    2  10        c
+
+    >>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+    >>> right
+       a  right_val
+    0  1          1
+    1  2          2
+    2  3          3
+    3  6          6
+    4  7          7
+
+    >>> pd.merge_asof(left, right, on="a")
+        a left_val  right_val
+    0   1        a          1
+    1   5        b          3
+    2  10        c          7
+
+    >>> pd.merge_asof(left, right, on="a", allow_exact_matches=False)
+        a left_val  right_val
+    0   1        a        NaN
+    1   5        b        3.0
+    2  10        c        7.0
+
+    >>> pd.merge_asof(left, right, on="a", direction="forward")
+        a left_val  right_val
+    0   1        a        1.0
+    1   5        b        6.0
+    2  10        c        NaN
+
+    >>> pd.merge_asof(left, right, on="a", direction="nearest")
+        a left_val  right_val
+    0   1        a          1
+    1   5        b          6
+    2  10        c          7
+
+    We can use indexed DataFrames as well.
+
+    >>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
+    >>> left
+       left_val
+    1         a
+    5         b
+    10        c
+
+    >>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
+    >>> right
+       right_val
+    1          1
+    2          2
+    3          3
+    6          6
+    7          7
+
+    >>> pd.merge_asof(left, right, left_index=True, right_index=True)
+       left_val  right_val
+    1         a          1
+    5         b          3
+    10        c          7
+
+    Here is a real-world times-series example
+
+    >>> quotes = pd.DataFrame(
+    ...     {
+    ...         "time": [
+    ...             pd.Timestamp("2016-05-25 13:30:00.023"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.023"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.030"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.041"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.048"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.049"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.072"),
+    ...             pd.Timestamp("2016-05-25 13:30:00.075")
+    ...         ],
+    ...         "ticker": [
+    ...                "GOOG",
+    ...                "MSFT",
+    ...                "MSFT",
+    ...                "MSFT",
+    ...                "GOOG",
+    ...                "AAPL",
+    ...                "GOOG",
+    ...                "MSFT"
+    ...            ],
+    ...            "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+    ...            "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
+    ...     }
+    ... )
+    >>> quotes
+                         time ticker     bid     ask
+    0 2016-05-25 13:30:00.023   GOOG  720.50  720.93
+    1 2016-05-25 13:30:00.023   MSFT   51.95   51.96
+    2 2016-05-25 13:30:00.030   MSFT   51.97   51.98
+    3 2016-05-25 13:30:00.041   MSFT   51.99   52.00
+    4 2016-05-25 13:30:00.048   GOOG  720.50  720.93
+    5 2016-05-25 13:30:00.049   AAPL   97.99   98.01
+    6 2016-05-25 13:30:00.072   GOOG  720.50  720.88
+    7 2016-05-25 13:30:00.075   MSFT   52.01   52.03
+
+    >>> trades = pd.DataFrame(
+    ...        {
+    ...            "time": [
+    ...                pd.Timestamp("2016-05-25 13:30:00.023"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.038"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.048"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.048"),
+    ...                pd.Timestamp("2016-05-25 13:30:00.048")
+    ...            ],
+    ...            "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+    ...            "price": [51.95, 51.95, 720.77, 720.92, 98.0],
+    ...            "quantity": [75, 155, 100, 100, 100]
+    ...        }
+    ...    )
+    >>> trades
+                         time ticker   price  quantity
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100
+
+    By default we are taking the asof of the quotes
+
+    >>> pd.merge_asof(trades, quotes, on="time", by="ticker")
+                         time ticker   price  quantity     bid     ask
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
+
+    We only asof within 2ms between the quote time and the trade time
+
+    >>> pd.merge_asof(
+    ...     trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
+    ... )
+                         time ticker   price  quantity     bid     ask
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155     NaN     NaN
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
+
+    We only asof within 10ms between the quote time and the trade time
+    and we exclude exact matches on time. However *prior* data will
+    propagate forward
+
+    >>> pd.merge_asof(
+    ...     trades,
+    ...     quotes,
+    ...     on="time",
+    ...     by="ticker",
+    ...     tolerance=pd.Timedelta("10ms"),
+    ...     allow_exact_matches=False
+    ... )
+                         time ticker   price  quantity     bid     ask
+    0 2016-05-25 13:30:00.023   MSFT   51.95        75     NaN     NaN
+    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
+    2 2016-05-25 13:30:00.048   GOOG  720.77       100     NaN     NaN
+    3 2016-05-25 13:30:00.048   GOOG  720.92       100     NaN     NaN
+    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
+    """
+    op = _AsOfMerge(
+        left,
+        right,
+        on=on,
+        left_on=left_on,
+        right_on=right_on,
+        left_index=left_index,
+        right_index=right_index,
+        by=by,
+        left_by=left_by,
+        right_by=right_by,
+        suffixes=suffixes,
+        how="asof",
+        tolerance=tolerance,
+        allow_exact_matches=allow_exact_matches,
+        direction=direction,
+    )
+    return op.get_result()
+
+
+# TODO: transformations??
+# TODO: only copy DataFrames when modification necessary
+class _MergeOperation:
+    """
+    Perform a database (SQL) merge operation between two DataFrame or Series
+    objects using either columns as keys or their row indexes
+    """
+
+    _merge_type = "merge"
+    how: JoinHow | Literal["asof"]
+    on: IndexLabel | None
+    # left_on/right_on may be None when passed, but in validate_specification
+    #  get replaced with non-None.
+    left_on: Sequence[Hashable | AnyArrayLike]
+    right_on: Sequence[Hashable | AnyArrayLike]
+    left_index: bool
+    right_index: bool
+    sort: bool
+    suffixes: Suffixes
+    copy: bool
+    indicator: str | bool
+    validate: str | None
+    join_names: list[Hashable]
+    right_join_keys: list[ArrayLike]
+    left_join_keys: list[ArrayLike]
+
+    def __init__(
+        self,
+        left: DataFrame | Series,
+        right: DataFrame | Series,
+        how: JoinHow | Literal["asof"] = "inner",
+        on: IndexLabel | AnyArrayLike | None = None,
+        left_on: IndexLabel | AnyArrayLike | None = None,
+        right_on: IndexLabel | AnyArrayLike | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        sort: bool = True,
+        suffixes: Suffixes = ("_x", "_y"),
+        indicator: str | bool = False,
+        validate: str | None = None,
+    ) -> None:
+        _left = _validate_operand(left)
+        _right = _validate_operand(right)
+        self.left = self.orig_left = _left
+        self.right = self.orig_right = _right
+        self.how = how
+
+        self.on = com.maybe_make_list(on)
+
+        self.suffixes = suffixes
+        self.sort = sort or how == "outer"
+
+        self.left_index = left_index
+        self.right_index = right_index
+
+        self.indicator = indicator
+
+        if not is_bool(left_index):
+            raise ValueError(
+                f"left_index parameter must be of type bool, not {type(left_index)}"
+            )
+        if not is_bool(right_index):
+            raise ValueError(
+                f"right_index parameter must be of type bool, not {type(right_index)}"
+            )
+
+        # GH 40993: raise when merging between different levels; enforced in 2.0
+        if _left.columns.nlevels != _right.columns.nlevels:
+            msg = (
+                "Not allowed to merge between different levels. "
+                f"({_left.columns.nlevels} levels on the left, "
+                f"{_right.columns.nlevels} on the right)"
+            )
+            raise MergeError(msg)
+
+        self.left_on, self.right_on = self._validate_left_right_on(left_on, right_on)
+
+        (
+            self.left_join_keys,
+            self.right_join_keys,
+            self.join_names,
+            left_drop,
+            right_drop,
+        ) = self._get_merge_keys()
+
+        if left_drop:
+            self.left = self.left._drop_labels_or_levels(left_drop)
+
+        if right_drop:
+            self.right = self.right._drop_labels_or_levels(right_drop)
+
+        self._maybe_require_matching_dtypes(self.left_join_keys, self.right_join_keys)
+        self._validate_tolerance(self.left_join_keys)
+
+        # validate the merge keys dtypes. We may need to coerce
+        # to avoid incompatible dtypes
+        self._maybe_coerce_merge_keys()
+
+        # If argument passed to validate,
+        # check if columns specified as unique
+        # are in fact unique.
+        if validate is not None:
+            self._validate_validate_kwd(validate)
+
+    def _maybe_require_matching_dtypes(
+        self, left_join_keys: list[ArrayLike], right_join_keys: list[ArrayLike]
+    ) -> None:
+        # Overridden by AsOfMerge
+        pass
+
+    def _validate_tolerance(self, left_join_keys: list[ArrayLike]) -> None:
+        # Overridden by AsOfMerge
+        pass
+
+    @final
+    def _reindex_and_concat(
+        self,
+        join_index: Index,
+        left_indexer: npt.NDArray[np.intp] | None,
+        right_indexer: npt.NDArray[np.intp] | None,
+        copy: bool | None,
+    ) -> DataFrame:
+        """
+        reindex along index and concat along columns.
+        """
+        # Take views so we do not alter the originals
+        left = self.left[:]
+        right = self.right[:]
+
+        llabels, rlabels = _items_overlap_with_suffix(
+            self.left._info_axis, self.right._info_axis, self.suffixes
+        )
+
+        if left_indexer is not None and not is_range_indexer(left_indexer, len(left)):
+            # Pinning the index here (and in the right code just below) is not
+            #  necessary, but makes the `.take` more performant if we have e.g.
+            #  a MultiIndex for left.index.
+            lmgr = left._mgr.reindex_indexer(
+                join_index,
+                left_indexer,
+                axis=1,
+                copy=False,
+                only_slice=True,
+                allow_dups=True,
+                use_na_proxy=True,
+            )
+            left = left._constructor_from_mgr(lmgr, axes=lmgr.axes)
+        left.index = join_index
+
+        if right_indexer is not None and not is_range_indexer(
+            right_indexer, len(right)
+        ):
+            rmgr = right._mgr.reindex_indexer(
+                join_index,
+                right_indexer,
+                axis=1,
+                copy=False,
+                only_slice=True,
+                allow_dups=True,
+                use_na_proxy=True,
+            )
+            right = right._constructor_from_mgr(rmgr, axes=rmgr.axes)
+        right.index = join_index
+
+        from pandas import concat
+
+        left.columns = llabels
+        right.columns = rlabels
+        result = concat([left, right], axis=1, copy=copy)
+        return result
+
+    def get_result(self, copy: bool | None = True) -> DataFrame:
+        if self.indicator:
+            self.left, self.right = self._indicator_pre_merge(self.left, self.right)
+
+        join_index, left_indexer, right_indexer = self._get_join_info()
+
+        result = self._reindex_and_concat(
+            join_index, left_indexer, right_indexer, copy=copy
+        )
+        result = result.__finalize__(self, method=self._merge_type)
+
+        if self.indicator:
+            result = self._indicator_post_merge(result)
+
+        self._maybe_add_join_keys(result, left_indexer, right_indexer)
+
+        self._maybe_restore_index_levels(result)
+
+        return result.__finalize__(self, method="merge")
+
+    @final
+    @cache_readonly
+    def _indicator_name(self) -> str | None:
+        if isinstance(self.indicator, str):
+            return self.indicator
+        elif isinstance(self.indicator, bool):
+            return "_merge" if self.indicator else None
+        else:
+            raise ValueError(
+                "indicator option can only accept boolean or string arguments"
+            )
+
+    @final
+    def _indicator_pre_merge(
+        self, left: DataFrame, right: DataFrame
+    ) -> tuple[DataFrame, DataFrame]:
+        columns = left.columns.union(right.columns)
+
+        for i in ["_left_indicator", "_right_indicator"]:
+            if i in columns:
+                raise ValueError(
+                    "Cannot use `indicator=True` option when "
+                    f"data contains a column named {i}"
+                )
+        if self._indicator_name in columns:
+            raise ValueError(
+                "Cannot use name of an existing column for indicator column"
+            )
+
+        left = left.copy()
+        right = right.copy()
+
+        left["_left_indicator"] = 1
+        left["_left_indicator"] = left["_left_indicator"].astype("int8")
+
+        right["_right_indicator"] = 2
+        right["_right_indicator"] = right["_right_indicator"].astype("int8")
+
+        return left, right
+
+    @final
+    def _indicator_post_merge(self, result: DataFrame) -> DataFrame:
+        result["_left_indicator"] = result["_left_indicator"].fillna(0)
+        result["_right_indicator"] = result["_right_indicator"].fillna(0)
+
+        result[self._indicator_name] = Categorical(
+            (result["_left_indicator"] + result["_right_indicator"]),
+            categories=[1, 2, 3],
+        )
+        result[self._indicator_name] = result[
+            self._indicator_name
+        ].cat.rename_categories(["left_only", "right_only", "both"])
+
+        result = result.drop(labels=["_left_indicator", "_right_indicator"], axis=1)
+        return result
+
+    @final
+    def _maybe_restore_index_levels(self, result: DataFrame) -> None:
+        """
+        Restore index levels specified as `on` parameters
+
+        Here we check for cases where `self.left_on` and `self.right_on` pairs
+        each reference an index level in their respective DataFrames. The
+        joined columns corresponding to these pairs are then restored to the
+        index of `result`.
+
+        **Note:** This method has side effects. It modifies `result` in-place
+
+        Parameters
+        ----------
+        result: DataFrame
+            merge result
+
+        Returns
+        -------
+        None
+        """
+        names_to_restore = []
+        for name, left_key, right_key in zip(
+            self.join_names, self.left_on, self.right_on
+        ):
+            if (
+                # Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
+                # type "Union[Hashable, ExtensionArray, Index, Series]"; expected
+                # "Hashable"
+                self.orig_left._is_level_reference(left_key)  # type: ignore[arg-type]
+                # Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
+                # type "Union[Hashable, ExtensionArray, Index, Series]"; expected
+                # "Hashable"
+                and self.orig_right._is_level_reference(
+                    right_key  # type: ignore[arg-type]
+                )
+                and left_key == right_key
+                and name not in result.index.names
+            ):
+                names_to_restore.append(name)
+
+        if names_to_restore:
+            result.set_index(names_to_restore, inplace=True)
+
+    @final
+    def _maybe_add_join_keys(
+        self,
+        result: DataFrame,
+        left_indexer: npt.NDArray[np.intp] | None,
+        right_indexer: npt.NDArray[np.intp] | None,
+    ) -> None:
+        left_has_missing = None
+        right_has_missing = None
+
+        assert all(isinstance(x, _known) for x in self.left_join_keys)
+
+        keys = zip(self.join_names, self.left_on, self.right_on)
+        for i, (name, lname, rname) in enumerate(keys):
+            if not _should_fill(lname, rname):
+                continue
+
+            take_left, take_right = None, None
+
+            if name in result:
+                if left_indexer is not None or right_indexer is not None:
+                    if name in self.left:
+                        if left_has_missing is None:
+                            left_has_missing = (
+                                False
+                                if left_indexer is None
+                                else (left_indexer == -1).any()
+                            )
+
+                        if left_has_missing:
+                            take_right = self.right_join_keys[i]
+
+                            if result[name].dtype != self.left[name].dtype:
+                                take_left = self.left[name]._values
+
+                    elif name in self.right:
+                        if right_has_missing is None:
+                            right_has_missing = (
+                                False
+                                if right_indexer is None
+                                else (right_indexer == -1).any()
+                            )
+
+                        if right_has_missing:
+                            take_left = self.left_join_keys[i]
+
+                            if result[name].dtype != self.right[name].dtype:
+                                take_right = self.right[name]._values
+
+            else:
+                take_left = self.left_join_keys[i]
+                take_right = self.right_join_keys[i]
+
+            if take_left is not None or take_right is not None:
+                if take_left is None:
+                    lvals = result[name]._values
+                elif left_indexer is None:
+                    lvals = take_left
+                else:
+                    # TODO: can we pin down take_left's type earlier?
+                    take_left = extract_array(take_left, extract_numpy=True)
+                    lfill = na_value_for_dtype(take_left.dtype)
+                    lvals = algos.take_nd(take_left, left_indexer, fill_value=lfill)
+
+                if take_right is None:
+                    rvals = result[name]._values
+                elif right_indexer is None:
+                    rvals = take_right
+                else:
+                    # TODO: can we pin down take_right's type earlier?
+                    taker = extract_array(take_right, extract_numpy=True)
+                    rfill = na_value_for_dtype(taker.dtype)
+                    rvals = algos.take_nd(taker, right_indexer, fill_value=rfill)
+
+                # if we have an all missing left_indexer
+                # make sure to just use the right values or vice-versa
+                if left_indexer is not None and (left_indexer == -1).all():
+                    key_col = Index(rvals, dtype=rvals.dtype, copy=False)
+                    result_dtype = rvals.dtype
+                elif right_indexer is not None and (right_indexer == -1).all():
+                    key_col = Index(lvals, dtype=lvals.dtype, copy=False)
+                    result_dtype = lvals.dtype
+                else:
+                    key_col = Index(lvals, dtype=lvals.dtype, copy=False)
+                    if left_indexer is not None:
+                        mask_left = left_indexer == -1
+                        key_col = key_col.where(~mask_left, rvals)
+                    result_dtype = find_common_type([lvals.dtype, rvals.dtype])
+                    if (
+                        lvals.dtype.kind == "M"
+                        and rvals.dtype.kind == "M"
+                        and result_dtype.kind == "O"
+                    ):
+                        # TODO(non-nano) Workaround for common_type not dealing
+                        # with different resolutions
+                        result_dtype = key_col.dtype
+
+                if result._is_label_reference(name):
+                    result[name] = result._constructor_sliced(
+                        key_col, dtype=result_dtype, index=result.index
+                    )
+                elif result._is_level_reference(name):
+                    if isinstance(result.index, MultiIndex):
+                        key_col.name = name
+                        idx_list = [
+                            result.index.get_level_values(level_name)
+                            if level_name != name
+                            else key_col
+                            for level_name in result.index.names
+                        ]
+
+                        result.set_index(idx_list, inplace=True)
+                    else:
+                        key_col.name = name
+                        result.index = key_col
+                else:
+                    result.insert(i, name or f"key_{i}", key_col)
+
+    def _get_join_indexers(
+        self,
+    ) -> tuple[npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        """return the join indexers"""
+        # make mypy happy
+        assert self.how != "asof"
+        return get_join_indexers(
+            self.left_join_keys, self.right_join_keys, sort=self.sort, how=self.how
+        )
+
+    @final
+    def _get_join_info(
+        self,
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        left_ax = self.left.index
+        right_ax = self.right.index
+
+        if self.left_index and self.right_index and self.how != "asof":
+            join_index, left_indexer, right_indexer = left_ax.join(
+                right_ax, how=self.how, return_indexers=True, sort=self.sort
+            )
+
+        elif self.right_index and self.how == "left":
+            join_index, left_indexer, right_indexer = _left_join_on_index(
+                left_ax, right_ax, self.left_join_keys, sort=self.sort
+            )
+
+        elif self.left_index and self.how == "right":
+            join_index, right_indexer, left_indexer = _left_join_on_index(
+                right_ax, left_ax, self.right_join_keys, sort=self.sort
+            )
+        else:
+            (left_indexer, right_indexer) = self._get_join_indexers()
+
+            if self.right_index:
+                if len(self.left) > 0:
+                    join_index = self._create_join_index(
+                        left_ax,
+                        right_ax,
+                        left_indexer,
+                        how="right",
+                    )
+                elif right_indexer is None:
+                    join_index = right_ax.copy()
+                else:
+                    join_index = right_ax.take(right_indexer)
+            elif self.left_index:
+                if self.how == "asof":
+                    # GH#33463 asof should always behave like a left merge
+                    join_index = self._create_join_index(
+                        left_ax,
+                        right_ax,
+                        left_indexer,
+                        how="left",
+                    )
+
+                elif len(self.right) > 0:
+                    join_index = self._create_join_index(
+                        right_ax,
+                        left_ax,
+                        right_indexer,
+                        how="left",
+                    )
+                elif left_indexer is None:
+                    join_index = left_ax.copy()
+                else:
+                    join_index = left_ax.take(left_indexer)
+            else:
+                n = len(left_ax) if left_indexer is None else len(left_indexer)
+                join_index = default_index(n)
+
+        return join_index, left_indexer, right_indexer
+
+    @final
+    def _create_join_index(
+        self,
+        index: Index,
+        other_index: Index,
+        indexer: npt.NDArray[np.intp] | None,
+        how: JoinHow = "left",
+    ) -> Index:
+        """
+        Create a join index by rearranging one index to match another
+
+        Parameters
+        ----------
+        index : Index
+            index being rearranged
+        other_index : Index
+            used to supply values not found in index
+        indexer : np.ndarray[np.intp] or None
+            how to rearrange index
+        how : str
+            Replacement is only necessary if indexer based on other_index.
+
+        Returns
+        -------
+        Index
+        """
+        if self.how in (how, "outer") and not isinstance(other_index, MultiIndex):
+            # if final index requires values in other_index but not target
+            # index, indexer may hold missing (-1) values, causing Index.take
+            # to take the final value in target index. So, we set the last
+            # element to be the desired fill value. We do not use allow_fill
+            # and fill_value because it throws a ValueError on integer indices
+            mask = indexer == -1
+            if np.any(mask):
+                fill_value = na_value_for_dtype(index.dtype, compat=False)
+                index = index.append(Index([fill_value]))
+        if indexer is None:
+            return index.copy()
+        return index.take(indexer)
+
+    @final
+    def _get_merge_keys(
+        self,
+    ) -> tuple[
+        list[ArrayLike],
+        list[ArrayLike],
+        list[Hashable],
+        list[Hashable],
+        list[Hashable],
+    ]:
+        """
+        Returns
+        -------
+        left_keys, right_keys, join_names, left_drop, right_drop
+        """
+        left_keys: list[ArrayLike] = []
+        right_keys: list[ArrayLike] = []
+        join_names: list[Hashable] = []
+        right_drop: list[Hashable] = []
+        left_drop: list[Hashable] = []
+
+        left, right = self.left, self.right
+
+        is_lkey = lambda x: isinstance(x, _known) and len(x) == len(left)
+        is_rkey = lambda x: isinstance(x, _known) and len(x) == len(right)
+
+        # Note that pd.merge_asof() has separate 'on' and 'by' parameters. A
+        # user could, for example, request 'left_index' and 'left_by'. In a
+        # regular pd.merge(), users cannot specify both 'left_index' and
+        # 'left_on'. (Instead, users have a MultiIndex). That means the
+        # self.left_on in this function is always empty in a pd.merge(), but
+        # a pd.merge_asof(left_index=True, left_by=...) will result in a
+        # self.left_on array with a None in the middle of it. This requires
+        # a work-around as designated in the code below.
+        # See _validate_left_right_on() for where this happens.
+
+        # ugh, spaghetti re #733
+        if _any(self.left_on) and _any(self.right_on):
+            for lk, rk in zip(self.left_on, self.right_on):
+                lk = extract_array(lk, extract_numpy=True)
+                rk = extract_array(rk, extract_numpy=True)
+                if is_lkey(lk):
+                    lk = cast(ArrayLike, lk)
+                    left_keys.append(lk)
+                    if is_rkey(rk):
+                        rk = cast(ArrayLike, rk)
+                        right_keys.append(rk)
+                        join_names.append(None)  # what to do?
+                    else:
+                        # Then we're either Hashable or a wrong-length arraylike,
+                        #  the latter of which will raise
+                        rk = cast(Hashable, rk)
+                        if rk is not None:
+                            right_keys.append(right._get_label_or_level_values(rk))
+                            join_names.append(rk)
+                        else:
+                            # work-around for merge_asof(right_index=True)
+                            right_keys.append(right.index._values)
+                            join_names.append(right.index.name)
+                else:
+                    if not is_rkey(rk):
+                        # Then we're either Hashable or a wrong-length arraylike,
+                        #  the latter of which will raise
+                        rk = cast(Hashable, rk)
+                        if rk is not None:
+                            right_keys.append(right._get_label_or_level_values(rk))
+                        else:
+                            # work-around for merge_asof(right_index=True)
+                            right_keys.append(right.index._values)
+                        if lk is not None and lk == rk:  # FIXME: what about other NAs?
+                            right_drop.append(rk)
+                    else:
+                        rk = cast(ArrayLike, rk)
+                        right_keys.append(rk)
+                    if lk is not None:
+                        # Then we're either Hashable or a wrong-length arraylike,
+                        #  the latter of which will raise
+                        lk = cast(Hashable, lk)
+                        left_keys.append(left._get_label_or_level_values(lk))
+                        join_names.append(lk)
+                    else:
+                        # work-around for merge_asof(left_index=True)
+                        left_keys.append(left.index._values)
+                        join_names.append(left.index.name)
+        elif _any(self.left_on):
+            for k in self.left_on:
+                if is_lkey(k):
+                    k = extract_array(k, extract_numpy=True)
+                    k = cast(ArrayLike, k)
+                    left_keys.append(k)
+                    join_names.append(None)
+                else:
+                    # Then we're either Hashable or a wrong-length arraylike,
+                    #  the latter of which will raise
+                    k = cast(Hashable, k)
+                    left_keys.append(left._get_label_or_level_values(k))
+                    join_names.append(k)
+            if isinstance(self.right.index, MultiIndex):
+                right_keys = [
+                    lev._values.take(lev_codes)
+                    for lev, lev_codes in zip(
+                        self.right.index.levels, self.right.index.codes
+                    )
+                ]
+            else:
+                right_keys = [self.right.index._values]
+        elif _any(self.right_on):
+            for k in self.right_on:
+                k = extract_array(k, extract_numpy=True)
+                if is_rkey(k):
+                    k = cast(ArrayLike, k)
+                    right_keys.append(k)
+                    join_names.append(None)
+                else:
+                    # Then we're either Hashable or a wrong-length arraylike,
+                    #  the latter of which will raise
+                    k = cast(Hashable, k)
+                    right_keys.append(right._get_label_or_level_values(k))
+                    join_names.append(k)
+            if isinstance(self.left.index, MultiIndex):
+                left_keys = [
+                    lev._values.take(lev_codes)
+                    for lev, lev_codes in zip(
+                        self.left.index.levels, self.left.index.codes
+                    )
+                ]
+            else:
+                left_keys = [self.left.index._values]
+
+        return left_keys, right_keys, join_names, left_drop, right_drop
+
+    @final
+    def _maybe_coerce_merge_keys(self) -> None:
+        # we have valid merges but we may have to further
+        # coerce these if they are originally incompatible types
+        #
+        # for example if these are categorical, but are not dtype_equal
+        # or if we have object and integer dtypes
+
+        for lk, rk, name in zip(
+            self.left_join_keys, self.right_join_keys, self.join_names
+        ):
+            if (len(lk) and not len(rk)) or (not len(lk) and len(rk)):
+                continue
+
+            lk = extract_array(lk, extract_numpy=True)
+            rk = extract_array(rk, extract_numpy=True)
+
+            lk_is_cat = isinstance(lk.dtype, CategoricalDtype)
+            rk_is_cat = isinstance(rk.dtype, CategoricalDtype)
+            lk_is_object_or_string = is_object_dtype(lk.dtype) or is_string_dtype(
+                lk.dtype
+            )
+            rk_is_object_or_string = is_object_dtype(rk.dtype) or is_string_dtype(
+                rk.dtype
+            )
+
+            # if either left or right is a categorical
+            # then the must match exactly in categories & ordered
+            if lk_is_cat and rk_is_cat:
+                lk = cast(Categorical, lk)
+                rk = cast(Categorical, rk)
+                if lk._categories_match_up_to_permutation(rk):
+                    continue
+
+            elif lk_is_cat or rk_is_cat:
+                pass
+
+            elif lk.dtype == rk.dtype:
+                continue
+
+            msg = (
+                f"You are trying to merge on {lk.dtype} and {rk.dtype} columns "
+                f"for key '{name}'. If you wish to proceed you should use pd.concat"
+            )
+
+            # if we are numeric, then allow differing
+            # kinds to proceed, eg. int64 and int8, int and float
+            # further if we are object, but we infer to
+            # the same, then proceed
+            if is_numeric_dtype(lk.dtype) and is_numeric_dtype(rk.dtype):
+                if lk.dtype.kind == rk.dtype.kind:
+                    continue
+
+                if isinstance(lk.dtype, ExtensionDtype) and not isinstance(
+                    rk.dtype, ExtensionDtype
+                ):
+                    ct = find_common_type([lk.dtype, rk.dtype])
+                    if isinstance(ct, ExtensionDtype):
+                        com_cls = ct.construct_array_type()
+                        rk = com_cls._from_sequence(rk, dtype=ct, copy=False)
+                    else:
+                        rk = rk.astype(ct)
+                elif isinstance(rk.dtype, ExtensionDtype):
+                    ct = find_common_type([lk.dtype, rk.dtype])
+                    if isinstance(ct, ExtensionDtype):
+                        com_cls = ct.construct_array_type()
+                        lk = com_cls._from_sequence(lk, dtype=ct, copy=False)
+                    else:
+                        lk = lk.astype(ct)
+
+                # check whether ints and floats
+                if is_integer_dtype(rk.dtype) and is_float_dtype(lk.dtype):
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        # error: Argument 1 to "astype" of "ndarray" has incompatible
+                        # type "Union[ExtensionDtype, Any, dtype[Any]]"; expected
+                        # "Union[dtype[Any], Type[Any], _SupportsDType[dtype[Any]]]"
+                        casted = lk.astype(rk.dtype)  # type: ignore[arg-type]
+
+                    mask = ~np.isnan(lk)
+                    match = lk == casted
+                    if not match[mask].all():
+                        warnings.warn(
+                            "You are merging on int and float "
+                            "columns where the float values "
+                            "are not equal to their int representation.",
+                            UserWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    continue
+
+                if is_float_dtype(rk.dtype) and is_integer_dtype(lk.dtype):
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        # error: Argument 1 to "astype" of "ndarray" has incompatible
+                        # type "Union[ExtensionDtype, Any, dtype[Any]]"; expected
+                        # "Union[dtype[Any], Type[Any], _SupportsDType[dtype[Any]]]"
+                        casted = rk.astype(lk.dtype)  # type: ignore[arg-type]
+
+                    mask = ~np.isnan(rk)
+                    match = rk == casted
+                    if not match[mask].all():
+                        warnings.warn(
+                            "You are merging on int and float "
+                            "columns where the float values "
+                            "are not equal to their int representation.",
+                            UserWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    continue
+
+                # let's infer and see if we are ok
+                if lib.infer_dtype(lk, skipna=False) == lib.infer_dtype(
+                    rk, skipna=False
+                ):
+                    continue
+
+            # Check if we are trying to merge on obviously
+            # incompatible dtypes GH 9780, GH 15800
+
+            # bool values are coerced to object
+            elif (lk_is_object_or_string and is_bool_dtype(rk.dtype)) or (
+                is_bool_dtype(lk.dtype) and rk_is_object_or_string
+            ):
+                pass
+
+            # object values are allowed to be merged
+            elif (lk_is_object_or_string and is_numeric_dtype(rk.dtype)) or (
+                is_numeric_dtype(lk.dtype) and rk_is_object_or_string
+            ):
+                inferred_left = lib.infer_dtype(lk, skipna=False)
+                inferred_right = lib.infer_dtype(rk, skipna=False)
+                bool_types = ["integer", "mixed-integer", "boolean", "empty"]
+                string_types = ["string", "unicode", "mixed", "bytes", "empty"]
+
+                # inferred bool
+                if inferred_left in bool_types and inferred_right in bool_types:
+                    pass
+
+                # unless we are merging non-string-like with string-like
+                elif (
+                    inferred_left in string_types and inferred_right not in string_types
+                ) or (
+                    inferred_right in string_types and inferred_left not in string_types
+                ):
+                    raise ValueError(msg)
+
+            # datetimelikes must match exactly
+            elif needs_i8_conversion(lk.dtype) and not needs_i8_conversion(rk.dtype):
+                raise ValueError(msg)
+            elif not needs_i8_conversion(lk.dtype) and needs_i8_conversion(rk.dtype):
+                raise ValueError(msg)
+            elif isinstance(lk.dtype, DatetimeTZDtype) and not isinstance(
+                rk.dtype, DatetimeTZDtype
+            ):
+                raise ValueError(msg)
+            elif not isinstance(lk.dtype, DatetimeTZDtype) and isinstance(
+                rk.dtype, DatetimeTZDtype
+            ):
+                raise ValueError(msg)
+            elif (
+                isinstance(lk.dtype, DatetimeTZDtype)
+                and isinstance(rk.dtype, DatetimeTZDtype)
+            ) or (lk.dtype.kind == "M" and rk.dtype.kind == "M"):
+                # allows datetime with different resolutions
+                continue
+            # datetime and timedelta not allowed
+            elif lk.dtype.kind == "M" and rk.dtype.kind == "m":
+                raise ValueError(msg)
+            elif lk.dtype.kind == "m" and rk.dtype.kind == "M":
+                raise ValueError(msg)
+
+            elif is_object_dtype(lk.dtype) and is_object_dtype(rk.dtype):
+                continue
+
+            # Houston, we have a problem!
+            # let's coerce to object if the dtypes aren't
+            # categorical, otherwise coerce to the category
+            # dtype. If we coerced categories to object,
+            # then we would lose type information on some
+            # columns, and end up trying to merge
+            # incompatible dtypes. See GH 16900.
+            if name in self.left.columns:
+                typ = cast(Categorical, lk).categories.dtype if lk_is_cat else object
+                self.left = self.left.copy()
+                self.left[name] = self.left[name].astype(typ)
+            if name in self.right.columns:
+                typ = cast(Categorical, rk).categories.dtype if rk_is_cat else object
+                self.right = self.right.copy()
+                self.right[name] = self.right[name].astype(typ)
+
+    def _validate_left_right_on(self, left_on, right_on):
+        left_on = com.maybe_make_list(left_on)
+        right_on = com.maybe_make_list(right_on)
+
+        # Hm, any way to make this logic less complicated??
+        if self.on is None and left_on is None and right_on is None:
+            if self.left_index and self.right_index:
+                left_on, right_on = (), ()
+            elif self.left_index:
+                raise MergeError("Must pass right_on or right_index=True")
+            elif self.right_index:
+                raise MergeError("Must pass left_on or left_index=True")
+            else:
+                # use the common columns
+                left_cols = self.left.columns
+                right_cols = self.right.columns
+                common_cols = left_cols.intersection(right_cols)
+                if len(common_cols) == 0:
+                    raise MergeError(
+                        "No common columns to perform merge on. "
+                        f"Merge options: left_on={left_on}, "
+                        f"right_on={right_on}, "
+                        f"left_index={self.left_index}, "
+                        f"right_index={self.right_index}"
+                    )
+                if (
+                    not left_cols.join(common_cols, how="inner").is_unique
+                    or not right_cols.join(common_cols, how="inner").is_unique
+                ):
+                    raise MergeError(f"Data columns not unique: {repr(common_cols)}")
+                left_on = right_on = common_cols
+        elif self.on is not None:
+            if left_on is not None or right_on is not None:
+                raise MergeError(
+                    'Can only pass argument "on" OR "left_on" '
+                    'and "right_on", not a combination of both.'
+                )
+            if self.left_index or self.right_index:
+                raise MergeError(
+                    'Can only pass argument "on" OR "left_index" '
+                    'and "right_index", not a combination of both.'
+                )
+            left_on = right_on = self.on
+        elif left_on is not None:
+            if self.left_index:
+                raise MergeError(
+                    'Can only pass argument "left_on" OR "left_index" not both.'
+                )
+            if not self.right_index and right_on is None:
+                raise MergeError('Must pass "right_on" OR "right_index".')
+            n = len(left_on)
+            if self.right_index:
+                if len(left_on) != self.right.index.nlevels:
+                    raise ValueError(
+                        "len(left_on) must equal the number "
+                        'of levels in the index of "right"'
+                    )
+                right_on = [None] * n
+        elif right_on is not None:
+            if self.right_index:
+                raise MergeError(
+                    'Can only pass argument "right_on" OR "right_index" not both.'
+                )
+            if not self.left_index and left_on is None:
+                raise MergeError('Must pass "left_on" OR "left_index".')
+            n = len(right_on)
+            if self.left_index:
+                if len(right_on) != self.left.index.nlevels:
+                    raise ValueError(
+                        "len(right_on) must equal the number "
+                        'of levels in the index of "left"'
+                    )
+                left_on = [None] * n
+        if len(right_on) != len(left_on):
+            raise ValueError("len(right_on) must equal len(left_on)")
+
+        return left_on, right_on
+
+    @final
+    def _validate_validate_kwd(self, validate: str) -> None:
+        # Check uniqueness of each
+        if self.left_index:
+            left_unique = self.orig_left.index.is_unique
+        else:
+            left_unique = MultiIndex.from_arrays(self.left_join_keys).is_unique
+
+        if self.right_index:
+            right_unique = self.orig_right.index.is_unique
+        else:
+            right_unique = MultiIndex.from_arrays(self.right_join_keys).is_unique
+
+        # Check data integrity
+        if validate in ["one_to_one", "1:1"]:
+            if not left_unique and not right_unique:
+                raise MergeError(
+                    "Merge keys are not unique in either left "
+                    "or right dataset; not a one-to-one merge"
+                )
+            if not left_unique:
+                raise MergeError(
+                    "Merge keys are not unique in left dataset; not a one-to-one merge"
+                )
+            if not right_unique:
+                raise MergeError(
+                    "Merge keys are not unique in right dataset; not a one-to-one merge"
+                )
+
+        elif validate in ["one_to_many", "1:m"]:
+            if not left_unique:
+                raise MergeError(
+                    "Merge keys are not unique in left dataset; not a one-to-many merge"
+                )
+
+        elif validate in ["many_to_one", "m:1"]:
+            if not right_unique:
+                raise MergeError(
+                    "Merge keys are not unique in right dataset; "
+                    "not a many-to-one merge"
+                )
+
+        elif validate in ["many_to_many", "m:m"]:
+            pass
+
+        else:
+            raise ValueError(
+                f'"{validate}" is not a valid argument. '
+                "Valid arguments are:\n"
+                '- "1:1"\n'
+                '- "1:m"\n'
+                '- "m:1"\n'
+                '- "m:m"\n'
+                '- "one_to_one"\n'
+                '- "one_to_many"\n'
+                '- "many_to_one"\n'
+                '- "many_to_many"'
+            )
+
+
+def get_join_indexers(
+    left_keys: list[ArrayLike],
+    right_keys: list[ArrayLike],
+    sort: bool = False,
+    how: JoinHow = "inner",
+) -> tuple[npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+    """
+
+    Parameters
+    ----------
+    left_keys : list[ndarray, ExtensionArray, Index, Series]
+    right_keys : list[ndarray, ExtensionArray, Index, Series]
+    sort : bool, default False
+    how : {'inner', 'outer', 'left', 'right'}, default 'inner'
+
+    Returns
+    -------
+    np.ndarray[np.intp] or None
+        Indexer into the left_keys.
+    np.ndarray[np.intp] or None
+        Indexer into the right_keys.
+    """
+    assert len(left_keys) == len(
+        right_keys
+    ), "left_keys and right_keys must be the same length"
+
+    # fast-path for empty left/right
+    left_n = len(left_keys[0])
+    right_n = len(right_keys[0])
+    if left_n == 0:
+        if how in ["left", "inner"]:
+            return _get_empty_indexer()
+        elif not sort and how in ["right", "outer"]:
+            return _get_no_sort_one_missing_indexer(right_n, True)
+    elif right_n == 0:
+        if how in ["right", "inner"]:
+            return _get_empty_indexer()
+        elif not sort and how in ["left", "outer"]:
+            return _get_no_sort_one_missing_indexer(left_n, False)
+
+    lkey: ArrayLike
+    rkey: ArrayLike
+    if len(left_keys) > 1:
+        # get left & right join labels and num. of levels at each location
+        mapped = (
+            _factorize_keys(left_keys[n], right_keys[n], sort=sort)
+            for n in range(len(left_keys))
+        )
+        zipped = zip(*mapped)
+        llab, rlab, shape = (list(x) for x in zipped)
+
+        # get flat i8 keys from label lists
+        lkey, rkey = _get_join_keys(llab, rlab, tuple(shape), sort)
+    else:
+        lkey = left_keys[0]
+        rkey = right_keys[0]
+
+    left = Index(lkey)
+    right = Index(rkey)
+
+    if (
+        left.is_monotonic_increasing
+        and right.is_monotonic_increasing
+        and (left.is_unique or right.is_unique)
+    ):
+        _, lidx, ridx = left.join(right, how=how, return_indexers=True, sort=sort)
+    else:
+        lidx, ridx = get_join_indexers_non_unique(
+            left._values, right._values, sort, how
+        )
+
+    if lidx is not None and is_range_indexer(lidx, len(left)):
+        lidx = None
+    if ridx is not None and is_range_indexer(ridx, len(right)):
+        ridx = None
+    return lidx, ridx
+
+
+def get_join_indexers_non_unique(
+    left: ArrayLike,
+    right: ArrayLike,
+    sort: bool = False,
+    how: JoinHow = "inner",
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    """
+    Get join indexers for left and right.
+
+    Parameters
+    ----------
+    left : ArrayLike
+    right : ArrayLike
+    sort : bool, default False
+    how : {'inner', 'outer', 'left', 'right'}, default 'inner'
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        Indexer into left.
+    np.ndarray[np.intp]
+        Indexer into right.
+    """
+    lkey, rkey, count = _factorize_keys(left, right, sort=sort)
+    if how == "left":
+        lidx, ridx = libjoin.left_outer_join(lkey, rkey, count, sort=sort)
+    elif how == "right":
+        ridx, lidx = libjoin.left_outer_join(rkey, lkey, count, sort=sort)
+    elif how == "inner":
+        lidx, ridx = libjoin.inner_join(lkey, rkey, count, sort=sort)
+    elif how == "outer":
+        lidx, ridx = libjoin.full_outer_join(lkey, rkey, count)
+    return lidx, ridx
+
+
+def restore_dropped_levels_multijoin(
+    left: MultiIndex,
+    right: MultiIndex,
+    dropped_level_names,
+    join_index: Index,
+    lindexer: npt.NDArray[np.intp],
+    rindexer: npt.NDArray[np.intp],
+) -> tuple[FrozenList, FrozenList, FrozenList]:
+    """
+    *this is an internal non-public method*
+
+    Returns the levels, labels and names of a multi-index to multi-index join.
+    Depending on the type of join, this method restores the appropriate
+    dropped levels of the joined multi-index.
+    The method relies on lindexer, rindexer which hold the index positions of
+    left and right, where a join was feasible
+
+    Parameters
+    ----------
+    left : MultiIndex
+        left index
+    right : MultiIndex
+        right index
+    dropped_level_names : str array
+        list of non-common level names
+    join_index : Index
+        the index of the join between the
+        common levels of left and right
+    lindexer : np.ndarray[np.intp]
+        left indexer
+    rindexer : np.ndarray[np.intp]
+        right indexer
+
+    Returns
+    -------
+    levels : list of Index
+        levels of combined multiindexes
+    labels : np.ndarray[np.intp]
+        labels of combined multiindexes
+    names : List[Hashable]
+        names of combined multiindex levels
+
+    """
+
+    def _convert_to_multiindex(index: Index) -> MultiIndex:
+        if isinstance(index, MultiIndex):
+            return index
+        else:
+            return MultiIndex.from_arrays([index._values], names=[index.name])
+
+    # For multi-multi joins with one overlapping level,
+    # the returned index if of type Index
+    # Assure that join_index is of type MultiIndex
+    # so that dropped levels can be appended
+    join_index = _convert_to_multiindex(join_index)
+
+    join_levels = join_index.levels
+    join_codes = join_index.codes
+    join_names = join_index.names
+
+    # Iterate through the levels that must be restored
+    for dropped_level_name in dropped_level_names:
+        if dropped_level_name in left.names:
+            idx = left
+            indexer = lindexer
+        else:
+            idx = right
+            indexer = rindexer
+
+        # The index of the level name to be restored
+        name_idx = idx.names.index(dropped_level_name)
+
+        restore_levels = idx.levels[name_idx]
+        # Inject -1 in the codes list where a join was not possible
+        # IOW indexer[i]=-1
+        codes = idx.codes[name_idx]
+        if indexer is None:
+            restore_codes = codes
+        else:
+            restore_codes = algos.take_nd(codes, indexer, fill_value=-1)
+
+        # error: Cannot determine type of "__add__"
+        join_levels = join_levels + [restore_levels]  # type: ignore[has-type]
+        join_codes = join_codes + [restore_codes]  # type: ignore[has-type]
+        join_names = join_names + [dropped_level_name]
+
+    return join_levels, join_codes, join_names
+
+
+class _OrderedMerge(_MergeOperation):
+    _merge_type = "ordered_merge"
+
+    def __init__(
+        self,
+        left: DataFrame | Series,
+        right: DataFrame | Series,
+        on: IndexLabel | None = None,
+        left_on: IndexLabel | None = None,
+        right_on: IndexLabel | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        suffixes: Suffixes = ("_x", "_y"),
+        fill_method: str | None = None,
+        how: JoinHow | Literal["asof"] = "outer",
+    ) -> None:
+        self.fill_method = fill_method
+        _MergeOperation.__init__(
+            self,
+            left,
+            right,
+            on=on,
+            left_on=left_on,
+            left_index=left_index,
+            right_index=right_index,
+            right_on=right_on,
+            how=how,
+            suffixes=suffixes,
+            sort=True,  # factorize sorts
+        )
+
+    def get_result(self, copy: bool | None = True) -> DataFrame:
+        join_index, left_indexer, right_indexer = self._get_join_info()
+
+        left_join_indexer: npt.NDArray[np.intp] | None
+        right_join_indexer: npt.NDArray[np.intp] | None
+
+        if self.fill_method == "ffill":
+            if left_indexer is None:
+                left_join_indexer = None
+            else:
+                left_join_indexer = libjoin.ffill_indexer(left_indexer)
+            if right_indexer is None:
+                right_join_indexer = None
+            else:
+                right_join_indexer = libjoin.ffill_indexer(right_indexer)
+        elif self.fill_method is None:
+            left_join_indexer = left_indexer
+            right_join_indexer = right_indexer
+        else:
+            raise ValueError("fill_method must be 'ffill' or None")
+
+        result = self._reindex_and_concat(
+            join_index, left_join_indexer, right_join_indexer, copy=copy
+        )
+        self._maybe_add_join_keys(result, left_indexer, right_indexer)
+
+        return result
+
+
+def _asof_by_function(direction: str):
+    name = f"asof_join_{direction}_on_X_by_Y"
+    return getattr(libjoin, name, None)
+
+
+class _AsOfMerge(_OrderedMerge):
+    _merge_type = "asof_merge"
+
+    def __init__(
+        self,
+        left: DataFrame | Series,
+        right: DataFrame | Series,
+        on: IndexLabel | None = None,
+        left_on: IndexLabel | None = None,
+        right_on: IndexLabel | None = None,
+        left_index: bool = False,
+        right_index: bool = False,
+        by=None,
+        left_by=None,
+        right_by=None,
+        suffixes: Suffixes = ("_x", "_y"),
+        how: Literal["asof"] = "asof",
+        tolerance=None,
+        allow_exact_matches: bool = True,
+        direction: str = "backward",
+    ) -> None:
+        self.by = by
+        self.left_by = left_by
+        self.right_by = right_by
+        self.tolerance = tolerance
+        self.allow_exact_matches = allow_exact_matches
+        self.direction = direction
+
+        # check 'direction' is valid
+        if self.direction not in ["backward", "forward", "nearest"]:
+            raise MergeError(f"direction invalid: {self.direction}")
+
+        # validate allow_exact_matches
+        if not is_bool(self.allow_exact_matches):
+            msg = (
+                "allow_exact_matches must be boolean, "
+                f"passed {self.allow_exact_matches}"
+            )
+            raise MergeError(msg)
+
+        _OrderedMerge.__init__(
+            self,
+            left,
+            right,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            how=how,
+            suffixes=suffixes,
+            fill_method=None,
+        )
+
+    def _validate_left_right_on(self, left_on, right_on):
+        left_on, right_on = super()._validate_left_right_on(left_on, right_on)
+
+        # we only allow on to be a single item for on
+        if len(left_on) != 1 and not self.left_index:
+            raise MergeError("can only asof on a key for left")
+
+        if len(right_on) != 1 and not self.right_index:
+            raise MergeError("can only asof on a key for right")
+
+        if self.left_index and isinstance(self.left.index, MultiIndex):
+            raise MergeError("left can only have one index")
+
+        if self.right_index and isinstance(self.right.index, MultiIndex):
+            raise MergeError("right can only have one index")
+
+        # set 'by' columns
+        if self.by is not None:
+            if self.left_by is not None or self.right_by is not None:
+                raise MergeError("Can only pass by OR left_by and right_by")
+            self.left_by = self.right_by = self.by
+        if self.left_by is None and self.right_by is not None:
+            raise MergeError("missing left_by")
+        if self.left_by is not None and self.right_by is None:
+            raise MergeError("missing right_by")
+
+        # GH#29130 Check that merge keys do not have dtype object
+        if not self.left_index:
+            left_on_0 = left_on[0]
+            if isinstance(left_on_0, _known):
+                lo_dtype = left_on_0.dtype
+            else:
+                lo_dtype = (
+                    self.left._get_label_or_level_values(left_on_0).dtype
+                    if left_on_0 in self.left.columns
+                    else self.left.index.get_level_values(left_on_0)
+                )
+        else:
+            lo_dtype = self.left.index.dtype
+
+        if not self.right_index:
+            right_on_0 = right_on[0]
+            if isinstance(right_on_0, _known):
+                ro_dtype = right_on_0.dtype
+            else:
+                ro_dtype = (
+                    self.right._get_label_or_level_values(right_on_0).dtype
+                    if right_on_0 in self.right.columns
+                    else self.right.index.get_level_values(right_on_0)
+                )
+        else:
+            ro_dtype = self.right.index.dtype
+
+        if (
+            is_object_dtype(lo_dtype)
+            or is_object_dtype(ro_dtype)
+            or is_string_dtype(lo_dtype)
+            or is_string_dtype(ro_dtype)
+        ):
+            raise MergeError(
+                f"Incompatible merge dtype, {repr(ro_dtype)} and "
+                f"{repr(lo_dtype)}, both sides must have numeric dtype"
+            )
+
+        # add 'by' to our key-list so we can have it in the
+        # output as a key
+        if self.left_by is not None:
+            if not is_list_like(self.left_by):
+                self.left_by = [self.left_by]
+            if not is_list_like(self.right_by):
+                self.right_by = [self.right_by]
+
+            if len(self.left_by) != len(self.right_by):
+                raise MergeError("left_by and right_by must be the same length")
+
+            left_on = self.left_by + list(left_on)
+            right_on = self.right_by + list(right_on)
+
+        return left_on, right_on
+
+    def _maybe_require_matching_dtypes(
+        self, left_join_keys: list[ArrayLike], right_join_keys: list[ArrayLike]
+    ) -> None:
+        # TODO: why do we do this for AsOfMerge but not the others?
+
+        def _check_dtype_match(left: ArrayLike, right: ArrayLike, i: int):
+            if left.dtype != right.dtype:
+                if isinstance(left.dtype, CategoricalDtype) and isinstance(
+                    right.dtype, CategoricalDtype
+                ):
+                    # The generic error message is confusing for categoricals.
+                    #
+                    # In this function, the join keys include both the original
+                    # ones of the merge_asof() call, and also the keys passed
+                    # to its by= argument. Unordered but equal categories
+                    # are not supported for the former, but will fail
+                    # later with a ValueError, so we don't *need* to check
+                    # for them here.
+                    msg = (
+                        f"incompatible merge keys [{i}] {repr(left.dtype)} and "
+                        f"{repr(right.dtype)}, both sides category, but not equal ones"
+                    )
+                else:
+                    msg = (
+                        f"incompatible merge keys [{i}] {repr(left.dtype)} and "
+                        f"{repr(right.dtype)}, must be the same type"
+                    )
+                raise MergeError(msg)
+
+        # validate index types are the same
+        for i, (lk, rk) in enumerate(zip(left_join_keys, right_join_keys)):
+            _check_dtype_match(lk, rk, i)
+
+        if self.left_index:
+            lt = self.left.index._values
+        else:
+            lt = left_join_keys[-1]
+
+        if self.right_index:
+            rt = self.right.index._values
+        else:
+            rt = right_join_keys[-1]
+
+        _check_dtype_match(lt, rt, 0)
+
+    def _validate_tolerance(self, left_join_keys: list[ArrayLike]) -> None:
+        # validate tolerance; datetime.timedelta or Timedelta if we have a DTI
+        if self.tolerance is not None:
+            if self.left_index:
+                lt = self.left.index._values
+            else:
+                lt = left_join_keys[-1]
+
+            msg = (
+                f"incompatible tolerance {self.tolerance}, must be compat "
+                f"with type {repr(lt.dtype)}"
+            )
+
+            if needs_i8_conversion(lt.dtype) or (
+                isinstance(lt, ArrowExtensionArray) and lt.dtype.kind in "mM"
+            ):
+                if not isinstance(self.tolerance, datetime.timedelta):
+                    raise MergeError(msg)
+                if self.tolerance < Timedelta(0):
+                    raise MergeError("tolerance must be positive")
+
+            elif is_integer_dtype(lt.dtype):
+                if not is_integer(self.tolerance):
+                    raise MergeError(msg)
+                if self.tolerance < 0:
+                    raise MergeError("tolerance must be positive")
+
+            elif is_float_dtype(lt.dtype):
+                if not is_number(self.tolerance):
+                    raise MergeError(msg)
+                # error: Unsupported operand types for > ("int" and "Number")
+                if self.tolerance < 0:  # type: ignore[operator]
+                    raise MergeError("tolerance must be positive")
+
+            else:
+                raise MergeError("key must be integer, timestamp or float")
+
+    def _convert_values_for_libjoin(
+        self, values: AnyArrayLike, side: str
+    ) -> np.ndarray:
+        # we require sortedness and non-null values in the join keys
+        if not Index(values).is_monotonic_increasing:
+            if isna(values).any():
+                raise ValueError(f"Merge keys contain null values on {side} side")
+            raise ValueError(f"{side} keys must be sorted")
+
+        if isinstance(values, ArrowExtensionArray):
+            values = values._maybe_convert_datelike_array()
+
+        if needs_i8_conversion(values.dtype):
+            values = values.view("i8")
+
+        elif isinstance(values, BaseMaskedArray):
+            # we've verified above that no nulls exist
+            values = values._data
+        elif isinstance(values, ExtensionArray):
+            values = values.to_numpy()
+
+        # error: Incompatible return value type (got "Union[ExtensionArray,
+        # Any, ndarray[Any, Any], ndarray[Any, dtype[Any]], Index, Series]",
+        # expected "ndarray[Any, Any]")
+        return values  # type: ignore[return-value]
+
+    def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+        """return the join indexers"""
+
+        # values to compare
+        left_values = (
+            self.left.index._values if self.left_index else self.left_join_keys[-1]
+        )
+        right_values = (
+            self.right.index._values if self.right_index else self.right_join_keys[-1]
+        )
+
+        # _maybe_require_matching_dtypes already checked for dtype matching
+        assert left_values.dtype == right_values.dtype
+
+        tolerance = self.tolerance
+        if tolerance is not None:
+            # TODO: can we reuse a tolerance-conversion function from
+            #  e.g. TimedeltaIndex?
+            if needs_i8_conversion(left_values.dtype) or (
+                isinstance(left_values, ArrowExtensionArray)
+                and left_values.dtype.kind in "mM"
+            ):
+                tolerance = Timedelta(tolerance)
+                # TODO: we have no test cases with PeriodDtype here; probably
+                #  need to adjust tolerance for that case.
+                if left_values.dtype.kind in "mM":
+                    # Make sure the i8 representation for tolerance
+                    #  matches that for left_values/right_values.
+                    if isinstance(left_values, ArrowExtensionArray):
+                        unit = left_values.dtype.pyarrow_dtype.unit
+                    else:
+                        unit = ensure_wrapped_if_datetimelike(left_values).unit
+                    tolerance = tolerance.as_unit(unit)
+
+                tolerance = tolerance._value
+
+        # initial type conversion as needed
+        left_values = self._convert_values_for_libjoin(left_values, "left")
+        right_values = self._convert_values_for_libjoin(right_values, "right")
+
+        # a "by" parameter requires special handling
+        if self.left_by is not None:
+            # remove 'on' parameter from values if one existed
+            if self.left_index and self.right_index:
+                left_join_keys = self.left_join_keys
+                right_join_keys = self.right_join_keys
+            else:
+                left_join_keys = self.left_join_keys[0:-1]
+                right_join_keys = self.right_join_keys[0:-1]
+
+            mapped = [
+                _factorize_keys(
+                    left_join_keys[n],
+                    right_join_keys[n],
+                    sort=False,
+                )
+                for n in range(len(left_join_keys))
+            ]
+
+            if len(left_join_keys) == 1:
+                left_by_values = mapped[0][0]
+                right_by_values = mapped[0][1]
+            else:
+                arrs = [np.concatenate(m[:2]) for m in mapped]
+                shape = tuple(m[2] for m in mapped)
+                group_index = get_group_index(
+                    arrs, shape=shape, sort=False, xnull=False
+                )
+                left_len = len(left_join_keys[0])
+                left_by_values = group_index[:left_len]
+                right_by_values = group_index[left_len:]
+
+            left_by_values = ensure_int64(left_by_values)
+            right_by_values = ensure_int64(right_by_values)
+
+            # choose appropriate function by type
+            func = _asof_by_function(self.direction)
+            return func(
+                left_values,
+                right_values,
+                left_by_values,
+                right_by_values,
+                self.allow_exact_matches,
+                tolerance,
+            )
+        else:
+            # choose appropriate function by type
+            func = _asof_by_function(self.direction)
+            return func(
+                left_values,
+                right_values,
+                None,
+                None,
+                self.allow_exact_matches,
+                tolerance,
+                False,
+            )
+
+
+def _get_multiindex_indexer(
+    join_keys: list[ArrayLike], index: MultiIndex, sort: bool
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    # left & right join labels and num. of levels at each location
+    mapped = (
+        _factorize_keys(index.levels[n]._values, join_keys[n], sort=sort)
+        for n in range(index.nlevels)
+    )
+    zipped = zip(*mapped)
+    rcodes, lcodes, shape = (list(x) for x in zipped)
+    if sort:
+        rcodes = list(map(np.take, rcodes, index.codes))
+    else:
+        i8copy = lambda a: a.astype("i8", subok=False, copy=True)
+        rcodes = list(map(i8copy, index.codes))
+
+    # fix right labels if there were any nulls
+    for i, join_key in enumerate(join_keys):
+        mask = index.codes[i] == -1
+        if mask.any():
+            # check if there already was any nulls at this location
+            # if there was, it is factorized to `shape[i] - 1`
+            a = join_key[lcodes[i] == shape[i] - 1]
+            if a.size == 0 or not a[0] != a[0]:
+                shape[i] += 1
+
+            rcodes[i][mask] = shape[i] - 1
+
+    # get flat i8 join keys
+    lkey, rkey = _get_join_keys(lcodes, rcodes, tuple(shape), sort)
+    return lkey, rkey
+
+
+def _get_empty_indexer() -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    """Return empty join indexers."""
+    return (
+        np.array([], dtype=np.intp),
+        np.array([], dtype=np.intp),
+    )
+
+
+def _get_no_sort_one_missing_indexer(
+    n: int, left_missing: bool
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    """
+    Return join indexers where all of one side is selected without sorting
+    and none of the other side is selected.
+
+    Parameters
+    ----------
+    n : int
+        Length of indexers to create.
+    left_missing : bool
+        If True, the left indexer will contain only -1's.
+        If False, the right indexer will contain only -1's.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        Left indexer
+    np.ndarray[np.intp]
+        Right indexer
+    """
+    idx = np.arange(n, dtype=np.intp)
+    idx_missing = np.full(shape=n, fill_value=-1, dtype=np.intp)
+    if left_missing:
+        return idx_missing, idx
+    return idx, idx_missing
+
+
+def _left_join_on_index(
+    left_ax: Index, right_ax: Index, join_keys: list[ArrayLike], sort: bool = False
+) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp]]:
+    if isinstance(right_ax, MultiIndex):
+        lkey, rkey = _get_multiindex_indexer(join_keys, right_ax, sort=sort)
+    else:
+        # error: Incompatible types in assignment (expression has type
+        # "Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series]",
+        # variable has type "ndarray[Any, dtype[signedinteger[Any]]]")
+        lkey = join_keys[0]  # type: ignore[assignment]
+        # error: Incompatible types in assignment (expression has type "Index",
+        # variable has type "ndarray[Any, dtype[signedinteger[Any]]]")
+        rkey = right_ax._values  # type: ignore[assignment]
+
+    left_key, right_key, count = _factorize_keys(lkey, rkey, sort=sort)
+    left_indexer, right_indexer = libjoin.left_outer_join(
+        left_key, right_key, count, sort=sort
+    )
+
+    if sort or len(left_ax) != len(left_indexer):
+        # if asked to sort or there are 1-to-many matches
+        join_index = left_ax.take(left_indexer)
+        return join_index, left_indexer, right_indexer
+
+    # left frame preserves order & length of its index
+    return left_ax, None, right_indexer
+
+
+def _factorize_keys(
+    lk: ArrayLike, rk: ArrayLike, sort: bool = True
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
+    """
+    Encode left and right keys as enumerated types.
+
+    This is used to get the join indexers to be used when merging DataFrames.
+
+    Parameters
+    ----------
+    lk : ndarray, ExtensionArray
+        Left key.
+    rk : ndarray, ExtensionArray
+        Right key.
+    sort : bool, defaults to True
+        If True, the encoding is done such that the unique elements in the
+        keys are sorted.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        Left (resp. right if called with `key='right'`) labels, as enumerated type.
+    np.ndarray[np.intp]
+        Right (resp. left if called with `key='right'`) labels, as enumerated type.
+    int
+        Number of unique elements in union of left and right labels.
+
+    See Also
+    --------
+    merge : Merge DataFrame or named Series objects
+        with a database-style join.
+    algorithms.factorize : Encode the object as an enumerated type
+        or categorical variable.
+
+    Examples
+    --------
+    >>> lk = np.array(["a", "c", "b"])
+    >>> rk = np.array(["a", "c"])
+
+    Here, the unique values are `'a', 'b', 'c'`. With the default
+    `sort=True`, the encoding will be `{0: 'a', 1: 'b', 2: 'c'}`:
+
+    >>> pd.core.reshape.merge._factorize_keys(lk, rk)
+    (array([0, 2, 1]), array([0, 2]), 3)
+
+    With the `sort=False`, the encoding will correspond to the order
+    in which the unique elements first appear: `{0: 'a', 1: 'c', 2: 'b'}`:
+
+    >>> pd.core.reshape.merge._factorize_keys(lk, rk, sort=False)
+    (array([0, 1, 2]), array([0, 1]), 3)
+    """
+    # TODO: if either is a RangeIndex, we can likely factorize more efficiently?
+
+    if (
+        isinstance(lk.dtype, DatetimeTZDtype) and isinstance(rk.dtype, DatetimeTZDtype)
+    ) or (lib.is_np_dtype(lk.dtype, "M") and lib.is_np_dtype(rk.dtype, "M")):
+        # Extract the ndarray (UTC-localized) values
+        # Note: we dont need the dtypes to match, as these can still be compared
+        lk, rk = cast("DatetimeArray", lk)._ensure_matching_resos(rk)
+        lk = cast("DatetimeArray", lk)._ndarray
+        rk = cast("DatetimeArray", rk)._ndarray
+
+    elif (
+        isinstance(lk.dtype, CategoricalDtype)
+        and isinstance(rk.dtype, CategoricalDtype)
+        and lk.dtype == rk.dtype
+    ):
+        assert isinstance(lk, Categorical)
+        assert isinstance(rk, Categorical)
+        # Cast rk to encoding so we can compare codes with lk
+
+        rk = lk._encode_with_my_categories(rk)
+
+        lk = ensure_int64(lk.codes)
+        rk = ensure_int64(rk.codes)
+
+    elif isinstance(lk, ExtensionArray) and lk.dtype == rk.dtype:
+        if (isinstance(lk.dtype, ArrowDtype) and is_string_dtype(lk.dtype)) or (
+            isinstance(lk.dtype, StringDtype) and lk.dtype.storage == "pyarrow"
+        ):
+            import pyarrow as pa
+            import pyarrow.compute as pc
+
+            len_lk = len(lk)
+            lk = lk._pa_array  # type: ignore[attr-defined]
+            rk = rk._pa_array  # type: ignore[union-attr]
+            dc = (
+                pa.chunked_array(lk.chunks + rk.chunks)  # type: ignore[union-attr]
+                .combine_chunks()
+                .dictionary_encode()
+            )
+
+            llab, rlab, count = (
+                pc.fill_null(dc.indices[slice(len_lk)], -1)
+                .to_numpy()
+                .astype(np.intp, copy=False),
+                pc.fill_null(dc.indices[slice(len_lk, None)], -1)
+                .to_numpy()
+                .astype(np.intp, copy=False),
+                len(dc.dictionary),
+            )
+
+            if sort:
+                uniques = dc.dictionary.to_numpy(zero_copy_only=False)
+                llab, rlab = _sort_labels(uniques, llab, rlab)
+
+            if dc.null_count > 0:
+                lmask = llab == -1
+                lany = lmask.any()
+                rmask = rlab == -1
+                rany = rmask.any()
+                if lany:
+                    np.putmask(llab, lmask, count)
+                if rany:
+                    np.putmask(rlab, rmask, count)
+                count += 1
+            return llab, rlab, count
+
+        if not isinstance(lk, BaseMaskedArray) and not (
+            # exclude arrow dtypes that would get cast to object
+            isinstance(lk.dtype, ArrowDtype)
+            and (
+                is_numeric_dtype(lk.dtype.numpy_dtype)
+                or is_string_dtype(lk.dtype)
+                and not sort
+            )
+        ):
+            lk, _ = lk._values_for_factorize()
+
+            # error: Item "ndarray" of "Union[Any, ndarray]" has no attribute
+            # "_values_for_factorize"
+            rk, _ = rk._values_for_factorize()  # type: ignore[union-attr]
+
+    if needs_i8_conversion(lk.dtype) and lk.dtype == rk.dtype:
+        # GH#23917 TODO: Needs tests for non-matching dtypes
+        # GH#23917 TODO: needs tests for case where lk is integer-dtype
+        #  and rk is datetime-dtype
+        lk = np.asarray(lk, dtype=np.int64)
+        rk = np.asarray(rk, dtype=np.int64)
+
+    klass, lk, rk = _convert_arrays_and_get_rizer_klass(lk, rk)
+
+    rizer = klass(max(len(lk), len(rk)))
+
+    if isinstance(lk, BaseMaskedArray):
+        assert isinstance(rk, BaseMaskedArray)
+        llab = rizer.factorize(lk._data, mask=lk._mask)
+        rlab = rizer.factorize(rk._data, mask=rk._mask)
+    elif isinstance(lk, ArrowExtensionArray):
+        assert isinstance(rk, ArrowExtensionArray)
+        # we can only get here with numeric dtypes
+        # TODO: Remove when we have a Factorizer for Arrow
+        llab = rizer.factorize(
+            lk.to_numpy(na_value=1, dtype=lk.dtype.numpy_dtype), mask=lk.isna()
+        )
+        rlab = rizer.factorize(
+            rk.to_numpy(na_value=1, dtype=lk.dtype.numpy_dtype), mask=rk.isna()
+        )
+    else:
+        # Argument 1 to "factorize" of "ObjectFactorizer" has incompatible type
+        # "Union[ndarray[Any, dtype[signedinteger[_64Bit]]],
+        # ndarray[Any, dtype[object_]]]"; expected "ndarray[Any, dtype[object_]]"
+        llab = rizer.factorize(lk)  # type: ignore[arg-type]
+        rlab = rizer.factorize(rk)  # type: ignore[arg-type]
+    assert llab.dtype == np.dtype(np.intp), llab.dtype
+    assert rlab.dtype == np.dtype(np.intp), rlab.dtype
+
+    count = rizer.get_count()
+
+    if sort:
+        uniques = rizer.uniques.to_array()
+        llab, rlab = _sort_labels(uniques, llab, rlab)
+
+    # NA group
+    lmask = llab == -1
+    lany = lmask.any()
+    rmask = rlab == -1
+    rany = rmask.any()
+
+    if lany or rany:
+        if lany:
+            np.putmask(llab, lmask, count)
+        if rany:
+            np.putmask(rlab, rmask, count)
+        count += 1
+
+    return llab, rlab, count
+
+
+def _convert_arrays_and_get_rizer_klass(
+    lk: ArrayLike, rk: ArrayLike
+) -> tuple[type[libhashtable.Factorizer], ArrayLike, ArrayLike]:
+    klass: type[libhashtable.Factorizer]
+    if is_numeric_dtype(lk.dtype):
+        if lk.dtype != rk.dtype:
+            dtype = find_common_type([lk.dtype, rk.dtype])
+            if isinstance(dtype, ExtensionDtype):
+                cls = dtype.construct_array_type()
+                if not isinstance(lk, ExtensionArray):
+                    lk = cls._from_sequence(lk, dtype=dtype, copy=False)
+                else:
+                    lk = lk.astype(dtype, copy=False)
+
+                if not isinstance(rk, ExtensionArray):
+                    rk = cls._from_sequence(rk, dtype=dtype, copy=False)
+                else:
+                    rk = rk.astype(dtype, copy=False)
+            else:
+                lk = lk.astype(dtype, copy=False)
+                rk = rk.astype(dtype, copy=False)
+        if isinstance(lk, BaseMaskedArray):
+            #  Invalid index type "type" for "Dict[Type[object], Type[Factorizer]]";
+            #  expected type "Type[object]"
+            klass = _factorizers[lk.dtype.type]  # type: ignore[index]
+        elif isinstance(lk.dtype, ArrowDtype):
+            klass = _factorizers[lk.dtype.numpy_dtype.type]
+        else:
+            klass = _factorizers[lk.dtype.type]
+
+    else:
+        klass = libhashtable.ObjectFactorizer
+        lk = ensure_object(lk)
+        rk = ensure_object(rk)
+    return klass, lk, rk
+
+
+def _sort_labels(
+    uniques: np.ndarray, left: npt.NDArray[np.intp], right: npt.NDArray[np.intp]
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
+    llength = len(left)
+    labels = np.concatenate([left, right])
+
+    _, new_labels = algos.safe_sort(uniques, labels, use_na_sentinel=True)
+    new_left, new_right = new_labels[:llength], new_labels[llength:]
+
+    return new_left, new_right
+
+
+def _get_join_keys(
+    llab: list[npt.NDArray[np.int64 | np.intp]],
+    rlab: list[npt.NDArray[np.int64 | np.intp]],
+    shape: Shape,
+    sort: bool,
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]:
+    # how many levels can be done without overflow
+    nlev = next(
+        lev
+        for lev in range(len(shape), 0, -1)
+        if not is_int64_overflow_possible(shape[:lev])
+    )
+
+    # get keys for the first `nlev` levels
+    stride = np.prod(shape[1:nlev], dtype="i8")
+    lkey = stride * llab[0].astype("i8", subok=False, copy=False)
+    rkey = stride * rlab[0].astype("i8", subok=False, copy=False)
+
+    for i in range(1, nlev):
+        with np.errstate(divide="ignore"):
+            stride //= shape[i]
+        lkey += llab[i] * stride
+        rkey += rlab[i] * stride
+
+    if nlev == len(shape):  # all done!
+        return lkey, rkey
+
+    # densify current keys to avoid overflow
+    lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)
+
+    llab = [lkey] + llab[nlev:]
+    rlab = [rkey] + rlab[nlev:]
+    shape = (count,) + shape[nlev:]
+
+    return _get_join_keys(llab, rlab, shape, sort)
+
+
+def _should_fill(lname, rname) -> bool:
+    if not isinstance(lname, str) or not isinstance(rname, str):
+        return True
+    return lname == rname
+
+
+def _any(x) -> bool:
+    return x is not None and com.any_not_none(*x)
+
+
+def _validate_operand(obj: DataFrame | Series) -> DataFrame:
+    if isinstance(obj, ABCDataFrame):
+        return obj
+    elif isinstance(obj, ABCSeries):
+        if obj.name is None:
+            raise ValueError("Cannot merge a Series without a name")
+        return obj.to_frame()
+    else:
+        raise TypeError(
+            f"Can only merge Series or DataFrame objects, a {type(obj)} was passed"
+        )
+
+
+def _items_overlap_with_suffix(
+    left: Index, right: Index, suffixes: Suffixes
+) -> tuple[Index, Index]:
+    """
+    Suffixes type validation.
+
+    If two indices overlap, add suffixes to overlapping entries.
+
+    If corresponding suffix is empty, the entry is simply converted to string.
+
+    """
+    if not is_list_like(suffixes, allow_sets=False) or isinstance(suffixes, dict):
+        raise TypeError(
+            f"Passing 'suffixes' as a {type(suffixes)}, is not supported. "
+            "Provide 'suffixes' as a tuple instead."
+        )
+
+    to_rename = left.intersection(right)
+    if len(to_rename) == 0:
+        return left, right
+
+    lsuffix, rsuffix = suffixes
+
+    if not lsuffix and not rsuffix:
+        raise ValueError(f"columns overlap but no suffix specified: {to_rename}")
+
+    def renamer(x, suffix: str | None):
+        """
+        Rename the left and right indices.
+
+        If there is overlap, and suffix is not None, add
+        suffix, otherwise, leave it as-is.
+
+        Parameters
+        ----------
+        x : original column name
+        suffix : str or None
+
+        Returns
+        -------
+        x : renamed column name
+        """
+        if x in to_rename and suffix is not None:
+            return f"{x}{suffix}"
+        return x
+
+    lrenamer = partial(renamer, suffix=lsuffix)
+    rrenamer = partial(renamer, suffix=rsuffix)
+
+    llabels = left._transform_index(lrenamer)
+    rlabels = right._transform_index(rrenamer)
+
+    dups = []
+    if not llabels.is_unique:
+        # Only warn when duplicates are caused because of suffixes, already duplicated
+        # columns in origin should not warn
+        dups = llabels[(llabels.duplicated()) & (~left.duplicated())].tolist()
+    if not rlabels.is_unique:
+        dups.extend(rlabels[(rlabels.duplicated()) & (~right.duplicated())].tolist())
+    if dups:
+        raise MergeError(
+            f"Passing 'suffixes' which cause duplicate columns {set(dups)} is "
+            f"not allowed.",
+        )
+
+    return llabels, rlabels
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/pivot.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/pivot.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2a915589cba756c16ecaa95c3d056bd7c902c68
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/pivot.py
@@ -0,0 +1,899 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import maybe_downcast_to_dtype
+from pandas.core.dtypes.common import (
+    is_list_like,
+    is_nested_list_like,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+import pandas.core.common as com
+from pandas.core.frame import _shared_docs
+from pandas.core.groupby import Grouper
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    get_objs_combined_axis,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.util import cartesian_product
+from pandas.core.series import Series
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        AggFuncType,
+        AggFuncTypeBase,
+        AggFuncTypeDict,
+        IndexLabel,
+    )
+
+    from pandas import DataFrame
+
+
+# Note: We need to make sure `frame` is imported before `pivot`, otherwise
+# _shared_docs['pivot_table'] will not yet exist.  TODO: Fix this dependency
+@Substitution("\ndata : DataFrame")
+@Appender(_shared_docs["pivot_table"], indents=1)
+def pivot_table(
+    data: DataFrame,
+    values=None,
+    index=None,
+    columns=None,
+    aggfunc: AggFuncType = "mean",
+    fill_value=None,
+    margins: bool = False,
+    dropna: bool = True,
+    margins_name: Hashable = "All",
+    observed: bool | lib.NoDefault = lib.no_default,
+    sort: bool = True,
+) -> DataFrame:
+    index = _convert_by(index)
+    columns = _convert_by(columns)
+
+    if isinstance(aggfunc, list):
+        pieces: list[DataFrame] = []
+        keys = []
+        for func in aggfunc:
+            _table = __internal_pivot_table(
+                data,
+                values=values,
+                index=index,
+                columns=columns,
+                fill_value=fill_value,
+                aggfunc=func,
+                margins=margins,
+                dropna=dropna,
+                margins_name=margins_name,
+                observed=observed,
+                sort=sort,
+            )
+            pieces.append(_table)
+            keys.append(getattr(func, "__name__", func))
+
+        table = concat(pieces, keys=keys, axis=1)
+        return table.__finalize__(data, method="pivot_table")
+
+    table = __internal_pivot_table(
+        data,
+        values,
+        index,
+        columns,
+        aggfunc,
+        fill_value,
+        margins,
+        dropna,
+        margins_name,
+        observed,
+        sort,
+    )
+    return table.__finalize__(data, method="pivot_table")
+
+
+def __internal_pivot_table(
+    data: DataFrame,
+    values,
+    index,
+    columns,
+    aggfunc: AggFuncTypeBase | AggFuncTypeDict,
+    fill_value,
+    margins: bool,
+    dropna: bool,
+    margins_name: Hashable,
+    observed: bool | lib.NoDefault,
+    sort: bool,
+) -> DataFrame:
+    """
+    Helper of :func:`pandas.pivot_table` for any non-list ``aggfunc``.
+    """
+    keys = index + columns
+
+    values_passed = values is not None
+    if values_passed:
+        if is_list_like(values):
+            values_multi = True
+            values = list(values)
+        else:
+            values_multi = False
+            values = [values]
+
+        # GH14938 Make sure value labels are in data
+        for i in values:
+            if i not in data:
+                raise KeyError(i)
+
+        to_filter = []
+        for x in keys + values:
+            if isinstance(x, Grouper):
+                x = x.key
+            try:
+                if x in data:
+                    to_filter.append(x)
+            except TypeError:
+                pass
+        if len(to_filter) < len(data.columns):
+            data = data[to_filter]
+
+    else:
+        values = data.columns
+        for key in keys:
+            try:
+                values = values.drop(key)
+            except (TypeError, ValueError, KeyError):
+                pass
+        values = list(values)
+
+    observed_bool = False if observed is lib.no_default else observed
+    grouped = data.groupby(keys, observed=observed_bool, sort=sort, dropna=dropna)
+    if observed is lib.no_default and any(
+        ping._passed_categorical for ping in grouped._grouper.groupings
+    ):
+        warnings.warn(
+            "The default value of observed=False is deprecated and will change "
+            "to observed=True in a future version of pandas. Specify "
+            "observed=False to silence this warning and retain the current behavior",
+            category=FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+    agged = grouped.agg(aggfunc)
+
+    if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
+        agged = agged.dropna(how="all")
+
+    table = agged
+
+    # GH17038, this check should only happen if index is defined (not None)
+    if table.index.nlevels > 1 and index:
+        # Related GH #17123
+        # If index_names are integers, determine whether the integers refer
+        # to the level position or name.
+        index_names = agged.index.names[: len(index)]
+        to_unstack = []
+        for i in range(len(index), len(keys)):
+            name = agged.index.names[i]
+            if name is None or name in index_names:
+                to_unstack.append(i)
+            else:
+                to_unstack.append(name)
+        table = agged.unstack(to_unstack, fill_value=fill_value)
+
+    if not dropna:
+        if isinstance(table.index, MultiIndex):
+            m = MultiIndex.from_arrays(
+                cartesian_product(table.index.levels), names=table.index.names
+            )
+            table = table.reindex(m, axis=0, fill_value=fill_value)
+
+        if isinstance(table.columns, MultiIndex):
+            m = MultiIndex.from_arrays(
+                cartesian_product(table.columns.levels), names=table.columns.names
+            )
+            table = table.reindex(m, axis=1, fill_value=fill_value)
+
+    if sort is True and isinstance(table, ABCDataFrame):
+        table = table.sort_index(axis=1)
+
+    if fill_value is not None:
+        table = table.fillna(fill_value)
+        if aggfunc is len and not observed and lib.is_integer(fill_value):
+            # TODO: can we avoid this?  this used to be handled by
+            #  downcast="infer" in fillna
+            table = table.astype(np.int64)
+
+    if margins:
+        if dropna:
+            data = data[data.notna().all(axis=1)]
+        table = _add_margins(
+            table,
+            data,
+            values,
+            rows=index,
+            cols=columns,
+            aggfunc=aggfunc,
+            observed=dropna,
+            margins_name=margins_name,
+            fill_value=fill_value,
+        )
+
+    # discard the top level
+    if values_passed and not values_multi and table.columns.nlevels > 1:
+        table.columns = table.columns.droplevel(0)
+    if len(index) == 0 and len(columns) > 0:
+        table = table.T
+
+    # GH 15193 Make sure empty columns are removed if dropna=True
+    if isinstance(table, ABCDataFrame) and dropna:
+        table = table.dropna(how="all", axis=1)
+
+    return table
+
+
+def _add_margins(
+    table: DataFrame | Series,
+    data: DataFrame,
+    values,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
+    fill_value=None,
+):
+    if not isinstance(margins_name, str):
+        raise ValueError("margins_name argument must be a string")
+
+    msg = f'Conflicting name "{margins_name}" in margins'
+    for level in table.index.names:
+        if margins_name in table.index.get_level_values(level):
+            raise ValueError(msg)
+
+    grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
+
+    if table.ndim == 2:
+        # i.e. DataFrame
+        for level in table.columns.names[1:]:
+            if margins_name in table.columns.get_level_values(level):
+                raise ValueError(msg)
+
+    key: str | tuple[str, ...]
+    if len(rows) > 1:
+        key = (margins_name,) + ("",) * (len(rows) - 1)
+    else:
+        key = margins_name
+
+    if not values and isinstance(table, ABCSeries):
+        # If there are no values and the table is a series, then there is only
+        # one column in the data. Compute grand margin and return it.
+        return table._append(table._constructor({key: grand_margin[margins_name]}))
+
+    elif values:
+        marginal_result_set = _generate_marginal_results(
+            table, data, values, rows, cols, aggfunc, observed, margins_name
+        )
+        if not isinstance(marginal_result_set, tuple):
+            return marginal_result_set
+        result, margin_keys, row_margin = marginal_result_set
+    else:
+        # no values, and table is a DataFrame
+        assert isinstance(table, ABCDataFrame)
+        marginal_result_set = _generate_marginal_results_without_values(
+            table, data, rows, cols, aggfunc, observed, margins_name
+        )
+        if not isinstance(marginal_result_set, tuple):
+            return marginal_result_set
+        result, margin_keys, row_margin = marginal_result_set
+
+    row_margin = row_margin.reindex(result.columns, fill_value=fill_value)
+    # populate grand margin
+    for k in margin_keys:
+        if isinstance(k, str):
+            row_margin[k] = grand_margin[k]
+        else:
+            row_margin[k] = grand_margin[k[0]]
+
+    from pandas import DataFrame
+
+    margin_dummy = DataFrame(row_margin, columns=Index([key])).T
+
+    row_names = result.index.names
+    # check the result column and leave floats
+
+    for dtype in set(result.dtypes):
+        if isinstance(dtype, ExtensionDtype):
+            # Can hold NA already
+            continue
+
+        cols = result.select_dtypes([dtype]).columns
+        margin_dummy[cols] = margin_dummy[cols].apply(
+            maybe_downcast_to_dtype, args=(dtype,)
+        )
+    result = result._append(margin_dummy)
+    result.index.names = row_names
+
+    return result
+
+
+def _compute_grand_margin(
+    data: DataFrame, values, aggfunc, margins_name: Hashable = "All"
+):
+    if values:
+        grand_margin = {}
+        for k, v in data[values].items():
+            try:
+                if isinstance(aggfunc, str):
+                    grand_margin[k] = getattr(v, aggfunc)()
+                elif isinstance(aggfunc, dict):
+                    if isinstance(aggfunc[k], str):
+                        grand_margin[k] = getattr(v, aggfunc[k])()
+                    else:
+                        grand_margin[k] = aggfunc[k](v)
+                else:
+                    grand_margin[k] = aggfunc(v)
+            except TypeError:
+                pass
+        return grand_margin
+    else:
+        return {margins_name: aggfunc(data.index)}
+
+
+def _generate_marginal_results(
+    table,
+    data: DataFrame,
+    values,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
+):
+    margin_keys: list | Index
+    if len(cols) > 0:
+        # need to "interleave" the margins
+        table_pieces = []
+        margin_keys = []
+
+        def _all_key(key):
+            return (key, margins_name) + ("",) * (len(cols) - 1)
+
+        if len(rows) > 0:
+            margin = data[rows + values].groupby(rows, observed=observed).agg(aggfunc)
+            cat_axis = 1
+
+            for key, piece in table.T.groupby(level=0, observed=observed):
+                piece = piece.T
+                all_key = _all_key(key)
+
+                # we are going to mutate this, so need to copy!
+                piece = piece.copy()
+                piece[all_key] = margin[key]
+
+                table_pieces.append(piece)
+                margin_keys.append(all_key)
+        else:
+            from pandas import DataFrame
+
+            cat_axis = 0
+            for key, piece in table.groupby(level=0, observed=observed):
+                if len(cols) > 1:
+                    all_key = _all_key(key)
+                else:
+                    all_key = margins_name
+                table_pieces.append(piece)
+                # GH31016 this is to calculate margin for each group, and assign
+                # corresponded key as index
+                transformed_piece = DataFrame(piece.apply(aggfunc)).T
+                if isinstance(piece.index, MultiIndex):
+                    # We are adding an empty level
+                    transformed_piece.index = MultiIndex.from_tuples(
+                        [all_key], names=piece.index.names + [None]
+                    )
+                else:
+                    transformed_piece.index = Index([all_key], name=piece.index.name)
+
+                # append piece for margin into table_piece
+                table_pieces.append(transformed_piece)
+                margin_keys.append(all_key)
+
+        if not table_pieces:
+            # GH 49240
+            return table
+        else:
+            result = concat(table_pieces, axis=cat_axis)
+
+        if len(rows) == 0:
+            return result
+    else:
+        result = table
+        margin_keys = table.columns
+
+    if len(cols) > 0:
+        row_margin = data[cols + values].groupby(cols, observed=observed).agg(aggfunc)
+        row_margin = row_margin.stack(future_stack=True)
+
+        # GH#26568. Use names instead of indices in case of numeric names
+        new_order_indices = [len(cols)] + list(range(len(cols)))
+        new_order_names = [row_margin.index.names[i] for i in new_order_indices]
+        row_margin.index = row_margin.index.reorder_levels(new_order_names)
+    else:
+        row_margin = data._constructor_sliced(np.nan, index=result.columns)
+
+    return result, margin_keys, row_margin
+
+
+def _generate_marginal_results_without_values(
+    table: DataFrame,
+    data: DataFrame,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
+):
+    margin_keys: list | Index
+    if len(cols) > 0:
+        # need to "interleave" the margins
+        margin_keys = []
+
+        def _all_key():
+            if len(cols) == 1:
+                return margins_name
+            return (margins_name,) + ("",) * (len(cols) - 1)
+
+        if len(rows) > 0:
+            margin = data.groupby(rows, observed=observed)[rows].apply(aggfunc)
+            all_key = _all_key()
+            table[all_key] = margin
+            result = table
+            margin_keys.append(all_key)
+
+        else:
+            margin = data.groupby(level=0, axis=0, observed=observed).apply(aggfunc)
+            all_key = _all_key()
+            table[all_key] = margin
+            result = table
+            margin_keys.append(all_key)
+            return result
+    else:
+        result = table
+        margin_keys = table.columns
+
+    if len(cols):
+        row_margin = data.groupby(cols, observed=observed)[cols].apply(aggfunc)
+    else:
+        row_margin = Series(np.nan, index=result.columns)
+
+    return result, margin_keys, row_margin
+
+
+def _convert_by(by):
+    if by is None:
+        by = []
+    elif (
+        is_scalar(by)
+        or isinstance(by, (np.ndarray, Index, ABCSeries, Grouper))
+        or callable(by)
+    ):
+        by = [by]
+    else:
+        by = list(by)
+    return by
+
+
+@Substitution("\ndata : DataFrame")
+@Appender(_shared_docs["pivot"], indents=1)
+def pivot(
+    data: DataFrame,
+    *,
+    columns: IndexLabel,
+    index: IndexLabel | lib.NoDefault = lib.no_default,
+    values: IndexLabel | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    columns_listlike = com.convert_to_list_like(columns)
+
+    # If columns is None we will create a MultiIndex level with None as name
+    # which might cause duplicated names because None is the default for
+    # level names
+    data = data.copy(deep=False)
+    data.index = data.index.copy()
+    data.index.names = [
+        name if name is not None else lib.no_default for name in data.index.names
+    ]
+
+    indexed: DataFrame | Series
+    if values is lib.no_default:
+        if index is not lib.no_default:
+            cols = com.convert_to_list_like(index)
+        else:
+            cols = []
+
+        append = index is lib.no_default
+        # error: Unsupported operand types for + ("List[Any]" and "ExtensionArray")
+        # error: Unsupported left operand type for + ("ExtensionArray")
+        indexed = data.set_index(
+            cols + columns_listlike, append=append  # type: ignore[operator]
+        )
+    else:
+        index_list: list[Index] | list[Series]
+        if index is lib.no_default:
+            if isinstance(data.index, MultiIndex):
+                # GH 23955
+                index_list = [
+                    data.index.get_level_values(i) for i in range(data.index.nlevels)
+                ]
+            else:
+                index_list = [
+                    data._constructor_sliced(data.index, name=data.index.name)
+                ]
+        else:
+            index_list = [data[idx] for idx in com.convert_to_list_like(index)]
+
+        data_columns = [data[col] for col in columns_listlike]
+        index_list.extend(data_columns)
+        multiindex = MultiIndex.from_arrays(index_list)
+
+        if is_list_like(values) and not isinstance(values, tuple):
+            # Exclude tuple because it is seen as a single column name
+            values = cast(Sequence[Hashable], values)
+            indexed = data._constructor(
+                data[values]._values, index=multiindex, columns=values
+            )
+        else:
+            indexed = data._constructor_sliced(data[values]._values, index=multiindex)
+    # error: Argument 1 to "unstack" of "DataFrame" has incompatible type "Union
+    # [List[Any], ExtensionArray, ndarray[Any, Any], Index, Series]"; expected
+    # "Hashable"
+    result = indexed.unstack(columns_listlike)  # type: ignore[arg-type]
+    result.index.names = [
+        name if name is not lib.no_default else None for name in result.index.names
+    ]
+
+    return result
+
+
+def crosstab(
+    index,
+    columns,
+    values=None,
+    rownames=None,
+    colnames=None,
+    aggfunc=None,
+    margins: bool = False,
+    margins_name: Hashable = "All",
+    dropna: bool = True,
+    normalize: bool | Literal[0, 1, "all", "index", "columns"] = False,
+) -> DataFrame:
+    """
+    Compute a simple cross tabulation of two (or more) factors.
+
+    By default, computes a frequency table of the factors unless an
+    array of values and an aggregation function are passed.
+
+    Parameters
+    ----------
+    index : array-like, Series, or list of arrays/Series
+        Values to group by in the rows.
+    columns : array-like, Series, or list of arrays/Series
+        Values to group by in the columns.
+    values : array-like, optional
+        Array of values to aggregate according to the factors.
+        Requires `aggfunc` be specified.
+    rownames : sequence, default None
+        If passed, must match number of row arrays passed.
+    colnames : sequence, default None
+        If passed, must match number of column arrays passed.
+    aggfunc : function, optional
+        If specified, requires `values` be specified as well.
+    margins : bool, default False
+        Add row/column margins (subtotals).
+    margins_name : str, default 'All'
+        Name of the row/column that will contain the totals
+        when margins is True.
+    dropna : bool, default True
+        Do not include columns whose entries are all NaN.
+    normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False
+        Normalize by dividing all values by the sum of values.
+
+        - If passed 'all' or `True`, will normalize over all values.
+        - If passed 'index' will normalize over each row.
+        - If passed 'columns' will normalize over each column.
+        - If margins is `True`, will also normalize margin values.
+
+    Returns
+    -------
+    DataFrame
+        Cross tabulation of the data.
+
+    See Also
+    --------
+    DataFrame.pivot : Reshape data based on column values.
+    pivot_table : Create a pivot table as a DataFrame.
+
+    Notes
+    -----
+    Any Series passed will have their name attributes used unless row or column
+    names for the cross-tabulation are specified.
+
+    Any input passed containing Categorical data will have **all** of its
+    categories included in the cross-tabulation, even if the actual data does
+    not contain any instances of a particular category.
+
+    In the event that there aren't overlapping indexes an empty DataFrame will
+    be returned.
+
+    Reference :ref:`the user guide <reshaping.crosstabulations>` for more examples.
+
+    Examples
+    --------
+    >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
+    ...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
+    >>> b = np.array(["one", "one", "one", "two", "one", "one",
+    ...               "one", "two", "two", "two", "one"], dtype=object)
+    >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
+    ...               "shiny", "dull", "shiny", "shiny", "shiny"],
+    ...              dtype=object)
+    >>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
+    b   one        two
+    c   dull shiny dull shiny
+    a
+    bar    1     2    1     0
+    foo    2     2    1     2
+
+    Here 'c' and 'f' are not represented in the data and will not be
+    shown in the output because dropna is True by default. Set
+    dropna=False to preserve categories with no data.
+
+    >>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
+    >>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
+    >>> pd.crosstab(foo, bar)
+    col_0  d  e
+    row_0
+    a      1  0
+    b      0  1
+    >>> pd.crosstab(foo, bar, dropna=False)
+    col_0  d  e  f
+    row_0
+    a      1  0  0
+    b      0  1  0
+    c      0  0  0
+    """
+    if values is None and aggfunc is not None:
+        raise ValueError("aggfunc cannot be used without values.")
+
+    if values is not None and aggfunc is None:
+        raise ValueError("values cannot be used without an aggfunc.")
+
+    if not is_nested_list_like(index):
+        index = [index]
+    if not is_nested_list_like(columns):
+        columns = [columns]
+
+    common_idx = None
+    pass_objs = [x for x in index + columns if isinstance(x, (ABCSeries, ABCDataFrame))]
+    if pass_objs:
+        common_idx = get_objs_combined_axis(pass_objs, intersect=True, sort=False)
+
+    rownames = _get_names(index, rownames, prefix="row")
+    colnames = _get_names(columns, colnames, prefix="col")
+
+    # duplicate names mapped to unique names for pivot op
+    (
+        rownames_mapper,
+        unique_rownames,
+        colnames_mapper,
+        unique_colnames,
+    ) = _build_names_mapper(rownames, colnames)
+
+    from pandas import DataFrame
+
+    data = {
+        **dict(zip(unique_rownames, index)),
+        **dict(zip(unique_colnames, columns)),
+    }
+    df = DataFrame(data, index=common_idx)
+
+    if values is None:
+        df["__dummy__"] = 0
+        kwargs = {"aggfunc": len, "fill_value": 0}
+    else:
+        df["__dummy__"] = values
+        kwargs = {"aggfunc": aggfunc}
+
+    # error: Argument 7 to "pivot_table" of "DataFrame" has incompatible type
+    # "**Dict[str, object]"; expected "Union[...]"
+    table = df.pivot_table(
+        "__dummy__",
+        index=unique_rownames,
+        columns=unique_colnames,
+        margins=margins,
+        margins_name=margins_name,
+        dropna=dropna,
+        observed=False,
+        **kwargs,  # type: ignore[arg-type]
+    )
+
+    # Post-process
+    if normalize is not False:
+        table = _normalize(
+            table, normalize=normalize, margins=margins, margins_name=margins_name
+        )
+
+    table = table.rename_axis(index=rownames_mapper, axis=0)
+    table = table.rename_axis(columns=colnames_mapper, axis=1)
+
+    return table
+
+
+def _normalize(
+    table: DataFrame, normalize, margins: bool, margins_name: Hashable = "All"
+) -> DataFrame:
+    if not isinstance(normalize, (bool, str)):
+        axis_subs = {0: "index", 1: "columns"}
+        try:
+            normalize = axis_subs[normalize]
+        except KeyError as err:
+            raise ValueError("Not a valid normalize argument") from err
+
+    if margins is False:
+        # Actual Normalizations
+        normalizers: dict[bool | str, Callable] = {
+            "all": lambda x: x / x.sum(axis=1).sum(axis=0),
+            "columns": lambda x: x / x.sum(),
+            "index": lambda x: x.div(x.sum(axis=1), axis=0),
+        }
+
+        normalizers[True] = normalizers["all"]
+
+        try:
+            f = normalizers[normalize]
+        except KeyError as err:
+            raise ValueError("Not a valid normalize argument") from err
+
+        table = f(table)
+        table = table.fillna(0)
+
+    elif margins is True:
+        # keep index and column of pivoted table
+        table_index = table.index
+        table_columns = table.columns
+        last_ind_or_col = table.iloc[-1, :].name
+
+        # check if margin name is not in (for MI cases) and not equal to last
+        # index/column and save the column and index margin
+        if (margins_name not in last_ind_or_col) & (margins_name != last_ind_or_col):
+            raise ValueError(f"{margins_name} not in pivoted DataFrame")
+        column_margin = table.iloc[:-1, -1]
+        index_margin = table.iloc[-1, :-1]
+
+        # keep the core table
+        table = table.iloc[:-1, :-1]
+
+        # Normalize core
+        table = _normalize(table, normalize=normalize, margins=False)
+
+        # Fix Margins
+        if normalize == "columns":
+            column_margin = column_margin / column_margin.sum()
+            table = concat([table, column_margin], axis=1)
+            table = table.fillna(0)
+            table.columns = table_columns
+
+        elif normalize == "index":
+            index_margin = index_margin / index_margin.sum()
+            table = table._append(index_margin)
+            table = table.fillna(0)
+            table.index = table_index
+
+        elif normalize == "all" or normalize is True:
+            column_margin = column_margin / column_margin.sum()
+            index_margin = index_margin / index_margin.sum()
+            index_margin.loc[margins_name] = 1
+            table = concat([table, column_margin], axis=1)
+            table = table._append(index_margin)
+
+            table = table.fillna(0)
+            table.index = table_index
+            table.columns = table_columns
+
+        else:
+            raise ValueError("Not a valid normalize argument")
+
+    else:
+        raise ValueError("Not a valid margins argument")
+
+    return table
+
+
+def _get_names(arrs, names, prefix: str = "row"):
+    if names is None:
+        names = []
+        for i, arr in enumerate(arrs):
+            if isinstance(arr, ABCSeries) and arr.name is not None:
+                names.append(arr.name)
+            else:
+                names.append(f"{prefix}_{i}")
+    else:
+        if len(names) != len(arrs):
+            raise AssertionError("arrays and names must have the same length")
+        if not isinstance(names, list):
+            names = list(names)
+
+    return names
+
+
+def _build_names_mapper(
+    rownames: list[str], colnames: list[str]
+) -> tuple[dict[str, str], list[str], dict[str, str], list[str]]:
+    """
+    Given the names of a DataFrame's rows and columns, returns a set of unique row
+    and column names and mappers that convert to original names.
+
+    A row or column name is replaced if it is duplicate among the rows of the inputs,
+    among the columns of the inputs or between the rows and the columns.
+
+    Parameters
+    ----------
+    rownames: list[str]
+    colnames: list[str]
+
+    Returns
+    -------
+    Tuple(Dict[str, str], List[str], Dict[str, str], List[str])
+
+    rownames_mapper: dict[str, str]
+        a dictionary with new row names as keys and original rownames as values
+    unique_rownames: list[str]
+        a list of rownames with duplicate names replaced by dummy names
+    colnames_mapper: dict[str, str]
+        a dictionary with new column names as keys and original column names as values
+    unique_colnames: list[str]
+        a list of column names with duplicate names replaced by dummy names
+
+    """
+
+    def get_duplicates(names):
+        seen: set = set()
+        return {name for name in names if name not in seen}
+
+    shared_names = set(rownames).intersection(set(colnames))
+    dup_names = get_duplicates(rownames) | get_duplicates(colnames) | shared_names
+
+    rownames_mapper = {
+        f"row_{i}": name for i, name in enumerate(rownames) if name in dup_names
+    }
+    unique_rownames = [
+        f"row_{i}" if name in dup_names else name for i, name in enumerate(rownames)
+    ]
+
+    colnames_mapper = {
+        f"col_{i}": name for i, name in enumerate(colnames) if name in dup_names
+    }
+    unique_colnames = [
+        f"col_{i}" if name in dup_names else name for i, name in enumerate(colnames)
+    ]
+
+    return rownames_mapper, unique_rownames, colnames_mapper, unique_colnames
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/reshape.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a49682d7c57c90ed26e890777758ad806bd961b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/reshape.py
@@ -0,0 +1,989 @@
+from __future__ import annotations
+
+import itertools
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+import pandas._libs.reshape as libreshape
+from pandas.errors import PerformanceWarning
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.cast import (
+    find_common_type,
+    maybe_promote,
+)
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_1d_only_ea_dtype,
+    is_integer,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.missing import notna
+
+import pandas.core.algorithms as algos
+from pandas.core.algorithms import (
+    factorize,
+    unique,
+)
+from pandas.core.arrays.categorical import factorize_from_iterable
+from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    RangeIndex,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.series import Series
+from pandas.core.sorting import (
+    compress_group_index,
+    decons_obs_group_ids,
+    get_compressed_ids,
+    get_group_index,
+    get_group_index_sorter,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Level,
+        npt,
+    )
+
+    from pandas.core.arrays import ExtensionArray
+    from pandas.core.indexes.frozen import FrozenList
+
+
+class _Unstacker:
+    """
+    Helper class to unstack data / pivot with multi-level index
+
+    Parameters
+    ----------
+    index : MultiIndex
+    level : int or str, default last level
+        Level to "unstack". Accepts a name for the level.
+    fill_value : scalar, optional
+        Default value to fill in missing values if subgroups do not have the
+        same set of labels. By default, missing values will be replaced with
+        the default fill value for that data type, NaN for float, NaT for
+        datetimelike, etc. For integer types, by default data will converted to
+        float and missing values will be set to NaN.
+    constructor : object
+        Pandas ``DataFrame`` or subclass used to create unstacked
+        response.  If None, DataFrame will be used.
+
+    Examples
+    --------
+    >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
+    ...                                    ('two', 'a'), ('two', 'b')])
+    >>> s = pd.Series(np.arange(1, 5, dtype=np.int64), index=index)
+    >>> s
+    one  a    1
+         b    2
+    two  a    3
+         b    4
+    dtype: int64
+
+    >>> s.unstack(level=-1)
+         a  b
+    one  1  2
+    two  3  4
+
+    >>> s.unstack(level=0)
+       one  two
+    a    1    3
+    b    2    4
+
+    Returns
+    -------
+    unstacked : DataFrame
+    """
+
+    def __init__(
+        self, index: MultiIndex, level: Level, constructor, sort: bool = True
+    ) -> None:
+        self.constructor = constructor
+        self.sort = sort
+
+        self.index = index.remove_unused_levels()
+
+        self.level = self.index._get_level_number(level)
+
+        # when index includes `nan`, need to lift levels/strides by 1
+        self.lift = 1 if -1 in self.index.codes[self.level] else 0
+
+        # Note: the "pop" below alters these in-place.
+        self.new_index_levels = list(self.index.levels)
+        self.new_index_names = list(self.index.names)
+
+        self.removed_name = self.new_index_names.pop(self.level)
+        self.removed_level = self.new_index_levels.pop(self.level)
+        self.removed_level_full = index.levels[self.level]
+        if not self.sort:
+            unique_codes = unique(self.index.codes[self.level])
+            self.removed_level = self.removed_level.take(unique_codes)
+            self.removed_level_full = self.removed_level_full.take(unique_codes)
+
+        # Bug fix GH 20601
+        # If the data frame is too big, the number of unique index combination
+        # will cause int32 overflow on windows environments.
+        # We want to check and raise an warning before this happens
+        num_rows = np.max([index_level.size for index_level in self.new_index_levels])
+        num_columns = self.removed_level.size
+
+        # GH20601: This forces an overflow if the number of cells is too high.
+        num_cells = num_rows * num_columns
+
+        # GH 26314: Previous ValueError raised was too restrictive for many users.
+        if num_cells > np.iinfo(np.int32).max:
+            warnings.warn(
+                f"The following operation may generate {num_cells} cells "
+                f"in the resulting pandas object.",
+                PerformanceWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        self._make_selectors()
+
+    @cache_readonly
+    def _indexer_and_to_sort(
+        self,
+    ) -> tuple[
+        npt.NDArray[np.intp],
+        list[np.ndarray],  # each has _some_ signed integer dtype
+    ]:
+        v = self.level
+
+        codes = list(self.index.codes)
+        levs = list(self.index.levels)
+        to_sort = codes[:v] + codes[v + 1 :] + [codes[v]]
+        sizes = tuple(len(x) for x in levs[:v] + levs[v + 1 :] + [levs[v]])
+
+        comp_index, obs_ids = get_compressed_ids(to_sort, sizes)
+        ngroups = len(obs_ids)
+
+        indexer = get_group_index_sorter(comp_index, ngroups)
+        return indexer, to_sort
+
+    @cache_readonly
+    def sorted_labels(self) -> list[np.ndarray]:
+        indexer, to_sort = self._indexer_and_to_sort
+        if self.sort:
+            return [line.take(indexer) for line in to_sort]
+        return to_sort
+
+    def _make_sorted_values(self, values: np.ndarray) -> np.ndarray:
+        if self.sort:
+            indexer, _ = self._indexer_and_to_sort
+
+            sorted_values = algos.take_nd(values, indexer, axis=0)
+            return sorted_values
+        return values
+
+    def _make_selectors(self):
+        new_levels = self.new_index_levels
+
+        # make the mask
+        remaining_labels = self.sorted_labels[:-1]
+        level_sizes = tuple(len(x) for x in new_levels)
+
+        comp_index, obs_ids = get_compressed_ids(remaining_labels, level_sizes)
+        ngroups = len(obs_ids)
+
+        comp_index = ensure_platform_int(comp_index)
+        stride = self.index.levshape[self.level] + self.lift
+        self.full_shape = ngroups, stride
+
+        selector = self.sorted_labels[-1] + stride * comp_index + self.lift
+        mask = np.zeros(np.prod(self.full_shape), dtype=bool)
+        mask.put(selector, True)
+
+        if mask.sum() < len(self.index):
+            raise ValueError("Index contains duplicate entries, cannot reshape")
+
+        self.group_index = comp_index
+        self.mask = mask
+        if self.sort:
+            self.compressor = comp_index.searchsorted(np.arange(ngroups))
+        else:
+            self.compressor = np.sort(np.unique(comp_index, return_index=True)[1])
+
+    @cache_readonly
+    def mask_all(self) -> bool:
+        return bool(self.mask.all())
+
+    @cache_readonly
+    def arange_result(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.bool_]]:
+        # We cache this for reuse in ExtensionBlock._unstack
+        dummy_arr = np.arange(len(self.index), dtype=np.intp)
+        new_values, mask = self.get_new_values(dummy_arr, fill_value=-1)
+        return new_values, mask.any(0)
+        # TODO: in all tests we have mask.any(0).all(); can we rely on that?
+
+    def get_result(self, values, value_columns, fill_value) -> DataFrame:
+        if values.ndim == 1:
+            values = values[:, np.newaxis]
+
+        if value_columns is None and values.shape[1] != 1:  # pragma: no cover
+            raise ValueError("must pass column labels for multi-column data")
+
+        values, _ = self.get_new_values(values, fill_value)
+        columns = self.get_new_columns(value_columns)
+        index = self.new_index
+
+        return self.constructor(
+            values, index=index, columns=columns, dtype=values.dtype
+        )
+
+    def get_new_values(self, values, fill_value=None):
+        if values.ndim == 1:
+            values = values[:, np.newaxis]
+
+        sorted_values = self._make_sorted_values(values)
+
+        # place the values
+        length, width = self.full_shape
+        stride = values.shape[1]
+        result_width = width * stride
+        result_shape = (length, result_width)
+        mask = self.mask
+        mask_all = self.mask_all
+
+        # we can simply reshape if we don't have a mask
+        if mask_all and len(values):
+            # TODO: Under what circumstances can we rely on sorted_values
+            #  matching values?  When that holds, we can slice instead
+            #  of take (in particular for EAs)
+            new_values = (
+                sorted_values.reshape(length, width, stride)
+                .swapaxes(1, 2)
+                .reshape(result_shape)
+            )
+            new_mask = np.ones(result_shape, dtype=bool)
+            return new_values, new_mask
+
+        dtype = values.dtype
+
+        # if our mask is all True, then we can use our existing dtype
+        if mask_all:
+            dtype = values.dtype
+            new_values = np.empty(result_shape, dtype=dtype)
+        else:
+            if isinstance(dtype, ExtensionDtype):
+                # GH#41875
+                # We are assuming that fill_value can be held by this dtype,
+                #  unlike the non-EA case that promotes.
+                cls = dtype.construct_array_type()
+                new_values = cls._empty(result_shape, dtype=dtype)
+                new_values[:] = fill_value
+            else:
+                dtype, fill_value = maybe_promote(dtype, fill_value)
+                new_values = np.empty(result_shape, dtype=dtype)
+                new_values.fill(fill_value)
+
+        name = dtype.name
+        new_mask = np.zeros(result_shape, dtype=bool)
+
+        # we need to convert to a basic dtype
+        # and possibly coerce an input to our output dtype
+        # e.g. ints -> floats
+        if needs_i8_conversion(values.dtype):
+            sorted_values = sorted_values.view("i8")
+            new_values = new_values.view("i8")
+        else:
+            sorted_values = sorted_values.astype(name, copy=False)
+
+        # fill in our values & mask
+        libreshape.unstack(
+            sorted_values,
+            mask.view("u1"),
+            stride,
+            length,
+            width,
+            new_values,
+            new_mask.view("u1"),
+        )
+
+        # reconstruct dtype if needed
+        if needs_i8_conversion(values.dtype):
+            # view as datetime64 so we can wrap in DatetimeArray and use
+            #  DTA's view method
+            new_values = new_values.view("M8[ns]")
+            new_values = ensure_wrapped_if_datetimelike(new_values)
+            new_values = new_values.view(values.dtype)
+
+        return new_values, new_mask
+
+    def get_new_columns(self, value_columns: Index | None):
+        if value_columns is None:
+            if self.lift == 0:
+                return self.removed_level._rename(name=self.removed_name)
+
+            lev = self.removed_level.insert(0, item=self.removed_level._na_value)
+            return lev.rename(self.removed_name)
+
+        stride = len(self.removed_level) + self.lift
+        width = len(value_columns)
+        propagator = np.repeat(np.arange(width), stride)
+
+        new_levels: FrozenList | list[Index]
+
+        if isinstance(value_columns, MultiIndex):
+            # error: Cannot determine type of "__add__"  [has-type]
+            new_levels = value_columns.levels + (  # type: ignore[has-type]
+                self.removed_level_full,
+            )
+            new_names = value_columns.names + (self.removed_name,)
+
+            new_codes = [lab.take(propagator) for lab in value_columns.codes]
+        else:
+            new_levels = [
+                value_columns,
+                self.removed_level_full,
+            ]
+            new_names = [value_columns.name, self.removed_name]
+            new_codes = [propagator]
+
+        repeater = self._repeater
+
+        # The entire level is then just a repetition of the single chunk:
+        new_codes.append(np.tile(repeater, width))
+        return MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+
+    @cache_readonly
+    def _repeater(self) -> np.ndarray:
+        # The two indices differ only if the unstacked level had unused items:
+        if len(self.removed_level_full) != len(self.removed_level):
+            # In this case, we remap the new codes to the original level:
+            repeater = self.removed_level_full.get_indexer(self.removed_level)
+            if self.lift:
+                repeater = np.insert(repeater, 0, -1)
+        else:
+            # Otherwise, we just use each level item exactly once:
+            stride = len(self.removed_level) + self.lift
+            repeater = np.arange(stride) - self.lift
+
+        return repeater
+
+    @cache_readonly
+    def new_index(self) -> MultiIndex:
+        # Does not depend on values or value_columns
+        result_codes = [lab.take(self.compressor) for lab in self.sorted_labels[:-1]]
+
+        # construct the new index
+        if len(self.new_index_levels) == 1:
+            level, level_codes = self.new_index_levels[0], result_codes[0]
+            if (level_codes == -1).any():
+                level = level.insert(len(level), level._na_value)
+            return level.take(level_codes).rename(self.new_index_names[0])
+
+        return MultiIndex(
+            levels=self.new_index_levels,
+            codes=result_codes,
+            names=self.new_index_names,
+            verify_integrity=False,
+        )
+
+
+def _unstack_multiple(
+    data: Series | DataFrame, clocs, fill_value=None, sort: bool = True
+):
+    if len(clocs) == 0:
+        return data
+
+    # NOTE: This doesn't deal with hierarchical columns yet
+
+    index = data.index
+    index = cast(MultiIndex, index)  # caller is responsible for checking
+
+    # GH 19966 Make sure if MultiIndexed index has tuple name, they will be
+    # recognised as a whole
+    if clocs in index.names:
+        clocs = [clocs]
+    clocs = [index._get_level_number(i) for i in clocs]
+
+    rlocs = [i for i in range(index.nlevels) if i not in clocs]
+
+    clevels = [index.levels[i] for i in clocs]
+    ccodes = [index.codes[i] for i in clocs]
+    cnames = [index.names[i] for i in clocs]
+    rlevels = [index.levels[i] for i in rlocs]
+    rcodes = [index.codes[i] for i in rlocs]
+    rnames = [index.names[i] for i in rlocs]
+
+    shape = tuple(len(x) for x in clevels)
+    group_index = get_group_index(ccodes, shape, sort=False, xnull=False)
+
+    comp_ids, obs_ids = compress_group_index(group_index, sort=False)
+    recons_codes = decons_obs_group_ids(comp_ids, obs_ids, shape, ccodes, xnull=False)
+
+    if not rlocs:
+        # Everything is in clocs, so the dummy df has a regular index
+        dummy_index = Index(obs_ids, name="__placeholder__")
+    else:
+        dummy_index = MultiIndex(
+            levels=rlevels + [obs_ids],
+            codes=rcodes + [comp_ids],
+            names=rnames + ["__placeholder__"],
+            verify_integrity=False,
+        )
+
+    if isinstance(data, Series):
+        dummy = data.copy()
+        dummy.index = dummy_index
+
+        unstacked = dummy.unstack("__placeholder__", fill_value=fill_value, sort=sort)
+        new_levels = clevels
+        new_names = cnames
+        new_codes = recons_codes
+    else:
+        if isinstance(data.columns, MultiIndex):
+            result = data
+            while clocs:
+                val = clocs.pop(0)
+                result = result.unstack(val, fill_value=fill_value, sort=sort)
+                clocs = [v if v < val else v - 1 for v in clocs]
+
+            return result
+
+        # GH#42579 deep=False to avoid consolidating
+        dummy_df = data.copy(deep=False)
+        dummy_df.index = dummy_index
+
+        unstacked = dummy_df.unstack(
+            "__placeholder__", fill_value=fill_value, sort=sort
+        )
+        if isinstance(unstacked, Series):
+            unstcols = unstacked.index
+        else:
+            unstcols = unstacked.columns
+        assert isinstance(unstcols, MultiIndex)  # for mypy
+        new_levels = [unstcols.levels[0]] + clevels
+        new_names = [data.columns.name] + cnames
+
+        new_codes = [unstcols.codes[0]]
+        new_codes.extend(rec.take(unstcols.codes[-1]) for rec in recons_codes)
+
+    new_columns = MultiIndex(
+        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+    )
+
+    if isinstance(unstacked, Series):
+        unstacked.index = new_columns
+    else:
+        unstacked.columns = new_columns
+
+    return unstacked
+
+
+def unstack(obj: Series | DataFrame, level, fill_value=None, sort: bool = True):
+    if isinstance(level, (tuple, list)):
+        if len(level) != 1:
+            # _unstack_multiple only handles MultiIndexes,
+            # and isn't needed for a single level
+            return _unstack_multiple(obj, level, fill_value=fill_value, sort=sort)
+        else:
+            level = level[0]
+
+    if not is_integer(level) and not level == "__placeholder__":
+        # check if level is valid in case of regular index
+        obj.index._get_level_number(level)
+
+    if isinstance(obj, DataFrame):
+        if isinstance(obj.index, MultiIndex):
+            return _unstack_frame(obj, level, fill_value=fill_value, sort=sort)
+        else:
+            return obj.T.stack(future_stack=True)
+    elif not isinstance(obj.index, MultiIndex):
+        # GH 36113
+        # Give nicer error messages when unstack a Series whose
+        # Index is not a MultiIndex.
+        raise ValueError(
+            f"index must be a MultiIndex to unstack, {type(obj.index)} was passed"
+        )
+    else:
+        if is_1d_only_ea_dtype(obj.dtype):
+            return _unstack_extension_series(obj, level, fill_value, sort=sort)
+        unstacker = _Unstacker(
+            obj.index, level=level, constructor=obj._constructor_expanddim, sort=sort
+        )
+        return unstacker.get_result(
+            obj._values, value_columns=None, fill_value=fill_value
+        )
+
+
+def _unstack_frame(
+    obj: DataFrame, level, fill_value=None, sort: bool = True
+) -> DataFrame:
+    assert isinstance(obj.index, MultiIndex)  # checked by caller
+    unstacker = _Unstacker(
+        obj.index, level=level, constructor=obj._constructor, sort=sort
+    )
+
+    if not obj._can_fast_transpose:
+        mgr = obj._mgr.unstack(unstacker, fill_value=fill_value)
+        return obj._constructor_from_mgr(mgr, axes=mgr.axes)
+    else:
+        return unstacker.get_result(
+            obj._values, value_columns=obj.columns, fill_value=fill_value
+        )
+
+
+def _unstack_extension_series(
+    series: Series, level, fill_value, sort: bool
+) -> DataFrame:
+    """
+    Unstack an ExtensionArray-backed Series.
+
+    The ExtensionDtype is preserved.
+
+    Parameters
+    ----------
+    series : Series
+        A Series with an ExtensionArray for values
+    level : Any
+        The level name or number.
+    fill_value : Any
+        The user-level (not physical storage) fill value to use for
+        missing values introduced by the reshape. Passed to
+        ``series.values.take``.
+    sort : bool
+        Whether to sort the resulting MuliIndex levels
+
+    Returns
+    -------
+    DataFrame
+        Each column of the DataFrame will have the same dtype as
+        the input Series.
+    """
+    # Defer to the logic in ExtensionBlock._unstack
+    df = series.to_frame()
+    result = df.unstack(level=level, fill_value=fill_value, sort=sort)
+
+    # equiv: result.droplevel(level=0, axis=1)
+    #  but this avoids an extra copy
+    result.columns = result.columns._drop_level_numbers([0])
+    return result
+
+
+def stack(frame: DataFrame, level=-1, dropna: bool = True, sort: bool = True):
+    """
+    Convert DataFrame to Series with multi-level Index. Columns become the
+    second level of the resulting hierarchical index
+
+    Returns
+    -------
+    stacked : Series or DataFrame
+    """
+
+    def stack_factorize(index):
+        if index.is_unique:
+            return index, np.arange(len(index))
+        codes, categories = factorize_from_iterable(index)
+        return categories, codes
+
+    N, K = frame.shape
+
+    # Will also convert negative level numbers and check if out of bounds.
+    level_num = frame.columns._get_level_number(level)
+
+    if isinstance(frame.columns, MultiIndex):
+        return _stack_multi_columns(
+            frame, level_num=level_num, dropna=dropna, sort=sort
+        )
+    elif isinstance(frame.index, MultiIndex):
+        new_levels = list(frame.index.levels)
+        new_codes = [lab.repeat(K) for lab in frame.index.codes]
+
+        clev, clab = stack_factorize(frame.columns)
+        new_levels.append(clev)
+        new_codes.append(np.tile(clab, N).ravel())
+
+        new_names = list(frame.index.names)
+        new_names.append(frame.columns.name)
+        new_index = MultiIndex(
+            levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+        )
+    else:
+        levels, (ilab, clab) = zip(*map(stack_factorize, (frame.index, frame.columns)))
+        codes = ilab.repeat(K), np.tile(clab, N).ravel()
+        new_index = MultiIndex(
+            levels=levels,
+            codes=codes,
+            names=[frame.index.name, frame.columns.name],
+            verify_integrity=False,
+        )
+
+    new_values: ArrayLike
+    if not frame.empty and frame._is_homogeneous_type:
+        # For homogeneous EAs, frame._values will coerce to object. So
+        # we concatenate instead.
+        dtypes = list(frame.dtypes._values)
+        dtype = dtypes[0]
+
+        if isinstance(dtype, ExtensionDtype):
+            arr = dtype.construct_array_type()
+            new_values = arr._concat_same_type(
+                [col._values for _, col in frame.items()]
+            )
+            new_values = _reorder_for_extension_array_stack(new_values, N, K)
+        else:
+            # homogeneous, non-EA
+            new_values = frame._values.ravel()
+
+    else:
+        # non-homogeneous
+        new_values = frame._values.ravel()
+
+    if dropna:
+        mask = notna(new_values)
+        new_values = new_values[mask]
+        new_index = new_index[mask]
+
+    return frame._constructor_sliced(new_values, index=new_index)
+
+
+def stack_multiple(frame: DataFrame, level, dropna: bool = True, sort: bool = True):
+    # If all passed levels match up to column names, no
+    # ambiguity about what to do
+    if all(lev in frame.columns.names for lev in level):
+        result = frame
+        for lev in level:
+            result = stack(result, lev, dropna=dropna, sort=sort)
+
+    # Otherwise, level numbers may change as each successive level is stacked
+    elif all(isinstance(lev, int) for lev in level):
+        # As each stack is done, the level numbers decrease, so we need
+        #  to account for that when level is a sequence of ints
+        result = frame
+        # _get_level_number() checks level numbers are in range and converts
+        # negative numbers to positive
+        level = [frame.columns._get_level_number(lev) for lev in level]
+
+        while level:
+            lev = level.pop(0)
+            result = stack(result, lev, dropna=dropna, sort=sort)
+            # Decrement all level numbers greater than current, as these
+            # have now shifted down by one
+            level = [v if v <= lev else v - 1 for v in level]
+
+    else:
+        raise ValueError(
+            "level should contain all level names or all level "
+            "numbers, not a mixture of the two."
+        )
+
+    return result
+
+
+def _stack_multi_column_index(columns: MultiIndex) -> MultiIndex:
+    """Creates a MultiIndex from the first N-1 levels of this MultiIndex."""
+    if len(columns.levels) <= 2:
+        return columns.levels[0]._rename(name=columns.names[0])
+
+    levs = [
+        [lev[c] if c >= 0 else None for c in codes]
+        for lev, codes in zip(columns.levels[:-1], columns.codes[:-1])
+    ]
+
+    # Remove duplicate tuples in the MultiIndex.
+    tuples = zip(*levs)
+    unique_tuples = (key for key, _ in itertools.groupby(tuples))
+    new_levs = zip(*unique_tuples)
+
+    # The dtype of each level must be explicitly set to avoid inferring the wrong type.
+    # See GH-36991.
+    return MultiIndex.from_arrays(
+        [
+            # Not all indices can accept None values.
+            Index(new_lev, dtype=lev.dtype) if None not in new_lev else new_lev
+            for new_lev, lev in zip(new_levs, columns.levels)
+        ],
+        names=columns.names[:-1],
+    )
+
+
+def _stack_multi_columns(
+    frame: DataFrame, level_num: int = -1, dropna: bool = True, sort: bool = True
+) -> DataFrame:
+    def _convert_level_number(level_num: int, columns: Index):
+        """
+        Logic for converting the level number to something we can safely pass
+        to swaplevel.
+
+        If `level_num` matches a column name return the name from
+        position `level_num`, otherwise return `level_num`.
+        """
+        if level_num in columns.names:
+            return columns.names[level_num]
+
+        return level_num
+
+    this = frame.copy(deep=False)
+    mi_cols = this.columns  # cast(MultiIndex, this.columns)
+    assert isinstance(mi_cols, MultiIndex)  # caller is responsible
+
+    # this makes life much simpler
+    if level_num != mi_cols.nlevels - 1:
+        # roll levels to put selected level at end
+        roll_columns = mi_cols
+        for i in range(level_num, mi_cols.nlevels - 1):
+            # Need to check if the ints conflict with level names
+            lev1 = _convert_level_number(i, roll_columns)
+            lev2 = _convert_level_number(i + 1, roll_columns)
+            roll_columns = roll_columns.swaplevel(lev1, lev2)
+        this.columns = mi_cols = roll_columns
+
+    if not mi_cols._is_lexsorted() and sort:
+        # Workaround the edge case where 0 is one of the column names,
+        # which interferes with trying to sort based on the first
+        # level
+        level_to_sort = _convert_level_number(0, mi_cols)
+        this = this.sort_index(level=level_to_sort, axis=1)
+        mi_cols = this.columns
+
+    mi_cols = cast(MultiIndex, mi_cols)
+    new_columns = _stack_multi_column_index(mi_cols)
+
+    # time to ravel the values
+    new_data = {}
+    level_vals = mi_cols.levels[-1]
+    level_codes = unique(mi_cols.codes[-1])
+    if sort:
+        level_codes = np.sort(level_codes)
+    level_vals_nan = level_vals.insert(len(level_vals), None)
+
+    level_vals_used = np.take(level_vals_nan, level_codes)
+    levsize = len(level_codes)
+    drop_cols = []
+    for key in new_columns:
+        try:
+            loc = this.columns.get_loc(key)
+        except KeyError:
+            drop_cols.append(key)
+            continue
+
+        # can make more efficient?
+        # we almost always return a slice
+        # but if unsorted can get a boolean
+        # indexer
+        if not isinstance(loc, slice):
+            slice_len = len(loc)
+        else:
+            slice_len = loc.stop - loc.start
+
+        if slice_len != levsize:
+            chunk = this.loc[:, this.columns[loc]]
+            chunk.columns = level_vals_nan.take(chunk.columns.codes[-1])
+            value_slice = chunk.reindex(columns=level_vals_used).values
+        else:
+            subset = this.iloc[:, loc]
+            dtype = find_common_type(subset.dtypes.tolist())
+            if isinstance(dtype, ExtensionDtype):
+                # TODO(EA2D): won't need special case, can go through .values
+                #  paths below (might change to ._values)
+                value_slice = dtype.construct_array_type()._concat_same_type(
+                    [x._values.astype(dtype, copy=False) for _, x in subset.items()]
+                )
+                N, K = subset.shape
+                idx = np.arange(N * K).reshape(K, N).T.ravel()
+                value_slice = value_slice.take(idx)
+            else:
+                value_slice = subset.values
+
+        if value_slice.ndim > 1:
+            # i.e. not extension
+            value_slice = value_slice.ravel()
+
+        new_data[key] = value_slice
+
+    if len(drop_cols) > 0:
+        new_columns = new_columns.difference(drop_cols)
+
+    N = len(this)
+
+    if isinstance(this.index, MultiIndex):
+        new_levels = list(this.index.levels)
+        new_names = list(this.index.names)
+        new_codes = [lab.repeat(levsize) for lab in this.index.codes]
+    else:
+        old_codes, old_levels = factorize_from_iterable(this.index)
+        new_levels = [old_levels]
+        new_codes = [old_codes.repeat(levsize)]
+        new_names = [this.index.name]  # something better?
+
+    new_levels.append(level_vals)
+    new_codes.append(np.tile(level_codes, N))
+    new_names.append(frame.columns.names[level_num])
+
+    new_index = MultiIndex(
+        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
+    )
+
+    result = frame._constructor(new_data, index=new_index, columns=new_columns)
+
+    if frame.columns.nlevels > 1:
+        desired_columns = frame.columns._drop_level_numbers([level_num]).unique()
+        if not result.columns.equals(desired_columns):
+            result = result[desired_columns]
+
+    # more efficient way to go about this? can do the whole masking biz but
+    # will only save a small amount of time...
+    if dropna:
+        result = result.dropna(axis=0, how="all")
+
+    return result
+
+
+def _reorder_for_extension_array_stack(
+    arr: ExtensionArray, n_rows: int, n_columns: int
+) -> ExtensionArray:
+    """
+    Re-orders the values when stacking multiple extension-arrays.
+
+    The indirect stacking method used for EAs requires a followup
+    take to get the order correct.
+
+    Parameters
+    ----------
+    arr : ExtensionArray
+    n_rows, n_columns : int
+        The number of rows and columns in the original DataFrame.
+
+    Returns
+    -------
+    taken : ExtensionArray
+        The original `arr` with elements re-ordered appropriately
+
+    Examples
+    --------
+    >>> arr = np.array(['a', 'b', 'c', 'd', 'e', 'f'])
+    >>> _reorder_for_extension_array_stack(arr, 2, 3)
+    array(['a', 'c', 'e', 'b', 'd', 'f'], dtype='<U1')
+
+    >>> _reorder_for_extension_array_stack(arr, 3, 2)
+    array(['a', 'd', 'b', 'e', 'c', 'f'], dtype='<U1')
+    """
+    # final take to get the order correct.
+    # idx is an indexer like
+    # [c0r0, c1r0, c2r0, ...,
+    #  c0r1, c1r1, c2r1, ...]
+    idx = np.arange(n_rows * n_columns).reshape(n_columns, n_rows).T.ravel()
+    return arr.take(idx)
+
+
+def stack_v3(frame: DataFrame, level: list[int]) -> Series | DataFrame:
+    if frame.columns.nunique() != len(frame.columns):
+        raise ValueError("Columns with duplicate values are not supported in stack")
+
+    # If we need to drop `level` from columns, it needs to be in descending order
+    drop_levnums = sorted(level, reverse=True)
+    stack_cols = frame.columns._drop_level_numbers(
+        [k for k in range(frame.columns.nlevels) if k not in level][::-1]
+    )
+    if len(level) > 1:
+        # Arrange columns in the order we want to take them, e.g. level=[2, 0, 1]
+        sorter = np.argsort(level)
+        ordered_stack_cols = stack_cols._reorder_ilevels(sorter)
+    else:
+        ordered_stack_cols = stack_cols
+
+    stack_cols_unique = stack_cols.unique()
+    ordered_stack_cols_unique = ordered_stack_cols.unique()
+
+    # Grab data for each unique index to be stacked
+    buf = []
+    for idx in stack_cols_unique:
+        if len(frame.columns) == 1:
+            data = frame.copy()
+        else:
+            # Take the data from frame corresponding to this idx value
+            if len(level) == 1:
+                idx = (idx,)
+            gen = iter(idx)
+            column_indexer = tuple(
+                next(gen) if k in level else slice(None)
+                for k in range(frame.columns.nlevels)
+            )
+            data = frame.loc[:, column_indexer]
+
+        if len(level) < frame.columns.nlevels:
+            data.columns = data.columns._drop_level_numbers(drop_levnums)
+        elif stack_cols.nlevels == 1:
+            if data.ndim == 1:
+                data.name = 0
+            else:
+                data.columns = RangeIndex(len(data.columns))
+        buf.append(data)
+
+    result: Series | DataFrame
+    if len(buf) > 0 and not frame.empty:
+        result = concat(buf)
+        ratio = len(result) // len(frame)
+    else:
+        # input is empty
+        if len(level) < frame.columns.nlevels:
+            # concat column order may be different from dropping the levels
+            new_columns = frame.columns._drop_level_numbers(drop_levnums).unique()
+        else:
+            new_columns = [0]
+        result = DataFrame(columns=new_columns, dtype=frame._values.dtype)
+        ratio = 0
+
+    if len(level) < frame.columns.nlevels:
+        # concat column order may be different from dropping the levels
+        desired_columns = frame.columns._drop_level_numbers(drop_levnums).unique()
+        if not result.columns.equals(desired_columns):
+            result = result[desired_columns]
+
+    # Construct the correct MultiIndex by combining the frame's index and
+    # stacked columns.
+    index_levels: list | FrozenList
+    if isinstance(frame.index, MultiIndex):
+        index_levels = frame.index.levels
+        index_codes = list(np.tile(frame.index.codes, (1, ratio)))
+    else:
+        codes, uniques = factorize(frame.index, use_na_sentinel=False)
+        index_levels = [uniques]
+        index_codes = list(np.tile(codes, (1, ratio)))
+    if isinstance(stack_cols, MultiIndex):
+        column_levels = ordered_stack_cols.levels
+        column_codes = ordered_stack_cols.drop_duplicates().codes
+    else:
+        column_levels = [ordered_stack_cols.unique()]
+        column_codes = [factorize(ordered_stack_cols_unique, use_na_sentinel=False)[0]]
+    column_codes = [np.repeat(codes, len(frame)) for codes in column_codes]
+    result.index = MultiIndex(
+        levels=index_levels + column_levels,
+        codes=index_codes + column_codes,
+        names=frame.index.names + list(ordered_stack_cols.names),
+        verify_integrity=False,
+    )
+
+    # sort result, but faster than calling sort_index since we know the order we need
+    len_df = len(frame)
+    n_uniques = len(ordered_stack_cols_unique)
+    indexer = np.arange(n_uniques)
+    idxs = np.tile(len_df * indexer, len_df) + np.repeat(np.arange(len_df), n_uniques)
+    result = result.take(idxs)
+
+    # Reshape/rename if needed and dropna
+    if result.ndim == 2 and frame.columns.nlevels == len(level):
+        if len(result.columns) == 0:
+            result = Series(index=result.index)
+        else:
+            result = result.iloc[:, 0]
+    if result.ndim == 1:
+        result.name = None
+
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/tile.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/tile.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b0c6fbb8e3bf69fcb60ce3257450260af2a9f6b
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/tile.py
@@ -0,0 +1,638 @@
+"""
+Quantilization functions and related stuff
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    Timedelta,
+    Timestamp,
+    lib,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_platform_int,
+    is_bool_dtype,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import isna
+
+from pandas import (
+    Categorical,
+    Index,
+    IntervalIndex,
+)
+import pandas.core.algorithms as algos
+from pandas.core.arrays.datetimelike import dtype_to_unit
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeObj,
+        IntervalLeftRight,
+    )
+
+
+def cut(
+    x,
+    bins,
+    right: bool = True,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+):
+    """
+    Bin values into discrete intervals.
+
+    Use `cut` when you need to segment and sort data values into bins. This
+    function is also useful for going from a continuous variable to a
+    categorical variable. For example, `cut` could convert ages to groups of
+    age ranges. Supports binning into an equal number of bins, or a
+    pre-specified array of bins.
+
+    Parameters
+    ----------
+    x : array-like
+        The input array to be binned. Must be 1-dimensional.
+    bins : int, sequence of scalars, or IntervalIndex
+        The criteria to bin by.
+
+        * int : Defines the number of equal-width bins in the range of `x`. The
+          range of `x` is extended by .1% on each side to include the minimum
+          and maximum values of `x`.
+        * sequence of scalars : Defines the bin edges allowing for non-uniform
+          width. No extension of the range of `x` is done.
+        * IntervalIndex : Defines the exact bins to be used. Note that
+          IntervalIndex for `bins` must be non-overlapping.
+
+    right : bool, default True
+        Indicates whether `bins` includes the rightmost edge or not. If
+        ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
+        indicate (1,2], (2,3], (3,4]. This argument is ignored when
+        `bins` is an IntervalIndex.
+    labels : array or False, default None
+        Specifies the labels for the returned bins. Must be the same length as
+        the resulting bins. If False, returns only integer indicators of the
+        bins. This affects the type of the output container (see below).
+        This argument is ignored when `bins` is an IntervalIndex. If True,
+        raises an error. When `ordered=False`, labels must be provided.
+    retbins : bool, default False
+        Whether to return the bins or not. Useful when bins is provided
+        as a scalar.
+    precision : int, default 3
+        The precision at which to store and display the bins labels.
+    include_lowest : bool, default False
+        Whether the first interval should be left-inclusive or not.
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+    ordered : bool, default True
+        Whether the labels are ordered or not. Applies to returned types
+        Categorical and Series (with Categorical dtype). If True,
+        the resulting categorical will be ordered. If False, the resulting
+        categorical will be unordered (labels must be provided).
+
+    Returns
+    -------
+    out : Categorical, Series, or ndarray
+        An array-like object representing the respective bin for each value
+        of `x`. The type depends on the value of `labels`.
+
+        * None (default) : returns a Series for Series `x` or a
+          Categorical for all other inputs. The values stored within
+          are Interval dtype.
+
+        * sequence of scalars : returns a Series for Series `x` or a
+          Categorical for all other inputs. The values stored within
+          are whatever the type in the sequence is.
+
+        * False : returns an ndarray of integers.
+
+    bins : numpy.ndarray or IntervalIndex.
+        The computed or specified bins. Only returned when `retbins=True`.
+        For scalar or sequence `bins`, this is an ndarray with the computed
+        bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For
+        an IntervalIndex `bins`, this is equal to `bins`.
+
+    See Also
+    --------
+    qcut : Discretize variable into equal-sized buckets based on rank
+        or based on sample quantiles.
+    Categorical : Array type for storing data that come from a
+        fixed set of values.
+    Series : One-dimensional array with axis labels (including time series).
+    IntervalIndex : Immutable Index implementing an ordered, sliceable set.
+
+    Notes
+    -----
+    Any NA values will be NA in the result. Out of bounds values will be NA in
+    the resulting Series or Categorical object.
+
+    Reference :ref:`the user guide <reshaping.tile.cut>` for more examples.
+
+    Examples
+    --------
+    Discretize into three equal-sized bins.
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3)
+    ... # doctest: +ELLIPSIS
+    [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+    Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3, retbins=True)
+    ... # doctest: +ELLIPSIS
+    ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+    Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+    array([0.994, 3.   , 5.   , 7.   ]))
+
+    Discovers the same bins, but assign them specific labels. Notice that
+    the returned Categorical's categories are `labels` and is ordered.
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]),
+    ...        3, labels=["bad", "medium", "good"])
+    ['bad', 'good', 'medium', 'medium', 'good', 'bad']
+    Categories (3, object): ['bad' < 'medium' < 'good']
+
+    ``ordered=False`` will result in unordered categories when labels are passed.
+    This parameter can be used to allow non-unique labels:
+
+    >>> pd.cut(np.array([1, 7, 5, 4, 6, 3]), 3,
+    ...        labels=["B", "A", "B"], ordered=False)
+    ['B', 'B', 'A', 'A', 'B', 'B']
+    Categories (2, object): ['A', 'B']
+
+    ``labels=False`` implies you just want the bins back.
+
+    >>> pd.cut([0, 1, 1, 2], bins=4, labels=False)
+    array([0, 1, 1, 3])
+
+    Passing a Series as an input returns a Series with categorical dtype:
+
+    >>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
+    ...               index=['a', 'b', 'c', 'd', 'e'])
+    >>> pd.cut(s, 3)
+    ... # doctest: +ELLIPSIS
+    a    (1.992, 4.667]
+    b    (1.992, 4.667]
+    c    (4.667, 7.333]
+    d     (7.333, 10.0]
+    e     (7.333, 10.0]
+    dtype: category
+    Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ...
+
+    Passing a Series as an input returns a Series with mapping value.
+    It is used to map numerically to intervals based on bins.
+
+    >>> s = pd.Series(np.array([2, 4, 6, 8, 10]),
+    ...               index=['a', 'b', 'c', 'd', 'e'])
+    >>> pd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
+    ... # doctest: +ELLIPSIS
+    (a    1.0
+     b    2.0
+     c    3.0
+     d    4.0
+     e    NaN
+     dtype: float64,
+     array([ 0,  2,  4,  6,  8, 10]))
+
+    Use `drop` optional when bins is not unique
+
+    >>> pd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
+    ...        right=False, duplicates='drop')
+    ... # doctest: +ELLIPSIS
+    (a    1.0
+     b    2.0
+     c    3.0
+     d    3.0
+     e    NaN
+     dtype: float64,
+     array([ 0,  2,  4,  6, 10]))
+
+    Passing an IntervalIndex for `bins` results in those categories exactly.
+    Notice that values not covered by the IntervalIndex are set to NaN. 0
+    is to the left of the first bin (which is closed on the right), and 1.5
+    falls between two bins.
+
+    >>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
+    >>> pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)
+    [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
+    Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]]
+    """
+    # NOTE: this binning code is changed a bit from histogram for var(x) == 0
+
+    original = x
+    x_idx = _preprocess_for_cut(x)
+    x_idx, _ = _coerce_to_type(x_idx)
+
+    if not np.iterable(bins):
+        bins = _nbins_to_bins(x_idx, bins, right)
+
+    elif isinstance(bins, IntervalIndex):
+        if bins.is_overlapping:
+            raise ValueError("Overlapping IntervalIndex is not accepted.")
+
+    else:
+        bins = Index(bins)
+        if not bins.is_monotonic_increasing:
+            raise ValueError("bins must increase monotonically.")
+
+    fac, bins = _bins_to_cuts(
+        x_idx,
+        bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        duplicates=duplicates,
+        ordered=ordered,
+    )
+
+    return _postprocess_for_cut(fac, bins, retbins, original)
+
+
+def qcut(
+    x,
+    q,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    duplicates: str = "raise",
+):
+    """
+    Quantile-based discretization function.
+
+    Discretize variable into equal-sized buckets based on rank or based
+    on sample quantiles. For example 1000 values for 10 quantiles would
+    produce a Categorical object indicating quantile membership for each data point.
+
+    Parameters
+    ----------
+    x : 1d ndarray or Series
+    q : int or list-like of float
+        Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately
+        array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles.
+    labels : array or False, default None
+        Used as labels for the resulting bins. Must be of the same length as
+        the resulting bins. If False, return only integer indicators of the
+        bins. If True, raises an error.
+    retbins : bool, optional
+        Whether to return the (bins, labels) or not. Can be useful if bins
+        is given as a scalar.
+    precision : int, optional
+        The precision at which to store and display the bins labels.
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+
+    Returns
+    -------
+    out : Categorical or Series or array of integers if labels is False
+        The return type (Categorical or Series) depends on the input: a Series
+        of type category if input is a Series else Categorical. Bins are
+        represented as categories when categorical data is returned.
+    bins : ndarray of floats
+        Returned only if `retbins` is True.
+
+    Notes
+    -----
+    Out of bounds values will be NA in the resulting Categorical object
+
+    Examples
+    --------
+    >>> pd.qcut(range(5), 4)
+    ... # doctest: +ELLIPSIS
+    [(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]
+    Categories (4, interval[float64, right]): [(-0.001, 1.0] < (1.0, 2.0] ...
+
+    >>> pd.qcut(range(5), 3, labels=["good", "medium", "bad"])
+    ... # doctest: +SKIP
+    [good, good, medium, bad, bad]
+    Categories (3, object): [good < medium < bad]
+
+    >>> pd.qcut(range(5), 4, labels=False)
+    array([0, 0, 1, 2, 3])
+    """
+    original = x
+    x_idx = _preprocess_for_cut(x)
+    x_idx, _ = _coerce_to_type(x_idx)
+
+    quantiles = np.linspace(0, 1, q + 1) if is_integer(q) else q
+
+    bins = x_idx.to_series().dropna().quantile(quantiles)
+
+    fac, bins = _bins_to_cuts(
+        x_idx,
+        Index(bins),
+        labels=labels,
+        precision=precision,
+        include_lowest=True,
+        duplicates=duplicates,
+    )
+
+    return _postprocess_for_cut(fac, bins, retbins, original)
+
+
+def _nbins_to_bins(x_idx: Index, nbins: int, right: bool) -> Index:
+    """
+    If a user passed an integer N for bins, convert this to a sequence of N
+    equal(ish)-sized bins.
+    """
+    if is_scalar(nbins) and nbins < 1:
+        raise ValueError("`bins` should be a positive integer.")
+
+    if x_idx.size == 0:
+        raise ValueError("Cannot cut empty array")
+
+    rng = (x_idx.min(), x_idx.max())
+    mn, mx = rng
+
+    if is_numeric_dtype(x_idx.dtype) and (np.isinf(mn) or np.isinf(mx)):
+        # GH#24314
+        raise ValueError(
+            "cannot specify integer `bins` when input data contains infinity"
+        )
+
+    if mn == mx:  # adjust end points before binning
+        if _is_dt_or_td(x_idx.dtype):
+            # using seconds=1 is pretty arbitrary here
+            # error: Argument 1 to "dtype_to_unit" has incompatible type
+            # "dtype[Any] | ExtensionDtype"; expected "DatetimeTZDtype | dtype[Any]"
+            unit = dtype_to_unit(x_idx.dtype)  # type: ignore[arg-type]
+            td = Timedelta(seconds=1).as_unit(unit)
+            # Use DatetimeArray/TimedeltaArray method instead of linspace
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "_generate_range"
+            bins = x_idx._values._generate_range(  # type: ignore[union-attr]
+                start=mn - td, end=mx + td, periods=nbins + 1, freq=None, unit=unit
+            )
+        else:
+            mn -= 0.001 * abs(mn) if mn != 0 else 0.001
+            mx += 0.001 * abs(mx) if mx != 0 else 0.001
+
+            bins = np.linspace(mn, mx, nbins + 1, endpoint=True)
+    else:  # adjust end points after binning
+        if _is_dt_or_td(x_idx.dtype):
+            # Use DatetimeArray/TimedeltaArray method instead of linspace
+
+            # error: Argument 1 to "dtype_to_unit" has incompatible type
+            # "dtype[Any] | ExtensionDtype"; expected "DatetimeTZDtype | dtype[Any]"
+            unit = dtype_to_unit(x_idx.dtype)  # type: ignore[arg-type]
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "_generate_range"
+            bins = x_idx._values._generate_range(  # type: ignore[union-attr]
+                start=mn, end=mx, periods=nbins + 1, freq=None, unit=unit
+            )
+        else:
+            bins = np.linspace(mn, mx, nbins + 1, endpoint=True)
+        adj = (mx - mn) * 0.001  # 0.1% of the range
+        if right:
+            bins[0] -= adj
+        else:
+            bins[-1] += adj
+
+    return Index(bins)
+
+
+def _bins_to_cuts(
+    x_idx: Index,
+    bins: Index,
+    right: bool = True,
+    labels=None,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+):
+    if not ordered and labels is None:
+        raise ValueError("'labels' must be provided if 'ordered = False'")
+
+    if duplicates not in ["raise", "drop"]:
+        raise ValueError(
+            "invalid value for 'duplicates' parameter, valid options are: raise, drop"
+        )
+
+    result: Categorical | np.ndarray
+
+    if isinstance(bins, IntervalIndex):
+        # we have a fast-path here
+        ids = bins.get_indexer(x_idx)
+        cat_dtype = CategoricalDtype(bins, ordered=True)
+        result = Categorical.from_codes(ids, dtype=cat_dtype, validate=False)
+        return result, bins
+
+    unique_bins = algos.unique(bins)
+    if len(unique_bins) < len(bins) and len(bins) != 2:
+        if duplicates == "raise":
+            raise ValueError(
+                f"Bin edges must be unique: {repr(bins)}.\n"
+                f"You can drop duplicate edges by setting the 'duplicates' kwarg"
+            )
+        bins = unique_bins
+
+    side: Literal["left", "right"] = "left" if right else "right"
+
+    try:
+        ids = bins.searchsorted(x_idx, side=side)
+    except TypeError as err:
+        # e.g. test_datetime_nan_error if bins are DatetimeArray and x_idx
+        #  is integers
+        if x_idx.dtype.kind == "m":
+            raise ValueError("bins must be of timedelta64 dtype") from err
+        elif x_idx.dtype.kind == bins.dtype.kind == "M":
+            raise ValueError(
+                "Cannot use timezone-naive bins with timezone-aware values, "
+                "or vice-versa"
+            ) from err
+        elif x_idx.dtype.kind == "M":
+            raise ValueError("bins must be of datetime64 dtype") from err
+        else:
+            raise
+    ids = ensure_platform_int(ids)
+
+    if include_lowest:
+        ids[x_idx == bins[0]] = 1
+
+    na_mask = isna(x_idx) | (ids == len(bins)) | (ids == 0)
+    has_nas = na_mask.any()
+
+    if labels is not False:
+        if not (labels is None or is_list_like(labels)):
+            raise ValueError(
+                "Bin labels must either be False, None or passed in as a "
+                "list-like argument"
+            )
+
+        if labels is None:
+            labels = _format_labels(
+                bins, precision, right=right, include_lowest=include_lowest
+            )
+        elif ordered and len(set(labels)) != len(labels):
+            raise ValueError(
+                "labels must be unique if ordered=True; pass ordered=False "
+                "for duplicate labels"
+            )
+        else:
+            if len(labels) != len(bins) - 1:
+                raise ValueError(
+                    "Bin labels must be one fewer than the number of bin edges"
+                )
+
+        if not isinstance(getattr(labels, "dtype", None), CategoricalDtype):
+            labels = Categorical(
+                labels,
+                categories=labels if len(set(labels)) == len(labels) else None,
+                ordered=ordered,
+            )
+        # TODO: handle mismatch between categorical label order and pandas.cut order.
+        np.putmask(ids, na_mask, 0)
+        result = algos.take_nd(labels, ids - 1)
+
+    else:
+        result = ids - 1
+        if has_nas:
+            result = result.astype(np.float64)
+            np.putmask(result, na_mask, np.nan)
+
+    return result, bins
+
+
+def _coerce_to_type(x: Index) -> tuple[Index, DtypeObj | None]:
+    """
+    if the passed data is of datetime/timedelta, bool or nullable int type,
+    this method converts it to numeric so that cut or qcut method can
+    handle it
+    """
+    dtype: DtypeObj | None = None
+
+    if _is_dt_or_td(x.dtype):
+        dtype = x.dtype
+    elif is_bool_dtype(x.dtype):
+        # GH 20303
+        x = x.astype(np.int64)
+    # To support cut and qcut for IntegerArray we convert to float dtype.
+    # Will properly support in the future.
+    # https://github.com/pandas-dev/pandas/pull/31290
+    # https://github.com/pandas-dev/pandas/issues/31389
+    elif isinstance(x.dtype, ExtensionDtype) and is_numeric_dtype(x.dtype):
+        x_arr = x.to_numpy(dtype=np.float64, na_value=np.nan)
+        x = Index(x_arr)
+
+    return Index(x), dtype
+
+
+def _is_dt_or_td(dtype: DtypeObj) -> bool:
+    # Note: the dtype here comes from an Index.dtype, so we know that that any
+    #  dt64/td64 dtype is of a supported unit.
+    return isinstance(dtype, DatetimeTZDtype) or lib.is_np_dtype(dtype, "mM")
+
+
+def _format_labels(
+    bins: Index,
+    precision: int,
+    right: bool = True,
+    include_lowest: bool = False,
+):
+    """based on the dtype, return our labels"""
+    closed: IntervalLeftRight = "right" if right else "left"
+
+    formatter: Callable[[Any], Timestamp] | Callable[[Any], Timedelta]
+
+    if _is_dt_or_td(bins.dtype):
+        # error: Argument 1 to "dtype_to_unit" has incompatible type
+        # "dtype[Any] | ExtensionDtype"; expected "DatetimeTZDtype | dtype[Any]"
+        unit = dtype_to_unit(bins.dtype)  # type: ignore[arg-type]
+        formatter = lambda x: x
+        adjust = lambda x: x - Timedelta(1, unit=unit).as_unit(unit)
+    else:
+        precision = _infer_precision(precision, bins)
+        formatter = lambda x: _round_frac(x, precision)
+        adjust = lambda x: x - 10 ** (-precision)
+
+    breaks = [formatter(b) for b in bins]
+    if right and include_lowest:
+        # adjust lhs of first interval by precision to account for being right closed
+        breaks[0] = adjust(breaks[0])
+
+    if _is_dt_or_td(bins.dtype):
+        # error: "Index" has no attribute "as_unit"
+        breaks = type(bins)(breaks).as_unit(unit)  # type: ignore[attr-defined]
+
+    return IntervalIndex.from_breaks(breaks, closed=closed)
+
+
+def _preprocess_for_cut(x) -> Index:
+    """
+    handles preprocessing for cut where we convert passed
+    input to array, strip the index information and store it
+    separately
+    """
+    # Check that the passed array is a Pandas or Numpy object
+    # We don't want to strip away a Pandas data-type here (e.g. datetimetz)
+    ndim = getattr(x, "ndim", None)
+    if ndim is None:
+        x = np.asarray(x)
+    if x.ndim != 1:
+        raise ValueError("Input array must be 1 dimensional")
+
+    return Index(x)
+
+
+def _postprocess_for_cut(fac, bins, retbins: bool, original):
+    """
+    handles post processing for the cut method where
+    we combine the index information if the originally passed
+    datatype was a series
+    """
+    if isinstance(original, ABCSeries):
+        fac = original._constructor(fac, index=original.index, name=original.name)
+
+    if not retbins:
+        return fac
+
+    if isinstance(bins, Index) and is_numeric_dtype(bins.dtype):
+        bins = bins._values
+
+    return fac, bins
+
+
+def _round_frac(x, precision: int):
+    """
+    Round the fractional part of the given number
+    """
+    if not np.isfinite(x) or x == 0:
+        return x
+    else:
+        frac, whole = np.modf(x)
+        if whole == 0:
+            digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision
+        else:
+            digits = precision
+        return np.around(x, digits)
+
+
+def _infer_precision(base_precision: int, bins: Index) -> int:
+    """
+    Infer an appropriate precision for _round_frac
+    """
+    for precision in range(base_precision, 20):
+        levels = np.asarray([_round_frac(b, precision) for b in bins])
+        if algos.unique(levels).size == bins.size:
+            return precision
+    return base_precision  # default
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/util.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..476e3922b6989e4267aeeafd5943a80c1599b1d5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/reshape/util.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.core.dtypes.common import is_list_like
+
+if TYPE_CHECKING:
+    from pandas._typing import NumpyIndexT
+
+
+def cartesian_product(X) -> list[np.ndarray]:
+    """
+    Numpy version of itertools.product.
+    Sometimes faster (for large inputs)...
+
+    Parameters
+    ----------
+    X : list-like of list-likes
+
+    Returns
+    -------
+    product : list of ndarrays
+
+    Examples
+    --------
+    >>> cartesian_product([list('ABC'), [1, 2]])
+    [array(['A', 'A', 'B', 'B', 'C', 'C'], dtype='<U1'), array([1, 2, 1, 2, 1, 2])]
+
+    See Also
+    --------
+    itertools.product : Cartesian product of input iterables.  Equivalent to
+        nested for-loops.
+    """
+    msg = "Input must be a list-like of list-likes"
+    if not is_list_like(X):
+        raise TypeError(msg)
+    for x in X:
+        if not is_list_like(x):
+            raise TypeError(msg)
+
+    if len(X) == 0:
+        return []
+
+    lenX = np.fromiter((len(x) for x in X), dtype=np.intp)
+    cumprodX = np.cumprod(lenX)
+
+    if np.any(cumprodX < 0):
+        raise ValueError("Product space too large to allocate arrays!")
+
+    a = np.roll(cumprodX, 1)
+    a[0] = 1
+
+    if cumprodX[-1] != 0:
+        b = cumprodX[-1] / cumprodX
+    else:
+        # if any factor is empty, the cartesian product is empty
+        b = np.zeros_like(cumprodX)
+
+    # error: Argument of type "int_" cannot be assigned to parameter "num" of
+    # type "int" in function "tile_compat"
+    return [
+        tile_compat(
+            np.repeat(x, b[i]),
+            np.prod(a[i]),
+        )
+        for i, x in enumerate(X)
+    ]
+
+
+def tile_compat(arr: NumpyIndexT, num: int) -> NumpyIndexT:
+    """
+    Index compat for np.tile.
+
+    Notes
+    -----
+    Does not support multi-dimensional `num`.
+    """
+    if isinstance(arr, np.ndarray):
+        return np.tile(arr, num)
+
+    # Otherwise we have an Index
+    taker = np.tile(np.arange(len(arr)), num)
+    return arr.take(taker)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/roperator.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/roperator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f320f4e9c6b984b64e0fc1268e50a8ad1a7e1fe
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/roperator.py
@@ -0,0 +1,62 @@
+"""
+Reversed Operations not available in the stdlib operator module.
+Defining these instead of using lambdas allows us to reference them by name.
+"""
+from __future__ import annotations
+
+import operator
+
+
+def radd(left, right):
+    return right + left
+
+
+def rsub(left, right):
+    return right - left
+
+
+def rmul(left, right):
+    return right * left
+
+
+def rdiv(left, right):
+    return right / left
+
+
+def rtruediv(left, right):
+    return right / left
+
+
+def rfloordiv(left, right):
+    return right // left
+
+
+def rmod(left, right):
+    # check if right is a string as % is the string
+    # formatting operation; this is a TypeError
+    # otherwise perform the op
+    if isinstance(right, str):
+        typ = type(left).__name__
+        raise TypeError(f"{typ} cannot perform the operation mod")
+
+    return right % left
+
+
+def rdivmod(left, right):
+    return divmod(right, left)
+
+
+def rpow(left, right):
+    return right**left
+
+
+def rand_(left, right):
+    return operator.and_(right, left)
+
+
+def ror_(left, right):
+    return operator.or_(right, left)
+
+
+def rxor(left, right):
+    return operator.xor(right, left)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sample.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..eebbed3512c4eca42e401c40605838c6f69011df
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sample.py
@@ -0,0 +1,154 @@
+"""
+Module containing utilities for NDFrame.sample() and .GroupBy.sample()
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import AxisInt
+
+    from pandas.core.generic import NDFrame
+
+
+def preprocess_weights(obj: NDFrame, weights, axis: AxisInt) -> np.ndarray:
+    """
+    Process and validate the `weights` argument to `NDFrame.sample` and
+    `.GroupBy.sample`.
+
+    Returns `weights` as an ndarray[np.float64], validated except for normalizing
+    weights (because that must be done groupwise in groupby sampling).
+    """
+    # If a series, align with frame
+    if isinstance(weights, ABCSeries):
+        weights = weights.reindex(obj.axes[axis])
+
+    # Strings acceptable if a dataframe and axis = 0
+    if isinstance(weights, str):
+        if isinstance(obj, ABCDataFrame):
+            if axis == 0:
+                try:
+                    weights = obj[weights]
+                except KeyError as err:
+                    raise KeyError(
+                        "String passed to weights not a valid column"
+                    ) from err
+            else:
+                raise ValueError(
+                    "Strings can only be passed to "
+                    "weights when sampling from rows on "
+                    "a DataFrame"
+                )
+        else:
+            raise ValueError(
+                "Strings cannot be passed as weights when sampling from a Series."
+            )
+
+    if isinstance(obj, ABCSeries):
+        func = obj._constructor
+    else:
+        func = obj._constructor_sliced
+
+    weights = func(weights, dtype="float64")._values
+
+    if len(weights) != obj.shape[axis]:
+        raise ValueError("Weights and axis to be sampled must be of same length")
+
+    if lib.has_infs(weights):
+        raise ValueError("weight vector may not include `inf` values")
+
+    if (weights < 0).any():
+        raise ValueError("weight vector many not include negative values")
+
+    missing = np.isnan(weights)
+    if missing.any():
+        # Don't modify weights in place
+        weights = weights.copy()
+        weights[missing] = 0
+    return weights
+
+
+def process_sampling_size(
+    n: int | None, frac: float | None, replace: bool
+) -> int | None:
+    """
+    Process and validate the `n` and `frac` arguments to `NDFrame.sample` and
+    `.GroupBy.sample`.
+
+    Returns None if `frac` should be used (variable sampling sizes), otherwise returns
+    the constant sampling size.
+    """
+    # If no frac or n, default to n=1.
+    if n is None and frac is None:
+        n = 1
+    elif n is not None and frac is not None:
+        raise ValueError("Please enter a value for `frac` OR `n`, not both")
+    elif n is not None:
+        if n < 0:
+            raise ValueError(
+                "A negative number of rows requested. Please provide `n` >= 0."
+            )
+        if n % 1 != 0:
+            raise ValueError("Only integers accepted as `n` values")
+    else:
+        assert frac is not None  # for mypy
+        if frac > 1 and not replace:
+            raise ValueError(
+                "Replace has to be set to `True` when "
+                "upsampling the population `frac` > 1."
+            )
+        if frac < 0:
+            raise ValueError(
+                "A negative number of rows requested. Please provide `frac` >= 0."
+            )
+
+    return n
+
+
+def sample(
+    obj_len: int,
+    size: int,
+    replace: bool,
+    weights: np.ndarray | None,
+    random_state: np.random.RandomState | np.random.Generator,
+) -> np.ndarray:
+    """
+    Randomly sample `size` indices in `np.arange(obj_len)`
+
+    Parameters
+    ----------
+    obj_len : int
+        The length of the indices being considered
+    size : int
+        The number of values to choose
+    replace : bool
+        Allow or disallow sampling of the same row more than once.
+    weights : np.ndarray[np.float64] or None
+        If None, equal probability weighting, otherwise weights according
+        to the vector normalized
+    random_state: np.random.RandomState or np.random.Generator
+        State used for the random sampling
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    if weights is not None:
+        weight_sum = weights.sum()
+        if weight_sum != 0:
+            weights = weights / weight_sum
+        else:
+            raise ValueError("Invalid weights: weights sum to zero")
+
+    return random_state.choice(obj_len, size=size, replace=replace, p=weights).astype(
+        np.intp, copy=False
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/series.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/series.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef0b41f3ae552221a9e3b186945f1f0fbee85b61
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/series.py
@@ -0,0 +1,6652 @@
+"""
+Data structure for 1-dimensional cross-sectional and time series data
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import operator
+import sys
+from textwrap import dedent
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+import weakref
+
+import numpy as np
+
+from pandas._config import (
+    using_copy_on_write,
+    warn_copy_on_write,
+)
+from pandas._config.config import _get_option
+
+from pandas._libs import (
+    lib,
+    properties,
+    reshape,
+)
+from pandas._libs.lib import is_range_indexer
+from pandas.compat import PYPY
+from pandas.compat._constants import (
+    REF_COUNT,
+    WARNING_CHECK_DISABLED,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.numpy import function as nv
+from pandas.errors import (
+    ChainedAssignmentError,
+    InvalidIndexError,
+    _chained_assignment_method_msg,
+    _chained_assignment_msg,
+    _chained_assignment_warning_method_msg,
+    _chained_assignment_warning_msg,
+    _check_cacher,
+)
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    deprecate_nonkeyword_arguments,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import (
+    validate_ascending,
+    validate_bool_kwarg,
+    validate_percentile,
+)
+
+from pandas.core.dtypes.astype import astype_is_view
+from pandas.core.dtypes.cast import (
+    LossySetitemError,
+    construct_1d_arraylike_from_scalar,
+    find_common_type,
+    infer_dtype_from,
+    maybe_box_native,
+    maybe_cast_pointwise_result,
+)
+from pandas.core.dtypes.common import (
+    is_dict_like,
+    is_integer,
+    is_iterator,
+    is_list_like,
+    is_object_dtype,
+    is_scalar,
+    pandas_dtype,
+    validate_all_hashable,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    ExtensionDtype,
+    SparseDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_hashable
+from pandas.core.dtypes.missing import (
+    isna,
+    na_value_for_dtype,
+    notna,
+    remove_na_arraylike,
+)
+
+from pandas.core import (
+    algorithms,
+    base,
+    common as com,
+    missing,
+    nanops,
+    ops,
+    roperator,
+)
+from pandas.core.accessor import CachedAccessor
+from pandas.core.apply import SeriesApply
+from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays.arrow import (
+    ListAccessor,
+    StructAccessor,
+)
+from pandas.core.arrays.categorical import CategoricalAccessor
+from pandas.core.arrays.sparse import SparseAccessor
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.construction import (
+    array as pd_array,
+    extract_array,
+    sanitize_array,
+)
+from pandas.core.generic import (
+    NDFrame,
+    make_doc,
+)
+from pandas.core.indexers import (
+    disallow_ndim_indexing,
+    unpack_1tuple,
+)
+from pandas.core.indexes.accessors import CombinedDatetimelikeProperties
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    default_index,
+    ensure_index,
+)
+import pandas.core.indexes.base as ibase
+from pandas.core.indexes.multi import maybe_droplevels
+from pandas.core.indexing import (
+    check_bool_indexer,
+    check_dict_or_set_indexers,
+)
+from pandas.core.internals import (
+    SingleArrayManager,
+    SingleBlockManager,
+)
+from pandas.core.methods import selectn
+from pandas.core.shared_docs import _shared_docs
+from pandas.core.sorting import (
+    ensure_key_mapped,
+    nargsort,
+)
+from pandas.core.strings.accessor import StringMethods
+from pandas.core.tools.datetimes import to_datetime
+
+import pandas.io.formats.format as fmt
+from pandas.io.formats.info import (
+    INFO_DOCSTRING,
+    SeriesInfo,
+    series_sub_kwargs,
+)
+import pandas.plotting
+
+if TYPE_CHECKING:
+    from pandas._libs.internals import BlockValuesRefs
+    from pandas._typing import (
+        AggFuncType,
+        AnyAll,
+        AnyArrayLike,
+        ArrayLike,
+        Axis,
+        AxisInt,
+        CorrelationMethod,
+        DropKeep,
+        Dtype,
+        DtypeObj,
+        FilePath,
+        Frequency,
+        IgnoreRaise,
+        IndexKeyFunc,
+        IndexLabel,
+        Level,
+        MutableMappingT,
+        NaPosition,
+        NumpySorter,
+        NumpyValueArrayLike,
+        QuantileInterpolation,
+        ReindexMethod,
+        Renamer,
+        Scalar,
+        Self,
+        SingleManager,
+        SortKind,
+        StorageOptions,
+        Suffixes,
+        ValueKeyFunc,
+        WriteBuffer,
+        npt,
+    )
+
+    from pandas.core.frame import DataFrame
+    from pandas.core.groupby.generic import SeriesGroupBy
+
+__all__ = ["Series"]
+
+_shared_doc_kwargs = {
+    "axes": "index",
+    "klass": "Series",
+    "axes_single_arg": "{0 or 'index'}",
+    "axis": """axis : {0 or 'index'}
+        Unused. Parameter needed for compatibility with DataFrame.""",
+    "inplace": """inplace : bool, default False
+        If True, performs operation inplace and returns None.""",
+    "unique": "np.ndarray",
+    "duplicated": "Series",
+    "optional_by": "",
+    "optional_reindex": """
+index : array-like, optional
+    New labels for the index. Preferably an Index object to avoid
+    duplicating data.
+axis : int or str, optional
+    Unused.""",
+}
+
+
+def _coerce_method(converter):
+    """
+    Install the scalar coercion methods.
+    """
+
+    def wrapper(self):
+        if len(self) == 1:
+            warnings.warn(
+                f"Calling {converter.__name__} on a single element Series is "
+                "deprecated and will raise a TypeError in the future. "
+                f"Use {converter.__name__}(ser.iloc[0]) instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return converter(self.iloc[0])
+        raise TypeError(f"cannot convert the series to {converter}")
+
+    wrapper.__name__ = f"__{converter.__name__}__"
+    return wrapper
+
+
+# ----------------------------------------------------------------------
+# Series class
+
+
+# error: Cannot override final attribute "ndim" (previously declared in base
+# class "NDFrame")
+# error: Cannot override final attribute "size" (previously declared in base
+# class "NDFrame")
+# definition in base class "NDFrame"
+class Series(base.IndexOpsMixin, NDFrame):  # type: ignore[misc]
+    """
+    One-dimensional ndarray with axis labels (including time series).
+
+    Labels need not be unique but must be a hashable type. The object
+    supports both integer- and label-based indexing and provides a host of
+    methods for performing operations involving the index. Statistical
+    methods from ndarray have been overridden to automatically exclude
+    missing data (currently represented as NaN).
+
+    Operations between Series (+, -, /, \\*, \\*\\*) align values based on their
+    associated index values-- they need not be the same length. The result
+    index will be the sorted union of the two indexes.
+
+    Parameters
+    ----------
+    data : array-like, Iterable, dict, or scalar value
+        Contains data stored in Series. If data is a dict, argument order is
+        maintained.
+    index : array-like or Index (1d)
+        Values must be hashable and have the same length as `data`.
+        Non-unique index values are allowed. Will default to
+        RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
+        and index is None, then the keys in the data are used as the index. If the
+        index is not None, the resulting Series is reindexed with the index values.
+    dtype : str, numpy.dtype, or ExtensionDtype, optional
+        Data type for the output Series. If not specified, this will be
+        inferred from `data`.
+        See the :ref:`user guide <basics.dtypes>` for more usages.
+    name : Hashable, default None
+        The name to give to the Series.
+    copy : bool, default False
+        Copy input data. Only affects Series or 1d ndarray input. See examples.
+
+    Notes
+    -----
+    Please reference the :ref:`User Guide <basics.series>` for more information.
+
+    Examples
+    --------
+    Constructing Series from a dictionary with an Index specified
+
+    >>> d = {'a': 1, 'b': 2, 'c': 3}
+    >>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
+    >>> ser
+    a   1
+    b   2
+    c   3
+    dtype: int64
+
+    The keys of the dictionary match with the Index values, hence the Index
+    values have no effect.
+
+    >>> d = {'a': 1, 'b': 2, 'c': 3}
+    >>> ser = pd.Series(data=d, index=['x', 'y', 'z'])
+    >>> ser
+    x   NaN
+    y   NaN
+    z   NaN
+    dtype: float64
+
+    Note that the Index is first build with the keys from the dictionary.
+    After this the Series is reindexed with the given Index values, hence we
+    get all NaN as a result.
+
+    Constructing Series from a list with `copy=False`.
+
+    >>> r = [1, 2]
+    >>> ser = pd.Series(r, copy=False)
+    >>> ser.iloc[0] = 999
+    >>> r
+    [1, 2]
+    >>> ser
+    0    999
+    1      2
+    dtype: int64
+
+    Due to input data type the Series has a `copy` of
+    the original data even though `copy=False`, so
+    the data is unchanged.
+
+    Constructing Series from a 1d ndarray with `copy=False`.
+
+    >>> r = np.array([1, 2])
+    >>> ser = pd.Series(r, copy=False)
+    >>> ser.iloc[0] = 999
+    >>> r
+    array([999,   2])
+    >>> ser
+    0    999
+    1      2
+    dtype: int64
+
+    Due to input data type the Series has a `view` on
+    the original data, so
+    the data is changed as well.
+    """
+
+    _typ = "series"
+    _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
+
+    _name: Hashable
+    _metadata: list[str] = ["_name"]
+    _internal_names_set = {"index", "name"} | NDFrame._internal_names_set
+    _accessors = {"dt", "cat", "str", "sparse"}
+    _hidden_attrs = (
+        base.IndexOpsMixin._hidden_attrs | NDFrame._hidden_attrs | frozenset([])
+    )
+
+    # similar to __array_priority__, positions Series after DataFrame
+    #  but before Index and ExtensionArray.  Should NOT be overridden by subclasses.
+    __pandas_priority__ = 3000
+
+    # Override cache_readonly bc Series is mutable
+    # error: Incompatible types in assignment (expression has type "property",
+    # base class "IndexOpsMixin" defined the type as "Callable[[IndexOpsMixin], bool]")
+    hasnans = property(  # type: ignore[assignment]
+        # error: "Callable[[IndexOpsMixin], bool]" has no attribute "fget"
+        base.IndexOpsMixin.hasnans.fget,  # type: ignore[attr-defined]
+        doc=base.IndexOpsMixin.hasnans.__doc__,
+    )
+    _mgr: SingleManager
+
+    # ----------------------------------------------------------------------
+    # Constructors
+
+    def __init__(
+        self,
+        data=None,
+        index=None,
+        dtype: Dtype | None = None,
+        name=None,
+        copy: bool | None = None,
+        fastpath: bool | lib.NoDefault = lib.no_default,
+    ) -> None:
+        if fastpath is not lib.no_default:
+            warnings.warn(
+                "The 'fastpath' keyword in pd.Series is deprecated and will "
+                "be removed in a future version.",
+                DeprecationWarning,
+                stacklevel=find_stack_level(),
+            )
+        else:
+            fastpath = False
+
+        allow_mgr = False
+        if (
+            isinstance(data, (SingleBlockManager, SingleArrayManager))
+            and index is None
+            and dtype is None
+            and (copy is False or copy is None)
+        ):
+            if not allow_mgr:
+                # GH#52419
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+            if using_copy_on_write():
+                data = data.copy(deep=False)
+            # GH#33357 called with just the SingleBlockManager
+            NDFrame.__init__(self, data)
+            if fastpath:
+                # e.g. from _box_col_values, skip validation of name
+                object.__setattr__(self, "_name", name)
+            else:
+                self.name = name
+            return
+
+        is_pandas_object = isinstance(data, (Series, Index, ExtensionArray))
+        data_dtype = getattr(data, "dtype", None)
+        original_dtype = dtype
+
+        if isinstance(data, (ExtensionArray, np.ndarray)):
+            if copy is not False and using_copy_on_write():
+                if dtype is None or astype_is_view(data.dtype, pandas_dtype(dtype)):
+                    data = data.copy()
+        if copy is None:
+            copy = False
+
+        # we are called internally, so short-circuit
+        if fastpath:
+            # data is a ndarray, index is defined
+            if not isinstance(data, (SingleBlockManager, SingleArrayManager)):
+                manager = _get_option("mode.data_manager", silent=True)
+                if manager == "block":
+                    data = SingleBlockManager.from_array(data, index)
+                elif manager == "array":
+                    data = SingleArrayManager.from_array(data, index)
+                allow_mgr = True
+            elif using_copy_on_write() and not copy:
+                data = data.copy(deep=False)
+
+            if not allow_mgr:
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
+            if copy:
+                data = data.copy()
+            # skips validation of the name
+            object.__setattr__(self, "_name", name)
+            NDFrame.__init__(self, data)
+            return
+
+        if isinstance(data, SingleBlockManager) and using_copy_on_write() and not copy:
+            data = data.copy(deep=False)
+
+            if not allow_mgr:
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
+        name = ibase.maybe_extract_name(name, data, type(self))
+
+        if index is not None:
+            index = ensure_index(index)
+
+        if dtype is not None:
+            dtype = self._validate_dtype(dtype)
+
+        if data is None:
+            index = index if index is not None else default_index(0)
+            if len(index) or dtype is not None:
+                data = na_value_for_dtype(pandas_dtype(dtype), compat=False)
+            else:
+                data = []
+
+        if isinstance(data, MultiIndex):
+            raise NotImplementedError(
+                "initializing a Series from a MultiIndex is not supported"
+            )
+
+        refs = None
+        if isinstance(data, Index):
+            if dtype is not None:
+                data = data.astype(dtype, copy=False)
+
+            if using_copy_on_write():
+                refs = data._references
+                data = data._values
+            else:
+                # GH#24096 we need to ensure the index remains immutable
+                data = data._values.copy()
+            copy = False
+
+        elif isinstance(data, np.ndarray):
+            if len(data.dtype):
+                # GH#13296 we are dealing with a compound dtype, which
+                #  should be treated as 2D
+                raise ValueError(
+                    "Cannot construct a Series from an ndarray with "
+                    "compound dtype.  Use DataFrame instead."
+                )
+        elif isinstance(data, Series):
+            if index is None:
+                index = data.index
+                data = data._mgr.copy(deep=False)
+            else:
+                data = data.reindex(index, copy=copy)
+                copy = False
+                data = data._mgr
+        elif isinstance(data, Mapping):
+            data, index = self._init_dict(data, index, dtype)
+            dtype = None
+            copy = False
+        elif isinstance(data, (SingleBlockManager, SingleArrayManager)):
+            if index is None:
+                index = data.index
+            elif not data.index.equals(index) or copy:
+                # GH#19275 SingleBlockManager input should only be called
+                # internally
+                raise AssertionError(
+                    "Cannot pass both SingleBlockManager "
+                    "`data` argument and a different "
+                    "`index` argument. `copy` must be False."
+                )
+
+            if not allow_mgr:
+                warnings.warn(
+                    f"Passing a {type(data).__name__} to {type(self).__name__} "
+                    "is deprecated and will raise in a future version. "
+                    "Use public APIs instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+                allow_mgr = True
+
+        elif isinstance(data, ExtensionArray):
+            pass
+        else:
+            data = com.maybe_iterable_to_list(data)
+            if is_list_like(data) and not len(data) and dtype is None:
+                # GH 29405: Pre-2.0, this defaulted to float.
+                dtype = np.dtype(object)
+
+        if index is None:
+            if not is_list_like(data):
+                data = [data]
+            index = default_index(len(data))
+        elif is_list_like(data):
+            com.require_length_match(data, index)
+
+        # create/copy the manager
+        if isinstance(data, (SingleBlockManager, SingleArrayManager)):
+            if dtype is not None:
+                data = data.astype(dtype=dtype, errors="ignore", copy=copy)
+            elif copy:
+                data = data.copy()
+        else:
+            data = sanitize_array(data, index, dtype, copy)
+
+            manager = _get_option("mode.data_manager", silent=True)
+            if manager == "block":
+                data = SingleBlockManager.from_array(data, index, refs=refs)
+            elif manager == "array":
+                data = SingleArrayManager.from_array(data, index)
+
+        NDFrame.__init__(self, data)
+        self.name = name
+        self._set_axis(0, index)
+
+        if original_dtype is None and is_pandas_object and data_dtype == np.object_:
+            if self.dtype != data_dtype:
+                warnings.warn(
+                    "Dtype inference on a pandas object "
+                    "(Series, Index, ExtensionArray) is deprecated. The Series "
+                    "constructor will keep the original dtype in the future. "
+                    "Call `infer_objects` on the result to get the old behavior.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    def _init_dict(
+        self, data: Mapping, index: Index | None = None, dtype: DtypeObj | None = None
+    ):
+        """
+        Derive the "_mgr" and "index" attributes of a new Series from a
+        dictionary input.
+
+        Parameters
+        ----------
+        data : dict or dict-like
+            Data used to populate the new Series.
+        index : Index or None, default None
+            Index for the new Series: if None, use dict keys.
+        dtype : np.dtype, ExtensionDtype, or None, default None
+            The dtype for the new Series: if None, infer from data.
+
+        Returns
+        -------
+        _data : BlockManager for the new Series
+        index : index for the new Series
+        """
+        keys: Index | tuple
+
+        # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
+        # raises KeyError), so we iterate the entire dict, and align
+        if data:
+            # GH:34717, issue was using zip to extract key and values from data.
+            # using generators in effects the performance.
+            # Below is the new way of extracting the keys and values
+
+            keys = tuple(data.keys())
+            values = list(data.values())  # Generating list of values- faster way
+        elif index is not None:
+            # fastpath for Series(data=None). Just use broadcasting a scalar
+            # instead of reindexing.
+            if len(index) or dtype is not None:
+                values = na_value_for_dtype(pandas_dtype(dtype), compat=False)
+            else:
+                values = []
+            keys = index
+        else:
+            keys, values = default_index(0), []
+
+        # Input is now list-like, so rely on "standard" construction:
+        s = Series(values, index=keys, dtype=dtype)
+
+        # Now we just make sure the order is respected, if any
+        if data and index is not None:
+            s = s.reindex(index, copy=False)
+        return s._mgr, s.index
+
+    # ----------------------------------------------------------------------
+
+    @property
+    def _constructor(self) -> Callable[..., Series]:
+        return Series
+
+    def _constructor_from_mgr(self, mgr, axes):
+        ser = Series._from_mgr(mgr, axes=axes)
+        ser._name = None  # caller is responsible for setting real name
+
+        if type(self) is Series:
+            # This would also work `if self._constructor is Series`, but
+            #  this check is slightly faster, benefiting the most-common case.
+            return ser
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.Series object.
+        return self._constructor(ser)
+
+    @property
+    def _constructor_expanddim(self) -> Callable[..., DataFrame]:
+        """
+        Used when a manipulation result has one higher dimension as the
+        original, such as Series.to_frame()
+        """
+        from pandas.core.frame import DataFrame
+
+        return DataFrame
+
+    def _constructor_expanddim_from_mgr(self, mgr, axes):
+        from pandas.core.frame import DataFrame
+
+        df = DataFrame._from_mgr(mgr, axes=mgr.axes)
+
+        if type(self) is Series:
+            # This would also work `if self._constructor_expanddim is DataFrame`,
+            #  but this check is slightly faster, benefiting the most-common case.
+            return df
+
+        # We assume that the subclass __init__ knows how to handle a
+        #  pd.DataFrame object.
+        return self._constructor_expanddim(df)
+
+    # types
+    @property
+    def _can_hold_na(self) -> bool:
+        return self._mgr._can_hold_na
+
+    # ndarray compatibility
+    @property
+    def dtype(self) -> DtypeObj:
+        """
+        Return the dtype object of the underlying data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.dtype
+        dtype('int64')
+        """
+        return self._mgr.dtype
+
+    @property
+    def dtypes(self) -> DtypeObj:
+        """
+        Return the dtype object of the underlying data.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.dtypes
+        dtype('int64')
+        """
+        # DataFrame compatibility
+        return self.dtype
+
+    @property
+    def name(self) -> Hashable:
+        """
+        Return the name of the Series.
+
+        The name of a Series becomes its index or column name if it is used
+        to form a DataFrame. It is also used whenever displaying the Series
+        using the interpreter.
+
+        Returns
+        -------
+        label (hashable object)
+            The name of the Series, also the column name if part of a DataFrame.
+
+        See Also
+        --------
+        Series.rename : Sets the Series name when given a scalar input.
+        Index.name : Corresponding Index property.
+
+        Examples
+        --------
+        The Series name can be set initially when calling the constructor.
+
+        >>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers')
+        >>> s
+        0    1
+        1    2
+        2    3
+        Name: Numbers, dtype: int64
+        >>> s.name = "Integers"
+        >>> s
+        0    1
+        1    2
+        2    3
+        Name: Integers, dtype: int64
+
+        The name of a Series within a DataFrame is its column name.
+
+        >>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
+        ...                   columns=["Odd Numbers", "Even Numbers"])
+        >>> df
+           Odd Numbers  Even Numbers
+        0            1             2
+        1            3             4
+        2            5             6
+        >>> df["Even Numbers"].name
+        'Even Numbers'
+        """
+        return self._name
+
+    @name.setter
+    def name(self, value: Hashable) -> None:
+        validate_all_hashable(value, error_name=f"{type(self).__name__}.name")
+        object.__setattr__(self, "_name", value)
+
+    @property
+    def values(self):
+        """
+        Return Series as ndarray or ndarray-like depending on the dtype.
+
+        .. warning::
+
+           We recommend using :attr:`Series.array` or
+           :meth:`Series.to_numpy`, depending on whether you need
+           a reference to the underlying data or a NumPy array.
+
+        Returns
+        -------
+        numpy.ndarray or ndarray-like
+
+        See Also
+        --------
+        Series.array : Reference to the underlying data.
+        Series.to_numpy : A NumPy array representing the underlying data.
+
+        Examples
+        --------
+        >>> pd.Series([1, 2, 3]).values
+        array([1, 2, 3])
+
+        >>> pd.Series(list('aabc')).values
+        array(['a', 'a', 'b', 'c'], dtype=object)
+
+        >>> pd.Series(list('aabc')).astype('category').values
+        ['a', 'a', 'b', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+
+        Timezone aware datetime data is converted to UTC:
+
+        >>> pd.Series(pd.date_range('20130101', periods=3,
+        ...                         tz='US/Eastern')).values
+        array(['2013-01-01T05:00:00.000000000',
+               '2013-01-02T05:00:00.000000000',
+               '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
+        """
+        return self._mgr.external_values()
+
+    @property
+    def _values(self):
+        """
+        Return the internal repr of this data (defined by Block.interval_values).
+        This are the values as stored in the Block (ndarray or ExtensionArray
+        depending on the Block class), with datetime64[ns] and timedelta64[ns]
+        wrapped in ExtensionArrays to match Index._values behavior.
+
+        Differs from the public ``.values`` for certain data types, because of
+        historical backwards compatibility of the public attribute (e.g. period
+        returns object ndarray and datetimetz a datetime64[ns] ndarray for
+        ``.values`` while it returns an ExtensionArray for ``._values`` in those
+        cases).
+
+        Differs from ``.array`` in that this still returns the numpy array if
+        the Block is backed by a numpy array (except for datetime64 and
+        timedelta64 dtypes), while ``.array`` ensures to always return an
+        ExtensionArray.
+
+        Overview:
+
+        dtype       | values        | _values       | array                 |
+        ----------- | ------------- | ------------- | --------------------- |
+        Numeric     | ndarray       | ndarray       | NumpyExtensionArray   |
+        Category    | Categorical   | Categorical   | Categorical           |
+        dt64[ns]    | ndarray[M8ns] | DatetimeArray | DatetimeArray         |
+        dt64[ns tz] | ndarray[M8ns] | DatetimeArray | DatetimeArray         |
+        td64[ns]    | ndarray[m8ns] | TimedeltaArray| TimedeltaArray        |
+        Period      | ndarray[obj]  | PeriodArray   | PeriodArray           |
+        Nullable    | EA            | EA            | EA                    |
+
+        """
+        return self._mgr.internal_values()
+
+    @property
+    def _references(self) -> BlockValuesRefs | None:
+        if isinstance(self._mgr, SingleArrayManager):
+            return None
+        return self._mgr._block.refs
+
+    # error: Decorated property not supported
+    @Appender(base.IndexOpsMixin.array.__doc__)  # type: ignore[misc]
+    @property
+    def array(self) -> ExtensionArray:
+        return self._mgr.array_values()
+
+    # ops
+    def ravel(self, order: str = "C") -> ArrayLike:
+        """
+        Return the flattened underlying data as an ndarray or ExtensionArray.
+
+        .. deprecated:: 2.2.0
+            Series.ravel is deprecated. The underlying array is already 1D, so
+            ravel is not necessary.  Use :meth:`to_numpy` for conversion to a numpy
+            array instead.
+
+        Returns
+        -------
+        numpy.ndarray or ExtensionArray
+            Flattened data of the Series.
+
+        See Also
+        --------
+        numpy.ndarray.ravel : Return a flattened array.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.ravel()  # doctest: +SKIP
+        array([1, 2, 3])
+        """
+        warnings.warn(
+            "Series.ravel is deprecated. The underlying array is already 1D, so "
+            "ravel is not necessary.  Use `to_numpy()` for conversion to a numpy "
+            "array instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        arr = self._values.ravel(order=order)
+        if isinstance(arr, np.ndarray) and using_copy_on_write():
+            arr.flags.writeable = False
+        return arr
+
+    def __len__(self) -> int:
+        """
+        Return the length of the Series.
+        """
+        return len(self._mgr)
+
+    def view(self, dtype: Dtype | None = None) -> Series:
+        """
+        Create a new view of the Series.
+
+        .. deprecated:: 2.2.0
+            ``Series.view`` is deprecated and will be removed in a future version.
+            Use :meth:`Series.astype` as an alternative to change the dtype.
+
+        This function will return a new Series with a view of the same
+        underlying values in memory, optionally reinterpreted with a new data
+        type. The new data type must preserve the same size in bytes as to not
+        cause index misalignment.
+
+        Parameters
+        ----------
+        dtype : data type
+            Data type object or one of their string representations.
+
+        Returns
+        -------
+        Series
+            A new Series object as a view of the same data in memory.
+
+        See Also
+        --------
+        numpy.ndarray.view : Equivalent numpy function to create a new view of
+            the same data in memory.
+
+        Notes
+        -----
+        Series are instantiated with ``dtype=float64`` by default. While
+        ``numpy.ndarray.view()`` will return a view with the same data type as
+        the original array, ``Series.view()`` (without specified dtype)
+        will try using ``float64`` and may fail if the original data type size
+        in bytes is not the same.
+
+        Examples
+        --------
+        Use ``astype`` to change the dtype instead.
+        """
+        warnings.warn(
+            "Series.view is deprecated and will be removed in a future version. "
+            "Use ``astype`` as an alternative to change the dtype.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        # self.array instead of self._values so we piggyback on NumpyExtensionArray
+        #  implementation
+        res_values = self.array.view(dtype)
+        res_ser = self._constructor(res_values, index=self.index, copy=False)
+        if isinstance(res_ser._mgr, SingleBlockManager):
+            blk = res_ser._mgr._block
+            blk.refs = cast("BlockValuesRefs", self._references)
+            blk.refs.add_reference(blk)
+        return res_ser.__finalize__(self, method="view")
+
+    # ----------------------------------------------------------------------
+    # NDArray Compat
+    def __array__(
+        self, dtype: npt.DTypeLike | None = None, copy: bool | None = None
+    ) -> np.ndarray:
+        """
+        Return the values as a NumPy array.
+
+        Users should not call this directly. Rather, it is invoked by
+        :func:`numpy.array` and :func:`numpy.asarray`.
+
+        Parameters
+        ----------
+        dtype : str or numpy.dtype, optional
+            The dtype to use for the resulting NumPy array. By default,
+            the dtype is inferred from the data.
+
+        copy : bool or None, optional
+            See :func:`numpy.asarray`.
+
+        Returns
+        -------
+        numpy.ndarray
+            The values in the series converted to a :class:`numpy.ndarray`
+            with the specified `dtype`.
+
+        See Also
+        --------
+        array : Create a new array from data.
+        Series.array : Zero-copy view to the array backing the Series.
+        Series.to_numpy : Series method for similar behavior.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+        >>> np.asarray(ser)
+        array([1, 2, 3])
+
+        For timezone-aware data, the timezones may be retained with
+        ``dtype='object'``
+
+        >>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
+        >>> np.asarray(tzser, dtype="object")
+        array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
+               Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
+              dtype=object)
+
+        Or the values may be localized to UTC and the tzinfo discarded with
+        ``dtype='datetime64[ns]'``
+
+        >>> np.asarray(tzser, dtype="datetime64[ns]")  # doctest: +ELLIPSIS
+        array(['1999-12-31T23:00:00.000000000', ...],
+              dtype='datetime64[ns]')
+        """
+        values = self._values
+        if copy is None:
+            # Note: branch avoids `copy=None` for NumPy 1.x support
+            arr = np.asarray(values, dtype=dtype)
+        else:
+            arr = np.array(values, dtype=dtype, copy=copy)
+
+        if copy is True:
+            return arr
+        if using_copy_on_write() and (
+            copy is False or astype_is_view(values.dtype, arr.dtype)
+        ):
+            arr = arr.view()
+            arr.flags.writeable = False
+        return arr
+
+    # ----------------------------------------------------------------------
+
+    def __column_consortium_standard__(self, *, api_version: str | None = None) -> Any:
+        """
+        Provide entry point to the Consortium DataFrame Standard API.
+
+        This is developed and maintained outside of pandas.
+        Please report any issues to https://github.com/data-apis/dataframe-api-compat.
+        """
+        dataframe_api_compat = import_optional_dependency("dataframe_api_compat")
+        return (
+            dataframe_api_compat.pandas_standard.convert_to_standard_compliant_column(
+                self, api_version=api_version
+            )
+        )
+
+    # ----------------------------------------------------------------------
+    # Unary Methods
+
+    # coercion
+    __float__ = _coerce_method(float)
+    __int__ = _coerce_method(int)
+
+    # ----------------------------------------------------------------------
+
+    # indexers
+    @property
+    def axes(self) -> list[Index]:
+        """
+        Return a list of the row axis labels.
+        """
+        return [self.index]
+
+    # ----------------------------------------------------------------------
+    # Indexing Methods
+
+    def _ixs(self, i: int, axis: AxisInt = 0) -> Any:
+        """
+        Return the i-th value or values in the Series by location.
+
+        Parameters
+        ----------
+        i : int
+
+        Returns
+        -------
+        scalar
+        """
+        return self._values[i]
+
+    def _slice(self, slobj: slice, axis: AxisInt = 0) -> Series:
+        # axis kwarg is retained for compat with NDFrame method
+        #  _slice is *always* positional
+        mgr = self._mgr.get_slice(slobj, axis=axis)
+        out = self._constructor_from_mgr(mgr, axes=mgr.axes)
+        out._name = self._name
+        return out.__finalize__(self)
+
+    def __getitem__(self, key):
+        check_dict_or_set_indexers(key)
+        key = com.apply_if_callable(key, self)
+
+        if key is Ellipsis:
+            if using_copy_on_write() or warn_copy_on_write():
+                return self.copy(deep=False)
+            return self
+
+        key_is_scalar = is_scalar(key)
+        if isinstance(key, (list, tuple)):
+            key = unpack_1tuple(key)
+
+        if is_integer(key) and self.index._should_fallback_to_positional:
+            warnings.warn(
+                # GH#50617
+                "Series.__getitem__ treating keys as positions is deprecated. "
+                "In a future version, integer keys will always be treated "
+                "as labels (consistent with DataFrame behavior). To access "
+                "a value by position, use `ser.iloc[pos]`",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self._values[key]
+
+        elif key_is_scalar:
+            return self._get_value(key)
+
+        # Convert generator to list before going through hashable part
+        # (We will iterate through the generator there to check for slices)
+        if is_iterator(key):
+            key = list(key)
+
+        if is_hashable(key) and not isinstance(key, slice):
+            # Otherwise index.get_value will raise InvalidIndexError
+            try:
+                # For labels that don't resolve as scalars like tuples and frozensets
+                result = self._get_value(key)
+
+                return result
+
+            except (KeyError, TypeError, InvalidIndexError):
+                # InvalidIndexError for e.g. generator
+                #  see test_series_getitem_corner_generator
+                if isinstance(key, tuple) and isinstance(self.index, MultiIndex):
+                    # We still have the corner case where a tuple is a key
+                    # in the first level of our MultiIndex
+                    return self._get_values_tuple(key)
+
+        if isinstance(key, slice):
+            # Do slice check before somewhat-costly is_bool_indexer
+            return self._getitem_slice(key)
+
+        if com.is_bool_indexer(key):
+            key = check_bool_indexer(self.index, key)
+            key = np.asarray(key, dtype=bool)
+            return self._get_rows_with_mask(key)
+
+        return self._get_with(key)
+
+    def _get_with(self, key):
+        # other: fancy integer or otherwise
+        if isinstance(key, ABCDataFrame):
+            raise TypeError(
+                "Indexing a Series with DataFrame is not "
+                "supported, use the appropriate DataFrame column"
+            )
+        elif isinstance(key, tuple):
+            return self._get_values_tuple(key)
+
+        elif not is_list_like(key):
+            # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684
+            return self.loc[key]
+
+        if not isinstance(key, (list, np.ndarray, ExtensionArray, Series, Index)):
+            key = list(key)
+
+        key_type = lib.infer_dtype(key, skipna=False)
+
+        # Note: The key_type == "boolean" case should be caught by the
+        #  com.is_bool_indexer check in __getitem__
+        if key_type == "integer":
+            # We need to decide whether to treat this as a positional indexer
+            #  (i.e. self.iloc) or label-based (i.e. self.loc)
+            if not self.index._should_fallback_to_positional:
+                return self.loc[key]
+            else:
+                warnings.warn(
+                    # GH#50617
+                    "Series.__getitem__ treating keys as positions is deprecated. "
+                    "In a future version, integer keys will always be treated "
+                    "as labels (consistent with DataFrame behavior). To access "
+                    "a value by position, use `ser.iloc[pos]`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return self.iloc[key]
+
+        # handle the dup indexing case GH#4246
+        return self.loc[key]
+
+    def _get_values_tuple(self, key: tuple):
+        # mpl hackaround
+        if com.any_none(*key):
+            # mpl compat if we look up e.g. ser[:, np.newaxis];
+            #  see tests.series.timeseries.test_mpl_compat_hack
+            # the asarray is needed to avoid returning a 2D DatetimeArray
+            result = np.asarray(self._values[key])
+            disallow_ndim_indexing(result)
+            return result
+
+        if not isinstance(self.index, MultiIndex):
+            raise KeyError("key of type tuple not found and not a MultiIndex")
+
+        # If key is contained, would have returned by now
+        indexer, new_index = self.index.get_loc_level(key)
+        new_ser = self._constructor(self._values[indexer], index=new_index, copy=False)
+        if isinstance(indexer, slice):
+            new_ser._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+        return new_ser.__finalize__(self)
+
+    def _get_rows_with_mask(self, indexer: npt.NDArray[np.bool_]) -> Series:
+        new_mgr = self._mgr.get_rows_with_mask(indexer)
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(self)
+
+    def _get_value(self, label, takeable: bool = False):
+        """
+        Quickly retrieve single value at passed index label.
+
+        Parameters
+        ----------
+        label : object
+        takeable : interpret the index as indexers, default False
+
+        Returns
+        -------
+        scalar value
+        """
+        if takeable:
+            return self._values[label]
+
+        # Similar to Index.get_value, but we do not fall back to positional
+        loc = self.index.get_loc(label)
+
+        if is_integer(loc):
+            return self._values[loc]
+
+        if isinstance(self.index, MultiIndex):
+            mi = self.index
+            new_values = self._values[loc]
+            if len(new_values) == 1 and mi.nlevels == 1:
+                # If more than one level left, we can not return a scalar
+                return new_values[0]
+
+            new_index = mi[loc]
+            new_index = maybe_droplevels(new_index, label)
+            new_ser = self._constructor(
+                new_values, index=new_index, name=self.name, copy=False
+            )
+            if isinstance(loc, slice):
+                new_ser._mgr.add_references(self._mgr)  # type: ignore[arg-type]
+            return new_ser.__finalize__(self)
+
+        else:
+            return self.iloc[loc]
+
+    def __setitem__(self, key, value) -> None:
+        warn = True
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= 3:
+                warnings.warn(
+                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
+                )
+        elif not PYPY and not WARNING_CHECK_DISABLED and not using_copy_on_write():
+            ctr = sys.getrefcount(self)
+            ref_count = 3
+            if not warn_copy_on_write() and _check_cacher(self):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count and (
+                warn_copy_on_write()
+                or (
+                    not warn_copy_on_write()
+                    and self._mgr.blocks[0].refs.has_reference()  # type: ignore[union-attr]
+                )
+            ):
+                warn = False
+                warnings.warn(
+                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
+                )
+
+        check_dict_or_set_indexers(key)
+        key = com.apply_if_callable(key, self)
+        cacher_needs_updating = self._check_is_chained_assignment_possible()
+
+        if key is Ellipsis:
+            key = slice(None)
+
+        if isinstance(key, slice):
+            indexer = self.index._convert_slice_indexer(key, kind="getitem")
+            return self._set_values(indexer, value, warn=warn)
+
+        try:
+            self._set_with_engine(key, value, warn=warn)
+        except KeyError:
+            # We have a scalar (or for MultiIndex or object-dtype, scalar-like)
+            #  key that is not present in self.index.
+            if is_integer(key):
+                if not self.index._should_fallback_to_positional:
+                    # GH#33469
+                    self.loc[key] = value
+                else:
+                    # positional setter
+                    # can't use _mgr.setitem_inplace yet bc could have *both*
+                    #  KeyError and then ValueError, xref GH#45070
+                    warnings.warn(
+                        # GH#50617
+                        "Series.__setitem__ treating keys as positions is deprecated. "
+                        "In a future version, integer keys will always be treated "
+                        "as labels (consistent with DataFrame behavior). To set "
+                        "a value by position, use `ser.iloc[pos] = value`",
+                        FutureWarning,
+                        stacklevel=find_stack_level(),
+                    )
+                    self._set_values(key, value)
+            else:
+                # GH#12862 adding a new key to the Series
+                self.loc[key] = value
+
+        except (TypeError, ValueError, LossySetitemError):
+            # The key was OK, but we cannot set the value losslessly
+            indexer = self.index.get_loc(key)
+            self._set_values(indexer, value)
+
+        except InvalidIndexError as err:
+            if isinstance(key, tuple) and not isinstance(self.index, MultiIndex):
+                # cases with MultiIndex don't get here bc they raise KeyError
+                # e.g. test_basic_getitem_setitem_corner
+                raise KeyError(
+                    "key of type tuple not found and not a MultiIndex"
+                ) from err
+
+            if com.is_bool_indexer(key):
+                key = check_bool_indexer(self.index, key)
+                key = np.asarray(key, dtype=bool)
+
+                if (
+                    is_list_like(value)
+                    and len(value) != len(self)
+                    and not isinstance(value, Series)
+                    and not is_object_dtype(self.dtype)
+                ):
+                    # Series will be reindexed to have matching length inside
+                    #  _where call below
+                    # GH#44265
+                    indexer = key.nonzero()[0]
+                    self._set_values(indexer, value)
+                    return
+
+                # otherwise with listlike other we interpret series[mask] = other
+                #  as series[mask] = other[mask]
+                try:
+                    self._where(~key, value, inplace=True, warn=warn)
+                except InvalidIndexError:
+                    # test_where_dups
+                    self.iloc[key] = value
+                return
+
+            else:
+                self._set_with(key, value, warn=warn)
+
+        if cacher_needs_updating:
+            self._maybe_update_cacher(inplace=True)
+
+    def _set_with_engine(self, key, value, warn: bool = True) -> None:
+        loc = self.index.get_loc(key)
+
+        # this is equivalent to self._values[key] = value
+        self._mgr.setitem_inplace(loc, value, warn=warn)
+
+    def _set_with(self, key, value, warn: bool = True) -> None:
+        # We got here via exception-handling off of InvalidIndexError, so
+        #  key should always be listlike at this point.
+        assert not isinstance(key, tuple)
+
+        if is_iterator(key):
+            # Without this, the call to infer_dtype will consume the generator
+            key = list(key)
+
+        if not self.index._should_fallback_to_positional:
+            # Regardless of the key type, we're treating it as labels
+            self._set_labels(key, value, warn=warn)
+
+        else:
+            # Note: key_type == "boolean" should not occur because that
+            #  should be caught by the is_bool_indexer check in __setitem__
+            key_type = lib.infer_dtype(key, skipna=False)
+
+            if key_type == "integer":
+                warnings.warn(
+                    # GH#50617
+                    "Series.__setitem__ treating keys as positions is deprecated. "
+                    "In a future version, integer keys will always be treated "
+                    "as labels (consistent with DataFrame behavior). To set "
+                    "a value by position, use `ser.iloc[pos] = value`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                self._set_values(key, value, warn=warn)
+            else:
+                self._set_labels(key, value, warn=warn)
+
+    def _set_labels(self, key, value, warn: bool = True) -> None:
+        key = com.asarray_tuplesafe(key)
+        indexer: np.ndarray = self.index.get_indexer(key)
+        mask = indexer == -1
+        if mask.any():
+            raise KeyError(f"{key[mask]} not in index")
+        self._set_values(indexer, value, warn=warn)
+
+    def _set_values(self, key, value, warn: bool = True) -> None:
+        if isinstance(key, (Index, Series)):
+            key = key._values
+
+        self._mgr = self._mgr.setitem(indexer=key, value=value, warn=warn)
+        self._maybe_update_cacher()
+
+    def _set_value(self, label, value, takeable: bool = False) -> None:
+        """
+        Quickly set single value at passed label.
+
+        If label is not contained, a new object is created with the label
+        placed at the end of the result index.
+
+        Parameters
+        ----------
+        label : object
+            Partial indexing with MultiIndex not allowed.
+        value : object
+            Scalar value.
+        takeable : interpret the index as indexers, default False
+        """
+        if not takeable:
+            try:
+                loc = self.index.get_loc(label)
+            except KeyError:
+                # set using a non-recursive method
+                self.loc[label] = value
+                return
+        else:
+            loc = label
+
+        self._set_values(loc, value)
+
+    # ----------------------------------------------------------------------
+    # Lookup Caching
+
+    @property
+    def _is_cached(self) -> bool:
+        """Return boolean indicating if self is cached or not."""
+        return getattr(self, "_cacher", None) is not None
+
+    def _get_cacher(self):
+        """return my cacher or None"""
+        cacher = getattr(self, "_cacher", None)
+        if cacher is not None:
+            cacher = cacher[1]()
+        return cacher
+
+    def _reset_cacher(self) -> None:
+        """
+        Reset the cacher.
+        """
+        if hasattr(self, "_cacher"):
+            del self._cacher
+
+    def _set_as_cached(self, item, cacher) -> None:
+        """
+        Set the _cacher attribute on the calling object with a weakref to
+        cacher.
+        """
+        if using_copy_on_write():
+            return
+        self._cacher = (item, weakref.ref(cacher))
+
+    def _clear_item_cache(self) -> None:
+        # no-op for Series
+        pass
+
+    def _check_is_chained_assignment_possible(self) -> bool:
+        """
+        See NDFrame._check_is_chained_assignment_possible.__doc__
+        """
+        if self._is_view and self._is_cached:
+            ref = self._get_cacher()
+            if ref is not None and ref._is_mixed_type:
+                self._check_setitem_copy(t="referent", force=True)
+            return True
+        return super()._check_is_chained_assignment_possible()
+
+    def _maybe_update_cacher(
+        self, clear: bool = False, verify_is_copy: bool = True, inplace: bool = False
+    ) -> None:
+        """
+        See NDFrame._maybe_update_cacher.__doc__
+        """
+        # for CoW, we never want to update the parent DataFrame cache
+        # if the Series changed, but don't keep track of any cacher
+        if using_copy_on_write():
+            return
+        cacher = getattr(self, "_cacher", None)
+        if cacher is not None:
+            ref: DataFrame = cacher[1]()
+
+            # we are trying to reference a dead referent, hence
+            # a copy
+            if ref is None:
+                del self._cacher
+            elif len(self) == len(ref) and self.name in ref.columns:
+                # GH#42530 self.name must be in ref.columns
+                # to ensure column still in dataframe
+                # otherwise, either self or ref has swapped in new arrays
+                ref._maybe_cache_changed(cacher[0], self, inplace=inplace)
+            else:
+                # GH#33675 we have swapped in a new array, so parent
+                #  reference to self is now invalid
+                ref._item_cache.pop(cacher[0], None)
+
+        super()._maybe_update_cacher(
+            clear=clear, verify_is_copy=verify_is_copy, inplace=inplace
+        )
+
+    # ----------------------------------------------------------------------
+    # Unsorted
+
+    def repeat(self, repeats: int | Sequence[int], axis: None = None) -> Series:
+        """
+        Repeat elements of a Series.
+
+        Returns a new Series where each element of the current Series
+        is repeated consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int or array of ints
+            The number of repetitions for each element. This should be a
+            non-negative integer. Repeating 0 times will return an empty
+            Series.
+        axis : None
+            Unused. Parameter needed for compatibility with DataFrame.
+
+        Returns
+        -------
+        Series
+            Newly created Series with repeated elements.
+
+        See Also
+        --------
+        Index.repeat : Equivalent function for Index.
+        numpy.repeat : Similar method for :class:`numpy.ndarray`.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'b', 'c'])
+        >>> s
+        0    a
+        1    b
+        2    c
+        dtype: object
+        >>> s.repeat(2)
+        0    a
+        0    a
+        1    b
+        1    b
+        2    c
+        2    c
+        dtype: object
+        >>> s.repeat([1, 2, 3])
+        0    a
+        1    b
+        1    b
+        2    c
+        2    c
+        2    c
+        dtype: object
+        """
+        nv.validate_repeat((), {"axis": axis})
+        new_index = self.index.repeat(repeats)
+        new_values = self._values.repeat(repeats)
+        return self._constructor(new_values, index=new_index, copy=False).__finalize__(
+            self, method="repeat"
+        )
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: Literal[False] = ...,
+        name: Level = ...,
+        inplace: Literal[False] = ...,
+        allow_duplicates: bool = ...,
+    ) -> DataFrame:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: Literal[True],
+        name: Level = ...,
+        inplace: Literal[False] = ...,
+        allow_duplicates: bool = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def reset_index(
+        self,
+        level: IndexLabel = ...,
+        *,
+        drop: bool = ...,
+        name: Level = ...,
+        inplace: Literal[True],
+        allow_duplicates: bool = ...,
+    ) -> None:
+        ...
+
+    def reset_index(
+        self,
+        level: IndexLabel | None = None,
+        *,
+        drop: bool = False,
+        name: Level = lib.no_default,
+        inplace: bool = False,
+        allow_duplicates: bool = False,
+    ) -> DataFrame | Series | None:
+        """
+        Generate a new DataFrame or Series with the index reset.
+
+        This is useful when the index needs to be treated as a column, or
+        when the index is meaningless and needs to be reset to the default
+        before another operation.
+
+        Parameters
+        ----------
+        level : int, str, tuple, or list, default optional
+            For a Series with a MultiIndex, only remove the specified levels
+            from the index. Removes all levels by default.
+        drop : bool, default False
+            Just reset the index, without inserting it as a column in
+            the new DataFrame.
+        name : object, optional
+            The name to use for the column containing the original Series
+            values. Uses ``self.name`` by default. This argument is ignored
+            when `drop` is True.
+        inplace : bool, default False
+            Modify the Series in place (do not create a new object).
+        allow_duplicates : bool, default False
+            Allow duplicate column labels to be created.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series or DataFrame or None
+            When `drop` is False (the default), a DataFrame is returned.
+            The newly created columns will come first in the DataFrame,
+            followed by the original Series values.
+            When `drop` is True, a `Series` is returned.
+            In either case, if ``inplace=True``, no value is returned.
+
+        See Also
+        --------
+        DataFrame.reset_index: Analogous function for DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4], name='foo',
+        ...               index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))
+
+        Generate a DataFrame with default index.
+
+        >>> s.reset_index()
+          idx  foo
+        0   a    1
+        1   b    2
+        2   c    3
+        3   d    4
+
+        To specify the name of the new column use `name`.
+
+        >>> s.reset_index(name='values')
+          idx  values
+        0   a       1
+        1   b       2
+        2   c       3
+        3   d       4
+
+        To generate a new Series with the default set `drop` to True.
+
+        >>> s.reset_index(drop=True)
+        0    1
+        1    2
+        2    3
+        3    4
+        Name: foo, dtype: int64
+
+        The `level` parameter is interesting for Series with a multi-level
+        index.
+
+        >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
+        ...           np.array(['one', 'two', 'one', 'two'])]
+        >>> s2 = pd.Series(
+        ...     range(4), name='foo',
+        ...     index=pd.MultiIndex.from_arrays(arrays,
+        ...                                     names=['a', 'b']))
+
+        To remove a specific level from the Index, use `level`.
+
+        >>> s2.reset_index(level='a')
+               a  foo
+        b
+        one  bar    0
+        two  bar    1
+        one  baz    2
+        two  baz    3
+
+        If `level` is not set, all levels are removed from the Index.
+
+        >>> s2.reset_index()
+             a    b  foo
+        0  bar  one    0
+        1  bar  two    1
+        2  baz  one    2
+        3  baz  two    3
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        if drop:
+            new_index = default_index(len(self))
+            if level is not None:
+                level_list: Sequence[Hashable]
+                if not isinstance(level, (tuple, list)):
+                    level_list = [level]
+                else:
+                    level_list = level
+                level_list = [self.index._get_level_number(lev) for lev in level_list]
+                if len(level_list) < self.index.nlevels:
+                    new_index = self.index.droplevel(level_list)
+
+            if inplace:
+                self.index = new_index
+            elif using_copy_on_write():
+                new_ser = self.copy(deep=False)
+                new_ser.index = new_index
+                return new_ser.__finalize__(self, method="reset_index")
+            else:
+                return self._constructor(
+                    self._values.copy(), index=new_index, copy=False, dtype=self.dtype
+                ).__finalize__(self, method="reset_index")
+        elif inplace:
+            raise TypeError(
+                "Cannot reset_index inplace on a Series to create a DataFrame"
+            )
+        else:
+            if name is lib.no_default:
+                # For backwards compatibility, keep columns as [0] instead of
+                #  [None] when self.name is None
+                if self.name is None:
+                    name = 0
+                else:
+                    name = self.name
+
+            df = self.to_frame(name)
+            return df.reset_index(
+                level=level, drop=drop, allow_duplicates=allow_duplicates
+            )
+        return None
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation for a particular Series.
+        """
+        # pylint: disable=invalid-repr-returned
+        repr_params = fmt.get_series_repr_params()
+        return self.to_string(**repr_params)
+
+    @overload
+    def to_string(
+        self,
+        buf: None = ...,
+        na_rep: str = ...,
+        float_format: str | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        length: bool = ...,
+        dtype=...,
+        name=...,
+        max_rows: int | None = ...,
+        min_rows: int | None = ...,
+    ) -> str:
+        ...
+
+    @overload
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str],
+        na_rep: str = ...,
+        float_format: str | None = ...,
+        header: bool = ...,
+        index: bool = ...,
+        length: bool = ...,
+        dtype=...,
+        name=...,
+        max_rows: int | None = ...,
+        min_rows: int | None = ...,
+    ) -> None:
+        ...
+
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        na_rep: str = "NaN",
+        float_format: str | None = None,
+        header: bool = True,
+        index: bool = True,
+        length: bool = False,
+        dtype: bool = False,
+        name: bool = False,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+    ) -> str | None:
+        """
+        Render a string representation of the Series.
+
+        Parameters
+        ----------
+        buf : StringIO-like, optional
+            Buffer to write to.
+        na_rep : str, optional
+            String representation of NaN to use, default 'NaN'.
+        float_format : one-parameter function, optional
+            Formatter function to apply to columns' elements if they are
+            floats, default None.
+        header : bool, default True
+            Add the Series header (index name).
+        index : bool, optional
+            Add index (row) labels, default True.
+        length : bool, default False
+            Add the Series length.
+        dtype : bool, default False
+            Add the Series dtype.
+        name : bool, default False
+            Add the Series name if not None.
+        max_rows : int, optional
+            Maximum number of rows to show before truncating. If None, show
+            all.
+        min_rows : int, optional
+            The number of rows to display in a truncated repr (when number
+            of rows is above `max_rows`).
+
+        Returns
+        -------
+        str or None
+            String representation of Series if ``buf=None``, otherwise None.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3]).to_string()
+        >>> ser
+        '0    1\\n1    2\\n2    3'
+        """
+        formatter = fmt.SeriesFormatter(
+            self,
+            name=name,
+            length=length,
+            header=header,
+            index=index,
+            dtype=dtype,
+            na_rep=na_rep,
+            float_format=float_format,
+            min_rows=min_rows,
+            max_rows=max_rows,
+        )
+        result = formatter.to_string()
+
+        # catch contract violations
+        if not isinstance(result, str):
+            raise AssertionError(
+                "result must be of type str, type "
+                f"of result is {repr(type(result).__name__)}"
+            )
+
+        if buf is None:
+            return result
+        else:
+            if hasattr(buf, "write"):
+                buf.write(result)
+            else:
+                with open(buf, "w", encoding="utf-8") as f:
+                    f.write(result)
+        return None
+
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        storage_options=_shared_docs["storage_options"],
+        examples=dedent(
+            """Examples
+            --------
+            >>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")
+            >>> print(s.to_markdown())
+            |    | animal   |
+            |---:|:---------|
+            |  0 | elk      |
+            |  1 | pig      |
+            |  2 | dog      |
+            |  3 | quetzal  |
+
+            Output markdown with a tabulate option.
+
+            >>> print(s.to_markdown(tablefmt="grid"))
+            +----+----------+
+            |    | animal   |
+            +====+==========+
+            |  0 | elk      |
+            +----+----------+
+            |  1 | pig      |
+            +----+----------+
+            |  2 | dog      |
+            +----+----------+
+            |  3 | quetzal  |
+            +----+----------+"""
+        ),
+    )
+    def to_markdown(
+        self,
+        buf: IO[str] | None = None,
+        mode: str = "wt",
+        index: bool = True,
+        storage_options: StorageOptions | None = None,
+        **kwargs,
+    ) -> str | None:
+        """
+        Print {klass} in Markdown-friendly format.
+
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        mode : str, optional
+            Mode in which file is opened, "wt" by default.
+        index : bool, optional, default True
+            Add index (row) labels.
+
+        {storage_options}
+
+        **kwargs
+            These parameters will be passed to `tabulate \
+                <https://pypi.org/project/tabulate>`_.
+
+        Returns
+        -------
+        str
+            {klass} in Markdown-friendly format.
+
+        Notes
+        -----
+        Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.
+
+        {examples}
+        """
+        return self.to_frame().to_markdown(
+            buf, mode=mode, index=index, storage_options=storage_options, **kwargs
+        )
+
+    # ----------------------------------------------------------------------
+
+    def items(self) -> Iterable[tuple[Hashable, Any]]:
+        """
+        Lazily iterate over (index, value) tuples.
+
+        This method returns an iterable tuple (index, value). This is
+        convenient if you want to create a lazy iterator.
+
+        Returns
+        -------
+        iterable
+            Iterable of tuples containing the (index, value) pairs from a
+            Series.
+
+        See Also
+        --------
+        DataFrame.items : Iterate over (column name, Series) pairs.
+        DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
+
+        Examples
+        --------
+        >>> s = pd.Series(['A', 'B', 'C'])
+        >>> for index, value in s.items():
+        ...     print(f"Index : {index}, Value : {value}")
+        Index : 0, Value : A
+        Index : 1, Value : B
+        Index : 2, Value : C
+        """
+        return zip(iter(self.index), iter(self))
+
+    # ----------------------------------------------------------------------
+    # Misc public methods
+
+    def keys(self) -> Index:
+        """
+        Return alias for index.
+
+        Returns
+        -------
+        Index
+            Index of the Series.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3], index=[0, 1, 2])
+        >>> s.keys()
+        Index([0, 1, 2], dtype='int64')
+        """
+        return self.index
+
+    @overload
+    def to_dict(
+        self, *, into: type[MutableMappingT] | MutableMappingT
+    ) -> MutableMappingT:
+        ...
+
+    @overload
+    def to_dict(self, *, into: type[dict] = ...) -> dict:
+        ...
+
+    # error: Incompatible default for argument "into" (default has type "type[
+    # dict[Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+    @deprecate_nonkeyword_arguments(
+        version="3.0", allowed_args=["self"], name="to_dict"
+    )
+    def to_dict(
+        self,
+        into: type[MutableMappingT]
+        | MutableMappingT = dict,  # type: ignore[assignment]
+    ) -> MutableMappingT:
+        """
+        Convert Series to {label -> value} dict or dict-like object.
+
+        Parameters
+        ----------
+        into : class, default dict
+            The collections.abc.MutableMapping subclass to use as the return
+            object. Can be the actual class or an empty instance of the mapping
+            type you want.  If you want a collections.defaultdict, you must
+            pass it initialized.
+
+        Returns
+        -------
+        collections.abc.MutableMapping
+            Key-value representation of Series.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.to_dict()
+        {0: 1, 1: 2, 2: 3, 3: 4}
+        >>> from collections import OrderedDict, defaultdict
+        >>> s.to_dict(into=OrderedDict)
+        OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])
+        >>> dd = defaultdict(list)
+        >>> s.to_dict(into=dd)
+        defaultdict(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
+        """
+        # GH16122
+        into_c = com.standardize_mapping(into)
+
+        if is_object_dtype(self.dtype) or isinstance(self.dtype, ExtensionDtype):
+            return into_c((k, maybe_box_native(v)) for k, v in self.items())
+        else:
+            # Not an object dtype => all types will be the same so let the default
+            # indexer return native python type
+            return into_c(self.items())
+
+    def to_frame(self, name: Hashable = lib.no_default) -> DataFrame:
+        """
+        Convert Series to DataFrame.
+
+        Parameters
+        ----------
+        name : object, optional
+            The passed name should substitute for the series name (if it has
+            one).
+
+        Returns
+        -------
+        DataFrame
+            DataFrame representation of Series.
+
+        Examples
+        --------
+        >>> s = pd.Series(["a", "b", "c"],
+        ...               name="vals")
+        >>> s.to_frame()
+          vals
+        0    a
+        1    b
+        2    c
+        """
+        columns: Index
+        if name is lib.no_default:
+            name = self.name
+            if name is None:
+                # default to [0], same as we would get with DataFrame(self)
+                columns = default_index(1)
+            else:
+                columns = Index([name])
+        else:
+            columns = Index([name])
+
+        mgr = self._mgr.to_2d_mgr(columns)
+        df = self._constructor_expanddim_from_mgr(mgr, axes=mgr.axes)
+        return df.__finalize__(self, method="to_frame")
+
+    def _set_name(
+        self, name, inplace: bool = False, deep: bool | None = None
+    ) -> Series:
+        """
+        Set the Series name.
+
+        Parameters
+        ----------
+        name : str
+        inplace : bool
+            Whether to modify `self` directly or return a copy.
+        deep : bool|None, default None
+            Whether to do a deep copy, a shallow copy, or Copy on Write(None)
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        ser = self if inplace else self.copy(deep and not using_copy_on_write())
+        ser.name = name
+        return ser
+
+    @Appender(
+        dedent(
+            """
+        Examples
+        --------
+        >>> ser = pd.Series([390., 350., 30., 20.],
+        ...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...                 name="Max Speed")
+        >>> ser
+        Falcon    390.0
+        Falcon    350.0
+        Parrot     30.0
+        Parrot     20.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(["a", "b", "a", "b"]).mean()
+        a    210.0
+        b    185.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).mean()
+        Falcon    370.0
+        Parrot     25.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(ser > 100).mean()
+        Max Speed
+        False     25.0
+        True     370.0
+        Name: Max Speed, dtype: float64
+
+        **Grouping by Indexes**
+
+        We can groupby different levels of a hierarchical index
+        using the `level` parameter:
+
+        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
+        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+        >>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
+        >>> ser
+        Animal  Type
+        Falcon  Captive    390.0
+                Wild       350.0
+        Parrot  Captive     30.0
+                Wild        20.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level=0).mean()
+        Animal
+        Falcon    370.0
+        Parrot     25.0
+        Name: Max Speed, dtype: float64
+        >>> ser.groupby(level="Type").mean()
+        Type
+        Captive    210.0
+        Wild       185.0
+        Name: Max Speed, dtype: float64
+
+        We can also choose to include `NA` in group keys or not by defining
+        `dropna` parameter, the default setting is `True`.
+
+        >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
+        >>> ser.groupby(level=0).sum()
+        a    3
+        b    3
+        dtype: int64
+
+        >>> ser.groupby(level=0, dropna=False).sum()
+        a    3
+        b    3
+        NaN  3
+        dtype: int64
+
+        >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
+        >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
+        >>> ser.groupby(["a", "b", "a", np.nan]).mean()
+        a    210.0
+        b    350.0
+        Name: Max Speed, dtype: float64
+
+        >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
+        a    210.0
+        b    350.0
+        NaN   20.0
+        Name: Max Speed, dtype: float64
+        """
+        )
+    )
+    @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
+    def groupby(
+        self,
+        by=None,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        as_index: bool = True,
+        sort: bool = True,
+        group_keys: bool = True,
+        observed: bool | lib.NoDefault = lib.no_default,
+        dropna: bool = True,
+    ) -> SeriesGroupBy:
+        from pandas.core.groupby.generic import SeriesGroupBy
+
+        if level is None and by is None:
+            raise TypeError("You have to supply one of 'by' and 'level'")
+        if not as_index:
+            raise TypeError("as_index=False only valid with DataFrame")
+        axis = self._get_axis_number(axis)
+
+        return SeriesGroupBy(
+            obj=self,
+            keys=by,
+            axis=axis,
+            level=level,
+            as_index=as_index,
+            sort=sort,
+            group_keys=group_keys,
+            observed=observed,
+            dropna=dropna,
+        )
+
+    # ----------------------------------------------------------------------
+    # Statistics, overridden ndarray methods
+
+    # TODO: integrate bottleneck
+    def count(self) -> int:
+        """
+        Return number of non-NA/null observations in the Series.
+
+        Returns
+        -------
+        int
+            Number of non-null values in the Series.
+
+        See Also
+        --------
+        DataFrame.count : Count non-NA cells for each column or row.
+
+        Examples
+        --------
+        >>> s = pd.Series([0.0, 1.0, np.nan])
+        >>> s.count()
+        2
+        """
+        return notna(self._values).sum().astype("int64")
+
+    def mode(self, dropna: bool = True) -> Series:
+        """
+        Return the mode(s) of the Series.
+
+        The mode is the value that appears most often. There can be multiple modes.
+
+        Always returns Series even if only one value is returned.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NaN/NaT.
+
+        Returns
+        -------
+        Series
+            Modes of the Series in sorted order.
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 4, 2, 2, 4, None])
+        >>> s.mode()
+        0    2.0
+        dtype: float64
+
+        More than one mode:
+
+        >>> s = pd.Series([2, 4, 8, 2, 4, None])
+        >>> s.mode()
+        0    2.0
+        1    4.0
+        dtype: float64
+
+        With and without considering null value:
+
+        >>> s = pd.Series([2, 4, None, None, 4, None])
+        >>> s.mode(dropna=False)
+        0   NaN
+        dtype: float64
+        >>> s = pd.Series([2, 4, None, None, 4, None])
+        >>> s.mode()
+        0    4.0
+        dtype: float64
+        """
+        # TODO: Add option for bins like value_counts()
+        values = self._values
+        if isinstance(values, np.ndarray):
+            res_values = algorithms.mode(values, dropna=dropna)
+        else:
+            res_values = values._mode(dropna=dropna)
+
+        # Ensure index is type stable (should always use int index)
+        return self._constructor(
+            res_values,
+            index=range(len(res_values)),
+            name=self.name,
+            copy=False,
+            dtype=self.dtype,
+        ).__finalize__(self, method="mode")
+
+    def unique(self) -> ArrayLike:  # pylint: disable=useless-parent-delegation
+        """
+        Return unique values of Series object.
+
+        Uniques are returned in order of appearance. Hash table-based unique,
+        therefore does NOT sort.
+
+        Returns
+        -------
+        ndarray or ExtensionArray
+            The unique values returned as a NumPy array. See Notes.
+
+        See Also
+        --------
+        Series.drop_duplicates : Return Series with duplicate values removed.
+        unique : Top-level unique method for any 1-d array-like object.
+        Index.unique : Return Index with unique values from an Index object.
+
+        Notes
+        -----
+        Returns the unique values as a NumPy array. In case of an
+        extension-array backed Series, a new
+        :class:`~api.extensions.ExtensionArray` of that type with just
+        the unique values is returned. This includes
+
+            * Categorical
+            * Period
+            * Datetime with Timezone
+            * Datetime without Timezone
+            * Timedelta
+            * Interval
+            * Sparse
+            * IntegerNA
+
+        See Examples section.
+
+        Examples
+        --------
+        >>> pd.Series([2, 1, 3, 3], name='A').unique()
+        array([2, 1, 3])
+
+        >>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
+        <DatetimeArray>
+        ['2016-01-01 00:00:00']
+        Length: 1, dtype: datetime64[ns]
+
+        >>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern')
+        ...            for _ in range(3)]).unique()
+        <DatetimeArray>
+        ['2016-01-01 00:00:00-05:00']
+        Length: 1, dtype: datetime64[ns, US/Eastern]
+
+        An Categorical will return categories in the order of
+        appearance and with the same dtype.
+
+        >>> pd.Series(pd.Categorical(list('baabc'))).unique()
+        ['b', 'a', 'c']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
+        ...                          ordered=True)).unique()
+        ['b', 'a', 'c']
+        Categories (3, object): ['a' < 'b' < 'c']
+        """
+        return super().unique()
+
+    @overload
+    def drop_duplicates(
+        self,
+        *,
+        keep: DropKeep = ...,
+        inplace: Literal[False] = ...,
+        ignore_index: bool = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self, *, keep: DropKeep = ..., inplace: Literal[True], ignore_index: bool = ...
+    ) -> None:
+        ...
+
+    @overload
+    def drop_duplicates(
+        self, *, keep: DropKeep = ..., inplace: bool = ..., ignore_index: bool = ...
+    ) -> Series | None:
+        ...
+
+    def drop_duplicates(
+        self,
+        *,
+        keep: DropKeep = "first",
+        inplace: bool = False,
+        ignore_index: bool = False,
+    ) -> Series | None:
+        """
+        Return Series with duplicate values removed.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            Method to handle dropping duplicates:
+
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        inplace : bool, default ``False``
+            If ``True``, performs operation inplace and returns None.
+
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or None
+            Series with duplicates dropped or None if ``inplace=True``.
+
+        See Also
+        --------
+        Index.drop_duplicates : Equivalent method on Index.
+        DataFrame.drop_duplicates : Equivalent method on DataFrame.
+        Series.duplicated : Related method on Series, indicating duplicate
+            Series values.
+        Series.unique : Return unique values as an array.
+
+        Examples
+        --------
+        Generate a Series with duplicated entries.
+
+        >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', 'hippo'],
+        ...               name='animal')
+        >>> s
+        0     llama
+        1       cow
+        2     llama
+        3    beetle
+        4     llama
+        5     hippo
+        Name: animal, dtype: object
+
+        With the 'keep' parameter, the selection behaviour of duplicated values
+        can be changed. The value 'first' keeps the first occurrence for each
+        set of duplicated entries. The default value of keep is 'first'.
+
+        >>> s.drop_duplicates()
+        0     llama
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+
+        The value 'last' for parameter 'keep' keeps the last occurrence for
+        each set of duplicated entries.
+
+        >>> s.drop_duplicates(keep='last')
+        1       cow
+        3    beetle
+        4     llama
+        5     hippo
+        Name: animal, dtype: object
+
+        The value ``False`` for parameter 'keep' discards all sets of
+        duplicated entries.
+
+        >>> s.drop_duplicates(keep=False)
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        result = super().drop_duplicates(keep=keep)
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if inplace:
+            self._update_inplace(result)
+            return None
+        else:
+            return result
+
+    def duplicated(self, keep: DropKeep = "first") -> Series:
+        """
+        Indicate duplicate Series values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        Series. Either all duplicates, all except the first or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            Method to handle dropping duplicates:
+
+            - 'first' : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - 'last' : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        Series[bool]
+            Series indicating whether each value has occurred in the
+            preceding values.
+
+        See Also
+        --------
+        Index.duplicated : Equivalent method on pandas.Index.
+        DataFrame.duplicated : Equivalent method on pandas.DataFrame.
+        Series.drop_duplicates : Remove duplicate values from Series.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set on False and all others on True:
+
+        >>> animals = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama'])
+        >>> animals.duplicated()
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        which is equivalent to
+
+        >>> animals.duplicated(keep='first')
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> animals.duplicated(keep='last')
+        0     True
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> animals.duplicated(keep=False)
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+        """
+        res = self._duplicated(keep=keep)
+        result = self._constructor(res, index=self.index, copy=False)
+        return result.__finalize__(self, method="duplicated")
+
+    def idxmin(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashable:
+        """
+        Return the row label of the minimum value.
+
+        If multiple values equal the minimum, the first row label with that
+        value is returned.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        Index
+            Label of the minimum value.
+
+        Raises
+        ------
+        ValueError
+            If the Series is empty.
+
+        See Also
+        --------
+        numpy.argmin : Return indices of the minimum values
+            along the given axis.
+        DataFrame.idxmin : Return index of first occurrence of minimum
+            over requested axis.
+        Series.idxmax : Return index *label* of the first occurrence
+            of maximum of values.
+
+        Notes
+        -----
+        This method is the Series version of ``ndarray.argmin``. This method
+        returns the label of the minimum, while ``ndarray.argmin`` returns
+        the position. To get the position, use ``series.values.argmin()``.
+
+        Examples
+        --------
+        >>> s = pd.Series(data=[1, None, 4, 1],
+        ...               index=['A', 'B', 'C', 'D'])
+        >>> s
+        A    1.0
+        B    NaN
+        C    4.0
+        D    1.0
+        dtype: float64
+
+        >>> s.idxmin()
+        'A'
+
+        If `skipna` is False and there is an NA value in the data,
+        the function returns ``nan``.
+
+        >>> s.idxmin(skipna=False)
+        nan
+        """
+        axis = self._get_axis_number(axis)
+        with warnings.catch_warnings():
+            # TODO(3.0): this catching/filtering can be removed
+            # ignore warning produced by argmin since we will issue a different
+            #  warning for idxmin
+            warnings.simplefilter("ignore")
+            i = self.argmin(axis, skipna, *args, **kwargs)
+
+        if i == -1:
+            # GH#43587 give correct NA value for Index.
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmin with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.index._na_value
+        return self.index[i]
+
+    def idxmax(self, axis: Axis = 0, skipna: bool = True, *args, **kwargs) -> Hashable:
+        """
+        Return the row label of the maximum value.
+
+        If multiple values equal the maximum, the first row label with that
+        value is returned.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire Series is NA, the result
+            will be NA.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        Index
+            Label of the maximum value.
+
+        Raises
+        ------
+        ValueError
+            If the Series is empty.
+
+        See Also
+        --------
+        numpy.argmax : Return indices of the maximum values
+            along the given axis.
+        DataFrame.idxmax : Return index of first occurrence of maximum
+            over requested axis.
+        Series.idxmin : Return index *label* of the first occurrence
+            of minimum of values.
+
+        Notes
+        -----
+        This method is the Series version of ``ndarray.argmax``. This method
+        returns the label of the maximum, while ``ndarray.argmax`` returns
+        the position. To get the position, use ``series.values.argmax()``.
+
+        Examples
+        --------
+        >>> s = pd.Series(data=[1, None, 4, 3, 4],
+        ...               index=['A', 'B', 'C', 'D', 'E'])
+        >>> s
+        A    1.0
+        B    NaN
+        C    4.0
+        D    3.0
+        E    4.0
+        dtype: float64
+
+        >>> s.idxmax()
+        'C'
+
+        If `skipna` is False and there is an NA value in the data,
+        the function returns ``nan``.
+
+        >>> s.idxmax(skipna=False)
+        nan
+        """
+        axis = self._get_axis_number(axis)
+        with warnings.catch_warnings():
+            # TODO(3.0): this catching/filtering can be removed
+            # ignore warning produced by argmax since we will issue a different
+            #  warning for argmax
+            warnings.simplefilter("ignore")
+            i = self.argmax(axis, skipna, *args, **kwargs)
+
+        if i == -1:
+            # GH#43587 give correct NA value for Index.
+            warnings.warn(
+                f"The behavior of {type(self).__name__}.idxmax with all-NA "
+                "values, or any-NA and skipna=False, is deprecated. In a future "
+                "version this will raise ValueError",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.index._na_value
+        return self.index[i]
+
+    def round(self, decimals: int = 0, *args, **kwargs) -> Series:
+        """
+        Round each value in a Series to the given number of decimals.
+
+        Parameters
+        ----------
+        decimals : int, default 0
+            Number of decimal places to round to. If decimals is negative,
+            it specifies the number of positions to the left of the decimal point.
+        *args, **kwargs
+            Additional arguments and keywords have no effect but might be
+            accepted for compatibility with NumPy.
+
+        Returns
+        -------
+        Series
+            Rounded values of the Series.
+
+        See Also
+        --------
+        numpy.around : Round values of an np.array.
+        DataFrame.round : Round values of a DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series([0.1, 1.3, 2.7])
+        >>> s.round()
+        0    0.0
+        1    1.0
+        2    3.0
+        dtype: float64
+        """
+        nv.validate_round(args, kwargs)
+        if self.dtype == "object":
+            raise TypeError("Expected numeric dtype, got object instead.")
+        new_mgr = self._mgr.round(decimals=decimals, using_cow=using_copy_on_write())
+        return self._constructor_from_mgr(new_mgr, axes=new_mgr.axes).__finalize__(
+            self, method="round"
+        )
+
+    @overload
+    def quantile(
+        self, q: float = ..., interpolation: QuantileInterpolation = ...
+    ) -> float:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: Sequence[float] | AnyArrayLike,
+        interpolation: QuantileInterpolation = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: float | Sequence[float] | AnyArrayLike = ...,
+        interpolation: QuantileInterpolation = ...,
+    ) -> float | Series:
+        ...
+
+    def quantile(
+        self,
+        q: float | Sequence[float] | AnyArrayLike = 0.5,
+        interpolation: QuantileInterpolation = "linear",
+    ) -> float | Series:
+        """
+        Return value at the given quantile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            The quantile(s) to compute, which can lie in range: 0 <= q <= 1.
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * (x-i)/(j-i)`, where `(x-i)/(j-i)` is
+                  the fractional part of the index surrounded by `i > j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+
+        Returns
+        -------
+        float or Series
+            If ``q`` is an array, a Series will be returned where the
+            index is ``q`` and the values are the quantiles, otherwise
+            a float will be returned.
+
+        See Also
+        --------
+        core.window.Rolling.quantile : Calculate the rolling quantile.
+        numpy.percentile : Returns the q-th percentile(s) of the array elements.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.quantile(.5)
+        2.5
+        >>> s.quantile([.25, .5, .75])
+        0.25    1.75
+        0.50    2.50
+        0.75    3.25
+        dtype: float64
+        """
+        validate_percentile(q)
+
+        # We dispatch to DataFrame so that core.internals only has to worry
+        #  about 2D cases.
+        df = self.to_frame()
+
+        result = df.quantile(q=q, interpolation=interpolation, numeric_only=False)
+        if result.ndim == 2:
+            result = result.iloc[:, 0]
+
+        if is_list_like(q):
+            result.name = self.name
+            idx = Index(q, dtype=np.float64)
+            return self._constructor(result, index=idx, name=self.name)
+        else:
+            # scalar
+            return result.iloc[0]
+
+    def corr(
+        self,
+        other: Series,
+        method: CorrelationMethod = "pearson",
+        min_periods: int | None = None,
+    ) -> float:
+        """
+        Compute correlation with `other` Series, excluding missing values.
+
+        The two `Series` objects are not required to be the same length and will be
+        aligned internally before the correlation function is applied.
+
+        Parameters
+        ----------
+        other : Series
+            Series with which to compute the correlation.
+        method : {'pearson', 'kendall', 'spearman'} or callable
+            Method used to compute correlation:
+
+            - pearson : Standard correlation coefficient
+            - kendall : Kendall Tau correlation coefficient
+            - spearman : Spearman rank correlation
+            - callable: Callable with input two 1d ndarrays and returning a float.
+
+            .. warning::
+                Note that the returned matrix from corr will have 1 along the
+                diagonals and will be symmetric regardless of the callable's
+                behavior.
+        min_periods : int, optional
+            Minimum number of observations needed to have a valid result.
+
+        Returns
+        -------
+        float
+            Correlation with other.
+
+        See Also
+        --------
+        DataFrame.corr : Compute pairwise correlation between columns.
+        DataFrame.corrwith : Compute pairwise correlation with another
+            DataFrame or Series.
+
+        Notes
+        -----
+        Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
+
+        * `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
+        * `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
+        * `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_
+
+        Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method.
+        ``corr()`` automatically considers values with matching indices.
+
+        Examples
+        --------
+        >>> def histogram_intersection(a, b):
+        ...     v = np.minimum(a, b).sum().round(decimals=1)
+        ...     return v
+        >>> s1 = pd.Series([.2, .0, .6, .2])
+        >>> s2 = pd.Series([.3, .6, .0, .1])
+        >>> s1.corr(s2, method=histogram_intersection)
+        0.3
+
+        Pandas auto-aligns the values with matching indices
+
+        >>> s1 = pd.Series([1, 2, 3], index=[0, 1, 2])
+        >>> s2 = pd.Series([1, 2, 3], index=[2, 1, 0])
+        >>> s1.corr(s2)
+        -1.0
+        """  # noqa: E501
+        this, other = self.align(other, join="inner", copy=False)
+        if len(this) == 0:
+            return np.nan
+
+        this_values = this.to_numpy(dtype=float, na_value=np.nan, copy=False)
+        other_values = other.to_numpy(dtype=float, na_value=np.nan, copy=False)
+
+        if method in ["pearson", "spearman", "kendall"] or callable(method):
+            return nanops.nancorr(
+                this_values, other_values, method=method, min_periods=min_periods
+            )
+
+        raise ValueError(
+            "method must be either 'pearson', "
+            "'spearman', 'kendall', or a callable, "
+            f"'{method}' was supplied"
+        )
+
+    def cov(
+        self,
+        other: Series,
+        min_periods: int | None = None,
+        ddof: int | None = 1,
+    ) -> float:
+        """
+        Compute covariance with Series, excluding missing values.
+
+        The two `Series` objects are not required to be the same length and
+        will be aligned internally before the covariance is calculated.
+
+        Parameters
+        ----------
+        other : Series
+            Series with which to compute the covariance.
+        min_periods : int, optional
+            Minimum number of observations needed to have a valid result.
+        ddof : int, default 1
+            Delta degrees of freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+
+        Returns
+        -------
+        float
+            Covariance between Series and other normalized by N-1
+            (unbiased estimator).
+
+        See Also
+        --------
+        DataFrame.cov : Compute pairwise covariance of columns.
+
+        Examples
+        --------
+        >>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
+        >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
+        >>> s1.cov(s2)
+        -0.01685762652715874
+        """
+        this, other = self.align(other, join="inner", copy=False)
+        if len(this) == 0:
+            return np.nan
+        this_values = this.to_numpy(dtype=float, na_value=np.nan, copy=False)
+        other_values = other.to_numpy(dtype=float, na_value=np.nan, copy=False)
+        return nanops.nancov(
+            this_values, other_values, min_periods=min_periods, ddof=ddof
+        )
+
+    @doc(
+        klass="Series",
+        extra_params="",
+        other_klass="DataFrame",
+        examples=dedent(
+            """
+        Difference with previous row
+
+        >>> s = pd.Series([1, 1, 2, 3, 5, 8])
+        >>> s.diff()
+        0    NaN
+        1    0.0
+        2    1.0
+        3    1.0
+        4    2.0
+        5    3.0
+        dtype: float64
+
+        Difference with 3rd previous row
+
+        >>> s.diff(periods=3)
+        0    NaN
+        1    NaN
+        2    NaN
+        3    2.0
+        4    4.0
+        5    6.0
+        dtype: float64
+
+        Difference with following row
+
+        >>> s.diff(periods=-1)
+        0    0.0
+        1   -1.0
+        2   -1.0
+        3   -2.0
+        4   -3.0
+        5    NaN
+        dtype: float64
+
+        Overflow in input dtype
+
+        >>> s = pd.Series([1, 0], dtype=np.uint8)
+        >>> s.diff()
+        0      NaN
+        1    255.0
+        dtype: float64"""
+        ),
+    )
+    def diff(self, periods: int = 1) -> Series:
+        """
+        First discrete difference of element.
+
+        Calculates the difference of a {klass} element compared with another
+        element in the {klass} (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference, accepts negative
+            values.
+        {extra_params}
+        Returns
+        -------
+        {klass}
+            First differences of the Series.
+
+        See Also
+        --------
+        {klass}.pct_change: Percent change over given number of periods.
+        {klass}.shift: Shift index by desired number of periods with an
+            optional time freq.
+        {other_klass}.diff: First discrete difference of object.
+
+        Notes
+        -----
+        For boolean dtypes, this uses :meth:`operator.xor` rather than
+        :meth:`operator.sub`.
+        The result is calculated according to current dtype in {klass},
+        however dtype of the result is always float64.
+
+        Examples
+        --------
+        {examples}
+        """
+        result = algorithms.diff(self._values, periods)
+        return self._constructor(result, index=self.index, copy=False).__finalize__(
+            self, method="diff"
+        )
+
+    def autocorr(self, lag: int = 1) -> float:
+        """
+        Compute the lag-N autocorrelation.
+
+        This method computes the Pearson correlation between
+        the Series and its shifted self.
+
+        Parameters
+        ----------
+        lag : int, default 1
+            Number of lags to apply before performing autocorrelation.
+
+        Returns
+        -------
+        float
+            The Pearson correlation between self and self.shift(lag).
+
+        See Also
+        --------
+        Series.corr : Compute the correlation between two Series.
+        Series.shift : Shift index by desired number of periods.
+        DataFrame.corr : Compute pairwise correlation of columns.
+        DataFrame.corrwith : Compute pairwise correlation between rows or
+            columns of two DataFrame objects.
+
+        Notes
+        -----
+        If the Pearson correlation is not well defined return 'NaN'.
+
+        Examples
+        --------
+        >>> s = pd.Series([0.25, 0.5, 0.2, -0.05])
+        >>> s.autocorr()  # doctest: +ELLIPSIS
+        0.10355...
+        >>> s.autocorr(lag=2)  # doctest: +ELLIPSIS
+        -0.99999...
+
+        If the Pearson correlation is not well defined, then 'NaN' is returned.
+
+        >>> s = pd.Series([1, 0, 0, 0])
+        >>> s.autocorr()
+        nan
+        """
+        return self.corr(cast(Series, self.shift(lag)))
+
+    def dot(self, other: AnyArrayLike) -> Series | np.ndarray:
+        """
+        Compute the dot product between the Series and the columns of other.
+
+        This method computes the dot product between the Series and another
+        one, or the Series and each columns of a DataFrame, or the Series and
+        each columns of an array.
+
+        It can also be called using `self @ other`.
+
+        Parameters
+        ----------
+        other : Series, DataFrame or array-like
+            The other object to compute the dot product with its columns.
+
+        Returns
+        -------
+        scalar, Series or numpy.ndarray
+            Return the dot product of the Series and other if other is a
+            Series, the Series of the dot product of Series and each rows of
+            other if other is a DataFrame or a numpy.ndarray between the Series
+            and each columns of the numpy array.
+
+        See Also
+        --------
+        DataFrame.dot: Compute the matrix product with the DataFrame.
+        Series.mul: Multiplication of series and other, element-wise.
+
+        Notes
+        -----
+        The Series and other has to share the same index if other is a Series
+        or a DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series([0, 1, 2, 3])
+        >>> other = pd.Series([-1, 2, -3, 4])
+        >>> s.dot(other)
+        8
+        >>> s @ other
+        8
+        >>> df = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]])
+        >>> s.dot(df)
+        0    24
+        1    14
+        dtype: int64
+        >>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]])
+        >>> s.dot(arr)
+        array([24, 14])
+        """
+        if isinstance(other, (Series, ABCDataFrame)):
+            common = self.index.union(other.index)
+            if len(common) > len(self.index) or len(common) > len(other.index):
+                raise ValueError("matrices are not aligned")
+
+            left = self.reindex(index=common, copy=False)
+            right = other.reindex(index=common, copy=False)
+            lvals = left.values
+            rvals = right.values
+        else:
+            lvals = self.values
+            rvals = np.asarray(other)
+            if lvals.shape[0] != rvals.shape[0]:
+                raise Exception(
+                    f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
+                )
+
+        if isinstance(other, ABCDataFrame):
+            return self._constructor(
+                np.dot(lvals, rvals), index=other.columns, copy=False
+            ).__finalize__(self, method="dot")
+        elif isinstance(other, Series):
+            return np.dot(lvals, rvals)
+        elif isinstance(rvals, np.ndarray):
+            return np.dot(lvals, rvals)
+        else:  # pragma: no cover
+            raise TypeError(f"unsupported type: {type(other)}")
+
+    def __matmul__(self, other):
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        return self.dot(other)
+
+    def __rmatmul__(self, other):
+        """
+        Matrix multiplication using binary `@` operator.
+        """
+        return self.dot(np.transpose(other))
+
+    @doc(base.IndexOpsMixin.searchsorted, klass="Series")
+    # Signature of "searchsorted" incompatible with supertype "IndexOpsMixin"
+    def searchsorted(  # type: ignore[override]
+        self,
+        value: NumpyValueArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        return base.IndexOpsMixin.searchsorted(self, value, side=side, sorter=sorter)
+
+    # -------------------------------------------------------------------
+    # Combination
+
+    def _append(
+        self, to_append, ignore_index: bool = False, verify_integrity: bool = False
+    ):
+        from pandas.core.reshape.concat import concat
+
+        if isinstance(to_append, (list, tuple)):
+            to_concat = [self]
+            to_concat.extend(to_append)
+        else:
+            to_concat = [self, to_append]
+        if any(isinstance(x, (ABCDataFrame,)) for x in to_concat[1:]):
+            msg = "to_append should be a Series or list/tuple of Series, got DataFrame"
+            raise TypeError(msg)
+        return concat(
+            to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity
+        )
+
+    @doc(
+        _shared_docs["compare"],
+        dedent(
+            """
+        Returns
+        -------
+        Series or DataFrame
+            If axis is 0 or 'index' the result will be a Series.
+            The resulting index will be a MultiIndex with 'self' and 'other'
+            stacked alternately at the inner level.
+
+            If axis is 1 or 'columns' the result will be a DataFrame.
+            It will have two columns namely 'self' and 'other'.
+
+        See Also
+        --------
+        DataFrame.compare : Compare with another DataFrame and show differences.
+
+        Notes
+        -----
+        Matching NaNs will not appear as a difference.
+
+        Examples
+        --------
+        >>> s1 = pd.Series(["a", "b", "c", "d", "e"])
+        >>> s2 = pd.Series(["a", "a", "c", "b", "e"])
+
+        Align the differences on columns
+
+        >>> s1.compare(s2)
+          self other
+        1    b     a
+        3    d     b
+
+        Stack the differences on indices
+
+        >>> s1.compare(s2, align_axis=0)
+        1  self     b
+           other    a
+        3  self     d
+           other    b
+        dtype: object
+
+        Keep all original rows
+
+        >>> s1.compare(s2, keep_shape=True)
+          self other
+        0  NaN   NaN
+        1    b     a
+        2  NaN   NaN
+        3    d     b
+        4  NaN   NaN
+
+        Keep all original rows and also all original values
+
+        >>> s1.compare(s2, keep_shape=True, keep_equal=True)
+          self other
+        0    a     a
+        1    b     a
+        2    c     c
+        3    d     b
+        4    e     e
+        """
+        ),
+        klass=_shared_doc_kwargs["klass"],
+    )
+    def compare(
+        self,
+        other: Series,
+        align_axis: Axis = 1,
+        keep_shape: bool = False,
+        keep_equal: bool = False,
+        result_names: Suffixes = ("self", "other"),
+    ) -> DataFrame | Series:
+        return super().compare(
+            other=other,
+            align_axis=align_axis,
+            keep_shape=keep_shape,
+            keep_equal=keep_equal,
+            result_names=result_names,
+        )
+
+    def combine(
+        self,
+        other: Series | Hashable,
+        func: Callable[[Hashable, Hashable], Hashable],
+        fill_value: Hashable | None = None,
+    ) -> Series:
+        """
+        Combine the Series with a Series or scalar according to `func`.
+
+        Combine the Series and `other` using `func` to perform elementwise
+        selection for combined Series.
+        `fill_value` is assumed when value is missing at some index
+        from one of the two objects being combined.
+
+        Parameters
+        ----------
+        other : Series or scalar
+            The value(s) to be combined with the `Series`.
+        func : function
+            Function that takes two scalars as inputs and returns an element.
+        fill_value : scalar, optional
+            The value to assume when an index is missing from
+            one Series or the other. The default specifies to use the
+            appropriate NaN value for the underlying dtype of the Series.
+
+        Returns
+        -------
+        Series
+            The result of combining the Series with the other object.
+
+        See Also
+        --------
+        Series.combine_first : Combine Series values, choosing the calling
+            Series' values first.
+
+        Examples
+        --------
+        Consider 2 Datasets ``s1`` and ``s2`` containing
+        highest clocked speeds of different birds.
+
+        >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})
+        >>> s1
+        falcon    330.0
+        eagle     160.0
+        dtype: float64
+        >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
+        >>> s2
+        falcon    345.0
+        eagle     200.0
+        duck       30.0
+        dtype: float64
+
+        Now, to combine the two datasets and view the highest speeds
+        of the birds across the two datasets
+
+        >>> s1.combine(s2, max)
+        duck        NaN
+        eagle     200.0
+        falcon    345.0
+        dtype: float64
+
+        In the previous example, the resulting value for duck is missing,
+        because the maximum of a NaN and a float is a NaN.
+        So, in the example, we set ``fill_value=0``,
+        so the maximum value returned will be the value from some dataset.
+
+        >>> s1.combine(s2, max, fill_value=0)
+        duck       30.0
+        eagle     200.0
+        falcon    345.0
+        dtype: float64
+        """
+        if fill_value is None:
+            fill_value = na_value_for_dtype(self.dtype, compat=False)
+
+        if isinstance(other, Series):
+            # If other is a Series, result is based on union of Series,
+            # so do this element by element
+            new_index = self.index.union(other.index)
+            new_name = ops.get_op_result_name(self, other)
+            new_values = np.empty(len(new_index), dtype=object)
+            with np.errstate(all="ignore"):
+                for i, idx in enumerate(new_index):
+                    lv = self.get(idx, fill_value)
+                    rv = other.get(idx, fill_value)
+                    new_values[i] = func(lv, rv)
+        else:
+            # Assume that other is a scalar, so apply the function for
+            # each element in the Series
+            new_index = self.index
+            new_values = np.empty(len(new_index), dtype=object)
+            with np.errstate(all="ignore"):
+                new_values[:] = [func(lv, other) for lv in self._values]
+            new_name = self.name
+
+        # try_float=False is to match agg_series
+        npvalues = lib.maybe_convert_objects(new_values, try_float=False)
+        # same_dtype here is a kludge to avoid casting e.g. [True, False] to
+        #  ["True", "False"]
+        same_dtype = isinstance(self.dtype, (StringDtype, CategoricalDtype))
+        res_values = maybe_cast_pointwise_result(
+            npvalues, self.dtype, same_dtype=same_dtype
+        )
+        return self._constructor(res_values, index=new_index, name=new_name, copy=False)
+
+    def combine_first(self, other) -> Series:
+        """
+        Update null elements with value in the same location in 'other'.
+
+        Combine two Series objects by filling null values in one Series with
+        non-null values from the other Series. Result index will be the union
+        of the two indexes.
+
+        Parameters
+        ----------
+        other : Series
+            The value(s) to be used for filling null values.
+
+        Returns
+        -------
+        Series
+            The result of combining the provided Series with the other object.
+
+        See Also
+        --------
+        Series.combine : Perform element-wise operation on two Series
+            using a given function.
+
+        Examples
+        --------
+        >>> s1 = pd.Series([1, np.nan])
+        >>> s2 = pd.Series([3, 4, 5])
+        >>> s1.combine_first(s2)
+        0    1.0
+        1    4.0
+        2    5.0
+        dtype: float64
+
+        Null values still persist if the location of that null value
+        does not exist in `other`
+
+        >>> s1 = pd.Series({'falcon': np.nan, 'eagle': 160.0})
+        >>> s2 = pd.Series({'eagle': 200.0, 'duck': 30.0})
+        >>> s1.combine_first(s2)
+        duck       30.0
+        eagle     160.0
+        falcon      NaN
+        dtype: float64
+        """
+        from pandas.core.reshape.concat import concat
+
+        if self.dtype == other.dtype:
+            if self.index.equals(other.index):
+                return self.mask(self.isna(), other)
+            elif self._can_hold_na and not isinstance(self.dtype, SparseDtype):
+                this, other = self.align(other, join="outer")
+                return this.mask(this.isna(), other)
+
+        new_index = self.index.union(other.index)
+
+        this = self
+        # identify the index subset to keep for each series
+        keep_other = other.index.difference(this.index[notna(this)])
+        keep_this = this.index.difference(keep_other)
+
+        this = this.reindex(keep_this, copy=False)
+        other = other.reindex(keep_other, copy=False)
+
+        if this.dtype.kind == "M" and other.dtype.kind != "M":
+            other = to_datetime(other)
+        combined = concat([this, other])
+        combined = combined.reindex(new_index, copy=False)
+        return combined.__finalize__(self, method="combine_first")
+
+    def update(self, other: Series | Sequence | Mapping) -> None:
+        """
+        Modify Series in place using values from passed Series.
+
+        Uses non-NA values from passed Series to make updates. Aligns
+        on index.
+
+        Parameters
+        ----------
+        other : Series, or object coercible into Series
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update(pd.Series([4, 5, 6]))
+        >>> s
+        0    4
+        1    5
+        2    6
+        dtype: int64
+
+        >>> s = pd.Series(['a', 'b', 'c'])
+        >>> s.update(pd.Series(['d', 'e'], index=[0, 2]))
+        >>> s
+        0    d
+        1    b
+        2    e
+        dtype: object
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update(pd.Series([4, 5, 6, 7, 8]))
+        >>> s
+        0    4
+        1    5
+        2    6
+        dtype: int64
+
+        If ``other`` contains NaNs the corresponding values are not updated
+        in the original Series.
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update(pd.Series([4, np.nan, 6]))
+        >>> s
+        0    4
+        1    2
+        2    6
+        dtype: int64
+
+        ``other`` can also be a non-Series object type
+        that is coercible into a Series
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update([4, np.nan, 6])
+        >>> s
+        0    4
+        1    2
+        2    6
+        dtype: int64
+
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.update({1: 9})
+        >>> s
+        0    1
+        1    9
+        2    3
+        dtype: int64
+        """
+        if not PYPY and not WARNING_CHECK_DISABLED and using_copy_on_write():
+            if sys.getrefcount(self) <= REF_COUNT:
+                warnings.warn(
+                    _chained_assignment_method_msg,
+                    ChainedAssignmentError,
+                    stacklevel=2,
+                )
+        elif (
+            not PYPY
+            and not WARNING_CHECK_DISABLED
+            and not using_copy_on_write()
+            and self._is_view_after_cow_rules()
+        ):
+            ctr = sys.getrefcount(self)
+            ref_count = REF_COUNT
+            if _check_cacher(self):
+                # see https://github.com/pandas-dev/pandas/pull/56060#discussion_r1399245221
+                ref_count += 1
+            if ctr <= ref_count:
+                warnings.warn(
+                    _chained_assignment_warning_method_msg,
+                    FutureWarning,
+                    stacklevel=2,
+                )
+
+        if not isinstance(other, Series):
+            other = Series(other)
+
+        other = other.reindex_like(self)
+        mask = notna(other)
+
+        self._mgr = self._mgr.putmask(mask=mask, new=other)
+        self._maybe_update_cacher()
+
+    # ----------------------------------------------------------------------
+    # Reindexing, sorting
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_values(
+        self,
+        *,
+        axis: Axis = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: bool = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        ignore_index: bool = ...,
+        key: ValueKeyFunc = ...,
+    ) -> Series | None:
+        ...
+
+    def sort_values(
+        self,
+        *,
+        axis: Axis = 0,
+        ascending: bool | Sequence[bool] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        ignore_index: bool = False,
+        key: ValueKeyFunc | None = None,
+    ) -> Series | None:
+        """
+        Sort by the values.
+
+        Sort a Series in ascending or descending order by some
+        criterion.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        ascending : bool or list of bools, default True
+            If True, sort values in ascending order, otherwise descending.
+        inplace : bool, default False
+            If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See also :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable  algorithms.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            If not None, apply the key function to the series values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect a
+            ``Series`` and return an array-like.
+
+        Returns
+        -------
+        Series or None
+            Series ordered by values or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.sort_index : Sort by the Series indices.
+        DataFrame.sort_values : Sort DataFrame by the values along either axis.
+        DataFrame.sort_index : Sort DataFrame by indices.
+
+        Examples
+        --------
+        >>> s = pd.Series([np.nan, 1, 3, 10, 5])
+        >>> s
+        0     NaN
+        1     1.0
+        2     3.0
+        3     10.0
+        4     5.0
+        dtype: float64
+
+        Sort values ascending order (default behaviour)
+
+        >>> s.sort_values(ascending=True)
+        1     1.0
+        2     3.0
+        4     5.0
+        3    10.0
+        0     NaN
+        dtype: float64
+
+        Sort values descending order
+
+        >>> s.sort_values(ascending=False)
+        3    10.0
+        4     5.0
+        2     3.0
+        1     1.0
+        0     NaN
+        dtype: float64
+
+        Sort values putting NAs first
+
+        >>> s.sort_values(na_position='first')
+        0     NaN
+        1     1.0
+        2     3.0
+        4     5.0
+        3    10.0
+        dtype: float64
+
+        Sort a series of strings
+
+        >>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
+        >>> s
+        0    z
+        1    b
+        2    d
+        3    a
+        4    c
+        dtype: object
+
+        >>> s.sort_values()
+        3    a
+        1    b
+        4    c
+        2    d
+        0    z
+        dtype: object
+
+        Sort using a key function. Your `key` function will be
+        given the ``Series`` of values and should return an array-like.
+
+        >>> s = pd.Series(['a', 'B', 'c', 'D', 'e'])
+        >>> s.sort_values()
+        1    B
+        3    D
+        0    a
+        2    c
+        4    e
+        dtype: object
+        >>> s.sort_values(key=lambda x: x.str.lower())
+        0    a
+        1    B
+        2    c
+        3    D
+        4    e
+        dtype: object
+
+        NumPy ufuncs work well here. For example, we can
+        sort by the ``sin`` of the value
+
+        >>> s = pd.Series([-4, -2, 0, 2, 4])
+        >>> s.sort_values(key=np.sin)
+        1   -2
+        4    4
+        2    0
+        0   -4
+        3    2
+        dtype: int64
+
+        More complicated user-defined functions can be used,
+        as long as they expect a Series and return an array-like
+
+        >>> s.sort_values(key=lambda x: (np.tan(x.cumsum())))
+        0   -4
+        3    2
+        4    4
+        1   -2
+        2    0
+        dtype: int64
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        # Validate the axis parameter
+        self._get_axis_number(axis)
+
+        # GH 5856/5853
+        if inplace and self._is_cached:
+            raise ValueError(
+                "This Series is a view of some other array, to "
+                "sort in-place you must create a copy"
+            )
+
+        if is_list_like(ascending):
+            ascending = cast(Sequence[bool], ascending)
+            if len(ascending) != 1:
+                raise ValueError(
+                    f"Length of ascending ({len(ascending)}) must be 1 for Series"
+                )
+            ascending = ascending[0]
+
+        ascending = validate_ascending(ascending)
+
+        if na_position not in ["first", "last"]:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        # GH 35922. Make sorting stable by leveraging nargsort
+        if key:
+            values_to_sort = cast(Series, ensure_key_mapped(self, key))._values
+        else:
+            values_to_sort = self._values
+        sorted_index = nargsort(values_to_sort, kind, bool(ascending), na_position)
+
+        if is_range_indexer(sorted_index, len(sorted_index)):
+            if inplace:
+                return self._update_inplace(self)
+            return self.copy(deep=None)
+
+        result = self._constructor(
+            self._values[sorted_index], index=self.index[sorted_index], copy=False
+        )
+
+        if ignore_index:
+            result.index = default_index(len(sorted_index))
+
+        if not inplace:
+            return result.__finalize__(self, method="sort_values")
+        self._update_inplace(result)
+        return None
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[True],
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: Literal[False] = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def sort_index(
+        self,
+        *,
+        axis: Axis = ...,
+        level: IndexLabel = ...,
+        ascending: bool | Sequence[bool] = ...,
+        inplace: bool = ...,
+        kind: SortKind = ...,
+        na_position: NaPosition = ...,
+        sort_remaining: bool = ...,
+        ignore_index: bool = ...,
+        key: IndexKeyFunc = ...,
+    ) -> Series | None:
+        ...
+
+    def sort_index(
+        self,
+        *,
+        axis: Axis = 0,
+        level: IndexLabel | None = None,
+        ascending: bool | Sequence[bool] = True,
+        inplace: bool = False,
+        kind: SortKind = "quicksort",
+        na_position: NaPosition = "last",
+        sort_remaining: bool = True,
+        ignore_index: bool = False,
+        key: IndexKeyFunc | None = None,
+    ) -> Series | None:
+        """
+        Sort Series by index labels.
+
+        Returns a new Series sorted by label if `inplace` argument is
+        ``False``, otherwise updates the original series and returns None.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        level : int, optional
+            If not None, sort on values in specified index level(s).
+        ascending : bool or list-like of bools, default True
+            Sort ascending vs. descending. When the index is a MultiIndex the
+            sort direction can be controlled for each level individually.
+        inplace : bool, default False
+            If True, perform operation in-place.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See also :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable algorithms. For
+            DataFrames, this option is only applied when sorting on a single
+            column or label.
+        na_position : {'first', 'last'}, default 'last'
+            If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.
+            Not implemented for MultiIndex.
+        sort_remaining : bool, default True
+            If True and sorting by level and index is multilevel, sort by other
+            levels too (in order) after sorting by specified level.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, …, n - 1.
+        key : callable, optional
+            If not None, apply the key function to the index values
+            before sorting. This is similar to the `key` argument in the
+            builtin :meth:`sorted` function, with the notable difference that
+            this `key` function should be *vectorized*. It should expect an
+            ``Index`` and return an ``Index`` of the same shape.
+
+        Returns
+        -------
+        Series or None
+            The original Series sorted by the labels or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.sort_index: Sort DataFrame by the index.
+        DataFrame.sort_values: Sort DataFrame by the value.
+        Series.sort_values : Sort Series by the value.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
+        >>> s.sort_index()
+        1    c
+        2    b
+        3    a
+        4    d
+        dtype: object
+
+        Sort Descending
+
+        >>> s.sort_index(ascending=False)
+        4    d
+        3    a
+        2    b
+        1    c
+        dtype: object
+
+        By default NaNs are put at the end, but use `na_position` to place
+        them at the beginning
+
+        >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan])
+        >>> s.sort_index(na_position='first')
+        NaN     d
+         1.0    c
+         2.0    b
+         3.0    a
+        dtype: object
+
+        Specify index level to sort
+
+        >>> arrays = [np.array(['qux', 'qux', 'foo', 'foo',
+        ...                     'baz', 'baz', 'bar', 'bar']),
+        ...           np.array(['two', 'one', 'two', 'one',
+        ...                     'two', 'one', 'two', 'one'])]
+        >>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays)
+        >>> s.sort_index(level=1)
+        bar  one    8
+        baz  one    6
+        foo  one    4
+        qux  one    2
+        bar  two    7
+        baz  two    5
+        foo  two    3
+        qux  two    1
+        dtype: int64
+
+        Does not sort by remaining levels when sorting by levels
+
+        >>> s.sort_index(level=1, sort_remaining=False)
+        qux  one    2
+        foo  one    4
+        baz  one    6
+        bar  one    8
+        qux  two    1
+        foo  two    3
+        baz  two    5
+        bar  two    7
+        dtype: int64
+
+        Apply a key function before sorting
+
+        >>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd'])
+        >>> s.sort_index(key=lambda x : x.str.lower())
+        A    1
+        b    2
+        C    3
+        d    4
+        dtype: int64
+        """
+
+        return super().sort_index(
+            axis=axis,
+            level=level,
+            ascending=ascending,
+            inplace=inplace,
+            kind=kind,
+            na_position=na_position,
+            sort_remaining=sort_remaining,
+            ignore_index=ignore_index,
+            key=key,
+        )
+
+    def argsort(
+        self,
+        axis: Axis = 0,
+        kind: SortKind = "quicksort",
+        order: None = None,
+        stable: None = None,
+    ) -> Series:
+        """
+        Return the integer indices that would sort the Series values.
+
+        Override ndarray.argsort. Argsorts the value, omitting NA/null values,
+        and places the result in the same locations as the non-NA values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        kind : {'mergesort', 'quicksort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable algorithms.
+        order : None
+            Has no effect but is accepted for compatibility with numpy.
+        stable : None
+            Has no effect but is accepted for compatibility with numpy.
+
+        Returns
+        -------
+        Series[np.intp]
+            Positions of values within the sort order with -1 indicating
+            nan values.
+
+        See Also
+        --------
+        numpy.ndarray.argsort : Returns the indices that would sort this array.
+
+        Examples
+        --------
+        >>> s = pd.Series([3, 2, 1])
+        >>> s.argsort()
+        0    2
+        1    1
+        2    0
+        dtype: int64
+        """
+        if axis != -1:
+            # GH#54257 We allow -1 here so that np.argsort(series) works
+            self._get_axis_number(axis)
+
+        values = self._values
+        mask = isna(values)
+
+        if mask.any():
+            # TODO(3.0): once this deprecation is enforced we can call
+            #  self.array.argsort directly, which will close GH#43840 and
+            #  GH#12694
+            warnings.warn(
+                "The behavior of Series.argsort in the presence of NA values is "
+                "deprecated. In a future version, NA values will be ordered "
+                "last instead of set to -1.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            result = np.full(len(self), -1, dtype=np.intp)
+            notmask = ~mask
+            result[notmask] = np.argsort(values[notmask], kind=kind)
+        else:
+            result = np.argsort(values, kind=kind)
+
+        res = self._constructor(
+            result, index=self.index, name=self.name, dtype=np.intp, copy=False
+        )
+        return res.__finalize__(self, method="argsort")
+
+    def nlargest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        """
+        Return the largest `n` elements.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many descending sorted values.
+        keep : {'first', 'last', 'all'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+            - ``all`` : keep all occurrences. This can result in a Series of
+              size larger than `n`.
+
+        Returns
+        -------
+        Series
+            The `n` largest values in the Series, sorted in decreasing order.
+
+        See Also
+        --------
+        Series.nsmallest: Get the `n` smallest elements.
+        Series.sort_values: Sort Series by values.
+        Series.head: Return the first `n` rows.
+
+        Notes
+        -----
+        Faster than ``.sort_values(ascending=False).head(n)`` for small `n`
+        relative to the size of the ``Series`` object.
+
+        Examples
+        --------
+        >>> countries_population = {"Italy": 59000000, "France": 65000000,
+        ...                         "Malta": 434000, "Maldives": 434000,
+        ...                         "Brunei": 434000, "Iceland": 337000,
+        ...                         "Nauru": 11300, "Tuvalu": 11300,
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
+        >>> s = pd.Series(countries_population)
+        >>> s
+        Italy       59000000
+        France      65000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        Iceland       337000
+        Nauru          11300
+        Tuvalu         11300
+        Anguilla       11300
+        Montserrat      5200
+        dtype: int64
+
+        The `n` largest elements where ``n=5`` by default.
+
+        >>> s.nlargest()
+        France      65000000
+        Italy       59000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        dtype: int64
+
+        The `n` largest elements where ``n=3``. Default `keep` value is 'first'
+        so Malta will be kept.
+
+        >>> s.nlargest(3)
+        France    65000000
+        Italy     59000000
+        Malta       434000
+        dtype: int64
+
+        The `n` largest elements where ``n=3`` and keeping the last duplicates.
+        Brunei will be kept since it is the last with value 434000 based on
+        the index order.
+
+        >>> s.nlargest(3, keep='last')
+        France      65000000
+        Italy       59000000
+        Brunei        434000
+        dtype: int64
+
+        The `n` largest elements where ``n=3`` with all duplicates kept. Note
+        that the returned Series has five elements due to the three duplicates.
+
+        >>> s.nlargest(3, keep='all')
+        France      65000000
+        Italy       59000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        dtype: int64
+        """
+        return selectn.SelectNSeries(self, n=n, keep=keep).nlargest()
+
+    def nsmallest(
+        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
+    ) -> Series:
+        """
+        Return the smallest `n` elements.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many ascending sorted values.
+        keep : {'first', 'last', 'all'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+            - ``all`` : keep all occurrences. This can result in a Series of
+              size larger than `n`.
+
+        Returns
+        -------
+        Series
+            The `n` smallest values in the Series, sorted in increasing order.
+
+        See Also
+        --------
+        Series.nlargest: Get the `n` largest elements.
+        Series.sort_values: Sort Series by values.
+        Series.head: Return the first `n` rows.
+
+        Notes
+        -----
+        Faster than ``.sort_values().head(n)`` for small `n` relative to
+        the size of the ``Series`` object.
+
+        Examples
+        --------
+        >>> countries_population = {"Italy": 59000000, "France": 65000000,
+        ...                         "Brunei": 434000, "Malta": 434000,
+        ...                         "Maldives": 434000, "Iceland": 337000,
+        ...                         "Nauru": 11300, "Tuvalu": 11300,
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
+        >>> s = pd.Series(countries_population)
+        >>> s
+        Italy       59000000
+        France      65000000
+        Brunei        434000
+        Malta         434000
+        Maldives      434000
+        Iceland       337000
+        Nauru          11300
+        Tuvalu         11300
+        Anguilla       11300
+        Montserrat      5200
+        dtype: int64
+
+        The `n` smallest elements where ``n=5`` by default.
+
+        >>> s.nsmallest()
+        Montserrat    5200
+        Nauru        11300
+        Tuvalu       11300
+        Anguilla     11300
+        Iceland     337000
+        dtype: int64
+
+        The `n` smallest elements where ``n=3``. Default `keep` value is
+        'first' so Nauru and Tuvalu will be kept.
+
+        >>> s.nsmallest(3)
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` and keeping the last
+        duplicates. Anguilla and Tuvalu will be kept since they are the last
+        with value 11300 based on the index order.
+
+        >>> s.nsmallest(3, keep='last')
+        Montserrat   5200
+        Anguilla    11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` with all duplicates kept. Note
+        that the returned Series has four elements due to the three duplicates.
+
+        >>> s.nsmallest(3, keep='all')
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        Anguilla    11300
+        dtype: int64
+        """
+        return selectn.SelectNSeries(self, n=n, keep=keep).nsmallest()
+
+    @doc(
+        klass=_shared_doc_kwargs["klass"],
+        extra_params=dedent(
+            """copy : bool, default True
+            Whether to copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``"""
+        ),
+        examples=dedent(
+            """\
+        Examples
+        --------
+        >>> s = pd.Series(
+        ...     ["A", "B", "A", "C"],
+        ...     index=[
+        ...         ["Final exam", "Final exam", "Coursework", "Coursework"],
+        ...         ["History", "Geography", "History", "Geography"],
+        ...         ["January", "February", "March", "April"],
+        ...     ],
+        ... )
+        >>> s
+        Final exam  History     January      A
+                    Geography   February     B
+        Coursework  History     March        A
+                    Geography   April        C
+        dtype: object
+
+        In the following example, we will swap the levels of the indices.
+        Here, we will swap the levels column-wise, but levels can be swapped row-wise
+        in a similar manner. Note that column-wise is the default behaviour.
+        By not supplying any arguments for i and j, we swap the last and second to
+        last indices.
+
+        >>> s.swaplevel()
+        Final exam  January     History         A
+                    February    Geography       B
+        Coursework  March       History         A
+                    April       Geography       C
+        dtype: object
+
+        By supplying one argument, we can choose which index to swap the last
+        index with. We can for example swap the first index with the last one as
+        follows.
+
+        >>> s.swaplevel(0)
+        January     History     Final exam      A
+        February    Geography   Final exam      B
+        March       History     Coursework      A
+        April       Geography   Coursework      C
+        dtype: object
+
+        We can also define explicitly which indices we want to swap by supplying values
+        for both i and j. Here, we for example swap the first and second indices.
+
+        >>> s.swaplevel(0, 1)
+        History     Final exam  January         A
+        Geography   Final exam  February        B
+        History     Coursework  March           A
+        Geography   Coursework  April           C
+        dtype: object"""
+        ),
+    )
+    def swaplevel(
+        self, i: Level = -2, j: Level = -1, copy: bool | None = None
+    ) -> Series:
+        """
+        Swap levels i and j in a :class:`MultiIndex`.
+
+        Default is to swap the two innermost levels of the index.
+
+        Parameters
+        ----------
+        i, j : int or str
+            Levels of the indices to be swapped. Can pass level name as string.
+        {extra_params}
+
+        Returns
+        -------
+        {klass}
+            {klass} with levels swapped in MultiIndex.
+
+        {examples}
+        """
+        assert isinstance(self.index, MultiIndex)
+        result = self.copy(deep=copy and not using_copy_on_write())
+        result.index = self.index.swaplevel(i, j)
+        return result
+
+    def reorder_levels(self, order: Sequence[Level]) -> Series:
+        """
+        Rearrange index levels using input order.
+
+        May not drop or duplicate levels.
+
+        Parameters
+        ----------
+        order : list of int representing new level order
+            Reference level by number or key.
+
+        Returns
+        -------
+        type of caller (new object)
+
+        Examples
+        --------
+        >>> arrays = [np.array(["dog", "dog", "cat", "cat", "bird", "bird"]),
+        ...           np.array(["white", "black", "white", "black", "white", "black"])]
+        >>> s = pd.Series([1, 2, 3, 3, 5, 2], index=arrays)
+        >>> s
+        dog   white    1
+              black    2
+        cat   white    3
+              black    3
+        bird  white    5
+              black    2
+        dtype: int64
+        >>> s.reorder_levels([1, 0])
+        white  dog     1
+        black  dog     2
+        white  cat     3
+        black  cat     3
+        white  bird    5
+        black  bird    2
+        dtype: int64
+        """
+        if not isinstance(self.index, MultiIndex):  # pragma: no cover
+            raise Exception("Can only reorder levels on a hierarchical axis.")
+
+        result = self.copy(deep=None)
+        assert isinstance(result.index, MultiIndex)
+        result.index = result.index.reorder_levels(order)
+        return result
+
+    def explode(self, ignore_index: bool = False) -> Series:
+        """
+        Transform each element of a list-like to a row.
+
+        Parameters
+        ----------
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        Series
+            Exploded lists to rows; index will be duplicated for these rows.
+
+        See Also
+        --------
+        Series.str.split : Split string values on specified separator.
+        Series.unstack : Unstack, a.k.a. pivot, Series with MultiIndex
+            to produce DataFrame.
+        DataFrame.melt : Unpivot a DataFrame from wide format to long format.
+        DataFrame.explode : Explode a DataFrame from list-like
+            columns to long format.
+
+        Notes
+        -----
+        This routine will explode list-likes including lists, tuples, sets,
+        Series, and np.ndarray. The result dtype of the subset rows will
+        be object. Scalars will be returned unchanged, and empty list-likes will
+        result in a np.nan for that row. In addition, the ordering of elements in
+        the output will be non-deterministic when exploding sets.
+
+        Reference :ref:`the user guide <reshaping.explode>` for more examples.
+
+        Examples
+        --------
+        >>> s = pd.Series([[1, 2, 3], 'foo', [], [3, 4]])
+        >>> s
+        0    [1, 2, 3]
+        1          foo
+        2           []
+        3       [3, 4]
+        dtype: object
+
+        >>> s.explode()
+        0      1
+        0      2
+        0      3
+        1    foo
+        2    NaN
+        3      3
+        3      4
+        dtype: object
+        """
+        if isinstance(self.dtype, ExtensionDtype):
+            values, counts = self._values._explode()
+        elif len(self) and is_object_dtype(self.dtype):
+            values, counts = reshape.explode(np.asarray(self._values))
+        else:
+            result = self.copy()
+            return result.reset_index(drop=True) if ignore_index else result
+
+        if ignore_index:
+            index: Index = default_index(len(values))
+        else:
+            index = self.index.repeat(counts)
+
+        return self._constructor(values, index=index, name=self.name, copy=False)
+
+    def unstack(
+        self,
+        level: IndexLabel = -1,
+        fill_value: Hashable | None = None,
+        sort: bool = True,
+    ) -> DataFrame:
+        """
+        Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
+
+        Parameters
+        ----------
+        level : int, str, or list of these, default last level
+            Level(s) to unstack, can pass level name.
+        fill_value : scalar value, default None
+            Value to use when replacing NaN values.
+        sort : bool, default True
+            Sort the level(s) in the resulting MultiIndex columns.
+
+        Returns
+        -------
+        DataFrame
+            Unstacked Series.
+
+        Notes
+        -----
+        Reference :ref:`the user guide <reshaping.stacking>` for more examples.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3, 4],
+        ...               index=pd.MultiIndex.from_product([['one', 'two'],
+        ...                                                 ['a', 'b']]))
+        >>> s
+        one  a    1
+             b    2
+        two  a    3
+             b    4
+        dtype: int64
+
+        >>> s.unstack(level=-1)
+             a  b
+        one  1  2
+        two  3  4
+
+        >>> s.unstack(level=0)
+           one  two
+        a    1    3
+        b    2    4
+        """
+        from pandas.core.reshape.reshape import unstack
+
+        return unstack(self, level, fill_value, sort)
+
+    # ----------------------------------------------------------------------
+    # function application
+
+    def map(
+        self,
+        arg: Callable | Mapping | Series,
+        na_action: Literal["ignore"] | None = None,
+    ) -> Series:
+        """
+        Map values of Series according to an input mapping or function.
+
+        Used for substituting each value in a Series with another value,
+        that may be derived from a function, a ``dict`` or
+        a :class:`Series`.
+
+        Parameters
+        ----------
+        arg : function, collections.abc.Mapping subclass or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        Series
+            Same index as caller.
+
+        See Also
+        --------
+        Series.apply : For applying more complex functions on a Series.
+        Series.replace: Replace values given in `to_replace` with `value`.
+        DataFrame.apply : Apply a function row-/column-wise.
+        DataFrame.map : Apply a function elementwise on a whole DataFrame.
+
+        Notes
+        -----
+        When ``arg`` is a dictionary, values in Series that are not in the
+        dictionary (as keys) are converted to ``NaN``. However, if the
+        dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e.
+        provides a method for default values), then this default is used
+        rather than ``NaN``.
+
+        Examples
+        --------
+        >>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
+        >>> s
+        0      cat
+        1      dog
+        2      NaN
+        3   rabbit
+        dtype: object
+
+        ``map`` accepts a ``dict`` or a ``Series``. Values that are not found
+        in the ``dict`` are converted to ``NaN``, unless the dict has a default
+        value (e.g. ``defaultdict``):
+
+        >>> s.map({'cat': 'kitten', 'dog': 'puppy'})
+        0   kitten
+        1    puppy
+        2      NaN
+        3      NaN
+        dtype: object
+
+        It also accepts a function:
+
+        >>> s.map('I am a {}'.format)
+        0       I am a cat
+        1       I am a dog
+        2       I am a nan
+        3    I am a rabbit
+        dtype: object
+
+        To avoid applying the function to missing values (and keep them as
+        ``NaN``) ``na_action='ignore'`` can be used:
+
+        >>> s.map('I am a {}'.format, na_action='ignore')
+        0     I am a cat
+        1     I am a dog
+        2            NaN
+        3  I am a rabbit
+        dtype: object
+        """
+        new_values = self._map_values(arg, na_action=na_action)
+        return self._constructor(new_values, index=self.index, copy=False).__finalize__(
+            self, method="map"
+        )
+
+    def _gotitem(self, key, ndim, subset=None) -> Self:
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            Requested ndim of result.
+        subset : object, default None
+            Subset to act on.
+        """
+        return self
+
+    _agg_see_also_doc = dedent(
+        """
+    See Also
+    --------
+    Series.apply : Invoke function on a Series.
+    Series.transform : Transform function producing a Series with like indexes.
+    """
+    )
+
+    _agg_examples_doc = dedent(
+        """
+    Examples
+    --------
+    >>> s = pd.Series([1, 2, 3, 4])
+    >>> s
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+
+    >>> s.agg('min')
+    1
+
+    >>> s.agg(['min', 'max'])
+    min   1
+    max   4
+    dtype: int64
+    """
+    )
+
+    @doc(
+        _shared_docs["aggregate"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+        see_also=_agg_see_also_doc,
+        examples=_agg_examples_doc,
+    )
+    def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
+        # Validate the axis parameter
+        self._get_axis_number(axis)
+
+        # if func is None, will switch to user-provided "named aggregation" kwargs
+        if func is None:
+            func = dict(kwargs.items())
+
+        op = SeriesApply(self, func, args=args, kwargs=kwargs)
+        result = op.agg()
+        return result
+
+    agg = aggregate
+
+    @doc(
+        _shared_docs["transform"],
+        klass=_shared_doc_kwargs["klass"],
+        axis=_shared_doc_kwargs["axis"],
+    )
+    def transform(
+        self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
+    ) -> DataFrame | Series:
+        # Validate axis argument
+        self._get_axis_number(axis)
+        ser = (
+            self.copy(deep=False)
+            if using_copy_on_write() or warn_copy_on_write()
+            else self
+        )
+        result = SeriesApply(ser, func=func, args=args, kwargs=kwargs).transform()
+        return result
+
+    def apply(
+        self,
+        func: AggFuncType,
+        convert_dtype: bool | lib.NoDefault = lib.no_default,
+        args: tuple[Any, ...] = (),
+        *,
+        by_row: Literal[False, "compat"] = "compat",
+        **kwargs,
+    ) -> DataFrame | Series:
+        """
+        Invoke function on values of Series.
+
+        Can be ufunc (a NumPy function that applies to the entire Series)
+        or a Python function that only works on single values.
+
+        Parameters
+        ----------
+        func : function
+            Python function or NumPy ufunc to apply.
+        convert_dtype : bool, default True
+            Try to find better dtype for elementwise function results. If
+            False, leave as dtype=object. Note that the dtype is always
+            preserved for some extension array dtypes, such as Categorical.
+
+            .. deprecated:: 2.1.0
+                ``convert_dtype`` has been deprecated. Do ``ser.astype(object).apply()``
+                instead if you want ``convert_dtype=False``.
+        args : tuple
+            Positional arguments passed to func after the series value.
+        by_row : False or "compat", default "compat"
+            If ``"compat"`` and func is a callable, func will be passed each element of
+            the Series, like ``Series.map``. If func is a list or dict of
+            callables, will first try to translate each func into pandas methods. If
+            that doesn't work, will try call to apply again with ``by_row="compat"``
+            and if that fails, will call apply again with ``by_row=False``
+            (backward compatible).
+            If False, the func will be passed the whole Series at once.
+
+            ``by_row`` has no effect when ``func`` is a string.
+
+            .. versionadded:: 2.1.0
+        **kwargs
+            Additional keyword arguments passed to func.
+
+        Returns
+        -------
+        Series or DataFrame
+            If func returns a Series object the result will be a DataFrame.
+
+        See Also
+        --------
+        Series.map: For element-wise operations.
+        Series.agg: Only perform aggregating type operations.
+        Series.transform: Only perform transforming type operations.
+
+        Notes
+        -----
+        Functions that mutate the passed object can produce unexpected
+        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+        for more details.
+
+        Examples
+        --------
+        Create a series with typical summer temperatures for each city.
+
+        >>> s = pd.Series([20, 21, 12],
+        ...               index=['London', 'New York', 'Helsinki'])
+        >>> s
+        London      20
+        New York    21
+        Helsinki    12
+        dtype: int64
+
+        Square the values by defining a function and passing it as an
+        argument to ``apply()``.
+
+        >>> def square(x):
+        ...     return x ** 2
+        >>> s.apply(square)
+        London      400
+        New York    441
+        Helsinki    144
+        dtype: int64
+
+        Square the values by passing an anonymous function as an
+        argument to ``apply()``.
+
+        >>> s.apply(lambda x: x ** 2)
+        London      400
+        New York    441
+        Helsinki    144
+        dtype: int64
+
+        Define a custom function that needs additional positional
+        arguments and pass these additional arguments using the
+        ``args`` keyword.
+
+        >>> def subtract_custom_value(x, custom_value):
+        ...     return x - custom_value
+
+        >>> s.apply(subtract_custom_value, args=(5,))
+        London      15
+        New York    16
+        Helsinki     7
+        dtype: int64
+
+        Define a custom function that takes keyword arguments
+        and pass these arguments to ``apply``.
+
+        >>> def add_custom_values(x, **kwargs):
+        ...     for month in kwargs:
+        ...         x += kwargs[month]
+        ...     return x
+
+        >>> s.apply(add_custom_values, june=30, july=20, august=25)
+        London      95
+        New York    96
+        Helsinki    87
+        dtype: int64
+
+        Use a function from the Numpy library.
+
+        >>> s.apply(np.log)
+        London      2.995732
+        New York    3.044522
+        Helsinki    2.484907
+        dtype: float64
+        """
+        return SeriesApply(
+            self,
+            func,
+            convert_dtype=convert_dtype,
+            by_row=by_row,
+            args=args,
+            kwargs=kwargs,
+        ).apply()
+
+    def _reindex_indexer(
+        self,
+        new_index: Index | None,
+        indexer: npt.NDArray[np.intp] | None,
+        copy: bool | None,
+    ) -> Series:
+        # Note: new_index is None iff indexer is None
+        # if not None, indexer is np.intp
+        if indexer is None and (
+            new_index is None or new_index.names == self.index.names
+        ):
+            if using_copy_on_write():
+                return self.copy(deep=copy)
+            if copy or copy is None:
+                return self.copy(deep=copy)
+            return self
+
+        new_values = algorithms.take_nd(
+            self._values, indexer, allow_fill=True, fill_value=None
+        )
+        return self._constructor(new_values, index=new_index, copy=False)
+
+    def _needs_reindex_multi(self, axes, method, level) -> bool:
+        """
+        Check if we do need a multi reindex; this is for compat with
+        higher dims.
+        """
+        return False
+
+    @overload
+    def rename(
+        self,
+        index: Renamer | Hashable | None = ...,
+        *,
+        axis: Axis | None = ...,
+        copy: bool = ...,
+        inplace: Literal[True],
+        level: Level | None = ...,
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def rename(
+        self,
+        index: Renamer | Hashable | None = ...,
+        *,
+        axis: Axis | None = ...,
+        copy: bool = ...,
+        inplace: Literal[False] = ...,
+        level: Level | None = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def rename(
+        self,
+        index: Renamer | Hashable | None = ...,
+        *,
+        axis: Axis | None = ...,
+        copy: bool = ...,
+        inplace: bool = ...,
+        level: Level | None = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series | None:
+        ...
+
+    def rename(
+        self,
+        index: Renamer | Hashable | None = None,
+        *,
+        axis: Axis | None = None,
+        copy: bool | None = None,
+        inplace: bool = False,
+        level: Level | None = None,
+        errors: IgnoreRaise = "ignore",
+    ) -> Series | None:
+        """
+        Alter Series index labels or name.
+
+        Function / dict values must be unique (1-to-1). Labels not contained in
+        a dict / Series will be left as-is. Extra labels listed don't throw an
+        error.
+
+        Alternatively, change ``Series.name`` with a scalar value.
+
+        See the :ref:`user guide <basics.rename>` for more.
+
+        Parameters
+        ----------
+        index : scalar, hashable sequence, dict-like or function optional
+            Functions or dict-like are transformations to apply to
+            the index.
+            Scalar or hashable sequence-like will alter the ``Series.name``
+            attribute.
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        copy : bool, default True
+            Also copy underlying data.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+        inplace : bool, default False
+            Whether to return a new Series. If True the value of copy is ignored.
+        level : int or level name, default None
+            In case of MultiIndex, only rename labels in the specified level.
+        errors : {'ignore', 'raise'}, default 'ignore'
+            If 'raise', raise `KeyError` when a `dict-like mapper` or
+            `index` contains labels that are not present in the index being transformed.
+            If 'ignore', existing keys will be renamed and extra keys will be ignored.
+
+        Returns
+        -------
+        Series or None
+            Series with index labels or name altered or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.rename : Corresponding DataFrame method.
+        Series.rename_axis : Set the name of the axis.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.rename("my_name")  # scalar, changes Series.name
+        0    1
+        1    2
+        2    3
+        Name: my_name, dtype: int64
+        >>> s.rename(lambda x: x ** 2)  # function, changes labels
+        0    1
+        1    2
+        4    3
+        dtype: int64
+        >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
+        0    1
+        3    2
+        5    3
+        dtype: int64
+        """
+        if axis is not None:
+            # Make sure we raise if an invalid 'axis' is passed.
+            axis = self._get_axis_number(axis)
+
+        if callable(index) or is_dict_like(index):
+            # error: Argument 1 to "_rename" of "NDFrame" has incompatible
+            # type "Union[Union[Mapping[Any, Hashable], Callable[[Any],
+            # Hashable]], Hashable, None]"; expected "Union[Mapping[Any,
+            # Hashable], Callable[[Any], Hashable], None]"
+            return super()._rename(
+                index,  # type: ignore[arg-type]
+                copy=copy,
+                inplace=inplace,
+                level=level,
+                errors=errors,
+            )
+        else:
+            return self._set_name(index, inplace=inplace, deep=copy)
+
+    @Appender(
+        """
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+
+        >>> s.set_axis(['a', 'b', 'c'], axis=0)
+        a    1
+        b    2
+        c    3
+        dtype: int64
+    """
+    )
+    @Substitution(
+        klass=_shared_doc_kwargs["klass"],
+        axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
+        extended_summary_sub="",
+        axis_description_sub="",
+        see_also_sub="",
+    )
+    @Appender(NDFrame.set_axis.__doc__)
+    def set_axis(
+        self,
+        labels,
+        *,
+        axis: Axis = 0,
+        copy: bool | None = None,
+    ) -> Series:
+        return super().set_axis(labels, axis=axis, copy=copy)
+
+    # error: Cannot determine type of 'reindex'
+    @doc(
+        NDFrame.reindex,  # type: ignore[has-type]
+        klass=_shared_doc_kwargs["klass"],
+        optional_reindex=_shared_doc_kwargs["optional_reindex"],
+    )
+    def reindex(  # type: ignore[override]
+        self,
+        index=None,
+        *,
+        axis: Axis | None = None,
+        method: ReindexMethod | None = None,
+        copy: bool | None = None,
+        level: Level | None = None,
+        fill_value: Scalar | None = None,
+        limit: int | None = None,
+        tolerance=None,
+    ) -> Series:
+        return super().reindex(
+            index=index,
+            method=method,
+            copy=copy,
+            level=level,
+            fill_value=fill_value,
+            limit=limit,
+            tolerance=tolerance,
+        )
+
+    @overload  # type: ignore[override]
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        axis: Axis = ...,
+        copy: bool = ...,
+        inplace: Literal[True],
+    ) -> None:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        axis: Axis = ...,
+        copy: bool = ...,
+        inplace: Literal[False] = ...,
+    ) -> Self:
+        ...
+
+    @overload
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = ...,
+        *,
+        index=...,
+        axis: Axis = ...,
+        copy: bool = ...,
+        inplace: bool = ...,
+    ) -> Self | None:
+        ...
+
+    @doc(NDFrame.rename_axis)
+    def rename_axis(
+        self,
+        mapper: IndexLabel | lib.NoDefault = lib.no_default,
+        *,
+        index=lib.no_default,
+        axis: Axis = 0,
+        copy: bool = True,
+        inplace: bool = False,
+    ) -> Self | None:
+        return super().rename_axis(
+            mapper=mapper,
+            index=index,
+            axis=axis,
+            copy=copy,
+            inplace=inplace,
+        )
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[True],
+        errors: IgnoreRaise = ...,
+    ) -> None:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: Literal[False] = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def drop(
+        self,
+        labels: IndexLabel = ...,
+        *,
+        axis: Axis = ...,
+        index: IndexLabel = ...,
+        columns: IndexLabel = ...,
+        level: Level | None = ...,
+        inplace: bool = ...,
+        errors: IgnoreRaise = ...,
+    ) -> Series | None:
+        ...
+
+    def drop(
+        self,
+        labels: IndexLabel | None = None,
+        *,
+        axis: Axis = 0,
+        index: IndexLabel | None = None,
+        columns: IndexLabel | None = None,
+        level: Level | None = None,
+        inplace: bool = False,
+        errors: IgnoreRaise = "raise",
+    ) -> Series | None:
+        """
+        Return Series with specified index labels removed.
+
+        Remove elements of a Series based on specifying the index labels.
+        When using a multi-index, labels on different levels can be removed
+        by specifying the level.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+            Index labels to drop.
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        index : single label or list-like
+            Redundant for application on Series, but 'index' can be used instead
+            of 'labels'.
+        columns : single label or list-like
+            No change is made to the Series; use 'index' or 'labels' instead.
+        level : int or level name, optional
+            For MultiIndex, level for which the labels will be removed.
+        inplace : bool, default False
+            If True, do operation inplace and return None.
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and only existing labels are dropped.
+
+        Returns
+        -------
+        Series or None
+            Series with specified index labels removed or None if ``inplace=True``.
+
+        Raises
+        ------
+        KeyError
+            If none of the labels are found in the index.
+
+        See Also
+        --------
+        Series.reindex : Return only specified index labels of Series.
+        Series.dropna : Return series without null values.
+        Series.drop_duplicates : Return Series with duplicate values removed.
+        DataFrame.drop : Drop specified labels from rows or columns.
+
+        Examples
+        --------
+        >>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C'])
+        >>> s
+        A  0
+        B  1
+        C  2
+        dtype: int64
+
+        Drop labels B en C
+
+        >>> s.drop(labels=['B', 'C'])
+        A  0
+        dtype: int64
+
+        Drop 2nd level label in MultiIndex Series
+
+        >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
+        ...                              ['speed', 'weight', 'length']],
+        ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
+        ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
+        >>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
+        ...               index=midx)
+        >>> s
+        llama   speed      45.0
+                weight    200.0
+                length      1.2
+        cow     speed      30.0
+                weight    250.0
+                length      1.5
+        falcon  speed     320.0
+                weight      1.0
+                length      0.3
+        dtype: float64
+
+        >>> s.drop(labels='weight', level=1)
+        llama   speed      45.0
+                length      1.2
+        cow     speed      30.0
+                length      1.5
+        falcon  speed     320.0
+                length      0.3
+        dtype: float64
+        """
+        return super().drop(
+            labels=labels,
+            axis=axis,
+            index=index,
+            columns=columns,
+            level=level,
+            inplace=inplace,
+            errors=errors,
+        )
+
+    def pop(self, item: Hashable) -> Any:
+        """
+        Return item and drops from series. Raise KeyError if not found.
+
+        Parameters
+        ----------
+        item : label
+            Index of the element that needs to be removed.
+
+        Returns
+        -------
+        Value that is popped from series.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1, 2, 3])
+
+        >>> ser.pop(0)
+        1
+
+        >>> ser
+        1    2
+        2    3
+        dtype: int64
+        """
+        return super().pop(item=item)
+
+    @doc(INFO_DOCSTRING, **series_sub_kwargs)
+    def info(
+        self,
+        verbose: bool | None = None,
+        buf: IO[str] | None = None,
+        max_cols: int | None = None,
+        memory_usage: bool | str | None = None,
+        show_counts: bool = True,
+    ) -> None:
+        return SeriesInfo(self, memory_usage).render(
+            buf=buf,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+
+    # TODO(3.0): this can be removed once GH#33302 deprecation is enforced
+    def _replace_single(self, to_replace, method: str, inplace: bool, limit):
+        """
+        Replaces values in a Series using the fill method specified when no
+        replacement value is given in the replace method
+        """
+
+        result = self if inplace else self.copy()
+
+        values = result._values
+        mask = missing.mask_missing(values, to_replace)
+
+        if isinstance(values, ExtensionArray):
+            # dispatch to the EA's _pad_mask_inplace method
+            values._fill_mask_inplace(method, limit, mask)
+        else:
+            fill_f = missing.get_fill_func(method)
+            fill_f(values, limit=limit, mask=mask)
+
+        if inplace:
+            return
+        return result
+
+    def memory_usage(self, index: bool = True, deep: bool = False) -> int:
+        """
+        Return the memory usage of the Series.
+
+        The memory usage can optionally include the contribution of
+        the index and of elements of `object` dtype.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Specifies whether to include the memory usage of the Series index.
+        deep : bool, default False
+            If True, introspect the data deeply by interrogating
+            `object` dtypes for system-level memory consumption, and include
+            it in the returned value.
+
+        Returns
+        -------
+        int
+            Bytes of memory consumed.
+
+        See Also
+        --------
+        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
+            array.
+        DataFrame.memory_usage : Bytes consumed by a DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series(range(3))
+        >>> s.memory_usage()
+        152
+
+        Not including the index gives the size of the rest of the data, which
+        is necessarily smaller:
+
+        >>> s.memory_usage(index=False)
+        24
+
+        The memory footprint of `object` values is ignored by default:
+
+        >>> s = pd.Series(["a", "b"])
+        >>> s.values
+        array(['a', 'b'], dtype=object)
+        >>> s.memory_usage()
+        144
+        >>> s.memory_usage(deep=True)
+        244
+        """
+        v = self._memory_usage(deep=deep)
+        if index:
+            v += self.index.memory_usage(deep=deep)
+        return v
+
+    def isin(self, values) -> Series:
+        """
+        Whether elements in Series are contained in `values`.
+
+        Return a boolean Series showing whether each element in the Series
+        matches an element in the passed sequence of `values` exactly.
+
+        Parameters
+        ----------
+        values : set or list-like
+            The sequence of values to test. Passing in a single string will
+            raise a ``TypeError``. Instead, turn a single string into a
+            list of one element.
+
+        Returns
+        -------
+        Series
+            Series of booleans indicating if each element is in values.
+
+        Raises
+        ------
+        TypeError
+          * If `values` is a string
+
+        See Also
+        --------
+        DataFrame.isin : Equivalent method on DataFrame.
+
+        Examples
+        --------
+        >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama',
+        ...                'hippo'], name='animal')
+        >>> s.isin(['cow', 'llama'])
+        0     True
+        1     True
+        2     True
+        3    False
+        4     True
+        5    False
+        Name: animal, dtype: bool
+
+        To invert the boolean values, use the ``~`` operator:
+
+        >>> ~s.isin(['cow', 'llama'])
+        0    False
+        1    False
+        2    False
+        3     True
+        4    False
+        5     True
+        Name: animal, dtype: bool
+
+        Passing a single string as ``s.isin('llama')`` will raise an error. Use
+        a list of one element instead:
+
+        >>> s.isin(['llama'])
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        5    False
+        Name: animal, dtype: bool
+
+        Strings and integers are distinct and are therefore not comparable:
+
+        >>> pd.Series([1]).isin(['1'])
+        0    False
+        dtype: bool
+        >>> pd.Series([1.1]).isin(['1.1'])
+        0    False
+        dtype: bool
+        """
+        result = algorithms.isin(self._values, values)
+        return self._constructor(result, index=self.index, copy=False).__finalize__(
+            self, method="isin"
+        )
+
+    def between(
+        self,
+        left,
+        right,
+        inclusive: Literal["both", "neither", "left", "right"] = "both",
+    ) -> Series:
+        """
+        Return boolean Series equivalent to left <= series <= right.
+
+        This function returns a boolean vector containing `True` wherever the
+        corresponding Series element is between the boundary values `left` and
+        `right`. NA values are treated as `False`.
+
+        Parameters
+        ----------
+        left : scalar or list-like
+            Left boundary.
+        right : scalar or list-like
+            Right boundary.
+        inclusive : {"both", "neither", "left", "right"}
+            Include boundaries. Whether to set each bound as closed or open.
+
+            .. versionchanged:: 1.3.0
+
+        Returns
+        -------
+        Series
+            Series representing whether each element is between left and
+            right (inclusive).
+
+        See Also
+        --------
+        Series.gt : Greater than of series and other.
+        Series.lt : Less than of series and other.
+
+        Notes
+        -----
+        This function is equivalent to ``(left <= ser) & (ser <= right)``
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 0, 4, 8, np.nan])
+
+        Boundary values are included by default:
+
+        >>> s.between(1, 4)
+        0     True
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        With `inclusive` set to ``"neither"`` boundary values are excluded:
+
+        >>> s.between(1, 4, inclusive="neither")
+        0     True
+        1    False
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        `left` and `right` can be any scalar value:
+
+        >>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
+        >>> s.between('Anna', 'Daniel')
+        0    False
+        1     True
+        2     True
+        3    False
+        dtype: bool
+        """
+        if inclusive == "both":
+            lmask = self >= left
+            rmask = self <= right
+        elif inclusive == "left":
+            lmask = self >= left
+            rmask = self < right
+        elif inclusive == "right":
+            lmask = self > left
+            rmask = self <= right
+        elif inclusive == "neither":
+            lmask = self > left
+            rmask = self < right
+        else:
+            raise ValueError(
+                "Inclusive has to be either string of 'both',"
+                "'left', 'right', or 'neither'."
+            )
+
+        return lmask & rmask
+
+    def case_when(
+        self,
+        caselist: list[
+            tuple[
+                ArrayLike | Callable[[Series], Series | np.ndarray | Sequence[bool]],
+                ArrayLike | Scalar | Callable[[Series], Series | np.ndarray],
+            ],
+        ],
+    ) -> Series:
+        """
+        Replace values where the conditions are True.
+
+        Parameters
+        ----------
+        caselist : A list of tuples of conditions and expected replacements
+            Takes the form:  ``(condition0, replacement0)``,
+            ``(condition1, replacement1)``, ... .
+            ``condition`` should be a 1-D boolean array-like object
+            or a callable. If ``condition`` is a callable,
+            it is computed on the Series
+            and should return a boolean Series or array.
+            The callable must not change the input Series
+            (though pandas doesn`t check it). ``replacement`` should be a
+            1-D array-like object, a scalar or a callable.
+            If ``replacement`` is a callable, it is computed on the Series
+            and should return a scalar or Series. The callable
+            must not change the input Series
+            (though pandas doesn`t check it).
+
+            .. versionadded:: 2.2.0
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.mask : Replace values where the condition is True.
+
+        Examples
+        --------
+        >>> c = pd.Series([6, 7, 8, 9], name='c')
+        >>> a = pd.Series([0, 0, 1, 2])
+        >>> b = pd.Series([0, 3, 4, 5])
+
+        >>> c.case_when(caselist=[(a.gt(0), a),  # condition, replacement
+        ...                       (b.gt(0), b)])
+        0    6
+        1    3
+        2    1
+        3    2
+        Name: c, dtype: int64
+        """
+        if not isinstance(caselist, list):
+            raise TypeError(
+                f"The caselist argument should be a list; instead got {type(caselist)}"
+            )
+
+        if not caselist:
+            raise ValueError(
+                "provide at least one boolean condition, "
+                "with a corresponding replacement."
+            )
+
+        for num, entry in enumerate(caselist):
+            if not isinstance(entry, tuple):
+                raise TypeError(
+                    f"Argument {num} must be a tuple; instead got {type(entry)}."
+                )
+            if len(entry) != 2:
+                raise ValueError(
+                    f"Argument {num} must have length 2; "
+                    "a condition and replacement; "
+                    f"instead got length {len(entry)}."
+                )
+        caselist = [
+            (
+                com.apply_if_callable(condition, self),
+                com.apply_if_callable(replacement, self),
+            )
+            for condition, replacement in caselist
+        ]
+        default = self.copy()
+        conditions, replacements = zip(*caselist)
+        common_dtypes = [infer_dtype_from(arg)[0] for arg in [*replacements, default]]
+        if len(set(common_dtypes)) > 1:
+            common_dtype = find_common_type(common_dtypes)
+            updated_replacements = []
+            for condition, replacement in zip(conditions, replacements):
+                if is_scalar(replacement):
+                    replacement = construct_1d_arraylike_from_scalar(
+                        value=replacement, length=len(condition), dtype=common_dtype
+                    )
+                elif isinstance(replacement, ABCSeries):
+                    replacement = replacement.astype(common_dtype)
+                else:
+                    replacement = pd_array(replacement, dtype=common_dtype)
+                updated_replacements.append(replacement)
+            replacements = updated_replacements
+            default = default.astype(common_dtype)
+
+        counter = reversed(range(len(conditions)))
+        for position, condition, replacement in zip(
+            counter, conditions[::-1], replacements[::-1]
+        ):
+            try:
+                default = default.mask(
+                    condition, other=replacement, axis=0, inplace=False, level=None
+                )
+            except Exception as error:
+                raise ValueError(
+                    f"Failed to apply condition{position} and replacement{position}."
+                ) from error
+        return default
+
+    # error: Cannot determine type of 'isna'
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def isna(self) -> Series:
+        return NDFrame.isna(self)
+
+    # error: Cannot determine type of 'isna'
+    @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def isnull(self) -> Series:
+        """
+        Series.isnull is an alias for Series.isna.
+        """
+        return super().isnull()
+
+    # error: Cannot determine type of 'notna'
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def notna(self) -> Series:
+        return super().notna()
+
+    # error: Cannot determine type of 'notna'
+    @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
+    def notnull(self) -> Series:
+        """
+        Series.notnull is an alias for Series.notna.
+        """
+        return super().notnull()
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        inplace: Literal[False] = ...,
+        how: AnyAll | None = ...,
+        ignore_index: bool = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def dropna(
+        self,
+        *,
+        axis: Axis = ...,
+        inplace: Literal[True],
+        how: AnyAll | None = ...,
+        ignore_index: bool = ...,
+    ) -> None:
+        ...
+
+    def dropna(
+        self,
+        *,
+        axis: Axis = 0,
+        inplace: bool = False,
+        how: AnyAll | None = None,
+        ignore_index: bool = False,
+    ) -> Series | None:
+        """
+        Return a new Series with missing values removed.
+
+        See the :ref:`User Guide <missing_data>` for more on which values are
+        considered missing, and how to work with missing data.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        inplace : bool, default False
+            If True, do operation inplace and return None.
+        how : str, optional
+            Not in use. Kept for compatibility.
+        ignore_index : bool, default ``False``
+            If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
+
+            .. versionadded:: 2.0.0
+
+        Returns
+        -------
+        Series or None
+            Series with NA entries dropped from it or None if ``inplace=True``.
+
+        See Also
+        --------
+        Series.isna: Indicate missing values.
+        Series.notna : Indicate existing (non-missing) values.
+        Series.fillna : Replace missing values.
+        DataFrame.dropna : Drop rows or columns which contain NA values.
+        Index.dropna : Drop missing indices.
+
+        Examples
+        --------
+        >>> ser = pd.Series([1., 2., np.nan])
+        >>> ser
+        0    1.0
+        1    2.0
+        2    NaN
+        dtype: float64
+
+        Drop NA values from a Series.
+
+        >>> ser.dropna()
+        0    1.0
+        1    2.0
+        dtype: float64
+
+        Empty strings are not considered NA values. ``None`` is considered an
+        NA value.
+
+        >>> ser = pd.Series([np.nan, 2, pd.NaT, '', None, 'I stay'])
+        >>> ser
+        0       NaN
+        1         2
+        2       NaT
+        3
+        4      None
+        5    I stay
+        dtype: object
+        >>> ser.dropna()
+        1         2
+        3
+        5    I stay
+        dtype: object
+        """
+        inplace = validate_bool_kwarg(inplace, "inplace")
+        ignore_index = validate_bool_kwarg(ignore_index, "ignore_index")
+        # Validate the axis parameter
+        self._get_axis_number(axis or 0)
+
+        if self._can_hold_na:
+            result = remove_na_arraylike(self)
+        else:
+            if not inplace:
+                result = self.copy(deep=None)
+            else:
+                result = self
+
+        if ignore_index:
+            result.index = default_index(len(result))
+
+        if inplace:
+            return self._update_inplace(result)
+        else:
+            return result
+
+    # ----------------------------------------------------------------------
+    # Time series-oriented methods
+
+    def to_timestamp(
+        self,
+        freq: Frequency | None = None,
+        how: Literal["s", "e", "start", "end"] = "start",
+        copy: bool | None = None,
+    ) -> Series:
+        """
+        Cast to DatetimeIndex of Timestamps, at *beginning* of period.
+
+        Parameters
+        ----------
+        freq : str, default frequency of PeriodIndex
+            Desired frequency.
+        how : {'s', 'e', 'start', 'end'}
+            Convention for converting period to timestamp; start of period
+            vs. end.
+        copy : bool, default True
+            Whether or not to return a copy.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        Series with DatetimeIndex
+
+        Examples
+        --------
+        >>> idx = pd.PeriodIndex(['2023', '2024', '2025'], freq='Y')
+        >>> s1 = pd.Series([1, 2, 3], index=idx)
+        >>> s1
+        2023    1
+        2024    2
+        2025    3
+        Freq: Y-DEC, dtype: int64
+
+        The resulting frequency of the Timestamps is `YearBegin`
+
+        >>> s1 = s1.to_timestamp()
+        >>> s1
+        2023-01-01    1
+        2024-01-01    2
+        2025-01-01    3
+        Freq: YS-JAN, dtype: int64
+
+        Using `freq` which is the offset that the Timestamps will have
+
+        >>> s2 = pd.Series([1, 2, 3], index=idx)
+        >>> s2 = s2.to_timestamp(freq='M')
+        >>> s2
+        2023-01-31    1
+        2024-01-31    2
+        2025-01-31    3
+        Freq: YE-JAN, dtype: int64
+        """
+        if not isinstance(self.index, PeriodIndex):
+            raise TypeError(f"unsupported Type {type(self.index).__name__}")
+
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+        new_index = self.index.to_timestamp(freq=freq, how=how)
+        setattr(new_obj, "index", new_index)
+        return new_obj
+
+    def to_period(self, freq: str | None = None, copy: bool | None = None) -> Series:
+        """
+        Convert Series from DatetimeIndex to PeriodIndex.
+
+        Parameters
+        ----------
+        freq : str, default None
+            Frequency associated with the PeriodIndex.
+        copy : bool, default True
+            Whether or not to return a copy.
+
+            .. note::
+                The `copy` keyword will change behavior in pandas 3.0.
+                `Copy-on-Write
+                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
+                will be enabled by default, which means that all methods with a
+                `copy` keyword will use a lazy copy mechanism to defer the copy and
+                ignore the `copy` keyword. The `copy` keyword will be removed in a
+                future version of pandas.
+
+                You can already get the future behavior and improvements through
+                enabling copy on write ``pd.options.mode.copy_on_write = True``
+
+        Returns
+        -------
+        Series
+            Series with index converted to PeriodIndex.
+
+        Examples
+        --------
+        >>> idx = pd.DatetimeIndex(['2023', '2024', '2025'])
+        >>> s = pd.Series([1, 2, 3], index=idx)
+        >>> s = s.to_period()
+        >>> s
+        2023    1
+        2024    2
+        2025    3
+        Freq: Y-DEC, dtype: int64
+
+        Viewing the index
+
+        >>> s.index
+        PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
+        """
+        if not isinstance(self.index, DatetimeIndex):
+            raise TypeError(f"unsupported Type {type(self.index).__name__}")
+
+        new_obj = self.copy(deep=copy and not using_copy_on_write())
+        new_index = self.index.to_period(freq=freq)
+        setattr(new_obj, "index", new_index)
+        return new_obj
+
+    # ----------------------------------------------------------------------
+    # Add index
+    _AXIS_ORDERS: list[Literal["index", "columns"]] = ["index"]
+    _AXIS_LEN = len(_AXIS_ORDERS)
+    _info_axis_number: Literal[0] = 0
+    _info_axis_name: Literal["index"] = "index"
+
+    index = properties.AxisProperty(
+        axis=0,
+        doc="""
+        The index (axis labels) of the Series.
+
+        The index of a Series is used to label and identify each element of the
+        underlying data. The index can be thought of as an immutable ordered set
+        (technically a multi-set, as it may contain duplicate labels), and is
+        used to index and align data in pandas.
+
+        Returns
+        -------
+        Index
+            The index labels of the Series.
+
+        See Also
+        --------
+        Series.reindex : Conform Series to new index.
+        Index : The base pandas index type.
+
+        Notes
+        -----
+        For more information on pandas indexing, see the `indexing user guide
+        <https://pandas.pydata.org/docs/user_guide/indexing.html>`__.
+
+        Examples
+        --------
+        To create a Series with a custom index and view the index labels:
+
+        >>> cities = ['Kolkata', 'Chicago', 'Toronto', 'Lisbon']
+        >>> populations = [14.85, 2.71, 2.93, 0.51]
+        >>> city_series = pd.Series(populations, index=cities)
+        >>> city_series.index
+        Index(['Kolkata', 'Chicago', 'Toronto', 'Lisbon'], dtype='object')
+
+        To change the index labels of an existing Series:
+
+        >>> city_series.index = ['KOL', 'CHI', 'TOR', 'LIS']
+        >>> city_series.index
+        Index(['KOL', 'CHI', 'TOR', 'LIS'], dtype='object')
+        """,
+    )
+
+    # ----------------------------------------------------------------------
+    # Accessor Methods
+    # ----------------------------------------------------------------------
+    str = CachedAccessor("str", StringMethods)
+    dt = CachedAccessor("dt", CombinedDatetimelikeProperties)
+    cat = CachedAccessor("cat", CategoricalAccessor)
+    plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
+    sparse = CachedAccessor("sparse", SparseAccessor)
+    struct = CachedAccessor("struct", StructAccessor)
+    list = CachedAccessor("list", ListAccessor)
+
+    # ----------------------------------------------------------------------
+    # Add plotting methods to Series
+    hist = pandas.plotting.hist_series
+
+    # ----------------------------------------------------------------------
+    # Template-Based Arithmetic/Comparison Methods
+
+    def _cmp_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+
+        if isinstance(other, Series) and not self._indexed_same(other):
+            raise ValueError("Can only compare identically-labeled Series objects")
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+
+        res_values = ops.comparison_op(lvalues, rvalues, op)
+
+        return self._construct_result(res_values, name=res_name)
+
+    def _logical_method(self, other, op):
+        res_name = ops.get_op_result_name(self, other)
+        self, other = self._align_for_op(other, align_asobject=True)
+
+        lvalues = self._values
+        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
+
+        res_values = ops.logical_op(lvalues, rvalues, op)
+        return self._construct_result(res_values, name=res_name)
+
+    def _arith_method(self, other, op):
+        self, other = self._align_for_op(other)
+        return base.IndexOpsMixin._arith_method(self, other, op)
+
+    def _align_for_op(self, right, align_asobject: bool = False):
+        """align lhs and rhs Series"""
+        # TODO: Different from DataFrame._align_for_op, list, tuple and ndarray
+        # are not coerced here
+        # because Series has inconsistencies described in GH#13637
+        left = self
+
+        if isinstance(right, Series):
+            # avoid repeated alignment
+            if not left.index.equals(right.index):
+                if align_asobject:
+                    if left.dtype not in (object, np.bool_) or right.dtype not in (
+                        object,
+                        np.bool_,
+                    ):
+                        warnings.warn(
+                            "Operation between Series with different indexes "
+                            "that are not of numpy boolean or object dtype "
+                            "will no longer return a numpy boolean result "
+                            "in a future version. "
+                            "Cast both Series to object type "
+                            "to maintain the prior behavior.",
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    # to keep original value's dtype for bool ops
+                    left = left.astype(object)
+                    right = right.astype(object)
+
+                left, right = left.align(right, copy=False)
+
+        return left, right
+
+    def _binop(self, other: Series, func, level=None, fill_value=None) -> Series:
+        """
+        Perform generic binary operation with optional fill value.
+
+        Parameters
+        ----------
+        other : Series
+        func : binary operator
+        fill_value : float or object
+            Value to substitute for NA/null values. If both Series are NA in a
+            location, the result will be NA regardless of the passed fill value.
+        level : int or level name, default None
+            Broadcast across a level, matching Index values on the
+            passed MultiIndex level.
+
+        Returns
+        -------
+        Series
+        """
+        this = self
+
+        if not self.index.equals(other.index):
+            this, other = self.align(other, level=level, join="outer", copy=False)
+
+        this_vals, other_vals = ops.fill_binop(this._values, other._values, fill_value)
+
+        with np.errstate(all="ignore"):
+            result = func(this_vals, other_vals)
+
+        name = ops.get_op_result_name(self, other)
+        out = this._construct_result(result, name)
+        return cast(Series, out)
+
+    def _construct_result(
+        self, result: ArrayLike | tuple[ArrayLike, ArrayLike], name: Hashable
+    ) -> Series | tuple[Series, Series]:
+        """
+        Construct an appropriately-labelled Series from the result of an op.
+
+        Parameters
+        ----------
+        result : ndarray or ExtensionArray
+        name : Label
+
+        Returns
+        -------
+        Series
+            In the case of __divmod__ or __rdivmod__, a 2-tuple of Series.
+        """
+        if isinstance(result, tuple):
+            # produced by divmod or rdivmod
+
+            res1 = self._construct_result(result[0], name=name)
+            res2 = self._construct_result(result[1], name=name)
+
+            # GH#33427 assertions to keep mypy happy
+            assert isinstance(res1, Series)
+            assert isinstance(res2, Series)
+            return (res1, res2)
+
+        # TODO: result should always be ArrayLike, but this fails for some
+        #  JSONArray tests
+        dtype = getattr(result, "dtype", None)
+        out = self._constructor(result, index=self.index, dtype=dtype, copy=False)
+        out = out.__finalize__(self)
+
+        # Set the result's name after __finalize__ is called because __finalize__
+        #  would set it back to self.name
+        out.name = name
+        return out
+
+    def _flex_method(self, other, op, *, level=None, fill_value=None, axis: Axis = 0):
+        if axis is not None:
+            self._get_axis_number(axis)
+
+        res_name = ops.get_op_result_name(self, other)
+
+        if isinstance(other, Series):
+            return self._binop(other, op, level=level, fill_value=fill_value)
+        elif isinstance(other, (np.ndarray, list, tuple)):
+            if len(other) != len(self):
+                raise ValueError("Lengths must be equal")
+            other = self._constructor(other, self.index, copy=False)
+            result = self._binop(other, op, level=level, fill_value=fill_value)
+            result._name = res_name
+            return result
+        else:
+            if fill_value is not None:
+                if isna(other):
+                    return op(self, fill_value)
+                self = self.fillna(fill_value)
+
+            return op(self, other)
+
+    @Appender(ops.make_flex_doc("eq", "series"))
+    def eq(
+        self,
+        other,
+        level: Level | None = None,
+        fill_value: float | None = None,
+        axis: Axis = 0,
+    ) -> Series:
+        return self._flex_method(
+            other, operator.eq, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("ne", "series"))
+    def ne(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.ne, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("le", "series"))
+    def le(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.le, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("lt", "series"))
+    def lt(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.lt, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("ge", "series"))
+    def ge(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.ge, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("gt", "series"))
+    def gt(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.gt, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("add", "series"))
+    def add(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.add, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("radd", "series"))
+    def radd(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.radd, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("sub", "series"))
+    def sub(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.sub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    subtract = sub
+
+    @Appender(ops.make_flex_doc("rsub", "series"))
+    def rsub(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rsub, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mul", "series"))
+    def mul(
+        self,
+        other,
+        level: Level | None = None,
+        fill_value: float | None = None,
+        axis: Axis = 0,
+    ) -> Series:
+        return self._flex_method(
+            other, operator.mul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    multiply = mul
+
+    @Appender(ops.make_flex_doc("rmul", "series"))
+    def rmul(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rmul, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("truediv", "series"))
+    def truediv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.truediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    div = truediv
+    divide = truediv
+
+    @Appender(ops.make_flex_doc("rtruediv", "series"))
+    def rtruediv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rtruediv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    rdiv = rtruediv
+
+    @Appender(ops.make_flex_doc("floordiv", "series"))
+    def floordiv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.floordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rfloordiv", "series"))
+    def rfloordiv(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rfloordiv, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("mod", "series"))
+    def mod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.mod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rmod", "series"))
+    def rmod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("pow", "series"))
+    def pow(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, operator.pow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rpow", "series"))
+    def rpow(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rpow, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("divmod", "series"))
+    def divmod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, divmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    @Appender(ops.make_flex_doc("rdivmod", "series"))
+    def rdivmod(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
+        return self._flex_method(
+            other, roperator.rdivmod, level=level, fill_value=fill_value, axis=axis
+        )
+
+    # ----------------------------------------------------------------------
+    # Reductions
+
+    def _reduce(
+        self,
+        op,
+        # error: Variable "pandas.core.series.Series.str" is not valid as a type
+        name: str,  # type: ignore[valid-type]
+        *,
+        axis: Axis = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        filter_type=None,
+        **kwds,
+    ):
+        """
+        Perform a reduction operation.
+
+        If we have an ndarray as a value, then simply perform the operation,
+        otherwise delegate to the object.
+        """
+        delegate = self._values
+
+        if axis is not None:
+            self._get_axis_number(axis)
+
+        if isinstance(delegate, ExtensionArray):
+            # dispatch to ExtensionArray interface
+            return delegate._reduce(name, skipna=skipna, **kwds)
+
+        else:
+            # dispatch to numpy arrays
+            if numeric_only and self.dtype.kind not in "iufcb":
+                # i.e. not is_numeric_dtype(self.dtype)
+                kwd_name = "numeric_only"
+                if name in ["any", "all"]:
+                    kwd_name = "bool_only"
+                # GH#47500 - change to TypeError to match other methods
+                raise TypeError(
+                    f"Series.{name} does not allow {kwd_name}={numeric_only} "
+                    "with non-numeric dtypes."
+                )
+            return op(delegate, skipna=skipna, **kwds)
+
+    @Appender(make_doc("any", ndim=1))
+    # error: Signature of "any" incompatible with supertype "NDFrame"
+    def any(  # type: ignore[override]
+        self,
+        *,
+        axis: Axis = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> bool:
+        nv.validate_logical_func((), kwargs, fname="any")
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        return self._reduce(
+            nanops.nanany,
+            name="any",
+            axis=axis,
+            numeric_only=bool_only,
+            skipna=skipna,
+            filter_type="bool",
+        )
+
+    @Appender(make_doc("all", ndim=1))
+    def all(
+        self,
+        axis: Axis = 0,
+        bool_only: bool = False,
+        skipna: bool = True,
+        **kwargs,
+    ) -> bool:
+        nv.validate_logical_func((), kwargs, fname="all")
+        validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        return self._reduce(
+            nanops.nanall,
+            name="all",
+            axis=axis,
+            numeric_only=bool_only,
+            skipna=skipna,
+            filter_type="bool",
+        )
+
+    @doc(make_doc("min", ndim=1))
+    def min(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.min(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("max", ndim=1))
+    def max(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.max(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("sum", ndim=1))
+    def sum(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return NDFrame.sum(self, axis, skipna, numeric_only, min_count, **kwargs)
+
+    @doc(make_doc("prod", ndim=1))
+    def prod(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        **kwargs,
+    ):
+        return NDFrame.prod(self, axis, skipna, numeric_only, min_count, **kwargs)
+
+    @doc(make_doc("mean", ndim=1))
+    def mean(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("median", ndim=1))
+    def median(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.median(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("sem", ndim=1))
+    def sem(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.sem(self, axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("var", ndim=1))
+    def var(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.var(self, axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("std", ndim=1))
+    def std(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.std(self, axis, skipna, ddof, numeric_only, **kwargs)
+
+    @doc(make_doc("skew", ndim=1))
+    def skew(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.skew(self, axis, skipna, numeric_only, **kwargs)
+
+    @doc(make_doc("kurt", ndim=1))
+    def kurt(
+        self,
+        axis: Axis | None = 0,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        **kwargs,
+    ):
+        return NDFrame.kurt(self, axis, skipna, numeric_only, **kwargs)
+
+    kurtosis = kurt
+    product = prod
+
+    @doc(make_doc("cummin", ndim=1))
+    def cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cummax", ndim=1))
+    def cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumsum", ndim=1))
+    def cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
+
+    @doc(make_doc("cumprod", 1))
+    def cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
+        return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/shared_docs.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/shared_docs.py
new file mode 100644
index 0000000000000000000000000000000000000000..25f7e7e9f832b0142af32acbb9690d0ae3f0b5e3
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/shared_docs.py
@@ -0,0 +1,952 @@
+from __future__ import annotations
+
+_shared_docs: dict[str, str] = {}
+
+_shared_docs[
+    "aggregate"
+] = """
+Aggregate using one or more operations over the specified axis.
+
+Parameters
+----------
+func : function, str, list or dict
+    Function to use for aggregating the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
+    - dict of axis labels -> functions, function names or list of such.
+{axis}
+*args
+    Positional arguments to pass to `func`.
+**kwargs
+    Keyword arguments to pass to `func`.
+
+Returns
+-------
+scalar, Series or DataFrame
+
+    The return can be:
+
+    * scalar : when Series.agg is called with single function
+    * Series : when DataFrame.agg is called with a single function
+    * DataFrame : when DataFrame.agg is called with several functions
+{see_also}
+Notes
+-----
+The aggregation operations are always performed over an axis, either the
+index (default) or the column axis. This behavior is different from
+`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
+`var`), where the default is to compute the aggregation of the flattened
+array, e.g., ``numpy.mean(arr_2d)`` as opposed to
+``numpy.mean(arr_2d, axis=0)``.
+
+`agg` is an alias for `aggregate`. Use the alias.
+
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+A passed user-defined-function will be passed a Series for evaluation.
+{examples}"""
+
+_shared_docs[
+    "compare"
+] = """
+Compare to another {klass} and show the differences.
+
+Parameters
+----------
+other : {klass}
+    Object to compare with.
+
+align_axis : {{0 or 'index', 1 or 'columns'}}, default 1
+    Determine which axis to align the comparison on.
+
+    * 0, or 'index' : Resulting differences are stacked vertically
+        with rows drawn alternately from self and other.
+    * 1, or 'columns' : Resulting differences are aligned horizontally
+        with columns drawn alternately from self and other.
+
+keep_shape : bool, default False
+    If true, all rows and columns are kept.
+    Otherwise, only the ones with different values are kept.
+
+keep_equal : bool, default False
+    If true, the result keeps values that are equal.
+    Otherwise, equal values are shown as NaNs.
+
+result_names : tuple, default ('self', 'other')
+    Set the dataframes names in the comparison.
+
+    .. versionadded:: 1.5.0
+"""
+
+_shared_docs[
+    "groupby"
+] = """
+Group %(klass)s using a mapper or by a Series of columns.
+
+A groupby operation involves some combination of splitting the
+object, applying a function, and combining the results. This can be
+used to group large amounts of data and compute operations on these
+groups.
+
+Parameters
+----------
+by : mapping, function, label, pd.Grouper or list of such
+    Used to determine the groups for the groupby.
+    If ``by`` is a function, it's called on each value of the object's
+    index. If a dict or Series is passed, the Series or dict VALUES
+    will be used to determine the groups (the Series' values are first
+    aligned; see ``.align()`` method). If a list or ndarray of length
+    equal to the selected axis is passed (see the `groupby user guide
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>`_),
+    the values are used as-is to determine the groups. A label or list
+    of labels may be passed to group by the columns in ``self``.
+    Notice that a tuple is interpreted as a (single) key.
+axis : {0 or 'index', 1 or 'columns'}, default 0
+    Split along rows (0) or columns (1). For `Series` this parameter
+    is unused and defaults to 0.
+
+    .. deprecated:: 2.1.0
+
+        Will be removed and behave like axis=0 in a future version.
+        For ``axis=1``, do ``frame.T.groupby(...)`` instead.
+
+level : int, level name, or sequence of such, default None
+    If the axis is a MultiIndex (hierarchical), group by a particular
+    level or levels. Do not specify both ``by`` and ``level``.
+as_index : bool, default True
+    Return object with group labels as the
+    index. Only relevant for DataFrame input. as_index=False is
+    effectively "SQL-style" grouped output. This argument has no effect
+    on filtrations (see the `filtrations in the user guide
+    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>`_),
+    such as ``head()``, ``tail()``, ``nth()`` and in transformations
+    (see the `transformations in the user guide
+    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>`_).
+sort : bool, default True
+    Sort group keys. Get better performance by turning this off.
+    Note this does not influence the order of observations within each
+    group. Groupby preserves the order of rows within each group. If False,
+    the groups will appear in the same order as they did in the original DataFrame.
+    This argument has no effect on filtrations (see the `filtrations in the user guide
+    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration>`_),
+    such as ``head()``, ``tail()``, ``nth()`` and in transformations
+    (see the `transformations in the user guide
+    <https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation>`_).
+
+    .. versionchanged:: 2.0.0
+
+        Specifying ``sort=False`` with an ordered categorical grouper will no
+        longer sort the values.
+
+group_keys : bool, default True
+    When calling apply and the ``by`` argument produces a like-indexed
+    (i.e. :ref:`a transform <groupby.transform>`) result, add group keys to
+    index to identify pieces. By default group keys are not included
+    when the result's index (and column) labels match the inputs, and
+    are included otherwise.
+
+    .. versionchanged:: 1.5.0
+
+       Warns that ``group_keys`` will no longer be ignored when the
+       result from ``apply`` is a like-indexed Series or DataFrame.
+       Specify ``group_keys`` explicitly to include the group keys or
+       not.
+
+    .. versionchanged:: 2.0.0
+
+       ``group_keys`` now defaults to ``True``.
+
+observed : bool, default False
+    This only applies if any of the groupers are Categoricals.
+    If True: only show observed values for categorical groupers.
+    If False: show all values for categorical groupers.
+
+    .. deprecated:: 2.1.0
+
+        The default value will change to True in a future version of pandas.
+
+dropna : bool, default True
+    If True, and if group keys contain NA values, NA values together
+    with row/column will be dropped.
+    If False, NA values will also be treated as the key in groups.
+
+Returns
+-------
+pandas.api.typing.%(klass)sGroupBy
+    Returns a groupby object that contains information about the groups.
+
+See Also
+--------
+resample : Convenience method for frequency conversion and resampling
+    of time series.
+
+Notes
+-----
+See the `user guide
+<https://pandas.pydata.org/pandas-docs/stable/groupby.html>`__ for more
+detailed usage and examples, including splitting an object into groups,
+iterating through groups, selecting a group, aggregation, and more.
+"""
+
+_shared_docs[
+    "melt"
+] = """
+Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
+
+This function is useful to massage a DataFrame into a format where one
+or more columns are identifier variables (`id_vars`), while all other
+columns, considered measured variables (`value_vars`), are "unpivoted" to
+the row axis, leaving just two non-identifier columns, 'variable' and
+'value'.
+
+Parameters
+----------
+id_vars : scalar, tuple, list, or ndarray, optional
+    Column(s) to use as identifier variables.
+value_vars : scalar, tuple, list, or ndarray, optional
+    Column(s) to unpivot. If not specified, uses all columns that
+    are not set as `id_vars`.
+var_name : scalar, default None
+    Name to use for the 'variable' column. If None it uses
+    ``frame.columns.name`` or 'variable'.
+value_name : scalar, default 'value'
+    Name to use for the 'value' column, can't be an existing column label.
+col_level : scalar, optional
+    If columns are a MultiIndex then use this level to melt.
+ignore_index : bool, default True
+    If True, original index is ignored. If False, the original index is retained.
+    Index labels will be repeated as necessary.
+
+Returns
+-------
+DataFrame
+    Unpivoted DataFrame.
+
+See Also
+--------
+%(other)s : Identical method.
+pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
+DataFrame.pivot : Return reshaped DataFrame organized
+    by given index / column values.
+DataFrame.explode : Explode a DataFrame from list-like
+        columns to long format.
+
+Notes
+-----
+Reference :ref:`the user guide <reshaping.melt>` for more examples.
+
+Examples
+--------
+>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
+...                    'B': {0: 1, 1: 3, 2: 5},
+...                    'C': {0: 2, 1: 4, 2: 6}})
+>>> df
+   A  B  C
+0  a  1  2
+1  b  3  4
+2  c  5  6
+
+>>> %(caller)sid_vars=['A'], value_vars=['B'])
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+
+>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+3  a        C      2
+4  b        C      4
+5  c        C      6
+
+The names of 'variable' and 'value' columns can be customized:
+
+>>> %(caller)sid_vars=['A'], value_vars=['B'],
+...         var_name='myVarname', value_name='myValname')
+   A myVarname  myValname
+0  a         B          1
+1  b         B          3
+2  c         B          5
+
+Original index values can be kept around:
+
+>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+0  a        C      2
+1  b        C      4
+2  c        C      6
+
+If you have multi-index columns:
+
+>>> df.columns = [list('ABC'), list('DEF')]
+>>> df
+   A  B  C
+   D  E  F
+0  a  1  2
+1  b  3  4
+2  c  5  6
+
+>>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
+   A variable  value
+0  a        B      1
+1  b        B      3
+2  c        B      5
+
+>>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
+  (A, D) variable_0 variable_1  value
+0      a          B          E      1
+1      b          B          E      3
+2      c          B          E      5
+"""
+
+_shared_docs[
+    "transform"
+] = """
+Call ``func`` on self producing a {klass} with the same axis shape as self.
+
+Parameters
+----------
+func : function, str, list-like or dict-like
+    Function to use for transforming the data. If a function, must either
+    work when passed a {klass} or when passed to {klass}.apply. If func
+    is both list-like and dict-like, dict-like behavior takes precedence.
+
+    Accepted combinations are:
+
+    - function
+    - string function name
+    - list-like of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
+    - dict-like of axis labels -> functions, function names or list-like of such.
+{axis}
+*args
+    Positional arguments to pass to `func`.
+**kwargs
+    Keyword arguments to pass to `func`.
+
+Returns
+-------
+{klass}
+    A {klass} that must have the same length as self.
+
+Raises
+------
+ValueError : If the returned {klass} has a different length than self.
+
+See Also
+--------
+{klass}.agg : Only perform aggregating type operations.
+{klass}.apply : Invoke function on a {klass}.
+
+Notes
+-----
+Functions that mutate the passed object can produce unexpected
+behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
+for more details.
+
+Examples
+--------
+>>> df = pd.DataFrame({{'A': range(3), 'B': range(1, 4)}})
+>>> df
+   A  B
+0  0  1
+1  1  2
+2  2  3
+>>> df.transform(lambda x: x + 1)
+   A  B
+0  1  2
+1  2  3
+2  3  4
+
+Even though the resulting {klass} must have the same length as the
+input {klass}, it is possible to provide several input functions:
+
+>>> s = pd.Series(range(3))
+>>> s
+0    0
+1    1
+2    2
+dtype: int64
+>>> s.transform([np.sqrt, np.exp])
+       sqrt        exp
+0  0.000000   1.000000
+1  1.000000   2.718282
+2  1.414214   7.389056
+
+You can call transform on a GroupBy object:
+
+>>> df = pd.DataFrame({{
+...     "Date": [
+...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
+...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
+...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
+... }})
+>>> df
+         Date  Data
+0  2015-05-08     5
+1  2015-05-07     8
+2  2015-05-06     6
+3  2015-05-05     1
+4  2015-05-08    50
+5  2015-05-07   100
+6  2015-05-06    60
+7  2015-05-05   120
+>>> df.groupby('Date')['Data'].transform('sum')
+0     55
+1    108
+2     66
+3    121
+4     55
+5    108
+6     66
+7    121
+Name: Data, dtype: int64
+
+>>> df = pd.DataFrame({{
+...     "c": [1, 1, 1, 2, 2, 2, 2],
+...     "type": ["m", "n", "o", "m", "m", "n", "n"]
+... }})
+>>> df
+   c type
+0  1    m
+1  1    n
+2  1    o
+3  2    m
+4  2    m
+5  2    n
+6  2    n
+>>> df['size'] = df.groupby('c')['type'].transform(len)
+>>> df
+   c type size
+0  1    m    3
+1  1    n    3
+2  1    o    3
+3  2    m    4
+4  2    m    4
+5  2    n    4
+6  2    n    4
+"""
+
+_shared_docs[
+    "storage_options"
+] = """storage_options : dict, optional
+    Extra options that make sense for a particular storage connection, e.g.
+    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
+    are forwarded to ``urllib.request.Request`` as header options. For other
+    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
+    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
+    details, and for more examples on storage options refer `here
+    <https://pandas.pydata.org/docs/user_guide/io.html?
+    highlight=storage_options#reading-writing-remote-files>`_."""
+
+_shared_docs[
+    "compression_options"
+] = """compression : str or dict, default 'infer'
+    For on-the-fly compression of the output data. If 'infer' and '%s' is
+    path-like, then detect compression from the following extensions: '.gz',
+    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
+    (otherwise no compression).
+    Set to ``None`` for no compression.
+    Can also be a dict with key ``'method'`` set
+    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
+    other key-value pairs are forwarded to
+    ``zipfile.ZipFile``, ``gzip.GzipFile``,
+    ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
+    ``tarfile.TarFile``, respectively.
+    As an example, the following could be passed for faster compression and to create
+    a reproducible gzip archive:
+    ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
+
+    .. versionadded:: 1.5.0
+        Added support for `.tar` files."""
+
+_shared_docs[
+    "decompression_options"
+] = """compression : str or dict, default 'infer'
+    For on-the-fly decompression of on-disk data. If 'infer' and '%s' is
+    path-like, then detect compression from the following extensions: '.gz',
+    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
+    (otherwise no compression).
+    If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
+    Set to ``None`` for no decompression.
+    Can also be a dict with key ``'method'`` set
+    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
+    other key-value pairs are forwarded to
+    ``zipfile.ZipFile``, ``gzip.GzipFile``,
+    ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or
+    ``tarfile.TarFile``, respectively.
+    As an example, the following could be passed for Zstandard decompression using a
+    custom compression dictionary:
+    ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
+
+    .. versionadded:: 1.5.0
+        Added support for `.tar` files."""
+
+_shared_docs[
+    "replace"
+] = """
+    Replace values given in `to_replace` with `value`.
+
+    Values of the {klass} are replaced with other values dynamically.
+    This differs from updating with ``.loc`` or ``.iloc``, which require
+    you to specify a location to update with some value.
+
+    Parameters
+    ----------
+    to_replace : str, regex, list, dict, Series, int, float, or None
+        How to find the values that will be replaced.
+
+        * numeric, str or regex:
+
+            - numeric: numeric values equal to `to_replace` will be
+              replaced with `value`
+            - str: string exactly matching `to_replace` will be replaced
+              with `value`
+            - regex: regexs matching `to_replace` will be replaced with
+              `value`
+
+        * list of str, regex, or numeric:
+
+            - First, if `to_replace` and `value` are both lists, they
+              **must** be the same length.
+            - Second, if ``regex=True`` then all of the strings in **both**
+              lists will be interpreted as regexs otherwise they will match
+              directly. This doesn't matter much for `value` since there
+              are only a few possible substitution regexes you can use.
+            - str, regex and numeric rules apply as above.
+
+        * dict:
+
+            - Dicts can be used to specify different replacement values
+              for different existing values. For example,
+              ``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and
+              'y' with 'z'. To use a dict in this way, the optional `value`
+              parameter should not be given.
+            - For a DataFrame a dict can specify that different values
+              should be replaced in different columns. For example,
+              ``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a'
+              and the value 'z' in column 'b' and replaces these values
+              with whatever is specified in `value`. The `value` parameter
+              should not be ``None`` in this case. You can treat this as a
+              special case of passing two lists except that you are
+              specifying the column to search in.
+            - For a DataFrame nested dictionaries, e.g.,
+              ``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column
+              'a' for the value 'b' and replace it with NaN. The optional `value`
+              parameter should not be specified to use a nested dict in this
+              way. You can nest regular expressions as well. Note that
+              column names (the top-level dictionary keys in a nested
+              dictionary) **cannot** be regular expressions.
+
+        * None:
+
+            - This means that the `regex` argument must be a string,
+              compiled regular expression, or list, dict, ndarray or
+              Series of such elements. If `value` is also ``None`` then
+              this **must** be a nested dictionary or Series.
+
+        See the examples section for examples of each of these.
+    value : scalar, dict, list, str, regex, default None
+        Value to replace any values matching `to_replace` with.
+        For a DataFrame a dict of values can be used to specify which
+        value to use for each column (columns not in the dict will not be
+        filled). Regular expressions, strings and lists or dicts of such
+        objects are also allowed.
+    {inplace}
+    limit : int, default None
+        Maximum size gap to forward or backward fill.
+
+        .. deprecated:: 2.1.0
+    regex : bool or same types as `to_replace`, default False
+        Whether to interpret `to_replace` and/or `value` as regular
+        expressions. Alternatively, this could be a regular expression or a
+        list, dict, or array of regular expressions in which case
+        `to_replace` must be ``None``.
+    method : {{'pad', 'ffill', 'bfill'}}
+        The method to use when for replacement, when `to_replace` is a
+        scalar, list or tuple and `value` is ``None``.
+
+        .. deprecated:: 2.1.0
+
+    Returns
+    -------
+    {klass}
+        Object after replacement.
+
+    Raises
+    ------
+    AssertionError
+        * If `regex` is not a ``bool`` and `to_replace` is not
+          ``None``.
+
+    TypeError
+        * If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
+        * If `to_replace` is a ``dict`` and `value` is not a ``list``,
+          ``dict``, ``ndarray``, or ``Series``
+        * If `to_replace` is ``None`` and `regex` is not compilable
+          into a regular expression or is a list, dict, ndarray, or
+          Series.
+        * When replacing multiple ``bool`` or ``datetime64`` objects and
+          the arguments to `to_replace` does not match the type of the
+          value being replaced
+
+    ValueError
+        * If a ``list`` or an ``ndarray`` is passed to `to_replace` and
+          `value` but they are not the same length.
+
+    See Also
+    --------
+    Series.fillna : Fill NA values.
+    DataFrame.fillna : Fill NA values.
+    Series.where : Replace values based on boolean condition.
+    DataFrame.where : Replace values based on boolean condition.
+    DataFrame.map: Apply a function to a Dataframe elementwise.
+    Series.map: Map values of Series according to an input mapping or function.
+    Series.str.replace : Simple string replacement.
+
+    Notes
+    -----
+    * Regex substitution is performed under the hood with ``re.sub``. The
+      rules for substitution for ``re.sub`` are the same.
+    * Regular expressions will only substitute on strings, meaning you
+      cannot provide, for example, a regular expression matching floating
+      point numbers and expect the columns in your frame that have a
+      numeric dtype to be matched. However, if those floating point
+      numbers *are* strings, then you can do this.
+    * This method has *a lot* of options. You are encouraged to experiment
+      and play with this method to gain intuition about how it works.
+    * When dict is used as the `to_replace` value, it is like
+      key(s) in the dict are the to_replace part and
+      value(s) in the dict are the value parameter.
+
+    Examples
+    --------
+
+    **Scalar `to_replace` and `value`**
+
+    >>> s = pd.Series([1, 2, 3, 4, 5])
+    >>> s.replace(1, 5)
+    0    5
+    1    2
+    2    3
+    3    4
+    4    5
+    dtype: int64
+
+    >>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
+    ...                    'B': [5, 6, 7, 8, 9],
+    ...                    'C': ['a', 'b', 'c', 'd', 'e']}})
+    >>> df.replace(0, 5)
+        A  B  C
+    0  5  5  a
+    1  1  6  b
+    2  2  7  c
+    3  3  8  d
+    4  4  9  e
+
+    **List-like `to_replace`**
+
+    >>> df.replace([0, 1, 2, 3], 4)
+        A  B  C
+    0  4  5  a
+    1  4  6  b
+    2  4  7  c
+    3  4  8  d
+    4  4  9  e
+
+    >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
+        A  B  C
+    0  4  5  a
+    1  3  6  b
+    2  2  7  c
+    3  1  8  d
+    4  4  9  e
+
+    >>> s.replace([1, 2], method='bfill')
+    0    3
+    1    3
+    2    3
+    3    4
+    4    5
+    dtype: int64
+
+    **dict-like `to_replace`**
+
+    >>> df.replace({{0: 10, 1: 100}})
+            A  B  C
+    0   10  5  a
+    1  100  6  b
+    2    2  7  c
+    3    3  8  d
+    4    4  9  e
+
+    >>> df.replace({{'A': 0, 'B': 5}}, 100)
+            A    B  C
+    0  100  100  a
+    1    1    6  b
+    2    2    7  c
+    3    3    8  d
+    4    4    9  e
+
+    >>> df.replace({{'A': {{0: 100, 4: 400}}}})
+            A  B  C
+    0  100  5  a
+    1    1  6  b
+    2    2  7  c
+    3    3  8  d
+    4  400  9  e
+
+    **Regular expression `to_replace`**
+
+    >>> df = pd.DataFrame({{'A': ['bat', 'foo', 'bait'],
+    ...                    'B': ['abc', 'bar', 'xyz']}})
+    >>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
+            A    B
+    0   new  abc
+    1   foo  new
+    2  bait  xyz
+
+    >>> df.replace({{'A': r'^ba.$'}}, {{'A': 'new'}}, regex=True)
+            A    B
+    0   new  abc
+    1   foo  bar
+    2  bait  xyz
+
+    >>> df.replace(regex=r'^ba.$', value='new')
+            A    B
+    0   new  abc
+    1   foo  new
+    2  bait  xyz
+
+    >>> df.replace(regex={{r'^ba.$': 'new', 'foo': 'xyz'}})
+            A    B
+    0   new  abc
+    1   xyz  new
+    2  bait  xyz
+
+    >>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
+            A    B
+    0   new  abc
+    1   new  new
+    2  bait  xyz
+
+    Compare the behavior of ``s.replace({{'a': None}})`` and
+    ``s.replace('a', None)`` to understand the peculiarities
+    of the `to_replace` parameter:
+
+    >>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
+
+    When one uses a dict as the `to_replace` value, it is like the
+    value(s) in the dict are equal to the `value` parameter.
+    ``s.replace({{'a': None}})`` is equivalent to
+    ``s.replace(to_replace={{'a': None}}, value=None, method=None)``:
+
+    >>> s.replace({{'a': None}})
+    0      10
+    1    None
+    2    None
+    3       b
+    4    None
+    dtype: object
+
+    When ``value`` is not explicitly passed and `to_replace` is a scalar, list
+    or tuple, `replace` uses the method parameter (default 'pad') to do the
+    replacement. So this is why the 'a' values are being replaced by 10
+    in rows 1 and 2 and 'b' in row 4 in this case.
+
+    >>> s.replace('a')
+    0    10
+    1    10
+    2    10
+    3     b
+    4     b
+    dtype: object
+
+        .. deprecated:: 2.1.0
+            The 'method' parameter and padding behavior are deprecated.
+
+    On the other hand, if ``None`` is explicitly passed for ``value``, it will
+    be respected:
+
+    >>> s.replace('a', None)
+    0      10
+    1    None
+    2    None
+    3       b
+    4    None
+    dtype: object
+
+        .. versionchanged:: 1.4.0
+            Previously the explicit ``None`` was silently ignored.
+
+    When ``regex=True``, ``value`` is not ``None`` and `to_replace` is a string,
+    the replacement will be applied in all columns of the DataFrame.
+
+    >>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
+    ...                    'B': ['a', 'b', 'c', 'd', 'e'],
+    ...                    'C': ['f', 'g', 'h', 'i', 'j']}})
+
+    >>> df.replace(to_replace='^[a-g]', value='e', regex=True)
+        A  B  C
+    0  0  e  e
+    1  1  e  e
+    2  2  e  h
+    3  3  e  i
+    4  4  e  j
+
+    If ``value`` is not ``None`` and `to_replace` is a dictionary, the dictionary
+    keys will be the DataFrame columns that the replacement will be applied.
+
+    >>> df.replace(to_replace={{'B': '^[a-c]', 'C': '^[h-j]'}}, value='e', regex=True)
+        A  B  C
+    0  0  e  f
+    1  1  e  g
+    2  2  e  e
+    3  3  d  e
+    4  4  e  e
+"""
+
+_shared_docs[
+    "idxmin"
+] = """
+    Return index of first occurrence of minimum over requested axis.
+
+    NA/null values are excluded.
+
+    Parameters
+    ----------
+    axis : {{0 or 'index', 1 or 'columns'}}, default 0
+        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+    skipna : bool, default True
+        Exclude NA/null values. If an entire row/column is NA, the result
+        will be NA.
+    numeric_only : bool, default {numeric_only_default}
+        Include only `float`, `int` or `boolean` data.
+
+        .. versionadded:: 1.5.0
+
+    Returns
+    -------
+    Series
+        Indexes of minima along the specified axis.
+
+    Raises
+    ------
+    ValueError
+        * If the row/column is empty
+
+    See Also
+    --------
+    Series.idxmin : Return index of the minimum element.
+
+    Notes
+    -----
+    This method is the DataFrame version of ``ndarray.argmin``.
+
+    Examples
+    --------
+    Consider a dataset containing food consumption in Argentina.
+
+    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
+    ...                     'co2_emissions': [37.2, 19.66, 1712]}},
+    ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+    >>> df
+                    consumption  co2_emissions
+    Pork                  10.51         37.20
+    Wheat Products       103.11         19.66
+    Beef                  55.48       1712.00
+
+    By default, it returns the index for the minimum value in each column.
+
+    >>> df.idxmin()
+    consumption                Pork
+    co2_emissions    Wheat Products
+    dtype: object
+
+    To return the index for the minimum value in each row, use ``axis="columns"``.
+
+    >>> df.idxmin(axis="columns")
+    Pork                consumption
+    Wheat Products    co2_emissions
+    Beef                consumption
+    dtype: object
+"""
+
+_shared_docs[
+    "idxmax"
+] = """
+    Return index of first occurrence of maximum over requested axis.
+
+    NA/null values are excluded.
+
+    Parameters
+    ----------
+    axis : {{0 or 'index', 1 or 'columns'}}, default 0
+        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+    skipna : bool, default True
+        Exclude NA/null values. If an entire row/column is NA, the result
+        will be NA.
+    numeric_only : bool, default {numeric_only_default}
+        Include only `float`, `int` or `boolean` data.
+
+        .. versionadded:: 1.5.0
+
+    Returns
+    -------
+    Series
+        Indexes of maxima along the specified axis.
+
+    Raises
+    ------
+    ValueError
+        * If the row/column is empty
+
+    See Also
+    --------
+    Series.idxmax : Return index of the maximum element.
+
+    Notes
+    -----
+    This method is the DataFrame version of ``ndarray.argmax``.
+
+    Examples
+    --------
+    Consider a dataset containing food consumption in Argentina.
+
+    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
+    ...                     'co2_emissions': [37.2, 19.66, 1712]}},
+    ...                   index=['Pork', 'Wheat Products', 'Beef'])
+
+    >>> df
+                    consumption  co2_emissions
+    Pork                  10.51         37.20
+    Wheat Products       103.11         19.66
+    Beef                  55.48       1712.00
+
+    By default, it returns the index for the maximum value in each column.
+
+    >>> df.idxmax()
+    consumption     Wheat Products
+    co2_emissions             Beef
+    dtype: object
+
+    To return the index for the maximum value in each row, use ``axis="columns"``.
+
+    >>> df.idxmax(axis="columns")
+    Pork              co2_emissions
+    Wheat Products     consumption
+    Beef              co2_emissions
+    dtype: object
+"""
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sorting.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sorting.py
new file mode 100644
index 0000000000000000000000000000000000000000..a431842218b3bbb97cb5373e03d2a31a4f69ca10
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sorting.py
@@ -0,0 +1,748 @@
+""" miscellaneous sorting / groupby utilities """
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    DefaultDict,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import (
+    algos,
+    hashtable,
+    lib,
+)
+from pandas._libs.hashtable import unique_label_indices
+
+from pandas.core.dtypes.common import (
+    ensure_int64,
+    ensure_platform_int,
+)
+from pandas.core.dtypes.generic import (
+    ABCMultiIndex,
+    ABCRangeIndex,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        IndexKeyFunc,
+        Level,
+        NaPosition,
+        Shape,
+        SortKind,
+        npt,
+    )
+
+    from pandas import (
+        MultiIndex,
+        Series,
+    )
+    from pandas.core.arrays import ExtensionArray
+    from pandas.core.indexes.base import Index
+
+
+def get_indexer_indexer(
+    target: Index,
+    level: Level | list[Level] | None,
+    ascending: list[bool] | bool,
+    kind: SortKind,
+    na_position: NaPosition,
+    sort_remaining: bool,
+    key: IndexKeyFunc,
+) -> npt.NDArray[np.intp] | None:
+    """
+    Helper method that return the indexer according to input parameters for
+    the sort_index method of DataFrame and Series.
+
+    Parameters
+    ----------
+    target : Index
+    level : int or level name or list of ints or list of level names
+    ascending : bool or list of bools, default True
+    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}
+    na_position : {'first', 'last'}
+    sort_remaining : bool
+    key : callable, optional
+
+    Returns
+    -------
+    Optional[ndarray[intp]]
+        The indexer for the new index.
+    """
+
+    # error: Incompatible types in assignment (expression has type
+    # "Union[ExtensionArray, ndarray[Any, Any], Index, Series]", variable has
+    # type "Index")
+    target = ensure_key_mapped(target, key, levels=level)  # type: ignore[assignment]
+    target = target._sort_levels_monotonic()
+
+    if level is not None:
+        _, indexer = target.sortlevel(
+            level,
+            ascending=ascending,
+            sort_remaining=sort_remaining,
+            na_position=na_position,
+        )
+    elif (np.all(ascending) and target.is_monotonic_increasing) or (
+        not np.any(ascending) and target.is_monotonic_decreasing
+    ):
+        # Check monotonic-ness before sort an index (GH 11080)
+        return None
+    elif isinstance(target, ABCMultiIndex):
+        codes = [lev.codes for lev in target._get_codes_for_sorting()]
+        indexer = lexsort_indexer(
+            codes, orders=ascending, na_position=na_position, codes_given=True
+        )
+    else:
+        # ascending can only be a Sequence for MultiIndex
+        indexer = nargsort(
+            target,
+            kind=kind,
+            ascending=cast(bool, ascending),
+            na_position=na_position,
+        )
+    return indexer
+
+
+def get_group_index(
+    labels, shape: Shape, sort: bool, xnull: bool
+) -> npt.NDArray[np.int64]:
+    """
+    For the particular label_list, gets the offsets into the hypothetical list
+    representing the totally ordered cartesian product of all possible label
+    combinations, *as long as* this space fits within int64 bounds;
+    otherwise, though group indices identify unique combinations of
+    labels, they cannot be deconstructed.
+    - If `sort`, rank of returned ids preserve lexical ranks of labels.
+      i.e. returned id's can be used to do lexical sort on labels;
+    - If `xnull` nulls (-1 labels) are passed through.
+
+    Parameters
+    ----------
+    labels : sequence of arrays
+        Integers identifying levels at each location
+    shape : tuple[int, ...]
+        Number of unique levels at each location
+    sort : bool
+        If the ranks of returned ids should match lexical ranks of labels
+    xnull : bool
+        If true nulls are excluded. i.e. -1 values in the labels are
+        passed through.
+
+    Returns
+    -------
+    An array of type int64 where two elements are equal if their corresponding
+    labels are equal at all location.
+
+    Notes
+    -----
+    The length of `labels` and `shape` must be identical.
+    """
+
+    def _int64_cut_off(shape) -> int:
+        acc = 1
+        for i, mul in enumerate(shape):
+            acc *= int(mul)
+            if not acc < lib.i8max:
+                return i
+        return len(shape)
+
+    def maybe_lift(lab, size: int) -> tuple[np.ndarray, int]:
+        # promote nan values (assigned -1 label in lab array)
+        # so that all output values are non-negative
+        return (lab + 1, size + 1) if (lab == -1).any() else (lab, size)
+
+    labels = [ensure_int64(x) for x in labels]
+    lshape = list(shape)
+    if not xnull:
+        for i, (lab, size) in enumerate(zip(labels, shape)):
+            labels[i], lshape[i] = maybe_lift(lab, size)
+
+    labels = list(labels)
+
+    # Iteratively process all the labels in chunks sized so less
+    # than lib.i8max unique int ids will be required for each chunk
+    while True:
+        # how many levels can be done without overflow:
+        nlev = _int64_cut_off(lshape)
+
+        # compute flat ids for the first `nlev` levels
+        stride = np.prod(lshape[1:nlev], dtype="i8")
+        out = stride * labels[0].astype("i8", subok=False, copy=False)
+
+        for i in range(1, nlev):
+            if lshape[i] == 0:
+                stride = np.int64(0)
+            else:
+                stride //= lshape[i]
+            out += labels[i] * stride
+
+        if xnull:  # exclude nulls
+            mask = labels[0] == -1
+            for lab in labels[1:nlev]:
+                mask |= lab == -1
+            out[mask] = -1
+
+        if nlev == len(lshape):  # all levels done!
+            break
+
+        # compress what has been done so far in order to avoid overflow
+        # to retain lexical ranks, obs_ids should be sorted
+        comp_ids, obs_ids = compress_group_index(out, sort=sort)
+
+        labels = [comp_ids] + labels[nlev:]
+        lshape = [len(obs_ids)] + lshape[nlev:]
+
+    return out
+
+
+def get_compressed_ids(
+    labels, sizes: Shape
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]:
+    """
+    Group_index is offsets into cartesian product of all possible labels. This
+    space can be huge, so this function compresses it, by computing offsets
+    (comp_ids) into the list of unique labels (obs_group_ids).
+
+    Parameters
+    ----------
+    labels : list of label arrays
+    sizes : tuple[int] of size of the levels
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+        comp_ids
+    np.ndarray[np.int64]
+        obs_group_ids
+    """
+    ids = get_group_index(labels, sizes, sort=True, xnull=False)
+    return compress_group_index(ids, sort=True)
+
+
+def is_int64_overflow_possible(shape: Shape) -> bool:
+    the_prod = 1
+    for x in shape:
+        the_prod *= int(x)
+
+    return the_prod >= lib.i8max
+
+
+def _decons_group_index(
+    comp_labels: npt.NDArray[np.intp], shape: Shape
+) -> list[npt.NDArray[np.intp]]:
+    # reconstruct labels
+    if is_int64_overflow_possible(shape):
+        # at some point group indices are factorized,
+        # and may not be deconstructed here! wrong path!
+        raise ValueError("cannot deconstruct factorized group indices!")
+
+    label_list = []
+    factor = 1
+    y = np.array(0)
+    x = comp_labels
+    for i in reversed(range(len(shape))):
+        labels = (x - y) % (factor * shape[i]) // factor
+        np.putmask(labels, comp_labels < 0, -1)
+        label_list.append(labels)
+        y = labels * factor
+        factor *= shape[i]
+    return label_list[::-1]
+
+
+def decons_obs_group_ids(
+    comp_ids: npt.NDArray[np.intp],
+    obs_ids: npt.NDArray[np.intp],
+    shape: Shape,
+    labels: Sequence[npt.NDArray[np.signedinteger]],
+    xnull: bool,
+) -> list[npt.NDArray[np.intp]]:
+    """
+    Reconstruct labels from observed group ids.
+
+    Parameters
+    ----------
+    comp_ids : np.ndarray[np.intp]
+    obs_ids: np.ndarray[np.intp]
+    shape : tuple[int]
+    labels : Sequence[np.ndarray[np.signedinteger]]
+    xnull : bool
+        If nulls are excluded; i.e. -1 labels are passed through.
+    """
+    if not xnull:
+        lift = np.fromiter(((a == -1).any() for a in labels), dtype=np.intp)
+        arr_shape = np.asarray(shape, dtype=np.intp) + lift
+        shape = tuple(arr_shape)
+
+    if not is_int64_overflow_possible(shape):
+        # obs ids are deconstructable! take the fast route!
+        out = _decons_group_index(obs_ids, shape)
+        return out if xnull or not lift.any() else [x - y for x, y in zip(out, lift)]
+
+    indexer = unique_label_indices(comp_ids)
+    return [lab[indexer].astype(np.intp, subok=False, copy=True) for lab in labels]
+
+
+def lexsort_indexer(
+    keys: Sequence[ArrayLike | Index | Series],
+    orders=None,
+    na_position: str = "last",
+    key: Callable | None = None,
+    codes_given: bool = False,
+) -> npt.NDArray[np.intp]:
+    """
+    Performs lexical sorting on a set of keys
+
+    Parameters
+    ----------
+    keys : Sequence[ArrayLike | Index | Series]
+        Sequence of arrays to be sorted by the indexer
+        Sequence[Series] is only if key is not None.
+    orders : bool or list of booleans, optional
+        Determines the sorting order for each element in keys. If a list,
+        it must be the same length as keys. This determines whether the
+        corresponding element in keys should be sorted in ascending
+        (True) or descending (False) order. if bool, applied to all
+        elements as above. if None, defaults to True.
+    na_position : {'first', 'last'}, default 'last'
+        Determines placement of NA elements in the sorted list ("last" or "first")
+    key : Callable, optional
+        Callable key function applied to every element in keys before sorting
+    codes_given: bool, False
+        Avoid categorical materialization if codes are already provided.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    from pandas.core.arrays import Categorical
+
+    if na_position not in ["last", "first"]:
+        raise ValueError(f"invalid na_position: {na_position}")
+
+    if isinstance(orders, bool):
+        orders = [orders] * len(keys)
+    elif orders is None:
+        orders = [True] * len(keys)
+
+    labels = []
+
+    for k, order in zip(keys, orders):
+        k = ensure_key_mapped(k, key)
+        if codes_given:
+            codes = cast(np.ndarray, k)
+            n = codes.max() + 1 if len(codes) else 0
+        else:
+            cat = Categorical(k, ordered=True)
+            codes = cat.codes
+            n = len(cat.categories)
+
+        mask = codes == -1
+
+        if na_position == "last" and mask.any():
+            codes = np.where(mask, n, codes)
+
+        # not order means descending
+        if not order:
+            codes = np.where(mask, codes, n - codes - 1)
+
+        labels.append(codes)
+
+    return np.lexsort(labels[::-1])
+
+
+def nargsort(
+    items: ArrayLike | Index | Series,
+    kind: SortKind = "quicksort",
+    ascending: bool = True,
+    na_position: str = "last",
+    key: Callable | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
+) -> npt.NDArray[np.intp]:
+    """
+    Intended to be a drop-in replacement for np.argsort which handles NaNs.
+
+    Adds ascending, na_position, and key parameters.
+
+    (GH #6399, #5231, #27237)
+
+    Parameters
+    ----------
+    items : np.ndarray, ExtensionArray, Index, or Series
+    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
+    ascending : bool, default True
+    na_position : {'first', 'last'}, default 'last'
+    key : Optional[Callable], default None
+    mask : Optional[np.ndarray[bool]], default None
+        Passed when called by ExtensionArray.argsort.
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+
+    if key is not None:
+        # see TestDataFrameSortKey, TestRangeIndex::test_sort_values_key
+        items = ensure_key_mapped(items, key)
+        return nargsort(
+            items,
+            kind=kind,
+            ascending=ascending,
+            na_position=na_position,
+            key=None,
+            mask=mask,
+        )
+
+    if isinstance(items, ABCRangeIndex):
+        return items.argsort(ascending=ascending)
+    elif not isinstance(items, ABCMultiIndex):
+        items = extract_array(items)
+    else:
+        raise TypeError(
+            "nargsort does not support MultiIndex. Use index.sort_values instead."
+        )
+
+    if mask is None:
+        mask = np.asarray(isna(items))
+
+    if not isinstance(items, np.ndarray):
+        # i.e. ExtensionArray
+        return items.argsort(
+            ascending=ascending,
+            kind=kind,
+            na_position=na_position,
+        )
+
+    idx = np.arange(len(items))
+    non_nans = items[~mask]
+    non_nan_idx = idx[~mask]
+
+    nan_idx = np.nonzero(mask)[0]
+    if not ascending:
+        non_nans = non_nans[::-1]
+        non_nan_idx = non_nan_idx[::-1]
+    indexer = non_nan_idx[non_nans.argsort(kind=kind)]
+    if not ascending:
+        indexer = indexer[::-1]
+    # Finally, place the NaNs at the end or the beginning according to
+    # na_position
+    if na_position == "last":
+        indexer = np.concatenate([indexer, nan_idx])
+    elif na_position == "first":
+        indexer = np.concatenate([nan_idx, indexer])
+    else:
+        raise ValueError(f"invalid na_position: {na_position}")
+    return ensure_platform_int(indexer)
+
+
+def nargminmax(values: ExtensionArray, method: str, axis: AxisInt = 0):
+    """
+    Implementation of np.argmin/argmax but for ExtensionArray and which
+    handles missing values.
+
+    Parameters
+    ----------
+    values : ExtensionArray
+    method : {"argmax", "argmin"}
+    axis : int, default 0
+
+    Returns
+    -------
+    int
+    """
+    assert method in {"argmax", "argmin"}
+    func = np.argmax if method == "argmax" else np.argmin
+
+    mask = np.asarray(isna(values))
+    arr_values = values._values_for_argsort()
+
+    if arr_values.ndim > 1:
+        if mask.any():
+            if axis == 1:
+                zipped = zip(arr_values, mask)
+            else:
+                zipped = zip(arr_values.T, mask.T)
+            return np.array([_nanargminmax(v, m, func) for v, m in zipped])
+        return func(arr_values, axis=axis)
+
+    return _nanargminmax(arr_values, mask, func)
+
+
+def _nanargminmax(values: np.ndarray, mask: npt.NDArray[np.bool_], func) -> int:
+    """
+    See nanargminmax.__doc__.
+    """
+    idx = np.arange(values.shape[0])
+    non_nans = values[~mask]
+    non_nan_idx = idx[~mask]
+
+    return non_nan_idx[func(non_nans)]
+
+
+def _ensure_key_mapped_multiindex(
+    index: MultiIndex, key: Callable, level=None
+) -> MultiIndex:
+    """
+    Returns a new MultiIndex in which key has been applied
+    to all levels specified in level (or all levels if level
+    is None). Used for key sorting for MultiIndex.
+
+    Parameters
+    ----------
+    index : MultiIndex
+        Index to which to apply the key function on the
+        specified levels.
+    key : Callable
+        Function that takes an Index and returns an Index of
+        the same shape. This key is applied to each level
+        separately. The name of the level can be used to
+        distinguish different levels for application.
+    level : list-like, int or str, default None
+        Level or list of levels to apply the key function to.
+        If None, key function is applied to all levels. Other
+        levels are left unchanged.
+
+    Returns
+    -------
+    labels : MultiIndex
+        Resulting MultiIndex with modified levels.
+    """
+
+    if level is not None:
+        if isinstance(level, (str, int)):
+            sort_levels = [level]
+        else:
+            sort_levels = level
+
+        sort_levels = [index._get_level_number(lev) for lev in sort_levels]
+    else:
+        sort_levels = list(range(index.nlevels))  # satisfies mypy
+
+    mapped = [
+        ensure_key_mapped(index._get_level_values(level), key)
+        if level in sort_levels
+        else index._get_level_values(level)
+        for level in range(index.nlevels)
+    ]
+
+    return type(index).from_arrays(mapped)
+
+
+def ensure_key_mapped(
+    values: ArrayLike | Index | Series, key: Callable | None, levels=None
+) -> ArrayLike | Index | Series:
+    """
+    Applies a callable key function to the values function and checks
+    that the resulting value has the same shape. Can be called on Index
+    subclasses, Series, DataFrames, or ndarrays.
+
+    Parameters
+    ----------
+    values : Series, DataFrame, Index subclass, or ndarray
+    key : Optional[Callable], key to be called on the values array
+    levels : Optional[List], if values is a MultiIndex, list of levels to
+    apply the key to.
+    """
+    from pandas.core.indexes.api import Index
+
+    if not key:
+        return values
+
+    if isinstance(values, ABCMultiIndex):
+        return _ensure_key_mapped_multiindex(values, key, level=levels)
+
+    result = key(values.copy())
+    if len(result) != len(values):
+        raise ValueError(
+            "User-provided `key` function must not change the shape of the array."
+        )
+
+    try:
+        if isinstance(
+            values, Index
+        ):  # convert to a new Index subclass, not necessarily the same
+            result = Index(result)
+        else:
+            # try to revert to original type otherwise
+            type_of_values = type(values)
+            #  error: Too many arguments for "ExtensionArray"
+            result = type_of_values(result)  # type: ignore[call-arg]
+    except TypeError:
+        raise TypeError(
+            f"User-provided `key` function returned an invalid type {type(result)} \
+            which could not be converted to {type(values)}."
+        )
+
+    return result
+
+
+def get_flattened_list(
+    comp_ids: npt.NDArray[np.intp],
+    ngroups: int,
+    levels: Iterable[Index],
+    labels: Iterable[np.ndarray],
+) -> list[tuple]:
+    """Map compressed group id -> key tuple."""
+    comp_ids = comp_ids.astype(np.int64, copy=False)
+    arrays: DefaultDict[int, list[int]] = defaultdict(list)
+    for labs, level in zip(labels, levels):
+        table = hashtable.Int64HashTable(ngroups)
+        table.map_keys_to_values(comp_ids, labs.astype(np.int64, copy=False))
+        for i in range(ngroups):
+            arrays[i].append(level[table.get_item(i)])
+    return [tuple(array) for array in arrays.values()]
+
+
+def get_indexer_dict(
+    label_list: list[np.ndarray], keys: list[Index]
+) -> dict[Hashable, npt.NDArray[np.intp]]:
+    """
+    Returns
+    -------
+    dict:
+        Labels mapped to indexers.
+    """
+    shape = tuple(len(x) for x in keys)
+
+    group_index = get_group_index(label_list, shape, sort=True, xnull=True)
+    if np.all(group_index == -1):
+        # Short-circuit, lib.indices_fast will return the same
+        return {}
+    ngroups = (
+        ((group_index.size and group_index.max()) + 1)
+        if is_int64_overflow_possible(shape)
+        else np.prod(shape, dtype="i8")
+    )
+
+    sorter = get_group_index_sorter(group_index, ngroups)
+
+    sorted_labels = [lab.take(sorter) for lab in label_list]
+    group_index = group_index.take(sorter)
+
+    return lib.indices_fast(sorter, group_index, keys, sorted_labels)
+
+
+# ----------------------------------------------------------------------
+# sorting levels...cleverly?
+
+
+def get_group_index_sorter(
+    group_index: npt.NDArray[np.intp], ngroups: int | None = None
+) -> npt.NDArray[np.intp]:
+    """
+    algos.groupsort_indexer implements `counting sort` and it is at least
+    O(ngroups), where
+        ngroups = prod(shape)
+        shape = map(len, keys)
+    that is, linear in the number of combinations (cartesian product) of unique
+    values of groupby keys. This can be huge when doing multi-key groupby.
+    np.argsort(kind='mergesort') is O(count x log(count)) where count is the
+    length of the data-frame;
+    Both algorithms are `stable` sort and that is necessary for correctness of
+    groupby operations. e.g. consider:
+        df.groupby(key)[col].transform('first')
+
+    Parameters
+    ----------
+    group_index : np.ndarray[np.intp]
+        signed integer dtype
+    ngroups : int or None, default None
+
+    Returns
+    -------
+    np.ndarray[np.intp]
+    """
+    if ngroups is None:
+        ngroups = 1 + group_index.max()
+    count = len(group_index)
+    alpha = 0.0  # taking complexities literally; there may be
+    beta = 1.0  # some room for fine-tuning these parameters
+    do_groupsort = count > 0 and ((alpha + beta * ngroups) < (count * np.log(count)))
+    if do_groupsort:
+        sorter, _ = algos.groupsort_indexer(
+            ensure_platform_int(group_index),
+            ngroups,
+        )
+        # sorter _should_ already be intp, but mypy is not yet able to verify
+    else:
+        sorter = group_index.argsort(kind="mergesort")
+    return ensure_platform_int(sorter)
+
+
+def compress_group_index(
+    group_index: npt.NDArray[np.int64], sort: bool = True
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]:
+    """
+    Group_index is offsets into cartesian product of all possible labels. This
+    space can be huge, so this function compresses it, by computing offsets
+    (comp_ids) into the list of unique labels (obs_group_ids).
+    """
+    if len(group_index) and np.all(group_index[1:] >= group_index[:-1]):
+        # GH 53806: fast path for sorted group_index
+        unique_mask = np.concatenate(
+            [group_index[:1] > -1, group_index[1:] != group_index[:-1]]
+        )
+        comp_ids = unique_mask.cumsum()
+        comp_ids -= 1
+        obs_group_ids = group_index[unique_mask]
+    else:
+        size_hint = len(group_index)
+        table = hashtable.Int64HashTable(size_hint)
+
+        group_index = ensure_int64(group_index)
+
+        # note, group labels come out ascending (ie, 1,2,3 etc)
+        comp_ids, obs_group_ids = table.get_labels_groupby(group_index)
+
+        if sort and len(obs_group_ids) > 0:
+            obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids)
+
+    return ensure_int64(comp_ids), ensure_int64(obs_group_ids)
+
+
+def _reorder_by_uniques(
+    uniques: npt.NDArray[np.int64], labels: npt.NDArray[np.intp]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.intp]]:
+    """
+    Parameters
+    ----------
+    uniques : np.ndarray[np.int64]
+    labels : np.ndarray[np.intp]
+
+    Returns
+    -------
+    np.ndarray[np.int64]
+    np.ndarray[np.intp]
+    """
+    # sorter is index where elements ought to go
+    sorter = uniques.argsort()
+
+    # reverse_indexer is where elements came from
+    reverse_indexer = np.empty(len(sorter), dtype=np.intp)
+    reverse_indexer.put(sorter, np.arange(len(sorter)))
+
+    mask = labels < 0
+
+    # move labels to right locations (ie, unsort ascending labels)
+    labels = reverse_indexer.take(labels)
+    np.putmask(labels, mask, -1)
+
+    # sort observed ids
+    uniques = uniques.take(sorter)
+
+    return uniques, labels
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sparse/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sparse/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sparse/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..6650a5c4e90a0f73a43e6e35cdd26c1189daf256
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/sparse/api.py
@@ -0,0 +1,5 @@
+from pandas.core.dtypes.dtypes import SparseDtype
+
+from pandas.core.arrays.sparse import SparseArray
+
+__all__ = ["SparseArray", "SparseDtype"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ce75f768c5d1dcd8586264fe1faf756d5d5e94
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/__init__.py
@@ -0,0 +1,28 @@
+"""
+Implementation of pandas.Series.str and its interface.
+
+* strings.accessor.StringMethods : Accessor for Series.str
+* strings.base.BaseStringArrayMethods: Mixin ABC for EAs to implement str methods
+
+Most methods on the StringMethods accessor follow the pattern:
+
+    1. extract the array from the series (or index)
+    2. Call that array's implementation of the string method
+    3. Wrap the result (in a Series, index, or DataFrame)
+
+Pandas extension arrays implementing string methods should inherit from
+pandas.core.strings.base.BaseStringArrayMethods. This is an ABC defining
+the various string methods. To avoid namespace clashes and pollution,
+these are prefixed with `_str_`. So ``Series.str.upper()`` calls
+``Series.array._str_upper()``. The interface isn't currently public
+to other string extension arrays.
+"""
+# Pandas current implementation is in ObjectStringArrayMixin. This is designed
+# to work on object-dtype ndarrays.
+#
+# BaseStringArrayMethods
+#  - ObjectStringArrayMixin
+#     - StringArray
+#     - NumpyExtensionArray
+#     - Categorical
+#     - ArrowStringArray
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/accessor.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/accessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..da17543f8470d2394ad7d9aac66c6db99b100df0
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/accessor.py
@@ -0,0 +1,3571 @@
+from __future__ import annotations
+
+import codecs
+from functools import wraps
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+from pandas._typing import (
+    AlignJoin,
+    DtypeObj,
+    F,
+    Scalar,
+    npt,
+)
+from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_integer,
+    is_list_like,
+    is_object_dtype,
+    is_re,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import NoNewAttributesMixin
+from pandas.core.construction import extract_array
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+_shared_docs: dict[str, str] = {}
+_cpython_optimized_encoders = (
+    "utf-8",
+    "utf8",
+    "latin-1",
+    "latin1",
+    "iso-8859-1",
+    "mbcs",
+    "ascii",
+)
+_cpython_optimized_decoders = _cpython_optimized_encoders + ("utf-16", "utf-32")
+
+
+def forbid_nonstring_types(
+    forbidden: list[str] | None, name: str | None = None
+) -> Callable[[F], F]:
+    """
+    Decorator to forbid specific types for a method of StringMethods.
+
+    For calling `.str.{method}` on a Series or Index, it is necessary to first
+    initialize the :class:`StringMethods` object, and then call the method.
+    However, different methods allow different input types, and so this can not
+    be checked during :meth:`StringMethods.__init__`, but must be done on a
+    per-method basis. This decorator exists to facilitate this process, and
+    make it explicit which (inferred) types are disallowed by the method.
+
+    :meth:`StringMethods.__init__` allows the *union* of types its different
+    methods allow (after skipping NaNs; see :meth:`StringMethods._validate`),
+    namely: ['string', 'empty', 'bytes', 'mixed', 'mixed-integer'].
+
+    The default string types ['string', 'empty'] are allowed for all methods.
+    For the additional types ['bytes', 'mixed', 'mixed-integer'], each method
+    then needs to forbid the types it is not intended for.
+
+    Parameters
+    ----------
+    forbidden : list-of-str or None
+        List of forbidden non-string types, may be one or more of
+        `['bytes', 'mixed', 'mixed-integer']`.
+    name : str, default None
+        Name of the method to use in the error message. By default, this is
+        None, in which case the name from the method being wrapped will be
+        copied. However, for working with further wrappers (like _pat_wrapper
+        and _noarg_wrapper), it is necessary to specify the name.
+
+    Returns
+    -------
+    func : wrapper
+        The method to which the decorator is applied, with an added check that
+        enforces the inferred type to not be in the list of forbidden types.
+
+    Raises
+    ------
+    TypeError
+        If the inferred type of the underlying data is in `forbidden`.
+    """
+    # deal with None
+    forbidden = [] if forbidden is None else forbidden
+
+    allowed_types = {"string", "empty", "bytes", "mixed", "mixed-integer"} - set(
+        forbidden
+    )
+
+    def _forbid_nonstring_types(func: F) -> F:
+        func_name = func.__name__ if name is None else name
+
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            if self._inferred_dtype not in allowed_types:
+                msg = (
+                    f"Cannot use .str.{func_name} with values of "
+                    f"inferred dtype '{self._inferred_dtype}'."
+                )
+                raise TypeError(msg)
+            return func(self, *args, **kwargs)
+
+        wrapper.__name__ = func_name
+        return cast(F, wrapper)
+
+    return _forbid_nonstring_types
+
+
+def _map_and_wrap(name: str | None, docstring: str | None):
+    @forbid_nonstring_types(["bytes"], name=name)
+    def wrapper(self):
+        result = getattr(self._data.array, f"_str_{name}")()
+        return self._wrap_result(
+            result, returns_string=name not in ("isnumeric", "isdecimal")
+        )
+
+    wrapper.__doc__ = docstring
+    return wrapper
+
+
+class StringMethods(NoNewAttributesMixin):
+    """
+    Vectorized string functions for Series and Index.
+
+    NAs stay NA unless handled otherwise by a particular method.
+    Patterned after Python's string methods, with some inspiration from
+    R's stringr package.
+
+    Examples
+    --------
+    >>> s = pd.Series(["A_Str_Series"])
+    >>> s
+    0    A_Str_Series
+    dtype: object
+
+    >>> s.str.split("_")
+    0    [A, Str, Series]
+    dtype: object
+
+    >>> s.str.replace("_", "")
+    0    AStrSeries
+    dtype: object
+    """
+
+    # Note: see the docstring in pandas.core.strings.__init__
+    # for an explanation of the implementation.
+    # TODO: Dispatch all the methods
+    # Currently the following are not dispatched to the array
+    # * cat
+    # * extractall
+
+    def __init__(self, data) -> None:
+        from pandas.core.arrays.string_ import StringDtype
+
+        self._inferred_dtype = self._validate(data)
+        self._is_categorical = isinstance(data.dtype, CategoricalDtype)
+        self._is_string = isinstance(data.dtype, StringDtype)
+        self._data = data
+
+        self._index = self._name = None
+        if isinstance(data, ABCSeries):
+            self._index = data.index
+            self._name = data.name
+
+        # ._values.categories works for both Series/Index
+        self._parent = data._values.categories if self._is_categorical else data
+        # save orig to blow up categoricals to the right type
+        self._orig = data
+        self._freeze()
+
+    @staticmethod
+    def _validate(data):
+        """
+        Auxiliary function for StringMethods, infers and checks dtype of data.
+
+        This is a "first line of defence" at the creation of the StringMethods-
+        object, and just checks that the dtype is in the
+        *union* of the allowed types over all string methods below; this
+        restriction is then refined on a per-method basis using the decorator
+        @forbid_nonstring_types (more info in the corresponding docstring).
+
+        This really should exclude all series/index with any non-string values,
+        but that isn't practical for performance reasons until we have a str
+        dtype (GH 9343 / 13877)
+
+        Parameters
+        ----------
+        data : The content of the Series
+
+        Returns
+        -------
+        dtype : inferred dtype of data
+        """
+        if isinstance(data, ABCMultiIndex):
+            raise AttributeError(
+                "Can only use .str accessor with Index, not MultiIndex"
+            )
+
+        # see _libs/lib.pyx for list of inferred types
+        allowed_types = ["string", "empty", "bytes", "mixed", "mixed-integer"]
+
+        data = extract_array(data)
+
+        values = getattr(data, "categories", data)  # categorical / normal
+
+        inferred_dtype = lib.infer_dtype(values, skipna=True)
+
+        if inferred_dtype not in allowed_types:
+            raise AttributeError("Can only use .str accessor with string values!")
+        return inferred_dtype
+
+    def __getitem__(self, key):
+        result = self._data.array._str_getitem(key)
+        return self._wrap_result(result)
+
+    def __iter__(self) -> Iterator:
+        raise TypeError(f"'{type(self).__name__}' object is not iterable")
+
+    def _wrap_result(
+        self,
+        result,
+        name=None,
+        expand: bool | None = None,
+        fill_value=np.nan,
+        returns_string: bool = True,
+        returns_bool: bool = False,
+        dtype=None,
+    ):
+        from pandas import (
+            Index,
+            MultiIndex,
+        )
+
+        if not hasattr(result, "ndim") or not hasattr(result, "dtype"):
+            if isinstance(result, ABCDataFrame):
+                result = result.__finalize__(self._orig, name="str")
+            return result
+        assert result.ndim < 3
+
+        # We can be wrapping a string / object / categorical result, in which
+        # case we'll want to return the same dtype as the input.
+        # Or we can be wrapping a numeric output, in which case we don't want
+        # to return a StringArray.
+        # Ideally the array method returns the right array type.
+        if expand is None:
+            # infer from ndim if expand is not specified
+            expand = result.ndim != 1
+        elif expand is True and not isinstance(self._orig, ABCIndex):
+            # required when expand=True is explicitly specified
+            # not needed when inferred
+            if isinstance(result.dtype, ArrowDtype):
+                import pyarrow as pa
+
+                from pandas.compat import pa_version_under11p0
+
+                from pandas.core.arrays.arrow.array import ArrowExtensionArray
+
+                value_lengths = pa.compute.list_value_length(result._pa_array)
+                max_len = pa.compute.max(value_lengths).as_py()
+                min_len = pa.compute.min(value_lengths).as_py()
+                if result._hasna:
+                    # ArrowExtensionArray.fillna doesn't work for list scalars
+                    result = ArrowExtensionArray(
+                        result._pa_array.fill_null([None] * max_len)
+                    )
+                if min_len < max_len:
+                    # append nulls to each scalar list element up to max_len
+                    if not pa_version_under11p0:
+                        result = ArrowExtensionArray(
+                            pa.compute.list_slice(
+                                result._pa_array,
+                                start=0,
+                                stop=max_len,
+                                return_fixed_size_list=True,
+                            )
+                        )
+                    else:
+                        all_null = np.full(max_len, fill_value=None, dtype=object)
+                        values = result.to_numpy()
+                        new_values = []
+                        for row in values:
+                            if len(row) < max_len:
+                                nulls = all_null[: max_len - len(row)]
+                                row = np.append(row, nulls)
+                            new_values.append(row)
+                        pa_type = result._pa_array.type
+                        result = ArrowExtensionArray(pa.array(new_values, type=pa_type))
+                if name is not None:
+                    labels = name
+                else:
+                    labels = range(max_len)
+                result = (
+                    pa.compute.list_flatten(result._pa_array)
+                    .to_numpy()
+                    .reshape(len(result), max_len)
+                )
+                result = {
+                    label: ArrowExtensionArray(pa.array(res))
+                    for label, res in zip(labels, result.T)
+                }
+            elif is_object_dtype(result):
+
+                def cons_row(x):
+                    if is_list_like(x):
+                        return x
+                    else:
+                        return [x]
+
+                result = [cons_row(x) for x in result]
+                if result and not self._is_string:
+                    # propagate nan values to match longest sequence (GH 18450)
+                    max_len = max(len(x) for x in result)
+                    result = [
+                        x * max_len if len(x) == 0 or x[0] is np.nan else x
+                        for x in result
+                    ]
+
+        if not isinstance(expand, bool):
+            raise ValueError("expand must be True or False")
+
+        if expand is False:
+            # if expand is False, result should have the same name
+            # as the original otherwise specified
+            if name is None:
+                name = getattr(result, "name", None)
+            if name is None:
+                # do not use logical or, _orig may be a DataFrame
+                # which has "name" column
+                name = self._orig.name
+
+        # Wait until we are sure result is a Series or Index before
+        # checking attributes (GH 12180)
+        if isinstance(self._orig, ABCIndex):
+            # if result is a boolean np.array, return the np.array
+            # instead of wrapping it into a boolean Index (GH 8875)
+            if is_bool_dtype(result):
+                return result
+
+            if expand:
+                result = list(result)
+                out: Index = MultiIndex.from_tuples(result, names=name)
+                if out.nlevels == 1:
+                    # We had all tuples of length-one, which are
+                    # better represented as a regular Index.
+                    out = out.get_level_values(0)
+                return out
+            else:
+                return Index(result, name=name, dtype=dtype)
+        else:
+            index = self._orig.index
+            # This is a mess.
+            _dtype: DtypeObj | str | None = dtype
+            vdtype = getattr(result, "dtype", None)
+            if _dtype is not None:
+                pass
+            elif self._is_string:
+                if is_bool_dtype(vdtype):
+                    _dtype = result.dtype
+                elif returns_string:
+                    _dtype = self._orig.dtype
+                else:
+                    _dtype = vdtype
+            elif vdtype is not None:
+                _dtype = vdtype
+
+            if expand:
+                cons = self._orig._constructor_expanddim
+                result = cons(result, columns=name, index=index, dtype=_dtype)
+            else:
+                # Must be a Series
+                cons = self._orig._constructor
+                result = cons(result, name=name, index=index, dtype=_dtype)
+            result = result.__finalize__(self._orig, method="str")
+            if name is not None and result.ndim == 1:
+                # __finalize__ might copy over the original name, but we may
+                # want the new name (e.g. str.extract).
+                result.name = name
+            return result
+
+    def _get_series_list(self, others):
+        """
+        Auxiliary function for :meth:`str.cat`. Turn potentially mixed input
+        into a list of Series (elements without an index must match the length
+        of the calling Series/Index).
+
+        Parameters
+        ----------
+        others : Series, DataFrame, np.ndarray, list-like or list-like of
+            Objects that are either Series, Index or np.ndarray (1-dim).
+
+        Returns
+        -------
+        list of Series
+            Others transformed into list of Series.
+        """
+        from pandas import (
+            DataFrame,
+            Series,
+        )
+
+        # self._orig is either Series or Index
+        idx = self._orig if isinstance(self._orig, ABCIndex) else self._orig.index
+
+        # Generally speaking, all objects without an index inherit the index
+        # `idx` of the calling Series/Index - i.e. must have matching length.
+        # Objects with an index (i.e. Series/Index/DataFrame) keep their own.
+        if isinstance(others, ABCSeries):
+            return [others]
+        elif isinstance(others, ABCIndex):
+            return [Series(others, index=idx, dtype=others.dtype)]
+        elif isinstance(others, ABCDataFrame):
+            return [others[x] for x in others]
+        elif isinstance(others, np.ndarray) and others.ndim == 2:
+            others = DataFrame(others, index=idx)
+            return [others[x] for x in others]
+        elif is_list_like(others, allow_sets=False):
+            try:
+                others = list(others)  # ensure iterators do not get read twice etc
+            except TypeError:
+                # e.g. ser.str, raise below
+                pass
+            else:
+                # in case of list-like `others`, all elements must be
+                # either Series/Index/np.ndarray (1-dim)...
+                if all(
+                    isinstance(x, (ABCSeries, ABCIndex, ExtensionArray))
+                    or (isinstance(x, np.ndarray) and x.ndim == 1)
+                    for x in others
+                ):
+                    los: list[Series] = []
+                    while others:  # iterate through list and append each element
+                        los = los + self._get_series_list(others.pop(0))
+                    return los
+                # ... or just strings
+                elif all(not is_list_like(x) for x in others):
+                    return [Series(others, index=idx)]
+        raise TypeError(
+            "others must be Series, Index, DataFrame, np.ndarray "
+            "or list-like (either containing only strings or "
+            "containing only objects of type Series/Index/"
+            "np.ndarray[1-dim])"
+        )
+
+    @forbid_nonstring_types(["bytes", "mixed", "mixed-integer"])
+    def cat(
+        self,
+        others=None,
+        sep: str | None = None,
+        na_rep=None,
+        join: AlignJoin = "left",
+    ) -> str | Series | Index:
+        """
+        Concatenate strings in the Series/Index with given separator.
+
+        If `others` is specified, this function concatenates the Series/Index
+        and elements of `others` element-wise.
+        If `others` is not passed, then all values in the Series/Index are
+        concatenated into a single string with a given `sep`.
+
+        Parameters
+        ----------
+        others : Series, Index, DataFrame, np.ndarray or list-like
+            Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and
+            other list-likes of strings must have the same length as the
+            calling Series/Index, with the exception of indexed objects (i.e.
+            Series/Index/DataFrame) if `join` is not None.
+
+            If others is a list-like that contains a combination of Series,
+            Index or np.ndarray (1-dim), then all elements will be unpacked and
+            must satisfy the above criteria individually.
+
+            If others is None, the method returns the concatenation of all
+            strings in the calling Series/Index.
+        sep : str, default ''
+            The separator between the different elements/columns. By default
+            the empty string `''` is used.
+        na_rep : str or None, default None
+            Representation that is inserted for all missing values:
+
+            - If `na_rep` is None, and `others` is None, missing values in the
+              Series/Index are omitted from the result.
+            - If `na_rep` is None, and `others` is not None, a row containing a
+              missing value in any of the columns (before concatenation) will
+              have a missing value in the result.
+        join : {'left', 'right', 'outer', 'inner'}, default 'left'
+            Determines the join-style between the calling Series/Index and any
+            Series/Index/DataFrame in `others` (objects without an index need
+            to match the length of the calling Series/Index). To disable
+            alignment, use `.values` on any Series/Index/DataFrame in `others`.
+
+        Returns
+        -------
+        str, Series or Index
+            If `others` is None, `str` is returned, otherwise a `Series/Index`
+            (same type as caller) of objects is returned.
+
+        See Also
+        --------
+        split : Split each string in the Series/Index.
+        join : Join lists contained as elements in the Series/Index.
+
+        Examples
+        --------
+        When not passing `others`, all values are concatenated into a single
+        string:
+
+        >>> s = pd.Series(['a', 'b', np.nan, 'd'])
+        >>> s.str.cat(sep=' ')
+        'a b d'
+
+        By default, NA values in the Series are ignored. Using `na_rep`, they
+        can be given a representation:
+
+        >>> s.str.cat(sep=' ', na_rep='?')
+        'a b ? d'
+
+        If `others` is specified, corresponding values are concatenated with
+        the separator. Result will be a Series of strings.
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',')
+        0    a,A
+        1    b,B
+        2    NaN
+        3    d,D
+        dtype: object
+
+        Missing values will remain missing in the result, but can again be
+        represented using `na_rep`
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')
+        0    a,A
+        1    b,B
+        2    -,C
+        3    d,D
+        dtype: object
+
+        If `sep` is not specified, the values are concatenated without
+        separation.
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')
+        0    aA
+        1    bB
+        2    -C
+        3    dD
+        dtype: object
+
+        Series with different indexes can be aligned before concatenation. The
+        `join`-keyword works as in other methods.
+
+        >>> t = pd.Series(['d', 'a', 'e', 'c'], index=[3, 0, 4, 2])
+        >>> s.str.cat(t, join='left', na_rep='-')
+        0    aa
+        1    b-
+        2    -c
+        3    dd
+        dtype: object
+        >>>
+        >>> s.str.cat(t, join='outer', na_rep='-')
+        0    aa
+        1    b-
+        2    -c
+        3    dd
+        4    -e
+        dtype: object
+        >>>
+        >>> s.str.cat(t, join='inner', na_rep='-')
+        0    aa
+        2    -c
+        3    dd
+        dtype: object
+        >>>
+        >>> s.str.cat(t, join='right', na_rep='-')
+        3    dd
+        0    aa
+        4    -e
+        2    -c
+        dtype: object
+
+        For more examples, see :ref:`here <text.concatenate>`.
+        """
+        # TODO: dispatch
+        from pandas import (
+            Index,
+            Series,
+            concat,
+        )
+
+        if isinstance(others, str):
+            raise ValueError("Did you mean to supply a `sep` keyword?")
+        if sep is None:
+            sep = ""
+
+        if isinstance(self._orig, ABCIndex):
+            data = Series(self._orig, index=self._orig, dtype=self._orig.dtype)
+        else:  # Series
+            data = self._orig
+
+        # concatenate Series/Index with itself if no "others"
+        if others is None:
+            # error: Incompatible types in assignment (expression has type
+            # "ndarray", variable has type "Series")
+            data = ensure_object(data)  # type: ignore[assignment]
+            na_mask = isna(data)
+            if na_rep is None and na_mask.any():
+                return sep.join(data[~na_mask])
+            elif na_rep is not None and na_mask.any():
+                return sep.join(np.where(na_mask, na_rep, data))
+            else:
+                return sep.join(data)
+
+        try:
+            # turn anything in "others" into lists of Series
+            others = self._get_series_list(others)
+        except ValueError as err:  # do not catch TypeError raised by _get_series_list
+            raise ValueError(
+                "If `others` contains arrays or lists (or other "
+                "list-likes without an index), these must all be "
+                "of the same length as the calling Series/Index."
+            ) from err
+
+        # align if required
+        if any(not data.index.equals(x.index) for x in others):
+            # Need to add keys for uniqueness in case of duplicate columns
+            others = concat(
+                others,
+                axis=1,
+                join=(join if join == "inner" else "outer"),
+                keys=range(len(others)),
+                sort=False,
+                copy=False,
+            )
+            data, others = data.align(others, join=join)
+            others = [others[x] for x in others]  # again list of Series
+
+        all_cols = [ensure_object(x) for x in [data] + others]
+        na_masks = np.array([isna(x) for x in all_cols])
+        union_mask = np.logical_or.reduce(na_masks, axis=0)
+
+        if na_rep is None and union_mask.any():
+            # no na_rep means NaNs for all rows where any column has a NaN
+            # only necessary if there are actually any NaNs
+            result = np.empty(len(data), dtype=object)
+            np.putmask(result, union_mask, np.nan)
+
+            not_masked = ~union_mask
+            result[not_masked] = cat_safe([x[not_masked] for x in all_cols], sep)
+        elif na_rep is not None and union_mask.any():
+            # fill NaNs with na_rep in case there are actually any NaNs
+            all_cols = [
+                np.where(nm, na_rep, col) for nm, col in zip(na_masks, all_cols)
+            ]
+            result = cat_safe(all_cols, sep)
+        else:
+            # no NaNs - can just concatenate
+            result = cat_safe(all_cols, sep)
+
+        out: Index | Series
+        if isinstance(self._orig.dtype, CategoricalDtype):
+            # We need to infer the new categories.
+            dtype = self._orig.dtype.categories.dtype
+        else:
+            dtype = self._orig.dtype
+        if isinstance(self._orig, ABCIndex):
+            # add dtype for case that result is all-NA
+            if isna(result).all():
+                dtype = object  # type: ignore[assignment]
+
+            out = Index(result, dtype=dtype, name=self._orig.name)
+        else:  # Series
+            res_ser = Series(
+                result, dtype=dtype, index=data.index, name=self._orig.name, copy=False
+            )
+            out = res_ser.__finalize__(self._orig, method="str_cat")
+        return out
+
+    _shared_docs[
+        "str_split"
+    ] = r"""
+    Split strings around given separator/delimiter.
+
+    Splits the string in the Series/Index from the %(side)s,
+    at the specified delimiter string.
+
+    Parameters
+    ----------
+    pat : str%(pat_regex)s, optional
+        %(pat_description)s.
+        If not specified, split on whitespace.
+    n : int, default -1 (all)
+        Limit number of splits in output.
+        ``None``, 0 and -1 will be interpreted as return all splits.
+    expand : bool, default False
+        Expand the split strings into separate columns.
+
+        - If ``True``, return DataFrame/MultiIndex expanding dimensionality.
+        - If ``False``, return Series/Index, containing lists of strings.
+    %(regex_argument)s
+    Returns
+    -------
+    Series, Index, DataFrame or MultiIndex
+        Type matches caller unless ``expand=True`` (see Notes).
+    %(raises_split)s
+    See Also
+    --------
+    Series.str.split : Split strings around given separator/delimiter.
+    Series.str.rsplit : Splits string around given separator/delimiter,
+        starting from the right.
+    Series.str.join : Join lists contained as elements in the Series/Index
+        with passed delimiter.
+    str.split : Standard library version for split.
+    str.rsplit : Standard library version for rsplit.
+
+    Notes
+    -----
+    The handling of the `n` keyword depends on the number of found splits:
+
+    - If found splits > `n`,  make first `n` splits only
+    - If found splits <= `n`, make all splits
+    - If for a certain row the number of found splits < `n`,
+      append `None` for padding up to `n` if ``expand=True``
+
+    If using ``expand=True``, Series and Index callers return DataFrame and
+    MultiIndex objects, respectively.
+    %(regex_pat_note)s
+    Examples
+    --------
+    >>> s = pd.Series(
+    ...     [
+    ...         "this is a regular sentence",
+    ...         "https://docs.python.org/3/tutorial/index.html",
+    ...         np.nan
+    ...     ]
+    ... )
+    >>> s
+    0                       this is a regular sentence
+    1    https://docs.python.org/3/tutorial/index.html
+    2                                              NaN
+    dtype: object
+
+    In the default setting, the string is split by whitespace.
+
+    >>> s.str.split()
+    0                   [this, is, a, regular, sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    Without the `n` parameter, the outputs of `rsplit` and `split`
+    are identical.
+
+    >>> s.str.rsplit()
+    0                   [this, is, a, regular, sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    The `n` parameter can be used to limit the number of splits on the
+    delimiter. The outputs of `split` and `rsplit` are different.
+
+    >>> s.str.split(n=2)
+    0                     [this, is, a regular sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    >>> s.str.rsplit(n=2)
+    0                     [this is a, regular, sentence]
+    1    [https://docs.python.org/3/tutorial/index.html]
+    2                                                NaN
+    dtype: object
+
+    The `pat` parameter can be used to split by other characters.
+
+    >>> s.str.split(pat="/")
+    0                         [this is a regular sentence]
+    1    [https:, , docs.python.org, 3, tutorial, index...
+    2                                                  NaN
+    dtype: object
+
+    When using ``expand=True``, the split elements will expand out into
+    separate columns. If NaN is present, it is propagated throughout
+    the columns during the split.
+
+    >>> s.str.split(expand=True)
+                                                   0     1     2        3         4
+    0                                           this    is     a  regular  sentence
+    1  https://docs.python.org/3/tutorial/index.html  None  None     None      None
+    2                                            NaN   NaN   NaN      NaN       NaN
+
+    For slightly more complex use cases like splitting the html document name
+    from a url, a combination of parameter settings can be used.
+
+    >>> s.str.rsplit("/", n=1, expand=True)
+                                        0           1
+    0          this is a regular sentence        None
+    1  https://docs.python.org/3/tutorial  index.html
+    2                                 NaN         NaN
+    %(regex_examples)s"""
+
+    @Appender(
+        _shared_docs["str_split"]
+        % {
+            "side": "beginning",
+            "pat_regex": " or compiled regex",
+            "pat_description": "String or regular expression to split on",
+            "regex_argument": """
+    regex : bool, default None
+        Determines if the passed-in pattern is a regular expression:
+
+        - If ``True``, assumes the passed-in pattern is a regular expression
+        - If ``False``, treats the pattern as a literal string.
+        - If ``None`` and `pat` length is 1, treats `pat` as a literal string.
+        - If ``None`` and `pat` length is not 1, treats `pat` as a regular expression.
+        - Cannot be set to False if `pat` is a compiled regex
+
+        .. versionadded:: 1.4.0
+         """,
+            "raises_split": """
+                      Raises
+                      ------
+                      ValueError
+                          * if `regex` is False and `pat` is a compiled regex
+                      """,
+            "regex_pat_note": """
+    Use of `regex =False` with a `pat` as a compiled regex will raise an error.
+            """,
+            "method": "split",
+            "regex_examples": r"""
+    Remember to escape special characters when explicitly using regular expressions.
+
+    >>> s = pd.Series(["foo and bar plus baz"])
+    >>> s.str.split(r"and|plus", expand=True)
+        0   1   2
+    0 foo bar baz
+
+    Regular expressions can be used to handle urls or file names.
+    When `pat` is a string and ``regex=None`` (the default), the given `pat` is compiled
+    as a regex only if ``len(pat) != 1``.
+
+    >>> s = pd.Series(['foojpgbar.jpg'])
+    >>> s.str.split(r".", expand=True)
+               0    1
+    0  foojpgbar  jpg
+
+    >>> s.str.split(r"\.jpg", expand=True)
+               0 1
+    0  foojpgbar
+
+    When ``regex=True``, `pat` is interpreted as a regex
+
+    >>> s.str.split(r"\.jpg", regex=True, expand=True)
+               0 1
+    0  foojpgbar
+
+    A compiled regex can be passed as `pat`
+
+    >>> import re
+    >>> s.str.split(re.compile(r"\.jpg"), expand=True)
+               0 1
+    0  foojpgbar
+
+    When ``regex=False``, `pat` is interpreted as the string itself
+
+    >>> s.str.split(r"\.jpg", regex=False, expand=True)
+                   0
+    0  foojpgbar.jpg
+    """,
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def split(
+        self,
+        pat: str | re.Pattern | None = None,
+        *,
+        n=-1,
+        expand: bool = False,
+        regex: bool | None = None,
+    ):
+        if regex is False and is_re(pat):
+            raise ValueError(
+                "Cannot use a compiled regex as replacement pattern with regex=False"
+            )
+        if is_re(pat):
+            regex = True
+        result = self._data.array._str_split(pat, n, expand, regex)
+        if self._data.dtype == "category":
+            dtype = self._data.dtype.categories.dtype
+        else:
+            dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    @Appender(
+        _shared_docs["str_split"]
+        % {
+            "side": "end",
+            "pat_regex": "",
+            "pat_description": "String to split on",
+            "regex_argument": "",
+            "raises_split": "",
+            "regex_pat_note": "",
+            "method": "rsplit",
+            "regex_examples": "",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rsplit(self, pat=None, *, n=-1, expand: bool = False):
+        result = self._data.array._str_rsplit(pat, n=n)
+        dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    _shared_docs[
+        "str_partition"
+    ] = """
+    Split the string at the %(side)s occurrence of `sep`.
+
+    This method splits the string at the %(side)s occurrence of `sep`,
+    and returns 3 elements containing the part before the separator,
+    the separator itself, and the part after the separator.
+    If the separator is not found, return %(return)s.
+
+    Parameters
+    ----------
+    sep : str, default whitespace
+        String to split on.
+    expand : bool, default True
+        If True, return DataFrame/MultiIndex expanding dimensionality.
+        If False, return Series/Index.
+
+    Returns
+    -------
+    DataFrame/MultiIndex or Series/Index of objects
+
+    See Also
+    --------
+    %(also)s
+    Series.str.split : Split strings around given separators.
+    str.partition : Standard library version.
+
+    Examples
+    --------
+
+    >>> s = pd.Series(['Linda van der Berg', 'George Pitt-Rivers'])
+    >>> s
+    0    Linda van der Berg
+    1    George Pitt-Rivers
+    dtype: object
+
+    >>> s.str.partition()
+            0  1             2
+    0   Linda     van der Berg
+    1  George      Pitt-Rivers
+
+    To partition by the last space instead of the first one:
+
+    >>> s.str.rpartition()
+                   0  1            2
+    0  Linda van der            Berg
+    1         George     Pitt-Rivers
+
+    To partition by something different than a space:
+
+    >>> s.str.partition('-')
+                        0  1       2
+    0  Linda van der Berg
+    1         George Pitt  -  Rivers
+
+    To return a Series containing tuples instead of a DataFrame:
+
+    >>> s.str.partition('-', expand=False)
+    0    (Linda van der Berg, , )
+    1    (George Pitt, -, Rivers)
+    dtype: object
+
+    Also available on indices:
+
+    >>> idx = pd.Index(['X 123', 'Y 999'])
+    >>> idx
+    Index(['X 123', 'Y 999'], dtype='object')
+
+    Which will create a MultiIndex:
+
+    >>> idx.str.partition()
+    MultiIndex([('X', ' ', '123'),
+                ('Y', ' ', '999')],
+               )
+
+    Or an index with tuples with ``expand=False``:
+
+    >>> idx.str.partition(expand=False)
+    Index([('X', ' ', '123'), ('Y', ' ', '999')], dtype='object')
+    """
+
+    @Appender(
+        _shared_docs["str_partition"]
+        % {
+            "side": "first",
+            "return": "3 elements containing the string itself, followed by two "
+            "empty strings",
+            "also": "rpartition : Split the string at the last occurrence of `sep`.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def partition(self, sep: str = " ", expand: bool = True):
+        result = self._data.array._str_partition(sep, expand)
+        if self._data.dtype == "category":
+            dtype = self._data.dtype.categories.dtype
+        else:
+            dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    @Appender(
+        _shared_docs["str_partition"]
+        % {
+            "side": "last",
+            "return": "3 elements containing two empty strings, followed by the "
+            "string itself",
+            "also": "partition : Split the string at the first occurrence of `sep`.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rpartition(self, sep: str = " ", expand: bool = True):
+        result = self._data.array._str_rpartition(sep, expand)
+        if self._data.dtype == "category":
+            dtype = self._data.dtype.categories.dtype
+        else:
+            dtype = object if self._data.dtype == object else None
+        return self._wrap_result(
+            result, expand=expand, returns_string=expand, dtype=dtype
+        )
+
+    def get(self, i):
+        """
+        Extract element from each component at specified position or with specified key.
+
+        Extract element from lists, tuples, dict, or strings in each element in the
+        Series/Index.
+
+        Parameters
+        ----------
+        i : int or hashable dict label
+            Position or key of element to extract.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = pd.Series(["String",
+        ...               (1, 2, 3),
+        ...               ["a", "b", "c"],
+        ...               123,
+        ...               -456,
+        ...               {1: "Hello", "2": "World"}])
+        >>> s
+        0                        String
+        1                     (1, 2, 3)
+        2                     [a, b, c]
+        3                           123
+        4                          -456
+        5    {1: 'Hello', '2': 'World'}
+        dtype: object
+
+        >>> s.str.get(1)
+        0        t
+        1        2
+        2        b
+        3      NaN
+        4      NaN
+        5    Hello
+        dtype: object
+
+        >>> s.str.get(-1)
+        0      g
+        1      3
+        2      c
+        3    NaN
+        4    NaN
+        5    None
+        dtype: object
+
+        Return element with given key
+
+        >>> s = pd.Series([{"name": "Hello", "value": "World"},
+        ...               {"name": "Goodbye", "value": "Planet"}])
+        >>> s.str.get('name')
+        0      Hello
+        1    Goodbye
+        dtype: object
+        """
+        result = self._data.array._str_get(i)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def join(self, sep: str):
+        """
+        Join lists contained as elements in the Series/Index with passed delimiter.
+
+        If the elements of a Series are lists themselves, join the content of these
+        lists using the delimiter passed to the function.
+        This function is an equivalent to :meth:`str.join`.
+
+        Parameters
+        ----------
+        sep : str
+            Delimiter to use between list entries.
+
+        Returns
+        -------
+        Series/Index: object
+            The list entries concatenated by intervening occurrences of the
+            delimiter.
+
+        Raises
+        ------
+        AttributeError
+            If the supplied Series contains neither strings nor lists.
+
+        See Also
+        --------
+        str.join : Standard library version of this method.
+        Series.str.split : Split strings around given separator/delimiter.
+
+        Notes
+        -----
+        If any of the list items is not a string object, the result of the join
+        will be `NaN`.
+
+        Examples
+        --------
+        Example with a list that contains non-string elements.
+
+        >>> s = pd.Series([['lion', 'elephant', 'zebra'],
+        ...                [1.1, 2.2, 3.3],
+        ...                ['cat', np.nan, 'dog'],
+        ...                ['cow', 4.5, 'goat'],
+        ...                ['duck', ['swan', 'fish'], 'guppy']])
+        >>> s
+        0        [lion, elephant, zebra]
+        1                [1.1, 2.2, 3.3]
+        2                [cat, nan, dog]
+        3               [cow, 4.5, goat]
+        4    [duck, [swan, fish], guppy]
+        dtype: object
+
+        Join all lists using a '-'. The lists containing object(s) of types other
+        than str will produce a NaN.
+
+        >>> s.str.join('-')
+        0    lion-elephant-zebra
+        1                    NaN
+        2                    NaN
+        3                    NaN
+        4                    NaN
+        dtype: object
+        """
+        result = self._data.array._str_join(sep)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na=lib.no_default,
+        regex: bool = True,
+    ):
+        r"""
+        Test if pattern or regex is contained within a string of a Series or Index.
+
+        Return boolean Series or Index based on whether a given pattern or regex is
+        contained within a string of a Series or Index.
+
+        Parameters
+        ----------
+        pat : str
+            Character sequence or regular expression.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the re module, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For the nullable
+            ``StringDtype``, ``pandas.NA`` is used. For the ``"str"`` dtype,
+            ``False`` is used.
+        regex : bool, default True
+            If True, assumes the pat is a regular expression.
+
+            If False, treats the pat as a literal string.
+
+        Returns
+        -------
+        Series or Index of boolean values
+            A Series or Index of boolean values indicating whether the
+            given pattern is contained within the string of each element
+            of the Series or Index.
+
+        See Also
+        --------
+        match : Analogous, but stricter, relying on re.match instead of re.search.
+        Series.str.startswith : Test if the start of each string element matches a
+            pattern.
+        Series.str.endswith : Same as startswith, but tests the end of string.
+
+        Examples
+        --------
+        Returning a Series of booleans using only a literal pattern.
+
+        >>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.nan])
+        >>> s1.str.contains('og', regex=False)
+        0    False
+        1     True
+        2    False
+        3    False
+        4      NaN
+        dtype: object
+
+        Returning an Index of booleans using only a literal pattern.
+
+        >>> ind = pd.Index(['Mouse', 'dog', 'house and parrot', '23.0', np.nan])
+        >>> ind.str.contains('23', regex=False)
+        Index([False, False, False, True, nan], dtype='object')
+
+        Specifying case sensitivity using `case`.
+
+        >>> s1.str.contains('oG', case=True, regex=True)
+        0    False
+        1    False
+        2    False
+        3    False
+        4      NaN
+        dtype: object
+
+        Specifying `na` to be `False` instead of `NaN` replaces NaN values
+        with `False`. If Series or Index does not contain NaN values
+        the resultant dtype will be `bool`, otherwise, an `object` dtype.
+
+        >>> s1.str.contains('og', na=False, regex=True)
+        0    False
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        Returning 'house' or 'dog' when either expression occurs in a string.
+
+        >>> s1.str.contains('house|dog', regex=True)
+        0    False
+        1     True
+        2     True
+        3    False
+        4      NaN
+        dtype: object
+
+        Ignoring case sensitivity using `flags` with regex.
+
+        >>> import re
+        >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True)
+        0    False
+        1    False
+        2     True
+        3    False
+        4      NaN
+        dtype: object
+
+        Returning any digit using regular expression.
+
+        >>> s1.str.contains('\\d', regex=True)
+        0    False
+        1    False
+        2    False
+        3     True
+        4      NaN
+        dtype: object
+
+        Ensure `pat` is a not a literal pattern when `regex` is set to True.
+        Note in the following example one might expect only `s2[1]` and `s2[3]` to
+        return `True`. However, '.0' as a regex matches any character
+        followed by a 0.
+
+        >>> s2 = pd.Series(['40', '40.0', '41', '41.0', '35'])
+        >>> s2.str.contains('.0', regex=True)
+        0     True
+        1     True
+        2    False
+        3     True
+        4    False
+        dtype: bool
+        """
+        if regex and re.compile(pat).groups:
+            warnings.warn(
+                "This pattern is interpreted as a regular expression, and has "
+                "match groups. To actually get the groups, use str.extract.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        result = self._data.array._str_contains(pat, case, flags, na, regex)
+        return self._wrap_result(result, fill_value=na, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def match(self, pat: str, case: bool = True, flags: int = 0, na=lib.no_default):
+        """
+        Determine if each string starts with a match of a regular expression.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            Character sequence or regular expression.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For the nullable
+            ``StringDtype``, ``pandas.NA`` is used. For the ``"str"`` dtype,
+            ``False`` is used.
+
+        Returns
+        -------
+        Series/Index/array of boolean values
+
+        See Also
+        --------
+        fullmatch : Stricter matching that requires the entire string to match.
+        contains : Analogous, but less strict, relying on re.search instead of
+            re.match.
+        extract : Extract matched groups.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["horse", "eagle", "donkey"])
+        >>> ser.str.match("e")
+        0   False
+        1   True
+        2   False
+        dtype: bool
+        """
+        result = self._data.array._str_match(pat, case=case, flags=flags, na=na)
+        return self._wrap_result(result, fill_value=na, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def fullmatch(self, pat, case: bool = True, flags: int = 0, na=lib.no_default):
+        """
+        Determine if each string entirely matches a regular expression.
+
+        Parameters
+        ----------
+        pat : str
+            Character sequence or regular expression.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For the nullable
+            ``StringDtype``, ``pandas.NA`` is used. For the ``"str"`` dtype,
+            ``False`` is used.
+
+        Returns
+        -------
+        Series/Index/array of boolean values
+
+        See Also
+        --------
+        match : Similar, but also returns `True` when only a *prefix* of the string
+            matches the regular expression.
+        extract : Extract matched groups.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["cat", "duck", "dove"])
+        >>> ser.str.fullmatch(r'd.+')
+        0   False
+        1    True
+        2    True
+        dtype: bool
+        """
+        result = self._data.array._str_fullmatch(pat, case=case, flags=flags, na=na)
+        return self._wrap_result(result, fill_value=na, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool | None = None,
+        flags: int = 0,
+        regex: bool = False,
+    ):
+        r"""
+        Replace each occurrence of pattern/regex in the Series/Index.
+
+        Equivalent to :meth:`str.replace` or :func:`re.sub`, depending on
+        the regex value.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            String can be a character sequence or regular expression.
+        repl : str or callable
+            Replacement string or a callable. The callable is passed the regex
+            match object and must return a replacement string to be used.
+            See :func:`re.sub`.
+        n : int, default -1 (all)
+            Number of replacements to make from start.
+        case : bool, default None
+            Determines if replace is case sensitive:
+
+            - If True, case sensitive (the default if `pat` is a string)
+            - Set to False for case insensitive
+            - Cannot be set if `pat` is a compiled regex.
+
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE. Cannot be set if `pat` is a compiled
+            regex.
+        regex : bool, default False
+            Determines if the passed-in pattern is a regular expression:
+
+            - If True, assumes the passed-in pattern is a regular expression.
+            - If False, treats the pattern as a literal string
+            - Cannot be set to False if `pat` is a compiled regex or `repl` is
+              a callable.
+
+        Returns
+        -------
+        Series or Index of object
+            A copy of the object with all matching occurrences of `pat` replaced by
+            `repl`.
+
+        Raises
+        ------
+        ValueError
+            * if `regex` is False and `repl` is a callable or `pat` is a compiled
+              regex
+            * if `pat` is a compiled regex and `case` or `flags` is set
+
+        Notes
+        -----
+        When `pat` is a compiled regex, all flags should be included in the
+        compiled regex. Use of `case`, `flags`, or `regex=False` with a compiled
+        regex will raise an error.
+
+        Examples
+        --------
+        When `pat` is a string and `regex` is True, the given `pat`
+        is compiled as a regex. When `repl` is a string, it replaces matching
+        regex patterns as with :meth:`re.sub`. NaN value(s) in the Series are
+        left as is:
+
+        >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f.', 'ba', regex=True)
+        0    bao
+        1    baz
+        2    NaN
+        dtype: object
+
+        When `pat` is a string and `regex` is False, every `pat` is replaced with
+        `repl` as with :meth:`str.replace`:
+
+        >>> pd.Series(['f.o', 'fuz', np.nan]).str.replace('f.', 'ba', regex=False)
+        0    bao
+        1    fuz
+        2    NaN
+        dtype: object
+
+        When `repl` is a callable, it is called on every `pat` using
+        :func:`re.sub`. The callable should expect one positional argument
+        (a regex object) and return a string.
+
+        To get the idea:
+
+        >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr, regex=True)
+        0    <re.Match object; span=(0, 1), match='f'>oo
+        1    <re.Match object; span=(0, 1), match='f'>uz
+        2                                            NaN
+        dtype: object
+
+        Reverse every lowercase alphabetic word:
+
+        >>> repl = lambda m: m.group(0)[::-1]
+        >>> ser = pd.Series(['foo 123', 'bar baz', np.nan])
+        >>> ser.str.replace(r'[a-z]+', repl, regex=True)
+        0    oof 123
+        1    rab zab
+        2        NaN
+        dtype: object
+
+        Using regex groups (extract second group and swap case):
+
+        >>> pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
+        >>> repl = lambda m: m.group('two').swapcase()
+        >>> ser = pd.Series(['One Two Three', 'Foo Bar Baz'])
+        >>> ser.str.replace(pat, repl, regex=True)
+        0    tWO
+        1    bAR
+        dtype: object
+
+        Using a compiled regex with flags
+
+        >>> import re
+        >>> regex_pat = re.compile(r'FUZ', flags=re.IGNORECASE)
+        >>> pd.Series(['foo', 'fuz', np.nan]).str.replace(regex_pat, 'bar', regex=True)
+        0    foo
+        1    bar
+        2    NaN
+        dtype: object
+        """
+        # Check whether repl is valid (GH 13438, GH 15055)
+        if not (isinstance(repl, str) or callable(repl)):
+            raise TypeError("repl must be a string or callable")
+
+        is_compiled_re = is_re(pat)
+        if regex or regex is None:
+            if is_compiled_re and (case is not None or flags != 0):
+                raise ValueError(
+                    "case and flags cannot be set when pat is a compiled regex"
+                )
+
+        elif is_compiled_re:
+            raise ValueError(
+                "Cannot use a compiled regex as replacement pattern with regex=False"
+            )
+        elif callable(repl):
+            raise ValueError("Cannot use a callable replacement when regex=False")
+
+        if case is None:
+            case = True
+
+        result = self._data.array._str_replace(
+            pat, repl, n=n, case=case, flags=flags, regex=regex
+        )
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def repeat(self, repeats):
+        """
+        Duplicate each string in the Series or Index.
+
+        Parameters
+        ----------
+        repeats : int or sequence of int
+            Same value for all (int) or different value per (sequence).
+
+        Returns
+        -------
+        Series or pandas.Index
+            Series or Index of repeated string objects specified by
+            input parameter repeats.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'b', 'c'])
+        >>> s
+        0    a
+        1    b
+        2    c
+        dtype: object
+
+        Single int repeats string in Series
+
+        >>> s.str.repeat(repeats=2)
+        0    aa
+        1    bb
+        2    cc
+        dtype: object
+
+        Sequence of int repeats corresponding string in Series
+
+        >>> s.str.repeat(repeats=[1, 2, 3])
+        0      a
+        1     bb
+        2    ccc
+        dtype: object
+        """
+        result = self._data.array._str_repeat(repeats)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        """
+        Pad strings in the Series/Index up to width.
+
+        Parameters
+        ----------
+        width : int
+            Minimum width of resulting string; additional characters will be filled
+            with character defined in `fillchar`.
+        side : {'left', 'right', 'both'}, default 'left'
+            Side from which to fill resulting string.
+        fillchar : str, default ' '
+            Additional character for filling, default is whitespace.
+
+        Returns
+        -------
+        Series or Index of object
+            Returns Series or Index with minimum number of char in object.
+
+        See Also
+        --------
+        Series.str.rjust : Fills the left side of strings with an arbitrary
+            character. Equivalent to ``Series.str.pad(side='left')``.
+        Series.str.ljust : Fills the right side of strings with an arbitrary
+            character. Equivalent to ``Series.str.pad(side='right')``.
+        Series.str.center : Fills both sides of strings with an arbitrary
+            character. Equivalent to ``Series.str.pad(side='both')``.
+        Series.str.zfill : Pad strings in the Series/Index by prepending '0'
+            character. Equivalent to ``Series.str.pad(side='left', fillchar='0')``.
+
+        Examples
+        --------
+        >>> s = pd.Series(["caribou", "tiger"])
+        >>> s
+        0    caribou
+        1      tiger
+        dtype: object
+
+        >>> s.str.pad(width=10)
+        0       caribou
+        1         tiger
+        dtype: object
+
+        >>> s.str.pad(width=10, side='right', fillchar='-')
+        0    caribou---
+        1    tiger-----
+        dtype: object
+
+        >>> s.str.pad(width=10, side='both', fillchar='-')
+        0    -caribou--
+        1    --tiger---
+        dtype: object
+        """
+        if not isinstance(fillchar, str):
+            msg = f"fillchar must be a character, not {type(fillchar).__name__}"
+            raise TypeError(msg)
+
+        if len(fillchar) != 1:
+            raise TypeError("fillchar must be a character, not str")
+
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_pad(width, side=side, fillchar=fillchar)
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "str_pad"
+    ] = """
+    Pad %(side)s side of strings in the Series/Index.
+
+    Equivalent to :meth:`str.%(method)s`.
+
+    Parameters
+    ----------
+    width : int
+        Minimum width of resulting string; additional characters will be filled
+        with ``fillchar``.
+    fillchar : str
+        Additional character for filling, default is whitespace.
+
+    Returns
+    -------
+    Series/Index of objects.
+
+    Examples
+    --------
+    For Series.str.center:
+
+    >>> ser = pd.Series(['dog', 'bird', 'mouse'])
+    >>> ser.str.center(8, fillchar='.')
+    0   ..dog...
+    1   ..bird..
+    2   .mouse..
+    dtype: object
+
+    For Series.str.ljust:
+
+    >>> ser = pd.Series(['dog', 'bird', 'mouse'])
+    >>> ser.str.ljust(8, fillchar='.')
+    0   dog.....
+    1   bird....
+    2   mouse...
+    dtype: object
+
+    For Series.str.rjust:
+
+    >>> ser = pd.Series(['dog', 'bird', 'mouse'])
+    >>> ser.str.rjust(8, fillchar='.')
+    0   .....dog
+    1   ....bird
+    2   ...mouse
+    dtype: object
+    """
+
+    @Appender(_shared_docs["str_pad"] % {"side": "left and right", "method": "center"})
+    @forbid_nonstring_types(["bytes"])
+    def center(self, width: int, fillchar: str = " "):
+        return self.pad(width, side="both", fillchar=fillchar)
+
+    @Appender(_shared_docs["str_pad"] % {"side": "right", "method": "ljust"})
+    @forbid_nonstring_types(["bytes"])
+    def ljust(self, width: int, fillchar: str = " "):
+        return self.pad(width, side="right", fillchar=fillchar)
+
+    @Appender(_shared_docs["str_pad"] % {"side": "left", "method": "rjust"})
+    @forbid_nonstring_types(["bytes"])
+    def rjust(self, width: int, fillchar: str = " "):
+        return self.pad(width, side="left", fillchar=fillchar)
+
+    @forbid_nonstring_types(["bytes"])
+    def zfill(self, width: int):
+        """
+        Pad strings in the Series/Index by prepending '0' characters.
+
+        Strings in the Series/Index are padded with '0' characters on the
+        left of the string to reach a total string length  `width`. Strings
+        in the Series/Index with length greater or equal to `width` are
+        unchanged.
+
+        Parameters
+        ----------
+        width : int
+            Minimum length of resulting string; strings with length less
+            than `width` be prepended with '0' characters.
+
+        Returns
+        -------
+        Series/Index of objects.
+
+        See Also
+        --------
+        Series.str.rjust : Fills the left side of strings with an arbitrary
+            character.
+        Series.str.ljust : Fills the right side of strings with an arbitrary
+            character.
+        Series.str.pad : Fills the specified sides of strings with an arbitrary
+            character.
+        Series.str.center : Fills both sides of strings with an arbitrary
+            character.
+
+        Notes
+        -----
+        Differs from :meth:`str.zfill` which has special handling
+        for '+'/'-' in the string.
+
+        Examples
+        --------
+        >>> s = pd.Series(['-1', '1', '1000', 10, np.nan])
+        >>> s
+        0      -1
+        1       1
+        2    1000
+        3      10
+        4     NaN
+        dtype: object
+
+        Note that ``10`` and ``NaN`` are not strings, therefore they are
+        converted to ``NaN``. The minus sign in ``'-1'`` is treated as a
+        special character and the zero is added to the right of it
+        (:meth:`str.zfill` would have moved it to the left). ``1000``
+        remains unchanged as it is longer than `width`.
+
+        >>> s.str.zfill(3)
+        0     -01
+        1     001
+        2    1000
+        3     NaN
+        4     NaN
+        dtype: object
+        """
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+        f = lambda x: x.zfill(width)
+        result = self._data.array._str_map(f)
+        return self._wrap_result(result)
+
+    def slice(self, start=None, stop=None, step=None):
+        """
+        Slice substrings from each element in the Series or Index.
+
+        Parameters
+        ----------
+        start : int, optional
+            Start position for slice operation.
+        stop : int, optional
+            Stop position for slice operation.
+        step : int, optional
+            Step size for slice operation.
+
+        Returns
+        -------
+        Series or Index of object
+            Series or Index from sliced substring from original string object.
+
+        See Also
+        --------
+        Series.str.slice_replace : Replace a slice with a string.
+        Series.str.get : Return element at position.
+            Equivalent to `Series.str.slice(start=i, stop=i+1)` with `i`
+            being the position.
+
+        Examples
+        --------
+        >>> s = pd.Series(["koala", "dog", "chameleon"])
+        >>> s
+        0        koala
+        1          dog
+        2    chameleon
+        dtype: object
+
+        >>> s.str.slice(start=1)
+        0        oala
+        1          og
+        2    hameleon
+        dtype: object
+
+        >>> s.str.slice(start=-1)
+        0           a
+        1           g
+        2           n
+        dtype: object
+
+        >>> s.str.slice(stop=2)
+        0    ko
+        1    do
+        2    ch
+        dtype: object
+
+        >>> s.str.slice(step=2)
+        0      kaa
+        1       dg
+        2    caeen
+        dtype: object
+
+        >>> s.str.slice(start=0, stop=5, step=3)
+        0    kl
+        1     d
+        2    cm
+        dtype: object
+
+        Equivalent behaviour to:
+
+        >>> s.str[0:5:3]
+        0    kl
+        1     d
+        2    cm
+        dtype: object
+        """
+        result = self._data.array._str_slice(start, stop, step)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def slice_replace(self, start=None, stop=None, repl=None):
+        """
+        Replace a positional slice of a string with another value.
+
+        Parameters
+        ----------
+        start : int, optional
+            Left index position to use for the slice. If not specified (None),
+            the slice is unbounded on the left, i.e. slice from the start
+            of the string.
+        stop : int, optional
+            Right index position to use for the slice. If not specified (None),
+            the slice is unbounded on the right, i.e. slice until the
+            end of the string.
+        repl : str, optional
+            String for replacement. If not specified (None), the sliced region
+            is replaced with an empty string.
+
+        Returns
+        -------
+        Series or Index
+            Same type as the original object.
+
+        See Also
+        --------
+        Series.str.slice : Just slicing without replacement.
+
+        Examples
+        --------
+        >>> s = pd.Series(['a', 'ab', 'abc', 'abdc', 'abcde'])
+        >>> s
+        0        a
+        1       ab
+        2      abc
+        3     abdc
+        4    abcde
+        dtype: object
+
+        Specify just `start`, meaning replace `start` until the end of the
+        string with `repl`.
+
+        >>> s.str.slice_replace(1, repl='X')
+        0    aX
+        1    aX
+        2    aX
+        3    aX
+        4    aX
+        dtype: object
+
+        Specify just `stop`, meaning the start of the string to `stop` is replaced
+        with `repl`, and the rest of the string is included.
+
+        >>> s.str.slice_replace(stop=2, repl='X')
+        0       X
+        1       X
+        2      Xc
+        3     Xdc
+        4    Xcde
+        dtype: object
+
+        Specify `start` and `stop`, meaning the slice from `start` to `stop` is
+        replaced with `repl`. Everything before or after `start` and `stop` is
+        included as is.
+
+        >>> s.str.slice_replace(start=1, stop=3, repl='X')
+        0      aX
+        1      aX
+        2      aX
+        3     aXc
+        4    aXde
+        dtype: object
+        """
+        result = self._data.array._str_slice_replace(start, stop, repl)
+        return self._wrap_result(result)
+
+    def decode(
+        self, encoding, errors: str = "strict", dtype: str | DtypeObj | None = None
+    ):
+        """
+        Decode character string in the Series/Index using indicated encoding.
+
+        Equivalent to :meth:`str.decode` in python2 and :meth:`bytes.decode` in
+        python3.
+
+        Parameters
+        ----------
+        encoding : str
+        errors : str, optional
+            Specifies the error handling scheme.
+            Possible values are those supported by :meth:`bytes.decode`.
+        dtype : str or dtype, optional
+            The dtype of the result. When not ``None``, must be either a string or
+            object dtype. When ``None``, the dtype of the result is determined by
+            ``pd.options.future.infer_string``.
+
+            .. versionadded:: 2.3.0
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        For Series:
+
+        >>> ser = pd.Series([b'cow', b'123', b'()'])
+        >>> ser.str.decode('ascii')
+        0   cow
+        1   123
+        2   ()
+        dtype: object
+        """
+        if dtype is not None and not is_string_dtype(dtype):
+            raise ValueError(f"dtype must be string or object, got {dtype=}")
+        if dtype is None and get_option("future.infer_string"):
+            dtype = "str"
+        # TODO: Add a similar _bytes interface.
+        if encoding in _cpython_optimized_decoders:
+            # CPython optimized implementation
+            f = lambda x: x.decode(encoding, errors)
+        else:
+            decoder = codecs.getdecoder(encoding)
+            f = lambda x: decoder(x, errors)[0]
+        arr = self._data.array
+        result = arr._str_map(f)
+        return self._wrap_result(result, dtype=dtype)
+
+    @forbid_nonstring_types(["bytes"])
+    def encode(self, encoding, errors: str = "strict"):
+        """
+        Encode character string in the Series/Index using indicated encoding.
+
+        Equivalent to :meth:`str.encode`.
+
+        Parameters
+        ----------
+        encoding : str
+        errors : str, optional
+
+        Returns
+        -------
+        Series/Index of objects
+
+        Examples
+        --------
+        >>> ser = pd.Series(['cow', '123', '()'])
+        >>> ser.str.encode(encoding='ascii')
+        0     b'cow'
+        1     b'123'
+        2      b'()'
+        dtype: object
+        """
+        result = self._data.array._str_encode(encoding, errors)
+        return self._wrap_result(result, returns_string=False)
+
+    _shared_docs[
+        "str_strip"
+    ] = r"""
+    Remove %(position)s characters.
+
+    Strip whitespaces (including newlines) or a set of specified characters
+    from each string in the Series/Index from %(side)s.
+    Replaces any non-strings in Series with NaNs.
+    Equivalent to :meth:`str.%(method)s`.
+
+    Parameters
+    ----------
+    to_strip : str or None, default None
+        Specifying the set of characters to be removed.
+        All combinations of this set of characters will be stripped.
+        If None then whitespaces are removed.
+
+    Returns
+    -------
+    Series or Index of object
+
+    See Also
+    --------
+    Series.str.strip : Remove leading and trailing characters in Series/Index.
+    Series.str.lstrip : Remove leading characters in Series/Index.
+    Series.str.rstrip : Remove trailing characters in Series/Index.
+
+    Examples
+    --------
+    >>> s = pd.Series(['1. Ant.  ', '2. Bee!\n', '3. Cat?\t', np.nan, 10, True])
+    >>> s
+    0    1. Ant.
+    1    2. Bee!\n
+    2    3. Cat?\t
+    3          NaN
+    4           10
+    5         True
+    dtype: object
+
+    >>> s.str.strip()
+    0    1. Ant.
+    1    2. Bee!
+    2    3. Cat?
+    3        NaN
+    4        NaN
+    5        NaN
+    dtype: object
+
+    >>> s.str.lstrip('123.')
+    0    Ant.
+    1    Bee!\n
+    2    Cat?\t
+    3       NaN
+    4       NaN
+    5       NaN
+    dtype: object
+
+    >>> s.str.rstrip('.!? \n\t')
+    0    1. Ant
+    1    2. Bee
+    2    3. Cat
+    3       NaN
+    4       NaN
+    5       NaN
+    dtype: object
+
+    >>> s.str.strip('123.!? \n\t')
+    0    Ant
+    1    Bee
+    2    Cat
+    3    NaN
+    4    NaN
+    5    NaN
+    dtype: object
+    """
+
+    @Appender(
+        _shared_docs["str_strip"]
+        % {
+            "side": "left and right sides",
+            "method": "strip",
+            "position": "leading and trailing",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def strip(self, to_strip=None):
+        result = self._data.array._str_strip(to_strip)
+        return self._wrap_result(result)
+
+    @Appender(
+        _shared_docs["str_strip"]
+        % {"side": "left side", "method": "lstrip", "position": "leading"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def lstrip(self, to_strip=None):
+        result = self._data.array._str_lstrip(to_strip)
+        return self._wrap_result(result)
+
+    @Appender(
+        _shared_docs["str_strip"]
+        % {"side": "right side", "method": "rstrip", "position": "trailing"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rstrip(self, to_strip=None):
+        result = self._data.array._str_rstrip(to_strip)
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "str_removefix"
+    ] = r"""
+    Remove a %(side)s from an object series.
+
+    If the %(side)s is not present, the original string will be returned.
+
+    Parameters
+    ----------
+    %(side)s : str
+        Remove the %(side)s of the string.
+
+    Returns
+    -------
+    Series/Index: object
+        The Series or Index with given %(side)s removed.
+
+    See Also
+    --------
+    Series.str.remove%(other_side)s : Remove a %(other_side)s from an object series.
+
+    Examples
+    --------
+    >>> s = pd.Series(["str_foo", "str_bar", "no_prefix"])
+    >>> s
+    0    str_foo
+    1    str_bar
+    2    no_prefix
+    dtype: object
+    >>> s.str.removeprefix("str_")
+    0    foo
+    1    bar
+    2    no_prefix
+    dtype: object
+
+    >>> s = pd.Series(["foo_str", "bar_str", "no_suffix"])
+    >>> s
+    0    foo_str
+    1    bar_str
+    2    no_suffix
+    dtype: object
+    >>> s.str.removesuffix("_str")
+    0    foo
+    1    bar
+    2    no_suffix
+    dtype: object
+    """
+
+    @Appender(
+        _shared_docs["str_removefix"] % {"side": "prefix", "other_side": "suffix"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def removeprefix(self, prefix: str):
+        result = self._data.array._str_removeprefix(prefix)
+        return self._wrap_result(result)
+
+    @Appender(
+        _shared_docs["str_removefix"] % {"side": "suffix", "other_side": "prefix"}
+    )
+    @forbid_nonstring_types(["bytes"])
+    def removesuffix(self, suffix: str):
+        result = self._data.array._str_removesuffix(suffix)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def wrap(self, width: int, **kwargs):
+        r"""
+        Wrap strings in Series/Index at specified line width.
+
+        This method has the same keyword parameters and defaults as
+        :class:`textwrap.TextWrapper`.
+
+        Parameters
+        ----------
+        width : int
+            Maximum line width.
+        expand_tabs : bool, optional
+            If True, tab characters will be expanded to spaces (default: True).
+        replace_whitespace : bool, optional
+            If True, each whitespace character (as defined by string.whitespace)
+            remaining after tab expansion will be replaced by a single space
+            (default: True).
+        drop_whitespace : bool, optional
+            If True, whitespace that, after wrapping, happens to end up at the
+            beginning or end of a line is dropped (default: True).
+        break_long_words : bool, optional
+            If True, then words longer than width will be broken in order to ensure
+            that no lines are longer than width. If it is false, long words will
+            not be broken, and some lines may be longer than width (default: True).
+        break_on_hyphens : bool, optional
+            If True, wrapping will occur preferably on whitespace and right after
+            hyphens in compound words, as it is customary in English. If false,
+            only whitespaces will be considered as potentially good places for line
+            breaks, but you need to set break_long_words to false if you want truly
+            insecable words (default: True).
+
+        Returns
+        -------
+        Series or Index
+
+        Notes
+        -----
+        Internally, this method uses a :class:`textwrap.TextWrapper` instance with
+        default settings. To achieve behavior matching R's stringr library str_wrap
+        function, use the arguments:
+
+        - expand_tabs = False
+        - replace_whitespace = True
+        - drop_whitespace = True
+        - break_long_words = False
+        - break_on_hyphens = False
+
+        Examples
+        --------
+        >>> s = pd.Series(['line to be wrapped', 'another line to be wrapped'])
+        >>> s.str.wrap(12)
+        0             line to be\nwrapped
+        1    another line\nto be\nwrapped
+        dtype: object
+        """
+        result = self._data.array._str_wrap(width, **kwargs)
+        return self._wrap_result(result)
+
+    @forbid_nonstring_types(["bytes"])
+    def get_dummies(self, sep: str = "|"):
+        """
+        Return DataFrame of dummy/indicator variables for Series.
+
+        Each string in Series is split by sep and returned as a DataFrame
+        of dummy/indicator variables.
+
+        Parameters
+        ----------
+        sep : str, default "|"
+            String to split on.
+
+        Returns
+        -------
+        DataFrame
+            Dummy variables corresponding to values of the Series.
+
+        See Also
+        --------
+        get_dummies : Convert categorical variable into dummy/indicator
+            variables.
+
+        Examples
+        --------
+        >>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies()
+           a  b  c
+        0  1  1  0
+        1  1  0  0
+        2  1  0  1
+
+        >>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
+           a  b  c
+        0  1  1  0
+        1  0  0  0
+        2  1  0  1
+        """
+        # we need to cast to Series of strings as only that has all
+        # methods available for making the dummies...
+        result, name = self._data.array._str_get_dummies(sep)
+        return self._wrap_result(
+            result,
+            name=name,
+            expand=True,
+            returns_string=False,
+        )
+
+    @forbid_nonstring_types(["bytes"])
+    def translate(self, table):
+        """
+        Map all characters in the string through the given mapping table.
+
+        Equivalent to standard :meth:`str.translate`.
+
+        Parameters
+        ----------
+        table : dict
+            Table is a mapping of Unicode ordinals to Unicode ordinals, strings, or
+            None. Unmapped characters are left untouched.
+            Characters mapped to None are deleted. :meth:`str.maketrans` is a
+            helper function for making translation tables.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> ser = pd.Series(["El niño", "Françoise"])
+        >>> mytable = str.maketrans({'ñ': 'n', 'ç': 'c'})
+        >>> ser.str.translate(mytable)
+        0   El nino
+        1   Francoise
+        dtype: object
+        """
+        result = self._data.array._str_translate(table)
+        dtype = object if self._data.dtype == "object" else None
+        return self._wrap_result(result, dtype=dtype)
+
+    @forbid_nonstring_types(["bytes"])
+    def count(self, pat, flags: int = 0):
+        r"""
+        Count occurrences of pattern in each string of the Series/Index.
+
+        This function is used to count the number of times a particular regex
+        pattern is repeated in each of the string elements of the
+        :class:`~pandas.Series`.
+
+        Parameters
+        ----------
+        pat : str
+            Valid regular expression.
+        flags : int, default 0, meaning no flags
+            Flags for the `re` module. For a complete list, `see here
+            <https://docs.python.org/3/howto/regex.html#compilation-flags>`_.
+        **kwargs
+            For compatibility with other string methods. Not used.
+
+        Returns
+        -------
+        Series or Index
+            Same type as the calling object containing the integer counts.
+
+        See Also
+        --------
+        re : Standard library module for regular expressions.
+        str.count : Standard library version, without regular expression support.
+
+        Notes
+        -----
+        Some characters need to be escaped when passing in `pat`.
+        eg. ``'$'`` has a special meaning in regex and must be escaped when
+        finding this literal character.
+
+        Examples
+        --------
+        >>> s = pd.Series(['A', 'B', 'Aaba', 'Baca', np.nan, 'CABA', 'cat'])
+        >>> s.str.count('a')
+        0    0.0
+        1    0.0
+        2    2.0
+        3    2.0
+        4    NaN
+        5    0.0
+        6    1.0
+        dtype: float64
+
+        Escape ``'$'`` to find the literal dollar sign.
+
+        >>> s = pd.Series(['$', 'B', 'Aab$', '$$ca', 'C$B$', 'cat'])
+        >>> s.str.count('\\$')
+        0    1
+        1    0
+        2    1
+        3    2
+        4    2
+        5    0
+        dtype: int64
+
+        This is also available on Index
+
+        >>> pd.Index(['A', 'A', 'Aaba', 'cat']).str.count('a')
+        Index([0, 0, 2, 1], dtype='int64')
+        """
+        result = self._data.array._str_count(pat, flags)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def startswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ) -> Series | Index:
+        """
+        Test if the start of each string element matches a pattern.
+
+        Equivalent to :meth:`str.startswith`.
+
+        Parameters
+        ----------
+        pat : str or tuple[str, ...]
+            Character sequence or tuple of strings. Regular expressions are not
+            accepted.
+        na : scalar, optional
+            Object shown if element tested is not a string. The default depends
+            on dtype of the array. For object-dtype, ``numpy.nan`` is used.
+            For the nullable ``StringDtype``, ``pandas.NA`` is used.
+            For the ``"str"`` dtype, ``False`` is used.
+
+        Returns
+        -------
+        Series or Index of bool
+            A Series of booleans indicating whether the given pattern matches
+            the start of each string element.
+
+        See Also
+        --------
+        str.startswith : Python standard library string method.
+        Series.str.endswith : Same as startswith, but tests the end of string.
+        Series.str.contains : Tests if string element contains a pattern.
+
+        Examples
+        --------
+        >>> s = pd.Series(['bat', 'Bear', 'cat', np.nan])
+        >>> s
+        0     bat
+        1    Bear
+        2     cat
+        3     NaN
+        dtype: object
+
+        >>> s.str.startswith('b')
+        0     True
+        1    False
+        2    False
+        3      NaN
+        dtype: object
+
+        >>> s.str.startswith(('b', 'B'))
+        0     True
+        1     True
+        2    False
+        3      NaN
+        dtype: object
+
+        Specifying `na` to be `False` instead of `NaN`.
+
+        >>> s.str.startswith('b', na=False)
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        if not isinstance(pat, (str, tuple)):
+            msg = f"expected a string or tuple, not {type(pat).__name__}"
+            raise TypeError(msg)
+        result = self._data.array._str_startswith(pat, na=na)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def endswith(
+        self, pat: str | tuple[str, ...], na: Scalar | lib.NoDefault = lib.no_default
+    ) -> Series | Index:
+        """
+        Test if the end of each string element matches a pattern.
+
+        Equivalent to :meth:`str.endswith`.
+
+        Parameters
+        ----------
+        pat : str or tuple[str, ...]
+            Character sequence or tuple of strings. Regular expressions are not
+            accepted.
+        na : scalar, optional
+            Object shown if element tested is not a string. The default depends
+            on dtype of the array. For object-dtype, ``numpy.nan`` is used.
+            For the nullable ``StringDtype``, ``pandas.NA`` is used.
+            For the ``"str"`` dtype, ``False`` is used.
+
+        Returns
+        -------
+        Series or Index of bool
+            A Series of booleans indicating whether the given pattern matches
+            the end of each string element.
+
+        See Also
+        --------
+        str.endswith : Python standard library string method.
+        Series.str.startswith : Same as endswith, but tests the start of string.
+        Series.str.contains : Tests if string element contains a pattern.
+
+        Examples
+        --------
+        >>> s = pd.Series(['bat', 'bear', 'caT', np.nan])
+        >>> s
+        0     bat
+        1    bear
+        2     caT
+        3     NaN
+        dtype: object
+
+        >>> s.str.endswith('t')
+        0     True
+        1    False
+        2    False
+        3      NaN
+        dtype: object
+
+        >>> s.str.endswith(('t', 'T'))
+        0     True
+        1    False
+        2     True
+        3      NaN
+        dtype: object
+
+        Specifying `na` to be `False` instead of `NaN`.
+
+        >>> s.str.endswith('t', na=False)
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        if not isinstance(pat, (str, tuple)):
+            msg = f"expected a string or tuple, not {type(pat).__name__}"
+            raise TypeError(msg)
+        result = self._data.array._str_endswith(pat, na=na)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def findall(self, pat, flags: int = 0):
+        """
+        Find all occurrences of pattern or regular expression in the Series/Index.
+
+        Equivalent to applying :func:`re.findall` to all the elements in the
+        Series/Index.
+
+        Parameters
+        ----------
+        pat : str
+            Pattern or regular expression.
+        flags : int, default 0
+            Flags from ``re`` module, e.g. `re.IGNORECASE` (default is 0, which
+            means no flags).
+
+        Returns
+        -------
+        Series/Index of lists of strings
+            All non-overlapping matches of pattern or regular expression in each
+            string of this Series/Index.
+
+        See Also
+        --------
+        count : Count occurrences of pattern or regular expression in each string
+            of the Series/Index.
+        extractall : For each string in the Series, extract groups from all matches
+            of regular expression and return a DataFrame with one row for each
+            match and one column for each group.
+        re.findall : The equivalent ``re`` function to all non-overlapping matches
+            of pattern or regular expression in string, as a list of strings.
+
+        Examples
+        --------
+        >>> s = pd.Series(['Lion', 'Monkey', 'Rabbit'])
+
+        The search for the pattern 'Monkey' returns one match:
+
+        >>> s.str.findall('Monkey')
+        0          []
+        1    [Monkey]
+        2          []
+        dtype: object
+
+        On the other hand, the search for the pattern 'MONKEY' doesn't return any
+        match:
+
+        >>> s.str.findall('MONKEY')
+        0    []
+        1    []
+        2    []
+        dtype: object
+
+        Flags can be added to the pattern or regular expression. For instance,
+        to find the pattern 'MONKEY' ignoring the case:
+
+        >>> import re
+        >>> s.str.findall('MONKEY', flags=re.IGNORECASE)
+        0          []
+        1    [Monkey]
+        2          []
+        dtype: object
+
+        When the pattern matches more than one string in the Series, all matches
+        are returned:
+
+        >>> s.str.findall('on')
+        0    [on]
+        1    [on]
+        2      []
+        dtype: object
+
+        Regular expressions are supported too. For instance, the search for all the
+        strings ending with the word 'on' is shown next:
+
+        >>> s.str.findall('on$')
+        0    [on]
+        1      []
+        2      []
+        dtype: object
+
+        If the pattern is found more than once in the same string, then a list of
+        multiple strings is returned:
+
+        >>> s.str.findall('b')
+        0        []
+        1        []
+        2    [b, b]
+        dtype: object
+        """
+        result = self._data.array._str_findall(pat, flags)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def extract(
+        self, pat: str, flags: int = 0, expand: bool = True
+    ) -> DataFrame | Series | Index:
+        r"""
+        Extract capture groups in the regex `pat` as columns in a DataFrame.
+
+        For each subject string in the Series, extract groups from the
+        first match of regular expression `pat`.
+
+        Parameters
+        ----------
+        pat : str
+            Regular expression pattern with capturing groups.
+        flags : int, default 0 (no flags)
+            Flags from the ``re`` module, e.g. ``re.IGNORECASE``, that
+            modify regular expression matching for things like case,
+            spaces, etc. For more details, see :mod:`re`.
+        expand : bool, default True
+            If True, return DataFrame with one column per capture group.
+            If False, return a Series/Index if there is one capture group
+            or DataFrame if there are multiple capture groups.
+
+        Returns
+        -------
+        DataFrame or Series or Index
+            A DataFrame with one row for each subject string, and one
+            column for each group. Any capture group names in regular
+            expression pat will be used for column names; otherwise
+            capture group numbers will be used. The dtype of each result
+            column is always object, even when no match is found. If
+            ``expand=False`` and pat has only one capture group, then
+            return a Series (if subject is a Series) or Index (if subject
+            is an Index).
+
+        See Also
+        --------
+        extractall : Returns all matches (not just the first match).
+
+        Examples
+        --------
+        A pattern with two groups will return a DataFrame with two columns.
+        Non-matches will be NaN.
+
+        >>> s = pd.Series(['a1', 'b2', 'c3'])
+        >>> s.str.extract(r'([ab])(\d)')
+            0    1
+        0    a    1
+        1    b    2
+        2  NaN  NaN
+
+        A pattern may contain optional groups.
+
+        >>> s.str.extract(r'([ab])?(\d)')
+            0  1
+        0    a  1
+        1    b  2
+        2  NaN  3
+
+        Named groups will become column names in the result.
+
+        >>> s.str.extract(r'(?P<letter>[ab])(?P<digit>\d)')
+        letter digit
+        0      a     1
+        1      b     2
+        2    NaN   NaN
+
+        A pattern with one group will return a DataFrame with one column
+        if expand=True.
+
+        >>> s.str.extract(r'[ab](\d)', expand=True)
+            0
+        0    1
+        1    2
+        2  NaN
+
+        A pattern with one group will return a Series if expand=False.
+
+        >>> s.str.extract(r'[ab](\d)', expand=False)
+        0      1
+        1      2
+        2    NaN
+        dtype: object
+        """
+        from pandas import DataFrame
+
+        if not isinstance(expand, bool):
+            raise ValueError("expand must be True or False")
+
+        regex = re.compile(pat, flags=flags)
+        if regex.groups == 0:
+            raise ValueError("pattern contains no capture groups")
+
+        if not expand and regex.groups > 1 and isinstance(self._data, ABCIndex):
+            raise ValueError("only one regex group is supported with Index")
+
+        obj = self._data
+        result_dtype = _result_dtype(obj)
+
+        returns_df = regex.groups > 1 or expand
+
+        if returns_df:
+            name = None
+            columns = _get_group_names(regex)
+
+            if obj.array.size == 0:
+                result = DataFrame(columns=columns, dtype=result_dtype)
+
+            else:
+                result_list = self._data.array._str_extract(
+                    pat, flags=flags, expand=returns_df
+                )
+
+                result_index: Index | None
+                if isinstance(obj, ABCSeries):
+                    result_index = obj.index
+                else:
+                    result_index = None
+
+                result = DataFrame(
+                    result_list, columns=columns, index=result_index, dtype=result_dtype
+                )
+
+        else:
+            name = _get_single_group_name(regex)
+            result = self._data.array._str_extract(pat, flags=flags, expand=returns_df)
+        return self._wrap_result(result, name=name, dtype=result_dtype)
+
+    @forbid_nonstring_types(["bytes"])
+    def extractall(self, pat, flags: int = 0) -> DataFrame:
+        r"""
+        Extract capture groups in the regex `pat` as columns in DataFrame.
+
+        For each subject string in the Series, extract groups from all
+        matches of regular expression pat. When each subject string in the
+        Series has exactly one match, extractall(pat).xs(0, level='match')
+        is the same as extract(pat).
+
+        Parameters
+        ----------
+        pat : str
+            Regular expression pattern with capturing groups.
+        flags : int, default 0 (no flags)
+            A ``re`` module flag, for example ``re.IGNORECASE``. These allow
+            to modify regular expression matching for things like case, spaces,
+            etc. Multiple flags can be combined with the bitwise OR operator,
+            for example ``re.IGNORECASE | re.MULTILINE``.
+
+        Returns
+        -------
+        DataFrame
+            A ``DataFrame`` with one row for each match, and one column for each
+            group. Its rows have a ``MultiIndex`` with first levels that come from
+            the subject ``Series``. The last level is named 'match' and indexes the
+            matches in each item of the ``Series``. Any capture group names in
+            regular expression pat will be used for column names; otherwise capture
+            group numbers will be used.
+
+        See Also
+        --------
+        extract : Returns first match only (not all matches).
+
+        Examples
+        --------
+        A pattern with one group will return a DataFrame with one column.
+        Indices with no matches will not appear in the result.
+
+        >>> s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
+        >>> s.str.extractall(r"[ab](\d)")
+                0
+        match
+        A 0      1
+          1      2
+        B 0      1
+
+        Capture group names are used for column names of the result.
+
+        >>> s.str.extractall(r"[ab](?P<digit>\d)")
+                digit
+        match
+        A 0         1
+          1         2
+        B 0         1
+
+        A pattern with two groups will return a DataFrame with two columns.
+
+        >>> s.str.extractall(r"(?P<letter>[ab])(?P<digit>\d)")
+                letter digit
+        match
+        A 0          a     1
+          1          a     2
+        B 0          b     1
+
+        Optional groups that do not match are NaN in the result.
+
+        >>> s.str.extractall(r"(?P<letter>[ab])?(?P<digit>\d)")
+                letter digit
+        match
+        A 0          a     1
+          1          a     2
+        B 0          b     1
+        C 0        NaN     1
+        """
+        # TODO: dispatch
+        return str_extractall(self._orig, pat, flags)
+
+    _shared_docs[
+        "find"
+    ] = """
+    Return %(side)s indexes in each strings in the Series/Index.
+
+    Each of returned indexes corresponds to the position where the
+    substring is fully contained between [start:end]. Return -1 on
+    failure. Equivalent to standard :meth:`str.%(method)s`.
+
+    Parameters
+    ----------
+    sub : str
+        Substring being searched.
+    start : int
+        Left edge index.
+    end : int
+        Right edge index.
+
+    Returns
+    -------
+    Series or Index of int.
+
+    See Also
+    --------
+    %(also)s
+
+    Examples
+    --------
+    For Series.str.find:
+
+    >>> ser = pd.Series(["cow_", "duck_", "do_ve"])
+    >>> ser.str.find("_")
+    0   3
+    1   4
+    2   2
+    dtype: int64
+
+    For Series.str.rfind:
+
+    >>> ser = pd.Series(["_cow_", "duck_", "do_v_e"])
+    >>> ser.str.rfind("_")
+    0   4
+    1   4
+    2   4
+    dtype: int64
+    """
+
+    @Appender(
+        _shared_docs["find"]
+        % {
+            "side": "lowest",
+            "method": "find",
+            "also": "rfind : Return highest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def find(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_find(sub, start, end)
+        return self._wrap_result(result, returns_string=False)
+
+    @Appender(
+        _shared_docs["find"]
+        % {
+            "side": "highest",
+            "method": "rfind",
+            "also": "find : Return lowest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rfind(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_rfind(sub, start=start, end=end)
+        return self._wrap_result(result, returns_string=False)
+
+    @forbid_nonstring_types(["bytes"])
+    def normalize(self, form):
+        """
+        Return the Unicode normal form for the strings in the Series/Index.
+
+        For more information on the forms, see the
+        :func:`unicodedata.normalize`.
+
+        Parameters
+        ----------
+        form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
+            Unicode form.
+
+        Returns
+        -------
+        Series/Index of objects
+
+        Examples
+        --------
+        >>> ser = pd.Series(['ñ'])
+        >>> ser.str.normalize('NFC') == ser.str.normalize('NFD')
+        0   False
+        dtype: bool
+        """
+        result = self._data.array._str_normalize(form)
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "index"
+    ] = """
+    Return %(side)s indexes in each string in Series/Index.
+
+    Each of the returned indexes corresponds to the position where the
+    substring is fully contained between [start:end]. This is the same
+    as ``str.%(similar)s`` except instead of returning -1, it raises a
+    ValueError when the substring is not found. Equivalent to standard
+    ``str.%(method)s``.
+
+    Parameters
+    ----------
+    sub : str
+        Substring being searched.
+    start : int
+        Left edge index.
+    end : int
+        Right edge index.
+
+    Returns
+    -------
+    Series or Index of object
+
+    See Also
+    --------
+    %(also)s
+
+    Examples
+    --------
+    For Series.str.index:
+
+    >>> ser = pd.Series(["horse", "eagle", "donkey"])
+    >>> ser.str.index("e")
+    0   4
+    1   0
+    2   4
+    dtype: int64
+
+    For Series.str.rindex:
+
+    >>> ser = pd.Series(["Deer", "eagle", "Sheep"])
+    >>> ser.str.rindex("e")
+    0   2
+    1   4
+    2   3
+    dtype: int64
+    """
+
+    @Appender(
+        _shared_docs["index"]
+        % {
+            "side": "lowest",
+            "similar": "find",
+            "method": "index",
+            "also": "rindex : Return highest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def index(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_index(sub, start=start, end=end)
+        return self._wrap_result(result, returns_string=False)
+
+    @Appender(
+        _shared_docs["index"]
+        % {
+            "side": "highest",
+            "similar": "rfind",
+            "method": "rindex",
+            "also": "index : Return lowest indexes in each strings.",
+        }
+    )
+    @forbid_nonstring_types(["bytes"])
+    def rindex(self, sub, start: int = 0, end=None):
+        if not isinstance(sub, str):
+            msg = f"expected a string object, not {type(sub).__name__}"
+            raise TypeError(msg)
+
+        result = self._data.array._str_rindex(sub, start=start, end=end)
+        return self._wrap_result(result, returns_string=False)
+
+    def len(self):
+        """
+        Compute the length of each element in the Series/Index.
+
+        The element may be a sequence (such as a string, tuple or list) or a collection
+        (such as a dictionary).
+
+        Returns
+        -------
+        Series or Index of int
+            A Series or Index of integer values indicating the length of each
+            element in the Series or Index.
+
+        See Also
+        --------
+        str.len : Python built-in function returning the length of an object.
+        Series.size : Returns the length of the Series.
+
+        Examples
+        --------
+        Returns the length (number of characters) in a string. Returns the
+        number of entries for dictionaries, lists or tuples.
+
+        >>> s = pd.Series(['dog',
+        ...                 '',
+        ...                 5,
+        ...                 {'foo' : 'bar'},
+        ...                 [2, 3, 5, 7],
+        ...                 ('one', 'two', 'three')])
+        >>> s
+        0                  dog
+        1
+        2                    5
+        3       {'foo': 'bar'}
+        4         [2, 3, 5, 7]
+        5    (one, two, three)
+        dtype: object
+        >>> s.str.len()
+        0    3.0
+        1    0.0
+        2    NaN
+        3    1.0
+        4    4.0
+        5    3.0
+        dtype: float64
+        """
+        result = self._data.array._str_len()
+        return self._wrap_result(result, returns_string=False)
+
+    _shared_docs[
+        "casemethods"
+    ] = """
+    Convert strings in the Series/Index to %(type)s.
+    %(version)s
+    Equivalent to :meth:`str.%(method)s`.
+
+    Returns
+    -------
+    Series or Index of object
+
+    See Also
+    --------
+    Series.str.lower : Converts all characters to lowercase.
+    Series.str.upper : Converts all characters to uppercase.
+    Series.str.title : Converts first character of each word to uppercase and
+        remaining to lowercase.
+    Series.str.capitalize : Converts first character to uppercase and
+        remaining to lowercase.
+    Series.str.swapcase : Converts uppercase to lowercase and lowercase to
+        uppercase.
+    Series.str.casefold: Removes all case distinctions in the string.
+
+    Examples
+    --------
+    >>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe'])
+    >>> s
+    0                 lower
+    1              CAPITALS
+    2    this is a sentence
+    3              SwApCaSe
+    dtype: object
+
+    >>> s.str.lower()
+    0                 lower
+    1              capitals
+    2    this is a sentence
+    3              swapcase
+    dtype: object
+
+    >>> s.str.upper()
+    0                 LOWER
+    1              CAPITALS
+    2    THIS IS A SENTENCE
+    3              SWAPCASE
+    dtype: object
+
+    >>> s.str.title()
+    0                 Lower
+    1              Capitals
+    2    This Is A Sentence
+    3              Swapcase
+    dtype: object
+
+    >>> s.str.capitalize()
+    0                 Lower
+    1              Capitals
+    2    This is a sentence
+    3              Swapcase
+    dtype: object
+
+    >>> s.str.swapcase()
+    0                 LOWER
+    1              capitals
+    2    THIS IS A SENTENCE
+    3              sWaPcAsE
+    dtype: object
+    """
+    # Types:
+    #   cases:
+    #       upper, lower, title, capitalize, swapcase, casefold
+    #   boolean:
+    #     isalpha, isnumeric isalnum isdigit isdecimal isspace islower isupper istitle
+    # _doc_args holds dict of strings to use in substituting casemethod docs
+    _doc_args: dict[str, dict[str, str]] = {}
+    _doc_args["lower"] = {"type": "lowercase", "method": "lower", "version": ""}
+    _doc_args["upper"] = {"type": "uppercase", "method": "upper", "version": ""}
+    _doc_args["title"] = {"type": "titlecase", "method": "title", "version": ""}
+    _doc_args["capitalize"] = {
+        "type": "be capitalized",
+        "method": "capitalize",
+        "version": "",
+    }
+    _doc_args["swapcase"] = {
+        "type": "be swapcased",
+        "method": "swapcase",
+        "version": "",
+    }
+    _doc_args["casefold"] = {
+        "type": "be casefolded",
+        "method": "casefold",
+        "version": "",
+    }
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["lower"])
+    @forbid_nonstring_types(["bytes"])
+    def lower(self):
+        result = self._data.array._str_lower()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["upper"])
+    @forbid_nonstring_types(["bytes"])
+    def upper(self):
+        result = self._data.array._str_upper()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["title"])
+    @forbid_nonstring_types(["bytes"])
+    def title(self):
+        result = self._data.array._str_title()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["capitalize"])
+    @forbid_nonstring_types(["bytes"])
+    def capitalize(self):
+        result = self._data.array._str_capitalize()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["swapcase"])
+    @forbid_nonstring_types(["bytes"])
+    def swapcase(self):
+        result = self._data.array._str_swapcase()
+        return self._wrap_result(result)
+
+    @Appender(_shared_docs["casemethods"] % _doc_args["casefold"])
+    @forbid_nonstring_types(["bytes"])
+    def casefold(self):
+        result = self._data.array._str_casefold()
+        return self._wrap_result(result)
+
+    _shared_docs[
+        "ismethods"
+    ] = """
+    Check whether all characters in each string are %(type)s.
+
+    This is equivalent to running the Python string method
+    :meth:`str.%(method)s` for each element of the Series/Index. If a string
+    has zero characters, ``False`` is returned for that check.
+
+    Returns
+    -------
+    Series or Index of bool
+        Series or Index of boolean values with the same length as the original
+        Series/Index.
+
+    See Also
+    --------
+    Series.str.isalpha : Check whether all characters are alphabetic.
+    Series.str.isnumeric : Check whether all characters are numeric.
+    Series.str.isalnum : Check whether all characters are alphanumeric.
+    Series.str.isdigit : Check whether all characters are digits.
+    Series.str.isdecimal : Check whether all characters are decimal.
+    Series.str.isspace : Check whether all characters are whitespace.
+    Series.str.islower : Check whether all characters are lowercase.
+    Series.str.isupper : Check whether all characters are uppercase.
+    Series.str.istitle : Check whether all characters are titlecase.
+
+    Examples
+    --------
+    **Checks for Alphabetic and Numeric Characters**
+
+    >>> s1 = pd.Series(['one', 'one1', '1', ''])
+
+    >>> s1.str.isalpha()
+    0     True
+    1    False
+    2    False
+    3    False
+    dtype: bool
+
+    >>> s1.str.isnumeric()
+    0    False
+    1    False
+    2     True
+    3    False
+    dtype: bool
+
+    >>> s1.str.isalnum()
+    0     True
+    1     True
+    2     True
+    3    False
+    dtype: bool
+
+    Note that checks against characters mixed with any additional punctuation
+    or whitespace will evaluate to false for an alphanumeric check.
+
+    >>> s2 = pd.Series(['A B', '1.5', '3,000'])
+    >>> s2.str.isalnum()
+    0    False
+    1    False
+    2    False
+    dtype: bool
+
+    **More Detailed Checks for Numeric Characters**
+
+    There are several different but overlapping sets of numeric characters that
+    can be checked for.
+
+    >>> s3 = pd.Series(['23', '³', '⅕', ''])
+
+    The ``s3.str.isdecimal`` method checks for characters used to form numbers
+    in base 10.
+
+    >>> s3.str.isdecimal()
+    0     True
+    1    False
+    2    False
+    3    False
+    dtype: bool
+
+    The ``s.str.isdigit`` method is the same as ``s3.str.isdecimal`` but also
+    includes special digits, like superscripted and subscripted digits in
+    unicode.
+
+    >>> s3.str.isdigit()
+    0     True
+    1     True
+    2    False
+    3    False
+    dtype: bool
+
+    The ``s.str.isnumeric`` method is the same as ``s3.str.isdigit`` but also
+    includes other characters that can represent quantities such as unicode
+    fractions.
+
+    >>> s3.str.isnumeric()
+    0     True
+    1     True
+    2     True
+    3    False
+    dtype: bool
+
+    **Checks for Whitespace**
+
+    >>> s4 = pd.Series([' ', '\\t\\r\\n ', ''])
+    >>> s4.str.isspace()
+    0     True
+    1     True
+    2    False
+    dtype: bool
+
+    **Checks for Character Case**
+
+    >>> s5 = pd.Series(['leopard', 'Golden Eagle', 'SNAKE', ''])
+
+    >>> s5.str.islower()
+    0     True
+    1    False
+    2    False
+    3    False
+    dtype: bool
+
+    >>> s5.str.isupper()
+    0    False
+    1    False
+    2     True
+    3    False
+    dtype: bool
+
+    The ``s5.str.istitle`` method checks for whether all words are in title
+    case (whether only the first letter of each word is capitalized). Words are
+    assumed to be as any sequence of non-numeric characters separated by
+    whitespace characters.
+
+    >>> s5.str.istitle()
+    0    False
+    1     True
+    2    False
+    3    False
+    dtype: bool
+    """
+    _doc_args["isalnum"] = {"type": "alphanumeric", "method": "isalnum"}
+    _doc_args["isalpha"] = {"type": "alphabetic", "method": "isalpha"}
+    _doc_args["isdigit"] = {"type": "digits", "method": "isdigit"}
+    _doc_args["isspace"] = {"type": "whitespace", "method": "isspace"}
+    _doc_args["islower"] = {"type": "lowercase", "method": "islower"}
+    _doc_args["isupper"] = {"type": "uppercase", "method": "isupper"}
+    _doc_args["istitle"] = {"type": "titlecase", "method": "istitle"}
+    _doc_args["isnumeric"] = {"type": "numeric", "method": "isnumeric"}
+    _doc_args["isdecimal"] = {"type": "decimal", "method": "isdecimal"}
+    # force _noarg_wrapper return type with dtype=np.dtype(bool) (GH 29624)
+
+    isalnum = _map_and_wrap(
+        "isalnum", docstring=_shared_docs["ismethods"] % _doc_args["isalnum"]
+    )
+    isalpha = _map_and_wrap(
+        "isalpha", docstring=_shared_docs["ismethods"] % _doc_args["isalpha"]
+    )
+    isdigit = _map_and_wrap(
+        "isdigit", docstring=_shared_docs["ismethods"] % _doc_args["isdigit"]
+    )
+    isspace = _map_and_wrap(
+        "isspace", docstring=_shared_docs["ismethods"] % _doc_args["isspace"]
+    )
+    islower = _map_and_wrap(
+        "islower", docstring=_shared_docs["ismethods"] % _doc_args["islower"]
+    )
+    isupper = _map_and_wrap(
+        "isupper", docstring=_shared_docs["ismethods"] % _doc_args["isupper"]
+    )
+    istitle = _map_and_wrap(
+        "istitle", docstring=_shared_docs["ismethods"] % _doc_args["istitle"]
+    )
+    isnumeric = _map_and_wrap(
+        "isnumeric", docstring=_shared_docs["ismethods"] % _doc_args["isnumeric"]
+    )
+    isdecimal = _map_and_wrap(
+        "isdecimal", docstring=_shared_docs["ismethods"] % _doc_args["isdecimal"]
+    )
+
+
+def cat_safe(list_of_columns: list[npt.NDArray[np.object_]], sep: str):
+    """
+    Auxiliary function for :meth:`str.cat`.
+
+    Same signature as cat_core, but handles TypeErrors in concatenation, which
+    happen if the arrays in list_of columns have the wrong dtypes or content.
+
+    Parameters
+    ----------
+    list_of_columns : list of numpy arrays
+        List of arrays to be concatenated with sep;
+        these arrays may not contain NaNs!
+    sep : string
+        The separator string for concatenating the columns.
+
+    Returns
+    -------
+    nd.array
+        The concatenation of list_of_columns with sep.
+    """
+    try:
+        result = cat_core(list_of_columns, sep)
+    except TypeError:
+        # if there are any non-string values (wrong dtype or hidden behind
+        # object dtype), np.sum will fail; catch and return with better message
+        for column in list_of_columns:
+            dtype = lib.infer_dtype(column, skipna=True)
+            if dtype not in ["string", "empty"]:
+                raise TypeError(
+                    "Concatenation requires list-likes containing only "
+                    "strings (or missing values). Offending values found in "
+                    f"column {dtype}"
+                ) from None
+    return result
+
+
+def cat_core(list_of_columns: list, sep: str):
+    """
+    Auxiliary function for :meth:`str.cat`
+
+    Parameters
+    ----------
+    list_of_columns : list of numpy arrays
+        List of arrays to be concatenated with sep;
+        these arrays may not contain NaNs!
+    sep : string
+        The separator string for concatenating the columns.
+
+    Returns
+    -------
+    nd.array
+        The concatenation of list_of_columns with sep.
+    """
+    if sep == "":
+        # no need to interleave sep if it is empty
+        arr_of_cols = np.asarray(list_of_columns, dtype=object)
+        return np.sum(arr_of_cols, axis=0)
+    list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
+    list_with_sep[::2] = list_of_columns
+    arr_with_sep = np.asarray(list_with_sep, dtype=object)
+    return np.sum(arr_with_sep, axis=0)
+
+
+def _result_dtype(arr):
+    # workaround #27953
+    # ideally we just pass `dtype=arr.dtype` unconditionally, but this fails
+    # when the list of values is empty.
+    from pandas.core.arrays.string_ import StringDtype
+
+    if isinstance(arr.dtype, (ArrowDtype, StringDtype)):
+        return arr.dtype
+    return object
+
+
+def _get_single_group_name(regex: re.Pattern) -> Hashable:
+    if regex.groupindex:
+        return next(iter(regex.groupindex))
+    else:
+        return None
+
+
+def _get_group_names(regex: re.Pattern) -> list[Hashable]:
+    """
+    Get named groups from compiled regex.
+
+    Unnamed groups are numbered.
+
+    Parameters
+    ----------
+    regex : compiled regex
+
+    Returns
+    -------
+    list of column labels
+    """
+    names = {v: k for k, v in regex.groupindex.items()}
+    return [names.get(1 + i, i) for i in range(regex.groups)]
+
+
+def str_extractall(arr, pat, flags: int = 0) -> DataFrame:
+    regex = re.compile(pat, flags=flags)
+    # the regex must contain capture groups.
+    if regex.groups == 0:
+        raise ValueError("pattern contains no capture groups")
+
+    if isinstance(arr, ABCIndex):
+        arr = arr.to_series().reset_index(drop=True).astype(arr.dtype)
+
+    columns = _get_group_names(regex)
+    match_list = []
+    index_list = []
+    is_mi = arr.index.nlevels > 1
+
+    for subject_key, subject in arr.items():
+        if isinstance(subject, str):
+            if not is_mi:
+                subject_key = (subject_key,)
+
+            for match_i, match_tuple in enumerate(regex.findall(subject)):
+                if isinstance(match_tuple, str):
+                    match_tuple = (match_tuple,)
+                na_tuple = [np.nan if group == "" else group for group in match_tuple]
+                match_list.append(na_tuple)
+                result_key = tuple(subject_key + (match_i,))
+                index_list.append(result_key)
+
+    from pandas import MultiIndex
+
+    index = MultiIndex.from_tuples(index_list, names=arr.index.names + ["match"])
+    dtype = _result_dtype(arr)
+
+    result = arr._constructor_expanddim(
+        match_list, index=index, columns=columns, dtype=dtype
+    )
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..316c86d152db32048f4de154ead0ec8fc76ec027
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/base.py
@@ -0,0 +1,266 @@
+from __future__ import annotations
+
+import abc
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+from pandas._libs import lib
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    import re
+
+    from pandas._typing import Scalar
+
+    from pandas import Series
+
+
+class BaseStringArrayMethods(abc.ABC):
+    """
+    Base class for extension arrays implementing string methods.
+
+    This is where our ExtensionArrays can override the implementation of
+    Series.str.<method>. We don't expect this to work with
+    3rd-party extension arrays.
+
+    * User calls Series.str.<method>
+    * pandas extracts the extension array from the Series
+    * pandas calls ``extension_array._str_<method>(*args, **kwargs)``
+    * pandas wraps the result, to return to the user.
+
+    See :ref:`Series.str` for the docstring of each method.
+    """
+
+    def _str_getitem(self, key):
+        if isinstance(key, slice):
+            return self._str_slice(start=key.start, stop=key.stop, step=key.step)
+        else:
+            return self._str_get(key)
+
+    @abc.abstractmethod
+    def _str_count(self, pat, flags: int = 0):
+        pass
+
+    @abc.abstractmethod
+    def _str_pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_contains(
+        self, pat, case: bool = True, flags: int = 0, na=None, regex: bool = True
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_startswith(self, pat, na=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_endswith(self, pat, na=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        pass
+
+    @abc.abstractmethod
+    def _str_match(
+        self,
+        pat: str,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_fullmatch(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_encode(self, encoding, errors: str = "strict"):
+        pass
+
+    @abc.abstractmethod
+    def _str_find(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_rfind(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_findall(self, pat, flags: int = 0):
+        pass
+
+    @abc.abstractmethod
+    def _str_get(self, i):
+        pass
+
+    @abc.abstractmethod
+    def _str_index(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_rindex(self, sub, start: int = 0, end=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_join(self, sep: str):
+        pass
+
+    @abc.abstractmethod
+    def _str_partition(self, sep: str, expand):
+        pass
+
+    @abc.abstractmethod
+    def _str_rpartition(self, sep: str, expand):
+        pass
+
+    @abc.abstractmethod
+    def _str_len(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_slice(self, start=None, stop=None, step=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_slice_replace(self, start=None, stop=None, repl=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_translate(self, table):
+        pass
+
+    @abc.abstractmethod
+    def _str_wrap(self, width: int, **kwargs):
+        pass
+
+    @abc.abstractmethod
+    def _str_get_dummies(self, sep: str = "|"):
+        pass
+
+    @abc.abstractmethod
+    def _str_isalnum(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isalpha(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isdecimal(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isdigit(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_islower(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isnumeric(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isspace(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_istitle(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_isupper(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_capitalize(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_casefold(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_title(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_swapcase(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_lower(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_upper(self):
+        pass
+
+    @abc.abstractmethod
+    def _str_normalize(self, form):
+        pass
+
+    @abc.abstractmethod
+    def _str_strip(self, to_strip=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_lstrip(self, to_strip=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_rstrip(self, to_strip=None):
+        pass
+
+    @abc.abstractmethod
+    def _str_removeprefix(self, prefix: str) -> Series:
+        pass
+
+    @abc.abstractmethod
+    def _str_removesuffix(self, suffix: str) -> Series:
+        pass
+
+    @abc.abstractmethod
+    def _str_split(
+        self, pat=None, n=-1, expand: bool = False, regex: bool | None = None
+    ):
+        pass
+
+    @abc.abstractmethod
+    def _str_rsplit(self, pat=None, n=-1):
+        pass
+
+    @abc.abstractmethod
+    def _str_extract(self, pat: str, flags: int = 0, expand: bool = True):
+        pass
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/object_array.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/object_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..05ffee662dbac69e2e942e37f0701d4b65adb6af
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/strings/object_array.py
@@ -0,0 +1,534 @@
+from __future__ import annotations
+
+import functools
+import re
+import textwrap
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+    cast,
+)
+import unicodedata
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+import pandas._libs.missing as libmissing
+import pandas._libs.ops as libops
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.missing import isna
+
+from pandas.core.strings.base import BaseStringArrayMethods
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from pandas._typing import (
+        NpDtype,
+        Scalar,
+    )
+
+    from pandas import Series
+
+
+class ObjectStringArrayMixin(BaseStringArrayMethods):
+    """
+    String Methods operating on object-dtype ndarrays.
+    """
+
+    def __len__(self) -> int:
+        # For typing, _str_map relies on the object being sized.
+        raise NotImplementedError
+
+    def _str_map(
+        self,
+        f,
+        na_value=lib.no_default,
+        dtype: NpDtype | None = None,
+        convert: bool = True,
+    ):
+        """
+        Map a callable over valid elements of the array.
+
+        Parameters
+        ----------
+        f : Callable
+            A function to call on each non-NA element.
+        na_value : Scalar, optional
+            The value to set for NA values. Might also be used for the
+            fill value if the callable `f` raises an exception.
+            This defaults to ``self.dtype.na_value`` which is ``np.nan``
+            for object-dtype and Categorical and ``pd.NA`` for StringArray.
+        dtype : Dtype, optional
+            The dtype of the result array.
+        convert : bool, default True
+            Whether to call `maybe_convert_objects` on the resulting ndarray
+        """
+        if dtype is None:
+            dtype = np.dtype("object")
+        if na_value is lib.no_default:
+            na_value = self.dtype.na_value  # type: ignore[attr-defined]
+
+        if not len(self):
+            return np.array([], dtype=dtype)
+
+        arr = np.asarray(self, dtype=object)
+        mask = isna(arr)
+        map_convert = convert and not np.all(mask)
+        try:
+            result = lib.map_infer_mask(arr, f, mask.view(np.uint8), map_convert)
+        except (TypeError, AttributeError) as err:
+            # Reraise the exception if callable `f` got wrong number of args.
+            # The user may want to be warned by this, instead of getting NaN
+            p_err = (
+                r"((takes)|(missing)) (?(2)from \d+ to )?\d+ "
+                r"(?(3)required )positional arguments?"
+            )
+
+            if len(err.args) >= 1 and re.search(p_err, err.args[0]):
+                # FIXME: this should be totally avoidable
+                raise err
+
+            def g(x):
+                # This type of fallback behavior can be removed once
+                # we remove object-dtype .str accessor.
+                try:
+                    return f(x)
+                except (TypeError, AttributeError):
+                    return na_value
+
+            return self._str_map(g, na_value=na_value, dtype=dtype)
+        if not isinstance(result, np.ndarray):
+            return result
+        if na_value is not np.nan:
+            np.putmask(result, mask, na_value)
+            if convert and result.dtype == object:
+                result = lib.maybe_convert_objects(result)
+        return result
+
+    def _str_count(self, pat, flags: int = 0):
+        regex = re.compile(pat, flags=flags)
+        f = lambda x: len(regex.findall(x))
+        return self._str_map(f, dtype="int64")
+
+    def _str_pad(
+        self,
+        width: int,
+        side: Literal["left", "right", "both"] = "left",
+        fillchar: str = " ",
+    ):
+        if side == "left":
+            f = lambda x: x.rjust(width, fillchar)
+        elif side == "right":
+            f = lambda x: x.ljust(width, fillchar)
+        elif side == "both":
+            f = lambda x: x.center(width, fillchar)
+        else:  # pragma: no cover
+            raise ValueError("Invalid side")
+        return self._str_map(f)
+
+    def _str_contains(
+        self,
+        pat,
+        case: bool = True,
+        flags: int = 0,
+        na=lib.no_default,
+        regex: bool = True,
+    ):
+        if regex:
+            if not case:
+                flags |= re.IGNORECASE
+
+            pat = re.compile(pat, flags=flags)
+
+            f = lambda x: pat.search(x) is not None
+        else:
+            if case:
+                f = lambda x: pat in x
+            else:
+                upper_pat = pat.upper()
+                f = lambda x: upper_pat in x.upper()
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                "Allowing a non-bool 'na' in obj.str.contains is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return self._str_map(f, na, dtype=np.dtype("bool"))
+
+    def _str_startswith(self, pat, na=lib.no_default):
+        f = lambda x: x.startswith(pat)
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                "Allowing a non-bool 'na' in obj.str.startswith is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_endswith(self, pat, na=lib.no_default):
+        f = lambda x: x.endswith(pat)
+        if na is not lib.no_default and not isna(na) and not isinstance(na, bool):
+            # GH#59561
+            warnings.warn(
+                "Allowing a non-bool 'na' in obj.str.endswith is deprecated "
+                "and will raise in a future version.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_replace(
+        self,
+        pat: str | re.Pattern,
+        repl: str | Callable,
+        n: int = -1,
+        case: bool = True,
+        flags: int = 0,
+        regex: bool = True,
+    ):
+        if case is False:
+            # add case flag, if provided
+            flags |= re.IGNORECASE
+
+        if regex or flags or callable(repl):
+            if not isinstance(pat, re.Pattern):
+                if regex is False:
+                    pat = re.escape(pat)
+                pat = re.compile(pat, flags=flags)
+
+            n = n if n >= 0 else 0
+            f = lambda x: pat.sub(repl=repl, string=x, count=n)
+        else:
+            f = lambda x: x.replace(pat, repl, n)
+
+        return self._str_map(f, dtype=str)
+
+    def _str_repeat(self, repeats: int | Sequence[int]):
+        if lib.is_integer(repeats):
+            rint = cast(int, repeats)
+
+            def scalar_rep(x):
+                try:
+                    return bytes.__mul__(x, rint)
+                except TypeError:
+                    return str.__mul__(x, rint)
+
+            return self._str_map(scalar_rep, dtype=str)
+        else:
+            from pandas.core.arrays.string_ import BaseStringArray
+
+            def rep(x, r):
+                if x is libmissing.NA:
+                    return x
+                try:
+                    return bytes.__mul__(x, r)
+                except TypeError:
+                    return str.__mul__(x, r)
+
+            result = libops.vec_binop(
+                np.asarray(self),
+                np.asarray(repeats, dtype=object),
+                rep,
+            )
+            if isinstance(self, BaseStringArray):
+                # Not going through map, so we have to do this here.
+                result = type(self)._from_sequence(result, dtype=self.dtype)
+            return result
+
+    def _str_match(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if not case:
+            flags |= re.IGNORECASE
+
+        regex = re.compile(pat, flags=flags)
+
+        f = lambda x: regex.match(x) is not None
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_fullmatch(
+        self,
+        pat: str | re.Pattern,
+        case: bool = True,
+        flags: int = 0,
+        na: Scalar | lib.NoDefault = lib.no_default,
+    ):
+        if not case:
+            flags |= re.IGNORECASE
+
+        regex = re.compile(pat, flags=flags)
+
+        f = lambda x: regex.fullmatch(x) is not None
+        return self._str_map(f, na_value=na, dtype=np.dtype(bool))
+
+    def _str_encode(self, encoding, errors: str = "strict"):
+        f = lambda x: x.encode(encoding, errors=errors)
+        return self._str_map(f, dtype=object)
+
+    def _str_find(self, sub, start: int = 0, end=None):
+        return self._str_find_(sub, start, end, side="left")
+
+    def _str_rfind(self, sub, start: int = 0, end=None):
+        return self._str_find_(sub, start, end, side="right")
+
+    def _str_find_(self, sub, start, end, side):
+        if side == "left":
+            method = "find"
+        elif side == "right":
+            method = "rfind"
+        else:  # pragma: no cover
+            raise ValueError("Invalid side")
+
+        if end is None:
+            f = lambda x: getattr(x, method)(sub, start)
+        else:
+            f = lambda x: getattr(x, method)(sub, start, end)
+        return self._str_map(f, dtype="int64")
+
+    def _str_findall(self, pat, flags: int = 0):
+        regex = re.compile(pat, flags=flags)
+        return self._str_map(regex.findall, dtype="object")
+
+    def _str_get(self, i):
+        def f(x):
+            if isinstance(x, dict):
+                return x.get(i)
+            elif len(x) > i >= -len(x):
+                return x[i]
+            return self.dtype.na_value  # type: ignore[attr-defined]
+
+        return self._str_map(f)
+
+    def _str_index(self, sub, start: int = 0, end=None):
+        if end:
+            f = lambda x: x.index(sub, start, end)
+        else:
+            f = lambda x: x.index(sub, start, end)
+        return self._str_map(f, dtype="int64")
+
+    def _str_rindex(self, sub, start: int = 0, end=None):
+        if end:
+            f = lambda x: x.rindex(sub, start, end)
+        else:
+            f = lambda x: x.rindex(sub, start, end)
+        return self._str_map(f, dtype="int64")
+
+    def _str_join(self, sep: str):
+        return self._str_map(sep.join)
+
+    def _str_partition(self, sep: str, expand):
+        result = self._str_map(lambda x: x.partition(sep), dtype="object")
+        return result
+
+    def _str_rpartition(self, sep: str, expand):
+        return self._str_map(lambda x: x.rpartition(sep), dtype="object")
+
+    def _str_len(self):
+        return self._str_map(len, dtype="int64")
+
+    def _str_slice(self, start=None, stop=None, step=None):
+        obj = slice(start, stop, step)
+        return self._str_map(lambda x: x[obj])
+
+    def _str_slice_replace(self, start=None, stop=None, repl=None):
+        if repl is None:
+            repl = ""
+
+        def f(x):
+            if x[start:stop] == "":
+                local_stop = start
+            else:
+                local_stop = stop
+            y = ""
+            if start is not None:
+                y += x[:start]
+            y += repl
+            if stop is not None:
+                y += x[local_stop:]
+            return y
+
+        return self._str_map(f)
+
+    def _str_split(
+        self,
+        pat: str | re.Pattern | None = None,
+        n=-1,
+        expand: bool = False,
+        regex: bool | None = None,
+    ):
+        if pat is None:
+            if n is None or n == 0:
+                n = -1
+            f = lambda x: x.split(pat, n)
+        else:
+            new_pat: str | re.Pattern
+            if regex is True or isinstance(pat, re.Pattern):
+                new_pat = re.compile(pat)
+            elif regex is False:
+                new_pat = pat
+            # regex is None so link to old behavior #43563
+            else:
+                if len(pat) == 1:
+                    new_pat = pat
+                else:
+                    new_pat = re.compile(pat)
+
+            if isinstance(new_pat, re.Pattern):
+                if n is None or n == -1:
+                    n = 0
+                f = lambda x: new_pat.split(x, maxsplit=n)
+            else:
+                if n is None or n == 0:
+                    n = -1
+                f = lambda x: x.split(pat, n)
+        return self._str_map(f, dtype=object)
+
+    def _str_rsplit(self, pat=None, n=-1):
+        if n is None or n == 0:
+            n = -1
+        f = lambda x: x.rsplit(pat, n)
+        return self._str_map(f, dtype="object")
+
+    def _str_translate(self, table):
+        return self._str_map(lambda x: x.translate(table))
+
+    def _str_wrap(self, width: int, **kwargs):
+        kwargs["width"] = width
+        tw = textwrap.TextWrapper(**kwargs)
+        return self._str_map(lambda s: "\n".join(tw.wrap(s)))
+
+    def _str_get_dummies(self, sep: str = "|"):
+        from pandas import Series
+
+        arr = Series(self).fillna("")
+        try:
+            arr = sep + arr + sep
+        except (TypeError, NotImplementedError):
+            arr = sep + arr.astype(str) + sep
+
+        tags: set[str] = set()
+        for ts in Series(arr, copy=False).str.split(sep):
+            tags.update(ts)
+        tags2 = sorted(tags - {""})
+
+        dummies = np.empty((len(arr), len(tags2)), dtype=np.int64)
+
+        def _isin(test_elements: str, element: str) -> bool:
+            return element in test_elements
+
+        for i, t in enumerate(tags2):
+            pat = sep + t + sep
+            dummies[:, i] = lib.map_infer(
+                arr.to_numpy(), functools.partial(_isin, element=pat)
+            )
+        return dummies, tags2
+
+    def _str_upper(self):
+        return self._str_map(lambda x: x.upper())
+
+    def _str_isalnum(self):
+        return self._str_map(str.isalnum, dtype="bool")
+
+    def _str_isalpha(self):
+        return self._str_map(str.isalpha, dtype="bool")
+
+    def _str_isdecimal(self):
+        return self._str_map(str.isdecimal, dtype="bool")
+
+    def _str_isdigit(self):
+        return self._str_map(str.isdigit, dtype="bool")
+
+    def _str_islower(self):
+        return self._str_map(str.islower, dtype="bool")
+
+    def _str_isnumeric(self):
+        return self._str_map(str.isnumeric, dtype="bool")
+
+    def _str_isspace(self):
+        return self._str_map(str.isspace, dtype="bool")
+
+    def _str_istitle(self):
+        return self._str_map(str.istitle, dtype="bool")
+
+    def _str_isupper(self):
+        return self._str_map(str.isupper, dtype="bool")
+
+    def _str_capitalize(self):
+        return self._str_map(str.capitalize)
+
+    def _str_casefold(self):
+        return self._str_map(str.casefold)
+
+    def _str_title(self):
+        return self._str_map(str.title)
+
+    def _str_swapcase(self):
+        return self._str_map(str.swapcase)
+
+    def _str_lower(self):
+        return self._str_map(str.lower)
+
+    def _str_normalize(self, form):
+        f = lambda x: unicodedata.normalize(form, x)
+        return self._str_map(f)
+
+    def _str_strip(self, to_strip=None):
+        return self._str_map(lambda x: x.strip(to_strip))
+
+    def _str_lstrip(self, to_strip=None):
+        return self._str_map(lambda x: x.lstrip(to_strip))
+
+    def _str_rstrip(self, to_strip=None):
+        return self._str_map(lambda x: x.rstrip(to_strip))
+
+    def _str_removeprefix(self, prefix: str) -> Series:
+        # outstanding question on whether to use native methods for users on Python 3.9+
+        # https://github.com/pandas-dev/pandas/pull/39226#issuecomment-836719770,
+        # in which case we could do return self._str_map(str.removeprefix)
+
+        def removeprefix(text: str) -> str:
+            if text.startswith(prefix):
+                return text[len(prefix) :]
+            return text
+
+        return self._str_map(removeprefix)
+
+    def _str_removesuffix(self, suffix: str) -> Series:
+        return self._str_map(lambda x: x.removesuffix(suffix))
+
+    def _str_extract(self, pat: str, flags: int = 0, expand: bool = True):
+        regex = re.compile(pat, flags=flags)
+        na_value = self.dtype.na_value  # type: ignore[attr-defined]
+
+        if not expand:
+
+            def g(x):
+                m = regex.search(x)
+                return m.groups()[0] if m else na_value
+
+            return self._str_map(g, convert=False)
+
+        empty_row = [na_value] * regex.groups
+
+        def f(x):
+            if not isinstance(x, str):
+                return empty_row
+            m = regex.search(x)
+            if m:
+                return [na_value if item is None else item for item in m.groups()]
+            else:
+                return empty_row
+
+        return [f(val) for val in np.asarray(self)]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/datetimes.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f700cfa631327fb09b1d6d50f86eefb212f3f87
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/datetimes.py
@@ -0,0 +1,1240 @@
+from __future__ import annotations
+
+from collections import abc
+from datetime import date
+from functools import partial
+from itertools import islice
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    TypedDict,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    lib,
+    tslib,
+)
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    Timedelta,
+    Timestamp,
+    astype_overflowsafe,
+    is_supported_dtype,
+    timezones as libtimezones,
+)
+from pandas._libs.tslibs.conversion import cast_from_unit_vectorized
+from pandas._libs.tslibs.parsing import (
+    DateParseError,
+    guess_datetime_format,
+)
+from pandas._libs.tslibs.strptime import array_strptime
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    DateTimeErrorChoices,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_float,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    DatetimeTZDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.arrays import (
+    DatetimeArray,
+    IntegerArray,
+    NumpyExtensionArray,
+)
+from pandas.core.algorithms import unique
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.datetimes import (
+    maybe_convert_dtype,
+    objects_to_datetime64,
+    tz_to_dtype,
+)
+from pandas.core.construction import extract_array
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._libs.tslibs.nattype import NaTType
+    from pandas._libs.tslibs.timedeltas import UnitChoices
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ---------------------------------------------------------------------
+# types used in annotations
+
+ArrayConvertible = Union[list, tuple, AnyArrayLike]
+Scalar = Union[float, str]
+DatetimeScalar = Union[Scalar, date, np.datetime64]
+
+DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
+
+DatetimeDictArg = Union[list[Scalar], tuple[Scalar, ...], AnyArrayLike]
+
+
+class YearMonthDayDict(TypedDict, total=True):
+    year: DatetimeDictArg
+    month: DatetimeDictArg
+    day: DatetimeDictArg
+
+
+class FulldatetimeDict(YearMonthDayDict, total=False):
+    hour: DatetimeDictArg
+    hours: DatetimeDictArg
+    minute: DatetimeDictArg
+    minutes: DatetimeDictArg
+    second: DatetimeDictArg
+    seconds: DatetimeDictArg
+    ms: DatetimeDictArg
+    us: DatetimeDictArg
+    ns: DatetimeDictArg
+
+
+DictConvertible = Union[FulldatetimeDict, "DataFrame"]
+start_caching_at = 50
+
+
+# ---------------------------------------------------------------------
+
+
+def _guess_datetime_format_for_array(arr, dayfirst: bool | None = False) -> str | None:
+    # Try to guess the format based on the first non-NaN element, return None if can't
+    if (first_non_null := tslib.first_non_null(arr)) != -1:
+        if type(first_non_nan_element := arr[first_non_null]) is str:  # noqa: E721
+            # GH#32264 np.str_ object
+            guessed_format = guess_datetime_format(
+                first_non_nan_element, dayfirst=dayfirst
+            )
+            if guessed_format is not None:
+                return guessed_format
+            # If there are multiple non-null elements, warn about
+            # how parsing might not be consistent
+            if tslib.first_non_null(arr[first_non_null + 1 :]) != -1:
+                warnings.warn(
+                    "Could not infer format, so each element will be parsed "
+                    "individually, falling back to `dateutil`. To ensure parsing is "
+                    "consistent and as-expected, please specify a format.",
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+    return None
+
+
+def should_cache(
+    arg: ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
+) -> bool:
+    """
+    Decides whether to do caching.
+
+    If the percent of unique elements among `check_count` elements less
+    than `unique_share * 100` then we can do caching.
+
+    Parameters
+    ----------
+    arg: listlike, tuple, 1-d array, Series
+    unique_share: float, default=0.7, optional
+        0 < unique_share < 1
+    check_count: int, optional
+        0 <= check_count <= len(arg)
+
+    Returns
+    -------
+    do_caching: bool
+
+    Notes
+    -----
+    By default for a sequence of less than 50 items in size, we don't do
+    caching; for the number of elements less than 5000, we take ten percent of
+    all elements to check for a uniqueness share; if the sequence size is more
+    than 5000, then we check only the first 500 elements.
+    All constants were chosen empirically by.
+    """
+    do_caching = True
+
+    # default realization
+    if check_count is None:
+        # in this case, the gain from caching is negligible
+        if len(arg) <= start_caching_at:
+            return False
+
+        if len(arg) <= 5000:
+            check_count = len(arg) // 10
+        else:
+            check_count = 500
+    else:
+        assert (
+            0 <= check_count <= len(arg)
+        ), "check_count must be in next bounds: [0; len(arg)]"
+        if check_count == 0:
+            return False
+
+    assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
+
+    try:
+        # We can't cache if the items are not hashable.
+        unique_elements = set(islice(arg, check_count))
+    except TypeError:
+        return False
+    if len(unique_elements) > check_count * unique_share:
+        do_caching = False
+    return do_caching
+
+
+def _maybe_cache(
+    arg: ArrayConvertible,
+    format: str | None,
+    cache: bool,
+    convert_listlike: Callable,
+) -> Series:
+    """
+    Create a cache of unique dates from an array of dates
+
+    Parameters
+    ----------
+    arg : listlike, tuple, 1-d array, Series
+    format : string
+        Strftime format to parse time
+    cache : bool
+        True attempts to create a cache of converted values
+    convert_listlike : function
+        Conversion function to apply on dates
+
+    Returns
+    -------
+    cache_array : Series
+        Cache of converted, unique dates. Can be empty
+    """
+    from pandas import Series
+
+    cache_array = Series(dtype=object)
+
+    if cache:
+        # Perform a quicker unique check
+        if not should_cache(arg):
+            return cache_array
+
+        if not isinstance(arg, (np.ndarray, ExtensionArray, Index, ABCSeries)):
+            arg = np.array(arg)
+
+        unique_dates = unique(arg)
+        if len(unique_dates) < len(arg):
+            cache_dates = convert_listlike(unique_dates, format)
+            # GH#45319
+            try:
+                cache_array = Series(cache_dates, index=unique_dates, copy=False)
+            except OutOfBoundsDatetime:
+                return cache_array
+            # GH#39882 and GH#35888 in case of None and NaT we get duplicates
+            if not cache_array.index.is_unique:
+                cache_array = cache_array[~cache_array.index.duplicated()]
+    return cache_array
+
+
+def _box_as_indexlike(
+    dt_array: ArrayLike, utc: bool = False, name: Hashable | None = None
+) -> Index:
+    """
+    Properly boxes the ndarray of datetimes to DatetimeIndex
+    if it is possible or to generic Index instead
+
+    Parameters
+    ----------
+    dt_array: 1-d array
+        Array of datetimes to be wrapped in an Index.
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+    name : string, default None
+        Name for a resulting index
+
+    Returns
+    -------
+    result : datetime of converted dates
+        - DatetimeIndex if convertible to sole datetime64 type
+        - general Index otherwise
+    """
+
+    if lib.is_np_dtype(dt_array.dtype, "M"):
+        tz = "utc" if utc else None
+        return DatetimeIndex(dt_array, tz=tz, name=name)
+    return Index(dt_array, name=name, dtype=dt_array.dtype)
+
+
+def _convert_and_box_cache(
+    arg: DatetimeScalarOrArrayConvertible,
+    cache_array: Series,
+    name: Hashable | None = None,
+) -> Index:
+    """
+    Convert array of dates with a cache and wrap the result in an Index.
+
+    Parameters
+    ----------
+    arg : integer, float, string, datetime, list, tuple, 1-d array, Series
+    cache_array : Series
+        Cache of converted, unique dates
+    name : string, default None
+        Name for a DatetimeIndex
+
+    Returns
+    -------
+    result : Index-like of converted dates
+    """
+    from pandas import Series
+
+    result = Series(arg, dtype=cache_array.index.dtype).map(cache_array)
+    return _box_as_indexlike(result._values, utc=False, name=name)
+
+
+def _convert_listlike_datetimes(
+    arg,
+    format: str | None,
+    name: Hashable | None = None,
+    utc: bool = False,
+    unit: str | None = None,
+    errors: DateTimeErrorChoices = "raise",
+    dayfirst: bool | None = None,
+    yearfirst: bool | None = None,
+    exact: bool = True,
+):
+    """
+    Helper function for to_datetime. Performs the conversions of 1D listlike
+    of dates
+
+    Parameters
+    ----------
+    arg : list, tuple, ndarray, Series, Index
+        date to be parsed
+    name : object
+        None or string for the Index name
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+    unit : str
+        None or string of the frequency of the passed data
+    errors : str
+        error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
+    dayfirst : bool
+        dayfirst parsing behavior from to_datetime
+    yearfirst : bool
+        yearfirst parsing behavior from to_datetime
+    exact : bool, default True
+        exact format matching behavior from to_datetime
+
+    Returns
+    -------
+    Index-like of parsed dates
+    """
+    if isinstance(arg, (list, tuple)):
+        arg = np.array(arg, dtype="O")
+    elif isinstance(arg, NumpyExtensionArray):
+        arg = np.array(arg)
+
+    arg_dtype = getattr(arg, "dtype", None)
+    # these are shortcutable
+    tz = "utc" if utc else None
+    if isinstance(arg_dtype, DatetimeTZDtype):
+        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
+            return DatetimeIndex(arg, tz=tz, name=name)
+        if utc:
+            arg = arg.tz_convert(None).tz_localize("utc")
+        return arg
+
+    elif isinstance(arg_dtype, ArrowDtype) and arg_dtype.type is Timestamp:
+        # TODO: Combine with above if DTI/DTA supports Arrow timestamps
+        if utc:
+            # pyarrow uses UTC, not lowercase utc
+            if isinstance(arg, Index):
+                arg_array = cast(ArrowExtensionArray, arg.array)
+                if arg_dtype.pyarrow_dtype.tz is not None:
+                    arg_array = arg_array._dt_tz_convert("UTC")
+                else:
+                    arg_array = arg_array._dt_tz_localize("UTC")
+                arg = Index(arg_array)
+            else:
+                # ArrowExtensionArray
+                if arg_dtype.pyarrow_dtype.tz is not None:
+                    arg = arg._dt_tz_convert("UTC")
+                else:
+                    arg = arg._dt_tz_localize("UTC")
+        return arg
+
+    elif lib.is_np_dtype(arg_dtype, "M"):
+        if not is_supported_dtype(arg_dtype):
+            # We go to closest supported reso, i.e. "s"
+            arg = astype_overflowsafe(
+                # TODO: looks like we incorrectly raise with errors=="ignore"
+                np.asarray(arg),
+                np.dtype("M8[s]"),
+                is_coerce=errors == "coerce",
+            )
+
+        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
+            return DatetimeIndex(arg, tz=tz, name=name)
+        elif utc:
+            # DatetimeArray, DatetimeIndex
+            return arg.tz_localize("utc")
+
+        return arg
+
+    elif unit is not None:
+        if format is not None:
+            raise ValueError("cannot specify both format and unit")
+        return _to_datetime_with_unit(arg, unit, name, utc, errors)
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError(
+            "arg must be a string, datetime, list, tuple, 1-d array, or Series"
+        )
+
+    # warn if passing timedelta64, raise for PeriodDtype
+    # NB: this must come after unit transformation
+    try:
+        arg, _ = maybe_convert_dtype(arg, copy=False, tz=libtimezones.maybe_get_tz(tz))
+    except TypeError:
+        if errors == "coerce":
+            npvalues = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
+            return DatetimeIndex(npvalues, name=name)
+        elif errors == "ignore":
+            idx = Index(arg, name=name)
+            return idx
+        raise
+
+    arg = ensure_object(arg)
+
+    if format is None:
+        format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
+
+    # `format` could be inferred, or user didn't ask for mixed-format parsing.
+    if format is not None and format != "mixed":
+        return _array_strptime_with_fallback(arg, name, utc, format, exact, errors)
+
+    result, tz_parsed = objects_to_datetime64(
+        arg,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        utc=utc,
+        errors=errors,
+        allow_object=True,
+    )
+
+    if tz_parsed is not None:
+        # We can take a shortcut since the datetime64 numpy array
+        # is in UTC
+        out_unit = np.datetime_data(result.dtype)[0]
+        dtype = cast(DatetimeTZDtype, tz_to_dtype(tz_parsed, out_unit))
+        dt64_values = result.view(f"M8[{dtype.unit}]")
+        dta = DatetimeArray._simple_new(dt64_values, dtype=dtype)
+        return DatetimeIndex._simple_new(dta, name=name)
+
+    return _box_as_indexlike(result, utc=utc, name=name)
+
+
+def _array_strptime_with_fallback(
+    arg,
+    name,
+    utc: bool,
+    fmt: str,
+    exact: bool,
+    errors: str,
+) -> Index:
+    """
+    Call array_strptime, with fallback behavior depending on 'errors'.
+    """
+    result, tz_out = array_strptime(arg, fmt, exact=exact, errors=errors, utc=utc)
+    if tz_out is not None:
+        unit = np.datetime_data(result.dtype)[0]
+        dtype = DatetimeTZDtype(tz=tz_out, unit=unit)
+        dta = DatetimeArray._simple_new(result, dtype=dtype)
+        if utc:
+            dta = dta.tz_convert("UTC")
+        return Index(dta, name=name)
+    elif result.dtype != object and utc:
+        unit = np.datetime_data(result.dtype)[0]
+        res = Index(result, dtype=f"M8[{unit}, UTC]", name=name)
+        return res
+    elif using_string_dtype() and result.dtype == object:
+        if lib.is_string_array(result):
+            return Index(result, dtype="str", name=name)
+    return Index(result, dtype=result.dtype, name=name)
+
+
+def _to_datetime_with_unit(arg, unit, name, utc: bool, errors: str) -> Index:
+    """
+    to_datetime specalized to the case where a 'unit' is passed.
+    """
+    arg = extract_array(arg, extract_numpy=True)
+
+    # GH#30050 pass an ndarray to tslib.array_with_unit_to_datetime
+    # because it expects an ndarray argument
+    if isinstance(arg, IntegerArray):
+        arr = arg.astype(f"datetime64[{unit}]")
+        tz_parsed = None
+    else:
+        arg = np.asarray(arg)
+
+        if arg.dtype.kind in "iu":
+            # Note we can't do "f" here because that could induce unwanted
+            #  rounding GH#14156, GH#20445
+            arr = arg.astype(f"datetime64[{unit}]", copy=False)
+            try:
+                arr = astype_overflowsafe(arr, np.dtype("M8[ns]"), copy=False)
+            except OutOfBoundsDatetime:
+                if errors == "raise":
+                    raise
+                arg = arg.astype(object)
+                return _to_datetime_with_unit(arg, unit, name, utc, errors)
+            tz_parsed = None
+
+        elif arg.dtype.kind == "f":
+            with np.errstate(over="raise"):
+                try:
+                    arr = cast_from_unit_vectorized(arg, unit=unit)
+                except OutOfBoundsDatetime:
+                    if errors != "raise":
+                        return _to_datetime_with_unit(
+                            arg.astype(object), unit, name, utc, errors
+                        )
+                    raise OutOfBoundsDatetime(
+                        f"cannot convert input with unit '{unit}'"
+                    )
+
+            arr = arr.view("M8[ns]")
+            tz_parsed = None
+        else:
+            arg = arg.astype(object, copy=False)
+            arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
+
+    if errors == "ignore":
+        # Index constructor _may_ infer to DatetimeIndex
+        result = Index._with_infer(arr, name=name)
+    else:
+        result = DatetimeIndex(arr, name=name)
+
+    if not isinstance(result, DatetimeIndex):
+        return result
+
+    # GH#23758: We may still need to localize the result with tz
+    # GH#25546: Apply tz_parsed first (from arg), then tz (from caller)
+    # result will be naive but in UTC
+    result = result.tz_localize("UTC").tz_convert(tz_parsed)
+
+    if utc:
+        if result.tz is None:
+            result = result.tz_localize("utc")
+        else:
+            result = result.tz_convert("utc")
+    return result
+
+
+def _adjust_to_origin(arg, origin, unit):
+    """
+    Helper function for to_datetime.
+    Adjust input argument to the specified origin
+
+    Parameters
+    ----------
+    arg : list, tuple, ndarray, Series, Index
+        date to be adjusted
+    origin : 'julian' or Timestamp
+        origin offset for the arg
+    unit : str
+        passed unit from to_datetime, must be 'D'
+
+    Returns
+    -------
+    ndarray or scalar of adjusted date(s)
+    """
+    if origin == "julian":
+        original = arg
+        j0 = Timestamp(0).to_julian_date()
+        if unit != "D":
+            raise ValueError("unit must be 'D' for origin='julian'")
+        try:
+            arg = arg - j0
+        except TypeError as err:
+            raise ValueError(
+                "incompatible 'arg' type for given 'origin'='julian'"
+            ) from err
+
+        # preemptively check this for a nice range
+        j_max = Timestamp.max.to_julian_date() - j0
+        j_min = Timestamp.min.to_julian_date() - j0
+        if np.any(arg > j_max) or np.any(arg < j_min):
+            raise OutOfBoundsDatetime(
+                f"{original} is Out of Bounds for origin='julian'"
+            )
+    else:
+        # arg must be numeric
+        if not (
+            (is_integer(arg) or is_float(arg)) or is_numeric_dtype(np.asarray(arg))
+        ):
+            raise ValueError(
+                f"'{arg}' is not compatible with origin='{origin}'; "
+                "it must be numeric with a unit specified"
+            )
+
+        # we are going to offset back to unix / epoch time
+        try:
+            offset = Timestamp(origin, unit=unit)
+        except OutOfBoundsDatetime as err:
+            raise OutOfBoundsDatetime(f"origin {origin} is Out of Bounds") from err
+        except ValueError as err:
+            raise ValueError(
+                f"origin {origin} cannot be converted to a Timestamp"
+            ) from err
+
+        if offset.tz is not None:
+            raise ValueError(f"origin offset {offset} must be tz-naive")
+        td_offset = offset - Timestamp(0)
+
+        # convert the offset to the unit of the arg
+        # this should be lossless in terms of precision
+        ioffset = td_offset // Timedelta(1, unit=unit)
+
+        # scalars & ndarray-like can handle the addition
+        if is_list_like(arg) and not isinstance(arg, (ABCSeries, Index, np.ndarray)):
+            arg = np.asarray(arg)
+        arg = arg + ioffset
+    return arg
+
+
+@overload
+def to_datetime(
+    arg: DatetimeScalar,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> Timestamp:
+    ...
+
+
+@overload
+def to_datetime(
+    arg: Series | DictConvertible,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> Series:
+    ...
+
+
+@overload
+def to_datetime(
+    arg: list | tuple | Index | ArrayLike,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> DatetimeIndex:
+    ...
+
+
+def to_datetime(
+    arg: DatetimeScalarOrArrayConvertible | DictConvertible,
+    errors: DateTimeErrorChoices = "raise",
+    dayfirst: bool = False,
+    yearfirst: bool = False,
+    utc: bool = False,
+    format: str | None = None,
+    exact: bool | lib.NoDefault = lib.no_default,
+    unit: str | None = None,
+    infer_datetime_format: lib.NoDefault | bool = lib.no_default,
+    origin: str = "unix",
+    cache: bool = True,
+) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
+    """
+    Convert argument to datetime.
+
+    This function converts a scalar, array-like, :class:`Series` or
+    :class:`DataFrame`/dict-like to a pandas datetime object.
+
+    Parameters
+    ----------
+    arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
+        The object to convert to a datetime. If a :class:`DataFrame` is provided, the
+        method expects minimally the following columns: :const:`"year"`,
+        :const:`"month"`, :const:`"day"`. The column "year"
+        must be specified in 4-digit format.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If :const:`'raise'`, then invalid parsing will raise an exception.
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`.
+        - If :const:`'ignore'`, then invalid parsing will return the input.
+    dayfirst : bool, default False
+        Specify a date parse order if `arg` is str or is list-like.
+        If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"`
+        is parsed as :const:`2012-11-10`.
+
+        .. warning::
+
+            ``dayfirst=True`` is not strict, but will prefer to parse
+            with day first.
+
+    yearfirst : bool, default False
+        Specify a date parse order if `arg` is str or is list-like.
+
+        - If :const:`True` parses dates with the year first, e.g.
+          :const:`"10/11/12"` is parsed as :const:`2010-11-12`.
+        - If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is
+          preceded (same as :mod:`dateutil`).
+
+        .. warning::
+
+            ``yearfirst=True`` is not strict, but will prefer to parse
+            with year first.
+
+    utc : bool, default False
+        Control timezone-related parsing, localization and conversion.
+
+        - If :const:`True`, the function *always* returns a timezone-aware
+          UTC-localized :class:`Timestamp`, :class:`Series` or
+          :class:`DatetimeIndex`. To do this, timezone-naive inputs are
+          *localized* as UTC, while timezone-aware inputs are *converted* to UTC.
+
+        - If :const:`False` (default), inputs will not be coerced to UTC.
+          Timezone-naive inputs will remain naive, while timezone-aware ones
+          will keep their time offsets. Limitations exist for mixed
+          offsets (typically, daylight savings), see :ref:`Examples
+          <to_datetime_tz_examples>` section for details.
+
+        .. warning::
+
+            In a future version of pandas, parsing datetimes with mixed time
+            zones will raise an error unless `utc=True`.
+            Please specify `utc=True` to opt in to the new behaviour
+            and silence this warning. To create a `Series` with mixed offsets and
+            `object` dtype, please use `apply` and `datetime.datetime.strptime`.
+
+        See also: pandas general documentation about `timezone conversion and
+        localization
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
+        #time-zone-handling>`_.
+
+    format : str, default None
+        The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
+        `strftime documentation
+        <https://docs.python.org/3/library/datetime.html
+        #strftime-and-strptime-behavior>`_ for more information on choices, though
+        note that :const:`"%f"` will parse all the way up to nanoseconds.
+        You can also pass:
+
+        - "ISO8601", to parse any `ISO8601 <https://en.wikipedia.org/wiki/ISO_8601>`_
+          time string (not necessarily in exactly the same format);
+        - "mixed", to infer the format for each element individually. This is risky,
+          and you should probably use it along with `dayfirst`.
+
+        .. note::
+
+            If a :class:`DataFrame` is passed, then `format` has no effect.
+
+    exact : bool, default True
+        Control how `format` is used:
+
+        - If :const:`True`, require an exact `format` match.
+        - If :const:`False`, allow the `format` to match anywhere in the target
+          string.
+
+        Cannot be used alongside ``format='ISO8601'`` or ``format='mixed'``.
+    unit : str, default 'ns'
+        The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
+        integer or float number. This will be based off the origin.
+        Example, with ``unit='ms'`` and ``origin='unix'``, this would calculate
+        the number of milliseconds to the unix epoch start.
+    infer_datetime_format : bool, default False
+        If :const:`True` and no `format` is given, attempt to infer the format
+        of the datetime strings based on the first non-NaN element,
+        and if it can be inferred, switch to a faster method of parsing them.
+        In some cases this can increase the parsing speed by ~5-10x.
+
+        .. deprecated:: 2.0.0
+            A strict version of this argument is now the default, passing it has
+            no effect.
+
+    origin : scalar, default 'unix'
+        Define the reference date. The numeric values would be parsed as number
+        of units (defined by `unit`) since this reference date.
+
+        - If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01.
+        - If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to
+          beginning of Julian Calendar. Julian day number :const:`0` is assigned
+          to the day starting at noon on January 1, 4713 BC.
+        - If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date
+          string), origin is set to Timestamp identified by origin.
+        - If a float or integer, origin is the difference
+          (in units determined by the ``unit`` argument) relative to 1970-01-01.
+    cache : bool, default True
+        If :const:`True`, use a cache of unique, converted dates to apply the
+        datetime conversion. May produce significant speed-up when parsing
+        duplicate date strings, especially ones with timezone offsets. The cache
+        is only used when there are at least 50 values. The presence of
+        out-of-bounds values will render the cache unusable and may slow down
+        parsing.
+
+    Returns
+    -------
+    datetime
+        If parsing succeeded.
+        Return type depends on input (types in parenthesis correspond to
+        fallback in case of unsuccessful timezone or out-of-range timestamp
+        parsing):
+
+        - scalar: :class:`Timestamp` (or :class:`datetime.datetime`)
+        - array-like: :class:`DatetimeIndex` (or :class:`Series` with
+          :class:`object` dtype containing :class:`datetime.datetime`)
+        - Series: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+        - DataFrame: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+
+    Raises
+    ------
+    ParserError
+        When parsing a date from string fails.
+    ValueError
+        When another datetime conversion error happens. For example when one
+        of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or
+        when a Timezone-aware :class:`datetime.datetime` is found in an array-like
+        of mixed time offsets, and ``utc=False``.
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_timedelta : Convert argument to timedelta.
+    convert_dtypes : Convert dtypes.
+
+    Notes
+    -----
+
+    Many input types are supported, and lead to different output types:
+
+    - **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime`
+      module or :mod:`numpy`). They are converted to :class:`Timestamp` when
+      possible, otherwise they are converted to :class:`datetime.datetime`.
+      None/NaN/null scalars are converted to :const:`NaT`.
+
+    - **array-like** can contain int, float, str, datetime objects. They are
+      converted to :class:`DatetimeIndex` when possible, otherwise they are
+      converted to :class:`Index` with :class:`object` dtype, containing
+      :class:`datetime.datetime`. None/NaN/null entries are converted to
+      :const:`NaT` in both cases.
+
+    - **Series** are converted to :class:`Series` with :class:`datetime64`
+      dtype when possible, otherwise they are converted to :class:`Series` with
+      :class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null
+      entries are converted to :const:`NaT` in both cases.
+
+    - **DataFrame/dict-like** are converted to :class:`Series` with
+      :class:`datetime64` dtype. For each row a datetime is created from assembling
+      the various dataframe columns. Column keys can be common abbreviations
+      like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or
+      plurals of the same.
+
+    The following causes are responsible for :class:`datetime.datetime` objects
+    being returned (possibly inside an :class:`Index` or a :class:`Series` with
+    :class:`object` dtype) instead of a proper pandas designated type
+    (:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series`
+    with :class:`datetime64` dtype):
+
+    - when any input element is before :const:`Timestamp.min` or after
+      :const:`Timestamp.max`, see `timestamp limitations
+      <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
+      #timeseries-timestamp-limits>`_.
+
+    - when ``utc=False`` (default) and the input is an array-like or
+      :class:`Series` containing mixed naive/aware datetime, or aware with mixed
+      time offsets. Note that this happens in the (quite frequent) situation when
+      the timezone has a daylight savings policy. In that case you may wish to
+      use ``utc=True``.
+
+    Examples
+    --------
+
+    **Handling various input formats**
+
+    Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys
+    can be common abbreviations like ['year', 'month', 'day', 'minute', 'second',
+    'ms', 'us', 'ns']) or plurals of the same
+
+    >>> df = pd.DataFrame({'year': [2015, 2016],
+    ...                    'month': [2, 3],
+    ...                    'day': [4, 5]})
+    >>> pd.to_datetime(df)
+    0   2015-02-04
+    1   2016-03-05
+    dtype: datetime64[ns]
+
+    Using a unix epoch time
+
+    >>> pd.to_datetime(1490195805, unit='s')
+    Timestamp('2017-03-22 15:16:45')
+    >>> pd.to_datetime(1490195805433502912, unit='ns')
+    Timestamp('2017-03-22 15:16:45.433502912')
+
+    .. warning:: For float arg, precision rounding might happen. To prevent
+        unexpected behavior use a fixed-width exact type.
+
+    Using a non-unix epoch origin
+
+    >>> pd.to_datetime([1, 2, 3], unit='D',
+    ...                origin=pd.Timestamp('1960-01-01'))
+    DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Differences with strptime behavior**
+
+    :const:`"%f"` will parse all the way up to nanoseconds.
+
+    >>> pd.to_datetime('2018-10-26 12:00:00.0000000011',
+    ...                format='%Y-%m-%d %H:%M:%S.%f')
+    Timestamp('2018-10-26 12:00:00.000000001')
+
+    **Non-convertible date/times**
+
+    Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`,
+    in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`.
+
+    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
+    NaT
+
+    .. _to_datetime_tz_examples:
+
+    **Timezones and time offsets**
+
+    The default behaviour (``utc=False``) is as follows:
+
+    - Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00:00', '2018-10-26 13:00:15'])
+    DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
+                  dtype='datetime64[ns]', freq=None)
+
+    - Timezone-aware inputs *with constant time offset* are converted to
+      timezone-aware :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500'])
+    DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
+                  dtype='datetime64[ns, UTC-05:00]', freq=None)
+
+    - However, timezone-aware inputs *with mixed time offsets* (for example
+      issued from a timezone with daylight savings, such as Europe/Paris)
+      are **not successfully converted** to a :class:`DatetimeIndex`.
+      Parsing datetimes with mixed time zones will show a warning unless
+      `utc=True`. If you specify `utc=False` the warning below will be shown
+      and a simple :class:`Index` containing :class:`datetime.datetime`
+      objects will be returned:
+
+    >>> pd.to_datetime(['2020-10-25 02:00 +0200',
+    ...                 '2020-10-25 04:00 +0100'])  # doctest: +SKIP
+    FutureWarning: In a future version of pandas, parsing datetimes with mixed
+    time zones will raise an error unless `utc=True`. Please specify `utc=True`
+    to opt in to the new behaviour and silence this warning. To create a `Series`
+    with mixed offsets and `object` dtype, please use `apply` and
+    `datetime.datetime.strptime`.
+    Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00],
+          dtype='object')
+
+    - A mix of timezone-aware and timezone-naive inputs is also converted to
+      a simple :class:`Index` containing :class:`datetime.datetime` objects:
+
+    >>> from datetime import datetime
+    >>> pd.to_datetime(["2020-01-01 01:00:00-01:00",
+    ...                 datetime(2020, 1, 1, 3, 0)])  # doctest: +SKIP
+    FutureWarning: In a future version of pandas, parsing datetimes with mixed
+    time zones will raise an error unless `utc=True`. Please specify `utc=True`
+    to opt in to the new behaviour and silence this warning. To create a `Series`
+    with mixed offsets and `object` dtype, please use `apply` and
+    `datetime.datetime.strptime`.
+    Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')
+
+    |
+
+    Setting ``utc=True`` solves most of the above issues:
+
+    - Timezone-naive inputs are *localized* as UTC
+
+    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Timezone-aware inputs are *converted* to UTC (the output represents the
+      exact same datetime, but viewed from the UTC time offset `+00:00`).
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
+    ...                utc=True)
+    DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Inputs can contain both string or datetime, the above
+      rules still apply
+
+    >>> pd.to_datetime(['2018-10-26 12:00', datetime(2020, 1, 1, 18)], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2020-01-01 18:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+    """
+    if exact is not lib.no_default and format in {"mixed", "ISO8601"}:
+        raise ValueError("Cannot use 'exact' when 'format' is 'mixed' or 'ISO8601'")
+    if infer_datetime_format is not lib.no_default:
+        warnings.warn(
+            "The argument 'infer_datetime_format' is deprecated and will "
+            "be removed in a future version. "
+            "A strict version of it is now the default, see "
+            "https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
+            "You can safely remove this argument.",
+            stacklevel=find_stack_level(),
+        )
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_datetime without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if arg is None:
+        return None
+
+    if origin != "unix":
+        arg = _adjust_to_origin(arg, origin, unit)
+
+    convert_listlike = partial(
+        _convert_listlike_datetimes,
+        utc=utc,
+        unit=unit,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        errors=errors,
+        exact=exact,
+    )
+    # pylint: disable-next=used-before-assignment
+    result: Timestamp | NaTType | Series | Index
+
+    if isinstance(arg, Timestamp):
+        result = arg
+        if utc:
+            if arg.tz is not None:
+                result = arg.tz_convert("utc")
+            else:
+                result = arg.tz_localize("utc")
+    elif isinstance(arg, ABCSeries):
+        cache_array = _maybe_cache(arg, format, cache, convert_listlike)
+        if not cache_array.empty:
+            result = arg.map(cache_array)
+        else:
+            values = convert_listlike(arg._values, format)
+            result = arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
+        result = _assemble_from_unit_mappings(arg, errors, utc)
+    elif isinstance(arg, Index):
+        cache_array = _maybe_cache(arg, format, cache, convert_listlike)
+        if not cache_array.empty:
+            result = _convert_and_box_cache(arg, cache_array, name=arg.name)
+        else:
+            result = convert_listlike(arg, format, name=arg.name)
+    elif is_list_like(arg):
+        try:
+            # error: Argument 1 to "_maybe_cache" has incompatible type
+            # "Union[float, str, datetime, List[Any], Tuple[Any, ...], ExtensionArray,
+            # ndarray[Any, Any], Series]"; expected "Union[List[Any], Tuple[Any, ...],
+            # Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series], Series]"
+            argc = cast(
+                Union[list, tuple, ExtensionArray, np.ndarray, "Series", Index], arg
+            )
+            cache_array = _maybe_cache(argc, format, cache, convert_listlike)
+        except OutOfBoundsDatetime:
+            # caching attempts to create a DatetimeIndex, which may raise
+            # an OOB. If that's the desired behavior, then just reraise...
+            if errors == "raise":
+                raise
+            # ... otherwise, continue without the cache.
+            from pandas import Series
+
+            cache_array = Series([], dtype=object)  # just an empty array
+        if not cache_array.empty:
+            result = _convert_and_box_cache(argc, cache_array)
+        else:
+            result = convert_listlike(argc, format)
+    else:
+        result = convert_listlike(np.array([arg]), format)[0]
+        if isinstance(arg, bool) and isinstance(result, np.bool_):
+            result = bool(result)  # TODO: avoid this kludge.
+
+    #  error: Incompatible return value type (got "Union[Timestamp, NaTType,
+    # Series, Index]", expected "Union[DatetimeIndex, Series, float, str,
+    # NaTType, None]")
+    return result  # type: ignore[return-value]
+
+
+# mappings for assembling units
+_unit_map = {
+    "year": "year",
+    "years": "year",
+    "month": "month",
+    "months": "month",
+    "day": "day",
+    "days": "day",
+    "hour": "h",
+    "hours": "h",
+    "minute": "m",
+    "minutes": "m",
+    "second": "s",
+    "seconds": "s",
+    "ms": "ms",
+    "millisecond": "ms",
+    "milliseconds": "ms",
+    "us": "us",
+    "microsecond": "us",
+    "microseconds": "us",
+    "ns": "ns",
+    "nanosecond": "ns",
+    "nanoseconds": "ns",
+}
+
+
+def _assemble_from_unit_mappings(arg, errors: DateTimeErrorChoices, utc: bool):
+    """
+    assemble the unit specified fields from the arg (DataFrame)
+    Return a Series for actual parsing
+
+    Parameters
+    ----------
+    arg : DataFrame
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+
+        - If :const:`'raise'`, then invalid parsing will raise an exception
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`
+        - If :const:`'ignore'`, then invalid parsing will return the input
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+
+    Returns
+    -------
+    Series
+    """
+    from pandas import (
+        DataFrame,
+        to_numeric,
+        to_timedelta,
+    )
+
+    arg = DataFrame(arg)
+    if not arg.columns.is_unique:
+        raise ValueError("cannot assemble with duplicate keys")
+
+    # replace passed unit with _unit_map
+    def f(value):
+        if value in _unit_map:
+            return _unit_map[value]
+
+        # m is case significant
+        if value.lower() in _unit_map:
+            return _unit_map[value.lower()]
+
+        return value
+
+    unit = {k: f(k) for k in arg.keys()}
+    unit_rev = {v: k for k, v in unit.items()}
+
+    # we require at least Ymd
+    required = ["year", "month", "day"]
+    req = sorted(set(required) - set(unit_rev.keys()))
+    if len(req):
+        _required = ",".join(req)
+        raise ValueError(
+            "to assemble mappings requires at least that "
+            f"[year, month, day] be specified: [{_required}] is missing"
+        )
+
+    # keys we don't recognize
+    excess = sorted(set(unit_rev.keys()) - set(_unit_map.values()))
+    if len(excess):
+        _excess = ",".join(excess)
+        raise ValueError(
+            f"extra keys have been passed to the datetime assemblage: [{_excess}]"
+        )
+
+    def coerce(values):
+        # we allow coercion to if errors allows
+        values = to_numeric(values, errors=errors)
+
+        # prevent overflow in case of int8 or int16
+        if is_integer_dtype(values.dtype):
+            values = values.astype("int64", copy=False)
+        return values
+
+    values = (
+        coerce(arg[unit_rev["year"]]) * 10000
+        + coerce(arg[unit_rev["month"]]) * 100
+        + coerce(arg[unit_rev["day"]])
+    )
+    try:
+        values = to_datetime(values, format="%Y%m%d", errors=errors, utc=utc)
+    except (TypeError, ValueError) as err:
+        raise ValueError(f"cannot assemble the datetimes: {err}") from err
+
+    units: list[UnitChoices] = ["h", "m", "s", "ms", "us", "ns"]
+    for u in units:
+        value = unit_rev.get(u)
+        if value is not None and value in arg:
+            try:
+                values += to_timedelta(coerce(arg[value]), unit=u, errors=errors)
+            except (TypeError, ValueError) as err:
+                raise ValueError(
+                    f"cannot assemble the datetimes [{value}]: {err}"
+                ) from err
+    return values
+
+
+__all__ = [
+    "DateParseError",
+    "should_cache",
+    "to_datetime",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/numeric.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca703e0362611b9b2ccecdbf282104ad450a8d83
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/numeric.py
@@ -0,0 +1,332 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.cast import maybe_downcast_numeric
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_decimal,
+    is_integer_dtype,
+    is_number,
+    is_numeric_dtype,
+    is_scalar,
+    is_string_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+from pandas.core.arrays import BaseMaskedArray
+from pandas.core.arrays.string_ import StringDtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DateTimeErrorChoices,
+        DtypeBackend,
+        npt,
+    )
+
+
+def to_numeric(
+    arg,
+    errors: DateTimeErrorChoices = "raise",
+    downcast: Literal["integer", "signed", "unsigned", "float"] | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+):
+    """
+    Convert argument to a numeric type.
+
+    The default return dtype is `float64` or `int64`
+    depending on the data supplied. Use the `downcast` parameter
+    to obtain other dtypes.
+
+    Please note that precision loss may occur if really large numbers
+    are passed in. Due to the internal limitations of `ndarray`, if
+    numbers smaller than `-9223372036854775808` (np.iinfo(np.int64).min)
+    or larger than `18446744073709551615` (np.iinfo(np.uint64).max) are
+    passed in, it is very likely they will be converted to float so that
+    they can be stored in an `ndarray`. These warnings apply similarly to
+    `Series` since it internally leverages `ndarray`.
+
+    Parameters
+    ----------
+    arg : scalar, list, tuple, 1-d array, or Series
+        Argument to be converted.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception.
+        - If 'coerce', then invalid parsing will be set as NaN.
+        - If 'ignore', then invalid parsing will return the input.
+
+        .. versionchanged:: 2.2
+
+        "ignore" is deprecated. Catch exceptions explicitly instead.
+
+    downcast : str, default None
+        Can be 'integer', 'signed', 'unsigned', or 'float'.
+        If not None, and if the data has been successfully cast to a
+        numerical dtype (or if the data was numeric to begin with),
+        downcast that resulting data to the smallest numerical dtype
+        possible according to the following rules:
+
+        - 'integer' or 'signed': smallest signed int dtype (min.: np.int8)
+        - 'unsigned': smallest unsigned int dtype (min.: np.uint8)
+        - 'float': smallest float dtype (min.: np.float32)
+
+        As this behaviour is separate from the core conversion to
+        numeric values, any errors raised during the downcasting
+        will be surfaced regardless of the value of the 'errors' input.
+
+        In addition, downcasting will only occur if the size
+        of the resulting data's dtype is strictly larger than
+        the dtype it is to be cast to, so if none of the dtypes
+        checked satisfy that specification, no downcasting will be
+        performed on the data.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    ret
+        Numeric if parsing succeeded.
+        Return type depends on input.  Series if Series, otherwise ndarray.
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_datetime : Convert argument to datetime.
+    to_timedelta : Convert argument to timedelta.
+    numpy.ndarray.astype : Cast a numpy array to a specified type.
+    DataFrame.convert_dtypes : Convert dtypes.
+
+    Examples
+    --------
+    Take separate series and convert to numeric, coercing when told to
+
+    >>> s = pd.Series(['1.0', '2', -3])
+    >>> pd.to_numeric(s)
+    0    1.0
+    1    2.0
+    2   -3.0
+    dtype: float64
+    >>> pd.to_numeric(s, downcast='float')
+    0    1.0
+    1    2.0
+    2   -3.0
+    dtype: float32
+    >>> pd.to_numeric(s, downcast='signed')
+    0    1
+    1    2
+    2   -3
+    dtype: int8
+    >>> s = pd.Series(['apple', '1.0', '2', -3])
+    >>> pd.to_numeric(s, errors='coerce')
+    0    NaN
+    1    1.0
+    2    2.0
+    3   -3.0
+    dtype: float64
+
+    Downcasting of nullable integer and floating dtypes is supported:
+
+    >>> s = pd.Series([1, 2, 3], dtype="Int64")
+    >>> pd.to_numeric(s, downcast="integer")
+    0    1
+    1    2
+    2    3
+    dtype: Int8
+    >>> s = pd.Series([1.0, 2.1, 3.0], dtype="Float64")
+    >>> pd.to_numeric(s, downcast="float")
+    0    1.0
+    1    2.1
+    2    3.0
+    dtype: Float32
+    """
+    if downcast not in (None, "integer", "signed", "unsigned", "float"):
+        raise ValueError("invalid downcasting method provided")
+
+    if errors not in ("ignore", "raise", "coerce"):
+        raise ValueError("invalid error value specified")
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_numeric without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    check_dtype_backend(dtype_backend)
+
+    is_series = False
+    is_index = False
+    is_scalars = False
+
+    if isinstance(arg, ABCSeries):
+        is_series = True
+        values = arg.values
+    elif isinstance(arg, ABCIndex):
+        is_index = True
+        if needs_i8_conversion(arg.dtype):
+            values = arg.view("i8")
+        else:
+            values = arg.values
+    elif isinstance(arg, (list, tuple)):
+        values = np.array(arg, dtype="O")
+    elif is_scalar(arg):
+        if is_decimal(arg):
+            return float(arg)
+        if is_number(arg):
+            return arg
+        is_scalars = True
+        values = np.array([arg], dtype="O")
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError("arg must be a list, tuple, 1-d array, or Series")
+    else:
+        values = arg
+
+    orig_values = values
+
+    # GH33013: for IntegerArray & FloatingArray extract non-null values for casting
+    # save mask to reconstruct the full array after casting
+    mask: npt.NDArray[np.bool_] | None = None
+    if isinstance(values, BaseMaskedArray):
+        mask = values._mask
+        values = values._data[~mask]
+
+    values_dtype = getattr(values, "dtype", None)
+    if isinstance(values_dtype, ArrowDtype):
+        mask = values.isna()
+        values = values.dropna().to_numpy()
+    new_mask: np.ndarray | None = None
+    if is_numeric_dtype(values_dtype):
+        pass
+    elif lib.is_np_dtype(values_dtype, "mM"):
+        values = values.view(np.int64)
+    else:
+        values = ensure_object(values)
+        coerce_numeric = errors not in ("ignore", "raise")
+        try:
+            values, new_mask = lib.maybe_convert_numeric(  # type: ignore[call-overload]
+                values,
+                set(),
+                coerce_numeric=coerce_numeric,
+                convert_to_masked_nullable=dtype_backend is not lib.no_default
+                or isinstance(values_dtype, StringDtype)
+                and values_dtype.na_value is libmissing.NA,
+            )
+        except (ValueError, TypeError):
+            if errors == "raise":
+                raise
+            values = orig_values
+
+    if new_mask is not None:
+        # Remove unnecessary values, is expected later anyway and enables
+        # downcasting
+        values = values[~new_mask]
+    elif (
+        dtype_backend is not lib.no_default
+        and new_mask is None
+        or isinstance(values_dtype, StringDtype)
+        and values_dtype.na_value is libmissing.NA
+    ):
+        new_mask = np.zeros(values.shape, dtype=np.bool_)
+
+    # attempt downcast only if the data has been successfully converted
+    # to a numerical dtype and if a downcast method has been specified
+    if downcast is not None and is_numeric_dtype(values.dtype):
+        typecodes: str | None = None
+
+        if downcast in ("integer", "signed"):
+            typecodes = np.typecodes["Integer"]
+        elif downcast == "unsigned" and (not len(values) or np.min(values) >= 0):
+            typecodes = np.typecodes["UnsignedInteger"]
+        elif downcast == "float":
+            typecodes = np.typecodes["Float"]
+
+            # pandas support goes only to np.float32,
+            # as float dtypes smaller than that are
+            # extremely rare and not well supported
+            float_32_char = np.dtype(np.float32).char
+            float_32_ind = typecodes.index(float_32_char)
+            typecodes = typecodes[float_32_ind:]
+
+        if typecodes is not None:
+            # from smallest to largest
+            for typecode in typecodes:
+                dtype = np.dtype(typecode)
+                if dtype.itemsize <= values.dtype.itemsize:
+                    values = maybe_downcast_numeric(values, dtype)
+
+                    # successful conversion
+                    if values.dtype == dtype:
+                        break
+
+    # GH33013: for IntegerArray, BooleanArray & FloatingArray need to reconstruct
+    # masked array
+    if (mask is not None or new_mask is not None) and not is_string_dtype(values.dtype):
+        if mask is None or (new_mask is not None and new_mask.shape == mask.shape):
+            # GH 52588
+            mask = new_mask
+        else:
+            mask = mask.copy()
+        assert isinstance(mask, np.ndarray)
+        data = np.zeros(mask.shape, dtype=values.dtype)
+        data[~mask] = values
+
+        from pandas.core.arrays import (
+            ArrowExtensionArray,
+            BooleanArray,
+            FloatingArray,
+            IntegerArray,
+        )
+
+        klass: type[IntegerArray | BooleanArray | FloatingArray]
+        if is_integer_dtype(data.dtype):
+            klass = IntegerArray
+        elif is_bool_dtype(data.dtype):
+            klass = BooleanArray
+        else:
+            klass = FloatingArray
+        values = klass(data, mask)
+
+        if dtype_backend == "pyarrow" or isinstance(values_dtype, ArrowDtype):
+            values = ArrowExtensionArray(values.__arrow_array__())
+
+    if is_series:
+        return arg._constructor(values, index=arg.index, name=arg.name)
+    elif is_index:
+        # because we want to coerce to numeric if possible,
+        # do not use _shallow_copy
+        from pandas import Index
+
+        return Index(values, name=arg.name)
+    elif is_scalars:
+        return values[0]
+    else:
+        return values
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/timedeltas.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/timedeltas.py
new file mode 100644
index 0000000000000000000000000000000000000000..d772c908c473109fcf7e37e06014b43226328e31
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/timedeltas.py
@@ -0,0 +1,283 @@
+"""
+timedelta support tools
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    NaT,
+    NaTType,
+)
+from pandas._libs.tslibs.timedeltas import (
+    Timedelta,
+    disallow_ambiguous_unit,
+    parse_timedelta_unit,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+
+from pandas.core.arrays.timedeltas import sequence_to_td64ns
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+    from datetime import timedelta
+
+    from pandas._libs.tslibs.timedeltas import UnitChoices
+    from pandas._typing import (
+        ArrayLike,
+        DateTimeErrorChoices,
+    )
+
+    from pandas import (
+        Index,
+        Series,
+        TimedeltaIndex,
+    )
+
+
+@overload
+def to_timedelta(
+    arg: str | float | timedelta,
+    unit: UnitChoices | None = ...,
+    errors: DateTimeErrorChoices = ...,
+) -> Timedelta:
+    ...
+
+
+@overload
+def to_timedelta(
+    arg: Series,
+    unit: UnitChoices | None = ...,
+    errors: DateTimeErrorChoices = ...,
+) -> Series:
+    ...
+
+
+@overload
+def to_timedelta(
+    arg: list | tuple | range | ArrayLike | Index,
+    unit: UnitChoices | None = ...,
+    errors: DateTimeErrorChoices = ...,
+) -> TimedeltaIndex:
+    ...
+
+
+def to_timedelta(
+    arg: str
+    | int
+    | float
+    | timedelta
+    | list
+    | tuple
+    | range
+    | ArrayLike
+    | Index
+    | Series,
+    unit: UnitChoices | None = None,
+    errors: DateTimeErrorChoices = "raise",
+) -> Timedelta | TimedeltaIndex | Series:
+    """
+    Convert argument to timedelta.
+
+    Timedeltas are absolute differences in times, expressed in difference
+    units (e.g. days, hours, minutes, seconds). This method converts
+    an argument from a recognized timedelta format / value into
+    a Timedelta type.
+
+    Parameters
+    ----------
+    arg : str, timedelta, list-like or Series
+        The data to be converted to timedelta.
+
+        .. versionchanged:: 2.0
+            Strings with units 'M', 'Y' and 'y' do not represent
+            unambiguous timedelta values and will raise an exception.
+
+    unit : str, optional
+        Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``.
+
+        Possible values:
+
+        * 'W'
+        * 'D' / 'days' / 'day'
+        * 'hours' / 'hour' / 'hr' / 'h' / 'H'
+        * 'm' / 'minute' / 'min' / 'minutes' / 'T'
+        * 's' / 'seconds' / 'sec' / 'second' / 'S'
+        * 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' / 'L'
+        * 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' / 'U'
+        * 'ns' / 'nanoseconds' / 'nano' / 'nanos' / 'nanosecond' / 'N'
+
+        Must not be specified when `arg` contains strings and ``errors="raise"``.
+
+        .. deprecated:: 2.2.0
+            Units 'H', 'T', 'S', 'L', 'U' and 'N' are deprecated and will be removed
+            in a future version. Please use 'h', 'min', 's', 'ms', 'us', and 'ns'
+            instead of 'H', 'T', 'S', 'L', 'U' and 'N'.
+
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception.
+        - If 'coerce', then invalid parsing will be set as NaT.
+        - If 'ignore', then invalid parsing will return the input.
+
+    Returns
+    -------
+    timedelta
+        If parsing succeeded.
+        Return type depends on input:
+
+        - list-like: TimedeltaIndex of timedelta64 dtype
+        - Series: Series of timedelta64 dtype
+        - scalar: Timedelta
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_datetime : Convert argument to datetime.
+    convert_dtypes : Convert dtypes.
+
+    Notes
+    -----
+    If the precision is higher than nanoseconds, the precision of the duration is
+    truncated to nanoseconds for string inputs.
+
+    Examples
+    --------
+    Parsing a single string to a Timedelta:
+
+    >>> pd.to_timedelta('1 days 06:05:01.00003')
+    Timedelta('1 days 06:05:01.000030')
+    >>> pd.to_timedelta('15.5us')
+    Timedelta('0 days 00:00:00.000015500')
+
+    Parsing a list or array of strings:
+
+    >>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
+    TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT],
+                   dtype='timedelta64[ns]', freq=None)
+
+    Converting numbers by specifying the `unit` keyword argument:
+
+    >>> pd.to_timedelta(np.arange(5), unit='s')
+    TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
+                    '0 days 00:00:03', '0 days 00:00:04'],
+                   dtype='timedelta64[ns]', freq=None)
+    >>> pd.to_timedelta(np.arange(5), unit='d')
+    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
+                   dtype='timedelta64[ns]', freq=None)
+    """
+    if unit is not None:
+        unit = parse_timedelta_unit(unit)
+        disallow_ambiguous_unit(unit)
+
+    if errors not in ("ignore", "raise", "coerce"):
+        raise ValueError("errors must be one of 'ignore', 'raise', or 'coerce'.")
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_timedelta without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if arg is None:
+        return arg
+    elif isinstance(arg, ABCSeries):
+        values = _convert_listlike(arg._values, unit=unit, errors=errors)
+        return arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, ABCIndex):
+        return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
+    elif isinstance(arg, np.ndarray) and arg.ndim == 0:
+        # extract array scalar and process below
+        # error: Incompatible types in assignment (expression has type "object",
+        # variable has type "Union[str, int, float, timedelta, List[Any],
+        # Tuple[Any, ...], Union[Union[ExtensionArray, ndarray[Any, Any]], Index,
+        # Series]]")  [assignment]
+        arg = lib.item_from_zerodim(arg)  # type: ignore[assignment]
+    elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
+        return _convert_listlike(arg, unit=unit, errors=errors)
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError(
+            "arg must be a string, timedelta, list, tuple, 1-d array, or Series"
+        )
+
+    if isinstance(arg, str) and unit is not None:
+        raise ValueError("unit must not be specified if the input is/contains a str")
+
+    # ...so it must be a scalar value. Return scalar.
+    return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors)
+
+
+def _coerce_scalar_to_timedelta_type(
+    r, unit: UnitChoices | None = "ns", errors: DateTimeErrorChoices = "raise"
+):
+    """Convert string 'r' to a timedelta object."""
+    result: Timedelta | NaTType
+
+    try:
+        result = Timedelta(r, unit)
+    except ValueError:
+        if errors == "raise":
+            raise
+        if errors == "ignore":
+            return r
+
+        # coerce
+        result = NaT
+
+    return result
+
+
+def _convert_listlike(
+    arg,
+    unit: UnitChoices | None = None,
+    errors: DateTimeErrorChoices = "raise",
+    name: Hashable | None = None,
+):
+    """Convert a list of objects to a timedelta index object."""
+    arg_dtype = getattr(arg, "dtype", None)
+    if isinstance(arg, (list, tuple)) or arg_dtype is None:
+        # This is needed only to ensure that in the case where we end up
+        #  returning arg (errors == "ignore"), and where the input is a
+        #  generator, we return a useful list-like instead of a
+        #  used-up generator
+        if not hasattr(arg, "__array__"):
+            arg = list(arg)
+        arg = np.array(arg, dtype=object)
+    elif isinstance(arg_dtype, ArrowDtype) and arg_dtype.kind == "m":
+        return arg
+
+    try:
+        td64arr = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]
+    except ValueError:
+        if errors == "ignore":
+            return arg
+        else:
+            # This else-block accounts for the cases when errors='raise'
+            # and errors='coerce'. If errors == 'raise', these errors
+            # should be raised. If errors == 'coerce', we shouldn't
+            # expect any errors to be raised, since all parsing errors
+            # cause coercion to pd.NaT. However, if an error / bug is
+            # introduced that causes an Exception to be raised, we would
+            # like to surface it.
+            raise
+
+    from pandas import TimedeltaIndex
+
+    value = TimedeltaIndex(td64arr, name=name)
+    return value
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/times.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/times.py
new file mode 100644
index 0000000000000000000000000000000000000000..d77bcc91df7096bfad4b3eddf6355435a4b9e7f6
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/tools/times.py
@@ -0,0 +1,168 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    time,
+)
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._libs.lib import is_list_like
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.generic import (
+    ABCIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import notna
+
+if TYPE_CHECKING:
+    from pandas._typing import DateTimeErrorChoices
+
+
+def to_time(
+    arg,
+    format: str | None = None,
+    infer_time_format: bool = False,
+    errors: DateTimeErrorChoices = "raise",
+):
+    """
+    Parse time strings to time objects using fixed strptime formats ("%H:%M",
+    "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
+    "%I%M%S%p")
+
+    Use infer_time_format if all the strings are in the same format to speed
+    up conversion.
+
+    Parameters
+    ----------
+    arg : string in time format, datetime.time, list, tuple, 1-d array,  Series
+    format : str, default None
+        Format used to convert arg into a time object.  If None, fixed formats
+        are used.
+    infer_time_format: bool, default False
+        Infer the time format based on the first non-NaN element.  If all
+        strings are in the same format, this will speed up conversion.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception
+        - If 'coerce', then invalid parsing will be set as None
+        - If 'ignore', then invalid parsing will return the input
+
+    Returns
+    -------
+    datetime.time
+    """
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_time without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    def _convert_listlike(arg, format):
+        if isinstance(arg, (list, tuple)):
+            arg = np.array(arg, dtype="O")
+
+        elif getattr(arg, "ndim", 1) > 1:
+            raise TypeError(
+                "arg must be a string, datetime, list, tuple, 1-d array, or Series"
+            )
+
+        arg = np.asarray(arg, dtype="O")
+
+        if infer_time_format and format is None:
+            format = _guess_time_format_for_array(arg)
+
+        times: list[time | None] = []
+        if format is not None:
+            for element in arg:
+                try:
+                    times.append(datetime.strptime(element, format).time())
+                except (ValueError, TypeError) as err:
+                    if errors == "raise":
+                        msg = (
+                            f"Cannot convert {element} to a time with given "
+                            f"format {format}"
+                        )
+                        raise ValueError(msg) from err
+                    if errors == "ignore":
+                        return arg
+                    else:
+                        times.append(None)
+        else:
+            formats = _time_formats[:]
+            format_found = False
+            for element in arg:
+                time_object = None
+                try:
+                    time_object = time.fromisoformat(element)
+                except (ValueError, TypeError):
+                    for time_format in formats:
+                        try:
+                            time_object = datetime.strptime(element, time_format).time()
+                            if not format_found:
+                                # Put the found format in front
+                                fmt = formats.pop(formats.index(time_format))
+                                formats.insert(0, fmt)
+                                format_found = True
+                            break
+                        except (ValueError, TypeError):
+                            continue
+
+                if time_object is not None:
+                    times.append(time_object)
+                elif errors == "raise":
+                    raise ValueError(f"Cannot convert arg {arg} to a time")
+                elif errors == "ignore":
+                    return arg
+                else:
+                    times.append(None)
+
+        return times
+
+    if arg is None:
+        return arg
+    elif isinstance(arg, time):
+        return arg
+    elif isinstance(arg, ABCSeries):
+        values = _convert_listlike(arg._values, format)
+        return arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, ABCIndex):
+        return _convert_listlike(arg, format)
+    elif is_list_like(arg):
+        return _convert_listlike(arg, format)
+
+    return _convert_listlike(np.array([arg]), format)[0]
+
+
+# Fixed time formats for time parsing
+_time_formats = [
+    "%H:%M",
+    "%H%M",
+    "%I:%M%p",
+    "%I%M%p",
+    "%H:%M:%S",
+    "%H%M%S",
+    "%I:%M:%S%p",
+    "%I%M%S%p",
+]
+
+
+def _guess_time_format_for_array(arr):
+    # Try to guess the format based on the first non-NaN element
+    non_nan_elements = notna(arr).nonzero()[0]
+    if len(non_nan_elements):
+        element = arr[non_nan_elements[0]]
+        for time_format in _time_formats:
+            try:
+                datetime.strptime(element, time_format)
+                return time_format
+            except ValueError:
+                pass
+
+    return None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/hashing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..4933de32125814baa6cc96926721c0c839540b2a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/hashing.py
@@ -0,0 +1,339 @@
+"""
+data hash pandas / numpy objects
+"""
+from __future__ import annotations
+
+import itertools
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.hashing import hash_object_array
+
+from pandas.core.dtypes.common import is_list_like
+from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Iterator,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        MultiIndex,
+        Series,
+    )
+
+
+# 16 byte long hashing key
+_default_hash_key = "0123456789123456"
+
+
+def combine_hash_arrays(
+    arrays: Iterator[np.ndarray], num_items: int
+) -> npt.NDArray[np.uint64]:
+    """
+    Parameters
+    ----------
+    arrays : Iterator[np.ndarray]
+    num_items : int
+
+    Returns
+    -------
+    np.ndarray[uint64]
+
+    Should be the same as CPython's tupleobject.c
+    """
+    try:
+        first = next(arrays)
+    except StopIteration:
+        return np.array([], dtype=np.uint64)
+
+    arrays = itertools.chain([first], arrays)
+
+    mult = np.uint64(1000003)
+    out = np.zeros_like(first) + np.uint64(0x345678)
+    last_i = 0
+    for i, a in enumerate(arrays):
+        inverse_i = num_items - i
+        out ^= a
+        out *= mult
+        mult += np.uint64(82520 + inverse_i + inverse_i)
+        last_i = i
+    assert last_i + 1 == num_items, "Fed in wrong num_items"
+    out += np.uint64(97531)
+    return out
+
+
+def hash_pandas_object(
+    obj: Index | DataFrame | Series,
+    index: bool = True,
+    encoding: str = "utf8",
+    hash_key: str | None = _default_hash_key,
+    categorize: bool = True,
+) -> Series:
+    """
+    Return a data hash of the Index/Series/DataFrame.
+
+    Parameters
+    ----------
+    obj : Index, Series, or DataFrame
+    index : bool, default True
+        Include the index in the hash (if Series/DataFrame).
+    encoding : str, default 'utf8'
+        Encoding for data & key when strings.
+    hash_key : str, default _default_hash_key
+        Hash_key for string key to encode.
+    categorize : bool, default True
+        Whether to first categorize object arrays before hashing. This is more
+        efficient when the array contains duplicate values.
+
+    Returns
+    -------
+    Series of uint64, same length as the object
+
+    Examples
+    --------
+    >>> pd.util.hash_pandas_object(pd.Series([1, 2, 3]))
+    0    14639053686158035780
+    1     3869563279212530728
+    2      393322362522515241
+    dtype: uint64
+    """
+    from pandas import Series
+
+    if hash_key is None:
+        hash_key = _default_hash_key
+
+    if isinstance(obj, ABCMultiIndex):
+        return Series(hash_tuples(obj, encoding, hash_key), dtype="uint64", copy=False)
+
+    elif isinstance(obj, ABCIndex):
+        h = hash_array(obj._values, encoding, hash_key, categorize).astype(
+            "uint64", copy=False
+        )
+        ser = Series(h, index=obj, dtype="uint64", copy=False)
+
+    elif isinstance(obj, ABCSeries):
+        h = hash_array(obj._values, encoding, hash_key, categorize).astype(
+            "uint64", copy=False
+        )
+        if index:
+            index_iter = (
+                hash_pandas_object(
+                    obj.index,
+                    index=False,
+                    encoding=encoding,
+                    hash_key=hash_key,
+                    categorize=categorize,
+                )._values
+                for _ in [None]
+            )
+            arrays = itertools.chain([h], index_iter)
+            h = combine_hash_arrays(arrays, 2)
+
+        ser = Series(h, index=obj.index, dtype="uint64", copy=False)
+
+    elif isinstance(obj, ABCDataFrame):
+        hashes = (
+            hash_array(series._values, encoding, hash_key, categorize)
+            for _, series in obj.items()
+        )
+        num_items = len(obj.columns)
+        if index:
+            index_hash_generator = (
+                hash_pandas_object(
+                    obj.index,
+                    index=False,
+                    encoding=encoding,
+                    hash_key=hash_key,
+                    categorize=categorize,
+                )._values
+                for _ in [None]
+            )
+            num_items += 1
+
+            # keep `hashes` specifically a generator to keep mypy happy
+            _hashes = itertools.chain(hashes, index_hash_generator)
+            hashes = (x for x in _hashes)
+        h = combine_hash_arrays(hashes, num_items)
+
+        ser = Series(h, index=obj.index, dtype="uint64", copy=False)
+    else:
+        raise TypeError(f"Unexpected type for hashing {type(obj)}")
+
+    return ser
+
+
+def hash_tuples(
+    vals: MultiIndex | Iterable[tuple[Hashable, ...]],
+    encoding: str = "utf8",
+    hash_key: str = _default_hash_key,
+) -> npt.NDArray[np.uint64]:
+    """
+    Hash an MultiIndex / listlike-of-tuples efficiently.
+
+    Parameters
+    ----------
+    vals : MultiIndex or listlike-of-tuples
+    encoding : str, default 'utf8'
+    hash_key : str, default _default_hash_key
+
+    Returns
+    -------
+    ndarray[np.uint64] of hashed values
+    """
+    if not is_list_like(vals):
+        raise TypeError("must be convertible to a list-of-tuples")
+
+    from pandas import (
+        Categorical,
+        MultiIndex,
+    )
+
+    if not isinstance(vals, ABCMultiIndex):
+        mi = MultiIndex.from_tuples(vals)
+    else:
+        mi = vals
+
+    # create a list-of-Categoricals
+    cat_vals = [
+        Categorical._simple_new(
+            mi.codes[level],
+            CategoricalDtype(categories=mi.levels[level], ordered=False),
+        )
+        for level in range(mi.nlevels)
+    ]
+
+    # hash the list-of-ndarrays
+    hashes = (
+        cat._hash_pandas_object(encoding=encoding, hash_key=hash_key, categorize=False)
+        for cat in cat_vals
+    )
+    h = combine_hash_arrays(hashes, len(cat_vals))
+
+    return h
+
+
+def hash_array(
+    vals: ArrayLike,
+    encoding: str = "utf8",
+    hash_key: str = _default_hash_key,
+    categorize: bool = True,
+) -> npt.NDArray[np.uint64]:
+    """
+    Given a 1d array, return an array of deterministic integers.
+
+    Parameters
+    ----------
+    vals : ndarray or ExtensionArray
+    encoding : str, default 'utf8'
+        Encoding for data & key when strings.
+    hash_key : str, default _default_hash_key
+        Hash_key for string key to encode.
+    categorize : bool, default True
+        Whether to first categorize object arrays before hashing. This is more
+        efficient when the array contains duplicate values.
+
+    Returns
+    -------
+    ndarray[np.uint64, ndim=1]
+        Hashed values, same length as the vals.
+
+    Examples
+    --------
+    >>> pd.util.hash_array(np.array([1, 2, 3]))
+    array([ 6238072747940578789, 15839785061582574730,  2185194620014831856],
+      dtype=uint64)
+    """
+    if not hasattr(vals, "dtype"):
+        raise TypeError("must pass a ndarray-like")
+
+    if isinstance(vals, ABCExtensionArray):
+        return vals._hash_pandas_object(
+            encoding=encoding, hash_key=hash_key, categorize=categorize
+        )
+
+    if not isinstance(vals, np.ndarray):
+        # GH#42003
+        raise TypeError(
+            "hash_array requires np.ndarray or ExtensionArray, not "
+            f"{type(vals).__name__}. Use hash_pandas_object instead."
+        )
+
+    return _hash_ndarray(vals, encoding, hash_key, categorize)
+
+
+def _hash_ndarray(
+    vals: np.ndarray,
+    encoding: str = "utf8",
+    hash_key: str = _default_hash_key,
+    categorize: bool = True,
+) -> npt.NDArray[np.uint64]:
+    """
+    See hash_array.__doc__.
+    """
+    dtype = vals.dtype
+
+    # _hash_ndarray only takes 64-bit values, so handle 128-bit by parts
+    if np.issubdtype(dtype, np.complex128):
+        hash_real = _hash_ndarray(vals.real, encoding, hash_key, categorize)
+        hash_imag = _hash_ndarray(vals.imag, encoding, hash_key, categorize)
+        return hash_real + 23 * hash_imag
+
+    # First, turn whatever array this is into unsigned 64-bit ints, if we can
+    # manage it.
+    if dtype == bool:
+        vals = vals.astype("u8")
+    elif issubclass(dtype.type, (np.datetime64, np.timedelta64)):
+        vals = vals.view("i8").astype("u8", copy=False)
+    elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8:
+        vals = vals.view(f"u{vals.dtype.itemsize}").astype("u8")
+    else:
+        # With repeated values, its MUCH faster to categorize object dtypes,
+        # then hash and rename categories. We allow skipping the categorization
+        # when the values are known/likely to be unique.
+        if categorize:
+            from pandas import (
+                Categorical,
+                Index,
+                factorize,
+            )
+
+            codes, categories = factorize(vals, sort=False)
+            dtype = CategoricalDtype(categories=Index(categories), ordered=False)
+            cat = Categorical._simple_new(codes, dtype)
+            return cat._hash_pandas_object(
+                encoding=encoding, hash_key=hash_key, categorize=False
+            )
+
+        try:
+            vals = hash_object_array(vals, hash_key, encoding)
+        except TypeError:
+            # we have mixed types
+            vals = hash_object_array(
+                vals.astype(str).astype(object), hash_key, encoding
+            )
+
+    # Then, redistribute these 64-bit ints within the space of 64-bit ints
+    vals ^= vals >> 30
+    vals *= np.uint64(0xBF58476D1CE4E5B9)
+    vals ^= vals >> 27
+    vals *= np.uint64(0x94D049BB133111EB)
+    vals ^= vals >> 31
+    return vals
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/numba_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/numba_.py
new file mode 100644
index 0000000000000000000000000000000000000000..4825c9fee24b1b1272c5f245a9c7ae49f53003fc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/util/numba_.py
@@ -0,0 +1,98 @@
+"""Common utilities for Numba operations"""
+from __future__ import annotations
+
+import types
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import NumbaUtilError
+
+GLOBAL_USE_NUMBA: bool = False
+
+
+def maybe_use_numba(engine: str | None) -> bool:
+    """Signal whether to use numba routines."""
+    return engine == "numba" or (engine is None and GLOBAL_USE_NUMBA)
+
+
+def set_use_numba(enable: bool = False) -> None:
+    global GLOBAL_USE_NUMBA
+    if enable:
+        import_optional_dependency("numba")
+    GLOBAL_USE_NUMBA = enable
+
+
+def get_jit_arguments(
+    engine_kwargs: dict[str, bool] | None = None, kwargs: dict | None = None
+) -> dict[str, bool]:
+    """
+    Return arguments to pass to numba.JIT, falling back on pandas default JIT settings.
+
+    Parameters
+    ----------
+    engine_kwargs : dict, default None
+        user passed keyword arguments for numba.JIT
+    kwargs : dict, default None
+        user passed keyword arguments to pass into the JITed function
+
+    Returns
+    -------
+    dict[str, bool]
+        nopython, nogil, parallel
+
+    Raises
+    ------
+    NumbaUtilError
+    """
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    nopython = engine_kwargs.get("nopython", True)
+    if kwargs and nopython:
+        raise NumbaUtilError(
+            "numba does not support kwargs with nopython=True: "
+            "https://github.com/numba/numba/issues/2916"
+        )
+    nogil = engine_kwargs.get("nogil", False)
+    parallel = engine_kwargs.get("parallel", False)
+    return {"nopython": nopython, "nogil": nogil, "parallel": parallel}
+
+
+def jit_user_function(func: Callable) -> Callable:
+    """
+    If user function is not jitted already, mark the user's function
+    as jitable.
+
+    Parameters
+    ----------
+    func : function
+        user defined function
+
+    Returns
+    -------
+    function
+        Numba JITed function, or function marked as JITable by numba
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    if numba.extending.is_jitted(func):
+        # Don't jit a user passed jitted function
+        numba_func = func
+    elif getattr(np, func.__name__, False) is func or isinstance(
+        func, types.BuiltinFunctionType
+    ):
+        # Not necessary to jit builtins or np functions
+        # This will mess up register_jitable
+        numba_func = func
+    else:
+        numba_func = numba.extending.register_jitable(func)
+
+    return numba_func
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..857e12e5467a6a7d2263d9add33e65b9499778fa
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/__init__.py
@@ -0,0 +1,23 @@
+from pandas.core.window.ewm import (
+    ExponentialMovingWindow,
+    ExponentialMovingWindowGroupby,
+)
+from pandas.core.window.expanding import (
+    Expanding,
+    ExpandingGroupby,
+)
+from pandas.core.window.rolling import (
+    Rolling,
+    RollingGroupby,
+    Window,
+)
+
+__all__ = [
+    "Expanding",
+    "ExpandingGroupby",
+    "ExponentialMovingWindow",
+    "ExponentialMovingWindowGroupby",
+    "Rolling",
+    "RollingGroupby",
+    "Window",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/common.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc8eddca09c84029b9ac92b658a47b45e4764414
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/common.py
@@ -0,0 +1,169 @@
+"""Common utility functions for rolling operations"""
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import cast
+
+import numpy as np
+
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core.indexes.api import MultiIndex
+
+
+def flex_binary_moment(arg1, arg2, f, pairwise: bool = False):
+    if isinstance(arg1, ABCSeries) and isinstance(arg2, ABCSeries):
+        X, Y = prep_binary(arg1, arg2)
+        return f(X, Y)
+
+    elif isinstance(arg1, ABCDataFrame):
+        from pandas import DataFrame
+
+        def dataframe_from_int_dict(data, frame_template) -> DataFrame:
+            result = DataFrame(data, index=frame_template.index)
+            if len(result.columns) > 0:
+                result.columns = frame_template.columns[result.columns]
+            else:
+                result.columns = frame_template.columns.copy()
+            return result
+
+        results = {}
+        if isinstance(arg2, ABCDataFrame):
+            if pairwise is False:
+                if arg1 is arg2:
+                    # special case in order to handle duplicate column names
+                    for i in range(len(arg1.columns)):
+                        results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])
+                    return dataframe_from_int_dict(results, arg1)
+                else:
+                    if not arg1.columns.is_unique:
+                        raise ValueError("'arg1' columns are not unique")
+                    if not arg2.columns.is_unique:
+                        raise ValueError("'arg2' columns are not unique")
+                    X, Y = arg1.align(arg2, join="outer")
+                    X, Y = prep_binary(X, Y)
+                    res_columns = arg1.columns.union(arg2.columns)
+                    for col in res_columns:
+                        if col in X and col in Y:
+                            results[col] = f(X[col], Y[col])
+                    return DataFrame(results, index=X.index, columns=res_columns)
+            elif pairwise is True:
+                results = defaultdict(dict)
+                for i in range(len(arg1.columns)):
+                    for j in range(len(arg2.columns)):
+                        if j < i and arg2 is arg1:
+                            # Symmetric case
+                            results[i][j] = results[j][i]
+                        else:
+                            results[i][j] = f(
+                                *prep_binary(arg1.iloc[:, i], arg2.iloc[:, j])
+                            )
+
+                from pandas import concat
+
+                result_index = arg1.index.union(arg2.index)
+                if len(result_index):
+                    # construct result frame
+                    result = concat(
+                        [
+                            concat(
+                                [results[i][j] for j in range(len(arg2.columns))],
+                                ignore_index=True,
+                            )
+                            for i in range(len(arg1.columns))
+                        ],
+                        ignore_index=True,
+                        axis=1,
+                    )
+                    result.columns = arg1.columns
+
+                    # set the index and reorder
+                    if arg2.columns.nlevels > 1:
+                        # mypy needs to know columns is a MultiIndex, Index doesn't
+                        # have levels attribute
+                        arg2.columns = cast(MultiIndex, arg2.columns)
+                        # GH 21157: Equivalent to MultiIndex.from_product(
+                        #  [result_index], <unique combinations of arg2.columns.levels>,
+                        # )
+                        # A normal MultiIndex.from_product will produce too many
+                        # combinations.
+                        result_level = np.tile(
+                            result_index, len(result) // len(result_index)
+                        )
+                        arg2_levels = (
+                            np.repeat(
+                                arg2.columns.get_level_values(i),
+                                len(result) // len(arg2.columns),
+                            )
+                            for i in range(arg2.columns.nlevels)
+                        )
+                        result_names = list(arg2.columns.names) + [result_index.name]
+                        result.index = MultiIndex.from_arrays(
+                            [*arg2_levels, result_level], names=result_names
+                        )
+                        # GH 34440
+                        num_levels = len(result.index.levels)
+                        new_order = [num_levels - 1] + list(range(num_levels - 1))
+                        result = result.reorder_levels(new_order).sort_index()
+                    else:
+                        result.index = MultiIndex.from_product(
+                            [range(len(arg2.columns)), range(len(result_index))]
+                        )
+                        result = result.swaplevel(1, 0).sort_index()
+                        result.index = MultiIndex.from_product(
+                            [result_index] + [arg2.columns]
+                        )
+                else:
+                    # empty result
+                    result = DataFrame(
+                        index=MultiIndex(
+                            levels=[arg1.index, arg2.columns], codes=[[], []]
+                        ),
+                        columns=arg2.columns,
+                        dtype="float64",
+                    )
+
+                # reset our index names to arg1 names
+                # reset our column names to arg2 names
+                # careful not to mutate the original names
+                result.columns = result.columns.set_names(arg1.columns.names)
+                result.index = result.index.set_names(
+                    result_index.names + arg2.columns.names
+                )
+
+                return result
+        else:
+            results = {
+                i: f(*prep_binary(arg1.iloc[:, i], arg2))
+                for i in range(len(arg1.columns))
+            }
+            return dataframe_from_int_dict(results, arg1)
+
+    else:
+        return flex_binary_moment(arg2, arg1, f)
+
+
+def zsqrt(x):
+    with np.errstate(all="ignore"):
+        result = np.sqrt(x)
+        mask = x < 0
+
+    if isinstance(x, ABCDataFrame):
+        if mask._values.any():
+            result[mask] = 0
+    else:
+        if mask.any():
+            result[mask] = 0
+
+    return result
+
+
+def prep_binary(arg1, arg2):
+    # mask out values, this also makes a common index...
+    X = arg1 + 0 * arg2
+    Y = arg2 + 0 * arg1
+
+    return X, Y
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/doc.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/doc.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a5cbc04921fadacf18a89608f2c0665bd8177e2
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/doc.py
@@ -0,0 +1,116 @@
+"""Any shareable docstring components for rolling/expanding/ewm"""
+from __future__ import annotations
+
+from textwrap import dedent
+
+from pandas.core.shared_docs import _shared_docs
+
+_shared_docs = dict(**_shared_docs)
+
+
+def create_section_header(header: str) -> str:
+    """Create numpydoc section header"""
+    return f"{header}\n{'-' * len(header)}\n"
+
+
+template_header = "\nCalculate the {window_method} {aggregation_description}.\n\n"
+
+template_returns = dedent(
+    """
+    Series or DataFrame
+        Return type is the same as the original object with ``np.float64`` dtype.\n
+    """
+).replace("\n", "", 1)
+
+template_see_also = dedent(
+    """
+    pandas.Series.{window_method} : Calling {window_method} with Series data.
+    pandas.DataFrame.{window_method} : Calling {window_method} with DataFrames.
+    pandas.Series.{agg_method} : Aggregating {agg_method} for Series.
+    pandas.DataFrame.{agg_method} : Aggregating {agg_method} for DataFrame.\n
+    """
+).replace("\n", "", 1)
+
+kwargs_numeric_only = dedent(
+    """
+    numeric_only : bool, default False
+        Include only float, int, boolean columns.
+
+        .. versionadded:: 1.5.0\n
+    """
+).replace("\n", "", 1)
+
+kwargs_scipy = dedent(
+    """
+    **kwargs
+        Keyword arguments to configure the ``SciPy`` weighted window type.\n
+    """
+).replace("\n", "", 1)
+
+window_apply_parameters = dedent(
+    """
+    func : function
+        Must produce a single value from an ndarray input if ``raw=True``
+        or a single value from a Series if ``raw=False``. Can also accept a
+        Numba JIT function with ``engine='numba'`` specified.
+
+    raw : bool, default False
+        * ``False`` : passes each row or column as a Series to the
+          function.
+        * ``True`` : the passed function will receive ndarray
+          objects instead.
+          If you are just applying a NumPy reduction function this will
+          achieve much better performance.
+
+    engine : str, default None
+        * ``'cython'`` : Runs rolling apply through C-extensions from cython.
+        * ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
+          Only available when ``raw`` is set to ``True``.
+        * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+    engine_kwargs : dict, default None
+        * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+        * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+          and ``parallel`` dictionary keys. The values must either be ``True`` or
+          ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+          ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+          applied to both the ``func`` and the ``apply`` rolling aggregation.
+
+    args : tuple, default None
+        Positional arguments to be passed into func.
+
+    kwargs : dict, default None
+        Keyword arguments to be passed into func.\n
+    """
+).replace("\n", "", 1)
+
+numba_notes = (
+    "See :ref:`window.numba_engine` and :ref:`enhancingperf.numba` for "
+    "extended documentation and performance considerations for the Numba engine.\n\n"
+)
+
+
+def window_agg_numba_parameters(version: str = "1.3") -> str:
+    return (
+        dedent(
+            """
+    engine : str, default None
+        * ``'cython'`` : Runs the operation through C-extensions from cython.
+        * ``'numba'`` : Runs the operation through JIT compiled code from numba.
+        * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
+
+          .. versionadded:: {version}.0
+
+    engine_kwargs : dict, default None
+        * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
+        * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+          and ``parallel`` dictionary keys. The values must either be ``True`` or
+          ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+          ``{{'nopython': True, 'nogil': False, 'parallel': False}}``
+
+          .. versionadded:: {version}.0\n
+    """
+        )
+        .replace("\n", "", 1)
+        .replace("{version}", version)
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/ewm.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/ewm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ebf32d3e536eee7233b911acb880f3d40521793
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/ewm.py
@@ -0,0 +1,1095 @@
+from __future__ import annotations
+
+import datetime
+from functools import partial
+from textwrap import dedent
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.tslibs import Timedelta
+import pandas._libs.window.aggregations as window_aggregations
+from pandas.util._decorators import doc
+
+from pandas.core.dtypes.common import (
+    is_datetime64_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.generic import ABCSeries
+from pandas.core.dtypes.missing import isna
+
+from pandas.core import common
+from pandas.core.arrays.datetimelike import dtype_to_unit
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    ExponentialMovingWindowIndexer,
+    GroupbyIndexer,
+)
+from pandas.core.util.numba_ import (
+    get_jit_arguments,
+    maybe_use_numba,
+)
+from pandas.core.window.common import zsqrt
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+)
+from pandas.core.window.numba_ import (
+    generate_numba_ewm_func,
+    generate_numba_ewm_table_func,
+)
+from pandas.core.window.online import (
+    EWMMeanState,
+    generate_online_numba_ewma_func,
+)
+from pandas.core.window.rolling import (
+    BaseWindow,
+    BaseWindowGroupby,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        TimedeltaConvertibleTypes,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+
+
+def get_center_of_mass(
+    comass: float | None,
+    span: float | None,
+    halflife: float | None,
+    alpha: float | None,
+) -> float:
+    valid_count = common.count_not_none(comass, span, halflife, alpha)
+    if valid_count > 1:
+        raise ValueError("comass, span, halflife, and alpha are mutually exclusive")
+
+    # Convert to center of mass; domain checks ensure 0 < alpha <= 1
+    if comass is not None:
+        if comass < 0:
+            raise ValueError("comass must satisfy: comass >= 0")
+    elif span is not None:
+        if span < 1:
+            raise ValueError("span must satisfy: span >= 1")
+        comass = (span - 1) / 2
+    elif halflife is not None:
+        if halflife <= 0:
+            raise ValueError("halflife must satisfy: halflife > 0")
+        decay = 1 - np.exp(np.log(0.5) / halflife)
+        comass = 1 / decay - 1
+    elif alpha is not None:
+        if alpha <= 0 or alpha > 1:
+            raise ValueError("alpha must satisfy: 0 < alpha <= 1")
+        comass = (1 - alpha) / alpha
+    else:
+        raise ValueError("Must pass one of comass, span, halflife, or alpha")
+
+    return float(comass)
+
+
+def _calculate_deltas(
+    times: np.ndarray | NDFrame,
+    halflife: float | TimedeltaConvertibleTypes | None,
+) -> npt.NDArray[np.float64]:
+    """
+    Return the diff of the times divided by the half-life. These values are used in
+    the calculation of the ewm mean.
+
+    Parameters
+    ----------
+    times : np.ndarray, Series
+        Times corresponding to the observations. Must be monotonically increasing
+        and ``datetime64[ns]`` dtype.
+    halflife : float, str, timedelta, optional
+        Half-life specifying the decay
+
+    Returns
+    -------
+    np.ndarray
+        Diff of the times divided by the half-life
+    """
+    unit = dtype_to_unit(times.dtype)
+    if isinstance(times, ABCSeries):
+        times = times._values
+    _times = np.asarray(times.view(np.int64), dtype=np.float64)
+    _halflife = float(Timedelta(halflife).as_unit(unit)._value)
+    return np.diff(_times) / _halflife
+
+
+class ExponentialMovingWindow(BaseWindow):
+    r"""
+    Provide exponentially weighted (EW) calculations.
+
+    Exactly one of ``com``, ``span``, ``halflife``, or ``alpha`` must be
+    provided if ``times`` is not provided. If ``times`` is provided,
+    ``halflife`` and one of ``com``, ``span`` or ``alpha`` may be provided.
+
+    Parameters
+    ----------
+    com : float, optional
+        Specify decay in terms of center of mass
+
+        :math:`\alpha = 1 / (1 + com)`, for :math:`com \geq 0`.
+
+    span : float, optional
+        Specify decay in terms of span
+
+        :math:`\alpha = 2 / (span + 1)`, for :math:`span \geq 1`.
+
+    halflife : float, str, timedelta, optional
+        Specify decay in terms of half-life
+
+        :math:`\alpha = 1 - \exp\left(-\ln(2) / halflife\right)`, for
+        :math:`halflife > 0`.
+
+        If ``times`` is specified, a timedelta convertible unit over which an
+        observation decays to half its value. Only applicable to ``mean()``,
+        and halflife value will not apply to the other functions.
+
+    alpha : float, optional
+        Specify smoothing factor :math:`\alpha` directly
+
+        :math:`0 < \alpha \leq 1`.
+
+    min_periods : int, default 0
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+    adjust : bool, default True
+        Divide by decaying adjustment factor in beginning periods to account
+        for imbalance in relative weightings (viewing EWMA as a moving average).
+
+        - When ``adjust=True`` (default), the EW function is calculated using weights
+          :math:`w_i = (1 - \alpha)^i`. For example, the EW moving average of the series
+          [:math:`x_0, x_1, ..., x_t`] would be:
+
+        .. math::
+            y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 -
+            \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
+
+        - When ``adjust=False``, the exponentially weighted function is calculated
+          recursively:
+
+        .. math::
+            \begin{split}
+                y_0 &= x_0\\
+                y_t &= (1 - \alpha) y_{t-1} + \alpha x_t,
+            \end{split}
+    ignore_na : bool, default False
+        Ignore missing values when calculating weights.
+
+        - When ``ignore_na=False`` (default), weights are based on absolute positions.
+          For example, the weights of :math:`x_0` and :math:`x_2` used in calculating
+          the final weighted average of [:math:`x_0`, None, :math:`x_2`] are
+          :math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
+          :math:`(1-\alpha)^2` and :math:`\alpha` if ``adjust=False``.
+
+        - When ``ignore_na=True``, weights are based
+          on relative positions. For example, the weights of :math:`x_0` and :math:`x_2`
+          used in calculating the final weighted average of
+          [:math:`x_0`, None, :math:`x_2`] are :math:`1-\alpha` and :math:`1` if
+          ``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if ``adjust=False``.
+
+    axis : {0, 1}, default 0
+        If ``0`` or ``'index'``, calculate across the rows.
+
+        If ``1`` or ``'columns'``, calculate across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+    times : np.ndarray, Series, default None
+
+        Only applicable to ``mean()``.
+
+        Times corresponding to the observations. Must be monotonically increasing and
+        ``datetime64[ns]`` dtype.
+
+        If 1-D array like, a sequence with the same shape as the observations.
+
+    method : str {'single', 'table'}, default 'single'
+        .. versionadded:: 1.4.0
+
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+        Only applicable to ``mean()``
+
+    Returns
+    -------
+    pandas.api.typing.ExponentialMovingWindow
+
+    See Also
+    --------
+    rolling : Provides rolling window calculations.
+    expanding : Provides expanding transformations.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations <window.exponentially_weighted>`
+    for further usage details and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    >>> df.ewm(com=0.5).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+    >>> df.ewm(alpha=2 / 3).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+
+    **adjust**
+
+    >>> df.ewm(com=0.5, adjust=True).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+    >>> df.ewm(com=0.5, adjust=False).mean()
+              B
+    0  0.000000
+    1  0.666667
+    2  1.555556
+    3  1.555556
+    4  3.650794
+
+    **ignore_na**
+
+    >>> df.ewm(com=0.5, ignore_na=True).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.225000
+    >>> df.ewm(com=0.5, ignore_na=False).mean()
+              B
+    0  0.000000
+    1  0.750000
+    2  1.615385
+    3  1.615385
+    4  3.670213
+
+    **times**
+
+    Exponentially weighted mean with weights calculated with a timedelta ``halflife``
+    relative to ``times``.
+
+    >>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
+    >>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
+              B
+    0  0.000000
+    1  0.585786
+    2  1.523889
+    3  1.523889
+    4  3.233686
+    """
+
+    _attributes = [
+        "com",
+        "span",
+        "halflife",
+        "alpha",
+        "min_periods",
+        "adjust",
+        "ignore_na",
+        "axis",
+        "times",
+        "method",
+    ]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | TimedeltaConvertibleTypes | None = None,
+        alpha: float | None = None,
+        min_periods: int | None = 0,
+        adjust: bool = True,
+        ignore_na: bool = False,
+        axis: Axis = 0,
+        times: np.ndarray | NDFrame | None = None,
+        method: str = "single",
+        *,
+        selection=None,
+    ) -> None:
+        super().__init__(
+            obj=obj,
+            min_periods=1 if min_periods is None else max(int(min_periods), 1),
+            on=None,
+            center=False,
+            closed=None,
+            method=method,
+            axis=axis,
+            selection=selection,
+        )
+        self.com = com
+        self.span = span
+        self.halflife = halflife
+        self.alpha = alpha
+        self.adjust = adjust
+        self.ignore_na = ignore_na
+        self.times = times
+        if self.times is not None:
+            if not self.adjust:
+                raise NotImplementedError("times is not supported with adjust=False.")
+            times_dtype = getattr(self.times, "dtype", None)
+            if not (
+                is_datetime64_dtype(times_dtype)
+                or isinstance(times_dtype, DatetimeTZDtype)
+            ):
+                raise ValueError("times must be datetime64 dtype.")
+            if len(self.times) != len(obj):
+                raise ValueError("times must be the same length as the object.")
+            if not isinstance(self.halflife, (str, datetime.timedelta, np.timedelta64)):
+                raise ValueError("halflife must be a timedelta convertible object")
+            if isna(self.times).any():
+                raise ValueError("Cannot convert NaT values to integer")
+            self._deltas = _calculate_deltas(self.times, self.halflife)
+            # Halflife is no longer applicable when calculating COM
+            # But allow COM to still be calculated if the user passes other decay args
+            if common.count_not_none(self.com, self.span, self.alpha) > 0:
+                self._com = get_center_of_mass(self.com, self.span, None, self.alpha)
+            else:
+                self._com = 1.0
+        else:
+            if self.halflife is not None and isinstance(
+                self.halflife, (str, datetime.timedelta, np.timedelta64)
+            ):
+                raise ValueError(
+                    "halflife can only be a timedelta convertible argument if "
+                    "times is not None."
+                )
+            # Without times, points are equally spaced
+            self._deltas = np.ones(
+                max(self.obj.shape[self.axis] - 1, 0), dtype=np.float64
+            )
+            self._com = get_center_of_mass(
+                # error: Argument 3 to "get_center_of_mass" has incompatible type
+                # "Union[float, Any, None, timedelta64, signedinteger[_64Bit]]";
+                # expected "Optional[float]"
+                self.com,
+                self.span,
+                self.halflife,  # type: ignore[arg-type]
+                self.alpha,
+            )
+
+    def _check_window_bounds(
+        self, start: np.ndarray, end: np.ndarray, num_vals: int
+    ) -> None:
+        # emw algorithms are iterative with each point
+        # ExponentialMovingWindowIndexer "bounds" are the entire window
+        pass
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        return ExponentialMovingWindowIndexer()
+
+    def online(
+        self, engine: str = "numba", engine_kwargs=None
+    ) -> OnlineExponentialMovingWindow:
+        """
+        Return an ``OnlineExponentialMovingWindow`` object to calculate
+        exponentially moving window aggregations in an online method.
+
+        .. versionadded:: 1.3.0
+
+        Parameters
+        ----------
+        engine: str, default ``'numba'``
+            Execution engine to calculate online aggregations.
+            Applies to all supported aggregation methods.
+
+        engine_kwargs : dict, default None
+            Applies to all supported aggregation methods.
+
+            * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
+              and ``parallel`` dictionary keys. The values must either be ``True`` or
+              ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
+              ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
+              applied to the function
+
+        Returns
+        -------
+        OnlineExponentialMovingWindow
+        """
+        return OnlineExponentialMovingWindow(
+            obj=self.obj,
+            com=self.com,
+            span=self.span,
+            halflife=self.halflife,
+            alpha=self.alpha,
+            min_periods=self.min_periods,
+            adjust=self.adjust,
+            ignore_na=self.ignore_na,
+            axis=self.axis,
+            times=self.times,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            selection=self._selection,
+        )
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.rolling.aggregate
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.ewm(alpha=0.5).mean()
+                  A         B         C
+        0  1.000000  4.000000  7.000000
+        1  1.666667  4.666667  7.666667
+        2  2.428571  5.428571  8.428571
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).mean()
+        0    1.000000
+        1    1.555556
+        2    2.147541
+        3    2.775068
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine=None,
+        engine_kwargs=None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "single":
+                func = generate_numba_ewm_func
+            else:
+                func = generate_numba_ewm_table_func
+            ewm_func = func(
+                **get_jit_arguments(engine_kwargs),
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=tuple(self._deltas),
+                normalize=True,
+            )
+            return self._apply(ewm_func, name="mean")
+        elif engine in ("cython", None):
+            if engine_kwargs is not None:
+                raise ValueError("cython engine does not accept engine_kwargs")
+
+            deltas = None if self.times is None else self._deltas
+            window_func = partial(
+                window_aggregations.ewm,
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=deltas,
+                normalize=True,
+            )
+            return self._apply(window_func, name="mean", numeric_only=numeric_only)
+        else:
+            raise ValueError("engine must be either 'numba' or 'cython'")
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).sum()
+        0    1.000
+        1    2.800
+        2    5.240
+        3    8.192
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine=None,
+        engine_kwargs=None,
+    ):
+        if not self.adjust:
+            raise NotImplementedError("sum is not implemented with adjust=False")
+        if maybe_use_numba(engine):
+            if self.method == "single":
+                func = generate_numba_ewm_func
+            else:
+                func = generate_numba_ewm_table_func
+            ewm_func = func(
+                **get_jit_arguments(engine_kwargs),
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=tuple(self._deltas),
+                normalize=False,
+            )
+            return self._apply(ewm_func, name="sum")
+        elif engine in ("cython", None):
+            if engine_kwargs is not None:
+                raise ValueError("cython engine does not accept engine_kwargs")
+
+            deltas = None if self.times is None else self._deltas
+            window_func = partial(
+                window_aggregations.ewm,
+                com=self._com,
+                adjust=self.adjust,
+                ignore_na=self.ignore_na,
+                deltas=deltas,
+                normalize=False,
+            )
+            return self._apply(window_func, name="sum", numeric_only=numeric_only)
+        else:
+            raise ValueError("engine must be either 'numba' or 'cython'")
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        bias : bool, default False
+            Use a standard estimation bias correction.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).std()
+        0         NaN
+        1    0.707107
+        2    0.995893
+        3    1.277320
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) standard deviation",
+        agg_method="std",
+    )
+    def std(self, bias: bool = False, numeric_only: bool = False):
+        if (
+            numeric_only
+            and self._selected_obj.ndim == 1
+            and not is_numeric_dtype(self._selected_obj.dtype)
+        ):
+            # Raise directly so error message says std instead of var
+            raise NotImplementedError(
+                f"{type(self).__name__}.std does not implement numeric_only"
+            )
+        return zsqrt(self.var(bias=bias, numeric_only=numeric_only))
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        bias : bool, default False
+            Use a standard estimation bias correction.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.ewm(alpha=.2).var()
+        0         NaN
+        1    0.500000
+        2    0.991803
+        3    1.631547
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) variance",
+        agg_method="var",
+    )
+    def var(self, bias: bool = False, numeric_only: bool = False):
+        window_func = window_aggregations.ewmcov
+        wfunc = partial(
+            window_func,
+            com=self._com,
+            adjust=self.adjust,
+            ignore_na=self.ignore_na,
+            bias=bias,
+        )
+
+        def var_func(values, begin, end, min_periods):
+            return wfunc(values, begin, end, min_periods, values)
+
+        return self._apply(var_func, name="var", numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        other : Series or DataFrame , optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndex DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        bias : bool, default False
+            Use a standard estimation bias correction.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4])
+        >>> ser2 = pd.Series([10, 11, 13, 16])
+        >>> ser1.ewm(alpha=.2).cov(ser2)
+        0         NaN
+        1    0.500000
+        2    1.524590
+        3    3.408836
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        bias: bool = False,
+        numeric_only: bool = False,
+    ):
+        from pandas import Series
+
+        self._validate_numeric_only("cov", numeric_only)
+
+        def cov_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+            result = window_aggregations.ewmcov(
+                x_array,
+                start,
+                end,
+                # error: Argument 4 to "ewmcov" has incompatible type
+                # "Optional[int]"; expected "int"
+                self.min_periods,  # type: ignore[arg-type]
+                y_array,
+                self._com,
+                self.adjust,
+                self.ignore_na,
+                bias,
+            )
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, cov_func, numeric_only
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """\
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndex DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        """
+        ),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4])
+        >>> ser2 = pd.Series([10, 11, 13, 16])
+        >>> ser1.ewm(alpha=.2).corr(ser2)
+        0         NaN
+        1    1.000000
+        2    0.982821
+        3    0.977802
+        dtype: float64
+        """
+        ),
+        window_method="ewm",
+        aggregation_description="(exponential weighted moment) sample correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        numeric_only: bool = False,
+    ):
+        from pandas import Series
+
+        self._validate_numeric_only("corr", numeric_only)
+
+        def cov_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+
+            def _cov(X, Y):
+                return window_aggregations.ewmcov(
+                    X,
+                    start,
+                    end,
+                    min_periods,
+                    Y,
+                    self._com,
+                    self.adjust,
+                    self.ignore_na,
+                    True,
+                )
+
+            with np.errstate(all="ignore"):
+                cov = _cov(x_array, y_array)
+                x_var = _cov(x_array, x_array)
+                y_var = _cov(y_array, y_array)
+                result = cov / zsqrt(x_var * y_var)
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, cov_func, numeric_only
+        )
+
+
+class ExponentialMovingWindowGroupby(BaseWindowGroupby, ExponentialMovingWindow):
+    """
+    Provide an exponential moving window groupby implementation.
+    """
+
+    _attributes = ExponentialMovingWindow._attributes + BaseWindowGroupby._attributes
+
+    def __init__(self, obj, *args, _grouper=None, **kwargs) -> None:
+        super().__init__(obj, *args, _grouper=_grouper, **kwargs)
+
+        if not obj.empty and self.times is not None:
+            # sort the times and recalculate the deltas according to the groups
+            groupby_order = np.concatenate(list(self._grouper.indices.values()))
+            self._deltas = _calculate_deltas(
+                self.times.take(groupby_order),
+                self.halflife,
+            )
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        window_indexer = GroupbyIndexer(
+            groupby_indices=self._grouper.indices,
+            window_indexer=ExponentialMovingWindowIndexer,
+        )
+        return window_indexer
+
+
+class OnlineExponentialMovingWindow(ExponentialMovingWindow):
+    def __init__(
+        self,
+        obj: NDFrame,
+        com: float | None = None,
+        span: float | None = None,
+        halflife: float | TimedeltaConvertibleTypes | None = None,
+        alpha: float | None = None,
+        min_periods: int | None = 0,
+        adjust: bool = True,
+        ignore_na: bool = False,
+        axis: Axis = 0,
+        times: np.ndarray | NDFrame | None = None,
+        engine: str = "numba",
+        engine_kwargs: dict[str, bool] | None = None,
+        *,
+        selection=None,
+    ) -> None:
+        if times is not None:
+            raise NotImplementedError(
+                "times is not implemented with online operations."
+            )
+        super().__init__(
+            obj=obj,
+            com=com,
+            span=span,
+            halflife=halflife,
+            alpha=alpha,
+            min_periods=min_periods,
+            adjust=adjust,
+            ignore_na=ignore_na,
+            axis=axis,
+            times=times,
+            selection=selection,
+        )
+        self._mean = EWMMeanState(
+            self._com, self.adjust, self.ignore_na, self.axis, obj.shape
+        )
+        if maybe_use_numba(engine):
+            self.engine = engine
+            self.engine_kwargs = engine_kwargs
+        else:
+            raise ValueError("'numba' is the only supported engine")
+
+    def reset(self) -> None:
+        """
+        Reset the state captured by `update` calls.
+        """
+        self._mean.reset()
+
+    def aggregate(self, func, *args, **kwargs):
+        raise NotImplementedError("aggregate is not implemented.")
+
+    def std(self, bias: bool = False, *args, **kwargs):
+        raise NotImplementedError("std is not implemented.")
+
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        numeric_only: bool = False,
+    ):
+        raise NotImplementedError("corr is not implemented.")
+
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        bias: bool = False,
+        numeric_only: bool = False,
+    ):
+        raise NotImplementedError("cov is not implemented.")
+
+    def var(self, bias: bool = False, numeric_only: bool = False):
+        raise NotImplementedError("var is not implemented.")
+
+    def mean(self, *args, update=None, update_times=None, **kwargs):
+        """
+        Calculate an online exponentially weighted mean.
+
+        Parameters
+        ----------
+        update: DataFrame or Series, default None
+            New values to continue calculating the
+            exponentially weighted mean from the last values and weights.
+            Values should be float64 dtype.
+
+            ``update`` needs to be ``None`` the first time the
+            exponentially weighted mean is calculated.
+
+        update_times: Series or 1-D np.ndarray, default None
+            New times to continue calculating the
+            exponentially weighted mean from the last values and weights.
+            If ``None``, values are assumed to be evenly spaced
+            in time.
+            This feature is currently unsupported.
+
+        Returns
+        -------
+        DataFrame or Series
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"a": range(5), "b": range(5, 10)})
+        >>> online_ewm = df.head(2).ewm(0.5).online()
+        >>> online_ewm.mean()
+              a     b
+        0  0.00  5.00
+        1  0.75  5.75
+        >>> online_ewm.mean(update=df.tail(3))
+                  a         b
+        2  1.615385  6.615385
+        3  2.550000  7.550000
+        4  3.520661  8.520661
+        >>> online_ewm.reset()
+        >>> online_ewm.mean()
+              a     b
+        0  0.00  5.00
+        1  0.75  5.75
+        """
+        result_kwargs = {}
+        is_frame = self._selected_obj.ndim == 2
+        if update_times is not None:
+            raise NotImplementedError("update_times is not implemented.")
+        update_deltas = np.ones(
+            max(self._selected_obj.shape[self.axis - 1] - 1, 0), dtype=np.float64
+        )
+        if update is not None:
+            if self._mean.last_ewm is None:
+                raise ValueError(
+                    "Must call mean with update=None first before passing update"
+                )
+            result_from = 1
+            result_kwargs["index"] = update.index
+            if is_frame:
+                last_value = self._mean.last_ewm[np.newaxis, :]
+                result_kwargs["columns"] = update.columns
+            else:
+                last_value = self._mean.last_ewm
+                result_kwargs["name"] = update.name
+            np_array = np.concatenate((last_value, update.to_numpy()))
+        else:
+            result_from = 0
+            result_kwargs["index"] = self._selected_obj.index
+            if is_frame:
+                result_kwargs["columns"] = self._selected_obj.columns
+            else:
+                result_kwargs["name"] = self._selected_obj.name
+            np_array = self._selected_obj.astype(np.float64, copy=False).to_numpy()
+        ewma_func = generate_online_numba_ewma_func(
+            **get_jit_arguments(self.engine_kwargs)
+        )
+        result = self._mean.run_ewm(
+            np_array if is_frame else np_array[:, np.newaxis],
+            update_deltas,
+            self.min_periods,
+            ewma_func,
+        )
+        if not is_frame:
+            result = result.squeeze()
+        result = result[result_from:]
+        result = self._selected_obj._constructor(result, **result_kwargs)
+        return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/expanding.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/expanding.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac10596ffc699c2b229f959b9c1b26393384b03
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/expanding.py
@@ -0,0 +1,964 @@
+from __future__ import annotations
+
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+from pandas.util._decorators import (
+    deprecate_kwarg,
+    doc,
+)
+
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    ExpandingIndexer,
+    GroupbyIndexer,
+)
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+    window_apply_parameters,
+)
+from pandas.core.window.rolling import (
+    BaseWindowGroupby,
+    RollingAndExpandingMixin,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        QuantileInterpolation,
+        WindowingRankType,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+
+
+class Expanding(RollingAndExpandingMixin):
+    """
+    Provide expanding window calculations.
+
+    Parameters
+    ----------
+    min_periods : int, default 1
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+    axis : int or str, default 0
+        If ``0`` or ``'index'``, roll across the rows.
+
+        If ``1`` or ``'columns'``, roll across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+    method : str {'single', 'table'}, default 'single'
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+        .. versionadded:: 1.3.0
+
+    Returns
+    -------
+    pandas.api.typing.Expanding
+
+    See Also
+    --------
+    rolling : Provides rolling window calculations.
+    ewm : Provides exponential weighted functions.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations <window.expanding>` for further usage details
+    and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    **min_periods**
+
+    Expanding sum with 1 vs 3 observations needed to calculate a value.
+
+    >>> df.expanding(1).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  3.0
+    4  7.0
+    >>> df.expanding(3).sum()
+         B
+    0  NaN
+    1  NaN
+    2  3.0
+    3  3.0
+    4  7.0
+    """
+
+    _attributes: list[str] = ["min_periods", "axis", "method"]
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        min_periods: int = 1,
+        axis: Axis = 0,
+        method: str = "single",
+        selection=None,
+    ) -> None:
+        super().__init__(
+            obj=obj,
+            min_periods=min_periods,
+            axis=axis,
+            method=method,
+            selection=selection,
+        )
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        return ExpandingIndexer()
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.aggregate : Similar DataFrame method.
+        pandas.Series.aggregate : Similar Series method.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.ewm(alpha=0.5).mean()
+                  A         B         C
+        0  1.000000  4.000000  7.000000
+        1  1.666667  4.666667  7.666667
+        2  2.428571  5.428571  8.428571
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().count()
+        a    1.0
+        b    2.0
+        c    3.0
+        d    4.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="count of non NaN observations",
+        agg_method="count",
+    )
+    def count(self, numeric_only: bool = False):
+        return super().count(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        window_apply_parameters,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().apply(lambda s: s.max() - 2 * s.min())
+        a   -1.0
+        b    0.0
+        c    1.0
+        d    2.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="custom aggregation function",
+        agg_method="apply",
+    )
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        return super().apply(
+            func,
+            raw=raw,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().sum()
+        a     1.0
+        b     3.0
+        c     6.0
+        d    10.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().sum(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([3, 2, 1, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().max()
+        a    3.0
+        b    3.0
+        c    3.0
+        d    4.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="maximum",
+        agg_method="max",
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().max(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([2, 3, 4, 1], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().min()
+        a    2.0
+        b    2.0
+        c    2.0
+        d    1.0
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="minimum",
+        agg_method="min",
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().min(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().mean()
+        a    1.0
+        b    1.5
+        c    2.0
+        d    2.5
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().mean(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser.expanding().median()
+        a    1.0
+        b    1.5
+        c    2.0
+        d    2.5
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="median",
+        agg_method="median",
+    )
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().median(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.std : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.std` is different
+        than the default ``ddof`` of 0 in :func:`numpy.std`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+
+        >>> s.expanding(3).std()
+        0         NaN
+        1         NaN
+        2    0.577350
+        3    0.957427
+        4    0.894427
+        5    0.836660
+        6    0.786796
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="standard deviation",
+        agg_method="std",
+    )
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().std(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.var : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.var` is different
+        than the default ``ddof`` of 0 in :func:`numpy.var`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+
+        >>> s.expanding(3).var()
+        0         NaN
+        1         NaN
+        2    0.333333
+        3    0.916667
+        4    0.800000
+        5    0.700000
+        6    0.619048
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="variance",
+        agg_method="var",
+    )
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().var(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.\n
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of one period is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([0, 1, 2, 3])
+
+        >>> s.expanding().sem()
+        0         NaN
+        1    0.707107
+        2    0.707107
+        3    0.745356
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="standard error of mean",
+        agg_method="sem",
+    )
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        return super().sem(ddof=ddof, numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.skew : Third moment of a probability density.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of three periods is required for the rolling calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([-1, 0, 2, -1, 2], index=['a', 'b', 'c', 'd', 'e'])
+        >>> ser.expanding().skew()
+        a         NaN
+        b         NaN
+        c    0.935220
+        d    1.414214
+        e    0.315356
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="unbiased skewness",
+        agg_method="skew",
+    )
+    def skew(self, numeric_only: bool = False):
+        return super().skew(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.kurtosis : Reference SciPy method.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of four periods is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        The example below will show a rolling calculation with a window size of
+        four matching the equivalent function call using `scipy.stats`.
+
+        >>> arr = [1, 2, 3, 4, 999]
+        >>> import scipy.stats
+        >>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}")
+        -1.200000
+        >>> print(f"{{scipy.stats.kurtosis(arr, bias=False):.6f}}")
+        4.999874
+        >>> s = pd.Series(arr)
+        >>> s.expanding(4).kurt()
+        0         NaN
+        1         NaN
+        2         NaN
+        3   -1.200000
+        4    4.999874
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="Fisher's definition of kurtosis without bias",
+        agg_method="kurt",
+    )
+    def kurt(self, numeric_only: bool = False):
+        return super().kurt(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        quantile : float
+            Quantile to compute. 0 <= quantile <= 1.
+
+            .. deprecated:: 2.1.0
+                This will be renamed to 'q' in a future version.
+        interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])
+        >>> ser.expanding(min_periods=4).quantile(.25)
+        a     NaN
+        b     NaN
+        c     NaN
+        d    1.75
+        e    2.00
+        f    2.25
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="quantile",
+        agg_method="quantile",
+    )
+    @deprecate_kwarg(old_arg_name="quantile", new_arg_name="q")
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        return super().quantile(
+            q=q,
+            interpolation=interpolation,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        ".. versionadded:: 1.4.0 \n\n",
+        create_section_header("Parameters"),
+        dedent(
+            """
+        method : {{'average', 'min', 'max'}}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 4, 2, 3, 5, 3])
+        >>> s.expanding().rank()
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    3.5
+        dtype: float64
+
+        >>> s.expanding().rank(method="max")
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    4.0
+        dtype: float64
+
+        >>> s.expanding().rank(method="min")
+        0    1.0
+        1    2.0
+        2    2.0
+        3    3.0
+        4    5.0
+        5    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="expanding",
+        aggregation_description="rank",
+        agg_method="rank",
+    )
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        return super().rank(
+            method=method,
+            ascending=ascending,
+            pct=pct,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
+        >>> ser1.expanding().cov(ser2)
+        a         NaN
+        b    0.500000
+        c    1.500000
+        d    3.333333
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().cov(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        dedent(
+            """
+        cov : Similar method to calculate covariance.
+        numpy.corrcoef : NumPy Pearson's correlation calculation.
+        """
+        ).replace("\n", "", 1),
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        This function uses Pearson's definition of correlation
+        (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
+
+        When `other` is not specified, the output will be self correlation (e.g.
+        all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
+        set to `True`.
+
+        Function will return ``NaN`` for correlations of equal valued sequences;
+        this is the result of a 0/0 division error.
+
+        When `pairwise` is set to `False`, only matching columns between `self` and
+        `other` will be used.
+
+        When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
+        with the original index on the first level, and the `other` DataFrame
+        columns on the second level.
+
+        In the case of missing elements, only complete pairwise observations
+        will be used.\n
+        """
+        ),
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
+        >>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
+        >>> ser1.expanding().corr(ser2)
+        a         NaN
+        b    1.000000
+        c    0.981981
+        d    0.975900
+        dtype: float64
+        """
+        ),
+        window_method="expanding",
+        aggregation_description="correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().corr(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+
+class ExpandingGroupby(BaseWindowGroupby, Expanding):
+    """
+    Provide a expanding groupby implementation.
+    """
+
+    _attributes = Expanding._attributes + BaseWindowGroupby._attributes
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        window_indexer = GroupbyIndexer(
+            groupby_indices=self._grouper.indices,
+            window_indexer=ExpandingIndexer,
+        )
+        return window_indexer
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/numba_.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/numba_.py
new file mode 100644
index 0000000000000000000000000000000000000000..9357945e78c631a3fada24ec3015ca0cf183b99c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/numba_.py
@@ -0,0 +1,351 @@
+from __future__ import annotations
+
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.util.numba_ import jit_user_function
+
+if TYPE_CHECKING:
+    from pandas._typing import Scalar
+
+
+@functools.cache
+def generate_numba_apply_func(
+    func: Callable[..., Scalar],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a numba jitted apply function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a rolling apply function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the rolling apply function.
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each window and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def roll_apply(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+        *args: Any,
+    ) -> np.ndarray:
+        result = np.empty(len(begin))
+        for i in numba.prange(len(result)):
+            start = begin[i]
+            stop = end[i]
+            window = values[start:stop]
+            count_nan = np.sum(np.isnan(window))
+            if len(window) - count_nan >= minimum_periods:
+                result[i] = numba_func(window, *args)
+            else:
+                result[i] = np.nan
+        return result
+
+    return roll_apply
+
+
+@functools.cache
+def generate_numba_ewm_func(
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+    com: float,
+    adjust: bool,
+    ignore_na: bool,
+    deltas: tuple,
+    normalize: bool,
+):
+    """
+    Generate a numba jitted ewm mean or sum function specified by values
+    from engine_kwargs.
+
+    Parameters
+    ----------
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+    com : float
+    adjust : bool
+    ignore_na : bool
+    deltas : tuple
+    normalize : bool
+
+    Returns
+    -------
+    Numba function
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def ewm(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+    ) -> np.ndarray:
+        result = np.empty(len(values))
+        alpha = 1.0 / (1.0 + com)
+        old_wt_factor = 1.0 - alpha
+        new_wt = 1.0 if adjust else alpha
+
+        for i in numba.prange(len(begin)):
+            start = begin[i]
+            stop = end[i]
+            window = values[start:stop]
+            sub_result = np.empty(len(window))
+
+            weighted = window[0]
+            nobs = int(not np.isnan(weighted))
+            sub_result[0] = weighted if nobs >= minimum_periods else np.nan
+            old_wt = 1.0
+
+            for j in range(1, len(window)):
+                cur = window[j]
+                is_observation = not np.isnan(cur)
+                nobs += is_observation
+                if not np.isnan(weighted):
+                    if is_observation or not ignore_na:
+                        if normalize:
+                            # note that len(deltas) = len(vals) - 1 and deltas[i]
+                            # is to be used in conjunction with vals[i+1]
+                            old_wt *= old_wt_factor ** deltas[start + j - 1]
+                        else:
+                            weighted = old_wt_factor * weighted
+                        if is_observation:
+                            if normalize:
+                                # avoid numerical errors on constant series
+                                if weighted != cur:
+                                    weighted = old_wt * weighted + new_wt * cur
+                                    if normalize:
+                                        weighted = weighted / (old_wt + new_wt)
+                                if adjust:
+                                    old_wt += new_wt
+                                else:
+                                    old_wt = 1.0
+                            else:
+                                weighted += cur
+                elif is_observation:
+                    weighted = cur
+
+                sub_result[j] = weighted if nobs >= minimum_periods else np.nan
+
+            result[start:stop] = sub_result
+
+        return result
+
+    return ewm
+
+
+@functools.cache
+def generate_numba_table_func(
+    func: Callable[..., np.ndarray],
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a numba jitted function to apply window calculations table-wise.
+
+    Func will be passed a M window size x N number of columns array, and
+    must return a 1 x N number of columns array. Func is intended to operate
+    row-wise, but the result will be transposed for axis=1.
+
+    1. jit the user's function
+    2. Return a rolling apply function with the jitted function inline
+
+    Parameters
+    ----------
+    func : function
+        function to be applied to each window and will be JITed
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    numba_func = jit_user_function(func)
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def roll_table(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+        *args: Any,
+    ):
+        result = np.empty((len(begin), values.shape[1]))
+        min_periods_mask = np.empty(result.shape)
+        for i in numba.prange(len(result)):
+            start = begin[i]
+            stop = end[i]
+            window = values[start:stop]
+            count_nan = np.sum(np.isnan(window), axis=0)
+            sub_result = numba_func(window, *args)
+            nan_mask = len(window) - count_nan >= minimum_periods
+            min_periods_mask[i, :] = nan_mask
+            result[i, :] = sub_result
+        result = np.where(min_periods_mask, result, np.nan)
+        return result
+
+    return roll_table
+
+
+# This function will no longer be needed once numba supports
+# axis for all np.nan* agg functions
+# https://github.com/numba/numba/issues/1269
+@functools.cache
+def generate_manual_numpy_nan_agg_with_axis(nan_func):
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=True, nogil=True, parallel=True)
+    def nan_agg_with_axis(table):
+        result = np.empty(table.shape[1])
+        for i in numba.prange(table.shape[1]):
+            partition = table[:, i]
+            result[i] = nan_func(partition)
+        return result
+
+    return nan_agg_with_axis
+
+
+@functools.cache
+def generate_numba_ewm_table_func(
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+    com: float,
+    adjust: bool,
+    ignore_na: bool,
+    deltas: tuple,
+    normalize: bool,
+):
+    """
+    Generate a numba jitted ewm mean or sum function applied table wise specified
+    by values from engine_kwargs.
+
+    Parameters
+    ----------
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+    com : float
+    adjust : bool
+    ignore_na : bool
+    deltas : tuple
+    normalize: bool
+
+    Returns
+    -------
+    Numba function
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def ewm_table(
+        values: np.ndarray,
+        begin: np.ndarray,
+        end: np.ndarray,
+        minimum_periods: int,
+    ) -> np.ndarray:
+        alpha = 1.0 / (1.0 + com)
+        old_wt_factor = 1.0 - alpha
+        new_wt = 1.0 if adjust else alpha
+        old_wt = np.ones(values.shape[1])
+
+        result = np.empty(values.shape)
+        weighted = values[0].copy()
+        nobs = (~np.isnan(weighted)).astype(np.int64)
+        result[0] = np.where(nobs >= minimum_periods, weighted, np.nan)
+        for i in range(1, len(values)):
+            cur = values[i]
+            is_observations = ~np.isnan(cur)
+            nobs += is_observations.astype(np.int64)
+            for j in numba.prange(len(cur)):
+                if not np.isnan(weighted[j]):
+                    if is_observations[j] or not ignore_na:
+                        if normalize:
+                            # note that len(deltas) = len(vals) - 1 and deltas[i]
+                            # is to be used in conjunction with vals[i+1]
+                            old_wt[j] *= old_wt_factor ** deltas[i - 1]
+                        else:
+                            weighted[j] = old_wt_factor * weighted[j]
+                        if is_observations[j]:
+                            if normalize:
+                                # avoid numerical errors on constant series
+                                if weighted[j] != cur[j]:
+                                    weighted[j] = (
+                                        old_wt[j] * weighted[j] + new_wt * cur[j]
+                                    )
+                                    if normalize:
+                                        weighted[j] = weighted[j] / (old_wt[j] + new_wt)
+                                if adjust:
+                                    old_wt[j] += new_wt
+                                else:
+                                    old_wt[j] = 1.0
+                            else:
+                                weighted[j] += cur[j]
+                elif is_observations[j]:
+                    weighted[j] = cur[j]
+
+            result[i] = np.where(nobs >= minimum_periods, weighted, np.nan)
+
+        return result
+
+    return ewm_table
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/online.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/online.py
new file mode 100644
index 0000000000000000000000000000000000000000..29d1f740e021fd30d3bfc7d72a5e6baefe62ac4c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/online.py
@@ -0,0 +1,118 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+
+def generate_online_numba_ewma_func(
+    nopython: bool,
+    nogil: bool,
+    parallel: bool,
+):
+    """
+    Generate a numba jitted groupby ewma function specified by values
+    from engine_kwargs.
+
+    Parameters
+    ----------
+    nopython : bool
+        nopython to be passed into numba.jit
+    nogil : bool
+        nogil to be passed into numba.jit
+    parallel : bool
+        parallel to be passed into numba.jit
+
+    Returns
+    -------
+    Numba function
+    """
+    if TYPE_CHECKING:
+        import numba
+    else:
+        numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
+    def online_ewma(
+        values: np.ndarray,
+        deltas: np.ndarray,
+        minimum_periods: int,
+        old_wt_factor: float,
+        new_wt: float,
+        old_wt: np.ndarray,
+        adjust: bool,
+        ignore_na: bool,
+    ):
+        """
+        Compute online exponentially weighted mean per column over 2D values.
+
+        Takes the first observation as is, then computes the subsequent
+        exponentially weighted mean accounting minimum periods.
+        """
+        result = np.empty(values.shape)
+        weighted_avg = values[0].copy()
+        nobs = (~np.isnan(weighted_avg)).astype(np.int64)
+        result[0] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
+
+        for i in range(1, len(values)):
+            cur = values[i]
+            is_observations = ~np.isnan(cur)
+            nobs += is_observations.astype(np.int64)
+            for j in numba.prange(len(cur)):
+                if not np.isnan(weighted_avg[j]):
+                    if is_observations[j] or not ignore_na:
+                        # note that len(deltas) = len(vals) - 1 and deltas[i] is to be
+                        # used in conjunction with vals[i+1]
+                        old_wt[j] *= old_wt_factor ** deltas[j - 1]
+                        if is_observations[j]:
+                            # avoid numerical errors on constant series
+                            if weighted_avg[j] != cur[j]:
+                                weighted_avg[j] = (
+                                    (old_wt[j] * weighted_avg[j]) + (new_wt * cur[j])
+                                ) / (old_wt[j] + new_wt)
+                            if adjust:
+                                old_wt[j] += new_wt
+                            else:
+                                old_wt[j] = 1.0
+                elif is_observations[j]:
+                    weighted_avg[j] = cur[j]
+
+            result[i] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
+
+        return result, old_wt
+
+    return online_ewma
+
+
+class EWMMeanState:
+    def __init__(self, com, adjust, ignore_na, axis, shape) -> None:
+        alpha = 1.0 / (1.0 + com)
+        self.axis = axis
+        self.shape = shape
+        self.adjust = adjust
+        self.ignore_na = ignore_na
+        self.new_wt = 1.0 if adjust else alpha
+        self.old_wt_factor = 1.0 - alpha
+        self.old_wt = np.ones(self.shape[self.axis - 1])
+        self.last_ewm = None
+
+    def run_ewm(self, weighted_avg, deltas, min_periods, ewm_func):
+        result, old_wt = ewm_func(
+            weighted_avg,
+            deltas,
+            min_periods,
+            self.old_wt_factor,
+            self.new_wt,
+            self.old_wt,
+            self.adjust,
+            self.ignore_na,
+        )
+        self.old_wt = old_wt
+        self.last_ewm = result[-1]
+        return result
+
+    def reset(self) -> None:
+        self.old_wt = np.ones(self.shape[self.axis - 1])
+        self.last_ewm = None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/rolling.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/rolling.py
new file mode 100644
index 0000000000000000000000000000000000000000..68cec16ec9eca8b34fadd9e67221219ebd7fe736
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/core/window/rolling.py
@@ -0,0 +1,2930 @@
+"""
+Provide a generic structure to support window functions,
+similar to how we have a Groupby object.
+"""
+from __future__ import annotations
+
+import copy
+from datetime import timedelta
+from functools import partial
+import inspect
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._libs.tslibs import (
+    BaseOffset,
+    Timedelta,
+    to_offset,
+)
+import pandas._libs.window.aggregations as window_aggregations
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import DataError
+from pandas.util._decorators import (
+    deprecate_kwarg,
+    doc,
+)
+
+from pandas.core.dtypes.common import (
+    ensure_float64,
+    is_bool,
+    is_integer,
+    is_numeric_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+from pandas.core.dtypes.missing import notna
+
+from pandas.core._numba import executor
+from pandas.core.algorithms import factorize
+from pandas.core.apply import ResamplerWindowApply
+from pandas.core.arrays import ExtensionArray
+from pandas.core.base import SelectionMixin
+import pandas.core.common as com
+from pandas.core.indexers.objects import (
+    BaseIndexer,
+    FixedWindowIndexer,
+    GroupbyIndexer,
+    VariableWindowIndexer,
+)
+from pandas.core.indexes.api import (
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+from pandas.core.reshape.concat import concat
+from pandas.core.util.numba_ import (
+    get_jit_arguments,
+    maybe_use_numba,
+)
+from pandas.core.window.common import (
+    flex_binary_moment,
+    zsqrt,
+)
+from pandas.core.window.doc import (
+    _shared_docs,
+    create_section_header,
+    kwargs_numeric_only,
+    kwargs_scipy,
+    numba_notes,
+    template_header,
+    template_returns,
+    template_see_also,
+    window_agg_numba_parameters,
+    window_apply_parameters,
+)
+from pandas.core.window.numba_ import (
+    generate_manual_numpy_nan_agg_with_axis,
+    generate_numba_apply_func,
+    generate_numba_table_func,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sized,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        Axis,
+        NDFrameT,
+        QuantileInterpolation,
+        WindowingRankType,
+        npt,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+    from pandas.core.groupby.ops import BaseGrouper
+
+from pandas.core.arrays.datetimelike import dtype_to_unit
+
+
+class BaseWindow(SelectionMixin):
+    """Provides utilities for performing windowing operations."""
+
+    _attributes: list[str] = []
+    exclusions: frozenset[Hashable] = frozenset()
+    _on: Index
+
+    def __init__(
+        self,
+        obj: NDFrame,
+        window=None,
+        min_periods: int | None = None,
+        center: bool | None = False,
+        win_type: str | None = None,
+        axis: Axis = 0,
+        on: str | Index | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+        method: str = "single",
+        *,
+        selection=None,
+    ) -> None:
+        self.obj = obj
+        self.on = on
+        self.closed = closed
+        self.step = step
+        self.window = window
+        self.min_periods = min_periods
+        self.center = center
+        self.win_type = win_type
+        self.axis = obj._get_axis_number(axis) if axis is not None else None
+        self.method = method
+        self._win_freq_i8: int | None = None
+        if self.on is None:
+            if self.axis == 0:
+                self._on = self.obj.index
+            else:
+                # i.e. self.axis == 1
+                self._on = self.obj.columns
+        elif isinstance(self.on, Index):
+            self._on = self.on
+        elif isinstance(self.obj, ABCDataFrame) and self.on in self.obj.columns:
+            self._on = Index(self.obj[self.on])
+        else:
+            raise ValueError(
+                f"invalid on specified as {self.on}, "
+                "must be a column (of DataFrame), an Index or None"
+            )
+
+        self._selection = selection
+        self._validate()
+
+    def _validate(self) -> None:
+        if self.center is not None and not is_bool(self.center):
+            raise ValueError("center must be a boolean")
+        if self.min_periods is not None:
+            if not is_integer(self.min_periods):
+                raise ValueError("min_periods must be an integer")
+            if self.min_periods < 0:
+                raise ValueError("min_periods must be >= 0")
+            if is_integer(self.window) and self.min_periods > self.window:
+                raise ValueError(
+                    f"min_periods {self.min_periods} must be <= window {self.window}"
+                )
+        if self.closed is not None and self.closed not in [
+            "right",
+            "both",
+            "left",
+            "neither",
+        ]:
+            raise ValueError("closed must be 'right', 'left', 'both' or 'neither'")
+        if not isinstance(self.obj, (ABCSeries, ABCDataFrame)):
+            raise TypeError(f"invalid type: {type(self)}")
+        if isinstance(self.window, BaseIndexer):
+            # Validate that the passed BaseIndexer subclass has
+            # a get_window_bounds with the correct signature.
+            get_window_bounds_signature = inspect.signature(
+                self.window.get_window_bounds
+            ).parameters.keys()
+            expected_signature = inspect.signature(
+                BaseIndexer().get_window_bounds
+            ).parameters.keys()
+            if get_window_bounds_signature != expected_signature:
+                raise ValueError(
+                    f"{type(self.window).__name__} does not implement "
+                    f"the correct signature for get_window_bounds"
+                )
+        if self.method not in ["table", "single"]:
+            raise ValueError("method must be 'table' or 'single")
+        if self.step is not None:
+            if not is_integer(self.step):
+                raise ValueError("step must be an integer")
+            if self.step < 0:
+                raise ValueError("step must be >= 0")
+
+    def _check_window_bounds(
+        self, start: np.ndarray, end: np.ndarray, num_vals: int
+    ) -> None:
+        if len(start) != len(end):
+            raise ValueError(
+                f"start ({len(start)}) and end ({len(end)}) bounds must be the "
+                f"same length"
+            )
+        if len(start) != (num_vals + (self.step or 1) - 1) // (self.step or 1):
+            raise ValueError(
+                f"start and end bounds ({len(start)}) must be the same length "
+                f"as the object ({num_vals}) divided by the step ({self.step}) "
+                f"if given and rounded up"
+            )
+
+    def _slice_axis_for_step(self, index: Index, result: Sized | None = None) -> Index:
+        """
+        Slices the index for a given result and the preset step.
+        """
+        return (
+            index
+            if result is None or len(result) == len(index)
+            else index[:: self.step]
+        )
+
+    def _validate_numeric_only(self, name: str, numeric_only: bool) -> None:
+        """
+        Validate numeric_only argument, raising if invalid for the input.
+
+        Parameters
+        ----------
+        name : str
+            Name of the operator (kernel).
+        numeric_only : bool
+            Value passed by user.
+        """
+        if (
+            self._selected_obj.ndim == 1
+            and numeric_only
+            and not is_numeric_dtype(self._selected_obj.dtype)
+        ):
+            raise NotImplementedError(
+                f"{type(self).__name__}.{name} does not implement numeric_only"
+            )
+
+    def _make_numeric_only(self, obj: NDFrameT) -> NDFrameT:
+        """Subset DataFrame to numeric columns.
+
+        Parameters
+        ----------
+        obj : DataFrame
+
+        Returns
+        -------
+        obj subset to numeric-only columns.
+        """
+        result = obj.select_dtypes(include=["number"], exclude=["timedelta"])
+        return result
+
+    def _create_data(self, obj: NDFrameT, numeric_only: bool = False) -> NDFrameT:
+        """
+        Split data into blocks & return conformed data.
+        """
+        # filter out the on from the object
+        if self.on is not None and not isinstance(self.on, Index) and obj.ndim == 2:
+            obj = obj.reindex(columns=obj.columns.difference([self.on]), copy=False)
+        if obj.ndim > 1 and (numeric_only or self.axis == 1):
+            # GH: 20649 in case of mixed dtype and axis=1 we have to convert everything
+            # to float to calculate the complete row at once. We exclude all non-numeric
+            # dtypes.
+            obj = self._make_numeric_only(obj)
+        if self.axis == 1:
+            obj = obj.astype("float64", copy=False)
+            obj._mgr = obj._mgr.consolidate()
+        return obj
+
+    def _gotitem(self, key, ndim, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : str / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        # create a new object to prevent aliasing
+        if subset is None:
+            subset = self.obj
+
+        # we need to make a shallow copy of ourselves
+        # with the same groupby
+        kwargs = {attr: getattr(self, attr) for attr in self._attributes}
+
+        selection = self._infer_selection(key, subset)
+        new_win = type(self)(subset, selection=selection, **kwargs)
+        return new_win
+
+    def __getattr__(self, attr: str):
+        if attr in self._internal_names_set:
+            return object.__getattribute__(self, attr)
+        if attr in self.obj:
+            return self[attr]
+
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{attr}'"
+        )
+
+    def _dir_additions(self):
+        return self.obj._dir_additions()
+
+    def __repr__(self) -> str:
+        """
+        Provide a nice str repr of our rolling object.
+        """
+        attrs_list = (
+            f"{attr_name}={getattr(self, attr_name)}"
+            for attr_name in self._attributes
+            if getattr(self, attr_name, None) is not None and attr_name[0] != "_"
+        )
+        attrs = ",".join(attrs_list)
+        return f"{type(self).__name__} [{attrs}]"
+
+    def __iter__(self) -> Iterator:
+        obj = self._selected_obj.set_axis(self._on)
+        obj = self._create_data(obj)
+        indexer = self._get_window_indexer()
+
+        start, end = indexer.get_window_bounds(
+            num_values=len(obj),
+            min_periods=self.min_periods,
+            center=self.center,
+            closed=self.closed,
+            step=self.step,
+        )
+        self._check_window_bounds(start, end, len(obj))
+
+        for s, e in zip(start, end):
+            result = obj.iloc[slice(s, e)]
+            yield result
+
+    def _prep_values(self, values: ArrayLike) -> np.ndarray:
+        """Convert input to numpy arrays for Cython routines"""
+        if needs_i8_conversion(values.dtype):
+            raise NotImplementedError(
+                f"ops for {type(self).__name__} for this "
+                f"dtype {values.dtype} are not implemented"
+            )
+        # GH #12373 : rolling functions error on float32 data
+        # make sure the data is coerced to float64
+        try:
+            if isinstance(values, ExtensionArray):
+                values = values.to_numpy(np.float64, na_value=np.nan)
+            else:
+                values = ensure_float64(values)
+        except (ValueError, TypeError) as err:
+            raise TypeError(f"cannot handle this type -> {values.dtype}") from err
+
+        # Convert inf to nan for C funcs
+        inf = np.isinf(values)
+        if inf.any():
+            values = np.where(inf, np.nan, values)
+
+        return values
+
+    def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:
+        # if we have an 'on' column we want to put it back into
+        # the results in the same location
+        from pandas import Series
+
+        if self.on is not None and not self._on.equals(obj.index):
+            name = self._on.name
+            extra_col = Series(self._on, index=self.obj.index, name=name, copy=False)
+            if name in result.columns:
+                # TODO: sure we want to overwrite results?
+                result[name] = extra_col
+            elif name in result.index.names:
+                pass
+            elif name in self._selected_obj.columns:
+                # insert in the same location as we had in _selected_obj
+                old_cols = self._selected_obj.columns
+                new_cols = result.columns
+                old_loc = old_cols.get_loc(name)
+                overlap = new_cols.intersection(old_cols[:old_loc])
+                new_loc = len(overlap)
+                result.insert(new_loc, name, extra_col)
+            else:
+                # insert at the end
+                result[name] = extra_col
+
+    @property
+    def _index_array(self) -> npt.NDArray[np.int64] | None:
+        # TODO: why do we get here with e.g. MultiIndex?
+        if isinstance(self._on, (PeriodIndex, DatetimeIndex, TimedeltaIndex)):
+            return self._on.asi8
+        elif isinstance(self._on.dtype, ArrowDtype) and self._on.dtype.kind in "mM":
+            return self._on.to_numpy(dtype=np.int64)
+        return None
+
+    def _resolve_output(self, out: DataFrame, obj: DataFrame) -> DataFrame:
+        """Validate and finalize result."""
+        if out.shape[1] == 0 and obj.shape[1] > 0:
+            raise DataError("No numeric types to aggregate")
+        if out.shape[1] == 0:
+            return obj.astype("float64")
+
+        self._insert_on_column(out, obj)
+        return out
+
+    def _get_window_indexer(self) -> BaseIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+        """
+        if isinstance(self.window, BaseIndexer):
+            return self.window
+        if self._win_freq_i8 is not None:
+            return VariableWindowIndexer(
+                index_array=self._index_array,
+                window_size=self._win_freq_i8,
+                center=self.center,
+            )
+        return FixedWindowIndexer(window_size=self.window)
+
+    def _apply_series(
+        self, homogeneous_func: Callable[..., ArrayLike], name: str | None = None
+    ) -> Series:
+        """
+        Series version of _apply_columnwise
+        """
+        obj = self._create_data(self._selected_obj)
+
+        if name == "count":
+            # GH 12541: Special case for count where we support date-like types
+            obj = notna(obj).astype(int)
+        try:
+            values = self._prep_values(obj._values)
+        except (TypeError, NotImplementedError) as err:
+            raise DataError("No numeric types to aggregate") from err
+
+        result = homogeneous_func(values)
+        index = self._slice_axis_for_step(obj.index, result)
+        return obj._constructor(result, index=index, name=obj.name)
+
+    def _apply_columnwise(
+        self,
+        homogeneous_func: Callable[..., ArrayLike],
+        name: str,
+        numeric_only: bool = False,
+    ) -> DataFrame | Series:
+        """
+        Apply the given function to the DataFrame broken down into homogeneous
+        sub-frames.
+        """
+        self._validate_numeric_only(name, numeric_only)
+        if self._selected_obj.ndim == 1:
+            return self._apply_series(homogeneous_func, name)
+
+        obj = self._create_data(self._selected_obj, numeric_only)
+        if name == "count":
+            # GH 12541: Special case for count where we support date-like types
+            obj = notna(obj).astype(int)
+            obj._mgr = obj._mgr.consolidate()
+
+        if self.axis == 1:
+            obj = obj.T
+
+        taker = []
+        res_values = []
+        for i, arr in enumerate(obj._iter_column_arrays()):
+            # GH#42736 operate column-wise instead of block-wise
+            # As of 2.0, hfunc will raise for nuisance columns
+            try:
+                arr = self._prep_values(arr)
+            except (TypeError, NotImplementedError) as err:
+                raise DataError(
+                    f"Cannot aggregate non-numeric type: {arr.dtype}"
+                ) from err
+            res = homogeneous_func(arr)
+            res_values.append(res)
+            taker.append(i)
+
+        index = self._slice_axis_for_step(
+            obj.index, res_values[0] if len(res_values) > 0 else None
+        )
+        df = type(obj)._from_arrays(
+            res_values,
+            index=index,
+            columns=obj.columns.take(taker),
+            verify_integrity=False,
+        )
+
+        if self.axis == 1:
+            df = df.T
+
+        return self._resolve_output(df, obj)
+
+    def _apply_tablewise(
+        self,
+        homogeneous_func: Callable[..., ArrayLike],
+        name: str | None = None,
+        numeric_only: bool = False,
+    ) -> DataFrame | Series:
+        """
+        Apply the given function to the DataFrame across the entire object
+        """
+        if self._selected_obj.ndim == 1:
+            raise ValueError("method='table' not applicable for Series objects.")
+        obj = self._create_data(self._selected_obj, numeric_only)
+        values = self._prep_values(obj.to_numpy())
+        values = values.T if self.axis == 1 else values
+        result = homogeneous_func(values)
+        result = result.T if self.axis == 1 else result
+        index = self._slice_axis_for_step(obj.index, result)
+        columns = (
+            obj.columns
+            if result.shape[1] == len(obj.columns)
+            else obj.columns[:: self.step]
+        )
+        out = obj._constructor(result, index=index, columns=columns)
+
+        return self._resolve_output(out, obj)
+
+    def _apply_pairwise(
+        self,
+        target: DataFrame | Series,
+        other: DataFrame | Series | None,
+        pairwise: bool | None,
+        func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series],
+        numeric_only: bool,
+    ) -> DataFrame | Series:
+        """
+        Apply the given pairwise function given 2 pandas objects (DataFrame/Series)
+        """
+        target = self._create_data(target, numeric_only)
+        if other is None:
+            other = target
+            # only default unset
+            pairwise = True if pairwise is None else pairwise
+        elif not isinstance(other, (ABCDataFrame, ABCSeries)):
+            raise ValueError("other must be a DataFrame or Series")
+        elif other.ndim == 2 and numeric_only:
+            other = self._make_numeric_only(other)
+
+        return flex_binary_moment(target, other, func, pairwise=bool(pairwise))
+
+    def _apply(
+        self,
+        func: Callable[..., Any],
+        name: str,
+        numeric_only: bool = False,
+        numba_args: tuple[Any, ...] = (),
+        **kwargs,
+    ):
+        """
+        Rolling statistical measure using supplied function.
+
+        Designed to be used with passed-in Cython array-based functions.
+
+        Parameters
+        ----------
+        func : callable function to apply
+        name : str,
+        numba_args : tuple
+            args to be passed when func is a numba func
+        **kwargs
+            additional arguments for rolling function and window function
+
+        Returns
+        -------
+        y : type of input
+        """
+        window_indexer = self._get_window_indexer()
+        min_periods = (
+            self.min_periods
+            if self.min_periods is not None
+            else window_indexer.window_size
+        )
+
+        def homogeneous_func(values: np.ndarray):
+            # calculation function
+
+            if values.size == 0:
+                return values.copy()
+
+            def calc(x):
+                start, end = window_indexer.get_window_bounds(
+                    num_values=len(x),
+                    min_periods=min_periods,
+                    center=self.center,
+                    closed=self.closed,
+                    step=self.step,
+                )
+                self._check_window_bounds(start, end, len(x))
+
+                return func(x, start, end, min_periods, *numba_args)
+
+            with np.errstate(all="ignore"):
+                result = calc(values)
+
+            return result
+
+        if self.method == "single":
+            return self._apply_columnwise(homogeneous_func, name, numeric_only)
+        else:
+            return self._apply_tablewise(homogeneous_func, name, numeric_only)
+
+    def _numba_apply(
+        self,
+        func: Callable[..., Any],
+        engine_kwargs: dict[str, bool] | None = None,
+        **func_kwargs,
+    ):
+        window_indexer = self._get_window_indexer()
+        min_periods = (
+            self.min_periods
+            if self.min_periods is not None
+            else window_indexer.window_size
+        )
+        obj = self._create_data(self._selected_obj)
+        if self.axis == 1:
+            obj = obj.T
+        values = self._prep_values(obj.to_numpy())
+        if values.ndim == 1:
+            values = values.reshape(-1, 1)
+        start, end = window_indexer.get_window_bounds(
+            num_values=len(values),
+            min_periods=min_periods,
+            center=self.center,
+            closed=self.closed,
+            step=self.step,
+        )
+        self._check_window_bounds(start, end, len(values))
+        # For now, map everything to float to match the Cython impl
+        # even though it is wrong
+        # TODO: Could preserve correct dtypes in future
+        # xref #53214
+        dtype_mapping = executor.float_dtype_mapping
+        aggregator = executor.generate_shared_aggregator(
+            func,
+            dtype_mapping,
+            is_grouped_kernel=False,
+            **get_jit_arguments(engine_kwargs),
+        )
+        result = aggregator(
+            values.T, start=start, end=end, min_periods=min_periods, **func_kwargs
+        ).T
+        result = result.T if self.axis == 1 else result
+        index = self._slice_axis_for_step(obj.index, result)
+        if obj.ndim == 1:
+            result = result.squeeze()
+            out = obj._constructor(result, index=index, name=obj.name)
+            return out
+        else:
+            columns = self._slice_axis_for_step(obj.columns, result.T)
+            out = obj._constructor(result, index=index, columns=columns)
+            return self._resolve_output(out, obj)
+
+    def aggregate(self, func, *args, **kwargs):
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if result is None:
+            return self.apply(func, raw=False, args=args, kwargs=kwargs)
+        return result
+
+    agg = aggregate
+
+
+class BaseWindowGroupby(BaseWindow):
+    """
+    Provide the groupby windowing facilities.
+    """
+
+    _grouper: BaseGrouper
+    _as_index: bool
+    _attributes: list[str] = ["_grouper"]
+
+    def __init__(
+        self,
+        obj: DataFrame | Series,
+        *args,
+        _grouper: BaseGrouper,
+        _as_index: bool = True,
+        **kwargs,
+    ) -> None:
+        from pandas.core.groupby.ops import BaseGrouper
+
+        if not isinstance(_grouper, BaseGrouper):
+            raise ValueError("Must pass a BaseGrouper object.")
+        self._grouper = _grouper
+        self._as_index = _as_index
+        # GH 32262: It's convention to keep the grouping column in
+        # groupby.<agg_func>, but unexpected to users in
+        # groupby.rolling.<agg_func>
+        obj = obj.drop(columns=self._grouper.names, errors="ignore")
+        # GH 15354
+        if kwargs.get("step") is not None:
+            raise NotImplementedError("step not implemented for groupby")
+        super().__init__(obj, *args, **kwargs)
+
+    def _apply(
+        self,
+        func: Callable[..., Any],
+        name: str,
+        numeric_only: bool = False,
+        numba_args: tuple[Any, ...] = (),
+        **kwargs,
+    ) -> DataFrame | Series:
+        result = super()._apply(
+            func,
+            name,
+            numeric_only,
+            numba_args,
+            **kwargs,
+        )
+        # Reconstruct the resulting MultiIndex
+        # 1st set of levels = group by labels
+        # 2nd set of levels = original DataFrame/Series index
+        grouped_object_index = self.obj.index
+        grouped_index_name = [*grouped_object_index.names]
+        groupby_keys = copy.copy(self._grouper.names)
+        result_index_names = groupby_keys + grouped_index_name
+
+        drop_columns = [
+            key
+            for key in self._grouper.names
+            if key not in self.obj.index.names or key is None
+        ]
+
+        if len(drop_columns) != len(groupby_keys):
+            # Our result will have still kept the column in the result
+            result = result.drop(columns=drop_columns, errors="ignore")
+
+        codes = self._grouper.codes
+        levels = copy.copy(self._grouper.levels)
+
+        group_indices = self._grouper.indices.values()
+        if group_indices:
+            indexer = np.concatenate(list(group_indices))
+        else:
+            indexer = np.array([], dtype=np.intp)
+        codes = [c.take(indexer) for c in codes]
+
+        # if the index of the original dataframe needs to be preserved, append
+        # this index (but reordered) to the codes/levels from the groupby
+        if grouped_object_index is not None:
+            idx = grouped_object_index.take(indexer)
+            if not isinstance(idx, MultiIndex):
+                idx = MultiIndex.from_arrays([idx])
+            codes.extend(list(idx.codes))
+            levels.extend(list(idx.levels))
+
+        result_index = MultiIndex(
+            levels, codes, names=result_index_names, verify_integrity=False
+        )
+
+        result.index = result_index
+        if not self._as_index:
+            result = result.reset_index(level=list(range(len(groupby_keys))))
+        return result
+
+    def _apply_pairwise(
+        self,
+        target: DataFrame | Series,
+        other: DataFrame | Series | None,
+        pairwise: bool | None,
+        func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series],
+        numeric_only: bool,
+    ) -> DataFrame | Series:
+        """
+        Apply the given pairwise function given 2 pandas objects (DataFrame/Series)
+        """
+        # Manually drop the grouping column first
+        target = target.drop(columns=self._grouper.names, errors="ignore")
+        result = super()._apply_pairwise(target, other, pairwise, func, numeric_only)
+        # 1) Determine the levels + codes of the groupby levels
+        if other is not None and not all(
+            len(group) == len(other) for group in self._grouper.indices.values()
+        ):
+            # GH 42915
+            # len(other) != len(any group), so must reindex (expand) the result
+            # from flex_binary_moment to a "transform"-like result
+            # per groupby combination
+            old_result_len = len(result)
+            result = concat(
+                [
+                    result.take(gb_indices).reindex(result.index)
+                    for gb_indices in self._grouper.indices.values()
+                ]
+            )
+
+            gb_pairs = (
+                com.maybe_make_list(pair) for pair in self._grouper.indices.keys()
+            )
+            groupby_codes = []
+            groupby_levels = []
+            # e.g. [[1, 2], [4, 5]] as [[1, 4], [2, 5]]
+            for gb_level_pair in map(list, zip(*gb_pairs)):
+                labels = np.repeat(np.array(gb_level_pair), old_result_len)
+                codes, levels = factorize(labels)
+                groupby_codes.append(codes)
+                groupby_levels.append(levels)
+        else:
+            # pairwise=True or len(other) == len(each group), so repeat
+            # the groupby labels by the number of columns in the original object
+            groupby_codes = self._grouper.codes
+            # error: Incompatible types in assignment (expression has type
+            # "List[Index]", variable has type "List[Union[ndarray, Index]]")
+            groupby_levels = self._grouper.levels  # type: ignore[assignment]
+
+            group_indices = self._grouper.indices.values()
+            if group_indices:
+                indexer = np.concatenate(list(group_indices))
+            else:
+                indexer = np.array([], dtype=np.intp)
+
+            if target.ndim == 1:
+                repeat_by = 1
+            else:
+                repeat_by = len(target.columns)
+            groupby_codes = [
+                np.repeat(c.take(indexer), repeat_by) for c in groupby_codes
+            ]
+        # 2) Determine the levels + codes of the result from super()._apply_pairwise
+        if isinstance(result.index, MultiIndex):
+            result_codes = list(result.index.codes)
+            result_levels = list(result.index.levels)
+            result_names = list(result.index.names)
+        else:
+            idx_codes, idx_levels = factorize(result.index)
+            result_codes = [idx_codes]
+            result_levels = [idx_levels]
+            result_names = [result.index.name]
+
+        # 3) Create the resulting index by combining 1) + 2)
+        result_codes = groupby_codes + result_codes
+        result_levels = groupby_levels + result_levels
+        result_names = self._grouper.names + result_names
+
+        result_index = MultiIndex(
+            result_levels, result_codes, names=result_names, verify_integrity=False
+        )
+        result.index = result_index
+        return result
+
+    def _create_data(self, obj: NDFrameT, numeric_only: bool = False) -> NDFrameT:
+        """
+        Split data into blocks & return conformed data.
+        """
+        # Ensure the object we're rolling over is monotonically sorted relative
+        # to the groups
+        # GH 36197
+        if not obj.empty:
+            groupby_order = np.concatenate(list(self._grouper.indices.values())).astype(
+                np.int64
+            )
+            obj = obj.take(groupby_order)
+        return super()._create_data(obj, numeric_only)
+
+    def _gotitem(self, key, ndim, subset=None):
+        # we are setting the index on the actual object
+        # here so our index is carried through to the selected obj
+        # when we do the splitting for the groupby
+        if self.on is not None:
+            # GH 43355
+            subset = self.obj.set_index(self._on)
+        return super()._gotitem(key, ndim, subset=subset)
+
+
+class Window(BaseWindow):
+    """
+    Provide rolling window calculations.
+
+    Parameters
+    ----------
+    window : int, timedelta, str, offset, or BaseIndexer subclass
+        Size of the moving window.
+
+        If an integer, the fixed number of observations used for
+        each window.
+
+        If a timedelta, str, or offset, the time period of each window. Each
+        window will be a variable sized based on the observations included in
+        the time-period. This is only valid for datetimelike indexes.
+        To learn more about the offsets & frequency strings, please see `this link
+        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+        If a BaseIndexer subclass, the window boundaries
+        based on the defined ``get_window_bounds`` method. Additional rolling
+        keyword arguments, namely ``min_periods``, ``center``, ``closed`` and
+        ``step`` will be passed to ``get_window_bounds``.
+
+    min_periods : int, default None
+        Minimum number of observations in window required to have a value;
+        otherwise, result is ``np.nan``.
+
+        For a window that is specified by an offset, ``min_periods`` will default to 1.
+
+        For a window that is specified by an integer, ``min_periods`` will default
+        to the size of the window.
+
+    center : bool, default False
+        If False, set the window labels as the right edge of the window index.
+
+        If True, set the window labels as the center of the window index.
+
+    win_type : str, default None
+        If ``None``, all points are evenly weighted.
+
+        If a string, it must be a valid `scipy.signal window function
+        <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>`__.
+
+        Certain Scipy window types require additional parameters to be passed
+        in the aggregation function. The additional parameters must match
+        the keywords specified in the Scipy window type method signature.
+
+    on : str, optional
+        For a DataFrame, a column label or Index level on which
+        to calculate the rolling window, rather than the DataFrame's index.
+
+        Provided integer column is ignored and excluded from result since
+        an integer index is not used to calculate the rolling window.
+
+    axis : int or str, default 0
+        If ``0`` or ``'index'``, roll across the rows.
+
+        If ``1`` or ``'columns'``, roll across the columns.
+
+        For `Series` this parameter is unused and defaults to 0.
+
+        .. deprecated:: 2.1.0
+
+            The axis keyword is deprecated. For ``axis=1``,
+            transpose the DataFrame first instead.
+
+    closed : str, default None
+        If ``'right'``, the first point in the window is excluded from calculations.
+
+        If ``'left'``, the last point in the window is excluded from calculations.
+
+        If ``'both'``, the no points in the window are excluded from calculations.
+
+        If ``'neither'``, the first and last points in the window are excluded
+        from calculations.
+
+        Default ``None`` (``'right'``).
+
+    step : int, default None
+
+        .. versionadded:: 1.5.0
+
+        Evaluate the window at every ``step`` result, equivalent to slicing as
+        ``[::step]``. ``window`` must be an integer. Using a step argument other
+        than None or 1 will produce a result with a different shape than the input.
+
+    method : str {'single', 'table'}, default 'single'
+
+        .. versionadded:: 1.3.0
+
+        Execute the rolling operation per single column or row (``'single'``)
+        or over the entire object (``'table'``).
+
+        This argument is only implemented when specifying ``engine='numba'``
+        in the method call.
+
+    Returns
+    -------
+    pandas.api.typing.Window or pandas.api.typing.Rolling
+        An instance of Window is returned if ``win_type`` is passed. Otherwise,
+        an instance of Rolling is returned.
+
+    See Also
+    --------
+    expanding : Provides expanding transformations.
+    ewm : Provides exponential weighted functions.
+
+    Notes
+    -----
+    See :ref:`Windowing Operations <window.generic>` for further usage details
+    and examples.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    **window**
+
+    Rolling sum with a window length of 2 observations.
+
+    >>> df.rolling(2).sum()
+         B
+    0  NaN
+    1  1.0
+    2  3.0
+    3  NaN
+    4  NaN
+
+    Rolling sum with a window span of 2 seconds.
+
+    >>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
+    ...                        index=[pd.Timestamp('20130101 09:00:00'),
+    ...                               pd.Timestamp('20130101 09:00:02'),
+    ...                               pd.Timestamp('20130101 09:00:03'),
+    ...                               pd.Timestamp('20130101 09:00:05'),
+    ...                               pd.Timestamp('20130101 09:00:06')])
+
+    >>> df_time
+                           B
+    2013-01-01 09:00:00  0.0
+    2013-01-01 09:00:02  1.0
+    2013-01-01 09:00:03  2.0
+    2013-01-01 09:00:05  NaN
+    2013-01-01 09:00:06  4.0
+
+    >>> df_time.rolling('2s').sum()
+                           B
+    2013-01-01 09:00:00  0.0
+    2013-01-01 09:00:02  1.0
+    2013-01-01 09:00:03  3.0
+    2013-01-01 09:00:05  NaN
+    2013-01-01 09:00:06  4.0
+
+    Rolling sum with forward looking windows with 2 observations.
+
+    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
+    >>> df.rolling(window=indexer, min_periods=1).sum()
+         B
+    0  1.0
+    1  3.0
+    2  2.0
+    3  4.0
+    4  4.0
+
+    **min_periods**
+
+    Rolling sum with a window length of 2 observations, but only needs a minimum of 1
+    observation to calculate a value.
+
+    >>> df.rolling(2, min_periods=1).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  2.0
+    4  4.0
+
+    **center**
+
+    Rolling sum with the result assigned to the center of the window index.
+
+    >>> df.rolling(3, min_periods=1, center=True).sum()
+         B
+    0  1.0
+    1  3.0
+    2  3.0
+    3  6.0
+    4  4.0
+
+    >>> df.rolling(3, min_periods=1, center=False).sum()
+         B
+    0  0.0
+    1  1.0
+    2  3.0
+    3  3.0
+    4  6.0
+
+    **step**
+
+    Rolling sum with a window length of 2 observations, minimum of 1 observation to
+    calculate a value, and a step of 2.
+
+    >>> df.rolling(2, min_periods=1, step=2).sum()
+         B
+    0  0.0
+    2  3.0
+    4  4.0
+
+    **win_type**
+
+    Rolling sum with a window length of 2, using the Scipy ``'gaussian'``
+    window type. ``std`` is required in the aggregation function.
+
+    >>> df.rolling(2, win_type='gaussian').sum(std=3)
+              B
+    0       NaN
+    1  0.986207
+    2  2.958621
+    3       NaN
+    4       NaN
+
+    **on**
+
+    Rolling sum with a window length of 2 days.
+
+    >>> df = pd.DataFrame({
+    ...     'A': [pd.to_datetime('2020-01-01'),
+    ...           pd.to_datetime('2020-01-01'),
+    ...           pd.to_datetime('2020-01-02'),],
+    ...     'B': [1, 2, 3], },
+    ...     index=pd.date_range('2020', periods=3))
+
+    >>> df
+                        A  B
+    2020-01-01 2020-01-01  1
+    2020-01-02 2020-01-01  2
+    2020-01-03 2020-01-02  3
+
+    >>> df.rolling('2D', on='A').sum()
+                        A    B
+    2020-01-01 2020-01-01  1.0
+    2020-01-02 2020-01-01  3.0
+    2020-01-03 2020-01-02  6.0
+    """
+
+    _attributes = [
+        "window",
+        "min_periods",
+        "center",
+        "win_type",
+        "axis",
+        "on",
+        "closed",
+        "step",
+        "method",
+    ]
+
+    def _validate(self):
+        super()._validate()
+
+        if not isinstance(self.win_type, str):
+            raise ValueError(f"Invalid win_type {self.win_type}")
+        signal = import_optional_dependency(
+            "scipy.signal.windows", extra="Scipy is required to generate window weight."
+        )
+        self._scipy_weight_generator = getattr(signal, self.win_type, None)
+        if self._scipy_weight_generator is None:
+            raise ValueError(f"Invalid win_type {self.win_type}")
+
+        if isinstance(self.window, BaseIndexer):
+            raise NotImplementedError(
+                "BaseIndexer subclasses not implemented with win_types."
+            )
+        if not is_integer(self.window) or self.window < 0:
+            raise ValueError("window must be an integer 0 or greater")
+
+        if self.method != "single":
+            raise NotImplementedError("'single' is the only supported method type.")
+
+    def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray:
+        """
+        Center the result in the window for weighted rolling aggregations.
+        """
+        if offset > 0:
+            lead_indexer = [slice(offset, None)]
+            result = np.copy(result[tuple(lead_indexer)])
+        return result
+
+    def _apply(
+        self,
+        func: Callable[[np.ndarray, int, int], np.ndarray],
+        name: str,
+        numeric_only: bool = False,
+        numba_args: tuple[Any, ...] = (),
+        **kwargs,
+    ):
+        """
+        Rolling with weights statistical measure using supplied function.
+
+        Designed to be used with passed-in Cython array-based functions.
+
+        Parameters
+        ----------
+        func : callable function to apply
+        name : str,
+        numeric_only : bool, default False
+            Whether to only operate on bool, int, and float columns
+        numba_args : tuple
+            unused
+        **kwargs
+            additional arguments for scipy windows if necessary
+
+        Returns
+        -------
+        y : type of input
+        """
+        # "None" not callable  [misc]
+        window = self._scipy_weight_generator(  # type: ignore[misc]
+            self.window, **kwargs
+        )
+        offset = (len(window) - 1) // 2 if self.center else 0
+
+        def homogeneous_func(values: np.ndarray):
+            # calculation function
+
+            if values.size == 0:
+                return values.copy()
+
+            def calc(x):
+                additional_nans = np.array([np.nan] * offset)
+                x = np.concatenate((x, additional_nans))
+                return func(
+                    x,
+                    window,
+                    self.min_periods if self.min_periods is not None else len(window),
+                )
+
+            with np.errstate(all="ignore"):
+                # Our weighted aggregations return memoryviews
+                result = np.asarray(calc(values))
+
+            if self.center:
+                result = self._center_window(result, offset)
+
+            return result
+
+        return self._apply_columnwise(homogeneous_func, name, numeric_only)[
+            :: self.step
+        ]
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.DataFrame.aggregate : Similar DataFrame method.
+        pandas.Series.aggregate : Similar Series method.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.rolling(2, win_type="boxcar").agg("mean")
+             A    B    C
+        0  NaN  NaN  NaN
+        1  1.5  4.5  7.5
+        2  2.5  5.5  8.5
+        """
+        ),
+        klass="Series/DataFrame",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if result is None:
+            # these must apply directly
+            result = func(self)
+
+        return result
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        <class 'pandas.core.window.rolling.Window'>
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method
+        (`sum` in this case):
+
+        >>> ser.rolling(2, win_type='gaussian').sum(std=3)
+        0         NaN
+        1    0.986207
+        2    5.917243
+        3    6.903450
+        4    9.862071
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window sum",
+        agg_method="sum",
+    )
+    def sum(self, numeric_only: bool = False, **kwargs):
+        window_func = window_aggregations.roll_weighted_sum
+        # error: Argument 1 to "_apply" of "Window" has incompatible type
+        # "Callable[[ndarray, ndarray, int], ndarray]"; expected
+        # "Callable[[ndarray, int, int], ndarray]"
+        return self._apply(
+            window_func,  # type: ignore[arg-type]
+            name="sum",
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        <class 'pandas.core.window.rolling.Window'>
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method:
+
+        >>> ser.rolling(2, win_type='gaussian').mean(std=3)
+        0    NaN
+        1    0.5
+        2    3.0
+        3    3.5
+        4    5.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window mean",
+        agg_method="mean",
+    )
+    def mean(self, numeric_only: bool = False, **kwargs):
+        window_func = window_aggregations.roll_weighted_mean
+        # error: Argument 1 to "_apply" of "Window" has incompatible type
+        # "Callable[[ndarray, ndarray, int], ndarray]"; expected
+        # "Callable[[ndarray, int, int], ndarray]"
+        return self._apply(
+            window_func,  # type: ignore[arg-type]
+            name="mean",
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        <class 'pandas.core.window.rolling.Window'>
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method:
+
+        >>> ser.rolling(2, win_type='gaussian').var(std=3)
+        0     NaN
+        1     0.5
+        2     8.0
+        3     4.5
+        4    18.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window variance",
+        agg_method="var",
+    )
+    def var(self, ddof: int = 1, numeric_only: bool = False, **kwargs):
+        window_func = partial(window_aggregations.roll_weighted_var, ddof=ddof)
+        kwargs.pop("name", None)
+        return self._apply(window_func, name="var", numeric_only=numeric_only, **kwargs)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        kwargs_scipy,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([0, 1, 5, 2, 8])
+
+        To get an instance of :class:`~pandas.core.window.rolling.Window` we need
+        to pass the parameter `win_type`.
+
+        >>> type(ser.rolling(2, win_type='gaussian'))
+        <class 'pandas.core.window.rolling.Window'>
+
+        In order to use the `SciPy` Gaussian window we need to provide the parameters
+        `M` and `std`. The parameter `M` corresponds to 2 in our example.
+        We pass the second parameter `std` as a parameter of the following method:
+
+        >>> ser.rolling(2, win_type='gaussian').std(std=3)
+        0         NaN
+        1    0.707107
+        2    2.828427
+        3    2.121320
+        4    4.242641
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="weighted window standard deviation",
+        agg_method="std",
+    )
+    def std(self, ddof: int = 1, numeric_only: bool = False, **kwargs):
+        return zsqrt(
+            self.var(ddof=ddof, name="std", numeric_only=numeric_only, **kwargs)
+        )
+
+
+class RollingAndExpandingMixin(BaseWindow):
+    def count(self, numeric_only: bool = False):
+        window_func = window_aggregations.roll_sum
+        return self._apply(window_func, name="count", numeric_only=numeric_only)
+
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        if args is None:
+            args = ()
+        if kwargs is None:
+            kwargs = {}
+
+        if not is_bool(raw):
+            raise ValueError("raw parameter must be `True` or `False`")
+
+        numba_args: tuple[Any, ...] = ()
+        if maybe_use_numba(engine):
+            if raw is False:
+                raise ValueError("raw must be `True` when using the numba engine")
+            numba_args = args
+            if self.method == "single":
+                apply_func = generate_numba_apply_func(
+                    func, **get_jit_arguments(engine_kwargs, kwargs)
+                )
+            else:
+                apply_func = generate_numba_table_func(
+                    func, **get_jit_arguments(engine_kwargs, kwargs)
+                )
+        elif engine in ("cython", None):
+            if engine_kwargs is not None:
+                raise ValueError("cython engine does not accept engine_kwargs")
+            apply_func = self._generate_cython_apply_func(args, kwargs, raw, func)
+        else:
+            raise ValueError("engine must be either 'numba' or 'cython'")
+
+        return self._apply(
+            apply_func,
+            name="apply",
+            numba_args=numba_args,
+        )
+
+    def _generate_cython_apply_func(
+        self,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+        raw: bool | np.bool_,
+        function: Callable[..., Any],
+    ) -> Callable[[np.ndarray, np.ndarray, np.ndarray, int], np.ndarray]:
+        from pandas import Series
+
+        window_func = partial(
+            window_aggregations.roll_apply,
+            args=args,
+            kwargs=kwargs,
+            raw=raw,
+            function=function,
+        )
+
+        def apply_func(values, begin, end, min_periods, raw=raw):
+            if not raw:
+                # GH 45912
+                values = Series(values, index=self._on, copy=False)
+            return window_func(values, begin, end, min_periods)
+
+        return apply_func
+
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nansum)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_sum
+
+                return self._numba_apply(sliding_sum, engine_kwargs)
+        window_func = window_aggregations.roll_sum
+        return self._apply(window_func, name="sum", numeric_only=numeric_only)
+
+    def max(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmax)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_min_max
+
+                return self._numba_apply(sliding_min_max, engine_kwargs, is_max=True)
+        window_func = window_aggregations.roll_max
+        return self._apply(window_func, name="max", numeric_only=numeric_only)
+
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmin)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_min_max
+
+                return self._numba_apply(sliding_min_max, engine_kwargs, is_max=False)
+        window_func = window_aggregations.roll_min
+        return self._apply(window_func, name="min", numeric_only=numeric_only)
+
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmean)
+                return self.apply(
+                    func,
+                    raw=True,
+                    engine=engine,
+                    engine_kwargs=engine_kwargs,
+                )
+            else:
+                from pandas.core._numba.kernels import sliding_mean
+
+                return self._numba_apply(sliding_mean, engine_kwargs)
+        window_func = window_aggregations.roll_mean
+        return self._apply(window_func, name="mean", numeric_only=numeric_only)
+
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmedian)
+            else:
+                func = np.nanmedian
+
+            return self.apply(
+                func,
+                raw=True,
+                engine=engine,
+                engine_kwargs=engine_kwargs,
+            )
+        window_func = window_aggregations.roll_median_c
+        return self._apply(window_func, name="median", numeric_only=numeric_only)
+
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                raise NotImplementedError("std not supported with method='table'")
+            from pandas.core._numba.kernels import sliding_var
+
+            return zsqrt(self._numba_apply(sliding_var, engine_kwargs, ddof=ddof))
+        window_func = window_aggregations.roll_var
+
+        def zsqrt_func(values, begin, end, min_periods):
+            return zsqrt(window_func(values, begin, end, min_periods, ddof=ddof))
+
+        return self._apply(
+            zsqrt_func,
+            name="std",
+            numeric_only=numeric_only,
+        )
+
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        if maybe_use_numba(engine):
+            if self.method == "table":
+                raise NotImplementedError("var not supported with method='table'")
+            from pandas.core._numba.kernels import sliding_var
+
+            return self._numba_apply(sliding_var, engine_kwargs, ddof=ddof)
+        window_func = partial(window_aggregations.roll_var, ddof=ddof)
+        return self._apply(
+            window_func,
+            name="var",
+            numeric_only=numeric_only,
+        )
+
+    def skew(self, numeric_only: bool = False):
+        window_func = window_aggregations.roll_skew
+        return self._apply(
+            window_func,
+            name="skew",
+            numeric_only=numeric_only,
+        )
+
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        # Raise here so error message says sem instead of std
+        self._validate_numeric_only("sem", numeric_only)
+        return self.std(numeric_only=numeric_only) / (
+            self.count(numeric_only=numeric_only) - ddof
+        ).pow(0.5)
+
+    def kurt(self, numeric_only: bool = False):
+        window_func = window_aggregations.roll_kurt
+        return self._apply(
+            window_func,
+            name="kurt",
+            numeric_only=numeric_only,
+        )
+
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        if q == 1.0:
+            window_func = window_aggregations.roll_max
+        elif q == 0.0:
+            window_func = window_aggregations.roll_min
+        else:
+            window_func = partial(
+                window_aggregations.roll_quantile,
+                quantile=q,
+                interpolation=interpolation,
+            )
+
+        return self._apply(window_func, name="quantile", numeric_only=numeric_only)
+
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        window_func = partial(
+            window_aggregations.roll_rank,
+            method=method,
+            ascending=ascending,
+            percentile=pct,
+        )
+
+        return self._apply(window_func, name="rank", numeric_only=numeric_only)
+
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        if self.step is not None:
+            raise NotImplementedError("step not implemented for cov")
+        self._validate_numeric_only("cov", numeric_only)
+
+        from pandas import Series
+
+        def cov_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+            self._check_window_bounds(start, end, len(x_array))
+
+            with np.errstate(all="ignore"):
+                mean_x_y = window_aggregations.roll_mean(
+                    x_array * y_array, start, end, min_periods
+                )
+                mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods)
+                mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods)
+                count_x_y = window_aggregations.roll_sum(
+                    notna(x_array + y_array).astype(np.float64), start, end, 0
+                )
+                result = (mean_x_y - mean_x * mean_y) * (count_x_y / (count_x_y - ddof))
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, cov_func, numeric_only
+        )
+
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        if self.step is not None:
+            raise NotImplementedError("step not implemented for corr")
+        self._validate_numeric_only("corr", numeric_only)
+
+        from pandas import Series
+
+        def corr_func(x, y):
+            x_array = self._prep_values(x)
+            y_array = self._prep_values(y)
+            window_indexer = self._get_window_indexer()
+            min_periods = (
+                self.min_periods
+                if self.min_periods is not None
+                else window_indexer.window_size
+            )
+            start, end = window_indexer.get_window_bounds(
+                num_values=len(x_array),
+                min_periods=min_periods,
+                center=self.center,
+                closed=self.closed,
+                step=self.step,
+            )
+            self._check_window_bounds(start, end, len(x_array))
+
+            with np.errstate(all="ignore"):
+                mean_x_y = window_aggregations.roll_mean(
+                    x_array * y_array, start, end, min_periods
+                )
+                mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods)
+                mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods)
+                count_x_y = window_aggregations.roll_sum(
+                    notna(x_array + y_array).astype(np.float64), start, end, 0
+                )
+                x_var = window_aggregations.roll_var(
+                    x_array, start, end, min_periods, ddof
+                )
+                y_var = window_aggregations.roll_var(
+                    y_array, start, end, min_periods, ddof
+                )
+                numerator = (mean_x_y - mean_x * mean_y) * (
+                    count_x_y / (count_x_y - ddof)
+                )
+                denominator = (x_var * y_var) ** 0.5
+                result = numerator / denominator
+            return Series(result, index=x.index, name=x.name, copy=False)
+
+        return self._apply_pairwise(
+            self._selected_obj, other, pairwise, corr_func, numeric_only
+        )
+
+
+class Rolling(RollingAndExpandingMixin):
+    _attributes: list[str] = [
+        "window",
+        "min_periods",
+        "center",
+        "win_type",
+        "axis",
+        "on",
+        "closed",
+        "step",
+        "method",
+    ]
+
+    def _validate(self):
+        super()._validate()
+
+        # we allow rolling on a datetimelike index
+        if (
+            self.obj.empty
+            or isinstance(self._on, (DatetimeIndex, TimedeltaIndex, PeriodIndex))
+            or (isinstance(self._on.dtype, ArrowDtype) and self._on.dtype.kind in "mM")
+        ) and isinstance(self.window, (str, BaseOffset, timedelta)):
+            self._validate_datetimelike_monotonic()
+
+            # this will raise ValueError on non-fixed freqs
+            try:
+                freq = to_offset(self.window)
+            except (TypeError, ValueError) as err:
+                raise ValueError(
+                    f"passed window {self.window} is not "
+                    "compatible with a datetimelike index"
+                ) from err
+            if isinstance(self._on, PeriodIndex):
+                # error: Incompatible types in assignment (expression has type
+                # "float", variable has type "Optional[int]")
+                self._win_freq_i8 = freq.nanos / (  # type: ignore[assignment]
+                    self._on.freq.nanos / self._on.freq.n
+                )
+            else:
+                try:
+                    unit = dtype_to_unit(self._on.dtype)  # type: ignore[arg-type]
+                except TypeError:
+                    # if not a datetime dtype, eg for empty dataframes
+                    unit = "ns"
+                self._win_freq_i8 = Timedelta(freq.nanos).as_unit(unit)._value
+
+            # min_periods must be an integer
+            if self.min_periods is None:
+                self.min_periods = 1
+
+            if self.step is not None:
+                raise NotImplementedError(
+                    "step is not supported with frequency windows"
+                )
+
+        elif isinstance(self.window, BaseIndexer):
+            # Passed BaseIndexer subclass should handle all other rolling kwargs
+            pass
+        elif not is_integer(self.window) or self.window < 0:
+            raise ValueError("window must be an integer 0 or greater")
+
+    def _validate_datetimelike_monotonic(self) -> None:
+        """
+        Validate self._on is monotonic (increasing or decreasing) and has
+        no NaT values for frequency windows.
+        """
+        if self._on.hasnans:
+            self._raise_monotonic_error("values must not have NaT")
+        if not (self._on.is_monotonic_increasing or self._on.is_monotonic_decreasing):
+            self._raise_monotonic_error("values must be monotonic")
+
+    def _raise_monotonic_error(self, msg: str):
+        on = self.on
+        if on is None:
+            if self.axis == 0:
+                on = "index"
+            else:
+                on = "column"
+        raise ValueError(f"{on} {msg}")
+
+    @doc(
+        _shared_docs["aggregate"],
+        see_also=dedent(
+            """
+        See Also
+        --------
+        pandas.Series.rolling : Calling object with Series data.
+        pandas.DataFrame.rolling : Calling object with DataFrame data.
+        """
+        ),
+        examples=dedent(
+            """
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+        >>> df
+           A  B  C
+        0  1  4  7
+        1  2  5  8
+        2  3  6  9
+
+        >>> df.rolling(2).sum()
+             A     B     C
+        0  NaN   NaN   NaN
+        1  3.0   9.0  15.0
+        2  5.0  11.0  17.0
+
+        >>> df.rolling(2).agg({"A": "sum", "B": "min"})
+             A    B
+        0  NaN  NaN
+        1  3.0  4.0
+        2  5.0  5.0
+        """
+        ),
+        klass="Series/Dataframe",
+        axis="",
+    )
+    def aggregate(self, func, *args, **kwargs):
+        return super().aggregate(func, *args, **kwargs)
+
+    agg = aggregate
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([2, 3, np.nan, 10])
+        >>> s.rolling(2).count()
+        0    NaN
+        1    2.0
+        2    1.0
+        3    1.0
+        dtype: float64
+        >>> s.rolling(3).count()
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        dtype: float64
+        >>> s.rolling(4).count()
+        0    NaN
+        1    NaN
+        2    NaN
+        3    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="count of non NaN observations",
+        agg_method="count",
+    )
+    def count(self, numeric_only: bool = False):
+        return super().count(numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        window_apply_parameters,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 6, 5, 4])
+        >>> ser.rolling(2).apply(lambda s: s.sum() - s.min())
+        0    NaN
+        1    6.0
+        2    6.0
+        3    5.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="custom aggregation function",
+        agg_method="apply",
+    )
+    def apply(
+        self,
+        func: Callable[..., Any],
+        raw: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        return super().apply(
+            func,
+            raw=raw,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+            args=args,
+            kwargs=kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 2, 3, 4, 5])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        4    5
+        dtype: int64
+
+        >>> s.rolling(3).sum()
+        0     NaN
+        1     NaN
+        2     6.0
+        3     9.0
+        4    12.0
+        dtype: float64
+
+        >>> s.rolling(3, center=True).sum()
+        0     NaN
+        1     6.0
+        2     9.0
+        3    12.0
+        4     NaN
+        dtype: float64
+
+        For DataFrame, each sum is computed column-wise.
+
+        >>> df = pd.DataFrame({{"A": s, "B": s ** 2}})
+        >>> df
+           A   B
+        0  1   1
+        1  2   4
+        2  3   9
+        3  4  16
+        4  5  25
+
+        >>> df.rolling(3).sum()
+              A     B
+        0   NaN   NaN
+        1   NaN   NaN
+        2   6.0  14.0
+        3   9.0  29.0
+        4  12.0  50.0
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="sum",
+        agg_method="sum",
+    )
+    def sum(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().sum(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 2, 3, 4])
+        >>> ser.rolling(2).max()
+        0    NaN
+        1    2.0
+        2    3.0
+        3    4.0
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="maximum",
+        agg_method="max",
+    )
+    def max(
+        self,
+        numeric_only: bool = False,
+        *args,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        **kwargs,
+    ):
+        return super().max(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        Performing a rolling minimum with a window size of 3.
+
+        >>> s = pd.Series([4, 3, 5, 2, 6])
+        >>> s.rolling(3).min()
+        0    NaN
+        1    NaN
+        2    3.0
+        3    2.0
+        4    2.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="minimum",
+        agg_method="min",
+    )
+    def min(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().min(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        The below examples will show rolling mean calculations with window sizes of
+        two and three, respectively.
+
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.rolling(2).mean()
+        0    NaN
+        1    1.5
+        2    2.5
+        3    3.5
+        dtype: float64
+
+        >>> s.rolling(3).mean()
+        0    NaN
+        1    NaN
+        2    2.0
+        3    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="mean",
+        agg_method="mean",
+    )
+    def mean(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().mean(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        window_agg_numba_parameters(),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        numba_notes,
+        create_section_header("Examples"),
+        dedent(
+            """
+        Compute the rolling median of a series with a window size of 3.
+
+        >>> s = pd.Series([0, 1, 2, 3, 4])
+        >>> s.rolling(3).median()
+        0    NaN
+        1    NaN
+        2    1.0
+        3    2.0
+        4    3.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="median",
+        agg_method="median",
+    )
+    def median(
+        self,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().median(
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.std : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.std` is different
+        than the default ``ddof`` of 0 in :func:`numpy.std`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+        >>> s.rolling(3).std()
+        0         NaN
+        1         NaN
+        2    0.577350
+        3    1.000000
+        4    1.000000
+        5    1.154701
+        6    0.000000
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="standard deviation",
+        agg_method="std",
+    )
+    def std(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().std(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        window_agg_numba_parameters("1.4"),
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "numpy.var : Equivalent method for NumPy array.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        The default ``ddof`` of 1 used in :meth:`Series.var` is different
+        than the default ``ddof`` of 0 in :func:`numpy.var`.
+
+        A minimum of one period is required for the rolling calculation.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
+        >>> s.rolling(3).var()
+        0         NaN
+        1         NaN
+        2    0.333333
+        3    1.000000
+        4    1.000000
+        5    1.333333
+        6    0.000000
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="variance",
+        agg_method="var",
+    )
+    def var(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        engine: Literal["cython", "numba"] | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+    ):
+        return super().var(
+            ddof=ddof,
+            numeric_only=numeric_only,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.skew : Third moment of a probability density.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        A minimum of three periods is required for the rolling calculation.\n
+        """
+        ),
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser = pd.Series([1, 5, 2, 7, 15, 6])
+        >>> ser.rolling(3).skew().round(6)
+        0         NaN
+        1         NaN
+        2    1.293343
+        3   -0.585583
+        4    0.670284
+        5    1.652317
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="unbiased skewness",
+        agg_method="skew",
+    )
+    def skew(self, numeric_only: bool = False):
+        return super().skew(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of one period is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([0, 1, 2, 3])
+        >>> s.rolling(2, min_periods=1).sem()
+        0         NaN
+        1    0.707107
+        2    0.707107
+        3    0.707107
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="standard error of mean",
+        agg_method="sem",
+    )
+    def sem(self, ddof: int = 1, numeric_only: bool = False):
+        # Raise here so error message says sem instead of std
+        self._validate_numeric_only("sem", numeric_only)
+        return self.std(numeric_only=numeric_only) / (
+            self.count(numeric_only) - ddof
+        ).pow(0.5)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        "scipy.stats.kurtosis : Reference SciPy method.\n",
+        template_see_also,
+        create_section_header("Notes"),
+        "A minimum of four periods is required for the calculation.\n\n",
+        create_section_header("Examples"),
+        dedent(
+            """
+        The example below will show a rolling calculation with a window size of
+        four matching the equivalent function call using `scipy.stats`.
+
+        >>> arr = [1, 2, 3, 4, 999]
+        >>> import scipy.stats
+        >>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}")
+        -1.200000
+        >>> print(f"{{scipy.stats.kurtosis(arr[1:], bias=False):.6f}}")
+        3.999946
+        >>> s = pd.Series(arr)
+        >>> s.rolling(4).kurt()
+        0         NaN
+        1         NaN
+        2         NaN
+        3   -1.200000
+        4    3.999946
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="Fisher's definition of kurtosis without bias",
+        agg_method="kurt",
+    )
+    def kurt(self, numeric_only: bool = False):
+        return super().kurt(numeric_only=numeric_only)
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        quantile : float
+            Quantile to compute. 0 <= quantile <= 1.
+
+            .. deprecated:: 2.1.0
+                This will be renamed to 'q' in a future version.
+        interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points `i` and `j`:
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 2, 3, 4])
+        >>> s.rolling(2).quantile(.4, interpolation='lower')
+        0    NaN
+        1    1.0
+        2    2.0
+        3    3.0
+        dtype: float64
+
+        >>> s.rolling(2).quantile(.4, interpolation='midpoint')
+        0    NaN
+        1    1.5
+        2    2.5
+        3    3.5
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="quantile",
+        agg_method="quantile",
+    )
+    @deprecate_kwarg(old_arg_name="quantile", new_arg_name="q")
+    def quantile(
+        self,
+        q: float,
+        interpolation: QuantileInterpolation = "linear",
+        numeric_only: bool = False,
+    ):
+        return super().quantile(
+            q=q,
+            interpolation=interpolation,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        ".. versionadded:: 1.4.0 \n\n",
+        create_section_header("Parameters"),
+        dedent(
+            """
+        method : {{'average', 'min', 'max'}}, default 'average'
+            How to rank the group of records that have the same value (i.e. ties):
+
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """
+        >>> s = pd.Series([1, 4, 2, 3, 5, 3])
+        >>> s.rolling(3).rank()
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        4    3.0
+        5    1.5
+        dtype: float64
+
+        >>> s.rolling(3).rank(method="max")
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        4    3.0
+        5    2.0
+        dtype: float64
+
+        >>> s.rolling(3).rank(method="min")
+        0    NaN
+        1    NaN
+        2    2.0
+        3    2.0
+        4    3.0
+        5    1.0
+        dtype: float64
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="rank",
+        agg_method="rank",
+    )
+    def rank(
+        self,
+        method: WindowingRankType = "average",
+        ascending: bool = True,
+        pct: bool = False,
+        numeric_only: bool = False,
+    ):
+        return super().rank(
+            method=method,
+            ascending=ascending,
+            pct=pct,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        template_see_also,
+        create_section_header("Examples"),
+        dedent(
+            """\
+        >>> ser1 = pd.Series([1, 2, 3, 4])
+        >>> ser2 = pd.Series([1, 4, 5, 8])
+        >>> ser1.rolling(2).cov(ser2)
+        0    NaN
+        1    1.5
+        2    0.5
+        3    1.5
+        dtype: float64
+        """
+        ),
+        window_method="rolling",
+        aggregation_description="sample covariance",
+        agg_method="cov",
+    )
+    def cov(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().cov(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+    @doc(
+        template_header,
+        create_section_header("Parameters"),
+        dedent(
+            """
+        other : Series or DataFrame, optional
+            If not supplied then will default to self and produce pairwise
+            output.
+        pairwise : bool, default None
+            If False then only matching columns between self and other will be
+            used and the output will be a DataFrame.
+            If True then all pairwise combinations will be calculated and the
+            output will be a MultiIndexed DataFrame in the case of DataFrame
+            inputs. In the case of missing elements, only complete pairwise
+            observations will be used.
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of elements.
+        """
+        ).replace("\n", "", 1),
+        kwargs_numeric_only,
+        create_section_header("Returns"),
+        template_returns,
+        create_section_header("See Also"),
+        dedent(
+            """
+        cov : Similar method to calculate covariance.
+        numpy.corrcoef : NumPy Pearson's correlation calculation.
+        """
+        ).replace("\n", "", 1),
+        template_see_also,
+        create_section_header("Notes"),
+        dedent(
+            """
+        This function uses Pearson's definition of correlation
+        (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
+
+        When `other` is not specified, the output will be self correlation (e.g.
+        all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
+        set to `True`.
+
+        Function will return ``NaN`` for correlations of equal valued sequences;
+        this is the result of a 0/0 division error.
+
+        When `pairwise` is set to `False`, only matching columns between `self` and
+        `other` will be used.
+
+        When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
+        with the original index on the first level, and the `other` DataFrame
+        columns on the second level.
+
+        In the case of missing elements, only complete pairwise observations
+        will be used.\n
+        """
+        ).replace("\n", "", 1),
+        create_section_header("Examples"),
+        dedent(
+            """
+        The below example shows a rolling calculation with a window size of
+        four matching the equivalent function call using :meth:`numpy.corrcoef`.
+
+        >>> v1 = [3, 3, 3, 5, 8]
+        >>> v2 = [3, 4, 4, 4, 8]
+        >>> np.corrcoef(v1[:-1], v2[:-1])
+        array([[1.        , 0.33333333],
+               [0.33333333, 1.        ]])
+        >>> np.corrcoef(v1[1:], v2[1:])
+        array([[1.       , 0.9169493],
+               [0.9169493, 1.       ]])
+        >>> s1 = pd.Series(v1)
+        >>> s2 = pd.Series(v2)
+        >>> s1.rolling(4).corr(s2)
+        0         NaN
+        1         NaN
+        2         NaN
+        3    0.333333
+        4    0.916949
+        dtype: float64
+
+        The below example shows a similar rolling calculation on a
+        DataFrame using the pairwise option.
+
+        >>> matrix = np.array([[51., 35.],
+        ...                    [49., 30.],
+        ...                    [47., 32.],
+        ...                    [46., 31.],
+        ...                    [50., 36.]])
+        >>> np.corrcoef(matrix[:-1, 0], matrix[:-1, 1])
+        array([[1.       , 0.6263001],
+               [0.6263001, 1.       ]])
+        >>> np.corrcoef(matrix[1:, 0], matrix[1:, 1])
+        array([[1.        , 0.55536811],
+               [0.55536811, 1.        ]])
+        >>> df = pd.DataFrame(matrix, columns=['X', 'Y'])
+        >>> df
+              X     Y
+        0  51.0  35.0
+        1  49.0  30.0
+        2  47.0  32.0
+        3  46.0  31.0
+        4  50.0  36.0
+        >>> df.rolling(4).corr(pairwise=True)
+                    X         Y
+        0 X       NaN       NaN
+          Y       NaN       NaN
+        1 X       NaN       NaN
+          Y       NaN       NaN
+        2 X       NaN       NaN
+          Y       NaN       NaN
+        3 X  1.000000  0.626300
+          Y  0.626300  1.000000
+        4 X  1.000000  0.555368
+          Y  0.555368  1.000000
+        """
+        ).replace("\n", "", 1),
+        window_method="rolling",
+        aggregation_description="correlation",
+        agg_method="corr",
+    )
+    def corr(
+        self,
+        other: DataFrame | Series | None = None,
+        pairwise: bool | None = None,
+        ddof: int = 1,
+        numeric_only: bool = False,
+    ):
+        return super().corr(
+            other=other,
+            pairwise=pairwise,
+            ddof=ddof,
+            numeric_only=numeric_only,
+        )
+
+
+Rolling.__doc__ = Window.__doc__
+
+
+class RollingGroupby(BaseWindowGroupby, Rolling):
+    """
+    Provide a rolling groupby implementation.
+    """
+
+    _attributes = Rolling._attributes + BaseWindowGroupby._attributes
+
+    def _get_window_indexer(self) -> GroupbyIndexer:
+        """
+        Return an indexer class that will compute the window start and end bounds
+
+        Returns
+        -------
+        GroupbyIndexer
+        """
+        rolling_indexer: type[BaseIndexer]
+        indexer_kwargs: dict[str, Any] | None = None
+        index_array = self._index_array
+        if isinstance(self.window, BaseIndexer):
+            rolling_indexer = type(self.window)
+            indexer_kwargs = self.window.__dict__.copy()
+            assert isinstance(indexer_kwargs, dict)  # for mypy
+            # We'll be using the index of each group later
+            indexer_kwargs.pop("index_array", None)
+            window = self.window
+        elif self._win_freq_i8 is not None:
+            rolling_indexer = VariableWindowIndexer
+            # error: Incompatible types in assignment (expression has type
+            # "int", variable has type "BaseIndexer")
+            window = self._win_freq_i8  # type: ignore[assignment]
+        else:
+            rolling_indexer = FixedWindowIndexer
+            window = self.window
+        window_indexer = GroupbyIndexer(
+            index_array=index_array,
+            window_size=window,
+            groupby_indices=self._grouper.indices,
+            window_indexer=rolling_indexer,
+            indexer_kwargs=indexer_kwargs,
+        )
+        return window_indexer
+
+    def _validate_datetimelike_monotonic(self):
+        """
+        Validate that each group in self._on is monotonic
+        """
+        # GH 46061
+        if self._on.hasnans:
+            self._raise_monotonic_error("values must not have NaT")
+        for group_indices in self._grouper.indices.values():
+            group_on = self._on.take(group_indices)
+            if not (
+                group_on.is_monotonic_increasing or group_on.is_monotonic_decreasing
+            ):
+                on = "index" if self.on is None else self.on
+                raise ValueError(
+                    f"Each group within {on} must be monotonic. "
+                    f"Sort the values in {on} first."
+                )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/errors/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/errors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..01094ba36b9dd5f3414c32a9a4f832b85902e021
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/errors/__init__.py
@@ -0,0 +1,850 @@
+"""
+Expose public exceptions & warnings
+"""
+from __future__ import annotations
+
+import ctypes
+
+from pandas._config.config import OptionError
+
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+)
+
+from pandas.util.version import InvalidVersion
+
+
+class IntCastingNaNError(ValueError):
+    """
+    Exception raised when converting (``astype``) an array with NaN to an integer type.
+
+    Examples
+    --------
+    >>> pd.DataFrame(np.array([[1, np.nan], [2, 3]]), dtype="i8")
+    Traceback (most recent call last):
+    IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer
+    """
+
+
+class NullFrequencyError(ValueError):
+    """
+    Exception raised when a ``freq`` cannot be null.
+
+    Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
+    ``PeriodIndex.shift``.
+
+    Examples
+    --------
+    >>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
+    >>> df.shift(2)
+    Traceback (most recent call last):
+    NullFrequencyError: Cannot shift with no freq
+    """
+
+
+class PerformanceWarning(Warning):
+    """
+    Warning raised when there is a possible performance impact.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"jim": [0, 0, 1, 1],
+    ...                    "joe": ["x", "x", "z", "y"],
+    ...                    "jolie": [1, 2, 3, 4]})
+    >>> df = df.set_index(["jim", "joe"])
+    >>> df
+              jolie
+    jim  joe
+    0    x    1
+         x    2
+    1    z    3
+         y    4
+    >>> df.loc[(1, 'z')]  # doctest: +SKIP
+    # PerformanceWarning: indexing past lexsort depth may impact performance.
+    df.loc[(1, 'z')]
+              jolie
+    jim  joe
+    1    z        3
+    """
+
+
+class UnsupportedFunctionCall(ValueError):
+    """
+    Exception raised when attempting to call a unsupported numpy function.
+
+    For example, ``np.cumsum(groupby_object)``.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"A": [0, 0, 1, 1],
+    ...                    "B": ["x", "x", "z", "y"],
+    ...                    "C": [1, 2, 3, 4]}
+    ...                   )
+    >>> np.cumsum(df.groupby(["A"]))
+    Traceback (most recent call last):
+    UnsupportedFunctionCall: numpy operations are not valid with groupby.
+    Use .groupby(...).cumsum() instead
+    """
+
+
+class UnsortedIndexError(KeyError):
+    """
+    Error raised when slicing a MultiIndex which has not been lexsorted.
+
+    Subclass of `KeyError`.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"cat": [0, 0, 1, 1],
+    ...                    "color": ["white", "white", "brown", "black"],
+    ...                    "lives": [4, 4, 3, 7]},
+    ...                   )
+    >>> df = df.set_index(["cat", "color"])
+    >>> df
+                lives
+    cat  color
+    0    white    4
+         white    4
+    1    brown    3
+         black    7
+    >>> df.loc[(0, "black"):(1, "white")]
+    Traceback (most recent call last):
+    UnsortedIndexError: 'Key length (2) was greater
+    than MultiIndex lexsort depth (1)'
+    """
+
+
+class ParserError(ValueError):
+    """
+    Exception that is raised by an error encountered in parsing file contents.
+
+    This is a generic error raised for errors encountered when functions like
+    `read_csv` or `read_html` are parsing contents of a file.
+
+    See Also
+    --------
+    read_csv : Read CSV (comma-separated) file into a DataFrame.
+    read_html : Read HTML table into a DataFrame.
+
+    Examples
+    --------
+    >>> data = '''a,b,c
+    ... cat,foo,bar
+    ... dog,foo,"baz'''
+    >>> from io import StringIO
+    >>> pd.read_csv(StringIO(data), skipfooter=1, engine='python')
+    Traceback (most recent call last):
+    ParserError: ',' expected after '"'. Error could possibly be due
+    to parsing errors in the skipped footer rows
+    """
+
+
+class DtypeWarning(Warning):
+    """
+    Warning raised when reading different dtypes in a column from a file.
+
+    Raised for a dtype incompatibility. This can happen whenever `read_csv`
+    or `read_table` encounter non-uniform dtypes in a column(s) of a given
+    CSV file.
+
+    See Also
+    --------
+    read_csv : Read CSV (comma-separated) file into a DataFrame.
+    read_table : Read general delimited file into a DataFrame.
+
+    Notes
+    -----
+    This warning is issued when dealing with larger files because the dtype
+    checking happens per chunk read.
+
+    Despite the warning, the CSV file is read with mixed types in a single
+    column which will be an object type. See the examples below to better
+    understand this issue.
+
+    Examples
+    --------
+    This example creates and reads a large CSV file with a column that contains
+    `int` and `str`.
+
+    >>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
+    ...                          ['1'] * 100000),
+    ...                    'b': ['b'] * 300000})  # doctest: +SKIP
+    >>> df.to_csv('test.csv', index=False)  # doctest: +SKIP
+    >>> df2 = pd.read_csv('test.csv')  # doctest: +SKIP
+    ... # DtypeWarning: Columns (0) have mixed types
+
+    Important to notice that ``df2`` will contain both `str` and `int` for the
+    same input, '1'.
+
+    >>> df2.iloc[262140, 0]  # doctest: +SKIP
+    '1'
+    >>> type(df2.iloc[262140, 0])  # doctest: +SKIP
+    <class 'str'>
+    >>> df2.iloc[262150, 0]  # doctest: +SKIP
+    1
+    >>> type(df2.iloc[262150, 0])  # doctest: +SKIP
+    <class 'int'>
+
+    One way to solve this issue is using the `dtype` parameter in the
+    `read_csv` and `read_table` functions to explicit the conversion:
+
+    >>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str})  # doctest: +SKIP
+
+    No warning was issued.
+    """
+
+
+class EmptyDataError(ValueError):
+    """
+    Exception raised in ``pd.read_csv`` when empty data or header is encountered.
+
+    Examples
+    --------
+    >>> from io import StringIO
+    >>> empty = StringIO()
+    >>> pd.read_csv(empty)
+    Traceback (most recent call last):
+    EmptyDataError: No columns to parse from file
+    """
+
+
+class ParserWarning(Warning):
+    """
+    Warning raised when reading a file that doesn't use the default 'c' parser.
+
+    Raised by `pd.read_csv` and `pd.read_table` when it is necessary to change
+    parsers, generally from the default 'c' parser to 'python'.
+
+    It happens due to a lack of support or functionality for parsing a
+    particular attribute of a CSV file with the requested engine.
+
+    Currently, 'c' unsupported options include the following parameters:
+
+    1. `sep` other than a single character (e.g. regex separators)
+    2. `skipfooter` higher than 0
+    3. `sep=None` with `delim_whitespace=False`
+
+    The warning can be avoided by adding `engine='python'` as a parameter in
+    `pd.read_csv` and `pd.read_table` methods.
+
+    See Also
+    --------
+    pd.read_csv : Read CSV (comma-separated) file into DataFrame.
+    pd.read_table : Read general delimited file into DataFrame.
+
+    Examples
+    --------
+    Using a `sep` in `pd.read_csv` other than a single character:
+
+    >>> import io
+    >>> csv = '''a;b;c
+    ...           1;1,8
+    ...           1;2,1'''
+    >>> df = pd.read_csv(io.StringIO(csv), sep='[;,]')  # doctest: +SKIP
+    ... # ParserWarning: Falling back to the 'python' engine...
+
+    Adding `engine='python'` to `pd.read_csv` removes the Warning:
+
+    >>> df = pd.read_csv(io.StringIO(csv), sep='[;,]', engine='python')
+    """
+
+
+class MergeError(ValueError):
+    """
+    Exception raised when merging data.
+
+    Subclass of ``ValueError``.
+
+    Examples
+    --------
+    >>> left = pd.DataFrame({"a": ["a", "b", "b", "d"],
+    ...                     "b": ["cat", "dog", "weasel", "horse"]},
+    ...                     index=range(4))
+    >>> right = pd.DataFrame({"a": ["a", "b", "c", "d"],
+    ...                      "c": ["meow", "bark", "chirp", "nay"]},
+    ...                      index=range(4)).set_index("a")
+    >>> left.join(right, on="a", validate="one_to_one",)
+    Traceback (most recent call last):
+    MergeError: Merge keys are not unique in left dataset; not a one-to-one merge
+    """
+
+
+class AbstractMethodError(NotImplementedError):
+    """
+    Raise this error instead of NotImplementedError for abstract methods.
+
+    Examples
+    --------
+    >>> class Foo:
+    ...     @classmethod
+    ...     def classmethod(cls):
+    ...         raise pd.errors.AbstractMethodError(cls, methodtype="classmethod")
+    ...     def method(self):
+    ...         raise pd.errors.AbstractMethodError(self)
+    >>> test = Foo.classmethod()
+    Traceback (most recent call last):
+    AbstractMethodError: This classmethod must be defined in the concrete class Foo
+
+    >>> test2 = Foo().method()
+    Traceback (most recent call last):
+    AbstractMethodError: This classmethod must be defined in the concrete class Foo
+    """
+
+    def __init__(self, class_instance, methodtype: str = "method") -> None:
+        types = {"method", "classmethod", "staticmethod", "property"}
+        if methodtype not in types:
+            raise ValueError(
+                f"methodtype must be one of {methodtype}, got {types} instead."
+            )
+        self.methodtype = methodtype
+        self.class_instance = class_instance
+
+    def __str__(self) -> str:
+        if self.methodtype == "classmethod":
+            name = self.class_instance.__name__
+        else:
+            name = type(self.class_instance).__name__
+        return f"This {self.methodtype} must be defined in the concrete class {name}"
+
+
+class NumbaUtilError(Exception):
+    """
+    Error raised for unsupported Numba engine routines.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"key": ["a", "a", "b", "b"], "data": [1, 2, 3, 4]},
+    ...                   columns=["key", "data"])
+    >>> def incorrect_function(x):
+    ...     return sum(x) * 2.7
+    >>> df.groupby("key").agg(incorrect_function, engine="numba")
+    Traceback (most recent call last):
+    NumbaUtilError: The first 2 arguments to incorrect_function
+    must be ['values', 'index']
+    """
+
+
+class DuplicateLabelError(ValueError):
+    """
+    Error raised when an operation would introduce duplicate labels.
+
+    Examples
+    --------
+    >>> s = pd.Series([0, 1, 2], index=['a', 'b', 'c']).set_flags(
+    ...     allows_duplicate_labels=False
+    ... )
+    >>> s.reindex(['a', 'a', 'b'])
+    Traceback (most recent call last):
+       ...
+    DuplicateLabelError: Index has duplicates.
+          positions
+    label
+    a        [0, 1]
+    """
+
+
+class InvalidIndexError(Exception):
+    """
+    Exception raised when attempting to use an invalid index key.
+
+    Examples
+    --------
+    >>> idx = pd.MultiIndex.from_product([["x", "y"], [0, 1]])
+    >>> df = pd.DataFrame([[1, 1, 2, 2],
+    ...                   [3, 3, 4, 4]], columns=idx)
+    >>> df
+        x       y
+        0   1   0   1
+    0   1   1   2   2
+    1   3   3   4   4
+    >>> df[:, 0]
+    Traceback (most recent call last):
+    InvalidIndexError: (slice(None, None, None), 0)
+    """
+
+
+class DataError(Exception):
+    """
+    Exceptionn raised when performing an operation on non-numerical data.
+
+    For example, calling ``ohlc`` on a non-numerical column or a function
+    on a rolling window.
+
+    Examples
+    --------
+    >>> ser = pd.Series(['a', 'b', 'c'])
+    >>> ser.rolling(2).sum()
+    Traceback (most recent call last):
+    DataError: No numeric types to aggregate
+    """
+
+
+class SpecificationError(Exception):
+    """
+    Exception raised by ``agg`` when the functions are ill-specified.
+
+    The exception raised in two scenarios.
+
+    The first way is calling ``agg`` on a
+    Dataframe or Series using a nested renamer (dict-of-dict).
+
+    The second way is calling ``agg`` on a Dataframe with duplicated functions
+    names without assigning column name.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
+    ...                    'B': range(5),
+    ...                    'C': range(5)})
+    >>> df.groupby('A').B.agg({'foo': 'count'}) # doctest: +SKIP
+    ... # SpecificationError: nested renamer is not supported
+
+    >>> df.groupby('A').agg({'B': {'foo': ['sum', 'max']}}) # doctest: +SKIP
+    ... # SpecificationError: nested renamer is not supported
+
+    >>> df.groupby('A').agg(['min', 'min']) # doctest: +SKIP
+    ... # SpecificationError: nested renamer is not supported
+    """
+
+
+class SettingWithCopyError(ValueError):
+    """
+    Exception raised when trying to set on a copied slice from a ``DataFrame``.
+
+    The ``mode.chained_assignment`` needs to be set to set to 'raise.' This can
+    happen unintentionally when chained indexing.
+
+    For more information on evaluation order,
+    see :ref:`the user guide<indexing.evaluation_order>`.
+
+    For more information on view vs. copy,
+    see :ref:`the user guide<indexing.view_versus_copy>`.
+
+    Examples
+    --------
+    >>> pd.options.mode.chained_assignment = 'raise'
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
+    >>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
+    ... # SettingWithCopyError: A value is trying to be set on a copy of a...
+    """
+
+
+class SettingWithCopyWarning(Warning):
+    """
+    Warning raised when trying to set on a copied slice from a ``DataFrame``.
+
+    The ``mode.chained_assignment`` needs to be set to set to 'warn.'
+    'Warn' is the default option. This can happen unintentionally when
+    chained indexing.
+
+    For more information on evaluation order,
+    see :ref:`the user guide<indexing.evaluation_order>`.
+
+    For more information on view vs. copy,
+    see :ref:`the user guide<indexing.view_versus_copy>`.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
+    >>> df.loc[0:3]['A'] = 'a' # doctest: +SKIP
+    ... # SettingWithCopyWarning: A value is trying to be set on a copy of a...
+    """
+
+
+class ChainedAssignmentError(Warning):
+    """
+    Warning raised when trying to set using chained assignment.
+
+    When the ``mode.copy_on_write`` option is enabled, chained assignment can
+    never work. In such a situation, we are always setting into a temporary
+    object that is the result of an indexing operation (getitem), which under
+    Copy-on-Write always behaves as a copy. Thus, assigning through a chain
+    can never update the original Series or DataFrame.
+
+    For more information on view vs. copy,
+    see :ref:`the user guide<indexing.view_versus_copy>`.
+
+    Examples
+    --------
+    >>> pd.options.mode.copy_on_write = True
+    >>> df = pd.DataFrame({'A': [1, 1, 1, 2, 2]}, columns=['A'])
+    >>> df["A"][0:3] = 10 # doctest: +SKIP
+    ... # ChainedAssignmentError: ...
+    >>> pd.options.mode.copy_on_write = False
+    """
+
+
+_chained_assignment_msg = (
+    "A value is trying to be set on a copy of a DataFrame or Series "
+    "through chained assignment.\n"
+    "When using the Copy-on-Write mode, such chained assignment never works "
+    "to update the original DataFrame or Series, because the intermediate "
+    "object on which we are setting values always behaves as a copy.\n\n"
+    "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform "
+    "the assignment in a single step.\n\n"
+    "See the caveats in the documentation: "
+    "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+    "indexing.html#returning-a-view-versus-a-copy"
+)
+
+
+_chained_assignment_method_msg = (
+    "A value is trying to be set on a copy of a DataFrame or Series "
+    "through chained assignment using an inplace method.\n"
+    "When using the Copy-on-Write mode, such inplace method never works "
+    "to update the original DataFrame or Series, because the intermediate "
+    "object on which we are setting values always behaves as a copy.\n\n"
+    "For example, when doing 'df[col].method(value, inplace=True)', try "
+    "using 'df.method({col: value}, inplace=True)' instead, to perform "
+    "the operation inplace on the original object.\n\n"
+)
+
+
+_chained_assignment_warning_msg = (
+    "ChainedAssignmentError: behaviour will change in pandas 3.0!\n"
+    "You are setting values through chained assignment. Currently this works "
+    "in certain cases, but when using Copy-on-Write (which will become the "
+    "default behaviour in pandas 3.0) this will never work to update the "
+    "original DataFrame or Series, because the intermediate object on which "
+    "we are setting values will behave as a copy.\n"
+    "A typical example is when you are setting values in a column of a "
+    "DataFrame, like:\n\n"
+    'df["col"][row_indexer] = value\n\n'
+    'Use `df.loc[row_indexer, "col"] = values` instead, to perform the '
+    "assignment in a single step and ensure this keeps updating the original `df`.\n\n"
+    "See the caveats in the documentation: "
+    "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
+    "indexing.html#returning-a-view-versus-a-copy\n"
+)
+
+
+_chained_assignment_warning_method_msg = (
+    "A value is trying to be set on a copy of a DataFrame or Series "
+    "through chained assignment using an inplace method.\n"
+    "The behavior will change in pandas 3.0. This inplace method will "
+    "never work because the intermediate object on which we are setting "
+    "values always behaves as a copy.\n\n"
+    "For example, when doing 'df[col].method(value, inplace=True)', try "
+    "using 'df.method({col: value}, inplace=True)' or "
+    "df[col] = df[col].method(value) instead, to perform "
+    "the operation inplace on the original object.\n\n"
+)
+
+
+def _check_cacher(obj):
+    # This is a mess, selection paths that return a view set the _cacher attribute
+    # on the Series; most of them also set _item_cache which adds 1 to our relevant
+    # reference count, but iloc does not, so we have to check if we are actually
+    # in the item cache
+    if hasattr(obj, "_cacher"):
+        parent = obj._cacher[1]()
+        # parent could be dead
+        if parent is None:
+            return False
+        if hasattr(parent, "_item_cache"):
+            if obj._cacher[0] in parent._item_cache:
+                # Check if we are actually the item from item_cache, iloc creates a
+                # new object
+                return obj is parent._item_cache[obj._cacher[0]]
+    return False
+
+
+class NumExprClobberingError(NameError):
+    """
+    Exception raised when trying to use a built-in numexpr name as a variable name.
+
+    ``eval`` or ``query`` will throw the error if the engine is set
+    to 'numexpr'. 'numexpr' is the default engine value for these methods if the
+    numexpr package is installed.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'abs': [1, 1, 1]})
+    >>> df.query("abs > 2") # doctest: +SKIP
+    ... # NumExprClobberingError: Variables in expression "(abs) > (2)" overlap...
+    >>> sin, a = 1, 2
+    >>> pd.eval("sin + a", engine='numexpr') # doctest: +SKIP
+    ... # NumExprClobberingError: Variables in expression "(sin) + (a)" overlap...
+    """
+
+
+class UndefinedVariableError(NameError):
+    """
+    Exception raised by ``query`` or ``eval`` when using an undefined variable name.
+
+    It will also specify whether the undefined variable is local or not.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.query("A > x") # doctest: +SKIP
+    ... # UndefinedVariableError: name 'x' is not defined
+    >>> df.query("A > @y") # doctest: +SKIP
+    ... # UndefinedVariableError: local variable 'y' is not defined
+    >>> pd.eval('x + 1') # doctest: +SKIP
+    ... # UndefinedVariableError: name 'x' is not defined
+    """
+
+    def __init__(self, name: str, is_local: bool | None = None) -> None:
+        base_msg = f"{repr(name)} is not defined"
+        if is_local:
+            msg = f"local variable {base_msg}"
+        else:
+            msg = f"name {base_msg}"
+        super().__init__(msg)
+
+
+class IndexingError(Exception):
+    """
+    Exception is raised when trying to index and there is a mismatch in dimensions.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.loc[..., ..., 'A'] # doctest: +SKIP
+    ... # IndexingError: indexer may only contain one '...' entry
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.loc[1, ..., ...] # doctest: +SKIP
+    ... # IndexingError: Too many indexers
+    >>> df[pd.Series([True], dtype=bool)] # doctest: +SKIP
+    ... # IndexingError: Unalignable boolean Series provided as indexer...
+    >>> s = pd.Series(range(2),
+    ...               index = pd.MultiIndex.from_product([["a", "b"], ["c"]]))
+    >>> s.loc["a", "c", "d"] # doctest: +SKIP
+    ... # IndexingError: Too many indexers
+    """
+
+
+class PyperclipException(RuntimeError):
+    """
+    Exception raised when clipboard functionality is unsupported.
+
+    Raised by ``to_clipboard()`` and ``read_clipboard()``.
+    """
+
+
+class PyperclipWindowsException(PyperclipException):
+    """
+    Exception raised when clipboard functionality is unsupported by Windows.
+
+    Access to the clipboard handle would be denied due to some other
+    window process is accessing it.
+    """
+
+    def __init__(self, message: str) -> None:
+        # attr only exists on Windows, so typing fails on other platforms
+        message += f" ({ctypes.WinError()})"  # type: ignore[attr-defined]
+        super().__init__(message)
+
+
+class CSSWarning(UserWarning):
+    """
+    Warning is raised when converting css styling fails.
+
+    This can be due to the styling not having an equivalent value or because the
+    styling isn't properly formatted.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'A': [1, 1, 1]})
+    >>> df.style.applymap(
+    ...     lambda x: 'background-color: blueGreenRed;'
+    ... ).to_excel('styled.xlsx')  # doctest: +SKIP
+    CSSWarning: Unhandled color format: 'blueGreenRed'
+    >>> df.style.applymap(
+    ...     lambda x: 'border: 1px solid red red;'
+    ... ).to_excel('styled.xlsx')  # doctest: +SKIP
+    CSSWarning: Unhandled color format: 'blueGreenRed'
+    """
+
+
+class PossibleDataLossError(Exception):
+    """
+    Exception raised when trying to open a HDFStore file when already opened.
+
+    Examples
+    --------
+    >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
+    >>> store.open("w") # doctest: +SKIP
+    ... # PossibleDataLossError: Re-opening the file [my-store] with mode [a]...
+    """
+
+
+class ClosedFileError(Exception):
+    """
+    Exception is raised when trying to perform an operation on a closed HDFStore file.
+
+    Examples
+    --------
+    >>> store = pd.HDFStore('my-store', 'a') # doctest: +SKIP
+    >>> store.close() # doctest: +SKIP
+    >>> store.keys() # doctest: +SKIP
+    ... # ClosedFileError: my-store file is not open!
+    """
+
+
+class IncompatibilityWarning(Warning):
+    """
+    Warning raised when trying to use where criteria on an incompatible HDF5 file.
+    """
+
+
+class AttributeConflictWarning(Warning):
+    """
+    Warning raised when index attributes conflict when using HDFStore.
+
+    Occurs when attempting to append an index with a different
+    name than the existing index on an HDFStore or attempting to append an index with a
+    different frequency than the existing index on an HDFStore.
+
+    Examples
+    --------
+    >>> idx1 = pd.Index(['a', 'b'], name='name1')
+    >>> df1 = pd.DataFrame([[1, 2], [3, 4]], index=idx1)
+    >>> df1.to_hdf('file', 'data', 'w', append=True)  # doctest: +SKIP
+    >>> idx2 = pd.Index(['c', 'd'], name='name2')
+    >>> df2 = pd.DataFrame([[5, 6], [7, 8]], index=idx2)
+    >>> df2.to_hdf('file', 'data', 'a', append=True)  # doctest: +SKIP
+    AttributeConflictWarning: the [index_name] attribute of the existing index is
+    [name1] which conflicts with the new [name2]...
+    """
+
+
+class DatabaseError(OSError):
+    """
+    Error is raised when executing sql with bad syntax or sql that throws an error.
+
+    Examples
+    --------
+    >>> from sqlite3 import connect
+    >>> conn = connect(':memory:')
+    >>> pd.read_sql('select * test', conn) # doctest: +SKIP
+    ... # DatabaseError: Execution failed on sql 'test': near "test": syntax error
+    """
+
+
+class PossiblePrecisionLoss(Warning):
+    """
+    Warning raised by to_stata on a column with a value outside or equal to int64.
+
+    When the column value is outside or equal to the int64 value the column is
+    converted to a float64 dtype.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"s": pd.Series([1, 2**53], dtype=np.int64)})
+    >>> df.to_stata('test') # doctest: +SKIP
+    ... # PossiblePrecisionLoss: Column converted from int64 to float64...
+    """
+
+
+class ValueLabelTypeMismatch(Warning):
+    """
+    Warning raised by to_stata on a category column that contains non-string values.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"categories": pd.Series(["a", 2], dtype="category")})
+    >>> df.to_stata('test') # doctest: +SKIP
+    ... # ValueLabelTypeMismatch: Stata value labels (pandas categories) must be str...
+    """
+
+
+class InvalidColumnName(Warning):
+    """
+    Warning raised by to_stata the column contains a non-valid stata name.
+
+    Because the column name is an invalid Stata variable, the name needs to be
+    converted.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"0categories": pd.Series([2, 2])})
+    >>> df.to_stata('test') # doctest: +SKIP
+    ... # InvalidColumnName: Not all pandas column names were valid Stata variable...
+    """
+
+
+class CategoricalConversionWarning(Warning):
+    """
+    Warning is raised when reading a partial labeled Stata file using a iterator.
+
+    Examples
+    --------
+    >>> from pandas.io.stata import StataReader
+    >>> with StataReader('dta_file', chunksize=2) as reader: # doctest: +SKIP
+    ...   for i, block in enumerate(reader):
+    ...      print(i, block)
+    ... # CategoricalConversionWarning: One or more series with value labels...
+    """
+
+
+class LossySetitemError(Exception):
+    """
+    Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
+
+    Notes
+    -----
+    This is an internal error.
+    """
+
+
+class NoBufferPresent(Exception):
+    """
+    Exception is raised in _get_data_buffer to signal that there is no requested buffer.
+    """
+
+
+class InvalidComparison(Exception):
+    """
+    Exception is raised by _validate_comparison_value to indicate an invalid comparison.
+
+    Notes
+    -----
+    This is an internal error.
+    """
+
+
+__all__ = [
+    "AbstractMethodError",
+    "AttributeConflictWarning",
+    "CategoricalConversionWarning",
+    "ClosedFileError",
+    "CSSWarning",
+    "DatabaseError",
+    "DataError",
+    "DtypeWarning",
+    "DuplicateLabelError",
+    "EmptyDataError",
+    "IncompatibilityWarning",
+    "IntCastingNaNError",
+    "InvalidColumnName",
+    "InvalidComparison",
+    "InvalidIndexError",
+    "InvalidVersion",
+    "IndexingError",
+    "LossySetitemError",
+    "MergeError",
+    "NoBufferPresent",
+    "NullFrequencyError",
+    "NumbaUtilError",
+    "NumExprClobberingError",
+    "OptionError",
+    "OutOfBoundsDatetime",
+    "OutOfBoundsTimedelta",
+    "ParserError",
+    "ParserWarning",
+    "PerformanceWarning",
+    "PossibleDataLossError",
+    "PossiblePrecisionLoss",
+    "PyperclipException",
+    "PyperclipWindowsException",
+    "SettingWithCopyError",
+    "SettingWithCopyWarning",
+    "SpecificationError",
+    "UndefinedVariableError",
+    "UnsortedIndexError",
+    "UnsupportedFunctionCall",
+    "ValueLabelTypeMismatch",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c804b81c49e7c8abb406f2132909df6036df1c09
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/__init__.py
@@ -0,0 +1,13 @@
+# ruff: noqa: TCH004
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # import modules that have public classes/functions
+    from pandas.io import (
+        formats,
+        json,
+        stata,
+    )
+
+    # mark only those modules as public
+    __all__ = ["formats", "json", "stata"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/_util.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..35fdfb1a9ee82b4e09afc2da35bb89cdc5f10739
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/_util.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat import (
+    pa_version_under18p0,
+    pa_version_under19p0,
+)
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    import pyarrow
+
+    from pandas._typing import DtypeBackend
+
+
+def _arrow_dtype_mapping() -> dict:
+    pa = import_optional_dependency("pyarrow")
+    return {
+        pa.int8(): pd.Int8Dtype(),
+        pa.int16(): pd.Int16Dtype(),
+        pa.int32(): pd.Int32Dtype(),
+        pa.int64(): pd.Int64Dtype(),
+        pa.uint8(): pd.UInt8Dtype(),
+        pa.uint16(): pd.UInt16Dtype(),
+        pa.uint32(): pd.UInt32Dtype(),
+        pa.uint64(): pd.UInt64Dtype(),
+        pa.bool_(): pd.BooleanDtype(),
+        pa.string(): pd.StringDtype(),
+        pa.float32(): pd.Float32Dtype(),
+        pa.float64(): pd.Float64Dtype(),
+        pa.string(): pd.StringDtype(),
+        pa.large_string(): pd.StringDtype(),
+    }
+
+
+def _arrow_string_types_mapper() -> Callable:
+    pa = import_optional_dependency("pyarrow")
+
+    mapping = {
+        pa.string(): pd.StringDtype(na_value=np.nan),
+        pa.large_string(): pd.StringDtype(na_value=np.nan),
+    }
+    if not pa_version_under18p0:
+        mapping[pa.string_view()] = pd.StringDtype(na_value=np.nan)
+
+    return mapping.get
+
+
+def arrow_table_to_pandas(
+    table: pyarrow.Table,
+    dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault = lib.no_default,
+    null_to_int64: bool = False,
+    to_pandas_kwargs: dict | None = None,
+) -> pd.DataFrame:
+    if to_pandas_kwargs is None:
+        to_pandas_kwargs = {}
+
+    pa = import_optional_dependency("pyarrow")
+
+    types_mapper: type[pd.ArrowDtype] | None | Callable
+    if dtype_backend == "numpy_nullable":
+        mapping = _arrow_dtype_mapping()
+        if null_to_int64:
+            # Modify the default mapping to also map null to Int64
+            # (to match other engines - only for CSV parser)
+            mapping[pa.null()] = pd.Int64Dtype()
+        types_mapper = mapping.get
+    elif dtype_backend == "pyarrow":
+        types_mapper = pd.ArrowDtype
+    elif using_string_dtype():
+        if pa_version_under19p0:
+            types_mapper = _arrow_string_types_mapper()
+        else:
+            types_mapper = None
+    elif dtype_backend is lib.no_default or dtype_backend == "numpy":
+        types_mapper = None
+    else:
+        raise NotImplementedError
+
+    df = table.to_pandas(types_mapper=types_mapper, **to_pandas_kwargs)
+    return df
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/api.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8b34a61dfc62992a37d9fab3263ee00a28d1fc
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/api.py
@@ -0,0 +1,65 @@
+"""
+Data IO api
+"""
+
+from pandas.io.clipboards import read_clipboard
+from pandas.io.excel import (
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+)
+from pandas.io.feather_format import read_feather
+from pandas.io.gbq import read_gbq
+from pandas.io.html import read_html
+from pandas.io.json import read_json
+from pandas.io.orc import read_orc
+from pandas.io.parquet import read_parquet
+from pandas.io.parsers import (
+    read_csv,
+    read_fwf,
+    read_table,
+)
+from pandas.io.pickle import (
+    read_pickle,
+    to_pickle,
+)
+from pandas.io.pytables import (
+    HDFStore,
+    read_hdf,
+)
+from pandas.io.sas import read_sas
+from pandas.io.spss import read_spss
+from pandas.io.sql import (
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+)
+from pandas.io.stata import read_stata
+from pandas.io.xml import read_xml
+
+__all__ = [
+    "ExcelFile",
+    "ExcelWriter",
+    "HDFStore",
+    "read_clipboard",
+    "read_csv",
+    "read_excel",
+    "read_feather",
+    "read_fwf",
+    "read_gbq",
+    "read_hdf",
+    "read_html",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_pickle",
+    "read_sas",
+    "read_spss",
+    "read_sql",
+    "read_sql_query",
+    "read_sql_table",
+    "read_stata",
+    "read_table",
+    "read_xml",
+    "to_pickle",
+]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/clipboard/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/clipboard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6491849925e863c35a98390a31729cb13e28ca19
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/clipboard/__init__.py
@@ -0,0 +1,747 @@
+"""
+Pyperclip
+
+A cross-platform clipboard module for Python,
+with copy & paste functions for plain text.
+By Al Sweigart al@inventwithpython.com
+Licence at LICENSES/PYPERCLIP_LICENSE
+
+Usage:
+  import pyperclip
+  pyperclip.copy('The text to be copied to the clipboard.')
+  spam = pyperclip.paste()
+
+  if not pyperclip.is_available():
+    print("Copy functionality unavailable!")
+
+On Windows, no additional modules are needed.
+On Mac, the pyobjc module is used, falling back to the pbcopy and pbpaste cli
+    commands. (These commands should come with OS X.).
+On Linux, install xclip, xsel, or wl-clipboard (for "wayland" sessions) via
+package manager.
+For example, in Debian:
+    sudo apt-get install xclip
+    sudo apt-get install xsel
+    sudo apt-get install wl-clipboard
+
+Otherwise on Linux, you will need the PyQt5 modules installed.
+
+This module does not work with PyGObject yet.
+
+Cygwin is currently not supported.
+
+Security Note: This module runs programs with these names:
+    - pbcopy
+    - pbpaste
+    - xclip
+    - xsel
+    - wl-copy/wl-paste
+    - klipper
+    - qdbus
+A malicious user could rename or add programs with these names, tricking
+Pyperclip into running them with whatever permissions the Python process has.
+
+"""
+
+__version__ = "1.8.2"
+
+
+import contextlib
+import ctypes
+from ctypes import (
+    c_size_t,
+    c_wchar,
+    c_wchar_p,
+    get_errno,
+    sizeof,
+)
+import os
+import platform
+from shutil import which as _executable_exists
+import subprocess
+import time
+import warnings
+
+from pandas.errors import (
+    PyperclipException,
+    PyperclipWindowsException,
+)
+from pandas.util._exceptions import find_stack_level
+
+# `import PyQt4` sys.exit()s if DISPLAY is not in the environment.
+# Thus, we need to detect the presence of $DISPLAY manually
+# and not load PyQt4 if it is absent.
+HAS_DISPLAY = os.getenv("DISPLAY")
+
+EXCEPT_MSG = """
+    Pyperclip could not find a copy/paste mechanism for your system.
+    For more information, please visit
+    https://pyperclip.readthedocs.io/en/latest/index.html#not-implemented-error
+    """
+
+ENCODING = "utf-8"
+
+
+class PyperclipTimeoutException(PyperclipException):
+    pass
+
+
+def _stringifyText(text) -> str:
+    acceptedTypes = (str, int, float, bool)
+    if not isinstance(text, acceptedTypes):
+        raise PyperclipException(
+            f"only str, int, float, and bool values "
+            f"can be copied to the clipboard, not {type(text).__name__}"
+        )
+    return str(text)
+
+
+def init_osx_pbcopy_clipboard():
+    def copy_osx_pbcopy(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        with subprocess.Popen(
+            ["pbcopy", "w"], stdin=subprocess.PIPE, close_fds=True
+        ) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_osx_pbcopy():
+        with subprocess.Popen(
+            ["pbpaste", "r"], stdout=subprocess.PIPE, close_fds=True
+        ) as p:
+            stdout = p.communicate()[0]
+        return stdout.decode(ENCODING)
+
+    return copy_osx_pbcopy, paste_osx_pbcopy
+
+
+def init_osx_pyobjc_clipboard():
+    def copy_osx_pyobjc(text):
+        """Copy string argument to clipboard"""
+        text = _stringifyText(text)  # Converts non-str values to str.
+        newStr = Foundation.NSString.stringWithString_(text).nsstring()
+        newData = newStr.dataUsingEncoding_(Foundation.NSUTF8StringEncoding)
+        board = AppKit.NSPasteboard.generalPasteboard()
+        board.declareTypes_owner_([AppKit.NSStringPboardType], None)
+        board.setData_forType_(newData, AppKit.NSStringPboardType)
+
+    def paste_osx_pyobjc():
+        """Returns contents of clipboard"""
+        board = AppKit.NSPasteboard.generalPasteboard()
+        content = board.stringForType_(AppKit.NSStringPboardType)
+        return content
+
+    return copy_osx_pyobjc, paste_osx_pyobjc
+
+
+def init_qt_clipboard():
+    global QApplication
+    # $DISPLAY should exist
+
+    # Try to import from qtpy, but if that fails try PyQt5 then PyQt4
+    try:
+        from qtpy.QtWidgets import QApplication
+    except ImportError:
+        try:
+            from PyQt5.QtWidgets import QApplication
+        except ImportError:
+            from PyQt4.QtGui import QApplication
+
+    app = QApplication.instance()
+    if app is None:
+        app = QApplication([])
+
+    def copy_qt(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        cb = app.clipboard()
+        cb.setText(text)
+
+    def paste_qt() -> str:
+        cb = app.clipboard()
+        return str(cb.text())
+
+    return copy_qt, paste_qt
+
+
+def init_xclip_clipboard():
+    DEFAULT_SELECTION = "c"
+    PRIMARY_SELECTION = "p"
+
+    def copy_xclip(text, primary=False):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        selection = DEFAULT_SELECTION
+        if primary:
+            selection = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xclip", "-selection", selection], stdin=subprocess.PIPE, close_fds=True
+        ) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_xclip(primary=False):
+        selection = DEFAULT_SELECTION
+        if primary:
+            selection = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xclip", "-selection", selection, "-o"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            stdout = p.communicate()[0]
+        # Intentionally ignore extraneous output on stderr when clipboard is empty
+        return stdout.decode(ENCODING)
+
+    return copy_xclip, paste_xclip
+
+
+def init_xsel_clipboard():
+    DEFAULT_SELECTION = "-b"
+    PRIMARY_SELECTION = "-p"
+
+    def copy_xsel(text, primary=False):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        selection_flag = DEFAULT_SELECTION
+        if primary:
+            selection_flag = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xsel", selection_flag, "-i"], stdin=subprocess.PIPE, close_fds=True
+        ) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_xsel(primary=False):
+        selection_flag = DEFAULT_SELECTION
+        if primary:
+            selection_flag = PRIMARY_SELECTION
+        with subprocess.Popen(
+            ["xsel", selection_flag, "-o"], stdout=subprocess.PIPE, close_fds=True
+        ) as p:
+            stdout = p.communicate()[0]
+        return stdout.decode(ENCODING)
+
+    return copy_xsel, paste_xsel
+
+
+def init_wl_clipboard():
+    PRIMARY_SELECTION = "-p"
+
+    def copy_wl(text, primary=False):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        args = ["wl-copy"]
+        if primary:
+            args.append(PRIMARY_SELECTION)
+        if not text:
+            args.append("--clear")
+            subprocess.check_call(args, close_fds=True)
+        else:
+            p = subprocess.Popen(args, stdin=subprocess.PIPE, close_fds=True)
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_wl(primary=False):
+        args = ["wl-paste", "-n"]
+        if primary:
+            args.append(PRIMARY_SELECTION)
+        p = subprocess.Popen(args, stdout=subprocess.PIPE, close_fds=True)
+        stdout, _stderr = p.communicate()
+        return stdout.decode(ENCODING)
+
+    return copy_wl, paste_wl
+
+
+def init_klipper_clipboard():
+    def copy_klipper(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        with subprocess.Popen(
+            [
+                "qdbus",
+                "org.kde.klipper",
+                "/klipper",
+                "setClipboardContents",
+                text.encode(ENCODING),
+            ],
+            stdin=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            p.communicate(input=None)
+
+    def paste_klipper():
+        with subprocess.Popen(
+            ["qdbus", "org.kde.klipper", "/klipper", "getClipboardContents"],
+            stdout=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            stdout = p.communicate()[0]
+
+        # Workaround for https://bugs.kde.org/show_bug.cgi?id=342874
+        # TODO: https://github.com/asweigart/pyperclip/issues/43
+        clipboardContents = stdout.decode(ENCODING)
+        # even if blank, Klipper will append a newline at the end
+        assert len(clipboardContents) > 0
+        # make sure that newline is there
+        assert clipboardContents.endswith("\n")
+        if clipboardContents.endswith("\n"):
+            clipboardContents = clipboardContents[:-1]
+        return clipboardContents
+
+    return copy_klipper, paste_klipper
+
+
+def init_dev_clipboard_clipboard():
+    def copy_dev_clipboard(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        if text == "":
+            warnings.warn(
+                "Pyperclip cannot copy a blank string to the clipboard on Cygwin. "
+                "This is effectively a no-op.",
+                stacklevel=find_stack_level(),
+            )
+        if "\r" in text:
+            warnings.warn(
+                "Pyperclip cannot handle \\r characters on Cygwin.",
+                stacklevel=find_stack_level(),
+            )
+
+        with open("/dev/clipboard", "w", encoding="utf-8") as fd:
+            fd.write(text)
+
+    def paste_dev_clipboard() -> str:
+        with open("/dev/clipboard", encoding="utf-8") as fd:
+            content = fd.read()
+        return content
+
+    return copy_dev_clipboard, paste_dev_clipboard
+
+
+def init_no_clipboard():
+    class ClipboardUnavailable:
+        def __call__(self, *args, **kwargs):
+            raise PyperclipException(EXCEPT_MSG)
+
+        def __bool__(self) -> bool:
+            return False
+
+    return ClipboardUnavailable(), ClipboardUnavailable()
+
+
+# Windows-related clipboard functions:
+class CheckedCall:
+    def __init__(self, f) -> None:
+        super().__setattr__("f", f)
+
+    def __call__(self, *args):
+        ret = self.f(*args)
+        if not ret and get_errno():
+            raise PyperclipWindowsException("Error calling " + self.f.__name__)
+        return ret
+
+    def __setattr__(self, key, value):
+        setattr(self.f, key, value)
+
+
+def init_windows_clipboard():
+    global HGLOBAL, LPVOID, DWORD, LPCSTR, INT
+    global HWND, HINSTANCE, HMENU, BOOL, UINT, HANDLE
+    from ctypes.wintypes import (
+        BOOL,
+        DWORD,
+        HANDLE,
+        HGLOBAL,
+        HINSTANCE,
+        HMENU,
+        HWND,
+        INT,
+        LPCSTR,
+        LPVOID,
+        UINT,
+    )
+
+    windll = ctypes.windll
+    msvcrt = ctypes.CDLL("msvcrt")
+
+    safeCreateWindowExA = CheckedCall(windll.user32.CreateWindowExA)
+    safeCreateWindowExA.argtypes = [
+        DWORD,
+        LPCSTR,
+        LPCSTR,
+        DWORD,
+        INT,
+        INT,
+        INT,
+        INT,
+        HWND,
+        HMENU,
+        HINSTANCE,
+        LPVOID,
+    ]
+    safeCreateWindowExA.restype = HWND
+
+    safeDestroyWindow = CheckedCall(windll.user32.DestroyWindow)
+    safeDestroyWindow.argtypes = [HWND]
+    safeDestroyWindow.restype = BOOL
+
+    OpenClipboard = windll.user32.OpenClipboard
+    OpenClipboard.argtypes = [HWND]
+    OpenClipboard.restype = BOOL
+
+    safeCloseClipboard = CheckedCall(windll.user32.CloseClipboard)
+    safeCloseClipboard.argtypes = []
+    safeCloseClipboard.restype = BOOL
+
+    safeEmptyClipboard = CheckedCall(windll.user32.EmptyClipboard)
+    safeEmptyClipboard.argtypes = []
+    safeEmptyClipboard.restype = BOOL
+
+    safeGetClipboardData = CheckedCall(windll.user32.GetClipboardData)
+    safeGetClipboardData.argtypes = [UINT]
+    safeGetClipboardData.restype = HANDLE
+
+    safeSetClipboardData = CheckedCall(windll.user32.SetClipboardData)
+    safeSetClipboardData.argtypes = [UINT, HANDLE]
+    safeSetClipboardData.restype = HANDLE
+
+    safeGlobalAlloc = CheckedCall(windll.kernel32.GlobalAlloc)
+    safeGlobalAlloc.argtypes = [UINT, c_size_t]
+    safeGlobalAlloc.restype = HGLOBAL
+
+    safeGlobalLock = CheckedCall(windll.kernel32.GlobalLock)
+    safeGlobalLock.argtypes = [HGLOBAL]
+    safeGlobalLock.restype = LPVOID
+
+    safeGlobalUnlock = CheckedCall(windll.kernel32.GlobalUnlock)
+    safeGlobalUnlock.argtypes = [HGLOBAL]
+    safeGlobalUnlock.restype = BOOL
+
+    wcslen = CheckedCall(msvcrt.wcslen)
+    wcslen.argtypes = [c_wchar_p]
+    wcslen.restype = UINT
+
+    GMEM_MOVEABLE = 0x0002
+    CF_UNICODETEXT = 13
+
+    @contextlib.contextmanager
+    def window():
+        """
+        Context that provides a valid Windows hwnd.
+        """
+        # we really just need the hwnd, so setting "STATIC"
+        # as predefined lpClass is just fine.
+        hwnd = safeCreateWindowExA(
+            0, b"STATIC", None, 0, 0, 0, 0, 0, None, None, None, None
+        )
+        try:
+            yield hwnd
+        finally:
+            safeDestroyWindow(hwnd)
+
+    @contextlib.contextmanager
+    def clipboard(hwnd):
+        """
+        Context manager that opens the clipboard and prevents
+        other applications from modifying the clipboard content.
+        """
+        # We may not get the clipboard handle immediately because
+        # some other application is accessing it (?)
+        # We try for at least 500ms to get the clipboard.
+        t = time.time() + 0.5
+        success = False
+        while time.time() < t:
+            success = OpenClipboard(hwnd)
+            if success:
+                break
+            time.sleep(0.01)
+        if not success:
+            raise PyperclipWindowsException("Error calling OpenClipboard")
+
+        try:
+            yield
+        finally:
+            safeCloseClipboard()
+
+    def copy_windows(text):
+        # This function is heavily based on
+        # http://msdn.com/ms649016#_win32_Copying_Information_to_the_Clipboard
+
+        text = _stringifyText(text)  # Converts non-str values to str.
+
+        with window() as hwnd:
+            # http://msdn.com/ms649048
+            # If an application calls OpenClipboard with hwnd set to NULL,
+            # EmptyClipboard sets the clipboard owner to NULL;
+            # this causes SetClipboardData to fail.
+            # => We need a valid hwnd to copy something.
+            with clipboard(hwnd):
+                safeEmptyClipboard()
+
+                if text:
+                    # http://msdn.com/ms649051
+                    # If the hMem parameter identifies a memory object,
+                    # the object must have been allocated using the
+                    # function with the GMEM_MOVEABLE flag.
+                    count = wcslen(text) + 1
+                    handle = safeGlobalAlloc(GMEM_MOVEABLE, count * sizeof(c_wchar))
+                    locked_handle = safeGlobalLock(handle)
+
+                    ctypes.memmove(
+                        c_wchar_p(locked_handle),
+                        c_wchar_p(text),
+                        count * sizeof(c_wchar),
+                    )
+
+                    safeGlobalUnlock(handle)
+                    safeSetClipboardData(CF_UNICODETEXT, handle)
+
+    def paste_windows():
+        with clipboard(None):
+            handle = safeGetClipboardData(CF_UNICODETEXT)
+            if not handle:
+                # GetClipboardData may return NULL with errno == NO_ERROR
+                # if the clipboard is empty.
+                # (Also, it may return a handle to an empty buffer,
+                # but technically that's not empty)
+                return ""
+            return c_wchar_p(handle).value
+
+    return copy_windows, paste_windows
+
+
+def init_wsl_clipboard():
+    def copy_wsl(text):
+        text = _stringifyText(text)  # Converts non-str values to str.
+        with subprocess.Popen(["clip.exe"], stdin=subprocess.PIPE, close_fds=True) as p:
+            p.communicate(input=text.encode(ENCODING))
+
+    def paste_wsl():
+        with subprocess.Popen(
+            ["powershell.exe", "-command", "Get-Clipboard"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            close_fds=True,
+        ) as p:
+            stdout = p.communicate()[0]
+        # WSL appends "\r\n" to the contents.
+        return stdout[:-2].decode(ENCODING)
+
+    return copy_wsl, paste_wsl
+
+
+# Automatic detection of clipboard mechanisms
+# and importing is done in determine_clipboard():
+def determine_clipboard():
+    """
+    Determine the OS/platform and set the copy() and paste() functions
+    accordingly.
+    """
+    global Foundation, AppKit, qtpy, PyQt4, PyQt5
+
+    # Setup for the CYGWIN platform:
+    if (
+        "cygwin" in platform.system().lower()
+    ):  # Cygwin has a variety of values returned by platform.system(),
+        # such as 'CYGWIN_NT-6.1'
+        # FIXME(pyperclip#55): pyperclip currently does not support Cygwin,
+        # see https://github.com/asweigart/pyperclip/issues/55
+        if os.path.exists("/dev/clipboard"):
+            warnings.warn(
+                "Pyperclip's support for Cygwin is not perfect, "
+                "see https://github.com/asweigart/pyperclip/issues/55",
+                stacklevel=find_stack_level(),
+            )
+            return init_dev_clipboard_clipboard()
+
+    # Setup for the WINDOWS platform:
+    elif os.name == "nt" or platform.system() == "Windows":
+        return init_windows_clipboard()
+
+    if platform.system() == "Linux":
+        if _executable_exists("wslconfig.exe"):
+            return init_wsl_clipboard()
+
+    # Setup for the macOS platform:
+    if os.name == "mac" or platform.system() == "Darwin":
+        try:
+            import AppKit
+            import Foundation  # check if pyobjc is installed
+        except ImportError:
+            return init_osx_pbcopy_clipboard()
+        else:
+            return init_osx_pyobjc_clipboard()
+
+    # Setup for the LINUX platform:
+    if HAS_DISPLAY:
+        if os.environ.get("WAYLAND_DISPLAY") and _executable_exists("wl-copy"):
+            return init_wl_clipboard()
+        if _executable_exists("xsel"):
+            return init_xsel_clipboard()
+        if _executable_exists("xclip"):
+            return init_xclip_clipboard()
+        if _executable_exists("klipper") and _executable_exists("qdbus"):
+            return init_klipper_clipboard()
+
+        try:
+            # qtpy is a small abstraction layer that lets you write applications
+            # using a single api call to either PyQt or PySide.
+            # https://pypi.python.org/project/QtPy
+            import qtpy  # check if qtpy is installed
+        except ImportError:
+            # If qtpy isn't installed, fall back on importing PyQt4.
+            try:
+                import PyQt5  # check if PyQt5 is installed
+            except ImportError:
+                try:
+                    import PyQt4  # check if PyQt4 is installed
+                except ImportError:
+                    pass  # We want to fail fast for all non-ImportError exceptions.
+                else:
+                    return init_qt_clipboard()
+            else:
+                return init_qt_clipboard()
+        else:
+            return init_qt_clipboard()
+
+    return init_no_clipboard()
+
+
+def set_clipboard(clipboard):
+    """
+    Explicitly sets the clipboard mechanism. The "clipboard mechanism" is how
+    the copy() and paste() functions interact with the operating system to
+    implement the copy/paste feature. The clipboard parameter must be one of:
+        - pbcopy
+        - pyobjc (default on macOS)
+        - qt
+        - xclip
+        - xsel
+        - klipper
+        - windows (default on Windows)
+        - no (this is what is set when no clipboard mechanism can be found)
+    """
+    global copy, paste
+
+    clipboard_types = {
+        "pbcopy": init_osx_pbcopy_clipboard,
+        "pyobjc": init_osx_pyobjc_clipboard,
+        "qt": init_qt_clipboard,  # TODO - split this into 'qtpy', 'pyqt4', and 'pyqt5'
+        "xclip": init_xclip_clipboard,
+        "xsel": init_xsel_clipboard,
+        "wl-clipboard": init_wl_clipboard,
+        "klipper": init_klipper_clipboard,
+        "windows": init_windows_clipboard,
+        "no": init_no_clipboard,
+    }
+
+    if clipboard not in clipboard_types:
+        allowed_clipboard_types = [repr(_) for _ in clipboard_types]
+        raise ValueError(
+            f"Argument must be one of {', '.join(allowed_clipboard_types)}"
+        )
+
+    # Sets pyperclip's copy() and paste() functions:
+    copy, paste = clipboard_types[clipboard]()
+
+
+def lazy_load_stub_copy(text):
+    """
+    A stub function for copy(), which will load the real copy() function when
+    called so that the real copy() function is used for later calls.
+
+    This allows users to import pyperclip without having determine_clipboard()
+    automatically run, which will automatically select a clipboard mechanism.
+    This could be a problem if it selects, say, the memory-heavy PyQt4 module
+    but the user was just going to immediately call set_clipboard() to use a
+    different clipboard mechanism.
+
+    The lazy loading this stub function implements gives the user a chance to
+    call set_clipboard() to pick another clipboard mechanism. Or, if the user
+    simply calls copy() or paste() without calling set_clipboard() first,
+    will fall back on whatever clipboard mechanism that determine_clipboard()
+    automatically chooses.
+    """
+    global copy, paste
+    copy, paste = determine_clipboard()
+    return copy(text)
+
+
+def lazy_load_stub_paste():
+    """
+    A stub function for paste(), which will load the real paste() function when
+    called so that the real paste() function is used for later calls.
+
+    This allows users to import pyperclip without having determine_clipboard()
+    automatically run, which will automatically select a clipboard mechanism.
+    This could be a problem if it selects, say, the memory-heavy PyQt4 module
+    but the user was just going to immediately call set_clipboard() to use a
+    different clipboard mechanism.
+
+    The lazy loading this stub function implements gives the user a chance to
+    call set_clipboard() to pick another clipboard mechanism. Or, if the user
+    simply calls copy() or paste() without calling set_clipboard() first,
+    will fall back on whatever clipboard mechanism that determine_clipboard()
+    automatically chooses.
+    """
+    global copy, paste
+    copy, paste = determine_clipboard()
+    return paste()
+
+
+def is_available() -> bool:
+    return copy != lazy_load_stub_copy and paste != lazy_load_stub_paste
+
+
+# Initially, copy() and paste() are set to lazy loading wrappers which will
+# set `copy` and `paste` to real functions the first time they're used, unless
+# set_clipboard() or determine_clipboard() is called first.
+copy, paste = lazy_load_stub_copy, lazy_load_stub_paste
+
+
+def waitForPaste(timeout=None):
+    """This function call blocks until a non-empty text string exists on the
+    clipboard. It returns this text.
+
+    This function raises PyperclipTimeoutException if timeout was set to
+    a number of seconds that has elapsed without non-empty text being put on
+    the clipboard."""
+    startTime = time.time()
+    while True:
+        clipboardText = paste()
+        if clipboardText != "":
+            return clipboardText
+        time.sleep(0.01)
+
+        if timeout is not None and time.time() > startTime + timeout:
+            raise PyperclipTimeoutException(
+                "waitForPaste() timed out after " + str(timeout) + " seconds."
+            )
+
+
+def waitForNewPaste(timeout=None):
+    """This function call blocks until a new text string exists on the
+    clipboard that is different from the text that was there when the function
+    was first called. It returns this text.
+
+    This function raises PyperclipTimeoutException if timeout was set to
+    a number of seconds that has elapsed without non-empty text being put on
+    the clipboard."""
+    startTime = time.time()
+    originalText = paste()
+    while True:
+        currentText = paste()
+        if currentText != originalText:
+            return currentText
+        time.sleep(0.01)
+
+        if timeout is not None and time.time() > startTime + timeout:
+            raise PyperclipTimeoutException(
+                "waitForNewPaste() timed out after " + str(timeout) + " seconds."
+            )
+
+
+__all__ = [
+    "copy",
+    "paste",
+    "waitForPaste",
+    "waitForNewPaste",
+    "set_clipboard",
+    "determine_clipboard",
+]
+
+# pandas aliases
+clipboard_get = paste
+clipboard_set = copy
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/clipboards.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/clipboards.py
new file mode 100644
index 0000000000000000000000000000000000000000..a15e37328e9fa95587d53b58b1af10e1e57fd60c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/clipboards.py
@@ -0,0 +1,197 @@
+""" io on the clipboard """
+from __future__ import annotations
+
+from io import StringIO
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.generic import ABCDataFrame
+
+from pandas import (
+    get_option,
+    option_context,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeBackend
+
+
+def read_clipboard(
+    sep: str = r"\s+",
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    **kwargs,
+):  # pragma: no cover
+    r"""
+    Read text from clipboard and pass to :func:`~pandas.read_csv`.
+
+    Parses clipboard contents similar to how CSV files are parsed
+    using :func:`~pandas.read_csv`.
+
+    Parameters
+    ----------
+    sep : str, default '\\s+'
+        A string or regex delimiter. The default of ``'\\s+'`` denotes
+        one or more whitespace characters.
+
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    **kwargs
+        See :func:`~pandas.read_csv` for the full argument list.
+
+    Returns
+    -------
+    DataFrame
+        A parsed :class:`~pandas.DataFrame` object.
+
+    See Also
+    --------
+    DataFrame.to_clipboard : Copy object to the system clipboard.
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+    read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+    >>> df.to_clipboard()  # doctest: +SKIP
+    >>> pd.read_clipboard()  # doctest: +SKIP
+         A  B  C
+    0    1  2  3
+    1    4  5  6
+    """
+    encoding = kwargs.pop("encoding", "utf-8")
+
+    # only utf-8 is valid for passed value because that's what clipboard
+    # supports
+    if encoding is not None and encoding.lower().replace("-", "") != "utf8":
+        raise NotImplementedError("reading from clipboard only supports utf-8 encoding")
+
+    check_dtype_backend(dtype_backend)
+
+    from pandas.io.clipboard import clipboard_get
+    from pandas.io.parsers import read_csv
+
+    text = clipboard_get()
+
+    # Try to decode (if needed, as "text" might already be a string here).
+    try:
+        text = text.decode(kwargs.get("encoding") or get_option("display.encoding"))
+    except AttributeError:
+        pass
+
+    # Excel copies into clipboard with \t separation
+    # inspect no more then the 10 first lines, if they
+    # all contain an equal number (>0) of tabs, infer
+    # that this came from excel and set 'sep' accordingly
+    lines = text[:10000].split("\n")[:-1][:10]
+
+    # Need to remove leading white space, since read_csv
+    # accepts:
+    #    a  b
+    # 0  1  2
+    # 1  3  4
+
+    counts = {x.lstrip(" ").count("\t") for x in lines}
+    if len(lines) > 1 and len(counts) == 1 and counts.pop() != 0:
+        sep = "\t"
+        # check the number of leading tabs in the first line
+        # to account for index columns
+        index_length = len(lines[0]) - len(lines[0].lstrip(" \t"))
+        if index_length != 0:
+            kwargs.setdefault("index_col", list(range(index_length)))
+
+    # Edge case where sep is specified to be None, return to default
+    if sep is None and kwargs.get("delim_whitespace") is None:
+        sep = r"\s+"
+
+    # Regex separator currently only works with python engine.
+    # Default to python if separator is multi-character (regex)
+    if len(sep) > 1 and kwargs.get("engine") is None:
+        kwargs["engine"] = "python"
+    elif len(sep) > 1 and kwargs.get("engine") == "c":
+        warnings.warn(
+            "read_clipboard with regex separator does not work properly with c engine.",
+            stacklevel=find_stack_level(),
+        )
+
+    return read_csv(StringIO(text), sep=sep, dtype_backend=dtype_backend, **kwargs)
+
+
+def to_clipboard(
+    obj, excel: bool | None = True, sep: str | None = None, **kwargs
+) -> None:  # pragma: no cover
+    """
+    Attempt to write text representation of object to the system clipboard
+    The clipboard can be then pasted into Excel for example.
+
+    Parameters
+    ----------
+    obj : the object to write to the clipboard
+    excel : bool, defaults to True
+            if True, use the provided separator, writing in a csv
+            format for allowing easy pasting into excel.
+            if False, write a string representation of the object
+            to the clipboard
+    sep : optional, defaults to tab
+    other keywords are passed to to_csv
+
+    Notes
+    -----
+    Requirements for your platform
+      - Linux: xclip, or xsel (with PyQt4 modules)
+      - Windows:
+      - OS X:
+    """
+    encoding = kwargs.pop("encoding", "utf-8")
+
+    # testing if an invalid encoding is passed to clipboard
+    if encoding is not None and encoding.lower().replace("-", "") != "utf8":
+        raise ValueError("clipboard only supports utf-8 encoding")
+
+    from pandas.io.clipboard import clipboard_set
+
+    if excel is None:
+        excel = True
+
+    if excel:
+        try:
+            if sep is None:
+                sep = "\t"
+            buf = StringIO()
+
+            # clipboard_set (pyperclip) expects unicode
+            obj.to_csv(buf, sep=sep, encoding="utf-8", **kwargs)
+            text = buf.getvalue()
+
+            clipboard_set(text)
+            return
+        except TypeError:
+            warnings.warn(
+                "to_clipboard in excel mode requires a single character separator.",
+                stacklevel=find_stack_level(),
+            )
+    elif sep is not None:
+        warnings.warn(
+            "to_clipboard with excel=False ignores the sep argument.",
+            stacklevel=find_stack_level(),
+        )
+
+    if isinstance(obj, ABCDataFrame):
+        # str(df) has various unhelpful defaults, like truncation
+        with option_context("display.max_colwidth", None):
+            objstr = obj.to_string(**kwargs)
+    else:
+        objstr = str(obj)
+    clipboard_set(objstr)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/common.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..72c9deeb54fc7aaab781b2870171cf983a47da1f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/common.py
@@ -0,0 +1,1267 @@
+"""Common IO api utilities"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import codecs
+from collections import defaultdict
+from collections.abc import (
+    Hashable,
+    Mapping,
+    Sequence,
+)
+import dataclasses
+import functools
+import gzip
+from io import (
+    BufferedIOBase,
+    BytesIO,
+    RawIOBase,
+    StringIO,
+    TextIOBase,
+    TextIOWrapper,
+)
+import mmap
+import os
+from pathlib import Path
+import re
+import tarfile
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    AnyStr,
+    DefaultDict,
+    Generic,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+from urllib.parse import (
+    urljoin,
+    urlparse as parse_url,
+    uses_netloc,
+    uses_params,
+    uses_relative,
+)
+import warnings
+import zipfile
+
+from pandas._typing import (
+    BaseBuffer,
+    ReadCsvBuffer,
+)
+from pandas.compat import (
+    get_bz2_file,
+    get_lzma_file,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_file_like,
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.generic import ABCMultiIndex
+
+from pandas.core.shared_docs import _shared_docs
+
+_VALID_URLS = set(uses_relative + uses_netloc + uses_params)
+_VALID_URLS.discard("")
+_RFC_3986_PATTERN = re.compile(r"^[A-Za-z][A-Za-z0-9+\-+.]*://")
+
+BaseBufferT = TypeVar("BaseBufferT", bound=BaseBuffer)
+
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionDict,
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import MultiIndex
+
+
+@dataclasses.dataclass
+class IOArgs:
+    """
+    Return value of io/common.py:_get_filepath_or_buffer.
+    """
+
+    filepath_or_buffer: str | BaseBuffer
+    encoding: str
+    mode: str
+    compression: CompressionDict
+    should_close: bool = False
+
+
+@dataclasses.dataclass
+class IOHandles(Generic[AnyStr]):
+    """
+    Return value of io/common.py:get_handle
+
+    Can be used as a context manager.
+
+    This is used to easily close created buffers and to handle corner cases when
+    TextIOWrapper is inserted.
+
+    handle: The file handle to be used.
+    created_handles: All file handles that are created by get_handle
+    is_wrapped: Whether a TextIOWrapper needs to be detached.
+    """
+
+    # handle might not implement the IO-interface
+    handle: IO[AnyStr]
+    compression: CompressionDict
+    created_handles: list[IO[bytes] | IO[str]] = dataclasses.field(default_factory=list)
+    is_wrapped: bool = False
+
+    def close(self) -> None:
+        """
+        Close all created buffers.
+
+        Note: If a TextIOWrapper was inserted, it is flushed and detached to
+        avoid closing the potentially user-created buffer.
+        """
+        if self.is_wrapped:
+            assert isinstance(self.handle, TextIOWrapper)
+            self.handle.flush()
+            self.handle.detach()
+            self.created_handles.remove(self.handle)
+        for handle in self.created_handles:
+            handle.close()
+        self.created_handles = []
+        self.is_wrapped = False
+
+    def __enter__(self) -> IOHandles[AnyStr]:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+def is_url(url: object) -> bool:
+    """
+    Check to see if a URL has a valid protocol.
+
+    Parameters
+    ----------
+    url : str or unicode
+
+    Returns
+    -------
+    isurl : bool
+        If `url` has a valid protocol return True otherwise False.
+    """
+    if not isinstance(url, str):
+        return False
+    return parse_url(url).scheme in _VALID_URLS
+
+
+@overload
+def _expand_user(filepath_or_buffer: str) -> str:
+    ...
+
+
+@overload
+def _expand_user(filepath_or_buffer: BaseBufferT) -> BaseBufferT:
+    ...
+
+
+def _expand_user(filepath_or_buffer: str | BaseBufferT) -> str | BaseBufferT:
+    """
+    Return the argument with an initial component of ~ or ~user
+    replaced by that user's home directory.
+
+    Parameters
+    ----------
+    filepath_or_buffer : object to be converted if possible
+
+    Returns
+    -------
+    expanded_filepath_or_buffer : an expanded filepath or the
+                                  input if not expandable
+    """
+    if isinstance(filepath_or_buffer, str):
+        return os.path.expanduser(filepath_or_buffer)
+    return filepath_or_buffer
+
+
+def validate_header_arg(header: object) -> None:
+    if header is None:
+        return
+    if is_integer(header):
+        header = cast(int, header)
+        if header < 0:
+            # GH 27779
+            raise ValueError(
+                "Passing negative integer to header is invalid. "
+                "For no header, use header=None instead"
+            )
+        return
+    if is_list_like(header, allow_sets=False):
+        header = cast(Sequence, header)
+        if not all(map(is_integer, header)):
+            raise ValueError("header must be integer or list of integers")
+        if any(i < 0 for i in header):
+            raise ValueError("cannot specify multi-index header with negative integers")
+        return
+    if is_bool(header):
+        raise TypeError(
+            "Passing a bool to header is invalid. Use header=None for no header or "
+            "header=int or list-like of ints to specify "
+            "the row(s) making up the column names"
+        )
+    # GH 16338
+    raise ValueError("header must be integer or list of integers")
+
+
+@overload
+def stringify_path(filepath_or_buffer: FilePath, convert_file_like: bool = ...) -> str:
+    ...
+
+
+@overload
+def stringify_path(
+    filepath_or_buffer: BaseBufferT, convert_file_like: bool = ...
+) -> BaseBufferT:
+    ...
+
+
+def stringify_path(
+    filepath_or_buffer: FilePath | BaseBufferT,
+    convert_file_like: bool = False,
+) -> str | BaseBufferT:
+    """
+    Attempt to convert a path-like object to a string.
+
+    Parameters
+    ----------
+    filepath_or_buffer : object to be converted
+
+    Returns
+    -------
+    str_filepath_or_buffer : maybe a string version of the object
+
+    Notes
+    -----
+    Objects supporting the fspath protocol are coerced
+    according to its __fspath__ method.
+
+    Any other object is passed through unchanged, which includes bytes,
+    strings, buffers, or anything else that's not even path-like.
+    """
+    if not convert_file_like and is_file_like(filepath_or_buffer):
+        # GH 38125: some fsspec objects implement os.PathLike but have already opened a
+        # file. This prevents opening the file a second time. infer_compression calls
+        # this function with convert_file_like=True to infer the compression.
+        return cast(BaseBufferT, filepath_or_buffer)
+
+    if isinstance(filepath_or_buffer, os.PathLike):
+        filepath_or_buffer = filepath_or_buffer.__fspath__()
+    return _expand_user(filepath_or_buffer)
+
+
+def urlopen(*args, **kwargs):
+    """
+    Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
+    the stdlib.
+    """
+    import urllib.request
+
+    return urllib.request.urlopen(*args, **kwargs)
+
+
+def is_fsspec_url(url: FilePath | BaseBuffer) -> bool:
+    """
+    Returns true if the given URL looks like
+    something fsspec can handle
+    """
+    return (
+        isinstance(url, str)
+        and bool(_RFC_3986_PATTERN.match(url))
+        and not url.startswith(("http://", "https://"))
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "filepath_or_buffer",
+)
+def _get_filepath_or_buffer(
+    filepath_or_buffer: FilePath | BaseBuffer,
+    encoding: str = "utf-8",
+    compression: CompressionOptions | None = None,
+    mode: str = "r",
+    storage_options: StorageOptions | None = None,
+) -> IOArgs:
+    """
+    If the filepath_or_buffer is a url, translate and return the buffer.
+    Otherwise passthrough.
+
+    Parameters
+    ----------
+    filepath_or_buffer : a url, filepath (str, py.path.local or pathlib.Path),
+                         or buffer
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    encoding : the encoding to use to decode bytes, default is 'utf-8'
+    mode : str, optional
+
+    {storage_options}
+
+
+    Returns the dataclass IOArgs.
+    """
+    filepath_or_buffer = stringify_path(filepath_or_buffer)
+
+    # handle compression dict
+    compression_method, compression = get_compression_method(compression)
+    compression_method = infer_compression(filepath_or_buffer, compression_method)
+
+    # GH21227 internal compression is not used for non-binary handles.
+    if compression_method and hasattr(filepath_or_buffer, "write") and "b" not in mode:
+        warnings.warn(
+            "compression has no effect when passing a non-binary object as input.",
+            RuntimeWarning,
+            stacklevel=find_stack_level(),
+        )
+        compression_method = None
+
+    compression = dict(compression, method=compression_method)
+
+    # bz2 and xz do not write the byte order mark for utf-16 and utf-32
+    # print a warning when writing such files
+    if (
+        "w" in mode
+        and compression_method in ["bz2", "xz"]
+        and encoding in ["utf-16", "utf-32"]
+    ):
+        warnings.warn(
+            f"{compression} will not write the byte order mark for {encoding}",
+            UnicodeWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    # Use binary mode when converting path-like objects to file-like objects (fsspec)
+    # except when text mode is explicitly requested. The original mode is returned if
+    # fsspec is not used.
+    fsspec_mode = mode
+    if "t" not in fsspec_mode and "b" not in fsspec_mode:
+        fsspec_mode += "b"
+
+    if isinstance(filepath_or_buffer, str) and is_url(filepath_or_buffer):
+        # TODO: fsspec can also handle HTTP via requests, but leaving this
+        # unchanged. using fsspec appears to break the ability to infer if the
+        # server responded with gzipped data
+        storage_options = storage_options or {}
+
+        # waiting until now for importing to match intended lazy logic of
+        # urlopen function defined elsewhere in this module
+        import urllib.request
+
+        # assuming storage_options is to be interpreted as headers
+        req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
+        with urlopen(req_info) as req:
+            content_encoding = req.headers.get("Content-Encoding", None)
+            if content_encoding == "gzip":
+                # Override compression based on Content-Encoding header
+                compression = {"method": "gzip"}
+            reader = BytesIO(req.read())
+        return IOArgs(
+            filepath_or_buffer=reader,
+            encoding=encoding,
+            compression=compression,
+            should_close=True,
+            mode=fsspec_mode,
+        )
+
+    if is_fsspec_url(filepath_or_buffer):
+        assert isinstance(
+            filepath_or_buffer, str
+        )  # just to appease mypy for this branch
+        # two special-case s3-like protocols; these have special meaning in Hadoop,
+        # but are equivalent to just "s3" from fsspec's point of view
+        # cc #11071
+        if filepath_or_buffer.startswith("s3a://"):
+            filepath_or_buffer = filepath_or_buffer.replace("s3a://", "s3://")
+        if filepath_or_buffer.startswith("s3n://"):
+            filepath_or_buffer = filepath_or_buffer.replace("s3n://", "s3://")
+        fsspec = import_optional_dependency("fsspec")
+
+        # If botocore is installed we fallback to reading with anon=True
+        # to allow reads from public buckets
+        err_types_to_retry_with_anon: list[Any] = []
+        try:
+            import_optional_dependency("botocore")
+            from botocore.exceptions import (
+                ClientError,
+                NoCredentialsError,
+            )
+
+            err_types_to_retry_with_anon = [
+                ClientError,
+                NoCredentialsError,
+                PermissionError,
+            ]
+        except ImportError:
+            pass
+
+        try:
+            file_obj = fsspec.open(
+                filepath_or_buffer, mode=fsspec_mode, **(storage_options or {})
+            ).open()
+        # GH 34626 Reads from Public Buckets without Credentials needs anon=True
+        except tuple(err_types_to_retry_with_anon):
+            if storage_options is None:
+                storage_options = {"anon": True}
+            else:
+                # don't mutate user input.
+                storage_options = dict(storage_options)
+                storage_options["anon"] = True
+            file_obj = fsspec.open(
+                filepath_or_buffer, mode=fsspec_mode, **(storage_options or {})
+            ).open()
+
+        return IOArgs(
+            filepath_or_buffer=file_obj,
+            encoding=encoding,
+            compression=compression,
+            should_close=True,
+            mode=fsspec_mode,
+        )
+    elif storage_options:
+        raise ValueError(
+            "storage_options passed with file object or non-fsspec file path"
+        )
+
+    if isinstance(filepath_or_buffer, (str, bytes, mmap.mmap)):
+        return IOArgs(
+            filepath_or_buffer=_expand_user(filepath_or_buffer),
+            encoding=encoding,
+            compression=compression,
+            should_close=False,
+            mode=mode,
+        )
+
+    # is_file_like requires (read | write) & __iter__ but __iter__ is only
+    # needed for read_csv(engine=python)
+    if not (
+        hasattr(filepath_or_buffer, "read") or hasattr(filepath_or_buffer, "write")
+    ):
+        msg = f"Invalid file path or buffer object type: {type(filepath_or_buffer)}"
+        raise ValueError(msg)
+
+    return IOArgs(
+        filepath_or_buffer=filepath_or_buffer,
+        encoding=encoding,
+        compression=compression,
+        should_close=False,
+        mode=mode,
+    )
+
+
+def file_path_to_url(path: str) -> str:
+    """
+    converts an absolute native path to a FILE URL.
+
+    Parameters
+    ----------
+    path : a path in native format
+
+    Returns
+    -------
+    a valid FILE URL
+    """
+    # lazify expensive import (~30ms)
+    from urllib.request import pathname2url
+
+    return urljoin("file:", pathname2url(path))
+
+
+extension_to_compression = {
+    ".tar": "tar",
+    ".tar.gz": "tar",
+    ".tar.bz2": "tar",
+    ".tar.xz": "tar",
+    ".gz": "gzip",
+    ".bz2": "bz2",
+    ".zip": "zip",
+    ".xz": "xz",
+    ".zst": "zstd",
+}
+_supported_compressions = set(extension_to_compression.values())
+
+
+def get_compression_method(
+    compression: CompressionOptions,
+) -> tuple[str | None, CompressionDict]:
+    """
+    Simplifies a compression argument to a compression method string and
+    a mapping containing additional arguments.
+
+    Parameters
+    ----------
+    compression : str or mapping
+        If string, specifies the compression method. If mapping, value at key
+        'method' specifies compression method.
+
+    Returns
+    -------
+    tuple of ({compression method}, Optional[str]
+              {compression arguments}, Dict[str, Any])
+
+    Raises
+    ------
+    ValueError on mapping missing 'method' key
+    """
+    compression_method: str | None
+    if isinstance(compression, Mapping):
+        compression_args = dict(compression)
+        try:
+            compression_method = compression_args.pop("method")
+        except KeyError as err:
+            raise ValueError("If mapping, compression must have key 'method'") from err
+    else:
+        compression_args = {}
+        compression_method = compression
+    return compression_method, compression_args
+
+
+@doc(compression_options=_shared_docs["compression_options"] % "filepath_or_buffer")
+def infer_compression(
+    filepath_or_buffer: FilePath | BaseBuffer, compression: str | None
+) -> str | None:
+    """
+    Get the compression method for filepath_or_buffer. If compression='infer',
+    the inferred compression method is returned. Otherwise, the input
+    compression method is returned unchanged, unless it's invalid, in which
+    case an error is raised.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str or file handle
+        File path or object.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    Returns
+    -------
+    string or None
+
+    Raises
+    ------
+    ValueError on invalid compression specified.
+    """
+    if compression is None:
+        return None
+
+    # Infer compression
+    if compression == "infer":
+        # Convert all path types (e.g. pathlib.Path) to strings
+        filepath_or_buffer = stringify_path(filepath_or_buffer, convert_file_like=True)
+        if not isinstance(filepath_or_buffer, str):
+            # Cannot infer compression of a buffer, assume no compression
+            return None
+
+        # Infer compression from the filename/URL extension
+        for extension, compression in extension_to_compression.items():
+            if filepath_or_buffer.lower().endswith(extension):
+                return compression
+        return None
+
+    # Compression has been specified. Check that it's valid
+    if compression in _supported_compressions:
+        return compression
+
+    valid = ["infer", None] + sorted(_supported_compressions)
+    msg = (
+        f"Unrecognized compression type: {compression}\n"
+        f"Valid compression types are {valid}"
+    )
+    raise ValueError(msg)
+
+
+def check_parent_directory(path: Path | str) -> None:
+    """
+    Check if parent directory of a file exists, raise OSError if it does not
+
+    Parameters
+    ----------
+    path: Path or str
+        Path to check parent directory of
+    """
+    parent = Path(path).parent
+    if not parent.is_dir():
+        raise OSError(rf"Cannot save file into a non-existent directory: '{parent}'")
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: Literal[False],
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[bytes]:
+    ...
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: Literal[True] = ...,
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[str]:
+    ...
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: bool = ...,
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[str] | IOHandles[bytes]:
+    ...
+
+
+@doc(compression_options=_shared_docs["compression_options"] % "path_or_buf")
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = None,
+    compression: CompressionOptions | None = None,
+    memory_map: bool = False,
+    is_text: bool = True,
+    errors: str | None = None,
+    storage_options: StorageOptions | None = None,
+) -> IOHandles[str] | IOHandles[bytes]:
+    """
+    Get file handle for given path/buffer and mode.
+
+    Parameters
+    ----------
+    path_or_buf : str or file handle
+        File path or object.
+    mode : str
+        Mode to open path_or_buf with.
+    encoding : str or None
+        Encoding to use.
+    {compression_options}
+
+           May be a dict with key 'method' as compression mode
+           and other keys as compression options if compression
+           mode is 'zip'.
+
+           Passing compression options as keys in dict is
+           supported for compression modes 'gzip', 'bz2', 'zstd' and 'zip'.
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    memory_map : bool, default False
+        See parsers._parser_params for more information. Only used by read_csv.
+    is_text : bool, default True
+        Whether the type of the content passed to the file/buffer is string or
+        bytes. This is not the same as `"b" not in mode`. If a string content is
+        passed to a binary file/buffer, a wrapper is inserted.
+    errors : str, default 'strict'
+        Specifies how encoding and decoding errors are to be handled.
+        See the errors argument for :func:`open` for a full list
+        of options.
+    storage_options: StorageOptions = None
+        Passed to _get_filepath_or_buffer
+
+    Returns the dataclass IOHandles
+    """
+    # Windows does not default to utf-8. Set to utf-8 for a consistent behavior
+    encoding = encoding or "utf-8"
+
+    errors = errors or "strict"
+
+    # read_csv does not know whether the buffer is opened in binary/text mode
+    if _is_binary_mode(path_or_buf, mode) and "b" not in mode:
+        mode += "b"
+
+    # validate encoding and errors
+    codecs.lookup(encoding)
+    if isinstance(errors, str):
+        codecs.lookup_error(errors)
+
+    # open URLs
+    ioargs = _get_filepath_or_buffer(
+        path_or_buf,
+        encoding=encoding,
+        compression=compression,
+        mode=mode,
+        storage_options=storage_options,
+    )
+
+    handle = ioargs.filepath_or_buffer
+    handles: list[BaseBuffer]
+
+    # memory mapping needs to be the first step
+    # only used for read_csv
+    handle, memory_map, handles = _maybe_memory_map(handle, memory_map)
+
+    is_path = isinstance(handle, str)
+    compression_args = dict(ioargs.compression)
+    compression = compression_args.pop("method")
+
+    # Only for write methods
+    if "r" not in mode and is_path:
+        check_parent_directory(str(handle))
+
+    if compression:
+        if compression != "zstd":
+            # compression libraries do not like an explicit text-mode
+            ioargs.mode = ioargs.mode.replace("t", "")
+        elif compression == "zstd" and "b" not in ioargs.mode:
+            # python-zstandard defaults to text mode, but we always expect
+            # compression libraries to use binary mode.
+            ioargs.mode += "b"
+
+        # GZ Compression
+        if compression == "gzip":
+            if isinstance(handle, str):
+                # error: Incompatible types in assignment (expression has type
+                # "GzipFile", variable has type "Union[str, BaseBuffer]")
+                handle = gzip.GzipFile(  # type: ignore[assignment]
+                    filename=handle,
+                    mode=ioargs.mode,
+                    **compression_args,
+                )
+            else:
+                handle = gzip.GzipFile(
+                    # No overload variant of "GzipFile" matches argument types
+                    # "Union[str, BaseBuffer]", "str", "Dict[str, Any]"
+                    fileobj=handle,  # type: ignore[call-overload]
+                    mode=ioargs.mode,
+                    **compression_args,
+                )
+
+        # BZ Compression
+        elif compression == "bz2":
+            # Overload of "BZ2File" to handle pickle protocol 5
+            # "Union[str, BaseBuffer]", "str", "Dict[str, Any]"
+            handle = get_bz2_file()(  # type: ignore[call-overload]
+                handle,
+                mode=ioargs.mode,
+                **compression_args,
+            )
+
+        # ZIP Compression
+        elif compression == "zip":
+            # error: Argument 1 to "_BytesZipFile" has incompatible type
+            # "Union[str, BaseBuffer]"; expected "Union[Union[str, PathLike[str]],
+            # ReadBuffer[bytes], WriteBuffer[bytes]]"
+            handle = _BytesZipFile(
+                handle, ioargs.mode, **compression_args  # type: ignore[arg-type]
+            )
+            if handle.buffer.mode == "r":
+                handles.append(handle)
+                zip_names = handle.buffer.namelist()
+                if len(zip_names) == 1:
+                    handle = handle.buffer.open(zip_names.pop())
+                elif not zip_names:
+                    raise ValueError(f"Zero files found in ZIP file {path_or_buf}")
+                else:
+                    raise ValueError(
+                        "Multiple files found in ZIP file. "
+                        f"Only one file per ZIP: {zip_names}"
+                    )
+
+        # TAR Encoding
+        elif compression == "tar":
+            compression_args.setdefault("mode", ioargs.mode)
+            if isinstance(handle, str):
+                handle = _BytesTarFile(name=handle, **compression_args)
+            else:
+                # error: Argument "fileobj" to "_BytesTarFile" has incompatible
+                # type "BaseBuffer"; expected "Union[ReadBuffer[bytes],
+                # WriteBuffer[bytes], None]"
+                handle = _BytesTarFile(
+                    fileobj=handle, **compression_args  # type: ignore[arg-type]
+                )
+            assert isinstance(handle, _BytesTarFile)
+            if "r" in handle.buffer.mode:
+                handles.append(handle)
+                files = handle.buffer.getnames()
+                if len(files) == 1:
+                    file = handle.buffer.extractfile(files[0])
+                    assert file is not None
+                    handle = file
+                elif not files:
+                    raise ValueError(f"Zero files found in TAR archive {path_or_buf}")
+                else:
+                    raise ValueError(
+                        "Multiple files found in TAR archive. "
+                        f"Only one file per TAR archive: {files}"
+                    )
+
+        # XZ Compression
+        elif compression == "xz":
+            # error: Argument 1 to "LZMAFile" has incompatible type "Union[str,
+            # BaseBuffer]"; expected "Optional[Union[Union[str, bytes, PathLike[str],
+            # PathLike[bytes]], IO[bytes]], None]"
+            handle = get_lzma_file()(
+                handle, ioargs.mode, **compression_args  # type: ignore[arg-type]
+            )
+
+        # Zstd Compression
+        elif compression == "zstd":
+            zstd = import_optional_dependency("zstandard")
+            if "r" in ioargs.mode:
+                open_args = {"dctx": zstd.ZstdDecompressor(**compression_args)}
+            else:
+                open_args = {"cctx": zstd.ZstdCompressor(**compression_args)}
+            handle = zstd.open(
+                handle,
+                mode=ioargs.mode,
+                **open_args,
+            )
+
+        # Unrecognized Compression
+        else:
+            msg = f"Unrecognized compression type: {compression}"
+            raise ValueError(msg)
+
+        assert not isinstance(handle, str)
+        handles.append(handle)
+
+    elif isinstance(handle, str):
+        # Check whether the filename is to be opened in binary mode.
+        # Binary mode does not support 'encoding' and 'newline'.
+        if ioargs.encoding and "b" not in ioargs.mode:
+            # Encoding
+            handle = open(
+                handle,
+                ioargs.mode,
+                encoding=ioargs.encoding,
+                errors=errors,
+                newline="",
+            )
+        else:
+            # Binary mode
+            handle = open(handle, ioargs.mode)
+        handles.append(handle)
+
+    # Convert BytesIO or file objects passed with an encoding
+    is_wrapped = False
+    if not is_text and ioargs.mode == "rb" and isinstance(handle, TextIOBase):
+        # not added to handles as it does not open/buffer resources
+        handle = _BytesIOWrapper(
+            handle,
+            encoding=ioargs.encoding,
+        )
+    elif is_text and (
+        compression or memory_map or _is_binary_mode(handle, ioargs.mode)
+    ):
+        if (
+            not hasattr(handle, "readable")
+            or not hasattr(handle, "writable")
+            or not hasattr(handle, "seekable")
+        ):
+            handle = _IOWrapper(handle)
+        # error: Argument 1 to "TextIOWrapper" has incompatible type
+        # "_IOWrapper"; expected "IO[bytes]"
+        handle = TextIOWrapper(
+            handle,  # type: ignore[arg-type]
+            encoding=ioargs.encoding,
+            errors=errors,
+            newline="",
+        )
+        handles.append(handle)
+        # only marked as wrapped when the caller provided a handle
+        is_wrapped = not (
+            isinstance(ioargs.filepath_or_buffer, str) or ioargs.should_close
+        )
+
+    if "r" in ioargs.mode and not hasattr(handle, "read"):
+        raise TypeError(
+            "Expected file path name or file-like object, "
+            f"got {type(ioargs.filepath_or_buffer)} type"
+        )
+
+    handles.reverse()  # close the most recently added buffer first
+    if ioargs.should_close:
+        assert not isinstance(ioargs.filepath_or_buffer, str)
+        handles.append(ioargs.filepath_or_buffer)
+
+    return IOHandles(
+        # error: Argument "handle" to "IOHandles" has incompatible type
+        # "Union[TextIOWrapper, GzipFile, BaseBuffer, typing.IO[bytes],
+        # typing.IO[Any]]"; expected "pandas._typing.IO[Any]"
+        handle=handle,  # type: ignore[arg-type]
+        # error: Argument "created_handles" to "IOHandles" has incompatible type
+        # "List[BaseBuffer]"; expected "List[Union[IO[bytes], IO[str]]]"
+        created_handles=handles,  # type: ignore[arg-type]
+        is_wrapped=is_wrapped,
+        compression=ioargs.compression,
+    )
+
+
+# error: Definition of "__enter__" in base class "IOBase" is incompatible
+# with definition in base class "BinaryIO"
+class _BufferedWriter(BytesIO, ABC):  # type: ignore[misc]
+    """
+    Some objects do not support multiple .write() calls (TarFile and ZipFile).
+    This wrapper writes to the underlying buffer on close.
+    """
+
+    buffer = BytesIO()
+
+    @abstractmethod
+    def write_to_buffer(self) -> None:
+        ...
+
+    def close(self) -> None:
+        if self.closed:
+            # already closed
+            return
+        if self.getbuffer().nbytes:
+            # write to buffer
+            self.seek(0)
+            with self.buffer:
+                self.write_to_buffer()
+        else:
+            self.buffer.close()
+        super().close()
+
+
+class _BytesTarFile(_BufferedWriter):
+    def __init__(
+        self,
+        name: str | None = None,
+        mode: Literal["r", "a", "w", "x"] = "r",
+        fileobj: ReadBuffer[bytes] | WriteBuffer[bytes] | None = None,
+        archive_name: str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.archive_name = archive_name
+        self.name = name
+        # error: Incompatible types in assignment (expression has type "TarFile",
+        # base class "_BufferedWriter" defined the type as "BytesIO")
+        self.buffer: tarfile.TarFile = tarfile.TarFile.open(  # type: ignore[assignment]
+            name=name,
+            mode=self.extend_mode(mode),
+            fileobj=fileobj,
+            **kwargs,
+        )
+
+    def extend_mode(self, mode: str) -> str:
+        mode = mode.replace("b", "")
+        if mode != "w":
+            return mode
+        if self.name is not None:
+            suffix = Path(self.name).suffix
+            if suffix in (".gz", ".xz", ".bz2"):
+                mode = f"{mode}:{suffix[1:]}"
+        return mode
+
+    def infer_filename(self) -> str | None:
+        """
+        If an explicit archive_name is not given, we still want the file inside the zip
+        file not to be named something.tar, because that causes confusion (GH39465).
+        """
+        if self.name is None:
+            return None
+
+        filename = Path(self.name)
+        if filename.suffix == ".tar":
+            return filename.with_suffix("").name
+        elif filename.suffix in (".tar.gz", ".tar.bz2", ".tar.xz"):
+            return filename.with_suffix("").with_suffix("").name
+        return filename.name
+
+    def write_to_buffer(self) -> None:
+        # TarFile needs a non-empty string
+        archive_name = self.archive_name or self.infer_filename() or "tar"
+        tarinfo = tarfile.TarInfo(name=archive_name)
+        tarinfo.size = len(self.getvalue())
+        self.buffer.addfile(tarinfo, self)
+
+
+class _BytesZipFile(_BufferedWriter):
+    def __init__(
+        self,
+        file: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
+        mode: str,
+        archive_name: str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        mode = mode.replace("b", "")
+        self.archive_name = archive_name
+
+        kwargs.setdefault("compression", zipfile.ZIP_DEFLATED)
+        # error: Incompatible types in assignment (expression has type "ZipFile",
+        # base class "_BufferedWriter" defined the type as "BytesIO")
+        self.buffer: zipfile.ZipFile = zipfile.ZipFile(  # type: ignore[assignment]
+            file, mode, **kwargs
+        )
+
+    def infer_filename(self) -> str | None:
+        """
+        If an explicit archive_name is not given, we still want the file inside the zip
+        file not to be named something.zip, because that causes confusion (GH39465).
+        """
+        if isinstance(self.buffer.filename, (os.PathLike, str)):
+            filename = Path(self.buffer.filename)
+            if filename.suffix == ".zip":
+                return filename.with_suffix("").name
+            return filename.name
+        return None
+
+    def write_to_buffer(self) -> None:
+        # ZipFile needs a non-empty string
+        archive_name = self.archive_name or self.infer_filename() or "zip"
+        self.buffer.writestr(archive_name, self.getvalue())
+
+
+class _IOWrapper:
+    # TextIOWrapper is overly strict: it request that the buffer has seekable, readable,
+    # and writable. If we have a read-only buffer, we shouldn't need writable and vice
+    # versa. Some buffers, are seek/read/writ-able but they do not have the "-able"
+    # methods, e.g., tempfile.SpooledTemporaryFile.
+    # If a buffer does not have the above "-able" methods, we simple assume they are
+    # seek/read/writ-able.
+    def __init__(self, buffer: BaseBuffer) -> None:
+        self.buffer = buffer
+
+    def __getattr__(self, name: str):
+        return getattr(self.buffer, name)
+
+    def readable(self) -> bool:
+        if hasattr(self.buffer, "readable"):
+            return self.buffer.readable()
+        return True
+
+    def seekable(self) -> bool:
+        if hasattr(self.buffer, "seekable"):
+            return self.buffer.seekable()
+        return True
+
+    def writable(self) -> bool:
+        if hasattr(self.buffer, "writable"):
+            return self.buffer.writable()
+        return True
+
+
+class _BytesIOWrapper:
+    # Wrapper that wraps a StringIO buffer and reads bytes from it
+    # Created for compat with pyarrow read_csv
+    def __init__(self, buffer: StringIO | TextIOBase, encoding: str = "utf-8") -> None:
+        self.buffer = buffer
+        self.encoding = encoding
+        # Because a character can be represented by more than 1 byte,
+        # it is possible that reading will produce more bytes than n
+        # We store the extra bytes in this overflow variable, and append the
+        # overflow to the front of the bytestring the next time reading is performed
+        self.overflow = b""
+
+    def __getattr__(self, attr: str):
+        return getattr(self.buffer, attr)
+
+    def read(self, n: int | None = -1) -> bytes:
+        assert self.buffer is not None
+        bytestring = self.buffer.read(n).encode(self.encoding)
+        # When n=-1/n greater than remaining bytes: Read entire file/rest of file
+        combined_bytestring = self.overflow + bytestring
+        if n is None or n < 0 or n >= len(combined_bytestring):
+            self.overflow = b""
+            return combined_bytestring
+        else:
+            to_return = combined_bytestring[:n]
+            self.overflow = combined_bytestring[n:]
+            return to_return
+
+
+def _maybe_memory_map(
+    handle: str | BaseBuffer, memory_map: bool
+) -> tuple[str | BaseBuffer, bool, list[BaseBuffer]]:
+    """Try to memory map file/buffer."""
+    handles: list[BaseBuffer] = []
+    memory_map &= hasattr(handle, "fileno") or isinstance(handle, str)
+    if not memory_map:
+        return handle, memory_map, handles
+
+    # mmap used by only read_csv
+    handle = cast(ReadCsvBuffer, handle)
+
+    # need to open the file first
+    if isinstance(handle, str):
+        handle = open(handle, "rb")
+        handles.append(handle)
+
+    try:
+        # open mmap and adds *-able
+        # error: Argument 1 to "_IOWrapper" has incompatible type "mmap";
+        # expected "BaseBuffer"
+        wrapped = _IOWrapper(
+            mmap.mmap(
+                handle.fileno(), 0, access=mmap.ACCESS_READ  # type: ignore[arg-type]
+            )
+        )
+    finally:
+        for handle in reversed(handles):
+            # error: "BaseBuffer" has no attribute "close"
+            handle.close()  # type: ignore[attr-defined]
+
+    return wrapped, memory_map, [wrapped]
+
+
+def file_exists(filepath_or_buffer: FilePath | BaseBuffer) -> bool:
+    """Test whether file exists."""
+    exists = False
+    filepath_or_buffer = stringify_path(filepath_or_buffer)
+    if not isinstance(filepath_or_buffer, str):
+        return exists
+    try:
+        exists = os.path.exists(filepath_or_buffer)
+        # gh-5874: if the filepath is too long will raise here
+    except (TypeError, ValueError):
+        pass
+    return exists
+
+
+def _is_binary_mode(handle: FilePath | BaseBuffer, mode: str) -> bool:
+    """Whether the handle is opened in binary mode"""
+    # specified by user
+    if "t" in mode or "b" in mode:
+        return "b" in mode
+
+    # exceptions
+    text_classes = (
+        # classes that expect string but have 'b' in mode
+        codecs.StreamWriter,
+        codecs.StreamReader,
+        codecs.StreamReaderWriter,
+    )
+    if issubclass(type(handle), text_classes):
+        return False
+
+    return isinstance(handle, _get_binary_io_classes()) or "b" in getattr(
+        handle, "mode", mode
+    )
+
+
+@functools.lru_cache
+def _get_binary_io_classes() -> tuple[type, ...]:
+    """IO classes that that expect bytes"""
+    binary_classes: tuple[type, ...] = (BufferedIOBase, RawIOBase)
+
+    # python-zstandard doesn't use any of the builtin base classes; instead we
+    # have to use the `zstd.ZstdDecompressionReader` class for isinstance checks.
+    # Unfortunately `zstd.ZstdDecompressionReader` isn't exposed by python-zstandard
+    # so we have to get it from a `zstd.ZstdDecompressor` instance.
+    # See also https://github.com/indygreg/python-zstandard/pull/165.
+    zstd = import_optional_dependency("zstandard", errors="ignore")
+    if zstd is not None:
+        with zstd.ZstdDecompressor().stream_reader(b"") as reader:
+            binary_classes += (type(reader),)
+
+    return binary_classes
+
+
+def is_potential_multi_index(
+    columns: Sequence[Hashable] | MultiIndex,
+    index_col: bool | Sequence[int] | None = None,
+) -> bool:
+    """
+    Check whether or not the `columns` parameter
+    could be converted into a MultiIndex.
+
+    Parameters
+    ----------
+    columns : array-like
+        Object which may or may not be convertible into a MultiIndex
+    index_col : None, bool or list, optional
+        Column or columns to use as the (possibly hierarchical) index
+
+    Returns
+    -------
+    bool : Whether or not columns could become a MultiIndex
+    """
+    if index_col is None or isinstance(index_col, bool):
+        index_col = []
+
+    return bool(
+        len(columns)
+        and not isinstance(columns, ABCMultiIndex)
+        and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
+    )
+
+
+def dedup_names(
+    names: Sequence[Hashable], is_potential_multiindex: bool
+) -> Sequence[Hashable]:
+    """
+    Rename column names if duplicates exist.
+
+    Currently the renaming is done by appending a period and an autonumeric,
+    but a custom pattern may be supported in the future.
+
+    Examples
+    --------
+    >>> dedup_names(["x", "y", "x", "x"], is_potential_multiindex=False)
+    ['x', 'y', 'x.1', 'x.2']
+    """
+    names = list(names)  # so we can index
+    counts: DefaultDict[Hashable, int] = defaultdict(int)
+
+    for i, col in enumerate(names):
+        cur_count = counts[col]
+
+        while cur_count > 0:
+            counts[col] = cur_count + 1
+
+            if is_potential_multiindex:
+                # for mypy
+                assert isinstance(col, tuple)
+                col = col[:-1] + (f"{col[-1]}.{cur_count}",)
+            else:
+                col = f"{col}.{cur_count}"
+            cur_count = counts[col]
+
+        names[i] = col
+        counts[col] = cur_count + 1
+
+    return names
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..275cbf0148f944eb04ca6c40c624cc5df77aa626
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/__init__.py
@@ -0,0 +1,19 @@
+from pandas.io.excel._base import (
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+)
+from pandas.io.excel._odswriter import ODSWriter as _ODSWriter
+from pandas.io.excel._openpyxl import OpenpyxlWriter as _OpenpyxlWriter
+from pandas.io.excel._util import register_writer
+from pandas.io.excel._xlsxwriter import XlsxWriter as _XlsxWriter
+
+__all__ = ["read_excel", "ExcelWriter", "ExcelFile"]
+
+
+register_writer(_OpenpyxlWriter)
+
+register_writer(_XlsxWriter)
+
+
+register_writer(_ODSWriter)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_base.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..786f719337b84a29e5b6ea7577edd412b596920f
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_base.py
@@ -0,0 +1,1659 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import datetime
+from functools import partial
+from io import BytesIO
+import os
+from textwrap import fill
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    Literal,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+import zipfile
+
+from pandas._config import config
+
+from pandas._libs import lib
+from pandas._libs.parsers import STR_NA_VALUES
+from pandas.compat._optional import (
+    get_version,
+    import_optional_dependency,
+)
+from pandas.errors import EmptyDataError
+from pandas.util._decorators import (
+    Appender,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float,
+    is_integer,
+    is_list_like,
+)
+
+from pandas.core.frame import DataFrame
+from pandas.core.shared_docs import _shared_docs
+from pandas.util.version import Version
+
+from pandas.io.common import (
+    IOHandles,
+    get_handle,
+    stringify_path,
+    validate_header_arg,
+)
+from pandas.io.excel._util import (
+    fill_mi_header,
+    get_default_engine,
+    get_writer,
+    maybe_convert_usecols,
+    pop_header_name,
+)
+from pandas.io.parsers import TextParser
+from pandas.io.parsers.readers import validate_integer
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+    from pandas._typing import (
+        DtypeArg,
+        DtypeBackend,
+        ExcelWriterIfSheetExists,
+        FilePath,
+        IntStrT,
+        ReadBuffer,
+        Self,
+        SequenceNotStr,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+_read_excel_doc = (
+    """
+Read an Excel file into a ``pandas`` ``DataFrame``.
+
+Supports `xls`, `xlsx`, `xlsm`, `xlsb`, `odf`, `ods` and `odt` file extensions
+read from a local filesystem or URL. Supports an option to read
+a single sheet or a list of sheets.
+
+Parameters
+----------
+io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object
+    Any valid string path is acceptable. The string could be a URL. Valid
+    URL schemes include http, ftp, s3, and file. For file URLs, a host is
+    expected. A local file could be: ``file://localhost/path/to/table.xlsx``.
+
+    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
+
+    By file-like object, we refer to objects with a ``read()`` method,
+    such as a file handle (e.g. via builtin ``open`` function)
+    or ``StringIO``.
+
+    .. deprecated:: 2.1.0
+        Passing byte strings is deprecated. To read from a
+        byte string, wrap it in a ``BytesIO`` object.
+sheet_name : str, int, list, or None, default 0
+    Strings are used for sheet names. Integers are used in zero-indexed
+    sheet positions (chart sheets do not count as a sheet position).
+    Lists of strings/integers are used to request multiple sheets.
+    Specify ``None`` to get all worksheets.
+
+    Available cases:
+
+    * Defaults to ``0``: 1st sheet as a `DataFrame`
+    * ``1``: 2nd sheet as a `DataFrame`
+    * ``"Sheet1"``: Load sheet with name "Sheet1"
+    * ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
+      as a dict of `DataFrame`
+    * ``None``: All worksheets.
+
+header : int, list of int, default 0
+    Row (0-indexed) to use for the column labels of the parsed
+    DataFrame. If a list of integers is passed those row positions will
+    be combined into a ``MultiIndex``. Use None if there is no header.
+names : array-like, default None
+    List of column names to use. If file contains no header row,
+    then you should explicitly pass header=None.
+index_col : int, str, list of int, default None
+    Column (0-indexed) to use as the row labels of the DataFrame.
+    Pass None if there is no such column.  If a list is passed,
+    those columns will be combined into a ``MultiIndex``.  If a
+    subset of data is selected with ``usecols``, index_col
+    is based on the subset.
+
+    Missing values will be forward filled to allow roundtripping with
+    ``to_excel`` for ``merged_cells=True``. To avoid forward filling the
+    missing values use ``set_index`` after reading the data instead of
+    ``index_col``.
+usecols : str, list-like, or callable, default None
+    * If None, then parse all columns.
+    * If str, then indicates comma separated list of Excel column letters
+      and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
+      both sides.
+    * If list of int, then indicates list of column numbers to be parsed
+      (0-indexed).
+    * If list of string, then indicates list of column names to be parsed.
+    * If callable, then evaluate each column name against it and parse the
+      column if the callable returns ``True``.
+
+    Returns a subset of the columns according to behavior above.
+dtype : Type name or dict of column -> type, default None
+    Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32}}
+    Use ``object`` to preserve data as stored in Excel and not interpret dtype,
+    which will necessarily result in ``object`` dtype.
+    If converters are specified, they will be applied INSTEAD
+    of dtype conversion.
+    If you use ``None``, it will infer the dtype of each column based on the data.
+engine : {{'openpyxl', 'calamine', 'odf', 'pyxlsb', 'xlrd'}}, default None
+    If io is not a buffer or path, this must be set to identify io.
+    Engine compatibility :
+
+    - ``openpyxl`` supports newer Excel file formats.
+    - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
+      and OpenDocument (.ods) file formats.
+    - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
+    - ``pyxlsb`` supports Binary Excel files.
+    - ``xlrd`` supports old-style Excel files (.xls).
+
+    When ``engine=None``, the following logic will be used to determine the engine:
+
+    - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
+      then `odf <https://pypi.org/project/odfpy/>`_ will be used.
+    - Otherwise if ``path_or_buffer`` is an xls format, ``xlrd`` will be used.
+    - Otherwise if ``path_or_buffer`` is in xlsb format, ``pyxlsb`` will be used.
+    - Otherwise ``openpyxl`` will be used.
+converters : dict, default None
+    Dict of functions for converting values in certain columns. Keys can
+    either be integers or column labels, values are functions that take one
+    input argument, the Excel cell content, and return the transformed
+    content.
+true_values : list, default None
+    Values to consider as True.
+false_values : list, default None
+    Values to consider as False.
+skiprows : list-like, int, or callable, optional
+    Line numbers to skip (0-indexed) or number of lines to skip (int) at the
+    start of the file. If callable, the callable function will be evaluated
+    against the row indices, returning True if the row should be skipped and
+    False otherwise. An example of a valid callable argument would be ``lambda
+    x: x in [0, 2]``.
+nrows : int, default None
+    Number of rows to parse.
+na_values : scalar, str, list-like, or dict, default None
+    Additional strings to recognize as NA/NaN. If dict passed, specific
+    per-column NA values. By default the following values are interpreted
+    as NaN: '"""
+    + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent="    ")
+    + """'.
+keep_default_na : bool, default True
+    Whether or not to include the default NaN values when parsing the data.
+    Depending on whether ``na_values`` is passed in, the behavior is as follows:
+
+    * If ``keep_default_na`` is True, and ``na_values`` are specified,
+      ``na_values`` is appended to the default NaN values used for parsing.
+    * If ``keep_default_na`` is True, and ``na_values`` are not specified, only
+      the default NaN values are used for parsing.
+    * If ``keep_default_na`` is False, and ``na_values`` are specified, only
+      the NaN values specified ``na_values`` are used for parsing.
+    * If ``keep_default_na`` is False, and ``na_values`` are not specified, no
+      strings will be parsed as NaN.
+
+    Note that if `na_filter` is passed in as False, the ``keep_default_na`` and
+    ``na_values`` parameters will be ignored.
+na_filter : bool, default True
+    Detect missing value markers (empty strings and the value of na_values). In
+    data without any NAs, passing ``na_filter=False`` can improve the
+    performance of reading a large file.
+verbose : bool, default False
+    Indicate number of NA values placed in non-numeric columns.
+parse_dates : bool, list-like, or dict, default False
+    The behavior is as follows:
+
+    * ``bool``. If True -> try parsing the index.
+    * ``list`` of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
+      each as a separate date column.
+    * ``list`` of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
+      a single date column.
+    * ``dict``, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
+      result 'foo'
+
+    If a column or index contains an unparsable date, the entire column or
+    index will be returned unaltered as an object data type. If you don`t want to
+    parse some cells as date just change their type in Excel to "Text".
+    For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.
+
+    Note: A fast-path exists for iso8601-formatted dates.
+date_parser : function, optional
+    Function to use for converting a sequence of string columns to an array of
+    datetime instances. The default uses ``dateutil.parser.parser`` to do the
+    conversion. Pandas will try to call `date_parser` in three different ways,
+    advancing to the next if an exception occurs: 1) Pass one or more arrays
+    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
+    string values from the columns defined by `parse_dates` into a single array
+    and pass that; and 3) call `date_parser` once for each row using one or
+    more strings (corresponding to the columns defined by `parse_dates`) as
+    arguments.
+
+    .. deprecated:: 2.0.0
+       Use ``date_format`` instead, or read in as ``object`` and then apply
+       :func:`to_datetime` as-needed.
+date_format : str or dict of column -> format, default ``None``
+   If used in conjunction with ``parse_dates``, will parse dates according to this
+   format. For anything more complex,
+   please read in as ``object`` and then apply :func:`to_datetime` as-needed.
+
+   .. versionadded:: 2.0.0
+thousands : str, default None
+    Thousands separator for parsing string columns to numeric.  Note that
+    this parameter is only necessary for columns stored as TEXT in Excel,
+    any numeric columns will automatically be parsed, regardless of display
+    format.
+decimal : str, default '.'
+    Character to recognize as decimal point for parsing string columns to numeric.
+    Note that this parameter is only necessary for columns stored as TEXT in Excel,
+    any numeric columns will automatically be parsed, regardless of display
+    format.(e.g. use ',' for European data).
+
+    .. versionadded:: 1.4.0
+
+comment : str, default None
+    Comments out remainder of line. Pass a character or characters to this
+    argument to indicate comments in the input file. Any data between the
+    comment string and the end of the current line is ignored.
+skipfooter : int, default 0
+    Rows at the end to skip (0-indexed).
+{storage_options}
+
+dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+    Back-end data type applied to the resultant :class:`DataFrame`
+    (still experimental). Behaviour is as follows:
+
+    * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+      (default).
+    * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+      DataFrame.
+
+    .. versionadded:: 2.0
+
+engine_kwargs : dict, optional
+    Arbitrary keyword arguments passed to excel engine.
+
+Returns
+-------
+DataFrame or dict of DataFrames
+    DataFrame from the passed in Excel file. See notes in sheet_name
+    argument for more information on when a dict of DataFrames is returned.
+
+See Also
+--------
+DataFrame.to_excel : Write DataFrame to an Excel file.
+DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
+read_csv : Read a comma-separated values (csv) file into DataFrame.
+read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+Notes
+-----
+For specific information on the methods used for each Excel engine, refer to the pandas
+:ref:`user guide <io.excel_reader>`
+
+Examples
+--------
+The file can be read using the file name as string or an open file object:
+
+>>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP
+       Name  Value
+0   string1      1
+1   string2      2
+2  #Comment      3
+
+>>> pd.read_excel(open('tmp.xlsx', 'rb'),
+...               sheet_name='Sheet3')  # doctest: +SKIP
+   Unnamed: 0      Name  Value
+0           0   string1      1
+1           1   string2      2
+2           2  #Comment      3
+
+Index and header can be specified via the `index_col` and `header` arguments
+
+>>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  # doctest: +SKIP
+     0         1      2
+0  NaN      Name  Value
+1  0.0   string1      1
+2  1.0   string2      2
+3  2.0  #Comment      3
+
+Column types are inferred but can be explicitly specified
+
+>>> pd.read_excel('tmp.xlsx', index_col=0,
+...               dtype={{'Name': str, 'Value': float}})  # doctest: +SKIP
+       Name  Value
+0   string1    1.0
+1   string2    2.0
+2  #Comment    3.0
+
+True, False, and NA values, and thousands separators have defaults,
+but can be explicitly specified, too. Supply the values you would like
+as strings or lists of strings!
+
+>>> pd.read_excel('tmp.xlsx', index_col=0,
+...               na_values=['string1', 'string2'])  # doctest: +SKIP
+       Name  Value
+0       NaN      1
+1       NaN      2
+2  #Comment      3
+
+Comment lines in the excel input file can be skipped using the
+``comment`` kwarg.
+
+>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP
+      Name  Value
+0  string1    1.0
+1  string2    2.0
+2     None    NaN
+"""
+)
+
+
+@overload
+def read_excel(
+    io,
+    # sheet name is str or int -> DataFrame
+    sheet_name: str | int = ...,
+    *,
+    header: int | Sequence[int] | None = ...,
+    names: SequenceNotStr[Hashable] | range | None = ...,
+    index_col: int | str | Sequence[int] | None = ...,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = ...,
+    dtype: DtypeArg | None = ...,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ...,
+    converters: dict[str, Callable] | dict[int, Callable] | None = ...,
+    true_values: Iterable[Hashable] | None = ...,
+    false_values: Iterable[Hashable] | None = ...,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
+    nrows: int | None = ...,
+    na_values=...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool = ...,
+    parse_dates: list | dict | bool = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: dict[Hashable, str] | str | None = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    comment: str | None = ...,
+    skipfooter: int = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_excel(
+    io,
+    # sheet name is list or None -> dict[IntStrT, DataFrame]
+    sheet_name: list[IntStrT] | None,
+    *,
+    header: int | Sequence[int] | None = ...,
+    names: SequenceNotStr[Hashable] | range | None = ...,
+    index_col: int | str | Sequence[int] | None = ...,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = ...,
+    dtype: DtypeArg | None = ...,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ...,
+    converters: dict[str, Callable] | dict[int, Callable] | None = ...,
+    true_values: Iterable[Hashable] | None = ...,
+    false_values: Iterable[Hashable] | None = ...,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
+    nrows: int | None = ...,
+    na_values=...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool = ...,
+    parse_dates: list | dict | bool = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: dict[Hashable, str] | str | None = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    comment: str | None = ...,
+    skipfooter: int = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> dict[IntStrT, DataFrame]:
+    ...
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+@Appender(_read_excel_doc)
+def read_excel(
+    io,
+    sheet_name: str | int | list[IntStrT] | None = 0,
+    *,
+    header: int | Sequence[int] | None = 0,
+    names: SequenceNotStr[Hashable] | range | None = None,
+    index_col: int | str | Sequence[int] | None = None,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = None,
+    dtype: DtypeArg | None = None,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = None,
+    converters: dict[str, Callable] | dict[int, Callable] | None = None,
+    true_values: Iterable[Hashable] | None = None,
+    false_values: Iterable[Hashable] | None = None,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+    nrows: int | None = None,
+    na_values=None,
+    keep_default_na: bool = True,
+    na_filter: bool = True,
+    verbose: bool = False,
+    parse_dates: list | dict | bool = False,
+    date_parser: Callable | lib.NoDefault = lib.no_default,
+    date_format: dict[Hashable, str] | str | None = None,
+    thousands: str | None = None,
+    decimal: str = ".",
+    comment: str | None = None,
+    skipfooter: int = 0,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    engine_kwargs: dict | None = None,
+) -> DataFrame | dict[IntStrT, DataFrame]:
+    check_dtype_backend(dtype_backend)
+    should_close = False
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    if not isinstance(io, ExcelFile):
+        should_close = True
+        io = ExcelFile(
+            io,
+            storage_options=storage_options,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+    elif engine and engine != io.engine:
+        raise ValueError(
+            "Engine should not be specified when passing "
+            "an ExcelFile - ExcelFile already has the engine set"
+        )
+
+    try:
+        data = io.parse(
+            sheet_name=sheet_name,
+            header=header,
+            names=names,
+            index_col=index_col,
+            usecols=usecols,
+            dtype=dtype,
+            converters=converters,
+            true_values=true_values,
+            false_values=false_values,
+            skiprows=skiprows,
+            nrows=nrows,
+            na_values=na_values,
+            keep_default_na=keep_default_na,
+            na_filter=na_filter,
+            verbose=verbose,
+            parse_dates=parse_dates,
+            date_parser=date_parser,
+            date_format=date_format,
+            thousands=thousands,
+            decimal=decimal,
+            comment=comment,
+            skipfooter=skipfooter,
+            dtype_backend=dtype_backend,
+        )
+    finally:
+        # make sure to close opened file handles
+        if should_close:
+            io.close()
+    return data
+
+
+_WorkbookT = TypeVar("_WorkbookT")
+
+
+class BaseExcelReader(Generic[_WorkbookT]):
+    book: _WorkbookT
+
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        # First argument can also be bytes, so create a buffer
+        if isinstance(filepath_or_buffer, bytes):
+            filepath_or_buffer = BytesIO(filepath_or_buffer)
+
+        self.handles = IOHandles(
+            handle=filepath_or_buffer, compression={"method": None}
+        )
+        if not isinstance(filepath_or_buffer, (ExcelFile, self._workbook_class)):
+            self.handles = get_handle(
+                filepath_or_buffer, "rb", storage_options=storage_options, is_text=False
+            )
+
+        if isinstance(self.handles.handle, self._workbook_class):
+            self.book = self.handles.handle
+        elif hasattr(self.handles.handle, "read"):
+            # N.B. xlrd.Book has a read attribute too
+            self.handles.handle.seek(0)
+            try:
+                self.book = self.load_workbook(self.handles.handle, engine_kwargs)
+            except Exception:
+                self.close()
+                raise
+        else:
+            raise ValueError(
+                "Must explicitly set engine if not passing in buffer or path for io."
+            )
+
+    @property
+    def _workbook_class(self) -> type[_WorkbookT]:
+        raise NotImplementedError
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> _WorkbookT:
+        raise NotImplementedError
+
+    def close(self) -> None:
+        if hasattr(self, "book"):
+            if hasattr(self.book, "close"):
+                # pyxlsb: opens a TemporaryFile
+                # openpyxl: https://stackoverflow.com/questions/31416842/
+                #     openpyxl-does-not-close-excel-workbook-in-read-only-mode
+                self.book.close()
+            elif hasattr(self.book, "release_resources"):
+                # xlrd
+                # https://github.com/python-excel/xlrd/blob/2.0.1/xlrd/book.py#L548
+                self.book.release_resources()
+        self.handles.close()
+
+    @property
+    def sheet_names(self) -> list[str]:
+        raise NotImplementedError
+
+    def get_sheet_by_name(self, name: str):
+        raise NotImplementedError
+
+    def get_sheet_by_index(self, index: int):
+        raise NotImplementedError
+
+    def get_sheet_data(self, sheet, rows: int | None = None):
+        raise NotImplementedError
+
+    def raise_if_bad_sheet_by_index(self, index: int) -> None:
+        n_sheets = len(self.sheet_names)
+        if index >= n_sheets:
+            raise ValueError(
+                f"Worksheet index {index} is invalid, {n_sheets} worksheets found"
+            )
+
+    def raise_if_bad_sheet_by_name(self, name: str) -> None:
+        if name not in self.sheet_names:
+            raise ValueError(f"Worksheet named '{name}' not found")
+
+    def _check_skiprows_func(
+        self,
+        skiprows: Callable,
+        rows_to_use: int,
+    ) -> int:
+        """
+        Determine how many file rows are required to obtain `nrows` data
+        rows when `skiprows` is a function.
+
+        Parameters
+        ----------
+        skiprows : function
+            The function passed to read_excel by the user.
+        rows_to_use : int
+            The number of rows that will be needed for the header and
+            the data.
+
+        Returns
+        -------
+        int
+        """
+        i = 0
+        rows_used_so_far = 0
+        while rows_used_so_far < rows_to_use:
+            if not skiprows(i):
+                rows_used_so_far += 1
+            i += 1
+        return i
+
+    def _calc_rows(
+        self,
+        header: int | Sequence[int] | None,
+        index_col: int | Sequence[int] | None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None,
+        nrows: int | None,
+    ) -> int | None:
+        """
+        If nrows specified, find the number of rows needed from the
+        file, otherwise return None.
+
+
+        Parameters
+        ----------
+        header : int, list of int, or None
+            See read_excel docstring.
+        index_col : int, str, list of int, or None
+            See read_excel docstring.
+        skiprows : list-like, int, callable, or None
+            See read_excel docstring.
+        nrows : int or None
+            See read_excel docstring.
+
+        Returns
+        -------
+        int or None
+        """
+        if nrows is None:
+            return None
+        if header is None:
+            header_rows = 1
+        elif is_integer(header):
+            header = cast(int, header)
+            header_rows = 1 + header
+        else:
+            header = cast(Sequence, header)
+            header_rows = 1 + header[-1]
+        # If there is a MultiIndex header and an index then there is also
+        # a row containing just the index name(s)
+        if is_list_like(header) and index_col is not None:
+            header = cast(Sequence, header)
+            if len(header) > 1:
+                header_rows += 1
+        if skiprows is None:
+            return header_rows + nrows
+        if is_integer(skiprows):
+            skiprows = cast(int, skiprows)
+            return header_rows + nrows + skiprows
+        if is_list_like(skiprows):
+
+            def f(skiprows: Sequence, x: int) -> bool:
+                return x in skiprows
+
+            skiprows = cast(Sequence, skiprows)
+            return self._check_skiprows_func(partial(f, skiprows), header_rows + nrows)
+        if callable(skiprows):
+            return self._check_skiprows_func(
+                skiprows,
+                header_rows + nrows,
+            )
+        # else unexpected skiprows type: read_excel will not optimize
+        # the number of rows read from file
+        return None
+
+    def parse(
+        self,
+        sheet_name: str | int | list[int] | list[str] | None = 0,
+        header: int | Sequence[int] | None = 0,
+        names: SequenceNotStr[Hashable] | range | None = None,
+        index_col: int | Sequence[int] | None = None,
+        usecols=None,
+        dtype: DtypeArg | None = None,
+        true_values: Iterable[Hashable] | None = None,
+        false_values: Iterable[Hashable] | None = None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+        nrows: int | None = None,
+        na_values=None,
+        verbose: bool = False,
+        parse_dates: list | dict | bool = False,
+        date_parser: Callable | lib.NoDefault = lib.no_default,
+        date_format: dict[Hashable, str] | str | None = None,
+        thousands: str | None = None,
+        decimal: str = ".",
+        comment: str | None = None,
+        skipfooter: int = 0,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        **kwds,
+    ):
+        validate_header_arg(header)
+        validate_integer("nrows", nrows)
+
+        ret_dict = False
+
+        # Keep sheetname to maintain backwards compatibility.
+        sheets: list[int] | list[str]
+        if isinstance(sheet_name, list):
+            sheets = sheet_name
+            ret_dict = True
+        elif sheet_name is None:
+            sheets = self.sheet_names
+            ret_dict = True
+        elif isinstance(sheet_name, str):
+            sheets = [sheet_name]
+        else:
+            sheets = [sheet_name]
+
+        # handle same-type duplicates.
+        sheets = cast(Union[list[int], list[str]], list(dict.fromkeys(sheets).keys()))
+
+        output = {}
+
+        last_sheetname = None
+        for asheetname in sheets:
+            last_sheetname = asheetname
+            if verbose:
+                print(f"Reading sheet {asheetname}")
+
+            if isinstance(asheetname, str):
+                sheet = self.get_sheet_by_name(asheetname)
+            else:  # assume an integer if not a string
+                sheet = self.get_sheet_by_index(asheetname)
+
+            file_rows_needed = self._calc_rows(header, index_col, skiprows, nrows)
+            data = self.get_sheet_data(sheet, file_rows_needed)
+            if hasattr(sheet, "close"):
+                # pyxlsb opens two TemporaryFiles
+                sheet.close()
+            usecols = maybe_convert_usecols(usecols)
+
+            if not data:
+                output[asheetname] = DataFrame()
+                continue
+
+            is_list_header = False
+            is_len_one_list_header = False
+            if is_list_like(header):
+                assert isinstance(header, Sequence)
+                is_list_header = True
+                if len(header) == 1:
+                    is_len_one_list_header = True
+
+            if is_len_one_list_header:
+                header = cast(Sequence[int], header)[0]
+
+            # forward fill and pull out names for MultiIndex column
+            header_names = None
+            if header is not None and is_list_like(header):
+                assert isinstance(header, Sequence)
+
+                header_names = []
+                control_row = [True] * len(data[0])
+
+                for row in header:
+                    if is_integer(skiprows):
+                        assert isinstance(skiprows, int)
+                        row += skiprows
+
+                    if row > len(data) - 1:
+                        raise ValueError(
+                            f"header index {row} exceeds maximum index "
+                            f"{len(data) - 1} of data.",
+                        )
+
+                    data[row], control_row = fill_mi_header(data[row], control_row)
+
+                    if index_col is not None:
+                        header_name, _ = pop_header_name(data[row], index_col)
+                        header_names.append(header_name)
+
+            # If there is a MultiIndex header and an index then there is also
+            # a row containing just the index name(s)
+            has_index_names = False
+            if is_list_header and not is_len_one_list_header and index_col is not None:
+                index_col_list: Sequence[int]
+                if isinstance(index_col, int):
+                    index_col_list = [index_col]
+                else:
+                    assert isinstance(index_col, Sequence)
+                    index_col_list = index_col
+
+                # We have to handle mi without names. If any of the entries in the data
+                # columns are not empty, this is a regular row
+                assert isinstance(header, Sequence)
+                if len(header) < len(data):
+                    potential_index_names = data[len(header)]
+                    potential_data = [
+                        x
+                        for i, x in enumerate(potential_index_names)
+                        if not control_row[i] and i not in index_col_list
+                    ]
+                    has_index_names = all(x == "" or x is None for x in potential_data)
+
+            if is_list_like(index_col):
+                # Forward fill values for MultiIndex index.
+                if header is None:
+                    offset = 0
+                elif isinstance(header, int):
+                    offset = 1 + header
+                else:
+                    offset = 1 + max(header)
+
+                # GH34673: if MultiIndex names present and not defined in the header,
+                # offset needs to be incremented so that forward filling starts
+                # from the first MI value instead of the name
+                if has_index_names:
+                    offset += 1
+
+                # Check if we have an empty dataset
+                # before trying to collect data.
+                if offset < len(data):
+                    assert isinstance(index_col, Sequence)
+
+                    for col in index_col:
+                        last = data[offset][col]
+
+                        for row in range(offset + 1, len(data)):
+                            if data[row][col] == "" or data[row][col] is None:
+                                data[row][col] = last
+                            else:
+                                last = data[row][col]
+
+            # GH 12292 : error when read one empty column from excel file
+            try:
+                parser = TextParser(
+                    data,
+                    names=names,
+                    header=header,
+                    index_col=index_col,
+                    has_index_names=has_index_names,
+                    dtype=dtype,
+                    true_values=true_values,
+                    false_values=false_values,
+                    skiprows=skiprows,
+                    nrows=nrows,
+                    na_values=na_values,
+                    skip_blank_lines=False,  # GH 39808
+                    parse_dates=parse_dates,
+                    date_parser=date_parser,
+                    date_format=date_format,
+                    thousands=thousands,
+                    decimal=decimal,
+                    comment=comment,
+                    skipfooter=skipfooter,
+                    usecols=usecols,
+                    dtype_backend=dtype_backend,
+                    **kwds,
+                )
+
+                output[asheetname] = parser.read(nrows=nrows)
+
+                if header_names:
+                    output[asheetname].columns = output[asheetname].columns.set_names(
+                        header_names
+                    )
+
+            except EmptyDataError:
+                # No Data, return an empty DataFrame
+                output[asheetname] = DataFrame()
+
+            except Exception as err:
+                err.args = (f"{err.args[0]} (sheet: {asheetname})", *err.args[1:])
+                raise err
+
+        if last_sheetname is None:
+            raise ValueError("Sheet name is an empty list")
+
+        if ret_dict:
+            return output
+        else:
+            return output[last_sheetname]
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+class ExcelWriter(Generic[_WorkbookT]):
+    """
+    Class for writing DataFrame objects into excel sheets.
+
+    Default is to use:
+
+    * `xlsxwriter <https://pypi.org/project/XlsxWriter/>`__ for xlsx files if xlsxwriter
+      is installed otherwise `openpyxl <https://pypi.org/project/openpyxl/>`__
+    * `odswriter <https://pypi.org/project/odswriter/>`__ for ods files
+
+    See ``DataFrame.to_excel`` for typical usage.
+
+    The writer should be used as a context manager. Otherwise, call `close()` to save
+    and close any opened file handles.
+
+    Parameters
+    ----------
+    path : str or typing.BinaryIO
+        Path to xls or xlsx or ods file.
+    engine : str (optional)
+        Engine to use for writing. If None, defaults to
+        ``io.excel.<extension>.writer``.  NOTE: can only be passed as a keyword
+        argument.
+    date_format : str, default None
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+    datetime_format : str, default None
+        Format string for datetime objects written into Excel files.
+        (e.g. 'YYYY-MM-DD HH:MM:SS').
+    mode : {{'w', 'a'}}, default 'w'
+        File mode to use (write or append). Append does not work with fsspec URLs.
+    {storage_options}
+
+    if_sheet_exists : {{'error', 'new', 'replace', 'overlay'}}, default 'error'
+        How to behave when trying to write to a sheet that already
+        exists (append mode only).
+
+        * error: raise a ValueError.
+        * new: Create a new sheet, with a name determined by the engine.
+        * replace: Delete the contents of the sheet before writing to it.
+        * overlay: Write contents to the existing sheet without first removing,
+          but possibly over top of, the existing contents.
+
+        .. versionadded:: 1.3.0
+
+        .. versionchanged:: 1.4.0
+
+           Added ``overlay`` option
+
+    engine_kwargs : dict, optional
+        Keyword arguments to be passed into the engine. These will be passed to
+        the following functions of the respective engines:
+
+        * xlsxwriter: ``xlsxwriter.Workbook(file, **engine_kwargs)``
+        * openpyxl (write mode): ``openpyxl.Workbook(**engine_kwargs)``
+        * openpyxl (append mode): ``openpyxl.load_workbook(file, **engine_kwargs)``
+        * odswriter: ``odf.opendocument.OpenDocumentSpreadsheet(**engine_kwargs)``
+
+        .. versionadded:: 1.3.0
+
+    Notes
+    -----
+    For compatibility with CSV writers, ExcelWriter serializes lists
+    and dicts to strings before writing.
+
+    Examples
+    --------
+    Default usage:
+
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    To write to separate sheets in a single file:
+
+    >>> df1 = pd.DataFrame([["AAA", "BBB"]], columns=["Spam", "Egg"])  # doctest: +SKIP
+    >>> df2 = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
+    ...     df1.to_excel(writer, sheet_name="Sheet1")  # doctest: +SKIP
+    ...     df2.to_excel(writer, sheet_name="Sheet2")  # doctest: +SKIP
+
+    You can set the date format or datetime format:
+
+    >>> from datetime import date, datetime  # doctest: +SKIP
+    >>> df = pd.DataFrame(
+    ...     [
+    ...         [date(2014, 1, 31), date(1999, 9, 24)],
+    ...         [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+    ...     ],
+    ...     index=["Date", "Datetime"],
+    ...     columns=["X", "Y"],
+    ... )  # doctest: +SKIP
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     date_format="YYYY-MM-DD",
+    ...     datetime_format="YYYY-MM-DD HH:MM:SS"
+    ... ) as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    You can also append to an existing Excel file:
+
+    >>> with pd.ExcelWriter("path_to_file.xlsx", mode="a", engine="openpyxl") as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet3")  # doctest: +SKIP
+
+    Here, the `if_sheet_exists` parameter can be set to replace a sheet if it
+    already exists:
+
+    >>> with ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     mode="a",
+    ...     engine="openpyxl",
+    ...     if_sheet_exists="replace",
+    ... ) as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet1")  # doctest: +SKIP
+
+    You can also write multiple DataFrames to a single sheet. Note that the
+    ``if_sheet_exists`` parameter needs to be set to ``overlay``:
+
+    >>> with ExcelWriter("path_to_file.xlsx",
+    ...     mode="a",
+    ...     engine="openpyxl",
+    ...     if_sheet_exists="overlay",
+    ... ) as writer:
+    ...     df1.to_excel(writer, sheet_name="Sheet1")
+    ...     df2.to_excel(writer, sheet_name="Sheet1", startcol=3)  # doctest: +SKIP
+
+    You can store Excel file in RAM:
+
+    >>> import io
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])
+    >>> buffer = io.BytesIO()
+    >>> with pd.ExcelWriter(buffer) as writer:
+    ...     df.to_excel(writer)
+
+    You can pack Excel file into zip archive:
+
+    >>> import zipfile  # doctest: +SKIP
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with zipfile.ZipFile("path_to_file.zip", "w") as zf:
+    ...     with zf.open("filename.xlsx", "w") as buffer:
+    ...         with pd.ExcelWriter(buffer) as writer:
+    ...             df.to_excel(writer)  # doctest: +SKIP
+
+    You can specify additional arguments to the underlying engine:
+
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     engine="xlsxwriter",
+    ...     engine_kwargs={{"options": {{"nan_inf_to_errors": True}}}}
+    ... ) as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    In append mode, ``engine_kwargs`` are passed through to
+    openpyxl's ``load_workbook``:
+
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     engine="openpyxl",
+    ...     mode="a",
+    ...     engine_kwargs={{"keep_vba": True}}
+    ... ) as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet2")  # doctest: +SKIP
+    """
+
+    # Defining an ExcelWriter implementation (see abstract methods for more...)
+
+    # - Mandatory
+    #   - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)``
+    #     --> called to write additional DataFrames to disk
+    #   - ``_supported_extensions`` (tuple of supported extensions), used to
+    #      check that engine supports the given extension.
+    #   - ``_engine`` - string that gives the engine name. Necessary to
+    #     instantiate class directly and bypass ``ExcelWriterMeta`` engine
+    #     lookup.
+    #   - ``save(self)`` --> called to save file to disk
+    # - Mostly mandatory (i.e. should at least exist)
+    #   - book, cur_sheet, path
+
+    # - Optional:
+    #   - ``__init__(self, path, engine=None, **kwargs)`` --> always called
+    #     with path as first argument.
+
+    # You also need to register the class with ``register_writer()``.
+    # Technically, ExcelWriter implementations don't need to subclass
+    # ExcelWriter.
+
+    _engine: str
+    _supported_extensions: tuple[str, ...]
+
+    def __new__(
+        cls,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> Self:
+        # only switch class if generic(ExcelWriter)
+        if cls is ExcelWriter:
+            if engine is None or (isinstance(engine, str) and engine == "auto"):
+                if isinstance(path, str):
+                    ext = os.path.splitext(path)[-1][1:]
+                else:
+                    ext = "xlsx"
+
+                try:
+                    engine = config.get_option(f"io.excel.{ext}.writer", silent=True)
+                    if engine == "auto":
+                        engine = get_default_engine(ext, mode="writer")
+                except KeyError as err:
+                    raise ValueError(f"No engine for filetype: '{ext}'") from err
+
+            # for mypy
+            assert engine is not None
+            #  error: Incompatible types in assignment (expression has type
+            #  "type[ExcelWriter[Any]]", variable has type "type[Self]")
+            cls = get_writer(engine)  # type: ignore[assignment]
+
+        return object.__new__(cls)
+
+    # declare external properties you can count on
+    _path = None
+
+    @property
+    def supported_extensions(self) -> tuple[str, ...]:
+        """Extensions that writer engine supports."""
+        return self._supported_extensions
+
+    @property
+    def engine(self) -> str:
+        """Name of engine."""
+        return self._engine
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        raise NotImplementedError
+
+    @property
+    def book(self) -> _WorkbookT:
+        """
+        Book instance. Class type will depend on the engine used.
+
+        This attribute can be used to access engine-specific features.
+        """
+        raise NotImplementedError
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        """
+        Write given formatted cells into Excel an excel sheet
+
+        Parameters
+        ----------
+        cells : generator
+            cell of formatted data to save to Excel sheet
+        sheet_name : str, default None
+            Name of Excel sheet, if None, then use self.cur_sheet
+        startrow : upper left cell row to dump data frame
+        startcol : upper left cell column to dump data frame
+        freeze_panes: int tuple of length 2
+            contains the bottom-most row and right-most column to freeze
+        """
+        raise NotImplementedError
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        raise NotImplementedError
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        # validate that this engine can handle the extension
+        if isinstance(path, str):
+            ext = os.path.splitext(path)[-1]
+            self.check_extension(ext)
+
+        # use mode to open the file
+        if "b" not in mode:
+            mode += "b"
+        # use "a" for the user to append data to excel but internally use "r+" to let
+        # the excel backend first read the existing file and then write any data to it
+        mode = mode.replace("a", "r+")
+
+        if if_sheet_exists not in (None, "error", "new", "replace", "overlay"):
+            raise ValueError(
+                f"'{if_sheet_exists}' is not valid for if_sheet_exists. "
+                "Valid options are 'error', 'new', 'replace' and 'overlay'."
+            )
+        if if_sheet_exists and "r+" not in mode:
+            raise ValueError("if_sheet_exists is only valid in append mode (mode='a')")
+        if if_sheet_exists is None:
+            if_sheet_exists = "error"
+        self._if_sheet_exists = if_sheet_exists
+
+        # cast ExcelWriter to avoid adding 'if self._handles is not None'
+        self._handles = IOHandles(
+            cast(IO[bytes], path), compression={"compression": None}
+        )
+        if not isinstance(path, ExcelWriter):
+            self._handles = get_handle(
+                path, mode, storage_options=storage_options, is_text=False
+            )
+        self._cur_sheet = None
+
+        if date_format is None:
+            self._date_format = "YYYY-MM-DD"
+        else:
+            self._date_format = date_format
+        if datetime_format is None:
+            self._datetime_format = "YYYY-MM-DD HH:MM:SS"
+        else:
+            self._datetime_format = datetime_format
+
+        self._mode = mode
+
+    @property
+    def date_format(self) -> str:
+        """
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+        """
+        return self._date_format
+
+    @property
+    def datetime_format(self) -> str:
+        """
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+        """
+        return self._datetime_format
+
+    @property
+    def if_sheet_exists(self) -> str:
+        """
+        How to behave when writing to a sheet that already exists in append mode.
+        """
+        return self._if_sheet_exists
+
+    def __fspath__(self) -> str:
+        return getattr(self._handles.handle, "name", "")
+
+    def _get_sheet_name(self, sheet_name: str | None) -> str:
+        if sheet_name is None:
+            sheet_name = self._cur_sheet
+        if sheet_name is None:  # pragma: no cover
+            raise ValueError("Must pass explicit sheet_name or set _cur_sheet property")
+        return sheet_name
+
+    def _value_with_fmt(
+        self, val
+    ) -> tuple[
+        int | float | bool | str | datetime.datetime | datetime.date, str | None
+    ]:
+        """
+        Convert numpy types to Python types for the Excel writers.
+
+        Parameters
+        ----------
+        val : object
+            Value to be written into cells
+
+        Returns
+        -------
+        Tuple with the first element being the converted value and the second
+            being an optional format
+        """
+        fmt = None
+
+        if is_integer(val):
+            val = int(val)
+        elif is_float(val):
+            val = float(val)
+        elif is_bool(val):
+            val = bool(val)
+        elif isinstance(val, datetime.datetime):
+            fmt = self._datetime_format
+        elif isinstance(val, datetime.date):
+            fmt = self._date_format
+        elif isinstance(val, datetime.timedelta):
+            val = val.total_seconds() / 86400
+            fmt = "0"
+        else:
+            val = str(val)
+
+        return val, fmt
+
+    @classmethod
+    def check_extension(cls, ext: str) -> Literal[True]:
+        """
+        checks that path's extension against the Writer's supported
+        extensions.  If it isn't supported, raises UnsupportedFiletypeError.
+        """
+        if ext.startswith("."):
+            ext = ext[1:]
+        if not any(ext in extension for extension in cls._supported_extensions):
+            raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
+        return True
+
+    # Allow use as a contextmanager
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def close(self) -> None:
+        """synonym for save, to make it more file-like"""
+        self._save()
+        self._handles.close()
+
+
+XLS_SIGNATURES = (
+    b"\x09\x00\x04\x00\x07\x00\x10\x00",  # BIFF2
+    b"\x09\x02\x06\x00\x00\x00\x10\x00",  # BIFF3
+    b"\x09\x04\x06\x00\x00\x00\x10\x00",  # BIFF4
+    b"\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1",  # Compound File Binary
+)
+ZIP_SIGNATURE = b"PK\x03\x04"
+PEEK_SIZE = max(map(len, XLS_SIGNATURES + (ZIP_SIGNATURE,)))
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def inspect_excel_format(
+    content_or_path: FilePath | ReadBuffer[bytes],
+    storage_options: StorageOptions | None = None,
+) -> str | None:
+    """
+    Inspect the path or content of an excel file and get its format.
+
+    Adopted from xlrd: https://github.com/python-excel/xlrd.
+
+    Parameters
+    ----------
+    content_or_path : str or file-like object
+        Path to file or content of file to inspect. May be a URL.
+    {storage_options}
+
+    Returns
+    -------
+    str or None
+        Format of file if it can be determined.
+
+    Raises
+    ------
+    ValueError
+        If resulting stream is empty.
+    BadZipFile
+        If resulting stream does not have an XLS signature and is not a valid zipfile.
+    """
+    if isinstance(content_or_path, bytes):
+        content_or_path = BytesIO(content_or_path)
+
+    with get_handle(
+        content_or_path, "rb", storage_options=storage_options, is_text=False
+    ) as handle:
+        stream = handle.handle
+        stream.seek(0)
+        buf = stream.read(PEEK_SIZE)
+        if buf is None:
+            raise ValueError("stream is empty")
+        assert isinstance(buf, bytes)
+        peek = buf
+        stream.seek(0)
+
+        if any(peek.startswith(sig) for sig in XLS_SIGNATURES):
+            return "xls"
+        elif not peek.startswith(ZIP_SIGNATURE):
+            return None
+
+        with zipfile.ZipFile(stream) as zf:
+            # Workaround for some third party files that use forward slashes and
+            # lower case names.
+            component_names = [
+                name.replace("\\", "/").lower() for name in zf.namelist()
+            ]
+
+        if "xl/workbook.xml" in component_names:
+            return "xlsx"
+        if "xl/workbook.bin" in component_names:
+            return "xlsb"
+        if "content.xml" in component_names:
+            return "ods"
+        return "zip"
+
+
+class ExcelFile:
+    """
+    Class for parsing tabular Excel sheets into DataFrame objects.
+
+    See read_excel for more documentation.
+
+    Parameters
+    ----------
+    path_or_buffer : str, bytes, path object (pathlib.Path or py._path.local.LocalPath),
+        A file-like object, xlrd workbook or openpyxl workbook.
+        If a string or path object, expected to be a path to a
+        .xls, .xlsx, .xlsb, .xlsm, .odf, .ods, or .odt file.
+    engine : str, default None
+        If io is not a buffer or path, this must be set to identify io.
+        Supported engines: ``xlrd``, ``openpyxl``, ``odf``, ``pyxlsb``, ``calamine``
+        Engine compatibility :
+
+        - ``xlrd`` supports old-style Excel files (.xls).
+        - ``openpyxl`` supports newer Excel file formats.
+        - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
+        - ``pyxlsb`` supports Binary Excel files.
+        - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
+          and OpenDocument (.ods) file formats.
+
+        .. versionchanged:: 1.2.0
+
+           The engine `xlrd <https://xlrd.readthedocs.io/en/latest/>`_
+           now only supports old-style ``.xls`` files.
+           When ``engine=None``, the following logic will be
+           used to determine the engine:
+
+           - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
+             then `odf <https://pypi.org/project/odfpy/>`_ will be used.
+           - Otherwise if ``path_or_buffer`` is an xls format,
+             ``xlrd`` will be used.
+           - Otherwise if ``path_or_buffer`` is in xlsb format,
+             `pyxlsb <https://pypi.org/project/pyxlsb/>`_ will be used.
+
+           .. versionadded:: 1.3.0
+
+           - Otherwise if `openpyxl <https://pypi.org/project/openpyxl/>`_ is installed,
+             then ``openpyxl`` will be used.
+           - Otherwise if ``xlrd >= 2.0`` is installed, a ``ValueError`` will be raised.
+
+           .. warning::
+
+            Please do not report issues when using ``xlrd`` to read ``.xlsx`` files.
+            This is not supported, switch to using ``openpyxl`` instead.
+    engine_kwargs : dict, optional
+        Arbitrary keyword arguments passed to excel engine.
+
+    Examples
+    --------
+    >>> file = pd.ExcelFile('myfile.xlsx')  # doctest: +SKIP
+    >>> with pd.ExcelFile("myfile.xls") as xls:  # doctest: +SKIP
+    ...     df1 = pd.read_excel(xls, "Sheet1")  # doctest: +SKIP
+    """
+
+    from pandas.io.excel._calamine import CalamineReader
+    from pandas.io.excel._odfreader import ODFReader
+    from pandas.io.excel._openpyxl import OpenpyxlReader
+    from pandas.io.excel._pyxlsb import PyxlsbReader
+    from pandas.io.excel._xlrd import XlrdReader
+
+    _engines: Mapping[str, Any] = {
+        "xlrd": XlrdReader,
+        "openpyxl": OpenpyxlReader,
+        "odf": ODFReader,
+        "pyxlsb": PyxlsbReader,
+        "calamine": CalamineReader,
+    }
+
+    def __init__(
+        self,
+        path_or_buffer,
+        engine: str | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        if engine is not None and engine not in self._engines:
+            raise ValueError(f"Unknown engine: {engine}")
+
+        # First argument can also be bytes, so create a buffer
+        if isinstance(path_or_buffer, bytes):
+            path_or_buffer = BytesIO(path_or_buffer)
+            warnings.warn(
+                "Passing bytes to 'read_excel' is deprecated and "
+                "will be removed in a future version. To read from a "
+                "byte string, wrap it in a `BytesIO` object.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        # Could be a str, ExcelFile, Book, etc.
+        self.io = path_or_buffer
+        # Always a string
+        self._io = stringify_path(path_or_buffer)
+
+        # Determine xlrd version if installed
+        if import_optional_dependency("xlrd", errors="ignore") is None:
+            xlrd_version = None
+        else:
+            import xlrd
+
+            xlrd_version = Version(get_version(xlrd))
+
+        if engine is None:
+            # Only determine ext if it is needed
+            ext: str | None
+            if xlrd_version is not None and isinstance(path_or_buffer, xlrd.Book):
+                ext = "xls"
+            else:
+                ext = inspect_excel_format(
+                    content_or_path=path_or_buffer, storage_options=storage_options
+                )
+                if ext is None:
+                    raise ValueError(
+                        "Excel file format cannot be determined, you must specify "
+                        "an engine manually."
+                    )
+
+            engine = config.get_option(f"io.excel.{ext}.reader", silent=True)
+            if engine == "auto":
+                engine = get_default_engine(ext, mode="reader")
+
+        assert engine is not None
+        self.engine = engine
+        self.storage_options = storage_options
+
+        self._reader = self._engines[engine](
+            self._io,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    def __fspath__(self):
+        return self._io
+
+    def parse(
+        self,
+        sheet_name: str | int | list[int] | list[str] | None = 0,
+        header: int | Sequence[int] | None = 0,
+        names: SequenceNotStr[Hashable] | range | None = None,
+        index_col: int | Sequence[int] | None = None,
+        usecols=None,
+        converters=None,
+        true_values: Iterable[Hashable] | None = None,
+        false_values: Iterable[Hashable] | None = None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+        nrows: int | None = None,
+        na_values=None,
+        parse_dates: list | dict | bool = False,
+        date_parser: Callable | lib.NoDefault = lib.no_default,
+        date_format: str | dict[Hashable, str] | None = None,
+        thousands: str | None = None,
+        comment: str | None = None,
+        skipfooter: int = 0,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        **kwds,
+    ) -> DataFrame | dict[str, DataFrame] | dict[int, DataFrame]:
+        """
+        Parse specified sheet(s) into a DataFrame.
+
+        Equivalent to read_excel(ExcelFile, ...)  See the read_excel
+        docstring for more info on accepted parameters.
+
+        Returns
+        -------
+        DataFrame or dict of DataFrames
+            DataFrame from the passed in Excel file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+        >>> df.to_excel('myfile.xlsx')  # doctest: +SKIP
+        >>> file = pd.ExcelFile('myfile.xlsx')  # doctest: +SKIP
+        >>> file.parse()  # doctest: +SKIP
+        """
+        return self._reader.parse(
+            sheet_name=sheet_name,
+            header=header,
+            names=names,
+            index_col=index_col,
+            usecols=usecols,
+            converters=converters,
+            true_values=true_values,
+            false_values=false_values,
+            skiprows=skiprows,
+            nrows=nrows,
+            na_values=na_values,
+            parse_dates=parse_dates,
+            date_parser=date_parser,
+            date_format=date_format,
+            thousands=thousands,
+            comment=comment,
+            skipfooter=skipfooter,
+            dtype_backend=dtype_backend,
+            **kwds,
+        )
+
+    @property
+    def book(self):
+        return self._reader.book
+
+    @property
+    def sheet_names(self):
+        return self._reader.sheet_names
+
+    def close(self) -> None:
+        """close io if necessary"""
+        self._reader.close()
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_calamine.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_calamine.py
new file mode 100644
index 0000000000000000000000000000000000000000..5259469f7a569a1913aa49635b3c14e89a18d157
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_calamine.py
@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Union,
+)
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+import pandas as pd
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from python_calamine import (
+        CalamineSheet,
+        CalamineWorkbook,
+    )
+
+    from pandas._typing import (
+        FilePath,
+        NaTType,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+    )
+
+_CellValue = Union[int, float, str, bool, time, date, datetime, timedelta]
+
+
+class CalamineReader(BaseExcelReader["CalamineWorkbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using calamine engine (xlsx/xls/xlsb/ods).
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path to be parsed or
+            an open readable stream.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("python_calamine")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[CalamineWorkbook]:
+        from python_calamine import CalamineWorkbook
+
+        return CalamineWorkbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs: Any
+    ) -> CalamineWorkbook:
+        from python_calamine import load_workbook
+
+        return load_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self) -> list[str]:
+        from python_calamine import SheetTypeEnum
+
+        return [
+            sheet.name
+            for sheet in self.book.sheets_metadata
+            if sheet.typ == SheetTypeEnum.WorkSheet
+        ]
+
+    def get_sheet_by_name(self, name: str) -> CalamineSheet:
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.get_sheet_by_name(name)
+
+    def get_sheet_by_index(self, index: int) -> CalamineSheet:
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.get_sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet: CalamineSheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar | NaTType | time]]:
+        def _convert_cell(value: _CellValue) -> Scalar | NaTType | time:
+            if isinstance(value, float):
+                val = int(value)
+                if val == value:
+                    return val
+                else:
+                    return value
+            elif isinstance(value, date):
+                return pd.Timestamp(value)
+            elif isinstance(value, timedelta):
+                return pd.Timedelta(value)
+            elif isinstance(value, time):
+                return value
+
+            return value
+
+        rows: list[list[_CellValue]] = sheet.to_python(
+            skip_empty_area=False, nrows=file_rows_needed
+        )
+        data = [[_convert_cell(cell) for cell in row] for row in rows]
+
+        return data
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_odfreader.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_odfreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..69b514da32857119f048a25f647d1002315a9889
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_odfreader.py
@@ -0,0 +1,253 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    FilePath,
+    ReadBuffer,
+    Scalar,
+    StorageOptions,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+import pandas as pd
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from odf.opendocument import OpenDocument
+
+    from pandas._libs.tslibs.nattype import NaTType
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+class ODFReader(BaseExcelReader["OpenDocument"]):
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Read tables out of OpenDocument formatted files.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path to be parsed or
+            an open readable stream.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("odf")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[OpenDocument]:
+        from odf.opendocument import OpenDocument
+
+        return OpenDocument
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> OpenDocument:
+        from odf.opendocument import load
+
+        return load(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def empty_value(self) -> str:
+        """Property for compat with other readers."""
+        return ""
+
+    @property
+    def sheet_names(self) -> list[str]:
+        """Return a list of sheet names present in the document"""
+        from odf.table import Table
+
+        tables = self.book.getElementsByType(Table)
+        return [t.getAttribute("name") for t in tables]
+
+    def get_sheet_by_index(self, index: int):
+        from odf.table import Table
+
+        self.raise_if_bad_sheet_by_index(index)
+        tables = self.book.getElementsByType(Table)
+        return tables[index]
+
+    def get_sheet_by_name(self, name: str):
+        from odf.table import Table
+
+        self.raise_if_bad_sheet_by_name(name)
+        tables = self.book.getElementsByType(Table)
+
+        for table in tables:
+            if table.getAttribute("name") == name:
+                return table
+
+        self.close()
+        raise ValueError(f"sheet {name} not found")
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar | NaTType]]:
+        """
+        Parse an ODF Table into a list of lists
+        """
+        from odf.table import (
+            CoveredTableCell,
+            TableCell,
+            TableRow,
+        )
+
+        covered_cell_name = CoveredTableCell().qname
+        table_cell_name = TableCell().qname
+        cell_names = {covered_cell_name, table_cell_name}
+
+        sheet_rows = sheet.getElementsByType(TableRow)
+        empty_rows = 0
+        max_row_len = 0
+
+        table: list[list[Scalar | NaTType]] = []
+
+        for sheet_row in sheet_rows:
+            sheet_cells = [
+                x
+                for x in sheet_row.childNodes
+                if hasattr(x, "qname") and x.qname in cell_names
+            ]
+            empty_cells = 0
+            table_row: list[Scalar | NaTType] = []
+
+            for sheet_cell in sheet_cells:
+                if sheet_cell.qname == table_cell_name:
+                    value = self._get_cell_value(sheet_cell)
+                else:
+                    value = self.empty_value
+
+                column_repeat = self._get_column_repeat(sheet_cell)
+
+                # Queue up empty values, writing only if content succeeds them
+                if value == self.empty_value:
+                    empty_cells += column_repeat
+                else:
+                    table_row.extend([self.empty_value] * empty_cells)
+                    empty_cells = 0
+                    table_row.extend([value] * column_repeat)
+
+            if max_row_len < len(table_row):
+                max_row_len = len(table_row)
+
+            row_repeat = self._get_row_repeat(sheet_row)
+            if len(table_row) == 0:
+                empty_rows += row_repeat
+            else:
+                # add blank rows to our table
+                table.extend([[self.empty_value]] * empty_rows)
+                empty_rows = 0
+                table.extend(table_row for _ in range(row_repeat))
+            if file_rows_needed is not None and len(table) >= file_rows_needed:
+                break
+
+        # Make our table square
+        for row in table:
+            if len(row) < max_row_len:
+                row.extend([self.empty_value] * (max_row_len - len(row)))
+
+        return table
+
+    def _get_row_repeat(self, row) -> int:
+        """
+        Return number of times this row was repeated
+        Repeating an empty row appeared to be a common way
+        of representing sparse rows in the table.
+        """
+        from odf.namespaces import TABLENS
+
+        return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1))
+
+    def _get_column_repeat(self, cell) -> int:
+        from odf.namespaces import TABLENS
+
+        return int(cell.attributes.get((TABLENS, "number-columns-repeated"), 1))
+
+    def _get_cell_value(self, cell) -> Scalar | NaTType:
+        from odf.namespaces import OFFICENS
+
+        if str(cell) == "#N/A":
+            return np.nan
+
+        cell_type = cell.attributes.get((OFFICENS, "value-type"))
+        if cell_type == "boolean":
+            if str(cell) == "TRUE":
+                return True
+            return False
+        if cell_type is None:
+            return self.empty_value
+        elif cell_type == "float":
+            # GH5394
+            cell_value = float(cell.attributes.get((OFFICENS, "value")))
+            val = int(cell_value)
+            if val == cell_value:
+                return val
+            return cell_value
+        elif cell_type == "percentage":
+            cell_value = cell.attributes.get((OFFICENS, "value"))
+            return float(cell_value)
+        elif cell_type == "string":
+            return self._get_cell_string_value(cell)
+        elif cell_type == "currency":
+            cell_value = cell.attributes.get((OFFICENS, "value"))
+            return float(cell_value)
+        elif cell_type == "date":
+            cell_value = cell.attributes.get((OFFICENS, "date-value"))
+            return pd.Timestamp(cell_value)
+        elif cell_type == "time":
+            stamp = pd.Timestamp(str(cell))
+            # cast needed here because Scalar doesn't include datetime.time
+            return cast(Scalar, stamp.time())
+        else:
+            self.close()
+            raise ValueError(f"Unrecognized type {cell_type}")
+
+    def _get_cell_string_value(self, cell) -> str:
+        """
+        Find and decode OpenDocument text:s tags that represent
+        a run length encoded sequence of space characters.
+        """
+        from odf.element import Element
+        from odf.namespaces import TEXTNS
+        from odf.office import Annotation
+        from odf.text import S
+
+        office_annotation = Annotation().qname
+        text_s = S().qname
+
+        value = []
+
+        for fragment in cell.childNodes:
+            if isinstance(fragment, Element):
+                if fragment.qname == text_s:
+                    spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
+                    value.append(" " * spaces)
+                elif fragment.qname == office_annotation:
+                    continue
+                else:
+                    # recursive impl needed in case of nested fragments
+                    # with multiple spaces
+                    # https://github.com/pandas-dev/pandas/pull/36175#discussion_r484639704
+                    value.append(self._get_cell_string_value(fragment))
+            else:
+                value.append(str(fragment).strip("\n"))
+        return "".join(value)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_odswriter.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_odswriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc7dca2d95b6b434279f8290fdf929e737f75459
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_odswriter.py
@@ -0,0 +1,357 @@
+from __future__ import annotations
+
+from collections import defaultdict
+import datetime
+import json
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    DefaultDict,
+    cast,
+    overload,
+)
+
+from pandas.io.excel._base import ExcelWriter
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+    from pandas.io.formats.excel import ExcelCell
+
+
+class ODSWriter(ExcelWriter):
+    _engine = "odf"
+    _supported_extensions = (".ods",)
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format=None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        from odf.opendocument import OpenDocumentSpreadsheet
+
+        if mode == "a":
+            raise ValueError("Append mode is not supported with odf!")
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+        self._book = OpenDocumentSpreadsheet(**engine_kwargs)
+
+        super().__init__(
+            path,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        self._style_dict: dict[str, str] = {}
+
+    @property
+    def book(self):
+        """
+        Book instance of class odf.opendocument.OpenDocumentSpreadsheet.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        from odf.table import Table
+
+        result = {
+            sheet.getAttribute("name"): sheet
+            for sheet in self.book.getElementsByType(Table)
+        }
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        for sheet in self.sheets.values():
+            self.book.spreadsheet.addElement(sheet)
+        self.book.save(self._handles.handle)
+
+    def _write_cells(
+        self,
+        cells: list[ExcelCell],
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        """
+        Write the frame cells using odf
+        """
+        from odf.table import (
+            Table,
+            TableCell,
+            TableRow,
+        )
+        from odf.text import P
+
+        sheet_name = self._get_sheet_name(sheet_name)
+        assert sheet_name is not None
+
+        if sheet_name in self.sheets:
+            wks = self.sheets[sheet_name]
+        else:
+            wks = Table(name=sheet_name)
+            self.book.spreadsheet.addElement(wks)
+
+        if validate_freeze_panes(freeze_panes):
+            freeze_panes = cast(tuple[int, int], freeze_panes)
+            self._create_freeze_panes(sheet_name, freeze_panes)
+
+        for _ in range(startrow):
+            wks.addElement(TableRow())
+
+        rows: DefaultDict = defaultdict(TableRow)
+        col_count: DefaultDict = defaultdict(int)
+
+        for cell in sorted(cells, key=lambda cell: (cell.row, cell.col)):
+            # only add empty cells if the row is still empty
+            if not col_count[cell.row]:
+                for _ in range(startcol):
+                    rows[cell.row].addElement(TableCell())
+
+            # fill with empty cells if needed
+            for _ in range(cell.col - col_count[cell.row]):
+                rows[cell.row].addElement(TableCell())
+                col_count[cell.row] += 1
+
+            pvalue, tc = self._make_table_cell(cell)
+            rows[cell.row].addElement(tc)
+            col_count[cell.row] += 1
+            p = P(text=pvalue)
+            tc.addElement(p)
+
+        # add all rows to the sheet
+        if len(rows) > 0:
+            for row_nr in range(max(rows.keys()) + 1):
+                wks.addElement(rows[row_nr])
+
+    def _make_table_cell_attributes(self, cell) -> dict[str, int | str]:
+        """Convert cell attributes to OpenDocument attributes
+
+        Parameters
+        ----------
+        cell : ExcelCell
+            Spreadsheet cell data
+
+        Returns
+        -------
+        attributes : Dict[str, Union[int, str]]
+            Dictionary with attributes and attribute values
+        """
+        attributes: dict[str, int | str] = {}
+        style_name = self._process_style(cell.style)
+        if style_name is not None:
+            attributes["stylename"] = style_name
+        if cell.mergestart is not None and cell.mergeend is not None:
+            attributes["numberrowsspanned"] = max(1, cell.mergestart)
+            attributes["numbercolumnsspanned"] = cell.mergeend
+        return attributes
+
+    def _make_table_cell(self, cell) -> tuple[object, Any]:
+        """Convert cell data to an OpenDocument spreadsheet cell
+
+        Parameters
+        ----------
+        cell : ExcelCell
+            Spreadsheet cell data
+
+        Returns
+        -------
+        pvalue, cell : Tuple[str, TableCell]
+            Display value, Cell value
+        """
+        from odf.table import TableCell
+
+        attributes = self._make_table_cell_attributes(cell)
+        val, fmt = self._value_with_fmt(cell.val)
+        pvalue = value = val
+        if isinstance(val, bool):
+            value = str(val).lower()
+            pvalue = str(val).upper()
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="boolean",
+                    booleanvalue=value,
+                    attributes=attributes,
+                ),
+            )
+        elif isinstance(val, datetime.datetime):
+            # Fast formatting
+            value = val.isoformat()
+            # Slow but locale-dependent
+            pvalue = val.strftime("%c")
+            return (
+                pvalue,
+                TableCell(valuetype="date", datevalue=value, attributes=attributes),
+            )
+        elif isinstance(val, datetime.date):
+            # Fast formatting
+            value = f"{val.year}-{val.month:02d}-{val.day:02d}"
+            # Slow but locale-dependent
+            pvalue = val.strftime("%x")
+            return (
+                pvalue,
+                TableCell(valuetype="date", datevalue=value, attributes=attributes),
+            )
+        elif isinstance(val, str):
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="string",
+                    stringvalue=value,
+                    attributes=attributes,
+                ),
+            )
+        else:
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="float",
+                    value=value,
+                    attributes=attributes,
+                ),
+            )
+
+    @overload
+    def _process_style(self, style: dict[str, Any]) -> str:
+        ...
+
+    @overload
+    def _process_style(self, style: None) -> None:
+        ...
+
+    def _process_style(self, style: dict[str, Any] | None) -> str | None:
+        """Convert a style dictionary to a OpenDocument style sheet
+
+        Parameters
+        ----------
+        style : Dict
+            Style dictionary
+
+        Returns
+        -------
+        style_key : str
+            Unique style key for later reference in sheet
+        """
+        from odf.style import (
+            ParagraphProperties,
+            Style,
+            TableCellProperties,
+            TextProperties,
+        )
+
+        if style is None:
+            return None
+        style_key = json.dumps(style)
+        if style_key in self._style_dict:
+            return self._style_dict[style_key]
+        name = f"pd{len(self._style_dict)+1}"
+        self._style_dict[style_key] = name
+        odf_style = Style(name=name, family="table-cell")
+        if "font" in style:
+            font = style["font"]
+            if font.get("bold", False):
+                odf_style.addElement(TextProperties(fontweight="bold"))
+        if "borders" in style:
+            borders = style["borders"]
+            for side, thickness in borders.items():
+                thickness_translation = {"thin": "0.75pt solid #000000"}
+                odf_style.addElement(
+                    TableCellProperties(
+                        attributes={f"border{side}": thickness_translation[thickness]}
+                    )
+                )
+        if "alignment" in style:
+            alignment = style["alignment"]
+            horizontal = alignment.get("horizontal")
+            if horizontal:
+                odf_style.addElement(ParagraphProperties(textalign=horizontal))
+            vertical = alignment.get("vertical")
+            if vertical:
+                odf_style.addElement(TableCellProperties(verticalalign=vertical))
+        self.book.styles.addElement(odf_style)
+        return name
+
+    def _create_freeze_panes(
+        self, sheet_name: str, freeze_panes: tuple[int, int]
+    ) -> None:
+        """
+        Create freeze panes in the sheet.
+
+        Parameters
+        ----------
+        sheet_name : str
+            Name of the spreadsheet
+        freeze_panes : tuple of (int, int)
+            Freeze pane location x and y
+        """
+        from odf.config import (
+            ConfigItem,
+            ConfigItemMapEntry,
+            ConfigItemMapIndexed,
+            ConfigItemMapNamed,
+            ConfigItemSet,
+        )
+
+        config_item_set = ConfigItemSet(name="ooo:view-settings")
+        self.book.settings.addElement(config_item_set)
+
+        config_item_map_indexed = ConfigItemMapIndexed(name="Views")
+        config_item_set.addElement(config_item_map_indexed)
+
+        config_item_map_entry = ConfigItemMapEntry()
+        config_item_map_indexed.addElement(config_item_map_entry)
+
+        config_item_map_named = ConfigItemMapNamed(name="Tables")
+        config_item_map_entry.addElement(config_item_map_named)
+
+        config_item_map_entry = ConfigItemMapEntry(name=sheet_name)
+        config_item_map_named.addElement(config_item_map_entry)
+
+        config_item_map_entry.addElement(
+            ConfigItem(name="HorizontalSplitMode", type="short", text="2")
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="VerticalSplitMode", type="short", text="2")
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(
+                name="HorizontalSplitPosition", type="int", text=str(freeze_panes[0])
+            )
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(
+                name="VerticalSplitPosition", type="int", text=str(freeze_panes[1])
+            )
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="PositionRight", type="int", text=str(freeze_panes[0]))
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="PositionBottom", type="int", text=str(freeze_panes[1]))
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_openpyxl.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_openpyxl.py
new file mode 100644
index 0000000000000000000000000000000000000000..c546443868a62aed062bf3fd41d80933e4fbc59e
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_openpyxl.py
@@ -0,0 +1,639 @@
+from __future__ import annotations
+
+import mmap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import (
+    BaseExcelReader,
+    ExcelWriter,
+)
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from openpyxl import Workbook
+    from openpyxl.descriptors.serialisable import Serialisable
+
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+
+class OpenpyxlWriter(ExcelWriter):
+    _engine = "openpyxl"
+    _supported_extensions = (".xlsx", ".xlsm")
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        # Use the openpyxl module as the Excel writer.
+        from openpyxl.workbook import Workbook
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+
+        super().__init__(
+            path,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        # ExcelWriter replaced "a" by "r+" to allow us to first read the excel file from
+        # the file and later write to it
+        if "r+" in self._mode:  # Load from existing workbook
+            from openpyxl import load_workbook
+
+            try:
+                self._book = load_workbook(self._handles.handle, **engine_kwargs)
+            except TypeError:
+                self._handles.handle.close()
+                raise
+            self._handles.handle.seek(0)
+        else:
+            # Create workbook object with default optimized_write=True.
+            try:
+                self._book = Workbook(**engine_kwargs)
+            except TypeError:
+                self._handles.handle.close()
+                raise
+
+            if self.book.worksheets:
+                self.book.remove(self.book.worksheets[0])
+
+    @property
+    def book(self) -> Workbook:
+        """
+        Book instance of class openpyxl.workbook.Workbook.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        result = {name: self.book[name] for name in self.book.sheetnames}
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        self.book.save(self._handles.handle)
+        if "r+" in self._mode and not isinstance(self._handles.handle, mmap.mmap):
+            # truncate file to the written content
+            self._handles.handle.truncate()
+
+    @classmethod
+    def _convert_to_style_kwargs(cls, style_dict: dict) -> dict[str, Serialisable]:
+        """
+        Convert a style_dict to a set of kwargs suitable for initializing
+        or updating-on-copy an openpyxl v2 style object.
+
+        Parameters
+        ----------
+        style_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'font'
+                'fill'
+                'border' ('borders')
+                'alignment'
+                'number_format'
+                'protection'
+
+        Returns
+        -------
+        style_kwargs : dict
+            A dict with the same, normalized keys as ``style_dict`` but each
+            value has been replaced with a native openpyxl style object of the
+            appropriate class.
+        """
+        _style_key_map = {"borders": "border"}
+
+        style_kwargs: dict[str, Serialisable] = {}
+        for k, v in style_dict.items():
+            k = _style_key_map.get(k, k)
+            _conv_to_x = getattr(cls, f"_convert_to_{k}", lambda x: None)
+            new_v = _conv_to_x(v)
+            if new_v:
+                style_kwargs[k] = new_v
+
+        return style_kwargs
+
+    @classmethod
+    def _convert_to_color(cls, color_spec):
+        """
+        Convert ``color_spec`` to an openpyxl v2 Color object.
+
+        Parameters
+        ----------
+        color_spec : str, dict
+            A 32-bit ARGB hex string, or a dict with zero or more of the
+            following keys.
+                'rgb'
+                'indexed'
+                'auto'
+                'theme'
+                'tint'
+                'index'
+                'type'
+
+        Returns
+        -------
+        color : openpyxl.styles.Color
+        """
+        from openpyxl.styles import Color
+
+        if isinstance(color_spec, str):
+            return Color(color_spec)
+        else:
+            return Color(**color_spec)
+
+    @classmethod
+    def _convert_to_font(cls, font_dict):
+        """
+        Convert ``font_dict`` to an openpyxl v2 Font object.
+
+        Parameters
+        ----------
+        font_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'name'
+                'size' ('sz')
+                'bold' ('b')
+                'italic' ('i')
+                'underline' ('u')
+                'strikethrough' ('strike')
+                'color'
+                'vertAlign' ('vertalign')
+                'charset'
+                'scheme'
+                'family'
+                'outline'
+                'shadow'
+                'condense'
+
+        Returns
+        -------
+        font : openpyxl.styles.Font
+        """
+        from openpyxl.styles import Font
+
+        _font_key_map = {
+            "sz": "size",
+            "b": "bold",
+            "i": "italic",
+            "u": "underline",
+            "strike": "strikethrough",
+            "vertalign": "vertAlign",
+        }
+
+        font_kwargs = {}
+        for k, v in font_dict.items():
+            k = _font_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            font_kwargs[k] = v
+
+        return Font(**font_kwargs)
+
+    @classmethod
+    def _convert_to_stop(cls, stop_seq):
+        """
+        Convert ``stop_seq`` to a list of openpyxl v2 Color objects,
+        suitable for initializing the ``GradientFill`` ``stop`` parameter.
+
+        Parameters
+        ----------
+        stop_seq : iterable
+            An iterable that yields objects suitable for consumption by
+            ``_convert_to_color``.
+
+        Returns
+        -------
+        stop : list of openpyxl.styles.Color
+        """
+        return map(cls._convert_to_color, stop_seq)
+
+    @classmethod
+    def _convert_to_fill(cls, fill_dict: dict[str, Any]):
+        """
+        Convert ``fill_dict`` to an openpyxl v2 Fill object.
+
+        Parameters
+        ----------
+        fill_dict : dict
+            A dict with one or more of the following keys (or their synonyms),
+                'fill_type' ('patternType', 'patterntype')
+                'start_color' ('fgColor', 'fgcolor')
+                'end_color' ('bgColor', 'bgcolor')
+            or one or more of the following keys (or their synonyms).
+                'type' ('fill_type')
+                'degree'
+                'left'
+                'right'
+                'top'
+                'bottom'
+                'stop'
+
+        Returns
+        -------
+        fill : openpyxl.styles.Fill
+        """
+        from openpyxl.styles import (
+            GradientFill,
+            PatternFill,
+        )
+
+        _pattern_fill_key_map = {
+            "patternType": "fill_type",
+            "patterntype": "fill_type",
+            "fgColor": "start_color",
+            "fgcolor": "start_color",
+            "bgColor": "end_color",
+            "bgcolor": "end_color",
+        }
+
+        _gradient_fill_key_map = {"fill_type": "type"}
+
+        pfill_kwargs = {}
+        gfill_kwargs = {}
+        for k, v in fill_dict.items():
+            pk = _pattern_fill_key_map.get(k)
+            gk = _gradient_fill_key_map.get(k)
+            if pk in ["start_color", "end_color"]:
+                v = cls._convert_to_color(v)
+            if gk == "stop":
+                v = cls._convert_to_stop(v)
+            if pk:
+                pfill_kwargs[pk] = v
+            elif gk:
+                gfill_kwargs[gk] = v
+            else:
+                pfill_kwargs[k] = v
+                gfill_kwargs[k] = v
+
+        try:
+            return PatternFill(**pfill_kwargs)
+        except TypeError:
+            return GradientFill(**gfill_kwargs)
+
+    @classmethod
+    def _convert_to_side(cls, side_spec):
+        """
+        Convert ``side_spec`` to an openpyxl v2 Side object.
+
+        Parameters
+        ----------
+        side_spec : str, dict
+            A string specifying the border style, or a dict with zero or more
+            of the following keys (or their synonyms).
+                'style' ('border_style')
+                'color'
+
+        Returns
+        -------
+        side : openpyxl.styles.Side
+        """
+        from openpyxl.styles import Side
+
+        _side_key_map = {"border_style": "style"}
+
+        if isinstance(side_spec, str):
+            return Side(style=side_spec)
+
+        side_kwargs = {}
+        for k, v in side_spec.items():
+            k = _side_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            side_kwargs[k] = v
+
+        return Side(**side_kwargs)
+
+    @classmethod
+    def _convert_to_border(cls, border_dict):
+        """
+        Convert ``border_dict`` to an openpyxl v2 Border object.
+
+        Parameters
+        ----------
+        border_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'left'
+                'right'
+                'top'
+                'bottom'
+                'diagonal'
+                'diagonal_direction'
+                'vertical'
+                'horizontal'
+                'diagonalUp' ('diagonalup')
+                'diagonalDown' ('diagonaldown')
+                'outline'
+
+        Returns
+        -------
+        border : openpyxl.styles.Border
+        """
+        from openpyxl.styles import Border
+
+        _border_key_map = {"diagonalup": "diagonalUp", "diagonaldown": "diagonalDown"}
+
+        border_kwargs = {}
+        for k, v in border_dict.items():
+            k = _border_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            if k in ["left", "right", "top", "bottom", "diagonal"]:
+                v = cls._convert_to_side(v)
+            border_kwargs[k] = v
+
+        return Border(**border_kwargs)
+
+    @classmethod
+    def _convert_to_alignment(cls, alignment_dict):
+        """
+        Convert ``alignment_dict`` to an openpyxl v2 Alignment object.
+
+        Parameters
+        ----------
+        alignment_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'horizontal'
+                'vertical'
+                'text_rotation'
+                'wrap_text'
+                'shrink_to_fit'
+                'indent'
+        Returns
+        -------
+        alignment : openpyxl.styles.Alignment
+        """
+        from openpyxl.styles import Alignment
+
+        return Alignment(**alignment_dict)
+
+    @classmethod
+    def _convert_to_number_format(cls, number_format_dict):
+        """
+        Convert ``number_format_dict`` to an openpyxl v2.1.0 number format
+        initializer.
+
+        Parameters
+        ----------
+        number_format_dict : dict
+            A dict with zero or more of the following keys.
+                'format_code' : str
+
+        Returns
+        -------
+        number_format : str
+        """
+        return number_format_dict["format_code"]
+
+    @classmethod
+    def _convert_to_protection(cls, protection_dict):
+        """
+        Convert ``protection_dict`` to an openpyxl v2 Protection object.
+
+        Parameters
+        ----------
+        protection_dict : dict
+            A dict with zero or more of the following keys.
+                'locked'
+                'hidden'
+
+        Returns
+        -------
+        """
+        from openpyxl.styles import Protection
+
+        return Protection(**protection_dict)
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        # Write the frame cells using openpyxl.
+        sheet_name = self._get_sheet_name(sheet_name)
+
+        _style_cache: dict[str, dict[str, Serialisable]] = {}
+
+        if sheet_name in self.sheets and self._if_sheet_exists != "new":
+            if "r+" in self._mode:
+                if self._if_sheet_exists == "replace":
+                    old_wks = self.sheets[sheet_name]
+                    target_index = self.book.index(old_wks)
+                    del self.book[sheet_name]
+                    wks = self.book.create_sheet(sheet_name, target_index)
+                elif self._if_sheet_exists == "error":
+                    raise ValueError(
+                        f"Sheet '{sheet_name}' already exists and "
+                        f"if_sheet_exists is set to 'error'."
+                    )
+                elif self._if_sheet_exists == "overlay":
+                    wks = self.sheets[sheet_name]
+                else:
+                    raise ValueError(
+                        f"'{self._if_sheet_exists}' is not valid for if_sheet_exists. "
+                        "Valid options are 'error', 'new', 'replace' and 'overlay'."
+                    )
+            else:
+                wks = self.sheets[sheet_name]
+        else:
+            wks = self.book.create_sheet()
+            wks.title = sheet_name
+
+        if validate_freeze_panes(freeze_panes):
+            freeze_panes = cast(tuple[int, int], freeze_panes)
+            wks.freeze_panes = wks.cell(
+                row=freeze_panes[0] + 1, column=freeze_panes[1] + 1
+            )
+
+        for cell in cells:
+            xcell = wks.cell(
+                row=startrow + cell.row + 1, column=startcol + cell.col + 1
+            )
+            xcell.value, fmt = self._value_with_fmt(cell.val)
+            if fmt:
+                xcell.number_format = fmt
+
+            style_kwargs: dict[str, Serialisable] | None = {}
+            if cell.style:
+                key = str(cell.style)
+                style_kwargs = _style_cache.get(key)
+                if style_kwargs is None:
+                    style_kwargs = self._convert_to_style_kwargs(cell.style)
+                    _style_cache[key] = style_kwargs
+
+            if style_kwargs:
+                for k, v in style_kwargs.items():
+                    setattr(xcell, k, v)
+
+            if cell.mergestart is not None and cell.mergeend is not None:
+                wks.merge_cells(
+                    start_row=startrow + cell.row + 1,
+                    start_column=startcol + cell.col + 1,
+                    end_column=startcol + cell.mergeend + 1,
+                    end_row=startrow + cell.mergestart + 1,
+                )
+
+                # When cells are merged only the top-left cell is preserved
+                # The behaviour of the other cells in a merged range is
+                # undefined
+                if style_kwargs:
+                    first_row = startrow + cell.row + 1
+                    last_row = startrow + cell.mergestart + 1
+                    first_col = startcol + cell.col + 1
+                    last_col = startcol + cell.mergeend + 1
+
+                    for row in range(first_row, last_row + 1):
+                        for col in range(first_col, last_col + 1):
+                            if row == first_row and col == first_col:
+                                # Ignore first cell. It is already handled.
+                                continue
+                            xcell = wks.cell(column=col, row=row)
+                            for k, v in style_kwargs.items():
+                                setattr(xcell, k, v)
+
+
+class OpenpyxlReader(BaseExcelReader["Workbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using openpyxl engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("openpyxl")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Workbook]:
+        from openpyxl import Workbook
+
+        return Workbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> Workbook:
+        from openpyxl import load_workbook
+
+        default_kwargs = {"read_only": True, "data_only": True, "keep_links": False}
+
+        return load_workbook(
+            filepath_or_buffer,
+            **(default_kwargs | engine_kwargs),
+        )
+
+    @property
+    def sheet_names(self) -> list[str]:
+        return [sheet.title for sheet in self.book.worksheets]
+
+    def get_sheet_by_name(self, name: str):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book[name]
+
+    def get_sheet_by_index(self, index: int):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.worksheets[index]
+
+    def _convert_cell(self, cell) -> Scalar:
+        from openpyxl.cell.cell import (
+            TYPE_ERROR,
+            TYPE_NUMERIC,
+        )
+
+        if cell.value is None:
+            return ""  # compat with xlrd
+        elif cell.data_type == TYPE_ERROR:
+            return np.nan
+        elif cell.data_type == TYPE_NUMERIC:
+            val = int(cell.value)
+            if val == cell.value:
+                return val
+            return float(cell.value)
+
+        return cell.value
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        if self.book.read_only:
+            sheet.reset_dimensions()
+
+        data: list[list[Scalar]] = []
+        last_row_with_data = -1
+        for row_number, row in enumerate(sheet.rows):
+            converted_row = [self._convert_cell(cell) for cell in row]
+            while converted_row and converted_row[-1] == "":
+                # trim trailing empty elements
+                converted_row.pop()
+            if converted_row:
+                last_row_with_data = row_number
+            data.append(converted_row)
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
+
+        # Trim trailing empty rows
+        data = data[: last_row_with_data + 1]
+
+        if len(data) > 0:
+            # extend rows to max width
+            max_width = max(len(data_row) for data_row in data)
+            if min(len(data_row) for data_row in data) < max_width:
+                empty_cell: list[Scalar] = [""]
+                data = [
+                    data_row + (max_width - len(data_row)) * empty_cell
+                    for data_row in data
+                ]
+
+        return data
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_pyxlsb.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_pyxlsb.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6e42616c20438fa4cab16e94b5d16a01c9c61df
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_pyxlsb.py
@@ -0,0 +1,127 @@
+# pyright: reportMissingImports=false
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from pyxlsb import Workbook
+
+    from pandas._typing import (
+        FilePath,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+    )
+
+
+class PyxlsbReader(BaseExcelReader["Workbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using pyxlsb engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object, or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("pyxlsb")
+        # This will call load_workbook on the filepath or buffer
+        # And set the result to the book-attribute
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Workbook]:
+        from pyxlsb import Workbook
+
+        return Workbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> Workbook:
+        from pyxlsb import open_workbook
+
+        # TODO: hack in buffer capability
+        # This might need some modifications to the Pyxlsb library
+        # Actual work for opening it is in xlsbpackage.py, line 20-ish
+
+        return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self) -> list[str]:
+        return self.book.sheets
+
+    def get_sheet_by_name(self, name: str):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.get_sheet(name)
+
+    def get_sheet_by_index(self, index: int):
+        self.raise_if_bad_sheet_by_index(index)
+        # pyxlsb sheets are indexed from 1 onwards
+        # There's a fix for this in the source, but the pypi package doesn't have it
+        return self.book.get_sheet(index + 1)
+
+    def _convert_cell(self, cell) -> Scalar:
+        # TODO: there is no way to distinguish between floats and datetimes in pyxlsb
+        # This means that there is no way to read datetime types from an xlsb file yet
+        if cell.v is None:
+            return ""  # Prevents non-named columns from not showing up as Unnamed: i
+        if isinstance(cell.v, float):
+            val = int(cell.v)
+            if val == cell.v:
+                return val
+            else:
+                return float(cell.v)
+
+        return cell.v
+
+    def get_sheet_data(
+        self,
+        sheet,
+        file_rows_needed: int | None = None,
+    ) -> list[list[Scalar]]:
+        data: list[list[Scalar]] = []
+        previous_row_number = -1
+        # When sparse=True the rows can have different lengths and empty rows are
+        # not returned. The cells are namedtuples of row, col, value (r, c, v).
+        for row in sheet.rows(sparse=True):
+            row_number = row[0].r
+            converted_row = [self._convert_cell(cell) for cell in row]
+            while converted_row and converted_row[-1] == "":
+                # trim trailing empty elements
+                converted_row.pop()
+            if converted_row:
+                data.extend([[]] * (row_number - previous_row_number - 1))
+                data.append(converted_row)
+                previous_row_number = row_number
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
+        if data:
+            # extend rows to max_width
+            max_width = max(len(data_row) for data_row in data)
+            if min(len(data_row) for data_row in data) < max_width:
+                empty_cell: list[Scalar] = [""]
+                data = [
+                    data_row + (max_width - len(data_row)) * empty_cell
+                    for data_row in data
+                ]
+        return data
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_util.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7a1fcb8052e391d0853be64866663f4e6de9d08
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_util.py
@@ -0,0 +1,334 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    MutableMapping,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    TypeVar,
+    overload,
+)
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+
+if TYPE_CHECKING:
+    from pandas.io.excel._base import ExcelWriter
+
+    ExcelWriter_t = type[ExcelWriter]
+    usecols_func = TypeVar("usecols_func", bound=Callable[[Hashable], object])
+
+_writers: MutableMapping[str, ExcelWriter_t] = {}
+
+
+def register_writer(klass: ExcelWriter_t) -> None:
+    """
+    Add engine to the excel writer registry.io.excel.
+
+    You must use this method to integrate with ``to_excel``.
+
+    Parameters
+    ----------
+    klass : ExcelWriter
+    """
+    if not callable(klass):
+        raise ValueError("Can only register callables as engines")
+    engine_name = klass._engine
+    _writers[engine_name] = klass
+
+
+def get_default_engine(ext: str, mode: Literal["reader", "writer"] = "reader") -> str:
+    """
+    Return the default reader/writer for the given extension.
+
+    Parameters
+    ----------
+    ext : str
+        The excel file extension for which to get the default engine.
+    mode : str {'reader', 'writer'}
+        Whether to get the default engine for reading or writing.
+        Either 'reader' or 'writer'
+
+    Returns
+    -------
+    str
+        The default engine for the extension.
+    """
+    _default_readers = {
+        "xlsx": "openpyxl",
+        "xlsm": "openpyxl",
+        "xlsb": "pyxlsb",
+        "xls": "xlrd",
+        "ods": "odf",
+    }
+    _default_writers = {
+        "xlsx": "openpyxl",
+        "xlsm": "openpyxl",
+        "xlsb": "pyxlsb",
+        "ods": "odf",
+    }
+    assert mode in ["reader", "writer"]
+    if mode == "writer":
+        # Prefer xlsxwriter over openpyxl if installed
+        xlsxwriter = import_optional_dependency("xlsxwriter", errors="warn")
+        if xlsxwriter:
+            _default_writers["xlsx"] = "xlsxwriter"
+        return _default_writers[ext]
+    else:
+        return _default_readers[ext]
+
+
+def get_writer(engine_name: str) -> ExcelWriter_t:
+    try:
+        return _writers[engine_name]
+    except KeyError as err:
+        raise ValueError(f"No Excel writer '{engine_name}'") from err
+
+
+def _excel2num(x: str) -> int:
+    """
+    Convert Excel column name like 'AB' to 0-based column index.
+
+    Parameters
+    ----------
+    x : str
+        The Excel column name to convert to a 0-based column index.
+
+    Returns
+    -------
+    num : int
+        The column index corresponding to the name.
+
+    Raises
+    ------
+    ValueError
+        Part of the Excel column name was invalid.
+    """
+    index = 0
+
+    for c in x.upper().strip():
+        cp = ord(c)
+
+        if cp < ord("A") or cp > ord("Z"):
+            raise ValueError(f"Invalid column name: {x}")
+
+        index = index * 26 + cp - ord("A") + 1
+
+    return index - 1
+
+
+def _range2cols(areas: str) -> list[int]:
+    """
+    Convert comma separated list of column names and ranges to indices.
+
+    Parameters
+    ----------
+    areas : str
+        A string containing a sequence of column ranges (or areas).
+
+    Returns
+    -------
+    cols : list
+        A list of 0-based column indices.
+
+    Examples
+    --------
+    >>> _range2cols('A:E')
+    [0, 1, 2, 3, 4]
+    >>> _range2cols('A,C,Z:AB')
+    [0, 2, 25, 26, 27]
+    """
+    cols: list[int] = []
+
+    for rng in areas.split(","):
+        if ":" in rng:
+            rngs = rng.split(":")
+            cols.extend(range(_excel2num(rngs[0]), _excel2num(rngs[1]) + 1))
+        else:
+            cols.append(_excel2num(rng))
+
+    return cols
+
+
+@overload
+def maybe_convert_usecols(usecols: str | list[int]) -> list[int]:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: list[str]) -> list[str]:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: usecols_func) -> usecols_func:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: None) -> None:
+    ...
+
+
+def maybe_convert_usecols(
+    usecols: str | list[int] | list[str] | usecols_func | None,
+) -> None | list[int] | list[str] | usecols_func:
+    """
+    Convert `usecols` into a compatible format for parsing in `parsers.py`.
+
+    Parameters
+    ----------
+    usecols : object
+        The use-columns object to potentially convert.
+
+    Returns
+    -------
+    converted : object
+        The compatible format of `usecols`.
+    """
+    if usecols is None:
+        return usecols
+
+    if is_integer(usecols):
+        raise ValueError(
+            "Passing an integer for `usecols` is no longer supported.  "
+            "Please pass in a list of int from 0 to `usecols` inclusive instead."
+        )
+
+    if isinstance(usecols, str):
+        return _range2cols(usecols)
+
+    return usecols
+
+
+@overload
+def validate_freeze_panes(freeze_panes: tuple[int, int]) -> Literal[True]:
+    ...
+
+
+@overload
+def validate_freeze_panes(freeze_panes: None) -> Literal[False]:
+    ...
+
+
+def validate_freeze_panes(freeze_panes: tuple[int, int] | None) -> bool:
+    if freeze_panes is not None:
+        if len(freeze_panes) == 2 and all(
+            isinstance(item, int) for item in freeze_panes
+        ):
+            return True
+
+        raise ValueError(
+            "freeze_panes must be of form (row, column) "
+            "where row and column are integers"
+        )
+
+    # freeze_panes wasn't specified, return False so it won't be applied
+    # to output sheet
+    return False
+
+
+def fill_mi_header(
+    row: list[Hashable], control_row: list[bool]
+) -> tuple[list[Hashable], list[bool]]:
+    """
+    Forward fill blank entries in row but only inside the same parent index.
+
+    Used for creating headers in Multiindex.
+
+    Parameters
+    ----------
+    row : list
+        List of items in a single row.
+    control_row : list of bool
+        Helps to determine if particular column is in same parent index as the
+        previous value. Used to stop propagation of empty cells between
+        different indexes.
+
+    Returns
+    -------
+    Returns changed row and control_row
+    """
+    last = row[0]
+    for i in range(1, len(row)):
+        if not control_row[i]:
+            last = row[i]
+
+        if row[i] == "" or row[i] is None:
+            row[i] = last
+        else:
+            control_row[i] = False
+            last = row[i]
+
+    return row, control_row
+
+
+def pop_header_name(
+    row: list[Hashable], index_col: int | Sequence[int]
+) -> tuple[Hashable | None, list[Hashable]]:
+    """
+    Pop the header name for MultiIndex parsing.
+
+    Parameters
+    ----------
+    row : list
+        The data row to parse for the header name.
+    index_col : int, list
+        The index columns for our data. Assumed to be non-null.
+
+    Returns
+    -------
+    header_name : str
+        The extracted header name.
+    trimmed_row : list
+        The original data row with the header name removed.
+    """
+    # Pop out header name and fill w/blank.
+    if is_list_like(index_col):
+        assert isinstance(index_col, Iterable)
+        i = max(index_col)
+    else:
+        assert not isinstance(index_col, Iterable)
+        i = index_col
+
+    header_name = row[i]
+    header_name = None if header_name == "" else header_name
+
+    return header_name, row[:i] + [""] + row[i + 1 :]
+
+
+def combine_kwargs(engine_kwargs: dict[str, Any] | None, kwargs: dict) -> dict:
+    """
+    Used to combine two sources of kwargs for the backend engine.
+
+    Use of kwargs is deprecated, this function is solely for use in 1.3 and should
+    be removed in 1.4/2.0. Also _base.ExcelWriter.__new__ ensures either engine_kwargs
+    or kwargs must be None or empty respectively.
+
+    Parameters
+    ----------
+    engine_kwargs: dict
+        kwargs to be passed through to the engine.
+    kwargs: dict
+        kwargs to be psased through to the engine (deprecated)
+
+    Returns
+    -------
+    engine_kwargs combined with kwargs
+    """
+    if engine_kwargs is None:
+        result = {}
+    else:
+        result = engine_kwargs.copy()
+    result.update(kwargs)
+    return result
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_xlrd.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_xlrd.py
new file mode 100644
index 0000000000000000000000000000000000000000..a444970792e6e65faf3d8947b721fff59487d994
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_xlrd.py
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+from datetime import time
+import math
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from xlrd import Book
+
+    from pandas._typing import (
+        Scalar,
+        StorageOptions,
+    )
+
+
+class XlrdReader(BaseExcelReader["Book"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using xlrd engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        err_msg = "Install xlrd >= 2.0.1 for xls Excel support"
+        import_optional_dependency("xlrd", extra=err_msg)
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Book]:
+        from xlrd import Book
+
+        return Book
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> Book:
+        from xlrd import open_workbook
+
+        if hasattr(filepath_or_buffer, "read"):
+            data = filepath_or_buffer.read()
+            return open_workbook(file_contents=data, **engine_kwargs)
+        else:
+            return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self):
+        return self.book.sheet_names()
+
+    def get_sheet_by_name(self, name):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.sheet_by_name(name)
+
+    def get_sheet_by_index(self, index):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        from xlrd import (
+            XL_CELL_BOOLEAN,
+            XL_CELL_DATE,
+            XL_CELL_ERROR,
+            XL_CELL_NUMBER,
+            xldate,
+        )
+
+        epoch1904 = self.book.datemode
+
+        def _parse_cell(cell_contents, cell_typ):
+            """
+            converts the contents of the cell into a pandas appropriate object
+            """
+            if cell_typ == XL_CELL_DATE:
+                # Use the newer xlrd datetime handling.
+                try:
+                    cell_contents = xldate.xldate_as_datetime(cell_contents, epoch1904)
+                except OverflowError:
+                    return cell_contents
+
+                # Excel doesn't distinguish between dates and time,
+                # so we treat dates on the epoch as times only.
+                # Also, Excel supports 1900 and 1904 epochs.
+                year = (cell_contents.timetuple())[0:3]
+                if (not epoch1904 and year == (1899, 12, 31)) or (
+                    epoch1904 and year == (1904, 1, 1)
+                ):
+                    cell_contents = time(
+                        cell_contents.hour,
+                        cell_contents.minute,
+                        cell_contents.second,
+                        cell_contents.microsecond,
+                    )
+
+            elif cell_typ == XL_CELL_ERROR:
+                cell_contents = np.nan
+            elif cell_typ == XL_CELL_BOOLEAN:
+                cell_contents = bool(cell_contents)
+            elif cell_typ == XL_CELL_NUMBER:
+                # GH5394 - Excel 'numbers' are always floats
+                # it's a minimal perf hit and less surprising
+                if math.isfinite(cell_contents):
+                    # GH54564 - don't attempt to convert NaN/Inf
+                    val = int(cell_contents)
+                    if val == cell_contents:
+                        cell_contents = val
+            return cell_contents
+
+        data = []
+
+        nrows = sheet.nrows
+        if file_rows_needed is not None:
+            nrows = min(nrows, file_rows_needed)
+        for i in range(nrows):
+            row = [
+                _parse_cell(value, typ)
+                for value, typ in zip(sheet.row_values(i), sheet.row_types(i))
+            ]
+            data.append(row)
+
+        return data
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a9789ccd0f3317c83b1542b823586d3419c485
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py
@@ -0,0 +1,284 @@
+from __future__ import annotations
+
+import json
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.io.excel._base import ExcelWriter
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+
+class _XlsxStyler:
+    # Map from openpyxl-oriented styles to flatter xlsxwriter representation
+    # Ordering necessary for both determinism and because some are keyed by
+    # prefixes of others.
+    STYLE_MAPPING: dict[str, list[tuple[tuple[str, ...], str]]] = {
+        "font": [
+            (("name",), "font_name"),
+            (("sz",), "font_size"),
+            (("size",), "font_size"),
+            (("color", "rgb"), "font_color"),
+            (("color",), "font_color"),
+            (("b",), "bold"),
+            (("bold",), "bold"),
+            (("i",), "italic"),
+            (("italic",), "italic"),
+            (("u",), "underline"),
+            (("underline",), "underline"),
+            (("strike",), "font_strikeout"),
+            (("vertAlign",), "font_script"),
+            (("vertalign",), "font_script"),
+        ],
+        "number_format": [(("format_code",), "num_format"), ((), "num_format")],
+        "protection": [(("locked",), "locked"), (("hidden",), "hidden")],
+        "alignment": [
+            (("horizontal",), "align"),
+            (("vertical",), "valign"),
+            (("text_rotation",), "rotation"),
+            (("wrap_text",), "text_wrap"),
+            (("indent",), "indent"),
+            (("shrink_to_fit",), "shrink"),
+        ],
+        "fill": [
+            (("patternType",), "pattern"),
+            (("patterntype",), "pattern"),
+            (("fill_type",), "pattern"),
+            (("start_color", "rgb"), "fg_color"),
+            (("fgColor", "rgb"), "fg_color"),
+            (("fgcolor", "rgb"), "fg_color"),
+            (("start_color",), "fg_color"),
+            (("fgColor",), "fg_color"),
+            (("fgcolor",), "fg_color"),
+            (("end_color", "rgb"), "bg_color"),
+            (("bgColor", "rgb"), "bg_color"),
+            (("bgcolor", "rgb"), "bg_color"),
+            (("end_color",), "bg_color"),
+            (("bgColor",), "bg_color"),
+            (("bgcolor",), "bg_color"),
+        ],
+        "border": [
+            (("color", "rgb"), "border_color"),
+            (("color",), "border_color"),
+            (("style",), "border"),
+            (("top", "color", "rgb"), "top_color"),
+            (("top", "color"), "top_color"),
+            (("top", "style"), "top"),
+            (("top",), "top"),
+            (("right", "color", "rgb"), "right_color"),
+            (("right", "color"), "right_color"),
+            (("right", "style"), "right"),
+            (("right",), "right"),
+            (("bottom", "color", "rgb"), "bottom_color"),
+            (("bottom", "color"), "bottom_color"),
+            (("bottom", "style"), "bottom"),
+            (("bottom",), "bottom"),
+            (("left", "color", "rgb"), "left_color"),
+            (("left", "color"), "left_color"),
+            (("left", "style"), "left"),
+            (("left",), "left"),
+        ],
+    }
+
+    @classmethod
+    def convert(cls, style_dict, num_format_str=None):
+        """
+        converts a style_dict to an xlsxwriter format dict
+
+        Parameters
+        ----------
+        style_dict : style dictionary to convert
+        num_format_str : optional number format string
+        """
+        # Create a XlsxWriter format object.
+        props = {}
+
+        if num_format_str is not None:
+            props["num_format"] = num_format_str
+
+        if style_dict is None:
+            return props
+
+        if "borders" in style_dict:
+            style_dict = style_dict.copy()
+            style_dict["border"] = style_dict.pop("borders")
+
+        for style_group_key, style_group in style_dict.items():
+            for src, dst in cls.STYLE_MAPPING.get(style_group_key, []):
+                # src is a sequence of keys into a nested dict
+                # dst is a flat key
+                if dst in props:
+                    continue
+                v = style_group
+                for k in src:
+                    try:
+                        v = v[k]
+                    except (KeyError, TypeError):
+                        break
+                else:
+                    props[dst] = v
+
+        if isinstance(props.get("pattern"), str):
+            # TODO: support other fill patterns
+            props["pattern"] = 0 if props["pattern"] == "none" else 1
+
+        for k in ["border", "top", "right", "bottom", "left"]:
+            if isinstance(props.get(k), str):
+                try:
+                    props[k] = [
+                        "none",
+                        "thin",
+                        "medium",
+                        "dashed",
+                        "dotted",
+                        "thick",
+                        "double",
+                        "hair",
+                        "mediumDashed",
+                        "dashDot",
+                        "mediumDashDot",
+                        "dashDotDot",
+                        "mediumDashDotDot",
+                        "slantDashDot",
+                    ].index(props[k])
+                except ValueError:
+                    props[k] = 2
+
+        if isinstance(props.get("font_script"), str):
+            props["font_script"] = ["baseline", "superscript", "subscript"].index(
+                props["font_script"]
+            )
+
+        if isinstance(props.get("underline"), str):
+            props["underline"] = {
+                "none": 0,
+                "single": 1,
+                "double": 2,
+                "singleAccounting": 33,
+                "doubleAccounting": 34,
+            }[props["underline"]]
+
+        # GH 30107 - xlsxwriter uses different name
+        if props.get("valign") == "center":
+            props["valign"] = "vcenter"
+
+        return props
+
+
+class XlsxWriter(ExcelWriter):
+    _engine = "xlsxwriter"
+    _supported_extensions = (".xlsx",)
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        # Use the xlsxwriter module as the Excel writer.
+        from xlsxwriter import Workbook
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+
+        if mode == "a":
+            raise ValueError("Append mode is not supported with xlsxwriter!")
+
+        super().__init__(
+            path,
+            engine=engine,
+            date_format=date_format,
+            datetime_format=datetime_format,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        try:
+            self._book = Workbook(self._handles.handle, **engine_kwargs)  # type: ignore[arg-type]
+        except TypeError:
+            self._handles.handle.close()
+            raise
+
+    @property
+    def book(self):
+        """
+        Book instance of class xlsxwriter.Workbook.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        result = self.book.sheetnames
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        self.book.close()
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        # Write the frame cells using xlsxwriter.
+        sheet_name = self._get_sheet_name(sheet_name)
+
+        wks = self.book.get_worksheet_by_name(sheet_name)
+        if wks is None:
+            wks = self.book.add_worksheet(sheet_name)
+
+        style_dict = {"null": None}
+
+        if validate_freeze_panes(freeze_panes):
+            wks.freeze_panes(*(freeze_panes))
+
+        for cell in cells:
+            val, fmt = self._value_with_fmt(cell.val)
+
+            stylekey = json.dumps(cell.style)
+            if fmt:
+                stylekey += fmt
+
+            if stylekey in style_dict:
+                style = style_dict[stylekey]
+            else:
+                style = self.book.add_format(_XlsxStyler.convert(cell.style, fmt))
+                style_dict[stylekey] = style
+
+            if cell.mergestart is not None and cell.mergeend is not None:
+                wks.merge_range(
+                    startrow + cell.row,
+                    startcol + cell.col,
+                    startrow + cell.mergestart,
+                    startcol + cell.mergeend,
+                    val,
+                    style,
+                )
+            else:
+                wks.write(startrow + cell.row, startcol + cell.col, val, style)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/feather_format.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/feather_format.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bdb732cb10de7c9622a10f6b353b766d049376c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/feather_format.py
@@ -0,0 +1,130 @@
+""" feather-format compat """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.api import DataFrame
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def to_feather(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes],
+    storage_options: StorageOptions | None = None,
+    **kwargs: Any,
+) -> None:
+    """
+    Write a DataFrame to the binary Feather format.
+
+    Parameters
+    ----------
+    df : DataFrame
+    path : str, path object, or file-like object
+    {storage_options}
+    **kwargs :
+        Additional keywords passed to `pyarrow.feather.write_feather`.
+
+    """
+    import_optional_dependency("pyarrow")
+    from pyarrow import feather
+
+    if not isinstance(df, DataFrame):
+        raise ValueError("feather only support IO with DataFrames")
+
+    with get_handle(
+        path, "wb", storage_options=storage_options, is_text=False
+    ) as handles:
+        feather.write_feather(df, handles.handle, **kwargs)
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_feather(
+    path: FilePath | ReadBuffer[bytes],
+    columns: Sequence[Hashable] | None = None,
+    use_threads: bool = True,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    """
+    Load a feather-format object from the file path.
+
+    Parameters
+    ----------
+    path : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function. The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be: ``file://localhost/path/to/table.feather``.
+    columns : sequence, default None
+        If not provided, all columns are read.
+    use_threads : bool, default True
+        Whether to parallelize reading using multiple threads.
+    {storage_options}
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    type of object stored in file
+
+    Examples
+    --------
+    >>> df = pd.read_feather("path/to/file.feather")  # doctest: +SKIP
+    """
+    import_optional_dependency("pyarrow")
+    from pyarrow import feather
+
+    # import utils to register the pyarrow extension types
+    import pandas.core.arrays.arrow.extension_types  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+    check_dtype_backend(dtype_backend)
+
+    with get_handle(
+        path, "rb", storage_options=storage_options, is_text=False
+    ) as handles:
+        if dtype_backend is lib.no_default and not using_string_dtype():
+            return feather.read_feather(
+                handles.handle, columns=columns, use_threads=bool(use_threads)
+            )
+
+        pa_table = feather.read_table(
+            handles.handle, columns=columns, use_threads=bool(use_threads)
+        )
+        return arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/__init__.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e56b1bc7ba4377cc5de9d68a1424524aef21cb5
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/__init__.py
@@ -0,0 +1,9 @@
+# ruff: noqa: TCH004
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # import modules that have public classes/functions
+    from pandas.io.formats import style
+
+    # and mark only those modules as public
+    __all__ = ["style"]
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/_color_data.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/_color_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e7cb7f29646eb11c0ec83d8a909a8cfd7953182
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/_color_data.py
@@ -0,0 +1,157 @@
+# GH37967: Enable the use of CSS named colors, as defined in
+# matplotlib.colors.CSS4_COLORS, when exporting to Excel.
+# This data has been copied here, instead of being imported from matplotlib,
+# not to have ``to_excel`` methods require matplotlib.
+# source: matplotlib._color_data (3.3.3)
+from __future__ import annotations
+
+CSS4_COLORS = {
+    "aliceblue": "F0F8FF",
+    "antiquewhite": "FAEBD7",
+    "aqua": "00FFFF",
+    "aquamarine": "7FFFD4",
+    "azure": "F0FFFF",
+    "beige": "F5F5DC",
+    "bisque": "FFE4C4",
+    "black": "000000",
+    "blanchedalmond": "FFEBCD",
+    "blue": "0000FF",
+    "blueviolet": "8A2BE2",
+    "brown": "A52A2A",
+    "burlywood": "DEB887",
+    "cadetblue": "5F9EA0",
+    "chartreuse": "7FFF00",
+    "chocolate": "D2691E",
+    "coral": "FF7F50",
+    "cornflowerblue": "6495ED",
+    "cornsilk": "FFF8DC",
+    "crimson": "DC143C",
+    "cyan": "00FFFF",
+    "darkblue": "00008B",
+    "darkcyan": "008B8B",
+    "darkgoldenrod": "B8860B",
+    "darkgray": "A9A9A9",
+    "darkgreen": "006400",
+    "darkgrey": "A9A9A9",
+    "darkkhaki": "BDB76B",
+    "darkmagenta": "8B008B",
+    "darkolivegreen": "556B2F",
+    "darkorange": "FF8C00",
+    "darkorchid": "9932CC",
+    "darkred": "8B0000",
+    "darksalmon": "E9967A",
+    "darkseagreen": "8FBC8F",
+    "darkslateblue": "483D8B",
+    "darkslategray": "2F4F4F",
+    "darkslategrey": "2F4F4F",
+    "darkturquoise": "00CED1",
+    "darkviolet": "9400D3",
+    "deeppink": "FF1493",
+    "deepskyblue": "00BFFF",
+    "dimgray": "696969",
+    "dimgrey": "696969",
+    "dodgerblue": "1E90FF",
+    "firebrick": "B22222",
+    "floralwhite": "FFFAF0",
+    "forestgreen": "228B22",
+    "fuchsia": "FF00FF",
+    "gainsboro": "DCDCDC",
+    "ghostwhite": "F8F8FF",
+    "gold": "FFD700",
+    "goldenrod": "DAA520",
+    "gray": "808080",
+    "green": "008000",
+    "greenyellow": "ADFF2F",
+    "grey": "808080",
+    "honeydew": "F0FFF0",
+    "hotpink": "FF69B4",
+    "indianred": "CD5C5C",
+    "indigo": "4B0082",
+    "ivory": "FFFFF0",
+    "khaki": "F0E68C",
+    "lavender": "E6E6FA",
+    "lavenderblush": "FFF0F5",
+    "lawngreen": "7CFC00",
+    "lemonchiffon": "FFFACD",
+    "lightblue": "ADD8E6",
+    "lightcoral": "F08080",
+    "lightcyan": "E0FFFF",
+    "lightgoldenrodyellow": "FAFAD2",
+    "lightgray": "D3D3D3",
+    "lightgreen": "90EE90",
+    "lightgrey": "D3D3D3",
+    "lightpink": "FFB6C1",
+    "lightsalmon": "FFA07A",
+    "lightseagreen": "20B2AA",
+    "lightskyblue": "87CEFA",
+    "lightslategray": "778899",
+    "lightslategrey": "778899",
+    "lightsteelblue": "B0C4DE",
+    "lightyellow": "FFFFE0",
+    "lime": "00FF00",
+    "limegreen": "32CD32",
+    "linen": "FAF0E6",
+    "magenta": "FF00FF",
+    "maroon": "800000",
+    "mediumaquamarine": "66CDAA",
+    "mediumblue": "0000CD",
+    "mediumorchid": "BA55D3",
+    "mediumpurple": "9370DB",
+    "mediumseagreen": "3CB371",
+    "mediumslateblue": "7B68EE",
+    "mediumspringgreen": "00FA9A",
+    "mediumturquoise": "48D1CC",
+    "mediumvioletred": "C71585",
+    "midnightblue": "191970",
+    "mintcream": "F5FFFA",
+    "mistyrose": "FFE4E1",
+    "moccasin": "FFE4B5",
+    "navajowhite": "FFDEAD",
+    "navy": "000080",
+    "oldlace": "FDF5E6",
+    "olive": "808000",
+    "olivedrab": "6B8E23",
+    "orange": "FFA500",
+    "orangered": "FF4500",
+    "orchid": "DA70D6",
+    "palegoldenrod": "EEE8AA",
+    "palegreen": "98FB98",
+    "paleturquoise": "AFEEEE",
+    "palevioletred": "DB7093",
+    "papayawhip": "FFEFD5",
+    "peachpuff": "FFDAB9",
+    "peru": "CD853F",
+    "pink": "FFC0CB",
+    "plum": "DDA0DD",
+    "powderblue": "B0E0E6",
+    "purple": "800080",
+    "rebeccapurple": "663399",
+    "red": "FF0000",
+    "rosybrown": "BC8F8F",
+    "royalblue": "4169E1",
+    "saddlebrown": "8B4513",
+    "salmon": "FA8072",
+    "sandybrown": "F4A460",
+    "seagreen": "2E8B57",
+    "seashell": "FFF5EE",
+    "sienna": "A0522D",
+    "silver": "C0C0C0",
+    "skyblue": "87CEEB",
+    "slateblue": "6A5ACD",
+    "slategray": "708090",
+    "slategrey": "708090",
+    "snow": "FFFAFA",
+    "springgreen": "00FF7F",
+    "steelblue": "4682B4",
+    "tan": "D2B48C",
+    "teal": "008080",
+    "thistle": "D8BFD8",
+    "tomato": "FF6347",
+    "turquoise": "40E0D0",
+    "violet": "EE82EE",
+    "wheat": "F5DEB3",
+    "white": "FFFFFF",
+    "whitesmoke": "F5F5F5",
+    "yellow": "FFFF00",
+    "yellowgreen": "9ACD32",
+}
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/console.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/console.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a6cbe07629031687c249f70b51bdfbe2dd84041
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/console.py
@@ -0,0 +1,94 @@
+"""
+Internal module for console introspection
+"""
+from __future__ import annotations
+
+from shutil import get_terminal_size
+
+
+def get_console_size() -> tuple[int | None, int | None]:
+    """
+    Return console size as tuple = (width, height).
+
+    Returns (None,None) in non-interactive session.
+    """
+    from pandas import get_option
+
+    display_width = get_option("display.width")
+    display_height = get_option("display.max_rows")
+
+    # Consider
+    # interactive shell terminal, can detect term size
+    # interactive non-shell terminal (ipnb/ipqtconsole), cannot detect term
+    # size non-interactive script, should disregard term size
+
+    # in addition
+    # width,height have default values, but setting to 'None' signals
+    # should use Auto-Detection, But only in interactive shell-terminal.
+    # Simple. yeah.
+
+    if in_interactive_session():
+        if in_ipython_frontend():
+            # sane defaults for interactive non-shell terminal
+            # match default for width,height in config_init
+            from pandas._config.config import get_default_val
+
+            terminal_width = get_default_val("display.width")
+            terminal_height = get_default_val("display.max_rows")
+        else:
+            # pure terminal
+            terminal_width, terminal_height = get_terminal_size()
+    else:
+        terminal_width, terminal_height = None, None
+
+    # Note if the User sets width/Height to None (auto-detection)
+    # and we're in a script (non-inter), this will return (None,None)
+    # caller needs to deal.
+    return display_width or terminal_width, display_height or terminal_height
+
+
+# ----------------------------------------------------------------------
+# Detect our environment
+
+
+def in_interactive_session() -> bool:
+    """
+    Check if we're running in an interactive shell.
+
+    Returns
+    -------
+    bool
+        True if running under python/ipython interactive shell.
+    """
+    from pandas import get_option
+
+    def check_main():
+        try:
+            import __main__ as main
+        except ModuleNotFoundError:
+            return get_option("mode.sim_interactive")
+        return not hasattr(main, "__file__") or get_option("mode.sim_interactive")
+
+    try:
+        # error: Name '__IPYTHON__' is not defined
+        return __IPYTHON__ or check_main()  # type: ignore[name-defined]
+    except NameError:
+        return check_main()
+
+
+def in_ipython_frontend() -> bool:
+    """
+    Check if we're inside an IPython zmq frontend.
+
+    Returns
+    -------
+    bool
+    """
+    try:
+        # error: Name 'get_ipython' is not defined
+        ip = get_ipython()  # type: ignore[name-defined]
+        return "zmq" in str(type(ip)).lower()
+    except NameError:
+        pass
+
+    return False
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/css.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/css.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccce60c00a9e02bf3bb7f21c5ec799b7123e8eed
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/css.py
@@ -0,0 +1,421 @@
+"""
+Utilities for interpreting CSS from Stylers for formatting non-HTML outputs.
+"""
+from __future__ import annotations
+
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+from pandas.errors import CSSWarning
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Iterable,
+        Iterator,
+    )
+
+
+def _side_expander(prop_fmt: str) -> Callable:
+    """
+    Wrapper to expand shorthand property into top, right, bottom, left properties
+
+    Parameters
+    ----------
+    side : str
+        The border side to expand into properties
+
+    Returns
+    -------
+        function: Return to call when a 'border(-{side}): {value}' string is encountered
+    """
+
+    def expand(self, prop, value: str) -> Generator[tuple[str, str], None, None]:
+        """
+        Expand shorthand property into side-specific property (top, right, bottom, left)
+
+        Parameters
+        ----------
+            prop (str): CSS property name
+            value (str): String token for property
+
+        Yields
+        ------
+            Tuple (str, str): Expanded property, value
+        """
+        tokens = value.split()
+        try:
+            mapping = self.SIDE_SHORTHANDS[len(tokens)]
+        except KeyError:
+            warnings.warn(
+                f'Could not expand "{prop}: {value}"',
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return
+        for key, idx in zip(self.SIDES, mapping):
+            yield prop_fmt.format(key), tokens[idx]
+
+    return expand
+
+
+def _border_expander(side: str = "") -> Callable:
+    """
+    Wrapper to expand 'border' property into border color, style, and width properties
+
+    Parameters
+    ----------
+    side : str
+        The border side to expand into properties
+
+    Returns
+    -------
+        function: Return to call when a 'border(-{side}): {value}' string is encountered
+    """
+    if side != "":
+        side = f"-{side}"
+
+    def expand(self, prop, value: str) -> Generator[tuple[str, str], None, None]:
+        """
+        Expand border into color, style, and width tuples
+
+        Parameters
+        ----------
+            prop : str
+                CSS property name passed to styler
+            value : str
+                Value passed to styler for property
+
+        Yields
+        ------
+            Tuple (str, str): Expanded property, value
+        """
+        tokens = value.split()
+        if len(tokens) == 0 or len(tokens) > 3:
+            warnings.warn(
+                f'Too many tokens provided to "{prop}" (expected 1-3)',
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        # TODO: Can we use current color as initial value to comply with CSS standards?
+        border_declarations = {
+            f"border{side}-color": "black",
+            f"border{side}-style": "none",
+            f"border{side}-width": "medium",
+        }
+        for token in tokens:
+            if token.lower() in self.BORDER_STYLES:
+                border_declarations[f"border{side}-style"] = token
+            elif any(ratio in token.lower() for ratio in self.BORDER_WIDTH_RATIOS):
+                border_declarations[f"border{side}-width"] = token
+            else:
+                border_declarations[f"border{side}-color"] = token
+            # TODO: Warn user if item entered more than once (e.g. "border: red green")
+
+        # Per CSS, "border" will reset previous "border-*" definitions
+        yield from self.atomize(border_declarations.items())
+
+    return expand
+
+
+class CSSResolver:
+    """
+    A callable for parsing and resolving CSS to atomic properties.
+    """
+
+    UNIT_RATIOS = {
+        "pt": ("pt", 1),
+        "em": ("em", 1),
+        "rem": ("pt", 12),
+        "ex": ("em", 0.5),
+        # 'ch':
+        "px": ("pt", 0.75),
+        "pc": ("pt", 12),
+        "in": ("pt", 72),
+        "cm": ("in", 1 / 2.54),
+        "mm": ("in", 1 / 25.4),
+        "q": ("mm", 0.25),
+        "!!default": ("em", 0),
+    }
+
+    FONT_SIZE_RATIOS = UNIT_RATIOS.copy()
+    FONT_SIZE_RATIOS.update(
+        {
+            "%": ("em", 0.01),
+            "xx-small": ("rem", 0.5),
+            "x-small": ("rem", 0.625),
+            "small": ("rem", 0.8),
+            "medium": ("rem", 1),
+            "large": ("rem", 1.125),
+            "x-large": ("rem", 1.5),
+            "xx-large": ("rem", 2),
+            "smaller": ("em", 1 / 1.2),
+            "larger": ("em", 1.2),
+            "!!default": ("em", 1),
+        }
+    )
+
+    MARGIN_RATIOS = UNIT_RATIOS.copy()
+    MARGIN_RATIOS.update({"none": ("pt", 0)})
+
+    BORDER_WIDTH_RATIOS = UNIT_RATIOS.copy()
+    BORDER_WIDTH_RATIOS.update(
+        {
+            "none": ("pt", 0),
+            "thick": ("px", 4),
+            "medium": ("px", 2),
+            "thin": ("px", 1),
+            # Default: medium only if solid
+        }
+    )
+
+    BORDER_STYLES = [
+        "none",
+        "hidden",
+        "dotted",
+        "dashed",
+        "solid",
+        "double",
+        "groove",
+        "ridge",
+        "inset",
+        "outset",
+        "mediumdashdot",
+        "dashdotdot",
+        "hair",
+        "mediumdashdotdot",
+        "dashdot",
+        "slantdashdot",
+        "mediumdashed",
+    ]
+
+    SIDE_SHORTHANDS = {
+        1: [0, 0, 0, 0],
+        2: [0, 1, 0, 1],
+        3: [0, 1, 2, 1],
+        4: [0, 1, 2, 3],
+    }
+
+    SIDES = ("top", "right", "bottom", "left")
+
+    CSS_EXPANSIONS = {
+        **{
+            (f"border-{prop}" if prop else "border"): _border_expander(prop)
+            for prop in ["", "top", "right", "bottom", "left"]
+        },
+        **{
+            f"border-{prop}": _side_expander(f"border-{{:s}}-{prop}")
+            for prop in ["color", "style", "width"]
+        },
+        "margin": _side_expander("margin-{:s}"),
+        "padding": _side_expander("padding-{:s}"),
+    }
+
+    def __call__(
+        self,
+        declarations: str | Iterable[tuple[str, str]],
+        inherited: dict[str, str] | None = None,
+    ) -> dict[str, str]:
+        """
+        The given declarations to atomic properties.
+
+        Parameters
+        ----------
+        declarations_str : str | Iterable[tuple[str, str]]
+            A CSS string or set of CSS declaration tuples
+            e.g. "font-weight: bold; background: blue" or
+            {("font-weight", "bold"), ("background", "blue")}
+        inherited : dict, optional
+            Atomic properties indicating the inherited style context in which
+            declarations_str is to be resolved. ``inherited`` should already
+            be resolved, i.e. valid output of this method.
+
+        Returns
+        -------
+        dict
+            Atomic CSS 2.2 properties.
+
+        Examples
+        --------
+        >>> resolve = CSSResolver()
+        >>> inherited = {'font-family': 'serif', 'font-weight': 'bold'}
+        >>> out = resolve('''
+        ...               border-color: BLUE RED;
+        ...               font-size: 1em;
+        ...               font-size: 2em;
+        ...               font-weight: normal;
+        ...               font-weight: inherit;
+        ...               ''', inherited)
+        >>> sorted(out.items())  # doctest: +NORMALIZE_WHITESPACE
+        [('border-bottom-color', 'blue'),
+         ('border-left-color', 'red'),
+         ('border-right-color', 'red'),
+         ('border-top-color', 'blue'),
+         ('font-family', 'serif'),
+         ('font-size', '24pt'),
+         ('font-weight', 'bold')]
+        """
+        if isinstance(declarations, str):
+            declarations = self.parse(declarations)
+        props = dict(self.atomize(declarations))
+        if inherited is None:
+            inherited = {}
+
+        props = self._update_initial(props, inherited)
+        props = self._update_font_size(props, inherited)
+        return self._update_other_units(props)
+
+    def _update_initial(
+        self,
+        props: dict[str, str],
+        inherited: dict[str, str],
+    ) -> dict[str, str]:
+        # 1. resolve inherited, initial
+        for prop, val in inherited.items():
+            if prop not in props:
+                props[prop] = val
+
+        new_props = props.copy()
+        for prop, val in props.items():
+            if val == "inherit":
+                val = inherited.get(prop, "initial")
+
+            if val in ("initial", None):
+                # we do not define a complete initial stylesheet
+                del new_props[prop]
+            else:
+                new_props[prop] = val
+        return new_props
+
+    def _update_font_size(
+        self,
+        props: dict[str, str],
+        inherited: dict[str, str],
+    ) -> dict[str, str]:
+        # 2. resolve relative font size
+        if props.get("font-size"):
+            props["font-size"] = self.size_to_pt(
+                props["font-size"],
+                self._get_font_size(inherited),
+                conversions=self.FONT_SIZE_RATIOS,
+            )
+        return props
+
+    def _get_font_size(self, props: dict[str, str]) -> float | None:
+        if props.get("font-size"):
+            font_size_string = props["font-size"]
+            return self._get_float_font_size_from_pt(font_size_string)
+        return None
+
+    def _get_float_font_size_from_pt(self, font_size_string: str) -> float:
+        assert font_size_string.endswith("pt")
+        return float(font_size_string.rstrip("pt"))
+
+    def _update_other_units(self, props: dict[str, str]) -> dict[str, str]:
+        font_size = self._get_font_size(props)
+        # 3. TODO: resolve other font-relative units
+        for side in self.SIDES:
+            prop = f"border-{side}-width"
+            if prop in props:
+                props[prop] = self.size_to_pt(
+                    props[prop],
+                    em_pt=font_size,
+                    conversions=self.BORDER_WIDTH_RATIOS,
+                )
+
+            for prop in [f"margin-{side}", f"padding-{side}"]:
+                if prop in props:
+                    # TODO: support %
+                    props[prop] = self.size_to_pt(
+                        props[prop],
+                        em_pt=font_size,
+                        conversions=self.MARGIN_RATIOS,
+                    )
+        return props
+
+    def size_to_pt(self, in_val, em_pt=None, conversions=UNIT_RATIOS) -> str:
+        def _error():
+            warnings.warn(
+                f"Unhandled size: {repr(in_val)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.size_to_pt("1!!default", conversions=conversions)
+
+        match = re.match(r"^(\S*?)([a-zA-Z%!].*)", in_val)
+        if match is None:
+            return _error()
+
+        val, unit = match.groups()
+        if val == "":
+            # hack for 'large' etc.
+            val = 1
+        else:
+            try:
+                val = float(val)
+            except ValueError:
+                return _error()
+
+        while unit != "pt":
+            if unit == "em":
+                if em_pt is None:
+                    unit = "rem"
+                else:
+                    val *= em_pt
+                    unit = "pt"
+                continue
+
+            try:
+                unit, mul = conversions[unit]
+            except KeyError:
+                return _error()
+            val *= mul
+
+        val = round(val, 5)
+        if int(val) == val:
+            size_fmt = f"{int(val):d}pt"
+        else:
+            size_fmt = f"{val:f}pt"
+        return size_fmt
+
+    def atomize(self, declarations: Iterable) -> Generator[tuple[str, str], None, None]:
+        for prop, value in declarations:
+            prop = prop.lower()
+            value = value.lower()
+            if prop in self.CSS_EXPANSIONS:
+                expand = self.CSS_EXPANSIONS[prop]
+                yield from expand(self, prop, value)
+            else:
+                yield prop, value
+
+    def parse(self, declarations_str: str) -> Iterator[tuple[str, str]]:
+        """
+        Generates (prop, value) pairs from declarations.
+
+        In a future version may generate parsed tokens from tinycss/tinycss2
+
+        Parameters
+        ----------
+        declarations_str : str
+        """
+        for decl in declarations_str.split(";"):
+            if not decl.strip():
+                continue
+            prop, sep, val = decl.partition(":")
+            prop = prop.strip().lower()
+            # TODO: don't lowercase case sensitive parts of values (strings)
+            val = val.strip().lower()
+            if sep:
+                yield prop, val
+            else:
+                warnings.warn(
+                    f"Ill-formatted attribute: expected a colon in {repr(decl)}",
+                    CSSWarning,
+                    stacklevel=find_stack_level(),
+                )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/csvs.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/csvs.py
new file mode 100644
index 0000000000000000000000000000000000000000..50503e862ef433901f40715987c2105f6f16263a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/csvs.py
@@ -0,0 +1,330 @@
+"""
+Module for formatting output data into CSV files.
+"""
+
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Iterator,
+    Sequence,
+)
+import csv as csvlib
+import os
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import writers as libwriters
+from pandas._typing import SequenceNotStr
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.generic import (
+    ABCDatetimeIndex,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCPeriodIndex,
+)
+from pandas.core.dtypes.missing import notna
+
+from pandas.core.indexes.api import Index
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        FloatFormatType,
+        IndexLabel,
+        StorageOptions,
+        WriteBuffer,
+        npt,
+    )
+
+    from pandas.io.formats.format import DataFrameFormatter
+
+
+_DEFAULT_CHUNKSIZE_CELLS = 100_000
+
+
+class CSVFormatter:
+    cols: npt.NDArray[np.object_]
+
+    def __init__(
+        self,
+        formatter: DataFrameFormatter,
+        path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] = "",
+        sep: str = ",",
+        cols: Sequence[Hashable] | None = None,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        encoding: str | None = None,
+        errors: str = "strict",
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        lineterminator: str | None = "\n",
+        chunksize: int | None = None,
+        quotechar: str | None = '"',
+        date_format: str | None = None,
+        doublequote: bool = True,
+        escapechar: str | None = None,
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        self.fmt = formatter
+
+        self.obj = self.fmt.frame
+
+        self.filepath_or_buffer = path_or_buf
+        self.encoding = encoding
+        self.compression: CompressionOptions = compression
+        self.mode = mode
+        self.storage_options = storage_options
+
+        self.sep = sep
+        self.index_label = self._initialize_index_label(index_label)
+        self.errors = errors
+        self.quoting = quoting or csvlib.QUOTE_MINIMAL
+        self.quotechar = self._initialize_quotechar(quotechar)
+        self.doublequote = doublequote
+        self.escapechar = escapechar
+        self.lineterminator = lineterminator or os.linesep
+        self.date_format = date_format
+        self.cols = self._initialize_columns(cols)
+        self.chunksize = self._initialize_chunksize(chunksize)
+
+    @property
+    def na_rep(self) -> str:
+        return self.fmt.na_rep
+
+    @property
+    def float_format(self) -> FloatFormatType | None:
+        return self.fmt.float_format
+
+    @property
+    def decimal(self) -> str:
+        return self.fmt.decimal
+
+    @property
+    def header(self) -> bool | SequenceNotStr[str]:
+        return self.fmt.header
+
+    @property
+    def index(self) -> bool:
+        return self.fmt.index
+
+    def _initialize_index_label(self, index_label: IndexLabel | None) -> IndexLabel:
+        if index_label is not False:
+            if index_label is None:
+                return self._get_index_label_from_obj()
+            elif not isinstance(index_label, (list, tuple, np.ndarray, ABCIndex)):
+                # given a string for a DF with Index
+                return [index_label]
+        return index_label
+
+    def _get_index_label_from_obj(self) -> Sequence[Hashable]:
+        if isinstance(self.obj.index, ABCMultiIndex):
+            return self._get_index_label_multiindex()
+        else:
+            return self._get_index_label_flat()
+
+    def _get_index_label_multiindex(self) -> Sequence[Hashable]:
+        return [name or "" for name in self.obj.index.names]
+
+    def _get_index_label_flat(self) -> Sequence[Hashable]:
+        index_label = self.obj.index.name
+        return [""] if index_label is None else [index_label]
+
+    def _initialize_quotechar(self, quotechar: str | None) -> str | None:
+        if self.quoting != csvlib.QUOTE_NONE:
+            # prevents crash in _csv
+            return quotechar
+        return None
+
+    @property
+    def has_mi_columns(self) -> bool:
+        return bool(isinstance(self.obj.columns, ABCMultiIndex))
+
+    def _initialize_columns(
+        self, cols: Iterable[Hashable] | None
+    ) -> npt.NDArray[np.object_]:
+        # validate mi options
+        if self.has_mi_columns:
+            if cols is not None:
+                msg = "cannot specify cols with a MultiIndex on the columns"
+                raise TypeError(msg)
+
+        if cols is not None:
+            if isinstance(cols, ABCIndex):
+                cols = cols._get_values_for_csv(**self._number_format)
+            else:
+                cols = list(cols)
+            self.obj = self.obj.loc[:, cols]
+
+        # update columns to include possible multiplicity of dupes
+        # and make sure cols is just a list of labels
+        new_cols = self.obj.columns
+        return new_cols._get_values_for_csv(**self._number_format)
+
+    def _initialize_chunksize(self, chunksize: int | None) -> int:
+        if chunksize is None:
+            return (_DEFAULT_CHUNKSIZE_CELLS // (len(self.cols) or 1)) or 1
+        return int(chunksize)
+
+    @property
+    def _number_format(self) -> dict[str, Any]:
+        """Dictionary used for storing number formatting settings."""
+        return {
+            "na_rep": self.na_rep,
+            "float_format": self.float_format,
+            "date_format": self.date_format,
+            "quoting": self.quoting,
+            "decimal": self.decimal,
+        }
+
+    @cache_readonly
+    def data_index(self) -> Index:
+        data_index = self.obj.index
+        if (
+            isinstance(data_index, (ABCDatetimeIndex, ABCPeriodIndex))
+            and self.date_format is not None
+        ):
+            data_index = Index(
+                [x.strftime(self.date_format) if notna(x) else "" for x in data_index]
+            )
+        elif isinstance(data_index, ABCMultiIndex):
+            data_index = data_index.remove_unused_levels()
+        return data_index
+
+    @property
+    def nlevels(self) -> int:
+        if self.index:
+            return getattr(self.data_index, "nlevels", 1)
+        else:
+            return 0
+
+    @property
+    def _has_aliases(self) -> bool:
+        return isinstance(self.header, (tuple, list, np.ndarray, ABCIndex))
+
+    @property
+    def _need_to_save_header(self) -> bool:
+        return bool(self._has_aliases or self.header)
+
+    @property
+    def write_cols(self) -> SequenceNotStr[Hashable]:
+        if self._has_aliases:
+            assert not isinstance(self.header, bool)
+            if len(self.header) != len(self.cols):
+                raise ValueError(
+                    f"Writing {len(self.cols)} cols but got {len(self.header)} aliases"
+                )
+            return self.header
+        else:
+            # self.cols is an ndarray derived from Index._get_values_for_csv,
+            #  so its entries are strings, i.e. hashable
+            return cast(SequenceNotStr[Hashable], self.cols)
+
+    @property
+    def encoded_labels(self) -> list[Hashable]:
+        encoded_labels: list[Hashable] = []
+
+        if self.index and self.index_label:
+            assert isinstance(self.index_label, Sequence)
+            encoded_labels = list(self.index_label)
+
+        if not self.has_mi_columns or self._has_aliases:
+            encoded_labels += list(self.write_cols)
+
+        return encoded_labels
+
+    def save(self) -> None:
+        """
+        Create the writer & save.
+        """
+        # apply compression and byte/text conversion
+        with get_handle(
+            self.filepath_or_buffer,
+            self.mode,
+            encoding=self.encoding,
+            errors=self.errors,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        ) as handles:
+            # Note: self.encoding is irrelevant here
+            self.writer = csvlib.writer(
+                handles.handle,
+                lineterminator=self.lineterminator,
+                delimiter=self.sep,
+                quoting=self.quoting,
+                doublequote=self.doublequote,
+                escapechar=self.escapechar,
+                quotechar=self.quotechar,
+            )
+
+            self._save()
+
+    def _save(self) -> None:
+        if self._need_to_save_header:
+            self._save_header()
+        self._save_body()
+
+    def _save_header(self) -> None:
+        if not self.has_mi_columns or self._has_aliases:
+            self.writer.writerow(self.encoded_labels)
+        else:
+            for row in self._generate_multiindex_header_rows():
+                self.writer.writerow(row)
+
+    def _generate_multiindex_header_rows(self) -> Iterator[list[Hashable]]:
+        columns = self.obj.columns
+        for i in range(columns.nlevels):
+            # we need at least 1 index column to write our col names
+            col_line = []
+            if self.index:
+                # name is the first column
+                col_line.append(columns.names[i])
+
+                if isinstance(self.index_label, list) and len(self.index_label) > 1:
+                    col_line.extend([""] * (len(self.index_label) - 1))
+
+            col_line.extend(columns._get_level_values(i))
+            yield col_line
+
+        # Write out the index line if it's not empty.
+        # Otherwise, we will print out an extraneous
+        # blank line between the mi and the data rows.
+        if self.encoded_labels and set(self.encoded_labels) != {""}:
+            yield self.encoded_labels + [""] * len(columns)
+
+    def _save_body(self) -> None:
+        nrows = len(self.data_index)
+        chunks = (nrows // self.chunksize) + 1
+        for i in range(chunks):
+            start_i = i * self.chunksize
+            end_i = min(start_i + self.chunksize, nrows)
+            if start_i >= end_i:
+                break
+            self._save_chunk(start_i, end_i)
+
+    def _save_chunk(self, start_i: int, end_i: int) -> None:
+        # create the data for a chunk
+        slicer = slice(start_i, end_i)
+        df = self.obj.iloc[slicer]
+
+        res = df._get_values_for_csv(**self._number_format)
+        data = list(res._iter_column_arrays())
+
+        ix = self.data_index[slicer]._get_values_for_csv(**self._number_format)
+        libwriters.write_csv_rows(
+            data,
+            ix,
+            self.nlevels,
+            self.cols,
+            self.writer,
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/excel.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/excel.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fd23cd7d918ad0efddb1088d79fd78f6079cca7
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/excel.py
@@ -0,0 +1,962 @@
+"""
+Utilities for conversion to writer-agnostic Excel representation.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import functools
+import itertools
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs.lib import is_list_like
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes import missing
+from pandas.core.dtypes.common import (
+    is_float,
+    is_scalar,
+)
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+)
+import pandas.core.common as com
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.formats._color_data import CSS4_COLORS
+from pandas.io.formats.css import (
+    CSSResolver,
+    CSSWarning,
+)
+from pandas.io.formats.format import get_level_lengths
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        FilePath,
+        IndexLabel,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+    from pandas import ExcelWriter
+
+
+class ExcelCell:
+    __fields__ = ("row", "col", "val", "style", "mergestart", "mergeend")
+    __slots__ = __fields__
+
+    def __init__(
+        self,
+        row: int,
+        col: int,
+        val,
+        style=None,
+        mergestart: int | None = None,
+        mergeend: int | None = None,
+    ) -> None:
+        self.row = row
+        self.col = col
+        self.val = val
+        self.style = style
+        self.mergestart = mergestart
+        self.mergeend = mergeend
+
+
+class CssExcelCell(ExcelCell):
+    def __init__(
+        self,
+        row: int,
+        col: int,
+        val,
+        style: dict | None,
+        css_styles: dict[tuple[int, int], list[tuple[str, Any]]] | None,
+        css_row: int,
+        css_col: int,
+        css_converter: Callable | None,
+        **kwargs,
+    ) -> None:
+        if css_styles and css_converter:
+            # Use dict to get only one (case-insensitive) declaration per property
+            declaration_dict = {
+                prop.lower(): val for prop, val in css_styles[css_row, css_col]
+            }
+            # Convert to frozenset for order-invariant caching
+            unique_declarations = frozenset(declaration_dict.items())
+            style = css_converter(unique_declarations)
+
+        super().__init__(row=row, col=col, val=val, style=style, **kwargs)
+
+
+class CSSToExcelConverter:
+    """
+    A callable for converting CSS declarations to ExcelWriter styles
+
+    Supports parts of CSS 2.2, with minimal CSS 3.0 support (e.g. text-shadow),
+    focusing on font styling, backgrounds, borders and alignment.
+
+    Operates by first computing CSS styles in a fairly generic
+    way (see :meth:`compute_css`) then determining Excel style
+    properties from CSS properties (see :meth:`build_xlstyle`).
+
+    Parameters
+    ----------
+    inherited : str, optional
+        CSS declarations understood to be the containing scope for the
+        CSS processed by :meth:`__call__`.
+    """
+
+    NAMED_COLORS = CSS4_COLORS
+
+    VERTICAL_MAP = {
+        "top": "top",
+        "text-top": "top",
+        "middle": "center",
+        "baseline": "bottom",
+        "bottom": "bottom",
+        "text-bottom": "bottom",
+        # OpenXML also has 'justify', 'distributed'
+    }
+
+    BOLD_MAP = {
+        "bold": True,
+        "bolder": True,
+        "600": True,
+        "700": True,
+        "800": True,
+        "900": True,
+        "normal": False,
+        "lighter": False,
+        "100": False,
+        "200": False,
+        "300": False,
+        "400": False,
+        "500": False,
+    }
+
+    ITALIC_MAP = {
+        "normal": False,
+        "italic": True,
+        "oblique": True,
+    }
+
+    FAMILY_MAP = {
+        "serif": 1,  # roman
+        "sans-serif": 2,  # swiss
+        "cursive": 4,  # script
+        "fantasy": 5,  # decorative
+    }
+
+    BORDER_STYLE_MAP = {
+        style.lower(): style
+        for style in [
+            "dashed",
+            "mediumDashDot",
+            "dashDotDot",
+            "hair",
+            "dotted",
+            "mediumDashDotDot",
+            "double",
+            "dashDot",
+            "slantDashDot",
+            "mediumDashed",
+        ]
+    }
+
+    # NB: Most of the methods here could be classmethods, as only __init__
+    #     and __call__ make use of instance attributes.  We leave them as
+    #     instancemethods so that users can easily experiment with extensions
+    #     without monkey-patching.
+    inherited: dict[str, str] | None
+
+    def __init__(self, inherited: str | None = None) -> None:
+        if inherited is not None:
+            self.inherited = self.compute_css(inherited)
+        else:
+            self.inherited = None
+        # We should avoid cache on the __call__ method.
+        # Otherwise once the method __call__ has been called
+        # garbage collection no longer deletes the instance.
+        self._call_cached = functools.cache(self._call_uncached)
+
+    compute_css = CSSResolver()
+
+    def __call__(
+        self, declarations: str | frozenset[tuple[str, str]]
+    ) -> dict[str, dict[str, str]]:
+        """
+        Convert CSS declarations to ExcelWriter style.
+
+        Parameters
+        ----------
+        declarations : str | frozenset[tuple[str, str]]
+            CSS string or set of CSS declaration tuples.
+            e.g. "font-weight: bold; background: blue" or
+            {("font-weight", "bold"), ("background", "blue")}
+
+        Returns
+        -------
+        xlstyle : dict
+            A style as interpreted by ExcelWriter when found in
+            ExcelCell.style.
+        """
+        return self._call_cached(declarations)
+
+    def _call_uncached(
+        self, declarations: str | frozenset[tuple[str, str]]
+    ) -> dict[str, dict[str, str]]:
+        properties = self.compute_css(declarations, self.inherited)
+        return self.build_xlstyle(properties)
+
+    def build_xlstyle(self, props: Mapping[str, str]) -> dict[str, dict[str, str]]:
+        out = {
+            "alignment": self.build_alignment(props),
+            "border": self.build_border(props),
+            "fill": self.build_fill(props),
+            "font": self.build_font(props),
+            "number_format": self.build_number_format(props),
+        }
+
+        # TODO: handle cell width and height: needs support in pandas.io.excel
+
+        def remove_none(d: dict[str, str | None]) -> None:
+            """Remove key where value is None, through nested dicts"""
+            for k, v in list(d.items()):
+                if v is None:
+                    del d[k]
+                elif isinstance(v, dict):
+                    remove_none(v)
+                    if not v:
+                        del d[k]
+
+        remove_none(out)
+        return out
+
+    def build_alignment(self, props: Mapping[str, str]) -> dict[str, bool | str | None]:
+        # TODO: text-indent, padding-left -> alignment.indent
+        return {
+            "horizontal": props.get("text-align"),
+            "vertical": self._get_vertical_alignment(props),
+            "wrap_text": self._get_is_wrap_text(props),
+        }
+
+    def _get_vertical_alignment(self, props: Mapping[str, str]) -> str | None:
+        vertical_align = props.get("vertical-align")
+        if vertical_align:
+            return self.VERTICAL_MAP.get(vertical_align)
+        return None
+
+    def _get_is_wrap_text(self, props: Mapping[str, str]) -> bool | None:
+        if props.get("white-space") is None:
+            return None
+        return bool(props["white-space"] not in ("nowrap", "pre", "pre-line"))
+
+    def build_border(
+        self, props: Mapping[str, str]
+    ) -> dict[str, dict[str, str | None]]:
+        return {
+            side: {
+                "style": self._border_style(
+                    props.get(f"border-{side}-style"),
+                    props.get(f"border-{side}-width"),
+                    self.color_to_excel(props.get(f"border-{side}-color")),
+                ),
+                "color": self.color_to_excel(props.get(f"border-{side}-color")),
+            }
+            for side in ["top", "right", "bottom", "left"]
+        }
+
+    def _border_style(self, style: str | None, width: str | None, color: str | None):
+        # convert styles and widths to openxml, one of:
+        #       'dashDot'
+        #       'dashDotDot'
+        #       'dashed'
+        #       'dotted'
+        #       'double'
+        #       'hair'
+        #       'medium'
+        #       'mediumDashDot'
+        #       'mediumDashDotDot'
+        #       'mediumDashed'
+        #       'slantDashDot'
+        #       'thick'
+        #       'thin'
+        if width is None and style is None and color is None:
+            # Return None will remove "border" from style dictionary
+            return None
+
+        if width is None and style is None:
+            # Return "none" will keep "border" in style dictionary
+            return "none"
+
+        if style in ("none", "hidden"):
+            return "none"
+
+        width_name = self._get_width_name(width)
+        if width_name is None:
+            return "none"
+
+        if style in (None, "groove", "ridge", "inset", "outset", "solid"):
+            # not handled
+            return width_name
+
+        if style == "double":
+            return "double"
+        if style == "dotted":
+            if width_name in ("hair", "thin"):
+                return "dotted"
+            return "mediumDashDotDot"
+        if style == "dashed":
+            if width_name in ("hair", "thin"):
+                return "dashed"
+            return "mediumDashed"
+        elif style in self.BORDER_STYLE_MAP:
+            # Excel-specific styles
+            return self.BORDER_STYLE_MAP[style]
+        else:
+            warnings.warn(
+                f"Unhandled border style format: {repr(style)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return "none"
+
+    def _get_width_name(self, width_input: str | None) -> str | None:
+        width = self._width_to_float(width_input)
+        if width < 1e-5:
+            return None
+        elif width < 1.3:
+            return "thin"
+        elif width < 2.8:
+            return "medium"
+        return "thick"
+
+    def _width_to_float(self, width: str | None) -> float:
+        if width is None:
+            width = "2pt"
+        return self._pt_to_float(width)
+
+    def _pt_to_float(self, pt_string: str) -> float:
+        assert pt_string.endswith("pt")
+        return float(pt_string.rstrip("pt"))
+
+    def build_fill(self, props: Mapping[str, str]):
+        # TODO: perhaps allow for special properties
+        #       -excel-pattern-bgcolor and -excel-pattern-type
+        fill_color = props.get("background-color")
+        if fill_color not in (None, "transparent", "none"):
+            return {"fgColor": self.color_to_excel(fill_color), "patternType": "solid"}
+
+    def build_number_format(self, props: Mapping[str, str]) -> dict[str, str | None]:
+        fc = props.get("number-format")
+        fc = fc.replace("§", ";") if isinstance(fc, str) else fc
+        return {"format_code": fc}
+
+    def build_font(
+        self, props: Mapping[str, str]
+    ) -> dict[str, bool | float | str | None]:
+        font_names = self._get_font_names(props)
+        decoration = self._get_decoration(props)
+        return {
+            "name": font_names[0] if font_names else None,
+            "family": self._select_font_family(font_names),
+            "size": self._get_font_size(props),
+            "bold": self._get_is_bold(props),
+            "italic": self._get_is_italic(props),
+            "underline": ("single" if "underline" in decoration else None),
+            "strike": ("line-through" in decoration) or None,
+            "color": self.color_to_excel(props.get("color")),
+            # shadow if nonzero digit before shadow color
+            "shadow": self._get_shadow(props),
+        }
+
+    def _get_is_bold(self, props: Mapping[str, str]) -> bool | None:
+        weight = props.get("font-weight")
+        if weight:
+            return self.BOLD_MAP.get(weight)
+        return None
+
+    def _get_is_italic(self, props: Mapping[str, str]) -> bool | None:
+        font_style = props.get("font-style")
+        if font_style:
+            return self.ITALIC_MAP.get(font_style)
+        return None
+
+    def _get_decoration(self, props: Mapping[str, str]) -> Sequence[str]:
+        decoration = props.get("text-decoration")
+        if decoration is not None:
+            return decoration.split()
+        else:
+            return ()
+
+    def _get_underline(self, decoration: Sequence[str]) -> str | None:
+        if "underline" in decoration:
+            return "single"
+        return None
+
+    def _get_shadow(self, props: Mapping[str, str]) -> bool | None:
+        if "text-shadow" in props:
+            return bool(re.search("^[^#(]*[1-9]", props["text-shadow"]))
+        return None
+
+    def _get_font_names(self, props: Mapping[str, str]) -> Sequence[str]:
+        font_names_tmp = re.findall(
+            r"""(?x)
+            (
+            "(?:[^"]|\\")+"
+            |
+            '(?:[^']|\\')+'
+            |
+            [^'",]+
+            )(?=,|\s*$)
+        """,
+            props.get("font-family", ""),
+        )
+
+        font_names = []
+        for name in font_names_tmp:
+            if name[:1] == '"':
+                name = name[1:-1].replace('\\"', '"')
+            elif name[:1] == "'":
+                name = name[1:-1].replace("\\'", "'")
+            else:
+                name = name.strip()
+            if name:
+                font_names.append(name)
+        return font_names
+
+    def _get_font_size(self, props: Mapping[str, str]) -> float | None:
+        size = props.get("font-size")
+        if size is None:
+            return size
+        return self._pt_to_float(size)
+
+    def _select_font_family(self, font_names: Sequence[str]) -> int | None:
+        family = None
+        for name in font_names:
+            family = self.FAMILY_MAP.get(name)
+            if family:
+                break
+
+        return family
+
+    def color_to_excel(self, val: str | None) -> str | None:
+        if val is None:
+            return None
+
+        if self._is_hex_color(val):
+            return self._convert_hex_to_excel(val)
+
+        try:
+            return self.NAMED_COLORS[val]
+        except KeyError:
+            warnings.warn(
+                f"Unhandled color format: {repr(val)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+        return None
+
+    def _is_hex_color(self, color_string: str) -> bool:
+        return bool(color_string.startswith("#"))
+
+    def _convert_hex_to_excel(self, color_string: str) -> str:
+        code = color_string.lstrip("#")
+        if self._is_shorthand_color(color_string):
+            return (code[0] * 2 + code[1] * 2 + code[2] * 2).upper()
+        else:
+            return code.upper()
+
+    def _is_shorthand_color(self, color_string: str) -> bool:
+        """Check if color code is shorthand.
+
+        #FFF is a shorthand as opposed to full #FFFFFF.
+        """
+        code = color_string.lstrip("#")
+        if len(code) == 3:
+            return True
+        elif len(code) == 6:
+            return False
+        else:
+            raise ValueError(f"Unexpected color {color_string}")
+
+
+class ExcelFormatter:
+    """
+    Class for formatting a DataFrame to a list of ExcelCells,
+
+    Parameters
+    ----------
+    df : DataFrame or Styler
+    na_rep: na representation
+    float_format : str, default None
+        Format string for floating point numbers
+    cols : sequence, optional
+        Columns to write
+    header : bool or sequence of str, default True
+        Write out column names. If a list of string is given it is
+        assumed to be aliases for the column names
+    index : bool, default True
+        output row names (index)
+    index_label : str or sequence, default None
+        Column label for index column(s) if desired. If None is given, and
+        `header` and `index` are True, then the index names are used. A
+        sequence should be given if the DataFrame uses MultiIndex.
+    merge_cells : bool, default False
+        Format MultiIndex and Hierarchical Rows as merged cells.
+    inf_rep : str, default `'inf'`
+        representation for np.inf values (which aren't representable in Excel)
+        A `'-'` sign will be added in front of -inf.
+    style_converter : callable, optional
+        This translates Styler styles (CSS) into ExcelWriter styles.
+        Defaults to ``CSSToExcelConverter()``.
+        It should have signature css_declarations string -> excel style.
+        This is only called for body cells.
+    """
+
+    max_rows = 2**20
+    max_cols = 2**14
+
+    def __init__(
+        self,
+        df,
+        na_rep: str = "",
+        float_format: str | None = None,
+        cols: Sequence[Hashable] | None = None,
+        header: Sequence[Hashable] | bool = True,
+        index: bool = True,
+        index_label: IndexLabel | None = None,
+        merge_cells: bool = False,
+        inf_rep: str = "inf",
+        style_converter: Callable | None = None,
+    ) -> None:
+        self.rowcounter = 0
+        self.na_rep = na_rep
+        if not isinstance(df, DataFrame):
+            self.styler = df
+            self.styler._compute()  # calculate applied styles
+            df = df.data
+            if style_converter is None:
+                style_converter = CSSToExcelConverter()
+            self.style_converter: Callable | None = style_converter
+        else:
+            self.styler = None
+            self.style_converter = None
+        self.df = df
+        if cols is not None:
+            # all missing, raise
+            if not len(Index(cols).intersection(df.columns)):
+                raise KeyError("passes columns are not ALL present dataframe")
+
+            if len(Index(cols).intersection(df.columns)) != len(set(cols)):
+                # Deprecated in GH#17295, enforced in 1.0.0
+                raise KeyError("Not all names specified in 'columns' are found")
+
+            self.df = df.reindex(columns=cols)
+
+        self.columns = self.df.columns
+        self.float_format = float_format
+        self.index = index
+        self.index_label = index_label
+        self.header = header
+        self.merge_cells = merge_cells
+        self.inf_rep = inf_rep
+
+    @property
+    def header_style(self) -> dict[str, dict[str, str | bool]]:
+        return {
+            "font": {"bold": True},
+            "borders": {
+                "top": "thin",
+                "right": "thin",
+                "bottom": "thin",
+                "left": "thin",
+            },
+            "alignment": {"horizontal": "center", "vertical": "top"},
+        }
+
+    def _format_value(self, val):
+        if is_scalar(val) and missing.isna(val):
+            val = self.na_rep
+        elif is_float(val):
+            if missing.isposinf_scalar(val):
+                val = self.inf_rep
+            elif missing.isneginf_scalar(val):
+                val = f"-{self.inf_rep}"
+            elif self.float_format is not None:
+                val = float(self.float_format % val)
+        if getattr(val, "tzinfo", None) is not None:
+            raise ValueError(
+                "Excel does not support datetimes with "
+                "timezones. Please ensure that datetimes "
+                "are timezone unaware before writing to Excel."
+            )
+        return val
+
+    def _format_header_mi(self) -> Iterable[ExcelCell]:
+        if self.columns.nlevels > 1:
+            if not self.index:
+                raise NotImplementedError(
+                    "Writing to Excel with MultiIndex columns and no "
+                    "index ('index'=False) is not yet implemented."
+                )
+
+        if not (self._has_aliases or self.header):
+            return
+
+        columns = self.columns
+        level_strs = columns._format_multi(
+            sparsify=self.merge_cells, include_names=False
+        )
+        level_lengths = get_level_lengths(level_strs)
+        coloffset = 0
+        lnum = 0
+
+        if self.index and isinstance(self.df.index, MultiIndex):
+            coloffset = len(self.df.index[0]) - 1
+
+        if self.merge_cells:
+            # Format multi-index as a merged cells.
+            for lnum, name in enumerate(columns.names):
+                yield ExcelCell(
+                    row=lnum,
+                    col=coloffset,
+                    val=name,
+                    style=self.header_style,
+                )
+
+            for lnum, (spans, levels, level_codes) in enumerate(
+                zip(level_lengths, columns.levels, columns.codes)
+            ):
+                values = levels.take(level_codes)
+                for i, span_val in spans.items():
+                    mergestart, mergeend = None, None
+                    if span_val > 1:
+                        mergestart, mergeend = lnum, coloffset + i + span_val
+                    yield CssExcelCell(
+                        row=lnum,
+                        col=coloffset + i + 1,
+                        val=values[i],
+                        style=self.header_style,
+                        css_styles=getattr(self.styler, "ctx_columns", None),
+                        css_row=lnum,
+                        css_col=i,
+                        css_converter=self.style_converter,
+                        mergestart=mergestart,
+                        mergeend=mergeend,
+                    )
+        else:
+            # Format in legacy format with dots to indicate levels.
+            for i, values in enumerate(zip(*level_strs)):
+                v = ".".join(map(pprint_thing, values))
+                yield CssExcelCell(
+                    row=lnum,
+                    col=coloffset + i + 1,
+                    val=v,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_columns", None),
+                    css_row=lnum,
+                    css_col=i,
+                    css_converter=self.style_converter,
+                )
+
+        self.rowcounter = lnum
+
+    def _format_header_regular(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            coloffset = 0
+
+            if self.index:
+                coloffset = 1
+                if isinstance(self.df.index, MultiIndex):
+                    coloffset = len(self.df.index.names)
+
+            colnames = self.columns
+            if self._has_aliases:
+                self.header = cast(Sequence, self.header)
+                if len(self.header) != len(self.columns):
+                    raise ValueError(
+                        f"Writing {len(self.columns)} cols "
+                        f"but got {len(self.header)} aliases"
+                    )
+                colnames = self.header
+
+            for colindex, colname in enumerate(colnames):
+                yield CssExcelCell(
+                    row=self.rowcounter,
+                    col=colindex + coloffset,
+                    val=colname,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_columns", None),
+                    css_row=0,
+                    css_col=colindex,
+                    css_converter=self.style_converter,
+                )
+
+    def _format_header(self) -> Iterable[ExcelCell]:
+        gen: Iterable[ExcelCell]
+
+        if isinstance(self.columns, MultiIndex):
+            gen = self._format_header_mi()
+        else:
+            gen = self._format_header_regular()
+
+        gen2: Iterable[ExcelCell] = ()
+
+        if self.df.index.names:
+            row = [x if x is not None else "" for x in self.df.index.names] + [
+                ""
+            ] * len(self.columns)
+            if functools.reduce(lambda x, y: x and y, (x != "" for x in row)):
+                gen2 = (
+                    ExcelCell(self.rowcounter, colindex, val, self.header_style)
+                    for colindex, val in enumerate(row)
+                )
+                self.rowcounter += 1
+        return itertools.chain(gen, gen2)
+
+    def _format_body(self) -> Iterable[ExcelCell]:
+        if isinstance(self.df.index, MultiIndex):
+            return self._format_hierarchical_rows()
+        else:
+            return self._format_regular_rows()
+
+    def _format_regular_rows(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            self.rowcounter += 1
+
+        # output index and index_label?
+        if self.index:
+            # check aliases
+            # if list only take first as this is not a MultiIndex
+            if self.index_label and isinstance(
+                self.index_label, (list, tuple, np.ndarray, Index)
+            ):
+                index_label = self.index_label[0]
+            # if string good to go
+            elif self.index_label and isinstance(self.index_label, str):
+                index_label = self.index_label
+            else:
+                index_label = self.df.index.names[0]
+
+            if isinstance(self.columns, MultiIndex):
+                self.rowcounter += 1
+
+            if index_label and self.header is not False:
+                yield ExcelCell(self.rowcounter - 1, 0, index_label, self.header_style)
+
+            # write index_values
+            index_values = self.df.index
+            if isinstance(self.df.index, PeriodIndex):
+                index_values = self.df.index.to_timestamp()
+
+            for idx, idxval in enumerate(index_values):
+                yield CssExcelCell(
+                    row=self.rowcounter + idx,
+                    col=0,
+                    val=idxval,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_index", None),
+                    css_row=idx,
+                    css_col=0,
+                    css_converter=self.style_converter,
+                )
+            coloffset = 1
+        else:
+            coloffset = 0
+
+        yield from self._generate_body(coloffset)
+
+    def _format_hierarchical_rows(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            self.rowcounter += 1
+
+        gcolidx = 0
+
+        if self.index:
+            index_labels = self.df.index.names
+            # check for aliases
+            if self.index_label and isinstance(
+                self.index_label, (list, tuple, np.ndarray, Index)
+            ):
+                index_labels = self.index_label
+
+            # MultiIndex columns require an extra row
+            # with index names (blank if None) for
+            # unambiguous round-trip, unless not merging,
+            # in which case the names all go on one row Issue #11328
+            if isinstance(self.columns, MultiIndex) and self.merge_cells:
+                self.rowcounter += 1
+
+            # if index labels are not empty go ahead and dump
+            if com.any_not_none(*index_labels) and self.header is not False:
+                for cidx, name in enumerate(index_labels):
+                    yield ExcelCell(self.rowcounter - 1, cidx, name, self.header_style)
+
+            if self.merge_cells:
+                # Format hierarchical rows as merged cells.
+                level_strs = self.df.index._format_multi(
+                    sparsify=True, include_names=False
+                )
+                level_lengths = get_level_lengths(level_strs)
+
+                for spans, levels, level_codes in zip(
+                    level_lengths, self.df.index.levels, self.df.index.codes
+                ):
+                    values = levels.take(
+                        level_codes,
+                        allow_fill=levels._can_hold_na,
+                        fill_value=levels._na_value,
+                    )
+
+                    for i, span_val in spans.items():
+                        mergestart, mergeend = None, None
+                        if span_val > 1:
+                            mergestart = self.rowcounter + i + span_val - 1
+                            mergeend = gcolidx
+                        yield CssExcelCell(
+                            row=self.rowcounter + i,
+                            col=gcolidx,
+                            val=values[i],
+                            style=self.header_style,
+                            css_styles=getattr(self.styler, "ctx_index", None),
+                            css_row=i,
+                            css_col=gcolidx,
+                            css_converter=self.style_converter,
+                            mergestart=mergestart,
+                            mergeend=mergeend,
+                        )
+                    gcolidx += 1
+
+            else:
+                # Format hierarchical rows with non-merged values.
+                for indexcolvals in zip(*self.df.index):
+                    for idx, indexcolval in enumerate(indexcolvals):
+                        yield CssExcelCell(
+                            row=self.rowcounter + idx,
+                            col=gcolidx,
+                            val=indexcolval,
+                            style=self.header_style,
+                            css_styles=getattr(self.styler, "ctx_index", None),
+                            css_row=idx,
+                            css_col=gcolidx,
+                            css_converter=self.style_converter,
+                        )
+                    gcolidx += 1
+
+        yield from self._generate_body(gcolidx)
+
+    @property
+    def _has_aliases(self) -> bool:
+        """Whether the aliases for column names are present."""
+        return is_list_like(self.header)
+
+    def _generate_body(self, coloffset: int) -> Iterable[ExcelCell]:
+        # Write the body of the frame data series by series.
+        for colidx in range(len(self.columns)):
+            series = self.df.iloc[:, colidx]
+            for i, val in enumerate(series):
+                yield CssExcelCell(
+                    row=self.rowcounter + i,
+                    col=colidx + coloffset,
+                    val=val,
+                    style=None,
+                    css_styles=getattr(self.styler, "ctx", None),
+                    css_row=i,
+                    css_col=colidx,
+                    css_converter=self.style_converter,
+                )
+
+    def get_formatted_cells(self) -> Iterable[ExcelCell]:
+        for cell in itertools.chain(self._format_header(), self._format_body()):
+            cell.val = self._format_value(cell.val)
+            yield cell
+
+    @doc(storage_options=_shared_docs["storage_options"])
+    def write(
+        self,
+        writer: FilePath | WriteExcelBuffer | ExcelWriter,
+        sheet_name: str = "Sheet1",
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+        engine: str | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        writer : path-like, file-like, or ExcelWriter object
+            File path or existing ExcelWriter
+        sheet_name : str, default 'Sheet1'
+            Name of sheet which will contain DataFrame
+        startrow :
+            upper left cell row to dump data frame
+        startcol :
+            upper left cell column to dump data frame
+        freeze_panes : tuple of integer (length 2), default None
+            Specifies the one-based bottommost row and rightmost column that
+            is to be frozen
+        engine : string, default None
+            write engine to use if writer is a path - you can also set this
+            via the options ``io.excel.xlsx.writer``,
+            or ``io.excel.xlsm.writer``.
+
+        {storage_options}
+
+        engine_kwargs: dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        from pandas.io.excel import ExcelWriter
+
+        num_rows, num_cols = self.df.shape
+        if num_rows > self.max_rows or num_cols > self.max_cols:
+            raise ValueError(
+                f"This sheet is too large! Your sheet size is: {num_rows}, {num_cols} "
+                f"Max sheet size is: {self.max_rows}, {self.max_cols}"
+            )
+
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        formatted_cells = self.get_formatted_cells()
+        if isinstance(writer, ExcelWriter):
+            need_save = False
+        else:
+            writer = ExcelWriter(
+                writer,
+                engine=engine,
+                storage_options=storage_options,
+                engine_kwargs=engine_kwargs,
+            )
+            need_save = True
+
+        try:
+            writer._write_cells(
+                formatted_cells,
+                sheet_name,
+                startrow=startrow,
+                startcol=startcol,
+                freeze_panes=freeze_panes,
+            )
+        finally:
+            # make sure to close opened file handles
+            if need_save:
+                writer.close()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/format.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/format.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcbee6ce2662f00646b20eba39d0509fb228db1a
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/format.py
@@ -0,0 +1,2058 @@
+"""
+Internal module for formatting output data in csv, html, xml,
+and latex files. This module also applies to display formatting.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Generator,
+    Hashable,
+    Mapping,
+    Sequence,
+)
+from contextlib import contextmanager
+from csv import QUOTE_NONE
+from decimal import Decimal
+from functools import partial
+from io import StringIO
+import math
+import re
+from shutil import get_terminal_size
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Final,
+    cast,
+)
+
+import numpy as np
+
+from pandas._config.config import (
+    get_option,
+    set_option,
+)
+
+from pandas._libs import lib
+from pandas._libs.missing import NA
+from pandas._libs.tslibs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+)
+from pandas._libs.tslibs.nattype import NaTType
+
+from pandas.core.dtypes.common import (
+    is_complex_dtype,
+    is_float,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    ensure_index,
+)
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+from pandas.core.reshape.concat import concat
+
+from pandas.io.common import (
+    check_parent_directory,
+    stringify_path,
+)
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Axes,
+        ColspaceArgType,
+        ColspaceType,
+        CompressionOptions,
+        FilePath,
+        FloatFormatType,
+        FormattersType,
+        IndexLabel,
+        SequenceNotStr,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+common_docstring: Final = """
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        columns : array-like, optional, default None
+            The subset of columns to write. Writes all columns by default.
+        col_space : %(col_space_type)s, optional
+            %(col_space)s.
+        header : %(header_type)s, optional
+            %(header)s.
+        index : bool, optional, default True
+            Whether to print index (row) labels.
+        na_rep : str, optional, default 'NaN'
+            String representation of ``NaN`` to use.
+        formatters : list, tuple or dict of one-param. functions, optional
+            Formatter functions to apply to columns' elements by position or
+            name.
+            The result of each function must be a unicode string.
+            List/tuple must be of length equal to the number of columns.
+        float_format : one-parameter function, optional, default None
+            Formatter function to apply to columns' elements if they are
+            floats. This function must return a unicode string and will be
+            applied only to the non-``NaN`` elements, with ``NaN`` being
+            handled by ``na_rep``.
+        sparsify : bool, optional, default True
+            Set to False for a DataFrame with a hierarchical index to print
+            every multiindex key at each row.
+        index_names : bool, optional, default True
+            Prints the names of the indexes.
+        justify : str, default None
+            How to justify the column labels. If None uses the option from
+            the print configuration (controlled by set_option), 'right' out
+            of the box. Valid values are
+
+            * left
+            * right
+            * center
+            * justify
+            * justify-all
+            * start
+            * end
+            * inherit
+            * match-parent
+            * initial
+            * unset.
+        max_rows : int, optional
+            Maximum number of rows to display in the console.
+        max_cols : int, optional
+            Maximum number of columns to display in the console.
+        show_dimensions : bool, default False
+            Display DataFrame dimensions (number of rows by number of columns).
+        decimal : str, default '.'
+            Character recognized as decimal separator, e.g. ',' in Europe.
+    """
+
+VALID_JUSTIFY_PARAMETERS = (
+    "left",
+    "right",
+    "center",
+    "justify",
+    "justify-all",
+    "start",
+    "end",
+    "inherit",
+    "match-parent",
+    "initial",
+    "unset",
+)
+
+return_docstring: Final = """
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns
+            None.
+    """
+
+
+class SeriesFormatter:
+    """
+    Implement the main logic of Series.to_string, which underlies
+    Series.__repr__.
+    """
+
+    def __init__(
+        self,
+        series: Series,
+        *,
+        length: bool | str = True,
+        header: bool = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        name: bool = False,
+        float_format: str | None = None,
+        dtype: bool = True,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+    ) -> None:
+        self.series = series
+        self.buf = StringIO()
+        self.name = name
+        self.na_rep = na_rep
+        self.header = header
+        self.length = length
+        self.index = index
+        self.max_rows = max_rows
+        self.min_rows = min_rows
+
+        if float_format is None:
+            float_format = get_option("display.float_format")
+        self.float_format = float_format
+        self.dtype = dtype
+        self.adj = printing.get_adjustment()
+
+        self._chk_truncate()
+
+    def _chk_truncate(self) -> None:
+        self.tr_row_num: int | None
+
+        min_rows = self.min_rows
+        max_rows = self.max_rows
+        # truncation determined by max_rows, actual truncated number of rows
+        # used below by min_rows
+        is_truncated_vertically = max_rows and (len(self.series) > max_rows)
+        series = self.series
+        if is_truncated_vertically:
+            max_rows = cast(int, max_rows)
+            if min_rows:
+                # if min_rows is set (not None or 0), set max_rows to minimum
+                # of both
+                max_rows = min(min_rows, max_rows)
+            if max_rows == 1:
+                row_num = max_rows
+                series = series.iloc[:max_rows]
+            else:
+                row_num = max_rows // 2
+                series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
+            self.tr_row_num = row_num
+        else:
+            self.tr_row_num = None
+        self.tr_series = series
+        self.is_truncated_vertically = is_truncated_vertically
+
+    def _get_footer(self) -> str:
+        name = self.series.name
+        footer = ""
+
+        index = self.series.index
+        if (
+            isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex))
+            and index.freq is not None
+        ):
+            footer += f"Freq: {index.freqstr}"
+
+        if self.name is not False and name is not None:
+            if footer:
+                footer += ", "
+
+            series_name = printing.pprint_thing(name, escape_chars=("\t", "\r", "\n"))
+            footer += f"Name: {series_name}"
+
+        if self.length is True or (
+            self.length == "truncate" and self.is_truncated_vertically
+        ):
+            if footer:
+                footer += ", "
+            footer += f"Length: {len(self.series)}"
+
+        if self.dtype is not False and self.dtype is not None:
+            dtype_name = getattr(self.tr_series.dtype, "name", None)
+            if dtype_name:
+                if footer:
+                    footer += ", "
+                footer += f"dtype: {printing.pprint_thing(dtype_name)}"
+
+        # level infos are added to the end and in a new line, like it is done
+        # for Categoricals
+        if isinstance(self.tr_series.dtype, CategoricalDtype):
+            level_info = self.tr_series._values._get_repr_footer()
+            if footer:
+                footer += "\n"
+            footer += level_info
+
+        return str(footer)
+
+    def _get_formatted_values(self) -> list[str]:
+        return format_array(
+            self.tr_series._values,
+            None,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            leading_space=self.index,
+        )
+
+    def to_string(self) -> str:
+        series = self.tr_series
+        footer = self._get_footer()
+
+        if len(series) == 0:
+            return f"{type(self.series).__name__}([], {footer})"
+
+        index = series.index
+        have_header = _has_names(index)
+        if isinstance(index, MultiIndex):
+            fmt_index = index._format_multi(include_names=True, sparsify=None)
+            adj = printing.get_adjustment()
+            fmt_index = adj.adjoin(2, *fmt_index).split("\n")
+        else:
+            fmt_index = index._format_flat(include_name=True)
+        fmt_values = self._get_formatted_values()
+
+        if self.is_truncated_vertically:
+            n_header_rows = 0
+            row_num = self.tr_row_num
+            row_num = cast(int, row_num)
+            width = self.adj.len(fmt_values[row_num - 1])
+            if width > 3:
+                dot_str = "..."
+            else:
+                dot_str = ".."
+            # Series uses mode=center because it has single value columns
+            # DataFrame uses mode=left
+            dot_str = self.adj.justify([dot_str], width, mode="center")[0]
+            fmt_values.insert(row_num + n_header_rows, dot_str)
+            fmt_index.insert(row_num + 1, "")
+
+        if self.index:
+            result = self.adj.adjoin(3, *[fmt_index[1:], fmt_values])
+        else:
+            result = self.adj.adjoin(3, fmt_values)
+
+        if self.header and have_header:
+            result = fmt_index[0] + "\n" + result
+
+        if footer:
+            result += "\n" + footer
+
+        return str("".join(result))
+
+
+def get_dataframe_repr_params() -> dict[str, Any]:
+    """Get the parameters used to repr(dataFrame) calls using DataFrame.to_string.
+
+    Supplying these parameters to DataFrame.to_string is equivalent to calling
+    ``repr(DataFrame)``. This is useful if you want to adjust the repr output.
+
+    .. versionadded:: 1.4.0
+
+    Example
+    -------
+    >>> import pandas as pd
+    >>>
+    >>> df = pd.DataFrame([[1, 2], [3, 4]])
+    >>> repr_params = pd.io.formats.format.get_dataframe_repr_params()
+    >>> repr(df) == df.to_string(**repr_params)
+    True
+    """
+    from pandas.io.formats import console
+
+    if get_option("display.expand_frame_repr"):
+        line_width, _ = console.get_console_size()
+    else:
+        line_width = None
+    return {
+        "max_rows": get_option("display.max_rows"),
+        "min_rows": get_option("display.min_rows"),
+        "max_cols": get_option("display.max_columns"),
+        "max_colwidth": get_option("display.max_colwidth"),
+        "show_dimensions": get_option("display.show_dimensions"),
+        "line_width": line_width,
+    }
+
+
+def get_series_repr_params() -> dict[str, Any]:
+    """Get the parameters used to repr(Series) calls using Series.to_string.
+
+    Supplying these parameters to Series.to_string is equivalent to calling
+    ``repr(series)``. This is useful if you want to adjust the series repr output.
+
+    .. versionadded:: 1.4.0
+
+    Example
+    -------
+    >>> import pandas as pd
+    >>>
+    >>> ser = pd.Series([1, 2, 3, 4])
+    >>> repr_params = pd.io.formats.format.get_series_repr_params()
+    >>> repr(ser) == ser.to_string(**repr_params)
+    True
+    """
+    width, height = get_terminal_size()
+    max_rows_opt = get_option("display.max_rows")
+    max_rows = height if max_rows_opt == 0 else max_rows_opt
+    min_rows = height if max_rows_opt == 0 else get_option("display.min_rows")
+
+    return {
+        "name": True,
+        "dtype": True,
+        "min_rows": min_rows,
+        "max_rows": max_rows,
+        "length": get_option("display.show_dimensions"),
+    }
+
+
+class DataFrameFormatter:
+    """
+    Class for processing dataframe formatting options and data.
+
+    Used by DataFrame.to_string, which backs DataFrame.__repr__.
+    """
+
+    __doc__ = __doc__ if __doc__ else ""
+    __doc__ += common_docstring + return_docstring
+
+    def __init__(
+        self,
+        frame: DataFrame,
+        columns: Axes | None = None,
+        col_space: ColspaceArgType | None = None,
+        header: bool | SequenceNotStr[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        justify: str | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool | str = False,
+        decimal: str = ".",
+        bold_rows: bool = False,
+        escape: bool = True,
+    ) -> None:
+        self.frame = frame
+        self.columns = self._initialize_columns(columns)
+        self.col_space = self._initialize_colspace(col_space)
+        self.header = header
+        self.index = index
+        self.na_rep = na_rep
+        self.formatters = self._initialize_formatters(formatters)
+        self.justify = self._initialize_justify(justify)
+        self.float_format = float_format
+        self.sparsify = self._initialize_sparsify(sparsify)
+        self.show_index_names = index_names
+        self.decimal = decimal
+        self.bold_rows = bold_rows
+        self.escape = escape
+        self.max_rows = max_rows
+        self.min_rows = min_rows
+        self.max_cols = max_cols
+        self.show_dimensions = show_dimensions
+
+        self.max_cols_fitted = self._calc_max_cols_fitted()
+        self.max_rows_fitted = self._calc_max_rows_fitted()
+
+        self.tr_frame = self.frame
+        self.truncate()
+        self.adj = printing.get_adjustment()
+
+    def get_strcols(self) -> list[list[str]]:
+        """
+        Render a DataFrame to a list of columns (as lists of strings).
+        """
+        strcols = self._get_strcols_without_index()
+
+        if self.index:
+            str_index = self._get_formatted_index(self.tr_frame)
+            strcols.insert(0, str_index)
+
+        return strcols
+
+    @property
+    def should_show_dimensions(self) -> bool:
+        return self.show_dimensions is True or (
+            self.show_dimensions == "truncate" and self.is_truncated
+        )
+
+    @property
+    def is_truncated(self) -> bool:
+        return bool(self.is_truncated_horizontally or self.is_truncated_vertically)
+
+    @property
+    def is_truncated_horizontally(self) -> bool:
+        return bool(self.max_cols_fitted and (len(self.columns) > self.max_cols_fitted))
+
+    @property
+    def is_truncated_vertically(self) -> bool:
+        return bool(self.max_rows_fitted and (len(self.frame) > self.max_rows_fitted))
+
+    @property
+    def dimensions_info(self) -> str:
+        return f"\n\n[{len(self.frame)} rows x {len(self.frame.columns)} columns]"
+
+    @property
+    def has_index_names(self) -> bool:
+        return _has_names(self.frame.index)
+
+    @property
+    def has_column_names(self) -> bool:
+        return _has_names(self.frame.columns)
+
+    @property
+    def show_row_idx_names(self) -> bool:
+        return all((self.has_index_names, self.index, self.show_index_names))
+
+    @property
+    def show_col_idx_names(self) -> bool:
+        return all((self.has_column_names, self.show_index_names, self.header))
+
+    @property
+    def max_rows_displayed(self) -> int:
+        return min(self.max_rows or len(self.frame), len(self.frame))
+
+    def _initialize_sparsify(self, sparsify: bool | None) -> bool:
+        if sparsify is None:
+            return get_option("display.multi_sparse")
+        return sparsify
+
+    def _initialize_formatters(
+        self, formatters: FormattersType | None
+    ) -> FormattersType:
+        if formatters is None:
+            return {}
+        elif len(self.frame.columns) == len(formatters) or isinstance(formatters, dict):
+            return formatters
+        else:
+            raise ValueError(
+                f"Formatters length({len(formatters)}) should match "
+                f"DataFrame number of columns({len(self.frame.columns)})"
+            )
+
+    def _initialize_justify(self, justify: str | None) -> str:
+        if justify is None:
+            return get_option("display.colheader_justify")
+        else:
+            return justify
+
+    def _initialize_columns(self, columns: Axes | None) -> Index:
+        if columns is not None:
+            cols = ensure_index(columns)
+            self.frame = self.frame[cols]
+            return cols
+        else:
+            return self.frame.columns
+
+    def _initialize_colspace(self, col_space: ColspaceArgType | None) -> ColspaceType:
+        result: ColspaceType
+
+        if col_space is None:
+            result = {}
+        elif isinstance(col_space, (int, str)):
+            result = {"": col_space}
+            result.update({column: col_space for column in self.frame.columns})
+        elif isinstance(col_space, Mapping):
+            for column in col_space.keys():
+                if column not in self.frame.columns and column != "":
+                    raise ValueError(
+                        f"Col_space is defined for an unknown column: {column}"
+                    )
+            result = col_space
+        else:
+            if len(self.frame.columns) != len(col_space):
+                raise ValueError(
+                    f"Col_space length({len(col_space)}) should match "
+                    f"DataFrame number of columns({len(self.frame.columns)})"
+                )
+            result = dict(zip(self.frame.columns, col_space))
+        return result
+
+    def _calc_max_cols_fitted(self) -> int | None:
+        """Number of columns fitting the screen."""
+        if not self._is_in_terminal():
+            return self.max_cols
+
+        width, _ = get_terminal_size()
+        if self._is_screen_narrow(width):
+            return width
+        else:
+            return self.max_cols
+
+    def _calc_max_rows_fitted(self) -> int | None:
+        """Number of rows with data fitting the screen."""
+        max_rows: int | None
+
+        if self._is_in_terminal():
+            _, height = get_terminal_size()
+            if self.max_rows == 0:
+                # rows available to fill with actual data
+                return height - self._get_number_of_auxiliary_rows()
+
+            if self._is_screen_short(height):
+                max_rows = height
+            else:
+                max_rows = self.max_rows
+        else:
+            max_rows = self.max_rows
+
+        return self._adjust_max_rows(max_rows)
+
+    def _adjust_max_rows(self, max_rows: int | None) -> int | None:
+        """Adjust max_rows using display logic.
+
+        See description here:
+        https://pandas.pydata.org/docs/dev/user_guide/options.html#frequently-used-options
+
+        GH #37359
+        """
+        if max_rows:
+            if (len(self.frame) > max_rows) and self.min_rows:
+                # if truncated, set max_rows showed to min_rows
+                max_rows = min(self.min_rows, max_rows)
+        return max_rows
+
+    def _is_in_terminal(self) -> bool:
+        """Check if the output is to be shown in terminal."""
+        return bool(self.max_cols == 0 or self.max_rows == 0)
+
+    def _is_screen_narrow(self, max_width) -> bool:
+        return bool(self.max_cols == 0 and len(self.frame.columns) > max_width)
+
+    def _is_screen_short(self, max_height) -> bool:
+        return bool(self.max_rows == 0 and len(self.frame) > max_height)
+
+    def _get_number_of_auxiliary_rows(self) -> int:
+        """Get number of rows occupied by prompt, dots and dimension info."""
+        dot_row = 1
+        prompt_row = 1
+        num_rows = dot_row + prompt_row
+
+        if self.show_dimensions:
+            num_rows += len(self.dimensions_info.splitlines())
+
+        if self.header:
+            num_rows += 1
+
+        return num_rows
+
+    def truncate(self) -> None:
+        """
+        Check whether the frame should be truncated. If so, slice the frame up.
+        """
+        if self.is_truncated_horizontally:
+            self._truncate_horizontally()
+
+        if self.is_truncated_vertically:
+            self._truncate_vertically()
+
+    def _truncate_horizontally(self) -> None:
+        """Remove columns, which are not to be displayed and adjust formatters.
+
+        Attributes affected:
+            - tr_frame
+            - formatters
+            - tr_col_num
+        """
+        assert self.max_cols_fitted is not None
+        col_num = self.max_cols_fitted // 2
+        if col_num >= 1:
+            left = self.tr_frame.iloc[:, :col_num]
+            right = self.tr_frame.iloc[:, -col_num:]
+            self.tr_frame = concat((left, right), axis=1)
+
+            # truncate formatter
+            if isinstance(self.formatters, (list, tuple)):
+                self.formatters = [
+                    *self.formatters[:col_num],
+                    *self.formatters[-col_num:],
+                ]
+        else:
+            col_num = cast(int, self.max_cols)
+            self.tr_frame = self.tr_frame.iloc[:, :col_num]
+        self.tr_col_num = col_num
+
+    def _truncate_vertically(self) -> None:
+        """Remove rows, which are not to be displayed.
+
+        Attributes affected:
+            - tr_frame
+            - tr_row_num
+        """
+        assert self.max_rows_fitted is not None
+        row_num = self.max_rows_fitted // 2
+        if row_num >= 1:
+            _len = len(self.tr_frame)
+            _slice = np.hstack([np.arange(row_num), np.arange(_len - row_num, _len)])
+            self.tr_frame = self.tr_frame.iloc[_slice]
+        else:
+            row_num = cast(int, self.max_rows)
+            self.tr_frame = self.tr_frame.iloc[:row_num, :]
+        self.tr_row_num = row_num
+
+    def _get_strcols_without_index(self) -> list[list[str]]:
+        strcols: list[list[str]] = []
+
+        if not is_list_like(self.header) and not self.header:
+            for i, c in enumerate(self.tr_frame):
+                fmt_values = self.format_col(i)
+                fmt_values = _make_fixed_width(
+                    strings=fmt_values,
+                    justify=self.justify,
+                    minimum=int(self.col_space.get(c, 0)),
+                    adj=self.adj,
+                )
+                strcols.append(fmt_values)
+            return strcols
+
+        if is_list_like(self.header):
+            # cast here since can't be bool if is_list_like
+            self.header = cast(list[str], self.header)
+            if len(self.header) != len(self.columns):
+                raise ValueError(
+                    f"Writing {len(self.columns)} cols "
+                    f"but got {len(self.header)} aliases"
+                )
+            str_columns = [[label] for label in self.header]
+        else:
+            str_columns = self._get_formatted_column_labels(self.tr_frame)
+
+        if self.show_row_idx_names:
+            for x in str_columns:
+                x.append("")
+
+        for i, c in enumerate(self.tr_frame):
+            cheader = str_columns[i]
+            header_colwidth = max(
+                int(self.col_space.get(c, 0)), *(self.adj.len(x) for x in cheader)
+            )
+            fmt_values = self.format_col(i)
+            fmt_values = _make_fixed_width(
+                fmt_values, self.justify, minimum=header_colwidth, adj=self.adj
+            )
+
+            max_len = max(*(self.adj.len(x) for x in fmt_values), header_colwidth)
+            cheader = self.adj.justify(cheader, max_len, mode=self.justify)
+            strcols.append(cheader + fmt_values)
+
+        return strcols
+
+    def format_col(self, i: int) -> list[str]:
+        frame = self.tr_frame
+        formatter = self._get_formatter(i)
+        return format_array(
+            frame.iloc[:, i]._values,
+            formatter,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            space=self.col_space.get(frame.columns[i]),
+            decimal=self.decimal,
+            leading_space=self.index,
+        )
+
+    def _get_formatter(self, i: str | int) -> Callable | None:
+        if isinstance(self.formatters, (list, tuple)):
+            if is_integer(i):
+                i = cast(int, i)
+                return self.formatters[i]
+            else:
+                return None
+        else:
+            if is_integer(i) and i not in self.columns:
+                i = self.columns[i]
+            return self.formatters.get(i, None)
+
+    def _get_formatted_column_labels(self, frame: DataFrame) -> list[list[str]]:
+        from pandas.core.indexes.multi import sparsify_labels
+
+        columns = frame.columns
+
+        if isinstance(columns, MultiIndex):
+            fmt_columns = columns._format_multi(sparsify=False, include_names=False)
+            fmt_columns = list(zip(*fmt_columns))
+            dtypes = self.frame.dtypes._values
+
+            # if we have a Float level, they don't use leading space at all
+            restrict_formatting = any(level.is_floating for level in columns.levels)
+            need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
+
+            def space_format(x, y):
+                if (
+                    y not in self.formatters
+                    and need_leadsp[x]
+                    and not restrict_formatting
+                ):
+                    return " " + y
+                return y
+
+            str_columns_tuple = list(
+                zip(*([space_format(x, y) for y in x] for x in fmt_columns))
+            )
+            if self.sparsify and len(str_columns_tuple):
+                str_columns_tuple = sparsify_labels(str_columns_tuple)
+
+            str_columns = [list(x) for x in zip(*str_columns_tuple)]
+        else:
+            fmt_columns = columns._format_flat(include_name=False)
+            dtypes = self.frame.dtypes
+            need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
+            str_columns = [
+                [" " + x if not self._get_formatter(i) and need_leadsp[x] else x]
+                for i, x in enumerate(fmt_columns)
+            ]
+        # self.str_columns = str_columns
+        return str_columns
+
+    def _get_formatted_index(self, frame: DataFrame) -> list[str]:
+        # Note: this is only used by to_string() and to_latex(), not by
+        # to_html(). so safe to cast col_space here.
+        col_space = {k: cast(int, v) for k, v in self.col_space.items()}
+        index = frame.index
+        columns = frame.columns
+        fmt = self._get_formatter("__index__")
+
+        if isinstance(index, MultiIndex):
+            fmt_index = index._format_multi(
+                sparsify=self.sparsify,
+                include_names=self.show_row_idx_names,
+                formatter=fmt,
+            )
+        else:
+            fmt_index = [
+                index._format_flat(include_name=self.show_row_idx_names, formatter=fmt)
+            ]
+
+        fmt_index = [
+            tuple(
+                _make_fixed_width(
+                    list(x), justify="left", minimum=col_space.get("", 0), adj=self.adj
+                )
+            )
+            for x in fmt_index
+        ]
+
+        adjoined = self.adj.adjoin(1, *fmt_index).split("\n")
+
+        # empty space for columns
+        if self.show_col_idx_names:
+            col_header = [str(x) for x in self._get_column_name_list()]
+        else:
+            col_header = [""] * columns.nlevels
+
+        if self.header:
+            return col_header + adjoined
+        else:
+            return adjoined
+
+    def _get_column_name_list(self) -> list[Hashable]:
+        names: list[Hashable] = []
+        columns = self.frame.columns
+        if isinstance(columns, MultiIndex):
+            names.extend("" if name is None else name for name in columns.names)
+        else:
+            names.append("" if columns.name is None else columns.name)
+        return names
+
+
+class DataFrameRenderer:
+    """Class for creating dataframe output in multiple formats.
+
+    Called in pandas.core.generic.NDFrame:
+        - to_csv
+        - to_latex
+
+    Called in pandas.core.frame.DataFrame:
+        - to_html
+        - to_string
+
+    Parameters
+    ----------
+    fmt : DataFrameFormatter
+        Formatter with the formatting options.
+    """
+
+    def __init__(self, fmt: DataFrameFormatter) -> None:
+        self.fmt = fmt
+
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        classes: str | list | tuple | None = None,
+        notebook: bool = False,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+    ) -> str | None:
+        """
+        Render a DataFrame to a html table.
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        encoding : str, default “utf-8”
+            Set character encoding.
+        classes : str or list-like
+            classes to include in the `class` attribute of the opening
+            ``<table>`` tag, in addition to the default "dataframe".
+        notebook : {True, False}, optional, default False
+            Whether the generated HTML is for IPython Notebook.
+        border : int
+            A ``border=border`` attribute is included in the opening
+            ``<table>`` tag. Default ``pd.options.display.html.border``.
+        table_id : str, optional
+            A css id is included in the opening `<table>` tag if specified.
+        render_links : bool, default False
+            Convert URLs to HTML links.
+        """
+        from pandas.io.formats.html import (
+            HTMLFormatter,
+            NotebookFormatter,
+        )
+
+        Klass = NotebookFormatter if notebook else HTMLFormatter
+
+        html_formatter = Klass(
+            self.fmt,
+            classes=classes,
+            border=border,
+            table_id=table_id,
+            render_links=render_links,
+        )
+        string = html_formatter.to_string()
+        return save_to_buffer(string, buf=buf, encoding=encoding)
+
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        line_width: int | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to a console-friendly tabular output.
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        encoding: str, default “utf-8”
+            Set character encoding.
+        line_width : int, optional
+            Width to wrap a line in characters.
+        """
+        from pandas.io.formats.string import StringFormatter
+
+        string_formatter = StringFormatter(self.fmt, line_width=line_width)
+        string = string_formatter.to_string()
+        return save_to_buffer(string, buf=buf, encoding=encoding)
+
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        sep: str = ",",
+        columns: Sequence[Hashable] | None = None,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        quotechar: str = '"',
+        lineterminator: str | None = None,
+        chunksize: int | None = None,
+        date_format: str | None = None,
+        doublequote: bool = True,
+        escapechar: str | None = None,
+        errors: str = "strict",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        """
+        Render dataframe as comma-separated file.
+        """
+        from pandas.io.formats.csvs import CSVFormatter
+
+        if path_or_buf is None:
+            created_buffer = True
+            path_or_buf = StringIO()
+        else:
+            created_buffer = False
+
+        csv_formatter = CSVFormatter(
+            path_or_buf=path_or_buf,
+            lineterminator=lineterminator,
+            sep=sep,
+            encoding=encoding,
+            errors=errors,
+            compression=compression,
+            quoting=quoting,
+            cols=columns,
+            index_label=index_label,
+            mode=mode,
+            chunksize=chunksize,
+            quotechar=quotechar,
+            date_format=date_format,
+            doublequote=doublequote,
+            escapechar=escapechar,
+            storage_options=storage_options,
+            formatter=self.fmt,
+        )
+        csv_formatter.save()
+
+        if created_buffer:
+            assert isinstance(path_or_buf, StringIO)
+            content = path_or_buf.getvalue()
+            path_or_buf.close()
+            return content
+
+        return None
+
+
+def save_to_buffer(
+    string: str,
+    buf: FilePath | WriteBuffer[str] | None = None,
+    encoding: str | None = None,
+) -> str | None:
+    """
+    Perform serialization. Write to buf or return as string if buf is None.
+    """
+    with _get_buffer(buf, encoding=encoding) as fd:
+        fd.write(string)
+        if buf is None:
+            # error: "WriteBuffer[str]" has no attribute "getvalue"
+            return fd.getvalue()  # type: ignore[attr-defined]
+        return None
+
+
+@contextmanager
+def _get_buffer(
+    buf: FilePath | WriteBuffer[str] | None, encoding: str | None = None
+) -> Generator[WriteBuffer[str], None, None] | Generator[StringIO, None, None]:
+    """
+    Context manager to open, yield and close buffer for filenames or Path-like
+    objects, otherwise yield buf unchanged.
+    """
+    if buf is not None:
+        buf = stringify_path(buf)
+    else:
+        buf = StringIO()
+
+    if encoding is None:
+        encoding = "utf-8"
+    elif not isinstance(buf, str):
+        raise ValueError("buf is not a file name and encoding is specified.")
+
+    if hasattr(buf, "write"):
+        # Incompatible types in "yield" (actual type "Union[str, WriteBuffer[str],
+        # StringIO]", expected type "Union[WriteBuffer[str], StringIO]")
+        yield buf  # type: ignore[misc]
+    elif isinstance(buf, str):
+        check_parent_directory(str(buf))
+        with open(buf, "w", encoding=encoding, newline="") as f:
+            # GH#30034 open instead of codecs.open prevents a file leak
+            #  if we have an invalid encoding argument.
+            # newline="" is needed to roundtrip correctly on
+            #  windows test_to_latex_filename
+            yield f
+    else:
+        raise TypeError("buf is not a file name and it has no write method")
+
+
+# ----------------------------------------------------------------------
+# Array formatters
+
+
+def format_array(
+    values: ArrayLike,
+    formatter: Callable | None,
+    float_format: FloatFormatType | None = None,
+    na_rep: str = "NaN",
+    digits: int | None = None,
+    space: str | int | None = None,
+    justify: str = "right",
+    decimal: str = ".",
+    leading_space: bool | None = True,
+    quoting: int | None = None,
+    fallback_formatter: Callable | None = None,
+) -> list[str]:
+    """
+    Format an array for printing.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    formatter
+    float_format
+    na_rep
+    digits
+    space
+    justify
+    decimal
+    leading_space : bool, optional, default True
+        Whether the array should be formatted with a leading space.
+        When an array as a column of a Series or DataFrame, we do want
+        the leading space to pad between columns.
+
+        When formatting an Index subclass
+        (e.g. IntervalIndex._get_values_for_csv), we don't want the
+        leading space since it should be left-aligned.
+    fallback_formatter
+
+    Returns
+    -------
+    List[str]
+    """
+    fmt_klass: type[_GenericArrayFormatter]
+    if lib.is_np_dtype(values.dtype, "M"):
+        fmt_klass = _Datetime64Formatter
+        values = cast(DatetimeArray, values)
+    elif isinstance(values.dtype, DatetimeTZDtype):
+        fmt_klass = _Datetime64TZFormatter
+        values = cast(DatetimeArray, values)
+    elif lib.is_np_dtype(values.dtype, "m"):
+        fmt_klass = _Timedelta64Formatter
+        values = cast(TimedeltaArray, values)
+    elif isinstance(values.dtype, ExtensionDtype):
+        fmt_klass = _ExtensionArrayFormatter
+    elif lib.is_np_dtype(values.dtype, "fc"):
+        fmt_klass = FloatArrayFormatter
+    elif lib.is_np_dtype(values.dtype, "iu"):
+        fmt_klass = _IntArrayFormatter
+    else:
+        fmt_klass = _GenericArrayFormatter
+
+    if space is None:
+        space = 12
+
+    if float_format is None:
+        float_format = get_option("display.float_format")
+
+    if digits is None:
+        digits = get_option("display.precision")
+
+    fmt_obj = fmt_klass(
+        values,
+        digits=digits,
+        na_rep=na_rep,
+        float_format=float_format,
+        formatter=formatter,
+        space=space,
+        justify=justify,
+        decimal=decimal,
+        leading_space=leading_space,
+        quoting=quoting,
+        fallback_formatter=fallback_formatter,
+    )
+
+    return fmt_obj.get_result()
+
+
+class _GenericArrayFormatter:
+    def __init__(
+        self,
+        values: ArrayLike,
+        digits: int = 7,
+        formatter: Callable | None = None,
+        na_rep: str = "NaN",
+        space: str | int = 12,
+        float_format: FloatFormatType | None = None,
+        justify: str = "right",
+        decimal: str = ".",
+        quoting: int | None = None,
+        fixed_width: bool = True,
+        leading_space: bool | None = True,
+        fallback_formatter: Callable | None = None,
+    ) -> None:
+        self.values = values
+        self.digits = digits
+        self.na_rep = na_rep
+        self.space = space
+        self.formatter = formatter
+        self.float_format = float_format
+        self.justify = justify
+        self.decimal = decimal
+        self.quoting = quoting
+        self.fixed_width = fixed_width
+        self.leading_space = leading_space
+        self.fallback_formatter = fallback_formatter
+
+    def get_result(self) -> list[str]:
+        fmt_values = self._format_strings()
+        return _make_fixed_width(fmt_values, self.justify)
+
+    def _format_strings(self) -> list[str]:
+        if self.float_format is None:
+            float_format = get_option("display.float_format")
+            if float_format is None:
+                precision = get_option("display.precision")
+                float_format = lambda x: _trim_zeros_single_float(
+                    f"{x: .{precision:d}f}"
+                )
+        else:
+            float_format = self.float_format
+
+        if self.formatter is not None:
+            formatter = self.formatter
+        elif self.fallback_formatter is not None:
+            formatter = self.fallback_formatter
+        else:
+            quote_strings = self.quoting is not None and self.quoting != QUOTE_NONE
+            formatter = partial(
+                printing.pprint_thing,
+                escape_chars=("\t", "\r", "\n"),
+                quote_strings=quote_strings,
+            )
+
+        def _format(x):
+            if self.na_rep is not None and is_scalar(x) and isna(x):
+                if x is None:
+                    return "None"
+                elif x is NA:
+                    return str(NA)
+                elif lib.is_float(x) and np.isinf(x):
+                    # TODO(3.0): this will be unreachable when use_inf_as_na
+                    #  deprecation is enforced
+                    return str(x)
+                elif x is NaT or isinstance(x, (np.datetime64, np.timedelta64)):
+                    return "NaT"
+                return self.na_rep
+            elif isinstance(x, PandasObject):
+                return str(x)
+            elif isinstance(x, StringDtype) and x.na_value is NA:
+                return repr(x)
+            else:
+                # object dtype
+                return str(formatter(x))
+
+        vals = self.values
+        if not isinstance(vals, np.ndarray):
+            raise TypeError(
+                "ExtensionArray formatting should use _ExtensionArrayFormatter"
+            )
+        inferred = lib.map_infer(vals, is_float)
+        is_float_type = (
+            inferred
+            # vals may have 2 or more dimensions
+            & np.all(notna(vals), axis=tuple(range(1, len(vals.shape))))
+        )
+        leading_space = self.leading_space
+        if leading_space is None:
+            leading_space = is_float_type.any()
+
+        fmt_values = []
+        for i, v in enumerate(vals):
+            if (not is_float_type[i] or self.formatter is not None) and leading_space:
+                fmt_values.append(f" {_format(v)}")
+            elif is_float_type[i]:
+                fmt_values.append(float_format(v))
+            else:
+                if leading_space is False:
+                    # False specifically, so that the default is
+                    # to include a space if we get here.
+                    tpl = "{v}"
+                else:
+                    tpl = " {v}"
+                fmt_values.append(tpl.format(v=_format(v)))
+
+        return fmt_values
+
+
+class FloatArrayFormatter(_GenericArrayFormatter):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # float_format is expected to be a string
+        # formatter should be used to pass a function
+        if self.float_format is not None and self.formatter is None:
+            # GH21625, GH22270
+            self.fixed_width = False
+            if callable(self.float_format):
+                self.formatter = self.float_format
+                self.float_format = None
+
+    def _value_formatter(
+        self,
+        float_format: FloatFormatType | None = None,
+        threshold: float | None = None,
+    ) -> Callable:
+        """Returns a function to be applied on each value to format it"""
+        # the float_format parameter supersedes self.float_format
+        if float_format is None:
+            float_format = self.float_format
+
+        # we are going to compose different functions, to first convert to
+        # a string, then replace the decimal symbol, and finally chop according
+        # to the threshold
+
+        # when there is no float_format, we use str instead of '%g'
+        # because str(0.0) = '0.0' while '%g' % 0.0 = '0'
+        if float_format:
+
+            def base_formatter(v):
+                assert float_format is not None  # for mypy
+                # error: "str" not callable
+                # error: Unexpected keyword argument "value" for "__call__" of
+                # "EngFormatter"
+                return (
+                    float_format(value=v)  # type: ignore[operator,call-arg]
+                    if notna(v)
+                    else self.na_rep
+                )
+
+        else:
+
+            def base_formatter(v):
+                return str(v) if notna(v) else self.na_rep
+
+        if self.decimal != ".":
+
+            def decimal_formatter(v):
+                return base_formatter(v).replace(".", self.decimal, 1)
+
+        else:
+            decimal_formatter = base_formatter
+
+        if threshold is None:
+            return decimal_formatter
+
+        def formatter(value):
+            if notna(value):
+                if abs(value) > threshold:
+                    return decimal_formatter(value)
+                else:
+                    return decimal_formatter(0.0)
+            else:
+                return self.na_rep
+
+        return formatter
+
+    def get_result_as_array(self) -> np.ndarray:
+        """
+        Returns the float values converted into strings using
+        the parameters given at initialisation, as a numpy array
+        """
+
+        def format_with_na_rep(values: ArrayLike, formatter: Callable, na_rep: str):
+            mask = isna(values)
+            formatted = np.array(
+                [
+                    formatter(val) if not m else na_rep
+                    for val, m in zip(values.ravel(), mask.ravel())
+                ]
+            ).reshape(values.shape)
+            return formatted
+
+        def format_complex_with_na_rep(
+            values: ArrayLike, formatter: Callable, na_rep: str
+        ):
+            real_values = np.real(values).ravel()  # type: ignore[arg-type]
+            imag_values = np.imag(values).ravel()  # type: ignore[arg-type]
+            real_mask, imag_mask = isna(real_values), isna(imag_values)
+            formatted_lst = []
+            for val, real_val, imag_val, re_isna, im_isna in zip(
+                values.ravel(),
+                real_values,
+                imag_values,
+                real_mask,
+                imag_mask,
+            ):
+                if not re_isna and not im_isna:
+                    formatted_lst.append(formatter(val))
+                elif not re_isna:  # xxx+nanj
+                    formatted_lst.append(f"{formatter(real_val)}+{na_rep}j")
+                elif not im_isna:  # nan[+/-]xxxj
+                    # The imaginary part may either start with a "-" or a space
+                    imag_formatted = formatter(imag_val).strip()
+                    if imag_formatted.startswith("-"):
+                        formatted_lst.append(f"{na_rep}{imag_formatted}j")
+                    else:
+                        formatted_lst.append(f"{na_rep}+{imag_formatted}j")
+                else:  # nan+nanj
+                    formatted_lst.append(f"{na_rep}+{na_rep}j")
+            return np.array(formatted_lst).reshape(values.shape)
+
+        if self.formatter is not None:
+            return format_with_na_rep(self.values, self.formatter, self.na_rep)
+
+        if self.fixed_width:
+            threshold = get_option("display.chop_threshold")
+        else:
+            threshold = None
+
+        # if we have a fixed_width, we'll need to try different float_format
+        def format_values_with(float_format):
+            formatter = self._value_formatter(float_format, threshold)
+
+            # default formatter leaves a space to the left when formatting
+            # floats, must be consistent for left-justifying NaNs (GH #25061)
+            na_rep = " " + self.na_rep if self.justify == "left" else self.na_rep
+
+            # different formatting strategies for complex and non-complex data
+            # need to distinguish complex and float NaNs (GH #53762)
+            values = self.values
+            is_complex = is_complex_dtype(values)
+
+            # separate the wheat from the chaff
+            if is_complex:
+                values = format_complex_with_na_rep(values, formatter, na_rep)
+            else:
+                values = format_with_na_rep(values, formatter, na_rep)
+
+            if self.fixed_width:
+                if is_complex:
+                    result = _trim_zeros_complex(values, self.decimal)
+                else:
+                    result = _trim_zeros_float(values, self.decimal)
+                return np.asarray(result, dtype="object")
+
+            return values
+
+        # There is a special default string when we are fixed-width
+        # The default is otherwise to use str instead of a formatting string
+        float_format: FloatFormatType | None
+        if self.float_format is None:
+            if self.fixed_width:
+                if self.leading_space is True:
+                    fmt_str = "{value: .{digits:d}f}"
+                else:
+                    fmt_str = "{value:.{digits:d}f}"
+                float_format = partial(fmt_str.format, digits=self.digits)
+            else:
+                float_format = self.float_format
+        else:
+            float_format = lambda value: self.float_format % value
+
+        formatted_values = format_values_with(float_format)
+
+        if not self.fixed_width:
+            return formatted_values
+
+        # we need do convert to engineering format if some values are too small
+        # and would appear as 0, or if some values are too big and take too
+        # much space
+
+        if len(formatted_values) > 0:
+            maxlen = max(len(x) for x in formatted_values)
+            too_long = maxlen > self.digits + 6
+        else:
+            too_long = False
+
+        abs_vals = np.abs(self.values)
+        # this is pretty arbitrary for now
+        # large values: more that 8 characters including decimal symbol
+        # and first digit, hence > 1e6
+        has_large_values = (abs_vals > 1e6).any()
+        has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()
+
+        if has_small_values or (too_long and has_large_values):
+            if self.leading_space is True:
+                fmt_str = "{value: .{digits:d}e}"
+            else:
+                fmt_str = "{value:.{digits:d}e}"
+            float_format = partial(fmt_str.format, digits=self.digits)
+            formatted_values = format_values_with(float_format)
+
+        return formatted_values
+
+    def _format_strings(self) -> list[str]:
+        return list(self.get_result_as_array())
+
+
+class _IntArrayFormatter(_GenericArrayFormatter):
+    def _format_strings(self) -> list[str]:
+        if self.leading_space is False:
+            formatter_str = lambda x: f"{x:d}".format(x=x)
+        else:
+            formatter_str = lambda x: f"{x: d}".format(x=x)
+        formatter = self.formatter or formatter_str
+        fmt_values = [formatter(x) for x in self.values]
+        return fmt_values
+
+
+class _Datetime64Formatter(_GenericArrayFormatter):
+    values: DatetimeArray
+
+    def __init__(
+        self,
+        values: DatetimeArray,
+        nat_rep: str = "NaT",
+        date_format: None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(values, **kwargs)
+        self.nat_rep = nat_rep
+        self.date_format = date_format
+
+    def _format_strings(self) -> list[str]:
+        """we by definition have DO NOT have a TZ"""
+        values = self.values
+
+        if self.formatter is not None:
+            return [self.formatter(x) for x in values]
+
+        fmt_values = values._format_native_types(
+            na_rep=self.nat_rep, date_format=self.date_format
+        )
+        return fmt_values.tolist()
+
+
+class _ExtensionArrayFormatter(_GenericArrayFormatter):
+    values: ExtensionArray
+
+    def _format_strings(self) -> list[str]:
+        values = self.values
+
+        formatter = self.formatter
+        fallback_formatter = None
+        if formatter is None:
+            fallback_formatter = values._formatter(boxed=True)
+
+        if isinstance(values, Categorical):
+            # Categorical is special for now, so that we can preserve tzinfo
+            array = values._internal_get_values()
+        else:
+            array = np.asarray(values, dtype=object)
+
+        fmt_values = format_array(
+            array,
+            formatter,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            digits=self.digits,
+            space=self.space,
+            justify=self.justify,
+            decimal=self.decimal,
+            leading_space=self.leading_space,
+            quoting=self.quoting,
+            fallback_formatter=fallback_formatter,
+        )
+        return fmt_values
+
+
+def format_percentiles(
+    percentiles: (np.ndarray | Sequence[float]),
+) -> list[str]:
+    """
+    Outputs rounded and formatted percentiles.
+
+    Parameters
+    ----------
+    percentiles : list-like, containing floats from interval [0,1]
+
+    Returns
+    -------
+    formatted : list of strings
+
+    Notes
+    -----
+    Rounding precision is chosen so that: (1) if any two elements of
+    ``percentiles`` differ, they remain different after rounding
+    (2) no entry is *rounded* to 0% or 100%.
+    Any non-integer is always rounded to at least 1 decimal place.
+
+    Examples
+    --------
+    Keeps all entries different after rounding:
+
+    >>> format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
+    ['1.999%', '2.001%', '50%', '66.667%', '99.99%']
+
+    No element is rounded to 0% or 100% (unless already equal to it).
+    Duplicates are allowed:
+
+    >>> format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
+    ['0%', '50%', '2.0%', '50%', '66.67%', '99.99%']
+    """
+    percentiles = np.asarray(percentiles)
+
+    # It checks for np.nan as well
+    if (
+        not is_numeric_dtype(percentiles)
+        or not np.all(percentiles >= 0)
+        or not np.all(percentiles <= 1)
+    ):
+        raise ValueError("percentiles should all be in the interval [0,1]")
+
+    percentiles = 100 * percentiles
+    prec = get_precision(percentiles)
+    percentiles_round_type = percentiles.round(prec).astype(int)
+
+    int_idx = np.isclose(percentiles_round_type, percentiles)
+
+    if np.all(int_idx):
+        out = percentiles_round_type.astype(str)
+        return [i + "%" for i in out]
+
+    unique_pcts = np.unique(percentiles)
+    prec = get_precision(unique_pcts)
+    out = np.empty_like(percentiles, dtype=object)
+    out[int_idx] = percentiles[int_idx].round().astype(int).astype(str)
+
+    out[~int_idx] = percentiles[~int_idx].round(prec).astype(str)
+    return [i + "%" for i in out]
+
+
+def get_precision(array: np.ndarray | Sequence[float]) -> int:
+    to_begin = array[0] if array[0] > 0 else None
+    to_end = 100 - array[-1] if array[-1] < 100 else None
+    diff = np.ediff1d(array, to_begin=to_begin, to_end=to_end)
+    diff = abs(diff)
+    prec = -np.floor(np.log10(np.min(diff))).astype(int)
+    prec = max(1, prec)
+    return prec
+
+
+def _format_datetime64(x: NaTType | Timestamp, nat_rep: str = "NaT") -> str:
+    if x is NaT:
+        return nat_rep
+
+    # Timestamp.__str__ falls back to datetime.datetime.__str__ = isoformat(sep=' ')
+    # so it already uses string formatting rather than strftime (faster).
+    return str(x)
+
+
+def _format_datetime64_dateonly(
+    x: NaTType | Timestamp,
+    nat_rep: str = "NaT",
+    date_format: str | None = None,
+) -> str:
+    if isinstance(x, NaTType):
+        return nat_rep
+
+    if date_format:
+        return x.strftime(date_format)
+    else:
+        # Timestamp._date_repr relies on string formatting (faster than strftime)
+        return x._date_repr
+
+
+def get_format_datetime64(
+    is_dates_only: bool, nat_rep: str = "NaT", date_format: str | None = None
+) -> Callable:
+    """Return a formatter callable taking a datetime64 as input and providing
+    a string as output"""
+
+    if is_dates_only:
+        return lambda x: _format_datetime64_dateonly(
+            x, nat_rep=nat_rep, date_format=date_format
+        )
+    else:
+        return lambda x: _format_datetime64(x, nat_rep=nat_rep)
+
+
+class _Datetime64TZFormatter(_Datetime64Formatter):
+    values: DatetimeArray
+
+    def _format_strings(self) -> list[str]:
+        """we by definition have a TZ"""
+        ido = self.values._is_dates_only
+        values = self.values.astype(object)
+        formatter = self.formatter or get_format_datetime64(
+            ido, date_format=self.date_format
+        )
+        fmt_values = [formatter(x) for x in values]
+
+        return fmt_values
+
+
+class _Timedelta64Formatter(_GenericArrayFormatter):
+    values: TimedeltaArray
+
+    def __init__(
+        self,
+        values: TimedeltaArray,
+        nat_rep: str = "NaT",
+        **kwargs,
+    ) -> None:
+        # TODO: nat_rep is never passed, na_rep is.
+        super().__init__(values, **kwargs)
+        self.nat_rep = nat_rep
+
+    def _format_strings(self) -> list[str]:
+        formatter = self.formatter or get_format_timedelta64(
+            self.values, nat_rep=self.nat_rep, box=False
+        )
+        return [formatter(x) for x in self.values]
+
+
+def get_format_timedelta64(
+    values: TimedeltaArray,
+    nat_rep: str | float = "NaT",
+    box: bool = False,
+) -> Callable:
+    """
+    Return a formatter function for a range of timedeltas.
+    These will all have the same format argument
+
+    If box, then show the return in quotes
+    """
+    even_days = values._is_dates_only
+
+    if even_days:
+        format = None
+    else:
+        format = "long"
+
+    def _formatter(x):
+        if x is None or (is_scalar(x) and isna(x)):
+            return nat_rep
+
+        if not isinstance(x, Timedelta):
+            x = Timedelta(x)
+
+        # Timedelta._repr_base uses string formatting (faster than strftime)
+        result = x._repr_base(format=format)
+        if box:
+            result = f"'{result}'"
+        return result
+
+    return _formatter
+
+
+def _make_fixed_width(
+    strings: list[str],
+    justify: str = "right",
+    minimum: int | None = None,
+    adj: printing._TextAdjustment | None = None,
+) -> list[str]:
+    if len(strings) == 0 or justify == "all":
+        return strings
+
+    if adj is None:
+        adjustment = printing.get_adjustment()
+    else:
+        adjustment = adj
+
+    max_len = max(adjustment.len(x) for x in strings)
+
+    if minimum is not None:
+        max_len = max(minimum, max_len)
+
+    conf_max = get_option("display.max_colwidth")
+    if conf_max is not None and max_len > conf_max:
+        max_len = conf_max
+
+    def just(x: str) -> str:
+        if conf_max is not None:
+            if (conf_max > 3) & (adjustment.len(x) > max_len):
+                x = x[: max_len - 3] + "..."
+        return x
+
+    strings = [just(x) for x in strings]
+    result = adjustment.justify(strings, max_len, mode=justify)
+    return result
+
+
+def _trim_zeros_complex(str_complexes: ArrayLike, decimal: str = ".") -> list[str]:
+    """
+    Separates the real and imaginary parts from the complex number, and
+    executes the _trim_zeros_float method on each of those.
+    """
+    real_part, imag_part = [], []
+    for x in str_complexes:
+        # Complex numbers are represented as "(-)xxx(+/-)xxxj"
+        # The split will give [{"", "-"}, "xxx", "+/-", "xxx", "j", ""]
+        # Therefore, the imaginary part is the 4th and 3rd last elements,
+        # and the real part is everything before the imaginary part
+        trimmed = re.split(r"([j+-])", x)
+        real_part.append("".join(trimmed[:-4]))
+        imag_part.append("".join(trimmed[-4:-2]))
+
+    # We want to align the lengths of the real and imaginary parts of each complex
+    # number, as well as the lengths the real (resp. complex) parts of all numbers
+    # in the array
+    n = len(str_complexes)
+    padded_parts = _trim_zeros_float(real_part + imag_part, decimal)
+    if len(padded_parts) == 0:
+        return []
+    padded_length = max(len(part) for part in padded_parts) - 1
+    padded = [
+        real_pt  # real part, possibly NaN
+        + imag_pt[0]  # +/-
+        + f"{imag_pt[1:]:>{padded_length}}"  # complex part (no sign), possibly nan
+        + "j"
+        for real_pt, imag_pt in zip(padded_parts[:n], padded_parts[n:])
+    ]
+    return padded
+
+
+def _trim_zeros_single_float(str_float: str) -> str:
+    """
+    Trims trailing zeros after a decimal point,
+    leaving just one if necessary.
+    """
+    str_float = str_float.rstrip("0")
+    if str_float.endswith("."):
+        str_float += "0"
+
+    return str_float
+
+
+def _trim_zeros_float(
+    str_floats: ArrayLike | list[str], decimal: str = "."
+) -> list[str]:
+    """
+    Trims the maximum number of trailing zeros equally from
+    all numbers containing decimals, leaving just one if
+    necessary.
+    """
+    trimmed = str_floats
+    number_regex = re.compile(rf"^\s*[\+-]?[0-9]+\{decimal}[0-9]*$")
+
+    def is_number_with_decimal(x) -> bool:
+        return re.match(number_regex, x) is not None
+
+    def should_trim(values: ArrayLike | list[str]) -> bool:
+        """
+        Determine if an array of strings should be trimmed.
+
+        Returns True if all numbers containing decimals (defined by the
+        above regular expression) within the array end in a zero, otherwise
+        returns False.
+        """
+        numbers = [x for x in values if is_number_with_decimal(x)]
+        return len(numbers) > 0 and all(x.endswith("0") for x in numbers)
+
+    while should_trim(trimmed):
+        trimmed = [x[:-1] if is_number_with_decimal(x) else x for x in trimmed]
+
+    # leave one 0 after the decimal points if need be.
+    result = [
+        x + "0" if is_number_with_decimal(x) and x.endswith(decimal) else x
+        for x in trimmed
+    ]
+    return result
+
+
+def _has_names(index: Index) -> bool:
+    if isinstance(index, MultiIndex):
+        return com.any_not_none(*index.names)
+    else:
+        return index.name is not None
+
+
+class EngFormatter:
+    """
+    Formats float values according to engineering format.
+
+    Based on matplotlib.ticker.EngFormatter
+    """
+
+    # The SI engineering prefixes
+    ENG_PREFIXES = {
+        -24: "y",
+        -21: "z",
+        -18: "a",
+        -15: "f",
+        -12: "p",
+        -9: "n",
+        -6: "u",
+        -3: "m",
+        0: "",
+        3: "k",
+        6: "M",
+        9: "G",
+        12: "T",
+        15: "P",
+        18: "E",
+        21: "Z",
+        24: "Y",
+    }
+
+    def __init__(
+        self, accuracy: int | None = None, use_eng_prefix: bool = False
+    ) -> None:
+        self.accuracy = accuracy
+        self.use_eng_prefix = use_eng_prefix
+
+    def __call__(self, num: float) -> str:
+        """
+        Formats a number in engineering notation, appending a letter
+        representing the power of 1000 of the original number. Some examples:
+        >>> format_eng = EngFormatter(accuracy=0, use_eng_prefix=True)
+        >>> format_eng(0)
+        ' 0'
+        >>> format_eng = EngFormatter(accuracy=1, use_eng_prefix=True)
+        >>> format_eng(1_000_000)
+        ' 1.0M'
+        >>> format_eng = EngFormatter(accuracy=2, use_eng_prefix=False)
+        >>> format_eng("-1e-6")
+        '-1.00E-06'
+
+        @param num: the value to represent
+        @type num: either a numeric value or a string that can be converted to
+                   a numeric value (as per decimal.Decimal constructor)
+
+        @return: engineering formatted string
+        """
+        dnum = Decimal(str(num))
+
+        if Decimal.is_nan(dnum):
+            return "NaN"
+
+        if Decimal.is_infinite(dnum):
+            return "inf"
+
+        sign = 1
+
+        if dnum < 0:  # pragma: no cover
+            sign = -1
+            dnum = -dnum
+
+        if dnum != 0:
+            pow10 = Decimal(int(math.floor(dnum.log10() / 3) * 3))
+        else:
+            pow10 = Decimal(0)
+
+        pow10 = pow10.min(max(self.ENG_PREFIXES.keys()))
+        pow10 = pow10.max(min(self.ENG_PREFIXES.keys()))
+        int_pow10 = int(pow10)
+
+        if self.use_eng_prefix:
+            prefix = self.ENG_PREFIXES[int_pow10]
+        elif int_pow10 < 0:
+            prefix = f"E-{-int_pow10:02d}"
+        else:
+            prefix = f"E+{int_pow10:02d}"
+
+        mant = sign * dnum / (10**pow10)
+
+        if self.accuracy is None:  # pragma: no cover
+            format_str = "{mant: g}{prefix}"
+        else:
+            format_str = f"{{mant: .{self.accuracy:d}f}}{{prefix}}"
+
+        formatted = format_str.format(mant=mant, prefix=prefix)
+
+        return formatted
+
+
+def set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> None:
+    """
+    Format float representation in DataFrame with SI notation.
+
+    Parameters
+    ----------
+    accuracy : int, default 3
+        Number of decimal digits after the floating point.
+    use_eng_prefix : bool, default False
+        Whether to represent a value with SI prefixes.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([1e-9, 1e-3, 1, 1e3, 1e6])
+    >>> df
+                  0
+    0  1.000000e-09
+    1  1.000000e-03
+    2  1.000000e+00
+    3  1.000000e+03
+    4  1.000000e+06
+
+    >>> pd.set_eng_float_format(accuracy=1)
+    >>> df
+             0
+    0  1.0E-09
+    1  1.0E-03
+    2  1.0E+00
+    3  1.0E+03
+    4  1.0E+06
+
+    >>> pd.set_eng_float_format(use_eng_prefix=True)
+    >>> df
+            0
+    0  1.000n
+    1  1.000m
+    2   1.000
+    3  1.000k
+    4  1.000M
+
+    >>> pd.set_eng_float_format(accuracy=1, use_eng_prefix=True)
+    >>> df
+          0
+    0  1.0n
+    1  1.0m
+    2   1.0
+    3  1.0k
+    4  1.0M
+
+    >>> pd.set_option("display.float_format", None)  # unset option
+    """
+    set_option("display.float_format", EngFormatter(accuracy, use_eng_prefix))
+
+
+def get_level_lengths(
+    levels: Any, sentinel: bool | object | str = ""
+) -> list[dict[int, int]]:
+    """
+    For each index in each level the function returns lengths of indexes.
+
+    Parameters
+    ----------
+    levels : list of lists
+        List of values on for level.
+    sentinel : string, optional
+        Value which states that no new index starts on there.
+
+    Returns
+    -------
+    Returns list of maps. For each level returns map of indexes (key is index
+    in row and value is length of index).
+    """
+    if len(levels) == 0:
+        return []
+
+    control = [True] * len(levels[0])
+
+    result = []
+    for level in levels:
+        last_index = 0
+
+        lengths = {}
+        for i, key in enumerate(level):
+            if control[i] and key == sentinel:
+                pass
+            else:
+                control[i] = False
+                lengths[last_index] = i - last_index
+                last_index = i
+
+        lengths[last_index] = len(level) - last_index
+
+        result.append(lengths)
+
+    return result
+
+
+def buffer_put_lines(buf: WriteBuffer[str], lines: list[str]) -> None:
+    """
+    Appends lines to a buffer.
+
+    Parameters
+    ----------
+    buf
+        The buffer to write to
+    lines
+        The lines to append.
+    """
+    if any(isinstance(x, str) for x in lines):
+        lines = [str(x) for x in lines]
+    buf.write("\n".join(lines))
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/html.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/html.py
new file mode 100644
index 0000000000000000000000000000000000000000..794ce77b3b45ec38d9fa58a708939e53bb8ae629
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/html.py
@@ -0,0 +1,646 @@
+"""
+Module for formatting output data in HTML.
+"""
+from __future__ import annotations
+
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Final,
+    cast,
+)
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+
+from pandas import (
+    MultiIndex,
+    option_context,
+)
+
+from pandas.io.common import is_url
+from pandas.io.formats.format import (
+    DataFrameFormatter,
+    get_level_lengths,
+)
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+    )
+
+
+class HTMLFormatter:
+    """
+    Internal class for formatting output data in html.
+    This class is intended for shared functionality between
+    DataFrame.to_html() and DataFrame._repr_html_().
+    Any logic in common with other output formatting methods
+    should ideally be inherited from classes in format.py
+    and this class responsible for only producing html markup.
+    """
+
+    indent_delta: Final = 2
+
+    def __init__(
+        self,
+        formatter: DataFrameFormatter,
+        classes: str | list[str] | tuple[str, ...] | None = None,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+    ) -> None:
+        self.fmt = formatter
+        self.classes = classes
+
+        self.frame = self.fmt.frame
+        self.columns = self.fmt.tr_frame.columns
+        self.elements: list[str] = []
+        self.bold_rows = self.fmt.bold_rows
+        self.escape = self.fmt.escape
+        self.show_dimensions = self.fmt.show_dimensions
+        if border is None or border is True:
+            border = cast(int, get_option("display.html.border"))
+        elif not border:
+            border = None
+
+        self.border = border
+        self.table_id = table_id
+        self.render_links = render_links
+
+        self.col_space = {}
+        is_multi_index = isinstance(self.columns, MultiIndex)
+        for column, value in self.fmt.col_space.items():
+            col_space_value = f"{value}px" if isinstance(value, int) else value
+            self.col_space[column] = col_space_value
+            # GH 53885: Handling case where column is index
+            # Flatten the data in the multi index and add in the map
+            if is_multi_index and isinstance(column, tuple):
+                for column_index in column:
+                    self.col_space[str(column_index)] = col_space_value
+
+    def to_string(self) -> str:
+        lines = self.render()
+        if any(isinstance(x, str) for x in lines):
+            lines = [str(x) for x in lines]
+        return "\n".join(lines)
+
+    def render(self) -> list[str]:
+        self._write_table()
+
+        if self.should_show_dimensions:
+            by = chr(215)  # ×  # noqa: RUF003
+            self.write(
+                f"<p>{len(self.frame)} rows {by} {len(self.frame.columns)} columns</p>"
+            )
+
+        return self.elements
+
+    @property
+    def should_show_dimensions(self) -> bool:
+        return self.fmt.should_show_dimensions
+
+    @property
+    def show_row_idx_names(self) -> bool:
+        return self.fmt.show_row_idx_names
+
+    @property
+    def show_col_idx_names(self) -> bool:
+        return self.fmt.show_col_idx_names
+
+    @property
+    def row_levels(self) -> int:
+        if self.fmt.index:
+            # showing (row) index
+            return self.frame.index.nlevels
+        elif self.show_col_idx_names:
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # If the row index is not displayed a column of
+            # blank cells need to be included before the DataFrame values.
+            return 1
+        # not showing (row) index
+        return 0
+
+    def _get_columns_formatted_values(self) -> Iterable:
+        return self.columns
+
+    @property
+    def is_truncated(self) -> bool:
+        return self.fmt.is_truncated
+
+    @property
+    def ncols(self) -> int:
+        return len(self.fmt.tr_frame.columns)
+
+    def write(self, s: Any, indent: int = 0) -> None:
+        rs = pprint_thing(s)
+        self.elements.append(" " * indent + rs)
+
+    def write_th(
+        self, s: Any, header: bool = False, indent: int = 0, tags: str | None = None
+    ) -> None:
+        """
+        Method for writing a formatted <th> cell.
+
+        If col_space is set on the formatter then that is used for
+        the value of min-width.
+
+        Parameters
+        ----------
+        s : object
+            The data to be written inside the cell.
+        header : bool, default False
+            Set to True if the <th> is for use inside <thead>.  This will
+            cause min-width to be set if there is one.
+        indent : int, default 0
+            The indentation level of the cell.
+        tags : str, default None
+            Tags to include in the cell.
+
+        Returns
+        -------
+        A written <th> cell.
+        """
+        col_space = self.col_space.get(s, None)
+
+        if header and col_space is not None:
+            tags = tags or ""
+            tags += f'style="min-width: {col_space};"'
+
+        self._write_cell(s, kind="th", indent=indent, tags=tags)
+
+    def write_td(self, s: Any, indent: int = 0, tags: str | None = None) -> None:
+        self._write_cell(s, kind="td", indent=indent, tags=tags)
+
+    def _write_cell(
+        self, s: Any, kind: str = "td", indent: int = 0, tags: str | None = None
+    ) -> None:
+        if tags is not None:
+            start_tag = f"<{kind} {tags}>"
+        else:
+            start_tag = f"<{kind}>"
+
+        if self.escape:
+            # escape & first to prevent double escaping of &
+            esc = {"&": r"&amp;", "<": r"&lt;", ">": r"&gt;"}
+        else:
+            esc = {}
+
+        rs = pprint_thing(s, escape_chars=esc).strip()
+
+        if self.render_links and is_url(rs):
+            rs_unescaped = pprint_thing(s, escape_chars={}).strip()
+            start_tag += f'<a href="{rs_unescaped}" target="_blank">'
+            end_a = "</a>"
+        else:
+            end_a = ""
+
+        self.write(f"{start_tag}{rs}{end_a}</{kind}>", indent)
+
+    def write_tr(
+        self,
+        line: Iterable,
+        indent: int = 0,
+        indent_delta: int = 0,
+        header: bool = False,
+        align: str | None = None,
+        tags: dict[int, str] | None = None,
+        nindex_levels: int = 0,
+    ) -> None:
+        if tags is None:
+            tags = {}
+
+        if align is None:
+            self.write("<tr>", indent)
+        else:
+            self.write(f'<tr style="text-align: {align};">', indent)
+        indent += indent_delta
+
+        for i, s in enumerate(line):
+            val_tag = tags.get(i, None)
+            if header or (self.bold_rows and i < nindex_levels):
+                self.write_th(s, indent=indent, header=header, tags=val_tag)
+            else:
+                self.write_td(s, indent, tags=val_tag)
+
+        indent -= indent_delta
+        self.write("</tr>", indent)
+
+    def _write_table(self, indent: int = 0) -> None:
+        _classes = ["dataframe"]  # Default class.
+        use_mathjax = get_option("display.html.use_mathjax")
+        if not use_mathjax:
+            _classes.append("tex2jax_ignore")
+        if self.classes is not None:
+            if isinstance(self.classes, str):
+                self.classes = self.classes.split()
+            if not isinstance(self.classes, (list, tuple)):
+                raise TypeError(
+                    "classes must be a string, list, "
+                    f"or tuple, not {type(self.classes)}"
+                )
+            _classes.extend(self.classes)
+
+        if self.table_id is None:
+            id_section = ""
+        else:
+            id_section = f' id="{self.table_id}"'
+
+        if self.border is None:
+            border_attr = ""
+        else:
+            border_attr = f' border="{self.border}"'
+
+        self.write(
+            f'<table{border_attr} class="{" ".join(_classes)}"{id_section}>',
+            indent,
+        )
+
+        if self.fmt.header or self.show_row_idx_names:
+            self._write_header(indent + self.indent_delta)
+
+        self._write_body(indent + self.indent_delta)
+
+        self.write("</table>", indent)
+
+    def _write_col_header(self, indent: int) -> None:
+        row: list[Hashable]
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        if isinstance(self.columns, MultiIndex):
+            template = 'colspan="{span:d}" halign="left"'
+
+            sentinel: lib.NoDefault | bool
+            if self.fmt.sparsify:
+                # GH3547
+                sentinel = lib.no_default
+            else:
+                sentinel = False
+            levels = self.columns._format_multi(sparsify=sentinel, include_names=False)
+            level_lengths = get_level_lengths(levels, sentinel)
+            inner_lvl = len(level_lengths) - 1
+            for lnum, (records, values) in enumerate(zip(level_lengths, levels)):
+                if is_truncated_horizontally:
+                    # modify the header lines
+                    ins_col = self.fmt.tr_col_num
+                    if self.fmt.sparsify:
+                        recs_new = {}
+                        # Increment tags after ... col.
+                        for tag, span in list(records.items()):
+                            if tag >= ins_col:
+                                recs_new[tag + 1] = span
+                            elif tag + span > ins_col:
+                                recs_new[tag] = span + 1
+                                if lnum == inner_lvl:
+                                    values = (
+                                        values[:ins_col] + ("...",) + values[ins_col:]
+                                    )
+                                else:
+                                    # sparse col headers do not receive a ...
+                                    values = (
+                                        values[:ins_col]
+                                        + (values[ins_col - 1],)
+                                        + values[ins_col:]
+                                    )
+                            else:
+                                recs_new[tag] = span
+                            # if ins_col lies between tags, all col headers
+                            # get ...
+                            if tag + span == ins_col:
+                                recs_new[ins_col] = 1
+                                values = values[:ins_col] + ("...",) + values[ins_col:]
+                        records = recs_new
+                        inner_lvl = len(level_lengths) - 1
+                        if lnum == inner_lvl:
+                            records[ins_col] = 1
+                    else:
+                        recs_new = {}
+                        for tag, span in list(records.items()):
+                            if tag >= ins_col:
+                                recs_new[tag + 1] = span
+                            else:
+                                recs_new[tag] = span
+                        recs_new[ins_col] = 1
+                        records = recs_new
+                        values = values[:ins_col] + ["..."] + values[ins_col:]
+
+                # see gh-22579
+                # Column Offset Bug with to_html(index=False) with
+                # MultiIndex Columns and Index.
+                # Initially fill row with blank cells before column names.
+                # TODO: Refactor to remove code duplication with code
+                # block below for standard columns index.
+                row = [""] * (self.row_levels - 1)
+                if self.fmt.index or self.show_col_idx_names:
+                    # see gh-22747
+                    # If to_html(index_names=False) do not show columns
+                    # index names.
+                    # TODO: Refactor to use _get_column_name_list from
+                    # DataFrameFormatter class and create a
+                    # _get_formatted_column_labels function for code
+                    # parity with DataFrameFormatter class.
+                    if self.fmt.show_index_names:
+                        name = self.columns.names[lnum]
+                        row.append(pprint_thing(name or ""))
+                    else:
+                        row.append("")
+
+                tags = {}
+                j = len(row)
+                for i, v in enumerate(values):
+                    if i in records:
+                        if records[i] > 1:
+                            tags[j] = template.format(span=records[i])
+                    else:
+                        continue
+                    j += 1
+                    row.append(v)
+                self.write_tr(row, indent, self.indent_delta, tags=tags, header=True)
+        else:
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # Initially fill row with blank cells before column names.
+            # TODO: Refactor to remove code duplication with code block
+            # above for columns MultiIndex.
+            row = [""] * (self.row_levels - 1)
+            if self.fmt.index or self.show_col_idx_names:
+                # see gh-22747
+                # If to_html(index_names=False) do not show columns
+                # index names.
+                # TODO: Refactor to use _get_column_name_list from
+                # DataFrameFormatter class.
+                if self.fmt.show_index_names:
+                    row.append(self.columns.name or "")
+                else:
+                    row.append("")
+            row.extend(self._get_columns_formatted_values())
+            align = self.fmt.justify
+
+            if is_truncated_horizontally:
+                ins_col = self.row_levels + self.fmt.tr_col_num
+                row.insert(ins_col, "...")
+
+            self.write_tr(row, indent, self.indent_delta, header=True, align=align)
+
+    def _write_row_header(self, indent: int) -> None:
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        row = [x if x is not None else "" for x in self.frame.index.names] + [""] * (
+            self.ncols + (1 if is_truncated_horizontally else 0)
+        )
+        self.write_tr(row, indent, self.indent_delta, header=True)
+
+    def _write_header(self, indent: int) -> None:
+        self.write("<thead>", indent)
+
+        if self.fmt.header:
+            self._write_col_header(indent + self.indent_delta)
+
+        if self.show_row_idx_names:
+            self._write_row_header(indent + self.indent_delta)
+
+        self.write("</thead>", indent)
+
+    def _get_formatted_values(self) -> dict[int, list[str]]:
+        with option_context("display.max_colwidth", None):
+            fmt_values = {i: self.fmt.format_col(i) for i in range(self.ncols)}
+        return fmt_values
+
+    def _write_body(self, indent: int) -> None:
+        self.write("<tbody>", indent)
+        fmt_values = self._get_formatted_values()
+
+        # write values
+        if self.fmt.index and isinstance(self.frame.index, MultiIndex):
+            self._write_hierarchical_rows(fmt_values, indent + self.indent_delta)
+        else:
+            self._write_regular_rows(fmt_values, indent + self.indent_delta)
+
+        self.write("</tbody>", indent)
+
+    def _write_regular_rows(
+        self, fmt_values: Mapping[int, list[str]], indent: int
+    ) -> None:
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        is_truncated_vertically = self.fmt.is_truncated_vertically
+
+        nrows = len(self.fmt.tr_frame)
+
+        if self.fmt.index:
+            fmt = self.fmt._get_formatter("__index__")
+            if fmt is not None:
+                index_values = self.fmt.tr_frame.index.map(fmt)
+            else:
+                # only reached with non-Multi index
+                index_values = self.fmt.tr_frame.index._format_flat(include_name=False)
+
+        row: list[str] = []
+        for i in range(nrows):
+            if is_truncated_vertically and i == (self.fmt.tr_row_num):
+                str_sep_row = ["..."] * len(row)
+                self.write_tr(
+                    str_sep_row,
+                    indent,
+                    self.indent_delta,
+                    tags=None,
+                    nindex_levels=self.row_levels,
+                )
+
+            row = []
+            if self.fmt.index:
+                row.append(index_values[i])
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # Add blank cell before data cells.
+            elif self.show_col_idx_names:
+                row.append("")
+            row.extend(fmt_values[j][i] for j in range(self.ncols))
+
+            if is_truncated_horizontally:
+                dot_col_ix = self.fmt.tr_col_num + self.row_levels
+                row.insert(dot_col_ix, "...")
+            self.write_tr(
+                row, indent, self.indent_delta, tags=None, nindex_levels=self.row_levels
+            )
+
+    def _write_hierarchical_rows(
+        self, fmt_values: Mapping[int, list[str]], indent: int
+    ) -> None:
+        template = 'rowspan="{span}" valign="top"'
+
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        is_truncated_vertically = self.fmt.is_truncated_vertically
+        frame = self.fmt.tr_frame
+        nrows = len(frame)
+
+        assert isinstance(frame.index, MultiIndex)
+        idx_values = frame.index._format_multi(sparsify=False, include_names=False)
+        idx_values = list(zip(*idx_values))
+
+        if self.fmt.sparsify:
+            # GH3547
+            sentinel = lib.no_default
+            levels = frame.index._format_multi(sparsify=sentinel, include_names=False)
+
+            level_lengths = get_level_lengths(levels, sentinel)
+            inner_lvl = len(level_lengths) - 1
+            if is_truncated_vertically:
+                # Insert ... row and adjust idx_values and
+                # level_lengths to take this into account.
+                ins_row = self.fmt.tr_row_num
+                inserted = False
+                for lnum, records in enumerate(level_lengths):
+                    rec_new = {}
+                    for tag, span in list(records.items()):
+                        if tag >= ins_row:
+                            rec_new[tag + 1] = span
+                        elif tag + span > ins_row:
+                            rec_new[tag] = span + 1
+
+                            # GH 14882 - Make sure insertion done once
+                            if not inserted:
+                                dot_row = list(idx_values[ins_row - 1])
+                                dot_row[-1] = "..."
+                                idx_values.insert(ins_row, tuple(dot_row))
+                                inserted = True
+                            else:
+                                dot_row = list(idx_values[ins_row])
+                                dot_row[inner_lvl - lnum] = "..."
+                                idx_values[ins_row] = tuple(dot_row)
+                        else:
+                            rec_new[tag] = span
+                        # If ins_row lies between tags, all cols idx cols
+                        # receive ...
+                        if tag + span == ins_row:
+                            rec_new[ins_row] = 1
+                            if lnum == 0:
+                                idx_values.insert(
+                                    ins_row, tuple(["..."] * len(level_lengths))
+                                )
+
+                            # GH 14882 - Place ... in correct level
+                            elif inserted:
+                                dot_row = list(idx_values[ins_row])
+                                dot_row[inner_lvl - lnum] = "..."
+                                idx_values[ins_row] = tuple(dot_row)
+                    level_lengths[lnum] = rec_new
+
+                level_lengths[inner_lvl][ins_row] = 1
+                for ix_col in fmt_values:
+                    fmt_values[ix_col].insert(ins_row, "...")
+                nrows += 1
+
+            for i in range(nrows):
+                row = []
+                tags = {}
+
+                sparse_offset = 0
+                j = 0
+                for records, v in zip(level_lengths, idx_values[i]):
+                    if i in records:
+                        if records[i] > 1:
+                            tags[j] = template.format(span=records[i])
+                    else:
+                        sparse_offset += 1
+                        continue
+
+                    j += 1
+                    row.append(v)
+
+                row.extend(fmt_values[j][i] for j in range(self.ncols))
+                if is_truncated_horizontally:
+                    row.insert(
+                        self.row_levels - sparse_offset + self.fmt.tr_col_num, "..."
+                    )
+                self.write_tr(
+                    row,
+                    indent,
+                    self.indent_delta,
+                    tags=tags,
+                    nindex_levels=len(levels) - sparse_offset,
+                )
+        else:
+            row = []
+            for i in range(len(frame)):
+                if is_truncated_vertically and i == (self.fmt.tr_row_num):
+                    str_sep_row = ["..."] * len(row)
+                    self.write_tr(
+                        str_sep_row,
+                        indent,
+                        self.indent_delta,
+                        tags=None,
+                        nindex_levels=self.row_levels,
+                    )
+
+                idx_values = list(
+                    zip(*frame.index._format_multi(sparsify=False, include_names=False))
+                )
+                row = []
+                row.extend(idx_values[i])
+                row.extend(fmt_values[j][i] for j in range(self.ncols))
+                if is_truncated_horizontally:
+                    row.insert(self.row_levels + self.fmt.tr_col_num, "...")
+                self.write_tr(
+                    row,
+                    indent,
+                    self.indent_delta,
+                    tags=None,
+                    nindex_levels=frame.index.nlevels,
+                )
+
+
+class NotebookFormatter(HTMLFormatter):
+    """
+    Internal class for formatting output data in html for display in Jupyter
+    Notebooks. This class is intended for functionality specific to
+    DataFrame._repr_html_() and DataFrame.to_html(notebook=True)
+    """
+
+    def _get_formatted_values(self) -> dict[int, list[str]]:
+        return {i: self.fmt.format_col(i) for i in range(self.ncols)}
+
+    def _get_columns_formatted_values(self) -> list[str]:
+        # only reached with non-Multi Index
+        return self.columns._format_flat(include_name=False)
+
+    def write_style(self) -> None:
+        # We use the "scoped" attribute here so that the desired
+        # style properties for the data frame are not then applied
+        # throughout the entire notebook.
+        template_first = """\
+            <style scoped>"""
+        template_last = """\
+            </style>"""
+        template_select = """\
+                .dataframe %s {
+                    %s: %s;
+                }"""
+        element_props = [
+            ("tbody tr th:only-of-type", "vertical-align", "middle"),
+            ("tbody tr th", "vertical-align", "top"),
+        ]
+        if isinstance(self.columns, MultiIndex):
+            element_props.append(("thead tr th", "text-align", "left"))
+            if self.show_row_idx_names:
+                element_props.append(
+                    ("thead tr:last-of-type th", "text-align", "right")
+                )
+        else:
+            element_props.append(("thead th", "text-align", "right"))
+        template_mid = "\n\n".join(template_select % t for t in element_props)
+        template = dedent(f"{template_first}\n{template_mid}\n{template_last}")
+        self.write(template)
+
+    def render(self) -> list[str]:
+        self.write("<div>")
+        self.write_style()
+        super().render()
+        self.write("</div>")
+        return self.elements
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/info.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/info.py
new file mode 100644
index 0000000000000000000000000000000000000000..552affbd053f2bed3f4d5f678ddf8eb293f65b01
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/info.py
@@ -0,0 +1,1101 @@
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import sys
+from textwrap import dedent
+from typing import TYPE_CHECKING
+
+from pandas._config import get_option
+
+from pandas.io.formats import format as fmt
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Iterator,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        Dtype,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+frame_max_cols_sub = dedent(
+    """\
+    max_cols : int, optional
+        When to switch from the verbose to the truncated output. If the
+        DataFrame has more than `max_cols` columns, the truncated output
+        is used. By default, the setting in
+        ``pandas.options.display.max_info_columns`` is used."""
+)
+
+
+show_counts_sub = dedent(
+    """\
+    show_counts : bool, optional
+        Whether to show the non-null counts. By default, this is shown
+        only if the DataFrame is smaller than
+        ``pandas.options.display.max_info_rows`` and
+        ``pandas.options.display.max_info_columns``. A value of True always
+        shows the counts, and False never shows the counts."""
+)
+
+
+frame_examples_sub = dedent(
+    """\
+    >>> int_values = [1, 2, 3, 4, 5]
+    >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+    >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
+    >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
+    ...                   "float_col": float_values})
+    >>> df
+        int_col text_col  float_col
+    0        1    alpha       0.00
+    1        2     beta       0.25
+    2        3    gamma       0.50
+    3        4    delta       0.75
+    4        5  epsilon       1.00
+
+    Prints information of all columns:
+
+    >>> df.info(verbose=True)
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 5 entries, 0 to 4
+    Data columns (total 3 columns):
+     #   Column     Non-Null Count  Dtype
+    ---  ------     --------------  -----
+     0   int_col    5 non-null      int64
+     1   text_col   5 non-null      object
+     2   float_col  5 non-null      float64
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 248.0+ bytes
+
+    Prints a summary of columns count and its dtypes but not per column
+    information:
+
+    >>> df.info(verbose=False)
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 5 entries, 0 to 4
+    Columns: 3 entries, int_col to float_col
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 248.0+ bytes
+
+    Pipe output of DataFrame.info to buffer instead of sys.stdout, get
+    buffer content and writes to a text file:
+
+    >>> import io
+    >>> buffer = io.StringIO()
+    >>> df.info(buf=buffer)
+    >>> s = buffer.getvalue()
+    >>> with open("df_info.txt", "w",
+    ...           encoding="utf-8") as f:  # doctest: +SKIP
+    ...     f.write(s)
+    260
+
+    The `memory_usage` parameter allows deep introspection mode, specially
+    useful for big DataFrames and fine-tune memory optimization:
+
+    >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    >>> df = pd.DataFrame({
+    ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+    ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+    ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    ... })
+    >>> df.info()
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Data columns (total 3 columns):
+     #   Column    Non-Null Count    Dtype
+    ---  ------    --------------    -----
+     0   column_1  1000000 non-null  object
+     1   column_2  1000000 non-null  object
+     2   column_3  1000000 non-null  object
+    dtypes: object(3)
+    memory usage: 22.9+ MB
+
+    >>> df.info(memory_usage='deep')
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Data columns (total 3 columns):
+     #   Column    Non-Null Count    Dtype
+    ---  ------    --------------    -----
+     0   column_1  1000000 non-null  object
+     1   column_2  1000000 non-null  object
+     2   column_3  1000000 non-null  object
+    dtypes: object(3)
+    memory usage: 165.9 MB"""
+)
+
+
+frame_see_also_sub = dedent(
+    """\
+    DataFrame.describe: Generate descriptive statistics of DataFrame
+        columns.
+    DataFrame.memory_usage: Memory usage of DataFrame columns."""
+)
+
+
+frame_sub_kwargs = {
+    "klass": "DataFrame",
+    "type_sub": " and columns",
+    "max_cols_sub": frame_max_cols_sub,
+    "show_counts_sub": show_counts_sub,
+    "examples_sub": frame_examples_sub,
+    "see_also_sub": frame_see_also_sub,
+    "version_added_sub": "",
+}
+
+
+series_examples_sub = dedent(
+    """\
+    >>> int_values = [1, 2, 3, 4, 5]
+    >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+    >>> s = pd.Series(text_values, index=int_values)
+    >>> s.info()
+    <class 'pandas.core.series.Series'>
+    Index: 5 entries, 1 to 5
+    Series name: None
+    Non-Null Count  Dtype
+    --------------  -----
+    5 non-null      object
+    dtypes: object(1)
+    memory usage: 80.0+ bytes
+
+    Prints a summary excluding information about its values:
+
+    >>> s.info(verbose=False)
+    <class 'pandas.core.series.Series'>
+    Index: 5 entries, 1 to 5
+    dtypes: object(1)
+    memory usage: 80.0+ bytes
+
+    Pipe output of Series.info to buffer instead of sys.stdout, get
+    buffer content and writes to a text file:
+
+    >>> import io
+    >>> buffer = io.StringIO()
+    >>> s.info(buf=buffer)
+    >>> s = buffer.getvalue()
+    >>> with open("df_info.txt", "w",
+    ...           encoding="utf-8") as f:  # doctest: +SKIP
+    ...     f.write(s)
+    260
+
+    The `memory_usage` parameter allows deep introspection mode, specially
+    useful for big Series and fine-tune memory optimization:
+
+    >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    >>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6))
+    >>> s.info()
+    <class 'pandas.core.series.Series'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Series name: None
+    Non-Null Count    Dtype
+    --------------    -----
+    1000000 non-null  object
+    dtypes: object(1)
+    memory usage: 7.6+ MB
+
+    >>> s.info(memory_usage='deep')
+    <class 'pandas.core.series.Series'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Series name: None
+    Non-Null Count    Dtype
+    --------------    -----
+    1000000 non-null  object
+    dtypes: object(1)
+    memory usage: 55.3 MB"""
+)
+
+
+series_see_also_sub = dedent(
+    """\
+    Series.describe: Generate descriptive statistics of Series.
+    Series.memory_usage: Memory usage of Series."""
+)
+
+
+series_sub_kwargs = {
+    "klass": "Series",
+    "type_sub": "",
+    "max_cols_sub": "",
+    "show_counts_sub": show_counts_sub,
+    "examples_sub": series_examples_sub,
+    "see_also_sub": series_see_also_sub,
+    "version_added_sub": "\n.. versionadded:: 1.4.0\n",
+}
+
+
+INFO_DOCSTRING = dedent(
+    """
+    Print a concise summary of a {klass}.
+
+    This method prints information about a {klass} including
+    the index dtype{type_sub}, non-null values and memory usage.
+    {version_added_sub}\
+
+    Parameters
+    ----------
+    verbose : bool, optional
+        Whether to print the full summary. By default, the setting in
+        ``pandas.options.display.max_info_columns`` is followed.
+    buf : writable buffer, defaults to sys.stdout
+        Where to send the output. By default, the output is printed to
+        sys.stdout. Pass a writable buffer if you need to further process
+        the output.
+    {max_cols_sub}
+    memory_usage : bool, str, optional
+        Specifies whether total memory usage of the {klass}
+        elements (including the index) should be displayed. By default,
+        this follows the ``pandas.options.display.memory_usage`` setting.
+
+        True always show memory usage. False never shows memory usage.
+        A value of 'deep' is equivalent to "True with deep introspection".
+        Memory usage is shown in human-readable units (base-2
+        representation). Without deep introspection a memory estimation is
+        made based in column dtype and number of rows assuming values
+        consume the same memory amount for corresponding dtypes. With deep
+        memory introspection, a real memory usage calculation is performed
+        at the cost of computational resources. See the
+        :ref:`Frequently Asked Questions <df-memory-usage>` for more
+        details.
+    {show_counts_sub}
+
+    Returns
+    -------
+    None
+        This method prints a summary of a {klass} and returns None.
+
+    See Also
+    --------
+    {see_also_sub}
+
+    Examples
+    --------
+    {examples_sub}
+    """
+)
+
+
+def _put_str(s: str | Dtype, space: int) -> str:
+    """
+    Make string of specified length, padding to the right if necessary.
+
+    Parameters
+    ----------
+    s : Union[str, Dtype]
+        String to be formatted.
+    space : int
+        Length to force string to be of.
+
+    Returns
+    -------
+    str
+        String coerced to given length.
+
+    Examples
+    --------
+    >>> pd.io.formats.info._put_str("panda", 6)
+    'panda '
+    >>> pd.io.formats.info._put_str("panda", 4)
+    'pand'
+    """
+    return str(s)[:space].ljust(space)
+
+
+def _sizeof_fmt(num: float, size_qualifier: str) -> str:
+    """
+    Return size in human readable format.
+
+    Parameters
+    ----------
+    num : int
+        Size in bytes.
+    size_qualifier : str
+        Either empty, or '+' (if lower bound).
+
+    Returns
+    -------
+    str
+        Size in human readable format.
+
+    Examples
+    --------
+    >>> _sizeof_fmt(23028, '')
+    '22.5 KB'
+
+    >>> _sizeof_fmt(23028, '+')
+    '22.5+ KB'
+    """
+    for x in ["bytes", "KB", "MB", "GB", "TB"]:
+        if num < 1024.0:
+            return f"{num:3.1f}{size_qualifier} {x}"
+        num /= 1024.0
+    return f"{num:3.1f}{size_qualifier} PB"
+
+
+def _initialize_memory_usage(
+    memory_usage: bool | str | None = None,
+) -> bool | str:
+    """Get memory usage based on inputs and display options."""
+    if memory_usage is None:
+        memory_usage = get_option("display.memory_usage")
+    return memory_usage
+
+
+class _BaseInfo(ABC):
+    """
+    Base class for DataFrameInfo and SeriesInfo.
+
+    Parameters
+    ----------
+    data : DataFrame or Series
+        Either dataframe or series.
+    memory_usage : bool or str, optional
+        If "deep", introspect the data deeply by interrogating object dtypes
+        for system-level memory consumption, and include it in the returned
+        values.
+    """
+
+    data: DataFrame | Series
+    memory_usage: bool | str
+
+    @property
+    @abstractmethod
+    def dtypes(self) -> Iterable[Dtype]:
+        """
+        Dtypes.
+
+        Returns
+        -------
+        dtypes : sequence
+            Dtype of each of the DataFrame's columns (or one series column).
+        """
+
+    @property
+    @abstractmethod
+    def dtype_counts(self) -> Mapping[str, int]:
+        """Mapping dtype - number of counts."""
+
+    @property
+    @abstractmethod
+    def non_null_counts(self) -> Sequence[int]:
+        """Sequence of non-null counts for all columns or column (if series)."""
+
+    @property
+    @abstractmethod
+    def memory_usage_bytes(self) -> int:
+        """
+        Memory usage in bytes.
+
+        Returns
+        -------
+        memory_usage_bytes : int
+            Object's total memory usage in bytes.
+        """
+
+    @property
+    def memory_usage_string(self) -> str:
+        """Memory usage in a form of human readable string."""
+        return f"{_sizeof_fmt(self.memory_usage_bytes, self.size_qualifier)}\n"
+
+    @property
+    def size_qualifier(self) -> str:
+        size_qualifier = ""
+        if self.memory_usage:
+            if self.memory_usage != "deep":
+                # size_qualifier is just a best effort; not guaranteed to catch
+                # all cases (e.g., it misses categorical data even with object
+                # categories)
+                if (
+                    "object" in self.dtype_counts
+                    or self.data.index._is_memory_usage_qualified()
+                ):
+                    size_qualifier = "+"
+        return size_qualifier
+
+    @abstractmethod
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None,
+        max_cols: int | None,
+        verbose: bool | None,
+        show_counts: bool | None,
+    ) -> None:
+        pass
+
+
+class DataFrameInfo(_BaseInfo):
+    """
+    Class storing dataframe-specific info.
+    """
+
+    def __init__(
+        self,
+        data: DataFrame,
+        memory_usage: bool | str | None = None,
+    ) -> None:
+        self.data: DataFrame = data
+        self.memory_usage = _initialize_memory_usage(memory_usage)
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        return _get_dataframe_dtype_counts(self.data)
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        """
+        Dtypes.
+
+        Returns
+        -------
+        dtypes
+            Dtype of each of the DataFrame's columns.
+        """
+        return self.data.dtypes
+
+    @property
+    def ids(self) -> Index:
+        """
+        Column names.
+
+        Returns
+        -------
+        ids : Index
+            DataFrame's column names.
+        """
+        return self.data.columns
+
+    @property
+    def col_count(self) -> int:
+        """Number of columns to be summarized."""
+        return len(self.ids)
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        """Sequence of non-null counts for all columns or column (if series)."""
+        return self.data.count()
+
+    @property
+    def memory_usage_bytes(self) -> int:
+        deep = self.memory_usage == "deep"
+        return self.data.memory_usage(index=True, deep=deep).sum()
+
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None,
+        max_cols: int | None,
+        verbose: bool | None,
+        show_counts: bool | None,
+    ) -> None:
+        printer = _DataFrameInfoPrinter(
+            info=self,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+        printer.to_buffer(buf)
+
+
+class SeriesInfo(_BaseInfo):
+    """
+    Class storing series-specific info.
+    """
+
+    def __init__(
+        self,
+        data: Series,
+        memory_usage: bool | str | None = None,
+    ) -> None:
+        self.data: Series = data
+        self.memory_usage = _initialize_memory_usage(memory_usage)
+
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None = None,
+        max_cols: int | None = None,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        if max_cols is not None:
+            raise ValueError(
+                "Argument `max_cols` can only be passed "
+                "in DataFrame.info, not Series.info"
+            )
+        printer = _SeriesInfoPrinter(
+            info=self,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+        printer.to_buffer(buf)
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        return [self.data.count()]
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        return [self.data.dtypes]
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        from pandas.core.frame import DataFrame
+
+        return _get_dataframe_dtype_counts(DataFrame(self.data))
+
+    @property
+    def memory_usage_bytes(self) -> int:
+        """Memory usage in bytes.
+
+        Returns
+        -------
+        memory_usage_bytes : int
+            Object's total memory usage in bytes.
+        """
+        deep = self.memory_usage == "deep"
+        return self.data.memory_usage(index=True, deep=deep)
+
+
+class _InfoPrinterAbstract:
+    """
+    Class for printing dataframe or series info.
+    """
+
+    def to_buffer(self, buf: WriteBuffer[str] | None = None) -> None:
+        """Save dataframe info into buffer."""
+        table_builder = self._create_table_builder()
+        lines = table_builder.get_lines()
+        if buf is None:  # pragma: no cover
+            buf = sys.stdout
+        fmt.buffer_put_lines(buf, lines)
+
+    @abstractmethod
+    def _create_table_builder(self) -> _TableBuilderAbstract:
+        """Create instance of table builder."""
+
+
+class _DataFrameInfoPrinter(_InfoPrinterAbstract):
+    """
+    Class for printing dataframe info.
+
+    Parameters
+    ----------
+    info : DataFrameInfo
+        Instance of DataFrameInfo.
+    max_cols : int, optional
+        When to switch from the verbose to the truncated output.
+    verbose : bool, optional
+        Whether to print the full summary.
+    show_counts : bool, optional
+        Whether to show the non-null counts.
+    """
+
+    def __init__(
+        self,
+        info: DataFrameInfo,
+        max_cols: int | None = None,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        self.info = info
+        self.data = info.data
+        self.verbose = verbose
+        self.max_cols = self._initialize_max_cols(max_cols)
+        self.show_counts = self._initialize_show_counts(show_counts)
+
+    @property
+    def max_rows(self) -> int:
+        """Maximum info rows to be displayed."""
+        return get_option("display.max_info_rows", len(self.data) + 1)
+
+    @property
+    def exceeds_info_cols(self) -> bool:
+        """Check if number of columns to be summarized does not exceed maximum."""
+        return bool(self.col_count > self.max_cols)
+
+    @property
+    def exceeds_info_rows(self) -> bool:
+        """Check if number of rows to be summarized does not exceed maximum."""
+        return bool(len(self.data) > self.max_rows)
+
+    @property
+    def col_count(self) -> int:
+        """Number of columns to be summarized."""
+        return self.info.col_count
+
+    def _initialize_max_cols(self, max_cols: int | None) -> int:
+        if max_cols is None:
+            return get_option("display.max_info_columns", self.col_count + 1)
+        return max_cols
+
+    def _initialize_show_counts(self, show_counts: bool | None) -> bool:
+        if show_counts is None:
+            return bool(not self.exceeds_info_cols and not self.exceeds_info_rows)
+        else:
+            return show_counts
+
+    def _create_table_builder(self) -> _DataFrameTableBuilder:
+        """
+        Create instance of table builder based on verbosity and display settings.
+        """
+        if self.verbose:
+            return _DataFrameTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+        elif self.verbose is False:  # specifically set to False, not necessarily None
+            return _DataFrameTableBuilderNonVerbose(info=self.info)
+        elif self.exceeds_info_cols:
+            return _DataFrameTableBuilderNonVerbose(info=self.info)
+        else:
+            return _DataFrameTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+
+
+class _SeriesInfoPrinter(_InfoPrinterAbstract):
+    """Class for printing series info.
+
+    Parameters
+    ----------
+    info : SeriesInfo
+        Instance of SeriesInfo.
+    verbose : bool, optional
+        Whether to print the full summary.
+    show_counts : bool, optional
+        Whether to show the non-null counts.
+    """
+
+    def __init__(
+        self,
+        info: SeriesInfo,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        self.info = info
+        self.data = info.data
+        self.verbose = verbose
+        self.show_counts = self._initialize_show_counts(show_counts)
+
+    def _create_table_builder(self) -> _SeriesTableBuilder:
+        """
+        Create instance of table builder based on verbosity.
+        """
+        if self.verbose or self.verbose is None:
+            return _SeriesTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+        else:
+            return _SeriesTableBuilderNonVerbose(info=self.info)
+
+    def _initialize_show_counts(self, show_counts: bool | None) -> bool:
+        if show_counts is None:
+            return True
+        else:
+            return show_counts
+
+
+class _TableBuilderAbstract(ABC):
+    """
+    Abstract builder for info table.
+    """
+
+    _lines: list[str]
+    info: _BaseInfo
+
+    @abstractmethod
+    def get_lines(self) -> list[str]:
+        """Product in a form of list of lines (strings)."""
+
+    @property
+    def data(self) -> DataFrame | Series:
+        return self.info.data
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        """Dtypes of each of the DataFrame's columns."""
+        return self.info.dtypes
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        """Mapping dtype - number of counts."""
+        return self.info.dtype_counts
+
+    @property
+    def display_memory_usage(self) -> bool:
+        """Whether to display memory usage."""
+        return bool(self.info.memory_usage)
+
+    @property
+    def memory_usage_string(self) -> str:
+        """Memory usage string with proper size qualifier."""
+        return self.info.memory_usage_string
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        return self.info.non_null_counts
+
+    def add_object_type_line(self) -> None:
+        """Add line with string representation of dataframe to the table."""
+        self._lines.append(str(type(self.data)))
+
+    def add_index_range_line(self) -> None:
+        """Add line with range of indices to the table."""
+        self._lines.append(self.data.index._summary())
+
+    def add_dtypes_line(self) -> None:
+        """Add summary line with dtypes present in dataframe."""
+        collected_dtypes = [
+            f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
+        ]
+        self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
+
+
+class _DataFrameTableBuilder(_TableBuilderAbstract):
+    """
+    Abstract builder for dataframe info table.
+
+    Parameters
+    ----------
+    info : DataFrameInfo.
+        Instance of DataFrameInfo.
+    """
+
+    def __init__(self, *, info: DataFrameInfo) -> None:
+        self.info: DataFrameInfo = info
+
+    def get_lines(self) -> list[str]:
+        self._lines = []
+        if self.col_count == 0:
+            self._fill_empty_info()
+        else:
+            self._fill_non_empty_info()
+        return self._lines
+
+    def _fill_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self._lines.append(f"Empty {type(self.data).__name__}\n")
+
+    @abstractmethod
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+
+    @property
+    def data(self) -> DataFrame:
+        """DataFrame."""
+        return self.info.data
+
+    @property
+    def ids(self) -> Index:
+        """Dataframe columns."""
+        return self.info.ids
+
+    @property
+    def col_count(self) -> int:
+        """Number of dataframe columns to be summarized."""
+        return self.info.col_count
+
+    def add_memory_usage_line(self) -> None:
+        """Add line containing memory usage."""
+        self._lines.append(f"memory usage: {self.memory_usage_string}")
+
+
+class _DataFrameTableBuilderNonVerbose(_DataFrameTableBuilder):
+    """
+    Dataframe info table builder for non-verbose output.
+    """
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_columns_summary_line()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    def add_columns_summary_line(self) -> None:
+        self._lines.append(self.ids._summary(name="Columns"))
+
+
+class _TableBuilderVerboseMixin(_TableBuilderAbstract):
+    """
+    Mixin for verbose info output.
+    """
+
+    SPACING: str = " " * 2
+    strrows: Sequence[Sequence[str]]
+    gross_column_widths: Sequence[int]
+    with_counts: bool
+
+    @property
+    @abstractmethod
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+
+    @property
+    def header_column_widths(self) -> Sequence[int]:
+        """Widths of header columns (only titles)."""
+        return [len(col) for col in self.headers]
+
+    def _get_gross_column_widths(self) -> Sequence[int]:
+        """Get widths of columns containing both headers and actual content."""
+        body_column_widths = self._get_body_column_widths()
+        return [
+            max(*widths)
+            for widths in zip(self.header_column_widths, body_column_widths)
+        ]
+
+    def _get_body_column_widths(self) -> Sequence[int]:
+        """Get widths of table content columns."""
+        strcols: Sequence[Sequence[str]] = list(zip(*self.strrows))
+        return [max(len(x) for x in col) for col in strcols]
+
+    def _gen_rows(self) -> Iterator[Sequence[str]]:
+        """
+        Generator function yielding rows content.
+
+        Each element represents a row comprising a sequence of strings.
+        """
+        if self.with_counts:
+            return self._gen_rows_with_counts()
+        else:
+            return self._gen_rows_without_counts()
+
+    @abstractmethod
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+
+    @abstractmethod
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+
+    def add_header_line(self) -> None:
+        header_line = self.SPACING.join(
+            [
+                _put_str(header, col_width)
+                for header, col_width in zip(self.headers, self.gross_column_widths)
+            ]
+        )
+        self._lines.append(header_line)
+
+    def add_separator_line(self) -> None:
+        separator_line = self.SPACING.join(
+            [
+                _put_str("-" * header_colwidth, gross_colwidth)
+                for header_colwidth, gross_colwidth in zip(
+                    self.header_column_widths, self.gross_column_widths
+                )
+            ]
+        )
+        self._lines.append(separator_line)
+
+    def add_body_lines(self) -> None:
+        for row in self.strrows:
+            body_line = self.SPACING.join(
+                [
+                    _put_str(col, gross_colwidth)
+                    for col, gross_colwidth in zip(row, self.gross_column_widths)
+                ]
+            )
+            self._lines.append(body_line)
+
+    def _gen_non_null_counts(self) -> Iterator[str]:
+        """Iterator with string representation of non-null counts."""
+        for count in self.non_null_counts:
+            yield f"{count} non-null"
+
+    def _gen_dtypes(self) -> Iterator[str]:
+        """Iterator with string representation of column dtypes."""
+        for dtype in self.dtypes:
+            yield pprint_thing(dtype)
+
+
+class _DataFrameTableBuilderVerbose(_DataFrameTableBuilder, _TableBuilderVerboseMixin):
+    """
+    Dataframe info table builder for verbose output.
+    """
+
+    def __init__(
+        self,
+        *,
+        info: DataFrameInfo,
+        with_counts: bool,
+    ) -> None:
+        self.info = info
+        self.with_counts = with_counts
+        self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
+        self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_columns_summary_line()
+        self.add_header_line()
+        self.add_separator_line()
+        self.add_body_lines()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    @property
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+        if self.with_counts:
+            return [" # ", "Column", "Non-Null Count", "Dtype"]
+        return [" # ", "Column", "Dtype"]
+
+    def add_columns_summary_line(self) -> None:
+        self._lines.append(f"Data columns (total {self.col_count} columns):")
+
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+        yield from zip(
+            self._gen_line_numbers(),
+            self._gen_columns(),
+            self._gen_dtypes(),
+        )
+
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+        yield from zip(
+            self._gen_line_numbers(),
+            self._gen_columns(),
+            self._gen_non_null_counts(),
+            self._gen_dtypes(),
+        )
+
+    def _gen_line_numbers(self) -> Iterator[str]:
+        """Iterator with string representation of column numbers."""
+        for i, _ in enumerate(self.ids):
+            yield f" {i}"
+
+    def _gen_columns(self) -> Iterator[str]:
+        """Iterator with string representation of column names."""
+        for col in self.ids:
+            yield pprint_thing(col)
+
+
+class _SeriesTableBuilder(_TableBuilderAbstract):
+    """
+    Abstract builder for series info table.
+
+    Parameters
+    ----------
+    info : SeriesInfo.
+        Instance of SeriesInfo.
+    """
+
+    def __init__(self, *, info: SeriesInfo) -> None:
+        self.info: SeriesInfo = info
+
+    def get_lines(self) -> list[str]:
+        self._lines = []
+        self._fill_non_empty_info()
+        return self._lines
+
+    @property
+    def data(self) -> Series:
+        """Series."""
+        return self.info.data
+
+    def add_memory_usage_line(self) -> None:
+        """Add line containing memory usage."""
+        self._lines.append(f"memory usage: {self.memory_usage_string}")
+
+    @abstractmethod
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+
+
+class _SeriesTableBuilderNonVerbose(_SeriesTableBuilder):
+    """
+    Series info table builder for non-verbose output.
+    """
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+
+class _SeriesTableBuilderVerbose(_SeriesTableBuilder, _TableBuilderVerboseMixin):
+    """
+    Series info table builder for verbose output.
+    """
+
+    def __init__(
+        self,
+        *,
+        info: SeriesInfo,
+        with_counts: bool,
+    ) -> None:
+        self.info = info
+        self.with_counts = with_counts
+        self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
+        self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_series_name_line()
+        self.add_header_line()
+        self.add_separator_line()
+        self.add_body_lines()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    def add_series_name_line(self) -> None:
+        self._lines.append(f"Series name: {self.data.name}")
+
+    @property
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+        if self.with_counts:
+            return ["Non-Null Count", "Dtype"]
+        return ["Dtype"]
+
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+        yield from self._gen_dtypes()
+
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+        yield from zip(
+            self._gen_non_null_counts(),
+            self._gen_dtypes(),
+        )
+
+
+def _get_dataframe_dtype_counts(df: DataFrame) -> Mapping[str, int]:
+    """
+    Create mapping between datatypes and their number of occurrences.
+    """
+    # groupby dtype.name to collect e.g. Categorical columns
+    return df.dtypes.value_counts().groupby(lambda x: x.name).sum()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/printing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/printing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cc9368f8846a6423655040673df283d111efeda
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/printing.py
@@ -0,0 +1,572 @@
+"""
+Printing tools.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import sys
+from typing import (
+    Any,
+    Callable,
+    TypeVar,
+    Union,
+)
+from unicodedata import east_asian_width
+
+from pandas._config import get_option
+
+from pandas.core.dtypes.inference import is_sequence
+
+from pandas.io.formats.console import get_console_size
+
+EscapeChars = Union[Mapping[str, str], Iterable[str]]
+_KT = TypeVar("_KT")
+_VT = TypeVar("_VT")
+
+
+def adjoin(space: int, *lists: list[str], **kwargs) -> str:
+    """
+    Glues together two sets of strings using the amount of space requested.
+    The idea is to prettify.
+
+    ----------
+    space : int
+        number of spaces for padding
+    lists : str
+        list of str which being joined
+    strlen : callable
+        function used to calculate the length of each str. Needed for unicode
+        handling.
+    justfunc : callable
+        function used to justify str. Needed for unicode handling.
+    """
+    strlen = kwargs.pop("strlen", len)
+    justfunc = kwargs.pop("justfunc", _adj_justify)
+
+    newLists = []
+    lengths = [max(map(strlen, x)) + space for x in lists[:-1]]
+    # not the last one
+    lengths.append(max(map(len, lists[-1])))
+    maxLen = max(map(len, lists))
+    for i, lst in enumerate(lists):
+        nl = justfunc(lst, lengths[i], mode="left")
+        nl = ([" " * lengths[i]] * (maxLen - len(lst))) + nl
+        newLists.append(nl)
+    toJoin = zip(*newLists)
+    return "\n".join("".join(lines) for lines in toJoin)
+
+
+def _adj_justify(texts: Iterable[str], max_len: int, mode: str = "right") -> list[str]:
+    """
+    Perform ljust, center, rjust against string or list-like
+    """
+    if mode == "left":
+        return [x.ljust(max_len) for x in texts]
+    elif mode == "center":
+        return [x.center(max_len) for x in texts]
+    else:
+        return [x.rjust(max_len) for x in texts]
+
+
+# Unicode consolidation
+# ---------------------
+#
+# pprinting utility functions for generating Unicode text or
+# bytes(3.x)/str(2.x) representations of objects.
+# Try to use these as much as possible rather than rolling your own.
+#
+# When to use
+# -----------
+#
+# 1) If you're writing code internal to pandas (no I/O directly involved),
+#    use pprint_thing().
+#
+#    It will always return unicode text which can handled by other
+#    parts of the package without breakage.
+#
+# 2) if you need to write something out to file, use
+#    pprint_thing_encoded(encoding).
+#
+#    If no encoding is specified, it defaults to utf-8. Since encoding pure
+#    ascii with utf-8 is a no-op you can safely use the default utf-8 if you're
+#    working with straight ascii.
+
+
+def _pprint_seq(
+    seq: Sequence, _nest_lvl: int = 0, max_seq_items: int | None = None, **kwds
+) -> str:
+    """
+    internal. pprinter for iterables. you should probably use pprint_thing()
+    rather than calling this directly.
+
+    bounds length of printed sequence, depending on options
+    """
+    if isinstance(seq, set):
+        fmt = "{{{body}}}"
+    else:
+        fmt = "[{body}]" if hasattr(seq, "__setitem__") else "({body})"
+
+    if max_seq_items is False:
+        nitems = len(seq)
+    else:
+        nitems = max_seq_items or get_option("max_seq_items") or len(seq)
+
+    s = iter(seq)
+    # handle sets, no slicing
+    r = [
+        pprint_thing(next(s), _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
+        for i in range(min(nitems, len(seq)))
+    ]
+    body = ", ".join(r)
+
+    if nitems < len(seq):
+        body += ", ..."
+    elif isinstance(seq, tuple) and len(seq) == 1:
+        body += ","
+
+    return fmt.format(body=body)
+
+
+def _pprint_dict(
+    seq: Mapping, _nest_lvl: int = 0, max_seq_items: int | None = None, **kwds
+) -> str:
+    """
+    internal. pprinter for iterables. you should probably use pprint_thing()
+    rather than calling this directly.
+    """
+    fmt = "{{{things}}}"
+    pairs = []
+
+    pfmt = "{key}: {val}"
+
+    if max_seq_items is False:
+        nitems = len(seq)
+    else:
+        nitems = max_seq_items or get_option("max_seq_items") or len(seq)
+
+    for k, v in list(seq.items())[:nitems]:
+        pairs.append(
+            pfmt.format(
+                key=pprint_thing(k, _nest_lvl + 1, max_seq_items=max_seq_items, **kwds),
+                val=pprint_thing(v, _nest_lvl + 1, max_seq_items=max_seq_items, **kwds),
+            )
+        )
+
+    if nitems < len(seq):
+        return fmt.format(things=", ".join(pairs) + ", ...")
+    else:
+        return fmt.format(things=", ".join(pairs))
+
+
+def pprint_thing(
+    thing: Any,
+    _nest_lvl: int = 0,
+    escape_chars: EscapeChars | None = None,
+    default_escapes: bool = False,
+    quote_strings: bool = False,
+    max_seq_items: int | None = None,
+) -> str:
+    """
+    This function is the sanctioned way of converting objects
+    to a string representation and properly handles nested sequences.
+
+    Parameters
+    ----------
+    thing : anything to be formatted
+    _nest_lvl : internal use only. pprint_thing() is mutually-recursive
+        with pprint_sequence, this argument is used to keep track of the
+        current nesting level, and limit it.
+    escape_chars : list or dict, optional
+        Characters to escape. If a dict is passed the values are the
+        replacements
+    default_escapes : bool, default False
+        Whether the input escape characters replaces or adds to the defaults
+    max_seq_items : int or None, default None
+        Pass through to other pretty printers to limit sequence printing
+
+    Returns
+    -------
+    str
+    """
+
+    def as_escaped_string(
+        thing: Any, escape_chars: EscapeChars | None = escape_chars
+    ) -> str:
+        translate = {"\t": r"\t", "\n": r"\n", "\r": r"\r"}
+        if isinstance(escape_chars, dict):
+            if default_escapes:
+                translate.update(escape_chars)
+            else:
+                translate = escape_chars
+            escape_chars = list(escape_chars.keys())
+        else:
+            escape_chars = escape_chars or ()
+
+        result = str(thing)
+        for c in escape_chars:
+            result = result.replace(c, translate[c])
+        return result
+
+    if hasattr(thing, "__next__"):
+        return str(thing)
+    elif isinstance(thing, dict) and _nest_lvl < get_option(
+        "display.pprint_nest_depth"
+    ):
+        result = _pprint_dict(
+            thing, _nest_lvl, quote_strings=True, max_seq_items=max_seq_items
+        )
+    elif is_sequence(thing) and _nest_lvl < get_option("display.pprint_nest_depth"):
+        result = _pprint_seq(
+            thing,
+            _nest_lvl,
+            escape_chars=escape_chars,
+            quote_strings=quote_strings,
+            max_seq_items=max_seq_items,
+        )
+    elif isinstance(thing, str) and quote_strings:
+        result = f"'{as_escaped_string(thing)}'"
+    else:
+        result = as_escaped_string(thing)
+
+    return result
+
+
+def pprint_thing_encoded(
+    object, encoding: str = "utf-8", errors: str = "replace"
+) -> bytes:
+    value = pprint_thing(object)  # get unicode representation of object
+    return value.encode(encoding, errors)
+
+
+def enable_data_resource_formatter(enable: bool) -> None:
+    if "IPython" not in sys.modules:
+        # definitely not in IPython
+        return
+    from IPython import get_ipython
+
+    ip = get_ipython()
+    if ip is None:
+        # still not in IPython
+        return
+
+    formatters = ip.display_formatter.formatters
+    mimetype = "application/vnd.dataresource+json"
+
+    if enable:
+        if mimetype not in formatters:
+            # define tableschema formatter
+            from IPython.core.formatters import BaseFormatter
+            from traitlets import ObjectName
+
+            class TableSchemaFormatter(BaseFormatter):
+                print_method = ObjectName("_repr_data_resource_")
+                _return_type = (dict,)
+
+            # register it:
+            formatters[mimetype] = TableSchemaFormatter()
+        # enable it if it's been disabled:
+        formatters[mimetype].enabled = True
+    # unregister tableschema mime-type
+    elif mimetype in formatters:
+        formatters[mimetype].enabled = False
+
+
+def default_pprint(thing: Any, max_seq_items: int | None = None) -> str:
+    return pprint_thing(
+        thing,
+        escape_chars=("\t", "\r", "\n"),
+        quote_strings=True,
+        max_seq_items=max_seq_items,
+    )
+
+
+def format_object_summary(
+    obj,
+    formatter: Callable,
+    is_justify: bool = True,
+    name: str | None = None,
+    indent_for_name: bool = True,
+    line_break_each_value: bool = False,
+) -> str:
+    """
+    Return the formatted obj as a unicode string
+
+    Parameters
+    ----------
+    obj : object
+        must be iterable and support __getitem__
+    formatter : callable
+        string formatter for an element
+    is_justify : bool
+        should justify the display
+    name : name, optional
+        defaults to the class name of the obj
+    indent_for_name : bool, default True
+        Whether subsequent lines should be indented to
+        align with the name.
+    line_break_each_value : bool, default False
+        If True, inserts a line break for each value of ``obj``.
+        If False, only break lines when the a line of values gets wider
+        than the display width.
+
+    Returns
+    -------
+    summary string
+    """
+    display_width, _ = get_console_size()
+    if display_width is None:
+        display_width = get_option("display.width") or 80
+    if name is None:
+        name = type(obj).__name__
+
+    if indent_for_name:
+        name_len = len(name)
+        space1 = f'\n{(" " * (name_len + 1))}'
+        space2 = f'\n{(" " * (name_len + 2))}'
+    else:
+        space1 = "\n"
+        space2 = "\n "  # space for the opening '['
+
+    n = len(obj)
+    if line_break_each_value:
+        # If we want to vertically align on each value of obj, we need to
+        # separate values by a line break and indent the values
+        sep = ",\n " + " " * len(name)
+    else:
+        sep = ","
+    max_seq_items = get_option("display.max_seq_items") or n
+
+    # are we a truncated display
+    is_truncated = n > max_seq_items
+
+    # adj can optionally handle unicode eastern asian width
+    adj = get_adjustment()
+
+    def _extend_line(
+        s: str, line: str, value: str, display_width: int, next_line_prefix: str
+    ) -> tuple[str, str]:
+        if adj.len(line.rstrip()) + adj.len(value.rstrip()) >= display_width:
+            s += line.rstrip()
+            line = next_line_prefix
+        line += value
+        return s, line
+
+    def best_len(values: list[str]) -> int:
+        if values:
+            return max(adj.len(x) for x in values)
+        else:
+            return 0
+
+    close = ", "
+
+    if n == 0:
+        summary = f"[]{close}"
+    elif n == 1 and not line_break_each_value:
+        first = formatter(obj[0])
+        summary = f"[{first}]{close}"
+    elif n == 2 and not line_break_each_value:
+        first = formatter(obj[0])
+        last = formatter(obj[-1])
+        summary = f"[{first}, {last}]{close}"
+    else:
+        if max_seq_items == 1:
+            # If max_seq_items=1 show only last element
+            head = []
+            tail = [formatter(x) for x in obj[-1:]]
+        elif n > max_seq_items:
+            n = min(max_seq_items // 2, 10)
+            head = [formatter(x) for x in obj[:n]]
+            tail = [formatter(x) for x in obj[-n:]]
+        else:
+            head = []
+            tail = [formatter(x) for x in obj]
+
+        # adjust all values to max length if needed
+        if is_justify:
+            if line_break_each_value:
+                # Justify each string in the values of head and tail, so the
+                # strings will right align when head and tail are stacked
+                # vertically.
+                head, tail = _justify(head, tail)
+            elif is_truncated or not (
+                len(", ".join(head)) < display_width
+                and len(", ".join(tail)) < display_width
+            ):
+                # Each string in head and tail should align with each other
+                max_length = max(best_len(head), best_len(tail))
+                head = [x.rjust(max_length) for x in head]
+                tail = [x.rjust(max_length) for x in tail]
+            # If we are not truncated and we are only a single
+            # line, then don't justify
+
+        if line_break_each_value:
+            # Now head and tail are of type List[Tuple[str]]. Below we
+            # convert them into List[str], so there will be one string per
+            # value. Also truncate items horizontally if wider than
+            # max_space
+            max_space = display_width - len(space2)
+            value = tail[0]
+            max_items = 1
+            for num_items in reversed(range(1, len(value) + 1)):
+                pprinted_seq = _pprint_seq(value, max_seq_items=num_items)
+                if len(pprinted_seq) < max_space:
+                    max_items = num_items
+                    break
+            head = [_pprint_seq(x, max_seq_items=max_items) for x in head]
+            tail = [_pprint_seq(x, max_seq_items=max_items) for x in tail]
+
+        summary = ""
+        line = space2
+
+        for head_value in head:
+            word = head_value + sep + " "
+            summary, line = _extend_line(summary, line, word, display_width, space2)
+
+        if is_truncated:
+            # remove trailing space of last line
+            summary += line.rstrip() + space2 + "..."
+            line = space2
+
+        for tail_item in tail[:-1]:
+            word = tail_item + sep + " "
+            summary, line = _extend_line(summary, line, word, display_width, space2)
+
+        # last value: no sep added + 1 space of width used for trailing ','
+        summary, line = _extend_line(summary, line, tail[-1], display_width - 2, space2)
+        summary += line
+
+        # right now close is either '' or ', '
+        # Now we want to include the ']', but not the maybe space.
+        close = "]" + close.rstrip(" ")
+        summary += close
+
+        if len(summary) > (display_width) or line_break_each_value:
+            summary += space1
+        else:  # one row
+            summary += " "
+
+        # remove initial space
+        summary = "[" + summary[len(space2) :]
+
+    return summary
+
+
+def _justify(
+    head: list[Sequence[str]], tail: list[Sequence[str]]
+) -> tuple[list[tuple[str, ...]], list[tuple[str, ...]]]:
+    """
+    Justify items in head and tail, so they are right-aligned when stacked.
+
+    Parameters
+    ----------
+    head : list-like of list-likes of strings
+    tail : list-like of list-likes of strings
+
+    Returns
+    -------
+    tuple of list of tuples of strings
+        Same as head and tail, but items are right aligned when stacked
+        vertically.
+
+    Examples
+    --------
+    >>> _justify([['a', 'b']], [['abc', 'abcd']])
+    ([('  a', '   b')], [('abc', 'abcd')])
+    """
+    combined = head + tail
+
+    # For each position for the sequences in ``combined``,
+    # find the length of the largest string.
+    max_length = [0] * len(combined[0])
+    for inner_seq in combined:
+        length = [len(item) for item in inner_seq]
+        max_length = [max(x, y) for x, y in zip(max_length, length)]
+
+    # justify each item in each list-like in head and tail using max_length
+    head_tuples = [
+        tuple(x.rjust(max_len) for x, max_len in zip(seq, max_length)) for seq in head
+    ]
+    tail_tuples = [
+        tuple(x.rjust(max_len) for x, max_len in zip(seq, max_length)) for seq in tail
+    ]
+    return head_tuples, tail_tuples
+
+
+class PrettyDict(dict[_KT, _VT]):
+    """Dict extension to support abbreviated __repr__"""
+
+    def __repr__(self) -> str:
+        return pprint_thing(self)
+
+
+class _TextAdjustment:
+    def __init__(self) -> None:
+        self.encoding = get_option("display.encoding")
+
+    def len(self, text: str) -> int:
+        return len(text)
+
+    def justify(self, texts: Any, max_len: int, mode: str = "right") -> list[str]:
+        """
+        Perform ljust, center, rjust against string or list-like
+        """
+        if mode == "left":
+            return [x.ljust(max_len) for x in texts]
+        elif mode == "center":
+            return [x.center(max_len) for x in texts]
+        else:
+            return [x.rjust(max_len) for x in texts]
+
+    def adjoin(self, space: int, *lists, **kwargs) -> str:
+        return adjoin(space, *lists, strlen=self.len, justfunc=self.justify, **kwargs)
+
+
+class _EastAsianTextAdjustment(_TextAdjustment):
+    def __init__(self) -> None:
+        super().__init__()
+        if get_option("display.unicode.ambiguous_as_wide"):
+            self.ambiguous_width = 2
+        else:
+            self.ambiguous_width = 1
+
+        # Definition of East Asian Width
+        # https://unicode.org/reports/tr11/
+        # Ambiguous width can be changed by option
+        self._EAW_MAP = {"Na": 1, "N": 1, "W": 2, "F": 2, "H": 1}
+
+    def len(self, text: str) -> int:
+        """
+        Calculate display width considering unicode East Asian Width
+        """
+        if not isinstance(text, str):
+            return len(text)
+
+        return sum(
+            self._EAW_MAP.get(east_asian_width(c), self.ambiguous_width) for c in text
+        )
+
+    def justify(
+        self, texts: Iterable[str], max_len: int, mode: str = "right"
+    ) -> list[str]:
+        # re-calculate padding space per str considering East Asian Width
+        def _get_pad(t):
+            return max_len - self.len(t) + len(t)
+
+        if mode == "left":
+            return [x.ljust(_get_pad(x)) for x in texts]
+        elif mode == "center":
+            return [x.center(_get_pad(x)) for x in texts]
+        else:
+            return [x.rjust(_get_pad(x)) for x in texts]
+
+
+def get_adjustment() -> _TextAdjustment:
+    use_east_asian_width = get_option("display.unicode.east_asian_width")
+    if use_east_asian_width:
+        return _EastAsianTextAdjustment()
+    else:
+        return _TextAdjustment()
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/string.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/string.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdad388592717dff79fde61bf35a12c0635034c1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/formats/string.py
@@ -0,0 +1,206 @@
+"""
+Module for formatting output data in console (to string).
+"""
+from __future__ import annotations
+
+from shutil import get_terminal_size
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    from pandas.io.formats.format import DataFrameFormatter
+
+
+class StringFormatter:
+    """Formatter for string representation of a dataframe."""
+
+    def __init__(self, fmt: DataFrameFormatter, line_width: int | None = None) -> None:
+        self.fmt = fmt
+        self.adj = fmt.adj
+        self.frame = fmt.frame
+        self.line_width = line_width
+
+    def to_string(self) -> str:
+        text = self._get_string_representation()
+        if self.fmt.should_show_dimensions:
+            text = f"{text}{self.fmt.dimensions_info}"
+        return text
+
+    def _get_strcols(self) -> list[list[str]]:
+        strcols = self.fmt.get_strcols()
+        if self.fmt.is_truncated:
+            strcols = self._insert_dot_separators(strcols)
+        return strcols
+
+    def _get_string_representation(self) -> str:
+        if self.fmt.frame.empty:
+            return self._empty_info_line
+
+        strcols = self._get_strcols()
+
+        if self.line_width is None:
+            # no need to wrap around just print the whole frame
+            return self.adj.adjoin(1, *strcols)
+
+        if self._need_to_wrap_around:
+            return self._join_multiline(strcols)
+
+        return self._fit_strcols_to_terminal_width(strcols)
+
+    @property
+    def _empty_info_line(self) -> str:
+        return (
+            f"Empty {type(self.frame).__name__}\n"
+            f"Columns: {pprint_thing(self.frame.columns)}\n"
+            f"Index: {pprint_thing(self.frame.index)}"
+        )
+
+    @property
+    def _need_to_wrap_around(self) -> bool:
+        return bool(self.fmt.max_cols is None or self.fmt.max_cols > 0)
+
+    def _insert_dot_separators(self, strcols: list[list[str]]) -> list[list[str]]:
+        str_index = self.fmt._get_formatted_index(self.fmt.tr_frame)
+        index_length = len(str_index)
+
+        if self.fmt.is_truncated_horizontally:
+            strcols = self._insert_dot_separator_horizontal(strcols, index_length)
+
+        if self.fmt.is_truncated_vertically:
+            strcols = self._insert_dot_separator_vertical(strcols, index_length)
+
+        return strcols
+
+    @property
+    def _adjusted_tr_col_num(self) -> int:
+        return self.fmt.tr_col_num + 1 if self.fmt.index else self.fmt.tr_col_num
+
+    def _insert_dot_separator_horizontal(
+        self, strcols: list[list[str]], index_length: int
+    ) -> list[list[str]]:
+        strcols.insert(self._adjusted_tr_col_num, [" ..."] * index_length)
+        return strcols
+
+    def _insert_dot_separator_vertical(
+        self, strcols: list[list[str]], index_length: int
+    ) -> list[list[str]]:
+        n_header_rows = index_length - len(self.fmt.tr_frame)
+        row_num = self.fmt.tr_row_num
+        for ix, col in enumerate(strcols):
+            cwidth = self.adj.len(col[row_num])
+
+            if self.fmt.is_truncated_horizontally:
+                is_dot_col = ix == self._adjusted_tr_col_num
+            else:
+                is_dot_col = False
+
+            if cwidth > 3 or is_dot_col:
+                dots = "..."
+            else:
+                dots = ".."
+
+            if ix == 0 and self.fmt.index:
+                dot_mode = "left"
+            elif is_dot_col:
+                cwidth = 4
+                dot_mode = "right"
+            else:
+                dot_mode = "right"
+
+            dot_str = self.adj.justify([dots], cwidth, mode=dot_mode)[0]
+            col.insert(row_num + n_header_rows, dot_str)
+        return strcols
+
+    def _join_multiline(self, strcols_input: Iterable[list[str]]) -> str:
+        lwidth = self.line_width
+        adjoin_width = 1
+        strcols = list(strcols_input)
+
+        if self.fmt.index:
+            idx = strcols.pop(0)
+            lwidth -= np.array([self.adj.len(x) for x in idx]).max() + adjoin_width
+
+        col_widths = [
+            np.array([self.adj.len(x) for x in col]).max() if len(col) > 0 else 0
+            for col in strcols
+        ]
+
+        assert lwidth is not None
+        col_bins = _binify(col_widths, lwidth)
+        nbins = len(col_bins)
+
+        str_lst = []
+        start = 0
+        for i, end in enumerate(col_bins):
+            row = strcols[start:end]
+            if self.fmt.index:
+                row.insert(0, idx)
+            if nbins > 1:
+                nrows = len(row[-1])
+                if end <= len(strcols) and i < nbins - 1:
+                    row.append([" \\"] + ["  "] * (nrows - 1))
+                else:
+                    row.append([" "] * nrows)
+            str_lst.append(self.adj.adjoin(adjoin_width, *row))
+            start = end
+        return "\n\n".join(str_lst)
+
+    def _fit_strcols_to_terminal_width(self, strcols: list[list[str]]) -> str:
+        from pandas import Series
+
+        lines = self.adj.adjoin(1, *strcols).split("\n")
+        max_len = Series(lines).str.len().max()
+        # plus truncate dot col
+        width, _ = get_terminal_size()
+        dif = max_len - width
+        # '+ 1' to avoid too wide repr (GH PR #17023)
+        adj_dif = dif + 1
+        col_lens = Series([Series(ele).str.len().max() for ele in strcols])
+        n_cols = len(col_lens)
+        counter = 0
+        while adj_dif > 0 and n_cols > 1:
+            counter += 1
+            mid = round(n_cols / 2)
+            mid_ix = col_lens.index[mid]
+            col_len = col_lens[mid_ix]
+            # adjoin adds one
+            adj_dif -= col_len + 1
+            col_lens = col_lens.drop(mid_ix)
+            n_cols = len(col_lens)
+
+        # subtract index column
+        max_cols_fitted = n_cols - self.fmt.index
+        # GH-21180. Ensure that we print at least two.
+        max_cols_fitted = max(max_cols_fitted, 2)
+        self.fmt.max_cols_fitted = max_cols_fitted
+
+        # Call again _truncate to cut frame appropriately
+        # and then generate string representation
+        self.fmt.truncate()
+        strcols = self._get_strcols()
+        return self.adj.adjoin(1, *strcols)
+
+
+def _binify(cols: list[int], line_width: int) -> list[int]:
+    adjoin_width = 1
+    bins = []
+    curr_width = 0
+    i_last_column = len(cols) - 1
+    for i, w in enumerate(cols):
+        w_adjoined = w + adjoin_width
+        curr_width += w_adjoined
+        if i_last_column == i:
+            wrap = curr_width + 1 > line_width and i > 0
+        else:
+            wrap = curr_width + 2 > line_width and i > 0
+        if wrap:
+            bins.append(i)
+            curr_width = w_adjoined
+
+    bins.append(len(cols))
+    return bins
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/gbq.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/gbq.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e4e0b7cef0a5fa66a70fa0ad70b52364b02091
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/gbq.py
@@ -0,0 +1,255 @@
+""" Google BigQuery support """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from google.auth.credentials import Credentials
+
+    from pandas import DataFrame
+
+
+def _try_import():
+    # since pandas is a dependency of pandas-gbq
+    # we need to import on first use
+    msg = (
+        "pandas-gbq is required to load data from Google BigQuery. "
+        "See the docs: https://pandas-gbq.readthedocs.io."
+    )
+    pandas_gbq = import_optional_dependency("pandas_gbq", extra=msg)
+    return pandas_gbq
+
+
+def read_gbq(
+    query: str,
+    project_id: str | None = None,
+    index_col: str | None = None,
+    col_order: list[str] | None = None,
+    reauth: bool = False,
+    auth_local_webserver: bool = True,
+    dialect: str | None = None,
+    location: str | None = None,
+    configuration: dict[str, Any] | None = None,
+    credentials: Credentials | None = None,
+    use_bqstorage_api: bool | None = None,
+    max_results: int | None = None,
+    progress_bar_type: str | None = None,
+) -> DataFrame:
+    """
+    Load data from Google BigQuery.
+
+    .. deprecated:: 2.2.0
+
+       Please use ``pandas_gbq.read_gbq`` instead.
+
+    This function requires the `pandas-gbq package
+    <https://pandas-gbq.readthedocs.io>`__.
+
+    See the `How to authenticate with Google BigQuery
+    <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
+    guide for authentication instructions.
+
+    Parameters
+    ----------
+    query : str
+        SQL-Like Query to return data values.
+    project_id : str, optional
+        Google BigQuery Account project ID. Optional when available from
+        the environment.
+    index_col : str, optional
+        Name of result column to use for index in results DataFrame.
+    col_order : list(str), optional
+        List of BigQuery column names in the desired order for results
+        DataFrame.
+    reauth : bool, default False
+        Force Google BigQuery to re-authenticate the user. This is useful
+        if multiple accounts are used.
+    auth_local_webserver : bool, default True
+        Use the `local webserver flow`_ instead of the `console flow`_
+        when getting user credentials.
+
+        .. _local webserver flow:
+            https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
+        .. _console flow:
+            https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
+
+        *New in version 0.2.0 of pandas-gbq*.
+
+        .. versionchanged:: 1.5.0
+           Default value is changed to ``True``. Google has deprecated the
+           ``auth_local_webserver = False`` `"out of band" (copy-paste)
+           flow
+           <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
+    dialect : str, default 'legacy'
+        Note: The default value is changing to 'standard' in a future version.
+
+        SQL syntax dialect to use. Value can be one of:
+
+        ``'legacy'``
+            Use BigQuery's legacy SQL dialect. For more information see
+            `BigQuery Legacy SQL Reference
+            <https://cloud.google.com/bigquery/docs/reference/legacy-sql>`__.
+        ``'standard'``
+            Use BigQuery's standard SQL, which is
+            compliant with the SQL 2011 standard. For more information
+            see `BigQuery Standard SQL Reference
+            <https://cloud.google.com/bigquery/docs/reference/standard-sql/>`__.
+    location : str, optional
+        Location where the query job should run. See the `BigQuery locations
+        documentation
+        <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
+        list of available locations. The location must match that of any
+        datasets used in the query.
+
+        *New in version 0.5.0 of pandas-gbq*.
+    configuration : dict, optional
+        Query config parameters for job processing.
+        For example:
+
+            configuration = {'query': {'useQueryCache': False}}
+
+        For more information see `BigQuery REST API Reference
+        <https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__.
+    credentials : google.auth.credentials.Credentials, optional
+        Credentials for accessing Google APIs. Use this parameter to override
+        default credentials, such as to use Compute Engine
+        :class:`google.auth.compute_engine.Credentials` or Service Account
+        :class:`google.oauth2.service_account.Credentials` directly.
+
+        *New in version 0.8.0 of pandas-gbq*.
+    use_bqstorage_api : bool, default False
+        Use the `BigQuery Storage API
+        <https://cloud.google.com/bigquery/docs/reference/storage/>`__ to
+        download query results quickly, but at an increased cost. To use this
+        API, first `enable it in the Cloud Console
+        <https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`__.
+        You must also have the `bigquery.readsessions.create
+        <https://cloud.google.com/bigquery/docs/access-control#roles>`__
+        permission on the project you are billing queries to.
+
+        This feature requires version 0.10.0 or later of the ``pandas-gbq``
+        package. It also requires the ``google-cloud-bigquery-storage`` and
+        ``fastavro`` packages.
+
+    max_results : int, optional
+        If set, limit the maximum number of rows to fetch from the query
+        results.
+
+    progress_bar_type : Optional, str
+        If set, use the `tqdm <https://tqdm.github.io/>`__ library to
+        display a progress bar while the data downloads. Install the
+        ``tqdm`` package to use this feature.
+
+        Possible values of ``progress_bar_type`` include:
+
+        ``None``
+            No progress bar.
+        ``'tqdm'``
+            Use the :func:`tqdm.tqdm` function to print a progress bar
+            to :data:`sys.stderr`.
+        ``'tqdm_notebook'``
+            Use the :func:`tqdm.tqdm_notebook` function to display a
+            progress bar as a Jupyter notebook widget.
+        ``'tqdm_gui'``
+            Use the :func:`tqdm.tqdm_gui` function to display a
+            progress bar as a graphical dialog box.
+
+    Returns
+    -------
+    df: DataFrame
+        DataFrame representing results of query.
+
+    See Also
+    --------
+    pandas_gbq.read_gbq : This function in the pandas-gbq library.
+    DataFrame.to_gbq : Write a DataFrame to Google BigQuery.
+
+    Examples
+    --------
+    Example taken from `Google BigQuery documentation
+    <https://cloud.google.com/bigquery/docs/pandas-gbq-migration>`_
+
+    >>> sql = "SELECT name FROM table_name WHERE state = 'TX' LIMIT 100;"
+    >>> df = pd.read_gbq(sql, dialect="standard")  # doctest: +SKIP
+    >>> project_id = "your-project-id"  # doctest: +SKIP
+    >>> df = pd.read_gbq(sql,
+    ...                  project_id=project_id,
+    ...                  dialect="standard"
+    ...                  )  # doctest: +SKIP
+    """
+    warnings.warn(
+        "read_gbq is deprecated and will be removed in a future version. "
+        "Please use pandas_gbq.read_gbq instead: "
+        "https://pandas-gbq.readthedocs.io/en/latest/api.html#pandas_gbq.read_gbq",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    pandas_gbq = _try_import()
+
+    kwargs: dict[str, str | bool | int | None] = {}
+
+    # START: new kwargs.  Don't populate unless explicitly set.
+    if use_bqstorage_api is not None:
+        kwargs["use_bqstorage_api"] = use_bqstorage_api
+    if max_results is not None:
+        kwargs["max_results"] = max_results
+
+    kwargs["progress_bar_type"] = progress_bar_type
+    # END: new kwargs
+
+    return pandas_gbq.read_gbq(
+        query,
+        project_id=project_id,
+        index_col=index_col,
+        col_order=col_order,
+        reauth=reauth,
+        auth_local_webserver=auth_local_webserver,
+        dialect=dialect,
+        location=location,
+        configuration=configuration,
+        credentials=credentials,
+        **kwargs,
+    )
+
+
+def to_gbq(
+    dataframe: DataFrame,
+    destination_table: str,
+    project_id: str | None = None,
+    chunksize: int | None = None,
+    reauth: bool = False,
+    if_exists: str = "fail",
+    auth_local_webserver: bool = True,
+    table_schema: list[dict[str, str]] | None = None,
+    location: str | None = None,
+    progress_bar: bool = True,
+    credentials: Credentials | None = None,
+) -> None:
+    warnings.warn(
+        "to_gbq is deprecated and will be removed in a future version. "
+        "Please use pandas_gbq.to_gbq instead: "
+        "https://pandas-gbq.readthedocs.io/en/latest/api.html#pandas_gbq.to_gbq",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    pandas_gbq = _try_import()
+    pandas_gbq.to_gbq(
+        dataframe,
+        destination_table,
+        project_id=project_id,
+        chunksize=chunksize,
+        reauth=reauth,
+        if_exists=if_exists,
+        auth_local_webserver=auth_local_webserver,
+        table_schema=table_schema,
+        location=location,
+        progress_bar=progress_bar,
+        credentials=credentials,
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/html.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/html.py
new file mode 100644
index 0000000000000000000000000000000000000000..4eeeb1b655f8ac55309edeacd593f5a5c2516678
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/html.py
@@ -0,0 +1,1259 @@
+"""
+:mod:`pandas.io.html` is a module containing functionality for dealing with
+HTML IO.
+
+"""
+
+from __future__ import annotations
+
+from collections import abc
+import numbers
+import re
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    cast,
+)
+import warnings
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    AbstractMethodError,
+    EmptyDataError,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import is_list_like
+
+from pandas import isna
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.multi import MultiIndex
+from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import (
+    file_exists,
+    get_handle,
+    is_file_like,
+    is_fsspec_url,
+    is_url,
+    stringify_path,
+    validate_header_arg,
+)
+from pandas.io.formats.printing import pprint_thing
+from pandas.io.parsers import TextParser
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        BaseBuffer,
+        DtypeBackend,
+        FilePath,
+        HTMLFlavors,
+        ReadBuffer,
+        StorageOptions,
+    )
+
+    from pandas import DataFrame
+
+#############
+# READ HTML #
+#############
+_RE_WHITESPACE = re.compile(r"[\r\n]+|\s{2,}")
+
+
+def _remove_whitespace(s: str, regex: Pattern = _RE_WHITESPACE) -> str:
+    """
+    Replace extra whitespace inside of a string with a single space.
+
+    Parameters
+    ----------
+    s : str or unicode
+        The string from which to remove extra whitespace.
+    regex : re.Pattern
+        The regular expression to use to remove extra whitespace.
+
+    Returns
+    -------
+    subd : str or unicode
+        `s` with all extra whitespace replaced with a single space.
+    """
+    return regex.sub(" ", s.strip())
+
+
+def _get_skiprows(skiprows: int | Sequence[int] | slice | None) -> int | Sequence[int]:
+    """
+    Get an iterator given an integer, slice or container.
+
+    Parameters
+    ----------
+    skiprows : int, slice, container
+        The iterator to use to skip rows; can also be a slice.
+
+    Raises
+    ------
+    TypeError
+        * If `skiprows` is not a slice, integer, or Container
+
+    Returns
+    -------
+    it : iterable
+        A proper iterator to use to skip rows of a DataFrame.
+    """
+    if isinstance(skiprows, slice):
+        start, step = skiprows.start or 0, skiprows.step or 1
+        return list(range(start, skiprows.stop, step))
+    elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
+        return cast("int | Sequence[int]", skiprows)
+    elif skiprows is None:
+        return 0
+    raise TypeError(f"{type(skiprows).__name__} is not a valid type for skipping rows")
+
+
+def _read(
+    obj: FilePath | BaseBuffer,
+    encoding: str | None,
+    storage_options: StorageOptions | None,
+) -> str | bytes:
+    """
+    Try to read from a url, file or string.
+
+    Parameters
+    ----------
+    obj : str, unicode, path object, or file-like object
+
+    Returns
+    -------
+    raw_text : str
+    """
+    text: str | bytes
+    if (
+        is_url(obj)
+        or hasattr(obj, "read")
+        or (isinstance(obj, str) and file_exists(obj))
+    ):
+        with get_handle(
+            obj, "r", encoding=encoding, storage_options=storage_options
+        ) as handles:
+            text = handles.handle.read()
+    elif isinstance(obj, (str, bytes)):
+        text = obj
+    else:
+        raise TypeError(f"Cannot read object of type '{type(obj).__name__}'")
+    return text
+
+
+class _HtmlFrameParser:
+    """
+    Base class for parsers that parse HTML into DataFrames.
+
+    Parameters
+    ----------
+    io : str or file-like
+        This can be either a string of raw HTML, a valid URL using the HTTP,
+        FTP, or FILE protocols or a file-like object.
+
+    match : str or regex
+        The text to match in the document.
+
+    attrs : dict
+        List of HTML <table> element attributes to match.
+
+    encoding : str
+        Encoding to be used by parser
+
+    displayed_only : bool
+        Whether or not items with "display:none" should be ignored
+
+    extract_links : {None, "all", "header", "body", "footer"}
+        Table elements in the specified section(s) with <a> tags will have their
+        href extracted.
+
+        .. versionadded:: 1.5.0
+
+    Attributes
+    ----------
+    io : str or file-like
+        raw HTML, URL, or file-like object
+
+    match : regex
+        The text to match in the raw HTML
+
+    attrs : dict-like
+        A dictionary of valid table attributes to use to search for table
+        elements.
+
+    encoding : str
+        Encoding to be used by parser
+
+    displayed_only : bool
+        Whether or not items with "display:none" should be ignored
+
+    extract_links : {None, "all", "header", "body", "footer"}
+        Table elements in the specified section(s) with <a> tags will have their
+        href extracted.
+
+        .. versionadded:: 1.5.0
+
+    Notes
+    -----
+    To subclass this class effectively you must override the following methods:
+        * :func:`_build_doc`
+        * :func:`_attr_getter`
+        * :func:`_href_getter`
+        * :func:`_text_getter`
+        * :func:`_parse_td`
+        * :func:`_parse_thead_tr`
+        * :func:`_parse_tbody_tr`
+        * :func:`_parse_tfoot_tr`
+        * :func:`_parse_tables`
+        * :func:`_equals_tag`
+    See each method's respective documentation for details on their
+    functionality.
+    """
+
+    def __init__(
+        self,
+        io: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
+        match: str | Pattern,
+        attrs: dict[str, str] | None,
+        encoding: str,
+        displayed_only: bool,
+        extract_links: Literal[None, "header", "footer", "body", "all"],
+        storage_options: StorageOptions = None,
+    ) -> None:
+        self.io = io
+        self.match = match
+        self.attrs = attrs
+        self.encoding = encoding
+        self.displayed_only = displayed_only
+        self.extract_links = extract_links
+        self.storage_options = storage_options
+
+    def parse_tables(self):
+        """
+        Parse and return all tables from the DOM.
+
+        Returns
+        -------
+        list of parsed (header, body, footer) tuples from tables.
+        """
+        tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
+        return (self._parse_thead_tbody_tfoot(table) for table in tables)
+
+    def _attr_getter(self, obj, attr):
+        """
+        Return the attribute value of an individual DOM node.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        attr : str or unicode
+            The attribute, such as "colspan"
+
+        Returns
+        -------
+        str or unicode
+            The attribute value.
+        """
+        # Both lxml and BeautifulSoup have the same implementation:
+        return obj.get(attr)
+
+    def _href_getter(self, obj) -> str | None:
+        """
+        Return a href if the DOM node contains a child <a> or None.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        Returns
+        -------
+        href : str or unicode
+            The href from the <a> child of the DOM node.
+        """
+        raise AbstractMethodError(self)
+
+    def _text_getter(self, obj):
+        """
+        Return the text of an individual DOM node.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        Returns
+        -------
+        text : str or unicode
+            The text from an individual DOM node.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_td(self, obj):
+        """
+        Return the td elements from a row element.
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM <tr> node.
+
+        Returns
+        -------
+        list of node-like
+            These are the elements of each row, i.e., the columns.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_thead_tr(self, table):
+        """
+        Return the list of thead row elements from the parsed table element.
+
+        Parameters
+        ----------
+        table : a table element that contains zero or more thead elements.
+
+        Returns
+        -------
+        list of node-like
+            These are the <tr> row elements of a table.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_tbody_tr(self, table):
+        """
+        Return the list of tbody row elements from the parsed table element.
+
+        HTML5 table bodies consist of either 0 or more <tbody> elements (which
+        only contain <tr> elements) or 0 or more <tr> elements. This method
+        checks for both structures.
+
+        Parameters
+        ----------
+        table : a table element that contains row elements.
+
+        Returns
+        -------
+        list of node-like
+            These are the <tr> row elements of a table.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_tfoot_tr(self, table):
+        """
+        Return the list of tfoot row elements from the parsed table element.
+
+        Parameters
+        ----------
+        table : a table element that contains row elements.
+
+        Returns
+        -------
+        list of node-like
+            These are the <tr> row elements of a table.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_tables(self, document, match, attrs):
+        """
+        Return all tables from the parsed DOM.
+
+        Parameters
+        ----------
+        document : the DOM from which to parse the table element.
+
+        match : str or regular expression
+            The text to search for in the DOM tree.
+
+        attrs : dict
+            A dictionary of table attributes that can be used to disambiguate
+            multiple tables on a page.
+
+        Raises
+        ------
+        ValueError : `match` does not match any text in the document.
+
+        Returns
+        -------
+        list of node-like
+            HTML <table> elements to be parsed into raw data.
+        """
+        raise AbstractMethodError(self)
+
+    def _equals_tag(self, obj, tag) -> bool:
+        """
+        Return whether an individual DOM node matches a tag
+
+        Parameters
+        ----------
+        obj : node-like
+            A DOM node.
+
+        tag : str
+            Tag name to be checked for equality.
+
+        Returns
+        -------
+        boolean
+            Whether `obj`'s tag name is `tag`
+        """
+        raise AbstractMethodError(self)
+
+    def _build_doc(self):
+        """
+        Return a tree-like object that can be used to iterate over the DOM.
+
+        Returns
+        -------
+        node-like
+            The DOM from which to parse the table element.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_thead_tbody_tfoot(self, table_html):
+        """
+        Given a table, return parsed header, body, and foot.
+
+        Parameters
+        ----------
+        table_html : node-like
+
+        Returns
+        -------
+        tuple of (header, body, footer), each a list of list-of-text rows.
+
+        Notes
+        -----
+        Header and body are lists-of-lists. Top level list is a list of
+        rows. Each row is a list of str text.
+
+        Logic: Use <thead>, <tbody>, <tfoot> elements to identify
+               header, body, and footer, otherwise:
+               - Put all rows into body
+               - Move rows from top of body to header only if
+                 all elements inside row are <th>
+               - Move rows from bottom of body to footer only if
+                 all elements inside row are <th>
+        """
+        header_rows = self._parse_thead_tr(table_html)
+        body_rows = self._parse_tbody_tr(table_html)
+        footer_rows = self._parse_tfoot_tr(table_html)
+
+        def row_is_all_th(row):
+            return all(self._equals_tag(t, "th") for t in self._parse_td(row))
+
+        if not header_rows:
+            # The table has no <thead>. Move the top all-<th> rows from
+            # body_rows to header_rows. (This is a common case because many
+            # tables in the wild have no <thead> or <tfoot>
+            while body_rows and row_is_all_th(body_rows[0]):
+                header_rows.append(body_rows.pop(0))
+
+        header = self._expand_colspan_rowspan(header_rows, section="header")
+        body = self._expand_colspan_rowspan(body_rows, section="body")
+        footer = self._expand_colspan_rowspan(footer_rows, section="footer")
+
+        return header, body, footer
+
+    def _expand_colspan_rowspan(
+        self, rows, section: Literal["header", "footer", "body"]
+    ):
+        """
+        Given a list of <tr>s, return a list of text rows.
+
+        Parameters
+        ----------
+        rows : list of node-like
+            List of <tr>s
+        section : the section that the rows belong to (header, body or footer).
+
+        Returns
+        -------
+        list of list
+            Each returned row is a list of str text, or tuple (text, link)
+            if extract_links is not None.
+
+        Notes
+        -----
+        Any cell with ``rowspan`` or ``colspan`` will have its contents copied
+        to subsequent cells.
+        """
+        all_texts = []  # list of rows, each a list of str
+        text: str | tuple
+        remainder: list[
+            tuple[int, str | tuple, int]
+        ] = []  # list of (index, text, nrows)
+
+        for tr in rows:
+            texts = []  # the output for this row
+            next_remainder = []
+
+            index = 0
+            tds = self._parse_td(tr)
+            for td in tds:
+                # Append texts from previous rows with rowspan>1 that come
+                # before this <td>
+                while remainder and remainder[0][0] <= index:
+                    prev_i, prev_text, prev_rowspan = remainder.pop(0)
+                    texts.append(prev_text)
+                    if prev_rowspan > 1:
+                        next_remainder.append((prev_i, prev_text, prev_rowspan - 1))
+                    index += 1
+
+                # Append the text from this <td>, colspan times
+                text = _remove_whitespace(self._text_getter(td))
+                if self.extract_links in ("all", section):
+                    href = self._href_getter(td)
+                    text = (text, href)
+                rowspan = int(self._attr_getter(td, "rowspan") or 1)
+                colspan = int(self._attr_getter(td, "colspan") or 1)
+
+                for _ in range(colspan):
+                    texts.append(text)
+                    if rowspan > 1:
+                        next_remainder.append((index, text, rowspan - 1))
+                    index += 1
+
+            # Append texts from previous rows at the final position
+            for prev_i, prev_text, prev_rowspan in remainder:
+                texts.append(prev_text)
+                if prev_rowspan > 1:
+                    next_remainder.append((prev_i, prev_text, prev_rowspan - 1))
+
+            all_texts.append(texts)
+            remainder = next_remainder
+
+        # Append rows that only appear because the previous row had non-1
+        # rowspan
+        while remainder:
+            next_remainder = []
+            texts = []
+            for prev_i, prev_text, prev_rowspan in remainder:
+                texts.append(prev_text)
+                if prev_rowspan > 1:
+                    next_remainder.append((prev_i, prev_text, prev_rowspan - 1))
+            all_texts.append(texts)
+            remainder = next_remainder
+
+        return all_texts
+
+    def _handle_hidden_tables(self, tbl_list, attr_name: str):
+        """
+        Return list of tables, potentially removing hidden elements
+
+        Parameters
+        ----------
+        tbl_list : list of node-like
+            Type of list elements will vary depending upon parser used
+        attr_name : str
+            Name of the accessor for retrieving HTML attributes
+
+        Returns
+        -------
+        list of node-like
+            Return type matches `tbl_list`
+        """
+        if not self.displayed_only:
+            return tbl_list
+
+        return [
+            x
+            for x in tbl_list
+            if "display:none"
+            not in getattr(x, attr_name).get("style", "").replace(" ", "")
+        ]
+
+
+class _BeautifulSoupHtml5LibFrameParser(_HtmlFrameParser):
+    """
+    HTML to DataFrame parser that uses BeautifulSoup under the hood.
+
+    See Also
+    --------
+    pandas.io.html._HtmlFrameParser
+    pandas.io.html._LxmlFrameParser
+
+    Notes
+    -----
+    Documentation strings for this class are in the base class
+    :class:`pandas.io.html._HtmlFrameParser`.
+    """
+
+    def _parse_tables(self, document, match, attrs):
+        element_name = "table"
+        tables = document.find_all(element_name, attrs=attrs)
+        if not tables:
+            raise ValueError("No tables found")
+
+        result = []
+        unique_tables = set()
+        tables = self._handle_hidden_tables(tables, "attrs")
+
+        for table in tables:
+            if self.displayed_only:
+                for elem in table.find_all("style"):
+                    elem.decompose()
+
+                for elem in table.find_all(style=re.compile(r"display:\s*none")):
+                    elem.decompose()
+
+            if table not in unique_tables and table.find(string=match) is not None:
+                result.append(table)
+            unique_tables.add(table)
+        if not result:
+            raise ValueError(f"No tables found matching pattern {repr(match.pattern)}")
+        return result
+
+    def _href_getter(self, obj) -> str | None:
+        a = obj.find("a", href=True)
+        return None if not a else a["href"]
+
+    def _text_getter(self, obj):
+        return obj.text
+
+    def _equals_tag(self, obj, tag) -> bool:
+        return obj.name == tag
+
+    def _parse_td(self, row):
+        return row.find_all(("td", "th"), recursive=False)
+
+    def _parse_thead_tr(self, table):
+        return table.select("thead tr")
+
+    def _parse_tbody_tr(self, table):
+        from_tbody = table.select("tbody tr")
+        from_root = table.find_all("tr", recursive=False)
+        # HTML spec: at most one of these lists has content
+        return from_tbody + from_root
+
+    def _parse_tfoot_tr(self, table):
+        return table.select("tfoot tr")
+
+    def _setup_build_doc(self):
+        raw_text = _read(self.io, self.encoding, self.storage_options)
+        if not raw_text:
+            raise ValueError(f"No text parsed from document: {self.io}")
+        return raw_text
+
+    def _build_doc(self):
+        from bs4 import BeautifulSoup
+
+        bdoc = self._setup_build_doc()
+        if isinstance(bdoc, bytes) and self.encoding is not None:
+            udoc = bdoc.decode(self.encoding)
+            from_encoding = None
+        else:
+            udoc = bdoc
+            from_encoding = self.encoding
+
+        soup = BeautifulSoup(udoc, features="html5lib", from_encoding=from_encoding)
+
+        for br in soup.find_all("br"):
+            br.replace_with("\n" + br.text)
+
+        return soup
+
+
+def _build_xpath_expr(attrs) -> str:
+    """
+    Build an xpath expression to simulate bs4's ability to pass in kwargs to
+    search for attributes when using the lxml parser.
+
+    Parameters
+    ----------
+    attrs : dict
+        A dict of HTML attributes. These are NOT checked for validity.
+
+    Returns
+    -------
+    expr : unicode
+        An XPath expression that checks for the given HTML attributes.
+    """
+    # give class attribute as class_ because class is a python keyword
+    if "class_" in attrs:
+        attrs["class"] = attrs.pop("class_")
+
+    s = " and ".join([f"@{k}={repr(v)}" for k, v in attrs.items()])
+    return f"[{s}]"
+
+
+_re_namespace = {"re": "http://exslt.org/regular-expressions"}
+
+
+class _LxmlFrameParser(_HtmlFrameParser):
+    """
+    HTML to DataFrame parser that uses lxml under the hood.
+
+    Warning
+    -------
+    This parser can only handle HTTP, FTP, and FILE urls.
+
+    See Also
+    --------
+    _HtmlFrameParser
+    _BeautifulSoupLxmlFrameParser
+
+    Notes
+    -----
+    Documentation strings for this class are in the base class
+    :class:`_HtmlFrameParser`.
+    """
+
+    def _href_getter(self, obj) -> str | None:
+        href = obj.xpath(".//a/@href")
+        return None if not href else href[0]
+
+    def _text_getter(self, obj):
+        return obj.text_content()
+
+    def _parse_td(self, row):
+        # Look for direct children only: the "row" element here may be a
+        # <thead> or <tfoot> (see _parse_thead_tr).
+        return row.xpath("./td|./th")
+
+    def _parse_tables(self, document, match, kwargs):
+        pattern = match.pattern
+
+        # 1. check all descendants for the given pattern and only search tables
+        # GH 49929
+        xpath_expr = f"//table[.//text()[re:test(., {repr(pattern)})]]"
+
+        # if any table attributes were given build an xpath expression to
+        # search for them
+        if kwargs:
+            xpath_expr += _build_xpath_expr(kwargs)
+
+        tables = document.xpath(xpath_expr, namespaces=_re_namespace)
+
+        tables = self._handle_hidden_tables(tables, "attrib")
+        if self.displayed_only:
+            for table in tables:
+                # lxml utilizes XPATH 1.0 which does not have regex
+                # support. As a result, we find all elements with a style
+                # attribute and iterate them to check for display:none
+                for elem in table.xpath(".//style"):
+                    elem.drop_tree()
+                for elem in table.xpath(".//*[@style]"):
+                    if "display:none" in elem.attrib.get("style", "").replace(" ", ""):
+                        elem.drop_tree()
+        if not tables:
+            raise ValueError(f"No tables found matching regex {repr(pattern)}")
+        return tables
+
+    def _equals_tag(self, obj, tag) -> bool:
+        return obj.tag == tag
+
+    def _build_doc(self):
+        """
+        Raises
+        ------
+        ValueError
+            * If a URL that lxml cannot parse is passed.
+
+        Exception
+            * Any other ``Exception`` thrown. For example, trying to parse a
+              URL that is syntactically correct on a machine with no internet
+              connection will fail.
+
+        See Also
+        --------
+        pandas.io.html._HtmlFrameParser._build_doc
+        """
+        from lxml.etree import XMLSyntaxError
+        from lxml.html import (
+            HTMLParser,
+            fromstring,
+            parse,
+        )
+
+        parser = HTMLParser(recover=True, encoding=self.encoding)
+
+        try:
+            if is_url(self.io):
+                with get_handle(
+                    self.io, "r", storage_options=self.storage_options
+                ) as f:
+                    r = parse(f.handle, parser=parser)
+            else:
+                # try to parse the input in the simplest way
+                r = parse(self.io, parser=parser)
+            try:
+                r = r.getroot()
+            except AttributeError:
+                pass
+        except (UnicodeDecodeError, OSError) as e:
+            # if the input is a blob of html goop
+            if not is_url(self.io):
+                r = fromstring(self.io, parser=parser)
+
+                try:
+                    r = r.getroot()
+                except AttributeError:
+                    pass
+            else:
+                raise e
+        else:
+            if not hasattr(r, "text_content"):
+                raise XMLSyntaxError("no text parsed from document", 0, 0, 0)
+
+        for br in r.xpath("*//br"):
+            br.tail = "\n" + (br.tail or "")
+
+        return r
+
+    def _parse_thead_tr(self, table):
+        rows = []
+
+        for thead in table.xpath(".//thead"):
+            rows.extend(thead.xpath("./tr"))
+
+            # HACK: lxml does not clean up the clearly-erroneous
+            # <thead><th>foo</th><th>bar</th></thead>. (Missing <tr>). Add
+            # the <thead> and _pretend_ it's a <tr>; _parse_td() will find its
+            # children as though it's a <tr>.
+            #
+            # Better solution would be to use html5lib.
+            elements_at_root = thead.xpath("./td|./th")
+            if elements_at_root:
+                rows.append(thead)
+
+        return rows
+
+    def _parse_tbody_tr(self, table):
+        from_tbody = table.xpath(".//tbody//tr")
+        from_root = table.xpath("./tr")
+        # HTML spec: at most one of these lists has content
+        return from_tbody + from_root
+
+    def _parse_tfoot_tr(self, table):
+        return table.xpath(".//tfoot//tr")
+
+
+def _expand_elements(body) -> None:
+    data = [len(elem) for elem in body]
+    lens = Series(data)
+    lens_max = lens.max()
+    not_max = lens[lens != lens_max]
+
+    empty = [""]
+    for ind, length in not_max.items():
+        body[ind] += empty * (lens_max - length)
+
+
+def _data_to_frame(**kwargs):
+    head, body, foot = kwargs.pop("data")
+    header = kwargs.pop("header")
+    kwargs["skiprows"] = _get_skiprows(kwargs["skiprows"])
+    if head:
+        body = head + body
+
+        # Infer header when there is a <thead> or top <th>-only rows
+        if header is None:
+            if len(head) == 1:
+                header = 0
+            else:
+                # ignore all-empty-text rows
+                header = [i for i, row in enumerate(head) if any(text for text in row)]
+
+    if foot:
+        body += foot
+
+    # fill out elements of body that are "ragged"
+    _expand_elements(body)
+    with TextParser(body, header=header, **kwargs) as tp:
+        return tp.read()
+
+
+_valid_parsers = {
+    "lxml": _LxmlFrameParser,
+    None: _LxmlFrameParser,
+    "html5lib": _BeautifulSoupHtml5LibFrameParser,
+    "bs4": _BeautifulSoupHtml5LibFrameParser,
+}
+
+
+def _parser_dispatch(flavor: HTMLFlavors | None) -> type[_HtmlFrameParser]:
+    """
+    Choose the parser based on the input flavor.
+
+    Parameters
+    ----------
+    flavor : {{"lxml", "html5lib", "bs4"}} or None
+        The type of parser to use. This must be a valid backend.
+
+    Returns
+    -------
+    cls : _HtmlFrameParser subclass
+        The parser class based on the requested input flavor.
+
+    Raises
+    ------
+    ValueError
+        * If `flavor` is not a valid backend.
+    ImportError
+        * If you do not have the requested `flavor`
+    """
+    valid_parsers = list(_valid_parsers.keys())
+    if flavor not in valid_parsers:
+        raise ValueError(
+            f"{repr(flavor)} is not a valid flavor, valid flavors are {valid_parsers}"
+        )
+
+    if flavor in ("bs4", "html5lib"):
+        import_optional_dependency("html5lib")
+        import_optional_dependency("bs4")
+    else:
+        import_optional_dependency("lxml.etree")
+    return _valid_parsers[flavor]
+
+
+def _print_as_set(s) -> str:
+    arg = ", ".join([pprint_thing(el) for el in s])
+    return f"{{{arg}}}"
+
+
+def _validate_flavor(flavor):
+    if flavor is None:
+        flavor = "lxml", "bs4"
+    elif isinstance(flavor, str):
+        flavor = (flavor,)
+    elif isinstance(flavor, abc.Iterable):
+        if not all(isinstance(flav, str) for flav in flavor):
+            raise TypeError(
+                f"Object of type {repr(type(flavor).__name__)} "
+                f"is not an iterable of strings"
+            )
+    else:
+        msg = repr(flavor) if isinstance(flavor, str) else str(flavor)
+        msg += " is not a valid flavor"
+        raise ValueError(msg)
+
+    flavor = tuple(flavor)
+    valid_flavors = set(_valid_parsers)
+    flavor_set = set(flavor)
+
+    if not flavor_set & valid_flavors:
+        raise ValueError(
+            f"{_print_as_set(flavor_set)} is not a valid set of flavors, valid "
+            f"flavors are {_print_as_set(valid_flavors)}"
+        )
+    return flavor
+
+
+def _parse(
+    flavor,
+    io,
+    match,
+    attrs,
+    encoding,
+    displayed_only,
+    extract_links,
+    storage_options,
+    **kwargs,
+):
+    flavor = _validate_flavor(flavor)
+    compiled_match = re.compile(match)  # you can pass a compiled regex here
+
+    retained = None
+    for flav in flavor:
+        parser = _parser_dispatch(flav)
+        p = parser(
+            io,
+            compiled_match,
+            attrs,
+            encoding,
+            displayed_only,
+            extract_links,
+            storage_options,
+        )
+
+        try:
+            tables = p.parse_tables()
+        except ValueError as caught:
+            # if `io` is an io-like object, check if it's seekable
+            # and try to rewind it before trying the next parser
+            if hasattr(io, "seekable") and io.seekable():
+                io.seek(0)
+            elif hasattr(io, "seekable") and not io.seekable():
+                # if we couldn't rewind it, let the user know
+                raise ValueError(
+                    f"The flavor {flav} failed to parse your input. "
+                    "Since you passed a non-rewindable file "
+                    "object, we can't rewind it to try "
+                    "another parser. Try read_html() with a different flavor."
+                ) from caught
+
+            retained = caught
+        else:
+            break
+    else:
+        assert retained is not None  # for mypy
+        raise retained
+
+    ret = []
+    for table in tables:
+        try:
+            df = _data_to_frame(data=table, **kwargs)
+            # Cast MultiIndex header to an Index of tuples when extracting header
+            # links and replace nan with None (therefore can't use mi.to_flat_index()).
+            # This maintains consistency of selection (e.g. df.columns.str[1])
+            if extract_links in ("all", "header") and isinstance(
+                df.columns, MultiIndex
+            ):
+                df.columns = Index(
+                    ((col[0], None if isna(col[1]) else col[1]) for col in df.columns),
+                    tupleize_cols=False,
+                )
+
+            ret.append(df)
+        except EmptyDataError:  # empty table
+            continue
+    return ret
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_html(
+    io: FilePath | ReadBuffer[str],
+    *,
+    match: str | Pattern = ".+",
+    flavor: HTMLFlavors | Sequence[HTMLFlavors] | None = None,
+    header: int | Sequence[int] | None = None,
+    index_col: int | Sequence[int] | None = None,
+    skiprows: int | Sequence[int] | slice | None = None,
+    attrs: dict[str, str] | None = None,
+    parse_dates: bool = False,
+    thousands: str | None = ",",
+    encoding: str | None = None,
+    decimal: str = ".",
+    converters: dict | None = None,
+    na_values: Iterable[object] | None = None,
+    keep_default_na: bool = True,
+    displayed_only: bool = True,
+    extract_links: Literal[None, "header", "footer", "body", "all"] = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    storage_options: StorageOptions = None,
+) -> list[DataFrame]:
+    r"""
+    Read HTML tables into a ``list`` of ``DataFrame`` objects.
+
+    Parameters
+    ----------
+    io : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a string ``read()`` function.
+        The string can represent a URL or the HTML itself. Note that
+        lxml only accepts the http, ftp and file url protocols. If you have a
+        URL that starts with ``'https'`` you might try removing the ``'s'``.
+
+        .. deprecated:: 2.1.0
+            Passing html literal strings is deprecated.
+            Wrap literal string/bytes input in ``io.StringIO``/``io.BytesIO`` instead.
+
+    match : str or compiled regular expression, optional
+        The set of tables containing text matching this regex or string will be
+        returned. Unless the HTML is extremely simple you will probably need to
+        pass a non-empty string here. Defaults to '.+' (match any non-empty
+        string). The default value will return all tables contained on a page.
+        This value is converted to a regular expression so that there is
+        consistent behavior between Beautiful Soup and lxml.
+
+    flavor : {{"lxml", "html5lib", "bs4"}} or list-like, optional
+        The parsing engine (or list of parsing engines) to use. 'bs4' and
+        'html5lib' are synonymous with each other, they are both there for
+        backwards compatibility. The default of ``None`` tries to use ``lxml``
+        to parse and if that fails it falls back on ``bs4`` + ``html5lib``.
+
+    header : int or list-like, optional
+        The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to
+        make the columns headers.
+
+    index_col : int or list-like, optional
+        The column (or list of columns) to use to create the index.
+
+    skiprows : int, list-like or slice, optional
+        Number of rows to skip after parsing the column integer. 0-based. If a
+        sequence of integers or a slice is given, will skip the rows indexed by
+        that sequence.  Note that a single element sequence means 'skip the nth
+        row' whereas an integer means 'skip n rows'.
+
+    attrs : dict, optional
+        This is a dictionary of attributes that you can pass to use to identify
+        the table in the HTML. These are not checked for validity before being
+        passed to lxml or Beautiful Soup. However, these attributes must be
+        valid HTML table attributes to work correctly. For example, ::
+
+            attrs = {{'id': 'table'}}
+
+        is a valid attribute dictionary because the 'id' HTML tag attribute is
+        a valid HTML attribute for *any* HTML tag as per `this document
+        <https://html.spec.whatwg.org/multipage/dom.html#global-attributes>`__. ::
+
+            attrs = {{'asdf': 'table'}}
+
+        is *not* a valid attribute dictionary because 'asdf' is not a valid
+        HTML attribute even if it is a valid XML attribute.  Valid HTML 4.01
+        table attributes can be found `here
+        <http://www.w3.org/TR/REC-html40/struct/tables.html#h-11.2>`__. A
+        working draft of the HTML 5 spec can be found `here
+        <https://html.spec.whatwg.org/multipage/tables.html>`__. It contains the
+        latest information on table attributes for the modern web.
+
+    parse_dates : bool, optional
+        See :func:`~read_csv` for more details.
+
+    thousands : str, optional
+        Separator to use to parse thousands. Defaults to ``','``.
+
+    encoding : str, optional
+        The encoding used to decode the web page. Defaults to ``None``.``None``
+        preserves the previous encoding behavior, which depends on the
+        underlying parser library (e.g., the parser library will try to use
+        the encoding provided by the document).
+
+    decimal : str, default '.'
+        Character to recognize as decimal point (e.g. use ',' for European
+        data).
+
+    converters : dict, default None
+        Dict of functions for converting values in certain columns. Keys can
+        either be integers or column labels, values are functions that take one
+        input argument, the cell (not column) content, and return the
+        transformed content.
+
+    na_values : iterable, default None
+        Custom NA values.
+
+    keep_default_na : bool, default True
+        If na_values are specified and keep_default_na is False the default NaN
+        values are overridden, otherwise they're appended to.
+
+    displayed_only : bool, default True
+        Whether elements with "display: none" should be parsed.
+
+    extract_links : {{None, "all", "header", "body", "footer"}}
+        Table elements in the specified section(s) with <a> tags will have their
+        href extracted.
+
+        .. versionadded:: 1.5.0
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    {storage_options}
+
+        .. versionadded:: 2.1.0
+
+    Returns
+    -------
+    dfs
+        A list of DataFrames.
+
+    See Also
+    --------
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+
+    Notes
+    -----
+    Before using this function you should read the :ref:`gotchas about the
+    HTML parsing libraries <io.html.gotchas>`.
+
+    Expect to do some cleanup after you call this function. For example, you
+    might need to manually assign column names if the column names are
+    converted to NaN when you pass the `header=0` argument. We try to assume as
+    little as possible about the structure of the table and push the
+    idiosyncrasies of the HTML contained in the table to the user.
+
+    This function searches for ``<table>`` elements and only for ``<tr>``
+    and ``<th>`` rows and ``<td>`` elements within each ``<tr>`` or ``<th>``
+    element in the table. ``<td>`` stands for "table data". This function
+    attempts to properly handle ``colspan`` and ``rowspan`` attributes.
+    If the function has a ``<thead>`` argument, it is used to construct
+    the header, otherwise the function attempts to find the header within
+    the body (by putting rows with only ``<th>`` elements into the header).
+
+    Similar to :func:`~read_csv` the `header` argument is applied
+    **after** `skiprows` is applied.
+
+    This function will *always* return a list of :class:`DataFrame` *or*
+    it will fail, e.g., it will *not* return an empty list.
+
+    Examples
+    --------
+    See the :ref:`read_html documentation in the IO section of the docs
+    <io.read_html>` for some examples of reading in HTML tables.
+    """
+    # Type check here. We don't want to parse only to fail because of an
+    # invalid value of an integer skiprows.
+    if isinstance(skiprows, numbers.Integral) and skiprows < 0:
+        raise ValueError(
+            "cannot skip rows starting from the end of the "
+            "data (you passed a negative value)"
+        )
+    if extract_links not in [None, "header", "footer", "body", "all"]:
+        raise ValueError(
+            "`extract_links` must be one of "
+            '{None, "header", "footer", "body", "all"}, got '
+            f'"{extract_links}"'
+        )
+
+    validate_header_arg(header)
+    check_dtype_backend(dtype_backend)
+
+    io = stringify_path(io)
+
+    if isinstance(io, str) and not any(
+        [
+            is_file_like(io),
+            file_exists(io),
+            is_url(io),
+            is_fsspec_url(io),
+        ]
+    ):
+        warnings.warn(
+            "Passing literal html to 'read_html' is deprecated and "
+            "will be removed in a future version. To read from a "
+            "literal string, wrap it in a 'StringIO' object.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    return _parse(
+        flavor=flavor,
+        io=io,
+        match=match,
+        header=header,
+        index_col=index_col,
+        skiprows=skiprows,
+        parse_dates=parse_dates,
+        thousands=thousands,
+        attrs=attrs,
+        encoding=encoding,
+        decimal=decimal,
+        converters=converters,
+        na_values=na_values,
+        keep_default_na=keep_default_na,
+        displayed_only=displayed_only,
+        extract_links=extract_links,
+        dtype_backend=dtype_backend,
+        storage_options=storage_options,
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/orc.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/orc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7f473a929568faa9a00886d627c65fa57a3aa18
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/orc.py
@@ -0,0 +1,228 @@
+""" orc compat """
+from __future__ import annotations
+
+import io
+from types import ModuleType
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.indexes.api import default_index
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import (
+    get_handle,
+    is_fsspec_url,
+)
+
+if TYPE_CHECKING:
+    import fsspec
+    import pyarrow.fs
+
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        WriteBuffer,
+    )
+
+    from pandas.core.frame import DataFrame
+
+
+def read_orc(
+    path: FilePath | ReadBuffer[bytes],
+    columns: list[str] | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    filesystem: pyarrow.fs.FileSystem | fsspec.spec.AbstractFileSystem | None = None,
+    **kwargs: Any,
+) -> DataFrame:
+    """
+    Load an ORC object from the file path, returning a DataFrame.
+
+    Parameters
+    ----------
+    path : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function. The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be:
+        ``file://localhost/path/to/table.orc``.
+    columns : list, default None
+        If not None, only these columns will be read from the file.
+        Output always follows the ordering of the file and not the columns list.
+        This mirrors the original behaviour of
+        :external+pyarrow:py:meth:`pyarrow.orc.ORCFile.read`.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    filesystem : fsspec or pyarrow filesystem, default None
+        Filesystem object to use when reading the parquet file.
+
+        .. versionadded:: 2.1.0
+
+    **kwargs
+        Any additional kwargs are passed to pyarrow.
+
+    Returns
+    -------
+    DataFrame
+
+    Notes
+    -----
+    Before using this function you should read the :ref:`user guide about ORC <io.orc>`
+    and :ref:`install optional dependencies <install.warn_orc>`.
+
+    If ``path`` is a URI scheme pointing to a local or remote file (e.g. "s3://"),
+    a ``pyarrow.fs`` filesystem will be attempted to read the file. You can also pass a
+    pyarrow or fsspec filesystem object into the filesystem keyword to override this
+    behavior.
+
+    Examples
+    --------
+    >>> result = pd.read_orc("example_pa.orc")  # doctest: +SKIP
+    """
+    # we require a newer version of pyarrow than we support for parquet
+
+    orc = import_optional_dependency("pyarrow.orc")
+
+    check_dtype_backend(dtype_backend)
+
+    with get_handle(path, "rb", is_text=False) as handles:
+        source = handles.handle
+        if is_fsspec_url(path) and filesystem is None:
+            pa = import_optional_dependency("pyarrow")
+            pa_fs = import_optional_dependency("pyarrow.fs")
+            try:
+                filesystem, source = pa_fs.FileSystem.from_uri(path)
+            except (TypeError, pa.ArrowInvalid):
+                pass
+
+        pa_table = orc.read_table(
+            source=source, columns=columns, filesystem=filesystem, **kwargs
+        )
+    return arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
+
+
+def to_orc(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes] | None = None,
+    *,
+    engine: Literal["pyarrow"] = "pyarrow",
+    index: bool | None = None,
+    engine_kwargs: dict[str, Any] | None = None,
+) -> bytes | None:
+    """
+    Write a DataFrame to the ORC format.
+
+    .. versionadded:: 1.5.0
+
+    Parameters
+    ----------
+    df : DataFrame
+        The dataframe to be written to ORC. Raises NotImplementedError
+        if dtype of one or more columns is category, unsigned integers,
+        intervals, periods or sparse.
+    path : str, file-like object or None, default None
+        If a string, it will be used as Root Directory path
+        when writing a partitioned dataset. By file-like object,
+        we refer to objects with a write() method, such as a file handle
+        (e.g. via builtin open function). If path is None,
+        a bytes object is returned.
+    engine : str, default 'pyarrow'
+        ORC library to use.
+    index : bool, optional
+        If ``True``, include the dataframe's index(es) in the file output. If
+        ``False``, they will not be written to the file.
+        If ``None``, similar to ``infer`` the dataframe's index(es)
+        will be saved. However, instead of being saved as values,
+        the RangeIndex will be stored as a range in the metadata so it
+        doesn't require much space and is faster. Other indexes will
+        be included as columns in the file output.
+    engine_kwargs : dict[str, Any] or None, default None
+        Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.
+
+    Returns
+    -------
+    bytes if no path argument is provided else None
+
+    Raises
+    ------
+    NotImplementedError
+        Dtype of one or more columns is category, unsigned integers, interval,
+        period or sparse.
+    ValueError
+        engine is not pyarrow.
+
+    Notes
+    -----
+    * Before using this function you should read the
+      :ref:`user guide about ORC <io.orc>` and
+      :ref:`install optional dependencies <install.warn_orc>`.
+    * This function requires `pyarrow <https://arrow.apache.org/docs/python/>`_
+      library.
+    * For supported dtypes please refer to `supported ORC features in Arrow
+      <https://arrow.apache.org/docs/cpp/orc.html#data-types>`__.
+    * Currently timezones in datetime columns are not preserved when a
+      dataframe is converted into ORC files.
+    """
+    if index is None:
+        index = df.index.names[0] is not None
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    # validate index
+    # --------------
+
+    # validate that we have only a default index
+    # raise on anything else as we don't serialize the index
+
+    if not df.index.equals(default_index(len(df))):
+        raise ValueError(
+            "orc does not support serializing a non-default index for the index; "
+            "you can .reset_index() to make the index into column(s)"
+        )
+
+    if df.index.name is not None:
+        raise ValueError("orc does not serialize index meta-data on a default index")
+
+    if engine != "pyarrow":
+        raise ValueError("engine must be 'pyarrow'")
+    engine = import_optional_dependency(engine, min_version="10.0.1")
+    pa = import_optional_dependency("pyarrow")
+    orc = import_optional_dependency("pyarrow.orc")
+
+    was_none = path is None
+    if was_none:
+        path = io.BytesIO()
+    assert path is not None  # For mypy
+    with get_handle(path, "wb", is_text=False) as handles:
+        assert isinstance(engine, ModuleType)  # For mypy
+        try:
+            orc.write_table(
+                engine.Table.from_pandas(df, preserve_index=index),
+                handles.handle,
+                **engine_kwargs,
+            )
+        except (TypeError, pa.ArrowNotImplementedError) as e:
+            raise NotImplementedError(
+                "The dtype of one or more columns is not supported yet."
+            ) from e
+
+    if was_none:
+        assert isinstance(path, io.BytesIO)  # For mypy
+        return path.getvalue()
+    return None
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/parquet.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/parquet.py
new file mode 100644
index 0000000000000000000000000000000000000000..01e320cdb1b72a4f9425de40721c43189b4c5ed8
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/parquet.py
@@ -0,0 +1,678 @@
+""" parquet compat """
+from __future__ import annotations
+
+import io
+import json
+import os
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Literal,
+)
+import warnings
+from warnings import (
+    catch_warnings,
+    filterwarnings,
+)
+
+from pandas._config.config import _get_option
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas import (
+    DataFrame,
+    get_option,
+)
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io._util import arrow_table_to_pandas
+from pandas.io.common import (
+    IOHandles,
+    get_handle,
+    is_fsspec_url,
+    is_url,
+    stringify_path,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+
+def get_engine(engine: str) -> BaseImpl:
+    """return our implementation"""
+    if engine == "auto":
+        engine = get_option("io.parquet.engine")
+
+    if engine == "auto":
+        # try engines in this order
+        engine_classes = [PyArrowImpl, FastParquetImpl]
+
+        error_msgs = ""
+        for engine_class in engine_classes:
+            try:
+                return engine_class()
+            except ImportError as err:
+                error_msgs += "\n - " + str(err)
+
+        raise ImportError(
+            "Unable to find a usable engine; "
+            "tried using: 'pyarrow', 'fastparquet'.\n"
+            "A suitable version of "
+            "pyarrow or fastparquet is required for parquet "
+            "support.\n"
+            "Trying to import the above resulted in these errors:"
+            f"{error_msgs}"
+        )
+
+    if engine == "pyarrow":
+        return PyArrowImpl()
+    elif engine == "fastparquet":
+        return FastParquetImpl()
+
+    raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
+
+
+def _get_path_or_handle(
+    path: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
+    fs: Any,
+    storage_options: StorageOptions | None = None,
+    mode: str = "rb",
+    is_dir: bool = False,
+) -> tuple[
+    FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any
+]:
+    """File handling for PyArrow."""
+    path_or_handle = stringify_path(path)
+    if fs is not None:
+        pa_fs = import_optional_dependency("pyarrow.fs", errors="ignore")
+        fsspec = import_optional_dependency("fsspec", errors="ignore")
+        if pa_fs is not None and isinstance(fs, pa_fs.FileSystem):
+            if storage_options:
+                raise NotImplementedError(
+                    "storage_options not supported with a pyarrow FileSystem."
+                )
+        elif fsspec is not None and isinstance(fs, fsspec.spec.AbstractFileSystem):
+            pass
+        else:
+            raise ValueError(
+                f"filesystem must be a pyarrow or fsspec FileSystem, "
+                f"not a {type(fs).__name__}"
+            )
+    if is_fsspec_url(path_or_handle) and fs is None:
+        if storage_options is None:
+            pa = import_optional_dependency("pyarrow")
+            pa_fs = import_optional_dependency("pyarrow.fs")
+
+            try:
+                fs, path_or_handle = pa_fs.FileSystem.from_uri(path)
+            except (TypeError, pa.ArrowInvalid):
+                pass
+        if fs is None:
+            fsspec = import_optional_dependency("fsspec")
+            fs, path_or_handle = fsspec.core.url_to_fs(
+                path_or_handle, **(storage_options or {})
+            )
+    elif storage_options and (not is_url(path_or_handle) or mode != "rb"):
+        # can't write to a remote url
+        # without making use of fsspec at the moment
+        raise ValueError("storage_options passed with buffer, or non-supported URL")
+
+    handles = None
+    if (
+        not fs
+        and not is_dir
+        and isinstance(path_or_handle, str)
+        and not os.path.isdir(path_or_handle)
+    ):
+        # use get_handle only when we are very certain that it is not a directory
+        # fsspec resources can also point to directories
+        # this branch is used for example when reading from non-fsspec URLs
+        handles = get_handle(
+            path_or_handle, mode, is_text=False, storage_options=storage_options
+        )
+        fs = None
+        path_or_handle = handles.handle
+    return path_or_handle, handles, fs
+
+
+class BaseImpl:
+    @staticmethod
+    def validate_dataframe(df: DataFrame) -> None:
+        if not isinstance(df, DataFrame):
+            raise ValueError("to_parquet only supports IO with DataFrames")
+
+    def write(self, df: DataFrame, path, compression, **kwargs):
+        raise AbstractMethodError(self)
+
+    def read(self, path, columns=None, **kwargs) -> DataFrame:
+        raise AbstractMethodError(self)
+
+
+class PyArrowImpl(BaseImpl):
+    def __init__(self) -> None:
+        import_optional_dependency(
+            "pyarrow", extra="pyarrow is required for parquet support."
+        )
+        import pyarrow.parquet
+
+        # import utils to register the pyarrow extension types
+        import pandas.core.arrays.arrow.extension_types  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+        self.api = pyarrow
+
+    def write(
+        self,
+        df: DataFrame,
+        path: FilePath | WriteBuffer[bytes],
+        compression: str | None = "snappy",
+        index: bool | None = None,
+        storage_options: StorageOptions | None = None,
+        partition_cols: list[str] | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> None:
+        self.validate_dataframe(df)
+
+        from_pandas_kwargs: dict[str, Any] = {"schema": kwargs.pop("schema", None)}
+        if index is not None:
+            from_pandas_kwargs["preserve_index"] = index
+
+        table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
+
+        if df.attrs:
+            df_metadata = {"PANDAS_ATTRS": json.dumps(df.attrs)}
+            existing_metadata = table.schema.metadata
+            merged_metadata = {**existing_metadata, **df_metadata}
+            table = table.replace_schema_metadata(merged_metadata)
+
+        path_or_handle, handles, filesystem = _get_path_or_handle(
+            path,
+            filesystem,
+            storage_options=storage_options,
+            mode="wb",
+            is_dir=partition_cols is not None,
+        )
+        if (
+            isinstance(path_or_handle, io.BufferedWriter)
+            and hasattr(path_or_handle, "name")
+            and isinstance(path_or_handle.name, (str, bytes))
+        ):
+            if isinstance(path_or_handle.name, bytes):
+                path_or_handle = path_or_handle.name.decode()
+            else:
+                path_or_handle = path_or_handle.name
+
+        try:
+            if partition_cols is not None:
+                # writes to multiple files under the given path
+                self.api.parquet.write_to_dataset(
+                    table,
+                    path_or_handle,
+                    compression=compression,
+                    partition_cols=partition_cols,
+                    filesystem=filesystem,
+                    **kwargs,
+                )
+            else:
+                # write to single output file
+                self.api.parquet.write_table(
+                    table,
+                    path_or_handle,
+                    compression=compression,
+                    filesystem=filesystem,
+                    **kwargs,
+                )
+        finally:
+            if handles is not None:
+                handles.close()
+
+    def read(
+        self,
+        path,
+        columns=None,
+        filters=None,
+        use_nullable_dtypes: bool = False,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        storage_options: StorageOptions | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> DataFrame:
+        kwargs["use_pandas_metadata"] = True
+
+        to_pandas_kwargs = {}
+
+        manager = _get_option("mode.data_manager", silent=True)
+        if manager == "array":
+            to_pandas_kwargs["split_blocks"] = True
+        path_or_handle, handles, filesystem = _get_path_or_handle(
+            path,
+            filesystem,
+            storage_options=storage_options,
+            mode="rb",
+        )
+        try:
+            pa_table = self.api.parquet.read_table(
+                path_or_handle,
+                columns=columns,
+                filesystem=filesystem,
+                filters=filters,
+                **kwargs,
+            )
+
+            with catch_warnings():
+                filterwarnings(
+                    "ignore",
+                    "make_block is deprecated",
+                    DeprecationWarning,
+                )
+                result = arrow_table_to_pandas(
+                    pa_table,
+                    dtype_backend=dtype_backend,
+                    to_pandas_kwargs=to_pandas_kwargs,
+                )
+
+            if manager == "array":
+                result = result._as_manager("array", copy=False)
+
+            if pa_table.schema.metadata:
+                if b"PANDAS_ATTRS" in pa_table.schema.metadata:
+                    df_metadata = pa_table.schema.metadata[b"PANDAS_ATTRS"]
+                    result.attrs = json.loads(df_metadata)
+            return result
+        finally:
+            if handles is not None:
+                handles.close()
+
+
+class FastParquetImpl(BaseImpl):
+    def __init__(self) -> None:
+        # since pandas is a dependency of fastparquet
+        # we need to import on first use
+        fastparquet = import_optional_dependency(
+            "fastparquet", extra="fastparquet is required for parquet support."
+        )
+        self.api = fastparquet
+
+    def write(
+        self,
+        df: DataFrame,
+        path,
+        compression: Literal["snappy", "gzip", "brotli"] | None = "snappy",
+        index=None,
+        partition_cols=None,
+        storage_options: StorageOptions | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> None:
+        self.validate_dataframe(df)
+
+        if "partition_on" in kwargs and partition_cols is not None:
+            raise ValueError(
+                "Cannot use both partition_on and "
+                "partition_cols. Use partition_cols for partitioning data"
+            )
+        if "partition_on" in kwargs:
+            partition_cols = kwargs.pop("partition_on")
+
+        if partition_cols is not None:
+            kwargs["file_scheme"] = "hive"
+
+        if filesystem is not None:
+            raise NotImplementedError(
+                "filesystem is not implemented for the fastparquet engine."
+            )
+
+        # cannot use get_handle as write() does not accept file buffers
+        path = stringify_path(path)
+        if is_fsspec_url(path):
+            fsspec = import_optional_dependency("fsspec")
+
+            # if filesystem is provided by fsspec, file must be opened in 'wb' mode.
+            kwargs["open_with"] = lambda path, _: fsspec.open(
+                path, "wb", **(storage_options or {})
+            ).open()
+        elif storage_options:
+            raise ValueError(
+                "storage_options passed with file object or non-fsspec file path"
+            )
+
+        with catch_warnings(record=True):
+            self.api.write(
+                path,
+                df,
+                compression=compression,
+                write_index=index,
+                partition_on=partition_cols,
+                **kwargs,
+            )
+
+    def read(
+        self,
+        path,
+        columns=None,
+        filters=None,
+        storage_options: StorageOptions | None = None,
+        filesystem=None,
+        **kwargs,
+    ) -> DataFrame:
+        parquet_kwargs: dict[str, Any] = {}
+        use_nullable_dtypes = kwargs.pop("use_nullable_dtypes", False)
+        dtype_backend = kwargs.pop("dtype_backend", lib.no_default)
+        # We are disabling nullable dtypes for fastparquet pending discussion
+        parquet_kwargs["pandas_nulls"] = False
+        if use_nullable_dtypes:
+            raise ValueError(
+                "The 'use_nullable_dtypes' argument is not supported for the "
+                "fastparquet engine"
+            )
+        if dtype_backend is not lib.no_default:
+            raise ValueError(
+                "The 'dtype_backend' argument is not supported for the "
+                "fastparquet engine"
+            )
+        if filesystem is not None:
+            raise NotImplementedError(
+                "filesystem is not implemented for the fastparquet engine."
+            )
+        path = stringify_path(path)
+        handles = None
+        if is_fsspec_url(path):
+            fsspec = import_optional_dependency("fsspec")
+
+            parquet_kwargs["fs"] = fsspec.open(path, "rb", **(storage_options or {})).fs
+        elif isinstance(path, str) and not os.path.isdir(path):
+            # use get_handle only when we are very certain that it is not a directory
+            # fsspec resources can also point to directories
+            # this branch is used for example when reading from non-fsspec URLs
+            handles = get_handle(
+                path, "rb", is_text=False, storage_options=storage_options
+            )
+            path = handles.handle
+
+        try:
+            parquet_file = self.api.ParquetFile(path, **parquet_kwargs)
+            return parquet_file.to_pandas(columns=columns, filters=filters, **kwargs)
+        finally:
+            if handles is not None:
+                handles.close()
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def to_parquet(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes] | None = None,
+    engine: str = "auto",
+    compression: str | None = "snappy",
+    index: bool | None = None,
+    storage_options: StorageOptions | None = None,
+    partition_cols: list[str] | None = None,
+    filesystem: Any = None,
+    **kwargs,
+) -> bytes | None:
+    """
+    Write a DataFrame to the parquet format.
+
+    Parameters
+    ----------
+    df : DataFrame
+    path : str, path object, file-like object, or None, default None
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``write()`` function. If None, the result is
+        returned as bytes. If a string, it will be used as Root Directory path
+        when writing a partitioned dataset. The engine fastparquet does not
+        accept file-like objects.
+    engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
+        Parquet library to use. If 'auto', then the option
+        ``io.parquet.engine`` is used. The default ``io.parquet.engine``
+        behavior is to try 'pyarrow', falling back to 'fastparquet' if
+        'pyarrow' is unavailable.
+
+        When using the ``'pyarrow'`` engine and no storage options are provided
+        and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
+        (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
+        Use the filesystem keyword with an instantiated fsspec filesystem
+        if you wish to use its implementation.
+    compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}},
+        default 'snappy'. Name of the compression to use. Use ``None``
+        for no compression.
+    index : bool, default None
+        If ``True``, include the dataframe's index(es) in the file output. If
+        ``False``, they will not be written to the file.
+        If ``None``, similar to ``True`` the dataframe's index(es)
+        will be saved. However, instead of being saved as values,
+        the RangeIndex will be stored as a range in the metadata so it
+        doesn't require much space and is faster. Other indexes will
+        be included as columns in the file output.
+    partition_cols : str or list, optional, default None
+        Column names by which to partition the dataset.
+        Columns are partitioned in the order they are given.
+        Must be None if path is not a string.
+    {storage_options}
+
+    filesystem : fsspec or pyarrow filesystem, default None
+        Filesystem object to use when reading the parquet file. Only implemented
+        for ``engine="pyarrow"``.
+
+        .. versionadded:: 2.1.0
+
+    kwargs
+        Additional keyword arguments passed to the engine
+
+    Returns
+    -------
+    bytes if no path argument is provided else None
+    """
+    if isinstance(partition_cols, str):
+        partition_cols = [partition_cols]
+    impl = get_engine(engine)
+
+    path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
+
+    impl.write(
+        df,
+        path_or_buf,
+        compression=compression,
+        index=index,
+        partition_cols=partition_cols,
+        storage_options=storage_options,
+        filesystem=filesystem,
+        **kwargs,
+    )
+
+    if path is None:
+        assert isinstance(path_or_buf, io.BytesIO)
+        return path_or_buf.getvalue()
+    else:
+        return None
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_parquet(
+    path: FilePath | ReadBuffer[bytes],
+    engine: str = "auto",
+    columns: list[str] | None = None,
+    storage_options: StorageOptions | None = None,
+    use_nullable_dtypes: bool | lib.NoDefault = lib.no_default,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    filesystem: Any = None,
+    filters: list[tuple] | list[list[tuple]] | None = None,
+    **kwargs,
+) -> DataFrame:
+    """
+    Load a parquet object from the file path, returning a DataFrame.
+
+    Parameters
+    ----------
+    path : str, path object or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function.
+        The string could be a URL. Valid URL schemes include http, ftp, s3,
+        gs, and file. For file URLs, a host is expected. A local file could be:
+        ``file://localhost/path/to/table.parquet``.
+        A file URL can also be a path to a directory that contains multiple
+        partitioned parquet files. Both pyarrow and fastparquet support
+        paths to directories as well as file URLs. A directory path could be:
+        ``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``.
+    engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
+        Parquet library to use. If 'auto', then the option
+        ``io.parquet.engine`` is used. The default ``io.parquet.engine``
+        behavior is to try 'pyarrow', falling back to 'fastparquet' if
+        'pyarrow' is unavailable.
+
+        When using the ``'pyarrow'`` engine and no storage options are provided
+        and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
+        (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
+        Use the filesystem keyword with an instantiated fsspec filesystem
+        if you wish to use its implementation.
+    columns : list, default=None
+        If not None, only these columns will be read from the file.
+    {storage_options}
+
+        .. versionadded:: 1.3.0
+
+    use_nullable_dtypes : bool, default False
+        If True, use dtypes that use ``pd.NA`` as missing value indicator
+        for the resulting DataFrame. (only applicable for the ``pyarrow``
+        engine)
+        As new dtypes are added that support ``pd.NA`` in the future, the
+        output with this option will change to use those dtypes.
+        Note: this is an experimental option, and behaviour (e.g. additional
+        support dtypes) may change without notice.
+
+        .. deprecated:: 2.0
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    filesystem : fsspec or pyarrow filesystem, default None
+        Filesystem object to use when reading the parquet file. Only implemented
+        for ``engine="pyarrow"``.
+
+        .. versionadded:: 2.1.0
+
+    filters : List[Tuple] or List[List[Tuple]], default None
+        To filter out data.
+        Filter syntax: [[(column, op, val), ...],...]
+        where op is [==, =, >, >=, <, <=, !=, in, not in]
+        The innermost tuples are transposed into a set of filters applied
+        through an `AND` operation.
+        The outer list combines these sets of filters through an `OR`
+        operation.
+        A single list of tuples can also be used, meaning that no `OR`
+        operation between set of filters is to be conducted.
+
+        Using this argument will NOT result in row-wise filtering of the final
+        partitions unless ``engine="pyarrow"`` is also specified.  For
+        other engines, filtering is only performed at the partition level, that is,
+        to prevent the loading of some row-groups and/or files.
+
+        .. versionadded:: 2.1.0
+
+    **kwargs
+        Any additional kwargs are passed to the engine.
+
+    Returns
+    -------
+    DataFrame
+
+    See Also
+    --------
+    DataFrame.to_parquet : Create a parquet object that serializes a DataFrame.
+
+    Examples
+    --------
+    >>> original_df = pd.DataFrame(
+    ...     {{"foo": range(5), "bar": range(5, 10)}}
+    ...    )
+    >>> original_df
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> df_parquet_bytes = original_df.to_parquet()
+    >>> from io import BytesIO
+    >>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes))
+    >>> restored_df
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> restored_df.equals(original_df)
+    True
+    >>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"])
+    >>> restored_bar
+        bar
+    0    5
+    1    6
+    2    7
+    3    8
+    4    9
+    >>> restored_bar.equals(original_df[['bar']])
+    True
+
+    The function uses `kwargs` that are passed directly to the engine.
+    In the following example, we use the `filters` argument of the pyarrow
+    engine to filter the rows of the DataFrame.
+
+    Since `pyarrow` is the default engine, we can omit the `engine` argument.
+    Note that the `filters` argument is implemented by the `pyarrow` engine,
+    which can benefit from multithreading and also potentially be more
+    economical in terms of memory.
+
+    >>> sel = [("foo", ">", 2)]
+    >>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel)
+    >>> restored_part
+        foo  bar
+    0    3    8
+    1    4    9
+    """
+
+    impl = get_engine(engine)
+
+    if use_nullable_dtypes is not lib.no_default:
+        msg = (
+            "The argument 'use_nullable_dtypes' is deprecated and will be removed "
+            "in a future version."
+        )
+        if use_nullable_dtypes is True:
+            msg += (
+                "Use dtype_backend='numpy_nullable' instead of use_nullable_dtype=True."
+            )
+        warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
+    else:
+        use_nullable_dtypes = False
+    check_dtype_backend(dtype_backend)
+
+    return impl.read(
+        path,
+        columns=columns,
+        filters=filters,
+        storage_options=storage_options,
+        use_nullable_dtypes=use_nullable_dtypes,
+        dtype_backend=dtype_backend,
+        filesystem=filesystem,
+        **kwargs,
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/pickle.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dae0e7106b69a471f0c2702158cfe0f11f0389c
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/pickle.py
@@ -0,0 +1,210 @@
+""" pickle compat """
+from __future__ import annotations
+
+import pickle
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+from pandas.compat import pickle_compat as pc
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadPickleBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "filepath_or_buffer",
+)
+def to_pickle(
+    obj: Any,
+    filepath_or_buffer: FilePath | WriteBuffer[bytes],
+    compression: CompressionOptions = "infer",
+    protocol: int = pickle.HIGHEST_PROTOCOL,
+    storage_options: StorageOptions | None = None,
+) -> None:
+    """
+    Pickle (serialize) object to file.
+
+    Parameters
+    ----------
+    obj : any object
+        Any python object.
+    filepath_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``write()`` function.
+        Also accepts URL. URL has to be of S3 or GCS.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    protocol : int
+        Int which indicates which protocol should be used by the pickler,
+        default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
+        values for this parameter depend on the version of Python. For Python
+        2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
+        For Python >= 3.4, 4 is a valid value. A negative value for the
+        protocol parameter is equivalent to setting its value to
+        HIGHEST_PROTOCOL.
+
+    {storage_options}
+
+        .. [1] https://docs.python.org/3/library/pickle.html
+
+    See Also
+    --------
+    read_pickle : Load pickled pandas object (or any object) from file.
+    DataFrame.to_hdf : Write DataFrame to an HDF5 file.
+    DataFrame.to_sql : Write DataFrame to a SQL database.
+    DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+
+    Examples
+    --------
+    >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})  # doctest: +SKIP
+    >>> original_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> pd.to_pickle(original_df, "./dummy.pkl")  # doctest: +SKIP
+
+    >>> unpickled_df = pd.read_pickle("./dummy.pkl")  # doctest: +SKIP
+    >>> unpickled_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    """  # noqa: E501
+    if protocol < 0:
+        protocol = pickle.HIGHEST_PROTOCOL
+
+    with get_handle(
+        filepath_or_buffer,
+        "wb",
+        compression=compression,
+        is_text=False,
+        storage_options=storage_options,
+    ) as handles:
+        # letting pickle write directly to the buffer is more memory-efficient
+        pickle.dump(obj, handles.handle, protocol=protocol)
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "filepath_or_buffer",
+)
+def read_pickle(
+    filepath_or_buffer: FilePath | ReadPickleBuffer,
+    compression: CompressionOptions = "infer",
+    storage_options: StorageOptions | None = None,
+) -> DataFrame | Series:
+    """
+    Load pickled pandas object (or any object) from file.
+
+    .. warning::
+
+       Loading pickled data received from untrusted sources can be
+       unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``readlines()`` function.
+        Also accepts URL. URL is not limited to S3 and GCS.
+
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    Returns
+    -------
+    same type as object stored in file
+
+    See Also
+    --------
+    DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
+    Series.to_pickle : Pickle (serialize) Series object to file.
+    read_hdf : Read HDF5 file into a DataFrame.
+    read_sql : Read SQL query or database table into a DataFrame.
+    read_parquet : Load a parquet object, returning a DataFrame.
+
+    Notes
+    -----
+    read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3
+    provided the object was serialized with to_pickle.
+
+    Examples
+    --------
+    >>> original_df = pd.DataFrame(
+    ...     {{"foo": range(5), "bar": range(5, 10)}}
+    ...    )  # doctest: +SKIP
+    >>> original_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    >>> pd.to_pickle(original_df, "./dummy.pkl")  # doctest: +SKIP
+
+    >>> unpickled_df = pd.read_pickle("./dummy.pkl")  # doctest: +SKIP
+    >>> unpickled_df  # doctest: +SKIP
+       foo  bar
+    0    0    5
+    1    1    6
+    2    2    7
+    3    3    8
+    4    4    9
+    """
+    excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError, TypeError)
+    with get_handle(
+        filepath_or_buffer,
+        "rb",
+        compression=compression,
+        is_text=False,
+        storage_options=storage_options,
+    ) as handles:
+        # 1) try standard library Pickle
+        # 2) try pickle_compat (older pandas version) to handle subclass changes
+        # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError
+
+        try:
+            # TypeError for Cython complaints about object.__new__ vs Tick.__new__
+            try:
+                with warnings.catch_warnings(record=True):
+                    # We want to silence any warnings about, e.g. moved modules.
+                    warnings.simplefilter("ignore", Warning)
+                    return pickle.load(handles.handle)
+            except excs_to_catch:
+                # e.g.
+                #  "No module named 'pandas.core.sparse.series'"
+                #  "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
+                return pc.load(handles.handle, encoding=None)
+        except UnicodeDecodeError:
+            # e.g. can occur for files written in py27; see GH#28645 and GH#31988
+            return pc.load(handles.handle, encoding="latin-1")
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/pytables.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/pytables.py
new file mode 100644
index 0000000000000000000000000000000000000000..c520b8d606ad755049464e6e5ffcaa11961e3577
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/pytables.py
@@ -0,0 +1,5532 @@
+"""
+High level interface to PyTables for reading and writing pandas data structures
+to disk
+"""
+from __future__ import annotations
+
+from contextlib import suppress
+import copy
+from datetime import (
+    date,
+    tzinfo,
+)
+import itertools
+import os
+import re
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Final,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import (
+    config,
+    get_option,
+    using_copy_on_write,
+    using_string_dtype,
+)
+
+from pandas._libs import (
+    lib,
+    writers as libwriters,
+)
+from pandas._libs.lib import is_string_array
+from pandas._libs.tslibs import timezones
+from pandas.compat import HAS_PYARROW
+from pandas.compat._optional import import_optional_dependency
+from pandas.compat.pickle_compat import patch_pickle
+from pandas.errors import (
+    AttributeConflictWarning,
+    ClosedFileError,
+    IncompatibilityWarning,
+    PerformanceWarning,
+    PossibleDataLossError,
+)
+from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_bool_dtype,
+    is_complex_dtype,
+    is_list_like,
+    is_string_dtype,
+    needs_i8_conversion,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.missing import array_equivalent
+
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    StringDtype,
+    TimedeltaIndex,
+    concat,
+    isna,
+)
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    PeriodArray,
+)
+from pandas.core.arrays.string_ import BaseStringArray
+import pandas.core.common as com
+from pandas.core.computation.pytables import (
+    PyTablesExpr,
+    maybe_expression,
+)
+from pandas.core.construction import (
+    array as pd_array,
+    extract_array,
+)
+from pandas.core.indexes.api import ensure_index
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+)
+
+from pandas.io.common import stringify_path
+from pandas.io.formats.printing import (
+    adjoin,
+    pprint_thing,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterator,
+        Sequence,
+    )
+    from types import TracebackType
+
+    from tables import (
+        Col,
+        File,
+        Node,
+    )
+
+    from pandas._typing import (
+        AnyArrayLike,
+        ArrayLike,
+        AxisInt,
+        DtypeArg,
+        FilePath,
+        Self,
+        Shape,
+        npt,
+    )
+
+    from pandas.core.internals import Block
+
+# versioning attribute
+_version = "0.15.2"
+
+# encoding
+_default_encoding = "UTF-8"
+
+
+def _ensure_decoded(s):
+    """if we have bytes, decode them to unicode"""
+    if isinstance(s, np.bytes_):
+        s = s.decode("UTF-8")
+    return s
+
+
+def _ensure_encoding(encoding: str | None) -> str:
+    # set the encoding if we need
+    if encoding is None:
+        encoding = _default_encoding
+
+    return encoding
+
+
+def _ensure_str(name):
+    """
+    Ensure that an index / column name is a str (python 3); otherwise they
+    may be np.string dtype. Non-string dtypes are passed through unchanged.
+
+    https://github.com/pandas-dev/pandas/issues/13492
+    """
+    if isinstance(name, str):
+        name = str(name)
+    return name
+
+
+Term = PyTablesExpr
+
+
+def _ensure_term(where, scope_level: int):
+    """
+    Ensure that the where is a Term or a list of Term.
+
+    This makes sure that we are capturing the scope of variables that are
+    passed create the terms here with a frame_level=2 (we are 2 levels down)
+    """
+    # only consider list/tuple here as an ndarray is automatically a coordinate
+    # list
+    level = scope_level + 1
+    if isinstance(where, (list, tuple)):
+        where = [
+            Term(term, scope_level=level + 1) if maybe_expression(term) else term
+            for term in where
+            if term is not None
+        ]
+    elif maybe_expression(where):
+        where = Term(where, scope_level=level)
+    return where if where is None or len(where) else None
+
+
+incompatibility_doc: Final = """
+where criteria is being ignored as this version [%s] is too old (or
+not-defined), read the file in and write it out to a new file to upgrade (with
+the copy_to method)
+"""
+
+attribute_conflict_doc: Final = """
+the [%s] attribute of the existing index is [%s] which conflicts with the new
+[%s], resetting the attribute to None
+"""
+
+performance_doc: Final = """
+your performance may suffer as PyTables will pickle object types that it cannot
+map directly to c-types [inferred_type->%s,key->%s] [items->%s]
+"""
+
+# formats
+_FORMAT_MAP = {"f": "fixed", "fixed": "fixed", "t": "table", "table": "table"}
+
+# axes map
+_AXES_MAP = {DataFrame: [0]}
+
+# register our configuration options
+dropna_doc: Final = """
+: boolean
+    drop ALL nan rows when appending to a table
+"""
+format_doc: Final = """
+: format
+    default format writing format, if None, then
+    put will default to 'fixed' and append will default to 'table'
+"""
+
+with config.config_prefix("io.hdf"):
+    config.register_option("dropna_table", False, dropna_doc, validator=config.is_bool)
+    config.register_option(
+        "default_format",
+        None,
+        format_doc,
+        validator=config.is_one_of_factory(["fixed", "table", None]),
+    )
+
+# oh the troubles to reduce import time
+_table_mod = None
+_table_file_open_policy_is_strict = False
+
+
+def _tables():
+    global _table_mod
+    global _table_file_open_policy_is_strict
+    if _table_mod is None:
+        import tables
+
+        _table_mod = tables
+
+        # set the file open policy
+        # return the file open policy; this changes as of pytables 3.1
+        # depending on the HDF5 version
+        with suppress(AttributeError):
+            _table_file_open_policy_is_strict = (
+                tables.file._FILE_OPEN_POLICY == "strict"
+            )
+
+    return _table_mod
+
+
+# interface to/from ###
+
+
+def to_hdf(
+    path_or_buf: FilePath | HDFStore,
+    key: str,
+    value: DataFrame | Series,
+    mode: str = "a",
+    complevel: int | None = None,
+    complib: str | None = None,
+    append: bool = False,
+    format: str | None = None,
+    index: bool = True,
+    min_itemsize: int | dict[str, int] | None = None,
+    nan_rep=None,
+    dropna: bool | None = None,
+    data_columns: Literal[True] | list[str] | None = None,
+    errors: str = "strict",
+    encoding: str = "UTF-8",
+) -> None:
+    """store this object, close it if we opened it"""
+    if append:
+        f = lambda store: store.append(
+            key,
+            value,
+            format=format,
+            index=index,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            dropna=dropna,
+            data_columns=data_columns,
+            errors=errors,
+            encoding=encoding,
+        )
+    else:
+        # NB: dropna is not passed to `put`
+        f = lambda store: store.put(
+            key,
+            value,
+            format=format,
+            index=index,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+            errors=errors,
+            encoding=encoding,
+            dropna=dropna,
+        )
+
+    path_or_buf = stringify_path(path_or_buf)
+    if isinstance(path_or_buf, str):
+        with HDFStore(
+            path_or_buf, mode=mode, complevel=complevel, complib=complib
+        ) as store:
+            f(store)
+    else:
+        f(path_or_buf)
+
+
+def read_hdf(
+    path_or_buf: FilePath | HDFStore,
+    key=None,
+    mode: str = "r",
+    errors: str = "strict",
+    where: str | list | None = None,
+    start: int | None = None,
+    stop: int | None = None,
+    columns: list[str] | None = None,
+    iterator: bool = False,
+    chunksize: int | None = None,
+    **kwargs,
+):
+    """
+    Read from the store, close it if we opened it.
+
+    Retrieve pandas object stored in file, optionally based on where
+    criteria.
+
+    .. warning::
+
+       Pandas uses PyTables for reading and writing HDF5 files, which allows
+       serializing object-dtype data with pickle when using the "fixed" format.
+       Loading pickled data received from untrusted sources can be unsafe.
+
+       See: https://docs.python.org/3/library/pickle.html for more.
+
+    Parameters
+    ----------
+    path_or_buf : str, path object, pandas.HDFStore
+        Any valid string path is acceptable. Only supports the local file system,
+        remote URLs and file-like objects are not supported.
+
+        If you want to pass in a path object, pandas accepts any
+        ``os.PathLike``.
+
+        Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
+
+    key : object, optional
+        The group identifier in the store. Can be omitted if the HDF file
+        contains a single pandas object.
+    mode : {'r', 'r+', 'a'}, default 'r'
+        Mode to use when opening the file. Ignored if path_or_buf is a
+        :class:`pandas.HDFStore`. Default is 'r'.
+    errors : str, default 'strict'
+        Specifies how encoding and decoding errors are to be handled.
+        See the errors argument for :func:`open` for a full list
+        of options.
+    where : list, optional
+        A list of Term (or convertible) objects.
+    start : int, optional
+        Row number to start selection.
+    stop  : int, optional
+        Row number to stop selection.
+    columns : list, optional
+        A list of columns names to return.
+    iterator : bool, optional
+        Return an iterator object.
+    chunksize : int, optional
+        Number of rows to include in an iteration when using an iterator.
+    **kwargs
+        Additional keyword arguments passed to HDFStore.
+
+    Returns
+    -------
+    object
+        The selected object. Return type depends on the object stored.
+
+    See Also
+    --------
+    DataFrame.to_hdf : Write a HDF file from a DataFrame.
+    HDFStore : Low-level access to HDF files.
+
+    Notes
+    -----
+    When ``errors="surrogatepass"``, ``pd.options.future.infer_string`` is true,
+    and PyArrow is installed, if a UTF-16 surrogate is encountered when decoding
+    to UTF-8, the resulting dtype will be
+    ``pd.StringDtype(storage="python", na_value=np.nan)``.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])  # doctest: +SKIP
+    >>> df.to_hdf('./store.h5', 'data')  # doctest: +SKIP
+    >>> reread = pd.read_hdf('./store.h5')  # doctest: +SKIP
+    """
+    if mode not in ["r", "r+", "a"]:
+        raise ValueError(
+            f"mode {mode} is not allowed while performing a read. "
+            f"Allowed modes are r, r+ and a."
+        )
+    # grab the scope
+    if where is not None:
+        where = _ensure_term(where, scope_level=1)
+
+    if isinstance(path_or_buf, HDFStore):
+        if not path_or_buf.is_open:
+            raise OSError("The HDFStore must be open for reading.")
+
+        store = path_or_buf
+        auto_close = False
+    else:
+        path_or_buf = stringify_path(path_or_buf)
+        if not isinstance(path_or_buf, str):
+            raise NotImplementedError(
+                "Support for generic buffers has not been implemented."
+            )
+        try:
+            exists = os.path.exists(path_or_buf)
+
+        # if filepath is too long
+        except (TypeError, ValueError):
+            exists = False
+
+        if not exists:
+            raise FileNotFoundError(f"File {path_or_buf} does not exist")
+
+        store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
+        # can't auto open/close if we are using an iterator
+        # so delegate to the iterator
+        auto_close = True
+
+    try:
+        if key is None:
+            groups = store.groups()
+            if len(groups) == 0:
+                raise ValueError(
+                    "Dataset(s) incompatible with Pandas data types, "
+                    "not table, or no datasets found in HDF5 file."
+                )
+            candidate_only_group = groups[0]
+
+            # For the HDF file to have only one dataset, all other groups
+            # should then be metadata groups for that candidate group. (This
+            # assumes that the groups() method enumerates parent groups
+            # before their children.)
+            for group_to_check in groups[1:]:
+                if not _is_metadata_of(group_to_check, candidate_only_group):
+                    raise ValueError(
+                        "key must be provided when HDF5 "
+                        "file contains multiple datasets."
+                    )
+            key = candidate_only_group._v_pathname
+        return store.select(
+            key,
+            where=where,
+            start=start,
+            stop=stop,
+            columns=columns,
+            iterator=iterator,
+            chunksize=chunksize,
+            auto_close=auto_close,
+        )
+    except (ValueError, TypeError, LookupError):
+        if not isinstance(path_or_buf, HDFStore):
+            # if there is an error, close the store if we opened it.
+            with suppress(AttributeError):
+                store.close()
+
+        raise
+
+
+def _is_metadata_of(group: Node, parent_group: Node) -> bool:
+    """Check if a given group is a metadata group for a given parent_group."""
+    if group._v_depth <= parent_group._v_depth:
+        return False
+
+    current = group
+    while current._v_depth > 1:
+        parent = current._v_parent
+        if parent == parent_group and current._v_name == "meta":
+            return True
+        current = current._v_parent
+    return False
+
+
+class HDFStore:
+    """
+    Dict-like IO interface for storing pandas objects in PyTables.
+
+    Either Fixed or Table format.
+
+    .. warning::
+
+       Pandas uses PyTables for reading and writing HDF5 files, which allows
+       serializing object-dtype data with pickle when using the "fixed" format.
+       Loading pickled data received from untrusted sources can be unsafe.
+
+       See: https://docs.python.org/3/library/pickle.html for more.
+
+    Parameters
+    ----------
+    path : str
+        File path to HDF5 file.
+    mode : {'a', 'w', 'r', 'r+'}, default 'a'
+
+        ``'r'``
+            Read-only; no data can be modified.
+        ``'w'``
+            Write; a new file is created (an existing file with the same
+            name would be deleted).
+        ``'a'``
+            Append; an existing file is opened for reading and writing,
+            and if the file does not exist it is created.
+        ``'r+'``
+            It is similar to ``'a'``, but the file must already exist.
+    complevel : int, 0-9, default None
+        Specifies a compression level for data.
+        A value of 0 or None disables compression.
+    complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
+        Specifies the compression library to be used.
+        These additional compressors for Blosc are supported
+        (default if no compressor specified: 'blosc:blosclz'):
+        {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
+         'blosc:zlib', 'blosc:zstd'}.
+        Specifying a compression library which is not available issues
+        a ValueError.
+    fletcher32 : bool, default False
+        If applying compression use the fletcher32 checksum.
+    **kwargs
+        These parameters will be passed to the PyTables open_file method.
+
+    Examples
+    --------
+    >>> bar = pd.DataFrame(np.random.randn(10, 4))
+    >>> store = pd.HDFStore('test.h5')
+    >>> store['foo'] = bar   # write to HDF5
+    >>> bar = store['foo']   # retrieve
+    >>> store.close()
+
+    **Create or load HDF5 file in-memory**
+
+    When passing the `driver` option to the PyTables open_file method through
+    **kwargs, the HDF5 file is loaded or created in-memory and will only be
+    written when closed:
+
+    >>> bar = pd.DataFrame(np.random.randn(10, 4))
+    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
+    >>> store['foo'] = bar
+    >>> store.close()   # only now, data is written to disk
+    """
+
+    _handle: File | None
+    _mode: str
+
+    def __init__(
+        self,
+        path,
+        mode: str = "a",
+        complevel: int | None = None,
+        complib=None,
+        fletcher32: bool = False,
+        **kwargs,
+    ) -> None:
+        if "format" in kwargs:
+            raise ValueError("format is not a defined argument for HDFStore")
+
+        tables = import_optional_dependency("tables")
+
+        if complib is not None and complib not in tables.filters.all_complibs:
+            raise ValueError(
+                f"complib only supports {tables.filters.all_complibs} compression."
+            )
+
+        if complib is None and complevel is not None:
+            complib = tables.filters.default_complib
+
+        self._path = stringify_path(path)
+        if mode is None:
+            mode = "a"
+        self._mode = mode
+        self._handle = None
+        self._complevel = complevel if complevel else 0
+        self._complib = complib
+        self._fletcher32 = fletcher32
+        self._filters = None
+        self.open(mode=mode, **kwargs)
+
+    def __fspath__(self) -> str:
+        return self._path
+
+    @property
+    def root(self):
+        """return the root node"""
+        self._check_if_open()
+        assert self._handle is not None  # for mypy
+        return self._handle.root
+
+    @property
+    def filename(self) -> str:
+        return self._path
+
+    def __getitem__(self, key: str):
+        return self.get(key)
+
+    def __setitem__(self, key: str, value) -> None:
+        self.put(key, value)
+
+    def __delitem__(self, key: str) -> None:
+        return self.remove(key)
+
+    def __getattr__(self, name: str):
+        """allow attribute access to get stores"""
+        try:
+            return self.get(name)
+        except (KeyError, ClosedFileError):
+            pass
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{name}'"
+        )
+
+    def __contains__(self, key: str) -> bool:
+        """
+        check for existence of this key
+        can match the exact pathname or the pathnm w/o the leading '/'
+        """
+        node = self.get_node(key)
+        if node is not None:
+            name = node._v_pathname
+            if key in (name, name[1:]):
+                return True
+        return False
+
+    def __len__(self) -> int:
+        return len(self.groups())
+
+    def __repr__(self) -> str:
+        pstr = pprint_thing(self._path)
+        return f"{type(self)}\nFile path: {pstr}\n"
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def keys(self, include: str = "pandas") -> list[str]:
+        """
+        Return a list of keys corresponding to objects stored in HDFStore.
+
+        Parameters
+        ----------
+
+        include : str, default 'pandas'
+                When kind equals 'pandas' return pandas objects.
+                When kind equals 'native' return native HDF5 Table objects.
+
+        Returns
+        -------
+        list
+            List of ABSOLUTE path-names (e.g. have the leading '/').
+
+        Raises
+        ------
+        raises ValueError if kind has an illegal value
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> store.get('data')  # doctest: +SKIP
+        >>> print(store.keys())  # doctest: +SKIP
+        ['/data1', '/data2']
+        >>> store.close()  # doctest: +SKIP
+        """
+        if include == "pandas":
+            return [n._v_pathname for n in self.groups()]
+
+        elif include == "native":
+            assert self._handle is not None  # mypy
+            return [
+                n._v_pathname for n in self._handle.walk_nodes("/", classname="Table")
+            ]
+        raise ValueError(
+            f"`include` should be either 'pandas' or 'native' but is '{include}'"
+        )
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self.keys())
+
+    def items(self) -> Iterator[tuple[str, list]]:
+        """
+        iterate on key->group
+        """
+        for g in self.groups():
+            yield g._v_pathname, g
+
+    def open(self, mode: str = "a", **kwargs) -> None:
+        """
+        Open the file in the specified mode
+
+        Parameters
+        ----------
+        mode : {'a', 'w', 'r', 'r+'}, default 'a'
+            See HDFStore docstring or tables.open_file for info about modes
+        **kwargs
+            These parameters will be passed to the PyTables open_file method.
+        """
+        tables = _tables()
+
+        if self._mode != mode:
+            # if we are changing a write mode to read, ok
+            if self._mode in ["a", "w"] and mode in ["r", "r+"]:
+                pass
+            elif mode in ["w"]:
+                # this would truncate, raise here
+                if self.is_open:
+                    raise PossibleDataLossError(
+                        f"Re-opening the file [{self._path}] with mode [{self._mode}] "
+                        "will delete the current file!"
+                    )
+
+            self._mode = mode
+
+        # close and reopen the handle
+        if self.is_open:
+            self.close()
+
+        if self._complevel and self._complevel > 0:
+            self._filters = _tables().Filters(
+                self._complevel, self._complib, fletcher32=self._fletcher32
+            )
+
+        if _table_file_open_policy_is_strict and self.is_open:
+            msg = (
+                "Cannot open HDF5 file, which is already opened, "
+                "even in read-only mode."
+            )
+            raise ValueError(msg)
+
+        self._handle = tables.open_file(self._path, self._mode, **kwargs)
+
+    def close(self) -> None:
+        """
+        Close the PyTables file handle
+        """
+        if self._handle is not None:
+            self._handle.close()
+        self._handle = None
+
+    @property
+    def is_open(self) -> bool:
+        """
+        return a boolean indicating whether the file is open
+        """
+        if self._handle is None:
+            return False
+        return bool(self._handle.isopen)
+
+    def flush(self, fsync: bool = False) -> None:
+        """
+        Force all buffered modifications to be written to disk.
+
+        Parameters
+        ----------
+        fsync : bool (default False)
+          call ``os.fsync()`` on the file handle to force writing to disk.
+
+        Notes
+        -----
+        Without ``fsync=True``, flushing may not guarantee that the OS writes
+        to disk. With fsync, the operation will block until the OS claims the
+        file has been written; however, other caching layers may still
+        interfere.
+        """
+        if self._handle is not None:
+            self._handle.flush()
+            if fsync:
+                with suppress(OSError):
+                    os.fsync(self._handle.fileno())
+
+    def get(self, key: str):
+        """
+        Retrieve pandas object stored in file.
+
+        Parameters
+        ----------
+        key : str
+
+        Returns
+        -------
+        object
+            Same type as object stored in file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> store.get('data')  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        """
+        with patch_pickle():
+            # GH#31167 Without this patch, pickle doesn't know how to unpickle
+            #  old DateOffset objects now that they are cdef classes.
+            group = self.get_node(key)
+            if group is None:
+                raise KeyError(f"No object named {key} in the file")
+            return self._read_group(group)
+
+    def select(
+        self,
+        key: str,
+        where=None,
+        start=None,
+        stop=None,
+        columns=None,
+        iterator: bool = False,
+        chunksize: int | None = None,
+        auto_close: bool = False,
+    ):
+        """
+        Retrieve pandas object stored in file, optionally based on where criteria.
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+        Parameters
+        ----------
+        key : str
+            Object being retrieved from file.
+        where : list or None
+            List of Term (or convertible) objects, optional.
+        start : int or None
+            Row number to start selection.
+        stop : int, default None
+            Row number to stop selection.
+        columns : list or None
+            A list of columns that if not None, will limit the return columns.
+        iterator : bool or False
+            Returns an iterator.
+        chunksize : int or None
+            Number or rows to include in iteration, return an iterator.
+        auto_close : bool or False
+            Should automatically close the store when finished.
+
+        Returns
+        -------
+        object
+            Retrieved object from file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> store.get('data')  # doctest: +SKIP
+        >>> print(store.keys())  # doctest: +SKIP
+        ['/data1', '/data2']
+        >>> store.select('/data1')  # doctest: +SKIP
+           A  B
+        0  1  2
+        1  3  4
+        >>> store.select('/data1', where='columns == A')  # doctest: +SKIP
+           A
+        0  1
+        1  3
+        >>> store.close()  # doctest: +SKIP
+        """
+        group = self.get_node(key)
+        if group is None:
+            raise KeyError(f"No object named {key} in the file")
+
+        # create the storer and axes
+        where = _ensure_term(where, scope_level=1)
+        s = self._create_storer(group)
+        s.infer_axes()
+
+        # function to call on iteration
+        def func(_start, _stop, _where):
+            return s.read(start=_start, stop=_stop, where=_where, columns=columns)
+
+        # create the iterator
+        it = TableIterator(
+            self,
+            s,
+            func,
+            where=where,
+            nrows=s.nrows,
+            start=start,
+            stop=stop,
+            iterator=iterator,
+            chunksize=chunksize,
+            auto_close=auto_close,
+        )
+
+        return it.get_result()
+
+    def select_as_coordinates(
+        self,
+        key: str,
+        where=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        return the selection as an Index
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+
+        Parameters
+        ----------
+        key : str
+        where : list of Term (or convertible) objects, optional
+        start : integer (defaults to None), row number to start selection
+        stop  : integer (defaults to None), row number to stop selection
+        """
+        where = _ensure_term(where, scope_level=1)
+        tbl = self.get_storer(key)
+        if not isinstance(tbl, Table):
+            raise TypeError("can only read_coordinates with a table")
+        return tbl.read_coordinates(where=where, start=start, stop=stop)
+
+    def select_column(
+        self,
+        key: str,
+        column: str,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        return a single column from the table. This is generally only useful to
+        select an indexable
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+        Parameters
+        ----------
+        key : str
+        column : str
+            The column of interest.
+        start : int or None, default None
+        stop : int or None, default None
+
+        Raises
+        ------
+        raises KeyError if the column is not found (or key is not a valid
+            store)
+        raises ValueError if the column can not be extracted individually (it
+            is part of a data block)
+
+        """
+        tbl = self.get_storer(key)
+        if not isinstance(tbl, Table):
+            raise TypeError("can only read_column with a table")
+        return tbl.read_column(column=column, start=start, stop=stop)
+
+    def select_as_multiple(
+        self,
+        keys,
+        where=None,
+        selector=None,
+        columns=None,
+        start=None,
+        stop=None,
+        iterator: bool = False,
+        chunksize: int | None = None,
+        auto_close: bool = False,
+    ):
+        """
+        Retrieve pandas objects from multiple tables.
+
+        .. warning::
+
+           Pandas uses PyTables for reading and writing HDF5 files, which allows
+           serializing object-dtype data with pickle when using the "fixed" format.
+           Loading pickled data received from untrusted sources can be unsafe.
+
+           See: https://docs.python.org/3/library/pickle.html for more.
+
+        Parameters
+        ----------
+        keys : a list of the tables
+        selector : the table to apply the where criteria (defaults to keys[0]
+            if not supplied)
+        columns : the columns I want back
+        start : integer (defaults to None), row number to start selection
+        stop  : integer (defaults to None), row number to stop selection
+        iterator : bool, return an iterator, default False
+        chunksize : nrows to include in iteration, return an iterator
+        auto_close : bool, default False
+            Should automatically close the store when finished.
+
+        Raises
+        ------
+        raises KeyError if keys or selector is not found or keys is empty
+        raises TypeError if keys is not a list or tuple
+        raises ValueError if the tables are not ALL THE SAME DIMENSIONS
+        """
+        # default to single select
+        where = _ensure_term(where, scope_level=1)
+        if isinstance(keys, (list, tuple)) and len(keys) == 1:
+            keys = keys[0]
+        if isinstance(keys, str):
+            return self.select(
+                key=keys,
+                where=where,
+                columns=columns,
+                start=start,
+                stop=stop,
+                iterator=iterator,
+                chunksize=chunksize,
+                auto_close=auto_close,
+            )
+
+        if not isinstance(keys, (list, tuple)):
+            raise TypeError("keys must be a list/tuple")
+
+        if not len(keys):
+            raise ValueError("keys must have a non-zero length")
+
+        if selector is None:
+            selector = keys[0]
+
+        # collect the tables
+        tbls = [self.get_storer(k) for k in keys]
+        s = self.get_storer(selector)
+
+        # validate rows
+        nrows = None
+        for t, k in itertools.chain([(s, selector)], zip(tbls, keys)):
+            if t is None:
+                raise KeyError(f"Invalid table [{k}]")
+            if not t.is_table:
+                raise TypeError(
+                    f"object [{t.pathname}] is not a table, and cannot be used in all "
+                    "select as multiple"
+                )
+
+            if nrows is None:
+                nrows = t.nrows
+            elif t.nrows != nrows:
+                raise ValueError("all tables must have exactly the same nrows!")
+
+        # The isinstance checks here are redundant with the check above,
+        #  but necessary for mypy; see GH#29757
+        _tbls = [x for x in tbls if isinstance(x, Table)]
+
+        # axis is the concentration axes
+        axis = {t.non_index_axes[0][0] for t in _tbls}.pop()
+
+        def func(_start, _stop, _where):
+            # retrieve the objs, _where is always passed as a set of
+            # coordinates here
+            objs = [
+                t.read(where=_where, columns=columns, start=_start, stop=_stop)
+                for t in tbls
+            ]
+
+            # concat and return
+            return concat(objs, axis=axis, verify_integrity=False)._consolidate()
+
+        # create the iterator
+        it = TableIterator(
+            self,
+            s,
+            func,
+            where=where,
+            nrows=nrows,
+            start=start,
+            stop=stop,
+            iterator=iterator,
+            chunksize=chunksize,
+            auto_close=auto_close,
+        )
+
+        return it.get_result(coordinates=True)
+
+    def put(
+        self,
+        key: str,
+        value: DataFrame | Series,
+        format=None,
+        index: bool = True,
+        append: bool = False,
+        complib=None,
+        complevel: int | None = None,
+        min_itemsize: int | dict[str, int] | None = None,
+        nan_rep=None,
+        data_columns: Literal[True] | list[str] | None = None,
+        encoding=None,
+        errors: str = "strict",
+        track_times: bool = True,
+        dropna: bool = False,
+    ) -> None:
+        """
+        Store object in HDFStore.
+
+        Parameters
+        ----------
+        key : str
+        value : {Series, DataFrame}
+        format : 'fixed(f)|table(t)', default is 'fixed'
+            Format to use when storing object in HDFStore. Value can be one of:
+
+            ``'fixed'``
+                Fixed format.  Fast writing/reading. Not-appendable, nor searchable.
+            ``'table'``
+                Table format.  Write as a PyTables Table structure which may perform
+                worse but allow more flexible operations like searching / selecting
+                subsets of the data.
+        index : bool, default True
+            Write DataFrame index as a column.
+        append : bool, default False
+            This will force Table format, append the input data to the existing.
+        data_columns : list of columns or True, default None
+            List of columns to create as data columns, or True to use all columns.
+            See `here
+            <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
+        encoding : str, default None
+            Provide an encoding for strings.
+        track_times : bool, default True
+            Parameter is propagated to 'create_table' method of 'PyTables'.
+            If set to False it enables to have the same h5 files (same hashes)
+            independent on creation time.
+        dropna : bool, default False, optional
+            Remove missing values.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        """
+        if format is None:
+            format = get_option("io.hdf.default_format") or "fixed"
+        format = self._validate_format(format)
+        self._write_to_group(
+            key,
+            value,
+            format=format,
+            index=index,
+            append=append,
+            complib=complib,
+            complevel=complevel,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+            encoding=encoding,
+            errors=errors,
+            track_times=track_times,
+            dropna=dropna,
+        )
+
+    def remove(self, key: str, where=None, start=None, stop=None) -> None:
+        """
+        Remove pandas object partially by specifying the where condition
+
+        Parameters
+        ----------
+        key : str
+            Node to remove or delete rows from
+        where : list of Term (or convertible) objects, optional
+        start : integer (defaults to None), row number to start selection
+        stop  : integer (defaults to None), row number to stop selection
+
+        Returns
+        -------
+        number of rows removed (or None if not a Table)
+
+        Raises
+        ------
+        raises KeyError if key is not a valid store
+
+        """
+        where = _ensure_term(where, scope_level=1)
+        try:
+            s = self.get_storer(key)
+        except KeyError:
+            # the key is not a valid store, re-raising KeyError
+            raise
+        except AssertionError:
+            # surface any assertion errors for e.g. debugging
+            raise
+        except Exception as err:
+            # In tests we get here with ClosedFileError, TypeError, and
+            #  _table_mod.NoSuchNodeError.  TODO: Catch only these?
+
+            if where is not None:
+                raise ValueError(
+                    "trying to remove a node with a non-None where clause!"
+                ) from err
+
+            # we are actually trying to remove a node (with children)
+            node = self.get_node(key)
+            if node is not None:
+                node._f_remove(recursive=True)
+                return None
+
+        # remove the node
+        if com.all_none(where, start, stop):
+            s.group._f_remove(recursive=True)
+
+        # delete from the table
+        else:
+            if not s.is_table:
+                raise ValueError(
+                    "can only remove with where on objects written as tables"
+                )
+            return s.delete(where=where, start=start, stop=stop)
+
+    def append(
+        self,
+        key: str,
+        value: DataFrame | Series,
+        format=None,
+        axes=None,
+        index: bool | list[str] = True,
+        append: bool = True,
+        complib=None,
+        complevel: int | None = None,
+        columns=None,
+        min_itemsize: int | dict[str, int] | None = None,
+        nan_rep=None,
+        chunksize: int | None = None,
+        expectedrows=None,
+        dropna: bool | None = None,
+        data_columns: Literal[True] | list[str] | None = None,
+        encoding=None,
+        errors: str = "strict",
+    ) -> None:
+        """
+        Append to Table in file.
+
+        Node must already exist and be Table format.
+
+        Parameters
+        ----------
+        key : str
+        value : {Series, DataFrame}
+        format : 'table' is the default
+            Format to use when storing object in HDFStore.  Value can be one of:
+
+            ``'table'``
+                Table format. Write as a PyTables Table structure which may perform
+                worse but allow more flexible operations like searching / selecting
+                subsets of the data.
+        index : bool, default True
+            Write DataFrame index as a column.
+        append       : bool, default True
+            Append the input data to the existing.
+        data_columns : list of columns, or True, default None
+            List of columns to create as indexed data columns for on-disk
+            queries, or True to use all columns. By default only the axes
+            of the object are indexed. See `here
+            <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
+        min_itemsize : dict of columns that specify minimum str sizes
+        nan_rep      : str to use as str nan representation
+        chunksize    : size to chunk the writing
+        expectedrows : expected TOTAL row size of this table
+        encoding     : default None, provide an encoding for str
+        dropna : bool, default False, optional
+            Do not write an ALL nan row to the store settable
+            by the option 'io.hdf.dropna_table'.
+
+        Notes
+        -----
+        Does *not* check if data being appended overlaps with existing
+        data in the table, so be careful
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df1, format='table')  # doctest: +SKIP
+        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
+        >>> store.append('data', df2)  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+           A  B
+        0  1  2
+        1  3  4
+        0  5  6
+        1  7  8
+        """
+        if columns is not None:
+            raise TypeError(
+                "columns is not a supported keyword in append, try data_columns"
+            )
+
+        if dropna is None:
+            dropna = get_option("io.hdf.dropna_table")
+        if format is None:
+            format = get_option("io.hdf.default_format") or "table"
+        format = self._validate_format(format)
+        self._write_to_group(
+            key,
+            value,
+            format=format,
+            axes=axes,
+            index=index,
+            append=append,
+            complib=complib,
+            complevel=complevel,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            chunksize=chunksize,
+            expectedrows=expectedrows,
+            dropna=dropna,
+            data_columns=data_columns,
+            encoding=encoding,
+            errors=errors,
+        )
+
+    def append_to_multiple(
+        self,
+        d: dict,
+        value,
+        selector,
+        data_columns=None,
+        axes=None,
+        dropna: bool = False,
+        **kwargs,
+    ) -> None:
+        """
+        Append to multiple tables
+
+        Parameters
+        ----------
+        d : a dict of table_name to table_columns, None is acceptable as the
+            values of one node (this will get all the remaining columns)
+        value : a pandas object
+        selector : a string that designates the indexable table; all of its
+            columns will be designed as data_columns, unless data_columns is
+            passed, in which case these are used
+        data_columns : list of columns to create as data columns, or True to
+            use all columns
+        dropna : if evaluates to True, drop rows from all tables if any single
+                 row in each table has all NaN. Default False.
+
+        Notes
+        -----
+        axes parameter is currently not accepted
+
+        """
+        if axes is not None:
+            raise TypeError(
+                "axes is currently not accepted as a parameter to append_to_multiple; "
+                "you can create the tables independently instead"
+            )
+
+        if not isinstance(d, dict):
+            raise ValueError(
+                "append_to_multiple must have a dictionary specified as the "
+                "way to split the value"
+            )
+
+        if selector not in d:
+            raise ValueError(
+                "append_to_multiple requires a selector that is in passed dict"
+            )
+
+        # figure out the splitting axis (the non_index_axis)
+        axis = next(iter(set(range(value.ndim)) - set(_AXES_MAP[type(value)])))
+
+        # figure out how to split the value
+        remain_key = None
+        remain_values: list = []
+        for k, v in d.items():
+            if v is None:
+                if remain_key is not None:
+                    raise ValueError(
+                        "append_to_multiple can only have one value in d that is None"
+                    )
+                remain_key = k
+            else:
+                remain_values.extend(v)
+        if remain_key is not None:
+            ordered = value.axes[axis]
+            ordd = ordered.difference(Index(remain_values))
+            ordd = sorted(ordered.get_indexer(ordd))
+            d[remain_key] = ordered.take(ordd)
+
+        # data_columns
+        if data_columns is None:
+            data_columns = d[selector]
+
+        # ensure rows are synchronized across the tables
+        if dropna:
+            idxs = (value[cols].dropna(how="all").index for cols in d.values())
+            valid_index = next(idxs)
+            for index in idxs:
+                valid_index = valid_index.intersection(index)
+            value = value.loc[valid_index]
+
+        min_itemsize = kwargs.pop("min_itemsize", None)
+
+        # append
+        for k, v in d.items():
+            dc = data_columns if k == selector else None
+
+            # compute the val
+            val = value.reindex(v, axis=axis)
+
+            filtered = (
+                {key: value for (key, value) in min_itemsize.items() if key in v}
+                if min_itemsize is not None
+                else None
+            )
+            self.append(k, val, data_columns=dc, min_itemsize=filtered, **kwargs)
+
+    def create_table_index(
+        self,
+        key: str,
+        columns=None,
+        optlevel: int | None = None,
+        kind: str | None = None,
+    ) -> None:
+        """
+        Create a pytables index on the table.
+
+        Parameters
+        ----------
+        key : str
+        columns : None, bool, or listlike[str]
+            Indicate which columns to create an index on.
+
+            * False : Do not create any indexes.
+            * True : Create indexes on all columns.
+            * None : Create indexes on all columns.
+            * listlike : Create indexes on the given columns.
+
+        optlevel : int or None, default None
+            Optimization level, if None, pytables defaults to 6.
+        kind : str or None, default None
+            Kind of index, if None, pytables defaults to "medium".
+
+        Raises
+        ------
+        TypeError: raises if the node is not a table
+        """
+        # version requirements
+        _tables()
+        s = self.get_storer(key)
+        if s is None:
+            return
+
+        if not isinstance(s, Table):
+            raise TypeError("cannot create table index on a Fixed format store")
+        s.create_index(columns=columns, optlevel=optlevel, kind=kind)
+
+    def groups(self) -> list:
+        """
+        Return a list of all the top-level nodes.
+
+        Each node returned is not a pandas storage object.
+
+        Returns
+        -------
+        list
+            List of objects.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> print(store.groups())  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        [/data (Group) ''
+          children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array),
+          'block0_items' (Array)]]
+        """
+        _tables()
+        self._check_if_open()
+        assert self._handle is not None  # for mypy
+        assert _table_mod is not None  # for mypy
+        return [
+            g
+            for g in self._handle.walk_groups()
+            if (
+                not isinstance(g, _table_mod.link.Link)
+                and (
+                    getattr(g._v_attrs, "pandas_type", None)
+                    or getattr(g, "table", None)
+                    or (isinstance(g, _table_mod.table.Table) and g._v_name != "table")
+                )
+            )
+        ]
+
+    def walk(self, where: str = "/") -> Iterator[tuple[str, list[str], list[str]]]:
+        """
+        Walk the pytables group hierarchy for pandas objects.
+
+        This generator will yield the group path, subgroups and pandas object
+        names for each group.
+
+        Any non-pandas PyTables objects that are not a group will be ignored.
+
+        The `where` group itself is listed first (preorder), then each of its
+        child groups (following an alphanumerical order) is also traversed,
+        following the same procedure.
+
+        Parameters
+        ----------
+        where : str, default "/"
+            Group where to start walking.
+
+        Yields
+        ------
+        path : str
+            Full path to a group (without trailing '/').
+        groups : list
+            Names (strings) of the groups contained in `path`.
+        leaves : list
+            Names (strings) of the pandas objects contained in `path`.
+
+        Examples
+        --------
+        >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df1, format='table')  # doctest: +SKIP
+        >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
+        >>> store.append('data', df2)  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        >>> for group in store.walk():  # doctest: +SKIP
+        ...     print(group)  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        """
+        _tables()
+        self._check_if_open()
+        assert self._handle is not None  # for mypy
+        assert _table_mod is not None  # for mypy
+
+        for g in self._handle.walk_groups(where):
+            if getattr(g._v_attrs, "pandas_type", None) is not None:
+                continue
+
+            groups = []
+            leaves = []
+            for child in g._v_children.values():
+                pandas_type = getattr(child._v_attrs, "pandas_type", None)
+                if pandas_type is None:
+                    if isinstance(child, _table_mod.group.Group):
+                        groups.append(child._v_name)
+                else:
+                    leaves.append(child._v_name)
+
+            yield (g._v_pathname.rstrip("/"), groups, leaves)
+
+    def get_node(self, key: str) -> Node | None:
+        """return the node with the key or None if it does not exist"""
+        self._check_if_open()
+        if not key.startswith("/"):
+            key = "/" + key
+
+        assert self._handle is not None
+        assert _table_mod is not None  # for mypy
+        try:
+            node = self._handle.get_node(self.root, key)
+        except _table_mod.exceptions.NoSuchNodeError:
+            return None
+
+        assert isinstance(node, _table_mod.Node), type(node)
+        return node
+
+    def get_storer(self, key: str) -> GenericFixed | Table:
+        """return the storer object for a key, raise if not in the file"""
+        group = self.get_node(key)
+        if group is None:
+            raise KeyError(f"No object named {key} in the file")
+
+        s = self._create_storer(group)
+        s.infer_axes()
+        return s
+
+    def copy(
+        self,
+        file,
+        mode: str = "w",
+        propindexes: bool = True,
+        keys=None,
+        complib=None,
+        complevel: int | None = None,
+        fletcher32: bool = False,
+        overwrite: bool = True,
+    ) -> HDFStore:
+        """
+        Copy the existing store to a new file, updating in place.
+
+        Parameters
+        ----------
+        propindexes : bool, default True
+            Restore indexes in copied file.
+        keys : list, optional
+            List of keys to include in the copy (defaults to all).
+        overwrite : bool, default True
+            Whether to overwrite (remove and replace) existing nodes in the new store.
+        mode, complib, complevel, fletcher32 same as in HDFStore.__init__
+
+        Returns
+        -------
+        open file handle of the new store
+        """
+        new_store = HDFStore(
+            file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32
+        )
+        if keys is None:
+            keys = list(self.keys())
+        if not isinstance(keys, (tuple, list)):
+            keys = [keys]
+        for k in keys:
+            s = self.get_storer(k)
+            if s is not None:
+                if k in new_store:
+                    if overwrite:
+                        new_store.remove(k)
+
+                data = self.select(k)
+                if isinstance(s, Table):
+                    index: bool | list[str] = False
+                    if propindexes:
+                        index = [a.name for a in s.axes if a.is_indexed]
+                    new_store.append(
+                        k,
+                        data,
+                        index=index,
+                        data_columns=getattr(s, "data_columns", None),
+                        encoding=s.encoding,
+                    )
+                else:
+                    new_store.put(k, data, encoding=s.encoding)
+
+        return new_store
+
+    def info(self) -> str:
+        """
+        Print detailed information on the store.
+
+        Returns
+        -------
+        str
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+        >>> store = pd.HDFStore("store.h5", 'w')  # doctest: +SKIP
+        >>> store.put('data', df)  # doctest: +SKIP
+        >>> print(store.info())  # doctest: +SKIP
+        >>> store.close()  # doctest: +SKIP
+        <class 'pandas.io.pytables.HDFStore'>
+        File path: store.h5
+        /data    frame    (shape->[2,2])
+        """
+        path = pprint_thing(self._path)
+        output = f"{type(self)}\nFile path: {path}\n"
+
+        if self.is_open:
+            lkeys = sorted(self.keys())
+            if len(lkeys):
+                keys = []
+                values = []
+
+                for k in lkeys:
+                    try:
+                        s = self.get_storer(k)
+                        if s is not None:
+                            keys.append(pprint_thing(s.pathname or k))
+                            values.append(pprint_thing(s or "invalid_HDFStore node"))
+                    except AssertionError:
+                        # surface any assertion errors for e.g. debugging
+                        raise
+                    except Exception as detail:
+                        keys.append(k)
+                        dstr = pprint_thing(detail)
+                        values.append(f"[invalid_HDFStore node: {dstr}]")
+
+                output += adjoin(12, keys, values)
+            else:
+                output += "Empty"
+        else:
+            output += "File is CLOSED"
+
+        return output
+
+    # ------------------------------------------------------------------------
+    # private methods
+
+    def _check_if_open(self) -> None:
+        if not self.is_open:
+            raise ClosedFileError(f"{self._path} file is not open!")
+
+    def _validate_format(self, format: str) -> str:
+        """validate / deprecate formats"""
+        # validate
+        try:
+            format = _FORMAT_MAP[format.lower()]
+        except KeyError as err:
+            raise TypeError(f"invalid HDFStore format specified [{format}]") from err
+
+        return format
+
+    def _create_storer(
+        self,
+        group,
+        format=None,
+        value: DataFrame | Series | None = None,
+        encoding: str = "UTF-8",
+        errors: str = "strict",
+    ) -> GenericFixed | Table:
+        """return a suitable class to operate"""
+        cls: type[GenericFixed | Table]
+
+        if value is not None and not isinstance(value, (Series, DataFrame)):
+            raise TypeError("value must be None, Series, or DataFrame")
+
+        pt = _ensure_decoded(getattr(group._v_attrs, "pandas_type", None))
+        tt = _ensure_decoded(getattr(group._v_attrs, "table_type", None))
+
+        # infer the pt from the passed value
+        if pt is None:
+            if value is None:
+                _tables()
+                assert _table_mod is not None  # for mypy
+                if getattr(group, "table", None) or isinstance(
+                    group, _table_mod.table.Table
+                ):
+                    pt = "frame_table"
+                    tt = "generic_table"
+                else:
+                    raise TypeError(
+                        "cannot create a storer if the object is not existing "
+                        "nor a value are passed"
+                    )
+            else:
+                if isinstance(value, Series):
+                    pt = "series"
+                else:
+                    pt = "frame"
+
+                # we are actually a table
+                if format == "table":
+                    pt += "_table"
+
+        # a storer node
+        if "table" not in pt:
+            _STORER_MAP = {"series": SeriesFixed, "frame": FrameFixed}
+            try:
+                cls = _STORER_MAP[pt]
+            except KeyError as err:
+                raise TypeError(
+                    f"cannot properly create the storer for: [_STORER_MAP] [group->"
+                    f"{group},value->{type(value)},format->{format}"
+                ) from err
+            return cls(self, group, encoding=encoding, errors=errors)
+
+        # existing node (and must be a table)
+        if tt is None:
+            # if we are a writer, determine the tt
+            if value is not None:
+                if pt == "series_table":
+                    index = getattr(value, "index", None)
+                    if index is not None:
+                        if index.nlevels == 1:
+                            tt = "appendable_series"
+                        elif index.nlevels > 1:
+                            tt = "appendable_multiseries"
+                elif pt == "frame_table":
+                    index = getattr(value, "index", None)
+                    if index is not None:
+                        if index.nlevels == 1:
+                            tt = "appendable_frame"
+                        elif index.nlevels > 1:
+                            tt = "appendable_multiframe"
+
+        _TABLE_MAP = {
+            "generic_table": GenericTable,
+            "appendable_series": AppendableSeriesTable,
+            "appendable_multiseries": AppendableMultiSeriesTable,
+            "appendable_frame": AppendableFrameTable,
+            "appendable_multiframe": AppendableMultiFrameTable,
+            "worm": WORMTable,
+        }
+        try:
+            cls = _TABLE_MAP[tt]
+        except KeyError as err:
+            raise TypeError(
+                f"cannot properly create the storer for: [_TABLE_MAP] [group->"
+                f"{group},value->{type(value)},format->{format}"
+            ) from err
+
+        return cls(self, group, encoding=encoding, errors=errors)
+
+    def _write_to_group(
+        self,
+        key: str,
+        value: DataFrame | Series,
+        format,
+        axes=None,
+        index: bool | list[str] = True,
+        append: bool = False,
+        complib=None,
+        complevel: int | None = None,
+        fletcher32=None,
+        min_itemsize: int | dict[str, int] | None = None,
+        chunksize: int | None = None,
+        expectedrows=None,
+        dropna: bool = False,
+        nan_rep=None,
+        data_columns=None,
+        encoding=None,
+        errors: str = "strict",
+        track_times: bool = True,
+    ) -> None:
+        # we don't want to store a table node at all if our object is 0-len
+        # as there are not dtypes
+        if getattr(value, "empty", None) and (format == "table" or append):
+            return
+
+        group = self._identify_group(key, append)
+
+        s = self._create_storer(group, format, value, encoding=encoding, errors=errors)
+        if append:
+            # raise if we are trying to append to a Fixed format,
+            #       or a table that exists (and we are putting)
+            if not s.is_table or (s.is_table and format == "fixed" and s.is_exists):
+                raise ValueError("Can only append to Tables")
+            if not s.is_exists:
+                s.set_object_info()
+        else:
+            s.set_object_info()
+
+        if not s.is_table and complib:
+            raise ValueError("Compression not supported on Fixed format stores")
+
+        # write the object
+        s.write(
+            obj=value,
+            axes=axes,
+            append=append,
+            complib=complib,
+            complevel=complevel,
+            fletcher32=fletcher32,
+            min_itemsize=min_itemsize,
+            chunksize=chunksize,
+            expectedrows=expectedrows,
+            dropna=dropna,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+            track_times=track_times,
+        )
+
+        if isinstance(s, Table) and index:
+            s.create_index(columns=index)
+
+    def _read_group(self, group: Node):
+        s = self._create_storer(group)
+        s.infer_axes()
+        return s.read()
+
+    def _identify_group(self, key: str, append: bool) -> Node:
+        """Identify HDF5 group based on key, delete/create group if needed."""
+        group = self.get_node(key)
+
+        # we make this assertion for mypy; the get_node call will already
+        # have raised if this is incorrect
+        assert self._handle is not None
+
+        # remove the node if we are not appending
+        if group is not None and not append:
+            self._handle.remove_node(group, recursive=True)
+            group = None
+
+        if group is None:
+            group = self._create_nodes_and_group(key)
+
+        return group
+
+    def _create_nodes_and_group(self, key: str) -> Node:
+        """Create nodes from key and return group name."""
+        # assertion for mypy
+        assert self._handle is not None
+
+        paths = key.split("/")
+        # recursively create the groups
+        path = "/"
+        for p in paths:
+            if not len(p):
+                continue
+            new_path = path
+            if not path.endswith("/"):
+                new_path += "/"
+            new_path += p
+            group = self.get_node(new_path)
+            if group is None:
+                group = self._handle.create_group(path, p)
+            path = new_path
+        return group
+
+
+class TableIterator:
+    """
+    Define the iteration interface on a table
+
+    Parameters
+    ----------
+    store : HDFStore
+    s     : the referred storer
+    func  : the function to execute the query
+    where : the where of the query
+    nrows : the rows to iterate on
+    start : the passed start value (default is None)
+    stop  : the passed stop value (default is None)
+    iterator : bool, default False
+        Whether to use the default iterator.
+    chunksize : the passed chunking value (default is 100000)
+    auto_close : bool, default False
+        Whether to automatically close the store at the end of iteration.
+    """
+
+    chunksize: int | None
+    store: HDFStore
+    s: GenericFixed | Table
+
+    def __init__(
+        self,
+        store: HDFStore,
+        s: GenericFixed | Table,
+        func,
+        where,
+        nrows,
+        start=None,
+        stop=None,
+        iterator: bool = False,
+        chunksize: int | None = None,
+        auto_close: bool = False,
+    ) -> None:
+        self.store = store
+        self.s = s
+        self.func = func
+        self.where = where
+
+        # set start/stop if they are not set if we are a table
+        if self.s.is_table:
+            if nrows is None:
+                nrows = 0
+            if start is None:
+                start = 0
+            if stop is None:
+                stop = nrows
+            stop = min(nrows, stop)
+
+        self.nrows = nrows
+        self.start = start
+        self.stop = stop
+
+        self.coordinates = None
+        if iterator or chunksize is not None:
+            if chunksize is None:
+                chunksize = 100000
+            self.chunksize = int(chunksize)
+        else:
+            self.chunksize = None
+
+        self.auto_close = auto_close
+
+    def __iter__(self) -> Iterator:
+        # iterate
+        current = self.start
+        if self.coordinates is None:
+            raise ValueError("Cannot iterate until get_result is called.")
+        while current < self.stop:
+            stop = min(current + self.chunksize, self.stop)
+            value = self.func(None, None, self.coordinates[current:stop])
+            current = stop
+            if value is None or not len(value):
+                continue
+
+            yield value
+
+        self.close()
+
+    def close(self) -> None:
+        if self.auto_close:
+            self.store.close()
+
+    def get_result(self, coordinates: bool = False):
+        #  return the actual iterator
+        if self.chunksize is not None:
+            if not isinstance(self.s, Table):
+                raise TypeError("can only use an iterator or chunksize on a table")
+
+            self.coordinates = self.s.read_coordinates(where=self.where)
+
+            return self
+
+        # if specified read via coordinates (necessary for multiple selections
+        if coordinates:
+            if not isinstance(self.s, Table):
+                raise TypeError("can only read_coordinates on a table")
+            where = self.s.read_coordinates(
+                where=self.where, start=self.start, stop=self.stop
+            )
+        else:
+            where = self.where
+
+        # directly return the result
+        results = self.func(self.start, self.stop, where)
+        self.close()
+        return results
+
+
+class IndexCol:
+    """
+    an index column description class
+
+    Parameters
+    ----------
+    axis   : axis which I reference
+    values : the ndarray like converted values
+    kind   : a string description of this type
+    typ    : the pytables type
+    pos    : the position in the pytables
+
+    """
+
+    is_an_indexable: bool = True
+    is_data_indexable: bool = True
+    _info_fields = ["freq", "tz", "index_name"]
+
+    def __init__(
+        self,
+        name: str,
+        values=None,
+        kind=None,
+        typ=None,
+        cname: str | None = None,
+        axis=None,
+        pos=None,
+        freq=None,
+        tz=None,
+        index_name=None,
+        ordered=None,
+        table=None,
+        meta=None,
+        metadata=None,
+    ) -> None:
+        if not isinstance(name, str):
+            raise ValueError("`name` must be a str.")
+
+        self.values = values
+        self.kind = kind
+        self.typ = typ
+        self.name = name
+        self.cname = cname or name
+        self.axis = axis
+        self.pos = pos
+        self.freq = freq
+        self.tz = tz
+        self.index_name = index_name
+        self.ordered = ordered
+        self.table = table
+        self.meta = meta
+        self.metadata = metadata
+
+        if pos is not None:
+            self.set_pos(pos)
+
+        # These are ensured as long as the passed arguments match the
+        #  constructor annotations.
+        assert isinstance(self.name, str)
+        assert isinstance(self.cname, str)
+
+    @property
+    def itemsize(self) -> int:
+        # Assumes self.typ has already been initialized
+        return self.typ.itemsize
+
+    @property
+    def kind_attr(self) -> str:
+        return f"{self.name}_kind"
+
+    def set_pos(self, pos: int) -> None:
+        """set the position of this column in the Table"""
+        self.pos = pos
+        if pos is not None and self.typ is not None:
+            self.typ._v_pos = pos
+
+    def __repr__(self) -> str:
+        temp = tuple(
+            map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind))
+        )
+        return ",".join(
+            [
+                f"{key}->{value}"
+                for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp)
+            ]
+        )
+
+    def __eq__(self, other: object) -> bool:
+        """compare 2 col items"""
+        return all(
+            getattr(self, a, None) == getattr(other, a, None)
+            for a in ["name", "cname", "axis", "pos"]
+        )
+
+    def __ne__(self, other) -> bool:
+        return not self.__eq__(other)
+
+    @property
+    def is_indexed(self) -> bool:
+        """return whether I am an indexed column"""
+        if not hasattr(self.table, "cols"):
+            # e.g. if infer hasn't been called yet, self.table will be None.
+            return False
+        return getattr(self.table.cols, self.cname).is_indexed
+
+    def convert(
+        self, values: np.ndarray, nan_rep, encoding: str, errors: str
+    ) -> tuple[np.ndarray, np.ndarray] | tuple[Index, Index]:
+        """
+        Convert the data from this selection to the appropriate pandas type.
+        """
+        assert isinstance(values, np.ndarray), type(values)
+
+        # values is a recarray
+        if values.dtype.fields is not None:
+            # Copy, otherwise values will be a view
+            # preventing the original recarry from being free'ed
+            values = values[self.cname].copy()
+
+        val_kind = _ensure_decoded(self.kind)
+        values = _maybe_convert(values, val_kind, encoding, errors)
+        kwargs = {}
+        kwargs["name"] = _ensure_decoded(self.index_name)
+
+        if self.freq is not None:
+            kwargs["freq"] = _ensure_decoded(self.freq)
+
+        factory: type[Index | DatetimeIndex] = Index
+        if lib.is_np_dtype(values.dtype, "M") or isinstance(
+            values.dtype, DatetimeTZDtype
+        ):
+            factory = DatetimeIndex
+        elif values.dtype == "i8" and "freq" in kwargs:
+            # PeriodIndex data is stored as i8
+            # error: Incompatible types in assignment (expression has type
+            # "Callable[[Any, KwArg(Any)], PeriodIndex]", variable has type
+            # "Union[Type[Index], Type[DatetimeIndex]]")
+            factory = lambda x, **kwds: PeriodIndex.from_ordinals(  # type: ignore[assignment]
+                x, freq=kwds.get("freq", None)
+            )._rename(
+                kwds["name"]
+            )
+
+        # making an Index instance could throw a number of different errors
+        try:
+            new_pd_index = factory(values, **kwargs)
+        except UnicodeEncodeError as err:
+            if (
+                errors == "surrogatepass"
+                and get_option("future.infer_string")
+                and str(err).endswith("surrogates not allowed")
+                and HAS_PYARROW
+            ):
+                new_pd_index = factory(
+                    values,
+                    dtype=StringDtype(storage="python", na_value=np.nan),
+                    **kwargs,
+                )
+            else:
+                raise
+        except ValueError:
+            # if the output freq is different that what we recorded,
+            # it should be None (see also 'doc example part 2')
+            if "freq" in kwargs:
+                kwargs["freq"] = None
+            new_pd_index = factory(values, **kwargs)
+        final_pd_index = _set_tz(new_pd_index, self.tz)
+        return final_pd_index, final_pd_index
+
+    def take_data(self):
+        """return the values"""
+        return self.values
+
+    @property
+    def attrs(self):
+        return self.table._v_attrs
+
+    @property
+    def description(self):
+        return self.table.description
+
+    @property
+    def col(self):
+        """return my current col description"""
+        return getattr(self.description, self.cname, None)
+
+    @property
+    def cvalues(self):
+        """return my cython values"""
+        return self.values
+
+    def __iter__(self) -> Iterator:
+        return iter(self.values)
+
+    def maybe_set_size(self, min_itemsize=None) -> None:
+        """
+        maybe set a string col itemsize:
+            min_itemsize can be an integer or a dict with this columns name
+            with an integer size
+        """
+        if _ensure_decoded(self.kind) == "string":
+            if isinstance(min_itemsize, dict):
+                min_itemsize = min_itemsize.get(self.name)
+
+            if min_itemsize is not None and self.typ.itemsize < min_itemsize:
+                self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos)
+
+    def validate_names(self) -> None:
+        pass
+
+    def validate_and_set(self, handler: AppendableTable, append: bool) -> None:
+        self.table = handler.table
+        self.validate_col()
+        self.validate_attr(append)
+        self.validate_metadata(handler)
+        self.write_metadata(handler)
+        self.set_attr()
+
+    def validate_col(self, itemsize=None):
+        """validate this column: return the compared against itemsize"""
+        # validate this column for string truncation (or reset to the max size)
+        if _ensure_decoded(self.kind) == "string":
+            c = self.col
+            if c is not None:
+                if itemsize is None:
+                    itemsize = self.itemsize
+                if c.itemsize < itemsize:
+                    raise ValueError(
+                        f"Trying to store a string with len [{itemsize}] in "
+                        f"[{self.cname}] column but\nthis column has a limit of "
+                        f"[{c.itemsize}]!\nConsider using min_itemsize to "
+                        "preset the sizes on these columns"
+                    )
+                return c.itemsize
+
+        return None
+
+    def validate_attr(self, append: bool) -> None:
+        # check for backwards incompatibility
+        if append:
+            existing_kind = getattr(self.attrs, self.kind_attr, None)
+            if existing_kind is not None and existing_kind != self.kind:
+                raise TypeError(
+                    f"incompatible kind in col [{existing_kind} - {self.kind}]"
+                )
+
+    def update_info(self, info) -> None:
+        """
+        set/update the info for this indexable with the key/value
+        if there is a conflict raise/warn as needed
+        """
+        for key in self._info_fields:
+            value = getattr(self, key, None)
+            idx = info.setdefault(self.name, {})
+
+            existing_value = idx.get(key)
+            if key in idx and value is not None and existing_value != value:
+                # frequency/name just warn
+                if key in ["freq", "index_name"]:
+                    ws = attribute_conflict_doc % (key, existing_value, value)
+                    warnings.warn(
+                        ws, AttributeConflictWarning, stacklevel=find_stack_level()
+                    )
+
+                    # reset
+                    idx[key] = None
+                    setattr(self, key, None)
+
+                else:
+                    raise ValueError(
+                        f"invalid info for [{self.name}] for [{key}], "
+                        f"existing_value [{existing_value}] conflicts with "
+                        f"new value [{value}]"
+                    )
+            elif value is not None or existing_value is not None:
+                idx[key] = value
+
+    def set_info(self, info) -> None:
+        """set my state from the passed info"""
+        idx = info.get(self.name)
+        if idx is not None:
+            self.__dict__.update(idx)
+
+    def set_attr(self) -> None:
+        """set the kind for this column"""
+        setattr(self.attrs, self.kind_attr, self.kind)
+
+    def validate_metadata(self, handler: AppendableTable) -> None:
+        """validate that kind=category does not change the categories"""
+        if self.meta == "category":
+            new_metadata = self.metadata
+            cur_metadata = handler.read_metadata(self.cname)
+            if (
+                new_metadata is not None
+                and cur_metadata is not None
+                and not array_equivalent(
+                    new_metadata, cur_metadata, strict_nan=True, dtype_equal=True
+                )
+            ):
+                raise ValueError(
+                    "cannot append a categorical with "
+                    "different categories to the existing"
+                )
+
+    def write_metadata(self, handler: AppendableTable) -> None:
+        """set the meta data"""
+        if self.metadata is not None:
+            handler.write_metadata(self.cname, self.metadata)
+
+
+class GenericIndexCol(IndexCol):
+    """an index which is not represented in the data of the table"""
+
+    @property
+    def is_indexed(self) -> bool:
+        return False
+
+    def convert(
+        self, values: np.ndarray, nan_rep, encoding: str, errors: str
+    ) -> tuple[Index, Index]:
+        """
+        Convert the data from this selection to the appropriate pandas type.
+
+        Parameters
+        ----------
+        values : np.ndarray
+        nan_rep : str
+        encoding : str
+        errors : str
+        """
+        assert isinstance(values, np.ndarray), type(values)
+
+        index = RangeIndex(len(values))
+        return index, index
+
+    def set_attr(self) -> None:
+        pass
+
+
+class DataCol(IndexCol):
+    """
+    a data holding column, by definition this is not indexable
+
+    Parameters
+    ----------
+    data   : the actual data
+    cname  : the column name in the table to hold the data (typically
+                values)
+    meta   : a string description of the metadata
+    metadata : the actual metadata
+    """
+
+    is_an_indexable = False
+    is_data_indexable = False
+    _info_fields = ["tz", "ordered"]
+
+    def __init__(
+        self,
+        name: str,
+        values=None,
+        kind=None,
+        typ=None,
+        cname: str | None = None,
+        pos=None,
+        tz=None,
+        ordered=None,
+        table=None,
+        meta=None,
+        metadata=None,
+        dtype: DtypeArg | None = None,
+        data=None,
+    ) -> None:
+        super().__init__(
+            name=name,
+            values=values,
+            kind=kind,
+            typ=typ,
+            pos=pos,
+            cname=cname,
+            tz=tz,
+            ordered=ordered,
+            table=table,
+            meta=meta,
+            metadata=metadata,
+        )
+        self.dtype = dtype
+        self.data = data
+
+    @property
+    def dtype_attr(self) -> str:
+        return f"{self.name}_dtype"
+
+    @property
+    def meta_attr(self) -> str:
+        return f"{self.name}_meta"
+
+    def __repr__(self) -> str:
+        temp = tuple(
+            map(
+                pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape)
+            )
+        )
+        return ",".join(
+            [
+                f"{key}->{value}"
+                for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp)
+            ]
+        )
+
+    def __eq__(self, other: object) -> bool:
+        """compare 2 col items"""
+        return all(
+            getattr(self, a, None) == getattr(other, a, None)
+            for a in ["name", "cname", "dtype", "pos"]
+        )
+
+    def set_data(self, data: ArrayLike) -> None:
+        assert data is not None
+        assert self.dtype is None
+
+        data, dtype_name = _get_data_and_dtype_name(data)
+
+        self.data = data
+        self.dtype = dtype_name
+        self.kind = _dtype_to_kind(dtype_name)
+
+    def take_data(self):
+        """return the data"""
+        return self.data
+
+    @classmethod
+    def _get_atom(cls, values: ArrayLike) -> Col:
+        """
+        Get an appropriately typed and shaped pytables.Col object for values.
+        """
+        dtype = values.dtype
+        # error: Item "ExtensionDtype" of "Union[ExtensionDtype, dtype[Any]]" has no
+        # attribute "itemsize"
+        itemsize = dtype.itemsize  # type: ignore[union-attr]
+
+        shape = values.shape
+        if values.ndim == 1:
+            # EA, use block shape pretending it is 2D
+            # TODO(EA2D): not necessary with 2D EAs
+            shape = (1, values.size)
+
+        if isinstance(values, Categorical):
+            codes = values.codes
+            atom = cls.get_atom_data(shape, kind=codes.dtype.name)
+        elif lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
+            atom = cls.get_atom_datetime64(shape)
+        elif lib.is_np_dtype(dtype, "m"):
+            atom = cls.get_atom_timedelta64(shape)
+        elif is_complex_dtype(dtype):
+            atom = _tables().ComplexCol(itemsize=itemsize, shape=shape[0])
+        elif is_string_dtype(dtype):
+            atom = cls.get_atom_string(shape, itemsize)
+        else:
+            atom = cls.get_atom_data(shape, kind=dtype.name)
+
+        return atom
+
+    @classmethod
+    def get_atom_string(cls, shape, itemsize):
+        return _tables().StringCol(itemsize=itemsize, shape=shape[0])
+
+    @classmethod
+    def get_atom_coltype(cls, kind: str) -> type[Col]:
+        """return the PyTables column class for this column"""
+        if kind.startswith("uint"):
+            k4 = kind[4:]
+            col_name = f"UInt{k4}Col"
+        elif kind.startswith("period"):
+            # we store as integer
+            col_name = "Int64Col"
+        else:
+            kcap = kind.capitalize()
+            col_name = f"{kcap}Col"
+
+        return getattr(_tables(), col_name)
+
+    @classmethod
+    def get_atom_data(cls, shape, kind: str) -> Col:
+        return cls.get_atom_coltype(kind=kind)(shape=shape[0])
+
+    @classmethod
+    def get_atom_datetime64(cls, shape):
+        return _tables().Int64Col(shape=shape[0])
+
+    @classmethod
+    def get_atom_timedelta64(cls, shape):
+        return _tables().Int64Col(shape=shape[0])
+
+    @property
+    def shape(self):
+        return getattr(self.data, "shape", None)
+
+    @property
+    def cvalues(self):
+        """return my cython values"""
+        return self.data
+
+    def validate_attr(self, append) -> None:
+        """validate that we have the same order as the existing & same dtype"""
+        if append:
+            existing_fields = getattr(self.attrs, self.kind_attr, None)
+            if existing_fields is not None and existing_fields != list(self.values):
+                raise ValueError("appended items do not match existing items in table!")
+
+            existing_dtype = getattr(self.attrs, self.dtype_attr, None)
+            if existing_dtype is not None and existing_dtype != self.dtype:
+                raise ValueError(
+                    "appended items dtype do not match existing items dtype in table!"
+                )
+
+    def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
+        """
+        Convert the data from this selection to the appropriate pandas type.
+
+        Parameters
+        ----------
+        values : np.ndarray
+        nan_rep :
+        encoding : str
+        errors : str
+
+        Returns
+        -------
+        index : listlike to become an Index
+        data : ndarraylike to become a column
+        """
+        assert isinstance(values, np.ndarray), type(values)
+
+        # values is a recarray
+        if values.dtype.fields is not None:
+            values = values[self.cname]
+
+        assert self.typ is not None
+        if self.dtype is None:
+            # Note: in tests we never have timedelta64 or datetime64,
+            #  so the _get_data_and_dtype_name may be unnecessary
+            converted, dtype_name = _get_data_and_dtype_name(values)
+            kind = _dtype_to_kind(dtype_name)
+        else:
+            converted = values
+            dtype_name = self.dtype
+            kind = self.kind
+
+        assert isinstance(converted, np.ndarray)  # for mypy
+
+        # use the meta if needed
+        meta = _ensure_decoded(self.meta)
+        metadata = self.metadata
+        ordered = self.ordered
+        tz = self.tz
+
+        assert dtype_name is not None
+        # convert to the correct dtype
+        dtype = _ensure_decoded(dtype_name)
+
+        # reverse converts
+        if dtype.startswith("datetime64"):
+            # recreate with tz if indicated
+            converted = _set_tz(converted, tz, coerce=True)
+
+        elif dtype == "timedelta64":
+            converted = np.asarray(converted, dtype="m8[ns]")
+        elif dtype == "date":
+            try:
+                converted = np.asarray(
+                    [date.fromordinal(v) for v in converted], dtype=object
+                )
+            except ValueError:
+                converted = np.asarray(
+                    [date.fromtimestamp(v) for v in converted], dtype=object
+                )
+
+        elif meta == "category":
+            # we have a categorical
+            categories = metadata
+            codes = converted.ravel()
+
+            # if we have stored a NaN in the categories
+            # then strip it; in theory we could have BOTH
+            # -1s in the codes and nulls :<
+            if categories is None:
+                # Handle case of NaN-only categorical columns in which case
+                # the categories are an empty array; when this is stored,
+                # pytables cannot write a zero-len array, so on readback
+                # the categories would be None and `read_hdf()` would fail.
+                categories = Index([], dtype=np.float64)
+            else:
+                mask = isna(categories)
+                if mask.any():
+                    categories = categories[~mask]
+                    codes[codes != -1] -= mask.astype(int).cumsum()._values
+
+            converted = Categorical.from_codes(
+                codes, categories=categories, ordered=ordered, validate=False
+            )
+
+        else:
+            try:
+                converted = converted.astype(dtype, copy=False)
+            except TypeError:
+                converted = converted.astype("O", copy=False)
+
+        # convert nans / decode
+        if _ensure_decoded(kind) == "string":
+            converted = _unconvert_string_array(
+                converted, nan_rep=nan_rep, encoding=encoding, errors=errors
+            )
+
+        return self.values, converted
+
+    def set_attr(self) -> None:
+        """set the data for this column"""
+        setattr(self.attrs, self.kind_attr, self.values)
+        setattr(self.attrs, self.meta_attr, self.meta)
+        assert self.dtype is not None
+        setattr(self.attrs, self.dtype_attr, self.dtype)
+
+
+class DataIndexableCol(DataCol):
+    """represent a data column that can be indexed"""
+
+    is_data_indexable = True
+
+    def validate_names(self) -> None:
+        if not is_string_dtype(Index(self.values).dtype):
+            # TODO: should the message here be more specifically non-str?
+            raise ValueError("cannot have non-object label DataIndexableCol")
+
+    @classmethod
+    def get_atom_string(cls, shape, itemsize):
+        return _tables().StringCol(itemsize=itemsize)
+
+    @classmethod
+    def get_atom_data(cls, shape, kind: str) -> Col:
+        return cls.get_atom_coltype(kind=kind)()
+
+    @classmethod
+    def get_atom_datetime64(cls, shape):
+        return _tables().Int64Col()
+
+    @classmethod
+    def get_atom_timedelta64(cls, shape):
+        return _tables().Int64Col()
+
+
+class GenericDataIndexableCol(DataIndexableCol):
+    """represent a generic pytables data column"""
+
+
+class Fixed:
+    """
+    represent an object in my store
+    facilitate read/write of various types of objects
+    this is an abstract base class
+
+    Parameters
+    ----------
+    parent : HDFStore
+    group : Node
+        The group node where the table resides.
+    """
+
+    pandas_kind: str
+    format_type: str = "fixed"  # GH#30962 needed by dask
+    obj_type: type[DataFrame | Series]
+    ndim: int
+    parent: HDFStore
+    is_table: bool = False
+
+    def __init__(
+        self,
+        parent: HDFStore,
+        group: Node,
+        encoding: str | None = "UTF-8",
+        errors: str = "strict",
+    ) -> None:
+        assert isinstance(parent, HDFStore), type(parent)
+        assert _table_mod is not None  # needed for mypy
+        assert isinstance(group, _table_mod.Node), type(group)
+        self.parent = parent
+        self.group = group
+        self.encoding = _ensure_encoding(encoding)
+        self.errors = errors
+
+    @property
+    def is_old_version(self) -> bool:
+        return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1
+
+    @property
+    def version(self) -> tuple[int, int, int]:
+        """compute and set our version"""
+        version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None))
+        try:
+            version = tuple(int(x) for x in version.split("."))
+            if len(version) == 2:
+                version = version + (0,)
+        except AttributeError:
+            version = (0, 0, 0)
+        return version
+
+    @property
+    def pandas_type(self):
+        return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None))
+
+    def __repr__(self) -> str:
+        """return a pretty representation of myself"""
+        self.infer_axes()
+        s = self.shape
+        if s is not None:
+            if isinstance(s, (list, tuple)):
+                jshape = ",".join([pprint_thing(x) for x in s])
+                s = f"[{jshape}]"
+            return f"{self.pandas_type:12.12} (shape->{s})"
+        return self.pandas_type
+
+    def set_object_info(self) -> None:
+        """set my pandas type & version"""
+        self.attrs.pandas_type = str(self.pandas_kind)
+        self.attrs.pandas_version = str(_version)
+
+    def copy(self) -> Fixed:
+        new_self = copy.copy(self)
+        return new_self
+
+    @property
+    def shape(self):
+        return self.nrows
+
+    @property
+    def pathname(self):
+        return self.group._v_pathname
+
+    @property
+    def _handle(self):
+        return self.parent._handle
+
+    @property
+    def _filters(self):
+        return self.parent._filters
+
+    @property
+    def _complevel(self) -> int:
+        return self.parent._complevel
+
+    @property
+    def _fletcher32(self) -> bool:
+        return self.parent._fletcher32
+
+    @property
+    def attrs(self):
+        return self.group._v_attrs
+
+    def set_attrs(self) -> None:
+        """set our object attributes"""
+
+    def get_attrs(self) -> None:
+        """get our object attributes"""
+
+    @property
+    def storable(self):
+        """return my storable"""
+        return self.group
+
+    @property
+    def is_exists(self) -> bool:
+        return False
+
+    @property
+    def nrows(self):
+        return getattr(self.storable, "nrows", None)
+
+    def validate(self, other) -> Literal[True] | None:
+        """validate against an existing storable"""
+        if other is None:
+            return None
+        return True
+
+    def validate_version(self, where=None) -> None:
+        """are we trying to operate on an old version?"""
+
+    def infer_axes(self) -> bool:
+        """
+        infer the axes of my storer
+        return a boolean indicating if we have a valid storer or not
+        """
+        s = self.storable
+        if s is None:
+            return False
+        self.get_attrs()
+        return True
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        raise NotImplementedError(
+            "cannot read on an abstract storer: subclasses should implement"
+        )
+
+    def write(self, obj, **kwargs) -> None:
+        raise NotImplementedError(
+            "cannot write on an abstract storer: subclasses should implement"
+        )
+
+    def delete(
+        self, where=None, start: int | None = None, stop: int | None = None
+    ) -> None:
+        """
+        support fully deleting the node in its entirety (only) - where
+        specification must be None
+        """
+        if com.all_none(where, start, stop):
+            self._handle.remove_node(self.group, recursive=True)
+            return None
+
+        raise TypeError("cannot delete on an abstract storer")
+
+
+class GenericFixed(Fixed):
+    """a generified fixed version"""
+
+    _index_type_map = {DatetimeIndex: "datetime", PeriodIndex: "period"}
+    _reverse_index_map = {v: k for k, v in _index_type_map.items()}
+    attributes: list[str] = []
+
+    # indexer helpers
+    def _class_to_alias(self, cls) -> str:
+        return self._index_type_map.get(cls, "")
+
+    def _alias_to_class(self, alias):
+        if isinstance(alias, type):  # pragma: no cover
+            # compat: for a short period of time master stored types
+            return alias
+        return self._reverse_index_map.get(alias, Index)
+
+    def _get_index_factory(self, attrs):
+        index_class = self._alias_to_class(
+            _ensure_decoded(getattr(attrs, "index_class", ""))
+        )
+
+        factory: Callable
+
+        if index_class == DatetimeIndex:
+
+            def f(values, freq=None, tz=None):
+                # data are already in UTC, localize and convert if tz present
+                dta = DatetimeArray._simple_new(
+                    values.values, dtype=values.dtype, freq=freq
+                )
+                result = DatetimeIndex._simple_new(dta, name=None)
+                if tz is not None:
+                    result = result.tz_localize("UTC").tz_convert(tz)
+                return result
+
+            factory = f
+        elif index_class == PeriodIndex:
+
+            def f(values, freq=None, tz=None):
+                dtype = PeriodDtype(freq)
+                parr = PeriodArray._simple_new(values, dtype=dtype)
+                return PeriodIndex._simple_new(parr, name=None)
+
+            factory = f
+        else:
+            factory = index_class
+
+        kwargs = {}
+        if "freq" in attrs:
+            kwargs["freq"] = attrs["freq"]
+            if index_class is Index:
+                # DTI/PI would be gotten by _alias_to_class
+                factory = TimedeltaIndex
+
+        if "tz" in attrs:
+            if isinstance(attrs["tz"], bytes):
+                # created by python2
+                kwargs["tz"] = attrs["tz"].decode("utf-8")
+            else:
+                # created by python3
+                kwargs["tz"] = attrs["tz"]
+            assert index_class is DatetimeIndex  # just checking
+
+        return factory, kwargs
+
+    def validate_read(self, columns, where) -> None:
+        """
+        raise if any keywords are passed which are not-None
+        """
+        if columns is not None:
+            raise TypeError(
+                "cannot pass a column specification when reading "
+                "a Fixed format store. this store must be selected in its entirety"
+            )
+        if where is not None:
+            raise TypeError(
+                "cannot pass a where specification when reading "
+                "from a Fixed format store. this store must be selected in its entirety"
+            )
+
+    @property
+    def is_exists(self) -> bool:
+        return True
+
+    def set_attrs(self) -> None:
+        """set our object attributes"""
+        self.attrs.encoding = self.encoding
+        self.attrs.errors = self.errors
+
+    def get_attrs(self) -> None:
+        """retrieve our attributes"""
+        self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
+        self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
+        for n in self.attributes:
+            setattr(self, n, _ensure_decoded(getattr(self.attrs, n, None)))
+
+    def write(self, obj, **kwargs) -> None:
+        self.set_attrs()
+
+    def read_array(self, key: str, start: int | None = None, stop: int | None = None):
+        """read an array for the specified node (off of group"""
+        import tables
+
+        node = getattr(self.group, key)
+        attrs = node._v_attrs
+
+        transposed = getattr(attrs, "transposed", False)
+
+        if isinstance(node, tables.VLArray):
+            ret = node[0][start:stop]
+            dtype = getattr(attrs, "value_type", None)
+            if dtype is not None:
+                ret = pd_array(ret, dtype=dtype)
+        else:
+            dtype = _ensure_decoded(getattr(attrs, "value_type", None))
+            shape = getattr(attrs, "shape", None)
+
+            if shape is not None:
+                # length 0 axis
+                ret = np.empty(shape, dtype=dtype)
+            else:
+                ret = node[start:stop]
+
+            if dtype and dtype.startswith("datetime64"):
+                # reconstruct a timezone if indicated
+                tz = getattr(attrs, "tz", None)
+                ret = _set_tz(ret, tz, coerce=True)
+
+            elif dtype == "timedelta64":
+                ret = np.asarray(ret, dtype="m8[ns]")
+
+        if transposed:
+            return ret.T
+        else:
+            return ret
+
+    def read_index(
+        self, key: str, start: int | None = None, stop: int | None = None
+    ) -> Index:
+        variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety"))
+
+        if variety == "multi":
+            return self.read_multi_index(key, start=start, stop=stop)
+        elif variety == "regular":
+            node = getattr(self.group, key)
+            index = self.read_index_node(node, start=start, stop=stop)
+            return index
+        else:  # pragma: no cover
+            raise TypeError(f"unrecognized index variety: {variety}")
+
+    def write_index(self, key: str, index: Index) -> None:
+        if isinstance(index, MultiIndex):
+            setattr(self.attrs, f"{key}_variety", "multi")
+            self.write_multi_index(key, index)
+        else:
+            setattr(self.attrs, f"{key}_variety", "regular")
+            converted = _convert_index("index", index, self.encoding, self.errors)
+
+            self.write_array(key, converted.values)
+
+            node = getattr(self.group, key)
+            node._v_attrs.kind = converted.kind
+            node._v_attrs.name = index.name
+
+            if isinstance(index, (DatetimeIndex, PeriodIndex)):
+                node._v_attrs.index_class = self._class_to_alias(type(index))
+
+            if isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
+                node._v_attrs.freq = index.freq
+
+            if isinstance(index, DatetimeIndex) and index.tz is not None:
+                node._v_attrs.tz = _get_tz(index.tz)
+
+    def write_multi_index(self, key: str, index: MultiIndex) -> None:
+        setattr(self.attrs, f"{key}_nlevels", index.nlevels)
+
+        for i, (lev, level_codes, name) in enumerate(
+            zip(index.levels, index.codes, index.names)
+        ):
+            # write the level
+            if isinstance(lev.dtype, ExtensionDtype):
+                raise NotImplementedError(
+                    "Saving a MultiIndex with an extension dtype is not supported."
+                )
+            level_key = f"{key}_level{i}"
+            conv_level = _convert_index(level_key, lev, self.encoding, self.errors)
+            self.write_array(level_key, conv_level.values)
+            node = getattr(self.group, level_key)
+            node._v_attrs.kind = conv_level.kind
+            node._v_attrs.name = name
+
+            # write the name
+            setattr(node._v_attrs, f"{key}_name{name}", name)
+
+            # write the labels
+            label_key = f"{key}_label{i}"
+            self.write_array(label_key, level_codes)
+
+    def read_multi_index(
+        self, key: str, start: int | None = None, stop: int | None = None
+    ) -> MultiIndex:
+        nlevels = getattr(self.attrs, f"{key}_nlevels")
+
+        levels = []
+        codes = []
+        names: list[Hashable] = []
+        for i in range(nlevels):
+            level_key = f"{key}_level{i}"
+            node = getattr(self.group, level_key)
+            lev = self.read_index_node(node, start=start, stop=stop)
+            levels.append(lev)
+            names.append(lev.name)
+
+            label_key = f"{key}_label{i}"
+            level_codes = self.read_array(label_key, start=start, stop=stop)
+            codes.append(level_codes)
+
+        return MultiIndex(
+            levels=levels, codes=codes, names=names, verify_integrity=True
+        )
+
+    def read_index_node(
+        self, node: Node, start: int | None = None, stop: int | None = None
+    ) -> Index:
+        data = node[start:stop]
+        # If the index was an empty array write_array_empty() will
+        # have written a sentinel. Here we replace it with the original.
+        if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
+            data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type)
+        kind = _ensure_decoded(node._v_attrs.kind)
+        name = None
+
+        if "name" in node._v_attrs:
+            name = _ensure_str(node._v_attrs.name)
+            name = _ensure_decoded(name)
+
+        attrs = node._v_attrs
+        factory, kwargs = self._get_index_factory(attrs)
+
+        if kind in ("date", "object"):
+            index = factory(
+                _unconvert_index(
+                    data, kind, encoding=self.encoding, errors=self.errors
+                ),
+                dtype=object,
+                **kwargs,
+            )
+        else:
+            try:
+                index = factory(
+                    _unconvert_index(
+                        data, kind, encoding=self.encoding, errors=self.errors
+                    ),
+                    **kwargs,
+                )
+            except UnicodeEncodeError as err:
+                if (
+                    self.errors == "surrogatepass"
+                    and get_option("future.infer_string")
+                    and str(err).endswith("surrogates not allowed")
+                    and HAS_PYARROW
+                ):
+                    index = factory(
+                        _unconvert_index(
+                            data, kind, encoding=self.encoding, errors=self.errors
+                        ),
+                        dtype=StringDtype(storage="python", na_value=np.nan),
+                        **kwargs,
+                    )
+                else:
+                    raise
+
+        index.name = name
+
+        return index
+
+    def write_array_empty(self, key: str, value: ArrayLike) -> None:
+        """write a 0-len array"""
+        # ugly hack for length 0 axes
+        arr = np.empty((1,) * value.ndim)
+        self._handle.create_array(self.group, key, arr)
+        node = getattr(self.group, key)
+        node._v_attrs.value_type = str(value.dtype)
+        node._v_attrs.shape = value.shape
+
+    def write_array(
+        self, key: str, obj: AnyArrayLike, items: Index | None = None
+    ) -> None:
+        # TODO: we only have a few tests that get here, the only EA
+        #  that gets passed is DatetimeArray, and we never have
+        #  both self._filters and EA
+
+        value = extract_array(obj, extract_numpy=True)
+
+        if key in self.group:
+            self._handle.remove_node(self.group, key)
+
+        # Transform needed to interface with pytables row/col notation
+        empty_array = value.size == 0
+        transposed = False
+
+        if isinstance(value.dtype, CategoricalDtype):
+            raise NotImplementedError(
+                "Cannot store a category dtype in a HDF5 dataset that uses format="
+                '"fixed". Use format="table".'
+            )
+        if not empty_array:
+            if hasattr(value, "T"):
+                # ExtensionArrays (1d) may not have transpose.
+                value = value.T
+                transposed = True
+
+        atom = None
+        if self._filters is not None:
+            with suppress(ValueError):
+                # get the atom for this datatype
+                atom = _tables().Atom.from_dtype(value.dtype)
+
+        if atom is not None:
+            # We only get here if self._filters is non-None and
+            #  the Atom.from_dtype call succeeded
+
+            # create an empty chunked array and fill it from value
+            if not empty_array:
+                ca = self._handle.create_carray(
+                    self.group, key, atom, value.shape, filters=self._filters
+                )
+                ca[:] = value
+
+            else:
+                self.write_array_empty(key, value)
+
+        elif value.dtype.type == np.object_:
+            # infer the type, warn if we have a non-string type here (for
+            # performance)
+            inferred_type = lib.infer_dtype(value, skipna=False)
+            if empty_array:
+                pass
+            elif inferred_type == "string":
+                pass
+            else:
+                ws = performance_doc % (inferred_type, key, items)
+                warnings.warn(ws, PerformanceWarning, stacklevel=find_stack_level())
+
+            vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
+            vlarr.append(value)
+
+        elif lib.is_np_dtype(value.dtype, "M"):
+            self._handle.create_array(self.group, key, value.view("i8"))
+            getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
+        elif isinstance(value.dtype, DatetimeTZDtype):
+            # store as UTC
+            # with a zone
+
+            # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
+            # attribute "asi8"
+            self._handle.create_array(
+                self.group, key, value.asi8  # type: ignore[union-attr]
+            )
+
+            node = getattr(self.group, key)
+            # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
+            # attribute "tz"
+            node._v_attrs.tz = _get_tz(value.tz)  # type: ignore[union-attr]
+            node._v_attrs.value_type = f"datetime64[{value.dtype.unit}]"
+        elif lib.is_np_dtype(value.dtype, "m"):
+            self._handle.create_array(self.group, key, value.view("i8"))
+            getattr(self.group, key)._v_attrs.value_type = "timedelta64"
+        elif isinstance(value, BaseStringArray):
+            vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
+            vlarr.append(value.to_numpy())
+            node = getattr(self.group, key)
+            node._v_attrs.value_type = str(value.dtype)
+        elif empty_array:
+            self.write_array_empty(key, value)
+        else:
+            self._handle.create_array(self.group, key, value)
+
+        getattr(self.group, key)._v_attrs.transposed = transposed
+
+
+class SeriesFixed(GenericFixed):
+    pandas_kind = "series"
+    attributes = ["name"]
+
+    name: Hashable
+
+    @property
+    def shape(self):
+        try:
+            return (len(self.group.values),)
+        except (TypeError, AttributeError):
+            return None
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> Series:
+        self.validate_read(columns, where)
+        index = self.read_index("index", start=start, stop=stop)
+        values = self.read_array("values", start=start, stop=stop)
+        try:
+            result = Series(values, index=index, name=self.name, copy=False)
+        except UnicodeEncodeError as err:
+            if (
+                self.errors == "surrogatepass"
+                and get_option("future.infer_string")
+                and str(err).endswith("surrogates not allowed")
+                and HAS_PYARROW
+            ):
+                result = Series(
+                    values,
+                    index=index,
+                    name=self.name,
+                    copy=False,
+                    dtype=StringDtype(storage="python", na_value=np.nan),
+                )
+            else:
+                raise
+        return result
+
+    def write(self, obj, **kwargs) -> None:
+        super().write(obj, **kwargs)
+        self.write_index("index", obj.index)
+        self.write_array("values", obj)
+        self.attrs.name = obj.name
+
+
+class BlockManagerFixed(GenericFixed):
+    attributes = ["ndim", "nblocks"]
+
+    nblocks: int
+
+    @property
+    def shape(self) -> Shape | None:
+        try:
+            ndim = self.ndim
+
+            # items
+            items = 0
+            for i in range(self.nblocks):
+                node = getattr(self.group, f"block{i}_items")
+                shape = getattr(node, "shape", None)
+                if shape is not None:
+                    items += shape[0]
+
+            # data shape
+            node = self.group.block0_values
+            shape = getattr(node, "shape", None)
+            if shape is not None:
+                shape = list(shape[0 : (ndim - 1)])
+            else:
+                shape = []
+
+            shape.append(items)
+
+            return shape
+        except AttributeError:
+            return None
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> DataFrame:
+        # start, stop applied to rows, so 0th axis only
+        self.validate_read(columns, where)
+        select_axis = self.obj_type()._get_block_manager_axis(0)
+
+        axes = []
+        for i in range(self.ndim):
+            _start, _stop = (start, stop) if i == select_axis else (None, None)
+            ax = self.read_index(f"axis{i}", start=_start, stop=_stop)
+            axes.append(ax)
+
+        items = axes[0]
+        dfs = []
+
+        for i in range(self.nblocks):
+            blk_items = self.read_index(f"block{i}_items")
+            values = self.read_array(f"block{i}_values", start=_start, stop=_stop)
+
+            columns = items[items.get_indexer(blk_items)]
+            df = DataFrame(values.T, columns=columns, index=axes[1], copy=False)
+            if (
+                using_string_dtype()
+                and isinstance(values, np.ndarray)
+                and is_string_array(values, skipna=True)
+            ):
+                df = df.astype(StringDtype(na_value=np.nan))
+            dfs.append(df)
+
+        if len(dfs) > 0:
+            out = concat(dfs, axis=1, copy=True)
+            if using_copy_on_write():
+                # with CoW, concat ignores the copy keyword. Here, we still want
+                # to copy to enforce optimized column-major layout
+                out = out.copy()
+            out = out.reindex(columns=items, copy=False)
+            return out
+
+        return DataFrame(columns=axes[0], index=axes[1])
+
+    def write(self, obj, **kwargs) -> None:
+        super().write(obj, **kwargs)
+
+        # TODO(ArrayManager) HDFStore relies on accessing the blocks
+        if isinstance(obj._mgr, ArrayManager):
+            obj = obj._as_manager("block")
+
+        data = obj._mgr
+        if not data.is_consolidated():
+            data = data.consolidate()
+
+        self.attrs.ndim = data.ndim
+        for i, ax in enumerate(data.axes):
+            if i == 0 and (not ax.is_unique):
+                raise ValueError("Columns index has to be unique for fixed format")
+            self.write_index(f"axis{i}", ax)
+
+        # Supporting mixed-type DataFrame objects...nontrivial
+        self.attrs.nblocks = len(data.blocks)
+        for i, blk in enumerate(data.blocks):
+            # I have no idea why, but writing values before items fixed #2299
+            blk_items = data.items.take(blk.mgr_locs)
+            self.write_array(f"block{i}_values", blk.values, items=blk_items)
+            self.write_index(f"block{i}_items", blk_items)
+
+
+class FrameFixed(BlockManagerFixed):
+    pandas_kind = "frame"
+    obj_type = DataFrame
+
+
+class Table(Fixed):
+    """
+    represent a table:
+        facilitate read/write of various types of tables
+
+    Attrs in Table Node
+    -------------------
+    These are attributes that are store in the main table node, they are
+    necessary to recreate these tables when read back in.
+
+    index_axes    : a list of tuples of the (original indexing axis and
+        index column)
+    non_index_axes: a list of tuples of the (original index axis and
+        columns on a non-indexing axis)
+    values_axes   : a list of the columns which comprise the data of this
+        table
+    data_columns  : a list of the columns that we are allowing indexing
+        (these become single columns in values_axes)
+    nan_rep       : the string to use for nan representations for string
+        objects
+    levels        : the names of levels
+    metadata      : the names of the metadata columns
+    """
+
+    pandas_kind = "wide_table"
+    format_type: str = "table"  # GH#30962 needed by dask
+    table_type: str
+    levels: int | list[Hashable] = 1
+    is_table = True
+
+    metadata: list
+
+    def __init__(
+        self,
+        parent: HDFStore,
+        group: Node,
+        encoding: str | None = None,
+        errors: str = "strict",
+        index_axes: list[IndexCol] | None = None,
+        non_index_axes: list[tuple[AxisInt, Any]] | None = None,
+        values_axes: list[DataCol] | None = None,
+        data_columns: list | None = None,
+        info: dict | None = None,
+        nan_rep=None,
+    ) -> None:
+        super().__init__(parent, group, encoding=encoding, errors=errors)
+        self.index_axes = index_axes or []
+        self.non_index_axes = non_index_axes or []
+        self.values_axes = values_axes or []
+        self.data_columns = data_columns or []
+        self.info = info or {}
+        self.nan_rep = nan_rep
+
+    @property
+    def table_type_short(self) -> str:
+        return self.table_type.split("_")[0]
+
+    def __repr__(self) -> str:
+        """return a pretty representation of myself"""
+        self.infer_axes()
+        jdc = ",".join(self.data_columns) if len(self.data_columns) else ""
+        dc = f",dc->[{jdc}]"
+
+        ver = ""
+        if self.is_old_version:
+            jver = ".".join([str(x) for x in self.version])
+            ver = f"[{jver}]"
+
+        jindex_axes = ",".join([a.name for a in self.index_axes])
+        return (
+            f"{self.pandas_type:12.12}{ver} "
+            f"(typ->{self.table_type_short},nrows->{self.nrows},"
+            f"ncols->{self.ncols},indexers->[{jindex_axes}]{dc})"
+        )
+
+    def __getitem__(self, c: str):
+        """return the axis for c"""
+        for a in self.axes:
+            if c == a.name:
+                return a
+        return None
+
+    def validate(self, other) -> None:
+        """validate against an existing table"""
+        if other is None:
+            return
+
+        if other.table_type != self.table_type:
+            raise TypeError(
+                "incompatible table_type with existing "
+                f"[{other.table_type} - {self.table_type}]"
+            )
+
+        for c in ["index_axes", "non_index_axes", "values_axes"]:
+            sv = getattr(self, c, None)
+            ov = getattr(other, c, None)
+            if sv != ov:
+                # show the error for the specific axes
+                # Argument 1 to "enumerate" has incompatible type
+                # "Optional[Any]"; expected "Iterable[Any]"  [arg-type]
+                for i, sax in enumerate(sv):  # type: ignore[arg-type]
+                    # Value of type "Optional[Any]" is not indexable  [index]
+                    oax = ov[i]  # type: ignore[index]
+                    if sax != oax:
+                        if c == "values_axes" and sax.kind != oax.kind:
+                            raise ValueError(
+                                f"Cannot serialize the column [{oax.values[0]}] "
+                                f"because its data contents are not [{sax.kind}] "
+                                f"but [{oax.kind}] object dtype"
+                            )
+                        raise ValueError(
+                            f"invalid combination of [{c}] on appending data "
+                            f"[{sax}] vs current table [{oax}]"
+                        )
+
+                # should never get here
+                raise Exception(
+                    f"invalid combination of [{c}] on appending data [{sv}] vs "
+                    f"current table [{ov}]"
+                )
+
+    @property
+    def is_multi_index(self) -> bool:
+        """the levels attribute is 1 or a list in the case of a multi-index"""
+        return isinstance(self.levels, list)
+
+    def validate_multiindex(
+        self, obj: DataFrame | Series
+    ) -> tuple[DataFrame, list[Hashable]]:
+        """
+        validate that we can store the multi-index; reset and return the
+        new object
+        """
+        levels = com.fill_missing_names(obj.index.names)
+        try:
+            reset_obj = obj.reset_index()
+        except ValueError as err:
+            raise ValueError(
+                "duplicate names/columns in the multi-index when storing as a table"
+            ) from err
+        assert isinstance(reset_obj, DataFrame)  # for mypy
+        return reset_obj, levels
+
+    @property
+    def nrows_expected(self) -> int:
+        """based on our axes, compute the expected nrows"""
+        return np.prod([i.cvalues.shape[0] for i in self.index_axes])
+
+    @property
+    def is_exists(self) -> bool:
+        """has this table been created"""
+        return "table" in self.group
+
+    @property
+    def storable(self):
+        return getattr(self.group, "table", None)
+
+    @property
+    def table(self):
+        """return the table group (this is my storable)"""
+        return self.storable
+
+    @property
+    def dtype(self):
+        return self.table.dtype
+
+    @property
+    def description(self):
+        return self.table.description
+
+    @property
+    def axes(self) -> itertools.chain[IndexCol]:
+        return itertools.chain(self.index_axes, self.values_axes)
+
+    @property
+    def ncols(self) -> int:
+        """the number of total columns in the values axes"""
+        return sum(len(a.values) for a in self.values_axes)
+
+    @property
+    def is_transposed(self) -> bool:
+        return False
+
+    @property
+    def data_orientation(self) -> tuple[int, ...]:
+        """return a tuple of my permutated axes, non_indexable at the front"""
+        return tuple(
+            itertools.chain(
+                [int(a[0]) for a in self.non_index_axes],
+                [int(a.axis) for a in self.index_axes],
+            )
+        )
+
+    def queryables(self) -> dict[str, Any]:
+        """return a dict of the kinds allowable columns for this object"""
+        # mypy doesn't recognize DataFrame._AXIS_NAMES, so we re-write it here
+        axis_names = {0: "index", 1: "columns"}
+
+        # compute the values_axes queryables
+        d1 = [(a.cname, a) for a in self.index_axes]
+        d2 = [(axis_names[axis], None) for axis, values in self.non_index_axes]
+        d3 = [
+            (v.cname, v) for v in self.values_axes if v.name in set(self.data_columns)
+        ]
+
+        return dict(d1 + d2 + d3)
+
+    def index_cols(self):
+        """return a list of my index cols"""
+        # Note: each `i.cname` below is assured to be a str.
+        return [(i.axis, i.cname) for i in self.index_axes]
+
+    def values_cols(self) -> list[str]:
+        """return a list of my values cols"""
+        return [i.cname for i in self.values_axes]
+
+    def _get_metadata_path(self, key: str) -> str:
+        """return the metadata pathname for this key"""
+        group = self.group._v_pathname
+        return f"{group}/meta/{key}/meta"
+
+    def write_metadata(self, key: str, values: np.ndarray) -> None:
+        """
+        Write out a metadata array to the key as a fixed-format Series.
+
+        Parameters
+        ----------
+        key : str
+        values : ndarray
+        """
+        self.parent.put(
+            self._get_metadata_path(key),
+            Series(values, copy=False),
+            format="table",
+            encoding=self.encoding,
+            errors=self.errors,
+            nan_rep=self.nan_rep,
+        )
+
+    def read_metadata(self, key: str):
+        """return the meta data array for this key"""
+        if getattr(getattr(self.group, "meta", None), key, None) is not None:
+            return self.parent.select(self._get_metadata_path(key))
+        return None
+
+    def set_attrs(self) -> None:
+        """set our table type & indexables"""
+        self.attrs.table_type = str(self.table_type)
+        self.attrs.index_cols = self.index_cols()
+        self.attrs.values_cols = self.values_cols()
+        self.attrs.non_index_axes = self.non_index_axes
+        self.attrs.data_columns = self.data_columns
+        self.attrs.nan_rep = self.nan_rep
+        self.attrs.encoding = self.encoding
+        self.attrs.errors = self.errors
+        self.attrs.levels = self.levels
+        self.attrs.info = self.info
+
+    def get_attrs(self) -> None:
+        """retrieve our attributes"""
+        self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or []
+        self.data_columns = getattr(self.attrs, "data_columns", None) or []
+        self.info = getattr(self.attrs, "info", None) or {}
+        self.nan_rep = getattr(self.attrs, "nan_rep", None)
+        self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
+        self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
+        self.levels: list[Hashable] = getattr(self.attrs, "levels", None) or []
+        self.index_axes = [a for a in self.indexables if a.is_an_indexable]
+        self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
+
+    def validate_version(self, where=None) -> None:
+        """are we trying to operate on an old version?"""
+        if where is not None:
+            if self.is_old_version:
+                ws = incompatibility_doc % ".".join([str(x) for x in self.version])
+                warnings.warn(
+                    ws,
+                    IncompatibilityWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    def validate_min_itemsize(self, min_itemsize) -> None:
+        """
+        validate the min_itemsize doesn't contain items that are not in the
+        axes this needs data_columns to be defined
+        """
+        if min_itemsize is None:
+            return
+        if not isinstance(min_itemsize, dict):
+            return
+
+        q = self.queryables()
+        for k in min_itemsize:
+            # ok, apply generally
+            if k == "values":
+                continue
+            if k not in q:
+                raise ValueError(
+                    f"min_itemsize has the key [{k}] which is not an axis or "
+                    "data_column"
+                )
+
+    @cache_readonly
+    def indexables(self):
+        """create/cache the indexables if they don't exist"""
+        _indexables = []
+
+        desc = self.description
+        table_attrs = self.table.attrs
+
+        # Note: each of the `name` kwargs below are str, ensured
+        #  by the definition in index_cols.
+        # index columns
+        for i, (axis, name) in enumerate(self.attrs.index_cols):
+            atom = getattr(desc, name)
+            md = self.read_metadata(name)
+            meta = "category" if md is not None else None
+
+            kind_attr = f"{name}_kind"
+            kind = getattr(table_attrs, kind_attr, None)
+
+            index_col = IndexCol(
+                name=name,
+                axis=axis,
+                pos=i,
+                kind=kind,
+                typ=atom,
+                table=self.table,
+                meta=meta,
+                metadata=md,
+            )
+            _indexables.append(index_col)
+
+        # values columns
+        dc = set(self.data_columns)
+        base_pos = len(_indexables)
+
+        def f(i, c):
+            assert isinstance(c, str)
+            klass = DataCol
+            if c in dc:
+                klass = DataIndexableCol
+
+            atom = getattr(desc, c)
+            adj_name = _maybe_adjust_name(c, self.version)
+
+            # TODO: why kind_attr here?
+            values = getattr(table_attrs, f"{adj_name}_kind", None)
+            dtype = getattr(table_attrs, f"{adj_name}_dtype", None)
+            # Argument 1 to "_dtype_to_kind" has incompatible type
+            # "Optional[Any]"; expected "str"  [arg-type]
+            kind = _dtype_to_kind(dtype)  # type: ignore[arg-type]
+
+            md = self.read_metadata(c)
+            # TODO: figure out why these two versions of `meta` dont always match.
+            #  meta = "category" if md is not None else None
+            meta = getattr(table_attrs, f"{adj_name}_meta", None)
+
+            obj = klass(
+                name=adj_name,
+                cname=c,
+                values=values,
+                kind=kind,
+                pos=base_pos + i,
+                typ=atom,
+                table=self.table,
+                meta=meta,
+                metadata=md,
+                dtype=dtype,
+            )
+            return obj
+
+        # Note: the definition of `values_cols` ensures that each
+        #  `c` below is a str.
+        _indexables.extend([f(i, c) for i, c in enumerate(self.attrs.values_cols)])
+
+        return _indexables
+
+    def create_index(
+        self, columns=None, optlevel=None, kind: str | None = None
+    ) -> None:
+        """
+        Create a pytables index on the specified columns.
+
+        Parameters
+        ----------
+        columns : None, bool, or listlike[str]
+            Indicate which columns to create an index on.
+
+            * False : Do not create any indexes.
+            * True : Create indexes on all columns.
+            * None : Create indexes on all columns.
+            * listlike : Create indexes on the given columns.
+
+        optlevel : int or None, default None
+            Optimization level, if None, pytables defaults to 6.
+        kind : str or None, default None
+            Kind of index, if None, pytables defaults to "medium".
+
+        Raises
+        ------
+        TypeError if trying to create an index on a complex-type column.
+
+        Notes
+        -----
+        Cannot index Time64Col or ComplexCol.
+        Pytables must be >= 3.0.
+        """
+        if not self.infer_axes():
+            return
+        if columns is False:
+            return
+
+        # index all indexables and data_columns
+        if columns is None or columns is True:
+            columns = [a.cname for a in self.axes if a.is_data_indexable]
+        if not isinstance(columns, (tuple, list)):
+            columns = [columns]
+
+        kw = {}
+        if optlevel is not None:
+            kw["optlevel"] = optlevel
+        if kind is not None:
+            kw["kind"] = kind
+
+        table = self.table
+        for c in columns:
+            v = getattr(table.cols, c, None)
+            if v is not None:
+                # remove the index if the kind/optlevel have changed
+                if v.is_indexed:
+                    index = v.index
+                    cur_optlevel = index.optlevel
+                    cur_kind = index.kind
+
+                    if kind is not None and cur_kind != kind:
+                        v.remove_index()
+                    else:
+                        kw["kind"] = cur_kind
+
+                    if optlevel is not None and cur_optlevel != optlevel:
+                        v.remove_index()
+                    else:
+                        kw["optlevel"] = cur_optlevel
+
+                # create the index
+                if not v.is_indexed:
+                    if v.type.startswith("complex"):
+                        raise TypeError(
+                            "Columns containing complex values can be stored but "
+                            "cannot be indexed when using table format. Either use "
+                            "fixed format, set index=False, or do not include "
+                            "the columns containing complex values to "
+                            "data_columns when initializing the table."
+                        )
+                    v.create_index(**kw)
+            elif c in self.non_index_axes[0][1]:
+                # GH 28156
+                raise AttributeError(
+                    f"column {c} is not a data_column.\n"
+                    f"In order to read column {c} you must reload the dataframe \n"
+                    f"into HDFStore and include {c} with the data_columns argument."
+                )
+
+    def _read_axes(
+        self, where, start: int | None = None, stop: int | None = None
+    ) -> list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]:
+        """
+        Create the axes sniffed from the table.
+
+        Parameters
+        ----------
+        where : ???
+        start : int or None, default None
+        stop : int or None, default None
+
+        Returns
+        -------
+        List[Tuple[index_values, column_values]]
+        """
+        # create the selection
+        selection = Selection(self, where=where, start=start, stop=stop)
+        values = selection.select()
+
+        results = []
+        # convert the data
+        for a in self.axes:
+            a.set_info(self.info)
+            res = a.convert(
+                values,
+                nan_rep=self.nan_rep,
+                encoding=self.encoding,
+                errors=self.errors,
+            )
+            results.append(res)
+
+        return results
+
+    @classmethod
+    def get_object(cls, obj, transposed: bool):
+        """return the data for this obj"""
+        return obj
+
+    def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):
+        """
+        take the input data_columns and min_itemize and create a data
+        columns spec
+        """
+        if not len(non_index_axes):
+            return []
+
+        axis, axis_labels = non_index_axes[0]
+        info = self.info.get(axis, {})
+        if info.get("type") == "MultiIndex" and data_columns:
+            raise ValueError(
+                f"cannot use a multi-index on axis [{axis}] with "
+                f"data_columns {data_columns}"
+            )
+
+        # evaluate the passed data_columns, True == use all columns
+        # take only valid axis labels
+        if data_columns is True:
+            data_columns = list(axis_labels)
+        elif data_columns is None:
+            data_columns = []
+
+        # if min_itemsize is a dict, add the keys (exclude 'values')
+        if isinstance(min_itemsize, dict):
+            existing_data_columns = set(data_columns)
+            data_columns = list(data_columns)  # ensure we do not modify
+            data_columns.extend(
+                [
+                    k
+                    for k in min_itemsize.keys()
+                    if k != "values" and k not in existing_data_columns
+                ]
+            )
+
+        # return valid columns in the order of our axis
+        return [c for c in data_columns if c in axis_labels]
+
+    def _create_axes(
+        self,
+        axes,
+        obj: DataFrame,
+        validate: bool = True,
+        nan_rep=None,
+        data_columns=None,
+        min_itemsize=None,
+    ):
+        """
+        Create and return the axes.
+
+        Parameters
+        ----------
+        axes: list or None
+            The names or numbers of the axes to create.
+        obj : DataFrame
+            The object to create axes on.
+        validate: bool, default True
+            Whether to validate the obj against an existing object already written.
+        nan_rep :
+            A value to use for string column nan_rep.
+        data_columns : List[str], True, or None, default None
+            Specify the columns that we want to create to allow indexing on.
+
+            * True : Use all available columns.
+            * None : Use no columns.
+            * List[str] : Use the specified columns.
+
+        min_itemsize: Dict[str, int] or None, default None
+            The min itemsize for a column in bytes.
+        """
+        if not isinstance(obj, DataFrame):
+            group = self.group._v_name
+            raise TypeError(
+                f"cannot properly create the storer for: [group->{group},"
+                f"value->{type(obj)}]"
+            )
+
+        # set the default axes if needed
+        if axes is None:
+            axes = [0]
+
+        # map axes to numbers
+        axes = [obj._get_axis_number(a) for a in axes]
+
+        # do we have an existing table (if so, use its axes & data_columns)
+        if self.infer_axes():
+            table_exists = True
+            axes = [a.axis for a in self.index_axes]
+            data_columns = list(self.data_columns)
+            nan_rep = self.nan_rep
+            # TODO: do we always have validate=True here?
+        else:
+            table_exists = False
+
+        new_info = self.info
+
+        assert self.ndim == 2  # with next check, we must have len(axes) == 1
+        # currently support on ndim-1 axes
+        if len(axes) != self.ndim - 1:
+            raise ValueError(
+                "currently only support ndim-1 indexers in an AppendableTable"
+            )
+
+        # create according to the new data
+        new_non_index_axes: list = []
+
+        # nan_representation
+        if nan_rep is None:
+            nan_rep = "nan"
+
+        # We construct the non-index-axis first, since that alters new_info
+        idx = next(x for x in [0, 1] if x not in axes)
+
+        a = obj.axes[idx]
+        # we might be able to change the axes on the appending data if necessary
+        append_axis = list(a)
+        if table_exists:
+            indexer = len(new_non_index_axes)  # i.e. 0
+            exist_axis = self.non_index_axes[indexer][1]
+            if not array_equivalent(
+                np.array(append_axis),
+                np.array(exist_axis),
+                strict_nan=True,
+                dtype_equal=True,
+            ):
+                # ahah! -> reindex
+                if array_equivalent(
+                    np.array(sorted(append_axis)),
+                    np.array(sorted(exist_axis)),
+                    strict_nan=True,
+                    dtype_equal=True,
+                ):
+                    append_axis = exist_axis
+
+        # the non_index_axes info
+        info = new_info.setdefault(idx, {})
+        info["names"] = list(a.names)
+        info["type"] = type(a).__name__
+
+        new_non_index_axes.append((idx, append_axis))
+
+        # Now we can construct our new index axis
+        idx = axes[0]
+        a = obj.axes[idx]
+        axis_name = obj._get_axis_name(idx)
+        new_index = _convert_index(axis_name, a, self.encoding, self.errors)
+        new_index.axis = idx
+
+        # Because we are always 2D, there is only one new_index, so
+        #  we know it will have pos=0
+        new_index.set_pos(0)
+        new_index.update_info(new_info)
+        new_index.maybe_set_size(min_itemsize)  # check for column conflicts
+
+        new_index_axes = [new_index]
+        j = len(new_index_axes)  # i.e. 1
+        assert j == 1
+
+        # reindex by our non_index_axes & compute data_columns
+        assert len(new_non_index_axes) == 1
+        for a in new_non_index_axes:
+            obj = _reindex_axis(obj, a[0], a[1])
+
+        transposed = new_index.axis == 1
+
+        # figure out data_columns and get out blocks
+        data_columns = self.validate_data_columns(
+            data_columns, min_itemsize, new_non_index_axes
+        )
+
+        frame = self.get_object(obj, transposed)._consolidate()
+
+        blocks, blk_items = self._get_blocks_and_items(
+            frame, table_exists, new_non_index_axes, self.values_axes, data_columns
+        )
+
+        # add my values
+        vaxes = []
+        for i, (blk, b_items) in enumerate(zip(blocks, blk_items)):
+            # shape of the data column are the indexable axes
+            klass = DataCol
+            name = None
+
+            # we have a data_column
+            if data_columns and len(b_items) == 1 and b_items[0] in data_columns:
+                klass = DataIndexableCol
+                name = b_items[0]
+                if not (name is None or isinstance(name, str)):
+                    # TODO: should the message here be more specifically non-str?
+                    raise ValueError("cannot have non-object label DataIndexableCol")
+
+            # make sure that we match up the existing columns
+            # if we have an existing table
+            existing_col: DataCol | None
+
+            if table_exists and validate:
+                try:
+                    existing_col = self.values_axes[i]
+                except (IndexError, KeyError) as err:
+                    raise ValueError(
+                        f"Incompatible appended table [{blocks}]"
+                        f"with existing table [{self.values_axes}]"
+                    ) from err
+            else:
+                existing_col = None
+
+            new_name = name or f"values_block_{i}"
+            data_converted = _maybe_convert_for_string_atom(
+                new_name,
+                blk.values,
+                existing_col=existing_col,
+                min_itemsize=min_itemsize,
+                nan_rep=nan_rep,
+                encoding=self.encoding,
+                errors=self.errors,
+                columns=b_items,
+            )
+            adj_name = _maybe_adjust_name(new_name, self.version)
+
+            typ = klass._get_atom(data_converted)
+            kind = _dtype_to_kind(data_converted.dtype.name)
+            tz = None
+            if getattr(data_converted, "tz", None) is not None:
+                tz = _get_tz(data_converted.tz)
+
+            meta = metadata = ordered = None
+            if isinstance(data_converted.dtype, CategoricalDtype):
+                ordered = data_converted.ordered
+                meta = "category"
+                metadata = np.asarray(data_converted.categories).ravel()
+            elif isinstance(blk.dtype, StringDtype):
+                meta = str(blk.dtype)
+
+            data, dtype_name = _get_data_and_dtype_name(data_converted)
+
+            col = klass(
+                name=adj_name,
+                cname=new_name,
+                values=list(b_items),
+                typ=typ,
+                pos=j,
+                kind=kind,
+                tz=tz,
+                ordered=ordered,
+                meta=meta,
+                metadata=metadata,
+                dtype=dtype_name,
+                data=data,
+            )
+            col.update_info(new_info)
+
+            vaxes.append(col)
+
+            j += 1
+
+        dcs = [col.name for col in vaxes if col.is_data_indexable]
+
+        new_table = type(self)(
+            parent=self.parent,
+            group=self.group,
+            encoding=self.encoding,
+            errors=self.errors,
+            index_axes=new_index_axes,
+            non_index_axes=new_non_index_axes,
+            values_axes=vaxes,
+            data_columns=dcs,
+            info=new_info,
+            nan_rep=nan_rep,
+        )
+        if hasattr(self, "levels"):
+            # TODO: get this into constructor, only for appropriate subclass
+            new_table.levels = self.levels
+
+        new_table.validate_min_itemsize(min_itemsize)
+
+        if validate and table_exists:
+            new_table.validate(self)
+
+        return new_table
+
+    @staticmethod
+    def _get_blocks_and_items(
+        frame: DataFrame,
+        table_exists: bool,
+        new_non_index_axes,
+        values_axes,
+        data_columns,
+    ):
+        # Helper to clarify non-state-altering parts of _create_axes
+
+        # TODO(ArrayManager) HDFStore relies on accessing the blocks
+        if isinstance(frame._mgr, ArrayManager):
+            frame = frame._as_manager("block")
+
+        def get_blk_items(mgr):
+            return [mgr.items.take(blk.mgr_locs) for blk in mgr.blocks]
+
+        mgr = frame._mgr
+        mgr = cast(BlockManager, mgr)
+        blocks: list[Block] = list(mgr.blocks)
+        blk_items: list[Index] = get_blk_items(mgr)
+
+        if len(data_columns):
+            # TODO: prove that we only get here with axis == 1?
+            #  It is the case in all extant tests, but NOT the case
+            #  outside this `if len(data_columns)` check.
+
+            axis, axis_labels = new_non_index_axes[0]
+            new_labels = Index(axis_labels).difference(Index(data_columns))
+            mgr = frame.reindex(new_labels, axis=axis)._mgr
+            mgr = cast(BlockManager, mgr)
+
+            blocks = list(mgr.blocks)
+            blk_items = get_blk_items(mgr)
+            for c in data_columns:
+                # This reindex would raise ValueError if we had a duplicate
+                #  index, so we can infer that (as long as axis==1) we
+                #  get a single column back, so a single block.
+                mgr = frame.reindex([c], axis=axis)._mgr
+                mgr = cast(BlockManager, mgr)
+                blocks.extend(mgr.blocks)
+                blk_items.extend(get_blk_items(mgr))
+
+        # reorder the blocks in the same order as the existing table if we can
+        if table_exists:
+            by_items = {
+                tuple(b_items.tolist()): (b, b_items)
+                for b, b_items in zip(blocks, blk_items)
+            }
+            new_blocks: list[Block] = []
+            new_blk_items = []
+            for ea in values_axes:
+                items = tuple(ea.values)
+                try:
+                    b, b_items = by_items.pop(items)
+                    new_blocks.append(b)
+                    new_blk_items.append(b_items)
+                except (IndexError, KeyError) as err:
+                    jitems = ",".join([pprint_thing(item) for item in items])
+                    raise ValueError(
+                        f"cannot match existing table structure for [{jitems}] "
+                        "on appending data"
+                    ) from err
+            blocks = new_blocks
+            blk_items = new_blk_items
+
+        return blocks, blk_items
+
+    def process_axes(self, obj, selection: Selection, columns=None) -> DataFrame:
+        """process axes filters"""
+        # make a copy to avoid side effects
+        if columns is not None:
+            columns = list(columns)
+
+        # make sure to include levels if we have them
+        if columns is not None and self.is_multi_index:
+            assert isinstance(self.levels, list)  # assured by is_multi_index
+            for n in self.levels:
+                if n not in columns:
+                    columns.insert(0, n)
+
+        # reorder by any non_index_axes & limit to the select columns
+        for axis, labels in self.non_index_axes:
+            obj = _reindex_axis(obj, axis, labels, columns)
+
+            def process_filter(field, filt, op):
+                for axis_name in obj._AXIS_ORDERS:
+                    axis_number = obj._get_axis_number(axis_name)
+                    axis_values = obj._get_axis(axis_name)
+                    assert axis_number is not None
+
+                    # see if the field is the name of an axis
+                    if field == axis_name:
+                        # if we have a multi-index, then need to include
+                        # the levels
+                        if self.is_multi_index:
+                            filt = filt.union(Index(self.levels))
+
+                        takers = op(axis_values, filt)
+                        return obj.loc(axis=axis_number)[takers]
+
+                    # this might be the name of a file IN an axis
+                    elif field in axis_values:
+                        # we need to filter on this dimension
+                        values = ensure_index(getattr(obj, field).values)
+                        filt = ensure_index(filt)
+
+                        # hack until we support reversed dim flags
+                        if isinstance(obj, DataFrame):
+                            axis_number = 1 - axis_number
+
+                        takers = op(values, filt)
+                        return obj.loc(axis=axis_number)[takers]
+
+                raise ValueError(f"cannot find the field [{field}] for filtering!")
+
+        # apply the selection filters (but keep in the same order)
+        if selection.filter is not None:
+            for field, op, filt in selection.filter.format():
+                obj = process_filter(field, filt, op)
+
+        return obj
+
+    def create_description(
+        self,
+        complib,
+        complevel: int | None,
+        fletcher32: bool,
+        expectedrows: int | None,
+    ) -> dict[str, Any]:
+        """create the description of the table from the axes & values"""
+        # provided expected rows if its passed
+        if expectedrows is None:
+            expectedrows = max(self.nrows_expected, 10000)
+
+        d = {"name": "table", "expectedrows": expectedrows}
+
+        # description from the axes & values
+        d["description"] = {a.cname: a.typ for a in self.axes}
+
+        if complib:
+            if complevel is None:
+                complevel = self._complevel or 9
+            filters = _tables().Filters(
+                complevel=complevel,
+                complib=complib,
+                fletcher32=fletcher32 or self._fletcher32,
+            )
+            d["filters"] = filters
+        elif self._filters is not None:
+            d["filters"] = self._filters
+
+        return d
+
+    def read_coordinates(
+        self, where=None, start: int | None = None, stop: int | None = None
+    ):
+        """
+        select coordinates (row numbers) from a table; return the
+        coordinates object
+        """
+        # validate the version
+        self.validate_version(where)
+
+        # infer the data kind
+        if not self.infer_axes():
+            return False
+
+        # create the selection
+        selection = Selection(self, where=where, start=start, stop=stop)
+        coords = selection.select_coords()
+        if selection.filter is not None:
+            for field, op, filt in selection.filter.format():
+                data = self.read_column(
+                    field, start=coords.min(), stop=coords.max() + 1
+                )
+                coords = coords[op(data.iloc[coords - coords.min()], filt).values]
+
+        return Index(coords)
+
+    def read_column(
+        self,
+        column: str,
+        where=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        return a single column from the table, generally only indexables
+        are interesting
+        """
+        # validate the version
+        self.validate_version()
+
+        # infer the data kind
+        if not self.infer_axes():
+            return False
+
+        if where is not None:
+            raise TypeError("read_column does not currently accept a where clause")
+
+        # find the axes
+        for a in self.axes:
+            if column == a.name:
+                if not a.is_data_indexable:
+                    raise ValueError(
+                        f"column [{column}] can not be extracted individually; "
+                        "it is not data indexable"
+                    )
+
+                # column must be an indexable or a data column
+                c = getattr(self.table.cols, column)
+                a.set_info(self.info)
+                col_values = a.convert(
+                    c[start:stop],
+                    nan_rep=self.nan_rep,
+                    encoding=self.encoding,
+                    errors=self.errors,
+                )
+                cvs = _set_tz(col_values[1], a.tz)
+                dtype = getattr(self.table.attrs, f"{column}_meta", None)
+                return Series(cvs, name=column, copy=False, dtype=dtype)
+
+        raise KeyError(f"column [{column}] not found in the table")
+
+
+class WORMTable(Table):
+    """
+    a write-once read-many table: this format DOES NOT ALLOW appending to a
+    table. writing is a one-time operation the data are stored in a format
+    that allows for searching the data on disk
+    """
+
+    table_type = "worm"
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        """
+        read the indices and the indexing array, calculate offset rows and return
+        """
+        raise NotImplementedError("WORMTable needs to implement read")
+
+    def write(self, obj, **kwargs) -> None:
+        """
+        write in a format that we can search later on (but cannot append
+        to): write out the indices and the values using _write_array
+        (e.g. a CArray) create an indexing table so that we can search
+        """
+        raise NotImplementedError("WORMTable needs to implement write")
+
+
+class AppendableTable(Table):
+    """support the new appendable table formats"""
+
+    table_type = "appendable"
+
+    # error: Signature of "write" incompatible with supertype "Fixed"
+    def write(  # type: ignore[override]
+        self,
+        obj,
+        axes=None,
+        append: bool = False,
+        complib=None,
+        complevel=None,
+        fletcher32=None,
+        min_itemsize=None,
+        chunksize: int | None = None,
+        expectedrows=None,
+        dropna: bool = False,
+        nan_rep=None,
+        data_columns=None,
+        track_times: bool = True,
+    ) -> None:
+        if not append and self.is_exists:
+            self._handle.remove_node(self.group, "table")
+
+        # create the axes
+        table = self._create_axes(
+            axes=axes,
+            obj=obj,
+            validate=append,
+            min_itemsize=min_itemsize,
+            nan_rep=nan_rep,
+            data_columns=data_columns,
+        )
+
+        for a in table.axes:
+            a.validate_names()
+
+        if not table.is_exists:
+            # create the table
+            options = table.create_description(
+                complib=complib,
+                complevel=complevel,
+                fletcher32=fletcher32,
+                expectedrows=expectedrows,
+            )
+
+            # set the table attributes
+            table.set_attrs()
+
+            options["track_times"] = track_times
+
+            # create the table
+            table._handle.create_table(table.group, **options)
+
+        # update my info
+        table.attrs.info = table.info
+
+        # validate the axes and set the kinds
+        for a in table.axes:
+            a.validate_and_set(table, append)
+
+        # add the rows
+        table.write_data(chunksize, dropna=dropna)
+
+    def write_data(self, chunksize: int | None, dropna: bool = False) -> None:
+        """
+        we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
+        """
+        names = self.dtype.names
+        nrows = self.nrows_expected
+
+        # if dropna==True, then drop ALL nan rows
+        masks = []
+        if dropna:
+            for a in self.values_axes:
+                # figure the mask: only do if we can successfully process this
+                # column, otherwise ignore the mask
+                mask = isna(a.data).all(axis=0)
+                if isinstance(mask, np.ndarray):
+                    masks.append(mask.astype("u1", copy=False))
+
+        # consolidate masks
+        if len(masks):
+            mask = masks[0]
+            for m in masks[1:]:
+                mask = mask & m
+            mask = mask.ravel()
+        else:
+            mask = None
+
+        # broadcast the indexes if needed
+        indexes = [a.cvalues for a in self.index_axes]
+        nindexes = len(indexes)
+        assert nindexes == 1, nindexes  # ensures we dont need to broadcast
+
+        # transpose the values so first dimension is last
+        # reshape the values if needed
+        values = [a.take_data() for a in self.values_axes]
+        values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values]
+        bvalues = []
+        for i, v in enumerate(values):
+            new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
+            bvalues.append(v.reshape(new_shape))
+
+        # write the chunks
+        if chunksize is None:
+            chunksize = 100000
+
+        rows = np.empty(min(chunksize, nrows), dtype=self.dtype)
+        chunks = nrows // chunksize + 1
+        for i in range(chunks):
+            start_i = i * chunksize
+            end_i = min((i + 1) * chunksize, nrows)
+            if start_i >= end_i:
+                break
+
+            self.write_data_chunk(
+                rows,
+                indexes=[a[start_i:end_i] for a in indexes],
+                mask=mask[start_i:end_i] if mask is not None else None,
+                values=[v[start_i:end_i] for v in bvalues],
+            )
+
+    def write_data_chunk(
+        self,
+        rows: np.ndarray,
+        indexes: list[np.ndarray],
+        mask: npt.NDArray[np.bool_] | None,
+        values: list[np.ndarray],
+    ) -> None:
+        """
+        Parameters
+        ----------
+        rows : an empty memory space where we are putting the chunk
+        indexes : an array of the indexes
+        mask : an array of the masks
+        values : an array of the values
+        """
+        # 0 len
+        for v in values:
+            if not np.prod(v.shape):
+                return
+
+        nrows = indexes[0].shape[0]
+        if nrows != len(rows):
+            rows = np.empty(nrows, dtype=self.dtype)
+        names = self.dtype.names
+        nindexes = len(indexes)
+
+        # indexes
+        for i, idx in enumerate(indexes):
+            rows[names[i]] = idx
+
+        # values
+        for i, v in enumerate(values):
+            rows[names[i + nindexes]] = v
+
+        # mask
+        if mask is not None:
+            m = ~mask.ravel().astype(bool, copy=False)
+            if not m.all():
+                rows = rows[m]
+
+        if len(rows):
+            self.table.append(rows)
+            self.table.flush()
+
+    def delete(self, where=None, start: int | None = None, stop: int | None = None):
+        # delete all rows (and return the nrows)
+        if where is None or not len(where):
+            if start is None and stop is None:
+                nrows = self.nrows
+                self._handle.remove_node(self.group, recursive=True)
+            else:
+                # pytables<3.0 would remove a single row with stop=None
+                if stop is None:
+                    stop = self.nrows
+                nrows = self.table.remove_rows(start=start, stop=stop)
+                self.table.flush()
+            return nrows
+
+        # infer the data kind
+        if not self.infer_axes():
+            return None
+
+        # create the selection
+        table = self.table
+        selection = Selection(self, where, start=start, stop=stop)
+        values = selection.select_coords()
+
+        # delete the rows in reverse order
+        sorted_series = Series(values, copy=False).sort_values()
+        ln = len(sorted_series)
+
+        if ln:
+            # construct groups of consecutive rows
+            diff = sorted_series.diff()
+            groups = list(diff[diff > 1].index)
+
+            # 1 group
+            if not len(groups):
+                groups = [0]
+
+            # final element
+            if groups[-1] != ln:
+                groups.append(ln)
+
+            # initial element
+            if groups[0] != 0:
+                groups.insert(0, 0)
+
+            # we must remove in reverse order!
+            pg = groups.pop()
+            for g in reversed(groups):
+                rows = sorted_series.take(range(g, pg))
+                table.remove_rows(
+                    start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1
+                )
+                pg = g
+
+            self.table.flush()
+
+        # return the number of rows removed
+        return ln
+
+
+class AppendableFrameTable(AppendableTable):
+    """support the new appendable table formats"""
+
+    pandas_kind = "frame_table"
+    table_type = "appendable_frame"
+    ndim = 2
+    obj_type: type[DataFrame | Series] = DataFrame
+
+    @property
+    def is_transposed(self) -> bool:
+        return self.index_axes[0].axis == 1
+
+    @classmethod
+    def get_object(cls, obj, transposed: bool):
+        """these are written transposed"""
+        if transposed:
+            obj = obj.T
+        return obj
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        # validate the version
+        self.validate_version(where)
+
+        # infer the data kind
+        if not self.infer_axes():
+            return None
+
+        result = self._read_axes(where=where, start=start, stop=stop)
+
+        info = (
+            self.info.get(self.non_index_axes[0][0], {})
+            if len(self.non_index_axes)
+            else {}
+        )
+
+        inds = [i for i, ax in enumerate(self.axes) if ax is self.index_axes[0]]
+        assert len(inds) == 1
+        ind = inds[0]
+
+        index = result[ind][0]
+
+        frames = []
+        for i, a in enumerate(self.axes):
+            if a not in self.values_axes:
+                continue
+            index_vals, cvalues = result[i]
+
+            # we could have a multi-index constructor here
+            # ensure_index doesn't recognized our list-of-tuples here
+            if info.get("type") != "MultiIndex":
+                cols = Index(index_vals)
+            else:
+                cols = MultiIndex.from_tuples(index_vals)
+
+            names = info.get("names")
+            if names is not None:
+                cols.set_names(names, inplace=True)
+
+            if self.is_transposed:
+                values = cvalues
+                index_ = cols
+                cols_ = Index(index, name=getattr(index, "name", None))
+            else:
+                values = cvalues.T
+                index_ = Index(index, name=getattr(index, "name", None))
+                cols_ = cols
+
+            # if we have a DataIndexableCol, its shape will only be 1 dim
+            if values.ndim == 1 and isinstance(values, np.ndarray):
+                values = values.reshape((1, values.shape[0]))
+
+            if isinstance(values, np.ndarray):
+                try:
+                    df = DataFrame(values.T, columns=cols_, index=index_, copy=False)
+                except UnicodeEncodeError as err:
+                    if (
+                        self.errors == "surrogatepass"
+                        and get_option("future.infer_string")
+                        and str(err).endswith("surrogates not allowed")
+                        and HAS_PYARROW
+                    ):
+                        df = DataFrame(
+                            values.T,
+                            columns=cols_,
+                            index=index_,
+                            copy=False,
+                            dtype=StringDtype(storage="python", na_value=np.nan),
+                        )
+                    else:
+                        raise
+            elif isinstance(values, Index):
+                df = DataFrame(values, columns=cols_, index=index_)
+            else:
+                # Categorical
+                df = DataFrame._from_arrays([values], columns=cols_, index=index_)
+            if not (using_string_dtype() and values.dtype.kind == "O"):
+                assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype)
+
+            # If str / string dtype is stored in meta, use that.
+            for column in cols_:
+                dtype = getattr(self.table.attrs, f"{column}_meta", None)
+                if dtype in ["str", "string"]:
+                    df[column] = df[column].astype(dtype)
+            frames.append(df)
+
+        if len(frames) == 1:
+            df = frames[0]
+        else:
+            df = concat(frames, axis=1)
+
+        selection = Selection(self, where=where, start=start, stop=stop)
+        # apply the selection filters & axis orderings
+        df = self.process_axes(df, selection=selection, columns=columns)
+        return df
+
+
+class AppendableSeriesTable(AppendableFrameTable):
+    """support the new appendable table formats"""
+
+    pandas_kind = "series_table"
+    table_type = "appendable_series"
+    ndim = 2
+    obj_type = Series
+
+    @property
+    def is_transposed(self) -> bool:
+        return False
+
+    @classmethod
+    def get_object(cls, obj, transposed: bool):
+        return obj
+
+    # error: Signature of "write" incompatible with supertype "Fixed"
+    def write(self, obj, data_columns=None, **kwargs) -> None:  # type: ignore[override]
+        """we are going to write this as a frame table"""
+        if not isinstance(obj, DataFrame):
+            name = obj.name or "values"
+            obj = obj.to_frame(name)
+        super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs)
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> Series:
+        is_multi_index = self.is_multi_index
+        if columns is not None and is_multi_index:
+            assert isinstance(self.levels, list)  # needed for mypy
+            for n in self.levels:
+                if n not in columns:
+                    columns.insert(0, n)
+        s = super().read(where=where, columns=columns, start=start, stop=stop)
+        if is_multi_index:
+            s.set_index(self.levels, inplace=True)
+
+        s = s.iloc[:, 0]
+
+        # remove the default name
+        if s.name == "values":
+            s.name = None
+        return s
+
+
+class AppendableMultiSeriesTable(AppendableSeriesTable):
+    """support the new appendable table formats"""
+
+    pandas_kind = "series_table"
+    table_type = "appendable_multiseries"
+
+    #  error: Signature of "write" incompatible with supertype "Fixed"
+    def write(self, obj, **kwargs) -> None:  # type: ignore[override]
+        """we are going to write this as a frame table"""
+        name = obj.name or "values"
+        newobj, self.levels = self.validate_multiindex(obj)
+        assert isinstance(self.levels, list)  # for mypy
+        cols = list(self.levels)
+        cols.append(name)
+        newobj.columns = Index(cols)
+        super().write(obj=newobj, **kwargs)
+
+
+class GenericTable(AppendableFrameTable):
+    """a table that read/writes the generic pytables table format"""
+
+    pandas_kind = "frame_table"
+    table_type = "generic_table"
+    ndim = 2
+    obj_type = DataFrame
+    levels: list[Hashable]
+
+    @property
+    def pandas_type(self) -> str:
+        return self.pandas_kind
+
+    @property
+    def storable(self):
+        return getattr(self.group, "table", None) or self.group
+
+    def get_attrs(self) -> None:
+        """retrieve our attributes"""
+        self.non_index_axes = []
+        self.nan_rep = None
+        self.levels = []
+
+        self.index_axes = [a for a in self.indexables if a.is_an_indexable]
+        self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
+        self.data_columns = [a.name for a in self.values_axes]
+
+    @cache_readonly
+    def indexables(self):
+        """create the indexables from the table description"""
+        d = self.description
+
+        # TODO: can we get a typ for this?  AFAICT it is the only place
+        #  where we aren't passing one
+        # the index columns is just a simple index
+        md = self.read_metadata("index")
+        meta = "category" if md is not None else None
+        index_col = GenericIndexCol(
+            name="index", axis=0, table=self.table, meta=meta, metadata=md
+        )
+
+        _indexables: list[GenericIndexCol | GenericDataIndexableCol] = [index_col]
+
+        for i, n in enumerate(d._v_names):
+            assert isinstance(n, str)
+
+            atom = getattr(d, n)
+            md = self.read_metadata(n)
+            meta = "category" if md is not None else None
+            dc = GenericDataIndexableCol(
+                name=n,
+                pos=i,
+                values=[n],
+                typ=atom,
+                table=self.table,
+                meta=meta,
+                metadata=md,
+            )
+            _indexables.append(dc)
+
+        return _indexables
+
+    # error: Signature of "write" incompatible with supertype "AppendableTable"
+    def write(self, **kwargs) -> None:  # type: ignore[override]
+        raise NotImplementedError("cannot write on an generic table")
+
+
+class AppendableMultiFrameTable(AppendableFrameTable):
+    """a frame with a multi-index"""
+
+    table_type = "appendable_multiframe"
+    obj_type = DataFrame
+    ndim = 2
+    _re_levels = re.compile(r"^level_\d+$")
+
+    @property
+    def table_type_short(self) -> str:
+        return "appendable_multi"
+
+    # error: Signature of "write" incompatible with supertype "Fixed"
+    def write(self, obj, data_columns=None, **kwargs) -> None:  # type: ignore[override]
+        if data_columns is None:
+            data_columns = []
+        elif data_columns is True:
+            data_columns = obj.columns.tolist()
+        obj, self.levels = self.validate_multiindex(obj)
+        assert isinstance(self.levels, list)  # for mypy
+        for n in self.levels:
+            if n not in data_columns:
+                data_columns.insert(0, n)
+        super().write(obj=obj, data_columns=data_columns, **kwargs)
+
+    def read(
+        self,
+        where=None,
+        columns=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ):
+        df = super().read(where=where, columns=columns, start=start, stop=stop)
+        df = df.set_index(self.levels)
+
+        # remove names for 'level_%d'
+        df.index = df.index.set_names(
+            [None if self._re_levels.search(name) else name for name in df.index.names]
+        )
+
+        return df
+
+
+def _reindex_axis(
+    obj: DataFrame, axis: AxisInt, labels: Index, other=None
+) -> DataFrame:
+    ax = obj._get_axis(axis)
+    labels = ensure_index(labels)
+
+    # try not to reindex even if other is provided
+    # if it equals our current index
+    if other is not None:
+        other = ensure_index(other)
+    if (other is None or labels.equals(other)) and labels.equals(ax):
+        return obj
+
+    labels = ensure_index(labels.unique())
+    if other is not None:
+        labels = ensure_index(other.unique()).intersection(labels, sort=False)
+    if not labels.equals(ax):
+        slicer: list[slice | Index] = [slice(None, None)] * obj.ndim
+        slicer[axis] = labels
+        obj = obj.loc[tuple(slicer)]
+    return obj
+
+
+# tz to/from coercion
+
+
+def _get_tz(tz: tzinfo) -> str | tzinfo:
+    """for a tz-aware type, return an encoded zone"""
+    zone = timezones.get_timezone(tz)
+    return zone
+
+
+@overload
+def _set_tz(
+    values: np.ndarray | Index, tz: str | tzinfo, coerce: bool = False
+) -> DatetimeIndex:
+    ...
+
+
+@overload
+def _set_tz(values: np.ndarray | Index, tz: None, coerce: bool = False) -> np.ndarray:
+    ...
+
+
+def _set_tz(
+    values: np.ndarray | Index, tz: str | tzinfo | None, coerce: bool = False
+) -> np.ndarray | DatetimeIndex:
+    """
+    coerce the values to a DatetimeIndex if tz is set
+    preserve the input shape if possible
+
+    Parameters
+    ----------
+    values : ndarray or Index
+    tz : str or tzinfo
+    coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
+    """
+    if isinstance(values, DatetimeIndex):
+        # If values is tzaware, the tz gets dropped in the values.ravel()
+        #  call below (which returns an ndarray).  So we are only non-lossy
+        #  if `tz` matches `values.tz`.
+        assert values.tz is None or values.tz == tz
+        if values.tz is not None:
+            return values
+
+    if tz is not None:
+        if isinstance(values, DatetimeIndex):
+            name = values.name
+        else:
+            name = None
+            values = values.ravel()
+
+        tz = _ensure_decoded(tz)
+        values = DatetimeIndex(values, name=name)
+        values = values.tz_localize("UTC").tz_convert(tz)
+    elif coerce:
+        values = np.asarray(values, dtype="M8[ns]")
+
+    # error: Incompatible return value type (got "Union[ndarray, Index]",
+    # expected "Union[ndarray, DatetimeIndex]")
+    return values  # type: ignore[return-value]
+
+
+def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol:
+    assert isinstance(name, str)
+
+    index_name = index.name
+    # error: Argument 1 to "_get_data_and_dtype_name" has incompatible type "Index";
+    # expected "Union[ExtensionArray, ndarray]"
+    converted, dtype_name = _get_data_and_dtype_name(index)  # type: ignore[arg-type]
+    kind = _dtype_to_kind(dtype_name)
+    atom = DataIndexableCol._get_atom(converted)
+
+    if (
+        lib.is_np_dtype(index.dtype, "iu")
+        or needs_i8_conversion(index.dtype)
+        or is_bool_dtype(index.dtype)
+    ):
+        # Includes Index, RangeIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex,
+        #  in which case "kind" is "integer", "integer", "datetime64",
+        #  "timedelta64", and "integer", respectively.
+        return IndexCol(
+            name,
+            values=converted,
+            kind=kind,
+            typ=atom,
+            freq=getattr(index, "freq", None),
+            tz=getattr(index, "tz", None),
+            index_name=index_name,
+        )
+
+    if isinstance(index, MultiIndex):
+        raise TypeError("MultiIndex not supported here!")
+
+    inferred_type = lib.infer_dtype(index, skipna=False)
+    # we won't get inferred_type of "datetime64" or "timedelta64" as these
+    #  would go through the DatetimeIndex/TimedeltaIndex paths above
+
+    values = np.asarray(index)
+
+    if inferred_type == "date":
+        converted = np.asarray([v.toordinal() for v in values], dtype=np.int32)
+        return IndexCol(
+            name, converted, "date", _tables().Time32Col(), index_name=index_name
+        )
+    elif inferred_type == "string":
+        converted = _convert_string_array(values, encoding, errors)
+        itemsize = converted.dtype.itemsize
+        return IndexCol(
+            name,
+            converted,
+            "string",
+            _tables().StringCol(itemsize),
+            index_name=index_name,
+        )
+
+    elif inferred_type in ["integer", "floating"]:
+        return IndexCol(
+            name, values=converted, kind=kind, typ=atom, index_name=index_name
+        )
+    else:
+        assert isinstance(converted, np.ndarray) and converted.dtype == object
+        assert kind == "object", kind
+        atom = _tables().ObjectAtom()
+        return IndexCol(name, converted, kind, atom, index_name=index_name)
+
+
+def _unconvert_index(data, kind: str, encoding: str, errors: str) -> np.ndarray | Index:
+    index: Index | np.ndarray
+
+    if kind.startswith("datetime64"):
+        if kind == "datetime64":
+            # created before we stored resolution information
+            index = DatetimeIndex(data)
+        else:
+            index = DatetimeIndex(data.view(kind))
+    elif kind == "timedelta64":
+        index = TimedeltaIndex(data)
+    elif kind == "date":
+        try:
+            index = np.asarray([date.fromordinal(v) for v in data], dtype=object)
+        except ValueError:
+            index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object)
+    elif kind in ("integer", "float", "bool"):
+        index = np.asarray(data)
+    elif kind in ("string"):
+        index = _unconvert_string_array(
+            data, nan_rep=None, encoding=encoding, errors=errors
+        )
+    elif kind == "object":
+        index = np.asarray(data[0])
+    else:  # pragma: no cover
+        raise ValueError(f"unrecognized index type {kind}")
+    return index
+
+
+def _maybe_convert_for_string_atom(
+    name: str,
+    bvalues: ArrayLike,
+    existing_col,
+    min_itemsize,
+    nan_rep,
+    encoding,
+    errors,
+    columns: list[str],
+):
+    if isinstance(bvalues.dtype, StringDtype):
+        # "ndarray[Any, Any]" has no attribute "to_numpy"
+        bvalues = bvalues.to_numpy()  # type: ignore[union-attr]
+    if bvalues.dtype != object:
+        return bvalues
+
+    bvalues = cast(np.ndarray, bvalues)
+
+    dtype_name = bvalues.dtype.name
+    inferred_type = lib.infer_dtype(bvalues, skipna=False)
+
+    if inferred_type == "date":
+        raise TypeError("[date] is not implemented as a table column")
+    if inferred_type == "datetime":
+        # after GH#8260
+        # this only would be hit for a multi-timezone dtype which is an error
+        raise TypeError(
+            "too many timezones in this block, create separate data columns"
+        )
+
+    if not (inferred_type == "string" or dtype_name == "object"):
+        return bvalues
+
+    mask = isna(bvalues)
+    data = bvalues.copy()
+    data[mask] = nan_rep
+
+    if existing_col and mask.any() and len(nan_rep) > existing_col.itemsize:
+        raise ValueError("NaN representation is too large for existing column size")
+
+    # see if we have a valid string type
+    inferred_type = lib.infer_dtype(data, skipna=False)
+    if inferred_type != "string":
+        # we cannot serialize this data, so report an exception on a column
+        # by column basis
+
+        # expected behaviour:
+        # search block for a non-string object column by column
+        for i in range(data.shape[0]):
+            col = data[i]
+            inferred_type = lib.infer_dtype(col, skipna=False)
+            if inferred_type != "string":
+                error_column_label = columns[i] if len(columns) > i else f"No.{i}"
+                raise TypeError(
+                    f"Cannot serialize the column [{error_column_label}]\n"
+                    f"because its data contents are not [string] but "
+                    f"[{inferred_type}] object dtype"
+                )
+
+    # itemsize is the maximum length of a string (along any dimension)
+
+    data_converted = _convert_string_array(data, encoding, errors).reshape(data.shape)
+    itemsize = data_converted.itemsize
+
+    # specified min_itemsize?
+    if isinstance(min_itemsize, dict):
+        min_itemsize = int(min_itemsize.get(name) or min_itemsize.get("values") or 0)
+    itemsize = max(min_itemsize or 0, itemsize)
+
+    # check for column in the values conflicts
+    if existing_col is not None:
+        eci = existing_col.validate_col(itemsize)
+        if eci is not None and eci > itemsize:
+            itemsize = eci
+
+    data_converted = data_converted.astype(f"|S{itemsize}", copy=False)
+    return data_converted
+
+
+def _convert_string_array(data: np.ndarray, encoding: str, errors: str) -> np.ndarray:
+    """
+    Take a string-like that is object dtype and coerce to a fixed size string type.
+
+    Parameters
+    ----------
+    data : np.ndarray[object]
+    encoding : str
+    errors : str
+        Handler for encoding errors.
+
+    Returns
+    -------
+    np.ndarray[fixed-length-string]
+    """
+    # encode if needed
+    if len(data):
+        data = (
+            Series(data.ravel(), copy=False, dtype="object")
+            .str.encode(encoding, errors)
+            ._values.reshape(data.shape)
+        )
+
+    # create the sized dtype
+    ensured = ensure_object(data.ravel())
+    itemsize = max(1, libwriters.max_len_string_array(ensured))
+
+    data = np.asarray(data, dtype=f"S{itemsize}")
+    return data
+
+
+def _unconvert_string_array(
+    data: np.ndarray, nan_rep, encoding: str, errors: str
+) -> np.ndarray:
+    """
+    Inverse of _convert_string_array.
+
+    Parameters
+    ----------
+    data : np.ndarray[fixed-length-string]
+    nan_rep : the storage repr of NaN
+    encoding : str
+    errors : str
+        Handler for encoding errors.
+
+    Returns
+    -------
+    np.ndarray[object]
+        Decoded data.
+    """
+    shape = data.shape
+    data = np.asarray(data.ravel(), dtype=object)
+
+    if len(data):
+        itemsize = libwriters.max_len_string_array(ensure_object(data))
+        dtype = f"U{itemsize}"
+
+        if isinstance(data[0], bytes):
+            ser = Series(data, copy=False).str.decode(
+                encoding, errors=errors, dtype="object"
+            )
+            data = ser.to_numpy()
+            data.flags.writeable = True
+        else:
+            data = data.astype(dtype, copy=False).astype(object, copy=False)
+
+    if nan_rep is None:
+        nan_rep = "nan"
+
+    libwriters.string_array_replace_from_nan_rep(data, nan_rep)
+    return data.reshape(shape)
+
+
+def _maybe_convert(values: np.ndarray, val_kind: str, encoding: str, errors: str):
+    assert isinstance(val_kind, str), type(val_kind)
+    if _need_convert(val_kind):
+        conv = _get_converter(val_kind, encoding, errors)
+        values = conv(values)
+    return values
+
+
+def _get_converter(kind: str, encoding: str, errors: str):
+    if kind == "datetime64":
+        return lambda x: np.asarray(x, dtype="M8[ns]")
+    elif "datetime64" in kind:
+        return lambda x: np.asarray(x, dtype=kind)
+    elif kind == "string":
+        return lambda x: _unconvert_string_array(
+            x, nan_rep=None, encoding=encoding, errors=errors
+        )
+    else:  # pragma: no cover
+        raise ValueError(f"invalid kind {kind}")
+
+
+def _need_convert(kind: str) -> bool:
+    if kind in ("datetime64", "string") or "datetime64" in kind:
+        return True
+    return False
+
+
+def _maybe_adjust_name(name: str, version: Sequence[int]) -> str:
+    """
+    Prior to 0.10.1, we named values blocks like: values_block_0 an the
+    name values_0, adjust the given name if necessary.
+
+    Parameters
+    ----------
+    name : str
+    version : Tuple[int, int, int]
+
+    Returns
+    -------
+    str
+    """
+    if isinstance(version, str) or len(version) < 3:
+        raise ValueError("Version is incorrect, expected sequence of 3 integers.")
+
+    if version[0] == 0 and version[1] <= 10 and version[2] == 0:
+        m = re.search(r"values_block_(\d+)", name)
+        if m:
+            grp = m.groups()[0]
+            name = f"values_{grp}"
+    return name
+
+
+def _dtype_to_kind(dtype_str: str) -> str:
+    """
+    Find the "kind" string describing the given dtype name.
+    """
+    dtype_str = _ensure_decoded(dtype_str)
+
+    if dtype_str.startswith(("string", "bytes")):
+        kind = "string"
+    elif dtype_str.startswith("float"):
+        kind = "float"
+    elif dtype_str.startswith("complex"):
+        kind = "complex"
+    elif dtype_str.startswith(("int", "uint")):
+        kind = "integer"
+    elif dtype_str.startswith("datetime64"):
+        kind = dtype_str
+    elif dtype_str.startswith("timedelta"):
+        kind = "timedelta64"
+    elif dtype_str.startswith("bool"):
+        kind = "bool"
+    elif dtype_str.startswith("category"):
+        kind = "category"
+    elif dtype_str.startswith("period"):
+        # We store the `freq` attr so we can restore from integers
+        kind = "integer"
+    elif dtype_str == "object":
+        kind = "object"
+    elif dtype_str == "str":
+        kind = "str"
+    else:
+        raise ValueError(f"cannot interpret dtype of [{dtype_str}]")
+
+    return kind
+
+
+def _get_data_and_dtype_name(data: ArrayLike):
+    """
+    Convert the passed data into a storable form and a dtype string.
+    """
+    if isinstance(data, Categorical):
+        data = data.codes
+
+    if isinstance(data.dtype, DatetimeTZDtype):
+        # For datetime64tz we need to drop the TZ in tests TODO: why?
+        dtype_name = f"datetime64[{data.dtype.unit}]"
+    else:
+        dtype_name = data.dtype.name
+
+    if data.dtype.kind in "mM":
+        data = np.asarray(data.view("i8"))
+        # TODO: we used to reshape for the dt64tz case, but no longer
+        #  doing that doesn't seem to break anything.  why?
+
+    elif isinstance(data, PeriodIndex):
+        data = data.asi8
+
+    data = np.asarray(data)
+    return data, dtype_name
+
+
+class Selection:
+    """
+    Carries out a selection operation on a tables.Table object.
+
+    Parameters
+    ----------
+    table : a Table object
+    where : list of Terms (or convertible to)
+    start, stop: indices to start and/or stop selection
+
+    """
+
+    def __init__(
+        self,
+        table: Table,
+        where=None,
+        start: int | None = None,
+        stop: int | None = None,
+    ) -> None:
+        self.table = table
+        self.where = where
+        self.start = start
+        self.stop = stop
+        self.condition = None
+        self.filter = None
+        self.terms = None
+        self.coordinates = None
+
+        if is_list_like(where):
+            # see if we have a passed coordinate like
+            with suppress(ValueError):
+                inferred = lib.infer_dtype(where, skipna=False)
+                if inferred in ("integer", "boolean"):
+                    where = np.asarray(where)
+                    if where.dtype == np.bool_:
+                        start, stop = self.start, self.stop
+                        if start is None:
+                            start = 0
+                        if stop is None:
+                            stop = self.table.nrows
+                        self.coordinates = np.arange(start, stop)[where]
+                    elif issubclass(where.dtype.type, np.integer):
+                        if (self.start is not None and (where < self.start).any()) or (
+                            self.stop is not None and (where >= self.stop).any()
+                        ):
+                            raise ValueError(
+                                "where must have index locations >= start and < stop"
+                            )
+                        self.coordinates = where
+
+        if self.coordinates is None:
+            self.terms = self.generate(where)
+
+            # create the numexpr & the filter
+            if self.terms is not None:
+                self.condition, self.filter = self.terms.evaluate()
+
+    def generate(self, where):
+        """where can be a : dict,list,tuple,string"""
+        if where is None:
+            return None
+
+        q = self.table.queryables()
+        try:
+            return PyTablesExpr(where, queryables=q, encoding=self.table.encoding)
+        except NameError as err:
+            # raise a nice message, suggesting that the user should use
+            # data_columns
+            qkeys = ",".join(q.keys())
+            msg = dedent(
+                f"""\
+                The passed where expression: {where}
+                            contains an invalid variable reference
+                            all of the variable references must be a reference to
+                            an axis (e.g. 'index' or 'columns'), or a data_column
+                            The currently defined references are: {qkeys}
+                """
+            )
+            raise ValueError(msg) from err
+
+    def select(self):
+        """
+        generate the selection
+        """
+        if self.condition is not None:
+            return self.table.table.read_where(
+                self.condition.format(), start=self.start, stop=self.stop
+            )
+        elif self.coordinates is not None:
+            return self.table.table.read_coordinates(self.coordinates)
+        return self.table.table.read(start=self.start, stop=self.stop)
+
+    def select_coords(self):
+        """
+        generate the selection
+        """
+        start, stop = self.start, self.stop
+        nrows = self.table.nrows
+        if start is None:
+            start = 0
+        elif start < 0:
+            start += nrows
+        if stop is None:
+            stop = nrows
+        elif stop < 0:
+            stop += nrows
+
+        if self.condition is not None:
+            return self.table.table.get_where_list(
+                self.condition.format(), start=start, stop=stop, sort=True
+            )
+        elif self.coordinates is not None:
+            return self.coordinates
+
+        return np.arange(start, stop)
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/spss.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/spss.py
new file mode 100644
index 0000000000000000000000000000000000000000..db31a07df79e6de2862e57fd75de0bd4b9c2455d
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/spss.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.inference import is_list_like
+
+from pandas.io.common import stringify_path
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from pathlib import Path
+
+    from pandas._typing import DtypeBackend
+
+    from pandas import DataFrame
+
+
+def read_spss(
+    path: str | Path,
+    usecols: Sequence[str] | None = None,
+    convert_categoricals: bool = True,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    """
+    Load an SPSS file from the file path, returning a DataFrame.
+
+    Parameters
+    ----------
+    path : str or Path
+        File path.
+    usecols : list-like, optional
+        Return a subset of the columns. If None, return all columns.
+    convert_categoricals : bool, default is True
+        Convert categorical columns into pd.Categorical.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    DataFrame
+
+    Examples
+    --------
+    >>> df = pd.read_spss("spss_data.sav")  # doctest: +SKIP
+    """
+    pyreadstat = import_optional_dependency("pyreadstat")
+    check_dtype_backend(dtype_backend)
+
+    if usecols is not None:
+        if not is_list_like(usecols):
+            raise TypeError("usecols must be list-like.")
+        usecols = list(usecols)  # pyreadstat requires a list
+
+    df, metadata = pyreadstat.read_sav(
+        stringify_path(path), usecols=usecols, apply_value_formats=convert_categoricals
+    )
+    df.attrs = metadata.__dict__
+    if dtype_backend is not lib.no_default:
+        df = df.convert_dtypes(dtype_backend=dtype_backend)
+    return df
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/sql.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/sql.py
new file mode 100644
index 0000000000000000000000000000000000000000..7027702a696feda183995a6346238e4076dbc069
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/sql.py
@@ -0,0 +1,2916 @@
+"""
+Collection of query wrappers / abstractions to both facilitate data
+retrieval and to reduce dependency on DB-specific API.
+"""
+
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from contextlib import (
+    ExitStack,
+    contextmanager,
+)
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+from functools import partial
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    AbstractMethodError,
+    DatabaseError,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    is_dict_like,
+    is_list_like,
+    is_object_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+
+from pandas import get_option
+from pandas.core.api import (
+    DataFrame,
+    Series,
+)
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.common import maybe_make_list
+from pandas.core.internals.construction import convert_object_array
+from pandas.core.tools.datetimes import to_datetime
+
+from pandas.io._util import arrow_table_to_pandas
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterator,
+        Mapping,
+    )
+
+    from sqlalchemy import Table
+    from sqlalchemy.sql.expression import (
+        Select,
+        TextClause,
+    )
+
+    from pandas._typing import (
+        DateTimeErrorChoices,
+        DtypeArg,
+        DtypeBackend,
+        IndexLabel,
+        Self,
+    )
+
+    from pandas import Index
+
+# -----------------------------------------------------------------------------
+# -- Helper functions
+
+
+def _process_parse_dates_argument(parse_dates):
+    """Process parse_dates argument for read_sql functions"""
+    # handle non-list entries for parse_dates gracefully
+    if parse_dates is True or parse_dates is None or parse_dates is False:
+        parse_dates = []
+
+    elif not hasattr(parse_dates, "__iter__"):
+        parse_dates = [parse_dates]
+    return parse_dates
+
+
+def _handle_date_column(
+    col, utc: bool = False, format: str | dict[str, Any] | None = None
+):
+    if isinstance(format, dict):
+        # GH35185 Allow custom error values in parse_dates argument of
+        # read_sql like functions.
+        # Format can take on custom to_datetime argument values such as
+        # {"errors": "coerce"} or {"dayfirst": True}
+        error: DateTimeErrorChoices = format.pop("errors", None) or "ignore"
+        if error == "ignore":
+            try:
+                return to_datetime(col, **format)
+            except (TypeError, ValueError):
+                # TODO: not reached 2023-10-27; needed?
+                return col
+        return to_datetime(col, errors=error, **format)
+    else:
+        # Allow passing of formatting string for integers
+        # GH17855
+        if format is None and (
+            issubclass(col.dtype.type, np.floating)
+            or issubclass(col.dtype.type, np.integer)
+        ):
+            format = "s"
+        if format in ["D", "d", "h", "m", "s", "ms", "us", "ns"]:
+            return to_datetime(col, errors="coerce", unit=format, utc=utc)
+        elif isinstance(col.dtype, DatetimeTZDtype):
+            # coerce to UTC timezone
+            # GH11216
+            return to_datetime(col, utc=True)
+        else:
+            return to_datetime(col, errors="coerce", format=format, utc=utc)
+
+
+def _parse_date_columns(data_frame, parse_dates):
+    """
+    Force non-datetime columns to be read as such.
+    Supports both string formatted and integer timestamp columns.
+    """
+    parse_dates = _process_parse_dates_argument(parse_dates)
+
+    # we want to coerce datetime64_tz dtypes for now to UTC
+    # we could in theory do a 'nice' conversion from a FixedOffset tz
+    # GH11216
+    for i, (col_name, df_col) in enumerate(data_frame.items()):
+        if isinstance(df_col.dtype, DatetimeTZDtype) or col_name in parse_dates:
+            try:
+                fmt = parse_dates[col_name]
+            except (KeyError, TypeError):
+                fmt = None
+            data_frame.isetitem(i, _handle_date_column(df_col, format=fmt))
+
+    return data_frame
+
+
+def _convert_arrays_to_dataframe(
+    data,
+    columns,
+    coerce_float: bool = True,
+    dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+) -> DataFrame:
+    content = lib.to_object_array_tuples(data)
+    arrays = convert_object_array(
+        list(content.T),
+        dtype=None,
+        coerce_float=coerce_float,
+        dtype_backend=dtype_backend,
+    )
+    if dtype_backend == "pyarrow":
+        pa = import_optional_dependency("pyarrow")
+
+        result_arrays = []
+        for arr in arrays:
+            pa_array = pa.array(arr, from_pandas=True)
+            if arr.dtype == "string":
+                # TODO: Arrow still infers strings arrays as regular strings instead
+                # of large_string, which is what we preserver everywhere else for
+                # dtype_backend="pyarrow". We may want to reconsider this
+                pa_array = pa_array.cast(pa.string())
+            result_arrays.append(ArrowExtensionArray(pa_array))
+        arrays = result_arrays  # type: ignore[assignment]
+    if arrays:
+        df = DataFrame(dict(zip(list(range(len(columns))), arrays)))
+        df.columns = columns
+        return df
+    else:
+        return DataFrame(columns=columns)
+
+
+def _wrap_result(
+    data,
+    columns,
+    index_col=None,
+    coerce_float: bool = True,
+    parse_dates=None,
+    dtype: DtypeArg | None = None,
+    dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+):
+    """Wrap result set of a SQLAlchemy query in a DataFrame."""
+    frame = _convert_arrays_to_dataframe(data, columns, coerce_float, dtype_backend)
+
+    if dtype:
+        frame = frame.astype(dtype)
+
+    frame = _parse_date_columns(frame, parse_dates)
+
+    if index_col is not None:
+        frame = frame.set_index(index_col)
+
+    return frame
+
+
+def _wrap_result_adbc(
+    df: DataFrame,
+    *,
+    index_col=None,
+    parse_dates=None,
+    dtype: DtypeArg | None = None,
+    dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+) -> DataFrame:
+    """Wrap result set of a SQLAlchemy query in a DataFrame."""
+    if dtype:
+        df = df.astype(dtype)
+
+    df = _parse_date_columns(df, parse_dates)
+
+    if index_col is not None:
+        df = df.set_index(index_col)
+
+    return df
+
+
+def execute(sql, con, params=None):
+    """
+    Execute the given SQL query using the provided connection object.
+
+    Parameters
+    ----------
+    sql : string
+        SQL query to be executed.
+    con : SQLAlchemy connection or sqlite3 connection
+        If a DBAPI2 object, only sqlite3 is supported.
+    params : list or tuple, optional, default: None
+        List of parameters to pass to execute method.
+
+    Returns
+    -------
+    Results Iterable
+    """
+    warnings.warn(
+        "`pandas.io.sql.execute` is deprecated and "
+        "will be removed in the future version.",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )  # GH50185
+    sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")
+
+    if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Engine)):
+        raise TypeError("pandas.io.sql.execute requires a connection")  # GH50185
+    with pandasSQL_builder(con, need_transaction=True) as pandas_sql:
+        return pandas_sql.execute(sql, params)
+
+
+# -----------------------------------------------------------------------------
+# -- Read and write to DataFrames
+
+
+@overload
+def read_sql_table(
+    table_name: str,
+    con,
+    schema=...,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    columns: list[str] | None = ...,
+    chunksize: None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_sql_table(
+    table_name: str,
+    con,
+    schema=...,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    columns: list[str] | None = ...,
+    chunksize: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> Iterator[DataFrame]:
+    ...
+
+
+def read_sql_table(
+    table_name: str,
+    con,
+    schema: str | None = None,
+    index_col: str | list[str] | None = None,
+    coerce_float: bool = True,
+    parse_dates: list[str] | dict[str, str] | None = None,
+    columns: list[str] | None = None,
+    chunksize: int | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame | Iterator[DataFrame]:
+    """
+    Read SQL database table into a DataFrame.
+
+    Given a table name and a SQLAlchemy connectable, returns a DataFrame.
+    This function does not support DBAPI connections.
+
+    Parameters
+    ----------
+    table_name : str
+        Name of SQL table in database.
+    con : SQLAlchemy connectable or str
+        A database URI could be provided as str.
+        SQLite DBAPI connection mode not supported.
+    schema : str, default None
+        Name of SQL schema in database to query (if database flavor
+        supports this). Uses default schema if None (default).
+    index_col : str or list of str, optional, default: None
+        Column(s) to set as index(MultiIndex).
+    coerce_float : bool, default True
+        Attempts to convert values of non-string, non-numeric objects (like
+        decimal.Decimal) to floating point. Can result in loss of Precision.
+    parse_dates : list or dict, default None
+        - List of column names to parse as dates.
+        - Dict of ``{column_name: format string}`` where format string is
+          strftime compatible in case of parsing string times or is one of
+          (D, s, ns, ms, us) in case of parsing integer timestamps.
+        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
+          to the keyword arguments of :func:`pandas.to_datetime`
+          Especially useful with databases without native Datetime support,
+          such as SQLite.
+    columns : list, default None
+        List of column names to select from SQL table.
+    chunksize : int, default None
+        If specified, returns an iterator where `chunksize` is the number of
+        rows to include in each chunk.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    DataFrame or Iterator[DataFrame]
+        A SQL table is returned as two-dimensional data structure with labeled
+        axes.
+
+    See Also
+    --------
+    read_sql_query : Read SQL query into a DataFrame.
+    read_sql : Read SQL query or database table into a DataFrame.
+
+    Notes
+    -----
+    Any datetime values with time zone information will be converted to UTC.
+
+    Examples
+    --------
+    >>> pd.read_sql_table('table_name', 'postgres:///db_name')  # doctest:+SKIP
+    """
+
+    check_dtype_backend(dtype_backend)
+    if dtype_backend is lib.no_default:
+        dtype_backend = "numpy"  # type: ignore[assignment]
+    assert dtype_backend is not lib.no_default
+
+    with pandasSQL_builder(con, schema=schema, need_transaction=True) as pandas_sql:
+        if not pandas_sql.has_table(table_name):
+            raise ValueError(f"Table {table_name} not found")
+
+        table = pandas_sql.read_table(
+            table_name,
+            index_col=index_col,
+            coerce_float=coerce_float,
+            parse_dates=parse_dates,
+            columns=columns,
+            chunksize=chunksize,
+            dtype_backend=dtype_backend,
+        )
+
+    if table is not None:
+        return table
+    else:
+        raise ValueError(f"Table {table_name} not found", con)
+
+
+@overload
+def read_sql_query(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params: list[Any] | Mapping[str, Any] | None = ...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    chunksize: None = ...,
+    dtype: DtypeArg | None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_sql_query(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params: list[Any] | Mapping[str, Any] | None = ...,
+    parse_dates: list[str] | dict[str, str] | None = ...,
+    chunksize: int = ...,
+    dtype: DtypeArg | None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> Iterator[DataFrame]:
+    ...
+
+
+def read_sql_query(
+    sql,
+    con,
+    index_col: str | list[str] | None = None,
+    coerce_float: bool = True,
+    params: list[Any] | Mapping[str, Any] | None = None,
+    parse_dates: list[str] | dict[str, str] | None = None,
+    chunksize: int | None = None,
+    dtype: DtypeArg | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame | Iterator[DataFrame]:
+    """
+    Read SQL query into a DataFrame.
+
+    Returns a DataFrame corresponding to the result set of the query
+    string. Optionally provide an `index_col` parameter to use one of the
+    columns as the index, otherwise default integer index will be used.
+
+    Parameters
+    ----------
+    sql : str SQL query or SQLAlchemy Selectable (select or text object)
+        SQL query to be executed.
+    con : SQLAlchemy connectable, str, or sqlite3 connection
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library. If a DBAPI2 object, only sqlite3 is supported.
+    index_col : str or list of str, optional, default: None
+        Column(s) to set as index(MultiIndex).
+    coerce_float : bool, default True
+        Attempts to convert values of non-string, non-numeric objects (like
+        decimal.Decimal) to floating point. Useful for SQL result sets.
+    params : list, tuple or mapping, optional, default: None
+        List of parameters to pass to execute method.  The syntax used
+        to pass parameters is database driver dependent. Check your
+        database driver documentation for which of the five syntax styles,
+        described in PEP 249's paramstyle, is supported.
+        Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
+    parse_dates : list or dict, default: None
+        - List of column names to parse as dates.
+        - Dict of ``{column_name: format string}`` where format string is
+          strftime compatible in case of parsing string times, or is one of
+          (D, s, ns, ms, us) in case of parsing integer timestamps.
+        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
+          to the keyword arguments of :func:`pandas.to_datetime`
+          Especially useful with databases without native Datetime support,
+          such as SQLite.
+    chunksize : int, default None
+        If specified, return an iterator where `chunksize` is the number of
+        rows to include in each chunk.
+    dtype : Type name or dict of columns
+        Data type for data or columns. E.g. np.float64 or
+        {'a': np.float64, 'b': np.int32, 'c': 'Int64'}.
+
+        .. versionadded:: 1.3.0
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    DataFrame or Iterator[DataFrame]
+
+    See Also
+    --------
+    read_sql_table : Read SQL database table into a DataFrame.
+    read_sql : Read SQL query or database table into a DataFrame.
+
+    Notes
+    -----
+    Any datetime values with time zone information parsed via the `parse_dates`
+    parameter will be converted to UTC.
+
+    Examples
+    --------
+    >>> from sqlalchemy import create_engine  # doctest: +SKIP
+    >>> engine = create_engine("sqlite:///database.db")  # doctest: +SKIP
+    >>> with engine.connect() as conn, conn.begin():  # doctest: +SKIP
+    ...     data = pd.read_sql_table("data", conn)  # doctest: +SKIP
+    """
+
+    check_dtype_backend(dtype_backend)
+    if dtype_backend is lib.no_default:
+        dtype_backend = "numpy"  # type: ignore[assignment]
+    assert dtype_backend is not lib.no_default
+
+    with pandasSQL_builder(con) as pandas_sql:
+        return pandas_sql.read_query(
+            sql,
+            index_col=index_col,
+            params=params,
+            coerce_float=coerce_float,
+            parse_dates=parse_dates,
+            chunksize=chunksize,
+            dtype=dtype,
+            dtype_backend=dtype_backend,
+        )
+
+
+@overload
+def read_sql(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params=...,
+    parse_dates=...,
+    columns: list[str] = ...,
+    chunksize: None = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    dtype: DtypeArg | None = None,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_sql(
+    sql,
+    con,
+    index_col: str | list[str] | None = ...,
+    coerce_float=...,
+    params=...,
+    parse_dates=...,
+    columns: list[str] = ...,
+    chunksize: int = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+    dtype: DtypeArg | None = None,
+) -> Iterator[DataFrame]:
+    ...
+
+
+def read_sql(
+    sql,
+    con,
+    index_col: str | list[str] | None = None,
+    coerce_float: bool = True,
+    params=None,
+    parse_dates=None,
+    columns: list[str] | None = None,
+    chunksize: int | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    dtype: DtypeArg | None = None,
+) -> DataFrame | Iterator[DataFrame]:
+    """
+    Read SQL query or database table into a DataFrame.
+
+    This function is a convenience wrapper around ``read_sql_table`` and
+    ``read_sql_query`` (for backward compatibility). It will delegate
+    to the specific function depending on the provided input. A SQL query
+    will be routed to ``read_sql_query``, while a database table name will
+    be routed to ``read_sql_table``. Note that the delegated function might
+    have more specific notes about their functionality not listed here.
+
+    Parameters
+    ----------
+    sql : str or SQLAlchemy Selectable (select or text object)
+        SQL query to be executed or a table name.
+    con : ADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible
+        for engine disposal and connection closure for the ADBC connection and
+        SQLAlchemy connectable; str connections are closed automatically. See
+        `here <https://docs.sqlalchemy.org/en/20/core/connections.html>`_.
+    index_col : str or list of str, optional, default: None
+        Column(s) to set as index(MultiIndex).
+    coerce_float : bool, default True
+        Attempts to convert values of non-string, non-numeric objects (like
+        decimal.Decimal) to floating point, useful for SQL result sets.
+    params : list, tuple or dict, optional, default: None
+        List of parameters to pass to execute method.  The syntax used
+        to pass parameters is database driver dependent. Check your
+        database driver documentation for which of the five syntax styles,
+        described in PEP 249's paramstyle, is supported.
+        Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
+    parse_dates : list or dict, default: None
+        - List of column names to parse as dates.
+        - Dict of ``{column_name: format string}`` where format string is
+          strftime compatible in case of parsing string times, or is one of
+          (D, s, ns, ms, us) in case of parsing integer timestamps.
+        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
+          to the keyword arguments of :func:`pandas.to_datetime`
+          Especially useful with databases without native Datetime support,
+          such as SQLite.
+    columns : list, default: None
+        List of column names to select from SQL table (only used when reading
+        a table).
+    chunksize : int, default None
+        If specified, return an iterator where `chunksize` is the
+        number of rows to include in each chunk.
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+    dtype : Type name or dict of columns
+        Data type for data or columns. E.g. np.float64 or
+        {'a': np.float64, 'b': np.int32, 'c': 'Int64'}.
+        The argument is ignored if a table is passed instead of a query.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    DataFrame or Iterator[DataFrame]
+
+    See Also
+    --------
+    read_sql_table : Read SQL database table into a DataFrame.
+    read_sql_query : Read SQL query into a DataFrame.
+
+    Examples
+    --------
+    Read data from SQL via either a SQL query or a SQL tablename.
+    When using a SQLite database only SQL queries are accepted,
+    providing only the SQL tablename will result in an error.
+
+    >>> from sqlite3 import connect
+    >>> conn = connect(':memory:')
+    >>> df = pd.DataFrame(data=[[0, '10/11/12'], [1, '12/11/10']],
+    ...                   columns=['int_column', 'date_column'])
+    >>> df.to_sql(name='test_data', con=conn)
+    2
+
+    >>> pd.read_sql('SELECT int_column, date_column FROM test_data', conn)
+       int_column date_column
+    0           0    10/11/12
+    1           1    12/11/10
+
+    >>> pd.read_sql('test_data', 'postgres:///db_name')  # doctest:+SKIP
+
+    Apply date parsing to columns through the ``parse_dates`` argument
+    The ``parse_dates`` argument calls ``pd.to_datetime`` on the provided columns.
+    Custom argument values for applying ``pd.to_datetime`` on a column are specified
+    via a dictionary format:
+
+    >>> pd.read_sql('SELECT int_column, date_column FROM test_data',
+    ...             conn,
+    ...             parse_dates={"date_column": {"format": "%d/%m/%y"}})
+       int_column date_column
+    0           0  2012-11-10
+    1           1  2010-11-12
+
+    .. versionadded:: 2.2.0
+
+       pandas now supports reading via ADBC drivers
+
+    >>> from adbc_driver_postgresql import dbapi  # doctest:+SKIP
+    >>> with dbapi.connect('postgres:///db_name') as conn:  # doctest:+SKIP
+    ...     pd.read_sql('SELECT int_column FROM test_data', conn)
+       int_column
+    0           0
+    1           1
+    """
+
+    check_dtype_backend(dtype_backend)
+    if dtype_backend is lib.no_default:
+        dtype_backend = "numpy"  # type: ignore[assignment]
+    assert dtype_backend is not lib.no_default
+
+    with pandasSQL_builder(con) as pandas_sql:
+        if isinstance(pandas_sql, SQLiteDatabase):
+            return pandas_sql.read_query(
+                sql,
+                index_col=index_col,
+                params=params,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                chunksize=chunksize,
+                dtype_backend=dtype_backend,
+                dtype=dtype,
+            )
+
+        try:
+            _is_table_name = pandas_sql.has_table(sql)
+        except Exception:
+            # using generic exception to catch errors from sql drivers (GH24988)
+            _is_table_name = False
+
+        if _is_table_name:
+            return pandas_sql.read_table(
+                sql,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                columns=columns,
+                chunksize=chunksize,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            return pandas_sql.read_query(
+                sql,
+                index_col=index_col,
+                params=params,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                chunksize=chunksize,
+                dtype_backend=dtype_backend,
+                dtype=dtype,
+            )
+
+
+def to_sql(
+    frame,
+    name: str,
+    con,
+    schema: str | None = None,
+    if_exists: Literal["fail", "replace", "append"] = "fail",
+    index: bool = True,
+    index_label: IndexLabel | None = None,
+    chunksize: int | None = None,
+    dtype: DtypeArg | None = None,
+    method: Literal["multi"] | Callable | None = None,
+    engine: str = "auto",
+    **engine_kwargs,
+) -> int | None:
+    """
+    Write records stored in a DataFrame to a SQL database.
+
+    Parameters
+    ----------
+    frame : DataFrame, Series
+    name : str
+        Name of SQL table.
+    con : ADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection
+        or sqlite3 DBAPI2 connection
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library.
+        If a DBAPI2 object, only sqlite3 is supported.
+    schema : str, optional
+        Name of SQL schema in database to write to (if database flavor
+        supports this). If None, use default schema (default).
+    if_exists : {'fail', 'replace', 'append'}, default 'fail'
+        - fail: If table exists, do nothing.
+        - replace: If table exists, drop it, recreate it, and insert data.
+        - append: If table exists, insert data. Create if does not exist.
+    index : bool, default True
+        Write DataFrame index as a column.
+    index_label : str or sequence, optional
+        Column label for index column(s). If None is given (default) and
+        `index` is True, then the index names are used.
+        A sequence should be given if the DataFrame uses MultiIndex.
+    chunksize : int, optional
+        Specify the number of rows in each batch to be written at a time.
+        By default, all rows will be written at once.
+    dtype : dict or scalar, optional
+        Specifying the datatype for columns. If a dictionary is used, the
+        keys should be the column names and the values should be the
+        SQLAlchemy types or strings for the sqlite3 fallback mode. If a
+        scalar is provided, it will be applied to all columns.
+    method : {None, 'multi', callable}, optional
+        Controls the SQL insertion clause used:
+
+        - None : Uses standard SQL ``INSERT`` clause (one per row).
+        - ``'multi'``: Pass multiple values in a single ``INSERT`` clause.
+        - callable with signature ``(pd_table, conn, keys, data_iter) -> int | None``.
+
+        Details and a sample callable implementation can be found in the
+        section :ref:`insert method <io.sql.method>`.
+    engine : {'auto', 'sqlalchemy'}, default 'auto'
+        SQL engine library to use. If 'auto', then the option
+        ``io.sql.engine`` is used. The default ``io.sql.engine``
+        behavior is 'sqlalchemy'
+
+        .. versionadded:: 1.3.0
+
+    **engine_kwargs
+        Any additional kwargs are passed to the engine.
+
+    Returns
+    -------
+    None or int
+        Number of rows affected by to_sql. None is returned if the callable
+        passed into ``method`` does not return an integer number of rows.
+
+        .. versionadded:: 1.4.0
+
+    Notes
+    -----
+    The returned rows affected is the sum of the ``rowcount`` attribute of ``sqlite3.Cursor``
+    or SQLAlchemy connectable. If using ADBC the returned rows are the result
+    of ``Cursor.adbc_ingest``. The returned value may not reflect the exact number of written
+    rows as stipulated in the
+    `sqlite3 <https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.rowcount>`__ or
+    `SQLAlchemy <https://docs.sqlalchemy.org/en/14/core/connections.html#sqlalchemy.engine.BaseCursorResult.rowcount>`__
+    """  # noqa: E501
+    if if_exists not in ("fail", "replace", "append"):
+        raise ValueError(f"'{if_exists}' is not valid for if_exists")
+
+    if isinstance(frame, Series):
+        frame = frame.to_frame()
+    elif not isinstance(frame, DataFrame):
+        raise NotImplementedError(
+            "'frame' argument should be either a Series or a DataFrame"
+        )
+
+    with pandasSQL_builder(con, schema=schema, need_transaction=True) as pandas_sql:
+        return pandas_sql.to_sql(
+            frame,
+            name,
+            if_exists=if_exists,
+            index=index,
+            index_label=index_label,
+            schema=schema,
+            chunksize=chunksize,
+            dtype=dtype,
+            method=method,
+            engine=engine,
+            **engine_kwargs,
+        )
+
+
+def has_table(table_name: str, con, schema: str | None = None) -> bool:
+    """
+    Check if DataBase has named table.
+
+    Parameters
+    ----------
+    table_name: string
+        Name of SQL table.
+    con: ADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library.
+        If a DBAPI2 object, only sqlite3 is supported.
+    schema : string, default None
+        Name of SQL schema in database to write to (if database flavor supports
+        this). If None, use default schema (default).
+
+    Returns
+    -------
+    boolean
+    """
+    with pandasSQL_builder(con, schema=schema) as pandas_sql:
+        return pandas_sql.has_table(table_name)
+
+
+table_exists = has_table
+
+
+def pandasSQL_builder(
+    con,
+    schema: str | None = None,
+    need_transaction: bool = False,
+) -> PandasSQL:
+    """
+    Convenience function to return the correct PandasSQL subclass based on the
+    provided parameters.  Also creates a sqlalchemy connection and transaction
+    if necessary.
+    """
+    import sqlite3
+
+    if isinstance(con, sqlite3.Connection) or con is None:
+        return SQLiteDatabase(con)
+
+    sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")
+
+    if isinstance(con, str) and sqlalchemy is None:
+        raise ImportError("Using URI string without sqlalchemy installed.")
+
+    if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Connectable)):
+        return SQLDatabase(con, schema, need_transaction)
+
+    adbc = import_optional_dependency("adbc_driver_manager.dbapi", errors="ignore")
+    if adbc and isinstance(con, adbc.Connection):
+        return ADBCDatabase(con)
+
+    warnings.warn(
+        "pandas only supports SQLAlchemy connectable (engine/connection) or "
+        "database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 "
+        "objects are not tested. Please consider using SQLAlchemy.",
+        UserWarning,
+        stacklevel=find_stack_level(),
+    )
+    return SQLiteDatabase(con)
+
+
+class SQLTable(PandasObject):
+    """
+    For mapping Pandas tables to SQL tables.
+    Uses fact that table is reflected by SQLAlchemy to
+    do better type conversions.
+    Also holds various flags needed to avoid having to
+    pass them between functions all the time.
+    """
+
+    # TODO: support for multiIndex
+
+    def __init__(
+        self,
+        name: str,
+        pandas_sql_engine,
+        frame=None,
+        index: bool | str | list[str] | None = True,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        prefix: str = "pandas",
+        index_label=None,
+        schema=None,
+        keys=None,
+        dtype: DtypeArg | None = None,
+    ) -> None:
+        self.name = name
+        self.pd_sql = pandas_sql_engine
+        self.prefix = prefix
+        self.frame = frame
+        self.index = self._index_name(index, index_label)
+        self.schema = schema
+        self.if_exists = if_exists
+        self.keys = keys
+        self.dtype = dtype
+
+        if frame is not None:
+            # We want to initialize based on a dataframe
+            self.table = self._create_table_setup()
+        else:
+            # no data provided, read-only mode
+            self.table = self.pd_sql.get_table(self.name, self.schema)
+
+        if self.table is None:
+            raise ValueError(f"Could not init table '{name}'")
+
+        if not len(self.name):
+            raise ValueError("Empty table name specified")
+
+    def exists(self):
+        return self.pd_sql.has_table(self.name, self.schema)
+
+    def sql_schema(self) -> str:
+        from sqlalchemy.schema import CreateTable
+
+        return str(CreateTable(self.table).compile(self.pd_sql.con))
+
+    def _execute_create(self) -> None:
+        # Inserting table into database, add to MetaData object
+        self.table = self.table.to_metadata(self.pd_sql.meta)
+        with self.pd_sql.run_transaction():
+            self.table.create(bind=self.pd_sql.con)
+
+    def create(self) -> None:
+        if self.exists():
+            if self.if_exists == "fail":
+                raise ValueError(f"Table '{self.name}' already exists.")
+            if self.if_exists == "replace":
+                self.pd_sql.drop_table(self.name, self.schema)
+                self._execute_create()
+            elif self.if_exists == "append":
+                pass
+            else:
+                raise ValueError(f"'{self.if_exists}' is not valid for if_exists")
+        else:
+            self._execute_create()
+
+    def _execute_insert(self, conn, keys: list[str], data_iter) -> int:
+        """
+        Execute SQL statement inserting data
+
+        Parameters
+        ----------
+        conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
+        keys : list of str
+           Column names
+        data_iter : generator of list
+           Each item contains a list of values to be inserted
+        """
+        data = [dict(zip(keys, row)) for row in data_iter]
+        result = conn.execute(self.table.insert(), data)
+        return result.rowcount
+
+    def _execute_insert_multi(self, conn, keys: list[str], data_iter) -> int:
+        """
+        Alternative to _execute_insert for DBs support multi-value INSERT.
+
+        Note: multi-value insert is usually faster for analytics DBs
+        and tables containing a few columns
+        but performance degrades quickly with increase of columns.
+
+        """
+
+        from sqlalchemy import insert
+
+        data = [dict(zip(keys, row)) for row in data_iter]
+        stmt = insert(self.table).values(data)
+        result = conn.execute(stmt)
+        return result.rowcount
+
+    def insert_data(self) -> tuple[list[str], list[np.ndarray]]:
+        if self.index is not None:
+            temp = self.frame.copy()
+            temp.index.names = self.index
+            try:
+                temp.reset_index(inplace=True)
+            except ValueError as err:
+                raise ValueError(f"duplicate name in index/columns: {err}") from err
+        else:
+            temp = self.frame
+
+        column_names = list(map(str, temp.columns))
+        ncols = len(column_names)
+        # this just pre-allocates the list: None's will be replaced with ndarrays
+        # error: List item 0 has incompatible type "None"; expected "ndarray"
+        data_list: list[np.ndarray] = [None] * ncols  # type: ignore[list-item]
+
+        for i, (_, ser) in enumerate(temp.items()):
+            if ser.dtype.kind == "M":
+                if isinstance(ser._values, ArrowExtensionArray):
+                    import pyarrow as pa
+
+                    if pa.types.is_date(ser.dtype.pyarrow_dtype):
+                        # GH#53854 to_pydatetime not supported for pyarrow date dtypes
+                        d = ser._values.to_numpy(dtype=object)
+                    else:
+                        with warnings.catch_warnings():
+                            warnings.filterwarnings("ignore", category=FutureWarning)
+                            # GH#52459 to_pydatetime will return Index[object]
+                            d = np.asarray(ser.dt.to_pydatetime(), dtype=object)
+                else:
+                    d = ser._values.to_pydatetime()
+            elif ser.dtype.kind == "m":
+                vals = ser._values
+                if isinstance(vals, ArrowExtensionArray):
+                    vals = vals.to_numpy(dtype=np.dtype("m8[ns]"))
+                # store as integers, see GH#6921, GH#7076
+                d = vals.view("i8").astype(object)
+            else:
+                d = ser._values.astype(object)
+
+            assert isinstance(d, np.ndarray), type(d)
+
+            if ser._can_hold_na:
+                # Note: this will miss timedeltas since they are converted to int
+                mask = isna(d)
+                d[mask] = None
+
+            data_list[i] = d
+
+        return column_names, data_list
+
+    def insert(
+        self,
+        chunksize: int | None = None,
+        method: Literal["multi"] | Callable | None = None,
+    ) -> int | None:
+        # set insert method
+        if method is None:
+            exec_insert = self._execute_insert
+        elif method == "multi":
+            exec_insert = self._execute_insert_multi
+        elif callable(method):
+            exec_insert = partial(method, self)
+        else:
+            raise ValueError(f"Invalid parameter `method`: {method}")
+
+        keys, data_list = self.insert_data()
+
+        nrows = len(self.frame)
+
+        if nrows == 0:
+            return 0
+
+        if chunksize is None:
+            chunksize = nrows
+        elif chunksize == 0:
+            raise ValueError("chunksize argument should be non-zero")
+
+        chunks = (nrows // chunksize) + 1
+        total_inserted = None
+        with self.pd_sql.run_transaction() as conn:
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, nrows)
+                if start_i >= end_i:
+                    break
+
+                chunk_iter = zip(*(arr[start_i:end_i] for arr in data_list))
+                num_inserted = exec_insert(conn, keys, chunk_iter)
+                # GH 46891
+                if num_inserted is not None:
+                    if total_inserted is None:
+                        total_inserted = num_inserted
+                    else:
+                        total_inserted += num_inserted
+        return total_inserted
+
+    def _query_iterator(
+        self,
+        result,
+        exit_stack: ExitStack,
+        chunksize: int | None,
+        columns,
+        coerce_float: bool = True,
+        parse_dates=None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ):
+        """Return generator through chunked result set."""
+        has_read_data = False
+        with exit_stack:
+            while True:
+                data = result.fetchmany(chunksize)
+                if not data:
+                    if not has_read_data:
+                        yield DataFrame.from_records(
+                            [], columns=columns, coerce_float=coerce_float
+                        )
+                    break
+
+                has_read_data = True
+                self.frame = _convert_arrays_to_dataframe(
+                    data, columns, coerce_float, dtype_backend
+                )
+
+                self._harmonize_columns(
+                    parse_dates=parse_dates, dtype_backend=dtype_backend
+                )
+
+                if self.index is not None:
+                    self.frame.set_index(self.index, inplace=True)
+
+                yield self.frame
+
+    def read(
+        self,
+        exit_stack: ExitStack,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        from sqlalchemy import select
+
+        if columns is not None and len(columns) > 0:
+            cols = [self.table.c[n] for n in columns]
+            if self.index is not None:
+                for idx in self.index[::-1]:
+                    cols.insert(0, self.table.c[idx])
+            sql_select = select(*cols)
+        else:
+            sql_select = select(self.table)
+        result = self.pd_sql.execute(sql_select)
+        column_names = result.keys()
+
+        if chunksize is not None:
+            return self._query_iterator(
+                result,
+                exit_stack,
+                chunksize,
+                column_names,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            data = result.fetchall()
+            self.frame = _convert_arrays_to_dataframe(
+                data, column_names, coerce_float, dtype_backend
+            )
+
+            self._harmonize_columns(
+                parse_dates=parse_dates, dtype_backend=dtype_backend
+            )
+
+            if self.index is not None:
+                self.frame.set_index(self.index, inplace=True)
+
+            return self.frame
+
+    def _index_name(self, index, index_label):
+        # for writing: index=True to include index in sql table
+        if index is True:
+            nlevels = self.frame.index.nlevels
+            # if index_label is specified, set this as index name(s)
+            if index_label is not None:
+                if not isinstance(index_label, list):
+                    index_label = [index_label]
+                if len(index_label) != nlevels:
+                    raise ValueError(
+                        "Length of 'index_label' should match number of "
+                        f"levels, which is {nlevels}"
+                    )
+                return index_label
+            # return the used column labels for the index columns
+            if (
+                nlevels == 1
+                and "index" not in self.frame.columns
+                and self.frame.index.name is None
+            ):
+                return ["index"]
+            else:
+                return com.fill_missing_names(self.frame.index.names)
+
+        # for reading: index=(list of) string to specify column to set as index
+        elif isinstance(index, str):
+            return [index]
+        elif isinstance(index, list):
+            return index
+        else:
+            return None
+
+    def _get_column_names_and_types(self, dtype_mapper):
+        column_names_and_types = []
+        if self.index is not None:
+            for i, idx_label in enumerate(self.index):
+                idx_type = dtype_mapper(self.frame.index._get_level_values(i))
+                column_names_and_types.append((str(idx_label), idx_type, True))
+
+        column_names_and_types += [
+            (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
+            for i in range(len(self.frame.columns))
+        ]
+
+        return column_names_and_types
+
+    def _create_table_setup(self):
+        from sqlalchemy import (
+            Column,
+            PrimaryKeyConstraint,
+            Table,
+        )
+        from sqlalchemy.schema import MetaData
+
+        column_names_and_types = self._get_column_names_and_types(self._sqlalchemy_type)
+
+        columns: list[Any] = [
+            Column(name, typ, index=is_index)
+            for name, typ, is_index in column_names_and_types
+        ]
+
+        if self.keys is not None:
+            if not is_list_like(self.keys):
+                keys = [self.keys]
+            else:
+                keys = self.keys
+            pkc = PrimaryKeyConstraint(*keys, name=self.name + "_pk")
+            columns.append(pkc)
+
+        schema = self.schema or self.pd_sql.meta.schema
+
+        # At this point, attach to new metadata, only attach to self.meta
+        # once table is created.
+        meta = MetaData()
+        return Table(self.name, meta, *columns, schema=schema)
+
+    def _harmonize_columns(
+        self,
+        parse_dates=None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> None:
+        """
+        Make the DataFrame's column types align with the SQL table
+        column types.
+        Need to work around limited NA value support. Floats are always
+        fine, ints must always be floats if there are Null values.
+        Booleans are hard because converting bool column with None replaces
+        all Nones with false. Therefore only convert bool if there are no
+        NA values.
+        Datetimes should already be converted to np.datetime64 if supported,
+        but here we also force conversion if required.
+        """
+        parse_dates = _process_parse_dates_argument(parse_dates)
+
+        for sql_col in self.table.columns:
+            col_name = sql_col.name
+            try:
+                df_col = self.frame[col_name]
+
+                # Handle date parsing upfront; don't try to convert columns
+                # twice
+                if col_name in parse_dates:
+                    try:
+                        fmt = parse_dates[col_name]
+                    except TypeError:
+                        fmt = None
+                    self.frame[col_name] = _handle_date_column(df_col, format=fmt)
+                    continue
+
+                # the type the dataframe column should have
+                col_type = self._get_dtype(sql_col.type)
+
+                if (
+                    col_type is datetime
+                    or col_type is date
+                    or col_type is DatetimeTZDtype
+                ):
+                    # Convert tz-aware Datetime SQL columns to UTC
+                    utc = col_type is DatetimeTZDtype
+                    self.frame[col_name] = _handle_date_column(df_col, utc=utc)
+                elif dtype_backend == "numpy" and col_type is float:
+                    # floats support NA, can always convert!
+                    self.frame[col_name] = df_col.astype(col_type, copy=False)
+                elif (
+                    using_string_dtype()
+                    and is_string_dtype(col_type)
+                    and is_object_dtype(self.frame[col_name])
+                ):
+                    self.frame[col_name] = df_col.astype(col_type, copy=False)
+                elif dtype_backend == "numpy" and len(df_col) == df_col.count():
+                    # No NA values, can convert ints and bools
+                    if col_type is np.dtype("int64") or col_type is bool:
+                        self.frame[col_name] = df_col.astype(col_type, copy=False)
+            except KeyError:
+                pass  # this column not in results
+
+    def _sqlalchemy_type(self, col: Index | Series):
+        dtype: DtypeArg = self.dtype or {}
+        if is_dict_like(dtype):
+            dtype = cast(dict, dtype)
+            if col.name in dtype:
+                return dtype[col.name]
+
+        # Infer type of column, while ignoring missing values.
+        # Needed for inserting typed data containing NULLs, GH 8778.
+        col_type = lib.infer_dtype(col, skipna=True)
+
+        from sqlalchemy.types import (
+            TIMESTAMP,
+            BigInteger,
+            Boolean,
+            Date,
+            DateTime,
+            Float,
+            Integer,
+            SmallInteger,
+            Text,
+            Time,
+        )
+
+        if col_type in ("datetime64", "datetime"):
+            # GH 9086: TIMESTAMP is the suggested type if the column contains
+            # timezone information
+            try:
+                # error: Item "Index" of "Union[Index, Series]" has no attribute "dt"
+                if col.dt.tz is not None:  # type: ignore[union-attr]
+                    return TIMESTAMP(timezone=True)
+            except AttributeError:
+                # The column is actually a DatetimeIndex
+                # GH 26761 or an Index with date-like data e.g. 9999-01-01
+                if getattr(col, "tz", None) is not None:
+                    return TIMESTAMP(timezone=True)
+            return DateTime
+        if col_type == "timedelta64":
+            warnings.warn(
+                "the 'timedelta' type is not supported, and will be "
+                "written as integer values (ns frequency) to the database.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            return BigInteger
+        elif col_type == "floating":
+            if col.dtype == "float32":
+                return Float(precision=23)
+            else:
+                return Float(precision=53)
+        elif col_type == "integer":
+            # GH35076 Map pandas integer to optimal SQLAlchemy integer type
+            if col.dtype.name.lower() in ("int8", "uint8", "int16"):
+                return SmallInteger
+            elif col.dtype.name.lower() in ("uint16", "int32"):
+                return Integer
+            elif col.dtype.name.lower() == "uint64":
+                raise ValueError("Unsigned 64 bit integer datatype is not supported")
+            else:
+                return BigInteger
+        elif col_type == "boolean":
+            return Boolean
+        elif col_type == "date":
+            return Date
+        elif col_type == "time":
+            return Time
+        elif col_type == "complex":
+            raise ValueError("Complex datatypes not supported")
+
+        return Text
+
+    def _get_dtype(self, sqltype):
+        from sqlalchemy.types import (
+            TIMESTAMP,
+            Boolean,
+            Date,
+            DateTime,
+            Float,
+            Integer,
+            String,
+        )
+
+        if isinstance(sqltype, Float):
+            return float
+        elif isinstance(sqltype, Integer):
+            # TODO: Refine integer size.
+            return np.dtype("int64")
+        elif isinstance(sqltype, TIMESTAMP):
+            # we have a timezone capable type
+            if not sqltype.timezone:
+                return datetime
+            return DatetimeTZDtype
+        elif isinstance(sqltype, DateTime):
+            # Caution: np.datetime64 is also a subclass of np.number.
+            return datetime
+        elif isinstance(sqltype, Date):
+            return date
+        elif isinstance(sqltype, Boolean):
+            return bool
+        elif isinstance(sqltype, String):
+            if using_string_dtype():
+                return StringDtype(na_value=np.nan)
+
+        return object
+
+
+class PandasSQL(PandasObject, ABC):
+    """
+    Subclasses Should define read_query and to_sql.
+    """
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(self, *args) -> None:
+        pass
+
+    def read_table(
+        self,
+        table_name: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def read_query(
+        self,
+        sql: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        pass
+
+    @abstractmethod
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool = True,
+        index_label=None,
+        schema=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        pass
+
+    @abstractmethod
+    def execute(self, sql: str | Select | TextClause, params=None):
+        pass
+
+    @abstractmethod
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        pass
+
+    @abstractmethod
+    def _create_sql_schema(
+        self,
+        frame: DataFrame,
+        table_name: str,
+        keys: list[str] | None = None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        pass
+
+
+class BaseEngine:
+    def insert_records(
+        self,
+        table: SQLTable,
+        con,
+        frame,
+        name: str,
+        index: bool | str | list[str] | None = True,
+        schema=None,
+        chunksize: int | None = None,
+        method=None,
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Inserts data into already-prepared table
+        """
+        raise AbstractMethodError(self)
+
+
+class SQLAlchemyEngine(BaseEngine):
+    def __init__(self) -> None:
+        import_optional_dependency(
+            "sqlalchemy", extra="sqlalchemy is required for SQL support."
+        )
+
+    def insert_records(
+        self,
+        table: SQLTable,
+        con,
+        frame,
+        name: str,
+        index: bool | str | list[str] | None = True,
+        schema=None,
+        chunksize: int | None = None,
+        method=None,
+        **engine_kwargs,
+    ) -> int | None:
+        from sqlalchemy import exc
+
+        try:
+            return table.insert(chunksize=chunksize, method=method)
+        except exc.StatementError as err:
+            # GH34431
+            # https://stackoverflow.com/a/67358288/6067848
+            msg = r"""(\(1054, "Unknown column 'inf(e0)?' in 'field list'"\))(?#
+            )|inf can not be used with MySQL"""
+            err_text = str(err.orig)
+            if re.search(msg, err_text):
+                raise ValueError("inf cannot be used with MySQL") from err
+            raise err
+
+
+def get_engine(engine: str) -> BaseEngine:
+    """return our implementation"""
+    if engine == "auto":
+        engine = get_option("io.sql.engine")
+
+    if engine == "auto":
+        # try engines in this order
+        engine_classes = [SQLAlchemyEngine]
+
+        error_msgs = ""
+        for engine_class in engine_classes:
+            try:
+                return engine_class()
+            except ImportError as err:
+                error_msgs += "\n - " + str(err)
+
+        raise ImportError(
+            "Unable to find a usable engine; "
+            "tried using: 'sqlalchemy'.\n"
+            "A suitable version of "
+            "sqlalchemy is required for sql I/O "
+            "support.\n"
+            "Trying to import the above resulted in these errors:"
+            f"{error_msgs}"
+        )
+
+    if engine == "sqlalchemy":
+        return SQLAlchemyEngine()
+
+    raise ValueError("engine must be one of 'auto', 'sqlalchemy'")
+
+
+class SQLDatabase(PandasSQL):
+    """
+    This class enables conversion between DataFrame and SQL databases
+    using SQLAlchemy to handle DataBase abstraction.
+
+    Parameters
+    ----------
+    con : SQLAlchemy Connectable or URI string.
+        Connectable to connect with the database. Using SQLAlchemy makes it
+        possible to use any DB supported by that library.
+    schema : string, default None
+        Name of SQL schema in database to write to (if database flavor
+        supports this). If None, use default schema (default).
+    need_transaction : bool, default False
+        If True, SQLDatabase will create a transaction.
+
+    """
+
+    def __init__(
+        self, con, schema: str | None = None, need_transaction: bool = False
+    ) -> None:
+        from sqlalchemy import create_engine
+        from sqlalchemy.engine import Engine
+        from sqlalchemy.schema import MetaData
+
+        # self.exit_stack cleans up the Engine and Connection and commits the
+        # transaction if any of those objects was created below.
+        # Cleanup happens either in self.__exit__ or at the end of the iterator
+        # returned by read_sql when chunksize is not None.
+        self.exit_stack = ExitStack()
+        if isinstance(con, str):
+            con = create_engine(con)
+            self.exit_stack.callback(con.dispose)
+        if isinstance(con, Engine):
+            con = self.exit_stack.enter_context(con.connect())
+        if need_transaction and not con.in_transaction():
+            self.exit_stack.enter_context(con.begin())
+        self.con = con
+        self.meta = MetaData(schema=schema)
+        self.returns_generator = False
+
+    def __exit__(self, *args) -> None:
+        if not self.returns_generator:
+            self.exit_stack.close()
+
+    @contextmanager
+    def run_transaction(self):
+        if not self.con.in_transaction():
+            with self.con.begin():
+                yield self.con
+        else:
+            yield self.con
+
+    def execute(self, sql: str | Select | TextClause, params=None):
+        """Simple passthrough to SQLAlchemy connectable"""
+        args = [] if params is None else [params]
+        if isinstance(sql, str):
+            return self.con.exec_driver_sql(sql, *args)
+        return self.con.execute(sql, *args)
+
+    def read_table(
+        self,
+        table_name: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL database table into a DataFrame.
+
+        Parameters
+        ----------
+        table_name : str
+            Name of SQL table in database.
+        index_col : string, optional, default: None
+            Column to set as index.
+        coerce_float : bool, default True
+            Attempts to convert values of non-string, non-numeric objects
+            (like decimal.Decimal) to floating point. This can result in
+            loss of precision.
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg}``, where the arg corresponds
+              to the keyword arguments of :func:`pandas.to_datetime`.
+              Especially useful with databases without native Datetime support,
+              such as SQLite.
+        columns : list, default: None
+            List of column names to select from SQL table.
+        schema : string, default None
+            Name of SQL schema in database to query (if database flavor
+            supports this).  If specified, this overwrites the default
+            schema of the SQL database object.
+        chunksize : int, default None
+            If specified, return an iterator where `chunksize` is the number
+            of rows to include in each chunk.
+        dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+            Back-end data type applied to the resultant :class:`DataFrame`
+            (still experimental). Behaviour is as follows:
+
+            * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+              (default).
+            * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+              DataFrame.
+
+            .. versionadded:: 2.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        pandas.read_sql_table
+        SQLDatabase.read_query
+
+        """
+        self.meta.reflect(bind=self.con, only=[table_name], views=True)
+        table = SQLTable(table_name, self, index=index_col, schema=schema)
+        if chunksize is not None:
+            self.returns_generator = True
+        return table.read(
+            self.exit_stack,
+            coerce_float=coerce_float,
+            parse_dates=parse_dates,
+            columns=columns,
+            chunksize=chunksize,
+            dtype_backend=dtype_backend,
+        )
+
+    @staticmethod
+    def _query_iterator(
+        result,
+        exit_stack: ExitStack,
+        chunksize: int,
+        columns,
+        index_col=None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ):
+        """Return generator through chunked result set"""
+        has_read_data = False
+        with exit_stack:
+            while True:
+                data = result.fetchmany(chunksize)
+                if not data:
+                    if not has_read_data:
+                        yield _wrap_result(
+                            [],
+                            columns,
+                            index_col=index_col,
+                            coerce_float=coerce_float,
+                            parse_dates=parse_dates,
+                            dtype=dtype,
+                            dtype_backend=dtype_backend,
+                        )
+                    break
+
+                has_read_data = True
+                yield _wrap_result(
+                    data,
+                    columns,
+                    index_col=index_col,
+                    coerce_float=coerce_float,
+                    parse_dates=parse_dates,
+                    dtype=dtype,
+                    dtype_backend=dtype_backend,
+                )
+
+    def read_query(
+        self,
+        sql: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL query into a DataFrame.
+
+        Parameters
+        ----------
+        sql : str
+            SQL query to be executed.
+        index_col : string, optional, default: None
+            Column name to use as index for the returned DataFrame object.
+        coerce_float : bool, default True
+            Attempt to convert values of non-string, non-numeric objects (like
+            decimal.Decimal) to floating point, useful for SQL result sets.
+        params : list, tuple or dict, optional, default: None
+            List of parameters to pass to execute method.  The syntax used
+            to pass parameters is database driver dependent. Check your
+            database driver documentation for which of the five syntax styles,
+            described in PEP 249's paramstyle, is supported.
+            Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg dict}``, where the arg dict
+              corresponds to the keyword arguments of
+              :func:`pandas.to_datetime` Especially useful with databases
+              without native Datetime support, such as SQLite.
+        chunksize : int, default None
+            If specified, return an iterator where `chunksize` is the number
+            of rows to include in each chunk.
+        dtype : Type name or dict of columns
+            Data type for data or columns. E.g. np.float64 or
+            {'a': np.float64, 'b': np.int32, 'c': 'Int64'}
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        read_sql_table : Read SQL database table into a DataFrame.
+        read_sql
+
+        """
+        result = self.execute(sql, params)
+        columns = result.keys()
+
+        if chunksize is not None:
+            self.returns_generator = True
+            return self._query_iterator(
+                result,
+                self.exit_stack,
+                chunksize,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            data = result.fetchall()
+            frame = _wrap_result(
+                data,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+            return frame
+
+    read_sql = read_query
+
+    def prep_table(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool | str | list[str] | None = True,
+        index_label=None,
+        schema=None,
+        dtype: DtypeArg | None = None,
+    ) -> SQLTable:
+        """
+        Prepares table in the database for data insertion. Creates it if needed, etc.
+        """
+        if dtype:
+            if not is_dict_like(dtype):
+                # error: Value expression in dictionary comprehension has incompatible
+                # type "Union[ExtensionDtype, str, dtype[Any], Type[object],
+                # Dict[Hashable, Union[ExtensionDtype, Union[str, dtype[Any]],
+                # Type[str], Type[float], Type[int], Type[complex], Type[bool],
+                # Type[object]]]]"; expected type "Union[ExtensionDtype, str,
+                # dtype[Any], Type[object]]"
+                dtype = {col_name: dtype for col_name in frame}  # type: ignore[misc]
+            else:
+                dtype = cast(dict, dtype)
+
+            from sqlalchemy.types import TypeEngine
+
+            for col, my_type in dtype.items():
+                if isinstance(my_type, type) and issubclass(my_type, TypeEngine):
+                    pass
+                elif isinstance(my_type, TypeEngine):
+                    pass
+                else:
+                    raise ValueError(f"The type of {col} is not a SQLAlchemy type")
+
+        table = SQLTable(
+            name,
+            self,
+            frame=frame,
+            index=index,
+            if_exists=if_exists,
+            index_label=index_label,
+            schema=schema,
+            dtype=dtype,
+        )
+        table.create()
+        return table
+
+    def check_case_sensitive(
+        self,
+        name: str,
+        schema: str | None,
+    ) -> None:
+        """
+        Checks table name for issues with case-sensitivity.
+        Method is called after data is inserted.
+        """
+        if not name.isdigit() and not name.islower():
+            # check for potentially case sensitivity issues (GH7815)
+            # Only check when name is not a number and name is not lower case
+            from sqlalchemy import inspect as sqlalchemy_inspect
+
+            insp = sqlalchemy_inspect(self.con)
+            table_names = insp.get_table_names(schema=schema or self.meta.schema)
+            if name not in table_names:
+                msg = (
+                    f"The provided table name '{name}' is not found exactly as "
+                    "such in the database after writing the table, possibly "
+                    "due to case sensitivity issues. Consider using lower "
+                    "case table names."
+                )
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool = True,
+        index_label=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Parameters
+        ----------
+        frame : DataFrame
+        name : string
+            Name of SQL table.
+        if_exists : {'fail', 'replace', 'append'}, default 'fail'
+            - fail: If table exists, do nothing.
+            - replace: If table exists, drop it, recreate it, and insert data.
+            - append: If table exists, insert data. Create if does not exist.
+        index : boolean, default True
+            Write DataFrame index as a column.
+        index_label : string or sequence, default None
+            Column label for index column(s). If None is given (default) and
+            `index` is True, then the index names are used.
+            A sequence should be given if the DataFrame uses MultiIndex.
+        schema : string, default None
+            Name of SQL schema in database to write to (if database flavor
+            supports this). If specified, this overwrites the default
+            schema of the SQLDatabase object.
+        chunksize : int, default None
+            If not None, then rows will be written in batches of this size at a
+            time.  If None, all rows will be written at once.
+        dtype : single type or dict of column name to SQL type, default None
+            Optional specifying the datatype for columns. The SQL type should
+            be a SQLAlchemy type. If all columns are of the same type, one
+            single value can be used.
+        method : {None', 'multi', callable}, default None
+            Controls the SQL insertion clause used:
+
+            * None : Uses standard SQL ``INSERT`` clause (one per row).
+            * 'multi': Pass multiple values in a single ``INSERT`` clause.
+            * callable with signature ``(pd_table, conn, keys, data_iter)``.
+
+            Details and a sample callable implementation can be found in the
+            section :ref:`insert method <io.sql.method>`.
+        engine : {'auto', 'sqlalchemy'}, default 'auto'
+            SQL engine library to use. If 'auto', then the option
+            ``io.sql.engine`` is used. The default ``io.sql.engine``
+            behavior is 'sqlalchemy'
+
+            .. versionadded:: 1.3.0
+
+        **engine_kwargs
+            Any additional kwargs are passed to the engine.
+        """
+        sql_engine = get_engine(engine)
+
+        table = self.prep_table(
+            frame=frame,
+            name=name,
+            if_exists=if_exists,
+            index=index,
+            index_label=index_label,
+            schema=schema,
+            dtype=dtype,
+        )
+
+        total_inserted = sql_engine.insert_records(
+            table=table,
+            con=self.con,
+            frame=frame,
+            name=name,
+            index=index,
+            schema=schema,
+            chunksize=chunksize,
+            method=method,
+            **engine_kwargs,
+        )
+
+        self.check_case_sensitive(name=name, schema=schema)
+        return total_inserted
+
+    @property
+    def tables(self):
+        return self.meta.tables
+
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        from sqlalchemy import inspect as sqlalchemy_inspect
+
+        insp = sqlalchemy_inspect(self.con)
+        return insp.has_table(name, schema or self.meta.schema)
+
+    def get_table(self, table_name: str, schema: str | None = None) -> Table:
+        from sqlalchemy import (
+            Numeric,
+            Table,
+        )
+
+        schema = schema or self.meta.schema
+        tbl = Table(table_name, self.meta, autoload_with=self.con, schema=schema)
+        for column in tbl.columns:
+            if isinstance(column.type, Numeric):
+                column.type.asdecimal = False
+        return tbl
+
+    def drop_table(self, table_name: str, schema: str | None = None) -> None:
+        schema = schema or self.meta.schema
+        if self.has_table(table_name, schema):
+            self.meta.reflect(
+                bind=self.con, only=[table_name], schema=schema, views=True
+            )
+            with self.run_transaction():
+                self.get_table(table_name, schema).drop(bind=self.con)
+            self.meta.clear()
+
+    def _create_sql_schema(
+        self,
+        frame: DataFrame,
+        table_name: str,
+        keys: list[str] | None = None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        table = SQLTable(
+            table_name,
+            self,
+            frame=frame,
+            index=False,
+            keys=keys,
+            dtype=dtype,
+            schema=schema,
+        )
+        return str(table.sql_schema())
+
+
+# ---- SQL without SQLAlchemy ---
+
+
+class ADBCDatabase(PandasSQL):
+    """
+    This class enables conversion between DataFrame and SQL databases
+    using ADBC to handle DataBase abstraction.
+
+    Parameters
+    ----------
+    con : adbc_driver_manager.dbapi.Connection
+    """
+
+    def __init__(self, con) -> None:
+        self.con = con
+
+    @contextmanager
+    def run_transaction(self):
+        with self.con.cursor() as cur:
+            try:
+                yield cur
+            except Exception:
+                self.con.rollback()
+                raise
+            self.con.commit()
+
+    def execute(self, sql: str | Select | TextClause, params=None):
+        if not isinstance(sql, str):
+            raise TypeError("Query must be a string unless using sqlalchemy.")
+        args = [] if params is None else [params]
+        cur = self.con.cursor()
+        try:
+            cur.execute(sql, *args)
+            return cur
+        except Exception as exc:
+            try:
+                self.con.rollback()
+            except Exception as inner_exc:  # pragma: no cover
+                ex = DatabaseError(
+                    f"Execution failed on sql: {sql}\n{exc}\nunable to rollback"
+                )
+                raise ex from inner_exc
+
+            ex = DatabaseError(f"Execution failed on sql '{sql}': {exc}")
+            raise ex from exc
+
+    def read_table(
+        self,
+        table_name: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        columns=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL database table into a DataFrame.
+
+        Parameters
+        ----------
+        table_name : str
+            Name of SQL table in database.
+        coerce_float : bool, default True
+            Raises NotImplementedError
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg}``, where the arg corresponds
+              to the keyword arguments of :func:`pandas.to_datetime`.
+              Especially useful with databases without native Datetime support,
+              such as SQLite.
+        columns : list, default: None
+            List of column names to select from SQL table.
+        schema : string, default None
+            Name of SQL schema in database to query (if database flavor
+            supports this).  If specified, this overwrites the default
+            schema of the SQL database object.
+        chunksize : int, default None
+            Raises NotImplementedError
+        dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+            Back-end data type applied to the resultant :class:`DataFrame`
+            (still experimental). Behaviour is as follows:
+
+            * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+              (default).
+            * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+              DataFrame.
+
+            .. versionadded:: 2.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        pandas.read_sql_table
+        SQLDatabase.read_query
+
+        """
+        if coerce_float is not True:
+            raise NotImplementedError(
+                "'coerce_float' is not implemented for ADBC drivers"
+            )
+        if chunksize:
+            raise NotImplementedError("'chunksize' is not implemented for ADBC drivers")
+
+        if columns:
+            if index_col:
+                index_select = maybe_make_list(index_col)
+            else:
+                index_select = []
+            to_select = index_select + columns
+            select_list = ", ".join(f'"{x}"' for x in to_select)
+        else:
+            select_list = "*"
+        if schema:
+            stmt = f"SELECT {select_list} FROM {schema}.{table_name}"
+        else:
+            stmt = f"SELECT {select_list} FROM {table_name}"
+
+        with self.con.cursor() as cur:
+            cur.execute(stmt)
+            pa_table = cur.fetch_arrow_table()
+            df = arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
+
+        return _wrap_result_adbc(
+            df,
+            index_col=index_col,
+            parse_dates=parse_dates,
+        )
+
+    def read_query(
+        self,
+        sql: str,
+        index_col: str | list[str] | None = None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        """
+        Read SQL query into a DataFrame.
+
+        Parameters
+        ----------
+        sql : str
+            SQL query to be executed.
+        index_col : string, optional, default: None
+            Column name to use as index for the returned DataFrame object.
+        coerce_float : bool, default True
+            Raises NotImplementedError
+        params : list, tuple or dict, optional, default: None
+            Raises NotImplementedError
+        parse_dates : list or dict, default: None
+            - List of column names to parse as dates.
+            - Dict of ``{column_name: format string}`` where format string is
+              strftime compatible in case of parsing string times, or is one of
+              (D, s, ns, ms, us) in case of parsing integer timestamps.
+            - Dict of ``{column_name: arg dict}``, where the arg dict
+              corresponds to the keyword arguments of
+              :func:`pandas.to_datetime` Especially useful with databases
+              without native Datetime support, such as SQLite.
+        chunksize : int, default None
+            Raises NotImplementedError
+        dtype : Type name or dict of columns
+            Data type for data or columns. E.g. np.float64 or
+            {'a': np.float64, 'b': np.int32, 'c': 'Int64'}
+
+            .. versionadded:: 1.3.0
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        read_sql_table : Read SQL database table into a DataFrame.
+        read_sql
+
+        """
+        if coerce_float is not True:
+            raise NotImplementedError(
+                "'coerce_float' is not implemented for ADBC drivers"
+            )
+        if params:
+            raise NotImplementedError("'params' is not implemented for ADBC drivers")
+        if chunksize:
+            raise NotImplementedError("'chunksize' is not implemented for ADBC drivers")
+
+        with self.con.cursor() as cur:
+            cur.execute(sql)
+            pa_table = cur.fetch_arrow_table()
+            df = arrow_table_to_pandas(pa_table, dtype_backend=dtype_backend)
+
+        return _wrap_result_adbc(
+            df,
+            index_col=index_col,
+            parse_dates=parse_dates,
+            dtype=dtype,
+        )
+
+    read_sql = read_query
+
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: Literal["fail", "replace", "append"] = "fail",
+        index: bool = True,
+        index_label=None,
+        schema: str | None = None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Parameters
+        ----------
+        frame : DataFrame
+        name : string
+            Name of SQL table.
+        if_exists : {'fail', 'replace', 'append'}, default 'fail'
+            - fail: If table exists, do nothing.
+            - replace: If table exists, drop it, recreate it, and insert data.
+            - append: If table exists, insert data. Create if does not exist.
+        index : boolean, default True
+            Write DataFrame index as a column.
+        index_label : string or sequence, default None
+            Raises NotImplementedError
+        schema : string, default None
+            Name of SQL schema in database to write to (if database flavor
+            supports this). If specified, this overwrites the default
+            schema of the SQLDatabase object.
+        chunksize : int, default None
+            Raises NotImplementedError
+        dtype : single type or dict of column name to SQL type, default None
+            Raises NotImplementedError
+        method : {None', 'multi', callable}, default None
+            Raises NotImplementedError
+        engine : {'auto', 'sqlalchemy'}, default 'auto'
+            Raises NotImplementedError if not set to 'auto'
+        """
+        if index_label:
+            raise NotImplementedError(
+                "'index_label' is not implemented for ADBC drivers"
+            )
+        if chunksize:
+            raise NotImplementedError("'chunksize' is not implemented for ADBC drivers")
+        if dtype:
+            raise NotImplementedError("'dtype' is not implemented for ADBC drivers")
+        if method:
+            raise NotImplementedError("'method' is not implemented for ADBC drivers")
+        if engine != "auto":
+            raise NotImplementedError(
+                "engine != 'auto' not implemented for ADBC drivers"
+            )
+
+        if schema:
+            table_name = f"{schema}.{name}"
+        else:
+            table_name = name
+
+        # pandas if_exists="append" will still create the
+        # table if it does not exist; ADBC is more explicit with append/create
+        # as applicable modes, so the semantics get blurred across
+        # the libraries
+        mode = "create"
+        if self.has_table(name, schema):
+            if if_exists == "fail":
+                raise ValueError(f"Table '{table_name}' already exists.")
+            elif if_exists == "replace":
+                with self.con.cursor() as cur:
+                    cur.execute(f"DROP TABLE {table_name}")
+            elif if_exists == "append":
+                mode = "append"
+
+        import pyarrow as pa
+
+        try:
+            tbl = pa.Table.from_pandas(frame, preserve_index=index)
+        except pa.ArrowNotImplementedError as exc:
+            raise ValueError("datatypes not supported") from exc
+
+        with self.con.cursor() as cur:
+            total_inserted = cur.adbc_ingest(
+                table_name=name, data=tbl, mode=mode, db_schema_name=schema
+            )
+
+        self.con.commit()
+        return total_inserted
+
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        meta = self.con.adbc_get_objects(
+            db_schema_filter=schema, table_name_filter=name
+        ).read_all()
+
+        for catalog_schema in meta["catalog_db_schemas"].to_pylist():
+            if not catalog_schema:
+                continue
+            for schema_record in catalog_schema:
+                if not schema_record:
+                    continue
+
+                for table_record in schema_record["db_schema_tables"]:
+                    if table_record["table_name"] == name:
+                        return True
+
+        return False
+
+    def _create_sql_schema(
+        self,
+        frame: DataFrame,
+        table_name: str,
+        keys: list[str] | None = None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        raise NotImplementedError("not implemented for adbc")
+
+
+# sqlite-specific sql strings and handler class
+# dictionary used for readability purposes
+_SQL_TYPES = {
+    "string": "TEXT",
+    "floating": "REAL",
+    "integer": "INTEGER",
+    "datetime": "TIMESTAMP",
+    "date": "DATE",
+    "time": "TIME",
+    "boolean": "INTEGER",
+}
+
+
+def _get_unicode_name(name: object):
+    try:
+        uname = str(name).encode("utf-8", "strict").decode("utf-8")
+    except UnicodeError as err:
+        raise ValueError(f"Cannot convert identifier to UTF-8: '{name}'") from err
+    return uname
+
+
+def _get_valid_sqlite_name(name: object):
+    # See https://stackoverflow.com/questions/6514274/how-do-you-escape-strings\
+    # -for-sqlite-table-column-names-in-python
+    # Ensure the string can be encoded as UTF-8.
+    # Ensure the string does not include any NUL characters.
+    # Replace all " with "".
+    # Wrap the entire thing in double quotes.
+
+    uname = _get_unicode_name(name)
+    if not len(uname):
+        raise ValueError("Empty table or column name specified")
+
+    nul_index = uname.find("\x00")
+    if nul_index >= 0:
+        raise ValueError("SQLite identifier cannot contain NULs")
+    return '"' + uname.replace('"', '""') + '"'
+
+
+class SQLiteTable(SQLTable):
+    """
+    Patch the SQLTable for fallback support.
+    Instead of a table variable just use the Create Table statement.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._register_date_adapters()
+
+    def _register_date_adapters(self) -> None:
+        # GH 8341
+        # register an adapter callable for datetime.time object
+        import sqlite3
+
+        # this will transform time(12,34,56,789) into '12:34:56.000789'
+        # (this is what sqlalchemy does)
+        def _adapt_time(t) -> str:
+            # This is faster than strftime
+            return f"{t.hour:02d}:{t.minute:02d}:{t.second:02d}.{t.microsecond:06d}"
+
+        # Also register adapters for date/datetime and co
+        # xref https://docs.python.org/3.12/library/sqlite3.html#adapter-and-converter-recipes
+        # Python 3.12+ doesn't auto-register adapters for us anymore
+
+        adapt_date_iso = lambda val: val.isoformat()
+        adapt_datetime_iso = lambda val: val.isoformat(" ")
+
+        sqlite3.register_adapter(time, _adapt_time)
+
+        sqlite3.register_adapter(date, adapt_date_iso)
+        sqlite3.register_adapter(datetime, adapt_datetime_iso)
+
+        convert_date = lambda val: date.fromisoformat(val.decode())
+        convert_timestamp = lambda val: datetime.fromisoformat(val.decode())
+
+        sqlite3.register_converter("date", convert_date)
+        sqlite3.register_converter("timestamp", convert_timestamp)
+
+    def sql_schema(self) -> str:
+        return str(";\n".join(self.table))
+
+    def _execute_create(self) -> None:
+        with self.pd_sql.run_transaction() as conn:
+            for stmt in self.table:
+                conn.execute(stmt)
+
+    def insert_statement(self, *, num_rows: int) -> str:
+        names = list(map(str, self.frame.columns))
+        wld = "?"  # wildcard char
+        escape = _get_valid_sqlite_name
+
+        if self.index is not None:
+            for idx in self.index[::-1]:
+                names.insert(0, idx)
+
+        bracketed_names = [escape(column) for column in names]
+        col_names = ",".join(bracketed_names)
+
+        row_wildcards = ",".join([wld] * len(names))
+        wildcards = ",".join([f"({row_wildcards})" for _ in range(num_rows)])
+        insert_statement = (
+            f"INSERT INTO {escape(self.name)} ({col_names}) VALUES {wildcards}"
+        )
+        return insert_statement
+
+    def _execute_insert(self, conn, keys, data_iter) -> int:
+        data_list = list(data_iter)
+        conn.executemany(self.insert_statement(num_rows=1), data_list)
+        return conn.rowcount
+
+    def _execute_insert_multi(self, conn, keys, data_iter) -> int:
+        data_list = list(data_iter)
+        flattened_data = [x for row in data_list for x in row]
+        conn.execute(self.insert_statement(num_rows=len(data_list)), flattened_data)
+        return conn.rowcount
+
+    def _create_table_setup(self):
+        """
+        Return a list of SQL statements that creates a table reflecting the
+        structure of a DataFrame.  The first entry will be a CREATE TABLE
+        statement while the rest will be CREATE INDEX statements.
+        """
+        column_names_and_types = self._get_column_names_and_types(self._sql_type_name)
+        escape = _get_valid_sqlite_name
+
+        create_tbl_stmts = [
+            escape(cname) + " " + ctype for cname, ctype, _ in column_names_and_types
+        ]
+
+        if self.keys is not None and len(self.keys):
+            if not is_list_like(self.keys):
+                keys = [self.keys]
+            else:
+                keys = self.keys
+            cnames_br = ", ".join([escape(c) for c in keys])
+            create_tbl_stmts.append(
+                f"CONSTRAINT {self.name}_pk PRIMARY KEY ({cnames_br})"
+            )
+        if self.schema:
+            schema_name = self.schema + "."
+        else:
+            schema_name = ""
+        create_stmts = [
+            "CREATE TABLE "
+            + schema_name
+            + escape(self.name)
+            + " (\n"
+            + ",\n  ".join(create_tbl_stmts)
+            + "\n)"
+        ]
+
+        ix_cols = [cname for cname, _, is_index in column_names_and_types if is_index]
+        if len(ix_cols):
+            cnames = "_".join(ix_cols)
+            cnames_br = ",".join([escape(c) for c in ix_cols])
+            create_stmts.append(
+                "CREATE INDEX "
+                + escape("ix_" + self.name + "_" + cnames)
+                + "ON "
+                + escape(self.name)
+                + " ("
+                + cnames_br
+                + ")"
+            )
+
+        return create_stmts
+
+    def _sql_type_name(self, col):
+        dtype: DtypeArg = self.dtype or {}
+        if is_dict_like(dtype):
+            dtype = cast(dict, dtype)
+            if col.name in dtype:
+                return dtype[col.name]
+
+        # Infer type of column, while ignoring missing values.
+        # Needed for inserting typed data containing NULLs, GH 8778.
+        col_type = lib.infer_dtype(col, skipna=True)
+
+        if col_type == "timedelta64":
+            warnings.warn(
+                "the 'timedelta' type is not supported, and will be "
+                "written as integer values (ns frequency) to the database.",
+                UserWarning,
+                stacklevel=find_stack_level(),
+            )
+            col_type = "integer"
+
+        elif col_type == "datetime64":
+            col_type = "datetime"
+
+        elif col_type == "empty":
+            col_type = "string"
+
+        elif col_type == "complex":
+            raise ValueError("Complex datatypes not supported")
+
+        if col_type not in _SQL_TYPES:
+            col_type = "string"
+
+        return _SQL_TYPES[col_type]
+
+
+class SQLiteDatabase(PandasSQL):
+    """
+    Version of SQLDatabase to support SQLite connections (fallback without
+    SQLAlchemy). This should only be used internally.
+
+    Parameters
+    ----------
+    con : sqlite connection object
+
+    """
+
+    def __init__(self, con) -> None:
+        self.con = con
+
+    @contextmanager
+    def run_transaction(self):
+        cur = self.con.cursor()
+        try:
+            yield cur
+            self.con.commit()
+        except Exception:
+            self.con.rollback()
+            raise
+        finally:
+            cur.close()
+
+    def execute(self, sql: str | Select | TextClause, params=None):
+        if not isinstance(sql, str):
+            raise TypeError("Query must be a string unless using sqlalchemy.")
+        args = [] if params is None else [params]
+        cur = self.con.cursor()
+        try:
+            cur.execute(sql, *args)
+            return cur
+        except Exception as exc:
+            try:
+                self.con.rollback()
+            except Exception as inner_exc:  # pragma: no cover
+                ex = DatabaseError(
+                    f"Execution failed on sql: {sql}\n{exc}\nunable to rollback"
+                )
+                raise ex from inner_exc
+
+            ex = DatabaseError(f"Execution failed on sql '{sql}': {exc}")
+            raise ex from exc
+
+    @staticmethod
+    def _query_iterator(
+        cursor,
+        chunksize: int,
+        columns,
+        index_col=None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ):
+        """Return generator through chunked result set"""
+        has_read_data = False
+        while True:
+            data = cursor.fetchmany(chunksize)
+            if type(data) == tuple:
+                data = list(data)
+            if not data:
+                cursor.close()
+                if not has_read_data:
+                    result = DataFrame.from_records(
+                        [], columns=columns, coerce_float=coerce_float
+                    )
+                    if dtype:
+                        result = result.astype(dtype)
+                    yield result
+                break
+
+            has_read_data = True
+            yield _wrap_result(
+                data,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+
+    def read_query(
+        self,
+        sql,
+        index_col=None,
+        coerce_float: bool = True,
+        parse_dates=None,
+        params=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        dtype_backend: DtypeBackend | Literal["numpy"] = "numpy",
+    ) -> DataFrame | Iterator[DataFrame]:
+        cursor = self.execute(sql, params)
+        columns = [col_desc[0] for col_desc in cursor.description]
+
+        if chunksize is not None:
+            return self._query_iterator(
+                cursor,
+                chunksize,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+        else:
+            data = self._fetchall_as_list(cursor)
+            cursor.close()
+
+            frame = _wrap_result(
+                data,
+                columns,
+                index_col=index_col,
+                coerce_float=coerce_float,
+                parse_dates=parse_dates,
+                dtype=dtype,
+                dtype_backend=dtype_backend,
+            )
+            return frame
+
+    def _fetchall_as_list(self, cur):
+        result = cur.fetchall()
+        if not isinstance(result, list):
+            result = list(result)
+        return result
+
+    def to_sql(
+        self,
+        frame,
+        name: str,
+        if_exists: str = "fail",
+        index: bool = True,
+        index_label=None,
+        schema=None,
+        chunksize: int | None = None,
+        dtype: DtypeArg | None = None,
+        method: Literal["multi"] | Callable | None = None,
+        engine: str = "auto",
+        **engine_kwargs,
+    ) -> int | None:
+        """
+        Write records stored in a DataFrame to a SQL database.
+
+        Parameters
+        ----------
+        frame: DataFrame
+        name: string
+            Name of SQL table.
+        if_exists: {'fail', 'replace', 'append'}, default 'fail'
+            fail: If table exists, do nothing.
+            replace: If table exists, drop it, recreate it, and insert data.
+            append: If table exists, insert data. Create if it does not exist.
+        index : bool, default True
+            Write DataFrame index as a column
+        index_label : string or sequence, default None
+            Column label for index column(s). If None is given (default) and
+            `index` is True, then the index names are used.
+            A sequence should be given if the DataFrame uses MultiIndex.
+        schema : string, default None
+            Ignored parameter included for compatibility with SQLAlchemy
+            version of ``to_sql``.
+        chunksize : int, default None
+            If not None, then rows will be written in batches of this
+            size at a time. If None, all rows will be written at once.
+        dtype : single type or dict of column name to SQL type, default None
+            Optional specifying the datatype for columns. The SQL type should
+            be a string. If all columns are of the same type, one single value
+            can be used.
+        method : {None, 'multi', callable}, default None
+            Controls the SQL insertion clause used:
+
+            * None : Uses standard SQL ``INSERT`` clause (one per row).
+            * 'multi': Pass multiple values in a single ``INSERT`` clause.
+            * callable with signature ``(pd_table, conn, keys, data_iter)``.
+
+            Details and a sample callable implementation can be found in the
+            section :ref:`insert method <io.sql.method>`.
+        """
+        if dtype:
+            if not is_dict_like(dtype):
+                # error: Value expression in dictionary comprehension has incompatible
+                # type "Union[ExtensionDtype, str, dtype[Any], Type[object],
+                # Dict[Hashable, Union[ExtensionDtype, Union[str, dtype[Any]],
+                # Type[str], Type[float], Type[int], Type[complex], Type[bool],
+                # Type[object]]]]"; expected type "Union[ExtensionDtype, str,
+                # dtype[Any], Type[object]]"
+                dtype = {col_name: dtype for col_name in frame}  # type: ignore[misc]
+            else:
+                dtype = cast(dict, dtype)
+
+            for col, my_type in dtype.items():
+                if not isinstance(my_type, str):
+                    raise ValueError(f"{col} ({my_type}) not a string")
+
+        table = SQLiteTable(
+            name,
+            self,
+            frame=frame,
+            index=index,
+            if_exists=if_exists,
+            index_label=index_label,
+            dtype=dtype,
+        )
+        table.create()
+        return table.insert(chunksize, method)
+
+    def has_table(self, name: str, schema: str | None = None) -> bool:
+        wld = "?"
+        query = f"""
+        SELECT
+            name
+        FROM
+            sqlite_master
+        WHERE
+            type IN ('table', 'view')
+            AND name={wld};
+        """
+
+        return len(self.execute(query, [name]).fetchall()) > 0
+
+    def get_table(self, table_name: str, schema: str | None = None) -> None:
+        return None  # not supported in fallback mode
+
+    def drop_table(self, name: str, schema: str | None = None) -> None:
+        drop_sql = f"DROP TABLE {_get_valid_sqlite_name(name)}"
+        self.execute(drop_sql)
+
+    def _create_sql_schema(
+        self,
+        frame,
+        table_name: str,
+        keys=None,
+        dtype: DtypeArg | None = None,
+        schema: str | None = None,
+    ) -> str:
+        table = SQLiteTable(
+            table_name,
+            self,
+            frame=frame,
+            index=False,
+            keys=keys,
+            dtype=dtype,
+            schema=schema,
+        )
+        return str(table.sql_schema())
+
+
+def get_schema(
+    frame,
+    name: str,
+    keys=None,
+    con=None,
+    dtype: DtypeArg | None = None,
+    schema: str | None = None,
+) -> str:
+    """
+    Get the SQL db table schema for the given frame.
+
+    Parameters
+    ----------
+    frame : DataFrame
+    name : str
+        name of SQL table
+    keys : string or sequence, default: None
+        columns to use a primary key
+    con: ADBC Connection, SQLAlchemy connectable, sqlite3 connection, default: None
+        ADBC provides high performance I/O with native type support, where available.
+        Using SQLAlchemy makes it possible to use any DB supported by that
+        library
+        If a DBAPI2 object, only sqlite3 is supported.
+    dtype : dict of column name to SQL type, default None
+        Optional specifying the datatype for columns. The SQL type should
+        be a SQLAlchemy type, or a string for sqlite3 fallback connection.
+    schema: str, default: None
+        Optional specifying the schema to be used in creating the table.
+    """
+    with pandasSQL_builder(con=con) as pandas_sql:
+        return pandas_sql._create_sql_schema(
+            frame, name, keys=keys, dtype=dtype, schema=schema
+        )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/stata.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/stata.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5057a66816386d344a3c9d2ced174ae1b4642e1
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/stata.py
@@ -0,0 +1,3768 @@
+"""
+Module contains tools for processing Stata files into DataFrames
+
+The StataReader below was originally written by Joe Presbrey as part of PyDTA.
+It has been extended and improved by Skipper Seabold from the Statsmodels
+project who also developed the StataWriter and was finally added to pandas in
+a once again improved version.
+
+You can find more information on http://presbrey.mit.edu/PyDTA and
+https://www.statsmodels.org/devel/
+"""
+from __future__ import annotations
+
+from collections import abc
+from datetime import (
+    datetime,
+    timedelta,
+)
+from io import BytesIO
+import os
+import struct
+import sys
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    AnyStr,
+    Callable,
+    Final,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.lib import infer_dtype
+from pandas._libs.writers import max_len_string_array
+from pandas.errors import (
+    CategoricalConversionWarning,
+    InvalidColumnName,
+    PossiblePrecisionLoss,
+    ValueLabelTypeMismatch,
+)
+from pandas.util._decorators import (
+    Appender,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_numeric_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas import (
+    Categorical,
+    DatetimeIndex,
+    NaT,
+    Timestamp,
+    isna,
+    to_datetime,
+    to_timedelta,
+)
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.range import RangeIndex
+from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+    from types import TracebackType
+    from typing import Literal
+
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        Self,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+_version_error = (
+    "Version of given Stata file is {version}. pandas supports importing "
+    "versions 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), "
+    "114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), 118 (Stata 14/15/16),"
+    "and 119 (Stata 15/16, over 32,767 variables)."
+)
+
+_statafile_processing_params1 = """\
+convert_dates : bool, default True
+    Convert date variables to DataFrame time values.
+convert_categoricals : bool, default True
+    Read value labels and convert columns to Categorical/Factor variables."""
+
+_statafile_processing_params2 = """\
+index_col : str, optional
+    Column to set as index.
+convert_missing : bool, default False
+    Flag indicating whether to convert missing values to their Stata
+    representations.  If False, missing values are replaced with nan.
+    If True, columns containing missing values are returned with
+    object data types and missing values are represented by
+    StataMissingValue objects.
+preserve_dtypes : bool, default True
+    Preserve Stata datatypes. If False, numeric data are upcast to pandas
+    default types for foreign data (float64 or int64).
+columns : list or None
+    Columns to retain.  Columns will be returned in the given order.  None
+    returns all columns.
+order_categoricals : bool, default True
+    Flag indicating whether converted categorical data are ordered."""
+
+_chunksize_params = """\
+chunksize : int, default None
+    Return StataReader object for iterations, returns chunks with
+    given number of lines."""
+
+_iterator_params = """\
+iterator : bool, default False
+    Return StataReader object."""
+
+_reader_notes = """\
+Notes
+-----
+Categorical variables read through an iterator may not have the same
+categories and dtype. This occurs when  a variable stored in a DTA
+file is associated to an incomplete set of value labels that only
+label a strict subset of the values."""
+
+_read_stata_doc = f"""
+Read Stata file into DataFrame.
+
+Parameters
+----------
+filepath_or_buffer : str, path object or file-like object
+    Any valid string path is acceptable. The string could be a URL. Valid
+    URL schemes include http, ftp, s3, and file. For file URLs, a host is
+    expected. A local file could be: ``file://localhost/path/to/table.dta``.
+
+    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
+
+    By file-like object, we refer to objects with a ``read()`` method,
+    such as a file handle (e.g. via builtin ``open`` function)
+    or ``StringIO``.
+{_statafile_processing_params1}
+{_statafile_processing_params2}
+{_chunksize_params}
+{_iterator_params}
+{_shared_docs["decompression_options"] % "filepath_or_buffer"}
+{_shared_docs["storage_options"]}
+
+Returns
+-------
+DataFrame or pandas.api.typing.StataReader
+
+See Also
+--------
+io.stata.StataReader : Low-level reader for Stata data files.
+DataFrame.to_stata: Export Stata data files.
+
+{_reader_notes}
+
+Examples
+--------
+
+Creating a dummy stata for this example
+
+>>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon', 'parrot'],
+...                     'speed': [350, 18, 361, 15]}})  # doctest: +SKIP
+>>> df.to_stata('animals.dta')  # doctest: +SKIP
+
+Read a Stata dta file:
+
+>>> df = pd.read_stata('animals.dta')  # doctest: +SKIP
+
+Read a Stata dta file in 10,000 line chunks:
+
+>>> values = np.random.randint(0, 10, size=(20_000, 1), dtype="uint8")  # doctest: +SKIP
+>>> df = pd.DataFrame(values, columns=["i"])  # doctest: +SKIP
+>>> df.to_stata('filename.dta')  # doctest: +SKIP
+
+>>> with pd.read_stata('filename.dta', chunksize=10000) as itr: # doctest: +SKIP
+>>>     for chunk in itr:
+...         # Operate on a single chunk, e.g., chunk.mean()
+...         pass  # doctest: +SKIP
+"""
+
+_read_method_doc = f"""\
+Reads observations from Stata file, converting them into a dataframe
+
+Parameters
+----------
+nrows : int
+    Number of lines to read from data file, if None read whole file.
+{_statafile_processing_params1}
+{_statafile_processing_params2}
+
+Returns
+-------
+DataFrame
+"""
+
+_stata_reader_doc = f"""\
+Class for reading Stata dta files.
+
+Parameters
+----------
+path_or_buf : path (string), buffer or path object
+    string, path object (pathlib.Path or py._path.local.LocalPath) or object
+    implementing a binary read() functions.
+{_statafile_processing_params1}
+{_statafile_processing_params2}
+{_chunksize_params}
+{_shared_docs["decompression_options"]}
+{_shared_docs["storage_options"]}
+
+{_reader_notes}
+"""
+
+
+_date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
+
+
+stata_epoch: Final = datetime(1960, 1, 1)
+
+
+def _stata_elapsed_date_to_datetime_vec(dates: Series, fmt: str) -> Series:
+    """
+    Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime
+
+    Parameters
+    ----------
+    dates : Series
+        The Stata Internal Format date to convert to datetime according to fmt
+    fmt : str
+        The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
+        Returns
+
+    Returns
+    -------
+    converted : Series
+        The converted dates
+
+    Examples
+    --------
+    >>> dates = pd.Series([52])
+    >>> _stata_elapsed_date_to_datetime_vec(dates , "%tw")
+    0   1961-01-01
+    dtype: datetime64[ns]
+
+    Notes
+    -----
+    datetime/c - tc
+        milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day
+    datetime/C - tC - NOT IMPLEMENTED
+        milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds
+    date - td
+        days since 01jan1960 (01jan1960 = 0)
+    weekly date - tw
+        weeks since 1960w1
+        This assumes 52 weeks in a year, then adds 7 * remainder of the weeks.
+        The datetime value is the start of the week in terms of days in the
+        year, not ISO calendar weeks.
+    monthly date - tm
+        months since 1960m1
+    quarterly date - tq
+        quarters since 1960q1
+    half-yearly date - th
+        half-years since 1960h1 yearly
+    date - ty
+        years since 0000
+    """
+    MIN_YEAR, MAX_YEAR = Timestamp.min.year, Timestamp.max.year
+    MAX_DAY_DELTA = (Timestamp.max - datetime(1960, 1, 1)).days
+    MIN_DAY_DELTA = (Timestamp.min - datetime(1960, 1, 1)).days
+    MIN_MS_DELTA = MIN_DAY_DELTA * 24 * 3600 * 1000
+    MAX_MS_DELTA = MAX_DAY_DELTA * 24 * 3600 * 1000
+
+    def convert_year_month_safe(year, month) -> Series:
+        """
+        Convert year and month to datetimes, using pandas vectorized versions
+        when the date range falls within the range supported by pandas.
+        Otherwise it falls back to a slower but more robust method
+        using datetime.
+        """
+        if year.max() < MAX_YEAR and year.min() > MIN_YEAR:
+            return to_datetime(100 * year + month, format="%Y%m")
+        else:
+            index = getattr(year, "index", None)
+            return Series([datetime(y, m, 1) for y, m in zip(year, month)], index=index)
+
+    def convert_year_days_safe(year, days) -> Series:
+        """
+        Converts year (e.g. 1999) and days since the start of the year to a
+        datetime or datetime64 Series
+        """
+        if year.max() < (MAX_YEAR - 1) and year.min() > MIN_YEAR:
+            return to_datetime(year, format="%Y") + to_timedelta(days, unit="d")
+        else:
+            index = getattr(year, "index", None)
+            value = [
+                datetime(y, 1, 1) + timedelta(days=int(d)) for y, d in zip(year, days)
+            ]
+            return Series(value, index=index)
+
+    def convert_delta_safe(base, deltas, unit) -> Series:
+        """
+        Convert base dates and deltas to datetimes, using pandas vectorized
+        versions if the deltas satisfy restrictions required to be expressed
+        as dates in pandas.
+        """
+        index = getattr(deltas, "index", None)
+        if unit == "d":
+            if deltas.max() > MAX_DAY_DELTA or deltas.min() < MIN_DAY_DELTA:
+                values = [base + timedelta(days=int(d)) for d in deltas]
+                return Series(values, index=index)
+        elif unit == "ms":
+            if deltas.max() > MAX_MS_DELTA or deltas.min() < MIN_MS_DELTA:
+                values = [
+                    base + timedelta(microseconds=(int(d) * 1000)) for d in deltas
+                ]
+                return Series(values, index=index)
+        else:
+            raise ValueError("format not understood")
+        base = to_datetime(base)
+        deltas = to_timedelta(deltas, unit=unit)
+        return base + deltas
+
+    # TODO(non-nano): If/when pandas supports more than datetime64[ns], this
+    #  should be improved to use correct range, e.g. datetime[Y] for yearly
+    bad_locs = np.isnan(dates)
+    has_bad_values = False
+    if bad_locs.any():
+        has_bad_values = True
+        dates._values[bad_locs] = 1.0  # Replace with NaT
+    dates = dates.astype(np.int64)
+
+    if fmt.startswith(("%tc", "tc")):  # Delta ms relative to base
+        base = stata_epoch
+        ms = dates
+        conv_dates = convert_delta_safe(base, ms, "ms")
+    elif fmt.startswith(("%tC", "tC")):
+        warnings.warn(
+            "Encountered %tC format. Leaving in Stata Internal Format.",
+            stacklevel=find_stack_level(),
+        )
+        conv_dates = Series(dates, dtype=object)
+        if has_bad_values:
+            conv_dates[bad_locs] = NaT
+        return conv_dates
+    # Delta days relative to base
+    elif fmt.startswith(("%td", "td", "%d", "d")):
+        base = stata_epoch
+        days = dates
+        conv_dates = convert_delta_safe(base, days, "d")
+    # does not count leap days - 7 days is a week.
+    # 52nd week may have more than 7 days
+    elif fmt.startswith(("%tw", "tw")):
+        year = stata_epoch.year + dates // 52
+        days = (dates % 52) * 7
+        conv_dates = convert_year_days_safe(year, days)
+    elif fmt.startswith(("%tm", "tm")):  # Delta months relative to base
+        year = stata_epoch.year + dates // 12
+        month = (dates % 12) + 1
+        conv_dates = convert_year_month_safe(year, month)
+    elif fmt.startswith(("%tq", "tq")):  # Delta quarters relative to base
+        year = stata_epoch.year + dates // 4
+        quarter_month = (dates % 4) * 3 + 1
+        conv_dates = convert_year_month_safe(year, quarter_month)
+    elif fmt.startswith(("%th", "th")):  # Delta half-years relative to base
+        year = stata_epoch.year + dates // 2
+        month = (dates % 2) * 6 + 1
+        conv_dates = convert_year_month_safe(year, month)
+    elif fmt.startswith(("%ty", "ty")):  # Years -- not delta
+        year = dates
+        first_month = np.ones_like(dates)
+        conv_dates = convert_year_month_safe(year, first_month)
+    else:
+        raise ValueError(f"Date fmt {fmt} not understood")
+
+    if has_bad_values:  # Restore NaT for bad values
+        conv_dates[bad_locs] = NaT
+
+    return conv_dates
+
+
+def _datetime_to_stata_elapsed_vec(dates: Series, fmt: str) -> Series:
+    """
+    Convert from datetime to SIF. https://www.stata.com/help.cgi?datetime
+
+    Parameters
+    ----------
+    dates : Series
+        Series or array containing datetime or datetime64[ns] to
+        convert to the Stata Internal Format given by fmt
+    fmt : str
+        The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
+    """
+    index = dates.index
+    NS_PER_DAY = 24 * 3600 * 1000 * 1000 * 1000
+    US_PER_DAY = NS_PER_DAY / 1000
+
+    def parse_dates_safe(
+        dates: Series, delta: bool = False, year: bool = False, days: bool = False
+    ):
+        d = {}
+        if lib.is_np_dtype(dates.dtype, "M"):
+            if delta:
+                time_delta = dates - Timestamp(stata_epoch).as_unit("ns")
+                d["delta"] = time_delta._values.view(np.int64) // 1000  # microseconds
+            if days or year:
+                date_index = DatetimeIndex(dates)
+                d["year"] = date_index._data.year
+                d["month"] = date_index._data.month
+            if days:
+                days_in_ns = dates._values.view(np.int64) - to_datetime(
+                    d["year"], format="%Y"
+                )._values.view(np.int64)
+                d["days"] = days_in_ns // NS_PER_DAY
+
+        elif infer_dtype(dates, skipna=False) == "datetime":
+            if delta:
+                delta = dates._values - stata_epoch
+
+                def f(x: timedelta) -> float:
+                    return US_PER_DAY * x.days + 1000000 * x.seconds + x.microseconds
+
+                v = np.vectorize(f)
+                d["delta"] = v(delta)
+            if year:
+                year_month = dates.apply(lambda x: 100 * x.year + x.month)
+                d["year"] = year_month._values // 100
+                d["month"] = year_month._values - d["year"] * 100
+            if days:
+
+                def g(x: datetime) -> int:
+                    return (x - datetime(x.year, 1, 1)).days
+
+                v = np.vectorize(g)
+                d["days"] = v(dates)
+        else:
+            raise ValueError(
+                "Columns containing dates must contain either "
+                "datetime64, datetime or null values."
+            )
+
+        return DataFrame(d, index=index)
+
+    bad_loc = isna(dates)
+    index = dates.index
+    if bad_loc.any():
+        if lib.is_np_dtype(dates.dtype, "M"):
+            dates._values[bad_loc] = to_datetime(stata_epoch)
+        else:
+            dates._values[bad_loc] = stata_epoch
+
+    if fmt in ["%tc", "tc"]:
+        d = parse_dates_safe(dates, delta=True)
+        conv_dates = d.delta / 1000
+    elif fmt in ["%tC", "tC"]:
+        warnings.warn(
+            "Stata Internal Format tC not supported.",
+            stacklevel=find_stack_level(),
+        )
+        conv_dates = dates
+    elif fmt in ["%td", "td"]:
+        d = parse_dates_safe(dates, delta=True)
+        conv_dates = d.delta // US_PER_DAY
+    elif fmt in ["%tw", "tw"]:
+        d = parse_dates_safe(dates, year=True, days=True)
+        conv_dates = 52 * (d.year - stata_epoch.year) + d.days // 7
+    elif fmt in ["%tm", "tm"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = 12 * (d.year - stata_epoch.year) + d.month - 1
+    elif fmt in ["%tq", "tq"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = 4 * (d.year - stata_epoch.year) + (d.month - 1) // 3
+    elif fmt in ["%th", "th"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = 2 * (d.year - stata_epoch.year) + (d.month > 6).astype(int)
+    elif fmt in ["%ty", "ty"]:
+        d = parse_dates_safe(dates, year=True)
+        conv_dates = d.year
+    else:
+        raise ValueError(f"Format {fmt} is not a known Stata date format")
+
+    conv_dates = Series(conv_dates, dtype=np.float64, copy=False)
+    missing_value = struct.unpack("<d", b"\x00\x00\x00\x00\x00\x00\xe0\x7f")[0]
+    conv_dates[bad_loc] = missing_value
+
+    return Series(conv_dates, index=index, copy=False)
+
+
+excessive_string_length_error: Final = """
+Fixed width strings in Stata .dta files are limited to 244 (or fewer)
+characters.  Column '{0}' does not satisfy this restriction. Use the
+'version=117' parameter to write the newer (Stata 13 and later) format.
+"""
+
+
+precision_loss_doc: Final = """
+Column converted from {0} to {1}, and some data are outside of the lossless
+conversion range. This may result in a loss of precision in the saved data.
+"""
+
+
+value_label_mismatch_doc: Final = """
+Stata value labels (pandas categories) must be strings. Column {0} contains
+non-string labels which will be converted to strings.  Please check that the
+Stata data file created has not lost information due to duplicate labels.
+"""
+
+
+invalid_name_doc: Final = """
+Not all pandas column names were valid Stata variable names.
+The following replacements have been made:
+
+    {0}
+
+If this is not what you expect, please make sure you have Stata-compliant
+column names in your DataFrame (strings only, max 32 characters, only
+alphanumerics and underscores, no Stata reserved words)
+"""
+
+
+categorical_conversion_warning: Final = """
+One or more series with value labels are not fully labeled. Reading this
+dataset with an iterator results in categorical variable with different
+categories. This occurs since it is not possible to know all possible values
+until the entire dataset has been read. To avoid this warning, you can either
+read dataset without an iterator, or manually convert categorical data by
+``convert_categoricals`` to False and then accessing the variable labels
+through the value_labels method of the reader.
+"""
+
+
+def _cast_to_stata_types(data: DataFrame) -> DataFrame:
+    """
+    Checks the dtypes of the columns of a pandas DataFrame for
+    compatibility with the data types and ranges supported by Stata, and
+    converts if necessary.
+
+    Parameters
+    ----------
+    data : DataFrame
+        The DataFrame to check and convert
+
+    Notes
+    -----
+    Numeric columns in Stata must be one of int8, int16, int32, float32 or
+    float64, with some additional value restrictions.  int8 and int16 columns
+    are checked for violations of the value restrictions and upcast if needed.
+    int64 data is not usable in Stata, and so it is downcast to int32 whenever
+    the value are in the int32 range, and sidecast to float64 when larger than
+    this range.  If the int64 values are outside of the range of those
+    perfectly representable as float64 values, a warning is raised.
+
+    bool columns are cast to int8.  uint columns are converted to int of the
+    same size if there is no loss in precision, otherwise are upcast to a
+    larger type.  uint64 is currently not supported since it is concerted to
+    object in a DataFrame.
+    """
+    ws = ""
+    # original, if small, if large
+    conversion_data: tuple[
+        tuple[type, type, type],
+        tuple[type, type, type],
+        tuple[type, type, type],
+        tuple[type, type, type],
+        tuple[type, type, type],
+    ] = (
+        (np.bool_, np.int8, np.int8),
+        (np.uint8, np.int8, np.int16),
+        (np.uint16, np.int16, np.int32),
+        (np.uint32, np.int32, np.int64),
+        (np.uint64, np.int64, np.float64),
+    )
+
+    float32_max = struct.unpack("<f", b"\xff\xff\xff\x7e")[0]
+    float64_max = struct.unpack("<d", b"\xff\xff\xff\xff\xff\xff\xdf\x7f")[0]
+
+    for col in data:
+        # Cast from unsupported types to supported types
+        is_nullable_int = (
+            isinstance(data[col].dtype, ExtensionDtype)
+            and data[col].dtype.kind in "iub"
+        )
+        # We need to find orig_missing before altering data below
+        orig_missing = data[col].isna()
+        if is_nullable_int:
+            fv = 0 if data[col].dtype.kind in "iu" else False
+            # Replace with NumPy-compatible column
+            data[col] = data[col].fillna(fv).astype(data[col].dtype.numpy_dtype)
+        elif isinstance(data[col].dtype, ExtensionDtype):
+            if getattr(data[col].dtype, "numpy_dtype", None) is not None:
+                data[col] = data[col].astype(data[col].dtype.numpy_dtype)
+            elif is_string_dtype(data[col].dtype):
+                # TODO could avoid converting string dtype to object here,
+                # but handle string dtype in _encode_strings
+                data[col] = data[col].astype("object")
+                # generate_table checks for None values
+                data.loc[data[col].isna(), col] = None
+
+        dtype = data[col].dtype
+        empty_df = data.shape[0] == 0
+        for c_data in conversion_data:
+            if dtype == c_data[0]:
+                if empty_df or data[col].max() <= np.iinfo(c_data[1]).max:
+                    dtype = c_data[1]
+                else:
+                    dtype = c_data[2]
+                if c_data[2] == np.int64:  # Warn if necessary
+                    if data[col].max() >= 2**53:
+                        ws = precision_loss_doc.format("uint64", "float64")
+
+                data[col] = data[col].astype(dtype)
+
+        # Check values and upcast if necessary
+
+        if dtype == np.int8 and not empty_df:
+            if data[col].max() > 100 or data[col].min() < -127:
+                data[col] = data[col].astype(np.int16)
+        elif dtype == np.int16 and not empty_df:
+            if data[col].max() > 32740 or data[col].min() < -32767:
+                data[col] = data[col].astype(np.int32)
+        elif dtype == np.int64:
+            if empty_df or (
+                data[col].max() <= 2147483620 and data[col].min() >= -2147483647
+            ):
+                data[col] = data[col].astype(np.int32)
+            else:
+                data[col] = data[col].astype(np.float64)
+                if data[col].max() >= 2**53 or data[col].min() <= -(2**53):
+                    ws = precision_loss_doc.format("int64", "float64")
+        elif dtype in (np.float32, np.float64):
+            if np.isinf(data[col]).any():
+                raise ValueError(
+                    f"Column {col} contains infinity or -infinity"
+                    "which is outside the range supported by Stata."
+                )
+            value = data[col].max()
+            if dtype == np.float32 and value > float32_max:
+                data[col] = data[col].astype(np.float64)
+            elif dtype == np.float64:
+                if value > float64_max:
+                    raise ValueError(
+                        f"Column {col} has a maximum value ({value}) outside the range "
+                        f"supported by Stata ({float64_max})"
+                    )
+        if is_nullable_int:
+            if orig_missing.any():
+                # Replace missing by Stata sentinel value
+                sentinel = StataMissingValue.BASE_MISSING_VALUES[data[col].dtype.name]
+                data.loc[orig_missing, col] = sentinel
+    if ws:
+        warnings.warn(
+            ws,
+            PossiblePrecisionLoss,
+            stacklevel=find_stack_level(),
+        )
+
+    return data
+
+
+class StataValueLabel:
+    """
+    Parse a categorical column and prepare formatted output
+
+    Parameters
+    ----------
+    catarray : Series
+        Categorical Series to encode
+    encoding : {"latin-1", "utf-8"}
+        Encoding to use for value labels.
+    """
+
+    def __init__(
+        self, catarray: Series, encoding: Literal["latin-1", "utf-8"] = "latin-1"
+    ) -> None:
+        if encoding not in ("latin-1", "utf-8"):
+            raise ValueError("Only latin-1 and utf-8 are supported.")
+        self.labname = catarray.name
+        self._encoding = encoding
+        categories = catarray.cat.categories
+        self.value_labels = enumerate(categories)
+
+        self._prepare_value_labels()
+
+    def _prepare_value_labels(self) -> None:
+        """Encode value labels."""
+
+        self.text_len = 0
+        self.txt: list[bytes] = []
+        self.n = 0
+        # Offsets (length of categories), converted to int32
+        self.off = np.array([], dtype=np.int32)
+        # Values, converted to int32
+        self.val = np.array([], dtype=np.int32)
+        self.len = 0
+
+        # Compute lengths and setup lists of offsets and labels
+        offsets: list[int] = []
+        values: list[float] = []
+        for vl in self.value_labels:
+            category: str | bytes = vl[1]
+            if not isinstance(category, str):
+                category = str(category)
+                warnings.warn(
+                    value_label_mismatch_doc.format(self.labname),
+                    ValueLabelTypeMismatch,
+                    stacklevel=find_stack_level(),
+                )
+            category = category.encode(self._encoding)
+            offsets.append(self.text_len)
+            self.text_len += len(category) + 1  # +1 for the padding
+            values.append(vl[0])
+            self.txt.append(category)
+            self.n += 1
+
+        if self.text_len > 32000:
+            raise ValueError(
+                "Stata value labels for a single variable must "
+                "have a combined length less than 32,000 characters."
+            )
+
+        # Ensure int32
+        self.off = np.array(offsets, dtype=np.int32)
+        self.val = np.array(values, dtype=np.int32)
+
+        # Total length
+        self.len = 4 + 4 + 4 * self.n + 4 * self.n + self.text_len
+
+    def generate_value_label(self, byteorder: str) -> bytes:
+        """
+        Generate the binary representation of the value labels.
+
+        Parameters
+        ----------
+        byteorder : str
+            Byte order of the output
+
+        Returns
+        -------
+        value_label : bytes
+            Bytes containing the formatted value label
+        """
+        encoding = self._encoding
+        bio = BytesIO()
+        null_byte = b"\x00"
+
+        # len
+        bio.write(struct.pack(byteorder + "i", self.len))
+
+        # labname
+        labname = str(self.labname)[:32].encode(encoding)
+        lab_len = 32 if encoding not in ("utf-8", "utf8") else 128
+        labname = _pad_bytes(labname, lab_len + 1)
+        bio.write(labname)
+
+        # padding - 3 bytes
+        for i in range(3):
+            bio.write(struct.pack("c", null_byte))
+
+        # value_label_table
+        # n - int32
+        bio.write(struct.pack(byteorder + "i", self.n))
+
+        # textlen  - int32
+        bio.write(struct.pack(byteorder + "i", self.text_len))
+
+        # off - int32 array (n elements)
+        for offset in self.off:
+            bio.write(struct.pack(byteorder + "i", offset))
+
+        # val - int32 array (n elements)
+        for value in self.val:
+            bio.write(struct.pack(byteorder + "i", value))
+
+        # txt - Text labels, null terminated
+        for text in self.txt:
+            bio.write(text + null_byte)
+
+        return bio.getvalue()
+
+
+class StataNonCatValueLabel(StataValueLabel):
+    """
+    Prepare formatted version of value labels
+
+    Parameters
+    ----------
+    labname : str
+        Value label name
+    value_labels: Dictionary
+        Mapping of values to labels
+    encoding : {"latin-1", "utf-8"}
+        Encoding to use for value labels.
+    """
+
+    def __init__(
+        self,
+        labname: str,
+        value_labels: dict[float, str],
+        encoding: Literal["latin-1", "utf-8"] = "latin-1",
+    ) -> None:
+        if encoding not in ("latin-1", "utf-8"):
+            raise ValueError("Only latin-1 and utf-8 are supported.")
+
+        self.labname = labname
+        self._encoding = encoding
+        self.value_labels = sorted(  # type: ignore[assignment]
+            value_labels.items(), key=lambda x: x[0]
+        )
+        self._prepare_value_labels()
+
+
+class StataMissingValue:
+    """
+    An observation's missing value.
+
+    Parameters
+    ----------
+    value : {int, float}
+        The Stata missing value code
+
+    Notes
+    -----
+    More information: <https://www.stata.com/help.cgi?missing>
+
+    Integer missing values make the code '.', '.a', ..., '.z' to the ranges
+    101 ... 127 (for int8), 32741 ... 32767  (for int16) and 2147483621 ...
+    2147483647 (for int32).  Missing values for floating point data types are
+    more complex but the pattern is simple to discern from the following table.
+
+    np.float32 missing values (float in Stata)
+    0000007f    .
+    0008007f    .a
+    0010007f    .b
+    ...
+    00c0007f    .x
+    00c8007f    .y
+    00d0007f    .z
+
+    np.float64 missing values (double in Stata)
+    000000000000e07f    .
+    000000000001e07f    .a
+    000000000002e07f    .b
+    ...
+    000000000018e07f    .x
+    000000000019e07f    .y
+    00000000001ae07f    .z
+    """
+
+    # Construct a dictionary of missing values
+    MISSING_VALUES: dict[float, str] = {}
+    bases: Final = (101, 32741, 2147483621)
+    for b in bases:
+        # Conversion to long to avoid hash issues on 32 bit platforms #8968
+        MISSING_VALUES[b] = "."
+        for i in range(1, 27):
+            MISSING_VALUES[i + b] = "." + chr(96 + i)
+
+    float32_base: bytes = b"\x00\x00\x00\x7f"
+    increment_32: int = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
+    for i in range(27):
+        key = struct.unpack("<f", float32_base)[0]
+        MISSING_VALUES[key] = "."
+        if i > 0:
+            MISSING_VALUES[key] += chr(96 + i)
+        int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment_32
+        float32_base = struct.pack("<i", int_value)
+
+    float64_base: bytes = b"\x00\x00\x00\x00\x00\x00\xe0\x7f"
+    increment_64 = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
+    for i in range(27):
+        key = struct.unpack("<d", float64_base)[0]
+        MISSING_VALUES[key] = "."
+        if i > 0:
+            MISSING_VALUES[key] += chr(96 + i)
+        int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment_64
+        float64_base = struct.pack("q", int_value)
+
+    BASE_MISSING_VALUES: Final = {
+        "int8": 101,
+        "int16": 32741,
+        "int32": 2147483621,
+        "float32": struct.unpack("<f", float32_base)[0],
+        "float64": struct.unpack("<d", float64_base)[0],
+    }
+
+    def __init__(self, value: float) -> None:
+        self._value = value
+        # Conversion to int to avoid hash issues on 32 bit platforms #8968
+        value = int(value) if value < 2147483648 else float(value)
+        self._str = self.MISSING_VALUES[value]
+
+    @property
+    def string(self) -> str:
+        """
+        The Stata representation of the missing value: '.', '.a'..'.z'
+
+        Returns
+        -------
+        str
+            The representation of the missing value.
+        """
+        return self._str
+
+    @property
+    def value(self) -> float:
+        """
+        The binary representation of the missing value.
+
+        Returns
+        -------
+        {int, float}
+            The binary representation of the missing value.
+        """
+        return self._value
+
+    def __str__(self) -> str:
+        return self.string
+
+    def __repr__(self) -> str:
+        return f"{type(self)}({self})"
+
+    def __eq__(self, other: object) -> bool:
+        return (
+            isinstance(other, type(self))
+            and self.string == other.string
+            and self.value == other.value
+        )
+
+    @classmethod
+    def get_base_missing_value(cls, dtype: np.dtype) -> float:
+        if dtype.type is np.int8:
+            value = cls.BASE_MISSING_VALUES["int8"]
+        elif dtype.type is np.int16:
+            value = cls.BASE_MISSING_VALUES["int16"]
+        elif dtype.type is np.int32:
+            value = cls.BASE_MISSING_VALUES["int32"]
+        elif dtype.type is np.float32:
+            value = cls.BASE_MISSING_VALUES["float32"]
+        elif dtype.type is np.float64:
+            value = cls.BASE_MISSING_VALUES["float64"]
+        else:
+            raise ValueError("Unsupported dtype")
+        return value
+
+
+class StataParser:
+    def __init__(self) -> None:
+        # type          code.
+        # --------------------
+        # str1        1 = 0x01
+        # str2        2 = 0x02
+        # ...
+        # str244    244 = 0xf4
+        # byte      251 = 0xfb  (sic)
+        # int       252 = 0xfc
+        # long      253 = 0xfd
+        # float     254 = 0xfe
+        # double    255 = 0xff
+        # --------------------
+        # NOTE: the byte type seems to be reserved for categorical variables
+        # with a label, but the underlying variable is -127 to 100
+        # we're going to drop the label and cast to int
+        self.DTYPE_MAP = dict(
+            [(i, np.dtype(f"S{i}")) for i in range(1, 245)]
+            + [
+                (251, np.dtype(np.int8)),
+                (252, np.dtype(np.int16)),
+                (253, np.dtype(np.int32)),
+                (254, np.dtype(np.float32)),
+                (255, np.dtype(np.float64)),
+            ]
+        )
+        self.DTYPE_MAP_XML: dict[int, np.dtype] = {
+            32768: np.dtype(np.uint8),  # Keys to GSO
+            65526: np.dtype(np.float64),
+            65527: np.dtype(np.float32),
+            65528: np.dtype(np.int32),
+            65529: np.dtype(np.int16),
+            65530: np.dtype(np.int8),
+        }
+        self.TYPE_MAP = list(tuple(range(251)) + tuple("bhlfd"))
+        self.TYPE_MAP_XML = {
+            # Not really a Q, unclear how to handle byteswap
+            32768: "Q",
+            65526: "d",
+            65527: "f",
+            65528: "l",
+            65529: "h",
+            65530: "b",
+        }
+        # NOTE: technically, some of these are wrong. there are more numbers
+        # that can be represented. it's the 27 ABOVE and BELOW the max listed
+        # numeric data type in [U] 12.2.2 of the 11.2 manual
+        float32_min = b"\xff\xff\xff\xfe"
+        float32_max = b"\xff\xff\xff\x7e"
+        float64_min = b"\xff\xff\xff\xff\xff\xff\xef\xff"
+        float64_max = b"\xff\xff\xff\xff\xff\xff\xdf\x7f"
+        self.VALID_RANGE = {
+            "b": (-127, 100),
+            "h": (-32767, 32740),
+            "l": (-2147483647, 2147483620),
+            "f": (
+                np.float32(struct.unpack("<f", float32_min)[0]),
+                np.float32(struct.unpack("<f", float32_max)[0]),
+            ),
+            "d": (
+                np.float64(struct.unpack("<d", float64_min)[0]),
+                np.float64(struct.unpack("<d", float64_max)[0]),
+            ),
+        }
+
+        self.OLD_TYPE_MAPPING = {
+            98: 251,  # byte
+            105: 252,  # int
+            108: 253,  # long
+            102: 254,  # float
+            100: 255,  # double
+        }
+
+        # These missing values are the generic '.' in Stata, and are used
+        # to replace nans
+        self.MISSING_VALUES = {
+            "b": 101,
+            "h": 32741,
+            "l": 2147483621,
+            "f": np.float32(struct.unpack("<f", b"\x00\x00\x00\x7f")[0]),
+            "d": np.float64(
+                struct.unpack("<d", b"\x00\x00\x00\x00\x00\x00\xe0\x7f")[0]
+            ),
+        }
+        self.NUMPY_TYPE_MAP = {
+            "b": "i1",
+            "h": "i2",
+            "l": "i4",
+            "f": "f4",
+            "d": "f8",
+            "Q": "u8",
+        }
+
+        # Reserved words cannot be used as variable names
+        self.RESERVED_WORDS = {
+            "aggregate",
+            "array",
+            "boolean",
+            "break",
+            "byte",
+            "case",
+            "catch",
+            "class",
+            "colvector",
+            "complex",
+            "const",
+            "continue",
+            "default",
+            "delegate",
+            "delete",
+            "do",
+            "double",
+            "else",
+            "eltypedef",
+            "end",
+            "enum",
+            "explicit",
+            "export",
+            "external",
+            "float",
+            "for",
+            "friend",
+            "function",
+            "global",
+            "goto",
+            "if",
+            "inline",
+            "int",
+            "local",
+            "long",
+            "NULL",
+            "pragma",
+            "protected",
+            "quad",
+            "rowvector",
+            "short",
+            "typedef",
+            "typename",
+            "virtual",
+            "_all",
+            "_N",
+            "_skip",
+            "_b",
+            "_pi",
+            "str#",
+            "in",
+            "_pred",
+            "strL",
+            "_coef",
+            "_rc",
+            "using",
+            "_cons",
+            "_se",
+            "with",
+            "_n",
+        }
+
+
+class StataReader(StataParser, abc.Iterator):
+    __doc__ = _stata_reader_doc
+
+    _path_or_buf: IO[bytes]  # Will be assigned by `_open_file`.
+
+    def __init__(
+        self,
+        path_or_buf: FilePath | ReadBuffer[bytes],
+        convert_dates: bool = True,
+        convert_categoricals: bool = True,
+        index_col: str | None = None,
+        convert_missing: bool = False,
+        preserve_dtypes: bool = True,
+        columns: Sequence[str] | None = None,
+        order_categoricals: bool = True,
+        chunksize: int | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        super().__init__()
+
+        # Arguments to the reader (can be temporarily overridden in
+        # calls to read).
+        self._convert_dates = convert_dates
+        self._convert_categoricals = convert_categoricals
+        self._index_col = index_col
+        self._convert_missing = convert_missing
+        self._preserve_dtypes = preserve_dtypes
+        self._columns = columns
+        self._order_categoricals = order_categoricals
+        self._original_path_or_buf = path_or_buf
+        self._compression = compression
+        self._storage_options = storage_options
+        self._encoding = ""
+        self._chunksize = chunksize
+        self._using_iterator = False
+        self._entered = False
+        if self._chunksize is None:
+            self._chunksize = 1
+        elif not isinstance(chunksize, int) or chunksize <= 0:
+            raise ValueError("chunksize must be a positive integer when set.")
+
+        # State variables for the file
+        self._close_file: Callable[[], None] | None = None
+        self._missing_values = False
+        self._can_read_value_labels = False
+        self._column_selector_set = False
+        self._value_labels_read = False
+        self._data_read = False
+        self._dtype: np.dtype | None = None
+        self._lines_read = 0
+
+        self._native_byteorder = _set_endianness(sys.byteorder)
+
+    def _ensure_open(self) -> None:
+        """
+        Ensure the file has been opened and its header data read.
+        """
+        if not hasattr(self, "_path_or_buf"):
+            self._open_file()
+
+    def _open_file(self) -> None:
+        """
+        Open the file (with compression options, etc.), and read header information.
+        """
+        if not self._entered:
+            warnings.warn(
+                "StataReader is being used without using a context manager. "
+                "Using StataReader as a context manager is the only supported method.",
+                ResourceWarning,
+                stacklevel=find_stack_level(),
+            )
+        handles = get_handle(
+            self._original_path_or_buf,
+            "rb",
+            storage_options=self._storage_options,
+            is_text=False,
+            compression=self._compression,
+        )
+        if hasattr(handles.handle, "seekable") and handles.handle.seekable():
+            # If the handle is directly seekable, use it without an extra copy.
+            self._path_or_buf = handles.handle
+            self._close_file = handles.close
+        else:
+            # Copy to memory, and ensure no encoding.
+            with handles:
+                self._path_or_buf = BytesIO(handles.handle.read())
+            self._close_file = self._path_or_buf.close
+
+        self._read_header()
+        self._setup_dtype()
+
+    def __enter__(self) -> Self:
+        """enter context manager"""
+        self._entered = True
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        if self._close_file:
+            self._close_file()
+
+    def close(self) -> None:
+        """Close the handle if its open.
+
+        .. deprecated: 2.0.0
+
+           The close method is not part of the public API.
+           The only supported way to use StataReader is to use it as a context manager.
+        """
+        warnings.warn(
+            "The StataReader.close() method is not part of the public API and "
+            "will be removed in a future version without notice. "
+            "Using StataReader as a context manager is the only supported method.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        if self._close_file:
+            self._close_file()
+
+    def _set_encoding(self) -> None:
+        """
+        Set string encoding which depends on file version
+        """
+        if self._format_version < 118:
+            self._encoding = "latin-1"
+        else:
+            self._encoding = "utf-8"
+
+    def _read_int8(self) -> int:
+        return struct.unpack("b", self._path_or_buf.read(1))[0]
+
+    def _read_uint8(self) -> int:
+        return struct.unpack("B", self._path_or_buf.read(1))[0]
+
+    def _read_uint16(self) -> int:
+        return struct.unpack(f"{self._byteorder}H", self._path_or_buf.read(2))[0]
+
+    def _read_uint32(self) -> int:
+        return struct.unpack(f"{self._byteorder}I", self._path_or_buf.read(4))[0]
+
+    def _read_uint64(self) -> int:
+        return struct.unpack(f"{self._byteorder}Q", self._path_or_buf.read(8))[0]
+
+    def _read_int16(self) -> int:
+        return struct.unpack(f"{self._byteorder}h", self._path_or_buf.read(2))[0]
+
+    def _read_int32(self) -> int:
+        return struct.unpack(f"{self._byteorder}i", self._path_or_buf.read(4))[0]
+
+    def _read_int64(self) -> int:
+        return struct.unpack(f"{self._byteorder}q", self._path_or_buf.read(8))[0]
+
+    def _read_char8(self) -> bytes:
+        return struct.unpack("c", self._path_or_buf.read(1))[0]
+
+    def _read_int16_count(self, count: int) -> tuple[int, ...]:
+        return struct.unpack(
+            f"{self._byteorder}{'h' * count}",
+            self._path_or_buf.read(2 * count),
+        )
+
+    def _read_header(self) -> None:
+        first_char = self._read_char8()
+        if first_char == b"<":
+            self._read_new_header()
+        else:
+            self._read_old_header(first_char)
+
+    def _read_new_header(self) -> None:
+        # The first part of the header is common to 117 - 119.
+        self._path_or_buf.read(27)  # stata_dta><header><release>
+        self._format_version = int(self._path_or_buf.read(3))
+        if self._format_version not in [117, 118, 119]:
+            raise ValueError(_version_error.format(version=self._format_version))
+        self._set_encoding()
+        self._path_or_buf.read(21)  # </release><byteorder>
+        self._byteorder = ">" if self._path_or_buf.read(3) == b"MSF" else "<"
+        self._path_or_buf.read(15)  # </byteorder><K>
+        self._nvar = (
+            self._read_uint16() if self._format_version <= 118 else self._read_uint32()
+        )
+        self._path_or_buf.read(7)  # </K><N>
+
+        self._nobs = self._get_nobs()
+        self._path_or_buf.read(11)  # </N><label>
+        self._data_label = self._get_data_label()
+        self._path_or_buf.read(19)  # </label><timestamp>
+        self._time_stamp = self._get_time_stamp()
+        self._path_or_buf.read(26)  # </timestamp></header><map>
+        self._path_or_buf.read(8)  # 0x0000000000000000
+        self._path_or_buf.read(8)  # position of <map>
+
+        self._seek_vartypes = self._read_int64() + 16
+        self._seek_varnames = self._read_int64() + 10
+        self._seek_sortlist = self._read_int64() + 10
+        self._seek_formats = self._read_int64() + 9
+        self._seek_value_label_names = self._read_int64() + 19
+
+        # Requires version-specific treatment
+        self._seek_variable_labels = self._get_seek_variable_labels()
+
+        self._path_or_buf.read(8)  # <characteristics>
+        self._data_location = self._read_int64() + 6
+        self._seek_strls = self._read_int64() + 7
+        self._seek_value_labels = self._read_int64() + 14
+
+        self._typlist, self._dtyplist = self._get_dtypes(self._seek_vartypes)
+
+        self._path_or_buf.seek(self._seek_varnames)
+        self._varlist = self._get_varlist()
+
+        self._path_or_buf.seek(self._seek_sortlist)
+        self._srtlist = self._read_int16_count(self._nvar + 1)[:-1]
+
+        self._path_or_buf.seek(self._seek_formats)
+        self._fmtlist = self._get_fmtlist()
+
+        self._path_or_buf.seek(self._seek_value_label_names)
+        self._lbllist = self._get_lbllist()
+
+        self._path_or_buf.seek(self._seek_variable_labels)
+        self._variable_labels = self._get_variable_labels()
+
+    # Get data type information, works for versions 117-119.
+    def _get_dtypes(
+        self, seek_vartypes: int
+    ) -> tuple[list[int | str], list[str | np.dtype]]:
+        self._path_or_buf.seek(seek_vartypes)
+        typlist = []
+        dtyplist = []
+        for _ in range(self._nvar):
+            typ = self._read_uint16()
+            if typ <= 2045:
+                typlist.append(typ)
+                dtyplist.append(str(typ))
+            else:
+                try:
+                    typlist.append(self.TYPE_MAP_XML[typ])  # type: ignore[arg-type]
+                    dtyplist.append(self.DTYPE_MAP_XML[typ])  # type: ignore[arg-type]
+                except KeyError as err:
+                    raise ValueError(f"cannot convert stata types [{typ}]") from err
+
+        return typlist, dtyplist  # type: ignore[return-value]
+
+    def _get_varlist(self) -> list[str]:
+        # 33 in order formats, 129 in formats 118 and 119
+        b = 33 if self._format_version < 118 else 129
+        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
+
+    # Returns the format list
+    def _get_fmtlist(self) -> list[str]:
+        if self._format_version >= 118:
+            b = 57
+        elif self._format_version > 113:
+            b = 49
+        elif self._format_version > 104:
+            b = 12
+        else:
+            b = 7
+
+        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
+
+    # Returns the label list
+    def _get_lbllist(self) -> list[str]:
+        if self._format_version >= 118:
+            b = 129
+        elif self._format_version > 108:
+            b = 33
+        else:
+            b = 9
+        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
+
+    def _get_variable_labels(self) -> list[str]:
+        if self._format_version >= 118:
+            vlblist = [
+                self._decode(self._path_or_buf.read(321)) for _ in range(self._nvar)
+            ]
+        elif self._format_version > 105:
+            vlblist = [
+                self._decode(self._path_or_buf.read(81)) for _ in range(self._nvar)
+            ]
+        else:
+            vlblist = [
+                self._decode(self._path_or_buf.read(32)) for _ in range(self._nvar)
+            ]
+        return vlblist
+
+    def _get_nobs(self) -> int:
+        if self._format_version >= 118:
+            return self._read_uint64()
+        else:
+            return self._read_uint32()
+
+    def _get_data_label(self) -> str:
+        if self._format_version >= 118:
+            strlen = self._read_uint16()
+            return self._decode(self._path_or_buf.read(strlen))
+        elif self._format_version == 117:
+            strlen = self._read_int8()
+            return self._decode(self._path_or_buf.read(strlen))
+        elif self._format_version > 105:
+            return self._decode(self._path_or_buf.read(81))
+        else:
+            return self._decode(self._path_or_buf.read(32))
+
+    def _get_time_stamp(self) -> str:
+        if self._format_version >= 118:
+            strlen = self._read_int8()
+            return self._path_or_buf.read(strlen).decode("utf-8")
+        elif self._format_version == 117:
+            strlen = self._read_int8()
+            return self._decode(self._path_or_buf.read(strlen))
+        elif self._format_version > 104:
+            return self._decode(self._path_or_buf.read(18))
+        else:
+            raise ValueError()
+
+    def _get_seek_variable_labels(self) -> int:
+        if self._format_version == 117:
+            self._path_or_buf.read(8)  # <variable_labels>, throw away
+            # Stata 117 data files do not follow the described format.  This is
+            # a work around that uses the previous label, 33 bytes for each
+            # variable, 20 for the closing tag and 17 for the opening tag
+            return self._seek_value_label_names + (33 * self._nvar) + 20 + 17
+        elif self._format_version >= 118:
+            return self._read_int64() + 17
+        else:
+            raise ValueError()
+
+    def _read_old_header(self, first_char: bytes) -> None:
+        self._format_version = int(first_char[0])
+        if self._format_version not in [104, 105, 108, 111, 113, 114, 115]:
+            raise ValueError(_version_error.format(version=self._format_version))
+        self._set_encoding()
+        self._byteorder = ">" if self._read_int8() == 0x1 else "<"
+        self._filetype = self._read_int8()
+        self._path_or_buf.read(1)  # unused
+
+        self._nvar = self._read_uint16()
+        self._nobs = self._get_nobs()
+
+        self._data_label = self._get_data_label()
+
+        self._time_stamp = self._get_time_stamp()
+
+        # descriptors
+        if self._format_version > 108:
+            typlist = [int(c) for c in self._path_or_buf.read(self._nvar)]
+        else:
+            buf = self._path_or_buf.read(self._nvar)
+            typlistb = np.frombuffer(buf, dtype=np.uint8)
+            typlist = []
+            for tp in typlistb:
+                if tp in self.OLD_TYPE_MAPPING:
+                    typlist.append(self.OLD_TYPE_MAPPING[tp])
+                else:
+                    typlist.append(tp - 127)  # bytes
+
+        try:
+            self._typlist = [self.TYPE_MAP[typ] for typ in typlist]
+        except ValueError as err:
+            invalid_types = ",".join([str(x) for x in typlist])
+            raise ValueError(f"cannot convert stata types [{invalid_types}]") from err
+        try:
+            self._dtyplist = [self.DTYPE_MAP[typ] for typ in typlist]
+        except ValueError as err:
+            invalid_dtypes = ",".join([str(x) for x in typlist])
+            raise ValueError(f"cannot convert stata dtypes [{invalid_dtypes}]") from err
+
+        if self._format_version > 108:
+            self._varlist = [
+                self._decode(self._path_or_buf.read(33)) for _ in range(self._nvar)
+            ]
+        else:
+            self._varlist = [
+                self._decode(self._path_or_buf.read(9)) for _ in range(self._nvar)
+            ]
+        self._srtlist = self._read_int16_count(self._nvar + 1)[:-1]
+
+        self._fmtlist = self._get_fmtlist()
+
+        self._lbllist = self._get_lbllist()
+
+        self._variable_labels = self._get_variable_labels()
+
+        # ignore expansion fields (Format 105 and later)
+        # When reading, read five bytes; the last four bytes now tell you
+        # the size of the next read, which you discard.  You then continue
+        # like this until you read 5 bytes of zeros.
+
+        if self._format_version > 104:
+            while True:
+                data_type = self._read_int8()
+                if self._format_version > 108:
+                    data_len = self._read_int32()
+                else:
+                    data_len = self._read_int16()
+                if data_type == 0:
+                    break
+                self._path_or_buf.read(data_len)
+
+        # necessary data to continue parsing
+        self._data_location = self._path_or_buf.tell()
+
+    def _setup_dtype(self) -> np.dtype:
+        """Map between numpy and state dtypes"""
+        if self._dtype is not None:
+            return self._dtype
+
+        dtypes = []  # Convert struct data types to numpy data type
+        for i, typ in enumerate(self._typlist):
+            if typ in self.NUMPY_TYPE_MAP:
+                typ = cast(str, typ)  # only strs in NUMPY_TYPE_MAP
+                dtypes.append((f"s{i}", f"{self._byteorder}{self.NUMPY_TYPE_MAP[typ]}"))
+            else:
+                dtypes.append((f"s{i}", f"S{typ}"))
+        self._dtype = np.dtype(dtypes)
+
+        return self._dtype
+
+    def _decode(self, s: bytes) -> str:
+        # have bytes not strings, so must decode
+        s = s.partition(b"\0")[0]
+        try:
+            return s.decode(self._encoding)
+        except UnicodeDecodeError:
+            # GH 25960, fallback to handle incorrect format produced when 117
+            # files are converted to 118 files in Stata
+            encoding = self._encoding
+            msg = f"""
+One or more strings in the dta file could not be decoded using {encoding}, and
+so the fallback encoding of latin-1 is being used.  This can happen when a file
+has been incorrectly encoded by Stata or some other software. You should verify
+the string values returned are correct."""
+            warnings.warn(
+                msg,
+                UnicodeWarning,
+                stacklevel=find_stack_level(),
+            )
+            return s.decode("latin-1")
+
+    def _read_value_labels(self) -> None:
+        self._ensure_open()
+        if self._value_labels_read:
+            # Don't read twice
+            return
+        if self._format_version <= 108:
+            # Value labels are not supported in version 108 and earlier.
+            self._value_labels_read = True
+            self._value_label_dict: dict[str, dict[float, str]] = {}
+            return
+
+        if self._format_version >= 117:
+            self._path_or_buf.seek(self._seek_value_labels)
+        else:
+            assert self._dtype is not None
+            offset = self._nobs * self._dtype.itemsize
+            self._path_or_buf.seek(self._data_location + offset)
+
+        self._value_labels_read = True
+        self._value_label_dict = {}
+
+        while True:
+            if self._format_version >= 117:
+                if self._path_or_buf.read(5) == b"</val":  # <lbl>
+                    break  # end of value label table
+
+            slength = self._path_or_buf.read(4)
+            if not slength:
+                break  # end of value label table (format < 117)
+            if self._format_version <= 117:
+                labname = self._decode(self._path_or_buf.read(33))
+            else:
+                labname = self._decode(self._path_or_buf.read(129))
+            self._path_or_buf.read(3)  # padding
+
+            n = self._read_uint32()
+            txtlen = self._read_uint32()
+            off = np.frombuffer(
+                self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n
+            )
+            val = np.frombuffer(
+                self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n
+            )
+            ii = np.argsort(off)
+            off = off[ii]
+            val = val[ii]
+            txt = self._path_or_buf.read(txtlen)
+            self._value_label_dict[labname] = {}
+            for i in range(n):
+                end = off[i + 1] if i < n - 1 else txtlen
+                self._value_label_dict[labname][val[i]] = self._decode(
+                    txt[off[i] : end]
+                )
+            if self._format_version >= 117:
+                self._path_or_buf.read(6)  # </lbl>
+        self._value_labels_read = True
+
+    def _read_strls(self) -> None:
+        self._path_or_buf.seek(self._seek_strls)
+        # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
+        self.GSO = {"0": ""}
+        while True:
+            if self._path_or_buf.read(3) != b"GSO":
+                break
+
+            if self._format_version == 117:
+                v_o = self._read_uint64()
+            else:
+                buf = self._path_or_buf.read(12)
+                # Only tested on little endian file on little endian machine.
+                v_size = 2 if self._format_version == 118 else 3
+                if self._byteorder == "<":
+                    buf = buf[0:v_size] + buf[4 : (12 - v_size)]
+                else:
+                    # This path may not be correct, impossible to test
+                    buf = buf[0:v_size] + buf[(4 + v_size) :]
+                v_o = struct.unpack("Q", buf)[0]
+            typ = self._read_uint8()
+            length = self._read_uint32()
+            va = self._path_or_buf.read(length)
+            if typ == 130:
+                decoded_va = va[0:-1].decode(self._encoding)
+            else:
+                # Stata says typ 129 can be binary, so use str
+                decoded_va = str(va)
+                # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
+            self.GSO[str(v_o)] = decoded_va
+
+    def __next__(self) -> DataFrame:
+        self._using_iterator = True
+        return self.read(nrows=self._chunksize)
+
+    def get_chunk(self, size: int | None = None) -> DataFrame:
+        """
+        Reads lines from Stata file and returns as dataframe
+
+        Parameters
+        ----------
+        size : int, defaults to None
+            Number of lines to read.  If None, reads whole file.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if size is None:
+            size = self._chunksize
+        return self.read(nrows=size)
+
+    @Appender(_read_method_doc)
+    def read(
+        self,
+        nrows: int | None = None,
+        convert_dates: bool | None = None,
+        convert_categoricals: bool | None = None,
+        index_col: str | None = None,
+        convert_missing: bool | None = None,
+        preserve_dtypes: bool | None = None,
+        columns: Sequence[str] | None = None,
+        order_categoricals: bool | None = None,
+    ) -> DataFrame:
+        self._ensure_open()
+
+        # Handle options
+        if convert_dates is None:
+            convert_dates = self._convert_dates
+        if convert_categoricals is None:
+            convert_categoricals = self._convert_categoricals
+        if convert_missing is None:
+            convert_missing = self._convert_missing
+        if preserve_dtypes is None:
+            preserve_dtypes = self._preserve_dtypes
+        if columns is None:
+            columns = self._columns
+        if order_categoricals is None:
+            order_categoricals = self._order_categoricals
+        if index_col is None:
+            index_col = self._index_col
+        if nrows is None:
+            nrows = self._nobs
+
+        # Handle empty file or chunk.  If reading incrementally raise
+        # StopIteration.  If reading the whole thing return an empty
+        # data frame.
+        if (self._nobs == 0) and nrows == 0:
+            self._can_read_value_labels = True
+            self._data_read = True
+            data = DataFrame(columns=self._varlist)
+            # Apply dtypes correctly
+            for i, col in enumerate(data.columns):
+                dt = self._dtyplist[i]
+                if isinstance(dt, np.dtype):
+                    if dt.char != "S":
+                        data[col] = data[col].astype(dt)
+            if columns is not None:
+                data = self._do_select_columns(data, columns)
+            return data
+
+        if (self._format_version >= 117) and (not self._value_labels_read):
+            self._can_read_value_labels = True
+            self._read_strls()
+
+        # Read data
+        assert self._dtype is not None
+        dtype = self._dtype
+        max_read_len = (self._nobs - self._lines_read) * dtype.itemsize
+        read_len = nrows * dtype.itemsize
+        read_len = min(read_len, max_read_len)
+        if read_len <= 0:
+            # Iterator has finished, should never be here unless
+            # we are reading the file incrementally
+            if convert_categoricals:
+                self._read_value_labels()
+            raise StopIteration
+        offset = self._lines_read * dtype.itemsize
+        self._path_or_buf.seek(self._data_location + offset)
+        read_lines = min(nrows, self._nobs - self._lines_read)
+        raw_data = np.frombuffer(
+            self._path_or_buf.read(read_len), dtype=dtype, count=read_lines
+        )
+
+        self._lines_read += read_lines
+        if self._lines_read == self._nobs:
+            self._can_read_value_labels = True
+            self._data_read = True
+        # if necessary, swap the byte order to native here
+        if self._byteorder != self._native_byteorder:
+            raw_data = raw_data.byteswap().view(raw_data.dtype.newbyteorder())
+
+        if convert_categoricals:
+            self._read_value_labels()
+
+        if len(raw_data) == 0:
+            data = DataFrame(columns=self._varlist)
+        else:
+            data = DataFrame.from_records(raw_data)
+            data.columns = Index(self._varlist)
+
+        # If index is not specified, use actual row number rather than
+        # restarting at 0 for each chunk.
+        if index_col is None:
+            data.index = RangeIndex(
+                self._lines_read - read_lines, self._lines_read
+            )  # set attr instead of set_index to avoid copy
+
+        if columns is not None:
+            data = self._do_select_columns(data, columns)
+
+        # Decode strings
+        for col, typ in zip(data, self._typlist):
+            if isinstance(typ, int):
+                data[col] = data[col].apply(self._decode)
+
+        data = self._insert_strls(data)
+
+        # Convert columns (if needed) to match input type
+        valid_dtypes = [i for i, dtyp in enumerate(self._dtyplist) if dtyp is not None]
+        object_type = np.dtype(object)
+        for idx in valid_dtypes:
+            dtype = data.iloc[:, idx].dtype
+            if dtype not in (object_type, self._dtyplist[idx]):
+                data.isetitem(idx, data.iloc[:, idx].astype(dtype))
+
+        data = self._do_convert_missing(data, convert_missing)
+
+        if convert_dates:
+            for i, fmt in enumerate(self._fmtlist):
+                if any(fmt.startswith(date_fmt) for date_fmt in _date_formats):
+                    data.isetitem(
+                        i, _stata_elapsed_date_to_datetime_vec(data.iloc[:, i], fmt)
+                    )
+
+        if convert_categoricals and self._format_version > 108:
+            data = self._do_convert_categoricals(
+                data, self._value_label_dict, self._lbllist, order_categoricals
+            )
+
+        if not preserve_dtypes:
+            retyped_data = []
+            convert = False
+            for col in data:
+                dtype = data[col].dtype
+                if dtype in (np.dtype(np.float16), np.dtype(np.float32)):
+                    dtype = np.dtype(np.float64)
+                    convert = True
+                elif dtype in (
+                    np.dtype(np.int8),
+                    np.dtype(np.int16),
+                    np.dtype(np.int32),
+                ):
+                    dtype = np.dtype(np.int64)
+                    convert = True
+                retyped_data.append((col, data[col].astype(dtype)))
+            if convert:
+                data = DataFrame.from_dict(dict(retyped_data))
+
+        if index_col is not None:
+            data = data.set_index(data.pop(index_col))
+
+        return data
+
+    def _do_convert_missing(self, data: DataFrame, convert_missing: bool) -> DataFrame:
+        # Check for missing values, and replace if found
+        replacements = {}
+        for i in range(len(data.columns)):
+            fmt = self._typlist[i]
+            if fmt not in self.VALID_RANGE:
+                continue
+
+            fmt = cast(str, fmt)  # only strs in VALID_RANGE
+            nmin, nmax = self.VALID_RANGE[fmt]
+            series = data.iloc[:, i]
+
+            # appreciably faster to do this with ndarray instead of Series
+            svals = series._values
+            missing = (svals < nmin) | (svals > nmax)
+
+            if not missing.any():
+                continue
+
+            if convert_missing:  # Replacement follows Stata notation
+                missing_loc = np.nonzero(np.asarray(missing))[0]
+                umissing, umissing_loc = np.unique(series[missing], return_inverse=True)
+                replacement = Series(series, dtype=object)
+                for j, um in enumerate(umissing):
+                    missing_value = StataMissingValue(um)
+
+                    loc = missing_loc[umissing_loc == j]
+                    replacement.iloc[loc] = missing_value
+            else:  # All replacements are identical
+                dtype = series.dtype
+                if dtype not in (np.float32, np.float64):
+                    dtype = np.float64
+                replacement = Series(series, dtype=dtype)
+                if not replacement._values.flags["WRITEABLE"]:
+                    # only relevant for ArrayManager; construction
+                    #  path for BlockManager ensures writeability
+                    replacement = replacement.copy()
+                # Note: operating on ._values is much faster than directly
+                # TODO: can we fix that?
+                replacement._values[missing] = np.nan
+            replacements[i] = replacement
+        if replacements:
+            for idx, value in replacements.items():
+                data.isetitem(idx, value)
+        return data
+
+    def _insert_strls(self, data: DataFrame) -> DataFrame:
+        if not hasattr(self, "GSO") or len(self.GSO) == 0:
+            return data
+        for i, typ in enumerate(self._typlist):
+            if typ != "Q":
+                continue
+            # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
+            data.isetitem(i, [self.GSO[str(k)] for k in data.iloc[:, i]])
+        return data
+
+    def _do_select_columns(self, data: DataFrame, columns: Sequence[str]) -> DataFrame:
+        if not self._column_selector_set:
+            column_set = set(columns)
+            if len(column_set) != len(columns):
+                raise ValueError("columns contains duplicate entries")
+            unmatched = column_set.difference(data.columns)
+            if unmatched:
+                joined = ", ".join(list(unmatched))
+                raise ValueError(
+                    "The following columns were not "
+                    f"found in the Stata data set: {joined}"
+                )
+            # Copy information for retained columns for later processing
+            dtyplist = []
+            typlist = []
+            fmtlist = []
+            lbllist = []
+            for col in columns:
+                i = data.columns.get_loc(col)
+                dtyplist.append(self._dtyplist[i])
+                typlist.append(self._typlist[i])
+                fmtlist.append(self._fmtlist[i])
+                lbllist.append(self._lbllist[i])
+
+            self._dtyplist = dtyplist
+            self._typlist = typlist
+            self._fmtlist = fmtlist
+            self._lbllist = lbllist
+            self._column_selector_set = True
+
+        return data[columns]
+
+    def _do_convert_categoricals(
+        self,
+        data: DataFrame,
+        value_label_dict: dict[str, dict[float, str]],
+        lbllist: Sequence[str],
+        order_categoricals: bool,
+    ) -> DataFrame:
+        """
+        Converts categorical columns to Categorical type.
+        """
+        if not value_label_dict:
+            return data
+        cat_converted_data = []
+        for col, label in zip(data, lbllist):
+            if label in value_label_dict:
+                # Explicit call with ordered=True
+                vl = value_label_dict[label]
+                keys = np.array(list(vl.keys()))
+                column = data[col]
+                key_matches = column.isin(keys)
+                if self._using_iterator and key_matches.all():
+                    initial_categories: np.ndarray | None = keys
+                    # If all categories are in the keys and we are iterating,
+                    # use the same keys for all chunks. If some are missing
+                    # value labels, then we will fall back to the categories
+                    # varying across chunks.
+                else:
+                    if self._using_iterator:
+                        # warn is using an iterator
+                        warnings.warn(
+                            categorical_conversion_warning,
+                            CategoricalConversionWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                    initial_categories = None
+                cat_data = Categorical(
+                    column, categories=initial_categories, ordered=order_categoricals
+                )
+                if initial_categories is None:
+                    # If None here, then we need to match the cats in the Categorical
+                    categories = []
+                    for category in cat_data.categories:
+                        if category in vl:
+                            categories.append(vl[category])
+                        else:
+                            categories.append(category)
+                else:
+                    # If all cats are matched, we can use the values
+                    categories = list(vl.values())
+                try:
+                    # Try to catch duplicate categories
+                    # TODO: if we get a non-copying rename_categories, use that
+                    cat_data = cat_data.rename_categories(categories)
+                except ValueError as err:
+                    vc = Series(categories, copy=False).value_counts()
+                    repeated_cats = list(vc.index[vc > 1])
+                    repeats = "-" * 80 + "\n" + "\n".join(repeated_cats)
+                    # GH 25772
+                    msg = f"""
+Value labels for column {col} are not unique. These cannot be converted to
+pandas categoricals.
+
+Either read the file with `convert_categoricals` set to False or use the
+low level interface in `StataReader` to separately read the values and the
+value_labels.
+
+The repeated labels are:
+{repeats}
+"""
+                    raise ValueError(msg) from err
+                # TODO: is the next line needed above in the data(...) method?
+                cat_series = Series(cat_data, index=data.index, copy=False)
+                cat_converted_data.append((col, cat_series))
+            else:
+                cat_converted_data.append((col, data[col]))
+        data = DataFrame(dict(cat_converted_data), copy=False)
+        return data
+
+    @property
+    def data_label(self) -> str:
+        """
+        Return data label of Stata file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([(1,)], columns=["variable"])
+        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
+        >>> data_label = "This is a data file."
+        >>> path = "/My_path/filename.dta"
+        >>> df.to_stata(path, time_stamp=time_stamp,    # doctest: +SKIP
+        ...             data_label=data_label,  # doctest: +SKIP
+        ...             version=None)  # doctest: +SKIP
+        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
+        ...     print(reader.data_label)  # doctest: +SKIP
+        This is a data file.
+        """
+        self._ensure_open()
+        return self._data_label
+
+    @property
+    def time_stamp(self) -> str:
+        """
+        Return time stamp of Stata file.
+        """
+        self._ensure_open()
+        return self._time_stamp
+
+    def variable_labels(self) -> dict[str, str]:
+        """
+        Return a dict associating each variable name with corresponding label.
+
+        Returns
+        -------
+        dict
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"])
+        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
+        >>> path = "/My_path/filename.dta"
+        >>> variable_labels = {"col_1": "This is an example"}
+        >>> df.to_stata(path, time_stamp=time_stamp,  # doctest: +SKIP
+        ...             variable_labels=variable_labels, version=None)  # doctest: +SKIP
+        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
+        ...     print(reader.variable_labels())  # doctest: +SKIP
+        {'index': '', 'col_1': 'This is an example', 'col_2': ''}
+        >>> pd.read_stata(path)  # doctest: +SKIP
+            index col_1 col_2
+        0       0    1    2
+        1       1    3    4
+        """
+        self._ensure_open()
+        return dict(zip(self._varlist, self._variable_labels))
+
+    def value_labels(self) -> dict[str, dict[float, str]]:
+        """
+        Return a nested dict associating each variable name to its value and label.
+
+        Returns
+        -------
+        dict
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"])
+        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
+        >>> path = "/My_path/filename.dta"
+        >>> value_labels = {"col_1": {3: "x"}}
+        >>> df.to_stata(path, time_stamp=time_stamp,  # doctest: +SKIP
+        ...             value_labels=value_labels, version=None)  # doctest: +SKIP
+        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
+        ...     print(reader.value_labels())  # doctest: +SKIP
+        {'col_1': {3: 'x'}}
+        >>> pd.read_stata(path)  # doctest: +SKIP
+            index col_1 col_2
+        0       0    1    2
+        1       1    x    4
+        """
+        if not self._value_labels_read:
+            self._read_value_labels()
+
+        return self._value_label_dict
+
+
+@Appender(_read_stata_doc)
+def read_stata(
+    filepath_or_buffer: FilePath | ReadBuffer[bytes],
+    *,
+    convert_dates: bool = True,
+    convert_categoricals: bool = True,
+    index_col: str | None = None,
+    convert_missing: bool = False,
+    preserve_dtypes: bool = True,
+    columns: Sequence[str] | None = None,
+    order_categoricals: bool = True,
+    chunksize: int | None = None,
+    iterator: bool = False,
+    compression: CompressionOptions = "infer",
+    storage_options: StorageOptions | None = None,
+) -> DataFrame | StataReader:
+    reader = StataReader(
+        filepath_or_buffer,
+        convert_dates=convert_dates,
+        convert_categoricals=convert_categoricals,
+        index_col=index_col,
+        convert_missing=convert_missing,
+        preserve_dtypes=preserve_dtypes,
+        columns=columns,
+        order_categoricals=order_categoricals,
+        chunksize=chunksize,
+        storage_options=storage_options,
+        compression=compression,
+    )
+
+    if iterator or chunksize:
+        return reader
+
+    with reader:
+        return reader.read()
+
+
+def _set_endianness(endianness: str) -> str:
+    if endianness.lower() in ["<", "little"]:
+        return "<"
+    elif endianness.lower() in [">", "big"]:
+        return ">"
+    else:  # pragma : no cover
+        raise ValueError(f"Endianness {endianness} not understood")
+
+
+def _pad_bytes(name: AnyStr, length: int) -> AnyStr:
+    """
+    Take a char string and pads it with null bytes until it's length chars.
+    """
+    if isinstance(name, bytes):
+        return name + b"\x00" * (length - len(name))
+    return name + "\x00" * (length - len(name))
+
+
+def _convert_datetime_to_stata_type(fmt: str) -> np.dtype:
+    """
+    Convert from one of the stata date formats to a type in TYPE_MAP.
+    """
+    if fmt in [
+        "tc",
+        "%tc",
+        "td",
+        "%td",
+        "tw",
+        "%tw",
+        "tm",
+        "%tm",
+        "tq",
+        "%tq",
+        "th",
+        "%th",
+        "ty",
+        "%ty",
+    ]:
+        return np.dtype(np.float64)  # Stata expects doubles for SIFs
+    else:
+        raise NotImplementedError(f"Format {fmt} not implemented")
+
+
+def _maybe_convert_to_int_keys(convert_dates: dict, varlist: list[Hashable]) -> dict:
+    new_dict = {}
+    for key in convert_dates:
+        if not convert_dates[key].startswith("%"):  # make sure proper fmts
+            convert_dates[key] = "%" + convert_dates[key]
+        if key in varlist:
+            new_dict.update({varlist.index(key): convert_dates[key]})
+        else:
+            if not isinstance(key, int):
+                raise ValueError("convert_dates key must be a column or an integer")
+            new_dict.update({key: convert_dates[key]})
+    return new_dict
+
+
+def _dtype_to_stata_type(dtype: np.dtype, column: Series) -> int:
+    """
+    Convert dtype types to stata types. Returns the byte of the given ordinal.
+    See TYPE_MAP and comments for an explanation. This is also explained in
+    the dta spec.
+    1 - 244 are strings of this length
+                         Pandas    Stata
+    251 - for int8      byte
+    252 - for int16     int
+    253 - for int32     long
+    254 - for float32   float
+    255 - for double    double
+
+    If there are dates to convert, then dtype will already have the correct
+    type inserted.
+    """
+    # TODO: expand to handle datetime to integer conversion
+    if dtype.type is np.object_:  # try to coerce it to the biggest string
+        # not memory efficient, what else could we
+        # do?
+        itemsize = max_len_string_array(ensure_object(column._values))
+        return max(itemsize, 1)
+    elif dtype.type is np.float64:
+        return 255
+    elif dtype.type is np.float32:
+        return 254
+    elif dtype.type is np.int32:
+        return 253
+    elif dtype.type is np.int16:
+        return 252
+    elif dtype.type is np.int8:
+        return 251
+    else:  # pragma : no cover
+        raise NotImplementedError(f"Data type {dtype} not supported.")
+
+
+def _dtype_to_default_stata_fmt(
+    dtype, column: Series, dta_version: int = 114, force_strl: bool = False
+) -> str:
+    """
+    Map numpy dtype to stata's default format for this type. Not terribly
+    important since users can change this in Stata. Semantics are
+
+    object  -> "%DDs" where DD is the length of the string.  If not a string,
+                raise ValueError
+    float64 -> "%10.0g"
+    float32 -> "%9.0g"
+    int64   -> "%9.0g"
+    int32   -> "%12.0g"
+    int16   -> "%8.0g"
+    int8    -> "%8.0g"
+    strl    -> "%9s"
+    """
+    # TODO: Refactor to combine type with format
+    # TODO: expand this to handle a default datetime format?
+    if dta_version < 117:
+        max_str_len = 244
+    else:
+        max_str_len = 2045
+        if force_strl:
+            return "%9s"
+    if dtype.type is np.object_:
+        itemsize = max_len_string_array(ensure_object(column._values))
+        if itemsize > max_str_len:
+            if dta_version >= 117:
+                return "%9s"
+            else:
+                raise ValueError(excessive_string_length_error.format(column.name))
+        return "%" + str(max(itemsize, 1)) + "s"
+    elif dtype == np.float64:
+        return "%10.0g"
+    elif dtype == np.float32:
+        return "%9.0g"
+    elif dtype == np.int32:
+        return "%12.0g"
+    elif dtype in (np.int8, np.int16):
+        return "%8.0g"
+    else:  # pragma : no cover
+        raise NotImplementedError(f"Data type {dtype} not supported.")
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "fname",
+)
+class StataWriter(StataParser):
+    """
+    A class for writing Stata binary dta files
+
+    Parameters
+    ----------
+    fname : path (string), buffer or path object
+        string, path object (pathlib.Path or py._path.local.LocalPath) or
+        object implementing a binary write() functions. If using a buffer
+        then the buffer will not be automatically closed after the file
+        is written.
+    data : DataFrame
+        Input to save
+    convert_dates : dict
+        Dictionary mapping columns containing datetime types to stata internal
+        format to use when writing the dates. Options are 'tc', 'td', 'tm',
+        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
+        Datetime columns that do not have a conversion type specified will be
+        converted to 'tc'. Raises NotImplementedError if a datetime column has
+        timezone information
+    write_index : bool
+        Write the index to Stata dataset.
+    byteorder : str
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+    time_stamp : datetime
+        A datetime to use as file creation date.  Default is the current time
+    data_label : str
+        A label for the data set.  Must be 80 characters or smaller.
+    variable_labels : dict
+        Dictionary containing columns as keys and variable labels as values.
+        Each label must be 80 characters or smaller.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    value_labels : dict of dicts
+        Dictionary containing columns as keys and dictionaries of column value
+        to labels as values. The combined length of all labels for a single
+        variable must be 32,000 characters or smaller.
+
+        .. versionadded:: 1.4.0
+
+    Returns
+    -------
+    writer : StataWriter instance
+        The StataWriter instance has a write_file method, which will
+        write the file to the given `fname`.
+
+    Raises
+    ------
+    NotImplementedError
+        * If datetimes contain timezone information
+    ValueError
+        * Columns listed in convert_dates are neither datetime64[ns]
+          or datetime
+        * Column dtype is not representable in Stata
+        * Column listed in convert_dates is not in DataFrame
+        * Categorical label contains more than 32,000 characters
+
+    Examples
+    --------
+    >>> data = pd.DataFrame([[1.0, 1]], columns=['a', 'b'])
+    >>> writer = StataWriter('./data_file.dta', data)
+    >>> writer.write_file()
+
+    Directly write a zip file
+    >>> compression = {{"method": "zip", "archive_name": "data_file.dta"}}
+    >>> writer = StataWriter('./data_file.zip', data, compression=compression)
+    >>> writer.write_file()
+
+    Save a DataFrame with dates
+    >>> from datetime import datetime
+    >>> data = pd.DataFrame([[datetime(2000,1,1)]], columns=['date'])
+    >>> writer = StataWriter('./date_data_file.dta', data, {{'date' : 'tw'}})
+    >>> writer.write_file()
+    """
+
+    _max_string_length = 244
+    _encoding: Literal["latin-1", "utf-8"] = "latin-1"
+
+    def __init__(
+        self,
+        fname: FilePath | WriteBuffer[bytes],
+        data: DataFrame,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: str | None = None,
+        time_stamp: datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        *,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        super().__init__()
+        self.data = data
+        self._convert_dates = {} if convert_dates is None else convert_dates
+        self._write_index = write_index
+        self._time_stamp = time_stamp
+        self._data_label = data_label
+        self._variable_labels = variable_labels
+        self._non_cat_value_labels = value_labels
+        self._value_labels: list[StataValueLabel] = []
+        self._has_value_labels = np.array([], dtype=bool)
+        self._compression = compression
+        self._output_file: IO[bytes] | None = None
+        self._converted_names: dict[Hashable, str] = {}
+        # attach nobs, nvars, data, varlist, typlist
+        self._prepare_pandas(data)
+        self.storage_options = storage_options
+
+        if byteorder is None:
+            byteorder = sys.byteorder
+        self._byteorder = _set_endianness(byteorder)
+        self._fname = fname
+        self.type_converters = {253: np.int32, 252: np.int16, 251: np.int8}
+
+    def _write(self, to_write: str) -> None:
+        """
+        Helper to call encode before writing to file for Python 3 compat.
+        """
+        self.handles.handle.write(to_write.encode(self._encoding))
+
+    def _write_bytes(self, value: bytes) -> None:
+        """
+        Helper to assert file is open before writing.
+        """
+        self.handles.handle.write(value)
+
+    def _prepare_non_cat_value_labels(
+        self, data: DataFrame
+    ) -> list[StataNonCatValueLabel]:
+        """
+        Check for value labels provided for non-categorical columns. Value
+        labels
+        """
+        non_cat_value_labels: list[StataNonCatValueLabel] = []
+        if self._non_cat_value_labels is None:
+            return non_cat_value_labels
+
+        for labname, labels in self._non_cat_value_labels.items():
+            if labname in self._converted_names:
+                colname = self._converted_names[labname]
+            elif labname in data.columns:
+                colname = str(labname)
+            else:
+                raise KeyError(
+                    f"Can't create value labels for {labname}, it wasn't "
+                    "found in the dataset."
+                )
+
+            if not is_numeric_dtype(data[colname].dtype):
+                # Labels should not be passed explicitly for categorical
+                # columns that will be converted to int
+                raise ValueError(
+                    f"Can't create value labels for {labname}, value labels "
+                    "can only be applied to numeric columns."
+                )
+            svl = StataNonCatValueLabel(colname, labels, self._encoding)
+            non_cat_value_labels.append(svl)
+        return non_cat_value_labels
+
+    def _prepare_categoricals(self, data: DataFrame) -> DataFrame:
+        """
+        Check for categorical columns, retain categorical information for
+        Stata file and convert categorical data to int
+        """
+        is_cat = [isinstance(dtype, CategoricalDtype) for dtype in data.dtypes]
+        if not any(is_cat):
+            return data
+
+        self._has_value_labels |= np.array(is_cat)
+
+        get_base_missing_value = StataMissingValue.get_base_missing_value
+        data_formatted = []
+        for col, col_is_cat in zip(data, is_cat):
+            if col_is_cat:
+                svl = StataValueLabel(data[col], encoding=self._encoding)
+                self._value_labels.append(svl)
+                dtype = data[col].cat.codes.dtype
+                if dtype == np.int64:
+                    raise ValueError(
+                        "It is not possible to export "
+                        "int64-based categorical data to Stata."
+                    )
+                values = data[col].cat.codes._values.copy()
+
+                # Upcast if needed so that correct missing values can be set
+                if values.max() >= get_base_missing_value(dtype):
+                    if dtype == np.int8:
+                        dtype = np.dtype(np.int16)
+                    elif dtype == np.int16:
+                        dtype = np.dtype(np.int32)
+                    else:
+                        dtype = np.dtype(np.float64)
+                    values = np.array(values, dtype=dtype)
+
+                # Replace missing values with Stata missing value for type
+                values[values == -1] = get_base_missing_value(dtype)
+                data_formatted.append((col, values))
+            else:
+                data_formatted.append((col, data[col]))
+        return DataFrame.from_dict(dict(data_formatted))
+
+    def _replace_nans(self, data: DataFrame) -> DataFrame:
+        # return data
+        """
+        Checks floating point data columns for nans, and replaces these with
+        the generic Stata for missing value (.)
+        """
+        for c in data:
+            dtype = data[c].dtype
+            if dtype in (np.float32, np.float64):
+                if dtype == np.float32:
+                    replacement = self.MISSING_VALUES["f"]
+                else:
+                    replacement = self.MISSING_VALUES["d"]
+                data[c] = data[c].fillna(replacement)
+
+        return data
+
+    def _update_strl_names(self) -> None:
+        """No-op, forward compatibility"""
+
+    def _validate_variable_name(self, name: str) -> str:
+        """
+        Validate variable names for Stata export.
+
+        Parameters
+        ----------
+        name : str
+            Variable name
+
+        Returns
+        -------
+        str
+            The validated name with invalid characters replaced with
+            underscores.
+
+        Notes
+        -----
+        Stata 114 and 117 support ascii characters in a-z, A-Z, 0-9
+        and _.
+        """
+        for c in name:
+            if (
+                (c < "A" or c > "Z")
+                and (c < "a" or c > "z")
+                and (c < "0" or c > "9")
+                and c != "_"
+            ):
+                name = name.replace(c, "_")
+        return name
+
+    def _check_column_names(self, data: DataFrame) -> DataFrame:
+        """
+        Checks column names to ensure that they are valid Stata column names.
+        This includes checks for:
+            * Non-string names
+            * Stata keywords
+            * Variables that start with numbers
+            * Variables with names that are too long
+
+        When an illegal variable name is detected, it is converted, and if
+        dates are exported, the variable name is propagated to the date
+        conversion dictionary
+        """
+        converted_names: dict[Hashable, str] = {}
+        columns = list(data.columns)
+        original_columns = columns[:]
+
+        duplicate_var_id = 0
+        for j, name in enumerate(columns):
+            orig_name = name
+            if not isinstance(name, str):
+                name = str(name)
+
+            name = self._validate_variable_name(name)
+
+            # Variable name must not be a reserved word
+            if name in self.RESERVED_WORDS:
+                name = "_" + name
+
+            # Variable name may not start with a number
+            if "0" <= name[0] <= "9":
+                name = "_" + name
+
+            name = name[: min(len(name), 32)]
+
+            if not name == orig_name:
+                # check for duplicates
+                while columns.count(name) > 0:
+                    # prepend ascending number to avoid duplicates
+                    name = "_" + str(duplicate_var_id) + name
+                    name = name[: min(len(name), 32)]
+                    duplicate_var_id += 1
+                converted_names[orig_name] = name
+
+            columns[j] = name
+
+        data.columns = Index(columns)
+
+        # Check date conversion, and fix key if needed
+        if self._convert_dates:
+            for c, o in zip(columns, original_columns):
+                if c != o:
+                    self._convert_dates[c] = self._convert_dates[o]
+                    del self._convert_dates[o]
+
+        if converted_names:
+            conversion_warning = []
+            for orig_name, name in converted_names.items():
+                msg = f"{orig_name}   ->   {name}"
+                conversion_warning.append(msg)
+
+            ws = invalid_name_doc.format("\n    ".join(conversion_warning))
+            warnings.warn(
+                ws,
+                InvalidColumnName,
+                stacklevel=find_stack_level(),
+            )
+
+        self._converted_names = converted_names
+        self._update_strl_names()
+
+        return data
+
+    def _set_formats_and_types(self, dtypes: Series) -> None:
+        self.fmtlist: list[str] = []
+        self.typlist: list[int] = []
+        for col, dtype in dtypes.items():
+            self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, self.data[col]))
+            self.typlist.append(_dtype_to_stata_type(dtype, self.data[col]))
+
+    def _prepare_pandas(self, data: DataFrame) -> None:
+        # NOTE: we might need a different API / class for pandas objects so
+        # we can set different semantics - handle this with a PR to pandas.io
+
+        data = data.copy()
+
+        if self._write_index:
+            temp = data.reset_index()
+            if isinstance(temp, DataFrame):
+                data = temp
+
+        # Ensure column names are strings
+        data = self._check_column_names(data)
+
+        # Check columns for compatibility with stata, upcast if necessary
+        # Raise if outside the supported range
+        data = _cast_to_stata_types(data)
+
+        # Replace NaNs with Stata missing values
+        data = self._replace_nans(data)
+
+        # Set all columns to initially unlabelled
+        self._has_value_labels = np.repeat(False, data.shape[1])
+
+        # Create value labels for non-categorical data
+        non_cat_value_labels = self._prepare_non_cat_value_labels(data)
+
+        non_cat_columns = [svl.labname for svl in non_cat_value_labels]
+        has_non_cat_val_labels = data.columns.isin(non_cat_columns)
+        self._has_value_labels |= has_non_cat_val_labels
+        self._value_labels.extend(non_cat_value_labels)
+
+        # Convert categoricals to int data, and strip labels
+        data = self._prepare_categoricals(data)
+
+        self.nobs, self.nvar = data.shape
+        self.data = data
+        self.varlist = data.columns.tolist()
+
+        dtypes = data.dtypes
+
+        # Ensure all date columns are converted
+        for col in data:
+            if col in self._convert_dates:
+                continue
+            if lib.is_np_dtype(data[col].dtype, "M"):
+                self._convert_dates[col] = "tc"
+
+        self._convert_dates = _maybe_convert_to_int_keys(
+            self._convert_dates, self.varlist
+        )
+        for key in self._convert_dates:
+            new_type = _convert_datetime_to_stata_type(self._convert_dates[key])
+            dtypes.iloc[key] = np.dtype(new_type)
+
+        # Verify object arrays are strings and encode to bytes
+        self._encode_strings()
+
+        self._set_formats_and_types(dtypes)
+
+        # set the given format for the datetime cols
+        if self._convert_dates is not None:
+            for key in self._convert_dates:
+                if isinstance(key, int):
+                    self.fmtlist[key] = self._convert_dates[key]
+
+    def _encode_strings(self) -> None:
+        """
+        Encode strings in dta-specific encoding
+
+        Do not encode columns marked for date conversion or for strL
+        conversion. The strL converter independently handles conversion and
+        also accepts empty string arrays.
+        """
+        convert_dates = self._convert_dates
+        # _convert_strl is not available in dta 114
+        convert_strl = getattr(self, "_convert_strl", [])
+        for i, col in enumerate(self.data):
+            # Skip columns marked for date conversion or strl conversion
+            if i in convert_dates or col in convert_strl:
+                continue
+            column = self.data[col]
+            dtype = column.dtype
+            # TODO could also handle string dtype here specifically
+            if dtype.type is np.object_:
+                inferred_dtype = infer_dtype(column, skipna=True)
+                if not ((inferred_dtype == "string") or len(column) == 0):
+                    col = column.name
+                    raise ValueError(
+                        f"""\
+Column `{col}` cannot be exported.\n\nOnly string-like object arrays
+containing all strings or a mix of strings and None can be exported.
+Object arrays containing only null values are prohibited. Other object
+types cannot be exported and must first be converted to one of the
+supported types."""
+                    )
+                encoded = self.data[col].str.encode(self._encoding)
+                # If larger than _max_string_length do nothing
+                if (
+                    max_len_string_array(ensure_object(encoded._values))
+                    <= self._max_string_length
+                ):
+                    self.data[col] = encoded
+
+    def write_file(self) -> None:
+        """
+        Export DataFrame object to Stata dta format.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"fully_labelled": [1, 2, 3, 3, 1],
+        ...                    "partially_labelled": [1.0, 2.0, np.nan, 9.0, np.nan],
+        ...                    "Y": [7, 7, 9, 8, 10],
+        ...                    "Z": pd.Categorical(["j", "k", "l", "k", "j"]),
+        ...                    })
+        >>> path = "/My_path/filename.dta"
+        >>> labels = {"fully_labelled": {1: "one", 2: "two", 3: "three"},
+        ...           "partially_labelled": {1.0: "one", 2.0: "two"},
+        ...           }
+        >>> writer = pd.io.stata.StataWriter(path,
+        ...                                  df,
+        ...                                  value_labels=labels)  # doctest: +SKIP
+        >>> writer.write_file()  # doctest: +SKIP
+        >>> df = pd.read_stata(path)  # doctest: +SKIP
+        >>> df  # doctest: +SKIP
+            index fully_labelled  partially_labeled  Y  Z
+        0       0            one                one  7  j
+        1       1            two                two  7  k
+        2       2          three                NaN  9  l
+        3       3          three                9.0  8  k
+        4       4            one                NaN 10  j
+        """
+        with get_handle(
+            self._fname,
+            "wb",
+            compression=self._compression,
+            is_text=False,
+            storage_options=self.storage_options,
+        ) as self.handles:
+            if self.handles.compression["method"] is not None:
+                # ZipFile creates a file (with the same name) for each write call.
+                # Write it first into a buffer and then write the buffer to the ZipFile.
+                self._output_file, self.handles.handle = self.handles.handle, BytesIO()
+                self.handles.created_handles.append(self.handles.handle)
+
+            try:
+                self._write_header(
+                    data_label=self._data_label, time_stamp=self._time_stamp
+                )
+                self._write_map()
+                self._write_variable_types()
+                self._write_varnames()
+                self._write_sortlist()
+                self._write_formats()
+                self._write_value_label_names()
+                self._write_variable_labels()
+                self._write_expansion_fields()
+                self._write_characteristics()
+                records = self._prepare_data()
+                self._write_data(records)
+                self._write_strls()
+                self._write_value_labels()
+                self._write_file_close_tag()
+                self._write_map()
+                self._close()
+            except Exception as exc:
+                self.handles.close()
+                if isinstance(self._fname, (str, os.PathLike)) and os.path.isfile(
+                    self._fname
+                ):
+                    try:
+                        os.unlink(self._fname)
+                    except OSError:
+                        warnings.warn(
+                            f"This save was not successful but {self._fname} could not "
+                            "be deleted. This file is not valid.",
+                            ResourceWarning,
+                            stacklevel=find_stack_level(),
+                        )
+                raise exc
+
+    def _close(self) -> None:
+        """
+        Close the file if it was created by the writer.
+
+        If a buffer or file-like object was passed in, for example a GzipFile,
+        then leave this file open for the caller to close.
+        """
+        # write compression
+        if self._output_file is not None:
+            assert isinstance(self.handles.handle, BytesIO)
+            bio, self.handles.handle = self.handles.handle, self._output_file
+            self.handles.handle.write(bio.getvalue())
+
+    def _write_map(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_file_close_tag(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_characteristics(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_strls(self) -> None:
+        """No-op, future compatibility"""
+
+    def _write_expansion_fields(self) -> None:
+        """Write 5 zeros for expansion fields"""
+        self._write(_pad_bytes("", 5))
+
+    def _write_value_labels(self) -> None:
+        for vl in self._value_labels:
+            self._write_bytes(vl.generate_value_label(self._byteorder))
+
+    def _write_header(
+        self,
+        data_label: str | None = None,
+        time_stamp: datetime | None = None,
+    ) -> None:
+        byteorder = self._byteorder
+        # ds_format - just use 114
+        self._write_bytes(struct.pack("b", 114))
+        # byteorder
+        self._write(byteorder == ">" and "\x01" or "\x02")
+        # filetype
+        self._write("\x01")
+        # unused
+        self._write("\x00")
+        # number of vars, 2 bytes
+        self._write_bytes(struct.pack(byteorder + "h", self.nvar)[:2])
+        # number of obs, 4 bytes
+        self._write_bytes(struct.pack(byteorder + "i", self.nobs)[:4])
+        # data label 81 bytes, char, null terminated
+        if data_label is None:
+            self._write_bytes(self._null_terminate_bytes(_pad_bytes("", 80)))
+        else:
+            self._write_bytes(
+                self._null_terminate_bytes(_pad_bytes(data_label[:80], 80))
+            )
+        # time stamp, 18 bytes, char, null terminated
+        # format dd Mon yyyy hh:mm
+        if time_stamp is None:
+            time_stamp = datetime.now()
+        elif not isinstance(time_stamp, datetime):
+            raise ValueError("time_stamp should be datetime type")
+        # GH #13856
+        # Avoid locale-specific month conversion
+        months = [
+            "Jan",
+            "Feb",
+            "Mar",
+            "Apr",
+            "May",
+            "Jun",
+            "Jul",
+            "Aug",
+            "Sep",
+            "Oct",
+            "Nov",
+            "Dec",
+        ]
+        month_lookup = {i + 1: month for i, month in enumerate(months)}
+        ts = (
+            time_stamp.strftime("%d ")
+            + month_lookup[time_stamp.month]
+            + time_stamp.strftime(" %Y %H:%M")
+        )
+        self._write_bytes(self._null_terminate_bytes(ts))
+
+    def _write_variable_types(self) -> None:
+        for typ in self.typlist:
+            self._write_bytes(struct.pack("B", typ))
+
+    def _write_varnames(self) -> None:
+        # varlist names are checked by _check_column_names
+        # varlist, requires null terminated
+        for name in self.varlist:
+            name = self._null_terminate_str(name)
+            name = _pad_bytes(name[:32], 33)
+            self._write(name)
+
+    def _write_sortlist(self) -> None:
+        # srtlist, 2*(nvar+1), int array, encoded by byteorder
+        srtlist = _pad_bytes("", 2 * (self.nvar + 1))
+        self._write(srtlist)
+
+    def _write_formats(self) -> None:
+        # fmtlist, 49*nvar, char array
+        for fmt in self.fmtlist:
+            self._write(_pad_bytes(fmt, 49))
+
+    def _write_value_label_names(self) -> None:
+        # lbllist, 33*nvar, char array
+        for i in range(self.nvar):
+            # Use variable name when categorical
+            if self._has_value_labels[i]:
+                name = self.varlist[i]
+                name = self._null_terminate_str(name)
+                name = _pad_bytes(name[:32], 33)
+                self._write(name)
+            else:  # Default is empty label
+                self._write(_pad_bytes("", 33))
+
+    def _write_variable_labels(self) -> None:
+        # Missing labels are 80 blank characters plus null termination
+        blank = _pad_bytes("", 81)
+
+        if self._variable_labels is None:
+            for i in range(self.nvar):
+                self._write(blank)
+            return
+
+        for col in self.data:
+            if col in self._variable_labels:
+                label = self._variable_labels[col]
+                if len(label) > 80:
+                    raise ValueError("Variable labels must be 80 characters or fewer")
+                is_latin1 = all(ord(c) < 256 for c in label)
+                if not is_latin1:
+                    raise ValueError(
+                        "Variable labels must contain only characters that "
+                        "can be encoded in Latin-1"
+                    )
+                self._write(_pad_bytes(label, 81))
+            else:
+                self._write(blank)
+
+    def _convert_strls(self, data: DataFrame) -> DataFrame:
+        """No-op, future compatibility"""
+        return data
+
+    def _prepare_data(self) -> np.rec.recarray:
+        data = self.data
+        typlist = self.typlist
+        convert_dates = self._convert_dates
+        # 1. Convert dates
+        if self._convert_dates is not None:
+            for i, col in enumerate(data):
+                if i in convert_dates:
+                    data[col] = _datetime_to_stata_elapsed_vec(
+                        data[col], self.fmtlist[i]
+                    )
+        # 2. Convert strls
+        data = self._convert_strls(data)
+
+        # 3. Convert bad string data to '' and pad to correct length
+        dtypes = {}
+        native_byteorder = self._byteorder == _set_endianness(sys.byteorder)
+        for i, col in enumerate(data):
+            typ = typlist[i]
+            if typ <= self._max_string_length:
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        "Downcasting object dtype arrays",
+                        category=FutureWarning,
+                    )
+                    dc = data[col].fillna("")
+                data[col] = dc.apply(_pad_bytes, args=(typ,))
+                stype = f"S{typ}"
+                dtypes[col] = stype
+                data[col] = data[col].astype(stype)
+            else:
+                dtype = data[col].dtype
+                if not native_byteorder:
+                    dtype = dtype.newbyteorder(self._byteorder)
+                dtypes[col] = dtype
+
+        return data.to_records(index=False, column_dtypes=dtypes)
+
+    def _write_data(self, records: np.rec.recarray) -> None:
+        self._write_bytes(records.tobytes())
+
+    @staticmethod
+    def _null_terminate_str(s: str) -> str:
+        s += "\x00"
+        return s
+
+    def _null_terminate_bytes(self, s: str) -> bytes:
+        return self._null_terminate_str(s).encode(self._encoding)
+
+
+def _dtype_to_stata_type_117(dtype: np.dtype, column: Series, force_strl: bool) -> int:
+    """
+    Converts dtype types to stata types. Returns the byte of the given ordinal.
+    See TYPE_MAP and comments for an explanation. This is also explained in
+    the dta spec.
+    1 - 2045 are strings of this length
+                Pandas    Stata
+    32768 - for object    strL
+    65526 - for int8      byte
+    65527 - for int16     int
+    65528 - for int32     long
+    65529 - for float32   float
+    65530 - for double    double
+
+    If there are dates to convert, then dtype will already have the correct
+    type inserted.
+    """
+    # TODO: expand to handle datetime to integer conversion
+    if force_strl:
+        return 32768
+    if dtype.type is np.object_:  # try to coerce it to the biggest string
+        # not memory efficient, what else could we
+        # do?
+        itemsize = max_len_string_array(ensure_object(column._values))
+        itemsize = max(itemsize, 1)
+        if itemsize <= 2045:
+            return itemsize
+        return 32768
+    elif dtype.type is np.float64:
+        return 65526
+    elif dtype.type is np.float32:
+        return 65527
+    elif dtype.type is np.int32:
+        return 65528
+    elif dtype.type is np.int16:
+        return 65529
+    elif dtype.type is np.int8:
+        return 65530
+    else:  # pragma : no cover
+        raise NotImplementedError(f"Data type {dtype} not supported.")
+
+
+def _pad_bytes_new(name: str | bytes, length: int) -> bytes:
+    """
+    Takes a bytes instance and pads it with null bytes until it's length chars.
+    """
+    if isinstance(name, str):
+        name = bytes(name, "utf-8")
+    return name + b"\x00" * (length - len(name))
+
+
+class StataStrLWriter:
+    """
+    Converter for Stata StrLs
+
+    Stata StrLs map 8 byte values to strings which are stored using a
+    dictionary-like format where strings are keyed to two values.
+
+    Parameters
+    ----------
+    df : DataFrame
+        DataFrame to convert
+    columns : Sequence[str]
+        List of columns names to convert to StrL
+    version : int, optional
+        dta version.  Currently supports 117, 118 and 119
+    byteorder : str, optional
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+
+    Notes
+    -----
+    Supports creation of the StrL block of a dta file for dta versions
+    117, 118 and 119.  These differ in how the GSO is stored.  118 and
+    119 store the GSO lookup value as a uint32 and a uint64, while 117
+    uses two uint32s. 118 and 119 also encode all strings as unicode
+    which is required by the format.  117 uses 'latin-1' a fixed width
+    encoding that extends the 7-bit ascii table with an additional 128
+    characters.
+    """
+
+    def __init__(
+        self,
+        df: DataFrame,
+        columns: Sequence[str],
+        version: int = 117,
+        byteorder: str | None = None,
+    ) -> None:
+        if version not in (117, 118, 119):
+            raise ValueError("Only dta versions 117, 118 and 119 supported")
+        self._dta_ver = version
+
+        self.df = df
+        self.columns = columns
+        self._gso_table = {"": (0, 0)}
+        if byteorder is None:
+            byteorder = sys.byteorder
+        self._byteorder = _set_endianness(byteorder)
+
+        gso_v_type = "I"  # uint32
+        gso_o_type = "Q"  # uint64
+        self._encoding = "utf-8"
+        if version == 117:
+            o_size = 4
+            gso_o_type = "I"  # 117 used uint32
+            self._encoding = "latin-1"
+        elif version == 118:
+            o_size = 6
+        else:  # version == 119
+            o_size = 5
+        self._o_offet = 2 ** (8 * (8 - o_size))
+        self._gso_o_type = gso_o_type
+        self._gso_v_type = gso_v_type
+
+    def _convert_key(self, key: tuple[int, int]) -> int:
+        v, o = key
+        return v + self._o_offet * o
+
+    def generate_table(self) -> tuple[dict[str, tuple[int, int]], DataFrame]:
+        """
+        Generates the GSO lookup table for the DataFrame
+
+        Returns
+        -------
+        gso_table : dict
+            Ordered dictionary using the string found as keys
+            and their lookup position (v,o) as values
+        gso_df : DataFrame
+            DataFrame where strl columns have been converted to
+            (v,o) values
+
+        Notes
+        -----
+        Modifies the DataFrame in-place.
+
+        The DataFrame returned encodes the (v,o) values as uint64s. The
+        encoding depends on the dta version, and can be expressed as
+
+        enc = v + o * 2 ** (o_size * 8)
+
+        so that v is stored in the lower bits and o is in the upper
+        bits. o_size is
+
+          * 117: 4
+          * 118: 6
+          * 119: 5
+        """
+        gso_table = self._gso_table
+        gso_df = self.df
+        columns = list(gso_df.columns)
+        selected = gso_df[self.columns]
+        col_index = [(col, columns.index(col)) for col in self.columns]
+        keys = np.empty(selected.shape, dtype=np.uint64)
+        for o, (idx, row) in enumerate(selected.iterrows()):
+            for j, (col, v) in enumerate(col_index):
+                val = row[col]
+                # Allow columns with mixed str and None (GH 23633)
+                val = "" if val is None else val
+                key = gso_table.get(val, None)
+                if key is None:
+                    # Stata prefers human numbers
+                    key = (v + 1, o + 1)
+                    gso_table[val] = key
+                keys[o, j] = self._convert_key(key)
+        for i, col in enumerate(self.columns):
+            gso_df[col] = keys[:, i]
+
+        return gso_table, gso_df
+
+    def generate_blob(self, gso_table: dict[str, tuple[int, int]]) -> bytes:
+        """
+        Generates the binary blob of GSOs that is written to the dta file.
+
+        Parameters
+        ----------
+        gso_table : dict
+            Ordered dictionary (str, vo)
+
+        Returns
+        -------
+        gso : bytes
+            Binary content of dta file to be placed between strl tags
+
+        Notes
+        -----
+        Output format depends on dta version.  117 uses two uint32s to
+        express v and o while 118+ uses a uint32 for v and a uint64 for o.
+        """
+        # Format information
+        # Length includes null term
+        # 117
+        # GSOvvvvooootllllxxxxxxxxxxxxxxx...x
+        #  3  u4  u4 u1 u4  string + null term
+        #
+        # 118, 119
+        # GSOvvvvooooooootllllxxxxxxxxxxxxxxx...x
+        #  3  u4   u8   u1 u4    string + null term
+
+        bio = BytesIO()
+        gso = bytes("GSO", "ascii")
+        gso_type = struct.pack(self._byteorder + "B", 130)
+        null = struct.pack(self._byteorder + "B", 0)
+        v_type = self._byteorder + self._gso_v_type
+        o_type = self._byteorder + self._gso_o_type
+        len_type = self._byteorder + "I"
+        for strl, vo in gso_table.items():
+            if vo == (0, 0):
+                continue
+            v, o = vo
+
+            # GSO
+            bio.write(gso)
+
+            # vvvv
+            bio.write(struct.pack(v_type, v))
+
+            # oooo / oooooooo
+            bio.write(struct.pack(o_type, o))
+
+            # t
+            bio.write(gso_type)
+
+            # llll
+            utf8_string = bytes(strl, "utf-8")
+            bio.write(struct.pack(len_type, len(utf8_string) + 1))
+
+            # xxx...xxx
+            bio.write(utf8_string)
+            bio.write(null)
+
+        return bio.getvalue()
+
+
+class StataWriter117(StataWriter):
+    """
+    A class for writing Stata binary dta files in Stata 13 format (117)
+
+    Parameters
+    ----------
+    fname : path (string), buffer or path object
+        string, path object (pathlib.Path or py._path.local.LocalPath) or
+        object implementing a binary write() functions. If using a buffer
+        then the buffer will not be automatically closed after the file
+        is written.
+    data : DataFrame
+        Input to save
+    convert_dates : dict
+        Dictionary mapping columns containing datetime types to stata internal
+        format to use when writing the dates. Options are 'tc', 'td', 'tm',
+        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
+        Datetime columns that do not have a conversion type specified will be
+        converted to 'tc'. Raises NotImplementedError if a datetime column has
+        timezone information
+    write_index : bool
+        Write the index to Stata dataset.
+    byteorder : str
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+    time_stamp : datetime
+        A datetime to use as file creation date.  Default is the current time
+    data_label : str
+        A label for the data set.  Must be 80 characters or smaller.
+    variable_labels : dict
+        Dictionary containing columns as keys and variable labels as values.
+        Each label must be 80 characters or smaller.
+    convert_strl : list
+        List of columns names to convert to Stata StrL format.  Columns with
+        more than 2045 characters are automatically written as StrL.
+        Smaller columns can be converted by including the column name.  Using
+        StrLs can reduce output file size when strings are longer than 8
+        characters, and either frequently repeated or sparse.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    value_labels : dict of dicts
+        Dictionary containing columns as keys and dictionaries of column value
+        to labels as values. The combined length of all labels for a single
+        variable must be 32,000 characters or smaller.
+
+        .. versionadded:: 1.4.0
+
+    Returns
+    -------
+    writer : StataWriter117 instance
+        The StataWriter117 instance has a write_file method, which will
+        write the file to the given `fname`.
+
+    Raises
+    ------
+    NotImplementedError
+        * If datetimes contain timezone information
+    ValueError
+        * Columns listed in convert_dates are neither datetime64[ns]
+          or datetime
+        * Column dtype is not representable in Stata
+        * Column listed in convert_dates is not in DataFrame
+        * Categorical label contains more than 32,000 characters
+
+    Examples
+    --------
+    >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c'])
+    >>> writer = pd.io.stata.StataWriter117('./data_file.dta', data)
+    >>> writer.write_file()
+
+    Directly write a zip file
+    >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
+    >>> writer = pd.io.stata.StataWriter117(
+    ...     './data_file.zip', data, compression=compression
+    ...     )
+    >>> writer.write_file()
+
+    Or with long strings stored in strl format
+    >>> data = pd.DataFrame([['A relatively long string'], [''], ['']],
+    ...                     columns=['strls'])
+    >>> writer = pd.io.stata.StataWriter117(
+    ...     './data_file_with_long_strings.dta', data, convert_strl=['strls'])
+    >>> writer.write_file()
+    """
+
+    _max_string_length = 2045
+    _dta_version = 117
+
+    def __init__(
+        self,
+        fname: FilePath | WriteBuffer[bytes],
+        data: DataFrame,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: str | None = None,
+        time_stamp: datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        convert_strl: Sequence[Hashable] | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        *,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        # Copy to new list since convert_strl might be modified later
+        self._convert_strl: list[Hashable] = []
+        if convert_strl is not None:
+            self._convert_strl.extend(convert_strl)
+
+        super().__init__(
+            fname,
+            data,
+            convert_dates,
+            write_index,
+            byteorder=byteorder,
+            time_stamp=time_stamp,
+            data_label=data_label,
+            variable_labels=variable_labels,
+            value_labels=value_labels,
+            compression=compression,
+            storage_options=storage_options,
+        )
+        self._map: dict[str, int] = {}
+        self._strl_blob = b""
+
+    @staticmethod
+    def _tag(val: str | bytes, tag: str) -> bytes:
+        """Surround val with <tag></tag>"""
+        if isinstance(val, str):
+            val = bytes(val, "utf-8")
+        return bytes("<" + tag + ">", "utf-8") + val + bytes("</" + tag + ">", "utf-8")
+
+    def _update_map(self, tag: str) -> None:
+        """Update map location for tag with file position"""
+        assert self.handles.handle is not None
+        self._map[tag] = self.handles.handle.tell()
+
+    def _write_header(
+        self,
+        data_label: str | None = None,
+        time_stamp: datetime | None = None,
+    ) -> None:
+        """Write the file header"""
+        byteorder = self._byteorder
+        self._write_bytes(bytes("<stata_dta>", "utf-8"))
+        bio = BytesIO()
+        # ds_format - 117
+        bio.write(self._tag(bytes(str(self._dta_version), "utf-8"), "release"))
+        # byteorder
+        bio.write(self._tag(byteorder == ">" and "MSF" or "LSF", "byteorder"))
+        # number of vars, 2 bytes in 117 and 118, 4 byte in 119
+        nvar_type = "H" if self._dta_version <= 118 else "I"
+        bio.write(self._tag(struct.pack(byteorder + nvar_type, self.nvar), "K"))
+        # 117 uses 4 bytes, 118 uses 8
+        nobs_size = "I" if self._dta_version == 117 else "Q"
+        bio.write(self._tag(struct.pack(byteorder + nobs_size, self.nobs), "N"))
+        # data label 81 bytes, char, null terminated
+        label = data_label[:80] if data_label is not None else ""
+        encoded_label = label.encode(self._encoding)
+        label_size = "B" if self._dta_version == 117 else "H"
+        label_len = struct.pack(byteorder + label_size, len(encoded_label))
+        encoded_label = label_len + encoded_label
+        bio.write(self._tag(encoded_label, "label"))
+        # time stamp, 18 bytes, char, null terminated
+        # format dd Mon yyyy hh:mm
+        if time_stamp is None:
+            time_stamp = datetime.now()
+        elif not isinstance(time_stamp, datetime):
+            raise ValueError("time_stamp should be datetime type")
+        # Avoid locale-specific month conversion
+        months = [
+            "Jan",
+            "Feb",
+            "Mar",
+            "Apr",
+            "May",
+            "Jun",
+            "Jul",
+            "Aug",
+            "Sep",
+            "Oct",
+            "Nov",
+            "Dec",
+        ]
+        month_lookup = {i + 1: month for i, month in enumerate(months)}
+        ts = (
+            time_stamp.strftime("%d ")
+            + month_lookup[time_stamp.month]
+            + time_stamp.strftime(" %Y %H:%M")
+        )
+        # '\x11' added due to inspection of Stata file
+        stata_ts = b"\x11" + bytes(ts, "utf-8")
+        bio.write(self._tag(stata_ts, "timestamp"))
+        self._write_bytes(self._tag(bio.getvalue(), "header"))
+
+    def _write_map(self) -> None:
+        """
+        Called twice during file write. The first populates the values in
+        the map with 0s.  The second call writes the final map locations when
+        all blocks have been written.
+        """
+        if not self._map:
+            self._map = {
+                "stata_data": 0,
+                "map": self.handles.handle.tell(),
+                "variable_types": 0,
+                "varnames": 0,
+                "sortlist": 0,
+                "formats": 0,
+                "value_label_names": 0,
+                "variable_labels": 0,
+                "characteristics": 0,
+                "data": 0,
+                "strls": 0,
+                "value_labels": 0,
+                "stata_data_close": 0,
+                "end-of-file": 0,
+            }
+        # Move to start of map
+        self.handles.handle.seek(self._map["map"])
+        bio = BytesIO()
+        for val in self._map.values():
+            bio.write(struct.pack(self._byteorder + "Q", val))
+        self._write_bytes(self._tag(bio.getvalue(), "map"))
+
+    def _write_variable_types(self) -> None:
+        self._update_map("variable_types")
+        bio = BytesIO()
+        for typ in self.typlist:
+            bio.write(struct.pack(self._byteorder + "H", typ))
+        self._write_bytes(self._tag(bio.getvalue(), "variable_types"))
+
+    def _write_varnames(self) -> None:
+        self._update_map("varnames")
+        bio = BytesIO()
+        # 118 scales by 4 to accommodate utf-8 data worst case encoding
+        vn_len = 32 if self._dta_version == 117 else 128
+        for name in self.varlist:
+            name = self._null_terminate_str(name)
+            name = _pad_bytes_new(name[:32].encode(self._encoding), vn_len + 1)
+            bio.write(name)
+        self._write_bytes(self._tag(bio.getvalue(), "varnames"))
+
+    def _write_sortlist(self) -> None:
+        self._update_map("sortlist")
+        sort_size = 2 if self._dta_version < 119 else 4
+        self._write_bytes(self._tag(b"\x00" * sort_size * (self.nvar + 1), "sortlist"))
+
+    def _write_formats(self) -> None:
+        self._update_map("formats")
+        bio = BytesIO()
+        fmt_len = 49 if self._dta_version == 117 else 57
+        for fmt in self.fmtlist:
+            bio.write(_pad_bytes_new(fmt.encode(self._encoding), fmt_len))
+        self._write_bytes(self._tag(bio.getvalue(), "formats"))
+
+    def _write_value_label_names(self) -> None:
+        self._update_map("value_label_names")
+        bio = BytesIO()
+        # 118 scales by 4 to accommodate utf-8 data worst case encoding
+        vl_len = 32 if self._dta_version == 117 else 128
+        for i in range(self.nvar):
+            # Use variable name when categorical
+            name = ""  # default name
+            if self._has_value_labels[i]:
+                name = self.varlist[i]
+            name = self._null_terminate_str(name)
+            encoded_name = _pad_bytes_new(name[:32].encode(self._encoding), vl_len + 1)
+            bio.write(encoded_name)
+        self._write_bytes(self._tag(bio.getvalue(), "value_label_names"))
+
+    def _write_variable_labels(self) -> None:
+        # Missing labels are 80 blank characters plus null termination
+        self._update_map("variable_labels")
+        bio = BytesIO()
+        # 118 scales by 4 to accommodate utf-8 data worst case encoding
+        vl_len = 80 if self._dta_version == 117 else 320
+        blank = _pad_bytes_new("", vl_len + 1)
+
+        if self._variable_labels is None:
+            for _ in range(self.nvar):
+                bio.write(blank)
+            self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
+            return
+
+        for col in self.data:
+            if col in self._variable_labels:
+                label = self._variable_labels[col]
+                if len(label) > 80:
+                    raise ValueError("Variable labels must be 80 characters or fewer")
+                try:
+                    encoded = label.encode(self._encoding)
+                except UnicodeEncodeError as err:
+                    raise ValueError(
+                        "Variable labels must contain only characters that "
+                        f"can be encoded in {self._encoding}"
+                    ) from err
+
+                bio.write(_pad_bytes_new(encoded, vl_len + 1))
+            else:
+                bio.write(blank)
+        self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
+
+    def _write_characteristics(self) -> None:
+        self._update_map("characteristics")
+        self._write_bytes(self._tag(b"", "characteristics"))
+
+    def _write_data(self, records) -> None:
+        self._update_map("data")
+        self._write_bytes(b"<data>")
+        self._write_bytes(records.tobytes())
+        self._write_bytes(b"</data>")
+
+    def _write_strls(self) -> None:
+        self._update_map("strls")
+        self._write_bytes(self._tag(self._strl_blob, "strls"))
+
+    def _write_expansion_fields(self) -> None:
+        """No-op in dta 117+"""
+
+    def _write_value_labels(self) -> None:
+        self._update_map("value_labels")
+        bio = BytesIO()
+        for vl in self._value_labels:
+            lab = vl.generate_value_label(self._byteorder)
+            lab = self._tag(lab, "lbl")
+            bio.write(lab)
+        self._write_bytes(self._tag(bio.getvalue(), "value_labels"))
+
+    def _write_file_close_tag(self) -> None:
+        self._update_map("stata_data_close")
+        self._write_bytes(bytes("</stata_dta>", "utf-8"))
+        self._update_map("end-of-file")
+
+    def _update_strl_names(self) -> None:
+        """
+        Update column names for conversion to strl if they might have been
+        changed to comply with Stata naming rules
+        """
+        # Update convert_strl if names changed
+        for orig, new in self._converted_names.items():
+            if orig in self._convert_strl:
+                idx = self._convert_strl.index(orig)
+                self._convert_strl[idx] = new
+
+    def _convert_strls(self, data: DataFrame) -> DataFrame:
+        """
+        Convert columns to StrLs if either very large or in the
+        convert_strl variable
+        """
+        convert_cols = [
+            col
+            for i, col in enumerate(data)
+            if self.typlist[i] == 32768 or col in self._convert_strl
+        ]
+
+        if convert_cols:
+            ssw = StataStrLWriter(data, convert_cols, version=self._dta_version)
+            tab, new_data = ssw.generate_table()
+            data = new_data
+            self._strl_blob = ssw.generate_blob(tab)
+        return data
+
+    def _set_formats_and_types(self, dtypes: Series) -> None:
+        self.typlist = []
+        self.fmtlist = []
+        for col, dtype in dtypes.items():
+            force_strl = col in self._convert_strl
+            fmt = _dtype_to_default_stata_fmt(
+                dtype,
+                self.data[col],
+                dta_version=self._dta_version,
+                force_strl=force_strl,
+            )
+            self.fmtlist.append(fmt)
+            self.typlist.append(
+                _dtype_to_stata_type_117(dtype, self.data[col], force_strl)
+            )
+
+
+class StataWriterUTF8(StataWriter117):
+    """
+    Stata binary dta file writing in Stata 15 (118) and 16 (119) formats
+
+    DTA 118 and 119 format files support unicode string data (both fixed
+    and strL) format. Unicode is also supported in value labels, variable
+    labels and the dataset label. Format 119 is automatically used if the
+    file contains more than 32,767 variables.
+
+    Parameters
+    ----------
+    fname : path (string), buffer or path object
+        string, path object (pathlib.Path or py._path.local.LocalPath) or
+        object implementing a binary write() functions. If using a buffer
+        then the buffer will not be automatically closed after the file
+        is written.
+    data : DataFrame
+        Input to save
+    convert_dates : dict, default None
+        Dictionary mapping columns containing datetime types to stata internal
+        format to use when writing the dates. Options are 'tc', 'td', 'tm',
+        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
+        Datetime columns that do not have a conversion type specified will be
+        converted to 'tc'. Raises NotImplementedError if a datetime column has
+        timezone information
+    write_index : bool, default True
+        Write the index to Stata dataset.
+    byteorder : str, default None
+        Can be ">", "<", "little", or "big". default is `sys.byteorder`
+    time_stamp : datetime, default None
+        A datetime to use as file creation date.  Default is the current time
+    data_label : str, default None
+        A label for the data set.  Must be 80 characters or smaller.
+    variable_labels : dict, default None
+        Dictionary containing columns as keys and variable labels as values.
+        Each label must be 80 characters or smaller.
+    convert_strl : list, default None
+        List of columns names to convert to Stata StrL format.  Columns with
+        more than 2045 characters are automatically written as StrL.
+        Smaller columns can be converted by including the column name.  Using
+        StrLs can reduce output file size when strings are longer than 8
+        characters, and either frequently repeated or sparse.
+    version : int, default None
+        The dta version to use. By default, uses the size of data to determine
+        the version. 118 is used if data.shape[1] <= 32767, and 119 is used
+        for storing larger DataFrames.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    value_labels : dict of dicts
+        Dictionary containing columns as keys and dictionaries of column value
+        to labels as values. The combined length of all labels for a single
+        variable must be 32,000 characters or smaller.
+
+        .. versionadded:: 1.4.0
+
+    Returns
+    -------
+    StataWriterUTF8
+        The instance has a write_file method, which will write the file to the
+        given `fname`.
+
+    Raises
+    ------
+    NotImplementedError
+        * If datetimes contain timezone information
+    ValueError
+        * Columns listed in convert_dates are neither datetime64[ns]
+          or datetime
+        * Column dtype is not representable in Stata
+        * Column listed in convert_dates is not in DataFrame
+        * Categorical label contains more than 32,000 characters
+
+    Examples
+    --------
+    Using Unicode data and column names
+
+    >>> from pandas.io.stata import StataWriterUTF8
+    >>> data = pd.DataFrame([[1.0, 1, 'ᴬ']], columns=['a', 'β', 'ĉ'])
+    >>> writer = StataWriterUTF8('./data_file.dta', data)
+    >>> writer.write_file()
+
+    Directly write a zip file
+    >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
+    >>> writer = StataWriterUTF8('./data_file.zip', data, compression=compression)
+    >>> writer.write_file()
+
+    Or with long strings stored in strl format
+
+    >>> data = pd.DataFrame([['ᴀ relatively long ŝtring'], [''], ['']],
+    ...                     columns=['strls'])
+    >>> writer = StataWriterUTF8('./data_file_with_long_strings.dta', data,
+    ...                          convert_strl=['strls'])
+    >>> writer.write_file()
+    """
+
+    _encoding: Literal["utf-8"] = "utf-8"
+
+    def __init__(
+        self,
+        fname: FilePath | WriteBuffer[bytes],
+        data: DataFrame,
+        convert_dates: dict[Hashable, str] | None = None,
+        write_index: bool = True,
+        byteorder: str | None = None,
+        time_stamp: datetime | None = None,
+        data_label: str | None = None,
+        variable_labels: dict[Hashable, str] | None = None,
+        convert_strl: Sequence[Hashable] | None = None,
+        version: int | None = None,
+        compression: CompressionOptions = "infer",
+        storage_options: StorageOptions | None = None,
+        *,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
+    ) -> None:
+        if version is None:
+            version = 118 if data.shape[1] <= 32767 else 119
+        elif version not in (118, 119):
+            raise ValueError("version must be either 118 or 119.")
+        elif version == 118 and data.shape[1] > 32767:
+            raise ValueError(
+                "You must use version 119 for data sets containing more than"
+                "32,767 variables"
+            )
+
+        super().__init__(
+            fname,
+            data,
+            convert_dates=convert_dates,
+            write_index=write_index,
+            byteorder=byteorder,
+            time_stamp=time_stamp,
+            data_label=data_label,
+            variable_labels=variable_labels,
+            value_labels=value_labels,
+            convert_strl=convert_strl,
+            compression=compression,
+            storage_options=storage_options,
+        )
+        # Override version set in StataWriter117 init
+        self._dta_version = version
+
+    def _validate_variable_name(self, name: str) -> str:
+        """
+        Validate variable names for Stata export.
+
+        Parameters
+        ----------
+        name : str
+            Variable name
+
+        Returns
+        -------
+        str
+            The validated name with invalid characters replaced with
+            underscores.
+
+        Notes
+        -----
+        Stata 118+ support most unicode characters. The only limitation is in
+        the ascii range where the characters supported are a-z, A-Z, 0-9 and _.
+        """
+        # High code points appear to be acceptable
+        for c in name:
+            if (
+                (
+                    ord(c) < 128
+                    and (c < "A" or c > "Z")
+                    and (c < "a" or c > "z")
+                    and (c < "0" or c > "9")
+                    and c != "_"
+                )
+                or 128 <= ord(c) < 192
+                or c in {"×", "÷"}  # noqa: RUF001
+            ):
+                name = name.replace(c, "_")
+
+        return name
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/xml.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/xml.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac497cd266027f7af71884996182e1725baba361
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/io/xml.py
@@ -0,0 +1,1177 @@
+"""
+:mod:``pandas.io.xml`` is a module for reading XML.
+"""
+
+from __future__ import annotations
+
+import io
+from os import PathLike
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+)
+import warnings
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import (
+    AbstractMethodError,
+    ParserError,
+)
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import is_list_like
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.common import (
+    file_exists,
+    get_handle,
+    infer_compression,
+    is_file_like,
+    is_fsspec_url,
+    is_url,
+    stringify_path,
+)
+from pandas.io.parsers import TextParser
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from xml.etree.ElementTree import Element
+
+    from lxml import etree
+
+    from pandas._typing import (
+        CompressionOptions,
+        ConvertersArg,
+        DtypeArg,
+        DtypeBackend,
+        FilePath,
+        ParseDatesArg,
+        ReadBuffer,
+        StorageOptions,
+        XMLParsers,
+    )
+
+    from pandas import DataFrame
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "path_or_buffer",
+)
+class _XMLFrameParser:
+    """
+    Internal subclass to parse XML into DataFrames.
+
+    Parameters
+    ----------
+    path_or_buffer : a valid JSON ``str``, path object or file-like object
+        Any valid string path is acceptable. The string could be a URL. Valid
+        URL schemes include http, ftp, s3, and file.
+
+    xpath : str or regex
+        The ``XPath`` expression to parse required set of nodes for
+        migration to :class:`~pandas.DataFrame`. ``etree`` supports limited ``XPath``.
+
+    namespaces : dict
+        The namespaces defined in XML document (``xmlns:namespace='URI'``)
+        as dicts with key being namespace and value the URI.
+
+    elems_only : bool
+        Parse only the child elements at the specified ``xpath``.
+
+    attrs_only : bool
+        Parse only the attributes at the specified ``xpath``.
+
+    names : list
+        Column names for :class:`~pandas.DataFrame` of parsed XML data.
+
+    dtype : dict
+        Data type for data or columns. E.g. {{'a': np.float64,
+        'b': np.int32, 'c': 'Int64'}}
+
+        .. versionadded:: 1.5.0
+
+    converters : dict, optional
+        Dict of functions for converting values in certain columns. Keys can
+        either be integers or column labels.
+
+        .. versionadded:: 1.5.0
+
+    parse_dates : bool or list of int or names or list of lists or dict
+        Converts either index or select columns to datetimes
+
+        .. versionadded:: 1.5.0
+
+    encoding : str
+        Encoding of xml object or document.
+
+    stylesheet : str or file-like
+        URL, file, file-like object, or a raw string containing XSLT,
+        ``etree`` does not support XSLT but retained for consistency.
+
+    iterparse : dict, optional
+        Dict with row element as key and list of descendant elements
+        and/or attributes as value to be retrieved in iterparsing of
+        XML document.
+
+        .. versionadded:: 1.5.0
+
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    See also
+    --------
+    pandas.io.xml._EtreeFrameParser
+    pandas.io.xml._LxmlFrameParser
+
+    Notes
+    -----
+    To subclass this class effectively you must override the following methods:`
+        * :func:`parse_data`
+        * :func:`_parse_nodes`
+        * :func:`_iterparse_nodes`
+        * :func:`_parse_doc`
+        * :func:`_validate_names`
+        * :func:`_validate_path`
+
+
+    See each method's respective documentation for details on their
+    functionality.
+    """
+
+    def __init__(
+        self,
+        path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
+        xpath: str,
+        namespaces: dict[str, str] | None,
+        elems_only: bool,
+        attrs_only: bool,
+        names: Sequence[str] | None,
+        dtype: DtypeArg | None,
+        converters: ConvertersArg | None,
+        parse_dates: ParseDatesArg | None,
+        encoding: str | None,
+        stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None,
+        iterparse: dict[str, list[str]] | None,
+        compression: CompressionOptions,
+        storage_options: StorageOptions,
+    ) -> None:
+        self.path_or_buffer = path_or_buffer
+        self.xpath = xpath
+        self.namespaces = namespaces
+        self.elems_only = elems_only
+        self.attrs_only = attrs_only
+        self.names = names
+        self.dtype = dtype
+        self.converters = converters
+        self.parse_dates = parse_dates
+        self.encoding = encoding
+        self.stylesheet = stylesheet
+        self.iterparse = iterparse
+        self.is_style = None
+        self.compression: CompressionOptions = compression
+        self.storage_options = storage_options
+
+    def parse_data(self) -> list[dict[str, str | None]]:
+        """
+        Parse xml data.
+
+        This method will call the other internal methods to
+        validate ``xpath``, names, parse and return specific nodes.
+        """
+
+        raise AbstractMethodError(self)
+
+    def _parse_nodes(self, elems: list[Any]) -> list[dict[str, str | None]]:
+        """
+        Parse xml nodes.
+
+        This method will parse the children and attributes of elements
+        in ``xpath``, conditionally for only elements, only attributes
+        or both while optionally renaming node names.
+
+        Raises
+        ------
+        ValueError
+            * If only elements and only attributes are specified.
+
+        Notes
+        -----
+        Namespace URIs will be removed from return node values. Also,
+        elements with missing children or attributes compared to siblings
+        will have optional keys filled with None values.
+        """
+
+        dicts: list[dict[str, str | None]]
+
+        if self.elems_only and self.attrs_only:
+            raise ValueError("Either element or attributes can be parsed not both.")
+        if self.elems_only:
+            if self.names:
+                dicts = [
+                    {
+                        **(
+                            {el.tag: el.text}
+                            if el.text and not el.text.isspace()
+                            else {}
+                        ),
+                        **{
+                            nm: ch.text if ch.text else None
+                            for nm, ch in zip(self.names, el.findall("*"))
+                        },
+                    }
+                    for el in elems
+                ]
+            else:
+                dicts = [
+                    {ch.tag: ch.text if ch.text else None for ch in el.findall("*")}
+                    for el in elems
+                ]
+
+        elif self.attrs_only:
+            dicts = [
+                {k: v if v else None for k, v in el.attrib.items()} for el in elems
+            ]
+
+        elif self.names:
+            dicts = [
+                {
+                    **el.attrib,
+                    **({el.tag: el.text} if el.text and not el.text.isspace() else {}),
+                    **{
+                        nm: ch.text if ch.text else None
+                        for nm, ch in zip(self.names, el.findall("*"))
+                    },
+                }
+                for el in elems
+            ]
+
+        else:
+            dicts = [
+                {
+                    **el.attrib,
+                    **({el.tag: el.text} if el.text and not el.text.isspace() else {}),
+                    **{ch.tag: ch.text if ch.text else None for ch in el.findall("*")},
+                }
+                for el in elems
+            ]
+
+        dicts = [
+            {k.split("}")[1] if "}" in k else k: v for k, v in d.items()} for d in dicts
+        ]
+
+        keys = list(dict.fromkeys([k for d in dicts for k in d.keys()]))
+        dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts]
+
+        if self.names:
+            dicts = [dict(zip(self.names, d.values())) for d in dicts]
+
+        return dicts
+
+    def _iterparse_nodes(self, iterparse: Callable) -> list[dict[str, str | None]]:
+        """
+        Iterparse xml nodes.
+
+        This method will read in local disk, decompressed XML files for elements
+        and underlying descendants using iterparse, a method to iterate through
+        an XML tree without holding entire XML tree in memory.
+
+        Raises
+        ------
+        TypeError
+            * If ``iterparse`` is not a dict or its dict value is not list-like.
+        ParserError
+            * If ``path_or_buffer`` is not a physical file on disk or file-like object.
+            * If no data is returned from selected items in ``iterparse``.
+
+        Notes
+        -----
+        Namespace URIs will be removed from return node values. Also,
+        elements with missing children or attributes in submitted list
+        will have optional keys filled with None values.
+        """
+
+        dicts: list[dict[str, str | None]] = []
+        row: dict[str, str | None] | None = None
+
+        if not isinstance(self.iterparse, dict):
+            raise TypeError(
+                f"{type(self.iterparse).__name__} is not a valid type for iterparse"
+            )
+
+        row_node = next(iter(self.iterparse.keys())) if self.iterparse else ""
+        if not is_list_like(self.iterparse[row_node]):
+            raise TypeError(
+                f"{type(self.iterparse[row_node])} is not a valid type "
+                "for value in iterparse"
+            )
+
+        if (not hasattr(self.path_or_buffer, "read")) and (
+            not isinstance(self.path_or_buffer, (str, PathLike))
+            or is_url(self.path_or_buffer)
+            or is_fsspec_url(self.path_or_buffer)
+            or (
+                isinstance(self.path_or_buffer, str)
+                and self.path_or_buffer.startswith(("<?xml", "<"))
+            )
+            or infer_compression(self.path_or_buffer, "infer") is not None
+        ):
+            raise ParserError(
+                "iterparse is designed for large XML files that are fully extracted on "
+                "local disk and not as compressed files or online sources."
+            )
+
+        iterparse_repeats = len(self.iterparse[row_node]) != len(
+            set(self.iterparse[row_node])
+        )
+
+        for event, elem in iterparse(self.path_or_buffer, events=("start", "end")):
+            curr_elem = elem.tag.split("}")[1] if "}" in elem.tag else elem.tag
+
+            if event == "start":
+                if curr_elem == row_node:
+                    row = {}
+
+            if row is not None:
+                if self.names and iterparse_repeats:
+                    for col, nm in zip(self.iterparse[row_node], self.names):
+                        if curr_elem == col:
+                            elem_val = elem.text if elem.text else None
+                            if elem_val not in row.values() and nm not in row:
+                                row[nm] = elem_val
+
+                        if col in elem.attrib:
+                            if elem.attrib[col] not in row.values() and nm not in row:
+                                row[nm] = elem.attrib[col]
+                else:
+                    for col in self.iterparse[row_node]:
+                        if curr_elem == col:
+                            row[col] = elem.text if elem.text else None
+                        if col in elem.attrib:
+                            row[col] = elem.attrib[col]
+
+            if event == "end":
+                if curr_elem == row_node and row is not None:
+                    dicts.append(row)
+                    row = None
+
+                elem.clear()
+                if hasattr(elem, "getprevious"):
+                    while (
+                        elem.getprevious() is not None and elem.getparent() is not None
+                    ):
+                        del elem.getparent()[0]
+
+        if dicts == []:
+            raise ParserError("No result from selected items in iterparse.")
+
+        keys = list(dict.fromkeys([k for d in dicts for k in d.keys()]))
+        dicts = [{k: d[k] if k in d.keys() else None for k in keys} for d in dicts]
+
+        if self.names:
+            dicts = [dict(zip(self.names, d.values())) for d in dicts]
+
+        return dicts
+
+    def _validate_path(self) -> list[Any]:
+        """
+        Validate ``xpath``.
+
+        This method checks for syntax, evaluation, or empty nodes return.
+
+        Raises
+        ------
+        SyntaxError
+            * If xpah is not supported or issues with namespaces.
+
+        ValueError
+            * If xpah does not return any nodes.
+        """
+
+        raise AbstractMethodError(self)
+
+    def _validate_names(self) -> None:
+        """
+        Validate names.
+
+        This method will check if names is a list-like and aligns
+        with length of parse nodes.
+
+        Raises
+        ------
+        ValueError
+            * If value is not a list and less then length of nodes.
+        """
+        raise AbstractMethodError(self)
+
+    def _parse_doc(
+        self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
+    ) -> Element | etree._Element:
+        """
+        Build tree from path_or_buffer.
+
+        This method will parse XML object into tree
+        either from string/bytes or file location.
+        """
+        raise AbstractMethodError(self)
+
+
+class _EtreeFrameParser(_XMLFrameParser):
+    """
+    Internal class to parse XML into DataFrames with the Python
+    standard library XML module: `xml.etree.ElementTree`.
+    """
+
+    def parse_data(self) -> list[dict[str, str | None]]:
+        from xml.etree.ElementTree import iterparse
+
+        if self.stylesheet is not None:
+            raise ValueError(
+                "To use stylesheet, you need lxml installed and selected as parser."
+            )
+
+        if self.iterparse is None:
+            self.xml_doc = self._parse_doc(self.path_or_buffer)
+            elems = self._validate_path()
+
+        self._validate_names()
+
+        xml_dicts: list[dict[str, str | None]] = (
+            self._parse_nodes(elems)
+            if self.iterparse is None
+            else self._iterparse_nodes(iterparse)
+        )
+
+        return xml_dicts
+
+    def _validate_path(self) -> list[Any]:
+        """
+        Notes
+        -----
+        ``etree`` supports limited ``XPath``. If user attempts a more complex
+        expression syntax error will raise.
+        """
+
+        msg = (
+            "xpath does not return any nodes or attributes. "
+            "Be sure to specify in `xpath` the parent nodes of "
+            "children and attributes to parse. "
+            "If document uses namespaces denoted with "
+            "xmlns, be sure to define namespaces and "
+            "use them in xpath."
+        )
+        try:
+            elems = self.xml_doc.findall(self.xpath, namespaces=self.namespaces)
+            children = [ch for el in elems for ch in el.findall("*")]
+            attrs = {k: v for el in elems for k, v in el.attrib.items()}
+
+            if elems is None:
+                raise ValueError(msg)
+
+            if elems is not None:
+                if self.elems_only and children == []:
+                    raise ValueError(msg)
+                if self.attrs_only and attrs == {}:
+                    raise ValueError(msg)
+                if children == [] and attrs == {}:
+                    raise ValueError(msg)
+
+        except (KeyError, SyntaxError):
+            raise SyntaxError(
+                "You have used an incorrect or unsupported XPath "
+                "expression for etree library or you used an "
+                "undeclared namespace prefix."
+            )
+
+        return elems
+
+    def _validate_names(self) -> None:
+        children: list[Any]
+
+        if self.names:
+            if self.iterparse:
+                children = self.iterparse[next(iter(self.iterparse))]
+            else:
+                parent = self.xml_doc.find(self.xpath, namespaces=self.namespaces)
+                children = parent.findall("*") if parent is not None else []
+
+            if is_list_like(self.names):
+                if len(self.names) < len(children):
+                    raise ValueError(
+                        "names does not match length of child elements in xpath."
+                    )
+            else:
+                raise TypeError(
+                    f"{type(self.names).__name__} is not a valid type for names"
+                )
+
+    def _parse_doc(
+        self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
+    ) -> Element:
+        from xml.etree.ElementTree import (
+            XMLParser,
+            parse,
+        )
+
+        handle_data = get_data_from_filepath(
+            filepath_or_buffer=raw_doc,
+            encoding=self.encoding,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        )
+
+        with preprocess_data(handle_data) as xml_data:
+            curr_parser = XMLParser(encoding=self.encoding)
+            document = parse(xml_data, parser=curr_parser)
+
+        return document.getroot()
+
+
+class _LxmlFrameParser(_XMLFrameParser):
+    """
+    Internal class to parse XML into :class:`~pandas.DataFrame` with third-party
+    full-featured XML library, ``lxml``, that supports
+    ``XPath`` 1.0 and XSLT 1.0.
+    """
+
+    def parse_data(self) -> list[dict[str, str | None]]:
+        """
+        Parse xml data.
+
+        This method will call the other internal methods to
+        validate ``xpath``, names, optionally parse and run XSLT,
+        and parse original or transformed XML and return specific nodes.
+        """
+        from lxml.etree import iterparse
+
+        if self.iterparse is None:
+            self.xml_doc = self._parse_doc(self.path_or_buffer)
+
+            if self.stylesheet:
+                self.xsl_doc = self._parse_doc(self.stylesheet)
+                self.xml_doc = self._transform_doc()
+
+            elems = self._validate_path()
+
+        self._validate_names()
+
+        xml_dicts: list[dict[str, str | None]] = (
+            self._parse_nodes(elems)
+            if self.iterparse is None
+            else self._iterparse_nodes(iterparse)
+        )
+
+        return xml_dicts
+
+    def _validate_path(self) -> list[Any]:
+        msg = (
+            "xpath does not return any nodes or attributes. "
+            "Be sure to specify in `xpath` the parent nodes of "
+            "children and attributes to parse. "
+            "If document uses namespaces denoted with "
+            "xmlns, be sure to define namespaces and "
+            "use them in xpath."
+        )
+
+        elems = self.xml_doc.xpath(self.xpath, namespaces=self.namespaces)
+        children = [ch for el in elems for ch in el.xpath("*")]
+        attrs = {k: v for el in elems for k, v in el.attrib.items()}
+
+        if elems == []:
+            raise ValueError(msg)
+
+        if elems != []:
+            if self.elems_only and children == []:
+                raise ValueError(msg)
+            if self.attrs_only and attrs == {}:
+                raise ValueError(msg)
+            if children == [] and attrs == {}:
+                raise ValueError(msg)
+
+        return elems
+
+    def _validate_names(self) -> None:
+        children: list[Any]
+
+        if self.names:
+            if self.iterparse:
+                children = self.iterparse[next(iter(self.iterparse))]
+            else:
+                children = self.xml_doc.xpath(
+                    self.xpath + "[1]/*", namespaces=self.namespaces
+                )
+
+            if is_list_like(self.names):
+                if len(self.names) < len(children):
+                    raise ValueError(
+                        "names does not match length of child elements in xpath."
+                    )
+            else:
+                raise TypeError(
+                    f"{type(self.names).__name__} is not a valid type for names"
+                )
+
+    def _parse_doc(
+        self, raw_doc: FilePath | ReadBuffer[bytes] | ReadBuffer[str]
+    ) -> etree._Element:
+        from lxml.etree import (
+            XMLParser,
+            fromstring,
+            parse,
+        )
+
+        handle_data = get_data_from_filepath(
+            filepath_or_buffer=raw_doc,
+            encoding=self.encoding,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        )
+
+        with preprocess_data(handle_data) as xml_data:
+            curr_parser = XMLParser(encoding=self.encoding)
+
+            if isinstance(xml_data, io.StringIO):
+                if self.encoding is None:
+                    raise TypeError(
+                        "Can not pass encoding None when input is StringIO."
+                    )
+
+                document = fromstring(
+                    xml_data.getvalue().encode(self.encoding), parser=curr_parser
+                )
+            else:
+                document = parse(xml_data, parser=curr_parser)
+
+        return document
+
+    def _transform_doc(self) -> etree._XSLTResultTree:
+        """
+        Transform original tree using stylesheet.
+
+        This method will transform original xml using XSLT script into
+        am ideally flatter xml document for easier parsing and migration
+        to Data Frame.
+        """
+        from lxml.etree import XSLT
+
+        transformer = XSLT(self.xsl_doc)
+        new_doc = transformer(self.xml_doc)
+
+        return new_doc
+
+
+def get_data_from_filepath(
+    filepath_or_buffer: FilePath | bytes | ReadBuffer[bytes] | ReadBuffer[str],
+    encoding: str | None,
+    compression: CompressionOptions,
+    storage_options: StorageOptions,
+) -> str | bytes | ReadBuffer[bytes] | ReadBuffer[str]:
+    """
+    Extract raw XML data.
+
+    The method accepts three input types:
+        1. filepath (string-like)
+        2. file-like object (e.g. open file object, StringIO)
+        3. XML string or bytes
+
+    This method turns (1) into (2) to simplify the rest of the processing.
+    It returns input types (2) and (3) unchanged.
+    """
+    if not isinstance(filepath_or_buffer, bytes):
+        filepath_or_buffer = stringify_path(filepath_or_buffer)
+
+    if (
+        isinstance(filepath_or_buffer, str)
+        and not filepath_or_buffer.startswith(("<?xml", "<"))
+    ) and (
+        not isinstance(filepath_or_buffer, str)
+        or is_url(filepath_or_buffer)
+        or is_fsspec_url(filepath_or_buffer)
+        or file_exists(filepath_or_buffer)
+    ):
+        with get_handle(
+            filepath_or_buffer,
+            "r",
+            encoding=encoding,
+            compression=compression,
+            storage_options=storage_options,
+        ) as handle_obj:
+            filepath_or_buffer = (
+                handle_obj.handle.read()
+                if hasattr(handle_obj.handle, "read")
+                else handle_obj.handle
+            )
+
+    return filepath_or_buffer
+
+
+def preprocess_data(data) -> io.StringIO | io.BytesIO:
+    """
+    Convert extracted raw data.
+
+    This method will return underlying data of extracted XML content.
+    The data either has a `read` attribute (e.g. a file object or a
+    StringIO/BytesIO) or is a string or bytes that is an XML document.
+    """
+
+    if isinstance(data, str):
+        data = io.StringIO(data)
+
+    elif isinstance(data, bytes):
+        data = io.BytesIO(data)
+
+    return data
+
+
+def _data_to_frame(data, **kwargs) -> DataFrame:
+    """
+    Convert parsed data to Data Frame.
+
+    This method will bind xml dictionary data of keys and values
+    into named columns of Data Frame using the built-in TextParser
+    class that build Data Frame and infers specific dtypes.
+    """
+
+    tags = next(iter(data))
+    nodes = [list(d.values()) for d in data]
+
+    try:
+        with TextParser(nodes, names=tags, **kwargs) as tp:
+            return tp.read()
+    except ParserError:
+        raise ParserError(
+            "XML document may be too complex for import. "
+            "Try to flatten document and use distinct "
+            "element and attribute names."
+        )
+
+
+def _parse(
+    path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
+    xpath: str,
+    namespaces: dict[str, str] | None,
+    elems_only: bool,
+    attrs_only: bool,
+    names: Sequence[str] | None,
+    dtype: DtypeArg | None,
+    converters: ConvertersArg | None,
+    parse_dates: ParseDatesArg | None,
+    encoding: str | None,
+    parser: XMLParsers,
+    stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None,
+    iterparse: dict[str, list[str]] | None,
+    compression: CompressionOptions,
+    storage_options: StorageOptions,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    **kwargs,
+) -> DataFrame:
+    """
+    Call internal parsers.
+
+    This method will conditionally call internal parsers:
+    LxmlFrameParser and/or EtreeParser.
+
+    Raises
+    ------
+    ImportError
+        * If lxml is not installed if selected as parser.
+
+    ValueError
+        * If parser is not lxml or etree.
+    """
+
+    p: _EtreeFrameParser | _LxmlFrameParser
+
+    if isinstance(path_or_buffer, str) and not any(
+        [
+            is_file_like(path_or_buffer),
+            file_exists(path_or_buffer),
+            is_url(path_or_buffer),
+            is_fsspec_url(path_or_buffer),
+        ]
+    ):
+        warnings.warn(
+            "Passing literal xml to 'read_xml' is deprecated and "
+            "will be removed in a future version. To read from a "
+            "literal string, wrap it in a 'StringIO' object.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if parser == "lxml":
+        lxml = import_optional_dependency("lxml.etree", errors="ignore")
+
+        if lxml is not None:
+            p = _LxmlFrameParser(
+                path_or_buffer,
+                xpath,
+                namespaces,
+                elems_only,
+                attrs_only,
+                names,
+                dtype,
+                converters,
+                parse_dates,
+                encoding,
+                stylesheet,
+                iterparse,
+                compression,
+                storage_options,
+            )
+        else:
+            raise ImportError("lxml not found, please install or use the etree parser.")
+
+    elif parser == "etree":
+        p = _EtreeFrameParser(
+            path_or_buffer,
+            xpath,
+            namespaces,
+            elems_only,
+            attrs_only,
+            names,
+            dtype,
+            converters,
+            parse_dates,
+            encoding,
+            stylesheet,
+            iterparse,
+            compression,
+            storage_options,
+        )
+    else:
+        raise ValueError("Values for parser can only be lxml or etree.")
+
+    data_dicts = p.parse_data()
+
+    return _data_to_frame(
+        data=data_dicts,
+        dtype=dtype,
+        converters=converters,
+        parse_dates=parse_dates,
+        dtype_backend=dtype_backend,
+        **kwargs,
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    decompression_options=_shared_docs["decompression_options"] % "path_or_buffer",
+)
+def read_xml(
+    path_or_buffer: FilePath | ReadBuffer[bytes] | ReadBuffer[str],
+    *,
+    xpath: str = "./*",
+    namespaces: dict[str, str] | None = None,
+    elems_only: bool = False,
+    attrs_only: bool = False,
+    names: Sequence[str] | None = None,
+    dtype: DtypeArg | None = None,
+    converters: ConvertersArg | None = None,
+    parse_dates: ParseDatesArg | None = None,
+    # encoding can not be None for lxml and StringIO input
+    encoding: str | None = "utf-8",
+    parser: XMLParsers = "lxml",
+    stylesheet: FilePath | ReadBuffer[bytes] | ReadBuffer[str] | None = None,
+    iterparse: dict[str, list[str]] | None = None,
+    compression: CompressionOptions = "infer",
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    r"""
+    Read XML document into a :class:`~pandas.DataFrame` object.
+
+    .. versionadded:: 1.3.0
+
+    Parameters
+    ----------
+    path_or_buffer : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a ``read()`` function. The string can be any valid XML
+        string or a path. The string can further be a URL. Valid URL schemes
+        include http, ftp, s3, and file.
+
+        .. deprecated:: 2.1.0
+            Passing xml literal strings is deprecated.
+            Wrap literal xml input in ``io.StringIO`` or ``io.BytesIO`` instead.
+
+    xpath : str, optional, default './\*'
+        The ``XPath`` to parse required set of nodes for migration to
+        :class:`~pandas.DataFrame`.``XPath`` should return a collection of elements
+        and not a single element. Note: The ``etree`` parser supports limited ``XPath``
+        expressions. For more complex ``XPath``, use ``lxml`` which requires
+        installation.
+
+    namespaces : dict, optional
+        The namespaces defined in XML document as dicts with key being
+        namespace prefix and value the URI. There is no need to include all
+        namespaces in XML, only the ones used in ``xpath`` expression.
+        Note: if XML document uses default namespace denoted as
+        `xmlns='<URI>'` without a prefix, you must assign any temporary
+        namespace prefix such as 'doc' to the URI in order to parse
+        underlying nodes and/or attributes. For example, ::
+
+            namespaces = {{"doc": "https://example.com"}}
+
+    elems_only : bool, optional, default False
+        Parse only the child elements at the specified ``xpath``. By default,
+        all child elements and non-empty text nodes are returned.
+
+    attrs_only :  bool, optional, default False
+        Parse only the attributes at the specified ``xpath``.
+        By default, all attributes are returned.
+
+    names :  list-like, optional
+        Column names for DataFrame of parsed XML data. Use this parameter to
+        rename original element names and distinguish same named elements and
+        attributes.
+
+    dtype : Type name or dict of column -> type, optional
+        Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
+        'c': 'Int64'}}
+        Use `str` or `object` together with suitable `na_values` settings
+        to preserve and not interpret dtype.
+        If converters are specified, they will be applied INSTEAD
+        of dtype conversion.
+
+        .. versionadded:: 1.5.0
+
+    converters : dict, optional
+        Dict of functions for converting values in certain columns. Keys can either
+        be integers or column labels.
+
+        .. versionadded:: 1.5.0
+
+    parse_dates : bool or list of int or names or list of lists or dict, default False
+        Identifiers to parse index or columns to datetime. The behavior is as follows:
+
+        * boolean. If True -> try parsing the index.
+        * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
+          each as a separate date column.
+        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
+          a single date column.
+        * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
+          result 'foo'
+
+        .. versionadded:: 1.5.0
+
+    encoding : str, optional, default 'utf-8'
+        Encoding of XML document.
+
+    parser : {{'lxml','etree'}}, default 'lxml'
+        Parser module to use for retrieval of data. Only 'lxml' and
+        'etree' are supported. With 'lxml' more complex ``XPath`` searches
+        and ability to use XSLT stylesheet are supported.
+
+    stylesheet : str, path object or file-like object
+        A URL, file-like object, or a raw string containing an XSLT script.
+        This stylesheet should flatten complex, deeply nested XML documents
+        for easier parsing. To use this feature you must have ``lxml`` module
+        installed and specify 'lxml' as ``parser``. The ``xpath`` must
+        reference nodes of transformed XML document generated after XSLT
+        transformation and not the original XML document. Only XSLT 1.0
+        scripts and not later versions is currently supported.
+
+    iterparse : dict, optional
+        The nodes or attributes to retrieve in iterparsing of XML document
+        as a dict with key being the name of repeating element and value being
+        list of elements or attribute names that are descendants of the repeated
+        element. Note: If this option is used, it will replace ``xpath`` parsing
+        and unlike ``xpath``, descendants do not need to relate to each other but can
+        exist any where in document under the repeating element. This memory-
+        efficient method should be used for very large XML files (500MB, 1GB, or 5GB+).
+        For example, ::
+
+            iterparse = {{"row_element": ["child_elem", "attr", "grandchild_elem"]}}
+
+        .. versionadded:: 1.5.0
+
+    {decompression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    {storage_options}
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    df
+        A DataFrame.
+
+    See Also
+    --------
+    read_json : Convert a JSON string to pandas object.
+    read_html : Read HTML tables into a list of DataFrame objects.
+
+    Notes
+    -----
+    This method is best designed to import shallow XML documents in
+    following format which is the ideal fit for the two-dimensions of a
+    ``DataFrame`` (row by column). ::
+
+            <root>
+                <row>
+                  <column1>data</column1>
+                  <column2>data</column2>
+                  <column3>data</column3>
+                  ...
+               </row>
+               <row>
+                  ...
+               </row>
+               ...
+            </root>
+
+    As a file format, XML documents can be designed any way including
+    layout of elements and attributes as long as it conforms to W3C
+    specifications. Therefore, this method is a convenience handler for
+    a specific flatter design and not all possible XML structures.
+
+    However, for more complex XML documents, ``stylesheet`` allows you to
+    temporarily redesign original document with XSLT (a special purpose
+    language) for a flatter version for migration to a DataFrame.
+
+    This function will *always* return a single :class:`DataFrame` or raise
+    exceptions due to issues with XML document, ``xpath``, or other
+    parameters.
+
+    See the :ref:`read_xml documentation in the IO section of the docs
+    <io.read_xml>` for more information in using this method to parse XML
+    files to DataFrames.
+
+    Examples
+    --------
+    >>> from io import StringIO
+    >>> xml = '''<?xml version='1.0' encoding='utf-8'?>
+    ... <data xmlns="http://example.com">
+    ...  <row>
+    ...    <shape>square</shape>
+    ...    <degrees>360</degrees>
+    ...    <sides>4.0</sides>
+    ...  </row>
+    ...  <row>
+    ...    <shape>circle</shape>
+    ...    <degrees>360</degrees>
+    ...    <sides/>
+    ...  </row>
+    ...  <row>
+    ...    <shape>triangle</shape>
+    ...    <degrees>180</degrees>
+    ...    <sides>3.0</sides>
+    ...  </row>
+    ... </data>'''
+
+    >>> df = pd.read_xml(StringIO(xml))
+    >>> df
+          shape  degrees  sides
+    0    square      360    4.0
+    1    circle      360    NaN
+    2  triangle      180    3.0
+
+    >>> xml = '''<?xml version='1.0' encoding='utf-8'?>
+    ... <data>
+    ...   <row shape="square" degrees="360" sides="4.0"/>
+    ...   <row shape="circle" degrees="360"/>
+    ...   <row shape="triangle" degrees="180" sides="3.0"/>
+    ... </data>'''
+
+    >>> df = pd.read_xml(StringIO(xml), xpath=".//row")
+    >>> df
+          shape  degrees  sides
+    0    square      360    4.0
+    1    circle      360    NaN
+    2  triangle      180    3.0
+
+    >>> xml = '''<?xml version='1.0' encoding='utf-8'?>
+    ... <doc:data xmlns:doc="https://example.com">
+    ...   <doc:row>
+    ...     <doc:shape>square</doc:shape>
+    ...     <doc:degrees>360</doc:degrees>
+    ...     <doc:sides>4.0</doc:sides>
+    ...   </doc:row>
+    ...   <doc:row>
+    ...     <doc:shape>circle</doc:shape>
+    ...     <doc:degrees>360</doc:degrees>
+    ...     <doc:sides/>
+    ...   </doc:row>
+    ...   <doc:row>
+    ...     <doc:shape>triangle</doc:shape>
+    ...     <doc:degrees>180</doc:degrees>
+    ...     <doc:sides>3.0</doc:sides>
+    ...   </doc:row>
+    ... </doc:data>'''
+
+    >>> df = pd.read_xml(StringIO(xml),
+    ...                  xpath="//doc:row",
+    ...                  namespaces={{"doc": "https://example.com"}})
+    >>> df
+          shape  degrees  sides
+    0    square      360    4.0
+    1    circle      360    NaN
+    2  triangle      180    3.0
+
+    >>> xml_data = '''
+    ...         <data>
+    ...            <row>
+    ...               <index>0</index>
+    ...               <a>1</a>
+    ...               <b>2.5</b>
+    ...               <c>True</c>
+    ...               <d>a</d>
+    ...               <e>2019-12-31 00:00:00</e>
+    ...            </row>
+    ...            <row>
+    ...               <index>1</index>
+    ...               <b>4.5</b>
+    ...               <c>False</c>
+    ...               <d>b</d>
+    ...               <e>2019-12-31 00:00:00</e>
+    ...            </row>
+    ...         </data>
+    ...         '''
+
+    >>> df = pd.read_xml(StringIO(xml_data),
+    ...                  dtype_backend="numpy_nullable",
+    ...                  parse_dates=["e"])
+    >>> df
+       index     a    b      c  d          e
+    0      0     1  2.5   True  a 2019-12-31
+    1      1  <NA>  4.5  False  b 2019-12-31
+    """
+    check_dtype_backend(dtype_backend)
+
+    return _parse(
+        path_or_buffer=path_or_buffer,
+        xpath=xpath,
+        namespaces=namespaces,
+        elems_only=elems_only,
+        attrs_only=attrs_only,
+        names=names,
+        dtype=dtype,
+        converters=converters,
+        parse_dates=parse_dates,
+        encoding=encoding,
+        parser=parser,
+        stylesheet=stylesheet,
+        iterparse=iterparse,
+        compression=compression,
+        storage_options=storage_options,
+        dtype_backend=dtype_backend,
+    )
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/pyproject.toml b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..d5a7f71391fef6e9797fb82700bb4021ab906984
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/pyproject.toml
@@ -0,0 +1,819 @@
+[build-system]
+# Minimum requirements for the build system to execute.
+# See https://github.com/scipy/scipy/pull/12940 for the AIX issue.
+requires = [
+    "meson-python>=0.13.1",
+    "meson>=1.2.1,<2",
+    "wheel",
+    "Cython<4.0.0a0",  # Note: sync with setup.py, environment.yml and asv.conf.json
+    # Force numpy higher than 2.0rc1, so that built wheels are compatible
+    # with both numpy 1 and 2
+    "numpy>=2.0",
+    "versioneer[toml]"
+]
+
+build-backend = "mesonpy"
+
+[project]
+name = 'pandas'
+dynamic = [
+  'version'
+]
+description = 'Powerful data structures for data analysis, time series, and statistics'
+readme = 'README.md'
+authors = [
+  { name = 'The Pandas Development Team', email='pandas-dev@python.org' },
+]
+license = {file = 'LICENSE'}
+requires-python = '>=3.9'
+dependencies = [
+  "numpy>=1.22.4; python_version<'3.11'",
+  "numpy>=1.23.2; python_version=='3.11'",
+  "numpy>=1.26.0; python_version>='3.12'",
+  "python-dateutil>=2.8.2",
+  "pytz>=2020.1",
+  "tzdata>=2022.7"
+]
+classifiers = [
+    'Development Status :: 5 - Production/Stable',
+    'Environment :: Console',
+    'Intended Audience :: Science/Research',
+    'License :: OSI Approved :: BSD License',
+    'Operating System :: OS Independent',
+    'Programming Language :: Cython',
+    'Programming Language :: Python',
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3 :: Only',
+    'Programming Language :: Python :: 3.9',
+    'Programming Language :: Python :: 3.10',
+    'Programming Language :: Python :: 3.11',
+    'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
+    'Programming Language :: Python :: 3.14',
+    'Topic :: Scientific/Engineering'
+]
+
+[project.urls]
+homepage = 'https://pandas.pydata.org'
+documentation = 'https://pandas.pydata.org/docs/'
+repository = 'https://github.com/pandas-dev/pandas'
+
+[project.entry-points."pandas_plotting_backends"]
+matplotlib = "pandas:plotting._matplotlib"
+
+[project.optional-dependencies]
+test = ['hypothesis>=6.46.1', 'pytest>=7.3.2', 'pytest-xdist>=2.2.0']
+pyarrow = ['pyarrow>=10.0.1']
+performance = ['bottleneck>=1.3.6', 'numba>=0.56.4', 'numexpr>=2.8.4']
+computation = ['scipy>=1.10.0', 'xarray>=2022.12.0']
+fss = ['fsspec>=2022.11.0']
+aws = ['s3fs>=2022.11.0']
+gcp = ['gcsfs>=2022.11.0', 'pandas-gbq>=0.19.0']
+excel = ['odfpy>=1.4.1', 'openpyxl>=3.1.0', 'python-calamine>=0.1.7', 'pyxlsb>=1.0.10', 'xlrd>=2.0.1', 'xlsxwriter>=3.0.5']
+parquet = ['pyarrow>=10.0.1']
+feather = ['pyarrow>=10.0.1']
+hdf5 = [# blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
+        #'blosc>=1.20.1',
+        'tables>=3.8.0']
+spss = ['pyreadstat>=1.2.0']
+postgresql = ['SQLAlchemy>=2.0.0', 'psycopg2>=2.9.6', 'adbc-driver-postgresql>=0.8.0']
+mysql = ['SQLAlchemy>=2.0.0', 'pymysql>=1.0.2']
+sql-other = ['SQLAlchemy>=2.0.0', 'adbc-driver-postgresql>=0.8.0', 'adbc-driver-sqlite>=0.8.0']
+html = ['beautifulsoup4>=4.11.2', 'html5lib>=1.1', 'lxml>=4.9.2']
+xml = ['lxml>=4.9.2']
+plot = ['matplotlib>=3.6.3']
+output-formatting = ['jinja2>=3.1.2', 'tabulate>=0.9.0']
+clipboard = ['PyQt5>=5.15.9', 'qtpy>=2.3.0']
+compression = ['zstandard>=0.19.0']
+consortium-standard = ['dataframe-api-compat>=0.1.7']
+all = ['adbc-driver-postgresql>=0.8.0',
+       'adbc-driver-sqlite>=0.8.0',
+       'beautifulsoup4>=4.11.2',
+       # blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
+       #'blosc>=1.21.3',
+       'bottleneck>=1.3.6',
+       'dataframe-api-compat>=0.1.7',
+       'fastparquet>=2022.12.0',
+       'fsspec>=2022.11.0',
+       'gcsfs>=2022.11.0',
+       'html5lib>=1.1',
+       'hypothesis>=6.46.1',
+       'jinja2>=3.1.2',
+       'lxml>=4.9.2',
+       'matplotlib>=3.6.3',
+       'numba>=0.56.4',
+       'numexpr>=2.8.4',
+       'odfpy>=1.4.1',
+       'openpyxl>=3.1.0',
+       'pandas-gbq>=0.19.0',
+       'psycopg2>=2.9.6',
+       'pyarrow>=10.0.1',
+       'pymysql>=1.0.2',
+       'PyQt5>=5.15.9',
+       'pyreadstat>=1.2.0',
+       'pytest>=7.3.2',
+       'pytest-xdist>=2.2.0',
+       'python-calamine>=0.1.7',
+       'pyxlsb>=1.0.10',
+       'qtpy>=2.3.0',
+       'scipy>=1.10.0',
+       's3fs>=2022.11.0',
+       'SQLAlchemy>=2.0.0',
+       'tables>=3.8.0',
+       'tabulate>=0.9.0',
+       'xarray>=2022.12.0',
+       'xlrd>=2.0.1',
+       'xlsxwriter>=3.0.5',
+       'zstandard>=0.19.0']
+
+# TODO: Remove after setuptools support is dropped.
+[tool.setuptools]
+include-package-data = true
+
+[tool.setuptools.packages.find]
+include = ["pandas", "pandas.*"]
+namespaces = false
+
+[tool.setuptools.exclude-package-data]
+"*" = ["*.c", "*.h"]
+
+# See the docstring in versioneer.py for instructions. Note that you must
+# re-run 'versioneer.py setup' after changing this section, and commit the
+# resulting files.
+[tool.versioneer]
+VCS = "git"
+style = "pep440"
+versionfile_source = "pandas/_version.py"
+versionfile_build = "pandas/_version.py"
+tag_prefix = "v"
+parentdir_prefix = "pandas-"
+
+[tool.meson-python.args]
+setup = ['--vsenv'] # For Windows
+
+[tool.cibuildwheel]
+skip = "cp38-* *_i686 *_ppc64le *_s390x"
+build-verbosity = "3"
+environment = {LDFLAGS="-Wl,--strip-all"}
+# pytz 2024.2 causing some failures
+test-requires = "hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytz<2024.2"
+test-command = """
+  PANDAS_CI='1' python -c 'import pandas as pd; \
+  pd.test(extra_args=["-m not clipboard and not single_cpu and not slow and not network and not db", "-n 2", "--no-strict-data-files"]); \
+  pd.test(extra_args=["-m not clipboard and single_cpu and not slow and not network and not db", "--no-strict-data-files"]);' \
+  """
+enable = ["cpython-freethreading"]
+before-build = "PACKAGE_DIR={package} bash {package}/scripts/cibw_before_build.sh"
+
+[tool.cibuildwheel.windows]
+before-build = "pip install delvewheel"
+repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel}"
+
+[[tool.cibuildwheel.overrides]]
+select = "*-manylinux_aarch64*"
+test-command = """
+  PANDAS_CI='1' python -c 'import pandas as pd; \
+  pd.test(extra_args=["-m not clipboard and not single_cpu and not slow and not network and not db and not fails_arm_wheels", "-n 2", "--no-strict-data-files"]); \
+  pd.test(extra_args=["-m not clipboard and single_cpu and not slow and not network and not db", "--no-strict-data-files"]);' \
+  """
+
+[[tool.cibuildwheel.overrides]]
+select = "*-musllinux*"
+before-test = "apk update && apk add musl-locales"
+
+[[tool.cibuildwheel.overrides]]
+select = "*-win*"
+# We test separately for Windows, since we use
+# the windowsservercore docker image to check if any dlls are
+# missing from the wheel
+test-command = ""
+
+[[tool.cibuildwheel.overrides]]
+# Don't strip wheels on macOS.
+# macOS doesn't support stripping wheels with linker
+# https://github.com/MacPython/numpy-wheels/pull/87#issuecomment-624878264
+select = "*-macosx*"
+environment = {CFLAGS="-g0"}
+
+[tool.black]
+target-version = ['py39', 'py310']
+required-version = '23.11.0'
+exclude = '''
+(
+    asv_bench/env
+  | \.egg
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.nox
+  | \.tox
+  | \.venv
+  | _build
+  | buck-out
+  | build
+  | dist
+  | setup.py
+)
+'''
+
+[tool.ruff]
+line-length = 88
+target-version = "py310"
+fix = true
+unfixable = []
+typing-modules = ["pandas._typing"]
+
+select = [
+  # pyflakes
+  "F",
+  # pycodestyle
+  "E", "W",
+  # flake8-2020
+  "YTT",
+  # flake8-bugbear
+  "B",
+  # flake8-quotes
+  "Q",
+  # flake8-debugger
+  "T10",
+  # flake8-gettext
+  "INT",
+  # pylint
+  "PL",
+  # misc lints
+  "PIE",
+  # flake8-pyi
+  "PYI",
+  # tidy imports
+  "TID",
+  # implicit string concatenation
+  "ISC",
+  # type-checking imports
+  "TCH",
+  # comprehensions
+  "C4",
+  # pygrep-hooks
+  "PGH",
+  # Ruff-specific rules
+  "RUF",
+  # flake8-bandit: exec-builtin
+  "S102",
+  # numpy-legacy-random
+  "NPY002",
+  # Perflint
+  "PERF",
+  # flynt
+  "FLY",
+  # flake8-logging-format
+  "G",
+  # flake8-future-annotations
+  "FA",
+]
+
+ignore = [
+  ### Intentionally disabled
+  # space before : (needed for how black formats slicing)
+  "E203",
+  # module level import not at top of file
+  "E402",
+  # do not assign a lambda expression, use a def
+  "E731",
+  # line break before binary operator
+  # "W503",  # not yet implemented
+  # line break after binary operator
+  # "W504",  # not yet implemented
+  # controversial
+  "B006",
+  # controversial
+  "B007",
+  # controversial
+  "B008",
+  # setattr is used to side-step mypy
+  "B009",
+  # getattr is used to side-step mypy
+  "B010",
+  # tests use assert False
+  "B011",
+  # tests use comparisons but not their returned value
+  "B015",
+  # false positives
+  "B019",
+  # Loop control variable overrides iterable it iterates
+  "B020",
+  # Function definition does not bind loop variable
+  "B023",
+  # Functions defined inside a loop must not use variables redefined in the loop
+  # "B301",  # not yet implemented
+  # Only works with python >=3.10
+  "B905",
+  # Too many arguments to function call
+  "PLR0913",
+  # Too many returns
+  "PLR0911",
+  # Too many branches
+  "PLR0912",
+  # Too many statements
+  "PLR0915",
+  # Redefined loop name
+  "PLW2901",
+  # Global statements are discouraged
+  "PLW0603",
+  # Docstrings should not be included in stubs
+  "PYI021",
+  # Use `typing.NamedTuple` instead of `collections.namedtuple`
+  "PYI024",
+  # No builtin `eval()` allowed
+  "PGH001",
+  # compare-to-empty-string
+  "PLC1901",
+  # while int | float can be shortened to float, the former is more explicit
+  "PYI041",
+  # incorrect-dict-iterator, flags valid Series.items usage
+  "PERF102",
+  # try-except-in-loop, becomes useless in Python 3.11
+  "PERF203",
+
+
+  ### TODO: Enable gradually
+  # Useless statement
+  "B018",
+  # Within an except clause, raise exceptions with ...
+  "B904",
+  # Magic number
+  "PLR2004",
+  # comparison-with-itself
+  "PLR0124",
+  # Consider `elif` instead of `else` then `if` to remove indentation level
+  "PLR5501",
+  # collection-literal-concatenation
+  "RUF005",
+  # pairwise-over-zipped (>=PY310 only)
+  "RUF007",
+  # explicit-f-string-type-conversion
+  "RUF010",
+  # mutable-class-default
+  "RUF012"
+]
+
+exclude = [
+  "doc/sphinxext/*.py",
+  "doc/build/*.py",
+  "doc/temp/*.py",
+  ".eggs/*.py",
+  # vendored files
+  "pandas/util/version/*",
+  "pandas/io/clipboard/__init__.py",
+  # exclude asv benchmark environments from linting
+  "env",
+]
+
+[tool.ruff.per-file-ignores]
+# relative imports allowed for asv_bench
+"asv_bench/*" = ["TID", "NPY002"]
+# to be enabled gradually
+"pandas/core/*" = ["PLR5501"]
+"pandas/tests/*" = ["B028", "FLY"]
+"scripts/*" = ["B028"]
+# Keep this one enabled
+"pandas/_typing.py" = ["TCH"]
+
+[tool.pylint.messages_control]
+max-line-length = 88
+disable = [
+ # intentionally turned off
+  "bad-mcs-classmethod-argument",
+  "broad-except",
+  "c-extension-no-member",
+  "comparison-with-itself",
+  "consider-using-enumerate",
+  "import-error",
+  "import-outside-toplevel",
+  "invalid-name",
+  "invalid-unary-operand-type",
+  "line-too-long",
+  "no-else-continue",
+  "no-else-raise",
+  "no-else-return",
+  "no-member",
+  "no-name-in-module",
+  "not-an-iterable",
+  "overridden-final-method",
+  "pointless-statement",
+  "redundant-keyword-arg",
+  "singleton-comparison",
+  "too-many-ancestors",
+  "too-many-arguments",
+  "too-many-boolean-expressions",
+  "too-many-branches",
+  "too-many-function-args",
+  "too-many-instance-attributes",
+  "too-many-locals",
+  "too-many-nested-blocks",
+  "too-many-public-methods",
+  "too-many-return-statements",
+  "too-many-statements",
+  "unexpected-keyword-arg",
+  "ungrouped-imports",
+  "unsubscriptable-object",
+  "unsupported-assignment-operation",
+  "unsupported-membership-test",
+  "unused-import",
+  "use-dict-literal",
+  "use-implicit-booleaness-not-comparison",
+  "use-implicit-booleaness-not-len",
+  "wrong-import-order",
+  "wrong-import-position",
+  "redefined-loop-name",
+
+ # misc
+  "abstract-class-instantiated",
+  "no-value-for-parameter",
+  "undefined-variable",
+  "unpacking-non-sequence",
+  "used-before-assignment",
+
+ # pylint type "C": convention, for programming standard violation
+  "missing-class-docstring",
+  "missing-function-docstring",
+  "missing-module-docstring",
+  "superfluous-parens",
+  "too-many-lines",
+  "unidiomatic-typecheck",
+  "unnecessary-dunder-call",
+  "unnecessary-lambda-assignment",
+
+  # pylint type "R": refactor, for bad code smell
+  "consider-using-with",
+  "cyclic-import",
+  "duplicate-code",
+  "inconsistent-return-statements",
+  "redefined-argument-from-local",
+  "too-few-public-methods",
+
+  # pylint type "W": warning, for python specific problems
+  "abstract-method",
+  "arguments-differ",
+  "arguments-out-of-order",
+  "arguments-renamed",
+  "attribute-defined-outside-init",
+  "broad-exception-raised",
+  "comparison-with-callable",
+  "dangerous-default-value",
+  "deprecated-module",
+  "eval-used",
+  "expression-not-assigned",
+  "fixme",
+  "global-statement",
+  "invalid-overridden-method",
+  "keyword-arg-before-vararg",
+  "possibly-unused-variable",
+  "protected-access",
+  "raise-missing-from",
+  "redefined-builtin",
+  "redefined-outer-name",
+  "self-cls-assignment",
+  "signature-differs",
+  "super-init-not-called",
+  "try-except-raise",
+  "unnecessary-lambda",
+  "unused-argument",
+  "unused-variable",
+  "using-constant-test",
+
+  # disabled on 2.3.x branch
+  "consider-using-in",
+  "simplifiable-if-expression",
+]
+
+[tool.pytest.ini_options]
+# sync minversion with pyproject.toml & install.rst
+minversion = "7.3.2"
+addopts = "--strict-markers --strict-config --capture=no --durations=30 --junitxml=test-data.xml"
+empty_parameter_set_mark = "fail_at_collect"
+xfail_strict = true
+testpaths = "pandas"
+doctest_optionflags = [
+  "NORMALIZE_WHITESPACE",
+  "IGNORE_EXCEPTION_DETAIL",
+  "ELLIPSIS",
+]
+filterwarnings = [
+  "error:::pandas",
+  "error::ResourceWarning",
+  "error::pytest.PytestUnraisableExceptionWarning",
+  # TODO(PY311-minimum): Specify EncodingWarning
+  # Ignore 3rd party EncodingWarning but raise on pandas'
+  "ignore:.*encoding.* argument not specified",
+  "error:.*encoding.* argument not specified::pandas",
+  "ignore:.*ssl.SSLSocket:pytest.PytestUnraisableExceptionWarning",
+  "ignore:.*ssl.SSLSocket:ResourceWarning",
+  # GH 44844: Can remove once minimum matplotlib version >= 3.7
+  "ignore:.*FileIO:pytest.PytestUnraisableExceptionWarning",
+  "ignore:.*BufferedRandom:ResourceWarning",
+  "ignore::ResourceWarning:asyncio",
+  # From plotting doctests
+  "ignore:More than 20 figures have been opened:RuntimeWarning",
+  # Will be fixed in numba 0.56: https://github.com/numba/numba/issues/7758
+  "ignore:`np.MachAr` is deprecated:DeprecationWarning:numba",
+  "ignore:.*urllib3:DeprecationWarning:botocore",
+  "ignore:Setuptools is replacing distutils.:UserWarning:_distutils_hack",
+  # https://github.com/PyTables/PyTables/issues/822
+  "ignore:a closed node found in the registry:UserWarning:tables",
+  "ignore:`np.object` is a deprecated:DeprecationWarning:tables",
+  "ignore:tostring:DeprecationWarning:tables",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:pandas_datareader",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:numexpr",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:fastparquet",
+  "ignore:distutils Version classes are deprecated:DeprecationWarning:fsspec",
+  # Can be removed once https://github.com/numpy/numpy/pull/24794 is merged
+  "ignore:.*In the future `np.long` will be defined as.*:FutureWarning",
+  # https://github.com/numpy/numpy/pull/29301
+  "ignore:.*align should be passed:",
+]
+junit_family = "xunit2"
+markers = [
+  "single_cpu: tests that should run on a single cpu only",
+  "slow: mark a test as slow",
+  "network: mark a test as network",
+  "db: tests requiring a database (mysql or postgres)",
+  "clipboard: mark a pd.read_clipboard test",
+  "arm_slow: mark a test as slow for arm64 architecture",
+  "skip_ubsan: Tests known to fail UBSAN check",
+  # TODO: someone should investigate this ...
+  # these tests only fail in the wheel builder and don't fail in regular
+  # ARM CI
+  "fails_arm_wheels: Tests that fail in the ARM wheel build only",
+]
+
+[tool.mypy]
+# Import discovery
+mypy_path = "typings"
+files = ["pandas", "typings"]
+namespace_packages = false
+explicit_package_bases = false
+ignore_missing_imports = true
+follow_imports = "normal"
+follow_imports_for_stubs = false
+no_site_packages = false
+no_silence_site_packages = false
+# Platform configuration
+python_version = "3.11"
+platform = "linux-64"
+# Disallow dynamic typing
+disallow_any_unimported = false # TODO
+disallow_any_expr = false # TODO
+disallow_any_decorated = false # TODO
+disallow_any_explicit = false # TODO
+disallow_any_generics = false # TODO
+disallow_subclassing_any = false # TODO
+# Untyped definitions and calls
+disallow_untyped_calls = true
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+disallow_untyped_decorators = true
+# None and Optional handling
+no_implicit_optional = true
+strict_optional = true
+# Configuring warnings
+warn_redundant_casts = true
+warn_unused_ignores = true
+warn_no_return = true
+warn_return_any = false # TODO
+warn_unreachable = false # GH#27396
+# Suppressing errors
+ignore_errors = false
+enable_error_code = "ignore-without-code"
+# Miscellaneous strictness flags
+allow_untyped_globals = false
+allow_redefinition = false
+local_partial_types = false
+implicit_reexport = true
+strict_equality = true
+# Configuring error messages
+show_error_context = false
+show_column_numbers = false
+show_error_codes = true
+
+[[tool.mypy.overrides]]
+module = [
+  "pandas._config.config", # TODO
+  "pandas._libs.*",
+  "pandas._testing.*", # TODO
+  "pandas.arrays", # TODO
+  "pandas.compat.numpy.function", # TODO
+  "pandas.compat._optional", # TODO
+  "pandas.compat.compressors", # TODO
+  "pandas.compat.pickle_compat", # TODO
+  "pandas.core._numba.executor", # TODO
+  "pandas.core.array_algos.datetimelike_accumulations", # TODO
+  "pandas.core.array_algos.masked_accumulations", # TODO
+  "pandas.core.array_algos.masked_reductions", # TODO
+  "pandas.core.array_algos.putmask", # TODO
+  "pandas.core.array_algos.quantile", # TODO
+  "pandas.core.array_algos.replace", # TODO
+  "pandas.core.array_algos.take", # TODO
+  "pandas.core.arrays.*", # TODO
+  "pandas.core.computation.*", # TODO
+  "pandas.core.dtypes.astype", # TODO
+  "pandas.core.dtypes.cast", # TODO
+  "pandas.core.dtypes.common", # TODO
+  "pandas.core.dtypes.concat", # TODO
+  "pandas.core.dtypes.dtypes", # TODO
+  "pandas.core.dtypes.generic", # TODO
+  "pandas.core.dtypes.inference", # TODO
+  "pandas.core.dtypes.missing", # TODO
+  "pandas.core.groupby.categorical", # TODO
+  "pandas.core.groupby.generic", # TODO
+  "pandas.core.groupby.grouper", # TODO
+  "pandas.core.groupby.groupby", # TODO
+  "pandas.core.groupby.ops", # TODO
+  "pandas.core.indexers.*", # TODO
+  "pandas.core.indexes.*", # TODO
+  "pandas.core.interchange.column", # TODO
+  "pandas.core.interchange.dataframe_protocol", # TODO
+  "pandas.core.interchange.from_dataframe", # TODO
+  "pandas.core.internals.*", # TODO
+  "pandas.core.methods.*", # TODO
+  "pandas.core.ops.array_ops", # TODO
+  "pandas.core.ops.common", # TODO
+  "pandas.core.ops.invalid", # TODO
+  "pandas.core.ops.mask_ops", # TODO
+  "pandas.core.ops.missing", # TODO
+  "pandas.core.reshape.*", # TODO
+  "pandas.core.strings.*", # TODO
+  "pandas.core.tools.*", # TODO
+  "pandas.core.window.common", # TODO
+  "pandas.core.window.ewm", # TODO
+  "pandas.core.window.expanding", # TODO
+  "pandas.core.window.numba_", # TODO
+  "pandas.core.window.online", # TODO
+  "pandas.core.window.rolling", # TODO
+  "pandas.core.accessor", # TODO
+  "pandas.core.algorithms", # TODO
+  "pandas.core.apply", # TODO
+  "pandas.core.arraylike", # TODO
+  "pandas.core.base", # TODO
+  "pandas.core.common", # TODO
+  "pandas.core.config_init", # TODO
+  "pandas.core.construction", # TODO
+  "pandas.core.flags", # TODO
+  "pandas.core.frame", # TODO
+  "pandas.core.generic", # TODO
+  "pandas.core.indexing", # TODO
+  "pandas.core.missing", # TODO
+  "pandas.core.nanops", # TODO
+  "pandas.core.resample", # TODO
+  "pandas.core.roperator", # TODO
+  "pandas.core.sample", # TODO
+  "pandas.core.series", # TODO
+  "pandas.core.sorting", # TODO
+  "pandas.errors", # TODO
+  "pandas.io.clipboard", # TODO
+  "pandas.io.excel._base", # TODO
+  "pandas.io.excel._odfreader", # TODO
+  "pandas.io.excel._odswriter", # TODO
+  "pandas.io.excel._openpyxl", # TODO
+  "pandas.io.excel._pyxlsb", # TODO
+  "pandas.io.excel._xlrd", # TODO
+  "pandas.io.excel._xlsxwriter", # TODO
+  "pandas.io.formats.console", # TODO
+  "pandas.io.formats.css", # TODO
+  "pandas.io.formats.excel", # TODO
+  "pandas.io.formats.format", # TODO
+  "pandas.io.formats.info", # TODO
+  "pandas.io.formats.printing", # TODO
+  "pandas.io.formats.style", # TODO
+  "pandas.io.formats.style_render", # TODO
+  "pandas.io.formats.xml", # TODO
+  "pandas.io.json.*", # TODO
+  "pandas.io.parsers.*", # TODO
+  "pandas.io.sas.sas_xport", # TODO
+  "pandas.io.sas.sas7bdat", # TODO
+  "pandas.io.clipboards", # TODO
+  "pandas.io.common", # TODO
+  "pandas.io.gbq", # TODO
+  "pandas.io.html", # TODO
+  "pandas.io.gbq", # TODO
+  "pandas.io.parquet", # TODO
+  "pandas.io.pytables", # TODO
+  "pandas.io.sql", # TODO
+  "pandas.io.stata", # TODO
+  "pandas.io.xml", # TODO
+  "pandas.plotting.*", # TODO
+  "pandas.tests.*",
+  "pandas.tseries.frequencies", # TODO
+  "pandas.tseries.holiday", # TODO
+  "pandas.util._decorators", # TODO
+  "pandas.util._doctools", # TODO
+  "pandas.util._print_versions", # TODO
+  "pandas.util._test_decorators", # TODO
+  "pandas.util._validators", # TODO
+  "pandas.util", # TODO
+  "pandas._version",
+  "pandas.conftest",
+  "pandas"
+]
+disallow_untyped_calls = false
+disallow_untyped_defs = false
+disallow_incomplete_defs = false
+
+[[tool.mypy.overrides]]
+module = [
+  "pandas.tests.*",
+  "pandas._version",
+  "pandas.io.clipboard",
+]
+check_untyped_defs = false
+
+[[tool.mypy.overrides]]
+module = [
+  "pandas.tests.apply.test_series_apply",
+  "pandas.tests.arithmetic.conftest",
+  "pandas.tests.arrays.sparse.test_combine_concat",
+  "pandas.tests.dtypes.test_common",
+  "pandas.tests.frame.methods.test_to_records",
+  "pandas.tests.groupby.test_rank",
+  "pandas.tests.groupby.transform.test_transform",
+  "pandas.tests.indexes.interval.test_interval",
+  "pandas.tests.indexing.test_categorical",
+  "pandas.tests.io.excel.test_writers",
+  "pandas.tests.reductions.test_reductions",
+  "pandas.tests.test_expressions",
+]
+ignore_errors = true
+
+# To be kept consistent with "Import Formatting" section in contributing.rst
+[tool.isort]
+known_pre_libs = "pandas._config"
+known_pre_core = ["pandas._libs", "pandas._typing", "pandas.util._*", "pandas.compat", "pandas.errors"]
+known_dtypes = "pandas.core.dtypes"
+known_post_core = ["pandas.tseries", "pandas.io", "pandas.plotting"]
+sections = ["FUTURE", "STDLIB", "THIRDPARTY" ,"PRE_LIBS" , "PRE_CORE", "DTYPES", "FIRSTPARTY", "POST_CORE", "LOCALFOLDER"]
+profile = "black"
+combine_as_imports = true
+force_grid_wrap = 2
+force_sort_within_sections = true
+skip_glob = "env"
+skip = "pandas/__init__.py"
+
+[tool.pyright]
+pythonVersion = "3.11"
+typeCheckingMode = "basic"
+useLibraryCodeForTypes = false
+include = ["pandas", "typings"]
+exclude = ["pandas/tests", "pandas/io/clipboard", "pandas/util/version", "pandas/core/_numba/extensions.py"]
+# enable subset of "strict"
+reportDuplicateImport = true
+reportInconsistentConstructor = true
+reportInvalidStubStatement = true
+reportOverlappingOverload = true
+reportPropertyTypeMismatch = true
+reportUntypedClassDecorator = true
+reportUntypedFunctionDecorator = true
+reportUntypedNamedTuple = true
+reportUnusedImport = true
+disableBytesTypePromotions = true
+# disable subset of "basic"
+reportGeneralTypeIssues = false
+reportMissingModuleSource = false
+reportOptionalCall = false
+reportOptionalIterable = false
+reportOptionalMemberAccess = false
+reportOptionalOperand = false
+reportOptionalSubscript = false
+reportPrivateImportUsage = false
+reportUnboundVariable = false
+
+[tool.coverage.run]
+branch = true
+omit = ["pandas/_typing.py", "pandas/_version.py"]
+plugins = ["Cython.Coverage"]
+source = ["pandas"]
+
+[tool.coverage.report]
+ignore_errors = false
+show_missing = true
+omit = ["pandas/_version.py"]
+exclude_lines = [
+  # Have to re-enable the standard pragma
+  "pragma: no cover",
+  # Don't complain about missing debug-only code:s
+  "def __repr__",
+  "if self.debug",
+  # Don't complain if tests don't hit defensive assertion code:
+  "raise AssertionError",
+  "raise NotImplementedError",
+  "AbstractMethodError",
+  # Don't complain if non-runnable code isn't run:
+  "if 0:",
+  "if __name__ == .__main__.:",
+  "if TYPE_CHECKING:",
+]
+
+[tool.coverage.html]
+directory = "coverage_html_report"
+
+[tool.codespell]
+ignore-words-list = "blocs, coo, hist, nd, sav, ser, recuse, nin, timere, expec, expecs"
+ignore-regex = 'https://([\w/\.])+'
diff --git a/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/testing.py b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/testing.py
new file mode 100644
index 0000000000000000000000000000000000000000..841b55df48556561904b9144a05f747d889ea621
--- /dev/null
+++ b/code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/testing.py
@@ -0,0 +1,18 @@
+"""
+Public testing utility functions.
+"""
+
+
+from pandas._testing import (
+    assert_extension_array_equal,
+    assert_frame_equal,
+    assert_index_equal,
+    assert_series_equal,
+)
+
+__all__ = [
+    "assert_extension_array_equal",
+    "assert_frame_equal",
+    "assert_series_equal",
+    "assert_index_equal",
+]